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    {
      "cell_type": "markdown",
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      "source": [
        "\n# Data Records\n\nData records are a tool for managing time series data collected\nover the course of a measurement. They store aligned time series data\nand meta data about the file in multiple csv files.\n\nAn ActiveRecord is a context manager that will automatically dump\nany data in the memory to the target directory in the event of\nan exception or error.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from rminstr.data_structures import ActiveRecord, ExistingRecord\nimport time\n\nwith ActiveRecord(\n    ['column_1', 'column_2'], output_dir='outputs', maxlen=100, minlen=10\n) as rec:\n    # add a single sample. Time stamps can be applied automatically\n    # or through the timestamp keyword.\n    rec.update('column_1', 0.1)\n\n    # add multiple samples all at once, time stamps need to be\n    # provided in this case\n    samples = []\n    timestamps = []\n    for i in range(50):\n        samples.append(i)\n        timestamps.append(time.time())\n    rec.array_update('column_2', samples, timestamps)\n\n    # The most recent sample in a column can be accessed via indexing\n\n    last_sample = rec['column_2']\n\n    # Columns can be indexed into by their timestamps\n\n    time_series = rec.get_time_series(\n        'column_2', t_min=timestamps[5], t_max=timestamps[7]\n    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To read the data back in, point the ``ExistingRecord`` class to\nthe metadata file produced by the ``ActiveRecord``.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "opened = ExistingRecord(f'outputs/{rec.metadata_file_name}')\n\n# Read each line in the record one at a time\n# and index into it like you would an active record\nwhile opened.read_next_line():\n    pass\nlast_sample = opened['column_1']\n\n\n# Read the entire record at once into a\n# dictionary of ``TimeSeries`` objects.\ncolumns = opened.batch_read()"
      ]
    }
  ],
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