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How to use func_kwargs method in assertpy

Best Python code snippet using assertpy_python

apply.py

Source:apply.py

1import pandas as pd234def apply_by(5    df: pd.core.frame.DataFrame,6    func,7    func_kwargs: dict = None,8    spiketimes_col: str = "spiketimes",9    spiketrain_col: str = "spiketrain",10    returned_colname: str = "apply_result",11):12    """13    Apply an arbitrary function to each spiketrain in a DataFrame.1415    The passed function should have a single return value for each spiketrain.1617    Args:18        df: A pandas DataFrame containing spiketimes indexed by spiketrain19        func: The function to apply to the data20        func_kwargs: dictionary of key-word arguments to be passed to the function21        spiketimes_col: The label of the column containing spiketimes22        spiketrain_col: The label of the column containing spiketrain identifiers23        return_colname: The label of the column in the returned DataFrame containing the function result24    Returns:25        A pandas DataFrame with columns {spiketrian_col and returned_colname}26    """27    if not func_kwargs:28        func_kwargs = {}29    res = (30        df.groupby(spiketrain_col)31        .apply(lambda x: func(x[spiketimes_col].values, **func_kwargs))32        .reset_index()33        .rename(columns={0: returned_colname})34    )35    if "level_1" in res.columns:36        res = res.rename(columns={"level_1": f"{returned_colname}_idx"})37    return res383940def apply_by_rolling(41    df: pd.core.frame.DataFrame,42    func,43    num_periods: int = 10,44    func_kwargs: dict = None,45    spiketimes_col: str = "spiketimes",46    spiketrain_col: str = "spiketrain",47    returned_colname: str = "rolling_result",48    copy: bool = True,49):50    """51    Apply a function in a roling window along each neuron in a dataframe5253    Args:54        df: A pandas DataFrame containing spiketimes indexed by spiketrain55        func: funtion to apply along the datafrmae56        num_period: The number of rows in the rolling window57        spiketimes_col: The label of the column containing spiketimes58        spiketrain_col: The label of the column containing spiketrain identifiers59        returned_colname: The label of the column in the returned DataFrame containing the function result60        copy: Whether make a copy of the passed to DataFrame before applying the function61    Returns:62        A copy of the passed DataFrame with returned_colname appended63    """64    original_index_name = df.index.name65    if not func_kwargs:66        func_kwargs = {}67    if copy:68        df = df.copy()69    tmp_res = (70        df.groupby(spiketrain_col)[spiketimes_col]71        .rolling(num_periods)72        .apply(lambda x: func(x.values, **func_kwargs), raw=True)73        .reset_index()74        .rename(columns={spiketimes_col: returned_colname})75        .set_index("level_1")76    )77    tmp_res.index.name = "index"78    tmp_res = pd.merge(df.reset_index(), tmp_res.reset_index()).set_index("index")79    tmp_res.index.name = original_index_name
...

bootstrap.py

Source:bootstrap.py

1import numpy as np2def bootstrap(data, n, axis=0, func=np.var, func_kwargs={"ddof": 1}):3    """Produce n bootstrap samples of data of the statistic given by func.4    Arguments5    ---------6    data : numpy.ndarray7        Data to resample.8    n : int9        Number of bootstrap trails.10    axis : int, optional11        Axis along which to resample. (Default ``0``).12    func : callable, optional13        Statistic to calculate. (Default ``numpy.var``).14    func_kwargs : dict, optional15        Dictionary with extra arguments for func. (Default ``{"ddof" : 1}``).16    Returns17    -------18    samples : numpy.ndarray19        Bootstrap samples of statistic func on the data.20    """21    if axis != 0:22        raise NotImplementedError("Only axis == 0 supported.")23    fiducial_output = func(data, axis=axis, **func_kwargs)24    if isinstance(data, list):25        assert all([d.shape[1:] == data[0].shape[1:] for d in data])26    samples = np.zeros((n, *fiducial_output.shape),27                       dtype=fiducial_output.dtype)28    for i in range(n):29        if isinstance(data, list):30            idx = [np.random.choice(d.shape[0], size=d.shape[0], replace=True)31                   for d in data]32            samples[i] = func([d[i] for d, i in zip(data, idx)],33                              axis=axis, **func_kwargs)34        else:35            idx = np.random.choice(data.shape[axis], size=data.shape[axis],36                                   replace=True)37            samples[i] = func(data[idx], axis=axis, **func_kwargs)38    return samples39def bootstrap_var(data, n, axis=0, func=np.var, func_kwargs={"ddof": 1}):40    """Calculate the variance of the statistic given by func.41    Arguments42    ---------43    data : numpy.ndarray44        Data to resample.45    n : int46        Number of bootstrap trails.47    axis : int, optional48        Axis along which to resample. (Default ``0``).49    func : callable, optional50        Statistic to calculate. (Default ``numpy.var``).51    func_kwargs : dict, optional52        Dictionary with extra arguments for func. (Default ``{"ddof" : 1}``).53    Returns54    -------55    var : numpy.ndarray56        Bootstrap variance of statistic func on the data.57    """58    samples = bootstrap(data, n, axis, func, func_kwargs)...

common_task.py

Source:common_task.py

1# -*- coding:utf-8 -*-2import sys3from .celery_worker import app4def normal_task(func, func_args=None, func_kwargs=None, **kwargs):5    """6    :param func: å½æ°å¯¹è±¡7    :param func_args: å½æ°çåæ° list8    :param func_kwargs: å½æ°çåæ° dict9    :param args: celery send_taskæ¹æ³çåæ°10    :param kwargs: celery send_taskæ¹æ³çåæ°11    :return:12    """13    # åæ°æ£æ¥14    if func_args is not None and not isinstance(func_args, list):15        raise Exception('invalid func_args type')16    if func_kwargs is not None and not isinstance(func_kwargs, dict):17        raise Exception('invalid func_kwargs type')18    args_list = [sys.path, func.__module__, func.__name__]19    if func_args is None:20        func_args = []21    func_args = args_list + func_args22    app.send_task('common_async_task', args=func_args, kwargs=func_kwargs, **kwargs)23def async_http_request(method, url, func_kwargs=None, **kwargs):24    """25    å¼æ¥httpè¯·æ±26    :param method: è¯·æ±ç±»å: "GET","POST","PATCH",...27    :param url: è¯·æ±çå°å28    :param func_kwargs: requestsåæ°29    :param kwargs: celeryåæ°30    :return:31    """32    if func_kwargs is not None and not isinstance(func_kwargs, dict):33        raise Exception('invalid func_kwargs type')...

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