From 39a60e2fa9fdee44c5a37da582c51f450ec5b414 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ant=C3=B3nio=20Brito?=
 <50997716+antmsbrito@users.noreply.github.com>
Date: Wed, 9 Aug 2023 14:43:59 +0100
Subject: [PATCH] Updated test notebook

---
 .../notebooks/le_interpolation_catmull_rom.ipynb | 16 +++++++---------
 .../le_interpolation_nearest_neighbor.ipynb      | 15 ++++++---------
 2 files changed, 13 insertions(+), 18 deletions(-)

diff --git a/tests/notebooks/le_interpolation_catmull_rom.ipynb b/tests/notebooks/le_interpolation_catmull_rom.ipynb
index 7f5c3527..56a9c5f7 100644
--- a/tests/notebooks/le_interpolation_catmull_rom.ipynb
+++ b/tests/notebooks/le_interpolation_catmull_rom.ipynb
@@ -6,20 +6,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "from nanopyx.liquid._le_interpolation_catmull_rom import ShiftAndMagnify\n",
     "from nanopyx.core.generate.noise_add_simplex import get_simplex_noise\n",
     "from nanopyx.core.transform.binning import rebin_2d\n",
     "\n",
-    "M = 11\n",
-    "# downloader = ExampleDataManager()\n",
-    "# z = downloader.get_ZipTiffIterator('SMLMS2013_HDTubulinAlexa647')\n",
-    "#image = z[0]\n",
+    "M = 2\n",
     "\n",
-    "image = get_simplex_noise(64*M, 32*M, amplitude=1000)\n",
+    "image = get_simplex_noise(64*M, 32*M, amplitude=1000).astype(np.float32)\n",
     "imageDownsampled = rebin_2d(image, M, mode=\"mean\")\n",
     "\n",
     "SM = ShiftAndMagnify(testing=True)\n",
-    "bench_values = SM.benchmark(image, 10, -10, 4, 4)\n",
+    "bench_values = SM.benchmark(image, 10, 0, 2, 2)\n",
     "\n",
     "images = []\n",
     "titles = []\n",
@@ -30,7 +28,7 @@
     "    run_times.append(run_time)\n",
     "    titles.append(title)\n",
     "    images.append(image[0,:,:])\n",
-    "    \n",
+    "        \n",
     "# show images in seaborn\n",
     "from matplotlib import pyplot as plt\n",
     "import seaborn as sns\n",
@@ -73,10 +71,10 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "image = np.repeat(get_simplex_noise(512, 512, amplitude=1000)[np.newaxis,:, :], 3, axis=0)\n",
+    "image = np.repeat(get_simplex_noise(256, 256, amplitude=1000)[np.newaxis,:, :], 3, axis=0)\n",
     "\n",
     "SM = ShiftAndMagnify(testing=True)\n",
-    "bench_values = SM.benchmark(image, 5, -5, 4, 4)\n",
+    "bench_values = SM.benchmark(image, 200, -200, 4, 4)\n",
     "\n",
     "images = []\n",
     "titles = []\n",
diff --git a/tests/notebooks/le_interpolation_nearest_neighbor.ipynb b/tests/notebooks/le_interpolation_nearest_neighbor.ipynb
index 61215718..61137758 100644
--- a/tests/notebooks/le_interpolation_nearest_neighbor.ipynb
+++ b/tests/notebooks/le_interpolation_nearest_neighbor.ipynb
@@ -8,16 +8,13 @@
    },
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "from nanopyx.liquid._le_interpolation_nearest_neighbor import ShiftAndMagnify\n",
     "from nanopyx.core.generate.noise_add_simplex import get_simplex_noise\n",
     "from nanopyx.core.transform.binning import rebin_2d\n",
     "\n",
-    "M = 21\n",
-    "# downloader = ExampleDataManager()\n",
-    "# z = downloader.get_ZipTiffIterator('SMLMS2013_HDTubulinAlexa647')\n",
-    "#image = z[0]\n",
-    "\n",
-    "image = get_simplex_noise(64*M, 32*M, amplitude=1000)\n",
+    "M = 2\n",
+    "image = get_simplex_noise(64*M, 32*M, amplitude=1000).astype(np.float32)\n",
     "imageDownsampled = rebin_2d(image, M, mode=\"mean\")\n",
     "\n",
     "SM = ShiftAndMagnify(testing=True)\n",
@@ -79,10 +76,10 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "image = np.repeat(get_simplex_noise(512, 512, amplitude=1000)[np.newaxis,:, :], 3, axis=0)\n",
-    "\n",
+    "image = np.repeat(get_simplex_noise(256, 256, amplitude=1000)[np.newaxis,:, :], 3, axis=0)\n",
+    "print(image.shape)\n",
     "SM = ShiftAndMagnify(testing=True)\n",
-    "bench_values = SM.benchmark(image, 5, -5, 4, 4)\n",
+    "bench_values = SM.benchmark(image, 200, -200, 4, 4)\n",
     "\n",
     "images = []\n",
     "titles = []\n",