settwi

import numpy as np
import matplotlib.pyplot as plt

def area_contour_image(img: np.ndarray, quantiles: list[float], ax=None, x=None, y=None, **contour_kwds):
    normalized_img = (img - np.min(img)) / (np.max(img) - np.min(img))
    flattened_img = normalized_img.flatten()

    # sort pixels: small areas first, then large areas
    sorted_values = np.sort(flattened_img)[::-1]

settwi

from astropy.io import fits
import astropy.table as atab
import astropy.time as atime
import astropy.units as u
import numpy as np
from stixdcpy import instrument as inst


def load_stix_pixel_data_to_spectrogram(fn: str) -> dict:
    pdat = load_stix_pixel_data(fn)

settwi

import astropy.units as u
import numpy as np
from sklearn import linear_model
import typing


@u.quantity_input
def decompose(
    counts: u.ct,
    energy_bins: u.keV,

settwi

import requests
import parse
import os
import sys


verbose = True


def vp(*args, **kw):

settwi

import numpy as np
import numpy.typing


'''
Perform a flux- (i.e. area-) conserving rebinning of a histogram,
given its edges and a set of new edges.

The __name__ ... section has an example using a power law.

settwi

from datetime import datetime
import functools
import multiprocessing as mp
import os
import pathlib
import sys

import imageio
import numpy as np 
import sunpy.map