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from typing import Tuple, Type, Any, Union, Optional | |
from pydantic import BaseModel | |
import numpy as np | |
def get_shape(data): | |
""" | |
Get the shape of various data structures: NumPy array, h5py dataset, Zarr dataset, or a nested list of arbitrary depth. | |
Parameters |
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import matplotlib.pyplot as plt | |
import numpy as np | |
from typing import List, Optional | |
def grouped_barplot( | |
data, | |
clabels: List[str], | |
xlabels: List[str], |
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from itertools import chain, repeat | |
from bisect import bisect_left | |
from datetime import datetime, timedelta | |
import numpy as np | |
import matplotlib.pyplot as plt | |
data = [ | |
{ |
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# first run: | |
# | |
#!git log --all --numstat --pretty=format:'--%h--%ad--%aN' --no-renames > git.log | |
import datetime | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
from pprint import pprint | |
import matplotlib.ticker |
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2022-06-01 11:34:17,098 [ INFO] Logs saved in /Users/bendichter/Library/Logs/dandi-cli/20220601153356Z-59646.log | |
(base) MacBook-Pro-3:~ bendichter$ dandi download https://dandiarchive.org/dandisets/000053/versions/"draft"/assets/?path="sub-npJ3/sub-npJ3_ses-20190504_behavior.nwb" | |
(base) MacBook-Pro-3:~ bendichter$ | |
(base) MacBook-Pro-3:~ bendichter$ | |
(base) MacBook-Pro-3:~ bendichter$ | |
(base) MacBook-Pro-3:~ bendichter$ dandi download https://dandiarchive.org/dandisets/000053/versions/"draft"/assets/?path="sub-npJ3/sub-npJ3_ses-20190504_behavior.nwb" | |
(base) MacBook-Pro-3:~ bendichter$ | |
(base) MacBook-Pro-3:~ bendichter$ cat /Users/bendichter/Library/Logs/dandi-cli/20220601153356Z-59646.log | |
2022-06-01T11:33:56-0400 [INFO ] dandi 59646:8602854912 dandi v0.40.0, hdmf v3.2.1, pynwb v2.0.1, h5py v3.1.0 | |
2022-06-01T11:33:56-0400 [INFO ] dandi 59646:8602854912 sys.argv = ['/Users/bendichter/opt/miniconda3/bin/dandi', 'download', 'https://dandiarchive.org/dandisets/000053/versions/draft/assets/?path=sub-npJ3/ |
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import h5py | |
def print_group(group: h5py.Group): | |
name = group.name.split('/')[-1] | |
return f"{name} ({len(group.keys())} objects)/" | |
def print_dataset(dataset: h5py.Dataset): | |
name = dataset.name.split('/')[-1] | |
return f"{name} {dataset.shape}, {dataset.dtype}" |
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from jinja2 import Template | |
_str = """## Converting data to NWB | |
Below is a collection of simple conversion scripts that are all tested against small | |
proprietary examples files. They are all optimized to handle very large data by iteratively | |
steping through large files and read/writing them one piece at a time. They also leverage | |
lossless compression within HDF5, which allows you to make large datasets smaller without | |
losing any data. We have seen this reduce large datasets by up to 66%! | |
{% for modality_name, modality_value in _dict.items() %} |
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