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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "confirmed-childhood",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",

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import pandas as pd
import numpy as np
import matplotlib.pyplot as pl
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
from sklearn.gaussian_process.kernels import DotProduct

def calculate(which, ax):
    print("Reading data...")
    if (which == 0):

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import napari
import numpy as np
import matplotlib.pyplot as pl
import h5py
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
#from qtpy.QtWidgets import QApplication, QMainWindow, QPushButton, QMenu, QVBoxLayout, QSizePolicy, QMessageBox, QWidget, QPushButton
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
import sys

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import numpy as np
import matplotlib.pyplot as pl 
import scipy.stats as st
import theano.tensor as tt
import theano.tensor.slinalg as sl
from ipdb import set_trace as stop
from sklearn.neighbors import NearestNeighbors

def chol_invert(A):
    """

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{
 "metadata": {
  "name": "Faraday rotation"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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{
 "metadata": {
  "name": "Bayesian linear regression"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {