Detailed_model_post_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D SEI Model Post-processing\n",
    "\n",
    "This notebook allows you to construct plots from your output data.\n",
    "\n",
    "## Identify the data location\n",
    "Start by naming the file(s) you want to process. \n",
    "This is the name of the folder inside the 'output' folder where the results are saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafolder = 'DOE_12092020_baseline'\n",
    "datafolder2 = 'DOE_12092020_thirdcond'\n",
    "datafolder3 = 'DOE_12092020_tenthcond'\n",
    "\n",
    "eps_min = 0.025\n",
    "fs = 12\n",
    "\n",
    "previous = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we'll load the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Cantera phases created. \n",
      "\n",
      "The species in the electrolyte are:\n",
      "bulk[C3H4O3]\n",
      "bulk[C4H8O3]\n",
      "LipElyt\n",
      "PF6mElyt\n",
      "C2H6O2(el)\n",
      "H2O(el)\n",
      "C2H4\n",
      "CO\n",
      "CO2\n",
      "\n",
      "\n",
      "The species in the SEI are:\n",
      "LEDC[SEI]\n",
      "Li2CO3[SEI]\n",
      "Li2O[SEI]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook  \n",
    "\n",
    "import os, csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# If you've run this block before, re-set chdir back to its original value, first:\n",
    "if previous:\n",
    "    os.chdir(cwd)\n",
    "    \n",
    "# Get the current working directory:\n",
    "cwd = os.getcwd()\n",
    "previous = 1\n",
    "os.chdir(cwd+'/output/'+datafolder)\n",
    "\n",
    "data = pd.read_csv('solution.csv',header=0)\n",
    "\n",
    "# Get variable names:\n",
    "f = open('names.csv','r')\n",
    "names = csv.reader(f)\n",
    "data_names = data.index.tolist()\n",
    "\n",
    "i=0\n",
    "for row in names:\n",
    "    data_names[i]=(str('\\t'.join(row)))\n",
    "    i+=1\n",
    "\n",
    "f.close()\n",
    "\n",
    "data.index = data_names\n",
    "\n",
    "from sei_1d_init import N_y, dy, nvars_node\n",
    "#elif homogeneous:\n",
    "#    from sei_1d_init import dy, nvars_node\n",
    "# Transpose the data so that each row is for a time, and each column represents a variable:\n",
    "data = data.T\n",
    "data\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "os.chdir(cwd)\n",
    "\n",
    "cwd = os.getcwd()\n",
    "previous = 1\n",
    "os.chdir(cwd+'/output/'+datafolder2)\n",
    "\n",
    "data2 = pd.read_csv('solution.csv',header=0)\n",
    "\n",
    "# Get variable names:\n",
    "f2 = open('names.csv','r')\n",
    "names2 = csv.reader(f2)\n",
    "data_names2 = data2.index.tolist()\n",
    "\n",
    "i=0\n",
    "for row in names2:\n",
    "    data_names2[i]=(str('\\t'.join(row)))\n",
    "    i+=1\n",
    "\n",
    "f2.close()\n",
    "\n",
    "data2.index = data_names2\n",
    "\n",
    "from sei_1d_init import N_y, dy, nvars_node\n",
    "#elif homogeneous:\n",
    "#    from sei_1d_init import dy, nvars_node\n",
    "# Transpose the data so that each row is for a time, and each column represents a variable:\n",
    "data2 = data2.T\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "os.chdir(cwd)\n",
    "\n",
    "cwd = os.getcwd()\n",
    "previous = 1\n",
    "os.chdir(cwd+'/output/'+datafolder3)\n",
    "\n",
    "data3 = pd.read_csv('solution.csv',header=0)\n",
    "\n",
    "# Get variable names:\n",
    "f3 = open('names.csv','r')\n",
    "names3 = csv.reader(f3)\n",
    "data_names3 = data3.index.tolist()\n",
    "\n",
    "i=0\n",
    "for row in names3:\n",
    "    data_names3[i]=(str('\\t'.join(row)))\n",
    "    i+=1\n",
    "\n",
    "f3.close()\n",
    "\n",
    "data3.index = data_names3\n",
    "\n",
    "from sei_1d_init import N_y, dy, nvars_node\n",
    "#elif homogeneous:\n",
    "#    from sei_1d_init import dy, nvars_node\n",
    "# Transpose the data so that each row is for a time, and each column represents a variable:\n",
    "data3 = data3.T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot SEI thickness vs. time.\n",
    "\n",
    "We start by finding the volumes at each time where $\\varepsilon_{\\rm SEI}$ is greater than a user-defined minimum. The SEI thickness is equal to the summed thickness of all but the final volume $\\left(n-1\\right)\\Delta y$, plus a fractional thickness $\\varepsilon_{\\rm SEI}(t)\\Delta y$ of the final such volume at time $t$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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": {
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\" width=\"350\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def thickness_calc(eps_min, data):\n",
    "    thickness = []\n",
    "    eps_SEI = data['eps SEI']\n",
    "\n",
    "    for j,eps in eps_SEI.iterrows():\n",
    "        grown = []\n",
    "        growth = eps[eps['eps SEI']>eps_min]\n",
    "        if growth.empty:\n",
    "            thickness.append(0.)\n",
    "        else:\n",
    "            #print(growth.iloc[-1])\n",
    "            thickness.append((growth.count()-1.+growth.iloc[-1])*dy)\n",
    "    return thickness   \n",
    "    \n",
    "\n",
    "t = np.asarray(data.index[:],dtype=float)\n",
    "y = np.asarray(thickness_calc(eps_min,data))\n",
    "t2 = np.asarray(data2.index[:],dtype=float)\n",
    "y2 = np.asarray(thickness_calc(eps_min,data2))\n",
    "t3 = np.asarray(data3.index[:],dtype=float)\n",
    "y3 = np.asarray(thickness_calc(eps_min,data3))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.15,0.2,0.75,0.75])\n",
    "fig.set_size_inches((3.5,2.25))\n",
    "\n",
    "plt.plot(t/3600,y*1e9,'k.',markersize=1.50)\n",
    "#plt.plot(t2/3600,y2*1e9,'b.',markersize=1.50)\n",
    "#plt.plot(t3/3600,y3*1e9,'g.',markersize=1.50)\n",
    "plt.xlim([0,1.0])\n",
    "plt.ylim([0,20])\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('SEI thickness (nm)')\n",
    "#plt.xticks(np.array([0.,1000.,2000.,3000.,4000.]))\n",
    "\n",
    "\n",
    "font = plt.matplotlib.font_manager.FontProperties(family='Times New Roman',size=fs)\n",
    "\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fs)\n",
    "    tick.label1.set_fontname('Times New Roman')\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(fs)\n",
    "    tick.label1.set_fontname('Times New Roman')\n",
    "\n",
    "ax.tick_params(axis=\"y\",direction=\"in\",length=5.5,right=True)\n",
    "ax.tick_params(axis=\"x\",direction=\"in\",length=5.5,top=True)\n",
    "ax.set_ylabel('SEI Thickness (nm)',fontsize=fs,family='Times New Roman',labelpad = 1.)\n",
    "ax.set_xlabel('Time (h)',fontsize=fs,family='Times New Roman')\n",
    "plt.savefig('Thickness_vs_time.pdf',format='pdf',dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set cwd back to original value\n",
    "os.chdir(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-83a332e90ca1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SEI'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprofiles\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mSVptr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ck sei'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0meps_k_sei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofiles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofiles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MARCUS\\anaconda3\\envs\\jup\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MARCUS\\anaconda3\\envs\\jup\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2698\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2699\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2700\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MARCUS\\anaconda3\\envs\\jup\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4130\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot label index with a null key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4132\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer_for\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4133\u001b[0m             return self.reindex_indexer(new_axis=self.items[indexer],\n\u001b[0;32m   4134\u001b[0m                                         \u001b[0mindexer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MARCUS\\anaconda3\\envs\\jup\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer_for\u001b[1;34m(self, target, **kwargs)\u001b[0m\n\u001b[0;32m   3384\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3385\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3386\u001b[1;33m         \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3387\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MARCUS\\anaconda3\\envs\\jup\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer_non_unique\u001b[1;34m(self, target)\u001b[0m\n\u001b[0;32m   3374\u001b[0m             \u001b[0mtgt_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ndarray_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3376\u001b[1;33m         \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtgt_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3377\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_ensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3378\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_indexer_non_unique\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    }
   ],
   "source": [
    "    from sei_1d_init import objs, params, voltage_lookup, SVptr\n",
    "    \n",
    "    sei = objs['SEI']\n",
    "    \n",
    "    profiles = data[-1,SVptr['Ck sei']]\n",
    "    eps_k_sei = np.zeros_like(profiles)\n",
    "    for i, p in enumerate(profiles):\n",
    "        eps_sei = data[-1,SVptr['eps sei'][i]]\n",
    "        vol_k = p*sei.partial_molar_volumes\n",
    "        v_tot = np.dot(p,sei.partial_molar_volumes)\n",
    "        eps_k_sei[i,:] = eps_sei*vol_k/v_tot\n",
    "    names = list()\n",
    "    \n",
    "    for i in range(sei.n_species):\n",
    "        names.append(sei.species_names[i])\n",
    "    names.append('eps elyte')\n",
    "    fig5, ax5 = plt.subplots(1, 1, figsize=(8, 7.2))\n",
    "    ax5.plot(1e9*np.arange(params['Ny'])/params['dyInv'],eps_k_sei)\n",
    "    ax5.plot(1e9*np.arange(params['Ny'])/params['dyInv'],1.-SV[-1,SVptr['eps sei']])\n",
    "    ax5.legend(names)\n",
    "    ax5.set_ylabel('Species volume fraction')\n",
    "    ax5.set_xlabel('SEI Depth (from anode, nm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2', 'phi SEI', 'phi elyte', 'eps SEI', 'LEDC[SEI]', 'Li2CO3[SEI]', 'Li2O[SEI]', 'bulk[C3H4O3]', 'bulk[C4H8O3]', 'LipElyt', 'PF6mElyt', 'C2H6O2(el)', 'H2O(el)', 'C2H4', 'CO', 'CO2']\n"
     ]
    }
   ],
   "source": [
    "print(data[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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