Added new demo

examples/PGURE-SVT HyperSpy Demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PGURE-SVT HyperSpy Demonstration\n",
+    "\n",
+    "### Tom Furnival ([[email protected]](mailto:[email protected]))\n",
+    "\n",
+    "HyperSpy is an open-source Python library that makes signal handling and processing straightforward in Python, with a friendly API. \n",
+    "\n",
+    "While you can use `pguresvt.pguresvt.SVT` to denoise a numpy array directly, `pguresvt.hspysvt.HSPYSVT` can directly denoise a HyperSpy signal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Configures the plotting backend\n",
+    "#%matplotlib inline\n",
+    "%matplotlib qt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "# Import the HyperSpy API\n",
+    "import hyperspy.api as hs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Import the HyperSpy wrapper for PGURE-SVT\n",
+    "from pguresvt import hspysvt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Simulated dataset\n",
+    "\n",
+    "First, we load the simulated dataset using HyperSpy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load example dataset\n",
+    "movie = hs.load(\"../test/examplesequence.tif\")\n",
+    "\n",
+    "# Truncate to 25 frames, and plot the result\n",
+    "movie = movie.inav[:25]\n",
+    "movie.plot(navigator='slider')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we corrupt it with a mixture of Poisson and Gaussian noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# First extract the data and rescale to [0,1] range\n",
+    "clean = movie._data_aligned_with_axes\n",
+    "clean = clean / np.amax(clean)\n",
+    "\n",
+    "# Detector gain\n",
+    "gain = 0.1\n",
+    "# Detector offset\n",
+    "offset = 0.1\n",
+    "# Detector variance\n",
+    "sigma = 0.1\n",
+    "\n",
+    "def addnoise(x):\n",
+    "    return gain * np.random.poisson(x / gain) + offset + sigma * np.random.randn()\n",
+    "addnoise = np.vectorize(addnoise, otypes=[np.float])\n",
+    "\n",
+    "noisy = addnoise(clean)\n",
+    "\n",
+    "noisy_movie = hs.signals.Image(noisy)\n",
+    "noisy_movie.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we initialise the SVT denoising function. The full list of options (with default values) is:\n",
+    "\n",
+    "```\n",
+    "hspysvt.HSPYSVT(patchsize=4,\n",
+    "                patchoverlap=1,\n",
+    "                length=15,\n",
+    "                optimize=True,\n",
+    "                threshold=0.5,\n",
+    "                estimatenoise=True,\n",
+    "                alpha=-1., \n",
+    "                mu=-1., \n",
+    "                sigma=-1.,                \n",
+    "                arpssize=7, \n",
+    "                tol=1e-7,\n",
+    "                median=5,\n",
+    "                hotpixelthreshold=10)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize with default parameters\n",
+    "svt = hspysvt.HSPYSVT(patchsize=4,\n",
+    "                      patchoverlap=2,\n",
+    "                      length=15,\n",
+    "                      threshold=0.5,\n",
+    "                      tol=1e-6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are able to run the denoising and plot the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Run the denoising\n",
+    "denoised_movie = svt.denoise(noisy_movie)\n",
+    "\n",
+    "# Plot denoised data\n",
+    "denoised_movie.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Time-resolved STEM image sequence\n",
+    "\n",
+    "First, we load the simulated dataset using HyperSpy, and then corrupt it with a mixture of Poisson and Gaussian noise.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
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+  },
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