notebooks/c3dp-Optimization_DAC+COlli-50mmAway_from_Sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parent_dir = os.path.abspath(os.pardir)\n",
    "libpath = os.path.join(parent_dir, 'c3dp')\n",
    "\n",
    "if not libpath in sys.path:\n",
    "    sys.path.insert(0, libpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fi0/anaconda2/envs/mcvine/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "/home/fi0/anaconda2/envs/mcvine/lib/python2.7/site-packages/mantid/kernel/__init__.py:17: RuntimeWarning: to-Python converter for std::vector<int, std::allocator<int> > already registered; second conversion method ignored.\n",
      "  from ._kernel import *\n",
      "/home/fi0/anaconda2/envs/mcvine/lib/python2.7/site-packages/mantid/kernel/__init__.py:17: RuntimeWarning: to-Python converter for std::vector<double, std::allocator<double> > already registered; second conversion method ignored.\n",
      "  from ._kernel import *\n"
     ]
    }
   ],
   "source": [
    "import optimization_mantidReduction_DAC as opt\n",
    "from sampleassembly_program import makeSAXML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colli=opt.PresureCell()\n",
    "colli.parameters(Snap_angle=False, coll_sim=True,ncount=1e7,source_file='../out/dac_without_collimator/dac_without_collimator.mcvine'\n",
    "                 , masking=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('vertical number of channels: ', 3.0)\n",
      "('vertical number of channels: ', 9.0)\n",
      "('vertical number of channels: ', 27.0)\n",
      "(768, 768)\n",
      "(768, 768)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fi0/anaconda2/envs/mcvine/lib/python2.7/site-packages/mcvine/run_script.py:125: UserWarning: Developer: <mccomposite.mccompositebp.CompositeNeutronScatterer object at 0x7f9194132470> does not implement method \"create_pps\"\n",
      "  warnings.warn('Developer: %s does not implement method \"create_pps\"' % comp)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAELCAYAAAA/cjqaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYBiGkWL+fyKhYRzCttTAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "collimator_front_end_from_center=0\n",
    "maximum_collimator_length = 60.\n",
    "param=[maximum_collimator_length, collimator_front_end_from_center]\n",
    "diffraction_pattern_colli = colli.diffraction_pattern_calculation (param )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mcvine",
   "language": "python",
   "name": "mcvine"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}