{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# pyright: reportUnusedExpression=false, reportDuplicateImport=false, reportUnusedImport=false, reportMissingImports=false, reportCallIssue=false, reportRedeclaration=false\n",
    "# mypy: disable-error-code=\"no-untyped-def, no-untyped-call, assignment, no-redef\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "# Why `tmmc-lnpy`?\n",
    "\n",
    ":::{note}\n",
    "While the distribution is named `tmmc-lnpy`, it provides the python package {mod}`lnpy`.  This naming was done to avoid conflicts with other packages on pypi.   The name of the module {mod}`lnpy` may be changed in the future.\n",
    ":::\n",
    "\n",
    "\n",
    "{mod}`lnpy` is a python package to analyze the main output of Grand Canonical Transition Matrix Monte Carlo (GC-TMMC) simulations: $\\ln \\Pi(N)$.  Hence the name. {mod}`lnpy` is designed to calculate a host of properties in the Canonical and Grand Canonical ensembles.   For a review of GC-TMMC and the property $\\ln \\Pi(N)$, look at some of the following references:\n",
    "\n",
    "\n",
    "* [Evaluating surface tension using grand-canonical transition-matrix Monte Carlo simulation and finite-size scaling](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.67.012102)\n",
    "* [Direct evaluation of multicomponent phase equilibria using flat-histogram methods](http://dx.doi.org/10.1063/1.2064628)\n",
    "* [Direct calculation of liquid--vapor phase equilibria from transition matrix Monte Carlo simulation](https://aip.scitation.org/doi/10.1063/1.1572463)\n",
    "* [Metastability and instability in the Lennard-Jones fluid investigated by transition-matrix Monte Carlo](https://pubs.acs.org/doi/10.1021/jp040218y)\n",
    "\n",
    "\n",
    "$\\Pi(N; \\mu, T, V)$ is the macrostate probability of observing $N$ particles at a given chemical potential $\\mu$, temperature $T$ and volume $V$.  We drop the explicit mention of $\\mu$, $V$ and $T$ below for simplicity.  This distribution is related to the grand canonical ensemble by:\n",
    "\n",
    "$$ \\Pi(N) = \\frac{\\exp(\\beta \\mu N) Q(N, V, T)}{\\Xi(\\mu, V, T)}$$\n",
    "\n",
    "Where $\\beta = 1 / (k_{\\rm B} T)$ is the inverse temperature, $k_{\\rm B}$ is Boltzmann's constant, $Q(N, V, T)$ is the canonical partition function, and $\\Xi(\\mu, V, T)$ is the grand canonical partition function.   GC-TMMC simulation provide a means to calculate the $\\ln \\Pi(N; \\mu, V, T)$ directly.  One of the truly excellent qualities of such simulations is that once $\\ln \\Pi$ is collected at a given chemical potential $\\mu_0$, it can be rescaled to any other chemical potential $\\mu$ through rescaling of the form:\n",
    "\n",
    "$$ \\ln \\Pi(N; \\mu, V, T) = \\ln \\Pi(N; \\mu_0, V, T) + \\beta N (\\mu - \\mu_0) + C $$\n",
    "\n",
    "where $C$ is a normalization constant which does not effect most calculated properties.  From $\\ln \\Pi(N)$, many thermodynamic properties can be calculated.  The grand potential $\\Omega$, and hence the system pressure $p$, can be obtained from \n",
    "\n",
    "$$ \\beta \\Omega = -\\beta p V = \\ln \\Pi(0) + \\ln \\left[\\sum_N \\Pi(N, \\mu, V, T) \\right]$$\n",
    "\n",
    "\n",
    "Also, grand canonical averages can be calculated from canonical averages using $\\ln \\Pi(N)$:\n",
    "\n",
    "$$ \\overline{X} = \\frac{\\sum_N \\Pi(N) X(N)}{\\sum_N \\Pi(N)} $$\n",
    "\n",
    "\n",
    "Plus, $\\ln \\Pi$ can be directly analyzed to identify unique phases, phase transitions, limits of stability, etc.  The package {mod}`lnpy` provides a simple means to perform all the above calculations simply, and quickly.  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "# Usage\n",
    "\n",
    "Lets consider a simple example: a Lennard-Jones (LJ) fluid at supercritical temperatures.  \n",
    "An example value of $\\ln \\Pi(N)$ (shortened to `lnPi`) can be loaded directly from {mod}`lnpy` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lnPi_data:[-1559.75 -1550.74 -1542.43 ...   -10.01   -11.02   -12.08]\n",
      "lnPi_mask:[False False False ... False False False]\n",
      "state_kws:{'beta': 0.6666666666666666, 'volume': 512.0}\n",
      "extra_kws:{'PE': array([    0.  ,     0.  ,    -0.02, ..., -1643.18, -1648.71, -1654.36])}\n",
      "lnz:[2.77]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import lnpy.examples\n",
    "\n",
    "data_dict = lnpy.examples.load_example_dict(\"lj_sup\")\n",
    "with np.printoptions(precision=2, suppress=True, threshold=5):\n",
    "    for k, v in data_dict.items():\n",
    "        print(f\"{k}:{v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "Here, we have the parameters\n",
    "\n",
    "* lnPi_data: the actual value of $\\ln \\Pi(N)$.\n",
    "* lnPi_mask: bool array.  where `mask == True`, values are 'masked out'\n",
    "* state_kws: dict of 'state' variables. Here, it include the inverse temperature 'beta' $=\\beta = 1/(k_{\\rm B} T)$\n",
    "  and the systems volume 'volume' $=V$\n",
    "* extra_kws: dict of 'extra' variables.  Here, it include 'PE', the canonical potential energy\n",
    "* lnz: log of activity $\\ln z = \\beta \\mu$, where $\\mu$ is chemical potential.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "## lnPiMasked\n",
    "\n",
    "The first important class is {class}`~lnpy.lnpidata.lnPiMasked`.  This class handles the basics of dealing with $\\ln \\Pi(N)$. To create it, to the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lnPi(lnz=[2.76512052])>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ref = lnpy.lnPiMasked.from_data(\n",
    "    lnz=data_dict[\"lnz\"],\n",
    "    lnz_data=data_dict[\"lnz\"],\n",
    "    data=data_dict[\"lnPi_data\"],\n",
    "    mask=data_dict[\"lnPi_mask\"],\n",
    "    state_kws=data_dict[\"state_kws\"],\n",
    "    extra_kws=data_dict[\"extra_kws\"],\n",
    ")\n",
    "ref"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "To access the underlying data, use the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x156f0b9d0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ref.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "## Reweighting results\n",
    "\n",
    "One of the amazing things about $\\ln \\Pi(N)$ calculated at some chemical potential $\\mu_{\\rm ref}$ is that it can be \n",
    "reweighted to any other chemical potential $\\mu$.  This is done with the formula\n",
    "\n",
    "$$\n",
    "\\ln \\Pi(N, \\mu) = \\ln \\Pi(N, \\mu_{\\rm ref}) + \\beta (\\mu - \\mu_{\\rm ref}) N\n",
    "$$\n",
    "\n",
    "To reweight data to another value of $\\ln z$, we use the {meth}`lnpy.lnpidata.lnPiMasked.reweight` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lnPi(lnz=[4.])>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157002b10>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new = ref.reweight(4.0)\n",
    "print(new)\n",
    "\n",
    "# compare to original\n",
    "plt.plot(ref.data, label=ref.lnz)\n",
    "plt.plot(new.data, label=new.lnz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "{class}`lnpy.lnpidata.lnPiMasked` has a variety of utilities to work with the $\\ln \\Pi(N)$ data. For example\n",
    "\n",
    "* zeromax: shift $\\ln \\Pi(N)$ such that maximum value is zero.  Useful for numerical stability\n",
    "* ma : `MaskedArray` view of data.  Used when constructing phases, and working with multicomponent data.\n",
    "* \n",
    "\n",
    "Check out the api docs for further details"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "## Calculating Ensemble properties\n",
    "\n",
    "\n",
    "$\\ln \\Pi(N)$ can be used to calculate a variety of properties in the Canonical and Grand Canonical ensembles.\n",
    "To make everything easier, the actual value of $\\ln \\Pi(N)$ is wrapped in an DataArray object, which provides some nice data goodies.  To access this view, use the {attr}`~lnpy.lnpidata.lnPiMasked.xce` or {attr}`~lnpy.lnpidata.lnPiMasked.xge` accessors (short for Xarray Canonical Ensemble and Xarray Grand canonical Ensemble).  For example, the canonical properties can be obtained as follows:\n",
    "\n",
    ":::{note}\n",
    "We only show a small subset of functionality.  For further information, see {class}`~lnpy.lnpidata.lnPiMasked`.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pressure&#x27; (n_0: 5)&gt; Size: 40B\n",
       "array([-0.        ,  0.00100796,  0.00319043,  0.00560728,  0.00813105])\n",
       "Coordinates:\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "  * n_0      (n_0) int64 40B 0 1 2 3 4\n",
       "Attributes:\n",
       "    long_name:  $p({\\bf n},V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pressure'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>n_0</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b817faff-03b7-4a43-abd0-8217959853b3' class='xr-array-in' type='checkbox' checked><label for='section-b817faff-03b7-4a43-abd0-8217959853b3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.0 0.001008 0.00319 0.005607 0.008131</span></div><div class='xr-array-data'><pre>array([-0.        ,  0.00100796,  0.00319043,  0.00560728,  0.00813105])</pre></div></div></li><li class='xr-section-item'><input id='section-2b62551a-8891-4785-8e40-b526eee809ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b62551a-8891-4785-8e40-b526eee809ac' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-92919093-7421-4786-97fe-d51d5a9698bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92919093-7421-4786-97fe-d51d5a9698bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e1a1f6-4e67-4933-aba3-280e99c9ce7f' class='xr-var-data-in' type='checkbox'><label for='data-23e1a1f6-4e67-4933-aba3-280e99c9ce7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-c346d50a-5cc7-4644-8245-a18344e4a0c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c346d50a-5cc7-4644-8245-a18344e4a0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e810a65-474a-4d8a-a89a-bbf4b9dbd746' class='xr-var-data-in' type='checkbox'><label for='data-3e810a65-474a-4d8a-a89a-bbf4b9dbd746' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>n_0</span></div><div class='xr-var-dims'>(n_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-d9dcd85b-b141-4cdd-ba0a-f66fc1dd2255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9dcd85b-b141-4cdd-ba0a-f66fc1dd2255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31e386df-decb-46cd-bad8-d2c146f81394' class='xr-var-data-in' type='checkbox'><label for='data-31e386df-decb-46cd-bad8-d2c146f81394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$n_0$</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a1ced1e-d405-4081-9777-4a679eab092b' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a1ced1e-d405-4081-9777-4a679eab092b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>n_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b33e7c8-abdb-4a48-bcd5-b50f1db49a95' class='xr-index-data-in' type='checkbox'/><label for='index-2b33e7c8-abdb-4a48-bcd5-b50f1db49a95' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;n_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea866b3d-278e-4efc-b6fd-9aedff66235f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea866b3d-278e-4efc-b6fd-9aedff66235f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$p({\\bf n},V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pressure' (n_0: 5)> Size: 40B\n",
       "array([-0.        ,  0.00100796,  0.00319043,  0.00560728,  0.00813105])\n",
       "Coordinates:\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "  * n_0      (n_0) int64 40B 0 1 2 3 4\n",
       "Attributes:\n",
       "    long_name:  $p({\\bf n},V,T)$"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pressure\n",
    "ref.xce.pressure().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;betaF&#x27; (n_0: 5)&gt; Size: 40B\n",
       "array([  0.        ,  -6.23817948, -11.78825896, -16.93743845,\n",
       "       -21.80391793])\n",
       "Coordinates:\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "  * n_0      (n_0) int64 40B 0 1 2 3 4\n",
       "Attributes:\n",
       "    dims_n:         [&#x27;n_0&#x27;]\n",
       "    dims_lnz:       [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:      [&#x27;component&#x27;]\n",
       "    dims_state:     [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    standard_name:  helmholtz_free_energy\n",
       "    long_name:      $\\beta F({\\bf n},V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'betaF'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>n_0</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ca907701-bf00-45e5-b91e-5bff4af8477f' class='xr-array-in' type='checkbox' checked><label for='section-ca907701-bf00-45e5-b91e-5bff4af8477f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 -6.238 -11.79 -16.94 -21.8</span></div><div class='xr-array-data'><pre>array([  0.        ,  -6.23817948, -11.78825896, -16.93743845,\n",
       "       -21.80391793])</pre></div></div></li><li class='xr-section-item'><input id='section-ad8ce4e1-dc76-44c2-bb7f-b80607b43776' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad8ce4e1-dc76-44c2-bb7f-b80607b43776' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-853afab5-2328-43b6-aed5-bcdef1c8a382' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-853afab5-2328-43b6-aed5-bcdef1c8a382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a4aa81-4caf-43cc-8b0b-1094298e1b47' class='xr-var-data-in' type='checkbox'><label for='data-71a4aa81-4caf-43cc-8b0b-1094298e1b47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-bd8847ca-2f90-4f9f-a938-b982b3963c5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd8847ca-2f90-4f9f-a938-b982b3963c5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d3c0d8f-502e-4db0-95da-e92a46825782' class='xr-var-data-in' type='checkbox'><label for='data-6d3c0d8f-502e-4db0-95da-e92a46825782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>n_0</span></div><div class='xr-var-dims'>(n_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-f6fd5b99-515d-4c3f-b1dd-df6704598633' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6fd5b99-515d-4c3f-b1dd-df6704598633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb7ea9f8-3d8d-47c6-a73c-314f3b02f622' class='xr-var-data-in' type='checkbox'><label for='data-cb7ea9f8-3d8d-47c6-a73c-314f3b02f622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$n_0$</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b918b77-f570-4de8-babb-ec2269eb43f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-3b918b77-f570-4de8-babb-ec2269eb43f8' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>n_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ccff5df-8bca-4700-bbcf-7b1ea44135f7' class='xr-index-data-in' type='checkbox'/><label for='index-8ccff5df-8bca-4700-bbcf-7b1ea44135f7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;n_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6ded539-3e35-4a5f-b49b-b04e2c75be03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6ded539-3e35-4a5f-b49b-b04e2c75be03' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>standard_name :</span></dt><dd>helmholtz_free_energy</dd><dt><span>long_name :</span></dt><dd>$\\beta F({\\bf n},V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'betaF' (n_0: 5)> Size: 40B\n",
       "array([  0.        ,  -6.23817948, -11.78825896, -16.93743845,\n",
       "       -21.80391793])\n",
       "Coordinates:\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "  * n_0      (n_0) int64 40B 0 1 2 3 4\n",
       "Attributes:\n",
       "    dims_n:         ['n_0']\n",
       "    dims_lnz:       ['lnz_0']\n",
       "    dims_comp:      ['component']\n",
       "    dims_state:     ['lnz_0', 'beta', 'volume']\n",
       "    standard_name:  helmholtz_free_energy\n",
       "    long_name:      $\\beta F({\\bf n},V,T)$"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  scaled Helmholtz free energy\n",
    "ref.xce.betaF().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "Look at the documentation for all the properties available.  To quickly construct a dataset of multiple properties, use the `table` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 10kB\n",
       "Dimensions:   (n_0: 436)\n",
       "Coordinates:\n",
       "    beta      float64 8B 0.6667\n",
       "    volume    float64 8B 512.0\n",
       "  * n_0       (n_0) int64 3kB 0 1 2 3 4 5 6 7 ... 429 430 431 432 433 434 435\n",
       "Data variables:\n",
       "    pressure  (n_0) float64 3kB -0.0 0.001008 0.00319 ... 5.786 5.854 5.893\n",
       "    betaF     (n_0) float64 3kB 0.0 -6.238 -11.79 ... -352.4 -348.7 -344.8\n",
       "Attributes:\n",
       "    long_name:  $p({\\bf n},V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c7ac6e7f-e6ed-4322-845c-2fd1b8aa5737' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c7ac6e7f-e6ed-4322-845c-2fd1b8aa5737' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>n_0</span>: 436</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e7f8b7b-c0f5-440e-8e99-53edfe6d8c2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e7f8b7b-c0f5-440e-8e99-53edfe6d8c2c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-c2df5e3f-19e5-43c8-a5fa-198a641b897e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2df5e3f-19e5-43c8-a5fa-198a641b897e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed95de5e-e382-4d09-8486-17280069b6db' class='xr-var-data-in' type='checkbox'><label for='data-ed95de5e-e382-4d09-8486-17280069b6db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-b11254b9-de37-47e4-9774-b06d1ea9a326' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b11254b9-de37-47e4-9774-b06d1ea9a326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33e3d92-ae3e-486b-92f6-66ec6c1e1202' class='xr-var-data-in' type='checkbox'><label for='data-d33e3d92-ae3e-486b-92f6-66ec6c1e1202' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>n_0</span></div><div class='xr-var-dims'>(n_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 431 432 433 434 435</div><input id='attrs-3a9f3f1e-2ee9-47eb-bde8-d9c565c53630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9f3f1e-2ee9-47eb-bde8-d9c565c53630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2a87af7-0bbb-4a05-8b40-5125aa0a0183' class='xr-var-data-in' type='checkbox'><label for='data-b2a87af7-0bbb-4a05-8b40-5125aa0a0183' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$n_0$</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 433, 434, 435])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f7f5bda-fea6-4627-9e91-a764be360549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f7f5bda-fea6-4627-9e91-a764be360549' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(n_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 0.001008 ... 5.854 5.893</div><input id='attrs-20a55bbc-b927-410a-a2ca-aacc4fa02ebe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a55bbc-b927-410a-a2ca-aacc4fa02ebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a862a05-16df-49ce-9767-eadc735402fd' class='xr-var-data-in' type='checkbox'><label for='data-6a862a05-16df-49ce-9767-eadc735402fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$p({\\bf n},V,T)$</dd></dl></div><div class='xr-var-data'><pre>array([-0.00000000e+00,  1.00795898e-03,  3.19042969e-03,  5.60727539e-03,\n",
       "        8.13105469e-03,  1.07110840e-02,  1.33245117e-02,  1.59626221e-02,\n",
       "        1.86035449e-02,  2.12531396e-02,  2.39138379e-02,  2.65724268e-02,\n",
       "        2.92237793e-02,  3.18847852e-02,  3.45402539e-02,  3.71912988e-02,\n",
       "        3.98437793e-02,  4.24513916e-02,  4.50499219e-02,  4.76741602e-02,\n",
       "        5.02820508e-02,  5.28891064e-02,  5.55045117e-02,  5.80831055e-02,\n",
       "        6.06579199e-02,  6.32146143e-02,  6.57673828e-02,  6.83276221e-02,\n",
       "        7.08482520e-02,  7.33880127e-02,  7.59249902e-02,  7.84426318e-02,\n",
       "        8.09484668e-02,  8.34514746e-02,  8.59810840e-02,  8.84699268e-02,\n",
       "        9.09261328e-02,  9.33975293e-02,  9.58728223e-02,  9.83368213e-02,\n",
       "        1.00797920e-01,  1.03231289e-01,  1.05678340e-01,  1.08122197e-01,\n",
       "        1.10520381e-01,  1.12914243e-01,  1.15324980e-01,  1.17722622e-01,\n",
       "        1.20133154e-01,  1.22532861e-01,  1.24934062e-01,  1.27297793e-01,\n",
       "        1.29691787e-01,  1.32085884e-01,  1.34399619e-01,  1.36757651e-01,\n",
       "        1.39123389e-01,  1.41462568e-01,  1.43806025e-01,  1.46144854e-01,\n",
       "        1.48474775e-01,  1.50824722e-01,  1.53090674e-01,  1.55359731e-01,\n",
       "        1.57746826e-01,  1.60114805e-01,  1.62430020e-01,  1.64709902e-01,\n",
       "        1.67055029e-01,  1.69411143e-01,  1.71716338e-01,  1.73960508e-01,\n",
       "        1.76163545e-01,  1.78509873e-01,  1.80828252e-01,  1.83095083e-01,\n",
       "        1.85397539e-01,  1.87673262e-01,  1.89966621e-01,  1.92227944e-01,\n",
       "...\n",
       "        2.41144821e+00,  2.43941208e+00,  2.46474643e+00,  2.49182352e+00,\n",
       "        2.52185649e+00,  2.55185917e+00,  2.58335501e+00,  2.61155211e+00,\n",
       "        2.64050670e+00,  2.67121149e+00,  2.70174746e+00,  2.73371677e+00,\n",
       "        2.76553628e+00,  2.79666366e+00,  2.82648006e+00,  2.85977036e+00,\n",
       "        2.89344684e+00,  2.92455606e+00,  2.95870041e+00,  2.99367556e+00,\n",
       "        3.02772368e+00,  3.06307060e+00,  3.09984945e+00,  3.13746029e+00,\n",
       "        3.17279385e+00,  3.20745304e+00,  3.24353609e+00,  3.28000669e+00,\n",
       "        3.31804157e+00,  3.35525206e+00,  3.39285756e+00,  3.43347425e+00,\n",
       "        3.47512361e+00,  3.51374995e+00,  3.55369184e+00,  3.59754333e+00,\n",
       "        3.63821462e+00,  3.67967000e+00,  3.72410434e+00,  3.76816885e+00,\n",
       "        3.80753144e+00,  3.85033347e+00,  3.89739490e+00,  3.94103914e+00,\n",
       "        3.98881466e+00,  4.03375941e+00,  4.07758450e+00,  4.12425794e+00,\n",
       "        4.17009086e+00,  4.21737066e+00,  4.26946438e+00,  4.31971126e+00,\n",
       "        4.36427285e+00,  4.41670709e+00,  4.46721311e+00,  4.51497176e+00,\n",
       "        4.56795852e+00,  4.62053264e+00,  4.67139978e+00,  4.71933167e+00,\n",
       "        4.77446846e+00,  4.83430838e+00,  4.88988394e+00,  4.94484867e+00,\n",
       "        4.99826279e+00,  5.05576434e+00,  5.11593608e+00,  5.17409159e+00,\n",
       "        5.23279714e+00,  5.29388224e+00,  5.35440210e+00,  5.41664957e+00,\n",
       "        5.47422335e+00,  5.53065666e+00,  5.59730717e+00,  5.65966039e+00,\n",
       "        5.72173746e+00,  5.78577560e+00,  5.85422369e+00,  5.89278464e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betaF</span></div><div class='xr-var-dims'>(n_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -6.238 -11.79 ... -348.7 -344.8</div><input id='attrs-4239317b-c140-4c4b-b41b-3f39b9fc00ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4239317b-c140-4c4b-b41b-3f39b9fc00ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93eb42c1-938e-4d5c-8150-55ea92f1f982' class='xr-var-data-in' type='checkbox'><label for='data-93eb42c1-938e-4d5c-8150-55ea92f1f982' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>standard_name :</span></dt><dd>helmholtz_free_energy</dd><dt><span>long_name :</span></dt><dd>$\\beta F({\\bf n},V,T)$</dd></dl></div><div class='xr-var-data'><pre>array([   0.        ,   -6.23817948,  -11.78825896,  -16.93743845,\n",
       "        -21.80391793,  -26.45169741,  -30.92217689,  -35.24305637,\n",
       "        -39.43488586,  -43.51427534,  -47.49263482,  -51.3802843 ,\n",
       "        -55.18540378,  -58.91534327,  -62.57494275,  -66.17037223,\n",
       "        -69.70504171,  -73.18350119,  -76.61015068,  -79.98718016,\n",
       "        -83.31692964,  -86.60257912,  -89.8454786 ,  -93.04802809,\n",
       "        -96.21263757,  -99.34036705, -102.43368653, -105.49306601,\n",
       "       -108.5203955 , -111.51717498, -114.48366446, -117.42170394,\n",
       "       -120.33183342, -123.21554291, -126.07309239, -128.90524187,\n",
       "       -131.71352135, -134.49843083, -137.26048032, -140.0003198 ,\n",
       "       -142.71866928, -145.41596876, -148.09328824, -150.75033773,\n",
       "       -153.38838721, -156.00779669, -158.60911617, -161.19235565,\n",
       "       -163.75846514, -166.30706462, -168.8393841 , -171.35487358,\n",
       "       -173.85522306, -176.33899255, -178.80819203, -181.26244151,\n",
       "       -183.70210099, -186.12725047, -188.53863996, -190.93596944,\n",
       "       -193.32005892, -195.6906584 , -198.04822788, -200.39366737,\n",
       "       -202.72645685, -205.04623633, -207.35395581, -209.64960529,\n",
       "       -211.93391478, -214.20585426, -216.46667374, -218.71597322,\n",
       "       -220.9550527 , -223.18332219, -225.40031167, -227.60704115,\n",
       "       -229.80326063, -231.98917011, -234.1650796 , -236.33078908,\n",
       "...\n",
       "       -517.57634594, -516.70443742, -515.8063279 , -514.88603639,\n",
       "       -513.93637587, -512.95905135, -511.95257783, -510.91577731,\n",
       "       -509.8561921 , -508.76502588, -507.64792636, -506.49973331,\n",
       "       -505.32308209, -504.11578421, -502.88146288, -501.61907936,\n",
       "       -500.32342584, -498.99915233, -497.64648581, -496.25981229,\n",
       "       -494.84338477, -493.39481425, -491.91429574, -490.39921822,\n",
       "       -488.8508627 , -487.27222118, -485.66168466, -484.01847615,\n",
       "       -482.34284843, -480.63193791, -478.89023839, -477.11274027,\n",
       "       -475.29930056, -473.44842984, -471.56710749, -469.6462433 ,\n",
       "       -467.68825729, -465.6964695 , -463.66638676, -461.59751234,\n",
       "       -459.49117603, -457.35434961, -455.17433569, -452.95667897,\n",
       "       -450.70189785, -448.40261144, -446.07120492, -443.6974159 ,\n",
       "       -441.28684118, -438.83551406, -436.34514405, -433.80612803,\n",
       "       -431.23135871, -428.61769449, -425.95531147, -423.25743386,\n",
       "       -420.51563787, -417.72985682, -414.90105215, -412.03128963,\n",
       "       -409.12353928, -406.16320457, -403.15736475, -400.10615663,\n",
       "       -397.01065581, -393.87252309, -390.68368018, -387.44808786,\n",
       "       -384.16527904, -380.83505572, -377.4540159 , -374.02668999,\n",
       "       -370.54602357, -367.02674195, -363.45618333, -359.83096831,\n",
       "       -356.1615208 , -352.43810628, -348.66757176, -344.83638224])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4365c36-5d66-4eee-8140-c366ea9554d9' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4365c36-5d66-4eee-8140-c366ea9554d9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>n_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6106e184-56c8-4b96-b395-b81360d13742' class='xr-index-data-in' type='checkbox'/><label for='index-6106e184-56c8-4b96-b395-b81360d13742' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       426, 427, 428, 429, 430, 431, 432, 433, 434, 435],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;n_0&#x27;, length=436))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed195209-936d-4e87-a011-7f251a9f431a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed195209-936d-4e87-a011-7f251a9f431a' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>$p({\\bf n},V,T)$</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset> Size: 10kB\n",
       "Dimensions:   (n_0: 436)\n",
       "Coordinates:\n",
       "    beta      float64 8B 0.6667\n",
       "    volume    float64 8B 512.0\n",
       "  * n_0       (n_0) int64 3kB 0 1 2 3 4 5 6 7 ... 429 430 431 432 433 434 435\n",
       "Data variables:\n",
       "    pressure  (n_0) float64 3kB -0.0 0.001008 0.00319 ... 5.786 5.854 5.893\n",
       "    betaF     (n_0) float64 3kB 0.0 -6.238 -11.79 ... -352.4 -348.7 -344.8\n",
       "Attributes:\n",
       "    long_name:  $p({\\bf n},V,T)$"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ref.xce.table(keys=[\"pressure\", \"betaF\"], default_keys=None)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "This can be easily converted to a `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18",
   "metadata": {},
   "outputs": [
    {
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       "         beta  volume  pressure      betaF\n",
       "n_0                                       \n",
       "0    0.666667   512.0 -0.000000   0.000000\n",
       "1    0.666667   512.0  0.001008  -6.238179\n",
       "2    0.666667   512.0  0.003190 -11.788259\n",
       "3    0.666667   512.0  0.005607 -16.937438\n",
       "4    0.666667   512.0  0.008131 -21.803918"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ds.to_dataframe().head()"
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  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "To access Grand Canonical properties, use the `xge` accessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pressure&#x27; ()&gt; Size: 8B\n",
       "array(4.57687966)\n",
       "Coordinates:\n",
       "    lnz_0    float64 8B 2.765\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      [&#x27;n_0&#x27;]\n",
       "    dims_lnz:    [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:   [&#x27;component&#x27;]\n",
       "    dims_state:  [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    long_name:   $p(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pressure'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0c42130c-3355-4f0a-a2a7-1f5acb76e176' class='xr-array-in' type='checkbox' checked><label for='section-0c42130c-3355-4f0a-a2a7-1f5acb76e176' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.577</span></div><div class='xr-array-data'><pre>array(4.57687966)</pre></div></div></li><li class='xr-section-item'><input id='section-6c36407a-f1dd-466f-a47b-b494153453c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c36407a-f1dd-466f-a47b-b494153453c9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lnz_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.765</div><input id='attrs-c28b8ddb-fb0c-4155-a820-69c619ada365' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c28b8ddb-fb0c-4155-a820-69c619ada365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d5d9e75-bd00-44f8-acf2-a95b92de57fa' class='xr-var-data-in' type='checkbox'><label for='data-8d5d9e75-bd00-44f8-acf2-a95b92de57fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2.76512052)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-c3f9c3ff-1fbf-47ea-9400-bb04921ef836' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3f9c3ff-1fbf-47ea-9400-bb04921ef836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4af940f4-ecde-4c72-b7b9-62ecff922cd2' class='xr-var-data-in' type='checkbox'><label for='data-4af940f4-ecde-4c72-b7b9-62ecff922cd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-dbf1e441-a962-4d71-8556-46c1a01c29a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbf1e441-a962-4d71-8556-46c1a01c29a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-545c1f6f-ae42-4b01-965e-db0993317a64' class='xr-var-data-in' type='checkbox'><label for='data-545c1f6f-ae42-4b01-965e-db0993317a64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e10343e4-2f1f-46d5-985d-6f44b8c271ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e10343e4-2f1f-46d5-985d-6f44b8c271ff' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-03275d76-909a-4ae3-b93b-db738901e73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03275d76-909a-4ae3-b93b-db738901e73f' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>long_name :</span></dt><dd>$p(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pressure' ()> Size: 8B\n",
       "array(4.57687966)\n",
       "Coordinates:\n",
       "    lnz_0    float64 8B 2.765\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      ['n_0']\n",
       "    dims_lnz:    ['lnz_0']\n",
       "    dims_comp:   ['component']\n",
       "    dims_state:  ['lnz_0', 'beta', 'volume']\n",
       "    long_name:   $p(\\mu,V,T)$"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref.xge.pressure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;betaF&#x27; ()&gt; Size: 8B\n",
       "array(-423.16867869)\n",
       "Coordinates:\n",
       "    lnz_0    float64 8B 2.765\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:         [&#x27;n_0&#x27;]\n",
       "    dims_lnz:       [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:      [&#x27;component&#x27;]\n",
       "    dims_state:     [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    standard_name:  helmholtz_free_energy\n",
       "    long_name:      $\\beta F(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'betaF'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3e3d31a9-e1f0-479a-87b6-dda6710b21e0' class='xr-array-in' type='checkbox' checked><label for='section-3e3d31a9-e1f0-479a-87b6-dda6710b21e0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-423.2</span></div><div class='xr-array-data'><pre>array(-423.16867869)</pre></div></div></li><li class='xr-section-item'><input id='section-07b8cb2e-4504-4d0f-8f00-33ad6c452d91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07b8cb2e-4504-4d0f-8f00-33ad6c452d91' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lnz_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.765</div><input id='attrs-8f4144a2-201e-422c-a297-538bc23de845' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f4144a2-201e-422c-a297-538bc23de845' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96696bbd-400f-4512-910a-69daaf0223df' class='xr-var-data-in' type='checkbox'><label for='data-96696bbd-400f-4512-910a-69daaf0223df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2.76512052)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-e91e65d8-1cac-41fd-b179-2800962570af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e91e65d8-1cac-41fd-b179-2800962570af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-914090d5-0227-4a6b-bb2c-08f4eddc1814' class='xr-var-data-in' type='checkbox'><label for='data-914090d5-0227-4a6b-bb2c-08f4eddc1814' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-ed9f69e7-64c5-4b39-b71c-f10ac0585a23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed9f69e7-64c5-4b39-b71c-f10ac0585a23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f2f304-749f-4229-8444-7da61ae178a4' class='xr-var-data-in' type='checkbox'><label for='data-37f2f304-749f-4229-8444-7da61ae178a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3b61cb2-e77b-4774-b6c7-dc52113e9018' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3b61cb2-e77b-4774-b6c7-dc52113e9018' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-9b0e5787-8bf5-4948-a086-8d1850a17d3d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b0e5787-8bf5-4948-a086-8d1850a17d3d' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>standard_name :</span></dt><dd>helmholtz_free_energy</dd><dt><span>long_name :</span></dt><dd>$\\beta F(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'betaF' ()> Size: 8B\n",
       "array(-423.16867869)\n",
       "Coordinates:\n",
       "    lnz_0    float64 8B 2.765\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:         ['n_0']\n",
       "    dims_lnz:       ['lnz_0']\n",
       "    dims_comp:      ['component']\n",
       "    dims_state:     ['lnz_0', 'beta', 'volume']\n",
       "    standard_name:  helmholtz_free_energy\n",
       "    long_name:      $\\beta F(\\mu,V,T)$"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref.xge.betaF()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22",
   "metadata": {},
   "source": [
    "Or, to calculate multiple properties, again, use the `table` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 40B\n",
       "Dimensions:   ()\n",
       "Coordinates:\n",
       "    lnz_0     float64 8B 2.765\n",
       "    beta      float64 8B 0.6667\n",
       "    volume    float64 8B 512.0\n",
       "Data variables:\n",
       "    pressure  float64 8B 4.577\n",
       "    betaF     float64 8B -423.2\n",
       "Attributes:\n",
       "    dims_n:      [&#x27;n_0&#x27;]\n",
       "    dims_lnz:    [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:   [&#x27;component&#x27;]\n",
       "    dims_state:  [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    long_name:   $p(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ffb82d2a-46b7-44a4-b521-f9f421e30603' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ffb82d2a-46b7-44a4-b521-f9f421e30603' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f3c30e9f-c2e2-4ba1-88bc-0fcc960eaae6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3c30e9f-c2e2-4ba1-88bc-0fcc960eaae6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lnz_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.765</div><input id='attrs-42f3a569-d091-4822-8f3f-c73a5e4c9b49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42f3a569-d091-4822-8f3f-c73a5e4c9b49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c91acdf4-adf2-445e-a9d3-60bcef591eed' class='xr-var-data-in' type='checkbox'><label for='data-c91acdf4-adf2-445e-a9d3-60bcef591eed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2.76512052)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-c8464ba7-7019-43d4-8e30-026628372773' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8464ba7-7019-43d4-8e30-026628372773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-251e0ce5-b62e-4361-8f74-ec46ae6a43e6' class='xr-var-data-in' type='checkbox'><label for='data-251e0ce5-b62e-4361-8f74-ec46ae6a43e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-26d38fc4-dfd1-4cbd-b1ff-02a8b9fa77c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26d38fc4-dfd1-4cbd-b1ff-02a8b9fa77c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24854017-e4c3-418f-9c30-86773c2413b7' class='xr-var-data-in' type='checkbox'><label for='data-24854017-e4c3-418f-9c30-86773c2413b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbd60001-28ad-42dc-9f21-b6d204a21ac4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbd60001-28ad-42dc-9f21-b6d204a21ac4' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.577</div><input id='attrs-92cc7c7a-5535-4594-8e36-0a75441b7b50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92cc7c7a-5535-4594-8e36-0a75441b7b50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d076cf6-85b4-4921-88ee-109c6cb5223e' class='xr-var-data-in' type='checkbox'><label for='data-9d076cf6-85b4-4921-88ee-109c6cb5223e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>long_name :</span></dt><dd>$p(\\mu,V,T)$</dd></dl></div><div class='xr-var-data'><pre>array(4.57687966)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betaF</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-423.2</div><input id='attrs-46a03552-4a28-44c5-ac78-0ae965ee4f16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46a03552-4a28-44c5-ac78-0ae965ee4f16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a9cb86f-ee5d-4334-b572-80ec9ec1dc52' class='xr-var-data-in' type='checkbox'><label for='data-0a9cb86f-ee5d-4334-b572-80ec9ec1dc52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>standard_name :</span></dt><dd>helmholtz_free_energy</dd><dt><span>long_name :</span></dt><dd>$\\beta F(\\mu,V,T)$</dd></dl></div><div class='xr-var-data'><pre>array(-423.16867869)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62bf3341-ea4c-4445-88d0-39d393aaa27d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62bf3341-ea4c-4445-88d0-39d393aaa27d' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1f0600f3-7708-497f-b413-de6e908d7b01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f0600f3-7708-497f-b413-de6e908d7b01' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>long_name :</span></dt><dd>$p(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 40B\n",
       "Dimensions:   ()\n",
       "Coordinates:\n",
       "    lnz_0     float64 8B 2.765\n",
       "    beta      float64 8B 0.6667\n",
       "    volume    float64 8B 512.0\n",
       "Data variables:\n",
       "    pressure  float64 8B 4.577\n",
       "    betaF     float64 8B -423.2\n",
       "Attributes:\n",
       "    dims_n:      ['n_0']\n",
       "    dims_lnz:    ['lnz_0']\n",
       "    dims_comp:   ['component']\n",
       "    dims_state:  ['lnz_0', 'beta', 'volume']\n",
       "    long_name:   $p(\\mu,V,T)$"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref.xge.table(keys=[\"pressure\", \"betaF\"], default_keys=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24",
   "metadata": {},
   "source": [
    "The great thing about using the xarray interface is that things like plotting become trivial.  For example, use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15700fa10>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref.xge.lnpi().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26",
   "metadata": {},
   "source": [
    "# Collections of lnPi Values\n",
    "\n",
    "Ok, we can reweight a $\\ln \\Pi(N)$ from one chemical potential to another, and calculate a variety of properties for a $\\ln \\Pi(N)$.  But what if we want to calculate properties at a variety of values of $\\ln z$.  We could do list comprehension, but that's slow, and can get unwieldy.  Instead, `lnpy` provides a helper class to deal with multiple `lnPi`s.  To use it, we must first have a {class}`~lnpy.segment.PhaseCreator` object.  We will discuss this object in detail later, but for now, know that for the simple case of a single component system that does not have any phase transitions, it suffices to use the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_creator = lnpy.PhaseCreator(nmax=1, ref=ref)\n",
    "\n",
    "build_phases = phase_creator.build_phases_mu([None])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28",
   "metadata": {},
   "source": [
    "To create a collection of with of `lnPi` at different values of `lnz`, use the {class}`lnpy.lnpiseries.lnPiCollection` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c64f97a0829247c990cd957abde28ddb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "build:   0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with lnpy.set_options(tqdm_leave=True):\n",
    "    c = lnpy.lnPiCollection.from_builder(\n",
    "        lnzs=np.linspace(-10, 3, 2000), build_phases=build_phases\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30",
   "metadata": {},
   "source": [
    "{class}`~lnpy.lnpiseries.lnPiCollection` is a wrapper around `pandas.Series`.  This provides an index for each {class}`~lnpy.lnpidata.lnPiMasked` instance.  This\n",
    "allows some niceties like indexing by value, groupby, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lnPi(lnz=[-9.99349675])>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select by position\n",
    "# scalar index gives object\n",
    "c.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0      phase\n",
       "-9.993497  0        [-9.993496748374188]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# array-like gives lnPiCollection instance\n",
    "(c.iloc[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  phase\n",
       "3.0    0        [3.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select by value.  Same as indexing into pandas.Series\n",
    "c.loc[[3.0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0     phase\n",
       "2.954477  0         [2.95447723861931]\n",
       "2.960980  0        [2.960980490245122]\n",
       "2.967484  0        [2.967483741870936]\n",
       "2.973987  0        [2.973986993496748]\n",
       "2.980490  0        [2.980490245122562]\n",
       "2.986993  0        [2.986993496748374]\n",
       "2.993497  0        [2.993496748374188]\n",
       "3.000000  0                      [3.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can also query\n",
    "c.query(\"lnz_0 > 2.95\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35",
   "metadata": {},
   "source": [
    "## Accessing properties of collection\n",
    "\n",
    "The really nice thing about {class}`~lnpy.lnpiseries.lnPiCollection` is that all the properties for *all* the {class}`~lnpy.lnpidata.lnPiMasked` objects can be calculated at once, in a vectorized way.  The same accessor {attr}`~lnpy.lnpiseries.lnPiCollection.xge` is available to {class}`~lnpy.lnpiseries.lnPiCollection` as for {class}`~lnpy.lnpidata.lnPiMasked`.  Note that the `xce` accessor is not available, as it only makes sense for a single value of `lnPiMasked`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b0c16f4ffb94df5936472160223dacc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pi_norm:   0%|          | 0/2000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pressure&#x27; (lnz_0: 2000, phase: 1)&gt; Size: 16kB\n",
       "array([[6.80940123e-05],\n",
       "       [6.85383141e-05],\n",
       "       [6.89855151e-05],\n",
       "       ...,\n",
       "       [4.84631185e+00],\n",
       "       [4.85425839e+00],\n",
       "       [4.86220769e+00]])\n",
       "Coordinates:\n",
       "  * lnz_0    (lnz_0) float64 16kB -10.0 -9.993 -9.987 -9.98 ... 2.987 2.993 3.0\n",
       "  * phase    (phase) int64 8B 0\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      [&#x27;n_0&#x27;]\n",
       "    dims_lnz:    [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:   [&#x27;component&#x27;]\n",
       "    dims_state:  [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    dims_rec:    [&#x27;sample&#x27;]\n",
       "    long_name:   $p(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pressure'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lnz_0</span>: 2000</li><li><span class='xr-has-index'>phase</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5fcd56c0-12d1-4017-abac-e88d5bca2c08' class='xr-array-in' type='checkbox' checked><label for='section-5fcd56c0-12d1-4017-abac-e88d5bca2c08' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.809e-05 6.854e-05 6.899e-05 6.944e-05 ... 4.838 4.846 4.854 4.862</span></div><div class='xr-array-data'><pre>array([[6.80940123e-05],\n",
       "       [6.85383141e-05],\n",
       "       [6.89855151e-05],\n",
       "       ...,\n",
       "       [4.84631185e+00],\n",
       "       [4.85425839e+00],\n",
       "       [4.86220769e+00]])</pre></div></div></li><li class='xr-section-item'><input id='section-225ee035-e93f-4329-b18b-e5dc21aca186' class='xr-section-summary-in' type='checkbox'  checked><label for='section-225ee035-e93f-4329-b18b-e5dc21aca186' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_0</span></div><div class='xr-var-dims'>(lnz_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.993 -9.987 ... 2.993 3.0</div><input id='attrs-4e178ec7-9bb1-4165-9a8a-ed0c1a39332f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e178ec7-9bb1-4165-9a8a-ed0c1a39332f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d89c7467-c98b-4827-b1e6-6389113bebdf' class='xr-var-data-in' type='checkbox'><label for='data-d89c7467-c98b-4827-b1e6-6389113bebdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.      ,  -9.993497,  -9.986993, ...,   2.986993,   2.993497,\n",
       "         3.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(phase)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-bd74e591-d738-4d3a-86c3-11f8b7fe743f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd74e591-d738-4d3a-86c3-11f8b7fe743f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a58a0e8-c804-4fee-802d-3a0b9a222a7c' class='xr-var-data-in' type='checkbox'><label for='data-3a58a0e8-c804-4fee-802d-3a0b9a222a7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6667</div><input id='attrs-f2bf328a-dffe-40cd-9de3-0b59cb112138' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2bf328a-dffe-40cd-9de3-0b59cb112138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac44106-4e9d-4095-95d7-68c8d89c2320' class='xr-var-data-in' type='checkbox'><label for='data-0ac44106-4e9d-4095-95d7-68c8d89c2320' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.66666667)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-c29863d0-5cd8-44ff-aaf1-15e27c237b2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c29863d0-5cd8-44ff-aaf1-15e27c237b2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7f3ce4-90b6-4055-a1d4-894f0436aeb4' class='xr-var-data-in' type='checkbox'><label for='data-5a7f3ce4-90b6-4055-a1d4-894f0436aeb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebb64ef3-a5a0-4dd7-ac78-2859b2989994' class='xr-section-summary-in' type='checkbox'  ><label for='section-ebb64ef3-a5a0-4dd7-ac78-2859b2989994' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lnz_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-daff003d-9abb-4665-b79b-ffb9299fa96b' class='xr-index-data-in' type='checkbox'/><label for='index-daff003d-9abb-4665-b79b-ffb9299fa96b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -10.0, -9.993496748374188, -9.986993496748374,\n",
       "       -9.980490245122562, -9.973986993496748, -9.967483741870936,\n",
       "       -9.960980490245122,  -9.95447723861931, -9.947973986993496,\n",
       "       -9.941470735367684,\n",
       "       ...\n",
       "        2.941470735367684, 2.9479739869934978,   2.95447723861931,\n",
       "        2.960980490245122,  2.967483741870936,  2.973986993496748,\n",
       "        2.980490245122562,  2.986993496748374,  2.993496748374188,\n",
       "                      3.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lnz_0&#x27;, length=2000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>phase</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afae2631-2957-47f1-b749-83ae2c416b80' class='xr-index-data-in' type='checkbox'/><label for='index-afae2631-2957-47f1-b749-83ae2c416b80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;phase&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6d969d5-e666-49d8-9113-0ffa5e164a44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6d969d5-e666-49d8-9113-0ffa5e164a44' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>$p(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pressure' (lnz_0: 2000, phase: 1)> Size: 16kB\n",
       "array([[6.80940123e-05],\n",
       "       [6.85383141e-05],\n",
       "       [6.89855151e-05],\n",
       "       ...,\n",
       "       [4.84631185e+00],\n",
       "       [4.85425839e+00],\n",
       "       [4.86220769e+00]])\n",
       "Coordinates:\n",
       "  * lnz_0    (lnz_0) float64 16kB -10.0 -9.993 -9.987 -9.98 ... 2.987 2.993 3.0\n",
       "  * phase    (phase) int64 8B 0\n",
       "    beta     float64 8B 0.6667\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      ['n_0']\n",
       "    dims_lnz:    ['lnz_0']\n",
       "    dims_comp:   ['component']\n",
       "    dims_state:  ['lnz_0', 'beta', 'volume']\n",
       "    dims_rec:    ['sample']\n",
       "    long_name:   $p(\\mu,V,T)$"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note the smart indexing.  The index is inherited from lnPiCollection.index\n",
    "c.xge.pressure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15780a510>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "c.xge.pressure().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38",
   "metadata": {},
   "source": [
    "This is much more convenient (and much faster) than doing something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157d83810>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Don't do things like this\n",
    "# It's super slow\n",
    "import xarray as xr\n",
    "\n",
    "pressures = [x.xge.pressure() for x in c]\n",
    "\n",
    "xr.concat(pressures, dim=\"lnz_0\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "# Loading examples\n",
    "\n",
    "lnPi comes with some pre-defined examples in the {mod}`~lnpy.examples` module.  We used it above to load a dictionary of data that we then turned into a {class}`~lnpy.lnpidata.lnPiMasked` object.  This can be streamlined using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lnPi(lnz=[2.76512052])>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lnpy.examples\n",
    "\n",
    "ref = lnpy.examples.load_example_lnpimasked(\"lj_sup\")\n",
    "ref"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {},
   "source": [
    "Alternatively, for some examples, you can load an object with contains a predefined {class}`~lnpy.segment.PhaseCreator` object.  We'll save this for later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43",
   "metadata": {},
   "source": [
    "# Single component system with phase transitions.\n",
    "\n",
    "Next, let's consider a system with a phase transition. First, lets load some example data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import lnpy\n",
    "import lnpy.examples\n",
    "\n",
    "# Subcritical LJ data\n",
    "ref = lnpy.examples.load_example_lnpimasked(\"lj_sub\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15784d450>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at the lnPi values\n",
    "ref.xge.lnpi().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46",
   "metadata": {},
   "source": [
    "Notice that this $\\ln \\Pi(N)$ has multiple maxima.  This indicates that multiple phases coexist.  \n",
    "So calculating properties from the total {mod}`~lnpy.lnpidata.lnPiMasked` doesn't make sense.  Instead, the {class}`~lnpy.lnpidata.lnPiMasked` should be divided into phases.  The division should be at the local minima in $\\ln \\Pi(N)$ (at approximately `n_0=100` in the example above).  To perform the segmentation, we turn back to the {class}`~lnpy.segment.PhaseCreator` object.  Let see how this works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This creates a `PhaseCreator` object\n",
    "phase_creator = lnpy.PhaseCreator(nmax=2, nmax_peak=4, ref=ref, merge_kws={\"efac\": 0.8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0      phase\n",
       "-3.609956  0        [-3.6099564097351307]\n",
       "           1        [-3.6099564097351307]\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call the `build_phases` method creates a lnPiCollection of phases\n",
    "p = phase_creator.build_phases()\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157fbc990>,\n",
       " <matplotlib.lines.Line2D at 0x15865cb90>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the lnpi values for each 'phase'\n",
    "p.xge.lnpi().plot(hue=\"lnz_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50",
   "metadata": {},
   "source": [
    "What just happened?  To segment $\\ln \\Pi(N)$ the code does the following:, we   For this we do the following\n",
    "\n",
    "1. Find the local maxima of the $\\ln \\Pi(N)$.  This is done using {func}`~lnpy.segment.peak_local_max_adaptive`, which is an an adaptive version of {func}`skimage.feature.peak_local_max`.  This finds at most `nmax_peak`.  Note that `nmax_peak` can be any number.\n",
    "2. Use the {func}`~skimage.segmentation.watershed` segmentation algorithm to segment $-\\ln \\Pi$ into regions about the local minima in $-\\ln \\Pi$ (local maxima in $\\ln \\Pi$). \n",
    "3. Remove any phases that have to low a transition energy.  We discuss this further below.\n",
    "4. If the number of 'phases' is greater than the maximum allowed number of phases.  Merge them.  This is done by analyzing the transition energy between phases.  Discussed further below.\n",
    "5. Merge phases that have same `phase_id`.  Discussed further below.\n",
    "6. Pass list of {class}`~lnpy.lnpidata.lnPiMasked` objects and created `index` to `phases_factory` function. Discussed further below.\n",
    "7. return result\n",
    "\n",
    "\n",
    "OK, that's a bunch of steps.  Let take them one at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51",
   "metadata": {},
   "source": [
    "## Local maxima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxima_marker = lnpy.segment.peak_local_max_adaptive(\n",
    "    ref.data, num_peaks_max=4, mask=~ref.mask, style=\"marker\"\n",
    ")\n",
    "maxima_index = np.where(maxima_marker > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a26e110>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ref.data)\n",
    "plt.plot(maxima_index[0], ref.data[maxima_index], marker=\"o\", ls=\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54",
   "metadata": {},
   "source": [
    "## Watershed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55",
   "metadata": {},
   "source": [
    "OK, great.  Now we can segment the data. `lnPi` provides a wrapper to {func}`skimage.segmentation.watershed` through {meth}`lnpy.segment.Segmenter.watershed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "56",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = lnpy.segment.Segmenter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = s.watershed(-ref.data, markers=maxima_marker, mask=~ref.mask)\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58",
   "metadata": {},
   "source": [
    "Each unique value in `labels` corresponds to a phase.  We can construct multiple `lnPiMasked` objects from labels using"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59",
   "metadata": {},
   "source": [
    "## local free energy\n",
    "\n",
    "Now, we look at the local free energy.  The local (scaled) free energy is defined as $w(N) = \\beta f(N) = -\\ln \\Pi(N)$. We consider the energy at the location of the local minima in $w$ (local maxima in $\\ln \\Pi$) versus the value of $w$ at the transition between phases (around $n=100$ in the figure above).  This is performed by the class {class}`lnpy.lnpienergy.wFreeEnergy`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lnpy.lnpienergy import wFreeEnergy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = wFreeEnergy.from_labels(data=ref.data, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[126.12754742],\n",
       "       [ -0.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the same as -lnPi[maxima]\n",
    "w.w_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126.12754742,  -0.        ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-ref.data[maxima_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[         inf, 132.72931142],\n",
       "       [132.72931142,          inf]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# w.w_tran[i, j] is the 'transition' energy going from phase i to phase j\n",
    "w.w_tran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 1): (87,)}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the transition between phases '0' and '1' is at index 87\n",
    "w.w_argtran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132.72931142169"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-ref.data[87]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[         inf,   6.601764  ],\n",
       "       [132.72931142,          inf]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the change in energy between the phases\n",
    "w.delta_w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68",
   "metadata": {},
   "source": [
    "This transition energy means the two phases are stable.  So move on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<lnPi(lnz=[-3.60995641])>, <lnPi(lnz=[-3.60995641])>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lnpis = ref.list_from_masks(w.masks)\n",
    "lnpis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70",
   "metadata": {},
   "source": [
    "## Construct collection\n",
    "Now these can be converted to a {class}`~lnpy.lnpiseries.lnPiCollection` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0      phase\n",
       "-3.609956  0        [-3.6099564097351307]\n",
       "           1        [-3.6099564097351307]\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = lnpy.lnPiCollection.from_list(lnpis, index=[0, 1])\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72",
   "metadata": {},
   "source": [
    "So, the segmentation can get pretty involved.  This is why there is the helper class {class}`~lnpy.segment.PhaseCreator`. There are a slew\n",
    "of options to the different routines.  Look at the docs for more information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73",
   "metadata": {},
   "source": [
    "## At a different $\\ln z$ value\n",
    "\n",
    "Let us instead consider a different value of chemical potential. One near the critical point.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  phase\n",
       "-3.49  0        [-3.49]\n",
       "       1        [-3.49]\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = phase_creator.build_phases(-3.49)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19a768c50>,\n",
       " <matplotlib.lines.Line2D at 0x15864f5d0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.xge.lnpi().plot(hue=\"phase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76",
   "metadata": {},
   "source": [
    "But lets look at the local free energy 'w' defined above.  This can be obtained by the accessor {attr}`~lnpy.lnpiseries.lnPiCollection.wfe` in {class}`~lnpy.lnpiseries.lnPiCollection`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lnz_0  phase\n",
       "-3.49  0        122.930891\n",
       "       1        -45.776165\n",
       "Name: w_min, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min(w) in each phase\n",
    "p.wfe.w_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lnz_0  phase  phase_nebr\n",
       "-3.49  0      0                    inf\n",
       "              1             123.785912\n",
       "       1      0             123.785912\n",
       "              1                    inf\n",
       "Name: w_tran, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transition w from phase to phase_nebr\n",
    "p.wfe.w_tran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lnz_0  phase  phase_nebr\n",
       "-3.49  0      0                    inf\n",
       "              1               0.855021\n",
       "       1      0             169.562078\n",
       "              1                    inf\n",
       "Name: delta_w, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# w_tran - w_min\n",
    "p.wfe.dw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80",
   "metadata": {},
   "source": [
    "We see that the $\\Delta w$ is pretty low at this value of $\\ln z$.  We passed in the parameter `efac=0.8` in the definition of `phase_creator`.  If we instead wish to remove phases with an energy this low, we can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  phase\n",
       "-3.49  0        [-3.49]\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = phase_creator.build_phases(-3.49, efac=0.9)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82",
   "metadata": {},
   "source": [
    "Now if the $\\Delta w$ between phases is smaller than `efac`, the phases are merged.  For more information, see that api docs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83",
   "metadata": {},
   "source": [
    "## Tagging phases\n",
    "\n",
    "{class}`~lnpy.segment.PhaseCreator` allows passing a callback `tag_phases` to attach a label to each phase.  By default, the phases are labeled by there list index.  This can be confusing, because over a range of $\\ln z$ values, different physical phases can have the same label.  For example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "84",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_creator = lnpy.PhaseCreator(nmax=2, nmax_peak=4, ref=ref, merge_kws={\"efac\": 0.8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19a7d6a90>,\n",
       " <matplotlib.lines.Line2D at 0x19a7c9b50>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    np.linspace(-10, -2, 50), build_phases=phase_creator.build_phases\n",
    ")\n",
    "\n",
    "# this looks weird because have multiple phases and not sure which is which\n",
    "c.xge.pressure().plot(hue=\"phase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86",
   "metadata": {},
   "source": [
    "Note that this can be fixed by considering 'stable' phases only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x158682290>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    c.xge.table([\"mask_stable\", \"pressure\"], default_keys=None)\n",
    "    .assign(pressure_stable=lambda x: x[\"pressure\"].where(x[\"mask_stable\"]))\n",
    "    .pressure_stable.max(\"phase\")\n",
    "    .plot()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88",
   "metadata": {},
   "source": [
    "There are two clear phases here.  It would be beneficial to label the liquid and vapor phases differently.\n",
    "So we define the callback tag_phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tag_phases(list_of_phases):\n",
    "    \"\"\"\n",
    "    Simple tag_phases callback\n",
    "\n",
    "    This looks at the local maximum of each lnPiMasked object.\n",
    "\n",
    "    If location of maximum < len(data)/2 -> phase = 0\n",
    "    else -> phase = 1\n",
    "\n",
    "    \"\"\"\n",
    "    if len(list_of_phases) > 2:\n",
    "        msg = \"bad tag function\"\n",
    "        raise ValueError(msg)\n",
    "    argmax0 = np.array([xx.local_argmax()[0] for xx in list_of_phases])\n",
    "    return np.where(argmax0 <= list_of_phases[0].shape[0] / 2, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "90",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_creator = lnpy.PhaseCreator(\n",
    "    nmax=2, nmax_peak=4, ref=ref, merge_kws={\"efac\": 0.8}, tag_phases=tag_phases\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19a92ef90>,\n",
       " <matplotlib.lines.Line2D at 0x19a92eb50>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    np.linspace(-10, -2, 50), build_phases=phase_creator.build_phases\n",
    ")\n",
    "\n",
    "c.xge.pressure().plot(hue=\"phase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92",
   "metadata": {},
   "source": [
    "# Calculation Binodal and spinodal\n",
    "\n",
    "The binodal and spinodal can be found using the {mod}`lnpy.stability` module.  This module adds accessors to {class}`~lnpy.lnpiseries.lnPiCollection` class.  A collection is needed to provide a decent guess for the location of the binodal and spinodal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# must import stability to add the accessors to lnPiCollection\n",
    "import lnpy.stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "94",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import lnpy\n",
    "import lnpy.examples\n",
    "\n",
    "# Subcritical LJ data\n",
    "ref = lnpy.examples.load_example_lnpimasked(\"lj_sub\")\n",
    "\n",
    "phase_creator = lnpy.PhaseCreator(\n",
    "    nmax=2, nmax_peak=4, ref=ref, merge_kws={\"efac\": 0.8}, tag_phases=tag_phases\n",
    ")\n",
    "\n",
    "# Here we need a scalar builder object.\n",
    "build_phases = phase_creator.build_phases_mu([None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial guess\n",
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    np.linspace(-10, -2, 10), build_phases=phase_creator.build_phases_mu([None])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96",
   "metadata": {},
   "source": [
    "The spinodal is calculates as the location where $\\Delta w$ equals some value.  Lets say we want to define the spinodal\n",
    "as the location where $\\Delta w = 1.0$.  We do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "97",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = c.spinodal(\n",
    "    phase_ids=2,  # the id's of the phases we're considering\n",
    "    build_phases=phase_creator.build_phases_mu([None]),\n",
    "    # this is the efac parameter for\n",
    "    build_kws={\"efac\": 0.5},\n",
    "    inplace=True,\n",
    "    as_dict=False,\n",
    "    efac=1.0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "spinodal  lnz_0      phase\n",
       "0         -3.494734  0        [-3.494734034735128]\n",
       "                     1        [-3.494734034735128]\n",
       "1         -4.379828  0        [-4.379828176267564]\n",
       "                     1        [-4.379828176267564]\n",
       "dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to access the data as a collection, use the `access` attribute\n",
    "c.spinodal.access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spinodal  lnz_0      phase  phase_nebr\n",
       "0         -3.494734  0      0                    inf\n",
       "                            1               1.000000\n",
       "                     1      0             168.038523\n",
       "                            1                    inf\n",
       "1         -4.379828  0      0                    inf\n",
       "                            1             142.179651\n",
       "                     1      0               1.000000\n",
       "                            1                    inf\n",
       "Name: delta_w, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# energetics\n",
    "# spinodal 0 is the limit of stability for phase 0 -> phase 1\n",
    "# spinodal 1 is the limit of stability for phase 1 -> phase 0\n",
    "c.spinodal.access.wfe.dw"
   ]
  },
  {
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   "id": "100",
   "metadata": {},
   "source": [
    "Now that we have the spinodal, we can calculation the binodal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "101",
   "metadata": {},
   "outputs": [],
   "source": [
    "bino = c.binodal(\n",
    "    phase_ids=[0, 1],\n",
    "    build_phases=phase_creator.build_phases_mu([None]),\n",
    "    build_kws={\"efac\": 0.5},\n",
    "    inplace=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "binodal  lnz_0      phase\n",
       "0        -3.966438  0        [-3.966438059499727]\n",
       "                    1        [-3.966438059499727]\n",
       "dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access as collection with the `access` attribute\n",
    "c.binodal.access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "103",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pressure&#x27; (binodal: 1, lnz_0: 1, phase: 2)&gt; Size: 16B\n",
       "array([[[0.01594237, 0.01594237]]])\n",
       "Coordinates:\n",
       "  * binodal  (binodal) int64 8B 0\n",
       "  * lnz_0    (lnz_0) float64 8B -3.966\n",
       "  * phase    (phase) int64 16B 0 1\n",
       "    beta     float64 8B 1.372\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      [&#x27;n_0&#x27;]\n",
       "    dims_lnz:    [&#x27;lnz_0&#x27;]\n",
       "    dims_comp:   [&#x27;component&#x27;]\n",
       "    dims_state:  [&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    dims_rec:    [&#x27;sample&#x27;]\n",
       "    long_name:   $p(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pressure'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>binodal</span>: 1</li><li><span class='xr-has-index'>lnz_0</span>: 1</li><li><span class='xr-has-index'>phase</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-66efb6b6-6e1f-455d-9346-30a9ed339b0f' class='xr-array-in' type='checkbox' checked><label for='section-66efb6b6-6e1f-455d-9346-30a9ed339b0f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.01594 0.01594</span></div><div class='xr-array-data'><pre>array([[[0.01594237, 0.01594237]]])</pre></div></div></li><li class='xr-section-item'><input id='section-cf962aa5-901c-4307-9e6d-bde61d8ee47e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf962aa5-901c-4307-9e6d-bde61d8ee47e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>binodal</span></div><div class='xr-var-dims'>(binodal)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a1685bb9-a664-4960-93a6-dc4ff03e47f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1685bb9-a664-4960-93a6-dc4ff03e47f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd465eb-a58c-412d-8455-d2ffa46e2395' class='xr-var-data-in' type='checkbox'><label for='data-5bd465eb-a58c-412d-8455-d2ffa46e2395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_0</span></div><div class='xr-var-dims'>(lnz_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.966</div><input id='attrs-7d8ccc24-6144-4560-9b02-458683080b26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d8ccc24-6144-4560-9b02-458683080b26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52dfd3ad-0858-49b5-a203-d1b487539d2f' class='xr-var-data-in' type='checkbox'><label for='data-52dfd3ad-0858-49b5-a203-d1b487539d2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3.966438])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(phase)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-0652e930-26b8-4056-829b-d7902c7afc10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0652e930-26b8-4056-829b-d7902c7afc10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9266f19-15ab-4988-a1c9-159f2eff0b57' class='xr-var-data-in' type='checkbox'><label for='data-c9266f19-15ab-4988-a1c9-159f2eff0b57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.372</div><input id='attrs-c3e533f5-17db-45a4-a0c3-741b4279268e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3e533f5-17db-45a4-a0c3-741b4279268e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e975a8bd-e9ff-4f4e-9a1c-cb62d54e8d4e' class='xr-var-data-in' type='checkbox'><label for='data-e975a8bd-e9ff-4f4e-9a1c-cb62d54e8d4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.37228802)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>512.0</div><input id='attrs-2fdbb38b-68fd-493b-ae9c-effd2a5b18f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fdbb38b-68fd-493b-ae9c-effd2a5b18f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb1f228-73b1-4554-8833-6ba19aba695a' class='xr-var-data-in' type='checkbox'><label for='data-ccb1f228-73b1-4554-8833-6ba19aba695a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(512.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c6bfcf9-5651-4c39-b10c-172f00f98271' class='xr-section-summary-in' type='checkbox'  ><label for='section-7c6bfcf9-5651-4c39-b10c-172f00f98271' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>binodal</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1ca7450-41ad-4c36-b3ce-0725d90f571d' class='xr-index-data-in' type='checkbox'/><label for='index-c1ca7450-41ad-4c36-b3ce-0725d90f571d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;binodal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lnz_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3257a80e-0f72-492a-9fe0-60425bc58b94' class='xr-index-data-in' type='checkbox'/><label for='index-3257a80e-0f72-492a-9fe0-60425bc58b94' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-3.966438059499727], dtype=&#x27;float64&#x27;, name=&#x27;lnz_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>phase</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fdccd382-9440-4ac7-a9aa-a2a286bcaece' class='xr-index-data-in' type='checkbox'/><label for='index-fdccd382-9440-4ac7-a9aa-a2a286bcaece' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;phase&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3221e3ca-597f-491c-9b1a-fb9448b249ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3221e3ca-597f-491c-9b1a-fb9448b249ac' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>$p(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pressure' (binodal: 1, lnz_0: 1, phase: 2)> Size: 16B\n",
       "array([[[0.01594237, 0.01594237]]])\n",
       "Coordinates:\n",
       "  * binodal  (binodal) int64 8B 0\n",
       "  * lnz_0    (lnz_0) float64 8B -3.966\n",
       "  * phase    (phase) int64 16B 0 1\n",
       "    beta     float64 8B 1.372\n",
       "    volume   float64 8B 512.0\n",
       "Attributes:\n",
       "    dims_n:      ['n_0']\n",
       "    dims_lnz:    ['lnz_0']\n",
       "    dims_comp:   ['component']\n",
       "    dims_state:  ['lnz_0', 'beta', 'volume']\n",
       "    dims_rec:    ['sample']\n",
       "    long_name:   $p(\\mu,V,T)$"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# at the binodal, the two phases should have equal pressure\n",
    "c.binodal.access.xge.pressure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104",
   "metadata": {},
   "source": [
    "To append spinodal/binodal to the dataset, use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0       phase\n",
       "-10.000000  0                      [-10.0]\n",
       "-9.111111   0          [-9.11111111111111]\n",
       "-8.222222   0         [-8.222222222222221]\n",
       "-7.333333   0         [-7.333333333333334]\n",
       "-6.444444   0         [-6.444444444444445]\n",
       "-5.555556   0         [-5.555555555555555]\n",
       "-4.666667   0         [-4.666666666666667]\n",
       "-3.777778   0        [-3.7777777777777786]\n",
       "            1        [-3.7777777777777786]\n",
       "-2.888889   1        [-2.8888888888888893]\n",
       "-2.000000   1                       [-2.0]\n",
       "-3.494734   0         [-3.494734034735128]\n",
       "            1         [-3.494734034735128]\n",
       "-4.379828   0         [-4.379828176267564]\n",
       "            1         [-4.379828176267564]\n",
       "-3.966438   0         [-3.966438059499727]\n",
       "            1         [-3.966438059499727]\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_total = c.append(c.spinodal.appender).append(c.binodal.appender)\n",
    "c_total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106",
   "metadata": {},
   "source": [
    "and if you'd like to sort the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0       phase\n",
       "-10.000000  0                      [-10.0]\n",
       "-9.111111   0          [-9.11111111111111]\n",
       "-8.222222   0         [-8.222222222222221]\n",
       "-7.333333   0         [-7.333333333333334]\n",
       "-6.444444   0         [-6.444444444444445]\n",
       "-5.555556   0         [-5.555555555555555]\n",
       "-4.666667   0         [-4.666666666666667]\n",
       "-4.379828   0         [-4.379828176267564]\n",
       "            1         [-4.379828176267564]\n",
       "-3.966438   0         [-3.966438059499727]\n",
       "            1         [-3.966438059499727]\n",
       "-3.777778   0        [-3.7777777777777786]\n",
       "            1        [-3.7777777777777786]\n",
       "-3.494734   0         [-3.494734034735128]\n",
       "            1         [-3.494734034735128]\n",
       "-2.888889   1        [-2.8888888888888893]\n",
       "-2.000000   1                       [-2.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_total.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108",
   "metadata": {},
   "source": [
    "# Multi component systems\n",
    "\n",
    "lnPi is designed to work with single and multicomponent systems.   Let's take a look at a system without phase transitions to start.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "109",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lnpy.examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "110",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = lnpy.examples.load_example_dict(\"ljmix_sup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19a72a1d0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = plt.subplots(figsize=(4, 4))\n",
    "a.imshow(data[\"lnPi_data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112",
   "metadata": {},
   "source": [
    "We have finite data along the upper corner of the matrix 'lnPi_data'. Therefore, the base `lnPiMasked` object, without splitting into phases, also will have a mask.  This is why everything was build up from the `numpy.ma.MaskedArray` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lnPi_data': array([[-759.26449941, -756.19423037, -753.77081946, ..., -424.28411489,\n",
       "         -425.66274776, -426.93571412],\n",
       "        [-755.52637941, -752.42375982, -749.96557613, ..., -418.59148639,\n",
       "         -419.73805248,           nan],\n",
       "        [-752.45715941, -749.31971691, -746.82151608, ..., -413.26963103,\n",
       "         -414.54424182,           nan],\n",
       "        ...,\n",
       "        [-104.40479985, -102.4117824 ,           nan, ...,           nan,\n",
       "                   nan,           nan],\n",
       "        [-105.98668385,           nan,           nan, ...,           nan,\n",
       "                   nan,           nan],\n",
       "        [-107.62349885,           nan,           nan, ...,           nan,\n",
       "                   nan,           nan]]),\n",
       " 'lnPi_mask': array([[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False,  True],\n",
       "        [False, False, False, ..., False, False,  True],\n",
       "        ...,\n",
       "        [False, False,  True, ...,  True,  True,  True],\n",
       "        [False,  True,  True, ...,  True,  True,  True],\n",
       "        [False,  True,  True, ...,  True,  True,  True]]),\n",
       " 'state_kws': {'temp': 0.8, 'beta': 1.25, 'volume': 512},\n",
       " 'extra_kws': {},\n",
       " 'lnz': array([-2.5, -2.5])}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "114",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = lnpy.lnPiMasked.from_data(\n",
    "    lnz=data[\"lnz\"],\n",
    "    lnz_data=data[\"lnz\"],\n",
    "    data=data[\"lnPi_data\"],\n",
    "    mask=data[\"lnPi_mask\"],\n",
    "    state_kws=data[\"state_kws\"],\n",
    "    extra_kws=data[\"extra_kws\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115",
   "metadata": {},
   "source": [
    "Note that we could have also done:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "116",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = lnpy.lnPiMasked.from_data(\n",
    "    lnz=data[\"lnz\"],\n",
    "    lnz_data=data[\"lnz\"],\n",
    "    data=data[\"lnPi_data\"],\n",
    "    mask=np.isnan(data[\"lnPi_data\"]),\n",
    "    state_kws=data[\"state_kws\"],\n",
    "    extra_kws=data[\"extra_kws\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117",
   "metadata": {},
   "source": [
    "## Considering lines of constant $\\ln z$ or constant $\\Delta \\ln z$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118",
   "metadata": {},
   "source": [
    "Considering a multicomponent system will hopefully help explain why some design choices were made for `lnpy`.\n",
    "\n",
    "It is common to want to consider a multicomponent system along lines of constant 'something'.  For example, we might want to consider a spectrum of values of $\\ln z_0$ while holding $\\ln z_1$ constant.  This can be accomplished using some built in {class}`~lnpy.segment.PhaseCreator` constructors. \n",
    "\n",
    "First, note that if you know that a particular system will not have a phase transition, specify `nmax=1`.  This will make the system skip the `lnPi` segmentation, which is the slowest process in analyzing $\\ln \\Pi(N)$.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "119",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_creator = lnpy.PhaseCreator(nmax=1, ref=ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120",
   "metadata": {},
   "source": [
    "If you just call the `phase_creator.build_phases` constructor, we have to specify the total (vector) value of $\\ln z$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  lnz_1  phase\n",
       "0.0    0.0    0        [0.0, 0.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase_creator.build_phases(lnz=[0.0, 0.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122",
   "metadata": {},
   "source": [
    "This is fine.  But things like {class}`~lnpy.lnpiseries.lnPiCollection`, {class}`~lnpy.stability.Spinodals`, and {class}`~lnpy.stability.Binodals` are setup to work with scalar values of $\\ln z$.  So, we have the following constructors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "123",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_phases = phase_creator.build_phases_mu([None, -1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  lnz_1  phase\n",
       "0.0    -1.0   0        [0.0, -1.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_phases(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125",
   "metadata": {},
   "source": [
    "{meth}`~lnpy.segment.PhaseCreator.build_phases_mu` returns a constructor at fixed values of $\\ln z$ for all but one component. The component given a value of None (the first component in the example above) is the one we can vary.  Then calling this constructor will make a new collection with the requested value of $\\ln z$ for the variable component and the fixed values of $\\ln z$ for other components as defined in the constructor.  \n",
    "\n",
    "We can use this to define a collection easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  lnz_1  phase\n",
       "-3.0   -1.0   0        [-3.0, -1.0]\n",
       "-1.5   -1.0   0        [-1.5, -1.0]\n",
       " 0.0   -1.0   0         [0.0, -1.0]\n",
       " 1.5   -1.0   0         [1.5, -1.0]\n",
       " 3.0   -1.0   0         [3.0, -1.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = lnpy.lnPiCollection.from_builder(np.linspace(-3, 3, 5), build_phases)\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  lnz_1  phase\n",
       "0.5    -3.0   0        [0.5, -3.0]\n",
       "       -1.5   0        [0.5, -1.5]\n",
       "        0.0   0         [0.5, 0.0]\n",
       "        1.5   0         [0.5, 1.5]\n",
       "        3.0   0         [0.5, 3.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# At a different fixed value.  This time at fixed lnz_0\n",
    "build_phases = phase_creator.build_phases_mu([0.5, None])\n",
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    lnzs=np.linspace(-3, 3, 5), build_phases=build_phases\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128",
   "metadata": {},
   "source": [
    "Similarly, we can create `lnPi`s at fixed value of $\\Delta \\ln z$, where $\\Delta \\ln z_k = \\ln z_k - \\ln z_f$ where $f$ is the index of the variable component using the {meth}`~lnpy.segment.PhaseCreator.build_phases_dmu` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0  lnz_1  phase\n",
       "-3.0   -2.0   0        [-3.0, -2.0]\n",
       "-1.5   -0.5   0        [-1.5, -0.5]\n",
       " 0.0    1.0   0          [0.0, 1.0]\n",
       " 1.5    2.5   0          [1.5, 2.5]\n",
       " 3.0    4.0   0          [3.0, 4.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed value of dlnz_1 = lnz_1 - lnz_0 = 1.0\n",
    "build_phases = phase_creator.build_phases_dmu([None, 1.0])\n",
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    lnzs=np.linspace(-3, 3, 5), build_phases=build_phases\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "130",
   "metadata": {},
   "outputs": [],
   "source": [
    "lnz_0 = c.get_index_level(\"lnz_0\")\n",
    "lnz_1 = c.get_index_level(\"lnz_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([1.0, 1.0, 1.0, 1.0, 1.0], dtype='float64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lnz_1 - lnz_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132",
   "metadata": {},
   "source": [
    "## Multicomponent system with phase transitions\n",
    "\n",
    "Next, we consider a multicomponent system "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lnPi(lnz=[0.5 0.5])>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref = lnpy.examples.load_example_lnpimasked(\"hsmix\")\n",
    "ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x19aac1ed0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref.xge.lnpi().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "135",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to tag 'LD' and 'HD' phases\n",
    "def tag_phases(x):\n",
    "    if len(x) > 2:\n",
    "        msg = \"bad tag function\"\n",
    "        raise ValueError(msg)\n",
    "    argmax0 = np.array([xx.local_argmax()[0] for xx in x])\n",
    "    return np.where(argmax0 <= x[0].shape[0] / 2, 0, 1)\n",
    "\n",
    "\n",
    "phase_creator = lnpy.PhaseCreator(\n",
    "    nmax=2, nmax_peak=4, ref=ref, tag_phases=tag_phases, merge_kws={\"efac\": 0.8}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class lnPiCollection>\n",
       "lnz_0      lnz_1  phase\n",
       "-5.000000  0.0    0                      [-5.0, 0.0]\n",
       "-4.795918  0.0    0        [-4.795918367346939, 0.0]\n",
       "-4.591837  0.0    0        [-4.591836734693878, 0.0]\n",
       "-4.387755  0.0    0        [-4.387755102040816, 0.0]\n",
       "-4.183673  0.0    0        [-4.183673469387755, 0.0]\n",
       "                                     ...            \n",
       " 4.183673  0.0    1         [4.183673469387756, 0.0]\n",
       " 4.387755  0.0    1         [4.387755102040817, 0.0]\n",
       " 4.591837  0.0    1         [4.591836734693878, 0.0]\n",
       " 4.795918  0.0    1         [4.795918367346939, 0.0]\n",
       " 5.000000  0.0    1                       [5.0, 0.0]\n",
       "Length: 65, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_phases = phase_creator.build_phases_mu([None, 0.0])\n",
    "\n",
    "c = lnpy.lnPiCollection.from_builder(\n",
    "    np.linspace(-5, 5, 50), build_phases, unstack=False\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137",
   "metadata": {},
   "source": [
    "Note that here we have used the `unstack=False` option.  This means that the results from {attr}`lnpy.lnpiseries.lnPiCollection.xge` will *not* be unstacked.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "138",
   "metadata": {},
   "outputs": [
    {
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       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;betaOmega&#x27; (sample: 65)&gt; Size: 520B\n",
       "array([-1246.12388491, -1246.12448998, -1246.12523222, -1246.12614278,\n",
       "       -1246.12725991, -1246.1286306 , -1246.13031256, -1246.13237676,\n",
       "       -1246.13491046, -1246.13802107, -1246.14184087, -1246.14653294,\n",
       "       -1246.15229859, -1246.1593867 , -1246.16810551, -1246.1788377 ,\n",
       "       -1246.19205972, -1010.9087438 , -1246.20836701, -1054.37219518,\n",
       "       -1246.22850704, -1098.99423283, -1246.25342379, -1144.4775693 ,\n",
       "       -1246.28431829, -1190.70729484, -1246.32273372, -1237.62335537,\n",
       "       -1246.37067871, -1285.1875377 , -1246.43081409, -1333.37342804,\n",
       "       -1246.50675309, -1382.15798324, -1246.60358765, -1431.51759825,\n",
       "       -1246.72896095, -1481.42620374, -1246.89609727, -1531.84252007,\n",
       "       -1247.1319439 , -1582.68976406, -1247.47204378, -1633.86017062,\n",
       "       -1247.86446355, -1685.24884688, -1736.77785101, -1788.39701972,\n",
       "       -1840.07559237, -1891.79461328, -1943.54206924, -1995.3100774 ,\n",
       "       -2047.093286  , -2098.88794914, -2150.69137525, -2202.50158717,\n",
       "       -2254.3171055 , -2306.13680626, -2357.95982463, -2409.78548824,\n",
       "       -2461.61326981, -2513.44275296, -2565.27360693, -2617.10556771,\n",
       "       -2668.9384238 ])\n",
       "Coordinates:\n",
       "  * sample   (sample) object 520B MultiIndex\n",
       "  * lnz_0    (sample) float64 520B -5.0 -4.796 -4.592 -4.388 ... 4.592 4.796 5.0\n",
       "  * lnz_1    (sample) float64 520B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "  * phase    (sample) int64 520B 0 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1 1\n",
       "    beta     float64 8B 1.0\n",
       "    volume   float64 8B 1.0\n",
       "Attributes:\n",
       "    dims_n:         [&#x27;n_0&#x27;, &#x27;n_1&#x27;]\n",
       "    dims_lnz:       [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]\n",
       "    dims_comp:      [&#x27;component&#x27;]\n",
       "    dims_state:     [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    dims_rec:       [&#x27;sample&#x27;]\n",
       "    standard_name:  grand_potential\n",
       "    long_name:      $\\beta \\Omega(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'betaOmega'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 65</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f15f18c5-51f0-4907-914c-c9c1a64bc504' class='xr-array-in' type='checkbox' checked><label for='section-f15f18c5-51f0-4907-914c-c9c1a64bc504' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.246e+03 -1.246e+03 -1.246e+03 ... -2.565e+03 -2.617e+03 -2.669e+03</span></div><div class='xr-array-data'><pre>array([-1246.12388491, -1246.12448998, -1246.12523222, -1246.12614278,\n",
       "       -1246.12725991, -1246.1286306 , -1246.13031256, -1246.13237676,\n",
       "       -1246.13491046, -1246.13802107, -1246.14184087, -1246.14653294,\n",
       "       -1246.15229859, -1246.1593867 , -1246.16810551, -1246.1788377 ,\n",
       "       -1246.19205972, -1010.9087438 , -1246.20836701, -1054.37219518,\n",
       "       -1246.22850704, -1098.99423283, -1246.25342379, -1144.4775693 ,\n",
       "       -1246.28431829, -1190.70729484, -1246.32273372, -1237.62335537,\n",
       "       -1246.37067871, -1285.1875377 , -1246.43081409, -1333.37342804,\n",
       "       -1246.50675309, -1382.15798324, -1246.60358765, -1431.51759825,\n",
       "       -1246.72896095, -1481.42620374, -1246.89609727, -1531.84252007,\n",
       "       -1247.1319439 , -1582.68976406, -1247.47204378, -1633.86017062,\n",
       "       -1247.86446355, -1685.24884688, -1736.77785101, -1788.39701972,\n",
       "       -1840.07559237, -1891.79461328, -1943.54206924, -1995.3100774 ,\n",
       "       -2047.093286  , -2098.88794914, -2150.69137525, -2202.50158717,\n",
       "       -2254.3171055 , -2306.13680626, -2357.95982463, -2409.78548824,\n",
       "       -2461.61326981, -2513.44275296, -2565.27360693, -2617.10556771,\n",
       "       -2668.9384238 ])</pre></div></div></li><li class='xr-section-item'><input id='section-8673932b-b133-4c46-a829-1d5884e176e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8673932b-b133-4c46-a829-1d5884e176e8' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-15875eca-5251-4c57-827c-e5e9c02e66d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15875eca-5251-4c57-827c-e5e9c02e66d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f8f7fe-2bf8-4ab5-a4e2-8d0473cd8079' class='xr-var-data-in' type='checkbox'><label for='data-97f8f7fe-2bf8-4ab5-a4e2-8d0473cd8079' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(-5.0, 0.0, 0), (-4.795918367346939, 0.0, 0),\n",
       "       (-4.591836734693878, 0.0, 0), (-4.387755102040816, 0.0, 0),\n",
       "       (-4.183673469387755, 0.0, 0), (-3.979591836734694, 0.0, 0),\n",
       "       (-3.7755102040816326, 0.0, 0), (-3.571428571428571, 0.0, 0),\n",
       "       (-3.36734693877551, 0.0, 0), (-3.163265306122449, 0.0, 0),\n",
       "       (-2.9591836734693877, 0.0, 0), (-2.7551020408163263, 0.0, 0),\n",
       "       (-2.5510204081632653, 0.0, 0), (-2.3469387755102042, 0.0, 0),\n",
       "       (-2.142857142857143, 0.0, 0), (-1.9387755102040813, 0.0, 0),\n",
       "       (-1.7346938775510203, 0.0, 0), (-1.7346938775510203, 0.0, 1),\n",
       "       (-1.5306122448979593, 0.0, 0), (-1.5306122448979593, 0.0, 1),\n",
       "       (-1.3265306122448979, 0.0, 0), (-1.3265306122448979, 0.0, 1),\n",
       "       (-1.1224489795918364, 0.0, 0), (-1.1224489795918364, 0.0, 1),\n",
       "       (-0.9183673469387754, 0.0, 0), (-0.9183673469387754, 0.0, 1),\n",
       "       (-0.7142857142857144, 0.0, 0), (-0.7142857142857144, 0.0, 1),\n",
       "       (-0.5102040816326525, 0.0, 0), (-0.5102040816326525, 0.0, 1),\n",
       "       (-0.3061224489795915, 0.0, 0), (-0.3061224489795915, 0.0, 1),\n",
       "       (-0.1020408163265305, 0.0, 0), (-0.1020408163265305, 0.0, 1),\n",
       "       (0.1020408163265305, 0.0, 0), (0.1020408163265305, 0.0, 1),\n",
       "       (0.3061224489795915, 0.0, 0), (0.3061224489795915, 0.0, 1),\n",
       "       (0.5102040816326534, 0.0, 0), (0.5102040816326534, 0.0, 1),\n",
       "       (0.7142857142857144, 0.0, 0), (0.7142857142857144, 0.0, 1),\n",
       "       (0.9183673469387754, 0.0, 0), (0.9183673469387754, 0.0, 1),\n",
       "       (1.1224489795918373, 0.0, 0), (1.1224489795918373, 0.0, 1),\n",
       "       (1.3265306122448983, 0.0, 1), (1.5306122448979593, 0.0, 1),\n",
       "       (1.7346938775510203, 0.0, 1), (1.9387755102040813, 0.0, 1),\n",
       "       (2.1428571428571432, 0.0, 1), (2.3469387755102042, 0.0, 1),\n",
       "       (2.5510204081632653, 0.0, 1), (2.755102040816327, 0.0, 1),\n",
       "       (2.959183673469388, 0.0, 1), (3.163265306122449, 0.0, 1),\n",
       "       (3.36734693877551, 0.0, 1), (3.571428571428571, 0.0, 1),\n",
       "       (3.775510204081632, 0.0, 1), (3.979591836734695, 0.0, 1),\n",
       "       (4.183673469387756, 0.0, 1), (4.387755102040817, 0.0, 1),\n",
       "       (4.591836734693878, 0.0, 1), (4.795918367346939, 0.0, 1), (5.0, 0.0, 1)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_0</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.0 -4.796 -4.592 ... 4.796 5.0</div><input id='attrs-618b7fe0-1a66-4912-b312-a2ba4687319b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-618b7fe0-1a66-4912-b312-a2ba4687319b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d4f245-3c15-4425-a0f9-536f567760ea' class='xr-var-data-in' type='checkbox'><label for='data-89d4f245-3c15-4425-a0f9-536f567760ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.      , -4.795918, -4.591837, -4.387755, -4.183673, -3.979592,\n",
       "       -3.77551 , -3.571429, -3.367347, -3.163265, -2.959184, -2.755102,\n",
       "       -2.55102 , -2.346939, -2.142857, -1.938776, -1.734694, -1.734694,\n",
       "       -1.530612, -1.530612, -1.326531, -1.326531, -1.122449, -1.122449,\n",
       "       -0.918367, -0.918367, -0.714286, -0.714286, -0.510204, -0.510204,\n",
       "       -0.306122, -0.306122, -0.102041, -0.102041,  0.102041,  0.102041,\n",
       "        0.306122,  0.306122,  0.510204,  0.510204,  0.714286,  0.714286,\n",
       "        0.918367,  0.918367,  1.122449,  1.122449,  1.326531,  1.530612,\n",
       "        1.734694,  1.938776,  2.142857,  2.346939,  2.55102 ,  2.755102,\n",
       "        2.959184,  3.163265,  3.367347,  3.571429,  3.77551 ,  3.979592,\n",
       "        4.183673,  4.387755,  4.591837,  4.795918,  5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-62fc0dba-7300-4138-80fe-6a8450677f7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62fc0dba-7300-4138-80fe-6a8450677f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b33f4bf8-8771-4214-814d-25f7bff7063b' class='xr-var-data-in' type='checkbox'><label for='data-b33f4bf8-8771-4214-814d-25f7bff7063b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-842c61f5-afcf-4352-9d08-0be37b6fa1cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-842c61f5-afcf-4352-9d08-0be37b6fa1cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2101fd0d-7c1d-4317-a861-69529804883f' class='xr-var-data-in' type='checkbox'><label for='data-2101fd0d-7c1d-4317-a861-69529804883f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-c51b943e-36b0-4e34-b253-65fe1799f97f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c51b943e-36b0-4e34-b253-65fe1799f97f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8273a56-129e-44b4-8811-5e963d14986c' class='xr-var-data-in' type='checkbox'><label for='data-f8273a56-129e-44b4-8811-5e963d14986c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-cdbc3e74-e69b-479e-baee-c6eb7c074142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdbc3e74-e69b-479e-baee-c6eb7c074142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e81daed-91a7-4f30-ab1e-68da61ce5dca' class='xr-var-data-in' type='checkbox'><label for='data-4e81daed-91a7-4f30-ab1e-68da61ce5dca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdcb69a0-d90d-41b8-a023-b6d40c9864ef' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdcb69a0-d90d-41b8-a023-b6d40c9864ef' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>lnz_0<br>lnz_1<br>phase</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-a511d444-09b2-4300-a173-99f49582b41a' class='xr-index-data-in' type='checkbox'/><label for='index-a511d444-09b2-4300-a173-99f49582b41a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(               -5.0, 0.0, 0),\n",
       "            ( -4.795918367346939, 0.0, 0),\n",
       "            ( -4.591836734693878, 0.0, 0),\n",
       "            ( -4.387755102040816, 0.0, 0),\n",
       "            ( -4.183673469387755, 0.0, 0),\n",
       "            ( -3.979591836734694, 0.0, 0),\n",
       "            (-3.7755102040816326, 0.0, 0),\n",
       "            ( -3.571428571428571, 0.0, 0),\n",
       "            (  -3.36734693877551, 0.0, 0),\n",
       "            ( -3.163265306122449, 0.0, 0),\n",
       "            (-2.9591836734693877, 0.0, 0),\n",
       "            (-2.7551020408163263, 0.0, 0),\n",
       "            (-2.5510204081632653, 0.0, 0),\n",
       "            (-2.3469387755102042, 0.0, 0),\n",
       "            ( -2.142857142857143, 0.0, 0),\n",
       "            (-1.9387755102040813, 0.0, 0),\n",
       "            (-1.7346938775510203, 0.0, 0),\n",
       "            (-1.7346938775510203, 0.0, 1),\n",
       "            (-1.5306122448979593, 0.0, 0),\n",
       "            (-1.5306122448979593, 0.0, 1),\n",
       "            (-1.3265306122448979, 0.0, 0),\n",
       "            (-1.3265306122448979, 0.0, 1),\n",
       "            (-1.1224489795918364, 0.0, 0),\n",
       "            (-1.1224489795918364, 0.0, 1),\n",
       "            (-0.9183673469387754, 0.0, 0),\n",
       "            (-0.9183673469387754, 0.0, 1),\n",
       "            (-0.7142857142857144, 0.0, 0),\n",
       "            (-0.7142857142857144, 0.0, 1),\n",
       "            (-0.5102040816326525, 0.0, 0),\n",
       "            (-0.5102040816326525, 0.0, 1),\n",
       "            (-0.3061224489795915, 0.0, 0),\n",
       "            (-0.3061224489795915, 0.0, 1),\n",
       "            (-0.1020408163265305, 0.0, 0),\n",
       "            (-0.1020408163265305, 0.0, 1),\n",
       "            ( 0.1020408163265305, 0.0, 0),\n",
       "            ( 0.1020408163265305, 0.0, 1),\n",
       "            ( 0.3061224489795915, 0.0, 0),\n",
       "            ( 0.3061224489795915, 0.0, 1),\n",
       "            ( 0.5102040816326534, 0.0, 0),\n",
       "            ( 0.5102040816326534, 0.0, 1),\n",
       "            ( 0.7142857142857144, 0.0, 0),\n",
       "            ( 0.7142857142857144, 0.0, 1),\n",
       "            ( 0.9183673469387754, 0.0, 0),\n",
       "            ( 0.9183673469387754, 0.0, 1),\n",
       "            ( 1.1224489795918373, 0.0, 0),\n",
       "            ( 1.1224489795918373, 0.0, 1),\n",
       "            ( 1.3265306122448983, 0.0, 1),\n",
       "            ( 1.5306122448979593, 0.0, 1),\n",
       "            ( 1.7346938775510203, 0.0, 1),\n",
       "            ( 1.9387755102040813, 0.0, 1),\n",
       "            ( 2.1428571428571432, 0.0, 1),\n",
       "            ( 2.3469387755102042, 0.0, 1),\n",
       "            ( 2.5510204081632653, 0.0, 1),\n",
       "            (  2.755102040816327, 0.0, 1),\n",
       "            (  2.959183673469388, 0.0, 1),\n",
       "            (  3.163265306122449, 0.0, 1),\n",
       "            (   3.36734693877551, 0.0, 1),\n",
       "            (  3.571428571428571, 0.0, 1),\n",
       "            (  3.775510204081632, 0.0, 1),\n",
       "            (  3.979591836734695, 0.0, 1),\n",
       "            (  4.183673469387756, 0.0, 1),\n",
       "            (  4.387755102040817, 0.0, 1),\n",
       "            (  4.591836734693878, 0.0, 1),\n",
       "            (  4.795918367346939, 0.0, 1),\n",
       "            (                5.0, 0.0, 1)],\n",
       "           name=&#x27;sample&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6dc83332-9239-4a75-b26c-9378b9f8323c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6dc83332-9239-4a75-b26c-9378b9f8323c' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>standard_name :</span></dt><dd>grand_potential</dd><dt><span>long_name :</span></dt><dd>$\\beta \\Omega(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'betaOmega' (sample: 65)> Size: 520B\n",
       "array([-1246.12388491, -1246.12448998, -1246.12523222, -1246.12614278,\n",
       "       -1246.12725991, -1246.1286306 , -1246.13031256, -1246.13237676,\n",
       "       -1246.13491046, -1246.13802107, -1246.14184087, -1246.14653294,\n",
       "       -1246.15229859, -1246.1593867 , -1246.16810551, -1246.1788377 ,\n",
       "       -1246.19205972, -1010.9087438 , -1246.20836701, -1054.37219518,\n",
       "       -1246.22850704, -1098.99423283, -1246.25342379, -1144.4775693 ,\n",
       "       -1246.28431829, -1190.70729484, -1246.32273372, -1237.62335537,\n",
       "       -1246.37067871, -1285.1875377 , -1246.43081409, -1333.37342804,\n",
       "       -1246.50675309, -1382.15798324, -1246.60358765, -1431.51759825,\n",
       "       -1246.72896095, -1481.42620374, -1246.89609727, -1531.84252007,\n",
       "       -1247.1319439 , -1582.68976406, -1247.47204378, -1633.86017062,\n",
       "       -1247.86446355, -1685.24884688, -1736.77785101, -1788.39701972,\n",
       "       -1840.07559237, -1891.79461328, -1943.54206924, -1995.3100774 ,\n",
       "       -2047.093286  , -2098.88794914, -2150.69137525, -2202.50158717,\n",
       "       -2254.3171055 , -2306.13680626, -2357.95982463, -2409.78548824,\n",
       "       -2461.61326981, -2513.44275296, -2565.27360693, -2617.10556771,\n",
       "       -2668.9384238 ])\n",
       "Coordinates:\n",
       "  * sample   (sample) object 520B MultiIndex\n",
       "  * lnz_0    (sample) float64 520B -5.0 -4.796 -4.592 -4.388 ... 4.592 4.796 5.0\n",
       "  * lnz_1    (sample) float64 520B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "  * phase    (sample) int64 520B 0 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1 1\n",
       "    beta     float64 8B 1.0\n",
       "    volume   float64 8B 1.0\n",
       "Attributes:\n",
       "    dims_n:         ['n_0', 'n_1']\n",
       "    dims_lnz:       ['lnz_0', 'lnz_1']\n",
       "    dims_comp:      ['component']\n",
       "    dims_state:     ['lnz_0', 'lnz_1', 'beta', 'volume']\n",
       "    dims_rec:       ['sample']\n",
       "    standard_name:  grand_potential\n",
       "    long_name:      $\\beta \\Omega(\\mu,V,T)$"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.xge.betaOmega()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139",
   "metadata": {},
   "source": [
    "To 'unstack' from 'sample' dimension to 'lnz_0', 'lnz_1', etc, you can call `unstack` directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "140",
   "metadata": {},
   "outputs": [
    {
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       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;betaOmega&#x27; (lnz_0: 50, lnz_1: 1, phase: 2)&gt; Size: 800B\n",
       "array([[[-1246.12388491,            nan]],\n",
       "\n",
       "       [[-1246.12448998,            nan]],\n",
       "\n",
       "       [[-1246.12523222,            nan]],\n",
       "\n",
       "       [[-1246.12614278,            nan]],\n",
       "\n",
       "       [[-1246.12725991,            nan]],\n",
       "\n",
       "       [[-1246.1286306 ,            nan]],\n",
       "\n",
       "       [[-1246.13031256,            nan]],\n",
       "\n",
       "       [[-1246.13237676,            nan]],\n",
       "\n",
       "       [[-1246.13491046,            nan]],\n",
       "\n",
       "       [[-1246.13802107,            nan]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[           nan, -2202.50158717]],\n",
       "\n",
       "       [[           nan, -2254.3171055 ]],\n",
       "\n",
       "       [[           nan, -2306.13680626]],\n",
       "\n",
       "       [[           nan, -2357.95982463]],\n",
       "\n",
       "       [[           nan, -2409.78548824]],\n",
       "\n",
       "       [[           nan, -2461.61326981]],\n",
       "\n",
       "       [[           nan, -2513.44275296]],\n",
       "\n",
       "       [[           nan, -2565.27360693]],\n",
       "\n",
       "       [[           nan, -2617.10556771]],\n",
       "\n",
       "       [[           nan, -2668.9384238 ]]])\n",
       "Coordinates:\n",
       "  * lnz_0    (lnz_0) float64 400B -5.0 -4.796 -4.592 -4.388 ... 4.592 4.796 5.0\n",
       "  * lnz_1    (lnz_1) float64 8B 0.0\n",
       "  * phase    (phase) int64 16B 0 1\n",
       "    beta     float64 8B 1.0\n",
       "    volume   float64 8B 1.0\n",
       "Attributes:\n",
       "    dims_n:         [&#x27;n_0&#x27;, &#x27;n_1&#x27;]\n",
       "    dims_lnz:       [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]\n",
       "    dims_comp:      [&#x27;component&#x27;]\n",
       "    dims_state:     [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    dims_rec:       [&#x27;sample&#x27;]\n",
       "    standard_name:  grand_potential\n",
       "    long_name:      $\\beta \\Omega(\\mu,V,T)$</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'betaOmega'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lnz_0</span>: 50</li><li><span class='xr-has-index'>lnz_1</span>: 1</li><li><span class='xr-has-index'>phase</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6353d70b-5555-422a-8694-84675a06dac6' class='xr-array-in' type='checkbox' checked><label for='section-6353d70b-5555-422a-8694-84675a06dac6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.246e+03 nan -1.246e+03 nan ... nan -2.617e+03 nan -2.669e+03</span></div><div class='xr-array-data'><pre>array([[[-1246.12388491,            nan]],\n",
       "\n",
       "       [[-1246.12448998,            nan]],\n",
       "\n",
       "       [[-1246.12523222,            nan]],\n",
       "\n",
       "       [[-1246.12614278,            nan]],\n",
       "\n",
       "       [[-1246.12725991,            nan]],\n",
       "\n",
       "       [[-1246.1286306 ,            nan]],\n",
       "\n",
       "       [[-1246.13031256,            nan]],\n",
       "\n",
       "       [[-1246.13237676,            nan]],\n",
       "\n",
       "       [[-1246.13491046,            nan]],\n",
       "\n",
       "       [[-1246.13802107,            nan]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[           nan, -2202.50158717]],\n",
       "\n",
       "       [[           nan, -2254.3171055 ]],\n",
       "\n",
       "       [[           nan, -2306.13680626]],\n",
       "\n",
       "       [[           nan, -2357.95982463]],\n",
       "\n",
       "       [[           nan, -2409.78548824]],\n",
       "\n",
       "       [[           nan, -2461.61326981]],\n",
       "\n",
       "       [[           nan, -2513.44275296]],\n",
       "\n",
       "       [[           nan, -2565.27360693]],\n",
       "\n",
       "       [[           nan, -2617.10556771]],\n",
       "\n",
       "       [[           nan, -2668.9384238 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-167602d3-d01f-44ff-a39e-b1af2ea6928d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-167602d3-d01f-44ff-a39e-b1af2ea6928d' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_0</span></div><div class='xr-var-dims'>(lnz_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.0 -4.796 -4.592 ... 4.796 5.0</div><input id='attrs-fd89c337-77c3-4bef-9840-cda7ee3fada6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd89c337-77c3-4bef-9840-cda7ee3fada6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a32900b-4ee8-42fb-8fc0-e31e7ff3c157' class='xr-var-data-in' type='checkbox'><label for='data-7a32900b-4ee8-42fb-8fc0-e31e7ff3c157' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.      , -4.795918, -4.591837, -4.387755, -4.183673, -3.979592,\n",
       "       -3.77551 , -3.571429, -3.367347, -3.163265, -2.959184, -2.755102,\n",
       "       -2.55102 , -2.346939, -2.142857, -1.938776, -1.734694, -1.530612,\n",
       "       -1.326531, -1.122449, -0.918367, -0.714286, -0.510204, -0.306122,\n",
       "       -0.102041,  0.102041,  0.306122,  0.510204,  0.714286,  0.918367,\n",
       "        1.122449,  1.326531,  1.530612,  1.734694,  1.938776,  2.142857,\n",
       "        2.346939,  2.55102 ,  2.755102,  2.959184,  3.163265,  3.367347,\n",
       "        3.571429,  3.77551 ,  3.979592,  4.183673,  4.387755,  4.591837,\n",
       "        4.795918,  5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_1</span></div><div class='xr-var-dims'>(lnz_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-6b02759b-03b9-4246-b8e1-ba1a6e6b1dc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b02759b-03b9-4246-b8e1-ba1a6e6b1dc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3807430-1fe3-4fd5-9d03-1500d39ae34c' class='xr-var-data-in' type='checkbox'><label for='data-e3807430-1fe3-4fd5-9d03-1500d39ae34c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(phase)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-415f7333-ba96-4746-9fd5-c89d3e7029dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-415f7333-ba96-4746-9fd5-c89d3e7029dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f36de276-e9b6-4747-885f-15872be7a55d' class='xr-var-data-in' type='checkbox'><label for='data-f36de276-e9b6-4747-885f-15872be7a55d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-9079da24-575b-497a-8288-fde7436ba300' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9079da24-575b-497a-8288-fde7436ba300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b5d1393-329e-4d64-8c61-d4c7bfbaebf9' class='xr-var-data-in' type='checkbox'><label for='data-9b5d1393-329e-4d64-8c61-d4c7bfbaebf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-8412e4ae-07b9-4ac5-9a88-29c856ab7287' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8412e4ae-07b9-4ac5-9a88-29c856ab7287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52ead014-9939-4b49-846a-d0679e431d95' class='xr-var-data-in' type='checkbox'><label for='data-52ead014-9939-4b49-846a-d0679e431d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d4ae495-6083-40b6-95ff-9fdca0ef673f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d4ae495-6083-40b6-95ff-9fdca0ef673f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lnz_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4603b85-95f5-4865-bf28-5163193964f5' class='xr-index-data-in' type='checkbox'/><label for='index-e4603b85-95f5-4865-bf28-5163193964f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               -5.0,  -4.795918367346939,  -4.591836734693878,\n",
       "        -4.387755102040816,  -4.183673469387755,  -3.979591836734694,\n",
       "       -3.7755102040816326,  -3.571428571428571,   -3.36734693877551,\n",
       "        -3.163265306122449, -2.9591836734693877, -2.7551020408163263,\n",
       "       -2.5510204081632653, -2.3469387755102042,  -2.142857142857143,\n",
       "       -1.9387755102040813, -1.7346938775510203, -1.5306122448979593,\n",
       "       -1.3265306122448979, -1.1224489795918364, -0.9183673469387754,\n",
       "       -0.7142857142857144, -0.5102040816326525, -0.3061224489795915,\n",
       "       -0.1020408163265305,  0.1020408163265305,  0.3061224489795915,\n",
       "        0.5102040816326534,  0.7142857142857144,  0.9183673469387754,\n",
       "        1.1224489795918373,  1.3265306122448983,  1.5306122448979593,\n",
       "        1.7346938775510203,  1.9387755102040813,  2.1428571428571432,\n",
       "        2.3469387755102042,  2.5510204081632653,   2.755102040816327,\n",
       "         2.959183673469388,   3.163265306122449,    3.36734693877551,\n",
       "         3.571428571428571,   3.775510204081632,   3.979591836734695,\n",
       "         4.183673469387756,   4.387755102040817,   4.591836734693878,\n",
       "         4.795918367346939,                 5.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lnz_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lnz_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f208cd1e-a7f3-4e76-975c-12366ccfacee' class='xr-index-data-in' type='checkbox'/><label for='index-f208cd1e-a7f3-4e76-975c-12366ccfacee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;lnz_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>phase</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8e7a169-3727-4648-bf2b-ebb9f81dd932' class='xr-index-data-in' type='checkbox'/><label for='index-b8e7a169-3727-4648-bf2b-ebb9f81dd932' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;phase&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dad063f9-262a-45e8-b6ea-09d7a54cd8ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dad063f9-262a-45e8-b6ea-09d7a54cd8ed' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>standard_name :</span></dt><dd>grand_potential</dd><dt><span>long_name :</span></dt><dd>$\\beta \\Omega(\\mu,V,T)$</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'betaOmega' (lnz_0: 50, lnz_1: 1, phase: 2)> Size: 800B\n",
       "array([[[-1246.12388491,            nan]],\n",
       "\n",
       "       [[-1246.12448998,            nan]],\n",
       "\n",
       "       [[-1246.12523222,            nan]],\n",
       "\n",
       "       [[-1246.12614278,            nan]],\n",
       "\n",
       "       [[-1246.12725991,            nan]],\n",
       "\n",
       "       [[-1246.1286306 ,            nan]],\n",
       "\n",
       "       [[-1246.13031256,            nan]],\n",
       "\n",
       "       [[-1246.13237676,            nan]],\n",
       "\n",
       "       [[-1246.13491046,            nan]],\n",
       "\n",
       "       [[-1246.13802107,            nan]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[           nan, -2202.50158717]],\n",
       "\n",
       "       [[           nan, -2254.3171055 ]],\n",
       "\n",
       "       [[           nan, -2306.13680626]],\n",
       "\n",
       "       [[           nan, -2357.95982463]],\n",
       "\n",
       "       [[           nan, -2409.78548824]],\n",
       "\n",
       "       [[           nan, -2461.61326981]],\n",
       "\n",
       "       [[           nan, -2513.44275296]],\n",
       "\n",
       "       [[           nan, -2565.27360693]],\n",
       "\n",
       "       [[           nan, -2617.10556771]],\n",
       "\n",
       "       [[           nan, -2668.9384238 ]]])\n",
       "Coordinates:\n",
       "  * lnz_0    (lnz_0) float64 400B -5.0 -4.796 -4.592 -4.388 ... 4.592 4.796 5.0\n",
       "  * lnz_1    (lnz_1) float64 8B 0.0\n",
       "  * phase    (phase) int64 16B 0 1\n",
       "    beta     float64 8B 1.0\n",
       "    volume   float64 8B 1.0\n",
       "Attributes:\n",
       "    dims_n:         ['n_0', 'n_1']\n",
       "    dims_lnz:       ['lnz_0', 'lnz_1']\n",
       "    dims_comp:      ['component']\n",
       "    dims_state:     ['lnz_0', 'lnz_1', 'beta', 'volume']\n",
       "    dims_rec:       ['sample']\n",
       "    standard_name:  grand_potential\n",
       "    long_name:      $\\beta \\Omega(\\mu,V,T)$"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.xge.betaOmega().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19abcc8d0>,\n",
       " <matplotlib.lines.Line2D at 0x19ab79790>]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.xge.betaOmega().unstack().plot(hue=\"phase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "142",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spinodal along line of constant lnz_2\n",
    "\n",
    "_ = c.spinodal(phase_ids=[0, 1], build_phases=build_phases, efac=1.0, inplace=True)\n",
    "_ = c.binodal(phase_ids=[0, 1], build_phases=build_phases, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "143",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create table for spinodal/binodal\n",
    "\n",
    "t_spin = c.spinodal.access.xge.table([\"molfrac\"], ref=ref)\n",
    "t_bino = c.binodal.access.xge.table([\"molfrac\"], ref=ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "144",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 368B\n",
       "Dimensions:        (sample: 4, component: 2)\n",
       "Coordinates:\n",
       "  * sample         (sample) object 32B MultiIndex\n",
       "  * spinodal       (sample) int64 32B 0 0 1 1\n",
       "  * lnz_0          (sample) float64 32B 1.202 1.202 -1.806 -1.806\n",
       "  * lnz_1          (sample) float64 32B 0.0 0.0 0.0 0.0\n",
       "  * phase          (sample) int64 32B 0 1 0 1\n",
       "    beta           float64 8B 1.0\n",
       "    volume         float64 8B 1.0\n",
       "Dimensions without coordinates: component\n",
       "Data variables:\n",
       "    edge_distance  (sample) float64 32B 31.11 1.0 31.11 29.7\n",
       "    molfrac        (sample, component) float64 64B 0.01297 0.987 ... 0.02102\n",
       "    nvec           (sample, component) float64 64B 2.715 206.5 ... 206.7 4.438\n",
       "    betapV         (sample) float64 32B 1.248e+03 1.705e+03 1.246e+03 996.1\n",
       "Attributes:\n",
       "    dims_n:      [&#x27;n_0&#x27;, &#x27;n_1&#x27;]\n",
       "    dims_lnz:    [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]\n",
       "    dims_comp:   [&#x27;component&#x27;]\n",
       "    dims_state:  [&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]\n",
       "    dims_rec:    [&#x27;sample&#x27;]\n",
       "    long_name:   distance from upper edge</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-42cdf7fd-2357-4d80-8e2c-00a810b49dd5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42cdf7fd-2357-4d80-8e2c-00a810b49dd5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 4</li><li><span>component</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4c0eb54b-3495-4f78-a42f-6b60a2587086' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c0eb54b-3495-4f78-a42f-6b60a2587086' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-bd7cdc29-6aff-4708-9d01-42e13a96d25c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd7cdc29-6aff-4708-9d01-42e13a96d25c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2294c4d4-165f-463c-97e1-94bfaef7a543' class='xr-var-data-in' type='checkbox'><label for='data-2294c4d4-165f-463c-97e1-94bfaef7a543' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(0, 1.2015754333333324, 0.0, 0), (0, 1.2015754333333324, 0.0, 1),\n",
       "       (1, -1.8057242053333276, 0.0, 0), (1, -1.8057242053333276, 0.0, 1)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spinodal</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 1 1</div><input id='attrs-25852891-3521-459d-a46b-2d2f37c667aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25852891-3521-459d-a46b-2d2f37c667aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e074947c-6856-4b5e-b8c1-71ead07fdb03' class='xr-var-data-in' type='checkbox'><label for='data-e074947c-6856-4b5e-b8c1-71ead07fdb03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_0</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.202 1.202 -1.806 -1.806</div><input id='attrs-04b28da7-9ae9-48ad-9dd4-80e8dc89bb93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04b28da7-9ae9-48ad-9dd4-80e8dc89bb93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dffc59c-fa88-488b-bc2c-4d699bf77a0d' class='xr-var-data-in' type='checkbox'><label for='data-3dffc59c-fa88-488b-bc2c-4d699bf77a0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.201575,  1.201575, -1.805724, -1.805724])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lnz_1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0</div><input id='attrs-83f9043b-72b0-4d58-b35d-6396a4c4ed91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f9043b-72b0-4d58-b35d-6396a4c4ed91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e34e647-0ee5-494b-8310-c1b070c3e25f' class='xr-var-data-in' type='checkbox'><label for='data-4e34e647-0ee5-494b-8310-c1b070c3e25f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 0 1</div><input id='attrs-8b73a425-2378-44c2-9ab1-4753e7e067ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b73a425-2378-44c2-9ab1-4753e7e067ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-760e137c-f6e8-4d40-8dc1-8ee16b5f893c' class='xr-var-data-in' type='checkbox'><label for='data-760e137c-f6e8-4d40-8dc1-8ee16b5f893c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-ce98e44f-cdfa-4711-b246-b5060e9bb8ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce98e44f-cdfa-4711-b246-b5060e9bb8ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9712648-ac14-4306-b0cb-2a7689c4daff' class='xr-var-data-in' type='checkbox'><label for='data-d9712648-ac14-4306-b0cb-2a7689c4daff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volume</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-e5cdabbe-4e8c-4baf-98f0-ac6b252dace3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5cdabbe-4e8c-4baf-98f0-ac6b252dace3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bbcc6fe-c5e6-40f8-af04-f3309ffb6a54' class='xr-var-data-in' type='checkbox'><label for='data-8bbcc6fe-c5e6-40f8-af04-f3309ffb6a54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff23adb9-c36a-4e80-a347-dfae88fae2c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff23adb9-c36a-4e80-a347-dfae88fae2c8' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>edge_distance</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>31.11 1.0 31.11 29.7</div><input id='attrs-33421bee-3b85-46ab-9bb7-9a2657792e22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33421bee-3b85-46ab-9bb7-9a2657792e22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0bce259-c909-427b-93f4-377fde33799a' class='xr-var-data-in' type='checkbox'><label for='data-f0bce259-c909-427b-93f4-377fde33799a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>distance from upper edge</dd></dl></div><div class='xr-var-data'><pre>array([31.11269837,  1.        , 31.11269837, 29.69848481])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>molfrac</span></div><div class='xr-var-dims'>(sample, component)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01297 0.987 1.0 ... 0.979 0.02102</div><input id='attrs-5586240d-355a-4873-aceb-926f2ca931ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5586240d-355a-4873-aceb-926f2ca931ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b57d1e87-86e8-4bb6-8820-de6933d0f40a' class='xr-var-data-in' type='checkbox'><label for='data-b57d1e87-86e8-4bb6-8820-de6933d0f40a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>${\\bf x}(\\mu,V,T)$</dd></dl></div><div class='xr-var-data'><pre>array([[1.29744853e-02, 9.87025515e-01],\n",
       "       [9.99985806e-01, 1.41935483e-05],\n",
       "       [3.17187024e-04, 9.99682813e-01],\n",
       "       [9.78978241e-01, 2.10217585e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nvec</span></div><div class='xr-var-dims'>(sample, component)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.715 206.5 252.4 ... 206.7 4.438</div><input id='attrs-571f8863-3d64-4245-99f1-36f77570f267' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571f8863-3d64-4245-99f1-36f77570f267' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a044ea0-33eb-48ca-87a2-523449e316ef' class='xr-var-data-in' type='checkbox'><label for='data-5a044ea0-33eb-48ca-87a2-523449e316ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>${\\bf n}(\\mu,V,T)$</dd></dl></div><div class='xr-var-data'><pre>array([[2.71467322e+00, 2.06516996e+02],\n",
       "       [2.52439940e+02, 3.58306932e-03],\n",
       "       [6.67557447e-02, 2.10395021e+02],\n",
       "       [2.06675356e+02, 4.43797344e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betapV</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.248e+03 1.705e+03 1.246e+03 996.1</div><input id='attrs-b0b0dbd1-b49c-4fe4-bbdb-702b0f542b2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0b0dbd1-b49c-4fe4-bbdb-702b0f542b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-634fa60a-87e8-41d1-a05b-fe6f2ac95c44' class='xr-var-data-in' type='checkbox'><label for='data-634fa60a-87e8-41d1-a05b-fe6f2ac95c44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>standard_name :</span></dt><dd>grand_potential</dd><dt><span>long_name :</span></dt><dd>$\\beta p(\\mu,V,T)V$</dd></dl></div><div class='xr-var-data'><pre>array([1248.00999152, 1705.21428595, 1246.18714097,  996.09560846])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72bca404-df93-46a5-bf54-1fe0f262d312' class='xr-section-summary-in' type='checkbox'  ><label for='section-72bca404-df93-46a5-bf54-1fe0f262d312' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample<br>spinodal<br>lnz_0<br>lnz_1<br>phase</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-7c4336a9-fef9-409e-b453-dcce9c2ee151' class='xr-index-data-in' type='checkbox'/><label for='index-7c4336a9-fef9-409e-b453-dcce9c2ee151' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(0,  1.2015754333333324, 0.0, 0),\n",
       "            (0,  1.2015754333333324, 0.0, 1),\n",
       "            (1, -1.8057242053333276, 0.0, 0),\n",
       "            (1, -1.8057242053333276, 0.0, 1)],\n",
       "           name=&#x27;sample&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee79e8d6-ce27-4a1d-87c8-4a54f25aab47' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee79e8d6-ce27-4a1d-87c8-4a54f25aab47' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>dims_n :</span></dt><dd>[&#x27;n_0&#x27;, &#x27;n_1&#x27;]</dd><dt><span>dims_lnz :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;]</dd><dt><span>dims_comp :</span></dt><dd>[&#x27;component&#x27;]</dd><dt><span>dims_state :</span></dt><dd>[&#x27;lnz_0&#x27;, &#x27;lnz_1&#x27;, &#x27;beta&#x27;, &#x27;volume&#x27;]</dd><dt><span>dims_rec :</span></dt><dd>[&#x27;sample&#x27;]</dd><dt><span>long_name :</span></dt><dd>distance from upper edge</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 368B\n",
       "Dimensions:        (sample: 4, component: 2)\n",
       "Coordinates:\n",
       "  * sample         (sample) object 32B MultiIndex\n",
       "  * spinodal       (sample) int64 32B 0 0 1 1\n",
       "  * lnz_0          (sample) float64 32B 1.202 1.202 -1.806 -1.806\n",
       "  * lnz_1          (sample) float64 32B 0.0 0.0 0.0 0.0\n",
       "  * phase          (sample) int64 32B 0 1 0 1\n",
       "    beta           float64 8B 1.0\n",
       "    volume         float64 8B 1.0\n",
       "Dimensions without coordinates: component\n",
       "Data variables:\n",
       "    edge_distance  (sample) float64 32B 31.11 1.0 31.11 29.7\n",
       "    molfrac        (sample, component) float64 64B 0.01297 0.987 ... 0.02102\n",
       "    nvec           (sample, component) float64 64B 2.715 206.5 ... 206.7 4.438\n",
       "    betapV         (sample) float64 32B 1.248e+03 1.705e+03 1.246e+03 996.1\n",
       "Attributes:\n",
       "    dims_n:      ['n_0', 'n_1']\n",
       "    dims_lnz:    ['lnz_0', 'lnz_1']\n",
       "    dims_comp:   ['component']\n",
       "    dims_state:  ['lnz_0', 'lnz_1', 'beta', 'volume']\n",
       "    dims_rec:    ['sample']\n",
       "    long_name:   distance from upper edge"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_spin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>spinodal</th>\n",
       "      <th>lnz_0</th>\n",
       "      <th>lnz_1</th>\n",
       "      <th>phase</th>\n",
       "      <th>beta</th>\n",
       "      <th>volume</th>\n",
       "      <th>edge_distance</th>\n",
       "      <th>molfrac</th>\n",
       "      <th>nvec</th>\n",
       "      <th>betapV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th>component</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.201575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.112698</td>\n",
       "      <td>0.012974</td>\n",
       "      <td>2.714673</td>\n",
       "      <td>1248.009992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.805724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.698485</td>\n",
       "      <td>0.978978</td>\n",
       "      <td>206.675356</td>\n",
       "      <td>996.095608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  spinodal     lnz_0  lnz_1  phase  beta  volume  \\\n",
       "sample component                                                   \n",
       "0      0                 0  1.201575    0.0      0   1.0     1.0   \n",
       "3      0                 1 -1.805724    0.0      1   1.0     1.0   \n",
       "\n",
       "                  edge_distance   molfrac        nvec       betapV  \n",
       "sample component                                                    \n",
       "0      0              31.112698  0.012974    2.714673  1248.009992  \n",
       "3      0              29.698485  0.978978  206.675356   996.095608  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_spin.reset_index(\"sample\").to_dataframe().query(\n",
    "    \"component==0 and spinodal==phase\"\n",
    ").dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146",
   "metadata": {},
   "source": [
    "doing this for multiple values of fixed `lnz_1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "147",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "\n",
    "\n",
    "def get_bin_spin1(lnz2, phase_creator, from_builder, from_builder_kws=None):\n",
    "    # reload stability here to make sure accessor available (also make sure black keeps this here.)\n",
    "\n",
    "    build_phases = phase_creator.build_phases_mu([None, lnz2])\n",
    "    lnzs = np.linspace(-8, 8, 20)\n",
    "    if from_builder_kws is None:\n",
    "        from_builder_kws = {}\n",
    "    c = from_builder(lnzs, build_phases, **from_builder_kws)\n",
    "    t_spin = None\n",
    "    t_bino = None\n",
    "\n",
    "    try:\n",
    "        c.spinodal(2, build_phases, inplace=True, unstack=False)\n",
    "        c.binodal(2, build_phases, inplace=True, unstack=False)\n",
    "        t_spin = c.spinodal.access.xge.table([\"molfrac\"], ref=ref)\n",
    "        t_bino = c.binodal.access.xge.table([\"molfrac\"], ref=ref)\n",
    "    except Exception:  # noqa: BLE001\n",
    "        pass\n",
    "    return t_spin, t_bino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "148",
   "metadata": {},
   "outputs": [],
   "source": [
    "out1 = Parallel(n_jobs=-1)(\n",
    "    delayed(get_bin_spin1)(lnz2, phase_creator, lnpy.lnPiCollection.from_builder)\n",
    "    for lnz2 in np.arange(-5, 5, 0.5)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "149",
   "metadata": {},
   "outputs": [],
   "source": [
    "spin1 = xr.concat([s for s, b in out1 if s is not None], \"sample\")\n",
    "bino1 = xr.concat([b for s, b in out1 if b is not None], \"sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_frame(df, **kws) -> None:\n",
    "    (\n",
    "        df.reset_index()\n",
    "        .set_index([\"molfrac\", \"phase\"])\n",
    "        .assign(pV=lambda x: x[\"betapV\"] / x[\"beta\"])[\"pV\"]\n",
    "        .to_xarray()\n",
    "        .plot(hue=\"phase\", **kws)\n",
    "    )\n",
    "\n",
    "\n",
    "plot_frame(\n",
    "    spin1.reset_index(\"sample\")\n",
    "    .to_dataframe()\n",
    "    .query(\"component==0 and spinodal==phase\")\n",
    "    .dropna(),\n",
    "    ls=\"--\",\n",
    "    color=\"k\",\n",
    ")\n",
    "plot_frame(\n",
    "    bino1.reset_index(\"sample\").to_dataframe().query(\"component==0\").dropna(), color=\"r\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "151",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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