S_T_beta_Adicora.ipynb 566 KB
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{
 "cells": [
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   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {
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   "source": [
    "%matplotlib notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {
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   "source": [
    "\n",
    "from scipy.stats import rv_continuous\n",
    "from scipy.interpolate import interp1d\n",
    "from matplotlib.patches import Circle\n",
    "from scipy.special import gamma\n",
    "import numpy as np\n",
    "import emcee\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from numpy import exp, sqrt\n",
    "from scipy.integrate import quad, dblquad, nquad\n",
    "import matplotlib.patches as patches\n",
    "from itertools import product\n",
    "from scipy.integrate import quad\n",
    "import scipy.optimize as optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn.neighbors import KDTree\n",
    "import sys\n",
    "import lmfit\n",
    "from py_unsio import *\n",
    "import pymc\n",
    "import os\n",
    "from scipy.integrate import simps\n",
    "from pymodelfit import FunctionModel1DAuto\n",
    "import wkbl\n",
    "from wkbl.astro.halo_info import *\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "from matplotlib import cm\n",
    "import wkbl.astro.nbody_essentials as nbe\n",
    "import cfalcon\n",
    "CF =cfalcon.CFalcon()\n",
    "import iminuit\n",
    "from iminuit import Minuit, describe, Struct\n",
    "import probfit\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import warnings\n",
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    "import glob\n",
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    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
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    "dmo = wkbl.astro.halo_info.Adicoradmo()\n",
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    "hydro = wkbl.astro.halo_info.AdicoraHydro()"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/OWN/Adicora/SF0/Stable/output_00041'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hydro.path"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {
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    "collapsed": false
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading Dark matter..\n",
      "loading Stars..\n",
      "loading Gas..\n",
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      "36839.426\n",
      "| r_200 = 211.52\n",
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      "| Diagonal matrix computed \n",
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      "|    | 19, 0, 0|\n",
      "| D =| 0, 15, 0|\n",
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      "|    | 0,  0, 4|\n"
     ]
    }
   ],
   "source": [
    "\n",
    "simname=hydro.name\n",
    "myhydro = wkbl.Galaxy_Hound(hydro.path)\n",
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    "print myhydro.dm.pos3d[:,0].max()\n",
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    "zoom_reg = np.where(myhydro.dm.mass==myhydro.dm.mass.min())\n",
    "nucenter = nbe.real_center(myhydro.dm.pos3d[zoom_reg], myhydro.dm.mass[zoom_reg])\n",
    "myhydro.center_shift(nucenter)\n",
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    "myhydro.r_virial(600,n=2.3)\n",
    "\n"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
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    "simname_nospace = list(simname)\n",
    "for i in range(len(simname)):\n",
    "    if simname[i]==\" \":\n",
    "        simname_nospace[i]=\"_\"\n",
    "simname_nospace = \"\".join(simname_nospace)\n"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "7679691 7679691\n"
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     ]
    }
   ],
   "source": [
    "pos_dm = np.array(myhydro.dm.pos3d.reshape(len(myhydro.dm.pos3d)*3),dtype=np.float32)\n",
    "pos_gs = np.array(myhydro.gs.pos3d.reshape(len(myhydro.gs.pos3d)*3),dtype=np.float32)\n",
    "pos_st = np.array(myhydro.st.pos3d.reshape(len(myhydro.st.pos3d)*3),dtype=np.float32)\n",
    "pos = np.concatenate((pos_dm, pos_st, pos_gs))\n",
    "phi_cord = np.concatenate((myhydro.dm.phi,myhydro.st.phi, myhydro.gs.phi))\n",
    "\n",
    "mass = np.concatenate((myhydro.dm.mass,myhydro.st.mass,myhydro.gs.mass))\n",
    "v = np.concatenate((myhydro.dm.v,myhydro.st.v,myhydro.gs.v))\n",
    "print len(mass)*3, len(pos)\n",
    "pos3d = pos.reshape(len(pos)/3,3)\n",
    "r2 = pos3d[:,0]**2 + pos3d[:,1]**2 +pos3d[:,2]**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ellipticity T and S"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_mat =  np.vectorize(nbe.m_matrix_for_r)"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {
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    "collapsed": false
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   },
   "outputs": [],
   "source": [
    "r = np.linspace(myhydro.gs.hsml.min(),2*myhydro.r200,300)\n",
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    "M_dm = np.array([nbe.m_matrix_for_r(myhydro,'halo',i)[0] for i in r])\n",
    "M_st = np.array([nbe.m_matrix_for_r(myhydro,'stars',i)[0] for i in r])\n",
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    "a_dm, b_dm, c_dm = np.sqrt(M_dm[:,0,0]), np.sqrt(M_dm[:,1,1]), np.sqrt(M_dm[:,2,2])\n",
    "a_st, b_st, c_st = np.sqrt(M_st[:,0,0]), np.sqrt(M_st[:,1,1]), np.sqrt(M_st[:,2,2])\n",
    "S_dm = c_dm/a_dm\n",
    "T_dm = ((a_dm**2) - (b_dm**2))/((a_dm**2) -(c_dm**2)) \n",
    "S_st = c_st/a_st\n",
    "T_st = ((a_st**2) - (b_st**2))/((a_st**2) -(c_st**2)) "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "outputing = open(\"../../datafiles/\"+simname_nospace+\"_S_and_T.txt\",\"w\")\n",
    "outputing.write(\"# \"+simname+\" ellipticity parameters\\n\")\n",
    "outputing.write(\"# r200 = {0:.2f} kpc\\n\".format(myhydro.r200))\n",
    "outputing.write(\"# format:\\n\")\n",
    "outputing.write(\"# r (kpc), S , T\\n\")\n",
    "for i in range(len(T_dm)):\n",
    "    outputing.write(\"{0:.2f} {1:.6f} {2:.6f} \\n\".format(r[i],S_dm[i],T_dm[i]))\n",
    "\n",
    "outputing.close()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
1017
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\">"
1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10,4])\n",
    "#ax.set_xlim(4,250)\n",
    "font=15\n",
    "ax.set_ylim(0,1.18)\n",
    "ax.set_xlim(0,410)\n",
    "ax.set_xlabel('r [kpc]',fontsize=font)\n",
    "ax.plot([4,5],[1e3,2e3],color='gray',linestyle='--',lw=2,label=\"S\")\n",
    "ax.plot([4,5],[1e3,2e3],color='gray',linestyle='-',lw=2,label=\"T\")\n",
    "ax.plot(r,T_dm,'-',color='#155c9e',lw=2,label=\"Dark matter\")\n",
    "ax.plot(r,S_dm,'--',color='#155c9e',lw=2)\n",
    "#ax.plot(r,T_st,'-',color='#a91e4f',lw=2,label='stars')\n",
    "#ax.plot(r,np.sqrt(S_st),'--',color='#a91e4f',lw=2)\n",
    "ax.axvline(x=myhydro.r200,color='k',linestyle='-.')\n",
    "ax.text(myhydro.r200+2,0.2,r\"R$_{200}$\",fontsize=font)\n",
    "ax.axvline(x=myhydro.r97,color='k',linestyle='-.')\n",
    "ax.text(myhydro.r97+2,0.2,r\"R$_{97}$\",fontsize=font)\n",
    "ax.axvline(x=8,color='k',linestyle='-.')\n",
    "ax.text(10,0.2,r\"R$_{\\odot}$\",fontsize=font)\n",
    "ax.text(50,0.9,r\"$\\rm \"+simname+\"$\",fontsize=1.5*font)\n",
    "\n",
    "\n",
    "\n",
    "legend = ax.legend(loc='upper right', ncol=2, shadow=False, fontsize=font)\n",
    "frame = legend.get_frame()\n",
    "ax.tick_params(axis='both', which='major', labelsize=15, size=5,width=1.2)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=15, size=3,width=1.2)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beta of r"
   ]
  },
  {
   "cell_type": "code",
1066
   "execution_count": null,
1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "point_num = 150\n",
    "r_beta = np.logspace(-1,np.log10(4*myhydro.r200),point_num)\n",
    "vphi = np.concatenate((myhydro.dm.vphi,myhydro.st.vphi,myhydro.gs.vphi))\n",
    "vtheta = np.concatenate((myhydro.dm.vtheta,myhydro.st.vtheta,myhydro.gs.vtheta))\n",
    "vr = np.concatenate((myhydro.dm.vr,myhydro.st.vr,myhydro.gs.vr))"
   ]
  },
  {
   "cell_type": "code",
1081
   "execution_count": null,
1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def beta_param(i):\n",
    "    condition = np.where((r2>r_beta[i]**2)&(r2<=r_beta[i+1]**2))\n",
    "    v_r = vr[condition]\n",
    "    v_phi = vphi[condition]\n",
    "    v_theta = vtheta[condition]\n",
    "    #print (np.std(v_phi))**2 ,(np.std(v_theta))**2 , (np.std(v_r))**2  \n",
    "    return 1 - ((np.std(v_phi))**2 +(np.std(v_theta))**2) / 2. / (np.std(v_r))**2  \n",
    "\n",
    "get_beta = np.vectorize(beta_param)"
   ]
  },
  {
   "cell_type": "code",
1100
   "execution_count": null,
1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "beta_r = get_beta(range(point_num-1))"
   ]
  },
  {
   "cell_type": "code",
1111
   "execution_count": null,
1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outputing = open(\"../../datafiles/\"+simname_nospace+\"_Beta.txt\",\"w\")\n",
    "outputing.write(\"# \"+simname+\" Beta parameters\\n\")\n",
    "outputing.write(\"# r200 = {0:.2f} kpc\\n\".format(myhydro.r200))\n",
    "outputing.write(\"# format:\\n\")\n",
    "outputing.write(\"# r (kpc), Beta(r) \\n\")\n",
    "for i in range(len(beta_r)):\n",
    "    outputing.write(\"{0:.2f} {1:.6f} \\n\".format(((r_beta[1:]+r_beta[:-1])/2.)[i],beta_r[i]))\n",
    "\n",
    "outputing.close()"
   ]
  },
  {
   "cell_type": "code",
1130
   "execution_count": null,
1131 1132 1133
   "metadata": {
    "collapsed": false
   },
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   "outputs": [],
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   "source": [
    "fig, ax = plt.subplots(figsize=[9,5])\n",
    "r_2 = 6.95\n",
    "ax.set_xscale('log')\n",
    "#ax.set_title(r\"$\\beta(r)$ parameter\", fontsize=1.5*font)\n",
    "ax.set_xlabel(r\"r [kpc]\",fontsize=font)\n",
    "ax.set_ylabel(r\"$\\beta(r)$\",fontsize=1.5*font)\n",
    "ax.set_ylim([-1.5,1])\n",
    "ax.plot((r_beta[1:]+r_beta[:-1])/2.,beta_r,lw=1.5)\n",
    "#ax.plot((r_beta[1:]+r_beta[:-1])/2/r_2,beta_r_2,lw=1.5)\n",
    "#ax.plot((r_beta[1:]+r_beta[:-1])/2/r_2,beta_r_3,lw=1.5)\n",
    "\n",
    "\n",
    "ax.axvline(x=myhydro.r200, color='k',lw=2,linestyle='--')\n",
    "ax.axvline(x=myhydro.r97, color='gray',lw=2,linestyle='--')\n",
    "\n",
    "ax.axvline(x=8/r_2, color='y',linestyle='--',lw=2,label=r'r$_{\\odot}$')\n",
    "fig.tight_layout()\n",
    "ax.tick_params(axis='both', which='major', labelsize=15, size=5,width=1.2)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=15, size=3,width=1.2)\n",
    "#plt.savefig(\"/home/arturo/Documents/git/LAMtoLUPM_latex/beta_r.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# F(v) moments"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 18,
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   "metadata": {
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    "collapsed": false
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   },
   "outputs": [],
   "source": [
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    "def moments(rmin,rmax):\n",
    "    selection= np.where((myhydro.dm.r>rmin)&(myhydro.dm.r<=rmax))\n",
    "    fdv, vs = np.histogram(myhydro.dm.v[selection],bins=50,normed=1)\n",
    "    vcenter = (vs[:-1]+vs[1:])/2.\n",
    "    m1 = simps(vcenter*fdv,x=vcenter)\n",
    "    m2 = simps((vcenter**2)*fdv,x=vcenter)\n",
    "    m3 = simps((vcenter**3)*fdv,x=vcenter)\n",
    "\n",
    "    return m1,m2,m3\n",
    "\n",
    "def moments2(rmin,rmax):\n",
    "    selection= np.where((myhydro.dm.r>rmin)&(myhydro.dm.r<=rmax))\n",
    "    fdv, vs = np.histogram(myhydro.dm.v[selection],bins=50,normed=1)\n",
    "    vcenter = (vs[:-1]+vs[1:])/2.\n",
    "    vw = vs[1:]-vs[:-1]\n",
    "    m1 = np.sum(vcenter*fdv*vw)\n",
    "    m2 = np.sum((vcenter**2)*fdv*vw)\n",
    "    m3 = np.sum((vcenter**3)*fdv*vw)\n",
    "\n",
    "    return m1,m2,m3\n",
    "\n",
    "def moments3(sim,rmin,rmax):\n",
    "    selection= np.where((sim.dm.r>rmin)&(sim.dm.r<=rmax))\n",
    "    npart = len(sim.dm.v[selection])\n",
    "    m_2 = np.sum(sim.dm.v[selection]**(-2))/npart\n",
    "    m_1 = np.sum(sim.dm.v[selection]**(-1))/npart\n",
    "    m1 = np.sum(sim.dm.v[selection])/npart\n",
    "    m2 = np.sum((sim.dm.v[selection])**2)/npart\n",
    "    std_2 = np.std(sim.dm.v[selection]**(-2))\n",
    "    std_1 = np.std(sim.dm.v[selection]**(-1))\n",
    "    std1 = np.std(sim.dm.v[selection])\n",
    "    std2 = np.std((sim.dm.v[selection])**2)\n",
    "    return m_2,m_1,m1, m2, npart, std_2, std_1, std1, std2\n",
    "\n",
    "fdv_moments = np.vectorize(moments)\n",
    "fdv_moments2 = np.vectorize(moments2)\n",
    "fdv_moments3 = np.vectorize(moments3)\n",
    "\n",
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    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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  },
  {
   "cell_type": "code",
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   "execution_count": 20,
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   "metadata": {
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    "collapsed": false
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   },
   "outputs": [],
   "source": [
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    "patho = \"/home/arturo/Documents/LAM/LAM2LUPM/speed/\"+hydro.namenospace\n",
    "cd = \"/home/arturo/Documents/LAM/LAM2LUPM/speed/Adicora/Eddington/v_minus1_av_Eddington_Adicora_DM_baryons_Rmax=1453.96kpc_no_divergence.txt\"\n",
    "v_2_av = np.loadtxt(glob.glob(patho+\"/Eddington/v_minus2_av_Eddington_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v_1_av = np.loadtxt(glob.glob(patho+\"/Eddington/v_minus1_av_Eddington_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v_av = np.loadtxt(glob.glob(patho+\"/Eddington/v_av_Eddington_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v2_av = np.loadtxt(glob.glob(patho+\"/Eddington/v_sq_av_Eddington_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
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    "\n",
    "\n",
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    "v_2_av_m = np.loadtxt(glob.glob(patho+\"/Maxwellian/v_minus2_av_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v_1_av_m = np.loadtxt(glob.glob(patho+\"/Maxwellian/v_minus1_av_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v_av_m = np.loadtxt(glob.glob(patho+\"/Maxwellian/v_av_\"+hydro.namenospace+\"_DM_bary*\")[0])\n",
    "v2_av_m = np.loadtxt(glob.glob(patho+\"/Maxwellian/v_sq_av_\"+hydro.namenospace+\"_DM_bary*\")[0])\n"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 21,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
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    "r_v = np.logspace(np.log10(4*myhydro.gs.hsml.min()),np.log10(3*myhydro.r200),150)\n",
    "#m1,m2,m3 = fdv_moments(r_v[:-1],r_v[1:])\n",
    "#mm1,mm2,mm3 = fdv_moments2(r_v[:-1],r_v[1:])\n",
    "m_2, m_1,m1, m2, n, std_2, std_1, std1, std2 = fdv_moments3(myhydro,r_v[:-1],r_v[1:])"
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   ]
  },
  {
   "cell_type": "code",
1261
   "execution_count": 23,
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   "metadata": {
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       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
2033
       "<img 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\">"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
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    "fig, ax = plt.subplots(4,1,fig