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from dask.distributed import Client

client = Client("tcp://127.0.0.1:8786")

print(client)

import dask

# generate random timeseries of data
df = dask.datasets.timeseries(

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### GET DASK CLIENT ###

import os
import coiled
cluster = coiled.Cluster(os.environ.get("COILED_CLUSTER_NAME"))
cluster.adapt()
client = cluster.get_client()
print("dask client:", client)

### USER CODE FOLLOWS ###

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print("Hello from Python!")

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import coiled

cluster = coiled.Cluster(...)

# this will send long-lived Application Default Credentials, with the OAuth scope needed for GEE
coiled.credentials.google.send_application_default_credentials(cluster, scopes=["https://www.googleapis.com/auth/earthengine"])

# plugin to init GEE on each worker 
from distributed.diagnostics.plugin import WorkerPlugin

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import pandas as pd
import requests

raw = requests.get("https://spot-bid-advisor.s3.amazonaws.com/spot-advisor-data.json").json()

ranges = {r["index"]: r["label"] for r in raw["ranges"]}
flat = []
for region in raw["spot_advisor"].keys():
    for instance in raw["spot_advisor"][region]["Linux"].keys():
        savings = raw["spot_advisor"][region]["Linux"][instance]["s"]

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"""
Script to make a labels dataset from a "tracking" h5 file.

> python tracking_h5_to_sleap.py path/to/tracking.h5 path/to/video.mp4
"""
import h5py
import numpy as np
from sleap import Labels, LabeledFrame, PredictedInstance, Skeleton, Video
from sleap.instance import Track

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from sleap import Labels

SCORE_THRESHOLD = 10

filename = "tests/data/json_format_v2/centered_pair_predictions.json"
out_filename = "tests/data/json_format_v2/centered_pair_predictions.high_scores.json"

labels = Labels.load_file(filename)

lf_inst_list = []

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"""
Outputs diagnostic information to help with debugging SLEAP installation.
"""

print_to_screen = False
output_text = ""


def print_(text=""):
    global output_text

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import attr
import os
import curses
from math import pi, cos, sin
import time
import random


@attr.s(auto_attribs=True)
class Board(object):