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import polars as pl
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from nominal.core import NominalClient
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df = pl.read_csv('hf://datasets/nominal-io/frosty-flight/frosty_flight_1k_rows.csv')
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df.write_csv('frosty_flight_1k_rows.csv')
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client = NominalClient.from_profile("default")  # replace with your profile name
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dataset = client.create_csv_dataset(
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    path = 'frosty_flight_1k_rows.csv',
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    name = 'Dataset for Asset: UAV Drone',
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    timestamp_column = 'source_time',
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    timestamp_type = 'iso_8601',
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)
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uav_drone_asset = client.create_asset(
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    name = 'Frosty Flight'
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)
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uav_drone_asset.add_dataset(
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    dataset = dataset,
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    data_scope_name = 'dataset'
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)
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print(uav_drone_asset.rid)

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import random
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def generate_sparkline(length=10):
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    """Generate a random sparkline in ASCII."""
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    data = [random.randint(1, 10) for _ in range(length)]
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    chars = "▁▂▃▄▅▆▇█"
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    max_value = max(data)
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    min_value = min(data)
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    def normalize(value):
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        if max_value == min_value:
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            return 0
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        return int((value - min_value) / (max_value - min_value) * (len(chars) - 1))
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    sparkline = ''.join(chars[normalize(value)] for value in data)
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    return sparkline

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logs = [
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    (df['source_time'][i], f"Log message {i} {generate_sparkline(random.randint(15, 40))}")
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    for i in range(len(df['source_time']))
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]
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for row in logs[:5]:
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    print(row)

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from nominal.core import NominalClient
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client = NominalClient.from_profile("default")
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log_set = client.create_log_set(
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    logs=logs,
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    name="Sparkline Logs",
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    description="description"
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)
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data_scope_name = "fun_with_logs"
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uav_drone_asset.add_log_set(data_scope_name, log_set)

1
from nominal.core import NominalClient
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client = NominalClient.from_profile("default")
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log = client.get_log_set("ri.data-source.cerulean-staging.log-set.8bc1f396-a42e-40db-9680-e686e288e45d")
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counter = 1
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for line in log.stream_logs():
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    counter += 1
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    print(line)
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    if counter > 5:
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        break