id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
167,465 | import pandas as pd
import plotly.graph_objects as go
from greykite.common.constants import ACTUAL_COL
from greykite.common.constants import ANOMALY_COL
from greykite.common.constants import END_TIME_COL
from greykite.common.constants import PREDICTED_ANOMALY_COL
from greykite.common.constants import PREDICTED_COL
from... | Utility function which overlayes the predicted anomalies or anomalies on the forecast vs actual plot. The function calls the internal function `~greykite.common.viz.timeseries_plotting.plot_forecast_vs_actual` and then adds markers on top. Parameters ---------- df : `pandas.DataFrame` The input dataframe. time_col : `s... |
167,466 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Plots multiple lines against the same x-axis values. The lines can partially share the x-axis values. See parameter descriptions for a running example. Parameters ---------- df : `pandas.DataFrame` Data frame with ``x_col`` and columns named by the keys in ``y_col_style_dict``, ``grouping_x_col``, ``grouping_y_col_styl... |
167,467 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Simple plot of univariate timeseries. Parameters ---------- df : `pandas.DataFrame` Data frame with ``x_col`` and ``y_col`` x_col: `str` x-axis column name, usually the time column y_col: `str` y-axis column name, the value the plot xlabel : `str` or None, default None x-axis label ylabel : `str` or None, default None ... |
167,468 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Extracts a column to group by from ``df``. Exactly one of ``groupby_time_feature``, ``groupby_sliding_window_size``, `groupby_custom_column` must be provided. Parameters ---------- df : 'pandas.DataFrame` Contains the univariate time series / forecast time_col : `str` The name of the time column of the univariate time ... |
167,469 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Groups ``df`` and evaluates a function on each group. The function takes a `pandas.DataFrame` and returns a scalar. Parameters ---------- df : `pandas.DataFrame` Input data. For example, univariate time series, or forecast result. Contains ``groupby_col`` and columns to apply ``grouping_func`` on. groupby_col : `str` C... |
167,470 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Flexible aggregation. Generates additional columns for evaluation via ``map_func_dict``, groups by ``groupby_col``, then aggregates according to ``agg_kwargs``. This function calls `pandas.DataFrame.apply` and `pandas.core.groupby.DataFrameGroupBy.agg` internally. Parameters ---------- df : `pandas.DataFrame` DataFrame... |
167,471 | import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import DEFAULT_PLOTLY_COLORS
from plotly.subplots import make_subplots
from greykite.common import constants as cst
from greykite.common.features.timeseries_features import build_time_features_df
from greykite.co... | Generic function to plot a dual y-axis plot. The x-axis is specified by ``x_col``. The left and right y-axes are specified by ``y_left_col`` and ``y_right_col`` respectively. If ``grouping_col`` is specified, then multiple pairs of curves are drawn, one for each level in ``grouping_col``. Parameters ---------- df : `pa... |
167,472 | import warnings
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `gen_moving_timeseries_forecast` function. Write a Python function `def gen_moving_timeseries_forecast( df, time_col, value_col, train_forecast_func, ... | Applies a forecast function (`train_forecast_func`) to many derived timeseries from `df` which are moving windows of `df`. For each derived series a model is trained and forecast is generated. It returns a `compare_df` to compare actuals and forecasts. Parameters ---------- df : `pandas.DataFrame` A data frame which in... |
167,473 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Returns the unique dictionaries in the input list, preserving the original order. Replaces ``unique_elements_in_list`` because `dict` is not hashable. Parameters ---------- array: `List` [`dict`] List of dictionaries. Returns ------- unique_array : `List` [`dict`] Unique dictionaries in `array`, preserving the order of... |
167,474 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
de... | Calls `~greykite.common.utils.python_utils.dictionary_values_to_lists` on the provided dictionary or on each item in a list of dictionaries. ``dictionary_values_to_lists`` returns a copy whose values are either lists, distributions with a ``rvs`` method, or None. Parameters ---------- hyperparameter_dicts : `dict` [`st... |
167,475 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Flattens an array by removing 1 level of nesting. Parameters ---------- array : `list` [`list`] List of lists. Returns ------- flat_arr : `list` Removes one level of nesting from the array. [[4], [3, 2], [1, [0]]] becomes [4, 3, 2, 1, [0]]. |
167,476 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Orders columns according to ``order_dict``. Can be used to order columns according to hierarchical constraints. Consider the tree where a parent is the sum of its children. Let a node's label be its BFS traversal order, with the root as 0. Use ``order_dict`` to map column names to these node labels, to get the datafram... |
167,477 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Returns a function that applies ``row_func`` to the selected ``cols``. Helper function for `~greykite.framework.output.univariate_forecast.UnivariateForecast.autocomplete_map_func_dict`. Parameters ---------- row_func : callable A function. cols : `list` [`str` or `int`] Names of the columns (or dictionary keys, list i... |
167,478 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Can be used to set the default value in a dataclass to a mutable value. Provides a factory function that returns a copy of the provided argument. Parameters ---------- mutable_default_value : Any The default value to use for the field. Returns ------- field : `dataclasses.field` Set the default value to this value. Exa... |
167,479 | import copy
import dataclasses
import functools
import math
import re
import warnings
from dataclasses import field
from typing import List
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pandas.testing import assert_index_equal
from pandas.testing import assert_series_equal
T... | Returns a decorator to ignore all warnings in the specified category. Parameters ---------- category : class Any warning that is a subclass of this category is ignored. Returns ------- decorator_ignore : function A decorator that ignores all warnings in the category. |
167,480 | import math
from datetime import timedelta
from greykite.common.constants import TIME_COL
from greykite.common.constants import VALUE_COL
from greykite.common.enums import SimpleTimeFrequencyEnum
from greykite.common.enums import TimeEnum
from greykite.common.features.timeseries_features import get_default_origin_for_t... | Returns the number of training points in `df`, the start year, and prediction end year Parameters ---------- df : `pandas.DataFrame` with columns [``time_col``, ``value_col``] Univariate timeseries data to forecast time_col : `str`, default ``TIME_COL`` in constants.py Name of timestamp column in df value_col : `str`, ... |
167,481 | import dataclasses
import functools
from typing import Dict
from typing import Optional
import numpy as np
import pandas as pd
from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
from greykite.common.constants import TimeFeaturesEnum
from greykite.common.features.timeseries_lags import ... | Gets extra predictor columns from the model components for :func:`~greykite.framework.templates.silverkite_templates.silverkite_template`. Parameters ---------- model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam` or None, default None Configuration of model growth, sea... |
167,482 | import dataclasses
import functools
from typing import Dict
from typing import Optional
import numpy as np
import pandas as pd
from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
from greykite.common.constants import TimeFeaturesEnum
from greykite.common.features.timeseries_lags import ... | Sets default values for ``model_components``. Parameters ---------- model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam` or None, default None Configuration of model growth, seasonality, events, etc. See :func:`~greykite.framework.templates.silverkite_templates.silverki... |
167,483 | import inspect
import os
import shutil
from collections import OrderedDict
import dill
from patsy.design_info import DesignInfo
The provided code snippet includes necessary dependencies for implementing the `recursive_rm_dir` function. Write a Python function `def recursive_rm_dir(dir_name)` to solve the following pro... | Recursively removes dirs and files in ``dir_name``. This functions removes everything in ``dir_name`` that it has permission to remove. This function is intended to remove the dumped directory. Do not use this function to remove other directories, unless you are sure to remove everything in the directory. Parameters --... |
167,484 | import inspect
import os
import shutil
from collections import OrderedDict
import dill
from patsy.design_info import DesignInfo
The provided code snippet includes necessary dependencies for implementing the `dump_obj` function. Write a Python function `def dump_obj( obj, dir_name, obj_name="obj... | Uses DFS to recursively dump an object to pickle files. Originally intended for dumping the `~greykite.framework.pipeline.pipeline.ForecastResult` instance, but could potentially used for other objects. For each object, if it's picklable, a file with {object_name}.pkl will be generated, otherwise, depending on its type... |
167,485 | import inspect
import os
import shutil
from collections import OrderedDict
import dill
from patsy.design_info import DesignInfo
The provided code snippet includes necessary dependencies for implementing the `load_obj` function. Write a Python function `def load_obj( dir_name, obj=None, load_des... | Loads the pickled files which are pickled by `~greykite.framework.templates.pickle_utils.dump_obj`. Originally intended for loading the `~greykite.framework.pipeline.pipeline.ForecastResult` instance, but could potentially used for other objects. Parameters ---------- dir_name : `str` The directory that stores the pick... |
167,486 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,487 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,488 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,489 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | Parses list of dictionaries, applying `f` to the dictionary values. All items must be dictionaries. |
167,490 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | Parses list of dictionaries or None elements, applying `f` to the dictionary values. If an element in the list is None, it is returned directly. |
167,491 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,492 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,493 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,494 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,495 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,496 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | Parses a list of floats |
167,497 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | Parses a list that contains lists of strings |
167,498 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,499 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | null |
167,500 | import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Type
from typing import TypeVar
from typing import Union
from typing import cast
from greykite.common.python_utils import assert... | Asserts equality between two instances of `ForecastConfig`. Raises an error in case of parameter mismatch. Parameters ---------- forecast_config_1: `ForecastConfig` First instance of the :class:`~greykite.framework.templates.model_templates.ForecastConfig` for comparing. forecast_config_2: `ForecastConfig` Second insta... |
167,501 | from typing import Optional
import pandas as pd
from greykite.common.logging import LoggingLevelEnum
from greykite.common.logging import log_message
from greykite.common.time_properties import infer_freq
from greykite.common.time_properties import min_gap_in_seconds
from greykite.framework.pipeline.utils import get_def... | Gets the most appropriate model template that fits the input df's frequency, forecast horizon and number of cv splits. We define the cv to be sufficient if both number of splits is at least 5 and the number of evaluated points is at least 30. Multi-template will be used only when cv is sufficient. Parameter --------- d... |
167,502 | import functools
import warnings
from dataclasses import dataclass
import pandas as pd
from sklearn import clone
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from greyki... | Decorator that validates inputs to forecast_pipeline function and sets defaults |
167,503 | import itertools
import os
import timeit
from pathlib import Path
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.tem... | Benchmarks silverkite template and returns the output as a list :param data_name: str Name of the dataset we are performing benchmarking on For real datasets, the data_name matches the corresponding filename in the data/ folder For simulated datasets, we follow the convention "<freq>_simulated" e.g. "daily_simulated" :... |
167,504 | import itertools
import os
import timeit
from pathlib import Path
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.tem... | Default parameter sets to framework.benchmark real datasets. The datasets are located in data folder. Every tuple has the following structure: (data_name, frequency, time_col, value_col, forecast_horizon) |
167,505 | import itertools
import os
import timeit
from pathlib import Path
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.tem... | Default parameter sets for benchmarking silverkite template |
167,506 | import itertools
import os
import timeit
from pathlib import Path
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.tem... | Default parameter sets for benchmarking |
167,507 | import itertools
import os
import timeit
from pathlib import Path
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.tem... | Default parameter sets to generate simulated data for benchmarking. The training periods and forecast horizon are chosen to complement default real datasets. Every tuple has the following structure: (data_name, frequency, training_periods, forecast_horizon) |
167,508 | import timeit
from typing import Dict
import pandas as pd
from tqdm.autonotebook import tqdm
from greykite.common.constants import TIME_COL
from greykite.common.logging import LoggingLevelEnum
from greykite.common.logging import log_message
from greykite.framework.pipeline.pipeline import forecast_pipeline
from greykit... | Runs ``forecast_pipeline`` on a rolling window basis. Parameters ---------- pipeline_params : `Dict` A dictionary containing the input to the :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`. tscv : `~greykite.sklearn.cross_validation.RollingTimeSeriesSplit` Cross-validation object that determines the ro... |
167,509 | import base64
from io import BytesIO
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from greykite.algo.changepoint.adalasso.changepoint_detector import ChangepointDetector
from greykite.algo.common.holiday_inferrer import HolidayInf... | Computes multiple exploratory data analysis (EDA) plots to visualize the metric in ``value_col``and aid in modeling. The EDA plots are written in an `html` file at ``output_path``. For details on how to interpret these EDA plots, check the tutorials. Parameters ---------- df : `pandas.DataFrame` Input timeseries. A dat... |
167,510 | import warnings
from functools import partial
import numpy as np
from greykite.common.constants import PREDICTED_COL
The provided code snippet includes necessary dependencies for implementing the `_cached_call` function. Write a Python function `def _cached_call(cache, estimator, method, *args, **kwargs)` to solve the... | Call estimator with method and args and kwargs. This code is private in scikit-learn 0.24, so it is copied here. |
167,511 | import datetime
import numpy as np
import pandas as pd
import plotly
import plotly.express as px
from greykite.common.constants import ANOMALY_COL
from greykite.common.constants import TIME_COL
from greykite.common.constants import VALUE_COL
from greykite.common.testing_utils import generate_df_for_tests
from greykite.... | null |
167,512 | import datetime
import numpy as np
import pandas as pd
import plotly
import plotly.express as px
from greykite.common.constants import ANOMALY_COL
from greykite.common.constants import TIME_COL
from greykite.common.constants import VALUE_COL
from greykite.common.testing_utils import generate_df_for_tests
from greykite.... | null |
167,513 | import datetime
import numpy as np
import pandas as pd
import plotly
import plotly.express as px
from greykite.common.constants import ANOMALY_COL
from greykite.common.constants import TIME_COL
from greykite.common.constants import VALUE_COL
from greykite.common.testing_utils import generate_df_for_tests
from greykite.... | null |
167,514 | import warnings
from collections import defaultdict
import plotly
import pandas as pd
from greykite.common.constants import TIME_COL
from greykite.common.constants import VALUE_COL
from greykite.framework.benchmark.data_loader_ts import DataLoader
from greykite.framework.input.univariate_time_series import UnivariateTi... | Generates model results summary. Parameters ---------- result : `ForecastResult` See :class:`~greykite.framework.pipeline.pipeline.ForecastResult` for documentation. Returns ------- Prints out model coefficients, cross-validation results, overall train/test evalautions. |
167,515 | import plotly
import warnings
import pandas as pd
from greykite.framework.benchmark.data_loader_ts import DataLoaderTS
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.... | Loads bike-sharing data and adds proper regressors. |
167,516 | import plotly
import warnings
import pandas as pd
from greykite.framework.benchmark.data_loader_ts import DataLoaderTS
from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.... | Fits a daily model for this use case. The daily model is a generic silverkite model with regressors. |
167,517 | import sys
from py12306.app import *
from py12306.helpers.cdn import Cdn
from py12306.log.common_log import CommonLog
from py12306.query.query import Query
from py12306.user.user import User
from py12306.web.web import Web
def test():
"""
功能检查
包含：
账号密码验证 (打码)
座位验证
乘客验证
语音验证码验... | null |
167,518 | import signal
import sys
from py12306.helpers.func import *
from py12306.config import Config
from py12306.helpers.notification import Notification
from py12306.log.common_log import CommonLog
from py12306.log.order_log import OrderLog
Config:
IS_DEBUG = False
# 查询任务
# 查询间隔
# 查询重试次数
... | null |
167,519 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.query import Query
from py12306.user.user import User
Config:
IS_DEBUG = False
# 查询任务
# 查询间隔
# 查询重试次数
# 用户心跳检测间隔
... | 状态统计 任务数量，用户数量，查询次数 节点信息（TODO） :return: |
167,520 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.query import Query
from py12306.user.user import User
class Cluster():
KEY_PREFIX = 'py12306_' # 目前只能手动
KEY_QUERY_COUNT = KEY_PREFIX + 'query_c... | 节点统计 节点数量，主节点，子节点列表 :return: |
167,521 | import json
import re
from flask import Blueprint, request, send_file
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.query import Query
from py12306.user.user import User
Config:
IS_DEBUG = False
# 查询任务
# 查询间隔
... | null |
167,522 | import json
import re
from flask import Blueprint, request, send_file
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.query import Query
from py12306.user.user import User
The provided code snippet includes necessary dependencies for imp... | 菜单列表 |
167,523 | import json
import re
from flask import Blueprint, request, send_file
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.query import Query
from py12306.user.user import User
The provided code snippet includes necessary dependencies for imp... | 操作列表 |
167,524 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required, create_access_token)
from py12306.config import Config
from py12306.helpers.func import str_to_time, timestamp_to_time
from py12306.user.job import UserJob
from py12306.user.user import User
Config:
IS... | 用户登录 :return: |
167,525 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required, create_access_token)
from py12306.config import Config
from py12306.helpers.func import str_to_time, timestamp_to_time
from py12306.user.job import UserJob
from py12306.user.user import User
def convert_job... | 用户任务列表 :return: |
167,526 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required, create_access_token)
from py12306.config import Config
from py12306.helpers.func import str_to_time, timestamp_to_time
from py12306.user.job import UserJob
from py12306.user.user import User
Config:
IS... | 获取用户信息 :return: |
167,527 | from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.query.job import Job
from py12306.query.query import Query
def convert_job_to_info(job: Job):
return {
'name': job.job_name,
'left_dates': ... | 查询任务列表 :return: |
167,528 | import linecache
from flask import Blueprint, request
from flask.json import jsonify
from flask_jwt_extended import (jwt_required)
from py12306.config import Config
from py12306.helpers.func import get_file_total_line_num, pick_file_lines
from py12306.log.common_log import CommonLog
from py12306.query.query import Quer... | 日志 :return: |
167,529 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
The provided code snippet includes necessary dependencies for implementing the `singleton` function. Write a Python function `def singleton(cls)` to solve... | 将一个类作为单例 来自 https://wiki.python.org/moin/PythonDecoratorLibrary#Singleton |
167,530 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
if isinstance(number, dict):
min = float(number.get('min'))
max = float(number.get('max'))
else:
min = number / 2
m... | null |
167,531 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
return round(random.uniform(interval.get('min'), interval.get('max')), decimal
def get_interval_num(interval, decimal=2):
return round(random.unif... | null |
167,532 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
sleep(second)
def stay_second(second, call_back=None):
sleep(second)
if call_back:
return call_back() | null |
167,533 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def current_thread_id():
return threading.current_thread().ident | null |
167,534 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def time_now():
return datetime.datetime.now() | null |
167,535 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def timestamp_to_time(timestamp):
time_struct = time.localtime(timestamp)
return time.strftime('%Y-%m-%d %H:%M:%S', time_struct)
def get_file_modi... | null |
167,536 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def touch_file(path):
with open(path, 'a'): pass | null |
167,537 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def str_to_time(str):
return datetime.datetime.strptime(str, '%Y-%m-%d %H:%M:%S.%f') | null |
167,538 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def time_int():
return int(time.time()) | null |
167,539 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def time_int_ms():
return int(time.time() * 1000) | null |
167,540 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
if isinstance(number, dict):
min = float(number.get('min'))
max = float(number.get('max'))
else:
min = number / 2
m... | null |
167,541 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
if isinstance(number, dict):
min = float(number.get('min'))
max = float(number.get('max'))
else:
min = number / 2
m... | null |
167,542 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
if isinstance(number, dict):
min = float(number.get('min'))
max = float(number.get('max'))
else:
min = number / 2
m... | null |
167,543 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def dict_find_key_by_value(data, value, default=None):
result = [k for k, v in data.items() if v == value]
return result.pop() if len(result) else... | null |
167,544 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def objects_find_object_by_key_value(objects, key, value, default=None):
result = [obj for obj in objects if getattr(obj, key) == value]
return re... | null |
167,545 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def dict_count_key_num(data: dict, key, like=False):
count = 0
for k in data.keys():
if like:
if k.find(key) >= 0: count += 1
... | null |
167,546 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def array_dict_find_by_key_value(data, key, value, default=None):
result = [v for k, v in enumerate(data) if key in v and v[key] == value]
return ... | null |
167,547 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def get_true_false_text(value, true='', false=''):
if value: return true
return false | null |
167,548 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def sleep_forever():
"""
当不是主线程时，假象停止
:return:
"""
if not is_main_thread():
while True: sleep(10000000)
class Const:
IS_TES... | null |
167,549 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def expand_class(cls, key, value, keep_old=True):
if (keep_old):
setattr(cls, 'old_' + key, getattr(cls, key))
setattr(cls, key, MethodTyp... | null |
167,550 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
if isinstance(number, dict):
min = float(number.get('min'))
max = float(number.get('max'))
else:
min = number / 2
m... | null |
167,551 | import datetime
import hashlib
import json
import os
import random
import threading
import functools
import time
from time import sleep
from types import MethodType
def md5(value):
return hashlib.md5(json.dumps(value).encode()).hexdigest() | null |
167,552 | import png
The provided code snippet includes necessary dependencies for implementing the `print_qrcode` function. Write a Python function `def print_qrcode(path)` to solve the following problem:
将二维码输出到控制台 需要终端尺寸足够大才能显示 :param path: 二维码图片路径 (PNG 格式) :return: None
Here is the function:
def print_qrcode(path):
""... | 将二维码输出到控制台 需要终端尺寸足够大才能显示 :param path: 二维码图片路径 (PNG 格式) :return: None |
167,553 | import base64
import logging
import os
import json
import boto3
import urllib3
import uuid
def lambda_handler(event, context):
urls = []
http = urllib3.PoolManager()
for i in range(10):
r = http.request('GET', 'http://thecatapi.com/api/images/get?size=medformat=src&type=png&api_key=8f7dc437-0b... | null |
167,554 | import os
import json
import uuid
import boto3
from PIL import Image
processed_bucket=os.environ['processed_bucket']
s3_client = boto3.client('s3')
def pixelate(pixelsize, image_path, pixelated_img_path):
img = Image.open(image_path)
temp_img = img.resize(pixelsize, Image.BILINEAR)
new_img = temp_img.resize(img.size... | null |
167,555 | from . import Image, ImageFile
from ._binary import i32be as i32
def _accept(prefix):
return len(prefix) >= 8 and i32(prefix, 0) >= 20 and i32(prefix, 4) in (1, 2) | null |
167,556 | from . import Image, ImageFile
from ._binary import o8
from ._binary import o16be as o16b
_Palm8BitColormapValues = (
(255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255),
(255, 51, 255), (255, 0, 255), (255, 255, 204), (255, 204, 204),
(255, 153, 204), (255, 102, 204), (255, 51, 204), (25... | null |
167,557 | from . import Image, ImageFile
from ._binary import o8
from ._binary import o16be as o16b
_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000}
_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00}
class ImageFile(Image.Image):
"""Base class for image file format han... | null |
167,558 | from . import Image, ImageFile
from ._binary import i16le as word
from ._binary import i32le as dword
from ._binary import si16le as short
from ._binary import si32le as _long
_handler = None
The provided code snippet includes necessary dependencies for implementing the `register_handler` function. Write a Python func... | Install application-specific WMF image handler. :param handler: Handler object. |
167,559 | from . import Image, ImageFile
from ._binary import i16le as word
from ._binary import i32le as dword
from ._binary import si16le as short
from ._binary import si32le as _long
def _accept(prefix):
return (
prefix[:6] == b"\xd7\xcd\xc6\x9a\x00\x00" or prefix[:4] == b"\x01\x00\x00\x00"
) | null |
167,560 | from . import Image, ImageFile
from ._binary import i16le as word
from ._binary import i32le as dword
from ._binary import si16le as short
from ._binary import si32le as _long
_handler = None
if hasattr(Image.core, "drawwmf"):
# install default handler (windows only)
register_handler(WmfHandler())
def _save(im... | null |
167,561 | import struct
from io import BytesIO
from . import Image, ImageFile
MAGIC = b"FTEX"
def _accept(prefix):
return prefix[:4] == MAGIC | null |
167,562 | from . import Image
from ._binary import i32le as i32
from .PcxImagePlugin import PcxImageFile
MAGIC = 0x3ADE68B1
def _accept(prefix):
return len(prefix) >= 4 and i32(prefix) == MAGIC | null |
167,563 | import calendar
import codecs
import collections
import mmap
import os
import re
import time
import zlib
PDFDocEncoding = {
0x16: "\u0017",
0x18: "\u02D8",
0x19: "\u02C7",
0x1A: "\u02C6",
0x1B: "\u02D9",
0x1C: "\u02DD",
0x1D: "\u02DB",
0x1E: "\u02DA",
0x1F: "\u02DC",
0x80: "\u202... | null |
167,564 | import calendar
import codecs
import collections
import mmap
import os
import re
import time
import zlib
class PdfFormatError(RuntimeError):
"""An error that probably indicates a syntactic or semantic error in the
PDF file structure"""
pass
def check_format_condition(condition, error_message):
if not c... | null |
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