cwindolf

%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle
from matplotlib.transforms import offset_copy
from matplotlib.patches import Ellipse, Rectangle, ConnectionPatch
from matplotlib.lines import Line2D
from matplotlib.legend_handler import HandlerTuple
import contextlib

plt.rc("figure", dpi=300)

cwindolf

def runs_to_ranges(x, one_more=False):
    ranges = []
    cur = x[0]
    b = x[0]
    for a, b in zip(x, x[1:]):
        assert b > a
        if b - a == 1:
            continue
        else:
            ranges.append(range(cur, a + 1 + one_more))

cwindolf

import matplotlib.pyplot as plt
import contextlib

@contextlib.contextmanager
def subplots(*args, **kwargs):
    fig, axes = plt.subplots(*args, **kwargs)
    try:
        yield fig, axes
    finally:
        plt.show()

cwindolf

import torch
from torch import nn
from tqdm.auto import trange


class PPCA(nn.Module):
    def __init__(self, d, c):
        super().__init__()
        self.d = d
        self.c = c

cwindolf

def sliding_mean_and_stddev_interval(
    axis, xs, ys, n_neighbors=30, linewidth=1, color="k", ci_alpha=0.25
):
    """Plot moving means and standard deviations"""
    _1 = np.ones(2 * n_neighbors + 1)
    _ys = np.nan_to_num(ys)
    sliding_sum = correlate1d(_ys, _1, mode="constant")
    sliding_N = correlate1d(np.isfinite(ys).astype(float), _1, mode="constant")
    sliding_mean = sliding_sum / sliding_N
    sliding_sumsq = correlate1d(_ys**2, _1, mode="constant")