h-mayorquin

import numpy as np
from sleap_io import (
    PredictedInstance,
    Skeleton,
    Track,
    Video,
    Node,
    LabeledFrame,
    Labels,
)

h-mayorquin

import numpy as np
from pathlib import Path

data_dir_path = Path("/home/heberto/Murthy-data-share/one2one-mapping")
tiff_dir_path = data_dir_path / "raw_data" / "calcium_imaging" / "example_tiffs"

tiff_file_path_list = list(tiff_dir_path.iterdir())
tiff_file_path = tiff_file_path_list[0]

h-mayorquin

import pickle
import timeit
from pathlib import Path
from statistics import mean, stdev

from spikeinterface.extractors import SpikeGLXRecordingExtractor
from spikeinterface.core import load_extractor, BaseRecording

def pickling(recording):
    pickled_recording = pickle.dumps(recording)

h-mayorquin

 - name: Installad datalad
  run: |
    pip install datalad-installer
    if [ ${{ runner.os }} = 'Linux' ]; then
      datalad-installer --sudo ok git-annex --method datalad/packages
    elif [ ${{ runner.os }} = 'macOS' ]; then
      datalad-installer --sudo ok git-annex --method brew
    elif [ ${{ runner.os }} = 'Windows' ]; then
      datalad-installer --sudo ok git-annex --method datalad/git-annex:release
    fi

h-mayorquin

import os
import h5py


def remove_fields(input_file, output_file, fields_to_remove):
    with h5py.File(input_file, "r") as input_h5:
        with h5py.File(output_file, "w") as output_h5:
            for key in input_h5.keys():
                if key not in fields_to_remove:
                    input_h5.copy(key, output_h5)

h-mayorquin

import tempfile
import os
import shutil

from pathlib import Path
from tempfile import tempdir

from spikeinterface.core.datasets import download_dataset
from spikeinterface.extractors import SpikeGLXRecordingExtractor
from spikeinterface.core.segmentutils import concatenate_recordings

h-mayorquin

     try:
            file_path.write_text(
                json.dumps(dump_dict, indent=4, cls=SIJsonEncoder),
                encoding='utf8',
            )
        except TypeError as e:
            accepted_type = [str, int, float, bool or None]
            def log_value_types_of_dict(dictionary):
                
                if isinstance(dictionary, dict):

h-mayorquin

from pathlib import Path
import time
import os
import mmap
import itertools

import numpy as np
import matplotlib.pyplot as plt
import psutil
from tqdm.auto import tqdm

h-mayorquin

import mmap
import os
from pathlib import Path

import numpy as np 
def count_int16_elements(file_path):
    file_size = os.path.getsize(file_path)
    int16_size = np.dtype(np.int16).itemsize
    return file_size // int16_size

h-mayorquin

def calculate_peaks_spike_recall(peaks, sorting_ground_truth, tolerance_ms=0.4):
    """
    Calculate the spike recall of the peaks (which are the output of a peak detection method)
    against a ground truth of spike_trains. This function is used to test the quality of a peak detection method
    in the context of a specific sorting. 
    
    Recall close to 1 means that all the spike in the spike_train are present in a peak whereas recall
    close to 0 means that no spike in the spike_train are present in a peak. 
    
    More technically, this calculates the number of True positives divided by