cwindolf

import tempfile
import imageio
from IPython.display import Image

def ipygif(z, fps=16, width=200, height=200):
    f = tempfile.NamedTemporaryFile(prefix="ipygif", suffix=".gif")
    z = z - z.min()
    z *= 255.0 / z.max()
    imageio.mimwrite(f.name, z.astype(np.uint8), fps=fps)
    return Image(filename=f.name, embed=True, width=width, height=height)

cwindolf

"""
Implements the nonparametric simultaneous confidence interval described
in Gao et al. [1], which modifies the Mandel-Betensky simultaneous CI [2]
such that it is admissible. Notation in the code below comes from [1].

Takes as input an array of bootstrap samples / replicates, returns lower
and upper bounds at 1-alpha confidence.

[1]: https://www.mdpi.com/2073-8994/13/7/1212
[2]: https://pubmed.ncbi.nlm.nih.gov/26452746/

cwindolf

import numpy as np
from .triginterp_inner import triginterp_inner


def triginterp(x, y, xi):
    """Trigonometric interpolation with zero-padding (lol, not periodic). Easy to change."""
    P = np.zeros_like(xi, dtype=np.float64)
    triginterp_inner(
        x.astype(np.float64), y.astype(np.float64), xi.astype(np.float64), P
    )

cwindolf

import torch
import torch.nn.functional as F


def translate(input, shifts, mode='bilinear', padding_mode='zeros'):
    """Translate a batch of images by a batch of xy shifts

    Arguments
    ---------
    input : torch.Tensor NCHW

cwindolf

# adapted from https://matplotlib.org/3.1.1/gallery/statistics/confidence_ellipse.html
import numpy as np
import matplotlib.pyplot as plt
import colorcet
from matplotlib.patches import Ellipse
import matplotlib.transforms as transforms


def cluster_scatter(xs, ys, ids, ax=None, n_std=2.0):
    ax = ax or plt.gca()

cwindolf

from pathlib import Path
import cmdstanpy
import ujson

def stanc(name, code, workdir=".stan"):
    Path(workdir).mkdir(exist_ok=True)
    path = Path(workdir) / f"{name}.stan"
    with open(path, "w") as f:
        f.write(code)
    model = cmdstanpy.CmdStanModel(stan_file=path, stanc_options={'warn-pedantic': True})

cwindolf

def tukey_scatter(x, y, iqrs=1.5, ax=None, **kwargs):
    ax = ax or plt.gca()
    x_25, x_75 = np.percentile(x, [25, 75])
    x_iqr = x_75 - x_25
    y_25, y_75 = np.percentile(x, [25, 75])
    y_iqr = y_75 - y_25
    inliers = np.flatnonzero(
        (x_25 - iqrs * x_iqr < x)
        & (x < x_75 + iqrs * x_iqr)
        & (y_25 - iqrs * y_iqr < y)

cwindolf

def regline(x, y, ax=None, **kwargs):
    b = ((x - x.mean()) * (y - y.mean())).sum() / np.square(x - x.mean()).sum()
    a = y.mean() - b * x.mean()
    x0, x1 = ax.get_xlim()
    r = np.corrcoef(x, y)[0, 1]
    ax.plot([x0, x1], [a + b * x0, a + b * x1], lw=1)
    ax.text(
        0.1,
        0.9,
        f"$\\rho={r:.2f}$",

cwindolf

try:
    import torch
    import torch.nn.functional as F
    HAVE_TORCH = True
except ImportError:
    HAVE_TORCH = False

def normxcorr1d(
    template,
    x,

cwindolf

# https://stackoverflow.com/a/74706214/3979938
from matplotlib.transforms import offset_copy

def inline_xlabel(ax, label):
    t = offset_copy(
        ax.transAxes,
        y=-(ax.xaxis.get_tick_padding() + ax.xaxis.get_tick_space()), 
        fig=fig,
        units='points'
    )