notebooks/PyBroMo - 1.2 Run simulation - Parallel.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "[PyBroMo](http://tritemio.github.io/PyBroMo/) - 1.2 Run the simulation - Parallel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> *This notebook is part of [PyBroMo](http://tritemio.github.io/PyBroMo/) a \n",
      "> python-based single-molecule Brownian motion diffusion simulator \n",
      "> that simulates confocal [smFRET](http://en.wikipedia.org/wiki/Single-molecule_FRET)\n",
      "> experiments. You can find the full list of notebooks in \n",
      "> [Usage Examples](http://tritemio.github.io/PyBroMo/#usage-examples).*"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Start the cluster"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> In this notebooks we run a a brownian motion simulation on a cluster.\n",
      "> The output of the simulation is the emission rate as a function of time.\n",
      ">\n",
      "> In the next notebook we will use this data to generate timestamps."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Follow the steps in [HOWTO CLUSTER SETUP.txt](files/HOWTO%20CLUSTER%20SETUP.txt) \n",
      "to configure and start an IPython cluster. There is another \n",
      "[txt file](files/HOWTO%20WIN7%20REMOTE.txt) explaining\n",
      "how to manage remotely windows machines (so you can start the enigines\n",
      "without walking in front of the remote PC).\n",
      "\n",
      "After the cluster is started, we can proceed running the inizialization script \n",
      "to load the PyBroMo software:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run -i load_bromo.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/anto/Documents/ucla/src/brownian\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and loading some IPython functions to manage the cluster:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "from IPython.utils.path import get_ipython_dir\n",
      "ipython_dir = get_ipython_dir() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PROFILE = 'parallel'\n",
      "rc = Client(ipython_dir+'/profile_%s/security/ipcontroller-client.json' %\\\n",
      "            PROFILE)\n",
      "dview = rc[:]\n",
      "dview.block = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc.ids"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[4, 5, 6, 7]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If the previous cell does not give an error should return a list of integers\n",
      "that are the ID of all the running engines (either remote or local).\n",
      "\n",
      "We are now ready to run the simulation in parallel."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Brownian Motion simulation"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Parameters definition"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To start a simulation we must load the functions and define the problem parameters. Some paramenters are already set in `brownian.py` but can be overwritten:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run -i brownian.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Simulation time step\n",
      "t_step = 0.5e-6   # seconds\n",
      "\n",
      "# Number of samples to simulate\n",
      "t_max = 10 # seconds\n",
      "\n",
      "# Particles definition\n",
      "P = gen_particles(30, box)\n",
      "\n",
      "# Object containing the simulation (parameters, functions, results)\n",
      "S = ParticlesSimulation(D=D, t_step=t_step, t_max=t_max, particles=P, \n",
      "                       box=box, psf=psf, ID=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can check the simulation parameters:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "S"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "Box: X 8.0um, Y 8.0um, Z 12.0um\n",
        "D 1.2e-11, #Particles 30, t_step 0.5us, t_max 10.0s EID_ID 0 0"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or have a compact string representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "S.compact_name()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID0-0'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Before running the simulation check how much RAM is requires with the current parameters:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "S.print_RAM()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Number of particles: 30\n",
        "  Number of time steps: 20000000\n",
        "  Emission array size: 152.0 MB (total_emission=True) \n",
        "  Emission array size: 4560.0 MB (total_emission=False)\n",
        "  Position array size: 13680.0 MB \n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Hardware limit"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the numbers above we see that the RAM usage can easily diverge. For long\n",
      "simulations we can save RAM discarding the trajectories and saving only the\n",
      "total emission (one array for all the particles).\n",
      "\n",
      "With this configuration a standard 4-core PC with 8GB of RAM and runing 4 engines,\n",
      "can easily perform 4 10s simulations in parallel, in about 8 minutes.\n",
      "In this way a single simulation run will generate 40s of simulated emission.\n",
      "In order to have longer times you can repeat the simulation with the same parameters\n",
      "but a different `ID`. The different IDs can be later composed when the timestamps \n",
      "are generated. In this way we can simulate any desired duration of an experiment\n",
      "making an efficient use of the hardware (we use all the cores)."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Running the simulation in parallel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare the engines (change folder, define a unique eid per engine, load functions):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px %reset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Push a variable with the engine ID to each engine\n",
      "dview.scatter('eid', rc.ids, flatten=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px eid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[4:1308]: \u001b[0m4"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[5:1308]: \u001b[0m5"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[6:1308]: \u001b[0m6"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[7:1308]: \u001b[0m7"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define here the folder where you put the **PyBroMo** software on the remote machines\n",
      "We can define different paths under Linux/Mac ('posix') or under Windows ('nt'):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "import os\n",
      "if os.name == 'posix':\n",
      "    BROWN_DIR = \"/home/anto/Documents/ucla/src/brownian/\"\n",
      "elif os.name == 'nt':\n",
      "    BROWN_DIR = r\"C:/Data/Antonio/software/Dropbox/brownian/\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px \n",
      "%cd $BROWN_DIR"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:4] C:\\Data\\Antonio\\software\\Dropbox\\brownian\n",
        "[stdout:5] C:\\Data\\Antonio\\software\\Dropbox\\brownian\n",
        "[stdout:6] C:\\Data\\Antonio\\software\\Dropbox\\brownian\n",
        "[stdout:7] C:\\Data\\Antonio\\software\\Dropbox\\brownian\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px run -i brownian.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now **push** the object `S` to the enignes so they can run the simulation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dview.push(dict(S=S))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "[None, None, None, None]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px S.EID = eid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px S"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[4:1313]: \u001b[0m\n",
        "Box: X 8.0um, Y 8.0um, Z 12.0um\n",
        "D 1.2e-11, #Particles 30, t_step 0.5us, t_max 10.0s EID_ID 4 0"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[5:1313]: \u001b[0m\n",
        "Box: X 8.0um, Y 8.0um, Z 12.0um\n",
        "D 1.2e-11, #Particles 30, t_step 0.5us, t_max 10.0s EID_ID 5 0"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[6:1313]: \u001b[0m\n",
        "Box: X 8.0um, Y 8.0um, Z 12.0um\n",
        "D 1.2e-11, #Particles 30, t_step 0.5us, t_max 10.0s EID_ID 6 0"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[7:1313]: \u001b[0m\n",
        "Box: X 8.0um, Y 8.0um, Z 12.0um\n",
        "D 1.2e-11, #Particles 30, t_step 0.5us, t_max 10.0s EID_ID 7 0"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run the brownian motion simulation on all the engines:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px S.sim_motion_em()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "[1944] Simulating particle 22 \n",
        "[1944] Simulating particle 23 \n",
        "[1944] Simulating particle 24 \n",
        "[1944] Simulating particle 25 \n",
        "[1944] Simulating particle 26 \n",
        "[1944] Simulating particle 27 \n",
        "[1944] Simulating particle 28 \n",
        "[1944] Simulating particle 29 \n",
        "[stdout:7] \n",
        "[3744] Simulating particle 0 \n",
        "[3744] Simulating particle 1 \n",
        "[3744] Simulating particle 2 \n",
        "[3744] Simulating particle 3 \n",
        "[3744] Simulating particle 4 \n",
        "[3744] Simulating particle 5 \n",
        "[3744] Simulating particle 6 \n",
        "[3744] Simulating particle 7 \n",
        "[3744] Simulating particle 8 \n",
        "[3744] Simulating particle 9 \n",
        "[3744] Simulating particle 10 \n",
        "[3744] Simulating particle 11 \n",
        "[3744] Simulating particle 12 \n",
        "[3744] Simulating particle 13 \n",
        "[3744] Simulating particle 14 \n",
        "[3744] Simulating particle 15 \n",
        "[3744] Simulating particle 16 \n",
        "[3744] Simulating particle 17 \n",
        "[3744] Simulating particle 18 \n",
        "[3744] Simulating particle 19 \n",
        "[3744] Simulating particle 20 \n",
        "[3744] Simulating particle 21 \n",
        "[3744] Simulating particle 22 \n",
        "[3744] Simulating particle 23 \n",
        "[3744] Simulating particle 24 \n",
        "[3744] Simulating particle 25 \n",
        "[3744] Simulating particle 26 \n",
        "[3744] Simulating particle 27 \n",
        "[3744] Simulating particle 28 \n",
        "[3744] Simulating particle 29 \n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!beep #Make some noise"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Save and load simulations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Save the simulation to a file (so it can be reloaded):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "S.dump(dir_=SIM_DATA_DIR)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:0] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID0-6.pickle\n",
        "[stdout:1] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID1-6.pickle\n",
        "[stdout:2] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID2-6.pickle\n",
        "[stdout:3] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID3-6.pickle\n",
        "[stdout:4] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID4-6.pickle\n",
        "[stdout:5] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID5-6.pickle\n",
        "[stdout:6] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID6-6.pickle\n",
        "[stdout:7] Saved to C:/Data/Antonio/data/sim/brownian/objects//bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID7-6.pickle\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the simulation and check that is the same as the one in memory:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "S2 = load_sim_id(ID=0, glob_str=\"*t_max10.0s*\", dir_=SIM_DATA_DIR)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:4] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID4-0.pickle\n",
        "[stdout:5] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID5-0.pickle\n",
        "[stdout:6] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID6-0.pickle\n",
        "[stdout:7] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID7-0.pickle\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "(S.em == S2.em).all()  # Return `True` if arrays equal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[0:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[1:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[2:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[3:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[4:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[5:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[6:35]: \u001b[0mTrue"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[7:35]: \u001b[0mTrue"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px del S2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some testing on the simulations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> *The following assumes you perfomed at least two simulations (with ID 0 and 1)\n",
      "> on the remote engines*\n",
      ">\n",
      "> This is a quick example. For a better description see the next notebook: \n",
      "> **2. Generate timestamps - Parallel**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we use the cluster to load the emission traces from file for two simulation set (ID=0 and ID=1). \n",
      "Then we generate the timestamps (`ph_times`) for the donor (D) and acceptor (D)\n",
      "channels."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IDs = [0,1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ID in IDs:\n",
      "    dview['ID'] = ID\n",
      "    %px S = load_sim_id(ID=ID, glob_str=\"*t_max10.0s*\", dir_=SIM_DATA_DIR)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:4] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID4-0.pickle\n",
        "[stdout:5] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID5-0.pickle\n",
        "[stdout:6] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID6-0.pickle\n",
        "[stdout:7] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID7-0.pickle\n",
        "[stdout:4] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID4-1.pickle\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:5] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID5-1.pickle\n",
        "[stdout:6] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID6-1.pickle\n",
        "[stdout:7] \n",
        "Loaded:\n",
        "C:/Data/Antonio/data/sim/brownian/objects\\bromo_sim_D1.2e-11_30P_64pM_step0.5us_t_max10.0s_ID7-1.pickle\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ph_times_d, t_tot, sim_name = parallel_gen_timestamps(dview, \n",
      "                                                 max_em_rate=1e6, bg_rate=2e3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ph_times_a, t_tot, sim_name = parallel_gen_timestamps(dview, \n",
      "                                                 max_em_rate=5e5, bg_rate=4e3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we merge the two arrays `ph_times_d` and `ph_times_a` in a single array `ph_times`\n",
      "and use a boolean mask (array of booleans) `a_em` to signal if each timestamp\n",
      "is from the **A**cceptor (`True`) or from the **D**onor (`False`)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ph_times, a_em = merge_DA_ph_times(ph_times_d, ph_times_a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Just test that the new arrays are consistent with the old ones:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(ph_times_d == ph_times[-a_em]).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(ph_times_a == ph_times[a_em]).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some timestamps plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%matplotlib qt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the full array of timestamps (the merged timestamps from the cluster):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ph_times)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "[<matplotlib.lines.Line2D at 0x55eee10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1JSocPb4PPviuxfOXv5x54bex72hjJrAzlzK5o0x2ibriH0plZTB2LKSlQXa2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\nGMpHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x55a3110>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(ph_times, bins=arange(0,4,1e-3), histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xo3e6oaEBAHD22VL60CEXtm4Fvv3t2Nav1p5NTS6/jybE2z5yeuhQdflle8rp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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x56061d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the emission for the simulation in **one** engine (no need to transfer the array):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px --target 5\n",
      "%matplotlib inline\n",
      "plot_emission(S, ms=1)\n",
      "plt.xlim(0,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l2giRiXAIkjkcPSA1dCSZ4XbuBNzdgWefFbsnhBiXQYXi8ePHiImJEbovxAC0\npsQxdhYXLwK3b0uzUND7gkNZ8M+gfRQDBw7E3r17he4LIYQQCTKoUCxbtgyRkZFo0qQJHBwc4ODg\nIIkvLrJEdB0bDmXBoSw4lAX/DBp6EvNsbEIIIeIyqFAAQEJCAo4dOwaZTIa+ffsiMjJSyH7pRDte\nafy1MsqCQ1lwKAv+GTT0NHv2bKxYsQIdO3ZEYGAgVqxYgQ8//FDovtWKLmhLCCHCM6hQ7N27FwcP\nHsS///1vjB07FomJifj555+F7lutHj0SuwfGR+OvHDGykOoh2vS+4EgtC6m+Z+rCoEIhk8lQUFCg\nmS4oKBDlPApCCCHGZ9A+ig8//BBdunTRjP0dPXoUixcvFrJfpAY0/sqhLDiUBYey4J9BhWLEiBHo\n27cvTp8+DZlMhpiYGLRu3VrovhFCCJEAvUNPly9fBgCcPXsWt2/fhlwuh4eHB27evIm0tLRaG09M\nTERAQAD8/f31ntl9+vRpWFtb48cff6xj9y2P1MZfxURZcCgLDmXBP71bFEuXLsXq1asxY8YMnfsk\njhw5UuNjlUolJk+ejKSkJHh4eKB79+6IiopCYGBgtflmzZqFAQMGgNVxr0+l3SaEEEIEordQrF69\nGkD9KvSpU6fg5+cHb29vAMDw4cORkJBQrVCsXLkSb7zxBk6fPl3n5zCHownqisZfOZQFh7LgUBb8\nM+iopx07dqCwsBAA8Omnn+L111+vdegpNzcXnp6emmm5XI7c3Nxq8yQkJGDixIkAxLkiLSGEEP0M\n2pn9ySefYMiQITh+/DgOHz6MmTNn4p133sGpU6dqfIwhC/1p06Zh8eLFkMlkYIzpHXoaPXo0ysu9\nkZMDLFvmhJCQEABhALgtHvWahDlPq/9WKABvb37aVyiSkZxc98e7uBj/9VeeVt9mvOcT9/Xqmz5/\n/jymTZsmmf6IOb1s2TKEhIRIpj/Jycm8fl71ta9QJGP06PUAoBnN4QUzQHBwMGOMsVmzZrHNmzcz\nxhgLCQnR+5iTJ0+y8PBwzfTChQvZ4sWLtebx8fFh3t7ezNvbm9nb27NWrVqxhISEam2pu7lvH2Pz\n5nG3//ST9rQlOHLkCGNM9br5eO3799e/nQsXxM1fnYWxzJvH2IoVRn1Kgxk7CymTWhZKJX+f15qo\n26/6HAYu4mtl0BaFh4cHxo8fj0OHDmH27NkoKSlBRUWF3sd069YNmZmZUCgUaNOmDbZt24b4+Hit\nea5fv67AvgjDAAAZL0lEQVT5e8yYMYiMjERUVJTBRe72bYNnNRvqtQhCWVRGWXAoC/4ZtI9i+/bt\nCA8Px8GDB+Hk5IT8/Hx8/vnneh9jbW2N2NhYhIeHo0OHDhg2bBgCAwMRFxeHuLg4XjqvVPLSDCEG\ns8QDKAgxaIvi9u3beOWVV9CkSRMcOXIE6enpGDVqVK2Pi4iIQEREhNZtEyZM0DnvunXrDOmKljt3\n6vwQk5dM3wesQVlwKAsOZcE/g7YoXn/9dVhbW+Pq1auYMGECcnJy8K9//UvovhFCCJEAgwqFlZWV\n5szpKVOm4PPPP8etW7eE7hvRgdaUOJQFh7LgSC0LcxiuNKhQ2NraYsuWLdi4cSMGDhwIACgrKxO0\nY/pU2gdOCCFEYAYVirVr1+LkyZOYM2cOfHx8cP36dbz11ltC961GllwoKp9DYOkoCw5lwaEs+GfQ\nzuyOHTti5cqVmum2bdti9uzZgnWqNvTNdvyhk+EJIbXRWyiGDBmCHTt2oFOnTtXuk8lkSE9PF6xj\nhnB2BvLzRe2C0Ult/FVMYmfxzTfAv/8NNGkiajcAiJ+FlFAW/NNbKJYvXw4A2LNnj1E6U1fNmlle\noeCbOexoE8udO8CDB9IoFIQISe8+ijZt2gBQXTPE29sbLi4ucHR01PyIzdlZ7B4YH42/cigLDmXB\nkVoWlVdmTXXFzKB9FHFxcZg3bx4aN24MKytVbZHJZFqX4CCEEFLd33+L3YOGM6hQfP7557h48SJc\nXV2F7k+dWOIWBY2/csTIQqprhPS+4FAW/DPo8Ni2bduiadOmQvelViUlqt/qI3XoiB1CCBGeQVsU\nixcvRs+ePdGzZ0/Y2toCUA09rVixQtDOVfXokVGfTpLoOjYcyoJDWXAoC/4ZVCjGjx+PF198EZ06\ndYKVlRUYY/RtdGaC/o0NI9WhKEL4ZFChUCqVWLp0qdB9MZh64Vb1sMSsLMDb27wXfrSmxKEsOJQF\nh7Lgn0H7KCIiIhAXF4dbt27h/v37mh+xVd1tsmEDUFwsTl8IIcRcGVQotmzZgsWLF6NXr17o2rWr\n5kcslry5L7VjxMVEWXAoCw5lwT+Dhp4UCoXA3SCEECJVercolixZovl7x44dWvd99NFHwvTIAFX3\nQSiVQGmpOH0xNhp/5VAWHMqCQ1nwT2+hiI+P1/y9cOFCrfv2798vTI/0yMzUffv//R+g/gpvc96R\nTQgxPTdvcn8rleL1oyEM2kchdXl5gPp7lMx9/wWNv3KkkIVU3m9SyEIqpJZF5fO/pPJ+qSuzKBSE\nGIupftAJaQi9O7PT09Ph4OAAAHj8+LHmb/U0MT4af+VQFhzKgiPlLBo1ErsH9aO3UChNcECN9lEQ\nQgi/THLoyZKLgdTGX8VEWXAoCw5lwT+TLBREGrKzxe4BIcQYqFCYGCmNv1ZUiPv8UspCbJQFh7Lg\nHxUKQgjhWXS07nMmTPWoOSoUJobGXzmUBYey4EglCxM8FqhGVCgIIURAproVUZlJFgpLPuqJxl85\nUshCKgsBKWQhFZQF/0yyUBBCiNSZ0wotFQoTI5XxVwAQ+7urxMhCKlsQVUnpfSE2yoJ/VChIvVW+\n2BkhxHyZRaF48kTsHhgP3+OvJ0/y2pxR0Vg0h7LgUBb8M8lCcfIkcPs2N11QIF5fCCHE3JlkoQCA\nP/8UuwfioPFXDmXBoSw4Us5Cqvu4aiNooUhMTERAQAD8/f0RExNT7f4ffvgBwcHB6Ny5M5599lmk\np6cL2R1CeGeqH3xC6kLvZcYbQqlUYvLkyUhKSoKHhwe6d++OqKgoBAYGauZp27Ytjh07hubNmyMx\nMRHjx49HSkqKUF0yC1IafxV7ISmlLMRGWXAoC/4JtkVx6tQp+Pn5wdvbGzY2Nhg+fDgSEhK05unZ\nsyeaN28OAHjmmWeQk5NTp+cwp+OUCSHmxZyWT4JtUeTm5sLT01MzLZfLkZqaWuP833//PV5++eUa\n7x89ejQUCm8AQJMmTnBxCUGfPmEAAIUiGQDg7a2aVo9RqtcszGla/bdCwd/rVSiSkZxc98cDxn/9\n2s+vnYnwzxcGxqTz+itPnz9/HtOmTZNMf8ScXrZsGUJCQkTtj0IBqN8fFy8m8/p5ren5vL3DoFAk\nY/To9QAAb29v8EXGmDADCLt27UJiYiJWr14NANi8eTNSU1OxcuXKavMeOXIE7777Ln777Tc4OztX\n76RMBsYYoqO52154AXB0BH76SXvemTMBe3s+X4m0JCcnIywsTJNF5UzqoyHtfPMNcOdOw/tQX+os\njKVqVtHRwLhxgIeH0bpQI2NnIWVSyCI6Gvj4Y8DaWrWMOn9edbv6NiGeT9ff6mVnQwm2ReHh4YHs\nSt9sk52dDblcXm2+9PR0jBs3DomJiTqLhD66Xr85be7pIvYHoDKxsxYzC6ldGVRK7wuxURb8E2wf\nRbdu3ZCZmQmFQoHS0lJs27YNUVFRWvPcuHEDr7/+OjZv3gw/P786tS+TAba21W8XewcrsQyPH4vd\nAyJV5rgMEqxQWFtbIzY2FuHh4ejQoQOGDRuGwMBAxMXFIS4uDgDwySefID8/HxMnTkRoaCiefvrp\nOj1Hq1ZC9FzaKo/PWzrKgkNZcCgL/gk29AQAERERiIiI0LptwoQJmr/XrFmDNWvWCNkFQgiRDFPd\n2jDZM7NrIva4udCkNP4q9ptezCzU7zOxM1CT0vtCbJQF/8yuUBBCiBRIZSWCDyZdKHRtPZjTP0cX\nGn/lUBYcyoJDWfDPpAuFuRcFQojpqbpcMofvbTHpQqEL7aMwHrGzlkIWUllZkUIWUiG1LMzhStcm\nWyiOHhW7B0TsQiEFGRli94AQ4ZlsoSgvt8wFFY2/cqSQhVS+XVEKWUgFZcE/ky0UhIjl+nXub6kM\nPREiJCoUJkZq469iEiuLjRtFeVq96H3BETsLfSsPprpiYXaF4u5dsXtALEFFheq3JQ5/EstjdoXi\n9m2xeyAsGn/liJnF1auiPbVO9L7gUBb8M7tCQYgxmOoQAiH1QYXCxIg9/lqZ2MMuUspCbJQFh7Lg\nHxUKQggxMamp3H4yYzC7QmHuQwJ8jr8eO9awx4u9RSHmWHRpqWhPrRONy3PEzsIYy6D9+4H8fOGf\nR83sCgUxnJBnFe/YId53aRvD8eNi94BInTmttFKhMDF8jr8K+Ua+dEm4ttXoO7M5NC7PkXIWplo8\nqFCQehN76EkKTPWDT8yLu7uw7VOhMDF8jr+a+kJO7LFoKaEsOGJnYcydzGr+/sK2b3aFwtQXfqbE\n2VnsHhgPLYeJoe7fN/5zCr3cM7tCUdWRI8CWLWL3gj9S2kfRvDk//agvY45FS71QSHlc3tgoC/6Z\nfaH4/Xfz+OIQKbLkfRSPH9c+z4EDwObNwveFEKGZXaGoupb88KE4/RAK7aPgiD0WXZuMDONdE0rq\nWRiT2FmY+udKF5MuFLWt0X75pXS+WEZqjh1r+JV2LXmLghBLYtKForbKXVRknH4YE1/jr5mZvDQj\nKhqL5lAWHKlkceqU2D3gj0kXCqmd9ERM1717wIMH/LZJW1yWLSmp+m18DksZ8/1l0oVC11Ck1K7B\nwzexx18rE3tByGcWK1cC339f98dJZWVFSu8LsVEW/DPpQqFrRzVdg8d4xD48lm+FhXV/THk5//0g\npo12ZkuMrjMgzf2DK5XxV0D8QiGFLMQ4C1cXKWQhFZQF/0y6UJhj5TYHR45wf9P/iBBh0PdRGMjS\nFkKPHwOHDiWjuJj/tuvzptO1j2L1au0jqrKz69+n2hhrLFrfJRn0bcEWFKh+G2M/hiWOy1+8qPtS\n9paSBX0fhYHELBRinJ+xfLnqciRffMF/24cO1f0xus5Ozs3VXjAac2fvkyeGnTFdVxcv1nyfISfU\nGfMDbUl27hS7B7VLSxOu7dxc1W/G6FpPeok5PrxokfEvDVJSAnh5hfHSVtU1/Vu36t7G/v3a08Yu\n3FXHojduBFas4KftVau4InDvXsPaMsbVAWhcniN2FpW/EGz3bsDOjpu+coW/5ykuVn3m5s/nr82a\nmHShEHvoyVgn9B061PC1UsaA27drvr8uC8OtW4FHj6pvLaj/H2KdDX//ft23KBjjhojUysuBmzdV\n39IHGLbTfu9eYM0a7XbV/vqrbn0i/DpyBNiwgZtOSBD2hFN9R17ysXWpfm/duGG8ZSAVChPw22/c\n8IdCkVyvNq5cAb77rub765LlH38AS5aotnB0tVF1wSsUPsaiN2wAli3Tvu2nn1S/1QXPz6/2dq5d\nA3JyuOnKhffOHcP78+gRcP684fOrWcq4vCGqZnH+PJCVxU2fO2faZ02rR1Ly8oCyMtXfQl/yX9BC\nkZiYiICAAPj7+yMmJkbnPFOnToW/vz+Cg4Nx7ty5OrWfl8dHL+vPGIVK/aZQvyFu367HUgTc2n9N\nw3WPHhnWjr41dmMPBZ6vzxK1CoWi+m1Vt64aNaq9nao79itvbfXsaXh/Tp/mClVd8JGFVDR0i7Rq\nFrrOuFd/noyh8nLi778b3p76vTVwIDf8LfTJr4IVCqVSicmTJyMxMREZGRmIj4/H5cuXtebZt28f\nrl69iszMTKxatQoTJ07k5bkzMnQfDcE3Y7zZ1GOax46pfpeU1G91XX0yWU1H6Ri6kNdV769fV324\nv/66+n2VN/n5VlBl08VKx7u5oqJum/uxsdX316xerf8xW7dyxUW98lL5vWFrq/qta6iSMe213cqH\nFtdF1SxMVWmpav9f5a2z2mzdqj1tSBZinVGvXgQ+fqw6KKU+w5I3b6p+t2kD7Nql+nv3bn76VxNr\noRo+deoU/Pz84O3tDQAYPnw4EhISEBgYqJln9+7dGDVqFADgmWeeQUFBAf7++2+4ublVa++PP1S/\nPT1rP+Ry+3ZeXoKWx49VCyIbG27n5IEDdVtb1Ed95IKVlerDsn+/agin8kKkIdRHNR09qn/robRU\n9bzffKOanjsX+PRT/YV340b9z/1//we89lr121NTgaZNgc6dq9938CDQu7dqbX7vXiA9XXX7vHk1\nrz2pP/wXLqhy7NQJ+OQT1W1TpwIuLvr7CVS/om7VhZAu6vcmoCqWM2Zo76/4+mtVv7/8UvXhHjpU\ntSUTFKTaD6JeGXB15R5z6RLQuHHtw17l5YD1P59ixoD161ULnzlzgAULjLPCVBeFharX1bix7vsX\nLlT9vn8fkMtV2bi4AC1b1txm5fwrO3dOtT9Cl+xs1QrE8uWqaV05VVSoDmhwdwfs7Q1baz9xovpt\nuj5v16+rdkavW2fY/6iwEFi6FIiMBPbsUd0WF6d7XiH+5zLGhBlA2blzJw4cOIDV/6yObd68Gamp\nqVi5cqVmnsjISHz44Yfo1asXAODFF19ETEwMunbtqt1JmQzz5qm62bUrcPasED1uuCZNADc31RtK\n/aaqvKC3tlYtKKytVR8ER0fVDqnK/Pz0H3L500+j8eqr66vd3rat6vfDh/xs3poCdRYuLqox2mvX\n9M/fsqXqA19RYX47mGt6X1Tl4gI0a6Za4anrSoj6PQZov8crUy9N1P8LHx/Dn8fdveaj71xdVQcV\nFBbWPuT800+jMWLEekEOlRaSmxv/n93582XgYxEv2BaFzMBBs6ovoqbHzZ9Pl+JUu3BBwPEcE0NZ\ncCgLDmXBL8EKhYeHB7IrjRFlZ2dDLpfrnScnJwceHh7V2hJoo4cQQogBBNuZ3a1bN2RmZkKhUKC0\ntBTbtm1DVFSU1jxRUVHY+M8Ad0pKCpycnHTunyCEECIewbYorK2tERsbi/DwcCiVSowdOxaBgYGI\n+2cPzIQJE/Dyyy9j37598PPzg52dHdatWydUdwghhNQXk7D9+/ez9u3bMz8/P7Z48WKxuyO4Gzdu\nsLCwMNahQwfWsWNHtnz5csYYY/fu3WMvvvgi8/f3Z/3792f5+fmaxyxcuJD5+fmx9u3bswMHDojV\ndcGUl5ezkJAQNnDgQMaY5WaRn5/PBg8ezAICAlhgYCBLSUmx2CwWLlzIOnTowIKCgtiIESNYSUmJ\nxWQxZswY1qpVKxYUFKS5rT6v/cyZMywoKIj5+fmxqVOn1vq8ki0U5eXlzNfXl2VlZbHS0lIWHBzM\nMjIyxO6WoG7dusXOnTvHGGOsqKiItWvXjmVkZLAPPviAxcTEMMYYW7x4MZs1axZjjLFLly6x4OBg\nVlpayrKyspivry9TKpWi9V8IX375JfvXv/7FIiMjGWPMYrMYOXIk+/777xljjJWVlbGCggKLzCIr\nK4v5+PiwkpISxhhjQ4cOZevXr7eYLI4dO8bS0tK0CkVdXntFRQVjjLHu3buz1NRUxhhjERERbP/+\n/XqfV7KF4sSJEyw8PFwzvWjRIrZo0SIRe2R8gwYNYocOHWLt27dnt2/fZoypikn79u0ZY6q1hcpb\nWuHh4ezkyZOi9FUI2dnZrF+/fuyXX37RbFFYYhYFBQXMx8en2u2WmMW9e/dYu3bt2P3791lZWRkb\nOHAgO3jwoEVlkZWVpVUo6vrab968yQICAjS3x8fHswkTJuh9Tsle6yk3Nxeenp6aablcjlz1dXUt\ngEKhwLlz5/DMM89onYTo5uaGv/852PrmzZtaR5KZW0bvv/8+Pv/8c1hVOuXaErPIyspCy5YtMWbM\nGHTp0gXjxo3Dw4cPLTILFxcXzJgxA0899RTatGkDJycn9O/f3yKzUKvra696u4eHR62ZSLZQGHoe\nhjkqLi7G4MGDsXz5cjg4OGjdJ5PJ9GZjLrn9/PPPaNWqFUJDQ2s8PNpSsigvL0daWhomTZqEtLQ0\n2NnZYfHixVrzWEoW165dw7Jly6BQKHDz5k0UFxdj8+bNWvNYSha61Pba60uyhcKQ8zDMUVlZGQYP\nHoy3334br776KgDVWsLtf64RfuvWLbRq1QqA4eehmKITJ05g9+7d8PHxwYgRI/DLL7/g7bfftsgs\n5HI55HI5unfvDgB44403kJaWhtatW1tcFmfOnEGvXr3QokULWFtb4/XXX8fJkyctMgu1unwm5HI5\nPDw8kFPpYlqGZCLZQmHIeRjmhjGGsWPHokOHDpg2bZrm9qioKGz45+p6GzZs0BSQqKgobN26FaWl\npcjKykJmZiaefvppUfrOt4ULFyI7OxtZWVnYunUrXnjhBWzatMkis2jdujU8PT3x5z+XCk1KSkLH\njh0RGRlpcVkEBAQgJSUFjx8/BmMMSUlJ6NChg0VmoVbXz0Tr1q3h6OiI1NRUMMawadMmzWNqxNcO\nFiHs27ePtWvXjvn6+rKFCxeK3R3B/frrr0wmk7Hg4GAWEhLCQkJC2P79+9m9e/dYv379dB7+tmDB\nAubr68vat2/PEhMTRey9cJKTkzVHPVlqFufPn2fdunVjnTt3Zq+99horKCiw2CxiYmI0h8eOHDmS\nlZaWWkwWw4cPZ+7u7szGxobJ5XK2du3aer129eGxvr6+bMqUKbU+r2AXBSSEEGIeJDv0RAghRBqo\nUBBCCNGLCgUhhBC9qFAQQgjRiwoFMWv37t1DaGgoQkND4e7uDrlcjtDQUDg4OGDy5MmCPGdsbCzW\nr1/f4HaGDh2KLL6+C5eQBqCjnojFmD9/PhwcHDB9+nTBnoMxhi5duuD06dOwtm7YVfwPHTqEPXv2\nYMWKFTz1jpD6oS0KYlHU60XJycmIjIwEAERHR2PUqFHo06cPvL298eOPP2LmzJno3LkzIiIiUF5e\nDgA4e/YswsLC0K1bNwwYMEBzNmxlv/32GwICAjRFIiwsDNOnT0f37t0RGBiI06dP47XXXkO7du0w\nd+5cAMDDhw/xyiuvICQkBJ06dcL27ds1j923b5/gmRBSGyoUhEB14b0jR45g9+7deOutt9C/f3+k\np6ejadOm2Lt3L8rKyjBlyhTs2rULZ86cwZgxYzBnzpxq7Rw/fhzdunXTTMtkMjRu3BinT5/GxIkT\nMWjQIHz33Xe4ePEi1q9fj/v37yMxMREeHh44f/48fv/9dwwYMAAAYGNjAw8PD1y+fNloORCiCxUK\nYvFkMhkiIiLQqFEjBAUFoaKiAuHh4QCATp06QaFQ4M8//8SlS5fw4osvIjQ0FAsWLNB5xc0bN27A\n3d1d6zb1pWeCgoIQFBQENzc32Nraom3btsjJyUHnzp1x6NAhzJ49G8ePH4ejo6PmsW3atIFCoRDu\nxRNiAMG+CpUQU2JrawsAsLKygo2NjeZ2KysrlJeXgzGGjh074sSJE7W2VXW3X+PGjTVtqf+u3La/\nvz/OnTuHvXv34uOPP0a/fv00w1KMMa3LrBMiBnoHEotnyPEc7du3R15eHlJSUgCorvKbkZFRbT4v\nLy+d+y70PfetW7fQpEkTvPnmm5g5cybS0tI099+6dQteXl4Gt0eIEGiLglgU9bX6K1+3v+o1/Kte\nz18mk8HGxgY7d+7E1KlT8eDBA5SXl+P9999Hhw4dtObt3bs3YmNja3xuXW3//vvv+OCDD2BlZQVb\nW1t8++23AFTFKCcnBwEBAQ170YQ0EB0eSwiP1IfHpqamaoaz6uvgwYPYu3cvli9fzlPvCKkfGnoi\nhEcymQzjxo3DDz/80OC21qxZg/fff5+HXhHSMLRFQQghRC/aoiCEEKIXFQpCCCF6UaEghBCiFxUK\nQgghelGhIIQQohcVCkIIIXr9P2T6iAV6FUb+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12b9f0b8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\u001b[0;31mOut[5:1317]: \u001b[0m(0, 1000)"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "plt.close('all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the emission for the simulation loaded in **ALL** the engines:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "%matplotlib inline\n",
      "plot_emission(S, ms=1)\n",
      "#plt.xlim(25,35)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[output:4]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vhfV6Hfz9gaeesn39xAkdioosn8f8ekaGDmfP2r9ufH6+5VeI37u01LX8jh/n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\nS/Tx9Q3XeGkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5cd0be0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[output:5]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hNTUVALBw4UJ4eHjg3Xff1aWJj49HRUWFbobAV155BcOGDcOzzz5raKRCgStX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x59c14e0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[output:6]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tk5g14KOL8nLAy8v6yZW69x0TA8ycCdSrp3+8fn3gn/+seY7hZ0dhTn/jxyvx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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5d2fc88>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[output:7]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C55d55ow45WqQoh//yhVxypWiLsSCYhocVC+ExezeUw899BBat26N4uJivP/+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\n6gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5cc0b38>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "How to remove remote simulation files"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> ***NOTE:*** *Here I'm just keeping track of some useful command*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px %cd 'C:\\Data\\Antonio\\data\\sim\\brownian\\traces_em'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[stdout:5] C:\\Data\\Antonio\\data\\sim\\brownian\\traces_em\n",
        "[stdout:6] C:\\Data\\Antonio\\data\\sim\\brownian\\traces_em\n",
        "[stdout:7] C:\\Data\\Antonio\\data\\sim\\brownian\\traces_em\n",
        "[stdout:8] C:\\Data\\Antonio\\data\\sim\\brownian\\traces_em\n"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px from glob import glob"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px --target 5\n",
      "# Execute this only on ONE engine\n",
      "for f in glob('*'):\n",
      "    print f\n",
      "    #os.remove(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"./styles/custom2.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:900px;\n",
        "        margin-left:8% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "        line-height: 145%;\n",
        "        font-size: 110%;\n",
        "        width:800px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    blockquote  p {\n",
        "\t/*margin: 1em 2em;*/\n",
        "\tline-height: 140%;\n",
        "    }\n",
        "\n",
        "    .rendered_html code {\n",
        "\tbackground-color: #f7f7f7;\n",
        "\tborder: 1px solid #dddddd;\n",
        "\tcolor: black;\n",
        "\tline-height: 1.1em;\n",
        "    \tpadding: 0.15em;\n",
        "\tfont-size: 85%;\n",
        "    }\n",
        "\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "    /*.prompt{\n",
        "        display: None;\n",
        "    }*/\n",
        "    \n",
        "</style>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML at 0x3121110>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}