u

Author: giovannivolpe

tutorials/3-advanced-topics/DTAT399D_backend.polynomials.ipynb

@@ -73,14 +73,14 @@
     "\n",
     "for l in range(3):\n",
     "    J_values = polynomials.besselj(l, x)\n",
-    "    plt.plot(x, J_values, label=f'Bessel J_{l}(x)')\n",
+    "    plt.plot(x, J_values, label=f\"Bessel J_{l}(x)\")\n",
     "\n",
-    "plt.title('Bessel Functions of the First Kind')\n",
+    "plt.title(\"Bessel Functions of the First Kind\")\n",
     "\n",
-    "plt.xlabel('x')\n",
+    "plt.xlabel(\"x\")\n",
     "plt.xlim(0, 20)\n",
     "\n",
-    "plt.ylabel('Function Value')\n",
+    "plt.ylabel(\"Function Value\")\n",
     "plt.ylim(-1, 1)\n",
     "\n",
     "plt.legend()\n",
@@ -111,14 +111,14 @@
     "\n",
     "for l in range(3):\n",
     "    Y_values = polynomials.bessely(l, x)\n",
-    "    plt.plot(x, Y_values, label=f'Bessel Y_{l}(x)')\n",
+    "    plt.plot(x, Y_values, label=f\"Bessel Y_{l}(x)\")\n",
     "\n",
-    "plt.title('Bessel Functions of the Second Kind')\n",
+    "plt.title(\"Bessel Functions of the Second Kind\")\n",
     "\n",
-    "plt.xlabel('x')\n",
+    "plt.xlabel(\"x\")\n",
     "plt.xlim(0, 20)\n",
     "\n",
-    "plt.ylabel('Function Value')\n",
+    "plt.ylabel(\"Function Value\")\n",
     "plt.ylim(-1, 1)\n",
     "\n",
     "plt.legend()\n",
@@ -158,14 +158,14 @@
     "\n",
     "for l in range(3):\n",
     "    RicJ_values = polynomials.ricbesj(l, x)\n",
-    "    plt.plot(x, RicJ_values, label=f'Riccati-Bessel J_{l}(x)')\n",
+    "    plt.plot(x, RicJ_values, label=f\"Riccati-Bessel J_{l}(x)\")\n",
     "\n",
-    "plt.title('Riccati-Bessel Functions of the First Kind')\n",
+    "plt.title(\"Riccati-Bessel Functions of the First Kind\")\n",
     "\n",
-    "plt.xlabel('x')\n",
+    "plt.xlabel(\"x\")\n",
     "plt.xlim(0, 20)\n",
     "\n",
-    "plt.ylabel('Function Value')\n",
+    "plt.ylabel(\"Function Value\")\n",
     "plt.ylim(-1.4, 1.4)\n",
     "\n",
     "plt.legend()\n",
@@ -204,13 +204,16 @@
     "\n",
     "for l in range(3):\n",
     "    RicY_values = polynomials.ricbesy(l, x)\n",
-    "    plt.plot(x, RicY_values, label=f'Riccati-Bessel Y_{l}(x)')\n",
+    "    plt.plot(x, RicY_values, label=f\"Riccati-Bessel Y_{l}(x)\")\n",
     "\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('Function Value')\n",
-    "plt.title('Riccati-Bessel Functions of the Second Kind')\n",
+    "plt.title(\"Riccati-Bessel Functions of the Second Kind\")\n",
+    "\n",
+    "plt.xlabel(\"x\")\n",
     "plt.xlim(0, 20)\n",
+    "\n",
+    "plt.ylabel(\"Function Value\")\n",
     "plt.ylim(-1.4, 1.4)\n",
+    "\n",
     "plt.legend()\n",
     "plt.grid()\n",
     "plt.show()"

tutorials/3-advanced-topics/DTAT399E_backend.mie.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,9 +43,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A coefficients: [1.04581094e-03-2.45891920e-02j 5.61754632e-06-3.39444972e-04j\n",
+      " 3.54498410e-08-2.25608771e-06j 1.33314410e-10-8.61596363e-09j\n",
+      " 3.25668489e-13-2.12414217e-11j]\n",
+      "B coefficients: [2.22095481e-05-8.65285036e-04j 1.48987829e-07-6.11710129e-06j\n",
+      " 5.86860069e-10-2.42490110e-08j 1.47867923e-12-6.12742871e-11j\n",
+      " 2.58354155e-15-1.07228248e-13j]\n"
+     ]
+    }
+   ],
    "source": [
     "from deeptrack.backend import mie\n",
     "\n",
@@ -70,15 +83,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A coefficients: [4.51815568e-03-4.14667025e-02j 9.00848393e-05-8.90449307e-04j\n",
+      " 1.30191942e-06-9.70936049e-06j 1.04767670e-08-6.35738175e-08j\n",
+      " 5.30930040e-11-2.77931029e-10j]\n",
+      "B coefficients: [2.08384699e-04-1.97056962e-03j 2.98780924e-06-2.15999592e-05j\n",
+      " 2.41258487e-08-1.40444143e-07j 1.22573034e-10-6.07956361e-10j\n",
+      " 4.27642496e-13-1.88504769e-12j]\n"
+     ]
+    }
+   ],
    "source": [
     "particle_radii = [0.5, 0.6, 0.7]\n",
-    "relative_refract_index = 1.5 + 0.01j\n",
+    "relative_refract_index = [1.5 + 0.01j, 1.3 + 0.02j, 1.1 + 0.03j]\n",
     "max_order = 5\n",
     "\n",
-    "A, B = mie.stratified_coefficients(relative_refract_index, particle_radii, max_order)\n",
+    "A, B = mie.stratified_coefficients(\n",
+    "    relative_refract_index, particle_radii, max_order,\n",
+    ")\n",
     "\n",
     "print(\"A coefficients:\", A)\n",
     "print(\"B coefficients:\", B)"
@@ -93,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -110,13 +138,13 @@
        "(-0.5, 99.5, 4.5, -0.5)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -151,7 +179,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "py_env_book",
    "language": "python",
    "name": "python3"
   },
@@ -165,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

tutorials/3-advanced-topics/DTAT399F_backend._config.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,23 +61,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "configuration.set_device(\"gpu\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or equivalently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_10742/2569479069.py:1: DeprecationWarning: (enable/disable)_gpu is deprecated. Use set_device instead\n",
-      "  configuration.enable_gpu()\n"
+      "/var/folders/nk/vplzgbvj0w1gpgk5h_j226480000gp/T/ipykernel_49153/3712292284.py:1: DeprecationWarning: (enable/disable)_gpu is deprecated. Use set_device instead\n",
+      "  configuration.enable_gpu()  # Deprecated but available for compatability.\n",
+      "/Users/giovannivolpe/Documents/GitHub/DeepTrack2/deeptrack/backend/_config.py:32: UserWarning: cupy not installed, CPU acceleration not enabled\n",
+      "  warnings.warn(\"cupy not installed, CPU acceleration not enabled\")\n"
      ]
     }
    ],
    "source": [
-    "configuration.set_device(\"gpu\")\n",
-    "\n",
-    "# Or equivalently:\n",
-    "configuration.enable_gpu() # Deprecated but available for compatability."
+    "configuration.enable_gpu()  # Deprecated but available for compatability."
    ]
   },
   {
@@ -90,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,13 +122,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "configuration.set_backend_torch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or equivalently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "configuration.set_backend_torch() \n",
-    "\n",
-    "# Or equivalently:\n",
     "configuration.set_backend(\"torch\")"
    ]
   },
@@ -128,28 +156,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<deeptrack.backend._config.Config.wrapper_enabled_context.<locals>.NullContext at 0x7f5419416a10>"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "configuration.enable_image_wrapper()"
+    "configuration.enable_image_wrapper();"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "py_env_book",
    "language": "python",
    "name": "python3"
   },
@@ -163,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,