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Anthony Scopatz committed 4556485

Have r-theta pixelizer.

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  • Parent commits 0cfd680

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Files changed (2)

File cylpixelizer.ipynb

+{
+ "metadata": {
+  "name": "cylpixelizer"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "from yt.mods import *\n",
+      "pf = load('cylindrical_data/nif2013_hdf5_plt_cnt_0006')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [WARNING  ] 2012-08-21 10:06:24,392 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [WARNING  ] 2012-08-21 10:06:24,393 integer runtime parameter forcedplotfilenumber overwrites a simulation scalar of the same name\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [WARNING  ] 2012-08-21 10:06:24,394 integer runtime parameter nbegin overwrites a simulation scalar of the same name\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [WARNING  ] 2012-08-21 10:06:24,394 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [INFO     ] 2012-08-21 10:06:24,402 Parameters: current_time              = 8.00057343882e-10\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [INFO     ] 2012-08-21 10:06:24,403 Parameters: domain_dimensions         = [48 96  1]\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [INFO     ] 2012-08-21 10:06:24,404 Parameters: domain_left_edge          = [ 0.     -1.2288  0.    ]\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [INFO     ] 2012-08-21 10:06:24,405 Parameters: domain_right_edge         = [ 1.2288      1.2288      6.28318531]\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "yt : [INFO     ] 2012-08-21 10:06:24,407 Parameters: cosmological_simulation   = 0.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sl = pf.h.slice(1, 0.0)\n",
+      "px = sl['px']\n",
+      "py = sl['py']\n",
+      "pdx = sl['pdx']\n",
+      "pdy = sl['pdy']\n",
+      "field = np.log10(sl['dens'])\n",
+      "\n",
+      "imax = px.argmax()\n",
+      "pxmax = px[imax] + pdx[imax]\n",
+      "\n",
+      "img = np.zeros((512, 512))\n",
+      "extents = [-pxmax, pxmax] * 2\n",
+      "dx = (extents[1] - extents[0])/ img.shape[0]\n",
+      "dy = (extents[3] - extents[2])/ img.shape[1]\n",
+      "\n",
+      "dthetamin = dx / pxmax\n",
+      "\n",
+      "for i in range(px.shape[0]):\n",
+      "    r0, theta0 = px[i], py[i]\n",
+      "    dr, dtheta = pdx[i], pdy[i]\n",
+      "    \n",
+      "    theta = theta0 - dtheta\n",
+      "    while theta < theta0 + dtheta:\n",
+      "        r = r0 - dr\n",
+      "        while r < r0 + dr:\n",
+      "            x, y = r * np.cos(theta), r * np.sin(theta)\n",
+      "            #pi, pj = int((x + r)/dx), int((y + r)/dy)\n",
+      "            pi, pj = int((x + pxmax)/dx), int((y + pxmax)/dy)\n",
+      "            img[pi, pj] = field[i]\n",
+      "            r += 0.5*dx \n",
+      "        theta += dthetamin\n",
+      "\n",
+      "plt.imshow(img, cmap='hot')\n",
+      "plt.colorbar()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 35,
+       "text": [
+        "<matplotlib.colorbar.Colorbar instance at 0x7f4b8e89d9e0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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yxlt7RDy8XVsArT0BAD95uUJ59pzhg6lB6LzR0I2oKStHGW2aKXo6JhsymZKb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\nSUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.imshow(img, cmap='hot')\n",
+      "plt.colorbar()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
+        "<matplotlib.colorbar.Colorbar instance at 0x7f4ba4d96f80>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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G0RXFDCVG+OC0tp5VCTajWB7qDdk22FoBHwZCVw+xL8oNZnpBrDsMdRfd1//a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+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

File cylpixelizer.py

+# -*- coding: utf-8 -*-
+# <nbformat>3.0</nbformat>
+
+# <codecell>
+
+import numpy as np
+import matplotlib.pyplot as plt
+from yt.mods import *
+pf = load('cylindrical_data/nif2013_hdf5_plt_cnt_0006')
+
+# <codecell>
+
+sl = pf.h.slice(1, 0.0)
+px = sl['px']
+py = sl['py']
+pdx = sl['pdx']
+pdy = sl['pdy']
+field = np.log10(sl['dens'])
+
+imax = px.argmax()
+pxmax = px[imax] + pdx[imax]
+
+img = np.zeros((512, 512))
+extents = [-pxmax, pxmax] * 2
+dx = (extents[1] - extents[0])/ img.shape[0]
+dy = (extents[3] - extents[2])/ img.shape[1]
+
+dthetamin = dx / pxmax
+
+for i in range(px.shape[0]):
+    r0, theta0 = px[i], py[i]
+    dr, dtheta = pdx[i], pdy[i]
+    
+    theta = theta0 - dtheta
+    while theta < theta0 + dtheta:
+        r = r0 - dr
+        while r < r0 + dr:
+            x, y = r * np.cos(theta), r * np.sin(theta)
+            #pi, pj = int((x + r)/dx), int((y + r)/dy)
+            pi, pj = int((x + pxmax)/dx), int((y + pxmax)/dy)
+            img[pi, pj] = field[i]
+            r += 0.5*dx 
+        theta += dthetamin
+
+plt.imshow(img, cmap='hot')
+plt.colorbar()
+
+# <codecell>
+
+plt.imshow(img, cmap='hot')
+plt.colorbar()
+
+# <codecell>
+
+