Reset23/the-stack-v2-python · Datasets at Hugging Face

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import typing import os import logging import argparse import warnings import inspect try: import apex # noqa: F401 APEX_FOUND = True except ImportError: APEX_FOUND = False from .registry import registry from . import training from . import utils CallbackList = typing.Sequence[typing.Callable] OutputDict = typing.Dict[str, typing.List[typing.Any]] logger = logging.getLogger(__name__) warnings.filterwarnings( # Ignore pytorch warning about loss gathering 'ignore', message='Was asked to gather along dimension 0', module='torch.nn.parallel') def create_base_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description='Parent parser for tape functions', add_help=False) parser.add_argument('model_type', help='Base model class to run') parser.add_argument('--model_config_file', default=None, type=utils.check_is_file, help='Config file for model') parser.add_argument('--vocab_file', default=None, help='Pretrained tokenizer vocab file') parser.add_argument('--output_dir', default='./results', type=str) parser.add_argument('--no_cuda', action='store_true', help='CPU-only flag') parser.add_argument('--seed', default=42, type=int, help='Random seed to use') parser.add_argument('--local_rank', type=int, default=-1, help='Local rank of process in distributed training. ' 'Set by launch script.') parser.add_argument('--tokenizer', choices=['iupac', 'unirep'], default='iupac', help='Tokenizes to use on the amino acid sequences') parser.add_argument('--num_workers', default=8, type=int, help='Number of workers to use for multi-threaded data loading') parser.add_argument('--log_level', default=logging.INFO, choices=['DEBUG', 'INFO', 'WARN', 'WARNING', 'ERROR', logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR], help="log level for the experiment") parser.add_argument('--debug', action='store_true', help='Run in debug mode') return parser def create_train_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description='Run Training on the TAPE datasets', parents=[base_parser]) parser.add_argument('task', choices=list(registry.task_name_mapping.keys()), help='TAPE Task to train/eval on') parser.add_argument('--learning_rate', default=1e-4, type=float, help='Learning rate') parser.add_argument('--batch_size', default=1024, type=int, help='Batch size') parser.add_argument('--data_dir', default='./data', type=utils.check_is_dir, help='Directory from which to load task data') parser.add_argument('--num_train_epochs', default=10, type=int, help='Number of training epochs') parser.add_argument('--num_steps_per_epoch', default=-1, type=int, help='Number of steps per epoch') parser.add_argument('--num_log_iter', default=20, type=int, help='Number of training steps per log iteration') parser.add_argument('--fp16', action='store_true', help='Whether to use fp16 weights') parser.add_argument('--warmup_steps', default=10000, type=int, help='Number of learning rate warmup steps') parser.add_argument('--gradient_accumulation_steps', default=1, type=int, help='Number of forward passes to make for each backwards pass') parser.add_argument('--loss_scale', default=0, type=int, help='Loss scaling. Only used during fp16 training.') parser.add_argument('--max_grad_norm', default=1.0, type=float, help='Maximum gradient norm') parser.add_argument('--exp_name', default=None, type=str, help='Name to give to this experiment') parser.add_argument('--from_pretrained', default=None, type=str, help='Directory containing config and pretrained model weights') parser.add_argument('--log_dir', default='./logs', type=str) parser.add_argument('--eval_freq', type=int, default=1, help="Frequency of eval pass. A value <= 0 means the eval pass is " "not run") parser.add_argument('--save_freq', default='improvement', type=utils.int_or_str, help="How often to save the model during training. Either an integer " "frequency or the string 'improvement'") parser.add_argument('--patience', default=-1, type=int, help="How many epochs without improvement to wait before ending " "training") parser.add_argument('--resume_from_checkpoint', action='store_true', help="whether to resume training from the checkpoint") parser.add_argument('--val_check_frac', default=1.0, type=float, help="Fraction of validation to check") return parser def create_eval_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description='Run Eval on the TAPE Datasets', parents=[base_parser]) parser.add_argument('task', choices=list(registry.task_name_mapping.keys()), help='TAPE Task to train/eval on') parser.add_argument('from_pretrained', type=str, help='Directory containing config and pretrained model weights') parser.add_argument('--batch_size', default=1024, type=int, help='Batch size') parser.add_argument('--data_dir', default='./data', type=utils.check_is_dir, help='Directory from which to load task data') parser.add_argument('--metrics', default=[], help=f'Metrics to run on the result. ' f'Choices: {list(registry.metric_name_mapping.keys())}', nargs='*') parser.add_argument('--split', default='test', type=str, help='Which split to run on') return parser def create_embed_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = argparse.ArgumentParser( description='Embed a set of proteins with a pretrained model', parents=[base_parser]) parser.add_argument('data_file', type=str, help='File containing set of proteins to embed') parser.add_argument('out_file', type=str, help='Name of output file') parser.add_argument('from_pretrained', type=str, help='Directory containing config and pretrained model weights') parser.add_argument('--batch_size', default=1024, type=int, help='Batch size') parser.add_argument('--full_sequence_embed', action='store_true', help='If true, saves an embedding at every amino acid position ' 'in the sequence. Note that this can take a large amount ' 'of disk space.') parser.set_defaults(task='embed') return parser def create_distributed_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(add_help=False, parents=[base_parser]) # typing.Optional arguments for the launch helper parser.add_argument("--nnodes", type=int, default=1, help="The number of nodes to use for distributed " "training") parser.add_argument("--node_rank", type=int, default=0, help="The rank of the node for multi-node distributed " "training") parser.add_argument("--nproc_per_node", type=int, default=1, help="The number of processes to launch on each node, " "for GPU training, this is recommended to be set " "to the number of GPUs in your system so that " "each process can be bound to a single GPU.") parser.add_argument("--master_addr", default="127.0.0.1", type=str, help="Master node (rank 0)'s address, should be either " "the IP address or the hostname of node 0, for " "single node multi-proc training, the " "--master_addr can simply be 127.0.0.1") parser.add_argument("--master_port", default=29500, type=int, help="Master node (rank 0)'s free port that needs to " "be used for communciation during distributed " "training") return parser def create_model_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = argparse.ArgumentParser(add_help=False, parents=[base_parser]) parser.add_argument('--model_args', nargs=argparse.REMAINDER, default=None) return parser def run_train(args: typing.Optional[argparse.Namespace] = None, env=None) -> None: if env is not None: os.environ = env if args is None: base_parser = create_base_parser() train_parser = create_train_parser(base_parser) model_parser = create_model_parser(train_parser) args = model_parser.parse_args() if args.gradient_accumulation_steps < 1: raise ValueError( f"Invalid gradient_accumulation_steps parameter: " f"{args.gradient_accumulation_steps}, should be >= 1") if (args.fp16 or args.local_rank != -1) and not APEX_FOUND: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex " "to use distributed and fp16 training.") arg_dict = vars(args) arg_names = inspect.getfullargspec(training.run_train).args missing = set(arg_names) - set(arg_dict.keys()) if missing: raise RuntimeError(f"Missing arguments: {missing}") train_args = {name: arg_dict[name] for name in arg_names} training.run_train(**train_args) def run_eval(args: typing.Optional[argparse.Namespace] = None) -> typing.Dict[str, float]: if args is None: base_parser = create_base_parser() parser = create_eval_parser(base_parser) parser = create_model_parser(parser) args = parser.parse_args() if args.from_pretrained is None: raise ValueError("Must specify pretrained model") if args.local_rank != -1: raise ValueError("TAPE does not support distributed validation pass") arg_dict = vars(args) arg_names = inspect.getfullargspec(training.run_eval).args missing = set(arg_names) - set(arg_dict.keys()) if missing: raise RuntimeError(f"Missing arguments: {missing}") eval_args = {name: arg_dict[name] for name in arg_names} return training.run_eval(**eval_args) def run_embed(args: typing.Optional[argparse.Namespace] = None) -> None: if args is None: base_parser = create_base_parser() parser = create_embed_parser(base_parser) parser = create_model_parser(parser) args = parser.parse_args() if args.from_pretrained is None: raise ValueError("Must specify pretrained model") if args.local_rank != -1: raise ValueError("TAPE does not support distributed validation pass") arg_dict = vars(args) arg_names = inspect.getfullargspec(training.run_embed).args missing = set(arg_names) - set(arg_dict.keys()) if missing: raise RuntimeError(f"Missing arguments: {missing}") embed_args = {name: arg_dict[name] for name in arg_names} training.run_embed(**embed_args) def run_train_distributed(args: typing.Optional[argparse.Namespace] = None) -> None: """Runs distributed training via multiprocessing. """ if args is None: base_parser = create_base_parser() distributed_parser = create_distributed_parser(base_parser) distributed_train_parser = create_train_parser(distributed_parser) parser = create_model_parser(distributed_train_parser) args = parser.parse_args() # Define the experiment name here, instead of dealing with barriers and communication # when getting the experiment name exp_name = utils.get_expname(args.exp_name, args.task, args.model_type) args.exp_name = exp_name utils.launch_process_group( run_train, args, args.nproc_per_node, args.nnodes, args.node_rank, args.master_addr, args.master_port) if __name__ == '__main__': run_train_distributed()

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from typing import Iterator, List import pytest from confluent_kafka import OFFSET_BEGINNING, SerializingProducer, TopicPartition from confluent_kafka.admin import AdminClient, NewTopic from confluent_kafka.avro import AvroConsumer from confluent_kafka.schema_registry import SchemaRegistryClient from confluent_kafka.schema_registry.avro import AvroSerializer from musekafka import consumers from musekafka.message import stream as message_stream GOOD_SCHEMA = """ { "type": "record", "name": "Test", "namespace": "musekafka.test", "fields": [ { "name": "test", "type": ["null", "string"], "default": null } ] } """ class Goofed(Exception): """Marker exception to assert test success.""" @pytest.fixture(scope="module") def producer(broker_host: str, registry: SchemaRegistryClient) -> SerializingProducer: return SerializingProducer( { "bootstrap.servers": broker_host, "value.serializer": AvroSerializer(GOOD_SCHEMA, registry), } ) @pytest.fixture(scope="module") def topics( broker_host: str, admin: AdminClient, registry: SchemaRegistryClient ) -> Iterator[List[str]]: # Seed data for the tests. We do this up-front, as otherwise the tests # will be too slow. topic_futures = admin.create_topics( [NewTopic("musekafka_consumer_test", 1, 1)], operation_timeout=20 ) for _, future in topic_futures.items(): future.result() # Block until the topic gets created. topics = list(topic_futures.keys()) yield topics admin.delete_topics(topics, operation_timeout=20) subjects = [f"{topic}-value" for topic in topics] for subj in registry.get_subjects(): if subj in subjects: registry.delete_subject(subj) @pytest.fixture(scope="module") def messages(producer: SerializingProducer, topics: List[str]) -> List[dict]: test_messages = [{"test": f"TESTING!{i}"} for i in range(4)] test_messages.append({"test": "FAIL."}) test_messages.extend([{"test": f"TESTING AFTER!{i}"} for i in range(4)]) for topic in topics: for message in test_messages: producer.produce(topic, value=message) return test_messages @pytest.fixture def app(broker_host: str, registry_url: str, topics: List[str]) -> consumers.App: consumer_app = consumers.App( "testconsumers", [broker_host], topics, consumer_cls=AvroConsumer, schema_registry_url=registry_url, app_mode=False, from_beginning=True, close_after_consume=False, timeout=10, ) yield consumer_app consumer_app.close() def test_app_stream(app: consumers.App, messages: List[dict]): """App.stream consumes all data up to count.""" with app.stream(count=len(messages)) as stream: actual_messages = [msg.value() for msg in stream] assert actual_messages == messages def test_app_batch(app: consumers.App, messages: List[dict]): """App.batch consumes a batch of messages.""" with app.batch(batch_size=2, batch_count=2) as batches: first_batch, second_batch = list(batches) assert [m.value() for m in first_batch] == messages[:2] assert [m.value() for m in second_batch] == messages[2:4] def test_app_stream_fail(app: consumers.App, messages: List[dict]): """App.stream exits on exception, and does not commit offsets.""" actual_messages = [] with pytest.raises(Goofed): with app.stream() as stream: for msg in stream: if msg.value()["test"] == "FAIL.": raise Goofed("Dun goofed.") actual_messages.append(msg.value()) # Only got the first four messages before we enocuntered an error. fail_msg_idx = messages.index({"test": "FAIL."}) assert actual_messages == messages[:fail_msg_idx] with pytest.raises(Goofed): with app.stream() as stream: for msg in stream: if msg.value()["test"] == "FAIL.": raise Goofed("Dun goofed.") actual_messages.append(msg.value()) # Should not have received any additional messages. assert actual_messages == messages[:fail_msg_idx] # Let's just consume the remaining messages to check that # we do consume everything. with app.stream(count=len(messages[fail_msg_idx:]) + 1) as stream: for msg in stream: actual_messages.append(msg.value()) assert actual_messages == messages def test_app_batch_fail(app: consumers.App, messages: List[dict]): """App.batch exits on exception, and does not commit offsets.""" actual_messages = [] with pytest.raises(Goofed): with app.batch(batch_size=4) as batch: for msgs in batch: for msg in msgs: if msg.value()["test"] == "FAIL.": raise Goofed("Dun goofed.") actual_messages.append(msg.value()) # Only got the first four messages before we enocuntered an error. fail_msg_idx = messages.index({"test": "FAIL."}) assert actual_messages == messages[:fail_msg_idx] with pytest.raises(Goofed): with app.batch(batch_size=4) as batch: for msgs in batch: for msg in msgs: if msg.value()["test"] == "FAIL.": raise Goofed("Dun goofed.") actual_messages.append(msg.value()) # Should not have received any additional messages. assert actual_messages == messages[:fail_msg_idx] # Let's just consume the remaining messages to check that # we do consume everything. with app.batch(batch_size=1, batch_count=len(messages[fail_msg_idx:]) + 1) as batch: for msgs in batch: for msg in msgs: actual_messages.append(msg.value()) assert actual_messages == messages def test_app_consume_from_end( app: consumers.App, topics: List[str], producer: SerializingProducer, messages: List[dict] ): """App.consume consumes from the tail end of the topic on first startup.""" app.from_beginning = False actual_messages = [] with app.consume(message_stream, timeout=0.5) as msgs: actual_messages.extend(msgs) # Will not have consumed anything, since we are tailing. assert not actual_messages # Now we add an additional message. We should consume it. for topic in topics: producer.produce(topic, value={"test": "An additional message!"}) with app.consume(message_stream, count=1) as msgs: actual_messages.extend([msg.value() for msg in msgs]) assert actual_messages == [{"test": "An additional message!"}] def test_app_consume_topic_partitions(app: consumers.App, topics: List[str], messages: List[dict]): """App.consume consumes starting from the given topic partitions.""" offset = len(messages) - 3 app.from_beginning = False app.topic_partitions = [TopicPartition(topic, 0, offset) for topic in topics] expected_messages = messages[offset:] actual_messages = [] with app.consume(message_stream, count=len(expected_messages)) as msgs: actual_messages.extend([msg.value() for msg in msgs]) assert actual_messages == expected_messages def test_app_mode(app: consumers.App): """App.consume raises SystemExit in app mode.""" app.app_mode = True with pytest.raises(SystemExit): with app.consume(message_stream, count=1) as msgs: list(msgs) def test_app_mode_exception(app: consumers.App, messages: List[dict]): """App.consume raises SystemExit in app mode if an underlying exception occurs.""" app.app_mode = True with pytest.raises(SystemExit): with app.consume(message_stream, count=len(messages)) as msgs: for msg in msgs: if msg.value()["test"] == "FAIL.": raise Goofed("Dun goofed.") def test_app_start_for_assign(app: consumers.App): """App.start assigns the consumer.""" app.start() assert app.started assert len(app.consumer.assignment()) == 1 def test_app_start_for_subscribe( app: consumers.App, topics: List[str], producer: SerializingProducer ): """App.start subscribes the consumer.""" def on_assign(consumer, _partitions): for topic in topics: producer.produce(topic, value={"test": "Assigned."}) app.from_beginning = False # on_assign is only called for subscribe, so this is a good # check that we subscribed rather than made an explicit assignment. app.on_assign = on_assign app.start() assert app.started, "consumer not started." success = False for msg in message_stream(app.consumer, timeout=20): if msg.value()["test"] == "Assigned.": success = True break assert success, "consumer not assigned." def test_app_stop_for_assign(app: consumers.App): """App.stop unassigns the consumer.""" # Need to start consumer for stop to have any impact. app.start() # assign will block, so we can immediately call stop. app.stop() assert not app.started, "app is still running." assert not app.consumer.assignment(), "consumer still has an assignment" def test_app_stop_for_subscribe( app: consumers.App, topics: List[str], producer: SerializingProducer ): """App.stop unassigns the consumer.""" subscribed = False # Need to start consumer for stop to have any impact. def on_assign(_consumer, _partitions): nonlocal subscribed subscribed = True for topic in topics: producer.produce(topic, value={"test": "Assigned."}) def on_revoke(consumer, partitions): for topic in topics: consumer.assign([TopicPartition(topic, 0, OFFSET_BEGINNING)]) producer.produce(topic, value={"test": "Revoked."}) # setting from_beginning to False will put us in subscribe mode. app.from_beginning = False app.on_assign = on_assign app.on_revoke = on_revoke app.start() for msg in message_stream(app.consumer, timeout=20): # Don't really care which message we get. Just need something. break assert subscribed, "consumer never got subscribed." app.stop() assert not app.started, "app is still running." for msg in message_stream(app.consumer, timeout=20): # Don't really care which message we get. Just need something. if msg.value() == {"test": "Revoked."}: subscribed = False break assert not subscribed, "consumer is still subscribed."

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"""django_mariadb_travis URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path urlpatterns = [ path('admin/', admin.site.urls), ]

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# -*- coding: utf-8 -*- __version__ = '0.1.0' default_app_config = 'hubspot_blog.apps.HubspotBlogConfig'

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#!/usr/bin/env python from __future__ import print_function import argparse import string import struct import sys from base64 import decode import win32api import win32file import pywintypes def CTL_CODE(DeviceType, Function, Method, Access): return (DeviceType << 16) | (Access << 14) | (Function << 2) | Method def USB_CTL(id): # CTL_CODE(FILE_DEVICE_USB, (id), METHOD_BUFFERED, FILE_ANY_ACCESS) return CTL_CODE(0x22, id, 0, 0) IOCTL_USB_GET_ROOT_HUB_NAME = USB_CTL(258) # HCD_GET_ROOT_HUB_NAME IOCTL_USB_GET_NODE_INFORMATION = USB_CTL(258) # USB_GET_NODE_INFORMATION IOCTL_USB_GET_NODE_CONNECTION_INFORMATION = USB_CTL(259) # USB_GET_NODE_CONNECTION_INFORMATION IOCTL_USB_GET_NODE_CONNECTION_DRIVERKEY_NAME = USB_CTL(264) # USB_GET_NODE_CONNECTION_DRIVERKEY_NAME IOCTL_USB_GET_NODE_CONNECTION_NAME = USB_CTL(261) # USB_GET_NODE_CONNECTION_NAME IOCTL_USB_GET_DESCRIPTOR_FROM_NODE_CONNECTION = USB_CTL(260) # USB_GET_DESCRIPTOR_FROM_NODE_CONNECTION USB_CONFIGURATION_DESCRIPTOR_TYPE = 2 USB_STRING_DESCRIPTOR_TYPE = 3 USB_INTERFACE_DESCRIPTOR_TYPE = 4 MAXIMUM_USB_STRING_LENGTH = 255 def open_dev(name): try: handle = win32file.CreateFile(name, win32file.GENERIC_WRITE, win32file.FILE_SHARE_WRITE, None, win32file.OPEN_EXISTING, 0, None) except pywintypes.error as e: return None return handle def get_root_hub_name(handle): buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_ROOT_HUB_NAME, None, 6, None) act_len, _ = struct.unpack('LH', buf) buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_ROOT_HUB_NAME, None, act_len, None) return buf[4:].decode('utf-16le') def get_driverkey_name(handle, index): key_name = chr(index) + '\0' * 9 try: buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_NODE_CONNECTION_DRIVERKEY_NAME, key_name, 10, None) except pywintypes.error as e: print(e.strerror, index) sys.exit(1) _, act_len, _ = struct.unpack('LLH', buf) buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_NODE_CONNECTION_DRIVERKEY_NAME, key_name, act_len, None) return buf[8:].decode('utf-16le') def get_ext_hub_name(handle, index): hub_name = chr(index) + '\0' * 9 buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_NODE_CONNECTION_NAME, hub_name, 10, None) _, act_len, _ = struct.unpack('LLH', buf) buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_NODE_CONNECTION_NAME, hub_name, act_len, None) return buf[8:].decode('utf-16le') def get_str_desc(handle, conn_idx, str_idx): req = struct.pack('LBBHHH', conn_idx, 0, 0, (USB_STRING_DESCRIPTOR_TYPE << 8) | str_idx, win32api.GetSystemDefaultLangID(), 12 + MAXIMUM_USB_STRING_LENGTH) try: buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_DESCRIPTOR_FROM_NODE_CONNECTION, req, 12 + MAXIMUM_USB_STRING_LENGTH, None) except pywintypes.error as e: return 'ERROR: no String Descriptor for index {}'.format(str_idx) if len(buf) > 16: return buf[14:].decode('utf-16le') return '' def exam_hub(name, verbose, level): handle = open_dev(r'\\.\{}'.format(name)) if not handle: print('Failed to open device {}'.format(name)) return buf = win32file.DeviceIoControl(handle, IOCTL_USB_GET_NODE_INFORMATION, None, 76, None) print_hub_ports(handle, ord(buf[6]), verbose, level) handle.close() def print_str_or_hex(to_be_print): if all(c in string.printable for c in to_be_print): print('"{}"'.format(to_be_print)) return print('Hex: ', end='') for x in to_be_print: print('{:02x} '.format(ord(x)), end='') print('') def print_hub_ports(handle, num_ports, verbose, level): for idx in range(1, num_ports + 1): info = chr(idx) + '\0' * 34 try: buf = win32file.DeviceIoControl(handle, (IOCTL_USB_GET_NODE_CONNECTION_INFORMATION), info, 34 + 11 * 30, None) except pywintypes.error as e: print(e.winerror, e.funcname, e.strerror) return _, vid, pid, vers, manu, prod, seri, _, ishub, _, stat = struct.unpack('=12sHHHBBB3s?6sL', buf[:35]) if ishub: if verbose: print('{} [Port{}] {}'.format(' ' * level, idx, 'USB Hub')) exam_hub(get_ext_hub_name(handle, idx), verbose, level) elif stat == 0 and verbose: print('{} [Port{}] {}'.format(' ' * level, idx, 'NoDeviceConnected')) elif stat == 1: if verbose or (manu != 0 or prod != 0 or seri != 0): print('{} [Port{}] {}'.format(' ' * level, idx, get_driverkey_name(handle, idx))) print('{} Vendor ID: 0x{:04X}'.format(' ' * level, vid)) print('{} Product ID: 0x{:04X}'.format(' ' * level, pid)) print('{} Device BCD: 0x{:04X}'.format(' ' * level, vers)) if manu != 0: print('{} Manufacturer (0x{:x}) -> '.format(' ' * level, manu), end='') print_str_or_hex(get_str_desc(handle, idx, manu)) if prod != 0: print('{} Product (0x{:x}) -> '.format(' ' * level, prod), end='') print_str_or_hex(get_str_desc(handle, idx, prod)) if seri != 0: print('{} Serial No (0x{:x}) -> '.format(' ' * level, seri), end='') print_str_or_hex(get_str_desc(handle, idx, seri)) def main(): parser = argparse.ArgumentParser() parser.add_argument('-v', '--verbose', action='store_true', help="Increase output verbosity.") args = parser.parse_args() for i in range(10): name = r"\\.\HCD{}".format(i) handle = open_dev(name) if not handle: continue root = get_root_hub_name(handle) print('{}RootHub: {}'.format('\n' if i != 0 else '', root)) # ext = get_ext_hub_name(handle, index) # print('{}ExtHub: {}'.format('\n' if i != 0 else '', ext)) dev_name = r'\\.\{}'.format(root) dev_handle = open_dev(dev_name) if not dev_handle: print('Failed to open device {}'.format(dev_name)) continue buf = win32file.DeviceIoControl(dev_handle, IOCTL_USB_GET_NODE_INFORMATION, None, 76, None) num = buf[6] print_hub_ports(dev_handle, num, args.verbose, 0) dev_handle.close() handle.close() if __name__ == '__main__': main()

[ "sunduoze@163.com" ]

sunduoze@163.com

9862630e7b2d92ec61b1b3fafa2d66bf2c38aecb

f275aead39428664296b54750fdb577799c4a928

/delete_motion_ve_votes.py

ef3cd0d743e0ffee14705b10d041067f11ddfce9

[]

no_license

KohoVolit/scraper-psp.cz

6ec46e8e08e60e9bbc0027c69390af7e70adc51c

f2f05522a758c7115fbe53da5b106f74054b95c5

refs/heads/master

2020-12-24T15:49:52.178424

2018-04-04T22:27:10

22,727,536

1

0

null

UTF-8

Python

false

235

py

import vpapi import authentication vpapi.parliament('cz/psp') vpapi.authorize(authentication.username,authentication.password) vpapi.timezone('Europe/Prague') vpapi.delete("votes") vpapi.delete("vote-events") vpapi.delete("motions")

[ "michal.skop@kohovolit.eu" ]

michal.skop@kohovolit.eu

39b4addcc99726b50837e3fd9fdbcd6e86573415

dd456df9c9e3b327463d8a729ab837ed262e4dce

/venv/Scripts/pip3.8-script.py

f255ad31ef96f95ed32417b9f68fd5e0943f719b

[]

no_license

Lelethu-Ndidi/secondDataTypeExercise

5262f39099fd4895d2e75f66808f5899e7856dcb

64b0c485ed1b56f3d6a25a4fe1196957a453f396

refs/heads/master

2021-03-07T11:20:12.396861

2020-03-10T11:55:24

2020-03-10T12:36:06

246,261,644

0

null

UTF-8

Python

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#!"C:\Users\User\PycharmProjects\DataType Ex2\venv\Scripts\python.exe" # EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3.8' __requires__ = 'pip==19.0.3' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==19.0.3', 'console_scripts', 'pip3.8')() )

[ "lelethundidi@gmail.com" ]

lelethundidi@gmail.com

c728711407cbf93363708bc7ebf19bea2a3b104a

6568474f411000b7863ac15071a51136fd0558d8

/ROC-probabilities_Ricardo Zamora Mennigke_Calibracion.py

e9e2fc06654eb836c28e1db43e2a76f365542382

[]

no_license

zamoraricardo15/Python-Model-Calibration-Methods

ea8769751aab72b61c9b12a2fd4e219b9dc655c1

d60a0560bab3c93764428218ee21479d452e8919

refs/heads/main

2023-06-20T08:13:41.747289

2021-07-17T15:52:21

386,977,037

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# -*- coding: utf-8 -*- """ Created on Thu Jul 23 23:20:41 2020 @author: rzamoram """ ##Pregunta 1 import numpy as np import pandas as pd import random as rd from sklearn.metrics import confusion_matrix from sklearn.metrics import roc_curve from sklearn.metrics import roc_auc_score import matplotlib.pyplot as plt import os from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import train_test_split from pandas import DataFrame from matplotlib import colors as mcolors import seaborn as sns pasada = os.getcwd() os.chdir("C:/Users/rzamoram/OneDrive - Intel Corporation/Documents/Machine Learning/Métodos Supervisados con Python/Clase 01") print(os.getcwd()) datos = pd.read_csv('tumores.csv',delimiter=',',decimal=".") datos['imagen'] = datos['imagen'].astype('category') print(datos.shape) print(datos.head()) print(datos.info()) def distribucion_variable_predecir(data:DataFrame,variable_predict:str): colors = list(dict(**mcolors.CSS4_COLORS)) df = pd.crosstab(index=data[variable_predict],columns="valor") / data[variable_predict].count() fig = plt.figure(figsize=(10,9)) g = fig.add_subplot(111) countv = 0 titulo = "Distribución de la variable %s" % variable_predict for i in range(df.shape[0]): g.barh(1,df.iloc[i],left = countv, align='center',color=colors[11+i],label= df.iloc[i].name) countv = countv + df.iloc[i] vals = g.get_xticks() g.set_xlim(0,1) g.set_yticklabels("") g.set_title(titulo) g.set_ylabel(variable_predict) g.set_xticklabels(['{:.0%}'.format(x) for x in vals]) countv = 0 for v in df.iloc[:,0]: g.text(np.mean([countv,countv+v]) - 0.03, 1 , '{:.1%}'.format(v), color='black', fontweight='bold') countv = countv + v g.legend(loc='upper center', bbox_to_anchor=(1.08, 1), shadow=True, ncol=1) distribucion_variable_predecir(datos,"tipo") def indices_general(MC, nombres = None): precision_global = np.sum(MC.diagonal()) / np.sum(MC) error_global = 1 - precision_global precision_categoria = pd.DataFrame(MC.diagonal()/np.sum(MC,axis = 1)).T precision_positiva = MC[1][1]/(MC[1][1] + MC[1][0]) precision_negativa = MC[0][0]/(MC[0][0] + MC[0][1]) falsos_positivos = 1 - precision_negativa falsos_negativos = 1 - precision_positiva asertividad_positiva = MC[1][1]/(MC[0][1] + MC[1][1]) asertividad_negativa = MC[0][0]/(MC[0][0] + MC[1][0]) if nombres!=None: precision_categoria.columns = nombres return {"Matriz de Confusión":MC, "Precisión Global":precision_global, "Error Global":error_global, "Precisión por categoría":precision_categoria, "Precision Positiva (PP)": precision_positiva, "Precision Negativa (PN)":precision_negativa, "Falsos Positivos(FP)": falsos_positivos, "Falsos Negativos (FN)": falsos_negativos, "Asertividad Positiva (AP)": asertividad_positiva, "Asertividad Negativa (NP)": asertividad_negativa} def poder_predictivo_categorica(data:DataFrame, var:str, variable_predict:str): df = pd.crosstab(index= data[var],columns=data[variable_predict]) df = df.div(df.sum(axis=1),axis=0) titulo = "Distribución de la variable %s según la variable %s" % (var,variable_predict) g = df.plot(kind='barh',stacked=True,legend = True, figsize = (10,9), \ xlim = (0,1),title = titulo, width = 0.8) vals = g.get_xticks() g.set_xticklabels(['{:.0%}'.format(x) for x in vals]) g.legend(loc='upper center', bbox_to_anchor=(1.08, 1), shadow=True, ncol=1) for bars in g.containers: plt.setp(bars, width=.9) for i in range(df.shape[0]): countv = 0 for v in df.iloc[i]: g.text(np.mean([countv,countv+v]) - 0.03, i , '{:.1%}'.format(v), color='black', fontweight='bold') countv = countv + v def poder_predictivo_numerica(data:DataFrame, var:str, variable_predict:str): sns.FacetGrid(data, hue=variable_predict, height=6).map(sns.kdeplot, var, shade=True).add_legend() from sklearn.ensemble import RandomForestClassifier def plotROC(real, prediccion, color = "red", label = None): fp_r, tp_r, umbral = roc_curve(real, prediccion) plt.plot(fp_r, tp_r, lw = 1, color = color, label = label) plt.plot([0, 1], [0, 1], lw = 1, color = "black") plt.xlabel("Tasa de Falsos Positivos") plt.ylabel("Tasa de Verdaderos Positivos") plt.title("Curva ROC") X = datos.iloc[:,1:17] print(X.head()) y = datos.iloc[:,17:18] print(y.head()) X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=0) instancia_svm = SVC(kernel = "rbf",gamma='scale',probability=True) instancia_svm.fit(X_train, y_train.iloc[:,0].values) #print("Probabilidad del No y del Si:\n",instancia_svm.predict_proba(X_test)) probabilidad = instancia_svm.predict_proba(X_test)[:, 1] #print("Probabilidad de Si (o sea del 1):\n",probabilidad) # Gráfico de la Curva ROC plt.figure(figsize=(10,10)) plotROC(y_test, probabilidad) instancia_bosques = RandomForestClassifier(n_estimators = 300, max_features = 3) instancia_bosques.fit(X_train, y_train.iloc[:,0].values) # Genera la Curva ROC para Bosques plt.figure(figsize=(10,10)) plotROC(y_test, instancia_bosques.predict_proba(X_test)[:, 1], color = "blue") instancia_bosques = RandomForestClassifier(n_estimators = 300, max_features = 3) instancia_bosques.fit(X_train, y_train.iloc[:,0].values) from sklearn.neighbors import KNeighborsClassifier instancia_knn = KNeighborsClassifier(n_neighbors=5) instancia_knn.fit(X_train, y_train.iloc[:,0].values) #plt.figure(figsize=(10,10)) #plotROC(y_test, instancia_knn.predict_proba(X_test)[:, 1], color = "blue") instancia_tree = DecisionTreeClassifier() instancia_tree.fit(X_train, y_train.iloc[:,0].values) instancia_ADA = AdaBoostClassifier(n_estimators=5) instancia_ADA.fit(X_train, y_train.iloc[:,0].values) from sklearn.ensemble import GradientBoostingClassifier instancia_XGB = GradientBoostingClassifier(n_estimators=5) instancia_XGB.fit(X_train, y_train.iloc[:,0].values) from sklearn.neural_network import MLPClassifier instancia_classifier = MLPClassifier(solver='lbfgs') instancia_classifier.fit(X_train, y_train.iloc[:,0].values) from sklearn.naive_bayes import GaussianNB instancia_bayes = GaussianNB() instancia_bayes.fit(X_train, y_train.iloc[:,0].values) from sklearn.discriminant_analysis import LinearDiscriminantAnalysis instancia_lda = LinearDiscriminantAnalysis(solver = 'lsqr', shrinkage = 'auto') instancia_lda.fit(X_train, y_train.iloc[:,0].values) from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis instancia_qda = QuadraticDiscriminantAnalysis() instancia_qda.fit(X_train, y_train.iloc[:,0].values) plt.figure(figsize=(10,10)) plotROC(y_test, instancia_svm.predict_proba(X_test)[:, 1], label = "SVM") plotROC(y_test, instancia_bosques.predict_proba(X_test)[:, 1], color = "blue", label= "Bosques Aleatorios") plotROC(y_test, instancia_knn.predict_proba(X_test)[:, 1], color = "red", label= "KNN") plotROC(y_test, instancia_tree.predict_proba(X_test)[:, 1], color = "#67E568", label= "Arboles Decision") #plotROC(y_test, instancia_knn.predict_proba(X_test)[:, 1], color = "#257F27", label= "KNN") plotROC(y_test, instancia_ADA.predict_proba(X_test)[:, 1], color = "#08420D", label= "ADA") plotROC(y_test, instancia_XGB.predict_proba(X_test)[:, 1], color = "#FFF000", label= "XGBoosting") plotROC(y_test, instancia_classifier.predict_proba(X_test)[:, 1], color = "#FFB62B", label= "Redes neuronales") plotROC(y_test, instancia_bayes.predict_proba(X_test)[:, 1], color = "#E56124", label= "Bayes") plotROC(y_test, instancia_lda.predict_proba(X_test)[:, 1], color = "#E53E30", label= "LDA") #plotROC(y_test, instancia_qda.predict_proba(X_test)[:, 1], color = "#7F2353", label= "QDA") plt.legend(loc = "lower right") bosques_area = roc_auc_score(y_test, instancia_bosques.predict_proba(X_test)[:, 1]) svm_area = roc_auc_score(y_test, instancia_svm.predict_proba(X_test)[:, 1]) knn_area = roc_auc_score(y_test, instancia_knn.predict_proba(X_test)[:, 1]) tree_area = roc_auc_score(y_test, instancia_tree.predict_proba(X_test)[:, 1]) knn_area = roc_auc_score(y_test, instancia_knn.predict_proba(X_test)[:, 1]) ADA_area = roc_auc_score(y_test, instancia_ADA.predict_proba(X_test)[:, 1]) XGB_area = roc_auc_score(y_test, instancia_XGB.predict_proba(X_test)[:, 1]) red_area = roc_auc_score(y_test, instancia_classifier.predict_proba(X_test)[:, 1]) bayes_area = roc_auc_score(y_test, instancia_bayes.predict_proba(X_test)[:, 1]) lda_area = roc_auc_score(y_test, instancia_lda.predict_proba(X_test)[:, 1]) #qda_area = roc_auc_score(y_test, instancia_qda.predict_proba(X_test)[:, 1]) print("Área bajo la curva ROC en Bosques Aleatorios: {:.3f}".format(bosques_area)) print("Área bajo la curva ROC en KNN: {:.3f}".format(knn_area)) print("Área bajo la curva ROC en Arboles Decision: {:.3f}".format(tree_area)) print("Área bajo la curva ROC en KNN: {:.3f}".format(knn_area)) print("Área bajo la curva ROC en ADA: {:.3f}".format(ADA_area)) print("Área bajo la curva ROC en XGB: {:.3f}".format(XGB_area)) print("Área bajo la curva ROC en red: {:.3f}".format(red_area)) print("Área bajo la curva ROC en bayes: {:.3f}".format(bayes_area)) print("Área bajo la curva ROC en LDA: {:.3f}".format(lda_area)) #print("Área bajo la curva ROC en QDA: {:.3f}".format(qda_area)) from sklearn.svm import SVC from sklearn.model_selection import train_test_split ##Pregunta 2 import os import pandas as pd pasada = os.getcwd() os.chdir("C:/Users/rzamoram/OneDrive - Intel Corporation/Documents/Machine Learning/Métodos NO Supervisados con Python/Clase 1") os.getcwd() ejemplo10 = pd.read_csv("SAheart.csv", delimiter = ';', decimal = ".", header = 0, index_col = 0) print(ejemplo10.head()) datos = pd.DataFrame(ejemplo10) def recodificar(col, nuevo_codigo): col_cod = pd.Series(col, copy=True) for llave, valor in nuevo_codigo.items(): col_cod.replace(llave, valor, inplace=True) return col_cod datos["famhist"] = recodificar(datos["famhist"], {'Present':1,'Absent':2}) datos["chd"] = recodificar(datos["chd"], {'No':0,'Si':1}) print(datos.head()) print(datos.dtypes) # Conviertiendo la variables en Dummy datos_dummy = pd.get_dummies(datos) print(datos_dummy.head()) print(datos_dummy.dtypes) X = datos.iloc[:,:8] print(X.head()) y = datos.iloc[:,8:9] print(y.head()) X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.80, random_state = 0) # bosques aleatorios modelo instancia_bosques = RandomForestClassifier(n_estimators = 300, max_features = 3) instancia_bosques.fit(X_train, y_train.iloc[:,0].values) prediccion = instancia_bosques.predict(X_test) MC = confusion_matrix(y_test, prediccion) indices = indices_general(MC,list(np.unique(y))) for k in indices: print("\n%s:\n%s"%(k,str(indices[k]))) print("Probabilidad del No y del Si:\n",instancia_bosques.predict_proba(X_test)) probabilidad = instancia_bosques.predict_proba(X_test)[:, 1] print("Probabilidad de Si (o sea del 1):\n",probabilidad) # regla de decision corte = [0.501, 0.502, 0.503, 0.504, 0.505, 0.506, 0.507, 0.508, 0.509] for c in corte: print("===========================") print("Probabilidad de Corte: ",c) prediccion = np.where(probabilidad > c, 1, 0) # Calidad de la predicción MC = confusion_matrix(y_test, prediccion) indices = indices_general(MC,list(np.unique(y))) for k in indices: print("\n%s:\n%s"%(k,str(indices[k]))) # XGB modelo instancia_XGB = GradientBoostingClassifier(n_estimators=5) instancia_XGB.fit(X_train, y_train.iloc[:,0].values) prediccion = instancia_XGB.predict(X_test) MC = confusion_matrix(y_test, prediccion) indices = indices_general(MC,list(np.unique(y))) for k in indices: print("\n%s:\n%s"%(k,str(indices[k]))) #print("Probabilidad del No y del Si:\n",instancia_XGB.predict_proba(X_test)) probabilidad = instancia_XGB.predict_proba(X_test)[:, 1] #print("Probabilidad de Si (o sea del 1):\n",probabilidad) # regla de decision corte = [0.501, 0.502, 0.503, 0.504, 0.505, 0.506, 0.507, 0.508, 0.509, 0.6, 0.601, 0.602, 0.603, 0.604, 0.605, 0.606, 0.607, 0.608, 0.609] for c in corte: print("===========================") print("Probabilidad de Corte: ",c) prediccion = np.where(probabilidad > c, 1, 0) # Calidad de la predicción MC = confusion_matrix(y_test, prediccion) indices = indices_general(MC,list(np.unique(y))) for k in indices: print("\n%s:\n%s"%(k,str(indices[k]))) ##Pregunta 3 from sklearn.metrics import roc_auc_score import matplotlib.pyplot as plt Clase = np.array([1,0,1,0,0,1,1,0,1,1]) Score = np.array([0.8,0.7,0.65,0.6,0.5,0.35,0.3,0.25,0.2,0.1]) # Graficamos ROC con usando roc_curve de sklearn fp_r, tp_r, umbral = roc_curve(Clase, Score) plt.figure(figsize=(10,10)) plt.plot(fp_r, tp_r, lw = 1, color = "red") plt.plot([0, 1], [0, 1], lw = 1, color = "black") plt.xlabel("Tasa de Falsos Positivos") plt.ylabel("Tasa de Verdaderos Positivos") plt.title("Curva ROC") # Graficamos puntos con el siguiente algoritmo i = 1 # Contador FP_r = -1 # Para que entre al condicional en la primera iteración TP_r = -1 # Para que entre al condicional en la primera iteración # linspace genera una sucesión de 201 números del 1 al 0 que equivale a una sucesión del 1 al 0 con paso de 0.005 for Umbral in np.linspace(1, 0, 201): Prediccion = np.where(Score >= Umbral, 1, 0) MC = confusion_matrix(Clase, Prediccion) if (FP_r != MC[0, 1] / sum(MC[0, ])) | (TP_r != MC[1, 1] / sum(MC[1, ])): FP_r = MC[0, 1] / sum(MC[0, ]) # Tasa de Falsos Positivos TP_r = MC[1, 1] / sum(MC[1, ]) # Tasa de Verdaderos Positivos # Graficamos punto plt.plot(FP_r, TP_r, "o", mfc = "none", color = "blue") plt.annotate(round(Umbral, 3), (FP_r + 0.01, TP_r - 0.02)) # Imprimimos resultado print("=====================") print("Punto i = ", i, "\n") print("Umbral = T = ", round(Umbral, 3), "\n") print("MC =") print(MC, "\n") print("Tasa FP = ", round(FP_r, 2), "\n") print("Tasa TP = ", round(TP_r, 2)) i = i + 1 ####### Clase = np.array([1,0,1,0,0,1,1,0,1,1]) Score = np.array([0.8,0.7,0.65,0.6,0.5,0.35,0.3,0.25,0.2,0.1]) fp_r, tp_r, umbral = roc_curve(Clase, Score) plt.figure(figsize=(10,10)) plt.plot(fp_r, tp_r, lw = 1, color = "red") plt.plot([0, 1], [0, 1], lw = 1, color = "black") plt.xlabel("Tasa de Falsos Positivos") plt.ylabel("Tasa de Verdaderos Positivos") plt.title("Curva ROC") # Aquí se inicializan para que de igual a la corrida a pie Umbral = min(Score) Paso = (max(Score) - min(Score)) / 10 N = 10 # ceros P = 10 # unos TP = 0 FP = 0 for i in range(0, 10): if Score[i] > Umbral: if Clase[i] == 1: TP = TP + 1 else: FP = FP + 1 else: if Clase[i] == 0: FP = FP + 1 else: TP = TP + 1 # Graficamos punto plt.plot(FP / N, TP / P, "o", mfc = "none", color = "blue") plt.annotate(i + 1, (FP / N + 0.01, TP / P - 0.02)) Umbral = Umbral + Paso

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import numpy as np import matplotlib.pyplot as plt import os import scipy.io as sio import hw3_functions as funct plt.ion() plt.style.use('ggplot') # define data path datapath = '../data/' ########### TEST 1 ############# # define filenames cam11_file = 'cam1_1.mat' cam21_file = 'cam2_1.mat' cam31_file = 'cam3_1.mat' # load mat files into python cam11 = sio.loadmat(os.path.join(datapath, cam11_file)) cam21 = sio.loadmat(os.path.join(datapath, cam21_file)) cam31 = sio.loadmat(os.path.join(datapath, cam31_file)) # pull camera data from mat files vid11 = cam11['vidFrames1_1'] vid21 = cam21['vidFrames2_1'] vid31 = cam31['vidFrames3_1'] # put camera data into greyscale vid11_grey = np.asarray([funct.rgb2grey(vid11[:,:,:,i]) for i in range(len(vid11[0,0,0,:]))]) vid21_grey = np.asarray([funct.rgb2grey(vid21[:,:,:,i]) for i in range(len(vid21[0,0,0,:]))]) vid31_grey = np.asarray([funct.rgb2grey(vid31[:,:,:,i]) for i in range(len(vid31[0,0,0,:]))]) # find indices of mass from each frame mindx11, mindy11 = funct.find_ind_and_plot(vid11_grey, 'plots/vid11_animation/fig', xmin=250, xmax=400, plot=False) mindx21, mindy21 = funct.find_ind_and_plot(vid21_grey, 'plots/vid21_animation/fig', xmin=200, xmax=400, plot=False) mindx31, mindy31 = funct.find_ind_and_plot(vid31_grey, 'plots/vid31_animation/fig', xmin=200, xmax=600, plot=False) # plot funct.plot_positions(mindx11, mindy11, mindx21, mindy21, mindx31, mindy31, '1') funct.plot_positions(mindx11, mindy11, mindx21[10:], mindy21[10:], mindx31, mindy31, '1_shifted') minlen = np.min([len(mindx11), len(mindx21[10:]), len(mindx31)]) X = np.zeros((6, minlen)) X[0,:] = mindx11[:minlen] X[1,:] = mindy11[:minlen] X[2,:] = mindx21[10:10+minlen] X[3,:] = mindy21[10:10+minlen] X[4,:] = mindx31[:minlen] X[5,:] = mindy31[:minlen] X = X - np.expand_dims(np.mean(X, axis=-1), axis=-1) ## python vs. matlab differences in svd function: https://stackoverflow.com/questions/50930899/svd-command-in-python-v-s-matlab [m, n] = X.shape U, Sdiag, VH = np.linalg.svd(X) V = VH.T pca1 = np.matmul(U.T, X) percentage1 = Sdiag**2/np.sum(Sdiag**2) ########### TEST 2 ############# # define filenames cam12_file = 'cam1_2.mat' cam22_file = 'cam2_2.mat' cam32_file = 'cam3_2.mat' # load mat files into python cam12 = sio.loadmat(os.path.join(datapath, cam12_file)) cam22 = sio.loadmat(os.path.join(datapath, cam22_file)) cam32 = sio.loadmat(os.path.join(datapath, cam32_file)) # pull camera data from mat files vid12 = cam12['vidFrames1_2'] vid22 = cam22['vidFrames2_2'] vid32 = cam32['vidFrames3_2'] # put camera data into greyscale vid12_grey = np.asarray([funct.rgb2grey(vid12[:,:,:,i]) for i in range(len(vid12[0,0,0,:]))]) vid22_grey = np.asarray([funct.rgb2grey(vid22[:,:,:,i]) for i in range(len(vid22[0,0,0,:]))]) vid32_grey = np.asarray([funct.rgb2grey(vid32[:,:,:,i]) for i in range(len(vid32[0,0,0,:]))]) # find indices of mass from each frame mindx12, mindy12 = funct.find_ind_and_plot(vid12_grey, 'plots/vid12_animation/fig', xmin=250, xmax=500, plot=False) mindx22, mindy22 = funct.find_ind_and_plot(vid22_grey, 'plots/vid22_animation/fig', xmin=200, xmax=450, plot=False) mindx32, mindy32 = funct.find_ind_and_plot(vid32_grey, 'plots/vid32_animation/fig', xmin=225, xmax=600, plot=False) # plot funct.plot_positions(mindx12, mindy12, mindx22, mindy22, mindx32, mindy32, '2') # svd calculation minlen = np.min([len(mindx12), len(mindx22), len(mindx32)]) X = np.zeros((6, minlen)) X[0,:] = mindx12[:minlen] X[1,:] = mindy12[:minlen] X[2,:] = mindx22[:minlen] X[3,:] = mindy22[:minlen] X[4,:] = mindx32[:minlen] X[5,:] = mindy32[:minlen] X = X - np.expand_dims(np.mean(X, axis=-1), axis=-1) [m, n] = X.shape U, Sdiag, VH = np.linalg.svd(X) V = VH.T #Xrank1_2 = np.matmul(np.expand_dims(U[:,0]*Sdiag[0],axis=-1), np.expand_dims(V[:,0], axis=-1).T) pca2 = np.matmul(U.T, X) percentage2 = Sdiag**2/np.sum(Sdiag**2) ########### TEST 3 ############# # define filenames cam13_file = 'cam1_3.mat' cam23_file = 'cam2_3.mat' cam33_file = 'cam3_3.mat' # load mat files into python cam13 = sio.loadmat(os.path.join(datapath, cam13_file)) cam23 = sio.loadmat(os.path.join(datapath, cam23_file)) cam33 = sio.loadmat(os.path.join(datapath, cam33_file)) # pull camera data from mat files vid13 = cam13['vidFrames1_3'] vid23 = cam23['vidFrames2_3'] vid33 = cam33['vidFrames3_3'] # put camera data into greyscale vid13_grey = np.asarray([funct.rgb2grey(vid13[:,:,:,i]) for i in range(len(vid13[0,0,0,:]))]) vid23_grey = np.asarray([funct.rgb2grey(vid23[:,:,:,i]) for i in range(len(vid23[0,0,0,:]))]) vid33_grey = np.asarray([funct.rgb2grey(vid33[:,:,:,i]) for i in range(len(vid33[0,0,0,:]))]) # find indices of mass from each frame mindx13, mindy13 = funct.find_ind_and_plot(vid13_grey, 'plots/vid13_animation/fig', xmin=250, xmax=450, plot=False) mindx23, mindy23 = funct.find_ind_and_plot(vid23_grey, 'plots/vid23_animation/fig', xmin=200, xmax=450, ymin=200, ymax=415, plot=False, restricty=True) mindx33, mindy33 = funct.find_ind_and_plot(vid33_grey, 'plots/vid33_animation/fig', xmin=225, xmax=600, plot=False) # plot funct.plot_positions(mindx13, mindy13, mindx23, mindy23, mindx33, mindy33, '3') # svd calculation minlen = np.min([len(mindx13), len(mindx23), len(mindx33)]) X = np.zeros((6, minlen)) X[0,:] = mindx13[:minlen] X[1,:] = mindy13[:minlen] X[2,:] = mindx23[:minlen] X[3,:] = mindy23[:minlen] X[4,:] = mindx33[:minlen] X[5,:] = mindy33[:minlen] X = X - np.expand_dims(np.mean(X, axis=-1), axis=-1) [m, n] = X.shape U, Sdiag, VH = np.linalg.svd(X) V = VH.T pca3 = np.matmul(U.T, X) percentage3 = Sdiag**2/np.sum(Sdiag**2) ########### TEST 4 ############# # define filenames cam14_file = 'cam1_4.mat' cam24_file = 'cam2_4.mat' cam34_file = 'cam3_4.mat' # load mat files into python cam14 = sio.loadmat(os.path.join(datapath, cam14_file)) cam24 = sio.loadmat(os.path.join(datapath, cam24_file)) cam34 = sio.loadmat(os.path.join(datapath, cam34_file)) # pull camera data from mat files vid14 = cam14['vidFrames1_4'] vid24 = cam24['vidFrames2_4'] vid34 = cam34['vidFrames3_4'] # put camera data into greyscale vid14_grey = np.asarray([funct.rgb2grey(vid14[:,:,:,i]) for i in range(len(vid14[0,0,0,:]))]) vid24_grey = np.asarray([funct.rgb2grey(vid24[:,:,:,i]) for i in range(len(vid24[0,0,0,:]))]) vid34_grey = np.asarray([funct.rgb2grey(vid34[:,:,:,i]) for i in range(len(vid34[0,0,0,:]))]) # find indices of mass from each frame mindx14, mindy14 = funct.find_ind_and_plot(vid14_grey, 'plots/vid14_animation/fig', xmin=300, xmax=500, plot=False) mindx24, mindy24 = funct.find_ind_and_plot(vid24_grey, 'plots/vid24_animation/fig', xmin=215, xmax=400, plot=False) mindx34, mindy34 = funct.find_ind_and_plot(vid34_grey, 'plots/vid34_animation/fig', xmin=200, xmax=600, plot=False) # plot funct.plot_positions(mindx14, mindy14, mindx24, mindy24, mindx34, mindy34, '4') # svd calculation minlen = np.min([len(mindx14), len(mindx24), len(mindx34)]) X = np.zeros((6, minlen)) X[0,:] = mindx14[:minlen] X[1,:] = mindy14[:minlen] X[2,:] = mindx24[:minlen] X[3,:] = mindy24[:minlen] X[4,:] = mindx34[:minlen] X[5,:] = mindy34[:minlen] X = X - np.expand_dims(np.mean(X, axis=-1), axis=-1) [m, n] = X.shape U, Sdiag, VH = np.linalg.svd(X) V = VH.T pca4 = np.matmul(U.T, X) percentage4 = Sdiag**2/np.sum(Sdiag**2) ### analysis figures ### fig, ax = plt.subplots(figsize=(12,7), nrows=2, ncols=2, sharex=False, sharey=True) ax[0,0].plot(mindx11-np.mean(mindx11), '--', color='tab:blue') ax[0,0].plot(mindy11-np.mean(mindy11),label='Camera 1', color='tab:blue') ax[0,0].plot(mindx21-np.mean(mindx21), '--', color='tab:green') ax[0,0].plot(mindy21-np.mean(mindy21), label='Camera 2', color='tab:green') ax[0,0].plot(mindx31-np.mean(mindx31), '--', color='tab:red') ax[0,0].plot(mindy31-np.mean(mindy31), label='Camera 3', color='tab:red') ax[0,0].legend(loc='best') ax[0,0].set_title('Ideal Case') ax[0,1].plot(mindx12-np.mean(mindx12), '--', color='tab:blue') ax[0,1].plot(mindy12-np.mean(mindy12),label='Camera 1', color='tab:blue') ax[0,1].plot(mindx22-np.mean(mindx22), '--', color='tab:green') ax[0,1].plot(mindy22-np.mean(mindy22), label='Camera 2', color='tab:green') ax[0,1].plot(mindx32-np.mean(mindx32), '--', color='tab:red') ax[0,1].plot(mindy32-np.mean(mindy32), label='Camera 3', color='tab:red') ax[0,1].set_title('Noisy Case') ax[1,0].plot(mindx13-np.mean(mindx13), '--', color='tab:blue') ax[1,0].plot(mindy13-np.mean(mindy13),label='Camera 1', color='tab:blue') ax[1,0].plot(mindx23-np.mean(mindx23), '--', color='tab:green') ax[1,0].plot(mindy23-np.mean(mindy23), label='Camera 2', color='tab:green') ax[1,0].plot(mindx33-np.mean(mindx33), '--', color='tab:red') ax[1,0].plot(mindy33-np.mean(mindy33), label='Camera 3', color='tab:red') ax[1,0].set_title('Horizontal Displacement Case') ax[1,1].plot(mindx14-np.mean(mindx14), '--', color='tab:blue') ax[1,1].plot(mindy14-np.mean(mindy14),label='Camera 1', color='tab:blue') ax[1,1].plot(mindx24-np.mean(mindx24), '--', color='tab:green') ax[1,1].plot(mindy24-np.mean(mindy24), label='Camera 2', color='tab:green') ax[1,1].plot(mindx34-np.mean(mindx34), '--', color='tab:red') ax[1,1].plot(mindy34-np.mean(mindy34), label='Camera 3', color='tab:red') ax[1,1].set_title('Horizontal Displacement and Rotation Case') fig.savefig('plots/positions.png') fig, ax = plt.subplots(figsize=(7,8.5), nrows=4, sharex=True) ax[0].plot(pca1[0,:], label='Mode 1', color='tab:purple') ax[0].plot(pca1[1,:], label='Mode 2', color='tab:cyan') ax[0].plot(pca1[2,:], label='Mode 3', color='tab:gray') ax[0].set_title('Ideal Case') ax[0].legend(loc='best') ax[0].set_ylabel('Position', fontsize=14) ax[1].plot(pca2[0,:], label='Mode 1', color='tab:purple') ax[1].plot(pca2[1,:], label='Mode 2', color='tab:cyan') ax[1].plot(pca2[2,:], label='Mode 3', color='tab:gray') ax[1].set_title('Noisy Case') #ax[1].legend(loc='best') ax[1].set_ylabel('Position', fontsize=14) ax[2].plot(pca3[0,:], label='Mode 1', color='tab:purple') ax[2].plot(pca3[1,:], label='Mode 2', color='tab:cyan') ax[2].plot(pca3[2,:], label='Mode 3', color='tab:gray') ax[2].set_title('Horizontal Displacement Case') #ax[2].legend(loc='best') ax[2].set_ylabel('Position', fontsize=14) ax[3].plot(pca4[0,:], label='Mode 1', color='tab:purple') ax[3].plot(pca4[1,:], label='Mode 2', color='tab:cyan') ax[3].plot(pca4[2,:], label='Mode 3', color='tab:gray') ax[3].set_title('Horizontal Displacement and Rotation Case') ax[3].set_xlabel('Frame', fontsize=14) ax[3].set_ylabel('Position', fontsize=14) #ax[3].legend(loc='best') fig.savefig('plots/PCA_comparison_modes.png') fig, ax = plt.subplots() ax.plot(np.arange(1,7,1), percentage1, '-^', label='Ideal Case') ax.plot(np.arange(1,7,1), percentage2, '-^', label='Noisy Case') ax.plot(np.arange(1,7,1), percentage3, '-^', label='Horizontal Displacement Case') ax.plot(np.arange(1,7,1), percentage4, '-^', label='Horizontal Displacement and Rotation Case') ax.legend(loc='best') ax.set_xlabel('Mode') ax.set_ylabel('Proportion of Variance') fig.savefig('plots/variance.png')

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import torch import torch.nn as nn import sys from torchvision import models from .Global_pooling import GlobalPool from .attention import AttentionMap from efficientnet_pytorch import EfficientNet from torch.autograd import Function import torch.nn.functional as F class ResNet18(nn.Module): """Model modified. The architecture of our model is the same as standard ResNet18 except the classifier layer which has an additional sigmoid function. """ def __init__(self, cfg): super(ResNet18, self).__init__() img_model = models.resnet18(pretrained=cfg.pretrained) self.cfg = cfg self.num_outputs = cfg.num_classes if cfg.multi_label else 1 img_model.avgpool = GlobalPool(cfg) img_model.fc = torch.nn.Sequential( nn.Dropout(0.5), nn.Linear(512, self.num_outputs), nn.Sigmoid() ) self.img_model = img_model def forward(self, x): """ :param x: input image [size N X H X W X C] :return x: probability of each disease [size N X 8] """ x = self.img_model(x) return x class DenseNet121(nn.Module): """Model modified. The architecture of our model is the same as standard DenseNet121 except the classifier layer which has an additional sigmoid function. Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf> """ def __init__(self, cfg): super(DenseNet121, self).__init__() self.img_model = models.densenet121(pretrained=cfg.pretrained) self.num_ftrs = self.img_model.classifier.in_features self.num_outputs = cfg.num_classes if cfg.multi_label else 1 self.cfg = cfg self.pool = GlobalPool(cfg) self.drop = nn.Dropout(0.5) self.img_model.classifier = torch.nn.Sequential(nn.Conv2d(self.num_ftrs, self.num_outputs, kernel_size=1, stride=1, padding=0, bias=True)) self.sig = nn.Sigmoid() if cfg.attention_map: self._init_attention_map() def _init_attention_map(self): setattr(self, "attention_map_1", AttentionMap(self.cfg, self.num_ftrs)) def forward(self, x): """ :param x: input image [size N X H X W X C] :return x: probability of each disease [size N X 8] """ feat_map = x # for k, v in self.img_model.features._modules.items(): # feat_map = v(feat_map) # if self.cfg.attention_map: # feat_map = self.attention_map(feat_map) feat_map = self.img_model.features(x) x = self.pool(feat_map) x = self.drop(x) x = self.img_model.classifier(x) x = self.sig(x) if len(x.shape) > 2: x = torch.squeeze(x, -1) if len(x.shape) > 2: x = torch.squeeze(x, -1) return x class PCAM_Model(nn.Module): """Probabilistic Class Activation Map, Ref; https://arxiv.org/pdf/2005.14480.pdf """ def __init__(self, cfg): super(PCAM_Model, self).__init__() self.cfg = cfg if self.cfg.backbone == 'densenet121': self.img_model = models.densenet121(pretrained=cfg.pretrained) self.num_ftrs = self.img_model.classifier.in_features elif self.cfg.backbone == 'ResNet18': self.img_model = models.resnet18(pretrained=cfg.pretrained) self.num_ftrs = 512 self.img_model = nn.Sequential( self.img_model.conv1, self.img_model.bn1, self.img_model.relu, self.img_model.maxpool, self.img_model.layer1, self.img_model.layer2, self.img_model.layer3, self.img_model.layer4) self.num_outputs = cfg.num_classes self.cfg.global_pool = 'PCAM' self.global_pool = GlobalPool(cfg) self.drop = nn.Dropout(0.0) self.sig = nn.Sigmoid() if cfg.attention_map: self._init_attention_map() self._init_classifier() def _init_classifier(self): for index in range((self.cfg.num_classes)): if self.cfg.backbone == 'ResNet18': setattr( self, "fc_" + str(index), nn.Conv2d( self.num_ftrs, 1, kernel_size=1, stride=1, padding=0, bias=True)) elif self.cfg.backbone == 'densenet121': setattr( self, "fc_" + str(index), nn.Conv2d( self.num_ftrs, 1, kernel_size=1, stride=1, padding=0, bias=True)) classifier = getattr(self, "fc_" + str(index)) if isinstance(classifier, nn.Conv2d): classifier.weight.data.normal_(0, 0.01) classifier.bias.data.zero_() def _init_attention_map(self): setattr(self, "attention_map", AttentionMap(self.cfg, self.num_ftrs)) def forward(self, x): """ :param x: input image [size N X H X W X C] :return: problity of each disease [size N X 8] """ if self.cfg.backbone == 'densenet121': feat_map = self.img_model.features(x) elif self.cfg.backbone == 'ResNet18': feat_map = self.img_model(x) logits = list() # [(N, H, W), (N, H, W),...] logit_maps = list() for index in range((self.cfg.num_classes)): if self.cfg.attention_map: feat_map = self.attention_map(feat_map) classifier = getattr(self, "fc_" + str(index)) # (N, 1, H, W) logit_map = classifier(feat_map) logit_maps.append(logit_map.squeeze()) # (N, C, 1, 1) feat = self.global_pool(feat_map, logit_map) feat = F.dropout(feat) # (N, num_class, 1, 1) logit = classifier(feat) # (N, num_class) logit = logit.squeeze(-1).squeeze(-1) logits.append(logit) logits = torch.cat(logits, dim=1) return logits, logit_maps class EfficientNet_model(nn.Module): def __init__(self, cfg): super(EfficientNet_model, self).__init__() self.img_model = EfficientNet.from_pretrained('efficientnet-b0') self.num_ftrs = 1280 self.cfg = cfg self.num_outputs = cfg.num_classes if cfg.multi_label else 1 self.pool = GlobalPool(cfg) self.drop = nn.Dropout(0.5) self.FF = torch.nn.Sequential(nn.Conv2d(self.num_ftrs, self.num_outputs, kernel_size=1, stride=1, padding=0, bias=True)) self.sig = nn.Sigmoid() def forward(self, x): """ :param image: input image [size N X H X W X C] :return: problity of each disease [size N X 8] """ feat_map = self.img_model.extract_features(x) x = self.pool(feat_map) x = self.drop(x) x = self.FF(x) x = torch.squeeze(x) x = self.sig(x) if len(x.shape) == 1: x = torch.unsqueeze(x, 0) return x class custom_xray(nn.Module): """ A custom model similar to ResNet architecture but smaller is size """ def __init__(self, cfg): """ Args : cfg :input configurations """ super(custom_xray, self).__init__() self.num_channels = 3 self.num_outputs = cfg.num_classes if cfg.multi_label else 1 self.first_layer = nn.Sequential( nn.Conv2d(3, self.num_channels * 5, kernel_size=7, stride=2, padding=3, bias=False), nn.BatchNorm2d(self.num_channels * 5), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1), ) self.num_channels = self.num_channels * 5 for x in range(4): if x == 0: coff = 1 else: coff = 1.5 layer = torch.nn.Sequential( nn.BatchNorm2d(int(self.num_channels * coff)), nn.ReLU(inplace=True), nn.Conv2d(int(self.num_channels * coff), self.num_channels * 2, kernel_size=3, stride=1, padding=1, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=1, stride=1, padding=0, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=3, stride=1, padding=1, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=1, stride=1, padding=0, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=1, stride=1, padding=0, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=1, stride=1, padding=0, bias=True), nn.BatchNorm2d(self.num_channels * 2), nn.ReLU(inplace=True), nn.Conv2d(self.num_channels * 2, self.num_channels * 2, kernel_size=1, stride=1, padding=0, bias=True), nn.Dropout(), nn.AvgPool2d(2, stride=2) ) setattr(self, 'layer_' + str(x), layer) skip_layer = torch.nn.Sequential( nn.BatchNorm2d(int(self.num_channels * coff)), nn.ReLU(inplace=True), nn.Conv2d(int(self.num_channels * coff), self.num_channels, kernel_size=1, stride=1, padding=0, bias=True), nn.AvgPool2d(2, stride=2) ) setattr(self, 'skiplayer_' + str(x), skip_layer) self.num_channels = self.num_channels * 2 self.pool = GlobalPool(cfg) self.drop = nn.Dropout(0.5) self.FF = torch.nn.Sequential(nn.Conv2d(int(self.num_channels * 1.5), self.num_outputs, kernel_size=1, stride=1, padding=0, bias=True)) self.sig = nn.Sigmoid() def forward(self, image): """ :param image: input image [size N X H X W X C] :return: problity of each disease [size N X 8] """ image = self.first_layer(image) for x_ in range(4): image_ = getattr(self, 'skiplayer_' + str(x_))(image) image = getattr(self, 'layer_' + str(x_))(image) image = torch.cat([image, image_], dim=1) image = self.pool(image) image = self.drop(image) image = self.FF(image).squeeze() output = self.sig(image) return output

[ "alinsteinjose@gmail.com" ]

alinsteinjose@gmail.com

d4629669e16ac320298aefd9df96cf73804d3c4a

711756b796d68035dc6a39060515200d1d37a274

/output_cog/optimized_43033.py

b05910d62650ebad3a06ec9bae8a3e8ca0a1252c

[]

no_license

batxes/exocyst_scripts

8b109c279c93dd68c1d55ed64ad3cca93e3c95ca

a6c487d5053b9b67db22c59865e4ef2417e53030

refs/heads/master

2020-06-16T20:16:24.840725

2016-11-30T16:23:16

75,075,164

0

null

UTF-8

Python

false

10,839

py

import _surface import chimera try: import chimera.runCommand except: pass from VolumePath import markerset as ms try: from VolumePath import Marker_Set, Link new_marker_set=Marker_Set except: from VolumePath import volume_path_dialog d= volume_path_dialog(True) new_marker_set= d.new_marker_set marker_sets={} surf_sets={} if "Cog2_GFPN" not in marker_sets: s=new_marker_set('Cog2_GFPN') marker_sets["Cog2_GFPN"]=s s= marker_sets["Cog2_GFPN"] mark=s.place_marker((630.119, 511.453, 576.726), (0.89, 0.1, 0.1), 18.4716) if "Cog2_0" not in marker_sets: s=new_marker_set('Cog2_0') marker_sets["Cog2_0"]=s s= marker_sets["Cog2_0"] mark=s.place_marker((585.061, 509.682, 526.489), (0.89, 0.1, 0.1), 17.1475) if "Cog2_1" not in marker_sets: s=new_marker_set('Cog2_1') marker_sets["Cog2_1"]=s s= marker_sets["Cog2_1"] mark=s.place_marker((521.572, 509.549, 475.752), (0.89, 0.1, 0.1), 17.1475) if "Cog2_GFPC" not in marker_sets: s=new_marker_set('Cog2_GFPC') marker_sets["Cog2_GFPC"]=s s= marker_sets["Cog2_GFPC"] mark=s.place_marker((514.774, 584.669, 593.047), (0.89, 0.1, 0.1), 18.4716) if "Cog2_Anch" not in marker_sets: s=new_marker_set('Cog2_Anch') marker_sets["Cog2_Anch"]=s s= marker_sets["Cog2_Anch"] mark=s.place_marker((398.748, 489.561, 324.612), (0.89, 0.1, 0.1), 18.4716) if "Cog3_GFPN" not in marker_sets: s=new_marker_set('Cog3_GFPN') marker_sets["Cog3_GFPN"]=s s= marker_sets["Cog3_GFPN"] mark=s.place_marker((594.944, 502.698, 547.787), (1, 1, 0), 18.4716) if "Cog3_0" not in marker_sets: s=new_marker_set('Cog3_0') marker_sets["Cog3_0"]=s s= marker_sets["Cog3_0"] mark=s.place_marker((595.68, 502, 549.422), (1, 1, 0.2), 17.1475) if "Cog3_1" not in marker_sets: s=new_marker_set('Cog3_1') marker_sets["Cog3_1"]=s s= marker_sets["Cog3_1"] mark=s.place_marker((590.851, 506.155, 576.512), (1, 1, 0.2), 17.1475) if "Cog3_2" not in marker_sets: s=new_marker_set('Cog3_2') marker_sets["Cog3_2"]=s s= marker_sets["Cog3_2"] mark=s.place_marker((579.508, 505.854, 602.118), (1, 1, 0.2), 17.1475) if "Cog3_3" not in marker_sets: s=new_marker_set('Cog3_3') marker_sets["Cog3_3"]=s s= marker_sets["Cog3_3"] mark=s.place_marker((581.34, 478.731, 594.646), (1, 1, 0.2), 17.1475) if "Cog3_4" not in marker_sets: s=new_marker_set('Cog3_4') marker_sets["Cog3_4"]=s s= marker_sets["Cog3_4"] mark=s.place_marker((565.109, 462.805, 611.033), (1, 1, 0.2), 17.1475) if "Cog3_5" not in marker_sets: s=new_marker_set('Cog3_5') marker_sets["Cog3_5"]=s s= marker_sets["Cog3_5"] mark=s.place_marker((562.847, 435.724, 603.464), (1, 1, 0.2), 17.1475) if "Cog3_GFPC" not in marker_sets: s=new_marker_set('Cog3_GFPC') marker_sets["Cog3_GFPC"]=s s= marker_sets["Cog3_GFPC"] mark=s.place_marker((622.894, 507.143, 551.239), (1, 1, 0.4), 18.4716) if "Cog3_Anch" not in marker_sets: s=new_marker_set('Cog3_Anch') marker_sets["Cog3_Anch"]=s s= marker_sets["Cog3_Anch"] mark=s.place_marker((507.324, 358.874, 651.796), (1, 1, 0.4), 18.4716) if "Cog4_GFPN" not in marker_sets: s=new_marker_set('Cog4_GFPN') marker_sets["Cog4_GFPN"]=s s= marker_sets["Cog4_GFPN"] mark=s.place_marker((423.381, 375.222, 468.659), (0, 0, 0.8), 18.4716) if "Cog4_0" not in marker_sets: s=new_marker_set('Cog4_0') marker_sets["Cog4_0"]=s s= marker_sets["Cog4_0"] mark=s.place_marker((423.381, 375.222, 468.659), (0, 0, 0.8), 17.1475) if "Cog4_1" not in marker_sets: s=new_marker_set('Cog4_1') marker_sets["Cog4_1"]=s s= marker_sets["Cog4_1"] mark=s.place_marker((444.891, 391.216, 477.849), (0, 0, 0.8), 17.1475) if "Cog4_2" not in marker_sets: s=new_marker_set('Cog4_2') marker_sets["Cog4_2"]=s s= marker_sets["Cog4_2"] mark=s.place_marker((466.761, 405.64, 488.571), (0, 0, 0.8), 17.1475) if "Cog4_3" not in marker_sets: s=new_marker_set('Cog4_3') marker_sets["Cog4_3"]=s s= marker_sets["Cog4_3"] mark=s.place_marker((488.345, 421.708, 497.487), (0, 0, 0.8), 17.1475) if "Cog4_4" not in marker_sets: s=new_marker_set('Cog4_4') marker_sets["Cog4_4"]=s s= marker_sets["Cog4_4"] mark=s.place_marker((510.58, 438.69, 502.338), (0, 0, 0.8), 17.1475) if "Cog4_5" not in marker_sets: s=new_marker_set('Cog4_5') marker_sets["Cog4_5"]=s s= marker_sets["Cog4_5"] mark=s.place_marker((533.123, 455.696, 503.421), (0, 0, 0.8), 17.1475) if "Cog4_6" not in marker_sets: s=new_marker_set('Cog4_6') marker_sets["Cog4_6"]=s s= marker_sets["Cog4_6"] mark=s.place_marker((557.926, 466.707, 510.956), (0, 0, 0.8), 17.1475) if "Cog4_GFPC" not in marker_sets: s=new_marker_set('Cog4_GFPC') marker_sets["Cog4_GFPC"]=s s= marker_sets["Cog4_GFPC"] mark=s.place_marker((358.514, 329.566, 603.332), (0, 0, 0.8), 18.4716) if "Cog4_Anch" not in marker_sets: s=new_marker_set('Cog4_Anch') marker_sets["Cog4_Anch"]=s s= marker_sets["Cog4_Anch"] mark=s.place_marker((758.231, 603.822, 421.217), (0, 0, 0.8), 18.4716) if "Cog5_GFPN" not in marker_sets: s=new_marker_set('Cog5_GFPN') marker_sets["Cog5_GFPN"]=s s= marker_sets["Cog5_GFPN"] mark=s.place_marker((551.323, 476.152, 468.534), (0.3, 0.3, 0.3), 18.4716) if "Cog5_0" not in marker_sets: s=new_marker_set('Cog5_0') marker_sets["Cog5_0"]=s s= marker_sets["Cog5_0"] mark=s.place_marker((551.323, 476.152, 468.534), (0.3, 0.3, 0.3), 17.1475) if "Cog5_1" not in marker_sets: s=new_marker_set('Cog5_1') marker_sets["Cog5_1"]=s s= marker_sets["Cog5_1"] mark=s.place_marker((554.703, 504.908, 470.795), (0.3, 0.3, 0.3), 17.1475) if "Cog5_2" not in marker_sets: s=new_marker_set('Cog5_2') marker_sets["Cog5_2"]=s s= marker_sets["Cog5_2"] mark=s.place_marker((545.665, 532.2, 474.802), (0.3, 0.3, 0.3), 17.1475) if "Cog5_3" not in marker_sets: s=new_marker_set('Cog5_3') marker_sets["Cog5_3"]=s s= marker_sets["Cog5_3"] mark=s.place_marker((523.908, 541.179, 491.62), (0.3, 0.3, 0.3), 17.1475) if "Cog5_GFPC" not in marker_sets: s=new_marker_set('Cog5_GFPC') marker_sets["Cog5_GFPC"]=s s= marker_sets["Cog5_GFPC"] mark=s.place_marker((601.262, 561.713, 583.895), (0.3, 0.3, 0.3), 18.4716) if "Cog5_Anch" not in marker_sets: s=new_marker_set('Cog5_Anch') marker_sets["Cog5_Anch"]=s s= marker_sets["Cog5_Anch"] mark=s.place_marker((429.12, 518.724, 408.184), (0.3, 0.3, 0.3), 18.4716) if "Cog6_GFPN" not in marker_sets: s=new_marker_set('Cog6_GFPN') marker_sets["Cog6_GFPN"]=s s= marker_sets["Cog6_GFPN"] mark=s.place_marker((581.344, 531.554, 544.402), (0.21, 0.49, 0.72), 18.4716) if "Cog6_0" not in marker_sets: s=new_marker_set('Cog6_0') marker_sets["Cog6_0"]=s s= marker_sets["Cog6_0"] mark=s.place_marker((581.249, 531.713, 544.571), (0.21, 0.49, 0.72), 17.1475) if "Cog6_1" not in marker_sets: s=new_marker_set('Cog6_1') marker_sets["Cog6_1"]=s s= marker_sets["Cog6_1"] mark=s.place_marker((553.972, 524.124, 546.396), (0.21, 0.49, 0.72), 17.1475) if "Cog6_2" not in marker_sets: s=new_marker_set('Cog6_2') marker_sets["Cog6_2"]=s s= marker_sets["Cog6_2"] mark=s.place_marker((528.843, 511.821, 542.697), (0.21, 0.49, 0.72), 17.1475) if "Cog6_3" not in marker_sets: s=new_marker_set('Cog6_3') marker_sets["Cog6_3"]=s s= marker_sets["Cog6_3"] mark=s.place_marker((543.289, 489.576, 552.432), (0.21, 0.49, 0.72), 17.1475) if "Cog6_4" not in marker_sets: s=new_marker_set('Cog6_4') marker_sets["Cog6_4"]=s s= marker_sets["Cog6_4"] mark=s.place_marker((553.515, 464.901, 561.789), (0.21, 0.49, 0.72), 17.1475) if "Cog6_5" not in marker_sets: s=new_marker_set('Cog6_5') marker_sets["Cog6_5"]=s s= marker_sets["Cog6_5"] mark=s.place_marker((567.605, 441.714, 569.925), (0.21, 0.49, 0.72), 17.1475) if "Cog6_6" not in marker_sets: s=new_marker_set('Cog6_6') marker_sets["Cog6_6"]=s s= marker_sets["Cog6_6"] mark=s.place_marker((583.014, 421.154, 581.709), (0.21, 0.49, 0.72), 17.1475) if "Cog6_GFPC" not in marker_sets: s=new_marker_set('Cog6_GFPC') marker_sets["Cog6_GFPC"]=s s= marker_sets["Cog6_GFPC"] mark=s.place_marker((614.392, 444.586, 505.477), (0.21, 0.49, 0.72), 18.4716) if "Cog6_Anch" not in marker_sets: s=new_marker_set('Cog6_Anch') marker_sets["Cog6_Anch"]=s s= marker_sets["Cog6_Anch"] mark=s.place_marker((549.816, 400.211, 659.848), (0.21, 0.49, 0.72), 18.4716) if "Cog7_GFPN" not in marker_sets: s=new_marker_set('Cog7_GFPN') marker_sets["Cog7_GFPN"]=s s= marker_sets["Cog7_GFPN"] mark=s.place_marker((614.126, 482.344, 478.893), (0.7, 0.7, 0.7), 18.4716) if "Cog7_0" not in marker_sets: s=new_marker_set('Cog7_0') marker_sets["Cog7_0"]=s s= marker_sets["Cog7_0"] mark=s.place_marker((595.247, 498.309, 487.311), (0.7, 0.7, 0.7), 17.1475) if "Cog7_1" not in marker_sets: s=new_marker_set('Cog7_1') marker_sets["Cog7_1"]=s s= marker_sets["Cog7_1"] mark=s.place_marker((554.02, 533.585, 507.429), (0.7, 0.7, 0.7), 17.1475) if "Cog7_2" not in marker_sets: s=new_marker_set('Cog7_2') marker_sets["Cog7_2"]=s s= marker_sets["Cog7_2"] mark=s.place_marker((512.331, 568.802, 525.914), (0.7, 0.7, 0.7), 17.1475) if "Cog7_GFPC" not in marker_sets: s=new_marker_set('Cog7_GFPC') marker_sets["Cog7_GFPC"]=s s= marker_sets["Cog7_GFPC"] mark=s.place_marker((574.576, 611.669, 553.431), (0.7, 0.7, 0.7), 18.4716) if "Cog7_Anch" not in marker_sets: s=new_marker_set('Cog7_Anch') marker_sets["Cog7_Anch"]=s s= marker_sets["Cog7_Anch"] mark=s.place_marker((408.788, 583.555, 528.966), (0.7, 0.7, 0.7), 18.4716) if "Cog8_0" not in marker_sets: s=new_marker_set('Cog8_0') marker_sets["Cog8_0"]=s s= marker_sets["Cog8_0"] mark=s.place_marker((486.777, 522.32, 566.41), (1, 0.5, 0), 17.1475) if "Cog8_1" not in marker_sets: s=new_marker_set('Cog8_1') marker_sets["Cog8_1"]=s s= marker_sets["Cog8_1"] mark=s.place_marker((496.443, 524.298, 540.008), (1, 0.5, 0), 17.1475) if "Cog8_2" not in marker_sets: s=new_marker_set('Cog8_2') marker_sets["Cog8_2"]=s s= marker_sets["Cog8_2"] mark=s.place_marker((502.84, 524.53, 512.223), (1, 0.5, 0), 17.1475) if "Cog8_3" not in marker_sets: s=new_marker_set('Cog8_3') marker_sets["Cog8_3"]=s s= marker_sets["Cog8_3"] mark=s.place_marker((495.136, 528.701, 483.861), (1, 0.5, 0), 17.1475) if "Cog8_4" not in marker_sets: s=new_marker_set('Cog8_4') marker_sets["Cog8_4"]=s s= marker_sets["Cog8_4"] mark=s.place_marker((511.917, 538.275, 460.872), (1, 0.5, 0), 17.1475) if "Cog8_5" not in marker_sets: s=new_marker_set('Cog8_5') marker_sets["Cog8_5"]=s s= marker_sets["Cog8_5"] mark=s.place_marker((535.611, 543.917, 443.776), (1, 0.5, 0), 17.1475) if "Cog8_GFPC" not in marker_sets: s=new_marker_set('Cog8_GFPC') marker_sets["Cog8_GFPC"]=s s= marker_sets["Cog8_GFPC"] mark=s.place_marker((589.32, 520.617, 500.38), (1, 0.6, 0.1), 18.4716) if "Cog8_Anch" not in marker_sets: s=new_marker_set('Cog8_Anch') marker_sets["Cog8_Anch"]=s s= marker_sets["Cog8_Anch"] mark=s.place_marker((492.954, 569.195, 378.67), (1, 0.6, 0.1), 18.4716) for k in surf_sets.keys(): chimera.openModels.add([surf_sets[k]])

[ "batxes@gmail.com" ]

batxes@gmail.com

70c0eebdf6fdce12711b9dca5bb4eaf6f545171f

0c6ae006fbcb27f60399917d29084ff42a6a96ad

/app2/populate/test.py

a045fa45611b0f95d6e1c1670cce702ead82834c

[]

no_license

ailan12345/app2

9ac54c287d90b86ca0726dde12d5dec62ae2f6eb

b9a00cb55a0d54198eb2030b158433ad73274a17

refs/heads/master

2020-03-17T09:42:11.457932

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from populate import base, admin, user admin.populate() user.populate()

[ "isccyut@isccyut-M11BB" ]

isccyut@isccyut-M11BB

ac1338440b36fda5674e33b1bf2e2085aaa9d69a

98e6328bc25a81bf2b492127b091e01c0dabb345

/graphs/data/2009-10-08/process.py

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[]

no_license

bernardokyotoku/Kyotoku-thesis

7d80bbcf8c93b3610262e4b3e186dbafb0eae29c

548eb33fc60f397f48c4999420e87c20737ee7f9

refs/heads/master

2016-09-06T01:12:54.305113

2011-04-14T14:15:56

1,613,873

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#!/usr/bin/env python from numpy import * from matplotlib.pyplot import * import scientific_utils as su T=range(1,11) data = loadtxt('./T1-0.dat') wavelength = data[:,0] figure(10) plot(wavelength,10*log10(data[:,1])) figure(2) mask =wavelength>1483 mask&=wavelength<1493 wavelength = wavelength[mask] data = wavelength for i in range(24,14,-1): for j in range(len(T)): filename = './T%d-%d.dat'%(T[j],i) print filename raw = loadtxt(filename) data = vstack((data,raw[mask,1])) wavelength = raw[mask,0] power = raw[mask,1] plot(wavelength,10*log10(power)) data = 40.5+10*log10(data) data[0,:]=wavelength savetxt('data1dB.dat',data.transpose(),fmt='%.8e') input()

[ "bernardo@kyotoku.org" ]

bernardo@kyotoku.org

8ad1db408f0ed4b1820869e77505354eb6f528a5

210fb7e5d8b588cb8796acd1699ba184a40566c3

/django/contrib/staticfiles/handlers.py

aba383f07ce604d41bc68f13f45aa78c81e01a6a

[]

no_license

suosuo1930/parse_django

2703f8195c4e0a75f604641b2f264e1f75a9ab86

ffaa299868dc1295aa665ad4b11d3f1e7cf0403f

refs/heads/master

2020-09-11T14:46:12.577076

2019-11-25T13:27:19

222,100,485

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from urllib.parse import urlparse from urllib.request import url2pathname from django.conf import settings from django.contrib.staticfiles import utils from django.contrib.staticfiles.views import serve from django.core.handlers.exception import response_for_exception from django.core.handlers.wsgi import WSGIHandler, get_path_info # 关键类 # Django 专门用来处理静态文件的类 class StaticFilesHandler(WSGIHandler): """ 拦截对静态文件目录的调用的WSGI中间件由STATIC_URL设置定义，并为这些文件提供服务 WSGI middleware that intercepts calls to the static files directory, as defined by the STATIC_URL setting, and serves those files. """ # May be used to differentiate between handler types (e.g. in a # request_finished signal) handles_files = True def __init__(self, application): print("3333333333333333333333333") self.application = application # self.application = <django.core.handlers.wsgi.WSGIHandler object at 0x000001AFF81CB8C8> self.base_url = urlparse(self.get_base_url()) # 静态文件 url 解析结果 # self.base_url == ParseResult(scheme='', netloc='', path='/static/', params='', query='', fragment='') # 例如 # base_url = urlparse('http://www.cwi.nl:80/%7Eguido/Python.html') # 则 base_url == ParseResult(scheme='http', netloc='www.cwi.nl:80', path='/%7Eguido/Python.html', # params='', query='', fragment='') super().__init__() def load_middleware(self): # Middleware are already loaded for self.application; no need to reload # them for self. pass def get_base_url(self): utils.check_settings() return settings.STATIC_URL def _should_handle(self, path): """ Check if the path should be handled. Ignore the path if: * the host is provided as part of the base_url * the request's path isn't under the media path (or equal) """ return path.startswith(self.base_url[2]) and not self.base_url[1] def file_path(self, url): """ Return the relative path to the media file on disk for the given URL. """ relative_url = url[len(self.base_url[2]):] return url2pathname(relative_url) def serve(self, request): """Serve the request path.""" return serve(request, self.file_path(request.path), insecure=True) def get_response(self, request): from django.http import Http404 if self._should_handle(request.path): try: return self.serve(request) except Http404 as e: return response_for_exception(request, e) return super().get_response(request) # 关键代码关键代码关键代码关键代码 def __call__(self, environ, start_response): # print("environ==", environ, "start_response==", start_response) # for k,v in enumerate(environ.items()): # print("{}---{}=={}".format(k, str(v[0]).lower(), v[1])) if not self._should_handle(get_path_info(environ)): # 执行 # get_path_info(environ) = "/" # 关键代码 return self.application(environ, start_response) return super().__call__(environ, start_response)

[ "1963275449@qq.com" ]

1963275449@qq.com

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/adapted_from_Allen_Institute/LGN_modifications/nwb_copy.py

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[ "BSD-3-Clause" ]

permissive

lenamyk/Exploring_the_Allen_model

c1903e36fe5321152bc70d14e54f61040c40233b

2da3a248a0ffc613b8019ba798e88bd2f71348b1

refs/heads/master

2022-04-26T07:26:36.258678

2020-05-02T18:39:01

258,632,148

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# Copyright 2017. Allen Institute. All rights reserved # # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the # following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following # disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import copy import numpy as np import os import h5py import time import uuid import tempfile from bmtk.analyzer.visualization.widgets import PlotWidget, MovieWidget import numpy as np __version__ = '0.1.0' allowed_dimensions = {'firing_rate': ('hertz',), 'time': ('second', 'millisecond'), 'brightness': ('intensity',), 'distance': ('pixel',), 'index': ('gid',), 'intensity': ('bit',None), 'voltage': ('volt',), 'current': ('ampere',), None: (None,), 'dev': ('dev',)} allowed_groups = {'firing_rate': ('firing_rate',), 'spike_train': ('index', 'time'), 'grayscale_movie': ('intensity',), 'time_series': ('voltage', 'current'), 'dev': ('dev',)} top_level_data = ['file_create_date', 'stimulus', 'acquisition', 'analysis', 'processing', 'epochs', 'general', 'session_description', 'nwb_version', 'identifier'] def open_file(file_name): return h5py.File(file_name) class Scale(object): def __init__(self, scale_range, dimension, unit): assert dimension in allowed_dimensions assert unit in allowed_dimensions[dimension] self.scale_range = scale_range self.dimension = dimension self.unit = unit self._hdf5_location = None def __eq__(self, other): d = self.dimension == other.dimension u = self.unit == other.unit s = np.allclose(self.scale_range, other.scale_range) return d and u and s @ property def data(self): return self.scale_range class DtScale(object): def __init__(self, dt, dimension, unit): assert dimension in allowed_dimensions assert unit in allowed_dimensions[dimension] self.dt = dt self.dimension = dimension self.unit = unit self._hdf5_location = None def __eq__(self, other): d = self.dimension == other.dimension u = self.unit == other.unit s = np.allclose(self.scale_range, other.scale_range) return d and u and s @ property def data(self): return self.dt class NullScale(object): def __init__(self): self._hdf5_location = None self.data = None self.dimension = None self.unit = None class Data(object): def __init__(self, data, dimension, unit, scales, metadata): assert dimension in allowed_dimensions assert unit in allowed_dimensions[dimension] if isinstance(scales, (Scale, DtScale)): assert len(data.shape) == 1 scales = (scales,) #for key in metadata.iterkeys(): for key in metadata.keys(): assert isinstance(key, (str, unicode)) for ii, scale in enumerate(scales): if isinstance(scale, Scale): assert len(scale.scale_range) == data.shape[ii] elif isinstance(scale, DtScale): assert isinstance(scale.dt, (float, np.float)) and scale.dt > 0 else: raise Exception if len(scales) == 0: scales = [NullScale()] metadata = copy.copy(metadata) self.data = data self.scales = scales self.dimension = dimension self.unit = unit self.metadata = metadata self._hdf5_location = None def __eq__(self, other): da = np.allclose(self.data, other.data) d = self.dimension == other.dimension u = self.unit == other.unit s = [s1 == s2 for s1, s2 in zip(self.scales, other.scales)].count(True) == len(self.scales) if len(self.metadata) != len(other.metadata): m = False else: try: sum = 0 for key in self.metadata.keys(): sum += other.metadata[key] == self.metadata[key] assert sum == len(self.metadata) m = True except: m = False return da and d and u and s and m @staticmethod def _get_from_group(object_class, parent_group, group_name, ii=0): data_group = parent_group['%s/%s' % (group_name, ii)] data, scales, dimension, unit, metadata = _get_data(data_group) assert dimension in allowed_groups[object_class.group] if unit == "None": unit = None scale_list = [] for scale in scales: if scale.attrs['type'] == 'Scale': curr_scale = Scale(scale, scale.attrs['dimension'], scale.attrs['unit']) elif scale.attrs['type'] == 'DtScale': curr_scale = DtScale(float(scale.value), scale.attrs['dimension'], scale.attrs['unit']) elif scale.attrs['type'] == 'NullScale': curr_scale = None else: raise Exception if curr_scale is not None: scale_list.append(curr_scale) if len(scale_list) == 1: scale_list = scale_list[0] return object_class(data, dimension=dimension, unit=unit, scale=scale_list, metadata=metadata) def add_to_stimulus(self, f, compression='gzip', compression_opts=4): self._add_to_group(f, 'stimulus', self.__class__.group, compression=compression, compression_opts=compression_opts) @classmethod def get_stimulus(cls, f, ii=None): if ii is None: return_data = [cls.get_stimulus(f, ii) for ii in range(len(f['stimulus/%s' % cls.group]))] if len(return_data) == 1: return_data = return_data[0] return return_data else: return Data._get_from_group(cls, f['stimulus'], cls.group, ii=ii) def add_to_acquisition(self, f, compression='gzip', compression_opts=4): self._add_to_group(f, 'acquisition', self.__class__.group, compression=compression, compression_opts=compression_opts) @classmethod def get_acquisition(cls, f, ii=None): if ii is None: return_data = [cls.get_acquisition(f, ii) for ii in range(len(f['acquisition/%s' % cls.group]))] if len(return_data) == 1: return_data = return_data[0] return return_data else: return Data._get_from_group(cls, f['acquisition'], cls.group, ii=ii) def add_to_processing(self, f, processing_submodule_name): if processing_submodule_name not in f['processing']: f['processing'].create_group(processing_submodule_name) return self._add_to_group(f, 'processing/%s' % processing_submodule_name, self.__class__.group) @classmethod def get_processing(cls, f, subgroup_name, ii=None): if ii is None: return_data = {} for ii in range(len(f['processing/%s/%s' % (subgroup_name, cls.group)])): return_data[ii] = cls.get_processing(f, subgroup_name, ii) return return_data else: return Data._get_from_group(cls, f['processing/%s' % subgroup_name], cls.group, ii=ii) def add_to_analysis(self, f, analysis_submodule_name): if analysis_submodule_name not in f['analysis']: f['analysis'].create_group(analysis_submodule_name) return self._add_to_group(f, 'analysis/%s' % analysis_submodule_name, self.__class__.group) @classmethod def get_analysis(cls, f, subgroup_name, ii=None): if ii is None: return [cls.get_analysis(f, ii, subgroup_name) for ii in range(len(f['analysis/%s/%s' % (subgroup_name, cls.group)]))] else: return Data._get_from_group(cls, f['analysis/%s' % subgroup_name], cls.group, ii=ii) def _add_to_group(self, f, parent_name, group_name, compression='gzip', compression_opts=4): assert group_name in allowed_groups assert self.dimension in allowed_groups[group_name] try: parent_group = f[parent_name] except ValueError: try: file_name = f.filename raise Exception('Parent group:%s not found in file %s' % parent_name, file_name) except ValueError: raise Exception('File not valid: %s' % f) if self.__class__.group in parent_group: subgroup = parent_group[self.__class__.group] int_group_name = str(len(subgroup)) else: subgroup = parent_group.create_group(self.__class__.group) int_group_name = '0' # Create external link: if isinstance(self.data, h5py.Dataset): if subgroup.file == self.data.file: raise NotImplementedError else: return _set_data_external_link(subgroup, int_group_name, self.data.parent) else: dataset_group = subgroup.create_group(int_group_name) # All this to allow do shared scale management: scale_group = None scale_list = [] for ii, scale in enumerate(self.scales): if isinstance(scale, (Scale, DtScale, NullScale)): if scale._hdf5_location is None: if scale_group is None: scale_group = dataset_group.create_group('scale') curr_scale = _set_scale(scale_group, 'dimension_%s' % ii, scale.data, scale.dimension, scale.unit, scale.__class__.__name__) scale._hdf5_location = curr_scale else: curr_scale = _set_scale(scale_group, 'dimension_%s' % ii, scale.data, scale.dimension, scale.unit, scale.__class__.__name__) scale._hdf5_location = curr_scale else: curr_scale = scale._hdf5_location elif isinstance(scale, h5py.Dataset): curr_scale = scale else: raise Exception scale_list.append(curr_scale) _set_data(subgroup, dataset_group.name, self.data, scale_list, self.dimension, self.unit, metadata=self.metadata, compression=compression, compression_opts=compression_opts) class FiringRate(Data): group = 'firing_rate' def __init__(self, data, **kwargs): dimension = 'firing_rate' unit = 'hertz' scale = kwargs.get('scale') metadata = kwargs.get('metadata', {}) assert isinstance(scale, (Scale, DtScale)) super(FiringRate, self).__init__(data, dimension, unit, scale, metadata) def get_widget(self, **kwargs): rate_data = self.data[:] t_range = self.scales[0].data[:] return PlotWidget(t_range, rate_data, metadata=self.metadata, **kwargs) class Dev(Data): group = 'dev' def __init__(self, data, **kwargs): dimension = kwargs.get('dimension') unit = kwargs.get('unit') scale = kwargs.get('scale') metadata = kwargs.get('metadata', {}) super(Dev, self).__init__(data, dimension, unit, scale, metadata) class TimeSeries(Data): group = 'time_series' def __init__(self, data, **kwargs): dimension = kwargs.get('dimension') unit = kwargs.get('unit') scale = kwargs.get('scale') metadata = kwargs.get('metadata', {}) assert isinstance(scale, (Scale, DtScale)) assert scale.dimension == 'time' super(TimeSeries, self).__init__(data, dimension, unit, scale, metadata) class SpikeTrain(Data): group = 'spike_train' def __init__(self, data, **kwargs): scales = kwargs.get('scale',[]) unit = kwargs.get('unit', 'gid') metadata = kwargs.get('metadata',{}) if isinstance(scales, Scale): super(SpikeTrain, self).__init__(data, 'index', unit, scales, metadata) elif len(scales) == 0: assert unit in allowed_dimensions['time'] scales = [] super(SpikeTrain, self).__init__(data, 'time', unit, scales, metadata) else: assert len(scales) == 1 and isinstance(scales[0], Scale) super(SpikeTrain, self).__init__(data, 'index', unit, scales, metadata) class GrayScaleMovie(Data): group = 'grayscale_movie' def __init__(self, data, **kwargs): dimension = 'intensity' unit = kwargs.get('unit', None) scale = kwargs.get('scale') metadata = kwargs.get('metadata', {}) super(GrayScaleMovie, self).__init__(data, dimension, unit, scale, metadata) def get_widget(self, ax=None): data = self.data[:] t_range = self.scales[0].data[:] return MovieWidget(t_range=t_range, data=data, ax=ax, metadata=self.metadata) def get_temp_file_name(): f = tempfile.NamedTemporaryFile(delete=False) temp_file_name = f.name f.close() os.remove(f.name) return temp_file_name def create_blank_file(save_file_name=None, force=False, session_description='', close=False): if save_file_name is None: save_file_name = get_temp_file_name() if not force: f = h5py.File(save_file_name, 'w-') else: if os.path.exists(save_file_name): os.remove(save_file_name) f = h5py.File(save_file_name, 'w') f.create_group('acquisition') f.create_group('analysis') f.create_group('epochs') f.create_group('general') f.create_group('processing') f.create_group('stimulus') f.create_dataset("file_create_date", data=np.string_(time.ctime())) f.create_dataset("session_description", data=session_description) f.create_dataset("nwb_version", data='iSee_%s' % __version__) f.create_dataset("identifier", data=str(uuid.uuid4())) if close: f.close() else: return f def assert_subgroup_exists(child_name, parent): try: assert child_name in parent except: raise RuntimeError('Group: %s has no subgroup %s' % (parent.name, child_name)) def _set_data_external_link(parent_group, dataset_name, data): parent_group[dataset_name] = h5py.ExternalLink(data.file.filename, data.name) def _set_scale_external_link(parent_group, name, scale): print(parent_group, name, scale) print(scale.file.filename, scale.name) parent_group[name] = h5py.ExternalLink(scale.file.filename, scale.name) return parent_group[name] def _set_data(parent_group, dataset_name, data, scales, dimension, unit, force=False, metadata={}, compression='gzip', compression_opts=4): # Check inputs: if isinstance(scales, h5py.Dataset): scales = (scales,) else: assert isinstance(scales, (list, tuple)) assert data.ndim == len(scales) assert dimension in allowed_dimensions assert unit in allowed_dimensions[dimension] for ii, scale in enumerate(scales): assert len(scale.shape) in (0, 1) check_dimension = str(scale.attrs['dimension']) if check_dimension == 'None': check_dimension = None check_unit = scale.attrs['unit'] if check_unit == 'None': check_unit = None assert check_dimension in allowed_dimensions assert check_unit in allowed_dimensions[check_dimension] if len(scale.shape) == 1: assert len(scale) == data.shape[ii] or len(scale) == 0 if dataset_name not in parent_group: dataset_group = parent_group.create_group(dataset_name) else: dataset_group = parent_group[dataset_name] for key, val in metadata.items(): assert key not in dataset_group.attrs dataset_group.attrs[key] = val if 'data' in dataset_group: if not force: raise IOError('Field "stimulus" of %s is not empty; override with force=True' % parent_group.name) else: del dataset_group['data'] dataset = dataset_group.create_dataset(name='data', data=data, compression=compression, compression_opts=compression_opts) for ii, scale in enumerate(scales): dataset.dims[ii].label = scale.attrs['dimension'] dataset.dims[ii].attach_scale(scale) # Modification made for making the model run with Python 3: dim_string = str(dimension) print('generating spikes') unit_string = str(unit) dim_string_conv = dim_string.encode('UTF-8') unit_string_conv = unit_string.encode('UTF-8') dataset.attrs.create('dimension', dim_string_conv) dataset.attrs.create('unit', unit_string_conv) return dataset def _set_scale(parent_group, name, scale, dimension, unit, scale_class_name): assert dimension in allowed_dimensions assert unit in allowed_dimensions[dimension] if scale is None: scale = parent_group.create_dataset(name=name, shape=(0,)) else: scale = np.array(scale) assert scale.ndim in (0, 1) scale = parent_group.create_dataset(name=name, data=scale) scale.attrs['dimension'] = str(dimension) scale.attrs['unit'] = str(unit) scale.attrs['type'] = scale_class_name return scale def _get_data(dataset_group): data = dataset_group['data'] dimension = dataset_group['data'].attrs['dimension'] unit = dataset_group['data'].attrs['unit'] scales = tuple([dim[0] for dim in dataset_group['data'].dims]) metadata = dict(dataset_group.attrs) return data, scales, dimension, unit, metadata def get_stimulus(f): category = 'stimulus' for parent_group in f[category]: for data_group in f[category][parent_group]: print(f[category][parent_group][data_group]) def add_external_links(parent_group, external_file_name, external_group_name_list=top_level_data): for subgroup in external_group_name_list: parent_group[subgroup] = h5py.ExternalLink(external_file_name, subgroup)

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lenamyk.noreply@github.com

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'web_back_end.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

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HOLMEScdk/SearchEngines

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# -*- coding: utf-8 -*- # __author__ = 'K_HOLMES_' # __time__ = '2018/4/16 10:45' import pymongo import time import datetime mongo_client = pymongo.MongoClient(host='localhost', port=27017) mongo_crawler = mongo_client.zhihu_crawler def search_all(): collection = mongo_crawler['person_json'] query = {} flag, timestamp = 0, '' try: with open('log_time.txt', 'r', encoding='utf-8') as f: timestamp = list(f.readlines())[-1][:-1] # get time except Exception as e: flag = 1 print(e) if not flag: # 对时间的判断在此 print(timestamp) query = {'Time': {'$gte': datetime.datetime(int(timestamp[:4]), int(timestamp[5:7]), int(timestamp[8:10]), int(timestamp[11:13]), int(timestamp[14:16]), int(timestamp[17:19])) } } projection = {'urlToken': True, 'name': True, 'Time': True} # 时间节点的选择上 res = collection.find(query, projection) res.sort('Time', pymongo.ASCENDING) cnt = res.count() print(cnt) return res, cnt def store_in_file(): res, cnt = search_all() with open('utlToken.txt', 'a+', encoding='utf-8') as f, open('user_name.txt', 'a+', encoding='utf-8') as f1, \ open('log_time.txt', 'a+', encoding='utf-8') as f2: for i, each in enumerate(res): f.write(each['urlToken'] + ' 4\n') f1.write(each['name'] + ' 4\n') if i == cnt - 1: f2.write(str(each['Time']) + '\n') def main(): store_in_file() if __name__ == '__main__': while True: t1 = time.time() main() print(time.time() - t1) time.sleep(3000)

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# -*- coding: utf-8 -*- def _to_int(s): try: return int(s) except ValueError: return s __version__ = "2.6.1" version_info = tuple(_to_int(s) for s in __version__.split("."))

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import requests import json # dataJson={'msgtype':'markdown','markdown':{ # "title":"回家过年了", # "text":"#### 回家过年了\n"+ # "> 我将搭乘今日19:18的高铁回家过年\n"+ # "> 提前祝大家新年快乐！！！\n"+ # "> 鼠年希望您有:\n"+ # "> “鼠”不尽的收获\n"+ # "> “鼠”不尽的幸福\n"+ # "> ![screenshot](http://img1.imgtn.bdimg.com/it/u=1966890294,2843363819&fm=11&gp=0.jpg)" # }} # result= requests.post(url='https://oapi.dingtalk.com/robot/send?access_token=d59d68d76f2bf3c80dacdc398a422d7e84a0477b5f53e431d97a1c080c791853', # data=json.dumps(dataJson), # headers={'Content-Type':'application/json'}) # print("成功")

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secretisdead/cogs

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import math from random import choice import discord from discord.ext import commands class fortunes: """port of imageboard style fortunes from notsecret irc bot""" def __init__(self, bot): self.bot = bot fortunes = [ 'Bad Luck', 'Average Luck', 'Good Luck', 'Excellent Luck', 'Reply hazy, try again', 'Godly Luck', 'Very Bad Luck', 'Outlook good', 'Better not tell you now', 'You will meet a dark handsome stranger', 'ｷﾀ━━━━━━(ﾟ∀ﾟ)━━━━━━ !!!!', '（　´_ゝ`）ﾌｰﾝ', 'Good news will come to you by mail', 'It\'s a secret to everybody', 'Bad luck and extreme misfortune will infest your pathetic soul for all eternity', '404 fortune not found', 'Don\'t look', 'Never draw', 'Never go outside', '（´・ω・｀）', 'ʕ •ᴥ• ʔ', 'I\'ll hear it directly thanks', 'Thanx :(', 'Whoever deserves it will get it', 'horse', 'Oh Shit', 'You died', 'You\'ve grown an inch', 'You\'ve shrunk an inch', 'O-oooooooooo AAAAE-A-A-I-A-U- JO-oooooooooooo AAE-O-A-A-U-U-A- E-eee-ee-eee AAAAE-A-E-I-E-A- JO-ooo-oo-oo-oo EEEEO-A-AAA-AAAA', 'Huh', '╰U╯', 'weehaw', ] def random_fortune(self): total_fortunes = len(self.fortunes) fortune = choice(self.fortunes) fortune_index = self.fortunes.index(fortune) c = (2 * math.pi * fortune_index / total_fortunes) r = int(127 + 127 * math.sin(c)) g = int(127 + 127 * math.sin(c + (2 / 3 * math.pi))) b = int(127 + 127 * math.sin(c + (4 / 3 * math.pi))) color = int('0x{:02X}{:02X}{:02X}'.format(r, g, b), 16) return (fortune, color) @commands.command(pass_context=True) async def fortune(self, ctx): fortune, color = self.random_fortune() description = 'Your fortune: ' + fortune embed = discord.Embed(colour=color, description=description) await self.bot.say(embed=embed) @commands.command() async def housetune(self): fortune, color = self.random_fortune() description = ( '┏┓\n' + '┃┃╱╲\n' + '┃╱╱╲╲\n' + '╱╱╭╮╲╲ \n' + '▔▏┗┛▕▔ \n' + '╱▔▔▔▔▔▔▔▔▔▔╲ \n' + '╱╱┏┳┓╭╮┏┳┓ ╲╲ \n' + '▔▏┗┻┛┃┃┗┻┛▕▔\n' + 'in this house ' + fortune + '\n' + '╱╱┏┳┓╭╮┏┳┓ ╲╲ \n' + '▔▏┗┻┛┃┃┗┻┛▕▔' ) embed = discord.Embed(colour=color, description=description) await self.bot.say(embed=embed) @commands.command() async def jortune(self): fortune, color = self.random_fortune() description = ( '```\n' + '　∧＿∧\n' + ' （｡･ω･｡)つ━☆・*。\n' + ' ⊂　　ノ　　　・゜+.\n' + ' 　しーＪ　　　°。+ *´¨)\n' + ' 　　　　　　　　　.· ´¸.·*´¨) ¸.·*¨)\n' + ' 　　　　　　　　　　(¸.·´ (¸.·’* ' + fortune + ' in jorts' + '```') embed = discord.Embed(colour=color, description=description) await self.bot.say(embed=embed) def setup(bot): bot.add_cog(fortunes(bot))

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secretisdead@gmail.com

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youjin827/flask_miniter

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import sys from flask_script import Manager from app import create_app from flask_twisted import flask_twisted from twisted.python import log if __name__ == "__main__" : app=create_app() twisted = Twisted(app) log.startLogging(sys.stdout) app.logger.info(f"Running the app..") manager = Manager(app) manager.run()

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""" Swap dims ========= Display a 4-D image and points layer and swap the displayed dimensions .. tags:: visualization-nD """ import numpy as np from skimage import data import napari blobs = np.stack( [ data.binary_blobs( length=128, blob_size_fraction=0.05, n_dim=3, volume_fraction=f ) for f in np.linspace(0.05, 0.5, 10) ], axis=0, ) viewer = napari.view_image(blobs.astype(float)) # add the points points = np.array( [ [0, 0, 0, 100], [0, 0, 50, 120], [1, 0, 100, 40], [2, 10, 110, 100], [9, 8, 80, 100], ] ) viewer.add_points( points, size=[0, 6, 10, 10], face_color='blue', out_of_slice_display=True ) viewer.dims.order = (0, 2, 1, 3) if __name__ == '__main__': napari.run()

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"""Unit tests for the bytes and bytearray types. XXX This is a mess. Common tests should be moved to buffer_tests.py, which itself ought to be unified with string_tests.py (and the latter should be modernized). """ import os import re import sys import copy import functools import pickle import tempfile import unittest import test.support import test.string_tests import test.buffer_tests if sys.flags.bytes_warning: def check_bytes_warnings(func): @functools.wraps(func) def wrapper(*args, **kw): with test.support.check_warnings(('', BytesWarning)): return func(*args, **kw) return wrapper else: # no-op def check_bytes_warnings(func): return func class Indexable: def __init__(self, value=0): self.value = value def __index__(self): return self.value class BaseBytesTest(unittest.TestCase): def test_basics(self): b = self.type2test() self.assertEqual(type(b), self.type2test) self.assertEqual(b.__class__, self.type2test) def test_copy(self): a = self.type2test(b"abcd") for copy_method in (copy.copy, copy.deepcopy): b = copy_method(a) self.assertEqual(a, b) self.assertEqual(type(a), type(b)) def test_empty_sequence(self): b = self.type2test() self.assertEqual(len(b), 0) self.assertRaises(IndexError, lambda: b[0]) self.assertRaises(IndexError, lambda: b[1]) self.assertRaises(IndexError, lambda: b[sys.maxsize]) self.assertRaises(IndexError, lambda: b[sys.maxsize+1]) self.assertRaises(IndexError, lambda: b[10**100]) self.assertRaises(IndexError, lambda: b[-1]) self.assertRaises(IndexError, lambda: b[-2]) self.assertRaises(IndexError, lambda: b[-sys.maxsize]) self.assertRaises(IndexError, lambda: b[-sys.maxsize-1]) self.assertRaises(IndexError, lambda: b[-sys.maxsize-2]) self.assertRaises(IndexError, lambda: b[-10**100]) def test_from_list(self): ints = list(range(256)) b = self.type2test(i for i in ints) self.assertEqual(len(b), 256) self.assertEqual(list(b), ints) def test_from_index(self): b = self.type2test([Indexable(), Indexable(1), Indexable(254), Indexable(255)]) self.assertEqual(list(b), [0, 1, 254, 255]) self.assertRaises(ValueError, self.type2test, [Indexable(-1)]) self.assertRaises(ValueError, self.type2test, [Indexable(256)]) def test_from_ssize(self): self.assertEqual(self.type2test(0), b'') self.assertEqual(self.type2test(1), b'\x00') self.assertEqual(self.type2test(5), b'\x00\x00\x00\x00\x00') self.assertRaises(ValueError, self.type2test, -1) self.assertEqual(self.type2test('0', 'ascii'), b'0') self.assertEqual(self.type2test(b'0'), b'0') self.assertRaises(OverflowError, self.type2test, sys.maxsize + 1) def test_constructor_type_errors(self): self.assertRaises(TypeError, self.type2test, 0.0) class C: pass self.assertRaises(TypeError, self.type2test, ["0"]) self.assertRaises(TypeError, self.type2test, [0.0]) self.assertRaises(TypeError, self.type2test, [None]) self.assertRaises(TypeError, self.type2test, [C()]) def test_constructor_value_errors(self): self.assertRaises(ValueError, self.type2test, [-1]) self.assertRaises(ValueError, self.type2test, [-sys.maxsize]) self.assertRaises(ValueError, self.type2test, [-sys.maxsize-1]) self.assertRaises(ValueError, self.type2test, [-sys.maxsize-2]) self.assertRaises(ValueError, self.type2test, [-10**100]) self.assertRaises(ValueError, self.type2test, [256]) self.assertRaises(ValueError, self.type2test, [257]) self.assertRaises(ValueError, self.type2test, [sys.maxsize]) self.assertRaises(ValueError, self.type2test, [sys.maxsize+1]) self.assertRaises(ValueError, self.type2test, [10**100]) def test_compare(self): b1 = self.type2test([1, 2, 3]) b2 = self.type2test([1, 2, 3]) b3 = self.type2test([1, 3]) self.assertEqual(b1, b2) self.assertTrue(b2 != b3) self.assertTrue(b1 <= b2) self.assertTrue(b1 <= b3) self.assertTrue(b1 < b3) self.assertTrue(b1 >= b2) self.assertTrue(b3 >= b2) self.assertTrue(b3 > b2) self.assertFalse(b1 != b2) self.assertFalse(b2 == b3) self.assertFalse(b1 > b2) self.assertFalse(b1 > b3) self.assertFalse(b1 >= b3) self.assertFalse(b1 < b2) self.assertFalse(b3 < b2) self.assertFalse(b3 <= b2) @check_bytes_warnings def test_compare_to_str(self): # Byte comparisons with unicode should always fail! # Test this for all expected byte orders and Unicode character # sizes. self.assertEqual(self.type2test(b"\0a\0b\0c") == "abc", False) self.assertEqual(self.type2test(b"\0\0\0a\0\0\0b\0\0\0c") == "abc", False) self.assertEqual(self.type2test(b"a\0b\0c\0") == "abc", False) self.assertEqual(self.type2test(b"a\0\0\0b\0\0\0c\0\0\0") == "abc", False) self.assertEqual(self.type2test() == str(), False) self.assertEqual(self.type2test() != str(), True) def test_reversed(self): input = list(map(ord, "Hello")) b = self.type2test(input) output = list(reversed(b)) input.reverse() self.assertEqual(output, input) def test_getslice(self): def by(s): return self.type2test(map(ord, s)) b = by("Hello, world") self.assertEqual(b[:5], by("Hello")) self.assertEqual(b[1:5], by("ello")) self.assertEqual(b[5:7], by(", ")) self.assertEqual(b[7:], by("world")) self.assertEqual(b[7:12], by("world")) self.assertEqual(b[7:100], by("world")) self.assertEqual(b[:-7], by("Hello")) self.assertEqual(b[-11:-7], by("ello")) self.assertEqual(b[-7:-5], by(", ")) self.assertEqual(b[-5:], by("world")) self.assertEqual(b[-5:12], by("world")) self.assertEqual(b[-5:100], by("world")) self.assertEqual(b[-100:5], by("Hello")) def test_extended_getslice(self): # Test extended slicing by comparing with list slicing. L = list(range(255)) b = self.type2test(L) indices = (0, None, 1, 3, 19, 100, -1, -2, -31, -100) for start in indices: for stop in indices: # Skip step 0 (invalid) for step in indices[1:]: self.assertEqual(b[start:stop:step], self.type2test(L[start:stop:step])) def test_encoding(self): sample = "Hello world\n\u1234\u5678\u9abc" for enc in ("utf8", "utf16"): b = self.type2test(sample, enc) self.assertEqual(b, self.type2test(sample.encode(enc))) self.assertRaises(UnicodeEncodeError, self.type2test, sample, "latin1") b = self.type2test(sample, "latin1", "ignore") self.assertEqual(b, self.type2test(sample[:-3], "utf-8")) def test_decode(self): sample = "Hello world\n\u1234\u5678\u9abc\def0\def0" for enc in ("utf8", "utf16"): b = self.type2test(sample, enc) self.assertEqual(b.decode(enc), sample) sample = "Hello world\n\x80\x81\xfe\xff" b = self.type2test(sample, "latin1") self.assertRaises(UnicodeDecodeError, b.decode, "utf8") self.assertEqual(b.decode("utf8", "ignore"), "Hello world\n") self.assertEqual(b.decode(errors="ignore", encoding="utf8"), "Hello world\n") def test_from_int(self): b = self.type2test(0) self.assertEqual(b, self.type2test()) b = self.type2test(10) self.assertEqual(b, self.type2test([0]*10)) b = self.type2test(10000) self.assertEqual(b, self.type2test([0]*10000)) def test_concat(self): b1 = self.type2test(b"abc") b2 = self.type2test(b"def") self.assertEqual(b1 + b2, b"abcdef") self.assertEqual(b1 + bytes(b"def"), b"abcdef") self.assertEqual(bytes(b"def") + b1, b"defabc") self.assertRaises(TypeError, lambda: b1 + "def") self.assertRaises(TypeError, lambda: "abc" + b2) def test_repeat(self): for b in b"abc", self.type2test(b"abc"): self.assertEqual(b * 3, b"abcabcabc") self.assertEqual(b * 0, b"") self.assertEqual(b * -1, b"") self.assertRaises(TypeError, lambda: b * 3.14) self.assertRaises(TypeError, lambda: 3.14 * b) # XXX Shouldn't bytes and bytearray agree on what to raise? with self.assertRaises((OverflowError, MemoryError)): c = b * sys.maxsize with self.assertRaises((OverflowError, MemoryError)): b *= sys.maxsize def test_repeat_1char(self): self.assertEqual(self.type2test(b'x')*100, self.type2test([ord('x')]*100)) def test_contains(self): b = self.type2test(b"abc") self.assertIn(ord('a'), b) self.assertIn(int(ord('a')), b) self.assertNotIn(200, b) self.assertRaises(ValueError, lambda: 300 in b) self.assertRaises(ValueError, lambda: -1 in b) self.assertRaises(TypeError, lambda: None in b) self.assertRaises(TypeError, lambda: float(ord('a')) in b) self.assertRaises(TypeError, lambda: "a" in b) for f in bytes, bytearray: self.assertIn(f(b""), b) self.assertIn(f(b"a"), b) self.assertIn(f(b"b"), b) self.assertIn(f(b"c"), b) self.assertIn(f(b"ab"), b) self.assertIn(f(b"bc"), b) self.assertIn(f(b"abc"), b) self.assertNotIn(f(b"ac"), b) self.assertNotIn(f(b"d"), b) self.assertNotIn(f(b"dab"), b) self.assertNotIn(f(b"abd"), b) def test_fromhex(self): self.assertRaises(TypeError, self.type2test.fromhex) self.assertRaises(TypeError, self.type2test.fromhex, 1) self.assertEqual(self.type2test.fromhex(''), self.type2test()) b = bytearray([0x1a, 0x2b, 0x30]) self.assertEqual(self.type2test.fromhex('1a2B30'), b) self.assertEqual(self.type2test.fromhex(' 1A 2B 30 '), b) self.assertEqual(self.type2test.fromhex('0000'), b'\0\0') self.assertRaises(TypeError, self.type2test.fromhex, b'1B') self.assertRaises(ValueError, self.type2test.fromhex, 'a') self.assertRaises(ValueError, self.type2test.fromhex, 'rt') self.assertRaises(ValueError, self.type2test.fromhex, '1a b cd') self.assertRaises(ValueError, self.type2test.fromhex, '\x00') self.assertRaises(ValueError, self.type2test.fromhex, '12 \x00 34') def test_join(self): self.assertEqual(self.type2test(b"").join([]), b"") self.assertEqual(self.type2test(b"").join([b""]), b"") for lst in [[b"abc"], [b"a", b"bc"], [b"ab", b"c"], [b"a", b"b", b"c"]]: lst = list(map(self.type2test, lst)) self.assertEqual(self.type2test(b"").join(lst), b"abc") self.assertEqual(self.type2test(b"").join(tuple(lst)), b"abc") self.assertEqual(self.type2test(b"").join(iter(lst)), b"abc") self.assertEqual(self.type2test(b".").join([b"ab", b"cd"]), b"ab.cd") # XXX more... def test_count(self): b = self.type2test(b'mississippi') self.assertEqual(b.count(b'i'), 4) self.assertEqual(b.count(b'ss'), 2) self.assertEqual(b.count(b'w'), 0) def test_startswith(self): b = self.type2test(b'hello') self.assertFalse(self.type2test().startswith(b"anything")) self.assertTrue(b.startswith(b"hello")) self.assertTrue(b.startswith(b"hel")) self.assertTrue(b.startswith(b"h")) self.assertFalse(b.startswith(b"hellow")) self.assertFalse(b.startswith(b"ha")) with self.assertRaises(TypeError) as cm: b.startswith([b'h']) exc = str(cm.exception) self.assertIn('bytes', exc) self.assertIn('tuple', exc) def test_endswith(self): b = self.type2test(b'hello') self.assertFalse(bytearray().endswith(b"anything")) self.assertTrue(b.endswith(b"hello")) self.assertTrue(b.endswith(b"llo")) self.assertTrue(b.endswith(b"o")) self.assertFalse(b.endswith(b"whello")) self.assertFalse(b.endswith(b"no")) with self.assertRaises(TypeError) as cm: b.endswith([b'o']) exc = str(cm.exception) self.assertIn('bytes', exc) self.assertIn('tuple', exc) def test_find(self): b = self.type2test(b'mississippi') self.assertEqual(b.find(b'ss'), 2) self.assertEqual(b.find(b'ss', 3), 5) self.assertEqual(b.find(b'ss', 1, 7), 2) self.assertEqual(b.find(b'ss', 1, 3), -1) self.assertEqual(b.find(b'w'), -1) self.assertEqual(b.find(b'mississippian'), -1) def test_rfind(self): b = self.type2test(b'mississippi') self.assertEqual(b.rfind(b'ss'), 5) self.assertEqual(b.rfind(b'ss', 3), 5) self.assertEqual(b.rfind(b'ss', 0, 6), 2) self.assertEqual(b.rfind(b'w'), -1) self.assertEqual(b.rfind(b'mississippian'), -1) def test_index(self): b = self.type2test(b'world') self.assertEqual(b.index(b'w'), 0) self.assertEqual(b.index(b'orl'), 1) self.assertRaises(ValueError, b.index, b'worm') self.assertRaises(ValueError, b.index, b'ldo') def test_rindex(self): # XXX could be more rigorous b = self.type2test(b'world') self.assertEqual(b.rindex(b'w'), 0) self.assertEqual(b.rindex(b'orl'), 1) self.assertRaises(ValueError, b.rindex, b'worm') self.assertRaises(ValueError, b.rindex, b'ldo') def test_replace(self): b = self.type2test(b'mississippi') self.assertEqual(b.replace(b'i', b'a'), b'massassappa') self.assertEqual(b.replace(b'ss', b'x'), b'mixixippi') def test_split(self): b = self.type2test(b'mississippi') self.assertEqual(b.split(b'i'), [b'm', b'ss', b'ss', b'pp', b'']) self.assertEqual(b.split(b'ss'), [b'mi', b'i', b'ippi']) self.assertEqual(b.split(b'w'), [b]) def test_split_whitespace(self): for b in (b' arf barf ', b'arf\tbarf', b'arf\nbarf', b'arf\rbarf', b'arf\fbarf', b'arf\vbarf'): b = self.type2test(b) self.assertEqual(b.split(), [b'arf', b'barf']) self.assertEqual(b.split(None), [b'arf', b'barf']) self.assertEqual(b.split(None, 2), [b'arf', b'barf']) for b in (b'a\x1Cb', b'a\x1Db', b'a\x1Eb', b'a\x1Fb'): b = self.type2test(b) self.assertEqual(b.split(), [b]) self.assertEqual(self.type2test(b' a bb c ').split(None, 0), [b'a bb c ']) self.assertEqual(self.type2test(b' a bb c ').split(None, 1), [b'a', b'bb c ']) self.assertEqual(self.type2test(b' a bb c ').split(None, 2), [b'a', b'bb', b'c ']) self.assertEqual(self.type2test(b' a bb c ').split(None, 3), [b'a', b'bb', b'c']) def test_split_string_error(self): self.assertRaises(TypeError, self.type2test(b'a b').split, ' ') def test_split_unicodewhitespace(self): b = self.type2test(b"\x09\x0A\x0B\x0C\x0D\x1C\x1D\x1E\x1F") self.assertEqual(b.split(), [b'\x1c\x1d\x1e\x1f']) def test_rsplit(self): b = self.type2test(b'mississippi') self.assertEqual(b.rsplit(b'i'), [b'm', b'ss', b'ss', b'pp', b'']) self.assertEqual(b.rsplit(b'ss'), [b'mi', b'i', b'ippi']) self.assertEqual(b.rsplit(b'w'), [b]) def test_rsplit_whitespace(self): for b in (b' arf barf ', b'arf\tbarf', b'arf\nbarf', b'arf\rbarf', b'arf\fbarf', b'arf\vbarf'): b = self.type2test(b) self.assertEqual(b.rsplit(), [b'arf', b'barf']) self.assertEqual(b.rsplit(None), [b'arf', b'barf']) self.assertEqual(b.rsplit(None, 2), [b'arf', b'barf']) self.assertEqual(self.type2test(b' a bb c ').rsplit(None, 0), [b' a bb c']) self.assertEqual(self.type2test(b' a bb c ').rsplit(None, 1), [b' a bb', b'c']) self.assertEqual(self.type2test(b' a bb c ').rsplit(None, 2), [b' a', b'bb', b'c']) self.assertEqual(self.type2test(b' a bb c ').rsplit(None, 3), [b'a', b'bb', b'c']) def test_rsplit_string_error(self): self.assertRaises(TypeError, self.type2test(b'a b').rsplit, ' ') def test_rsplit_unicodewhitespace(self): b = self.type2test(b"\x09\x0A\x0B\x0C\x0D\x1C\x1D\x1E\x1F") self.assertEqual(b.rsplit(), [b'\x1c\x1d\x1e\x1f']) def test_partition(self): b = self.type2test(b'mississippi') self.assertEqual(b.partition(b'ss'), (b'mi', b'ss', b'issippi')) self.assertEqual(b.partition(b'w'), (b'mississippi', b'', b'')) def test_rpartition(self): b = self.type2test(b'mississippi') self.assertEqual(b.rpartition(b'ss'), (b'missi', b'ss', b'ippi')) self.assertEqual(b.rpartition(b'i'), (b'mississipp', b'i', b'')) self.assertEqual(b.rpartition(b'w'), (b'', b'', b'mississippi')) def test_pickling(self): for proto in range(pickle.HIGHEST_PROTOCOL + 1): for b in b"", b"a", b"abc", b"\xffab\x80", b"\0\0\377\0\0": b = self.type2test(b) ps = pickle.dumps(b, proto) q = pickle.loads(ps) self.assertEqual(b, q) def test_strip(self): b = self.type2test(b'mississippi') self.assertEqual(b.strip(b'i'), b'mississipp') self.assertEqual(b.strip(b'm'), b'ississippi') self.assertEqual(b.strip(b'pi'), b'mississ') self.assertEqual(b.strip(b'im'), b'ssissipp') self.assertEqual(b.strip(b'pim'), b'ssiss') self.assertEqual(b.strip(b), b'') def test_lstrip(self): b = self.type2test(b'mississippi') self.assertEqual(b.lstrip(b'i'), b'mississippi') self.assertEqual(b.lstrip(b'm'), b'ississippi') self.assertEqual(b.lstrip(b'pi'), b'mississippi') self.assertEqual(b.lstrip(b'im'), b'ssissippi') self.assertEqual(b.lstrip(b'pim'), b'ssissippi') def test_rstrip(self): b = self.type2test(b'mississippi') self.assertEqual(b.rstrip(b'i'), b'mississipp') self.assertEqual(b.rstrip(b'm'), b'mississippi') self.assertEqual(b.rstrip(b'pi'), b'mississ') self.assertEqual(b.rstrip(b'im'), b'mississipp') self.assertEqual(b.rstrip(b'pim'), b'mississ') def test_strip_whitespace(self): b = self.type2test(b' \t\n\r\f\vabc \t\n\r\f\v') self.assertEqual(b.strip(), b'abc') self.assertEqual(b.lstrip(), b'abc \t\n\r\f\v') self.assertEqual(b.rstrip(), b' \t\n\r\f\vabc') def test_strip_bytearray(self): self.assertEqual(self.type2test(b'abc').strip(memoryview(b'ac')), b'b') self.assertEqual(self.type2test(b'abc').lstrip(memoryview(b'ac')), b'bc') self.assertEqual(self.type2test(b'abc').rstrip(memoryview(b'ac')), b'ab') def test_strip_string_error(self): self.assertRaises(TypeError, self.type2test(b'abc').strip, 'b') self.assertRaises(TypeError, self.type2test(b'abc').lstrip, 'b') self.assertRaises(TypeError, self.type2test(b'abc').rstrip, 'b') def test_ord(self): b = self.type2test(b'\0A\x7f\x80\xff') self.assertEqual([ord(b[i:i+1]) for i in range(len(b))], [0, 65, 127, 128, 255]) def test_maketrans(self): transtable = b'\000\001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037 !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`xyzdefghijklmnopqrstuvwxyz{|}~\177\200\201\202\203\204\205\206\207\210\211\212\213\214\215\216\217\220\221\222\223\224\225\226\227\230\231\232\233\234\235\236\237\240\241\242\243\244\245\246\247\250\251\252\253\254\255\256\257\260\261\262\263\264\265\266\267\270\271\272\273\274\275\276\277\300\301\302\303\304\305\306\307\310\311\312\313\314\315\316\317\320\321\322\323\324\325\326\327\330\331\332\333\334\335\336\337\340\341\342\343\344\345\346\347\350\351\352\353\354\355\356\357\360\361\362\363\364\365\366\367\370\371\372\373\374\375\376\377' self.assertEqual(self.type2test.maketrans(b'abc', b'xyz'), transtable) transtable = b'\000\001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037 !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\177\200\201\202\203\204\205\206\207\210\211\212\213\214\215\216\217\220\221\222\223\224\225\226\227\230\231\232\233\234\235\236\237\240\241\242\243\244\245\246\247\250\251\252\253\254\255\256\257\260\261\262\263\264\265\266\267\270\271\272\273\274\275\276\277\300\301\302\303\304\305\306\307\310\311\312\313\314\315\316\317\320\321\322\323\324\325\326\327\330\331\332\333\334\335\336\337\340\341\342\343\344\345\346\347\350\351\352\353\354\355\356\357\360\361\362\363\364\365\366\367\370\371\372\373\374xyz' self.assertEqual(self.type2test.maketrans(b'\375\376\377', b'xyz'), transtable) self.assertRaises(ValueError, self.type2test.maketrans, b'abc', b'xyzq') self.assertRaises(TypeError, self.type2test.maketrans, 'abc', 'def') def test_none_arguments(self): # issue 11828 b = self.type2test(b'hello') l = self.type2test(b'l') h = self.type2test(b'h') x = self.type2test(b'x') o = self.type2test(b'o') self.assertEqual(2, b.find(l, None)) self.assertEqual(3, b.find(l, -2, None)) self.assertEqual(2, b.find(l, None, -2)) self.assertEqual(0, b.find(h, None, None)) self.assertEqual(3, b.rfind(l, None)) self.assertEqual(3, b.rfind(l, -2, None)) self.assertEqual(2, b.rfind(l, None, -2)) self.assertEqual(0, b.rfind(h, None, None)) self.assertEqual(2, b.index(l, None)) self.assertEqual(3, b.index(l, -2, None)) self.assertEqual(2, b.index(l, None, -2)) self.assertEqual(0, b.index(h, None, None)) self.assertEqual(3, b.rindex(l, None)) self.assertEqual(3, b.rindex(l, -2, None)) self.assertEqual(2, b.rindex(l, None, -2)) self.assertEqual(0, b.rindex(h, None, None)) self.assertEqual(2, b.count(l, None)) self.assertEqual(1, b.count(l, -2, None)) self.assertEqual(1, b.count(l, None, -2)) self.assertEqual(0, b.count(x, None, None)) self.assertEqual(True, b.endswith(o, None)) self.assertEqual(True, b.endswith(o, -2, None)) self.assertEqual(True, b.endswith(l, None, -2)) self.assertEqual(False, b.endswith(x, None, None)) self.assertEqual(True, b.startswith(h, None)) self.assertEqual(True, b.startswith(l, -2, None)) self.assertEqual(True, b.startswith(h, None, -2)) self.assertEqual(False, b.startswith(x, None, None)) def test_find_etc_raise_correct_error_messages(self): # issue 11828 b = self.type2test(b'hello') x = self.type2test(b'x') self.assertRaisesRegex(TypeError, r'\bfind\b', b.find, x, None, None, None) self.assertRaisesRegex(TypeError, r'\brfind\b', b.rfind, x, None, None, None) self.assertRaisesRegex(TypeError, r'\bindex\b', b.index, x, None, None, None) self.assertRaisesRegex(TypeError, r'\brindex\b', b.rindex, x, None, None, None) self.assertRaisesRegex(TypeError, r'\bcount\b', b.count, x, None, None, None) self.assertRaisesRegex(TypeError, r'\bstartswith\b', b.startswith, x, None, None, None) self.assertRaisesRegex(TypeError, r'\bendswith\b', b.endswith, x, None, None, None) class BytesTest(BaseBytesTest): type2test = bytes def test_buffer_is_readonly(self): fd = os.dup(sys.stdin.fileno()) with open(fd, "rb", buffering=0) as f: self.assertRaises(TypeError, f.readinto, b"") def test_custom(self): class A: def __bytes__(self): return b'abc' self.assertEqual(bytes(A()), b'abc') class A: pass self.assertRaises(TypeError, bytes, A()) class A: def __bytes__(self): return None self.assertRaises(TypeError, bytes, A()) # Test PyBytes_FromFormat() @test.support.impl_detail("don't test cpyext here") def test_from_format(self): test.support.import_module('ctypes') from ctypes import pythonapi, py_object, c_int, c_char_p PyBytes_FromFormat = pythonapi.PyBytes_FromFormat PyBytes_FromFormat.restype = py_object self.assertEqual(PyBytes_FromFormat(b'format'), b'format') self.assertEqual(PyBytes_FromFormat(b'%'), b'%') self.assertEqual(PyBytes_FromFormat(b'%%'), b'%') self.assertEqual(PyBytes_FromFormat(b'%%s'), b'%s') self.assertEqual(PyBytes_FromFormat(b'[%%]'), b'[%]') self.assertEqual(PyBytes_FromFormat(b'%%%c', c_int(ord('_'))), b'%_') self.assertEqual(PyBytes_FromFormat(b'c:%c', c_int(255)), b'c:\xff') self.assertEqual(PyBytes_FromFormat(b's:%s', c_char_p(b'cstr')), b's:cstr') class ByteArrayTest(BaseBytesTest): type2test = bytearray def test_nohash(self): self.assertRaises(TypeError, hash, bytearray()) def test_bytearray_api(self): short_sample = b"Hello world\n" sample = short_sample + b"\0"*(20 - len(short_sample)) tfn = tempfile.mktemp() try: # Prepare with open(tfn, "wb") as f: f.write(short_sample) # Test readinto with open(tfn, "rb") as f: b = bytearray(20) n = f.readinto(b) self.assertEqual(n, len(short_sample)) self.assertEqual(list(b), list(sample)) # Test writing in binary mode with open(tfn, "wb") as f: f.write(b) with open(tfn, "rb") as f: self.assertEqual(f.read(), sample) # Text mode is ambiguous; don't test finally: try: os.remove(tfn) except os.error: pass def test_reverse(self): b = bytearray(b'hello') self.assertEqual(b.reverse(), None) self.assertEqual(b, b'olleh') b = bytearray(b'hello1') # test even number of items b.reverse() self.assertEqual(b, b'1olleh') b = bytearray() b.reverse() self.assertFalse(b) def test_regexps(self): def by(s): return bytearray(map(ord, s)) b = by("Hello, world") self.assertEqual(re.findall(br"\w+", b), [by("Hello"), by("world")]) def test_setitem(self): b = bytearray([1, 2, 3]) b[1] = 100 self.assertEqual(b, bytearray([1, 100, 3])) b[-1] = 200 self.assertEqual(b, bytearray([1, 100, 200])) b[0] = Indexable(10) self.assertEqual(b, bytearray([10, 100, 200])) try: b[3] = 0 self.fail("Didn't raise IndexError") except IndexError: pass try: b[-10] = 0 self.fail("Didn't raise IndexError") except IndexError: pass try: b[0] = 256 self.fail("Didn't raise ValueError") except ValueError: pass try: b[0] = Indexable(-1) self.fail("Didn't raise ValueError") except ValueError: pass try: b[0] = None self.fail("Didn't raise TypeError") except TypeError: pass def test_delitem(self): b = bytearray(range(10)) del b[0] self.assertEqual(b, bytearray(range(1, 10))) del b[-1] self.assertEqual(b, bytearray(range(1, 9))) del b[4] self.assertEqual(b, bytearray([1, 2, 3, 4, 6, 7, 8])) def test_setslice(self): b = bytearray(range(10)) self.assertEqual(list(b), list(range(10))) b[0:5] = bytearray([1, 1, 1, 1, 1]) self.assertEqual(b, bytearray([1, 1, 1, 1, 1, 5, 6, 7, 8, 9])) del b[0:-5] self.assertEqual(b, bytearray([5, 6, 7, 8, 9])) b[0:0] = bytearray([0, 1, 2, 3, 4]) self.assertEqual(b, bytearray(range(10))) b[-7:-3] = bytearray([100, 101]) self.assertEqual(b, bytearray([0, 1, 2, 100, 101, 7, 8, 9])) b[3:5] = [3, 4, 5, 6] self.assertEqual(b, bytearray(range(10))) b[3:0] = [42, 42, 42] self.assertEqual(b, bytearray([0, 1, 2, 42, 42, 42, 3, 4, 5, 6, 7, 8, 9])) def test_extended_set_del_slice(self): indices = (0, None, 1, 3, 19, 300, 1<<333, -1, -2, -31, -300) for start in indices: for stop in indices: # Skip invalid step 0 for step in indices[1:]: L = list(range(255)) b = bytearray(L) # Make sure we have a slice of exactly the right length, # but with different data. data = L[start:stop:step] data.reverse() L[start:stop:step] = data b[start:stop:step] = data self.assertEqual(b, bytearray(L)) del L[start:stop:step] del b[start:stop:step] self.assertEqual(b, bytearray(L)) def test_setslice_trap(self): # This test verifies that we correctly handle assigning self # to a slice of self (the old Lambert Meertens trap). b = bytearray(range(256)) b[8:] = b self.assertEqual(b, bytearray(list(range(8)) + list(range(256)))) def test_iconcat(self): b = bytearray(b"abc") b1 = b b += b"def" self.assertEqual(b, b"abcdef") self.assertEqual(b, b1) self.assertTrue(b is b1) b += b"xyz" self.assertEqual(b, b"abcdefxyz") try: b += "" except TypeError: pass else: self.fail("bytes += unicode didn't raise TypeError") def test_irepeat(self): b = bytearray(b"abc") b1 = b b *= 3 self.assertEqual(b, b"abcabcabc") self.assertEqual(b, b1) self.assertTrue(b is b1) def test_irepeat_1char(self): b = bytearray(b"x") b1 = b b *= 100 self.assertEqual(b, b"x"*100) self.assertEqual(b, b1) self.assertTrue(b is b1) @test.support.impl_detail("undocumented bytes.__alloc__()") def test_alloc(self): b = bytearray() alloc = b.__alloc__() self.assertTrue(alloc >= 0) seq = [alloc] for i in range(100): b += b"x" alloc = b.__alloc__() self.assertTrue(alloc >= len(b)) if alloc not in seq: seq.append(alloc) def test_extend(self): orig = b'hello' a = bytearray(orig) a.extend(a) self.assertEqual(a, orig + orig) self.assertEqual(a[5:], orig) a = bytearray(b'') # Test iterators that don't have a __length_hint__ a.extend(map(int, orig * 25)) a.extend(int(x) for x in orig * 25) self.assertEqual(a, orig * 50) self.assertEqual(a[-5:], orig) a = bytearray(b'') a.extend(iter(map(int, orig * 50))) self.assertEqual(a, orig * 50) self.assertEqual(a[-5:], orig) a = bytearray(b'') a.extend(list(map(int, orig * 50))) self.assertEqual(a, orig * 50) self.assertEqual(a[-5:], orig) a = bytearray(b'') self.assertRaises(ValueError, a.extend, [0, 1, 2, 256]) self.assertRaises(ValueError, a.extend, [0, 1, 2, -1]) self.assertEqual(len(a), 0) a = bytearray(b'') a.extend([Indexable(ord('a'))]) self.assertEqual(a, b'a') def test_remove(self): b = bytearray(b'hello') b.remove(ord('l')) self.assertEqual(b, b'helo') b.remove(ord('l')) self.assertEqual(b, b'heo') self.assertRaises(ValueError, lambda: b.remove(ord('l'))) self.assertRaises(ValueError, lambda: b.remove(400)) self.assertRaises(TypeError, lambda: b.remove('e')) # remove first and last b.remove(ord('o')) b.remove(ord('h')) self.assertEqual(b, b'e') self.assertRaises(TypeError, lambda: b.remove(b'e')) b.remove(Indexable(ord('e'))) self.assertEqual(b, b'') def test_pop(self): b = bytearray(b'world') self.assertEqual(b.pop(), ord('d')) self.assertEqual(b.pop(0), ord('w')) self.assertEqual(b.pop(-2), ord('r')) self.assertRaises(IndexError, lambda: b.pop(10)) self.assertRaises(IndexError, lambda: bytearray().pop()) # test for issue #6846 self.assertEqual(bytearray(b'\xff').pop(), 0xff) def test_nosort(self): self.assertRaises(AttributeError, lambda: bytearray().sort()) def test_append(self): b = bytearray(b'hell') b.append(ord('o')) self.assertEqual(b, b'hello') self.assertEqual(b.append(100), None) b = bytearray() b.append(ord('A')) self.assertEqual(len(b), 1) self.assertRaises(TypeError, lambda: b.append(b'o')) b = bytearray() b.append(Indexable(ord('A'))) self.assertEqual(b, b'A') def test_insert(self): b = bytearray(b'msssspp') b.insert(1, ord('i')) b.insert(4, ord('i')) b.insert(-2, ord('i')) b.insert(1000, ord('i')) self.assertEqual(b, b'mississippi') self.assertRaises(TypeError, lambda: b.insert(0, b'1')) b = bytearray() b.insert(0, Indexable(ord('A'))) self.assertEqual(b, b'A') def test_copied(self): # Issue 4348. Make sure that operations that don't mutate the array # copy the bytes. b = bytearray(b'abc') self.assertFalse(b is b.replace(b'abc', b'cde', 0)) t = bytearray([i for i in range(256)]) x = bytearray(b'') self.assertFalse(x is x.translate(t)) def test_partition_bytearray_doesnt_share_nullstring(self): a, b, c = bytearray(b"x").partition(b"y") self.assertEqual(b, b"") self.assertEqual(c, b"") self.assertTrue(b is not c) b += b"!" self.assertEqual(c, b"") a, b, c = bytearray(b"x").partition(b"y") self.assertEqual(b, b"") self.assertEqual(c, b"") # Same for rpartition b, c, a = bytearray(b"x").rpartition(b"y") self.assertEqual(b, b"") self.assertEqual(c, b"") self.assertTrue(b is not c) b += b"!" self.assertEqual(c, b"") c, b, a = bytearray(b"x").rpartition(b"y") self.assertEqual(b, b"") self.assertEqual(c, b"") @test.support.impl_detail( "resizing semantics of CPython rely on refcounting") def test_resize_forbidden(self): # #4509: can't resize a bytearray when there are buffer exports, even # if it wouldn't reallocate the underlying buffer. # Furthermore, no destructive changes to the buffer may be applied # before raising the error. b = bytearray(range(10)) v = memoryview(b) def resize(n): b[1:-1] = range(n + 1, 2*n - 1) resize(10) orig = b[:] self.assertRaises(BufferError, resize, 11) self.assertEqual(b, orig) self.assertRaises(BufferError, resize, 9) self.assertEqual(b, orig) self.assertRaises(BufferError, resize, 0) self.assertEqual(b, orig) # Other operations implying resize self.assertRaises(BufferError, b.pop, 0) self.assertEqual(b, orig) self.assertRaises(BufferError, b.remove, b[1]) self.assertEqual(b, orig) def delitem(): del b[1] self.assertRaises(BufferError, delitem) self.assertEqual(b, orig) # deleting a non-contiguous slice def delslice(): b[1:-1:2] = b"" self.assertRaises(BufferError, delslice) self.assertEqual(b, orig) @test.support.impl_detail("resizing semantics", cpython=False) def test_resize_forbidden_non_cpython(self): # on non-CPython implementations, we cannot prevent changes to # bytearrays just because there are buffers around. Instead, # we get (on PyPy) a buffer that follows the changes and resizes. b = bytearray(range(10)) v = memoryview(b) b[5] = 99 self.assertIn(v[5], (99, bytes([99]))) b[5] = 100 b += b b += b b += b self.assertEquals(len(v), 80) self.assertIn(v[5], (100, bytes([100]))) self.assertIn(v[79], (9, bytes([9]))) del b[10:] self.assertRaises(IndexError, lambda: v[10]) self.assertEquals(len(v), 10) class AssortedBytesTest(unittest.TestCase): # # Test various combinations of bytes and bytearray # @check_bytes_warnings def test_repr_str(self): for f in str, repr: self.assertEqual(f(bytearray()), "bytearray(b'')") self.assertEqual(f(bytearray([0])), "bytearray(b'\\x00')") self.assertEqual(f(bytearray([0, 1, 254, 255])), "bytearray(b'\\x00\\x01\\xfe\\xff')") self.assertEqual(f(b"abc"), "b'abc'") self.assertEqual(f(b"'"), '''b"'"''') # ''' self.assertEqual(f(b"'\""), r"""b'\'"'""") # ' def test_compare_bytes_to_bytearray(self): self.assertEqual(b"abc" == bytes(b"abc"), True) self.assertEqual(b"ab" != bytes(b"abc"), True) self.assertEqual(b"ab" <= bytes(b"abc"), True) self.assertEqual(b"ab" < bytes(b"abc"), True) self.assertEqual(b"abc" >= bytes(b"ab"), True) self.assertEqual(b"abc" > bytes(b"ab"), True) self.assertEqual(b"abc" != bytes(b"abc"), False) self.assertEqual(b"ab" == bytes(b"abc"), False) self.assertEqual(b"ab" > bytes(b"abc"), False) self.assertEqual(b"ab" >= bytes(b"abc"), False) self.assertEqual(b"abc" < bytes(b"ab"), False) self.assertEqual(b"abc" <= bytes(b"ab"), False) self.assertEqual(bytes(b"abc") == b"abc", True) self.assertEqual(bytes(b"ab") != b"abc", True) self.assertEqual(bytes(b"ab") <= b"abc", True) self.assertEqual(bytes(b"ab") < b"abc", True) self.assertEqual(bytes(b"abc") >= b"ab", True) self.assertEqual(bytes(b"abc") > b"ab", True) self.assertEqual(bytes(b"abc") != b"abc", False) self.assertEqual(bytes(b"ab") == b"abc", False) self.assertEqual(bytes(b"ab") > b"abc", False) self.assertEqual(bytes(b"ab") >= b"abc", False) self.assertEqual(bytes(b"abc") < b"ab", False) self.assertEqual(bytes(b"abc") <= b"ab", False) def test_doc(self): self.assertIsNotNone(bytearray.__doc__) self.assertTrue(bytearray.__doc__.startswith("bytearray("), bytearray.__doc__) self.assertIsNotNone(bytes.__doc__) self.assertTrue(bytes.__doc__.startswith("bytes("), bytes.__doc__) def test_from_bytearray(self): sample = bytes(b"Hello world\n\x80\x81\xfe\xff") buf = memoryview(sample) b = bytearray(buf) self.assertEqual(b, bytearray(sample)) @check_bytes_warnings def test_to_str(self): self.assertEqual(str(b''), "b''") self.assertEqual(str(b'x'), "b'x'") self.assertEqual(str(b'\x80'), "b'\\x80'") self.assertEqual(str(bytearray(b'')), "bytearray(b'')") self.assertEqual(str(bytearray(b'x')), "bytearray(b'x')") self.assertEqual(str(bytearray(b'\x80')), "bytearray(b'\\x80')") def test_literal(self): tests = [ (b"Wonderful spam", "Wonderful spam"), (br"Wonderful spam too", "Wonderful spam too"), (b"\xaa\x00\000\200", "\xaa\x00\000\200"), (br"\xaa\x00\000\200", r"\xaa\x00\000\200"), ] for b, s in tests: self.assertEqual(b, bytearray(s, 'latin-1')) for c in range(128, 256): self.assertRaises(SyntaxError, eval, 'b"%s"' % chr(c)) def test_translate(self): b = b'hello' ba = bytearray(b) rosetta = bytearray(range(0, 256)) rosetta[ord('o')] = ord('e') c = b.translate(rosetta, b'l') self.assertEqual(b, b'hello') self.assertEqual(c, b'hee') c = ba.translate(rosetta, b'l') self.assertEqual(ba, b'hello') self.assertEqual(c, b'hee') c = b.translate(None, b'e') self.assertEqual(c, b'hllo') c = ba.translate(None, b'e') self.assertEqual(c, b'hllo') self.assertRaises(TypeError, b.translate, None, None) self.assertRaises(TypeError, ba.translate, None, None) def test_split_bytearray(self): self.assertEqual(b'a b'.split(memoryview(b' ')), [b'a', b'b']) def test_rsplit_bytearray(self): self.assertEqual(b'a b'.rsplit(memoryview(b' ')), [b'a', b'b']) def test_return_self(self): # bytearray.replace must always return a new bytearray b = bytearray() self.assertFalse(b.replace(b'', b'') is b) def test_compare(self): if sys.flags.bytes_warning: def bytes_warning(): return test.support.check_warnings(('', BytesWarning)) with bytes_warning(): b'' == '' with bytes_warning(): b'' != '' with bytes_warning(): bytearray(b'') == '' with bytes_warning(): bytearray(b'') != '' else: self.skipTest("BytesWarning is needed for this test: use -bb option") # Optimizations: # __iter__? (optimization) # __reversed__? (optimization) # XXX More string methods? (Those that don't use character properties) # There are tests in string_tests.py that are more # comprehensive for things like split, partition, etc. # Unfortunately they are all bundled with tests that # are not appropriate for bytes # I've started porting some of those into bytearray_tests.py, we should port # the rest that make sense (the code can be cleaned up to use modern # unittest methods at the same time). class BytearrayPEP3137Test(unittest.TestCase, test.buffer_tests.MixinBytesBufferCommonTests): def marshal(self, x): return bytearray(x) def test_returns_new_copy(self): val = self.marshal(b'1234') # On immutable types these MAY return a reference to themselves # but on mutable types like bytearray they MUST return a new copy. for methname in ('zfill', 'rjust', 'ljust', 'center'): method = getattr(val, methname) newval = method(3) self.assertEqual(val, newval) self.assertTrue(val is not newval, methname+' returned self on a mutable object') for expr in ('val.split()[0]', 'val.rsplit()[0]', 'val.partition(b".")[0]', 'val.rpartition(b".")[2]', 'val.splitlines()[0]', 'val.replace(b"", b"")'): newval = eval(expr) self.assertEqual(val, newval) self.assertTrue(val is not newval, expr+' returned val on a mutable object') class FixedStringTest(test.string_tests.BaseTest): def fixtype(self, obj): if isinstance(obj, str): return obj.encode("utf-8") return super().fixtype(obj) # Currently the bytes containment testing uses a single integer # value. This may not be the final design, but until then the # bytes section with in a bytes containment not valid def test_contains(self): pass def test_expandtabs(self): pass def test_upper(self): pass def test_lower(self): pass class ByteArrayAsStringTest(FixedStringTest): type2test = bytearray class BytesAsStringTest(FixedStringTest): type2test = bytes class SubclassTest(unittest.TestCase): def test_basic(self): self.assertTrue(issubclass(self.subclass2test, self.type2test)) self.assertIsInstance(self.subclass2test(), self.type2test) a, b = b"abcd", b"efgh" _a, _b = self.subclass2test(a), self.subclass2test(b) # test comparison operators with subclass instances self.assertTrue(_a == _a) self.assertTrue(_a != _b) self.assertTrue(_a < _b) self.assertTrue(_a <= _b) self.assertTrue(_b >= _a) self.assertTrue(_b > _a) self.assertTrue(_a is not a) # test concat of subclass instances self.assertEqual(a + b, _a + _b) self.assertEqual(a + b, a + _b) self.assertEqual(a + b, _a + b) # test repeat self.assertTrue(a*5 == _a*5) def test_join(self): # Make sure join returns a NEW object for single item sequences # involving a subclass. # Make sure that it is of the appropriate type. s1 = self.subclass2test(b"abcd") s2 = self.type2test().join([s1]) self.assertTrue(s1 is not s2) self.assertTrue(type(s2) is self.type2test, type(s2)) # Test reverse, calling join on subclass s3 = s1.join([b"abcd"]) self.assertTrue(type(s3) is self.type2test) def test_pickle(self): a = self.subclass2test(b"abcd") a.x = 10 a.y = self.subclass2test(b"efgh") for proto in range(pickle.HIGHEST_PROTOCOL + 1): b = pickle.loads(pickle.dumps(a, proto)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(a.y, b.y) self.assertEqual(type(a), type(b)) self.assertEqual(type(a.y), type(b.y)) def test_copy(self): a = self.subclass2test(b"abcd") a.x = 10 a.y = self.subclass2test(b"efgh") for copy_method in (copy.copy, copy.deepcopy): b = copy_method(a) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(a.y, b.y) self.assertEqual(type(a), type(b)) self.assertEqual(type(a.y), type(b.y)) class ByteArraySubclass(bytearray): pass class BytesSubclass(bytes): pass class ByteArraySubclassTest(SubclassTest): type2test = bytearray subclass2test = ByteArraySubclass def test_init_override(self): class subclass(bytearray): def __init__(me, newarg=1, *args, **kwargs): bytearray.__init__(me, *args, **kwargs) x = subclass(4, b"abcd") x = subclass(4, source=b"abcd") self.assertEqual(x, b"abcd") x = subclass(newarg=4, source=b"abcd") self.assertEqual(x, b"abcd") class BytesSubclassTest(SubclassTest): type2test = bytes subclass2test = BytesSubclass def test_main(): test.support.run_unittest( BytesTest, AssortedBytesTest, BytesAsStringTest, ByteArrayTest, ByteArrayAsStringTest, BytesSubclassTest, ByteArraySubclassTest, BytearrayPEP3137Test) if __name__ == "__main__": test_main()

[ "thezhangwei@gmail.com" ]

thezhangwei@gmail.com

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# This file is protected via CODEOWNERS __version__ = "1.26.12"

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from django.http import HttpResponse, HttpResponseServerError from django.urls import path, include from django_dynamic_path import DynamicPath def confirm_args(request, *args, **kwargs): if len(args) == 2 and args[0] == 'a' and kwargs['b'] == 2 : return HttpResponse() raise RuntimeError('did not get expected args/kwargs') def do_nothing(request, *args, **kwargs): pass def interact(path): import code; code.interact(local=locals()) urlpatterns = [ path('path_before/', lambda r: HttpResponse('path before'), name='path_before_name'), DynamicPath( lambda path: path == 'bar/' and ((), {}), lambda r: HttpResponse('bar from dynamic'), ), DynamicPath( lambda path: path == 'baz/' and (('a', 'b'), {'a': 1, 'b': 2}), confirm_args, ), DynamicPath( lambda path: path == 'list_args/' and ([1, 2], {}), do_nothing, ), path('included/', include([DynamicPath( lambda path: path == 'baz/' and (('a', 'b'), {'a': 1, 'b': 2}), confirm_args, )])), path('path_after/', lambda r: HttpResponse('path after'), name='path_after_name'), path('path_after/<value>/', lambda r: HttpResponse('path after'), name='path_after_name'), ]

[ "alex@quadrant.net" ]

alex@quadrant.net

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 1.11.14. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'u23lydl-udf8m*(&p7(y176uul&jxr$m2)1pttf)@=t$p*sbw(' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['/Users/mohan/Documents/GitHub/PythonProjects/FirstDjangoProj/mysite/templates/'], #'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'static'), #'/var/www/static/', ] STATIC_ROOT = os.path.join(BASE_DIR, 'static_cdn') #https://www.youtube.com/watch?v=YH-ipgxlJzs - steps for python manage.py collectstatic MEDIA_ROOT = os.path.join(BASE_DIR, 'media_cdn')

[ "mohan.kris.lakshmanan@gmail.com" ]

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from scipy.io import wavfile from scipy import signal from numpy import mean, std from matplotlib import pyplot as plt def keystroke_starts(wav_filename): c_minFreqHz = 400 c_maxFreqHz = 12000 freq, data = wavfile.read(wav_filename) # select only the left channel for now leftData = data[:,0] numSamples = len(data) # # coefficients = abs(fft.rfft(data)) # frequencies = fft.rfftfreq(numSamples, 1/freq) # frequencies = [(i,f) for i,f in enumerate(frequencies) if c_minFreqHz < f < c_maxFreqHz] # plt.plot([f[1] for f in frequencies], coefficients[[f[0] for f in frequencies]]) # plt.show() # energy = sum(coefficients[[f[0] for f in frequencies]]) # print(energy) f, t, Sxx = signal.spectrogram(leftData, fs=freq, nperseg=440) freqs = [(index, freq) for index, freq in enumerate(f) if c_minFreqHz < freq < c_maxFreqHz] energies = [sum(col) for col in zip(*Sxx[[f[0] for f in freqs]])] threshold = mean(energies) + 1.5*std(energies) #plt.plot(t, energies) energies = [(i,e) for i, e in enumerate(energies) if e > threshold] print(len(energies)) print(t[[e[0] for e in energies]]) #plt.plot(t[[e[0] for e in energies]],[e[1] for e in energies]) #plt.show() return (freq*(t[[e[0] for e in energies]])).tolist() # for e in energies: # wavfile.write(str(e[0]) + '.wav', freq, data[range(freq*t[e[0]], freq*t[e[0]])]) # print(t[e[0]]) # #for i in range(100,200): # plt.plot([f[1] for f in freqs], Sxx[[f[0] for f in freqs], i]) #lt.show() if __name__=="__main__": wav_filename = "../resources/abc.wav" starts = keystroke_starts(wav_filename) print(starts)

[ "github@harishlall.com" ]

github@harishlall.com

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#!/usr/bin/env python # coding: utf-8 # In[ ]: import json import plotly import pandas as pd from nltk.stem import WordNetLemmatizer from nltk.tokenize import word_tokenize from flask import Flask from flask import render_template, request, jsonify from plotly.graph_objs import Bar from sklearn.externals import joblib from sqlalchemy import create_engine import numpy as np import pandas as pd import sys import os import re from sqlalchemy import create_engine import pickle from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.metrics import fbeta_score, make_scorer from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import GridSearchCV from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer from sklearn.multioutput import MultiOutputClassifier from sklearn.base import BaseEstimator,TransformerMixin app = Flask(__name__) def tokenize(text): text = re.sub(r"[^a-zA-Z0-9]", " ", text) tokens = word_tokenize(text) lemmatizer = WordNetLemmatizer() clean_tokens = [] for tok in tokens: clean_tok = lemmatizer.lemmatize(tok).lower().strip() clean_tokens.append(clean_tok) return clean_tokens # load data engine = create_engine('sqlite:///../data/DisasterResponse.db') df = pd.read_sql_table('Database Project_2', engine) # load model model = joblib.load("../models/classifier.pkl") # index webpage displays cool visuals and receives user input text for model @app.route('/') @app.route('/index') def index(): # extract data needed for visuals # TODO: Below is an example - modify to extract data for your own visuals genre_counts = df.groupby('genre').count()['message'] genre_names = list(genre_counts.index) y_labels = df.iloc[:,4:].columns y_values = (df.iloc[:,4:] == 1).sum().values # create visuals # TODO: Below is an example - modify to create your own visuals graphs = [ { 'data': [ Bar( x=genre_names, y=genre_counts ) ], 'layout': { 'title': 'Distribution of Message Genres', 'yaxis': { 'title': "Count" }, 'xaxis': { 'title': "Genre" } } }, { 'data': [ Bar( x=y_values, y=y_labels, orientation='h' ) ], 'layout': { 'title': 'Distribution of Targets', 'yaxis': { 'title': "Frequency" }, 'xaxis': { 'title': "Categories" } } } ] # encode plotly graphs in JSON ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)] graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder) # render web page with plotly graphs return render_template('master.html', ids=ids, graphJSON=graphJSON) # web page that handles user query and displays model results @app.route('/go') def go(): # save user input in query query = request.args.get('query', '') # use model to predict classification for query classification_labels = model.predict([query])[0] classification_results = dict(zip(df.columns[4:], classification_labels)) # This will render the go.html Please see that file. return render_template( 'go.html', query=query, classification_result=classification_results ) def main(): app.run(host='0.0.0.0', port=3001, debug=True) if __name__ == '__main__': main()

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#!/usr/bin/env python #------------------------------------------------------------------------------ # importDialogue.py - Imports a Flix text file and assigns dialogue to the panels #------------------------------------------------------------------------------ # Copyright (c) 2014 The Foundry Visionmongers Ltd. All Rights Reserved. #------------------------------------------------------------------------------ import os import urllib2, urllib import copy import math from flix.fileServices import FileService from flix.fileServices.repathDefault import RepathDefault from flix.plugins.toMov import ToMov from flix.core2.shotCutList import ShotCutList from flix.gui.fileBrowserTypes import kBrowseTypeLoadFiles from flix.utilities.log import log as log from flix.utilities.osUtils import OSUtils from flix.utilities.editUtils import EditUtils import flix.exceptions from flix.plugins.pluginDecorator import PluginDecorator from flix.core2.mode import Mode from flix.web.serverSession import ServerSession from flix.core2.markerList import MarkerList from flix.web.serverFlixFunctions import ServerFlixFunctions import flix.core2.shotCutList import flix.remote import flix.logger from xml.sax.saxutils import escape import xml.etree.ElementTree as ET # ----------------------------------------------------- # Global Variables # ----------------------------------------------------- serverSession = ServerSession() fileService = FileService() class ImportDialogue(PluginDecorator): #-------------------------------------------------------------------------- # object #-------------------------------------------------------------------------- def __init__(self): self.fileService = fileService self.rePath = RepathDefault() self.shotList = '' self.serverFlixFunctions = ServerFlixFunctions() # load the icon iconPath = Mode().get('[FLIX_CONFIG_FOLDER]')+'/plugins/icons/custom_icon.png' icon = self.fileService.loadByteArray(iconPath) self.init(label ='Import Dialogue', icon =icon, tooltip ='Import dialogue to current edit', group ='Export', pluginPath='flixConfig.plugins.importDialogue.ImportDialogue') #-------------------------------------------------------------------------- # executions #-------------------------------------------------------------------------- def execute(self, shotCutList, selection, additionalData=None): dialogueFile = self.loadFileBrowser() if not dialogueFile or not os.path.exists(dialogueFile) or not dialogueFile[-4:] == ".txt": raise flix.exceptions.FlixException("No valid text file selected.") self.shotList = flix.core2.shotCutList.ShotCutList.fromFile(shotCutList.defaultPath()) panels = self.getPanelSetupList() with open(dialogueFile) as f: dialogueFileContent = f.readlines() panelDialogueLines = self.getPanelDialogueLines(panels, dialogueFileContent) data = [] data.append('<Recipies>') for panel in self.shotList: recipe = panel.recipe if not panel.isMarker(): dialogueLines = panelDialogueLines[recipe.getShortLabelName()] if not dialogueLines == [-1]: newDialogue = u"" for line in range(dialogueLines[0], dialogueLines[1]+1): newDialogue += dialogueFileContent[line].strip("\t") poses = recipe.getPoses() poseXML = poses[0]['poseXML'] poseXML.attrib['dialogue'] = newDialogue recipe.saveRecipeFiles() data.append('<Setup show="%(show)s" sequence="%(sequence)s" beat="%(beat)s" setup="%(setup)s" version="%(version)s">' % recipe.getProperties()) data.append(ET.tostring(recipe.getMultiTrackXML()) + "</Setup>") data.append("</Recipies>") dataString = "".join(data) log("DATASTRING: %s" % dataString) self.serverFlixFunctions.addFeedback('reloadSetupsMultiTracks', dataString) def loadFileBrowser(self): output = OSUtils.runFileBrowser(kBrowseTypeLoadFiles, 'Select the text file', '/', 'dialogue') return escape(output[0].decode('utf-8')) def getPanelSetupList(self): """ Returns a list of panels with their 'short label name' (e.g. '0010-2' for 'hum_p_0010_v2') """ panelSetupList = [] for panel in self.shotList: panelSetupList.append(panel.recipe.getShortLabelName()) return panelSetupList def getPanelDialogueLines(self, panels, dialogueFileContent): """ Returns a dictionary listing the first and last dialogue lines for each panel """ panelDialogueLines = {} previousPanel = None for panel in panels: # Panels with no dialogue get set to -1 panelDialogueLines[panel] = [-1] # Ignore Markers if "marker" in panel: continue panelName = "%s:\n" % panel # If this panel has dialogue associated to it in the text file if panelName in dialogueFileContent: # Record the first line of dialogue for this panel panelDialogueLines[panel] = [dialogueFileContent.index(panelName) + 1] if previousPanel is not None: # Record the last line of dialogue for the previous panel panelDialogueLines[previousPanel].append(dialogueFileContent.index(panelName) - 2) if panel == panels[-1]: panelDialogueLines[panel].append(len(dialogueFileContent)-1) else: previousPanel = panel log("PANELDIALOGUELINES: %s" % panelDialogueLines) return panelDialogueLines

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# Write a program that outputs even if a number is even and odd if a number is odd. Think about what if the value is neither even nor odd (the value can be fractional or it can even not be a number at all). num = int(input("Enter a number")) if (num % 2) == 0: print("number is even") elif (num % 2) == 1: print("number is odd")

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# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from arch.api.utils import log_utils from playhouse.pool import PooledMySQLDatabase from arch.task_manager.settings import DATABASE from peewee import Model, CharField, IntegerField, BigIntegerField, DateTimeField, TextField, CompositeKey import datetime import sys import inspect LOGGER = log_utils.getLogger() data_base_config = DATABASE.copy() # TODO: create instance according to the engine engine = data_base_config.pop("engine") db_name = data_base_config.pop("name") DB = PooledMySQLDatabase(db_name, **data_base_config) def close_db(db): try: if db: db.close() except Exception as e: LOGGER.exception(e) @DB.connection_context() def init_tables(): members = inspect.getmembers(sys.modules[__name__], inspect.isclass) table_objs = [] for name, obj in members: if obj != DataBaseModel and issubclass(obj, DataBaseModel): table_objs.append(obj) DB.create_tables(table_objs) class DataBaseModel(Model): class Meta: database = DB def to_json(self): return self.__dict__['__data__'] def save(self, *args, **kwargs): if hasattr(self, "update_date"): self.update_date = datetime.datetime.now() super(DataBaseModel, self).save(*args, **kwargs) class JobInfo(DataBaseModel): job_id = CharField(max_length=50) name = CharField(max_length=100, null=True, default='') task = CharField(max_length=50, null=True, index=True) module = CharField(max_length=50, null=True, index=True) initiator = IntegerField(null=True, index=True) role = CharField(max_length=50, null=True, default='', index=True) party_id = IntegerField(null=True, default=0, index=True) config = TextField(null=True) all_party = TextField(null=True) pid = IntegerField(null=True, index=True) status = CharField(max_length=50, null=True, default='ready') # waiting/ready/start/running/success/failed/partial/deleted set_status = CharField(max_length=50, null=True) # ready/running/success/failed/partial/deleted progress = IntegerField(null=True, default=0) current_step = CharField(max_length=100, null=True, index=True) create_date = DateTimeField(index=True) update_date = DateTimeField(index=True) begin_date = DateTimeField(null=True, index=True) end_date = DateTimeField(null=True, index=True) elapsed = BigIntegerField(null=True) class Meta: db_table = "job_info" primary_key = CompositeKey('job_id', 'role', 'party_id') class JobQueue(DataBaseModel): job_id = CharField(max_length=50) role = CharField(max_length=50, null=True, default='', index=True) party_id = IntegerField(null=True, default=0, index=True) config = TextField(null=True) status = CharField(max_length=50, null=True, default='ready') # waiting/ready/start/running/success/failed/partial/deleted pid = IntegerField(null=True, index=True) create_date = DateTimeField(index=True) update_date = DateTimeField(index=True) class Meta: db_table = "job_queue" primary_key = CompositeKey('job_id', 'role', 'party_id') init_tables()

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def send_message(message, recipient): return True

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from urllib import request class HtmlDownloader(object): def download(self, url): if url is None: return None response = request.urlopen(url) if response.getcode() == 200: return response else: return None

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""" Image Utils Handles preprocessing images before they are sent to the server """ import os.path, base64, re, warnings from six import BytesIO, string_types, PY3 from PIL import Image from indicoio.utils.errors import IndicoError B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)") def file_exists(filename): """ Check if a file exists (and don't error out on unicode inputs) """ try: return os.path.isfile(filename) except (UnicodeDecodeError, UnicodeEncodeError, ValueError): return False def image_preprocess(image, size=None, min_axis=None, batch=False): """ Takes an image and prepares it for sending to the api including resizing and image data/structure standardizing. """ if batch: return [image_preprocess(img, size=size, min_axis=min_axis, batch=False) for img in image] if isinstance(image, string_types): b64_or_url = re.sub('^data:image/.+;base64,', '', image) if file_exists(image): # check type of element out_image = Image.open(image) else: return b64_or_url elif isinstance(image, Image.Image): out_image = image elif type(image).__name__ == "ndarray": # image is from numpy/scipy if "float" in str(image.dtype) and image.min() >= 0 and image.max() <= 1: image *= 255. try: out_image = Image.fromarray(image.astype("uint8")) except TypeError as e: raise IndicoError("Please ensure the numpy array is acceptable by PIL. Values must be between 0 and 1 or between 0 and 255 in greyscale, rgb, or rgba format.") else: raise IndicoError("Image must be a filepath, url, base64 encoded string, or a numpy array") if size or min_axis: out_image = resize_image(out_image, size, min_axis) # convert to base64 temp_output = BytesIO() out_image.save(temp_output, format='PNG') temp_output.seek(0) output_s = temp_output.read() return base64.b64encode(output_s).decode('utf-8') if PY3 else base64.b64encode(output_s) def resize_image(image, size, min_axis): if min_axis: min_idx, other_idx = (0,1) if image.size[0] < image.size[1] else (1,0) aspect = image.size[other_idx]/float(image.size[min_idx]) if aspect > 10: warnings.warn( "An aspect ratio greater than 10:1 is not recommended", Warning ) size_arr = [0,0] size_arr[min_idx] = size size_arr[other_idx] = int(size * aspect) image = image.resize(tuple(size_arr)) elif size: image = image.resize(size) return image def get_list_dimensions(_list): """ Takes a nested list and returns the size of each dimension followed by the element type in the list """ if isinstance(_list, list) or isinstance(_list, tuple): return [len(_list)] + get_list_dimensions(_list[0]) return [] def get_element_type(_list, dimens): """ Given the dimensions of a nested list and the list, returns the type of the elements in the inner list. """ elem = _list for _ in range(len(dimens)): elem = elem[0] return type(elem)

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### Figure S7 - Obenhaus et al. # panel D: anatomical masks # Plots an overlay of anatomical masks as # annotated by the experimenter import sys, os import os.path from pathlib import Path from matplotlib.figure import Figure import numpy as np import pandas as pd import datajoint as dj import cmasher as cmr # Make plots pretty import seaborn as sns sns.set(style='white') # Prevent bug in figure export as pdf: import matplotlib as mpl mpl.rcParams['pdf.fonttype'] = 42 ##### IMPORTS ######################################################################################### from matplotlib import pyplot as plt from matplotlib.ticker import MaxNLocator sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from helpers_topography.fov_stitching import make_composite_fov from dj_schemas.anatomical_alignment import get_aligned_anatomical_masks from skimage.exposure import rescale_intensity from PIL import Image, ImageCms import colorsys rgb_to_hsv = np.vectorize(colorsys.rgb_to_hsv) hsv_to_rgb = np.vectorize(colorsys.hsv_to_rgb) ##### LOAD SCHEMA COMPONENTS ########################################################################## from dj_schemas.dj_conn import * ##### EXPORT LOCATION ################################################################################# figure_dir = 'YOUR_EXPORT_DIRECTORY/' figure_dir = Path(figure_dir) #### HELPERS FOR CMYK -> RGB conversion ############################################################### def _convert_to_8bit(data): ''' Convert numpy array from arbitrary range to np.unit8 ''' data = np.nan_to_num(data) data = rescale_intensity(data, in_range='image', out_range=np.uint8) data = data.astype(np.uint8) return np.nan_to_num(data) def _shift_hue(arr, hout, ): r, g, b, a = np.rollaxis(arr, axis=-1) h, s, v = rgb_to_hsv(r, g, b) h += hout r, g, b = hsv_to_rgb(h, s, v) arr = np.dstack((r, g, b, a)) return arr def _colorize(image, hue_change, ): ''' Hue change PIL image `image` with the given `hue_change` (hue within 0-360) returns another PIL image ''' img = image.convert('RGBA') arr = np.array(np.asarray(img).astype('float')) hue_shifted = _shift_hue(arr, hue_change/360., ) new_img = Image.fromarray(hue_shifted.astype('uint8'), 'RGBA') return new_img ####################################################################################################### def _plot_fov_and_masks(animal_name, projection_short, ssim_thresh, center_plane=0, plot_mask_fov='fov', percentile=99.8, size_scalebar=50., ): ''' Anatomical FOV + PLOTTING OF ANATOMICAL MASKS! ''' assert plot_mask_fov in ['fov','mask'], f'Choose "fov" or "mask" for plot_mask_fov' # Retrieve sessions and plot FOV composite session_filter = (Session.proj(..., metasession_ref='metasession_name') & f'animal_name = "{animal_name}"' & FilteredSessions) metasessions = (AlignmentFOV.proj(diff_ssim = 'ssim_warped-ssim_original') & session_filter & f'diff_ssim > {ssim_thresh}' & f'projection_short = "{projection_short}"') alignment_composite, x_min, x_max, y_max, y_min = make_composite_fov(metasessions, center_plane = center_plane, padding_composite = 5., add_edge=False, ) if plot_mask_fov == 'mask': # Retrieve anatomical masks (manual annotations) region_masks_mec = get_aligned_anatomical_masks(animal_name=animal_name, region='mec_label', projection_short=projection_short, edge=False ) region_masks_pas = get_aligned_anatomical_masks(animal_name=animal_name, region='pas_label', projection_short=projection_short, edge=False ) # Normalize region_masks_mec /= region_masks_mec.max() region_masks_pas /=region_masks_pas.max() # Create a CMYK stack from those masks, # filling "C", and "Y" and leaving the others at zero # See also my gist: # https://gist.github.com/horsto/072758ba4b6a292264cd65a9a98a80d2 all_masks_cmyk = np.stack([region_masks_mec, np.zeros_like(region_masks_mec), region_masks_pas, np.zeros_like(region_masks_mec) ], axis=2) all_masks_cmyk8bit = _convert_to_8bit(all_masks_cmyk) # profiles cmyk_profile = '/Users/hotte/Documents/python/hotte-dj-moser-imaging/color_profiles/USWebCoatedSWOP.icc' rgb_profile = '/Users/hotte/Documents/python/hotte-dj-moser-imaging/color_profiles/sRGB Color Space Profile.icm' img_pil = Image.fromarray(all_masks_cmyk8bit, mode='CMYK') img = ImageCms.profileToProfile(img_pil, cmyk_profile, rgb_profile, renderingIntent=0, outputMode='RGB') # HUE CHANGE img_hue = _colorize(img, hue_change=185) # ... convert back to RGB image all_masks_rgb = np.array(img_hue) # Plot ... cmap = 'Greys' figure = plt.figure(figsize=(10,10)) ax = figure.add_subplot(111) if plot_mask_fov == 'fov': ax.imshow(alignment_composite, vmin=0, vmax=np.nanpercentile(alignment_composite, percentile), cmap=cmap) else: ax.imshow(all_masks_rgb) # Scalebar ... if size_scalebar is not None: ax.plot([x_max-size_scalebar-10,x_max-size_scalebar-10+size_scalebar], [y_max-10,y_max-10], lw=3, color='k', solid_capstyle='butt' ) ax.set_xlim([x_min,x_max]), ax.set_ylim([y_max,y_min]) # Construct title projection_key = (metasessions * FOVProjParam).fetch('projection_key')[0] ax.get_xaxis().set_ticks([]); ax.get_yaxis().set_ticks([]) sns.despine(left=True, bottom=True) print(f'Animal {animal_name} (n={len(metasessions)+1} sessions) {projection_key}') print('Saving figure under {}'.format(str(figure_dir))) figure.savefig(figure_dir / f'{animal_name} composite {plot_mask_fov}.png', bbox_inches='tight', dpi=300) if __name__ == "__main__": #### PLOT COMPOSITE FOV EXAMPLE ##################################################### ssim_thresh = 0. # This is: [ssim_warped-ssim_original] in AlignmentFOV center_plane = 0 animal_name = '88592' projection_short = 'mean_im2' # FOVProjParam() percentile = 99.8 mask_fov = 'fov' # 'mask' or 'fov' - either plot the anatomical fov defined by projection_short # or the annotated region masks for MEC / PAS only size_scalebar = 50. print('Plotting (anatomical) FOV composite') _plot_fov_and_masks(animal_name, projection_short, ssim_thresh, center_plane=0, plot_mask_fov=mask_fov, percentile=percentile, size_scalebar=size_scalebar, ) print('Success.')

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no_license

yzhh029/wikitrending

cc190b8f78022d1e16749a59e36ae603031d054f

158d565d3cb7f292928f81e7c054cec30d9b9354

refs/heads/master

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#!/usr/bin/env python import sys current_name = None current_count = 0 current_date = None word = None for line in sys.stdin: line = line.strip() rd = line.split('\t') if len(rd) != 3: continue name, views, date = rd[0], rd[1], rd[2] try: count = int(views) except ValueError: continue if current_name == name and current_date == date: current_count += count else: if current_name: print '%s\t%s\t%s' % (current_name, current_count, current_date) current_count = count current_name = name current_date = date if current_name == name: print '%s\t%s\t%s' % (current_name, current_count, current_date)

[ "yzhh029@gmail.com" ]

yzhh029@gmail.com

a1f18c3115ef61507d291c92a1fb592e652a1b31

86d7526f06552f0b46d539b41ac147c5daddb1d6

/flipkartgrid.py

8ad77183727fe0149002bdca04fd083497d88e93

[]

no_license

ShrungDN/Flipkart-Grid-3.0

3a6b35317bee6a392f01377a5f23f228c651caa5

b4f9c48606e045d718fbc6ec1d10df254ca969a2

refs/heads/main

2023-08-10T19:09:16.522837

2021-10-03T11:04:52

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# -*- coding: utf-8 -*- """FlipkartGrid.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/16vpyuFlG8XAHVhG2uDySuP_ilW6S1xLs # Main Code (Run From Here) ## Initial Steps (used to get the start points, checkpoints and destination points) """ # importing libraries import cv2 import matplotlib.pyplot as plt import numpy as np from numpy.linalg import norm from math import atan2, cos, sin, sqrt, pi def get_start_points(w, h): a = h / 10 b1 = np.array([8.5*a, 0.5*a]) b2 = b1 + np.array([a, 0]) b3 = b2 + np.array([a, 0]) b4 = b3 + np.array([a, 0]) return [ [int(x) for x in b1], [int(x) for x in b2], [int(x) for x in b3], [int(x) for x in b4] ] def get_destinations(w, h): a = h / 10 d1 = np.array([3*a//2, 17*a//2], np.uint8) d2 = d1 + np.array([0, a]) d3 = d2 + np.array([17*a, 0]) d4 = d3 + np.array([0, -a]) return [ [int(x) for x in d1], [int(x) for x in d2], [int(x) for x in d3], [int(x) for x in d4] ] def get_checkpoints(w, h): a = h / 10 c1 = np.array([17*a//2, 17*a//2], np.uint8) c2 = c1 + np.array([a, a]) c3 = c2 + np.array([a, 0]) c4 = c3 + np.array([a, -a]) return [ [int(x) for x in c1], [int(x) for x in c2], [int(x) for x in c3], [int(x) for x in c4] ] """# HSV Calculator (used to detect the two markers on the bot)""" #HSV FINDER TOOL def nothing(x): pass def create_Trackbars(trackbarwindow = 'Controls',num_masks=1): lower = [0,0,0] upper = [180,255,255] #HUE cv2.createTrackbar('lh_mask1',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('uh_mask1',trackbarwindow,upper[0],179,nothing) #Saturation cv2.createTrackbar('ls_mask1',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('us_mask1',trackbarwindow,upper[1],255,nothing) #Value cv2.createTrackbar('lv_mask1',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('uv_mask1',trackbarwindow,upper[2],255,nothing) #Same for Mask2 if num_masks==2: cv2.createTrackbar('lh_mask2',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('uh_mask2',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('ls_mask2',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('us_mask2',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('lv_mask2',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('uv_mask2',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('save',trackbarwindow,0,1,nothing) #cv2.createTrackbar('mode',trackbarwindow,0,3,nothing) def get_mask_3d(mask_number,hsv_img,trackbarwindow):#Here, 3d indicates that the shape of the mask is a tuple lh = cv2.getTrackbarPos('lh_mask{}'.format(mask_number),trackbarwindow) uh = cv2.getTrackbarPos('uh_mask{}'.format(mask_number),trackbarwindow) ls = cv2.getTrackbarPos('ls_mask{}'.format(mask_number),trackbarwindow) us = cv2.getTrackbarPos('us_mask{}'.format(mask_number),trackbarwindow) lv = cv2.getTrackbarPos('lv_mask{}'.format(mask_number),trackbarwindow) uv = cv2.getTrackbarPos('uv_mask{}'.format(mask_number),trackbarwindow) lower = np.array([lh,ls,lv]) upper = np.array([uh,us,uv]) mask = cv2.inRange(hsv_img,lower,upper) mask_3d = cv2.cvtColor(mask,cv2.COLOR_GRAY2BGR) return mask_3d,[lower,upper] def get_hsv_limits(img,window_name="Controls"): cv2.namedWindow(window_name) create_Trackbars(window_name,num_masks=1) hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) while True: mask,hsv_limits = get_mask_3d(1,hsv,window_name) mask_gray = cv2.cvtColor(mask,cv2.COLOR_BGR2GRAY) contours,h = cv2.findContours(mask_gray.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) if contours: max_area_cnt = max(contours, key = cv2.contourArea) peri = cv2.arcLength(max_area_cnt, True) approx = cv2.approxPolyDP(max_area_cnt, 0.02 * peri, True) pnts = approx.squeeze() img_contours = img.copy() cv2.drawContours(img_contours,[pnts],-1,(0,0,255),15) else: img_contours = img.copy() img_and_mask = np.hstack((img,mask)) img_and_mask = cv2.resize(img_and_mask,None,fx = 0.4,fy=0.4) cv2.imshow(window_name,img_and_mask) key = cv2.waitKey(10) if key == ord('s'): break cv2.destroyAllWindows() return hsv_limits # [lower,upper] """# Other Helper Functions ### Finding centroids of the markers and finding their orientations """ #returns the centroid of the detected marker on the bot which is later used to find the orientation def get_centroid(bot_img,hsv_limits,draw_bb,marker_color): hsv_bot_img = cv2.cvtColor(bot_img, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_bot_img,hsv_limits[0],hsv_limits[1]) contours,h = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) marker_detected = False if contours: max_area_cnt = max(contours, key = cv2.contourArea) M = cv2.moments(max_area_cnt) if M['m00']==0: marker_detected = False else: cx = int(M['m10']/M['m00']) cy = int(M['m01']/M['m00']) if draw_bb: bot_img = cv2.drawContours(bot_img,[max_area_cnt],-1,marker_color,2) marker_detected = True if marker_detected == True: return (cx,cy) else: return -1 def get_angle(img,previous_angle,hsv_limits1,hsv_limits,draw_bb = True): c1 = get_centroid(img,hsv_limits1,draw_bb,marker_color=(255,0,0)) c2 = get_centroid(img,hsv_limits2,draw_bb,marker_color=(0,255,255)) if c1 == -1 or c2 == -1: angle = previous_angle else: angle = round((atan2(c1[1]-c2[1],c2[0]-c1[0])*180/pi)) return angle """### Get Command Function to give commands to the bot""" from math import atan2,pi def getcommand(loc, bot_orientation, dst): x,y = loc x1,y1 = dst target_orientation = (atan2((y-y1),(x1-x)))*180/pi diff_angle = target_orientation-bot_orientation #conveting negative difference in angle to positive if diff_angle<0: diff_angle += 360 #if the bot's orientation is within 20 degrees of the required orientation, we go straight if diff_angle <= 20: return 'F' #the difference angle is measured from the bots orientation vector to the destination direction vector #if it is within 180 degrees, the command is to take a left elif 20<diff_angle < 180: return 'L' else: return 'R' """### Used to warp an image when 4 points are given (used when cropping the arena) """ def order_points(pts): #parameters: pts 4x2 np.ndarray #returns: 4x2 np.ndarray # The function initialises a list of coordinates that will be ordered # such that the first entry in the list is the top-left, # the second entry is the top-right, the third is the # bottom-right, and the fourth is the bottom-left #*************************************************************# rect = np.zeros((4, 2), dtype = "float32") # the top-left point will have the smallest sum, whereas # the bottom-right point will have the largest sum s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # now, compute the difference between the points, the # top-right point will have the smallest difference, # whereas the bottom-left will have the largest difference diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] # return the ordered coordinates return rect def four_point_transform(image, pts): #returns a warped image #parameters: image (np.ndarray) and the 4 pts np.ndarray of size (4,2) # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped """# Execution""" video1 = "Moving Bot.mp4" video2 = "total_arcade2.mp4" video3 = "total_arcade3.mp4" video4 = "total_arcade_endless.mp4" cap = cv2.VideoCapture(video4) # Check if camera opened successfully if (cap.isOpened()== False): print("Error opening video file") cv2.namedWindow('Frame') grid_coordinates = [] def save_points(event, x, y, flags, params): if event == cv2.EVENT_LBUTTONDOWN: grid_coordinates.append((x,y)) ################################# Locking and Cropping the Arena ########################### print("Arena Locking Stage") while(cap.isOpened()): # Capture frame-by-frame ret, frame = cap.read() if ret == True: # Display the resulting frame if len(grid_coordinates)!=0: for point in grid_coordinates: cv2.circle(frame, point, radius=2, color=(0,0,255), thickness=4) cv2.setMouseCallback('Frame', save_points) cv2.imshow("Frame",frame) key = cv2.waitKey(25) #clear the points if c is pressed if key == ord('c'): grid_coordinates =[] if key == ord('f'): w = frame.shape[1] h = frame.shape[0] grid_coordinates=[(0,0),(0,h),(w,0),(w,h)] break if key & 0xFF == ord('s'): break if key &0xFF == ord('p'): ##use this only for saving a photo after choosing 4 points cv2.imwrite("grid_cropped{}.jpeg".format(np.random.randint(0,100)),four_point_transform(frame,np.array(grid_coordinates))) # Break the loop else: break cv2.destroyAllWindows() print("Arena Locking Done\n") ################################### Selecting BOTS to be tracked ############################# if len(grid_coordinates)!=4: print("Didnt choose 4 points correctly. Choosing entire frame") cap.release() cv2.destroyAllWindows() else: print("Drawing bounding boxes") TrDict = {'csrt': cv2.legacy.TrackerCSRT_create, 'kcf' : cv2.legacy.TrackerKCF_create, } trackers = cv2.legacy.MultiTracker_create() ret,frame_roi = cap.read() frame = four_point_transform(frame_roi,np.array(grid_coordinates)) bot_largest_dim = 0 fraction = 0.9 num_bots=1 for i in range(num_bots): if ret: cv2.imshow('Frame',frame) bbi = cv2.selectROI('Frame',frame) bot_largest_dim = max(bbi[2:]) tracker_i = TrDict['csrt']() trackers.add(tracker_i,frame,bbi) cv2.waitKey(1) else: print("Cap not reading in ROI Selection Stage") break bot_largest_dim = int(fraction*bot_largest_dim) #The extra region around the bot for detection of the bot ################################ HSV LIMITS SELECTION ########################## bot_img_t0 = frame[bbi[1]:bbi[1]+bbi[3],bbi[0]:bbi[0]+bbi[2]] bot_img_t0 = frame[:,:] cv2.imshow("bot_img_t0",bot_img_t0) hsv_limits1 = get_hsv_limits(bot_img_t0,"Finding HSV Limits of Marker 1") hsv_limits2 = get_hsv_limits(bot_img_t0,"Finding HSV Limits of Marker 2") ############################# Tracking and Velocity Publishing ############################## print("\nBOT Tracking...") angles = [-90 for i in range(num_bots)] bot_imgs = [0 for i in range(num_bots)] while True: ret, frame = cap.read() if not ret: print("Not able to read in Tracking Stage") break frame = four_point_transform(frame,np.array(grid_coordinates)) #frame_c = frame.copy() #cv2.imshow("orig",frame_c) (success, boxes) = trackers.update(frame) height,width,_ = frame.shape for i in range(len(boxes)): box = boxes[i] (x, y, w, h) = [int(a) for a in box] bot_centroid = (int(x + w/2), int(y + h/2)) search_rect = [bot_centroid[0],bot_centroid[1], bot_centroid[0]+2*bot_largest_dim,bot_centroid[1]+2*bot_largest_dim] frame_padded = np.stack([np.pad(frame[:,:,c], bot_largest_dim, mode='constant', constant_values=0) for c in range(3)], axis=2) bot_imgs[i] = frame_padded[search_rect[1]:search_rect[3],search_rect[0]:search_rect[2]] angles[i] = get_angle(bot_imgs[i],angles[i],hsv_limits1,hsv_limits2,draw_bb = True) destination_point = (int(width/2),35) cv2.putText(frame,str(destination_point),destination_point,cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2) cv2.circle(frame,destination_point , radius=4, color=(0,0,255), thickness=4) #cv2.putText(frame,str(angles[i]),bot_centroid,cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2) cv2.circle(frame,bot_centroid , radius=4, color=(0,0,150), thickness=4) command = getcommand(bot_centroid,angles[i],destination_point) cv2.putText(frame,str(command),(bot_centroid[0]+10,bot_centroid[1]+10),cv2.FONT_HERSHEY_SIMPLEX,1,(0,150,0),2) bot_img = np.hstack(bot_imgs) cv2.imshow('Frame',frame) cv2.imshow('bot_img',bot_img) #cv2.imshow('bot_with_triangle',img_with_triangle) key = cv2.waitKey(1) & 0xFF if key == ord('q'): break cap.release() cv2.destroyAllWindows() ''' """# Other redundant/supplementary code (below) # Video Reading Code And Saving Snapshots """ import cv2 import numpy as np import time # Create a video capture object, in this case we are reading the video from a file vid_capture = cv2.VideoCapture("total_arcade_endless.mp4") if (vid_capture.isOpened() == False): print("Error opening the video file") else: fps = vid_capture.get(5) print('Frames per second : ', fps,'FPS') # Get frame count # You can replace 7 with CAP_PROP_FRAME_COUNT as well, they are enumerations frame_count = vid_capture.get(7) print('Frame count : ', frame_count) vid_capture.set(cv2.CAP_PROP_FPS, 1) fps = vid_capture.get(5) print('Frames per second : ', fps,'FPS') while(vid_capture.isOpened()): # vid_capture.read() methods returns a tuple, first element is a bool # and the second is frame ret, frame = vid_capture.read() if ret == True: cv2.imshow('Frame',frame) # 20 is in milliseconds, try to increase the value, say 50 and observe key = cv2.waitKey(20) # time.sleep(0.1) if key == ord('q'): break if key == ord('s'): cv2.imwrite("triangle{}.jpeg".format(np.random.randint(0,100)),frame) break else: break # Release the video capture object vid_capture.release() cv2.destroyAllWindows() """# Arena Locking Tool and Mapping (Ignore this)""" import cv2 import numpy as np import matplotlib.pyplot as plt #HSV FINDER TOOL def nothing(x): pass def create_Trackbars(trackbarwindow = 'Controls'): lower = [0,0,0] upper = [180,255,255] #HUE cv2.createTrackbar('lh_mask1',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('uh_mask1',trackbarwindow,upper[0],179,nothing) #Saturation cv2.createTrackbar('ls_mask1',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('us_mask1',trackbarwindow,upper[1],255,nothing) #Value cv2.createTrackbar('lv_mask1',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('uv_mask1',trackbarwindow,upper[2],255,nothing) #Same for Mask2 cv2.createTrackbar('lh_mask2',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('uh_mask2',trackbarwindow,lower[0],179,nothing) cv2.createTrackbar('ls_mask2',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('us_mask2',trackbarwindow,lower[1],255,nothing) cv2.createTrackbar('lv_mask2',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('uv_mask2',trackbarwindow,lower[2],255,nothing) cv2.createTrackbar('save',trackbarwindow,0,1,nothing) #cv2.createTrackbar('mode',trackbarwindow,0,3,nothing) def get_mask_3d(mask_number,hsv_img,trackbarwindow):#Here, 3d indicates that the shape of the mask is a tuple lh = cv2.getTrackbarPos('lh_mask{}'.format(mask_number),trackbarwindow) uh = cv2.getTrackbarPos('uh_mask{}'.format(mask_number),trackbarwindow) ls = cv2.getTrackbarPos('ls_mask{}'.format(mask_number),trackbarwindow) us = cv2.getTrackbarPos('us_mask{}'.format(mask_number),trackbarwindow) lv = cv2.getTrackbarPos('lv_mask{}'.format(mask_number),trackbarwindow) uv = cv2.getTrackbarPos('uv_mask{}'.format(mask_number),trackbarwindow) lower = np.array([lh,ls,lv]) upper = np.array([uh,us,uv]) mask = cv2.inRange(hsv_img,lower,upper) mask_3d = cv2.cvtColor(mask,cv2.COLOR_GRAY2BGR) return mask_3d def visualise_contours(img,contours,thickness=3): img_c = img.copy() cv2.drawContours(img_c,contours,-1,(255,0,0),thickness) plt.imshow(img_c,cmap='gray') plt.show() return img_c def get_points_for_warping(qmask): qmask_gray = cv2.cvtColor(qmask,cv2.COLOR_BGR2GRAY) contours,h = cv2.findContours(qmask_gray.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) max_area_cnt = max(contours, key = cv2.contourArea) peri = cv2.arcLength(max_area_cnt, True) approx = cv2.approxPolyDP(max_area_cnt, 0.02 * peri, True) pnts = approx.squeeze() if pnts.shape !=(4,2): visualise_contours(img,[contours]) return pnts,contours def order_points(pts): #parameters: pts 4x2 np.ndarray #returns: 4x2 np.ndarray # The function initialises a list of coordinates that will be ordered # such that the first entry in the list is the top-left, # the second entry is the top-right, the third is the # bottom-right, and the fourth is the bottom-left #*************************************************************# rect = np.zeros((4, 2), dtype = "float32") # the top-left point will have the smallest sum, whereas # the bottom-right point will have the largest sum s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # now, compute the difference between the points, the # top-right point will have the smallest difference, # whereas the bottom-left will have the largest difference diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] # return the ordered coordinates return rect def four_point_transform(image, pts): #returns a warped image #parameters: image (np.ndarray) and the 4 pts np.ndarray of size (4,2) # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped def initiate_mapping(cap): if cap.isOpened()==False: print('Could not access feed') return trackbarwindow = 'Controls' cv2.namedWindow(trackbarwindow) create_Trackbars(trackbarwindow) pnts = np.array([[0,0],[0,1],[1,0],[1,1]]) while True: ret,img = cap.read() hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) save = cv2.getTrackbarPos('save',trackbarwindow) mask1 = get_mask_3d(1,hsv_img,trackbarwindow) mask2 = get_mask_3d(2,hsv_img,trackbarwindow) mask = cv2.bitwise_or(mask1,mask2) mask1_and_mask2 = np.hstack((mask1,mask2)) img_and_mask = np.hstack((img,mask)) stacked = np.vstack((img_and_mask,mask1_and_mask2)) stacked = cv2.resize(stacked,None,fx=0.25,fy=0.25) cv2.imshow("Mask",stacked) mask_gray = cv2.cvtColor(mask,cv2.COLOR_BGR2GRAY) contours,h = cv2.findContours(mask_gray.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) if contours: max_area_cnt = max(contours, key = cv2.contourArea) peri = cv2.arcLength(max_area_cnt, True) approx = cv2.approxPolyDP(max_area_cnt, 0.02 * peri, True) pnts = approx.squeeze() img_contours = img.copy() cv2.drawContours(img_contours,[pnts],-1,(0,0,255),15) img_contours = cv2.resize(img_contours,None,fx = 0.7,fy=0.7) cv2.imshow('Grid Recognition',img_contours) else: cv2.imshow('Grid Recognition',img.copy()) warped = four_point_transform(img,pnts) warped = cv2.resize(warped,None,fx = 0.7,fy=0.7) cv2.imshow('Warped',warped) key = cv2.waitKey(10) if save ==1: mask_no = np.random.randint(1,10000) cv2.imwrite('mask_{}.jpg'.format(mask_no),mask) break if key == ord('s'): break cv2.destroyAllWindows() while cap.isOpened(): ret,img = cap.read() warped = four_point_transform(img,pnts) cv2.imshow('Grid',warped) gray_warped = cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY) #ret,binary_warped = cv2.threshold(gray_warped,200,255,cv2.THRESH_BINARY) gray_warped = cv2.resize(gray_warped, None,fx=2,fy=2) cv2.imshow('Grid_Binary',gray_warped) if cv2.waitKey(10)==ord('q'): break cv2.destroyAllWindows() cap.release() cap = cv2.VideoCapture(0) cap.isOpened() initiate_mapping(cap) cv2.destroyAllWindows() cap.release() def get_orientation_rough2(bot_img): gray = cv2.cvtColor(bot_img, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (7, 7), 0) (T, mask) = cv2.threshold(blurred, 200, 255,cv2.THRESH_BINARY_INV) contours,h = cv2.findContours(255-mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) img_contours = bot_img.copy() if contours: max_area_cnt = max(contours, key = cv2.contourArea) peri = cv2.arcLength(max_area_cnt, True) approx = cv2.approxPolyDP(max_area_cnt, 0.02 * peri, True) pnts = approx.squeeze() cv2.drawContours(img_contours,[pnts],-1,(0,0,255),3) # angle = round(getOrientation(max_area_cnt, img_contours)*180/3.141) return img_contours#,angle def get_orientation_rough1(bot_img): gray = cv2.cvtColor(bot_img, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (7, 7), 0) (T, mask) = cv2.threshold(blurred, 200, 255,cv2.THRESH_BINARY_INV) contours,h = cv2.findContours(255-mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) max_area_cnt = max(contours, key = cv2.contourArea) img_c = bot_img.copy() cv2.drawContours(img_c,max_area_cnt,-1,(255,0,0),2) angle = round(getOrientation(max_area_cnt, img_c)*180/3.141) return angle,img_c (int(w/2),int(h/2)) h,w cv2.destroyAllWindows() cap.release() hsv_limits1 = get_hsv_limits(img,"Finding HSV Limits of Marker 1") hsv_limits2 = get_hsv_limits(img,"Finding HSV Limits of Marker 2") plt.imshow(img) get_angle(img,0,hsv_limits1,hsv_limits2) ret,thresh = cv2.threshold(img,127,255,0) print(thresh.shape) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) mask = cv2.inRange(hsv_img,) mask_3d = cv2.cvtColor(mask,cv2.COLOR_GRAY2BGR) import cv2 import numpy as np img = cv2.imread("grid_cropped5.jpeg") cv2.imshow("img",img) cv2.waitKey(0) cv2.destroyAllWindows() w = img.shape[1] h = img.shape[0] get_start_points(w,h) get_checkpoints(w,h) get_destinations(w,h) for i, b in enumerate(get_start_points(w, h)): x, y = b cv2.circle(img, (x, y), 2, (255, 0, 0), -1) cv2.putText(img, f's{i+1}', (x+1, y+1), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255, 0, 0), 1) for i, c in enumerate(get_checkpoints(w, h)): x, y = c cv2.circle(img, (x, y), 2, (0, 0, 255), -1) cv2.putText(img, f'c{i+1}', (x+1, y+1), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (0, 0, 255), 1) for i, d in enumerate(get_destinations(w, h)): x, y = d cv2.circle(img, (x, y), 2, (0, 255, 0), -1) cv2.putText(img, f'd{i+1}', (x+1, y+1), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (0, 255, 0), 1) #cv2.imwrite('RESULT.png', img) cv2.imshow('Platform with points', img) cv2.waitKey(0) cv2.destroyAllWindows() getcommand((0,10),180,(10,10)) """# Pose Estimation using triangle marker Getting Orientation from Bot Image """ import cv2 import matplotlib.pyplot as plt import numpy as np from numpy.linalg import norm from math import atan2, cos, sin, sqrt, pi def get_pose(img,previous_angle,bot_centroid): gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (7, 7), 0) (T, mask) = cv2.threshold(blurred,230,255,cv2.THRESH_BINARY) contours,h = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) img_contours = img.copy() detected = False if len(contours)!=0: required_pnts=[] max_area = 0 for cnt in contours: peri = cv2.arcLength(cnt,True) approx = cv2.approxPolyDP(cnt, 0.09 * peri, True) area = cv2.contourArea(approx) pnts = approx.squeeze() #np.ndarray (n,1 size) if len(pnts)==3: if area<max_area: pass else: detected = True required_pnts = pnts max_area = area if detected: cv2.drawContours(img_contours,[required_pnts],-1,(0,0,255),1) max_norm = 0 max_points = [] l1 = norm(required_pnts[0]-required_pnts[1]) l2 = norm(required_pnts[1]-required_pnts[2]) l3 = norm(required_pnts[2]-required_pnts[0]) if l1<=l2 and l1<=l3: mid_point = (required_pnts[0]+required_pnts[1])/2 top_point = required_pnts[2] if l2<=l1 and l2<=l3: mid_point = (required_pnts[1]+required_pnts[2])/2 top_point = required_pnts[0] if l3<=l1 and l3<=l2: mid_point = (required_pnts[2]+required_pnts[0])/2 top_point = required_pnts[1] mid_point = mid_point.astype(np.uint32) angle = round(atan2(mid_point[1]-top_point[1],top_point[0]-mid_point[0])*180/pi) centroid = (mid_point+top_point)/2 centroid = centroid.astype(np.uint32) else: angle= previous_angle centroid = bot_centroid return angle,centroid,img_contours img = cv2.imread("5x5.png") img_text = img.copy() plt.imshow(img) angle, centroid,img_contours = get_pose(img,0,(10,10)) cv2.putText(img_text,str(angle),(10000,100),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),2) plt.imshow(img_contours) plt.figure() plt.imshow(img_text) cv2.waitKey(0) cv2.destroyAllWindows() """# Code to Determine Orientation using PCA""" from math import atan2, cos, sin, sqrt, pi def drawAxis(img, p_, q_, color, scale): p = list(p_) q = list(q_) ## [visualization1] angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0])) # Here we lengthen the arrow by a factor of scale q[0] = p[0] - scale * hypotenuse * cos(angle) q[1] = p[1] - scale * hypotenuse * sin(angle) cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA) # create the arrow hooks p[0] = q[0] + 9 * cos(angle + pi / 4) p[1] = q[1] + 9 * sin(angle + pi / 4) cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA) p[0] = q[0] + 9 * cos(angle - pi / 4) p[1] = q[1] + 9 * sin(angle - pi / 4) cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA) ## [visualization1] def getOrientation(pts, img): ## [pca] # Construct a buffer used by the pca analysis sz = len(pts) data_pts = np.empty((sz, 2), dtype=np.float64) for i in range(data_pts.shape[0]): data_pts[i,0] = pts[i,0,0] data_pts[i,1] = pts[i,0,1] # Perform PCA analysis mean = np.empty((0)) mean, eigenvectors, eigenvalues = cv2.PCACompute2(data_pts, mean) # Store the center of the object cntr = (int(mean[0,0]), int(mean[0,1])) ## [pca] ## [visualization] # Draw the principal components #cv2.circle(img, cntr, 3, (255, 0, 255), 2) p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0]) p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0]) drawAxis(img, cntr, p1, (255, 255, 0), 1) drawAxis(img, cntr, p2, (0, 0, 255), 5) angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians ## [visualization] # Label with the rotation angle #label = str(-int(np.rad2deg(angle)) - 90) + " degrees" #textbox = cv2.rectangle(img, (cntr[0], cntr[1]-25), (cntr[0] + 250, cntr[1] + 10), (255,255,255), -1) #cv2.putText(img, label, (cntr[0], cntr[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA) return angle import cv2 cap = cv2.VideoCapture(0) # Check if camera opened successfully if (cap.isOpened()== False): print("Error opening video file") while(cap.isOpened()): # Capture frame-by-frame ret, img = cap.read() if ret == True: # Display the resulting frame cv2.imshow("Frame",img) # Convert image to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert image to binary _, bw = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Find all the contours in the thresholded image contours, _ = cv2.findContours(bw, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) for i, c in enumerate(contours): # Calculate the area of each contour area = cv2.contourArea(c) # Ignore contours that are too small or too large if area < 3700 or 100000 < area: continue # Draw each contour only for visualisation purposes cv2.drawContours(img, contours, i, (0, 0, 255), 2) # Find the orientation of each shape #getOrientation(c, img) cv2.imshow('Output Image', img) key = cv2.waitKey(25) if key & 0xFF == ord('q'): break # Break the loop else: break cv2.destroyAllWindows() # Load the image img = cv2.imread("bot.png") # Was the image there? if img is None: print("Error: File not found") exit(0) cv2.imshow('Input Image', img) # Convert image to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert image to binary _, bw = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Find all the contours in the thresholded image contours, _ = cv2.findContours(bw, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) for i, c in enumerate(contours): # Calculate the area of each contour area = cv2.contourArea(c) # Ignore contours that are too small or too large if area < 3700 or 100000 < area: continue # Draw each contour only for visualisation purposes cv2.drawContours(img, contours, i, (0, 0, 255), 2) # Find the orientation of each shape #getOrientation(c, img) cv2.imshow('Output Image', img) cv2.waitKey(0) cv2.destroyAllWindows() '''

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from novathousands.driver import NovaThousandsDriver as ThousandsDriver

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import matplotlib.pyplot as plt import matplotlib.colors as colors import matplotlib.patches as mpatches from matplotlib import style import numpy as np style.use('fivethirtyeight') np_members = np.array(df.Member_Months * .01) col = df.Select.map({'On':'b', 'Off':'r'}) red_patch = mpatches.Patch(color='red', label='Off Exchange') blue_patch = mpatches.Patch(color='blue', label='On Exchange') # green_patch = mpatches.Patch(color='green', label='On Exchange (Select Network*)') # yellow_patch = mpatches.Patch(color='yellow', label='Off Exchange (Select Network*)') df.plot.scatter('AV', 'Plan_Adjusted_Index_Rate', s=np_members, marker='o', c=col, edgecolor='black') plt.xlabel('Actuarial Value (AV)') plt.ylabel('Plan Adjusted Index Rate') plt.title('Kaiser Permanente\nVisualizing Plan Popularity (Marker Size)\nMeasured in Member Months') plt.grid(True) plt.legend(handles=[red_patch, blue_patch, green_patch, yellow_patch]) plt.show()

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lukkyjay@gmail.com

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from itertools import count from math import exp from operator import ne from random import random import numpy as np class NeuralNet: def __init__(self, units=None, eta=1, steps=1e6): self.eta = eta self.steps = steps input_count, *hidden_counts, output_count = units self.inputs = [InputNode(eta) for _ in range(input_count)] self.hidden_layers = [[Node(eta) for _ in range(nth_layer)] for nth_layer in hidden_counts] self.outputs = [OutputNode(eta) for _ in range(output_count)] self.layers = [self.inputs] + self.hidden_layers + [self.outputs] self.init_connections() self.trained = False def init_connections(self): """ Connects each node in all non output layers to the i+1th layer """ for layer, next_layer in zip(self.layers, self.layers[1:]): for node in layer: node.set_next_layer(next_layer) def clear_nodes(self): """ Clears out all inputs and outputs from each node in the net """ for layer in self.layers: for node in layer: node.clear() def train(self, data, results): """ Trains this neural network on data and results :param data: N*M data vector, where M is the number of input nodes :param results: N*K data vector, where K is the number of output nodes """ counter = count() while self.has_error(data, results): if next(counter) % 10000 == 0: print("current is at", next(counter)) self.stochastic_descent(data, results) if self.has_error(data, results): print("Iterations maxed out") self.trained = True def has_error(self, data, results): """ Determines if this network produces any errors when predicting the results for data :param data: N*M data vector, where M is the number of input nodes :param results: N*K data vector, where K is the number of output nodes :return: True if any elements in data are misclassified """ for row, result in zip(data, results): if any(map(ne, row, self.predict(row))): return True return False def stochastic_descent(self, data, results): """ Runs one cycle of the backprop error algorithm on data and results :param data: N*M data vector, where M is the number of input nodes :param results: N*K data vector, where K is the number of output nodes """ for row, result in zip(data, results): #print(row) self.predict(row) self.backprop(result) def predict(self, row): """ Runs the feed forward algorithm on the given row :param row: 1*M data vector, where M is the number of input nodes :return: predicted output for row """ self.clear_nodes() for element, input_node in zip(row, self.inputs): input_node.set_input(element) for layer in self.layers: for node in layer: node.feed_forward(print_hidden=self.trained) return [0 if out.output < 0.8 else 1 for out in self.outputs] def backprop(self, result): """ Backpropogates the error of this net given that the result should be result :param result: 1*K data vector, where K is the number of output nodes """ for resultum, output in zip(result, self.outputs): #print("output errors") output.calc_error(resultum) for layer in (self.hidden_layers[::-1] + [self.inputs]): #print("new layer errors and weights") for node in layer: node.calc_error() node.update_weights() class Node: def __init__(self, eta): self.eta = eta self.output = self.input = self.error = 0 self.ws = self.next_layer = None self.bias = random() def set_next_layer(self, layer): """ Initializes the reference to the next layer, and creates a random weight vector :param layer: list of nodes that belong to the next layer """ self.next_layer = layer self.ws = np.random.random(len(layer)) #self.ws = np.zeros(len(layer)) #self.ws.fill(.1) def set_input(self, input): """ Set the input to this node :param input: float value """ raise AttributeError("Hidden node's input must be set by other nodes") def clear(self): """ clear the input, output and error of this node """ self.input = self.output = self.error = 0 def calc_output(self): """ Set the output value of this node using it's input field """ #print("input was", self.input, "bias", self.bias) self.output = 1 / (1 + exp(-(self.input + self.bias))) #print("output is", self.output) def feed_forward(self, print_hidden=False): """ Adds to the inputs of each node in the next layer the output of this node times the weight from this to that node :param #print_hidden: if true, #print to the console the output value of this hidden node """ self.calc_output() if print_hidden and self.output != self.input: print(self.output) for wij, node in zip(self.ws, self.next_layer): node.input += (self.output * wij) #print("weights are", self.ws) def calc_error(self, *args): """ Calculates the error of this node based on the error of nodes in the next layer :param args: unused here """ self.error = (self.output * (1 - self.output) * sum(node.error * wij for wij, node in zip(self.ws, self.next_layer))) #print("error is", self.error) def update_weights(self): """ Based on the error of each node in the next layer and this node's input, updates the weight from this node to that node """ for j, node in enumerate(self.next_layer): self.ws[j] += self.eta * (node.error * self.output) #print("bias was", self.bias, "eta is", self.eta) self.bias += (self.error * self.eta) #print("bias became", self.bias) #print("weights are", self.ws) class InputNode(Node): def set_input(self, input): """ OVERRIDE: Input nodes can set their own input :param input: a float value to to set as input """ self.input = input def calc_output(self): """ OVERRIDE: Input node's outputs are their inputs """ self.output = self.input def calc_error(self): """ OVERRIDE: input nodes have no error """ self.error = 0 class OutputNode(Node): def set_next_layer(self, layer): """ OVERRIDE: output nodes have no next layer :param layer: next layer """ raise AttributeError("Output nodes do not have a next layer") def calc_error(self, result, *args): """ OVERRIDE: The error of an output node is directly dependent on the output result :param result: bit (0 or 1) :param args: unused """ self.error = self.output * (1 - self.output) * (result - self.output) #print("output error is", self.error) def feed_forward(self, print_hidden=False): """ OVERRIDE: Output nodes only need to calculate their input :param #print_hidden: unused """ self.calc_output()

[ "jamesscottmcnamara@gmail.com" ]

jamesscottmcnamara@gmail.com

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webiumsk/WOT-0.9.15.1

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# 2016.08.04 19:57:02 Střední Evropa (letní čas) # Embedded file name: scripts/common/Lib/macurl2path.py """Macintosh-specific module for conversion between pathnames and URLs. Do not import directly; use urllib instead.""" import urllib import os __all__ = ['url2pathname', 'pathname2url'] def url2pathname(pathname): """OS-specific conversion from a relative URL of the 'file' scheme to a file system path; not recommended for general use.""" tp = urllib.splittype(pathname)[0] if tp and tp != 'file': raise RuntimeError, 'Cannot convert non-local URL to pathname' if pathname[:3] == '///': pathname = pathname[2:] elif pathname[:2] == '//': raise RuntimeError, 'Cannot convert non-local URL to pathname' components = pathname.split('/') i = 0 while i < len(components): if components[i] == '.': del components[i] elif components[i] == '..' and i > 0 and components[i - 1] not in ('', '..'): del components[i - 1:i + 1] i = i - 1 elif components[i] == '' and i > 0 and components[i - 1] != '': del components[i] else: i = i + 1 if not components[0]: rv = ':'.join(components[1:]) else: i = 0 while i < len(components) and components[i] == '..': components[i] = '' i = i + 1 rv = ':' + ':'.join(components) return urllib.unquote(rv) def pathname2url(pathname): """OS-specific conversion from a file system path to a relative URL of the 'file' scheme; not recommended for general use.""" if '/' in pathname: raise RuntimeError, 'Cannot convert pathname containing slashes' components = pathname.split(':') if components[0] == '': del components[0] if components[-1] == '': del components[-1] for i in range(len(components)): if components[i] == '': components[i] = '..' components = map(_pncomp2url, components) if os.path.isabs(pathname): return '/' + '/'.join(components) else: return '/'.join(components) def _pncomp2url(component): component = urllib.quote(component[:31], safe='') return component # okay decompyling c:\Users\PC\wotsources\files\originals\res_bw\scripts\common\lib\macurl2path.pyc # decompiled 1 files: 1 okay, 0 failed, 0 verify failed # 2016.08.04 19:57:02 Střední Evropa (letní čas)

[ "info@webium.sk" ]

info@webium.sk

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/Clase 3/ejemplos_comprension.py

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agustinc24/datascience_unsam

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import csv def leer_camion(nombre_archivo): '''Lee un archivo y lo almacena en un diccionario, tambien calcula el precio y lo devuelve''' f = open(nombre_archivo, 'r') rows = csv.reader(f) next(f) lista_camion = [] for row in rows: camion = { 'Nombre': row[0], 'Cajones': int(row[1]), 'Precio': float(row[2]) } lista_camion.append(dict(camion)) f.close() return(lista_camion) camion = leer_camion('C:/Users/elcav/OneDrive/Documentos/Python/Curso Unsam/ejercicios_python/Data/camion.csv') nombre_cajones = [(s['Nombre'], s['Cajones']) for s in camion] #Devuelve una lista de tuplas (nombre, cajon) de la lista camion #print(nombre_cajones) nombres = {s['Nombre'] for s in camion} #Devuelve un conjunto de los nombres de la lista camiob #print("\n",nombres) stock = {Nombre: 0 for Nombre in nombres} #Creo un diccionario por compresion con pares 'clave:valor' iniciandolos en 0 for s in camion: stock[s['Nombre']] += s['Cajones'] #Le agrega la cantidad de cajones al elemento buscado print("\n", stock)

[ "agustin.cavalotto@gmail.com" ]

agustin.cavalotto@gmail.com

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/tests/test_commit_hooks.py

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dekoza/commit-hooks

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from commit_hooks import __version__ def test_version(): assert __version__ == '0.1.0'

[ "dominik@kozaczko.info" ]

dominik@kozaczko.info

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IMDCGP105-1819/portfolio-ShmigabornDungiedoo

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refs/heads/master

2020-03-30T01:18:38.020979

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def fib(n): if n==0 or n==1: return 1 else: return fib(n-1)+fib(n-2) n=int(input("number: ")) print(fib(n)) for n in range (1, 21): print(fib(n))

[ "relonet95@gmail.com" ]

relonet95@gmail.com

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/cheese_doodle.py

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[]

no_license

nagaem/learn-to-git

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def cheese(n): return n**2

[ "stephan@echosec.net" ]

stephan@echosec.net

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/lispy/lis.py

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nettee/code_history

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# Peter Norvig # (How to Write a (Lisp) Interpreter (in Python)) import sys import math import operator def add_globals(env): env.update(vars(math)) env.update({ '+' : operator.add, '-' : operator.sub, '*' : operator.mul, '/' : operator.truediv }) return env class Env(dict): """An environment: a dict of {'var':val} pairs, with an outer Env. """ def __init__(self, parms=(), args=(), outer=None): self.update(zip(parms, args)) self.outer = outer def find(self, var): "Find the innermost Env where var appears." if var in self: return self elif self.outer is None: print("Env error") else: return self.outer.find(var) global_env = add_globals(Env()) def eval_(x, env=global_env): "Evaluate an expression in an environment." if isinstance(x, Symbol): # variable reference return env.find(x)[x] elif not isinstance(x, list): # constant literal return x elif x[0] == 'quote': # (quote exp) (_, exp) = x return exp elif x[0] == 'if': # (if test conseq alt) (_, test, conseq, alt) = x if eval_(test, env): return eval_(conseq, env) else: return eval_(alt, env) elif x[0] == 'define': (_, var, exp) = x env[var] = eval_(exp, env) elif x[0] == 'lambda': (_, vars, exp) = x def func_(*args): return eval_(exp, Env(vars, args, env)) return func_ else: # (proc exp*) exps = [eval_(exp, env) for exp in x] proc = exps.pop(0) return proc(*exps) def parse(s): return read_from(tokenize(s)) def tokenize(s): "Convert a string into a list of tokens." ss = s.replace('(', ' ( ').replace(')', ' ) ') return ss.split() def read_from(tokens): "Read an expression from a seq of tokens." if not(tokens): print("read token error") t = tokens.pop(0) if t == '(': L = [] while tokens[0] != ')': L.append(read_from(tokens)) tokens.pop(0) # pop off ')' return L elif t == ')': print("unexpected )") else: return atom(t) def atom(token): "Numbers become numbers; every other token is a symbol." try: return int(token) except ValueError: try: return float(token) except ValueError: return Symbol(token) class Symbol(str): def __init__(self, s): self = s def to_string(exp): if isinstance(exp, list): return '(' + ' '.join(to_string(e) for e in exp) + ')' else: return str(exp) def repl(prompt='lis.py> '): while True: val = eval_(parse(input(prompt))) if val is not None: print(to_string(val)) if __name__ == '__main__': repl()

[ "xf0718@gmail.com" ]

xf0718@gmail.com

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/roughWork/exp7.py

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no_license

meghanads/pydlcs

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2021-01-17T09:31:53.145526

2013-06-22T21:57:14

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# CIRCUIT: T flip Flop # from sequence import * sim = SIMU('sim1',start = 0,plots = 1, debug =1, pclk = 1 , step =0, pannotate = 1) I = Istream('IN', fname = 'inp', stream =1) O = Ostream('OUT', stream = 1) TF = TFlipFlop('TF') sim.clk_out.connect([I.clk_in, TF.C, O.clk_in]) I.data_out.connect([TF.T]) TF.Q.connect([O.data_in]) sim.addplot([I.data,O.data]) sim.addpname(["input","output"]) #sim.addplot([I.data]) #sim.addpname(["input"]) sim.start = 1 sim.simulate()

[ "bharat@localhost" ]

bharat@localhost

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/poly_kernel_sketch/common.py

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2023-04-07T19:43:54.483068

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# coding=utf-8 # Copyright 2022 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Common methods shared by MNIST and ImageNet experiments.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import errno import getpass import numpy as np import tensorflow.compat.v1 as tf import matplotlib.pyplot as plt # mkdir -p in Python >2.5 def mkdir_p(path): try: os.makedirs(path, mode=0o755) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise # Returns path to postfix under user's Unix home directory. def make_experiment_dir(postfix): home = os.path.expanduser('~') exp_dir = os.path.join(home, postfix) mkdir_p(exp_dir) return exp_dir # appends .png to file name def save_fig(folder, filename): if folder is None: return filename_out = os.path.join(folder, filename + '.png') print('saving {}'.format(filename_out)) with open(filename_out, 'w') as out_file: plt.savefig(out_file) # appends .txt to file name def save_array(x, folder, filename, formatting): if folder is None: return filename_out = os.path.join(folder, filename + '.txt') print('saving {}'.format(filename_out)) with open(filename_out, 'w') as out_file: np.savetxt(out_file, x, fmt=formatting) def load_array(filename): with open(filename, 'r') as f: return np.loadtxt(f) # count parameters for svd truncation def count_parameters_list(k_values, nrows, ncols): new_list = [] for k in k_values: new_k = count_parameters(k, nrows, ncols) new_list.append(new_k) return new_list # number of parameters when nrows-by-ncols matrix is approximated # with product of nrows-by-rank and rank-by-ncolds matrix. def count_parameters(rank, nrows, ncols): return (nrows + ncols) * rank # Return one random rademacher matrix def fully_random_rademacher_matrix(nrows, ncols): plus_minus_one = np.array([-1, 1], dtype=np.float32) return np.random.choice(plus_minus_one, (nrows, ncols)) # Return a rank-1 Rademacher matrix def rank1_rademacher(nrows, ncols): plus_minus_one = np.array([-1, 1], dtype=np.float32) column_vector = np.random.choice(plus_minus_one, (nrows, 1)) row_vector = np.random.choice(plus_minus_one, (1, ncols)) # Plain * is quicker than equivalent np.dot(column_vector, row_vector) return column_vector * row_vector # Sketch matrix A def sketch_matrix(A, sketch_type, k): tf.logging.info('sketch_matrix %s %d', sketch_type, k) h1 = A.shape[0] h2 = A.shape[1] # Numpy defaults to int64 or float64 (double precision). # Computing with float32 (single precision) is quicker. A_hat = np.zeros((h1, h2), dtype=np.float32) for i in range(0, k): tf.logging.log_every_n(tf.logging.INFO, 'sketch_matrix %s iter %d/%d', 1000, sketch_type, i, k) # generate random matrix if sketch_type == 'arora': mat = fully_random_rademacher_matrix(h1, h2) elif sketch_type == 'our_sketch': mat = rank1_rademacher(h1, h2) else: print('wrong sketch_type variable') return -1 # get coefficient coefficient = np.dot(np.ravel(A), np.ravel(mat)) # add coefficient*matrix to A_hat A_hat += coefficient * mat tf.logging.info('Done sketch_matrix %s %d', sketch_type, k) return (1.0 / k) * A_hat # Return truncated svd of A, where only the top k components are used. # Adding --copt=-mavx --copt=-mavx2 --copt=-mfma compiler flags # speeds up svd by almost 2x. However it makes sketching, which is dominant, # a tiny bit slower and hence it's not worth it. def truncated_svd(A, k): tf.logging.info('Computing SVD ...') u, s, v = np.linalg.svd(A, full_matrices=False) u_trunc = u[:, 0:k] s_trunc = s[0:k] v_trunc = v[0:k, :] A_hat = np.dot(u_trunc, np.dot(np.diag(s_trunc), v_trunc)) tf.logging.info('Done computing SVD ...') return A_hat # num_params is rank for SVD, number of coefficients for sketches. def compress(A, compression_type, num_params): if compression_type == 'svd': A_hat = truncated_svd(A, num_params) elif compression_type == 'our_sketch' or compression_type == 'arora': A_hat = sketch_matrix(A, compression_type, num_params) else: print('Error: wrong compression type. Must be svd, our_sketch, or arora.') return A_hat # return singular values of A sorted in descending order def singular_values(A): u, s, v = np.linalg.svd(A) sing = sorted(s, reverse=True) return sing def plot_and_save_singular_values(s, folder, fn, nrows, ncols): x = range(1, len(s) + 1) y = sorted(s, reverse=True) title = 'Singular values\ndim = (' + str(nrows) + 'x' + str(ncols) + ')' plt.plot(x, y) plt.title(title) plt.tight_layout() save_fig(folder, fn) save_array(np.array(s), folder, fn + '_vals', '%.18e')

[ "copybara-worker@google.com" ]

copybara-worker@google.com

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/vindula/tile/browser/accordion.py

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no_license

vindula/vindula.tile

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refs/heads/master

2016-09-10T22:31:12.217220

2015-11-25T16:26:12

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# coding: utf-8 from five import grok from vindula.tile.browser.baseview import BaseView grok.templatedir('templates') class AccordionView(BaseView): grok.name('accordion-view') def get_itens_abas(self): context = self.context current_user = self.p_membership.getAuthenticatedMember() L = [] itens = self.portal_catalog(**{'sort_on': 'getObjPositionInParent', 'portal_type':['TileAccordionItem',], 'path':{'query':'/'.join(context.getPhysicalPath()), 'depth': 1} }) for t in itens: t = t.getObject() if not t.getExcludeFromNav() and self.has_public_or_permission(current_user, t): L.append(t) return L

[ "cesaraugusto@liberiun.com" ]

cesaraugusto@liberiun.com

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/chess/player.py

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no_license

DomhnallBoyle/python-games

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refs/heads/master

2021-01-18T16:02:20.601949

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class Player(object): def __init__(self): self.name = self._get_player_name() self.pieces = [] self.score = 0 def _get_player_name(self): name = raw_input('Enter a name for player: ') while len(name) <= 0: name = raw_input('Enter a name for player: ') return name def make_move(self): pass

[ "domhnallboyle@gmail.com" ]

domhnallboyle@gmail.com

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/sft/config-sample/old_keras_agents/agents/lasagne_complex.py

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[ "MIT" ]

permissive

kevinkepp/search-for-this

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refs/heads/master

2021-09-04T02:49:53.551799

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from lasagne.layers import DenseLayer, ConcatLayer, FlattenLayer from lasagne.nonlinearities import linear, rectify from lasagne.updates import rmsprop import sft.agent.DeepQAgentGpu import sft.agent.model.LasagneMlpModel import sft.eps.Linear import sft.reward.TargetMiddle from sft.log.AgentLogger import AgentLogger from .. import world from ..world import * logger = AgentLogger(__name__) action_hist_len = 4 action_hist_size = Size(action_hist_len, world.nb_actions) epsilon_update = sft.eps.Linear.Linear( start=1, end=0.1, steps=epochs * 4/5 ) def build_network(net_in_views, net_in_actions): net_views_out = FlattenLayer(net_in_views) net_actions_out = FlattenLayer(net_in_actions) net_concat = ConcatLayer([net_views_out, net_actions_out]) net_hid = DenseLayer(net_concat, num_units=256, nonlinearity=rectify) net_hid2 = DenseLayer(net_hid, num_units=64, nonlinearity=rectify) net_out = DenseLayer(net_hid2, num_units=4, nonlinearity=linear) return net_out def optimizer(loss, params): return rmsprop(loss, params, learning_rate=0.00025, rho=0.9, epsilon=1e-8 ) batch_size = 32 model = sft.agent.model.LasagneMlpModel.LasagneMlpModel( logger=logger, batch_size=batch_size, discount=0.99, actions=actions, view_size=world.view_size, action_hist_size=action_hist_size, network_builder=build_network, optimizer=optimizer, clone_interval=500 ) agent = sft.agent.DeepQAgentGpu.DeepQAgentGpu( logger=logger, actions=actions, batch_size=batch_size, buffer_size=100000, start_learn=1000, learn_interval=1, view_size=world.view_size, action_hist_size=action_hist_size, model=model ) reward = sft.reward.TargetMiddle.TargetMiddle( reward_target=1, reward_not_target=0 )

[ "dev@kevinkepp.de" ]

dev@kevinkepp.de

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/utilities/gitall.py

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[]

no_license

MetaCell/HNN-UI

d111bf0eaccc78252fd37cd74fef79eac67e59d6

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refs/heads/master

2020-04-01T10:00:33.640897

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py

#!/usr/bin/python # # Utility script for mass git operatiosn on HNN-UI # Usage: # gitall branches: print current branch of each repo # # gitall checkout <branch> : checkout <branch> on each repo # # gitall pull <remote> <branch> : execute git pull on each repo # # gitall fetch <remote> <branch> : execute git fetch on each repo # # import subprocess import sys config = { "repos": [ { "name": "HNN_UI", "path": "..", "url": "https://github.com/MetaCell/HNN-UI" }, { "name": "org.geppetto.frontend.jupyter", "path": "../org.geppetto.frontend.jupyter", "url": "https://github.com/openworm/org.geppetto.frontend.jupyter" }, { "name": "org.geppetto.frontend", "path": "../hnn_ui/geppetto/", "url": "https://github.com/openworm/org.geppetto.frontend" }, { "name": "Geppetto HNN extension", "path": "../hnn_ui/geppetto/src/main/webapp/extensions/geppetto-hnn/", "url": "https://github.com/MetaCell/geppetto-hnn" } ] } def incorrectInput(argv, msg): print(msg) sys.exit() def main(argv): command = [] if (len(argv) == 0): incorrectInput(argv, 'Too few paramaters') elif (argv[0] == 'push'): command = ['git', 'push', argv[1], argv[2]] elif (argv[0] == 'add'): command = ['git', 'add', argv[1]] elif (argv[0] == 'commit'): command = ['git', 'commit', argv[1], argv[2]] elif (argv[0] == 'branches'): command = ['git', 'rev-parse', '--abbrev-ref', 'HEAD'] elif (argv[0] == 'reset'): command = ['git', 'reset', '--hard', 'HEAD'] elif (argv[0] == 'status'): command = ['git', argv[0]] elif (argv[0] == 'remote'): command = ['git', 'remote', '-v'] elif (argv[0] == 'diff'): command = ['git', 'diff'] elif (argv[0] == 'checkout'): if (len(argv) == 2): command = ['git', 'checkout', argv[1]] elif (len(argv) == 3): command = ['git', 'checkout', argv[1], argv[2]] else: incorrectInput(argv, 'Expected <=3 paramaters') elif (argv[0] == 'pull' or argv[0] == 'fetch'): if (len(argv) == 1): command = ['git', argv[0]] elif (len(argv) == 2): command = ['git', argv[0], argv[1]] elif (len(argv) == 3): command = ['git', argv[0], argv[1], argv[2]] else: incorrectInput(argv, 'Too many paramaters') else: incorrectInput(argv, 'Unrecognized command') for repo in config['repos']: try: print(repo['name']) print(subprocess.check_output(command, cwd=repo['path']).decode('utf-8')) except: print("Error -- trying next repo") if __name__ == "__main__": main(sys.argv[1:])

[ "afonsobspinto@gmail.com" ]

afonsobspinto@gmail.com

647917924cd80707d6b2baa922c9d4558992a1a0

d13c098738df6e94332b84a63beb2e75a7036f7e

/Visualization/conv_filter_visualization.py

54feff0be31e8c595acf57dc5c1b060de47f1065

[]

no_license

ma-xu/DNN

0d4962647f3b2ce0d35d3d50a026a4ad7f6871ee

c05bba778810e61e821bcb9093434a1ff9f0c258

refs/heads/master

2022-03-20T12:15:42.966469

2019-12-05T03:40:00

null

0

null

UTF-8

Python

false

9,759

py

########################################### ### Copy for keras examples at github ########################################### """ #Visualization of the filters of VGG16, via gradient ascent in input space. This script can run on CPU in a few minutes. Results example: ![Visualization](http://i.imgur.com/4nj4KjN.jpg) """ from __future__ import print_function import time import numpy as np from PIL import Image as pil_image from keras.preprocessing.image import save_img from keras import layers from keras.applications import vgg16 from keras import backend as K def normalize(x): """utility function to normalize a tensor. # Arguments x: An input tensor. # Returns The normalized input tensor. """ return x / (K.sqrt(K.mean(K.square(x))) + K.epsilon()) def deprocess_image(x): """utility function to convert a float array into a valid uint8 image. # Arguments x: A numpy-array representing the generated image. # Returns A processed numpy-array, which could be used in e.g. imshow. """ # normalize tensor: center on 0., ensure std is 0.25 x -= x.mean() x /= (x.std() + K.epsilon()) x *= 0.25 # clip to [0, 1] x += 0.5 x = np.clip(x, 0, 1) # convert to RGB array x *= 255 if K.image_data_format() == 'channels_first': x = x.transpose((1, 2, 0)) x = np.clip(x, 0, 255).astype('uint8') return x def process_image(x, former): """utility function to convert a valid uint8 image back into a float array. Reverses `deprocess_image`. # Arguments x: A numpy-array, which could be used in e.g. imshow. former: The former numpy-array. Need to determine the former mean and variance. # Returns A processed numpy-array representing the generated image. """ if K.image_data_format() == 'channels_first': x = x.transpose((2, 0, 1)) return (x / 255 - 0.5) * 4 * former.std() + former.mean() def visualize_layer(model, layer_name, step=1., epochs=15, upscaling_steps=9, upscaling_factor=1.2, output_dim=(412, 412), filter_range=(0, None)): """Visualizes the most relevant filters of one conv-layer in a certain model. # Arguments model: The model containing layer_name. layer_name: The name of the layer to be visualized. Has to be a part of model. step: step size for gradient ascent. epochs: Number of iterations for gradient ascent. upscaling_steps: Number of upscaling steps. Starting image is in this case (80, 80). upscaling_factor: Factor to which to slowly upgrade the image towards output_dim. output_dim: [img_width, img_height] The output image dimensions. filter_range: Tupel[lower, upper] Determines the to be computed filter numbers. If the second value is `None`, the last filter will be inferred as the upper boundary. """ def _generate_filter_image(input_img, layer_output, filter_index): """Generates image for one particular filter. # Arguments input_img: The input-image Tensor. layer_output: The output-image Tensor. filter_index: The to be processed filter number. Assumed to be valid. #Returns Either None if no image could be generated. or a tuple of the image (array) itself and the last loss. """ s_time = time.time() # we build a loss function that maximizes the activation # of the nth filter of the layer considered if K.image_data_format() == 'channels_first': loss = K.mean(layer_output[:, filter_index, :, :]) else: loss = K.mean(layer_output[:, :, :, filter_index]) # we compute the gradient of the input picture wrt this loss grads = K.gradients(loss, input_img)[0] # normalization trick: we normalize the gradient grads = normalize(grads) # this function returns the loss and grads given the input picture iterate = K.function([input_img], [loss, grads]) # we start from a gray image with some random noise intermediate_dim = tuple( int(x / (upscaling_factor ** upscaling_steps)) for x in output_dim) if K.image_data_format() == 'channels_first': input_img_data = np.random.random( (1, 3, intermediate_dim[0], intermediate_dim[1])) else: input_img_data = np.random.random( (1, intermediate_dim[0], intermediate_dim[1], 3)) input_img_data = (input_img_data - 0.5) * 20 + 128 # Slowly upscaling towards the original size prevents # a dominating high-frequency of the to visualized structure # as it would occur if we directly compute the 412d-image. # Behaves as a better starting point for each following dimension # and therefore avoids poor local minima for up in reversed(range(upscaling_steps)): # we run gradient ascent for e.g. 20 steps for _ in range(epochs): loss_value, grads_value = iterate([input_img_data]) input_img_data += grads_value * step # some filters get stuck to 0, we can skip them if loss_value <= K.epsilon(): return None # Calulate upscaled dimension intermediate_dim = tuple( int(x / (upscaling_factor ** up)) for x in output_dim) # Upscale img = deprocess_image(input_img_data[0]) img = np.array(pil_image.fromarray(img).resize(intermediate_dim, pil_image.BICUBIC)) input_img_data = [process_image(img, input_img_data[0])] # decode the resulting input image img = deprocess_image(input_img_data[0]) e_time = time.time() print('Costs of filter {:3}: {:5.0f} ( {:4.2f}s )'.format(filter_index, loss_value, e_time - s_time)) return img, loss_value def _draw_filters(filters, n=None): """Draw the best filters in a nxn grid. # Arguments filters: A List of generated images and their corresponding losses for each processed filter. n: dimension of the grid. If none, the largest possible square will be used """ if n is None: n = int(np.floor(np.sqrt(len(filters)))) # the filters that have the highest loss are assumed to be better-looking. # we will only keep the top n*n filters. filters.sort(key=lambda x: x[1], reverse=True) filters = filters[:n * n] # build a black picture with enough space for # e.g. our 8 x 8 filters of size 412 x 412, with a 5px margin in between MARGIN = 5 width = n * output_dim[0] + (n - 1) * MARGIN height = n * output_dim[1] + (n - 1) * MARGIN stitched_filters = np.zeros((width, height, 3), dtype='uint8') # fill the picture with our saved filters for i in range(n): for j in range(n): img, _ = filters[i * n + j] width_margin = (output_dim[0] + MARGIN) * i height_margin = (output_dim[1] + MARGIN) * j stitched_filters[ width_margin: width_margin + output_dim[0], height_margin: height_margin + output_dim[1], :] = img # save the result to disk save_img('vgg_{0:}_{1:}x{1:}.png'.format(layer_name, n), stitched_filters) # this is the placeholder for the input images assert len(model.inputs) == 1 input_img = model.inputs[0] # get the symbolic outputs of each "key" layer (we gave them unique names). layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]]) output_layer = layer_dict[layer_name] assert isinstance(output_layer, layers.Conv2D) # Compute to be processed filter range filter_lower = filter_range[0] filter_upper = (filter_range[1] if filter_range[1] is not None else len(output_layer.get_weights()[1])) assert(filter_lower >= 0 and filter_upper <= len(output_layer.get_weights()[1]) and filter_upper > filter_lower) print('Compute filters {:} to {:}'.format(filter_lower, filter_upper)) # iterate through each filter and generate its corresponding image processed_filters = [] for f in range(filter_lower, filter_upper): img_loss = _generate_filter_image(input_img, output_layer.output, f) if img_loss is not None: processed_filters.append(img_loss) print('{} filter processed.'.format(len(processed_filters))) # Finally draw and store the best filters to disk _draw_filters(processed_filters) if __name__ == '__main__': # the name of the layer we want to visualize # (see model definition at keras/applications/vgg16.py) LAYER_NAME = 'block5_conv1' # build the VGG16 network with ImageNet weights vgg = vgg16.VGG16(weights='imagenet', include_top=False) print('Model loaded.') vgg.summary() # example function call visualize_layer(vgg, LAYER_NAME)

[ "xuma@my.unt.edu" ]

xuma@my.unt.edu

f419e81f514108bf11109e0849632fd4c5d074f3

d7d25574246fd8585396a02ebd2ca8450e49b082

/leetcode-py/leetcode84.py

1824ee7477f31f88d415aa6aff3453e23e14c6cb

[]

no_license

cicihou/LearningProject

b6b1de2300e574835f253935d0c0ae693b194020

3a5649357e0f21cbbc5e238351300cd706d533b3

refs/heads/master

2022-12-04T06:18:14.856766

2022-11-29T08:54:16

141,606,471

0

null

UTF-8

Python

false

1,494

py

''' 视频：https://www.youtube.com/watch?v=vcv3REtIvEo monostack 1. find the nearest left bar with height < current bar 2. find the nearest right bar with height < current bar 分别从左向右，以及右向左维护两个单调栈，monostack 左边向右边时：碰到比左边或者比右边小的，就 pop 掉当前的数，单调栈中存且只存比 cur 的 height 小的 index，我们单独维护一个数组，存 nearest left bar less than cur 的 index + 1 值右边向左边时：相反最后求出每个index可以生成的 histogram 的最大值，(right - left + 1) * bar_height 这题关于单调栈，挺难的，我不是很理解 ''' class Solution: def largestRectangleArea(self, heights: List[int]) -> int: ''' method 1 :param heights: :return: ''' n = len(heights) left, right = [0] * n, [0] * n monostack = list() for i in range(n): while monostack and heights[monostack[-1]] >= heights[i]: monostack.pop() left[i] = monostack[-1] if monostack else -1 monostack.append(i) monostack = list() for i in range(n-1, -1, -1): while monostack and heights[monostack[-1]] >= heights[i]: monostack.pop() right[i] = monostack[-1] if monostack else n monostack.append(i) res = max((right[i] - left[i] - 1) * heights[i] for i in range(n)) return res

[ "houxi_zuel@163.com" ]

houxi_zuel@163.com

6aa93f8ca984578884a7b5542a708073c3d95d26

12289eecb8ad582578e5722a52e519cbd38c3ab3

/app/auth/forms.py

e128e95ec44f4b5d935f2fca9b0a9b7aaef9879a

[ "MIT" ]

permissive

kuangzhehuangjian/TaskScheduler

fde409820f4fdbd9bff36f5c181943dd8f5d1601

18f25ae1b3ffd14d3c9c736e0e63104df13acb8f

refs/heads/main

2023-08-21T00:30:31.184742

2021-10-27T02:29:55

null

0

null

UTF-8

Python

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py

# -*- coding: utf-8 -*- # @Author: guomaoqiu # @File Name: forms.py # @Date: 2019-03-14 09:51:08 # @Last Modified by: guomaoqiu # @Last Modified time: 2019-05-22 17:59:46 from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, BooleanField, SubmitField from wtforms.validators import Required, Length, Email, Regexp, EqualTo from wtforms import ValidationError from ..models import User class LoginForm(FlaskForm): email = StringField('邮箱', default="root@email.com", validators=[Required(), Length(1, 64), Email()]) password = StringField('密码', default="123456", validators=[Required()]) remember_me = BooleanField('保持登录') submit = SubmitField('登录') class RegistrationForm(FlaskForm): email = StringField('邮箱', validators=[Required(), Length(1, 64), Email()]) username = StringField('用户名', validators=[ Required(), Length(1, 64), Regexp('^[A-Za-z][A-Za-z0-9_.]*$', 0, 'Usernames must have only letters, ' 'numbers, dots or underscores')]) password = PasswordField('密码', validators=[ Required(), EqualTo('password2', message='Passwords must match.')]) password2 = PasswordField('确认密码', validators=[Required()]) submit = SubmitField('注册') def validate_email(self, field): if User.query.filter_by(email=field.data).first(): raise ValidationError('Email already registered.') def validate_username(self, field): if User.query.filter_by(username=field.data).first(): raise ValidationError('Username already in use.') class ChangePasswordForm(FlaskForm): old_password = PasswordField('Old password', validators=[Required()]) password = PasswordField('New password', validators=[ Required(), EqualTo('password2', message='Passwords must match')]) password2 = PasswordField('Confirm new password', validators=[Required()]) submit = SubmitField('Update Password') class PasswordResetRequestForm(FlaskForm): email = StringField('Email', validators=[Required(), Length(1, 64), Email()]) submit = SubmitField('Reset Password') class PasswordResetForm(FlaskForm): email = StringField('Email', validators=[Required(), Length(1, 64), Email()]) password = PasswordField('New Password', validators=[ Required(), EqualTo('password2', message='Passwords must match')]) password2 = PasswordField('Confirm password', validators=[Required()]) submit = SubmitField('Reset Password') def validate_email(self, field): if User.query.filter_by(email=field.data).first() is None: raise ValidationError('Unknown email address.') class ChangeEmailForm(FlaskForm): email = StringField('New Email', validators=[Required(), Length(1, 64), Email()]) password = PasswordField('Password', validators=[Required()]) submit = SubmitField('Update Email Address') def validate_email(self, field): if User.query.filter_by(email=field.data).first(): raise ValidationError('Email already registered.')

[ "870709864@qq.com" ]

870709864@qq.com

1d4a746c351655e76b9f92ea08e41692ab68a1fa

9dbb2c18651df570ded1d4a5fd53e1c36c359afa

/基础心法第1层--炼体/计算个人所得税.py

c7e091cbecaf593c6318e4a87949f194d690d8a6

[]

no_license

zhanrui2011/Python-100-Days-Study-Notes

a9ca7ef9dc58b7959329f1db85feaf119ad78005

18bcc3e5905c6c50470d70ba5d3ac41c0cd62556

refs/heads/master

2020-06-26T15:09:18.402828

2019-07-30T14:18:47

199,668,645

2

0

null

2019-07-30T14:32:03

null

UTF-8

Python

false

683

py

""" 输入月收入和五险一金计算个人所得税 """ salary = float(input('本月收入: ')) insurance = float(input('五险一金: ')) diff = salary - insurance - 3500 if diff <= 0: rate = 0 deduction = 0 elif diff < 1500: rate = 0.03 deduction = 0 elif diff < 4500: rate = 0.1 deduction = 105 elif diff < 9000: rate = 0.2 deduction = 555 elif diff < 35000: rate = 0.25 deduction = 1005 elif diff < 55000: rate = 0.3 deduction = 2755 elif diff < 80000: rate = 0.35 deduction = 5505 else: rate = 0.45 deduction = 13505 tax = abs(diff * rate - deduction) print('个人所得税: ￥%.2f元' % tax) print('实际到手收入: ￥%.2f元' % (diff + 3500 - tax))

[ "Vhzix24@gmail.com" ]

Vhzix24@gmail.com

2e6e6d70f0b9d008c58b9603581611fe2e94b17f

b1b4dc2c530b56a9abbc6b144c3ca3f5a6d11e70

/twisted_/UDP.py

e56e81deb2f409b7f9ab7d7464fe929c25dbbe04

[]

no_license

BorisovDima/_ex

d38cff25575bfc6d1906940d40091fb6946cd829

6c14d661b8e051f8ec385cb09997da1fa781431a

refs/heads/master

2020-05-07T18:13:38.304305

2019-08-23T10:59:09

180,757,813

0

null

UTF-8

Python

false

1,157

py

from twisted.internet.protocol import DatagramProtocol from twisted.internet import reactor class UDP(DatagramProtocol): def stopProtocol(self): print('END') def startProtocol(self): print(self.transport) print('Start') def datagramReceived(self, datagram, addr): print(datagram, addr) addr[1] self.transport.write(b'%s - %s' % (datagram, str(addr).encode()), addr) d = reactor.resolve('google.com') d.addCallback(lambda r: print(r)) # reactor.listenUDP(8001, UDP()) # reactor.run() ################## my socket import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.bind(('0.0.0.0', 8000)) s.setblocking(False) # reactor.adoptDatagramPort(s.fileno(), socket.AF_INET, UDP()) # reactor.run() ################## MULTICAST class MultiUDP(DatagramProtocol): def startProtocol(self): self.transport.setTTL(5) self.transport.joinGroup("228.0.0.5") def datagramReceived(self, datagram, addr): print(datagram, addr) self.transport.write(b"Server: Pong", addr) reactor.listenMulticast(9999, MultiUDP(), listenMultiple=True) reactor.run()

[ "you@example.com" ]

you@example.com

6eb055534e069a46fb58ca98949e9dd2924f5ec0

87ba91313fb206e8974fcd0be9676eca6f15fb09

/w3.py

fb91c18243898c02ade224ad8576bd3fdb6be77e

[]

no_license

tsly2015/Notes

afd55b806184a3332676c7d15bb485c87d1aa6e3

81b1eaa7c5931798bfeb0f0a48929c5d354d8be8

refs/heads/master

2021-01-19T20:18:34.817264

2014-11-28T03:42:43

null

0

null

UTF-8

Python

false

2,686

py

from collections import namedtuple # make a basic Link class Link = namedtuple('Link', ['id', 'submitter_id', 'submitted_time', 'votes', 'title', 'url']) # list of Links to work with links = [ Link(0, 60398, 1334014208.0, 109, "C overtakes Java as the No. 1 programming language in the TIOBE index.", "http://pixelstech.net/article/index.php?id=1333969280")] def query(): for l in links: if l.id == 15: return l.votes def query(): submissions = [] for l in links: if l.submitter_id == 62443: submissions.append(l) submissions.sort(key = lambda x: x.submitted_time) return submissions import sqlite3 db = sqlite3.connect(':memory:') db.execute('create table links ' + '(id integer, submitter_id integer, submitted_time integer, ' + 'votes integer, title text, url text)') for l in links: db.execute('insert into links values (?, ?, ?, ?, ?, ?)', l) def query(): c = db.execute("select * from links") results = c.fetchall() return results def query(): c = db.execute("select * from links") for link_tuple in c: link = Link(*link_tuple) print link.votes print link_tuple def query(): c = db.execute("select * from links where id = 2") #select * from links where submitter_id = 62443 and votes > 1000 link = Link(*c.fetchone()) return link.votes #return link.id def query(): results = [] c = db.execute("select * from links where submitter_id = 62443 order by submitted_time asc") #for link_tuple in c: # link = Link(*link_tuple) # results.append(link.id) results = [t[0] for t in c] return results print query() def build_link_index(): index = {} for l in links: index[l.id] = l return index def link_by_id(link_id): for l in links: if l.id == link_id: return l link_index = build_link_index() def link_by_id(link_id): return link_index.get[link_id] def add_new_link(link): links.append(link) link_index[link.id] = link import os import webapp2 import jinja2 from google.appengine.ext import db template_dir = os.path.join(os.path.dirname(__file__), 'templates') jinja_env = jinja2.Environment(loader = jinja2.FileSystemLoader(template_dir), autoescape=True) class Handler(webapp2.RequestHandler): def write(self, *a, **kw): self.response.out.write(*a, **kw) def render_str(self, template, **params): t = jinja_env.get_template(template) return t.render(params) def render(self, template, **kw): self.write(self.render_str(template, **kw)) class MainPage(Handler): def get(self): self.write("asciichan!") app = webapp2.WSGIApplication([('/', MainPage)], debug=True)

[ "zhenw.wang@gmail.com" ]

zhenw.wang@gmail.com

ce3a35be3812760d489066c9e18242b176ff7c97

35da3082f950aff29c309e78567fc4a539ba654d

/ventas_api/ventas_api/settings.py

cc3c4183066291dd26a42700ee910a3ace782dcb

[]

no_license

EdvinCV/E-Commerce

1f125948488f2fde422cc01b30790464629373e9

fbbdc4dbe8d6c28b985be0e20c69323ab98d80b5

refs/heads/master

2023-01-11T15:46:27.494404

2020-11-14T20:54:39

312,899,904

0

null

UTF-8

Python

false

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py

""" Django settings for ventas_api project. Generated by 'django-admin startproject' using Django 3.1.3. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '(*wn3@h7)*kx=_=+na*9$ho(#d7993a47wfve9-qs1%vierq76' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Change default user model. AUTH_USER_MODEL = 'usuarios.Usuario' # Application definition INSTALLED_APPS = [ # Local Apps 'usuarios.apps.UsuariosConfig', 'ventas.apps.VentasConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'corsheaders' ] REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': [ 'rest_framework.authentication.TokenAuthentication', # <-- And here ], } MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'ventas_api.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'ventas_api.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' CORS_ORIGIN_ALLOW_ALL = True # If this is used then `CORS_ORIGIN_WHITELIST` will not have any effect CORS_ALLOW_CREDENTIALS = True CORS_ORIGIN_WHITELIST = [ 'http://localhost:3030', ] # If this is used, then not need to use `CORS_ORIGIN_ALLOW_ALL = True` CORS_ORIGIN_REGEX_WHITELIST = [ 'http://localhost:3030', ]

[ "edvinc97@gmail.com" ]

edvinc97@gmail.com

34dc5f92471ec178e2a5420ed1d5d28ca9cbbbad

044822487dc3ef7683887ca72ac46f0a361dcd3c

/rateapp/forms.py

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[]

no_license

jer1ck0/Currencies

e41ca590e374403227e44eaaf8cdae33dd39192d

794e61fe8d30426c630f26697496d16d33335c9d

refs/heads/master

2023-06-23T11:17:57.454335

2021-05-12T06:55:20

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from django import forms class RequestForm(forms.Form): first_currency = forms.CharField() second_currency = forms.CharField() period = forms.DateTimeField(required=False) class CurrencyForm(forms.Form): currency_ident = forms.CharField() currency_rate = forms.CharField() period = forms.DateTimeField()

[ "jer1ck0@lex.lebedev.example.com" ]

jer1ck0@lex.lebedev.example.com

b5054b5f42826f0d2b5346bf1680095877833494

ca31b0082522bd107510946fa4f9acf83536e50b

/biocoder/apps/common/view.py

7e4ee6374d080aaf4cfe532093d0109d368fb299

[ "Apache-2.0" ]

permissive

east301/biocoder-server

3e850d5164f13cdcab1dd06482434666e86643bf

e7b937e99608fa18675c972ec4377d946896cbb1

refs/heads/master

2020-12-04T01:06:12.268189

2016-08-28T12:00:34

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# # (c) 2016 biocoder developers # # This file is part of biocoder, # released under Apache License Version 2.0 (http://www.apache.org/licenses/LICENCE). # import jsonschema from braces.views import LoginRequiredMixin, StaffuserRequiredMixin from django.core.exceptions import SuspiciousOperation # ================================================================================ # validation # ================================================================================ class JSONSchemaValidationMixin(object): def dispatch(self, request, *args, **kwargs): # if request.method == 'POST': try: jsonschema.validate(request.POST, self.schema) except ValueError as exc: raise SuspiciousOperation('Failed to validate request parameters.') from exc # return super(JSONSchemaValidationMixin, self).dispatch(request, *args, **kwargs) class DjangoFormValidationMixin(object): def __init__(self, *args, **kwargs): super(DjangoFormValidationMixin, self).__init__(*args, **kwargs) self.form_validator = None def dispatch(self, request, *args, **kwargs): # if request.method == 'POST': self.form_validator = self.form_validator_class(request.POST, request.FILES) if not self.form_validator.is_valid(): raise SuspiciousOperation('Failed to validate request parameters.') # return super(DjangoFormValidationMixin, self).dispatch(request, *args, **kwargs) # ================================================================================ # authentication # ================================================================================ class StaffUserRequiredMixin(LoginRequiredMixin, StaffuserRequiredMixin): pass

[ "tadaka@sb.ecei.tohoku.ac.jp" ]

tadaka@sb.ecei.tohoku.ac.jp

37e09223e7074ba03f04238608d482908fc0a19a

62261d7fbd2feab54b1b5f9f0fef33fd784873f9

/src/deepfool.py

8b318f762dff20a8b7b10e9f0e718ca60b020cb4

[]

no_license

ychnlgy/DeepConsensus-experimental-FROZEN

50ebfe25b33ce8715fb9f24285969831cef311f2

904ae3988fee1df20273e002ba53a49a0d811192

refs/heads/master

2020-03-31T06:18:38.029959

2018-12-02T05:04:52

151,976,523

1

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import numpy as np from torch.autograd import Variable import torch as torch import copy from torch.autograd.gradcheck import zero_gradients def deepfool(image, net, num_classes=10, overshoot=0.02, max_iter=50): """ :param image: Image of size HxWx3 :param net: network (input: images, output: values of activation **BEFORE** softmax). :param num_classes: num_classes (limits the number of classes to test against, by default = 10) :param overshoot: used as a termination criterion to prevent vanishing updates (default = 0.02). :param max_iter: maximum number of iterations for deepfool (default = 50) :return: minimal perturbation that fools the classifier, number of iterations that it required, new estimated_label and perturbed image """ is_cuda = torch.cuda.is_available() if is_cuda: #print("Using GPU") image = image.cuda() net = net.cuda() else: print("Using CPU") f_image = net.forward(Variable(image[None, :, :, :], requires_grad=True)).data.cpu().numpy().flatten() I = (np.array(f_image)).flatten().argsort()[::-1] I = I[0:num_classes] label = I[0] input_shape = image.cpu().numpy().shape pert_image = copy.deepcopy(image) w = np.zeros(input_shape) r_tot = np.zeros(input_shape) loop_i = 0 x = Variable(pert_image[None, :], requires_grad=True) fs = net.forward(x) fs_list = [fs[0,I[k]] for k in range(num_classes)] k_i = label while k_i == label and loop_i < max_iter: pert = np.inf fs[0, I[0]].backward(retain_graph=True) grad_orig = x.grad.data.cpu().numpy().copy() for k in range(1, num_classes): zero_gradients(x) fs[0, I[k]].backward(retain_graph=True) cur_grad = x.grad.data.cpu().numpy().copy() # set new w_k and new f_k w_k = cur_grad - grad_orig f_k = (fs[0, I[k]] - fs[0, I[0]]).data.cpu().numpy() pert_k = abs(f_k)/np.linalg.norm(w_k.flatten()) # determine which w_k to use if pert_k < pert: pert = pert_k w = w_k # compute r_i and r_tot # Added 1e-4 for numerical stability r_i = (pert+1e-4) * w / np.linalg.norm(w) r_tot = np.float32(r_tot + r_i) if is_cuda: pert_image = image + (1+overshoot)*torch.from_numpy(r_tot).cuda() else: pert_image = image + (1+overshoot)*torch.from_numpy(r_tot) x = Variable(pert_image, requires_grad=True) fs = net.forward(x) k_i = np.argmax(fs.data.cpu().numpy().flatten()) loop_i += 1 r_tot = (1+overshoot)*r_tot return r_tot, loop_i, label, k_i, pert_image

[ "ychnlgy@gmail.com" ]

ychnlgy@gmail.com

cebbf6f6e8fc9240025c732d1601f9e7e36ed81b

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/javbus/javbus/settings.py

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[]

no_license

SakuraMaaya/JavbusScrapy

ff3a269a7a87e4e6c8c33231221b9378637c797b

f714304cd057b38e1f6ef3b37ec25d77e2816979

refs/heads/master

2020-03-13T16:09:41.959870

2018-08-10T02:05:55

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3

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# -*- coding: utf-8 -*- # Scrapy settings for javbus project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://doc.scrapy.org/en/latest/topics/settings.html # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html # https://doc.scrapy.org/en/latest/topics/spider-middleware.html import os BOT_NAME = 'javbus' SPIDER_MODULES = ['javbus.spiders'] NEWSPIDER_MODULE = 'javbus.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'javbus (+http://www.yourdomain.com)' DOWNLOAD_DELAY = 1 # 250 ms of delay # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: DEFAULT_REQUEST_HEADERS = { "method":"GET", "scheme":"https", # "accept":"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8", # "accept-encoding":"gzip, deflate, sdch, br", # "accept-language":"zh-CN,zh;q=0.8", # "cache-control":"no-cache", # 'pragma':'no-cache', 'user-agent':'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:48.0) Gecko/20100101 Firefox/48.0' } # Enable or disable spider middlewares # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'javbus.middlewares.JavbusSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'javbus.middlewares.JavbusDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://doc.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html project_dir = os.path.abspath(os.path.dirname(__file__)) IMAGES_STORE = os.path.join(project_dir,'images') IMAGES_EXPIRES = 90 #90天内抓取的都不会被重抓 AUTOTHROTTLE_ENABLED = True AUTOTHROTTLE_START_DELAY = 3.0 AUTOTHROTTLE_MAX_DELAY = 30.0 CONCURRENT_REQUESTS = 5 # Enable and configure the AutoThrottle extension (disabled by default) # See https://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

[ "czkme@qq.com" ]

czkme@qq.com

77d6bcfd63501931a3dd644c5d7be0143c691f69

82b9c39b136428f5d2e05e14a61c8a535fd7d81c

/net/binary_test.py

c1a09faae0e2446fb973e8cafe0adb994ef9c2f4

[]

no_license

DongminK/python

a6889c4deed48738fbb554b78341315955fe2813

6a88ba11f65d430e2109864ea20a7b6e1632e342

refs/heads/master

2020-03-16T17:21:20.784177

2018-05-29T09:06:17

132,828,302

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## STR import struct from insoft.openmanager.message.packet import Packet str_name = "kim dong min" str_bin = bytes(str_name, "utf-8") print("BIN : %s" % str_bin) print("LENGTH : %d" % len(str_bin)) print("STR : %s" % str_bin.decode()) print("") ## INT float_name = 4 float_bin = float_name.to_bytes(4, "big") print("BIN : %s" % float_bin) print("LENGTH : %d" % len(float_bin)) print("INT : %s" % int.from_bytes(float_bin, "big")) print("") test_bin = bytes(b'\x0b') # Server ID print(len(test_bin)) print(int.from_bytes(test_bin, "big")) test_bin = bytes(b'\x00') # Flag print(len(test_bin)) print(int.from_bytes(test_bin, "big")) test_bin = bytes(b'\x00\x04\x93\xec') # Request ID print(len(test_bin)) print(int.from_bytes(test_bin, "big")) # AGENT 6 BYTE # LONG ## INT float_name = 8 float_bin = float_name.to_bytes(8, "big") print("BIN : %s" % float_bin) print("LENGTH : %d" % len(float_bin)) print("LONG : %s" % int.from_bytes(float_bin, "big")) print("") # FLOAT p = struct.pack('f', 3.141592654) print(p, len(p), struct.unpack('f', p)) print(struct.unpack('!i', b'\x00\x00\x00\x06')) print(struct.unpack_from('!I', bytes(b'\x0b\x00\x00\x04\x93\xec'), 2))

[ "kdm0228@gmail.com" ]

kdm0228@gmail.com

e98c27e3713f47c89125bc6ab3b5d0e5b25d33b7

bf20eeafee9a9ba4639d3d8c3a917604721dfa20

/cloudy/db/pgpool.py

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[ "MIT" ]

permissive

un33k/python-cloudy

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fb20f45b782cf81a1924cc93ce61d11c8fc27859

refs/heads/master

2023-03-31T08:57:27.235184

2023-03-19T01:12:21

6,311,992

1

2

MIT

2018-12-16T02:02:52

2012-10-20T17:59:03

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py

import os import re import sys import time from operator import itemgetter from fabric.api import local from fabric.api import run from fabric.api import task from fabric.api import sudo from fabric.api import put from fabric.api import env from fabric.api import settings from fabric.api import hide from fabric.contrib import files from cloudy.db.psql import db_psql_default_installed_version from cloudy.sys.etc import sys_etc_git_commit from cloudy.util.common import sys_restart_service def db_pgpool2_install(): """ Install pgpool2 - Ex: (cmd:)""" # requirements requirements = '%s' % ' '.join([ 'pgpool2', ]) # install requirements sudo('apt -y install {}'.format(requirements)) sys_etc_git_commit('Installed pgpool2') def db_pgpool2_configure(dbhost='', dbport=5432, localport=5432): """ Install pgpool2 - Ex: (cmd:)""" cfgdir = os.path.join(os.path.dirname( __file__), '../cfg') localcfg = os.path.expanduser(os.path.join(cfgdir, 'pgpool2/pgpool.conf')) remotecfg = '/etc/pgpool2/pgpool.conf' sudo('rm -rf ' + remotecfg) put(localcfg, remotecfg, use_sudo=True) sudo('sed -i "s/dbhost/{}/g" {}'.format(dbhost, remotecfg)) sudo('sed -i "s/dbport/{}/g" {}'.format(dbport, remotecfg)) sudo('sed -i "s/localport/{}/g" {}'.format(localport, remotecfg)) localdefault = os.path.expanduser(os.path.join(cfgdir, 'pgpool2/default-pgpool2')) remotedefault = '/etc/default/pgpool2' sudo('rm -rf ' + remotedefault) put(localdefault, remotedefault, use_sudo=True) sys_etc_git_commit('Configured pgpool2') sys_restart_service('pgpool2')

[ "val@neekware.com" ]

val@neekware.com

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/farm_management/lands/models/paddocks.py

ff9517f19bb80fe11a79efe7f6a45050a6e52a78

[ "MIT" ]

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alexanders0/farm-management

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refs/heads/main

2023-07-18T08:51:20.876231

2021-09-01T04:22:59

384,026,009

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"""Paddocks model.""" # Django from django.db import models # Utilities from farm_management.utils.models import FarmModel class Paddock(FarmModel): """Paddock model.""" name = models.CharField('paddock name', max_length=150) description = models.CharField('paddock description', max_length=150) meassure = models.FloatField(null=True) is_active = models.BooleanField( 'active status', default=False, help_text='It is used to know if a group of animals is currently using the paddock.' ) land = models.ForeignKey( 'lands.Land', on_delete=models.CASCADE ) def __str__(self): """Return paddock name.""" return self.name

[ "alexandersn059@gmail.com" ]

alexandersn059@gmail.com

4867499bf7288a93391ea017ca3fdc81362a0829

acb8e84e3b9c987fcab341f799f41d5a5ec4d587

/langs/4/jhm.py

8a63df3857f86c108b49d23e9c21fe4735ab4a02

[]

no_license

G4te-Keep3r/HowdyHackers

46bfad63eafe5ac515da363e1c75fa6f4b9bca32

fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2

refs/heads/master

2020-08-01T12:08:10.782018

2016-11-13T20:45:50

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import sys def printFunction(lineRemaining): if lineRemaining[0] == '"' and lineRemaining[-1] == '"': if len(lineRemaining) > 2: #data to print lineRemaining = lineRemaining[1:-1] print ' '.join(lineRemaining) else: print def main(fileName): with open(fileName) as f: for line in f: data = line.split() if data[0] == 'jHM': printFunction(data[1:]) else: print 'ERROR' return if __name__ == '__main__': main(sys.argv[1])

[ "juliettaylorswift@gmail.com" ]

juliettaylorswift@gmail.com

0ff03af25c4cf1f6e46d865b868f3d0af8f04e17

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/distribution_features.py

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[]

no_license

bons-ai/NASA

bba28bccc3b793ddae2f18a86aa48a91dcb0a878

bf93d2f0bd05e0740bca66702b0409b8cf697807

refs/heads/master

2020-08-21T09:21:00.597056

2019-10-19T22:09:31

216,129,907

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import numpy as np from sklearn.decomposition import NMF import matplotlib.pyplot as plt import umap import hdbscan import json filename = "hist_0530_03_09.npy" data = np.load(filename, allow_pickle=True).item() hist = np.array(data["hist"]) h = np.array(data["h"]) pos = np.array(data["pos"]) reducer = umap.UMAP() embedding = reducer.fit_transform(hist) clusterer = hdbscan.HDBSCAN(algorithm='best', alpha=1.0, approx_min_span_tree=True, gen_min_span_tree=False, leaf_size=40, metric='euclidean', min_cluster_size=15, min_samples=None, p=None) clusterer.fit(embedding) # # print(clusterer.labels_) # # plt.figure() # plt.scatter(embedding[:, 0], embedding[:, 1], c=clusterer.labels_) # plt.show() # # plt.figure(figsize=(12,12)) # plt.scatter(pos[0, :], pos[1,:], c=clusterer.labels_) # plt.colorbar() # plt.show() results = [] for n in range(pos.shape[1]): results.append({"coordinates": list(pos[:, n]), "type": float(clusterer.labels_[n])}) full_data = {"points": results, "minLat": np.min(pos[0, :]), "maxLat": np.max(pos[0, :]), "minLong": np.min(pos[1, :]), "maxLong": np.max(pos[1, :])} with open("results.json", "w", encoding='utf-8') as f: json.dump(full_data, f)

[ "mohamed.bahdine.1@ulaval.ca" ]

mohamed.bahdine.1@ulaval.ca

ce293f3f21f040c9819e5a37cc9389a60372c6ce

086be2c7d301aee05126964b381ae9a3563183f8

/persistence/memory-map.py

98382a34be776929ddc1304171dd48c29aea34d5

[]

no_license

wkrea/DownTheRabbitHole

fe4ca2aa6141ee482a0f2c649e19243b0431a2f1

60fe7601d1725e7458a882de661fa1412fe37fe3

refs/heads/master

2022-01-29T15:33:53.338936

2019-03-20T19:12:44

null

0

null

UTF-8

Python

false

3,040

py

""" This module writes a matrix of numbers of int and long types to a file in binary format. It mmaps that file, and allows efficient alters-in-place through the api. """ import mmap import struct import os def format_to_size(fmt): if fmt == 'i': return 4 elif fmt == 'l': return 8 def get_column_offset(col_index, row_format): offset = 0 for idx, fmt in enumerate(row_format): if idx == col_index: return offset offset += format_to_size(fmt) def get_row_length(row_format): return sum([format_to_size(fmt) for fmt in row_format]) def pack_row(row, row_format): packed = [] for fmt, value in zip(row_format, row): packed.append(struct.pack(fmt, value)) return "".join(packed) def unpack_row(row, row_format): unpacked = [] for idx, fmt in enumerate(row_format): offset = get_column_offset(idx, row_format) size = format_to_size(fmt) unpacked.append(struct.unpack(fmt, row[offset:offset+size])[0]) return unpacked def write_block(blk, row_format): with open(FILENAME, 'w+') as fp: for row in blk: fp.write(pack_row(row, row_format)) def file_to_block(filename, row_format): packed, idx, row_length = [], 0, get_row_length(row_format) with open(filename, 'r+') as fp: mapped = mmap.mmap(fp.fileno(), 0) while True: row = mapped[idx*row_length:(idx+1)*row_length] if not row: break yield unpack_row(row, row_format) idx += 1 def print_file(filename, row_format): packed, idx, row_length = [], 0, get_row_length(row_format) with open(filename, 'r+') as fp: mapped = mmap.mmap(fp.fileno(), 0) while True: row = mapped[idx*row_length:(idx+1)*row_length] if not row: break print(unpack_row(row, row_format)) idx += 1 def batch_increment_column(filename, row_format, column_id, incrby): idx = 0 row_length = get_row_length(row_format) col_offset = get_column_offset(column_id, row_format) col_size = format_to_size(row_format[column_id]) with open(filename, 'r+') as fp: mapped = mmap.mmap(fp.fileno(), 0) while True: row = mapped[idx*row_length:(idx+1)*row_length] if not row: break updated = unpack_row(row, row_format)[column_id] + incrby start = idx*row_length + col_offset stop = start + col_size idx += 1 mapped[start:stop] = struct.pack('i', updated) mapped.flush() os.fsync(fp.fileno()) if __name__ == '__main__': ROW_FORMAT = 'liili' BIGINT, INT = 8, 4 FILENAME = 'myfile.dat' block = [ [10e12, 55, 74, 1234, 45376], [2.1e14, 62, 2462, 5678, 324], [4.3e10, 12, 24, 91011, 2346], [8e15, 15, 768, 12131, 23] ] write_block(block, ROW_FORMAT) print_file(FILENAME, ROW_FORMAT) batch_increment_column(FILENAME, ROW_FORMAT, 1, 2)

[ "andrew.kelleher@venmo.com" ]

andrew.kelleher@venmo.com

122aa3f61736137c1b3bfcdc6d80491b14a6acbd

e01d9449d415a37f3afa2b20be6ba4fcbfef5650

/Version 2/Menu.py

7d2be37bb7bf944441d923ec5cf9219af0430f8b

[]

no_license

cramos5/Inova-Class-Extraction

082f0c38a48444f9443730a1d127aeb971b4bee0

75e4f2cc4ea16aad13dcdc88cd8b5c85696a00a4

refs/heads/master

2022-02-06T21:46:46.207031

2017-08-03T18:04:28

null

0

null

UTF-8

Python

false

594

py

import os def Menu_Instructions(): print("******************************************************************************") print("Set Up Instructions:\n1) Generate Class Screening Report from Careworks") print("Excel file should have the Class ID in the A Column\n") print("2) Save generated report as a xlsx file within Excel\n") print("3) Place new file in the same location as this program file\n") print("******************************************************************************") name = input("Please enter any key to continue") os.system('cls')

[ "cr4mos@gmail.com" ]

cr4mos@gmail.com

a12d1c1d69d2f4a99bc1bedd4191dadd159c9ae1

0d26474cc10de46cfe0dcc45c57ab299c7b515bc

/propagsim/cp/classes.py

825f3b4e324e7af22f83f4d1be99794cdb41fa5e

[ "MIT" ]

permissive

payoto/py-propagsim

3f735283c51764836ae170f391225f7661f57f23

d704796c77bc6c921c5f14fab1e27aaadbdd640e

refs/heads/master

2021-04-20T13:36:42.030817

2020-05-17T01:37:26

249,688,117

0

null

2020-03-24T11:18:35

null

UTF-8

Python

false

26,787

py

import cupy as cp import numpy as np import os, pickle from time import time from utils import get_least_severe_state, squarify, get_square_sampling_probas, get_cell_sampling_probas, vectorized_choice, group_max, append, repeat, sum_by_group class State: def __init__(self, id, name, contagiousity, sensitivity, severity): """ A state can be carried by an agent. It makes the agent accordingly contagious, sensitive and in a severe state. """ self.id = id self.name = name self.contagiousity = contagiousity self.sensitivity = sensitivity self.severity = severity def __str__(self): return self.name def get_id(self): return self.id def get_name(self): return self.name def get_contagiousity(self): return self.contagiousity def get_sensitivity(self): return self.sensitivity def get_severity(self): return self.severity class Agent: def __init__(self, id, p_move, states, transitions, durations, current_state, home_cell_id, current_state_duration=0, been_infected=0): self.id = id self.p_move = p_move self.states = states self.transitions = transitions self.durations = durations self.current_state = current_state self.home_cell_id = home_cell_id self.current_state_duration = current_state_duration # how long the agent has been in this state self.been_infected = been_infected self.least_state = get_least_severe_state(states) def get_id(self): return self.id def get_p_move(self): return self.p_move def set_p_move(self, p_move): self.p_move = p_move def get_states(self): return self.states def set_states(self, states): return self.states def get_transitions(self): return self.transitions def set_transitions(self, transitions): self.transitions = transitions def get_transitions_id(self): return self.transitions.get_id() def get_transitions_arr(self): return self.transitions.get_arr() def get_durations(self): return self.durations def set_durations(self, durations): self.durations = durations def get_current_state_id(self): return self.current_state.get_id() def get_current_state_duration(self): return self.current_state_duration def set_current_state(self, current_state): self.current_state = current_state def get_home_cell_id(self): return self.home_cell_id def set_home_cell_id(self, home_cell_id): self.home_cell_id = home_cell_id def get_least_state_id(self): return self.least_state.get_id() def get_severity(self): return self.current_state.get_severity() class Transitions: def __init__(self, id, arr): self.id = id self.arr = arr.astype(cp.float32) def get_id(self): return self.id def get_arr(self): return self.arr class Cell: def __init__(self, id, position, attractivity, unsafety): """A cell is figuratively a place where several agents can be together and possibly get infected from an infected agent in the cell. A cell has also a geographic `position` (Euclidean coordinates) and an `attractivity` influencing the probability of the agents in other cells to move in this cell. """ self.id = id self.position = position self.attractivity = attractivity self.unsafety = unsafety def get_id(self): return self.id def get_position(self): return self.position def set_position(self, position): self.position = position def get_attractivity(self): return self.attractivity def set_attractivity(self, attractivity): self.attractivity = attractivity def get_unsafety(self): return self.unsafety def set_unsafety(self, unsafety): self.unsafety = unsafety class Map: def __init__(self, cell_ids, attractivities, unsafeties, xcoords, ycoords, unique_state_ids, unique_contagiousities, unique_sensitivities, unique_severities, transitions, agent_ids, home_cell_ids, p_moves, least_state_ids, current_state_ids, current_state_durations, durations, transitions_ids, dscale=1, current_period=0, verbose=0): """ A map contains a list of `cells`, `agents` and an implementation of the way agents can move from a cell to another. `possible_states` must be distinct. We let each the possibility for each agent to have its own least severe state to make the model more flexible. Default parameter set to None in order to be able to create an empty map and load it from disk `dcale` allows to weight the importance of the distance vs. attractivity for the moves to cells """ self.current_period = current_period self.verbose = verbose self.dscale = dscale self.n_infected_period = 0 # For cells self.cell_ids = cell_ids self.attractivities = attractivities self.unsafeties = unsafeties self.xcoords = xcoords self.ycoords = ycoords # For states self.unique_state_ids = unique_state_ids self.unique_contagiousities = unique_contagiousities self.unique_sensitivities = unique_sensitivities self.unique_severities = unique_severities self.transitions = transitions # For agents self.agent_ids = agent_ids self.home_cell_ids = home_cell_ids self.p_moves = p_moves self.least_state_ids = least_state_ids self.current_state_ids = current_state_ids self.current_state_durations = current_state_durations # how long the agents are already in their current state self.durations = cp.squeeze(durations) # 2d, one row for each agent self.transitions_ids = transitions_ids # for cells: cell_ids, attractivities, unsafeties, xcoords, ycoords # for states: unique_contagiousities, unique_sensitivities, unique_severities, transitions # for agents: home_cell_ids, p_moves, least_state_ids, current_state_ids, current_state_durations, durations (3d) <<<<<<< HEAD ======= >>>>>>> 024791b60731bd81bf57a6c52f3f58c77cab4579 # Compute inter-squares proba transition matrix self.coords_squares, self.square_ids_cells = squarify(xcoords, ycoords) self.set_attractivities(attractivities) # the first cells in parameter `cells`must be home cell, otherwise modify here self.agent_squares = self.square_ids_cells[self.home_cell_ids] cp.cuda.Stream.null.synchronize() # Re-order transitions by ids order = cp.argsort(self.transitions_ids) self.transitions_ids = self.transitions_ids[order] self.transitions = cp.dstack(self.transitions) self.transitions = self.transitions[:,:, order] cp.cuda.Stream.null.synchronize() # Compute upfront cumulated sum self.transitions = cp.cumsum(self.transitions, axis=1) # Compute probas_move for agent selection # Define variable for monitoring the propagation (r factor, contagion chain) self.n_contaminated_period = 0 # number of agent contaminated during current period self.n_diseased_period = self.get_n_diseased() self.r_factors = cp.array([]) # TODO: Contagion chains # Define arrays for agents state transitions self.infecting_agents, self.infected_agents, self.infected_periods = cp.array([]), cp.array([]), cp.array([]) def contaminate(self, selected_agents, selected_cells): """ both arguments have same length. If an agent with sensitivity > 0 is in the same cell than an agent with contagiousity > 0: possibility of contagion """ t_start = time() i = 0 t0 = time() selected_unsafeties = self.unsafeties[selected_cells] selected_agents = selected_agents.astype(cp.uint32) selected_states = self.current_state_ids[selected_agents] selected_contagiousities = self.unique_contagiousities[selected_states] selected_sensitivities = self.unique_sensitivities[selected_states] print(f'ttt first part contaminate: {time() - t0}') # Find cells where max contagiousity == 0 (no contagiousity can happen there) t0 = time() cont_sens = cp.multiply(selected_contagiousities, selected_sensitivities) print(f'ttt group max sensitivities: {time() - t0}') # Combine them if cp.max(cont_sens) == 0: return t0 = time() mask_zero = (cont_sens > 0) selected_agents = selected_agents[mask_zero] selected_contagiousities = selected_contagiousities[mask_zero] selected_sensitivities = selected_sensitivities[mask_zero] selected_cells = selected_cells[mask_zero] selected_unsafeties = selected_unsafeties[mask_zero] print(f'ttt mask zero all: {time() - t0}') # Compute proportion (contagious agent) / (non contagious agent) by cell t0 = time() _, n_contagious_by_cell = cp.unique(selected_cells[selected_contagiousities > 0], return_counts=True) _, n_non_contagious_by_cell = cp.unique(selected_cells[selected_contagiousities == 0], return_counts=True) print(f'ttt non contagious: {time() - t0}') i += 1 t0 = time() p_contagious = cp.divide(n_contagious_by_cell, n_non_contagious_by_cell) n_selected_agents = selected_agents.shape[0] print(f'ttt p_contagious: {time() - t0}') if self.verbose > 1: print(f'{n_selected_agents} selected agents after removing cells with max sensitivity or max contagiousity==0') if n_selected_agents == 0: return # Find for each cell which agent has the max contagiousity inside (it will be the contaminating agent) t0 = time() max_contagiousities, mask_max_contagiousities = group_max(data=selected_contagiousities, groups=selected_cells) print(f'ttt max contagious: {time() - t0}') t0 = time() infecting_agents = selected_agents[mask_max_contagiousities] selected_contagiousities = selected_contagiousities[mask_max_contagiousities] print(f'ttt mask max contagious: {time() - t0}') # Select agents that can be potentially infected ("pinfected") and corresponding variables t0 = time() pinfected_mask = (selected_sensitivities > 0) pinfected_agents = selected_agents[pinfected_mask] selected_sensitivities = selected_sensitivities[pinfected_mask] selected_unsafeties = selected_unsafeties[pinfected_mask] selected_cells = selected_cells[pinfected_mask] print(f'ttt p_infected_mask: {time() - t0}') # Group `selected_cells` and expand `infecting_agents` and `selected_contagiousities` accordingly # There is one and only one infecting agent by pinselected_agentsfected_cell so #`counts` == #`infecting_agents` t0 = time() _, inverse = cp.unique(selected_cells, return_inverse=True) print(f'ttt inverse select cell: {time() - t0}') # TODO: ACHTUNG: count repeat replace by inverse here t0 = time() infecting_agents = infecting_agents[inverse] selected_contagiousities = selected_contagiousities[inverse] p_contagious = p_contagious[inverse] print(f'ttt p_contagious inverse: {time() - t0}') # Compute contagions t0 = time() res = cp.multiply(selected_contagiousities, selected_sensitivities) res = cp.multiply(res, selected_unsafeties) print(f'ttt cp.multiply: {time() - t0}') # Modifiy probas contamination according to `p_contagious` t0 = time() mask_p = (p_contagious < 1) res[mask_p] = cp.multiply(res[mask_p], p_contagious[mask_p]) res[~mask_p] = 1 - cp.divide(1 - res[~mask_p], p_contagious[~mask_p]) print(f'ttt res mask p: {time() - t0}') t0 = time() draw = cp.random.uniform(size=infecting_agents.shape[0]) draw = (draw < res) infecting_agents = infecting_agents[draw] infected_agents = pinfected_agents[draw] n_infected_agents = infected_agents.shape[0] print(f'ttt n_infected draw: {time() - t0}') if self.verbose > 1: print(f'Infecting and infected agents should be all different, are they? {((infecting_agents == infected_agents).sum() == 0)}') print(f'Number of infected agents: {n_infected_agents}') t0 = time() self.current_state_ids[infected_agents] = self.least_state_ids[infected_agents] self.current_state_durations[infected_agents] = 0 self.n_infected_period += n_infected_agents self.infecting_agents = append(self.infecting_agents, infecting_agents) self.infected_agents = append(self.infected_agents, infected_agents) self.infected_periods = append(self.infected_periods, cp.multiply(cp.ones(n_infected_agents), self.current_period)) print(f'ttt final: {time() - t0}') print(f'contaminate computed in {time() - t_start}') def move_agents(self, selected_agents): """ First select the square where they move and then the cell inside the square """ t0 = time() selected_agents = selected_agents.astype(cp.uint32) agents_squares_to_move = self.agent_squares[selected_agents] """ order = cp.argsort(agents_squares_to_move) selected_agents = selected_agents[order] agents_squares_to_move = agents_squares_to_move[order] # Compute number of agents by square unique_square_ids, counts = cp.unique(agents_squares_to_move, return_counts=True) # Select only rows corresponding to squares where there are agents to move square_sampling_ps = self.square_sampling_probas[unique_square_ids,:] # Apply "repeat sample" trick square_sampling_ps = cp.repeat(square_sampling_ps, counts.tolist(), axis=0) """ square_sampling_ps = self.square_sampling_probas[agents_squares_to_move,:] # Chose one square for each row (agent), considering each row as a sample proba selected_squares = vectorized_choice(square_sampling_ps) """ order = cp.argsort(selected_squares) selected_agents = selected_agents[order] selected_squares = selected_squares[order] """ if self.verbose > 1: print(f'{(agents_squares_to_move != selected_squares).sum()}/{selected_agents.shape[0]} agents moving outside of their square') # Now select cells in the squares where the agents move # ACHTUNG: change unique repeat to inverse unique_selected_squares, inverse = cp.unique(selected_squares, return_inverse=True) # unique_selected_squares = unique_selected_squares.astype(cp.uint16) cell_sampling_ps = self.cell_sampling_probas[unique_selected_squares,:] cell_sampling_ps = cell_sampling_ps[inverse,:] """ cell_sampling_ps = cp.repeat(cell_sampling_ps, counts.tolist(), axis=0) cell_sampling_ps = cell_sampling_ps.astype(cp.float16) # float16 to avoid max memory error, precision should be enough """ selected_cells = vectorized_choice(cell_sampling_ps) # Now we have like "cell 2 in square 1, cell n in square 2 etc." we have to go back to the actual cell id index_shift = self.cell_index_shift[selected_squares].astype(cp.uint32) selected_cells = cp.add(selected_cells, index_shift) # return selected_agents since it has been re-ordered print(f'move_agents computed in {time() - t0}') return selected_agents, selected_cells def make_move(self): """ determine which agents to move, then move hem and proceed to the contamination process """ probas_move = cp.multiply(self.p_moves.flatten(), 1 - self.unique_severities[self.current_state_ids]) draw = cp.random.uniform(size=probas_move.shape[0]) t0 = time() draw = (draw < probas_move) print(f't draw: {time() - t0}') t0 = time() selected_agents = self.agent_ids[draw] print(f't selected: {time() - t0}') t0 = time() selected_agents, selected_cells = self.move_agents(selected_agents) print(f't move_agents(): {time() - t0}') if self.verbose > 1: print(f'{selected_agents.shape[0]} agents selected for moving') t0 = time() self.contaminate(selected_agents, selected_cells) print(f't contaminate(): {time() - t0}') def forward_all_cells(self): """ move all agents in map one time step forward """ agents_durations = self.durations[cp.arange(0, self.durations.shape[0]), self.current_state_ids].flatten() print(f'DEBUG: agents_durations.shape: {agents_durations.shape}, self.durations.shape: {self.durations.shape}, self.current_state_ids.shape: {self.current_state_ids.shape}') to_transit = (self.current_state_durations == agents_durations) self.current_state_durations += 1 to_transit = self.agent_ids[to_transit] self.transit_states(to_transit) # Contamination at home by end of the period self.contaminate(self.agent_ids, self.home_cell_ids) # Update r and associated variables r = self.n_infected_period / self.n_diseased_period if self.n_diseased_period > 0 else 0 r = cp.array([r]) if self.verbose > 1: print(f'period {self.current_period}: r={r}') self.r_factors = append(self.r_factors, r) self.n_diseased_period = self.get_n_diseased() self.n_infected_period = 0 #Move one period forward self.current_period += 1 def transit_states(self, agent_ids_transit): if agent_ids_transit.shape[0] == 0: return t0 = time() agent_ids_transit = agent_ids_transit.astype(cp.uint32) agent_current_states = self.current_state_ids[agent_ids_transit] agent_transitions = self.transitions_ids[agent_current_states] # Select rows corresponding to transitions to do transitions = self.transitions[agent_current_states,:,agent_transitions] # Select new states according to transition matrix new_states = vectorized_choice(transitions) self.change_state_agents(agent_ids_transit, new_states) print(f'transit_states computed in {time() - t0}s') def get_states_numbers(self): """ For all possible states, return the number of agents in the map in this state returns a numpy array consisting in 2 columns: the first is the state id and the second, the number of agents currently in this state on the map """ state_ids, n_agents = cp.unique(self.current_state_ids, return_counts=True) return state_ids, n_agents def get_n_diseased(self): return ((self.unique_severities[self.current_state_ids] > 0) & (self.unique_severities[self.current_state_ids] < 1)).sum() def get_r_factors(self): return self.r_factors def get_contamination_chain(self): return self.infecting_agents, self.infected_agents, self.infected_periods def change_state_agents(self, agent_ids, new_state_ids): """ switch `agent_ids` to `new_state_ids` """ self.current_state_ids[agent_ids] = new_state_ids self.current_state_durations[agent_ids] = 0 ### Persistence methods def save(self, savedir): """ persist map in `savedir` """ if not os.path.isdir(savedir): os.makedirs(savedir) # Persist arrays dsave = {} dsave['unique_state_ids'] = self.unique_state_ids, dsave['unique_contagiousities'] = self.unique_contagiousities, dsave['unique_sensitivities'] = self.unique_sensitivities, dsave['unique_severities'] = self.unique_severities, dsave['cell_ids'] = self.cell_ids, dsave['unsafeties'] = self.unsafeties, dsave['square_sampling_probas'] = self.square_sampling_probas, dsave['eligible_cells'] = self.eligible_cells, dsave['coords_squares'] = self.coords_squares, dsave['square_ids_cells'] = self.square_ids_cells, dsave['cell_sampling_probas'] = self.cell_sampling_probas, dsave['cell_index_shift'] = self.cell_index_shift, dsave['agent_ids'] = self.agent_ids, dsave['p_moves'] = self.p_moves, dsave['least_state_ids'] = self.least_state_ids, dsave['unique_state_ids'] = self.unique_state_ids, dsave['home_cell_ids'] = self.home_cell_ids, dsave['current_state_ids'] = self.current_state_ids, dsave['current_state_durations'] = self.current_state_durations, dsave['agent_squares'] = self.agent_squares dsave['transitions'] = self.transitions, dsave['transitions_ids'] = self.transitions_ids, dsave['durations'] = self.durations, dsave['r_factors'] = self.r_factors, dsave['infecting_agents'] = self.infecting_agents, dsave['infected_agents'] = self.infected_agents, dsave['infected_periods'] = self.infected_periods for fname, arr in dsave.items(): filepath = os.path.join(savedir, f'{fname}.npy') cp.save(filepath, arr) # Persist scalars and other parameters sdict = {} sdict['current_period'] = self.current_period sdict['verbose'] = self.verbose sdict['dcale'] = self.dscale sdict['n_infected_period'] = self.n_infected_period sdict['n_diseased_period'] = self.n_diseased_period sdict_path = os.path.join(savedir, 'params.pkl') with open(sdict_path, 'wb') as f: pickle.dump(sdict, f, protocol=pickle.HIGHEST_PROTOCOL) if self.verbose > 0: print(f'Map persisted under folder: {savedir}') def load(self, savedir): """ load map that has been persisted in `savedir` through `self.save()` """ if not os.path.isdir(savedir): print(f'{savedir} is not a path') self.unique_state_ids = cp.squeeze(cp.load(os.path.join(savedir, 'unique_state_ids.npy'))) self.unique_contagiousities = cp.squeeze(cp.load(os.path.join(savedir, 'unique_contagiousities.npy'))) self.unique_sensitivities = cp.squeeze(cp.load(os.path.join(savedir, 'unique_sensitivities.npy'))) self.unique_severities = cp.squeeze(cp.load(os.path.join(savedir, 'unique_severities.npy'))) self.cell_ids = cp.squeeze(cp.load(os.path.join(savedir, 'cell_ids.npy'))) self.unsafeties = cp.squeeze(cp.load(os.path.join(savedir, 'unsafeties.npy'))) self.square_sampling_probas = cp.squeeze(cp.load(os.path.join(savedir, 'square_sampling_probas.npy'))) self.eligible_cells = cp.squeeze(cp.load(os.path.join(savedir, 'eligible_cells.npy'))) self.coords_squares = cp.squeeze(cp.load(os.path.join(savedir, 'coords_squares.npy'))) self.square_ids_cells = cp.squeeze(cp.load(os.path.join(savedir, 'square_ids_cells.npy'))) self.cell_sampling_probas = cp.squeeze(cp.load(os.path.join(savedir, 'cell_sampling_probas.npy'))) self.cell_index_shift = cp.squeeze(cp.load(os.path.join(savedir, 'cell_index_shift.npy'))) self.agent_ids = cp.squeeze(cp.load(os.path.join(savedir, 'agent_ids.npy'))) self.p_moves = cp.squeeze(cp.load(os.path.join(savedir, 'p_moves.npy'))) self.least_state_ids = cp.squeeze(cp.load(os.path.join(savedir, 'least_state_ids.npy'))) self.unique_state_ids = cp.squeeze(cp.load(os.path.join(savedir, 'unique_state_ids.npy'))) self.home_cell_ids = cp.squeeze(cp.load(os.path.join(savedir, 'home_cell_ids.npy'))) self.current_state_ids = cp.squeeze(cp.load(os.path.join(savedir, 'current_state_ids.npy'))) self.current_state_durations = cp.squeeze(cp.load(os.path.join(savedir, 'current_state_durations.npy'))) self.agent_squares = cp.squeeze(cp.load(os.path.join(savedir, 'agent_squares.npy'))) self.transitions = cp.squeeze(cp.load(os.path.join(savedir, 'transitions.npy'))) self.transitions_ids = cp.squeeze(cp.load(os.path.join(savedir, 'transitions_ids.npy'))) self.durations = cp.squeeze(cp.load(os.path.join(savedir, 'durations.npy'))) self.r_factors = cp.squeeze(cp.load(os.path.join(savedir, 'r_factors.npy'))) self.infecting_agents = cp.squeeze(cp.load(os.path.join(savedir, 'infecting_agents.npy'))) self.infected_agents = cp.squeeze(cp.load(os.path.join(savedir, 'infected_agents.npy'))) self.infected_periods = cp.squeeze(cp.load(os.path.join(savedir, 'infected_periods.npy'))) sdict_path = os.path.join(savedir, 'params.pkl') with open(sdict_path, 'rb') as f: sdict = pickle.load(f) self.current_period = sdict['current_period'] self.verbose = sdict['verbose'] self.dscale = sdict['dcale'] self.n_infected_period = sdict['n_infected_period'] self.n_diseased_period = sdict['n_diseased_period'] # For calibration: reset parameters that can change due to public policies def set_p_moves(self, p_moves): self.p_moves = p_moves def set_unsafeties(self, unsafeties): self.unsafeties = unsafeties def set_attractivities(self, attractivities): self.square_sampling_probas = get_square_sampling_probas(attractivities, self.square_ids_cells, self.coords_squares, self.dscale) mask_eligible = cp.where(attractivities > 0)[0] # only cells with attractivity > 0 are eligible for a move self.eligible_cells = self.cell_ids[mask_eligible] # Compute square to cell transition matrix self.cell_sampling_probas, self.cell_index_shift = get_cell_sampling_probas(attractivities[mask_eligible], self.square_ids_cells[mask_eligible]) # Compute upfront cumulated sum of sampling matrices self.square_sampling_probas = cp.cumsum(self.square_sampling_probas, axis=1) self.cell_sampling_probas = cp.cumsum(self.cell_sampling_probas, axis=1) <<<<<<< HEAD ======= >>>>>>> 024791b60731bd81bf57a6c52f3f58c77cab4579

[ "egregius314@gmail.com" ]

egregius314@gmail.com

224852df1c4b1e762780d1e5dd02015ea14faa4d

5ffd3067814644928625e4dd53323dd8ee986390

/main.py

3dee01369a87bbd63d1859635dcbfc64371e2a7f

[]

no_license

rynkevich/image-filtering

359717b3a60acf7eca317f2de1335fc46241162e

f996b292e7ce4fe76355f2d9d28505f0c591ae9d

refs/heads/master

2020-08-02T00:01:03.453078

2019-09-26T19:41:41

211,167,429

0

null

UTF-8

Python

false

1,663

py

import sys import matplotlib.pyplot as plt import imageio import numpy as np from filters import MovingAverageFilter, MedianFilter from filters.EdgeDetectingFilter import EdgeDetectingFilter VALID_ARGC = 3 WINDOW_SIZE = 3 def main(): if len(sys.argv) < VALID_ARGC: print('Usage: main.py <path_to_image> (average|median|sobel|prewitt)') return original_img = imageio.imread(sys.argv[1]) img_to_filter = original_img is_grayscale = False image_filter = None filter_name = str.lower(sys.argv[2]) if filter_name == 'average': image_filter = MovingAverageFilter(WINDOW_SIZE) elif filter_name == 'median': image_filter = MedianFilter(WINDOW_SIZE) elif filter_name == 'sobel' or filter_name == 'prewitt': is_grayscale = True img_to_filter = rgb_to_grayscale(original_img) image_filter = EdgeDetectingFilter(use_prewitt=filter_name == 'prewitt') else: raise NotImplementedError(f'Filter "{filter_name}" is not supported') filtered_img = image_filter.apply(img_to_filter) fig, axes = plt.subplots(1, 2, figsize=(8, 3)) fig.edgecolor = 'black' fig.canvas.set_window_title('Image Filtering') show_img(axes[0], original_img, 'Original', is_grayscale) show_img(axes[1], filtered_img, 'Filtered', is_grayscale) plt.tight_layout() plt.show() def rgb_to_grayscale(image): return np.dot(image[..., :3], [0.3, 0.587, 0.114]) def show_img(ax, img, title, is_grayscale): ax.set_title(title) ax.imshow(img, cmap=(plt.get_cmap('gray') if is_grayscale else None)) ax.axis('off') if __name__ == '__main__': main()

[ "rynkevich@icloud.com" ]

rynkevich@icloud.com

ec886a50104eadb4bb7095b7baddd9867b7f611a

12ac898e8db7ac325e42c9d6c6c875aee78da655

/StockApp/StockQuotes/migrations/0009_buystockmodel.py

5bdc79480f464e464d08fd48054c223caf87ee44

[]

no_license

Teri-Ed/Project-4

121d0e5fcccf89489bbd4d7ec670f57508839dbc

518f47416fe687c8fbc52535fe07a552d969ed9d

refs/heads/main

2023-01-01T20:01:55.988283

2020-10-17T23:13:54

300,732,534

0

null

UTF-8

Python

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650

py

# Generated by Django 3.1.1 on 2020-10-11 23:17 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('StockQuotes', '0008_auto_20201011_0813'), ] operations = [ migrations.CreateModel( name='BuyStockModel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('price', models.CharField(max_length=100)), ('quantity', models.CharField(max_length=100)), ], ), ]

[ "teri.edwards@outlook.com" ]

teri.edwards@outlook.com

ef23d977d1e039a74262aed9f8df4b6a55bfd2df

dd00894931c9d1764bdf129418da1bd7e711fb99

/kb/groups/migrations/0005_group_imported.py

55a4a034ec85004b0eb13c3afd4a5f433d110c36

[]

no_license

johnfelipe/edubox-platform

b8c69112c36d8ec971db1537f18d1e0e1ea7a8c5

d5cdcc351cabb86ae2f36d3eae0921f63d41383c

refs/heads/master

2021-01-18T11:13:20.931521

2016-05-23T15:11:08

null

0

null

UTF-8

Python

false

403

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('groups', '0004_auto_20160215_1236'), ] operations = [ migrations.AddField( model_name='group', name='imported', field=models.BooleanField(default=False), ), ]

[ "tim.doolan@gmail.com" ]

tim.doolan@gmail.com

71ad75ed4334334155ffd4dc39edc9241ddd1ede

70ba7a0cf6d1018152fedf2ea1ae3d758eb75571

/bin/python-config

768319652562c971781d1adb69eb490d1dd44575

[]

no_license

alexdsole/venv

a9205d1dd814b44cc5886da26b69c6427e230b93

a8d2f708a33b2ce2bf578d256387b5cc6eff3021

refs/heads/master

2021-01-01T19:14:46.817101

2017-07-27T15:12:50

98,550,691

0

null

UTF-8

Python

false

2,363

#!/home/alexis/Documentos/Consensus-master/venv/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "adavisole@gmail.com" ]

adavisole@gmail.com

64111bc8816975c4707095c57cce2ccf946afbdb

39095f7c7fafefcd99589ed77a34d6811c87040f

/scripts/get_south_beach_latest.py

634089860e1d4c42c9cf5c7371ed281e108d4e51

[]

no_license

dlenz/home-assistant-config

03656605e08982f5d851de3c9d9fdca78f6f84d5

19d132ca20a9aabb7b90d115bfd6d499c1fee110

refs/heads/master

2020-03-08T11:30:25.571752

2018-08-10T17:26:21

128,099,741

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null

UTF-8

Python

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745

py

#!/usr/bin/python3 import urllib3 import json import requests import os http = urllib3.PoolManager() r = http.request('GET', 'http://video-monitoring.com/beachcams/boca/latest.json') latest = json.loads(r.data.decode('utf-8')) base_url = 'http://video-monitoring.com/beachcams/boca/' img_url = latest['s4']['hr'] complete_url = base_url + img_url with open('/home/rosie/docker/volumes/home-assistant-config/config/www/pictures/south_beach_latest.jpg', 'wb') as handle: response = requests.get(complete_url, stream=True) if not response.ok: print(response) for block in response.iter_content(1024): if not block: break handle.write(block)

[ "captainkrypto@gmail.com" ]

captainkrypto@gmail.com

1f93a53a75605cde0bdc649c9dc7878d4fb215bb

2ff7e53d5e512cd762217ca54317982e07a2bb0c

/eve-8.51.857815/encodings/cp1258.py

f667aa8a98b642a6aa031749d9cd65a1e7e5b80d

[]

no_license

nanxijw/Clara-Pretty-One-Dick

66d3d69426642b79e8fd4cc8e0bec23adeeca6d6

50de3488a2140343c364efc2615cf6e67f152be0

refs/heads/master

2021-01-19T09:25:07.555284

2015-02-17T21:49:33

null

0

null

UTF-8

Python

false

2,058

py

#Embedded file name: encodings\cp1258.py """ Python Character Mapping Codec cp1258 generated from 'MAPPINGS/VENDORS/MICSFT/WINDOWS/CP1258.TXT' with gencodec.py. """ import codecs class Codec(codecs.Codec): def encode(self, input, errors = 'strict'): return codecs.charmap_encode(input, errors, encoding_table) def decode(self, input, errors = 'strict'): return codecs.charmap_decode(input, errors, decoding_table) class IncrementalEncoder(codecs.IncrementalEncoder): def encode(self, input, final = False): return codecs.charmap_encode(input, self.errors, encoding_table)[0] class IncrementalDecoder(codecs.IncrementalDecoder): def decode(self, input, final = False): return codecs.charmap_decode(input, self.errors, decoding_table)[0] class StreamWriter(Codec, codecs.StreamWriter): pass class StreamReader(Codec, codecs.StreamReader): pass def getregentry(): return codecs.CodecInfo(name='cp1258', encode=Codec().encode, decode=Codec().decode, incrementalencoder=IncrementalEncoder, incrementaldecoder=IncrementalDecoder, streamreader=StreamReader, streamwriter=StreamWriter) decoding_table = u'\x00\x01\x02\x03\x04\x05\x06\x07\x08\t\n\x0b\x0c\r\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\x7f\u20ac\ufffe\u201a\u0192\u201e\u2026\u2020\u2021\u02c6\u2030\ufffe\u2039\u0152\ufffe\ufffe\ufffe\ufffe\u2018\u2019\u201c\u201d\u2022\u2013\u2014\u02dc\u2122\ufffe\u203a\u0153\ufffe\ufffe\u0178\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\xaa\xab\xac\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\u0102\xc4\xc5\xc6\xc7\xc8\xc9\xca\xcb\u0300\xcd\xce\xcf\u0110\xd1\u0309\xd3\xd4\u01a0\xd6\xd7\xd8\xd9\xda\xdb\xdc\u01af\u0303\xdf\xe0\xe1\xe2\u0103\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\u0301\xed\xee\xef\u0111\xf1\u0323\xf3\xf4\u01a1\xf6\xf7\xf8\xf9\xfa\xfb\xfc\u01b0\u20ab\xff' encoding_table = codecs.charmap_build(decoding_table)

[ "billchang.e@gmail.com" ]

billchang.e@gmail.com

8b52332672b8f363f40ce770b90d8da2b98dcd3f

b30a1c6298191571cbfbd9a0be20e1654625e621

/브루트포스/2231_분해합.py

4e7977d8f99e2cc0de4793a366bfca1e2db47b1e

[]

no_license

ChoneungSon/BeakJoon

5b07d58f97b377984d205d6b481289dc2e6041fe

a313848cb657dfa7c63a8ad86789e92f7a491748

refs/heads/master

2021-01-04T16:28:05.892412

2020-07-18T14:43:26

240,634,502

0

null

UTF-8

Python

false

204

py

import sys n = int(sys.stdin.readline()) for i in range(1, n+1): m = i s = i while m != 0: s += m % 10 m //= 10 if s == n: print(i) break else: print(0)

[ "thschsmd@naver.com" ]

thschsmd@naver.com

d31974c2a03e29a8f1c935202d55f90607461b20

9fac825a83f90b98702f150fbce6a4e5bc06ed1e

/test_mining.py

2593e63dff302ddf63fa57e8a7fda5f087743bba

[]

no_license

jiatujiashuai/PRP

ab3a235e1c8120685a870bdcc0dfdcfe102f6d9b

df7a9507e7d72cc20f9772387b35a65a4e9c3889

refs/heads/master

2020-03-16T15:28:20.695212

2018-05-09T10:48:03

null

0

null

UTF-8

Python

false

720

py

from pymongo import MongoClient def test_mining(Ix,Iy): client=MongoClient('mongodb://172.17.0.3:27017/') encrypted=client.testdata.encrypted N=encrypted.count() transaction=encrypted.find() support_x,support_xy=0,0 for i in range(N): sub_support_x=1 for item in Ix: sub_support_x*=int(transaction[i]['item'+str(item)]) sub_support_xy=sub_support_x for item in Iy: sub_support_xy*=int(transaction[i]['item'+str(item)]) support_x+=sub_support_x support_xy+=sub_support_xy with open('calculation.dat','w') as f: f.write("%s\n%s\n%s" %(support_xy,support_x,N)) test_mining([0,1,2],[3])

[ "noreply@github.com" ]

jiatujiashuai.noreply@github.com

21a83673b88c2aef4df0eb031e6282b87c5895e0

fbf832e1aafdd6382f11b0e736faf98765acfe6b

/deployRiaz/deployRiaz/urls.py

b6b75d584789a217db9b86723795f79da64ff753

[]

no_license

rashedoz/DeployRiaz

6c8d1f2dcfc782c59b652b219e21dc504ad60591

10465f5c73906417b934acb25c444d6d64074b67

refs/heads/master

2020-03-17T09:09:34.483087

2018-05-15T05:47:06

133,463,655

0

null

UTF-8

Python

false

800

py

"""deployRiaz URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path,include urlpatterns = [ path('admin/', admin.site.urls), path('home/',include('home.urls')), ]

[ "noreply@github.com" ]

rashedoz.noreply@github.com

bf1597e673d16b97b990e25232920df6cf6c03d1

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/TubesSA_2Bot.py

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azrielnaufal/Tic-Tac-Toe

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2023-06-05T21:48:18.960345

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# MODULES import pygame, sys import numpy as np # initializes pygame pygame.init() # --------- # CONSTANTS # --------- WIDTH = 600 HEIGHT = 600 LINE_WIDTH = 15 # tebal garis WIN_LINE_WIDTH = 15 # tebal coretan garis yg menang BOARD_ROWS = 3 BOARD_COLS = 3 SQUARE_SIZE = 200 # ukuran masing2 kotak -> square_size^2 CIRCLE_RADIUS = 60 # radius lingkaran CIRCLE_WIDTH = 30 # tebal garis lingkaran CROSS_WIDTH = 25 # tebal garis silang SPACE = 30 # bot = 2 player = 1 # we assign rgb value (red green blue) RED = (255, 0, 0) GREEN = (0,255,0) BLUE = (0,0,255) BG_COLOR = (145, 121, 250) #ganti warna background LINE_COLOR = (94, 23, 235) #ganti warna garis pada background CIRCLE_COLOR = (239, 231, 200) CROSS_COLOR = (66, 66, 66) # ------ # SCREEN # ------ screen = pygame.display.set_mode( (WIDTH, HEIGHT) ) pygame.display.set_caption('Backtracking Tic Tac Toe') screen.fill( BG_COLOR ) mainClock = pygame.time.Clock() # ------------- # CONSOLE BOARD # ------------- board = np.zeros( (BOARD_ROWS, BOARD_COLS) ) # --------- # FUNCTIONS # --------- def draw_lines(): # untuk gambar garis2 kotak # 1 horizontal pygame.draw.line( screen, LINE_COLOR, (0, SQUARE_SIZE), (WIDTH, SQUARE_SIZE), LINE_WIDTH ) # 2 horizontal pygame.draw.line( screen, LINE_COLOR, (0, 2 * SQUARE_SIZE), (WIDTH, 2 * SQUARE_SIZE), LINE_WIDTH ) # 1 vertical pygame.draw.line( screen, LINE_COLOR, (SQUARE_SIZE, 0), (SQUARE_SIZE, HEIGHT), LINE_WIDTH ) # 2 vertical pygame.draw.line( screen, LINE_COLOR, (2 * SQUARE_SIZE, 0), (2 * SQUARE_SIZE, HEIGHT), LINE_WIDTH ) def draw_figures(): # untuk gambar simbol player dan bot ( O DAN X ) for row in range(BOARD_ROWS): for col in range(BOARD_COLS): if board[row][col] == 1: pygame.draw.circle( screen, CIRCLE_COLOR, (int( col * SQUARE_SIZE + SQUARE_SIZE//2 ), int( row * SQUARE_SIZE + SQUARE_SIZE//2 )), CIRCLE_RADIUS, CIRCLE_WIDTH ) elif board[row][col] == 2: pygame.draw.line( screen, CROSS_COLOR, (col * SQUARE_SIZE + SPACE, row * SQUARE_SIZE + SQUARE_SIZE - SPACE), (col * SQUARE_SIZE + SQUARE_SIZE - SPACE, row * SQUARE_SIZE + SPACE), CROSS_WIDTH ) pygame.draw.line( screen, CROSS_COLOR, (col * SQUARE_SIZE + SPACE, row * SQUARE_SIZE + SPACE), (col * SQUARE_SIZE + SQUARE_SIZE - SPACE, row * SQUARE_SIZE + SQUARE_SIZE - SPACE), CROSS_WIDTH ) print(board) def mark_square(row, col, player): # assign 1 kotak, terisi oleh player yang mana (1 , 2 ,...) if available_square(row,col): board[row][col] = player def available_square(row, col): # -> if kotaknya available maka -> return True return board[row][col] == 0 def is_board_full(): # cek apakah semua kotak penuh ato gaa, kalo penuh -> return True for row in range(BOARD_ROWS): for col in range(BOARD_COLS): if board[row][col] == 0: return False return True def check_win(player): # nge cek udh ada yang menang belum # vertical win check for col in range(BOARD_COLS): if board[0][col] == player and board[1][col] == player and board[2][col] == player: return True # horizontal win check for row in range(BOARD_ROWS): if board[row][0] == player and board[row][1] == player and board[row][2] == player: return True # asc diagonal win check if board[2][0] == player and board[1][1] == player and board[0][2] == player: return True # desc diagonal win chek if board[0][0] == player and board[1][1] == player and board[2][2] == player: return True return False #bikin coretan garis kalo berhasil menang def draw_vertical_winning_line(col, player): posX = col * SQUARE_SIZE + SQUARE_SIZE//2 if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (posX, 15), (posX, HEIGHT - 15), LINE_WIDTH ) def draw_horizontal_winning_line(row, player): posY = row * SQUARE_SIZE + SQUARE_SIZE//2 if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, posY), (WIDTH - 15, posY), WIN_LINE_WIDTH ) def draw_asc_diagonal(player): if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, HEIGHT - 15), (WIDTH - 15, 15), WIN_LINE_WIDTH ) def draw_desc_diagonal(player): if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, 15), (WIDTH - 15, HEIGHT - 15), WIN_LINE_WIDTH ) def winning_Line(player): # vertical win check for col in range(BOARD_COLS): if board[0][col] == player and board[1][col] == player and board[2][col] == player: draw_vertical_winning_line(col, player) # horizontal win check for row in range(BOARD_ROWS): if board[row][0] == player and board[row][1] == player and board[row][2] == player: draw_horizontal_winning_line(row, player) # asc diagonal win check if board[2][0] == player and board[1][1] == player and board[0][2] == player: draw_asc_diagonal(player) # desc diagonal win chek if board[0][0] == player and board[1][1] == player and board[2][2] == player: draw_desc_diagonal(player) return def restart(): screen.fill( BG_COLOR ) draw_lines() for row in range(BOARD_ROWS): for col in range(BOARD_COLS): board[row][col] = 0 def checkDraw(): for row in range(0,BOARD_ROWS): for col in range(0,BOARD_COLS): if (board[row][col] == 0): return False return True def compMove_X(): bestScore = -800 bestMove_row = -1 bestMove_col = -1 for i in range(0,BOARD_ROWS): for y in range(0,BOARD_COLS): if (board[i][y] == 0): board[i][y] = bot score = minimax(bot,board, 0, False) board[i][y] = 0 if (score > bestScore): bestScore = score bestMove_row = i bestMove_col = y mark_square(bestMove_row,bestMove_col,bot) return def compMove_O(): bestScore = -800 bestMove_row = -1 bestMove_col = -1 for i in range(0,BOARD_ROWS): for y in range(0,BOARD_COLS): if (board[i][y] == 0): board[i][y] = player score = minimax(player,board, 0, False) board[i][y] = 0 if (score > bestScore): bestScore = score bestMove_row = i bestMove_col = y mark_square(bestMove_row,bestMove_col,player) return def minimax(user, board, depth, isMaximizing): if user == player: minVal = bot elif user == bot: minVal = player if (check_win(user)): return 1 elif (check_win(minVal)): return -1 elif (checkDraw()): return 0 if (isMaximizing): bestScore = -800 for i in range(0,BOARD_ROWS): for y in range(0,BOARD_COLS): if (board[i][y] == 0): board[i][y] = user score = minimax(user,board, depth + 1, False) board[i][y] = 0 if (score > bestScore): bestScore = score return bestScore else: bestScore = 0 for i in range(0,BOARD_ROWS): for y in range(0,BOARD_COLS): if (board[i][y] == 0): board[i][y] = minVal score = minimax(user,board, depth + 1, True) board[i][y] = 0 if (score < bestScore): bestScore = score return bestScore def main_2Bot(): draw_lines() game_over = False running = False while not running: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() if event.type == pygame.KEYDOWN: if event.key == pygame.K_r: restart() main_2Bot() if event.key == pygame.K_s: if not game_over: compMove_O() if check_win(1): game_over = True winning_Line(1) compMove_X() if check_win(2): game_over = True winning_Line(2) draw_figures() if event.key == pygame.K_ESCAPE: running = True pygame.display.update() mainClock.tick(60) if __name__ == "__main__": main_2Bot()

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/blogsite/blog/templatetags/blog_tags.py

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from django import template from django.db.models import Count from django.utils.safestring import mark_safe import markdown from ..models import Post register = template.Library() @register.simple_tag def total_posts(): return Post.published.count() @register.inclusion_tag('blog/post/latest_posts.html') def show_latest_posts(count=5): latest_posts = Post.published.order_by('-publish')[:count] return {'latest_posts': latest_posts} @register.simple_tag def get_most_commented_posts(count=5): return Post.published.annotate( total_comments=Count('comments') ).order_by('-total_comments')[:count] @register.filter(name='markdown') def markdown_format(text): return mark_safe(markdown.markdown(text)) # some problems?

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ch1huizong@gmail.com

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/todo/views.py

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akiyoko/django-todo-sample

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from django.http import HttpResponse, HttpResponseRedirect from django.http.response import Http404 from django.template.response import TemplateResponse from django.utils import timezone from django.views import View from .forms import TodoForm from .models import Todo # Step 1. はじめての画面表示 # def todo_list(request): # if request.method == 'GET': # today = timezone.localdate() # return HttpResponse('今日は {} です。'.format(today)) # Step 2. クラスベースで書き直す # class TodoListView(View): # def get(self, request, *args, **kwargs): # today = timezone.localdate() # return HttpResponse('今日は {} です。'.format(today)) # Step 3. HTMLを使ったレスポンスを返す # class TodoListView(View): # def get(self, request, *args, **kwargs): # today = timezone.localdate() # return HttpResponse(""" # <html> # <body> # <h1>TODOリスト</h1> # <p>今日は {} です。</p> # </body> # </html> # """.format(today)) # Step 4. テンプレートを使う # class TodoListView(View): # def get(self, request, *args, **kwargs): # today = timezone.localdate() # context = { # 'today': today, # } # return TemplateResponse(request, 'todo/todo_list_step4.html', context) # Step 5. モデルを使う class TodoListView(View): def get(self, request, *args, **kwargs): today = timezone.localdate() todo_list = Todo.objects.order_by('expiration_date') context = { 'today': today, 'todo_list': todo_list, } # return TemplateResponse(request, 'todo/todo_list_step5.html', context) return TemplateResponse(request, 'todo/todo_list.html', context) # Step 6. TODO追加画面を作成する class TodoCreateView(View): def get(self, request, *args, **kwargs): # 空のフォームを作成して画面に表示 context = { 'form': TodoForm(), } return TemplateResponse(request, 'todo/todo_create.html', context) def post(self, request, *args, **kwargs): # リクエストパラメータからフォームを作成 form = TodoForm(request.POST) # フォームを使ってバリデーション if not form.is_valid(): # バリデーションNGの場合はリクエスト元の画面のテンプレートを再表示 context = { 'form': form, } return TemplateResponse(request, 'todo/todo_create.html', context) # オブジェクトを保存 form.save() # TODOリスト画面にリダイレクト return HttpResponseRedirect('/todo/') # Step 7. TODO変更画面を作成する class TodoUpdateView(View): def get(self, request, pk, *args, **kwargs): # 対象レコードを取得 try: todo = Todo.objects.get(pk=pk) except Todo.DoesNotExist: raise Http404 # 対象レコードからフォームを作成して画面に表示 context = { 'form': TodoForm(instance=todo), } return TemplateResponse(request, 'todo/todo_update.html', context) def post(self, request, pk, *args, **kwargs): # 対象レコードを取得 try: todo = Todo.objects.get(pk=pk) except Todo.DoesNotExist: raise Http404 # リクエストパラメータと対象レコードからフォームを作成 form = TodoForm(request.POST, instance=todo) # フォームを使ってバリデーション if not form.is_valid(): # バリデーションNGの場合はリクエスト元の画面のテンプレートを再表示 context = { 'form': form, } return TemplateResponse(request, 'todo/todo_update.html', context) # オブジェクトを更新 form.save() # TODOリスト画面にリダイレクト return HttpResponseRedirect('/todo/')

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xuanhan863/HyperGAN

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import argparse import os import tensorflow as tf import hypergan as hg import hyperchamber as hc from hypergan.loaders import * from hypergan.samplers.common import * from hypergan.util.globals import * def parse_args(): parser = argparse.ArgumentParser(description='Train a colorizer!', add_help=True) parser.add_argument('directory', action='store', type=str, help='The location of your data. Subdirectories are treated as different classes. You must have at least 1 subdirectory.') parser.add_argument('--batch_size', '-b', type=int, default=32, help='Number of samples to include in each batch. If using batch norm, this needs to be preserved when in server mode') parser.add_argument('--crop', type=bool, default=False, help='If your images are perfectly sized you can skip cropping.') parser.add_argument('--device', '-d', type=str, default='/gpu:0', help='In the form "/gpu:0", "/cpu:0", etc. Always use a GPU (or TPU) to train') parser.add_argument('--format', '-f', type=str, default='png', help='jpg or png') parser.add_argument('--sample_every', type=int, default=50, help='Samples the model every n epochs.') parser.add_argument('--save_every', type=int, default=30000, help='Saves the model every n epochs.') parser.add_argument('--size', '-s', type=str, default='64x64x3', help='Size of your data. For images it is widthxheightxchannels.') parser.add_argument('--use_hc_io', type=bool, default=False, help='Set this to no unless you are feeling experimental.') return parser.parse_args() def sampler(name, sess, config): generator = get_tensor("g")[0] y_t = get_tensor("y") z_t = get_tensor("z") x_t = get_tensor('x') mask_t = get_tensor('mask') fltr_x_t = get_tensor('xfiltered') x = sess.run([x_t]) x = np.tile(x[0][0], [config['batch_size'],1,1,1]) s = [int(x) for x in mask_t.get_shape()] #mask = np.zeros([s[0], s[1]//2, s[2]//2, s[3]]) #constants = (1,1) #mask = np.pad(mask, ((0,0),(s[1]//4,s[1]//4),(s[2]//4,s[2]//4),(0,0)),'constant', constant_values=constants) print("Set up mask") sample, bw_x = sess.run([generator, fltr_x_t], {x_t: x})#, mask_t: mask}) stacks = [] stacks.append([x[0], bw_x[0], sample[0], sample[1], sample[2], sample[3]]) for i in range(4): stacks.append([sample[i*6+4+j] for j in range(6)]) images = np.vstack([np.hstack(s) for s in stacks]) plot(config, images, name) def add_inpaint(gan, net): x = get_tensor('x') mask = get_tensor('mask') s = [int(x) for x in net.get_shape()] shape = [s[1], s[2]] x = tf.image.resize_images(x, shape, 1) mask = tf.image.resize_images(mask, shape, 1) print("Created bw ", x) x = x*mask#tf.image.rgb_to_grayscale(x) #x += tf.random_normal(x.get_shape(), mean=0, stddev=1e-1, dtype=config['dtype']) return x def add_original_x(gan, net): x = get_tensor('x') mask = get_tensor('mask') s = [int(x) for x in net.get_shape()] shape = [s[1], s[2]] mask = tf.image.resize_images(mask, shape, 1) x = tf.image.resize_images(x, shape, 1) #xx += tf.random_normal(xx.get_shape(), mean=0, stddev=config['noise_stddev'], dtype=root_config['dtype']) x = x*mask return x args = parse_args() width = int(args.size.split("x")[0]) height = int(args.size.split("x")[1]) channels = int(args.size.split("x")[2]) selector = hg.config.selector(args) config = selector.random_config() config_filename = os.path.expanduser('~/.hypergan/configs/inpainting.json') config = selector.load_or_create_config(config_filename, config) #TODO add this option to D #TODO add this option to G config['generator.layer_filter'] = add_inpaint config['discriminators'][0]['layer_filter'] = add_original_x # TODO refactor, shared in CLI config['dtype']=tf.float32 config['batch_size'] = args.batch_size x,y,f,num_labels,examples_per_epoch = image_loader.labelled_image_tensors_from_directory( args.directory, config['batch_size'], channels=channels, format=args.format, crop=args.crop, width=width, height=height) config['y_dims']=num_labels config['x_dims']=[height,width] config['channels']=channels config['model']='inpainting' config = hg.config.lookup_functions(config) initial_graph = { 'x':x, 'y':y, 'f':f, 'num_labels':num_labels, 'examples_per_epoch':examples_per_epoch } shape = [config['batch_size'], config['x_dims'][0], config['x_dims'][1], config['channels']] mask = tf.ones([shape[1], shape[2], shape[3]]) scaling = 0.6 mask = tf.image.central_crop(mask, scaling) print(mask.get_shape()) left = (shape[1]*scaling)//2 * 0.75 top = (shape[2]*scaling)//2 * 0.75 mask = tf.image.pad_to_bounding_box(mask, int(top), int(left), shape[1], shape[2]) mask = (1.0-mask) #mask = tf.random_uniform(shape, -1, 1) #mask = tf.greater(mask, 0) mask = tf.cast(mask, tf.float32) set_tensor('mask', mask) gan = hg.GAN(config, initial_graph) save_file = os.path.expanduser("~/.hypergan/saves/inpainting.ckpt") gan.load_or_initialize_graph(save_file) tf.train.start_queue_runners(sess=gan.sess) for i in range(100000): d_loss, g_loss = gan.train() if i % args.save_every == 0 and i > 0: print("Saving " + save_file) gan.save(save_file) if i % args.sample_every == 0 and i > 0: print("Sampling "+str(i)) sample_file = "samples/"+str(i)+".png" gan.sample_to_file(sample_file, sampler=sampler) if args.use_hc_io: hc.io.sample(config, [{"image":sample_file, "label": 'sample'}]) tf.reset_default_graph() self.sess.close()

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class ErrorResponse(Exception): def __init__(self, error): self.error = error def __str__(self): return self.error

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import math import torch.nn as nn import torch.utils.model_zoo as model_zoo import torch.nn.init as init __all__ = [ 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19_bn', 'vgg19', ] model_urls = { 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth', 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth', 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', } class VGG(nn.Module): def __init__(self, features, num_classes=1000): super(VGG, self).__init__() self.classes = num_classes self.features = features self.classifier = nn.Linear(512, num_classes) self._initialize_weights() def forward(self, x): x = self.features(x) if self.classes != 10 and self.classes != 100: x = nn.AvgPool2d(4)(x) else: x = nn.AvgPool2d(2)(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * (m.in_channels) m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): n = m.weight.size(1) m.weight.data.normal_(0, 0.01) m.bias.data.zero_() def make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) cfg = { 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512], 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512], 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512], 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512], # 'E': [64, 128, 'M', 128, 256, 'M', 64, 128, 256, 512, 1024, 'M', 64, 128, 256, 512, 1024, 2048,'M',256, 512, 1024, 512,'M'] } def vgg11(**kwargs): """VGG 11-layer model (configuration "A") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG(make_layers(cfg['A']), **kwargs) return model def vgg11_bn(**kwargs): """VGG 11-layer model (configuration "A") with batch normalization""" model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs) return model def vgg13(**kwargs): """VGG 13-layer model (configuration "B") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG(make_layers(cfg['B']), **kwargs) return model def vgg13_bn(**kwargs): """VGG 13-layer model (configuration "B") with batch normalization""" model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs) return model def vgg16(**kwargs): """VGG 16-layer model (configuration "D") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG(make_layers(cfg['D']), **kwargs) return model def vgg16_bn(**kwargs): """VGG 16-layer model (configuration "D") with batch normalization""" model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs) return model def vgg19(**kwargs): """VGG 19-layer model (configuration "E") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = VGG(make_layers(cfg['E']), **kwargs) return model def vgg19_bn(**kwargs): """VGG 19-layer model (configuration 'E') with batch normalization""" model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs) return model

[ "jtsu@pku.edu.cn" ]

jtsu@pku.edu.cn

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/inst/scripts/fix-realign-cigars

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lianos/TagSeq-original

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533c5e351d96ff958e2bc1b1ed7828df402cf881

refs/heads/master

2021-01-21T11:09:14.368272

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#!/usr/bin/env python import sys, os, time, re from optparse import OptionParser ## The hela passeq always has screwed up cigars, so look for alignments with ## X0:i:N (>1) and ghetto-fix the CIGAR (turn it into *) ## 11M1D24M ATTTTTTAAAACCAGAACATTTATGCATGACTAATCGTTG csplit = re.compile('[MIDNSHP=X]') def cigar_correct_fancy(cigar, sequence): ## Tried to verify the cigar string against the length of the sequence by ## counting the I's and D's -- not exactly working. splits = csplit.split(cigar) ciglen = sum([int(x) for x in splits[0:len(splits)-1]]) return ciglen == len(sequence) def cigar_correct(cigar, sequence): return ('D' not in cigar) and ('I' not in cigar) if __name__ == '__main__': usage = "%prog: IN.sam OUT.sam" parser = OptionParser(usage=usage) (options,args) = parser.parse_args() infile = open(args[0], 'r') outfile = open(args[1], 'w') n_nuked = 0 t = time.time() for idx,line in enumerate(infile): if line.startswith("@") or "X0:i:1" in line: outfile.write(line) continue info = line.split("\t") cigar = info[5] sequence = info[9] if not cigar_correct(cigar, sequence): info[5] = '*' n_nuked += 1 line = '\t'.join(info) outfile.write(line) sys.stdout.write("Removed " + str(n_nuked) + " cigars\n") sys.stdout.write("Took " + str(time.time() - t) + " seconds\n")

[ "slianoglou@gmail.com" ]

slianoglou@gmail.com

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/support/Text2vec/text2vec.py

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brandeis-machine-learning/InfectionMechanismSpectrumPrediction

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import spacy from gensim.corpora import Dictionary from gensim.models.tfidfmodel import TfidfModel from gensim import models from gensim.matutils import sparse2full import numpy as np import en_core_web_sm # text2vec methods class Text2vec: def __init__(self, doc_list): # Initialize self.doc_list = doc_list self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list) # Functions to lemmatise docs def _keep_token(self, t): return (t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)) def _lemmatize_doc(self, doc): return [t.lemma_ for t in doc if self._keep_token(t)] # Gensim to create a dictionary and filter out stop and infrequent words (lemmas). def _get_docs_dict(self, docs): docs_dict = Dictionary(docs) # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out. # docs_dict.filter_extremes(no_below=5, no_above=0.2) docs_dict.compactify() return docs_dict # Preprocess docs def _preprocess(self, doc_list): # Load spacy model nlp = en_core_web_sm.load() # lemmatise docs docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list] # Get docs dictionary docs_dict = self._get_docs_dict(docs) return nlp, docs, docs_dict # Gensim can again be used to create a bag-of-words representation of each document, # build the TF-IDF model, # and compute the TF-IDF vector for each document. def _get_tfidf(self, docs, docs_dict): docs_corpus = [docs_dict.doc2bow(doc) for doc in docs] model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict) docs_tfidf = model_tfidf[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf]) return docs_vecs # Get avg w2v for one document def _document_vector(self, doc, docs_dict, nlp): # remove out-of-vocabulary words doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id] return np.mean(doc_vector, axis=0) # Get a TF-IDF weighted Glove vector summary for document list # Input: a list of documents, Output: Matrix of vector for all the documents def tfidf_weighted_wv(self): # tf-idf docs_vecs = self._get_tfidf(self.docs, self.docs_dict) # Load glove embedding vector for each TF-IDF term tfidf_emb_vecs = np.vstack([self.nlp(self.docs_dict[i]).vector for i in range(len(self.docs_dict))]) # To get a TF-IDF weighted Glove vector summary of each document, # we just need to matrix multiply docs_vecs with tfidf_emb_vecs docs_emb = np.dot(docs_vecs, tfidf_emb_vecs) return docs_emb # Get average vector for document list def avg_wv(self): docs_vecs = np.vstack([self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]) return docs_vecs # Get TF-IDF vector for document list def get_tfidf(self): docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs] model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict) docs_tfidf = model_tfidf[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf]) return docs_vecs # Get Latent Semantic Indexing(LSI) vector for document list def get_lsi(self, num_topics=300): docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs] model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict) docs_lsi = model_lsi[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi]) return docs_vecs # Get Random Projections(RP) vector for document list def get_rp(self): docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs] model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict) docs_rp = model_rp[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp]) return docs_vecs # Get Latent Dirichlet Allocation(LDA) vector for document list def get_lda(self, num_topics=100): docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs] model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict) docs_lda = model_lda[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda]) return docs_vecs # Get Hierarchical Dirichlet Process(HDP) vector for document list def get_hdp(self): docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs] model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict) docs_hdp = model_hdp[docs_corpus] docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp]) return docs_vecs

[ "43837579+SupremeEthan@users.noreply.github.com" ]

43837579+SupremeEthan@users.noreply.github.com

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/v1/transactions/migrations/0001_initial.py

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[]

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kdaang/DigiWallet

c050bc7c865decd912cea9c59434443cf61f2aad

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refs/heads/master

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# Generated by Django 2.2.7 on 2019-11-24 21:00 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Transaction', fields=[ ('transaction_id', models.BigAutoField(primary_key=True, serialize=False, unique=True)), ('date_created', models.DateTimeField(auto_now_add=True)), ('total', models.BigIntegerField()), ('from_user', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='from_user', to=settings.AUTH_USER_MODEL)), ('to_user', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='to_user', to=settings.AUTH_USER_MODEL)), ], ), ]

[ "k9dang@uwaterloo.ca" ]

k9dang@uwaterloo.ca

fe87873efdf8ddedf99385dac9968b5ca8a5cc81

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/neo_browser.py

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bhavyejain/Gtk-Web-Browser

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refs/heads/master

2021-04-13T14:00:55.806585

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import sys, gi gi.require_version("Gtk", "3.0") # GUI toolkit gi.require_version("WebKit2", "4.0") # Web content engine from gi.repository import Gtk, WebKit2 as wk, Gdk class BrowserTab(Gtk.VBox): def __init__(self, *args, **kwargs): super(BrowserTab, self).__init__(*args, **kwargs) self.web_view = wk.WebView() # initialize webview self.web_view.load_uri('https://google.com') # default homepage for every tab self.show() button_go = Gtk.ToolButton(Gtk.STOCK_APPLY); # go button widget self.button_back = Gtk.ToolButton(Gtk.STOCK_GO_BACK) # back button widget self.button_forward = Gtk.ToolButton(Gtk.STOCK_GO_FORWARD) # forward button widget self.button_refresh = Gtk.ToolButton(Gtk.STOCK_REFRESH) # refresh button widget self.address_bar = Gtk.Entry() # address bar entry widget button_go.connect("clicked", self.load_page) # trigger: click self.address_bar.connect("activate", self.load_page) # trigger: enter self.button_back.connect("clicked", lambda x : self.web_view.go_back()) # trigger: click self.button_forward.connect("clicked", lambda x : self.web_view.go_forward()) # trigger: click self.button_refresh.connect("clicked", lambda x : self.web_view.reload()) # trigger: click url_box = Gtk.HBox() # create url bar url_box.pack_start(self.button_back, False, False, 0) url_box.pack_start(self.button_forward, False, False, 0) url_box.pack_start(self.button_refresh, False, False, 0) url_box.pack_start(self.address_bar, True, True, 0) url_box.pack_start(button_go, False, False, 0) scrolled_window = Gtk.ScrolledWindow() # scrolling window widget scrolled_window.add(self.web_view) # add web_view to scrolled window find_box = Gtk.HBox() # find text dialog self.find_controller = self.web_view.get_find_controller() button_close = Gtk.ToolButton(Gtk.STOCK_CLOSE) # close the find dialog button_next = Gtk.ToolButton(Gtk.STOCK_GO_DOWN) # find next button_prev = Gtk.ToolButton(Gtk.STOCK_GO_UP) # find previous self.find_entry = Gtk.Entry() # text to find button_close.connect("clicked", lambda x : find_box.hide()) self.find_entry.connect("activate", self.find_text) button_next.connect("clicked", self.find_text_next) button_prev.connect("clicked", self.find_text_prev) # attach UI elements to find dialog find_box.pack_start(button_close, False, False, 0) find_box.pack_start(self.find_entry, False, False, 0) find_box.pack_start(button_prev, False, False, 0) find_box.pack_start(button_next, False, False, 0) self.find_box = find_box # add everything to browser tab self.pack_start(url_box, False, False, 0) self.pack_start(find_box, False, False, 0) self.pack_start(scrolled_window, True, True, 0) url_box.show_all() scrolled_window.show_all() def load_page(self, widget): # load page from URI url = self.address_bar.get_text() if url.startswith("http://") or url.startswith("https://"): self.web_view.load_uri(url) else: url = "https://" + url self.address_bar.set_text(url) self.web_view.load_uri(url) def find_text(self, widget): self.find_controller.search(self.find_entry.get_text(), 0, 1) def find_text_next(self, widget): self.find_controller.search_next() def find_text_prev(self, widget): self.find_controller.search_previous() class Browser(Gtk.Window): def __init__(self, *args, **kwargs): super(Browser, self).__init__(*args, **kwargs) self.set_title("My Browser") # set title of window self.set_icon_from_file('images/icon.png') # set icon image file self.set_default_size(600, 600) self.tool_bar = Gtk.HBox() # create horizontal box for tool bar self.button_new_tab = Gtk.ToolButton(Gtk.STOCK_ADD) # create new tab self.button_close_tab = Gtk.ToolButton(Gtk.STOCK_CLOSE) # close current tab self.button_find = Gtk.ToolButton(Gtk.STOCK_FIND) # show find dialog self.button_home = Gtk.ToolButton(Gtk.STOCK_HOME) self.button_new_tab.connect("clicked", self.open_new_tab) self.button_close_tab.connect("clicked", self.close_current_tab) self.button_find.connect("clicked", self.raise_find_dialog) self.button_home.connect("clicked", self.goto_home) self.tool_bar.pack_start(self.button_new_tab, False, False, 0) self.tool_bar.pack_start(self.button_close_tab, False, False, 0) self.tool_bar.pack_start(self.button_find, False, False, 0) self.tool_bar.pack_start(self.button_home, False, False, 0) # create notebook and tabs self.notebook = Gtk.Notebook() self.notebook.set_scrollable(True) self.tabs = [] # list of tuples : each tuple represents a tab (tab, label) self.set_size_request(600, 600) # create a first empty browser tab self.tabs.append((self.create_tab(), Gtk.Label("New Tab"))) self.notebook.insert_page(self.tabs[0][0], self.tabs[0][1], 0) # connect signals self.connect("destroy", Gtk.main_quit) self.notebook.connect("switch-page", self.tab_changed) self.vbox_container = Gtk.VBox() # pack tool bar and notebook in a vertical box self.vbox_container.pack_start(self.tool_bar, False, False, 0) self.vbox_container.pack_start(self.notebook, True, True, 0) # add vertical box to the Window self.add(self.vbox_container) # show widgets self.tool_bar.show_all() self.notebook.show() self.vbox_container.show() self.show() def tab_changed(self, notebook, current_page, index): if not index: return title = self.tabs[index][0].web_view.get_title() if title: self.set_title("Neo Browser - " + title) def title_changed(self, web_view, frame): current_page = self.notebook.get_current_page() counter = 0 for tab, label in self.tabs: if tab.web_view is web_view: label.set_text(tab.web_view.get_title()) if counter == current_page: self.tab_changed(None, None, counter) break counter += 1 def create_tab(self): tab = BrowserTab() tab.web_view.connect("notify::title", self.title_changed) return tab def close_current_tab(self, widget): if self.notebook.get_n_pages() == 1: return page = self.notebook.get_current_page() current_tab = self.tabs.pop(page) self.notebook.remove(current_tab[0]) def open_new_tab(self, widget): current_page = self.notebook.get_current_page() page_tuple = (self.create_tab(), Gtk.Label("New Tab")) self.tabs.insert(current_page + 1, page_tuple) self.notebook.insert_page(page_tuple[0], page_tuple[1], current_page + 1) self.notebook.set_current_page(current_page + 1) def raise_find_dialog(self, widget): current_page = self.notebook.get_current_page() self.tabs[current_page][0].find_box.show_all() self.tabs[current_page][0].find_entry.grab_focus() def goto_home(self, widget): current_page = self.notebook.get_current_page() self.tabs[current_page][0].web_view.load_uri("https://www.google.com/") if __name__ == "__main__": Gtk.init(sys.argv) browser = Browser() Gtk.main()

[ "bhavyej@gmail.com" ]

bhavyej@gmail.com

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refs/heads/master

2020-09-25T05:58:24.320811

2019-12-19T22:19:53

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## Import Module import matplotlib.pyplot as plt import seaborn as sns; sns.set() import pandas as pd import numpy as np import io from sklearn import datasets, svm, metrics from sklearn.preprocessing import StandardScaler from sklearn.metrics import recall_score, precision_score ################################################################ ## This is used to search the critical chemical shift for ## prediciting base pair. ## neighbors = 1 (best f1-score) : 0.9229 +/- 0.0851 ## LOO model ## sklearn: clf = svm.SVC(gamma='auto') ################################################################ ################################################################ ## Read in and pre-process chemical shift data ################################################################ c=pd.read_csv('final_training.csv', sep=" ") cs_data=c.replace({'stack':1}) # Replace stack with 1 cs_data=cs_data.replace({'non-stack':0}) # Replace non-stack with 0 cs_data=cs_data.drop(columns=['base_pairing', 'orientation', 'sugar_puckering', 'pseudoknot']) if 'Unnamed: 0' in cs_data.columns: cs_data=cs_data.drop(columns=['Unnamed: 0']) cs_data=cs_data.rename(columns={'stacking':'class'}) ## Creat chemical shift list cs_list = list(cs_data.columns) cs_list.remove('id') cs_list.remove('resid') cs_list.remove('resname') cs_list.remove('class') num = len(cs_list) ################################################################ ## Global variables and functions ################################################################ NUMBER_CHEMICAL_SHIFT_TYPE = 18 neighbors = np.loadtxt("neighbors", dtype = int) f1 = np.loadtxt("f1", dtype = float) recall = np.loadtxt("recall", dtype = float) precision = np.loadtxt("precision", dtype = float) def get_cs_all(cs_all, id): ''' This function gets chemical shifts for a particular RNA. ''' return(cs_all[cs_all.id == id]) def get_cs_residues(cs_i, resid, dummy=0): ''' This function return an array contining the chemical shifts for a particular residues in an RNA. ''' cs_tmp=cs_i[(cs_i.resid == resid)].drop(['id', 'resid', 'resname', 'class'], axis=1) info_tmp=cs_i[(cs_i.resid == resid)] if (cs_tmp.shape[0] != 1): return(dummy*np.ones(shape=(1, NUMBER_CHEMICAL_SHIFT_TYPE))) else: return(cs_tmp.values) def get_resnames(cs_i, resid, dummy="UNK"): ''' This function returns the residue name for specified residue (resid) ''' cs_tmp=cs_i[(cs_i.resid == resid)] if (cs_tmp.shape[0] != 1): return(dummy) else: return(cs_tmp['resname'].values[0]) def get_cs_features(cs_i, resid, neighbors): ''' This function return chemical shifts and resnames for residues (resid) and its neighbors ''' cs=[] resnames=[] for i in range(resid-neighbors, resid+neighbors+1): cs.append(get_cs_residues(cs_i, i)) resnames.append(get_resnames(cs_i, i)) return(resnames, np.array(cs)) def get_columns_name(neighbors=3, chemical_shift_types = NUMBER_CHEMICAL_SHIFT_TYPE): ''' Helper function that writes out the required column names ''' #tmp=2*neighbors+1 #neighbors=1 columns=['id', 'resname', 'resid', 'class'] for i in range(0, neighbors*NUMBER_CHEMICAL_SHIFT_TYPE): columns.append(i) return(columns) def write_out_resname(neighbors=1): ''' Helper function that writes out the column names associated resnames for a given residue and its neighbors ''' colnames = [] for i in range(1-neighbors-1, neighbors+1): if i < 0: colnames.append('R%s'%i) elif i > 0: colnames.append('R+%s'%i) else: colnames.append('R') return(colnames) def get_cs_features_rna(cs, neighbors=1, retain = ['id', 'class', 'resid']): ''' This function generates the complete required data frame an RNA ''' all_features = [] all_resnames = [] for resid in cs['resid'].unique(): resnames, features = get_cs_features(cs, resid, neighbors) all_features.append(features.flatten()) all_resnames.append(resnames) all_resnames = pd.DataFrame(all_resnames, dtype='object', columns = write_out_resname(neighbors)) all_features = pd.DataFrame(all_features, dtype='object') info = pd.DataFrame(cs[retain].values, dtype='object', columns = retain) return(pd.concat([info, all_resnames, all_features], axis=1)) def get_cs_features_rna_all(cs, neighbors): ''' This function generate a pandas dataframe containing training data for all RNAs Each row in the data frame should contain the class and chemical shifts for given residue and neighbors in a given RNA. ''' cs_new=pd.DataFrame() for pdbid in cs['id'].unique()[0 :]: tmp=get_cs_features_rna(get_cs_all(cs, id=pdbid), neighbors) cs_new=pd.concat([cs_new, tmp], axis=0) return(cs_new) ################################################################ ## Build model and test ################################################################ drop_names = ['id', 'class', 'resid'] target_name = 'class' col = 2*neighbors + 1 totalscore = [] totalrecall = [] totalprecision = [] for atom in cs_list: print(f"[SET UP DATA]: The chemical shift dropped is {atom}") tmp_c = cs_data.drop(atom, axis=1) cs_all = get_cs_features_rna_all(tmp_c, neighbors=neighbors) score = [] recall = [] precision = [] for pdbid in cs_all['id'].unique()[0 :]: print(f"[INFO]: Now predict RNA --> {pdbid}") ## Prepare test set test = cs_all[cs_all.id == pdbid] tmp = test.drop(drop_names, axis=1) tmp_testX = tmp.iloc[:, col :] tmp_testy = test[target_name] testX = tmp_testX.values testy = tmp_testy.values id = pd.unique(test.id) print(f"[INFO]: Test set --> {id}") ## Prepare training set train = cs_all[cs_all.id != pdbid] tmp = train.drop(drop_names, axis=1) tmp_trainX = tmp.iloc[:, col :] tmp_trainy = train[target_name] trainX = tmp_trainX.values trainy = tmp_trainy.values id = pd.unique(train.id) print(f"[INFO]: Test set --> {id}") ## Normalization of the training set and test set scaler = StandardScaler() scaler.fit(trainX) trainX_scaled = scaler.transform(trainX) testX_scaled = scaler.transform(testX) print(f"[INFO]: Scale the features") ## Train model clf = svm.SVC(gamma='auto') clf.fit(trainX_scaled, np.int_(trainy)) ## Test model predicted = clf.predict(testX_scaled) print(f"[INFO]: Running prediction") ## Recall recall.append(recall_score(np.int_(testy), predicted)) ## Precision precision.append(precision_score(np.int_(testy), predicted)) ## f1-score score.append(metrics.f1_score(np.int_(testy),predicted)) print(" ") ## Total f1-score totalscore.append(score) totalrecall.append(recall) totalprecision.append(precision) print(f"[INFO]: Now appending recall to total recall") print(f"[INFO]: Now appending f1-score to total score") print(f"[INFO]: Now appending precision to total precision") print(" ") print(" ") ################################################################ ## Prediction analysis ################################################################ ## Prepare data totalscore = np.asarray(totalscore) totalrecall = np.asarray(totalrecall) totalprecision = np.asarray(totalprecision) totalscore = totalscore.reshape(num,-1) totalrecall = totalrecall.reshape(num,-1) totalprecision = totalprecision.reshape(num,-1) pdbid_list = cs_data['id'].unique()[0 :] average_name = ['Average of f1 score'] std_name = ['Std of f1 score'] ## Analyze prediction result print(f"[ANALYSIS RESULT]: LOO model result") i = 0 average_f1 = [] average_recall = [] average_precision = [] std_f1 = [] std_recall = [] std_precision = [] while i < num : score = totalscore[i,:] recall = totalrecall[i,:] precision = totalprecision[i,:] average_f1.append(np.average(score)) average_recall.append(np.average(recall)) average_precision.append(np.average(precision)) std_f1.append(np.std(score)) std_recall.append(np.std(recall)) std_precision.append(np.std(precision)) print(f"[INFO]: The chemical shift {cs_list[i]} is dropped -->") print(f"[ANALYSIS RESULT]: The average f1-score is: {average_f1[i]} +/- {std_f1[i]}") print(f"[ANALYSIS RESULT]: The average recall is: {average_recall[i]} +/- {std_recall[i]}") print(f"[ANALYSIS RESULT]: The average precision is: {average_precision[i]} +/- {std_precision[i]}") print(" ") i += 1 ## Save f1-score data to a csv print(f"[INFO]: Save f1-score data") tmp_score = pd.DataFrame(totalscore, dtype = 'object', columns = pdbid_list, index = cs_list) tmp_average = pd.DataFrame(average_f1, dtype = 'object', columns = ['Average of f1-score'], index = cs_list) tmp_std = pd.DataFrame(std_f1, dtype = 'object', columns = ['Std of f1-score'], index = cs_list) tmp = pd.concat([tmp_score, tmp_average], axis=1) all_score = pd.concat([tmp, tmp_std], axis=1) all_score.to_csv('all_score.csv', sep=' ') ## Save recall data to a csv print(f"[INFO]: Save recall data") tmp_recall = pd.DataFrame(totalrecall, dtype = 'object', columns = pdbid_list, index = cs_list) tmp_average = pd.DataFrame(average_recall, dtype = 'object', columns = ['Average of recall'], index = cs_list) tmp_std = pd.DataFrame(std_recall, dtype = 'object', columns = ['Std of recall'], index = cs_list) tmp = pd.concat([tmp_score, tmp_average], axis=1) all_recall = pd.concat([tmp, tmp_std], axis=1) all_recall.to_csv('all_recall.csv', sep=' ') ## Save precision data to a csv print(f"[INFO]: Save recall data") tmp_recall = pd.DataFrame(totalprecision, dtype = 'object', columns = pdbid_list, index = cs_list) tmp_average = pd.DataFrame(average_precision, dtype = 'object', columns = ['Average of precision'], index = cs_list) tmp_std = pd.DataFrame(std_precision, dtype = 'object', columns = ['Std of precision'], index = cs_list) tmp = pd.concat([tmp_score, tmp_average], axis=1) all_precision = pd.concat([tmp, tmp_std], axis=1) all_precision.to_csv('all_precision.csv', sep=' ') ## Plot heatmap print(f"[INFO]: Plotting heatmap") delta_score = totalscore - f1 delta_score = delta_score * 100 plt.figure(figsize = (30,15)) ax = sns.heatmap(delta_score, center = 0, xticklabels = pdbid_list, yticklabels = cs_list) ax.set_title("f1 heatmap") plt.savefig("f1.png") delta_recall = totalrecall - recall delta_recall = delta_recall * 100 plt.figure(figsize = (30,15)) ax = sns.heatmap(delta_recall, center = 0, xticklabels = pdbid_list, yticklabels = cs_list) ax.set_title("recall heatmap") plt.savefig("recall.png") delta_precision = totalprecision - precision delta_precision = delta_precision * 100 plt.figure(figsize = (30,15)) ax = sns.heatmap(delta_precision, center = 0, xticklabels = pdbid_list, yticklabels = cs_list) ax.set_title("precision heatmap") plt.savefig("precision.png")

[ "tmstew@umich.edu" ]

tmstew@umich.edu

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/map_render.py

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[]

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MishaBlin/openworld

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2023-02-20T00:48:20.054869

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import pygame as pg import pytmx # rendering tiled map using pytmx class TiledMap: def __init__(self, filename): tm = pytmx.load_pygame(filename, pixelalpha=True) self.width = tm.width * tm.tilewidth self.height = tm.height * tm.tileheight self.tmxdata = tm self.players = pytmx.TiledImageLayer self.objects = pytmx.TiledImageLayer def render(self, surface, sender): if sender == 'map': ti = self.tmxdata.get_tile_image_by_gid for layer in self.tmxdata.visible_layers: if isinstance(layer, pytmx.TiledTileLayer): for x, y, gid, in layer: tile = ti(gid) if tile: surface.blit(tile, (x * self.tmxdata.tilewidth, y * self.tmxdata.tileheight)) def make_map(self): temp_surface = pg.Surface((self.width, self.height)) self.render(temp_surface, 'map') return temp_surface # making camera class Camera: def __init__(self, width, height): self.camera = pg.Rect(0, 0, width, height) self.width = width self.height = height infoObject = pg.display.Info() self.sizes = (infoObject.current_w, infoObject.current_h) def apply(self, entity): return entity.rect.move(self.camera.topleft) def apply_rect(self, rect): return rect.move(self.camera.topleft) # updating camera def update(self, target): x = -target.rect.x + int(self.sizes[0] / 2) y = -target.rect.y + int(self.sizes[1] / 2) x = min(0, x) y = min(0, y) x = max(-(self.width - self.sizes[0]), x) y = max(-(self.height - self.sizes[1]), y) self.camera = pg.Rect(x, y, self.width, self.height)

[ "wildline007@gmail.com" ]

wildline007@gmail.com

156e0b33b99981c5f144b23232e2749c57a87a7e

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/contacts/models.py

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[]

no_license

kartik6314/job-portal

4ff88a62e9101e5f1e611bbd4825cf4e3874a5aa

84066ca90fbade4d05996c30e8e155cb01129db7

refs/heads/main

2023-08-03T19:03:08.516253

2021-09-14T18:04:45

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from django.db import models from datetime import datetime class Contact(models.Model): name = models.CharField(max_length=200) company = models.CharField(max_length=200) email = models.CharField(max_length=100) phone = models.CharField(max_length=11) message = models.TextField(blank=True) contact_date = models.DateTimeField(default=datetime.now, blank=True) user_id = models.IntegerField(blank=True) def __str__(self): return self.name

[ "carishmashukla28@gmail.com" ]

carishmashukla28@gmail.com

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/kpis/tests/test_informe_diario.py

5f5c2f3e41a4590b79384e58f598426454ecfa6b

[]

no_license

Amalio769/kpis_mto

3a3c876e1a426b229d3d2f0e2509d531d06b335e

3f6349305375fb1f8c83fbc1171af9fdd8b3a098

refs/heads/master

2023-08-12T16:42:44.312130

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py

# -*- coding: utf-8 -*- """ Created on Sat Dec 19 07:44:50 2020 @author: C48142 """ def main(): import kpis.informes.diario.salida_datos as ids import kpis.sap.zpm_report_mwo as sap import kpis.configuracion.config as cfg import webbrowser as wb FECHA_INI = "01.1.2021" FECHA_FIN = "26.1.2021" #sap.zpm_report_mwo_id(FECHA_INI,FECHA_FIN,cfg.PATH_INFORME_DIARIO,"INFORME_DIARIO") filename_woe = "INFORME_DIARIO_ERRORES" ids.procesar_informe(cfg.PATH_INFORME_DIARIO,\ filename_woe,\ FECHA_INI,\ FECHA_FIN,\ cfg.ID_ERRORES) wb.open(cfg.PATH_INFORME_DIARIO + filename_woe + '.html') if __name__ == '__main__': main() else: print("Ha ocurrido un error.")

[ "amalio.rete-campos@airbus.com" ]

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[ "Apache-2.0" ]

permissive

googleapis/google-cloud-python

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2023-09-04T09:09:07.852632

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# -*- coding: utf-8 -*- # Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Generated code. DO NOT EDIT! # # Snippet for UpdateScanConfig # NOTE: This snippet has been automatically generated for illustrative purposes only. # It may require modifications to work in your environment. # To install the latest published package dependency, execute the following: # python3 -m pip install google-cloud-websecurityscanner # [START websecurityscanner_v1_generated_WebSecurityScanner_UpdateScanConfig_async] # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import websecurityscanner_v1 async def sample_update_scan_config(): # Create a client client = websecurityscanner_v1.WebSecurityScannerAsyncClient() # Initialize request argument(s) request = websecurityscanner_v1.UpdateScanConfigRequest( ) # Make the request response = await client.update_scan_config(request=request) # Handle the response print(response) # [END websecurityscanner_v1_generated_WebSecurityScanner_UpdateScanConfig_async]

[ "noreply@github.com" ]

googleapis.noreply@github.com

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/aula1_3.py

5f532e692ed152efff0f4dacba5d7c87e8e6b591

[]

no_license

flaviogpacheco/python-520

7518acb86ff76f3fda16bf9e6efdda9aca8f22d9

b89592135536386f0505e5cdc9ec3623fd5f67a8

refs/heads/master

2020-04-22T18:56:04.116634

2019-02-21T01:28:15

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UTF-8

Python

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py

#!/usr/bin/python3 # ==, !=, <, <=, >, >= # Se o numero resultado da soma for maior 100 # Escrever: "Que numero grandão..." # Caso contrário: "Que numero pequeno..." n1 = int(input('Digite o primeiro número: ')) n2 = int(input('Digite o segundo número: ')) n3 = n1 + n2 print(n3) if n3 > 100: print('Que número grandão...') elif n3 == 50: print('...') else: print('Que número pequeno...')

[ "flaviogpacheco@hotmail.com" ]

flaviogpacheco@hotmail.com

667b07d36ccd852870139d650b98432ed3d4a7dd

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2600/60667/294166.py

b8720ad05b9b04434393c63233f70af47f569c70

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

553

py

n = int(input()) nums = list(map(int, input().split())) sequence = list(map(int, input().split())) temp = [] for i in range(n): temp.append(sequence.index(i+1)) temp.sort() nums[sequence.index(i+1)] = 0 maximum = 0 first = sum(nums[:temp[0]]) if first > maximum: maximum = first last = sum(nums[temp[-1]+1:]) if last > maximum: maximum = last if len(temp) > 1: for j in range(len(temp)-1): t = sum(nums[temp[j]+1:temp[j+1]]) maximum = max(t, maximum) print(maximum)

[ "1069583789@qq.com" ]

1069583789@qq.com

a4abc79e2a76dcc110d7c8a6cf7736b6fad9ebb6

92e290932cba0e045c86b26de13fcc902e920d83

/demo/bbxpy/image.py

6728dda0528de2685e0e0386dbf68267b1cce8f6

[]

no_license

HorizonXP/blackberry-py

4f9615f2637c1e9bc81b712172c41d13ce6e1726

ee9823b5f1ce1efa6531005df60b246a4147c4a2

refs/heads/master

2020-05-27T13:08:56.497247

2013-08-30T06:56:03

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2013-08-29T08:57:05

2013-01-20T08:27:19

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UTF-8

Python

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'''Image class to load and render bitmaps.''' from ctypes import byref, cast, c_int, c_void_p, POINTER void_pp = POINTER(c_void_p) from bbxpy.wrapped.screen import * from bbxpy.wrapped.img import * __all__ = ['Image'] class Image: def __init__(self, context): self._context = context self._pixmap = None def load(self, path): ilib = img_lib_t() rc = img_lib_attach(byref(ilib)) if rc: raise RuntimeError(rc) img = img_t() # 24-bits/pixel BGR format, little-endian img.format = IMG_FMT_PKLE_XRGB8888 img.flags |= IMG_FORMAT # set up decoder to load image into new pixmap self._pixmap = screen_pixmap_t() rc = screen_create_pixmap(byref(self._pixmap), self._context) if rc: raise RuntimeError(rc) callouts = img_decode_callouts_t() callouts.setup_f = decode_setup callouts.abort_f = decode_abort callouts.data = cast(self._pixmap, POINTER(c_uint)) rc = img_load_file(ilib, path, byref(callouts), byref(img)) if rc: raise RuntimeError(rc) #~ print('img_load_file', rc) #~ print('img is %d x %d x %d' % (img.w, img.h, IMG_FMT_BPP(img.format))) self.width = img.w self.height = img.h img_lib_detach(ilib) def draw(self, x, y, buffer, scale=1.0): pixbuf = screen_buffer_t() rc = screen_get_pixmap_property_pv(self._pixmap, SCREEN_PROPERTY_RENDER_BUFFERS, cast(byref(pixbuf), void_pp)) if rc: raise RuntimeError(rc) # prepare window to receive image in blit hg = [ SCREEN_BLIT_SOURCE_WIDTH, self.width, SCREEN_BLIT_SOURCE_HEIGHT, self.height, SCREEN_BLIT_DESTINATION_X, int(x), SCREEN_BLIT_DESTINATION_Y, int(y), SCREEN_BLIT_DESTINATION_WIDTH, int(self.width * scale), SCREEN_BLIT_DESTINATION_HEIGHT, int(self.height * scale), SCREEN_BLIT_TRANSPARENCY, SCREEN_TRANSPARENCY_SOURCE_OVER, SCREEN_BLIT_END ] hg = (c_int * len(hg))(*hg) rc = screen_blit(self._context, buffer, pixbuf, hg) if rc: raise RuntimeError(rc) #~ img_decode_setup_f = CFUNCTYPE(c_int, POINTER(c_uint), POINTER(img_t), c_uint) #~ static int decode_setup(uintptr_t data, img_t *img, unsigned flags): def decode_setup(data, img, flags): pixmap = cast(data, screen_pixmap_t) buffer = screen_buffer_t() # set up pixmap buffer for NATIVE usage so we can blit from it usage = c_int(SCREEN_USAGE_NATIVE) rc = screen_set_pixmap_property_iv(pixmap, SCREEN_PROPERTY_USAGE, byref(usage)) #~ print('screen_set_pixmap_property_iv', rc) if rc: raise RuntimeError(rc) img = img.contents size = (c_int * 2)(img.w, img.h) print('decode: image size', img.w, img.h) rc = screen_set_pixmap_property_iv(pixmap, SCREEN_PROPERTY_BUFFER_SIZE, cast(byref(size), POINTER(c_int))) if rc: raise RuntimeError(rc) # set format to have alpha channel for our blitting format = c_int(SCREEN_FORMAT_RGBA8888) rc = screen_set_pixmap_property_iv(pixmap, SCREEN_PROPERTY_FORMAT, byref(format)) if rc: raise RuntimeError(rc) rc = screen_create_pixmap_buffer(pixmap) #~ print('screen_create_pixmap_buffer', rc) if rc: raise RuntimeError(rc) rc = screen_get_pixmap_property_pv(pixmap, SCREEN_PROPERTY_RENDER_BUFFERS, cast(byref(buffer), void_pp)) #~ print('screen_get_pixmap_property_pv', rc) if rc: raise RuntimeError(rc) rc = screen_get_buffer_property_pv(buffer, SCREEN_PROPERTY_POINTER, cast(byref(img.access.direct.data), void_pp)) #~ print('screen_get_buffer_property_pv', rc) if rc: raise RuntimeError(rc) # could use something like offset = img_t.access.offset + img_t.access.direct.offset + img_t.access.direct.stride.offset # then cast(byref(img, offset), POINTER(c_int)) stride_val = c_int() rc = screen_get_buffer_property_iv(buffer, SCREEN_PROPERTY_STRIDE, byref(stride_val)) if rc: raise RuntimeError(rc) img.access.direct.stride = stride_val.value #~ print('stride', stride_val) img.flags |= IMG_DIRECT return IMG_ERR_OK decode_setup = img_decode_setup_f(decode_setup) #~ img_decode_abort_f = CFUNCTYPE(None, POINTER(c_uint), POINTER(img_t)) def decode_abort(data, img): #~ print('decode_abort') pixmap = cast(data, screen_pixmap_t) rc = screen_destroy_pixmap_buffer(pixmap) if rc: raise RuntimeError(rc) decode_abort = img_decode_abort_f(decode_abort) # EOF

[ "peter@engcorp.com" ]

peter@engcorp.com

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/fluo/utils/deprecation.py

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rsalmaso/django-fluo

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refs/heads/main

2023-01-12T01:37:06.975318

2022-12-30T22:08:40

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# Copyright (C) 2007-2022, Raffaele Salmaso <raffaele@salmaso.org> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from __future__ import annotations from django.utils.deprecation import RenameMethodsBase __all__ = [ "RemovedIn030Warning", "RemovedInNextVersionWarning", "RenameMethodsBase", ] class RemovedIn030Warning(DeprecationWarning): pass # django <= 1.8 aliases RemovedInNextVersionWarning to RemovedInDjango19Warning # when drop support for django <= 1.8 use django.utils.deprecation.RemovedInNextVersionWarning class RemovedInNextVersionWarning(DeprecationWarning): pass

[ "raffaele@salmaso.org" ]

raffaele@salmaso.org