xszheng2020/the_stack_dedup_python_hits_1 · Datasets at Hugging Face

76dead288194d6b5e50fd079f21d614687299cb8

1,085

py

Python

src/lennybot/model/plan.py

raynigon/lenny-bot

d906a25dc28d9102829d3d6265d300f65406db02

[ "Apache-2.0" ]

1

2021-12-15T14:03:54.000Z

src/lennybot/model/plan.py

raynigon/lenny-bot

d906a25dc28d9102829d3d6265d300f65406db02

[ "Apache-2.0" ]

1

2021-12-15T14:02:57.000Z

2021-12-15T17:44:26.000Z

src/lennybot/model/plan.py

raynigon/lennybot

79bee9a834f885a0da2484b239cf6efaf9cb9e4e

[ "Apache-2.0" ]

null

from typing import Any, List from ..actions.iaction import IAction from ..model.state import LennyBotState class LennyBotPlan: def __init__(self, state: LennyBotState, actions: List[IAction]) -> None: self._state = state self._actions = actions @property def applications(self) -> List[str]: result = [] for action in self._actions: result.append(action.application) return list(set(result)) @property def actions(self) -> List[IAction]: return self._actions @property def state(self) -> LennyBotState: return self._state def source_version(self, application: str) -> str: for action in self._actions: if action.application != application: continue return action.source_version return None def target_version(self, application: str) -> str: for action in self._actions: if action.application != application: continue return action.target_version return None

25.232558

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5.730435

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0.08346

0.050076

0.068285

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0.30349

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1,085

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0.548387

0

null

0

null

0

1

0

1

76dfdcc4b341cedf794e7489e27908f2ae58e24b

10,024

py

Python

mllib/nlp/seq2seq.py

pmaxit/dlnotebooks

5e5a161bbd9d0753850029be29e1488b8858ecd5

[ "Apache-2.0" ]

null

mllib/nlp/seq2seq.py

pmaxit/dlnotebooks

5e5a161bbd9d0753850029be29e1488b8858ecd5

[ "Apache-2.0" ]

null

mllib/nlp/seq2seq.py

pmaxit/dlnotebooks

5e5a161bbd9d0753850029be29e1488b8858ecd5

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/01_seq2seq.ipynb (unless otherwise specified). __all__ = ['Encoder', 'NewDecoder', 'Seq2Seq'] # Cell from torch import nn from torch import optim import torch import torch.nn.functional as F from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence # Cell class Encoder(nn.Module): def __init__(self, input_size, embedding_size, hidden_size, num_layers=2, p=0.1): super(Encoder, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.dropout = nn.Dropout(p) self.embedding = nn.Embedding(input_size, embedding_size) self.rnn = nn.LSTM(embedding_size, hidden_size, num_layers, dropout=p,batch_first=False) def forward(self, x, x_len): # x shape (seq_length, N) embedding = self.dropout(self.embedding(x)) # embedding shape : (seq_length, N, embedding_size) x_packed = pack_padded_sequence(embedding, x_len.cpu(), batch_first=False, enforce_sorted=False) output_packed, (hidden,cell) = self.rnn(x_packed) # irrelevant because we are interested only in hidden state #output_padded, output_lengths = pad_packed_sequence(output_packed, batch_first=True) # output is irrelevant, context vector is important return hidden,cell # Cell class NewDecoder(nn.Module): def __init__(self, hidden_size, embedding_size, output_size, n_layers=1, dropout_p=0.1): super(NewDecoder, self).__init__() # Define parameters self.hidden_size = hidden_size self.output_size = output_size self.n_layers =n_layers self.dropout_p = dropout_p # Define layers self.embedding = nn.Embedding(output_size, embedding_size) self.dropout=nn.Dropout(dropout_p) self.rnn = nn.LSTM(embedding_size, hidden_size, n_layers, dropout=dropout_p, batch_first=False) self.out = nn.Linear(hidden_size, output_size) def forward(self, word_input, last_hidden, encoder_outputs): # Note that we will only be running forward for a single decoder time step, but will # use all encoder outputs word_input = word_input.unsqueeze(0) # we are not using encoder_outputs here word_embedded = self.embedding(word_input) # 1 X B word_embedded = self.dropout(word_embedded) # 1 X B X emb_length # Combine embedded input word and hidden vector, run through RNN output, hidden = self.rnn(word_embedded, last_hidden) # 1 X B X hidden predictions = self.out(output) # 1, B, out #output = F.log_softmax(predictions) return predictions, hidden # Cell import random import pytorch_lightning as pl import pytorch_lightning.metrics.functional as plfunc from pytorch_lightning.loggers import TensorBoardLogger # Cell class Seq2Seq(pl.LightningModule): """ Encoder decoder pytorch lightning module for training seq2seq model with teacher forcing Module try to learn mapping from one sequence to another """ @staticmethod def add_model_specific_args(parent_parser): parser = ArgumentParser(parents=[parent_parser], add_help=False) parser.add_argument("--emb_dim", type=int, default=32) parser.add_argument('--hidden_dim', type=int, default=64) parser.add_argument('--dropout', type=float, default=0.1) return parser def __init__(self, input_vocab_size, output_vocab_size, padding_index = 0, emb_dim = 8, hidden_dim=32, dropout=0.1, max_length=20, **kwargs): super().__init__() # dynamic, based on tokenizer vocab size defined in datamodule self.input_dim = input_vocab_size self.output_dim = output_vocab_size self.enc_emb_dim = emb_dim self.dec_emb_dim = emb_dim self.enc_hid_dim = hidden_dim self.dec_hid_dim = hidden_dim self.enc_dropout = dropout self.dec_dropout = dropout self.pad_idx = padding_index self.num_layers = 2 self.max_length =10 self.save_hyperparameters() self.max_epochs= kwargs.get('max_epochs',5) self.learning_rate = 0.0005 self._loss = nn.CrossEntropyLoss(ignore_index=self.pad_idx) self.encoder = Encoder( self.input_dim, self.enc_emb_dim, self.enc_hid_dim, self.num_layers, self.enc_dropout ) self.decoder = NewDecoder( self.enc_hid_dim, self.dec_emb_dim, self.output_dim, self.num_layers, self.dec_dropout ) self._init_weights() def _init_weights(self): for name, param in self.named_parameters(): if "weight" in name: nn.init.normal_(param.data, mean=0, std=0.01) else: nn.init.constant_(param.data, 0) def create_mask(self, src): mask = (src != self.pad_idx).permute(1, 0) return mask def forward(self, src_seq, source_len, trg_seq, teacher_force_ratio=0.5): """ teacher_force_ratio is used to help in decoding. In starting, original input token will be sent as input token """ source = src_seq.transpose(0, 1) target_len = self.max_length if trg_seq is not None: target = trg_seq.transpose(0, 1) target_len = target.shape[0] batch_size = source.shape[1] target_vocab_size = self.output_dim outputs = torch.zeros(target_len, batch_size, target_vocab_size).to(self.device) encoder_hidden = self.encoder(source, source_len) # mask = [batch_size, src len] # without sos token at the beginning and eos token at the end #x = target[0,:] decoder_input = torch.ones(batch_size).long().to(self.device) decoder_hidden = encoder_hidden encoder_outputs = None for t in range(target_len): decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs) outputs[t] = decoder_output #(N, english_vocab_size) #best_guess = output.argmax(1) topv, topi = decoder_output.topk(1) decoder_input = topi.squeeze().detach() decoder_input = target[t] if random.random() < teacher_force_ratio and target is not None else decoder_input return outputs def loss(self, logits, target): return self._loss(logits, target) def configure_optimizers(self): optimizer = optim.AdamW(self.parameters(), lr=self.learning_rate) lr_scheduler = { 'scheduler': optim.lr_scheduler.OneCycleLR( optimizer, max_lr = self.learning_rate, steps_per_epoch = 3379, epochs=self.max_epochs, anneal_strategy='linear', final_div_factor=1000, pct_start = 0.01 ), "name": "learning_rate", "interval":"step", "frequency": 1 } return [optimizer],[lr_scheduler] def training_step(self, batch, batch_idx): src_seq, trg_seq, src_lengths = batch['src'],batch['trg'], batch['src_len'] output = self.forward(src_seq, src_lengths,trg_seq) # do not know if this is a problem, loss will be computed with sos token # without sos token at the beginning and eos token at the end output = output.view(-1, self.output_dim) trg_seq = trg_seq.transpose(0, 1) trg = trg_seq.reshape(-1) loss = self.loss(output, trg) self.log('train_loss',loss.item(), on_step = True, on_epoch=True, prog_bar = True, logger=True) return loss def validation_step(self, batch,batch_idx): """ validation is in eval model so we do not have to use placeholder input sensors""" src_seq, trg_seq, src_lengths = batch['src'],batch['trg'], batch['src_len'] outputs = self.forward(src_seq, src_lengths, trg_seq, 0) logits = outputs[1:].view(-1, self.output_dim) trg = trg_seq[1:].reshape(-1) loss = self.loss(logits, trg) pred_seq = outputs[1:].argmax(2) # seq_len*batch_size*vocab_size -> seq_len * batch_size # change layout: sesq_len * batch_size -> batch_size * seq_len pred_seq = pred_seq.T # change layout: seq_len * batch_size -> batch_size * seq_len trg_batch = trg_seq[1:].T # compare list of predicted ids for all sequences in a batch to targets acc = plfunc.accuracy(pred_seq.reshape(-1), trg_batch.reshape(-1)) # need to cast to list of predicted sequences ( as list of token ids ) [ seq_tok1, seqtok2] predicted_ids - pred_seq.tolist() # need to add additional dim to each target reference sequence in order to # conver to format needed by blue_score_func # [seq1=[[reference1],[reference2]], seq2=[reference1]] target_ids = torch.unsqueeze(trg_batch, 1).tolist() bleu_score - plfunc.nlp.bleu_score(predicted_ids, target_ids, n_gram=3).to(self.device) self.log( 'val_loss', loss, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True) self.log( "val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True ) self.log( "val_bleu_idx", bleu_score, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True ) return loss, acc, bleu_score

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0.01184

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0.101996

0.092862

0.064107

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0.289206

10,024

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0.816702

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null

0

1

0

null

0

1

76e58be1ebfa1f5a2978f0298b22ab49d27824a1

386

py

Python

initdb.py

dasmerlon/flunky-bot

19dff5a74bee6685e806f98c3f877216ef454a5d

[ "MIT" ]

null

initdb.py

dasmerlon/flunky-bot

19dff5a74bee6685e806f98c3f877216ef454a5d

[ "MIT" ]

null

initdb.py

dasmerlon/flunky-bot

19dff5a74bee6685e806f98c3f877216ef454a5d

[ "MIT" ]

null

#!/bin/env python """Drop and create a new database with schema.""" from sqlalchemy_utils.functions import database_exists, create_database, drop_database from flunkybot.db import engine, base from flunkybot.models import * # noqa db_url = engine.url if database_exists(db_url): drop_database(db_url) create_database(db_url) base.metadata.drop_all() base.metadata.create_all()

22.705882

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0.790155

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0.465517

0.068493

0.089041

0

0.11658

386

16

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0.168394

0

1

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false

0

0.333333

0

0.333333

0

null

0

null

0

1

0

1

76ebcd294c425806f2a19ba5ab050dfad80e8987

826

py

Python

trabalho-numerico/tridimensional.py

heissonwillen/tcm

71da46489f12e64b50436b17447721cb8f7eaf09

[ "MIT" ]

null

trabalho-numerico/tridimensional.py

heissonwillen/tcm

71da46489f12e64b50436b17447721cb8f7eaf09

[ "MIT" ]

null

trabalho-numerico/tridimensional.py

heissonwillen/tcm

71da46489f12e64b50436b17447721cb8f7eaf09

[ "MIT" ]

null

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np import os import contorno from constantes import INTERVALOS, PASSOS, TAMANHO_BARRA, DELTA_T, DELTA_X z_temp = contorno.p_3 TAMANHO_BARRA = 2 x = np.linspace(0.0, TAMANHO_BARRA, INTERVALOS+1) y = np.linspace(0.0, DELTA_T, PASSOS+1) z = [] for k in range(PASSOS+1): z_k = np.copy(z_temp) z.append(z_k) for i in range(1, INTERVALOS): z_temp[i] = z_k[i] + (DELTA_T/(DELTA_X**2)) * (z_k[i+1]-2*z_k[i]+z_k[i-1]) z = np.asarray(z) x, y = np.meshgrid(x, y) fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm, antialiased=False) ax.set_xlabel('x') ax.set_ylabel('t') ax.set_zlabel('T(x,t)') fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()

22.944444

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3.43125

0.39375

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0.043716

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0.02845

0.14891

826

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1

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false

0.111111

0.259259

0

0.259259

0

null

0

null

0

1

0

1

76f0f94143a86c5bd1bdfebcc7fe3a026073720d

860

py

Python

SVM/SVM_12_Quiz.py

rohit517/Intro-to-machine-learning-Udacity

d0b2cc6cac1cb3408b274225cecd4afcea4ee30f

[ "MIT" ]

null

SVM/SVM_12_Quiz.py

rohit517/Intro-to-machine-learning-Udacity

d0b2cc6cac1cb3408b274225cecd4afcea4ee30f

[ "MIT" ]

null

SVM/SVM_12_Quiz.py

rohit517/Intro-to-machine-learning-Udacity

d0b2cc6cac1cb3408b274225cecd4afcea4ee30f

[ "MIT" ]

null

import sys from class_vis import prettyPicture from prep_terrain_data import makeTerrainData import matplotlib.pyplot as plt import copy import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() ########################## SVM ################################# ### we handle the import statement and SVC creation for you here from sklearn.svm import SVC clf = SVC(kernel="linear") #### now your job is to fit the classifier #### using the training features/labels, and to #### make a set of predictions on the test data clf.fit(features_train,labels_train) pred = clf.predict(features_test) #### store your predictions in a list named pred from sklearn.metrics import accuracy_score acc = accuracy_score(pred, labels_test) def submitAccuracy(): return acc

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null

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1

0

1

76f2637d428beecc1c55ba4761f8ecce6c4c4884

26,267

py

Python

runtime/python/Lib/site-packages/isort/output.py

hwaipy/InteractionFreeNode

88642b68430f57b028fd0f276a5709f89279e30d

[ "MIT" ]

4

2021-10-20T12:39:09.000Z

2022-02-26T15:02:08.000Z

runtime/python/Lib/site-packages/isort/output.py

hwaipy/InteractionFreeNode

88642b68430f57b028fd0f276a5709f89279e30d

[ "MIT" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

runtime/python/Lib/site-packages/isort/output.py

hwaipy/InteractionFreeNode

88642b68430f57b028fd0f276a5709f89279e30d

[ "MIT" ]

3

2021-08-28T14:22:36.000Z

2021-10-06T18:59:41.000Z

import copy import itertools from functools import partial from typing import Any, Iterable, List, Optional, Set, Tuple, Type from isort.format import format_simplified from . import parse, sorting, wrap from .comments import add_to_line as with_comments from .identify import STATEMENT_DECLARATIONS from .settings import DEFAULT_CONFIG, Config def sorted_imports( parsed: parse.ParsedContent, config: Config = DEFAULT_CONFIG, extension: str = "py", import_type: str = "import", ) -> str: """Adds the imports back to the file. (at the index of the first import) sorted alphabetically and split between groups """ if parsed.import_index == -1: return _output_as_string(parsed.lines_without_imports, parsed.line_separator) formatted_output: List[str] = parsed.lines_without_imports.copy() remove_imports = [format_simplified(removal) for removal in config.remove_imports] sections: Iterable[str] = itertools.chain(parsed.sections, config.forced_separate) if config.no_sections: parsed.imports["no_sections"] = {"straight": {}, "from": {}} base_sections: Tuple[str, ...] = () for section in sections: if section == "FUTURE": base_sections = ("FUTURE",) continue parsed.imports["no_sections"]["straight"].update( parsed.imports[section].get("straight", {}) ) parsed.imports["no_sections"]["from"].update(parsed.imports[section].get("from", {})) sections = base_sections + ("no_sections",) output: List[str] = [] seen_headings: Set[str] = set() pending_lines_before = False for section in sections: straight_modules = parsed.imports[section]["straight"] if not config.only_sections: straight_modules = sorting.sort( config, straight_modules, key=lambda key: sorting.module_key( key, config, section_name=section, straight_import=True ), reverse=config.reverse_sort, ) from_modules = parsed.imports[section]["from"] if not config.only_sections: from_modules = sorting.sort( config, from_modules, key=lambda key: sorting.module_key(key, config, section_name=section), reverse=config.reverse_sort, ) if config.star_first: star_modules = [] other_modules = [] for module in from_modules: if "*" in parsed.imports[section]["from"][module]: star_modules.append(module) else: other_modules.append(module) from_modules = star_modules + other_modules straight_imports = _with_straight_imports( parsed, config, straight_modules, section, remove_imports, import_type ) from_imports = _with_from_imports( parsed, config, from_modules, section, remove_imports, import_type ) lines_between = [""] * ( config.lines_between_types if from_modules and straight_modules else 0 ) if config.from_first: section_output = from_imports + lines_between + straight_imports else: section_output = straight_imports + lines_between + from_imports if config.force_sort_within_sections: # collapse comments comments_above = [] new_section_output: List[str] = [] for line in section_output: if not line: continue if line.startswith("#"): comments_above.append(line) elif comments_above: new_section_output.append(_LineWithComments(line, comments_above)) comments_above = [] else: new_section_output.append(line) # only_sections options is not imposed if force_sort_within_sections is True new_section_output = sorting.sort( config, new_section_output, key=partial(sorting.section_key, config=config), reverse=config.reverse_sort, ) # uncollapse comments section_output = [] for line in new_section_output: comments = getattr(line, "comments", ()) if comments: section_output.extend(comments) section_output.append(str(line)) section_name = section no_lines_before = section_name in config.no_lines_before if section_output: if section_name in parsed.place_imports: parsed.place_imports[section_name] = section_output continue section_title = config.import_headings.get(section_name.lower(), "") if section_title and section_title not in seen_headings: if config.dedup_headings: seen_headings.add(section_title) section_comment = f"# {section_title}" if section_comment not in parsed.lines_without_imports[0:1]: # pragma: no branch section_output.insert(0, section_comment) if pending_lines_before or not no_lines_before: output += [""] * config.lines_between_sections output += section_output pending_lines_before = False else: pending_lines_before = pending_lines_before or not no_lines_before if config.ensure_newline_before_comments: output = _ensure_newline_before_comment(output) while output and output[-1].strip() == "": output.pop() # pragma: no cover while output and output[0].strip() == "": output.pop(0) if config.formatting_function: output = config.formatting_function( parsed.line_separator.join(output), extension, config ).splitlines() output_at = 0 if parsed.import_index < parsed.original_line_count: output_at = parsed.import_index formatted_output[output_at:0] = output if output: imports_tail = output_at + len(output) while [ character.strip() for character in formatted_output[imports_tail : imports_tail + 1] ] == [""]: formatted_output.pop(imports_tail) if len(formatted_output) > imports_tail: next_construct = "" tail = formatted_output[imports_tail:] for index, line in enumerate(tail): # pragma: no branch should_skip, in_quote, *_ = parse.skip_line( line, in_quote="", index=len(formatted_output), section_comments=config.section_comments, needs_import=False, ) if not should_skip and line.strip(): if ( line.strip().startswith("#") and len(tail) > (index + 1) and tail[index + 1].strip() ): continue next_construct = line break if in_quote: # pragma: no branch next_construct = line break if config.lines_after_imports != -1: formatted_output[imports_tail:0] = [ "" for line in range(config.lines_after_imports) ] elif extension != "pyi" and next_construct.startswith(STATEMENT_DECLARATIONS): formatted_output[imports_tail:0] = ["", ""] else: formatted_output[imports_tail:0] = [""] if parsed.place_imports: new_out_lines = [] for index, line in enumerate(formatted_output): new_out_lines.append(line) if line in parsed.import_placements: new_out_lines.extend(parsed.place_imports[parsed.import_placements[line]]) if ( len(formatted_output) <= (index + 1) or formatted_output[index + 1].strip() != "" ): new_out_lines.append("") formatted_output = new_out_lines return _output_as_string(formatted_output, parsed.line_separator) def _with_from_imports( parsed: parse.ParsedContent, config: Config, from_modules: Iterable[str], section: str, remove_imports: List[str], import_type: str, ) -> List[str]: output: List[str] = [] for module in from_modules: if module in remove_imports: continue import_start = f"from {module} {import_type} " from_imports = list(parsed.imports[section]["from"][module]) if ( not config.no_inline_sort or (config.force_single_line and module not in config.single_line_exclusions) ) and not config.only_sections: from_imports = sorting.sort( config, from_imports, key=lambda key: sorting.module_key( key, config, True, config.force_alphabetical_sort_within_sections, section_name=section, ), reverse=config.reverse_sort, ) if remove_imports: from_imports = [ line for line in from_imports if f"{module}.{line}" not in remove_imports ] sub_modules = [f"{module}.{from_import}" for from_import in from_imports] as_imports = { from_import: [ f"{from_import} as {as_module}" for as_module in parsed.as_map["from"][sub_module] ] for from_import, sub_module in zip(from_imports, sub_modules) if sub_module in parsed.as_map["from"] } if config.combine_as_imports and not ("*" in from_imports and config.combine_star): if not config.no_inline_sort: for as_import in as_imports: if not config.only_sections: as_imports[as_import] = sorting.sort(config, as_imports[as_import]) for from_import in copy.copy(from_imports): if from_import in as_imports: idx = from_imports.index(from_import) if parsed.imports[section]["from"][module][from_import]: from_imports[(idx + 1) : (idx + 1)] = as_imports.pop(from_import) else: from_imports[idx : (idx + 1)] = as_imports.pop(from_import) only_show_as_imports = False comments = parsed.categorized_comments["from"].pop(module, ()) above_comments = parsed.categorized_comments["above"]["from"].pop(module, None) while from_imports: if above_comments: output.extend(above_comments) above_comments = None if "*" in from_imports and config.combine_star: import_statement = wrap.line( with_comments( _with_star_comments(parsed, module, list(comments or ())), f"{import_start}*", removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) from_imports = [ from_import for from_import in from_imports if from_import in as_imports ] only_show_as_imports = True elif config.force_single_line and module not in config.single_line_exclusions: import_statement = "" while from_imports: from_import = from_imports.pop(0) single_import_line = with_comments( comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) comment = ( parsed.categorized_comments["nested"].get(module, {}).pop(from_import, None) ) if comment: single_import_line += ( f"{comments and ';' or config.comment_prefix} " f"{comment}" ) if from_import in as_imports: if ( parsed.imports[section]["from"][module][from_import] and not only_show_as_imports ): output.append( wrap.line(single_import_line, parsed.line_separator, config) ) from_comments = parsed.categorized_comments["straight"].get( f"{module}.{from_import}" ) if not config.only_sections: output.extend( with_comments( from_comments, wrap.line( import_start + as_import, parsed.line_separator, config ), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for as_import in sorting.sort(config, as_imports[from_import]) ) else: output.extend( with_comments( from_comments, wrap.line( import_start + as_import, parsed.line_separator, config ), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for as_import in as_imports[from_import] ) else: output.append(wrap.line(single_import_line, parsed.line_separator, config)) comments = None else: while from_imports and from_imports[0] in as_imports: from_import = from_imports.pop(0) if not config.only_sections: as_imports[from_import] = sorting.sort(config, as_imports[from_import]) from_comments = ( parsed.categorized_comments["straight"].get(f"{module}.{from_import}") or [] ) if ( parsed.imports[section]["from"][module][from_import] and not only_show_as_imports ): specific_comment = ( parsed.categorized_comments["nested"] .get(module, {}) .pop(from_import, None) ) if specific_comment: from_comments.append(specific_comment) output.append( wrap.line( with_comments( from_comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) ) from_comments = [] for as_import in as_imports[from_import]: specific_comment = ( parsed.categorized_comments["nested"] .get(module, {}) .pop(as_import, None) ) if specific_comment: from_comments.append(specific_comment) output.append( wrap.line( with_comments( from_comments, import_start + as_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) ) from_comments = [] if "*" in from_imports: output.append( with_comments( _with_star_comments(parsed, module, []), f"{import_start}*", removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) ) from_imports.remove("*") for from_import in copy.copy(from_imports): comment = ( parsed.categorized_comments["nested"].get(module, {}).pop(from_import, None) ) if comment: from_imports.remove(from_import) if from_imports: use_comments = [] else: use_comments = comments comments = None single_import_line = with_comments( use_comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) single_import_line += ( f"{use_comments and ';' or config.comment_prefix} " f"{comment}" ) output.append(wrap.line(single_import_line, parsed.line_separator, config)) from_import_section = [] while from_imports and ( from_imports[0] not in as_imports or ( config.combine_as_imports and parsed.imports[section]["from"][module][from_import] ) ): from_import_section.append(from_imports.pop(0)) if config.combine_as_imports: comments = (comments or []) + list( parsed.categorized_comments["from"].pop(f"{module}.__combined_as__", ()) ) import_statement = with_comments( comments, import_start + (", ").join(from_import_section), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) if not from_import_section: import_statement = "" do_multiline_reformat = False force_grid_wrap = config.force_grid_wrap if force_grid_wrap and len(from_import_section) >= force_grid_wrap: do_multiline_reformat = True if len(import_statement) > config.line_length and len(from_import_section) > 1: do_multiline_reformat = True # If line too long AND have imports AND we are # NOT using GRID or VERTICAL wrap modes if ( len(import_statement) > config.line_length and len(from_import_section) > 0 and config.multi_line_output not in (wrap.Modes.GRID, wrap.Modes.VERTICAL) # type: ignore ): do_multiline_reformat = True if do_multiline_reformat: import_statement = wrap.import_statement( import_start=import_start, from_imports=from_import_section, comments=comments, line_separator=parsed.line_separator, config=config, ) if config.multi_line_output == wrap.Modes.GRID: # type: ignore other_import_statement = wrap.import_statement( import_start=import_start, from_imports=from_import_section, comments=comments, line_separator=parsed.line_separator, config=config, multi_line_output=wrap.Modes.VERTICAL_GRID, # type: ignore ) if ( max( len(import_line) for import_line in import_statement.split(parsed.line_separator) ) > config.line_length ): import_statement = other_import_statement if not do_multiline_reformat and len(import_statement) > config.line_length: import_statement = wrap.line(import_statement, parsed.line_separator, config) if import_statement: output.append(import_statement) return output def _with_straight_imports( parsed: parse.ParsedContent, config: Config, straight_modules: Iterable[str], section: str, remove_imports: List[str], import_type: str, ) -> List[str]: output: List[str] = [] as_imports = any((module in parsed.as_map["straight"] for module in straight_modules)) # combine_straight_imports only works for bare imports, 'as' imports not included if config.combine_straight_imports and not as_imports: if not straight_modules: return [] above_comments: List[str] = [] inline_comments: List[str] = [] for module in straight_modules: if module in parsed.categorized_comments["above"]["straight"]: above_comments.extend(parsed.categorized_comments["above"]["straight"].pop(module)) if module in parsed.categorized_comments["straight"]: inline_comments.extend(parsed.categorized_comments["straight"][module]) combined_straight_imports = ", ".join(straight_modules) if inline_comments: combined_inline_comments = " ".join(inline_comments) else: combined_inline_comments = "" output.extend(above_comments) if combined_inline_comments: output.append( f"{import_type} {combined_straight_imports} # {combined_inline_comments}" ) else: output.append(f"{import_type} {combined_straight_imports}") return output for module in straight_modules: if module in remove_imports: continue import_definition = [] if module in parsed.as_map["straight"]: if parsed.imports[section]["straight"][module]: import_definition.append((f"{import_type} {module}", module)) import_definition.extend( (f"{import_type} {module} as {as_import}", f"{module} as {as_import}") for as_import in parsed.as_map["straight"][module] ) else: import_definition.append((f"{import_type} {module}", module)) comments_above = parsed.categorized_comments["above"]["straight"].pop(module, None) if comments_above: output.extend(comments_above) output.extend( with_comments( parsed.categorized_comments["straight"].get(imodule), idef, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for idef, imodule in import_definition ) return output def _output_as_string(lines: List[str], line_separator: str) -> str: return line_separator.join(_normalize_empty_lines(lines)) def _normalize_empty_lines(lines: List[str]) -> List[str]: while lines and lines[-1].strip() == "": lines.pop(-1) lines.append("") return lines class _LineWithComments(str): comments: List[str] def __new__( cls: Type["_LineWithComments"], value: Any, comments: List[str] ) -> "_LineWithComments": instance = super().__new__(cls, value) instance.comments = comments return instance def _ensure_newline_before_comment(output: List[str]) -> List[str]: new_output: List[str] = [] def is_comment(line: Optional[str]) -> bool: return line.startswith("#") if line else False for line, prev_line in zip(output, [None] + output): # type: ignore if is_comment(line) and prev_line != "" and not is_comment(prev_line): new_output.append("") new_output.append(line) return new_output def _with_star_comments(parsed: parse.ParsedContent, module: str, comments: List[str]) -> List[str]: star_comment = parsed.categorized_comments["nested"].get(module, {}).pop("*", None) if star_comment: return comments + [star_comment] return comments

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0.516199

2,439

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0.204355

0

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0

null

0

null

0

1

0

1

76f6512f7d0f9be2b22c77b6be1aa4a85a8c2498

1,530

py

Python

utils/setAddress.py

wedvjin/rs485-moist-sensor

90930a34d0e6ec977f6083e70cc4bd931d7453fb

[ "Apache-2.0" ]

1

2019-03-04T13:24:42.000Z

utils/setAddress.py

wedvjin/rs485-moist-sensor

90930a34d0e6ec977f6083e70cc4bd931d7453fb

[ "Apache-2.0" ]

null

utils/setAddress.py

wedvjin/rs485-moist-sensor

90930a34d0e6ec977f6083e70cc4bd931d7453fb

[ "Apache-2.0" ]

null

#!/usr/bin/python """Looks for sensor on the bus and changes it's address to the one specified on command line""" import argparse import minimalmodbus import serial from time import sleep parser = argparse.ArgumentParser() parser.add_argument('address', metavar='ADDR', type=int, choices=range(1, 248), help='An address to set') args = parser.parse_args() ADDRESS1 = 1 ADDRESS2 = args.address minimalmodbus.CLOSE_PORT_AFTER_EACH_CALL = True minimalmodbus.PARITY=serial.PARITY_NONE minimalmodbus.STOPBITS = 2 minimalmodbus.BAUDRATE=19200 minimalmodbus.CLOSE_PORT_AFTER_EACH_CALL = True def scanModbus(): for i in range(1, 248): try: print('Trying address: ' + str(i)) sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=i) addressRead = sensor.read_register(0, functioncode=3) if(i == addressRead): print('FOUND!') return (True, i) except (IOError): print("nope...") pass return (False, 0) # sensor.debug=True (found, i) = scanModbus() if found: print('Found sensor at address: ' + str(i)) try: sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=i) print("writing new address: " + str(ADDRESS2)) sensor.write_register(0, value=ADDRESS2, functioncode=6) sleep(0.2) sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=ADDRESS2) print("reading address from holding register: ") print(sensor.read_register(0, functioncode=3)) except: print "Could not change the address. Check your connections" else: print('No sensor on the bus found')

27.321429

105

0.733333

207

1,530

5.352657

0.483092

0.027076

0.07852

0.086643

0.268051

0.16426

0

0.02439

0.142484

1,530

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null

0.214286

0

null

0

null

0

1

0

1

76f7e1b302002b518c986240747a14b0f7bf282f

4,291

py

Python

src/manifest.py

silent1mezzo/lightsaber

e470be7fb84b810fe846ff0ede78d06bf69cd5e3

[ "MIT" ]

13

2020-08-12T12:04:19.000Z

2022-03-12T03:53:07.000Z

src/manifest.py

silent1mezzo/lightsaber

e470be7fb84b810fe846ff0ede78d06bf69cd5e3

[ "MIT" ]

46

2020-09-03T06:00:18.000Z

2022-03-25T10:03:53.000Z

src/manifest.py

silent1mezzo/lightsaber

e470be7fb84b810fe846ff0ede78d06bf69cd5e3

[ "MIT" ]

3

2021-08-11T19:12:37.000Z

2021-11-09T15:19:59.000Z

MANIFEST = { "hilt": { "h1": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (110, 111)}}, "colours": { "primary": (216, 216, 216), # d8d8d8 "secondary": (141, 141, 141), # 8d8d8d "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal/Salvaged materials", }, "h2": { "offsets": {"blade": 20, "button": {"x": (8, 8), "y": (100, 105)}}, "colours": { "primary": (112, 112, 112), # 707070 "secondary": (0, 0, 0), # 000000 "tertiary": (212, 175, 55), # 000000 }, "length": 24, "materials": "Alloy metal and carbon composite", }, "h3": { "offsets": {"blade": 0, "button": {"x": (10, 10), "y": (100, 118)}}, "colours": { "primary": (157, 157, 157), # 707070 "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal", }, "h4": { "offsets": {"blade": 7, "button": {"x": (8, 9), "y": (92, 100)}}, "colours": { "primary": (0, 0, 0), # 000000 "secondary": (157, 157, 157), # 9d9d9d "tertiary": (180, 97, 19), # b46113 }, "length": 13, "materials": "Alloy metal", }, "h5": { "offsets": {"blade": 0, "button": {"x": (8, 8), "y": (92, 105)}}, "colours": { "primary": (111, 111, 111), # 6f6f6f "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal", }, "h6": { "offsets": {"blade": 2, "button": {"x": (8, 9), "y": (112, 113)}}, "colours": { "primary": (120, 120, 120), # 787878 "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 22, "materials": "Alloy metal/Salvaged materials", }, "h7": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (105, 113)}}, "colours": { "primary": (192, 192, 192), # c0c0c0 "secondary": (255, 215, 0), # ffd700 "tertiary": (0, 0, 0), # 000000 }, "length": 22, "materials": "Alloy metal and Gold", }, "h8": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (100, 111)}}, "colours": { "primary": (216, 216, 216), # d8d8d8 "secondary": (180, 97, 19), # b46113 "tertiary": (0, 0, 0), # 000000 }, "length": 24, "materials": "Alloy metal/Copper", }, }, "blade": { "b1": {"colour": "Red", "crystal": "Adegan crystal", "type": "Sith"}, "b2": {"colour": "Blue", "crystal": "Zophis crystal", "type": "Jedi"}, "b3": {"colour": "Green", "crystal": "Nishalorite stone", "type": "Jedi"}, "b4": {"colour": "Yellow", "crystal": "Kimber stone", "type": "Jedi"}, "b5": {"colour": "White", "crystal": "Dragite gem", "type": "Jedi"}, "b6": {"colour": "Purple", "crystal": "Krayt dragon pearl", "type": "Jedi"}, "b7": {"colour": "Blue/Green", "crystal": "Dantari crystal", "type": "Jedi"}, "b8": { "colour": "Orange", "crystal": ["Ilum crystal", "Ultima Pearl"], "type": "Sith", }, "b9": { "colour": "Black", "crystal": "Obsidian", "type": ["Jedi", "Mandalorian"], }, }, "pommel": { "p1": {"length": 5,}, "p2": {"length": 14,}, "p3": {"length": 3,}, "p4": {"length": 8,}, "p5": {"length": 5,}, "p6": {"length": 5,}, "p7": {"length": 8,}, }, # These are lightsabers for a specific Jedi or Sith. Should use their name instead of "unique_urls": {""}, }

37.313043

89

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384

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0.346354

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0

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false

0

null

0

1

0

null

0

1

76f8632c56e75a6a31f710898b1568e855cfd849

9,238

py

Python

apps/interactor/tests/commander/commands/test_animations.py

Djelibeybi/photons

bc0aa91771d8e88fd3c691fb58f18cb876f292ec

[ "MIT" ]

51

2020-07-03T08:34:48.000Z

2022-03-16T10:56:08.000Z

apps/interactor/tests/commander/commands/test_animations.py

delfick/photons

bc0aa91771d8e88fd3c691fb58f18cb876f292ec

[ "MIT" ]

81

2020-07-03T08:13:59.000Z

2022-03-31T23:02:54.000Z

apps/interactor/tests/commander/commands/test_animations.py

Djelibeybi/photons

bc0aa91771d8e88fd3c691fb58f18cb876f292ec

[ "MIT" ]

8

2020-07-24T23:48:20.000Z

2021-05-24T17:20:16.000Z

# coding: spec from interactor.commander.store import store, load_commands from photons_app.mimic.event import Events from photons_app import helpers as hp from photons_canvas.points.simple_messages import Set64 from unittest import mock import pytest @pytest.fixture() def store_clone(): load_commands() return store.clone() @pytest.fixture() def final_future(): fut = hp.create_future() try: yield fut finally: fut.cancel() @pytest.fixture() async def sender(devices, final_future): async with devices.for_test(final_future) as sender: yield sender @pytest.fixture() async def make_server(store_clone, server_wrapper, FakeTime, MockedCallLater, sender, final_future): with FakeTime() as t: async with MockedCallLater(t) as m: async with server_wrapper(store_clone, sender, final_future) as server: yield server, m @pytest.fixture() def server(make_server): return make_server[0] @pytest.fixture() def m(make_server): return make_server[1] @pytest.fixture(autouse=True) def set_async_timeout(request): request.applymarker(pytest.mark.async_timeout(15)) describe "Animation Commands": async it "can get info and help", server, m: await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/help"}, ) assert b"Available animations include" in got assert b"* dice" in got assert b"To see options for a particular animation, run this again" in got got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/help", "args": {"animation_name": "dice"}}, ) assert b"dice animation" in got assert b"This animation has the following options:" in got assert b"colour range options" in got async it "can control an animation", server, m: await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) identity = "first" got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"identity": identity}}, ) assert "animations" in got assert got["animations"] == [identity] assert got["started"] == identity identity2 = "second" got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"identity": identity2}}, ) assert "animations" in got identities = [identity, identity2] assert got["animations"] == identities assert got["started"] == identity2 info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info == {"animations": {identity: mock.ANY, identity2: mock.ANY}, "paused": []} # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity}}, json_output={"animations": identities, "paused": [identity], "pausing": [identity]}, ) await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity2}}, json_output={ "animations": identities, "paused": identities, "pausing": [identity2], }, ) # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": identity2}}, json_output={ "animations": identities, "paused": [identity], "resuming": [identity2], }, ) # pause multiple await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identities}}, json_output={"animations": identities, "paused": identities, "pausing": identities}, ) # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": identities}}, json_output={ "animations": identities, "paused": [], "resuming": identities, }, ) # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity}}, json_output={"animations": identities, "paused": [identity], "pausing": [identity]}, ) # info info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info["animations"] == {identity: mock.ANY, identity2: mock.ANY} assert info["paused"] == [identity] # stop await server.assertCommand( "/v1/lifx/command", {"command": "animation/stop", "args": {"stop": identity}}, json_output={ "animations": [identity, identity2], "paused": [identity], "stopping": [identity], }, ) await m.add(0.5) # info info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info["animations"] == {identity2: mock.ANY} assert info["paused"] == [] async it "pausing an animation actually pauses the animation", devices, server, m: tile = devices["tile"] io = tile.io["MEMORY"] store = devices.store(tile) store.clear() first_set_64 = tile.attrs.event_waiter.wait_for_incoming(io, Set64) # start got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"animations": [["balls", {"every": 3}]]}}, ) identity = got["started"] await first_set_64 now = store.count(Events.INCOMING(tile, io, pkt=Set64)) assert now > 0 await m.add(5) now2 = store.count(Events.INCOMING(tile, io, pkt=Set64)) assert now2 > now identity = got["started"] await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) > now # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": [identity]}}, ) await m.add(5) store.clear() await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) == 0 # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": [identity]}}, ) await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) > 0 # stop await server.assertCommand( "/v1/lifx/command", {"command": "animation/stop", "args": {"stop": [identity]}}, ) store.clear() await m.add(5) store.clear() await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) == 0 # info await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) async it "can get information", server, m: # start got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"animations": [["balls", {"every": 0.3}]]}}, ) identity = got["started"] info = await server.assertCommand("/v1/lifx/command", {"command": "animation/info"}) assert info["paused"] == [] assert identity in info["animations"] assert info["animations"][identity]["animations_ran"] == 1 assert info["animations"][identity]["current_animation"] == { "name": "balls", "options": { "ball_colors": "<ManyColor:[((0, 360), (1000.0, 1000.0), (1000.0, 1000.0), (3500.0, 3500.0))]>", "fade_amount": 0.02, "num_balls": 5, "rate": "<Rate 0.9 -> 1>", }, "started": mock.ANY, } assert info["animations"][identity]["options"]["combined"] assert "unlocked" in info["animations"][identity]["options"]["pauser"] assert info["animations"][identity]["options"]["noisy_network"] == 0 specific = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info", "args": {"identity": identity}} ) info["animations"][identity]["current_animation"]["started"] = mock.ANY assert info["animations"][identity] == specific

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1

76f93238491c8f0f67d7813df6d0b4a6c7ed0a80

245

py

Python

.ipython/profile_pytube/startup/init.py

showa-yojyo/dotfiles

994cc7df0643d69f62cb59550bdd48a42751c345

[ "MIT" ]

null

.ipython/profile_pytube/startup/init.py

showa-yojyo/dotfiles

994cc7df0643d69f62cb59550bdd48a42751c345

[ "MIT" ]

3

2018-03-27T14:10:18.000Z

2018-03-30T14:06:11.000Z

.ipython/profile_pytube/startup/init.py

showa-yojyo/dotfiles

994cc7df0643d69f62cb59550bdd48a42751c345

[ "MIT" ]

null

from pytube import YouTube def download_video(watch_url): yt = YouTube(watch_url) (yt.streams .filter(progressive=True, file_extension='mp4') .order_by('resolution') .desc() .first() .download())

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0

1

0

null

0

null

0

1

0a017ba6441979fea8dcb4bd6912e6e472b2970d

456

py

Python

brokenChains/migrations/0003_auto_20181106_1819.py

bunya017/brokenChains

3e20c834efd7f0ade8e3abe7acf547c093f76758

[ "MIT" ]

1

2018-12-07T09:15:57.000Z

brokenChains/migrations/0003_auto_20181106_1819.py

bunya017/brokenChains

3e20c834efd7f0ade8e3abe7acf547c093f76758

[ "MIT" ]

null

brokenChains/migrations/0003_auto_20181106_1819.py

bunya017/brokenChains

3e20c834efd7f0ade8e3abe7acf547c093f76758

[ "MIT" ]

null

# Generated by Django 2.1.1 on 2018-11-06 17:19 from django.conf import settings from django.db import migrations class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('brokenChains', '0002_auto_20181106_1723'), ] operations = [ migrations.AlterUniqueTogether( name='habit', unique_together={('owner', 'name')}, ), ]

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null

0

1

0a03afbc022ab3ed1e3b4074455a3f3fdefc3a2e

1,189

py

Python

app/modules/ai_lab/migrations/0003_ailabcasestudy.py

nickmoreton/nhsx-website

2397d1308376c02b75323d30e6bc916af0daac9d

[ "MIT" ]

50

2019-04-04T17:50:00.000Z

2021-08-05T15:08:37.000Z

app/modules/ai_lab/migrations/0003_ailabcasestudy.py

nickmoreton/nhsx-website

2397d1308376c02b75323d30e6bc916af0daac9d

[ "MIT" ]

434

2019-04-04T18:25:32.000Z

2022-03-31T18:23:37.000Z

app/modules/ai_lab/migrations/0003_ailabcasestudy.py

nhsx-mirror/nhsx-website

2133b4e275ca35ff77f7d6874e809f139ec4bf86

[ "MIT" ]

23

2019-04-04T09:52:07.000Z

2021-04-11T07:41:47.000Z

# Generated by Django 3.0.4 on 2020-07-14 11:00 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("core", "0026_auto_20200713_1535"), ("ai_lab", "0002_ailabusecase"), ] operations = [ migrations.CreateModel( name="AiLabCaseStudy", fields=[ ( "articlepage_ptr", models.OneToOneField( auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to="core.ArticlePage", ), ), ( "use_case", models.ForeignKey( on_delete=django.db.models.deletion.PROTECT, to="ai_lab.AiLabUseCase", ), ), ], options={"abstract": False,}, bases=("core.articlepage", models.Model), ), ]

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null

0

1

0

null

0

1

0a03cda07d112635217a5bbdc7ec5274c0658a7a

3,258

py

Python

requests/UpdateWorkbookConnectionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

1

2019-06-08T22:19:40.000Z

requests/UpdateWorkbookConnectionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

null

requests/UpdateWorkbookConnectionRequest.py

divinorum-webb/python-tableau-api

9d3f130d63b15307ad2b23e2273b52790b8d9018

[ "Apache-2.0" ]

null

from .BaseRequest import BaseRequest class UpdateWorkbookConnectionRequest(BaseRequest): """ Update workbook connection request for sending API requests to Tableau Server. :param ts_connection: The Tableau Server connection object. :type ts_connection: class :param server_address: The new server for the connection. :type server_address: string :param port: The new port for the connection. :type port: string :param connection_username: The new username for the connection. :type connection_username: string :param connection_password: The new password for the connection. :type connection_password: string :param embed_password_flag: Boolean; True to embed the password in the connection, False otherwise. :type embed_password_flag: boolean """ def __init__(self, ts_connection, server_address=None, port=None, connection_username=None, connection_password=None, embed_password_flag=None): super().__init__(ts_connection) self._server_address = server_address self._port = port self._connection_username = connection_username self._connection_password = connection_password self._embed_password_flag = embed_password_flag self.base_update_workbook_connection_request @property def optional_parameter_keys(self): return [ 'serverAddress', 'serverPort', 'userName', 'password', 'embedPassword' ] @property def optional_parameter_values_exist(self): return [ self._server_address, self._port, self._connection_username, self._connection_password, True if self._embed_password_flag is not None else None ] @property def optional_parameter_values(self): return [ self._server_address, self._port, self._connection_username, self._connection_password, self._embed_password_flag ] @property def base_update_workbook_connection_request(self): self._request_body.update({'connection': {}}) return self._request_body @property def modified_update_workbook_connection_request(self): if any(self.optional_parameter_values_exist): self._request_body['connection'].update( self._get_parameters_dict(self.optional_parameter_keys, self.optional_parameter_values)) return self._request_body @staticmethod def _get_parameters_dict(param_keys, param_values): """Override the inherited _get_parameters_dict() method to allow passing boolean values directly""" params_dict = {} for i, key in enumerate(param_keys): if param_values[i] is not None: params_dict.update({key: param_values[i]}) return params_dict def get_request(self): return self.modified_update_workbook_connection_request

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0

null

0

null

0

1

0

1

0a0e5c306cd6cb5140e3d9096d9aec435b5e905a

637

py

Python

src/plat/index_news_remove.py

jack139/cnnc

c32611ec01af50bedb67dcd4c8a28e4b0c7a9aef

[ "BSD-2-Clause" ]

null

src/plat/index_news_remove.py

jack139/cnnc

c32611ec01af50bedb67dcd4c8a28e4b0c7a9aef

[ "BSD-2-Clause" ]

null

src/plat/index_news_remove.py

jack139/cnnc

c32611ec01af50bedb67dcd4c8a28e4b0c7a9aef

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # import web import time from bson.objectid import ObjectId from config import setting import helper db = setting.db_web # 删除聊天规则 url = ('/plat/index_news_remove') class handler: def GET(self): if not helper.logged(helper.PRIV_USER, 'TALKBOT'): raise web.seeother('/') render = helper.create_render() user_data = web.input(news_id='') if user_data.news_id == '': return render.info('参数错误！') db.index_news.delete_one({'_id':ObjectId(user_data.news_id)}) return render.info('成功删除！', '/plat/index_news')

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null

0

null

0

1

0

1

0a114ea68c2fa1e2738f0d3ff99019e72e2ea941

1,074

py

Python

sitewebapp/migrations/0011_auto_20210130_0150.py

deucaleon18/debsoc-nitdgp-website

41bd6ade7f4af143ef34aff01848f830cc533add

[ "MIT" ]

2

2020-12-05T05:34:56.000Z

2020-12-09T10:27:43.000Z

sitewebapp/migrations/0011_auto_20210130_0150.py

deucaleon18/debsoc-nitdgp-website

41bd6ade7f4af143ef34aff01848f830cc533add

[ "MIT" ]

3

2021-06-28T16:47:23.000Z

2021-06-28T16:48:51.000Z

sitewebapp/migrations/0011_auto_20210130_0150.py

deucaleon18/debsoc-nitdgp-website

41bd6ade7f4af143ef34aff01848f830cc533add

[ "MIT" ]

9

2021-01-29T17:06:30.000Z

2021-08-21T18:23:26.000Z

# Generated by Django 2.2.15 on 2021-01-29 20:20 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('sitewebapp', '0010_auditionanswers_auditionquestions_audtionrounds_candidates'), ] operations = [ migrations.CreateModel( name='auditionRounds', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('roundno', models.IntegerField(default=1)), ('candidate', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='inductees', to='sitewebapp.Candidates')), ], ), migrations.AlterField( model_name='auditionquestions', name='round', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='round', to='sitewebapp.auditionRounds'), ), migrations.DeleteModel( name='audtionRounds', ), ]

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0

null

0

1

0

null

0

1

0a1a359a4636f368d0f28057e4bf1af274c7fb79

3,332

py

Python

influxdb_service_sdk/model/container/resource_requirements_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

5

2019-07-31T04:11:05.000Z

2021-01-07T03:23:20.000Z

influxdb_service_sdk/model/container/resource_requirements_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

influxdb_service_sdk/model/container/resource_requirements_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: resource_requirements.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from influxdb_service_sdk.model.container import resource_list_pb2 as influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='resource_requirements.proto', package='container', syntax='proto3', serialized_options=_b('ZCgo.easyops.local/contracts/protorepo-models/easyops/model/container'), serialized_pb=_b('\n\x1bresource_requirements.proto\x12\tcontainer\x1a\x38influxdb_service_sdk/model/container/resource_list.proto\"j\n\x14ResourceRequirements\x12\'\n\x06limits\x18\x01 \x01(\x0b\x32\x17.container.ResourceList\x12)\n\x08requests\x18\x02 \x01(\x0b\x32\x17.container.ResourceListBEZCgo.easyops.local/contracts/protorepo-models/easyops/model/containerb\x06proto3') , dependencies=[influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2.DESCRIPTOR,]) _RESOURCEREQUIREMENTS = _descriptor.Descriptor( name='ResourceRequirements', full_name='container.ResourceRequirements', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='limits', full_name='container.ResourceRequirements.limits', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='requests', full_name='container.ResourceRequirements.requests', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=100, serialized_end=206, ) _RESOURCEREQUIREMENTS.fields_by_name['limits'].message_type = influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2._RESOURCELIST _RESOURCEREQUIREMENTS.fields_by_name['requests'].message_type = influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2._RESOURCELIST DESCRIPTOR.message_types_by_name['ResourceRequirements'] = _RESOURCEREQUIREMENTS _sym_db.RegisterFileDescriptor(DESCRIPTOR) ResourceRequirements = _reflection.GeneratedProtocolMessageType('ResourceRequirements', (_message.Message,), { 'DESCRIPTOR' : _RESOURCEREQUIREMENTS, '__module__' : 'resource_requirements_pb2' # @@protoc_insertion_point(class_scope:container.ResourceRequirements) }) _sym_db.RegisterMessage(ResourceRequirements) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

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null

0

1

0

null

0

1

0a1cc533cda21da8b86ba8309652b8179ef12637

1,371

py

Python

Episode11-Menu/Pygame/explosion.py

Inksaver/Shmup_With_Pygame_Love2D_Monogame

84838516d9dd9d6639b1b699dca546bfdfec73dc

[ "CC0-1.0" ]

1

2022-02-01T04:05:04.000Z

Episode11-Menu/Pygame/explosion.py

Inksaver/Shmup_With_Pygame_Love2D_Monogame

84838516d9dd9d6639b1b699dca546bfdfec73dc

[ "CC0-1.0" ]

null

Episode11-Menu/Pygame/explosion.py

Inksaver/Shmup_With_Pygame_Love2D_Monogame

84838516d9dd9d6639b1b699dca546bfdfec73dc

[ "CC0-1.0" ]

null

import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list of 8 images self.centre = centre # use for all frames self.key = key # key used later self.image = images[key][0] # set to first image in the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set centre for all frames self.frame = 0 # no of first frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): # check if end of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current frame

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null

0

1

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1

0a21ba878c2e6396a56688811ff51897970088c4

3,361

py

Python

tinc/tests/parameter_space_test.py

AlloSphere-Research-Group/tinc-python

4c3390df9911a391833244de1eb1d33a2e19d330

[ "BSD-3-Clause" ]

1

2020-11-23T22:42:50.000Z

tinc/tests/parameter_space_test.py

AlloSphere-Research-Group/tinc-python

4c3390df9911a391833244de1eb1d33a2e19d330

[ "BSD-3-Clause" ]

null

tinc/tests/parameter_space_test.py

AlloSphere-Research-Group/tinc-python

4c3390df9911a391833244de1eb1d33a2e19d330

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Jun 14 11:49:43 2021 @author: Andres """ import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter("param1") p2 = Parameter("param2") ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter("param1") p1.values = [0, 1,2,3,4] p2 = Parameter("param2") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter("param1") p1.values = [0, 1,2,3,4] p2 = Parameter("param2") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) ps.enable_cache("ps_test") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter("dim1") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter("dim2") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter("dim3") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace("ps") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template("file_%%dim1%%_%%dim2:INDEX%%") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter("dim1") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = ["0.1_1" ,"0.1_2","0.2_1" ,"0.2_2", "0.3_1" ,"0.3_2"] dim2 = Parameter("dim2") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = ["0.1_1", "0.2_1", "0.3_1", "0.1_2", "0.2_2", "0.3_2"] ps = ParameterSpace("ps") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.1_1") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.2_1") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.1_2") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.2_2") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.3_2") if __name__ == '__main__': unittest.main()

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null

0

1

0a2ad964a50ee086e447a623b3863c7fbb9ef26a

1,977

py

Python

src/com/python/email/send_mail.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

src/com/python/email/send_mail.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

src/com/python/email/send_mail.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

''' Created on 2016年8月10日 @author: Administrator ''' from email import encoders from email.header import Header from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from email.mime.multipart import MIMEBase from email.utils import parseaddr, formataddr import smtplib def _format_addr(s): name, addr = parseaddr(s) return formataddr((Header(name, 'utf-8').encode(), addr)) from_addr = '[email protected]'#input('From: ') password = input('Password: ') to_addr = '[email protected]'#input('To: ') smtp_server = 'smtp.163.com'#input('SMTP server: ') # 发送纯文本邮件 # msg = MIMEText('hello, send by Python...', 'plain', 'utf-8') # 发送HTML邮件 # msg = MIMEText('<html><body><h1>Hello</h1>' + # '<p>send by <a href="http://www.python.org">Python</a>...</p>' + # '</body></html>', 'html', 'utf-8') # 发送带附件的邮件 # 邮件对象: msg = MIMEMultipart() msg['From'] = _format_addr('Python爱好者 <%s>' % from_addr) msg['To'] = _format_addr('管理员 <%s>' % to_addr) msg['Subject'] = Header('来自SMTP的问候……', 'utf-8').encode() # 邮件正文是MIMEText: msg.attach(MIMEText('send with file...', 'plain', 'utf-8')) # 添加附件就是加上一个MIMEBase，从本地读取一个图片: with open('D:/pythonWorkspace/pthonDemo/src/com/python/email/test.jpg', 'rb') as f: # 设置附件的MIME和文件名，这里是png类型: mime = MIMEBase('image', 'png', filename='test.png') # 加上必要的头信息: mime.add_header('Content-Disposition', 'attachment', filename='test.png') mime.add_header('Content-ID', '<0>') mime.add_header('X-Attachment-Id', '0') # 把附件的内容读进来: mime.set_payload(f.read()) # 用Base64编码: encoders.encode_base64(mime) # 添加到MIMEMultipart: msg.attach(mime) msg['From'] = _format_addr('Python爱好者 <%s>' % from_addr) msg['To'] = _format_addr('管理员 <%s>' % to_addr) msg['Subject'] = Header('来自SMTP的问候……', 'utf-8').encode() server = smtplib.SMTP(smtp_server, 25) server.set_debuglevel(1) server.login(from_addr, password) server.sendmail(from_addr, [to_addr], msg.as_string()) server.quit()

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1

0a36ce830d4011a6336f73093bb61b54abdb2cbd

7,782

py

Python

pypy/interpreter/test/test_generator.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

381

2018-08-18T03:37:22.000Z

2022-02-06T23:57:36.000Z

pypy/interpreter/test/test_generator.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

16

2018-09-22T18:12:47.000Z

2022-02-22T20:03:59.000Z

pypy/interpreter/test/test_generator.py

m4sterchain/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

[ "Apache-2.0", "OpenSSL" ]

30

2018-08-20T03:16:34.000Z

2022-01-12T17:39:22.000Z

class AppTestGenerator: def test_generator(self): def f(): yield 1 assert f().next() == 1 def test_generator2(self): def f(): yield 1 g = f() assert g.next() == 1 raises(StopIteration, g.next) def test_attributes(self): def f(): yield 1 assert g.gi_running g = f() assert g.gi_code is f.__code__ assert g.__name__ == 'f' assert g.gi_frame is not None assert not g.gi_running g.next() assert not g.gi_running raises(StopIteration, g.next) assert not g.gi_running assert g.gi_frame is None assert g.gi_code is f.__code__ assert g.__name__ == 'f' def test_generator3(self): def f(): yield 1 g = f() assert list(g) == [1] def test_generator4(self): def f(): yield 1 g = f() assert [x for x in g] == [1] def test_generator5(self): d = {} exec """if 1: def f(): v = (yield ) yield v g = f() g.next() """ in d g = d['g'] assert g.send(42) == 42 def test_throw1(self): def f(): yield 2 g = f() # two arguments version raises(NameError, g.throw, NameError, "Error") def test_throw2(self): def f(): yield 2 g = f() # single argument version raises(NameError, g.throw, NameError("Error")) def test_throw3(self): def f(): try: yield 1 yield 2 except: yield 3 g = f() assert g.next() == 1 assert g.throw(NameError("Error")) == 3 raises(StopIteration, g.next) def test_throw4(self): d = {} exec """if 1: def f(): try: yield 1 v = (yield 2) except: yield 3 g = f() """ in d g = d['g'] assert g.next() == 1 assert g.next() == 2 assert g.throw(NameError("Error")) == 3 raises(StopIteration, g.next) def test_throw5(self): def f(): try: yield 1 except: x = 3 try: yield x except: pass g = f() g.next() # String exceptions are not allowed anymore raises(TypeError, g.throw, "Error") assert g.throw(Exception) == 3 raises(StopIteration, g.throw, Exception) def test_throw6(self): def f(): yield 2 g = f() raises(NameError, g.throw, NameError, "Error", None) def test_throw_fail(self): def f(): yield 1 g = f() raises(TypeError, g.throw, NameError("Error"), "error") def test_throw_fail2(self): def f(): yield 1 g = f() raises(TypeError, g.throw, list()) def test_throw_fail3(self): def f(): yield 1 g = f() raises(TypeError, g.throw, NameError("Error"), None, "not tb object") def test_throw_finishes_generator(self): def f(): yield 1 g = f() assert g.gi_frame is not None raises(ValueError, g.throw, ValueError) assert g.gi_frame is None def test_throw_bug(self): def f(): try: x.throw(IndexError) # => "generator already executing" except ValueError: yield 1 x = f() res = list(x) assert res == [1] def test_throw_on_finished_generator(self): def f(): yield 1 g = f() res = g.next() assert res == 1 raises(StopIteration, g.next) raises(NameError, g.throw, NameError) def test_close(self): def f(): yield 1 g = f() assert g.close() is None def test_close2(self): def f(): try: yield 1 except GeneratorExit: raise StopIteration g = f() g.next() assert g.close() is None def test_close3(self): def f(): try: yield 1 except GeneratorExit: raise NameError g = f() g.next() raises(NameError, g.close) def test_close_fail(self): def f(): try: yield 1 except GeneratorExit: yield 2 g = f() g.next() raises(RuntimeError, g.close) def test_close_on_collect(self): ## we need to exec it, else it won't run on python2.4 d = {} exec """ def f(): try: yield finally: f.x = 42 """.strip() in d g = d['f']() g.next() del g import gc gc.collect() assert d['f'].x == 42 def test_generator_raises_typeerror(self): def f(): yield 1 g = f() raises(TypeError, g.send) # one argument required raises(TypeError, g.send, 1) # not started, must send None def test_generator_explicit_stopiteration(self): def f(): yield 1 raise StopIteration g = f() assert [x for x in g] == [1] def test_generator_propagate_stopiteration(self): def f(): it = iter([1]) while 1: yield it.next() g = f() assert [x for x in g] == [1] def test_generator_restart(self): def g(): i = me.next() yield i me = g() raises(ValueError, me.next) def test_generator_expression(self): exec "res = sum(i*i for i in range(5))" assert res == 30 def test_generator_expression_2(self): d = {} exec """ def f(): total = sum(i for i in [x for x in z]) return total, x z = [1, 2, 7] res = f() """ in d assert d['res'] == (10, 7) def test_repr(self): def myFunc(): yield 1 g = myFunc() r = repr(g) assert r.startswith("<generator object myFunc at 0x") assert list(g) == [1] assert repr(g) == r def test_unpackiterable_gen(self): g = (i*i for i in range(-5, 3)) assert set(g) == set([0, 1, 4, 9, 16, 25]) assert set(g) == set() assert set(i for i in range(0)) == set() def test_explicit_stop_iteration_unpackiterable(self): def f(): yield 1 raise StopIteration assert tuple(f()) == (1,) def test_exception_is_cleared_by_yield(self): def f(): try: foobar except NameError: yield 5 raise # should raise "no active exception to re-raise" gen = f() next(gen) # --> 5 try: next(gen) except TypeError: pass def test_multiple_invalid_sends(self): def mygen(): yield 42 g = mygen() raises(TypeError, g.send, 2) raises(TypeError, g.send, 2) def test_should_not_inline(space): from pypy.interpreter.generator import should_not_inline w_co = space.appexec([], '''(): def g(x): yield x + 5 return g.__code__ ''') assert should_not_inline(w_co) == False w_co = space.appexec([], '''(): def g(x): yield x + 5 yield x + 6 return g.__code__ ''') assert should_not_inline(w_co) == True

24.092879

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null

0

1

0

1

0a3a46f51a8f874a867b535822da740830faf6e6

966

py

Python

cybox/common/location.py

tirkarthi/python-cybox

a378deb68b3ac56360c5cc35ff5aad1cd3dcab83

[ "BSD-3-Clause" ]

40

2015-03-05T18:22:51.000Z

2022-03-06T07:29:25.000Z

cybox/common/location.py

tirkarthi/python-cybox

a378deb68b3ac56360c5cc35ff5aad1cd3dcab83

[ "BSD-3-Clause" ]

106

2015-01-12T18:52:20.000Z

2021-04-25T22:57:52.000Z

cybox/common/location.py

tirkarthi/python-cybox

a378deb68b3ac56360c5cc35ff5aad1cd3dcab83

[ "BSD-3-Clause" ]

30

2015-03-25T07:24:40.000Z

2021-07-23T17:10:11.000Z

# Copyright (c) 2017, The MITRE Corporation. All rights reserved. # See LICENSE.txt for complete terms. from mixbox import entities, fields import cybox import cybox.bindings.cybox_common as common_binding class LocationFactory(entities.EntityFactory): @classmethod def entity_class(cls, key): return cybox.lookup_extension(key, default=Location) class Location(entities.Entity): _binding = common_binding _binding_class = common_binding.LocationType _namespace = 'http://cybox.mitre.org/common-2' _XSI_TYPE = None # overridden by subclasses id_ = fields.IdrefField("id") idref = fields.IdrefField("idref") name = fields.TypedField("Name") def to_dict(self): d = super(Location, self).to_dict() if self._XSI_TYPE: d["xsi:type"] = self._XSI_TYPE return d @staticmethod def lookup_class(xsi_type): return cybox.lookup_extension(xsi_type, default=Location)

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null

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null

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1

0a3bec6c960ec5a80b8e4e32d4669b80255b605f

1,114

py

Python

app/rss_feeder_api/migrations/0003_auto_20200813_1623.py

RSaab/rss-scraper

9bf608878e7d08fea6508ae90b27f1c226b313f1

[ "MIT" ]

null

app/rss_feeder_api/migrations/0003_auto_20200813_1623.py

RSaab/rss-scraper

9bf608878e7d08fea6508ae90b27f1c226b313f1

[ "MIT" ]

null

app/rss_feeder_api/migrations/0003_auto_20200813_1623.py

RSaab/rss-scraper

9bf608878e7d08fea6508ae90b27f1c226b313f1

[ "MIT" ]

null

# Generated by Django 3.1 on 2020-08-13 16:23 from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('rss_feeder_api', '0002_feed_subtitle'), ] operations = [ migrations.AlterModelOptions( name='entry', options={'ordering': ('-updated_at',), 'verbose_name_plural': 'entries'}, ), migrations.AlterModelOptions( name='feed', options={'ordering': ('-updated_at',), 'verbose_name': 'Feed', 'verbose_name_plural': 'Feeds'}, ), migrations.AddField( model_name='entry', name='created_at', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='entry', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterUniqueTogether( name='entry', unique_together={('guid',)}, ), ]

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null

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1

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null

0

1

0a4049bea9cce33edfb9f0362df0cd2e91b7aa1a

335

py

Python

reo/migrations/0121_merge_20211001_1841.py

NREL/REopt_API

fbc70f3b0cdeec9ee220266d6b3b0c5d64f257a6

[ "BSD-3-Clause" ]

7

2022-01-29T12:10:10.000Z

2022-03-28T13:45:20.000Z

reo/migrations/0121_merge_20211001_1841.py

NREL/reopt_api

fbc70f3b0cdeec9ee220266d6b3b0c5d64f257a6

[ "BSD-3-Clause" ]

12

2022-02-01T18:23:18.000Z

2022-03-31T17:22:17.000Z

reo/migrations/0121_merge_20211001_1841.py

NREL/REopt_API

fbc70f3b0cdeec9ee220266d6b3b0c5d64f257a6

[ "BSD-3-Clause" ]

3

2022-02-08T19:44:40.000Z

2022-03-12T11:05:36.000Z

# Generated by Django 3.1.13 on 2021-10-01 18:41 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reo', '0117_financialmodel_generator_fuel_escalation_pct'), ('reo', '0120_auto_20210927_2046'), ('reo', '0121_auto_20211012_0305') ] operations = [ ]

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0

null

0

1

0

null

0

1

0a42fad82c7026120ddbfdc222f7f45f5ba001fc

8,219

py

Python

seqenv/ontology.py

xapple/seqenv

a898b936b64b51340f439b05fc8909f4ed826247

[ "MIT" ]

7

2016-12-02T09:28:00.000Z

2021-11-04T13:47:16.000Z

seqenv/ontology.py

xapple/seqenv

a898b936b64b51340f439b05fc8909f4ed826247

[ "MIT" ]

7

2016-04-07T17:00:50.000Z

2018-05-14T12:16:06.000Z

seqenv/ontology.py

xapple/seqenv

a898b936b64b51340f439b05fc8909f4ed826247

[ "MIT" ]

4

2016-03-15T16:41:12.000Z

2021-12-06T09:30:35.000Z

# Built-in modules # # Internal modules # from seqenv import module_dir from seqenv.common.cache import property_cached # Third party modules # import sh, networkx import matplotlib.colors # A list of envos to help test this module # test_envos = [ "ENVO:00000033", "ENVO:00000043", "ENVO:00000067", "ENVO:00000143", "ENVO:00000210", "ENVO:00000215", "ENVO:00000475", ] ################################################################################ class Ontology(object): """A object that gives you access to the graph (network with nodes and edges) of the ENVO ontology from the OBO file's path. Other libraries not used here that could be added: * graphviz: http://graphviz.readthedocs.org/en/latest/api.html#digraph * pydot: https://github.com/erocarrera/pydot """ def __init__(self, path=None): """Give the path to the OBO file""" if path is None: path = module_dir + 'data_envo/envo.obo' self.path = path # --------------------------- In this section --------------------------- # # orange_obo # goatools # orange_network # pygraphviz # networkx @property_cached def orange_obo(self): """The ontology loaded by the `orange` library. * http://orange.biolab.si * http://orange-bioinformatics.readthedocs.org/en/latest/ * https://github.com/biolab/orange-bio * https://bitbucket.org/biolab/orange-bioinformatics To install: $ pip install Orange-Bioinformatics """ from orangecontrib.bio.ontology import OBOOntology return OBOOntology(self.path) @property_cached def goatools(self): """The network loaded into goatools' format. * https://github.com/tanghaibao/goatools To install: $ pip install goatools """ from goatools import obo_parser return obo_parser.GODag(self.path) @property_cached def orange_network(self): """The network converted to `orange network` format. Doesn't seem to work until they update PyPI. * https://bitbucket.org/biolab/orange-network/ * http://orange-network.readthedocs.org/en/latest/ To install: $ pip install orange-network """ return self.orange_obo.to_network() @property_cached def pygraphviz(self): """The network converted to `pygraphviz` format. * http://pygraphviz.github.io/documentation/pygraphviz-1.3rc1/ To install: $ pip install pygraphviz """ g = self.orange_obo.to_graphviz() assert g.is_directed() assert g.is_strict() return g @property_cached def networkx(self): """The network converted to `networkx` format. Seems like it looses directionality. * https://networkx.readthedocs.org/en/stable/ To install: $ pip install networkx """ g = self.orange_obo.to_networkx() assert networkx.is_directed_acyclic_graph(g) return g # --------------------------- In this section --------------------------- # # test # get_subgraph # add_weights # draw_to_pdf # write_to_dot def get_subgraph(self, envos=None): """Given a list of ENVO terms, get the subgraph that contains them all and all their ancestors, up to the root. Outputs a networkx DiGraph object.""" # Testing mode # if envos is None: envos = test_envos # All nodes # nodes = set(n for e in envos for n in networkx.descendants(self.networkx, e)) nodes.update(envos) nodes = list(nodes) # Return # return self.networkx.subgraph(nodes) def add_weights(self, g, weights=None): """Input a networkx DiGraph object. Outputs a pygraphviz AGraph object.""" g = networkx.nx_agraph.to_agraph(g) if weights is None: return g for envo in weights: node = g.get_node(envo) weight = weights[envo] color = matplotlib.colors.rgb2hex((1.0, 1.0 - weight, 0.0)) node.attr['fillcolor'] = color return g def add_style(self, g): """Input a pygraphviz AGraph object. Outputs a pygraphviz AGraph object.""" for node in g.nodes(): text = node.attr['name'] node.attr['label'] = text.replace(' ','\\n') node.attr['name'] = '' node.attr['shape'] = 'Mrecord' node.attr['style'] = 'filled' # To add the envo id to each node, uncomment: #envo = node.attr['label'] #node.attr['label'] = "{<f0> %s|<f1> %s}" % (envo, text) for edge in g.edges(): if edge.attr['label'] == 'located_in': edge.attr['color'] = 'turquoise4' edge.attr['label'] = '' return g def write_to_dot(self, g, path): """Input a pygraphviz AGraph object.""" with open(path, 'w') as handle: handle.write(g.to_string()) def add_legend(self, path): """Input the path to a dot file.""" legend_txt = """ digraph { rankdir=LR node [shape=plaintext,fontname="helvetica"] subgraph cluster_01 { label = "NB: darker nodes weigh more"; key [label=<<table border="0" cellpadding="2" cellspacing="0" cellborder="0"> <tr><td align="right" port="i1">Is</td></tr> <tr><td align="right" port="i2">Part</td></tr> <tr><td align="right" port="i3">Located</td></tr> </table>>]; key2 [label=<<table border="0" cellpadding="2" cellspacing="0" cellborder="0"> <tr><td port="i1">a</td></tr> <tr><td port="i2">of</td></tr> <tr><td port="i3">in</td></tr> </table>>]; key:i1:e -> key2:i1:w [color=red]; key:i2:e -> key2:i2:w [color=blue]; key:i3:e -> key2:i3:w [color=turquoise4]; }""" orig_txt = [line.rstrip('\n') for line in open(path, 'r') if line] new_text = [line.lstrip() for line in legend_txt.split('\n') if line] new_text = '\n'.join(new_text + orig_txt[2:]) with open(path, 'w') as handle: handle.write(new_text) def draw_to_pdf(self, in_path, out_path): """Input a path to a dot file.""" sh.dot(in_path, '-Tpdf', '-o', out_path) # --------------------------- In this section --------------------------- # # descends def descends(self, e, root): """Does the envo term `e` descend from the node `root`? Returns True or False.""" # Auto conversion # if isinstance(e, int): e = "ENVO:%08d" % e if isinstance(root, int): root = "ENVO:%08d" % root # Return # return e in networkx.ancestors(self.networkx, root) # --------------------------- In this section --------------------------- # # print_test # draw_with_networkx # draw_with_pygraphviz def print_test(self, e=None): """Just a method to see a bit how the different libraries work.""" # Test node # if e is None: e = test_envos[0] # Goa # print "Goa: " print self.goatools[e] # Pygraphviz # print "pygraphviz: " print self.pygraphviz[e] print self.pygraphviz.successors(e) print self.pygraphviz.predecessors(e) print self.pygraphviz.get_node(e) # Networkx # import networkx print "networkx: " print self.networkx[e] print self.networkx.successors(e) print self.networkx.predecessors(e) print networkx.ancestors(self.networkx, e) # same as predecessors print networkx.descendants(self.networkx, e) # almost as child_to_parents def draw_with_networkx(self, g, path): """Input a networkx DiGraph object.""" from matplotlib import pyplot networkx.draw(g) pyplot.savefig(path) pyplot.close() def draw_with_pygraphviz(self, g, path): """Input a pygraphviz AGraph object.""" with open(path, 'w') as handle: handle.write(g.to_string())

35.426724

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0.565397

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null

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null

0

1

0

1

0a4ab6a6c7a8f22ae4262d99f43041e035e6b535

602

py

Python

project/settings/production.py

chiehtu/kissaten

a7aad01de569107d5fd5ed2cd781bca6e5750871

[ "MIT" ]

null

project/settings/production.py

chiehtu/kissaten

a7aad01de569107d5fd5ed2cd781bca6e5750871

[ "MIT" ]

null

project/settings/production.py

chiehtu/kissaten

a7aad01de569107d5fd5ed2cd781bca6e5750871

[ "MIT" ]

null

from .base import * SECRET_KEY = get_env_var('SECRET_KEY') CSRF_COOKIE_SECURE = True SESSION_COOKIE_SECURE = True TEMPLATE_LOADERS = ( ('django.template.loaders.cached.Loader', ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', )), ) EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_HOST_USER = get_env_var('EMAIL_HOST_USER') EMAIL_HOST_PASSWORD = get_env_var('EMAIL_HOST_PASSWORD') EMAIL_PORT = 587 EMAIL_USE_TLS = True DEFAULT_FROM_EMAIL = '' USERENA_USE_HTTPS = True

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null

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1

0

1

0a4b453e9f68bd48c8b434b43c7c61e7c47c248d

3,400

py

Python

modelflow/graph_viz_from_outputs.py

ModelFlow/modelflow

c2b720b2da8bb17462baff5c00bbe942644474b0

[ "MIT" ]

6

2020-07-28T19:58:28.000Z

2021-05-01T18:51:37.000Z

modelflow/graph_viz_from_outputs.py

ModelFlow/modelflow

c2b720b2da8bb17462baff5c00bbe942644474b0

[ "MIT" ]

81

2020-07-30T07:08:10.000Z

2021-07-28T02:17:43.000Z

modelflow/graph_viz_from_outputs.py

ModelFlow/modelflow

c2b720b2da8bb17462baff5c00bbe942644474b0

[ "MIT" ]

null

import pandas as pd import argparse import json try: from graphviz import Digraph except: print("Note: Optional graphviz not installed") def generate_graph(df, graph_format='pdf'): g = Digraph('ModelFlow', filename='modelflow.gv', engine='neato', format=graph_format) g.attr(overlap='false') g.attr(splines='true') column_names = df.columns states = [] g.attr('node', shape='ellipse') for column_name in column_names: if column_name[:6] == 'state_': states.append((column_name[6:], column_name)) g.node(column_name[6:]) models = [] g.attr('node', shape='box') for column_name in column_names: if column_name[:6] != 'state_': models.append((column_name.split('_')[0], column_name)) g.node(column_name.split('_')[0]) for column_name in column_names: if column_name[:6] != 'state_': parts = column_name.split('_') state = '_'.join(parts[1:])[6:-7] print(parts[0], state, df[column_name].min(), df[column_name].max()) if df[column_name].min() < 0 and df[column_name].max() <= 0: g.edge(state, parts[0]) elif df[column_name].min() >= 0 and df[column_name].max() > 0: g.edge(parts[0], state) else: g.edge(parts[0], state) g.edge(state, parts[0]) if graph_format == 'json': # TODO: THIS DOES NOT WORK FOR MULTIPLE MODELFLOWS with open('modelflow.gv.json', 'r') as f: return json.load(f) else: g.view() def generate_react_flow_chart(outputs): df = pd.DataFrame() for key, value in outputs['output_states'].items(): df[key] = value['data'] return generate_react_flow_chart_from_df(df) def generate_react_flow_chart_from_df(df): column_names = df.columns nodes = {} # Elipses for column_name in column_names: if column_name[:6] == 'state_': nodes[column_name[6:]] = dict(name=column_name[6:], kind='elipse') # Boxes for column_name in column_names: if column_name[:6] != 'state_': nodes[column_name.split('_')[0]] = dict(name=column_name.split('_')[0], kind='box') edges = [] for column_name in column_names: if column_name[:6] != 'state_': parts = column_name.split('_') name1 = parts[0] state = '_'.join(parts[1:])[6:-7] # print(name1, state, df[column_name].min(), # df[column_name].max()) if df[column_name].min() < 0 and df[column_name].max() <= 0: edges.append([state, name1, 'one_way']) elif df[column_name].min() >= 0 and df[column_name].max() > 0: edges.append([name1, state, 'one_way']) else: edges.append([name1, state, 'both']) return dict(nodes=list(nodes.values()), edges=edges) def main(args): df = pd.read_csv(args.output_file) # generate_graph(df) generate_react_flow_chart_from_df(df) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate Graph Viz') parser.add_argument('-f', '--output_file', type=str, help='The output file to generate a graph of', required=True) args = parser.parse_args() main(args)

32.380952

95

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3,400

4.188341

0.262332

0.192719

0.077088

0.04818

0.457173

0.413276

0.38651

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0

0.015783

0.273235

3,400

104

96

32.692308

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0.045

0

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1

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0

1

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false

0

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0

0.141026

0.025641

0

null

0

1

0

null

0

1

0a4ed29474e7c8d2e3be0b36b2cae77e32eb65c8

376

py

Python

controller/base_service.py

oopsteams/pansite

11896842da66efc72c26eab071f7f802b982f435

[ "MIT" ]

null

controller/base_service.py

oopsteams/pansite

11896842da66efc72c26eab071f7f802b982f435

[ "MIT" ]

1

2021-06-02T01:00:41.000Z

controller/base_service.py

oopsteams/pansite

11896842da66efc72c26eab071f7f802b982f435

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created by susy at 2019/11/8 """ from dao.dao import DataDao import pytz from dao.models import PanAccounts from cfg import PAN_SERVICE, MASTER_ACCOUNT_ID class BaseService: def __init__(self): self.default_tz = pytz.timezone('Asia/Chongqing') # self.pan_acc: PanAccounts = DataDao.pan_account_list(MASTER_ACCOUNT_ID, False)

23.5

88

0.726064

54

376

4.814815

0.648148

0.053846

0.115385

0

0.025559

0.167553

376

15

89

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0.805112

0.345745

0

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0

1

0.142857

false

0

0.571429

0

0.857143

0

null

0

null

0

1

0

1

0a554fb894afeaf01a54f7e6b34139ca26334475

862

py

Python

dbschema/revertDBinstall.py

leschzinerlab/myami-3.2-freeHand

974b8a48245222de0d9cfb0f433533487ecce60d

[ "MIT" ]

null

dbschema/revertDBinstall.py

leschzinerlab/myami-3.2-freeHand

974b8a48245222de0d9cfb0f433533487ecce60d

[ "MIT" ]

null

dbschema/revertDBinstall.py

leschzinerlab/myami-3.2-freeHand

974b8a48245222de0d9cfb0f433533487ecce60d

[ "MIT" ]

1

2019-09-05T20:58:37.000Z

#!/usr/bin/env python from sinedon import dbupgrade, dbconfig import updatelib project_dbupgrade = dbupgrade.DBUpgradeTools('projectdata', drop=True) if __name__ == "__main__": updatelib_inst = updatelib.UpdateLib(project_dbupgrade) checkout_version = raw_input('Revert to checkout version, for example, 2.1 -->') if checkout_version != 'trunk': try: map((lambda x:int(x)),checkout_version.split('.')[:2]) except: print "valid versions are 'trunk', '2.1', or '2.1.2' etc" raise checkout_revision = int(raw_input('Revert to checkout revision, for example, 16500 -->')) updatelib_inst.updateDatabaseVersion(checkout_version) print "\033[35mVersion Updated in the database %s\033[0m" % checkout_version updatelib_inst.updateDatabaseRevision(checkout_revision) print "\033[35mRevision Updated in the database as %d\033[0m" % checkout_revision

41.047619

90

0.759861

114

862

5.54386

0.517544

0.142405

0.079114

0.050633

0.075949

0

0.040789

0.118329

862

20

91

43.1

0.790789

0.023202

0

0.326992

0

null

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0.117647

null

0.176471

0

null

0

null

0

1

0

1

0a56c8065ff434f391ba424536df2984e5ef9221

3,396

py

Python

notebooks/classical_clustering.py

prise6/smart-iss-posts

fc913078e7fbe6343fd36ec6ca9852322247da5d

[ "MIT" ]

null

notebooks/classical_clustering.py

prise6/smart-iss-posts

fc913078e7fbe6343fd36ec6ca9852322247da5d

[ "MIT" ]

10

2020-01-28T23:15:20.000Z

2022-03-12T00:12:31.000Z

notebooks/classical_clustering.py

prise6/smart-iss-posts

fc913078e7fbe6343fd36ec6ca9852322247da5d

[ "MIT" ]

null

#%% [markdown] # # Clustering classique #%% [markdown] # ## import classique import os #%% %load_ext autoreload %autoreload 2 os.chdir('/home/jovyan/work') #%% [markdown] # ## Import iss #%% from iss.tools import Config from iss.tools import Tools from iss.models import SimpleConvAutoEncoder from iss.clustering import ClassicalClustering from iss.clustering import AdvancedClustering from dotenv import find_dotenv, load_dotenv import numpy as np #%% [markdown] # ## Chargement de la config #%% load_dotenv(find_dotenv()) cfg = Config(project_dir = os.getenv("PROJECT_DIR"), mode = os.getenv("MODE")) #%% [markdown] # ## Chargement du modèle #%% ## charger le modèle model_type = 'simple_conv' cfg.get('models')[model_type]['model_name'] = 'model_colab' model = SimpleConvAutoEncoder(cfg.get('models')[model_type]) #%% [markdown] ## Chargement des images #%% filenames = Tools.list_directory_filenames('data/processed/models/autoencoder/train/k/') generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (27, 48), batch = 496, nb_batch = 10) #%% pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model) #%% intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], 3*6*16)) #%% [markdown] # ## ACP # Réduction de la dimension #%% clustering = ClassicalClustering(cfg.get('clustering')['classical'], pictures_id, intermediate_output) #%% clustering.compute_pca() #%% [markdown] # ## Kmeans # Premiers clusters #%% clustering.compute_kmeans() clustering.compute_kmeans_centers() #%% [markdown] # ## CAH # Seconds clusters #%% clustering.compute_cah() clustering.compute_cah_labels() #%% [markdown] # ## Résultats #%% [markdown] # ### Clusters intermediaires #%% fig = plt.figure(1, figsize=(12, 7)) plt.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = clustering.kmeans_labels) #%% [markdown] # ### Clusters finaux #%% plt.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = clustering.final_labels) #%% [markdown] # ### Sauvegarde des modèles #%% clustering.save() #%% # clustering = ClassicalClustering(cfg.get('clustering')['classical']) clustering.load() #%% [markdown] # ## Visualisation des clusters #%% def select_cluster(clustering, id_cluster): return [os.path.join('data/processed/models/autoencoder/train/k/', res[0] + '.jpg') for res in clustering.get_zip_results() if res[2] == id_cluster] #%% from IPython.display import Image #%% for cl in range(0,19): print("Cluster %s" % (cl)) res_tmp = select_cluster(clustering, cl) print(len(res_tmp)) image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp[:100]] # img = Tools.display_mosaic(image_array, nrow = 10) # fig = plt.figure(1, figsize=(12, 7)) # plt.imshow(img, aspect = 'auto') # plt.show() #%% [markdown] # ## Zoom sur le cluster 0 #%% res_tmp = select_cluster(clustering, 1) #%% print(len(res_tmp)) image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp] #%% Tools.display_mosaic(image_array, nrow = 18) #%% col = [1 if l == 1 else 0 for l in clustering.kmeans_labels] plt.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = col) #%% plt.scatter(clustering.pca_reduction[np.array(col) == 1, 0], clustering.pca_reduction[np.array(col) == 1, 1])

22.196078

152

0.707008

441

3,396

5.272109

0.342404

0.044731

0.075699

0.03957

0.343226

0.295914

0.190968

0.162581

0.141505

0

0.019614

0.12927

3,396

152

153

22.342105

0.766655

0.232332

0

0.043478

0

0.076801

0.033426

0

null

0

0.195652

null

0.065217

0

null

0

null

0

1

0

1

0a57479ced46772f03d9c9dc023a3217a695d37d

345

py

Python

lambdataalchemani/lambda_test.py

Full-Data-Alchemist/lambdata-Mani-alch

90dcbc091d8f9841d5a1046e64437058a4156dc5

[ "MIT" ]

null

lambdataalchemani/lambda_test.py

Full-Data-Alchemist/lambdata-Mani-alch

90dcbc091d8f9841d5a1046e64437058a4156dc5

[ "MIT" ]

null

lambdataalchemani/lambda_test.py

Full-Data-Alchemist/lambdata-Mani-alch

90dcbc091d8f9841d5a1046e64437058a4156dc5

[ "MIT" ]

null

""" """ import unittest from example_module import COLORS, increment class ExampleTest(unittest.TestCase): """ #TODO """ def test_increment(self): x0 = 0 y0 = increment(x0) #y0 == 1 self.assertEqual(y0, 1) x1 = 100 y1 = increment(x1) #y1 == 101 self.assertEqual(y1, 101)

15.681818

44

0.550725

39

345

4.820513

0.589744

0.031915

0

0.094421

0.324638

345

21

45

16.428571

0.712446

0.063768

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false

0

0.2

0

0.4

0

null

0

null

0

1

0

1

0a585a8c735b3266210fbee5416e533aa2feb0c6

8,847

py

Python

desktop/core/src/desktop/auth/views.py

bopopescu/hue-5

665c275d0c0570b1a4a34a293503cc72ec35695c

[ "Apache-2.0" ]

1

2018-05-07T05:40:36.000Z

desktop/core/src/desktop/auth/views.py

lockhart39/HueQualityAndIngestionApp

c75e55a43a8bdeb7aa0f5bf2101ec72b01dcac1c

[ "Apache-2.0" ]

null

desktop/core/src/desktop/auth/views.py

lockhart39/HueQualityAndIngestionApp

c75e55a43a8bdeb7aa0f5bf2101ec72b01dcac1c

[ "Apache-2.0" ]

1

2022-03-21T09:41:35.000Z

#!/usr/bin/env python # Licensed to Cloudera, Inc. under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. Cloudera, Inc. licenses this file # to you 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. try: import oauth2 as oauth except: oauth = None import cgi import logging import urllib from datetime import datetime from axes.decorators import watch_login import django.contrib.auth.views from django.core import urlresolvers from django.core.exceptions import SuspiciousOperation from django.contrib.auth import login, get_backends, authenticate from django.contrib.auth.models import User from django.contrib.sessions.models import Session from django.http import HttpResponseRedirect from django.utils.translation import ugettext as _ from desktop.auth import forms as auth_forms from desktop.lib.django_util import render from desktop.lib.django_util import login_notrequired from desktop.lib.django_util import JsonResponse from desktop.log.access import access_warn, last_access_map from desktop.conf import LDAP, OAUTH, DEMO_ENABLED from hadoop.fs.exceptions import WebHdfsException from useradmin.models import get_profile from useradmin.views import ensure_home_directory, require_change_password LOG = logging.getLogger(__name__) def get_current_users(): """Return dictionary of User objects and a dictionary of the user's IP address and last access time""" current_users = { } for session in Session.objects.all(): try: uid = session.get_decoded().get(django.contrib.auth.SESSION_KEY) except SuspiciousOperation: # If secret_key changed, this resolution won't work. uid = None if uid is not None: try: userobj = User.objects.get(pk=uid) current_users[userobj] = last_access_map.get(userobj.username, { }) except User.DoesNotExist: LOG.debug("User with id=%d does not exist" % uid) return current_users def first_login_ever(): backends = get_backends() for backend in backends: if hasattr(backend, 'is_first_login_ever') and backend.is_first_login_ever(): return True return False def get_backend_names(): return get_backends and [backend.__class__.__name__ for backend in get_backends()] @login_notrequired @watch_login def dt_login(request, from_modal=False): redirect_to = request.REQUEST.get('next', '/') is_first_login_ever = first_login_ever() backend_names = get_backend_names() is_active_directory = 'LdapBackend' in backend_names and ( bool(LDAP.NT_DOMAIN.get()) or bool(LDAP.LDAP_SERVERS.get()) ) if is_active_directory: UserCreationForm = auth_forms.LdapUserCreationForm AuthenticationForm = auth_forms.LdapAuthenticationForm else: UserCreationForm = auth_forms.UserCreationForm AuthenticationForm = auth_forms.AuthenticationForm if request.method == 'POST': request.audit = { 'operation': 'USER_LOGIN', 'username': request.POST.get('username') } # For first login, need to validate user info! first_user_form = is_first_login_ever and UserCreationForm(data=request.POST) or None first_user = first_user_form and first_user_form.is_valid() if first_user or not is_first_login_ever: auth_form = AuthenticationForm(data=request.POST) if auth_form.is_valid(): # Must login by using the AuthenticationForm. # It provides 'backends' on the User object. user = auth_form.get_user() userprofile = get_profile(user) login(request, user) if request.session.test_cookie_worked(): request.session.delete_test_cookie() auto_create_home_backends = ['AllowAllBackend', 'LdapBackend', 'SpnegoDjangoBackend'] if is_first_login_ever or any(backend in backend_names for backend in auto_create_home_backends): # Create home directory for first user. try: ensure_home_directory(request.fs, user.username) except (IOError, WebHdfsException), e: LOG.error(_('Could not create home directory.'), exc_info=e) request.error(_('Could not create home directory.')) if require_change_password(userprofile): return HttpResponseRedirect(urlresolvers.reverse('useradmin.views.edit_user', kwargs={'username': user.username})) userprofile.first_login = False userprofile.last_activity = datetime.now() userprofile.save() msg = 'Successful login for user: %s' % user.username request.audit['operationText'] = msg access_warn(request, msg) if from_modal or request.REQUEST.get('fromModal', 'false') == 'true': return JsonResponse({'auth': True}) else: return HttpResponseRedirect(redirect_to) else: request.audit['allowed'] = False msg = 'Failed login for user: %s' % request.POST.get('username') request.audit['operationText'] = msg access_warn(request, msg) if from_modal or request.REQUEST.get('fromModal', 'false') == 'true': return JsonResponse({'auth': False}) else: first_user_form = None auth_form = AuthenticationForm() if DEMO_ENABLED.get() and not 'admin' in request.REQUEST: user = authenticate(username=request.user.username, password='HueRocks') login(request, user) ensure_home_directory(request.fs, user.username) return HttpResponseRedirect(redirect_to) if not from_modal: request.session.set_test_cookie() renderable_path = 'login.mako' if from_modal: renderable_path = 'login_modal.mako' return render(renderable_path, request, { 'action': urlresolvers.reverse('desktop.auth.views.dt_login'), 'form': first_user_form or auth_form, 'next': redirect_to, 'first_login_ever': is_first_login_ever, 'login_errors': request.method == 'POST', 'backend_names': backend_names, 'active_directory': is_active_directory }) def dt_logout(request, next_page=None): """Log out the user""" username = request.user.get_username() request.audit = { 'username': username, 'operation': 'USER_LOGOUT', 'operationText': 'Logged out user: %s' % username } backends = get_backends() if backends: for backend in backends: if hasattr(backend, 'logout'): response = backend.logout(request, next_page) if response: return response return django.contrib.auth.views.logout(request, next_page) def profile(request): """ Dumps JSON for user-profile information. """ return render(None, request, _profile_dict(request.user)) def _profile_dict(user): return dict( username=user.username, first_name=user.first_name, last_name=user.last_name, last_login=str(user.last_login), # datetime object needs to be converted email=user.email) # OAuth is based on Twitter as example. @login_notrequired def oauth_login(request): assert oauth is not None consumer = oauth.Consumer(OAUTH.CONSUMER_KEY.get(), OAUTH.CONSUMER_SECRET.get()) client = oauth.Client(consumer) resp, content = client.request(OAUTH.REQUEST_TOKEN_URL.get(), "POST", body=urllib.urlencode({ 'oauth_callback': 'http://' + request.get_host() + '/login/oauth_authenticated/' })) if resp['status'] != '200': raise Exception(_("Invalid response from OAuth provider: %s") % resp) request.session['request_token'] = dict(cgi.parse_qsl(content)) url = "%s?oauth_token=%s" % (OAUTH.AUTHENTICATE_URL.get(), request.session['request_token']['oauth_token']) return HttpResponseRedirect(url) @login_notrequired def oauth_authenticated(request): consumer = oauth.Consumer(OAUTH.CONSUMER_KEY.get(), OAUTH.CONSUMER_SECRET.get()) token = oauth.Token(request.session['request_token']['oauth_token'], request.session['request_token']['oauth_token_secret']) client = oauth.Client(consumer, token) resp, content = client.request(OAUTH.ACCESS_TOKEN_URL.get(), "GET") if resp['status'] != '200': raise Exception(_("Invalid response from OAuth provider: %s") % resp) access_token = dict(cgi.parse_qsl(content)) user = authenticate(access_token=access_token) login(request, user) redirect_to = request.REQUEST.get('next', '/') return HttpResponseRedirect(redirect_to)

34.158301

126

0.722505

1,136

8,847

5.442782

0.242958

0.019408

0.022643

0.018114

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0.123403

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0.096232

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0

0.001513

0.178026

8,847

258

127

34.290698

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null

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0.131429

null

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null

0

null

0

1

0

1

0a5cd9823d91b39775866f431a665d36a045cbd2

2,450

py

Python

Code/all-starter-code/search.py

diyarkudrat/CS-1.3-Core-Data-Structures

7d7d48ad7913cded7b0ea75ced144d0a08989924

[ "MIT" ]

null

Code/all-starter-code/search.py

diyarkudrat/CS-1.3-Core-Data-Structures

7d7d48ad7913cded7b0ea75ced144d0a08989924

[ "MIT" ]

null

Code/all-starter-code/search.py

diyarkudrat/CS-1.3-Core-Data-Structures

7d7d48ad7913cded7b0ea75ced144d0a08989924

[ "MIT" ]

null

#!python """ ANNOTATE FUNCTIONS WITH TIME AND SPACE COMPLEXITY!!!!! """ def linear_search(array, item): """return the first index of item in array or None if item is not found""" return linear_search_iterative(array, item) # return linear_search_recursive(array, item) def linear_search_iterative(array, item): """Time complexity: O(n) because you iterate through n amount of items in array Space Complexity: O(n) because there are n amount of items""" # loop over all array values until item is found for index, value in enumerate(array): #O(n) if item == value: #O(1) return index # found O(1) return None # not found O(1) def linear_search_recursive(array, item, index=0): """Time complexity: O(n) because you are returning the function continuously until index equals to nth-item """ if len(array) <= index: return index if array[index] == item: return index else: return linear_search_recursive(array, item, index + 1) def binary_search(array, item): """return the index of item in sorted array or None if item is not found""" return binary_search_iterative(array, item) # return binary_search_recursive(array, item) def binary_search_iterative(array, item): """Time Complexity: O(log*n) because you are constantly dividing the length of array by 2 until array length is 1 Space Complexity: O(1) """ left, right = 0, len(array) - 1 if len(array) == 0: return None while left <= right: middle = left + (right - left) // 2 if item == array[middle]: return middle elif item > array[middle]: left = middle + 1 else: right = middle - 1 return None def binary_search_recursive(array, item, left=None, right=None): """Time Complexity: O(log*n) Space Complexity: 0(log*n) recursion call stack space""" # TODO: implement binary search recursively here if left is None and right is None: left, right = 0, len(array) - 1 middle = left + (right - left) // 2 if left > right: return None if array[middle] == item: return middle elif item > array[middle]: return binary_search_recursive(array, item, middle + 1, right) else: return binary_search_recursive(array, item, left, middle - 1)

27.222222

117

0.628571

335

2,450

4.525373

0.223881

0.077177

0.092348

0.110818

0.48285

0.384565

0.094987

0.043536

0

0.011306

0.277959

2,450

89

118

27.52809

0.845676

0.365306

0

0.439024

0

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1

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false

0

0.487805

0

null

0

null

0

1

0

1

0a61c9cfc48e56723e2d98bba70acd01045f443c

1,357

py

Python

cv_recommender/account/urls.py

hhhameem/CV-Recommender

b85d53934f0d888835ab8201be388d7d69f0693d

[ "MIT" ]

1

2021-09-14T17:40:17.000Z

cv_recommender/account/urls.py

mjohra/Cv-Recommender-Python-Django

d231092f7bd989b513210dd6031fb23e28bd5dfe

[ "MIT" ]

1

2021-03-31T17:45:15.000Z

cv_recommender/account/urls.py

mjohra/Cv-Recommender-Python-Django

d231092f7bd989b513210dd6031fb23e28bd5dfe

[ "MIT" ]

1

2021-03-31T16:58:50.000Z

from django.urls import path from django.contrib.auth import views as auth_views from . import views urlpatterns = [ path('register/', views.register, name='register'), path('login/', views.userlogin, name='login'), path('logout/', views.userlogout, name='logout'), path('password_change/', auth_views.PasswordChangeView.as_view(), name='password_change'), path('password_change/done/', auth_views.PasswordChangeDoneView.as_view(), name='password_change_done'), path('password_reset/', auth_views.PasswordResetView.as_view(), name='password_reset'), path('password_reset/done/', auth_views.PasswordResetDoneView.as_view(), name='password_reset_done'), path('reset/<uidb64>/<token>/', auth_views.PasswordResetConfirmView.as_view(), name='password_reset_confirm'), path('reset/done/', auth_views.PasswordResetCompleteView.as_view(), name='password_reset_complete'), path('applicantdashboard/', views.applicantdashboard, name='applicantdashboard'), path('recruiterdashboard/', views.recruiterdashboard, name='recruiterdashboard'), path('applicantdashboard/profile-edit/', views.applicantedit, name='editapplicantprofile'), path('recruiterdashboard/profile-edit/', views.recruiteredit, name='editrecruiterprofile'), ]

45.233333

82

0.709654

137

1,357

6.832117

0.284672

0.067308

0.064103

0.115385

0.149573

0

0.001729

0.147384

1,357

29

83

46.793103

0.80726

0

0.322771

0.112749

0

1

0

false

0.428571

0.107143

0

0.107143

0

null

0

null

0

1

0

1

0a658f2185402efce42f9a0cf262eb928b7b63f0

1,650

py

Python

modules/models.py

sbj-ss/github-watcher

7d7c4d2a0a6a014b93a2168dc6e508b2b867a414

[ "MIT" ]

null

modules/models.py

sbj-ss/github-watcher

7d7c4d2a0a6a014b93a2168dc6e508b2b867a414

[ "MIT" ]

null

modules/models.py

sbj-ss/github-watcher

7d7c4d2a0a6a014b93a2168dc6e508b2b867a414

[ "MIT" ]

null

from dataclasses import asdict, dataclass from typing import Any, Dict, List, Type @dataclass(frozen=True) class StatsBaseModel: """Base model for various reports""" @classmethod def key(cls: Type) -> str: name = cls.__name__ return name[0].lower() + name[1:] def to_table(self) -> List[str]: raise NotImplementedError def to_dict(self) -> Dict[str, Any]: return asdict(self) @dataclass(frozen=True) class Contributor: name: str commit_count: int @dataclass(frozen=True) class ContributorStats(StatsBaseModel): contributors: List[Contributor] def to_table(self) -> List[str]: return [ 'Most active contributors:', '-------------------------', 'Name' + (' ' * 20) + 'Commits', ] + [f'{c.name.ljust(24)}{c.commit_count}' for c in self.contributors] @dataclass(frozen=True) class PullRequestStats(StatsBaseModel): open_count: int closed_count: int old_count: int def to_table(self) -> List[str]: return [ 'Pull requests:', '--------------', 'Open Closed Old', f'{str(self.open_count).ljust(8)}{str(self.closed_count).ljust(8)}{str(self.old_count).ljust(8)}' ] @dataclass(frozen=True) class IssueStats(StatsBaseModel): open_count: int closed_count: int old_count: int def to_table(self) -> List[str]: return [ 'Issues:', '-------', 'Open Closed Old', f'{str(self.open_count).ljust(8)}{str(self.closed_count).ljust(8)}{str(self.old_count).ljust(8)}' ]

25

109

0.577576

191

1,650

4.86911

0.303665

0.060215

0.070968

0.129032

0.393548

0.370968

0.341935

0

0.009844

0.261212

1,650

65

110

25.384615

0.753076

0.018182

0

0.44898

0

0.040816

0.225527

0.153036

0

1

0.122449

false

0

0.040816

0.081633

0.55102

0

null

0

null

0

1

0

1

6a55c2af9ac7243f141edb694902ca98eb95a939

278

py

Python

ReadSymLink.py

ohel/pyorbital-gizmod-tweaks

4c02783d1c6287df508351467a5c203a11430b07

[ "Unlicense" ]

null

ReadSymLink.py

ohel/pyorbital-gizmod-tweaks

4c02783d1c6287df508351467a5c203a11430b07

[ "Unlicense" ]

null

ReadSymLink.py

ohel/pyorbital-gizmod-tweaks

4c02783d1c6287df508351467a5c203a11430b07

[ "Unlicense" ]

null

import os def readlinkabs(l): """ Return an absolute path for the destination of a symlink """ if not (os.path.islink(l)): return None p = os.readlink(l) if os.path.isabs(p): return p return os.path.join(os.path.dirname(l), p)

18.533333

48

0.582734

43

278

3.767442

0.55814

0.148148

0

0.294964

278

14

49

19.857143

0.826531

0.205036

0

1

0.125

false

0

0.125

0

0.625

0

null

0

null

0

1

0

1

6a57cefd47f3150e0a9d0bbdcd3affcfe90d72c9

15,520

py

Python

legtool/tabs/servo_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

10

2015-09-23T19:28:06.000Z

2021-04-27T02:32:27.000Z

legtool/tabs/servo_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

null

legtool/tabs/servo_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

9

2015-10-16T07:26:18.000Z

2021-01-13T07:18:35.000Z

# Copyright 2014 Josh Pieper, [email protected]. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import trollius as asyncio from trollius import Task, From, Return import PySide.QtCore as QtCore import PySide.QtGui as QtGui from ..servo import selector from .common import BoolContext from . import gazebo_config_dialog def spawn(callback): def start(): Task(callback()) return start class ServoTab(object): def __init__(self, ui, status): self.ui = ui self.status = status self.servo_controls = [] self.monitor_thread = None self.servo_model = '' self.servo_name_map = {} self.ui.statusText.setText('not connected') self.ui.connectButton.clicked.connect( spawn(self.handle_connect_clicked)) self.ui.typeCombo.currentIndexChanged.connect(self.handle_type_change) self.handle_type_change() self.ui.configureGazeboButton.clicked.connect( self.handle_configure_gazebo) servo_layout = QtGui.QVBoxLayout() servo_layout.setSpacing(0) servo_layout.setContentsMargins(0, 0, 0, 0) self.ui.scrollContents.setLayout(servo_layout) self.ui.servoCountSpin.valueChanged.connect(self.handle_servo_count) self.handle_servo_count() self.ui.powerCombo.currentIndexChanged.connect( spawn(self.handle_power)) self.ui.captureCurrentButton.clicked.connect( spawn(self.handle_capture_current)) self.update_connected(False) self.ui.addPoseButton.clicked.connect(self.handle_add_pose) self.ui.removePoseButton.clicked.connect(self.handle_remove_pose) self.ui.moveToPoseButton.clicked.connect( spawn(self.handle_move_to_pose)) self.ui.updatePoseButton.clicked.connect(self.handle_update_pose) self.ui.poseList.currentItemChanged.connect( self.handle_poselist_current_changed) self.controller = None self.servo_update = BoolContext() def resizeEvent(self, event): pass def poses(self): result = [] for i in range(self.ui.poseList.count()): result.append(self.ui.poseList.item(i).text()) return result def pose(self, name): for i in range(self.ui.poseList.count()): if self.ui.poseList.item(i).text() == name: return self.ui.poseList.item(i).data(QtCore.Qt.UserRole) return dict([(i, 0.0) for i in range(self.ui.servoCountSpin.value())]) @asyncio.coroutine def handle_connect_clicked(self): val = self.ui.typeCombo.currentText().lower() try: self.controller = yield From( selector.select_servo( val, serial_port=self.ui.serialPortCombo.currentText(), model_name=self.servo_model, servo_name_map=self.servo_name_map)) self.ui.statusText.setText('connected') self.update_connected(True) except Exception as e: self.ui.statusText.setText('error: %s' % str(e)) self.update_connected(False) def handle_type_change(self): val = self.ui.typeCombo.currentText().lower() self.ui.serialPortCombo.setEnabled(val == 'herkulex') self.ui.configureGazeboButton.setEnabled(val == 'gazebo') def handle_configure_gazebo(self): servo_name_map = self.servo_name_map.copy() for x in range(self.ui.servoCountSpin.value()): if not x in servo_name_map: servo_name_map[x] = '' dialog = gazebo_config_dialog.GazeboConfigDialog( self.servo_model, servo_name_map) dialog.setModal(True) result = dialog.exec_() if result == QtGui.QDialog.Rejected: return self.servo_model = dialog.model_name() self.servo_name_map = dialog.servo_name_map() def handle_servo_count(self): count = self.ui.servoCountSpin.value() while len(self.servo_controls) > count: # Remove the last one last = self.servo_controls[-1] widget = last['widget'] self.ui.scrollContents.layout().removeWidget(widget) widget.deleteLater() self.servo_controls = self.servo_controls[:-1] while len(self.servo_controls) < count: # Add a new one. servo_id = len(self.servo_controls) label = QtGui.QLabel() label.setText('ID %d:' % servo_id) slider = QtGui.QSlider(QtCore.Qt.Horizontal) slider.setRange(-180, 180) doublespin = QtGui.QDoubleSpinBox() doublespin.setRange(-180, 180) doublespin.setDecimals(1) save = QtGui.QPushButton() save.setText("Save") move = QtGui.QPushButton() move.setText("Move") current = QtGui.QLabel() current.setText('N/A') current.setMinimumWidth(60) widget = QtGui.QWidget() layout = QtGui.QHBoxLayout(widget) layout.addWidget(label) layout.addWidget(slider) layout.addWidget(doublespin) layout.addWidget(save) layout.addWidget(move) layout.addWidget(current) slider.valueChanged.connect( functools.partial(self.handle_servo_slider, servo_id)) doublespin.valueChanged.connect( functools.partial(self.handle_servo_spin, servo_id)) save.clicked.connect( functools.partial(self.handle_servo_save, servo_id)) move.clicked.connect( functools.partial(self.handle_servo_move, servo_id)) self.ui.scrollContents.layout().addWidget(widget) self.servo_controls.append({ 'widget': widget, 'label': label, 'slider': slider, 'doublespin': doublespin, 'save': save, 'move': move, 'current': current}) @asyncio.coroutine def handle_power(self): text = self.ui.powerCombo.currentText().lower() value = None if text == 'free': value = selector.POWER_FREE elif text == 'brake': value = selector.POWER_BRAKE elif text == 'drive': value = selector.POWER_ENABLE else: raise NotImplementedError() yield From(self.controller.enable_power(value)) def update_connected(self, value): self.ui.controlGroup.setEnabled(value) self.ui.posesGroup.setEnabled(value) if self.monitor_thread is not None: self.monitor_thread.cancel() self.monitor_thread = None if value: self.handle_power() self.monitor_thread = Task(self.monitor_status()) @asyncio.coroutine def monitor_status(self): voltages = {} temperatures = {} ident = 0 while True: if (self.controller is not None and hasattr(self.controller, 'get_voltage')): try: ident = (ident + 1) % len(self.servo_controls) this_voltage = yield From( self.controller.get_voltage([ident])) voltages.update(this_voltage) # Get all temperatures. this_temp = yield From( self.controller.get_temperature([ident])) temperatures.update(this_temp) def non_None(value): return [x for x in value if x is not None] message = "Servo status: " if len(non_None(voltages.values())): message += "%.1f/%.1fV" % ( min(non_None(voltages.values())), max(non_None(voltages.values()))) if len(non_None(temperatures.values())): message += " %.1f/%.1fC" % ( min(non_None(temperatures.values())), max(non_None(temperatures.values()))) self.status.showMessage(message, 10000) except Exception as e: traceback.print_exc() print "Error reading servo:", type(e), e yield From(asyncio.sleep(2.0)) @asyncio.coroutine def set_single_pose(self, servo_id, value): yield From( self.controller.set_single_pose(servo_id, value, pose_time=0.2)) def handle_servo_slider(self, servo_id, event): if self.servo_update.value: return with self.servo_update: control = self.servo_controls[servo_id] value = control['slider'].value() control['doublespin'].setValue(value) Task(self.set_single_pose(servo_id, value)) def handle_servo_spin(self, servo_id, event): if self.servo_update.value: return with self.servo_update: control = self.servo_controls[servo_id] value = control['doublespin'].value() control['slider'].setSliderPosition(int(value)) Task(self.set_single_pose(servo_id, value)) def handle_servo_save(self, servo_id): if self.ui.poseList.currentRow() < 0: return current_data = self.ui.poseList.currentItem().data( QtCore.Qt.UserRole) current_data[servo_id] = ( self.servo_controls[servo_id]['doublespin'].value()) self.ui.poseList.currentItem().setData( QtCore.Qt.UserRole, current_data) self.handle_poselist_current_changed(None, None) def handle_servo_move(self, servo_id): if self.ui.poseList.currentRow() < 0: return data = self.ui.poseList.currentItem().data(QtCore.Qt.UserRole) self.servo_controls[servo_id]['doublespin'].setValue(data[servo_id]) @asyncio.coroutine def handle_capture_current(self): with self.servo_update: results = yield From( self.controller.get_pose(range(len(self.servo_controls)))) for ident, angle in results.iteritems(): if angle is None: continue control = self.servo_controls[ident] control['slider'].setSliderPosition(int(angle)) control['doublespin'].setValue(angle) def add_list_pose(self, name): self.ui.poseList.addItem(name) item = self.ui.poseList.item(self.ui.poseList.count() - 1) item.setFlags(QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEditable | QtCore.Qt.ItemIsSelectable) return item def get_new_pose_name(self): poses = set([self.ui.poseList.item(x).text() for x in range(self.ui.poseList.count())]) count = 0 while True: name = 'new_pose_%d' % count if name not in poses: return name count += 1 def generate_pose_data(self): return dict( [ (i, control['doublespin'].value()) for i, control in enumerate(self.servo_controls) ]) def handle_add_pose(self): pose_name = self.get_new_pose_name() item = self.add_list_pose(pose_name) item.setData(QtCore.Qt.UserRole, self.generate_pose_data()) self.ui.poseList.editItem(item) def handle_remove_pose(self): if self.ui.poseList.currentRow() < 0: return pose_name = self.ui.poseList.currentItem().text() del self.poses[pose_name] self.ui.poseList.takeItem(self.ui.poseList.currentRow()) @asyncio.coroutine def handle_move_to_pose(self): if self.ui.poseList.currentRow() < 0: return values = self.ui.poseList.currentItem().data(QtCore.Qt.UserRole) yield From(self.controller.set_pose(values, pose_time=1.0)) with self.servo_update: for ident, angle_deg in values.iteritems(): control = self.servo_controls[ident] control['slider'].setSliderPosition(int(angle_deg)) control['doublespin'].setValue(angle_deg) def handle_update_pose(self): if self.ui.poseList.currentRow() < 0: return self.ui.poseList.currentItem().setData( QtCore.Qt.UserRole, self.generate_pose_data()) self.handle_poselist_current_changed(None, None) def handle_poselist_current_changed(self, current, previous): if self.ui.poseList.currentRow() < 0: return data = self.ui.poseList.currentItem().data(QtCore.Qt.UserRole) for i, control in enumerate(self.servo_controls): control['current'].setText('%.1f' % data[i]) def read_settings(self, config): if not config.has_section('servo'): return self.ui.typeCombo.setCurrentIndex(config.getint('servo', 'type')) self.ui.serialPortCombo.setEditText(config.get('servo', 'port')) self.ui.servoCountSpin.setValue(config.getint('servo', 'count')) self.servo_model = config.get('servo', 'model') if config.has_section('servo.names'): self.servo_name_map = {} for name, value in config.items('servo.names'): self.servo_name_map[int(name)] = value if config.has_section('servo.poses'): for name, value in config.items('servo.poses'): this_data = {} for element in value.split(','): ident, angle_deg = element.split('=') this_data[int(ident)] = float(angle_deg) item = self.add_list_pose(name) item.setData(QtCore.Qt.UserRole, this_data) def write_settings(self, config): config.add_section('servo') config.add_section('servo.poses') config.add_section('servo.names') config.set('servo', 'type', self.ui.typeCombo.currentIndex()) config.set('servo', 'port', self.ui.serialPortCombo.currentText()) config.set('servo', 'count', self.ui.servoCountSpin.value()) config.set('servo', 'model', self.servo_model) for key, value in self.servo_name_map.iteritems(): config.set('servo.names', str(key), value) for row in range(self.ui.poseList.count()): item = self.ui.poseList.item(row) pose_name = item.text() values = item.data(QtCore.Qt.UserRole) config.set( 'servo.poses', pose_name, ','.join(['%d=%.2f' % (ident, angle_deg) for ident, angle_deg in values.iteritems()]))

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0.045313

0.014286

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0.252232

0.206027

0.152567

0.11529

0.086496

0

0.005517

0.299227

15,520

436

79

35.59633

0.818316

0.040013

0

0.168675

0

0.032926

0

null

0.003012

0.024096

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0

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null

0

1

0

1

6a5a09a1f1eb09c5b1fb6c4e179dd1021a0b354e

47,088

py

Python

perturbed_images_generation_multiProcess.py

gwxie/Synthesize-Distorted-Image-and-Its-Control-Points

ed6de3e05a7ee1f3aecf65fcbb87c11d2ede41e7

[ "Apache-2.0" ]

8

2022-03-27T18:37:57.000Z

2022-03-30T09:17:26.000Z

perturbed_images_generation_multiProcess.py

gwxie/Synthesize-Distorted-Image-and-Its-Control-Points

ed6de3e05a7ee1f3aecf65fcbb87c11d2ede41e7

[ "Apache-2.0" ]

null

perturbed_images_generation_multiProcess.py

gwxie/Synthesize-Distorted-Image-and-Its-Control-Points

ed6de3e05a7ee1f3aecf65fcbb87c11d2ede41e7

[ "Apache-2.0" ]

1

2022-03-31T02:22:58.000Z

''' GuoWang xie set up :2020-1-9 intergrate img and label into one file -- fiducial1024_v1 ''' import argparse import sys, os import pickle import random import collections import json import numpy as np import scipy.io as io import scipy.misc as m import matplotlib.pyplot as plt import glob import math import time import threading import multiprocessing as mp from multiprocessing import Pool import re import cv2 # sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN import utils def getDatasets(dir): return os.listdir(dir) class perturbed(utils.BasePerturbed): def __init__(self, path, bg_path, save_path, save_suffix): self.path = path self.bg_path = bg_path self.save_path = save_path self.save_suffix = save_suffix def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'): origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR) save_img_shape = [512*2, 480*2] # 320 # reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1]) reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02]) # reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18]) # reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09]) base_img_shrink = save_img_shape[0] - reduce_value # enlarge_img_shrink = [1024, 768] # enlarge_img_shrink = [896, 672] # 420 enlarge_img_shrink = [512*4, 480*4] # 420 # enlarge_img_shrink = [896*2, 768*2] # 420 # enlarge_img_shrink = [896, 768] # 420 # enlarge_img_shrink = [768, 576] # 420 # enlarge_img_shrink = [640, 480] # 420 '''''' im_lr = origin_img.shape[0] im_ud = origin_img.shape[1] reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1]) # reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14]) if im_lr > im_ud: im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2) im_lr = save_img_shape[0] - reduce_value else: base_img_shrink = save_img_shape[1] - reduce_value im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2) im_ud = base_img_shrink if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5: repeat_time = min(repeat_time, 8) edge_padding = 3 im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1 im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1 im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64) im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64) # im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1 # im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1 # im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64) # im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64) im_x, im_y = np.meshgrid(im_hight, im_wide) segment_x = (im_lr) // (fiducial_points-1) segment_y = (im_ud) // (fiducial_points-1) # plt.plot(im_x, im_y, # color='limegreen', # marker='.', # linestyle='') # plt.grid(True) # plt.show() self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC) perturbed_bg_ = getDatasets(self.bg_path) perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_) perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR) mesh_shape = self.origin_img.shape[:2] self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img) # self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img) self.new_shape = self.synthesis_perturbed_img.shape[:2] perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA) origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2) pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2) self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2)) # self.perturbed_xy_ = pixel_position.copy().astype(np.float32) # fiducial_points_grid = origin_pixel_position[im_x, im_y] self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2)) x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape) origin_pixel_position += [x_min, y_min] x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1]) x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16) y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16) x_min += x_shift x_max += x_shift y_min += y_shift y_max += y_shift '''im_x,y''' im_x += x_min im_y += y_min self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy() synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy() foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16) foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16) foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label # synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max) # synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max) '''*****************************************************************''' is_normalizationFun_mixture = self.is_perform(0.2, 0.8) # if not is_normalizationFun_mixture: normalizationFun_0_1 = False # normalizationFun_0_1 = self.is_perform(0.5, 0.5) if fold_curve == 'fold': fold_curve_random = True # is_normalizationFun_mixture = False normalizationFun_0_1 = self.is_perform(0.2, 0.8) if is_normalizationFun_mixture: alpha_perturbed = random.randint(80, 120) / 100 else: if normalizationFun_0_1 and repeat_time < 8: alpha_perturbed = random.randint(50, 70) / 100 else: alpha_perturbed = random.randint(70, 130) / 100 else: fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99) alpha_perturbed = random.randint(80, 160) / 100 # is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99) synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256) # synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16) synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label) alpha_perturbed_change = self.is_perform(0.5, 0.5) p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9) for repeat_i in range(repeat_time): if alpha_perturbed_change: if fold_curve == 'fold': if is_normalizationFun_mixture: alpha_perturbed = random.randint(80, 120) / 100 else: if normalizationFun_0_1 and repeat_time < 8: alpha_perturbed = random.randint(50, 70) / 100 else: alpha_perturbed = random.randint(70, 130) / 100 else: alpha_perturbed = random.randint(80, 160) / 100 '''''' linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1] linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1] linspace_x_seq = [1, 2, 3] linspace_y_seq = [1, 2, 3] r_x = random.choice(linspace_x_seq) r_y = random.choice(linspace_y_seq) perturbed_p = np.array( [random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10), random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10 if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice: linspace_x_seq.remove(r_x) linspace_y_seq.remove(r_y) r_x = random.choice(linspace_x_seq) r_y = random.choice(linspace_y_seq) perturbed_pp = np.array( [random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10), random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10 # perturbed_p, perturbed_pp = np.array( # [random.randint(0, self.new_shape[0] * 10) / 10, # random.randint(0, self.new_shape[1] * 10) / 10]) \ # , np.array([random.randint(0, self.new_shape[0] * 10) / 10, # random.randint(0, self.new_shape[1] * 10) / 10]) # perturbed_p, perturbed_pp = np.array( # [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10, # random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \ # , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10, # random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) '''''' perturbed_vp = perturbed_pp - perturbed_p perturbed_vp_norm = np.linalg.norm(perturbed_vp) perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm '''''' # perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100]) # perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100]) if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7): # perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100]) perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100]) # perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100]) else: # perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100]) # perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100]) perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100]) # perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100]) # perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10]) '''''' if fold_curve == 'fold': if is_normalizationFun_mixture: if self.is_perform(0.5, 0.5): perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line)) else: perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2)) else: if normalizationFun_0_1: perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2) else: perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line)) else: if is_normalizationFun_mixture: if self.is_perform(0.5, 0.5): perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line)) else: perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2)) else: if normalizationFun_0_1: perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2) else: perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line)) '''''' if fold_curve_random: # omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed) # omega_perturbed = alpha_perturbed**perturbed_d omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed) else: omega_perturbed = 1 - perturbed_d ** alpha_perturbed '''shadow''' if self.is_perform(0.6, 0.4): synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255) '''''' if relativeShift_position in ['position', 'relativeShift_v2']: self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0) else: print('relativeShift_position error') exit() ''' flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape( self.new_shape[0] * self.new_shape[1], 2) vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position) wts_sum = np.abs(wts).sum(-1) # flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts) wts = wts[wts_sum <= 1, :] vtx = vtx[wts_sum <= 1, :] synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1, :] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts) synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1, :] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts) foreORbackground_label = np.zeros(self.new_shape) foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts) foreORbackground_label[foreORbackground_label < 0.99] = 0 foreORbackground_label[foreORbackground_label >= 0.99] = 1 # synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8) synthesis_perturbed_label[:, :, 0] *= foreORbackground_label synthesis_perturbed_label[:, :, 1] *= foreORbackground_label synthesis_perturbed_img[:, :, 0] *= foreORbackground_label synthesis_perturbed_img[:, :, 1] *= foreORbackground_label synthesis_perturbed_img[:, :, 2] *= foreORbackground_label self.synthesis_perturbed_img = synthesis_perturbed_img self.synthesis_perturbed_label = synthesis_perturbed_label ''' '''perspective''' perspective_shreshold = random.randint(26, 36)*10 # 280 x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold) pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]]) e_1_ = x_max_per - x_min_per e_2_ = y_max_per - y_min_per e_3_ = e_2_ e_4_ = e_1_ perspective_shreshold_h = e_1_*0.02 perspective_shreshold_w = e_2_*0.02 a_min_, a_max_ = 70, 110 # if self.is_perform(1, 0): if fold_curve == 'curve' and self.is_perform(0.5, 0.5): if self.is_perform(0.5, 0.5): while True: pts2 = np.around( np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold], [x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold], [x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold], [x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right e_1 = np.linalg.norm(pts2[0]-pts2[1]) e_2 = np.linalg.norm(pts2[0]-pts2[2]) e_3 = np.linalg.norm(pts2[1]-pts2[3]) e_4 = np.linalg.norm(pts2[2]-pts2[3]) if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \ e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \ abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w: a0_, a1_, a2_, a3_ = self.get_angle_4(pts2) if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_): break else: while True: pts2 = np.around( np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold], [x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold], [x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold], [x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]])) e_1 = np.linalg.norm(pts2[0]-pts2[1]) e_2 = np.linalg.norm(pts2[0]-pts2[2]) e_3 = np.linalg.norm(pts2[1]-pts2[3]) e_4 = np.linalg.norm(pts2[2]-pts2[3]) if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \ e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \ abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w: a0_, a1_, a2_, a3_ = self.get_angle_4(pts2) if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_): break else: while True: pts2 = np.around(np.float32([[x_min_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold], [x_max_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold], [x_min_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold], [x_max_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold]])) e_1 = np.linalg.norm(pts2[0]-pts2[1]) e_2 = np.linalg.norm(pts2[0]-pts2[2]) e_3 = np.linalg.norm(pts2[1]-pts2[3]) e_4 = np.linalg.norm(pts2[2]-pts2[3]) if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \ e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \ abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w: a0_, a1_, a2_, a3_ = self.get_angle_4(pts2) if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_): break M = cv2.getPerspectiveTransform(pts1, pts2) one = np.ones((self.new_shape[0], self.new_shape[1], 1), dtype=np.int16) matr = np.dstack((pixel_position, one)) new = np.dot(M, matr.reshape(-1, 3).T).T.reshape(self.new_shape[0], self.new_shape[1], 3) x = new[:, :, 0]/new[:, :, 2] y = new[:, :, 1]/new[:, :, 2] perturbed_xy_ = np.dstack((x, y)) # perturbed_xy_round_int = np.around(cv2.bilateralFilter(perturbed_xy_round_int, 9, 75, 75)) # perturbed_xy_round_int = np.around(cv2.blur(perturbed_xy_, (17, 17))) # perturbed_xy_round_int = cv2.blur(perturbed_xy_round_int, (17, 17)) # perturbed_xy_round_int = cv2.GaussianBlur(perturbed_xy_round_int, (7, 7), 0) perturbed_xy_ = perturbed_xy_-np.min(perturbed_xy_.T.reshape(2, -1), 1) # perturbed_xy_round_int = np.around(perturbed_xy_round_int-np.min(perturbed_xy_round_int.T.reshape(2, -1), 1)).astype(np.int16) self.perturbed_xy_ += perturbed_xy_ '''perspective end''' '''to img''' flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape( self.new_shape[0] * self.new_shape[1], 2) # self.perturbed_xy_ = cv2.blur(self.perturbed_xy_, (7, 7)) self.perturbed_xy_ = cv2.GaussianBlur(self.perturbed_xy_, (7, 7), 0) '''get fiducial points''' fiducial_points_coordinate = self.perturbed_xy_[im_x, im_y] vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position) wts_sum = np.abs(wts).sum(-1) # flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts) wts = wts[wts_sum <= 1, :] vtx = vtx[wts_sum <= 1, :] synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1, :] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts) synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1, :] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts) foreORbackground_label = np.zeros(self.new_shape) foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts) foreORbackground_label[foreORbackground_label < 0.99] = 0 foreORbackground_label[foreORbackground_label >= 0.99] = 1 self.synthesis_perturbed_img = synthesis_perturbed_img self.synthesis_perturbed_label = synthesis_perturbed_label self.foreORbackground_label = foreORbackground_label '''draw fiducial points stepSize = 0 fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_img.copy() for l in fiducial_points_coordinate.astype(np.int64).reshape(-1,2): cv2.circle(fiducial_points_synthesis_perturbed_img, (l[1] + math.ceil(stepSize / 2), l[0] + math.ceil(stepSize / 2)), 5, (0, 0, 255), -1) cv2.imwrite('/lustre/home/gwxie/program/project/unwarp/unwarp_perturbed/TPS/img/cv_TPS_large.jpg', fiducial_points_synthesis_perturbed_img) ''' '''clip''' perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = -1, -1, self.new_shape[0], self.new_shape[1] for x in range(self.new_shape[0] // 2, perturbed_x_max): if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and perturbed_x_max - 1 > x: perturbed_x_max = x break for x in range(self.new_shape[0] // 2, perturbed_x_min, -1): if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and x > 0: perturbed_x_min = x break for y in range(self.new_shape[1] // 2, perturbed_y_max): if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and perturbed_y_max - 1 > y: perturbed_y_max = y break for y in range(self.new_shape[1] // 2, perturbed_y_min, -1): if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and y > 0: perturbed_y_min = y break if perturbed_x_min == 0 or perturbed_x_max == self.new_shape[0] or perturbed_y_min == self.new_shape[1] or perturbed_y_max == self.new_shape[1]: raise Exception('clip error') if perturbed_x_max - perturbed_x_min < im_lr//2 or perturbed_y_max - perturbed_y_min < im_ud//2: raise Exception('clip error') perfix_ = self.save_suffix+'_'+str(m)+'_'+str(n) is_shrink = False if perturbed_x_max - perturbed_x_min > save_img_shape[0] or perturbed_y_max - perturbed_y_min > save_img_shape[1]: is_shrink = True synthesis_perturbed_img = cv2.resize(self.synthesis_perturbed_img[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR) synthesis_perturbed_label = cv2.resize(self.synthesis_perturbed_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR) foreORbackground_label = cv2.resize(self.foreORbackground_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR) foreORbackground_label[foreORbackground_label < 0.99] = 0 foreORbackground_label[foreORbackground_label >= 0.99] = 1 '''shrink fiducial points''' center_x_l, center_y_l = perturbed_x_min + (perturbed_x_max - perturbed_x_min) // 2, perturbed_y_min + (perturbed_y_max - perturbed_y_min) // 2 fiducial_points_coordinate_copy = fiducial_points_coordinate.copy() shrink_x = im_lr/(perturbed_x_max - perturbed_x_min) shrink_y = im_ud/(perturbed_y_max - perturbed_y_min) fiducial_points_coordinate *= [shrink_x, shrink_y] center_x_l *= shrink_x center_y_l *= shrink_y # fiducial_points_coordinate[1:, 1:] *= [shrink_x, shrink_y] # fiducial_points_coordinate[1:, :1, 0] *= shrink_x # fiducial_points_coordinate[:1, 1:, 1] *= shrink_y # perturbed_x_min_copy, perturbed_y_min_copy, perturbed_x_max_copy, perturbed_y_max_copy = perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = self.adjust_position_v2(0, 0, im_lr, im_ud, self.new_shape) self.synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256) self.synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label) self.foreORbackground_label = np.zeros_like(self.foreORbackground_label) self.synthesis_perturbed_img[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_img self.synthesis_perturbed_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_label self.foreORbackground_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max] = foreORbackground_label center_x, center_y = perturbed_x_min + (perturbed_x_max - perturbed_x_min) // 2, perturbed_y_min + (perturbed_y_max - perturbed_y_min) // 2 if is_shrink: fiducial_points_coordinate += [center_x-center_x_l, center_y-center_y_l] '''draw fiducial points stepSize = 0 fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_img.copy() for l in fiducial_points_coordinate.astype(np.int64).reshape(-1, 2): cv2.circle(fiducial_points_synthesis_perturbed_img, (l[1] + math.ceil(stepSize / 2), l[0] + math.ceil(stepSize / 2)), 5, (0, 0, 255), -1) cv2.imwrite('/lustre/home/gwxie/program/project/unwarp/unwarp_perturbed/TPS/img/cv_TPS_small.jpg',fiducial_points_synthesis_perturbed_img) ''' self.new_shape = save_img_shape self.synthesis_perturbed_img = self.synthesis_perturbed_img[ center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2, center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2, :].copy() self.synthesis_perturbed_label = self.synthesis_perturbed_label[ center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2, center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2, :].copy() self.foreORbackground_label = self.foreORbackground_label[ center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2, center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2].copy() perturbed_x_ = max(self.new_shape[0] - (perturbed_x_max - perturbed_x_min), 0) perturbed_x_min = perturbed_x_ // 2 perturbed_x_max = self.new_shape[0] - perturbed_x_ // 2 if perturbed_x_%2 == 0 else self.new_shape[0] - (perturbed_x_ // 2 + 1) perturbed_y_ = max(self.new_shape[1] - (perturbed_y_max - perturbed_y_min), 0) perturbed_y_min = perturbed_y_ // 2 perturbed_y_max = self.new_shape[1] - perturbed_y_ // 2 if perturbed_y_%2 == 0 else self.new_shape[1] - (perturbed_y_ // 2 + 1) '''clip perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = -1, -1, self.new_shape[0], self.new_shape[1] for x in range(self.new_shape[0] // 2, perturbed_x_max): if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and perturbed_x_max - 1 > x: perturbed_x_max = x break for x in range(self.new_shape[0] // 2, perturbed_x_min, -1): if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and x > 0: perturbed_x_min = x break for y in range(self.new_shape[1] // 2, perturbed_y_max): if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and perturbed_y_max - 1 > y: perturbed_y_max = y break for y in range(self.new_shape[1] // 2, perturbed_y_min, -1): if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and y > 0: perturbed_y_min = y break center_x, center_y = perturbed_x_min+(perturbed_x_max - perturbed_x_min)//2, perturbed_y_min+(perturbed_y_max - perturbed_y_min)//2 perfix_ = self.save_suffix+'_'+str(m)+'_'+str(n) self.new_shape = save_img_shape perturbed_x_ = max(self.new_shape[0] - (perturbed_x_max - perturbed_x_min), 0) perturbed_x_min = perturbed_x_ // 2 perturbed_x_max = self.new_shape[0] - perturbed_x_ // 2 if perturbed_x_%2 == 0 else self.new_shape[0] - (perturbed_x_ // 2 + 1) perturbed_y_ = max(self.new_shape[1] - (perturbed_y_max - perturbed_y_min), 0) perturbed_y_min = perturbed_y_ // 2 perturbed_y_max = self.new_shape[1] - perturbed_y_ // 2 if perturbed_y_%2 == 0 else self.new_shape[1] - (perturbed_y_ // 2 + 1) self.synthesis_perturbed_img = self.synthesis_perturbed_img[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2, :].copy() self.synthesis_perturbed_label = self.synthesis_perturbed_label[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2, :].copy() self.foreORbackground_label = self.foreORbackground_label[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2].copy() ''' '''save''' pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2) if relativeShift_position == 'relativeShift_v2': self.synthesis_perturbed_label -= pixel_position fiducial_points_coordinate -= [center_x - self.new_shape[0] // 2, center_y - self.new_shape[1] // 2] self.synthesis_perturbed_label[:, :, 0] *= self.foreORbackground_label self.synthesis_perturbed_label[:, :, 1] *= self.foreORbackground_label self.synthesis_perturbed_img[:, :, 0] *= self.foreORbackground_label self.synthesis_perturbed_img[:, :, 1] *= self.foreORbackground_label self.synthesis_perturbed_img[:, :, 2] *= self.foreORbackground_label ''' synthesis_perturbed_img_filter = self.synthesis_perturbed_img.copy() synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0) # if self.is_perform(0.9, 0.1) or repeat_time > 5: # # if self.is_perform(0.1, 0.9) and repeat_time > 9: # # synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (7, 7), 0) # # else: # synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (5, 5), 0) # else: # synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0) self.synthesis_perturbed_img[self.foreORbackground_label == 1] = synthesis_perturbed_img_filter[self.foreORbackground_label == 1] ''' ''' perturbed_bg_img = perturbed_bg_img.astype(np.float32) perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label self.synthesis_perturbed_img += perturbed_bg_img HSV perturbed_bg_img = perturbed_bg_img.astype(np.float32) if self.is_perform(0.1, 0.9): if self.is_perform(0.2, 0.8): synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy() synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_RGB2HSV) H_, S_, V_ = (random.random()-0.2)*20, (random.random()-0.2)/8, (random.random()-0.2)*20 synthesis_perturbed_img_clip_HSV[:, :, 0], synthesis_perturbed_img_clip_HSV[:, :, 1], synthesis_perturbed_img_clip_HSV[:, :, 2] = synthesis_perturbed_img_clip_HSV[:, :, 0]-H_, synthesis_perturbed_img_clip_HSV[:, :, 1]-S_, synthesis_perturbed_img_clip_HSV[:, :, 2]-V_ synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_HSV2RGB) perturbed_bg_img[:, :, 0] *= 1-self.foreORbackground_label perturbed_bg_img[:, :, 1] *= 1-self.foreORbackground_label perturbed_bg_img[:, :, 2] *= 1-self.foreORbackground_label synthesis_perturbed_img_clip_HSV += perturbed_bg_img self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV else: perturbed_bg_img_HSV = perturbed_bg_img perturbed_bg_img_HSV = cv2.cvtColor(perturbed_bg_img_HSV, cv2.COLOR_RGB2HSV) H_, S_, V_ = (random.random()-0.5)*20, (random.random()-0.5)/8, (random.random()-0.2)*20 perturbed_bg_img_HSV[:, :, 0], perturbed_bg_img_HSV[:, :, 1], perturbed_bg_img_HSV[:, :, 2] = perturbed_bg_img_HSV[:, :, 0]-H_, perturbed_bg_img_HSV[:, :, 1]-S_, perturbed_bg_img_HSV[:, :, 2]-V_ perturbed_bg_img_HSV = cv2.cvtColor(perturbed_bg_img_HSV, cv2.COLOR_HSV2RGB) perturbed_bg_img_HSV[:, :, 0] *= 1-self.foreORbackground_label perturbed_bg_img_HSV[:, :, 1] *= 1-self.foreORbackground_label perturbed_bg_img_HSV[:, :, 2] *= 1-self.foreORbackground_label self.synthesis_perturbed_img += perturbed_bg_img_HSV # self.synthesis_perturbed_img[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771] else: synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy() perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label synthesis_perturbed_img_clip_HSV += perturbed_bg_img # synthesis_perturbed_img_clip_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img[np.sum(self.synthesis_perturbed_img, 2) == 771] synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_RGB2HSV) H_, S_, V_ = (random.random()-0.5)*20, (random.random()-0.5)/10, (random.random()-0.4)*20 synthesis_perturbed_img_clip_HSV[:, :, 0], synthesis_perturbed_img_clip_HSV[:, :, 1], synthesis_perturbed_img_clip_HSV[:, :, 2] = synthesis_perturbed_img_clip_HSV[:, :, 0]-H_, synthesis_perturbed_img_clip_HSV[:, :, 1]-S_, synthesis_perturbed_img_clip_HSV[:, :, 2]-V_ synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_HSV2RGB) self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV ''' '''HSV_v2''' perturbed_bg_img = perturbed_bg_img.astype(np.float32) # if self.is_perform(1, 0): # if self.is_perform(1, 0): if self.is_perform(0.1, 0.9): if self.is_perform(0.2, 0.8): synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy() synthesis_perturbed_img_clip_HSV = self.HSV_v1(synthesis_perturbed_img_clip_HSV) perturbed_bg_img[:, :, 0] *= 1-self.foreORbackground_label perturbed_bg_img[:, :, 1] *= 1-self.foreORbackground_label perturbed_bg_img[:, :, 2] *= 1-self.foreORbackground_label synthesis_perturbed_img_clip_HSV += perturbed_bg_img self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV else: perturbed_bg_img_HSV = perturbed_bg_img perturbed_bg_img_HSV = self.HSV_v1(perturbed_bg_img_HSV) perturbed_bg_img_HSV[:, :, 0] *= 1-self.foreORbackground_label perturbed_bg_img_HSV[:, :, 1] *= 1-self.foreORbackground_label perturbed_bg_img_HSV[:, :, 2] *= 1-self.foreORbackground_label self.synthesis_perturbed_img += perturbed_bg_img_HSV # self.synthesis_perturbed_img[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771] else: synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy() perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label synthesis_perturbed_img_clip_HSV += perturbed_bg_img synthesis_perturbed_img_clip_HSV = self.HSV_v1(synthesis_perturbed_img_clip_HSV) self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV '''''' # cv2.imwrite(self.save_path+'clip/'+perfix_+'_'+fold_curve+str(perturbed_time)+'-'+str(repeat_time)+'.png', synthesis_perturbed_img_clip) self.synthesis_perturbed_img[self.synthesis_perturbed_img < 0] = 0 self.synthesis_perturbed_img[self.synthesis_perturbed_img > 255] = 255 self.synthesis_perturbed_img = np.around(self.synthesis_perturbed_img).astype(np.uint8) label = np.zeros_like(self.synthesis_perturbed_img, dtype=np.float32) label[:, :, :2] = self.synthesis_perturbed_label label[:, :, 2] = self.foreORbackground_label # grey = np.around(self.synthesis_perturbed_img[:, :, 0] * 0.2989 + self.synthesis_perturbed_img[:, :, 1] * 0.5870 + self.synthesis_perturbed_img[:, :, 0] * 0.1140).astype(np.int16) # synthesis_perturbed_grey = np.concatenate((grey.reshape(self.new_shape[0], self.new_shape[1], 1), label), axis=2) synthesis_perturbed_color = np.concatenate((self.synthesis_perturbed_img, label), axis=2) self.synthesis_perturbed_color = np.zeros_like(synthesis_perturbed_color, dtype=np.float32) # self.synthesis_perturbed_grey = np.zeros_like(synthesis_perturbed_grey, dtype=np.float32) reduce_value_x = int(round(min((random.random() / 2) * (self.new_shape[0] - (perturbed_x_max - perturbed_x_min)), min(reduce_value, reduce_value_v2)))) reduce_value_y = int(round(min((random.random() / 2) * (self.new_shape[1] - (perturbed_y_max - perturbed_y_min)), min(reduce_value, reduce_value_v2)))) perturbed_x_min = max(perturbed_x_min - reduce_value_x, 0) perturbed_x_max = min(perturbed_x_max + reduce_value_x, self.new_shape[0]) perturbed_y_min = max(perturbed_y_min - reduce_value_y, 0) perturbed_y_max = min(perturbed_y_max + reduce_value_y, self.new_shape[1]) if im_lr >= im_ud: self.synthesis_perturbed_color[:, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_color[:, perturbed_y_min:perturbed_y_max, :] # self.synthesis_perturbed_grey[:, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_grey[:, perturbed_y_min:perturbed_y_max, :] else: self.synthesis_perturbed_color[perturbed_x_min:perturbed_x_max, :, :] = synthesis_perturbed_color[perturbed_x_min:perturbed_x_max, :, :] # self.synthesis_perturbed_grey[perturbed_x_min:perturbed_x_max, :, :] = synthesis_perturbed_grey[perturbed_x_min:perturbed_x_max, :, :] '''blur''' if self.is_perform(0.1, 0.9): synthesis_perturbed_img_filter = self.synthesis_perturbed_color[:, :, :3].copy() if self.is_perform(0.1, 0.9): synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (5, 5), 0) else: synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0) if self.is_perform(0.5, 0.5): self.synthesis_perturbed_color[:, :, :3][self.synthesis_perturbed_color[:, :, 5] == 1] = synthesis_perturbed_img_filter[self.synthesis_perturbed_color[:, :, 5] == 1] else: self.synthesis_perturbed_color[:, :, :3] = synthesis_perturbed_img_filter fiducial_points_coordinate = fiducial_points_coordinate[:, :, ::-1] '''draw fiducial points''' stepSize = 0 fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_color[:, :, :3].copy() for l in fiducial_points_coordinate.astype(np.int64).reshape(-1, 2): cv2.circle(fiducial_points_synthesis_perturbed_img, (l[0] + math.ceil(stepSize / 2), l[1] + math.ceil(stepSize / 2)), 2, (0, 0, 255), -1) cv2.imwrite(self.save_path + 'fiducial_points/' + perfix_ + '_' + fold_curve + '.png', fiducial_points_synthesis_perturbed_img) cv2.imwrite(self.save_path + 'png/' + perfix_ + '_' + fold_curve + '.png', self.synthesis_perturbed_color[:, :, :3]) '''forward-begin''' self.forward_mapping = np.full((save_img_shape[0], save_img_shape[1], 2), 0, dtype=np.float32) forward_mapping = np.full((save_img_shape[0], save_img_shape[1], 2), 0, dtype=np.float32) forward_position = (self.synthesis_perturbed_color[:, :, 3:5] + pixel_position)[self.synthesis_perturbed_color[:, :, 5] != 0, :] flat_position = np.argwhere(np.zeros(save_img_shape, dtype=np.uint32) == 0) vtx, wts = self.interp_weights(forward_position, flat_position) wts_sum = np.abs(wts).sum(-1) wts = wts[wts_sum <= 1, :] vtx = vtx[wts_sum <= 1, :] flat_position_forward = flat_position.reshape(save_img_shape[0], save_img_shape[1], 2)[self.synthesis_perturbed_color[:, :, 5] != 0, :] forward_mapping.reshape(save_img_shape[0] * save_img_shape[1], 2)[wts_sum <= 1, :] = self.interpolate(flat_position_forward, vtx, wts) forward_mapping = forward_mapping.reshape(save_img_shape[0], save_img_shape[1], 2) mapping_x_min_, mapping_y_min_, mapping_x_max_, mapping_y_max_ = self.adjust_position_v2(0, 0, im_lr, im_ud, self.new_shape) shreshold_zoom_out = 2 mapping_x_min = mapping_x_min_ + shreshold_zoom_out mapping_y_min = mapping_y_min_ + shreshold_zoom_out mapping_x_max = mapping_x_max_ - shreshold_zoom_out mapping_y_max = mapping_y_max_ - shreshold_zoom_out self.forward_mapping[mapping_x_min:mapping_x_max, mapping_y_min:mapping_y_max] = forward_mapping[mapping_x_min:mapping_x_max, mapping_y_min:mapping_y_max] self.scan_img = np.full((save_img_shape[0], save_img_shape[1], 3), 0, dtype=np.float32) self.scan_img[mapping_x_min_:mapping_x_max_, mapping_y_min_:mapping_y_max_] = self.origin_img self.origin_img = self.scan_img # flat_img = np.full((save_img_shape[0], save_img_shape[1], 3), 0, dtype=np.float32) # cv2.remap(self.synthesis_perturbed_color[:, :, :3], self.forward_mapping[:, :, 1], self.forward_mapping[:, :, 0], cv2.INTER_LINEAR, flat_img) # cv2.imwrite(self.save_path + 'outputs/1.jpg', flat_img) '''forward-end''' synthesis_perturbed_data = { 'fiducial_points': fiducial_points_coordinate, 'segment': np.array((segment_x, segment_y)) } cv2.imwrite(self.save_path + 'png/' + perfix_ + '_' + fold_curve + '.png', self.synthesis_perturbed_color[:, :, :3]) with open(self.save_path+'color/'+perfix_+'_'+fold_curve+'.gw', 'wb') as f: pickle_perturbed_data = pickle.dumps(synthesis_perturbed_data) f.write(pickle_perturbed_data) # with open(self.save_path+'grey/'+perfix_+'_'+fold_curve+'.gw', 'wb') as f: # pickle_perturbed_data = pickle.dumps(self.synthesis_perturbed_grey) # f.write(pickle_perturbed_data) # cv2.imwrite(self.save_path+'grey_im/'+perfix_+'_'+fold_curve+'.png', self.synthesis_perturbed_color[:, :, :1]) # cv2.imwrite(self.save_path + 'scan/' + self.save_suffix + '_' + str(m) + '.png', self.origin_img) trian_t = time.time() - begin_train mm, ss = divmod(trian_t, 60) hh, mm = divmod(mm, 60) print(str(m)+'_'+str(n)+'_'+fold_curve+' '+str(repeat_time)+" Time : %02d:%02d:%02d\n" % (hh, mm, ss)) def multiThread(m, n, img_path_, bg_path_, save_path, save_suffix): saveFold = perturbed(img_path_, bg_path_, save_path, save_suffix) saveCurve = perturbed(img_path_, bg_path_, save_path, save_suffix) repeat_time = min(max(round(np.random.normal(10, 3)), 5), 16) fold = threading.Thread(target=saveFold.save_img, args=(m, n, 'fold', repeat_time, 'relativeShift_v2'), name='fold') curve = threading.Thread(target=saveCurve.save_img, args=(m, n, 'curve', repeat_time, 'relativeShift_v2'), name='curve') fold.start() curve.start() curve.join() fold.join() def xgw(args): path = args.path bg_path = args.bg_path if args.output_path is None: save_path = '/lustre/home/gwxie/data/unwarp_new/train/general1024/general1024_v1/' else: save_path = args.output_path # if not os.path.exists(save_path + 'grey/'): # os.makedirs(save_path + 'grey/') if not os.path.exists(save_path + 'color/'): os.makedirs(save_path + 'color/') if not os.path.exists(save_path + 'fiducial_points/'): os.makedirs(save_path + 'fiducial_points/') if not os.path.exists(save_path + 'png/'): os.makedirs(save_path + 'png/') if not os.path.exists(save_path + 'scan/'): os.makedirs(save_path + 'scan/') if not os.path.exists(save_path + 'outputs/'): os.makedirs(save_path + 'outputs/') save_suffix = str.split(args.path, '/')[-2] all_img_path = getDatasets(path) all_bgImg_path = getDatasets(bg_path) global begin_train begin_train = time.time() fiducial_points = 61 # 31 process_pool = Pool(2) for m, img_path in enumerate(all_img_path): for n in range(args.sys_num): img_path_ = path+img_path bg_path_ = bg_path+random.choice(all_bgImg_path)+'/' for m_n in range(10): try: saveFold = perturbed(img_path_, bg_path_, save_path, save_suffix) saveCurve = perturbed(img_path_, bg_path_, save_path, save_suffix) repeat_time = min(max(round(np.random.normal(12, 4)), 1), 18) # repeat_time = min(max(round(np.random.normal(8, 4)), 1), 12) # random.randint(1, 2) # min(max(round(np.random.normal(8, 4)), 1), 12) process_pool.apply_async(func=saveFold.save_img, args=(m, n, 'fold', repeat_time, fiducial_points, 'relativeShift_v2')) repeat_time = min(max(round(np.random.normal(8, 4)), 1), 13) # repeat_time = min(max(round(np.random.normal(6, 4)), 1), 10) process_pool.apply_async(func=saveCurve.save_img, args=(m, n, 'curve', repeat_time, fiducial_points, 'relativeShift_v2')) except BaseException as err: print(err) continue break # print('end') process_pool.close() process_pool.join() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Hyperparams') parser.add_argument('--path', default='./scan/new/', type=str, help='the path of origin img.') parser.add_argument('--bg_path', default='./background/', type=str, help='the path of bg img.') parser.add_argument('--output_path', default='./output/', type=str, help='the path of origin img.') # parser.set_defaults(output_path='test') parser.add_argument('--count_from', '-p', default=0, type=int, metavar='N', help='print frequency (default: 10)') # print frequency parser.add_argument('--repeat_T', default=0, type=int) parser.add_argument('--sys_num', default=6, type=int) args = parser.parse_args() xgw(args)

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6a5ce615b33cd197b365d6e3673610f15fbcf59b

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py

Python

assignment1/cs231n/classifiers/neural_net.py

zeevikal/CS231n-spring2018

50691a947b877047099e7a1fe99a3fdea4a4fcf8

[ "MIT" ]

null

assignment1/cs231n/classifiers/neural_net.py

zeevikal/CS231n-spring2018

50691a947b877047099e7a1fe99a3fdea4a4fcf8

[ "MIT" ]

3

2019-12-09T06:04:00.000Z

2019-12-09T06:05:23.000Z

assignment1/cs231n/classifiers/neural_net.py

zeevikal/CS231n-spring2018

50691a947b877047099e7a1fe99a3fdea4a4fcf8

[ "MIT" ]

null

from __future__ import print_function import numpy as np import matplotlib.pyplot as plt class TwoLayerNet(object): """ A two-layer fully-connected neural network. The net has an input dimension of N, a hidden layer dimension of H, and performs classification over C classes. We train the network with a softmax loss function and L2 regularization on the weight matrices. The network uses a ReLU nonlinearity after the first fully connected layer. In other words, the network has the following architecture: input - fully connected layer - ReLU - fully connected layer - softmax The outputs of the second fully-connected layer are the scores for each class. """ def __init__(self, input_size, hidden_size, output_size, std=1e-4): """ Initialize the model. Weights are initialized to small random values and biases are initialized to zero. Weights and biases are stored in the variable self.params, which is a dictionary with the following keys W1: First layer weights; has shape (D, H) b1: First layer biases; has shape (H,) W2: Second layer weights; has shape (H, C) b2: Second layer biases; has shape (C,) Inputs: - input_size: The dimension D of the input data. - hidden_size: The number of neurons H in the hidden layer. - output_size: The number of classes C. """ self.params = {} self.params['W1'] = std * np.random.randn(input_size, hidden_size) self.params['b1'] = np.zeros(hidden_size) self.params['W2'] = std * np.random.randn(hidden_size, output_size) self.params['b2'] = np.zeros(output_size) def loss(self, X, y=None, reg=0.0): """ Compute the loss and gradients for a two layer fully connected neural network. Inputs: - X: Input data of shape (N, D). Each X[i] is a training sample. - y: Vector of training labels. y[i] is the label for X[i], and each y[i] is an integer in the range 0 <= y[i] < C. This parameter is optional; if it is not passed then we only return scores, and if it is passed then we instead return the loss and gradients. - reg: Regularization strength. Returns: If y is None, return a matrix scores of shape (N, C) where scores[i, c] is the score for class c on input X[i]. If y is not None, instead return a tuple of: - loss: Loss (data loss and regularization loss) for this batch of training samples. - grads: Dictionary mapping parameter names to gradients of those parameters with respect to the loss function; has the same keys as self.params. """ # Unpack variables from the params dictionary W1, b1 = self.params['W1'], self.params['b1'] W2, b2 = self.params['W2'], self.params['b2'] N, D = X.shape # Compute the forward pass scores = None ####################################################################### # TODO: Perform the forward pass, computing the class scores for the # # input. Store the result in the scores variable, which should be an # # array of shape (N, C). # ####################################################################### scores1 = X.dot(W1) + b1 # FC1 X2 = np.maximum(0, scores1) # ReLU FC1 scores = X2.dot(W2) + b2 # FC2 ####################################################################### # END OF YOUR CODE # ####################################################################### # If the targets are not given then jump out, we're done if y is None: return scores scores -= np.max(scores) # Fix Number instability scores_exp = np.exp(scores) probs = scores_exp / np.sum(scores_exp, axis=1, keepdims=True) # Compute the loss loss = None ####################################################################### # TODO: Finish the forward pass, and compute the loss. This should # # include both the data loss and L2 regularization for W1 and W2. # # Store the result in the variable loss, which should be a scalar. Use# # the Softmax classifier loss. # ####################################################################### correct_probs = -np.log(probs[np.arange(N), y]) # L_i = -log(e^correct_score/sum(e^scores))) = -log(correct_probs) loss = np.sum(correct_probs) loss /= N # L2 regularization WRT W1 and W2 loss += reg * (np.sum(W1 * W1) + np.sum(W2 * W2)) ####################################################################### # END OF YOUR CODE # ####################################################################### # Backward pass: compute gradients grads = {} ############################################################################# # TODO: Compute the backward pass, computing the derivatives of the weights # # and biases. Store the results in the grads dictionary. For example, # # grads['W1'] should store the gradient on W1, and be a matrix of same size # ############################################################################# # gradient of loss_i WRT scores_k # dL_i/ds_k = probs_k-1(y_i == k) # this means the gradient is the score for "other" classes and score-1 # for the target class d_scores = probs.copy() d_scores[np.arange(N), y] -= 1 d_scores /= N # W2 were multiplied with X2, by chain rule and multiplication # derivative, WRT W2 we need to multiply downstream derivative by X2 d_W2 = X2.T.dot(d_scores) # b2 was added, so it's d is 1 but we must multiply it with chain rule # (downstream), in this case d_scores d_b2 = np.sum(d_scores, axis=0) # W1 is upstream of X2, so we continue this way d_X2 = d_scores.dot(W2.T) # ReLU derivative is 1 for > 0, else 0 d_scores1 = d_X2 * (scores1 > 0) d_W1 = X.T.dot(d_scores1) # b1 gradient d_b1 = d_scores1.sum(axis=0) # regularization gradient (reg*W2^2) d_W2 += reg * 2 * W2 d_W1 += reg * 2 * W1 grads['W1'] = d_W1 grads['b1'] = d_b1 grads['W2'] = d_W2 grads['b2'] = d_b2 ####################################################################### # END OF YOUR CODE # ####################################################################### return loss, grads def train(self, X, y, X_val, y_val, learning_rate=1e-3, learning_rate_decay=0.95, reg=5e-6, num_iters=100, batch_size=200, verbose=False): """ Train this neural network using stochastic gradient descent. Inputs: - X: A numpy array of shape (N, D) giving training data. - y: A numpy array f shape (N,) giving training labels; y[i] = c means that X[i] has label c, where 0 <= c < C. - X_val: A numpy array of shape (N_val, D) giving validation data. - y_val: A numpy array of shape (N_val,) giving validation labels. - learning_rate: Scalar giving learning rate for optimization. - learning_rate_decay: Scalar giving factor used to decay the learning rate after each epoch. - reg: Scalar giving regularization strength. - num_iters: Number of steps to take when optimizing. - batch_size: Number of training examples to use per step. - verbose: boolean; if true print progress during optimization. """ num_train = X.shape[0] iterations_per_epoch = max(num_train / batch_size, 1) # Use SGD to optimize the parameters in self.model loss_history = [] train_acc_history = [] val_acc_history = [] for it in range(num_iters): X_batch = None y_batch = None ################################################################### # TODO: Create a random minibatch of training data and labels, # # storing them in X_batch and y_batch respectively. # ################################################################### # random indexes to sample training data/labels sample_idx = np.random.choice(num_train, batch_size, replace=True) X_batch = X[sample_idx] y_batch = y[sample_idx] ################################################################### # END OF YOUR CODE # ################################################################### # Compute loss and gradients using the current minibatch loss, grads = self.loss(X_batch, y=y_batch, reg=reg) loss_history.append(loss) ################################################################### # TODO: Use the gradients in the grads dictionary to update the # # parameters of the network (stored in the dictionary self.params)# # using stochastic gradient descent. You'll need to use the # # gradients stored in the grads dictionary defined above. # ################################################################### # For each weight in network parameters, update it with the # corresponding calculated gradient for key in self.params: self.params[key] -= learning_rate * grads[key] ################################################################### # END OF YOUR CODE # ################################################################### if verbose and it % 100 == 0: print('iteration %d / %d: loss %f' % (it, num_iters, loss)) # Every epoch, check train and val accuracy and decay learning rate if it % iterations_per_epoch == 0: # Check accuracy train_acc = (self.predict(X_batch) == y_batch).mean() val_acc = (self.predict(X_val) == y_val).mean() train_acc_history.append(train_acc) val_acc_history.append(val_acc) # Decay learning rate learning_rate *= learning_rate_decay return { 'loss_history': loss_history, 'train_acc_history': train_acc_history, 'val_acc_history': val_acc_history, } def predict(self, X): """ Use the trained weights of this two-layer network to predict labels for data points. For each data point we predict scores for each of the C classes, and assign each data point to the class with the highest score Inputs: - X: A numpy array of shape (N, D) giving N D-dimensional data points to classify. Returns: - y_pred: A numpy array of shape (N,) giving predicted labels for each of the elements of X. For all i, y_pred[i] = c means that X[i] is predicted to have class c, where 0 <= c < C. """ y_pred = None ####################################################################### # TODO: Implement this function; it should be VERY simple! # ####################################################################### y_pred = np.argmax(self.loss(X), axis=1) ####################################################################### # END OF YOUR CODE # ####################################################################### return y_pred

45.854478

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0.487509

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0.013313

0.084144

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268

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45.854478

0.679082

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0.024691

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null

0

null

0

1

0

1

6a60c251c96da7b05351011b63ba88125eca7fb7

9,790

py

Python

sdk/python/pulumi_azure_native/storage/storage_account_static_website.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/storage/storage_account_static_website.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/storage/storage_account_static_website.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = ['StorageAccountStaticWebsiteArgs', 'StorageAccountStaticWebsite'] @pulumi.input_type class StorageAccountStaticWebsiteArgs: def __init__(__self__, *, account_name: pulumi.Input[str], resource_group_name: pulumi.Input[str], error404_document: Optional[pulumi.Input[str]] = None, index_document: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a StorageAccountStaticWebsite resource. :param pulumi.Input[str] account_name: The name of the storage account within the specified resource group. :param pulumi.Input[str] resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. :param pulumi.Input[str] error404_document: The absolute path to a custom webpage that should be used when a request is made which does not correspond to an existing file. :param pulumi.Input[str] index_document: The webpage that Azure Storage serves for requests to the root of a website or any sub-folder. For example, 'index.html'. The value is case-sensitive. """ pulumi.set(__self__, "account_name", account_name) pulumi.set(__self__, "resource_group_name", resource_group_name) if error404_document is not None: pulumi.set(__self__, "error404_document", error404_document) if index_document is not None: pulumi.set(__self__, "index_document", index_document) @property @pulumi.getter(name="accountName") def account_name(self) -> pulumi.Input[str]: """ The name of the storage account within the specified resource group. """ return pulumi.get(self, "account_name") @account_name.setter def account_name(self, value: pulumi.Input[str]): pulumi.set(self, "account_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The name of the resource group within the user's subscription. The name is case insensitive. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="error404Document") def error404_document(self) -> Optional[pulumi.Input[str]]: """ The absolute path to a custom webpage that should be used when a request is made which does not correspond to an existing file. """ return pulumi.get(self, "error404_document") @error404_document.setter def error404_document(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "error404_document", value) @property @pulumi.getter(name="indexDocument") def index_document(self) -> Optional[pulumi.Input[str]]: """ The webpage that Azure Storage serves for requests to the root of a website or any sub-folder. For example, 'index.html'. The value is case-sensitive. """ return pulumi.get(self, "index_document") @index_document.setter def index_document(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "index_document", value) class StorageAccountStaticWebsite(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, account_name: Optional[pulumi.Input[str]] = None, error404_document: Optional[pulumi.Input[str]] = None, index_document: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): """ Enables the static website feature of a storage account. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] account_name: The name of the storage account within the specified resource group. :param pulumi.Input[str] error404_document: The absolute path to a custom webpage that should be used when a request is made which does not correspond to an existing file. :param pulumi.Input[str] index_document: The webpage that Azure Storage serves for requests to the root of a website or any sub-folder. For example, 'index.html'. The value is case-sensitive. :param pulumi.Input[str] resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. """ ... @overload def __init__(__self__, resource_name: str, args: StorageAccountStaticWebsiteArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Enables the static website feature of a storage account. :param str resource_name: The name of the resource. :param StorageAccountStaticWebsiteArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(StorageAccountStaticWebsiteArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, account_name: Optional[pulumi.Input[str]] = None, error404_document: Optional[pulumi.Input[str]] = None, index_document: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = StorageAccountStaticWebsiteArgs.__new__(StorageAccountStaticWebsiteArgs) if account_name is None and not opts.urn: raise TypeError("Missing required property 'account_name'") __props__.__dict__["account_name"] = account_name __props__.__dict__["error404_document"] = error404_document __props__.__dict__["index_document"] = index_document if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["container_name"] = None super(StorageAccountStaticWebsite, __self__).__init__( 'azure-native:storage:StorageAccountStaticWebsite', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'StorageAccountStaticWebsite': """ Get an existing StorageAccountStaticWebsite resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = StorageAccountStaticWebsiteArgs.__new__(StorageAccountStaticWebsiteArgs) __props__.__dict__["container_name"] = None __props__.__dict__["error404_document"] = None __props__.__dict__["index_document"] = None return StorageAccountStaticWebsite(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="containerName") def container_name(self) -> pulumi.Output[str]: """ The name of the container to upload blobs to. """ return pulumi.get(self, "container_name") @property @pulumi.getter(name="error404Document") def error404_document(self) -> pulumi.Output[Optional[str]]: """ The absolute path to a custom webpage that should be used when a request is made which does not correspond to an existing file. """ return pulumi.get(self, "error404_document") @property @pulumi.getter(name="indexDocument") def index_document(self) -> pulumi.Output[Optional[str]]: """ The webpage that Azure Storage serves for requests to the root of a website or any sub-folder. For example, 'index.html'. The value is case-sensitive. """ return pulumi.get(self, "index_document")

48.226601

199

0.674157

1,146

9,790

5.49651

0.140489

0.054136

0.066677

0.048897

0.653755

0.575806

0.548976

0.502461

0.480235

0.422448

0

0.008201

0.240245

9,790

202

200

48.465347

0.838666

0.309806

0

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1

0

0.130107

0.024199

0

1

0.137097

false

0.008065

0.040323

0

0.258065

0

null

0

1

0

null

0

1

6a61c6ef3ad58f9b8003931de1870b0f5ad404c7

1,247

py

Python

python/example_code/s3/s3-python-example-get-bucket-policy.py

onehitcombo/aws-doc-sdk-examples

03e2e0c5dee75c5decbbb99e849c51417521fd82

[ "Apache-2.0" ]

3

2021-01-19T20:23:17.000Z

2021-01-19T21:38:59.000Z

python/example_code/s3/s3-python-example-get-bucket-policy.py

onehitcombo/aws-doc-sdk-examples

03e2e0c5dee75c5decbbb99e849c51417521fd82

[ "Apache-2.0" ]

null

python/example_code/s3/s3-python-example-get-bucket-policy.py

onehitcombo/aws-doc-sdk-examples

03e2e0c5dee75c5decbbb99e849c51417521fd82

[ "Apache-2.0" ]

2

2019-12-27T13:58:00.000Z

2020-05-21T18:35:40.000Z

# Copyright 2010-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # This file is licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. A copy of the # License is located at # # http://aws.amazon.com/apache2.0/ # # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS # OF ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. import boto3 # Create an S3 client s3 = boto3.client('s3') # Call to S3 to retrieve the policy for the given bucket result = s3.get_bucket_policy(Bucket='my-bucket') print(result) # snippet-comment:[These are tags for the AWS doc team's sample catalog. Do not remove.] # snippet-sourcedescription:[s3-python-example-get-bucket-policy.py demonstrates how to list the Amazon S3 Buckets in your account.] # snippet-keyword:[Python] # snippet-keyword:[AWS SDK for Python (Boto3)] # snippet-keyword:[Code Sample] # snippet-keyword:[Amazon S3] # snippet-service:[s3] # snippet-sourcetype:[full-example] # snippet-sourcedate:[2018-06-25] # snippet-sourceauthor:[jschwarzwalder (AWS)]

35.628571

133

0.735365

186

1,247

4.919355

0.586022

0.054645

0.021858

0

0.03074

0.165196

1,247

34

134

36.676471

0.848223

0.847634

0

0.083969

0

1

0

false

0

0.25

0

0.25

0

null

0

1

0

null

0

1

6a630004921c5a5ff2ec4e4b2d0a96b0bf000baa

897

py

Python

data_io/util/value_blob_erosion.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

null

data_io/util/value_blob_erosion.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

4

2016-04-22T15:39:21.000Z

2016-11-15T21:23:58.000Z

data_io/util/value_blob_erosion.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

4

2017-05-12T00:17:55.000Z

2019-07-01T19:23:32.000Z

import numpy as np from scipy import ndimage def erode_value_blobs(array, steps=1, values_to_ignore=tuple(), new_value=0): unique_values = list(np.unique(array)) all_entries_to_keep = np.zeros(shape=array.shape, dtype=np.bool) for unique_value in unique_values: entries_of_this_value = array == unique_value if unique_value in values_to_ignore: all_entries_to_keep = np.logical_or(entries_of_this_value, all_entries_to_keep) else: eroded_unique_indicator = ndimage.binary_erosion(entries_of_this_value, iterations=steps) all_entries_to_keep = np.logical_or(eroded_unique_indicator, all_entries_to_keep) result = array * all_entries_to_keep if new_value != 0: eroded_entries = np.logical_not(all_entries_to_keep) new_values = new_value * eroded_entries result += new_values return result

42.714286

101

0.733556

132

897

4.560606

0.340909

0.116279

0.139535

0.186047

0.162791

0.089701

0

0.004172

0.198439

897

20

102

44.85

0.833102

0

1

0.055556

false

0

0.111111

0

0.222222

0

null

0

1

0

null

0

1

6a6b124cb7b2cd1d6d09ae5b84d5b49e63612508

679

py

Python

test_f_login_andy.py

KotoLLC/peacenik-tests

760f7799ab2b9312fe0cce373890195151c48fce

[ "Apache-2.0" ]

null

test_f_login_andy.py

KotoLLC/peacenik-tests

760f7799ab2b9312fe0cce373890195151c48fce

[ "Apache-2.0" ]

null

test_f_login_andy.py

KotoLLC/peacenik-tests

760f7799ab2b9312fe0cce373890195151c48fce

[ "Apache-2.0" ]

null

from helpers import * def test_f_login_andy(): url = "http://central.orbits.local/rpc.AuthService/Login" raw_payload = {"name": "andy","password": "12345"} payload = json.dumps(raw_payload) headers = {'Content-Type': 'application/json'} # convert dict to json by json.dumps() for body data. response = requests.request("POST", url, headers=headers, data=payload) save_cookies(response.cookies,"cookies.txt") # Validate response headers and body contents, e.g. status code. assert response.status_code == 200 # print full request and response pretty_print_request(response.request) pretty_print_response(response)

35.736842

75

0.696613

86

679

5.372093

0.604651

0.04329

0

0.014467

0.185567

679

19

76

35.736842

0.820976

0.216495

0

0.213611

0

0.090909

1

0.090909

false

0.090909

0

0.181818

0

null

0

null

0

1

0

1

6a6dcc4d9c3e1b2437b6c8b26173ce12b1dfa929

7,761

py

Python

week2/Assignment2Answer.py

RayshineRen/Introduction_to_Data_Science_in_Python

b19aa781a8f8d0e25853c4e86dadd4c9bebbcd71

[ "MIT" ]

1

2020-09-22T15:06:02.000Z

week2/Assignment2Answer.py

RayshineRen/Introduction_to_Data_Science_in_Python

b19aa781a8f8d0e25853c4e86dadd4c9bebbcd71

[ "MIT" ]

1

2020-11-03T14:11:02.000Z

2020-11-03T14:24:50.000Z

week2/Assignment2Answer.py

RayshineRen/Introduction_to_Data_Science_in_Python

b19aa781a8f8d0e25853c4e86dadd4c9bebbcd71

[ "MIT" ]

2

2020-09-22T05:27:09.000Z

2020-11-05T10:39:49.000Z

# -*- coding: utf-8 -*- """ Created on Fri Sep 18 21:56:15 2020 @author: Ray @email: [email protected] @wechat: RayTing0305 """ ''' Question 1 Write a function called proportion_of_education which returns the proportion of children in the dataset who had a mother with the education levels equal to less than high school (<12), high school (12), more than high school but not a college graduate (>12) and college degree. This function should return a dictionary in the form of (use the correct numbers, do not round numbers): {"less than high school":0.2, "high school":0.4, "more than high school but not college":0.2, "college":0.2} ''' import scipy.stats as stats import numpy as np import pandas as pd df = pd.read_csv("./assets/NISPUF17.csv") def proportion_of_education(): # your code goes here # YOUR CODE HERE df_edu = df.EDUC1 edu_list = [1, 2, 3, 4] zero_df = pd.DataFrame(np.zeros((df_edu.shape[0], len(edu_list))), columns=edu_list) for edu in edu_list: zero_df[edu][df_edu==edu]=1 #zero_df sum_ret = zero_df.sum(axis=0) name_l = ["less than high school", "high school", "more than high school but not college", "college"] rat = sum_ret.values/sum(sum_ret.values) dic = dict() for i in range(4): dic[name_l[i]] = rat[i] return dic raise NotImplementedError() assert type(proportion_of_education())==type({}), "You must return a dictionary." assert len(proportion_of_education()) == 4, "You have not returned a dictionary with four items in it." assert "less than high school" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys." assert "high school" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys." assert "more than high school but not college" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys." assert "college" in proportion_of_education().keys(), "You have not returned a dictionary with the correct" ''' Question 2 Let's explore the relationship between being fed breastmilk as a child and getting a seasonal influenza vaccine from a healthcare provider. Return a tuple of the average number of influenza vaccines for those children we know received breastmilk as a child and those who know did not. This function should return a tuple in the form (use the correct numbers: (2.5, 0.1) ''' def average_influenza_doses(): # YOUR CODE HERE #是否喂养母乳 fed_breastmilk = list(df.groupby(by='CBF_01')) be_fed_breastmilk = fed_breastmilk[0][1] not_fed_breastmilk = fed_breastmilk[1][1] #喂养母乳的influenza数目 be_fed_breastmilk_influenza = be_fed_breastmilk.P_NUMFLU num_be_fed_breastmilk_influenza = be_fed_breastmilk_influenza.dropna().mean() #未喂养母乳的influenza数目 not_be_fed_breastmilk_influenza = not_fed_breastmilk.P_NUMFLU num_not_be_fed_breastmilk_influenza = not_be_fed_breastmilk_influenza.dropna().mean() return num_be_fed_breastmilk_influenza, num_not_be_fed_breastmilk_influenza raise NotImplementedError() assert len(average_influenza_doses())==2, "Return two values in a tuple, the first for yes and the second for no." ''' Question 3 It would be interesting to see if there is any evidence of a link between vaccine effectiveness and sex of the child. Calculate the ratio of the number of children who contracted chickenpox but were vaccinated against it (at least one varicella dose) versus those who were vaccinated but did not contract chicken pox. Return results by sex. This function should return a dictionary in the form of (use the correct numbers): {"male":0.2, "female":0.4} Note: To aid in verification, the chickenpox_by_sex()['female'] value the autograder is looking for starts with the digits 0.0077. ''' def chickenpox_by_sex(): # YOUR CODE HERE #是否感染Varicella cpox = df.HAD_CPOX #cpox.value_counts() cpox_group = list(df.groupby(by='HAD_CPOX')) have_cpox = cpox_group[0][1] not_have_cpox = cpox_group[1][1] #男女分开 have_cpox_group = list(have_cpox.groupby(by='SEX')) not_have_cpox_group = list(not_have_cpox.groupby(by='SEX')) have_cpox_boy = have_cpox_group[0][1] have_cpox_girl = have_cpox_group[1][1] not_have_cpox_boy = not_have_cpox_group[0][1] not_have_cpox_girl = not_have_cpox_group[1][1] #接种感染 #have_cpox_boy_injected = have_cpox_boy[(have_cpox_boy['P_NUMMMR']>0) | (have_cpox_boy['P_NUMVRC']>0)] have_cpox_boy_injected = have_cpox_boy[(have_cpox_boy['P_NUMVRC']>0)] num_have_cpox_boy_injected = have_cpox_boy_injected.count()['SEQNUMC'] have_cpox_girl_injected = have_cpox_girl[(have_cpox_girl['P_NUMVRC']>0)] num_have_cpox_girl_injected = have_cpox_girl_injected.count()['SEQNUMC'] #接种未感染 not_have_cpox_boy_injected = not_have_cpox_boy[(not_have_cpox_boy['P_NUMVRC']>0)] num_not_have_cpox_boy_injected = not_have_cpox_boy_injected.count()['SEQNUMC'] not_have_cpox_girl_injected = not_have_cpox_girl[(not_have_cpox_girl['P_NUMVRC']>0)] num_not_have_cpox_girl_injected = not_have_cpox_girl_injected.count()['SEQNUMC'] #计算比例 ratio_boy = num_have_cpox_boy_injected / num_not_have_cpox_boy_injected ratio_girl = num_have_cpox_girl_injected / num_not_have_cpox_girl_injected dic = {} dic['male'] = ratio_boy dic['female'] = ratio_girl return dic raise NotImplementedError() assert len(chickenpox_by_sex())==2, "Return a dictionary with two items, the first for males and the second for females." ''' Question 4 A correlation is a statistical relationship between two variables. If we wanted to know if vaccines work, we might look at the correlation between the use of the vaccine and whether it results in prevention of the infection or disease [1]. In this question, you are to see if there is a correlation between having had the chicken pox and the number of chickenpox vaccine doses given (varicella). Some notes on interpreting the answer. The had_chickenpox_column is either 1 (for yes) or 2 (for no), and the num_chickenpox_vaccine_column is the number of doses a child has been given of the varicella vaccine. A positive correlation (e.g., corr > 0) means that an increase in had_chickenpox_column (which means more no’s) would also increase the values of num_chickenpox_vaccine_column (which means more doses of vaccine). If there is a negative correlation (e.g., corr < 0), it indicates that having had chickenpox is related to an increase in the number of vaccine doses. Also, pval is the probability that we observe a correlation between had_chickenpox_column and num_chickenpox_vaccine_column which is greater than or equal to a particular value occurred by chance. A small pval means that the observed correlation is highly unlikely to occur by chance. In this case, pval should be very small (will end in e-18 indicating a very small number). [1] This isn’t really the full picture, since we are not looking at when the dose was given. It’s possible that children had chickenpox and then their parents went to get them the vaccine. Does this dataset have the data we would need to investigate the timing of the dose? ''' def corr_chickenpox(): cpox = df[(df.P_NUMVRC).notnull()] have_cpox = cpox[(cpox.HAD_CPOX==1) | (cpox.HAD_CPOX==2)] df1=pd.DataFrame({"had_chickenpox_column":have_cpox.HAD_CPOX, "num_chickenpox_vaccine_column":have_cpox.P_NUMVRC}) corr, pval=stats.pearsonr(df1["had_chickenpox_column"],df1["num_chickenpox_vaccine_column"]) return corr raise NotImplementedError()

53.895833

576

0.74024

1,237

7,761

4.43169

0.23848

0.065669

0.038125

0.031193

0.356804

0.270522

0.212149

0.12915

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0.017069

0.177168

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0.203759

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0.042857

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null

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null

0

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1

6a6f28bb63a4999e5f2dcb27c1de7d562bafcd05

1,664

py

Python

Experimente/Experiment ID 8/run-cifar10-v7.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

6

2021-11-03T07:20:48.000Z

2021-11-10T08:20:44.000Z

Experimente/Experiment ID 8/run-cifar10-v7.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

1

2021-11-02T21:10:51.000Z

2021-11-02T21:11:05.000Z

Experimente/Experiment ID 8/run-cifar10-v7.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

null

from __future__ import print_function from keras.datasets import mnist from keras.datasets import cifar10 from keras.utils.np_utils import to_categorical import numpy as np from keras import backend as K from evolution import Evolution from genome_handler import GenomeHandler import tensorflow as tf #import mlflow.keras #import mlflow #import mlflow.tensorflow #mlflow.tensorflow.autolog() #mlflow.keras.autolog() print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) K.set_image_data_format("channels_last") #(x_train, y_train), (x_test, y_test) = mnist.load_data() (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2],x_train.shape[3]).astype('float32') / 255 x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], x_test.shape[3]).astype('float32') / 255 # nCLasses y_train = to_categorical(y_train) y_test = to_categorical(y_test) #y_train.shape dataset = ((x_train, y_train), (x_test, y_test)) genome_handler = GenomeHandler(max_conv_layers=4, max_dense_layers=2, # includes final dense layer max_filters=512, max_dense_nodes=1024, input_shape=x_train.shape[1:], n_classes=10) evo = Evolution(genome_handler, data_path="log/evo_cifar10_gen40_pop10_e20.csv") model = evo.run(dataset=dataset, num_generations=40, pop_size=10, epochs=20,metric='acc') #epochs=10,metric='loss') print(model.summary())

37.818182

120

0.676683

239

1,664

4.435146

0.380753

0.056604

0.051887

0.033962

0.103774

0.062264

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0.038139

0.212139

1,664

44

121

37.818182

0.770404

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0.310345

0.103448

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null

0

null

0

1

0

1

6a725ee4987cc406e04ed4e04ead31dbd1e9b6ea

1,088

py

Python

To-D0-App-main/base/views.py

shagun-agrawal/To-Do-App

083081690fe9d291f13c0452a695a092b7544ab2

[ "MIT" ]

1

2021-04-08T14:12:38.000Z

To-D0-App-main/base/views.py

shagun-agrawal/To-Do-App

083081690fe9d291f13c0452a695a092b7544ab2

[ "MIT" ]

null

To-D0-App-main/base/views.py

shagun-agrawal/To-Do-App

083081690fe9d291f13c0452a695a092b7544ab2

[ "MIT" ]

null

from django.shortcuts import render from django.views.generic.list import ListView from django.views.generic.detail import DetailView from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.urls import reverse_lazy from django.contrib.auth.views import LoginView from .models import Task # Create your views here. class CustomLoginView(LoginView): template_name='base/login.html' fiels='__all__' redirect_auhenticated_user = True def get_success_url(self): return reverse_lazy('tasks') class TaskList(ListView): model = Task context_object_name = 'tasks' class TaskDetail(DetailView): model = Task context_object_name = 'task' class TaskCreate(CreateView): model = Task fields = '__all__' success_url = reverse_lazy('tasks') class TaskUpdate(UpdateView): model = Task fields = '__all__' success_url = reverse_lazy('tasks') class TaskDelete(DeleteView): model = Task context_object_name='Task' success_url = reverse_lazy('tasks')

24.727273

73

0.714154

128

1,088

5.828125

0.429688

0.080429

0.085791

0.088472

0.281501

0.211796

0.131367

0

0.204963

1,088

43

74

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null

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null

0

1

0

1

6a75c6bcf2a235fe76f46e51c4cc31283811626a

2,534

py

Python

simulation/dataset_G_1q_X_Z_N1.py

eperrier/QDataSet

383b38b9b4166848f72fac0153800525e66b477b

[ "MIT" ]

42

2021-08-17T02:27:59.000Z

2022-03-26T16:00:57.000Z

simulation/dataset_G_1q_X_Z_N1.py

eperrier/QDataSet

383b38b9b4166848f72fac0153800525e66b477b

[ "MIT" ]

1

2021-09-25T11:15:20.000Z

2021-09-27T04:18:25.000Z

simulation/dataset_G_1q_X_Z_N1.py

eperrier/QDataSet

383b38b9b4166848f72fac0153800525e66b477b

[ "MIT" ]

6

2021-08-17T02:28:04.000Z

2022-03-22T07:11:48.000Z

############################################## """ This module generate a dataset """ ############################################## # preample import numpy as np from utilites import Pauli_operators, simulate, CheckNoise ################################################ # meta parameters name = "G_1q_X_Z_N1" ################################################ # quantum parameters dim = 2 # dimension of the system Omega = 12 # qubit energy gap static_operators = [0.5*Pauli_operators[3]*Omega] # drift Hamiltonian dynamic_operators = [0.5*Pauli_operators[1]] # control Hamiltonian noise_operators = [0.5*Pauli_operators[3]] # noise Hamiltonian initial_states = [ np.array([[0.5,0.5],[0.5,0.5]]), np.array([[0.5,-0.5],[-0.5,0.5]]), np.array([[0.5,-0.5j],[0.5j,0.5]]),np.array([[0.5,0.5j],[-0.5j,0.5]]), np.array([[1,0],[0,0]]), np.array([[0,0],[0,1]]) ] # intial state of qubit measurement_operators = Pauli_operators[1:] # measurement operators ################################################## # simulation parameters T = 1 # Evolution time M = 1024 # Number of time steps num_ex = 10000 # Number of examples batch_size = 50 # batch size for TF ################################################## # noise parameters K = 2000 # Number of realzations noise_profile = [1] # Noise type ################################################### # control parameters pulse_shape = "Gaussian" # Control pulse shape num_pulses = 5 # Number of pulses per sequence #################################################### # Generate the dataset sim_parameters = dict( [(k,eval(k)) for k in ["name", "dim", "Omega", "static_operators", "dynamic_operators", "noise_operators", "measurement_operators", "initial_states", "T", "M", "num_ex", "batch_size", "K", "noise_profile", "pulse_shape", "num_pulses"] ]) CheckNoise(sim_parameters) simulate(sim_parameters) ####################################################

56.311111

261

0.404893

228

2,534

4.359649

0.372807

0.030181

0.024145

0.148893

0.123742

0.071429

0

0.042815

0.327151

2,534

45

262

56.311111

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0

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false

0

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0

0.08

0

null

0

1

0

null

0

1

6a7641f27315b4a34aa454452b185ab3ffeddc05

505

py

Python

user_service/user_service/api.py

Ziang-Lu/Flask-Blog

8daf901a0ea0e079ad24a61fd7f16f1298514d4c

[ "MIT" ]

null

user_service/user_service/api.py

Ziang-Lu/Flask-Blog

8daf901a0ea0e079ad24a61fd7f16f1298514d4c

[ "MIT" ]

2

2020-06-09T08:40:42.000Z

2021-04-30T21:20:35.000Z

user_service/user_service/api.py

Ziang-Lu/Flask-Blog

8daf901a0ea0e079ad24a61fd7f16f1298514d4c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ API definition module. """ from flask import Blueprint from flask_restful import Api from .resources.user import UserAuth, UserItem, UserList, UserFollow # Create an API-related blueprint api_bp = Blueprint(name='api', import_name=__name__) api = Api(api_bp) api.add_resource(UserList, '/users') api.add_resource(UserItem, '/users/<int:id>') api.add_resource(UserAuth, '/user-auth') api.add_resource( UserFollow, '/user-follow/<int:follower_id>/<followed_username>' )

22.954545

68

0.740594

69

505

5.217391

0.463768

0.066667

0.155556

0

0.002227

0.110891

505

21

69

24.047619

0.799555

0.152475

0

0.200477

0.119332

0

1

0

false

0

0.363636

0

0.363636

0.181818

0

null

0

null

0

1

0

1

6a77df2fb34c60a66cb0710a264af376f888be93

2,112

py

Python

advanced/itertools_funcs.py

ariannasg/python3-essential-training

9b52645f5ccb57d2bda5d5f4a3053681a026450a

[ "MIT" ]

1

2020-06-02T08:37:41.000Z

advanced/itertools_funcs.py

ariannasg/python3-training

9b52645f5ccb57d2bda5d5f4a3053681a026450a

[ "MIT" ]

null

advanced/itertools_funcs.py

ariannasg/python3-training

9b52645f5ccb57d2bda5d5f4a3053681a026450a

[ "MIT" ]

null

#!usr/bin/env python3 import itertools # itertools is a module that's not technically a set of built-in functions but # it is part of the standard library that comes with python. # it's useful for for creating and using iterators. def main(): print('some infinite iterators') # cycle iterator can be used to cycle over a collection over and over seq1 = ["Joe", "John", "Mike"] cycle1 = itertools.cycle(seq1) print(next(cycle1)) print(next(cycle1)) print(next(cycle1)) print(next(cycle1)) print(next(cycle1)) # use count to create a simple counter count1 = itertools.count(100, 3) print(next(count1)) print(next(count1)) print(next(count1)) print('some non-infinite iterators') values = [10, 5, 20, 30, 40, 50, 40, 30] # accumulate creates an iterator that accumulates/aggregates values print(list(itertools.accumulate(values))) # this defaults to addition print(list(itertools.accumulate(values, max))) print(list(itertools.accumulate(values, min))) # use chain to connect sequences together x = itertools.chain('ABCD', '1234') print(list(x)) # dropwhile and takewhile will return values until # a certain condition is met that stops them. they are similar to the # filter built-in function. # dropwhile will drop the values from the sequence as long as the # condition of the function is true and then returns the rest of values print(list(itertools.dropwhile(is_less_than_forty, values))) # takewhile will keep the values from the sequence as long as the # condition of the function is true and then stops giving data print(list(itertools.takewhile(is_less_than_forty, values))) def is_less_than_forty(x): return x < 40 if __name__ == "__main__": main() # CONSOLE OUTPUT: # some infinite iterators # Joe # John # Mike # Joe # John # 100 # 103 # 106 # some non-infinite iterators # [10, 15, 35, 65, 105, 155, 195, 225] # [10, 10, 20, 30, 40, 50, 50, 50] # [10, 5, 5, 5, 5, 5, 5, 5] # ['A', 'B', 'C', 'D', '1', '2', '3', '4'] # [40, 50, 40, 30] # [10, 5, 20, 30]

29.333333

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2,112

4.376947

0.420561

0.051246

0.053381

0.05694

0.296085

0.193594

0.188612

0.153025

0

0.066547

0.210227

2,112

71

79

29.746479

0.775779

0.529356

0

0.296296

0

0.080125

0

1

0.074074

false

0

0.037037

0.148148

0.592593

0

null

0

null

0

1

0

1

6a782fcc9b346f1edc133e8b8d12314c1cc0a5ff

421

py

Python

aula 05/model/Pessoa.py

Azenha/AlgProg2

062b5caac24435717074a18a7499f80130489a46

[ "MIT" ]

null

aula 05/model/Pessoa.py

Azenha/AlgProg2

062b5caac24435717074a18a7499f80130489a46

[ "MIT" ]

null

aula 05/model/Pessoa.py

Azenha/AlgProg2

062b5caac24435717074a18a7499f80130489a46

[ "MIT" ]

null

class Pessoa: def __init__(self, codigo, nome, endereco, telefone): self.__codigo = int(codigo) self.nome = str(nome) self._endereco = str(endereco) self.__telefone = str(telefone) def imprimeNome(self): print(f"Você pode chamar essa pessoa de {self.nome}.") def __imprimeTelefone(self): print(f"Você pode ligar para esta pessoa no número {self.__telefone}.")

35.083333

79

0.650831

53

421

4.924528

0.471698

0.076628

0.10728

0.137931

0

0.239905

421

12

79

35.083333

0.815625

0

0.248815

0

1

0.3

false

0

0.4

0.2

0

null

0

null

0

1

0

1

6a78c857a857449cf31704c6af0759d610215a2d

25,852

py

Python

pypyrus_logbook/logger.py

t3eHawk/pypyrus_logbook

bd647a1c355b07e8df28c0d7298fcfe68cd9572e

[ "MIT" ]

null

pypyrus_logbook/logger.py

t3eHawk/pypyrus_logbook

bd647a1c355b07e8df28c0d7298fcfe68cd9572e

[ "MIT" ]

null

pypyrus_logbook/logger.py

t3eHawk/pypyrus_logbook

bd647a1c355b07e8df28c0d7298fcfe68cd9572e

[ "MIT" ]

2

2019-02-06T08:05:43.000Z

2019-02-06T08:06:35.000Z

import atexit import datetime as dt import os import platform import pypyrus_logbook as logbook import sys import time import traceback from .conf import all_loggers from .formatter import Formatter from .header import Header from .output import Root from .record import Record from .sysinfo import Sysinfo class Logger(): """This class represents a single logger. Logger by it self is a complex set of methods, items and commands that together gives funcionality for advanced logging in different outputs: console, file, email, database table, HTML document - and using information from diffrent inputs: user messages, traceback, frames, user parameters, execution arguments and systems descriptors. Each logger must have an unique name which will help to identify it. Main application logger will have the same name as a python script file. It can be accessed by native logbook methods or by calling `getlogger()` method with no name. Parameters ---------- name : str, optional The argument is used te define `name` attribute app : str, optional The argument is used to set the `app` attribute. desc : str, optional The argument is used to set the `desc` attribute. version : str, optional The argument is used to set the `version` attribute. status : bool, optional The argument is used to open or close output `root`. console : bool, optional The argument is used to open or close output `console`. file : bool, optional The argument is used to open or close output `file`. email : bool, optional The argument is used to open or close output `email`. html : bool, optional The argument is used to open or close output `html`. table : bool, optional The argument is used to open or close output `table`. directory : str, optional The argument is used to set logging file folder. filename : str, optional The argument is used to set logging file name. extension : str, optional The argument is used to set logging file extension. smtp : dict, optional The argument is used to configure SMTP connection. db : dict, optional The argument is used to configure DB connection. format : str, optional The argument is used to set record template. info : bool, optional The argument is used to filter info records. The default is True. debug : bool, optional The argument is used to filter debug records. The default is False. warning : bool, optional The argument is used to filter warning records. The default is True. error : bool, optional The argument is used to filter error records. The default is True. critical : bool, optional The argument is used to filter critical records. The default is True. alarming : bool, optional The argument is used to enable or disable alarming mechanism. The default is True. control : bool, optional The argument is used to enable or disable execution break in case on error. The default is True. maxsize : int or bool, optional The argument is used to define maximum size of output file. Must be presented as number of bytes. The default is 10 Mb. maxdays : int or bool, optional The argument is used to define maximum number of days that will be logged to same file. The default is 1 which means that new output file will be opened at each 00:00:00. maxlevel : int or bool, optional The argument is used to define the break error level (WARNING = 0, ERRROR = 1, CRITICAL = 2). All that higher the break level will interrupt application execution. The default is 1. maxerrors : int or bool, optional The argument is used to define maximun number of errors. The default is False which means it is disabled. Attributes ---------- name : str Name of the logger. app : str Name of the application that we are logging. desc : str Description of the application that we are logging. version : str Version of the application that we are logging. start_date : datetime.datetime Date when logging was started. rectypes : dict All available record types. Keys are used in `Logger` write methods as `rectype` argument. Values are used in formatting. So if you wish to modify `rectype` form then edit appropriate one here. If you wish to use own record types then just add it to that dictinary. By default we provide the next few record types: +---------+---------+ | Key | Value | +=========+=========+ |none |NONE | +---------+---------+ |info |INFO | +---------+---------+ |debug |DEBUG | +---------+---------+ |warning |WARNING | +---------+---------+ |error |ERROR | +---------+---------+ |critical |CRITICAL | +---------+---------+ messages : dict Messages that are printed with some `Logger` methods like `ok()`, `success()`, `fail()`. If you wish to modify the text of this messages just edit the value of appropriate item. with_errors : int The flag shows that logger catched errors in the application during its execution. count_errors : int Number of errors that logger catched in the application during its execution. filters : dict Record types filters. To filter record type just set corresponding item value to False. root : pypyrus_logbook.output.Root The output `Root` object. console : pypyrus_logbook.output.Console The output `Console` object. Shortcut for `Logger.root.console`. file : pypyrus_logbook.output.File The output file. Shortcut for `Logger.output.file`. email : pypyrus_logbook.output.Email The output email. Shortcut for `Logger.output.email`. html: pypyrus_logbook.output.HTML The output HTML document. Shortcut for `Logger.output.html`. table: pypyrus_logbook.output.Table The output table. Shortcut for `Logger.output.table`. formatter : pypyrus_logbook.formatter.Formatter Logger formatter which sets all formatting configuration like record template, error message template, line length etc. sysinfo : pypyrus_logbook.sysinfo.Sysinfo Special input object which parse different inputs includeing system specifications, flag arguments, execution parameters, user parameters and environment variables and transforms all of that to `Dataset` object. Through the `Dataset` object data can be easily accessed by get item operation or by point like `sysinfo.desc['hostname']` or `sysinfo.desc.hostname`. header : pypyrus_logbook.header.Header The header that can be printed to the writable output. """ def __init__(self, name=None, app=None, desc=None, version=None, status=True, console=True, file=True, email=False, html=False, table=False, directory=None, filename=None, extension=None, smtp=None, db=None, format=None, info=True, debug=False, warning=True, error=True, critical=True, alarming=True, control=True, maxsize=(1024*1024*10), maxdays=1, maxlevel=2, maxerrors=False): # Unique name of the logger. self._name = name # Attributes describing the application. self.app = None self.desc = None self.version = None # Some logger important attributes self._start_date = dt.datetime.now() self.rectypes = {'none': 'NONE', 'info': 'INFO', 'debug': 'DEBUG', 'warning': 'WARNING', 'error': 'ERROR', 'critical': 'CRITICAL'} self.messages = {'ok': 'OK', 'success': 'SUCCESS', 'fail': 'FAIL'} self._with_error = False self._count_errors = 0 # Complete the initial configuration. self.configure(app=app, desc=desc, version=version, status=status, console=console, file=file, email=email, html=html, table=table, directory=directory, filename=filename, extension=extension, smtp=smtp, db=db, format=format, info=info, debug=debug, warning=warning, error=error, critical=critical, alarming=alarming, control=control, maxsize=maxsize, maxdays=maxdays, maxlevel=maxlevel, maxerrors=maxerrors) # Output shortcuts. self.console = self.root.console self.file = self.root.file self.email = self.root.email self.html = self.root.html self.table = self.root.table # Set exit function. atexit.register(self._exit) # Add creating logger to special all_loggers dictinary. all_loggers[self._name] = self pass def __str__(self): return f'<Logger object "{self._name}">' __repr__ = __str__ @property def name(self): """Unique logger name.""" return self._name @property def start_date(self): """Logging start date.""" return self._start_date @property def with_error(self): """Flag that shows was an error or not.""" return self._with_error @property def count_errors(self): """The number of occured errors.""" return self._count_errors def configure(self, app=None, desc=None, version=None, status=None, console=None, file=None, email=None, html=None, table=None, directory=None, filename=None, extension=None, smtp=None, db=None, format=None, info=None, debug=None, warning=None, error=None, critical=None, alarming=None, control=None, maxsize=None, maxdays=None, maxlevel=None, maxerrors=None): """Main method to configure the logger and all its attributes. This is an only one right way to customize logger. Parameters are the same as for creatrion. Parameters ---------- app : str, optional The argument is used to set the `app` attribute. desc : str, optional The argument is used to set the `desc` attribute. version : str, optional The argument is used to set the `version` attribute. status : bool, optional The argument is used to open or close output `root`. console : bool, optional The argument is used to open or close output `console`. file : bool, optional The argument is used to open or close output `file`. email : bool, optional The argument is used to open or close output `email`. html : bool, optional The argument is used to open or close output `html`. table : bool, optional The argument is used to open or close output `table`. directory : str, optional The argument is used to set logging file folder. filename : str, optional The argument is used to set logging file name. extension : str, optional The argument is used to set logging file extension. smtp : dict, optional The argument is used to configure SMTP connection. db : dict, optional The argument is used to configure DB connection. format : str, optional The argument is used to set record template. info : bool, optional The argument is used to filter info records. debug : bool, optional The argument is used to filter debug records. warning : bool, optional The argument is used to filter warning records. error : bool, optional The argument is used to filter error records. critical : bool, optional The argument is used to filter critical records. alarming : bool, optional The argument is used to enable or disable alarming mechanism. control : bool, optional The argument is used to enable or disable execution break in case on error. maxsize : int or bool, optional The argument is used to define maximum size of output file. maxdays : int or bool, optional The argument is used to define maximum number of days that will be logged to same file. maxlevel : int or bool, optional The argument is used to define the break error level. maxerrors : int or bool, optional The argument is used to define maximun number of errors. """ if isinstance(app, str) is True: self.app = app if isinstance(desc, str) is True: self.desc = desc if isinstance(version, (str, int, float)) is True: self.version = version # Build the output root if it is not exists. In other case modify # existing output if it is requested. if hasattr(self, 'root') is False: self.root = Root(self, console=console, file=file, email=email, html=html, table=table, status=status, directory=directory, filename=filename, extension=extension, smtp=smtp, db=db) else: for key, value in {'console': console, 'file': file, 'email': email, 'html': html, 'table': table}.items(): if value is True: getattr(self.root, key).open() if key == 'file': getattr(self.root, key).new() elif value is False: getattr(self.root, key).close() # Customize output file path. path = {} if directory is not None: path['dir'] = directory if filename is not None: path['name'] = filename if extension is not None: path['ext'] = extension if len(path) > 0: self.root.file.configure(**path) # Customize SMTP server. if isinstance(smtp, dict) is True: self.root.email.configure(**smtp) # Customize database connection. if isinstance(db, dict) is True: self.root.table.configure(**db) # Create formatter in case it is not exists yet or just customize it. # Parameter format can be either string or dictionary. # When it is string then it must describe records format. # When it is dictionary it can contaion any parameter of formatter # that must be customized. if isinstance(format, str) is True: format = {'record': format} if hasattr(self, 'formatter') is False: format = {} if isinstance(format, dict) is False else format self.formatter = Formatter(**format) elif isinstance(format, dict) is True: self.formatter.configure(**format) # Create or customize record type filters. if hasattr(self, 'filters') is False: self.filters = {} for key, value in {'info': info, 'debug': debug, 'error': error, 'warning': warning, 'critical': critical}.items(): if isinstance(value, bool) is True: self.filters[key] = value # Customize limits and parameters of execution behaviour. if isinstance(maxsize, (int, float, bool)) is True: self._maxsize = maxsize if isinstance(maxdays, (int, float, bool)) is True: self._maxdays = maxdays self.__calculate_restart_date() if isinstance(maxlevel, (int, float, bool)) is True: self._maxlevel = maxlevel if isinstance(maxerrors, (int, float, bool)) is True: self._maxerrors = maxerrors if isinstance(alarming, bool) is True: self._alarming = alarming if isinstance(control, bool) is True: self._control = control # Initialize sysinfo instance when not exists. if hasattr(self, 'sysinfo') is False: self.sysinfo = Sysinfo(self) # Initialize header instance when not exists. if hasattr(self, 'header') is False: self.header = Header(self) pass def write(self, record): """Direct write to the output. Parameters ---------- record : Record The argument is used to send it to the output `root`. """ self.__check_file_stats() self.root.write(record) pass def record(self, rectype, message, error=False, **kwargs): """Basic method to write records. Parameters ---------- rectype : str By default method creates the record with the type NONE. That can be changed but depends on available record types. All registered record types are stored in the instance attribute rectypes. If you wish to use own record type or change the presentaion of exeisting one then edit this dictinary. message : str The message that must be written. error : bool, optional If record is error then set that parameter to `True`. **kwargs The keyword arguments used for additional forms (variables) for record and message formatting. """ if self.filters.get(rectype, True) is True: record = Record(self, rectype, message, error=error, **kwargs) self.write(record) pass def info(self, message, **kwargs): """Send INFO record to output.""" rectype = 'info' self.record(rectype, message, **kwargs) pass def debug(self, message, **kwargs): """Send DEBUG record to the output.""" rectype = 'debug' self.record(rectype, message, **kwargs) pass def error(self, message=None, rectype='error', format=None, alarming=False, level=1, **kwargs): """Send ERROR record to the output. If exception in current traceback exists then method will format the exception according to `formatter.error` string presentation. If `formatter.error` is set to `False` the exception will be just printed in original Python style. Also method will send an alarm if alarming attribute is `True`, email output is enabled and SMTP server is configurated. If one of the limit triggers worked then application will be aborted. Parameters ---------- message : str, optional The message that must be written instead of exception. rectype : str, optional The type of error according to `rectypes` dictionary. format : str, optional The format of the error message. alarming : bool The argument is used to enable or disable the alarming mechanism for this certain call. level : int The argument is used to describe the error level. **kwargs The keyword arguments used for additional forms (variables) for record and message formatting. """ self._with_error = True self._count_errors += 1 format = self.formatter.error if format is None else format # Parse the error. err_type, err_value, err_tb = sys.exc_info() if message is None and err_type is not None: if isinstance(format, str) is True: err_name = err_type.__name__ err_value = err_value for tb in traceback.walk_tb(err_tb): f_code = tb[0].f_code err_file = os.path.abspath(f_code.co_filename) err_line = tb[1] err_obj = f_code.co_name self.record(rectype, message, error=True, err_name=err_name, err_value=err_value, err_file=err_file, err_line=err_line, err_obj=err_obj, **kwargs) elif format is False: exception = traceback.format_exception(err_type, err_value, err_tb) message = '\n' message += ''.join(exception) self.record(rectype, message, **kwargs) else: message = message or '' self.record(rectype, message, **kwargs) # Break execution in case of critical error if permitted. # The alarm will be generated at exit if it is configured. if self._control is True: if level >= self._maxlevel: sys.exit() if self._maxerrors is not False: if self._count_errors > self._maxerrors: sys.exit() # Send alarm if execution was not aborted but alarm is needed. if alarming is True: self.root.email.alarm() pass def warning(self, message=None, **kwargs): """Send WARNING error record to the output.""" self.error(message, rectype='warning', level=0, **kwargs) pass def critical(self, message=None, **kwargs): """Send CRITICAL error record to the output.""" self.error(message, rectype='critical', level=2, **kwargs) pass def head(self): """Send header to the output.""" string = self.header.create() self.write(string) pass def subhead(self, string): """Send subheader as upper-case text between two border lines to the output. Parameters ---------- string : str The text that will be presented as subheader. """ bound = f'{self.formatter.div*self.formatter.length}\n' string = f'{bound}\t{string}\n{bound}'.upper() self.write(string) pass def line(self, message): """Send raw text with the new line to the output. Parameters ---------- message : str The message that must be written. """ self.write(f'{message}\n') pass def bound(self, div=None, length=None): """Write horizontal border in the output. Useful when need to separate different blocks of information. Parameters ---------- div : str, optional Symbol that is used to bulid the bound. length : int, optional Lenght of the bound. """ border = self.formatter.div * self.formatter.length self.write(border + '\n') pass def blank(self, number=1): """Write blank lines in the output. Parameters ---------- number : int, optional The number of the blank lines that must be written. """ string = '\n'*number self.write(string) pass def ok(self, **kwargs): """Print INFO message with OK.""" rectype = 'info' message = self.messages['ok'] self.record(rectype, message, **kwargs) pass def success(self, **kwargs): """Print INFO message with SUCCESS.""" rectype = 'info' message = self.messages['success'] self.record(rectype, message, **kwargs) pass def fail(self, **kwargs): """Print INFO message with FAIL.""" rectype = 'info' message = self.messages['fail'] self.record(rectype, message, **kwargs) pass def restart(self): """Restart logging. Will open new file.""" self._start_date = dt.datetime.now() self.__calculate_restart_date() if self.root.file.status is True: self.root.file.new() if self.header.used is True: self.head() pass def send(self, *args, **kwargs): """Send email message. Note that SMTP server connection must be configured. """ self.root.email.send(*args, **kwargs) pass def set(self, **kwargs): """Update values in table. Note that DB connection must be configured. """ self.root.table.write(**kwargs) pass def _exit(self): # Inform about the error. if self._alarming is True and self._with_error is True: self.root.email.alarm() pass def __calculate_restart_date(self): """Calculate the date when logger must be restarted according to maxdays parameter. """ self.__restart_date = (self._start_date + dt.timedelta(days=self._maxdays)) pass def __check_file_stats(self): """Check the output file statistics to catch when current file must be closed and new one must be opened. """ if self.root.file.status is True: if self._maxsize is not False: if self.root.file.size is not None: if self.root.file.size > self._maxsize: self.restart() return if self._maxdays is not False: if self.__restart_date.day == dt.datetime.now().day: self.restart() return

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0

null

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1

6a79e21ee2f5d7ad67e69bd27f9206807683db56

488

py

Python

darling_ansible/python_venv/lib/python3.7/site-packages/oci/object_storage/transfer/constants.py

revnav/sandbox

f9c8422233d093b76821686b6c249417502cf61d

[ "Apache-2.0" ]

null

darling_ansible/python_venv/lib/python3.7/site-packages/oci/object_storage/transfer/constants.py

revnav/sandbox

f9c8422233d093b76821686b6c249417502cf61d

[ "Apache-2.0" ]

null

darling_ansible/python_venv/lib/python3.7/site-packages/oci/object_storage/transfer/constants.py

revnav/sandbox

f9c8422233d093b76821686b6c249417502cf61d

[ "Apache-2.0" ]

1

2020-06-25T03:12:58.000Z

# coding: utf-8 # Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. MEBIBYTE = 1024 * 1024 STREAMING_DEFAULT_PART_SIZE = 10 * MEBIBYTE DEFAULT_PART_SIZE = 128 * MEBIBYTE OBJECT_USE_MULTIPART_SIZE = 128 * MEBIBYTE

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false

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1

0

null

0

1

6a7ebe45370c220d4cb3303c8715bdc2a5f264ae

7,074

py

Python

python/sdk/client/api/log_api.py

ashwinath/merlin

087a7fa6fb21e4c771d64418bd58873175226ca1

[ "Apache-2.0" ]

null

python/sdk/client/api/log_api.py

ashwinath/merlin

087a7fa6fb21e4c771d64418bd58873175226ca1

[ "Apache-2.0" ]

null

python/sdk/client/api/log_api.py

ashwinath/merlin

087a7fa6fb21e4c771d64418bd58873175226ca1

[ "Apache-2.0" ]

null

# coding: utf-8 """ Merlin API Guide for accessing Merlin's model management, deployment, and serving functionalities # noqa: E501 OpenAPI spec version: 0.7.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from client.api_client import ApiClient class LogApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def logs_get(self, name, pod_name, namespace, cluster, **kwargs): # noqa: E501 """Retrieve log from a container # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.logs_get(name, pod_name, namespace, cluster, async_req=True) >>> result = thread.get() :param async_req bool :param str name: (required) :param str pod_name: (required) :param str namespace: (required) :param str cluster: (required) :param str follow: :param str limit_bytes: :param str pretty: :param str previous: :param str since_seconds: :param str since_time: :param str tail_lines: :param str timestamps: :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.logs_get_with_http_info(name, pod_name, namespace, cluster, **kwargs) # noqa: E501 else: (data) = self.logs_get_with_http_info(name, pod_name, namespace, cluster, **kwargs) # noqa: E501 return data def logs_get_with_http_info(self, name, pod_name, namespace, cluster, **kwargs): # noqa: E501 """Retrieve log from a container # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.logs_get_with_http_info(name, pod_name, namespace, cluster, async_req=True) >>> result = thread.get() :param async_req bool :param str name: (required) :param str pod_name: (required) :param str namespace: (required) :param str cluster: (required) :param str follow: :param str limit_bytes: :param str pretty: :param str previous: :param str since_seconds: :param str since_time: :param str tail_lines: :param str timestamps: :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['name', 'pod_name', 'namespace', 'cluster', 'follow', 'limit_bytes', 'pretty', 'previous', 'since_seconds', 'since_time', 'tail_lines', 'timestamps'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method logs_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'name' is set if ('name' not in params or params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `logs_get`") # noqa: E501 # verify the required parameter 'pod_name' is set if ('pod_name' not in params or params['pod_name'] is None): raise ValueError("Missing the required parameter `pod_name` when calling `logs_get`") # noqa: E501 # verify the required parameter 'namespace' is set if ('namespace' not in params or params['namespace'] is None): raise ValueError("Missing the required parameter `namespace` when calling `logs_get`") # noqa: E501 # verify the required parameter 'cluster' is set if ('cluster' not in params or params['cluster'] is None): raise ValueError("Missing the required parameter `cluster` when calling `logs_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'name' in params: query_params.append(('name', params['name'])) # noqa: E501 if 'pod_name' in params: query_params.append(('pod_name', params['pod_name'])) # noqa: E501 if 'namespace' in params: query_params.append(('namespace', params['namespace'])) # noqa: E501 if 'cluster' in params: query_params.append(('cluster', params['cluster'])) # noqa: E501 if 'follow' in params: query_params.append(('follow', params['follow'])) # noqa: E501 if 'limit_bytes' in params: query_params.append(('limit_bytes', params['limit_bytes'])) # noqa: E501 if 'pretty' in params: query_params.append(('pretty', params['pretty'])) # noqa: E501 if 'previous' in params: query_params.append(('previous', params['previous'])) # noqa: E501 if 'since_seconds' in params: query_params.append(('since_seconds', params['since_seconds'])) # noqa: E501 if 'since_time' in params: query_params.append(('since_time', params['since_time'])) # noqa: E501 if 'tail_lines' in params: query_params.append(('tail_lines', params['tail_lines'])) # noqa: E501 if 'timestamps' in params: query_params.append(('timestamps', params['timestamps'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['Bearer'] # noqa: E501 return self.api_client.call_api( '/logs', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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null

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1

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null

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1

6a7f701b1440f625bfec8817f0a39a899231c69f

105,704

py

Python

tencentcloud/dbbrain/v20210527/models.py

lleiyyang/tencentcloud-sdk-python

e6e6a4ce89286673b2322ae92d3c2fbf8665aa0b

[ "Apache-2.0" ]

465

2018-04-27T09:54:59.000Z

2022-03-29T02:18:01.000Z

tencentcloud/dbbrain/v20210527/models.py

lleiyyang/tencentcloud-sdk-python

e6e6a4ce89286673b2322ae92d3c2fbf8665aa0b

[ "Apache-2.0" ]

91

2018-04-27T09:48:11.000Z

2022-03-12T08:04:04.000Z

tencentcloud/dbbrain/v20210527/models.py

lleiyyang/tencentcloud-sdk-python

e6e6a4ce89286673b2322ae92d3c2fbf8665aa0b

[ "Apache-2.0" ]

232

2018-05-02T08:02:46.000Z

2022-03-30T08:02:48.000Z

# -*- coding: utf8 -*- # Copyright (c) 2017-2021 THL A29 Limited, a Tencent company. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from tencentcloud.common.abstract_model import AbstractModel class AddUserContactRequest(AbstractModel): """AddUserContact请求参数结构体 """ def __init__(self): r""" :param Name: 联系人姓名，由中英文、数字、空格、!@#$%^&*()_+-=（）组成，不能以下划线开头，长度在20以内。 :type Name: str :param ContactInfo: 邮箱地址，支持大小写字母、数字、下划线及@字符，不能以下划线开头，邮箱地址不可重复。 :type ContactInfo: str :param Product: 服务产品类型，固定值："mysql"。 :type Product: str """ self.Name = None self.ContactInfo = None self.Product = None def _deserialize(self, params): self.Name = params.get("Name") self.ContactInfo = params.get("ContactInfo") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class AddUserContactResponse(AbstractModel): """AddUserContact返回参数结构体 """ def __init__(self): r""" :param Id: 添加成功的联系人id。 :type Id: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Id = None self.RequestId = None def _deserialize(self, params): self.Id = params.get("Id") self.RequestId = params.get("RequestId") class ContactItem(AbstractModel): """联系人contact描述。 """ def __init__(self): r""" :param Id: 联系人id。 :type Id: int :param Name: 联系人姓名。 :type Name: str :param Mail: 联系人绑定的邮箱。 :type Mail: str """ self.Id = None self.Name = None self.Mail = None def _deserialize(self, params): self.Id = params.get("Id") self.Name = params.get("Name") self.Mail = params.get("Mail") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateDBDiagReportTaskRequest(AbstractModel): """CreateDBDiagReportTask请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param StartTime: 开始时间，如“2020-11-08T14:00:00+08:00”。 :type StartTime: str :param EndTime: 结束时间，如“2020-11-09T14:00:00+08:00”。 :type EndTime: str :param SendMailFlag: 是否发送邮件: 0 - 否，1 - 是。 :type SendMailFlag: int :param ContactPerson: 接收邮件的联系人ID数组。 :type ContactPerson: list of int :param ContactGroup: 接收邮件的联系组ID数组。 :type ContactGroup: list of int :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认值为"mysql"。 :type Product: str """ self.InstanceId = None self.StartTime = None self.EndTime = None self.SendMailFlag = None self.ContactPerson = None self.ContactGroup = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.SendMailFlag = params.get("SendMailFlag") self.ContactPerson = params.get("ContactPerson") self.ContactGroup = params.get("ContactGroup") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateDBDiagReportTaskResponse(AbstractModel): """CreateDBDiagReportTask返回参数结构体 """ def __init__(self): r""" :param AsyncRequestId: 异步任务的请求 ID，可使用此 ID 查询异步任务的执行结果。注意：此字段可能返回 null，表示取不到有效值。 :type AsyncRequestId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.AsyncRequestId = None self.RequestId = None def _deserialize(self, params): self.AsyncRequestId = params.get("AsyncRequestId") self.RequestId = params.get("RequestId") class CreateDBDiagReportUrlRequest(AbstractModel): """CreateDBDiagReportUrl请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param AsyncRequestId: 健康报告相应的任务ID，可通过DescribeDBDiagReportTasks查询。 :type AsyncRequestId: int :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL；"cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.AsyncRequestId = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.AsyncRequestId = params.get("AsyncRequestId") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateDBDiagReportUrlResponse(AbstractModel): """CreateDBDiagReportUrl返回参数结构体 """ def __init__(self): r""" :param ReportUrl: 健康报告浏览地址。 :type ReportUrl: str :param ExpireTime: 健康报告浏览地址到期时间戳（秒）。 :type ExpireTime: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.ReportUrl = None self.ExpireTime = None self.RequestId = None def _deserialize(self, params): self.ReportUrl = params.get("ReportUrl") self.ExpireTime = params.get("ExpireTime") self.RequestId = params.get("RequestId") class CreateMailProfileRequest(AbstractModel): """CreateMailProfile请求参数结构体 """ def __init__(self): r""" :param ProfileInfo: 邮件配置内容。 :type ProfileInfo: :class:`tencentcloud.dbbrain.v20210527.models.ProfileInfo` :param ProfileLevel: 配置级别，支持值包括："User" - 用户级别，"Instance" - 实例级别，其中数据库巡检邮件配置为用户级别，定期生成邮件配置为实例级别。 :type ProfileLevel: str :param ProfileName: 配置名称，需要保持唯一性，数据库巡检邮件配置名称自拟；定期生成邮件配置命名格式："scheduler_" + {instanceId}，如"schduler_cdb-test"。 :type ProfileName: str :param ProfileType: 配置类型，支持值包括："dbScan_mail_configuration" - 数据库巡检邮件配置，"scheduler_mail_configuration" - 定期生成邮件配置。 :type ProfileType: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL。 :type Product: str :param BindInstanceIds: 配置绑定的实例ID，当配置级别为"Instance"时需要传入且只能为一个实例；当配置级别为“User”时，此参数不填。 :type BindInstanceIds: list of str """ self.ProfileInfo = None self.ProfileLevel = None self.ProfileName = None self.ProfileType = None self.Product = None self.BindInstanceIds = None def _deserialize(self, params): if params.get("ProfileInfo") is not None: self.ProfileInfo = ProfileInfo() self.ProfileInfo._deserialize(params.get("ProfileInfo")) self.ProfileLevel = params.get("ProfileLevel") self.ProfileName = params.get("ProfileName") self.ProfileType = params.get("ProfileType") self.Product = params.get("Product") self.BindInstanceIds = params.get("BindInstanceIds") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateMailProfileResponse(AbstractModel): """CreateMailProfile返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class CreateSchedulerMailProfileRequest(AbstractModel): """CreateSchedulerMailProfile请求参数结构体 """ def __init__(self): r""" :param WeekConfiguration: 取值范围1-7，分别代表周一至周日。 :type WeekConfiguration: list of int :param ProfileInfo: 邮件配置内容。 :type ProfileInfo: :class:`tencentcloud.dbbrain.v20210527.models.ProfileInfo` :param ProfileName: 配置名称，需要保持唯一性，定期生成邮件配置命名格式："scheduler_" + {instanceId}，如"schduler_cdb-test"。 :type ProfileName: str :param BindInstanceId: 配置订阅的实例ID。 :type BindInstanceId: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str """ self.WeekConfiguration = None self.ProfileInfo = None self.ProfileName = None self.BindInstanceId = None self.Product = None def _deserialize(self, params): self.WeekConfiguration = params.get("WeekConfiguration") if params.get("ProfileInfo") is not None: self.ProfileInfo = ProfileInfo() self.ProfileInfo._deserialize(params.get("ProfileInfo")) self.ProfileName = params.get("ProfileName") self.BindInstanceId = params.get("BindInstanceId") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateSchedulerMailProfileResponse(AbstractModel): """CreateSchedulerMailProfile返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class CreateSecurityAuditLogExportTaskRequest(AbstractModel): """CreateSecurityAuditLogExportTask请求参数结构体 """ def __init__(self): r""" :param SecAuditGroupId: 安全审计组Id。 :type SecAuditGroupId: str :param StartTime: 导出日志开始时间，例如2020-12-28 00:00:00。 :type StartTime: str :param EndTime: 导出日志结束时间，例如2020-12-28 01:00:00。 :type EndTime: str :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL。 :type Product: str :param DangerLevels: 日志风险等级列表，支持值包括：0 无风险；1 低风险；2 中风险；3 高风险。 :type DangerLevels: list of int """ self.SecAuditGroupId = None self.StartTime = None self.EndTime = None self.Product = None self.DangerLevels = None def _deserialize(self, params): self.SecAuditGroupId = params.get("SecAuditGroupId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.Product = params.get("Product") self.DangerLevels = params.get("DangerLevels") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class CreateSecurityAuditLogExportTaskResponse(AbstractModel): """CreateSecurityAuditLogExportTask返回参数结构体 """ def __init__(self): r""" :param AsyncRequestId: 日志导出任务Id。 :type AsyncRequestId: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.AsyncRequestId = None self.RequestId = None def _deserialize(self, params): self.AsyncRequestId = params.get("AsyncRequestId") self.RequestId = params.get("RequestId") class DeleteSecurityAuditLogExportTasksRequest(AbstractModel): """DeleteSecurityAuditLogExportTasks请求参数结构体 """ def __init__(self): r""" :param SecAuditGroupId: 安全审计组Id。 :type SecAuditGroupId: str :param AsyncRequestIds: 日志导出任务Id列表，接口会忽略不存在或已删除的任务Id。 :type AsyncRequestIds: list of int non-negative :param Product: 服务产品类型，支持值： "mysql" - 云数据库 MySQL。 :type Product: str """ self.SecAuditGroupId = None self.AsyncRequestIds = None self.Product = None def _deserialize(self, params): self.SecAuditGroupId = params.get("SecAuditGroupId") self.AsyncRequestIds = params.get("AsyncRequestIds") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DeleteSecurityAuditLogExportTasksResponse(AbstractModel): """DeleteSecurityAuditLogExportTasks返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class DescribeAllUserContactRequest(AbstractModel): """DescribeAllUserContact请求参数结构体 """ def __init__(self): r""" :param Product: 服务产品类型，固定值：mysql。 :type Product: str :param Names: 联系人名数组，支持模糊搜索。 :type Names: list of str """ self.Product = None self.Names = None def _deserialize(self, params): self.Product = params.get("Product") self.Names = params.get("Names") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeAllUserContactResponse(AbstractModel): """DescribeAllUserContact返回参数结构体 """ def __init__(self): r""" :param TotalCount: 联系人的总数量。 :type TotalCount: int :param Contacts: 联系人的信息。注意：此字段可能返回 null，表示取不到有效值。 :type Contacts: list of ContactItem :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.Contacts = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") if params.get("Contacts") is not None: self.Contacts = [] for item in params.get("Contacts"): obj = ContactItem() obj._deserialize(item) self.Contacts.append(obj) self.RequestId = params.get("RequestId") class DescribeAllUserGroupRequest(AbstractModel): """DescribeAllUserGroup请求参数结构体 """ def __init__(self): r""" :param Product: 服务产品类型，固定值：mysql。 :type Product: str :param Names: 联系组名称数组，支持模糊搜索。 :type Names: list of str """ self.Product = None self.Names = None def _deserialize(self, params): self.Product = params.get("Product") self.Names = params.get("Names") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeAllUserGroupResponse(AbstractModel): """DescribeAllUserGroup返回参数结构体 """ def __init__(self): r""" :param TotalCount: 组总数。 :type TotalCount: int :param Groups: 组信息。注意：此字段可能返回 null，表示取不到有效值。 :type Groups: list of GroupItem :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.Groups = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") if params.get("Groups") is not None: self.Groups = [] for item in params.get("Groups"): obj = GroupItem() obj._deserialize(item) self.Groups.append(obj) self.RequestId = params.get("RequestId") class DescribeDBDiagEventRequest(AbstractModel): """DescribeDBDiagEvent请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param EventId: 事件 ID 。通过“获取实例诊断历史DescribeDBDiagHistory”获取。 :type EventId: int :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.EventId = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.EventId = params.get("EventId") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeDBDiagEventResponse(AbstractModel): """DescribeDBDiagEvent返回参数结构体 """ def __init__(self): r""" :param DiagItem: 诊断项。 :type DiagItem: str :param DiagType: 诊断类型。 :type DiagType: str :param EventId: 事件 ID 。 :type EventId: int :param Explanation: 诊断事件详情，若无附加解释信息则输出为空。 :type Explanation: str :param Outline: 诊断概要。 :type Outline: str :param Problem: 诊断出的问题。 :type Problem: str :param Severity: 严重程度。严重程度分为5级，按影响程度从高至低分别为：1：致命，2：严重，3：告警，4：提示，5：健康。 :type Severity: int :param StartTime: 开始时间 :type StartTime: str :param Suggestions: 诊断建议，若无建议则输出为空。 :type Suggestions: str :param Metric: 保留字段。注意：此字段可能返回 null，表示取不到有效值。 :type Metric: str :param EndTime: 结束时间。 :type EndTime: str :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.DiagItem = None self.DiagType = None self.EventId = None self.Explanation = None self.Outline = None self.Problem = None self.Severity = None self.StartTime = None self.Suggestions = None self.Metric = None self.EndTime = None self.RequestId = None def _deserialize(self, params): self.DiagItem = params.get("DiagItem") self.DiagType = params.get("DiagType") self.EventId = params.get("EventId") self.Explanation = params.get("Explanation") self.Outline = params.get("Outline") self.Problem = params.get("Problem") self.Severity = params.get("Severity") self.StartTime = params.get("StartTime") self.Suggestions = params.get("Suggestions") self.Metric = params.get("Metric") self.EndTime = params.get("EndTime") self.RequestId = params.get("RequestId") class DescribeDBDiagHistoryRequest(AbstractModel): """DescribeDBDiagHistory请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param StartTime: 开始时间，如“2019-09-10 12:13:14”。 :type StartTime: str :param EndTime: 结束时间，如“2019-09-11 12:13:14”，结束时间与开始时间的间隔最大可为2天。 :type EndTime: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.StartTime = None self.EndTime = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeDBDiagHistoryResponse(AbstractModel): """DescribeDBDiagHistory返回参数结构体 """ def __init__(self): r""" :param Events: 事件描述。 :type Events: list of DiagHistoryEventItem :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Events = None self.RequestId = None def _deserialize(self, params): if params.get("Events") is not None: self.Events = [] for item in params.get("Events"): obj = DiagHistoryEventItem() obj._deserialize(item) self.Events.append(obj) self.RequestId = params.get("RequestId") class DescribeDBDiagReportTasksRequest(AbstractModel): """DescribeDBDiagReportTasks请求参数结构体 """ def __init__(self): r""" :param StartTime: 第一个任务的开始时间，用于范围查询，时间格式如：2019-09-10 12:13:14。 :type StartTime: str :param EndTime: 最后一个任务的开始时间，用于范围查询，时间格式如：2019-09-10 12:13:14。 :type EndTime: str :param InstanceIds: 实例ID数组，用于筛选指定实例的任务列表。 :type InstanceIds: list of str :param Sources: 任务的触发来源，支持的取值包括："DAILY_INSPECTION" - 实例巡检；"SCHEDULED" - 定时生成；"MANUAL" - 手动触发。 :type Sources: list of str :param HealthLevels: 报告的健康等级，支持的取值包括："HEALTH" - 健康；"SUB_HEALTH" - 亚健康；"RISK" - 危险；"HIGH_RISK" - 高危。 :type HealthLevels: str :param TaskStatuses: 任务的状态，支持的取值包括："created" - 新建；"chosen" - 待执行； "running" - 执行中；"failed" - 失败；"finished" - 已完成。 :type TaskStatuses: str :param Offset: 偏移量，默认0。 :type Offset: int :param Limit: 返回数量，默认20，最大值为100。 :type Limit: int :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL；"cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str """ self.StartTime = None self.EndTime = None self.InstanceIds = None self.Sources = None self.HealthLevels = None self.TaskStatuses = None self.Offset = None self.Limit = None self.Product = None def _deserialize(self, params): self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.InstanceIds = params.get("InstanceIds") self.Sources = params.get("Sources") self.HealthLevels = params.get("HealthLevels") self.TaskStatuses = params.get("TaskStatuses") self.Offset = params.get("Offset") self.Limit = params.get("Limit") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeDBDiagReportTasksResponse(AbstractModel): """DescribeDBDiagReportTasks返回参数结构体 """ def __init__(self): r""" :param TotalCount: 任务总数目。 :type TotalCount: int :param Tasks: 任务列表。 :type Tasks: list of HealthReportTask :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.Tasks = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") if params.get("Tasks") is not None: self.Tasks = [] for item in params.get("Tasks"): obj = HealthReportTask() obj._deserialize(item) self.Tasks.append(obj) self.RequestId = params.get("RequestId") class DescribeDBSpaceStatusRequest(AbstractModel): """DescribeDBSpaceStatus请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param RangeDays: 时间段天数，截止日期为当日，默认为7天。 :type RangeDays: int :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.RangeDays = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.RangeDays = params.get("RangeDays") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeDBSpaceStatusResponse(AbstractModel): """DescribeDBSpaceStatus返回参数结构体 """ def __init__(self): r""" :param Growth: 磁盘增长量(MB)。 :type Growth: int :param Remain: 磁盘剩余(MB)。 :type Remain: int :param Total: 磁盘总量(MB)。 :type Total: int :param AvailableDays: 预计可用天数。 :type AvailableDays: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Growth = None self.Remain = None self.Total = None self.AvailableDays = None self.RequestId = None def _deserialize(self, params): self.Growth = params.get("Growth") self.Remain = params.get("Remain") self.Total = params.get("Total") self.AvailableDays = params.get("AvailableDays") self.RequestId = params.get("RequestId") class DescribeDiagDBInstancesRequest(AbstractModel): """DescribeDiagDBInstances请求参数结构体 """ def __init__(self): r""" :param IsSupported: 是否是DBbrain支持的实例，固定传 true。 :type IsSupported: bool :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str :param Offset: 分页参数，偏移量。 :type Offset: int :param Limit: 分页参数，分页值，最大值为100。 :type Limit: int :param InstanceNames: 根据实例名称条件查询。 :type InstanceNames: list of str :param InstanceIds: 根据实例ID条件查询。 :type InstanceIds: list of str :param Regions: 根据地域条件查询。 :type Regions: list of str """ self.IsSupported = None self.Product = None self.Offset = None self.Limit = None self.InstanceNames = None self.InstanceIds = None self.Regions = None def _deserialize(self, params): self.IsSupported = params.get("IsSupported") self.Product = params.get("Product") self.Offset = params.get("Offset") self.Limit = params.get("Limit") self.InstanceNames = params.get("InstanceNames") self.InstanceIds = params.get("InstanceIds") self.Regions = params.get("Regions") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeDiagDBInstancesResponse(AbstractModel): """DescribeDiagDBInstances返回参数结构体 """ def __init__(self): r""" :param TotalCount: 实例总数。 :type TotalCount: int :param DbScanStatus: 全实例巡检状态：0：开启全实例巡检；1：未开启全实例巡检。 :type DbScanStatus: int :param Items: 实例相关信息。 :type Items: list of InstanceInfo :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.DbScanStatus = None self.Items = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") self.DbScanStatus = params.get("DbScanStatus") if params.get("Items") is not None: self.Items = [] for item in params.get("Items"): obj = InstanceInfo() obj._deserialize(item) self.Items.append(obj) self.RequestId = params.get("RequestId") class DescribeHealthScoreRequest(AbstractModel): """DescribeHealthScore请求参数结构体 """ def __init__(self): r""" :param InstanceId: 需要获取健康得分的实例ID。 :type InstanceId: str :param Time: 获取健康得分的时间，时间格式如：2019-09-10 12:13:14。 :type Time: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Time = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Time = params.get("Time") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeHealthScoreResponse(AbstractModel): """DescribeHealthScore返回参数结构体 """ def __init__(self): r""" :param Data: 健康得分以及异常扣分项。 :type Data: :class:`tencentcloud.dbbrain.v20210527.models.HealthScoreInfo` :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Data = None self.RequestId = None def _deserialize(self, params): if params.get("Data") is not None: self.Data = HealthScoreInfo() self.Data._deserialize(params.get("Data")) self.RequestId = params.get("RequestId") class DescribeMailProfileRequest(AbstractModel): """DescribeMailProfile请求参数结构体 """ def __init__(self): r""" :param ProfileType: 配置类型，支持值包括："dbScan_mail_configuration" - 数据库巡检邮件配置，"scheduler_mail_configuration" - 定期生成邮件配置。 :type ProfileType: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str :param Offset: 分页偏移量。 :type Offset: int :param Limit: 分页单位，最大支持50。 :type Limit: int :param ProfileName: 根据邮件配置名称查询，定期发送的邮件配置名称遵循："scheduler_"+{instanceId}的规则。 :type ProfileName: str """ self.ProfileType = None self.Product = None self.Offset = None self.Limit = None self.ProfileName = None def _deserialize(self, params): self.ProfileType = params.get("ProfileType") self.Product = params.get("Product") self.Offset = params.get("Offset") self.Limit = params.get("Limit") self.ProfileName = params.get("ProfileName") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeMailProfileResponse(AbstractModel): """DescribeMailProfile返回参数结构体 """ def __init__(self): r""" :param ProfileList: 邮件配置详情。注意：此字段可能返回 null，表示取不到有效值。 :type ProfileList: list of UserProfile :param TotalCount: 邮件模版总数。注意：此字段可能返回 null，表示取不到有效值。 :type TotalCount: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.ProfileList = None self.TotalCount = None self.RequestId = None def _deserialize(self, params): if params.get("ProfileList") is not None: self.ProfileList = [] for item in params.get("ProfileList"): obj = UserProfile() obj._deserialize(item) self.ProfileList.append(obj) self.TotalCount = params.get("TotalCount") self.RequestId = params.get("RequestId") class DescribeMySqlProcessListRequest(AbstractModel): """DescribeMySqlProcessList请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param ID: 线程的ID，用于筛选线程列表。 :type ID: int :param User: 线程的操作账号名，用于筛选线程列表。 :type User: str :param Host: 线程的操作主机地址，用于筛选线程列表。 :type Host: str :param DB: 线程的操作数据库，用于筛选线程列表。 :type DB: str :param State: 线程的操作状态，用于筛选线程列表。 :type State: str :param Command: 线程的执行类型，用于筛选线程列表。 :type Command: str :param Time: 线程的操作时长最小值，单位秒，用于筛选操作时长大于该值的线程列表。 :type Time: int :param Info: 线程的操作语句，用于筛选线程列表。 :type Info: str :param Limit: 返回数量，默认20。 :type Limit: int :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL；"cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.ID = None self.User = None self.Host = None self.DB = None self.State = None self.Command = None self.Time = None self.Info = None self.Limit = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.ID = params.get("ID") self.User = params.get("User") self.Host = params.get("Host") self.DB = params.get("DB") self.State = params.get("State") self.Command = params.get("Command") self.Time = params.get("Time") self.Info = params.get("Info") self.Limit = params.get("Limit") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeMySqlProcessListResponse(AbstractModel): """DescribeMySqlProcessList返回参数结构体 """ def __init__(self): r""" :param ProcessList: 实时线程列表。 :type ProcessList: list of MySqlProcess :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.ProcessList = None self.RequestId = None def _deserialize(self, params): if params.get("ProcessList") is not None: self.ProcessList = [] for item in params.get("ProcessList"): obj = MySqlProcess() obj._deserialize(item) self.ProcessList.append(obj) self.RequestId = params.get("RequestId") class DescribeSecurityAuditLogDownloadUrlsRequest(AbstractModel): """DescribeSecurityAuditLogDownloadUrls请求参数结构体 """ def __init__(self): r""" :param SecAuditGroupId: 安全审计组Id。 :type SecAuditGroupId: str :param AsyncRequestId: 异步任务Id。 :type AsyncRequestId: int :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL。 :type Product: str """ self.SecAuditGroupId = None self.AsyncRequestId = None self.Product = None def _deserialize(self, params): self.SecAuditGroupId = params.get("SecAuditGroupId") self.AsyncRequestId = params.get("AsyncRequestId") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeSecurityAuditLogDownloadUrlsResponse(AbstractModel): """DescribeSecurityAuditLogDownloadUrls返回参数结构体 """ def __init__(self): r""" :param Urls: 导出结果的COS链接列表。当结果集很大时，可能会切分为多个url下载。 :type Urls: list of str :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Urls = None self.RequestId = None def _deserialize(self, params): self.Urls = params.get("Urls") self.RequestId = params.get("RequestId") class DescribeSecurityAuditLogExportTasksRequest(AbstractModel): """DescribeSecurityAuditLogExportTasks请求参数结构体 """ def __init__(self): r""" :param SecAuditGroupId: 安全审计组Id。 :type SecAuditGroupId: str :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL。 :type Product: str :param AsyncRequestIds: 日志导出任务Id列表。 :type AsyncRequestIds: list of int non-negative :param Offset: 偏移量，默认0。 :type Offset: int :param Limit: 返回数量，默认20，最大值为100。 :type Limit: int """ self.SecAuditGroupId = None self.Product = None self.AsyncRequestIds = None self.Offset = None self.Limit = None def _deserialize(self, params): self.SecAuditGroupId = params.get("SecAuditGroupId") self.Product = params.get("Product") self.AsyncRequestIds = params.get("AsyncRequestIds") self.Offset = params.get("Offset") self.Limit = params.get("Limit") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeSecurityAuditLogExportTasksResponse(AbstractModel): """DescribeSecurityAuditLogExportTasks返回参数结构体 """ def __init__(self): r""" :param Tasks: 安全审计日志导出任务列表。 :type Tasks: list of SecLogExportTaskInfo :param TotalCount: 安全审计日志导出任务总数。 :type TotalCount: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Tasks = None self.TotalCount = None self.RequestId = None def _deserialize(self, params): if params.get("Tasks") is not None: self.Tasks = [] for item in params.get("Tasks"): obj = SecLogExportTaskInfo() obj._deserialize(item) self.Tasks.append(obj) self.TotalCount = params.get("TotalCount") self.RequestId = params.get("RequestId") class DescribeSlowLogTimeSeriesStatsRequest(AbstractModel): """DescribeSlowLogTimeSeriesStats请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param StartTime: 开始时间，如“2019-09-10 12:13:14”。 :type StartTime: str :param EndTime: 结束时间，如“2019-09-10 12:13:14”，结束时间与开始时间的间隔最大可为7天。 :type EndTime: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.StartTime = None self.EndTime = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeSlowLogTimeSeriesStatsResponse(AbstractModel): """DescribeSlowLogTimeSeriesStats返回参数结构体 """ def __init__(self): r""" :param Period: 柱间单位时间间隔，单位为秒。 :type Period: int :param TimeSeries: 单位时间间隔内慢日志数量统计。 :type TimeSeries: list of TimeSlice :param SeriesData: 单位时间间隔内的实例 cpu 利用率监控数据。 :type SeriesData: :class:`tencentcloud.dbbrain.v20210527.models.MonitorMetricSeriesData` :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Period = None self.TimeSeries = None self.SeriesData = None self.RequestId = None def _deserialize(self, params): self.Period = params.get("Period") if params.get("TimeSeries") is not None: self.TimeSeries = [] for item in params.get("TimeSeries"): obj = TimeSlice() obj._deserialize(item) self.TimeSeries.append(obj) if params.get("SeriesData") is not None: self.SeriesData = MonitorMetricSeriesData() self.SeriesData._deserialize(params.get("SeriesData")) self.RequestId = params.get("RequestId") class DescribeSlowLogTopSqlsRequest(AbstractModel): """DescribeSlowLogTopSqls请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param StartTime: 开始时间，如“2019-09-10 12:13:14”。 :type StartTime: str :param EndTime: 截止时间，如“2019-09-11 10:13:14”，截止时间与开始时间的间隔小于7天。 :type EndTime: str :param SortBy: 排序键，目前支持 QueryTime,ExecTimes,RowsSent,LockTime以及RowsExamined 等排序键，默认为QueryTime。 :type SortBy: str :param OrderBy: 排序方式，支持ASC（升序）以及DESC（降序），默认为DESC。 :type OrderBy: str :param Limit: 返回数量，默认为20，最大值为100。 :type Limit: int :param Offset: 偏移量，默认为0。 :type Offset: int :param SchemaList: 数据库名称数组。 :type SchemaList: list of SchemaItem :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.StartTime = None self.EndTime = None self.SortBy = None self.OrderBy = None self.Limit = None self.Offset = None self.SchemaList = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.SortBy = params.get("SortBy") self.OrderBy = params.get("OrderBy") self.Limit = params.get("Limit") self.Offset = params.get("Offset") if params.get("SchemaList") is not None: self.SchemaList = [] for item in params.get("SchemaList"): obj = SchemaItem() obj._deserialize(item) self.SchemaList.append(obj) self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeSlowLogTopSqlsResponse(AbstractModel): """DescribeSlowLogTopSqls返回参数结构体 """ def __init__(self): r""" :param TotalCount: 符合条件的记录总数。 :type TotalCount: int :param Rows: 慢日志 top sql 列表 :type Rows: list of SlowLogTopSqlItem :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.Rows = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") if params.get("Rows") is not None: self.Rows = [] for item in params.get("Rows"): obj = SlowLogTopSqlItem() obj._deserialize(item) self.Rows.append(obj) self.RequestId = params.get("RequestId") class DescribeSlowLogUserHostStatsRequest(AbstractModel): """DescribeSlowLogUserHostStats请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param StartTime: 查询范围的开始时间，时间格式如：2019-09-10 12:13:14。 :type StartTime: str :param EndTime: 查询范围的结束时间，时间格式如：2019-09-10 12:13:14。 :type EndTime: str :param Product: 服务产品类型，支持值："mysql" - 云数据库 MySQL；"cynosdb" - 云数据库 TDSQL-C for MySQL，默认为"mysql"。 :type Product: str :param Md5: SOL模板的MD5值 :type Md5: str """ self.InstanceId = None self.StartTime = None self.EndTime = None self.Product = None self.Md5 = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.Product = params.get("Product") self.Md5 = params.get("Md5") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeSlowLogUserHostStatsResponse(AbstractModel): """DescribeSlowLogUserHostStats返回参数结构体 """ def __init__(self): r""" :param TotalCount: 来源地址数目。 :type TotalCount: int :param Items: 各来源地址的慢日志占比详情列表。 :type Items: list of SlowLogHost :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TotalCount = None self.Items = None self.RequestId = None def _deserialize(self, params): self.TotalCount = params.get("TotalCount") if params.get("Items") is not None: self.Items = [] for item in params.get("Items"): obj = SlowLogHost() obj._deserialize(item) self.Items.append(obj) self.RequestId = params.get("RequestId") class DescribeTopSpaceSchemaTimeSeriesRequest(AbstractModel): """DescribeTopSpaceSchemaTimeSeries请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param Limit: 返回的Top库数量，最大值为100，默认为20。 :type Limit: int :param SortBy: 筛选Top库所用的排序字段，可选字段包含DataLength、IndexLength、TotalLength、DataFree、FragRatio、TableRows、PhysicalFileSize（仅云数据库 MySQL实例支持），云数据库 MySQL实例默认为 PhysicalFileSize，其他产品实例默认为TotalLength。 :type SortBy: str :param StartDate: 开始日期，如“2021-01-01”，最早为当日的前第29天，默认为截止日期的前第6天。 :type StartDate: str :param EndDate: 截止日期，如“2021-01-01”，最早为当日的前第29天，默认为当日。 :type EndDate: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Limit = None self.SortBy = None self.StartDate = None self.EndDate = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Limit = params.get("Limit") self.SortBy = params.get("SortBy") self.StartDate = params.get("StartDate") self.EndDate = params.get("EndDate") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeTopSpaceSchemaTimeSeriesResponse(AbstractModel): """DescribeTopSpaceSchemaTimeSeries返回参数结构体 """ def __init__(self): r""" :param TopSpaceSchemaTimeSeries: 返回的Top库空间统计信息的时序数据列表。 :type TopSpaceSchemaTimeSeries: list of SchemaSpaceTimeSeries :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TopSpaceSchemaTimeSeries = None self.RequestId = None def _deserialize(self, params): if params.get("TopSpaceSchemaTimeSeries") is not None: self.TopSpaceSchemaTimeSeries = [] for item in params.get("TopSpaceSchemaTimeSeries"): obj = SchemaSpaceTimeSeries() obj._deserialize(item) self.TopSpaceSchemaTimeSeries.append(obj) self.RequestId = params.get("RequestId") class DescribeTopSpaceSchemasRequest(AbstractModel): """DescribeTopSpaceSchemas请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param Limit: 返回的Top库数量，最大值为100，默认为20。 :type Limit: int :param SortBy: 筛选Top库所用的排序字段，可选字段包含DataLength、IndexLength、TotalLength、DataFree、FragRatio、TableRows、PhysicalFileSize（仅云数据库 MySQL实例支持），云数据库 MySQL实例默认为 PhysicalFileSize，其他产品实例默认为TotalLength。 :type SortBy: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Limit = None self.SortBy = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Limit = params.get("Limit") self.SortBy = params.get("SortBy") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeTopSpaceSchemasResponse(AbstractModel): """DescribeTopSpaceSchemas返回参数结构体 """ def __init__(self): r""" :param TopSpaceSchemas: 返回的Top库空间统计信息列表。 :type TopSpaceSchemas: list of SchemaSpaceData :param Timestamp: 采集库空间数据的时间戳（秒）。 :type Timestamp: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TopSpaceSchemas = None self.Timestamp = None self.RequestId = None def _deserialize(self, params): if params.get("TopSpaceSchemas") is not None: self.TopSpaceSchemas = [] for item in params.get("TopSpaceSchemas"): obj = SchemaSpaceData() obj._deserialize(item) self.TopSpaceSchemas.append(obj) self.Timestamp = params.get("Timestamp") self.RequestId = params.get("RequestId") class DescribeTopSpaceTableTimeSeriesRequest(AbstractModel): """DescribeTopSpaceTableTimeSeries请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param Limit: 返回的Top表数量，最大值为100，默认为20。 :type Limit: int :param SortBy: 筛选Top表所用的排序字段，可选字段包含DataLength、IndexLength、TotalLength、DataFree、FragRatio、TableRows、PhysicalFileSize，默认为 PhysicalFileSize。 :type SortBy: str :param StartDate: 开始日期，如“2021-01-01”，最早为当日的前第29天，默认为截止日期的前第6天。 :type StartDate: str :param EndDate: 截止日期，如“2021-01-01”，最早为当日的前第29天，默认为当日。 :type EndDate: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Limit = None self.SortBy = None self.StartDate = None self.EndDate = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Limit = params.get("Limit") self.SortBy = params.get("SortBy") self.StartDate = params.get("StartDate") self.EndDate = params.get("EndDate") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeTopSpaceTableTimeSeriesResponse(AbstractModel): """DescribeTopSpaceTableTimeSeries返回参数结构体 """ def __init__(self): r""" :param TopSpaceTableTimeSeries: 返回的Top表空间统计信息的时序数据列表。 :type TopSpaceTableTimeSeries: list of TableSpaceTimeSeries :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TopSpaceTableTimeSeries = None self.RequestId = None def _deserialize(self, params): if params.get("TopSpaceTableTimeSeries") is not None: self.TopSpaceTableTimeSeries = [] for item in params.get("TopSpaceTableTimeSeries"): obj = TableSpaceTimeSeries() obj._deserialize(item) self.TopSpaceTableTimeSeries.append(obj) self.RequestId = params.get("RequestId") class DescribeTopSpaceTablesRequest(AbstractModel): """DescribeTopSpaceTables请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例 ID 。 :type InstanceId: str :param Limit: 返回的Top表数量，最大值为100，默认为20。 :type Limit: int :param SortBy: 筛选Top表所用的排序字段，可选字段包含DataLength、IndexLength、TotalLength、DataFree、FragRatio、TableRows、PhysicalFileSize（仅云数据库 MySQL实例支持），云数据库 MySQL实例默认为 PhysicalFileSize，其他产品实例默认为TotalLength。 :type SortBy: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Limit = None self.SortBy = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Limit = params.get("Limit") self.SortBy = params.get("SortBy") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeTopSpaceTablesResponse(AbstractModel): """DescribeTopSpaceTables返回参数结构体 """ def __init__(self): r""" :param TopSpaceTables: 返回的Top表空间统计信息列表。 :type TopSpaceTables: list of TableSpaceData :param Timestamp: 采集表空间数据的时间戳（秒）。 :type Timestamp: int :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.TopSpaceTables = None self.Timestamp = None self.RequestId = None def _deserialize(self, params): if params.get("TopSpaceTables") is not None: self.TopSpaceTables = [] for item in params.get("TopSpaceTables"): obj = TableSpaceData() obj._deserialize(item) self.TopSpaceTables.append(obj) self.Timestamp = params.get("Timestamp") self.RequestId = params.get("RequestId") class DescribeUserSqlAdviceRequest(AbstractModel): """DescribeUserSqlAdvice请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param SqlText: SQL语句。 :type SqlText: str :param Schema: 库名。 :type Schema: str """ self.InstanceId = None self.SqlText = None self.Schema = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.SqlText = params.get("SqlText") self.Schema = params.get("Schema") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class DescribeUserSqlAdviceResponse(AbstractModel): """DescribeUserSqlAdvice返回参数结构体 """ def __init__(self): r""" :param Advices: SQL优化建议，可解析为JSON数组，无需优化时输出为空。 :type Advices: str :param Comments: SQL优化建议备注，可解析为String数组，无需优化时输出为空。 :type Comments: str :param SqlText: SQL语句。 :type SqlText: str :param Schema: 库名。 :type Schema: str :param Tables: 相关表的DDL信息，可解析为JSON数组。 :type Tables: str :param SqlPlan: SQL执行计划，可解析为JSON，无需优化时输出为空。 :type SqlPlan: str :param Cost: SQL优化后的成本节约详情，可解析为JSON，无需优化时输出为空。 :type Cost: str :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Advices = None self.Comments = None self.SqlText = None self.Schema = None self.Tables = None self.SqlPlan = None self.Cost = None self.RequestId = None def _deserialize(self, params): self.Advices = params.get("Advices") self.Comments = params.get("Comments") self.SqlText = params.get("SqlText") self.Schema = params.get("Schema") self.Tables = params.get("Tables") self.SqlPlan = params.get("SqlPlan") self.Cost = params.get("Cost") self.RequestId = params.get("RequestId") class DiagHistoryEventItem(AbstractModel): """实例诊断历史事件 """ def __init__(self): r""" :param DiagType: 诊断类型。 :type DiagType: str :param EndTime: 结束时间。 :type EndTime: str :param StartTime: 开始时间。 :type StartTime: str :param EventId: 事件唯一ID 。 :type EventId: int :param Severity: 严重程度。严重程度分为5级，按影响程度从高至低分别为：1：致命，2：严重，3：告警，4：提示，5：健康。 :type Severity: int :param Outline: 诊断概要。 :type Outline: str :param DiagItem: 诊断项说明。 :type DiagItem: str :param InstanceId: 实例 ID 。 :type InstanceId: str :param Metric: 保留字段。注意：此字段可能返回 null，表示取不到有效值。 :type Metric: str :param Region: 地域。 :type Region: str """ self.DiagType = None self.EndTime = None self.StartTime = None self.EventId = None self.Severity = None self.Outline = None self.DiagItem = None self.InstanceId = None self.Metric = None self.Region = None def _deserialize(self, params): self.DiagType = params.get("DiagType") self.EndTime = params.get("EndTime") self.StartTime = params.get("StartTime") self.EventId = params.get("EventId") self.Severity = params.get("Severity") self.Outline = params.get("Outline") self.DiagItem = params.get("DiagItem") self.InstanceId = params.get("InstanceId") self.Metric = params.get("Metric") self.Region = params.get("Region") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class EventInfo(AbstractModel): """异常事件信息。 """ def __init__(self): r""" :param EventId: 事件 ID 。 :type EventId: int :param DiagType: 诊断类型。 :type DiagType: str :param StartTime: 开始时间。 :type StartTime: str :param EndTime: 结束时间。 :type EndTime: str :param Outline: 概要。 :type Outline: str :param Severity: 严重程度。严重程度分为5级，按影响程度从高至低分别为：1：致命，2：严重，3：告警，4：提示，5：健康。 :type Severity: int :param ScoreLost: 扣分。 :type ScoreLost: int :param Metric: 保留字段。 :type Metric: str :param Count: 告警数目。 :type Count: int """ self.EventId = None self.DiagType = None self.StartTime = None self.EndTime = None self.Outline = None self.Severity = None self.ScoreLost = None self.Metric = None self.Count = None def _deserialize(self, params): self.EventId = params.get("EventId") self.DiagType = params.get("DiagType") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.Outline = params.get("Outline") self.Severity = params.get("Severity") self.ScoreLost = params.get("ScoreLost") self.Metric = params.get("Metric") self.Count = params.get("Count") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class GroupItem(AbstractModel): """描述组信息。 """ def __init__(self): r""" :param Id: 组id。 :type Id: int :param Name: 组名称。 :type Name: str :param MemberCount: 组成员数量。 :type MemberCount: int """ self.Id = None self.Name = None self.MemberCount = None def _deserialize(self, params): self.Id = params.get("Id") self.Name = params.get("Name") self.MemberCount = params.get("MemberCount") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class HealthReportTask(AbstractModel): """健康报告任务详情。 """ def __init__(self): r""" :param AsyncRequestId: 异步任务请求 ID。 :type AsyncRequestId: int :param Source: 任务的触发来源，支持的取值包括："DAILY_INSPECTION" - 实例巡检；"SCHEDULED" - 定时生成；"MANUAL" - 手动触发。 :type Source: str :param Progress: 任务完成进度，单位%。 :type Progress: int :param CreateTime: 任务创建时间。 :type CreateTime: str :param StartTime: 任务开始执行时间。 :type StartTime: str :param EndTime: 任务完成执行时间。 :type EndTime: str :param InstanceInfo: 任务所属实例的基础信息。 :type InstanceInfo: :class:`tencentcloud.dbbrain.v20210527.models.InstanceBasicInfo` :param HealthStatus: 健康报告中的健康信息。 :type HealthStatus: :class:`tencentcloud.dbbrain.v20210527.models.HealthStatus` """ self.AsyncRequestId = None self.Source = None self.Progress = None self.CreateTime = None self.StartTime = None self.EndTime = None self.InstanceInfo = None self.HealthStatus = None def _deserialize(self, params): self.AsyncRequestId = params.get("AsyncRequestId") self.Source = params.get("Source") self.Progress = params.get("Progress") self.CreateTime = params.get("CreateTime") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") if params.get("InstanceInfo") is not None: self.InstanceInfo = InstanceBasicInfo() self.InstanceInfo._deserialize(params.get("InstanceInfo")) if params.get("HealthStatus") is not None: self.HealthStatus = HealthStatus() self.HealthStatus._deserialize(params.get("HealthStatus")) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class HealthScoreInfo(AbstractModel): """获取健康得分返回的详情。 """ def __init__(self): r""" :param IssueTypes: 异常详情。 :type IssueTypes: list of IssueTypeInfo :param EventsTotalCount: 异常事件总数。 :type EventsTotalCount: int :param HealthScore: 健康得分。 :type HealthScore: int :param HealthLevel: 健康等级, 如："HEALTH", "SUB_HEALTH", "RISK", "HIGH_RISK"。 :type HealthLevel: str """ self.IssueTypes = None self.EventsTotalCount = None self.HealthScore = None self.HealthLevel = None def _deserialize(self, params): if params.get("IssueTypes") is not None: self.IssueTypes = [] for item in params.get("IssueTypes"): obj = IssueTypeInfo() obj._deserialize(item) self.IssueTypes.append(obj) self.EventsTotalCount = params.get("EventsTotalCount") self.HealthScore = params.get("HealthScore") self.HealthLevel = params.get("HealthLevel") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class HealthStatus(AbstractModel): """实例健康详情。 """ def __init__(self): r""" :param HealthScore: 健康分数，满分100。 :type HealthScore: int :param HealthLevel: 健康等级，取值包括："HEALTH" - 健康；"SUB_HEALTH" - 亚健康；"RISK"- 危险；"HIGH_RISK" - 高危。 :type HealthLevel: str :param ScoreLost: 总扣分分数。 :type ScoreLost: int :param ScoreDetails: 扣分详情。注意：此字段可能返回 null，表示取不到有效值。 :type ScoreDetails: list of ScoreDetail """ self.HealthScore = None self.HealthLevel = None self.ScoreLost = None self.ScoreDetails = None def _deserialize(self, params): self.HealthScore = params.get("HealthScore") self.HealthLevel = params.get("HealthLevel") self.ScoreLost = params.get("ScoreLost") if params.get("ScoreDetails") is not None: self.ScoreDetails = [] for item in params.get("ScoreDetails"): obj = ScoreDetail() obj._deserialize(item) self.ScoreDetails.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class InstanceBasicInfo(AbstractModel): """实例基础信息。 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param InstanceName: 实例名称。 :type InstanceName: str :param Vip: 实例内网IP。 :type Vip: str :param Vport: 实例内网Port。 :type Vport: int :param Product: 实例产品。 :type Product: str :param EngineVersion: 实例引擎版本。 :type EngineVersion: str """ self.InstanceId = None self.InstanceName = None self.Vip = None self.Vport = None self.Product = None self.EngineVersion = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.InstanceName = params.get("InstanceName") self.Vip = params.get("Vip") self.Vport = params.get("Vport") self.Product = params.get("Product") self.EngineVersion = params.get("EngineVersion") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class InstanceConfs(AbstractModel): """实例配置。 """ def __init__(self): r""" :param DailyInspection: 数据库巡检开关, Yes/No。 :type DailyInspection: str :param OverviewDisplay: 实例概览开关，Yes/No。 :type OverviewDisplay: str """ self.DailyInspection = None self.OverviewDisplay = None def _deserialize(self, params): self.DailyInspection = params.get("DailyInspection") self.OverviewDisplay = params.get("OverviewDisplay") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class InstanceInfo(AbstractModel): """查询实例列表，返回实例的相关信息的对象。 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param InstanceName: 实例名称。 :type InstanceName: str :param Region: 实例所属地域。 :type Region: str :param HealthScore: 健康得分。 :type HealthScore: int :param Product: 所属产品。 :type Product: str :param EventCount: 异常事件数量。 :type EventCount: int :param InstanceType: 实例类型：1:MASTER；2:DR，3：RO，4:SDR。 :type InstanceType: int :param Cpu: 核心数。 :type Cpu: int :param Memory: 内存，单位MB。 :type Memory: int :param Volume: 硬盘存储，单位GB。 :type Volume: int :param EngineVersion: 数据库版本。 :type EngineVersion: str :param Vip: 内网地址。 :type Vip: str :param Vport: 内网端口。 :type Vport: int :param Source: 接入来源。 :type Source: str :param GroupId: 分组ID。 :type GroupId: str :param GroupName: 分组组名。 :type GroupName: str :param Status: 实例状态：0：发货中；1：运行正常；4：销毁中；5：隔离中。 :type Status: int :param UniqSubnetId: 子网统一ID。 :type UniqSubnetId: str :param DeployMode: cdb类型。 :type DeployMode: str :param InitFlag: cdb实例初始化标志：0：未初始化；1：已初始化。 :type InitFlag: int :param TaskStatus: 任务状态。 :type TaskStatus: int :param UniqVpcId: 私有网络统一ID。 :type UniqVpcId: str :param InstanceConf: 实例巡检/概览的状态。 :type InstanceConf: :class:`tencentcloud.dbbrain.v20210527.models.InstanceConfs` :param DeadlineTime: 资源到期时间。 :type DeadlineTime: str :param IsSupported: 是否是DBbrain支持的实例。 :type IsSupported: bool :param SecAuditStatus: 实例安全审计日志开启状态：ON：安全审计开启；OFF：未开启安全审计。 :type SecAuditStatus: str :param AuditPolicyStatus: 实例审计日志开启状态，ALL_AUDIT：开启全审计；RULE_AUDIT：开启规则审计；UNBOUND：未开启审计。 :type AuditPolicyStatus: str :param AuditRunningStatus: 实例审计日志运行状态：normal：运行中； paused：欠费暂停。 :type AuditRunningStatus: str """ self.InstanceId = None self.InstanceName = None self.Region = None self.HealthScore = None self.Product = None self.EventCount = None self.InstanceType = None self.Cpu = None self.Memory = None self.Volume = None self.EngineVersion = None self.Vip = None self.Vport = None self.Source = None self.GroupId = None self.GroupName = None self.Status = None self.UniqSubnetId = None self.DeployMode = None self.InitFlag = None self.TaskStatus = None self.UniqVpcId = None self.InstanceConf = None self.DeadlineTime = None self.IsSupported = None self.SecAuditStatus = None self.AuditPolicyStatus = None self.AuditRunningStatus = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.InstanceName = params.get("InstanceName") self.Region = params.get("Region") self.HealthScore = params.get("HealthScore") self.Product = params.get("Product") self.EventCount = params.get("EventCount") self.InstanceType = params.get("InstanceType") self.Cpu = params.get("Cpu") self.Memory = params.get("Memory") self.Volume = params.get("Volume") self.EngineVersion = params.get("EngineVersion") self.Vip = params.get("Vip") self.Vport = params.get("Vport") self.Source = params.get("Source") self.GroupId = params.get("GroupId") self.GroupName = params.get("GroupName") self.Status = params.get("Status") self.UniqSubnetId = params.get("UniqSubnetId") self.DeployMode = params.get("DeployMode") self.InitFlag = params.get("InitFlag") self.TaskStatus = params.get("TaskStatus") self.UniqVpcId = params.get("UniqVpcId") if params.get("InstanceConf") is not None: self.InstanceConf = InstanceConfs() self.InstanceConf._deserialize(params.get("InstanceConf")) self.DeadlineTime = params.get("DeadlineTime") self.IsSupported = params.get("IsSupported") self.SecAuditStatus = params.get("SecAuditStatus") self.AuditPolicyStatus = params.get("AuditPolicyStatus") self.AuditRunningStatus = params.get("AuditRunningStatus") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class IssueTypeInfo(AbstractModel): """指标信息。 """ def __init__(self): r""" :param IssueType: 指标分类：AVAILABILITY：可用性，MAINTAINABILITY：可维护性，PERFORMANCE，性能，RELIABILITY可靠性。 :type IssueType: str :param Events: 异常事件。 :type Events: list of EventInfo :param TotalCount: 异常事件总数。 :type TotalCount: int """ self.IssueType = None self.Events = None self.TotalCount = None def _deserialize(self, params): self.IssueType = params.get("IssueType") if params.get("Events") is not None: self.Events = [] for item in params.get("Events"): obj = EventInfo() obj._deserialize(item) self.Events.append(obj) self.TotalCount = params.get("TotalCount") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class KillMySqlThreadsRequest(AbstractModel): """KillMySqlThreads请求参数结构体 """ def __init__(self): r""" :param InstanceId: 实例ID。 :type InstanceId: str :param Stage: kill会话任务的阶段，取值包括："Prepare"-准备阶段，"Commit"-提交阶段。 :type Stage: str :param Threads: 需要kill的sql会话ID列表，此参数用于Prepare阶段。 :type Threads: list of int :param SqlExecId: 执行ID，此参数用于Commit阶段。 :type SqlExecId: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL，默认为"mysql"。 :type Product: str """ self.InstanceId = None self.Stage = None self.Threads = None self.SqlExecId = None self.Product = None def _deserialize(self, params): self.InstanceId = params.get("InstanceId") self.Stage = params.get("Stage") self.Threads = params.get("Threads") self.SqlExecId = params.get("SqlExecId") self.Product = params.get("Product") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class KillMySqlThreadsResponse(AbstractModel): """KillMySqlThreads返回参数结构体 """ def __init__(self): r""" :param Threads: kill完成的sql会话ID列表。 :type Threads: list of int :param SqlExecId: 执行ID， Prepare阶段的任务输出，用于Commit阶段中指定执行kill操作的会话ID。注意：此字段可能返回 null，表示取不到有效值。 :type SqlExecId: str :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.Threads = None self.SqlExecId = None self.RequestId = None def _deserialize(self, params): self.Threads = params.get("Threads") self.SqlExecId = params.get("SqlExecId") self.RequestId = params.get("RequestId") class MailConfiguration(AbstractModel): """邮件发送配置 """ def __init__(self): r""" :param SendMail: 是否开启邮件发送: 0, 否; 1, 是。 :type SendMail: int :param Region: 地域配置, 如["ap-guangzhou", "ap-shanghai"]。巡检的邮件发送模版，配置需要发送巡检邮件的地域；订阅的邮件发送模版，配置当前订阅实例的所属地域。 :type Region: list of str :param HealthStatus: 发送指定的健康等级的报告, 如["HEALTH", "SUB_HEALTH", "RISK", "HIGH_RISK"]。 :type HealthStatus: list of str :param ContactPerson: 联系人id, 联系人/联系组不能都为空。 :type ContactPerson: list of int :param ContactGroup: 联系组id, 联系人/联系组不能都为空。 :type ContactGroup: list of int """ self.SendMail = None self.Region = None self.HealthStatus = None self.ContactPerson = None self.ContactGroup = None def _deserialize(self, params): self.SendMail = params.get("SendMail") self.Region = params.get("Region") self.HealthStatus = params.get("HealthStatus") self.ContactPerson = params.get("ContactPerson") self.ContactGroup = params.get("ContactGroup") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class ModifyDiagDBInstanceConfRequest(AbstractModel): """ModifyDiagDBInstanceConf请求参数结构体 """ def __init__(self): r""" :param InstanceConfs: 实例配置，包括巡检、概览开关等。 :type InstanceConfs: :class:`tencentcloud.dbbrain.v20210527.models.InstanceConfs` :param Regions: 生效实例地域，取值为"All"，代表全地域。 :type Regions: str :param Product: 服务产品类型，支持值包括： "mysql" - 云数据库 MySQL， "cynosdb" - 云数据库 CynosDB for MySQL。 :type Product: str :param InstanceIds: 指定更改巡检状态的实例ID。 :type InstanceIds: list of str """ self.InstanceConfs = None self.Regions = None self.Product = None self.InstanceIds = None def _deserialize(self, params): if params.get("InstanceConfs") is not None: self.InstanceConfs = InstanceConfs() self.InstanceConfs._deserialize(params.get("InstanceConfs")) self.Regions = params.get("Regions") self.Product = params.get("Product") self.InstanceIds = params.get("InstanceIds") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class ModifyDiagDBInstanceConfResponse(AbstractModel): """ModifyDiagDBInstanceConf返回参数结构体 """ def __init__(self): r""" :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.RequestId = None def _deserialize(self, params): self.RequestId = params.get("RequestId") class MonitorFloatMetric(AbstractModel): """监控数据（浮点型） """ def __init__(self): r""" :param Metric: 指标名称。 :type Metric: str :param Unit: 指标单位。 :type Unit: str :param Values: 指标值。注意：此字段可能返回 null，表示取不到有效值。 :type Values: list of float """ self.Metric = None self.Unit = None self.Values = None def _deserialize(self, params): self.Metric = params.get("Metric") self.Unit = params.get("Unit") self.Values = params.get("Values") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class MonitorFloatMetricSeriesData(AbstractModel): """单位时间间隔内的监控指标数据（浮点型） """ def __init__(self): r""" :param Series: 监控指标。 :type Series: list of MonitorFloatMetric :param Timestamp: 监控指标对应的时间戳。 :type Timestamp: list of int """ self.Series = None self.Timestamp = None def _deserialize(self, params): if params.get("Series") is not None: self.Series = [] for item in params.get("Series"): obj = MonitorFloatMetric() obj._deserialize(item) self.Series.append(obj) self.Timestamp = params.get("Timestamp") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class MonitorMetric(AbstractModel): """监控数据 """ def __init__(self): r""" :param Metric: 指标名称。 :type Metric: str :param Unit: 指标单位。 :type Unit: str :param Values: 指标值。注意：此字段可能返回 null，表示取不到有效值。 :type Values: list of float """ self.Metric = None self.Unit = None self.Values = None def _deserialize(self, params): self.Metric = params.get("Metric") self.Unit = params.get("Unit") self.Values = params.get("Values") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class MonitorMetricSeriesData(AbstractModel): """单位时间间隔内的监控指标数据 """ def __init__(self): r""" :param Series: 监控指标。 :type Series: list of MonitorMetric :param Timestamp: 监控指标对应的时间戳。 :type Timestamp: list of int """ self.Series = None self.Timestamp = None def _deserialize(self, params): if params.get("Series") is not None: self.Series = [] for item in params.get("Series"): obj = MonitorMetric() obj._deserialize(item) self.Series.append(obj) self.Timestamp = params.get("Timestamp") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class MySqlProcess(AbstractModel): """关系型数据库线程 """ def __init__(self): r""" :param ID: 线程ID。 :type ID: str :param User: 线程的操作账号名。 :type User: str :param Host: 线程的操作主机地址。 :type Host: str :param DB: 线程的操作数据库。 :type DB: str :param State: 线程的操作状态。 :type State: str :param Command: 线程的执行类型。 :type Command: str :param Time: 线程的操作时长，单位秒。 :type Time: str :param Info: 线程的操作语句。 :type Info: str """ self.ID = None self.User = None self.Host = None self.DB = None self.State = None self.Command = None self.Time = None self.Info = None def _deserialize(self, params): self.ID = params.get("ID") self.User = params.get("User") self.Host = params.get("Host") self.DB = params.get("DB") self.State = params.get("State") self.Command = params.get("Command") self.Time = params.get("Time") self.Info = params.get("Info") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class ProfileInfo(AbstractModel): """用户配置的信息 """ def __init__(self): r""" :param Language: 语言, 如"zh"。 :type Language: str :param MailConfiguration: 邮件模板的内容。 :type MailConfiguration: :class:`tencentcloud.dbbrain.v20210527.models.MailConfiguration` """ self.Language = None self.MailConfiguration = None def _deserialize(self, params): self.Language = params.get("Language") if params.get("MailConfiguration") is not None: self.MailConfiguration = MailConfiguration() self.MailConfiguration._deserialize(params.get("MailConfiguration")) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SchemaItem(AbstractModel): """SchemaItem数组 """ def __init__(self): r""" :param Schema: 数据库名称 :type Schema: str """ self.Schema = None def _deserialize(self, params): self.Schema = params.get("Schema") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SchemaSpaceData(AbstractModel): """库空间统计数据。 """ def __init__(self): r""" :param TableSchema: 库名。 :type TableSchema: str :param DataLength: 数据空间（MB）。 :type DataLength: float :param IndexLength: 索引空间（MB）。 :type IndexLength: float :param DataFree: 碎片空间（MB）。 :type DataFree: float :param TotalLength: 总使用空间（MB）。 :type TotalLength: float :param FragRatio: 碎片率（%）。 :type FragRatio: float :param TableRows: 行数。 :type TableRows: int :param PhysicalFileSize: 库中所有表对应的独立物理文件大小加和（MB）。注意：此字段可能返回 null，表示取不到有效值。 :type PhysicalFileSize: float """ self.TableSchema = None self.DataLength = None self.IndexLength = None self.DataFree = None self.TotalLength = None self.FragRatio = None self.TableRows = None self.PhysicalFileSize = None def _deserialize(self, params): self.TableSchema = params.get("TableSchema") self.DataLength = params.get("DataLength") self.IndexLength = params.get("IndexLength") self.DataFree = params.get("DataFree") self.TotalLength = params.get("TotalLength") self.FragRatio = params.get("FragRatio") self.TableRows = params.get("TableRows") self.PhysicalFileSize = params.get("PhysicalFileSize") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SchemaSpaceTimeSeries(AbstractModel): """库空间时序数据 """ def __init__(self): r""" :param TableSchema: 库名 :type TableSchema: str :param SeriesData: 单位时间间隔内的空间指标数据。 :type SeriesData: :class:`tencentcloud.dbbrain.v20210527.models.MonitorMetricSeriesData` """ self.TableSchema = None self.SeriesData = None def _deserialize(self, params): self.TableSchema = params.get("TableSchema") if params.get("SeriesData") is not None: self.SeriesData = MonitorMetricSeriesData() self.SeriesData._deserialize(params.get("SeriesData")) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class ScoreDetail(AbstractModel): """扣分详情。 """ def __init__(self): r""" :param IssueType: 扣分项分类，取值包括：可用性、可维护性、性能及可靠性。 :type IssueType: str :param ScoreLost: 扣分总分。 :type ScoreLost: int :param ScoreLostMax: 扣分总分上限。 :type ScoreLostMax: int :param Items: 扣分项列表。注意：此字段可能返回 null，表示取不到有效值。 :type Items: list of ScoreItem """ self.IssueType = None self.ScoreLost = None self.ScoreLostMax = None self.Items = None def _deserialize(self, params): self.IssueType = params.get("IssueType") self.ScoreLost = params.get("ScoreLost") self.ScoreLostMax = params.get("ScoreLostMax") if params.get("Items") is not None: self.Items = [] for item in params.get("Items"): obj = ScoreItem() obj._deserialize(item) self.Items.append(obj) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class ScoreItem(AbstractModel): """诊断扣分项。 """ def __init__(self): r""" :param DiagItem: 异常诊断项名称。 :type DiagItem: str :param IssueType: 诊断项分类，取值包括：可用性、可维护性、性能及可靠性。 :type IssueType: str :param TopSeverity: 健康等级，取值包括：信息、提示、告警、严重、致命。 :type TopSeverity: str :param Count: 该异常诊断项出现次数。 :type Count: int :param ScoreLost: 扣分分数。 :type ScoreLost: int """ self.DiagItem = None self.IssueType = None self.TopSeverity = None self.Count = None self.ScoreLost = None def _deserialize(self, params): self.DiagItem = params.get("DiagItem") self.IssueType = params.get("IssueType") self.TopSeverity = params.get("TopSeverity") self.Count = params.get("Count") self.ScoreLost = params.get("ScoreLost") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SecLogExportTaskInfo(AbstractModel): """安全审计日志导出任务信息 """ def __init__(self): r""" :param AsyncRequestId: 异步任务Id。 :type AsyncRequestId: int :param StartTime: 任务开始时间。注意：此字段可能返回 null，表示取不到有效值。 :type StartTime: str :param EndTime: 任务结束时间。注意：此字段可能返回 null，表示取不到有效值。 :type EndTime: str :param CreateTime: 任务创建时间。 :type CreateTime: str :param Status: 任务状态。 :type Status: str :param Progress: 任务执行进度。 :type Progress: int :param LogStartTime: 导出日志开始时间。注意：此字段可能返回 null，表示取不到有效值。 :type LogStartTime: str :param LogEndTime: 导出日志结束时间。注意：此字段可能返回 null，表示取不到有效值。 :type LogEndTime: str :param TotalSize: 日志文件总大小，单位KB。注意：此字段可能返回 null，表示取不到有效值。 :type TotalSize: int :param DangerLevels: 风险等级列表。0 无风险；1 低风险；2 中风险；3 高风险。注意：此字段可能返回 null，表示取不到有效值。 :type DangerLevels: list of int non-negative """ self.AsyncRequestId = None self.StartTime = None self.EndTime = None self.CreateTime = None self.Status = None self.Progress = None self.LogStartTime = None self.LogEndTime = None self.TotalSize = None self.DangerLevels = None def _deserialize(self, params): self.AsyncRequestId = params.get("AsyncRequestId") self.StartTime = params.get("StartTime") self.EndTime = params.get("EndTime") self.CreateTime = params.get("CreateTime") self.Status = params.get("Status") self.Progress = params.get("Progress") self.LogStartTime = params.get("LogStartTime") self.LogEndTime = params.get("LogEndTime") self.TotalSize = params.get("TotalSize") self.DangerLevels = params.get("DangerLevels") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SlowLogHost(AbstractModel): """慢日志来源地址详情。 """ def __init__(self): r""" :param UserHost: 来源地址。 :type UserHost: str :param Ratio: 该来源地址的慢日志数目占总数目的比例，单位%。 :type Ratio: float :param Count: 该来源地址的慢日志数目。 :type Count: int """ self.UserHost = None self.Ratio = None self.Count = None def _deserialize(self, params): self.UserHost = params.get("UserHost") self.Ratio = params.get("Ratio") self.Count = params.get("Count") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class SlowLogTopSqlItem(AbstractModel): """慢日志TopSql """ def __init__(self): r""" :param LockTime: sql总锁等待时间，单位秒 :type LockTime: float :param LockTimeMax: 最大锁等待时间，单位秒 :type LockTimeMax: float :param LockTimeMin: 最小锁等待时间，单位秒 :type LockTimeMin: float :param RowsExamined: 总扫描行数 :type RowsExamined: int :param RowsExaminedMax: 最大扫描行数 :type RowsExaminedMax: int :param RowsExaminedMin: 最小扫描行数 :type RowsExaminedMin: int :param QueryTime: 总耗时，单位秒 :type QueryTime: float :param QueryTimeMax: 最大执行时间，单位秒 :type QueryTimeMax: float :param QueryTimeMin: 最小执行时间，单位秒 :type QueryTimeMin: float :param RowsSent: 总返回行数 :type RowsSent: int :param RowsSentMax: 最大返回行数 :type RowsSentMax: int :param RowsSentMin: 最小返回行数 :type RowsSentMin: int :param ExecTimes: 执行次数 :type ExecTimes: int :param SqlTemplate: sql模板 :type SqlTemplate: str :param SqlText: 带参数SQL（随机） :type SqlText: str :param Schema: 数据库名 :type Schema: str :param QueryTimeRatio: 总耗时占比，单位% :type QueryTimeRatio: float :param LockTimeRatio: sql总锁等待时间占比，单位% :type LockTimeRatio: float :param RowsExaminedRatio: 总扫描行数占比，单位% :type RowsExaminedRatio: float :param RowsSentRatio: 总返回行数占比，单位% :type RowsSentRatio: float :param QueryTimeAvg: 平均执行时间，单位秒 :type QueryTimeAvg: float :param RowsSentAvg: 平均返回行数 :type RowsSentAvg: float :param LockTimeAvg: 平均锁等待时间，单位秒 :type LockTimeAvg: float :param RowsExaminedAvg: 平均扫描行数 :type RowsExaminedAvg: float :param Md5: SOL模板的MD5值 :type Md5: str """ self.LockTime = None self.LockTimeMax = None self.LockTimeMin = None self.RowsExamined = None self.RowsExaminedMax = None self.RowsExaminedMin = None self.QueryTime = None self.QueryTimeMax = None self.QueryTimeMin = None self.RowsSent = None self.RowsSentMax = None self.RowsSentMin = None self.ExecTimes = None self.SqlTemplate = None self.SqlText = None self.Schema = None self.QueryTimeRatio = None self.LockTimeRatio = None self.RowsExaminedRatio = None self.RowsSentRatio = None self.QueryTimeAvg = None self.RowsSentAvg = None self.LockTimeAvg = None self.RowsExaminedAvg = None self.Md5 = None def _deserialize(self, params): self.LockTime = params.get("LockTime") self.LockTimeMax = params.get("LockTimeMax") self.LockTimeMin = params.get("LockTimeMin") self.RowsExamined = params.get("RowsExamined") self.RowsExaminedMax = params.get("RowsExaminedMax") self.RowsExaminedMin = params.get("RowsExaminedMin") self.QueryTime = params.get("QueryTime") self.QueryTimeMax = params.get("QueryTimeMax") self.QueryTimeMin = params.get("QueryTimeMin") self.RowsSent = params.get("RowsSent") self.RowsSentMax = params.get("RowsSentMax") self.RowsSentMin = params.get("RowsSentMin") self.ExecTimes = params.get("ExecTimes") self.SqlTemplate = params.get("SqlTemplate") self.SqlText = params.get("SqlText") self.Schema = params.get("Schema") self.QueryTimeRatio = params.get("QueryTimeRatio") self.LockTimeRatio = params.get("LockTimeRatio") self.RowsExaminedRatio = params.get("RowsExaminedRatio") self.RowsSentRatio = params.get("RowsSentRatio") self.QueryTimeAvg = params.get("QueryTimeAvg") self.RowsSentAvg = params.get("RowsSentAvg") self.LockTimeAvg = params.get("LockTimeAvg") self.RowsExaminedAvg = params.get("RowsExaminedAvg") self.Md5 = params.get("Md5") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class TableSpaceData(AbstractModel): """库表空间统计数据。 """ def __init__(self): r""" :param TableName: 表名。 :type TableName: str :param TableSchema: 库名。 :type TableSchema: str :param Engine: 库表的存储引擎。 :type Engine: str :param DataLength: 数据空间（MB）。 :type DataLength: float :param IndexLength: 索引空间（MB）。 :type IndexLength: float :param DataFree: 碎片空间（MB）。 :type DataFree: float :param TotalLength: 总使用空间（MB）。 :type TotalLength: float :param FragRatio: 碎片率（%）。 :type FragRatio: float :param TableRows: 行数。 :type TableRows: int :param PhysicalFileSize: 表对应的独立物理文件大小（MB）。 :type PhysicalFileSize: float """ self.TableName = None self.TableSchema = None self.Engine = None self.DataLength = None self.IndexLength = None self.DataFree = None self.TotalLength = None self.FragRatio = None self.TableRows = None self.PhysicalFileSize = None def _deserialize(self, params): self.TableName = params.get("TableName") self.TableSchema = params.get("TableSchema") self.Engine = params.get("Engine") self.DataLength = params.get("DataLength") self.IndexLength = params.get("IndexLength") self.DataFree = params.get("DataFree") self.TotalLength = params.get("TotalLength") self.FragRatio = params.get("FragRatio") self.TableRows = params.get("TableRows") self.PhysicalFileSize = params.get("PhysicalFileSize") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class TableSpaceTimeSeries(AbstractModel): """库表空间时序数据 """ def __init__(self): r""" :param TableName: 表名。 :type TableName: str :param TableSchema: 库名。 :type TableSchema: str :param Engine: 库表的存储引擎。 :type Engine: str :param SeriesData: 单位时间间隔内的空间指标数据。 :type SeriesData: :class:`tencentcloud.dbbrain.v20210527.models.MonitorFloatMetricSeriesData` """ self.TableName = None self.TableSchema = None self.Engine = None self.SeriesData = None def _deserialize(self, params): self.TableName = params.get("TableName") self.TableSchema = params.get("TableSchema") self.Engine = params.get("Engine") if params.get("SeriesData") is not None: self.SeriesData = MonitorFloatMetricSeriesData() self.SeriesData._deserialize(params.get("SeriesData")) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class TimeSlice(AbstractModel): """单位时间间隔内的慢日志统计 """ def __init__(self): r""" :param Count: 总数 :type Count: int :param Timestamp: 统计开始时间 :type Timestamp: int """ self.Count = None self.Timestamp = None def _deserialize(self, params): self.Count = params.get("Count") self.Timestamp = params.get("Timestamp") memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set)) class UserProfile(AbstractModel): """用户配置的相关信息，包括邮件配置。 """ def __init__(self): r""" :param ProfileId: 配置的id。注意：此字段可能返回 null，表示取不到有效值。 :type ProfileId: str :param ProfileType: 配置类型，支持值包括："dbScan_mail_configuration" - 数据库巡检邮件配置，"scheduler_mail_configuration" - 定期生成邮件配置。注意：此字段可能返回 null，表示取不到有效值。 :type ProfileType: str :param ProfileLevel: 配置级别，支持值包括："User" - 用户级别，"Instance" - 实例级别，其中数据库巡检邮件配置为用户级别，定期生成邮件配置为实例级别。注意：此字段可能返回 null，表示取不到有效值。 :type ProfileLevel: str :param ProfileName: 配置名称。注意：此字段可能返回 null，表示取不到有效值。 :type ProfileName: str :param ProfileInfo: 配置详情。 :type ProfileInfo: :class:`tencentcloud.dbbrain.v20210527.models.ProfileInfo` """ self.ProfileId = None self.ProfileType = None self.ProfileLevel = None self.ProfileName = None self.ProfileInfo = None def _deserialize(self, params): self.ProfileId = params.get("ProfileId") self.ProfileType = params.get("ProfileType") self.ProfileLevel = params.get("ProfileLevel") self.ProfileName = params.get("ProfileName") if params.get("ProfileInfo") is not None: self.ProfileInfo = ProfileInfo() self.ProfileInfo._deserialize(params.get("ProfileInfo")) memeber_set = set(params.keys()) for name, value in vars(self).items(): if name in memeber_set: memeber_set.remove(name) if len(memeber_set) > 0: warnings.warn("%s fileds are useless." % ",".join(memeber_set))

31.310427

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0.597149

10,779

105,704

5.781705

0.088505

0.064264

0.015532

0.016945

0.658077

0.609153

0.559009

0.541391

0.510069

0.499318

0

0.007231

0.293518

105,704

3,376

196

31.310427

0.827299

0.307793

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0.159856

0

null

0

1

0

null

0

1

6a836399736ccfbfdcec602215566bd6e9ae598c

2,201

py

Python

melisa/utils/snowflake.py

MelisaDev/melisa

53fee10d8c1bf4dd716bc90096c16f096e11bfbf

[ "MIT" ]

5

2022-03-11T19:51:28.000Z

2022-03-13T16:28:58.000Z

melisa/utils/snowflake.py

jungledev1/melisa

835e4b644e50b5038599ecbd1bfa510a0d3200e9

[ "MIT" ]

2

2022-03-19T18:09:39.000Z

2022-03-23T12:18:49.000Z

melisa/utils/snowflake.py

jungledev1/melisa

835e4b644e50b5038599ecbd1bfa510a0d3200e9

[ "MIT" ]

1

2022-03-23T07:30:04.000Z

# Copyright MelisaDev 2022 - Present # Full MIT License can be found in `LICENSE.txt` at the project root. from __future__ import annotations class Snowflake(int): """ Discord utilizes Twitter's snowflake format for uniquely identifiable descriptors (IDs). These IDs are guaranteed to be unique across all of Discord, except in some unique scenarios in which child objects share their parent's ID. Because Snowflake IDs are up to 64 bits in size (e.g. a uint64), they are always returned as strings in the HTTP API to prevent integer overflows in some languages. See Gateway ETF/JSON for more information regarding Gateway encoding. Read more here: https://discord.com/developers/docs/reference#snowflakes """ _MAX_VALUE: int = 9223372036854775807 _MIN_VALUE: int = 0 def __init__(self, _): super().__init__() if self < self._MIN_VALUE: raise ValueError("snowflake value should be greater than or equal to 0.") if self > self._MAX_VALUE: raise ValueError( "snowflake value should be less than or equal to 9223372036854775807." ) @classmethod def __factory__(cls, string: str) -> Snowflake: return cls.from_string(string) @classmethod def from_string(cls, string: str): """Initialize a new Snowflake from a string. Parameters ---------- string: :class:`str` The snowflake as a string. """ return Snowflake(int(string)) @property def timestamp(self) -> int: """ Milliseconds since Discord Epoch, the first second of 2015 or 1420070400000. """ return self >> 22 @property def worker_id(self) -> int: """Internal worker ID""" return (self >> 17) % 16 @property def process_id(self) -> int: """Internal process ID""" return (self >> 12) % 16 @property def increment(self) -> int: """For every ID that is generated on that process, this number is incremented""" return self % 2048 @property def unix(self) -> int: return self.timestamp + 1420070400000

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2,201

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2,201

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0.060606

0.575758

0

null

0

null

0

1

0

1

6a89b2893b587e6d66f6aa207ca89999bce84710

846

py

Python

utils/config.py

jtr109/Alpha2kindle

a411d05cafa9036a732eeb75fa13f68963f254e3

[ "MIT" ]

null

utils/config.py

jtr109/Alpha2kindle

a411d05cafa9036a732eeb75fa13f68963f254e3

[ "MIT" ]

null

utils/config.py

jtr109/Alpha2kindle

a411d05cafa9036a732eeb75fa13f68963f254e3

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os class BaseConf(object): HEADERS = { "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) " "AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/55.0.2883.95 " "Safari/537.36", "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,en;q=0.6,zh-TW;q=0.4", "Cache-Control": "max-age=0", } class TestConf(BaseConf): REDIS_URL = "redis://:{password}@{hostname}:{port}/{db_number}".format( password=os.environ.get("REDIS_PWD"), hostname='127.0.0.1', port=6379, db_number=0 ) CURCONF = TestConf

27.290323

75

0.51773

110

846

3.927273

0.663636

0.023148

0.013889

0

0.082631

0.299054

846

30

76

28.2

0.645868

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0

1

0

false

0.090909

0.045455

0

0.227273

0

null

0

null

0

1

0

1

6a8c916961dcdf5b4bdd11f085941afc268401f1

771

py

Python

inventory/admin.py

shakyasaijal/businessAnalytics

9312bae79709387c6eadd50f87f6be85bd52c396

[ "BSD-3-Clause" ]

null

inventory/admin.py

shakyasaijal/businessAnalytics

9312bae79709387c6eadd50f87f6be85bd52c396

[ "BSD-3-Clause" ]

8

2021-03-30T13:03:11.000Z

2022-03-12T00:20:13.000Z

inventory/admin.py

shakyasaijal/businessAnalytics

9312bae79709387c6eadd50f87f6be85bd52c396

[ "BSD-3-Clause" ]

null

from django.contrib import admin from . import models class SupplierAdmin(admin.ModelAdmin): list_display = ('supplier_name', 'contact', ) search_fields = ['supplier_name', 'contact', ] admin.site.register(models.Suppliers, SupplierAdmin) class InventoryUserAdmin(admin.ModelAdmin): list_display = ('employee_name', 'user_type') search_fields = ['employee_name', 'user_type'] list_filter = ("user_type",) admin.site.register(models.InventoryUser, InventoryUserAdmin) class ProductsAdmin(admin.ModelAdmin): list_display = ('name', 'quantity', 'cost_price', 'selling_price',) search_fields = ['name', 'quantity', 'cost_price', 'selling_price',] list_filter = ("branch", "supplier",) admin.site.register(models.Product, ProductsAdmin)

32.125

72

0.731518

84

771

6.488095

0.380952

0.082569

0.104587

0.143119

0.121101

0

0.124514

771

23

73

33.521739

0.807407

0

0.229572

0

1

0

false

0

0.125

0

0.8125

0

null

0

null

0

1

0

1

6a8e7fcaf4ca3d67de4aab013987d7db788188b5

252

py

Python

pyqtgraph/examples/template.py

secantsquared/pyqtgraph

3ef7f5b91639543e43bcd66a84290fb9bc18fc5c

[ "MIT" ]

null

pyqtgraph/examples/template.py

secantsquared/pyqtgraph

3ef7f5b91639543e43bcd66a84290fb9bc18fc5c

[ "MIT" ]

null

pyqtgraph/examples/template.py

secantsquared/pyqtgraph

3ef7f5b91639543e43bcd66a84290fb9bc18fc5c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Description of example """ import pyqtgraph as pg from pyqtgraph.Qt import QtCore, QtGui, mkQApp import numpy as np app = mkQApp() # win.setWindowTitle('pyqtgraph example: ____') if __name__ == '__main__': pg.exec()

15.75

47

0.68254

32

252

5

0.75

0

0.004808

0.174603

252

15

48

16.8

0.764423

0.361111

0

0.052632

0

1

0

false

0

0.5

0

0.5

0

null

0

null

0

1

0

1

6a8f3e25920be24fb569cc55eff90ae879efa647

73,328

py

Python

ambari-server/src/test/python/stacks/2.0.6/common/test_stack_advisor.py

cas-packone/ambari-chs

68033fbd4b810b6642853f2ad9128cbbd4e0cb7b

[ "Apache-2.0" ]

3

2019-06-20T11:49:36.000Z

2020-12-11T10:44:29.000Z

ambari-server/src/test/python/stacks/2.0.6/common/test_stack_advisor.py

cas-packone/ambari-chs

68033fbd4b810b6642853f2ad9128cbbd4e0cb7b

[ "Apache-2.0" ]

null

ambari-server/src/test/python/stacks/2.0.6/common/test_stack_advisor.py

cas-packone/ambari-chs

68033fbd4b810b6642853f2ad9128cbbd4e0cb7b

[ "Apache-2.0" ]

1

2019-03-20T08:36:17.000Z

''' Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import socket from unittest import TestCase from mock.mock import patch, MagicMock class TestHDP206StackAdvisor(TestCase): def setUp(self): import imp import os testDirectory = os.path.dirname(os.path.abspath(__file__)) stackAdvisorPath = os.path.join(testDirectory, '../../../../../main/resources/stacks/stack_advisor.py') hdp206StackAdvisorPath = os.path.join(testDirectory, '../../../../../main/resources/stacks/HDP/2.0.6/services/stack_advisor.py') hdp206StackAdvisorClassName = 'HDP206StackAdvisor' with open(stackAdvisorPath, 'rb') as fp: stack_advisor = imp.load_module( 'stack_advisor', fp, stackAdvisorPath, ('.py', 'rb', imp.PY_SOURCE) ) with open(hdp206StackAdvisorPath, 'rb') as fp: self.stack_advisor_impl = imp.load_module('stack_advisor_impl', fp, hdp206StackAdvisorPath, ('.py', 'rb', imp.PY_SOURCE)) clazz = getattr(self.stack_advisor_impl, hdp206StackAdvisorClassName) self.stackAdvisor = clazz() self.maxDiff = None # substitute method in the instance self.get_system_min_uid_real = self.stackAdvisor.get_system_min_uid self.stackAdvisor.get_system_min_uid = self.get_system_min_uid_magic @patch('__builtin__.open') @patch('os.path.exists') def get_system_min_uid_magic(self, exists_mock, open_mock): class MagicFile(object): def read(self): return """ #test line UID_MIN 200 UID_MIN 500 """ def __exit__(self, exc_type, exc_val, exc_tb): pass def __enter__(self): return self exists_mock.return_value = True open_mock.return_value = MagicFile() return self.get_system_min_uid_real() def test_recommendationCardinalityALL(self): servicesInfo = [ { "name": "GANGLIA", "components": [{"name": "GANGLIA_MONITOR", "cardinality": "ALL", "category": "SLAVE", "is_master": False}] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.recommendComponentLayout(services, hosts) expectedComponentsHostsMap = { "GANGLIA_MONITOR": ["host1", "host2"] } self.assertHostLayout(expectedComponentsHostsMap, result) def test_recommendOnAllHosts(self): """ Recommend on all hosts for cardinality ALL even if the component has been installed in the cluster before """ servicesInfo = [ { "name": "GANGLIA", "components": [{"name": "GANGLIA_MONITOR", "cardinality": "ALL", "category": "SLAVE", "is_master": False, "hostnames": ["host1"]}] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.recommendComponentLayout(services, hosts) expectedComponentsHostsMap = { "GANGLIA_MONITOR": ["host1", "host2"] } self.assertHostLayout(expectedComponentsHostsMap, result) def test_recommendationIsNotPreferableOnAmbariServer(self): servicesInfo = [ { "name": "GANGLIA", "components": [{"name": "GANGLIA_SERVER", "cardinality": "ALL", "category": "MASTER", "is_master": True}] } ] services = self.prepareServices(servicesInfo) localhost = socket.getfqdn() hosts = self.prepareHosts([localhost, "host2"]) result = self.stackAdvisor.recommendComponentLayout(services, hosts) expectedComponentsHostsMap = { "GANGLIA_SERVER": ["host2"] } self.assertHostLayout(expectedComponentsHostsMap, result) def test_validationNamenodeAndSecondaryNamenode2Hosts_noMessagesForSameHost(self): servicesInfo = [ { "name": "HDFS", "components": [ {"name": "NAMENODE", "cardinality": "1-2", "category": "MASTER", "is_master": True, "hostnames": ["host1"]}, {"name": "SECONDARY_NAMENODE", "cardinality": "1", "category": "MASTER", "is_master": True, "hostnames": ["host1"]}] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Host is not used", "level": "ERROR", "host": "host2"} ] self.assertValidationResult(expectedItems, result) def test_validationCardinalityALL(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_MONITOR", "display_name": "Ganglia Monitor", "cardinality": "ALL", "category": "SLAVE", "is_master": False, "hostnames": ["host1"]}, {"name": "GANGLIA_SERVER", "display_name": "Ganglia Server", "cardinality": "1-2", "category": "MASTER", "is_master": True, "hostnames": ["host2", "host1"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Ganglia Monitor component should be installed on all hosts in cluster.", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationCardinalityExactAmount(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_MONITOR", "display_name": "Ganglia Monitor", "cardinality": "2", "category": "SLAVE", "is_master": False, "hostnames": ["host1"]}, {"name": "GANGLIA_SERVER", "display_name": "Ganglia Server", "cardinality": "2", "category": "MASTER", "is_master": True, "hostnames": ["host2", "host1"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Exactly 2 Ganglia Monitor components should be installed in cluster.", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationCardinalityAtLeast(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_MONITOR", "display_name": "Ganglia Monitor", "cardinality": "1+", "category": "SLAVE", "is_master": False, "hostnames": ["host1"]}, {"name": "GANGLIA_SERVER", "display_name": "Ganglia Server", "cardinality": "3+", "category": "MASTER", "is_master": True, "hostnames": ["host2", "host1"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "At least 3 Ganglia Server components should be installed in cluster.", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationWarnMessagesIfLessThanDefault(self): servicesInfo = [ { "name": "YARN", "components": [] } ] services = self.prepareServices(servicesInfo) services["configurations"] = {"yarn-site":{"properties":{"yarn.nodemanager.resource.memory-mb": "0", "yarn.scheduler.minimum-allocation-mb": "str"}}} hosts = self.prepareHosts([]) result = self.stackAdvisor.validateConfigurations(services, hosts) expectedItems = [ {"message": "Value is less than the recommended default of 512", "level": "WARN"}, {'message': 'Value should be set for yarn.nodemanager.linux-container-executor.group', 'level': 'ERROR'}, {"message": "Value should be integer", "level": "ERROR"}, {"message": "Value should be set", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationYARNServicecheckQueueName(self): servicesInfo = [ { "name": "YARN", "components": [] } ] services = self.prepareServices(servicesInfo) services["configurations"] = {"yarn-env":{"properties":{"service_check.queue.name": "default"}}, "capacity-scheduler":{"properties":{"capacity-scheduler": "yarn.scheduler.capacity.root.queues=ndfqueue\n"}}} hosts = self.prepareHosts([]) result = self.stackAdvisor.validateConfigurations(services, hosts) expectedItems = [ {'message': 'Queue is not exist, or not corresponds to existing YARN leaf queue', 'level': 'ERROR'} ] self.assertValidationResult(expectedItems, result) services["configurations"]["yarn-env"]["properties"]["service_check.queue.name"] = "ndfqueue" expectedItems = [] result = self.stackAdvisor.validateConfigurations(services, hosts) self.assertValidationResult(expectedItems, result) def test_validationMinMax(self): configurations = { "mapred-site": { "properties": { "mapreduce.task.io.sort.mb": "4096", "some_float_value": "0.5", "no_min_or_max_attribute_property": "STRING_VALUE" } } } recommendedDefaults = { "mapred-site": { "properties": { "mapreduce.task.io.sort.mb": "2047", "some_float_value": "0.8", "no_min_or_max_attribute_property": "STRING_VALUE" }, "property_attributes": { 'mapreduce.task.io.sort.mb': {'maximum': '2047'}, 'some_float_value': {'minimum': '0.8'} } } } items = [] self.stackAdvisor.validateMinMax(items, recommendedDefaults, configurations) expectedItems = [ { 'message': 'Value is greater than the recommended maximum of 2047 ', 'level': 'WARN', 'config-type': 'mapred-site', 'config-name': 'mapreduce.task.io.sort.mb', 'type': 'configuration' }, { 'message': 'Value is less than the recommended minimum of 0.8 ', 'level': 'WARN', 'config-type': 'mapred-site', 'config-name': 'some_float_value', 'type': 'configuration' } ] self.assertEquals(expectedItems, items) def test_validationHostIsNotUsedForNonValuableComponent(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_MONITOR", "cardinality": "ALL", "category": "SLAVE", "is_master": False, "hostnames": ["host1", "host2"]}, {"name": "GANGLIA_SERVER", "cardinality": "1", "category": "MASTER", "is_master": True, "hostnames": ["host2"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Host is not used", "host": "host1", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationCardinality01TwoHostsAssigned(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_SERVER", "display_name": "Ganglia Server", "cardinality": "0-1", "category": "MASTER", "is_master": True, "hostnames": ["host1", "host2"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Between 0 and 1 Ganglia Server components should be installed in cluster.", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_validationHostIsNotUsed(self): servicesInfo = [ { "name": "GANGLIA", "components": [ {"name": "GANGLIA_SERVER", "cardinality": "1", "category": "MASTER", "is_master": True, "hostnames": ["host1"]} ] } ] services = self.prepareServices(servicesInfo) hosts = self.prepareHosts(["host1", "host2"]) result = self.stackAdvisor.validateComponentLayout(services, hosts) expectedItems = [ {"message": "Host is not used", "host": "host2", "level": "ERROR"} ] self.assertValidationResult(expectedItems, result) def test_getConfigurationClusterSummary_withHBaseAnd6gbRam(self): servicesList = ["HBASE"] components = [] hosts = { "items" : [ { "Hosts" : { "cpu_count" : 8, "total_mem" : 6291456, "disk_info" : [ {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"} ] } } ] } expected = { "hBaseInstalled": True, "components": components, "cpu": 8, "disk": 8, "ram": 6, "reservedRam": 2, "hbaseRam": 1, "minContainerSize": 512, "totalAvailableRam": 3072, "containers": 6, "ramPerContainer": 512, "mapMemory": 512, "reduceMemory": 512, "amMemory": 512, "referenceHost": hosts["items"][0]["Hosts"] } # Test - Cluster data with 1 host result = self.stackAdvisor.getConfigurationClusterSummary(servicesList, hosts, components, None) self.assertEquals(result, expected) # Test - Cluster data with 2 hosts - pick minimum memory servicesList.append("YARN") services = services = {"services": [{"StackServices": {"service_name" : "YARN", "service_version" : "2.6.0.2.2" }, "components":[ { "StackServiceComponents":{ "advertise_version":"true", "cardinality":"1+", "component_category":"SLAVE", "component_name":"NODEMANAGER", "custom_commands":[ ], "display_name":"NodeManager", "is_client":"false", "is_master":"false", "service_name":"YARN", "stack_name":"HDP", "stack_version":"2.2", "hostnames":[ "host1", "host2" ] }, "dependencies":[ ] } ], }], "configurations": {} } hosts["items"][0]["Hosts"]["host_name"] = "host1" hosts["items"].append({ "Hosts": { "cpu_count" : 4, "total_mem" : 500000, "host_name" : "host2", "disk_info" : [ {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"} ] } }) expected["referenceHost"] = hosts["items"][1]["Hosts"] expected["referenceNodeManagerHost"] = hosts["items"][1]["Hosts"] expected["amMemory"] = 170.66666666666666 expected["containers"] = 3.0 expected["cpu"] = 4 expected["totalAvailableRam"] = 512 expected["mapMemory"] = 170 expected["minContainerSize"] = 256 expected["reduceMemory"] = 170.66666666666666 expected["ram"] = 0 expected["ramPerContainer"] = 170.66666666666666 expected["reservedRam"] = 1 result = self.stackAdvisor.getConfigurationClusterSummary(servicesList, hosts, components, services) self.assertEquals(result, expected) def test_getConfigurationClusterSummary_withHBaseAnd48gbRam(self): servicesList = ["HBASE"] components = [] hosts = { "items" : [ { "Hosts" : { "cpu_count" : 6, "total_mem" : 50331648, "disk_info" : [ {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"} ] } } ] } expected = { "hBaseInstalled": True, "components": components, "cpu": 6, "disk": 6, "ram": 48, "reservedRam": 6, "hbaseRam": 8, "minContainerSize": 2048, "totalAvailableRam": 34816, "containers": 11, "ramPerContainer": 3072, "mapMemory": 3072, "reduceMemory": 3072, "amMemory": 3072, "referenceHost": hosts["items"][0]["Hosts"] } result = self.stackAdvisor.getConfigurationClusterSummary(servicesList, hosts, components, None) self.assertEquals(result, expected) def test_recommendStormConfigurations(self): # no AMS configurations = {} services = { "services": [ ], "configurations": configurations } expected = { "storm-site": { "properties": { } }, } self.stackAdvisor.recommendStormConfigurations(configurations, None, services, None) self.assertEquals(configurations, expected) # with AMS configurations = {} services = { "services": [ { "StackServices": { "service_name": "AMBARI_METRICS" } } ], "configurations": configurations } expected = { "storm-site": { "properties": { "metrics.reporter.register": "org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter" } }, } self.stackAdvisor.recommendStormConfigurations(configurations, None, services, None) self.assertEquals(configurations, expected) def test_recommendYARNConfigurations(self): configurations = {} services = {"configurations": configurations, "services": []} clusterData = { "containers" : 5, "ramPerContainer": 256 } expected = { "yarn-env": { "properties": { "min_user_id": "500", 'service_check.queue.name': 'default' } }, "yarn-site": { "properties": { "yarn.nodemanager.linux-container-executor.group": "hadoop", "yarn.nodemanager.resource.memory-mb": "1280", "yarn.scheduler.minimum-allocation-mb": "256", "yarn.scheduler.maximum-allocation-mb": "1280" } } } self.stackAdvisor.recommendYARNConfigurations(configurations, clusterData, services, None) self.assertEquals(configurations, expected) def test_recommendMapReduce2Configurations_mapMemoryLessThan2560(self): configurations = {} clusterData = { "mapMemory": 567, "reduceMemory": 345.6666666666666, "amMemory": 123.54 } expected = { "mapred-site": { "properties": { 'mapreduce.job.queuename': 'default', "yarn.app.mapreduce.am.resource.mb": "123", "yarn.app.mapreduce.am.command-opts": "-Xmx99m", "mapreduce.map.memory.mb": "567", "mapreduce.reduce.memory.mb": "345", "mapreduce.map.java.opts": "-Xmx454m", "mapreduce.reduce.java.opts": "-Xmx277m", "mapreduce.task.io.sort.mb": "227" } } } self.stackAdvisor.recommendMapReduce2Configurations(configurations, clusterData, None, None) self.assertEquals(configurations, expected) def test_getConfigurationClusterSummary_noHostsWithoutHBase(self): servicesList = [] components = [] hosts = { "items" : [] } result = self.stackAdvisor.getConfigurationClusterSummary(servicesList, hosts, components, None) expected = { "hBaseInstalled": False, "components": components, "cpu": 0, "disk": 0, "ram": 0, "reservedRam": 1, "hbaseRam": 1, "minContainerSize": 256, "totalAvailableRam": 512, "containers": 3, "ramPerContainer": 170.66666666666666, "mapMemory": 170, "reduceMemory": 170.66666666666666, "amMemory": 170.66666666666666 } self.assertEquals(result, expected) def prepareHosts(self, hostsNames): hosts = { "items": [] } for hostName in hostsNames: nextHost = {"Hosts":{"host_name" : hostName}} hosts["items"].append(nextHost) return hosts def prepareServices(self, servicesInfo): services = { "Versions" : { "stack_name" : "HDP", "stack_version" : "2.0.6" } } services["services"] = [] for serviceInfo in servicesInfo: nextService = {"StackServices":{"service_name" : serviceInfo["name"]}} nextService["components"] = [] for component in serviceInfo["components"]: nextComponent = { "StackServiceComponents": { "component_name": component["name"], "cardinality": component["cardinality"], "component_category": component["category"], "is_master": component["is_master"] } } try: nextComponent["StackServiceComponents"]["hostnames"] = component["hostnames"] except KeyError: nextComponent["StackServiceComponents"]["hostnames"] = [] try: nextComponent["StackServiceComponents"]["display_name"] = component["display_name"] except KeyError: nextComponent["StackServiceComponents"]["display_name"] = component["name"] nextService["components"].append(nextComponent) services["services"].append(nextService) return services def assertHostLayout(self, componentsHostsMap, recommendation): blueprintMapping = recommendation["recommendations"]["blueprint"]["host_groups"] bindings = recommendation["recommendations"]["blueprint_cluster_binding"]["host_groups"] actualComponentHostsMap = {} for hostGroup in blueprintMapping: hostGroupName = hostGroup["name"] hostsInfos = [binding["hosts"] for binding in bindings if binding["name"] == hostGroupName][0] hosts = [info["fqdn"] for info in hostsInfos] for component in hostGroup["components"]: componentName = component["name"] try: actualComponentHostsMap[componentName] except KeyError, err: actualComponentHostsMap[componentName] = [] for host in hosts: if host not in actualComponentHostsMap[componentName]: actualComponentHostsMap[componentName].append(host) for componentName in componentsHostsMap.keys(): expectedHosts = componentsHostsMap[componentName] actualHosts = actualComponentHostsMap[componentName] self.checkEqual(expectedHosts, actualHosts) def checkEqual(self, l1, l2): if not len(l1) == len(l2) or not sorted(l1) == sorted(l2): raise AssertionError("list1={0}, list2={1}".format(l1, l2)) def assertValidationResult(self, expectedItems, result): actualItems = [] for item in result["items"]: next = {"message": item["message"], "level": item["level"]} try: next["host"] = item["host"] except KeyError, err: pass actualItems.append(next) self.checkEqual(expectedItems, actualItems) def test_recommendHbaseConfigurations(self): servicesList = ["HBASE"] configurations = {} components = [] host_item = { "Hosts" : { "cpu_count" : 6, "total_mem" : 50331648, "disk_info" : [ {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"}, {"mountpoint" : "/"}, {"mountpoint" : "/dev/shm"}, {"mountpoint" : "/vagrant"} ] } } hosts = { "items" : [host_item for i in range(1, 300)] } services = { "services" : [ ], "configurations": { "hbase-site": { "properties": { "hbase.superuser": "hbase" } }, "hbase-env": { "properties": { "hbase_user": "hbase123" } } } } expected = { 'hbase-site': { 'properties': { 'hbase.superuser': 'hbase123' } }, "hbase-env": { "properties": { "hbase_master_heapsize": "4096", "hbase_regionserver_heapsize": "8192", } } } clusterData = self.stackAdvisor.getConfigurationClusterSummary(servicesList, hosts, components, None) self.assertEquals(clusterData['hbaseRam'], 8) self.stackAdvisor.recommendHbaseConfigurations(configurations, clusterData, services, hosts) self.assertEquals(configurations, expected) def test_recommendRangerConfigurations(self): clusterData = {} # Recommend for not existing DB_FLAVOR and http enabled, HDP-2.3 services = { "Versions" : { "stack_version" : "2.3", }, "services": [ { "StackServices": { "service_name": "RANGER", "service_version": "0.5.0" }, "components": [ { "StackServiceComponents": { "component_name": "RANGER_ADMIN", "hostnames": ["host1"] } } ] }, { "StackServices": { "service_name": "HDFS" }, "components": [ { "StackServiceComponents": { "component_name": "NAMENODE", "hostnames": ["host1"] } } ] } ], "configurations": { "admin-properties": { "properties": { "DB_FLAVOR": "NOT_EXISTING", } }, "ranger-admin-site": { "properties": { "ranger.service.http.port": "7777", "ranger.service.http.enabled": "true", } } } } expected = { "admin-properties": { "properties": { "policymgr_external_url": "http://host1:7777" } } } recommendedConfigurations = {} self.stackAdvisor.recommendRangerConfigurations(recommendedConfigurations, clusterData, services, None) self.assertEquals(recommendedConfigurations, expected, "Test for not existing DB_FLAVOR and http enabled, HDP-2.3") # Recommend for DB_FLAVOR POSTGRES and https enabled, HDP-2.3 configurations = { "admin-properties": { "properties": { "DB_FLAVOR": "POSTGRES", } }, "ranger-admin-site": { "properties": { "ranger.service.https.port": "7777", "ranger.service.http.enabled": "false", } } } services['configurations'] = configurations expected = { "admin-properties": { "properties": { "policymgr_external_url": "https://host1:7777" } } } recommendedConfigurations = {} self.stackAdvisor.recommendRangerConfigurations(recommendedConfigurations, clusterData, services, None) self.assertEquals(recommendedConfigurations, expected, "Test for DB_FLAVOR POSTGRES and https enabled, HDP-2.3") # Recommend for DB_FLAVOR ORACLE and https enabled, HDP-2.2 configurations = { "admin-properties": { "properties": { "DB_FLAVOR": "ORACLE", } }, "ranger-site": { "properties": { "http.enabled": "false", "https.service.port": "8888", } } } services['configurations'] = configurations expected = { "admin-properties": { "properties": { "policymgr_external_url": "https://host1:8888" } }, "ranger-env": {"properties": {}} } recommendedConfigurations = {} services['services'][0]['StackServices']['service_version'] = "0.4.0" self.stackAdvisor.recommendRangerConfigurations(recommendedConfigurations, clusterData, services, None) self.assertEquals(recommendedConfigurations, expected, "Test for DB_FLAVOR ORACLE and https enabled, HDP-2.2") # Test Recommend LDAP values services["ambari-server-properties"] = { "ambari.ldap.isConfigured" : "true", "authentication.ldap.bindAnonymously" : "false", "authentication.ldap.baseDn" : "dc=apache,dc=org", "authentication.ldap.groupNamingAttr" : "cn", "authentication.ldap.primaryUrl" : "c6403.ambari.apache.org:636", "authentication.ldap.userObjectClass" : "posixAccount", "authentication.ldap.secondaryUrl" : "c6403.ambari.apache.org:636", "authentication.ldap.usernameAttribute" : "uid", "authentication.ldap.dnAttribute" : "dn", "authentication.ldap.useSSL" : "true", "authentication.ldap.managerPassword" : "/etc/ambari-server/conf/ldap-password.dat", "authentication.ldap.groupMembershipAttr" : "memberUid", "authentication.ldap.groupObjectClass" : "posixGroup", "authentication.ldap.managerDn" : "uid=hdfs,ou=people,ou=dev,dc=apache,dc=org" } services["configurations"] = {} expected = { 'admin-properties': { 'properties': { 'policymgr_external_url': 'http://host1:6080', } }, 'ranger-env': {'properties': {}}, 'usersync-properties': { 'properties': { 'SYNC_LDAP_URL': 'ldaps://c6403.ambari.apache.org:636', 'SYNC_LDAP_BIND_DN': 'uid=hdfs,ou=people,ou=dev,dc=apache,dc=org', 'SYNC_LDAP_USER_OBJECT_CLASS': 'posixAccount', 'SYNC_LDAP_USER_NAME_ATTRIBUTE': 'uid' } } } recommendedConfigurations = {} self.stackAdvisor.recommendRangerConfigurations(recommendedConfigurations, clusterData, services, None) self.assertEquals(recommendedConfigurations, expected, "Test Recommend LDAP values") # Test Ranger Audit properties del services["ambari-server-properties"] services["configurations"] = { "core-site": { "properties": { "fs.defaultFS": "hdfs://host1:8080", } }, "ranger-env": { "properties": { "xasecure.audit.destination.db": "true", "xasecure.audit.destination.hdfs":"false", "xasecure.audit.destination.hdfs.dir":"hdfs://localhost:8020/ranger/audit/%app-type%/%time:yyyyMMdd%" } }, "ranger-hdfs-plugin-properties": { "properties": {} } } expected = { 'admin-properties': { 'properties': { 'policymgr_external_url': 'http://host1:6080' } }, 'ranger-hdfs-plugin-properties': { 'properties': { 'XAAUDIT.HDFS.IS_ENABLED': 'false', 'XAAUDIT.HDFS.DESTINATION_DIRECTORY': 'hdfs://host1:8080/ranger/audit/%app-type%/%time:yyyyMMdd%', 'XAAUDIT.DB.IS_ENABLED': 'true' } }, 'ranger-env': { 'properties': { 'xasecure.audit.destination.hdfs.dir': 'hdfs://host1:8080/ranger/audit/%app-type%/%time:yyyyMMdd%' } } } recommendedConfigurations = {} self.stackAdvisor.recommendRangerConfigurations(recommendedConfigurations, clusterData, services, None) self.assertEquals(recommendedConfigurations, expected, "Test Ranger Audit properties") def test_recommendHDFSConfigurations(self): configurations = { "hadoop-env": { "properties": { "hdfs_user": "hdfs", "proxyuser_group": "users" } }, "hive-env": { "properties": { "webhcat_user": "webhcat", "hive_user": "hive" } }, "oozie-env": { "properties": { "oozie_user": "oozie" } }, "falcon-env": { "properties": { "falcon_user": "falcon" } } } hosts = { "items": [ { "href": "/api/v1/hosts/host1", "Hosts": { "cpu_count": 1, "host_name": "c6401.ambari.apache.org", "os_arch": "x86_64", "os_type": "centos6", "ph_cpu_count": 1, "public_host_name": "c6401.ambari.apache.org", "rack_info": "/default-rack", "total_mem": 2097152, "disk_info": [{ "size": '8', "mountpoint": "/" }] } }, { "href": "/api/v1/hosts/host2", "Hosts": { "cpu_count": 1, "host_name": "c6402.ambari.apache.org", "os_arch": "x86_64", "os_type": "centos6", "ph_cpu_count": 1, "public_host_name": "c6402.ambari.apache.org", "rack_info": "/default-rack", "total_mem": 1048576, "disk_info": [{ "size": '8', "mountpoint": "/" }] } }, ]} services = { "services": [ { "StackServices": { "service_name": "HDFS" }, "components": [] }, { "StackServices": { "service_name": "FALCON" }, "components": [] }, { "StackServices": { "service_name": "HIVE" }, "components": [{ "href": "/api/v1/stacks/HDP/versions/2.0.6/services/HIVE/components/HIVE_SERVER", "StackServiceComponents": { "advertise_version": "true", "cardinality": "1", "component_category": "MASTER", "component_name": "HIVE_SERVER", "custom_commands": [], "display_name": "Hive Server", "is_client": "false", "is_master": "true", "service_name": "HIVE", "stack_name": "HDP", "stack_version": "2.0.6", "hostnames": ["c6401.ambari.apache.org","c6402.ambari.apache.org"] }}, { "href": "/api/v1/stacks/HDP/versions/2.0.6/services/HIVE/components/WEBHCAT_SERVER", "StackServiceComponents": { "advertise_version": "true", "cardinality": "1", "component_category": "MASTER", "component_name": "WEBHCAT_SERVER", "custom_commands": [], "display_name": "WebHCat Server", "is_client": "false", "is_master": "true", "service_name": "HIVE", "stack_name": "HDP", "stack_version": "2.0.6", "hostnames": ["c6401.ambari.apache.org", "c6402.ambari.apache.org"] }}] }, { "StackServices": { "service_name": "OOZIE" }, "components": [{ "href": "/api/v1/stacks/HDP/versions/2.0.6/services/HIVE/components/OOZIE_SERVER", "StackServiceComponents": { "advertise_version": "true", "cardinality": "1", "component_category": "MASTER", "component_name": "OOZIE_SERVER", "custom_commands": [], "display_name": "Oozie Server", "is_client": "false", "is_master": "true", "service_name": "HIVE", "stack_name": "HDP", "stack_version": "2.0.6", "hostnames": ["c6401.ambari.apache.org", "c6402.ambari.apache.org"] }, }] }], "configurations": configurations, "ambari-server-properties": {"ambari-server.user":"ambari_user"} } clusterData = { "totalAvailableRam": 2048 } ambariHostName = socket.getfqdn() expected = {'oozie-env': {'properties': {'oozie_user': 'oozie'}}, 'core-site': {'properties': {'hadoop.proxyuser.ambari_user.groups': '*', 'hadoop.proxyuser.ambari_user.hosts': ambariHostName, 'hadoop.proxyuser.oozie.groups': '*', 'hadoop.proxyuser.hive.groups': '*', 'hadoop.proxyuser.webhcat.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.falcon.hosts': '*', 'hadoop.proxyuser.webhcat.groups': '*', 'hadoop.proxyuser.hdfs.groups': '*', 'hadoop.proxyuser.hdfs.hosts': '*', 'hadoop.proxyuser.hive.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.oozie.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.falcon.groups': '*'}}, 'falcon-env': {'properties': {'falcon_user': 'falcon'}}, 'hdfs-site': {'properties': {'dfs.datanode.data.dir': '/hadoop/hdfs/data', 'dfs.datanode.du.reserved': '1024'}}, 'hive-env': {'properties': {'hive_user': 'hive', 'webhcat_user': 'webhcat'}}, 'hadoop-env': {'properties': {'hdfs_user': 'hdfs', 'namenode_heapsize': '1024', 'proxyuser_group': 'users', 'namenode_opt_maxnewsize': '256', 'namenode_opt_newsize': '256'}}} self.stackAdvisor.recommendHDFSConfigurations(configurations, clusterData, services, hosts) self.assertEquals(configurations, expected) configurations["hadoop-env"]["properties"]['hdfs_user'] = "hdfs1" changedConfigurations = [{"type":"hadoop-env", "name":"hdfs_user", "old_value":"hdfs"}] services["changed-configurations"] = changedConfigurations services['configurations'] = configurations expected = {'oozie-env': {'properties': {'oozie_user': 'oozie'}}, 'core-site': {'properties': {'hadoop.proxyuser.ambari_user.groups': '*', 'hadoop.proxyuser.ambari_user.hosts': ambariHostName, 'hadoop.proxyuser.oozie.groups': '*', 'hadoop.proxyuser.hive.groups': '*', 'hadoop.proxyuser.hdfs1.groups': '*', 'hadoop.proxyuser.hdfs1.hosts': '*', 'hadoop.proxyuser.webhcat.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.falcon.hosts': '*', 'hadoop.proxyuser.webhcat.groups': '*', 'hadoop.proxyuser.hdfs.groups': '*', 'hadoop.proxyuser.hdfs.hosts': '*', 'hadoop.proxyuser.hive.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.oozie.hosts': 'c6401.ambari.apache.org,c6402.ambari.apache.org', 'hadoop.proxyuser.falcon.groups': '*'}, 'property_attributes': {'hadoop.proxyuser.hdfs.groups': {'delete': 'true'}, 'hadoop.proxyuser.hdfs.hosts': {'delete': 'true'}}}, 'falcon-env': {'properties': {'falcon_user': 'falcon'}}, 'hive-env': {'properties': {'hive_user': 'hive', 'webhcat_user': 'webhcat'}}, 'hdfs-site': {'properties': {'dfs.datanode.data.dir': '/hadoop/hdfs/data', 'dfs.datanode.du.reserved': '1024'}}, 'hadoop-env': {'properties': {'hdfs_user': 'hdfs1', 'namenode_heapsize': '1024', 'proxyuser_group': 'users', 'namenode_opt_maxnewsize': '256', 'namenode_opt_newsize': '256'}}} self.stackAdvisor.recommendHDFSConfigurations(configurations, clusterData, services, hosts) self.assertEquals(configurations, expected) # Verify dfs.namenode.rpc-address is recommended to be deleted when NN HA configurations["hdfs-site"]["properties"]['dfs.internal.nameservices'] = "mycluster" configurations["hdfs-site"]["properties"]['dfs.ha.namenodes.mycluster'] = "nn1,nn2" services['configurations'] = configurations expected["hdfs-site"] = { 'properties': { 'dfs.datanode.data.dir': '/hadoop/hdfs/data', 'dfs.datanode.du.reserved': '1024', 'dfs.internal.nameservices': 'mycluster', 'dfs.ha.namenodes.mycluster': 'nn1,nn2' }, 'property_attributes': { 'dfs.namenode.rpc-address': { 'delete': 'true' } } } self.stackAdvisor.recommendHDFSConfigurations(configurations, clusterData, services, hosts) self.assertEquals(configurations, expected) def test_getHostNamesWithComponent(self): services = { "services": [ { "StackServices": { "service_name": "SERVICE" }, "components": [ { "StackServiceComponents": { "component_name": "COMPONENT", "hostnames": ["host1","host2","host3"] } } ] } ], "configurations": {} } result = self.stackAdvisor.getHostNamesWithComponent("SERVICE","COMPONENT", services) expected = ["host1","host2","host3"] self.assertEquals(result, expected) def test_getZKHostPortString(self): configurations = { "zoo.cfg": { "properties": { 'clientPort': "2183" } } } services = { "services": [ { "StackServices": { "service_name": "ZOOKEEPER" }, "components": [ { "StackServiceComponents": { "component_name": "ZOOKEEPER_SERVER", "hostnames": ["zk.host1","zk.host2","zk.host3"] } }, { "StackServiceComponents": { "component_name": "ZOOKEEPER_CLIENT", "hostnames": ["host1"] } } ] } ], "configurations": configurations } result = self.stackAdvisor.getZKHostPortString(services) expected = "zk.host1:2183,zk.host2:2183,zk.host3:2183" self.assertEquals(result, expected) def test_validateHDFSConfigurations(self): configurations = {} services = '' hosts = '' #Default configuration recommendedDefaults = {'dfs.datanode.du.reserved': '1024'} properties = {'dfs.datanode.du.reserved': '1024'} res = self.stackAdvisor.validateHDFSConfigurations(properties, recommendedDefaults, configurations, services, hosts) self.assertFalse(res) #Value is less then expected recommendedDefaults = {'dfs.datanode.du.reserved': '1024'} properties = {'dfs.datanode.du.reserved': '512'} res = self.stackAdvisor.validateHDFSConfigurations(properties, recommendedDefaults, configurations, services, hosts) self.assertTrue(res) #Value is begger then expected recommendedDefaults = {'dfs.datanode.du.reserved': '1024'} properties = {'dfs.datanode.du.reserved': '2048'} res = self.stackAdvisor.validateHDFSConfigurations(properties, recommendedDefaults, configurations, services, hosts) self.assertFalse(res) def test_validateHDFSConfigurationsEnv(self): configurations = {} # 1) ok: namenode_heapsize > recommended recommendedDefaults = {'namenode_heapsize': '1024', 'namenode_opt_newsize' : '256', 'namenode_opt_maxnewsize' : '256'} properties = {'namenode_heapsize': '2048', 'namenode_opt_newsize' : '300', 'namenode_opt_maxnewsize' : '300'} res_expected = [] res = self.stackAdvisor.validateHDFSConfigurationsEnv(properties, recommendedDefaults, configurations, '', '') self.assertEquals(res, res_expected) # 2) fail: namenode_heapsize, namenode_opt_maxnewsize < recommended properties['namenode_heapsize'] = '1022' properties['namenode_opt_maxnewsize'] = '255' res_expected = [{'config-type': 'hadoop-env', 'message': 'Value is less than the recommended default of 1024', 'type': 'configuration', 'config-name': 'namenode_heapsize', 'level': 'WARN'}, {'config-name': 'namenode_opt_maxnewsize', 'config-type': 'hadoop-env', 'level': 'WARN', 'message': 'Value is less than the recommended default of 256', 'type': 'configuration'}] res = self.stackAdvisor.validateHDFSConfigurationsEnv(properties, recommendedDefaults, configurations, '', '') self.assertEquals(res, res_expected) def test_validateAmsHbaseSiteConfigurations(self): configurations = { "hdfs-site": { "properties": { 'dfs.datanode.data.dir': "/hadoop/data" } }, "core-site": { "properties": { "fs.defaultFS": "hdfs://c6401.ambari.apache.org:8020" } }, "ams-site": { "properties": { "timeline.metrics.service.operation.mode": "embedded" } } } recommendedDefaults = { 'hbase.rootdir': 'file:///var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir': '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } properties = { 'hbase.rootdir': 'file:///var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir' : '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } host = { "href" : "/api/v1/hosts/host1", "Hosts" : { "cpu_count" : 1, "host_name" : "host1", "os_arch" : "x86_64", "os_type" : "centos6", "ph_cpu_count" : 1, "public_host_name" : "host1", "rack_info" : "/default-rack", "total_mem" : 2097152, "disk_info": [ { "available": str(15<<30), # 15 GB "type": "ext4", "mountpoint": "/" } ] } } hosts = { "items" : [ host ] } services = { "services": [ { "StackServices": { "service_name": "AMBARI_METRICS" }, "components": [ { "StackServiceComponents": { "component_name": "METRICS_COLLECTOR", "hostnames": ["host1"] } }, { "StackServiceComponents": { "component_name": "METRICS_MONITOR", "hostnames": ["host1"] } } ] }, { "StackServices": { "service_name": "HDFS" }, "components": [ { "StackServiceComponents": { "component_name": "DATANODE", "hostnames": ["host1"] } } ] } ], "configurations": configurations } # only 1 partition, enough disk space, no warnings res = self.stackAdvisor.validateAmsHbaseSiteConfigurations(properties, recommendedDefaults, configurations, services, hosts) expected = [] self.assertEquals(res, expected) # 1 partition, no enough disk space host['Hosts']['disk_info'] = [ { "available" : '1', "type" : "ext4", "mountpoint" : "/" } ] res = self.stackAdvisor.validateAmsHbaseSiteConfigurations(properties, recommendedDefaults, configurations, services, hosts) expected = [ {'config-name': 'hbase.rootdir', 'config-type': 'ams-hbase-site', 'level': 'WARN', 'message': 'Ambari Metrics disk space requirements not met. ' '\nRecommended disk space for partition / is 10G', 'type': 'configuration' } ] self.assertEquals(res, expected) # 2 partitions host['Hosts']['disk_info'] = [ { "available": str(15<<30), # 15 GB "type" : "ext4", "mountpoint" : "/grid/0" }, { "available" : str(15<<30), # 15 GB "type" : "ext4", "mountpoint" : "/" } ] recommendedDefaults = { 'hbase.rootdir': 'file:///grid/0/var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir': '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } properties = { 'hbase.rootdir': 'file:///grid/0/var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir' : '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } res = self.stackAdvisor.validateAmsHbaseSiteConfigurations(properties, recommendedDefaults, configurations, services, hosts) expected = [] self.assertEquals(res, expected) # dfs.dir & hbase.rootdir crosscheck + root partition + hbase.rootdir == hbase.tmp.dir warnings properties = { 'hbase.rootdir': 'file:///var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir' : '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } res = self.stackAdvisor.validateAmsHbaseSiteConfigurations(properties, recommendedDefaults, configurations, services, hosts) expected = [ { 'config-name': 'hbase.rootdir', 'config-type': 'ams-hbase-site', 'level': 'WARN', 'message': 'It is not recommended to use root partition for hbase.rootdir', 'type': 'configuration' }, { 'config-name': 'hbase.tmp.dir', 'config-type': 'ams-hbase-site', 'level': 'WARN', 'message': 'Consider not using / partition for storing metrics temporary data. ' '/ partition is already used as hbase.rootdir to store metrics data', 'type': 'configuration' }, { 'config-name': 'hbase.rootdir', 'config-type': 'ams-hbase-site', 'level': 'WARN', 'message': 'Consider not using / partition for storing metrics data. ' '/ is already used by datanode to store HDFS data', 'type': 'configuration' } ] self.assertEquals(res, expected) # incorrect hbase.rootdir in distributed mode properties = { 'hbase.rootdir': 'file:///grid/0/var/lib/ambari-metrics-collector/hbase', 'hbase.tmp.dir' : '/var/lib/ambari-metrics-collector/hbase', 'hbase.cluster.distributed': 'false' } configurations['ams-site']['properties']['timeline.metrics.service.operation.mode'] = 'distributed' res = self.stackAdvisor.validateAmsHbaseSiteConfigurations(properties, recommendedDefaults, configurations, services, hosts) expected = [ { 'config-name': 'hbase.rootdir', 'config-type': 'ams-hbase-site', 'level': 'WARN', 'message': 'In distributed mode hbase.rootdir should point to HDFS.', 'type': 'configuration' }, { 'config-name': 'hbase.cluster.distributed', 'config-type': 'ams-hbase-site', 'level': 'ERROR', 'message': 'hbase.cluster.distributed property should be set to true for distributed mode', 'type': 'configuration' } ] self.assertEquals(res, expected) def test_validateStormSiteConfigurations(self): configurations = { "storm-site": { "properties": { 'metrics.reporter.register': "org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter" } } } recommendedDefaults = { 'metrics.reporter.register': 'org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter', } properties = { 'metrics.reporter.register': 'org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter', } services = { "services": [ { "StackServices": { "service_name": "AMBARI_METRICS" } } ], "configurations": configurations } # positive res = self.stackAdvisor.validateStormConfigurations(properties, recommendedDefaults, configurations, services, None) expected = [] self.assertEquals(res, expected) properties['metrics.reporter.register'] = '' res = self.stackAdvisor.validateStormConfigurations(properties, recommendedDefaults, configurations, services, None) expected = [ {'config-name': 'metrics.reporter.register', 'config-type': 'storm-site', 'level': 'WARN', 'message': 'Should be set to org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter ' 'to report the metrics to Ambari Metrics service.', 'type': 'configuration' } ] self.assertEquals(res, expected) def test_getHostsWithComponent(self): services = {"services": [{"StackServices": {"service_name" : "HDFS", "service_version" : "2.6.0.2.2" }, "components":[ { "href":"/api/v1/stacks/HDP/versions/2.2/services/HDFS/components/DATANODE", "StackServiceComponents":{ "advertise_version":"true", "cardinality":"1+", "component_category":"SLAVE", "component_name":"DATANODE", "custom_commands":[ ], "display_name":"DataNode", "is_client":"false", "is_master":"false", "service_name":"HDFS", "stack_name":"HDP", "stack_version":"2.2", "hostnames":[ "host1", "host2" ] }, "dependencies":[ ] }, { "href":"/api/v1/stacks/HDP/versions/2.2/services/HDFS/components/JOURNALNODE", "StackServiceComponents":{ "advertise_version":"true", "cardinality":"0+", "component_category":"SLAVE", "component_name":"JOURNALNODE", "custom_commands":[ ], "display_name":"JournalNode", "is_client":"false", "is_master":"false", "service_name":"HDFS", "stack_name":"HDP", "stack_version":"2.2", "hostnames":[ "host1" ] }, "dependencies":[ { "href":"/api/v1/stacks/HDP/versions/2.2/services/HDFS/components/JOURNALNODE/dependencies/HDFS_CLIENT", "Dependencies":{ "component_name":"HDFS_CLIENT", "dependent_component_name":"JOURNALNODE", "dependent_service_name":"HDFS", "stack_name":"HDP", "stack_version":"2.2" } } ] }, { "href":"/api/v1/stacks/HDP/versions/2.2/services/HDFS/components/NAMENODE", "StackServiceComponents":{ "advertise_version":"true", "cardinality":"1-2", "component_category":"MASTER", "component_name":"NAMENODE", "custom_commands":[ "DECOMMISSION", "REBALANCEHDFS" ], "display_name":"NameNode", "is_client":"false", "is_master":"true", "service_name":"HDFS", "stack_name":"HDP", "stack_version":"2.2", "hostnames":[ "host2" ] }, "dependencies":[ ] }, ], }], "configurations": {} } hosts = { "items" : [ { "href" : "/api/v1/hosts/host1", "Hosts" : { "cpu_count" : 1, "host_name" : "host1", "os_arch" : "x86_64", "os_type" : "centos6", "ph_cpu_count" : 1, "public_host_name" : "host1", "rack_info" : "/default-rack", "total_mem" : 2097152 } }, { "href" : "/api/v1/hosts/host2", "Hosts" : { "cpu_count" : 1, "host_name" : "host2", "os_arch" : "x86_64", "os_type" : "centos6", "ph_cpu_count" : 1, "public_host_name" : "host2", "rack_info" : "/default-rack", "total_mem" : 1048576 } }, ] } datanodes = self.stackAdvisor.getHostsWithComponent("HDFS", "DATANODE", services, hosts) self.assertEquals(len(datanodes), 2) self.assertEquals(datanodes, hosts["items"]) datanode = self.stackAdvisor.getHostWithComponent("HDFS", "DATANODE", services, hosts) self.assertEquals(datanode, hosts["items"][0]) namenodes = self.stackAdvisor.getHostsWithComponent("HDFS", "NAMENODE", services, hosts) self.assertEquals(len(namenodes), 1) # [host2] self.assertEquals(namenodes, [hosts["items"][1]]) namenode = self.stackAdvisor.getHostWithComponent("HDFS", "NAMENODE", services, hosts) # host2 self.assertEquals(namenode, hosts["items"][1]) # not installed nodemanager = self.stackAdvisor.getHostWithComponent("YARN", "NODEMANAGER", services, hosts) self.assertEquals(nodemanager, None) # unknown component unknown_component = self.stackAdvisor.getHostWithComponent("YARN", "UNKNOWN", services, hosts) self.assertEquals(nodemanager, None) # unknown service unknown_component = self.stackAdvisor.getHostWithComponent("UNKNOWN", "NODEMANAGER", services, hosts) self.assertEquals(nodemanager, None) def test_mergeValidators(self): childValidators = { "HDFS": {"hdfs-site": "validateHDFSConfigurations2.3"}, "HIVE": {"hiveserver2-site": "validateHiveServer2Configurations2.3"}, "HBASE": {"hbase-site": "validateHBASEConfigurations2.3", "newconf": "new2.3"}, "NEWSERVICE" : {"newserviceconf": "abc2.3"} } parentValidators = { "HDFS": {"hdfs-site": "validateHDFSConfigurations2.2", "hadoop-env": "validateHDFSConfigurationsEnv2.2"}, "YARN": {"yarn-env": "validateYARNEnvConfigurations2.2"}, "HIVE": {"hiveserver2-site": "validateHiveServer2Configurations2.2", "hive-site": "validateHiveConfigurations2.2", "hive-env": "validateHiveConfigurationsEnv2.2"}, "HBASE": {"hbase-site": "validateHBASEConfigurations2.2", "hbase-env": "validateHBASEEnvConfigurations2.2"}, "MAPREDUCE2": {"mapred-site": "validateMapReduce2Configurations2.2"}, "TEZ": {"tez-site": "validateTezConfigurations2.2"} } expected = { "HDFS": {"hdfs-site": "validateHDFSConfigurations2.3", "hadoop-env": "validateHDFSConfigurationsEnv2.2"}, "YARN": {"yarn-env": "validateYARNEnvConfigurations2.2"}, "HIVE": {"hiveserver2-site": "validateHiveServer2Configurations2.3", "hive-site": "validateHiveConfigurations2.2", "hive-env": "validateHiveConfigurationsEnv2.2"}, "HBASE": {"hbase-site": "validateHBASEConfigurations2.3", "hbase-env": "validateHBASEEnvConfigurations2.2", "newconf": "new2.3"}, "MAPREDUCE2": {"mapred-site": "validateMapReduce2Configurations2.2"}, "TEZ": {"tez-site": "validateTezConfigurations2.2"}, "NEWSERVICE" : {"newserviceconf": "abc2.3"} } self.stackAdvisor.mergeValidators(parentValidators, childValidators) self.assertEquals(expected, parentValidators) def test_getProperMountPoint(self): hostInfo = None self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) hostInfo = {"some_key": []} self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) hostInfo["disk_info"] = [] self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # root mountpoint with low space available hostInfo["disk_info"].append( { "available" : "1", "type" : "ext4", "mountpoint" : "/" } ) self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # tmpfs with more space available hostInfo["disk_info"].append( { "available" : "2", "type" : "tmpfs", "mountpoint" : "/dev/shm" } ) self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # /boot with more space available hostInfo["disk_info"].append( { "available" : "3", "type" : "tmpfs", "mountpoint" : "/boot/grub" } ) self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # /boot with more space available hostInfo["disk_info"].append( { "available" : "4", "type" : "tmpfs", "mountpoint" : "/mnt/external_hdd" } ) self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # virtualbox fs with more space available hostInfo["disk_info"].append( { "available" : "5", "type" : "vboxsf", "mountpoint" : "/vagrant" } ) self.assertEquals(["/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # proper mountpoint with more space available hostInfo["disk_info"].append( { "available" : "6", "type" : "ext4", "mountpoint" : "/grid/0" } ) self.assertEquals(["/grid/0", "/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) # proper mountpoint with more space available hostInfo["disk_info"].append( { "available" : "7", "type" : "ext4", "mountpoint" : "/grid/1" } ) self.assertEquals(["/grid/1", "/grid/0", "/"], self.stackAdvisor.getPreferredMountPoints(hostInfo)) def test_validateNonRootFs(self): hostInfo = {"disk_info": [ { "available" : "2", "type" : "ext4", "mountpoint" : "/" } ]} properties = {"property1": "file:///var/dir"} recommendedDefaults = {"property1": "file:///var/dir"} # only / mountpoint - no warning self.assertTrue(self.stackAdvisor.validatorNotRootFs(properties, recommendedDefaults, 'property1', hostInfo) == None) # More preferable /grid/0 mountpoint - warning hostInfo["disk_info"].append( { "available" : "3", "type" : "ext4", "mountpoint" : "/grid/0" } ) recommendedDefaults = {"property1": "file:///grid/0/var/dir"} warn = self.stackAdvisor.validatorNotRootFs(properties, recommendedDefaults, 'property1', hostInfo) self.assertTrue(warn != None) self.assertEquals({'message': 'It is not recommended to use root partition for property1', 'level': 'WARN'}, warn) # Set by user /var mountpoint, which is non-root , but not preferable - no warning hostInfo["disk_info"].append( { "available" : "1", "type" : "ext4", "mountpoint" : "/var" } ) self.assertTrue(self.stackAdvisor.validatorNotRootFs(properties, recommendedDefaults, 'property1', hostInfo) == None) def test_validatorEnoughDiskSpace(self): reqiuredDiskSpace = 1048576 errorMsg = "Ambari Metrics disk space requirements not met. \n" \ "Recommended disk space for partition / is 1G" # local FS, enough space hostInfo = {"disk_info": [ { "available" : "1048578", "type" : "ext4", "mountpoint" : "/" } ]} properties = {"property1": "file:///var/dir"} self.assertTrue(self.stackAdvisor.validatorEnoughDiskSpace(properties, 'property1', hostInfo, reqiuredDiskSpace) == None) # local FS, no enough space hostInfo = {"disk_info": [ { "available" : "1", "type" : "ext4", "mountpoint" : "/" } ]} warn = self.stackAdvisor.validatorEnoughDiskSpace(properties, 'property1', hostInfo, reqiuredDiskSpace) self.assertTrue(warn != None) self.assertEquals({'message': errorMsg, 'level': 'WARN'}, warn) # non-local FS, HDFS properties = {"property1": "hdfs://h1"} self.assertTrue(self.stackAdvisor.validatorEnoughDiskSpace(properties, 'property1', hostInfo, reqiuredDiskSpace) == None) # non-local FS, WASB properties = {"property1": "wasb://h1"} self.assertTrue(self.stackAdvisor.validatorEnoughDiskSpace(properties, 'property1', hostInfo, reqiuredDiskSpace) == None) def test_round_to_n(self): self.assertEquals(self.stack_advisor_impl.round_to_n(0), 0) self.assertEquals(self.stack_advisor_impl.round_to_n(1000), 1024) self.assertEquals(self.stack_advisor_impl.round_to_n(2000), 2048) self.assertEquals(self.stack_advisor_impl.round_to_n(4097), 4096) def test_getMountPointForDir(self): self.assertEquals(self.stack_advisor_impl.getMountPointForDir("/var/log", ["/"]), "/") self.assertEquals(self.stack_advisor_impl.getMountPointForDir("/var/log", ["/var", "/"]), "/var") self.assertEquals(self.stack_advisor_impl.getMountPointForDir("file:///var/log", ["/var", "/"]), "/var") self.assertEquals(self.stack_advisor_impl.getMountPointForDir("hdfs:///hdfs_path", ["/var", "/"]), None) self.assertEquals(self.stack_advisor_impl.getMountPointForDir("relative/path", ["/var", "/"]), None) def test_getValidatorEqualsToRecommendedItem(self): properties = {"property1": "value1"} recommendedDefaults = {"property1": "value1"} self.assertEquals(self.stackAdvisor.validatorEqualsToRecommendedItem(properties, recommendedDefaults, "property1"), None) properties = {"property1": "value1"} recommendedDefaults = {"property1": "value2"} expected = {'message': 'It is recommended to set value value2 for property property1', 'level': 'WARN'} self.assertEquals(self.stackAdvisor.validatorEqualsToRecommendedItem(properties, recommendedDefaults, "property1"), expected) properties = {} recommendedDefaults = {"property1": "value2"} expected = {'level': 'ERROR', 'message': 'Value should be set for property1'} self.assertEquals(self.stackAdvisor.validatorEqualsToRecommendedItem(properties, recommendedDefaults, "property1"), expected) properties = {"property1": "value1"} recommendedDefaults = {} expected = {'level': 'ERROR', 'message': 'Value should be recommended for property1'} self.assertEquals(self.stackAdvisor.validatorEqualsToRecommendedItem(properties, recommendedDefaults, "property1"), expected) def test_getServicesSiteProperties(self): import imp, os testDirectory = os.path.dirname(os.path.abspath(__file__)) hdp206StackAdvisorPath = os.path.join(testDirectory, '../../../../../main/resources/stacks/HDP/2.0.6/services/stack_advisor.py') stack_advisor = imp.load_source('stack_advisor', hdp206StackAdvisorPath) services = { "services": [ { "StackServices": { "service_name": "RANGER" }, "components": [ { "StackServiceComponents": { "component_name": "RANGER_ADMIN", "hostnames": ["host1"] } } ] }, ], "configurations": { "admin-properties": { "properties": { "DB_FLAVOR": "NOT_EXISTING", } }, "ranger-admin-site": { "properties": { "ranger.service.http.port": "7777", "ranger.service.http.enabled": "true", } } } } expected = { "ranger.service.http.port": "7777", "ranger.service.http.enabled": "true", } siteProperties = stack_advisor.getServicesSiteProperties(services, "ranger-admin-site") self.assertEquals(siteProperties, expected) def test_createComponentLayoutRecommendations_addService_1freeHost(self): """ Test that already installed slaves are not added to any free hosts (not having any component installed) as part of recommendation received during Add service operation. For already installed services, recommendation for installed components should match the existing layout """ services = { "services" : [ { "StackServices" : { "service_name" : "HDFS" }, "components" : [ { "StackServiceComponents" : { "cardinality" : "1+", "component_category" : "SLAVE", "component_name" : "DATANODE", "hostnames" : [ "c6401.ambari.apache.org" ] } } ] } ] } hosts = self.prepareHosts(["c6401.ambari.apache.org", "c6402.ambari.apache.org"]) recommendations = self.stackAdvisor.createComponentLayoutRecommendations(services, hosts) """ Recommendation received should be as below: { 'blueprint': { 'host_groups': [{ 'name': 'host-group-1', 'components': [] }, { 'name': 'host-group-2', 'components': [{ 'name': 'DATANODE' }] }] }, 'blueprint_cluster_binding': { 'host_groups': [{ 'hosts': [{ 'fqdn': 'c6402.ambari.apache.org' }], 'name': 'host-group-1' }, { 'hosts': [{ 'fqdn': 'c6401.ambari.apache.org' }], 'name': 'host-group-2' }] } } """ # Assert that the list is empty for host-group-1 self.assertFalse(recommendations['blueprint']['host_groups'][0]['components']) # Assert that DATANODE is placed on host-group-2 self.assertEquals(recommendations['blueprint']['host_groups'][1]['components'][0]['name'], 'DATANODE')

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6a8fddf8511ca7d429d8644119f475536d5dae17

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py

Python

main.py

ThomasDLi/simple-photo-editor

f8b3f1025155e2542b93b94c12d607b9b5e45731

[ "MIT" ]

1

2021-05-21T19:21:26.000Z

main.py

ThomasDLi/simple-photo-editor

f8b3f1025155e2542b93b94c12d607b9b5e45731

[ "MIT" ]

null

main.py

ThomasDLi/simple-photo-editor

f8b3f1025155e2542b93b94c12d607b9b5e45731

[ "MIT" ]

null

from PIL import Image, ImageEnhance user_account_name = "Thomas.Li26" def main(): mode = input("Specify image editing mode. Type DEEPFRY, STRETCH, BRIGHTNESS, SHARPEN, or INVERT: ") if mode == "DEEPFRY": DEEPFRY() if mode == "STRETCH": STRETCH() if mode == "INVERT": INVERT() if mode == "BRIGHTNESS": BRIGHTNESS() if mode == "SHARPEN": SHARPEN() def DEEPFRY(): img = input("Insert the name of an image found in the Downloads folder (for example: Image.png): ") im = Image.open(r"C:\Users\{}\Downloads\{}".format(user_account_name, img)) enhancer = ImageEnhance.Contrast(im) factor = float(input("Specify deepfry amount (0-100): ")) im_output = enhancer.enhance(factor) im_output.save('more-contrast-image.png') im_output.show() def STRETCH(): img = input("Insert the name of an image found in the Downloads folder (for example: Image.png): ") im = Image.open(r"C:\Users\{}\Downloads\{}".format(user_account_name, img)) factor = int(input("Specify width: ")) factor2 = int(input("Specify height: ")) im_output = im.resize((factor,factor2)) im_output.save('more-contrast-image.png') im_output.show() def INVERT(): img = input("Insert the name of an image found in the Downloads folder (for example: Image.png): ") im = Image.open(r"C:\Users\{}\Downloads\{}".format(user_account_name, img)) enhancer = ImageEnhance.Contrast(im) im_output = enhancer.enhance(-1) im_output.save('more-contrast-image.png') im_output.show() def BRIGHTNESS(): img = input("Insert the name of an image found in the Downloads folder (for example: Image.png): ") im = Image.open(r"C:\Users\{}\Downloads\{}".format(user_account_name, img)) enhancer = ImageEnhance.Brightness(im) factor = float(input("Specify brightness amount: ")) im_output = enhancer.enhance(factor) im_output.save('more-contrast-image.png') im_output.show() def SHARPEN(): img = input("Insert the name of an image found in the Downloads folder (for example: Image.png): ") im = Image.open(r"C:\Users\{}\Downloads\{}".format(user_account_name, img)) enhancer = ImageEnhance.Sharpness(im) factor = float(input("Specify sharpening amount: ")) im_output = enhancer.enhance(factor) im_output.save('more-contrast-image.png') im_output.show() if __name__ == "__main__": main()

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0.650442

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4.852761

0.190184

0.075853

0.063211

0.053729

0.710493

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2,486

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null

0

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null

0

1

6a9907c6e19624e9a00da0b3cff99ba87e746680

3,206

py

Python

models2.py

Lydia-Tan/MindLife

644f1a3834f337d51c99650c3924df99c5200d06

[ "MIT" ]

1

2020-01-20T19:49:07.000Z

models2.py

lindaweng/Mindlife

30be070b39728fb3fe149d4c95e5bce280a3b6a7

[ "MIT" ]

null

models2.py

lindaweng/Mindlife

30be070b39728fb3fe149d4c95e5bce280a3b6a7

[ "MIT" ]

null

import nltk import re import sys from sys import argv from nltk.sentiment.vader import SentimentIntensityAnalyzer def ajay(ans): ajay = SentimentIntensityAnalyzer() completeScore = 0 questionWeights = [0.05, 0.20, 0.05, 0.05, 0.05, 0.20, 0.05, 0.05, 0.20, 0.10] print ans ansList = ans.split("$") for j in range(10): print ansList[j] for i in range(10): results = [] score = 0 count = 0 # print (count) for paragraph in ansList: for line in paragraph: #Split Paragraph on basis of '.' or ? or !. for l in re.split(r"\.|\?|\!",paragraph): # print(l) ss = ajay.polarity_scores(l) results.append(ss); # print(ss['compound']) score += ss['compound'] count += 1 completeScore += (score/count)*questionWeights[i] #print(completeScore) if (completeScore >= 0.1): return "False Alarm! You don't have Depression." elif (completeScore >= -0.1): return ("Seasonal affective disorder (SAD). This type of depression " + "emerges as days get shorter in the fall and winter. The mood " + "change may result from alterations in the body's natural daily " + "rhythms, in the eyes' sensitivity to light, or in how chemical " + "messengers like serotonin and melatonin function. The leading " + "treatment is light therapy, which involves daily sessions sitting " + "close to an especially intense light source. The usual treatments " + "for depression, such as psychotherapy and medication, may also be " + "effective."); elif (completeScore >= -0.4): return ("Persistent depressive disorder. Formerly called dysthymia, this " + "type of depression refers to low mood that has lasted for at least " + "two years but may not reach the intensity of major depression. Many " + "people with this type of depression type are able to function day to " + "but feel low or joyless much of the time. Some depressive symptoms, " + "such as appetite and sleep changes, low energy, low self-esteem, or " + "hopelessness, are usually part of the picture.") else: return ("The classic depression type, major depression is a state where a dark " + "mood is all-consuming and one loses interest in activities, even ones " + "that are usually pleasurable. Symptoms of this type of depression " + "include trouble sleeping, changes in appetite or weight, loss of energy, " + "and feeling worthless. Thoughts of death or suicide may occur. It is " + "usually treated with psychotherapy and medication. For some people with " + "severe depression that isn't alleviated with psychotherapy or antidepressant " + "medications, electroconvulsive therapy may be effective.")

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0.013333

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0.020648

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3,206

62

99

51.709677

0.859221

0.033063

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0.510204

0

null

0

0.092593

null

0.037037

0

null

0

null

0

1

0

1

6a9d299ac035789dcfbdc5b67b56e5ebe19176e2

33,321

py

Python

bin/ADFRsuite/CCSBpckgs/mglutil/gui/BasicWidgets/Tk/Dial.py

AngelRuizMoreno/Jupyter_Dock_devel

6d23bc174d5294d1e9909a0a1f9da0713042339e

[ "MIT" ]

null

bin/ADFRsuite/CCSBpckgs/mglutil/gui/BasicWidgets/Tk/Dial.py

AngelRuizMoreno/Jupyter_Dock_devel

6d23bc174d5294d1e9909a0a1f9da0713042339e

[ "MIT" ]

null

bin/ADFRsuite/CCSBpckgs/mglutil/gui/BasicWidgets/Tk/Dial.py

AngelRuizMoreno/Jupyter_Dock_devel

6d23bc174d5294d1e9909a0a1f9da0713042339e

[ "MIT" ]

1

2021-11-04T21:48:14.000Z

################################################################################ ## ## This library is free software; you can redistribute it and/or ## modify it under the terms of the GNU Lesser General Public ## License as published by the Free Software Foundation; either ## version 2.1 of the License, or (at your option) any later version. ## ## This library is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ## Lesser General Public License for more details. ## ## You should have received a copy of the GNU Lesser General Public ## License along with this library; if not, write to the Free Software ## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA ## ## (C) Copyrights Dr. Michel F. Sanner and TSRI 2016 ## ################################################################################ ######################################################################### # # Date: Mai 2001 Authors: Michel Sanner, Daniel Stoffler # # [email protected] # [email protected] # # Copyright: Michel Sanner, Daniel Stoffler and TSRI # ######################################################################### import Tkinter import math import types import sys import os from mglutil.util.callback import CallbackManager from mglutil.util.misc import ensureFontCase from optionsPanel import OptionsPanel from KeyboardEntry import KeyboardEntry class Dial(Tkinter.Frame, KeyboardEntry): """This class implements a Dial widget. The widget has a pointer that can be moved around a circle. The range corresponding to one full turn can be specified as well as the min and max values that are allowed. By defaults these are set to None meaning that there is no min and no max. One turn corresponds to 360 units by default. A dial can also operate in discrete mode (if self.increment is set to x). In this mode the values will be restrained to be multiples of self.increment. The Widget has a Callback manager. Callback functions get called at every value change if self.contiguous is set to 1, else they get called when the mouse button is released. They always get called with the current value as an argument. An optional label can be displayed at the center of the Dial widget. The size of the dial has to be specified at instanciation. Other parameters can be set after the widget has been created. The widget tried to adjust automatically the size of the arrow according to the size of the dial. The widget has a configure() method: type, min, max, increment, precision, showLabel, value, continuous, oneTurn can be set this way. master, labCfg and size can be passed only to the constructor. a lock() method is used to disable the various gui components of the options panel. Usage: <instance>.lock(<component>=<value>) components see configure(). value is 0 or 1. 1 disables, 0 enables. Setting values with increment enabled: if using the method set(), the actual value will 'snap' to the next increment. i.e., if the value is set to 3, and the increment is set to 2, setting the value to 6 will actually result in 7 (3,5,7,9,.....) To still be able to set the value, disregarding the current active increment, the set method understands the optional keyword force=True, i.e. dial.set(<value>, force=True)), which will set the value to <value>. The increment will now be added to this new <value> """ def __init__(self, master=None, type='float', labCfg={'fg':'black','side':'left', 'text':None}, min=None, max=None, increment=.0, precision=2, showLabel=1, value=0.0, continuous=1, oneTurn=360., size=50, callback=None, lockMin=0, lockBMin=0, lockMax=0, lockBMax=0, lockIncrement=0, lockBIncrement=0, lockPrecision=0, lockShowLabel=0, lockValue=0, lockType=0, lockContinuous=0, lockOneTurn=0, **kw): Tkinter.Frame.__init__(self, master) Tkinter.Pack.config(self) self.callbacks = CallbackManager() # object to manage callback # functions. They get called with the # current value as an argument # initialize various attributes with default values self.precision = 2 # decimal places self.min = None # minimum value self.max = None # maximum value self.increment = increment # value increment self.minOld = 0. # used to store old values self.maxOld = 0. self.incrementOld = increment self.size = 50 # defines widget size self.offsetValue = 0. # used to set increment correctly self.lab = None # label self.callback = None # user specified callback self.opPanel = None # option panel widget self.oneTurn = 360. # value increment for 1 full turn self.value = 0.0 # current value of widget self.oldValue = 0.0 # old value of widget self.showLabel = 1 # turn on to display label on self.continuous = 1 # set to 1 to call callbacks at # each value change, else gets called # on button release event self.angle = 0. # angle corresponding to value self.labCfg = labCfg # Tkinter Label options self.labelFont = ( ensureFontCase('helvetica'), 14, 'bold') # label font self.labelColor = 'yellow' # label color self.canvas = None # the canvas to create the widget in self.usedArcColor = '#aaaaaa' # filled arc color of used portion self.unusedArcColor = '#cccccc' # filled arc color of unused portion self.pyOver180 = math.pi/180.0 # constants used in various places self.threeSixtyOver1turn = 1 self.piOver1turn = math.pi/360. self.lockMin = lockMin # lock<X> vars are used in self.lock() self.lockMax = lockMax # to lock/unlock entries in optionpanel self.lockIncrement = lockIncrement self.lockBMin = lockBMin self.lockBMax = lockBMax self.lockBIncrement = lockBIncrement self.lockPrecision = lockPrecision self.lockShowLabel = lockShowLabel self.lockValue = lockValue self.lockType = lockType self.lockContinuous = lockContinuous self.lockOneTurn = lockOneTurn self.setArrow() # configure with user-defined values self.setSize(size) self.setCallback(callback) self.setContinuous(continuous) self.setType(type) self.setPrecision(precision) self.setOneTurn(oneTurn) self.setMin(min) self.setMax(max) self.setIncrement(increment) self.setShowLabel(showLabel) self.setValue(value) self.setLabel(self.labCfg) self.createCanvas(master) canvas = self.canvas canvas.bind("<ButtonPress-1>", self.mouseDown) canvas.bind("<ButtonRelease-1>", self.mouseUp) canvas.bind("<B1-Motion>", self.mouseMove) canvas.bind("<Button-3>", self.toggleOptPanel) if os.name == 'nt': #sys.platform == 'win32': canvas.bind("<MouseWheel>", self.mouseWheel) else: canvas.bind("<Button-4>", self.mouseWheel) canvas.bind("<Button-5>", self.mouseWheel) KeyboardEntry.__init__(self, (canvas,), self.setFromEntry) self.opPanel = OptionsPanel(master = self, title="Dial Options") ## if self.callback: ## self.callbacks.AddCallback(self.callback) def setFromEntry(self, valueString): try: self.set(self.type(valueString)) except ValueError: # fixme we would like to pop this up in a window maybe import traceback traceback.print_stack() traceback.print_exc() def handleKeyStroke(self, event): # handle key strokes for numbers only in widget keyboard entry label key = event.keysym if key.isdigit() or key=='period' or key=='minus' or key=='plus': if key == 'period': key = '.' elif key == 'minus': key = '-' elif key == 'plus': key = '+' self.typedValue += key self.typedValueTK.configure(text=self.typedValue) else: KeyboardEntry.handleKeyStroke(self, event) def setSize(self, size): """Set widget size. Size must be of type int and greater than 0""" assert isinstance(size, types.IntType),\ "Illegal size: expected type %s, got %s"%(type(1), type(size) ) assert size > 0, "Illegal size: must be > 0, got %s"%size self.size = size def setCallback(self, cb): """Set widget callback. Must be callable function. Callback is called every time the widget value is set/modified""" assert cb is None or callable(cb) or type(cb) is types.ListType,\ "Illegal callback: must be either None or callable, or list. Got %s"%cb if cb is None: return elif type(cb) is types.ListType: for func in cb: assert callable(func), "Illegal callback must be callable. Got %s"%func self.callbacks.AddCallback(func) else: self.callbacks.AddCallback(cb) self.callback = cb def toggleOptPanel(self, event=None): if self.opPanel.flag: self.opPanel.Dismiss_cb() else: if not hasattr(self.opPanel, 'optionsForm'): self.opPanel.displayPanel(create=1) else: self.opPanel.displayPanel(create=0) def setArrow(self, size=None): if size is not None: self.setSize(size) aS = self.size/40 self.arrowLength = max(3, 3*aS) # arrow head length self.arrowWidth = max(2, aS) # half the arrow body width self.arrowBorderwidth = max(1, self.arrowWidth/2) # width of arrow # shadow lines self.arrowHeadWidth = 2*self.arrowWidth # width of arrow head base def mouseDown(self, event): # remember where the mouse went down self.lastx = event.x self.lasty = event.y def mouseUp(self, event): # call callbacks if not in continuous mode if not self.continuous: self.callbacks.CallCallbacks(self.opPanel.valInput.get()) if self.showLabel == 2: # no widget labels on mouse release self.canvas.itemconfigure(self.labelId2, text='') self.canvas.itemconfigure(self.labelId, text='') def mouseMove(self, event): dx = event.x-self.xm dy = self.ym-event.y n = math.sqrt(dx*dx+dy*dy) if n == 0.0: v = [0.0, 0.0] else: v = [dx/n, dy/n] # find the cosine of the angle between new hand position and previous # hand position ma = v[0]*self.vector[0] + v[1]*self.vector[1] # assure no rounding errors if ma > 1.0: ma = 1.0 elif ma < -1.0: ma = -1.0 # compute angle increment compared to current vector ang = math.acos(ma) # find the sign of the rotation, sign of z component of vector prod. oldv = self.vector normz = oldv[0]*v[1] - oldv[1]*v[0] if normz>0: ang = -1. * ang # compute the new value val = self.value + ang*self.oneTurnOver2pi self.set(val) self.lastx = event.x self.lasty = event.y def mouseWheel(self, event): #print "mouseWheel", event, event.num if os.name == 'nt': #sys.platform == 'win32': if event.delta > 0: lEventNum = 4 else: lEventNum = 5 else: lEventNum = event.num if lEventNum == 4: self.set(self.value+self.oneTurn) else: self.set(self.value-self.oneTurn) def get(self): return self.type(self.value) def printLabel(self): if self.canvas is None: return self.canvas.itemconfigure(self.labelId2, text=self.labelFormat%self.value)#newVal) self.canvas.itemconfigure(self.labelId, text=self.labelFormat%self.value)#newVal) def set(self, val, update=1, force=0): # if force is set to 1, we call this method regardless of the # widget configuration. This is for example the case if the dial # is set to continuous=0, but the value is set in the options panel # snap to closest increment if self.increment is not None and self.increment != 0. and not force: offset = self.offsetValue%self.increment dval = round(val/self.increment) * self.increment if val < dval: dval = dval + offset - self.increment else: dval = dval + offset if self.min is not None and dval < self.min: dval = self.min elif self.max is not None and dval > self.max: dval = self.max # recompute vector and angle corresponding to val self.angle = (dval%self.oneTurn)*self.threeSixtyOver1turn if dval <0.0: self.angle = self.angle - 360.0 a = self.angle*self.pyOver180 self.vector = [math.sin(a), math.cos(a)] self.value = dval self.offsetValue = dval else: # 'regular' mode, i.e. no step-wise increment if self.min is not None and val < self.min: val = self.min elif self.max is not None and val > self.max: val = self.max # recompute vector and angle corresponding to val self.angle = (val%self.oneTurn)*self.threeSixtyOver1turn if val <0.0: self.angle = self.angle - 360.0 a = self.angle*self.pyOver180 self.vector = [math.sin(a), math.cos(a)] self.value = val self.offsetValue = val #update arrow in display self.drawArrow() newVal = self.get() if self.continuous or force: if update and self.oldValue != newVal or force: self.oldValue = newVal self.callbacks.CallCallbacks(newVal) if self.showLabel==2: self.printLabel() else: if self.showLabel==2: self.printLabel() if self.showLabel==1: self.printLabel() if self.opPanel: self.opPanel.valInput.set(self.labelFormat%newVal) def drawArrow(self): if self.canvas is None: return # end point x1 = self.xm + self.vector[0]*self.rad y1 = self.ym - self.vector[1]*self.rad # point at arrow head base xb = self.xm + self.vector[0]*self.radNoArrow yb = self.xm - self.vector[1]*self.radNoArrow # vector orthogonal to arrow n = [-self.vector[1], -self.vector[0]] pts1 = [ self.xm+n[0]*self.arrowWidth, self.ym+n[1]*self.arrowWidth, xb+n[0]*self.arrowWidth, yb+n[1]*self.arrowWidth, xb+n[0]*self.arrowHeadWidth, yb+n[1]*self.arrowHeadWidth, x1, y1 ] pts2 = [ x1, y1, xb-n[0]*self.arrowHeadWidth, yb-n[1]*self.arrowHeadWidth, xb-n[0]*self.arrowWidth, yb-n[1]*self.arrowWidth, self.xm-n[0]*self.arrowWidth, self.ym-n[1]*self.arrowWidth ] canvas = self.canvas if self.vector[0] > 0.0: col1 = '#DDDDDD' col2 = 'black' else: col1 = 'black' col2 = '#DDDDDD' apply( canvas.coords, (self.arrowPolId,) + tuple(pts1+pts2) ) apply( canvas.coords, (self.arrowPolborder1,) + tuple(pts1) ) canvas.itemconfigure( self.arrowPolborder1, fill=col1 ) apply( canvas.coords, (self.arrowPolborder2,) + tuple(pts2) ) canvas.itemconfigure( self.arrowPolborder2, fill=col2 ) canvas.itemconfigure(self.arcId, extent = 0.0-self.angle) def createCanvas(self, master): size = self.size self.frame = Tkinter.Frame(self, borderwidth=3, relief='sunken') self.canvas = Tkinter.Canvas(self.frame, width=size+2, height=size+2) self.xm = self.ym = size/2+2 self.rad = size/2 self.radNoArrow = self.rad-self.arrowLength self.vector = [0, 1] x1 = self.xm + self.vector[0]*self.rad y1 = self.ym + self.vector[1]*self.rad canvas = self.canvas self.circleId = canvas.create_oval(2,2,size,size, width=1, fill=self.unusedArcColor) self.arcId = canvas.create_arc(2,2,size,size, start=90., extent=0, fill=self.usedArcColor) canvas.create_line(2, self.ym, size+2, self.ym) canvas.create_line(self.xm, 2, self.ym, size+2) self.arrowPolId = canvas.create_polygon( 0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0, fill='gray75' ) self.arrowPolborder1 = canvas.create_line( 0,0,0,0,0,0,0,0, fill='black', width = self.arrowBorderwidth) self.arrowPolborder2 = canvas.create_line( 0,0,0,0,0,0,0,0, fill='white', width = self.arrowBorderwidth ) r = size/20 off = self.arrowBorderwidth canvas.create_oval(self.xm-r,self.ym-r-off/2,self.xm+r,self.ym+r-off/2, fill='#DDDDDD', outline='white') canvas.create_oval(self.xm-r,self.ym-r+off,self.xm+r,self.ym+r+off, fill='black', outline='black') canvas.create_oval(self.xm-r,self.ym-r,self.xm+r,self.ym+r, fill='gray70', outline='#DDDDDD') self.labelId2 = canvas.create_text(self.xm+2, self.ym+2, fill='black', justify='center', text='', font = self.labelFont) self.labelId = canvas.create_text(self.xm, self.ym, fill=self.labelColor, justify='center', text='', font = self.labelFont) self.drawArrow() self.opPanel = OptionsPanel(master = self, title="Dial Options") # pack em up self.canvas.pack(side=Tkinter.TOP) self.frame.pack(expand=1, fill='x') self.toggleWidgetLabel(self.showLabel) def toggleWidgetLabel(self, val): if val == 0: # no widget labels self.showLabel=0 self.canvas.itemconfigure(self.labelId2, text='') self.canvas.itemconfigure(self.labelId, text='') if val == 1: # show always widget labels self.showLabel=1 self.printLabel() if val == 2: # show widget labels only when mouse moves self.showLabel=2 self.canvas.itemconfigure(self.labelId2, text='') self.canvas.itemconfigure(self.labelId, text='') def setValue(self, val): if type(val) == types.StringType: val = float(val) assert type(val) in [types.IntType, types.FloatType],\ "Illegal type for value: expected %s or %s, got %s"%( type(1), type(1.0), type(val) ) # setValue does NOT call a callback! if self.min is not None and val < self.min: val = self.min if self.max is not None and val > self.max: val = self.max self.value = self.type(val) self.offsetValue=self.value self.oldValue = self.value #update arrow in display self.angle = (self.value%self.oneTurn)*self.threeSixtyOver1turn if self.value <0.0: self.angle = self.angle - 360.0 a = self.angle*self.pyOver180 self.vector = [math.sin(a), math.cos(a)] self.drawArrow() if self.showLabel == 1: self.printLabel() if self.opPanel: self.opPanel.valInput.set(self.labelFormat%self.value) def setLabel(self, labCfg): self.labCfg = labCfg text = labCfg.get('text', None) if text is None or text=='': return d={} for k, w in self.labCfg.items(): if k == 'side': continue else: d[k] = w if not 'side' in self.labCfg.keys(): self.labCfg['side'] = 'left' if not self.lab: self.lab = Tkinter.Label(self, d) self.lab.pack(side=self.labCfg['side']) self.lab.bind("<Button-3>", self.toggleOptPanel) else: self.lab.configure(text) ##################################################################### # the 'configure' methods: ##################################################################### def configure(self, **kw): for key,value in kw.items(): # the 'set' parameter callbacks if key=='labCfg': self.setLabel(value) elif key=='type': self.setType(value) elif key=='min': self.setMin(value) elif key=='max': self.setMax(value) elif key=='increment': self.setIncrement(value) elif key=='precision': self.setPrecision(value) elif key=='showLabel': self.setShowLabel(value) elif key=='continuous': self.setContinuous(value) elif key=='oneTurn': self.setOneTurn(value) # the 'lock' entries callbacks elif key=='lockType': self.lockTypeCB(value) elif key=='lockMin': self.lockMinCB(value) elif key=='lockBMin': self.lockBMinCB(value) elif key=='lockMax': self.lockMaxCB(value) elif key=='lockBMax': self.lockBMaxCB(value) elif key=='lockIncrement': self.lockIncrementCB(value) elif key=='lockBIncrement': self.lockBIncrementCB(value) elif key=='lockPrecision': self.lockPrecisionCB(value) elif key=='lockShowLabel': self.lockShowLabelCB(value) elif key=='lockValue': self.lockValueCB(value) elif key=='lockContinuous': self.lockContinuousCB(value) elif key=='lockOneTurn': self.lockOneTurnCB(value) def setType(self, Type): assert type(Type) in [types.StringType, types.TypeType],\ "Illegal type for datatype. Expected %s or %s, got %s"%( type('a'), type(type), type(Type) ) if type(Type) == type(""): # type str assert Type in ('int','float'),\ "Illegal type descriptor. Expected 'int' or 'float', got '%s'"%Type self.type = eval(Type) else: self.type = Type if self.type == int: self.labelFormat = "%d" self.int_value = self.value else: self.labelFormat = "%."+str(self.precision)+"f" if hasattr(self.opPanel, 'optionsForm'): w = self.opPanel.idf.entryByName['togIntFloat']['widget'] if self.type == int: w.setvalue('int') elif self.type == 'float': w.setvalue('float') if self.opPanel: self.opPanel.updateDisplay() # and update the printed label if self.canvas and self.showLabel == 1: self.printLabel() def setMin(self, min): if min is not None: assert type(min) in [types.IntType, types.FloatType],\ "Illegal type for minimum. Expected type %s or %s, got %s"%( type(0), type(0.0), type(min) ) if self.max and min > self.max: min = self.max self.min = self.type(min) if self.showLabel == 1: self.printLabel() if self.value < self.min: self.set(self.min) if hasattr(self.opPanel, 'optionsForm'): self.opPanel.minInput.set(self.labelFormat%self.min) self.opPanel.toggleMin.set(1) self.opPanel.min_entry.configure(state='normal', fg='gray0') self.minOld = self.min else: self.min = None if hasattr(self.opPanel, 'optionsForm'): self.opPanel.toggleMin.set(0) self.opPanel.min_entry.configure(state='disabled', fg='gray40') def setMax(self, max): if max is not None: assert type(max) in [types.IntType, types.FloatType],\ "Illegal type for maximum. Expected type %s or %s, got %s"%( type(0), type(0.0), type(max) ) if self.min and max < self.min: max = self.min self.max = self.type(max) if self.showLabel == 1: self.printLabel() if self.value > self.max: self.set(self.max) if hasattr(self.opPanel, 'optionsForm'): self.opPanel.maxInput.set(self.labelFormat%self.max) self.opPanel.toggleMax.set(1) self.opPanel.max_entry.configure(state='normal', fg='gray0') self.maxOld = self.max else: self.max = None if hasattr(self.opPanel, 'optionsForm'): self.opPanel.toggleMax.set(0) self.opPanel.max_entry.configure(state='disabled', fg='gray40') def setIncrement(self, incr): if incr is not None: assert type(incr) in [types.IntType, types.FloatType],\ "Illegal type for increment. Expected type %s or %s, got %s"%( type(0), type(0.0), type(incr) ) self.increment = self.type(incr) self.offsetValue = self.value self.incrementOld = self.increment if hasattr(self.opPanel, 'optionsForm'): self.opPanel.incrInput.set(self.labelFormat%self.increment) self.opPanel.toggleIncr.set(1) self.opPanel.incr_entry.configure(state='normal', fg='gray0') else: self.increment = self.type(0) if hasattr(self.opPanel, 'optionsForm'): self.opPanel.toggleIncr.set(0) self.opPanel.incrInput.set(self.labelFormat%0) self.opPanel.incr_entry.configure(state='disabled', fg='gray40') def setPrecision(self, val): assert type(val) in [types.IntType, types.FloatType],\ "Illegal type for precision. Expected type %s or %s, got %s"%( type(0), type(0.0), type(val) ) val = int(val) if val > 10: val = 10 if val < 1: val = 1 self.precision = val if self.type == float: self.labelFormat = "%."+str(self.precision)+"f" else: self.labelFormat = "%d" if hasattr(self.opPanel, 'optionsForm'): w = self.opPanel.idf.entryByName['selPrec']['widget'] w.setvalue(val) if self.opPanel: self.opPanel.updateDisplay() # and update the printed label if self.canvas and self.showLabel == 1: self.printLabel() def setContinuous(self, cont): """ cont can be None, 0 or 1 """ assert cont in [None, 0, 1],\ "Illegal value for continuous: expected None, 0 or 1, got %s"%cont if cont != 1: cont = None self.continuous = cont if hasattr(self.opPanel, 'optionsForm'): w = self.opPanel.idf.entryByName['togCont']['widget'] if cont: w.setvalue('on')#i=1 else: w.setvalue('off')#i=0 if self.opPanel: self.opPanel.updateDisplay() def setShowLabel(self, val): """Show label can be 0, 1 or 2 0: no label 1: label is always shown 2: show label only when value changes""" assert val in [0,1,2],\ "Illegal value for showLabel. Expected 0, 1 or 2, got %s"%val if val != 0 and val != 1 and val != 2: print "Illegal value. Must be 0, 1 or 2" return self.showLabel = val self.toggleWidgetLabel(val) if hasattr(self.opPanel, 'optionsForm'): w = self.opPanel.idf.entryByName['togLabel']['widget'] if self.showLabel == 0: label = 'never' elif self.showLabel == 1: label = 'always' elif self.showLabel == 2: label = 'move' w.setvalue(label) if self.opPanel: self.opPanel.updateDisplay() def setOneTurn(self, oneTurn): assert type(oneTurn) in [types.IntType, types.FloatType],\ "Illegal type for oneTurn. Expected %s or %s, got %s"%( type(0), type(0.0), type(oneTurn) ) self.oneTurn = oneTurn self.threeSixtyOver1turn = 360./oneTurn self.piOver1turn = math.pi/oneTurn self.oneTurnOver2pi = oneTurn / (2*math.pi) if self.opPanel: self.opPanel.updateDisplay() ##################################################################### # the 'lock' methods: ##################################################################### def lockTypeCB(self, mode): if mode != 0: mode = 1 self.lockType = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockMinCB(self, mode): #min entry field if mode != 0: mode = 1 self.lockMin = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockBMinCB(self, mode): # min checkbutton if mode != 0: mode = 1 self.lockBMin = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockMaxCB(self, mode): # max entry field if mode != 0: mode = 1 self.lockMax = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockBMaxCB(self, mode): # max checkbutton if mode != 0: mode = 1 self.lockBMax = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockIncrementCB(self, mode): # increment entry field if mode != 0: mode = 1 self.lockIncrement = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockBIncrementCB(self, mode): # increment checkbutton if mode != 0: mode = 1 self.lockBIncrement = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockPrecisionCB(self, mode): if mode != 0: mode = 1 self.lockPrecision = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockShowLabelCB(self, mode): if mode != 0: mode = 1 self.lockShowLabel = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockValueCB(self, mode): if mode != 0: mode = 1 self.lockValue = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockContinuousCB(self, mode): if mode != 0: mode = 1 self.lockContinuous = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() def lockOneTurnCB(self, mode): if mode != 0: mode = 1 self.lockOneTurn = mode if hasattr(self.opPanel, 'optionsForm'): self.opPanel.lockUnlockDisplay() if __name__ == '__main__': def foo(val): print val d = Dial(size=50) d.configure(showLabel=1) d.callbacks.AddCallback(foo)

36.376638

87

0.547643

3,868

33,321

4.706825

0.152792

0.046523

0.004779

0.005273

0.32341

0.297484

0.258376

0.209162

0.176261

0.161046

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0.018954

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33,321

915

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null

0.023295

0

null

0

null

0

1

0

1

6a9d42bd307c1507375c76e403f46b3901bbf76d

3,560

py

Python

qt-creator-opensource-src-4.6.1/scripts/checkInstalledFiles.py

kevinlq/Qt-Creator-Opensource-Study

b8cadff1f33f25a5d4ef33ed93f661b788b1ba0f

[ "MIT" ]

5

2018-12-22T14:49:13.000Z

2022-01-13T07:21:46.000Z

qt-creator-opensource-src-4.6.1/scripts/checkInstalledFiles.py

kevinlq/Qt-Creator-Opensource-Study

b8cadff1f33f25a5d4ef33ed93f661b788b1ba0f

[ "MIT" ]

null

qt-creator-opensource-src-4.6.1/scripts/checkInstalledFiles.py

kevinlq/Qt-Creator-Opensource-Study

b8cadff1f33f25a5d4ef33ed93f661b788b1ba0f

[ "MIT" ]

8

2018-07-17T03:55:48.000Z

2021-12-22T06:37:53.000Z

#!/usr/bin/env python ############################################################################ # # Copyright (C) 2016 The Qt Company Ltd. # Contact: https://www.qt.io/licensing/ # # This file is part of Qt Creator. # # Commercial License Usage # Licensees holding valid commercial Qt licenses may use this file in # accordance with the commercial license agreement provided with the # Software or, alternatively, in accordance with the terms contained in # a written agreement between you and The Qt Company. For licensing terms # and conditions see https://www.qt.io/terms-conditions. For further # information use the contact form at https://www.qt.io/contact-us. # # GNU General Public License Usage # Alternatively, this file may be used under the terms of the GNU # General Public License version 3 as published by the Free Software # Foundation with exceptions as appearing in the file LICENSE.GPL3-EXCEPT # included in the packaging of this file. Please review the following # information to ensure the GNU General Public License requirements will # be met: https://www.gnu.org/licenses/gpl-3.0.html. # ############################################################################ import os import sys import stat import difflib import inspect import getopt def referenceFile(): if sys.platform.startswith('linux'): filename = 'makeinstall.linux' elif sys.platform.startswith('win'): filename = 'makeinstall.windows' elif sys.platform == 'darwin': filename = 'makeinstall.darwin' else: print "Unsupported platform: ", sys.platform sys.exit(-1) scriptDir = os.path.dirname(inspect.getfile(inspect.currentframe())) return os.path.join(scriptDir,'..','tests', 'reference', filename) def readReferenceFile(): # read file with old diff f = open(referenceFile(), 'r'); filelist = [] for line in f: filelist.append(line) f.close() return filelist def generateReference(rootdir): fileDict = {} for root, subFolders, files in os.walk(rootdir): for file in (subFolders + files): f = os.path.join(root,file) perm = os.stat(f).st_mode & 0777 if os.path.getsize(f) == 0: print "'%s' is empty!" % f fileDict[f[len(rootdir)+1:]] = perm # generate new list formattedlist = [] for name, perm in sorted(fileDict.iteritems()): formattedlist.append("%o %s\n"% (perm, name)) return formattedlist; def usage(): print "Usage: %s [-g | --generate] <dir>" % os.path.basename(sys.argv[0]) def main(): generateMode = False try: opts, args = getopt.gnu_getopt(sys.argv[1:], 'hg', ['help', 'generate']) except: print str(err) usage() sys.exit(2) for o, a in opts: if o in ('-h', '--help'): usage() sys.exit(0) if o in ('-g', '--generate'): generateMode = True if len(args) != 1: usage() sys.exit(2) rootdir = args[0] if generateMode: f = open(referenceFile(), 'w') for item in generateReference(rootdir): f.write(item) f.close() print "Do not forget to commit", referenceFile() else: hasDiff = False for line in difflib.unified_diff(readReferenceFile(), generateReference(rootdir), fromfile=referenceFile(), tofile="generated"): sys.stdout.write(line) hasDiff = True if hasDiff: sys.exit(1) if __name__ == "__main__": main()

31.504425

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4.91344

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0.013908

0.01669

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0

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0.235674

3,560

112

137

31.785714

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null

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null

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0

null

0

null

0

1

0

1

6aa62343269180c72e1026d8bfdc9d3fa9196b1e

7,448

py

Python

gluon/contrib/pbkdf2_ctypes.py

Cwlowe/web2py

6ae4c3c274be1026cbc45b0fcd8d1180c74b9070

[ "BSD-3-Clause" ]

9

2018-04-19T05:08:30.000Z

2021-11-23T07:36:58.000Z

gluon/contrib/pbkdf2_ctypes.py

mohit3011/Quiz-Mate

17988a623abde439aef2b43fc8dc3162b5cae15e

[ "BSD-3-Clause" ]

98

2017-11-02T19:00:44.000Z

2022-03-22T16:15:39.000Z

gluon/contrib/pbkdf2_ctypes.py

mohit3011/Quiz-Mate

17988a623abde439aef2b43fc8dc3162b5cae15e

[ "BSD-3-Clause" ]

9

2017-10-24T21:53:36.000Z

2021-11-23T07:36:59.000Z

# -*- coding: utf-8 -*- """ pbkdf2_ctypes ~~~~~~ Fast pbkdf2. This module implements pbkdf2 for Python using crypto lib from openssl or commoncrypto. Note: This module is intended as a plugin replacement of pbkdf2.py by Armin Ronacher. Git repository: $ git clone https://github.com/michele-comitini/pbkdf2_ctypes.git :copyright: Copyright (c) 2013: Michele Comitini <[email protected]> :license: LGPLv3 """ import ctypes import ctypes.util import hashlib import platform import os.path import binascii import sys __all__ = ['pkcs5_pbkdf2_hmac', 'pbkdf2_bin', 'pbkdf2_hex'] __version__ = '0.99.3' def _commoncrypto_hashlib_to_crypto_map_get(hashfunc): hashlib_to_crypto_map = {hashlib.sha1: 1, hashlib.sha224: 2, hashlib.sha256: 3, hashlib.sha384: 4, hashlib.sha512: 5} crypto_hashfunc = hashlib_to_crypto_map.get(hashfunc) if crypto_hashfunc is None: raise ValueError('Unkwnown digest %s' % hashfunc) return crypto_hashfunc def _commoncrypto_pbkdf2(data, salt, iterations, digest, keylen): """Common Crypto compatibile wrapper """ c_hashfunc = ctypes.c_uint32(_commoncrypto_hashlib_to_crypto_map_get(digest)) c_pass = ctypes.c_char_p(data) c_passlen = ctypes.c_size_t(len(data)) c_salt = ctypes.c_char_p(salt) c_saltlen = ctypes.c_size_t(len(salt)) c_iter = ctypes.c_uint(iterations) c_keylen = ctypes.c_size_t(keylen) c_buff = ctypes.create_string_buffer(keylen) crypto.CCKeyDerivationPBKDF.restype = ctypes.c_int crypto.CCKeyDerivationPBKDF.argtypes = [ctypes.c_uint32, ctypes.c_char_p, ctypes.c_size_t, ctypes.c_char_p, ctypes.c_size_t, ctypes.c_uint32, ctypes.c_uint, ctypes.c_char_p, ctypes.c_size_t] ret = crypto.CCKeyDerivationPBKDF(2, # hardcoded 2-> PBKDF2 c_pass, c_passlen, c_salt, c_saltlen, c_hashfunc, c_iter, c_buff, c_keylen) return (1 - ret, c_buff) def _openssl_hashlib_to_crypto_map_get(hashfunc): hashlib_to_crypto_map = {hashlib.md5: crypto.EVP_md5, hashlib.sha1: crypto.EVP_sha1, hashlib.sha256: crypto.EVP_sha256, hashlib.sha224: crypto.EVP_sha224, hashlib.sha384: crypto.EVP_sha384, hashlib.sha512: crypto.EVP_sha512} crypto_hashfunc = hashlib_to_crypto_map.get(hashfunc) if crypto_hashfunc is None: raise ValueError('Unkwnown digest %s' % hashfunc) crypto_hashfunc.restype = ctypes.c_void_p return crypto_hashfunc() def _openssl_pbkdf2(data, salt, iterations, digest, keylen): """OpenSSL compatibile wrapper """ c_hashfunc = ctypes.c_void_p(_openssl_hashlib_to_crypto_map_get(digest)) c_pass = ctypes.c_char_p(data) c_passlen = ctypes.c_int(len(data)) c_salt = ctypes.c_char_p(salt) c_saltlen = ctypes.c_int(len(salt)) c_iter = ctypes.c_int(iterations) c_keylen = ctypes.c_int(keylen) c_buff = ctypes.create_string_buffer(keylen) # PKCS5_PBKDF2_HMAC(const char *pass, int passlen, # const unsigned char *salt, int saltlen, int iter, # const EVP_MD *digest, # int keylen, unsigned char *out); crypto.PKCS5_PBKDF2_HMAC.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.c_char_p, ctypes.c_int, ctypes.c_int, ctypes.c_void_p, ctypes.c_int, ctypes.c_char_p] crypto.PKCS5_PBKDF2_HMAC.restype = ctypes.c_int err = crypto.PKCS5_PBKDF2_HMAC(c_pass, c_passlen, c_salt, c_saltlen, c_iter, c_hashfunc, c_keylen, c_buff) return (err, c_buff) try: # check that we have proper OpenSSL or Common Crypto on the system. system = platform.system() if system == 'Windows': if platform.architecture()[0] == '64bit': libname = ctypes.util.find_library('libeay64') if not libname: raise OSError('Library not found') crypto = ctypes.CDLL(libname) else: libname = ctypes.util.find_library('libeay32') if not libname: raise OSError('Library libeay32 not found.') crypto = ctypes.CDLL(libname) _pbkdf2_hmac = _openssl_pbkdf2 crypto.PKCS5_PBKDF2_HMAC # test compatibility elif system == 'Darwin': # think different(TM)! i.e. break things! if [int(x) for x in platform.mac_ver()[0].split('.')] < [10, 7, 0]: raise OSError('OS X Version too old %s < 10.7.0' % platform.mac_ver()[0]) libname = ctypes.util.find_library('System') if not libname: raise OSError('Library not found') crypto = ctypes.CDLL(os.path.basename(libname)) _pbkdf2_hmac = _commoncrypto_pbkdf2 else: libname = ctypes.util.find_library('crypto') if not libname: raise OSError('Library crypto not found.') crypto = ctypes.CDLL(os.path.basename(libname)) _pbkdf2_hmac = _openssl_pbkdf2 crypto.PKCS5_PBKDF2_HMAC # test compatibility except (OSError, AttributeError): _, e, _ = sys.exc_info() raise ImportError('Cannot find a compatible cryptographic library ' 'on your system. %s' % e) def pkcs5_pbkdf2_hmac(data, salt, iterations=1000, keylen=24, hashfunc=None): if hashfunc is None: hashfunc = hashlib.sha1 err, c_buff = _pbkdf2_hmac(data, salt, iterations, hashfunc, keylen) if err == 0: raise ValueError('wrong parameters') return c_buff.raw[:keylen] def pbkdf2_hex(data, salt, iterations=1000, keylen=24, hashfunc=None): return binascii.hexlify(pkcs5_pbkdf2_hmac(data, salt, iterations, keylen, hashfunc)) def pbkdf2_bin(data, salt, iterations=1000, keylen=24, hashfunc=None): return pkcs5_pbkdf2_hmac(data, salt, iterations, keylen, hashfunc) if __name__ == '__main__': try: crypto.SSLeay_version.restype = ctypes.c_char_p print(crypto.SSLeay_version(0)) except: pass import platform if platform.python_version_tuple() < ('3', '0', '0'): def bytes(*args): return str(args[0]) for h in [hashlib.sha1, hashlib.sha224, hashlib.sha256, hashlib.sha384, hashlib.sha512]: print(binascii.hexlify(pkcs5_pbkdf2_hmac(bytes('secret', 'utf-8') * 11, bytes('salt', 'utf-8'), hashfunc=h)))

38.194872

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0

null

0

null

0

1

0

1

6aa7fd8436efabe5593a8174e9772f897fb7aec0

4,465

py

Python

sympy/polys/tests/test_sqfreetools.py

eriknw/sympy

b7544e2bb74c011f6098a7e886fd77f41776c2c4

[ "BSD-3-Clause" ]

7

2015-01-14T06:55:33.000Z

2018-08-11T14:43:52.000Z

sympy/polys/tests/test_sqfreetools.py

pbeltran/sympy-1

94f92b36731c2bebe6de1037c063c2a258a8a399

[ "BSD-3-Clause" ]

1

2018-02-19T04:56:04.000Z

sympy/polys/tests/test_sqfreetools.py

pbeltran/sympy-1

94f92b36731c2bebe6de1037c063c2a258a8a399

[ "BSD-3-Clause" ]

1

2016-04-24T14:39:22.000Z

"""Tests for square-free decomposition algorithms and related tools. """ from sympy.polys.rings import ring from sympy.polys.domains import FF, ZZ, QQ from sympy.polys.polyclasses import DMP from sympy.polys.specialpolys import f_polys from sympy.utilities.pytest import raises f_0, f_1, f_2, f_3, f_4, f_5, f_6 = f_polys() def test_dup_sqf(): R, x = ring("x", ZZ) assert R.dup_sqf_part(0) == 0 assert R.dup_sqf_p(0) is True assert R.dup_sqf_part(7) == 1 assert R.dup_sqf_p(7) is True assert R.dup_sqf_part(2*x + 2) == x + 1 assert R.dup_sqf_p(2*x + 2) is True assert R.dup_sqf_part(x**3 + x + 1) == x**3 + x + 1 assert R.dup_sqf_p(x**3 + x + 1) is True assert R.dup_sqf_part(-x**3 + x + 1) == x**3 - x - 1 assert R.dup_sqf_p(-x**3 + x + 1) is True assert R.dup_sqf_part(2*x**3 + 3*x**2) == 2*x**2 + 3*x assert R.dup_sqf_p(2*x**3 + 3*x**2) is False assert R.dup_sqf_part(-2*x**3 + 3*x**2) == 2*x**2 - 3*x assert R.dup_sqf_p(-2*x**3 + 3*x**2) is False assert R.dup_sqf_list(0) == (0, []) assert R.dup_sqf_list(1) == (1, []) assert R.dup_sqf_list(x) == (1, [(x, 1)]) assert R.dup_sqf_list(2*x**2) == (2, [(x, 2)]) assert R.dup_sqf_list(3*x**3) == (3, [(x, 3)]) assert R.dup_sqf_list(-x**5 + x**4 + x - 1) == \ (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)]) assert R.dup_sqf_list(x**8 + 6*x**6 + 12*x**4 + 8*x**2) == \ ( 1, [(x, 2), (x**2 + 2, 3)]) assert R.dup_sqf_list(2*x**2 + 4*x + 2) == (2, [(x + 1, 2)]) R, x = ring("x", QQ) assert R.dup_sqf_list(2*x**2 + 4*x + 2) == (2, [(x + 1, 2)]) R, x = ring("x", FF(2)) assert R.dup_sqf_list(x**2 + 1) == (1, [(x + 1, 2)]) R, x = ring("x", FF(3)) assert R.dup_sqf_list(x**10 + 2*x**7 + 2*x**4 + x) == \ (1, [(x, 1), (x + 1, 3), (x + 2, 6)]) R1, x = ring("x", ZZ) R2, y = ring("y", FF(3)) f = x**3 + 1 g = y**3 + 1 assert R1.dup_sqf_part(f) == f assert R2.dup_sqf_part(g) == y + 1 assert R1.dup_sqf_p(f) is True assert R2.dup_sqf_p(g) is False R, x, y = ring("x,y", ZZ) A = x**4 - 3*x**2 + 6 D = x**6 - 5*x**4 + 5*x**2 + 4 f, g = D, R.dmp_sub(A, R.dmp_mul(R.dmp_diff(D, 1), y)) res = R.dmp_resultant(f, g) h = (4*y**2 + 1).drop(x) assert R.drop(x).dup_sqf_list(res) == (45796, [(h, 3)]) R, x = ring("x", ZZ["t"]) assert R.dup_sqf_list_include(DMP([1, 0, 0, 0], ZZ)*x**2) == \ [(DMP([1, 0, 0, 0], ZZ), 1), (DMP([1], ZZ)*x, 2)] def test_dmp_sqf(): R, x, y = ring("x,y", ZZ) assert R.dmp_sqf_part(0) == 0 assert R.dmp_sqf_p(0) is True assert R.dmp_sqf_part(7) == 1 assert R.dmp_sqf_p(7) is True assert R.dmp_sqf_list(3) == (3, []) assert R.dmp_sqf_list_include(3) == [(3, 1)] R, x, y, z = ring("x,y,z", ZZ) assert R.dmp_sqf_p(f_0) is True assert R.dmp_sqf_p(f_0**2) is False assert R.dmp_sqf_p(f_1) is True assert R.dmp_sqf_p(f_1**2) is False assert R.dmp_sqf_p(f_2) is True assert R.dmp_sqf_p(f_2**2) is False assert R.dmp_sqf_p(f_3) is True assert R.dmp_sqf_p(f_3**2) is False assert R.dmp_sqf_p(f_5) is False assert R.dmp_sqf_p(f_5**2) is False assert R.dmp_sqf_p(f_4) is True assert R.dmp_sqf_part(f_4) == -f_4 assert R.dmp_sqf_part(f_5) == x + y - z R, x, y, z, t = ring("x,y,z,t", ZZ) assert R.dmp_sqf_p(f_6) is True assert R.dmp_sqf_part(f_6) == f_6 R, x = ring("x", ZZ) f = -x**5 + x**4 + x - 1 assert R.dmp_sqf_list(f) == (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)]) assert R.dmp_sqf_list_include(f) == [(-x**3 - x**2 - x - 1, 1), (x - 1, 2)] R, x, y = ring("x,y", ZZ) f = -x**5 + x**4 + x - 1 assert R.dmp_sqf_list(f) == (-1, [(x**3 + x**2 + x + 1, 1), (x - 1, 2)]) assert R.dmp_sqf_list_include(f) == [(-x**3 - x**2 - x - 1, 1), (x - 1, 2)] f = -x**2 + 2*x - 1 assert R.dmp_sqf_list_include(f) == [(-1, 1), (x - 1, 2)] R, x, y = ring("x,y", FF(2)) raises(NotImplementedError, lambda: R.dmp_sqf_list(y**2 + 1)) def test_dup_gff_list(): R, x = ring("x", ZZ) f = x**5 + 2*x**4 - x**3 - 2*x**2 assert R.dup_gff_list(f) == [(x, 1), (x + 2, 4)] g = x**9 - 20*x**8 + 166*x**7 - 744*x**6 + 1965*x**5 - 3132*x**4 + 2948*x**3 - 1504*x**2 + 320*x assert R.dup_gff_list(g) == [(x**2 - 5*x + 4, 1), (x**2 - 5*x + 4, 2), (x, 3)] raises(ValueError, lambda: R.dup_gff_list(0))

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0.07767

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null

0

null

0

1

0

1

6aa848925fe885025486d711e7226e473656a954

1,377

py

Python

ezno_convert/enums.py

ofersadan85/ezno_convert

4c5cf7d41c72698e5486068673f170d968a9de27

[ "MIT" ]

2

2021-02-07T21:27:04.000Z

2021-03-13T06:47:25.000Z

ezno_convert/enums.py

ofersadan85/ezno_convert

4c5cf7d41c72698e5486068673f170d968a9de27

[ "MIT" ]

1

2021-02-10T05:45:00.000Z

ezno_convert/enums.py

ofersadan85/ezno_convert

4c5cf7d41c72698e5486068673f170d968a9de27

[ "MIT" ]

null

import enum from typing import Union @enum.unique class PPT(enum.Enum): # Source: https://docs.microsoft.com/en-us/office/vba/api/powerpoint.ppsaveasfiletype AnimatedGIF = 40 BMP = 19 Default = 11 EMF = 23 External = 64000 GIF = 16 JPG = 17 META = 15 MP4 = 39 OpenPresentation = 35 PDF = 32 PNG = 18 Presentation = 1 RTF = 6 SHOW = 7 Template = 5 TIF = 21 WMV = 37 XPS = 33 app = 'Powerpoint.Application' extensions = ('.ppt', '.pptx') @enum.unique class WORD(enum.Enum): # Source: https://docs.microsoft.com/en-us/office/vba/api/word.wdsaveformat DosText = 4 DosTextLineBreaks = 5 FilteredHTML = 10 FlatXML = 19 OpenDocumentText = 23 HTML = 8 RTF = 6 Template = 1 Text = 2 TextLineBreaks = 3 UnicodeText = 7 WebArchive = 9 XML = 11 Document97 = 0 DocumentDefault = 16 PDF = 17 XPS = 18 app = 'Word.Application' extensions = ('.doc', '.docx') @enum.unique class XL(enum.Enum): # Source: https://docs.microsoft.com/en-us/office/vba/api/excel.xlfixedformattype # TODO: Implement "SaveAs" methods, see: https://docs.microsoft.com/en-us/office/vba/api/excel.workbook.saveas PDF = 0 XPS = 1 app = 'Excel.Application' extensions = ('.xls', '.xlsx') enum_types = Union[PPT, WORD, XL]

20.863636

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0.924528

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null

0

null

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1

0

1

6aa897704d8b8b96376b6c78aa9de27ecec18071

378

py

Python

app/django_first/news/migrations/0002_movies_year.py

vvuri/flask_pipeline

d3f283b8a6a6239e56d85e67dbe3edce55bcb980

[ "MIT" ]

null

app/django_first/news/migrations/0002_movies_year.py

vvuri/flask_pipeline

d3f283b8a6a6239e56d85e67dbe3edce55bcb980

[ "MIT" ]

null

app/django_first/news/migrations/0002_movies_year.py

vvuri/flask_pipeline

d3f283b8a6a6239e56d85e67dbe3edce55bcb980

[ "MIT" ]

null

# Generated by Django 4.0.1 on 2022-01-19 23:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('news', '0001_initial'), ] operations = [ migrations.AddField( model_name='movies', name='year', field=models.CharField(max_length=4, null=True), ), ]

19.894737

60

0.582011

42

378

5.166667

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0

0.075188

0.296296

378

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0

0.333333

0

null

0

1

0

null

0

1

6aaa29259fb6e01655aa91ee60654bb2eceee036

1,271

py

Python

gjqyxyxxcxxt/gjqyxyxxcxxt/queue_companies.py

AisinoPythonTeam/PythonAiniso

983a29962752679d8cc26a2c3cdb0ba8fcfa3f02

[ "Apache-2.0" ]

null

gjqyxyxxcxxt/gjqyxyxxcxxt/queue_companies.py

AisinoPythonTeam/PythonAiniso

983a29962752679d8cc26a2c3cdb0ba8fcfa3f02

[ "Apache-2.0" ]

null

gjqyxyxxcxxt/gjqyxyxxcxxt/queue_companies.py

AisinoPythonTeam/PythonAiniso

983a29962752679d8cc26a2c3cdb0ba8fcfa3f02

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import pymysql import sys, os, json, time, pymongo app_dir = os.path.abspath("../") sys.path.append(app_dir) from gjqyxyxxcxxt import settings from gjqyxyxxcxxt.database.my_redis import QueueRedis conn = None def connect_db(): global conn conn = pymysql.connect(host="172.16.16.15",port=3306,user="root",passwd="A1s1n0@zxyc#3",db="ixinnuo_sjcj",charset="utf8") return def get_req_from_db(): global conn cursor = conn.cursor() cursor.execute('select id, entname from req where status=0 order by id limit 10') results = cursor.fetchall() companies = [] for res in results: company = {} company['id'] = res[0] company['name'] = res[1] companies.append(company) return companies def main(): my_queue = QueueRedis() result = my_queue.get_queue_length(settings.COMPANIES) print result #mq 里存在数据则，3秒后退出 if result: time.sleep(3) exit() time.sleep(3) global conn connect_db() source = get_req_from_db() for id_name in source: message = json.dumps(id_name) my_queue.send_to_queue(settings.COMPANIES, message) conn.close() print '成功添加队列%s条数据!!!' % len(source) if __name__ == '__main__': main()

24.921569

125

0.650669

173

1,271

4.612717

0.508671

0.037594

0.030075

0

0.027328

0.222659

1,271

50

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0

0.112916

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null

0.02439

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0

null

0

null

0

1

0

1

6ab5293b9595b159942c1bb0c1e2bfcef5e08aec

1,029

py

Python

solutions/PE4.py

KerimovEmil/ProjectEuler

bc9cb682181c1ac7889ee57c36d32beae7b441a8

[ "MIT" ]

1

2022-01-22T19:48:44.000Z

solutions/PE4.py

KerimovEmil/ProjectEuler

bc9cb682181c1ac7889ee57c36d32beae7b441a8

[ "MIT" ]

null

solutions/PE4.py

KerimovEmil/ProjectEuler

bc9cb682181c1ac7889ee57c36d32beae7b441a8

[ "MIT" ]

null

""" PROBLEM A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99. Find the largest palindrome made from the product of two 3-digit numbers. ANSWER: 906609 Solve time ~ 0.760 seconds """ from itertools import product import unittest from util.utils import timeit class Problem4: def __init__(self, num_digits): self.lower = 10 ** (num_digits - 1) - 1 self.upper = 10 ** num_digits - 1 @staticmethod def is_palindrome(num): return str(num) == str(num)[::-1] @timeit def solve(self): pds = [] for i, j in product(range(self.lower, self.upper), repeat=2): if self.is_palindrome(i * j): pds.append(i * j) return max(pds) class Solution4(unittest.TestCase): def setUp(self): self.problem = Problem4(3) def test_solution(self): self.assertEqual(906609, self.problem.solve()) if __name__ == '__main__': unittest.main()

21.4375

114

0.640428

144

1,029

4.458333

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0.062305

0.074766

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115

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0.783854

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false

0

0.125

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0.5

0

null

0

null

0

1

0

1

6ab606d6bade1bb254f8ee2b1905c9d3d07e2051

11,447

py

Python

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

[ "MIT" ]

null

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

[ "MIT" ]

null

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

[ "MIT" ]

null

import pandas as pd import numpy as np import re import pickle # plotting import seaborn as sns import matplotlib.pyplot as plt # Tune learning_rate from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold # First XGBoost model for MBTI dataset from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score ##### Compute list of subject with Type | list of comments from nltk.stem import PorterStemmer, WordNetLemmatizer from nltk.corpus import stopwords from nltk import word_tokenize import nltk nltk.download('wordnet') from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer from sklearn.manifold import TSNE #타입을 숫자로 변환 def get_types(row): t=row['type'] I = 0; N = 0 T = 0; J = 0 if t[0] == 'I': I = 1 elif t[0] == 'E': I = 0 else: print('I-E incorrect') if t[1] == 'N': N = 1 elif t[1] == 'S': N = 0 else: print('N-S incorrect') if t[2] == 'T': T = 1 elif t[2] == 'F': T = 0 else: print('T-F incorrect') if t[3] == 'J': J = 1 elif t[3] == 'P': J = 0 else: print('J-P incorrect') return pd.Series( {'IE':I, 'NS':N , 'TF': T, 'JP': J }) #딕셔너리파일 설정 b_Pers = {'I':0, 'E':1, 'N':0, 'S':1, 'F':0, 'T':1, 'J':0, 'P':1} #리스트를 두개씩 묶어서 리스트로 만듬 b_Pers_list = [{0:'I', 1:'E'}, {0:'N', 1:'S'}, {0:'F', 1:'T'}, {0:'J', 1:'P'}] def translate_personality(personality): # transform mbti to binary vector return [b_Pers[l] for l in personality] def translate_back(personality): # transform binary vector to mbti personality s = "" for i, l in enumerate(personality): s += b_Pers_list[i][l] return s # We want to remove these from the psosts unique_type_list = ['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP', 'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ'] unique_type_list = [x.lower() for x in unique_type_list] # Lemmatize stemmer = PorterStemmer() lemmatiser = WordNetLemmatizer() # Cache the stop words for speed cachedStopWords = stopwords.words("english") def pre_process_data(data, remove_stop_words=True, remove_mbti_profiles=True): list_personality = [] list_posts = [] len_data = len(data) i=0 for row in data.iterrows(): i+=1 if (i % 500 == 0 or i == 1 or i == len_data): print("%s of %s rows" % (i, len_data)) ##### Remove and clean comments posts = row[1].posts temp = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', posts) temp = re.sub("[^a-zA-Z]", " ", temp) temp = re.sub(' +', ' ', temp).lower() if remove_stop_words: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ') if w not in cachedStopWords]) else: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ')]) if remove_mbti_profiles: for t in unique_type_list: temp = temp.replace(t,"") type_labelized = translate_personality(row[1].type) list_personality.append(type_labelized) list_posts.append(temp) list_posts = np.array(list_posts) list_personality = np.array(list_personality) return list_posts, list_personality # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject_2020_12_09/essayai/mbti_1.csv') data = pd.read_csv('./mbti/mbti_1.csv') # get_types 함수 적용 data = data.join(data.apply (lambda row: get_types (row),axis=1)) # load with open('./mbti/list_posts.pickle', 'rb') as f: list_posts = pickle.load(f) # load with open('./mbti/list_personality.pickle', 'rb') as f: list_personality = pickle.load(f) # # Posts to a matrix of token counts cntizer = CountVectorizer(analyzer="word", max_features=1500, tokenizer=None, preprocessor=None, stop_words=None, max_df=0.7, min_df=0.1) # Learn the vocabulary dictionary and return term-document matrix print("CountVectorizer...") X_cnt = cntizer.fit_transform(list_posts) ################################################# #save!!! model X_cnt import pickle # save # with open('./essayai/ai_character/mbti/data_X_cnt.pickle', 'wb') as f: # pickle.dump(X_cnt, f, pickle.HIGHEST_PROTOCOL) # load with open('./mbti/data_X_cnt.pickle', 'rb') as f: X_cnt = pickle.load(f) ################################################# # Transform the count matrix to a normalized tf or tf-idf representation tfizer = TfidfTransformer() print("Tf-idf...") # Learn the idf vector (fit) and transform a count matrix to a tf-idf representation X_tfidf = tfizer.fit_transform(X_cnt).toarray() # load with open('./mbti/data.pickle', 'rb') as f: X_tfidf = pickle.load(f) def mbti_classify(text): type_indicators = [ "IE: Introversion (I) / Extroversion (E)", "NS: Intuition (N) – Sensing (S)", "FT: Feeling (F) - Thinking (T)", "JP: Judging (J) – Perceiving (P)" ] # Posts in tf-idf representation X = X_tfidf my_posts = str(text) # The type is just a dummy so that the data prep fucntion can be reused mydata = pd.DataFrame(data={'type': ['INFJ'], 'posts': [my_posts]}) my_posts, dummy = pre_process_data(mydata, remove_stop_words=True) my_X_cnt = cntizer.transform(my_posts) my_X_tfidf = tfizer.transform(my_X_cnt).toarray() # setup parameters for xgboost param = {} param['n_estimators'] = 200 param['max_depth'] = 2 param['nthread'] = 8 param['learning_rate'] = 0.2 result = [] # Let's train type indicator individually for l in range(len(type_indicators)): print("%s ..." % (type_indicators[l])) Y = list_personality[:,l] # split data into train and test sets seed = 7 test_size = 0.33 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed) # fit model on training data model = XGBClassifier(**param) model.fit(X_train, y_train) # make predictions for my data y_pred = model.predict(my_X_tfidf) result.append(y_pred[0]) # print("* %s prediction: %s" % (type_indicators[l], y_pred)) print("The result is: ", translate_back(result)) #결과를 리스트에 담고 Result_list = list(translate_back(result)) #mbit 결과값에 따라 내용 print 하기 # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject/essayai/mbti_exp.csv') data = pd.read_csv('./mbti/mbti_exp.csv') #새로운 데이터프레임을 만들어서 계산된 값을 추가할 예정 df2 = pd.DataFrame(index=range(0,4),columns=['Type', 'Explain']) #리스트에서 한글자씩 불러와서 데이터프레임의 값을 출력하면 됨 for i in range(0, len(Result_list)): type = Result_list[i] for j in range(0, len(data)): if type == data.iloc[j,0]: break is_mbti = data.iloc[j,2] df2.iloc[i, [0,1]] = [type, is_mbti] print(df2) return df2 # my_posts = """Describe a place or environment where you are perfectly content. What do you do or experience there, and why is it meaningful to you? 644 words out of 650 Gettysburg, a small town in the middle of Pennsylvania, was the sight of the largest, bloodiest battle in the Civil War. Something about these hallowed grounds draws me back every year for a three day camping trip with my family over Labor Day weekend. Every year, once school starts, I count the days until I take that three and half hour drive from Pittsburgh to Gettysburg. Each year, we leave after school ends on Friday and arrive in Gettysburg with just enough daylight to pitch the tents and cook up a quick dinner on the campfire. As more of the extended family arrives, we circle around the campfire and find out what is new with everyone. The following morning, everyone is up by nine and helping to make breakfast which is our best meal of the day while camping. Breakfast will fuel us for the day as we hike the vast battlefields. My Uncle Mark, my twin brother, Andrew, and I like to take charge of the family tour since we have the most passion and knowledge about the battle. I have learned so much from the stories Mark tells us while walking on the tours. Through my own research during these last couple of trips, I did some of the explaining about the events that occurred during the battle 150 years ago. My fondest experience during one trip was when we decided to go off of the main path to find a carving in a rock from a soldier during the battle. Mark had read about the carving in one of his books about Gettysburg, and we were determined to locate it. After almost an hour of scanning rocks in the area, we finally found it with just enough daylight to read what it said. After a long day of exploring the battlefield, we went back to the campsite for some 'civil war' stew. There is nothing special about the stew, just meat, vegetables and gravy, but for whatever reason, it is some of the best stew I have ever eaten. For the rest of the night, we enjoy the company of our extended family. My cousins, my brother and I listen to the stories from Mark and his friends experiences' in the military. After the parents have gone to bed, we stay up talking with each other, inching closer and closer to the fire as it gets colder. Finally, we creep back into our tents, trying to be as quiet as possible to not wake our parents. The next morning we awake red-eyed from the lack of sleep and cook up another fantastic breakfast. Unfortunately, after breakfast we have to pack up and head back to Pittsburgh. It will be another year until I visit Gettysburg again. There is something about that time I spend in Gettysburg that keeps me coming back to visit. For one, it is just a fun, relaxing time I get to spend with my family. This trip also fulfills my love for the outdoors. From sitting by the campfire and falling asleep to the chirp of the crickets, that is my definition of a perfect weekend. Gettysburg is also an interesting place to go for Civil War buffs like me. While walking down the Union line or walking Pickett's Charge, I imagine how the battle would have been played out around me. Every year when I visit Gettysburg, I learn more facts and stories about the battle, soldiers and generally about the Civil War. While I am in Gettysburg, I am perfectly content, passionate about the history and just enjoying the great outdoors with my family. This drive to learn goes beyond just my passion for history but applies to all of the math, science and business classes I have taken and clubs I am involved in at school. Every day, I am genuinely excited to learn. # """ # test = mbti_classify(my_posts) # print ('check') # test # print ('check2')

41.625455

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0.653621

1,750

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null

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null

0

1

6ac1a5f132a19c0dca01d22ddfd3613255dba8b5

4,258

py

Python

wce_triage/ops/create_image_runner.py

pfrouleau/wce-triage-v2

25610cda55f5cb2170e13e121ae1cbaa92ef7626

[ "MIT" ]

3

2019-07-25T03:24:23.000Z

2021-06-23T14:01:34.000Z

wce_triage/ops/create_image_runner.py

pfrouleau/wce-triage-v2

25610cda55f5cb2170e13e121ae1cbaa92ef7626

[ "MIT" ]

1

2019-12-20T16:04:19.000Z

wce_triage/ops/create_image_runner.py

pfrouleau/wce-triage-v2

25610cda55f5cb2170e13e121ae1cbaa92ef7626

[ "MIT" ]

2

2019-07-25T03:24:26.000Z

2021-02-14T05:27:11.000Z

#!/usr/bin/env python3 # # Create disk image # import re, sys, traceback from .tasks import task_fetch_partitions, task_refresh_partitions, task_mount, task_remove_persistent_rules, task_remove_logs, task_fsck, task_shrink_partition, task_expand_partition, task_unmount from .partclone_tasks import task_create_disk_image from .ops_ui import console_ui from ..components.disk import create_storage_instance from .runner import Runner from ..lib.disk_images import make_disk_image_name from .json_ui import json_ui from ..lib.util import init_triage_logger, is_block_device # "Waiting", "Prepare", "Preflight", "Running", "Success", "Failed"] my_messages = { "Waiting": "Saving disk is waiting.", "Prepare": "Savign disk is preparing.", "Preflight": "Saving disk is preparing.", "Running": "{step} of {steps}: Running {task}", "Success": "Saving disk completed successfully.", "Failed": "Saving disk failed." } # class ImageDiskRunner(Runner): '''Runner for creating disk image. does fsck, shrink partition, create disk image and resize the file system back to the max. For now, this is only dealing with the EXT4 linux partition. ''' # FIXME: If I want to make this to a generic clone app, I need to deal with all of partitions on the disk. # One step at a time. def __init__(self, ui, runner_id, disk, destdir, suggestedname=None, partition_id='Linux'): super().__init__(ui, runner_id) self.time_estimate = 600 self.disk = disk self.partition_id = partition_id self.destdir = destdir self.imagename = make_disk_image_name(destdir, suggestedname) pass def prepare(self): super().prepare() # self.tasks.append(task_mount_nfs_destination(self, "Mount the destination volume")) self.tasks.append(task_fetch_partitions("Fetch partitions", self.disk)) self.tasks.append(task_refresh_partitions("Refresh partition information", self.disk)) self.tasks.append(task_mount("Mount the target disk", disk=self.disk, partition_id=self.partition_id)) self.tasks.append(task_remove_persistent_rules("Remove persistent rules", disk=self.disk, partition_id=self.partition_id)) self.tasks.append(task_remove_logs("Remove/Clean Logs", disk=self.disk, partition_id=self.partition_id)) task = task_unmount("Unmount target", disk=self.disk, partition_id=self.partition_id) task.set_teardown_task() self.tasks.append(task) self.tasks.append(task_fsck("fsck partition", disk=self.disk, partition_id=self.partition_id)) self.tasks.append(task_shrink_partition("Shrink partition to smallest", disk=self.disk, partition_id=self.partition_id)) self.tasks.append(task_create_disk_image("Create disk image", disk=self.disk, partition_id=self.partition_id, imagename=self.imagename)) task = task_expand_partition("Expand the partion back", disk=self.disk, partition_id=self.partition_id) task.set_teardown_task() self.tasks.append(task) pass pass if __name__ == "__main__": tlog = init_triage_logger() if len(sys.argv) == 1: print( 'Unloader: devicename part destdir') sys.exit(0) # NOTREACHED pass devname = sys.argv[1] if not is_block_device(devname): print( '%s is not a block device.' % devname) sys.exit(1) # NOTREACHED pass part = sys.argv[2] # This is a partition id destdir = sys.argv[3] # Destination directory disk = create_storage_instance(devname) # Preflight is for me to see the tasks. http server runs this with json_ui. do_it = True if destdir == "preflight": ui = console_ui() do_it = False pass elif destdir == "testflight": ui = console_ui() do_it = True pass else: ui = json_ui(wock_event="saveimage", message_catalog=my_messages) pass if re.match(part, '\d+'): part = int(part) pass runner_id = disk.device_name runner = ImageDiskRunner(ui, runner_id, disk, destdir, partition_id=part) try: runner.prepare() runner.preflight() runner.explain() runner.run() sys.exit(0) # NOTREACHED except Exception as exc: sys.stderr.write(traceback.format_exc(exc) + "\n") sys.exit(1) # NOTREACHED pass pass

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593

4,258

4.903879

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0.079436

0.067056

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0.134801

0

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0.180601

4,258

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0.829751

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0

null

0

null

0

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0

1

6ac3173f834c06ec5469554b76a1d8e391432cee

5,171

py

Python

demos/chicken_pasta/chicken_pasta.py

icaros-usc/wecook

27bbb6b78a48e04765a87d33cc8a5d3748d2d4cc

[ "BSD-3-Clause" ]

15

2019-09-15T05:24:19.000Z

2021-02-26T20:31:19.000Z

demos/chicken_pasta/chicken_pasta.py

icaros-usc/wecook

27bbb6b78a48e04765a87d33cc8a5d3748d2d4cc

[ "BSD-3-Clause" ]

16

2019-10-10T23:27:00.000Z

2020-05-14T02:30:56.000Z

demos/chicken_pasta/chicken_pasta.py

icaros-usc/wecook

27bbb6b78a48e04765a87d33cc8a5d3748d2d4cc

[ "BSD-3-Clause" ]

2

2020-02-01T16:31:29.000Z

2020-04-07T21:00:04.000Z

#!/usr/bin/env python3 import rospy from wecook.msg import ActionMsg, TaskMsg, SceneMsg, ObjectMsg, ContainingMsg, AgentMsg def talker(): pub = rospy.Publisher('WeCookDispatch', TaskMsg, queue_size=10) rospy.init_node('wecook_chicken_pasta', anonymous=True) scene_msg = SceneMsg([ObjectMsg('wall0', 'package://wecook_assets/data/furniture/wall.urdf', [0.75, 0.05, 0., 0., 0., 0., 1.]), ObjectMsg('wall1', 'package://wecook_assets/data/furniture/wall.urdf', [-0.85, 1.45, 0., 0., 0., 0.707, 0.707]), ObjectMsg('counter0', 'package://wecook_assets/data/furniture/kitchen_counter.urdf', [0.3, 0., 0., 0., 0., 0., 1.]), ObjectMsg('counter1', 'package://wecook_assets/data/furniture/kitchen_counter.urdf', [0., 1.0, 0., 0., 0., 0.707, 0.707]), ObjectMsg('sink0', 'package://wecook_assets/data/furniture/sink_counter.urdf', [-1.3, 1.05, 0., 0., 0., 0.707, 0.707]), ObjectMsg('shelf0', 'package://wecook_assets/data/furniture/bookcase.urdf', [0.3, -1.05, 0., 0., 0., 0., 1.]), ObjectMsg('stove0', 'package://wecook_assets/data/objects/stove.urdf', [-0.35, 0.95, 0.75, 0., 0., 0., 1.]), ObjectMsg('pot0', 'package://wecook_assets/data/objects/cooking_pot.urdf', [0.35, 1.1, 0.75, 0., 0., 0., 1.]), ObjectMsg('skillet0', 'package://wecook_assets/data/objects/skillet.urdf', [0.3, 0.7, 0.75, 0., 0., -0.707, .707]), ObjectMsg('cutting_board0', 'package://wecook_assets/data/objects/cutting_board.urdf', [0.3, -0.3, 0.75, 0., 0., 0., 1.]), ObjectMsg('knife0', 'package://wecook_assets/data/objects/knife_big.urdf', [0.215, -0.55, 0.775, 0., 0., 0., 1.]), ObjectMsg('plate0', 'package://wecook_assets/data/objects/plate.urdf', [0.3, 0.075, 0.75, 0., 0., 0., 1.]), ObjectMsg('bowl0', 'package://wecook_assets/data/objects/bowl_green.urdf', [0.45, 0.375, 0.75, 0., 0., 0., 1.]), ObjectMsg('bowl1', 'package://wecook_assets/data/objects/bowl_green.urdf', [0.15, 0.375, 0.75, 0., 0., 0., 1.]), ObjectMsg('oil0', 'package://wecook_assets/data/objects/olive_oil.urdf', [0., 1.15, 0.75, 0., 0., 0.707, 0.707]), ObjectMsg('salt0', 'package://wecook_assets/data/objects/salt.urdf', [0., 1.0, 0.75, 0., 0., 0.707, 0.707]), ObjectMsg('pepper0', 'package://wecook_assets/data/objects/black_pepper.urdf', [0., 0.9, 0.75, 0., 0., 0.707, 0.707]), ObjectMsg('chicken0', 'package://wecook_assets/data/food/chicken.urdf', [0.3, 0.075, 0.757, 0., 0., 0., 1.]), ObjectMsg('lime0', 'package://wecook_assets/data/food/lime.urdf', [0.3, -0.3, 0.757, 0., 0., 0., 1.]), ObjectMsg('pasta0', 'package://wecook_assets/data/food/pasta.urdf', [0.45, 0.375, 0.757, 0., 0., 0., 1.])], [ContainingMsg(['plate0', 'chicken0']), ContainingMsg(['bowl0', 'pasta0'])]) task_msg = TaskMsg(scene_msg, [ActionMsg(['p1'], 'cut', ['plate0'], 'knife0', ['lime0'])], [AgentMsg('p1', 'r', [0., 0., 0.75, 0., 0., 0., 0.])], "", "", "follow", "RRTConnect", False) # sleeping 10 seconds to publish rospy.sleep(1) pub.publish(task_msg) if __name__ == '__main__': try: talker() except rospy.ROSInterruptException: pass

55.010638

98

0.375169

482

5,171

3.927386

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0.058109

0.049128

0.243001

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0.349709

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0.231379

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0

0.11743

0.47631

5,171

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99

55.602151

0.58161

0.010056

0

0.097561

0

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0

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false

0.012195

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0

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0

null

0

1

0

null

0

1

6ac3c0aa131a8fbf4b061367a8fbb2e23790a4c8

3,777

py

Python

metricbeat/module/postgresql/test_postgresql.py

SHolzhauer/beats

39679a536a22e8a0d7534a2475504488909d19fd

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

4

2020-11-17T06:29:30.000Z

2021-08-08T11:56:01.000Z

metricbeat/module/postgresql/test_postgresql.py

SHolzhauer/beats

39679a536a22e8a0d7534a2475504488909d19fd

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

36

2021-02-02T14:18:40.000Z

2022-03-20T15:07:30.000Z

metricbeat/module/postgresql/test_postgresql.py

SHolzhauer/beats

39679a536a22e8a0d7534a2475504488909d19fd

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

6

2021-03-10T05:38:32.000Z

2021-08-16T13:11:19.000Z

import metricbeat import os import pytest import sys import unittest class Test(metricbeat.BaseTest): COMPOSE_SERVICES = ['postgresql'] def common_checks(self, output): # Ensure no errors or warnings exist in the log. self.assert_no_logged_warnings() for evt in output: top_level_fields = metricbeat.COMMON_FIELDS + ["postgresql"] self.assertCountEqual(self.de_dot(top_level_fields), evt.keys()) self.assert_fields_are_documented(evt) def get_hosts(self): username = "postgres" host = self.compose_host() dsn = "postgres://{}?sslmode=disable".format(host) return ( [dsn], username, os.getenv("POSTGRESQL_PASSWORD"), ) @unittest.skipUnless(metricbeat.INTEGRATION_TESTS, "integration test") @pytest.mark.tag('integration') def test_activity(self): """ PostgreSQL module outputs an event. """ hosts, username, password = self.get_hosts() self.render_config_template(modules=[{ "name": "postgresql", "metricsets": ["activity"], "hosts": hosts, "username": username, "password": password, "period": "5s" }]) proc = self.start_beat() self.wait_until(lambda: self.output_lines() > 0) proc.check_kill_and_wait() output = self.read_output_json() self.common_checks(output) for evt in output: assert "name" in evt["postgresql"]["activity"]["database"] assert "oid" in evt["postgresql"]["activity"]["database"] assert "state" in evt["postgresql"]["activity"] @unittest.skipUnless(metricbeat.INTEGRATION_TESTS, "integration test") @pytest.mark.tag('integration') def test_database(self): """ PostgreSQL module outputs an event. """ hosts, username, password = self.get_hosts() self.render_config_template(modules=[{ "name": "postgresql", "metricsets": ["database"], "hosts": hosts, "username": username, "password": password, "period": "5s" }]) proc = self.start_beat() self.wait_until(lambda: self.output_lines() > 0) proc.check_kill_and_wait() output = self.read_output_json() self.common_checks(output) for evt in output: assert "name" in evt["postgresql"]["database"] assert "oid" in evt["postgresql"]["database"] assert "blocks" in evt["postgresql"]["database"] assert "rows" in evt["postgresql"]["database"] assert "conflicts" in evt["postgresql"]["database"] assert "deadlocks" in evt["postgresql"]["database"] @unittest.skipUnless(metricbeat.INTEGRATION_TESTS, "integration test") @pytest.mark.tag('integration') def test_bgwriter(self): """ PostgreSQL module outputs an event. """ hosts, username, password = self.get_hosts() self.render_config_template(modules=[{ "name": "postgresql", "metricsets": ["bgwriter"], "hosts": hosts, "username": username, "password": password, "period": "5s" }]) proc = self.start_beat() self.wait_until(lambda: self.output_lines() > 0) proc.check_kill_and_wait() output = self.read_output_json() self.common_checks(output) for evt in output: assert "checkpoints" in evt["postgresql"]["bgwriter"] assert "buffers" in evt["postgresql"]["bgwriter"] assert "stats_reset" in evt["postgresql"]["bgwriter"]

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null

0

null

0

1

0

1

6ac4ca9b00a8492410dc6166ad36ac8d64fdcffc

2,337

py

Python

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

[ "BSD-3-Clause" ]

1

2021-03-24T13:00:14.000Z

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

[ "BSD-3-Clause" ]

null

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2018-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import os from packaging import version from datadog_checks.base.utils.common import get_docker_hostname HERE = os.path.dirname(os.path.abspath(__file__)) ROOT = os.path.dirname(os.path.dirname(HERE)) RABBITMQ_VERSION_RAW = os.environ['RABBITMQ_VERSION'] RABBITMQ_VERSION = version.parse(RABBITMQ_VERSION_RAW) CHECK_NAME = 'rabbitmq' HOST = get_docker_hostname() PORT = 15672 URL = 'http://{}:{}/api/'.format(HOST, PORT) CONFIG = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues': ['test1'], 'tags': ["tag1:1", "tag2"], 'exchanges': ['test1'], } CONFIG_NO_NODES = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues': ['test1'], 'tags': ["tag1:1", "tag2"], 'exchanges': ['test1'], 'collect_node_metrics': False, } CONFIG_REGEX = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues_regexes': [r'test\d+'], 'exchanges_regexes': [r'test\d+'], } CONFIG_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['/', 'myvhost'], } CONFIG_WITH_FAMILY = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'tag_families': True, 'queues_regexes': [r'(test)\d+'], 'exchanges_regexes': [r'(test)\d+'], } CONFIG_DEFAULT_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['/', 'test'], } CONFIG_TEST_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['test', 'test2'], } EXCHANGE_MESSAGE_STATS = { 'ack': 1.0, 'ack_details': {'rate': 1.0}, 'confirm': 1.0, 'confirm_details': {'rate': 1.0}, 'deliver_get': 1.0, 'deliver_get_details': {'rate': 1.0}, 'publish': 1.0, 'publish_details': {'rate': 1.0}, 'publish_in': 1.0, 'publish_in_details': {'rate': 1.0}, 'publish_out': 1.0, 'publish_out_details': {'rate': 1.0}, 'return_unroutable': 1.0, 'return_unroutable_details': {'rate': 1.0}, 'redeliver': 1.0, 'redeliver_details': {'rate': 1.0}, }

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null

0

null

0

1

0

1

6ac55faf90a367de65f30a569842061f13204e0c

2,952

py

Python

module1-introduction-to-sql/query.py

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

8a6b3fd14b6a6833ee3a14b2d8a7db3bee494a14

[ "MIT" ]

null

module1-introduction-to-sql/query.py

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

8a6b3fd14b6a6833ee3a14b2d8a7db3bee494a14

[ "MIT" ]

null

module1-introduction-to-sql/query.py

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

8a6b3fd14b6a6833ee3a14b2d8a7db3bee494a14

[ "MIT" ]

null

# Look at the charactercreator_character table # GET_CHARACTERS = """ # SELECT * # FROM charactercreator_character; # """ # How many total Characters are there? (302) TOTAL_CHARACTERS = """ SELECT COUNT(*) as number_of_characters FROM charactercreator_character; """ # How many of each specific subclass? # TOTAL_SUBCLASS = """ # SELECT # (SELECT COUNT(*) FROM charactercreator_necromancer) AS necros, # (SELECT COUNT(*) FROM charactercreator_mage) AS mages, # (SELECT COUNT(*) FROM charactercreator_thief) AS thiefs, # (SELECT COUNT(*) FROM charactercreator_cleric) AS clerics, # (SELECT COUNT(*) FROM charactercreator_fighter) AS fighters; # """ CLASS = "SELECT COUNT(*) FROM charactercreator_" # How many total Items? (174) TOTAL_ITEMS = """ SELECT COUNT(item_id) as items FROM armory_item; """ # How many of the Items are weapons? (37) WEAPONS = """ SELECT COUNT(item_ptr_id) FROM armory_weapon; """ # How many of the items are not weapons? (137) NON_WEAPONS = """ SELECT COUNT(items.name) FROM armory_item as items WHERE items.item_id NOT IN( SELECT armory_weapon.item_ptr_id FROM armory_weapon); """ # How many Items does each character have? (Return first 20 rows) CHARACTER_ITEMS = """ SELECT character.name as "character_name", COUNT(inventory.id) as "#_of_items" FROM charactercreator_character AS character, charactercreator_character_inventory AS inventory WHERE character.character_id = inventory.character_id GROUP BY character.name ORDER BY character.name LIMIT 20; """ # How many Weapons does each character have? (Return first 20 rows) CHARACTER_WEAPONS = """ SELECT character.name as "character_name", COUNT(weapon.item_ptr_id) as "#_of_weapons" FROM charactercreator_character AS character, charactercreator_character_inventory AS inventory, armory_weapon as weapon WHERE character.character_id = inventory.character_id AND inventory.item_id = weapon.item_ptr_id GROUP BY character.name ORDER BY character.name LIMIT 20; """ # On average, how many Items does each Character have? (3.02) AVG_CHARACTER_ITEMS = """ SELECT AVG("#_of_items") as "avg_#_of_items" FROM ( SELECT COUNT(inventory.id) AS "#_of_items" FROM charactercreator_character AS character, charactercreator_character_inventory AS inventory WHERE character.character_id = inventory.character_id GROUP BY character.name ); """ # On average, how many Weapons does each character have? (0.67) AVG_CHARACTER_WEAPONS = """ SELECT AVG(weapon_count) as avg_weapons_per_char FROM ( SELECT character.character_id, COUNT(DISTINCT weapon.item_ptr_id) as weapon_count FROM charactercreator_character AS character LEFT JOIN charactercreator_character_inventory inventory -- characters may have zero items ON character.character_id = inventory.character_id LEFT JOIN armory_weapon weapon -- many items are not weapons, so only retain weapons ON inventory.item_id = weapon.item_ptr_id GROUP BY character.character_id ) subq; """

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0

false

0

null

0

1

0

null

0

1

6ac7d878414c23d75e260d1c447ced1efb264340

2,420

py

Python

events_page/app.py

los-verdes/lv-event-pagenerator

88416b626ff2dca6e2d71fa60bff4823954b3131

[ "MIT" ]

null

events_page/app.py

los-verdes/lv-event-pagenerator

88416b626ff2dca6e2d71fa60bff4823954b3131

[ "MIT" ]

7

2022-01-16T15:36:40.000Z

2022-01-25T22:02:12.000Z

events_page/app.py

los-verdes/lv-event-pagenerator

88416b626ff2dca6e2d71fa60bff4823954b3131

[ "MIT" ]

null

#!/usr/bin/env python from zoneinfo import ZoneInfo import flask from dateutil.parser import parse from flask_assets import Bundle, Environment from logzero import logger, setup_logger from webassets.filter import get_filter from config import cfg from apis import calendar as gcal setup_logger(name=__name__) app = flask.Flask(__name__) libsass = get_filter( "libsass", as_output=True, style="compressed", ) assets = Environment(app) # create an Environment instance bundles = { # define nested Bundle "style": Bundle( "scss/*.scss", filters=(libsass), output="style.css", ) } assets.register(bundles) @app.route("/") def events(): return flask.render_template( "index.html", calendar=gcal.load_calendar( service=gcal.build_service(), calendar_id=cfg.calendar_id, ), ) @app.template_filter() def parse_tz_datetime(datetime_str): return parse(datetime_str).replace(tzinfo=ZoneInfo(app.config["display_timezone"])) @app.template_filter() def replace_tz(datetime_obj): return datetime_obj.replace(tzinfo=ZoneInfo(app.config["display_timezone"])) @app.template_filter() def hex2rgb(hex, alpha=None): """Convert a string to all caps.""" if not hex.startswith("#"): return hex h = hex.lstrip("#") try: rgb = tuple(int(h[i : i + 2], 16) for i in (0, 2, 4)) # noqa except Exception as err: logger.exception(f"unable to convert {hex=} to rgb: {err}") return h if alpha is None: return f"rgb({rgb[0]}, {rgb[1]}, {rgb[2]})" else: return f"rgba({rgb[0]}, {rgb[1]}, {rgb[2]}, {alpha})" def get_base_url(): if prefix := cfg.gcs_bucket_prefix: return f"https://{cfg.hostname}/{prefix}" return f"https://{cfg.hostname}" def create_app(): cfg.load() # TODO: do this default settings thing better? default_app_config = dict( display_timezone=cfg.display_timezone, FREEZER_BASE_URL=get_base_url(), FREEZER_STATIC_IGNORE=["*.scss", ".webassets-cache/*", ".DS_Store"], FREEZER_RELATIVE_URLS=False, FREEZER_REMOVE_EXTRA_FILES=True, ) logger.info(f"create_app() => {default_app_config=}") app.config.update(default_app_config) return app if __name__ == "__main__": app = create_app() app.run( host="0.0.0.0", debug=True, )

24.444444

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false

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0.32

0

null

0

null

0

1

0

1

6ac9be98a456dcdce40e3c4f391cc313ab62f054

13,522

py

Python

sdk/python/pulumi_google_native/healthcare/v1beta1/user_data_mapping.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

44

2021-04-18T23:00:48.000Z

2022-02-14T17:43:15.000Z

sdk/python/pulumi_google_native/healthcare/v1beta1/user_data_mapping.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

354

2021-04-16T16:48:39.000Z

2022-03-31T17:16:39.000Z

sdk/python/pulumi_google_native/healthcare/v1beta1/user_data_mapping.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

8

2021-04-24T17:46:51.000Z

2022-01-05T10:40:21.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ._inputs import * __all__ = ['UserDataMappingArgs', 'UserDataMapping'] @pulumi.input_type class UserDataMappingArgs: def __init__(__self__, *, consent_store_id: pulumi.Input[str], data_id: pulumi.Input[str], dataset_id: pulumi.Input[str], user_id: pulumi.Input[str], location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, resource_attributes: Optional[pulumi.Input[Sequence[pulumi.Input['AttributeArgs']]]] = None): """ The set of arguments for constructing a UserDataMapping resource. :param pulumi.Input[str] data_id: A unique identifier for the mapped resource. :param pulumi.Input[str] user_id: User's UUID provided by the client. :param pulumi.Input[str] name: Resource name of the User data mapping, of the form `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/consentStores/{consent_store_id}/userDataMappings/{user_data_mapping_id}`. :param pulumi.Input[Sequence[pulumi.Input['AttributeArgs']]] resource_attributes: Attributes of the resource. Only explicitly set attributes are displayed here. Attribute definitions with defaults set implicitly apply to these User data mappings. Attributes listed here must be single valued, that is, exactly one value is specified for the field "values" in each Attribute. """ pulumi.set(__self__, "consent_store_id", consent_store_id) pulumi.set(__self__, "data_id", data_id) pulumi.set(__self__, "dataset_id", dataset_id) pulumi.set(__self__, "user_id", user_id) if location is not None: pulumi.set(__self__, "location", location) if name is not None: pulumi.set(__self__, "name", name) if project is not None: pulumi.set(__self__, "project", project) if resource_attributes is not None: pulumi.set(__self__, "resource_attributes", resource_attributes) @property @pulumi.getter(name="consentStoreId") def consent_store_id(self) -> pulumi.Input[str]: return pulumi.get(self, "consent_store_id") @consent_store_id.setter def consent_store_id(self, value: pulumi.Input[str]): pulumi.set(self, "consent_store_id", value) @property @pulumi.getter(name="dataId") def data_id(self) -> pulumi.Input[str]: """ A unique identifier for the mapped resource. """ return pulumi.get(self, "data_id") @data_id.setter def data_id(self, value: pulumi.Input[str]): pulumi.set(self, "data_id", value) @property @pulumi.getter(name="datasetId") def dataset_id(self) -> pulumi.Input[str]: return pulumi.get(self, "dataset_id") @dataset_id.setter def dataset_id(self, value: pulumi.Input[str]): pulumi.set(self, "dataset_id", value) @property @pulumi.getter(name="userId") def user_id(self) -> pulumi.Input[str]: """ User's UUID provided by the client. """ return pulumi.get(self, "user_id") @user_id.setter def user_id(self, value: pulumi.Input[str]): pulumi.set(self, "user_id", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Resource name of the User data mapping, of the form `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/consentStores/{consent_store_id}/userDataMappings/{user_data_mapping_id}`. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @property @pulumi.getter(name="resourceAttributes") def resource_attributes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['AttributeArgs']]]]: """ Attributes of the resource. Only explicitly set attributes are displayed here. Attribute definitions with defaults set implicitly apply to these User data mappings. Attributes listed here must be single valued, that is, exactly one value is specified for the field "values" in each Attribute. """ return pulumi.get(self, "resource_attributes") @resource_attributes.setter def resource_attributes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['AttributeArgs']]]]): pulumi.set(self, "resource_attributes", value) class UserDataMapping(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, consent_store_id: Optional[pulumi.Input[str]] = None, data_id: Optional[pulumi.Input[str]] = None, dataset_id: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, resource_attributes: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['AttributeArgs']]]]] = None, user_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Creates a new User data mapping in the parent consent store. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] data_id: A unique identifier for the mapped resource. :param pulumi.Input[str] name: Resource name of the User data mapping, of the form `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/consentStores/{consent_store_id}/userDataMappings/{user_data_mapping_id}`. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['AttributeArgs']]]] resource_attributes: Attributes of the resource. Only explicitly set attributes are displayed here. Attribute definitions with defaults set implicitly apply to these User data mappings. Attributes listed here must be single valued, that is, exactly one value is specified for the field "values" in each Attribute. :param pulumi.Input[str] user_id: User's UUID provided by the client. """ ... @overload def __init__(__self__, resource_name: str, args: UserDataMappingArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Creates a new User data mapping in the parent consent store. :param str resource_name: The name of the resource. :param UserDataMappingArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(UserDataMappingArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, consent_store_id: Optional[pulumi.Input[str]] = None, data_id: Optional[pulumi.Input[str]] = None, dataset_id: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, resource_attributes: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['AttributeArgs']]]]] = None, user_id: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = UserDataMappingArgs.__new__(UserDataMappingArgs) if consent_store_id is None and not opts.urn: raise TypeError("Missing required property 'consent_store_id'") __props__.__dict__["consent_store_id"] = consent_store_id if data_id is None and not opts.urn: raise TypeError("Missing required property 'data_id'") __props__.__dict__["data_id"] = data_id if dataset_id is None and not opts.urn: raise TypeError("Missing required property 'dataset_id'") __props__.__dict__["dataset_id"] = dataset_id __props__.__dict__["location"] = location __props__.__dict__["name"] = name __props__.__dict__["project"] = project __props__.__dict__["resource_attributes"] = resource_attributes if user_id is None and not opts.urn: raise TypeError("Missing required property 'user_id'") __props__.__dict__["user_id"] = user_id __props__.__dict__["archive_time"] = None __props__.__dict__["archived"] = None super(UserDataMapping, __self__).__init__( 'google-native:healthcare/v1beta1:UserDataMapping', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'UserDataMapping': """ Get an existing UserDataMapping resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = UserDataMappingArgs.__new__(UserDataMappingArgs) __props__.__dict__["archive_time"] = None __props__.__dict__["archived"] = None __props__.__dict__["data_id"] = None __props__.__dict__["name"] = None __props__.__dict__["resource_attributes"] = None __props__.__dict__["user_id"] = None return UserDataMapping(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="archiveTime") def archive_time(self) -> pulumi.Output[str]: """ Indicates the time when this mapping was archived. """ return pulumi.get(self, "archive_time") @property @pulumi.getter def archived(self) -> pulumi.Output[bool]: """ Indicates whether this mapping is archived. """ return pulumi.get(self, "archived") @property @pulumi.getter(name="dataId") def data_id(self) -> pulumi.Output[str]: """ A unique identifier for the mapped resource. """ return pulumi.get(self, "data_id") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource name of the User data mapping, of the form `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/consentStores/{consent_store_id}/userDataMappings/{user_data_mapping_id}`. """ return pulumi.get(self, "name") @property @pulumi.getter(name="resourceAttributes") def resource_attributes(self) -> pulumi.Output[Sequence['outputs.AttributeResponse']]: """ Attributes of the resource. Only explicitly set attributes are displayed here. Attribute definitions with defaults set implicitly apply to these User data mappings. Attributes listed here must be single valued, that is, exactly one value is specified for the field "values" in each Attribute. """ return pulumi.get(self, "resource_attributes") @property @pulumi.getter(name="userId") def user_id(self) -> pulumi.Output[str]: """ User's UUID provided by the client. """ return pulumi.get(self, "user_id")

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0.060045

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0.265306

0

null

0

1

0

null

0

1

6aca7a5f520c3a19c81c989f925529d891ca4d67

661

py

Python

_doc/sphinxdoc/source/conf.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

null

_doc/sphinxdoc/source/conf.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

null

_doc/sphinxdoc/source/conf.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

null

import sys import os import sphinx_rtd_theme source_path = os.path.normpath( os.path.join( os.path.abspath( os.path.split(__file__)[0]))) try: from conf_base import * except ImportError: sys.path.append(source_path) from conf_base import * html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] templates_path = [os.path.join(source_path, 'phdoc_static')] html_static_path = [os.path.join(source_path, 'phdoc_static')] if not os.path.exists(templates_path[0]): raise FileNotFoundError(templates_path[0]) blog_root = "http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/"

25.423077

74

0.741301

98

661

4.673469

0.428571

0.091703

0.078603

0.152838

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661

25

75

26.44

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0.315789

0

null

0

null

0

1

0

1

6acb7ed968b97603aa5b744b910e0997b0f3f62d

561

py

Python

server/api/migrations/0002_auto_20201011_1053.py

ShahriarDhruvo/WebTech_Assignment2

845d198a91b1dcc8ed149362499754167fca419d

[ "MIT" ]

null

server/api/migrations/0002_auto_20201011_1053.py

ShahriarDhruvo/WebTech_Assignment2

845d198a91b1dcc8ed149362499754167fca419d

[ "MIT" ]

null

server/api/migrations/0002_auto_20201011_1053.py

ShahriarDhruvo/WebTech_Assignment2

845d198a91b1dcc8ed149362499754167fca419d

[ "MIT" ]

null

# Generated by Django 3.1.2 on 2020-10-11 10:53 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('api', '0001_initial'), ] operations = [ migrations.AlterField( model_name='task', name='author', field=models.CharField(default='Anonymous', max_length=100), ), migrations.AlterField( model_name='task', name='deadline', field=models.DateTimeField(default='2020-10-11 10:53'), ), ]

23.375

72

0.57041

58

561

5.448276

0.637931

0.037975

0.050633

0.063291

0.310127

0.234177

0

0.086957

0.30303

561

23

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24.391304

0.721228

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0.235294

0

null

0

1

0

null

0

1

6acc395ad3bfafbc612c2d532d32bbb5ce80e13f

4,123

py

Python

flink-ai-flow/lib/notification_service/notification_service/mongo_event_storage.py

lisy09/flink-ai-extended

011a5a332f7641f66086653e715d0596eab2e107

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2021-08-06T04:24:36.000Z

flink-ai-flow/lib/notification_service/notification_service/mongo_event_storage.py

sentimentist/flink-ai-extended

689d000f2db8919fd80e0725a1609918ca4a26f4

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

flink-ai-flow/lib/notification_service/notification_service/mongo_event_storage.py

sentimentist/flink-ai-extended

689d000f2db8919fd80e0725a1609918ca4a26f4

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2021-05-20T02:17:11.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import time import socket from collections import Iterable from typing import Union, Tuple from mongoengine import connect from notification_service.event_storage import BaseEventStorage from notification_service.base_notification import BaseEvent from notification_service.mongo_notification import MongoEvent class MongoEventStorage(BaseEventStorage): def __init__(self, *args, **kwargs): self.db_conn = self.setup_connection(**kwargs) self.server_ip = socket.gethostbyname(socket.gethostname()) def setup_connection(self, **kwargs): db_conf = { "host": kwargs.get("host"), "port": kwargs.get("port"), "db": kwargs.get("db"), } username = kwargs.get("username", None) password = kwargs.get("password", None) authentication_source = kwargs.get("authentication_source", "admin") if (username or password) and not (username and password): raise Exception("Please provide valid username and password") if username and password: db_conf.update({ "username": username, "password": password, "authentication_source": authentication_source }) return connect(**db_conf) def get_latest_version(self, key: str, namespace: str = None): mongo_events = MongoEvent.get_by_key(key, 0, 1, "-version") if not mongo_events: return 0 return mongo_events[0].version def add_event(self, event: BaseEvent, uuid: str): kwargs = { "server_ip": self.server_ip, "create_time": int(time.time() * 1000), "event_type": event.event_type, "key": event.key, "value": event.value, "context": event.context, "namespace": event.namespace, "sender": event.sender, "uuid": uuid } mongo_event = MongoEvent(**kwargs) mongo_event.save() mongo_event.reload() event.create_time = mongo_event.create_time event.version = mongo_event.version return event def list_events(self, key: Union[str, Tuple[str]], version: int = None, event_type: str = None, start_time: int = None, namespace: str = None, sender: str = None): key = None if key == "" else key version = None if version == 0 else version event_type = None if event_type == "" else event_type namespace = None if namespace == "" else namespace sender = None if sender == "" else sender if isinstance(key, str): key = (key,) elif isinstance(key, Iterable): key = tuple(key) res = MongoEvent.get_base_events(key, version, event_type, start_time, namespace, sender) return res def list_all_events(self, start_time: int): res = MongoEvent.get_base_events_by_time(start_time) return res def list_all_events_from_version(self, start_version: int, end_version: int = None): res = MongoEvent.get_base_events_by_version(start_version, end_version) return res def clean_up(self): MongoEvent.delete_by_client(self.server_ip)

38.175926

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4,123

107

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0

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1

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false

0.063291

0.101266

0

0.303797

0

null

0

null

0

1

0

1

6acc7db3216417c3207f16b6723988768ff50b66

711

py

Python

src/unicon/plugins/confd/csp/__init__.py

tahigash/unicon.plugins

1b43a5a61244ea9312387fd855442ace37c65db9

[ "Apache-2.0" ]

1

2021-02-25T19:36:56.000Z

src/unicon/plugins/confd/csp/__init__.py

tahigash/unicon.plugins

1b43a5a61244ea9312387fd855442ace37c65db9

[ "Apache-2.0" ]

null

src/unicon/plugins/confd/csp/__init__.py

tahigash/unicon.plugins

1b43a5a61244ea9312387fd855442ace37c65db9

[ "Apache-2.0" ]

null

__author__ = "Dave Wapstra <[email protected]>" from unicon.plugins.confd import ConfdServiceList, ConfdConnection, ConfdConnectionProvider from .statemachine import CspStateMachine from .settings import CspSettings from . import service_implementation as csp_svc class CspServiceList(ConfdServiceList): def __init__(self): super().__init__() delattr(self, 'cli_style') self.reload = csp_svc.Reload class CspSingleRPConnection(ConfdConnection): os = 'confd' series = 'csp' chassis_type = 'single_rp' state_machine_class = CspStateMachine connection_provider_class = ConfdConnectionProvider subcommand_list = CspServiceList settings = CspSettings()

28.44

91

0.759494

70

711

7.385714

0.657143

0.023211

0

0.168776

711

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92

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0

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false

0

0.222222

0

0.777778

0

null

0

null

0

1

0

1

6accba984dd52f022ed6544e1f7ad42db7180437

665

py

Python

setup.py

rrwen/search_google

e647868ba5da2803e787a3c06b32e09452068736

[ "MIT" ]

15

2017-08-24T18:44:55.000Z

2021-02-01T22:07:53.000Z

setup.py

rrwen/search_google

e647868ba5da2803e787a3c06b32e09452068736

[ "MIT" ]

5

2017-09-05T12:25:09.000Z

2021-10-18T06:45:24.000Z

setup.py

rrwen/search_google

e647868ba5da2803e787a3c06b32e09452068736

[ "MIT" ]

1

2018-02-20T13:44:44.000Z

# -*- coding: utf-8 -*- from setuptools import setup import search_google as package def readme(): with open('README.rst') as f: return ''.join(f.readlines()[11:]) setup( name=package.__name__, version=package.__version__, description=package.__description__, long_description=readme(), author=package.__author__, author_email=package.__email__, license=package.__license__, url=package.__url__, download_url=package.__download_url__, keywords =package. __keywords__, entry_points=package.__entry_points__, packages=package.__packages__, package_data=package.__package_data__, install_requires=package.__install_requires__ )

24.62963

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77

665

5.844156

0.480519

0.044444

0

0.005119

0.118797

665

26

48

25.576923

0.762799

0.031579

0

0.015576

0

1

0.047619

true

0

0.095238

0

0.190476

0

null

0

null

0

1

0

1

6ad2141e919181f75e53ccffa43344d1aae6eea7

346

py

Python

main.py

BenG49/sudoku

e4b14655e23d04c161feb16ceb1338537f519bdb

[ "MIT" ]

null

main.py

BenG49/sudoku

e4b14655e23d04c161feb16ceb1338537f519bdb

[ "MIT" ]

null

main.py

BenG49/sudoku

e4b14655e23d04c161feb16ceb1338537f519bdb

[ "MIT" ]

null

from sudoku import Sudoku def main(): s = Sudoku.parse( ''' ------------- | |2 | | | | 6 |4 3| | | 5| 7 | ------------- | 7 | 2|8 | |51 | 4|9 | | 9| 3| | ------------- | | 9| | | 2| | 98| | 83|1 |2 | ------------- ''' ) print(s) print(s.solve()) if __name__ == '__main__': main()

12.814815

26

0.297688

38

346

2.5

0.605263

0.126316

0

0.105505

0.369942

346

26

27

13.307692

0.330275

0

0.062016

0

1

0.125

false

0

0.125

0

0.25

0

null

0

1

0

null

0

1

6ad3007b95e5d17415b05151d343ee3326e45e1d

2,157

py

Python

experiment/diabetes/accuracy_info.py

leandro-santiago/bloomwisard

4c02610c4ef2d2cf8424797c8a815da182ca2383

[ "MIT" ]

2

2020-10-25T17:01:10.000Z

2020-12-04T14:26:26.000Z

experiment/diabetes/accuracy_info.py

leandro-santiago/bloomwisard

4c02610c4ef2d2cf8424797c8a815da182ca2383

[ "MIT" ]

null

experiment/diabetes/accuracy_info.py

leandro-santiago/bloomwisard

4c02610c4ef2d2cf8424797c8a815da182ca2383

[ "MIT" ]

null

import numpy as np import sys from timeit import default_timer as timer sys.path.append("../../") from core import wnn from encoding import thermometer from encoding import util #Load Diabetes data base_path = "../../dataset/diabetes/" #2/3 Test bits_encoding = 20 train_data, train_label, test_data, test_label, data_min, data_max = util.load_3data(base_path) ths = [] for i in range(len(data_max)): ths.append(thermometer.Thermometer(data_min[i], data_max[i], bits_encoding)) train_bin = [] test_bin = [] i = 0 for data in train_data: train_bin.append(np.array([], dtype=bool)) t = 0 for v in data: binarr = ths[t].binarize(v) train_bin[i] = np.append(train_bin[i], binarr) t += 1 i += 1 i = 0 for data in test_data: test_bin.append(np.array([], dtype=bool)) t = 0 for v in data: binarr = ths[t].binarize(v) test_bin[i] = np.append(test_bin[i], binarr) t += 1 i += 1 #print test_label #Wisard num_classes = 2 tuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30] acc_list = [] test_length = len(test_label) entry_size = len(train_bin[0]) #print entry_size for t in tuple_list: wisard = wnn.Wisard(entry_size, t, num_classes) wisard.train(train_bin, train_label) rank_result = wisard.rank(test_bin) num_hits = 0 for i in range(test_length): if rank_result[i] == test_label[i]: num_hits += 1 acc_list.append(float(num_hits)/float(test_length)) #Bloom Wisard btuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 40, 56] bacc_list = [] #capacity = len(train_bin) capacity = 10 print capacity for t in btuple_list: bwisard = wnn.BloomWisard(entry_size, t, num_classes, capacity) bwisard.train(train_bin, train_label) rank_result = bwisard.rank(test_bin) num_hits = 0 for i in range(test_length): if rank_result[i] == test_label[i]: num_hits += 1 bacc_list.append(float(num_hits)/float(test_length)) print "Tuples=", tuple_list print "Wisard Accuracy=", acc_list print "Tuples=", btuple_list print "BloomWisard Accuracy=",bacc_list

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0

null

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null

0.078125

0

null

0

null

0

1

0

1

6adc3f2423ac6cf2c778f44e1751ae2e595e05f5

74,159

py

Python

jss_figures_replication_script.py

Cole-vJ/AdvEMDpy

160cd44b371a2c8aa66961f23062c1d7305dd728

[ "Unlicense" ]

null

jss_figures_replication_script.py

Cole-vJ/AdvEMDpy

160cd44b371a2c8aa66961f23062c1d7305dd728

[ "Unlicense" ]

null

jss_figures_replication_script.py

Cole-vJ/AdvEMDpy

160cd44b371a2c8aa66961f23062c1d7305dd728

[ "Unlicense" ]

null

# ________ # / # \ / # \ / # \/ import random import textwrap import emd_mean import AdvEMDpy import emd_basis import emd_utils import numpy as np import pandas as pd import cvxpy as cvx import seaborn as sns import matplotlib.pyplot as plt from scipy.integrate import odeint from scipy.ndimage import gaussian_filter from emd_utils import time_extension, Utility from scipy.interpolate import CubicSpline from emd_hilbert import Hilbert, hilbert_spectrum from emd_preprocess import Preprocess from emd_mean import Fluctuation from AdvEMDpy import EMD # alternate packages from PyEMD import EMD as pyemd0215 import emd as emd040 sns.set(style='darkgrid') pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001) pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time) pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series) # plot 0 - addition fig = plt.figure(figsize=(9, 4)) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('First Iteration of Sifting Algorithm') plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1) plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()], c='r', label=r'$M(t_i)$', zorder=2) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4) plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()], c='c', label=r'$m(t_j)$', zorder=3) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5) plt.yticks(ticks=[-2, -1, 0, 1, 2]) plt.xticks(ticks=[0, np.pi, 2 * np.pi], labels=[r'0', r'$\pi$', r'$2\pi$']) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/pseudo_algorithm.png') plt.show() knots = np.arange(12) time = np.linspace(0, 11, 1101) basis = emd_basis.Basis(time=time, time_series=time) b_spline_basis = basis.cubic_b_spline(knots) chsi_basis = basis.chsi_basis(knots) # plot 1 plt.title('Non-Natural Cubic B-Spline Bases at Boundary') plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $') plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $') plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $') plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $') plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $') plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $']) plt.xlim(4.4, 6.6) plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.legend(loc='upper left') plt.savefig('jss_figures/boundary_bases.png') plt.show() # plot 1a - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) knots_uniform = np.linspace(0, 2 * np.pi, 51) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0] fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Uniform Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Uniform Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Uniform Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots_uniform)): axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_uniform.png') plt.show() # plot 1b - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=1, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Statically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Statically Optimised Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Statically Optimised Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots)): axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_1.png') plt.show() # plot 1c - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=2, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Dynamically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Dynamically Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Dynamically Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots[i])): axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_2.png') plt.show() # plot 1d - addition window = 81 fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Filtering Demonstration') axs[1].set_title('Zoomed Region') preprocess_time = pseudo_alg_time.copy() np.random.seed(1) random.seed(1) preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time)) for i in random.sample(range(1000), 500): preprocess_time_series[i] += np.random.normal(0, 1) preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series) axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_filter.png') plt.show() # plot 1e - addition fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Smoothing Demonstration') axs[1].set_title('Zoomed Region') axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[0].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) downsampled_and_decimated = preprocess.downsample() axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 11)) downsampled = preprocess.downsample(decimate=False) axs[0].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[1].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 13)) axs[1].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_smooth.png') plt.show() # plot 2 fig, axs = plt.subplots(1, 2, sharey=True) axs[0].set_title('Cubic B-Spline Bases') axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1') axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2') axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3') axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4') axs[0].legend(loc='upper left') axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].set_xticks([5, 6]) axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[0].set_xlim(4.5, 6.5) axs[1].set_title('Cubic Hermite Spline Bases') axs[1].plot(time, chsi_basis[10, :].T, '--') axs[1].plot(time, chsi_basis[11, :].T, '--') axs[1].plot(time, chsi_basis[12, :].T, '--') axs[1].plot(time, chsi_basis[13, :].T, '--') axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].set_xticks([5, 6]) axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[1].set_xlim(4.5, 6.5) plt.savefig('jss_figures/comparing_bases.png') plt.show() # plot 3 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_dash = maxima_y[-1] * np.ones_like(max_dash_time) min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_dash = minima_y[-1] * np.ones_like(min_dash_time) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) max_discard = maxima_y[-1] max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1] max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101) max_discard_dash = max_discard * np.ones_like(max_discard_dash_time) dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101) dash_2 = np.linspace(minima_y[-1], max_discard, 101) end_point_time = time[-1] end_point = time_series[-1] time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101) time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101))) time_series_anti_reflect = time_series_reflect[0] - time_series_reflect utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect) anti_max_bool = utils.max_bool_func_1st_order_fd() anti_max_point_time = time_reflect[anti_max_bool] anti_max_point = time_series_anti_reflect[anti_max_bool] utils = emd_utils.Utility(time=time, time_series=time_series_reflect) no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()] no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()] point_1 = 5.4 length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101) length_distance_time = point_1 * np.pi * np.ones_like(length_distance) length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101) length_top = maxima_y[-1] * np.ones_like(length_time) length_bottom = minima_y[-1] * np.ones_like(length_time) point_2 = 5.2 length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101) length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2) length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101) length_top_2 = time_series[-1] * np.ones_like(length_time_2) length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2) symmetry_axis_1_time = minima_x[-1] * np.ones(101) symmetry_axis_2_time = time[-1] * np.ones(101) symmetry_axis = np.linspace(-2, 2, 101) end_time = np.linspace(time[-1] - width, time[-1] + width, 101) end_signal = time_series[-1] * np.ones_like(end_time) anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101) anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Symmetry Edge Effects Example') plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10)) plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2, label=textwrap.fill('Anti-symmetric signal', 10)) plt.plot(max_dash_time, max_dash, 'k-') plt.plot(min_dash_time, min_dash, 'k-') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(length_distance_time, length_distance, 'k--') plt.plot(length_distance_time_2, length_distance_2, 'k--') plt.plot(length_time, length_top, 'k-') plt.plot(length_time, length_bottom, 'k-') plt.plot(length_time_2, length_top_2, 'k-') plt.plot(length_time_2, length_bottom_2, 'k-') plt.plot(end_time, end_signal, 'k-') plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1) plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1) plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1) plt.text(5.1 * np.pi, -0.7, r'$\beta$L') plt.text(5.34 * np.pi, -0.05, 'L') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10)) plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10)) plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10)) plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_symmetry_anti.png') plt.show() # plot 4 a = 0.21 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1) max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1) min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1) min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101) dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101) s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1]) slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1 max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1) max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101) dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101) dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101) s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1]) slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2 min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1) min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101) dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time) dash_4 = np.linspace(slope_based_maximum, slope_based_minimum) maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101) maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash) maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash) maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash) maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101) maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time) minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101) minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash) minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash) minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash) minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101) minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time) # slightly edit signal to make difference between slope-based method and improved slope-based method more clear time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \ time_series[time == minima_x[-1]] improved_slope_based_maximum_time = time[-1] improved_slope_based_maximum = time_series[-1] improved_slope_based_minimum_time = slope_based_minimum_time improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time - improved_slope_based_maximum_time) min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101) min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4) dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101) dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101) ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 0.9 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Slope-Based Edge Effects Example') plt.plot(max_dash_time_1, max_dash_1, 'k-') plt.plot(max_dash_time_2, max_dash_2, 'k-') plt.plot(max_dash_time_3, max_dash_3, 'k-') plt.plot(min_dash_time_1, min_dash_1, 'k-') plt.plot(min_dash_time_2, min_dash_2, 'k-') plt.plot(min_dash_time_3, min_dash_3, 'k-') plt.plot(min_dash_time_4, min_dash_4, 'k-') plt.plot(maxima_dash_time_1, maxima_dash, 'k-') plt.plot(maxima_dash_time_2, maxima_dash, 'k-') plt.plot(maxima_dash_time_3, maxima_dash, 'k-') plt.plot(minima_dash_time_1, minima_dash, 'k-') plt.plot(minima_dash_time_2, minima_dash, 'k-') plt.plot(minima_dash_time_3, minima_dash, 'k-') plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.30 * np.pi, 0.35, r'$s_1$') plt.text(4.43 * np.pi, -0.20, r'$s_2$') plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$') plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]), -0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]), 1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.plot(minima_line_dash_time, minima_line_dash, 'k--') plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(dash_3_time, dash_3, 'k--') plt.plot(dash_4_time, dash_4, 'k--') plt.plot(dash_final_time, dash_final, 'k--') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4, label=textwrap.fill('Slope-based maximum', 11)) plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4, label=textwrap.fill('Slope-based minimum', 11)) plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4, label=textwrap.fill('Improved slope-based maximum', 11)) plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4, label=textwrap.fill('Improved slope-based minimum', 11)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_slope_based.png') plt.show() # plot 5 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2 A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2 P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2]) P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1]) Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1] Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1] Coughlin_time = Huang_time Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0])) Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) Average_max = (maxima_y[-2] + maxima_y[-1]) / 2 Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) Average_min = (minima_y[-2] + minima_y[-1]) / 2 utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave) Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd() Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd() utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave) Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd() Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd() Huang_max_time = Huang_time[Huang_max_bool] Huang_max = Huang_wave[Huang_max_bool] Huang_min_time = Huang_time[Huang_min_bool] Huang_min = Huang_wave[Huang_min_bool] Coughlin_max_time = Coughlin_time[Coughlin_max_bool] Coughlin_max = Coughlin_wave[Coughlin_max_bool] Coughlin_min_time = Coughlin_time[Coughlin_min_bool] Coughlin_min = Coughlin_wave[Coughlin_min_bool] max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101) max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time) min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101) min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) min_2_x = minima_y[-2] * np.ones_like(min_2_x_time) dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101) dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x) max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y) min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) min_2_y_time = minima_x[-2] * np.ones_like(min_2_y) dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101) dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time) max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time) min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) min_1_x = minima_y[-1] * np.ones_like(min_1_x_time) dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101) dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x) max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y) min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) min_1_y_time = minima_x[-1] * np.ones_like(min_1_y) dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101) dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Characteristic Wave Effects Example') plt.plot(time, time_series, LineWidth=2, label='Signal') plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10)) plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10)) plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4, label=textwrap.fill('Coughlin maximum', 14)) plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4, label=textwrap.fill('Coughlin minimum', 14)) plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4, label=textwrap.fill('Average maximum', 14)) plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4, label=textwrap.fill('Average minimum', 14)) plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14)) plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14)) plt.plot(max_2_x_time, max_2_x, 'k-') plt.plot(max_2_x_time_side, max_2_x, 'k-') plt.plot(min_2_x_time, min_2_x, 'k-') plt.plot(min_2_x_time_side, min_2_x, 'k-') plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--') plt.text(5.16 * np.pi, 0.85, r'$2a_2$') plt.plot(max_2_y_time, max_2_y, 'k-') plt.plot(max_2_y_time, max_2_y_side, 'k-') plt.plot(min_2_y_time, min_2_y, 'k-') plt.plot(min_2_y_time, min_2_y_side, 'k-') plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--') plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$') plt.plot(max_1_x_time, max_1_x, 'k-') plt.plot(max_1_x_time_side, max_1_x, 'k-') plt.plot(min_1_x_time, min_1_x, 'k-') plt.plot(min_1_x_time_side, min_1_x, 'k-') plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--') plt.text(5.42 * np.pi, -0.1, r'$2a_1$') plt.plot(max_1_y_time, max_1_y, 'k-') plt.plot(max_1_y_time, max_1_y_side, 'k-') plt.plot(min_1_y_time, min_1_y, 'k-') plt.plot(min_1_y_time, min_1_y_side, 'k-') plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--') plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$') plt.xlim(3.9 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_characteristic_wave.png') plt.show() # plot 6 t = np.linspace(5, 95, 100) signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200) util_nn = emd_utils.Utility(time=t, time_series=signal_orig) maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()] minima = signal_orig[util_nn.min_bool_func_1st_order_fd()] cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima) cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima) time = np.linspace(0, 5 * np.pi, 1001) lsq_signal = np.cos(time) + np.cos(5 * time) knots = np.linspace(0, 5 * np.pi, 101) time_extended = time_extension(time) time_series_extended = np.zeros_like(time_extended) / 0 time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal neural_network_m = 200 neural_network_k = 100 # forward -> P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))] P[-1, col] = 1 # for additive constant t = lsq_signal[-neural_network_m:] # test - top seed_weights = np.ones(neural_network_k) / neural_network_k weights = 0 * seed_weights.copy() train_input = P[:-1, :] lr = 0.01 for iterations in range(1000): output = np.matmul(weights, train_input) error = (t - output) gradients = error * (- train_input) # guess average gradients average_gradients = np.mean(gradients, axis=1) # steepest descent max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients))) adjustment = - lr * average_gradients # adjustment = - lr * max_gradient_vector weights += adjustment # test - bottom weights_right = np.hstack((weights, 0)) max_count_right = 0 min_count_right = 0 i_right = 0 while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1): time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \ sum(weights_right * np.hstack((time_series_extended[ int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right): int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1))) i_right += 1 if i_right > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_right += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_right += 1 # backward <- P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)] P[-1, col] = 1 # for additive constant t = lsq_signal[:neural_network_m] vx = cvx.Variable(int(neural_network_k + 1)) objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary prob = cvx.Problem(objective) result = prob.solve(verbose=True, solver=cvx.ECOS) weights_left = np.array(vx.value) max_count_left = 0 min_count_left = 0 i_left = 0 while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1): time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \ 2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left): int(len(lsq_signal) - 1 - i_left + neural_network_k)], 1))) + 1 i_left += 1 if i_left > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_left += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_left += 1 lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal) utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended) maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()] maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()] maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1] maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1] minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()] minima_time = time[lsq_utils.min_bool_func_1st_order_fd()] minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Single Neuron Neural Network Example') plt.plot(time, lsq_signal, zorder=2, label='Signal') plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12)) plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima') plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima') plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3, label=textwrap.fill('Extrapolated maxima', 12)) plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4, label=textwrap.fill('Extrapolated minima', 12)) plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k', label=textwrap.fill('Neural network inputs', 13)) plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k') plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed', label=textwrap.fill('Neural network targets', 13)) plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed') plt.xlim(3.4 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/neural_network.png') plt.show() # plot 6a np.random.seed(0) time = np.linspace(0, 5 * np.pi, 1001) knots_51 = np.linspace(0, 5 * np.pi, 51) time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time) noise = np.random.normal(0, 1, len(time_series)) time_series += noise advemdpy = EMD(time=time, time_series=time_series) imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3, edge_effect='symmetric_anchor', verbose=False)[:3] knots_31 = np.linspace(0, 5 * np.pi, 31) imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2, edge_effect='symmetric_anchor', verbose=False)[:3] knots_11 = np.linspace(0, 5 * np.pi, 11) imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1, edge_effect='symmetric_anchor', verbose=False)[:3] fig, axs = plt.subplots(3, 1) plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40)) plt.subplots_adjust(hspace=0.1) axs[0].plot(time, time_series, label='Time series') axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21)) print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}') for knot in knots_51: axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[0].set_xticklabels(['', '', '', '', '', '']) axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--') axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--') axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--') axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region') box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[1].plot(time, time_series, label='Time series') axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19)) axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19)) print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}') for knot in knots_31: axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[1].set_xticklabels(['', '', '', '', '', '']) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height]) axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--') axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--') axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--') axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region') axs[2].plot(time, time_series, label='Time series') axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots') axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots') axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots') print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}') for knot in knots_11: axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$']) box_2 = axs[2].get_position() axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height]) axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--') axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--') axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--') axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region') plt.savefig('jss_figures/DFA_different_trends.png') plt.show() # plot 6b fig, axs = plt.subplots(3, 1) plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40)) plt.subplots_adjust(hspace=0.1) axs[0].plot(time, time_series, label='Time series') axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21)) for knot in knots_51: axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[0].set_xticklabels(['', '', '', '', '', '']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[0].set_ylim(-5.5, 5.5) axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi) axs[1].plot(time, time_series, label='Time series') axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19)) axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19)) for knot in knots_31: axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[1].set_xticklabels(['', '', '', '', '', '']) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height]) axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[1].set_ylim(-5.5, 5.5) axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi) axs[2].plot(time, time_series, label='Time series') axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots') axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots') axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots') for knot in knots_11: axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[2].set_xticks([np.pi, (3 / 2) * np.pi]) axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$']) box_2 = axs[2].get_position() axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height]) axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[2].set_ylim(-5.5, 5.5) axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi) plt.savefig('jss_figures/DFA_different_trends_zoomed.png') plt.show() hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False) # plot 6c ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 0.9 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50)) x_hs, y, z = hs_ouputs z_min, z_max = 0, np.abs(z).max() ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max) ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3) ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3) ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3) ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi]) ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$']) plt.ylabel(r'Frequency (rad.s$^{-1}$)') plt.xlabel('Time (s)') box_0 = ax.get_position() ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/DFA_hilbert_spectrum.png') plt.show() # plot 6c time = np.linspace(0, 5 * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) knots = np.linspace(0, 5 * np.pi, 51) fluc = Fluctuation(time=time, time_series=time_series) max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False) max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True) min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False) min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True) util = Utility(time=time, time_series=time_series) maxima = util.max_bool_func_1st_order_fd() minima = util.min_bool_func_1st_order_fd() ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50)) plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2) plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10) plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10) plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange') plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red') plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan') plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue') for knot in knots[:-1]: plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1) plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1) plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) plt.xlim(-0.25 * np.pi, 5.25 * np.pi) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png') plt.show() # plot 7 a = 0.25 width = 0.2 time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001) knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] inflection_bool = utils.inflection_point() inflection_x = time[inflection_bool] inflection_y = time_series[inflection_bool] fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series) maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False, smoothing_penalty=0.2, edge_effect='none', spline_method='b_spline')[0] maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True, smoothing_penalty=0.2, edge_effect='none', spline_method='b_spline')[0] minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False, smoothing_penalty=0.2, edge_effect='none', spline_method='b_spline')[0] minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True, smoothing_penalty=0.2, edge_effect='none', spline_method='b_spline')[0] inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots, smooth=True, smoothing_penalty=0.2, technique='inflection_points')[0] binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots, smooth=True, smoothing_penalty=0.2, technique='binomial_average', order=21, increment=20)[0] derivative_of_lsq = utils.derivative_forward_diff() derivative_time = time[:-1] derivative_knots = np.linspace(knots[0], knots[-1], 31) # change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging) emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq) imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots, knot_time=derivative_time, text=False, verbose=False)[0][1, :] utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative) optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \ np.r_[utils.zero_crossing() == 1, False] optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \ np.r_[utils.zero_crossing() == 1, False] EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima', optimal_maxima, optimal_minima, smooth=False, smoothing_penalty=0.2, edge_effect='none')[0] EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima', optimal_maxima, optimal_minima, smooth=False, smoothing_penalty=0.2, edge_effect='none')[0] ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Detrended Fluctuation Analysis Examples') plt.plot(time, time_series, LineWidth=2, label='Time series') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4, label=textwrap.fill('Optimal maxima', 10)) plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4, label=textwrap.fill('Optimal minima', 10)) plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10)) plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10)) plt.plot(time, minima_envelope, c='darkblue') plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue') plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10)) plt.plot(time, minima_envelope_smooth, c='darkred') plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred') plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10)) plt.plot(time, EEMD_minima_envelope, c='darkgreen') plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen') plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10)) plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10)) plt.plot(time, np.cos(time), c='black', label='True mean') plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) plt.xlim(-0.25 * np.pi, 5.25 * np.pi) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/detrended_fluctuation_analysis.png') plt.show() # Duffing Equation Example def duffing_equation(xy, ts): gamma = 0.1 epsilon = 1 omega = ((2 * np.pi) / 25) return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * np.cos(omega * ts)] t = np.linspace(0, 150, 1501) XY0 = [1, 1] solution = odeint(duffing_equation, XY0, t) x = solution[:, 0] dxdt = solution[:, 1] x_points = [0, 50, 100, 150] x_names = {0, 50, 100, 150} y_points_1 = [-2, 0, 2] y_points_2 = [-1, 0, 1] fig, axs = plt.subplots(2, 1) plt.subplots_adjust(hspace=0.2) axs[0].plot(t, x) axs[0].set_title('Duffing Equation Displacement') axs[0].set_ylim([-2, 2]) axs[0].set_xlim([0, 150]) axs[1].plot(t, dxdt) axs[1].set_title('Duffing Equation Velocity') axs[1].set_ylim([-1.5, 1.5]) axs[1].set_xlim([0, 150]) axis = 0 for ax in axs.flat: ax.label_outer() if axis == 0: ax.set_ylabel('x(t)') ax.set_yticks(y_points_1) if axis == 1: ax.set_ylabel(r'$ \dfrac{dx(t)}{dt} $') ax.set(xlabel='t') ax.set_yticks(y_points_2) ax.set_xticks(x_points) ax.set_xticklabels(x_names) axis += 1 plt.savefig('jss_figures/Duffing_equation.png') plt.show() # compare other packages Duffing - top pyemd = pyemd0215() py_emd = pyemd(x) IP, IF, IA = emd040.spectra.frequency_transform(py_emd.T, 10, 'hilbert') freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100) hht = emd040.spectra.hilberthuang(IF, IA, freq_edges) hht = gaussian_filter(hht, sigma=1) ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 1.0 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using PyEMD 0.2.10', 40)) plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht)))) plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15)) plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15)) plt.xticks([0, 50, 100, 150]) plt.yticks([0, 0.1, 0.2]) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)') box_0 = ax.get_position() ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/Duffing_equation_ht_pyemd.png') plt.show() plt.show() emd_sift = emd040.sift.sift(x) IP, IF, IA = emd040.spectra.frequency_transform(emd_sift, 10, 'hilbert') freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100) hht = emd040.spectra.hilberthuang(IF, IA, freq_edges) hht = gaussian_filter(hht, sigma=1) ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 1.0 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using emd 0.3.3', 40)) plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht)))) plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15)) plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15)) plt.xticks([0, 50, 100, 150]) plt.yticks([0, 0.1, 0.2]) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)') box_0 = ax.get_position() ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/Duffing_equation_ht_emd.png') plt.show() # compare other packages Duffing - bottom emd_duffing = AdvEMDpy.EMD(time=t, time_series=x) emd_duff, emd_ht_duff, emd_if_duff, _, _, _, _ = emd_duffing.empirical_mode_decomposition(verbose=False) fig, axs = plt.subplots(2, 1) plt.subplots_adjust(hspace=0.3) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) axs[0].plot(t, emd_duff[1, :], label='AdvEMDpy') axs[0].plot(t, py_emd[0, :], '--', label='PyEMD 0.2.10') axs[0].plot(t, emd_sift[:, 0], '--', label='emd 0.3.3') axs[0].set_title('IMF 1') axs[0].set_ylim([-2, 2]) axs[0].set_xlim([0, 150]) axs[1].plot(t, emd_duff[2, :], label='AdvEMDpy') print(f'AdvEMDpy driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_duff[2, :])), 3)}') axs[1].plot(t, py_emd[1, :], '--', label='PyEMD 0.2.10') print(f'PyEMD driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - py_emd[1, :])), 3)}') axs[1].plot(t, emd_sift[:, 1], '--', label='emd 0.3.3') print(f'emd driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_sift[:, 1])), 3)}') axs[1].plot(t, 0.1 * np.cos(0.04 * 2 * np.pi * t), '--', label=r'$0.1$cos$(0.08{\pi}t)$') axs[1].set_title('IMF 2') axs[1].set_ylim([-0.2, 0.4]) axs[1].set_xlim([0, 150]) axis = 0 for ax in axs.flat: ax.label_outer() if axis == 0: ax.set_ylabel(r'$\gamma_1(t)$') ax.set_yticks([-2, 0, 2]) if axis == 1: ax.set_ylabel(r'$\gamma_2(t)$') ax.set_yticks([-0.2, 0, 0.2]) box_0 = ax.get_position() ax.set_position([box_0.x0, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) ax.set_xticks(x_points) ax.set_xticklabels(x_names) axis += 1 plt.savefig('jss_figures/Duffing_equation_imfs.png') plt.show() hs_ouputs = hilbert_spectrum(t, emd_duff, emd_ht_duff, emd_if_duff, max_frequency=1.3, plot=False) ax = plt.subplot(111) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using AdvEMDpy', 40)) x, y, z = hs_ouputs y = y / (2 * np.pi) z_min, z_max = 0, np.abs(z).max() figure_size = plt.gcf().get_size_inches() factor = 1.0 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) ax.pcolormesh(x, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max) plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15)) plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15)) plt.xticks([0, 50, 100, 150]) plt.yticks([0, 0.1, 0.2]) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)') box_0 = ax.get_position() ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/Duffing_equation_ht.png') plt.show() # Carbon Dioxide Concentration Example CO2_data = pd.read_csv('Data/co2_mm_mlo.csv', header=51) plt.plot(CO2_data['month'], CO2_data['decimal date']) plt.title(textwrap.fill('Mean Monthly Concentration of Carbon Dioxide in the Atmosphere', 35)) plt.ylabel('Parts per million') plt.xlabel('Time (years)') plt.savefig('jss_figures/CO2_concentration.png') plt.show() signal = CO2_data['decimal date'] signal = np.asarray(signal) time = CO2_data['month'] time = np.asarray(time) # compare other packages Carbon Dioxide - top pyemd = pyemd0215() py_emd = pyemd(signal) IP, IF, IA = emd040.spectra.frequency_transform(py_emd[:2, :].T, 12, 'hilbert') print(f'PyEMD annual frequency error: {np.round(sum(np.abs(IF[:, 0] - np.ones_like(IF[:, 0]))), 3)}') freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 2, 100) hht = emd040.spectra.hilberthuang(IF, IA, freq_edges) hht = gaussian_filter(hht, sigma=1) fig, ax = plt.subplots() figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using PyEMD 0.2.10', 45)) plt.ylabel('Frequency (year$^{-1}$)') plt.xlabel('Time (years)') plt.pcolormesh(time, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht)))) plt.plot(time, np.ones_like(time), 'k--', label=textwrap.fill('Annual cycle', 10)) box_0 = ax.get_position() ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/CO2_Hilbert_pyemd.png') plt.show() emd_sift = emd040.sift.sift(signal) IP, IF, IA = emd040.spectra.frequency_transform(emd_sift[:, :1], 12, 'hilbert') print(f'emd annual frequency error: {np.round(sum(np.abs(IF - np.ones_like(IF)))[0], 3)}') freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 2, 100) hht = emd040.spectra.hilberthuang(IF, IA, freq_edges) hht = gaussian_filter(hht, sigma=1) fig, ax = plt.subplots() figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using emd 0.3.3', 45)) plt.ylabel('Frequency (year$^{-1}$)') plt.xlabel('Time (years)') plt.pcolormesh(time, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht)))) plt.plot(time, np.ones_like(time), 'k--', label=textwrap.fill('Annual cycle', 10)) box_0 = ax.get_position() ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/CO2_Hilbert_emd.png') plt.show() # compare other packages Carbon Dioxide - bottom knots = np.linspace(time[0], time[-1], 200) emd_example = AdvEMDpy.EMD(time=time, time_series=signal) imfs, hts, ifs, _, _, _, _ = \ emd_example.empirical_mode_decomposition(knots=knots, knot_time=time, verbose=False) print(f'AdvEMDpy annual frequency error: {np.round(sum(np.abs(ifs[1, :] / (2 * np.pi) - np.ones_like(ifs[1, :]))), 3)}') fig, axs = plt.subplots(2, 2) plt.subplots_adjust(hspace=0.5) axs[0, 0].plot(time, signal) axs[0, 1].plot(time, signal) axs[0, 1].plot(time, imfs[0, :], label='Smoothed') axs[0, 1].legend(loc='lower right') axs[1, 0].plot(time, imfs[1, :]) axs[1, 1].plot(time, imfs[2, :]) axis = 0 for ax in axs.flat: if axis == 0: ax.set(ylabel=R'C0$_2$ concentration') if axis == 1: pass if axis == 2: ax.set(ylabel=R'C0$_2$ concentration') ax.set(xlabel='Time (years)') if axis == 3: ax.set(xlabel='Time (years)') axis += 1 plt.gcf().subplots_adjust(bottom=0.15) axs[0, 0].set_title(r'Original CO$_2$ Concentration') axs[0, 1].set_title('Smoothed CO$_2$ Concentration') axs[1, 0].set_title('IMF 1') axs[1, 1].set_title('Residual') plt.gcf().subplots_adjust(bottom=0.15) plt.savefig('jss_figures/CO2_EMD.png') plt.show() hs_ouputs = hilbert_spectrum(time, imfs, hts, ifs, max_frequency=10, which_imfs=[1], plot=False) x_hs, y, z = hs_ouputs y = y / (2 * np.pi) z_min, z_max = 0, np.abs(z).max() fig, ax = plt.subplots() figure_size = plt.gcf().get_size_inches() factor = 0.7 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max) ax.set_title(textwrap.fill(r'Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using AdvEMDpy', 40)) plt.ylabel('Frequency (year$^{-1}$)') plt.xlabel('Time (years)') plt.plot(x_hs[0, :], np.ones_like(x_hs[0, :]), 'k--', label=textwrap.fill('Annual cycle', 10)) ax.axis([x_hs.min(), x_hs.max(), y.min(), y.max()]) box_0 = ax.get_position() ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/CO2_Hilbert.png') plt.show()

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migrations/versions/0158_remove_rate_limit_default.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

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migrations/versions/0158_remove_rate_limit_default.py

cds-snc/notification-api

b1c1064f291eb860b494c3fa65ac256ad70bf47c

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migrations/versions/0158_remove_rate_limit_default.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

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2020-01-24T19:56:43.000Z

2022-01-27T21:36:53.000Z

""" Revision ID: 0158_remove_rate_limit_default Revises: 0157_add_rate_limit_to_service Create Date: 2018-01-09 14:33:08.313893 """ import sqlalchemy as sa from alembic import op revision = "0158_remove_rate_limit_default" down_revision = "0157_add_rate_limit_to_service" def upgrade(): op.execute("ALTER TABLE services ALTER rate_limit DROP DEFAULT") op.execute("ALTER TABLE services_history ALTER rate_limit DROP DEFAULT") def downgrade(): op.execute("ALTER TABLE services ALTER rate_limit SET DEFAULT '3000'") op.execute("ALTER TABLE services_history ALTER rate_limit SET DEFAULT '3000'")

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backend/core/api_urls.py

albeiks/omaralbeik.com

8d096130393919612863aac6280dffaf6e00961d

[ "MIT" ]

10

2020-05-05T16:20:04.000Z

2021-07-22T15:15:13.000Z

backend/core/api_urls.py

albeiks/omaralbeik.com

8d096130393919612863aac6280dffaf6e00961d

[ "MIT" ]

null

backend/core/api_urls.py

albeiks/omaralbeik.com

8d096130393919612863aac6280dffaf6e00961d

[ "MIT" ]

1

2020-05-06T22:31:48.000Z

from django.conf.urls import url, include from core.routers import OptionalTrailingSlashRouter from blog import views as blogViews from snippets import views as snippetsViews from projects import views as projectsViews from tags import views as tagsViews from contents import views as contentsViews from contact import views as contactViews router = OptionalTrailingSlashRouter() router.register(r"blog", blogViews.PostViewSet) router.register(r"snippets", snippetsViews.SnippetViewSet) router.register(r"languages", snippetsViews.ProgrammingLanguageViewSet) router.register(r"projects", projectsViews.ProjectViewSet) router.register(r"tags", tagsViews.TagViewSet) router.register(r"contents", contentsViews.ContentViewSet) router.register(r"contact", contactViews.MessageViewSet) # List or url patterns for the api subdomain urlpatterns = [ url(r"^v2/", include(router.urls)), ]

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Python

tools/perf/contrib/oop_raster/oop_raster.py

zipated/src

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2022-03-31T08:39:18.000Z

tools/perf/contrib/oop_raster/oop_raster.py

cangulcan/src

2b8388091c71e442910a21ada3d97ae8bc1845d3

[ "BSD-3-Clause" ]

395

2020-04-18T08:22:18.000Z

2021-12-08T13:04:49.000Z

tools/perf/contrib/oop_raster/oop_raster.py

cangulcan/src

2b8388091c71e442910a21ada3d97ae8bc1845d3

[ "BSD-3-Clause" ]

338

2020-04-18T08:03:10.000Z

2022-03-29T12:33:22.000Z

# Copyright 2018 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from benchmarks import smoothness,thread_times import page_sets from telemetry import benchmark # pylint: disable=protected-access def CustomizeBrowserOptionsForOopRasterization(options): """Enables flags needed for out of process rasterization.""" options.AppendExtraBrowserArgs('--force-gpu-rasterization') options.AppendExtraBrowserArgs('--enable-oop-rasterization') @benchmark.Owner(emails=['[email protected]']) class SmoothnessOopRasterizationTop25(smoothness._Smoothness): """Measures rendering statistics for the top 25 with oop rasterization. """ tag = 'oop_rasterization' page_set = page_sets.Top25SmoothPageSet def SetExtraBrowserOptions(self, options): CustomizeBrowserOptionsForOopRasterization(options) @classmethod def Name(cls): return 'smoothness.oop_rasterization.top_25_smooth' @benchmark.Owner(emails=['[email protected]']) class ThreadTimesOopRasterKeyMobile(thread_times._ThreadTimes): """Measure timeline metrics for key mobile pages while using out of process raster.""" tag = 'oop_rasterization' page_set = page_sets.KeyMobileSitesSmoothPageSet options = {'story_tag_filter': 'fastpath'} def SetExtraBrowserOptions(self, options): super(ThreadTimesOopRasterKeyMobile, self).SetExtraBrowserOptions(options) CustomizeBrowserOptionsForOopRasterization(options) @classmethod def Name(cls): return 'thread_times.oop_rasterization.key_mobile'

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Python

jnpy/experiments/Qt/pyqtgraph_tutorial/codeloop_org_materials/c4_drawing_curves.py

jojoquant/jnpy

c874060af4b129ae09cee9f8542517b7b2f6573b

[ "MIT" ]

5

2020-05-19T07:32:39.000Z

2022-03-14T09:09:48.000Z

jnpy/experiments/Qt/pyqtgraph_tutorial/codeloop_org_materials/c4_drawing_curves.py

jojoquant/jnpy

c874060af4b129ae09cee9f8542517b7b2f6573b

[ "MIT" ]

null

jnpy/experiments/Qt/pyqtgraph_tutorial/codeloop_org_materials/c4_drawing_curves.py

jojoquant/jnpy

c874060af4b129ae09cee9f8542517b7b2f6573b

[ "MIT" ]

3

2020-04-02T08:30:17.000Z

2020-05-03T12:12:05.000Z

# !/usr/bin/env python3 # -*- coding:utf-8 -*- # @Datetime : 2019/11/14 上午2:26 # @Author : Fangyang # @Software : PyCharm import sys from PyQt5.QtWidgets import QApplication import pyqtgraph as pg import numpy as np app = QApplication(sys.argv) x = np.arange(1000) y = np.random.normal(size=(3, 1000)) plotWidget = pg.plot(title='Three plot curves') for i in range(3): plotWidget.plot(x, y[i], pen=(i, 3)) status = app.exec_() sys.exit(status) if __name__ == '__main__': pass

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Python

sdk/python/pulumi_kubernetes/coordination/v1/_inputs.py

polivbr/pulumi-kubernetes

36a5fb34240a38a60b52a5f4e55e66e248d9305f

[ "Apache-2.0" ]

277

2018-06-18T14:57:09.000Z

2022-03-29T04:05:06.000Z

sdk/python/pulumi_kubernetes/coordination/v1/_inputs.py

polivbr/pulumi-kubernetes

36a5fb34240a38a60b52a5f4e55e66e248d9305f

[ "Apache-2.0" ]

1,447

2018-06-20T00:58:34.000Z

2022-03-31T21:28:43.000Z

sdk/python/pulumi_kubernetes/coordination/v1/_inputs.py

polivbr/pulumi-kubernetes

36a5fb34240a38a60b52a5f4e55e66e248d9305f

[ "Apache-2.0" ]

95

2018-06-30T03:30:05.000Z

2022-03-29T04:05:09.000Z

# coding=utf-8 # *** WARNING: this file was generated by pulumigen. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ... import meta as _meta __all__ = [ 'LeaseSpecArgs', 'LeaseArgs', ] @pulumi.input_type class LeaseSpecArgs: def __init__(__self__, *, acquire_time: Optional[pulumi.Input[str]] = None, holder_identity: Optional[pulumi.Input[str]] = None, lease_duration_seconds: Optional[pulumi.Input[int]] = None, lease_transitions: Optional[pulumi.Input[int]] = None, renew_time: Optional[pulumi.Input[str]] = None): """ LeaseSpec is a specification of a Lease. :param pulumi.Input[str] acquire_time: acquireTime is a time when the current lease was acquired. :param pulumi.Input[str] holder_identity: holderIdentity contains the identity of the holder of a current lease. :param pulumi.Input[int] lease_duration_seconds: leaseDurationSeconds is a duration that candidates for a lease need to wait to force acquire it. This is measure against time of last observed RenewTime. :param pulumi.Input[int] lease_transitions: leaseTransitions is the number of transitions of a lease between holders. :param pulumi.Input[str] renew_time: renewTime is a time when the current holder of a lease has last updated the lease. """ if acquire_time is not None: pulumi.set(__self__, "acquire_time", acquire_time) if holder_identity is not None: pulumi.set(__self__, "holder_identity", holder_identity) if lease_duration_seconds is not None: pulumi.set(__self__, "lease_duration_seconds", lease_duration_seconds) if lease_transitions is not None: pulumi.set(__self__, "lease_transitions", lease_transitions) if renew_time is not None: pulumi.set(__self__, "renew_time", renew_time) @property @pulumi.getter(name="acquireTime") def acquire_time(self) -> Optional[pulumi.Input[str]]: """ acquireTime is a time when the current lease was acquired. """ return pulumi.get(self, "acquire_time") @acquire_time.setter def acquire_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "acquire_time", value) @property @pulumi.getter(name="holderIdentity") def holder_identity(self) -> Optional[pulumi.Input[str]]: """ holderIdentity contains the identity of the holder of a current lease. """ return pulumi.get(self, "holder_identity") @holder_identity.setter def holder_identity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "holder_identity", value) @property @pulumi.getter(name="leaseDurationSeconds") def lease_duration_seconds(self) -> Optional[pulumi.Input[int]]: """ leaseDurationSeconds is a duration that candidates for a lease need to wait to force acquire it. This is measure against time of last observed RenewTime. """ return pulumi.get(self, "lease_duration_seconds") @lease_duration_seconds.setter def lease_duration_seconds(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "lease_duration_seconds", value) @property @pulumi.getter(name="leaseTransitions") def lease_transitions(self) -> Optional[pulumi.Input[int]]: """ leaseTransitions is the number of transitions of a lease between holders. """ return pulumi.get(self, "lease_transitions") @lease_transitions.setter def lease_transitions(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "lease_transitions", value) @property @pulumi.getter(name="renewTime") def renew_time(self) -> Optional[pulumi.Input[str]]: """ renewTime is a time when the current holder of a lease has last updated the lease. """ return pulumi.get(self, "renew_time") @renew_time.setter def renew_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "renew_time", value) @pulumi.input_type class LeaseArgs: def __init__(__self__, *, api_version: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']] = None, spec: Optional[pulumi.Input['LeaseSpecArgs']] = None): """ Lease defines a lease concept. :param pulumi.Input[str] api_version: APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources :param pulumi.Input[str] kind: Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds :param pulumi.Input['_meta.v1.ObjectMetaArgs'] metadata: More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata :param pulumi.Input['LeaseSpecArgs'] spec: Specification of the Lease. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status """ if api_version is not None: pulumi.set(__self__, "api_version", 'coordination.k8s.io/v1') if kind is not None: pulumi.set(__self__, "kind", 'Lease') if metadata is not None: pulumi.set(__self__, "metadata", metadata) if spec is not None: pulumi.set(__self__, "spec", spec) @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[pulumi.Input[str]]: """ APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources """ return pulumi.get(self, "api_version") @api_version.setter def api_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "api_version", value) @property @pulumi.getter def kind(self) -> Optional[pulumi.Input[str]]: """ Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds """ return pulumi.get(self, "kind") @kind.setter def kind(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kind", value) @property @pulumi.getter def metadata(self) -> Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]: """ More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata """ return pulumi.get(self, "metadata") @metadata.setter def metadata(self, value: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]): pulumi.set(self, "metadata", value) @property @pulumi.getter def spec(self) -> Optional[pulumi.Input['LeaseSpecArgs']]: """ Specification of the Lease. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status """ return pulumi.get(self, "spec") @spec.setter def spec(self, value: Optional[pulumi.Input['LeaseSpecArgs']]): pulumi.set(self, "spec", value)

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0a7c48d84a538009f1d4846a3bf1ffec3626caf1

1,005

py

Python

Components/Align All Components.py

davidtahim/Glyphs-Scripts

5ed28805b5fe03c63d904ad2f79117844c22aa44

[ "Apache-2.0" ]

1

2021-09-04T18:41:30.000Z

Components/Align All Components.py

davidtahim/Glyphs-Scripts

5ed28805b5fe03c63d904ad2f79117844c22aa44

[ "Apache-2.0" ]

null

Components/Align All Components.py

davidtahim/Glyphs-Scripts

5ed28805b5fe03c63d904ad2f79117844c22aa44

[ "Apache-2.0" ]

null

#MenuTitle: Align All Components # -*- coding: utf-8 -*- __doc__=""" Fakes auto-alignment in glyphs that cannot be auto-aligned. """ import GlyphsApp thisFont = Glyphs.font # frontmost font thisFontMaster = thisFont.selectedFontMaster # active master thisFontMasterID = thisFont.selectedFontMaster.id # active master listOfSelectedLayers = thisFont.selectedLayers # active layers of selected glyphs def process( thisLayer ): advance = 0.0 for thisComponent in thisLayer.components: thisComponent.position = NSPoint( advance, 0.0 ) advance += thisComponent.component.layers[thisFontMasterID].width thisLayer.width = advance thisFont.disableUpdateInterface() # suppresses UI updates in Font View for thisLayer in listOfSelectedLayers: thisGlyph = thisLayer.parent print "Aligning components in:", thisGlyph.name thisGlyph.beginUndo() # begin undo grouping process( thisLayer ) thisGlyph.endUndo() # end undo grouping thisFont.enableUpdateInterface() # re-enables UI updates in Font View

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null

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1

0a7cd64e2508df91e539f1a6f804bc5eb4b0ea83

12,372

py

Python

audio/audio_server.py

artigianitecnologici/marrtino_apps

b58bf4daa1d06db2f1c8a47be02b29948d41f48d

[ "BSD-4-Clause" ]

null

audio/audio_server.py

artigianitecnologici/marrtino_apps

b58bf4daa1d06db2f1c8a47be02b29948d41f48d

[ "BSD-4-Clause" ]

null

audio/audio_server.py

artigianitecnologici/marrtino_apps

b58bf4daa1d06db2f1c8a47be02b29948d41f48d

[ "BSD-4-Clause" ]

null

# Only PCM 16 bit wav 44100 Hz - Use audacity or sox to convert audio files. # WAV generation # Synth # sox -n --no-show-progress -G --channels 1 -r 44100 -b 16 -t wav bip.wav synth 0.25 sine 800 # sox -n --no-show-progress -G --channels 1 -r 44100 -b 16 -t wav bop.wav synth 0.25 sine 400 # Voices # pico2wave -l "it-IT" -w start.wav "Bene! Si Parte!" # Then convert wav files to to 44100 Hz # Note: some initial sound may not be played. # alsaaudio examples # https://larsimmisch.github.io/pyalsaaudio/libalsaaudio.html import threading import time import socket import sys, os, platform import re import wave import argparse import rospy use_sound_play = False use_alsaaudio = True try: from sound_play.msg import SoundRequest from sound_play.libsoundplay import SoundClient except: print('ROS package sound_play required.') print('Install with: sudo apt-get install ros-kinetic-audio-common libasound2') use_sound_play = False #sys.exit(0) try: import sox except: print('sox required. Install with: pip install --user sox') sys.exit(0) try: import alsaaudio except: print('alsaaudio required. Install with: pip install --user pyalsaaudio') use_alsaaudio = False #sys.exit(0) from asr_server import ASRServer SOUNDS_DIR = "sounds/" # dir with sounds soundfile = None # sound file tts_server = None asr_server = None def TTS_callback(in_data, frame_count, time_info, status): global soundfile if (soundfile==None): return (None, True) else: data = soundfile.readframes(frame_count) return (data, pyaudio.paContinue) class TTSServer(threading.Thread): def __init__(self, port, output_device): global use_alsaaudio, use_sound_play threading.Thread.__init__(self) # Initialize audio player self.streaming = False self.output_device = output_device self.soundhandle = None m = platform.machine() print "Machine type:" , m if (m[0:3]=='arm'): use_sound_play = False if (use_sound_play): os.system('roslaunch sound_play.launch &') time.sleep(5) rospy.init_node('sound_client', disable_signals=True) use_alsaaudio = False elif (use_alsaaudio): self.init_alsaaudio() else: print('Cannot initializa audio interface') # Create a TCP/IP socket self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self.sock.settimeout(3) # Bind the socket to the port server_address = ('', port) self.sock.bind(server_address) self.sock.listen(1) print "TTS Server running on port ", port, " ..." self.dorun = True self.connection = None # Dictionary of sounds self.Sounds = {} self.Sounds['bip'] = wave.open(SOUNDS_DIR+'bip.wav', 'rb') self.idcache = 0 def init_alsaaudio(self): print("Audio devices available") pp = alsaaudio.pcms() if (self.output_device=='sysdefault'): # select proper sysdefault name for l in pp: print(' %s' %l) if (l[0:10]=='sysdefault'): print "choose ",l self.output_device = l # choose default device break print("Audio device used: %s" %self.output_device) self.aa_stream = None retry = 3 while retry>0: try: self.aa_stream = alsaaudio.PCM(alsaaudio.PCM_PLAYBACK, alsaaudio.PCM_NORMAL, self.output_device) retry = 0 except Exception as e: print(e) retry -= 1 time.sleep(2) if self.aa_stream == None: retry = 3 while retry>0: try: self.output_device='default' print("Audio device used: %s" %self.output_device) self.aa_stream = alsaaudio.PCM(alsaaudio.PCM_PLAYBACK, alsaaudio.PCM_NORMAL, self.output_device) retry = 0 except Exception as e: print(e) retry -= 1 time.sleep(2) self.audio_rate = 44100 self.periodsize = self.audio_rate / 8 if self.aa_stream != None: self.aa_stream.setformat(alsaaudio.PCM_FORMAT_S16_LE) self.aa_stream.setchannels(1) self.aa_stream.setrate(self.audio_rate) self.aa_stream.setperiodsize(self.periodsize) def stop(self): self.dorun = False def connect(self): connected = False while (self.dorun and not connected): try: # print 'Waiting for a connection ...' # Wait for a connection self.connection, client_address = self.sock.accept() self.connection.settimeout(3) # timeout when listening (exit with CTRL+C) connected = True print 'TTS Server Connection from ', client_address except: pass #print "Listen again ..." def reply(self,mstr): if (self.connection != None): try: mstr = mstr.encode('utf-8') self.connection.send(mstr+'\n\r') except: print('Connection closed') def setVolume(self,volperc): # volume in percentag [0-100] cmdstr = 'amixer set PCM %d%%' %volperc os.system(cmdstr) def run(self): global asr_server if (use_sound_play and self.soundhandle == None): self.soundhandle = SoundClient() time.sleep(3) self.setVolume(99) # set volume (99% = +3 dB) #print('bip') #self.play('bip') #time.sleep(3) self.say('Hello!', 'en') self.say('Audio server is running.', 'en') time.sleep(3) while (self.dorun): self.connect() try: # Receive the data in small chunks while (self.dorun): try: data = self.connection.recv(320) data = data.strip() except socket.timeout: data = "***" except: data = None if (data!=None and data !="" and data!="***"): if data!="ASR": print 'TTS Received [%s]' % data if (data.startswith('TTS')): lang = 'en-US' # default language strsay = data[4:] if (data[3]=='['): vd = re.split('\[|\]',data) lang = vd[1] strsay = vd[2] self.say(strsay,lang) self.reply('OK') elif (data=="ASR"): #print('asr request') bh = asr_server.get_asr() self.reply(bh) if bh!='': print('ASR sent [%s]' %bh) elif (data.startswith('SOUND')): self.play(data[6:]) # play this sound self.reply('OK') #print 'sending data back to the client' #self.connection.sendall("OK") else: print('Message not understood: %s' %data) self.reply('ERR') elif (data == None or data==""): break finally: print 'TTS Server Connection closed.' # Clean up the connection if (self.connection != None): self.connection.close() self.connection = None self.say('Audio server has been closed.', 'en') time.sleep(2) self.aa_stream = None def say(self, data, lang): print 'Say ',data if (use_sound_play): voice = 'voice_kal_diphone' volume = 1.0 print 'Saying: %s' % data print 'Voice: %s' % voice print 'Volume: %s' % volume self.soundhandle.say(data, voice, volume) rospy.sleep(3) elif (use_alsaaudio): cachefile = 'cache'+str(self.idcache) self.idcache = (self.idcache+1)%10 tmpfile = "/tmp/cache.wav" ofile = "%s%s.wav" %(SOUNDS_DIR, cachefile) cmd = 'rm %s %s' %(tmpfile, ofile) os.system(cmd) if (lang=='en'): lang = 'en-US' elif (len(lang)==2): lang = lang+'-'+lang.upper() time.sleep(0.2) cmd = 'pico2wave -l "%s" -w %s " , %s"' %(lang,tmpfile, data) print cmd os.system(cmd) time.sleep(0.2) # convert samplerate tfm = sox.Transformer() tfm.rate(samplerate=self.audio_rate) tfm.build(tmpfile, ofile) time.sleep(0.2) self.play(cachefile) else: print('Cannot play audio. No infrastructure available.') def play(self, name): if (use_alsaaudio): print('Playing %s ...' %name) soundfile = None i = 0 while (i<3): #((not name in self.Sounds) and (i<3)): try: soundfile = wave.open(SOUNDS_DIR+name+".wav", 'rb') #self.Sounds[name] = soundfile except: print "File %s%s.wav not found." %(SOUNDS_DIR,name) time.sleep(1) i += 1 if (soundfile != None and use_alsaaudio): #(name in self.Sounds): self.playwav_aa(soundfile) print('Play completed.') def playwav_aa(self, soundfile): soundfile.setpos(0) data = soundfile.readframes(self.periodsize) while (len(data)>0): # print('stream data %d' %(len(data))) if self.aa_stream != None: self.aa_stream.write(data) data = soundfile.readframes(self.periodsize) # def playwav_pa(self, sfile): # global soundfile # self.streaming = True # self.stream = self.pa.open(format = 8, #self.pa.get_format_from_width(f.getsampwidth#()), # channels = 1, #f.getnchannels(), # rate = 44100, #f.getframerate(), # output = True, # stream_callback = TTS_callback, # output_device_index = self.output_device) # soundfile = sfile # soundfile.setpos(0) # self.stream.start_stream() # while self.stream.is_active(): # time.sleep(1.0) # self.stream.stop_stream() # self.stream.close() # self.streaming = False if __name__ == "__main__": parser = argparse.ArgumentParser(description='audio_server') parser.add_argument('-ttsport', type=int, help='TTS server port [default: 9001]', default=9001) parser.add_argument('-asrport', type=int, help='ASR server port [default: 9002]', default=9002) parser.add_argument('-device', type=str, help='audio device [default: \'sysdefault\']', default='sysdefault') args = parser.parse_args() tts_server = TTSServer(args.ttsport,args.device) asr_server = ASRServer(args.asrport) tts_server.start() time.sleep(1) asr_server.start() run = True while (run): try: time.sleep(3) #if (not tts_server.streaming): # cmd = 'play -n --no-show-progress -r 44100 -c1 synth 0.1 sine 50 vol 0.01' # keep sound alive # os.system(cmd) except KeyboardInterrupt: print "Exit" run = False tts_server.stop() asr_server.stop() sys.exit(0)

30.93

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4.585777

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0.020144

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0.076739

0

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null

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null

0

1

0

1

0a7f1dd168a64e7f7f19d3324731c892ec275922

1,845

py

Python

patch.py

silverhikari/romtools

2a09290fef85f35502a95c5c2874317029f0439c

[ "Apache-2.0" ]

5

2018-02-02T06:36:56.000Z

2020-12-21T20:17:20.000Z

patch.py

silverhikari/romtools

2a09290fef85f35502a95c5c2874317029f0439c

[ "Apache-2.0" ]

8

2017-10-10T17:50:47.000Z

2021-06-02T00:02:58.000Z

patch.py

silverhikari/romtools

2a09290fef85f35502a95c5c2874317029f0439c

[ "Apache-2.0" ]

2

2017-10-10T20:15:24.000Z

2021-12-17T04:50:16.000Z

""" Utils for creating xdelta patches. """ import logging from subprocess import check_output, CalledProcessError from shutil import copyfile from os import remove, path class PatchChecksumError(Exception): def __init__(self, message, errors): super(PatchChecksumError, self).__init__(message) class Patch: # TODO: Abstract out the need for "edited" by just copying the original # file. def __init__(self, original, filename, edited=None, xdelta_dir='.'): self.original = original self.edited = edited self.filename = filename # Need to have this absolute path for xdelta3 to be found. self.xdelta_path = path.join(xdelta_dir, 'xdelta3') # self.xdelta_path = 'xdelta3' def create(self): if self.edited is None: raise Exception cmd = [ self.xdelta_path, '-f', '-s', self.original, self.edited, self.filename, ] print(cmd) logging.info(cmd) try: check_output(cmd) except CalledProcessError as e: raise Exception(e.output) def apply(self): if not self.edited: copyfile(self.original, self.original + "_temp") self.edited = self.original self.original = self.original + "_temp" cmd = [ self.xdelta_path, '-f', '-d', '-s', self.original, self.filename, self.edited, ] logging.info(cmd) try: check_output(cmd) except CalledProcessError: raise PatchChecksumError('Target file had incorrect checksum', []) finally: if self.original.endswith('_temp'): remove(self.original)

27.132353

78

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1,845

5.352632

0.378947

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0.070796

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0

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0.344173

1,845

67

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0

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false

0

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0.019231

0

null

0

null

0

1

0

1

0a854fbf5fe92dd3c9a7f42e69f796c6cc578917

333

py

Python

bluebottle/tasks/migrations/0012_merge.py

terrameijar/bluebottle

b4f5ba9c4f03e678fdd36091b29240307ea69ffd

[ "BSD-3-Clause" ]

10

2015-05-28T18:26:40.000Z

2021-09-06T10:07:03.000Z

bluebottle/tasks/migrations/0012_merge.py

terrameijar/bluebottle

b4f5ba9c4f03e678fdd36091b29240307ea69ffd

[ "BSD-3-Clause" ]

762

2015-01-15T10:00:59.000Z

2022-03-31T15:35:14.000Z

bluebottle/tasks/migrations/0012_merge.py

terrameijar/bluebottle

b4f5ba9c4f03e678fdd36091b29240307ea69ffd

[ "BSD-3-Clause" ]

9

2015-02-20T13:19:30.000Z

2022-03-08T14:09:17.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.9.6 on 2016-09-27 15:35 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('tasks', '0011_auto_20160919_1508'), ('tasks', '0011_auto_20160920_1019'), ] operations = [ ]

19.588235

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false

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0.222222

0

0.555556

0

null

0

1

0

null

0

1

0a85751a815d71753d3e2aaa3ccbd06b815ba219

5,200

py

Python

bat_train/evaluate.py

bgotthold-usgs/batdetect

0d4a70f1cda9f6104f6f785f0d953f802fddf0f1

[ "BSD-Source-Code" ]

59

2018-03-05T08:58:59.000Z

2022-03-19T17:33:14.000Z

bat_train/evaluate.py

bgotthold-usgs/batdetect

0d4a70f1cda9f6104f6f785f0d953f802fddf0f1

[ "BSD-Source-Code" ]

11

2018-03-16T21:46:51.000Z

2021-12-14T16:07:55.000Z

bat_train/evaluate.py

bgotthold-usgs/batdetect

0d4a70f1cda9f6104f6f785f0d953f802fddf0f1

[ "BSD-Source-Code" ]

24

2018-03-15T14:48:08.000Z

2022-01-09T01:12:51.000Z

import numpy as np from sklearn.metrics import roc_curve, auc def compute_error_auc(op_str, gt, pred, prob): # classification error pred_int = (pred > prob).astype(np.int) class_acc = (pred_int == gt).mean() * 100.0 # ROC - area under curve fpr, tpr, thresholds = roc_curve(gt, pred) roc_auc = auc(fpr, tpr) print op_str, ', class acc = %.3f, ROC AUC = %.3f' % (class_acc, roc_auc) #return class_acc, roc_auc def calc_average_precision(recall, precision): precision[np.isnan(precision)] = 0 recall[np.isnan(recall)] = 0 # pascal'12 way mprec = np.hstack((0, precision, 0)) mrec = np.hstack((0, recall, 1)) for ii in range(mprec.shape[0]-2, -1,-1): mprec[ii] = np.maximum(mprec[ii], mprec[ii+1]) inds = np.where(np.not_equal(mrec[1:], mrec[:-1]))[0]+1 ave_prec = ((mrec[inds] - mrec[inds-1])*mprec[inds]).sum() return ave_prec def remove_end_preds(nms_pos_o, nms_prob_o, gt_pos_o, durations, win_size): # this filters out predictions and gt that are close to the end # this is a bit messy because of the shapes of gt_pos_o nms_pos = [] nms_prob = [] gt_pos = [] for ii in range(len(nms_pos_o)): valid_time = durations[ii] - win_size gt_cur = gt_pos_o[ii] if gt_cur.shape[0] > 0: gt_pos.append(gt_cur[:, 0][gt_cur[:, 0] < valid_time][..., np.newaxis]) else: gt_pos.append(gt_cur) valid_preds = nms_pos_o[ii] < valid_time nms_pos.append(nms_pos_o[ii][valid_preds]) nms_prob.append(nms_prob_o[ii][valid_preds, 0][..., np.newaxis]) return nms_pos, nms_prob, gt_pos def prec_recall_1d(nms_pos_o, nms_prob_o, gt_pos_o, durations, detection_overlap, win_size, remove_eof=True): """ nms_pos, nms_prob, and gt_pos are lists of numpy arrays specifying detection position, detection probability and GT position. Each list entry is a different file. Each entry in nms_pos is an array of length num_entries. For nms_prob and gt_pos its an array of size (num_entries, 1). durations is a array of the length of the number of files with each entry containing that file length in seconds. detection_overlap determines if a prediction is counted as correct or not. win_size is used to ignore predictions and ground truth at the end of an audio file. returns precision: fraction of retrieved instances that are relevant. recall: fraction of relevant instances that are retrieved. """ if remove_eof: # filter out the detections in both ground truth and predictions that are too # close to the end of the file - dont count them during eval nms_pos, nms_prob, gt_pos = remove_end_preds(nms_pos_o, nms_prob_o, gt_pos_o, durations, win_size) else: nms_pos = nms_pos_o nms_prob = nms_prob_o gt_pos = gt_pos_o # loop through each file true_pos = [] # correctly predicts the ground truth false_pos = [] # says there is a detection but isn't for ii in range(len(nms_pos)): num_preds = nms_pos[ii].shape[0] if num_preds > 0: # check to make sure it contains something num_gt = gt_pos[ii].shape[0] # for each set of predictions label them as true positive or false positive (i.e. 1-tp) tp = np.zeros(num_preds) distance_to_gt = np.abs(gt_pos[ii].ravel()-nms_pos[ii].ravel()[:, np.newaxis]) within_overlap = (distance_to_gt <= detection_overlap) # remove duplicate detections - assign to valid detection with highest prob for jj in range(num_gt): inds = np.where(within_overlap[:, jj])[0] # get the indices of all valid predictions if inds.shape[0] > 0: max_prob = np.argmax(nms_prob[ii][inds]) selected_pred = inds[max_prob] within_overlap[selected_pred, :] = False tp[selected_pred] = 1 # set as true positives true_pos.append(tp) false_pos.append(1 - tp) # calc precision and recall - sort confidence in descending order # PASCAL style conf = np.concatenate(nms_prob)[:, 0] num_gt = np.concatenate(gt_pos).shape[0] inds = np.argsort(conf)[::-1] true_pos_cat = np.concatenate(true_pos)[inds].astype(float) false_pos_cat = np.concatenate(false_pos)[inds].astype(float) # i.e. 1-true_pos_cat if (conf == conf[0]).sum() == conf.shape[0]: # all the probability values are the same therefore we will not sweep # the curve and instead will return a single value true_pos_sum = true_pos_cat.sum() false_pos_sum = false_pos_cat.sum() recall = np.asarray([true_pos_sum / float(num_gt)]) precision = np.asarray([(true_pos_sum / (false_pos_sum + true_pos_sum))]) elif inds.shape[0] > 0: # otherwise produce a list of values true_pos_cum = np.cumsum(true_pos_cat) false_pos_cum = np.cumsum(false_pos_cat) recall = true_pos_cum / float(num_gt) precision = (true_pos_cum / (false_pos_cum + true_pos_cum)) return precision, recall

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0.054426

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0.012323

0.250962

5,200

133

110

39.097744

0.808472

0.180962

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0.009756

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null

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1

0a86094f8b6e8a0e12d48278a3971b48591f4ec2

27,399

py

Python

azure-mgmt/tests/test_mgmt_network.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

2

2020-07-29T14:22:17.000Z

2020-11-06T18:47:40.000Z

azure-mgmt/tests/test_mgmt_network.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

1

2016-08-01T07:37:04.000Z

azure-mgmt/tests/test_mgmt_network.py

SUSE/azure-sdk-for-python

324f99d26dd6f4ee9793b9bf1d4d5f928e4b6c2f

[ "MIT" ]

1

2020-12-12T21:04:41.000Z

# coding: utf-8 #------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. #-------------------------------------------------------------------------- import unittest import azure.mgmt.network.models from testutils.common_recordingtestcase import record from tests.mgmt_testcase import HttpStatusCode, AzureMgmtTestCase class MgmtNetworkTest(AzureMgmtTestCase): def setUp(self): super(MgmtNetworkTest, self).setUp() self.network_client = self.create_mgmt_client( azure.mgmt.network.NetworkManagementClient ) if not self.is_playback(): self.create_resource_group() @record def test_network_interface_card(self): vnet_name = self.get_resource_name('pyvnet') subnet_name = self.get_resource_name('pysubnet') nic_name = self.get_resource_name('pynic') # Create VNet async_vnet_creation = self.network_client.virtual_networks.create_or_update( self.group_name, vnet_name, { 'location': self.region, 'address_space': { 'address_prefixes': ['10.0.0.0/16'] } } ) async_vnet_creation.wait() # Create Subnet async_subnet_creation = self.network_client.subnets.create_or_update( self.group_name, vnet_name, subnet_name, {'address_prefix': '10.0.0.0/24'} ) subnet_info = async_subnet_creation.result() # Create NIC async_nic_creation = self.network_client.network_interfaces.create_or_update( self.group_name, nic_name, { 'location': self.region, 'ip_configurations': [{ 'name': 'MyIpConfig', 'subnet': { 'id': subnet_info.id } }] } ) nic_info = async_nic_creation.result() nic_info = self.network_client.network_interfaces.get( self.group_name, nic_info.name ) nics = list(self.network_client.network_interfaces.list( self.group_name )) self.assertEqual(len(nics), 1) nics = list(self.network_client.network_interfaces.list_all()) self.assertGreater(len(nics), 0) async_delete = self.network_client.network_interfaces.delete( self.group_name, nic_info.name ) async_delete.wait() @record def test_load_balancers(self): public_ip_name = self.get_resource_name('pyipname') frontend_ip_name = self.get_resource_name('pyfipname') addr_pool_name = self.get_resource_name('pyapname') probe_name = self.get_resource_name('pyprobename') lb_name = self.get_resource_name('pylbname') front_end_id = ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/frontendIPConfigurations/{}').format( self.settings.SUBSCRIPTION_ID, self.group_name, lb_name, frontend_ip_name ) back_end_id = ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/backendAddressPools/{}').format( self.settings.SUBSCRIPTION_ID, self.group_name, lb_name, addr_pool_name ) probe_id = ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/probes/{}').format( self.settings.SUBSCRIPTION_ID, self.group_name, lb_name, probe_name ) # Create PublicIP public_ip_parameters = { 'location': self.region, 'public_ip_allocation_method': 'static', 'idle_timeout_in_minutes': 4 } async_publicip_creation = self.network_client.public_ip_addresses.create_or_update( self.group_name, public_ip_name, public_ip_parameters ) public_ip_info = async_publicip_creation.result() # Building a FrontEndIpPool frontend_ip_configurations = [{ 'name': frontend_ip_name, 'private_ip_allocation_method': 'Dynamic', 'public_ip_address': { 'id': public_ip_info.id } }] # Building a BackEnd adress pool backend_address_pools = [{ 'name': addr_pool_name }] # Building a HealthProbe probes = [{ 'name': probe_name, 'protocol': 'Http', 'port': 80, 'interval_in_seconds': 15, 'number_of_probes': 4, 'request_path': 'healthprobe.aspx' }] # Building a LoadBalancer rule load_balancing_rules = [{ 'name': 'azure-sample-lb-rule', 'protocol': 'tcp', 'frontend_port': 80, 'backend_port': 80, 'idle_timeout_in_minutes': 4, 'enable_floating_ip': False, 'load_distribution': 'Default', 'frontend_ip_configuration': { 'id': front_end_id }, 'backend_address_pool': { 'id': back_end_id }, 'probe': { 'id': probe_id } }] # Building InboundNATRule1 inbound_nat_rules = [{ 'name': 'azure-sample-netrule1', 'protocol': 'tcp', 'frontend_port': 21, 'backend_port': 22, 'enable_floating_ip': False, 'idle_timeout_in_minutes': 4, 'frontend_ip_configuration': { 'id': front_end_id } }] # Building InboundNATRule2 inbound_nat_rules.append({ 'name': 'azure-sample-netrule2', 'protocol': 'tcp', 'frontend_port': 23, 'backend_port': 22, 'enable_floating_ip': False, 'idle_timeout_in_minutes': 4, 'frontend_ip_configuration': { 'id': front_end_id } }) # Creating Load Balancer lb_async_creation = self.network_client.load_balancers.create_or_update( self.group_name, lb_name, { 'location': self.region, 'frontend_ip_configurations': frontend_ip_configurations, 'backend_address_pools': backend_address_pools, 'probes': probes, 'load_balancing_rules': load_balancing_rules, 'inbound_nat_rules' :inbound_nat_rules } ) lb_info = lb_async_creation.result() # Get it lb_info = self.network_client.load_balancers.get( self.group_name, lb_name ) # List all lbs = self.network_client.load_balancers.list_all() lbs = list(lbs) self.assertGreater(len(lbs), 0) # List RG lbs = self.network_client.load_balancers.list(self.group_name) lbs = list(lbs) self.assertGreater(len(lbs), 0) # Delete async_lb_delete = self.network_client.load_balancers.delete( self.group_name, lb_name ) async_lb_delete.wait() @record def test_public_ip_addresses(self): public_ip_name = self.get_resource_name('pyipname') params_create = azure.mgmt.network.models.PublicIPAddress( location=self.region, public_ip_allocation_method=azure.mgmt.network.models.IPAllocationMethod.dynamic, tags={ 'key': 'value', }, ) result_create = self.network_client.public_ip_addresses.create_or_update( self.group_name, public_ip_name, params_create, ) result_create.wait() # AzureOperationPoller #self.assertEqual(result_create.status_code, HttpStatusCode.OK) result_get = self.network_client.public_ip_addresses.get( self.group_name, public_ip_name, ) #self.assertEqual(result_get.status_code, HttpStatusCode.OK) self.assertEqual(result_get.location, self.region) self.assertEqual(result_get.tags['key'], 'value') result_list = self.network_client.public_ip_addresses.list(self.group_name) #self.assertEqual(result_list.status_code, HttpStatusCode.OK) result_list = list(result_list) self.assertEqual(len(result_list), 1) result_list_all = self.network_client.public_ip_addresses.list_all() #self.assertEqual(result_list_all.status_code, HttpStatusCode.OK) result_list_all = list(result_list_all) self.assertGreater(len(result_list_all), 0) result_delete = self.network_client.public_ip_addresses.delete( self.group_name, public_ip_name, ) result_delete.wait() # AzureOperationPoller #self.assertEqual(result_delete.status_code, HttpStatusCode.OK) result_list = self.network_client.public_ip_addresses.list(self.group_name) #self.assertEqual(result_list.status_code, HttpStatusCode.OK) result_list = list(result_list) self.assertEqual(len(result_list), 0) @record def test_virtual_networks(self): network_name = self.get_resource_name('pyvnet') subnet1_name = self.get_resource_name('pyvnetsubnetone') subnet2_name = self.get_resource_name('pyvnetsubnettwo') params_create = azure.mgmt.network.models.VirtualNetwork( location=self.region, address_space=azure.mgmt.network.models.AddressSpace( address_prefixes=[ '10.0.0.0/16', ], ), dhcp_options=azure.mgmt.network.models.DhcpOptions( dns_servers=[ '10.1.1.1', '10.1.2.4', ], ), subnets=[ azure.mgmt.network.models.Subnet( name=subnet1_name, address_prefix='10.0.1.0/24', ), azure.mgmt.network.models.Subnet( name=subnet2_name, address_prefix='10.0.2.0/24', ), ], ) result_create = self.network_client.virtual_networks.create_or_update( self.group_name, network_name, params_create, ) vnet = result_create.result() vnet = self.network_client.virtual_networks.get( self.group_name, vnet.name, ) ip_availability = self.network_client.virtual_networks.check_ip_address_availability( self.group_name, vnet.name, '10.0.1.35' # Should be available since new VNet sor Subnet 1 ) self.assertTrue(ip_availability.available) result_list = list(self.network_client.virtual_networks.list( self.group_name, )) self.assertEqual(len(result_list), 1) result_list_all = list(self.network_client.virtual_networks.list_all()) async_delete = self.network_client.virtual_networks.delete( self.group_name, network_name, ) async_delete.wait() @record def test_dns_availability(self): result_check = self.network_client.check_dns_name_availability( self.region, 'pydomain', ) #self.assertEqual(result_check.status_code, HttpStatusCode.OK) self.assertTrue(result_check) @record def test_subnets(self): network_name = self.get_resource_name('pysubnet') subnet1_name = self.get_resource_name('pysubnetone') subnet2_name = self.get_resource_name('pysubnettwo') params_create = azure.mgmt.network.models.VirtualNetwork( location=self.region, address_space=azure.mgmt.network.models.AddressSpace( address_prefixes=[ '10.0.0.0/16', ], ), dhcp_options=azure.mgmt.network.models.DhcpOptions( dns_servers=[ '10.1.1.1', '10.1.2.4', ], ), subnets=[ azure.mgmt.network.models.Subnet( name=subnet1_name, address_prefix='10.0.1.0/24', ), ], ) result_create = self.network_client.virtual_networks.create_or_update( self.group_name, network_name, params_create, ) result_create.wait() # AzureOperationPoller params_create = azure.mgmt.network.models.Subnet( name=subnet2_name, address_prefix='10.0.2.0/24', ) result_create = self.network_client.subnets.create_or_update( self.group_name, network_name, subnet2_name, params_create, ) result_create.wait() # AzureOperationPoller result_get = self.network_client.virtual_networks.get( self.group_name, network_name, ) self.assertEqual(len(result_get.subnets), 2) result_get = self.network_client.subnets.get( self.group_name, network_name, subnet2_name, ) result_list = self.network_client.subnets.list( self.group_name, network_name, ) subnets = list(result_list) result_delete = self.network_client.subnets.delete( self.group_name, network_name, subnet2_name, ) result_delete.wait() @record def test_network_security_groups(self): security_group_name = self.get_resource_name('pysecgroup') security_rule_name = self.get_resource_name('pysecgrouprule') params_create = azure.mgmt.network.models.NetworkSecurityGroup( location=self.region, security_rules=[ azure.mgmt.network.models.SecurityRule( name=security_rule_name, access=azure.mgmt.network.models.SecurityRuleAccess.allow, description='Test security rule', destination_address_prefix='*', destination_port_range='123-3500', direction=azure.mgmt.network.models.SecurityRuleDirection.inbound, priority=500, protocol=azure.mgmt.network.models.SecurityRuleProtocol.tcp, source_address_prefix='*', source_port_range='655', ), ], ) result_create = self.network_client.network_security_groups.create_or_update( self.group_name, security_group_name, params_create, ) result_create.wait() # AzureOperationPoller result_get = self.network_client.network_security_groups.get( self.group_name, security_group_name, ) result_list = list(self.network_client.network_security_groups.list( self.group_name, )) self.assertEqual(len(result_list), 1) result_list_all = list(self.network_client.network_security_groups.list_all()) # Security Rules new_security_rule_name = self.get_resource_name('pynewrule') async_security_rule = self.network_client.security_rules.create_or_update( self.group_name, security_group_name, new_security_rule_name, { 'access':azure.mgmt.network.models.SecurityRuleAccess.allow, 'description':'New Test security rule', 'destination_address_prefix':'*', 'destination_port_range':'123-3500', 'direction':azure.mgmt.network.models.SecurityRuleDirection.outbound, 'priority':400, 'protocol':azure.mgmt.network.models.SecurityRuleProtocol.tcp, 'source_address_prefix':'*', 'source_port_range':'655', } ) security_rule = async_security_rule.result() security_rule = self.network_client.security_rules.get( self.group_name, security_group_name, security_rule.name ) self.assertEqual(security_rule.name, new_security_rule_name) new_security_rules = list(self.network_client.security_rules.list( self.group_name, security_group_name )) self.assertEqual(len(new_security_rules), 2) result_delete = self.network_client.security_rules.delete( self.group_name, security_group_name, new_security_rule_name ) result_delete.wait() # Delete NSG result_delete = self.network_client.network_security_groups.delete( self.group_name, security_group_name, ) result_delete.wait() @record def test_routes(self): route_table_name = self.get_resource_name('pyroutetable') route_name = self.get_resource_name('pyroute') async_route_table = self.network_client.route_tables.create_or_update( self.group_name, route_table_name, {'location': self.region} ) route_table = async_route_table.result() route_table = self.network_client.route_tables.get( self.group_name, route_table.name ) self.assertEqual(route_table.name, route_table_name) route_tables = list(self.network_client.route_tables.list( self.group_name )) self.assertEqual(len(route_tables), 1) route_tables = list(self.network_client.route_tables.list_all()) self.assertGreater(len(route_tables), 0) async_route = self.network_client.routes.create_or_update( self.group_name, route_table.name, route_name, { 'address_prefix': '10.1.0.0/16', 'next_hop_type': 'None' } ) route = async_route.result() route = self.network_client.routes.get( self.group_name, route_table.name, route.name ) self.assertEqual(route.name, route_name) routes = list(self.network_client.routes.list( self.group_name, route_table.name )) self.assertEqual(len(routes), 1) async_route_delete = self.network_client.routes.delete( self.group_name, route_table.name, route.name ) async_route_delete.wait() async_route_table_delete = self.network_client.route_tables.delete( self.group_name, route_table_name ) async_route_table_delete.wait() @record def test_usages(self): usages = list(self.network_client.usages.list(self.region)) self.assertGreater(len(usages), 1) self.assertTrue(all(hasattr(u, 'name') for u in usages)) @record def test_express_route_service_providers(self): ersp = list(self.network_client.express_route_service_providers.list()) self.assertGreater(len(ersp), 0) self.assertTrue(all(hasattr(u, 'bandwidths_offered') for u in ersp)) @record def test_express_route_circuit(self): express_route_name = self.get_resource_name('pyexpressroute') async_express_route = self.network_client.express_route_circuits.create_or_update( self.group_name, express_route_name, { "location": self.region, "sku": { "name": "Standard_MeteredData", "tier": "Standard", "family": "MeteredData" }, "service_provider_properties": { "service_provider_name": "Comcast", "peering_location": "Chicago", "bandwidth_in_mbps": 100 } } ) express_route = async_express_route.result() express_route = self.network_client.express_route_circuits.get( self.group_name, express_route_name ) routes = list(self.network_client.express_route_circuits.list( self.group_name )) self.assertEqual(len(routes), 1) routes = list(self.network_client.express_route_circuits.list_all()) self.assertGreater(len(routes), 0) stats = self.network_client.express_route_circuits.get_stats( self.group_name, express_route_name ) self.assertIsNotNone(stats) async_peering = self.network_client.express_route_circuit_peerings.create_or_update( self.group_name, express_route_name, 'AzurePublicPeering', { "peering_type": "AzurePublicPeering", "peer_asn": 100, "primary_peer_address_prefix": "192.168.1.0/30", "secondary_peer_address_prefix": "192.168.2.0/30", "vlan_id": 200, } ) peering = async_peering.result() peering = self.network_client.express_route_circuit_peerings.get( self.group_name, express_route_name, 'AzurePublicPeering' ) peerings = list(self.network_client.express_route_circuit_peerings.list( self.group_name, express_route_name )) self.assertEqual(len(peerings), 1) stats = self.network_client.express_route_circuits.get_peering_stats( self.group_name, express_route_name, 'AzurePublicPeering' ) self.assertIsNotNone(stats) auth_name = self.get_resource_name('pyauth') async_auth = self.network_client.express_route_circuit_authorizations.create_or_update( self.group_name, express_route_name, auth_name, {} ) auth = async_auth.result() auth = self.network_client.express_route_circuit_authorizations.get( self.group_name, express_route_name, auth_name ) auths = list(self.network_client.express_route_circuit_authorizations.list( self.group_name, express_route_name )) self.assertEqual(len(auths), 1) async_auth = self.network_client.express_route_circuit_authorizations.delete( self.group_name, express_route_name, auth_name ) async_auth.wait() async_peering = self.network_client.express_route_circuit_peerings.delete( self.group_name, express_route_name, 'AzurePublicPeering' ) async_peering.wait() async_express_route = self.network_client.express_route_circuits.delete( self.group_name, express_route_name ) async_express_route.wait() @record def test_virtual_network_gateway_operations(self): # https://docs.microsoft.com/en-us/azure/vpn-gateway/vpn-gateway-howto-site-to-site-resource-manager-portal vnet_name = self.get_resource_name('pyvirtnet') fe_name = self.get_resource_name('pysubnetfe') be_name = self.get_resource_name('pysubnetbe') gateway_name = self.get_resource_name('pysubnetga') # Create VNet async_vnet_creation = self.network_client.virtual_networks.create_or_update( self.group_name, vnet_name, { 'location': self.region, 'address_space': { 'address_prefixes': [ '10.11.0.0/16', '10.12.0.0/16' ] } } ) async_vnet_creation.wait() # Create Front End Subnet async_subnet_creation = self.network_client.subnets.create_or_update( self.group_name, vnet_name, fe_name, {'address_prefix': '10.11.0.0/24'} ) fe_subnet_info = async_subnet_creation.result() # Create Back End Subnet async_subnet_creation = self.network_client.subnets.create_or_update( self.group_name, vnet_name, be_name, {'address_prefix': '10.12.0.0/24'} ) be_subnet_info = async_subnet_creation.result() # Create Gateway Subnet async_subnet_creation = self.network_client.subnets.create_or_update( self.group_name, vnet_name, 'GatewaySubnet', {'address_prefix': '10.12.255.0/27'} ) gateway_subnet_info = async_subnet_creation.result() # Public IP Address public_ip_name = self.get_resource_name('pyipname') params_create = azure.mgmt.network.models.PublicIPAddress( location=self.region, public_ip_allocation_method=azure.mgmt.network.models.IPAllocationMethod.dynamic, tags={ 'key': 'value', }, ) result_create = self.network_client.public_ip_addresses.create_or_update( self.group_name, public_ip_name, params_create, ) public_ip_address = result_create.result() # Gateway itself vng_name = self.get_resource_name('pyvng') gw_params = { 'location': self.region, 'gateway_type': 'VPN', 'vpn_type': 'RouteBased', 'enable_bgp': False, 'sku': { 'tier': 'Standard', 'capacity': 2, 'name': 'Standard'}, 'ip_configurations':[{ 'name': 'default', 'private_ip_allocation_method': 'Dynamic', 'subnet': { 'id': gateway_subnet_info.id }, 'public_ip_address': { 'id': public_ip_address.id } }], } async_create = self.network_client.virtual_network_gateways.create_or_update( self.group_name, vng_name, gw_params ) vng = async_create.result() self.assertEquals(vng.name, vng_name) #------------------------------------------------------------------------------ if __name__ == '__main__': unittest.main()

33.454212

115

0.567904

2,714

27,399

5.389831

0.119381

0.057151

0.085999

0.036369

0.688474

0.577864

0.510938

0.401217

0.331966

0.267774

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0.013431

0.334209

27,399

818

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null

0

null

0

null

0

1

0

1

0a8741dde6ef103d06812289a7da5d5ee4748c1d

2,427

py

Python

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

[ "MIT" ]

null

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

[ "MIT" ]

null

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

[ "MIT" ]

null

from pathlib import Path import pickle import tkinter as tk import tkinter.filedialog def open_dialog(**opt): """Parameters ---------- Options will be passed to `tkinter.filedialog.askopenfilename`. See also tkinter's document. Followings are example of frequently used options. - filetypes=[(label, ext), ...] - label: str - ext: str, semicolon separated extentions - initialdir: str, default Path.cwd() - multiple: bool, default False Returns -------- filename, str """ root = tk.Tk() root.withdraw() root.wm_attributes("-topmost", True) opt_default = dict(initialdir=Path.cwd()) _opt = dict(opt_default, **opt) return tk.filedialog.askopenfilename(**_opt) def saveas_dialog(**opt): """Parameters ---------- Options will be passed to `tkinter.filedialog.asksaveasfilename`. See also tkinter's document. Followings are example of frequently used options. - filetypes=[(label, ext), ...] - label: str - ext: str, semicolon separated extentions - initialdir: str, default Path.cwd() - initialfile: str, default isn't set Returns -------- filename, str """ root = tk.Tk() root.withdraw() root.wm_attributes("-topmost", True) opt_default = dict(initialdir=Path.cwd()) _opt = dict(opt_default, **opt) return tk.filedialog.asksaveasfilename(**_opt) def load_pickle_with_dialog(mode='rb', **opt): """Load a pickled object with a filename assigned by tkinter's open dialog. kwargs will be passed to saveas_dialog. """ opt_default = dict(filetypes=[('pickled data', '*.pkl'), ('all', '*')]) _opt = dict(opt_default, **opt) fn = open_dialog(**_opt) if fn == '': # canceled return None with Path(fn).open(mode) as f: data = pickle.load(f) return data def dump_pickle_with_dialog(obj, mode='wb', **opt): """Pickle an object with a filename assigned by tkinter's saveas dialog. kwargs will be passed to saveas_dialog. Returns -------- filename: str """ opt_default = dict(filetypes=[('pickled data', '*.pkl'), ('all', '*')]) _opt = dict(opt_default, **opt) fn = saveas_dialog(**_opt) if fn == '': # canceled return '' # note: 上書き確認はtkinterがやってくれるのでここではチェックしない with Path(fn).open(mode) as f: pickle.dump(obj, f) return fn

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null

0

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null

0

1

0a89d9e3455e77e62d24b044c32fc90cbc464fc1

368

py

Python

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

[ "MIT" ]

7

2019-08-04T20:37:55.000Z

2020-03-05T08:36:10.000Z

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

[ "MIT" ]

1

2019-10-21T05:43:28.000Z

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

[ "MIT" ]

null

import setuptools setuptools.setup( name = 'sili-canvas', version = '0.0.1', license = 'MIT', url = 'https://github.com/SilicalNZ/canvas', description = 'A series of easy to use classes to perform complex 2D array transformations', long_description = '', author = 'SilicalNZ', packages = ['canvas', 'canvas.common', 'canvas.tools'] )

26.285714

96

0.649457

43

368

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0.206522

368

13

97

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0.459239

0

1

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true

0

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0.090909

0

null

0

null

0

1

0

1

0a8b4fc2b42148f674fa2146ee9800ea9e96f927

2,614

py

Python

surname_rnn/surname/containers.py

sudarshan85/nlpbook

41e59d706fb31f5185a0133789639ccffbddb41f

[ "Apache-2.0" ]

null

surname_rnn/surname/containers.py

sudarshan85/nlpbook

41e59d706fb31f5185a0133789639ccffbddb41f

[ "Apache-2.0" ]

null

surname_rnn/surname/containers.py

sudarshan85/nlpbook

41e59d706fb31f5185a0133789639ccffbddb41f

[ "Apache-2.0" ]

null

#!/usr/bin/env python import pandas as pd from pathlib import Path from torch.utils.data import DataLoader class ModelContainer(object): def __init__(self, model, optimizer, loss_fn, scheduler=None): self.model = model self.optimizer = optimizer self.loss_fn = loss_fn self.scheduler = scheduler class DataContainer(object): def __init__(self, df_with_split: pd.DataFrame, dataset_class, vectorizer_file: Path, batch_size: int, with_test=True, is_load: bool=True) -> None: self.train_df = df_with_split.loc[df_with_split['split'] == 'train'] self.val_df = df_with_split.loc[df_with_split['split'] == 'val'] self._bs = batch_size self.with_test = with_test self.is_load = is_load self._lengths = {'train_size': len(self.train_df), 'val_size': len(self.val_df)} self._n_batches = [self._lengths['train_size'] // self._bs, self._lengths['val_size'] // self._bs] if not self.is_load: print("Creating and saving vectorizer") train_ds = dataset_class.load_data_and_create_vectorizer(self.train_df) train_ds.save_vectorizer(vectorizer_file) self.train_ds = dataset_class.load_data_and_vectorizer_from_file(self.train_df, vectorizer_file) self.vectorizer = self.train_ds.vectorizer self.surname_vocab = self.vectorizer.surname_vocab self.nationality_vocab = self.vectorizer.nationality_vocab self.train_dl = DataLoader(self.train_ds, self._bs, shuffle=True, drop_last=True) self.val_ds = dataset_class.load_data_and_vectorizer(self.val_df, self.vectorizer) self.val_dl = DataLoader(self.val_ds, self._bs, shuffle=True, drop_last=True) if self.with_test: self.test_df = df_with_split.loc[df_with_split['split'] == 'test'] self._lengths['test_size'] = len(self.test_df) self._n_batches.append(self._lengths['test_size'] // self._bs) self.test_ds = dataset_class.load_data_and_vectorizer(self.test_df, self.vectorizer) self.test_dl = DataLoader(self.test_ds, self._bs, shuffle=True, drop_last=True) def get_loaders(self): return self.train_dl, self.val_dl, self.test_dl @property def train_batches(self): return self._n_batches[0] @property def val_batches(self): return self._n_batches[1] @property def test_batches(self): if not self.with_test: raise NameError("No test dataset was provided") return self._n_batches[2] @property def vocab_size(self): return len(self.surname_vocab) @property def n_classes(self): return len(self.nationality_vocab) @property def sizes(self): return self._lengths

33.512821

100

0.729533

389

2,614

4.570694

0.210797

0.045557

0.043307

0.040495

0.22216

0.189539

0.150169

0.053993

0

0.001366

0.159908

2,614

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33.948052

0.808288

0.007651

0

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0

0.053627

0

1

0.152542

false

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0.101695

0.355932

0.016949

0

null

0

null

0

1

0

1

0a95cfa206f2acf8636e2a3399ef4362d43aa15a

3,092

py

Python

pybm/commands/compare.py

nicholasjng/pybm

13e256ca5c2c8239f9d611b9849dab92f70b2834

[ "Apache-2.0" ]

12

2021-10-10T20:00:07.000Z

2022-02-09T11:29:07.000Z

pybm/commands/compare.py

nicholasjng/pybm

13e256ca5c2c8239f9d611b9849dab92f70b2834

[ "Apache-2.0" ]

20

2021-10-13T09:37:20.000Z

2022-03-07T15:14:00.000Z

pybm/commands/compare.py

nicholasjng/pybm

13e256ca5c2c8239f9d611b9849dab92f70b2834

[ "Apache-2.0" ]

1

2022-02-09T10:09:41.000Z

from typing import List from pybm import PybmConfig from pybm.command import CLICommand from pybm.config import get_reporter_class from pybm.exceptions import PybmError from pybm.reporters import BaseReporter from pybm.status_codes import ERROR, SUCCESS from pybm.util.path import get_subdirs class CompareCommand(CLICommand): """ Report benchmark results from specified sources. """ usage = "pybm compare <run> <anchor-ref> <compare-refs> [<options>]\n" def __init__(self): super(CompareCommand, self).__init__(name="compare") self.config = PybmConfig.load() def add_arguments(self): self.parser.add_argument( "run", type=str, metavar="<run>", help="Benchmark run to report results for. " "To report the preceding run, use the " '"latest" keyword. To report results ' "of the n-th preceding run " "(i.e., n runs ago), " 'use the "latest^{n}" syntax.', ) self.parser.add_argument( "refs", nargs="+", metavar="<refs>", help="Benchmarked refs to compare. The first " "given ref will be treated as the " "anchor ref, relative to which all " "differences are reported. An error is " "raised if any of the given " "refs are not present in the run.", ) reporter: BaseReporter = get_reporter_class(config=self.config) reporter_args = reporter.additional_arguments() if reporter_args: reporter_name = self.config.get_value("reporter.name") reporter_group_desc = ( f"Additional options from configured reporter class {reporter_name!r}" ) reporter_group = self.parser.add_argument_group(reporter_group_desc) # add builder-specific options into the group for arg in reporter_args: reporter_group.add_argument(arg.pop("flags"), **arg) def run(self, args: List[str]) -> int: if not args: self.parser.print_help() return ERROR self.add_arguments() options = self.parser.parse_args(args) reporter: BaseReporter = get_reporter_class(config=self.config) # TODO: Parse run to fit schema run = options.run refs: List[str] = options.refs result_dir = reporter.result_dir # TODO: Make this dynamic to support other run identifiers result = sorted(get_subdirs(result_dir))[-1] result_path = result_dir / result if result_path.exists(): reporter.compare( *refs, result=result, target_filter=options.target_filter, benchmark_filter=options.benchmark_filter, context_filter=options.context_filter, ) else: raise PybmError( f"No benchmark results found for the requested run {run!r}." ) return SUCCESS

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86

0.597025

351

3,092

5.11396

0.361823

0.031198

0.026741

0.035097

0.057939

0

0.000474

0.317917

3,092

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0

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0

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0

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0

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0.042254

false

0

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0

0.211268

0.014085

0

null

0

null

0

1

0

1

0a9605df608e45d997ef3a777c5490c843c12343

1,728

py

Python

dddm/recoil_rates/halo.py

JoranAngevaare/dddm

3461e37984bac4d850beafecc9d1881b84fb226c

[ "MIT" ]

null

dddm/recoil_rates/halo.py

JoranAngevaare/dddm

3461e37984bac4d850beafecc9d1881b84fb226c

[ "MIT" ]

85

2021-09-20T12:08:53.000Z

2022-03-30T12:48:06.000Z

dddm/recoil_rates/halo.py

JoranAngevaare/dddm

3461e37984bac4d850beafecc9d1881b84fb226c

[ "MIT" ]

null

""" For a given detector get a WIMPrate for a given detector (not taking into account any detector effects """ import numericalunits as nu import wimprates as wr import dddm export, __all__ = dddm.exporter() @export class SHM: """ class used to pass a halo model to the rate computation must contain: :param v_esc -- escape velocity (multiplied by units) :param rho_dm -- density in mass/volume of dark matter at the Earth (multiplied by units) The standard halo model also allows variation of v_0 :param v_0 -- v0 of the velocity distribution (multiplied by units) :function velocity_dist -- function taking v,t giving normalised velocity distribution in earth rest-frame. """ def __init__(self, v_0=None, v_esc=None, rho_dm=None): self.v_0 = 230 * nu.km / nu.s if v_0 is None else v_0 self.v_esc = 544 * nu.km / nu.s if v_esc is None else v_esc self.rho_dm = (0.3 * nu.GeV / nu.c0 ** 2 / nu.cm ** 3 if rho_dm is None else rho_dm) def __str__(self): # Standard Halo Model (shm) return 'shm' def velocity_dist(self, v, t): """ Get the velocity distribution in units of per velocity, :param v: v is in units of velocity :return: observed velocity distribution at earth """ return wr.observed_speed_dist(v, t, self.v_0, self.v_esc) def parameter_dict(self): """Return a dict of readable parameters of the current settings""" return dict( v_0=self.v_0 / (nu.km / nu.s), v_esc=self.v_esc / (nu.km / nu.s), rho_dm=self.rho_dm / (nu.GeV / nu.c0 ** 2 / nu.cm ** 3), )

33.882353

97

0.618634

271

1,728

3.793358

0.346863

0.01751

0.023346

0.027237

0.068093

0.048638

0.029183

0

0.019608

0.291667

1,728

50

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34.56

0.820261

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0

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0

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0.190476

false

0

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0.047619

0.52381

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null

0

null

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