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import numpy as np
import random

from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec, NWBDtypeSpec
from pynwb import get_class, load_namespaces, NWBFile, NWBHDF5IO

from datetime import datetime

ns_path = "soltesz.namespace.yaml"
ext_source = "soltesz.extensions.yaml"

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from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec, NWBAttributeSpec
from pynwb.form.spec import RefSpec
from pynwb import register_class, load_namespaces, NWBFile, NWBHDF5IO
from pynwb.form.utils import docval
from pynwb.file import Subject, NWBContainer, MultiContainerInterface, NWBDataInterface
from pynwb.device import Device



from datetime import datetime

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from ipywidgets import VBox, Button, HTML
import time
from threading import Thread
from queue import Queue


class MyWidget(VBox):
    def __init__(self):
        super(MyWidget, self).__init__()
        

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# MIT LICENSE


import datetime
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

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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):
    """

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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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## 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 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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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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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):