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

create_animated_quiver_on_image_using_napari.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ff2cc68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# napari attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "380d28db",
   "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": 14,
   "id": "c2031266",
   "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": 41,
   "id": "15679e5b",
   "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",
    "\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_qt5agg import FigureCanvas\n",
    "from matplotlib.figure import Figure\n",
    "\n",
    "NUM_ITER = 500\n",
    "\n",
    "# build the plot, but don't display it yet\n",
    "# — we'll add it to the napari viewer later\n",
    "with plt.style.context('dark_background'):\n",
    "    loss_canvas = FigureCanvas(Figure(figsize=(5, 3)))\n",
    "    loss_axes = loss_canvas.figure.subplots()\n",
    "    lines = loss_axes.plot([], [])  # make empty plot\n",
    "    loss_axes.set_xlim(0, NUM_ITER)\n",
    "    loss_axes.set_xlabel('batch number')\n",
    "    loss_axes.set_ylabel('loss')\n",
    "    loss_canvas.figure.tight_layout()\n",
    "    loss_line = lines[0]\n",
    "\n",
    "# Napari's threading utilities, created by Talley Lambert, allow *yielding*\n",
    "# of values during a thread's execution, and connecting those yielded\n",
    "\n",
    "\n",
    "# ewer.add_vectors(vectors, edge_width=0.4, name='vectors', edge_colormap='bwr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6da294b0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'nilearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [42]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfigure\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Figure\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnilearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnilearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m surface\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nilearn'"
     ]
    }
   ],
   "source": [
    "import napari\n",
    "import matplotlib as mpl\n",
    "from matplotlib.backends.backend_qt5agg import FigureCanvas\n",
    "from matplotlib.figure import Figure\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from nilearn import datasets\n",
    "from nilearn import surface\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "nki_dataset = datasets.fetch_surf_nki_enhanced(n_subjects=1)\n",
    "fsaverage = datasets.fetch_surf_fsaverage()\n",
    "\n",
    "brain_vertices, brain_faces = surface.load_surf_data(fsaverage['pial_left'])\n",
    "timeseries = surface.load_surf_data(nki_dataset['func_left'][0])\n",
    "timeseries = timeseries.transpose((1, 0))\n",
    "\n",
    "def axis_changed(event):\n",
    "\n",
    "    idx_max = viewer.dims.range[0][1]\n",
    "    idx = viewer.dims.current_step[0]\n",
    "\n",
    "    if idx < idx_max and layer.visible:\n",
    "        random_number = np.random.random()\n",
    "        mappable1.set_clim(random_number, random_number+1)\n",
    "        mappable2.set_clim(random_number, random_number+1)\n",
    "        mappable3.set_clim(random_number, random_number+1)\n",
    "        # comment next line to check performance\n",
    "        colorbars_widget.draw_idle()\n",
    "\n",
    "\n",
    "viewer = napari.Viewer(ndisplay=3)\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(3, 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.jet\n",
    "    mappable1 = mpl.cm.ScalarMappable(cmap=cmap1)\n",
    "    colorbar1 = fig.colorbar(mappable1, cax=ax1, orientation='horizontal')\n",
    "    colorbar1.set_label('jet')\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.axis.connect(axis_changed)\n",
    "\n",
    "colorbars_widget.setFixedWidth(300)\n",
    "colorbars_widget.setFixedHeight(200)\n",
    "\n",
    "colorbars_dock = viewer.window.add_dock_widget(colorbars_widget, name='Colorbars', area = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f8dca141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Using cached lxml-4.9.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.9 MB)\n",
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     ]
    }
   ],
   "source": [
    "!pip install nilearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6340090",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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