3D-CDI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib nbagg\n",
    "%load_ext pyopencl.ipython_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy\n",
    "from matplotlib.pyplot import subplots\n",
    "import glob\n",
    "import fabio\n",
    "import h5py\n",
    "import hdf5plugin\n",
    "import pyopencl as cl\n",
    "from pyopencl import array as cla\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  811 frames\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"PYOPENCL_CTX\"]=\"0:0\"\n",
    "frames = glob.glob('/mnt/data/ID10/CDI/SiO2msgel3_cand1/img_*.edf')\n",
    "nframes = len(frames)\n",
    "shape = 568, 568\n",
    "volume = (568, 568, 568)\n",
    "center = (284, 284)\n",
    "pixel_size = numpy.float32(55e-6)\n",
    "distance = numpy.float32(3.3)\n",
    "dummy = numpy.float32(0.0)\n",
    "print(\"Processing \",nframes, \"frames\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyopencl.Context at 0x237d030 on <pyopencl.Device 'GeForce GT 1030' on 'NVIDIA CUDA' at 0x19b8eb0>>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctx = cl.create_some_context(interactive=False)\n",
    "queue = cl.CommandQueue(ctx)\n",
    "ctx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cl_kernel \n",
    "\n",
    "\n",
    "// Function to perform an atom addition in global memory (does not exist in OpenCL)\n",
    "inline void atomic_add_global_float(volatile global float *addr, float val)\n",
    "{\n",
    "   union {\n",
    "       uint  u32;\n",
    "       float f32;\n",
    "   } next, expected, current;\n",
    "   current.f32    = *addr;\n",
    "   do {\n",
    "       expected.f32 = current.f32;\n",
    "       next.f32     = expected.f32 + val;\n",
    "       current.u32  = atomic_cmpxchg( (volatile global uint *)addr,\n",
    "                                      expected.u32, next.u32);\n",
    "   } while( current.u32 != expected.u32 );\n",
    "}\n",
    "\n",
    "// Performs the centering/scaling in the detector plane. Image flipping must be implemented here\n",
    "float2 inline calc_position_real(float2 index,\n",
    "                                 float2 center,\n",
    "                                 float pixel_size)\n",
    "{\n",
    "    return (index - center) * pixel_size;\n",
    "}\n",
    "\n",
    "// Transforms a 2D position in the image into a 3D coordinate in the volume\n",
    "float3 inline calc_position_rec(float2 index,\n",
    "                                float2 center,\n",
    "                                float pixel_size,\n",
    "                                float distance,\n",
    "                                float3 Rx,\n",
    "                                float3 Ry,\n",
    "                                float3 Rz)\n",
    "{\n",
    "    float2 pos2 = calc_position_real(index, center, pixel_size);\n",
    "    float d = sqrt(distance*distance + dot(pos2, pos2));\n",
    "    float3 pos3 = (float3)(pos2.x/d, pos2.y/d, distance/d-1.0f);\n",
    "    float scale = distance*distance/pixel_size;\n",
    "    return scale * (float3)(dot(Rx, pos3), dot(Ry, pos3), dot(Rz, pos3));\n",
    "}\n",
    "\n",
    "//Storage for that many voxel per pixel\n",
    "#define STORAGE_SIZE 64\n",
    "\n",
    "kernel void regid_CDI(global float* image,\n",
    "                      const  int    height,\n",
    "                      const  int    width,\n",
    "                      const  float  dummy,\n",
    "                      const  float  pixel_size,\n",
    "                      const  float  distance,\n",
    "                      const  float  phi,\n",
    "                      const  float  center_x,\n",
    "                      const  float  center_y,\n",
    "                      global float* signal,\n",
    "                      global int*   norm,\n",
    "                      const  int    shape,\n",
    "                      int    oversampling)\n",
    "{\n",
    "    int tmp, shape_2, i, j, k;\n",
    "    size_t where_in, where_out;\n",
    "    float value, delta;\n",
    "    float2 pos2, center = (float2)(center_x, center_y);\n",
    "    float3 Rx, Ry, Rz, recip;\n",
    "    \n",
    "    //This is local storage of voxels to be written\n",
    "    int last=0;\n",
    "    size_t index[STORAGE_SIZE];\n",
    "    float2 store[STORAGE_SIZE];\n",
    "    \n",
    "    \n",
    "    \n",
    "    if ((get_global_id(0)>=height) || (get_global_id(1)>=width))\n",
    "        return;\n",
    "    \n",
    "    where_in = width*get_global_id(0)+get_global_id(1);\n",
    "    shape_2 = shape/2;\n",
    "    oversampling = (oversampling<1?1:oversampling);\n",
    "    delta = 1.0f / oversampling;\n",
    "    {   \n",
    "        float cos_phi, sin_phi;\n",
    "        cos_phi = cos(phi*M_PI_F/180.0f);\n",
    "        sin_phi = sin(phi*M_PI_F/180.0f);\n",
    "        Rx = (float3)(cos_phi, 0.0f, sin_phi);\n",
    "        Ry = (float3)(0.0f, 1.0f, 0.0f);\n",
    "        Rz = (float3)(-sin_phi, 0.0f, cos_phi);\n",
    "        \n",
    "    }\n",
    "    \n",
    "    // No oversampling for now\n",
    "    //this is the center of the pixel\n",
    "    //pos2 = (float2)(get_global_id(1)+0.5f, get_global_id(0) + 0.5f); \n",
    "    \n",
    "    \n",
    "    value = image[where_in];\n",
    "    \n",
    "    //dynamic masking\n",
    "    if (value == dummy) return;\n",
    "    \n",
    "    //Basic oversampling    \n",
    "    for (i=0; i<oversampling; i++)\n",
    "    {\n",
    "        for (j=0; j<oversampling; j++)\n",
    "        {\n",
    "            pos2 = (float2)(get_global_id(1) + (i + 0.5f)*delta, \n",
    "                            get_global_id(0) + (j + 0.5f)*delta); \n",
    "            recip = calc_position_rec(pos2, center, pixel_size, distance, Rx, Ry, Rz);\n",
    "            \n",
    "    \n",
    "            tmp = (int)recip.x + shape/2;\n",
    "            if ((tmp>=0) && (tmp<shape))\n",
    "            {\n",
    "                where_out = tmp;\n",
    "                tmp = (int)recip.y + shape_2;\n",
    "                if ((tmp>=0) && (tmp<shape))\n",
    "                {\n",
    "                    where_out += tmp * shape;\n",
    "                    tmp = (int)recip.z + shape_2;\n",
    "                    if ((tmp>=0) && (tmp<shape))\n",
    "                    {\n",
    "                        where_out += ((long)tmp) * shape * shape;                          \n",
    "                        signal[where_out] += value;\n",
    "                        norm[where_out] += 1.0f;\n",
    "                        \n",
    "                        //storage locally\n",
    "                        int found = 0;\n",
    "                        for (k=0; k<last; k++)\n",
    "                        {\n",
    "                            if (where_out == index[k])\n",
    "                            {\n",
    "                                    store[k] += (float2)(value, 1.0f);\n",
    "                                    found = 1;\n",
    "                                    k = last;\n",
    "                            }\n",
    "                        }\n",
    "                        if (found == 0)\n",
    "                        {\n",
    "                            if (last >= STORAGE_SIZE)\n",
    "                                printf(\"Too many voxels covered by pixel\\n\");\n",
    "                            else\n",
    "                            {\n",
    "                                index[last] = where_out;\n",
    "                                store[last] = (float2)(value, 1.0f);\n",
    "                                last++;\n",
    "                            }\n",
    "                        }  \n",
    "                    }\n",
    "                }               \n",
    "            }            \n",
    "        }\n",
    "    }\n",
    "    // Finally we update the global memory with atomic writes\n",
    "    for (k=0; k<last; k++)\n",
    "    {\n",
    "        atomic_add_global_float(&signal[index[k]], store[k].s0);\n",
    "        atomic_add(&norm[index[k]], (int)store[k].s1);\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_d = cla.empty(queue, shape, dtype=numpy.float32)\n",
    "signal_d = cla.empty(queue, volume, dtype=numpy.float32)\n",
    "norm_d = cla.empty(queue, volume, dtype=numpy.int32)\n",
    "ws = (8,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-44.6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def meas_phi(f):\n",
    "    return numpy.float32(f.header.get(\"motor_pos\").split()[f.header.get(\"motor_mne\").split().index(\"ths\")])\n",
    "meas_phi(fabio.open(frames[51]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 32s, sys: 33.5 s, total: 2min 6s\n",
      "Wall time: 2min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "oversampling = numpy.int32(8)\n",
    "signal_d.fill(0.0)\n",
    "norm_d.fill(0)\n",
    "_center = numpy.float32(center[0]),numpy.float32(center[1])\n",
    "_shape = numpy.int32(shape[1]),numpy.int32(shape[0])\n",
    "_volume_shape = numpy.int32(volume[0])\n",
    "ldphi = numpy.linspace(0, 0.2, oversampling//2,endpoint=False,dtype=numpy.float32)\n",
    "for i in frames:\n",
    "    f = fabio.open(i)\n",
    "    image_d.set(f.data)\n",
    "    phi = meas_phi(f)\n",
    "    for dphi in ldphi:\n",
    "        evt = regid_CDI(queue, shape, ws,\n",
    "                                image_d.data,\n",
    "                                *_shape,\n",
    "                                dummy,\n",
    "                                pixel_size,\n",
    "                                distance,\n",
    "                                phi+dphi,\n",
    "                                *_center,\n",
    "                                signal_d.data,\n",
    "                                norm_d.data,\n",
    "                                _volume_shape,\n",
    "                                oversampling)\n",
    "evt.wait()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.05, 0.1 , 0.15], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#norm_d.get().max()\n",
    "ldphi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-39933.2625"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Not enough memory on GPU\n",
    "volume_h=signal_d.get()/norm_d.get()\n",
    "volume_h[numpy.isfinite(volume_h)].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afc4d01ecea64e20af4fbe9eebc8582e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "560efa4eebcd4f3e99e95ff9bd8ba363"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1, 3, figsize=(9,3))\n",
    "x=y=z=255\n",
    "i0=ax[0].imshow(volume_h[:,:,x])\n",
    "i1=ax[1].imshow(volume_h[:,y,:])\n",
    "i2=ax[2].imshow(volume_h[z,:,:])\n",
    "x = widgets.IntSlider(x, 0,volume[2]-1)\n",
    "y = widgets.IntSlider(y, 0,volume[1]-1)\n",
    "z = widgets.IntSlider(z, 0,volume[0]-1)\n",
    "ui = widgets.HBox([x, y, z])\n",
    "def update(x, y, z):\n",
    "    i0.set_data(volume_h[:,:,x])\n",
    "    i1.set_data(volume_h[:,y,:])\n",
    "    i2.set_data(volume_h[z,:,:])\n",
    "    fig.canvas.draw()\n",
    "    fig.canvas.flush_events()\n",
    "\n",
    "out = widgets.interactive_output(update, {'x': x, 'y': y, 'z': z})\n",
    "\n",
    "display(ui, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30424.105, 66.97335278860352)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_d.get().mean(), norm_d.get().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(\"regrid.h5\", mode=\"w\") as h:\n",
    "    h.create_dataset(\"SiO2msgel3\", data=volume_h, **hdf5plugin.Bitshuffle())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cl_kernel \n",
    "\n",
    "// Function to perform an atom addition in global memory (does not exist in OpenCL)\n",
    "inline void atomic_add_global_float(volatile global float *addr, float val)\n",
    "{\n",
    "   union {\n",
    "       uint  u32;\n",
    "       float f32;\n",
    "   } next, expected, current;\n",
    "   current.f32    = *addr;\n",
    "   do {\n",
    "       expected.f32 = current.f32;\n",
    "       next.f32     = expected.f32 + val;\n",
    "       current.u32  = atomic_cmpxchg( (volatile global uint *)addr,\n",
    "                                      expected.u32, next.u32);\n",
    "   } while( current.u32 != expected.u32 );\n",
    "}\n",
    "\n",
    "// Performs the centering/scaling in the detector plane. Image flipping must be implemented here\n",
    "float2 inline calc_position_real(float2 index,\n",
    "                                 float2 center,\n",
    "                                 float pixel_size)\n",
    "{\n",
    "    return (index - center) * pixel_size;\n",
    "}\n",
    "\n",
    "// Transforms a 2D position in the image into a 3D coordinate in the volume\n",
    "float3 inline calc_position_rec(float2 index,\n",
    "                                float2 center,\n",
    "                                float pixel_size,\n",
    "                                float distance,\n",
    "                                float3 Rx,\n",
    "                                float3 Ry,\n",
    "                                float3 Rz)\n",
    "{\n",
    "    float2 pos2 = calc_position_real(index, center, pixel_size);\n",
    "    float d = sqrt(distance*distance + dot(pos2, pos2));\n",
    "    float3 pos3 = (float3)(pos2.x/d, pos2.y/d, distance/d-1.0f);\n",
    "    float scale = distance*distance/pixel_size;\n",
    "    return scale * (float3)(dot(Rx, pos3), dot(Ry, pos3), dot(Rz, pos3));\n",
    "}\n",
    "\n",
    "\n",
    "//Perform a bilinear interpolation of image with 3 channels\n",
    "static float3 bilinear3(float2 target,       //expected coordinated\n",
    "                        local float3* patch, //patch, section of the image in shared memory\n",
    "                        int2 size,           //size of the patch \n",
    "                        int mode) //mode = 1 bilinear or 0 for nearest\n",
    "{\n",
    "    int tx_prev = (int) target.x,\n",
    "        tx_next = tx_prev + 1,\n",
    "        ty_prev = (int) target.y,\n",
    "        ty_next = ty_prev + 1;\n",
    "\n",
    "    float3 interp;\n",
    "\n",
    "    if (0.0f <= target.x && target.x < (size.x - 1) && 0.0f <= target.y && target.y < (size.y - 1) )\n",
    "    {\n",
    "        if (mode == 0) \n",
    "        {   \n",
    "            //nearest neighbour interpolation\n",
    "            interp = patch[(int)(target.y + 0.5) * size.x + (int)(target.x + 0.5)];\n",
    "        }\n",
    "        else\n",
    "        {   //bilinear interpolation: read 4 neighbours\n",
    "            float3 image_p = patch[ ty_prev*size.x + tx_prev],\n",
    "                   image_x = patch[ ty_prev*size.x + tx_next],\n",
    "                   image_y = patch[ ty_next*size.x + tx_prev],\n",
    "                   image_n = patch[ ty_next*size.x + tx_next];\n",
    "\n",
    "             // this guard should not be needed\n",
    "            if (tx_next >= size.x) \n",
    "            {\n",
    "                image_x = image_p;\n",
    "                image_n = image_y;\n",
    "            }\n",
    "            if (ty_next >= size.y) {\n",
    "                image_y = image_p;\n",
    "                image_n = image_x;\n",
    "            }\n",
    "\n",
    "            //bilinear interpolation\n",
    "            float3 interp1 = (tx_next - target.x) * image_p + (target.x - tx_prev) * image_x,\n",
    "                   interp2 = (tx_next - target.x) * image_y + (target.x - tx_prev) * image_n;\n",
    "\n",
    "            interp = (ty_next - target.y) * interp1 + (target.y - ty_prev) * interp2;\n",
    "\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    return interp;\n",
    "}\n",
    "\n",
    "/* Performs the regridding of an image on a 3D volume\n",
    " *\n",
    " * 2D kernel, one thread per input pixel. Scatter-like kernel with atomics.\n",
    " * \n",
    " * pixel start at 0 and finish at 1, the center is at 0.5\n",
    " * thread ids follow the memory location convention (zyx) not the math x,y,z convention \n",
    " *   \n",
    " * Collborative calculation of the 3D position of the kernel (shared memory)\n",
    " * Linear interpolation within every pixel from the corner coordinates\n",
    " * \n",
    " * Needs in shared memory: (wg0+1)*(wg1+1)*4*4  = 17424 for a 32x32 kernel\n",
    " */ \n",
    "\n",
    "\n",
    "kernel void regid_CDI_shared(global float* image,\n",
    "                             const  int height,\n",
    "                             const  int width,\n",
    "                             const  float pixel_size,\n",
    "                             const  float distance,\n",
    "                             const  float phi,\n",
    "                             const  float center_x,\n",
    "                             const  float center_y,\n",
    "                             global float *signal,\n",
    "                             global float *norm,\n",
    "                             const  int shape,\n",
    "                                    int oversampling,\n",
    "                             local float3* corners)\n",
    "{\n",
    "    int tmp, shape_2, i, j, tid;\n",
    "    size_t where_in, where_out;\n",
    "    float value, cos_phi, sin_phi, delta, start;\n",
    "    float2 pos2, center = (float2)(center_x, center_y);\n",
    "    float3 Rx, Ry, Rz, recip, A, B, C, D;\n",
    "    int2 shared_shape = (int2)(1 + get_local_size(0), 1 + get_local_size(1));\n",
    "    \n",
    "    where_in = width*get_global_id(0)+get_global_id(1);\n",
    "    shape_2 = shape/2;\n",
    "    oversampling = (oversampling<1?1:oversampling);\n",
    "    start = 0.5f / oversampling;\n",
    "    delta = 2 * start;\n",
    "    \n",
    "    cos_phi = cos(phi*M_PI_F/180.0f);\n",
    "    sin_phi = sin(phi*M_PI_F/180.0f);\n",
    "    Rx = (float3)(cos_phi, 0.0f, sin_phi);\n",
    "    Ry = (float3)(0.0f, 1.0f, 0.0f);\n",
    "    Rz = (float3)(-sin_phi, 0.0f, cos_phi);\n",
    "\n",
    "    //Populate the shared memory\n",
    "    {\n",
    "        int tid = get_local_size(1)*get_global_id(0)+get_global_id(1);\n",
    "        int ws = get_local_size(1)*get_local_size(0);\n",
    "        int shared_size = shared_shape.s0*shared_shape.s1;\n",
    "        for (i=0; i<shared_size; i+=ws)\n",
    "        {\n",
    "            int x, y, idx;\n",
    "            idx = i+tid;\n",
    "            if (idx<shared_size)\n",
    "            {\n",
    "                x = get_local_size(1)*get_group_id(1) + idx%shared_shape.s1;\n",
    "                y = get_local_size(0)*get_group_id(0) + idx/shared_shape.s1;\n",
    "                corners[i] = calc_position_rec((float2)(x,y), center, pixel_size, distance, Rx, Ry, Rz);\n",
    "            }\n",
    "        }\n",
    "        barrier(CLK_LOCAL_MEM_FENCE);\n",
    "    }\n",
    "    \n",
    "    // No processing for invalide pixels\n",
    "    if ((get_global_id(0)>=height) || (get_global_id(1)>=width))\n",
    "        return;\n",
    "\n",
    "    //manage masked/invalid pixels here\n",
    "    value = image[where_in];\n",
    "    if (!isfinite(value))\n",
    "        return;\n",
    "    \n",
    "    //Basic oversampling    \n",
    "    for (i=0; i<oversampling; i++)\n",
    "    {\n",
    "        for (j=0; j<oversampling; j++)\n",
    "        {\n",
    "            // bilinear interpolation ...\n",
    "            float2 target = (float2)(start + get_local_id(0) + i*delta,\n",
    "                                     start + get_local_id(1) + j*delta);\n",
    "            recip = bilinear3(target,      //expected coordinated\n",
    "                              corners,     //section of the image in shared memory\n",
    "                              shared_shape,//size of the shared memory \n",
    "                              1);           //mode = 1 bilinear or 0 for nearest            \n",
    "    \n",
    "            tmp = (int)recip.x + shape/2;\n",
    "            if ((tmp>=0) && (tmp<shape))\n",
    "            {\n",
    "                where_out = tmp;\n",
    "                tmp = (int)recip.y + shape_2;\n",
    "                if ((tmp>=0) && (tmp<shape))\n",
    "                {\n",
    "                    where_out += tmp * shape;\n",
    "                    tmp = (int)recip.z + shape_2;\n",
    "                    if ((tmp>=0) && (tmp<shape))\n",
    "                    {\n",
    "                        where_out += ((long)tmp) * shape * shape;  \n",
    "                        atomic_add_global_float(&signal[where_out], value);\n",
    "                        atomic_add_global_float(&norm[where_out], 1.0f);\n",
    "                        \n",
    "                        //signal[where_out] += value;\n",
    "                        //norm[where_out] += 1.0f;\n",
    "                    }\n",
    "                }               \n",
    "            }            \n",
    "        }\n",
    "    }\n",
    "}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}