Migrated from GitHub

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@@ -58,3 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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+data/data/DSMO/DSMO_init.gpx filter=lfs diff=lfs merge=lfs -text
+data/data/DSMO/dsmo.gpx filter=lfs diff=lfs merge=lfs -text
+data/data/LiCoO2/LiCoO2_init.gpx filter=lfs diff=lfs merge=lfs -text
+data/data/Y2O3/Y2O3.gpx filter=lfs diff=lfs merge=lfs -text
+data/data/Y2O3/Y2O3_init.gpx filter=lfs diff=lfs merge=lfs -text

data/1_Y2O3.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BBO-Rietveld Y2O3\n",
+    "\n",
+    "In this notebook, you can see how to automate Rietveld refinement with black-box optimization.  \n",
+    "Run the following cells to run the optimization.\n",
+    "\n",
+    "## Documentations\n",
+    "- [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)\n",
+    "- [GSASIIscriptable: Scripting Interface](https://gsas-ii.readthedocs.io/en/latest/GSASIIscriptable.html)\n",
+    "- [Optuna](https://optuna.readthedocs.io/en/stable/)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import packages\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "from multiprocessing import Process, Queue\n",
+    "import pandas as pd\n",
+    "import optuna\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "from dataclasses import dataclass\n",
+    "sys.path.append('/root/g2full/GSASII/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configurations\n",
+    "RANDOM_SEED = 1024\n",
+    "STUDY_NAME = 'Y2O3'\n",
+    "DATA_DIR = 'data/' + STUDY_NAME\n",
+    "# all output files include GSAS project file (*.gpx) will be saved in WORK_DIR\n",
+    "WORK_DIR = 'work/' + STUDY_NAME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make directories\n",
+    "! rm -f $WORK_DIR/$STUDY_NAME*\n",
+    "! mkdir -p $WORK_DIR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class ProjectConfig:\n",
+    "    work_dir: str\n",
+    "    random_seed: int\n",
+    "    data_dir: str\n",
+    "    cif_file: str\n",
+    "    csv_file: str\n",
+    "    prm_file: str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Project:\n",
+    "    def __init__(self, config: ProjectConfig, trial_number: int):\n",
+    "        import GSASIIscriptable as G2sc\n",
+    "        self.gpx = G2sc.G2Project(newgpx=os.path.join(\n",
+    "            config.work_dir, f'project_seed{config.random_seed}_trial_{trial_number}.gpx'))\n",
+    "\n",
+    "        self.hist1 = self.gpx.add_powder_histogram(\n",
+    "            os.path.join(config.data_dir, config.csv_file), os.path.join(config.data_dir, config.prm_file))\n",
+    "        \n",
+    "        self.phase0 = self.gpx.add_phase(os.path.join(config.data_dir, config.cif_file),\n",
+    "                                         phasename=config.cif_file.split(\".cif\")[0],\n",
+    "                                         histograms=[self.hist1])\n",
+    "        \n",
+    "        self.hist1.data['Instrument Parameters'][0]['I(L2)/I(L1)'] = [0.5, 0.5, 0]\n",
+    "\n",
+    "        # Set to use iso\n",
+    "        for val in self.phase0.data['Atoms']:\n",
+    "            val[9] = 'I'\n",
+    "\n",
+    "    def refine_and_calc_Rwp(self, param_dict):\n",
+    "        self.gpx.do_refinements([param_dict])\n",
+    "        for hist in self.gpx.histograms():\n",
+    "            _, Rwp = hist.name, hist.get_wR()\n",
+    "        return Rwp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example usage:\n",
+    "config = ProjectConfig(\n",
+    "    work_dir=WORK_DIR,\n",
+    "    random_seed=RANDOM_SEED,\n",
+    "    data_dir=DATA_DIR,\n",
+    "    cif_file='Y2O3.cif',\n",
+    "    csv_file='Y2O3.csv',\n",
+    "    prm_file='INST_XRY.PRM'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the objective function, first, configurations for the following parameters are sampled.\n",
+    "Then, the crystal structure is refined by GSAS-II.\n",
+    "Finally, the calculated Rwp is returned.\n",
+    "\n",
+    "| Parameter                                 | Parameter type   | Search space                                                                                                      |\n",
+    "|-------------------------------------------|------------------|-------------------------------------------------------------------------------------------------------------------|\n",
+    "| initial 2theta bound refinement           | categorical      | {True, False}                                                                                                      |\n",
+    "| initial 2theta lower bound (deg.)         | real             | [15, 130]                                                                                                          |\n",
+    "| initial 2theta upper bound (deg.)         | real/conditional | [l + 20, 150] (l is the initial 2theta lower bound)                                                                |\n",
+    "| background function                       | categorical      | {Chebyshev, cosine, Q^2 power series, Q^-2 power series, linear interpolate, inverse interpolate, log interpolate} |\n",
+    "| degree of background function             | initegral        | [1, 15]                                                                                                            |\n",
+    "| instrument zero position refinement       | categorical      | {True, False}                                                                                                      |\n",
+    "| sample shift X refinement                 | categorical      | {True, False}                                                                                                      |\n",
+    "| sample shift Y refinement                 | categorical      | {True, False}                                                                                                      |\n",
+    "| scale factor refinement                   | categorical      | {True, False}                                                                                                      |\n",
+    "| peak shape refinement (Gaussian: U, V, W) | categorical      | {True, False}                                                                                                      |\n",
+    "| peak shape refinement (Lorentzian: X, Y)  | categorical      | {True, False}                                                                                                      |\n",
+    "| peak shape refinement (SH/L)              | categorical      | {True, False}                                                                                                      |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(trial, config):\n",
+    "    \"\"\"\n",
+    "    objective function for Optuna.\n",
+    "    \n",
+    "    You can apply black-box optimization for other data analysis \n",
+    "    by modifying this function for that task.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    trial : optuna.trial object\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    Rwp : float\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    ### define search space ###\n",
+    "    # Limits (acute angle)\n",
+    "    limits_lb = trial.suggest_float('Limits lower bound', 15, 130)\n",
+    "    limits_ub = trial.suggest_float('Limits upper bound', limits_lb + 20, 150)\n",
+    "    limits_refine = trial.suggest_categorical('limits refine', [True, False])\n",
+    "    refdict0 = {'set': {'Limits': [limits_lb, limits_ub]}, 'refine': limits_refine}\n",
+    "\n",
+    "    # Background\n",
+    "    background_type = trial.suggest_categorical(\n",
+    "        'Background type', ['chebyschev',\n",
+    "                            'cosine',\n",
+    "                            'Q^2 power series',\n",
+    "                            'Q^-2 power series',\n",
+    "                            'lin interpolate',\n",
+    "                            'inv interpolate',\n",
+    "                            'log interpolate'])\n",
+    "    no_coeffs = trial.suggest_int('Number of coefficients', 1, 15)  # [1, 15]\n",
+    "    background_refine = trial.suggest_categorical('Background refine', [True, False])\n",
+    "    refdict0bg_h = {\n",
+    "        'set': {\n",
+    "            'Background': {\n",
+    "                'type': background_type,\n",
+    "                'no. coeffs': no_coeffs,\n",
+    "                'refine': background_refine\n",
+    "            }\n",
+    "        }\n",
+    "    }\n",
+    "\n",
+    "    # Instrument parameters\n",
+    "    instrument_parameters1_refine = []\n",
+    "    for p in ['Zero']:\n",
+    "        if trial.suggest_categorical('Instrument_parameters refine %s' % (p), [True, False]):\n",
+    "            instrument_parameters1_refine.append(p)\n",
+    "    refdict1_h = {'set': {'Cell': True, 'Instrument Parameters': instrument_parameters1_refine}}\n",
+    "\n",
+    "    sample_parameters1_refine =[]\n",
+    "    for p in ['DisplaceX', 'DisplaceY', 'Scale']:\n",
+    "        if trial.suggest_categorical('Sample_parameters refine %s' % (p), [True, False]):\n",
+    "            sample_parameters1_refine.append(p)\n",
+    "    refdict1_h2 = {\"set\": {'Sample Parameters':sample_parameters1_refine }}\n",
+    "\n",
+    "    instrument_parameters2_refine = []\n",
+    "    for p in ['U', 'V', 'W', 'X', 'Y', 'SH/L']:\n",
+    "        if trial.suggest_categorical('Peakshape_parameters refine %s' % (p), [True, False]):\n",
+    "            instrument_parameters2_refine.append(p)\n",
+    "    refdict2_h = {'set': {'Instrument Parameters': instrument_parameters2_refine}}\n",
+    "\n",
+    "    refdict3_h = {'set': {'Atoms': {'all': 'XU'}}}\n",
+    "\n",
+    "    # Limits (wide angle)\n",
+    "    refdict_fin_h = {'set': {'Limits': [15, 150]}, 'refine': True}\n",
+    "\n",
+    "    # Evaluate\n",
+    "    refine_params_list = [refdict0,\n",
+    "                          refdict0bg_h,\n",
+    "                          refdict1_h,\n",
+    "                          refdict1_h2,\n",
+    "                          refdict2_h,\n",
+    "                          refdict3_h,\n",
+    "                          refdict_fin_h]\n",
+    "    \n",
+    "    def evaluate(config, trial_number, refine_params_list):\n",
+    "        ERROR_PENALTY = 1e9      \n",
+    "        try:\n",
+    "            print(config)\n",
+    "            print(trial_number)\n",
+    "            print(refine_params_list)\n",
+    "            # todo: ここを実装（trial_number）を渡す\n",
+    "            project = Project(config, trial_number)\n",
+    "            for params in refine_params_list:\n",
+    "                Rwp = project.refine_and_calc_Rwp(params)                    \n",
+    "            # validate Uiso >= 0\n",
+    "            phase_Y2O3 = project.gpx.phases()[0]\n",
+    "            u_iso_list = [atom.uiso for atom in phase_Y2O3.atoms()]\n",
+    "            if min(u_iso_list) < 0:\n",
+    "                # Uiso < 0\n",
+    "                Rwp = ERROR_PENALTY\n",
+    "            return Rwp\n",
+    "            \n",
+    "        except Exception as e:\n",
+    "            # Refinement failed\n",
+    "            print(e, file=sys.stderr)\n",
+    "            return ERROR_PENALTY\n",
+    "            \n",
+    "    Rwp = evaluate(config, trial.number, refine_params_list)\n",
+    "    \n",
+    "    return Rwp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-04-03 12:39:27,443] Using an existing study with name 'Y2O3_seed1024' instead of creating a new one.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create Optuna study\n",
+    "\n",
+    "study = optuna.create_study(study_name=STUDY_NAME + '_seed%s' % (RANDOM_SEED),\n",
+    "                            storage=f\"sqlite:///{config.work_dir}/history_sqlite.db\", \n",
+    "                            load_if_exists=True,\n",
+    "                            sampler=optuna.samplers.TPESampler(n_startup_trials=20, seed=RANDOM_SEED))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run 200 refinements to find the best configuration. It may take abount an hour to complete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Optimize\n",
+    "study.optimize(func=lambda trial: objective(trial, config), n_trials=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Results\n",
+    "df = study.trials_dataframe()\n",
+    "df.columns = [''.join(col).replace('params', '').strip() for col in df.columns.values]\n",
+    "df.rename(columns={'value':'Rwp', 'number':'trial'}, inplace=True)\n",
+    "df.sort_values('Rwp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Limits lower bound': 91.81741956486644,\n",
+       " 'Limits upper bound': 124.16688310979242,\n",
+       " 'limits refine': False,\n",
+       " 'Background type': 'log interpolate',\n",
+       " 'Number of coefficients': 14,\n",
+       " 'Background refine': True,\n",
+       " 'Instrument_parameters refine Zero': True,\n",
+       " 'Sample_parameters refine DisplaceX': True,\n",
+       " 'Sample_parameters refine DisplaceY': True,\n",
+       " 'Sample_parameters refine Scale': False,\n",
+       " 'Peakshape_parameters refine U': False,\n",
+       " 'Peakshape_parameters refine V': False,\n",
+       " 'Peakshape_parameters refine W': True,\n",
+       " 'Peakshape_parameters refine X': True,\n",
+       " 'Peakshape_parameters refine Y': True,\n",
+       " 'Peakshape_parameters refine SH/L': False}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Best configuration\n",
+    "study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.946615605079828"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Best Rwp\n",
+    "study.best_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Rwp plot\n",
+    "def rwp_plot():\n",
+    "    minvalues = [df.iloc[0]['Rwp']]\n",
+    "    for i in range(1, df.shape[0]):\n",
+    "        minvalues.append(min(minvalues[-1], df.iloc[i]['Rwp']))\n",
+    "    minvalues = pd.DataFrame(minvalues)\n",
+    "    \n",
+    "    minvalues.plot(legend=None)\n",
+    "    plt.ylim([6, 16])\n",
+    "    plt.grid(color='#cccccc')\n",
+    "    plt.ylabel('$R_{wp}$')\n",
+    "    plt.xlabel('Number of trials')\n",
+    "    plt.show()\n",
+    "    \n",
+    "rwp_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Rietveld plot\n",
+    "def rietveld_plot():\n",
+    "    import GSASIIscriptable as G2sc\n",
+    "\n",
+    "    gpx = G2sc.G2Project(\n",
+    "        f'{WORK_DIR}/project_seed{RANDOM_SEED}_trial_{study.best_trial.number}.gpx')\n",
+    "\n",
+    "    hist1 = gpx.histograms()[0]\n",
+    "    phase0 = gpx.phases()[0]\n",
+    "\n",
+    "    hist = hist1\n",
+    "    i = 5\n",
+    "    two_theta = hist.getdata(\"X\")[::i]\n",
+    "    Yobs = hist.getdata(\"Yobs\")[::i]\n",
+    "    Ycalc = hist.getdata(\"Ycalc\")[::i]\n",
+    "    bg = hist.getdata(\"Background\")[::i]\n",
+    "    residual = hist.getdata(\"Residual\")[::i]\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    gs = GridSpec(5, 1, figure=fig)\n",
+    "    ax1 = fig.add_subplot(gs[:4, :])\n",
+    "    ax2 = fig.add_subplot(gs[4, :])\n",
+    "    fig.subplots_adjust(hspace=0)\n",
+    "    ax1.grid(color='#cccccc')\n",
+    "\n",
+    "    ax1.scatter(two_theta, Yobs, marker='P', lw=0.0001, c='Black', label='XRD (Obs)')\n",
+    "    ax1.plot(two_theta, Ycalc, label='XRD (Calc)')\n",
+    "    ax1.plot(two_theta, bg, color='red', label='Background (Calc)')\n",
+    "    ax1.set_ylabel('Intensity')\n",
+    "    ax1.legend()\n",
+    "    ax2.plot(two_theta, residual, color='blue')\n",
+    "    plt.setp(ax1.get_xticklabels(), visible=False);\n",
+    "    ax2.set_ylim(-6600, 6600)\n",
+    "    plt.xlabel(r'$2\\theta$ (deg.)')\n",
+    "    ax2.set_ylabel('Residual')\n",
+    "    ax1.set_xlim(15, 150)\n",
+    "    ax2.set_xlim(15, 150)\n",
+    "    plt.show()\n",
+    "    \n",
+    "rietveld_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/LICENSE

@@ -0,0 +1,201 @@
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+      of any other Contributor, and only if You agree to indemnify,
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+
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+   you may not use this file except in compliance with the License.
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

data/README.md

@@ -0,0 +1,104 @@
+# BBO-Rietveld
+Implementation of BBO-Rietveld, automated crystal structure analysis method based on blackbox optimisation.  
+![The schematic figure of BBO-Rietveld](https://gist.githubusercontent.com/resnant/32aed77b71f71d798a847fab16431315/raw/cadacc758e000e1dc7d7e4d0253512f8b83b4e88/bbo_rietveld_schematic.jpg)
+
+## Citation
+Ozaki, Y., Suzuki, Y., Hawai, T., Saito, K., Onishi, M., and Ono, K.  
+Automated crystal structure analysis based on blackbox optimisation.  
+<i>npj Computational Materials</i> <b>6</b>, 75 (2020).  
+[https://doi.org/10.1038/s41524-020-0330-9](https://doi.org/10.1038/s41524-020-0330-9)
+
+## Requirements
+
+- [Docker](https://www.docker.com/)
+  - If you are new to Docker, please setup Docker Desktop for your computer first.
+  - Here are the documents for Windows and Mac:
+    - https://docs.docker.com/docker-for-windows/
+    - https://docs.docker.com/docker-for-mac/
+  - We have verified our software works on macOS(10.14) and Ubuntu(18.04). We recommend macOS or Ubuntu to run this software, but it should work on any computer that Docker running.
+
+## Usage
+__Note: We suppose you are using macOS or Ubuntu and can run docker command without `sudo` permissions. If you are using Windows, we strongly recommend using WSL2 terminal instead of Command prompt or Windows PowerShell with regard to compatibility.__
+
+1. First, clone this repository to your computer.
+
+2. Pull bbo-rietveld docker image on terminal. 
+```sh
+docker pull resnant/bbo-rietveld:v1.1
+```
+
+If needed, you can build bbo-rietveld docker image from `Dockerfile` on your computer.  
+```sh
+# Please make sure run following command in this directory
+docker build ./docker -t bbo-rietveld
+```
+In recent versions of GSAS, conda may not be able to resolve the dependencies and GSAS installation may fail.  
+https://subversion.xray.aps.anl.gov/trac/pyGSAS/wiki/InstallConda  
+
+<!-- For more information on how to use Docker image -->
+To use your own docker image, you need to specify your docker image in `run.sh`. Please refer to [run.sh](https://github.com/quantumbeam/BBO-Rietveld/blob/8d4533dc1c436f227205e225fc4bb0c3f6402edf/run.sh#L4).
+
+3. Run bbo-rietveld container.
+```sh
+./run.sh
+```
+or 
+```bash
+docker run --rm -it -p 8888:8888 -v $PWD:/workspace bbo-rietveld:v1 bash
+jupyter lab --allow-root --ip 0.0.0.0
+```
+
+4. Open http://127.0.0.1:8888/?token={TOKEN} in your browser, and you can see a window of JupyterLab. The token will be displayed on your terminal like this:
+```
+...
+
+[I 11:23:41.325 LabApp] The Jupyter Notebook is running at:
+[I 11:23:41.325 LabApp] http://900cc3c9314c:8888/?token=126c79b021d40344592d5b066225b32474487cb69f711ffe
+[I 11:23:41.325 LabApp]  or http://127.0.0.1:8888/?token=126c79b021d40344592d5b066225b32474487cb69f711ffe
+
+...
+```
+In this case, you should open `http://127.0.0.1:8888/?token=126c79b021d40344592d5b066225b32474487cb69f711ffe`.
+
+5. Then, open `1_Y2O3.ipynb` or other notebooks on JupyterLab and run notebook cells.
+
+
+## For expert users
+If you are already familiar with Python, you can open notebooks (*.ipynb files) directly in your environment and run it.
+It requires following packages:
+- [GSAS](https://gsas-ii.readthedocs.io/en/latest/GSASIIscriptable.html)
+- [Optuna](https://optuna.readthedocs.io/en/stable/)(newer than 3.5)
+- Jupyter Lab or Jupyter Notebook
+- Pandas
+- matplotlib
+
+In the notebook, you should set `DATA_DIR` and `WORK_DIR` appropriately for your directory.
+
+
+## Trouble shooting
+If you have any problems or questions, please open an issue on GitHub or contact Yuta Suzuki (resnant [at] outlook.jp) by email. 
+_Whenever possible, please ask your question via an issue rather than an email._ This will help others who encounter similar problems.
+
+### FAQ
+- If port `8888` occupied by other services, following error occurs when you start `run.sh`:
+  
+  ```Error starting userland proxy: listen tcp 0.0.0.0:8888: bind: address already in use.```
+  
+  - Please open `run.sh` with a texteditor and modify port binding setting to other port, `18888`, for example:
+```
+docker run --rm -v ${SCRIPT_DIR}/:/bbo_rietveld -p 18888:8888 -it bbo-rietveld
+```
+  - Then, Open `http://127.0.0.1:18888/?token={TOKEN}` in your browser to open Jupyter Notebook.
+
+- If you encounter `Unable to find image 'resnant/bbo-rietveld:v1.0' locally` error when you run `run.sh`:
+  - Please run `docker image list` on your terminal and make sure that there is `resnant/bbo-rietveld` on REPOSITORY like following.
+```sh
+resnant@cosmos:~$ docker image list
+REPOSITORY              TAG                             IMAGE ID            CREATED             SIZE
+resnant/bbo-rietveld    v1.1                            ab342a0ba172        1 months ago        5.24GB
+```
+
+## Licence
+This software is distributed under Apache 2.0 licence for research and education purposes. If you want to use this code for a commercial purpose, please contact the corresponding author.
+
+The diffraction and crystal structure dataset in `data/` directory is distributed under its original licences. See [README of the dataset](data/README.md) for details.

data/YOUR_MATERIAL.ipynb

@@ -0,0 +1,274 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import packages\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "from multiprocessing import Process, Queue\n",
+    "import pandas as pd\n",
+    "import optuna\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "\n",
+    "sys.path.append('/opt/conda/GSASII/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configurations\n",
+    "\n",
+    "### Change here ###\n",
+    "STUDY_NAME = 'YOUR_MATERIAL'\n",
+    "RANDOM_SEED = 1024\n",
+    "\n",
+    "DATA_DIR = '/bbo_rietveld/data/' + STUDY_NAME\n",
+    "# all output files include GSAS project file (*.gpx) will be saved in WORK_DIR\n",
+    "WORK_DIR = '/bbo_rietveld/work/' + STUDY_NAME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make directories\n",
+    "! rm -f $WORK_DIR/$STUDY_NAME*\n",
+    "! mkdir -p $WORK_DIR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ProjectBBO:\n",
+    "    def __init__(self, trial_number):\n",
+    "        import GSASIIscriptable as G2sc\n",
+    "        import shutil\n",
+    "        \n",
+    "        # Create a project with a default project name\n",
+    "        ### Change here ###\n",
+    "        shutil.copyfile(DATA_DIR+'/'+'YOUR_PROJECT_FILE.gpx', \n",
+    "                        WORK_DIR+'/'+'BBO_seed{0}_trial_{1}.gpx'.format(RANDOM_SEED, trial_number))\n",
+    "        \n",
+    "        self.gpx = G2sc.G2Project(gpxfile=os.path.join(WORK_DIR, 'BBO_seed{0}_trial_{1}.gpx'.format(RANDOM_SEED, trial_number)))\n",
+    "\n",
+    "        # Add two histograms to the project\n",
+    "        self.hist1 = self.gpx.histograms()[0]\n",
+    "        self.phase0 = self.gpx.phases()[0]\n",
+    "        self.hist1.data['Instrument Parameters'][0]['I(L2)/I(L1)'] = [0.5, 0.5, 0]\n",
+    "\n",
+    "        # Set to use iso\n",
+    "        for val in self.phase0.data['Atoms']:\n",
+    "            val[9] = 'I'\n",
+    "\n",
+    "    def refine_and_calc_Rwp(self, param_dict):\n",
+    "        self.gpx.do_refinements([param_dict])\n",
+    "        for hist in self.gpx.histograms():\n",
+    "            _, Rwp = hist.name, hist.get_wR()\n",
+    "        return Rwp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(trial):\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    trial : optuna.trial object\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    Rwp : float\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Here, you should define search space and perform the refinement.\n",
+    "    # Please see other notebook.\n",
+    "    # Copy and paste from other notebook and some modifications would be enough.\n",
+    "    \n",
+    "    return Rwp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Optuna study\n",
+    "study = optuna.create_study(study_name=STUDY_NAME + '_seed%s' % (RANDOM_SEED),\n",
+    "                            sampler=optuna.samplers.TPESampler(n_startup_trials=20, seed=RANDOM_SEED))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run 200 refinements to find the best configuration. It may take abount an hour to complete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optimize\n",
+    "study.optimize(objective, n_trials=200, n_jobs=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Results\n",
+    "df = study.trials_dataframe()\n",
+    "df.columns = [' '.join(col).replace('params', '').strip() for col in df.columns.values]\n",
+    "df.rename(columns={'value':'Rwp', 'number':'trial'}, inplace=True)\n",
+    "df.drop(columns=['state', 'system_attrs _number'], inplace=True)\n",
+    "df.sort_values('Rwp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Best configuration\n",
+    "study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Best Rwp\n",
+    "study.best_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rwp plot\n",
+    "def rwp_plot():\n",
+    "    minvalues = [df.iloc[0]['Rwp']]\n",
+    "    for i in range(1, df.shape[0]):\n",
+    "        minvalues.append(min(minvalues[-1], df.iloc[i]['Rwp']))\n",
+    "    minvalues = pd.DataFrame(minvalues)\n",
+    "    \n",
+    "    minvalues.plot(legend=None)\n",
+    "#     plt.ylim([6, 16])\n",
+    "    plt.grid(color='#cccccc')\n",
+    "    plt.ylabel('$R_{wp}$')\n",
+    "    plt.xlabel('Number of trials')\n",
+    "    plt.show()\n",
+    "    \n",
+    "rwp_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rietveld plot\n",
+    "def rietveld_plot():\n",
+    "    import GSASIIscriptable as G2sc\n",
+    "\n",
+    "    gpx = G2sc.G2Project(\n",
+    "        '%s/%s_seed%s_trial_%s.gpx' % (WORK_DIR, STUDY_NAME, RANDOM_SEED, study.best_trial.number))\n",
+    "\n",
+    "    hist1 = gpx.histograms()[0]\n",
+    "    phase0 = gpx.phases()[0]\n",
+    "\n",
+    "    hist = hist1\n",
+    "    i = 5\n",
+    "    two_theta = hist.getdata(\"X\")[::i]\n",
+    "    Yobs = hist.getdata(\"Yobs\")[::i]\n",
+    "    Ycalc = hist.getdata(\"Ycalc\")[::i]\n",
+    "    bg = hist.getdata(\"Background\")[::i]\n",
+    "    residual = hist.getdata(\"Residual\")[::i]\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    gs = GridSpec(5, 1, figure=fig)\n",
+    "    ax1 = fig.add_subplot(gs[:4, :])\n",
+    "    ax2 = fig.add_subplot(gs[4, :])\n",
+    "    fig.subplots_adjust(hspace=0)\n",
+    "    ax1.grid(color='#cccccc')\n",
+    "\n",
+    "    ax1.scatter(two_theta, Yobs, marker='P', lw=0.0001, c='Black', label='XRD (Obs)')\n",
+    "    ax1.plot(two_theta, Ycalc, label='XRD (Calc)')\n",
+    "    ax1.plot(two_theta, bg, color='red', label='Background (Calc)')\n",
+    "    ax1.set_ylabel('Intensity')\n",
+    "    ax1.legend()\n",
+    "    ax2.plot(two_theta, residual, color='blue')\n",
+    "    plt.setp(ax1.get_xticklabels(), visible=False);\n",
+    "    # ax2.set_ylim(-6600, 6600)\n",
+    "    plt.xlabel(r'$2\\theta$ (deg.)')\n",
+    "    ax2.set_ylabel('Residual')\n",
+    "    # change 2theta range according to your data\n",
+    "    ax1.set_xlim(15, 150)\n",
+    "    ax2.set_xlim(15, 150)\n",
+    "    plt.show()\n",
+    "    \n",
+    "rietveld_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/data/DSMO/DSMO_init.gpx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b82823a27dee925e327e2dc5b82d1ea254a95a8e219366fa7061c3eccbfd9660
+size 312853

data/data/DSMO/INST_XRY.PRM

@@ -0,0 +1,10 @@
+            123456789012345678901234567890123456789012345678901234567890
+INS   BANK      1                                                               
+INS   HTYPE   PXCR                                                              
+INS  1 IRAD     3                                                               
+INS  1 ICONS  1.540500  1.544300       0.0         0       0.7    0       0.5   
+INS  1I HEAD  DUMMY INCIDENT SPECTRUM FOR X-RAY DIFFRACTOMETER
+INS  1I ITYP    0    0.0000  180.0000         1                                 
+INS  1PRCF1     3    8      0.01                                                
+INS  1PRCF11   2.000000E+00  -2.000000E+00   5.000000E+00   0.100000E+00        
+INS  1PRCF12   0.000000E+00   0.000000E+00   0.100000E-01   0.100000E-01        

data/data/DSMO/dsmo-raw.cif

@@ -0,0 +1,501 @@
+##############################################################################
+###      FullProf-generated CIF output file  (version: May 2011)           ###
+###      Template of CIF submission form for structure report              ###
+##############################################################################
+ 
+# This file has been generated using FullProf.2k taking one example of 
+# structure report provided by Acta Cryst. It is given as a 'template' with 
+# filled structural items. Many other items are left unfilled and it is the  
+# responsibility of the user to properly fill or suppress them. In principle 
+# all question marks '?' should be replaced by the appropriate text or 
+# numerical value depending on the kind of CIF item. 
+# See the document: cif_core.dic (URL: http://www.iucr.org) for details. 
+ 
+# Please notify any error or suggestion to: 
+#           Juan Rodriguez-Carvajal (jrc@ill.eu)
+# Improvements will be progressively added as needed.
+ 
+#=============================================================================
+ data_global
+#=============================================================================
+ 
+# PROCESSING SUMMARY (IUCr Office Use Only)
+ 
+_journal_data_validation_number      ?
+ 
+_journal_date_recd_electronic        ?
+_journal_date_to_coeditor            ?
+_journal_date_from_coeditor          ?
+_journal_date_accepted               ?
+_journal_date_printers_first         ?
+_journal_date_printers_final         ?
+_journal_date_proofs_out             ?
+_journal_date_proofs_in              ?
+_journal_coeditor_name               ?
+_journal_coeditor_code               ?
+_journal_coeditor_notes
+; ?
+;
+_journal_techeditor_code             ?
+_journal_techeditor_notes
+; ?
+;
+_journal_coden_ASTM                  ?
+_journal_name_full                   ?
+_journal_year                        ?
+_journal_volume                      ?
+_journal_issue                       ?
+_journal_page_first                  ?
+_journal_page_last                   ?
+_journal_paper_category              ?
+_journal_suppl_publ_number           ?
+_journal_suppl_publ_pages            ?
+ 
+#=============================================================================
+ 
+# 1. SUBMISSION DETAILS
+ 
+_publ_contact_author_name            ?  # Name of author for correspondence
+_publ_contact_author_address            # Address of author for correspondence
+; ?
+;
+_publ_contact_author_email           ?
+_publ_contact_author_fax             ?
+_publ_contact_author_phone           ?
+ 
+_publ_contact_letter
+; ?
+;
+ 
+_publ_requested_journal              ?
+_publ_requested_coeditor_name        ?
+_publ_requested_category             ?   # Acta C: one of CI/CM/CO/FI/FM/FO
+ 
+ 
+# Definition of non standard CIF items (Reliability indices used in FULLPROF) 
+ 
+loop_ 
+_publ_manuscript_incl_extra_item
+_publ_manuscript_incl_extra_info
+_publ_manuscript_incl_extra_defn
+#      Name                               Explanation                 Standard?
+#     ------                              -----------                 ---------
+ '_pd_proc_ls_prof_cR_factor'    'Prof. R-factor CORRECTED for background'   no
+ '_pd_proc_ls_prof_cwR_factor'   'wProf.R-factor CORRECTED for background'   no
+ '_pd_proc_ls_prof_cwR_expected' 'wProf.Expected CORRECTED for background'   no
+ '_pd_proc_ls_prof_chi2'         'Chi-square for all considered points'      no
+ '_pd_proc_ls_prof_echi2'        'Chi-2 for points with Bragg contribution'  no
+#=============================================================================
+ 
+# 3. TITLE AND AUTHOR LIST
+ 
+_publ_section_title
+; '  A single-crystal infrared spectroscopic and X-ray diffraction study of'
+;
+_publ_section_title_footnote
+;
+;
+ 
+# The loop structure below should contain the names and addresses of all 
+# authors, in the required order of publication. Repeat as necessary.
+ 
+loop_
+    _publ_author_name
+    _publ_author_footnote
+    _publ_author_address
+?                                   #<--'Last name, first name' 
+; ?
+;
+; ?
+;
+ 
+#=============================================================================
+ 
+# 4. TEXT
+ 
+_publ_section_synopsis
+;  ?
+;
+_publ_section_abstract
+; ?
+;          
+_publ_section_comment
+; ?
+;
+_publ_section_exptl_prep      # Details of the preparation of the sample(s)
+                              # should be given here. 
+; ?
+;
+_publ_section_exptl_refinement
+; ?
+;
+_publ_section_references
+; ?
+;
+_publ_section_figure_captions
+; ?
+;
+_publ_section_acknowledgements
+; ?
+;
+ 
+#=============================================================================
+ 
+#=============================================================================
+# If more than one structure is reported, the remaining sections should be 
+# completed per structure. For each data set, replace the '?' in the
+# data_? line below by a unique identifier.
+ 
+data_ A single-crystal infrared spe
+ 
+#=============================================================================
+ 
+# 5. CHEMICAL DATA
+ 
+_chemical_name_systematic
+; ?
+;
+_chemical_name_common             ?
+_chemical_formula_moiety          ?
+_chemical_formula_structural      ?
+_chemical_formula_analytical      ?
+_chemical_formula_iupac           ?
+_chemical_formula_sum             ?
+_chemical_formula_weight          ?
+_chemical_melting_point           ?
+_chemical_compound_source         ?       # for minerals and 
+                                          # natural products
+ 
+loop_
+    _atom_type_symbol               
+    _atom_type_scat_Cromer_Mann_a1 
+    _atom_type_scat_Cromer_Mann_b1 
+    _atom_type_scat_Cromer_Mann_a2 
+    _atom_type_scat_Cromer_Mann_b2 
+    _atom_type_scat_Cromer_Mann_a3 
+    _atom_type_scat_Cromer_Mann_b3 
+    _atom_type_scat_Cromer_Mann_a4 
+    _atom_type_scat_Cromer_Mann_b4 
+    _atom_type_scat_Cromer_Mann_c 
+    _atom_type_scat_dispersion_real 
+    _atom_type_scat_dispersion_imag 
+    _atom_type_scat_source          
+mn    11.28190   5.34090   7.35730   0.34320   3.01930  17.86740
+       2.24410  83.75430   1.08960  -0.56800   2.80800
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+dy    26.50700   2.18020  17.63830   0.20217  14.55960  12.18990
+       2.96577 111.87400   4.29728 -10.42300   9.74800
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+sr    17.56630   1.55640   9.81840  14.09880   5.42200   0.16640
+       2.66940 132.37601   2.50640  -0.46500   1.82000
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+o      3.04850  13.27710   2.28680   5.70110   1.54630   0.32390
+       0.86700  32.90890   0.25080   0.04700   0.03200
+ International_Tables_for_Crystallography_Vol.C(1991)_Tables_6.1.1.4_and_6.1.1.5
+ 
+#=============================================================================
+ 
+# 6. POWDER SPECIMEN AND CRYSTAL DATA
+ 
+_symmetry_cell_setting                Orthorhombic
+_symmetry_space_group_name_H-M       'P b n m'
+_symmetry_space_group_name_Hall      '-P 2c 2ab'
+ 
+loop_
+    _symmetry_equiv_pos_as_xyz   #<--must include 'x,y,z'
+'x,y,z'
+'x+1/2,-y+1/2,-z'
+'-x+1/2,y+1/2,-z+1/2'
+'-x,-y,z+1/2'
+'-x,-y,-z'
+'-x+1/2,y+1/2,z'
+'x+1/2,-y+1/2,z+1/2'
+'x,y,-z+1/2'
+ 
+_cell_length_a                       5.40201
+_cell_length_b                       5.41494
+_cell_length_c                       7.62832
+_cell_angle_alpha                    90.00000
+_cell_angle_beta                     90.00000
+_cell_angle_gamma                    90.00000
+_cell_volume                         223.14005
+_cell_formula_units_Z                ?
+_cell_measurement_temperature        ?
+_cell_special_details
+; ?
+;
+# The next three fields give the specimen dimensions in mm.  The equatorial
+# plane contains the incident and diffracted beam.
+ 
+_pd_spec_size_axial               ?       # perpendicular to 
+                                          # equatorial plane
+_pd_spec_size_equat               ?       # parallel to 
+                                          # scattering vector
+                                          # in transmission
+_pd_spec_size_thick               ?       # parallel to 
+                                          # scattering vector
+                                          # in reflection
+ 
+# The next five fields are character fields that describe the specimen.
+ 
+_pd_spec_mounting                         # This field should be
+                                          # used to give details of the 
+                                          # container.
+; ?
+;
+_pd_spec_mount_mode               ?       # options are 'reflection'
+                                          # or 'transmission'
+_pd_spec_shape                    ?       # options are 'cylinder' 
+                                          # 'flat_sheet' or 'irregular'
+_pd_char_particle_morphology      ?
+_pd_char_colour                   ?       # use ICDD colour descriptions
+ 
+# The following three fields describe the preparation of the specimen.
+# The cooling rate is in K/min.  The pressure at which the sample was 
+# prepared is in kPa.  The temperature of preparation is in K.        
+ 
+_pd_prep_cool_rate                ?
+_pd_prep_pressure                 ?
+_pd_prep_temperature              ?
+ 
+# The next four fields are normally only needed for transmission experiments.
+ 
+_exptl_absorpt_coefficient_mu        ?
+_exptl_absorpt_correction_type       ?
+_exptl_absorpt_process_details       ?
+_exptl_absorpt_correction_T_min      ?
+_exptl_absorpt_correction_T_max      ?
+ 
+#=============================================================================
+ 
+# 7. EXPERIMENTAL DATA
+ 
+_exptl_special_details
+; ?
+;
+ 
+# The following item is used to identify the equipment used to record 
+# the powder pattern when the diffractogram was measured at a laboratory 
+# other than the authors' home institution, e.g. when neutron or synchrotron
+# radiation is used.
+ 
+_pd_instr_location
+; ?
+;
+_pd_calibration_special_details           # description of the method used
+                                          # to calibrate the instrument
+; ?
+;
+ 
+_diffrn_ambient_temperature          ?
+_diffrn_source              ? # Put here: 'rotating-anode X-ray tube' or similar 
+_diffrn_radiation_type     'X-ray' 
+_diffrn_source_target       ? # Put here the chemical symbol of the anode
+ 
+_diffrn_radiation_monochromator      ?
+_diffrn_measurement_device_type      ?
+_diffrn_measurement_method           ?
+_diffrn_detector_area_resol_mean     ? # Not in version 2.0.1
+_diffrn_detector                     ?
+_diffrn_detector_type                ? # make or model of detector
+_pd_meas_scan_method      ?   # options are 'step', 'cont',
+                              # 'tof', 'fixed' or
+                              # 'disp' (= dispersive)
+_pd_meas_special_details
+;  ?
+;
+ 
+#  The following four items give details of the measured (not processed)
+#  powder pattern.  Angles are in degrees.
+ 
+_pd_meas_number_of_points         5495
+_pd_meas_2theta_range_min         10.12000
+_pd_meas_2theta_range_max         120.00000
+_pd_meas_2theta_range_inc         0.020006
+ 
+#=============================================================================
+ 
+# 8. REFINEMENT DATA
+ 
+_refine_special_details
+; ?
+;
+ 
+# Use the next field to give any special details about the fitting of the
+# powder pattern.
+ 
+_pd_proc_ls_special_details
+; ?
+;
+ 
+# The next three items are given as text.
+ 
+_pd_proc_ls_profile_function      ?
+_pd_proc_ls_background_function   ?
+_pd_proc_ls_pref_orient_corr
+; ?
+;
+ 
+#  The following profile R-factors are NOT CORRECTED for background
+#  The sum is extended to all non-excluded points.
+#  These are the current CIF standard
+ 
+_pd_proc_ls_prof_R_factor                6.8453
+_pd_proc_ls_prof_wR_factor               9.7298
+_pd_proc_ls_prof_wR_expected             5.2718
+ 
+#  The following profile R-factors are CORRECTED for background
+#  The sum is extended to all non-excluded points.
+#  These items are not in the current CIF standard, but are defined above
+ 
+_pd_proc_ls_prof_cR_factor               10.1463
+_pd_proc_ls_prof_cwR_factor              12.5354
+_pd_proc_ls_prof_cwR_expected             6.7919
+ 
+#  The following items are not in the CIF standard, but are defined above
+ 
+_pd_proc_ls_prof_chi2                 3.4064
+_pd_proc_ls_prof_echi2                3.6503
+ 
+#  Items related to LS refinement
+ 
+_refine_ls_R_I_factor                 3.7639
+_refine_ls_number_reflns                   383
+_refine_ls_number_parameters                19
+_refine_ls_number_restraints                 0
+ 
+# The following four items apply to angular dispersive measurements.
+# 2theta minimum, maximum and increment (in degrees) are for the 
+# intensities used in the refinement.
+ 
+_pd_proc_2theta_range_min               10.1565
+_pd_proc_2theta_range_max              120.0365
+_pd_proc_2theta_range_inc              0.020006
+_pd_proc_wavelength                    1.540560
+ 
+_pd_block_diffractogram_id        ?  # The id used for the block containing
+                                     # the powder pattern profile (section 11)
+ 
+# Give appropriate details in the next two text fields.
+ 
+_pd_proc_info_excluded_regions    ?
+_pd_proc_info_data_reduction      ?
+ 
+# The following items are used to identify the programs used.
+ 
+_computing_data_collection           ?
+_computing_structure_solution        ?
+_computing_structure_refinement      FULLPROF
+_computing_molecular_graphics        ?
+_computing_publication_material      ?
+ 
+#=============================================================================
+ 
+# 9. ATOMIC COORDINATES AND DISPLACEMENT PARAMETERS
+ 
+loop_
+    _atom_site_label
+    _atom_site_fract_x
+    _atom_site_fract_y
+    _atom_site_fract_z
+    _atom_site_U_iso_or_equiv
+    _atom_site_occupancy
+    _atom_site_adp_type              # Not in version 2.0.1
+    _atom_site_type_symbol
+ Mn1  0.00000  0.50000  0.00000  0.00148  1.00000 Uani Mn  
+ Dy1 -0.0027(6)  0.02575(15)  0.25000  0.00916  0.49043 Uani Dy  
+ Sr1 -0.0027(6)  0.02575(15)  0.25000  0.00446  0.49043 Uani Sr  
+ O1  0.543(6) -0.0129(19)  0.25000  0.10572  1.00006 Uani O   
+ O2  0.2677(18)  0.2774(17)  0.0351(9)  0.00546  1.06274 Uani O   
+ 
+loop_
+    _atom_site_aniso_label 
+    _atom_site_aniso_U_11  
+    _atom_site_aniso_U_22  
+    _atom_site_aniso_U_33  
+    _atom_site_aniso_U_12  
+    _atom_site_aniso_U_13  
+    _atom_site_aniso_U_23  
+    _atom_site_aniso_type_symbol
+ Mn1 -0.00200 -0.00076  0.00719 -0.00529 -0.00159  0.00115  Mn  
+ Dy1  0.00686  0.01866  0.00198  0.01226  0.00000  0.00000  Dy  
+ Sr1  0.00251 -0.00233  0.01321  0.00353  0.00021  0.00000  Sr  
+ O1  0.21605  0.08627  0.01483 -0.10071  0.00000  0.00000  O   
+ O2 -0.00438  0.02460 -0.00383  0.00317  0.01111 -0.00435  O   
+ 
+# Note: if the displacement parameters were refined anisotropically
+# the U matrices should be given as for single-crystal studies.
+ 
+#=============================================================================
+ 
+# 10. DISTANCES AND ANGLES / MOLECULAR GEOMETRY 
+ 
+_geom_special_details                ?
+ 
+loop_
+    _geom_bond_atom_site_label_1  
+    _geom_bond_atom_site_label_2  
+    _geom_bond_site_symmetry_1    
+    _geom_bond_site_symmetry_2    
+    _geom_bond_distance           
+    _geom_bond_publ_flag          
+    ?   ?   ?   ?   ?   ?
+ 
+loop_
+    _geom_contact_atom_site_label_1 
+    _geom_contact_atom_site_label_2 
+    _geom_contact_distance          
+    _geom_contact_site_symmetry_1   
+    _geom_contact_site_symmetry_2   
+    _geom_contact_publ_flag         
+    ?   ?   ?   ?   ?   ?
+ 
+loop_
+_geom_angle_atom_site_label_1 
+_geom_angle_atom_site_label_2 
+_geom_angle_atom_site_label_3 
+_geom_angle_site_symmetry_1   
+_geom_angle_site_symmetry_2   
+_geom_angle_site_symmetry_3   
+_geom_angle                   
+_geom_angle_publ_flag         
+?   ?   ?   ?   ?   ?   ?   ?
+ 
+loop_
+_geom_torsion_atom_site_label_1 
+_geom_torsion_atom_site_label_2 
+_geom_torsion_atom_site_label_3 
+_geom_torsion_atom_site_label_4 
+_geom_torsion_site_symmetry_1   
+_geom_torsion_site_symmetry_2   
+_geom_torsion_site_symmetry_3   
+_geom_torsion_site_symmetry_4   
+_geom_torsion                   
+_geom_torsion_publ_flag         
+?   ?   ?   ?   ?   ?   ?   ?   ?   ?
+ 
+loop_
+_geom_hbond_atom_site_label_D 
+_geom_hbond_atom_site_label_H 
+_geom_hbond_atom_site_label_A 
+_geom_hbond_site_symmetry_D   
+_geom_hbond_site_symmetry_H   
+_geom_hbond_site_symmetry_A   
+_geom_hbond_distance_DH       
+_geom_hbond_distance_HA       
+_geom_hbond_distance_DA       
+_geom_hbond_angle_DHA         
+_geom_hbond_publ_flag         
+?   ?   ?   ?   ?   ?   ?   ?   ?   ?   ?
+ 
+#=============================================================================
+ 
+#=============================================================================
+# Additional structures (last six sections and associated data_? identifiers) 
+# may be added at this point.                                                 
+#=============================================================================
+ 
+# The following lines are used to test the character set of files sent by     
+# network email or other means. They are not part of the CIF data set.        
+# abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789              
+# !@#$%^&*()_+{}:"~<>?|\-=[];'`,./ 

data/data/DSMO/dsmo.cif

@@ -0,0 +1,44 @@
+#======================================================================
+
+# CRYSTAL DATA
+
+#----------------------------------------------------------------------
+
+data_VESTA_phase_1
+
+
+_chemical_name_common                  'A single-crystal infrared spe'
+_cell_length_a                         5.40201
+_cell_length_b                         5.41494
+_cell_length_c                         7.62832
+_cell_angle_alpha                      90
+_cell_angle_beta                       90
+_cell_angle_gamma                      90
+_space_group_name_H-M_alt              'P b n m'
+_space_group_IT_number                 62
+
+loop_
+_space_group_symop_operation_xyz
+   'x, y, z'
+   '-x, -y, -z'
+   'x+1/2, -y+1/2, -z'
+   '-x+1/2, y+1/2, z'
+   '-x, -y, z+1/2'
+   'x, y, -z+1/2'
+   '-x+1/2, y+1/2, -z+1/2'
+   'x+1/2, -y+1/2, z+1/2'
+
+loop_
+   _atom_site_label
+   _atom_site_occupancy
+   _atom_site_fract_x
+   _atom_site_fract_y
+   _atom_site_fract_z
+   _atom_site_adp_type
+   _atom_site_U_iso_or_equiv
+   _atom_site_type_symbol
+   Mn1        1.0     0.000000      0.500000      0.000000     Uiso  0.001477 Mn
+   Dy1        0.5    -0.0027(7)     0.02575(16)   0.250000     Uiso  0.009167 Dy
+   Sr1        0.5    -0.0027(7)     0.02575(16)   0.250000     Uiso  0.004463 Sr
+   O1         1.0     0.543(6)     -0.013(2)      0.250000     Uiso  0.105717 O
+   O2         1.0     0.2677(18)    0.2774(17)    0.0351(9)    Uiso  0.005463 O

data/data/DSMO/dsmo.csv

@@ -0,0 +1,5501 @@
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data/data/DSMO/dsmo.gpx

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+oid sha256:da429eec3c6224ceb04573222911e6a420a8132c4f62c17129f087c6f27171ba
+size 313078

data/data/LiCoO2/COD/1533825.cif

@@ -0,0 +1,96 @@
+#------------------------------------------------------------------------------
+#$Date: 2016-02-13 21:28:24 +0200 (Sat, 13 Feb 2016) $
+#$Revision: 176429 $
+#$URL: file:///home/coder/svn-repositories/cod/cif/1/53/38/1533825.cif $
+#------------------------------------------------------------------------------
+#
+# This file is available in the Crystallography Open Database (COD),
+# http://www.crystallography.net/
+#
+# All data on this site have been placed in the public domain by the
+# contributors.
+#
+data_1533825
+loop_
+_publ_author_name
+'Hong Jin K'
+'Oh Seung M'
+_publ_section_title
+;
+ Crystal structure and electrochemical performance of Li Ni1-x Cox O2 (0
+ <= x < 1.0) according to Co substitution
+;
+_journal_name_full
+'Journal of the Korean Electrochemical Society'
+_journal_page_first              1
+_journal_page_last               5
+_journal_volume                  6
+_journal_year                    2003
+_chemical_formula_sum            'Co Li O2'
+_chemical_name_systematic        'Li (Co O2)'
+_space_group_IT_number           166
+_symmetry_space_group_name_Hall  '-R 3 2"'
+_symmetry_space_group_name_H-M   'R -3 m :H'
+_cell_angle_alpha                90
+_cell_angle_beta                 90
+_cell_angle_gamma                120
+_cell_formula_units_Z            3
+_cell_length_a                   2.814
+_cell_length_b                   2.814
+_cell_length_c                   14.048
+_cell_volume                     96.337
+_citation_journal_id_ASTM        JKESFC
+_cod_data_source_file            HongJinK_JKESFC_2003_777.cif
+_cod_data_source_block           Co1Li1O2
+_cod_original_cell_volume        96.33704
+_cod_original_formula_sum        'Co1 Li1 O2'
+_cod_database_code               1533825
+loop_
+_symmetry_equiv_pos_as_xyz
+x,y,z
+-y,x-y,z
+-x+y,-x,z
+y,x,-z
+-x,-x+y,-z
+x-y,-y,-z
+-x,-y,-z
+y,-x+y,-z
+x-y,x,-z
+-y,-x,z
+x,x-y,z
+-x+y,y,z
+x+2/3,y+1/3,z+1/3
+-y+2/3,x-y+1/3,z+1/3
+-x+y+2/3,-x+1/3,z+1/3
+y+2/3,x+1/3,-z+1/3
+-x+2/3,-x+y+1/3,-z+1/3
+x-y+2/3,-y+1/3,-z+1/3
+-x+2/3,-y+1/3,-z+1/3
+y+2/3,-x+y+1/3,-z+1/3
+x-y+2/3,x+1/3,-z+1/3
+-y+2/3,-x+1/3,z+1/3
+x+2/3,x-y+1/3,z+1/3
+-x+y+2/3,y+1/3,z+1/3
+x+1/3,y+2/3,z+2/3
+-y+1/3,x-y+2/3,z+2/3
+-x+y+1/3,-x+2/3,z+2/3
+y+1/3,x+2/3,-z+2/3
+-x+1/3,-x+y+2/3,-z+2/3
+x-y+1/3,-y+2/3,-z+2/3
+-x+1/3,-y+2/3,-z+2/3
+y+1/3,-x+y+2/3,-z+2/3
+x-y+1/3,x+2/3,-z+2/3
+-y+1/3,-x+2/3,z+2/3
+x+1/3,x-y+2/3,z+2/3
+-x+y+1/3,y+2/3,z+2/3
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_occupancy
+_atom_site_U_iso_or_equiv
+O1 O-2 0 0 0.2268 1 0.0
+Co1 Co+3 0 0 0 1 0.0
+Li1 Li+1 0 0 0.5 1 0.0

data/data/LiCoO2/COD/1550397.cif

@@ -0,0 +1,113 @@
+#------------------------------------------------------------------------------
+#$Date: 2019-01-10 12:36:08 +0200 (Thu, 10 Jan 2019) $
+#$Revision: 212886 $
+#$URL: file:///home/coder/svn-repositories/cod/cif/1/55/03/1550397.cif $
+#------------------------------------------------------------------------------
+#
+# This file is available in the Crystallography Open Database (COD),
+# http://www.crystallography.net/
+#
+# All data on this site have been placed in the public domain by the
+# contributors.
+#
+data_1550397
+loop_
+_publ_author_name
+'Rosolen JM'
+'Ballirano P'
+'Berrettoni M'
+'Decker F'
+'Gregorkiewitz M'
+_publ_contact_author_email       gregormigu@gmail.com
+_publ_contact_author_name        'M Gregorkiewitz'
+_publ_section_title
+;
+ Structural Assessment of the Electrochemical Performance of LixCoO2
+ Membrane Electrodes by X-Ray Diffraction and Absorpion Refinements
+;
+_journal_name_full               Ionics
+_journal_page_first              345
+_journal_page_last               354
+_journal_volume                  3
+_journal_year                    1997
+_chemical_compound_source        synthetic
+_chemical_formula_sum            'Co Li O2'
+_chemical_formula_weight         97.87
+_chemical_name_systematic
+;
+Lithium Cobalt Oxide
+;
+_space_group_IT_number           166
+_symmetry_Int_Tables_number      166
+_symmetry_space_group_name_Hall  '-R 3 2"'
+_symmetry_space_group_name_H-M   'R -3 m :H'
+_cell_angle_alpha                90
+_cell_angle_beta                 90
+_cell_angle_gamma                120
+_cell_formula_units_Z            3
+_cell_length_a                   2.8185(1)
+_cell_length_b                   2.8185(1)
+_cell_length_c                   14.0664(2)
+_cell_volume                     96.772(5)
+_diffrn_radiation_monochromator  graphite
+_diffrn_radiation_type           'Cu K\a'
+_diffrn_radiation_wavelength     1.5406
+_pd_instr_geometry               Bragg-Brentano
+_pd_phase_name                   'Lithium Cobalt Oxide LixCoO2, x=1.00'
+_refine_ls_goodness_of_fit_all   1.04
+_refine_ls_R_I_factor            0.039
+_cod_data_source_file            gregorkiewitz97Li1.00CoO2LC.txt
+_cod_data_source_block           VESTA_phase_1
+_cod_original_cell_volume        96.775(3)
+_cod_original_formula_sum        'Li1.00 Co O2'
+_cod_database_code               1550397
+loop_
+_symmetry_equiv_pos_as_xyz
+x,y,z
+-y,x-y,z
+-x+y,-x,z
+y,x,-z
+-x,-x+y,-z
+x-y,-y,-z
+-x,-y,-z
+y,-x+y,-z
+x-y,x,-z
+-y,-x,z
+x,x-y,z
+-x+y,y,z
+x+2/3,y+1/3,z+1/3
+-y+2/3,x-y+1/3,z+1/3
+-x+y+2/3,-x+1/3,z+1/3
+y+2/3,x+1/3,-z+1/3
+-x+2/3,-x+y+1/3,-z+1/3
+x-y+2/3,-y+1/3,-z+1/3
+-x+2/3,-y+1/3,-z+1/3
+y+2/3,-x+y+1/3,-z+1/3
+x-y+2/3,x+1/3,-z+1/3
+-y+2/3,-x+1/3,z+1/3
+x+2/3,x-y+1/3,z+1/3
+-x+y+2/3,y+1/3,z+1/3
+x+1/3,y+2/3,z+2/3
+-y+1/3,x-y+2/3,z+2/3
+-x+y+1/3,-x+2/3,z+2/3
+y+1/3,x+2/3,-z+2/3
+-x+1/3,-x+y+2/3,-z+2/3
+x-y+1/3,-y+2/3,-z+2/3
+-x+1/3,-y+2/3,-z+2/3
+y+1/3,-x+y+2/3,-z+2/3
+x-y+1/3,x+2/3,-z+2/3
+-y+1/3,-x+2/3,z+2/3
+x+1/3,x-y+2/3,z+2/3
+-x+y+1/3,y+2/3,z+2/3
+loop_
+_atom_site_label
+_atom_site_occupancy
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_adp_type
+_atom_site_U_iso_or_equiv
+_atom_site_type_symbol
+Li 1.0 0 0 0.5 Uiso 0.015(2) Li
+Co 1.0 0 0 0 Uiso 0.019(1) Co
+O1 1.0 0 0 0.2592(1) Uiso 0.023(1) O

data/data/LiCoO2/COD/4505482.cif

@@ -0,0 +1,384 @@
+#------------------------------------------------------------------------------
+#$Date: 2016-03-24 02:37:24 +0200 (Thu, 24 Mar 2016) $
+#$Revision: 179589 $
+#$URL: file:///home/coder/svn-repositories/cod/cif/4/50/54/4505482.cif $
+#------------------------------------------------------------------------------
+#
+# This file is available in the Crystallography Open Database (COD),
+# http://www.crystallography.net/
+#
+# All data on this site have been placed in the public domain by the
+# contributors.
+#
+data_4505482
+loop_
+_publ_author_name
+'Lin, Qiyin'
+'Li, Qing'
+'Gray, Kenneth E.'
+'Mitchell, John F.'
+_publ_section_title
+;
+ Vapor Growth and Chemical Delithiation of Stoichiometric LiCoO2Crystals
+;
+_journal_issue                   3
+_journal_name_full               'Crystal Growth & Design'
+_journal_page_first              1232
+_journal_paper_doi               10.1021/cg201238n
+_journal_volume                  12
+_journal_year                    2012
+_chemical_formula_sum            'Co Li O2'
+_chemical_formula_weight         97.87
+_chemical_name_systematic
+; 
+? 
+;
+_space_group_IT_number           166
+_symmetry_cell_setting           trigonal
+_symmetry_space_group_name_Hall  '-R 3 2"'
+_symmetry_space_group_name_H-M   'R -3 m :H'
+_atom_sites_solution_primary     direct
+_atom_sites_solution_secondary   difmap
+_audit_creation_method           SHELXL-97
+_cell_angle_alpha                90.00
+_cell_angle_beta                 90.00
+_cell_angle_gamma                120.00
+_cell_formula_units_Z            3
+_cell_length_a                   2.8150(3)
+_cell_length_b                   2.8150(3)
+_cell_length_c                   14.0516(6)
+_cell_measurement_reflns_used    1478
+_cell_measurement_temperature    298(2)
+_cell_measurement_theta_max      34.34
+_cell_measurement_theta_min      4.35
+_cell_volume                     96.430(15)
+_computing_cell_refinement       'STOE X-AREA'
+_computing_data_collection       'STOE X-AREA'
+_computing_data_reduction        'STOE X-RED'
+_computing_structure_refinement  'SHELXL-97 (Sheldrick, 2008)'
+_computing_structure_solution    'SHELXS-97 (Sheldrick, 2008)'
+_diffrn_ambient_temperature      298(2)
+_diffrn_detector_area_resol_mean 6.67
+_diffrn_measured_fraction_theta_full 1.000
+_diffrn_measured_fraction_theta_max 1.000
+_diffrn_measurement_device_type  'STOE IPDS 2T'
+_diffrn_measurement_method       'rotation method'
+_diffrn_radiation_monochromator  graphite
+_diffrn_radiation_source         'fine-focus sealed tube'
+_diffrn_radiation_type           MoK\a
+_diffrn_radiation_wavelength     0.71073
+_diffrn_reflns_av_R_equivalents  0.0425
+_diffrn_reflns_av_sigmaI/netI    0.0153
+_diffrn_reflns_limit_h_max       4
+_diffrn_reflns_limit_h_min       -4
+_diffrn_reflns_limit_k_max       4
+_diffrn_reflns_limit_k_min       -3
+_diffrn_reflns_limit_l_max       22
+_diffrn_reflns_limit_l_min       -22
+_diffrn_reflns_number            776
+_diffrn_reflns_theta_full        34.28
+_diffrn_reflns_theta_max         34.28
+_diffrn_reflns_theta_min         4.35
+_exptl_absorpt_coefficient_mu    12.685
+_exptl_absorpt_correction_T_max  0.9785
+_exptl_absorpt_correction_T_min  0.4141
+_exptl_absorpt_correction_type   integration
+_exptl_absorpt_process_details   '(Coppens, 1970)'
+_exptl_crystal_colour            black
+_exptl_crystal_density_diffrn    5.056
+_exptl_crystal_density_method    'not measured'
+_exptl_crystal_description       plate
+_exptl_crystal_F_000             138
+_exptl_crystal_size_max          0.05
+_exptl_crystal_size_mid          0.02
+_exptl_crystal_size_min          0.01
+_refine_diff_density_max         0.635
+_refine_diff_density_min         -1.270
+_refine_diff_density_rms         0.207
+_refine_ls_extinction_coef       0.020(7)
+_refine_ls_extinction_method     none
+_refine_ls_goodness_of_fit_ref   1.353
+_refine_ls_matrix_type           full
+_refine_ls_number_parameters     8
+_refine_ls_number_reflns         71
+_refine_ls_number_restraints     0
+_refine_ls_restrained_S_all      1.353
+_refine_ls_R_factor_all          0.0190
+_refine_ls_R_factor_gt           0.0190
+_refine_ls_shift/su_max          0.000
+_refine_ls_shift/su_mean         0.000
+_refine_ls_structure_factor_coef Fsqd
+_refine_ls_weighting_details
+'calc w=1/[\s^2^(Fo^2^)+(0.0280P)^2^+0.1931P] where P=(Fo^2^+2Fc^2^)/3'
+_refine_ls_weighting_scheme      calc
+_refine_ls_wR_factor_gt          0.0487
+_refine_ls_wR_factor_ref         0.0487
+_reflns_number_gt                71
+_reflns_number_total             71
+_reflns_threshold_expression     >2sigma(I)
+_cod_data_source_file            cg201238n_si_001.cif
+_cod_data_source_block           I
+_cod_original_sg_symbol_H-M      'R -3 m'
+_cod_database_code               4505482
+loop_
+_symmetry_equiv_pos_as_xyz
+'x, y, z'
+'-y, x-y, z'
+'-x+y, -x, z'
+'y, x, -z'
+'x-y, -y, -z'
+'-x, -x+y, -z'
+'x+2/3, y+1/3, z+1/3'
+'-y+2/3, x-y+1/3, z+1/3'
+'-x+y+2/3, -x+1/3, z+1/3'
+'y+2/3, x+1/3, -z+1/3'
+'x-y+2/3, -y+1/3, -z+1/3'
+'-x+2/3, -x+y+1/3, -z+1/3'
+'x+1/3, y+2/3, z+2/3'
+'-y+1/3, x-y+2/3, z+2/3'
+'-x+y+1/3, -x+2/3, z+2/3'
+'y+1/3, x+2/3, -z+2/3'
+'x-y+1/3, -y+2/3, -z+2/3'
+'-x+1/3, -x+y+2/3, -z+2/3'
+'-x, -y, -z'
+'y, -x+y, -z'
+'x-y, x, -z'
+'-y, -x, z'
+'-x+y, y, z'
+'x, x-y, z'
+'-x+2/3, -y+1/3, -z+1/3'
+'y+2/3, -x+y+1/3, -z+1/3'
+'x-y+2/3, x+1/3, -z+1/3'
+'-y+2/3, -x+1/3, z+1/3'
+'-x+y+2/3, y+1/3, z+1/3'
+'x+2/3, x-y+1/3, z+1/3'
+'-x+1/3, -y+2/3, -z+2/3'
+'y+1/3, -x+y+2/3, -z+2/3'
+'x-y+1/3, x+2/3, -z+2/3'
+'-y+1/3, -x+2/3, z+2/3'
+'-x+y+1/3, y+2/3, z+2/3'
+'x+1/3, x-y+2/3, z+2/3'
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+_atom_site_symmetry_multiplicity
+_atom_site_calc_flag
+_atom_site_refinement_flags
+Li1 Li 0.0000 0.0000 0.0000 0.014(2) Uani 1 12 d S
+Co1 Co 0.0000 0.0000 0.5000 0.0047(2) Uani 1 12 d S
+O1 O 0.0000 0.0000 0.2397(2) 0.0070(5) Uani 1 6 d S
+loop_
+_atom_site_aniso_label
+_atom_site_aniso_U_11
+_atom_site_aniso_U_22
+_atom_site_aniso_U_33
+_atom_site_aniso_U_23
+_atom_site_aniso_U_13
+_atom_site_aniso_U_12
+Li1 0.015(4) 0.015(4) 0.012(4) 0.000 0.000 0.0076(18)
+Co1 0.0039(3) 0.0039(3) 0.0065(3) 0.000 0.000 0.00193(14)
+O1 0.0058(8) 0.0058(8) 0.0095(10) 0.000 0.000 0.0029(4)
+loop_
+_atom_type_symbol
+_atom_type_description
+_atom_type_scat_dispersion_real
+_atom_type_scat_dispersion_imag
+_atom_type_scat_source
+Li Li -0.0003 0.0001 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4'
+O O 0.0106 0.0060 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4'
+Co Co 0.3494 0.9721 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4'
+loop_
+_geom_angle_atom_site_label_1
+_geom_angle_atom_site_label_2
+_geom_angle_atom_site_label_3
+_geom_angle
+_geom_angle_site_symmetry_1
+_geom_angle_site_symmetry_3
+O1 Li1 O1 180.00(12) 25 13_444
+O1 Li1 O1 84.59(9) 25 25_445
+O1 Li1 O1 95.41(9) 13_444 25_445
+O1 Li1 O1 95.41(9) 25 13_554
+O1 Li1 O1 84.59(9) 13_444 13_554
+O1 Li1 O1 180.00(12) 25_445 13_554
+O1 Li1 O1 84.59(9) 25 25_455
+O1 Li1 O1 95.41(9) 13_444 25_455
+O1 Li1 O1 84.59(9) 25_445 25_455
+O1 Li1 O1 95.41(9) 13_554 25_455
+O1 Li1 O1 95.41(9) 25 13_544
+O1 Li1 O1 84.59(9) 13_444 13_544
+O1 Li1 O1 95.41(9) 25_445 13_544
+O1 Li1 O1 84.59(9) 13_554 13_544
+O1 Li1 O1 180.00(12) 25_455 13_544
+O1 Li1 Li1 132.30(4) 25 1_445
+O1 Li1 Li1 47.70(4) 13_444 1_445
+O1 Li1 Li1 47.70(4) 25_445 1_445
+O1 Li1 Li1 132.30(4) 13_554 1_445
+O1 Li1 Li1 90.0 25_455 1_445
+O1 Li1 Li1 90.0 13_544 1_445
+O1 Li1 Li1 47.70(4) 25 1_665
+O1 Li1 Li1 132.30(4) 13_444 1_665
+O1 Li1 Li1 132.30(4) 25_445 1_665
+O1 Li1 Li1 47.70(4) 13_554 1_665
+O1 Li1 Li1 90.0 25_455 1_665
+O1 Li1 Li1 90.0 13_544 1_665
+Li1 Li1 Li1 180.0 1_445 1_665
+O1 Li1 Li1 90.0 25 1_565
+O1 Li1 Li1 90.0 13_444 1_565
+O1 Li1 Li1 132.30(4) 25_445 1_565
+O1 Li1 Li1 47.70(4) 13_554 1_565
+O1 Li1 Li1 47.70(4) 25_455 1_565
+O1 Li1 Li1 132.30(4) 13_544 1_565
+Li1 Li1 Li1 120.0 1_445 1_565
+Li1 Li1 Li1 60.0 1_665 1_565
+O1 Li1 Li1 132.30(4) 25 1_455
+O1 Li1 Li1 47.70(4) 13_444 1_455
+O1 Li1 Li1 90.0 25_445 1_455
+O1 Li1 Li1 90.0 13_554 1_455
+O1 Li1 Li1 47.70(4) 25_455 1_455
+O1 Li1 Li1 132.30(4) 13_544 1_455
+Li1 Li1 Li1 60.0 1_445 1_455
+Li1 Li1 Li1 120.0 1_665 1_455
+Li1 Li1 Li1 60.0 1_565 1_455
+O1 Li1 Li1 47.70(4) 25 1_655
+O1 Li1 Li1 132.30(4) 13_444 1_655
+O1 Li1 Li1 90.0 25_445 1_655
+O1 Li1 Li1 90.0 13_554 1_655
+O1 Li1 Li1 132.30(4) 25_455 1_655
+O1 Li1 Li1 47.70(4) 13_544 1_655
+Li1 Li1 Li1 120.0 1_445 1_655
+Li1 Li1 Li1 60.0 1_665 1_655
+Li1 Li1 Li1 120.0 1_565 1_655
+Li1 Li1 Li1 180.0 1_455 1_655
+O1 Li1 Li1 90.0 25 1_545
+O1 Li1 Li1 90.0 13_444 1_545
+O1 Li1 Li1 47.70(4) 25_445 1_545
+O1 Li1 Li1 132.30(4) 13_554 1_545
+O1 Li1 Li1 132.30(4) 25_455 1_545
+O1 Li1 Li1 47.70(4) 13_544 1_545
+Li1 Li1 Li1 60.0 1_445 1_545
+Li1 Li1 Li1 120.0 1_665 1_545
+Li1 Li1 Li1 180.0 1_565 1_545
+Li1 Li1 Li1 120.0 1_455 1_545
+Li1 Li1 Li1 60.0 1_655 1_545
+O1 Co1 O1 180.0 31_445 7
+O1 Co1 O1 94.17(10) 31_445 31
+O1 Co1 O1 85.83(10) 7 31
+O1 Co1 O1 85.83(10) 31_445 7_445
+O1 Co1 O1 94.17(10) 7 7_445
+O1 Co1 O1 180.0 31 7_445
+O1 Co1 O1 94.17(10) 31_445 31_545
+O1 Co1 O1 85.83(10) 7 31_545
+O1 Co1 O1 94.17(10) 31 31_545
+O1 Co1 O1 85.83(10) 7_445 31_545
+O1 Co1 O1 85.83(10) 31_445 7_455
+O1 Co1 O1 94.17(10) 7 7_455
+O1 Co1 O1 85.83(10) 31 7_455
+O1 Co1 O1 94.17(10) 7_445 7_455
+O1 Co1 O1 180.00(14) 31_545 7_455
+O1 Co1 Co1 137.09(5) 31_445 1_665
+O1 Co1 Co1 42.91(5) 7 1_665
+O1 Co1 Co1 42.91(5) 31 1_665
+O1 Co1 Co1 137.09(5) 7_445 1_665
+O1 Co1 Co1 90.0 31_545 1_665
+O1 Co1 Co1 90.0 7_455 1_665
+O1 Co1 Co1 42.91(5) 31_445 1_445
+O1 Co1 Co1 137.09(5) 7 1_445
+O1 Co1 Co1 137.09(5) 31 1_445
+O1 Co1 Co1 42.91(5) 7_445 1_445
+O1 Co1 Co1 90.000(1) 31_545 1_445
+O1 Co1 Co1 90.0 7_455 1_445
+Co1 Co1 Co1 180.0 1_665 1_445
+O1 Co1 Co1 90.0 31_445 1_545
+O1 Co1 Co1 90.0 7 1_545
+O1 Co1 Co1 137.09(5) 31 1_545
+O1 Co1 Co1 42.91(5) 7_445 1_545
+O1 Co1 Co1 42.91(5) 31_545 1_545
+O1 Co1 Co1 137.09(5) 7_455 1_545
+Co1 Co1 Co1 120.0 1_665 1_545
+Co1 Co1 Co1 60.0 1_445 1_545
+O1 Co1 Co1 42.91(5) 31_445 1_455
+O1 Co1 Co1 137.09(5) 7 1_455
+O1 Co1 Co1 90.0 31 1_455
+O1 Co1 Co1 90.0 7_445 1_455
+O1 Co1 Co1 137.09(5) 31_545 1_455
+O1 Co1 Co1 42.91(5) 7_455 1_455
+Co1 Co1 Co1 120.0 1_665 1_455
+Co1 Co1 Co1 60.0 1_445 1_455
+Co1 Co1 Co1 120.0 1_545 1_455
+O1 Co1 Co1 137.09(5) 31_445 1_655
+O1 Co1 Co1 42.91(5) 7 1_655
+O1 Co1 Co1 90.0 31 1_655
+O1 Co1 Co1 90.0 7_445 1_655
+O1 Co1 Co1 42.91(5) 31_545 1_655
+O1 Co1 Co1 137.09(5) 7_455 1_655
+Co1 Co1 Co1 60.0 1_665 1_655
+Co1 Co1 Co1 120.0 1_445 1_655
+Co1 Co1 Co1 60.0 1_545 1_655
+Co1 Co1 Co1 180.0 1_455 1_655
+O1 Co1 Co1 90.0 31_445 1_565
+O1 Co1 Co1 90.0 7 1_565
+O1 Co1 Co1 42.91(5) 31 1_565
+O1 Co1 Co1 137.09(5) 7_445 1_565
+O1 Co1 Co1 137.09(5) 31_545 1_565
+O1 Co1 Co1 42.91(5) 7_455 1_565
+Co1 Co1 Co1 60.0 1_665 1_565
+Co1 Co1 Co1 120.0 1_445 1_565
+Co1 Co1 Co1 180.0 1_545 1_565
+Co1 Co1 Co1 60.0 1_455 1_565
+Co1 Co1 Co1 120.0 1_655 1_565
+Co1 O1 Co1 94.17(10) 13_444 13_554
+Co1 O1 Co1 94.17(10) 13_444 13_544
+Co1 O1 Co1 94.17(10) 13_554 13_544
+Co1 O1 Li1 173.25(13) 13_444 7
+Co1 O1 Li1 90.419(13) 13_554 7
+Co1 O1 Li1 90.420(13) 13_544 7
+Co1 O1 Li1 90.419(13) 13_444 7_445
+Co1 O1 Li1 173.25(13) 13_554 7_445
+Co1 O1 Li1 90.420(13) 13_544 7_445
+Li1 O1 Li1 84.60(9) 7 7_445
+Co1 O1 Li1 90.420(13) 13_444 7_455
+Co1 O1 Li1 90.420(13) 13_554 7_455
+Co1 O1 Li1 173.25(13) 13_544 7_455
+Li1 O1 Li1 84.60(9) 7 7_455
+Li1 O1 Li1 84.60(9) 7_445 7_455
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+Li1 O1 2.0915(18) 25
+Li1 O1 2.0915(18) 13_444
+Li1 O1 2.0915(18) 25_445
+Li1 O1 2.0915(18) 13_554
+Li1 O1 2.0915(18) 25_455
+Li1 O1 2.0915(18) 13_544
+Li1 Li1 2.8150(3) 1_445
+Li1 Li1 2.8150(3) 1_665
+Li1 Li1 2.8150(3) 1_565
+Li1 Li1 2.8150(3) 1_455
+Li1 Li1 2.8150(3) 1_655
+Li1 Li1 2.8150(3) 1_545
+Co1 O1 1.9218(15) 31_445
+Co1 O1 1.9218(15) 7
+Co1 O1 1.9218(15) 31
+Co1 O1 1.9218(15) 7_445
+Co1 O1 1.9218(15) 31_545
+Co1 O1 1.9218(15) 7_455
+Co1 Co1 2.8150(3) 1_665
+Co1 Co1 2.8150(3) 1_445
+Co1 Co1 2.8150(3) 1_545
+Co1 Co1 2.8150(3) 1_455
+Co1 Co1 2.8150(3) 1_655
+Co1 Co1 2.8150(3) 1_565
+O1 Co1 1.9218(15) 13_444
+O1 Co1 1.9218(15) 13_554
+O1 Co1 1.9218(15) 13_544
+O1 Li1 2.0914(18) 7
+O1 Li1 2.0914(18) 7_445
+O1 Li1 2.0914(18) 7_455

data/data/LiCoO2/LiCoO2_init.gpx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18e08863e891ffdde89f8650b6903d9b86c6c1106033e18a2490e307199d0d3f
+size 229130

data/data/LiCoO2/Powder-data-Bruker/LiCoO2.UXD

@@ -0,0 +1,520 @@
+; E:\COD-all\Powbase-to-COD\fullprof\LiCoO2\LiCoO2.RAW - (Diffrac AT V1) File converted by XCH V1.0
+_FILEVERSION=1
+_SAMPLE='LICoO2'
+_WL1=1.540600
+_WL2=1.544430
+_WL3=1.392600
+_WLRATIO=0.514000
+_ANODE='Cu'
+; (Data for Range number 1)
+_DRIVE='COUPLED'
+_STEPTIME=12.500000
+_STEPSIZE=0.030000
+_STEPMODE='C'
+_START=15.985000
+_2THETA=15.985000
+_THETA=0.000000
+_KHI=0.000000
+_PHI=0.000000
+_COUNTS
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data/data/LiCoO2/Powder-data-Bruker/LiCoO2.dat

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+      416       466       431       408       391       419       419       393
+      418       415       419       423       467       470       571       586
+      760       949      1005       821       672       667       734       853
+      769       660       651       572       647       832      1110      1545
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+      403       401       404       383       391       357       423       402
+      437       418       459       447       437       460       470       516
+      559       539       529       569       553       605       749       880
+     1166      1750      2952      4200      4427      3198      2041      1601
+     1955      2497      2699      2126      1297       842       687       570
+      587       554       533       542       496       516       493       517
+      519       571       531       591       618       746       876      1357
+     2157      3405      3845      3093      1890      1463      1462      2026
+     2375      2186      1372       880       652       585       530       505
+      501       473       467       454       424       429       415       402
+      425       422       407       412       372       438       416       427
+      398       397       415       411       395       404       403       367
+      430       410       395       402       409       428       371       386
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+      398       361       392       407       427       401       449       368
+      387       404       418       439       470       442       455       469
+      476       488       508       551       605       738       968      1476
+     2262      2918      2458      1705      1099      1000      1253      1685
+     1829      1476       917       657       575       519       454       481
+      438       424       415       371       425       375       394       403
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+      403       406       401       383       420       391       386       390
+      424       400       379       405       429       362       400       402
+      378       376       377       386       402       378       390       377
+      400       390       365       391       383       385       380       386
+      383       385       388       353       407       379       383       370
+      375       382       368       374       411       412       391       394
+      389       379       373       392       389       375       364       355
+      429       372       375       363       370       385       365       378
+      350       382       393       352       361       412       374       359
+      403       376       387       375       363       363       399       394
+      386       393       385       380       371       388       393       364
+      376       380       377       390       371       347       345       387
+      373       368       372       363       389       384       384       383
+      368       363       372       418       374       405       381       373
+      380       393       420       413       423       455       430       391
+      422       439       468       522       540       695       865       956
+      881       691       616       665       804      1074      1269      1212
+      937       736       627       604       632       802       775       677
+      632       518       465       441       460       441       479       537
+      592       744      1015      1130      1047       885       627       551
+      561       652       753       859       821       680       550       598
+      741       731       736       580       476       474       442       507
+      572       577       587       511       446       417       452       392
+      383       398       358       369       396       358       369       379
+      374       391       386       381       380       392       395       354
+      364       378       364       385       399       369       368       385
+      373       349       359       388       370       387       373       396
+      357       380       390       340       395       410       401       387
+      366       379       403       414       383       385       394       405
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+      420       404       406       368       411       431       439       391
+      465       528       603       755      1109      1204      1026       748
+      597       498       519       623       726       858       849       665
+      474       445       445       391       398       384       413       394
+      358       389       401       366       385       372       405       399
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+      373       373       424       366       417       423       440       438
+      407       454       466       414       443       450       498       520
+      555       724      1142      1754      2194      1917      1394       968
+      715       668       706       919      1254      1398      1166       869
+      640       514       476       445       451       402       427       398
+      397       405       387       373       388       411       415       405
+      413       401       358       398       356       383       378       360
+      398       381       385       374       383       385       364       426
+      386       407       379       416       386       393       414       419
+      435       448       443       395       492       487       519       591
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+      388       369       420       383       366       362       394       380
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+     1426      1224       934       704       667       643       572       577
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+      371       370       360       341       371       395       369       381
+      375       396       382       356       405       374       387       398
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+      358       367       375       369       377       361       345       375
+      371       351       375       368       394       389       349       389
+      377       359       386       368       378       373       368       365
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+      407       378       363       378       371       345       371       374
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+      348       366       391       397       379       368       352       382
+      390       346       379       358       352       428       392       384
+      398       412       360       372       387       350       396       370
+      354       383       339       351       383       340       345       366
+      398       384       378       399       385       377       401       369
+      419       366       436       395       401       434       456       444
+      439       484       514       536       597       699       854       926
+      978       962       891       734       679       600       553       563
+      489       477       439       467       418       440       461       391
+      464       493       510       566       614       737       747       734
+      679       601       570       567       476       504       438       396
+      426       400       394       392       401       374       364       386
+      427       380       358       398       382       364       368       350
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+      382       388       391       342       381       330       381       383
+      379       395       398       381       335       348       363       346
+      401

data/data/README.md

@@ -0,0 +1,20 @@
+# Dataset
+We provide the diffraction and crystallography dataset for Y<sub>2</sub>O<sub>3</sub>, Dy<sub>0.5</sub>Sr<sub>0.5</sub>MnO<sub>3</sub> (DSMO), and LiCoO<sub>2</sub>.
+
+These are distributed according to its original licence.
+
+## Y2O3 and DSMO
+- Data source
+  - AutoFP repository
+    - https://github.com/xpclove/autofp/tree/master/example
+- Licence
+  - GPL v3
+- Citation
+  - Cui, X. et al. AutoFP: a GUI for highly automated Rietveld refinement using an expert system algorithm based on  FullProf. J Appl Crystallogr <b>48</b>, 1581–1586 (2015).
+
+## LiCoO2
+- Data source
+  - PowBase
+    - http://www.cristal.org/powbase/index.html
+- Licence
+  - Copyleft

data/data/Y2O3/INST_XRY.PRM

@@ -0,0 +1,10 @@
+            123456789012345678901234567890123456789012345678901234567890
+INS   BANK      1                                                               
+INS   HTYPE   PXCR                                                              
+INS  1 IRAD     3                                                               
+INS  1 ICONS  1.540500  1.544300       0.0         0       0.7    0       0.5   
+INS  1I HEAD  DUMMY INCIDENT SPECTRUM FOR X-RAY DIFFRACTOMETER
+INS  1I ITYP    0    0.0000  180.0000         1                                 
+INS  1PRCF1     3    8      0.01                                                
+INS  1PRCF11   2.000000E+00  -2.000000E+00   5.000000E+00   0.100000E+00        
+INS  1PRCF12   0.000000E+00   0.000000E+00   0.100000E-01   0.100000E-01        

data/data/Y2O3/Y2O3.cif

@@ -0,0 +1,131 @@
+###############################################################################
+# Santos, C.;Strecker, K.;Suzuki, P.A.;Kycia, S.;Silva, O.M.M.;Silva, C\
+#.R.M. (2005)
+# Materials Research Bulletin 40, 1094-1103
+# Stabilization of alpha-SIALONs using a rare-earth mixed oxide (RE2 O3\
+#) as sintering additive
+# 
+# CIF by ICSD-for-WWW,  Copyright 2003 FIZ-Karlsruhe & A.W.Hewat (hewat@ill.fr)
+# NOT TO BE PUBLISHED IN ANY FORM. See http://icsd.ill.fr/icsd/conditions.html
+###############################################################################
+
+data_153500-ICSD
+_database_code_ICSD                153500
+_audit_creation_date               2006-10-01
+_chemical_name_systematic
+'Yttrium oxide'
+_chemical_formula_structural       
+'Y2 O3'
+_chemical_formula_sum              
+'O3 Y2'
+_publ_section_title                
+'O3 Y2'
+loop_
+_citation_id
+_citation_journal_abbrev
+_citation_year
+_citation_journal_volume
+_citation_page_first
+_citation_page_last
+_citation_journal_id_ASTM
+primary 'Materials Research Bulletin' 2005 40 1094 1103 MRBUAC
+
+loop_
+_publ_author_name
+Santos, C.;Strecker, K.;Suzuki, P.A.;Kycia, S.;Silva, O.M.M.;Silva, C\
+.R.M.
+_cell_length_a                     10.60796(3)
+_cell_length_b                     10.60796(3)
+_cell_length_c                     10.60796(3)
+_cell_angle_alpha                  90.
+_cell_angle_beta                   90.
+_cell_angle_gamma                  90.
+_cell_volume                       1193.71
+_cell_formula_units_Z              16
+_symmetry_space_group_name_H-M     'I a -3'
+_symmetry_Int_Tables_number        206
+_refine_ls_R_factor_all            0.092300
+loop_
+
+_symmetry_equiv_pos_site_id
+_symmetry_equiv_pos_as_xyz
+   1   '-z, x, y+1/2'
+   2   'z, x+1/2, -y'
+   3   'z+1/2, -x, y'
+   4   '-z, -x, -y'
+   5   'y+1/2, -z, x'
+   6   '-y, z, x+1/2'
+   7   'y, z+1/2, -x'
+   8   '-y, -z, -x'
+   9   'x, y+1/2, -z'
+  10   'x+1/2, -y, z'
+  11   '-x, y, z+1/2'
+  12   '-x, -y, -z'
+  13   'z, -x, -y+1/2'
+  14   '-z, -x+1/2, y'
+  15   '-z+1/2, x, -y'
+  16   'z, x, y'
+  17   '-y+1/2, z, -x'
+  18   'y, -z, -x+1/2'
+  19   '-y, -z+1/2, x'
+  20   'y, z, x'
+  21   '-x, -y+1/2, z'
+  22   '-x+1/2, y, -z'
+  23   'x, -y, -z+1/2'
+  24   'x, y, z'
+  25   '-z+1/2, x+1/2, y'
+  26   'z+1/2, x, -y+1/2'
+  27   'z, -x+1/2, y+1/2'
+  28   '-z+1/2, -x+1/2, -y+1/2'
+  29   'y, -z+1/2, x+1/2'
+  30   '-y+1/2, z+1/2, x'
+  31   'y+1/2, z, -x+1/2'
+  32   '-y+1/2, -z+1/2, -x+1/2'
+  33   'x+1/2, y, -z+1/2'
+  34   'x, -y+1/2, z+1/2'
+  35   '-x+1/2, y+1/2, z'
+  36   '-x+1/2, -y+1/2, -z+1/2'
+  37   'z+1/2, -x+1/2, -y'
+  38   '-z+1/2, -x, y+1/2'
+  39   '-z, x+1/2, -y+1/2'
+  40   'z+1/2, x+1/2, y+1/2'
+  41   '-y, z+1/2, -x+1/2'
+  42   'y+1/2, -z+1/2, -x'
+  43   '-y+1/2, -z, x+1/2'
+  44   'y+1/2, z+1/2, x+1/2'
+  45   '-x+1/2, -y, z+1/2'
+  46   '-x, y+1/2, -z+1/2'
+  47   'x+1/2, -y+1/2, -z'
+  48   'x+1/2, y+1/2, z+1/2'
+loop_
+_atom_type_symbol
+_atom_type_oxidation_number
+Y3+     3.
+O2-     -2.
+ loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_symmetry_multiplicity
+_atom_site_Wyckoff_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_B_iso_or_equiv
+_atom_site_occupancy
+Y1 Y3+ 8 b 0.250 0.250 0.250 0.0 1.
+Y2 Y3+ 24 d 0.9673(1) 0. 0.250 0.0 1.
+O1 O2- 48 e 0.3915(8) 0.1538(8) 0.3800(8) 0.0 1.
+loop_
+_atom_site_aniso_label
+_atom_site_aniso_type_symbol
+_atom_site_aniso_U_11
+_atom_site_aniso_U_22
+_atom_site_aniso_U_33
+_atom_site_aniso_U_12
+_atom_site_aniso_U_13
+_atom_site_aniso_U_23
+Y1 Y3+ 0. 0. 0. 0 0 0
+Y2 Y3+ 0. 0. 0. 0 0 0
+O1 O2- 0. 0. 0. 0 0 0
+#End of data_153500-ICSD
+

data/data/Y2O3/Y2O3.gpx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8d83f89d46f63f5fbb77a02efab3dd76e3a42842592bb0da9c7dbec0edc981f
+size 592390

data/data/Y2O3/Y2O3_init.gpx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8d83f89d46f63f5fbb77a02efab3dd76e3a42842592bb0da9c7dbec0edc981f
+size 592390

data/docker/Dockerfile

@@ -0,0 +1,20 @@
+FROM continuumio/miniconda3:23.10.0-1
+
+RUN apt update && apt install curl libgtk2.0-dev libgtk-3-dev libjpeg-dev libtiff-dev libsdl1.2-dev libgstreamer-plugins-base1.0-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev tk-dev libgfortran5 -y
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libffi.so.7
+
+RUN conda install python=3.10 -y
+RUN pip install optuna jupyterlab ipython matplotlib pandas numpy scikit-learn pymatgen PyOpenGL requests
+RUN pip install -U \
+    -f https://extras.wxpython.org/wxPython4/extras/linux/gtk3/debian-11/ \
+    wxPython
+
+RUN curl https://subversion.xray.aps.anl.gov/admin_pyGSAS/downloads/gsas2full-Latest-Linux-x86_64.sh > /tmp/gsas2full-Latest-Linux-x86_64.sh
+RUN echo -e "\n" | bash /tmp/gsas2full-Latest-Linux-x86_64.sh -b -p ~/g2full
+RUN echo -e "\n" | python ~/g2full/GSASII/bootstrap.py
+
+ENV PATH="/root/g2full/GSASII/:$PATH"
+
+RUN jupyter lab --generate-config \
+    && sed -i -e "s/#c.NotebookApp.ip = 'localhost'/c.NotebookApp.ip = '0.0.0.0'/g" ~/.jupyter/jupyter_lab_config.py \
+    && sed -i -e "s/#c.NotebookApp.port = 8888/c.NotebookApp.port = 8888/g" ~/.jupyter/jupyter_lab_config.py

data/run.sh

@@ -0,0 +1,4 @@
+#!/bin/sh
+
+SCRIPT_DIR=$(cd $(dirname $0); pwd)
+docker run --rm -v ${SCRIPT_DIR}/:/workspace -p 8888:8888 -it resnant/bbo-rietveld:v1.1 jupyter lab --allow-root --ip 0.0.0.0