0.538

_proc/bounding_boxes.ipynb

@@ -99,8 +99,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[07/25/24 07:17:46] WARNING  Unable to load torch and dependent libraries from torch-snippets.                                                        loader.py:<module>:108\n",
-      "                             Functionalities might be limited. pip install lovely-tensors in case there are torch related errors                                            \n"
+      "\u001b[2;36m[08/17/24 12:32:44]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;31mWARNING \u001b[0m Unable to load torch and dependent libraries from torch-snippets.                                                        \u001b]8;id=7281;file:///Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/loader.py:108\u001b\\\u001b[2mloader.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=181415;file:///Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/loader.py:108#<module>:108\u001b\\\u001b[2m<module>:108\u001b[0m\u001b]8;;\u001b\\\n",
+      "\u001b[2;36m                    \u001b[0m         Functionalities might be limited. pip install lovely-tensors in case there are torch related errors                      \u001b[2m                      \u001b[0m\n"
      ]
     }
    ],

_proc/decorators.ipynb

@@ -282,7 +282,7 @@
       "OUTPUTS:\n",
       "int: 21\n",
       "══════════════════════════════════════════════════════════════════\n",
-      "[07/25/24 07:17:48] INFO     foo took 1.01 seconds to execute                                                                      <ipython-input-1-6ac2073623b5>:wrapper:47\n"
+      "\u001b[2;36m[08/17/24 12:32:46]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;33mINFO    \u001b[0m foo took \u001b[1;36m1.00\u001b[0m seconds to execute                                                                      \u001b]8;id=974847;file://<ipython-input-1-6ac2073623b5>:47\u001b\\\u001b[2m<ipython-input-1-6ac2073623b5>\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=366818;file://<ipython-input-1-6ac2073623b5>:47#wrapper:47\u001b\\\u001b[2mwrapper:47\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     {

_proc/jupyter_notebook.ipynb

@@ -219,15 +219,15 @@
        "### display_dfs_side_by_side\n",
        "\n",
        ">      display_dfs_side_by_side (*args, titles=<itertools.cycle object at\n",
-       ">                                0x138a37040>, max_rows=50)"
+       ">                                0x12fc26800>, max_rows=50)"
       ],
       "text/plain": [
        "---\n",
        "\n",
        "### display_dfs_side_by_side\n",
        "\n",
        ">      display_dfs_side_by_side (*args, titles=<itertools.cycle object at\n",
-       ">                                0x138a37040>, max_rows=50)"
+       ">                                0x12fc26800>, max_rows=50)"
       ]
      },
      "execution_count": 6,

_proc/paths.ipynb

@@ -1664,9 +1664,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ts",
+   "display_name": "base",
    "language": "python",
-   "name": "ts"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1678,12 +1678,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "0a92af74ccf65e106ae96852a45a9b0a5e11a3c414a16fc8bb2d134868e55bfd"
-   }
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

changelog.md

@@ -1,4 +1,13 @@
 # Changelog
+#### 0.538
+🎉 `tryy` a new decorator to wrap try except with optional default return as well as print stack trace if need be
+✨ `Timer` can given time-remaning approximation based on both instantaneous or average speed
+✨ `logging_mode` functions can be used as both context managers and decorators
+✨ better formatting for `now()`
+✨ `show` auto generates a title if possible
+✨ `show` function includes a `framecount` parameter to specify which frame of the stack it should search for the title. See its usage in `show_big_dataframe`
+✨ `yolo_2_df` and `df_2_yolo` functions in adapters
+
 #### 0.537
 ✨ `AD` let's you know if a collection is list, set or tuple
 ✨ NEW DEFAULT: use `info` column to show text else `text`

nbs/adapters.ipynb

@@ -324,6 +324,54 @@
     "            Warn(f'{e} @ {item[\"@name\"]}')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | export\n",
+    "# | hide\n",
+    "def df_2_yolo(df, h, w, class2id, class_column):\n",
+    "    yolo_data = []\n",
+    "    for index, row in df.iterrows():\n",
+    "        cls = row[class_column]\n",
+    "        if cls not in class2id:\n",
+    "            continue\n",
+    "        class_id = class2id[cls]\n",
+    "        x_center = (row[\"x\"] + row[\"X\"]) / 2 / w\n",
+    "        y_center = (row[\"y\"] + row[\"Y\"]) / 2 / h\n",
+    "        width = (row[\"X\"] - row[\"x\"]) / w\n",
+    "        height = (row[\"Y\"] - row[\"y\"]) / h\n",
+    "        yolo_data.append(\n",
+    "            f\"{class_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\"\n",
+    "        )\n",
+    "    return yolo_data\n",
+    "\n",
+    "\n",
+    "def yolo_2_df(yolo, h, w, id2class, class_column):\n",
+    "    data = []\n",
+    "\n",
+    "    for line in yolo:\n",
+    "        class_id, x_center, y_center, width, height = map(float, line.split())\n",
+    "        class_name = id2class[int(class_id)]\n",
+    "\n",
+    "        x_center *= w\n",
+    "        y_center *= h\n",
+    "        width *= w\n",
+    "        height *= h\n",
+    "\n",
+    "        x_min = int(x_center - (width / 2))\n",
+    "        y_min = int(y_center - (height / 2))\n",
+    "        x_max = int(x_center + (width / 2))\n",
+    "        y_max = int(y_center + (height / 2))\n",
+    "\n",
+    "        data.append([x_min, y_min, x_max, y_max, class_name])\n",
+    "\n",
+    "    df = pd.DataFrame(data, columns=[\"x\", \"y\", \"X\", \"Y\", class_column])\n",
+    "    return df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

nbs/jupyter_notebook.ipynb

@@ -185,7 +185,7 @@
     "        \"display.max_rows\",\n",
     "        max_rows,\n",
     "    ):\n",
-    "        show(df, max_rows=max_rows)"
+    "        show(df, max_rows=max_rows, frame_count=2)"
    ]
   },
   {

nbs/logging.ipynb

@@ -834,15 +834,39 @@
     "\n",
     "\n",
     "@contextmanager\n",
-    "def logger_mode(level):\n",
+    "def _logger_mode_context(level):\n",
+    "    lv = get_logger_level()\n",
     "    try:\n",
-    "        lv = get_logger_level()\n",
     "        reset_logger(level.upper())\n",
     "        yield\n",
     "    finally:\n",
     "        reset_logger(lv.upper())\n",
     "\n",
     "\n",
+    "def logger_mode(level):\n",
+    "    if callable(level):\n",
+    "        # Used as a decorator without arguments\n",
+    "        func = level\n",
+    "\n",
+    "        @wraps(func)\n",
+    "        def wrapper(*args, **kwargs):\n",
+    "            with _logger_mode_context(\"DEBUG\"):\n",
+    "                return func(*args, **kwargs)\n",
+    "\n",
+    "        return wrapper\n",
+    "    else:\n",
+    "        # Used as a decorator with arguments or as a context manager\n",
+    "        def decorator(func):\n",
+    "            @wraps(func)\n",
+    "            def wrapper(*args, **kwargs):\n",
+    "                with _logger_mode_context(level):\n",
+    "                    return func(*args, **kwargs)\n",
+    "\n",
+    "            return wrapper\n",
+    "\n",
+    "        return _logger_mode_context(level) if not callable(level) else decorator\n",
+    "\n",
+    "\n",
     "def in_logger_mode(level):\n",
     "    return get_logger_level() == level\n",
     "\n",