diff --git a/largvis.ipynb b/largvis.ipynb index 4a8b10e..1decc29 100644 --- a/largvis.ipynb +++ b/largvis.ipynb @@ -2,43 +2,46 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Collecting matplotlib==2.0.2\n", - " Downloading https://files.pythonhosted.org/packages/2c/69/362c0a5eca37225c7d451e6c57f07c4fee85a5b6cb3aedf16fe649f411ea/matplotlib-2.0.2-cp27-cp27mu-manylinux1_x86_64.whl (14.5MB)\n", - "\u001b[K 100% |████████████████████████████████| 14.5MB 88kB/s eta 0:00:01\n", - "\u001b[?25hCollecting subprocess32 (from matplotlib==2.0.2)\n", - "Collecting numpy>=1.7.1 (from matplotlib==2.0.2)\n", + "Collecting matplotlib\n", + " Using cached https://files.pythonhosted.org/packages/9d/40/5ba7d4a3f80d39d409f21899972596bf62c8606f1406a825029649eaa439/matplotlib-2.2.5-cp27-cp27mu-manylinux1_x86_64.whl\n", + "Collecting subprocess32 (from matplotlib)\n", + "Collecting backports.functools-lru-cache (from matplotlib)\n", + " Using cached https://files.pythonhosted.org/packages/da/d1/080d2bb13773803648281a49e3918f65b31b7beebf009887a529357fd44a/backports.functools_lru_cache-1.6.1-py2.py3-none-any.whl\n", + "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib)\n", + " Using cached https://files.pythonhosted.org/packages/8a/bb/488841f56197b13700afd5658fc279a2025a39e22449b7cf29864669b15d/pyparsing-2.4.7-py2.py3-none-any.whl\n", + "Collecting numpy>=1.7.1 (from matplotlib)\n", " Using cached https://files.pythonhosted.org/packages/3a/5f/47e578b3ae79e2624e205445ab77a1848acdaa2929a00eeef6b16eaaeb20/numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl\n", - "Collecting cycler>=0.10 (from matplotlib==2.0.2)\n", + "Collecting cycler>=0.10 (from matplotlib)\n", " Using cached https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n", - "Collecting python-dateutil 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https://files.pythonhosted.org/packages/e7/f9/f0b53f88060247251bf481fa6ea62cd0d25bf1b11a87888e53ce5b7c8ad2/pytz-2019.3-py2.py3-none-any.whl\n", - "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 (from matplotlib==2.0.2)\n", - " Using cached https://files.pythonhosted.org/packages/8a/bb/488841f56197b13700afd5658fc279a2025a39e22449b7cf29864669b15d/pyparsing-2.4.7-py2.py3-none-any.whl\n", - "Installing collected packages: subprocess32, numpy, six, cycler, python-dateutil, functools32, pytz, pyparsing, matplotlib\n", - "Successfully installed cycler-0.10.0 functools32-3.2.3.post2 matplotlib-2.2.5 numpy-1.16.6 pyparsing-2.4.7 python-dateutil-2.8.1 pytz-2019.3 six-1.14.0 subprocess32-3.5.4\n" + "Collecting setuptools (from kiwisolver>=1.0.1->matplotlib)\n", + " Using cached https://files.pythonhosted.org/packages/ab/b5/3679d7c98be5b65fa5522671ef437b792d909cf3908ba54fe9eca5d2a766/setuptools-44.1.0-py2.py3-none-any.whl\n", + "Installing collected packages: subprocess32, backports.functools-lru-cache, pyparsing, numpy, six, cycler, setuptools, kiwisolver, python-dateutil, pytz, matplotlib\n", + "Successfully installed backports.functools-lru-cache-1.6.1 cycler-0.10.0 kiwisolver-1.1.0 matplotlib-2.2.5 numpy-1.16.6 pyparsing-2.4.7 python-dateutil-2.8.1 pytz-2019.3 setuptools-44.1.0 six-1.14.0 subprocess32-3.5.4\n" ] } ], "source": [ - "!pip install --user matplotlib==2.0.2" + "!pip install --user matplotlib" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-04-11T16:50:43.663008Z", @@ -52,18 +55,7 @@ "id": "HZcWwetbhvIU", "outputId": "83ed7ff1-a4d9-4d3c-fb54-647db5caf4dd" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ilona/.local/lib/python2.7/site-packages/matplotlib/colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.\n", - " not cbook.is_string_like(colors[0]):\n", - "/home/ilona/.local/lib/python2.7/site-packages/matplotlib/cm.py:131: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.\n", - " if not cbook.is_string_like(name):\n" - ] - } - ], + "outputs": [], "source": [ "import umap\n", "import sklearn.datasets\n", @@ -93,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-04-11T16:50:46.681280Z", @@ -121,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-04-11T16:51:48.804474Z", @@ -192,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -224,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -280,22 +272,18 @@ " label.append(0)\n", " all_data.setdefault(label[i-1], []).append((float(vec[-2]), float(vec[-1])))\n", "\n", - " #colors = plt.cm.rainbow(np.linspace(0, 1, len(all_data)))\n", + " colors = plt.cm.rainbow(np.linspace(0, 1, len(all_data)))\n", "\n", " for color, ll in zip(colors, sorted(all_data.keys())):\n", " x = [t[0] for t in all_data[ll]]\n", " y = [t[1] for t in all_data[ll]]\n", " plt.plot(x, y, '.', color = color, markersize = 1)\n", - " #if args.range != '':\n", - " #l = abs(float(args.range))\n", - " #plt.xlim(-l, l)\n", - " #plt.ylim(-l, l)\n", " plt.savefig(output_plot, dpi = 500)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", @@ -316,31 +304,30 @@ "import LargeVis\n", "\n", "LargeVis.loadfile(\"input.txt\")\n", - "Y = LargeVis.run()\n", + "Y = LargeVis.run(3)\n", "LargeVis.save(\"output.txt\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'module' object has no attribute '_putmask'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_LargeVis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"output.txt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"input_labels.txt\"\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[0malmost_one\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnextafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_putmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxa\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malmost_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 488\u001b[0m \u001b[0;31m# The following clip is fast, and prevents possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m \u001b[0;31m# conversion of large positive values to negative integers.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute '_putmask'" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "plot_LargeVis(\"output.txt\", \"input_labels.txt\", \"plot.jpg\")" + "plot_LargeVis(\"output.txt\", \"input_labels.txt\", \"plot_copy.jpg\")" ] }, { @@ -363,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2020-04-11T16:51:24.131177Z", @@ -373,7 +360,34 @@ "colab_type": "code", "id": "s-P5hJ_DhvJW" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ilona/.local/lib/python2.7/site-packages/sklearn/datasets/openml.py:61: RuntimeWarning: Invalid cache, redownloading file\n", + " RuntimeWarning)\n" + ] + }, + { + "ename": "UnicodeDecodeError", + "evalue": "'ascii' codec can't decode byte 0xe2 in position 54: ordinal not in range(128)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfmnist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_openml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fashion-MNIST'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/datasets/openml.pyc\u001b[0m in \u001b[0;36mfetch_openml\u001b[0;34m(name, version, data_id, data_home, target_column, cache, return_X_y)\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;31m# obtain the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 654\u001b[0m arff = _download_data_arff(data_description['file_id'], return_sparse,\n\u001b[0;32m--> 655\u001b[0;31m data_home)\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0;31m# nominal attributes is a dict mapping from the attribute name to the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/datasets/openml.pyc\u001b[0m in \u001b[0;36m_download_data_arff\u001b[0;34m(file_id, sparse, data_home, encode_nominal)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marff_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_arff_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/datasets/openml.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m()\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/datasets/openml.pyc\u001b[0m in \u001b[0;36m_arff_load\u001b[0;34m()\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0mencode_nominal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_nominal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m )\n\u001b[1;32m 406\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/externals/_arff.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, encode_nominal, return_type)\u001b[0m\n\u001b[1;32m 1057\u001b[0m \u001b[0mdecoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArffDecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1058\u001b[0m return decoder.decode(fp, encode_nominal=encode_nominal,\n\u001b[0;32m-> 1059\u001b[0;31m return_type=return_type)\n\u001b[0m\u001b[1;32m 1060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_nominal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDENSE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/externals/_arff.pyc\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, encode_nominal, return_type)\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 891\u001b[0m return self._decode(s, encode_nominal=encode_nominal,\n\u001b[0;32m--> 892\u001b[0;31m matrix_type=return_type)\n\u001b[0m\u001b[1;32m 893\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mArffException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/ilona/.local/lib/python2.7/site-packages/sklearn/externals/_arff.pyc\u001b[0m in \u001b[0;36m_decode\u001b[0;34m(self, s, encode_nominal, matrix_type)\u001b[0m\n\u001b[1;32m 801\u001b[0m \u001b[0;31m# DESCRIPTION -----------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mu_row\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_TK_DESCRIPTION\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mSTATE\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_TK_DESCRIPTION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'description'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_comment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 804\u001b[0m \u001b[0;31m# -----------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xe2 in position 54: ordinal not in range(128)" + ] + } + ], "source": [ "fmnist = sklearn.datasets.fetch_openml('Fashion-MNIST')" ] diff --git a/output.txt b/output.txt index bd7a175..8baad1c 100644 --- a/output.txt +++ b/output.txt @@ -1,2001 +1,2001 @@ 2000 2 --242.144394 -654.408875 --252.226807 -664.012573 --250.855728 -640.478638 --229.962479 -647.245300 --244.108353 -641.382690 --226.746307 -653.153748 --242.180511 -649.107422 --241.993134 -657.207336 --241.543213 -648.029663 --247.473618 -634.038574 --244.216507 -658.559326 --233.125580 -662.173340 --247.470596 -655.137939 --241.000046 -670.119812 --241.158096 -648.252258 --235.658005 -635.750488 --231.126114 -654.836365 --235.276001 -656.181824 --242.585434 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-642.541809 --230.906158 -646.890198 --252.827820 -640.721130 --229.704681 -643.747498 --240.093185 -667.790527 --251.962372 -663.602173 --246.823547 -639.912476 --233.280426 -662.217285 --239.803925 -669.073364 --234.868088 -647.005798 --253.962982 -659.467957 --249.561783 -662.573914 --240.595688 -648.991699 --234.126129 -637.032227 --240.029587 -647.644714 --240.359970 -669.518982 --245.697159 -654.433411 --250.149490 -663.746155 --233.144577 -651.100525 --232.684982 -646.837158 --233.147354 -648.196594 --234.125244 -637.718567 --247.752457 -657.956909 --249.919632 -663.887024 --230.359711 -655.755127 --240.711090 -669.869385 --240.895477 -635.118225 --237.303802 -655.764771 --248.366898 -655.043701 --241.043655 -637.910767 --253.009811 -660.696167 --246.312271 -634.309387 --241.481598 -669.569702 --235.137711 -634.493774 --249.746521 -641.002075 --240.466171 -669.577637 --235.921204 -653.728760 --249.880234 -663.251160 --240.251740 -637.074951 --236.621353 -654.855774 --245.896088 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-660.716553 --248.437866 -635.618164 --237.045670 -641.766235 --233.903885 -638.038940 --248.300446 -637.522705 --227.872360 -652.154785 --246.743164 -652.154175 --233.786880 -661.702209 --235.200516 -654.553528 --242.041977 -668.025879 --242.894897 -639.883301 --245.526230 -656.313232 --238.225449 -648.033264 --241.808609 -669.822449 --236.973495 -647.876343 --238.500580 -640.464478 --242.305801 -634.305176 --240.214676 -669.633484 --254.068710 -663.717346 --241.331314 -658.809814 --250.967575 -644.283325 --227.996826 -652.503601 --244.049484 -651.021423 --238.210907 -658.224609 --245.398193 -641.981079 --229.523209 -643.556335 --251.678436 -640.702332 --240.289276 -669.144348 --248.265961 -636.056641 --241.115311 -638.152588 --234.567261 -642.854614 --249.570633 -663.157043 --245.811981 -641.930481 --228.186066 -652.545532 --242.270203 -634.194580 --233.306244 -662.419373 --238.075790 -647.159119 --234.020447 -662.069458 --244.866394 -643.020752 --233.677124 -647.860168 --229.476395 -657.573486 --242.058167 -656.829041 --237.395615 -649.657227 --241.287125 -656.455261 --247.589478 -657.883362 --241.906036 -638.681091 --241.649689 -648.679199 --235.511765 -634.317017 --245.044388 -668.243713 --228.944901 -652.270691 --239.999176 -654.056030 --228.123840 -652.083984 --230.369293 -650.423096 --233.733170 -661.756287 --250.795319 -662.524231 --235.293671 -634.379944 --249.649216 -640.427002 --237.946930 -639.000427 --241.698898 -632.749939 --235.171600 -656.289001 --246.928391 -654.167786 --227.557434 -652.378906 --241.845154 -649.104431 --229.643890 -647.143372 --235.496155 -657.039612 --241.398178 -656.607422 --242.595947 -667.558838 --231.174423 -646.689392 --253.796066 -661.621094 --236.536972 -633.351746 --236.539459 -648.384216 --253.744629 -660.787476 --250.941406 -660.080566 --236.450256 -648.457703 --242.059708 -646.994141 --228.817642 -654.115906 --241.150696 -633.191162 --242.425430 -658.420959 --249.867554 -660.557739 --247.336533 -637.902405 --244.477615 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