notebooks/demo_2d_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 2D data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from pyflint.pyflint import FlintSignal, Flint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will use 2d data from a T1(Inversion Recovery)T2 experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_2d = np.loadtxt(Path('./example_data') / \"T1IRT2.dat\", delimiter = \" \")\n",
    "t1_axis = np.loadtxt(Path('./example_data') / \"T1IRT2_t1axis.dat\")\n",
    "t2_axis = np.loadtxt(Path('./example_data') / \"T1IRT2_t2axis.dat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform ILT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert data to Flint signal format and initialise the Flint class. The ILT distribution is obtained by calling the `solve_flint` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lipschitz constant found: 78646960.22347136\n"
     ]
    },
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   ],
   "source": [
    "signal = FlintSignal.load_from_data(signal_2d, t1_axis, t2_axis)\n",
    "flint = Flint(signal, \n",
    "              kernel_shape = [32, 32], \n",
    "              kernel_name = \"T1IRT2\", \n",
    "              alpha = 1e-1, \n",
    "              t1range = [1e-4, 10e0], \n",
    "              t2range = [1e-4, 10e0], \n",
    "              )\n",
    "flint.solve_flint()\n",
    "flint.plot()"
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  }
 ],
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