{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Test of the `tshirt` Spectroscopic Pipeline\n", "This tutorial describes the basics of how to use the `tshirt` spectroscopic pipeline. This can also be run to test that the code has installed correctly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, make sure the environment is loaded within bash, if it is not already set. Then start a python session or Juypyter notebook\n", "\n", "```\n", "conda activate astroconda\n", "jupyter notebook\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Code and Run Extraction\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the code and set up the example parameters. The example parameter set is available at \n", "https://github.com/eas342/tshirt/blob/master/tshirt/parameters/spec_params/example_spec_parameters.yaml
\n", "This will extract spectra from the images in `../example_data/spec_time_series/`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "On 0 of 4\n" ] } ], "source": [ "from tshirt.pipeline import spec_pipeline\n", "spec = spec_pipeline.spec()\n", "spec.do_extraction(useMultiprocessing=True)\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can plot an example spectrom from the time series." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "spec.plot_one_spec()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`plot_wavebin_series` will plot the time series for wavelength-binned data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/everettschlawin/es_programs/tshirt/tshirt/pipeline/spec_pipeline.py:1407: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { "image/png": 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ej9gtpjcefpFXbnyW7a8kn5ftr2zllRuf5Y2HX6xzZK2rFp+VXhOEpOeAL0bEExHxeERsk/TTfougjlydUTwHHXUsx591NiPGjAWJEWPGcvxZZ/uKrs5evf15YlvXuy/Gth28evvz9QnIavJZqaaKaRtwrKTDgD+NiLeAif0WQR25OqOYPGK3eEpXDtWWW23k/VmpphfTmxHxSZJ7Qt8jaV96mOW1kbg6w6w6A0dl32Wxu3JrDtVcQQggIv5R0oMkYyJG5xpVjXgCMrPq7HHCFF658dku1UwaPIA9TphSv6Asd9UkiPJ7SC+TdALwufxCqi1XZ5j1rtRbyb2YWktPNww6MCJWAmslHVKxuCkaqc2servN2tsJocX0dAXxFeBPgH/KWBbAcblEZGZmhdDTDYP+JP3p+herKQ/IMiuGnqqY5vb0xoi4sf/DsVZXGpBVagwtDcgCnCTMaqynKqY5PSwLwAnC+l1PA7KcIMxqq6cqpjNrGYgZeECWWZFU080VSScD7weGlcoiYmEV7zsRuAwYCFwVERdXLN8XuJpkAsCNwOkRsUbSscClZaseCJwWETdVE681roGjhmYmAw/IMqu9auZi+g7wSeAvSAbN/RGwbxXvGwhcAZwEzAA+ld6Nrtxi4JqImAksBBYBRMTyiDg4Ig4m6S31JnBHtQdljWuPE6agwV3/LT0gy6w+qplq44iIOAP474j4W+BwYHIV7zsUWBURq9P5m64FTqlYZwawLH2+PGM5wCeA2yLizSr2aQ1ut1l7M2ru9J1XDANHDWXU3OlufzCrg2qqmDanP9+UNAF4GdivivdNBF4oe70GOKxinUeBeSTVUB8HRkjaKyJeLlvnNOCfs3Yg6SzgLIB99tmnipCsEXhAVjGtWLGCZcuWsWnTJkaOHMns2bOZOXNmvcOyHFWTIH4qaRRwCfAQSQ+mq6p4nzLKKif5mw9cLunzwN3AWqBz5wak8cAHSeZ/eufGIq4ErgRoa2trigkEzYpoxYoVLF26lG3btgGwadMmli5dCuAkUUd5jxnqNUFExEXp0xvS+0AMi4hqbjm6hq5VUZOAdRXbXgfMBZC0OzCvYtunAj+JiG1V7M/McrJs2bKdyaFk27ZtLFu2zAmiTmoxZqjXBJE2Np8MTCmtL4mIyKz2KfMAMF3SfiRXBqcBn67Y9hhgY0TsABaQ9Ggq96m0PDcetWvWu02bss8Juyu3/NVizFA1VUxLgS3AY8COXtbdKSI6JZ1NUj00ELg6Ip6QtBBoj4glwDHAIklBUsX0pdL7JU0huQL5ebX77CuP2i0m13UXz8iRIzOTwciRI+sQjUFtxgxVkyAmpd1Q+ywibgVurSgrnz78euD6bt77PDnfuc6jdovHdd3FNHv27C5/F4DBgwcze/bsOkbV2moxZqiabq63STq+3/ZYIB61Wzw91XVb/cycOZM5c+bsvGIYOXIkc+bMcdKuo1qMGarmCuJ+4CeSBpDcn1pARMQe/RZFnXjUbvG4rru4Zs6c6YRQILW4iVM1CeKfSAbHPRYRTdWV1LdRLB7XdZtVL+8xQ9VUMT0LPN5syQE8areIZs+ezeDBg7uUua7brD6quYLoAO6SdBuwsz6mim6uDcGjdoulVIXhXkxm9VdNgviv9DEkfZjlynXdZsXQY4JIB8ntHhFfrVE8ZmZWED22QUTEduCQGsViZmYFUk0V0yOSlgD/DrxRKvQ9qS0vN6zfyKLVHazduo2JQwezYOp45o0bXe+wzFpONQliNMkU38eVlfme1JaLG9ZvZP7TL7B5R9Jpbs3Wbcx/Opk13knCrLaqmc3V96a2mlm0umNncijZvCNYtLrDCcKsxqq55egkST+R9KKk30q6QdKkWgRnrWft1uyZ3bsrN7P8VDNQ7nvAEmACyeR5S9Mys343blBnn8qtdjrW38y99x7Fsv94H/feexQd62+ud0iWs2oSxNiI+F5EdKaP7wNjc47LWtSp8W8MiS1dyobEFk6Nf6tTRAZJcli58ny2bF0HBFu2rmPlyvOdJJpcNQniJUmnSxqYPk4nabQ263eHdv6UL/JtxsSLEDsYEy/yRb7NoZ0/rXdoLW31c4vZsWNzl7IdOzaz+rnFdYrIaqGaXkx/DFwOXErSe+m+tMys3w0bOp4jt/6CI/lFRfmEOkVkAFu2dvSp3JpDr1cQEfGbiPhoRIyNiL0j4mMR8etaBGetZ+q0+QwYMLxL2YABw5k6bX6dIjJIEndfyq05dHsFIemC7paR3A/iohzisRY3ftwpQFKlsWVrB8OGjmfqtPk7y60+pk6bz8qV53epZnLibn49VTG9kVG2G/AFYC+gKRKE739cPOPHneKEUDBO3MWU9/dXtwkiIv6p9FzSCOAc4EzgWpKbCDU83//YrHpO3MVSi++vHtsgJI2W9HfACpJkckhE/K+IeLFf9l5nvv+xmTWqWnx/9dQGcQkwF7gS+GBEvN5vey0I3//YzBpVLb6/erqC+ArJ6On/DayT9Gr6eE3Sq/0WQR11d59j3//YzIquFt9f3SaIiBgQEcMjYkRE7FH2GBERe/RbBHXk+x+bWaOqxfdXNQPlmpbvf2xmjaoW31+KiN7X2tWNSycClwEDgasi4uKK5fsCV5PM7bQROD0i1qTL9gGuAiaTjOD+SEQ8392+2traor29PY/DMDNrWpIejIi2rGXVzMW0qzsdCFwBnATMAD4laUbFaouBayJiJrAQWFS27Brgkog4CDgUaIqeU2aN6ob1G2m77wnGL3+Etvue4Ib1G+sdkuUstwRB8qW+KiJWR8RbJOMnKjtRzwBKfbKWl5aniWRQRNwJEBGvR8SbOcZqZj0o3elvzdZtBG/f6c9JornlmSAmAi+UvV6TlpV7FJiXPv84MELSXsD+wCuSbpT0sKRL0iuSLiSdJaldUvuGDRtyOAQzg57v9GfNK88EoYyyygaP+cDRkh4GjgbWAp0kjedHpct/D5gKfP4dG4u4MiLaIqJt7FjfosIsL77TX2vKM0GsIWlgLpkErCtfISLWRcTciJgFnJ+WbUrf+3BaPdUJ3AQckmOsZtaDiUMH96ncmkOeCeIBYLqk/SQNAU4juXXpTpLGSCrFsICkR1PpvXtKKl0WHAc8mWOsZtaDBVPHM3xA10qB4QPEgqme7ruZ5ZYg0jP/s4HbgaeA6yLiCUkLJX00Xe0Y4GlJzwDvBb6evnc7SfXSMkmPkVRX/Z+8YjWzns0bN5rFB0xm0tDBCJg0dDCLD5jMvHGj6x2a5SjXcRC15HEQZmZ9V5dxEGZm1ticIMzMLJMThJmZZXKCMDOzTC09mytAx/qbfZ9dM7MMLZ0gOtbfzMqV57Njx2YAtmxdx8qV5wM4SZhZy2vpKqbVzy3emRxKduzYzOrnFtcpIjOz4mjpBLFla/ZEY92Vm5m1kpauYho2dDxbtq7LLDczK7q821Bb+gpi6rT5DBgwvEvZgAHDmTptfp0iMjOrTqkNNTnJjZ1tqB3rb+63fbR0ghg/7hQOPPDrDBs6ARDDhk7gwAO/7gZqMyu8WrShtnQVEyRJwgnBzBpNLdpQW/oKwsysUXXXVtqfbahOEGZmDagWbagtX8VkZtaISlXjefZicoIwM2tQebehuorJzMwyOUGYmVkmJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaWyQnCzMwy5ZogJJ0o6WlJqyR9LWP5vpKWSVoh6S5Jk8qWbZf0SPpYkleMN6zfSNt9TzB++SO03fcEN6zfmNeuzMwaSm5TbUgaCFwBfBhYAzwgaUlEPFm22mLgmoj4gaTjgEXAZ9NlmyPi4LzigyQ5zH/6BTbvCADWbN3G/KdfAGDeuNF57trMrPDyvII4FFgVEasj4i3gWqBy0pAZwLL0+fKM5blatLpjZ3Io2bwjWLTa96Q2M8szQUwEXih7vSYtK/coMC99/nFghKS90tfDJLVLul/Sx7J2IOmsdJ32DRs29DnAtVu39anczKyV5JkglFEWFa/nA0dLehg4GlgLdKbL9omINuDTwDckTXvHxiKujIi2iGgbO3ZsnwOcOHRwn8rNzFpJngliDTC57PUkYF35ChGxLiLmRsQs4Py0bFNpWfpzNXAXMKu/A1wwdTzDB3TNY8MHiAVT+++OTGZmjSrPBPEAMF3SfpKGAKcBXXojSRojqRTDAuDqtHxPSUNL6wBHAuWN2/1i3rjRLD5gMpOGDkbApKGDWXzAZDdQm5mRYy+miOiUdDZwOzAQuDoinpC0EGiPiCXAMcAiSQHcDXwpfftBwL9K2kGSxC6u6P3Ub+aNG+2EYGaWQRGVzQKNqa2tLdrb2+sdhplZQ5H0YNre+w4eSW1mZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8vkBGFmZpmcIMzMLFNuczGZmVm+bli/kUWrO1i7dRsThw5mwdTx/Tq3nBOEmVkDqsUtk13FZGbWgGpxy2QnCDOzBlSLWyY7QZiZNaBa3DLZCcLMrAHV4pbJbqQ2M2tApYZo92IyM7N3yPuWya5iMjOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8ukiOh9rQYgaQPw637c5BjgpX7cXlH4uBqLj6vxNNqx7RsRY7MWNE2C6G+S2iOird5x9DcfV2PxcTWeZjo2VzGZmVkmJwgzM8vkBNG9K+sdQE58XI3Fx9V4mubY3AZhZmaZfAVhZmaZnCDMzCxT0yYISSdKelrSKklfy1j+l5KelLRC0jJJ+6blx0p6pOyxRdLH0mU/TLf5uKSrJQ1Oy4+RtKnsPRc0w3GVHdsjkp6Q9PNmOC5JXy1b/3FJ2yXlMiVmjY9rpKSlkh5N/15n5nFMdTq2PSX9JN3Wf0r6QIMd13fTv8sKSddL2j0tHyrpx+m+fiVpSl7HtUsioukewEDgOWAqMAR4FJhRsc6xwHvS538O/DhjO6OBjWXrfQRQ+vgR8Odp+THAT5vwuEYBTwL7pK/3bobjqnjPHOA/muG4gL8C/iF9PjZ9z5AmObZLgL9Jnx8ILGuw49qjbNk/A19Ln/9P4Dvp89OytlXPR7NeQRwKrIqI1RHxFnAtcEr5ChGxPCLeTF/eD0zK2M4ngNtK60XErZEC/rOb9+Sp1sf1aeDGiPhNut6L/X5EiXr+vT5F8kWUh1ofVwAjJAnYneQLqrO/DypV62ObASxL11kJTJH03v4+KPI7rlcB0r/NcJK/Fem2f5A+vx6Yna5TCM2aICYCL5S9XpOWdecLwG0Z5aeR8eWRXvZ+FvhZWfHh6SXkbZLe3/eQq1Lr49of2FPSXZIelHTGLkXdu3r8vZD0HuBE4IY+xlutWh/X5cBBwDrgMeCciNjR97CrUutjexSYmy47FNiXfE7QcjsuSd8D1pNcAX2rcn8R0QlsAvbalcDz0Kx3lMvKwJn9eSWdDrQBR1eUjwc+CNye8bZ/Ae6OiHvS1w+RzGfyuqSPADcB03cx9p7U+rgGAb8LzCY56/mlpPsj4pldC79btT6ukjnAvRGxsc8RV6fWx3UC8AhwHDANuFPSPaWz135W62O7GLhM0iMkye9h8rk6yu24IuJMSQNJksMnge/1ZX/10KxXEGuAyWWvJ5GcVXUh6Q+B84GPRsTWisWnAj+JiG0V7/kbkvrdvyyVRcSrEfF6+vxWYLCkMf1xIBVqelzp/n4WEW9ExEvA3cDvvOujeKdaH1dJ5tlrP6r1cZ1JUiUYEbEK+C+Ss9U81OMzdmZEHAyckS7/r/44kAq5HRdARGwHfgzMq9yfpEHASJKqwWLY1caLIj9IznxXA/vxdkPT+yvWmUXSGDW9m23cDxxbUfZF4D5geEX5ON4edHgo8JvS6wY/roNI6n0HAe8BHgc+0OjHlS4rfRB3a6L/w28DF6bP3wusBcY0ybGNIm1wB/4EuKZRjovkKuF9Zc8XA4vT11+iayP1dXn9P+7S76PeAeR2YElviGfSP+T5adlCkowP8P+A35Jckj8CLCl775T0wzWgYpud6fZK77kgLT8beCL9Z7ofOKIZjitd9lWSnkyPA+c20XF9Hri2yf4PJwB3kFTBPA6c3kTHdjjwLLASuBHYs1GOi6Sm5t6yv8sPSdOPZXQAAATASURBVHs1AcOAfwdWkTTKT837f7IvD0+1YWZmmZq1DcLMzN4lJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCsMKQtFfZTJjrJa0te31fTvucJemq9PnnJV2ePr+wbP/PSrpR0ow+bvs9km6RtDKdXfXijHU+ISkktaWvp0jaXHbc3ylb91OSHktnBP1ZaTCmpNGS7kzjvFPSnmm5JH0znSl0haRDyra1vWwfS7qJP3OmUUkflPT9vvwurDE5QVhhRMTLEXFwJKNlvwNcWnodEUfktNu/4u15cSqV9j+dZPTrf0ga28ftL46IA0kGVx0p6aTSAkkjgC8Dv6p4z3Nlx/1n6bqDgMtIBmDNBFaQjL8B+BrJ7KbTSQY2lqaoPolkypfpwFkkA+lKNpft46PdxP4F4L8j4n3ApcA/AETEY8AkSfv08XdhDcYJwhqCpNfTn8dI+rmk6yQ9I+liSZ9Rco+AxyRNS9cbK+kGSQ+kjyMztjkCmBkRj/a2/4j4MckgtE9XG3NEvBkRy9Pnb5HM2VU+wdxFwD8CW6rYXGkK7N0kCdiDt6eAKJ8R9AfAx8rKr4nE/cCodJ6gavU00+hSkpG/1sScIKwR/Q5wDsmEaJ8F9o+IQ4GrgL9I17mM5Arg90jmvbkqYzttJCNbq/UQuzi3kaRRJJMDLktfzwImR8RPM1bfT9LDaSI8CiCSeX3+nGQ07jqS6a+/m67/3ojoSNfrAPZOy3uamXSYpHZJ9yu9qU2GnmYabQeOqvb4rTE162yu1tweKH0hSnqO5Mweki/PY9PnfwjMePuElz0kjYiI18q2Mx7Y0If97tI8/Wn10I+Ab0bEakkDSKpsPp+xegfJDZpelvS7wE1Kpo/fTJIgZpHMFfQtYAHwd32MtzR1wj4RsU7SVJKqs8ci4rk+vP9Fkqk9rIn5CsIaUfnsmTvKXu/g7ZOeAcDhZfXsEyuSAyRfusP6sN9ZwFPlBZImlzX2/lk377sSeDYivpG+HgF8ALhL0vPAh4AlktoiYmtEvAwQEQ+SzAe0P3BwWvZcJPPjXAeU2mV+W6o6Sn+WbuzU7cykEVH6uRq4Kz22Sj3NNDqM5PdnTcwJwprVHbzdiIukgzPWeQp4XzUbkzQPOJ6K6cEj4oWyJPSdjPf9HckX67ll79kUEWMiYkpETCGZ4PGjEdGetp0MTN87laSBeTXJBHAzyhrJP8zbyWoJ8Ln0+eeAm8vKz0h7M30I2BQRHUru7zw03ccY4EiSCRkrlW/3EyS3Zi1dQexP36rnrAG5isma1ZeBKyStIPk/vxvocoYfESsljSyrehpE16uT85TcFGY3ki/D4yKi6iopSZNI7hmwEngore66PCKy2kNK/gBYKKkT2A78WaQ3NJL0t8DdkrYBv+btKqqLgeskfYFkqvk/SstvJZmZdBXwJsn9IiCZxv1fJe0gOUm8OCKeTPexEGiPiCUkbRz/V9IqkiuH8kbpY4Fbqv1dWGPybK7W0iSdB7wWEVdJupSkKuhf6h1XkaVXHz8Hfj9tvLYm5QRhLU3SMJIz7k+T3CBmbkRsqm9UxSZpOjAxIu6qdyyWLycIMzPL5EZqMzPL5ARhZmaZnCDMzCyTE4SZmWVygjAzs0z/H5x5i1xTTD8kAAAAAElFTkSuQmCC\n", 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