Logging¶

We want to use the python logging module as much as possible to traceback errors and document the pipeline processes. Furthermore, we want to configure each logger with the RichHandler, which prettyfies the output with rich.

Setup Guide¶

Currently, all setup related to logging is found in the darts.utils.logging.py file. It contains two functions:

A setup function which sets the log-level for all darts.* logger and add default options to xarray and pytorch to supress arrays. See how to supress arrays.
A function which adds a file and a rich log handler.

Both functions are used in the CLI setup but can also be called from e.g. a notebook. The recommended approach for handling logging within a notebook is the following:

import logging
from rich.logging import RichHandler
from darts.utils.logging import setup_logging

setup_logging()
logging.basicConfig(
    level=logging.INFO,
    format="%(message)s",
    datefmt="[%X]",
    handlers=[RichHandler(rich_tracebacks=True)],
)

This way the notebook won't spam logfiles everywhere and we still have control over our rich handler.

Usage Guide¶

For logging inside a darts-package should be done without any further configuration:

import logging

logger = logging.getLogger(__name__.replace("darts_", "darts.")) # don't replace __name__

Logging at the top-level darts package can just use a __name__ logger:

import loggin

logger = logging.getLogger(__name__) # don't replace __name__

Supressing Arrays¶

When printing or logging large numpy arrays a lot of numbers get truncated, however the array still takes a lot of space. Using lovely_numpy and lovely_tensor can help here:

import numyp as np
import torch
import xarray as xr
from lovely_numpy import lo
from lovely_tensors import monkey_patch

monkey_patch()
xr.set_options(display_expand_data=False)

a = np.zeros((8, 1024, 1024))
la = lo(a)
da = xr.DataArray(a)
t = torch.tensor(a)

logger.warning(la)
logger.warning(da)
logger.warning(t)