fastplotlib · kushalkolar · Jul 6, 2023 · Jun 29, 2023 · Jun 29, 2023 · Jun 30, 2023
diff --git a/examples/notebooks/heatmap.ipynb b/examples/notebooks/heatmap.ipynb
@@ -20,17 +20,15 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import fastplotlib as fpl\n",
-    "\n",
-    "from tqdm import tqdm"
+    "import fastplotlib as fpl"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "908f93f8-68c3-4a36-8f40-e0aab560955d",
    "metadata": {},
    "source": [
-    "## Generate some random neural-like data"
+    "## Generate some sine and cosine data"
    ]
   },
   {
@@ -42,70 +40,47 @@
    },
    "outputs": [],
    "source": [
-    "def generate_traces(n_components, n_frames):\n",
-    "    n_frames = n_frames + 50\n",
-    "    n_components = n_components\n",
-    "    \n",
-    "    out = np.zeros((n_components, n_frames), dtype=np.float16)\n",
-    "    \n",
-    "    xs = np.arange(0, 50, 1)\n",
-    "    # exponential decay\n",
-    "    _lambda = 0.1\n",
-    "    ys = np.e**-(_lambda * xs)\n",
-    "    \n",
-    "    for component_num in tqdm(range(n_components)):\n",
-    "        time_step = 0\n",
-    "        while time_step < n_frames - 50:\n",
-    "            firing_prop = np.random.randint(0, 20)\n",
-    "            if np.random.poisson() > firing_prop:\n",
-    "                out[component_num, time_step:min(time_step + 50, n_frames - 1)] = ys.astype(np.float16)\n",
-    "                time_step += 100\n",
-    "            else:\n",
-    "                time_step += 2\n",
-    "                \n",
-    "    return out[:, :n_frames - 50]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc1070d9-f9e9-405f-939c-a130cc5c456a",
-   "metadata": {},
-   "source": [
-    "Generate an array that is `10,000 x 30,000`, this may take a few minutes"
+    "xs = np.linspace(0, 50, 10_000)\n",
+    "\n",
+    "sine_data = np.sin(xs)\n",
+    "\n",
+    "cosine_data = np.cos(xs)\n",
+    "\n",
+    "data = np.vstack([(sine_data, cosine_data) for i in range(5)])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8a1b83f6-c0d8-4237-abd6-b483e7d978ee",
+   "id": "02b072eb-2909-40c8-8739-950f07efbbc2",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "temporal = generate_traces(10_000, 30_000)"
+    "data.shape"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f89bd740-7397-43e7-9e66-d6cfb14de884",
+   "id": "84deb31b-5464-4cce-a938-694371011021",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
     "plot = fpl.Plot()\n",
     "\n",
-    "plot.add_heatmap(temporal, cmap=\"viridis\")\n",
+    "plot.add_heatmap(data, cmap=\"viridis\")\n",
     "\n",
     "plot.show(maintain_aspect=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84deb31b-5464-4cce-a938-694371011021",
+   "id": "df3f8994-0f5b-4578-a36d-4cd9bf0733c0",
    "metadata": {},
    "outputs": [],
    "source": []

diff --git a/fastplotlib/graphics/image.py b/fastplotlib/graphics/image.py
@@ -500,8 +500,8 @@ def __init__(
             img.row_chunk_index = chunk[0]
             img.col_chunk_index = chunk[1]
 
-            img.position_x = x_pos
-            img.position_y = y_pos
+            img.world.x = x_pos
+            img.world.y = y_pos
 
             self.world_object.add(img)