fschwar4

import gc
import os
import subprocess
import time

import memray
import numpy as np
import numpy.lib.format
from datetime import datetime as dt

fschwar4

import pandas as pd


## CALCIUM CORRECTION (based on Albumin)
# cave: check the units of Calcium and Albumin
# Calcium is given in mmol/l; Albumin in g/l
# equation following Payne 1973; https://doi.org/10.1136/bmj.4.5893.643
# Adjusted calcium = calcium - albumin + 4.0 (if albumin in g/dL and calcium in mg/dL)
# Calcium conversions: 1 mg/dL = 0.25 mmol/L; 1 mmol/L = 4 mg/dL
# Calcium corrected [mmol/l] = Calcium measured [mmol/l] – (0,025 x Albumin [g/l]) + 1

fschwar4

import h5py as h5


file_path = 'data.h5'
stream_path = '/Data/Recording_0/AnalogStream/Stream_'


# set boundaries for data extraction
start_s = 0  # start second
end_s = 10  # end second

fschwar4

def addLoggingLevel(levelName, levelNum, methodName=None):
    """Comprehensively adds a new logging level to the `logging` module and the
    currently configured logging class.

    `levelName` becomes an attribute of the `logging` module with the value
    `levelNum`. `methodName` becomes a convenience method for both `logging`
    itself and the class returned by `logging.getLoggerClass()` (usually just
    `logging.Logger`). If `methodName` is not specified, `levelName.lower()` is
    used.

fschwar4

from typing import Dict, Optional
from pathlib import Path

class MyClass:
    @staticmethod
    def get_git_infos() -> Dict[str, Optional[str]]:
        """Extract relevant git information for reproducibility.
        
        This function should help to increase reproducibility of the results,
        since it is than known which code version was used for the respective

fschwar4

import numpy as np
from numpy.typing import NDArray

def sinc_interpolation(x: NDArray, s: NDArray, u: NDArray) -> NDArray:
            """Whittaker–Shannon or sinc or bandlimited interpolation.

            Args:
                x (NDArray): signal to be interpolated, can be 1D or 2D
                s (NDArray): time points of x (*s* for *samples*) 
                u (NDArray): time points of y (*u* for *upsampled*)

fschwar4

import numpy as np

def cv(spike_train: np.ndarray) -> float:
        """Calculate coefficient of variation (Cv) of the interspike intervals.
    
        $$\displaystyle{Cv = \frac{\sigma_{ISI}}{\mu_{ISI}}}$$

        Args:
            spike_train (np.ndarray): Spike train.