xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

018e37a3271bbe0ac811dfe2f2b0248dd13424ad

5,123

py

Python

tests/ut/python/dataset_deprecated/test_map.py

httpsgithu/mindspore

c29d6bb764e233b427319cb89ba79e420f1e2c64

[ "Apache-2.0" ]

1

2022-02-23T09:13:43.000Z

tests/ut/python/dataset_deprecated/test_map.py

949144093/mindspore

c29d6bb764e233b427319cb89ba79e420f1e2c64

[ "Apache-2.0" ]

null

tests/ut/python/dataset_deprecated/test_map.py

949144093/mindspore

c29d6bb764e233b427319cb89ba79e420f1e2c64

[ "Apache-2.0" ]

null

# Copyright 2022 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import pytest import mindspore.dataset as ds from mindspore.dataset.transforms import c_transforms from mindspore.dataset.transforms import py_transforms import mindspore.dataset.vision.c_transforms as c_vision import mindspore.dataset.vision.py_transforms as py_vision DATA_DIR = "../data/dataset/testPK/data" def test_map_c_transform_exception(): """ Feature: test c error op def Description: op defined like c_vision.HWC2CHW Expectation: success """ data_set = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=1, shuffle=True) train_image_size = 224 mean = [0.485 * 255, 0.456 * 255, 0.406 * 255] std = [0.229 * 255, 0.224 * 255, 0.225 * 255] # define map operations random_crop_decode_resize_op = c_vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)) random_horizontal_flip_op = c_vision.RandomHorizontalFlip(prob=0.5) normalize_op = c_vision.Normalize(mean=mean, std=std) hwc2chw_op = c_vision.HWC2CHW # exception data_set = data_set.map(operations=random_crop_decode_resize_op, input_columns="image", num_parallel_workers=1) data_set = data_set.map(operations=random_horizontal_flip_op, input_columns="image", num_parallel_workers=1) data_set = data_set.map(operations=normalize_op, input_columns="image", num_parallel_workers=1) with pytest.raises(ValueError) as info: data_set = data_set.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=1) assert "Parameter operations's element of method map should be a " in str(info.value) # compose exception with pytest.raises(ValueError) as info: c_transforms.Compose([ c_vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)), c_vision.RandomHorizontalFlip, c_vision.Normalize(mean=mean, std=std), c_vision.HWC2CHW()]) assert " should be a " in str(info.value) # randomapply exception with pytest.raises(ValueError) as info: c_transforms.RandomApply([ c_vision.RandomCropDecodeResize, c_vision.RandomHorizontalFlip(prob=0.5), c_vision.Normalize(mean=mean, std=std), c_vision.HWC2CHW()]) assert " should be a " in str(info.value) # randomchoice exception with pytest.raises(ValueError) as info: c_transforms.RandomChoice([ c_vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)), c_vision.RandomHorizontalFlip(prob=0.5), c_vision.Normalize, c_vision.HWC2CHW()]) assert " should be a " in str(info.value) def test_map_py_transform_exception(): """ Feature: test python error op def Description: op defined like py_vision.RandomHorizontalFlip Expectation: success """ data_set = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=1, shuffle=True) # define map operations decode_op = py_vision.Decode() random_horizontal_flip_op = py_vision.RandomHorizontalFlip # exception to_tensor_op = py_vision.ToTensor() trans = [decode_op, random_horizontal_flip_op, to_tensor_op] with pytest.raises(ValueError) as info: data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=1) assert "Parameter operations's element of method map should be a " in str(info.value) # compose exception with pytest.raises(ValueError) as info: py_transforms.Compose([ py_vision.Decode, py_vision.RandomHorizontalFlip(), py_vision.ToTensor()]) assert " should be a " in str(info.value) # randomapply exception with pytest.raises(ValueError) as info: py_transforms.RandomApply([ py_vision.Decode(), py_vision.RandomHorizontalFlip, py_vision.ToTensor()]) assert " should be a " in str(info.value) # randomchoice exception with pytest.raises(ValueError) as info: py_transforms.RandomChoice([ py_vision.Decode(), py_vision.RandomHorizontalFlip(), py_vision.ToTensor]) assert " should be a " in str(info.value) if __name__ == '__main__': test_map_c_transform_exception() test_map_py_transform_exception()

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null

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null

0

1

0

018ecde16201a4f4c059f4251f120ee69a80438a

7,685

py

Python

overlays/holo-nixpkgs/hpos-admin/hpos-admin.py

samrose/holo-nixpkgs

057c92fcef9934d1ba2310e77579b78e61271a59

[ "MIT" ]

null

overlays/holo-nixpkgs/hpos-admin/hpos-admin.py

samrose/holo-nixpkgs

057c92fcef9934d1ba2310e77579b78e61271a59

[ "MIT" ]

null

overlays/holo-nixpkgs/hpos-admin/hpos-admin.py

samrose/holo-nixpkgs

057c92fcef9934d1ba2310e77579b78e61271a59

[ "MIT" ]

null

from base64 import b64encode from flask import Flask, jsonify, request from functools import reduce from gevent import subprocess, pywsgi, queue, socket, spawn, lock from gevent.subprocess import CalledProcessError from hashlib import sha512 from pathlib import Path from tempfile import mkstemp import json import os import subprocess import toml import requests import asyncio import websockets PROFILES_TOML_PATH = '/etc/nixos/hpos-admin-features.toml' app = Flask(__name__) rebuild_queue = queue.PriorityQueue() state_lock = lock.Semaphore() def rebuild_worker(): while True: (_, cmd) = rebuild_queue.get() rebuild_queue.queue.clear() subprocess.run(cmd) def rebuild(priority, args): rebuild_queue.put((priority, ['nixos-rebuild', 'switch'] + args)) def get_state_path(): hpos_config_file_symlink = os.getenv('HPOS_CONFIG_PATH') hpos_config_file = os.path.realpath(hpos_config_file_symlink) return hpos_config_file def get_state_data(): with open(get_state_path(), 'r') as f: return json.loads(f.read()) def cas_hash(data): dump = json.dumps(data, separators=(',', ':'), sort_keys=True) return b64encode(sha512(dump.encode()).digest()).decode() @app.route('/config', methods=['GET']) def get_settings(): return jsonify(get_state_data()['v1']['settings']) def replace_file_contents(path, data): fd, tmp_path = mkstemp(dir=os.path.dirname(path)) with open(fd, 'w') as f: f.write(data) os.rename(tmp_path, path) @app.route('/config', methods=['PUT']) def put_settings(): with state_lock: state = get_state_data() expected_cas = cas_hash(state['v1']['settings']) received_cas = request.headers.get('x-hpos-admin-cas') if received_cas != expected_cas: app.logger.warning('CAS mismatch: {} != {}'.format(received_cas, expected_cas)) return '', 409 state['v1']['settings'] = request.get_json(force=True) state_json = json.dumps(state, indent=2) try: subprocess.run(['hpos-config-is-valid'], check=True, input=state_json, text=True) except CalledProcessError: return '', 400 replace_file_contents(get_state_path(), state_json) # FIXME: see next FIXME # rebuild(priority=5, args=[]) return '', 200 # Toggling HPOS features def read_profiles(): if Path(PROFILES_TOML_PATH).is_file(): return toml.load(PROFILES_TOML_PATH) else: return {} def write_profiles(profiles): with open(PROFILES_TOML_PATH, 'w') as f: f.write(toml.dumps(profiles)) def set_feature_state(profile, feature, enable = True): profiles = read_profiles() profiles.update({ profile: { 'features': { feature: { 'enable': enable } } } }) write_profiles(profiles) return jsonify({ 'enabled': enable }) @app.route('/profiles', methods=['GET']) def get_profiles(): return jsonify({ 'profiles': read_profiles() }) @app.route('/profiles/<profile>/features/<feature>', methods=['GET']) def get_feature_state(profile, feature): profiles = read_profiles() keys = [profile, 'features', feature, 'enable'] enabled = reduce(lambda d, key: d.get(key) if d else None, keys, profiles) or False return jsonify({ 'enabled': enabled }) @app.route('/profiles/<profile>/features/<feature>', methods=['PUT']) def enable_feature(profile, feature): return set_feature_state(profile, feature, True) @app.route('/profiles/<profile>/features/<feature>', methods=['DELETE']) def disable_feature(profile, feature): return set_feature_state(profile, feature, False) def hosted_happs(): conductor_config = toml.load('/var/lib/holochain-conductor/conductor-config.toml') return [dna for dna in conductor_config['dnas'] if dna['holo-hosted']] def hosted_instances(): conductor_config = toml.load('/var/lib/holochain-conductor/conductor-config.toml') return [instance for instance in conductor_config['instances'] if instance['holo-hosted']] async def hc_call(method, params): uri = "ws://localhost:42222" m = { 'jsonrpc': '2.0', 'id': '0', 'method': method, 'params': params } data = json.dumps(m, indent=2) async with websockets.connect(uri) as websocket: await websocket.send(bytes(data,encoding="utf-8")) response = await websocket.recv() return json.loads(response) TRAFFIC_NULL_STATE = {'start_date': None, 'total_zome_calls':0, 'value': []} def get_traffic_service_logger_call(instance_id): response = asyncio.get_event_loop().run_until_complete(hc_call('call', { "instance_id": instance_id ,"zome": "service", "function": "get_traffic", "args": {"filter": "DAY"} })) if 'result' in response: return json.loads(response['result'])['Ok'] else: return TRAFFIC_NULL_STATE @app.route('/hosted_happs', methods=['GET']) def get_hosted_happs(): hosted_happs_list = hosted_happs() hosted_instances_list = hosted_instances() if len(hosted_happs_list) > 0: for hosted_happ in hosted_happs_list: if len(hosted_instances_list) > 0: num_instances = sum(hosted_happ['id'] in hosted_instance['id'] for hosted_instance in hosted_instances_list) hosted_happ['stats'] = {"traffic": get_traffic_service_logger_call(hosted_happ['id']+"::servicelogger")} else: num_instances = 0 hosted_happ['stats'] = {"traffic": TRAFFIC_NULL_STATE} hosted_happ['number_instances'] = num_instances return jsonify({ 'hosted_happs': hosted_happs_list }) def hydra_channel(): with open('/root/.nix-channels') as f: channel_url = f.read() return channel_url.split('/')[6] def hydra_revision(): channel = hydra_channel() eval_url = 'https://hydra.holo.host/jobset/holo-nixpkgs/' + channel + '/latest-eval' headers = { 'Content-Type': 'application/json', 'Accept': 'application/json' } eval_summary = requests.get(eval_url, headers=headers).json() return eval_summary['jobsetevalinputs']['holo-nixpkgs']['revision'] def local_revision(): try: with open('/root/.nix-revision') as f: local_revision = f.read() except: local_revision = 'unversioned' return local_revision def zerotier_info(): proc = subprocess.run(['zerotier-cli', '-j', 'info'], capture_output=True, check=True) return json.loads(proc.stdout) @app.route('/status', methods=['GET']) def status(): return jsonify({ 'holo_nixpkgs':{ 'channel': { 'name': hydra_channel(), 'rev': hydra_revision() }, 'current_system': { 'rev': local_revision() } }, 'zerotier': zerotier_info() }) @app.route('/upgrade', methods=['POST']) def upgrade(): # FIXME: calling nixos-rebuild fails # rebuild(priority=1, args=['--upgrade']) return '', 503 # service unavailable @app.route('/reset', methods=['POST']) def reset(): try: subprocess.run(['hpos-reset'], check=True) except CalledProcessError: return '', 500 def unix_socket(path): sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) if os.path.exists(path): os.remove(path) sock.bind(path) sock.listen() return sock if __name__ == '__main__': spawn(rebuild_worker) pywsgi.WSGIServer(unix_socket('/run/hpos-admin.sock'), app).serve_forever()

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0.645413

930

7,685

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0.016747

0.013607

0.013398

0.114298

0.081851

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0.030144

0

0.007935

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7,685

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0

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0.033156

0

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0

1

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false

0

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0

null

0

null

0

1

0

0196ed4e4760ab9bf312a9416801f8b71d0a5124

2,471

py

Python

downloader/download.py

inverthermit/sec_edgar_analysis

ffdf43b30ab53b0a024790757c8ef0c989acf67a

[ "MIT" ]

1

2018-02-03T00:28:53.000Z

downloader/download.py

inverthermit/sec_edgar_analysis

ffdf43b30ab53b0a024790757c8ef0c989acf67a

[ "MIT" ]

null

downloader/download.py

inverthermit/sec_edgar_analysis

ffdf43b30ab53b0a024790757c8ef0c989acf67a

[ "MIT" ]

null

import urllib import time from multiprocessing.dummy import Pool as ThreadPool excelFolder = 'F://SecExcelDownload2/' compListUrl = 'C://Users/l1111/Desktop/AlphaCapture/downloadFileUrl.txt' successFile = excelFolder+'/success.txt' failFile = excelFolder+'/fail.txt' logFile = excelFolder+'/log.txt' def getAlreadyDownload(): lineList = [] count = 0 with open(successFile) as f: for line in f: line = line.strip() lineList.append(line) return lineList downloadedList = getAlreadyDownload() def downloadFile(line): compName = line.split(',')[0] cik = line.split(',')[1] doc = line.split(',')[2] url = line.split(',')[3] if url in downloadedList: return 0 fileURLOpener = urllib.URLopener() try: fileURLOpener.retrieve(url,excelFolder+compName+'-'+cik+'-'+doc+'.xlsx' ) with open(successFile, "a") as myfile: myfile.write(url+'\n') except: print('Error: not a xlsx file. Downloading xls file') try: fileURLOpener.retrieve(url.replace('.xlsx','.xls'), excelFolder+compName+'-'+cik+'-'+doc+'.xls') with open(successFile, "a") as myfile: myfile.write(url+'\n') except: print('Error: download failed') with open(failFile, "a") as myfile: myfile.write(url+'\n') def slowSingleThread(): lineList = [] count = 0 with open(compListUrl) as f: for line in f: line = line.strip() lineList.append(line) # downloadFile(line) # break # print(len(lineList)) # total = len(lineList) # with open(compListUrl) as f: # for line in f: # line = line.strip() # with open(logFile, "a") as myfile: # myfile.write(str(count)+'/'+str(total)+':'+line+'\n') # count+=1 # downloadFile(line) for line in lineList: downloadFile(line) def fastMultiThread(): lineList = [] count = 0 with open(compListUrl) as f: for line in f: line = line.strip() lineList.append(line) # make the Pool of workers pool = ThreadPool(10) # open the urls in their own threads # and return the results results = pool.map(downloadFile, lineList) # close the pool and wait for the work to finish pool.close() pool.join() fastMultiThread() # slowSingleThread()

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2,471

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0.33574

0.044352

0.031185

0.02772

0.295911

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0.250173

0

0.00905

0.2845

2,471

86

109

28.732558

0.807127

0.180089

0

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0

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1

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false

0

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0.033333

0

null

0

null

0

1

0

0197d01c354f66f49415a9ef3d542eb61ea7a772

20,495

py

Python

HOST/py/tc_TcpEcho.py

cloudFPGA/cFp_HelloKale

949f8c3005d2824b8bc65345b77ea97bd0b6e692

[ "Apache-2.0" ]

null

HOST/py/tc_TcpEcho.py

cloudFPGA/cFp_HelloKale

949f8c3005d2824b8bc65345b77ea97bd0b6e692

[ "Apache-2.0" ]

6

2022-01-22T10:04:18.000Z

2022-02-01T21:28:19.000Z

HOST/py/tc_TcpEcho.py

cloudFPGA/cFp_HelloKale

949f8c3005d2824b8bc65345b77ea97bd0b6e692

[ "Apache-2.0" ]

null

#/* # * Copyright 2016 -- 2021 IBM Corporation # * # * Licensed under the Apache License, Version 2.0 (the "License"); # * you may not use this file except in compliance with the License. # * You may obtain a copy of the License at # * # * http://www.apache.org/licenses/LICENSE-2.0 # * # * Unless required by applicable law or agreed to in writing, software # * distributed under the License is distributed on an "AS IS" BASIS, # * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # * See the License for the specific language governing permissions and # * limitations under the License. # * # ***************************************************************************** # * @file : tc_TcpEcho.py # * @brief : A multi-threaded script to send and receive traffic on the # * TCP connection of an FPGA module. # * # * System: : cloudFPGA # * Component : cFp_BringUp/ROLE # * Language : Python 3 # * # ***************************************************************************** # ### REQUIRED PYTHON PACKAGES ################################################ import argparse import datetime import errno import filecmp import socket import threading import time # ### REQUIRED TESTCASE MODULES ############################################### from tc_utils import * # ### GLOBAL VARIABLES ######################################################## gEchoRxPath = './echoRx.dat' gEchoTxPath = './echoTx.dat' def tcp_tx(sock, message, count, verbose=False): """TCP Tx Thread. :param sock, the socket to send to. :param message, the random string to sent. :param count, the number of segments to send. :param verbose, enables verbosity. :return None""" if verbose: print("The following message of %d bytes will be sent out %d times:\n Message=%s\n" % (len(message), count, message.decode('ascii'))) # Create a Tx Reference File echoTxFile = open(gEchoTxPath, 'w') if count <= 1000: loop = 0 while loop < count: echoTxFile.write(message.decode('ascii')) loop += 1 # Start Data Transmission loop = 0 startTime = datetime.datetime.now() while loop < count: try: sock.sendall(message) finally: pass loop += 1 endTime = datetime.datetime.now() elapseTime = endTime - startTime; bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024) print("##################################################") print("#### TCP TX DONE with bandwidth = %6.1f Mb/s ####" % bandwidth) print("##################################################") print() # Close the Tx Reference File echoTxFile.close() # Push a few more bytes to force the FPGA to flush its buffers try: sock.sendall(message) finally: pass def tcp_rx(sock, message, count, verbose): """TCP Rx Thread. :param sock, the socket to receive from. :param message, the expected string message to be received. :param count, the number of segment to receive. :param verbose, enables verbosity. :return None""" # Create an Rx Test File echoRxFile = open(gEchoRxPath, 'w') # Start Data Reception loop = 0 rxBytes = 0 expectedBytes = count*len(message) startTime = datetime.datetime.now() while rxBytes < expectedBytes: try: data = sock.recv(expectedBytes - rxBytes) rxBytes += len(data) if count <= 1000: echoRxFile.write(data.decode('ascii')) except socket.error as exc: print("[EXCEPTION] Socket error while receiving :: %s" % exc) else: if verbose: print("Loop=%d | RxBytes=%d" % (loop, rxBytes)) loop += 1 endTime = datetime.datetime.now() elapseTime = endTime - startTime bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024) print("##################################################") print("#### TCP RX DONE with bandwidth = %6.1f Mb/s ####" % bandwidth) print("##################################################") print() # Close the Rx Test File echoRxFile.close() def waitUntilSocketPairCanBeReused(ipFpga, portFpga): """Check and wait until the a socket pair can be reused. [INFO] When a client or a server initiates an active close, then the same destination socket (i.e. the same IP address / TCP port number) cannot be re-used immediately because of security issues. Therefore, a closed connection must linger in a 'TIME_WAIT' or 'FIN_WAIT' state for as long as 2xMSL (Maximum Segment Lifetime), which corresponds to twice the time a TCP segment might exist in the internet system. The MSL is arbitrarily defined to be 2 minutes long. :param ipFpga: the IP address of FPGA. :param portFpga: the TCP port of the FPGA. :return: nothing """ wait = True # NETSTAT example: rc = os.system("netstat | grep '10.12.200.163:8803' | grep TIME_WAIT") cmdStr = "netstat | grep \'" + str(ipFpga) + ":" + str(portFpga) + "\' | grep \'TIME_WAIT\|FIN_WAIT\' " while wait: rc = os.system(cmdStr) if rc == 0: print("[INFO] Cannot reuse this socket as long as it is in the \'TIME_WAIT\' or \'FIN_WAIT\' state.") print(" Let's sleep for 5 sec...") time.sleep(5) else: wait = False def tcp_txrx_loop(sock, message, count, verbose=False): """TCP Tx-Rx Single-Thread Loop. :param sock The socket to send/receive to/from. :param message The message string to sent. :param count The number of segments send. :param verbose Enables verbosity. :return None""" if verbose: print("[INFO] The following message of %d bytes will be sent out %d times:\n Message=%s\n" % (len(message), count, message.decode('ascii'))) nrErr = 0 txMssgCnt = 0 rxMssgCnt = 0 rxByteCnt = 0 txStream = "" rxStream = "" # Init the Tx reference stream for i in range(count): txStream = txStream + message.decode('ascii') startTime = datetime.datetime.now() while rxByteCnt < (count * len(message)): if txMssgCnt < count: # Send a new message # ------------------------ try: tcpSock.sendall(message) txMssgCnt += 1 finally: pass # Receive a segment # -------------------- try: data = tcpSock.recv(len(message)) rxByteCnt += len(data) rxMssgCnt += 1 if verbose: print("%d:%s" % (rxMssgCnt, data.decode('ascii'))) except IOError as e: # On non blocking connections - when there are no incoming data, error is going to be # raised. Some operating systems will indicate that using AGAIN, and some using # WOULDBLOCK error code. We are going to check for both - if one of them - that's # expected, means no incoming data, continue as normal. If we got different error code, # something happened if e.errno != errno.EAGAIN and e.errno != errno.EWOULDBLOCK: print('[ERROR] Socket reading error: {}'.format(str(e))) exit(1) # We just did not receive anything continue except socket.error as exc: # Any other exception print("[EXCEPTION] Socket error while receiving :: %s" % exc) # exit(1) finally: pass rxStream = rxStream + data.decode('ascii') endTime = datetime.datetime.now() if verbose: print("\n") # Compare Tx and Rx stream if rxStream != txStream: print(" KO | Received stream = %s" % data.decode('ascii')) print(" | Expected stream = %s" % rxStream) nrErr += 1 elif verbose: print(" OK | Received %d bytes in %d messages." % (rxByteCnt, rxMssgCnt)) elapseTime = endTime - startTime; bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024) print("[INFO] Transferred a total of %d bytes." % rxByteCnt) print("#####################################################") print("#### TCP Tx/Rx DONE with bandwidth = %6.1f Mb/s ####" % bandwidth) print("#####################################################") print() def tcp_txrx_ramp(sock, message, count, verbose=False): """TCP Tx-Rx Single-Thread Ramp. :param sock The socket to send/receive to/from. :param message The message string to sent. :param count The number of segments to send. :param verbose Enables verbosity. :return None""" if verbose: print("[INFO] The following message of %d bytes will be sent out incrementally %d times:\n Message=%s\n" % (len(message), count, message.decode('ascii'))) nrErr = 0 loop = 0 rxByteCnt = 0 startTime = datetime.datetime.now() while loop < count: i = 1 while i <= len(message): subMsg = message[0:i] # Send datagram # ------------------- try: tcpSock.sendall(subMsg) finally: pass # Receive datagram # ------------------- try: data = tcpSock.recv(len(subMsg)) rxByteCnt += len(data) if data == subMsg: if verbose: print("Loop=%d | RxBytes=%d" % (loop, len(data))) else: print("Loop=%d | RxBytes=%d" % (loop, len(data))) print(" KO | Received Message=%s" % data.decode('ascii')) print(" | Expecting Message=%s" % subMsg) nrErr += 1 except IOError as e: # On non blocking connections - when there are no incoming data, error is going to be raised # Some operating systems will indicate that using AGAIN, and some using WOULDBLOCK error code # We are going to check for both - if one of them - that's expected, means no incoming data, # continue as normal. If we got different error code - something happened if e.errno != errno.EAGAIN and e.errno != errno.EWOULDBLOCK: print('[ERROR] Socket reading error: {}'.format(str(e))) exit(1) # We just did not receive anything continue except socket.error as exc: # Any other exception print("[EXCEPTION] Socket error while receiving :: %s" % exc) # exit(1) finally: pass i += 1 loop += 1 endTime = datetime.datetime.now() elapseTime = endTime - startTime bandwidth = (rxByteCnt * 8 * count * 1.0) / (elapseTime.total_seconds() * 1024 * 1024) megaBytes = (rxByteCnt * 1.0) / (1024 * 1024 * 1.0) print("[INFO] Transferred a total of %.1f MB." % megaBytes) print("#####################################################") print("#### TCP Tx/Rx DONE with bandwidth = %6.1f Mb/s ####" % bandwidth) print("#####################################################") print() ############################################################################### # # # MAIN # # # ############################################################################### rc = 0 # STEP-1: Parse the command line strings into Python objects # ----------------------------------------------------------------------------- parser = argparse.ArgumentParser(description='A script to send/receive TCP data to/from an FPGA module.') parser.add_argument('-fi', '--fpga_ipv4', type=str, default='', help='The destination IPv4 address of the FPGA (a.k.a image_ip / e.g. 10.12.200.163)') parser.add_argument('-fp', '--fpga_port', type=int, default=8803, help='The TCP destination port of the FPGA (default is 8803)') parser.add_argument('-ii', '--inst_id', type=int, default=0, help='The instance ID assigned by the cloudFPGA Resource Manager (range is 1-32)') parser.add_argument('-lc', '--loop_count', type=int, default=10, help='The number of times to run run the test (default is 10)') parser.add_argument('-mi', '--mngr_ipv4', type=str, default='10.12.0.132', help='The IP address of the cloudFPGA Resource Manager (default is 10.12.0.132)') parser.add_argument('-mp', '--mngr_port', type=int, default=8080, help='The TCP port of the cloudFPGA Resource Manager (default is 8080)') parser.add_argument('-mt', '--multi_threading', action="store_true", help='Enable multi_threading') parser.add_argument('-sd', '--seed', type=int, default=-1, help='The initial number to seed the pseudo-random number generator.') parser.add_argument('-sz', '--size', type=int, default=-1, help='The size of the segment to generate.') parser.add_argument('-un', '--user_name', type=str, default='', help='A user name as used to log in ZYC2 (.e.g \'fab\')') parser.add_argument('-up', '--user_passwd', type=str, default='', help='The ZYC2 password attached to the user name') parser.add_argument('-v', '--verbose', action="store_true", help='Enable verbosity') args = parser.parse_args() if args.user_name == '' or args.user_passwd == '': print("\nWARNING: You must provide a ZYC2 user name and the corresponding password for this script to execute.\n") exit(1) # STEP-2a: Retrieve the IP address of the FPGA module (this will be the SERVER) # ------------------------------------------------------------------------------ ipFpga = getFpgaIpv4(args) # STEP-2b: Retrieve the instance Id assigned by the cloudFPGA Resource Manager # ----------------------------------------------------------------------------- instId = getInstanceId(args) # STEP-2c: Retrieve the IP address of the cF Resource Manager # ----------------------------------------------------------------------------- ipResMngr = getResourceManagerIpv4(args) # STEP-3a: Retrieve the TCP port of the FPGA server # ----------------------------------------------------------------------------- portFpga = getFpgaPort(args) # STEP-3b: Retrieve the TCP port of the cloudFPGA Resource Manager # ----------------------------------------------------------------------------- portResMngr = getResourceManagerPort(args) # STEP-?: Configure the application registers # ----------------------------------------------------------------------------- # TODO print("\nNow: Configuring the application registers.") # TODO tcpEchoPathThruMode = (0x0 << 4) # See DIAG_CTRL_2 register # STEP-4: Trigger the FPGA role to restart (i.e. perform SW reset of the role) # ----------------------------------------------------------------------------- restartApp(instId, ipResMngr, portResMngr, args.user_name, args.user_passwd) # STEP-5: Ping the FPGA # ----------------------------------------------------------------------------- pingFpga(ipFpga) # STEP-6a: Set the FPGA socket association # ----------------------------------------------------------------------------- tcpDP = 8803 # 8803=0x2263 and 0x6322=25378 fpgaAssociation = (str(ipFpga), tcpDP) # STEP-6b: Set the HOST socket association (optional) # Info: Linux selects a source port from an ephemeral port range, which by # default is a set to range from 32768 to 61000. You can check it # with the command: # > cat /proc/sys/net/ipv4/ip_local_port_range # If we want to force the source port ourselves, we must use the # "bind before connect" trick. # ----------------------------------------------------------------------------- if 0: tcpSP = tcpDP + 49152 # 8803 + 0xC000 hostAssociation = (ipSaStr, tcpSP) # STEP-7: Wait until the current socket can be reused # ----------------------------------------------------------------------------- if 0: waitUntilSocketPairCanBeReused(ipFpga, portFpga) # STEP-8a: Create a TCP/IP socket for the TCP/IP connection # ----------------------------------------------------------------------------- try: tcpSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) except Exception as exc: print("[EXCEPTION] %s" % exc) exit(1) # Step-8b: Allow this socket to be re-used and disable the Nagle's algorithm # ---------------------------------------------------------------------------- tcpSock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) tcpSock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, True) # STEP-8c: Bind before connect (optional). # This trick enables us to ask the kernel to select a specific source IP and # source PORT by calling bind() before calling connect(). # ----------------------------------------------------------------------------- if 0: try: tcpSock.bind(hostAssociation) print('Binding the socket address of the HOST to {%s, %d}' % hostAssociation) except Exception as exc: print("[EXCEPTION] %s" % exc) exit(1) # STEP-9: Connect to the remote FPGA # ----------------------------------------------------------------------------- try: tcpSock.connect(fpgaAssociation) except Exception as exc: print("[EXCEPTION] %s" % exc) exit(1) else: print('\nSuccessful connection with socket address of FPGA at {%s, %d} \n' % fpgaAssociation) # STEP-10: Setup the test # ------------------------------- print("[INFO] Testcase `%s` is run with:" % (os.path.basename(__file__))) seed = args.seed if seed == -1: seed = random.randint(0, 100000) random.seed(seed) print("\t\t seed = %d" % seed) size = args.size if size == -1: size = random.randint(1, ZYC2_MSS) elif size > ZYC2_MSS: print('\nERROR: ') print("[ERROR] This test-case expects the transfer of segment which are less or equal to MSS (.i.e %d bytes).\n" % ZYC2_MSS) exit(1) print("\t\t size = %d" % size) count = args.loop_count print("\t\t loop = %d" % count) if seed % 1: message = str_static_gen(size) else: message = str_rand_gen(size) verbose = args.verbose print("[INFO] This testcase is sending traffic from HOST-to-FPGA and back from FPGA-to-HOST.") if args.multi_threading: print("[INFO] This run is executed in multi-threading mode.\n") # STEP-11: Create Rx and Tx threads # ---------------------------------- tx_thread = threading.Thread(target=tcp_tx, args=(tcpSock, message, count, args.verbose)) rx_thread = threading.Thread(target=tcp_rx, args=(tcpSock, message, count, args.verbose)) # STEP-12: Start the threads # --------------------------- tx_thread.start() rx_thread.start() # STEP-13: Wait for threads to terminate # ---------------------------------------- tx_thread.join() rx_thread.join() # STEP-14: Compare Rx and Tx files # ---------------------------------------- result = filecmp.cmp(gEchoTxPath, gEchoRxPath, shallow=False) if not result: print("\n[ERROR] Rx file \'%s\' differs from Tx file \'%s\'.\n" % (gEchoRxPath, gEchoTxPath)) rc = 1 else: os.remove(gEchoRxPath) os.remove(gEchoTxPath) else: print("[INFO] The run is executed in single-threading mode.\n") # STEP-11: Set the socket in non-blocking mode # ---------------------------------------------- tcpSock.setblocking(False) tcpSock.settimeout(5) if seed == 0: tcp_txrx_ramp(tcpSock, message, count, args.verbose) else: tcp_txrx_loop(tcpSock, message, count, args.verbose) # STEP-14: Close socket # ----------------------- time.sleep(2) tcpSock.close() exit(rc)

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null

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null

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0

019ba56645c86cd5f76a825624bb4c712e44806d

967

py

Python

ymir/command/mir/scm/__init__.py

Zhang-SJ930104/ymir

dd6481be6f229ade4cf8fba64ef44a15357430c4

[ "Apache-2.0" ]

64

2021-11-15T03:48:00.000Z

2022-03-25T07:08:46.000Z

ymir/command/mir/scm/__init__.py

Zhang-SJ930104/ymir

dd6481be6f229ade4cf8fba64ef44a15357430c4

[ "Apache-2.0" ]

35

2021-11-23T04:14:35.000Z

2022-03-26T09:03:43.000Z

ymir/command/mir/scm/__init__.py

Aryalfrat/ymir

d4617ed00ef67a77ab4e1944763f608bface4be6

[ "Apache-2.0" ]

57

2021-11-11T10:15:40.000Z

2022-03-29T07:27:54.000Z

import os from mir.scm.cmd import CmdScm from mir.tools.code import MirCode from mir.tools.errors import MirRuntimeError def Scm(root_dir: str, scm_executable: str = None) -> CmdScm: """Returns SCM instance that corresponds to a repo at the specified path. Args: root_dir (str): path to a root directory of the repo. scm_excutable(str): "git". Returns: mir.scm.cmd.BaseScm: SCM instance. """ if scm_executable not in ["git"]: raise MirRuntimeError(error_code=MirCode.RC_CMD_INVALID_ARGS, error_message=f"args error: expected git, not {scm_executable}") if not os.path.exists(root_dir): os.makedirs(root_dir) if not os.path.isdir(root_dir): raise MirRuntimeError(error_code=MirCode.RC_CMD_INVALID_ARGS, error_message=f"can not create dir: {root_dir}") return CmdScm(root_dir, scm_executable)

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94

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null

0

null

0

1

0

6d67721cacf03f0b19c2edfa7d94b286095c3b16

724

py

Python

readability_transformers/features/lf/utils.py

OneTheta/readability-transformers

3c122c98a90c67add8eafad16563b269d5e3124a

[ "Apache-2.0" ]

1

2022-01-26T10:55:59.000Z

readability_transformers/features/lf/utils.py

OneTheta/readability-transformers

3c122c98a90c67add8eafad16563b269d5e3124a

[ "Apache-2.0" ]

null

readability_transformers/features/lf/utils.py

OneTheta/readability-transformers

3c122c98a90c67add8eafad16563b269d5e3124a

[ "Apache-2.0" ]

2

2021-10-14T22:53:57.000Z

2022-01-26T10:53:32.000Z

""" Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment Page: utils.py License: CC-BY-SA 4.0 Original Author: Bruce W. Lee (이웅성) @brucewlee Affiliation 1: LXPER AI, Seoul, South Korea Affiliation 2: University of Pennsylvania, PA, USA Contributing Author: - Affiliation : - """ import re import math def division(x, y): try: result = x/y except: result = 0 return result def nan_check(result): for key in result: if math.isnan(float(result[key])): result[key] = 0 return result def count_syllables(word:str): return len( re.findall('(?!e$)[aeiouy]+', word, re.I) + re.findall('^[^aeiouy]*e$', word, re.I) )

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0.444444

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null

0

null

0

1

0

6d69e794272f0966fc5025bf7ae39b7bd8cdeaea

1,173

py

Python

src/ex05/bwfilter.py

satvik007/Scanner_OP

c146f67e3851cd537d62989842abfee7d34de2c0

[ "MIT" ]

null

src/ex05/bwfilter.py

satvik007/Scanner_OP

c146f67e3851cd537d62989842abfee7d34de2c0

[ "MIT" ]

null

src/ex05/bwfilter.py

satvik007/Scanner_OP

c146f67e3851cd537d62989842abfee7d34de2c0

[ "MIT" ]

1

2021-05-10T10:14:27.000Z

# Usage: # python bwfilter.py --input=./data/test1.jpg import cv2 import numpy as np import argparse def parse_args(): parser = argparse.ArgumentParser(add_help=True, description='testing B&W filter') required_named = parser.add_argument_group('required named arguments') required_named.add_argument('-i', '--input', type=str, help='path of the input image', required=True) return parser.parse_args() def show_img(img): cv2.namedWindow("output", cv2.WINDOW_NORMAL) cv2.resizeWindow('output', 900, 900) cv2.imshow("output", img) cv2.waitKey(0) cv2.destroyAllWindows() def bwfilter(bwimg): # blur the image bwimg = cv2.GaussianBlur(bwimg,(7,7),7) cv2.imwrite('blur.png', bwimg) bwimg = cv2.bilateralFilter(bwimg,9,75,75) cv2.imwrite('bilat.png', bwimg) # adaptive threshholding bwimg = cv2.adaptiveThreshold(bwimg,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2) cv2.imwrite('adaptth.png', bwimg) return bwimg if __name__=='__main__': args = parse_args() img = cv2.imread(args.input, 0) bwimg = bwfilter(img) show_img(bwimg) cv2.imwrite('bwimg.png', bwimg)

26.066667

105

0.6948

160

1,173

4.94375

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0.041026

0.168798

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false

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0.107143

0

0.285714

0

null

0

null

0

1

0

6d6f7df41b42997a8642b881e20683971aa08d5d

1,065

py

Python

resotolib/test/test_graph_extensions.py

someengineering/resoto

ee17313f5376e9797ed305e7fdb62d40139a6608

[ "Apache-2.0" ]

126

2022-01-13T18:22:03.000Z

2022-03-31T11:03:14.000Z

resotolib/test/test_graph_extensions.py

someengineering/resoto

ee17313f5376e9797ed305e7fdb62d40139a6608

[ "Apache-2.0" ]

110

2022-01-13T22:27:55.000Z

2022-03-30T22:26:50.000Z

resotolib/test/test_graph_extensions.py

someengineering/resoto

ee17313f5376e9797ed305e7fdb62d40139a6608

[ "Apache-2.0" ]

8

2022-01-15T10:28:16.000Z

2022-03-30T16:38:21.000Z

from networkx import DiGraph from pytest import fixture from resotolib.graph.graph_extensions import dependent_node_iterator @fixture def graph() -> DiGraph: g = DiGraph() for i in range(1, 14): g.add_node(i) g.add_edges_from([(1, 2), (1, 3), (2, 3)]) # island 1 g.add_edges_from([(4, 5), (4, 6), (6, 7)]) # island 2 g.add_edges_from( [(8, 9), (9, 10), (9, 11), (8, 12), (12, 11), (12, 13)] ) # island 3 return g def test_reversed_directed_traversal(graph: DiGraph): result = list(dependent_node_iterator(graph)) assert len(result) == 3 # 3 steps to complete assert result == [ [3, 5, 7, 10, 11, 13], # step 1 [2, 6, 9, 12], # step 2 [1, 4, 8], # step 3 ] def test_delete_nodes(graph: DiGraph): to_delete = graph.copy() for parallel in dependent_node_iterator(graph): for node in parallel: to_delete.remove_node(node) assert len(to_delete.nodes) == 0 def test_empty_graph(): assert list(dependent_node_iterator(DiGraph())) == []

26.625

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0.605634

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0.341615

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0.135922

0.063107

0

0.075094

0.249765

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1

0.133333

false

0

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0

0.266667

0

null

0

null

0

1

0

6d72243ededbe77bc217b7d87bcc872254da5cff

3,806

py

Python

cross_sums.py

minddrive/random_math

b5dececaf48ec80d8250d0f5fde0485e1b9e73c2

[ "MIT" ]

null

cross_sums.py

minddrive/random_math

b5dececaf48ec80d8250d0f5fde0485e1b9e73c2

[ "MIT" ]

null

cross_sums.py

minddrive/random_math

b5dececaf48ec80d8250d0f5fde0485e1b9e73c2

[ "MIT" ]

null

#!/usr/bin/env python3.4 import functools @functools.total_ordering class CrossSum: def __init__(self, digits, total=0, addends=''): self._digits = digits self._base = len(digits) self.total = total self.addends = addends def _convert_total(self): num_total = self.total total_digits = [] while num_total: total_digits.append(self._digits[num_total % self._base]) num_total //= self._base return ''.join(total_digits[::-1]) # This assumes that the new addend is larger than others in the sum def add_addend(self, addend): d = self._digits.index(addend) return CrossSum(self._digits, self.total + d, self.addends + addend) def has_total(self, total): if isinstance(total, str): total_str = total total = 0 for digit in total_str: total = total * self._base + self._digits.index(digit) return self.total == total def has_addends(self, addends): if not isinstance(addends, set): addends = set(addends) return set(self.addends).issuperset(addends) @property def num_addends(self): return len(self.addends) @staticmethod def _is_valid_operand(other): return hasattr(other, 'total') and hasattr(other, 'addends') def __eq__(self, other): if not self._is_valid_operand(other): return NotImplemented return (self.total, self.addends) == (other.total, other.addends) def __lt__(self, other): if not self._is_valid_operand(other): return NotImplemented return (self.total, self.addends) < (other.total, other.addends) def __repr__(self): return ("<CrossSum(digits='%s', total='%s', addends='%s'>" % (self._digits, self._convert_total(), self.addends)) def __str__(self): total_str = self._convert_total() return '%s = %s' % (total_str, ' + '.join(self.addends)) class CrossSums: def __init__(self, digits='0123456789', cross_sums=None): self._digits = digits self._base = len(digits) if cross_sums is None: cross_sums = [CrossSum(digits)] for digit in digits[1:]: cross_sums += [cs.add_addend(digit) for cs in cross_sums] cross_sums = [cs for cs in cross_sums if cs.num_addends > 1] self._cross_sums = sorted(cross_sums) def filter(self, total=None, num_addends=None, addends=None): cross_sums = self._cross_sums if total: cross_sums = [cs for cs in cross_sums if cs.has_total(total)] if num_addends: cross_sums = [cs for cs in cross_sums if cs.num_addends == num_addends] if addends: addends = set(addends) cross_sums = [cs for cs in cross_sums if cs.has_addends(addends)] return CrossSums(self._digits, cross_sums) @property def max_sum(self): return self._cross_sums[-1].total def __iter__(self): return self._cross_sums.__iter__() def __len__(self): return len(self._cross_sums) if __name__ == '__main__': doz_sums = CrossSums('0123456789XE') print('Sums totalling 15:') for ds in doz_sums.filter(total='15'): print(' ', ds) print('\nSums containing addends 3-X inclusive:') for ds in doz_sums.filter(addends='3456789X'): print(' ', ds) print('\nSums containing ten addends:') for ds in doz_sums.filter(num_addends=10): print(' ', ds) print('\nSums totaling 1X with five addends including 2 and 3:') for ds in doz_sums.filter(total='1X', num_addends=5, addends='23'): print(' ', ds)

27.781022

77

0.607725

486

3,806

4.49177

0.197531

0.086578

0.030234

0.027485

0.310124

0.245534

0.22721

0.174072

0

0.017903

0.280872

3,806

136

78

27.985294

0.779686

0.023384

0

0.175824

0

0.071332

0.005922

0

1

0.175824

false

0

0.010989

0.065934

0.384615

0.087912

0

null

0

null

0

1

0

6d7355fa775ea3bb8dea2a5a98443123ea1e47bf

1,177

py

Python

functions.py

XomaDev/asteroid-bot

2e0743fc3c51027b54b8f2e9aedf632395fdbc31

[ "Apache-2.0" ]

null

functions.py

XomaDev/asteroid-bot

2e0743fc3c51027b54b8f2e9aedf632395fdbc31

[ "Apache-2.0" ]

2

2021-05-12T05:37:24.000Z

2021-06-02T05:39:21.000Z

functions.py

XomaDev/asteroid-bot

2e0743fc3c51027b54b8f2e9aedf632395fdbc31

[ "Apache-2.0" ]

5

2021-05-12T11:39:09.000Z

2021-10-06T06:49:05.000Z

import base64 import re def encode(text): return base64.b64encode(text.encode("ASCII")).decode() def enhanceText(text): text = text.replace('.', '.', text.count('.')).replace(',', ', ', text.count(',')) text = " ".join(text.split()).replace(" . ", ". ") return text def stylish_text(text): text = text.lower() style_text = list('𝗮𝗯𝗰𝗱𝗲𝗳𝗴𝗵𝗶𝗷𝗸𝗹𝗺𝗻𝗼𝗽𝗾𝗿𝘀𝘁𝘂𝘃𝘄𝘅𝘆𝘇') normal_text = list('abcdefghijklmnopqrstuvwxyz') result = [] for char in list(text): if char in normal_text: result.append(style_text[normal_text.index(char)]) else: result.append(char) return ''.join(result) def checkForURLs(string): regex = r"(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|$([^\s()<>]+|(\([^\s()<>]+$))*\))+(?:$([^\s()<>]+|(\([^\s()<>]+$))*\)|[^\s`!()\[\]{};:'\".,<>?«»“”‘’]))" url = re.findall(regex, string) return [x[0] for x in url] def replace_special_slash(text): characters = '!@#$%^&*()-+?_=,<>/".' + "''" new_string = "" for i in text: if i in characters: new_string += '\\' new_string += i return new_string

25.586957

197

0.514019

135

1,177

4.4

0.407407

0.06734

0.020202

0.010101

0

0.013963

0.209006

1,177

45

198

26.155556

0.621912

0

0.032258

0.2226

0.203908

0.032258

0

1

0.16129

false

0

0.064516

0.032258

0.387097

0

null

0

null

0

1

0

6d768c2ea44a94e626129ce1ad7462b5def358ad

1,697

py

Python

docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py

whoopnip/fin-model-course

e6c5ae313bba601c4aca0f334818b61cc0393118

[ "MIT" ]

5

2020-08-29T15:28:39.000Z

2021-12-01T16:53:25.000Z

docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py

whoopnip/fin-model-course

e6c5ae313bba601c4aca0f334818b61cc0393118

[ "MIT" ]

16

2020-02-26T16:03:47.000Z

2021-06-15T15:17:37.000Z

docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py

whoopnip/fin-model-course

e6c5ae313bba601c4aca0f334818b61cc0393118

[ "MIT" ]

3

2021-01-22T19:38:36.000Z

2021-09-28T08:14:00.000Z

import xlwings as xw import random import pandas as pd @xw.func @xw.arg('num_periods', numbers=int) @xw.ret(expand='table') def n_random_normal(mean, stdev, num_periods, horizontal=False): random_values = [] for i in range(num_periods): num = random.normalvariate(mean, stdev) if not horizontal: num = [num] random_values.append(num) return random_values @xw.func @xw.arg('nper', numbers=int) @xw.ret(expand='horizontal') def n_random_uniform(bot, top, nper): nums = [] for i in range(nper): num = random.uniform(bot, top) nums.append(num) return nums def capm(risk_free, beta, market_ret, epsilon): return risk_free + beta * (market_ret - risk_free) + epsilon def capm_auto_epsilon(risk_free, beta, market_ret, epsilon_stdev): epsilon = random.normalvariate(0, epsilon_stdev) return capm(risk_free, beta, market_ret, epsilon) @xw.func @xw.arg('betas', expand='horizontal') @xw.arg('epsilon_stdevs', expand='horizontal') @xw.arg('market_rets', expand='vertical') @xw.arg('num_assets', numbers=int) @xw.ret(expand='table', index=False) def multi_capm(risk_free, betas, market_rets, epsilon_stdevs, num_assets): df = pd.DataFrame() for i in range(num_assets): beta = betas[i] epsilon_stdev = epsilon_stdevs[i] returns = [capm_auto_epsilon(risk_free, beta, market_ret, epsilon_stdev) for market_ret in market_rets] df[f'Asset {i + 1}'] = returns return df @xw.func @xw.arg('data', pd.DataFrame, expand='table', index=False) @xw.ret(expand='table') def correlations(data): return data.corr()

27.819672

112

0.659988

239

1,697

4.514644

0.259414

0.032437

0.055607

0.083411

0.281742

0.196478

0.148285

0.088971

0

0.001493

0.210371

1,697

60

113

28.283333

0.803731

0

0.12766

0

0.079462

0

1

0.12766

false

0

0.06383

0.042553

0.319149

0

null

0

null

0

1

0

6d7f27d4b21a33d924da32d2c0841520bdc52d0d

4,635

py

Python

shapey/utils/customdataset.py

njw0709/ShapeY

f2272f799fe779c3e4b3d0d06e88ecde9e4b039c

[ "MIT" ]

1

2022-03-22T17:19:57.000Z

shapey/utils/customdataset.py

njw0709/ShapeY

f2272f799fe779c3e4b3d0d06e88ecde9e4b039c

[ "MIT" ]

null

shapey/utils/customdataset.py

njw0709/ShapeY

f2272f799fe779c3e4b3d0d06e88ecde9e4b039c

[ "MIT" ]

null

import torchvision.datasets as datasets from torch.utils.data import Dataset from itertools import combinations import math import psutil class CombinationDataset(Dataset): def __init__(self, dataset): self.dataset = dataset self.comb = list(combinations(dataset, 2)) def __getitem__(self, index): img1, img2 = self.comb[index] return img1, img2 def __len__(self): return len(self.comb) def cut_dataset(self, index): self.comb = self.comb[index:] class ImageFolderWithPaths(datasets.ImageFolder): """Custom dataset that includes image file paths. Extends torchvision.datasets.ImageFolder """ # override the __getitem__ method. this is the method that dataloader calls def __getitem__(self, index): # this is what ImageFolder normally returns original_tuple = super(ImageFolderWithPaths, self).__getitem__(index) # the image file path path = self.imgs[index][0] # make a new tuple that includes original and the path tuple_with_path = original_tuple + (path,) return tuple_with_path class FeatureTensorDatasetWithImgName(Dataset): def __init__(self, feature_tensor, img_name_array): self.feature_tensor = feature_tensor self.imgnames = img_name_array def __getitem__(self, index): feat = self.feature_tensor[index, :] imgname = self.imgnames[index] return imgname, feat def __len__(self): return len(self.imgnames) class PermutationIndexDataset(Dataset): def __init__(self, datalen): self.datalen = datalen def __getitem__(self, index): idx1 = int(math.floor(index / self.datalen)) idx2 = index % self.datalen return idx1, idx2 class OriginalandPostProcessedPairsDataset(Dataset): def __init__(self, original_feat_dataset, postprocessed_feat_dataset): self.original = original_feat_dataset self.postprocessed = postprocessed_feat_dataset self.datalen = len(self.postprocessed) def __getitem__(self, index): idx1 = int(math.floor(index / self.datalen)) idx2 = index % self.datalen s1 = self.original[idx1] s2 = self.postprocessed[idx2] return (idx1, s1), (idx2, s2) def __len__(self): return len(self.original) ** 2 class PermutationPairsDataset(Dataset): def __init__(self, original_feat_dataset, postprocessed=None): self.original = original_feat_dataset self.datalen = len(self.original) self.postprocessed = postprocessed def __getitem__(self, index): idx1 = int(math.floor(index / self.datalen)) idx2 = index % self.datalen s1 = self.original[idx1] if self.postprocessed is not None: s2 = self.postprocessed[idx2] else: s2 = self.original[idx2] return (idx1, s1), (idx2, s2) def __len__(self): return len(self.original) ** 2 class HDFDataset(Dataset): def __init__(self, hdfstore, mem_usage=0.85): self.hdfstore = hdfstore self.datalen = len(self.hdfstore) self.pull_data_to_cache(mem_usage) if not self.all_in_cache: print("initializing placeholder cache list") self.cache_length = int( psutil.virtual_memory().available * 0.85 / self.hdfstore[0].nbytes ) self.in_cache_idx = [None] * self.cache_length self.in_cache = [None] * self.cache_length self.cache_counter = 0 def __getitem__(self, index): if not self.all_in_cache: if index in self.in_cache_idx: return self.in_cache[self.in_cache_idx.index(index)] else: self.in_cache_idx[self.cache_counter] = index data = self.hdfstore[index] self.in_cache[self.cache_counter] = data self.cache_counter += 1 self.cache_counter %= self.cache_length return data return self.hdfstore[index] def __len__(self): return self.datalen def pull_data_to_cache(self, mem_usage): single_row = self.hdfstore[0] if ( psutil.virtual_memory().available * mem_usage < single_row.nbytes * self.datalen ): print("Not enough memory to pull data to cache") self.all_in_cache = False else: print("Pulling data to cache") self.hdfstore = self.hdfstore[:] self.all_in_cache = True print("Done pulling data to cache")

32.1875

82

0.63754

541

4,635

5.186691

0.203327

0.050962

0.034925

0.047398

0.275837

0.245902

0.175695

0.140057

0

0.013103

0.275512

4,635

143

83

32.412587

0.822513

0.059763

0

0.305556

0

0.02788

0

1

0.185185

false

0

0.046296

0.425926

0.037037

0

null

0

null

0

1

0

6d82b1b31f8d0b6b84847768e733ad87e9b8137d

19,761

py

Python

ehlit/writer/dump.py

lefta/reflex-prototype

9d9a34e222d9782815da529a8e2daa575c7c3eba

[ "MIT" ]

1

2019-03-29T14:06:00.000Z

ehlit/writer/dump.py

lefta/ehlit-prototype

9d9a34e222d9782815da529a8e2daa575c7c3eba

[ "MIT" ]

null

ehlit/writer/dump.py

lefta/ehlit-prototype

9d9a34e222d9782815da529a8e2daa575c7c3eba

[ "MIT" ]

null

# Copyright © 2017-2019 Cedric Legrand # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice (including the next # paragraph) shall be included in all copies or substantial portions of the # Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import logging from typing import Callable, cast, List, Sequence, Union from ehlit.parser.c_header import CDefine, CMacroFunction, CAnyType from ehlit.parser.ast import ( Alias, AnonymousArray, Array, ArrayAccess, Assignment, AST, BoolValue, Cast, Char, ClassMethod, ClassProperty, CompoundIdentifier, Condition, ControlStructure, DecimalNumber, Declaration, Dtor, EhClass, EhEnum, EhUnion, EnumField, Expression, ForDoLoop, FunctionCall, Function, FunctionType, HeapAlloc, HeapDealloc, Identifier, Include, Import, InitializationList, Namespace, Node, NullValue, Number, Operator, PrefixOperatorValue, ReferenceToType, ReferenceToValue, Return, Sizeof, Statement, String, Struct, SuffixOperatorValue, SwitchCase, SwitchCaseBody, SwitchCaseTest, Symbol, TemplatedIdentifier, VariableAssignment, VariableDeclaration ) IndentedFnType = Callable[['DumpWriter', Union[Node, str]], None] def indent(fn: IndentedFnType) -> Callable[..., None]: def fn_wrapper(cls: 'DumpWriter', node: Union[Node, str], is_next: bool = True) -> None: cls.increment_prefix(is_next) fn(cls, node) cls.decrement_prefix() return fn_wrapper class DumpWriter: def __init__(self, ast: AST) -> None: self.prefix: str = '' logging.debug('') logging.debug('--- AST ---') i: int = 0 count: int = len(ast) self.prev_have_next: bool = count > 1 self.upd_prefix: bool = False while i < count: self.print_node(ast[i], i < count - 1) i += 1 def dump(self, string: str) -> None: logging.debug('%s%s', self.prefix, string) def decrement_prefix(self) -> None: self.prefix = self.prefix[:-3] self.upd_prefix = False def increment_prefix(self, is_next: bool) -> None: if self.upd_prefix: self.prefix = self.prefix[:-3] if self.prev_have_next: self.prefix += '\u2502 ' else: self.prefix += ' ' self.upd_prefix = False self.prev_have_next = is_next if is_next: self.prefix += '\u251c' + '\u2500 ' else: self.prefix += '\u2514' + '\u2500 ' self.upd_prefix = True def print_node(self, node: Node, is_next: bool = True) -> None: func = getattr(self, 'dump' + type(node).__name__) func(node, is_next) def print_node_list(self, string: str, lst: Sequence[Node], is_next: bool = True) -> None: self.increment_prefix(is_next) self.dump(string) i: int = 0 cnt: int = len(lst) while i < cnt: self.print_node(lst[i], i < cnt - 1) i += 1 self.decrement_prefix() @indent def print_str(self, s: Union[Node, str]) -> None: s = cast(str, s) self.dump(s) @indent def dumpInclude(self, inc: Union[Node, str]) -> None: inc = cast(Include, inc) self.dump('Include') self.print_str('Path: {}'.format(inc.lib)) self.print_node_list('Symbols found', inc.syms, False) @indent def dumpImport(self, node: Union[Node, str]) -> None: node = cast(Import, node) self.dump('Import') self.print_str('Path: {}'.format(node.lib)) self.print_node_list('Symbols found', node.syms, False) def dump_declaration(self, decl: Union[Node, str], is_next: bool = True) -> None: decl = cast(Declaration, decl) self.print_node(decl.typ_src, decl.sym is not None or is_next) if decl.sym is not None: self.print_node(decl.sym, is_next) def dump_variable_declaration(self, cls_name: str, decl: VariableDeclaration) -> None: self.dump(cls_name) if decl.private: self.print_str('Modifiers: private') if decl.static: self.print_str('Modifiers: static') if decl.assign is not None: self.dump_declaration(decl) self.print_node(decl.assign, False) else: self.dump_declaration(decl, False) @indent def dumpVariableDeclaration(self, decl: Union[Node, str]) -> None: decl = cast(VariableDeclaration, decl) self.dump_variable_declaration('VariableDeclaration', decl) def dump_function(self, cls_name: str, fun: Function) -> None: self.dump(cls_name) if fun.body_str is None: self.print_str('Declaration') if fun.sym is not None: self.print_node(fun.sym) self.dump_qualifiers(fun) self.print_node(fun.typ, fun.body_str is not None) if fun.body_str is not None: self.print_node_list('Body', fun.body, False) @indent def dumpFunction(self, fun: Union[Node, str]) -> None: fun = cast(Function, fun) self.dump_function('Function', fun) @indent def dumpStatement(self, stmt: Union[Node, str]) -> None: stmt = cast(Statement, stmt) self.dump('Statement') self.print_node(stmt.expr, False) def dumpExpression(self, expr: Union[Node, str], is_next: bool) -> None: expr = cast(Expression, expr) self.print_node_list('Expression', expr.contents, is_next) def dumpInitializationList(self, node: Union[Node, str], is_next: bool) -> None: node = cast(InitializationList, node) self.print_node_list('InitializerList', node.contents, is_next) @indent def dumpCast(self, node: Union[Node, str]) -> None: node = cast(Cast, node) self.dump('Cast') self.print_node(node.types[0]) self.print_node(node.arg, False) @indent def dumpFunctionCall(self, call: Union[Node, str]) -> None: call = cast(FunctionCall, call) self.dump('FunctionCall') self.print_node(call.sym) if call.cast: self.increment_prefix(True) self.dump('Automatic cast') self.print_node(call.cast, False) self.decrement_prefix() self.print_node_list('Arguments', call.args, False) @indent def dumpArrayAccess(self, arr: Union[Node, str]) -> None: arr = cast(ArrayAccess, arr) self.dump('ArrayAccess') self.print_node(arr.idx) self.print_node(arr.child, False) @indent def dumpVariableAssignment(self, assign: Union[Node, str]) -> None: assign = cast(VariableAssignment, assign) self.dump('VariableAssignment') self.print_node(assign.var) self.print_node(assign.assign, False) @indent def dumpAssignment(self, assign: Union[Node, str]) -> None: assign = cast(Assignment, assign) self.dump('Assignment') if assign.operator is not None: self.print_node(assign.operator) self.print_node(assign.expr, False) @indent def dumpControlStructure(self, struct: Union[Node, str]) -> None: struct = cast(ControlStructure, struct) self.dump('ControlStructure: ' + struct.name) if struct.cond is not None: self.print_node(struct.cond) self.print_node_list("ControlStructureBody", struct.body, False) def dumpDoWhileLoop(self, node: Union[Node, str], is_next: bool) -> None: self.dumpControlStructure(node, is_next) @indent def dumpForDoLoop(self, node: Union[Node, str]) -> None: node = cast(ForDoLoop, node) self.dump('ControlStructure: ' + node.name) self.print_node(node.cond) self.print_node_list("Initializers", node.initializers) self.print_node_list("Actions", node.actions) self.print_node_list("ControlStructureBody", node.body, False) def dumpCondition(self, cond: Union[Node, str], is_next: bool) -> None: cond = cast(Condition, cond) self.print_node_list("ConditionBranches", cond.branches, is_next) @indent def dumpSwitchCase(self, node: Union[Node, str]) -> None: node = cast(SwitchCase, node) self.dump('Case') self.print_node_list('Tests', node.cases) self.print_node(node.body, False) def dumpSwitchCaseTest(self, node: Union[Node, str], is_next: bool) -> None: node = cast(SwitchCaseTest, node) if node.test is not None: self.print_node(node.test, is_next) else: self.print_str('default', is_next) def dumpSwitchCaseBody(self, node: Union[Node, str], _: bool) -> None: node = cast(SwitchCaseBody, node) self.print_str('Falls through: ' + ('yes' if node.fallthrough else 'no')) self.print_node_list('Body', node.body, False) @indent def dumpReturn(self, ret: Union[Node, str]) -> None: ret = cast(Return, ret) self.dump('Return') if ret.expr is not None: self.print_node(ret.expr, False) def dump_qualifiers(self, node: Union[Symbol, Function]) -> None: qualifiers: List[str] = [] if node.qualifiers.is_const: qualifiers.append('const') if node.qualifiers.is_volatile: qualifiers.append('volatile') if node.qualifiers.is_restricted: qualifiers.append('restrict') if node.qualifiers.is_inline: qualifiers.append('inline') if node.qualifiers.is_private: qualifiers.append('private') if len(qualifiers) != 0: self.print_str('Modifiers: {}'.format(', '.join(qualifiers))) @indent def dumpReferenceToType(self, ref: Union[Node, str]) -> None: ref = cast(ReferenceToType, ref) self.dump('Reference') self.dump_qualifiers(ref) self.print_node(ref.child, False) @indent def dumpReferenceToValue(self, ref: Union[Node, str]) -> None: ref = cast(ReferenceToValue, ref) self.dump('Reference') self.print_node(ref.child, False) @indent def dumpOperator(self, op: Union[Node, str]) -> None: op = cast(Operator, op) self.dump('Operator: ' + op.op) @indent def dumpArray(self, arr: Union[Node, str]) -> None: arr = cast(Array, arr) self.dump('Array') if arr.length is not None: self.print_str('Sub-type:') self.increment_prefix(True) self.print_node(arr.child, False) if arr.length is not None: self.decrement_prefix() self.print_str('Length:', False) self.increment_prefix(False) self.print_node(arr.length, False) self.decrement_prefix() @indent def dumpFunctionType(self, node: Union[Node, str]) -> None: node = cast(FunctionType, node) self.dump('FunctionType') self.print_node(node.ret, len(node.args) != 0 or node.is_variadic) if len(node.args) != 0: self.print_node_list('Arguments:', node.args, node.is_variadic) if node.is_variadic: if node.variadic_type is None: self.print_str('Variadic (C)', False) else: self.print_str('Variadic:', False) self.increment_prefix(False) self.print_node(node.variadic_type, False) self.decrement_prefix() def dumpCompoundIdentifier(self, node: Union[Node, str], is_next: bool) -> None: node = cast(CompoundIdentifier, node) self.increment_prefix(is_next) self.dump('CompoundIdentifier') self.dump_qualifiers(node) i = 0 while i < len(node.elems): self.print_node(node.elems[i], i < len(node.elems) - 1) i += 1 self.decrement_prefix() @indent def dumpIdentifier(self, node: Union[Node, str]) -> None: node = cast(Identifier, node) self.dump('Identifier: ' + node.name) @indent def dumpTemplatedIdentifier(self, node: Union[Node, str]) -> None: node = cast(TemplatedIdentifier, node) self.dump('TemplatedIdentifier: ' + node.name) self.print_node_list('Types', node.types, False) @indent def dumpHeapAlloc(self, node: Union[Node, str]) -> None: node = cast(HeapAlloc, node) self.dump('HeapAlloc') self.print_node(node.sym) self.print_node_list('Arguments', node.args, False) @indent def dumpHeapDealloc(self, node: Union[Node, str]) -> None: node = cast(HeapDealloc, node) self.dump('HeapDealloc') self.print_node(node.sym) @indent def dumpNumber(self, num: Union[Node, str]) -> None: num = cast(Number, num) self.dump('Number: ' + num.num) @indent def dumpDecimalNumber(self, node: Union[Node, str]) -> None: node = cast(DecimalNumber, node) self.dump('DecimalNumber: ' + node.num) @indent def dumpChar(self, char: Union[Node, str]) -> None: char = cast(Char, char) self.dump('Character: ' + char.char) @indent def dumpString(self, string: Union[Node, str]) -> None: string = cast(String, string) self.dump('String: ' + string.string) @indent def dumpNullValue(self, stmt: Union[Node, str]) -> None: stmt = cast(NullValue, stmt) self.dump('NullValue') @indent def dumpBoolValue(self, node: Union[Node, str]) -> None: node = cast(BoolValue, node) self.dump('BoolValue: ' + 'true' if node.val is True else 'false') @indent def dumpPrefixOperatorValue(self, val: Union[Node, str]) -> None: val = cast(PrefixOperatorValue, val) self.dump('PrefixOperatorValue') self.print_str('Operator: %s' % val.op) self.print_node(val.val, False) @indent def dumpSuffixOperatorValue(self, val: Union[Node, str]) -> None: val = cast(SuffixOperatorValue, val) self.dump('SuffixOperatorValue') self.print_str('Operator: %s' % val.op) self.print_node(val.val, False) @indent def dumpAnonymousArray(self, node: Union[Node, str]) -> None: node = cast(AnonymousArray, node) self.dump('AnonymousArray') self.print_node_list('Contents:', node.contents, False) @indent def dumpSizeof(self, node: Union[Node, str]) -> None: node = cast(Sizeof, node) self.dump('Sizeof') self.print_node(node.sz_typ, False) @indent def dumpAlias(self, node: Union[Node, str]) -> None: node = cast(Alias, node) self.dump('Alias') self.print_str('From:') self.increment_prefix(True) self.print_node(node.src_sym, False) self.decrement_prefix() self.print_str('To:', False) self.increment_prefix(False) self.print_node(node.dst, False) self.decrement_prefix() @indent def dumpStruct(self, node: Union[Node, str]) -> None: node = cast(Struct, node) self.dump('Struct') self.print_node(node.sym) if node.fields is None: self.print_str('Forward declaration', False) else: self.print_node_list('Fields', node.fields, False) @indent def dumpEhUnion(self, node: Union[Node, str]) -> None: node = cast(EhUnion, node) self.dump('Union') self.print_node(node.sym) if node.fields is None: self.print_str('Forward declaration', False) else: self.print_node_list('Fields', node.fields, False) @indent def dumpClassMethod(self, node: Union[Node, str]) -> None: node = cast(ClassMethod, node) self.dump_function('ClassMethod', node) @indent def dumpCtor(self, node: Union[Node, str]) -> None: node = cast(ClassMethod, node) self.dump('Constructor') self.dump_qualifiers(node) assert isinstance(node.typ, FunctionType) if len(node.typ.args) != 0: self.print_node_list('Arguments:', node.typ.args) if node.typ.is_variadic: self.print_str('Variadic:') self.increment_prefix(False) assert node.typ.variadic_type is not None self.print_node(node.typ.variadic_type, False) self.decrement_prefix() self.print_node_list('Body', node.body, False) @indent def dumpDtor(self, node: Union[Node, str]) -> None: node = cast(Dtor, node) self.dump('Destructor') self.dump_qualifiers(node) self.print_node_list('Body', node.body, False) @indent def dumpClassProperty(self, node: Union[Node, str]) -> None: node = cast(ClassProperty, node) self.dump_variable_declaration('ClassProperty', node) @indent def dumpEhClass(self, node: Union[Node, str]) -> None: node = cast(EhClass, node) self.dump('Class') self.print_node(node.sym) if node.contents is None: self.print_str('Forward declaration', False) else: self.print_node_list('Properties', node.properties) self.print_node_list('Methods', node.methods, len(node.ctors) != 0) for ctor in node.ctors: self.print_node(ctor, ctor != node.ctors[-1] or node.dtor is not None) if node.dtor is not None: self.print_node(node.dtor, False) @indent def dumpEhEnum(self, node: Union[Node, str]) -> None: node = cast(EhEnum, node) self.dump('Enum') self.print_node(node.sym) if node.fields is None: self.print_str('Forward declaration', False) else: self.print_node_list('Fields', node.fields, False) @indent def dumpEnumField(self, node: Union[Node, str]) -> None: node = cast(EnumField, node) self.dump(node.name) @indent def dumpNamespace(self, node: Union[Node, str]) -> None: node = cast(Namespace, node) self.dump('Namespace') self.print_node(node.sym) self.print_node_list('Contents', node.contents, False) @indent def dumpCDefine(self, node: Union[Node, str]) -> None: node = cast(CDefine, node) self.dump('C define') if node.sym is not None: self.print_node(node.sym, False) @indent def dumpCMacroFunction(self, node: Union[Node, str]) -> None: node = cast(CMacroFunction, node) self.dump('C function macro') if node.sym is not None: self.print_node(node.sym) assert isinstance(node.typ, FunctionType) self.print_str('Arg count: {}'.format(len(node.typ.args)), False) @indent def dumpCAnyType(self, node: Union[Node, str]) -> None: node = cast(CAnyType, node) self.dump('No type')

36.730483

100

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19,761

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0.002242

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0.219731

0

null

0

null

0

1

0

6d833ac6a830a6bbcae3005bb5acb8b96a7801b5

1,859

py

Python

tutorials/04_Tutorial_Boolean.py

lmidolo/samplemaker

8211af0e4cea60aea8f5720d5ff0ee532c442123

[ "BSD-3-Clause" ]

null

tutorials/04_Tutorial_Boolean.py

lmidolo/samplemaker

8211af0e4cea60aea8f5720d5ff0ee532c442123

[ "BSD-3-Clause" ]

null

tutorials/04_Tutorial_Boolean.py

lmidolo/samplemaker

8211af0e4cea60aea8f5720d5ff0ee532c442123

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ 04_Tutorial_Boolean """ # In this tutorial we learn how to do boolean operations between groups of # polygons # Let's import basic stuff import samplemaker.layout as smlay # used for layout import samplemaker.makers as sm # used for drawing # Create a simple mask layout themask = smlay.Mask("04_Tutorial_Boolean") # Empty geometry geomE = sm.GeomGroup() # Let's make a large box box0 = sm.make_rect(0,0,100,100,layer=1) # And some text, because text is complex polygons! text0 = sm.make_text(0, 0, "DIFF", 10, 2,angle=30,to_poly=True,layer=1) # Let's take the boolean difference box-text bdiff = box0.copy() # Note that boolean operations alter the original element so we need to make a copy first bdiff.boolean_difference(text0, 1, 1) # The first integer is the layer from which you should subtract and the second is the subtracted layer # Now bdiff is box-text geomE+=bdiff # Now let's try intersection (AND operation) # Let's use two overlapping texts, slighlty larger text1 = sm.make_text(0,0,"DIFF",11,3,angle=30,to_poly=True,layer=1) text1.boolean_intersection(text0, 1, 1) text1.translate(100, 0) geomE+=text1 # XOR is also quite useful, only keeps parts that are not in both text2 = sm.make_text(50,0,"XOR",10,1,angle=0,to_poly=True,layer=1) text2.boolean_xor(box0, 1, 1) text2.translate(200, 0) geomE+=text2 # Trapezoid slicing, useful for some e-beam export trapz = text2.copy() trapz.trapezoids(1) trapz.translate(150, 0) geomE+=trapz # Union, we could re-unite all trapezoids in the previous uni1 = trapz.copy() uni1.boolean_union(1) uni1.translate(150, 0) geomE+=uni1 # Just for fun, outlining the last result out1 = uni1.copy() out1.poly_outlining(1, 1) out1.translate(150, 0) geomE+=out1 # Let's add all to main cell themask.addToMainCell(geomE) # Export to GDS themask.exportGDS() # Finished!

26.557143

109

0.743948

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4.285266

0.438871

0.017557

0.021946

0.032919

0.068764

0.057059

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0

0.057935

0.145777

1,859

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26.557143

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0.064516

0

null

0

null

0

1

0

6d8a66fbf9d684f0b5b7c285749ed54196898dec

1,211

py

Python

day6.py

aslttml/30days-of-code

be6c894f8df4913413b7e6d9a6b0585e5884d35d

[ "MIT" ]

null

day6.py

aslttml/30days-of-code

be6c894f8df4913413b7e6d9a6b0585e5884d35d

[ "MIT" ]

null

day6.py

aslttml/30days-of-code

be6c894f8df4913413b7e6d9a6b0585e5884d35d

[ "MIT" ]

null

#!/bin/python3 import math import os import random import re import sys import string if __name__ == '__main__': try: t = int(input().strip()) except: print('Invalid input.') if t>=1 and t<=10: for a0 in range(t): s = input().strip() index = 0 if len(s)>=2 and len(s)<=10000: while index<len(s): #Loop should quit when it reaches the last character, which has an index of (length-1) if index<2: odd = s[index] even = s[index + 1] elif index>=2: odd = odd + s[index] #If string length is an odd number loop should stop at the even index #Trying to add another character will give an IndexError if index<len(s)-1: even = even + s[index + 1] index = index + 2 print(odd + ' ' + even) else: print('Constraint error. String is either too long or too short.') a0 = a0 + 1 else: print('Constraint error.')

31.051282

102

0.456647

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1,211

3.758621

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0.029358

0.033028

0.040367

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0.45417

1,211

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0.193548

0.129032

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null

0

null

0

1

0

6d8cd75b30233cb95ea2e1005dd56109d735bde6

2,377

py

Python

FindAndReplaceByProjectWithExclusions.py

Zlatov/FindAndReplaceByProjectWithExclusions

f1209696d960bd1471420ed18f4e71e03b3df1b5

[ "MIT" ]

null

FindAndReplaceByProjectWithExclusions.py

Zlatov/FindAndReplaceByProjectWithExclusions

f1209696d960bd1471420ed18f4e71e03b3df1b5

[ "MIT" ]

null

FindAndReplaceByProjectWithExclusions.py

Zlatov/FindAndReplaceByProjectWithExclusions

f1209696d960bd1471420ed18f4e71e03b3df1b5

[ "MIT" ]

null

import sublime, sublime_plugin import os import json class FindAndReplaceByProjectWithExclusions(sublime_plugin.TextCommand): print('reloading FindAndReplaceByProjectWithExclusions') def run(self, edit, from_current_file_path=None): # Текущее окно сублайма window = self.view.window() # В окне - проект, берём его настройки dict_project = window.project_data() # Определим exclusions - безопасным извлечением интересуемой настройки из многоуровнего словаря # через get. exclusions_list = dict_project.get('settings', {}).get("find_and_replace_by_project_with_exclusions") exclusions = None if exclusions_list is not None: exclusions = ', '.join('-' + exclusion for exclusion in exclusions_list) # Определим project_path - первый путь из прикреплённых папок в файле # проекта. sublime_project_file_path = window.project_file_name() is_project = sublime_project_file_path is not None project_path = None if is_project and dict_project is not None and 'folders' in dict_project and dict_project['folders'][0] is not None: relative_first_folder_path = dict_project['folders'][0]['path'] if relative_first_folder_path == '.' or relative_first_folder_path == './': relative_first_folder_path = '' project_path = os.path.join(os.path.dirname(sublime_project_file_path), relative_first_folder_path) # Определим dir_path - путь к директории текущего открытого файла (если # таковой открыт). dir_path = None file_path = self.view.file_name() if file_path is not None: dir_path = os.path.dirname(file_path) # Бизнес логика # Определение пути поиска по исходным данным search_path = "" if from_current_file_path == True and dir_path is not None: search_path = dir_path elif is_project and project_path is not None: search_path = project_path elif is_project: search_path = "<project>" # Дополнение пути поиска исключенем where_string = search_path if exclusions is not None: where_string = search_path + ", " + exclusions # Аргументы для открытия панели panel_args = { "panel": "find_in_files", "regex": False, "where": where_string } # Показываем панель с настройками в panel_args self.view.window().run_command( "show_panel", panel_args )

34.449275

120

0.715187

304

2,377

5.315789

0.361842

0.039604

0.044554

0.071163

0.082921

0.028465

0

0.00106

0.206563

2,377

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121

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0.855779

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0

1

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false

0

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0.116279

0.023256

0

null

0

null

0

1

0

6d8df2d3b1dcbda991e01aea09990e08fc942cf3

1,566

py

Python

schnell-nautilus.py

umangrajpara99/OpenInSchnell

5d48be8741f130471c892f1e77f19b9dad70a882

[ "MIT" ]

null

schnell-nautilus.py

umangrajpara99/OpenInSchnell

5d48be8741f130471c892f1e77f19b9dad70a882

[ "MIT" ]

null

schnell-nautilus.py

umangrajpara99/OpenInSchnell

5d48be8741f130471c892f1e77f19b9dad70a882

[ "MIT" ]

null

# Schnell Nautilus Extension # # Place me in ~/.local/share/nautilus-python/extensions/, # ensure you have python-nautilus package, restrart Nautilus, and enjoy :) from gi import require_version require_version('Gtk', '3.0') require_version('Nautilus', '3.0') from gi.repository import Nautilus, GObject from subprocess import call import os # path to schnell schnell = 'schnell' # what name do you want to see in the context menu? schnellname = 'Schnell' # always create new window? NEWWINDOW = False class SchnellExtension(GObject.GObject, Nautilus.MenuProvider): def schnellname(self, menu, files): safepaths = '' for file in files: filepath = file.get_location().get_path() safepaths += '"' + filepath + '" ' # If one of the files we are trying to open is a folder # create a new instance of schnell call(schnell + ' ' + safepaths + '&', shell=True) def get_file_items(self, window, files): item = Nautilus.MenuItem( name='SchnellOpen', label='Open In ' + schnellname, tip='Opens the selected files with Schnell' ) item.connect('activate', self.schnellname, files) return [item] def get_background_items(self, window, file_): item = Nautilus.MenuItem( name='SchnellOpenBackground', label='Open in ' + schnellname, tip='Opens Schnell in the current directory' ) item.connect('activate', self.schnellname, [file_]) return [item]

27.473684

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0.483516

0.042553

0.030395

0.048632

0.129686

0.06079

0

0.00346

0.261814

1,566

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0.850346

0.213282

0

0.125

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0.017185

0

1

0.09375

false

0

0.125

0

0.3125

0

null

0

null

0

1

0

6d9062da7cb5be608d72b13c37aef7c0131a8035

1,891

py

Python

Cogs/StaticMethods.py

pajratbej/hetman

1da634cdb94221bb81ceb0c29467cccce640bbb6

[ "MIT" ]

2

2019-12-19T17:11:29.000Z

2020-02-22T17:55:13.000Z

Cogs/StaticMethods.py

pajratbej/hetman

1da634cdb94221bb81ceb0c29467cccce640bbb6

[ "MIT" ]

5

2019-12-08T21:42:12.000Z

2022-03-11T23:58:29.000Z

Cogs/StaticMethods.py

pajratbej/hetman

1da634cdb94221bb81ceb0c29467cccce640bbb6

[ "MIT" ]

null

from pymongo import MongoClient import random as r import os client = MongoClient(os.environ["MONGO_LAB"]) db = client.get_database("hetmanbot") collection = db['data_base'] class StaticMethods(): @staticmethod def push_record(name, txt, number): records = collection.find_one({"document_id": number}) for i in records[name]: if txt[10:] == str(list(i.values()))[2:-2]: return "Ten cytat już istnieje" else: size = len(records[name]) print(records[name]) collection.update({"document_id": number}, {'$push': {name: {str(size): txt[10:]}}}) print("Dodano nowy cytat") return "Dodano nowy cytat" @staticmethod def get_random_record(name, number): records = collection.find_one({"document_id": number}) for i in records[name]: str(list(i.values()))[2:-2] return str(list(r.choice(records[name]).values())) @staticmethod def get_specific_record(name, number, r_number): records = collection.find_one({"document_id": number}) return str(list(records[name][r_number].values())) @staticmethod def number_of_quotes(name, number): records = collection.find_one({"document_id": number}) size = len(records[name]) return size @staticmethod def replace(string): collection.update({"document_id": 3}, {'$set' : {"plan": string}}) @staticmethod def getPlan(): record = collection.find_one({"document_id": 3}) return record["plan"] @staticmethod def getGame(): record = collection.find_one({"document_id":3}) return record["game"] @staticmethod def setGame(string): collection.update({"document_id": 3},{'$set': {"game": string}})

31.516667

100

0.589635

214

1,891

5.084112

0.313084

0.082721

0.09375

0.137868

0.398897

0.292279

0.25

0.115809

0

0.008677

0.268641

1,891

59

101

32.050847

0.778019

0

0.375

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0.111581

0

1

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false

0

0.0625

0

0.395833

0.041667

0

null

0

null

0

1

0

6d9276de4eb719b6800f06060463d545ce0e50b7

702

py

Python

article/admin.py

SeddonShen/TimePill

8b2c4dc2c129f440d67e1dba1ab16591057b65f7

[ "Apache-2.0" ]

4

2021-12-26T04:39:06.000Z

2021-12-29T16:57:36.000Z

article/admin.py

SeddonShen/TimePill

8b2c4dc2c129f440d67e1dba1ab16591057b65f7

[ "Apache-2.0" ]

null

article/admin.py

SeddonShen/TimePill

8b2c4dc2c129f440d67e1dba1ab16591057b65f7

[ "Apache-2.0" ]

null

from django.contrib import admin # Register your models here. from . import models from .models import Article, Comment class ArticleAdmin(admin.ModelAdmin): fieldsets = [ (None, {'fields': ['id']}), (None, {'fields': ['title', 'content', 'status']}) ] list_display = ( ['expire_time', 'diary_type', 'square_open', 'add_date', 'mod_date', 'id', 'title', 'content', 'status', 'author_id']) # title = title, # content = content, # square_open = square_open, # expire_time = expire_time, # status = status, # author_id_id = user_id, # diary_type = diary_type, # admin.site.register(Article, ArticleAdmin) admin.site.register(Comment) admin.site.register(Article)

25.071429

108

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0.07947

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702

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0.778351

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false

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0.461538

0

null

0

null

0

1

0

6d961a7fca3206cd16ef0e5d9d5a6b6cd7a06634

32,545

py

Python

neurokit2/ecg/ecg_findpeaks.py

vansjyo/NeuroKit

238cd3d89467f7922c68a3a4c1f44806a8466922

[ "MIT" ]

null

neurokit2/ecg/ecg_findpeaks.py

vansjyo/NeuroKit

238cd3d89467f7922c68a3a4c1f44806a8466922

[ "MIT" ]

null

neurokit2/ecg/ecg_findpeaks.py

vansjyo/NeuroKit

238cd3d89467f7922c68a3a4c1f44806a8466922

[ "MIT" ]

null

# - * - coding: utf-8 - * - import numpy as np import pandas as pd import matplotlib.pyplot as plt import scipy.signal from ..signal import signal_smooth from ..signal import signal_zerocrossings def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False): """Find R-peaks in an ECG signal. Low-level function used by `ecg_peaks()` to identify R-peaks in an ECG signal using a different set of algorithms. See `ecg_peaks()` for details. Parameters ---------- ecg_cleaned : list, array or Series The cleaned ECG channel as returned by `ecg_clean()`. sampling_rate : int The sampling frequency of `ecg_signal` (in Hz, i.e., samples/second). Defaults to 1000. method : string The algorithm to be used for R-peak detection. Can be one of 'neurokit' (default), 'pamtompkins1985', 'hamilton2002', 'christov2004', 'gamboa2008', 'elgendi2010', 'engzeemod2012', 'kalidas2017', 'martinez2003' or 'rodrigues2020'. show : bool If True, will return a plot to visualizing the thresholds used in the algorithm. Useful for debugging. Returns ------- info : dict A dictionary containing additional information, in this case the samples at which R-peaks occur, accessible with the key "ECG_R_Peaks". See Also -------- ecg_clean, signal_fixpeaks, ecg_peaks, ecg_rate, ecg_process, ecg_plot Examples -------- >>> import neurokit2 as nk >>> >>> ecg = nk.ecg_simulate(duration=10, sampling_rate=1000) >>> cleaned = nk.ecg_clean(ecg, sampling_rate=1000) >>> info = nk.ecg_findpeaks(cleaned) >>> nk.events_plot(info["ECG_R_Peaks"], cleaned) >>> >>> # Different methods >>> neurokit = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="neurokit"), method="neurokit") >>> pantompkins1985 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="pantompkins1985"), method="pantompkins1985") >>> hamilton2002 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="hamilton2002"), method="hamilton2002") >>> christov2004 = nk.ecg_findpeaks(cleaned, method="christov2004") >>> gamboa2008 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="gamboa2008"), method="gamboa2008") >>> elgendi2010 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="elgendi2010"), method="elgendi2010") >>> engzeemod2012 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="engzeemod2012"), method="engzeemod2012") >>> kalidas2017 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="kalidas2017"), method="kalidas2017") >>> martinez2003 = nk.ecg_findpeaks(cleaned, method="martinez2003") >>> >>> # Visualize >>> nk.events_plot([neurokit["ECG_R_Peaks"], pantompkins1985["ECG_R_Peaks"], hamilton2002["ECG_R_Peaks"], christov2004["ECG_R_Peaks"], gamboa2008["ECG_R_Peaks"], elgendi2010["ECG_R_Peaks"], engzeemod2012["ECG_R_Peaks"], kalidas2017["ECG_R_Peaks"]], martinez2003["ECG_R_Peaks"]], cleaned) References -------------- - Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology. PhD ThesisUniversidade. - W. Zong, T. Heldt, G.B. Moody, and R.G. Mark. An open-source algorithm to detect onset of arterial blood pressure pulses. In Computers in Cardiology, 2003, pages 259–262, 2003. - Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002. - Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm. In: IEEE Transactions on Biomedical Engineering BME-32.3 (1985), pp. 230–236. - C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp. in Cardiology, vol. 6, pp. 37-42, 1979 - A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012. """ # Try retrieving right column if isinstance(ecg_cleaned, pd.DataFrame): try: ecg_cleaned = ecg_cleaned["ECG_Clean"] except NameError: try: ecg_cleaned = ecg_cleaned["ECG_Raw"] except NameError: ecg_cleaned = ecg_cleaned["ECG"] method = method.lower() # remove capitalised letters # Run peak detection algorithm if method in ["nk", "nk2", "neurokit", "neurokit2"]: rpeaks = _ecg_findpeaks_neurokit(ecg_cleaned, sampling_rate, show=show) elif method in ["pantompkins", "pantompkins1985"]: rpeaks = _ecg_findpeaks_pantompkins(ecg_cleaned, sampling_rate) elif method in ["gamboa2008", "gamboa"]: rpeaks = _ecg_findpeaks_gamboa(ecg_cleaned, sampling_rate) elif method in ["ssf", "slopesumfunction", "zong", "zong2003"]: rpeaks = _ecg_findpeaks_ssf(ecg_cleaned, sampling_rate) elif method in ["hamilton", "hamilton2002"]: rpeaks = _ecg_findpeaks_hamilton(ecg_cleaned, sampling_rate) elif method in ["christov", "christov2004"]: rpeaks = _ecg_findpeaks_christov(ecg_cleaned, sampling_rate) elif method in ["engzee", "engzee2012", "engzeemod", "engzeemod2012"]: rpeaks = _ecg_findpeaks_engzee(ecg_cleaned, sampling_rate) elif method in ["elgendi", "elgendi2010"]: rpeaks = _ecg_findpeaks_elgendi(ecg_cleaned, sampling_rate) elif method in ["kalidas2017", "swt", "kalidas", "kalidastamil", "kalidastamil2017"]: rpeaks = _ecg_findpeaks_kalidas(ecg_cleaned, sampling_rate) elif method in ["martinez2003", "martinez"]: rpeaks = _ecg_findpeaks_WT(ecg_cleaned, sampling_rate) elif method in ["rodrigues2020", "rodrigues", "asi"]: rpeaks = _ecg_findpeaks_rodrigues(ecg_cleaned, sampling_rate) else: raise ValueError("NeuroKit error: ecg_findpeaks(): 'method' should be " "one of 'neurokit' or 'pamtompkins'.") # Prepare output. info = {"ECG_R_Peaks": rpeaks} return info # ============================================================================= # NeuroKit # ============================================================================= def _ecg_findpeaks_neurokit(signal, sampling_rate=1000, smoothwindow=.1, avgwindow=.75, gradthreshweight=1.5, minlenweight=0.4, mindelay=0.3, show=False): """ All tune-able parameters are specified as keyword arguments. The `signal` must be the highpass-filtered raw ECG with a lowcut of .5 Hz. """ if show is True: fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True) # Compute the ECG's gradient as well as the gradient threshold. Run with # show=True in order to get an idea of the threshold. grad = np.gradient(signal) absgrad = np.abs(grad) smooth_kernel = int(np.rint(smoothwindow * sampling_rate)) avg_kernel = int(np.rint(avgwindow * sampling_rate)) smoothgrad = signal_smooth(absgrad, kernel="boxcar", size=smooth_kernel) avggrad = signal_smooth(smoothgrad, kernel="boxcar", size=avg_kernel) gradthreshold = gradthreshweight * avggrad mindelay = int(np.rint(sampling_rate * mindelay)) if show is True: ax1.plot(signal) ax2.plot(smoothgrad) ax2.plot(gradthreshold) # Identify start and end of QRS complexes. qrs = smoothgrad > gradthreshold beg_qrs = np.where(np.logical_and(np.logical_not(qrs[0:-1]), qrs[1:]))[0] end_qrs = np.where(np.logical_and(qrs[0:-1], np.logical_not(qrs[1:])))[0] # Throw out QRS-ends that precede first QRS-start. end_qrs = end_qrs[end_qrs > beg_qrs[0]] # Identify R-peaks within QRS (ignore QRS that are too short). num_qrs = min(beg_qrs.size, end_qrs.size) min_len = np.mean(end_qrs[:num_qrs] - beg_qrs[:num_qrs]) * minlenweight peaks = [0] for i in range(num_qrs): beg = beg_qrs[i] end = end_qrs[i] len_qrs = end - beg if len_qrs < min_len: continue if show is True: ax2.axvspan(beg, end, facecolor="m", alpha=0.5) # Find local maxima and their prominence within QRS. data = signal[beg:end] locmax, props = scipy.signal.find_peaks(data, prominence=(None, None)) if locmax.size > 0: # Identify most prominent local maximum. peak = beg + locmax[np.argmax(props["prominences"])] # Enforce minimum delay between peaks. if peak - peaks[-1] > mindelay: peaks.append(peak) peaks.pop(0) if show is True: ax1.scatter(peaks, signal[peaks], c="r") peaks = np.asarray(peaks).astype(int) # Convert to int return peaks # ============================================================================= # Pan & Tompkins (1985) # ============================================================================= def _ecg_findpeaks_pantompkins(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm. In: IEEE Transactions on Biomedical Engineering BME-32.3 (1985), pp. 230–236. """ diff = np.diff(signal) squared = diff * diff N = int(0.12 * sampling_rate) mwa = _ecg_findpeaks_MWA(squared, N) mwa[:int(0.2 * sampling_rate)] = 0 mwa_peaks = _ecg_findpeaks_peakdetect(mwa, sampling_rate) mwa_peaks = np.array(mwa_peaks, dtype='int') return mwa_peaks # ============================================================================= # Hamilton (2002) # ============================================================================= def _ecg_findpeaks_hamilton(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002. """ diff = abs(np.diff(signal)) b = np.ones(int(0.08 * sampling_rate)) b = b/int(0.08 * sampling_rate) a = [1] ma = scipy.signal.lfilter(b, a, diff) ma[0:len(b) * 2] = 0 n_pks = [] n_pks_ave = 0.0 s_pks = [] s_pks_ave = 0.0 QRS = [0] RR = [] RR_ave = 0.0 th = 0.0 i = 0 idx = [] peaks = [] for i in range(len(ma)): if i > 0 and i < len(ma) - 1: if ma[i-1] < ma[i] and ma[i + 1] < ma[i]: peak = i peaks.append(i) if ma[peak] > th and (peak-QRS[-1]) > 0.3 * sampling_rate: QRS.append(peak) idx.append(i) s_pks.append(ma[peak]) if len(n_pks) > 8: s_pks.pop(0) s_pks_ave = np.mean(s_pks) if RR_ave != 0.0: if QRS[-1]-QRS[-2] > 1.5 * RR_ave: missed_peaks = peaks[idx[-2] + 1:idx[-1]] for missed_peak in missed_peaks: if missed_peak - peaks[idx[-2]] > int(0.360 * sampling_rate) and ma[missed_peak] > 0.5 * th: QRS.append(missed_peak) QRS.sort() break if len(QRS) > 2: RR.append(QRS[-1]-QRS[-2]) if len(RR) > 8: RR.pop(0) RR_ave = int(np.mean(RR)) else: n_pks.append(ma[peak]) if len(n_pks) > 8: n_pks.pop(0) n_pks_ave = np.mean(n_pks) th = n_pks_ave + 0.45 * (s_pks_ave-n_pks_ave) i += 1 QRS.pop(0) QRS = np.array(QRS, dtype='int') return QRS # ============================================================================= # Slope Sum Function (SSF) - Zong et al. (2003) # ============================================================================= def _ecg_findpeaks_ssf(signal, sampling_rate=1000, threshold=20, before=0.03, after=0.01): """ From https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L448 - W. Zong, T. Heldt, G.B. Moody, and R.G. Mark. An open-source algorithm to detect onset of arterial blood pressure pulses. In Computers in Cardiology, 2003, pages 259–262, 2003. """ # TODO: Doesn't really seems to work # convert to samples winB = int(before * sampling_rate) winA = int(after * sampling_rate) Rset = set() length = len(signal) # diff dx = np.diff(signal) dx[dx >= 0] = 0 dx = dx ** 2 # detection idx, = np.nonzero(dx > threshold) idx0 = np.hstack(([0], idx)) didx = np.diff(idx0) # search sidx = idx[didx > 1] for item in sidx: a = item - winB if a < 0: a = 0 b = item + winA if b > length: continue r = np.argmax(signal[a:b]) + a Rset.add(r) # output rpeaks = list(Rset) rpeaks.sort() rpeaks = np.array(rpeaks, dtype='int') return rpeaks # ============================================================================= # Christov (2004) # ============================================================================= def _ecg_findpeaks_christov(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - Ivaylo I. Christov, Real time electrocardiogram QRS detection using combined adaptive threshold, BioMedical Engineering OnLine 2004, vol. 3:28, 2004. """ total_taps = 0 b = np.ones(int(0.02 * sampling_rate)) b = b/int(0.02 * sampling_rate) total_taps += len(b) a = [1] MA1 = scipy.signal.lfilter(b, a, signal) b = np.ones(int(0.028 * sampling_rate)) b = b/int(0.028 * sampling_rate) total_taps += len(b) a = [1] MA2 = scipy.signal.lfilter(b, a, MA1) Y = [] for i in range(1, len(MA2)-1): diff = abs(MA2[i + 1]-MA2[i-1]) Y.append(diff) b = np.ones(int(0.040 * sampling_rate)) b = b/int(0.040 * sampling_rate) total_taps += len(b) a = [1] MA3 = scipy.signal.lfilter(b, a, Y) MA3[0:total_taps] = 0 ms50 = int(0.05 * sampling_rate) ms200 = int(0.2 * sampling_rate) ms1200 = int(1.2 * sampling_rate) ms350 = int(0.35 * sampling_rate) M = 0 newM5 = 0 M_list = [] MM = [] M_slope = np.linspace(1.0, 0.6, ms1200-ms200) F = 0 F_list = [] R = 0 RR = [] Rm = 0 R_list = [] MFR = 0 MFR_list = [] QRS = [] for i in range(len(MA3)): # M if i < 5 * sampling_rate: M = 0.6 * np.max(MA3[:i + 1]) MM.append(M) if len(MM) > 5: MM.pop(0) elif QRS and i < QRS[-1] + ms200: newM5 = 0.6 * np.max(MA3[QRS[-1]:i]) if newM5 > 1.5 * MM[-1]: newM5 = 1.1 * MM[-1] elif QRS and i == QRS[-1] + ms200: if newM5 == 0: newM5 = MM[-1] MM.append(newM5) if len(MM) > 5: MM.pop(0) M = np.mean(MM) elif QRS and i > QRS[-1] + ms200 and i < QRS[-1] + ms1200: M = np.mean(MM) * M_slope[i-(QRS[-1] + ms200)] elif QRS and i > QRS[-1] + ms1200: M = 0.6 * np.mean(MM) # F if i > ms350: F_section = MA3[i-ms350:i] max_latest = np.max(F_section[-ms50:]) max_earliest = np.max(F_section[:ms50]) F = F + ((max_latest-max_earliest)/150.0) # R if QRS and i < QRS[-1] + int((2.0/3.0 * Rm)): R = 0 elif QRS and i > QRS[-1] + int((2.0/3.0 * Rm)) and i < QRS[-1] + Rm: dec = (M-np.mean(MM))/1.4 R = 0 + dec MFR = M + F + R M_list.append(M) F_list.append(F) R_list.append(R) MFR_list.append(MFR) if not QRS and MA3[i] > MFR: QRS.append(i) elif QRS and i > QRS[-1] + ms200 and MA3[i] > MFR: QRS.append(i) if len(QRS) > 2: RR.append(QRS[-1] - QRS[-2]) if len(RR) > 5: RR.pop(0) Rm = int(np.mean(RR)) QRS.pop(0) QRS = np.array(QRS, dtype='int') return QRS # ============================================================================= # Gamboa (2008) # ============================================================================= def _ecg_findpeaks_gamboa(signal, sampling_rate=1000, tol=0.002): """ From https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L834 - Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology. PhD ThesisUniversidade. """ # convert to samples v_100ms = int(0.1 * sampling_rate) v_300ms = int(0.3 * sampling_rate) hist, edges = np.histogram(signal, 100, density=True) TH = 0.01 F = np.cumsum(hist) v0 = edges[np.nonzero(F > TH)[0][0]] v1 = edges[np.nonzero(F < (1 - TH))[0][-1]] nrm = max([abs(v0), abs(v1)]) norm_signal = signal / float(nrm) d2 = np.diff(norm_signal, 2) b = np.nonzero((np.diff(np.sign(np.diff(-d2)))) == -2)[0] + 2 b = np.intersect1d(b, np.nonzero(-d2 > tol)[0]) if len(b) < 3: rpeaks = [] else: b = b.astype('float') rpeaks = [] previous = b[0] for i in b[1:]: if i - previous > v_300ms: previous = i rpeaks.append(np.argmax(signal[int(i):int(i + v_100ms)]) + i) rpeaks = sorted(list(set(rpeaks))) rpeaks = np.array(rpeaks, dtype='int') return rpeaks # ============================================================================= # Engzee Modified (2012) # ============================================================================= def _ecg_findpeaks_engzee(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp. in Cardiology, vol. 6, pp. 37-42, 1979 - A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012. """ engzee_fake_delay = 0 diff = np.zeros(len(signal)) for i in range(4, len(diff)): diff[i] = signal[i]-signal[i-4] ci = [1, 4, 6, 4, 1] low_pass = scipy.signal.lfilter(ci, 1, diff) low_pass[:int(0.2 * sampling_rate)] = 0 ms200 = int(0.2 * sampling_rate) ms1200 = int(1.2 * sampling_rate) ms160 = int(0.16 * sampling_rate) neg_threshold = int(0.01 * sampling_rate) M = 0 M_list = [] neg_m = [] MM = [] M_slope = np.linspace(1.0, 0.6, ms1200-ms200) QRS = [] r_peaks = [] counter = 0 thi_list = [] thi = False thf_list = [] thf = False for i in range(len(low_pass)): # M if i < 5 * sampling_rate: M = 0.6 * np.max(low_pass[:i + 1]) MM.append(M) if len(MM) > 5: MM.pop(0) elif QRS and i < QRS[-1] + ms200: newM5 = 0.6 * np.max(low_pass[QRS[-1]:i]) if newM5 > 1.5 * MM[-1]: newM5 = 1.1 * MM[-1] elif QRS and i == QRS[-1] + ms200: MM.append(newM5) if len(MM) > 5: MM.pop(0) M = np.mean(MM) elif QRS and i > QRS[-1] + ms200 and i < QRS[-1] + ms1200: M = np.mean(MM) * M_slope[i-(QRS[-1] + ms200)] elif QRS and i > QRS[-1] + ms1200: M = 0.6 * np.mean(MM) M_list.append(M) neg_m.append(-M) if not QRS and low_pass[i] > M: QRS.append(i) thi_list.append(i) thi = True elif QRS and i > QRS[-1] + ms200 and low_pass[i] > M: QRS.append(i) thi_list.append(i) thi = True if thi and i < thi_list[-1] + ms160: if low_pass[i] < -M and low_pass[i-1] > -M: # thf_list.append(i) thf = True if thf and low_pass[i] < -M: thf_list.append(i) counter += 1 elif low_pass[i] > -M and thf: counter = 0 thi = False thf = False elif thi and i > thi_list[-1] + ms160: counter = 0 thi = False thf = False if counter > neg_threshold: unfiltered_section = signal[thi_list[-1] - int(0.01 * sampling_rate):i] r_peaks.append(engzee_fake_delay + np.argmax(unfiltered_section) + thi_list[-1] - int(0.01 * sampling_rate)) counter = 0 thi = False thf = False r_peaks = np.array(r_peaks, dtype='int') return r_peaks # ============================================================================= # Stationary Wavelet Transform (SWT) - Kalidas and Tamil (2017) # ============================================================================= def _ecg_findpeaks_kalidas(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - Vignesh Kalidas and Lakshman Tamil (2017). Real-time QRS detector using Stationary Wavelet Transform for Automated ECG Analysis. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). Uses the Pan and Tompkins thresolding. """ # Try loading pywt try: import pywt except ImportError: raise ImportError("NeuroKit error: ecg_findpeaks(): the 'PyWavelets' " "module is required for this method to run. ", "Please install it first (`pip install PyWavelets`).") swt_level = 3 padding = -1 for i in range(1000): if (len(signal) + i) % 2 ** swt_level == 0: padding = i break if padding > 0: signal = np.pad(signal, (0, padding), 'edge') elif padding == -1: print("Padding greater than 1000 required\n") swt_ecg = pywt.swt(signal, 'db3', level=swt_level) swt_ecg = np.array(swt_ecg) swt_ecg = swt_ecg[0, 1, :] squared = swt_ecg * swt_ecg f1 = 0.01/sampling_rate f2 = 10/sampling_rate b, a = scipy.signal.butter(3, [f1 * 2, f2 * 2], btype='bandpass') filtered_squared = scipy.signal.lfilter(b, a, squared) filt_peaks = _ecg_findpeaks_peakdetect(filtered_squared, sampling_rate) filt_peaks = np.array(filt_peaks, dtype='int') return filt_peaks # ============================================================================= # Elgendi et al. (2010) # ============================================================================= def _ecg_findpeaks_elgendi(signal, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ - Elgendi, Mohamed & Jonkman, Mirjam & De Boer, Friso. (2010). Frequency Bands Effects on QRS Detection. The 3rd International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS2010). 428-431. """ window1 = int(0.12 * sampling_rate) mwa_qrs = _ecg_findpeaks_MWA(abs(signal), window1) window2 = int(0.6 * sampling_rate) mwa_beat = _ecg_findpeaks_MWA(abs(signal), window2) blocks = np.zeros(len(signal)) block_height = np.max(signal) for i in range(len(mwa_qrs)): if mwa_qrs[i] > mwa_beat[i]: blocks[i] = block_height else: blocks[i] = 0 QRS = [] for i in range(1, len(blocks)): if blocks[i-1] == 0 and blocks[i] == block_height: start = i elif blocks[i-1] == block_height and blocks[i] == 0: end = i-1 if end-start > int(0.08 * sampling_rate): detection = np.argmax(signal[start:end + 1]) + start if QRS: if detection-QRS[-1] > int(0.3 * sampling_rate): QRS.append(detection) else: QRS.append(detection) QRS = np.array(QRS, dtype='int') return QRS # ============================================================================= # Continuous Wavelet Transform (CWT) - Martinez et al. (2003) # ============================================================================= # def _ecg_findpeaks_WT(signal, sampling_rate=1000): # Try loading pywt try: import pywt except ImportError: raise ImportError("NeuroKit error: ecg_delineator(): the 'PyWavelets' " "module is required for this method to run. ", "Please install it first (`pip install PyWavelets`).") # first derivative of the Gaissian signal scales = np.array([1, 2, 4, 8, 16]) cwtmatr, freqs = pywt.cwt(signal, scales, 'gaus1', sampling_period=1.0/sampling_rate) # For wt of scale 2^4 signal_4 = cwtmatr[4, :] epsilon_4 = np.sqrt(np.mean(np.square(signal_4))) peaks_4, _ = scipy.signal.find_peaks(np.abs(signal_4), height=epsilon_4) # For wt of scale 2^3 signal_3 = cwtmatr[3, :] epsilon_3 = np.sqrt(np.mean(np.square(signal_3))) peaks_3, _ = scipy.signal.find_peaks(np.abs(signal_3), height=epsilon_3) # Keep only peaks_3 that are nearest to peaks_4 peaks_3_keep = np.zeros_like(peaks_4) for i in range(len(peaks_4)): peaks_distance = abs(peaks_4[i] - peaks_3) peaks_3_keep[i] = peaks_3[np.argmin(peaks_distance)] # For wt of scale 2^2 signal_2 = cwtmatr[2, :] epsilon_2 = np.sqrt(np.mean(np.square(signal_2))) peaks_2, _ = scipy.signal.find_peaks(np.abs(signal_2), height=epsilon_2) # Keep only peaks_2 that are nearest to peaks_3 peaks_2_keep = np.zeros_like(peaks_4) for i in range(len(peaks_4)): peaks_distance = abs(peaks_3_keep[i] - peaks_2) peaks_2_keep[i] = peaks_2[np.argmin(peaks_distance)] # For wt of scale 2^1 signal_1 = cwtmatr[1, :] epsilon_1 = np.sqrt(np.mean(np.square(signal_1))) peaks_1, _ = scipy.signal.find_peaks(np.abs(signal_1), height=epsilon_1) # Keep only peaks_1 that are nearest to peaks_2 peaks_1_keep = np.zeros_like(peaks_4) for i in range(len(peaks_4)): peaks_distance = abs(peaks_2_keep[i] - peaks_1) peaks_1_keep[i] = peaks_1[np.argmin(peaks_distance)] # Find R peaks max_R_peak_dist = int(0.1 * sampling_rate) rpeaks = [] for index_cur, index_next in zip(peaks_1_keep[:-1], peaks_1_keep[1:]): correct_sign = signal_1[index_cur] < 0 and signal_1[index_next] > 0 # limit 1 near = (index_next - index_cur) < max_R_peak_dist # limit 2 if near and correct_sign: rpeaks.append(signal_zerocrossings( signal_1[index_cur:index_next])[0] + index_cur) rpeaks = np.array(rpeaks, dtype='int') return rpeaks # ============================================================================= # ASI (FSM based 2020) # ============================================================================= def _ecg_findpeaks_rodrigues(signal, sampling_rate=1000): """ Segmenter by Tiago Rodrigues, inspired by on Gutierrez-Rivas (2015) and Sadhukhan (2012). References ---------- - Gutiérrez-Rivas, R., García, J. J., Marnane, W. P., & Hernández, A. (2015). Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sensors Journal, 15(10), 6036-6043. - Sadhukhan, D., & Mitra, M. (2012). R-peak detection algorithm for ECG using double difference and RR interval processing. Procedia Technology, 4, 873-877. """ N = int(np.round(3 * sampling_rate/128)) Nd = N-1 Pth = (0.7 * sampling_rate) / 128+2.7 # Pth = 3, optimal for fs = 250 Hz Rmin = 0.26 rpeaks = [] i = 1 tf = len(signal) Ramptotal = 0 # Double derivative squared diff_ecg = [signal[i] - signal[i - Nd] for i in range(Nd, len(signal))] ddiff_ecg = [diff_ecg[i] - diff_ecg[i - 1] for i in range(1, len(diff_ecg))] squar = np.square(ddiff_ecg) # Integrate moving window b = np.array(np.ones(N)) a = [1] processed_ecg = scipy.signal.lfilter(b, a, squar) # R-peak finder FSM while i < tf - sampling_rate: # ignore last second of recording # State 1: looking for maximum tf1 = np.round(i + Rmin*sampling_rate) Rpeakamp = 0 while i < tf1: # Rpeak amplitude and position if processed_ecg[i] > Rpeakamp: Rpeakamp = processed_ecg[i] rpeakpos = i + 1 i += 1 Ramptotal = (19 / 20) * Ramptotal + (1 / 20) * Rpeakamp rpeaks.append(rpeakpos) # State 2: waiting state d = tf1 - rpeakpos tf2 = i + np.round(0.2*2 - d) while i <= tf2: i += 1 # State 3: decreasing threshold Thr = Ramptotal while processed_ecg[i] < Thr: Thr = Thr * np.exp(-Pth / sampling_rate) i += 1 return rpeaks # ============================================================================= # Utilities # ============================================================================= def _ecg_findpeaks_MWA(signal, window_size): """ From https://github.com/berndporr/py-ecg-detectors/ """ mwa = np.zeros(len(signal)) sums = np.cumsum(signal) def get_mean(begin, end): if begin == 0: return sums[end - 1] / end dif = sums[end - 1] - sums[begin - 1] return dif / (end - begin) for i in range(len(signal)): if i < window_size: section = signal[0:i] else: section = get_mean(i - window_size, i) if i != 0: mwa[i] = np.mean(section) else: mwa[i] = signal[i] return mwa def _ecg_findpeaks_peakdetect(detection, sampling_rate=1000): """ From https://github.com/berndporr/py-ecg-detectors/ """ min_distance = int(0.25 * sampling_rate) signal_peaks = [0] noise_peaks = [] SPKI = 0.0 NPKI = 0.0 threshold_I1 = 0.0 threshold_I2 = 0.0 RR_missed = 0 index = 0 indexes = [] missed_peaks = [] peaks = [] for i in range(len(detection)): if i > 0 and i < len(detection) - 1: if detection[i-1] < detection[i] and detection[i + 1] < detection[i]: peak = i peaks.append(i) if detection[peak] > threshold_I1 and (peak - signal_peaks[-1]) > 0.3 * sampling_rate: signal_peaks.append(peak) indexes.append(index) SPKI = 0.125 * detection[signal_peaks[-1]] + 0.875 * SPKI if RR_missed != 0: if signal_peaks[-1] - signal_peaks[-2] > RR_missed: missed_section_peaks = peaks[indexes[-2] + 1:indexes[-1]] missed_section_peaks2 = [] for missed_peak in missed_section_peaks: if missed_peak - signal_peaks[-2] > min_distance and signal_peaks[-1] - missed_peak > min_distance and detection[missed_peak] > threshold_I2: missed_section_peaks2.append(missed_peak) if len(missed_section_peaks2) > 0: missed_peak = missed_section_peaks2[np.argmax(detection[missed_section_peaks2])] missed_peaks.append(missed_peak) signal_peaks.append(signal_peaks[-1]) signal_peaks[-2] = missed_peak else: noise_peaks.append(peak) NPKI = 0.125 * detection[noise_peaks[-1]] + 0.875 * NPKI threshold_I1 = NPKI + 0.25 * (SPKI - NPKI) threshold_I2 = 0.5 * threshold_I1 if len(signal_peaks) > 8: RR = np.diff(signal_peaks[-9:]) RR_ave = int(np.mean(RR)) RR_missed = int(1.66 * RR_ave) index = index + 1 signal_peaks.pop(0) return signal_peaks

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0.540636

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4.082534

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0.053597

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0.010343

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0.316232

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null

0

null

0

1

0

6d96c1cfb476a1c31417724d0d6d9bf4095e9439

1,157

py

Python

tinynn/converter/operators/base.py

www516717402/TinyNeuralNetwork

23e7931b4377462fad94a9ab0651b6d9a346252d

[ "MIT" ]

1

2022-01-11T06:40:13.000Z

tinynn/converter/operators/base.py

kingkie/TinyNeuralNetwork

9b4313bbe6fb46d602681b69799e4725eef4d71b

[ "MIT" ]

null

tinynn/converter/operators/base.py

kingkie/TinyNeuralNetwork

9b4313bbe6fb46d602681b69799e4725eef4d71b

[ "MIT" ]

1

2021-12-20T07:21:37.000Z

import inspect import sys from enum import IntEnum from tflite.ActivationFunctionType import ActivationFunctionType from tflite.BuiltinOperator import BuiltinOperator # In Python 3.6, we cannot make ExtendedOperator derive from IntEnum if sys.version_info >= (3, 7): bases = (IntEnum, ) else: bases = () class _ExtendedOperatorBase(BuiltinOperator, *bases): INPUT_NODE = -1 OUTPUT_NODE = -2 CONSTANT_NODE = -3 BATCH_NORM = -10 GENERIC_CONV = -11 GENERIC_DECONV = -12 def type_name(self): return self.name.replace('_NODE', '') # In Python 3.6, the elements in the parent class are not collected in IntEnum, # so we have to do that dynamically. if sys.version_info >= (3, 7): ExtendedOperator = _ExtendedOperatorBase else: ExtendedOperator = IntEnum('ExtendedOperator', dict( filter(lambda x: not x[0].startswith('__'), inspect.getmembers(_ExtendedOperatorBase)))) FUSE_ACTIVATION_MAP = {BuiltinOperator.RELU: ActivationFunctionType.RELU, BuiltinOperator.RELU6: ActivationFunctionType.RELU6, BuiltinOperator.TANH: ActivationFunctionType.TANH}

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1,157

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0.199654

1,157

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0.538462

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null

0

null

0

1

0

6d986eb3521f1a36cc7b07b20157b53df24adc51

852

py

Python

ihome/apps/homes/urls.py

Noah-Smith-wgp/rentinghouse

22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3

[ "MIT" ]

null

ihome/apps/homes/urls.py

Noah-Smith-wgp/rentinghouse

22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3

[ "MIT" ]

null

ihome/apps/homes/urls.py

Noah-Smith-wgp/rentinghouse

22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3

[ "MIT" ]

null

from django.conf.urls import url from rest_framework.routers import DefaultRouter from apps.homes import views urlpatterns = [ url(r'^areas/$', views.AreaAPIView.as_view()), # url(r'^houses/$', views.HouseAPIView.as_view()), # 我的房屋列表 url(r'^user/houses/$', views.HouseListView.as_view()), # 首页房屋模块 url(r'^houses/index/$', views.HouseIndexView.as_view()), # 房屋详情页面 url(r'^houses/(?P<house_id>\d+)/$', views.HouseDetailView.as_view()), ] router = DefaultRouter() # # 首页房屋推荐 # router.register(r'houses/index', views.HouseIndexViewSet, basename='index') # urlpatterns += router.urls # 发布房源房屋数据搜索 router.register(r'houses', views.HouseAPIView, basename='houses') urlpatterns += router.urls # 上传房源图片 router.register(r'houses/(?P<house_id>\d+)/images', views.HouseImageView, basename='images') urlpatterns += router.urls

29.37931

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0.70892

107

852

5.570093

0.401869

0.07047

0.050336

0.105705

0.053691

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0.118545

852

28

93

30.428571

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1

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false

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0.214286

0

0.214286

0

null

0

null

0

1

0

6d9999a24bad3d878ecc89ba34c9037a6d5b672e

646

py

Python

encryption/validation/ssl_client.py

TheConner/intl-iot

e7f0d7e96392acec900f29eb95cbbf5cb8d8db66

[ "Apache-2.0" ]

46

2019-09-19T05:03:56.000Z

2022-03-07T05:55:12.000Z

encryption/validation/ssl_client.py

dng24/intl-iot

84d46012afce5c7473d0cc9b82dc9e3aef069bbf

[ "Apache-2.0" ]

null

encryption/validation/ssl_client.py

dng24/intl-iot

84d46012afce5c7473d0cc9b82dc9e3aef069bbf

[ "Apache-2.0" ]

23

2019-09-18T02:04:59.000Z

2022-03-07T05:55:13.000Z

import socket import ssl import sys hostname = '127.0.0.1' if len(sys.argv) < 2: exit(0) inputfile = sys.argv[1] print('\tRead file %s' % inputfile) # msg = b"HEAD / HTTP /1.0\r\nHost: linuxfr.org\r\n\r\n" msg = open(inputfile).read() msg = bytes(msg.encode()) context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT) context.load_verify_locations('rootCA.pem') with socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0) as sock: with context.wrap_socket(sock, server_hostname=hostname) as ssock: ssock.connect((hostname, 8443)) # cert = ssock.getpeercert() ssock.sendall(msg) print('\tSent %s .+' % msg[:10])

26.916667

70

0.676471

99

646

4.333333

0.575758

0.032634

0

0.033395

0.165635

646

23

71

28.086957

0.762523

0.125387

0

0.080071

0

1

0

false

0

0.176471

0

0.176471

0.117647

0

null

0

null

0

1

0

6d9d4c3a43bd4e12042fd3d32b8a804be12b5ec6

429

py

Python

solutions/1209_remove_all_adjacent_duplicates_in_string_ii.py

YiqunPeng/leetcode_pro

7e6376984f9baec49a5e827d98330fe3d1b656f0

[ "MIT" ]

null

solutions/1209_remove_all_adjacent_duplicates_in_string_ii.py

YiqunPeng/leetcode_pro

7e6376984f9baec49a5e827d98330fe3d1b656f0

[ "MIT" ]

null

solutions/1209_remove_all_adjacent_duplicates_in_string_ii.py

YiqunPeng/leetcode_pro

7e6376984f9baec49a5e827d98330fe3d1b656f0

[ "MIT" ]

null

class Solution: def removeDuplicates(self, s: str, k: int) -> str: """Stack. Running time: O(n) where n is the length of s. """ st = [['#', 0]] for c in s: if st[-1][0] == c: st[-1][1] += 1 if st[-1][1] == k: st.pop() else: st.append([c, 1]) return ''.join([i[0] * i[1] for i in st])

26.8125

54

0.361305

59

429

2.627119

0.542373

0.058065

0.064516

0

0.047619

0.461538

429

15

55

28.6

0.623377

0.125874

0

0.002841

0

1

0.090909

false

0

0.272727

0

null

0

null

0

1

0

6da36de83dd56e3ca84e1de8b7ae22701073bf6d

528

py

Python

parte2/alternativeq2.py

ronaldbrito/PDS

58c8f9737e4cc5872a27e7b778a43def5e3e11f4

[ "MIT" ]

1

2019-03-16T01:49:11.000Z

parte2/alternativeq2.py

heliomeiralins/pds

58c8f9737e4cc5872a27e7b778a43def5e3e11f4

[ "MIT" ]

null

parte2/alternativeq2.py

heliomeiralins/pds

58c8f9737e4cc5872a27e7b778a43def5e3e11f4

[ "MIT" ]

null

import numpy as np from scipy.misc import imread, imsave from scipy import ndimage img = imread('doc1.bmp') def f(x): ret = x * 255 / 150 if ret > 255: ret = 255 return ret F = np.vectorize(f) treated_img = F(img) imsave('treated_doc.bmp', treated_img) mask = treated_img < treated_img.mean() label_im, nb_labels = ndimage.label(mask) sizes = ndimage.sum(mask, label_im, range(nb_labels + 1)) print(nb_labels) print(sum(sizes > 1)) print(sum(sizes > 2)) print(sum(sizes > 5)) print(sum(sizes > 10))

17.6

57

0.676136

88

528

3.943182

0.431818

0.115274

0.149856

0

0.044393

0.189394

528

29

58

18.206897

0.766355

0

0.043561

0

1

0.05

false

0

0.15

0

0.25

0

null

0

null

0

1

0

6da7a648349b63e6ebd5bddae98e78d24000ce56

2,617

py

Python

module2-sql-for-analysis/insert_rpg_thief.py

KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases

4a690cd8e651161296d7aec2af86a56c499d6801

[ "MIT" ]

null

module2-sql-for-analysis/insert_rpg_thief.py

KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases

4a690cd8e651161296d7aec2af86a56c499d6801

[ "MIT" ]

null

module2-sql-for-analysis/insert_rpg_thief.py

KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases

4a690cd8e651161296d7aec2af86a56c499d6801

[ "MIT" ]

null

import os from dotenv import load_dotenv import sqlite3 import psycopg2 from psycopg2.extras import execute_values load_dotenv() # looks inside the .env file for some env vars # passes env var values to python var DB_HOST = os.getenv("DB_HOST", default="OOPS") DB_NAME = os.getenv("DB_NAME", default="OOPS") DB_USER = os.getenv("DB_USER", default="OOPS") DB_PASSWORD = os.getenv("DB_PASSWORD", default="OOPS") # what is the filepath to connect to our sqlite database? DB_FILEPATH = os.path.join(os.path.dirname(__file__), "..", "module1-introduction-to-sql", "rpg_db.sqlite3") class SqliteService_thief(): def __init__(self, db_filepath=DB_FILEPATH): self.connection = sqlite3.connect(db_filepath) self.cursor = self.connection.cursor() def fetch_characters_thief(self): return self.cursor.execute("SELECT * FROM charactercreator_thief;").fetchall() class ElephantSQLService_thief(): def __init__(self): self.connection = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST) self.cursor = self.connection.cursor() def create_characters_thief_table(self): create_query = """ DROP TABLE IF EXISTS characters_thief; -- allows this to be run idempotently, avoids psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "characters_thief_pkey" DETAIL: Key (character_id)=(1) already exists. CREATE TABLE IF NOT EXISTS characters_thief ( character_ptr_id INT, is_sneaking INT, energy INT ); """ print(create_query) self.cursor.execute(create_query) self.connection.commit() def insert_characters_thief(self, characters_thief): """ Param characters_thief needs to be a list of tuples, each representing a row to insert (each should have each column) """ insertion_query = """ INSERT INTO characters_thief (character_ptr_id, is_sneaking, energy) VALUES %s """ execute_values(self.cursor, insertion_query, characters_thief) self.connection.commit() if __name__ == "__main__": # # EXTRACT (AND MAYBE TRANSFORM IF NECESSARY) # sqlite_service = SqliteService_thief() characters_thief = sqlite_service.fetch_characters_thief() print(type(characters_thief), len(characters_thief)) print(type(characters_thief[0]), characters_thief[0]) # # LOAD # pg_service = ElephantSQLService_thief() pg_service.create_characters_thief_table() pg_service.insert_characters_thief(characters_thief)

42.209677

244

0.708445

328

2,617

5.375

0.365854

0.161656

0.022689

0.018151

0.114577

0.081679

0

0.005231

0.196408

2,617

62

245

42.209677

0.833096

0.115781

0

0.125

0

0.020833

0.286027

0.045992

0

1

0.104167

false

0.041667

0.104167

0.020833

0.270833

0.0625

0

null

0

null

0

1

0

6dabb2d9a3beda1b3745a3582f1367443e6ae076

4,626

py

Python

src/data_prep_uci.py

akumesi48/hyper-genetic

6e1ec16b31bb2259d4a325e08779d5668750a635

[ "MIT" ]

null

src/data_prep_uci.py

akumesi48/hyper-genetic

6e1ec16b31bb2259d4a325e08779d5668750a635

[ "MIT" ]

null

src/data_prep_uci.py

akumesi48/hyper-genetic

6e1ec16b31bb2259d4a325e08779d5668750a635

[ "MIT" ]

null

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedKFold, KFold def cv_index(n_fold, feature, label): skf = KFold(n_fold, shuffle=True, random_state=7840) index_list = [] for i, j in skf.split(feature, label): index_list.append((i, j)) return index_list def data_selector(data_name): if data_name == 'cmc': return x_train_cmc, x_test_cmc, y_train_cmc, y_test_cmc, index_cmc elif data_name == 'setap': return x_train_setap, x_test_setap, y_train_setap, y_test_setap, index_setap elif data_name == 'audit': return x_train_audit, x_test_audit, y_train_audit, y_test_audit, index_audit elif data_name == 'titanic': return x_train_tt, x_test_tt, y_train_tt, y_test_tt, index_tt elif data_name == 'dota': return x_train_dota, x_test_dota, y_train_dota, y_test_dota, index_dota no_of_folds = 3 # Dataset cmc data_cmc = pd.read_csv("data/cmc.data", header=None) data_cmc[9] = np.where(data_cmc[9] == 1, 0, 1) data_cmc_label = data_cmc.pop(9) x_train_cmc, x_test_cmc, y_train_cmc, y_test_cmc = train_test_split(data_cmc, data_cmc_label, random_state=7840, test_size=0.25) index_cmc = cv_index(no_of_folds, x_train_cmc, y_train_cmc) # Dataset SETAP data_setap = pd.read_csv("data/setap.csv") data_setap['label'] = np.where(data_setap['label'] == 'A', 0, 1) data_setap_label = data_setap.pop('label') x_train_setap, x_test_setap, y_train_setap, y_test_setap = train_test_split(data_setap, data_setap_label, random_state=7840, test_size=0.25) index_setap = cv_index(no_of_folds, x_train_setap, y_train_setap) # Dataset audit data_audit = pd.read_csv("data/audit_risk.csv") data_audit['LOCATION_ID'] = pd.to_numeric(data_audit['LOCATION_ID'], errors='coerce') data_audit['LOCATION_ID'] = data_audit['LOCATION_ID'].fillna(data_audit['LOCATION_ID'].mode()[0]) data_audit['Money_Value'] = data_audit['Money_Value'].fillna(data_audit['Money_Value'].mean()) data_audit_label = data_audit.pop('Risk') x_train_audit, x_test_audit, y_train_audit, y_test_audit = train_test_split(data_audit, data_audit_label, random_state=7840, test_size=0.25,) index_audit = cv_index(no_of_folds, x_train_audit, y_train_audit) # Dataset titanic data_tt = pd.read_csv("data/titanic_train.csv") data_tt['Age'] = data_tt['Age'].fillna(data_tt['Age'].mean()) data_tt['Embarked'] = data_tt['Embarked'].fillna(data_tt['Embarked'].mode()[0]) data_tt['Pclass'] = data_tt['Pclass'].apply(str) for col in data_tt.dtypes[data_tt.dtypes == 'object'].index: for_dummy = data_tt.pop(col) data_tt = pd.concat([data_tt, pd.get_dummies(for_dummy, prefix=col)], axis=1) data_tt_labels = data_tt.pop('Survived') x_train_tt, x_test_tt, y_train_tt, y_test_tt = train_test_split(data_tt, data_tt_labels, random_state=7840, test_size=0.25) index_tt = cv_index(no_of_folds, x_train_tt, y_train_tt) # Dataset DotA2 x_train_dota = pd.read_csv("data/dota2Train.csv", header=None) x_train_dota[0] = np.where(x_train_dota[0] == 1, 1, 0) y_train_dota = x_train_dota.pop(0) x_test_dota = pd.read_csv("data/dota2Test.csv", header=None) x_test_dota[0] = np.where(x_test_dota[0] == 1, 1, 0) y_test_dota = x_test_dota.pop(0) index_dota = cv_index(no_of_folds, x_train_dota, y_train_dota) # for train_index, test_index in skf.split(x_train, y_train): # train_feature, test_feature = x_train.iloc[train_index], x_train.iloc[test_index] # train_label, test_label = y_train.iloc[train_index], y_train.iloc[test_index] # print(train_gbm(train_feature, train_label, test_feature, test_label)) # skf = KFold(5) # train_index = [] # test_index = [] # index_list = [] # for i, j in skf.split(x_train_cmc, y_train_cmc): # index_list.append((i, j))

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null

0

null

0

1

0

6dad2a388f6001f81a7db3ec98fd61ac8d241fec

935

py

Python

ncdoublescrape/__main__.py

hancush/ncdoublescrape

ea64277514ddff04e634bb464dd5ea6bf05226ae

[ "BSD-3-Clause" ]

null

ncdoublescrape/__main__.py

hancush/ncdoublescrape

ea64277514ddff04e634bb464dd5ea6bf05226ae

[ "BSD-3-Clause" ]

null

ncdoublescrape/__main__.py

hancush/ncdoublescrape

ea64277514ddff04e634bb464dd5ea6bf05226ae

[ "BSD-3-Clause" ]

null

import argparse import importlib import logging import sys logger = logging.getLogger() COMMAND_MODULES = ( 'ncdoublescrape.scrape', ) def main(): parser = argparse.ArgumentParser('ncds', description='A janky NCAA scraper') subparsers = parser.add_subparsers(dest='subcommand') subcommands = {} for module in COMMAND_MODULES: try: command = importlib.import_module(module).Command(subparsers) except ImportError as e: logger.error('exception "%s" prevented loading of %s module', e, module) else: subcommands[command.name] = command args, other = parser.parse_known_args() if not args.subcommand: parser.print_help() else: try: subcommands[args.subcommand].handle(args, other) except Exception as e: logger.critical(str(e)) sys.exit(1) if __name__ == '__main__': main()

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935

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null

0

null

0

1

0

6daefd5d0ce54a9298c815543a7ce9308e437d8f

5,037

py

Python

src/lambda_function.py

sd11/react-aws-s3-rekognition

f37ea4ef0242f8c650380ab0c060e0bddb4ff432

[ "Unlicense" ]

null

src/lambda_function.py

sd11/react-aws-s3-rekognition

f37ea4ef0242f8c650380ab0c060e0bddb4ff432

[ "Unlicense" ]

null

src/lambda_function.py

sd11/react-aws-s3-rekognition

f37ea4ef0242f8c650380ab0c060e0bddb4ff432

[ "Unlicense" ]

null

from __future__ import print_function import boto3 from decimal import Decimal import json import urllib from botocore.vendored import requests print('Loading function') rekognition = boto3.client('rekognition') s3 = boto3.resource("s3") # --------------- Helper Functions to call Rekognition APIs ------------------ def detect_faces(bucket, key): response = rekognition.detect_faces(Image={"S3Object": {"Bucket": bucket, "Name": key}}) return response def detect_labels(bucket, key): response = rekognition.detect_labels(Image={"S3Object": {"Bucket": bucket, "Name": key}}) # Sample code to write response to DynamoDB table 'MyTable' with 'PK' as Primary Key. # Note: role used for executing this Lambda function should have write access to the table. #table = boto3.resource('dynamodb').Table('MyTable') #labels = [{'Confidence': Decimal(str(label_prediction['Confidence'])), 'Name': label_prediction['Name']} for label_prediction in response['Labels']] #table.put_item(Item={'PK': key, 'Labels': labels}) return response def index_faces(bucket, key): # Note: Collection has to be created upfront. Use CreateCollection API to create a collecion. #rekognition.create_collection(CollectionId='BLUEPRINT_COLLECTION') response = rekognition.index_faces(Image={"S3Object": {"Bucket": bucket, "Name": key}}, CollectionId="BLUEPRINT_COLLECTION") return response def find_recipes(ingredients): payload = { 'ingredients': ingredients, 'number': 2, 'ranking': '1', 'apiKey': '8bce36150747496f98b2c81860545458' } recipes = requests.get('https://api.spoonacular.com/recipes/findByIngredients', params=payload) return recipes.json() # --------------- Main handler ------------------ def lambda_handler(event, context): '''Demonstrates S3 trigger that uses Rekognition APIs to detect faces, labels and index faces in S3 Object. ''' print("Received event: " + json.dumps(event, indent=2)) # Get the object from the event bucket = event['Records'][0]['s3']['bucket']['name'] key = urllib.unquote_plus(event['Records'][0]['s3']['object']['key'].encode('utf8')) try: # Calls rekognition DetectFaces API to detect faces in S3 object #response = detect_faces(bucket, key) # Calls rekognition DetectLabels API to detect labels in S3 object response = detect_labels(bucket, key) # Calls rekognition IndexFaces API to detect faces in S3 object and index faces into specified collection #response = index_faces(bucket, key) # Print response to console. print('Detected labels for ' + key) print() ingredients = "" for label in response['Labels']: encodedName = label['Name'].encode('utf-8') if len(ingredients): ingredients = ingredients + ", " + encodedName else: ingredients = encodedName # print ("Label: " + label['Name']) # print ("Confidence: " + str(label['Confidence'])) # print ("Instances:") #for instance in label['Instances']: # print (" Bounding box") # print (" Top: " + str(instance['BoundingBox']['Top'])) # print (" Left: " + str(instance['BoundingBox']['Left'])) # print (" Width: " + str(instance['BoundingBox']['Width'])) # print (" Height: " + str(instance['BoundingBox']['Height'])) # print (" Confidence: " + str(instance['Confidence'])) # print() # print ("Parents:") # for parent in label['Parents']: # print (" " + parent['Name']) # print ("----------") # print () recipes = find_recipes(ingredients) #return recipes #print(ingredients) #print(recipes) recipeResponse = [] for recipe in recipes: recipeIngredients = [] for usedIngredient in recipe['usedIngredients']: recipeIngredients.append({ 'name': usedIngredient['name'], 'servingSize': str(usedIngredient['amount']) + ' ' + usedIngredient['unit'] }) recipeResponse.append({ 'title': recipe['title'], 'image': recipe['image'], 'ingredients': recipeIngredients }) responseData = { 'ingredients': ingredients, 'recipes': recipeResponse } if responseData: print(s3) obj = s3.Object('groupneuralnetworkrecipebucket1','recipes.json') obj.put(Body=json.dumps(responseData)) return responseData except Exception as e: print(e) print("Error processing object {} from bucket {}. ".format(key, bucket) + "Make sure your object and bucket exist and your bucket is in the same region as this function.") raise e

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5.967936

0.328657

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0.018805

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0.08865

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0.04231

0

0.014065

0.266031

5,037

135

154

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0.791453

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0

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0

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0

1

0.074627

false

0

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0

0.238806

0.119403

0

null

0

null

0

1

0

6dafe2f9f75624f4e6bac8d36ece57fee0f45bc2

3,836

py

Python

main_server.py

tenzindayoe/example

edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89

[ "MIT" ]

null

main_server.py

tenzindayoe/example

edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89

[ "MIT" ]

null

main_server.py

tenzindayoe/example

edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89

[ "MIT" ]

null

import socket import sqlite3 def Main(): port = 4000 s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM) print(socket.gethostbyaddr(socket.gethostname())) s.bind((socket.gethostbyaddr(),port)) print("Server started") while True: reg_db = sqlite3.connect("reg_db.db") reg_db_cur = reg_db.cursor() unp_db = sqlite3.connect("unp_db.db") unp_db_cur = unp_db.cursor() '''first quadrant table : f_q_table second quadrant table : s_q_table third quadrant table : t_q_table fourth quadrant table : fourth_q_table''' data, addr = s.recvfrom(1024) data = data.decode('utf-8') data = eval(data) if data[0] == "acc": data.remove(data[0]) username = data[0].lower() email = data[1].lower() email = email.replace("@","_") password = data[2].lower() success = None unp_db_cur.execute("SELECT email FROM accounts where email = "+"'"+email+"'") res = unp_db_cur.fetchall() if len(res) == 0: try: unp_db_cur.execute("INSERT INTO accounts values("+"'"+email+"',"+" '"+username+"', "+"'"+password+"')") unp_db.commit() unp_db_cur.execute("SELECT * FROM accounts") print(unp_db_cur.fetchall()) s.sendto("ACCOUNT CREATED".encode("utf-8"),addr) except: print("DATABASE REGISTRATION ERROR") elif len(res) ==1: print("email associated with account already exist") elif data[1] == "ch_acc_cred": email_ch = data[0][0].replace("@","_") name_ch = data[0][1] pw_ch = data[0][2] unp_db_cur.execute("SELECT * FROM accounts WHERE email ="+"'"+email_ch+"'") ch_res = unp_db_cur.fetchall() if ch_res == [] : print("no accounts associated with the email") s.sendto("error_lia".encode("utf-8"), addr) elif ch_res[0][2]== pw_ch: print("Login success") s.sendto(str(["verified",ch_res[0][1]]).encode("utf-8"),addr) elif data[0] == "tr_reg": lat = data[1][0] lon = data[1][1] if lon >= 0 and lat >= 0: print("INSERT INTO f_q_table values('"+str(lon)+"', '"+str(lat)+")") reg_db_cur.execute("INSERT INTO f_q_table values('"+str(lon)+"', '"+str(lat)+"')") else: print("message from : ", addr) print("message : ",data) temp = data # the data from the client is of the form [[lo,lo*],[la,la*]] lo = temp[0][0] lo_p = temp[0][1] la = temp[1][0] la_p = temp[1][1] text = "" if lo >= 0 and la >=0: text = "FIRST QUADRANT" print(text) reg_db_cur.execute("SELECT DISTINCT * FROM f_q_table WHERE lon > %s AND lon < %s AND lat > %s AND lat < %s"%(lo,lo_p,la,la_p)) coordinates = reg_db_cur.fetchall() coordinates = str(coordinates) print("sending : ", coordinates) s.sendto(coordinates.encode('utf-8'), addr) elif lo_p < 0 and la >= 0: text = "SECOND QUADRANT" print(text) elif lo_p < 0 and la_p < 0: text = "THIRD QUADRANT" print(text) elif lo >= 0 and la_p < 0: text = "FOURTH QUADRANT" print(text) else: text = "unidentified... but will find tomorrow" if __name__ == "__main__": Main().run()

29.282443

142

0.489572

462

3,836

3.896104

0.251082

0.033333

0.035556

0.033333

0.236111

0.123333

0.075556

0.038889

0

0.023583

0.369917

3,836

130

143

29.507692

0.721142

0.015381

0

0.071429

0

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0.186544

0

1

0.011905

false

0.02381

0

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0.178571

0

null

0

null

0

1

0

6db40aaa8cd6b1e406d9fcd14ef25634a3d1ada0

2,274

py

Python

db/division.py

leaffan/pynhldb

a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1

[ "MIT" ]

3

2017-02-01T15:37:23.000Z

2017-08-31T20:41:46.000Z

db/division.py

leaffan/pynhldb

a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1

[ "MIT" ]

41

2017-09-13T02:13:21.000Z

2018-11-07T03:29:39.000Z

db/division.py

leaffan/pynhldb

a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1

[ "MIT" ]

1

2017-03-09T14:58:39.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import datetime from .common import Base, session_scope from .team import Team class Division(Base): __tablename__ = 'divisions' __autoload__ = True HUMAN_READABLE = 'division' def __init__(self, name, season, teams, conference=None): self.division_name = name self.season = season self.teams = list() for t in teams: self.teams.append(t.team_id) self.conference = conference @classmethod def get_divisions_and_teams(cls, season=None): if season is None: now = datetime.datetime.now() season = now.year - 1 if now.month <= 6 else now.year division_dict = dict() with session_scope() as session: divs = session.query(Division).filter( Division.season == season).all() for d in divs: teams = list() for team_id in d.teams: team = Team.find_by_id(team_id) teams.append(team) division_dict[d.division_name] = teams return division_dict def __str__(self): if self.conference: base_information_str = "%s Division (%s Conference) %s:" % ( self.division_name, self.conference, self.season) else: base_information_str = "%s Division %s:" % ( self.division_name, self.season) team_information_str = "\n\t+ ".join( sorted([Team.find_by_id(team_id).name for team_id in self.teams])) return "\n\t+ ".join((base_information_str, team_information_str)) def __gt__(self, other): if None in (self.conference, other.conference): return self.division_name > other.division_name else: return ( self.conference, self.division_name ) > ( other.conference, other.division_name) def __lt__(self, other): if None in (self.conference, other.conference): return self.division_name < other.division_name else: return ( self.conference, self.division_name ) < ( other.conference, other.division_name)

30.32

78

0.575198

259

2,274

4.803089

0.266409

0.115756

0.090032

0.067524

0.359325

0.326367

0.252412

0

0.001963

0.328056

2,274

74

79

30.72973

0.812173

0.01847

0

0.196429

0

0.033632

0

1

0.089286

false

0

0.053571

0

0.321429

0

null

0

null

0

1

0

6db421aa4d6562b0233d7c1b87bdca893ef23405

1,338

py

Python

tests/test_config.py

regro/runthis-server

26d6551560bd6ddabdb9b360ecd327460dfd779a

[ "BSD-3-Clause" ]

2

2019-11-13T23:19:13.000Z

2019-11-15T21:01:51.000Z

tests/test_config.py

regro/runthis-server

26d6551560bd6ddabdb9b360ecd327460dfd779a

[ "BSD-3-Clause" ]

null

tests/test_config.py

regro/runthis-server

26d6551560bd6ddabdb9b360ecd327460dfd779a

[ "BSD-3-Clause" ]

null

import pytest from ruamel import yaml from runthis.server.config import Config, get_config_from_yaml @pytest.fixture def config_obj(tmpdir): return Config( tty_server="ttyd", command="xonsh", docker=False, docker_image="myimage", keyfile="/path/to/privkey.pem", ) def test_fields(config_obj): assert config_obj.tty_server == "ttyd" assert config_obj.command == "xonsh" assert not config_obj.docker assert config_obj.docker_image == "myimage" assert config_obj.keyfile == "/path/to/privkey.pem" DICT_CONFIG_CONTENT = dict( tty_server="tty-server", command="myshell", docker=True, docker_image="img", host="8.8.8.8", certfile="/path/to/cert.pem", ) @pytest.mark.parametrize( "config_content", [DICT_CONFIG_CONTENT, {"runthis": DICT_CONFIG_CONTENT}] ) def test_populate_config_by_yaml(config_content, tmpdir): yaml_path = tmpdir.join("TEST.yaml") yaml_path.write(yaml.dump(config_content)) config_obj = get_config_from_yaml(str(yaml_path)) assert config_obj.tty_server == "tty-server" assert config_obj.command == "myshell" assert config_obj.docker assert config_obj.docker_image == "img" assert config_obj.host == "8.8.8.8" assert config_obj.certfile == "/path/to/cert.pem"

26.76

77

0.684604

180

1,338

4.838889

0.266667

0.144661

0.172216

0.072331

0.268657

0.094145

0

0.007401

0.192078

1,338

49

78

27.306122

0.798335

0

0.142003

0

0.282051

1

0.076923

false

0

0.076923

0.025641

0.179487

0

null

0

null

0

1

0

6db4535a87906f105783cb4e0f22471fe703aef0

290

py

Python

src/constants.py

halilyaman/UlasimdaYapayZekaYarismasi

e9f024454470ad6f40653583f3d7f24cdd4f4fd9

[ "MIT" ]

1

2021-09-23T22:34:12.000Z

src/constants.py

halilyaman/UlasimdaYapayZekaYarismasi

e9f024454470ad6f40653583f3d7f24cdd4f4fd9

[ "MIT" ]

null

src/constants.py

halilyaman/UlasimdaYapayZekaYarismasi

e9f024454470ad6f40653583f3d7f24cdd4f4fd9

[ "MIT" ]

null

# DISCLAIMER TO CONTEST TEAMS : DO NOT MAKE CHANGES IN THIS FILE. classes = { "Tasit": 0, "Insan": 1, "UAP": 2, "UAI": 3, } landing_statuses = { "Inilebilir": "1", "Inilemez": "0", "Inis Alani Degil": "-1" } base_url = "http://192.168.1.10:3000"

19.333333

67

0.527586

38

290

3.973684

0.894737

0

0.097087

0.289655

290

14

68

20.714286

0.635922

0.22069

0

0.371429

0

1

0

false

0

null

0

null

0

1

0

6db6887534c339671321ea2ad6c3cae9fe067123

2,345

py

Python

setup.py

Ezbob/dgDynamic

394de1c138c1517c4cdfead879c43db189752d92

[ "MIT" ]

null

setup.py

Ezbob/dgDynamic

394de1c138c1517c4cdfead879c43db189752d92

[ "MIT" ]

null

setup.py

Ezbob/dgDynamic

394de1c138c1517c4cdfead879c43db189752d92

[ "MIT" ]

null

from setuptools import setup from setuptools.command.install import install import os import sys import atexit if __name__ == '__main__': package_name = 'dgdynamic' excludes = [ '__pycache__', 'StochKit' ] extras = [ 'default_config.ini', 'spim.ocaml', 'stochkit.tar.gz' ] def find_package_dirs(package_dir_path, excludes): return [path for path, dirs, files in os.walk(package_dir_path) if not any(exclude_name in path for exclude_name in excludes)] def get_requirements(): with open('requirements.txt', mode="r") as file: return list(map(str.strip, file)) package_dirs = find_package_dirs(package_name, excludes) internal_python_paths = { ".".join(p_name.split('/')): p_name for p_name in package_dirs } class CustomInstall(install): def run(self): def _post_install(): def find_module_path(): for p in sys.path: if os.path.isdir(p) and package_name in os.listdir(p): return os.path.join(p, package_name) install_path = find_module_path() stochkit2_plugin_path = os.path.join(install_path, "plugins/stochastic/stochkit2/") stochkit2_tar_path = os.path.join(stochkit2_plugin_path, "stochkit.tar.gz") stochkit2_installer_path = os.path.join(stochkit2_plugin_path, "StochKit") os.system("tar xvzf " + stochkit2_tar_path + " -C " + stochkit2_plugin_path) os.system("cd " + stochkit2_installer_path + " && ./install.sh") atexit.register(_post_install) install.run(self) setup( cmdclass={'install': CustomInstall}, name=package_name, version='1.0.0', description='Dynamic simulation library for the MØD graph transformation framework', url='https://bitbucket.org/Ezben/dgdynamic', author='Anders Busch', author_email='andersbusch@gmail.com', license='MIT', package_dir=internal_python_paths, include_package_data=True, package_data={'': extras}, packages=list(internal_python_paths.keys()), install_requires=get_requirements(), zip_safe=False )

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null

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null

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1

0

6db73ff6f5328cc7f6a179960f5ceb876377c833

5,543

py

Python

Robotics/src/otonomsesli.py

ahmetakif/Voice-Controlled-Raspberry-Pi-Robot

00dcc15dfbb7441d6403fb0467b2144e8750cc0c

[ "Apache-2.0" ]

5

2019-08-21T08:08:27.000Z

2021-06-14T06:56:50.000Z

Robotics/src/otonomsesli.py

ahmetakif/Voice-Controlled-Raspberry-Pi-Robot

00dcc15dfbb7441d6403fb0467b2144e8750cc0c

[ "Apache-2.0" ]

null

Robotics/src/otonomsesli.py

ahmetakif/Voice-Controlled-Raspberry-Pi-Robot

00dcc15dfbb7441d6403fb0467b2144e8750cc0c

[ "Apache-2.0" ]

2

2019-08-21T08:16:58.000Z

2021-04-07T11:56:11.000Z

import os import RPi.GPIO as gpio import time from mesafe import distance motorhizi = 1 aci2 = aci3 = aci4 = 6 aci = 5.5 in4 = 26 in3 = 4 in2 = 12 in1 = 8 solled = 9 sagled = 11 gpio.setwarnings(False) def init(): gpio.setwarnings(False) gpio.setmode(gpio.BCM) gpio.setup(22,gpio.OUT) gpio.setup(27,gpio.OUT) gpio.setup(17,gpio.OUT) gpio.setup(18,gpio.OUT) gpio.setup(in4,gpio.OUT) gpio.setup(in3,gpio.OUT) gpio.setup(in2,gpio.OUT) gpio.setup(in1,gpio.OUT) gpio.setup(21,gpio.OUT) gpio.setup(solled,gpio.OUT) gpio.setup(sagled,gpio.OUT) gpio.setup(23,gpio.IN) gpio.setup(24,gpio.IN) gpio.output(22,0) gpio.output(18,0) gpio.output(17,0) gpio.output(27,0) gpio.output(in4,0) gpio.output(in3,0) gpio.output(in2,0) gpio.output(in1,0) gpio.output(21,0) gpio.output(solled,0) gpio.output(sagled,0) def ileri(tf,ff): init() gpio.output(17,0) gpio.output(22,0) ip = gpio.PWM(27,50) ip2 = gpio.PWM(18,50) ip.start(ff) ip2.start(ff) tf = float(tf) tf = tf / motorhizi time.sleep(tf) gpio.cleanup() def geri(tf,ff): init() gpio.output(18,0) gpio.output(27,0) gp = gpio.PWM(22,50) gp2 = gpio.PWM(17,50) gp.start(ff) gp2.start(ff) tf = float(tf) tf = tf / motorhizi time.sleep(tf) gpio.cleanup() def sol(tf,ff): init() gpio.output(17,0) gpio.output(27,0) sp = gpio.PWM(22,50) sp2 = gpio.PWM(18,50) sp.start(ff) sp2.start(ff) tf = float(tf) tf = tf / motorhizi time.sleep(tf) gpio.cleanup() def sag(tf,ff): init() gpio.output(18,0) gpio.output(22,0) sap = gpio.PWM(27,50) sap2 = gpio.PWM(17,50) sap.start(ff) sap2.start(ff) tf = float(tf) tf = tf / motorhizi time.sleep(tf) gpio.cleanup() def dur(): init() gpio.output(22,0) gpio.output(17,0) gpio.output(18,0) gpio.output(27,0) gpio.cleanup() def adim1(tf,y): init() if (y == 1): # sol gpio.output(in1,1) gpio.output(in2,0) gpio.output(in3,0) gpio.output(in4,0) if (y == 0): # sag gpio.output(in1,0) gpio.output(in2,0) gpio.output(in3,0) gpio.output(in4,1) time.sleep(tf) gpio.cleanup() def adim2(tf,y): init() if (y == 1): # sol gpio.output(in1,0) gpio.output(in2,1) gpio.output(in3,0) gpio.output(in4,0) if (y == 0): # sag gpio.output(in1,0) gpio.output(in2,0) gpio.output(in3,1) gpio.output(in4,0) time.sleep(tf) gpio.cleanup() def adim3(tf,y): init() if (y == 1): # sol gpio.output(in1,0) gpio.output(in2,0) gpio.output(in3,1) gpio.output(in4,0) if (y == 0): # sag gpio.output(in1,0) gpio.output(in2,1) gpio.output(in3,0) gpio.output(in4,0) time.sleep(tf) gpio.cleanup() def adim4(tf,y): init() if (y == 1): # sol gpio.output(in1,0) gpio.output(in2,0) gpio.output(in3,0) gpio.output(in4,1) if (y == 0): # sag gpio.output(in1,1) gpio.output(in2,0) gpio.output(in3,0) gpio.output(in4,0) time.sleep(tf) gpio.cleanup() def stepper(tf,ff,yf): ff = float(ff) ff = ff / 1000 if (yf == 0): # sag for i in range(0,tf): adim1(ff,0) adim2(ff,0) adim3(ff,0) adim4(ff,0) if (yf == 1): # sol for i in range(0,tf): adim1(ff,1) adim2(ff,1) adim3(ff,1) adim4(ff,1) def servo(tf): gpio.setmode(gpio.BCM) gpio.setup(5,gpio.OUT) p = gpio.PWM(5,50) p.start(5.5) p.ChangeDutyCycle(tf) time.sleep(0.7) gpio.cleanup() def servo2(tf): gpio.setmode(gpio.BCM) gpio.setup(6,gpio.OUT) p2 = gpio.PWM(6,50) p2.start(6) p2.ChangeDutyCycle(tf) time.sleep(0.7) gpio.cleanup() def servo3(tf): gpio.setmode(gpio.BCM) gpio.setup(20,gpio.OUT) p3 = gpio.PWM(20,50) p3.start(6) p3.ChangeDutyCycle(tf) time.sleep(0.7) gpio.cleanup() def servo4(tf): gpio.setmode(gpio.BCM) gpio.setup(16,gpio.OUT) p3 = gpio.PWM(16,50) p3.start(6) p3.ChangeDutyCycle(tf) time.sleep(0.7) gpio.cleanup() def ses(tf,ff): init() sp = gpio.PWM(21,ff) sp.start(70) time.sleep(tf) gpio.cleanup() def led(ff,tf,sf): init() sp = gpio.PWM(solled,500) sap = gpio.PWM(sagled,500) if (sf == 0): sp.start(ff) time.sleep(tf) gpio.cleanup() elif (sf == 1): sap.start(ff) time.sleep(tf) gpio.cleanup() elif (sf == 2): sp.start(ff) sap.start(ff) time.sleep(tf) gpio.cleanup() print (" ") print ("otonomgorev yazilimi google speech api sesli komutlari ile robotun otonom hareket etmesi için yazilmistir") print (" ") time.sleep(1) def cizgi(lf): os.system("aplay -vv /home/pi/Robotics/PortalTurret/Turret_active.wav &") int = 0 for int in range(1,lf): init() if (gpio.input(23) == 0 and gpio.input(24) == 0): ileri(0.1,100) elif (gpio.input(23) == 1 and gpio.input(24) == 0): sol(0.1,100) elif (gpio.input(22) == 0 and gpio.input(24) == 1): sag(0.1,100) else: dur() dur() main() aci2 = aci3 = aci4 = 6 aci = 5.5

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null

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null

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0

6db82d6e06cc11fb7b83d45d0342ef4c6c52a44f

6,093

py

Python

experiments/livecell/validate_model.py

JonasHell/torch-em

2e008e0cd2f0ea6681581374fce4f9f47b986d55

[ "MIT" ]

13

2021-03-09T21:31:09.000Z

2022-03-21T05:24:26.000Z

experiments/livecell/validate_model.py

JonasHell/torch-em

2e008e0cd2f0ea6681581374fce4f9f47b986d55

[ "MIT" ]

16

2021-03-02T23:19:34.000Z

2022-03-25T19:43:41.000Z

experiments/livecell/validate_model.py

JonasHell/torch-em

2e008e0cd2f0ea6681581374fce4f9f47b986d55

[ "MIT" ]

4

2021-05-18T08:29:33.000Z

2022-02-11T12:16:20.000Z

import argparse import os from glob import glob from pathlib import Path import imageio import h5py import pandas as pd from bioimageio.core import load_resource_description from bioimageio.core.prediction import predict_with_padding from bioimageio.core.prediction_pipeline import create_prediction_pipeline from elf.evaluation import mean_average_precision from torch_em.util.segmentation import (connected_components_with_boundaries, mutex_watershed, size_filter) from tqdm import tqdm from xarray import DataArray try: import napari except ImportError: napari = None def segment(prediction_pipeline, path, out_path, view, offsets=None, strides=None, min_seg_size=50): image = imageio.imread(path) assert image.ndim == 2 input_ = DataArray(image[None, None], dims=prediction_pipeline.input_specs[0].axes) padding = {"x": 16, "y": 16} prediction = predict_with_padding(prediction_pipeline, input_, padding)[0][0] foreground, prediction = prediction[0], prediction[1:] if offsets is None: assert prediction.shape[0] == 1, f"{prediction.shape}" prediction = prediction[0] assert foreground.shape == prediction.shape seg = connected_components_with_boundaries(foreground, prediction) else: assert len(offsets) == prediction.shape[0] mask = foreground > 0.5 seg = mutex_watershed(prediction, offsets, mask=mask, strides=strides) seg = size_filter(seg, min_seg_size, hmap=prediction, with_background=True) # implement more postprocessing? # - merge noisy foreground prediction (that only have very weak boundary predictions) into the background if out_path is not None: with h5py.File(out_path, "w") as f: f.create_dataset("prediction", data=prediction, compression="gzip") f.create_dataset("foreground", data=foreground, compression="gzip") f.create_dataset("segmentation", data=seg, compression="gzip") if view: assert napari is not None v = napari.Viewer() v.add_image(image) v.add_image(foreground) v.add_image(prediction) v.add_labels(seg) napari.run() return seg def validate(seg, gt_path): gt = imageio.imread(gt_path) assert gt.shape == seg.shape map_, scores = mean_average_precision(seg, gt, return_aps=True) # map, iou50, iou75, iou90 return [map_, scores[0], scores[5], scores[-1]] def run_prediction(model_path, input_files, target_files, output_folder, view, min_seg_size, device): model = load_resource_description(model_path) offsets, strides = None, None if "mws" in model.config: offsets = model.config["mws"]["offsets"] strides = [4, 4] if output_folder is not None: os.makedirs(output_folder, exist_ok=True) validation_results = [] devices = None if device is None else [device] with create_prediction_pipeline(bioimageio_model=model, devices=devices) as pp: for in_path, target_path in tqdm(zip(input_files, target_files), total=len(input_files)): fname = str(Path(in_path).stem) out_path = None if output_folder is None else os.path.join(output_folder, f"{fname}.h5") seg = segment(pp, in_path, out_path, view, offsets=offsets, strides=strides, min_seg_size=min_seg_size) if target_path: val = validate(seg, target_path) validation_results.append([fname] + val) if validation_results: cols = ["name", "mAP", "IoU50", "IoU75", "IoU90"] validation_results = pd.DataFrame(validation_results, columns=cols) print("Validation results averaged over all", len(input_files), "images:") print(validation_results[cols[1:]].mean(axis=0)) return validation_results # TODO needs update for live-cell data structure def _load_data(input_folder, ext): input_data = glob(os.path.join(input_folder, "images", f"*.{ext}")) input_data.sort() if os.path.exists(os.path.join(input_folder, "masks")): input_target = glob(os.path.join(input_folder, "masks", f"*.{ext}")) input_target.sort() else: input_target = [None] * len(input_data) assert len(input_data) == len(input_target) return input_data, input_target def main(): parser = argparse.ArgumentParser( "Run prediction and segmentation with a bioimagie.io model and save or validate the results." "If 'output_folder' is passed, the results will be saved as hdf5 files with keys:" "prediction: the affinity or boundary predictions" "foreground: the foreground predictions" "segmentation: the nucleus instance segmentation" ) parser.add_argument("-m", "--model", required=True, help="Path to the bioimage.io model.") parser.add_argument("-i", "--input_folder", required=True, help="The root input folder with subfolders 'images' and (optionally) 'masks'") parser.add_argument("--ext", default="tif", help="The file extension of the input files.") parser.add_argument("-o", "--output_folder", default=None, help="Where to save the results.") parser.add_argument("-v", "--view", default=0, help="Whether to show segmentation results (needs napari).", type=int) parser.add_argument("--min_seg_size", default=25, type=int) parser.add_argument("--device", default=None, help="The device used for inference.") parser.add_argument("--save_path", "-s", default=None, help="Where to save a csv with the validation results.") args = parser.parse_args() input_files, target_files = _load_data(args.input_folder, args.ext) res = run_prediction(args.model, input_files, target_files, args.output_folder, view=bool(args.view), min_seg_size=args.min_seg_size, device=args.device) if args.save_path is not None: assert res is not None res.to_csv(args.save_path, index=False) if __name__ == "__main__": main()

42.02069

115

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5.002491

0.265255

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0.070451

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0

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6,093

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0

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0

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0

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0.068376

1

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false

0.008547

0.136752

0

0.213675

0.017094

0

null

0

null

0

1

0

6dbfcff192ece7ab414ec98a8d97692a952d7bdd

7,506

py

Python

main.py

qmn/pershing

ec3cc87d9bfb7ca0cf1da1449d695c36df548309

[ "BSD-2-Clause" ]

16

2017-05-20T05:30:59.000Z

2022-02-08T05:41:52.000Z

main.py

qmn/pershing

ec3cc87d9bfb7ca0cf1da1449d695c36df548309

[ "BSD-2-Clause" ]

null

main.py

qmn/pershing

ec3cc87d9bfb7ca0cf1da1449d695c36df548309

[ "BSD-2-Clause" ]

3

2016-09-18T15:55:37.000Z

2020-12-27T15:36:09.000Z

#!/usr/bin/env python2.7 from __future__ import print_function import json import sys import numpy as np import os.path import time from math import ceil import argparse import nbt from util import blif, cell, cell_library from placer import placer from router import router, extractor, minetime from vis import png from inserter import inserter def underline_print(s): print() print(s) print("-" * len(s)) if __name__ == "__main__": placements = None dimensions = None routing = None # Create parser parser = argparse.ArgumentParser(description="An automatic place-and-route tool for Minecraft Redstone circuits.") parser.add_argument('blif', metavar="<input BLIF file>") parser.add_argument('-o', '--output_dir', metavar="output_directory", dest="output_dir") parser.add_argument('--library', metavar="library_file", dest="library_file", default="lib/quan.yaml") parser.add_argument('--placements', metavar="placements_file", dest="placements_file", help="Use this placements file rather than creating one. Must be previously generated from the supplied BLIF.") parser.add_argument('--routings', metavar="routings_file", dest="routings_file", help="Use this routings file rather than creating one. Must be previously generated from the supplied BLIF and placements JSON.") parser.add_argument('--world', metavar="world_folder", dest="world_folder", help="Place the extracted redstone circuit layout in this world.") args = parser.parse_args() # Load placements, if provided if args.placements_file is not None: print("Using placements file:", args.placements_file) with open(args.placements_file) as f: placements = json.loads(f.readline()) dimensions = json.loads(f.readline()) # Load library file with open(args.library_file) as f: cell_lib = cell_library.load(f) # Load BLIF with open(args.blif) as f: blif = blif.load(f) # Result directory if args.output_dir is not None: if os.path.isabs(args.output_dir): result_dir = args.output_dir else: result_dir = os.path.abspath(args.output_dir) else: result_base, _ = os.path.splitext(args.blif) result_dir = os.path.abspath(result_base + "_result") # Try making the directory if not os.path.exists(result_dir): try: os.mkdir(result_dir) print("Made result dir: ", result_dir) except OSError as e: print(e) pregenerated_cells = cell_library.pregenerate_cells(cell_lib, pad=1) placer = placer.GridPlacer(blif, pregenerated_cells, grid_spacing=5) start_time = time.time() print("Started", time.strftime("%c", time.localtime(start_time))) # PLACE ============================================================= if placements is None: underline_print("Performing Initial Placement...") placements, dimensions = placer.initial_placement() score = placer.score(placements, dimensions) print("Initial Placement Penalty:", score) underline_print("Doing Placement...") # Place cells T_0 = 250 iterations = 2000 new_placements = placer.simulated_annealing_placement(placements, dimensions, T_0, iterations) placements, dimensions = placer.shrink(new_placements) # Place pins and resize placements += placer.place_pins(dimensions) placements, dimensions = placer.shrink(placements) # print(new_placements) print("Placed", len(placements), "cells") with open(os.path.join(result_dir, "placements.json"), "w") as f: json.dump(placements, f) f.write("\n") json.dump(dimensions, f) # Visualize this layout layout = placer.placement_to_layout(dimensions, placements) png.layout_to_png(layout, filename_base=os.path.join(result_dir, "composite")) print("Dimensions:", dimensions) # ROUTE ============================================================= underline_print("Doing Routing...") placements, dimensions = placer.shrink(placements) layout = placer.placement_to_layout(dimensions, placements) router = router.Router(blif, pregenerated_cells) # Load routings, if provided if args.routings_file is not None: print("Using routings file:", args.routings_file) with open(args.routings_file) as f: routing = router.deserialize_routing(f) if routing is None: blocks, data = layout print("Doing initial routing...") routing = router.initial_routing(placements, blocks.shape) print("done.") routing = router.re_route(routing, layout) # Preserve routing with open(os.path.join(result_dir, "routing.json"), "w") as f: router.serialize_routing(routing, dimensions, f) print("Routed", len(routing), "nets") # EXTRACT =========================================================== underline_print("Doing Extraction...") extractor = extractor.Extractor(blif, pregenerated_cells) extracted_routing = extractor.extract_routing(routing) extracted_layout = extractor.extract_layout(extracted_routing, layout) with open(os.path.join(result_dir, "extraction.json"), "w") as f: blocks, data = extracted_layout json.dump(blocks.tolist(), f) json.dump(data.tolist(), f) print("Wrote extraction to extraction.json") # VISUALIZE ========================================================= underline_print("Doing Visualization...") # Get the pins pins = placer.locate_circuit_pins(placements) # png.nets_to_png(layout, routing) png_fn = os.path.join(result_dir, "layout.png") png.layout_to_composite(extracted_layout, pins=pins).save(png_fn) print("Image written to ", png_fn) # MINETIME ========================================================= underline_print("Doing Timing Analysis with MineTime...") mt = minetime.MineTime() path_delays = mt.compute_combinational_delay(placements, extracted_routing, cell_lib) print("Path delays:") for path_delay, path in sorted(path_delays, key=lambda x: x[0], reverse=True): print(path_delay, " ", " -> ".join(path)) print() crit_delay, crit_path = max(path_delays, key=lambda x: x[0]) print("Critical path delay: {} ticks".format(crit_delay)) print("Minimum period: {:.2f} s".format(crit_delay * 0.05)) print("Maximum frequency: {:.4f} Hz".format(1./(crit_delay * 0.05))) underline_print("Design Statistics") blocks, _ = layout print("Layout size: {} x {} x {}".format(blocks.shape[0], blocks.shape[1], blocks.shape[2])) print(" Blocks placed: {}".format(sum(blocks.flat != 0))) print() print("Total nets: {}".format(len(extracted_routing))) print(" Segments routed: {}".format(sum(len(net["segments"]) for net in extracted_routing.itervalues()))) print() end_time = time.time() print("Finished", time.strftime("%c", time.localtime(end_time)), "(took", ceil(end_time - start_time), "s)") # INSERTION ======================================================== if args.world_folder is not None: underline_print("Inserting Design into Minecraft World...") world = nbt.world.WorldFolder(args.world_folder) inserter.insert_extracted_layout(world, extracted_layout, offset=(4, 0, 0))

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7,506

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0

1

0.007519

false

0

0.105263

0

0.112782

0.293233

0

null

0

null

0

1

0

6dc096d4b45dd4acd7b5d28912a696afdc093628

296

py

Python

logtest.py

jonathanstrong/log-viewer

83374de21ce807709217e3fffa87b75265b3edd6

[ "MIT" ]

1

2017-03-09T01:18:06.000Z

logtest.py

jonathanstrong/log-viewer

83374de21ce807709217e3fffa87b75265b3edd6

[ "MIT" ]

null

logtest.py

jonathanstrong/log-viewer

83374de21ce807709217e3fffa87b75265b3edd6

[ "MIT" ]

null

import logging import logging.handlers import time logger = logging.getLogger(__name__) handler = logging.handlers.SocketHandler('localhost', 9033) stream = logging.StreamHandler() logger.addHandler(handler) logger.addHandler(stream) while True: logger.warning('ping') time.sleep(.001)

22.769231

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0

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296

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24.666667

0.833333

0

0.043919

0

1

0

false

0

0.272727

0

0.272727

0

null

0

null

0

1

0

6dc161a661b51dac6f78e1e2949123e1dfec52e8

5,032

py

Python

text-builder.py

guskma/text-builder

e9de6178ef5ce71a6f022b7932d40a906200578e

[ "MIT" ]

null

text-builder.py

guskma/text-builder

e9de6178ef5ce71a6f022b7932d40a906200578e

[ "MIT" ]

null

text-builder.py

guskma/text-builder

e9de6178ef5ce71a6f022b7932d40a906200578e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function from argparse import ArgumentParser from collections import OrderedDict import jinja2 import csv import sys import os.path import re def store_keyval(src_dict, key, val): if key is None: return val if type(src_dict) is not OrderedDict: src_dict = OrderedDict() matched = re.match(r'^([^\.\[\]]+?)(?:\[([\d]+|@?)\])?(?:\.(.+))?$', key) if matched is None: print(f'Invalid key name: {key}') return src_dict key_name = matched.group(1) key_index_str = matched.group(2) key_dict = matched.group(3) is_array = key_index_str is not None is_dict = key_dict is not None key_exists = key_name in src_dict.keys() if is_array and not key_exists: src_dict[key_name] = [None] elif is_dict and not key_exists: src_dict[key_name] = OrderedDict() if is_array: if key_index_str == '@': key_index = len(src_dict[key_name]) - 1 elif not key_index_str and src_dict[key_name][0] is None: key_index = 0 elif not key_index_str: key_index = len(src_dict[key_name]) else: key_index = int(key_index_str) key_len = len(src_dict[key_name]) if key_len < key_index + 1: src_dict[key_name].extend([None] * (key_index - key_len + 1)) src_dict[key_name][key_index] = store_keyval(src_dict[key_name][key_index], key_dict, val) elif is_dict: src_dict[key_name] = store_keyval(src_dict[key_name], key_dict, val) else: src_dict[key_name] = val return src_dict def build_templates(args): if args.DEBUG: print('* === text-builder execute. ===') templateLoader = jinja2.FileSystemLoader(searchpath='.', encoding=args.ENCODING) templateEnv = jinja2.Environment( loader=templateLoader ) templateEnv.trim_blocks = True newline = args.NEWLINE.replace(r'\r', "\r").replace(r'\n', "\n") if args.DEBUG: sys.stdout.write('* Loading INVENTORY file ... ') f = open(args.INVENTORY, 'rt', encoding=args.ENCODING, newline=newline) if args.DEBUG: print('Done.') try: if args.DEBUG: print('* Loading header.') reader = list(csv.reader(f)) header = reader.pop(0) header_cols = len(header) parsed_files = 0 for row in reader: if args.DEBUG: sys.stdout.write(f'* Building row({parsed_files + 2}): ') dict_row = OrderedDict() cols = len(row) for i in range(cols if cols > header_cols else header_cols): if header_cols <= i: continue elif cols <= i: col = "" else: col = row[i] dict_row = store_keyval(dict_row, header[i], col) if args.DEBUG: print(dict_row) template = templateEnv.get_template(args.TEMPLATE) outputText = template.render(dict_row) output_dir = args.OUTPUTS_DIR if 'output_dir' in dict_row and dict_row['output_dir'].strip() != '': output_dir = f"{output_dir}/{dict_row['output_dir'].strip()}" os.makedirs(output_dir, exist_ok=True) filename = dict_row['filename'] if 'filename' in dict_row else f"parsed_{parsed_files}.txt" output_filename = f"{output_dir}/{filename}" with open(output_filename, 'w', newline=newline, encoding=args.ENCODING) as output_file: output_file.write(outputText) print("wrote file: %s" % output_filename) parsed_files += 1 print(f"\nDone. output {parsed_files} files in \"{output_dir}\" directory.") finally: f.close() def cmd_options(): usage = f"text-builder <TEMPLATE> <INVENTORY> [-ehno]" argparser = ArgumentParser(usage=usage) argparser.add_argument( 'TEMPLATE', type=str, help='Template text file.') argparser.add_argument( 'INVENTORY', type=str, help='Paramaters CSV file.') argparser.add_argument( '-d', '--debug', dest='DEBUG', action='store_true', help='Output debug message.') argparser.add_argument( '-e', '--encoding', type=str, dest='ENCODING', default='cp932', help='Set encoding charset of template and inventory file. (default: "cp932")') argparser.add_argument( '-n', '--new-line', type=str, dest='NEWLINE', default="\r\n", help='Set new line charcode. (default: "\\r\\n")') argparser.add_argument( '-o', '--output-path', type=str, default='output', dest='OUTPUTS_DIR', help='Set output files path.') args = argparser.parse_args() return args if __name__ == "__main__": args = cmd_options() build_templates(args)

29.775148

103

0.584261

629

5,032

4.45787

0.235294

0.044936

0.049929

0.064907

0.136947

0.093795

0.066334

0.046362

0.025678

0

0.00615

0.289149

5,032

168

104

29.952381

0.777747

0.008347

0

0.165414

0

0.152366

0.027666

0

1

0.022556

false

0

0.06015

0

0.112782

0.06015

0

null

0

null

0

1

0

6dc1f2e93753dd6196949aaa91494539baeb31b1

2,077

py

Python

hard_grasp.py

bionicdl-sustech/AmphibiousManipulation

397c7dfef6b4dda178a567c36aabfe0f4b05b821

[ "MIT" ]

null

hard_grasp.py

bionicdl-sustech/AmphibiousManipulation

397c7dfef6b4dda178a567c36aabfe0f4b05b821

[ "MIT" ]

null

hard_grasp.py

bionicdl-sustech/AmphibiousManipulation

397c7dfef6b4dda178a567c36aabfe0f4b05b821

[ "MIT" ]

null

import yaml import os import sys import time import numpy as np import cv2 as cv from franka.FrankaController import FrankaController def read_cfg(path): with open(path, 'r') as stream: out = yaml.safe_load(stream) return out if __name__ == '__main__': ROOT = os.path.dirname(os.path.abspath(__file__)) sys.path.append(ROOT) cfg = read_cfg(ROOT + '/config/grasping _colorseg.yaml') arm = FrankaController(ROOT + '/config/franka.yaml') # grasping config initial_pose = cfg['initial_position'] initial_pose[2] -= 0.3 check_position = cfg['check_position'] drop_position = cfg['drop_position'] grasp_pre_offset = cfg['grasp_prepare_offset'] effector_offset = cfg['effector_offset'] check_threshold = cfg['check_threshold'] attmp_num = cfg['attmp_num'] print("Moving to initial position...") arm.move_p(initial_pose) print("Moving to initial position... Done") stored_exception = None arm.move_p(initial_pose) current_num = 0 while current_num < attmp_num: try: if stored_exception: break target_in_base = drop_position.copy() target_in_base[2] -= 0.37 prepare_pos = [target_in_base[0], target_in_base[1], target_in_base[2] + grasp_pre_offset + effector_offset, 3.14, 0, 0] arm.move_p(prepare_pos) arm.gripperOpen() arm.move_p([target_in_base[0], target_in_base[1], target_in_base[2] + effector_offset, 3.14, 0, 0]) arm.gripperGrasp(width=0.05, force=2) time.sleep(0.5) # Move to check position # arm.move_p(check_position) arm.move_p(initial_pose) # Move to drop position and drop object arm.move_p(drop_position) arm.gripperOpen() # Back to initial position arm.move_p(initial_pose) current_num += 1 except KeyboardInterrupt: stored_exception = sys.exc_info() cv.destroyAllWindows()

25.9625

132

0.629273

271

2,077

4.535055

0.328413

0.045566

0.052075

0.264443

0.211554

0.189585

0.12205

0.063466

0

0.021136

0.271064

2,077

79

133

26.291139

0.790621

0.061627

0

0.12

0

0.115286

0

1

0.02

false

0

0.14

0

0.18

0.04

0

null

0

null

0

1

0

6dc710027aba9309fc1e58e6facfd13ec0253ff6

731

py

Python

Domain_Knowledge_based/stanfordNER.py

Mount428/Hate-Speech-Detection

f8644844dda954ebd169aeec54cb4c7361d88a09

[ "MIT" ]

null

Domain_Knowledge_based/stanfordNER.py

Mount428/Hate-Speech-Detection

f8644844dda954ebd169aeec54cb4c7361d88a09

[ "MIT" ]

null

Domain_Knowledge_based/stanfordNER.py

Mount428/Hate-Speech-Detection

f8644844dda954ebd169aeec54cb4c7361d88a09

[ "MIT" ]

null

from nltk.tag import StanfordNERTagger import pandas as pd from sklearn.metrics import f1_score, confusion_matrix from loader import Load train, test = Load('c') ner = StanfordNERTagger('./stanford-ner-2018-10-16/classifiers/english.all.3class.distsim.crf.ser.gz', './stanford-ner-2018-10-16/stanford-ner.jar') data = train data['tweet'] = ner.tag_sents(data['tweet'].str.split(' ')) pred = [] for i, d in data.iterrows(): tweet = d['tweet'] tag = 'IND' for w in tweet: if w[1] == 'ORGANIZATION': tag = 'GRP' # elif w[1] == 'PEOPLE': # tag = 'IND' pred.append(tag) print(confusion_matrix(data['label'], pred)) print(f1_score(data['label'], pred, average='macro'))

22.84375

148

0.641587

104

731

4.461538

0.548077

0.071121

0.064655

0.073276

0.081897

0

0.035473

0.19015

731

31

149

23.580645

0.748311

0.051984

0

0.055556

0.24238

0.169811

0

1

0

false

0

0.222222

0

0.222222

0.111111

0

null

0

null

0

1

0

6dc79c5bcc11f79748762758e10ea27d0fe9f70f

41,355

py

Python

pyrif/FuXi/Rete/Network.py

mpetyx/pyrif

2f7ba863030d7337bb39ad502d1e09e26ac950d2

[ "MIT" ]

null

pyrif/FuXi/Rete/Network.py

mpetyx/pyrif

2f7ba863030d7337bb39ad502d1e09e26ac950d2

[ "MIT" ]

null

pyrif/FuXi/Rete/Network.py

mpetyx/pyrif

2f7ba863030d7337bb39ad502d1e09e26ac950d2

[ "MIT" ]

null

""" ==================================================================================== A Rete Network Building and 'Evaluation' Implementation for RDFLib Graphs of Notation 3 rules. The DLP implementation uses this network to automatically building RETE decision trees for OWL forms of DLP Uses Python hashing mechanism to maximize the efficiency of the built pattern network. The network : - compiles an RDFLib N3 rule graph into AlphaNode and BetaNode instances - takes a fact (or the removal of a fact, perhaps?) and propagates down, starting from its alpha nodes - stores inferred triples in provided triple source (an RDFLib graph) or a temporary IOMemory Graph by default """ from itertools import chain import sys import time from pprint import pprint try: from functools import reduce except ImportError: pass try: from io import StringIO except ImportError: from StringIO import StringIO from .BetaNode import ( BetaNode, LEFT_MEMORY, RIGHT_MEMORY, PartialInstantiation, ) from .AlphaNode import ( AlphaNode, BuiltInAlphaNode, ReteToken, ) from FuXi.Horn import ( ComplementExpansion, DATALOG_SAFETY_NONE, DATALOG_SAFETY_STRICT, DATALOG_SAFETY_LOOSE, ) from FuXi.Syntax.InfixOWL import Class from FuXi.Horn.PositiveConditions import ( Exists, GetUterm, Or, SetOperator, Uniterm, ) from FuXi.DLP import ( MapDLPtoNetwork, non_DHL_OWL_Semantics, ) from FuXi.DLP.ConditionalAxioms import AdditionalRules from .Util import ( generateTokenSet, renderNetwork, xcombine, ) from rdflib.graph import ( ConjunctiveGraph, Graph, ReadOnlyGraphAggregate, ) from rdflib.namespace import NamespaceManager from rdflib import ( BNode, Literal, Namespace, RDF, RDFS, URIRef, Variable, ) from rdflib import py3compat from rdflib.util import first from .ReteVocabulary import RETE_NS from .RuleStore import ( Formula, N3Builtin, N3RuleStore, ) OWL_NS = Namespace("http://www.w3.org/2002/07/owl#") Any = None LOG = Namespace("http://www.w3.org/2000/10/swap/log#") #From itertools recipes def iteritems(mapping): return list(zip(iter(mapping.keys()), iter(mapping.values()))) def any(seq, pred=None): """Returns True if pred(x) is true for at least one element in the iterable""" for elem in filter(pred, seq): return True return False class HashablePatternList(object): """ A hashable list of N3 statements which are patterns of a rule. Order is disregarded by sorting based on unicode value of the concatenation of the term strings (in both triples and function builtins invokations). This value is also used for the hash. In this way, patterns with the same terms but in different order are considered equivalent and share the same Rete nodes >>> nodes = {} >>> a = HashablePatternList([(Variable('X'), Literal(1), Literal(2))]) >>> nodes[a] = 1 >>> nodes[HashablePatternList([None]) + a] = 2 >>> b = HashablePatternList([(Variable('Y'), Literal(1), Literal(2))]) >>> b in a #doctest: +SKIP True >>> a == b #doctest: +SKIP True """ def __init__(self, items=None, skipBNodes=False): self.skipBNodes = skipBNodes if items: self._l = items else: self._l = [] def _hashRulePattern(self, item): """ Generates a unique hash for RDF triples and N3 builtin invokations. The hash function consists of the hash of the terms concatenated in order """ if isinstance(item, tuple): return reduce(lambda x, y: x + y, [ i for i in item if not self.skipBNodes or not isinstance(i, BNode) ]) elif isinstance(item, N3Builtin): return reduce(lambda x, y: x + y, [item.argument, item.result]) def __len__(self): return len(self._l) def __getslice__(self, beginIdx, endIdx): return HashablePatternList(self._l[beginIdx:endIdx]) def __hash__(self): if self._l: _concatPattern = [pattern and self._hashRulePattern(pattern) or "None" for pattern in self._l] #nulify the impact of order in patterns _concatPattern.sort() return hash(reduce(lambda x, y: x + y, _concatPattern)) else: return hash(None) def __add__(self, other): assert isinstance(other, HashablePatternList), other return HashablePatternList(self._l + other._l) def __repr__(self): return repr(self._l) def extend(self, other): assert isinstance(other, HashablePatternList), other self._l.extend(other._l) def append(self, other): self._l.append(other) def __iter__(self): return iter(self._l) def __eq__(self, other): return hash(self) == hash(other) def _mulPatternWithSubstitutions(tokens, consequent, termNode): """ Takes a set of tokens and a pattern and returns an iterator over consequent triples, created by applying all the variable substitutions in the given tokens against the pattern >>> aNode = AlphaNode((Variable('S'), Variable('P'), Variable('O'))) >>> token1 = ReteToken((URIRef('urn:uuid:alpha'), OWL_NS.differentFrom, URIRef('urn:uuid:beta'))) >>> token2 = ReteToken((URIRef('urn:uuid:beta'), OWL_NS.differentFrom, URIRef('urn:uuid:alpha'))) >>> token1 = token1.bindVariables(aNode) >>> token2 = token2.bindVariables(aNode) >>> inst = PartialInstantiation([token1, token2]) """ # success = False for binding in tokens.bindings: tripleVals = [] # if any(consequent, # lambda term:isinstance(term, Variable) and term not in binding):# not mismatchedTerms: # return # else: for term in consequent: if isinstance(term, (Variable, BNode)) and term in binding: #try: tripleVals.append(binding[term]) #except: # pass else: tripleVals.append(term) yield tuple(tripleVals), binding class InferredGoal(Exception): def __init__(self, msg): self.msg = msg def __repr__(self): return "Goal inferred.: %" % self.msg class ReteNetwork: """ The Rete network. The constructor takes an N3 rule graph, an identifier (a BNode by default), an initial Set of Rete tokens that serve as the 'working memory', and an rdflib Graph to add inferred triples to - by forward-chaining via Rete evaluation algorithm), """ def __init__(self, ruleStore, name=None, initialWorkingMemory=None, inferredTarget=None, nsMap={}, graphVizOutFile=None, dontFinalize=False, goal=None): self.leanCheck = {} self.goal = goal self.nsMap = nsMap self.name = name and name or BNode() self.nodes = {} self.alphaPatternHash = {} self.ruleSet = set() for alphaPattern in xcombine(('1', '0'), ('1', '0'), ('1', '0')): self.alphaPatternHash[tuple(alphaPattern)] = {} if inferredTarget is None: self.inferredFacts = Graph() namespace_manager = NamespaceManager(self.inferredFacts) for k, v in list(nsMap.items()): namespace_manager.bind(k, v) self.inferredFacts.namespace_manager = namespace_manager else: self.inferredFacts = inferredTarget self.workingMemory = initialWorkingMemory and initialWorkingMemory or set() self.proofTracers = {} self.terminalNodes = set() self.instantiations = {} start = time.time() self.ruleStore = ruleStore self.justifications = {} self.dischargedBindings = {} if not dontFinalize: self.ruleStore._finalize() self.filteredFacts = Graph() #'Universal truths' for a rule set are rules where the LHS is empty. # Rather than automatically adding them to the working set, alpha nodes are 'notified' # of them, so they can be checked for while performing inter element tests. self.universalTruths = [] from FuXi.Horn.HornRules import Ruleset self.rules = set() self.negRules = set() for rule in Ruleset(n3Rules=self.ruleStore.rules, nsMapping=self.nsMap): import warnings warnings.warn( "Rules in a network should be built *after* construction via " + " self.buildNetworkClause(HornFromN3(n3graph)) for instance", DeprecationWarning, 2) self.buildNetworkFromClause(rule) self.alphaNodes = [node for node in list(self.nodes.values()) if isinstance(node, AlphaNode)] self.alphaBuiltInNodes = [node for node in list(self.nodes.values()) if isinstance(node, BuiltInAlphaNode)] self._setupDefaultRules() if initialWorkingMemory: start = time.time() self.feedFactsToAdd(initialWorkingMemory) print("Time to calculate closure on working memory: %s m seconds" % ( (time.time() - start) * 1000)) if graphVizOutFile: print("Writing out RETE network to ", graphVizOutFile) renderNetwork(self, nsMap=nsMap).write(graphVizOutFile) def getNsBindings(self, nsMgr): for prefix, Uri in nsMgr.namespaces(): self.nsMap[prefix] = Uri def buildFilterNetworkFromClause(self, rule): lhs = BNode() rhs = BNode() builtins = [] for term in rule.formula.body: if isinstance(term, N3Builtin): #We want to move builtins to the 'end' of the body #so they only apply to the terminal nodes of #the corresponding network builtins.append(term) else: self.ruleStore.formulae.setdefault(lhs, Formula(lhs)).append(term.toRDFTuple()) for builtin in builtins: self.ruleStore.formulae.setdefault(lhs, Formula(lhs)).append(builtin.toRDFTuple()) nonEmptyHead = False for term in rule.formula.head: nonEmptyHead = True assert not hasattr(term, 'next') assert isinstance(term, Uniterm) self.ruleStore.formulae.setdefault(rhs, Formula(rhs)).append(term.toRDFTuple()) assert nonEmptyHead, "Filters must conclude something." self.ruleStore.rules.append((self.ruleStore.formulae[lhs], self.ruleStore.formulae[rhs])) tNode = self.buildNetwork(iter(self.ruleStore.formulae[lhs]), iter(self.ruleStore.formulae[rhs]), rule, aFilter=True) self.alphaNodes = [node for node in list(self.nodes.values()) if isinstance(node, AlphaNode)] self.rules.add(rule) return tNode def buildNetworkFromClause(self, rule): lhs = BNode() rhs = BNode() builtins = [] for term in rule.formula.body: if isinstance(term, N3Builtin): #We want to move builtins to the 'end' of the body #so they only apply to the terminal nodes of #the corresponding network builtins.append(term) else: self.ruleStore.formulae.setdefault(lhs, Formula(lhs)).append(term.toRDFTuple()) for builtin in builtins: self.ruleStore.formulae.setdefault(lhs, Formula(lhs)).append(builtin.toRDFTuple()) nonEmptyHead = False for term in rule.formula.head: nonEmptyHead = True assert not hasattr(term, 'next') assert isinstance(term, Uniterm) self.ruleStore.formulae.setdefault(rhs, Formula(rhs)).append(term.toRDFTuple()) if not nonEmptyHead: import warnings warnings.warn( "Integrity constraints (rules with empty heads) are not supported: %s" % rule, SyntaxWarning, 2) return self.ruleStore.rules.append((self.ruleStore.formulae[lhs], self.ruleStore.formulae[rhs])) tNode = self.buildNetwork(iter(self.ruleStore.formulae[lhs]), iter(self.ruleStore.formulae[rhs]), rule) self.alphaNodes = [node for node in list(self.nodes.values()) if isinstance(node, AlphaNode)] self.rules.add(rule) return tNode def calculateStratifiedModel(self, database): """ Stratified Negation Semantics for DLP using SPARQL to handle the negation """ if not self.negRules: return from FuXi.DLP.Negation import StratifiedSPARQL # from FuXi.Rete.Magic import PrettyPrintRule import copy noNegFacts = 0 for i in self.negRules: #Evaluate the Graph pattern, and instanciate the head of the rule with #the solutions returned nsMapping = dict([(v, k) for k, v in list(self.nsMap.items())]) sel, compiler = StratifiedSPARQL(i, nsMapping) query = compiler.compile(sel) i.stratifiedQuery = query vars = sel.projection unionClosureG = self.closureGraph(database) for rt in unionClosureG.query(query): solutions = {} if isinstance(rt, tuple): solutions.update(dict([(vars[idx], i) for idx, i in enumerate(rt)])) else: solutions[vars[0]] = rt i.solutions = solutions head = copy.deepcopy(i.formula.head) head.ground(solutions) fact = head.toRDFTuple() self.inferredFacts.add(fact) self.feedFactsToAdd(generateTokenSet([fact])) noNegFacts += 1 #Now we need to clear assertions that cross the individual, concept, relation divide # toRemove = [] for s, p, o in self.inferredFacts.triples((None, RDF.type, None)): if s in unionClosureG.predicates() or\ s in [_s for _s, _p, _o in unionClosureG.triples_choices( (None, RDF.type, [OWL_NS.Class, OWL_NS.Restriction]))]: self.inferredFacts.remove((s, p, o)) return noNegFacts def setupDescriptionLogicProgramming(self, owlN3Graph, expanded=[], addPDSemantics=True, classifyTBox=False, constructNetwork=True, derivedPreds=[], ignoreNegativeStratus=False, safety=DATALOG_SAFETY_NONE): rt = [rule for rule in MapDLPtoNetwork(self, owlN3Graph, complementExpansions=expanded, constructNetwork=constructNetwork, derivedPreds=derivedPreds, ignoreNegativeStratus=ignoreNegativeStratus, safety=safety)] if ignoreNegativeStratus: rules, negRules = rt rules = set(rules) self.negRules = set(negRules) else: rules = set(rt) if constructNetwork: self.rules.update(rules) additionalRules = set(AdditionalRules(owlN3Graph)) if addPDSemantics: from FuXi.Horn.HornRules import HornFromN3 additionalRules.update(HornFromN3(StringIO(non_DHL_OWL_Semantics))) if constructNetwork: for rule in additionalRules: self.buildNetwork(iter(rule.formula.body), iter(rule.formula.head), rule) self.rules.add(rule) else: rules.update(additionalRules) if constructNetwork: rules = self.rules # noRules = len(rules) if classifyTBox: self.feedFactsToAdd(generateTokenSet(owlN3Graph)) # print("##### DLP rules fired against OWL/RDF TBOX", self) return rules def reportSize(self, tokenSizeThreshold=1200, stream=sys.stdout): for pattern, node in list(self.nodes.items()): if isinstance(node, BetaNode): for largeMem in [i for i in iter(node.memories.values()) if len(i) > tokenSizeThreshold]: if largeMem: print("Large apha node memory extent: ") pprint(pattern) print(len(largeMem)) def reportConflictSet(self, closureSummary=False, stream=sys.stdout): tNodeOrder = [tNode for tNode in self.terminalNodes if self.instantiations.get(tNode, 0)] tNodeOrder.sort(key=lambda x: self.instantiations[x], reverse=True) for termNode in tNodeOrder: print(termNode) print("\t", termNode.clauseRepresentation()) print("\t\t%s instantiations" % self.instantiations[termNode]) if closureSummary: print(self.inferredFacts.serialize( destination=stream, format='turtle')) def parseN3Logic(self, src): store = N3RuleStore(additionalBuiltins=self.ruleStore.filters) Graph(store).parse(src, format='n3') store._finalize() assert len(store.rules), "There are no rules passed in." from FuXi.Horn.HornRules import Ruleset for rule in Ruleset(n3Rules=store.rules, nsMapping=self.nsMap): self.buildNetwork(iter(rule.formula.body), iter(rule.formula.head), rule) self.rules.add(rule) self.alphaNodes = [node for node in list(self.nodes.values()) if isinstance(node, AlphaNode)] self.alphaBuiltInNodes = [node for node in list(self.nodes.values()) if isinstance(node, BuiltInAlphaNode)] def __repr__(self): total = 0 for node in list(self.nodes.values()): if isinstance(node, BetaNode): total += len(node.memories[LEFT_MEMORY]) total += len(node.memories[RIGHT_MEMORY]) return "<Network: %s rules, %s nodes, %s tokens in working memory, %s inferred tokens>" % ( len(self.terminalNodes), len(self.nodes), total, len(self.inferredFacts)) def closureGraph(self, sourceGraph, readOnly=True, store=None): if readOnly: if store is None and not sourceGraph: store = Graph().store store = store is None and sourceGraph.store or store roGraph = ReadOnlyGraphAggregate([sourceGraph, self.inferredFacts], store=store) roGraph.namespace_manager = NamespaceManager(roGraph) for srcGraph in [sourceGraph, self.inferredFacts]: for prefix, uri in srcGraph.namespaces(): roGraph.namespace_manager.bind(prefix, uri) return roGraph else: cg = ConjunctiveGraph() cg += sourceGraph cg += self.inferredFacts return cg def _setupDefaultRules(self): """ Checks every alpha node to see if it may match against a 'universal truth' (one w/out a LHS) """ for node in list(self.nodes.values()): if isinstance(node, AlphaNode): node.checkDefaultRule(self.universalTruths) def clear(self): self.nodes = {} self.alphaPatternHash = {} self.rules = set() for alphaPattern in xcombine(('1', '0'), ('1', '0'), ('1', '0')): self.alphaPatternHash[tuple(alphaPattern)] = {} self.proofTracers = {} self.terminalNodes = set() self.justifications = {} self._resetinstantiationStats() self.workingMemory = set() self.dischargedBindings = {} def reset(self, newinferredFacts=None): "Reset the network by emptying the memory associated with all Beta Nodes nodes" for node in list(self.nodes.values()): if isinstance(node, BetaNode): node.memories[LEFT_MEMORY].reset() node.memories[RIGHT_MEMORY].reset() self.justifications = {} self.proofTracers = {} self.inferredFacts = newinferredFacts if newinferredFacts is not None else Graph() self.workingMemory = set() self._resetinstantiationStats() def fireConsequent(self, tokens, termNode, debug=False): """ "In general, a p-node also contains a specifcation of what production it corresponds to | the name of the production, its right-hand-side actions, etc. A p-node may also contain information about the names of the variables that occur in the production. Note that variable names are not mentioned in any of the Rete node data structures we describe in this chapter. This is intentional |it enables nodes to be shared when two productions have conditions with the same basic form, but with different variable names." Takes a set of tokens and the terminal Beta node they came from and fires the inferred statements using the patterns associated with the terminal node. Statements that have been previously inferred or already exist in the working memory are not asserted """ if debug: print("%s from %s" % (tokens, termNode)) # newTokens = [] termNode.instanciatingTokens.add(tokens) def iterCondition(condition): if isinstance(condition, Exists): return condition.formula return isinstance(condition, SetOperator) and condition or iter([condition]) def extractVariables(term, existential=True): if isinstance(term, existential and BNode or Variable): yield term elif isinstance(term, Uniterm): for t in term.toRDFTuple(): if isinstance(t, existential and BNode or Variable): yield t #replace existentials in the head with new BNodes! BNodeReplacement = {} for rule in termNode.rules: if isinstance(rule.formula.head, Exists): for bN in rule.formula.head.declare: if not isinstance(rule.formula.body, Exists) or \ bN not in rule.formula.body.declare: BNodeReplacement[bN] = BNode() for rhsTriple in termNode.consequent: if BNodeReplacement: rhsTriple = tuple([BNodeReplacement.get(term, term) for term in rhsTriple]) if debug: if not tokens.bindings: tokens._generateBindings() key = tuple([None if isinstance(item, BNode) else item for item in rhsTriple]) override, executeFn = termNode.executeActions.get(key, (None, None)) if override: #There is an execute action associated with this production #that is attaced to the given consequent triple and #is meant to perform all of the production duties #(bypassing the inference of triples, etc.) executeFn(termNode, None, tokens, None, debug) else: for inferredTriple, binding in _mulPatternWithSubstitutions(tokens, rhsTriple, termNode): if [term for term in inferredTriple if isinstance(term, Variable)]: #Unfullfilled bindings (skip non-ground head literals) if executeFn: #The indicated execute action is supposed to be triggered #when the indicates RHS triple is inferred for the #(even if it is not ground) executeFn(termNode, inferredTriple, tokens, binding, debug) continue # if rhsTriple[1].find('subClassOf_derived')+1:import pdb;pdb.set_trace() inferredToken = ReteToken(inferredTriple) self.proofTracers.setdefault(inferredTriple, []).append(binding) self.justifications.setdefault(inferredTriple, set()).add(termNode) if termNode.filter and inferredTriple not in self.filteredFacts: self.filteredFacts.add(inferredTriple) if inferredTriple not in self.inferredFacts and inferredToken not in self.workingMemory: # if (rhsTriple == (Variable('A'), RDFS.RDFSNS['subClassOf_derived'], Variable('B'))): # import pdb;pdb.set_trace() if debug: print("Inferred triple: ", inferredTriple, " from ", termNode.clauseRepresentation()) inferredToken.debug = True self.inferredFacts.add(inferredTriple) self.addWME(inferredToken) currIdx = self.instantiations.get(termNode, 0) currIdx += 1 self.instantiations[termNode] = currIdx if executeFn: #The indicated execute action is supposed to be triggered #when the indicates RHS triple is inferred for the #first time executeFn(termNode, inferredTriple, tokens, binding, debug) if self.goal is not None and self.goal in self.inferredFacts: raise InferredGoal("Proved goal " + repr(self.goal)) else: if debug: print("Inferred triple skipped: ", inferredTriple) if executeFn: #The indicated execute action is supposed to be triggered #when the indicates RHS triple is inferred for the #first time executeFn(termNode, inferredTriple, tokens, binding, debug) def addWME(self, wme): """ procedure add-wme (w: WME) exhaustive hash table versiong let v1, v2, and v3 be the symbols in the three fields of w alpha-mem = lookup-in-hash-table (v1, v2, v3) if alpha-mem then alpha-memory-activation (alpha-mem, w) alpha-mem = lookup-in-hash-table (v1, v2, *) if alpha-mem then alpha-memory-activation (alpha-mem, w) alpha-mem = lookup-in-hash-table (v1, *, v3) if alpha-mem then alpha-memory-activation (alpha-mem, w) ... alpha-mem = lookup-in-hash-table (*, *, *) if alpha-mem then alpha-memory-activation (alpha-mem, w) end """ # print(wme.asTuple()) for termComb, termDict in iteritems(self.alphaPatternHash): for alphaNode in termDict.get(wme.alphaNetworkHash(termComb), []): # print("\t## Activated AlphaNode ##") # print("\t\t", termComb, wme.alphaNetworkHash(termComb)) # print("\t\t", alphaNode) alphaNode.activate(wme.unboundCopy()) def feedFactsToAdd(self, tokenIterator): """ Feeds the network an iterator of facts / tokens which are fed to the alpha nodes which propagate the matching process through the network """ for token in tokenIterator: self.workingMemory.add(token) # print(token.unboundCopy().bindingDict) self.addWME(token) def _findPatterns(self, patternList): rt = [] for betaNodePattern, alphaNodePatterns in \ [(patternList.__getslice__(0, -i), patternList.__getslice__(-i, len(patternList))) for i in range(1, len(patternList))]: # [(patternList[:-i], patternList[-i:]) for i in xrange(1, len(patternList))]: assert isinstance(betaNodePattern, HashablePatternList) assert isinstance(alphaNodePatterns, HashablePatternList) if betaNodePattern in self.nodes: rt.append(betaNodePattern) rt.extend([HashablePatternList([aPattern]) for aPattern in alphaNodePatterns]) return rt for alphaNodePattern in patternList: rt.append(HashablePatternList([alphaNodePattern])) return rt def createAlphaNode(self, currentPattern): """ """ if isinstance(currentPattern, N3Builtin): node = BuiltInAlphaNode(currentPattern) else: node = AlphaNode(currentPattern, self.ruleStore.filters) self.alphaPatternHash[node.alphaNetworkHash()].setdefault(node.alphaNetworkHash(groundTermHash=True), []).append(node) if not isinstance(node, BuiltInAlphaNode) and node.builtin: s, p, o = currentPattern node = BuiltInAlphaNode(N3Builtin(p, self.ruleStore.filters[p](s, o), s, o)) return node def _resetinstantiationStats(self): self.instantiations = dict([(tNode, 0) for tNode in self.terminalNodes]) def checkDuplicateRules(self): checkedClauses = {} for tNode in self.terminalNodes: for rule in tNode.rules: collision = checkedClauses.get(rule.formula) assert collision is None, "%s collides with %s" % ( tNode, checkedClauses[rule.formula]) checkedClauses.setdefault(tNode.rule.formula, []).append(tNode) def registerReteAction(self, headTriple, override, executeFn): """ Register the given execute function for any rule with the given head using the override argument to determine whether or not the action completely handles the firing of the rule. The signature of the execute action is as follows: def someExecuteAction(tNode, inferredTriple, token, binding): .. pass .. """ for tNode in self.terminalNodes: for rule in tNode.rules: if not isinstance(rule.formula.head, (Exists, Uniterm)): continue headTriple = GetUterm(rule.formula.head).toRDFTuple() headTriple = tuple( [None if isinstance(item, BNode) else item for item in headTriple]) tNode.executeActions[headTriple] = (override, executeFn) def buildNetwork(self, lhsIterator, rhsIterator, rule, aFilter=False): """ Takes an iterator of triples in the LHS of an N3 rule and an iterator of the RHS and extends the Rete network, building / reusing Alpha and Beta nodes along the way (via a dictionary mapping of patterns to the built nodes) """ matchedPatterns = HashablePatternList() attachedPatterns = [] # hasBuiltin = False LHS = [] while True: try: currentPattern = next(lhsIterator) if py3compat.PY3 else lhsIterator.next() #The LHS isn't done yet, stow away the current pattern #We need to convert the Uniterm into a triple if isinstance(currentPattern, Uniterm): currentPattern = currentPattern.toRDFTuple() LHS.append(currentPattern) except StopIteration: #The LHS is done, need to initiate second pass to recursively build join / beta #nodes towards a terminal node #We need to convert the Uniterm into a triple consequents = [isinstance(fact, Uniterm) and fact.toRDFTuple() or fact for fact in rhsIterator] if matchedPatterns and matchedPatterns in self.nodes: attachedPatterns.append(matchedPatterns) elif matchedPatterns: rt = self._findPatterns(matchedPatterns) attachedPatterns.extend(rt) if len(attachedPatterns) == 1: node = self.nodes[attachedPatterns[0]] if isinstance(node, BetaNode): terminalNode = node else: paddedLHSPattern = HashablePatternList([None]) + attachedPatterns[0] terminalNode = self.nodes.get(paddedLHSPattern) if terminalNode is None: #New terminal node terminalNode = BetaNode(None, node, aPassThru=True) self.nodes[paddedLHSPattern] = terminalNode node.connectToBetaNode(terminalNode, RIGHT_MEMORY) terminalNode.consequent.update(consequents) terminalNode.rules.add(rule) terminalNode.antecedent = rule.formula.body terminalNode.network = self terminalNode.headAtoms.update(rule.formula.head) terminalNode.filter = aFilter self.terminalNodes.add(terminalNode) else: moveToEnd = [] # endIdx = len(attachedPatterns) - 1 finalPatternList = [] for idx, pattern in enumerate(attachedPatterns): assert isinstance(pattern, HashablePatternList), repr(pattern) currNode = self.nodes[pattern] if (isinstance(currNode, BuiltInAlphaNode) or isinstance(currNode, BetaNode) and currNode.fedByBuiltin): moveToEnd.append(pattern) else: finalPatternList.append(pattern) terminalNode = self.attachBetaNodes(chain(finalPatternList, moveToEnd)) terminalNode.consequent.update(consequents) terminalNode.rules.add(rule) terminalNode.antecedent = rule.formula.body terminalNode.network = self terminalNode.headAtoms.update(rule.formula.head) terminalNode.filter = aFilter self.terminalNodes.add(terminalNode) self._resetinstantiationStats() #self.checkDuplicateRules() return terminalNode if HashablePatternList([currentPattern]) in self.nodes: #Current pattern matches an existing alpha node matchedPatterns.append(currentPattern) elif matchedPatterns in self.nodes: #preceding patterns match an existing join/beta node newNode = self.createAlphaNode(currentPattern) if len(matchedPatterns) == 1 \ and HashablePatternList([None]) + matchedPatterns in self.nodes: existingNode = self.nodes[HashablePatternList([None]) + matchedPatterns] newBetaNode = BetaNode(existingNode, newNode) self.nodes[HashablePatternList([None]) + \ matchedPatterns + \ HashablePatternList([currentPattern])] = newBetaNode matchedPatterns = HashablePatternList([None]) + \ matchedPatterns + \ HashablePatternList([currentPattern]) else: existingNode = self.nodes[matchedPatterns] newBetaNode = BetaNode(existingNode, newNode) self.nodes[matchedPatterns + \ HashablePatternList([currentPattern])] = newBetaNode matchedPatterns.append(currentPattern) self.nodes[HashablePatternList([currentPattern])] = newNode newBetaNode.connectIncomingNodes(existingNode, newNode) #Extend the match list with the current pattern and add it #to the list of attached patterns for the second pass attachedPatterns.append(matchedPatterns) matchedPatterns = HashablePatternList() else: #The current pattern is not in the network and the match list isn't #either. Add an alpha node newNode = self.createAlphaNode(currentPattern) self.nodes[HashablePatternList([currentPattern])] = newNode #Add to list of attached patterns for the second pass attachedPatterns.append(HashablePatternList([currentPattern])) def attachBetaNodes(self, patternIterator, lastBetaNodePattern=None): """ The second 'pass' in the Rete network compilation algorithm: Attaches Beta nodes to the alpha nodes associated with all the patterns in a rule's LHS recursively towards a 'root' Beta node - the terminal node for the rule. This root / terminal node is returned """ try: nextPattern = next(patternIterator) if py3compat.PY3 else patternIterator.next() except StopIteration: assert lastBetaNodePattern if lastBetaNodePattern: return self.nodes[lastBetaNodePattern] else: assert len(self.universalTruths), "should be empty LHSs" terminalNode = BetaNode(None, None, aPassThru=True) self.nodes[HashablePatternList([None])] = terminalNode return terminalNode # raise Exception("Ehh. Why are we here?") if lastBetaNodePattern: firstNode = self.nodes[lastBetaNodePattern] secondNode = self.nodes[nextPattern] newBNodePattern = lastBetaNodePattern + nextPattern newBetaNode = BetaNode(firstNode, secondNode) self.nodes[newBNodePattern] = newBetaNode else: firstNode = self.nodes[nextPattern] oldAnchor = self.nodes.get(HashablePatternList([None]) + nextPattern) if not oldAnchor: if isinstance(firstNode, AlphaNode): newfirstNode = BetaNode(None, firstNode, aPassThru=True) newfirstNode.connectIncomingNodes(None, firstNode) self.nodes[HashablePatternList([None]) + nextPattern] = newfirstNode else: newfirstNode = firstNode else: newfirstNode = oldAnchor firstNode = newfirstNode secondPattern = next(patternIterator) if py3compat.PY3 else patternIterator.next() secondNode = self.nodes[secondPattern] newBetaNode = BetaNode(firstNode, secondNode) newBNodePattern = HashablePatternList([None]) + nextPattern + secondPattern self.nodes[newBNodePattern] = newBetaNode newBetaNode.connectIncomingNodes(firstNode, secondNode) return self.attachBetaNodes(patternIterator, newBNodePattern) def ComplementExpand(tBoxGraph, complementAnnotation): complementExpanded = [] for negativeClass in tBoxGraph.subjects(predicate=OWL_NS.complementOf): containingList = first(tBoxGraph.subjects(RDF.first, negativeClass)) prevLink = None while containingList: prevLink = containingList containingList = first(tBoxGraph.subjects(RDF.rest, containingList)) if prevLink: for s, p, o in tBoxGraph.triples_choices((None, [OWL_NS.intersectionOf, OWL_NS.unionOf], prevLink)): if (s, complementAnnotation, None) in tBoxGraph: continue _class = Class(s) complementExpanded.append(s) print("Added %s to complement expansion" % _class) ComplementExpansion(_class) def test(): import doctest doctest.testmod() if __name__ == '__main__': test() # from FuXi.Rete.Network import iteritems # from FuXi.Rete.Network import any # from FuXi.Rete.Network import ComplementExpand # from FuXi.Rete.Network import HashablePatternList # from FuXi.Rete.Network import InferredGoal # from FuXi.Rete.Network import ReteNetwork

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null

0

null

0

1

0

6dcbed133eb3a7b3cdb7874d0063d3eca6ce4f69

792

py

Python

flask_sqlalchemy/app.py

andreeaionescu/graphql-example

ceeff3888ea87312d4df138093d7f6fcaa1ae973

[ "MIT" ]

null

flask_sqlalchemy/app.py

andreeaionescu/graphql-example

ceeff3888ea87312d4df138093d7f6fcaa1ae973

[ "MIT" ]

null

flask_sqlalchemy/app.py

andreeaionescu/graphql-example

ceeff3888ea87312d4df138093d7f6fcaa1ae973

[ "MIT" ]

null

''' Unlike a RESTful API, there is only a single URL from which GraphQL is accessed. We are going to use Flask to create a server that expose the GraphQL schema under /graphql and a interface for querying it easily: GraphiQL (also under /graphql when accessed by a browser). ''' from flask import Flask from flask_graphql import GraphQLView from flask_sqlalchemy.models import db_session from flask_sqlalchemy.schema import schema, Department app = Flask(__name__) app.debug = True app.add_url_rule( '/graphql', view_func=GraphQLView.as_view( 'graphql', schema=schema, graphiql=True # for having the GraphiQL interface ) ) @app.teardown_appcontext def shutdown_session(exception=None): db_session.remove() if __name__ == '__main__': app.run()

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null

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0

6dcf6d8a2796f5c3d07f258930f33ef3e7528467

4,021

py

Python

src/skansensor/datacollector.py

fadykuzman/Rodeo-App

2972b371ed38fad4f93e6afcb699b51cec865510

[ "BSD-3-Clause" ]

null

src/skansensor/datacollector.py

fadykuzman/Rodeo-App

2972b371ed38fad4f93e6afcb699b51cec865510

[ "BSD-3-Clause" ]

null

src/skansensor/datacollector.py

fadykuzman/Rodeo-App

2972b371ed38fad4f93e6afcb699b51cec865510

[ "BSD-3-Clause" ]

null

""" This class searches for data in a hierarchy of folders and sorts them in a list. Attributes to the data are: path: path to the raw data file type: whether from Picarro, DropSense sensors. data: the data set after reading with the modules: read_dropsense read_picarro Please refer to the documentation of the above data reading modules to know the data structure of the resultant datasets """ import os from collections import namedtuple import skansensor.skansensor as ss class DataCollector: dir_list = [] def __init__(self): pass def get_files(self, path = '.'): """ Loops through a folder hierarchy from a given path. If no path is given, it searches through the current directory. The method returns a namedtuple of: path, kind of file/folder, and if a file, the extension Parameters: ------------- path: path to the parent directory to loop through. Default is current directory returns: ------------- dir_list: a list of all files or folders as a namedtuple. Attributes of the namedtuple are: path: path to file or folder whatis: dir or data whatext: what extension the data file has. (only dropsense and picarro) """ # Opens an instance of a directory with os.scandir(path) as it: # loop through all items in a directory for entry in it: cat = namedtuple('cat', ['path', 'whatis', 'whatext']) if entry.is_dir(): cat.path = entry.path cat.whatis = 'dir' self.dir_list.append(cat) self.get_files(cat.path) else: filename, fileext = os.path.splitext(entry.path) if ( (fileext == '.mta') or (fileext == '.mtc') or (fileext == '.mtzc') ): cat.path = entry.path cat.whatis = 'data' cat.whatext = fileext self.dir_list.append(cat) elif fileext == '.dat': cat.path = entry.path cat.whatis = 'data' cat.whatext = fileext self.dir_list.append(cat) else: pass return self.dir_list def collect(self, dir_list): """ Parameters: ------------ dir_list: the list of files and folders. Expected the result of the method get_files() returns: ------------ data_list: a list of dictionaries that contain data read from data files. Refer to 'skansensor' module for the data structure. """ datalist = [] for a in dir_list: if a.whatis == 'data': if (a.whatext == '.mta') or (a.whatext == '.mtc') or (a.whatext == '.mtzc'): d = { 'path' : a.path, 'type' : 'dropsense', 'data' : ss.read_dropsense(a.path) } elif a.whatext == '.dat': d = { 'path' : a.path, 'type' : 'picarro', 'data' : ss.read_picarro(a.path) } if d not in datalist: datalist.append(d) return datalist

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0.437453

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4,021

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0.278772

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null

0

null

0

1

0

6dd2c58e17cfa913515f063e288c4ff6d601590c

875

py

Python

pybuildtool/core/context.py

dozymoe/PyBuildTool

d938a8d6335b801e102159e82a6e0002dfaa1b1a

[ "MIT" ]

5

2017-02-10T07:54:49.000Z

2017-07-11T09:14:26.000Z

pybuildtool/core/context.py

dozymoe/PyBuildTool

d938a8d6335b801e102159e82a6e0002dfaa1b1a

[ "MIT" ]

null

pybuildtool/core/context.py

dozymoe/PyBuildTool

d938a8d6335b801e102159e82a6e0002dfaa1b1a

[ "MIT" ]

1

2017-05-21T20:35:10.000Z

import os from waflib import Context, Errors # pylint:disable=import-error class WatchContext(Context.Context): cmd = 'watch' fun = 'watch' variant = '' def __init__(self, **kw): super().__init__(**kw) self.top_dir = kw.get('top_dir', Context.top_dir) self.out_dir = kw.get('out_dir', Context.out_dir) if not(os.path.isabs(self.top_dir) and os.path.isabs(self.out_dir)): raise Errors.WafError('The project was not configured: ' +\ 'run "waf configure" first!') self.path = self.srcnode = self.root.find_dir(self.top_dir) self.bldnode = self.root.make_node(self.variant_dir) def get_variant_dir(self): if not self.variant: return self.out_dir return os.path.join(self.out_dir, self.variant) variant_dir = property(get_variant_dir, None)

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0.238857

875

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false

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0

0.55

0

null

0

null

0

1

0

6dd5f7a4941e24796fe51eb9276d1b79188a16ea

15,499

py

Python

au/fixtures/dataset.py

pwais/au2018

edd224e5fb649b9f0095ffad39b94f72f73e4853

[ "Apache-2.0" ]

null

au/fixtures/dataset.py

pwais/au2018

edd224e5fb649b9f0095ffad39b94f72f73e4853

[ "Apache-2.0" ]

3

2019-01-05T22:43:37.000Z

2019-01-26T05:45:01.000Z

au/fixtures/dataset.py

pwais/au2018

edd224e5fb649b9f0095ffad39b94f72f73e4853

[ "Apache-2.0" ]

1

2020-05-03T21:10:03.000Z

import io import os from collections import OrderedDict import imageio import numpy as np from au import conf from au.util import create_log from au import util ## ## Images ## class ImageRow(object): """For expected usage, see `test_imagerow_demo`""" # NB: While pyspark uses cloudpickle for *user code*, it uses normal # pickle for *data*, so the contents of ImageRow instances must be # pickle-able. I.e. attributes cannot be free functions, since only # cloudpickle can serialize functions. FMI: # http://apache-spark-user-list.1001560.n3.nabble.com/pyspark-serializer-can-t-handle-functions-td7650.html # https://github.com/apache/spark/blob/c3c45cbd76d91d591d98cf8411fcfd30079f5969/python/pyspark/worker.py#L50 # https://github.com/apache/spark/blob/c3c45cbd76d91d591d98cf8411fcfd30079f5969/python/pyspark/worker.py#L359 __slots__ = ( 'dataset', 'split', 'uri', '_image_bytes', # NB: see property image_bytes '_cached_image_arr', # TODO: use __ for privates .. idk '_cached_image_fobj', '_arr_factory', # NB: must be a callable *object*; see above 'label', 'attrs', # '_label_bytes', # NB: see property label and label_bytes # '_cached_label', # '_cached_label_arr', # '_cached_label_fobj', ) DEFAULT_PQ_PARTITION_COLS = ['dataset', 'split'] # NB: must be a list and not a tuple due to pyarrow c++ api # Old pickle API requires __{get,set}state__ for classes that define # __slots__. Some part of Spark uses this API for serializatio, so we # provide an impl. def __getstate__(self): return {'as_tuple': self.astuple()} def __setstate__(self, d): for k, v in zip(self.__slots__, d['as_tuple']): setattr(self, k, v) # self._image_bytes = d.get('image_bytes', self._image_bytes) def __init__(self, **kwargs): for k in self.__slots__: setattr(self, k, kwargs.get(k, '')) if ('_image_bytes' not in kwargs and kwargs.get('image_bytes', '') is not ''): self._image_bytes = kwargs['image_bytes'] # if ('_label_bytes' not in kwargs and # kwargs.get('label_bytes', '') is not ''): # self._label_bytes = kwargs['label_bytes'] def astuple(self): return tuple(getattr(self, k) for k in self.__slots__) def __lt__(self, other): # Important! Otherwise Python might break ties in unexpected ways return self.astuple() < other.astuple() @staticmethod def from_np_img_labels(np_img, label='', **kwargs): row = ImageRow(**kwargs) row._cached_image_arr = np_img row.label = label return row @staticmethod def wrap_factory(np_img_factory, **kwargs): row = ImageRow(**kwargs) row._arr_factory = np_img_factory return row @staticmethod def from_path(path, **kwargs): # NB: The ImageRow instance will be a flyweight for the image data row = ImageRow(uri=path, **kwargs) row._cached_image_fobj = open(path, 'rb') return row def to_dict(self): attrs = [] for k in self.__slots__: if not k.startswith('_'): # pyarrow + python 2.7 -> str gets interpreted as binary # https://stackoverflow.com/a/49507268 # Can skip for python3 ... v = getattr(self, k) if isinstance(v, basestring): v = unicode(v.encode('utf-8')) attrs.append((k, v)) elif k == '_image_bytes': attrs.append(('image_bytes', bytearray(self.image_bytes))) # NB: must be bytearray to support parquet / pyspark type inference # elif k == '_label_bytes': # attrs.append(('label_bytes', self.label_bytes)) return OrderedDict(attrs) def as_numpy(self): if self._cached_image_arr is '': if self._arr_factory is not '': self._cached_image_arr = self._arr_factory() else: image_bytes = self.image_bytes if image_bytes is '': # Can't make an array return np.array([]) self._cached_image_arr = imageio.imread(io.BytesIO(image_bytes)) return self._cached_image_arr @property def image_bytes(self): if self._image_bytes is '': # Read lazily if self._arr_factory is not '' and self._cached_image_arr is '': self._cached_image_arr = self._arr_factory() if self._cached_image_arr is not '': buf = io.BytesIO() imageio.imwrite(buf, self._cached_image_arr, format='png') self._image_bytes = buf.getvalue() elif self._cached_image_fobj is not '': self._image_bytes = self._cached_image_fobj.read() self._cached_image_fobj = '' return self._image_bytes # @property # def label_bytes(self): # if self._label_bytes is '': # # Read lazily # if self._cached_label_arr is not '': # buf = io.BytesIO() # imageio.imwrite(buf, self._cached_image_arr, format='png') # self._image_bytes = buf.getvalue() # elif self._cached_image_fobj is not '': # self._image_bytes = self._cached_image_fobj.read() # self._cached_image_fobj = '' # return self._image_bytes # # @property # def label(self): # if self._cached_label is '': # if self.label_encoding == 'json': # # # if self._label is '': # # Read lazily # if self._cached_label_arr is not '': # buf = io.BytesIO() # imageio.imwrite(buf, self._cached_label_arr, format='png') # self._label_bytes = buf.getvalue() # elif self._cached_label_fobj is not '': # self._label_bytes = self._cached_label_fobj.read() # self._cached_label_fobj = '' # return self._label_bytes def fname(self): has_fnamable_label = ( self.label is not '' and isinstance(self.label, (basestring, int, float))) toks = ( self.dataset, self.split, 'label_%s' % str(self.label).replace(' ', '-') if has_fnamable_label else '', self.uri.split('/')[-1] if self.uri else '', ) fname = '-'.join(str(tok) for tok in toks if tok) + '.png' return fname def to_debug(self, fname=''): """Convenience for dumping an image to a place on disk where the user can view locally (e.g. using Apple Finder file preview, Ubuntu image browser, an nginx instance pointed at the folder, etc). FMI see conf.AU_CACHE_TMP """ if self.image_bytes == '': return None dest = os.path.join(conf.AU_CACHE_TMP, self.fname()) util.mkdir(conf.AU_CACHE_TMP) with open(dest, 'wb') as f: f.write(self.image_bytes) return dest @staticmethod def rows_from_images_dir(img_dir, pattern='*', **kwargs): import pathlib2 as pathlib log = create_log() log.info("Reading images from dir %s ..." % img_dir) paths = pathlib.Path(img_dir).glob(pattern) n = 0 for path in paths: path = str(path) # pathlib uses PosixPath thingies ... yield ImageRow.from_path(path, **kwargs) n += 1 if (n % 100) == 0: log.info("... read %s paths ..." % n) log.info("... read %s total paths." % n) @staticmethod def from_pandas(df, **kwargs): for row in df.to_dict(orient='records'): row.update(**kwargs) yield ImageRow(**row) @staticmethod def write_to_parquet( rows, dest_dir, rows_per_file=-1, partition_cols=DEFAULT_PQ_PARTITION_COLS, compression='lz4', spark=None): is_rdd, is_pyspark_df = False, False try: import pyspark.rdd import pyspark.sql is_rdd = isinstance(rows, pyspark.rdd.RDD) is_pyspark_df = isinstance(rows, pyspark.sql.dataframe.DataFrame) if is_pyspark_df: df = rows except ImportError: pass if is_rdd: assert spark is not None from pyspark.sql import Row # RDD[ImageRow] -> DataFrame[ImageRow] rows_rdd = rows.map(lambda r: Row(**r.to_dict())) df = spark.createDataFrame(rows_rdd) is_pyspark_df = True if is_pyspark_df: util.log.info("Writing parquet to %s ..." % dest_dir) df.printSchema() # NB: can't .show() b/c of binary data df.write.parquet( dest_dir, mode='append', partitionBy=partition_cols, compression=compression) util.log.info("... done! Wrote to %s ." % dest_dir) else: # Use Pyarrow to write Parquet in this process import pandas as pd import pyarrow as pa import pyarrow.parquet as pq log = create_log() if rows_per_file >= 1: irows = util.ichunked(rows, rows_per_file) else: rows = list(rows) if not rows: return irows = iter([rows]) util.log.info("Writing parquet to %s ..." % dest_dir) for row_chunk in irows: r = row_chunk[0] # Pandas wants dicts if isinstance(r, ImageRow): row_chunk = [r.to_dict() for r in row_chunk] df = pd.DataFrame(row_chunk) table = pa.Table.from_pandas(df) util.mkdir(dest_dir) pq.write_to_dataset( table, dest_dir, partition_cols=partition_cols, preserve_index=False, # Don't care about pandas index compression='snappy', # NB: pyarrow lz4 is totes broken https://github.com/apache/arrow/issues/3491 flavor='spark') util.log.info("... wrote %s rows ..." % len(row_chunk)) util.log.info("... done writing to %s ." % dest_dir) @staticmethod def write_to_pngs(rows, dest_root=None): dest_root = dest_root or conf.AU_DATA_CACHE util.log.info("Writing PNGs to %s ..." % dest_root) n = 0 for row in rows: dest_dir = os.path.join( dest_root, row.dataset or 'default_dataset', row.split or 'default_split') util.mkdir(dest_dir) fname = row.fname() dest = os.path.join(dest_dir, fname) with open(dest, 'wb') as f: f.write(row.image_bytes) n += 1 if n % 100 == 0: util.log.info("... write %s PNGs ..." % n) util.log.info("... wrote %s total PNGs to %s ." % (n, dest_root)) ## Ops & Utils import cv2 def _make_have_target_chan(img, nchan): shape = img.shape if len(shape) == 2: img = np.expand_dims(img, axis=-1) elif len(shape) != 3: raise ValueError("Hmm input image has shape: %s" % (shape,)) shape = img.shape if shape[-1] == nchan: return img elif nchan == 1: if len(shape) == 3 and shape[-1] == 3: # Make the image greyscale img2 = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) return np.expand_dims(img2, axis=-1) else: raise ValueError("TODO input image has != 3 chan %s" % (shape,)) elif nchan == 3: if len(shape) == 3 and shape[-1] == 1: # Repeate the grey channel to create an RGB image # (or BGR or who knows) img = np.squeeze(img, axis=-1) return np.stack([img, img, img], axis=-1) else: raise ValueError("TODO input image has != 1 chan %s" % (shape,)) else: raise ValueError("TODO idk yet %s %s" % (nchan, shape,)) class FillNormalized(object): def __init__(self, target_hw=None, target_nchan=None, norm_func=None): self.norm_func = norm_func self.target_hw = target_hw self.target_nchan = target_nchan self.thruput = util.ThruputObserver( name='FillNormalized', log_on_del=True) def __call__(self, row): self.thruput.start_block() normalized = row.as_numpy() bytes_in = normalized.nbytes if self.target_hw is not None: h, w = self.target_hw normalized = cv2.resize(normalized, (w, h)) # Sneaky, opencv! if self.target_nchan is not None: normalized = _make_have_target_chan(normalized, self.target_nchan) if self.norm_func is not None: normalized = self.norm_func(normalized) row.attrs = row.attrs or {} row.attrs.update({ 'normalized': normalized, }) self.thruput.stop_block(n=1, num_bytes=bytes_in) return row ## ## Tables of images ## class ImageTable(object): """A (partitioned Parquet) table of images (perhaps use one table per dataset / label type).""" TABLE_NAME = 'default' ROWS_PER_FILE = 100 @classmethod def setup(cls, spark=None): """Subclasses should override to create a dataset from scratch (e.g. download images, create a table, etc). The base class is just a bunch of images from ImageNet. """ if os.path.exists(cls.table_root()): util.log.info( "Skipping setup for %s, %s exists." % ( cls.TABLE_NAME, cls.table_root())) return rows = ImageRow.rows_from_images_dir( conf.AU_IMAGENET_SAMPLE_IMGS_DIR, dataset=cls.TABLE_NAME, split='__default') rows = list(rows) import json with open(conf.AU_IMAGENET_SAMPLE_LABELS_PATH, 'rb') as f: fname_to_label = json.load(f) for row in rows: fname = row.uri.split('/')[-1] row.label = fname_to_label[fname] cls.save_to_image_table(rows) @classmethod def table_root(cls): return os.path.join(conf.AU_TABLE_CACHE, cls.TABLE_NAME) @classmethod def save_to_image_table(cls, rows): dest = os.path.join(conf.AU_TABLE_CACHE, cls.TABLE_NAME) if not os.path.exists(dest): return ImageRow.write_to_parquet( rows, cls.table_root(), rows_per_file=cls.ROWS_PER_FILE) @classmethod def get_rows_by_uris(cls, uris): import pandas as pd import pyarrow.parquet as pq pa_table = pq.read_table(cls.table_root()) df = pa_table.to_pandas() matching = df[df.uri.isin(uris)] return list(ImageRow.from_pandas(matching)) @classmethod def iter_all_rows(cls): """Convenience method (mainly for testing) using Pandas""" import pandas as pd import pyarrow.parquet as pq pa_table = pq.read_table(cls.table_root()) df = pa_table.to_pandas() for row in ImageRow.from_pandas(df): yield row @classmethod def as_imagerow_rdd(cls, spark): df = spark.read.parquet(cls.table_root()) row_rdd = df.rdd.map(lambda row: ImageRow(**row.asDict())) return row_rdd # @classmethod # def show_stats(cls, spark=None): # # @staticmethod # def write_tf_dataset_to_parquet( # dataset, # dest_dir, # """ make a dataset for 1-channel mnist things make a dataset for our handful of images try to coerce dataset from mscoco make one for bbd100k record activations for mnist then for mobilenet on bdd100k / mscoco take note of deeplab inference: https://colab.research.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb#scrollTo=edGukUHXyymr and we'll wanna add maskrcnn mebbe ? SPARK_LOCAL_IP=127.0.0.1 $SPARK_HOME/bin/pyspark --packages databricks:tensorframes:0.5.0-s_2.11 --packages databricks:spark-deep-learning:1.2.0-spark2.3-s_2.11 class DatasetFactoryBase(object): class ParamsBase(object): def __init__(self): self.BASE_DIR = '' @classmethod def create_dataset(cls): pass @classmethod def get_ctx_for_entry(cls, entry_id): pass """

28.595941

160

0.623653

2,107

15,499

4.375415

0.21215

0.029287

0.024406

0.017572

0.194707

0.164335

0.142206

0.124851

0.119861

0.103373

0

0.013811

0.261888

15,499

542

161

28.595941

0.792045

0.24079

0

0.213592

0

0.066036

0

0.001845

0.003236

1

0.084142

false

0.003236

0.071197

0.012945

0.252427

0.003236

0

null

0

null

0

1

0

6dd85933e9edf9c201a35fe12dd563f0c97ddb8b

417

py

Python

Python Fundamentals/Regular Expressions/More Exercises/Task05.py

DonikaChervenkova/SoftUni

bff579c037ec48f39ed193b34bc3502a32e90732

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Fundamentals/Regular Expressions/More Exercise/Task05.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Fundamentals/Regular Expressions/More Exercise/Task05.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2021-12-04T12:30:57.000Z

import re title_regex = r'<title>([^<>]*)<\/title>' info = input() title = re.findall(title_regex, info) title = ''.join(title) print(f"Title: {title}") body_regex = r'<body>.*<\/body>' body = re.findall(body_regex, info) body = ''.join(body) content_regex = r">([^><]*)<" content = re.findall(content_regex, body) content = ''.join(content) content = content.replace('\\n', '') print(f'Content: {content}')

17.375

41

0.630695

56

417

4.589286

0.267857

0.070039

0

0.1247

417

24

42

17.375

0.70411

0

0.203349

0.057416

0

1

0

false

0

0.071429

0

0.071429

0.142857

0

null

0

null

0

1

0

6ddb9acc38ebe942d14b28143c5d4ded77045159

320

py

Python

code_references/graph.py

nathanShepherd/Intelligent-Interface

4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593

[ "MIT" ]

3

2018-03-26T21:08:45.000Z

2018-11-16T21:16:57.000Z

code_references/graph.py

nathanShepherd/Intelligent-Interface

4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593

[ "MIT" ]

null

code_references/graph.py

nathanShepherd/Intelligent-Interface

4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593

[ "MIT" ]

2

2018-03-26T21:08:51.000Z

2020-05-06T09:22:52.000Z

# Testing various methods to graph with matplotlib # Developed by Nathan Shepherd import numpy as np import matplotlib.pyplot as plt n = 100 y = [round(np.random.normal(scale=n/10)) for _ in range(n)] x = [i for i in range(-n, n)] _y = [] for i in range(-n, n): _y.append(y.count(i)) plt.plot(x, _y) plt.show()

18.823529

59

0.671875

60

320

3.516667

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0.113744

0.104265

0.132701

0

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0.1875

320

16

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20

0.792308

0.240625

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1

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false

0

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0.2

0

null

0

null

0

1

0

6ddc0994a3a96b2f0830b2f0c227911b21552e3f

1,741

py

Python

scripts/taxonomy_frequency.py

STRIDES-Codes/Exploring-the-Microbiome-

bd29c8c74d8f40a58b63db28815acb4081f20d6b

[ "MIT" ]

null

scripts/taxonomy_frequency.py

STRIDES-Codes/Exploring-the-Microbiome-

bd29c8c74d8f40a58b63db28815acb4081f20d6b

[ "MIT" ]

null

scripts/taxonomy_frequency.py

STRIDES-Codes/Exploring-the-Microbiome-

bd29c8c74d8f40a58b63db28815acb4081f20d6b

[ "MIT" ]

2

2021-06-05T07:40:20.000Z

2021-06-05T08:02:58.000Z

import sys from Bio import Entrez from collections import Counter import pandas as pd ########################################### def get_tax_id(species): species = species.replace(" ", "+").strip() search = Entrez.esearch(term = species, db = "taxonomy", retmode = "xml") record = Entrez.read(search) return record['IdList'][0] ############################################### def get_tax_data(taxid): search = Entrez.efetch(id = taxid, db = "taxonomy", retmode = "xml") return Entrez.read(search) ############################################### def tax_to_freq(in_file,out_file): Entrez.email = 'idrissi.azami.abdellah@gmail.com' print('Reading input file .....') with open (in_file,'r', encoding='utf-8') as inpt: sps = [] content = inpt.readlines() print('Extracting nodes ...') for i in content: if '# Model Data:' in i: sp = i.split ('|')[1].split('|')[0].replace('_',' ') sps.append(sp) print('Counting nodes....') counter = dict(Counter(sps)) total = 0 for i in counter: try: taxid = get_tax_id(i) taxdata = get_tax_data(taxid) total = total + counter[i] except: pass print ('Retriving taxonomy ...') tax = [] for i in counter: try: taxid = get_tax_id(i) taxdata = get_tax_data(taxid) lineage = {d['Rank']:d['ScientificName'] for d in taxdata[0]['LineageEx'] if d['Rank'] in ['superkingdom','phylum','class','order', 'family','genus','species']} lineage['Strain']=i lineage['#Hits']=counter[i] lineage['Frequency (%)']=(counter[i]/total)*100 tax.append(lineage) except: pass df = pd.DataFrame(tax) df.to_csv(out_file,sep='\t',index=False) return df

31.654545

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0.2

0.08

0

null

0

null

0

1

0

6dddfed29dba919369dee25697fc63339866499f

12,754

py

Python

gipf/GipfLogic.py

callix2/alphaZero-gipf

fd8dac7606611126d2d14beca0333b53bd5ee995

[ "MIT" ]

null

gipf/GipfLogic.py

callix2/alphaZero-gipf

fd8dac7606611126d2d14beca0333b53bd5ee995

[ "MIT" ]

null

gipf/GipfLogic.py

callix2/alphaZero-gipf

fd8dac7606611126d2d14beca0333b53bd5ee995

[ "MIT" ]

null

''' Author: Eric P. Nichols Date: Feb 8, 2008. Board class. Board data: 1=white, -1=black, 0=empty first dim is column , 2nd is row: pieces[1][7] is the square in column 2, at the opposite end of the board in row 8. Squares are stored and manipulated as (x,y) tuples. x is the column, y is the row. ''' import numpy as np class Board(): # list of all 6 directions on the board, as (x,y) offsets __directions = [(2,0),(-2,0),(1,1),(1,-1),(-1,1),(-1,-1)] # list of all entries of the matrix, which are actually spots on the board actBoard = [(2,3),(3,2),(3,4),(4,1),(4,3),(4,5),(5,2),(5,4),(6,1),(6,3),(6,5),(7,2),(7,4),(8,1),(8,3),(8,5),(9,2),(9,4),(10,3)] # list of all starting Points on the board startingPoints = [(0,3),(1,2),(1,4),(2,1),(2,5),(3,0),(3,6),(5,0),(5,6),(7,0),(7,6),(9,0),(9,6),(10,1),(10,5),(11,2),(11,4),(12,3)] # dictionary for the translation of the spot names into the entries of the matrix (as tuple) move_dict = {"a1": (9,0), "a2": (7,0), "a3": (5,0), "a4": (3,0), "b1": (10,1), "b2": (8,1), "b3": (6,1), "b4": (4,1), "b5": (2,1), "c1": (11,2), "c2": (9,2), "c5": (3,2), "c6": (1,2), "d1": (12,3), "d2": (10,3), "d6": (2,3), "d7": (0,3), "e1": (11,4), "e2": (9,4), "e5": (3,4), "e6": (1,4), "f1": (10,5), "f2": (8,5), "f3": (6,5), "f4": (4,5), "f5": (2,5), "g1": (9,6), "g2": (7,6), "g3": (5,6), "g4": (3,6)} def __init__(self, n): "Set up initial board configuration." self.n = n # Create the empty board array. self.pieces = [None]*self.n # rows: mini: 13, normal: 17 for i in range(self.n): self.pieces[i] = [0]*(int(self.n//(1.8))) # columns: mini: 13//1.8=7 normal: 17//1.8=9 #Set up reserve in board corner self.pieces[0][0] = 5 self.pieces[0][2] = 5 # Set up the initial 6 pieces. self.pieces[4][1] = 1 self.pieces[4][5] = 1 self.pieces[10][3] = 1 self.pieces[8][1] = -1 self.pieces[8][5] = -1 self.pieces[2][3] = -1 """ #Testfall Sym self.pieces[8][1] = 1 self.pieces[10][3] = 1 self.pieces[4][5] = 1 self.pieces[2][3] = -1 self.pieces[7][4] = -1 self.pieces[8][5] = -1 #Testfall A self.pieces[8][1] = -1 self.pieces[7][2] = -1 self.pieces[4][3] = -1 self.pieces[10][3] = 1 self.pieces[8][3] = 1 self.pieces[4][5] = 1 self.pieces[5][4] = 1 #Testfall B self.pieces[7][2] = 1 self.pieces[6][1] = 1 self.pieces[10][3] = 1 self.pieces[8][3] = -1 self.pieces[4][3] = -1 self.pieces[2][3] = -1 #Testfall C self.pieces[4][1] = 1 self.pieces[5][2] = -1 self.pieces[10][3] = 1 self.pieces[4][3] = -1 self.pieces[2][3] = -1 #Testfall D self.pieces[6][1] = -1 self.pieces[7][2] = -1 self.pieces[9][4] = 1 self.pieces[10][3] = -1 self.pieces[6][3] = -1 self.pieces[4][3] = -1 self.pieces[2][3] = 1 """ # add [][] indexer syntax to the Board def __getitem__(self, index): return self.pieces[index] def __setitem__(self, index, color): self.pieces[index] = color def get_actBoard(self): if self.n == 13: return self.actBoard else: pass # return actBoard + ext def get_startingPoints(self): if self.n == 13: return self.startingPoints else: pass # return actBoard + ext @staticmethod def translate_move(move): """Returns a tuple of the spot names as a tuple of the matrix """ try: move_new = (Board.move_dict[move[0]],Board.move_dict[move[1]]) return move_new except KeyError: 'Invalid Field' def get_legal_moves(self): """Returns all the legal moves """ moves = set() # stores the legal moves. # discover the possible moves for every starting point for start in self.startingPoints: newmoves = self.get_moves_for_dot(start)[1],[2] moves.update(newmoves) return list(moves) def get_legal_moves_binary(self): """Returns all the legal moves """ moves = [] # stores the legal moves. # discover the possible moves for every starting point for start in self.startingPoints: newmoves = self.get_moves_for_dot(start)[2] moves.extend(newmoves) return moves def get_all_moves(self): """Returns all the legal moves """ moves = [] # stores the legal moves. # discover the possible moves for every starting point for start in self.startingPoints: newmoves = self.get_moves_for_dot(start)[1] moves.extend(newmoves) return moves def get_moves_for_dot(self, dot): """Returns all the legal moves that use the given dot as a base. """ # search all possible directions. legal_moves = [] all_moves = [] all_moves_binary = [] for direction in self.__directions: target = tuple(np.add(dot, direction)) if target in self.actBoard: move = (dot, target) all_moves.append(move) if self.check_move(target, direction): legal_moves.append(move) all_moves_binary.extend([1]) else: all_moves_binary.extend([0]) # return the generated move list return legal_moves, all_moves, all_moves_binary def check_move(self, target, direction): """Returns True if there is a free field along the given direction if not returns Flase because the move is not valid """ s = target while s in self.actBoard: if self[s] == 0: return True s = tuple(np.add(s, direction)) return False def execute_move(self, action, curPlayer): """Performs the given move on the board; does not remove pieces! color gives the color of the piece to play (1=white,-1=black) """ all_moves = self.get_all_moves() move = all_moves[action] start=move[0] target=move[1] direction = tuple(np.subtract(target, start)) s=target # Runs up to a gap and places the piece there while s in self.actBoard: if self[s] == 0: break s = tuple(np.add(s, direction)) self[start]=curPlayer # Runs in opposite direction and moves the pieces while s in self.actBoard: s_prev = tuple(np.subtract(s, direction)) s_prev_color = self[s_prev] self[s]= s_prev_color s = tuple(np.subtract(s, direction)) self[s]=0 # Decreases reserve #players[color+1].dec_reserve() def remove_lines(self, curPlayer): """Checks for each field whether a row of four results. If so, removes the entire line """ #prüfen ob mehrere 4er, wenn ja zuerst den der spielenden Farbe, wenn immer noch mehrere zuerst den der mehr schlägt rows = [] add_reserve = [0, None, 0] for spot in self.actBoard: new_row = self.discover_row_of_4(spot) if new_row and new_row not in rows: rows.append(new_row) while len(rows)>1: #mehrere rows rows_of_color = [] #alle rows der aktuellen Farbe (haben vorrang) index_max = None for row in rows: row_color = self[list(row)[0]] if row_color == curPlayer: rows_of_color.append(row) if len(rows_of_color)>1: #mehrere rows der aktiven Farbe #prüfen welche die meisten schlägt c = [None]*len(rows_of_color) for index, row in enumerate(rows_of_color): c[index] = self.get_hit_count(row) index_max = np.argmax(c) add_reserve = np.add(add_reserve, self.remove_line(rows_of_color[index_max]), where=[1,0,1]) elif len(rows_of_color)>0: #nur eine row der aktiven Farbe add_reserve = np.add(add_reserve, self.remove_line(rows_of_color[0]), where=[1,0,1]) else: #mehrer rows der anderen Farbe und keine der aktiven #prüfen welche die meisten schlägt c = [None]*len(rows) for index, row in enumerate(rows): c[index] = self.get_hit_count(row) index_max = np.argmax(c) add_reserve = np.add(add_reserve, self.remove_line(rows[index_max]), where=[1,0,1]) #prüfe ob rows noch aktuell rows = self.check_rows(rows) if len(rows)>0: #nur eine row (egal welche Farbe) add_reserve = np.add(add_reserve, self.remove_line(rows[0]), where=[1,0,1]) return add_reserve def check_rows(self, rows): rows_new = rows.copy() for row in rows: for spot in row: if self[spot] == 0: rows_new.remove(row) break return rows_new def get_hit_count(self, row): count = 0 row = list(row) color_of_row = self[row[0]] direction = tuple(np.subtract(row[0], row[1])) s = row[0] # Runs from the first of the 4 in one direction of the line while s in self.actBoard: if self[s] == 0: break else: color = self[s] if color != color_of_row: count += 1 #self[s] = 0 s = tuple(np.add(s, direction)) # Runs in the opposite direction s = tuple(np.subtract(row[0], direction)) while s in self.actBoard: if self[s] == 0: break else: color = self[s] if color != color_of_row: count += 1 #self[s] = 0 s = tuple(np.subtract(s, direction)) return count def discover_row_of_4(self, spot): """Examines all directions for the given spot to see if a row of four exists If found returns a array of the four, otherwise returns False """ color = self[spot] for direction in self.__directions: row_of_4 = [] #set() #weil unorderd #row_of_4.update([spot]) row_of_4.append(spot) s = tuple(np.add(spot, direction)) while s in self.actBoard: if self[s] == 0 or self[s] != color: break else: #row_of_4.update([s]) row_of_4.append(s) s = tuple(np.add(s, direction)) if len(row_of_4)>2: #GipfMini: 3; Normal: 4 row_of_4.sort() return row_of_4 def remove_line(self, row_of_4): """Removes the 4 pieces and the pieces that form a direct extension of these 4 The pieces with the color of the 4 return to his reserve """ add_reserve = [0, None, 0] row_of_4 = list(row_of_4) color_of_4 = self[row_of_4[0]] direction = tuple(np.subtract(row_of_4[0], row_of_4[1])) s = row_of_4[0] # Runs from the first of the 4 in one direction of the line while s in self.actBoard: if self[s] == 0: break else: color = self[s] if color == color_of_4: add_reserve[color+1]+=1 #players[color+1].inc_reserve() self[s] = 0 s = tuple(np.add(s, direction)) # Runs in the opposite direction s = tuple(np.subtract(row_of_4[0], direction)) while s in self.actBoard: if self[s] == 0: break else: color = self[s] if color == color_of_4: add_reserve[color+1]+=1 #players[color+1].inc_reserve() self[s] = 0 s = tuple(np.subtract(s, direction)) return add_reserve

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0.509644

1,779

12,754

3.550871

0.156268

0.06807

0.053981

0.026595

0.436758

0.40019

0.364413

0.309799

0.296818

0.257084

0

0.054965

0.365219

12,754

352

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36.232955

0.725296

0.225576

0

0.38835

0

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0

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false

0.009709

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0.184466

0

null

0

null

0

1

0

6dde1212ba406b7fb0a629963b918fa2448f2579

2,533

py

Python

pcwg/reporting/colour.py

lcameron05/PCWG

8ae8ea7d644aa5bec0d1651101d83d8f17994f4b

[ "MIT" ]

14

2015-01-15T12:40:51.000Z

2019-06-14T16:10:08.000Z

pcwg/reporting/colour.py

lzhiwen3090/PCWG

795e3ea267c7b87187dce04721c91a9d9c7999a7

[ "MIT" ]

121

2015-01-06T11:31:25.000Z

2018-05-29T21:13:23.000Z

pcwg/reporting/colour.py

lzhiwen3090/PCWG

795e3ea267c7b87187dce04721c91a9d9c7999a7

[ "MIT" ]

26

2015-01-15T12:41:09.000Z

2019-04-11T14:45:32.000Z

import xlwt class ColourGradient: def __init__(self, minimum, maximum, interval, book): self.levels = {} self.minimum = minimum self.maximum = maximum dataRange = maximum - minimum steps = int(dataRange / interval) + 1 if (steps >= 4): steps_4 = steps / 4 else: steps_4 = 1 for i in range(steps): if (i <= steps_4): red = 255 elif (i > steps_4 and i <= steps_4 * 2): red = 255 - (255 / steps_4) * (i - steps_4) elif (i > steps_4 * 2 and i <= steps_4 * 3): red = (255 / 2 / steps_4) * (i - steps_4 * 2) elif i < steps: red = (255 / 2) - (255 / 2 / steps_4) * (i - steps_4 * 3) else: red = 0 if (i <= steps_4): green = (255 / steps_4) * i elif (i > steps_4 and i <= steps_4 * 2): green = 255 - (255 / steps_4) * (i - steps_4) elif (i > steps_4 * 2 and i <= steps_4 * 3): green = (255 / steps_4) * (i - steps_4 * 2) else: green = 255 if (i <= steps_4): blue = 0 elif (i > steps_4 and i <= steps_4 * 2): blue = 0 + (255 / steps_4) * (i - steps_4) elif i < steps: blue = 255 - (255 / steps_4 / 2) * (i - steps_4 * 2) else: blue = 0 red = abs(red) green = abs(green) blue = abs(blue) if (red > 255): red = 255 if (green > 255): green = 255 if (blue > 255): blue = 255 value = self.roundValue(minimum + i * interval) excelIndex = 8 + i colourName = "custom_colour_%d" % excelIndex xlwt.add_palette_colour(colourName, excelIndex) book.set_colour_RGB(excelIndex, red, green, blue) style = xlwt.easyxf('pattern: pattern solid, fore_colour %s' % colourName, num_format_str='0%') self.levels[value] = (red, green, blue, value, excelIndex, colourName, style) def roundValue(self, value): return round(value, 2) def getStyle(self, value): value = max(self.minimum, value) value = min(self.maximum, value) return self.levels[self.roundValue(value)][6]

32.474359

107

0.446901

293

2,533

3.716724

0.204778

0.176309

0.128558

0.05877

0.242424

0.209366

0.192837

0.161616

0.077135

0

0.082979

0.443348

2,533

77

108

32.896104

0.689362

0

0.275862

0

0.022108

0

1

0.051724

false

0

0.017241

0.12069

0

null

0

null

0

1

0

6ddf36ca544a3c8ccbf3d16260d57ec9db94a87c

2,021

py

Python

amap_distance_matrix/schemas/amap.py

Euraxluo/distance_matrix

680e3147c263ea5f1abb26998aeb0b1985442a4b

[ "MIT" ]

1

2022-03-15T06:47:36.000Z

amap_distance_matrix/schemas/amap.py

Euraxluo/distance_matrix

680e3147c263ea5f1abb26998aeb0b1985442a4b

[ "MIT" ]

null

amap_distance_matrix/schemas/amap.py

Euraxluo/distance_matrix

680e3147c263ea5f1abb26998aeb0b1985442a4b

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Time: 2022-03-01 15:43 # Copyright (c) 2022 # author: Euraxluo from typing import * from amap_distance_matrix.helper import haversine,format_loc class AMapDefaultResultRouteStep(object): def __init__(self, start: str, end: str): self.polyline: str self.instruction = "到达途经地" self.orientation = "北" self.road = "road" self.distance = haversine(format_loc(start),format_loc(end))*1.5 self.tolls = "0" self.toll_distance = "0" self.toll_road = [] self.duration = self.distance/(25000/60/60) self.action = [] self.assistant_action = "到达途经地" self.tmcs: List self.polyline = start + ";" + end self.tmcs = [ { "lcode": [], "distance": "0", "status": "畅通", "polyline": self.polyline } ] class AMapDefaultResultPath(object): def __init__(self, steps: List[AMapDefaultResultRouteStep]): self.distance = "0" self.duration = "0" self.strategy = "速度最快" self.tolls = "0" self.toll_distance = "0" self.steps = [i.__dict__ for i in steps] self.restriction = "0" self.traffic_lights = "0" class AMapDefaultResultRoute(object): def __init__(self, paths: AMapDefaultResultPath): self.origin = "0" self.destination = "0" self.taxi_cost = "0" self.paths = [paths.__dict__] class AMapDefaultResult(object): def __init__(self, points: List[str]): self.status = "1" self.info = "OK" self.infocode = "10000" self.count = "1" self.route: AMapDefaultResultRoute steps = [] for i, point in enumerate(points): if i == 0: continue steps.append(AMapDefaultResultRouteStep(start=points[i - 1], end=point)) self.route = AMapDefaultResultRoute(paths=AMapDefaultResultPath(steps=steps)).__dict__

29.720588

94

0.573973

213

2,021

5.262911

0.389671

0.044603

0.046387

0.06066

0.055308

0

0.034801

0.303315

2,021

67

95

30.164179

0.761364

0.040079

0

0.074074

0

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0

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0.074074

false

0

0.037037

0

0.185185

0

null

0

null

0

1

0

6de2331479d616c60c982b16b354a172879db20e

393

py

Python

at_learner_core/at_learner_core/models/init_model.py

hieuvecto/CASIA-SURF_CeFA

71dfd846ce968b3ed26974392a6e0c9b40aa12ae

[ "MIT" ]

133

2020-03-03T03:58:04.000Z

2022-03-28T21:42:36.000Z

at_learner_core/at_learner_core/models/init_model.py

lucaslu1987/CASIA-SURF_CeFA

205d3d976523ed0c15d1e709ed7f21d50d7cf19b

[ "MIT" ]

24

2020-03-13T09:30:09.000Z

2022-03-22T07:47:15.000Z

at_learner_core/at_learner_core/models/init_model.py

lucaslu1987/CASIA-SURF_CeFA

205d3d976523ed0c15d1e709ed7f21d50d7cf19b

[ "MIT" ]

29

2020-03-10T06:46:45.000Z

2022-01-29T15:35:21.000Z

from .wrappers import SimpleClassifierWrapper def get_wrapper(config, wrapper_func=None): if wrapper_func is not None: wrapper = wrapper_func(config) elif config.wrapper_config.wrapper_name == 'SimpleClassifierWrapper': wrapper = SimpleClassifierWrapper(config.wrapper_config) else: raise Exception('Unknown wrapper architecture type') return wrapper

32.75

73

0.750636

42

393

6.857143

0.52381

0.180556

0.138889

0

0.183206

393

11

74

35.727273

0.897196

0

0.142494

0.058524

0

1

0.111111

false

0

0.111111

0

0.333333

0

null

0

null

0

1

0

6de2eb54f1f884015cd25862ba629bbde92b8312

11,739

py

Python

register.py

khvmaths/Register_UM_Crawl

2741bfe9267e9ad068b438b27141cfc664f140f2

[ "MIT" ]

null

register.py

khvmaths/Register_UM_Crawl

2741bfe9267e9ad068b438b27141cfc664f140f2

[ "MIT" ]

null

register.py

khvmaths/Register_UM_Crawl

2741bfe9267e9ad068b438b27141cfc664f140f2

[ "MIT" ]

null

from bs4 import BeautifulSoup from urllib.request import Request,urlopen from urllib.error import HTTPError from PyQt5 import QtCore, QtGui, QtWidgets, Qt import sys import threading import datetime import win32con import os import struct import time import pyttsx3 from win32api import * from win32gui import * class Ui_Form(object): def setupUi(self, Form): Form.setObjectName("Form") Form.resize(1236, 996) self.groupBox = QtWidgets.QGroupBox(Form) self.groupBox.setGeometry(QtCore.QRect(10, 10, 361, 831)) self.groupBox.setObjectName("groupBox") self.tableWidget = QtWidgets.QTableWidget(self.groupBox) self.tableWidget.setGeometry(QtCore.QRect(10, 30, 341, 791)) self.tableWidget.setSizeAdjustPolicy(QtWidgets.QAbstractScrollArea.AdjustToContents) self.tableWidget.setObjectName("tableWidget") self.tableWidget.setColumnCount(0) self.tableWidget.setRowCount(0) self.tableWidget.horizontalHeader().setCascadingSectionResizes(True) self.groupBox_2 = QtWidgets.QGroupBox(Form) self.groupBox_2.setGeometry(QtCore.QRect(380, 10, 681, 831)) self.groupBox_2.setObjectName("groupBox_2") self.tableWidget_2 = QtWidgets.QTableWidget(self.groupBox_2) self.tableWidget_2.setGeometry(QtCore.QRect(10, 30, 651, 791)) self.tableWidget_2.setSizeAdjustPolicy(QtWidgets.QAbstractScrollArea.AdjustToContents) self.tableWidget_2.setObjectName("tableWidget_2") self.tableWidget_2.setColumnCount(0) self.tableWidget_2.setRowCount(0) self.tableWidget_2.horizontalHeader().setCascadingSectionResizes(True) self.groupBox_3 = QtWidgets.QGroupBox(Form) self.groupBox_3.setGeometry(QtCore.QRect(1070, 10, 141, 80)) self.groupBox_3.setObjectName("groupBox_3") self.pushButton = QtWidgets.QPushButton(self.groupBox_3) self.pushButton.setGeometry(QtCore.QRect(10, 50, 111, 28)) self.pushButton.setObjectName("pushButton") self.lineEdit = QtWidgets.QLineEdit(self.groupBox_3) self.lineEdit.setGeometry(QtCore.QRect(10, 20, 113, 22)) self.lineEdit.setObjectName("lineEdit") self.groupBox_4 = QtWidgets.QGroupBox(Form) self.groupBox_4.setGeometry(QtCore.QRect(1070, 100, 141, 61)) self.groupBox_4.setObjectName("groupBox_4") self.lineEdit_2 = QtWidgets.QLineEdit(self.groupBox_4) self.lineEdit_2.setGeometry(QtCore.QRect(10, 20, 113, 22)) self.lineEdit_2.setText("") self.lineEdit_2.setObjectName("lineEdit_2") self.groupBox_5 = QtWidgets.QGroupBox(Form) self.groupBox_5.setGeometry(QtCore.QRect(1070, 170, 141, 51)) self.groupBox_5.setObjectName("groupBox_5") self.lineEdit_3 = QtWidgets.QLineEdit(self.groupBox_5) self.lineEdit_3.setGeometry(QtCore.QRect(10, 20, 113, 22)) self.lineEdit_3.setText("") self.lineEdit_3.setObjectName("lineEdit_3") self.groupBox_6 = QtWidgets.QGroupBox(Form) self.groupBox_6.setGeometry(QtCore.QRect(1070, 230, 141, 51)) self.groupBox_6.setObjectName("groupBox_6") self.lineEdit_4 = QtWidgets.QLineEdit(self.groupBox_6) self.lineEdit_4.setGeometry(QtCore.QRect(10, 20, 113, 22)) self.lineEdit_4.setText("") self.lineEdit_4.setObjectName("lineEdit_4") self.groupBox_7 = QtWidgets.QGroupBox(Form) self.groupBox_7.setGeometry(QtCore.QRect(10, 840, 1051, 151)) self.groupBox_7.setObjectName("groupBox_7") self.listWidget = QtWidgets.QListWidget(self.groupBox_7) self.listWidget.setGeometry(QtCore.QRect(10, 20, 1021, 121)) self.listWidget.setObjectName("listWidget") self.label = QtWidgets.QLabel(Form) self.label.setGeometry(QtCore.QRect(1090, 930, 131, 41)) font = QtGui.QFont() font.setPointSize(10) font.setBold(True) font.setWeight(75) self.label.setFont(font) self.label.setObjectName("label") self.retranslateUi(Form) QtCore.QMetaObject.connectSlotsByName(Form) def retranslateUi(self, Form): _translate = QtCore.QCoreApplication.translate Form.setWindowTitle(_translate("Form", "REGISTER UM")) self.groupBox.setTitle(_translate("Form", "Elective")) self.tableWidget.setSortingEnabled(True) self.groupBox_2.setTitle(_translate("Form", "KoK")) self.tableWidget_2.setSortingEnabled(True) self.groupBox_3.setTitle(_translate("Form", "Set Timer (sec)")) self.pushButton.setText(_translate("Form", "START!!")) self.lineEdit.setText(_translate("Form", "5")) self.groupBox_4.setTitle(_translate("Form", "Targeted Elective 1")) self.groupBox_5.setTitle(_translate("Form", "Targeted Elective 2")) self.groupBox_6.setTitle(_translate("Form", "Targeted KoK 1")) self.groupBox_7.setTitle(_translate("Form", "Command")) self.label.setText(_translate("Form", "A program by\n" "hongvin")) class WindowsBalloonTip: def __init__(self): message_map = { win32con.WM_DESTROY: self.OnDestroy, } # Register the Window class. wc = WNDCLASS() self.hinst = wc.hInstance = GetModuleHandle(None) wc.lpszClassName = "PythonTaskbar" wc.lpfnWndProc = message_map # could also specify a wndproc. self.classAtom = RegisterClass(wc) def ShowWindow(self,title, msg): # Create the Window. style = win32con.WS_OVERLAPPED | win32con.WS_SYSMENU self.hwnd = CreateWindow( self.classAtom, "Taskbar", style, \ 0, 0, win32con.CW_USEDEFAULT, win32con.CW_USEDEFAULT, \ 0, 0, self.hinst, None) UpdateWindow(self.hwnd) #iconPathName = os.path.abspath(os.path.join( sys.path[0], "favicon.ico" )) icon_flags = win32con.LR_LOADFROMFILE | win32con.LR_DEFAULTSIZE hicon = LoadIcon(0, win32con.IDI_APPLICATION) flags = NIF_ICON | NIF_MESSAGE | NIF_TIP nid = (self.hwnd, 0, flags, win32con.WM_USER+20, hicon, "tooltip") Shell_NotifyIcon(NIM_ADD, nid) Shell_NotifyIcon(NIM_MODIFY, \ (self.hwnd, 0, NIF_INFO, win32con.WM_USER+20,\ hicon, "Balloon tooltip",msg,200,title)) # self.show_balloon(title, msg) DestroyWindow(self.hwnd) def OnDestroy(self, hwnd, msg, wparam, lparam): nid = (self.hwnd, 0) Shell_NotifyIcon(NIM_DELETE, nid) PostQuitMessage(0) # Terminate the app. w=WindowsBalloonTip() engine = pyttsx3.init() def TTS(text,grp=""): split=" ".join(text) if grp=="": engine.say('Found!'+split) else: engine.say('Found!' + split+'Group '+str(grp)) engine.runAndWait() class App(QtWidgets.QMainWindow,Ui_Form): def __init__(self): super(self.__class__,self).__init__() self.setupUi(self) self.pushButton.clicked.connect(self.startengine) def startengine(self): self.listWidget.scrollToBottom() self.lineEdit.setEnabled(False) timeout=float(self.lineEdit.text()) time_now=time.time() e1=self.lineEdit_2.text() e2=self.lineEdit_3.text() k1=self.lineEdit_4.text() e1b=False e2b=False k1b=False courses=['','',''] url = 'http://register.um.edu.my/el_kosong_bi.asp' request = Request(url) try: self.tableWidget.setEnabled(True) json = urlopen(request).read().decode() soup = BeautifulSoup(json,"html.parser") a = soup.find_all('div') self.tableWidget.setColumnCount(3) self.tableWidget.setRowCount((len(a)-1)/3) self.tableWidget.setHorizontalHeaderLabels(["Subject Code","Group","Vacant"]) j=-1 k=0 for i in range(0,len(a)): if i%3==0: j+=1 k=0 self.tableWidget.setItem(j,k,QtWidgets.QTableWidgetItem(a[i].text)) if e1==a[i].text: self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: Matched found for targeted elective '+a[i].text+' (Group '+a[i+1].text+')') e1b=True courses[0]=(a[i].text+'(G'+a[i+1].text+')') TTS(a[i].text,a[i+1].text) if e2==a[i].text: self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: Matched found for targeted elective '+a[i].text+' (Group '+a[i+1].text+')') e2b=True courses[1]=(a[i].text+'(G'+a[i+1].text+')') TTS(a[i].text,a[i+1].text) k+=1 except Exception as e: print("Error",e) self.tableWidget.setEnabled(False) self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: Error occured at Elective') self.lineEdit.setEnabled(True) url = 'http://register.um.edu.my/kok_kosong_bi.asp' request = Request(url) try: self.tableWidget_2.setEnabled(True) json = urlopen(request).read().decode() soup = BeautifulSoup(json,"html.parser") a = soup.find_all('td') self.tableWidget_2.setColumnCount(3) self.tableWidget_2.setRowCount((len(a)-10)/4) self.tableWidget_2.setHorizontalHeaderLabels(["Subject Code","Course Name","Vacant"]) j=-1 k=0 m=1 for i in range(9,len(a)-1): if (i-m)%4==0: j+=1 k=0 continue if a[i].text=='Bil' or a[i].text=='Code' or a[i].text=='Description' or a[i].text=='Vacant': m=2 if a[i].text=='Vacant': j-=1 self.tableWidget_2.setRowCount(((len(a)-10)/4)-1) continue if k1==a[i].text: self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: Matched found for targeted KoK '+a[i].text) k1b=True courses[2]=(a[i].text) TTS(a[i].text) self.tableWidget_2.setItem(j,k,QtWidgets.QTableWidgetItem(a[i].text)) k+=1 except Exception as e: print("Error",e) self.tableWidget.setEnabled(False) self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: Error occured at KoK') self.lineEdit.setEnabled(True) self.tableWidget.resizeColumnsToContents() self.tableWidget_2.resizeColumnsToContents() self.listWidget.scrollToBottom() self.listWidget.addItem('['+str(datetime.datetime.now().time())+']: List refreshed') if e1b==True or e2b==True or k1b==True: w.ShowWindow("Matching course found!","Course Code: {0} {1} {2}".format(*courses)) next_time=time_now+timeout t=threading.Timer(next_time-time.time(),self.startengine) t.daemon=True t.start() def main(): app=QtWidgets.QApplication(sys.argv) form=App() form.show() app.exec_() if __name__ == '__main__': main()

43.639405

164

0.597155

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11,739

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0.224164

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0.016604

0.034955

0.31838

0.205797

0.176668

0.148849

0.110399

0

0.042606

0.274214

11,739

268

165

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0.763263

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false

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0.008299

0

null

0

null

0

1

0

6de329be760fa541cce9f8961d309f42264a1df3

1,604

py

Python

tests/utils.py

ofek/hatch-containers

dd57acc812db8e62994f2b00160a05292d5f35c1

[ "MIT" ]

3

2021-12-29T06:44:41.000Z

2022-02-28T09:27:20.000Z

tests/utils.py

ofek/hatch-containers

dd57acc812db8e62994f2b00160a05292d5f35c1

[ "MIT" ]

null

tests/utils.py

ofek/hatch-containers

dd57acc812db8e62994f2b00160a05292d5f35c1

[ "MIT" ]

null

# SPDX-FileCopyrightText: 2021-present Ofek Lev <oss@ofek.dev> # # SPDX-License-Identifier: MIT import subprocess from textwrap import dedent as _dedent import tomli import tomli_w def dedent(text): return _dedent(text[1:]) def check_container_output(container_name, command): return subprocess.check_output(['docker', 'exec', container_name, *command]).decode('utf-8') def container_exists(container_name): output = ( subprocess.check_output(['docker', 'ps', '-a', '--format', '{{.Names}}', '--filter', f'name={container_name}']) .strip() .decode('utf-8') ) return any(line.strip() == container_name for line in output.splitlines()) def container_running(container_name): output = ( subprocess.check_output(['docker', 'ps', '--format', '{{.Names}}', '--filter', f'name={container_name}']) .strip() .decode('utf-8') ) return any(line.strip() == container_name for line in output.splitlines()) def update_project_environment(project, name, config): project_file = project.root / 'pyproject.toml' with open(str(project_file), 'r', encoding='utf-8') as f: raw_config = tomli.loads(f.read()) env_config = raw_config.setdefault('tool', {}).setdefault('hatch', {}).setdefault('envs', {}).setdefault(name, {}) env_config.update(config) project.config.envs[name] = project.config.envs.get(name, project.config.envs['default']).copy() project.config.envs[name].update(env_config) with open(str(project_file), 'w', encoding='utf-8') as f: f.write(tomli_w.dumps(raw_config))

30.846154

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0.667082

204

1,604

5.098039

0.357843

0.1

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0.077885

0.373077

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0.209615

0

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1,604

51

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0.40625

0

null

0

null

0

1

0

6de4785de957dcd93698e538204a1309d3d31d03

881

py

Python

Question Set 3 - (Functions)/Version 1/main.py

Randula98/Python-For-Beginners

e41a6014be882f01c6ccdcbe2167e2b581646eee

[ "MIT" ]

6

2021-12-14T17:52:11.000Z

2021-12-19T20:22:44.000Z

Question Set 3 - (Functions)/Version 1/main.py

GIHAA/Python-For-Beginners

e41a6014be882f01c6ccdcbe2167e2b581646eee

[ "MIT" ]

null

Question Set 3 - (Functions)/Version 1/main.py

GIHAA/Python-For-Beginners

e41a6014be882f01c6ccdcbe2167e2b581646eee

[ "MIT" ]

2

2021-12-19T18:50:30.000Z

2022-01-01T23:05:18.000Z

#define calcIncrement function def calcIncrement(salary , noOfYearsWorked): if(noOfYearsWorked > 2): increment = (salary * 10 / 100) else: increment = 0 return increment #define calcTotalSalary function def calcTotalSalary(salary , increment): total = salary + increment return total #get user inputs for salary salary = input("Enter Salary : ") salary = float(salary) #get user inputs for number of years worked years = input("Enter no of years worked : ") years = int(years) #calculate the increment by passing the given values to the function increment = calcIncrement(salary , years) #calculate the total salary by passing the given values to the function totalSalary = calcTotalSalary(salary , increment) #display the increment and the total salary print("Increment : " + str(increment)) print("Total Salary : " + str(totalSalary))

26.69697

71

0.732123

108

881

5.972222

0.37963

0.068217

0.093023

0.049612

0.111628

0

0.00979

0.188422

881

32

72

27.53125

0.892308

0.348468

0

0.121908

0

1

0.117647

false

0

0.235294

0.117647

0

null

0

null

0

1

0

6de56b62032d39a0f8c492c5d736fc6926aeb427

2,576

py

Python

setup.py

vomaufgang/publish

6e610c055118f9761d49962a12d9095cf2936386

[ "MIT" ]

1

2019-08-19T01:45:29.000Z

setup.py

vomaufgang/publish

6e610c055118f9761d49962a12d9095cf2936386

[ "MIT" ]

11

2019-08-18T09:31:10.000Z

2021-01-27T19:02:53.000Z

setup.py

vomaufgang/publish

6e610c055118f9761d49962a12d9095cf2936386

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # anited. publish - Python package with cli to turn markdown files into ebooks # Copyright (c) 2014 Christopher Knörndel # # Distributed under the MIT License # (license terms are at http://opensource.org/licenses/MIT). """Setup script for easy_install and pip.""" import sys import codecs import os.path MIN_SUPPORTED_PYTHON_VERSION = (3, 6) if sys.version_info < MIN_SUPPORTED_PYTHON_VERSION: sys.exit('Sorry, Python < {} is not supported.'.format( '.'.join(map(str, MIN_SUPPORTED_PYTHON_VERSION)) )) try: from setuptools import setup except ImportError: from distutils.core import setup def read(rel_path): """Reads the contents of the file atthe relative path `rel_path`. """ here = os.path.abspath(os.path.dirname(__file__)) with codecs.open(os.path.join(here, rel_path), 'r') as file_: return file_.read() def get_version(rel_path): """Gets the version number declared in the `__version__` constant of the Python file at `rel_path`. """ for line in read(rel_path).splitlines(): if line.startswith('__version__'): delim = '"' if '"' in line else "'" return line.split(delim)[1] raise RuntimeError("Unable to find version string.") README = open('README.md').read() VERSION = get_version('publish/__init__.py') REQUIREMENTS = open('requirements.txt').readlines() DEV_REQUIREMENTS = open('dev-requirements.txt').readlines()[1:] setup( name='anited-publish', version=VERSION, description='Python package with command line interface to turn markdown ' 'files into ebooks.', long_description=README, long_description_content_type='text/markdown', author='Christopher Knörndel', author_email='cknoerndel@anited.de', url='https://gitlab.com/anited/publish', packages=[ 'publish', ], package_data={ 'publish': ['template.jinja', 'VERSION'] }, entry_points={ 'console_scripts': [ 'publish = publish.cli:main' ] }, python_requires=">=3.6", install_requires=REQUIREMENTS, tests_require=DEV_REQUIREMENTS, extras_require={ 'dev': DEV_REQUIREMENTS }, license="MIT", zip_safe=False, keywords='publish', classifiers=[ 'Intended Audience :: End Users/Desktop', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], )

28

78

0.65295

309

2,576

5.271845

0.511327

0.025783

0.033149

0.046041

0.035605

0

0.007921

0.215839

2,576

91

79

28.307692

0.798515

0.177795

0

0.03125

0

0.285235

0

1

0.03125

false

0

0.09375

0

0.15625

0

null

0

null

0

1

0

6de7ad1350d5b902468a609df5d16498912264b6

1,347

py

Python

examples/DGL/alagnn.py

dongzizhu/GraphGallery

c65eab42daeb52de5019609fe7b368e30863b4ae

[ "MIT" ]

1

2020-07-29T08:00:32.000Z

examples/DGL/alagnn.py

dongzizhu/GraphGallery

c65eab42daeb52de5019609fe7b368e30863b4ae

[ "MIT" ]

null

examples/DGL/alagnn.py

dongzizhu/GraphGallery

c65eab42daeb52de5019609fe7b368e30863b4ae

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 import random import math import torch import dgl import graphgallery from graphgallery.datasets import Planetoid print("GraphGallery version: ", graphgallery.__version__) print("PyTorch version: ", torch.__version__) print("DGL version: ", dgl.__version__) ''' Load Datasets - cora/citeseer/pubmed ''' data = Planetoid('cora', root="~/GraphData/datasets/", verbose=False) graph = data.graph device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # splits = data.split_nodes() graphgallery.set_backend("dgl") # experimental setup in # `When Do GNNs Work: Understanding and Improving Neighborhood Aggregation # <https://www.ijcai.org/Proceedings/2020/0181.pdf>` random.seed(2020) split = 0.01 n_nodes = graph.num_nodes sample_size = math.ceil(n_nodes * split) train_idx = random.sample(range(n_nodes - 1000), sample_size) train_nodes = [idx if idx < 500 else idx + 1000 for idx in train_idx] test_nodes = list(range(500, 1500)) from graphgallery.gallery.nodeclas import ALaGCN, ALaGAT # trainer = ALaGAT(device=device, seed=123).setup_graph(graph).build() trainer = ALaGCN(device=device, seed=123).setup_graph(graph).build() trainer.fit(train_nodes, verbose=1) results = trainer.evaluate(test_nodes) print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')

32.071429

83

0.76095

192

1,347

5.1875

0.489583

0.018072

0.032129

0.038153

0.092369

0

0.035626

0.103935

1,347

41

84

32.853659

0.789561

0.205642

0

0.149166

0.042198

0

1

0

false

0

0.28

0

0.28

0.16

0

null

0

null

0

1

0

6dea6c9087b914af198d695841147682ba1f18e7

1,453

py

Python

garden_test/setup.py

jad-b/garden

44169c57fdaa08e0edd751d7459da99334e97323

[ "MIT" ]

null

garden_test/setup.py

jad-b/garden

44169c57fdaa08e0edd751d7459da99334e97323

[ "MIT" ]

null

garden_test/setup.py

jad-b/garden

44169c57fdaa08e0edd751d7459da99334e97323

[ "MIT" ]

null

import subprocess import os from setuptools import setup, find_packages def readme(): with open('README.md') as _file: return _file.read() def requirements(): reqs_file = 'reqs.txt' if os.path.isfile(reqs_file): with open('reqs.txt') as reqs: return [line.strip() for line in reqs if line and not line.startswith('#')] return [] def latest_git_tag(): try: tag = subprocess.check_output( ['git', 'describe', '--abbrev=0', '--tags'] ).decode().rstrip() except subprocess.CalledProcessError: return '0.0.0' return tag setup( name='garden_test', version=latest_git_tag(), long_description=readme(), description='Python package for testing garden', author='Jeremy Dobbins-Bucklad', author_email='j.american.db@gmail.com', url='https://github.com/jad-b/garden', install_requires=requirements(), packages = find_packages(), package_dir = {'garden': 'garden_test'}, py_modules=['testfile'], entry_points={ 'garden.bump': ['garden_test = garden_test.bump:Bumper.bump'], }, zip_safe=False, include_package_data=True, classifiers=( 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Natural Language :: English', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5' ), )

26.907407

70

0.618032

166

1,453

5.26506

0.590361

0.045767

0.02746

0.059497

0

0.007266

0.242257

1,453

53

71

27.415094

0.786558

0

0.295251

0.0351

0

1

0.065217

false

0

0.065217

0

0.23913

0

null

0

null

0

1

0

6debe89876c11c370db73006de84c2358493d8ef

19,992

py

Python

test/coco_save.py

ZCDu/CenternessNet

03f5d01999a4e1595eaceef9f62b4450ed017843

[ "MIT" ]

null

test/coco_save.py

ZCDu/CenternessNet

03f5d01999a4e1595eaceef9f62b4450ed017843

[ "MIT" ]

null

test/coco_save.py

ZCDu/CenternessNet

03f5d01999a4e1595eaceef9f62b4450ed017843

[ "MIT" ]

null

import os import cv2 import pdb import json import copy import numpy as np import torch from PIL import Image, ImageDraw, ImageFont import matplotlib.pyplot as plt import matplotlib import math from tqdm import tqdm from config import system_configs from utils import crop_image, normalize_ from external.nms import soft_nms, soft_nms_merge import pdb colours = np.random.rand(80, 3) def _rescale_dets(detections, ratios, borders, sizes): xs, ys = detections[..., 0:4:2], detections[..., 1:4:2] xs /= ratios[:, 1][:, None, None] ys /= ratios[:, 0][:, None, None] xs -= borders[:, 2][:, None, None] ys -= borders[:, 0][:, None, None] tx_inds = xs[:, :, 0] <= -5 bx_inds = xs[:, :, 1] >= sizes[0, 1] + 5 ty_inds = ys[:, :, 0] <= -5 by_inds = ys[:, :, 1] >= sizes[0, 0] + 5 np.clip(xs, 0, sizes[:, 1][:, None, None], out=xs) np.clip(ys, 0, sizes[:, 0][:, None, None], out=ys) detections[:, tx_inds[0, :], 4] = -1 detections[:, bx_inds[0, :], 4] = -1 detections[:, ty_inds[0, :], 4] = -1 detections[:, by_inds[0, :], 4] = -1 def save_image(data, fn): sizes = np.shape(data) height = float(sizes[0]) width = float(sizes[1]) fig = plt.figure() fig.set_size_inches(width / height, 1, forward=False) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) ax.imshow(data) plt.savefig(fn, dpi=height) plt.close() def kp_decode(nnet, images, K, ae_threshold=0.5, kernel=3): detections, center = nnet.test([images], ae_threshold=ae_threshold, K=K, kernel=kernel) detections = detections.data.cpu().numpy() center = center.data.cpu().numpy() return detections, center def kp_detection(db, nnet, result_dir, debug=False, decode_func=kp_decode): debug_dir = os.path.join(result_dir, "debug") if not os.path.exists(debug_dir): os.makedirs(debug_dir) if db.split != "trainval": db_inds = db.db_inds[:100] if debug else db.db_inds else: db_inds = db.db_inds[:100] if debug else db.db_inds[:5000] num_images = db_inds.size K = db.configs["top_k"] ae_threshold = db.configs["ae_threshold"] nms_kernel = db.configs["nms_kernel"] scales = db.configs["test_scales"] weight_exp = db.configs["weight_exp"] merge_bbox = db.configs["merge_bbox"] categories = db.configs["categories"] nms_threshold = db.configs["nms_threshold"] max_per_image = db.configs["max_per_image"] nms_algorithm = { "nms": 0, "linear_soft_nms": 1, "exp_soft_nms": 2 }[db.configs["nms_algorithm"]] top_bboxes = {} num_images = 1 for root, dirs, files in os.walk( "/media/dl/train_disk/zcdu/work/CenterNet/pic"): for f in files: #db_ind = db_inds[ind] #image_id = db.image_ids(db_ind) #image_file = db.image_file(db_ind) #name = os.path.join(root, f) #print('name':name) #image_file = os.path.join('/media/dl/train_disk/zcdu/work/CenterNet', # name) image_file = os.path.join(root, f) print("image:", image_file) image = cv2.imread(image_file) height, width = image.shape[0:2] detections = [] center_points = [] for scale in scales: new_height = int(height * scale) new_width = int(width * scale) new_center = np.array([new_height // 2, new_width // 2]) inp_height = new_height | 127 inp_width = new_width | 127 images = np.zeros((1, 3, inp_height, inp_width), dtype=np.float32) ratios = np.zeros((1, 2), dtype=np.float32) borders = np.zeros((1, 4), dtype=np.float32) sizes = np.zeros((1, 2), dtype=np.float32) out_height, out_width = (inp_height + 1) // 4, (inp_width + 1) // 4 height_ratio = out_height / inp_height width_ratio = out_width / inp_width resized_image = cv2.resize(image, (new_width, new_height)) resized_image, border, offset = crop_image( resized_image, new_center, [inp_height, inp_width]) resized_image = resized_image / 255. normalize_(resized_image, db.mean, db.std) images[0] = resized_image.transpose((2, 0, 1)) borders[0] = border sizes[0] = [int(height * scale), int(width * scale)] ratios[0] = [height_ratio, width_ratio] images = np.concatenate((images, images[:, :, :, ::-1]), axis=0) images = torch.from_numpy(images) dets, center = decode_func(nnet, images, K, ae_threshold=ae_threshold, kernel=nms_kernel) dets = dets.reshape(2, -1, 8) center = center.reshape(2, -1, 4) dets[1, :, [0, 2]] = out_width - dets[1, :, [2, 0]] center[1, :, [0]] = out_width - center[1, :, [0]] dets = dets.reshape(1, -1, 8) center = center.reshape(1, -1, 4) _rescale_dets(dets, ratios, borders, sizes) center[..., [0]] /= ratios[:, 1][:, None, None] center[..., [1]] /= ratios[:, 0][:, None, None] center[..., [0]] -= borders[:, 2][:, None, None] center[..., [1]] -= borders[:, 0][:, None, None] np.clip(center[..., [0]], 0, sizes[:, 1][:, None, None], out=center[..., [0]]) np.clip(center[..., [1]], 0, sizes[:, 0][:, None, None], out=center[..., [1]]) dets[:, :, 0:4] /= scale center[:, :, 0:2] /= scale if scale == 1: center_points.append(center) detections.append(dets) detections = np.concatenate(detections, axis=1) center_points = np.concatenate(center_points, axis=1) classes = detections[..., -1] classes = classes[0] detections = detections[0] center_points = center_points[0] valid_ind = detections[:, 4] > -1 valid_detections = detections[valid_ind] box_width = valid_detections[:, 2] - valid_detections[:, 0] box_height = valid_detections[:, 3] - valid_detections[:, 1] s_ind = (box_width * box_height <= 22500) l_ind = (box_width * box_height > 22500) s_detections = valid_detections[s_ind] l_detections = valid_detections[l_ind] s_left_x = (2 * s_detections[:, 0] + s_detections[:, 2]) / 3 s_right_x = (s_detections[:, 0] + 2 * s_detections[:, 2]) / 3 s_top_y = (2 * s_detections[:, 1] + s_detections[:, 3]) / 3 s_bottom_y = (s_detections[:, 1] + 2 * s_detections[:, 3]) / 3 s_temp_score = copy.copy(s_detections[:, 4]) s_detections[:, 4] = -1 center_x = center_points[:, 0][:, np.newaxis] center_y = center_points[:, 1][:, np.newaxis] s_left_x = s_left_x[np.newaxis, :] s_right_x = s_right_x[np.newaxis, :] s_top_y = s_top_y[np.newaxis, :] s_bottom_y = s_bottom_y[np.newaxis, :] ind_lx = (center_x - s_left_x) > 0 ind_rx = (center_x - s_right_x) < 0 ind_ty = (center_y - s_top_y) > 0 ind_by = (center_y - s_bottom_y) < 0 ind_cls = (center_points[:, 2][:, np.newaxis] - s_detections[:, -1][np.newaxis, :]) == 0 ind_s_new_score = np.max( ((ind_lx + 0) & (ind_rx + 0) & (ind_ty + 0) & (ind_by + 0) & (ind_cls + 0)), axis=0) == 1 index_s_new_score = np.argmax( ((ind_lx + 0) & (ind_rx + 0) & (ind_ty + 0) & (ind_by + 0) & (ind_cls + 0))[:, ind_s_new_score], axis=0) s_detections[:, 4][ind_s_new_score] = ( s_temp_score[ind_s_new_score] * 2 + center_points[index_s_new_score, 3]) / 3 l_left_x = (3 * l_detections[:, 0] + 2 * l_detections[:, 2]) / 5 l_right_x = (2 * l_detections[:, 0] + 3 * l_detections[:, 2]) / 5 l_top_y = (3 * l_detections[:, 1] + 2 * l_detections[:, 3]) / 5 l_bottom_y = (2 * l_detections[:, 1] + 3 * l_detections[:, 3]) / 5 l_temp_score = copy.copy(l_detections[:, 4]) l_detections[:, 4] = -1 center_x = center_points[:, 0][:, np.newaxis] center_y = center_points[:, 1][:, np.newaxis] l_left_x = l_left_x[np.newaxis, :] l_right_x = l_right_x[np.newaxis, :] l_top_y = l_top_y[np.newaxis, :] l_bottom_y = l_bottom_y[np.newaxis, :] ind_lx = (center_x - l_left_x) > 0 ind_rx = (center_x - l_right_x) < 0 ind_ty = (center_y - l_top_y) > 0 ind_by = (center_y - l_bottom_y) < 0 ind_cls = (center_points[:, 2][:, np.newaxis] - l_detections[:, -1][np.newaxis, :]) == 0 ind_l_new_score = np.max( ((ind_lx + 0) & (ind_rx + 0) & (ind_ty + 0) & (ind_by + 0) & (ind_cls + 0)), axis=0) == 1 index_l_new_score = np.argmax( ((ind_lx + 0) & (ind_rx + 0) & (ind_ty + 0) & (ind_by + 0) & (ind_cls + 0))[:, ind_l_new_score], axis=0) l_detections[:, 4][ind_l_new_score] = ( l_temp_score[ind_l_new_score] * 2 + center_points[index_l_new_score, 3]) / 3 detections = np.concatenate([l_detections, s_detections], axis=0) detections = detections[np.argsort(-detections[:, 4])] classes = detections[..., -1] #for i in range(detections.shape[0]): # box_width = detections[i,2]-detections[i,0] # box_height = detections[i,3]-detections[i,1] # if box_width*box_height<=22500 and detections[i,4]!=-1: # left_x = (2*detections[i,0]+1*detections[i,2])/3 # right_x = (1*detections[i,0]+2*detections[i,2])/3 # top_y = (2*detections[i,1]+1*detections[i,3])/3 # bottom_y = (1*detections[i,1]+2*detections[i,3])/3 # temp_score = copy.copy(detections[i,4]) # detections[i,4] = -1 # for j in range(center_points.shape[0]): # if (classes[i] == center_points[j,2])and \ # (center_points[j,0]>left_x and center_points[j,0]< right_x) and \ # ((center_points[j,1]>top_y and center_points[j,1]< bottom_y)): # detections[i,4] = (temp_score*2 + center_points[j,3])/3 # break # elif box_width*box_height > 22500 and detections[i,4]!=-1: # left_x = (3*detections[i,0]+2*detections[i,2])/5 # right_x = (2*detections[i,0]+3*detections[i,2])/5 # top_y = (3*detections[i,1]+2*detections[i,3])/5 # bottom_y = (2*detections[i,1]+3*detections[i,3])/5 # temp_score = copy.copy(detections[i,4]) # detections[i,4] = -1 # for j in range(center_points.shape[0]): # if (classes[i] == center_points[j,2])and \ # (center_points[j,0]>left_x and center_points[j,0]< right_x) and \ # ((center_points[j,1]>top_y and center_points[j,1]< bottom_y)): # detections[i,4] = (temp_score*2 + center_points[j,3])/3 # break # reject detections with negative scores keep_inds = (detections[:, 4] > -1) detections = detections[keep_inds] classes = classes[keep_inds] image_id = 0 top_bboxes[image_id] = {} for j in range(categories): keep_inds = (classes == j) top_bboxes[image_id][j + 1] = detections[keep_inds][:, 0:7].astype( np.float32) if merge_bbox: soft_nms_merge(top_bboxes[image_id][j + 1], Nt=nms_threshold, method=nms_algorithm, weight_exp=weight_exp) else: soft_nms(top_bboxes[image_id][j + 1], Nt=nms_threshold, method=nms_algorithm) top_bboxes[image_id][j + 1] = top_bboxes[image_id][j + 1][:, 0:5] scores = np.hstack([ top_bboxes[image_id][j][:, -1] for j in range(1, categories + 1) ]) if len(scores) > max_per_image: kth = len(scores) - max_per_image thresh = np.partition(scores, kth)[kth] for j in range(1, categories + 1): keep_inds = (top_bboxes[image_id][j][:, -1] >= thresh) top_bboxes[image_id][j] = top_bboxes[image_id][j][ keep_inds] if debug: #image_file = db.image_file(db_ind) #image_file = os.path.join( # "/media/dl/train_disk/zcdu/work/CenterNet", name) image = cv2.imread(image_file) im = image[:, :, (2, 1, 0)] fig, ax = plt.subplots(figsize=(12, 12)) fig = ax.imshow(im, aspect='equal') plt.axis('off') fig.axes.get_xaxis().set_visible(False) fig.axes.get_yaxis().set_visible(False) #bboxes = {} for j in range(1, categories + 1): keep_inds = (top_bboxes[image_id][j][:, -1] >= 0.4) cat_name = db.class_name(j) bboxes = top_bboxes[image_id][j][keep_inds] if len(bboxes) > 3: bboxes = select_box(bboxes) #print('test select_box bboxes later:', bboxes.shape) if len(bboxes) == 0: continue p1 = 0 for bbox in bboxes: p1 += 1 bbox = bbox[0:4].astype(np.int32) xmin = bbox[0] ymin = bbox[1] xmax = bbox[2] ymax = bbox[3] #if (xmax - xmin) * (ymax - ymin) > 5184: ax.add_patch( plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, edgecolor=colours[j - 1], linewidth=4.0)) ax.text(xmin + 1, ymin - 3, '{:s}'.format(cat_name), bbox=dict(facecolor=colours[j - 1], ec='black', lw=2, alpha=0.5), fontsize=15, color='white', weight='bold') print("count:!!!!!!!!", p1) out_name = f.replace('jpg', 'pdf') debug_file1 = os.path.join( "/media/dl/train_disk/zcdu/work/CenterNet", "result", "centernet_lite", "{}".format(out_name)) debug_file2 = os.path.join( "/media/dl/train_disk/zcdu/work/CenterNet", "result", "centernet_lite", f) plt.savefig(debug_file1) plt.savefig(debug_file2) plt.close() debug_file = os.path.join( "/media/dl/train_disk/zcdu/work/CenterNet", "result", "centernet_lite", '{}'.format(f)) cv2.imwrite(debug_file, image, [int(cv2.IMWRITE_JPEG_QUALITY), 100]) #result_json = os.path.join(result_dir, "results.json") #detections = db.convert_to_coco(top_bboxes) #with open(result_json, "w") as f: # json.dump(detections, f) #cls_ids = list(range(1, categories + 1)) #image_ids = [db.image_ids(ind) for ind in db_inds] #db.evaluate(result_json, cls_ids, image_ids) print('successful!!!!') return 0 def select_box(boxes): length = len(boxes) if length > 3: print('test coco_Save boxes:', boxes) areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) #print('test coco_Save areas:', areas) #print('test coco_Save areas:', areas.shape) max_index = np.argsort(-areas)[0] max_area = areas[max_index] print('select_box max_index:', boxes[max_index]) count_m = 0 for i in range(length): if i == max_index: continue else: if (int(boxes[i][0]) >= int(boxes[max_index][0]) and int(boxes[i][1]) >= int(boxes[max_index][1]) and int(boxes[i][2]) <= int(boxes[max_index][2]) and int(boxes[i][3]) <= int(boxes[max_index][3])): print('test inside max_area:', boxes[i]) count_m = 1 break #for m in range(length): # if (math.isclose(boxes[m][0], # boxes[max_index][0], # abs_tol=0.00001) # and math.isclose(boxes[m][1], # boxes[max_index][1], # abs_tol=0.00001)): # count_m = 1 # break # elif (math.isclose(boxes[m][2], # boxes[max_index][2], # abs_tol=0.00001) # and math.isclose(boxes[m][3], # boxes[max_index][3], # abs_tol=0.00001)): # count_m = 1 # break # else: # continue if (count_m == 1): print('test coco delete!!!') boxes = np.delete(boxes, max_index, axis=0) count_m = 0 return boxes def testing(db, nnet, result_dir, debug=False): return globals()[system_configs.sampling_function](db, nnet, result_dir, debug=debug)

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0.456633

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3.71404

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0.018089

0.022263

0.355635

0.300093

0.250232

0.206169

0.187848

0.180427

0

0.041217

0.411415

19,992

468

90

42.717949

0.69168

0.146609

0

0.126844

0

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false

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null

0

null

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1

0

6dee89b3a35ae4ccdd9553002d458259723951b4

31,184

py

Python

Apps/polls/Views/CourseArrangement.py

shadowofgost/WebEngineering

693af827e3458806cdace959262cf393d29f6504

[ "Apache-2.0" ]

1

2021-04-05T05:40:17.000Z

Apps/polls/Views/CourseArrangement.py

shadowofgost/WebEngineering

693af827e3458806cdace959262cf393d29f6504

[ "Apache-2.0" ]

null

Apps/polls/Views/CourseArrangement.py

shadowofgost/WebEngineering

693af827e3458806cdace959262cf393d29f6504

[ "Apache-2.0" ]

null

from django.http import HttpResponse from django.db.models import Q from drf_yasg.utils import swagger_auto_schema from drf_yasg.openapi import Parameter, Schema, Response, TYPE_INTEGER, TYPE_OBJECT, TYPE_STRING, IN_QUERY from json import dumps from .. import models from .Public import responses_success, responses_fail, get_request_args, data_page_response, content_type_tmp, post_search, put_success, put_error, post_error, data_base_error_specific, patch_success, patch_error, id_error, delete_schema from rest_framework.views import APIView from django.views.decorators.csrf import csrf_exempt class CourseArrangement(APIView): ''' list list all information about Equipment ''' data_schema = { 'id': Schema( title='课程id', description='课程id,其中课程id是唯一的标识', type=TYPE_INTEGER, format='int32', enum=None, ), 'id_curricula__name': Schema( title='课程名称', description='课程名称是课程安排表对应的课程课程名称', type=TYPE_STRING, format='string', enum=None, ), 'timebegin': Schema( title='课程开时间 ', description=' 项目开始时间记录最后更新时间；（2000-1-1 0:0:0 经过的秒），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'timeend': Schema( title='课程结束时间', description='项目结束时间记录最后更新时间；（2000-1-1 0:0:0 经过的秒），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'id_location__name': Schema( title=' 课程所在教室的地点的名称 ', description='课程所在教室的地点的名称 ', type=TYPE_STRING, format='string', enum=None, ), 'id_speaker__name': Schema( title='主讲人', description='主讲人也就是课程老师的姓名', type=TYPE_STRING, format='string', enum=None, ), 'attr': Schema( title='课程属性', description='1代表实验类型、2代表普通上课类型、3讲座考勤类型，必须有值', type=TYPE_INTEGER, format='int32', enum=[1, 2, 3], ), 'charge': Schema( title=' 是否收费的字段 ', description=' 免费0、收费1、开放2，必须有值 ', type=TYPE_INTEGER, format='int32', enum=[0, 1, 2], ), 'pwaccess': Schema( title='派位', description='不派位0、刷卡派位1（派位指用户刷卡时系统指定座位），必须有值', type=TYPE_INTEGER, format='int32', enum=[0, 1], ), 'pwcontinuous': Schema( title='派位连续性', description='连续派位0、随机派位1，必须有值', type=TYPE_INTEGER, format='int32', enum=[0, 1], ), 'pwdirection': Schema( title='排位顺序', description='顺序派位0、逆序派位1（当设置为随机派位时本功能无效），必须有值', type=TYPE_INTEGER, format='int32', enum=[0, 1], ), 'dooropen': Schema( title='是否开门', description='匹配的用户刷卡是否开门，0开门，1不开门', type=TYPE_INTEGER, format='int32', enum=[0, 1], ), 'timebegincheckbegin': Schema( title='最早开始考勤的最早时间', description=' 安排考勤开始的最早时间（单位为分钟，0代表无效），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'timebegincheckend': Schema( title='最早签到结束时间 ', description=' 安排考勤开始的最迟时间（单位为分钟，0代表无效），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'timeendcheckbegin': Schema( title='考勤结束的最早时间（签退） ', description=' 安排考勤结束的最早时间（单位为分钟，0代表无效），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'timeendcheckend': Schema( title='考勤结束的最迟时间（签退）', description=' 安排考勤结束的最迟时间（单位为分钟，0代表无效），必须有值', type=TYPE_INTEGER, format='int32', enum=None, ), 'listdepts': Schema( title=' 参加本安排的学生部门列表 ', description=' 参加本安排的学生部门列表 ', type=TYPE_STRING, format='string', enum=None, ), 'rangeusers': Schema( title='参加本安排的学生学号列表（与RangeUser为相加的关系）', description='参加本安排的学生学号列表（与RangeUser为相加的关系）', type=TYPE_STRING, format='string', enum=None, ), 'rangeequs': Schema( title=' 座位表 ', description=' 课程使用的座位范围列表 ', type=TYPE_STRING, format='string', enum=None, ), 'listplaces': Schema( title=' 课程使用的地点 ', description=' 课程使用的地点列表（与课程使用的座位范围列表为相加的关系）', type=TYPE_STRING, format='string', enum=None, ), 'mapuser2equ': Schema( title='学生和座位对应表', description='学生和座位对应表', type=TYPE_STRING, format='string', enum=None, ), 'aboutspeaker': Schema( title='本课程主讲人介绍', description=' 本课程主讲人也就是上课老师的介绍', type=TYPE_STRING, format='string', enum=None, ), 'rem': Schema( title='课程介绍', description='课程内容的介绍', type=TYPE_STRING, format='string', enum=None, ), 'timeupdate': Schema( title='update time ', description=' 记录最后更新时间；（2000-1-1 0:0:0 经过的秒），必须有值 ', type=TYPE_INTEGER, format='int32', enum=None, ), 'idmanager__name': Schema( title=' 更新信息的管理员的姓名 ', description=' 更新信息的管理员的姓名 ', type=TYPE_STRING, format='string', enum=None, ) } data_schema_present = Schema( title='查询成功的返回', description='查询成功返回的函数值', type=TYPE_OBJECT, # 类型 properties=data_schema ) get_responses_success = Schema( title='成功获取查询数据', description='这个接口用于展示成功获取全部数据的格式', type=TYPE_OBJECT, properties={ 'page': Schema( title='页码', description='用于表示展示的页码数', type=TYPE_INTEGER, format='int32', ), 'limits': Schema( title='页码', description='用于表示每页展示的行数', type=TYPE_INTEGER, format='int32', ), 'error_code': Schema( title='是否有报错数据', description='用于传达是否有报错数据，0表示没有报错数据，1表示有报错数据', type=TYPE_INTEGER, format='int32', ), 'data': Schema( title='数据', description='用于传递查询到的全部数据', type=TYPE_OBJECT, properties=[data_schema_present, data_schema_present] ), } ) CourseInformation_get_responses_success = Response( description='查询课程信息成功的响应', schema=get_responses_success, examples=None, ) CourseInformation_get_responses_fail = Response( description='查询课程信息失败的响应', schema=responses_fail, examples={ 'error_code': 1, 'message': '查询课程信息失败' } ) page_get_parammeter = Parameter( name='page', in_=IN_QUERY, description='查询时设定的页码数', required=True, type=TYPE_INTEGER, format='int32', ) limits_get_parammeter = Parameter( name='limits', in_=IN_QUERY, description='查询时设定的每页行数', required=True, type=TYPE_INTEGER, format='int32', ) @swagger_auto_schema( request_body=None, manual_parameters=[ page_get_parammeter, limits_get_parammeter], operation_id=None, operation_description='这个端口用于查询课程信息', operation_summary=None, security=None, responses={ 200: CourseInformation_get_responses_success, 401: CourseInformation_get_responses_fail }, tags=None) @get_request_args @csrf_exempt def get(self, request, args, session): is_login = request.COOKIES.get('is_login') if not request.session.get(is_login, None): return HttpResponse(dumps({'code': 0}), content_type=content_type_tmp, charset='utf-8') pages = int(args.get('page', 1)) limits = int(args.get('limits', 20)) data_equipment = models.TCyplan.objects.all().values('id', 'id_curricula__name', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager__name', 'mapuser2equ', 'aboutspeaker', 'rem').distinct().order_by('id') return data_page_response(data_equipment, pages, limits) ''' list list all information about Equipment ''' CourseArrangement_post_request_body = Schema( title='查询课程安排所需要的信息', # 标题 description=' 输入查询字符串用于查询课程安排信息 ', # 接口描述 type=TYPE_OBJECT, # 类型 "object" ,"string" ,"number" ,"integer" ,"boolean" ,"array"" ,"boolean" ,"array" ,"file" format=None, # 格式 date,date-time,password,binary,bytes,float,double,int32,int64,email,ipv4, ipv6, uri, uuid, slug, decimal enum=None, # [列表]列举参数的请求值 pattern=None, # 当 format为 string是才填此项 # 当 type为object时，为dict对象 {'str1': Schema对象, 'str2': SchemaRef对象} properties=post_search, required=['input_string', 'page', 'limits'], # [必须的属性列表] items=None, # 当type是array时，填此项 ) CourseArrangement_post_responses_success = Response( description='查询课程安排表成功的响应', schema=get_responses_success, examples={ 'error_code': 0, 'message': '查询成功' }) CourseArrangement_post_responses_fail = Response( description='查询课程安排表失败的响应', schema=responses_fail, examples={ 'error_code': 1, 'message': post_error }) @swagger_auto_schema( request_body=CourseArrangement_post_request_body, manual_parameters=None, operation_id=None, operation_description='这个端口用于查询课程安排表（某些条件下的课程安排表）', operation_summary=None, security=None, responses={ 201: CourseArrangement_post_responses_success, 400: CourseArrangement_post_responses_fail }, tags=None) @get_request_args @csrf_exempt def post(self, request, args, session): is_login = request.COOKIES.get('is_login') if not request.session.get(is_login, None): return HttpResponse(dumps({'code': 0}), content_type=content_type_tmp, charset='utf-8') input_string = args.get('input_string', None) pages = int(args.get('page', 1)) limits = int(args.get('limits', 20)) if input_string == None: data_equipment = models.TCyplan.objects.all().values( 'id', 'id_curricula__name', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager__name', 'mapuser2equ', 'aboutspeaker', 'rem' ).distinct().order_by('id') else: input_string = input_string.strip() try: test_input = eval(input_string) except: test_input = input_string if isinstance(test_input, int): data_equipment = models.TCyplan.objects.filter( Q(id=test_input) | Q(id_curricula=test_input) | Q(timebegin=test_input) | Q(timeend=test_input) | Q(id_location=test_input) | Q(id_speaker=test_input) | Q(attr=test_input) | Q(charge=test_input) | Q(pwaccess=test_input) | Q(pwcontinuous=test_input) | Q(pwdirection=test_input) | Q(dooropen=test_input) | Q(timebegincheckbegin=test_input) | Q(timebegincheckend=test_input) | Q(timeendcheckbegin=test_input) | Q(timeendcheckend=test_input) | Q(timeupdate=test_input) | Q(idmanager=test_input)).values( 'id', 'id_curricula', 'timebegin', 'timeend', 'id_location', 'id_speaker', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager', 'mapuser2equ', 'aboutspeaker', 'rem' ).distinct().order_by('id') else: data_equipment = models.TCyplan.objects.filter( Q(rem__icontains=test_input) | Q(rangeequs__icontains=test_input) | Q(rangeequs__icontains=test_input) | Q(listdepts__icontains=test_input) | Q(listplaces__icontains=test_input) | Q(mapuser2equ__icontains=test_input) | Q(aboutspeaker__icontains=test_input) | Q(idmanager__name__icontains=test_input) | Q(id_location__name__icontains=test_input) | Q(id_speaker__name__icontains=test_input)).values( 'id', 'id_curricula', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager__name', 'mapuser2equ', 'aboutspeaker', 'rem', ).distinct().order_by('id') return data_page_response(data_equipment, pages, limits) ''' list list all information about Equipment ''' CourseArrangement_put_request_body = Schema( title=' 增加课程安排表需要的数据 ', # 标题 description='向数据库增加课程安排表需要的数据和字段', # 接口描述 type=TYPE_OBJECT, # 类型 "object" ,"string" ,"number" ,"integer" ,"boolean" ,"array" ,"file" properties=data_schema, required=[ 'id', 'id_curricula__name', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager__name', 'mapuser2equ', 'aboutspeaker', 'rem'] ) CourseArrangement_put_responses_success = Response( description='增加课程安排表数据成功的响应', schema=responses_success, examples={ 'error_code': 0, 'message': put_success }) CourseArrangement_put_responses_fail = Response( description='增加课程安排表数据失败的响应', schema=responses_fail, examples={ 'error_code': 1, 'message': put_error }) @swagger_auto_schema( request_body=CourseArrangement_put_request_body, manual_parameters=None, operation_id=None, operation_description='这个端口用于向数据库增加课程安排表的数据', operation_summary=None, security=None, responses={ 201: CourseArrangement_put_responses_success, 400: CourseArrangement_put_responses_fail }, tags=None) @get_request_args @csrf_exempt def put(self, request, args, session): field_name = [ 'id', 'id_curricula__name', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'timeupdate', 'idmanager__name', 'aboutspeaker', 'rem' ] is_login = request.COOKIES.get('is_login') if not request.session.get(is_login, None): return HttpResponse(dumps({'code': 0}), content_type=content_type_tmp, charset='utf-8') variable_name = locals() for i in field_name: variable_name[i] = args.get(i, 0) user_id = request.COOKIES.get('user_id') user_id = request.session.get(user_id) variable_name['idmanager'] = user_id del variable_name['idmanager__name'] if isinstance(variable_name['id_location__name'], int) and isinstance(variable_name['id_speaker__name'], int) and isinstance(variable_name['id_curricula__name'], int): variable_name['id_location'] = variable_name['id_location__name'] variable_name['id_speaker'] = variable_name['id_speaker__name'] variable_name['id_curricula'] = variable_name['id_curricula__name'] else: return HttpResponse(dumps( {'error_code': 1, 'message': '请确保所填的id类数据是数字'}), content_type=content_type_tmp, charset='utf-8') # 批量命名变量 try: curricula_object = models.TCycurricula.objects.get( id=variable_name.get('id_curricula')) location_object = models.TCylocation.objects.get( id=variable_name.get('id_location')) speaker_object = models.TCyuser.objects.get( id=variable_name.get('id_speaker')) idmanager_object = models.TCyuser.objects.get( id=variable_name.get('idmanager')) ueses_tmp = models.TCyplan.objects.create( id=variable_name.get('id'), id_curricula=curricula_object, id_location=location_object, id_speaker=speaker_object, timebegin=variable_name.get('timebegin'), timeend=variable_name.get('timeend'), attr=variable_name.get('attr'), charge=variable_name.get('charge'), pwaccess=variable_name.get('pwaccess'), pwcontinuous=variable_name.get('pwcontinuous'), pwdirection=variable_name.get('pwdirection'), dooropen=variable_name.get('dooropen'), timebegincheckbegin=variable_name.get('timebegincheckbegin'), timebegincheckend=variable_name.get('timebegincheckend'), timeendcheckbegin=variable_name.get('timeendcheckbegin'), timeendcheckend=variable_name.get('timeendcheckend'), rangeusers=variable_name.get('rangeusers'), listdepts=variable_name.get('listdepts'), rangeequs=variable_name.get('rangeequs'), timeupdate=variable_name.get('timeupdate'), listplaces=variable_name.get('listplaces'), idmanager=idmanager_object, mapuser2equ=variable_name.get('mapuser2equ'), aboutspeaker=variable_name.get('aboutspeaker'), rem=variable_name.get('rem')) return HttpResponse(dumps({'error_code': 0, 'message': put_success}), content_type=content_type_tmp, charset='utf-8') except Exception as error: return HttpResponse(dumps( {'error_code': 1, 'message': data_base_error_specific + str(error)}), content_type=content_type_tmp, charset='utf-8') ''' list list all information about Equipment ''' CourseArrangement_patch_request_body = Schema( title=' 修改课程安排表所需要的数据 ', # 标题 description=' 修改课程安排表 ', # 接口描述 type=TYPE_OBJECT, # 类型 "object" ,"string" ,"number" ,"integer" ,"boolean" ,"array" ,"file" format=None, # 格式 date,date-time,password,binary,bytes,float,double,int32,int64,email,ipv4, ipv6, uri, uuid, slug, decimal enum=None, # [列表]列举参数的请求值 pattern=None, # 当 format为 string是才填此项 # 当 type为object时，为dict对象 {'str1': Schema对象, 'str2': SchemaRef对象} properties=data_schema, required=['id'], # [必须的属性列表] items=None, # 当type是array时，填此项 ) CourseArrangement_patch_responses_success = Response( description='修改课程安排表成功的响应', schema=responses_success, examples={ 'error_code': 0, 'message': patch_success }) CourseArrangement_patch_responses_fail = Response( description='修改课程安排表失败的响应', schema=responses_fail, examples={ 'error_code': 1, 'message': patch_error }) @swagger_auto_schema(request_body=CourseArrangement_patch_request_body, manual_parameters=None, operation_id=None, operation_description='这个端口用于修改课程安排表的数据', operation_summary=None, security=None, responses={ 201: CourseArrangement_patch_responses_success, 400: CourseArrangement_patch_responses_fail }, tags=None) @get_request_args @csrf_exempt def patch(self, request, args, session): is_login = request.COOKIES.get('is_login') if not request.session.get(is_login, None): return HttpResponse(dumps({'code': 0}), content_type=content_type_tmp, charset='utf-8') id_equipment = args.get('id') data_equipment_initial = list( models.TCyplan.objects.filter(id=id_equipment).values( 'id', 'id_curricula', 'timebegin', 'timeend', 'id_location', 'id_speaker', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager', 'mapuser2equ', 'aboutspeaker', 'rem')) if data_equipment_initial == []: return HttpResponse(dumps({'error_code': 1, 'message': id_error}), content_type=content_type_tmp, charset='utf-8') data_equipment = data_equipment_initial[0] field_name = [ 'id', 'id_curricula__name', 'timebegin', 'timeend', 'id_location__name', 'id_speaker__name', 'attr', 'charge', 'pwaccess', 'pwcontinuous', 'pwdirection', 'dooropen', 'timebegincheckbegin', 'timebegincheckend', 'timeendcheckbegin', 'timeendcheckend', 'rangeusers', 'listdepts', 'rangeequs', 'timeupdate', 'listplaces', 'idmanager__name', 'mapuser2equ', 'aboutspeaker', 'rem' ] args['id_curricula'] = args.get( 'id_curricula__name', data_equipment['id_curricula']) args['id_location'] = args.get( 'id_location__name', data_equipment['id_location']) args['id_speaker'] = args.get( 'id_speaker__name', data_equipment['id_speaker']) args['idmanager'] = args.get( 'idmanager__name', data_equipment['idmanager']) if isinstance(args['id_location__name'], int) and isinstance(args['id_speaker__name'], int) and isinstance(args['id_curricula__name'], int) and isinstance(args['idmanager__name'], int): args['id_location'] = args['id_location__name'] args['id_speaker'] = args['id_speaker__name'] args['id_curricula'] = args['id_curricula__name'] args['idmanager'] = args['idmanager__name'] else: return HttpResponse(dumps( {'error_code': 1, 'message': '请确保所填的id类数据是数字'}), content_type=content_type_tmp, charset='utf-8') variable_name = locals() for i in field_name: if args[i] == 0: variable_name[i] = data_equipment[i] else: variable_name[i] = args.get(i, data_equipment[i]) user_id = request.COOKIES.get('user_id') user_id = request.session.get(user_id) variable_name['idmanager'] = user_id try: models.TCyplan.objects.filter(id=id_equipment).update( id=variable_name.get('id'), id_curricula=variable_name.get('id_curricula'), id_location=variable_name.get('id_location'), id_speaker=variable_name.get('id_speaker'), timebegin=variable_name.get('timebegin'), timeend=variable_name.get('timeend'), attr=variable_name.get('attr'), charge=variable_name.get('charge'), pwaccess=variable_name.get('pwaccess'), pwcontinuous=variable_name.get('pwcontinuous'), pwdirection=variable_name.get('pwdirection'), dooropen=variable_name.get('dooropen'), timebegincheckbegin=variable_name.get('timebegincheckbegin'), timebegincheckend=variable_name.get('timebegincheckend'), timeendcheckbegin=variable_name.get('timeendcheckbegin'), timeendcheckend=variable_name.get('timeendcheckend'), rangeusers=variable_name.get('rangeusers'), listdepts=variable_name.get('listdepts'), rangeequs=variable_name.get('rangeequs'), timeupdate=variable_name.get('timeupdate'), listplaces=variable_name.get('listplaces'), idmanager=variable_name.get('idmanager'), mapuser2equ=variable_name.get('mapuser2equ'), aboutspeaker=variable_name.get('aboutspeaker'), rem=variable_name.get('rem')) return HttpResponse(dumps({'message': '修改课程安排表成功'}), content_type=content_type_tmp, charset='utf-8') except Exception as error: return HttpResponse(dumps( {'error_code': 1, 'message': data_base_error_specific + str(error)}), content_type=content_type_tmp, charset='utf-8') APIView_delete_request_body = Schema( title=' 删除数据库中的信息 ', # 标题 description='删除数据库中具体的id名称', # 接口描述 type=TYPE_OBJECT, # 类型 "object" ,"string" ,"number" ,"integer" ,"boolean" ,"array" ,"file" format=None, # 格式 date,date-time,password,binary,bytes,float,double,int32,int64,email,ipv4, ipv6, uri, uuid, slug, decimal enum=None, # [列表]列举参数的请求值 pattern=None, # 当 format为 string是才填此项 # 当 type为object时，为dict对象 {'str1': Schema对象, 'str2': SchemaRef对象} properties=delete_schema, required=['ids'], # [必须的属性列表] items=None, # 当type是array时，填此项 ) APIView_delete_responses_success = Response( description='APIView_delete_responses is success', schema=responses_success, examples={ 'error_code': 0, 'message': '删除成功' } ) APIView_delete_responses_fail = Response( description='APIView_delete_responses is failure', schema=responses_fail, examples={ 'error_code': 1, 'message': '删除失败，请输入正确的id' } ) @ swagger_auto_schema( request_body=APIView_delete_request_body, manual_parameters=None, operation_id=None, operation_description='api是用来删除数据库中的给定字段', operation_summary=None, security=None, responses={ 204: APIView_delete_request_body, 500: APIView_delete_request_body }, tags=None) @ get_request_args def delete(self, request, args, session): is_login = request.COOKIES.get('is_login') if not request.session.get(is_login, None): return HttpResponse(dumps({'code': 0}), content_type=content_type_tmp, charset='utf-8') variable_name = locals() delete_data = args.get('ids') numbers_id = len(delete_data) for i in range(numbers_id): variable_name['id_'+str(i)] = delete_data[i].get('data_id') try: for i in range(numbers_id): models.TCyplan.objects.filter( id=variable_name.get('id_'+str(i), 'id_1')).delete() return HttpResponse(dumps({'error_code': 0, 'message': '数据删除成功'}), content_type=content_type_tmp, charset='utf-8') except Exception as error: return HttpResponse(dumps({'error_code': 1, 'message': data_base_error_specific + str(error)}), content_type=content_type_tmp, charset='utf-8')

38.594059

455

0.536942

2,703

31,184

5.938217

0.118017

0.052333

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null

0

null

0

1

0

6defdfc013df6a621f25fd5ffba934ad58dd3acd

3,312

py

Python

lights.py

team-7108/computer-vision-tutorials

cfb7e455b5d8bba8779c440907344d9763573f57

[ "MIT" ]

3

2018-09-12T02:56:46.000Z

2020-11-13T13:48:44.000Z

lights.py

team-7108/computer-vision-tutorials

cfb7e455b5d8bba8779c440907344d9763573f57

[ "MIT" ]

null

lights.py

team-7108/computer-vision-tutorials

cfb7e455b5d8bba8779c440907344d9763573f57

[ "MIT" ]

1

2020-11-13T13:48:45.000Z

# Import OpenCV module import cv2 # Import numpy for array operations import numpy as np image = cv2.imread('images/five_cubes.jpeg') # Show the image cv2.imshow('Image',image) # Resize the image if it is too big, also helps to speed up the processing image = cv2.resize(image, (600, 600)) cv2.imshow('Resized Image',image) # Equalizing histograms, we try to reduce the effect of light here image = cv2.cvtColor(image,cv2.COLOR_BGR2YUV) channel = cv2.split(image) cv2.equalizeHist(channel[0], channel[0]) cv2.merge(channel,image) image = cv2.cvtColor(image,cv2.COLOR_YUV2BGR) cv2.imshow('Normalized Image',image) # This is a dummy function needed for creating trackbars def nothing(x): pass # Create a window named 'Colorbars' cv2.namedWindow('Colorbars') # Assign strings for ease of coding bh='Blue High' bl='Blue Low' gh='Green High' gl='Green Low' rh='Red High' rl='Red Low' wnd = 'Colorbars' # Begin Creating trackbars for each BGR value cv2.createTrackbar(bl, wnd, 0, 255, nothing) cv2.createTrackbar(bh, wnd, 149, 255, nothing) cv2.createTrackbar(gl, wnd, 156, 255, nothing) cv2.createTrackbar(gh, wnd, 255, 255, nothing) cv2.createTrackbar(rl, wnd, 199, 255, nothing) cv2.createTrackbar(rh, wnd, 255, 255, nothing) while True: mergedImage = np.zeros((600,150,3), np.uint8) # Split image into four pieces and merge again for i in range(0,4): resizedImage = image[0:600, i*150:(i+1)*150] cv2.imshow("cropped", resizedImage) bLow = cv2.getTrackbarPos(bl, wnd) bHigh = cv2.getTrackbarPos(bh, wnd) gLow = cv2.getTrackbarPos(gl, wnd) gHigh = cv2.getTrackbarPos(gh, wnd) rLow = cv2.getTrackbarPos(rl, wnd) rHigh = cv2.getTrackbarPos(rh, wnd) rgbLow=np.array([bLow,gLow,rLow]) rgbHigh=np.array([bHigh,gHigh,rHigh]) maskedImage = cv2.inRange(resizedImage, rgbLow, rgbHigh) cv2.imshow('Masked Image', maskedImage) kernel = np.ones((15,15),np.uint8) # the first morphological transformation is called opening, it will sweep out extra lone pixels around the image openedImage = cv2.morphologyEx(maskedImage, cv2.MORPH_OPEN, kernel) cv2.imshow("Open Image", openedImage) outImage = resizedImage.copy() try: contourImage, contours, hierarchy = cv2.findContours(openedImage.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnt = contours[0] print(cnt) # contours are the points on the outline of the image # bounding rectangle is the minimum rectangle that includes all the contours # this bounding rectangle is perpendicular to image x,y,w,h = cv2.boundingRect(cnt) # We mark that bounding rectangle with green cv2.rectangle(outImage,(x,y),(x+w,y+h),(255,0,0),4) except: pass cv2.imshow("Bboxed",outImage) mergedImage = np.concatenate((mergedImage,outImage), axis=1) mergedImage = mergedImage[0:600, 150:750] cv2.imshow("Merged",mergedImage) keyPressed = cv2.waitKey(1) # Look for keys to be pressed if keyPressed == 27: # if the key is ESC, check the ASCII table, 27 = ESC break # Exit the loop cv2.destroyAllWindows() # Destroy the windows and close the program

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null

0

null

0

1

0

6df0403cfe638d0fa7c9fc0942bb17cdd38113df

569

py

Python

exastics/publish_github_api_releases.py

exastro-suite/exastics

de6193159943319333abc2688f543e7424810823

[ "Apache-2.0" ]

null

exastics/publish_github_api_releases.py

exastro-suite/exastics

de6193159943319333abc2688f543e7424810823

[ "Apache-2.0" ]

1

2020-10-25T08:30:59.000Z

exastics/publish_github_api_releases.py

exastro-suite/exastics

de6193159943319333abc2688f543e7424810823

[ "Apache-2.0" ]

8

2020-10-09T13:11:08.000Z

2021-11-04T06:26:27.000Z

import exastics.collect import pathlib import sys import urllib.parse if __name__ == '__main__': github_account = sys.argv[1] github_repository = sys.argv[2] url_parts = ( 'https', 'api.github.com', urllib.parse.quote(f'/repos/{github_account}/{github_repository}/releases'), '', '', '' ) headers = { 'Accept': 'application/vnd.github.v3+json' } output_dir = pathlib.PurePath(github_repository, 'github-releases') exastics.collect.publish_api(url_parts, headers, output_dir)

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0.2

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null

0

null

0

1

0

6df0ee5285eb665d18e287fcf75e62d896c148dd

1,471

py

Python

cohesity_management_sdk/models/rpo_schedule.py

sachinthakare-cohesity/management-sdk-python

c95f67b7d387d5bab8392be43190e598280ae7b5

[ "MIT" ]

null

cohesity_management_sdk/models/rpo_schedule.py

sachinthakare-cohesity/management-sdk-python

c95f67b7d387d5bab8392be43190e598280ae7b5

[ "MIT" ]

null

cohesity_management_sdk/models/rpo_schedule.py

sachinthakare-cohesity/management-sdk-python

c95f67b7d387d5bab8392be43190e598280ae7b5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright 2019 Cohesity Inc. class RPOSchedule(object): """Implementation of the 'RPO Schedule.' model. Specifies an RPO Schedule. Attributes: rpo_inteval_minutes (long|int): If this field is set, then at any point, a recovery point should be available not older than the given interval minutes. """ # Create a mapping from Model property names to API property names _names = { "rpo_inteval_minutes":'rpoIntevalMinutes' } def __init__(self, rpo_inteval_minutes=None): """Constructor for the RPOSchedule class""" # Initialize members of the class self.rpo_inteval_minutes = rpo_inteval_minutes @classmethod def from_dictionary(cls, dictionary): """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class. """ if dictionary is None: return None # Extract variables from the dictionary rpo_inteval_minutes = dictionary.get('rpoIntevalMinutes') # Return an object of this model return cls(rpo_inteval_minutes)

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0.428571

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null

0

null

0

1

0

6df1040ee952e6ce1e567165568234bfbe1f725c

5,834

py

Python

Gh compilation files/text.py

ibois-epfl/Manis-timber-plate-joinery-solver

fecdb1dfe23348de261f034f85baf24ac396e8cc

[ "MIT" ]

3

2021-10-19T11:55:59.000Z

2022-02-04T15:29:04.000Z

Gh compilation files/text.py

ibois-epfl/Manis-timber-plate-joinery-solver

fecdb1dfe23348de261f034f85baf24ac396e8cc

[ "MIT" ]

null

Gh compilation files/text.py

ibois-epfl/Manis-timber-plate-joinery-solver

fecdb1dfe23348de261f034f85baf24ac396e8cc

[ "MIT" ]

null

"""Export a text file.""" from ghpythonlib.componentbase import dotnetcompiledcomponent as component import Grasshopper, GhPython import System import os import datetime __author__ = "Nicolas Rogeau" __laboratory__ = "IBOIS, Laboratory for Timber Construction" __university__ = "EPFL, Ecole Polytechnique Federale de Lausanne" __funding__ = "NCCR Digital Fabrication, ETH Zurich" __version__ = "2021.09" class MyComponent(component): def __new__(cls): instance = Grasshopper.Kernel.GH_Component.__new__(cls, "Export Text File", "TextOut", """Export a text file.""", "Manis", "Utility") return instance def get_ComponentGuid(self): return System.Guid("02ba4a11-7b1c-48b3-8376-55637e7a1ed2") def SetUpParam(self, p, name, nickname, description): p.Name = name p.NickName = nickname p.Description = description p.Optional = True def RegisterInputParams(self, pManager): p = Grasshopper.Kernel.Parameters.Param_Boolean() self.SetUpParam(p, "run", "run", "Export file if True.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_String() self.SetUpParam(p, "text", "text", "Text to export.") p.Access = Grasshopper.Kernel.GH_ParamAccess.list self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_String() self.SetUpParam(p, "folder", "folder", "Folder path.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_String() self.SetUpParam(p, "name", "name", "File name.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_String() self.SetUpParam(p, "extension", "extension", "(Optional) Custom file extension.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_Boolean() self.SetUpParam(p, "date", "date", "(Optional) Add the date of today to the file name.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) p = Grasshopper.Kernel.Parameters.Param_Boolean() self.SetUpParam(p, "x", "incremental", "(Optional) Check for existing file with the same name and increment if necessary.") p.Access = Grasshopper.Kernel.GH_ParamAccess.item self.Params.Input.Add(p) def RegisterOutputParams(self, pManager): pass def SolveInstance(self, DA): p0 = self.marshal.GetInput(DA, 0) p1 = self.marshal.GetInput(DA, 1) p2 = self.marshal.GetInput(DA, 2) p3 = self.marshal.GetInput(DA, 3) p4 = self.marshal.GetInput(DA, 4) p5 = self.marshal.GetInput(DA, 5) p6 = self.marshal.GetInput(DA, 6) result = self.RunScript(p0, p1, p2, p3, p4, p5, p6) def get_Internal_Icon_24x24(self): o = "iVBORw0KGgoAAAANSUhEUgAAABgAAAAYCAYAAADgdz34AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAJOgAACToAYJjBRwAAACKSURBVEhL7c3RCoQwDETR/v9Pq7dOSoygFDrggweGrhM2aT+3Ta8NB6xH4oDtyAZeZbl+APxWbvJwOlnqLzReg33KUAcj0We5rwmp61Sf6jeie5pV9Mr7Acz2YHbk/UB0T7OKXrn+Od4w+w06pVO9BvuUIZfTyVK/jFZ7lsO6HNblsC6HdfkXtLYDh4phuyx2L58AAAAASUVORK5CYII=" return System.Drawing.Bitmap(System.IO.MemoryStream(System.Convert.FromBase64String(o))) def RunScript(self, run, text, folder, name, extension, date, incremental): inc = incremental ext = extension if run is True: # gh doc path ghP = self.LocalScope.ghdoc.Path # folder and file name if name == None: name = 'this_script_has_no_name' if folder == None: folder = os.path.dirname(os.path.realpath(ghP)) outputName = folder + '\\' + str(name) # date if date is True: date = datetime.datetime.today() outputName += '_' + str(date.year) + '_' + str(date.month) + '_' + str(date.day) # extension if ext == None: ext = '.txt' # avoid overwrite if inc is True: i = 0 iter = outputName + '_' + str(i) while os.path.exists(iter + str(ext)) and i<100: #safety i += 1 iter = outputName + '_' + str(i) outputName = iter outputName += str(ext) # create file myFile = open(outputName,'w') # pass values to file if text != None: for i in range(len(text)): myFile.write(str(text[i])) if i != len(text)-1: myFile.write('\n') # close file myFile.close() # confirm file write if os.stat(outputName).st_size > 0: print('File successfully written as ' + outputName) else: print('output file is empty - check your values') return class AssemblyInfo(GhPython.Assemblies.PythonAssemblyInfo): def get_AssemblyName(self): return "Text File Output" def get_AssemblyDescription(self): return """""" def get_AssemblyVersion(self): return "0.1" def get_AuthorName(self): return "Nicolas Rogeau" def get_Id(self): return System.Guid("bc9186be-9321-4eb3-ba5e-58a615f66a50")

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0

null

0

null

0

1

0

6df14ec0665b31a613e368f74d43196adfd0df56

877

py

Python

setup.py

anthonytw/dutyroll

489dd452ba614a2214756eba0831b33111187225

[ "MIT" ]

2

2019-01-22T20:44:03.000Z

2019-11-30T07:59:32.000Z

setup.py

anthonytw/dutyroll

489dd452ba614a2214756eba0831b33111187225

[ "MIT" ]

null

setup.py

anthonytw/dutyroll

489dd452ba614a2214756eba0831b33111187225

[ "MIT" ]

null

import sys from packaging.version import LegacyVersion from skbuild.exceptions import SKBuildError from skbuild.cmaker import get_cmake_version from skbuild import setup setup_requires = [] # Require pytest-runner only when running tests. if any(arg in sys.argv for arg in ('pytest', 'test')): setup_requires.append('pytest-runner>=2.0') # Add CMake as a build requirement if cmake is not installed or is too low a version. try: if LegacyVersion(get_cmake_version()) < LegacyVersion('3.10'): setup_requires.append('cmake') except SKBuildError: setup_requires.append('cmake') setup( name='dutyroll', version='1.0.1', description='Parallel implementation of rolling window duty cycle.', author='"Anthony Wertz"<awertz@cmu.edu>', license='MIT', packages=['dutyroll'], tests_require=['pytest'], setup_requires=setup_requires )

28.290323

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0.217391

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null

0

null

0

1

0

6df2b3c71a785d6478f03f2023bb542307a17b8f

1,195

py

Python

crits/locations/forms.py

dutrow/crits

6b357daa5c3060cf622d3a3b0c7b41a9ca69c049

[ "MIT" ]

738

2015-01-02T12:39:55.000Z

2022-03-23T11:05:51.000Z

crits/locations/forms.py

deadbits/crits

154097a1892e9d3960d6faaed4bd2e912a196a47

[ "MIT" ]

605

2015-01-01T01:03:39.000Z

2021-11-17T18:51:07.000Z

crits/locations/forms.py

deadbits/crits

154097a1892e9d3960d6faaed4bd2e912a196a47

[ "MIT" ]

316

2015-01-07T12:35:01.000Z

2022-03-30T04:44:30.000Z

from django import forms from crits.locations.location import Location from crits.core.handlers import get_item_names class AddLocationForm(forms.Form): """ Django form for adding a location to a TLO. The list of names comes from :func:`get_item_names`. """ error_css_class = 'error' required_css_class = 'required' location_type = forms.ChoiceField(widget=forms.Select, required=True) country = forms.ChoiceField(widget=forms.Select, required=True) description = forms.CharField( widget=forms.TextInput(attrs={'size': '50'}), required=False) latitude = forms.CharField( widget=forms.TextInput(attrs={'size': '50'}), required=False) longitude = forms.CharField( widget=forms.TextInput(attrs={'size': '50'}), required=False) def __init__(self, *args, **kwargs): super(AddLocationForm, self).__init__(*args, **kwargs) self.fields['location_type'].choices = [ ('Originated From', 'Originated From'), ('Destined For', 'Destined For'), ] self.fields['country'].choices = [ (c.name, c.name) for c in get_item_names(Location, True)]

34.142857

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null

0

null

0

1

0

6df4ba8add8eb7e8c911008f72f03e4dab32f5ab

3,641

py

Python

utils/utils_preprocess_v3.py

microsoft/normalized_trend_filtering

eb73f124243dfc3dc610abba35a3ad1a6303a227

[ "MIT" ]

2

2021-09-06T14:04:17.000Z

2021-11-09T11:55:10.000Z

utils/utils_preprocess_v3.py

microsoft/normalized_trend_filtering

eb73f124243dfc3dc610abba35a3ad1a6303a227

[ "MIT" ]

null

utils/utils_preprocess_v3.py

microsoft/normalized_trend_filtering

eb73f124243dfc3dc610abba35a3ad1a6303a227

[ "MIT" ]

1

2021-11-10T11:44:36.000Z

import pandas as pd import numpy as np import sys import os import itertools import pandas as pd import os from tqdm import tqdm_notebook, tnrange import numpy as np import networkx as nx import seaborn as sns import matplotlib.pyplot as plt from scipy.optimize import minimize import scipy from sklearn import linear_model from sklearn.preprocessing import StandardScaler import cvxpy as cp from scipy.sparse import csr_matrix, vstack, hstack from copy import deepcopy module_path = os.path.abspath(os.path.join('..')) def getReducedGraph(sample_nodes, graph_nodes, interactome): ''' Reduce graph with only intersection nodes from sample and interactome. ''' #find intersection between sample nodes and graph nodes sample_nodes = set(sample_nodes) graph_nodes = set(graph_nodes) intersection_nodes = sample_nodes.intersection(graph_nodes) print('Number of Intersection Nodes : ', len(intersection_nodes)) g = [] for line in tqdm_notebook(range(len(interactome))): if (interactome.iloc[line]['node1'] in intersection_nodes and interactome.iloc[line]['node2'] in intersection_nodes): g.append(interactome.iloc[line]) return pd.DataFrame(g) def getNodeCharacterization(g, sample_nodes): ''' Characterizes nodes based on if node is connected or orphan ''' connected_nodes = set(g.nodes()) orphan_nodes = set(sample_nodes) - connected_nodes return connected_nodes, orphan_nodes def getDataSorting(connected_nodes, sample_df): ''' Sorts covariant matrix such that connected nodes are first followed by orphan nodes and nodes not in interactome ''' sample_df_sorted = deepcopy(sample_df) sample_df_sorted['IN_INTERACTOME'] = sample_df["node"].isin(list(connected_nodes)).tolist() sample_df_sorted = sample_df_sorted.sort_values(by="IN_INTERACTOME", ascending=False).reset_index(drop=True) #get dictionary to map node to number num_to_node = {} for i,nod in enumerate(sample_df_sorted['node'].tolist()): num_to_node[i] = nod #get ordered list of nodes in interactome ordered_nodelist = sample_df_sorted.loc[sample_df_sorted['IN_INTERACTOME'] == True]['node'].tolist() #delete 'IN_INTERACTOME' column sample_df_sorted = sample_df_sorted.drop(columns = ['IN_INTERACTOME', 'node']) return sample_df_sorted, ordered_nodelist, num_to_node def getLaplacian(g, ordered_nodelist, orphan_nodes): ''' Calculates laplacian matrix with respect to ordering of covariant matrix ''' L_norm = nx.normalized_laplacian_matrix(g, nodelist = ordered_nodelist, weight = 'confidence') L = nx.laplacian_matrix(g, nodelist = ordered_nodelist, weight = 'confidence') return csr_matrix(scipy.linalg.block_diag(L.todense(),np.eye(len(orphan_nodes)))), \ csr_matrix(scipy.linalg.block_diag(L_norm.todense(),np.eye(len(orphan_nodes)))) class Preprocessing(): def __init__(self): self.g = None self.connected_nodes = None self.orphan_nodes = None self.sorted_X = None self.ordered_nodelist = None self.num_to_node = None self.L = None self.L_norm = None def transform(self,X_nodes, graph_nodes, graph, X, save_location, load_graph = False): if load_graph == False: self.g = getReducedGraph(X_nodes, graph_nodes, graph) self.g.to_csv(save_location, header=None, index=None, sep='\t') self.g = nx.read_edgelist(save_location, data=(('confidence',float),)) self.connected_nodes, self.orphan_nodes = \ getNodeCharacterization(self.g, X_nodes) self.sorted_X, self.ordered_nodelist, self.num_to_node = \ getDataSorting(self.connected_nodes,X) self.L, self.L_norm = getLaplacian(self.g, self.ordered_nodelist, self.orphan_nodes, )

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null

0

null

0

1

0

6df5916ec657908f3c7be4eae54758a97075100c

791

py

Python

server.py

marwano/remoterobot

80409bde8e20de2b9fe97a8f214295aa5290decd

[ "BSD-3-Clause" ]

1

2019-05-26T10:41:07.000Z

server.py

marwano/remoterobot

80409bde8e20de2b9fe97a8f214295aa5290decd

[ "BSD-3-Clause" ]

1

2018-02-28T23:47:23.000Z

server.py

marwano/remoterobot

80409bde8e20de2b9fe97a8f214295aa5290decd

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import tornado.ioloop import tornado.web import json import logging from uf.wrapper.swift_api import SwiftAPI class MainHandler(tornado.web.RequestHandler): def initialize(self, swift): self.swift = swift def post(self): data = json.loads(self.request.body.decode()) logging.info(repr(data)) func = getattr(self.swift, data['action']) results = func(**data['kwargs']) self.write(json.dumps(dict(results=results))) def main(): logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO) swift = SwiftAPI() app = tornado.web.Application([('/', MainHandler, dict(swift=swift))]) app.listen(8000) tornado.ioloop.IOLoop.current().start() if __name__ == '__main__': main()

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0

0.409091

0

null

0

null

0

1

0

6df62870aa4daf08157f0c702682542e2f8979fe

2,765

py

Python

open/core/betterself/serializers/daily_productivity_log_serializers.py

lawrendran/open

d136f694bafab647722c78be6f39ec79d589f774

[ "MIT" ]

105

2019-06-01T08:34:47.000Z

2022-03-15T11:48:36.000Z

open/core/betterself/serializers/daily_productivity_log_serializers.py

lawrendran/open

d136f694bafab647722c78be6f39ec79d589f774

[ "MIT" ]

111

2019-06-04T15:34:14.000Z

2022-03-12T21:03:20.000Z

open/core/betterself/serializers/daily_productivity_log_serializers.py

lawrendran/open

d136f694bafab647722c78be6f39ec79d589f774

[ "MIT" ]

26

2019-09-04T06:06:12.000Z

2022-01-03T03:40:11.000Z

from rest_framework.exceptions import ValidationError from rest_framework.fields import DateField, ChoiceField, CharField from open.core.betterself.constants import ( BETTERSELF_LOG_INPUT_SOURCES, WEB_INPUT_SOURCE, ) from open.core.betterself.models.daily_productivity_log import DailyProductivityLog from open.core.betterself.serializers.mixins import ( BaseCreateUpdateSerializer, BaseModelReadSerializer, ) from open.core.betterself.serializers.validators import ModelValidatorsMixin from open.utilities.date_and_time import ( format_datetime_to_human_readable, yyyy_mm_dd_format_1, ) class DailyProductivityLogReadSerializer(BaseModelReadSerializer): class Meta: model = DailyProductivityLog fields = ( "uuid", "source", "date", "very_productive_time_minutes", "productive_time_minutes", "neutral_time_minutes", "distracting_time_minutes", "very_distracting_time_minutes", "notes", "mistakes", "created", "modified", "display_name", "pomodoro_count", ) def get_display_name(self, instance): model = self.Meta.model model_name = model._meta.verbose_name time_label = instance.date serialized_time = format_datetime_to_human_readable( time_label, yyyy_mm_dd_format_1 ) display_name = f"{model_name} | Date: {serialized_time}" return display_name class DailyProductivityLogCreateUpdateSerializer( BaseCreateUpdateSerializer, ModelValidatorsMixin ): # allow an regular isoformat of milliseconds also be passed date = DateField(input_formats=["iso-8601"]) source = ChoiceField(choices=BETTERSELF_LOG_INPUT_SOURCES, default=WEB_INPUT_SOURCE) mistakes = CharField(trim_whitespace=True, default="", allow_blank=True) class Meta: model = DailyProductivityLog fields = ( "source", "date", "very_productive_time_minutes", "productive_time_minutes", "neutral_time_minutes", "distracting_time_minutes", "very_distracting_time_minutes", "pomodoro_count", "notes", "mistakes", "user", ) def validate(self, validated_data): user = self.context["request"].user is_creating_instance = not self.instance if is_creating_instance: if self.Meta.model.objects.filter( user=user, date=validated_data["date"], ).exists(): raise ValidationError(f"Fields user and date need to be unique!") return validated_data

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2,765

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0

0.243243

0

null

0

null

0

1

0

6df927163bf069ad2144fb5439fa950c5da79469

1,409

py

Python

Sources/Mavsdk/proto/pb_plugins/setup.py

obe711/MAVSDK-Swift

3ed35bbb57754824f8235f9acf828c73cc10b72b

[ "BSD-3-Clause" ]

null

Sources/Mavsdk/proto/pb_plugins/setup.py

obe711/MAVSDK-Swift

3ed35bbb57754824f8235f9acf828c73cc10b72b

[ "BSD-3-Clause" ]

null

Sources/Mavsdk/proto/pb_plugins/setup.py

obe711/MAVSDK-Swift

3ed35bbb57754824f8235f9acf828c73cc10b72b

[ "BSD-3-Clause" ]

null

import os import subprocess import sys from distutils.command.build import build from distutils.spawn import find_executable from setuptools import setup def parse_requirements(filename): """ Helper which parses requirement_?.*.txt files :param filename: relative path, e.g. `./requirements.txt` :returns: List of requirements """ # Get absolute filepath filepath = os.path.join(os.getcwd(), filename) # Check if file exists if not os.path.exists(filepath): print("[!] File {} not found".format(filename)) return [] # Parse install requirements with open(filepath, encoding="utf-8") as f: return [requires.strip() for requires in f.readlines()] setup( name="protoc-gen-mavsdk", version="1.0.1", description="Protoc plugin used to generate MAVSDK bindings", url="https://github.com/mavlink/MAVSDK-Proto", maintainer="Jonas Vautherin, Julian Oes", maintainer_email="jonas.vautherin@gmail.com, julian@oes.ch", classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", ], packages=["protoc_gen_mavsdk"], install_requires=parse_requirements("requirements.txt"), entry_points={ "console_scripts": [ "protoc-gen-mavsdk= protoc_gen_mavsdk.__main__:main" ] } )

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5.640244

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0

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0.030303

0

null

0

null

0

1

0

6dfb865d03b79b2e933642d474e469577e44cc93

690

py

Python

count.py

sunray97/countrows_excel

7a95e0f6901051942615c6c16d15fee8e6fd4ded

[ "MIT" ]

null

count.py

sunray97/countrows_excel

7a95e0f6901051942615c6c16d15fee8e6fd4ded

[ "MIT" ]

null

count.py

sunray97/countrows_excel

7a95e0f6901051942615c6c16d15fee8e6fd4ded

[ "MIT" ]

null

import xlrd import os import sys # rootdir = 'D:/工作/code/electric/' rootdir = sys.argv[1] xlrd.Book.encoding = "gbk" sumnum=0 filenum = 0 list = os.listdir(rootdir) #列出文件夹下所有的目录与文件 for i in range(0,len(list)): path = os.path.join(rootdir,list[i]) if os.path.isfile(path): print('正在处理：'+path) data = xlrd.open_workbook(path) table = data.sheet_by_index(0) # table = data.sheet_by_name(u'Sheet1') nrows = table.nrows data.release_resources() sumnum=sumnum+nrows filenum=filenum+1 print('-------------------------------------------------------------------------') print('共有%d个文件'%filenum) print('共有%d行记录'%sumnum)

28.75

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0.52809

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0.082474

0

0.012567

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690

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false

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0.190476

0

null

0

null

0

1

0

6dfbe53d06b44a62a35e17a43905a5b258b2a411

1,442

py

Python

0_mesh2html/preprocess_segments.py

ygCoconut/volume2stl

bd95fc39620afd21ce08c8c805ac213583d9daaa

[ "MIT" ]

null

0_mesh2html/preprocess_segments.py

ygCoconut/volume2stl

bd95fc39620afd21ce08c8c805ac213583d9daaa

[ "MIT" ]

null

0_mesh2html/preprocess_segments.py

ygCoconut/volume2stl

bd95fc39620afd21ce08c8c805ac213583d9daaa

[ "MIT" ]

null

''' 0 Preprocess segments: - - specify segments you want to process - dilate slightly the segments - create mask for dilation. - np.unique(my_masked_id) --> select only part with biggest uc - eliminates ouliers too disconnected/far from main structure ''' import numpy as np import h5py from scipy.ndimage import binary_dilation, label from tqdm import tqdm def writeh5_file(file, filename=None): hf = h5py.File(filename, 'w') hf.create_dataset('main', data=file) hf.close() if __name__=='__main__': print('start') segpath = '/n/pfister_lab2/Lab/donglai/mito/db/30um_human/seg_64nm.h5' savepath = '/n/pfister_lab2/Lab/nils/snowproject/seg_64nm_maindendrite.h5' seg = h5py.File(segpath, 'r') seg = np.array(seg['main'], np.uint32) # x y z dendrite_ids = np.loadtxt('seg_spiny_v2.txt', int) for i, did in enumerate(tqdm(dendrite_ids)): # dil = binary_dilation(seg==did)*did # find all components of the dendrite, tolerate tiny gaps s = np.ones((3, 3, 3), int) dil, nf = label((seg==did)*did, structure=s) # find main component ui, uc = np.unique(dil, return_counts=True) uc = uc[ui>0]; ui = ui[ui>0] max_id = ui[np.argmax(uc)] # remove non-main components from segmentation seg[seg==did] = 0 seg[dil==max_id] = did writeh5_file(seg, savepath) print('start')

27.730769

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0.640777

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0.020089

0.026786

0.033482

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0.23509

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0.789665

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0

1

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0.08

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null

0

null

0

1

0

6dfdee78a36f76a22a8222a5f71ca90b9c824b58

2,665

py

Python

branch/runner.py

sahibsin/Pruning

acc1db31c19c8b23599950cec4fe6399513ed306

[ "MIT" ]

null

branch/runner.py

sahibsin/Pruning

acc1db31c19c8b23599950cec4fe6399513ed306

[ "MIT" ]

null

branch/runner.py

sahibsin/Pruning

acc1db31c19c8b23599950cec4fe6399513ed306

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import argparse from dataclasses import dataclass import sys from cli import arg_utils from foundations.runner import Runner from branch import registry @dataclass class BranchRunner(Runner): """A meta-runner that calls the branch-specific runner.""" runner: Runner @staticmethod def description(): return "Run a branch." @staticmethod def add_args(parser): # Produce help text for selecting the branch. helptext = '='*82 + '\nOpenLTH: A Library for Research on Lottery Tickets and Beyond\n' + '-'*82 runner_name = arg_utils.maybe_get_arg('runner', positional=True, position=1) # If the runner name is not present. if runner_name is None or runner_name not in registry.registered_runners(): helptext = '\nChoose a runner on which to branch:\n' helptext += '\n'.join([f' * {sys.argv[0]} branch {runner}' for runner in registry.registered_runners()]) helptext += '\n' + '='*82 print(helptext) sys.exit(1) # If the branch name is not present. branch_names = registry.registered_branches(runner_name) branch_name = arg_utils.maybe_get_arg('branch', positional=True, position=2) if branch_name is None or branch_name not in branch_names: helptext += '\nChoose a branch to run:' for bn in branch_names: helptext += "\n * {} {} {} [...] => {}".format( sys.argv[0], sys.argv[1], bn, registry.get(runner_name, bn).description()) helptext += '\n' + '='*82 print(helptext) sys.exit(1) # Add the arguments for the branch. parser.add_argument('runner_name', type=str) parser.add_argument('branch_name', type=str) registry.get(runner_name, branch_name).add_args(parser) @staticmethod def create_from_args(args: argparse.Namespace): runner_name = arg_utils.maybe_get_arg('runner', positional=True, position=1) branch_name = arg_utils.maybe_get_arg('branch', positional=True, position=2) return BranchRunner(registry.get(runner_name, branch_name).create_from_args(args)) def display_output_location(self): self.runner.display_output_location() def run(self) -> None: self.runner.run() class LotteryBranch(BranchRunner): @staticmethod def description(): return "Run a lottery branch."

36.013514

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0.139172

0

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0.253283

2,665

73

120

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0

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0.122449

0.040816

0.367347

0.040816

0

null

0

null

0

1

0

6dff005711decae58a77ac5da887759206c11424

946

py

Python

wulinfeng/L3/WordDic/AQICity.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

2

2018-03-29T08:26:17.000Z

2019-06-17T10:56:19.000Z

wulinfeng/L3/WordDic/AQICity.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

1

2022-03-22T20:26:08.000Z

wulinfeng/L3/WordDic/AQICity.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

1

2019-02-18T10:44:20.000Z

import requests # 导入requests 库 from bs4 import BeautifulSoup import urllib.error import re class AQICityClass(object): def cityAQI(self,url,cityName,header={}): try: urlName = url + cityName + '.html' r = requests.get(urlName, header) except urllib.error.URLError as e: print("获取空气质量数据请求出错") except Exception as e: print('获取空气质量数据函数出现异常') resp = BeautifulSoup(r.text, 'html.parser') all_div = [] for tag in resp.find_all('div', class_='span12 data'): all_div = tag.findAll('div') all_divValues = [] for div in all_div: value = div.find('div', class_='value') if value != None: title = value.text.strip() #取第一个<a>的文本数据 print(title.replace("\n", "")) return title.replace("\n", "") break

32.62069

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0.564356

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0

0.004951

0.359408

946

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0

null

0

null

0

1

0

6dff00eda6e7e13b33088c5ae46ed97a3a4cc3ce

1,464

py

Python

setup.py

hiradyazdan/nginx-amplify-agent-health-check

7aa0fa2aba082491b1b47c2b6189a9266245f647

[ "MIT" ]

2

2018-05-23T17:34:28.000Z

2018-07-09T21:55:53.000Z

setup.py

hiradyazdan/nginx-amplify-agent-health-check

7aa0fa2aba082491b1b47c2b6189a9266245f647

[ "MIT" ]

null

setup.py

hiradyazdan/nginx-amplify-agent-health-check

7aa0fa2aba082491b1b47c2b6189a9266245f647

[ "MIT" ]

null

from setuptools import setup classifiers = [ 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: POSIX' ] + [ ('Programming Language :: Python :: %s' % x) for x in '2.7'.split() ] test_requirements = [ 'pytest', 'pytest-cov', 'coveralls', 'mock', 'numpy', # Only their Exceptions 'setuptools', 'psutil', 'requests' ] with open('README.rst', 'r') as f: long_description = f.read() setup( name='nginx-amplify-agent-health-check', version='0.1.6', description='Static and Dynamic Analysis for nginx-amplify-agent Health Status', long_description=long_description, url='https://github.com/hiradyazdan/nginx-amplify-agent-health-check', author='Hirad Yazdanpanah', author_email='hirad.y@gmail.com', license='MIT', platforms=["linux"], packages=['amplifyhealthcheck'], entry_points={ 'console_scripts': [ 'amphc=amplifyhealthcheck.cli:init_cli' ] }, classifiers=classifiers, keywords="nginx amplify nginx-amplify nginx-configuration health-check metrics", install_requires=[ 'psutil', 'setuptools', 'ntplib', 'crossplane', 'requests' ], setup_requires=['pytest-runner'], tests_require=test_requirements, extras_require={ 'test': test_requirements, }, python_requires='==2.7.*', zip_safe=False )

24

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0.06243

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1,464

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0

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false

0

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0

0.018519

0

null

0

null

0

1

0

6dffaf2548225e608c4b2975db6390a9dca03d10

2,849

py

Python

sherlock_scripts/pythonhops/sherlock_combine_restarts.py

apoletayev/anomalous_ion_conduction

badb91e971e4a5263a433cfa9fcbf914d53ed2a1

[ "MIT" ]

2

2021-05-20T03:49:51.000Z

2021-06-21T08:41:10.000Z

sherlock_scripts/pythonhops/sherlock_combine_restarts.py

apoletayev/anomalous_ion_conduction

badb91e971e4a5263a433cfa9fcbf914d53ed2a1

[ "MIT" ]

null

sherlock_scripts/pythonhops/sherlock_combine_restarts.py

apoletayev/anomalous_ion_conduction

badb91e971e4a5263a433cfa9fcbf914d53ed2a1

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Sep 3 01:41:52 2020 Combines LAMMPS output files coming from a series of restarts with a * wildcard. This works on expanded (mode scalar) fixes from LAMMPS where each line is a time. The overlapping values of times due to restarts are averaged, but they should be identical. Required command-line args : filenames= , Optional command-line args : file_out= , @author: andreypoletaev """ # ============================================================================= # %% Imports and constants # ============================================================================= import pandas as pd import sys from glob import glob # ============================================================================= # %% parse input and combine # ============================================================================= ## Parse inputs. Format: key=value options = dict([ (x.split('=')[0],x.split('=')[1]) for x in sys.argv[1:] ]) keys = list(options.keys()) # print(options) assert 'filenames' in keys, 'please pass filenames=... [path] as command-line option' # template = f'/*_vacf_{int(options["duration"])}ps.csv' if 'template' not in keys else options['template'] file_out = options['filenames'].replace('*','') if 'file_out' not in keys else options['file_out'] print('looking for files that look like this: '+options['filenames'], flush=True) output = pd.DataFrame() counter = 0 files_to_combine = sorted(glob(options['filenames'])) assert len(files_to_combine) > 1, 'Only one file fits the bill, skipping combining.' print(files_to_combine, flush=True) for fin in files_to_combine: try: ## read the header for column names fp = open(fin, 'r') line1 = fp.readline() line2 = fp.readline() fp.close() colnames = line2[:-1].split(' ')[1:] ## read the actual numbers df = pd.read_csv(fin, skiprows=1, sep=' ') # colnames = df.iloc[0,1:-1].tolist() df = df.iloc[:, :-1] df.columns = colnames df = df.apply(pd.to_numeric) # print(df.columns) # print(df.head(5)) # print(df.dtypes) # print(df.head()) if len(df) > 0: output = output.append(df, ignore_index=True) counter += 1 print(f'appended data from file #{counter} : {fin}', flush=True) except: print(f'could not load / add {fin}', flush=True) ## ensemble-average in all cases - but not always the first thing output = output.groupby('TimeStep').agg('mean').reset_index().rename(columns={'TimeStep':line1[:-1]+'\n# '+'TimeStep'}) # output.TimeStep = output.TimeStep.astype(int) ## write file normally output.to_csv(file_out, index=False, float_format='%.6g', sep=' ')

32.011236

119

0.562654

355

2,849

4.456338

0.495775

0.022124

0.035398

0.016435

0.025284

0

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0.199017

2,849

89

120

32.011236

0.678352

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0

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0

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1

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false

0.032258

0.096774

0

0.096774

0.129032

0

null

0

null

0

1

0

a3009f5a1a8c11a46a1920015fba53e4cf3ae345

1,875

py

Python

app.py

zorro1992/task-app-devops

48312e53ce5711ce0d9508b481e73f78df411dd2

[ "MIT" ]

1

2021-08-19T11:54:08.000Z

app.py

zorro1992/task-app-devops

48312e53ce5711ce0d9508b481e73f78df411dd2

[ "MIT" ]

null

app.py

zorro1992/task-app-devops

48312e53ce5711ce0d9508b481e73f78df411dd2

[ "MIT" ]

null

""" app """ from flask import Flask, render_template, request, redirect, url_for from flask_sqlalchemy import SQLAlchemy app = Flask(__name__) # /// = relative path, //// = absolute path app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///db.sqlite' app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(app) class Todo(db.Model): """A dummy docstring.""" id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(100)) complete = db.Column(db.Boolean) def pub1(self): """A dummy docstring.""" print("") def pub2(self): """A dummy docstring.""" print("") # Edit endpoint @app.route("/edit") def home1(): """A dummy docstring.""" todo_list = Todo.query.all() return render_template("base.html", todo_list=todo_list) # Default home endpoint @app.route("/") def list1(): """A dummy docstring.""" todo_list = Todo.query.all() return render_template("list.html", todo_list=todo_list) # Add endpoint @app.route("/add", methods=["POST"]) def add(): """A dummy docstring.""" title = request.form.get("title") new_todo = Todo(title=title, complete=False) db.session.add(new_todo) db.session.commit() return redirect(url_for("home1")) # Update endpoint @app.route("/update/<int:todo_id>") def update(todo_id): """A dummy docstring.""" todo = Todo.query.filter_by(id=todo_id).first() todo.complete = not todo.complete db.session.commit() return redirect(url_for("home1")) # Delete endpoint @app.route("/delete/<int:todo_id>") def delete(todo_id): """A dummy docstring.""" todo = Todo.query.filter_by(id=todo_id).first() db.session.delete(todo) db.session.commit() return redirect(url_for("home1")) # Main function if __name__ == "__main__": db.create_all() app.run(host="0.0.0.0", debug=True)

25

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0.6544

255

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0.045455

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null

0

null

0

1

0

a304245df7598c6937f92e93f9b38b346d5b4c9a

2,009

py

Python

app/models/version.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

3

2019-09-05T05:28:49.000Z

2020-06-10T09:03:37.000Z

app/models/version.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

null

app/models/version.py

akashtalole/python-flask-restful-api

475d8fd7be1724183716a197aac4257f8fbbeac4

[ "MIT" ]

null

from sqlalchemy.orm import backref from app.models import db class Version(db.Model): """Version model class""" __tablename__ = 'versions' id = db.Column(db.Integer, primary_key=True) event_id = db.Column(db.Integer, db.ForeignKey('events.id', ondelete='CASCADE')) events = db.relationship("Event", backref=backref('version', uselist=False)) event_ver = db.Column(db.Integer, nullable=False, default=0) sessions_ver = db.Column(db.Integer, nullable=False, default=0) speakers_ver = db.Column(db.Integer, nullable=False, default=0) tracks_ver = db.Column(db.Integer, nullable=False, default=0) sponsors_ver = db.Column(db.Integer, nullable=False, default=0) microlocations_ver = db.Column(db.Integer, nullable=False, default=0) def __init__(self, event_id=None, event_ver=None, sessions_ver=None, speakers_ver=None, tracks_ver=None, sponsors_ver=None, microlocations_ver=None): self.event_id = event_id self.event_ver = event_ver self.sessions_ver = sessions_ver self.speakers_ver = speakers_ver self.tracks_ver = tracks_ver self.sponsors_ver = sponsors_ver self.microlocations_ver = microlocations_ver def __repr__(self): return '<Version %r>' % self.id def __str__(self): return self.__repr__() @property def serialize(self): """Return object data in easily serializable format""" return { 'version': [ {'id': self.id, 'event_id': self.event_id, 'event_ver': self.event_ver, 'sessions_ver': self.sessions_ver, 'speakers_ver': self.speakers_ver, 'tracks_ver': self.tracks_ver, 'sponsors_ver': self.sponsors_ver, 'microlocations_ver': self.microlocations_ver} ] }

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4.982759

0.224138

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0.117647

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2,009

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35.875

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0.434783

0

null

0

null

0

1

0

a304eeaa7c9f4ed5704a6d6deba75d5ddfdbb3d1

346

py

Python

code-tk/scrollbar.py

shilpasayura/bk

2b0a1aa9300da80e201264bcf80226b3c5ff4ad6

[ "MIT" ]

4

2018-09-08T10:30:27.000Z

2021-07-23T07:59:24.000Z

code-tk/scrollbar.py

shilpasayura/bk

2b0a1aa9300da80e201264bcf80226b3c5ff4ad6

[ "MIT" ]

null

code-tk/scrollbar.py

shilpasayura/bk

2b0a1aa9300da80e201264bcf80226b3c5ff4ad6

[ "MIT" ]

6

2018-09-07T05:54:17.000Z

2021-07-23T07:59:25.000Z

from tkinter import * import tkinter root = Tk() scrollbar = Scrollbar(root) scrollbar.pack( side = RIGHT, fill=Y ) mylist = Listbox(root, yscrollcommand = scrollbar.set ) for line in range(100): mylist.insert(END, "Line number : " + str(line)) mylist.pack( side = LEFT, fill = BOTH ) scrollbar.config( command = mylist.yview ) mainloop()

21.625

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0.705202

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346

5.304348

0.652174

0.065574

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0.010381

0.16474

346

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0.83391

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0.040462

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1

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false

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0.181818

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0.181818

0

null

0

null

0

1

0

a3052e2c0e4e4d32b495f5d940bc6dff09090dc4

1,742

py

Python

Solutions/2021/13.py

Azurealistic/Winter

4ef5d1fde10f9ba769c33597e1269f161068f18b

[ "Unlicense" ]

1

2021-12-18T20:02:57.000Z

Solutions/2021/13.py

Azurealistic/Winter

4ef5d1fde10f9ba769c33597e1269f161068f18b

[ "Unlicense" ]

null

Solutions/2021/13.py

Azurealistic/Winter

4ef5d1fde10f9ba769c33597e1269f161068f18b

[ "Unlicense" ]

null

# Advent of Code 2021 - Day: 13 # Imports (Always imports data based on the folder and file name) from aocd import data, submit def solve(data): # Parse input # Split the input into two lists, based on where the empty line is # Find the index of the line that is '', and use that to split the list # Return the two lists coordinates, instructions = data.strip().split("\n\n") coordinates = [[int(x) for x in ln.split(",")] for ln in coordinates.strip().split("\n")] instructions = [ln.split() for ln in instructions.strip().split("\n")] for iteration, fold in enumerate(instructions): direction, location = fold[-1].split('=') location = int(location) points = set() # PLace the point based on the current fold. for (x, y) in coordinates: if direction == 'y': if y < location: points.add((x, y)) else: points.add((x, location - (y - location))) elif direction == 'x': if x < location: points.add((x, y)) else: points.add((location - (x - location), y)) coordinates = points if iteration == 0: print("Star 1:", len(coordinates)) submit(len(coordinates), part="a", day=13, year=2021) grid = [] for n in range(10): grid.append(list(" " * 80)) for (x, y) in coordinates: grid[y][x] = '█' # Print the grid, by using each row as a string, and then joining them with newlines, only include rows that have a '#' and print up to the final '#' print("Star 2:") print("\n".join(["".join(row) for row in grid if '█' in row])) # This has to be manually submitted, because it's a visual representation of the grid. submit("RHALRCRA", part="b", day=13, year=2021) # Solution def main(): solve(data) # Call the main function. if __name__ == '__main__': main()

29.525424

150

0.647532

272

1,742

4.125

0.411765

0.00713

0.029412

0.02139

0.114082

0.057041

0

0.018786

0.205511

1,742

59

151

29.525424

0.790462

0.326636

0

0.166667

0

0.042241

0

1

0.055556

false

0

0.027778

0

0.083333

0

null

0

null

0

1

0

a308348c02f7d05a6bdcec5e102eab0f328f25f9

1,329

py

Python

biosimulators_utils/archive/utils.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

2

2021-06-02T13:26:34.000Z

2021-12-27T23:12:47.000Z

biosimulators_utils/archive/utils.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

102

2020-12-06T19:47:43.000Z

2022-03-31T12:56:17.000Z

biosimulators_utils/archive/utils.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

4

2021-01-27T19:56:34.000Z

2022-02-03T21:08:20.000Z

""" Utilities for creating archives :Author: Jonathan Karr <karr@mssm.edu> :Date: 2020-12-06 :Copyright: 2020, Center for Reproducible Biomedical Modeling :License: MIT """ from .data_model import Archive, ArchiveFile import glob import os __all__ = ['build_archive_from_paths'] def build_archive_from_paths(path_patterns, rel_path=None, recursive=True): """ Build an archive from a list of glob path patterns Args: path_patterns (:obj:`list` of :obj:`str`): glob path patterns for files to bundle into an archive rel_path (:obj:`str`, optional): if provided, set the archive file names to their path relative to this path recursive (:obj:`bool`, optional): if :obj:`True`, match the path patterns recursively Returns: :obj:`Archive`: archive """ archive = Archive() for path_pattern in path_patterns: for local_path in glob.glob(path_pattern, recursive=recursive): if os.path.isfile(local_path): if rel_path: archive_path = os.path.relpath(local_path, rel_path) else: archive_path = local_path archive.files.append(ArchiveFile( local_path=local_path, archive_path=archive_path, )) return archive

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0.645598

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1,329

4.928571

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0.054348

0.050725

0

0.012333

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1,329

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false

0

0.166667

0

0.277778

0

null

0

null

0

1

0

096461150c75c546d91d335a2584ba96fe70e040

845

py

Python

v1/Commit.py

gzc/gitstats

d6e41c4f7ad5c3d754ef872fa9e615b88df0ccb8

[ "MIT" ]

26

2017-06-11T05:44:25.000Z

2021-02-20T12:21:22.000Z

v1/Commit.py

gzc/gitstats

d6e41c4f7ad5c3d754ef872fa9e615b88df0ccb8

[ "MIT" ]

1

2020-04-22T15:48:19.000Z

2020-04-22T15:52:51.000Z

v1/Commit.py

gzc/gitstats

d6e41c4f7ad5c3d754ef872fa9e615b88df0ccb8

[ "MIT" ]

1

2020-10-20T04:46:11.000Z

""" This class represents the info of one commit """ from Change import *; class Commit: def __init__(self, hash, author, authorEmail, date, commitMessage): self.hash = hash; self.author = author; self.authorEmail = authorEmail self.date = date; self.commitMessage = commitMessage; self.changes = None; self.linesAdded = 0; self.linesDeleted = 0; self.filesAdded = 0; self.filesDeleted = 0; def __str__(self): return ('commit hash {0}\ncommit author {1}\ncommit author email {2}\n' 'commit date {3}\n{4} lines added, {5} lines deleted\n' '{6} files added, {7} files deleted\n'). \ format(self.hash, self.author, self.authorEmail, self.date, self.linesAdded, self.linesDeleted, self.filesAdded, self.filesDeleted)

31.296296

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0.622485

101

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5.128713

0.425743

0.046332

0.054054

0

0.019169

0.259172

845

26

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32.5

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0

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false

0

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0.263158

0

null

0

null

0

1

0

0966490b7f876064ed7777de569aec9aeed5aa61

3,758

py

Python

htdocs/plotting/auto/scripts100/p172.py

trentford/iem

7264d24f2d79a3cd69251a09758e6531233a732f

[ "MIT" ]

null

htdocs/plotting/auto/scripts100/p172.py

trentford/iem

7264d24f2d79a3cd69251a09758e6531233a732f

[ "MIT" ]

null

htdocs/plotting/auto/scripts100/p172.py

trentford/iem

7264d24f2d79a3cd69251a09758e6531233a732f

[ "MIT" ]

null

"""YTD precip""" import calendar import datetime from pandas.io.sql import read_sql from pyiem.util import get_autoplot_context, get_dbconn from pyiem.plot.use_agg import plt from pyiem.network import Table as NetworkTable def get_description(): """ Return a dict describing how to call this plotter """ desc = dict() desc['data'] = True desc['description'] = """This chart presents year to date accumulated precipitation for a station of your choice. The year with the highest and lowest accumulation is shown along with the envelop of observations and long term average. You can optionally plot up to three additional years of your choice. """ thisyear = datetime.date.today().year desc['arguments'] = [ dict(type='station', name='station', default='IA2203', label='Select Station:', network='IACLIMATE'), dict(type='year', name='year1', default=thisyear, label='Additional Year to Plot:'), dict(type='year', name='year2', optional=True, default=(thisyear - 1), label='Additional Year to Plot: (optional)'), dict(type='year', name='year3', optional=True, default=(thisyear - 2), label='Additional Year to Plot: (optional)'), ] return desc def plotter(fdict): """ Go """ pgconn = get_dbconn('coop') ctx = get_autoplot_context(fdict, get_description()) station = ctx['station'] network = ctx['network'] year1 = ctx.get('year1') year2 = ctx.get('year2') year3 = ctx.get('year3') nt = NetworkTable(network) table = "alldata_%s" % (station[:2],) df = read_sql(""" WITH years as (SELECT distinct year from """ + table + """ WHERE station = %s and sday = '0101') SELECT day, sday, year, precip, sum(precip) OVER (PARTITION by year ORDER by day ASC) as accum from """ + table + """ WHERE station = %s and year in (select year from years) ORDER by day ASC """, pgconn, params=(station, station), index_col='day') if df.empty: raise ValueError("No data found!") (fig, ax) = plt.subplots(1, 1) # Average jday = df[['sday', 'accum']].groupby('sday').mean() ax.plot(range(1, len(jday.index)+1), jday['accum'], lw=2, zorder=5, color='k', label='Average - %.2f' % (jday['accum'].iloc[-1],)) # Min and Max jmin = df[['sday', 'accum']].groupby('sday').min() jmax = df[['sday', 'accum']].groupby('sday').max() ax.fill_between(range(1, len(jday.index)+1), jmin['accum'], jmax['accum'], zorder=2, color='tan') # find max year plotted = [] for year, color in zip([df['accum'].idxmax().year, df[df['sday'] == '1231']['accum'].idxmin().year, year1, year2, year3], ['b', 'brown', 'r', 'g', 'purple']): if year is None or year in plotted: continue plotted.append(year) df2 = df[df['year'] == year] ax.plot(range(1, len(df2.index)+1), df2['accum'], label='%s - %.2f' % (year, df2['accum'].iloc[-1]), color=color, lw=2) ax.set_title(("Year to Date Accumulated Precipitation\n" "[%s] %s (%s-%s)" ) % (station, nt.sts[station]['name'], nt.sts[station]['archive_begin'].year, datetime.date.today().year)) ax.set_ylabel("Precipitation [inch]") ax.grid(True) ax.legend(loc=2) ax.set_xlim(1, 366) ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365)) ax.set_xticklabels(calendar.month_abbr[1:]) return fig, df if __name__ == '__main__': plotter(dict())

37.207921

78

0.575838

486

3,758

4.393004

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0.014052

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0.072131

0

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0.262906

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100

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0

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0

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false

0

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0

0.121951

0

null

0

null

0

1

0

096a5172854a6f7ee1cbbe59f19ac4a86d87ac0c

1,684

py

Python

Steganalysis-CNN/dataload.py

1129ljc/video-interpolation-detection

eb2931269b2ac19af28de750f0b719fb0d66aaef

[ "Apache-2.0" ]

2

2022-03-29T06:46:21.000Z

2022-03-30T09:13:10.000Z

Steganalysis-CNN/dataload.py

1129ljc/video-interpolation-detection

eb2931269b2ac19af28de750f0b719fb0d66aaef

[ "Apache-2.0" ]

null

Steganalysis-CNN/dataload.py

1129ljc/video-interpolation-detection

eb2931269b2ac19af28de750f0b719fb0d66aaef

[ "Apache-2.0" ]

null

''' @Time : 2021/9/3 9:42 @Author : ljc @FileName: dataload.py @Software: PyCharm ''' import os import json import cv2 import numpy as np import torch from PIL import Image from torchvision import transforms from torch.utils.data import DataLoader from torch.utils.data import Dataset transform = transforms.Compose( [ # transforms.Resize(size=(224, 224)), transforms.ToTensor(), # transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ] ) class DataSet(Dataset): def __init__(self, data_file_path): super(Dataset, self).__init__() self.json_file_path = data_file_path assert os.path.isfile(data_file_path), print('The dataset json file cannot be read') with open(data_file_path, 'r', encoding='utf8')as fp: data = fp.readlines() self.image_path_list = [] self.image_label_list = [] for i in range(len(data)): line = data[i].split(' ') self.image_path_list.append(line[0]) self.image_label_list.append(int(line[1][0:-1])) self.image_num = len(self.image_path_list) def __len__(self): return self.image_num def __getitem__(self, item): image_file = self.image_path_list[item] label = self.image_label_list[item] label_torch = torch.tensor(label) # image_torch = transform(Image.open(image_file).convert('RGB')) image_torch = torch.from_numpy(np.array(Image.open(image_file).convert('RGB'))) image_torch = image_torch.permute(2, 0, 1).float() image_torch = torch.unsqueeze(image_torch[0, :, :], dim=0) return image_file, image_torch, label_torch

30.618182

92

0.647862

236

1,684

4.385593

0.355932

0.078261

0.014493

0.019324

0.131401

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0.011594

0

0.029163

0.226247

1,684

54

93

31.185185

0.765157

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false

0

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0.026316

0.394737

0.026316

0

null

0

null

0

1

0

096b3b878c08f6ba21432355cfef1328654cf1dc

23,998

py

Python

run.py

kampta/PatchVAE

816f4b49fd8b836641d7e1068c1e802ae0453742

[ "MIT" ]

9

2020-10-29T11:56:53.000Z

2021-11-21T14:34:38.000Z

run.py

kampta/PatchVAE

816f4b49fd8b836641d7e1068c1e802ae0453742

[ "MIT" ]

null

run.py

kampta/PatchVAE

816f4b49fd8b836641d7e1068c1e802ae0453742

[ "MIT" ]

2

2020-10-29T03:40:31.000Z

2021-01-31T20:04:49.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ run.py Code to run the PatchVAE on different datasets Usage: # Run with default arguments on mnist python run.py Basic VAE borrowed from https://github.com/pytorch/examples/tree/master/vae """ __author__ = "Kamal Gupta" __email__ = "kampta@cs.umd.edu" __version__ = "0.1" import sys from collections import OrderedDict import shutil import numpy as np import torch import torch.nn as nn from torchvision.utils import make_grid from utils import Timer from utils.torchsummary import summary from utils.commons import data_loaders, load_vae_model, count_parameters, EdgeWeights from loss import BetaVaeLoss, VaeConcreteLoss, BetaVaeConcreteLoss,\ BetaVaeConcretePartsLoss, BetaVaeConcretePartsEntropyLoss, DiscLoss from model import Discriminator import utils.commons as commons from torch.utils.tensorboard import SummaryWriter def train_vaegan(data_loader, model_d, model_v, opt_d, opt_v, d_loss_fn, v_loss_fn, writer): model_v.train() model_d.train() fwd_clock = Timer() bwd_clock = Timer() num_batches = args.img_per_epoch // args.batch_size data_iterator = iter(data_loader) overall_losses = OrderedDict() # for batch_idx, (x, _) in enumerate(data_loader): for batch_idx in range(num_batches): batch_losses = OrderedDict() try: x, _ = next(data_iterator) except StopIteration: data_iterator = iter(data_loader) continue x = x.to(args.device) ######################################################## # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) ####################################################### # train with real model_d.zero_grad() real_x = x real_y = torch.ones(x.size(0)).cuda() outputs = model_d(real_x) err_d_real = d_loss_fn(outputs.squeeze(), real_y.squeeze()) err_d_real.backward() batch_losses['err_d_real'] = err_d_real.item() batch_losses['d_x'] = outputs.data.mean() # train with fake fake_y = torch.zeros(x.size(0)).cuda() x_tilde, z_app_mean, z_app_var, z_vis_mean = model_v(x, args.temp) # recon_x, _ = x_tilde outputs = model_d(x_tilde.detach()) err_d_fake = d_loss_fn(outputs.squeeze(), fake_y.squeeze()) err_d_fake.backward() batch_losses['err_d_fake'] = err_d_fake.item() batch_losses['d_v1'] = outputs.data.mean() opt_d.step() ########################### # (2) Update G network: VAE ########################### model_v.zero_grad() loss, loss_dict = v_loss_fn( x_tilde, x, z_app_mean, z_app_var, z_vis_mean, categorical=args.categorical, py=args.py, beta_p=args.beta_p, beta_a=args.beta_a, beta_v=args.beta_v, beta_ea=args.beta_ea, beta_ew=args.beta_ew ) loss.backward() for loss_key, loss_value in loss_dict.items(): batch_losses[loss_key] = loss_value.item() opt_v.step() ############################ # (3) Update G network: maximize log(D(G(z))) ########################### x_tilde, z_app_mean, z_app_var, z_vis_mean = model_v(x, args.temp) # recon_x, _ = x_tilde outputs = model_d(x_tilde) real_y.fill_(1) err_g = d_loss_fn(outputs.squeeze(), real_y.squeeze()) err_g.backward() batch_losses['err_g'] = err_g.item() batch_losses['d_v2'] = outputs.data.mean() opt_v.step() # Logs for loss_key, loss_value in batch_losses.items(): writer.add_scalar('loss/train/' + loss_key, loss_value, args.steps) overall_losses[loss_key] = overall_losses[loss_key] + loss_value \ if loss_key in overall_losses else loss_value args.steps += 1 if args.steps % 1000 == 1: args.temp = max(args.temp * np.exp(-args.anneal * args.steps), args.min_temp) if batch_idx % args.log_interval != 0: continue logstr = '\t'.join(['{}: {:0.4f}'.format(k, v) for k, v in batch_losses.items()]) print('[{}/{} ({:0.0f}%)]\t{}'.format(batch_idx, num_batches, 100. * batch_idx / num_batches, logstr)) overall_losses = OrderedDict([(k, v / num_batches) for k, v in overall_losses.items()]) logstr = '\t'.join(['{}: {:0.4f}'.format(k, v) for k, v in overall_losses.items()]) print('[End of train epoch]\t# steps: {}\t# images: {}, temp: {:0.2f}'.format( args.steps, num_batches * args.batch_size, args.temp)) print(logstr) print('[End of train epoch]\t# calls: {}, Fwd: {:.3f} ms\tBwd: {:.3f} ms'.format( fwd_clock.calls, 1000 * fwd_clock.average_time, 1000 * bwd_clock.average_time)) return overall_losses def train(data_loader, model, optimizer, loss_function, writer): model.train() fwd_clock = Timer() bwd_clock = Timer() losses = OrderedDict() losses['loss'] = 0 num_batches = args.img_per_epoch // args.batch_size data_iterator = iter(data_loader) for batch_idx in range(num_batches): try: x, _ = next(data_iterator) x = x.to(args.device) optimizer.zero_grad() # Forward Pass fwd_clock.tic() x_tilde, z_app_mean, z_app_var, z_vis_mean = model(x, args.temp) # Compute Loss loss, loss_dict = loss_function( x_tilde, x, z_app_mean, z_app_var, z_vis_mean, categorical=args.categorical, py=args.py, beta_p=args.beta_p, beta_a=args.beta_a, beta_v=args.beta_v, beta_ea=args.beta_ea, beta_ew=args.beta_ew ) fwd_clock.toc() # Backprop bwd_clock.tic() loss.backward() bwd_clock.toc() # Update Adam optimizer.step() # Logs losses['loss'] += loss.item() writer.add_scalar('loss/train/loss', loss.item(), args.steps) for loss_key, loss_value in loss_dict.items(): writer.add_scalar('loss/train/' + loss_key, loss_value.item(), args.steps) losses[loss_key] = losses[loss_key] + loss_value.item() \ if loss_key in losses else loss_value.item() args.steps += 1 if args.steps % 1000 == 1: args.temp = max(args.temp * np.exp(-args.anneal * args.steps), args.min_temp) if batch_idx % args.log_interval != 0: continue logstr = '\t'.join(['{}: {:0.4f}'.format(k, v.item()) for k, v in loss_dict.items()]) print('[{}/{} ({:0.0f}%)]\t{}'.format(batch_idx, num_batches, 100. * batch_idx / num_batches, logstr)) except StopIteration: data_iterator = iter(data_loader) losses = OrderedDict([(k, v / num_batches) for k, v in losses.items()]) logstr = '\t'.join(['{}: {:0.4f}'.format(k, v) for k, v in losses.items()]) print('[End of train epoch]\t# steps: {}\t# images: {}, temp: {:0.2f}'.format( args.steps, num_batches * args.batch_size, args.temp)) print(logstr) print('[End of train epoch]\t# calls: {}, Fwd: {:.3f} ms\tBwd: {:.3f} ms'.format( fwd_clock.calls, 1000 * fwd_clock.average_time, 1000 * bwd_clock.average_time)) return losses['loss'] def test(data_loader, model, loss_function, writer): model.eval() losses = OrderedDict() losses['loss'] = 0 data_iterator = iter(data_loader) with torch.no_grad(): for batch_idx, (x, _) in enumerate(data_iterator): x = x.to(args.device) x_tilde, z_app_mean, z_app_var, z_vis_mean = model(x, args.temp) loss, loss_dict = loss_function( x_tilde, x, z_app_mean, z_app_var, z_vis_mean, categorical=args.categorical, py=args.py, beta_p=args.beta_p, beta_a=args.beta_a, beta_v=args.beta_v, beta_ea=args.beta_ea, beta_ew=args.beta_ew ) losses['loss'] += loss.item() for loss_key, loss_value in loss_dict.items(): losses[loss_key] = losses[loss_key] + loss_value.item() \ if loss_key in losses else loss_value.item() losses = OrderedDict([(k, v / (batch_idx+1)) for k, v in losses.items()]) logstr = '\t'.join(['{}: {:0.4f}'.format(k, v) for k, v in losses.items()]) print('[End of test epoch]') print(logstr) # Logs for loss_key, loss_value in losses.items(): writer.add_scalar('loss/test/' + loss_key, loss_value, args.steps) return losses['loss'] def plot_graph(height, width, channels, model, writer): fake = torch.from_numpy(np.random.randn(args.batch_size, channels, height, width).astype(np.float32)) fake = fake.to(args.device) writer.add_graph(model, fake) def main(): np.random.seed(args.seed) torch.manual_seed(args.seed) args.steps = 0 writer = SummaryWriter(args.log_dir) save_filename = args.model_dir train_loader, test_loader, (channels, height, width), num_classes, _ = \ data_loaders(args.dataset, data_folder=args.data_folder, classify=False, size=args.size, inet=args.inet, batch_size=args.batch_size, num_workers=args.workers) # Fixed images for Tensorboard fixed_images, _ = next(iter(test_loader)) fixed_images = fixed_images.to(args.device) fixed_grid = make_grid(commons.unnorm(fixed_images).cpu().data, nrow=32, pad_value=1) writer.add_image('original', fixed_grid, 0) # build a VAE model vae_model, _ = load_vae_model((channels, height, width), args.arch, encoder_arch=args.encoder_arch, decoder_arch=args.decoder_arch, hidden_size=args.hidden_size, num_parts=args.num_parts, base_depth=args.ngf, independent=args.independent, hard=args.hard, categorical=args.categorical, scale=args.scale, device=args.device) args.py = 1 / args.num_parts if args.py is None else args.py if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs vae_model = nn.DataParallel(vae_model) vae_model.to(args.device) if args.pretrained is not None: print("Loading pretrained model from %s" % args.pretrained) pretrained_dict = torch.load(args.pretrained, map_location=args.device) if type(pretrained_dict) == OrderedDict: vae_model.load_state_dict(pretrained_dict) elif 'vae_dict' in pretrained_dict: vae_model.load_state_dict(pretrained_dict['vae_dict']) else: print('debug') sys.exit(0) # Generate samples only, no training if args.evaluate: with torch.no_grad(): # Reconstructions after current epoch if torch.cuda.device_count() > 1: reconstructions = vae_model.module.get_reconstructions( fixed_images, temp=args.temp) else: reconstructions = vae_model.get_reconstructions( fixed_images, temp=args.temp) for key in reconstructions: grid = make_grid(reconstructions[key].cpu(), nrow=32, pad_value=1) writer.add_image(key, grid, 0) # Random samples after current epoch if torch.cuda.device_count() > 1: random_samples = vae_model.module.get_random_samples(py=args.py) else: random_samples = vae_model.get_random_samples(py=args.py) for key in random_samples: grid = make_grid(random_samples[key].cpu(), nrow=32, pad_value=1) writer.add_image(key, grid, 0) sys.exit(0) opt_v = torch.optim.Adam(vae_model.parameters(), lr=args.lr, betas=(0.5, 0.999)) recon_mask = None if args.recon_mask == 'edge': recon_mask = EdgeWeights(nc=channels, scale=args.scale) if args.arch == 'vae': loss_function = BetaVaeLoss(beta=args.beta_a, mask_nn=recon_mask) elif args.arch == 'convvae': loss_function = VaeConcreteLoss( beta_v=args.beta_v, py=args.py, categorical=args.categorical, mask_nn=recon_mask ) elif args.arch == 'patchy': if args.beta_p == 0. and args.beta_ea == 0. and args.beta_ew == 0.: loss_function = BetaVaeConcreteLoss( beta_a=args.beta_a, beta_v=args.beta_v, py=args.py, categorical=args.categorical, mask_nn=recon_mask ) elif args.beta_ea == 0. and args.beta_ew == 0.: loss_function = BetaVaeConcretePartsLoss( beta_a=args.beta_a, beta_v=args.beta_v, beta_p=args.beta_p, py=args.py, categorical=args.categorical, ) else: loss_function = BetaVaeConcretePartsEntropyLoss( beta_a=args.beta_a, beta_v=args.beta_v, beta_p=args.beta_p, beta_ea=args.beta_ea, beta_ew=args.beta_ew, py=args.py, categorical=args.categorical, ) else: print('Unknown model architecture: %s' % args.arch) sys.exit(0) if args.gan: gan_model = Discriminator(height, nc=channels, ndf=args.ndf, scale=args.scale).to(args.device) opt_d = torch.optim.Adam(gan_model.parameters(), lr=args.lr, betas=(0.5, 0.999)) d_loss_fn = DiscLoss(args.beta_g) # test after seeing approx. every 50000 images # num_epochs = (args.num_epochs * len(train_loader.dataset)) // 50000 for epoch in range(1, args.num_epochs + 1): print("================== Epoch: {} ==================".format(epoch)) if args.gan: train_loss = train_vaegan(train_loader, gan_model, vae_model, opt_d, opt_v, d_loss_fn, loss_function, writer) else: train_loss = train(train_loader, vae_model, opt_v, loss_function, writer) test_loss = test(test_loader, vae_model, loss_function, writer) if epoch == 1: best_loss = test_loss if epoch % args.save_interval != 0: continue # Save model with torch.no_grad(): # Reconstructions after current epoch if torch.cuda.device_count() > 1: reconstructions = vae_model.module.get_reconstructions( fixed_images, temp=args.temp) else: reconstructions = vae_model.get_reconstructions( fixed_images, temp=args.temp) for key in reconstructions: grid = make_grid(reconstructions[key].cpu(), nrow=32, pad_value=1, normalize=True) writer.add_image(key, grid, epoch) # Random samples after current epoch if torch.cuda.device_count() > 1: random_samples = vae_model.module.get_random_samples(py=args.py) else: random_samples = vae_model.get_random_samples(py=args.py) for key in random_samples: grid = make_grid(random_samples[key].cpu(), nrow=32, pad_value=1, normalize=True) writer.add_image(key, grid, epoch) f = '{0}/model_{1}.pt'.format(save_filename, epoch) save_state = { 'args': args, 'vae_dict': vae_model.state_dict(), 'loss': train_loss, } if args.gan: save_state['disc_dict'] = gan_model.state_dict() torch.save(save_state, f) if test_loss < best_loss: best_loss = test_loss shutil.copyfile(f, '{0}/best.pt'.format(save_filename)) print("Model saved at: {0}/best.pt".format(save_filename)) print("# Parameters: {}".format(count_parameters(vae_model))) if torch.cuda.device_count() > 1: summary(vae_model.module, (channels, height, width)) else: summary(vae_model, (channels, height, width)) if __name__ == '__main__': import argparse import os parser = argparse.ArgumentParser(description='Patchy VAE') # Dataset parser.add_argument('--dataset', type=str, default='cifar100', help='name of the dataset (default: cifar100)') parser.add_argument('--data-folder', type=str, default='./data', help='name of the data folder (default: ./data)') parser.add_argument('--workers', type=int, default=4, help='number of threads (default: 4)') parser.add_argument('--pretrained', default=None, help='path of pre-trained model') parser.add_argument('--evaluate', action='store_true', default=False, help='just sample no training (default: False)') parser.add_argument('--size', type=int, default=64, help='size of image (default: 64)') parser.add_argument('--inet', default=False, action='store_true', help='Whether or not to do imagenet normalization') # Model parser.add_argument('--arch', type=str, default='patchy', help='model architecture (default: patchy)') parser.add_argument('--encoder-arch', type=str, default='resnet', help='encoder architecture (default: resnet)') parser.add_argument('--decoder-arch', type=str, default='pyramid', help='decoder architecture (default: pyramid)') parser.add_argument('--independent', action='store_true', default=False, help='independent decoders (default: False)') parser.add_argument('--ngf', type=int, default=64, help='depth of first layer of encoder (default: 64)') # Optimization parser.add_argument('--recon-mask', type=str, default=None, help="Use 'edge' mask for improved reconstruction (default: None.)") parser.add_argument('--batch-size', type=int, default=128, help='batch size (default: 128)') parser.add_argument('--img-per-epoch', type=int, default=50000, help='images per epoch (default: 50000)') parser.add_argument('--num-epochs', type=int, default=30, help='number of epochs (default: 30)') parser.add_argument('--no-cuda', action='store_true', default=False, help='enables CUDA training (default: False)') parser.add_argument('--lr', type=float, default=1e-4, help='learning rate for Adam optimizer (default: 1e-4)') parser.add_argument('--beta-a', type=float, default=1.0, help='contribution of KLD App loss (default: 1.0)') parser.add_argument('--beta-v', type=float, default=10., help='contribution of KLD Vis loss (default: 10.)') parser.add_argument('--beta-p', type=float, default=0., help='contribution of MSE Parts loss (default: 0.)') parser.add_argument('--beta-ea', type=float, default=0., help='contribution of Entropy Across loss (default: 0.)') parser.add_argument('--beta-ew', type=float, default=0., help='contribution of Entropy Within loss (default: 0.)') # GAN parser.add_argument('--gan', action='store_true', default=False, help='enable gan (default: False)') parser.add_argument('--ndf', type=int, default=64, help='depth of first layer of discrimnator (default: 64)') parser.add_argument('--beta-g', type=float, default=1.0, help='contribution of GAN loss (default: 0.)') # Latent space parser.add_argument('--scale', type=int, default=8, help='scale down by (default: 8)') parser.add_argument('--num-parts', type=int, default=16, help='number of parts (default: 16)') parser.add_argument('--hidden-size', type=int, default=6, help='size of the latent vectors (default: 6)') parser.add_argument('--py', type=float, default=None, help='part visibility prior (default: 1 / num_parts)') parser.add_argument('--categorical', action='store_true', default=False, help='take only 1 part per location (default: False)') # Annealing parser.add_argument('--hard', action='store_true', default=False, help='hard samples from bernoulli (default: False)') parser.add_argument('--temp', type=float, default=1.0, help='Initial temperature (default: 1.0)') parser.add_argument('--anneal', type=float, default=0.00003, help='Anneal rate (default: 00003)') parser.add_argument('--min-temp', type=float, default=0.1, help='minimum temperature') # Miscellaneous parser.add_argument('--debug-grad', action='store_true', default=False, help='debug gradients (default: False)') parser.add_argument('--output-folder', type=str, default='./scratch', help='name of the output folder (default: ./scratch)') parser.add_argument('--seed', type=int, default=1, help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=50, help='how many batches to wait before logging training status') parser.add_argument('--save-interval', type=int, default=1, help='how many batches to wait before logging training status') args = parser.parse_args() print("All arguments") print(args) print("PID: ", os.getpid()) args.cuda = not args.no_cuda and torch.cuda.is_available() args.device = torch.device("cuda:0" if args.cuda and torch.cuda.is_available() else "cpu") # Slurm if 'SLURM_JOB_NAME' in os.environ and 'SLURM_JOB_ID' in os.environ: # running with sbatch and not srun if os.environ['SLURM_JOB_NAME'] != 'bash': args.output_folder = os.path.join(args.output_folder, os.environ['SLURM_JOB_ID']) print("SLURM_JOB_ID: ", os.environ['SLURM_JOB_ID']) else: args.output_folder = os.path.join(args.output_folder, str(os.getpid())) else: args.output_folder = os.path.join(args.output_folder, str(os.getpid())) # Create logs and models folder if they don't exist if not os.path.exists(args.output_folder): print("Creating output directory: %s" % args.output_folder) os.makedirs(args.output_folder) log_dir = os.path.join(args.output_folder, 'logs') if not os.path.exists(log_dir): print("Creating log directory: %s" % log_dir) os.makedirs(log_dir) model_dir = os.path.join(args.output_folder, 'models') if not os.path.exists(model_dir): print("Creating model directory: %s" % model_dir) os.makedirs(model_dir) args.log_dir = log_dir args.model_dir = model_dir main()

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0

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40.130435

0.764592

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096bb8869bace9e3c4b6964fc661952242355ebd

11,602

py

Python

membership/management/commands/csvbills.py

guaq/sikteeri

9a80790666edaa058e9cb42cb9e78626cfc0e565

[ "MIT" ]

null

membership/management/commands/csvbills.py

guaq/sikteeri

9a80790666edaa058e9cb42cb9e78626cfc0e565

[ "MIT" ]

null

membership/management/commands/csvbills.py

guaq/sikteeri

9a80790666edaa058e9cb42cb9e78626cfc0e565

[ "MIT" ]

null

# encoding: UTF-8 from __future__ import with_statement import logging import codecs import csv import os from datetime import datetime, timedelta from decimal import Decimal from django.db.models import Q, Sum from django.core.management.base import BaseCommand from django.core.exceptions import ObjectDoesNotExist from django.utils.translation import ugettext as _ from django.contrib.auth.models import User from membership.models import Bill, BillingCycle, Payment from membership.utils import log_change from optparse import make_option logger = logging.getLogger("membership.csvbills") class UTF8Recoder: """ Iterator that reads an encoded stream and reencodes the input to UTF-8 <http://docs.python.org/library/csv.html#examples> """ def __init__(self, f, encoding): self.reader = codecs.getreader(encoding)(f) def __iter__(self): return self def next(self): return self.reader.next().encode("utf-8") class UnicodeReader: """ A CSV reader which will iterate over lines in the CSV file "f", which is encoded in the given encoding. <http://docs.python.org/library/csv.html#examples> """ def __init__(self, f, dialect=csv.excel, encoding="utf-8", **kwds): f = UTF8Recoder(f, encoding) self.reader = csv.reader(f, dialect=dialect, **kwds) def next(self): row = self.reader.next() return [unicode(s, "utf-8") for s in row] def __iter__(self): return self class UnicodeDictReader(UnicodeReader): """A CSV reader which stores the headers from the first line """ def __init__(self, *args, **kw): UnicodeReader.__init__(self, *args, **kw) # Read headers from first line self.headers = map(lambda x: x.strip(), UnicodeReader.next(self)) def next(self): row = UnicodeReader.next(self) return dict(zip(self.headers, row)) class RequiredFieldNotFoundException(Exception): pass class DuplicateColumnException(Exception): pass class PaymentFromFutureException(Exception): pass class BillDictReader(UnicodeDictReader): REQUIRED_COLUMNS = ['date', 'amount', 'transaction'] CSV_TRANSLATION = {} def __init__(self, f, delimiter=';', encoding="iso8859-1", *args, **kw): UnicodeDictReader.__init__(self, f, delimiter=delimiter, encoding=encoding, *args, **kw) # Translate headers h = self.headers for i in xrange(0, len(h)): self.headers[i] = self._get_translation(h[i]) # Check that all required columns exist in the header for name in self.REQUIRED_COLUMNS: if name not in self.headers: error = "CSV format is invalid: missing field '%s'." % name raise RequiredFieldNotFoundException(error) # Check that each field is unique for name in self.headers: if self.headers.count(name) != 1: error = "The field '%s' occurs multiple times in the header" raise DuplicateColumnException(error) def _get_translation(self, h): """ Function for custom translations """ return self.CSV_TRANSLATION.get(h, h) def _get_row(self, row): """ Function for custom data processing """ return row def next(self): row = self._get_row(UnicodeDictReader.next(self)) if len(row) == 0: return None row['amount'] = Decimal(row['amount'].replace(",", ".")) row['date'] = datetime.strptime(row['date'], "%d.%m.%Y") row['reference'] = row['reference'].replace(' ', '').lstrip('0') row['transaction'] = row['transaction'].replace(' ', '').replace('/', '') if row.has_key('value_date'): row['value_date'] = datetime.strptime(row['value_date'], "%d.%m.%Y") return row class OpDictReader(BillDictReader): '''Reader for Osuuspankki CSV file format The module converts Osuuspankki CSV format data into a more usable form.''' # If these fields are not found on the first line, an exception is raised REQUIRED_COLUMNS = ['date', 'amount', 'transaction'] # Translation table from Osuuspankki CSV format to short names OP_CSV_TRANSLATION = {u'Kirjauspäivä' : 'date', u'Arvopäivä' : 'value_date', u'Tap.pv' : 'date', # old format u'Määrä EUROA' : 'amount', u'Määrä' : 'amount', u'Tapahtumalajikoodi' : 'event_type_code', u'Selitys' : 'event_type_description', u'Saaja/Maksaja' : 'fromto', u'Saajan tilinumero' : 'account', # old format u'Saajan tilinumero ja pankin BIC' : 'account', u'Viite' : 'reference', u'Viesti' : 'message', u'Arkistotunnus' : 'transaction', # old format u'Arkistointitunnus' : 'transaction'} def _get_translation(self, h): # Quick and dirty, OP changes this field name too often! if h.startswith(u"Määrä"): return "amount" return self.OP_CSV_TRANSLATION.get(h, h) class ProcountorDictReader(BillDictReader): REQUIRED_COLUMNS = ['date', 'amount', 'transaction'] CSV_TRANSLATION = {u'Kirjauspäivä' : 'date', u'Arvopäivä' : 'value_date', u'Maksupäivä' : 'date', u'Maksu' : 'amount', u'Summa' : 'amount', u'Kirjausselite' : 'event_type_description', u'Maksaja' : 'fromto', u'Nimi' : 'fromto', u'Tilinro' : 'account', u'Viesti' : 'message', u'Viitenumero' : 'reference', u'Arkistointitunnus' : 'transaction', u'Oikea viite' : 'real_reference', } def _get_row(self, row): if 'real_reference' in row: row['reference'] = row['real_reference'] return row def row_to_payment(row): try: p = Payment.objects.get(transaction_id__exact=row['transaction']) return p except Payment.DoesNotExist: p = Payment(payment_day=min(datetime.now(), row['date']), amount=row['amount'], type=row['event_type_description'], payer_name=row['fromto'], reference_number=row['reference'], message=row['message'], transaction_id=row['transaction']) return p def attach_payment_to_cycle(payment, user=None): """ Outside of this module, this function is mainly used by generate_test_data.py. """ if payment.ignore == True or payment.billingcycle != None: raise Exception("Unexpected function call. This shouldn't happen.") reference = payment.reference_number cycle = BillingCycle.objects.get(reference_number=reference) if cycle.is_paid == False or cycle.amount_paid() < cycle.sum: payment.attach_to_cycle(cycle, user=user) else: # Don't attach a payment to a cycle with enough payments payment.comment = _('duplicate payment') payment.duplicate = True log_user = User.objects.get(id=1) log_change(payment, log_user, change_message="Payment not attached due to duplicate payment") payment.save() return None return cycle def process_payments(reader, user=None): """ Actual CSV file processing logic """ return_messages = [] num_attached = num_notattached = 0 sum_attached = sum_notattached = 0 for row in reader: if row == None: continue if row['amount'] < 0: # Transaction is paid by us, ignored continue # Payment in future more than 1 day is a fatal error if row['date'] > datetime.now() + timedelta(days=1): raise PaymentFromFutureException("Payment date in future") payment = row_to_payment(row) # Do nothing if this payment has already been assigned or ignored if payment.billingcycle or payment.ignore: continue try: cycle = attach_payment_to_cycle(payment, user=user) if cycle: msg = _("Attached payment %(payment)s to cycle %(cycle)s") % { 'payment': unicode(payment), 'cycle': unicode(cycle)} logger.info(msg) return_messages.append((None, None, msg)) num_attached = num_attached + 1 sum_attached = sum_attached + payment.amount else: # Payment not attached to cycle because enough payments were attached msg = _("Billing cycle already paid for %s. Payment not attached.") % payment return_messages.append((None, None, msg)) logger.info(msg) num_notattached = num_notattached + 1 sum_notattached = sum_notattached + payment.amount except BillingCycle.DoesNotExist: # Failed to find cycle for this reference number if not payment.id: payment.save() # Only save if object not in database yet logger.warning("No billing cycle found for %s" % payment.reference_number) return_messages.append((None, payment.id, _("No billing cycle found for %s") % payment)) num_notattached = num_notattached + 1 sum_notattached = sum_notattached + payment.amount log_message ="Processed %s payments total %.2f EUR. Unidentified payments: %s (%.2f EUR)" % \ (num_attached + num_notattached, sum_attached + sum_notattached, num_notattached, \ sum_notattached) logger.info(log_message) return_messages.append((None, None, log_message)) return return_messages def process_op_csv(file_handle, user=None): logger.info("Starting OP payment CSV processing...") reader = OpDictReader(file_handle) return process_payments(reader) def process_procountor_csv(file_handle, user=None): logger.info("Starting procountor payment CSV processing...") reader = ProcountorDictReader(file_handle) return process_payments(reader) class Command(BaseCommand): args = '<csvfile> [<csvfile> ...]' help = 'Read a CSV list of payment transactions' option_list = BaseCommand.option_list + ( make_option('--procountor', dest='procountor', default=None, action="store_true", help='Use procountor import csv format'), ) def handle(self, *args, **options): for csvfile in args: logger.info("Starting the processing of file %s." % os.path.abspath(csvfile)) # Exceptions of process_csv are fatal in command line run with open(csvfile, 'r') as file_handle: if options['procountor']: process_procountor_csv(file_handle) else: process_op_csv(file_handle) logger.info("Done processing file %s." % os.path.abspath(csvfile))

38.039344

104

0.589036

1,275

11,602

5.228235

0.253333

0.008401

0.006601

0.014401

0.180918

0.122412

0.085209

0.063306

0.051605

0

0.00337

0.309516

11,602

304

105

38.164474

0.828735

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false

0.014151

0.075472

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0.349057

0

null

0

null

0

1

0

096c49cec3a4f594f36896910c20f3ffbf6d0451

1,962

py

Python

apps/site/api/serializers/dataset_serializer.py

LocalGround/localground

aa5a956afe7a84a7763a3b23d62a9fd925831cd7

[ "Apache-2.0" ]

9

2015-05-29T22:22:20.000Z

2022-02-01T20:39:00.000Z

apps/site/api/serializers/dataset_serializer.py

LocalGround/localground

aa5a956afe7a84a7763a3b23d62a9fd925831cd7

[ "Apache-2.0" ]

143

2015-01-22T15:03:40.000Z

2020-06-27T01:55:29.000Z

apps/site/api/serializers/dataset_serializer.py

LocalGround/localground

aa5a956afe7a84a7763a3b23d62a9fd925831cd7

[ "Apache-2.0" ]

5

2015-03-16T20:51:49.000Z

2017-02-07T20:48:49.000Z

from localground.apps.site.api.serializers.base_serializer import \ BaseSerializer, NamedSerializerMixin, ProjectSerializerMixin from localground.apps.site.api.serializers.field_serializer import \ FieldSerializer from django.conf import settings from rest_framework import serializers from localground.apps.site import models class DatasetSerializerList( NamedSerializerMixin, ProjectSerializerMixin, BaseSerializer): data_url = serializers.SerializerMethodField() fields_url = serializers.SerializerMethodField() def create(self, validated_data): # Call the Dataset's custom create method, which creates # 2 fields "for free": Name and Description: description = serializers.CharField( source='description', required=False, allow_null=True, label='description', style={'base_template': 'textarea.html', 'rows': 5}, allow_blank=True ) validated_data.update(self.get_presave_create_dictionary()) self.instance = models.Dataset.create(**validated_data) return self.instance class Meta: model = models.Dataset fields = BaseSerializer.field_list + \ ('id', 'name', 'description', 'tags', 'url') + \ ProjectSerializerMixin.field_list + ('data_url', 'fields_url') depth = 0 def get_data_url(self, obj): return '%s/api/0/datasets/%s/data/' % (settings.SERVER_URL, obj.pk) def get_fields_url(self, obj): return '%s/api/0/datasets/%s/fields/' % (settings.SERVER_URL, obj.pk) class DatasetSerializerDetail(DatasetSerializerList): fields = serializers.SerializerMethodField('get_dataset_fields') class Meta: model = models.Dataset fields = DatasetSerializerList.Meta.fields + ('fields',) depth = 0 def get_dataset_fields(self, obj): return FieldSerializer( obj.fields, many=True, context={'request': {}}).data

37.730769

87

0.690622

207

1,962

6.410628

0.386473

0.039186

0.042954

0.051997

0.183873

0.150716

0.045215

0

0.003849

0.205403

1,962

51

88

38.470588

0.847338

0.049439

0

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0

0.096133

0.029001

0

1

0.102564

false

0

0.128205

0.076923

0.512821

0

null

0

null

0

1

0

096c52364e36a63ef84c11f7cd157e7b506deae2

1,447

py

Python

example/0_Basic_usage_of_the_library/python_pyppeteer/7_PageClass_Cookie.py

RecluseXU/learning_spider

45fa790ed7970be57a21b40817cc66856de3d99b

[ "MIT" ]

38

2020-08-30T11:41:53.000Z

2022-03-23T04:30:26.000Z

example/0_Basic_usage_of_the_library/python_pyppeteer/7_PageClass_Cookie.py

AndersonHJB/learning_spider

b855b7808fb5268e9564180cf73ba5b1fb133f58

[ "MIT" ]

2

2021-08-20T16:34:12.000Z

2021-10-08T11:06:41.000Z

example/0_Basic_usage_of_the_library/python_pyppeteer/7_PageClass_Cookie.py

AndersonHJB/learning_spider

b855b7808fb5268e9564180cf73ba5b1fb133f58

[ "MIT" ]

10

2020-11-24T09:15:42.000Z

2022-02-25T06:05:16.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @File : 7_PageClass_Cookie.py @Time : 2020-8-23 01:33:25 @Author : Recluse Xu @Version : 1.0 @Contact : 444640050@qq.com @Desc : 页面类 Page Class 官方文档：https://miyakogi.github.io/pyppeteer/reference.html#pyppeteer.page.Page.target Page类提供了与标签交互的方法，一个浏览器可以有多个Page对象 ''' # here put the import lib import asyncio from pyppeteer import launch async def main(): browser = await launch({ 'headless': False, 'ignorehttpserrrors': True, 'viewport': {'width': 1280, 'height': 800}, 'autoClose': True, }) page = await browser.newPage() await page.goto('http://www.baidu.com') # Page.cookies(*urls) → dict # 获取Cookie # 如果指定url那就返回那个url的Cookie，没指定就返回当前页面Cookie c = await page.cookies() print(c) # Page.deleteCookie(*cookies) # 删除Cookie # cookies可以填入的参数 # name (str): 必须传入 # url (str) # domain (str) # path (str) # secure (bool) await page.deleteCookie({'name': 'BAIDUID'}) # Page.setCookie(*cookies) → None[source] # 设置Cookie # 可选Cookie的参数： # name (str): required # value (str): required # url (str) # domain (str) # path (str) # expires (number): Unix time in seconds # httpOnly (bool) # secure (bool) # sameSite (str): 'Strict' or 'Lax' asyncio.get_event_loop().run_until_complete(main())

23.721311

87

0.601935

164

1,447

5.286585

0.70122

0.031142

0.027682

0.034602

0.05075

0

0.030698

0.257084

1,447

61

88

23.721311

0.773953

0.56669

0

0.143098

0

1

0

false

0

0.133333

0

0.133333

0.066667

0

null

0

null

0

1

0

09706c6eb6ce8046078f05dc861a923a4dfa7d00

736

py

Python

ML/Computer Vision/Lab7_face_detection_real_time.py

richeyphu/ITE-425

4210b692609fa04cdd00b76a45d9e1e5baacd6e3

[ "MIT" ]

null

ML/Computer Vision/Lab7_face_detection_real_time.py

richeyphu/ITE-425

4210b692609fa04cdd00b76a45d9e1e5baacd6e3

[ "MIT" ]

null

ML/Computer Vision/Lab7_face_detection_real_time.py

richeyphu/ITE-425

4210b692609fa04cdd00b76a45d9e1e5baacd6e3

[ "MIT" ]

null

import cv2 faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") #capture = cv2.VideoCapture(0) capture = cv2.VideoCapture('Elon Musk 320.mp4') while True: _, frame = capture.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #faces = faceCascade.detectMultiScale(gray, 1.1, 4) faces = faceCascade.detectMultiScale( gray, scaleFactor=1.1, minNeighbors=5, #minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE ) for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) cv2.imshow('Image', frame) keyboard = cv2.waitKey(30 & 0xff) if keyboard==27: break capture.release()

30.666667

99

0.639946

94

736

4.946809

0.585106

0.043011

0.094624

0.154839

0

0.066318

0.221467

736

24

100

30.666667

0.745201

0.130435

0

0.092683

0.056911

0

0.006504

0

1

0

false

0

0.052632

0

0.052632

0

null

0

null

0

1

0

0970b9ad7614a84f468d83f8de90de992c7f521f

1,110

py

Python

setup.py

tabac/cprofilev

dd9ee42ef8e68d08dbdde88ddce854aac55ef934

[ "MIT" ]

null

setup.py

tabac/cprofilev

dd9ee42ef8e68d08dbdde88ddce854aac55ef934

[ "MIT" ]

null

setup.py

tabac/cprofilev

dd9ee42ef8e68d08dbdde88ddce854aac55ef934

[ "MIT" ]

1

2019-09-15T12:56:29.000Z

from setuptools import setup import sys if sys.version_info < (2,5): raise NotImplementedError( "Sorry, you need at least Python 2.5 to use cprofilev.") VERSION = '1.0.4' __doc__ = """\ An easier way to use cProfile. Outputs a simpler html view of profiled stats. Able to show stats while the code is still running! """ setup( name='CProfileV', version=VERSION, url='https://github.com/ymichael/cprofilev', author='Michael Yong', author_email='wrong92@gmail.com', py_modules=['cprofilev'], entry_points=""" [console_scripts] cprofilev = cprofilev:main """, install_requires=["bottle"], license='MIT', description='An easier way to use cProfile', long_description=__doc__, classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'Environment :: Web Environment', 'Framework :: Bottle', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Topic :: Software Development :: Testing', ] )

24.130435

64

0.636937

125

1,110

5.536

0.704

0.021676

0.031792

0.037572

0.069364

0

0.01182

0.237838

1,110

45

65

24.666667

0.806147

0

0.551351

0

1

0

false

0

0.054054

0

0.054054

0

null

0

null

0

1

0

09722db03d2e3d65cdf0b22fef132df0fab89e4d

5,947

py

Python

classification.py

Sigmoid-Frontsquat-LLC/classification-model-backend

7366302063315a245b7ab20219fb22ecf67bd377

[ "MIT" ]

null

classification.py

Sigmoid-Frontsquat-LLC/classification-model-backend

7366302063315a245b7ab20219fb22ecf67bd377

[ "MIT" ]

null

classification.py

Sigmoid-Frontsquat-LLC/classification-model-backend

7366302063315a245b7ab20219fb22ecf67bd377

[ "MIT" ]

null

import sys # this is for extracting command line arguments. def parse_activator(flag, value): if flag[1] == 'a': return (True, value) else: return (False,None) pass def parse_optimizer(flag, value): if flag[1] == 'o': return (True, value) else: return (False,None) pass def parse_source(flag, value): if flag[1] == 's': return (True, value) else: return (False,None) pass activator = '' optimizer = '' source = '' if len(sys.argv) == 1 or (len(sys.argv) - 1) % 2 != 0: raise ValueError("Usage: [-s image] [-a activator] [-o optimizer]") else: # could this be done better? # sure, but this works for now... for i in range(1, len(sys.argv) - 1): flag = sys.argv[i] value = sys.argv[i + 1] isActivator, act = parse_activator(flag, value) if isActivator: if act != '-o': activator = act continue isOptimizer, opt = parse_optimizer(flag, value) if isOptimizer: optimizer = opt continue isSource, so = parse_source(flag, value) if isSource: source = so continue pass pass # naive check to ensure no argument is left unfilled if len(activator) == 0 or len(optimizer) == 0 or len(source) == 0 : raise ValueError("Usage: [-s image] [-a activator] [-o optimizer]") # exit(0) ############# Classification Logic ################## import pandas as pd import io import requests import numpy as np import os import logging import json import shutil from sklearn.model_selection import train_test_split from sklearn import metrics import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.applications.vgg16 import VGG16 from PIL import Image, ImageFile, ImageEnhance from matplotlib.pyplot import imshow import requests from io import BytesIO import matplotlib.image as mpimg import matplotlib.pyplot as plt ####### warning messages not printed ####### logging.disable(logging.WARNING) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # class labels are as follows for the cifar10 # airplane : 0 # automobile : 1 # bird : 2 # cat : 3 # deer : 4 # dog : 5 # frog : 6 # horse : 7 # ship : 8 # truck : 9 class_labels = ['airplane','automobile','bird','cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] num_classes = 10 # Image preprocessing img = Image.open(source) img = img.resize((32,32)) enhancer = ImageEnhance.Sharpness(img) enhanced_im = enhancer.enhance(10.0) enhanced_im.save('resized.jpg') img_array = np.asarray(enhanced_im) img_array = img_array / 255 input_shape = (32,32,3) # reshape for model # original model was trained with (32,32,3) img_array = img_array.reshape((1,32,32,3)) modelo = Sequential() modelo.add(Conv2D(32, (3, 3), activation=activator, padding='same', input_shape=input_shape)) modelo.add(Conv2D(32, (3, 3), activation=activator, padding='same')) modelo.add(Conv2D(32, (3, 3), activation=activator, padding='same')) modelo.add(MaxPooling2D((3, 3))) modelo.add(Dropout(0.2)) modelo.add(Conv2D(64, (3, 3), activation=activator, padding='same')) modelo.add(Conv2D(64, (3, 3), activation=activator, padding='same')) modelo.add(Conv2D(64, (3, 3), activation=activator, padding='same')) modelo.add(MaxPooling2D((3, 3))) modelo.add(Dropout(0.2)) modelo.add(Conv2D(128, (3, 3), activation=activator, padding='same')) modelo.add(Conv2D(128, (3, 3), activation=activator, padding='same')) modelo.add(MaxPooling2D((3, 3))) modelo.add(Flatten()) modelo.add(Dense(128, activation=activator)) modelo.add(Dropout(0.2)) modelo.add(Dense(10, activation='softmax')) modelo.compile(loss='categorical_crossentropy',optimizer=optimizer) # validate the 'activator' pass # validate the 'optimizer' pass # Load weights based on activator and optimizer # probably not needed as we are already passing the optimizer as a variable if optimizer == 'adam': # compile with adam modelo.compile(loss='categorical_crossentropy',optimizer=optimizer) # activator if activator == 'relu': # load adam-relu modelo.load_weights('dnn/relu-adam2.hdf5') elif activator == 'sigmoid': # load sigmoid-adam modelo.load_weights('dnn/sigmoid-adam2.hdf5') elif activator == 'tanh': # load tanh-adam modelo.load_weights('dnn/tanh-adam2.hdf5') else: print('error') elif optimizer == 'sgd': # compile with sgd modelo.compile(loss='categorical_crossentropy',optimizer=optimizer) if activator == 'relu': # load relu-sgd modelo.load_weights('dnn/relu-sgd2.hdf5') elif activator == 'sigmoid': # load sigmoid-sgd modelo.load_weights('dnn/sigmoid-sgd2.hdf5') elif activator == 'tanh': # load tanh-sgd modelo.load_weights('dnn/tanh-sgd2.hdf5') else: print('error') # Get the classification ############# classification ############## pred = modelo.predict(img_array) pred = pred[0] pred_class = class_labels[np.argmax(pred)] ############# JSON ############### # classification = {k:v for k,v in zip(class_labels,pred)} classification = [ { class_labels[0] : pred[0] }, { class_labels[1] : pred[1] }, { class_labels[2] : pred[2] }, { class_labels[3] : pred[3] }, { class_labels[4] : pred[4] }, { class_labels[5] : pred[5] }, { class_labels[6] : pred[6] }, { class_labels[7] : pred[7] }, { class_labels[8] : pred[8] }, { class_labels[9] : pred[9] }, ] ########## output ################ print(classification)

23.230469

102

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768

5,947

4.865885

0.276042

0.040942

0.032111

0.044956

0.386674

0.283918

0.249666

0.195879

0.18571

0

0.033398

0.214562

5,947

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103

23.321569

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0.044586

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0.019108

0

null

0

null

0

1

0

0972614a80b05e57c1220dbf0ff54e2fa988f86e

7,658

py

Python

wizard/gui/destination_manager.py

Wizard-collab/wizard_2

a2cb23362e178a0205f6dd0b9b4328c329b5b142

[ "MIT" ]

1

2021-10-13T15:07:32.000Z

wizard/gui/destination_manager.py

Wizard-collab/wizard_2

a2cb23362e178a0205f6dd0b9b4328c329b5b142

[ "MIT" ]

null

wizard/gui/destination_manager.py

Wizard-collab/wizard_2

a2cb23362e178a0205f6dd0b9b4328c329b5b142

[ "MIT" ]

null

# coding: utf-8 # Author: Leo BRUNEL # Contact: contact@leobrunel.com # Python modules from PyQt5 import QtWidgets, QtCore, QtGui from PyQt5.QtCore import pyqtSignal import logging # Wizard modules from wizard.core import assets from wizard.core import project from wizard.vars import ressources # Wizard gui modules from wizard.gui import gui_utils from wizard.gui import gui_server logger = logging.getLogger(__name__) class destination_manager(QtWidgets.QWidget): def __init__(self, export_id, parent=None): super(destination_manager, self).__init__(parent) self.setWindowIcon(QtGui.QIcon(ressources._wizard_ico_)) self.setWindowTitle(f"Wizard - Destination manager") self.references_ids = dict() self.export_id = export_id self.fill_thread = fill_thread(self) self.build_ui() self.connect_functions() self.refresh() def build_ui(self): self.setMinimumSize(QtCore.QSize(800,500)) self.main_layout = QtWidgets.QVBoxLayout() self.main_layout.setContentsMargins(0,0,0,0) self.main_layout.setSpacing(0) self.setLayout(self.main_layout) self.header = QtWidgets.QWidget() self.header.setObjectName('transparent_widget') self.header_layout = QtWidgets.QHBoxLayout() self.header_layout.setSpacing(6) self.header.setLayout(self.header_layout) self.main_layout.addWidget(self.header) self.header_label = QtWidgets.QLabel() self.header_layout.addWidget(self.header_label) self.content_widget = QtWidgets.QWidget() self.content_widget.setObjectName('dark_widget') self.content_layout = QtWidgets.QVBoxLayout() self.content_layout.setSpacing(6) self.content_widget.setLayout(self.content_layout) self.main_layout.addWidget(self.content_widget) self.list_view = QtWidgets.QTreeWidget() self.list_view.setContextMenuPolicy(QtCore.Qt.CustomContextMenu) self.list_view.setObjectName('tree_as_list_widget') self.list_view.setColumnCount(2) self.list_view.setHeaderLabels(['Destination', 'Referenced version']) self.list_view.header().resizeSection(0, 450) self.list_view.setIndentation(0) self.list_view.setAlternatingRowColors(True) self.list_view.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection) self.content_layout.addWidget(self.list_view) self.buttons_widget = QtWidgets.QWidget() self.buttons_widget.setObjectName('transparent_widget') self.buttons_layout = QtWidgets.QHBoxLayout() self.buttons_layout.setContentsMargins(0,0,0,0) self.buttons_layout.setSpacing(6) self.buttons_widget.setLayout(self.buttons_layout) self.content_layout.addWidget(self.buttons_widget) self.buttons_layout.addSpacerItem(QtWidgets.QSpacerItem(0,0, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Fixed)) self.remove_selection_button = QtWidgets.QPushButton() gui_utils.application_tooltip(self.remove_selection_button, "Remove selected references") self.remove_selection_button.setFixedSize(35,35) self.remove_selection_button.setIconSize(QtCore.QSize(25,25)) self.remove_selection_button.setIcon(QtGui.QIcon(ressources._tool_archive_)) self.buttons_layout.addWidget(self.remove_selection_button) self.update_button = QtWidgets.QPushButton() gui_utils.application_tooltip(self.update_button, "Update selected references") self.update_button.setFixedSize(35,35) self.update_button.setIconSize(QtCore.QSize(25,25)) self.update_button.setIcon(QtGui.QIcon(ressources._tool_update_)) self.buttons_layout.addWidget(self.update_button) def connect_functions(self): self.fill_thread.data_signal.connect(self.update_reference) self.remove_selection_button.clicked.connect(self.remove_selection) self.update_button.clicked.connect(self.update_selection) def refresh(self): self.header_label.setText(assets.instance_to_string(('export', self.export_id))) reference_rows = project.get_references_by_export(self.export_id) project_references_id = [] for reference_row in reference_rows: project_references_id.append(reference_row['id']) if reference_row['id'] not in self.references_ids.keys(): target_item = custom_target_item(reference_row, self.list_view.invisibleRootItem()) self.references_ids[reference_row['id']] = target_item references_list_ids = list(self.references_ids.keys()) for reference_id in references_list_ids: if reference_id not in project_references_id: self.remove_reference_item(reference_id) self.fill_thread.update_reference_rows(self.export_id, reference_rows) def remove_reference_item(self, reference_id): if reference_id in self.references_ids.keys(): item = self.references_ids[reference_id] self.list_view.invisibleRootItem().removeChild(item) del self.references_ids[reference_id] def remove_selection(self): selected_items = self.list_view.selectedItems() for selected_item in selected_items: project.remove_reference(selected_item.reference_row['id']) gui_server.refresh_team_ui() def update_selection(self): selected_items = self.list_view.selectedItems() for selected_item in selected_items: reference_id = selected_item.reference_row['id'] assets.set_reference_last_version(reference_id) gui_server.refresh_team_ui() def update_reference(self, data_tuple): if data_tuple[0] in self.references_ids.keys(): self.references_ids[data_tuple[0]].update(data_tuple) class custom_target_item(QtWidgets.QTreeWidgetItem): def __init__(self, reference_row, parent=None): super(custom_target_item, self).__init__(parent) self.reference_row = reference_row bold_font=QtGui.QFont() bold_font.setBold(True) self.setFont(0, bold_font) def update(self, data_tuple): self.setText(0, data_tuple[1]) self.setText(1, data_tuple[2]) if data_tuple[3]: self.setForeground(1, QtGui.QBrush(QtGui.QColor('#9ce87b'))) else: self.setForeground(1, QtGui.QBrush(QtGui.QColor('#f79360'))) class fill_thread(QtCore.QThread): data_signal = pyqtSignal(tuple) def __init__(self, parent = None): super(fill_thread, self).__init__(parent) self.export_id = None self.references_rows = [] self.running = False def update_reference_rows(self, export_id, reference_rows): self.references_rows = reference_rows self.export_id = export_id self.running = True self.start() def run(self): if self.running: default_export_version_id = project.get_default_export_version(self.export_id, 'id') for reference_row in self.references_rows: work_env_string = assets.instance_to_string(('work_env', reference_row['work_env_id'])) export_version_row = project.get_export_version_data(reference_row['export_version_id']) if default_export_version_id != export_version_row['id']: up_to_date = 0 else: up_to_date = 1 self.data_signal.emit((reference_row['id'], work_env_string, export_version_row['name'], up_to_date))

42.076923

131

0.70619

921

7,658

5.563518

0.192182

0.021858

0.032787

0.034153

0.253513

0.162373

0.125683

0.083528

0.031616

0

0.010616

0.200444

7,658

181

132

42.309392

0.826229

0.014625

0

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0.090278

false

0

0.055556

0

0.173611

0

null

0

null

0

1

0

0972acc5adf0761541464f2087b1feb90d1044ab

1,591

py

Python

algotrading/agents/six_month_cycle_agent.py

vrishank97/AlgoTrading

41dd44f73d97267283032ed433dd0bfb3bd6c638

[ "MIT" ]

92

2018-12-21T11:21:17.000Z

2022-03-27T13:01:45.000Z

build/lib/algotrader/agents/six_month_cycle_agent.py

ajmal017/AlgoTrading-5

41dd44f73d97267283032ed433dd0bfb3bd6c638

[ "MIT" ]

3

2018-12-19T16:33:36.000Z

2019-05-28T10:08:40.000Z

build/lib/algotrader/agents/six_month_cycle_agent.py

ajmal017/AlgoTrading-5

41dd44f73d97267283032ed433dd0bfb3bd6c638

[ "MIT" ]

34

2019-05-28T21:31:51.000Z

2022-02-06T20:25:54.000Z

from .BaseAgent import BaseAgent import pandas as pd import numpy as np from itertools import islice class SixMonthCycle_Agent(BaseAgent): def __init__(self, window_size, small, large, signal, up, down): super().__init__(window_size) self.up = up self.down = down self.large = large self.small = small self.signal = signal self.window_size = window_size def get_macd_signal(self): memory_slice = list(islice(self.memory, self.window_size - self.large, self.window_size)) memory_slice = pd.DataFrame(memory_slice) df_memory = pd.DataFrame(memory_slice) df_macd = df_memory.ewm(span=self.small, adjust=False).mean() - df_memory.ewm(span=self.large, adjust=False).mean() signal = df_macd.ewm(span=self.signal, adjust=False).mean()[0][self.large - 1] macd = df_macd[0][self.large - 1] if macd >= (1 + self.up)*(signal): return "buy" elif macd <= (1 - self.down)*(signal): return "sell" else: return "hold" def step(self, price, date): self.memory.append(price) if len(self.memory)<self.window_size: return 0 date = list(map(int, date.split("-"))) month = date[1] macd_signal = self.get_macd_signal() # Buy in november if month > 10 or month < 5 and macd_signal == "buy": return 1 # Sell in may if month > 4 and month < 11 and macd_signal == "sell": return -1 # Hold return 0

29.462963

123

0.588938

211

1,591

4.28436

0.298578

0.077434

0.044248

0.14823

0

0.015247

0.299183

1,591

54

124

29.462963

0.795516

0.020113

0

0.052632

0

0.012211

0

1

0.078947

false

0

0.105263

0

0.394737

0

null

0

null

0

1

0

0973f9ff4b18475410c9ec73581276a3c910551c

485

py

Python

modu/linear_regression/linear_regression_cost_plot.py

godong9/ml

2c735376f4366000685cd97de5df31aabc1c597e

[ "MIT" ]

null

modu/linear_regression/linear_regression_cost_plot.py

godong9/ml

2c735376f4366000685cd97de5df31aabc1c597e

[ "MIT" ]

null

modu/linear_regression/linear_regression_cost_plot.py

godong9/ml

2c735376f4366000685cd97de5df31aabc1c597e

[ "MIT" ]

null

import tensorflow as tf import matplotlib.pyplot as plt X = [1, 2, 3] Y = [1, 2, 3] W = tf.placeholder(tf.float32) hypothesis = X * W cost = tf.reduce_mean(tf.square(hypothesis - Y)) sess = tf.Session() sess.run(tf.global_variables_initializer()) W_val = [] cost_val = [] for i in range(-30, 50): feed_W = i * 0.1 curr_cost, curr_W = sess.run([cost, W], feed_dict={W: feed_W}) W_val.append(curr_W) cost_val.append(curr_cost) plt.plot(W_val, cost_val) plt.show()

18.653846

66

0.665979

88

485

3.488636

0.454545

0.039088

0.019544

0.071661

0

0.035088

0.17732

485

26

67

18.653846

0.734336

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

09744b77cc03b6489272302bf793ec2a6a1a7ea2

2,078

py

Python

dyno_pods/test/test_multisampling.py

louisXW/PODS-DYNO

5cd3cced8f0556a5c42d9021ff1d965880f360dd

[ "MIT" ]

null

dyno_pods/test/test_multisampling.py

louisXW/PODS-DYNO

5cd3cced8f0556a5c42d9021ff1d965880f360dd

[ "MIT" ]

1

2022-03-24T18:17:50.000Z

dyno_pods/test/test_multisampling.py

louisXW/PODS-DYNO

5cd3cced8f0556a5c42d9021ff1d965880f360dd

[ "MIT" ]

1

2021-08-01T12:57:30.000Z

""" .. module:: test_multisampling :synopsis: Test multisampling strategy .. moduleauthor:: David Eriksson <dme65@cornell.edu> """ from pySOT import Ackley, CandidateDYCORS, GeneticAlgorithm, \ MultiStartGradient, SyncStrategyNoConstraints, \ RBFInterpolant, CubicKernel, LinearTail, \ SymmetricLatinHypercube, MultiSampling from poap.controller import SerialController import numpy as np import os.path import logging def main(): if not os.path.exists("./logfiles"): os.makedirs("logfiles") if os.path.exists("./logfiles/test_multisampling.log"): os.remove("./logfiles/test_multisampling.log") logging.basicConfig(filename="./logfiles/test_multisampling.log", level=logging.INFO) print("\nNumber of threads: 1") print("Maximum number of evaluations: 500") print("Sampling method: CandidateDYCORS, Genetic Algorithm, Multi-Start Gradient") print("Experimental design: Latin Hypercube") print("Surrogate: Cubic RBF") nthreads = 1 maxeval = 500 nsamples = nthreads data = Ackley(dim=10) print(data.info) # Create a strategy and a controller sampling_method = [CandidateDYCORS(data=data, numcand=100*data.dim), GeneticAlgorithm(data=data), MultiStartGradient(data=data)] controller = SerialController(data.objfunction) controller.strategy = \ SyncStrategyNoConstraints( worker_id=0, data=data, maxeval=maxeval, nsamples=nsamples, response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval), exp_design=SymmetricLatinHypercube(dim=data.dim, npts=2*(data.dim + 1)), sampling_method=MultiSampling(sampling_method, [0, 1, 0, 2])) result = controller.run() best, xbest = result.value, result.params[0] print('Best value: {0}'.format(best)) print('Best solution: {0}\n'.format( np.array_str(xbest, max_line_width=np.inf, precision=5, suppress_small=True))) if __name__ == '__main__': main()

34.065574

95

0.682387

223

2,078

6.26009

0.502242

0.060888

0.053725

0.060172

0

0.015691

0.202599

2,078

60

96

34.633333

0.826795

0.076997

0

0.180628

0.051832

0

1

0.023256

false

0

0.116279

0

0.139535

0.186047

0

null

0

null

0

1

0

09745d72d59a6783162603fbdf15fcd5912b5ca1

1,172

py

Python

play_game.py

sanderland/SelfplayLab

4ce5b8ffd8cfb5465196dddaa0142b2843570b98

[ "MIT" ]

2

2020-12-10T17:11:23.000Z

2021-05-09T04:14:00.000Z

play_game.py

sanderland/SelfplayLab

4ce5b8ffd8cfb5465196dddaa0142b2843570b98

[ "MIT" ]

null

play_game.py

sanderland/SelfplayLab

4ce5b8ffd8cfb5465196dddaa0142b2843570b98

[ "MIT" ]

2

2021-05-09T04:14:05.000Z

2021-05-09T04:14:34.000Z

import torch import argparse from selfplaylab.game.go import CaptureGoState, PixelCaptureGoState, GoState from selfplaylab.game.gomoku import GoMokuState, GoMokuStateAugmented, TicTacToe, TicTacToeAugmented from selfplaylab.game.nim import NimState from selfplaylab.game.othello import OthelloState from selfplaylab.play import play_game parser = argparse.ArgumentParser(description="Self-play visualization.") parser.add_argument("--game", type=str, help="Game to play") parser.add_argument("--tag", type=str, help="Tag for experiment", default="") args = parser.parse_args() game = args.game if game == "cg": game_class = CaptureGoState elif game == "pxcg": game_class = PixelCaptureGoState elif game == "nim": game_class = NimState elif game == "oth": game_class = OthelloState else: raise Exception("unknown game") net = game_class.create_net(tag=args.tag) options = {} print(f"Loaded net {net.metadata['filename']} on cuda? {net.device}") temp_fn = lambda mv: 1.0 if mv < 2 else 0.1 with torch.no_grad(): game_states = play_game( net_evaluator=net.evaluate_sample, game_class=game_class, temperature=temp_fn, verbose=True, )

31.675676

100

0.75256

157

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5.496815

0.477707

0.073001

0.088065

0

0.004926

0.133959

1,172

36

101

32.555556

0.84532

0

0.12628

0.022184

0

1

0

false

0

0.233333

0

0.233333

0.033333

0

null

0

null

0

1

0

09761ad36a8d0fda1e1934f1d5836f763526e5ae

558

py

Python

tests/test_version.py

rndazurescript/Coco2CustomVision

c189109413b185a77f5d1de51fb2dbcc96139ff6

[ "MIT" ]

null

tests/test_version.py

rndazurescript/Coco2CustomVision

c189109413b185a77f5d1de51fb2dbcc96139ff6

[ "MIT" ]

1

2022-02-23T13:01:38.000Z

tests/test_version.py

rndazurescript/Coco2CustomVision

c189109413b185a77f5d1de51fb2dbcc96139ff6

[ "MIT" ]

null

import re def try_parse_int(s, base=10, val=None): try: return int(s, base) except ValueError: return val def test_version(): """Test version string""" from coco2customvision import __version__ version_parts = re.split("[.-]", __version__) if __version__ != "UNKNOWN": assert 3 <= len(version_parts), "must have at least Major.minor.patch" assert all( not try_parse_int(i) is None for i in version_parts[:2] ), f"Version Major.minor must be 2 integers. Received {__version__}"

26.571429

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4.453333

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0.107784

0.065868

0

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558

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79

27.9

0.784689

0.03405

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0.142857

1

0.142857

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0

0.142857

0

0.428571

0

null

0

null

0

1

0

0976f09aff61c07c694ac80f44c6f37d65e2b8b2

1,266

py

Python

Python/Pages/ITProPage.py

hirokundayon/koedo

1d6fc0bb6045edb24253f039628104256896bd1a

[ "Apache-2.0" ]

1

2019-02-04T15:13:51.000Z

Python/Pages/ITProPage.py

hirokundayon/koedo

1d6fc0bb6045edb24253f039628104256896bd1a

[ "Apache-2.0" ]

null

Python/Pages/ITProPage.py

hirokundayon/koedo

1d6fc0bb6045edb24253f039628104256896bd1a

[ "Apache-2.0" ]

1

2018-02-26T15:12:04.000Z

# -*- coding: utf-8 -*- from Pages.PageObject import PageObject import time class ITProPage(PageObject): firstHandle = "" secondHandle = "" def __init__(self, driver): PageObject.__init__(self, driver) def click_picture(self): self.firstHandle = self.driver.window_handles[0] picture =\ self.waiting_element_by_xpath("//img[@alt=\"小江戸らぐ\"]") #self.driver.save_screenshot("C:\\home\\hirofumi\\koedo\\a.jpg") self.click(picture) for handle in self.driver.window_handles: if handle != self.firstHandle: self.secondHandle = handle self.driver.switch_to_window(self.secondHandle) picture =\ self.waiting_element_by_xpath("//img[@src=\"koedlug.jpg\"]") time.sleep(5) return self def quit(self): self.driver.switch_to_window(self.secondHandle) self.driver.close() self.driver.switch_to_window(self.firstHandle) self.driver.quit() def click_PC_button(self): PC_button =\ self.waiting_element_by_xpath("//img[@src=\"/images/n/itpro/2010/leaf/btn_pc.gif\"]") self.click(PC_button) return self

32.461538

98

0.598736

143

1,266

5.076923

0.405594

0.137741

0.078512

0.082645

0.292011

0.253444

0

0.007634

0.275671

1,266

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99

33.315789

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0.2

0

0.034211

0

1

0.133333

false

0

0.066667

0

0.366667

0

null

0

null

0

1

0

097a67824a1ea5f6c93e2208bf5602c06cf66bd7

9,891

py

Python

Chapter05/5B_MnA/5B_MnAPrediction.py

uyenphuong18406/Hands-On-Artificial-Intelligence-for-Banking

3a10a14194368478bb8b78d3d17e9c6a7b7253db

[ "MIT" ]

115

2020-06-18T15:00:58.000Z

2022-03-02T10:13:19.000Z

Chapter05/5B_MnA/5B_MnAPrediction.py

uyenphuong18406/Hands-On-Artificial-Intelligence-for-Banking

3a10a14194368478bb8b78d3d17e9c6a7b7253db

[ "MIT" ]

2

2020-11-06T11:02:31.000Z

2021-01-22T12:44:35.000Z

Chapter05/5B_MnA/5B_MnAPrediction.py

uyenphuong18406/Hands-On-Artificial-Intelligence-for-Banking

3a10a14194368478bb8b78d3d17e9c6a7b7253db

[ "MIT" ]

60

2020-07-22T14:53:10.000Z

2022-03-23T10:17:59.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- QUANDLKEY = '<Enter your Quandl APT key here>' """ Created on Fri Oct 5 23:24:35 2018 @author: jeff """ '''************************************* #1. Import libraries and define key variables ''' import pandas as pd import numpy as np import quandl import matplotlib.pyplot as plt from sklearn.metrics import classification_report,roc_curve, auc,confusion_matrix,f1_score from sklearn.model_selection import train_test_split from sklearn import tree from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import StandardScaler import pickle import graphviz #KPI keys quandl.ApiConfig.api_key = QUANDLKEY '''************************************* #2. Definition of functions ''' #2a.Download tickers def download_tkr(tkr): record_db_events_gp = pd.DataFrame() record_db_financials=quandl.get_table('SHARADAR/SF1', calendardate={'gte': '2008-12-31'}, ticker=tkr, dimension='MRY') record_db_financials['year'] = record_db_financials['reportperiod'].dt.year record_db_financials['year_1'] = record_db_financials['year']+1 record_db_events=quandl.get_table('SHARADAR/EVENTS', ticker=tkr) tmp_series = record_db_events['eventcodes'].str.contains('21') record_db_events= record_db_events[tmp_series] record_db_events['year'] = record_db_events.date.dt.year record_db_events= record_db_events.drop(['date'],axis=1) record_db_events_gp = record_db_events.groupby(['ticker','year'],as_index=False).count() combined_pd = pd.merge(record_db_financials,record_db_events_gp,how ='left',left_on='year_1',right_on='year') #convert all events to 1 and NaN combined_pd.loc[combined_pd['eventcodes']>1,'eventcodes'] = 1 X = record_db_financials.iloc[:,6:-5] Y = combined_pd.iloc[:,-1] return combined_pd, X, Y #tkr = 'AMZN' #df_tmp = download_tkr(tkr) #2b.Train tree def train_tree(X,Y,ind): print('Decision Tree') #split the dataset into training set and testing set X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0) min_leaf_size = int(len(X_train) * 0.01) tree_clf = tree.DecisionTreeClassifier(min_samples_leaf=min_leaf_size) #preprocessing the data scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) #fit the training data to the model tree_clf.fit(X_train,Y_train) ##metric 1: roc Y_score_tree = tree_clf.predict(X_test) fpr, tpr, thresholds = roc_curve(Y_test,Y_score_tree, pos_label=1) roc_auc = auc(fpr,tpr) lw=2 plt.figure() plt.plot(fpr,tpr,color='darkorange',lw=lw,label='ROC curve (area = %0.2f)' %roc_auc) plt.plot([0,1],[0,1],color='navy',lw=lw,linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic - Decision Tree '+ind) plt.legend(loc="lower right") plt.savefig(ind+'_DT.png') ##metric 2: Confusion matrix Y_pred_tree = tree_clf.predict(X_test) confusion_matrix_tree = confusion_matrix(Y_test, Y_pred_tree) print(confusion_matrix_tree) print(classification_report(Y_test, Y_pred_tree)) #common standard to compare across models f1_clf = f1_score(Y_test, Y_pred_tree, average='weighted') ##save model f_tree = open(ind+'_tree_clf.pkl',"wb+") pickle.dump(tree_clf, f_tree) f_tree.close() f_tree_sc = open(ind+'_tree_scaler.pkl',"wb+") pickle.dump(scaler, f_tree_sc) f_tree_sc.close() return tree_clf,f1_clf ##2C Neural Network #2Ci. Grid search that simulate the performance of different neural network design def grid_search(X_train,X_test, Y_train,Y_test,num_training_sample): best_f1 = 0 best_hidden_layers_list = [] best_hidden_layers_tuple = () #various depth for depth in range(1,5): print('Depth = '+str(depth)) for layer_size in range(1,8): neuron_cnt = 0 hidden_layers_list = [] i = 0 while i<depth: hidden_layers_list.append(layer_size) neuron_cnt += layer_size i+=1 #pruning - to avoid over-training if num_training_sample<neuron_cnt: break hidden_layers_tuple = tuple(hidden_layers_list) nn_clf = MLPClassifier(alpha=1e-5, hidden_layer_sizes=hidden_layers_tuple, random_state=1) nn_clf.fit(X_train,Y_train) Y_pred = nn_clf.predict(X_test) temp_f1 = f1_score(Y_test, Y_pred, average='weighted') if temp_f1 > best_f1: best_f1 = temp_f1 best_hidden_layers_list = hidden_layers_list best_hidden_layers_tuple = hidden_layers_tuple print(best_hidden_layers_list) return best_hidden_layers_list,best_hidden_layers_tuple #2Cii. Train Neural Network def train_NN(X,Y,ind): print('Neural Network') #split the dataset into training set and testing set X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0) #preprocessing the data scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) num_training_sample = len(X_train) best_hidden_layers_list,best_hidden_layers_tuple = grid_search(X_train, X_test, Y_train, Y_test,num_training_sample) nn_clf = MLPClassifier(alpha=1e-5, hidden_layer_sizes=best_hidden_layers_tuple, random_state=1) #fit the training data to the model nn_clf.fit(X_train,Y_train) ##metric 1: roc Y_score_nn = nn_clf.predict(X_test) fpr, tpr, thresholds = roc_curve(Y_test,Y_score_nn, pos_label=1) roc_auc = auc(fpr,tpr) lw=2 plt.figure() plt.plot(fpr,tpr,color='darkorange',lw=lw,label='ROC curve (area = %0.2f)' %roc_auc) plt.plot([0,1],[0,1],color='navy',lw=lw,linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic - Neural Network '+ind) plt.legend(loc="lower right") #plt.show() plt.savefig(ind+'_NN.png') ##metric 2: Confusion matrix Y_pred_tree = nn_clf.predict(X_test) confusion_matrix_tree = confusion_matrix(Y_test, Y_pred_tree) print(confusion_matrix_tree) print(classification_report(Y_test, Y_pred_tree)) #common standard to compare across models #f1_clf = f1_score(Y_test, Y_score_nn, average='binary') f1_clf = f1_score(Y_test, Y_score_nn, average='weighted') ##save model f_nn = open(ind+'_nn_clf_.pkl',"wb+") pickle.dump(nn_clf, f_nn) f_nn.close() f_nn_sc = open(ind+'_nn_scaler.pkl',"wb+") pickle.dump(scaler, f_nn_sc) f_nn_sc.close() return nn_clf, f1_clf '''************************************* 3. Execute the program #3a. filter the industry in scope ''' groupby_fld = 'sicsector' min_size = 30 df_tkr = pd.read_csv('industry_tickers_list.csv') dict_ind_tkr = {} f1_list = [] df_tkr_ind = pd.DataFrame() df_tkr_ind['cnt'] = df_tkr.groupby(groupby_fld)['ticker'].count() df_tkr_ind_select = df_tkr_ind[df_tkr_ind['cnt']>=min_size] list_scope = list(df_tkr_ind_select.index) #collect ticker in each industry for index, row in df_tkr.iterrows(): ind = row[groupby_fld] tkr = row['ticker'] if ind in list_scope: if ind in dict_ind_tkr: dict_ind_tkr[ind].append(tkr) else: dict_ind_tkr[ind] = [tkr] #loop through the dictionary - one industry at a time for ind, list_tkr in dict_ind_tkr.items(): df_X = pd.DataFrame({}) df_Y = pd.DataFrame({}) print(ind) #Go through the ticker list to Download data from source #loop through tickers from that industry for tkr in list_tkr: print(tkr) try: df_tmp,X_tmp,Y_tmp = download_tkr(tkr) except Exception: continue if len(df_X)==0: #df_all = df_tmp df_X = X_tmp df_Y = Y_tmp else: #df_all = pd.concat([df_all,df_tmp]) df_X = pd.concat([df_X,X_tmp]) df_Y = pd.concat([df_Y,Y_tmp]) ''' ************************************* 3b. prepare features for clustering for the industry ''' #convert to float and calc the difference across rows df_X = df_X.astype(float) df_Y = df_Y.astype(float) #remove zero records df_X = df_X.replace([np.inf ], 999999999) df_X = df_X.fillna(0) df_Y = df_Y.fillna(0) #neural network nn_clf,f1_score_temp = train_NN(df_X,df_Y,ind) f1_list.append(f1_score_temp) nn_clf.get_params() #decision tree try: tree_clf,f1_score_temp = train_tree(df_X,df_Y,ind) except Exception: continue f1_list.append(f1_score_temp) tree_clf.get_params() ''' #3c. Visualize the result ''' fields_list = df_tmp.columns print('********************') print('f1 of the models') print(f1_list) print('********************') #for visualization of decision tree x_feature_name = fields_list[6:-8] y_target_name = fields_list[-1] d_tree_out_file = 'decision_tree_'+ind dot_data = tree.export_graphviz(tree_clf, out_file=None, feature_names=x_feature_name, class_names=y_target_name, filled=True, rounded=True, special_characters=True) graph = graphviz.Source(dot_data) graph.render(d_tree_out_file)

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