{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 6.3\n", "\n", "For this practical you will need the Shock response data for sand. This can be found in the file [sand_swdb.txt](https://github.com/ggorman/Introduction-to-stats-for-geoscientists/blob/gh-pages/data/sand_swdb.txt), taken from the shock wave database.\n", "\n", "## Exercise 6.3.1\n", "* Open up the text file and have a look around, see what you're working with.\n", "* Read in the file sand_swdb.txt into two arrays called pressure and energy.\n", " * Do NOT use recfromcsv to do this! numpy has other methods too, like genfromtxt, which recfromcsv is based on.\n", "* Remember you do not want any 'commented' lines in the text file to be included, how would you do this?\n", "\n", "## Exercise 6.3.2\n", "\n", "* Make a scatter plot of pressure against energy.\n", "* Label the axes appropriately (include units).\n", "* Perform a linear regression on the data, and overlay the best fit line on the plot (as a dashed line).\n", "* Add a legend to the plot and label the best fit line with its r-value. e.g. r = 0.5.\n", "* Add (or change) the title of your plot to be y = {}x + {} and fill with the fit parameters.\n", "\n", "## BONUS ROUND (Harder)\n", "\n", "* Remake your plot, but this time with vertical errorbars of 10% on every point.\n", "\n", "For this practical you should make extensive use of the matplotlib documentation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#\r\n", "#Silica (Sand), R0= 2.140 g/cc\r\n", "#\r\n", "# P E-E0 \r\n", "11.560 1.066 \r\n", "19.698 2.060 \r\n", " 7.457 0.572 \r\n", " 5.920 0.673 \r\n", " 5.786 0.684 \r\n", " 8.700 1.296 \r\n", " 9.321 1.445 \r\n", "15.045 2.486 \r\n", "15.144 2.531 \r\n", "\r\n", "#References:\r\n", "# M. van Thiel (Ed.), Compendium of shock wave data, (Livermore: Lawrence\r\n", "# Livermore Laboratory Report UCRL-50108, 1977), 356-357\r\n", "#\r\n", "#Denotions:\r\n", "#R0 - normal density of substance, g/cc\r\n", "#P - pressure behind shock front, GPa\r\n", "#E-E0 - specific energy behind shock front, kJ/g\r\n" ] }, { "data": { "image/png": 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L5a2WkQm6PUf30OCVBp7hpUOW0qVRl2yWMKZoy2sto0Cf0ZckIreISBkRCROR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\n/7333tNzzz1XDx48qHv37tWLL77YM29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JjgnpJuBouIUZRmlk+W/LafdeO6/71a6v8uDFD0ZRIsMITKg9hPrA68AlOAph\nMfB3Vd0WQdmsh2AUaxb+spBLx1/qdb99zdvc1fquHFIYRsFgs4wMo4jw7dZv+fMHmdt1Teg1gf4J\n/aMokVHaiKjJSERqAnfi7EPkTaOqA8Mt0DBKKtM2TqPHxz287onXT+SGP90QRYkMIzxCHUP4Emdj\nu2+B9MiJYxjFj8/WfcZfJv3F657adyo9zuuRQwrDKJqEOoawRlVb5hqxgDGTkVGUmZA8gVun3Op1\nz+4/mysaXhFFiQzDIdKzjKaJyFWqOiPcAgyjpPH2928zaPogr3vBbQvoFN8pihIZRsEQag/hMFAR\nOAGcwlmcprZSOfrYEv7CY9TSUTw4K3O66PI7ltP27LZRlMgwAmOzjIxSvbtjJHlhwQs8Oe9Jr3vN\n39aQUDuiazINI19ExGQkIk1V9ScRuTBQeCgH5BhGcWXInCG8uChzS4n196zn/Jrn55DCMIo3uW1u\n946q3iUi8wIEq6peFjnRrIcQLtZDKBgemPkAbyx/w+vePHgzjao3iqJEhhEeRdJkJCJjgGuAFFVt\n4fqNBHrgjEdsAQao6qEg6U0hhIEphPxxx9Q7GLN6jNe9/e/bOafKOVGUyDDyRsQVgoh0IPvCtAm5\npOkIHAEm+CiEK4C5qpohIi852ejjQdKbQggDUwh5o++kvny67lOve+dDO6lT2TbzNYovkV6p/AHQ\nCFhD5sI0BXJUCKq6SETi/fy+9XEuBfqELK1hFCDXfHQN0zdlHk255+E91KxYM4cUhlGyCXUdQhug\nWQRe1wfi7KBqGIWCqtJ5fGcWbl/o9dv/6H6qxlaNolSGUTQIVSH8CNTGOSSnQBCRIcApVf2ooPI0\njGCoKq3fac3q3au9foceO0Tl0ytHUSrDKFrkNu30KxzTUGVgvYgsxxkMBkBVe+alUBG5DbgKyHWW\n0rBhw7zXiYmJJNpqqyykp6czefIsxo9fDJTl6qvTGDCgI717dyMmJiba4kWdDM3gvH+fx+Z9m71+\nR584SoVyFaIolWEULElJSSR5Vqjmg9ymnXbOKbGqzs+1AOcsha9UtbnrvhL4F3Cpqv6RS1obVM6B\nPXv20LPnMyQnX09qaiLuAnJiY5NISJjE1KlDqVWrVpSljA7pGemc9epZ7Dm6x+uXOiSV08ueHkWp\nDKNwKIxZRvFAE1X9VkQqAGVU9XAuaT4CEoEaQAowFHgCOA3wKIOlqnpPkPSmEIKQkZFBhw6DWbZs\nJM6uIv63AqUgAAAgAElEQVQcpV27R1iyZHSp6imcSj9F1RFVOXbqGADly5bn4GMHKVemXJQlM4zC\nI6IKQUTuBO4CqqtqIxFpAvyfql4evqhhCGcKISiTJs2kf/9YUlO7BI0TGzuXDz88Se/eVxaiZNHh\nRNoJYl+I9bprVqjJrn/sokxMmShKZRjRIa8KIdRXx3txjs88BKCqm4DSaYsoIowbt8g1EwUnNbUL\nY8cuzDFOcef4qePIM+JVBo2rNyb96XT2/HOPKQPDCJNQZxmdUNWTIo7CEZGyOIPNRpQ4dqwszphB\nTogbr+Rx5OQRKg/PnCHUsnZLVt21Ck8bNQwjfEJ9WswXkSeA8iLyZ+Ae4KvIiWXkRoUKaTg6OacH\noLrxSg4HUg9QbUQ1r7tTvU7Mv22+KQLDKABCHUOIAW4HuuI8gWYB70XawG9jCMEpbWMIe4/tpebL\nmauIuzfuzoyb7bwmwwhEYcwyOg1oivNa+rOqngy3sHAxhRCc0jLLaPeR3dT5V+a+Qjc0u4GJN0yM\nokSGUfSJ9Cyjq4H/w9mdVIAGwN9UdWa4BYYlnCmEHMlch9DH7Sl41iHMIyHh82K9DmHHwR3UG1XP\n6x7YciBjrh2TQwrDMDxEWiH8BFyjqptddyNguqo2DVvScIQzhZArGRkZfPHFLMaNW8T06c5K5YED\nO9GrV9di2TPYun8rjd7IPHtg8EWDeaP7GzmkMAzDn0grhBWq2tbHLcByX79IYAohPIrz9tc/7f2J\n89/MPI3ssUseY/gVw6MokWEUXyJ1hGZv9/J7EZkBTMQZQ7gBWBG2lIbhxw8pP5Dwf5nnEz+b+CxP\ndX4qihIZRuklt2mnPXyuUwDP3ka/A7HZoxtGaKz4bQUXvXeR1/2vrv/ioYsfiqJEhmFE9AjN/GIm\no/AoDiajRdsX0WlcJ6/7P1f/h0FtBkVRIsMoeUT0xDTDyC9zts7hig+u8LrHXzueW1veGkWJDMPw\nxxSCEVGmb5zONR9f43V/0ucTbrzgxihKZBhGMEwhGBFh0vpJ3PDZDV731L5T6XFejxxSGIYRbUJS\nCCJyJvAicJaqdheRZsDFqmorhYws/PeH/9L/i/5e96y/zqJro65RlMgwjFAJdR3CTGAcMERVE9zd\nTld7TkGLmHA2qJwrSUnOx3PtOWE0MTHzujB4d+W73DXtLq97/m3zuTT+0sITwDAML4WyME1EVqtq\nK9dvjaq2zCXdGOAaIEVVW7h+1YBPgXhgG/AXVT0YJL0phCLO60tf5++z/u51L719Ke3qtouiRIZh\nRHqW0VERqYF7BoKItAcCPsT9GAeMBib4+D0GfKuqI0XkUeBx188oRry48EWGzB3ida/+22pa1s7x\n/SBXikpvxzBKK6H2EC7EebBfAPwI1ASuV9UfQkgbD3zl00P4CeisqikiUhtICrYnkvUQih5Pzn2S\nFxa+4HWvu2cdzWo2K/ByisOaCsMoqkSsh+CehRCLs0r5PJwtNX9W1VNhS+lQS1VTAFR1t4gUz+04\nSxkPfv0go5aN8ro3Dd5E4+qNoyiRYRgFTa4KQVUzRORNd+xgXQRkyPE9cNiwYd7rxMREEs12UKjc\nOfVO3lv9nte97YFtxFeNj6JEhmH4k5SURJLH3poPQjUZvQJ8B0wO14YTwGS0AUj0MRnNU9Xzg6Q1\nk1GU6Pd5Pz7+8WOv+7eHfuOsymcVWvlmMjKMvBPpQeW/AQ8BaSKSinsSi6rGhSIbWQ/+nQrcBowA\nbgW+DFlaI+L0+LgH0zZO87r3PLyHmhVr5pDCMIySQkQ3txORj4BEoAbObqlDgSnAZ8A5wC84004P\nBElvPYRCQFXp8n4X5v8y3+u375F9VCtfLYdUkcV6CIaRdyLaQxCRgCuMVHVBTulUtV+QoCuC+BuF\niKrS5t02rNq1yut36LFDVD69clTkSU9PZ/LkWYwfvxhwTn8bMKAjvXt3K5anvxlGcSPUMYSvfJyx\nwEXASlW9LFKCueVaDyECZGgGTf/dlE37Nnn9jj5xlArlKkRNpszzoa8nNTWRzPOhk0hImFSsz4c2\njMImoiuVAxR2DjBKVfuEnTi8ckwhFCDpGenUfa0uu4/s9vodH3Kc2LJ5O+uooBaSZWRk0KHDYJYt\nGwlUDBDjKO3aPcKSJaOtp2AYIVDYCkGAdapa8CuSspZjCqEASMtIo+pLVTl66igAp5U5jSOPH6Fc\nmXIFVkZ+bP6TJs2kf/9YUlO7BI0TGzuXDz88Se/eV+ZRQsMoPUR6DGE0mesFYoCWwKrgKYyiwMn0\nk5z+/Oled43yNUh5OIUyMWWiKFV2xo1bRGrq8znGSU3twtixT5pCMIwIEuq00+99rtOAj1V1cQTk\nMQqA46eOU+HFzPGABlUbsPn+zcRI0TS3HDtWlqwzkwMhbjzDMCJFSP8wVX3fc+3uVnpOxCQy8syR\nk0eoPDxzhlCLM1uw5m9rcCx8RZcKFdJwOqA5yaluPMMwIkWoJqMkoKcbfyWwR0SWqOqDEZTNCJFj\np45R8cXMwdiO9Tqy4LYFRV4ReBgwoCNz5yblMoYwj4EDOxWiVIZR+gjVhlBFVQ8BvYEJqtoOuDxy\nYhmhcPjEYVq/09qrDLo26ooOVRYOWFgoyiA9PZ3PPpvB1VcPAYZy9dVDmDRpJhkZGWHl07t3NxIS\nJgFHg8Q4SkLC5/TqZSevGUYkCXUdwlqgK/A+zqlpK0TkB8/+RBETzmYZBeRA6gE6jOnAhr0bAHjl\nz6/wjw7/KFQZCnrdQGZ+fdyegie/eSQkfG7rEAwjDCJ9YtoNwFPAIlW9R0QaAi/bOoTC5Y9jf9Dm\n3TZsO7ANgH93/zf3XnRvocsRqXUDGRkZfPHFLMaNW8T06c5K5YEDO9GrV1dbf2AYYVCo6xAKC1MI\nDilHUkj4vwRSjqYA8F6P97j9wtujJk9hrBuwvYwMI+/kVSGE9NolIiNFJE5EyonIHBH5XUT+Gr6Y\nRjjsPLyTqi9Vpfa/apNyNIUPrvsAHapRVQbgWTeQmGMcZ93AwsIRyDCMAiHUid1dVfUREbkO2IYz\nuLwA+G+kBCvNbD+4nXNHn8uJ9BMAfHbDZ1zf7PooS5WJrRswjJJJqP9YT7yrgc9U9WBxmdJYnNi6\nfyuN3mjkdU/tO5Ue5/WIokSBsXUDhlEyCVUhTBORn4DjwN0iUhNIjZxYpYuf9/5M0zebet1f3/w1\n3Rp3i6JEOWPrBgyjZBLyoLKIVAcOqmq6iFQEKqvq7tzS5Uu4Ej6o/OOeH2n+n+Ze97xb55FYPzF6\nAoVIpGYZFdTuqYZR2on0tNMKOEdo1lPVu0SkCXCeqk7LJWlOeT4I3A5kAGuBAap60i9OiVQIq3et\n5sJ3LvS6Fw9cTIdzOkRRovCxdQOGUXSJtEL4FGfLiltU9QJXQSxR1ZbhiwoichawCGiqqifd/Ker\n6gS/eCVKISz7dRntx7T3upffsZy2Z7eNokT5w9YNGEbRJNIK4XtVbSMiq1W1leuXrKoJeZDVoxC+\nw9lG+zDwBfC6qn7rFy+oQihO5oVF2xfRaVymPX3131bTsnaedGmRxdYNGEbRIaLnIQAnRaQ87pkI\nItIIOBFuYR5UdaeI/AvYDhwDvvFXBrnh++AXyVQORYk5W+dwxQeZx0evu2cdzWpG9EwhwzCMPBOq\nQhgKfA2cIyIfApcAt+W1UBGpClwLxAMHgUki0k9VP/KPO2zYMO91YmIiiUXt9T8AMzfN5KqPrvK6\nN963kSY1mkRRIsMwSjJJSUkkFcBbca4mI/e4zLo4b/LtcUYPl6rq3jwXKnI90E1V73Td/YF2qnqf\nX7yQxhCKirliyk9TuO7T6wCIkRg2D95Mg2oNoixV4VBUfgPDMCJoMlJVFZEZqtocmJ4n6bKzHWgv\nIrE4pqfLgRXhZJCens7kybMYP34x4AxoDhjQkd69uxX6gOanP35K38/7AlCxXEU23LuBc6qU/DOE\nfMdxOncGT2euKI7jGIaRO6EOKr8P/FtVw3po55LnUKAvcApYDdyhqqf84gTsIRT01st5ZULyBG6d\ncivgnFe89u611KlcJ+LlGoZh5ESkZxn9BDTB2cfoKO4TOBrnIURqUVQ4vLvyXe6adhcAdePqsuqu\nVdSsWDMiZRmGYYRLpBVCfCB/Vf0l3ALDIZBCKIytl4Mxetlo7v/6fgCaVG/C0juWUr189QItwzAM\nI79EZAzBtfEPAhrjrCYeo6pR3bHM2Xr5+RzjOFsvP1lgCuHlxS/zyLePANC8VnMWDlhIldgqBZK3\nYRhGUSG3QeX3cWz8C4HuQDPggUgLlROFufXys/OfZWjSUAAuOvsi5twyh0qnVcp3voZhGEWR3J6a\nzdzZRYjIGGB55EXKmUhvvayqPDHnCV5a/BIAneM7M/PmmZQvVz5P+RmGYRQXclMI3lk/qppWFM5A\niNTWy6rKQ7MeYtSyUQB0a9SNL/t+yellT8+XvIZhGMWFHAeVRSQdZ1YROK/k5XEWqHlmGcVFVLhC\nmGWUoRncM/0e3l75NgDXnnctn93wGeXKlCuAOzAMwyh8IjrLKFrkvg4h71svp2ekM3DqQCYkOxus\n9r2gLx9c9wFlY+zYR8MwijelSiFA3rdeTstIo9/n/fhs/WcADGg5gHd7vEuZmDIRuQfDMIzCptQp\nhKzxct9H52T6SfpM7MO0jc6ZPoNaD+LNq98kRmzffsMwShaR3v662JKalkqPj3vw7VZnd+2H2j/E\nK11foSgMkBuGYRQlSqxCOHbqGF0/6MriHYsBGNJpCM91ec4UgWEYRhCKrcko2IlpF3U8zNNbu7By\n10oAnk18lqc6PxVpUQ3DMIoMpXoMAeBA6gE6jOnAhr0bAHj5zy/zcIeHIymeYRhGkaTUjiHsO76P\nNu+04X8H/gfA6O6jue+i+3JJZRiGYfhT7HsI8oyjBN/t8S53XHhHjnGDmZnsQBfDMEoSpdZklKEZ\nYU8dTUqCLl1g6FBTDIZhlDyKnclIRKoA7wEXABnAQFVdFm4+4SgD/2M3V6xIY/78jsydW/jHbhpZ\nqV+/Pr/8EtHjNQyjxBEfH8+2bdsKLL+o9RBEZDwwX1XHiUhZoIKqHvKLE/Kgcm4EO3YTkmjXrvCO\n3TQC477RRFsMwyhWBPvfFCuTkYjEAatVtVEu8QpEIWTfEC8dmAU4PQVIpXHjTWzYMJGyZYv9OHux\nxBSCYYRPQSuEaNlJGgB7RWSciKwSkXdEJGIHDkyePIvk5OtxlMEe4H6cjVufB54BXmLz5nto1uxW\n9uzZEykxDMMwijTReh0uC1wI3Kuq34vIKOAxYKh/xGHDhnmvExMTSczDqG/msZsZOArAf+tsAa5g\n06aL6dkz9K2zDcMwigJJSUkkeaZQ5oNomYzOBL5T1YauuyPwqKr28ItXICajLl2GkpT0DDATiAVy\nOlxnLh9+eLLAzmM2QsNMRoYRPiXCZKSqKcAOETnX9bocWB+p8jKP3VwEJOYYNzW1C2PHLoyUKIaR\njQYNGjB37txoi2EYURtDAMeQ/6GIrAESgBcjVdCAAR2JjU3CsVTlpjSFY8dsYNkovZw8eZKBAwdS\npUoVzjrrLF577bUc448ePZqGDRtStWpVLrroIhYvXuwNGzBgAKeffjpxcXFUrlyZuLi4gG+0EyZM\nICYmhrFjx3r91q1bx5VXXknNmjUpUyb7eSX79+/nuuuuo1KlSjRo0ICPP/44S/jEiRNp1qwZVapU\n4YILLuDLL7/MlsepU6c4//zzqVevXhb/+vXrU6FCBeLi4oiLi+PKKwNbDAYOHEhMTAxbt24N6Z43\nbdpEr169qFWrFmeccQbdu3dn48aNWfJ88sknqVu3LtWqVeOyyy5j/fqIvStnI2oKQVWTVbWtqrZU\n1d6qejBSZfXu3Y2EhElAKk5PIUfJ3B6FYWQnPT092iJEnKFDh7JlyxZ27NjB3LlzGTlyJN98803A\nuMuXL+fxxx9n8uTJHDhwgIEDB3Lddddleeg/+uijHDp0iMOHD3Po0KFsOw4fOHCA4cOHc8EFF2Tx\nL1euHDfeeGMWJeHLPffcQ2xsLL///jv//e9/ufvuu9mwwdnLbOfOnfTv359Ro0Zx8OBBRo4cSb9+\n/di7d2+WPEaOHMmZZ56ZLW8RYfr06Rw6dIhDhw7x9ddfZ4uzePFitm7dGnAH5WD3fODAAa699lo2\nbtxISkoKbdu25dprr/WmmzhxIuPHj2fx4sXs27eP9u3b079//4D3HxFUtch+HPEKhpSUFG3c+DqF\n2eocpxP4Exs7Rz//fGaBlWuERkH+1gVN/fr1dcSIEdqiRQuNjY3V9PT0LOE7d+7U8uXL6/79+71+\nq1at0jPOOEPT0tJ0y5Ytetlll2mNGjW0Zs2aevPNN+vBgwez5D9nzhxVVb3tttv0qaee8oYlJSVp\n3bp1s5TVp08frVmzpjZs2FDfeOONAr/fs846S7/99luv++mnn9abbropYNxPP/1U27Vr53UfPXpU\nRUR3796tqtnvJxCDBg3S//znP5qYmKhjxozJFr5582aNiYnJ4nf06FE97bTTdPPmzV6/W265RR9/\n/HFVVV22bJmeeeaZWdLUrFlTly5d6nVv3bpVmzVrpl9//bWec845WeL6/iaBSEtL01atWunatWtV\nRHTLli3esFDu2cO+fftURHTfvn2qqjpixAi98cYbveHr1q3T8uXLB00f7H/j+of9zC01U2lq1arF\nhg0TadJkHHA0SKyjJCR8Tq9eXQtTNKMY8MknnzBz5kwOHDiQbQZanTp16NChA59//rnX7+OPP+aG\nG26gTJkyqCpPPPEEu3fvZsOGDfz6669ZZs/lhuftUlXp0aMHrVq1YteuXcyZM4fXX3+d2bNnB0w3\nYsQIqlWrRvXq1alWrVqW6+rVqwdMc+DAAXbt2kWLFi28fgkJCaxbty5g/O7du5Oens7y5cvJyMhg\nzJgxtGrVKstb91tvvcUZZ5xB27ZtmTx5cpb0y5cvZ+XKlQwaNCjk+gDYuHEj5cqVo1GjzKVMvnK2\nadOG888/n2nTppGRkcGUKVOIjY3Ncl/3338/w4cPJzY2NmAZN998M2eeeSZXXnklP/zwQ5awV199\nlcTExGy9mlDu2Zf58+dTp04dqlWrBkDfvn3ZsmULmzZt4tSpU4wfP57u3buHVikFQV60SGF9iMBb\nY0pKirZrd4/CHIUMt2eQobGxc7Rdu3s0JSWlwMs0cie335phFMgnL9SvX1/Hjx+fY5z33ntPL7vs\nMq/7nHPO0YULFwaMO2XKFL3wwguz5J9TD8Hz9rp06VKNj4/Pktfw4cN14MCBYd1PTuzYsUNjYmL0\nxIkTXr/Zs2drgwYNgqZ58cUXtVy5clquXDmtWbOmfv/9996w1atX6759+zQ9PV1nzJihlStX1iVL\nlqiqanp6urZp00aXL1+uqhpWD2HhwoVap06dLH7vvvuudunSxeseM2aMVqpUScuWLasVK1bUGTNm\neMMmT56sV111lapmrWMPS5Ys0dTUVD1+/LgOHz5ca9eu7e3Vbd++XZs0aaKHDx9WVc3WQ8jpnn3Z\nsWOHnn322frpp596/U6ePKkPPPCAioiWK1dOGzZsqNu2bcuW1kOw/w157CFE/aGfo3AFrBDmzVMd\nOlT16afTtVmzGdqkyRMKT2v79k/o55/PzGYKMAqPSCj/gqJ+/fpZTCgffvihVqpUSStXrux9qOzf\nv18rVKigu3fv1qSkJK1fv743fkpKivbt21fPPvtsrVKlilaqVEnr1auXJf9QFMLEiRO1bNmyWq1a\nNa1WrZpWrVpV4+Li9Jprrimwe92/f7/GxMTo77//7vWbNGmStmjRImD8d999V5s0aeI13Xz99dd6\n5pln6q5duwLGHzRokD788MOqqvrGG2/o7bff7g0LRyGsXr1aK1asmMXvlVde0Z49e6qqo8Rq1Kih\nq1atUlXVFStWaJ06dTQ5OVmPHj2aReZ58+ZlUwj+NG3aVKdNm6aqqn369NEPPvjAG+avEHK6Zw97\n9uzRZs2a6fDhw7P4DxkyRDt06KA7d+7U9PR0HT9+vDZo0ECPHz8eMO+CVgilajpN5m6mMTzzTHeg\nOyLw3XdRFcsoBvgOHPbr149+/fplCa9atSpdu3blk08+YcOGDfTt29cb9sQTTxATE8O6deuoUqUK\nX375JYMHDw5YTsWKFTl27JjXvWvXLu/1OeecQ8OGDfn5559Dknn48OG8+OKL2QY9VRUR4dChQ9nS\nVK1alTp16pCcnMzll18OQHJyMn/6058ClpGcnEyPHj28pptu3bpRp04dlixZQu/evbPF9503P3fu\nXBYsWMD06dMB2LdvH2vWrGHNmjW88cYbOd7bueeeS1paGlu2bPGW7StncnIynTt3plWrVoBjQmrX\nrh3ffvstqsovv/xCp06dUFVOnjzJwYMHOeuss1i6dGm2GUf+cs+ZM4fFixfzz3/+0xt+8cUX8/rr\nr2f53QOlBccs161bN3r16sVjjz2WrT7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%cat sand_swdb.txt\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import stats\n", "\n", "data = np.genfromtxt('sand_swdb.txt', comments='#')\n", "# Genfromtxt will use a delimiter of a space or tab by default, and specifying\n", "# comments as '#' means it will ignore any line in the text file that starts with\n", "# '#' like in a python script. This reads the data in as a 2D numpy array.\n", "\n", "\"\"\"BONUS POINTS\"\"\"\n", "# If you read the data in using the 'usecols' argument that is particularly good! e.g.\n", "P = np.genfromtxt('sand_swdb.txt', comments='#', usecols=0)\n", "E = np.genfromtxt('sand_swdb.txt', comments='#', usecols=1)\n", "# However, either way works fine\n", "\"\"\"############\"\"\"\n", "\n", "\n", "Pressure = data[:, 0]\n", "Energy = data[:, 1]\n", "# Split the data into two arrays\n", "\n", "M, C, R, P, err = stats.linregress(Energy,Pressure)\n", "# Linear regression on energy and pressure, to give coefficients of M and C\n", "# from y = Mx + C; R-value, p-value and the standard error.\n", "\n", "plt.figure()\n", "# Start the figure\n", "plt.plot(Energy, Pressure, linestyle=' ', marker='o', markersize=10)\n", "# Plot Pressure against Energy. No line, circle markers, size 10\n", "plt.plot(Energy, Energy*M+C, linestyle='-', label = 'r-value = {}'.format(R))\n", "# Plot the best-fit line, use the Energy values on the x-axis and calculate\n", "# the corresponding y-values using y = mx + c. \n", "# Label this with the r-value, and use conditional formatting to insert it.\n", "plt.xlabel('Specific Energy behind Shock [kJ/g]')\n", "plt.ylabel('Pressure behind Shock [GPa]')\n", "# Label the axes, based on data in the text file\n", "plt.title('Best fit: y = {}x + {}'.format(M,C))\n", "# Use conditional formatting to label the title with the equation fo the best-fit line\n", "plt.legend(loc='best')\n", "# Add a legend, this will contain all the labels from each 'plot' function.\n", "# EXTRA: add a 'loc' argument to give a location for the legend. 'best' puts it in the\n", "# best possible place, to display as much data as possible.\n", "\n", "plt.figure()\n", "plt.errorbar(Energy, Pressure, yerr=Pressure*.1, linestyle=' ', marker='o', markersize=10)\n", "# To add errorbars, use the errorbar function, and now just add the argument 'yerr'. To make\n", "# the errorbars 10% simply multiply the Pressure values by 0.1 and assign this to yerr.\n", "# The errorbars will be +/- 10%.\n", "plt.plot(Energy, Energy*M+C, linestyle='-', label = 'r-value = {}'.format(R))\n", "plt.xlabel('Specific Energy behind Shock [kJ/g]')\n", "plt.ylabel('Pressure behind Shock [GPa]')\n", "plt.title('Best fit: y = {}x + {}'.format(M,C))\n", "plt.legend(loc='best')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }