{
"metadata": {
"name": "",
"signature": "sha256:35865b002209fd7bb16dba34f63c100ac305a34e223da23b65ab93b98ea213c2"
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Importing Matplotlib's pylab"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pylab is a nice interface to all that is matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Should plots pop up as you type? Use interactive on/off plt.ion() or plt.ioff()\n",
"plt.ion()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# if you want the plots to appear in the notebook\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Basic Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Basic components of the figure environment:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image(url='http://matplotlib.org/_images/fig_map.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several ways to make a figure:\n",
"(for documentation type e.g, plt.figure?)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# basic\n",
"fig = plt.figure()\n",
"ax = plt.axes()\n",
"# plt.figure?\n",
"# plt.axes?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# my favorite:\n",
"fig, ax = plt.subplots()\n",
"# plt.subplots?\n",
"# fig, ax = plt.subplots(ncols=2)\n",
"# fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# clear them:\n",
"plt.close() # close the most recent window\n",
"plt.close('all')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define something to plot\n",
"y = np.sin(np.linspace(0, 2*np.pi, 20))\n",
"x = np.arange(20)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Line Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" '-' solid line style\n",
" '--' dashed line style\n",
" '-.' dash-dot line style\n",
" ':' dotted line style\n",
" 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try these:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(14, 14))\n",
"# plt.plot?\n",
"ax[0, 0].plot(x, y)\n",
"ax[0, 1].plot(x, y, ':')\n",
"ax[0, 2].plot(x, y, linestyle=':')\n",
"ax[1, 0].plot(x, y, linestyle='steps')\n",
"ax[1, 1].plot(x, y, linestyle='steps-pre:')\n",
"ax[1, 2].plot(x, y, linestyle='steps-pre-')\n",
"ax[1, 2].plot(x, y, linestyle='steps-mid-')\n",
"ax[1, 2].plot(x, y, linestyle='steps-post-')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
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BmpWUMGMfpffekxYtsk4B5EdJiXWC7Cgqkl56yToFkigRzc68eb6g7LKLdZJs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\nmTUMAAAAAElFTkSuQmCC\n",
"text": [
""
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Scatter plots using markers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ``'.'`` point marker\n",
" ``','`` pixel marker\n",
" ``'o'`` circle marker\n",
" ``'*'`` star marker\n",
" ``'x'`` x marker\n",
" ``'D'`` diamond marker\n",
" ``'d'`` thin_diamond marker\n",
"
\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(ncols=2, figsize=(14, 4))\n",
"ax[0].plot(x, y, 'o')\n",
"ax[1].plot(x, y, 'o', ms=30, mec='white', alpha=0.3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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HxnC4G+Hx44zOYUpneNiOiUr6+6Xjx70lyCgJ6hpKsAMAgAft7aTtRbi2tlx6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\n/wOSZj4qcn2PKgAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Scatter using plt.scatter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scatter is useful because you can set the individual color or marker sizes as arrays."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A scatter plot that uses colors picked by the y value (you must set the color map)\n",
"fig, ax = plt.subplots(ncols=2, figsize=(14,4))\n",
"\n",
"ax[0].scatter(x, y, c=y, cmap=plt.cm.RdYlBu, s=150)\n",
"\n",
"# Same as above, with the marker size set by the array z\n",
"z = np.random.random(20) * 1000\n",
"ax[1].scatter(x, y, c=y, cmap=plt.cm.RdYlBu, s=z)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8d420+Gjatm1rgKxMo2jRopw6cQyHlBDOrP6QhOjwHI+lqipBZ3ZyZPEoiri7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\nXh6FhYU+i5k+ffpQGBtLVlFRlX3sc7uZn3eAoddfX6tYD2c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KU5VTriqjPFU55aqKBSpP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\nIv1/HN1RbLZ7w9EAAAAASUVORK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Error Bars"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xerr = np.sqrt(x)\n",
"yerr = 0.1\n",
"fig, ax = plt.subplots(ncols=3, figsize=(16, 4))\n",
"ax[0].errorbar(x, y, xerr=xerr, yerr=yerr)\n",
"# no error caps, color the error bars differently than the data:\n",
"ax[1].errorbar(x, y, xerr=xerr, yerr=yerr, capsize=0, ecolor='gray')\n",
"# don't plot values? Just error bars? set fmt='none'\n",
"ax[2].errorbar(x, y, xerr=xerr, yerr=yerr, capsize=0, ecolor='gray', fmt='none')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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PBg6UzjrLLWOz666+o8mvVMrNBhpFUYw7mzkeUqlUqFO6k+tiZt99pY02kp58\nUjrwQN/R5F8qlVJlZaXvMJrs3Xd30DPP9FFFRUXk8l0ikcj4PUyuA/IjarmuWkVFhe8QMpZNrpNy\nz3fG5vA1pjGmh6Rya23fquvnS1prrb0ybZubJKWstfdUXV8gKWGt/VetfdlcYkH+jBkjffqpNHmy\n70jyq3pSqaiwVtptN+mttxTJ1oZ8PN/GGFlrvX90JdfF0623StOmSY884jsSHHCA1KbNNE2bdpjv\nULwg1wGlo6KiQmVlZb7D8CbTfJdrN+C5knY0xmxnjGkp6ShJ02ttM13S8VXB9ZD0Ze2EhrAMGybd\nd5+bnRn+PP10NFtUY4pcF0MsYxOGt9+W5s2Tdt31Ld+hgFwHIDA5FavW2tWSTpP0uKS3Jd1rrZ1v\njBlmjBlWtc3fJX1gjHlP0mRJw3OMGQXWvr3029/GbxmbqPU+ql6LM6qi9nw3hFwXT61aSX/8I8vY\n+DZxovuStEWLmE5FHyHkOgChyakbcD7RXSQsL70kHXGE9P77LCfkw8KFUq9e0qJFUuvW0ewGnA+h\ndI3LJ3JdWD7+WNpjD/de22QT39GUnuXLpZ/9TJo/X5o8uXS7xpHrgNJBN+DM8l2uEywhprp1k7ba\nSho3Tlq92t2WStW0liWT8Wo5C82kSa7Fp1Ur35EA8dapk9Snj3T77TVLCqVSKSaCKZJbbpEOPVTq\n0MF3JEDpIdchCihWUa8RI9wHiWeecdeNid3amUH66ivprrukN97wHQlQGkaMkIYOlYYPl5o1kyor\nK/kAVwSrVrku2A8/7DsSoDSR6xAFuU6whBgbOFBasMBNfIHimTLFzYy5DavWAUWRvowNimfaNLfm\n8F57+Y4EABCqoFpWq5e5oItpGFq2lE45xXVJvflm39GUhjVr3PM9enTN+yGR4L0BFJIxbjKziRPj\nueZqqCZMkEaNqrmeyGZxZgCIGHJdZoIsVrG+9PGixTR0qNSli3TFFcU/dl18PQ/F8sgjUrt2rjsi\ngOIZPFg67zyWsSmWl16SFi+W+vevuY3uiABKAbkuM3QDjghfY0Xbt3eTX4SyjE3cx8xed510+um+\nowBKD8vYFNeECW5CqxZBfWUOAAgNxSoaNWIEH+CK4Y03pHfflQYN8h0JUJpOOUW64w7phx9a+g4l\n1pYskR591H05AABAQ1hnNSKSSamy0ncU/iUS8W1d/cMfpJ/+VLrwQt+RhIO1B0tPKpVSpcdkd999\ng9Sp08cWGgalAAAcCUlEQVTq0WOOtxgkN6Yprl3FLrlE+uIL6c9/9h1JOMh1pcd3rgtFnHMd6pZp\nvqNYjYjycr9jeh980LX4rV3rJiPxxffzUCjLlkmdO7uW1XbtfEcTDj7AodhmzZIOOmiFli3bjC6q\nBfD9924G4MpKNx8CHHIdfKioqFBZWZnvMFBiMs13dANGkwwY4H6/8ILfOOJq8mS3VBCFKuBXjx7S\nJpt8rYce8h1JPN19t1uqhkIVANAUFKsR4buHRPPm7vf48X7j8P08FMLKldKNNzKxEhCKXr1e0Pjx\nEo1C+WWtm1iJXAcAaCqK1YgIpUj75z9dV1VfQnke8umBB6SddpJ22813JAAkqXPnd/Tll9LMmb4j\niZfnnnPdgA84wHckAICooFhFRk4+Wbr2Wt9RRFcqVTPuNpmUysqks8+W+vTxGxeAGs2aSWed5b8n\nSVykqmbFq16aqxmfPADEUCquM4B6xr8MZOTUU6V77nETAiFzyWRNsVpZKfXtK220kXTeeZ4DA7CO\n44+XZs+WFizwHUn0VVZW6sMPXUv18cf7jgYACoPZnQuDYhUZad9eOuIIlhzIlwkT3Dq21WOCAYSh\nVStp+HDpmmt8RxIP118vDRkitWnjOxIAQJQwMT8alErVrGuaSLgWwZYtXZF1zjnuAx2y98QT0s03\n+44CQLpEIiHJFaudO0uXXuq+qEN2fvihpaZOlV55xXckANJV5zogZBSraFAyWfekRh9/LN1xhzRs\nWLEjqlsqFc3Jl447Ttp0U99RNE1Un2MgU9UL1LdrJx19tHTDDdKYMX5jirLXXttd++3n1lcFEI4k\n/9QRAXQDRlZGj5b+9Cdp7VrfkThRG9P+7bfu94gRfuPIRNSeYyAfzjxTuummmvcsMrN2rTR79t4s\nVwMAyArFKrKy777Sj38sTZ/uO5Jouusu93uHHfzGAaBhnTtLvXpJt93mO5JoeuwxqWXLlerd23ck\nAIAoMjaQVc+NMTaUWNA0990nTZwoPf+870hc91QmYSusRKL4ravGGFlrTXGPWljkuuh54QXXZf/d\nd/1PhpZKpSI14+TUqcdrzz1f0+67v+E7lCZLJBJF7x5JrgPWFbVcF1VRyHcUq8ja6tXSjjtKf/ub\ntM8+fmOpXg4mCu691603OGuWFKU/eR/PMR/gEIqePd3aqwMH+o4kOp5/3i1Vc+yxl2rMmIt9hxM0\nch0QfRUVFSorK/MdRvAyzXd0A0bWWrRw47n+9CffkUTH2rXS2LHSRRf5jgRAJkaPlq6+OlpfMPk2\ndqxbQ7p580AmNwAARA7FKnLy+9+7rqHvv+83jqhMaDd9ulv6p18/35FkLirPMVAI/ftLX3zhugSj\ncXPmSG+/LZ1wgu9IAABRxtI1yMnGG0tDh7pWh913d7elL3FS39I3+RaFQspat17jRRdJJoKdvaLw\nHAOF0ry5NGqUNH68m3BJcmOqWPqhbmPHurW4N9zQdyQAckWug08Uq8jZiBHSzjtLf/mLtMUWrhBj\nmZP1PfaYtHKla6EBED0nnujGbb/7rpsluLKykg9wdXjtNWnuXDc+H0D0kevgE92AkbOOHaXDD5du\nvNF3JOFKb1VtxrsOiKTWraWTT5auvdZ3JGEbO9b1tmnVynckAICo42Mz8mLUKOn666Xvv/cdSZie\neUZasUIaNMh3JAByceqprsVw2TLfkYTprbekmTOlYcN8RwIAiAO6ASMvdtlF6tpVuusu35GE6dJL\npQsucB/iqrtIJxI1S8EUa2wvgNy0by8dcYT05z/7jiRMl18unXGG1KZNzW2JRMJfQABQJOS6wsi6\nWDXGbC7pXkk/kfSRpCOttV/Wsd1Hkv4jaY2kVdba7tkeE/VLn9TIl9GjpVNO8RtDbSE8LzNnSp98\nIh1zjFvux3c8yAy5DrWNGiX96lfSH//I973pFi6Unnhi/SEhjHWLDvIdkD1yXWHk0g34PElPWms7\nS3q66npdrKSktXZPklnhhDChUTK57rfpIQjhebn0Uun8812hikgi12EdO+0k9eghvf767r5DCcrl\nl0unnSZtuqnvSJAD8h2AoORSrB4q6faqy7dLGtDAthFcqAOZMsa1rkpuQiFIs2dL77wjHX+870iQ\nA3Id1jN6tPTCCz21apXvSMLw4YduHemRI31HghyR7wAExdgsqwpjzApr7WZVl42k5dXXa233gaSv\n5LqKTLbW3lLP/my2scC1alZW+o4iPImE39bVQw6R+vWThg/3F0OUGWNkrfX6gYhcF55UKqXKABLe\nHXccqy5dFqh797m+Q1EikfDaBW3YMKltW+myy7yFEGkh5LqqOPKW78h1uQsl14XEd65D7jLNdw12\nTDTGPCmpQx13XZh+xVprjTH1ZaRe1tolxph2kp40xiyw1s6sa8Py6tlm5Pp988fYdMlkGF1eJdfC\n2qWLNG+e/66vaX9SRffqq+7ngQf8xRA1qVRKKQ9/yOS6aAnlOVuyZLL+7/+G6Z57DtYmm/iOxp/F\ni6X773e9SNA0vnKdVNx8R67LTSjPWUVFhcrKynyHgYjKNd/l0rK6QG68wlJjTEdJz1pruzTymDJJ\n31hr/1THfXwDl4Pycr+FWTpjXIvm734nnXSS31iK8bykUjVfFKRP6PTMM9Jhh0lnnlnY48dZCK0N\n5DrUp6KiQgsXlmmHHcLJv8WSSqX+9yF65EipZUtp/Hi/MUVZCLmuKo685TtyXXyUcrGanuuQH5nm\nu1zGrE6XdELV5RMkPVxHMK2NMZtUXW4j6QBJ83I4JuoR2vvoqqvch7f//tdvHMV4XpLJmqK4stL9\nHjRIevdd1hqMCXId6jV2rDRpkrR0qe9Iiqu6a+LSpW7Jsur5ChB55DsgDd2w/culWL1C0m+MMe9K\n+nXVdRljtjLGPFq1TQdJM40xr0maLekRa+0TuQSMuoVWrHbvLvXuLV13nd84fD0vl13mWlRbt/Zz\nfOQVuQ712m476YQTpDFjfEfix/jx0rHHSh3q6lSKKCLfAQhK1iMKrbXLJe1fx+2fSTq46vIHkvbI\nOjpE2mWXueUdhg6V2rXzHU3xvPOO9PTT0s03+44E+UCuQ2MuvNCN0z/jDKlzZ9/RFM+yZdKUKdIb\nb/iOBPlCvgMQmlxaVoEG7bCDNHiw6yZXSsaNk0aMUElPuAKUki22cN1gL7jAdyTFdd110pFHStts\n4zsSAEBceZ6rFXGRPslQIlEz2civf+0mWTr9dOmnP81+36F1c27IjBnS++/7jqJpovbcAiFJJBL/\nuzxypGtVnTXL9SiJu+++20i33CLN9b9qD4ACS891QLFRrCIvksn6i54333Td5O6+O7t9R62gOuUU\n6cc/9h1F00TtuQVCkj5DZKtWbtzqOee4idaM93ldC2v27L116KHS9tv7jgRAoTEbLnyiGzAKbtQo\n6bnn4v8N/KJF7vcZZ/iNA4Afxx8vrVghPfKI70gK68svpTlzupVct2cAQPFlvc5qvrEeV7zdfLN0\nzz1u4qFMWxySSddSgfxLJGq6b4colLUH84lcF2+PPiqdfbabdKhFhn2XUqlUJJZJmDHjYBkjHXLI\no41vHIhEIhF06xC5DqUkKrk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"text": [
""
]
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Histograms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib has a plt.hist function that can create histograms and plot them."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.random.normal(size=10000)\n",
"y = np.random.normal(size=10000)\n",
"\n",
"\n",
"fig, ax = plt.subplots(ncols=2, figsize=(14, 4))\n",
"h, bins, p = ax[0].hist(x, bins=20)\n",
"\n",
"h, xe, ye, p = ax[1].hist2d(x, y, bins=20, cmap=plt.cm.RdYlBu)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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AAGigk8PSAAAAAKDr5JotDQAAtG/mzDRxZiTYRTlv3vhtJmJWom2av2R+kjijTx1OEscv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\njCfVlMmppoKef0K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"text": [
""
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Personally, I prefer to use np.histogram with plt.plot using linestyle='steps-pre' since I'm usually using the binned data at some other point in the code. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots()\n",
"h, bins = np.histogram(x, bins=20)\n",
"ax.plot(bins[1:], h, linestyle='steps-pre')\n",
"plt.draw()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text": [
""
]
}
],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Plotting with Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take any galaxy data file that ends with 'gst.fits'\n",
"\n",
"http://archive.stsci.edu/prepds/angst/datalist.html\n",
"\n",
"I'll be using this for this lesson:\n",
"http://archive.stsci.edu/pub/hlsp/angst/acs/hlsp_angst_hst_acs-wfc_10605-ugc-5336_f555w-f814w_v1_gst.fits\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from astropy.io import fits\n",
"# replace this with the file you downloaded\n",
"fitsfile = 'hlsp_angst_hst_acs-wfc_10605-ugc-5336_f555w-f814w_v1_gst.fits'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hdu = fits.open(fitsfile)\n",
"data = hdu[1].data\n",
"photsys = hdu[0].header['CAMERA'] # survey used ACS and WFPC2\n",
"\n",
"# the magnitude fields in the fits file are named MAG[1 or 2]_[ACS or WFPC2] \n",
"mag1 = data['MAG1_%s' % photsys]\n",
"mag2 = data['MAG2_%s' % photsys]\n",
"\n",
"mag1_err = data['MAG1_ERR']\n",
"mag2_err = data['MAG2_ERR']\n",
"\n",
"color = mag1 - mag2\n",
"color_err = np.sqrt(data['MAG1_ERR'] ** 2 + data['MAG2_ERR'] ** 2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Scatter: Color Magnitude Diagram"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# these fits file contains stars that are recovered in only one filter\n",
"# stars not recovered are given values >= 90. No need to plot them, messes up the autoscaling.\n",
"good, = np.nonzero((np.abs(color) < 30) & (np.abs(mag2) < 30))\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"ax.plot(color[good], mag2[good], '.', color='black', ms=3)\n",
"\n",
"# CMDs have yaxis reversed, a simple way to reverse axis depend on autoscaling\n",
"ax.set_ylim(ax.get_ylim()[::-1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"(30.0, 18.0)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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2XJKY/13S4/jOYunxctQ1OOmKYxDRY1jjrtQKbwjhdZJ+VNLfijF+KIRwKekt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\neAEAAHrk/wfN5satxTlB6QAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Error Bars: On the Color Magnitude Diagram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With fmt = 'none'
we can simply overplot error bars and no replot the data. This is a good option for a general CMD function with the option of overploting uncertainties"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"ax.plot(color[good], mag2[good], '.', color='black', ms=3)\n",
"\n",
"# CMDs have yaxis reversed, a simple way to reverse axis depend on autoscaling\n",
"ax.set_ylim(ax.get_ylim()[::-1])\n",
"\n",
"ax.errorbar(color[good], mag2[good], fmt='none', lw=1, xerr=color_err[good],\n",
" yerr=mag2_err[good], capsize=0, ecolor='gray')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QUF1dXeratWv6c9mXUkrvoxrnc/LkyYq3lTWbHDt2TJ8zABUOh1VPT49qaWnR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\n",
"text": [
""
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"1D Histogram: Luminosity Function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"binsize = 0.1\n",
"mbin = np.arange(mag2.min(), mag2.max(), binsize) \n",
"lf, bins = np.histogram(mag2, bins=mbin)\n",
"fig, ax = plt.subplots(ncols=2, figsize=(14, 4))\n",
"\n",
"[ax[i].plot(bins[1:], lf, linestyle='steps-pre') for i in range(2)]\n",
"\n",
"# maybe you want a log scale\n",
"for i, yscale in enumerate(['log', 'linear']):\n",
" ax[i].set_yscale(yscale)\n",
" ax[i].set_title(yscale)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"2D Histogram with Imshow: Hess Diagram"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cbin = np.arange(color.min(), color.max(), binsize)\n",
"hess, cbin, mbin = np.histogram2d(color, mag2, bins=[cbin, mbin])\n",
"\n",
"# Set the extent of the image\n",
"extent = [np.min(cbin), np.max(cbin), np.max(mbin), np.min(mbin)]\n",
"\n",
"fig, ax = plt.subplots(ncols=2, figsize=(14, 4))\n",
"\n",
"im = ax[0].imshow(hess.T, cmap=plt.cm.gray, interpolation='nearest',\n",
" extent=extent, aspect='auto')\n",
"\n",
"# Perhaps you want log bin counts\n",
"from matplotlib.colors import LogNorm\n",
"im = ax[1].imshow(hess.T, cmap=plt.cm.gray, interpolation='nearest',\n",
" extent=extent, aspect='auto', norm=LogNorm())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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+qshBpjy9GkOcB1VuY3xuMx39iVREJUZQ1LyvdknEMai5+sEHH6za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\nAACAzuGLCgAAAIDO4YsKAAAAgM75f3FtJrDwbsqsAAAAAElFTkSuQmCC\n",
"text": [
""
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Customizations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib has a few ways of customizing the plotting environment. \n",
"+ rcParams: Global default settings \n",
"+ mplstyle files: Quick runtime edits of rcParams"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# some code for the next sections\n",
"import os\n",
"\n",
"def directory_check(directory):\n",
" \"\"\"check if a directory exists, if not print the mkdir command to create it\"\"\"\n",
" if not os.path.isdir(directory):\n",
" print 'Need to make the directory.'\n",
" print 'mkdir', directory\n",
" else:\n",
" print '%s exists' % directory\n",
"\n",
"def plot_demo():\n",
" \"\"\"make a plot for a demo\"\"\"\n",
" fig, ax = plt.subplots()\n",
" ax.plot(np.sin(np.linspace(0, 2*np.pi)))\n",
" ax.plot(np.sin(np.linspace(0, 2*np.pi)), 'o')\n",
" return ax"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"rcParams"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see what the current setup is, find the matplotlibrc file in your matplotlib data path: install_dir/python?.?/site-packages/matplotlib/"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'matplotlib data path:', mpl.get_data_path()\n",
"mplrc = os.path.join(mpl.get_data_path(), 'matplotlibrc')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"matplotlib data path: /Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# In terminal use 'cat' to see the contents of the matplotlibrc file, or open it.\n",
"print '$ cat %s' % mplrc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"$ cat /Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make changes to matplotlibrc, you will first have to copy it to your matplotlib user directory, or any changes will be overwritten with future matplotlib installs/upgrades. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Quick check if you may need to create the directory .matplotlib\n",
"user_mpl_path = os.path.join(os.environ['HOME'], '.matplotlib')\n",
"directory_check(user_mpl_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/rosenfield/.matplotlib exists\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the default matplotlibrc to your matplotlib user directory and then follow directions within the matplotlibrc file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'cp %s %s/matplotlibrc' % (mplrc, user_mpl_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cp /Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc /Users/rosenfield/.matplotlib/matplotlibrc\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see all available options (uncomment):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#plt.rcParams.keys()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Mpl Styles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Say you want to plot a few different styles, one for your paper, another for your presenation. In that case, you don't need to make many changes to the matplotlibrc file. You can create your own style files that overide rcParams, either for a given plot command, or thoughout an entire script.\n",
"\n",
"Some styles are available by default:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print plt.style.available"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'grayscale', u'bmh', u'dark_background', u'ggplot', u'fivethirtyeight', u'presentation']\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"to set a default style for the entire session:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# default parameters\n",
"plot_demo()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set all future plots to use bmh\n",
"plt.style.use('bmh')\n",
"plot_demo()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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L0FJdo89d8zFjaBIRyuPqjM+MOdKXPn1LuPQg+4OJsfjX0uW1Cd9FMMYumxgL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\nfZxdynHHmPKO1PQtUse2SCw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"text": [
""
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use a style once, and go back to default style afterwards:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# plot each of the available styles\n",
"for style in plt.style.available:\n",
" with plt.style.context(style):\n",
" ax = plot_demo()\n",
" ax.set_title(style)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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7FiUlJZBIJEhISMDHH3/c5Od16tQJAwYMMCYkm0D9Si1z50I9ffOrr77Cv//9\nb9TV1eGjjz4y63caio4LLa7kQj1tODo6GiKRyCQry4wq+teuXcPTTz8NLy8v8Pl8hIaG4vTp0zr7\nZGVlYfTo0QCAnj174sGDB6ioqND7eS+//DL27NnD2iVzxLbNnz8fPB4Pe/bsQXFxMdPhEI5rOG3Y\nycnJZEuKjSr65eXl6NSpk+a1p6cnysvLn7hPWVlZk59JS+aoX9mQJXPRvXt3TJgwAXV1ddi0aZPF\nvrel6LjQ4kIuxGIx/Pz8dLaZoj5aZJmCemSyGo/H07tfRkYGOnbsiNraWmzatAnBwcGaX+PU/5Hp\nNbdeq1nq+5KSknDw4EFs3boVI0aMQEREBKP//oav8/LyGP/vwZbXeXl5rIrHHK9LSko01z9LSkoA\nAJ07d0ZxcTFiY2MBoNEPhZYwagxDfn4+du/ejfXr1wMAUlNTYWdnp3MxNyUlBcHBwQgNDQUAvPba\na9i0aRPc3Nx0PmvkyJF46623ANDcfMKsUaNGIT09HatXr8aiRYuYDodw1JQpU9CxY8dG2x+vjxYd\nwxAUFITi4mLcuXMHdXV1yMjIQEhIiM4+ISEhOH78OIB/fkg4Ojo2KvgN0TRNwrTk5GQAwKZNm/D3\n338zHA3hqq5duyI9PV1nmynqo1FF397eHnFxcViwYAFiY2MxcuRI+Pr64vDhwzh8+DAAYPDgwfD2\n9sbUqVOxceNGvPfee01+HtemaTbl8dYGlzGRi9DQUAwYMABlZWX44osvLP79TaHjQsvWc6FUKnH0\n6FEUFRXh999/N+m0YaN7+oMGDcKgQYN0tkVGRuq8njt3bos+i1o6hA14PB6Sk5MxefJkrF27Fj/9\n9BP4fD5N4CQWc/ToUeTn56Nr1674/vvvTTqHjO43ZyH1xRzCXC4EAgECAwPxwgsvaLalpKQAAGOF\nn44LLVvORcPBkwkJCSYfPMmqMQyEsIVEItEp+AAtJyaW8fPPP+Ps2bNwc3PDm2++afLPp6LPQrbe\nr2wNpnLBxgmcdFxo2XIuzD14koo+IXrQBE7ChLy8PHz33Xfo0KED3nnnHbN8BxV9FrLlfmVrMZUL\nfRM4r169yuhyYjoutGw1F+vWrQNg3sGTdNpCiB4NJ3DeuHEDN27cQLdu3Wj1DjGbmzdv4sCBA+Dz\n+YiPjzfb99CZPgvZcr+ytZjMhXoC55EjRyCTyXD27FlcuHCBsXjouNCypVyoJ2lOnDgRbm5uCA8P\nR9euXc19Y+PcAAAX/UlEQVT2fVT0CXkCDw8PzJgxA4D2IhshptBwkuaAAQMQFRWFR48emXXSsFGz\nd0xp5MiRjQazEcIWf/75JwIDA6FQKPDbb7+hW7duTIdEbIBIJNK7Qqc188csOnuHEK7o2rUrRCIR\nlEolax+yQqwPE0uDqeizkC31K43FplwsWLAAPB4Pu3fv1oy6tSQ25YJptpILJpYGU9EnpIV69OiB\n8ePHQy6XN/vYT0JaauLEiY1aM+aeNEw9fUJa4ZdffsHAgQPh6OiIoqIiuLq6Mh0SsWKbN29GYmIi\n/Pz8MGDAAPD5fIhEolYtDaaePiFmNGDAAISHh+PBgwfYtm0b0+EQKyaXy/HRRx+hvr4eGzZsQFpa\nGsRisdnvBaGiz0K20q80BTbmYuHCheDz+di+fTsmTZoEkUhk1iV2amzMBVNsIRdisRjFxcXo1auX\n5rGclkB35BLSSnK5HN27d8fw4cM125geu0ysi0Kh0NzzsXDhQtjZWe78m3r6hLSSKdZWE27bv38/\nJk+ejICAAPz2229Grdahnj4hZsbGscvEeqhUKqxZswYAkJSUZPHJrVT0WcgW+pWmwsZcMDV2mY25\nYIo15+LYsWO4ePEivL298frrr1v8+6noE9JK+sYuX7x4kdGxy8Q6qFQqrF69GgCQmJiIdu3aWTwG\n6ukTYgCpVAqJRIK//voLly9fhpOTE37//XeLXpAj1iczMxMjR46Em5sbbt26BUdHR6M/s7U9fVq9\nQ4gBhEIhhEKhZiVPQUEBDh06hAkTJjAdGmEhqVQKsViM3NxceHh4YNy4cSYp+Iag0xIWsuZ+pamx\nPRdt27ZFUlISAGDVqlVm/W2V7bmwJGvKRcPxycOGDUNUVBT+/PNPi9zboQ8VfUKMNG3aNHh5eSEv\nLw/Hjh1jOhzCMmKxGH5+fjrbAgMDIZFIGImHij4L2erzPw1hDblo37495s+fDwBYuXKl2c72rSEX\nlmJNuWDbEl8q+oSYwMyZM+Hu7o7s7GycPHmS6XAIizC1xLcpVPRZyJr6leZmLbkQCASah1mvWrXK\nLN9hLbmwBGvKxfDhw5Genq6zzdzjk5tDRZ8QE5kzZw6cnZ1x6tQpjB07FtHR0RYbxkbY6+TJkygq\nKsL58+dRU1MDmUyGhIQExuY00Tp9QkwoJiYGWVlZCA8P12z7448/EB8fT8PYOCg/Px+9e/dGmzZt\ncPPmTXTp0sXk30GzdwhhkFwu1yn4AODn58fYSg3CrNWrV0OlUmH69OlmKfiGoKLPQtbUrzQ3a8tF\nU3fkmmKlhrXlwpysIRe//fYb/vd//xdt2rRBcnIy0+FoUNEnxITYtlKDMGf16tVQKpV444034OPj\nw3Q4GlT0Wcia1iCbm7XlQt8wNlOt1LC2XJgT23Nx48YNpKamgs/nY+HChUyHo4NOPwgxIfXF2l27\nduH06dOora3F+++/TxdxOUZ9lh8bG9voblym0Zk+C1lDv9JSrDEXQqEQ+/fvR0xMDMrLy002msEa\nc2EubM2FVCrFhAkTIJVK4enpiREjRjAdUiNU9Akxk8TERDg6OuL777/HmTNnmA6HmJl6sJqHhwci\nIiIwfvx47Nu3j3X3aVDRZyG29ystyZpz4e7ujri4OADAsmXLjP48a86FqbExF/oGq7FxuS4VfULM\nKCEhAR07dsQPP/yA06dPMx0OMSO2DVZrChV9FmJrv5IJ1p4LNzc3vPfeewCMP9u39lyYEhtz8ejR\nI73b2bZcl4o+IWY2b948ODs748SJE/jpp5+YDoeYSXV1NasGqzWFij4LsbFfyRRbyIWrqyvmzZsH\nwLizfVvIhamwLRcXLlxARkYGbt++jYqKClYMVmsKDVwjxAKqq6vh6+uLNm3aYMCAAXB3d0dMTAzr\nCgIxTGRkJI4cOYK5c+di06ZNFv1uGrhmA9jYr2SKreTi7Nmz6N69O6KiouDj4wOBQICUlJRWLeez\nlVyYAptycfbsWRw5cgQdOnRg3d23+lDRJ8QCxGIx+vfvr7ONjcv5SOstWbIEAPDee+/hqaeeYjia\nJ6Oiz0Js61cyyVZyYYrlfLaSC1NgSy4yMjJw4sQJODs7IzExkelwWoSKPiEWQNM3bY9KpcLixYsB\nAPPnz4erqyvDEbUMFX0WYlO/kmm2kgt90zczMzMxefLkFn+GreTCFNiQi2PHjuHMmTPw9PTE3Llz\nmQ6nxQw+zaipqcGKFStw9+5deHl5YdmyZXB0dGy03+TJkyEQCGBnZwc+n4/t27cbFTAh1ki9Skci\nkaCurg7Z2dn466+/UFhYyHBkpLWkUin27t2Lc+fOwcPDA6+88ore2sdWBi/Z/PTTT+Hs7Izo6Gik\npaXh/v37eOuttxrtFx0djR07dsDJyanZz6Mlm4RLvvnmG4wfPx6enp64efMmOnbsyHRIpAXUQ9Ua\nztgpLCxk9kHnllqymZWVpflHCoVCnDp1qsl9qZgTouvf//43hgwZgrKyMqSkpDAdDmkhfUPV/P39\nrWoVlsFFv7KyEm5ubgD+ueOwsrJS7348Hg+JiYmYOXMmjh49aujXcQob+pVsYau54PF4WLduHQBg\nw4YNKC0tfeLfsdVcGIKpXFjLULXmNNvTT0xMREVFRaPt06dP13nN4/HA4/H0fsaWLVvg7u6Oqqoq\nJCYmwsfHB3379tW7b8OnzLi4uCA4OFizNEv9H5lec+u1GlviMfXrcePG4ejRo5g1axbi4uKa3T8v\nL4/xeNnyOi8vj5HvV3ctSkpKAACdO3cGAJSXlyMzM9Mi8WRmZmL37t0AYNBTuQzu6b/22mvYtGkT\n3NzccO/ePcybNw979+5t9u/s2bMHDg4OmDhxYqP3qKdPuOjSpUt47rnnwOfzcfXqVQQGBjIdEmlG\ndHQ0cnJyEB4ertnGmZ5+SEiI5hZyqVSKYcOGNdrn0aNHkMlkAIC///4b586dg7+/v6FfSYjN6dOn\nD1577TWoVCqMHz8e0dHREIlErHvaEgH++OMPHDp0CEVFRbh9+zarh6o1x+CiP2XKFPzyyy+IiYlB\nbm4upkyZAuCfX3OSk5MBABUVFYiLi8Obb76Jt99+G0OGDMHAgQNNE7kNe7y1wWVcyEVYWBh8fX0x\nZMgQODk5NTmXhwu5aCkmcrFo0SLU1tZi4sSJOHz4MNLS0iAWi62q4ANGrNN3cnLCxo0bG2338PDA\n2rVrAfzT79q5c6fh0RHCAVKpFGFhYTrb1HN5rK2g2KqcnBykpaWhXbt2WLNmDdPhGIXuyGUh9cUb\nwo1ctHRFCBdy0VKWzIVKpUJ8fDyAfx6I4+vra7HvNgcq+oQwjObysNvXX3+N06dPw9PT0ypGJz8J\nFX0Wot6tFhdyoW8uz6+//troMXtcyEVLWSoXcrkcSUlJAIDly5c/cbKANaBTCUIY1nAuT1lZGc6f\nP49Hjx6hd+/eDEfGXVKpFGKxGLdu3UJ1dTX8/f31jpmxRvS4REJYJioqCocOHYJIJIJYLGY6HM7R\nN1/nypUrWLp0KSsvrNPjEgmxchs3bkS7du0gkUiQlZXFdDico2++Tq9evaxqvk5zqOizEPVutbiY\nC39/f8yfPx8AEBcXp1ndw8V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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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HZc8/Tu/87SS2CNywP2OSvhCmSoiLYmxNF0Xtr99C+EPtJM0RaZncvesEczNP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\n0uvZ0rwPnx3I55qerS/wDs7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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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qxUswt7SEh/9ErsMhROd4uZ6+ROKEmNi1dbZxG7AQcXHXdR0aMTCOA/ph7s8b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\nXqpKra8UycnJzNbWlgFgbdu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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Hjl3oUz37uAGIXsrs40SWZDP3mGqOlgDgLl9H9GrLxQ7UMmZ1c4V38qba2ce9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\ntZvWaI0OTFlZWfD19QUA+Pr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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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hvTBr9EAEGJci/8s5SJ07EpmHjdi9YxtscbEgcyIW5kMszIe+8OokDfj4449x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\noQuQrODyYk3ef/99oYBxcHDA6tWrWcAYERYxpBXuv5Ae5kRaDJUPPz8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"text": [
""
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# back to bmh\n",
"plot_demo()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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L0FJdo89d8zFjaBIRyuPqjM+MOdKXPn1LuPQg+4OJsfjX0uW1Cd9FMMYumxgL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\nRK5CYII=\n",
"text": [
""
]
}
],
"prompt_number": 29
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Make your own style file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like the matplotlibrc file, the easiest way to make your own style file is to copy an existing one, place it in your matplotlib user directory, and edit at will."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The style files are located in install_dir/python?.?/site-packages/matplotlib/mpl-data/stylelib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"mpl defualt style files, see http://matplotlib.org/examples/style_sheets/index.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stylelib_path = os.path.join(mpl.get_data_path(), 'stylelib')\n",
"print 'mpl style path:', stylelib_path\n",
"print 'mpl style files:', os.listdir(stylelib_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"mpl style path: /Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib\n",
"mpl style files: [u'bmh.mplstyle', u'dark_background.mplstyle', u'fivethirtyeight.mplstyle', u'ggplot.mplstyle', u'grayscale.mplstyle']\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open them, check them out, copy your favorite to the directory keeping the .mplstyle file extension\n",
"\n",
"~/.matplotlib/stylelib/[YOUR_CHOSEN_NAME].mplstyle
\n",
" NOTE: ~/.matplotlib/stylelib/ files overide those in mpl-data/stylelib of the same name \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Quick check if you may need to create the directory stylelib\n",
"user_stylelib_path = os.path.join(os.environ['HOME'], '.matplotlib', 'stylelib')\n",
"directory_check(user_stylelib_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/rosenfield/.matplotlib/stylelib exists\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the terminal, copy one of these to your user directory "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# for copy and paste ease, here are the full paths\n",
"for s in os.listdir(stylelib_path):\n",
" print os.path.join(stylelib_path, s)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib/bmh.mplstyle\n",
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib/dark_background.mplstyle\n",
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib/fivethirtyeight.mplstyle\n",
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib/ggplot.mplstyle\n",
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/mpl-data/stylelib/grayscale.mplstyle\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"An example presentation syle that I adapted from the fivethirtyeight style"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%file presentation.mplstyle\n",
"# Author: Phil Rosenfield\n",
"# Adapted from: \n",
"# Author: Cameron Davidson-Pilon, replicated styles from FiveThirtyEight.com\n",
"# See https://www.dataorigami.net/blogs/fivethirtyeight-mpl\n",
"\n",
"lines.linewidth: 3\n",
"lines.solid_capstyle: butt\n",
"\n",
"legend.frameon: False\n",
"legend.numpoints: 1\n",
"legend.fontsize: 16\n",
"\n",
"axes.color_cycle: 000000, 30a2da, fc4f30, e5ae38, 6d904f, 8b8b8b\n",
"axes.axisbelow: true\n",
"axes.edgecolor: f0f0f0\n",
"axes.linewidth: 3.0\n",
"axes.titlesize: 24\n",
"axes.labelsize: 20\n",
"\n",
"patch.edgecolor: f0f0f0\n",
"patch.linewidth: 0.5\n",
"\n",
"font.size: 14.0\n",
"\n",
"figure.figsize: 8, 8 # square figures (inches)\n",
"figure.subplot.left: 0.08\n",
"figure.subplot.right: 0.95 \n",
"figure.subplot.bottom: 0.07\n",
"figure.facecolor: ffffff\n",
"\n",
"\n",
"xtick.labelsize : 16\n",
"ytick.labelsize : 16\n",
"\n",
"text.usetex: True\n",
"text.latex.unicode: False\n",
"\n",
"image.aspect: 1.0\n",
"image.cmap: Blues"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting presentation.mplstyle\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can copy that to your directory (you may need to restart this kernel to have python find it):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'cp %s %s/' % (os.path.join(os.getcwd(), 'presentation.mplstyle'), user_stylelib_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cp /Users/rosenfield/Work/action/aas/scwkshop/2015-01-03-aas/intermediate/matplotlib/presentation.mplstyle /Users/rosenfield/.matplotlib/stylelib/\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Putting it all Together"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a script that pulls together many topics discussed today and yesterday."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%file data_plots.py\n",
"#!/usr/bin/env python\n",
"\"\"\" \n",
"Script to make CMD, LF, or Hess diagram from a binary fits table\n",
"Written by: Phil Rosenfield\n",
"\"\"\"\n",
"import argparse\n",
"from astropy.io import fits\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import sys\n",
"\n",
"def plot_cmd(color, mag, color_err=None, mag_err=None, ax=None):\n",
" '''\n",
" Plot a Color Magnitude diagram with uncertainties\n",
" \n",
" Parameters\n",
" ----------\n",
" color, mag : color and magnitude arrays\n",
" color_err, mag_err: uncertainties in color and mag\n",
" \n",
" ax : axes instance\n",
" \n",
" Returns\n",
" -------\n",
" ax : axes instance\n",
" '''\n",
"\n",
" if ax is None:\n",
" fig, ax = plt.subplots(figsize=(8, 8))\n",
"\n",
" ax.plot(color, mag, '.', ms=3)\n",
"\n",
" if color_err is not None and mag_err is not None:\n",
" ax.errorbar(color, mag, fmt='none', lw=1, xerr=color_err,\n",
" yerr=mag_err, capsize=0, ecolor='gray')\n",
"\n",
" # reverse yaxis\n",
" ax.set_ylim(ax.get_ylim()[::-1])\n",
" return ax\n",
"\n",
"\n",
"def plot_lf(mag, binsize, mbin=None, yscale='log'):\n",
" \"\"\"\n",
" Make a Luminosty function (binned magnitude) plot\n",
" \n",
" Parameters\n",
" ----------\n",
" mag : array\n",
" magnitude array to be binned\n",
" binsize : float\n",
" width of magnitude bins\n",
" mbin : array\n",
" right edges of magnitude bins\n",
" yscale : str\n",
" plt.set_yscale option\n",
"\n",
" Returns\n",
" -------\n",
" ax : axes instance\n",
" \"\"\"\n",
" if mbin is None:\n",
" mbin = np.arange(mag.min(), mag.max(), binsize)\n",
" \n",
" lf, bins = np.histogram(mag, bins=mbin)\n",
" fig, ax = plt.subplots()\n",
" \n",
" ax.plot(bins[1:], lf, linestyle='steps-pre')\n",
" ax.set_yscale(yscale)\n",
" return ax\n",
" \n",
"def make_hess(color, mag, binsize, cbinsize=None, mbin=None, cbin=None):\n",
" \"\"\"\n",
" Compute a hess diagram (surface-density CMD) on photometry data.\n",
"\n",
" Parameters\n",
" ---------\n",
" color : array\n",
" color values\n",
"\n",
" mag : array\n",
" magnitude values\n",
"\n",
" binsize, cbinsize: float, float\n",
" width of mag, color bins in magnitudes\n",
"\n",
" cbin : array\n",
" the right edges of the color bins\n",
"\n",
" mbin : array\n",
" the right edges of the magnitude bins\n",
"\n",
" Returns\n",
" -------\n",
" cbin : array\n",
" the centers of the color bins\n",
"\n",
" mbin : array\n",
" the centers of the magnitude bins\n",
"\n",
" hess : 2d array\n",
" The Hess diagram values\n",
" \"\"\"\n",
"\n",
" if mbin is None:\n",
" mbin = np.arange(mag.min(), mag.max(), binsize)\n",
"\n",
" if cbin is None:\n",
" if cbinsize is None:\n",
" cbinsize = binsize\n",
" cbin = np.arange(color.min(), color.max(), cbinsize)\n",
"\n",
" hess, cbin, mbin = np.histogram2d(color, mag, bins=[cbin, mbin])\n",
" return hess, cbin, mbin\n",
"\n",
"\n",
"def plot_hess(color, mag, binsize, ax=None, colorbar=False,\n",
" vmin=None, vmax=None, cbinsize=None, im_kwargs={}):\n",
" \"\"\"\n",
" Plot a hess diagram with imshow.\n",
" \n",
" Parameters\n",
" ----------\n",
" color : array\n",
" color array to be binned\n",
" mag : array\n",
" magnitude array to be binned\n",
"\n",
" binsize, cbinsize : float, float\n",
" width of magnitude, color bins\n",
" \n",
" colorbar : bool\n",
" option to also plot the colorbar\n",
" \n",
" vmin, vmax : float, float or None, None\n",
" lower and upper limits sent to matplotlib.colors.LogNorm\n",
" \n",
" im_kwargs : dict\n",
" dictionary passed to imshow by default:\n",
" defaults = {'norm': LogNorm(vmin=vmin, vmax=vmax),\n",
" 'cmap': plt.cm.gray,\n",
" 'interpolation': 'nearest',\n",
" 'extent' [limits of mag and color]\n",
" 'aspect': 'auto'}\n",
" Returns\n",
" -------\n",
" ax : axes instance\n",
" \"\"\"\n",
" from matplotlib.colors import LogNorm\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
"\n",
" hess, cbin, mbin = make_hess(color, mag, binsize, cbinsize=cbinsize)\n",
" extent = [np.min(cbin), np.max(cbin), np.max(mbin), np.min(mbin)]\n",
" vmax = vmax or hess.max()\n",
"\n",
" defaults = {'norm': LogNorm(vmin=vmin, vmax=vmax),\n",
" 'cmap': plt.cm.gray,\n",
" 'interpolation': 'nearest',\n",
" 'extent': extent,\n",
" 'aspect': 'auto'}\n",
"\n",
" kwargs = dict(defaults.items() + im_kwargs.items())\n",
"\n",
" im = ax.imshow(hess.T, **kwargs)\n",
"\n",
" if colorbar is True:\n",
" plt.colorbar(im)\n",
"\n",
" return ax\n",
"\n",
"\n",
"def load_data(fitsfile, yfilt='I'):\n",
" \"\"\"\n",
" Load color, magnitude and uncertainties from binary fits table\n",
" \n",
" Parameters\n",
" ----------\n",
" fitsfile : string or file object\n",
" path to binary fits table or object to be read by astropy.io.fits\n",
" \n",
" yfilt : string\n",
" filter to use as mag (V or I)\n",
"\n",
" Returns\n",
" -------\n",
" color, mag : arrays of color and magnitude\n",
" color_error, mag_err : arrays of summed quadriture uncertainies and magnitude uncertainties\n",
" \"\"\"\n",
"\n",
" hdu = fits.open(fitsfile)\n",
" data = hdu[1].data\n",
" photsys = hdu[0].header['CAMERA']\n",
"\n",
" # the magnitude fields in the fits file are named MAG{1,2}_[photsys]\n",
" mag1 = data['MAG1_%s' % photsys]\n",
" mag2 = data['MAG2_%s' % photsys]\n",
" color = mag1 - mag2\n",
"\n",
" color_err = np.sqrt(data['MAG1_ERR'] ** 2 + data['MAG2_ERR'] ** 2)\n",
"\n",
" # choose what gets the yaxis V or I\n",
" if yfilt.upper() == 'I':\n",
" mag = mag2\n",
" ymag = 'MAG2'\n",
" else:\n",
" mag = mag1\n",
" ymag = 'MAG1'\n",
"\n",
" # the error fields in the fits file are named [MAG]_ERR\n",
" mag_err = data['%s_ERR' % ymag]\n",
"\n",
" return color, mag, color_err, mag_err\n",
"\n",
" \n",
"def main(argv):\n",
" parser = argparse.ArgumentParser(description=\"Generate a plot of a fits file\")\n",
"\n",
" parser.add_argument('-p', '--plottype', type=str, default='cmd',\n",
" help='which plot to make: CMD, hess, or LF')\n",
"\n",
" parser.add_argument('-f', '--filters', type=str, default=None,\n",
" help='comma separated V and I filter names for plot labels')\n",
"\n",
" parser.add_argument('-y', '--yfilter', type=str, default='I',\n",
" help='plot V or I for LF or on y axis for cmd, Hess')\n",
"\n",
" parser.add_argument('-m', '--binsize', type=float, default=0.05,\n",
" help='hess diagram or LF mag binsize')\n",
"\n",
" parser.add_argument('-c', '--cbinsize', type=float, default=0.1,\n",
" help='hess diagram color binsize')\n",
"\n",
" parser.add_argument('-xlim', '--xlim', type=list, default=None,\n",
" help='comma separated x axis min, max')\n",
"\n",
" parser.add_argument('-ylim', '--ylim', type=list, default=None,\n",
" help='comma separated y axis min, max')\n",
"\n",
" parser.add_argument('-yscale', '--yscale', type=str, default='log',\n",
" help='y axis scale for LF')\n",
"\n",
" parser.add_argument('-x', '--colorbar', action='store_true',\n",
" help='add the hess diagram colorbar')\n",
"\n",
" parser.add_argument('-outfile', '--outfile', type=str, default='data_plot.png',\n",
" help='the name of the output file')\n",
"\n",
" parser.add_argument('-style', '--style', type=str, default='ggplot',\n",
" choices=plt.style.available,\n",
" help='the name of the matplotlib style')\n",
"\n",
" parser.add_argument('file', type=argparse.FileType('r'),\n",
" help='the name of the fits file')\n",
"\n",
" args = parser.parse_args(argv)\n",
"\n",
" # set the plot style\n",
" plt.style.use(args.style)\n",
"\n",
" if args.filters is not None:\n",
" filter1, filter2 = args.filters.split(',')\n",
" else:\n",
" print('warning: using V, I as default filter names')\n",
" filter1 = 'V'\n",
" filter2 = 'I'\n",
" \n",
" yfilt = args.yfilter\n",
" \n",
" color, mag, color_err, mag_err = load_data(args.file, yfilt=yfilt)\n",
" # the fits file contains stars that are recovered in only one filter\n",
" # stars not recovered are given values >= 90. No need to plot em.\n",
" good, = np.nonzero((np.abs(color) < 30) & (np.abs(mag) < 30)) \n",
" \n",
" if args.plottype.lower() == 'cmd': \n",
" ax = plot_cmd(color[good], mag[good], color_err=color_err[good], mag_err=mag_err[good])\n",
"\n",
" if args.plottype.lower() == 'hess':\n",
" ax = plot_hess(color[good], mag[good], colorbar=args.colorbar,\n",
" binsize=args.binsize, cbinsize=args.cbinsize)\n",
"\n",
" if args.plottype.lower() == 'lf':\n",
" ax = plot_lf(mag[good], args.binsize, yscale=args.yscale)\n",
"\n",
" # make axis labels\n",
" ax.set_xlabel(r'$%s$' % yfilt)\n",
" ax.set_ylabel(r'$\\#$')\n",
" else:\n",
" # make axis labels for cmd, hess\n",
" if args.filters is not None:\n",
" if yfilt == 'I':\n",
" yfilt = filter2\n",
" else:\n",
" yfilt = filter1\n",
"\n",
" ax.set_ylabel(r'$%s$' % yfilt)\n",
" ax.set_xlabel(r'$%s-%s$' % (filter1, filter2))\n",
"\n",
" if args.ylim is not None:\n",
" ylim = np.array(''.join(args.ylim).split(','), dtype=float)\n",
" ax.set_ylim(ylim)\n",
" \n",
" if args.xlim is not None:\n",
" xlim = np.array(''.join(args.xlim).split(','), dtype=float)\n",
" ax.set_xlim(xlim)\n",
"\n",
" plt.savefig(args.outfile)\n",
" print('wrote %s' % args.outfile)\n",
"\n",
"if __name__ == \"__main__\":\n",
" main(sys.argv[1:])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting data_plots.py\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! chmod u+x data_plots.py"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!./data_plots.py -h"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"usage: data_plots.py [-h] [-p PLOTTYPE] [-f FILTERS] [-y YFILTER] [-m BINSIZE]\r\n",
" [-c CBINSIZE] [-xlim XLIM] [-ylim YLIM] [-yscale YSCALE]\r\n",
" [-x] [-outfile OUTFILE]\r\n",
" [-style {grayscale,bmh,dark_background,ggplot,fivethirtyeight,presentation}]\r\n",
" file\r\n",
"\r\n",
"Generate a plot of a fits file\r\n",
"\r\n",
"positional arguments:\r\n",
" file the name of the fits file\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n",
" -p PLOTTYPE, --plottype PLOTTYPE\r\n",
" which plot to make: CMD, hess, or LF\r\n",
" -f FILTERS, --filters FILTERS\r\n",
" comma separated V and I filter names for plot labels\r\n",
" -y YFILTER, --yfilter YFILTER\r\n",
" plot V or I for LF or on y axis for cmd, Hess\r\n",
" -m BINSIZE, --binsize BINSIZE\r\n",
" hess diagram or LF mag binsize\r\n",
" -c CBINSIZE, --cbinsize CBINSIZE\r\n",
" hess diagram color binsize\r\n",
" -xlim XLIM,"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" --xlim XLIM\r\n",
" comma separated x axis min, max\r\n",
" -ylim YLIM, --ylim YLIM\r\n",
" comma separated y axis min, max\r\n",
" -yscale YSCALE, --yscale YSCALE\r\n",
" y axis scale for LF\r\n",
" -x, --colorbar add the hess diagram colorbar\r\n",
" -outfile OUTFILE, --outfile OUTFILE\r\n",
" the name of the output file\r\n",
" -style {grayscale,bmh,dark_background,ggplot,fivethirtyeight,presentation}, --style {grayscale,bmh,dark_background,ggplot,fivethirtyeight,presentation}\r\n",
" the name of the matplotlib style\r\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ./data_plots.py -x -p hess -style presentation -f F555W,F814W -outfile hess_presentation.png hlsp_angst_hst_acs-wfc_10605-ugc-5336_f555w-f814w_v1_gst.fits"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"wrote hess_presentation.png\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Backends"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(IMO this is an advanced topic) Read more here\n",
"http://matplotlib.org/faq/usage_faq.html#what-is-a-backend
\n",
"\n",
"Real quickly, if you need to run a python script over ssh you might not want plots to be popping up, especially if you don't have X forwarding enabled. In these cases, specify the backend as \"Agg\".\n",
"\n",
"You must put this statement above any other matplotlib import"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib as mpl\n",
"mpl.use('Agg')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/Users/rosenfield/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:1312: UserWarning: This call to matplotlib.use() has no effect\n",
"because the backend has already been chosen;\n",
"matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
"or matplotlib.backends is imported for the first time.\n",
"\n",
" warnings.warn(_use_error_msg)\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Resources"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Matplotlib plotting Gallaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://matplotlib.org/gallery.html\n",
"+ http://www.labri.fr/perso/nrougier/coding/gallery/\n",
"+ http://www.astroml.org/examples/index.html"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"More on matplotlib styles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://matplotlib.org/examples/style_sheets/index.html\n",
"+ http://stanford.edu/~mwaskom/software/seaborn/index.html\n",
"
and a cool example using seaborn:\n",
"+ http://nxn.se/post/97650612370/high-contrast-stacked-distribution-plots"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Pythonic access to color palettes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://matplotlib.org/users/colormaps.html\n",
"+ http://nbviewer.ipython.org/github/jiffyclub/brewer2mpl/blob/master/demo/brewer2mpl_maps.ipynb\n",
" "
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Simulate colorblindness "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://www.vischeck.com"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Find color palettes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://colrd.com\n",
"+ http://www.colourlovers.com"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"General Python Resources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://docs.python-guide.org/en/latest/ \n",
"
Especially, \n",
"+ http://docs.python-guide.org/en/latest/writing/style/#general-concepts"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Python tutorials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ http://learnpythonthehardway.org/book/intro.html"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Interfaces with Matplotlib"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Aplpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://aplpy.github.io"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"AstroML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://www.astroml.org/examples/index.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
}
],
"metadata": {}
}
]
}