From 42909d4cc72938b9080f34a4c77213e77462ceb2 Mon Sep 17 00:00:00 2001 From: David A Minton Date: Fri, 6 Aug 2021 11:59:16 -0400 Subject: [PATCH] Updated example Notebook after testing --- .../swiftest_vs_swifter.ipynb | 99 +++++++++++++------ 1 file changed, 69 insertions(+), 30 deletions(-) diff --git a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb index 22e1403d8..cb6b9ecd7 100644 --- a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb +++ b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb @@ -42,24 +42,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reading Swiftest file param.swiftest.in\n" - ] - }, - { - "ename": "ValueError", - "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 4 and the array at index 5 has size 1", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mswiftestsim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mswiftest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"param.swiftest.in\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mswiftestsim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbin2xr\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[0m", - "\u001b[0;32m~/git/swiftest/python/swiftest/swiftest/simulation_class.py\u001b[0m in \u001b[0;36mbin2xr\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mdef\u001b[0m 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**kwargs)\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 4 and the array at index 5 has size 1" + "Reading Swiftest file param.swiftest.in\n", + "Reading in time 1.000e+00\n", + "Creating Dataset\n", + "Successfully converted 1462 output frames.\n", + "Swiftest simulation data stored as xarray DataSet .ds\n" ] } ], @@ -70,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -108,9 +95,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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ULTFIuhU4ChgiqQW4AhgIEBHTgNnAJ4CFwBrg7GrFZmZm76taYoiIyT1sD+DLVQrHzPqKl8TI1OTJk3nooYdYvnw5I0aM4Morr2TKlCm9KrOWmpLMzKxEt956a+Zl1lLns5k1gUbtY2gkTgxmZtZJj01JkkYWWdY7EZHtIF4zazgdE9xcYahZxfQx/ISku2hjH2OQrIV0cwYxmZlZH+oxMUTE0fnPSdoxIt6oTEhm1sjcx1D7yu1j+HymUZhZ03FiqF3lJoYTJZ0vaa9MozGzhudlt7OzePFijj76aMaOHcs+++zDtddem0m55c5jOAn4IDBJ0u4RcU4m0ZhZw2vvfPYEt94bMGAA3/72txk/fjwrV67kwAMP5Nhjj2XvvffuXbnlvCgi3gTuTn/MzKwP7LTTTuy0U3LbmkGDBjF27Fhee+21vkkMkm4AtoyIsyQdFxH39ioKM2s6DdfHcNdl8Maz2Za5475w/DVF7fryyy/z1FNPccghh/T6bcvtY1gHLEp/P6bXUZiZWdlWrVrFySefzHe/+1223nrrXpdXbh/DGmAbSQOBYifAmZk17nDVIq/ss7Z+/XpOPvlkzjjjDE466aRMyiw3MbwFvAfcADySSSRmZlaSiGDKlCmMHTuWiy++OLNyS2pKkjRY0o+Bk9OnbgYmZBaNmTU8L4mRnUceeYSf/vSnPPDAAxxwwAEccMABzJ49u9flllRjiIh3JF0DjAKWA/sBv+l1FGZmVrIjjjji/USboXKakqYAL0XEPcCTGcdjZg2uYfsYGkg5ieFt4Nx01vPTwNyIeCrbsMzMrK+UnBgi4mpJ9wMvAAcARwJODGZWFM98rn0lJwZJVwH9gbkktYWHMo7JzJqAm5JqV8kT3CLicuA6YCVwsqQfFftaSRMlLZC0UNJlBbZvI+l3kp6WNF/S2aXGZ2ZmvVPuPIYvAT+MiKLXSpLUn2Tew7FAC/CEpFkR8VzObl8GnouIT0kaCiyQ9LOIWFdmnGZWY9z5XPvKXRJjBnCepG9JOqDI1xwMLIyIRemJfiZwYt4+AQxS0vi4FclEug1lxmhm1tDWrl3LwQcfzP77788+++zDFVdckUm55SaGfyCpbQwgaVYqxnBgcc7jlvS5XNcDY4ElwLPAhRHRll+QpKmS5kias2zZslJjN7M+5M7n7Gy66aY88MADPP3008ydO5e7776bxx57rNfllpsYXgQ2A+6IiCOLfE2hb0H+zIyPk3RqDyMZ8XS9pC4rQkXE9IiYEBEThg4dWnTQZmaNRBJbbbUVkKyZtH79+kwSbrl9DPNJrv6nSPpWRBxUxGtagJ1zHo8gqRnkOhu4JpJLioWSXgLGAI+XGaeZ1ZhGvYPbNx7/Bv/z1v9kWuaY7cZw6cGXbnSf1tZWDjzwQBYuXMiXv/zlPl12e0+SIavTSU7mxXgC2EPSaEmbAKcBs/L2eRX4KICkDwB78f7y3mZmlqd///7MnTuXlpYWHn/8cebNm9frMsutMYwB7iUZZfQKSZ/DRkXEBknnA/eQJJUZETFf0rnp9mnA14CbJD1L0vR0aUQsLzNGM6tBjToqqacr+0obPHgwRx11FHfffTfjxo3rVVnlJobBwKXAJSRrJxUlImYDs/Oem5bz+xLguDJjMjNrKsuWLWPgwIEMHjyY9957j/vuu49LL+19gio3MVwFjImIBZK6jBoyM+uORyVl5/XXX+cLX/gCra2ttLW18dnPfpZPfvKTvS63qMSQTk5rAf41Im6MiJb0MRHRZQazmVlPGq0pqS/st99+PPVU9kvVFdX5HBGtwDxgt8wjMDOzmlJKU9IWwCWSjuX9YaYREfmzl83MuuWmpNpXSmI4NP13fPoDXSeomZlZnSslMYyuWBRm1jQadYJbIyk6MUTEK5UMxMzMakO5M5/NzMrSqBPcGokTg5lZnWttbeWDH/xgJnMYoIzEIOlTmbyzmTUlj0rK3rXXXsvYsWMzK6+cGsO/ZfbuZmbWKy0tLfz+97/nnHPOyazMcpbEcJov0c+e/xlPvvlkUfvOXz6f4YOGM3jTwZUNqo4N7DeQi8ZfxE5b7dTxXETwzSe+yZtr3uzDyOrTCbuewEdHfjTTMtuijW8+8U2WrlnaZdur774KNF4fwxtf/zp/ez7bZbc3HTuGHf/5nze6z0UXXcQ3v/lNVq5cmdn7lpMYPNasRDfNv4nV61bzgS0/0OO+S1YvYcnqJew+ePcqRFZ/1ret55V3X+GwYYdx4u7vz61csXYFtzx/C0M2H+KkWoLFKxezdsPazBPDW2vf4mfP/4yhmw9lm0236bL98OGHM2iTQZm+ZzO688472WGHHTjwwAN56KGHMiu33EX0rARt0cbHdvkYVx1+VY/7/n7R79lhix04aMdi7n3UfF5b9RoTfz2x27Hw5+53LqeOObXKUdWvyXdOpo3s18Fs70c4d/9z+exen828/FrU05V9JTzyyCPMmjWL2bNns3btWt59913OPPNMbrnlll6V61FJVRAR9FNx/9Un7HqCk4JVTT/16ziJZ6ktvVW7O5gr6+qrr6alpYWXX36ZmTNncswxx/Q6KUB5icGNuCVqizb/gWSkvV06/2TmkS7lkdRxEs+S5yrUt5KbkiLi2EoE0siCoJ8rZ1aDKlVjaOfEUD1HHXUURx11VCZl+WxVBW3RVnRTkm1cdycar79Tnn7qV9E+Btfg6pPPVlXQGq1ODFaT+qmfm5Ksi7LOVpIuzvl9r+zCaUyldD7bxrVfgebXEHyFWp5+VDYxWH0q6WwlabCkHwOfkfT3ko4Air61p6SJkhZIWiip4OskHSVprqT5kv6rlPhqlTufrVZVrPPZibquldT5HBHvAGdLOgF4AzgO+E0xr03vG30DcCzJ/aKfkDQrIp7L2Wcw8H1gYkS8KmmHUuKrVe58zl6XUUluuihLpTqf/XnUt3LPVh8hGbb6IZITfTEOBhZGxKKIWAfMBPJvC3o68JuIeBUgIrrOp69D7ny2WlWpGkN7S5JrDPWp3JnPg4FLgUuAKUW+ZjiwOOdxC3BI3j57AgMlPQQMAq6NiJvzC5I0FZgKMHLkyFLi7hNODNnpmMfQTRu2r1BL01/9KzMqyTWGqhk1ahSDBg2if//+DBgwgDlz5vS6zHITw1XAmIhYIKnYb1Whb0j+X/cA4EDgo8DmwJ8kPRYRL3R6UcR0YDrAhAkTar6Xy4khOz1dgfoKtTT9qHBTkj+PqnjwwQcZMmRIZuWVlRgiooXkip+IKLbzuQXYOefxCGBJgX2WR8RqYLWkh4H9gReoY+58zl53o5KsNBXvfHaNoS6VlRgk3QBsGRFnSTouIu4t4mVPAHtIGg28BpxG0qeQ6w7gekkDgE1Impq+U06MtSIiks5n1xgy0e2SGG66KEs/9aM1WjMvtxk/jz/+8gWWL16VaZlDdt6KD392z43uI4njjjsOSXzpS19i6tSpvX7fcpuS1vH+mknHAD0mhojYIOl84B6gPzAjIuZLOjfdPi0inpd0N/AM0AbcGBHzyoyxJrT/gXhUktWiio1K8nDVqnnkkUcYNmwYS5cu5dhjj2XMmDEceeSRvSqz3MSwBthG0kCg6N7fiJgNzM57blre428B3yozrprjVSaz1d3/oydUlUfInc8Z6enKvlKGDRsGwA477MCkSZN4/PHHe50Yyr2MfQt4kWRewiO9iqDBtV859Vf/Po7ErKv+6l/RGkMT5YU+sXr16o47t61evZp7772XcePG9brckmoM6QS07wB7AbcAN1P8cNWm1N5+6xpDtrzsdja87HZ9e/PNN5k0aRIAGzZs4PTTT2fixIm9Lrfkmc+SrgFGAcuB/Shy5nOzav+jc+ez1SIvolffdt11V55++unMyy2nj2EK8FJE3AMUd4f7JubO52x1N8HNJ6Ly9FO/ivTPuAZX38pJDG8D56arqj4NzI2Ip7INq3G489lqmVBFhqvmlm/1p5w7uF0t6X6SSWcHAEcCTgzdcFNStrpbdttr85THTUlWSMmJQdJVJPMQ5pLUFh7KOKaG0l6ldmKwWlTpeQzOC/WpnBrD5ZIuJxnqerKk3SLi77IPrT5d9OBFPPLa+yN426+cPFw1G575nL1l7y3j+RXPM3b7sZmV6c+jvpU7wW0GcA6wJcn9Eyw1f8V8RgwawYeHf7jjuQH9BvCxXT7Wh1E1jp46n600Hxr2IX79l1/z6spXnRisQ7mJ4R9IlsUYAFxL0s9gJH0K+w3dj4snXNzzzmZ9bM/ByWzdzJuT3OdTNe+88w7nnHMO8+bNQxIzZszg0EMP7VWZ5SaGF4E9gDsi4n/3KoIGExG+SqqgbpfE8PDIsrT/f2XdAd0xTNt9axV34YUXMnHiRG677TbWrVvHmjVrel1muZ/afOABYIqkJ3odRQNpjVb/MVjdaP+uZj1k1cugV8e7777Lww8/zJQpyQIUm2yyCYMHD+51ueXWGHYjmc8wPf3XUhFeYrsa3PmcjfbvatZ9NO0L8zXT5/HgTdNZ+sqiTMvcYZddOfqs7pfRXrRoEUOHDuXss8/m6aef5sADD+Taa69lyy237NX7lnsGWxwRs4CFwPO9iqDBtNHWVH8MVt/aE0PmTUlu2quKDRs28Oc//5nzzjuPp556ii233JJrrrmm1+WWW2OYKOkFktVVXyHpjDZ8G89K626Cm2sM5WlfqqVSTT/N9Hls7Mq+UkaMGMGIESM45JBDADjllFMySQzlnsEGA5cClwB/63UUDcRNSVZPKt353EyJoS/suOOO7LzzzixYsACA+++/n7333rvX5ZZbY7gKGBMRCyRVbqGVOuQaQ2V1N8HNwyPL09GUlPHNejzzuXq+973vccYZZ7Bu3Tp23XVXfvzjH/e6zKISg6T+QAvwrxFxY0S0pI+JiMt6HUUDcWKwetLR+ZxxU5JrDNVzwAEHMGfOnEzLLOoMFhGtwDyS0Ui2EW3R5qvWCvKy29lq//+q1HBV/y3Up1KakrYALpF0LLAkfS4i4sTsw6pfbbT53gsV1OOJxuehkrSv4eU+BstVyhnsUJI/u/HAJ3N+iiZpoqQFkhZK6rYJStJBklolnVJK+bXAnc99wxOqytMxysujkixHKTWG0b15o7Sf4gbgWJL+iSckzYqI5wrs9w3gnt68X19xH0NleXXVbHkegxXS4xlM0khJI0nGfXT5ad8uaeseijoYWBgRiyJiHTATKNQMdQHwa2BpCcdREyKCwDUGqx+Vmvns1W7rWzE1hp+QJIGNpf4AbgJu3sg+w4HFOY9bgENyd5A0HJgEHAMcVERsNcW38awedz5no/3/y30MlqvHxBARR2f0XoW+IfmXFd8FLo2I1o2dXCVNBaYCjBw5MqPweq99LLg7n61euCmpvi1YsIBTTz214/GiRYu46qqruOiii3pVbrkT3MrRAuyc83gE749uajcBmJl+mYYAn5C0ISJuz90pIqaTLODHhAkTaqbO6tt4Vl63S2L4RFSWiiUGL7tdFXvttRdz584FoLW1leHDhzNp0qRel1vNxPAEsIek0cBrwGnA6bk7RERHB7ekm4A785NCLXNTktWbitcY3JRUNffffz+77bYbu+yyS6/LqlpiiIgNks4nGW3UH5gREfMlnZtun1atWCql/Y/L93eunG6XxMjbbsWp2JIYTdj5/M7vXmTdktWZlrnJsC0Z/Kni5hXPnDmTyZMnZ/K+1awxEBGzgdl5zxVMCBFxVjViylJ7YnD12epFT4m2XG7aq65169Yxa9Ysrr766kzKq2piaHTNeHOSavOy29mShJBHJWWg2Cv7SrjrrrsYP348H/jABzIpz5e2GXLns9WjfuqXeWJo10yJoS/deuutmTUjgWsMBb2x+g0Wr1zc8455Vq5bCbj6XEndnWi8zHP5WqOVJ954gjXr17DFwC0AWLthLfOWz+tUM9u0/6aMGzKuqAufJauSAYf+W6i8NWvW8Ic//IEf/vCHmZXpxFDAefedx8J3Fpb9+kGbDMowGsvV0xWor1BLt+2m2zJ32VymPzOdiw68CIAZ82bwg6d/0GXfaR+bxuHDD++xzG888Q0AthzYu3sPW8+22GILVqxYkWmZTgwFrFq/isOHHc6UfaeU/NoB/Qaw75B9KxCV5epurSQr3c9P+DnH/+Z4Vq1f1fHcynUr2XzA5tzw0RsAaFnZwuWPXt5pn43ZtP+m7DdkP0Zv06sl1qyPODEUEBEM2XwIB+1Yd6tyNL60QuDO5+yMGDSC7TfbvlM/QxAM6Deg429g20237Xi+GP3UjzHbjck+WKsK95IWEITbRq2p5HdA568S3PH3UGzFLNy/UM+cGAoJjyyqVb7nc2VI6lQbaIvON5zqbphwd4Jw7a2O+exXgL/U1mzyawz5N5wqdSKca931zYmhAHdk1i7f87ky+tE5MbRGa+HEUGyNIXxxVc+cGAqI8NWONRdJXTqfc/8G3JRUu77zne+wzz77MG7cOCZPnszatWt7XaYTQwH+UtcuL7tdGf3Vf+Odz6U2Jfniqipee+01rrvuOubMmcO8efNobW1l5syZvS7XiaEAV4Ot2fRTv04n/S6dz6U2Jfniqmo2bNjAe++9x4YNG1izZg3Dhg3rdZmex1CAO85qlyg8bNL9Qr0jqdPS212u+PX+88WIiKZbnuSuu+7ijTfeyLTMHXfckeOPP77b7cOHD+crX/kKI0eOZPPNN+e4447juOOO6/X7usZQgE8y1mzyO5/baOt0XxHXGGrT22+/zR133MFLL73EkiVLWL16Nbfcckuvy3WNoQA3JdUuL7tdGfmdz21thSe4lTRctck+i41d2VfKfffdx+jRoxk6dCgAJ510Eo8++ihnnnlmr8p1jaEANyXVL39u5eky85m2zqOSSj3Je+ZzVYwcOZLHHnuMNWvWEBHcf//9jB07ttflOjEUEr7yrHXdjUqy8rjzuT4dcsghnHLKKYwfP559992XtrY2pk6d2uty3ZRUQBBeEqOGCfmezxnrp34b7XwupynJquPKK6/kyiuvzLRMn/0K8Jfamk2Xzufo3PncrpSZz764ql/+5Arw5Jzalr/gG7jzube6NCXRzQS3UpqS/DdUt6qaGCRNlLRA0kJJlxXYfoakZ9KfRyXtX8342rl91JqNJFqjteNxW7SV3ZTUMQu9Sf6Gar1/q5z4qpYYJPUHbgCOB/YGJkvaO2+3l4CPRMR+wNeA6dWKL1+zfKnrUaE+Bt/zuXd66nwupVmomWpvm222GStWrKjZ5BARrFixgs0226yk11Wz8/lgYGFELAKQNBM4EXiufYeIeDRn/8eAEVWMr4ObkqzZiK4znwslg1JqDE2QFxgxYgQtLS0sW7asr0Pp1mabbcaIEaWdSquZGIYDi3MetwCHbGT/KcBdhTZImgpMhWQcb9bclFTbCn02zXSVWgmF7uBWaB5DMX0MzfRZDBw4kNGjG+++1tXsYyj0LSn4LZN0NEliuLTQ9oiYHhETImJC+4y/LDXjOi/W3Pqrf5fO505LYpSw7HYzJYZGVc0aQwuwc87jEcCS/J0k7QfcCBwfESuqFFsnbbT5S13L5GW3s9Zj53P695Bbq+iWb7Na96qZGJ4A9pA0GngNOA04PXcHSSOB3wCfi4gXqhhbZ575bE2mn/rx2qrX+PacbwPw6ruvMnLr95tpS/l7cI2h/lUtMUTEBknnA/cA/YEZETFf0rnp9mnA5cD2wPfTq40NETGhWjF2xOox2DWt4Kgkn4x6Ze/t9+appU/xiwW/6Hhu7Hbvr7lT0nBVXHurd1VdEiMiZgOz856blvP7OcA51YypEHc+1zbRdYJb7jYr3YXjL+TC8Rf2uF9RfQw1OnTTiueZzwV4uKpZZ+XUGLwkRv3yJ1eAawy1reCSGO58rqiShqs22cznRuTE0A2fYMze587n5uLEkMdXO7VPyXjVTrwibmWVtVaSL67qlhNDHl/tmHVVzsxnq19ODHmaaZ2XeuVlt/uOZz43ByeGPP5Sm3XlpqTm4sSQx4mhPnS37LZPRpXRPvS0lGYi/w3VLyeGfF7nxayLkkYlOUnXPSeGPK4x1L5CM5/9uVVWR+dzCRPcrH45MeTxOi+1b2OfjT+3Ckn/W4tZXdVJuv45MeRp/+L7S13bulyV+iK1osqZ+ewlMeqXP7k8bh+tfV5dtfp8B7fm4sTQDX+pzd7XcaFURM3MF1f1z4khj692at/G7vlsleGZz83FiSGPr3bMuirpns/+G6p7Tgx5fLVTB3zP5z5T0h3cXOuuW04MefylNitsY3fO625/q09ODHl85Vn7PCqpb0hd/98L8d9Q/XNi6IZPMGadFfs34SRd/6qaGCRNlLRA0kJJlxXYLknXpdufkTS+mvGBr3bqQaFltzu2+WRUMcU2Jbmfrv5VLTFI6g/cABwP7A1MlrR33m7HA3ukP1OBH1Qrvna+2jErzE1JzUPFfNCZvJF0KPDViPh4+vifACLi6px9fgg8FBG3po8XAEdFxOvdlTthwoSYM2dOyfHc8k9fZ+kAt6SZWf0a8p74/L9fWtZrJT0ZERMKbRvQq6hKMxxYnPO4BTikiH2GA50Sg6SpJDUKRo4cWVYwAzcdwOZ/28hibGWVatXS3eWMP7fKKuUy0p9F5Q1UZS7sq5kYCn1P8o+qmH2IiOnAdEhqDOUEc+pXLynnZWZmDa+abSktwM45j0cAS8rYx8zMKqiaieEJYA9JoyVtApwGzMrbZxbw+XR00oeAv26sf8HMzLJXtaakiNgg6XzgHqA/MCMi5ks6N90+DZgNfAJYCKwBzq5WfGZmlqhmHwMRMZvk5J/73LSc3wP4cjVjMjOzzjxe08zMOnFiMDOzTpwYzMysEycGMzPrpGpLYlSKpGXAK2W+fAiwPMNw6oGPuTn4mJtDb455l4gYWmhD3SeG3pA0p7u1QhqVj7k5+JibQ6WO2U1JZmbWiRODmZl10uyJYXpfB9AHfMzNwcfcHCpyzE3dx2BmZl01e43BzMzyODGYmVknTZEYJE2UtEDSQkmXFdguSdel25+RNL4v4sxSEcd8Rnqsz0h6VNL+fRFnlno65pz9DpLUKumUasZXCcUcs6SjJM2VNF/Sf1U7xqwV8d3eRtLvJD2dHnNdr9IsaYakpZLmdbM9+/NXRDT0D8kS3y8CuwKbAE8De+ft8wngLpI7yH0I+H99HXcVjvkwYNv09+Ob4Zhz9nuAZJXfU/o67ip8zoOB54CR6eMd+jruKhzzPwPfSH8fCrwFbNLXsffimI8ExgPzutme+fmrGWoMBwMLI2JRRKwDZgIn5u1zInBzJB4DBkvaqdqBZqjHY46IRyPi7fThYyR3y6tnxXzOABcAvwaWVjO4CinmmE8HfhMRrwJERL0fdzHHHMAgSQK2IkkMG6obZnYi4mGSY+hO5uevZkgMw4HFOY9b0udK3aeelHo8U0iuOOpZj8csaTgwCZhGYyjmc94T2FbSQ5KelPT5qkVXGcUc8/XAWJLbAj8LXBgRbdUJr09kfv6q6o16+ogKPJc/RreYfepJ0ccj6WiSxHBERSOqvGKO+bvApRHRmlxM1r1ijnkAcCDwUWBz4E+SHouIFyodXIUUc8wfB+YCxwC7AX+Q9MeIeLfCsfWVzM9fzZAYWoCdcx6PILmSKHWfelLU8UjaD7gROD4iVlQptkop5pgnADPTpDAE+ISkDRFxe1UizF6x3+3lEbEaWC3pYWB/oF4TQzHHfDZwTSQN8AslvQSMAR6vTohVl/n5qxmakp4A9pA0WtImwGnArLx9ZgGfT3v3PwT8NSJer3agGerxmCWNBH4DfK6Orx5z9XjMETE6IkZFxCjgNuDv6zgpQHHf7TuAD0saIGkL4BDg+SrHmaVijvlVkhoSkj4A7AUsqmqU1ZX5+avhawwRsUHS+cA9JCMaZkTEfEnnptunkYxQ+QSwEFhDcsVRt4o85suB7YHvp1fQG6KOV6Ys8pgbSjHHHBHPS7obeAZoA26MiILDHutBkZ/z14CbJD1L0sxyaUTU7XLckm4FjgKGSGoBrgAGQuXOX14Sw8zMOmmGpiQzMyuBE4OZmXXixGBmZp04MZiZWSdODGZm1okTg1kOSYMl/X3O42GSbqvQe31a0uU97PPvko6pxPubdcfDVc1ySBoF3BkR46rwXo8C/2tjY+wl7QL8KCKOq3Q8Zu1cYzDr7Bpgt/T+Bd+SNKp9HXxJZ0m6PV3r/yVJ50u6WNJTkh6TtF26326S7k4XrfujpDH5byJpT+BvEbFc0qC0vIHptq0lvSxpYES8Amwvaccq/h9Yk3NiMOvsMuDFiDggIv5Pge3jSJayPhj4N2BNRHwQ+BPQvnLpdOCCiDgQ+Arw/QLlHA78GSAiVgIPASek204Dfh0R69PHf073N6uKhl8SwyxjD6Yn8pWS/gr8Ln3+WWA/SVuR3ATpVzkruG5aoJydgGU5j28ELgFuJ1nS4O9yti0FhmV1AGY9cWIwK83fcn5vy3ncRvL31A94JyIO6KGc94Bt2h9ExCNps9VHgP556xltlu5vVhVuSjLrbCUwqNwXp2v+vyTpM9BxP95C99N+Htg977mbgVuBH+c9vydQtwvfWf1xYjDLkd6X4hFJ8yR9q8xizgCmSHoamE/hW4w+DHxQne8Y9DNgW5LkAEDaIb07MKfMWMxK5uGqZn1E0rXA7yLivvTxKcCJEfG5nH0mAeMj4l/7KExrQu5jMOs7Xye5cQ6SvgccT7Kufq4BwLerHJc1OdcYzMysE/cxmJlZJ04MZmbWiRODmZl14sRgZmadODGYmVkn/x8VXXi/EtP9gwAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "swiftdiff['dr'].sel(id=plidx).plot.line(x=\"time (y)\", ax=ax)\n", @@ -122,9 +122,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "swiftdiff['dr'].sel(id=tpidx).plot.line(x=\"time (y)\", ax=ax)\n", @@ -135,9 +148,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "swiftdiff['dv'].sel(id=plidx).plot.line(x=\"time (y)\", ax=ax)\n", @@ -148,9 +174,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "swiftdiff['dv'].sel(id=tpidx).plot.line(x=\"time (y)\", ax=ax)\n",