Create colored vector field on image background using napari, based on https://napari.org/stable/gallery/add_vectors_color_by_angle.html

another_attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b455d2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# napari attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d18965e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "import numpy as np\n",
    "from skimage import data\n",
    "\n",
    "# create vector data\n",
    "n = 250\n",
    "vectors = np.zeros((n, 2, 2), dtype=np.float32)\n",
    "phi_space = np.linspace(0, 4 * np.pi, n)\n",
    "radius_space = np.linspace(0, 100, n)\n",
    "# assign x-y projection\n",
    "vectors[:, 1, 0] = radius_space * np.cos(phi_space)\n",
    "vectors[:, 1, 1] = radius_space * np.sin(phi_space)\n",
    "# assign x-y position\n",
    "vectors[:, 0] = vectors[:, 1] + 256\n",
    "\n",
    "# add the image\n",
    "viewer = napari.view_image(data.camera(), name='photographer')\n",
    "# add the vectors\n",
    "vectors_layer = viewer.add_vectors(vectors, edge_width=3, edge_colormap = 'RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c2b618d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "from skimage import data\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# create the viewer and window\n",
    "viewer = napari.Viewer()\n",
    "\n",
    "layer = viewer.add_image(data.camera(), name='photographer')\n",
    "\n",
    "# sample vector coord-like data\n",
    "n = 300\n",
    "pos = np.zeros((n, 2, 2), dtype=np.float32)\n",
    "phi_space = np.linspace(0, 4 * np.pi, n)\n",
    "radius_space = np.linspace(0, 100, n)\n",
    "\n",
    "# assign x-y position\n",
    "pos[:, 0, 0] = radius_space * np.cos(phi_space) + 300\n",
    "pos[:, 0, 1] = radius_space * np.sin(phi_space) + 256\n",
    "\n",
    "# assign x-y projection\n",
    "pos[:, 1, 0] = 2 * radius_space * np.cos(phi_space)\n",
    "pos[:, 1, 1] = 2 * radius_space * np.sin(phi_space)\n",
    "\n",
    "# make the angle feature, range 0-2pi\n",
    "angle = np.mod(phi_space, 2 * np.pi)\n",
    "\n",
    "# create a feature that is true for all angles  > pi\n",
    "pos_angle = angle > np.pi\n",
    "\n",
    "\n",
    "\n",
    "# create the features dictionary.\n",
    "features = {\n",
    "    'angle': angle,\n",
    "    'pos_angle': pos_angle,\n",
    "    'magnitude': radius_space,\n",
    "}\n",
    "\n",
    "# add the vectors\n",
    "layer = viewer.add_vectors(\n",
    "    pos,\n",
    "    edge_width=3,\n",
    "    features=features,\n",
    "    edge_color='magnitude',\n",
    "    edge_colormap='husl',\n",
    "    name='vectors'\n",
    ")\n",
    "\n",
    "# set the edge color mode to colormap\n",
    "layer.edge_color_mode = 'colormap'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd0a0c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer = napari.Viewer()\n",
    "layer = viewer.add_image(a, name='a')\n",
    "\n",
    "# viewer.add_image(b, name ='b')\n",
    "\n",
    "s = 10\n",
    "\n",
    "vectors = np.zeros((len(u.flatten()), 2, 2), dtype=np.float32)\n",
    "vectors[:,0,1] = x.flatten()\n",
    "vectors[:,0,0] = y.flatten()\n",
    "\n",
    "\n",
    "u[np.isnan(u)] = 0\n",
    "v[np.isnan(v)] = 0\n",
    "\n",
    "vectors[:,1,1] = s*u.flatten()\n",
    "vectors[:,1,0] = s*v.flatten()\n",
    "\n",
    "magnitude = np.sqrt(u.flatten()**2 + v.flatten()**2)\n",
    "angle = np.arctan2(u.flatten(),v.flatten())\n",
    "\n",
    "# create the features dictionary.\n",
    "features = {\n",
    "    'angle': angle,\n",
    "    # 'pos_angle': pos_angle,\n",
    "    'magnitude': magnitude,\n",
    "}\n",
    "\n",
    "\n",
    "# add the vectors\n",
    "layer = viewer.add_vectors(\n",
    "    vectors,\n",
    "    edge_width=3,\n",
    "    features=features,\n",
    "    edge_color='magnitude',\n",
    "    edge_colormap='husl',\n",
    "    name='vectors'\n",
    ")\n",
    "\n",
    "# set the edge color mode to colormap\n",
    "layer.edge_color_mode = 'colormap'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "16d3ba68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/.edm/envs/napari/lib/python3.8/site-packages/ipykernel/eventloops.py:107: UserWarning: constrained_layout not applied because axes sizes collapsed to zero.  Try making figure larger or axes decorations smaller.\n",
      "  app.exec_()\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Viewing mass spec imaging data in napari.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import napari\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_qt5agg import FigureCanvas\n",
    "from matplotlib.figure import Figure\n",
    "\n",
    "\n",
    "# # load the data\n",
    "# cube = np.load('datacube.npy')\n",
    "# peaks = np.load('peaklist.npy')\n",
    "# mz = peaks[0]\n",
    "# thresh = np.load('hsr_thresholds.npy')\n",
    "# cubet = np.transpose(cube, (2, 0, 1))\n",
    "# cubet_norm = cubet / thresh[:, np.newaxis, np.newaxis]\n",
    "\n",
    "\n",
    "# # create the viewer\n",
    "# viewer = napari.view_image(cubet_norm)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "viewer = napari.Viewer()\n",
    "layer = viewer.add_image(a, name='a')\n",
    "\n",
    "# viewer.add_image(b, name ='b')\n",
    "\n",
    "s = 10\n",
    "\n",
    "vectors = np.zeros((len(u.flatten()), 2, 2), dtype=np.float32)\n",
    "vectors[:,0,1] = x.flatten()\n",
    "vectors[:,0,0] = y.flatten()\n",
    "\n",
    "\n",
    "u[np.isnan(u)] = 0\n",
    "v[np.isnan(v)] = 0\n",
    "\n",
    "vectors[:,1,1] = s*u.flatten()\n",
    "vectors[:,1,0] = s*v.flatten()\n",
    "\n",
    "magnitude = np.sqrt(u.flatten()**2 + v.flatten()**2)\n",
    "angle = np.arctan2(u.flatten(),v.flatten())\n",
    "\n",
    "# create the features dictionary.\n",
    "features = {\n",
    "    'angle': angle,\n",
    "    # 'pos_angle': pos_angle,\n",
    "    'magnitude': magnitude,\n",
    "}\n",
    "\n",
    "\n",
    "# add the vectors\n",
    "layer = viewer.add_vectors(\n",
    "    vectors,\n",
    "    edge_width=3,\n",
    "    features=features,\n",
    "    edge_color='magnitude',\n",
    "    edge_colormap='bwr',\n",
    "    name='vectors'\n",
    ")\n",
    "\n",
    "# set the edge color mode to colormap\n",
    "layer.edge_color_mode = 'colormap'\n",
    "\n",
    "with plt.style.context('dark_background'):\n",
    "\n",
    "    colorbars_widget = FigureCanvas(Figure(constrained_layout=True))\n",
    "    fig = colorbars_widget.figure\n",
    "\n",
    "    gs = fig.add_gridspec(1, 1)\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    # ax2 = fig.add_subplot(gs[1, 0])\n",
    "    # ax3 = fig.add_subplot(gs[2, 0])\n",
    "\n",
    "    cmap1 = mpl.cm.bwr\n",
    "    mappable1 = mpl.cm.ScalarMappable(cmap=cmap1)\n",
    "    colorbar1 = fig.colorbar(mappable1, cax=ax1, orientation='vertical')\n",
    "    colorbar1.set_label('husl')\n",
    "\n",
    "#     cmap2 = mpl.cm.gist_rainbow\n",
    "#     mappable2 = mpl.cm.ScalarMappable(cmap=cmap2)\n",
    "#     colorbar2 = fig.colorbar(mappable2, cax=ax2, orientation='horizontal')\n",
    "#     colorbar2.set_label('gist_rainbow')\n",
    "\n",
    "#     cmap3 = mpl.cm.viridis\n",
    "#     mappable3 = mpl.cm.ScalarMappable(cmap=cmap3)\n",
    "#     colorbar3 = fig.colorbar(mappable3, cax=ax3, orientation='horizontal')\n",
    "#     colorbar3.set_label('viridis')\n",
    "\n",
    "\n",
    "# layer = viewer.add_surface((brain_vertices, brain_faces, timeseries))\n",
    "# layer.colormap = cmap1.name\n",
    "\n",
    "viewer.dims.events.connect(axis_changed)\n",
    "\n",
    "colorbars_widget.setFixedWidth(30)\n",
    "# colorbars_widget.setFixedHeight(200)\n",
    "\n",
    "colorbars_dock = viewer.window.add_dock_widget(colorbars_widget, name='Colorbars', area = 'right')\n",
    "\n",
    "# # create a function to update the plot\n",
    "# def update_plot(step_event):\n",
    "#     steps = step_event.value\n",
    "#     slice_num = steps[0]\n",
    "#     x = mz[slice_num]\n",
    "#     current_pos = position_line.get_data()[0][0]\n",
    "#     if x == current_pos:\n",
    "#         return\n",
    "#     position_line.set_data([x, x], [0, 1])\n",
    "#     coord_str, mz_str = title.get_text().split(';')\n",
    "#     title.set_text(coord_str + '; ' + f'm/z={x:.3f}')\n",
    "#     mz_canvas.draw_idle()\n",
    "\n",
    "\n",
    "# # connect the function to the dims axis\n",
    "# viewer.dims.events.current_step.connect(update_plot)\n",
    "\n",
    "# # grab the image layer\n",
    "# layer = viewer.layers[0]\n",
    "\n",
    "\n",
    "# # add a click callback to the layer to update the spectrum being viewed\n",
    "# @layer.mouse_drag_callbacks.append\n",
    "# def update_intensity(layer, event):\n",
    "#     xs, ys = intensity_line.get_data()\n",
    "#     coords_full = tuple(np.round(layer.coordinates).astype(int))\n",
    "#     if all(coords_full[i] in range(cubet.shape[i])\n",
    "#             for i in range(cubet.ndim)):\n",
    "#         coords = coords_full[1:]  # rows, columns\n",
    "#         new_ys = cube[coords]\n",
    "#         intensity_line.set_data(xs, new_ys)\n",
    "#         coord_str, mz_str = title.get_text().split(';')\n",
    "#         title.set_text(str(coords) + '; ' + mz_str)\n",
    "#         mz_canvas.draw_idle()\n",
    "\n",
    "# napari.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70580c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from matplotlib.backends.backend_qt5agg import FigureCanvas\n",
    "# from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "# from matplotlib.figure import Figure\n",
    "# import matplotlib.pyplot as plt\n",
    "# import napari\n",
    "\n",
    "# %gui qt\n",
    "\n",
    "# def get_colour(layer,track_IDs):\n",
    "    \n",
    "#     colors = np.ones((max(track_IDs)+1,4))\n",
    "    \n",
    "#     for cell in track_IDs:\n",
    "#         colors[cell] = layer.get_color(cell)\n",
    "#     colors[0] = [0,0,0,1]\n",
    "#     newcmp = ListedColormap(colors)\n",
    "    \n",
    "#     return newcmp\n",
    "\n",
    "# def update_time(event):\n",
    "#     static_ax.ti_vline.set_xdata([event.value,event.value])\n",
    "#     static_ax.figure.canvas.draw()\n",
    "\n",
    "# # data variables\n",
    "# # tracks_img = fov.tracks_curated.mod\n",
    "# # tracks_length = fov.tracks_curated.tracks_length\n",
    "# # cellIDs = fov.tracks_curated.cellIDs\n",
    "\n",
    "# viewer = napari.view_labels(tracks_img,name='Curation',opacity=1)\n",
    "\n",
    "# #generate colourmap\n",
    "# newcmp = get_colour(viewer.layers['Curation'],cellIDs)\n",
    "\n",
    "# cellIDs_text = [str(int(integer)) for integer in cellIDs]\n",
    "\n",
    "# with plt.style.context('dark_background'):\n",
    "#     plt.rcParams['figure.dpi'] = 110\n",
    "#     mpl_widget = FigureCanvas(Figure(figsize=(8.5, 13)))\n",
    "#     static_ax = mpl_widget.figure.subplots()\n",
    "#     static_ax.imshow(tracks_length,cmap=newcmp,aspect='auto')\n",
    "#     static_ax.set(xlim=(0, 120), ylim=len(cellIDs))\n",
    "#     static_ax.ti_vline = static_ax.axvline(0,0,len(cellIDs)+0.5,color='yellow',linewidth=4)\n",
    "#     static_ax.set_xticks(np.arange(0, 121, 6))\n",
    "#     static_ax.set_yticks(np.arange(0, len(cellIDs)+1, 1))\n",
    "#     static_ax.set_yticklabels(cellIDs_text)\n",
    "#     static_ax.tick_params(labelright=True, right=True,labeltop=True,top=True,colors='white')\n",
    "#     mpl_widget.figure.tight_layout()\n",
    "    \n",
    "# mywidget = viewer.window.add_dock_widget(mpl_widget,name='tracks_length',area='right',shortcut='t')\n",
    "\n",
    "# viewer.dims.events.axis.connect(update_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c0d03b01",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'flux_from_day' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [59]\u001b[0m, in \u001b[0;36m<cell line: 58>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     54\u001b[0m update \u001b[38;5;241m=\u001b[39m partial(update_artists, artists\u001b[38;5;241m=\u001b[39martists,\n\u001b[1;32m     55\u001b[0m                  lambdas\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m3298\u001b[39m, \u001b[38;5;241m9700\u001b[39m, \u001b[38;5;241m2.5\u001b[39m))\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# 4. Generate the animation\u001b[39;00m\n\u001b[1;32m     58\u001b[0m anim \u001b[38;5;241m=\u001b[39m animation\u001b[38;5;241m.\u001b[39mFuncAnimation(\n\u001b[1;32m     59\u001b[0m     fig\u001b[38;5;241m=\u001b[39mfig,\n\u001b[1;32m     60\u001b[0m     func\u001b[38;5;241m=\u001b[39mupdate,\n\u001b[1;32m     61\u001b[0m     frames\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m     62\u001b[0m     init_func\u001b[38;5;241m=\u001b[39minit,\n\u001b[0;32m---> 63\u001b[0m     save_count\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m),\n\u001b[1;32m     64\u001b[0m     repeat_delay\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5000\u001b[39m,\n\u001b[1;32m     65\u001b[0m )\n\u001b[1;32m     67\u001b[0m \u001b[38;5;66;03m# 5. Save the animation\u001b[39;00m\n\u001b[1;32m     68\u001b[0m anim\u001b[38;5;241m.\u001b[39msave(\n\u001b[1;32m     69\u001b[0m   filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/tmp/sn2011fe_spectral_time_series.mp4\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     70\u001b[0m   fps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m24\u001b[39m,\n\u001b[1;32m     71\u001b[0m   extra_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-vcodec\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlibx264\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m     72\u001b[0m   dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m,\n\u001b[1;32m     73\u001b[0m )\n",
      "Input \u001b[0;32mIn [59]\u001b[0m, in \u001b[0;36mframe_iter\u001b[0;34m(from_day, until_day)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m\"\"\"Iterate through the days of the spectra and return\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;124;03mflux and day number.\u001b[39;00m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m day \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(from_day, until_day):\n\u001b[0;32m---> 28\u001b[0m     flux \u001b[38;5;241m=\u001b[39m \u001b[43mflux_from_day\u001b[49m(day)\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;66;03m# Yield events so the function can be looped over\u001b[39;00m\n\u001b[1;32m     30\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m (flux, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDay: \u001b[39m\u001b[38;5;132;01m{day}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(day))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'flux_from_day' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "from functools import partial\n",
    "from matplotlib import animation\n",
    "\n",
    "def init_fig(fig, ax, artists):\n",
    "    \"\"\"Initialize the figure, used to draw the first\n",
    "    frame for the animation.\n",
    "    \"\"\"\n",
    "    # Set the axis and plot titles\n",
    "    ax.set_title(\"Supernova 2011fe Spectrum\", fontsize=22)\n",
    "    ax.set_xlabel(\"Wavelength [Å]\", fontsize=20)\n",
    "    FLUX_LABEL = \"Flux [erg s$^{-1}$ cm$^{-2}$ Å$^{-1}$]\"\n",
    "    ax.set_ylabel(FLUX_LABEL, fontsize=20)\n",
    "\n",
    "    # Set the axis range\n",
    "    plt.xlim(3000, 10000)\n",
    "    plt.ylim(0, 1.25e-12)\n",
    "\n",
    "    # Must return the list of artists, but we use a pass\n",
    "    # through so that they aren't created multiple times\n",
    "    return artists\n",
    "\n",
    "def frame_iter(from_day, until_day):\n",
    "    \"\"\"Iterate through the days of the spectra and return\n",
    "    flux and day number.\n",
    "    \"\"\"\n",
    "    for day in range(from_day, until_day):\n",
    "        flux = flux_from_day(day)\n",
    "        # Yield events so the function can be looped over\n",
    "        yield (flux, \"Day: {day}\".format(day))\n",
    "\n",
    "def update_artists(frames, artists, lambdas):\n",
    "    \"\"\"Update artists with data from each frame.\"\"\"\n",
    "    flux, day = frames\n",
    "\n",
    "    artists.flux_line.set_data(lambdas, flux)\n",
    "    artists.day.set_text(day)\n",
    "\n",
    "    \n",
    "\n",
    "# 1. Create the plot\n",
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "# 2. Initialize the artists with empty data\n",
    "Artists = namedtuple(\"Artists\", (\"flux_line\", \"day\"))\n",
    "artists = Artists(\n",
    "    plt.plot([], [], animated=True)[0],\n",
    "    ax.text(x=0.987, y=0.955, s=\"\"),\n",
    ")\n",
    "\n",
    "# 3. Apply the three plotting functions written above\n",
    "init = partial(init_fig, fig=fig, ax=ax, artists=artists)\n",
    "step = partial(frame_iter, from_day=-15, until_day=25)\n",
    "update = partial(update_artists, artists=artists,\n",
    "                 lambdas=np.arange(3298, 9700, 2.5))\n",
    "\n",
    "# 4. Generate the animation\n",
    "anim = animation.FuncAnimation(\n",
    "    fig=fig,\n",
    "    func=update,\n",
    "    frames=step,\n",
    "    init_func=init,\n",
    "    save_count=len(list(step())),\n",
    "    repeat_delay=5000,\n",
    ")\n",
    "\n",
    "# 5. Save the animation\n",
    "anim.save(\n",
    "  filename='/tmp/sn2011fe_spectral_time_series.mp4',\n",
    "  fps=24,\n",
    "  extra_args=['-vcodec', 'libx264'],\n",
    "  dpi=300,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b316278",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68b650a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

create_animated_quiver_on_image_using_napari.ipynb