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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 os
import json
import requests
from tqdm import tqdm
from urllib.request import urlretrieve

BASE_URL = 'https://api.figshare.com/v2'

def download_collection(collection_id, destination):
    

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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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## set up env via bash:
conda create -n stream_nwb -c conda-forge -y python=3.7 hdf5=1.12.0 jupyter pip
conda activate stream_nwb
pip install nwbwidgets
pip install git+https://github.com/NeurodataWithoutBorders/pynwb
pip install git+https://github.com/hdmf-dev/hdmf
git clone https://github.com/satra/h5py.git
cd h5py
HDF5_DIR=~/Users/bendichter/opt/anaconda3/envs/stream_nwb/ pip install --force-reinstall --no-binary=h5py .

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from datetime import datetime, timedelta
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import numpy as np
import dash_core_components as dcc
import dash_html_components as html
import dash

def discrete_colorscale(bvals, colors):
    """

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from datetime import datetime, timedelta
import plotly.graph_objs as go
import numpy as np
import dash_core_components as dcc
import dash_html_components as html
import dash


def display_eletrode_turns(all_turn_data):
    """