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import pandas as pd


def format_best(to_bold, numeric_df=None, order="max", format_str="\\bfseries{{{}}}"):
    """Format the row with the best value within each column of a pandas DataFrame in a particular way.

    Args:
        to_bold: pandas.DataFrame to operate on in-place.
        numeric_df: Optional pandas.DataFrame from which the best rows should be infered.
        order: Define what is the row that should be formatted. Supported: `max`, `min`, `second_highest`, `second_lowest`. If it is a list then it is interpreted in a per column way, otherwise applies to all columns.

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def brier_decomposition(labels, probs):
    """Compute the decompositon of the Brier score into its three components
    uncertainty (UNC), reliability (REL) and resolution (RES).

    Brier score is given by `BS = REL - RES + UNC`. The decomposition requires partioning
    into discrete events. Partioning into probability classes `M_k` is done for `p_k > p_i`
    for all `i!=k`. This induces a error when compared to the Brier score.

    For more information on the partioning see
    Murphy, A. H. (1973). A New Vector Partition of the Probability Score, Journal of Applied Meteorology and Climatology, 12(4)

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import matplotlib.pyplot as plt

import seaborn as sns
sns.set()
sns.set_style("ticks")
sns.set_context("paper", font_scale=1.0)

params = {
    'text.usetex' : True,
    'font.size' : 11,

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class GradNormLogger:

    def __init__(self):
        self.grad_norms = defaultdict(list)

    def update(self, model: torch.nn.Module, norm_type: float = 2.):
        total_norm = 0
        for name, p in model.named_parameters():
            if p.requires_grad:
                try:

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dpi = 80

# What size does the figure need to be in inches to fit the image?
figsize = width / float(dpi), height / float(dpi)

# Create a figure of the right size with one axes that takes up the full figure
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0, 0, 1, 1])
ax.set_aspect('equal', adjustable='box')