diff --git a/Images_doku/Y_axis/img.png b/Images_doku/Y_axis/img.png new file mode 100644 index 0000000..25d22db Binary files /dev/null and b/Images_doku/Y_axis/img.png differ diff --git a/notebooks/Analytics.ipynb b/notebooks/Analytics.ipynb index 1237abf..f1f9b7b 100644 --- a/notebooks/Analytics.ipynb +++ b/notebooks/Analytics.ipynb @@ -5,8 +5,8 @@ "id": "ca3c9c7af43b4e58", "metadata": { "ExecuteTime": { - "end_time": "2025-08-27T10:57:48.085022Z", - "start_time": "2025-08-27T10:57:48.021846Z" + "end_time": "2025-08-27T12:35:47.776234Z", + "start_time": "2025-08-27T12:35:47.771287Z" } }, "source": [ @@ -75,7 +75,7 @@ ] } ], - "execution_count": 1 + "execution_count": 5 }, { "cell_type": "markdown", @@ -91,10 +91,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "52db5e2c12fea30c", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-27T14:37:34.034238Z", + "start_time": "2025-08-27T14:37:33.943025Z" + } + }, "source": [ "data_folder = r'C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\data\\Temp'\n", "%matplotlib widget\n", @@ -122,7 +125,7 @@ " step = max((ind_max - ind_min) // 1000, 1)\n", "\n", " for i in range(5):\n", - " if i >2: #(i==2): # 2 ist raum\n", + " if i>2: #(i==2): # 2 ist raum\n", " line, = ax.plot(times[ind_min:ind_max:step],\n", " temps[i][ind_min:ind_max:step],\n", " label=labels[i], color=colors[i])\n", @@ -162,14 +165,40 @@ "#update_plot()\n", "#plt.tight_layout()\n", "#fig_temp.canvas.draw_idle()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ], + "image/png": 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+ "text/html": [ + "\n", + "
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+ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "plt.style.use('_mpl-gallery')\n", "\n", "# === CONFIGURATION ===\n", @@ -530,13 +562,42 @@ "\n", "\n", "\n", - " fig, axs = plt.subplot_mosaic([['histx', '.'],\n", - " ['scatter', 'histy']],\n", - " figsize=(6, 6),\n", - " width_ratios=(4, 1), height_ratios=(1, 4),\n", - " layout='constrained')\n", + " fig, ax = plt.subplots(figsize=(5.5, 5.5))\n", + "\n", + " # the scatter plot:\n", + " ax.scatter(x, y)\n", + "\n", + " # Set aspect of the main Axes.\n", + " ax.set_aspect(1.)\n", + "\n", + " # create new Axes on the right and on the top of the current Axes\n", + " divider = make_axes_locatable(ax)\n", + " # below height and pad are in inches\n", + " ax_histx = divider.append_axes(\"top\", 1.2, pad=0.1, sharex=ax)\n", + " ax_histy = divider.append_axes(\"right\", 1.2, pad=0.1, sharey=ax)\n", + "\n", + " # make some labels invisible\n", + " ax_histx.xaxis.set_tick_params(labelbottom=False)\n", + " ax_histy.yaxis.set_tick_params(labelleft=False)\n", + "\n", + " # now determine nice limits by hand:\n", + " binwidth = 0.15\n", + " xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))\n", + " xymin = min(np.min(np.abs(x)), np.min(np.abs(y)))\n", + " limP = (int(xymax/binwidth) +0.01)*binwidth\n", + " limM = (int(xymin/binwidth) -0.01)*binwidth\n", + "\n", + " bins = np.arange(limM, limP + binwidth, binwidth)\n", + " ax_histx.hist(x, bins=bins)\n", + " ax_histy.hist(y, bins=bins, orientation='horizontal')\n", + "\n", + " # the xaxis of ax_histx and yaxis of ax_histy are shared with ax,\n", + " # thus there is no need to manually adjust the xlim and ylim of these\n", + " # axis.\n", + "\n", + " ax_histx.set_yticks([0, 50, 100])\n", + " ax_histy.set_xticks([0, 50, 100])\n", "\n", - " scatter_hist(x, y, axs['scatter'], axs['histx'], axs['histy'])\n", " plt.show()\n", "\n", "\n", @@ -556,15 +617,15 @@ " print(conf_path)\n", " x = x_vals1*get_pixel_size(rf\"{path}\\conf_{conf_path}.json\")\n", " y = y_vals1*get_pixel_size(rf\"{path}\\conf_{conf_path}.json\")\n", - " dt = 50\n", + " dt = 60\n", " t = np.arange(0, len(x)*dt, dt)\n", "\n", "\n", - " fig, (ax0, ax1) = plt.subplots(2, 1, layout='constrained')\n", - " ax0.plot(t, x)\n", + " fig, (ax0, ax1) = plt.subplots(2, 1, layout='constrained', figsize=(10, 6))\n", + " ax0.plot(t, y)\n", " ax0.set_xlabel('Time (s)')\n", " ax0.set_ylabel('Signal')\n", - " ax1.psd(x, NFFT=512, Fs=1 / dt)\n", + " ax1.psd(y, NFFT=512, Fs=1 / dt)\n", "\n", " plt.show()\n", "\n", @@ -644,8 +705,10 @@ " conf_path = path.split(\"\\\\\")[-1]\n", " conf_path = conf_path.split(\"_\")[0]\n", " print(conf_path)\n", + " \"\"\"a = 300\n", + " b = 400\"\"\"\n", " a = 0\n", - " b = 5000\n", + " b = 3000\n", " # Convert pixel values and slice the relevant time window\n", " scale = get_pixel_size(rf\"{path}\\conf_{conf_path}.json\")\n", " x = (x_vals1 * scale)[a:b]\n", @@ -666,7 +729,6 @@ "\n", " x_high_std = rolling_avg_x['x'].to_numpy() + rolling_std_x['x'].to_numpy()\n", " x_low_std = rolling_avg_x['x'].to_numpy() - rolling_std_x['x'].to_numpy()\n", - "\n", " # Compute statistics\n", " stdx = np.mean(rolling_std_x)\n", " stdy = np.mean(rolling_std_y)\n", @@ -679,17 +741,16 @@ "\n", "\n", " # Create subplots\n", - " fig_raw, (ax1_raw, ax2_raw) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n", + " fig_raw, (ax1_raw, ax2_raw) = plt.subplots(2, 1, figsize=(10, 6), sharex=True,sharey=True)\n", + "\n", + " # Optionally add statistics as a text box\n", + " props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n", + " ax1_raw.text(0.02, 0.95, textstra, transform=ax1_raw.transAxes, fontsize=10,\n", + " verticalalignment='top', bbox=props)\n", "\n", " # The first items are for padding and the second items are for the Axes.\n", - "# sizes are in inch.\n", - " h = [Size.Fixed(1.0), Size.Fixed(4.5)]\n", - " v = [Size.Fixed(0.7), Size.Fixed(5.)]\n", - "\n", - " divider = Divider(fig, (0, 0, 1, 1), h, v, aspect=False)\n", - " ax_newY = divider.append_axes(\"top\", 1.2, pad=0.1, sharex=ax)\n", " # Plot x over time with mean and ±std deviation\n", - " ax_newY.plot(times1, rolling_avg_x['x'].to_numpy(),linewidth= 0.5 , label=\"X Position\")\n", + " ax1_raw.plot(times1, rolling_avg_x['x'].to_numpy(),linewidth= 0.5 , label=\"X Position\")\n", "\n", " ax1_raw.fill_between(times1, x_high_std, x_low_std,linewidth=0, alpha=0.5)\n", "\n", @@ -704,8 +765,7 @@ " ax1_raw.set_ylabel('X Position')\n", " ax1_raw.legend()\n", " ax1_raw.margins(0.05)\n", - " ax1_raw.yaxis.set_major_locator(plt.MultipleLocator(1))\n", - " ax1_raw.set_box_aspect(1)\n", + "\n", "\n", "\n", "\n", @@ -719,8 +779,7 @@ " ax2_raw.set_ylabel('Y Position')\n", " ax2_raw.legend()\n", " ax2_raw.margins(0.05)\n", - " ax2_raw.yaxis.set_major_locator(plt.MultipleLocator(1))\n", - " ax2_raw.set_box_aspect(1)\n", + "\n", "\n", " plt.tight_layout()\n", "\n", @@ -746,7 +805,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3a860630410a4cfcb2f55e4511710cbe" + "model_id": "7b2b9390d9a942ff9038164b0383a1dd" } }, "metadata": {}, @@ -760,7 +819,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "bb24fa0dbd304873b025f7957c517efc" + "model_id": "75e234dc24534f0092569e3be4a1dcb7" } }, "metadata": {}, @@ -774,7 +833,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2f1d4406c3ed4508b4e1eab64fea70f5" + "model_id": "8500d61523174a7f9cfc3f8b74469db8" } }, "metadata": {}, @@ -788,7 +847,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c42126e8a0324f4199b85bcc2b9017b8" + "model_id": "21fe85cd72ca4f1a9105fbde1fc903f1" } }, "metadata": {}, @@ -802,7 +861,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f5e2893192104b48877ec46a94ba7c75" + "model_id": "20443d6eac6b4398837155afe50ce255" } }, "metadata": {}, @@ -816,7 +875,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e485ed555621483f9b6ed25f2e555b6e" + "model_id": "2b9ce7b34af74059892f9af0f2c3fdfc" } }, "metadata": {}, @@ -830,7 +889,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "52c0404941f542559fc0f9fe728d3ecf" + "model_id": "ef45e1fa8a5f4569b3f86dd22da682ea" } }, "metadata": {}, @@ -844,7 +903,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0211dd6f3508451090a2300965116758" + "model_id": "27ea21f821cf4e99ab78a489dd13d028" } }, "metadata": {}, @@ -858,7 +917,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b2c500783d614ca096711141d1be2a32" + "model_id": "8634dd665b144bfdbc756caf7edbe1f3" } }, "metadata": {}, @@ -872,52 +931,81 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "265f769f0da844b8ab06a8b5d8ca6dec" + "model_id": "a03b5cb540ce4b6ea9db265321006a98" } }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 29 + "execution_count": 44 }, { "cell_type": "code", - "execution_count": null, "id": "9f3a5361dcc6f7f7", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-27T12:28:49.764119Z", + "start_time": "2025-08-27T12:28:49.719053Z" + } + }, "source": [ - " import matplotlib.pyplot as plt\n", - "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "\n", - "np.random.seed(19680801)\n", + "from mpl_toolkits.axes_grid1 import Divider, Size\n", + "fig, (ax1, ax2) = plt.subplots(2, 1,figsize=(10, 6) )\n", "\n", - "fig, ax = plt.subplots()\n", - "x = 30*np.random.randn(10000)\n", - "mu = x.mean()\n", - "median = np.median(x)\n", - "sigma = x.std()\n", - "textstr = \"hello\"\n", + "# The first items are for padding and the second items are for the Axes.\n", + "# sizes are in inch.\n", + "h = [Size.Fixed(1.0), Size.Fixed(4.5)]\n", + "v = [Size.Fixed(0.7), Size.Fixed(5.)]\n", "\n", - "ax.hist(x, 50)\n", - "# these are matplotlib.patch.Patch properties\n", - "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n", - "\n", - "# place a text box in upper left in axes coords\n", - "ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,\n", - " verticalalignment='top', bbox=props)\n", + "divider = Divider(fig, (0, 0, 1, 1), h, v, aspect=False)\n", + "# The width and height of the rectangle are ignored.\n", + "ax2.set_axes_locator(plt.FixedLocator([0.5, 1.5, 2.5, 3.5]))\n", + "ax1.set_axes_locator(plt.FixedLocator([0.5, 1.5, 2.5, 3.5]))\n", "\n", + "ax1.plot([1, 2, 3], [1, 2, 3], 'k--')\n", + "ax1.plot([4, 5, 6], [4, 5, 6], 'k--')\n", "plt.show()" - ] + ], + "outputs": [ + { + "ename": "TypeError", + "evalue": "FixedLocator.__call__() takes 1 positional argument but 3 were given", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mTypeError\u001B[39m Traceback (most recent call last)", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\IPython\\core\\formatters.py:402\u001B[39m, in \u001B[36mBaseFormatter.__call__\u001B[39m\u001B[34m(self, obj)\u001B[39m\n\u001B[32m 400\u001B[39m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[32m 401\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m402\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mprinter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 403\u001B[39m \u001B[38;5;66;03m# Finally look for special method names\u001B[39;00m\n\u001B[32m 404\u001B[39m method = get_real_method(obj, \u001B[38;5;28mself\u001B[39m.print_method)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170\u001B[39m, in \u001B[36mprint_figure\u001B[39m\u001B[34m(fig, fmt, bbox_inches, base64, **kwargs)\u001B[39m\n\u001B[32m 167\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mmatplotlib\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mbackend_bases\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m FigureCanvasBase\n\u001B[32m 168\u001B[39m FigureCanvasBase(fig)\n\u001B[32m--> \u001B[39m\u001B[32m170\u001B[39m \u001B[43mfig\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcanvas\u001B[49m\u001B[43m.\u001B[49m\u001B[43mprint_figure\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbytes_io\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 171\u001B[39m data = bytes_io.getvalue()\n\u001B[32m 172\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m fmt == \u001B[33m'\u001B[39m\u001B[33msvg\u001B[39m\u001B[33m'\u001B[39m:\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\matplotlib\\backend_bases.py:2155\u001B[39m, in \u001B[36mFigureCanvasBase.print_figure\u001B[39m\u001B[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001B[39m\n\u001B[32m 2152\u001B[39m \u001B[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001B[39;00m\n\u001B[32m 2153\u001B[39m \u001B[38;5;66;03m# so that we can inject the orientation\u001B[39;00m\n\u001B[32m 2154\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(renderer, \u001B[33m\"\u001B[39m\u001B[33m_draw_disabled\u001B[39m\u001B[33m\"\u001B[39m, nullcontext)():\n\u001B[32m-> \u001B[39m\u001B[32m2155\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mfigure\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 2156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m bbox_inches:\n\u001B[32m 2157\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m bbox_inches == \u001B[33m\"\u001B[39m\u001B[33mtight\u001B[39m\u001B[33m\"\u001B[39m:\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\matplotlib\\artist.py:94\u001B[39m, in \u001B[36m_finalize_rasterization..draw_wrapper\u001B[39m\u001B[34m(artist, renderer, *args, **kwargs)\u001B[39m\n\u001B[32m 92\u001B[39m \u001B[38;5;129m@wraps\u001B[39m(draw)\n\u001B[32m 93\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdraw_wrapper\u001B[39m(artist, renderer, *args, **kwargs):\n\u001B[32m---> \u001B[39m\u001B[32m94\u001B[39m result = \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 95\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m renderer._rasterizing:\n\u001B[32m 96\u001B[39m renderer.stop_rasterizing()\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\matplotlib\\artist.py:71\u001B[39m, in \u001B[36mallow_rasterization..draw_wrapper\u001B[39m\u001B[34m(artist, renderer)\u001B[39m\n\u001B[32m 68\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 69\u001B[39m renderer.start_filter()\n\u001B[32m---> \u001B[39m\u001B[32m71\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 72\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m 73\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m artist.get_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\matplotlib\\figure.py:3246\u001B[39m, in \u001B[36mFigure.draw\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m 3242\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[32m 3244\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m._render_lock:\n\u001B[32m-> \u001B[39m\u001B[32m3246\u001B[39m artists = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_get_draw_artists\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 3247\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 3248\u001B[39m renderer.open_group(\u001B[33m'\u001B[39m\u001B[33mfigure\u001B[39m\u001B[33m'\u001B[39m, gid=\u001B[38;5;28mself\u001B[39m.get_gid())\n", + "\u001B[36mFile \u001B[39m\u001B[32m~\\Python_Projects\\StagePerformaceDocu\\.venv\\Lib\\site-packages\\matplotlib\\figure.py:166\u001B[39m, in \u001B[36mFigureBase._get_draw_artists\u001B[39m\u001B[34m(self, renderer)\u001B[39m\n\u001B[32m 164\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m ax \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._localaxes:\n\u001B[32m 165\u001B[39m locator = ax.get_axes_locator()\n\u001B[32m--> \u001B[39m\u001B[32m166\u001B[39m ax.apply_aspect(\u001B[43mlocator\u001B[49m\u001B[43m(\u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m locator \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m 168\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m child \u001B[38;5;129;01min\u001B[39;00m ax.get_children():\n\u001B[32m 169\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(child, \u001B[33m'\u001B[39m\u001B[33mapply_aspect\u001B[39m\u001B[33m'\u001B[39m):\n", + "\u001B[31mTypeError\u001B[39m: FixedLocator.__call__() takes 1 positional argument but 3 were given" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": null, "id": "a51da887d5028934", "metadata": {}, + "source": [], "outputs": [], - "source": [] + "execution_count": null } ], "metadata": {