+
diff --git a/jnb-tools/1_cif_to_jcpds/Convert_CIF_to_JCPDS.ipynb b/jnb-tools/1_cif_to_jcpds/Convert_CIF_to_JCPDS.ipynb
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@@ -0,0 +1,837 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Convert CIF to JCPDS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* This notebook shows how to calculate a theoretical diffraction pattern using `pymatgen`. \n",
+ "* This also aims to show how to read `CIF` files, convert them to `JCPDS`. \n",
+ "* Note that `ds_jcpds` is differernt from that in `PeakPo`, but it produces readable jcpds for PeakPo. \n",
+ "* Some `jcpds` files can be downloaded from: https://github.com/SHDShim/JCPDS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is CIF file\n",
+ "\n",
+ "https://en.wikipedia.org/wiki/Crystallographic_Information_File\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "./cif/MgSiO3_bm.cif\n"
+ ]
+ }
+ ],
+ "source": [
+ "%ls ./cif/*.cif"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "data_global\n",
+ "_chemical_name_mineral 'Bridgmanite'\n",
+ "loop_\n",
+ "_publ_author_name\n",
+ "'Horiuchi H'\n",
+ "'Ito E'\n",
+ "'Weidner D J'\n",
+ "_journal_name_full 'American Mineralogist'\n",
+ "_journal_volume 72 \n",
+ "_journal_year 1987\n",
+ "_journal_page_first 357\n",
+ "_journal_page_last 360\n",
+ "_publ_section_title\n",
+ ";\n",
+ " Perovskite-type MgSiO3: Single-crystal X-ray diffraction study\n",
+ ";\n",
+ "_database_code_amcsd 0001071\n",
+ "_chemical_compound_source 'Synthetic'\n",
+ "_chemical_formula_sum 'Mg Si O3'\n",
+ "_cell_length_a 4.7754\n",
+ "_cell_length_b 4.9292\n",
+ "_cell_length_c 6.8969\n",
+ "_cell_angle_alpha 90\n",
+ "_cell_angle_beta 90\n",
+ "_cell_angle_gamma 90\n",
+ "_cell_volume 162.345\n",
+ "_exptl_crystal_density_diffrn 4.107\n",
+ "_symmetry_space_group_name_H-M 'P b n m'\n",
+ "loop_\n",
+ "_space_group_symop_operation_xyz\n",
+ " 'x,y,z'\n",
+ " 'x,y,1/2-z'\n",
+ " '-x,-y,1/2+z'\n",
+ " '1/2+x,1/2-y,1/2+z'\n",
+ " '1/2-x,1/2+y,1/2-z'\n",
+ " '1/2-x,1/2+y,z'\n",
+ " '1/2+x,1/2-y,-z'\n",
+ " '-x,-y,-z'\n",
+ "loop_\n",
+ "_atom_site_label\n",
+ "_atom_site_fract_x\n",
+ "_atom_site_fract_y\n",
+ "_atom_site_fract_z\n",
+ "Mg 0.51410 0.55600 0.25000\n",
+ "Si 0.50000 0.00000 0.50000\n",
+ "O1 0.10280 0.46600 0.25000\n",
+ "O2 0.19610 0.20140 0.55310\n",
+ "loop_\n",
+ "_atom_site_aniso_label\n",
+ "_atom_site_aniso_U_11\n",
+ "_atom_site_aniso_U_22\n",
+ "_atom_site_aniso_U_33\n",
+ "_atom_site_aniso_U_12\n",
+ "_atom_site_aniso_U_13\n",
+ "_atom_site_aniso_U_23\n",
+ "Mg 0.00555 0.00565 0.00619 0.00052 0.00000 0.00000\n",
+ "Si 0.00342 0.00367 0.00241 -0.00005 0.00003 -0.00016\n",
+ "O1 0.00434 0.00581 0.00217 0.00013 0.00000 0.00000\n",
+ "O2 0.00430 0.00449 0.00431 0.00083 0.00048 0.00083\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%cat ./cif/MgSiO3_bm.cif"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is a JCPDS file\n",
+ "\n",
+ "What is lacking in cif?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is `pymatgen`?\n",
+ "\n",
+ "https://pymatgen.org"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pymatgen as mg\n",
+ "from pymatgen import Lattice, Structure\n",
+ "from pymatgen.analysis.diffraction.xrd import XRDCalculator\n",
+ "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'2019.4.11'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mg.__version__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This works with `pymatgen` version `2019.4.11`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`ds_jcpds` is written by Dan Shim for making a jcpds file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PeakPo.icns \u001b[1m\u001b[34mds_cake\u001b[m\u001b[m/ peakpo-mac.spec\n",
+ "\u001b[1m\u001b[32mPeakPo.ico\u001b[m\u001b[m* \u001b[1m\u001b[34mds_jcpds\u001b[m\u001b[m/ peakpo-win.spec\n",
+ "__init__.py \u001b[1m\u001b[34mds_powdiff\u001b[m\u001b[m/ peakpo.py\n",
+ "__main__.py \u001b[1m\u001b[34mds_section\u001b[m\u001b[m/ \u001b[1m\u001b[34mtemporary_pkpo\u001b[m\u001b[m/\n",
+ "\u001b[1m\u001b[34m__pycache__\u001b[m\u001b[m/ dum.ppss \u001b[1m\u001b[34mutils\u001b[m\u001b[m/\n",
+ "\u001b[1m\u001b[34mbuild\u001b[m\u001b[m/ error.log version.py\n",
+ "citation.py \u001b[1m\u001b[34mmodel\u001b[m\u001b[m/ \u001b[1m\u001b[34mview\u001b[m\u001b[m/\n",
+ "\u001b[1m\u001b[34mcontrol\u001b[m\u001b[m/ \u001b[1m\u001b[34mmplstyle\u001b[m\u001b[m/\n",
+ "\u001b[1m\u001b[34mdist\u001b[m\u001b[m/ peakpo-mac-app.spec\n"
+ ]
+ }
+ ],
+ "source": [
+ "%ls ../../peakpo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('../../peakpo/')\n",
+ "sys.path.append('../local_modules/')\n",
+ "import ds_jcpds\n",
+ "import quick_plots as quick"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Input parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MgSiO3_bm.cif\n"
+ ]
+ }
+ ],
+ "source": [
+ "%ls ./cif/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fn_cif = \"./cif/MgSiO3_bm.cif\"\n",
+ "fn_jcpds = './jcpds/MgSiO3-bm.jcpds'\n",
+ "comments_jcpds = \"Bridgmanite\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Parameters for the equation of state of bridgmanite."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k0 = 260. # 200.\n",
+ "k0p = 4.00 # 4.\n",
+ "alpha = 3.16e-5 # 1.e-5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wl_xray = 0.3344\n",
+ "xrange = (0,40)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read CIF"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `cif` file below was downloaded from American mineralogist crystal structure database."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "run_control": {
+ "marked": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "material = mg.Structure.from_file(fn_cif)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get some parameters in CIF"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unit-cell volume = 162.345450996792\n",
+ "Density = 4.107275060713392 g cm^-3\n",
+ "Chemical formula = Mg4 Si4 O12\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Unit-cell volume = ', material.volume)\n",
+ "print('Density = ', material.density)\n",
+ "print('Chemical formula = ', material.formula)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Lattice parameters = 4.7754 4.9292 6.8969 90.0 90.0 90.0\n",
+ "orthorhombic\n"
+ ]
+ }
+ ],
+ "source": [
+ "lattice = material.lattice\n",
+ "print('Lattice parameters = ', lattice.a, lattice.b, lattice.c, \\\n",
+ " lattice.alpha, lattice.beta, lattice.gamma)\n",
+ "crystal_system = SpacegroupAnalyzer(material).get_crystal_system()\n",
+ "print(crystal_system)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get diffraction pattern"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c = XRDCalculator(wavelength=wl_xray)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pattern = c.get_pattern(material, two_theta_range = xrange)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extract twotheta, d-sp, int, hkl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, 0, 1)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pattern.hkls[0][0]['hkl']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "722"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pattern.hkls.__len__()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h = []; k = []; l = []\n",
+ "for i in range(pattern.hkls.__len__()):\n",
+ " h.append(pattern.hkls[i][0]['hkl'][0])\n",
+ " k.append(pattern.hkls[i][0]['hkl'][1])\n",
+ " l.append(pattern.hkls[i][0]['hkl'][2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[5.55821414 3.44845 8.23481259 0. 0. 2. ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "d_lines = [pattern.x, pattern.d_hkls, pattern.y, h, k, l ]\n",
+ "diff_lines = np.transpose(np.asarray(d_lines))\n",
+ "print(diff_lines[1,:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Table output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can make a nice looking table using the `pandas` package. `pandas` is more than looking-good table producer. It is a powerful statistics package popular in data science."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Two Theta \n",
+ " d-spacing \n",
+ " intensity \n",
+ " h \n",
+ " k \n",
+ " l \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 4.881525 \n",
+ " 3.926131 \n",
+ " 0.815361 \n",
+ " 1.0 \n",
+ " 0.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 5.558214 \n",
+ " 3.448450 \n",
+ " 8.234813 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 2.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 5.588459 \n",
+ " 3.429802 \n",
+ " 20.916571 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 6.241956 \n",
+ " 3.071022 \n",
+ " 8.938528 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 7.779938 \n",
+ " 2.464600 \n",
+ " 24.497625 \n",
+ " 0.0 \n",
+ " 2.0 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Two Theta d-spacing intensity h k l\n",
+ "0 4.881525 3.926131 0.815361 1.0 0.0 1.0\n",
+ "1 5.558214 3.448450 8.234813 0.0 0.0 2.0\n",
+ "2 5.588459 3.429802 20.916571 1.0 1.0 0.0\n",
+ "3 6.241956 3.071022 8.938528 1.0 1.0 1.0\n",
+ "4 7.779938 2.464600 24.497625 0.0 2.0 0.0"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "table = pd.DataFrame(data = diff_lines, # values\n",
+ " columns=['Two Theta', 'd-spacing', 'intensity', 'h', 'k', 'l']) # 1st row as the column names\n",
+ "table.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot peak positions generated from pymatgen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "plot_diffpattern() takes 2 positional arguments but 3 were given",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m
\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mquick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_diffpattern\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_lines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff_lines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mTypeError\u001b[0m: plot_diffpattern() takes 2 positional arguments but 3 were given"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 194,
+ "width": 485
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(8,3))\n",
+ "quick.plot_diffpattern(ax, [0], [0])\n",
+ "ax.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Convert to JCPDS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Setup an `jcpds` object from a `cif` file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "material_jcpds = ds_jcpds.JCPDS()\n",
+ "material_jcpds.set_from_cif(fn_cif, k0, k0p, \\\n",
+ " thermal_expansion=alpha, \n",
+ " two_theta_range=xrange)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Calculate diffraction pattern at a pressure."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "material_jcpds.cal_dsp(pressure = 100.)\n",
+ "dl = material_jcpds.get_DiffractionLines()\n",
+ "tth, inten = material_jcpds.get_tthVSint(wl_xray)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f, ax = plt.subplots(2, 1, figsize=(7,3), sharex=True)\n",
+ "ax[0].vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')\n",
+ "ax[1].vlines(tth, 0., inten, color = 'r')\n",
+ "ax[0].set_xlim(7.5,9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Save to a JCPDS file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "material_jcpds.write_to_file(fn_jcpds, comments=comments_jcpds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%cat {fn_jcpds}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Read back the written JCPDS for test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "material_test = ds_jcpds.JCPDS(filename = fn_jcpds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Calculate a pattern at a pressure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "material_test.cal_dsp(pressure = 100.)\n",
+ "material_test.get_DiffractionLines()\n",
+ "tth, inten = material_test.get_tthVSint(wl_xray)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = plt.figure(figsize=(8,3))\n",
+ "plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b', label='0 GPa')\n",
+ "plt.vlines(tth, 0., inten, color = 'r', label='100 GPa')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.7.0"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 0,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "nav_menu": {},
+ "toc": {
+ "navigate_menu": true,
+ "number_sections": true,
+ "sideBar": true,
+ "threshold": 6,
+ "toc_cell": false,
+ "toc_section_display": "block",
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/1_cif_to_jcpds/Convert_CIF_to_JCPDS.py b/jnb-tools/1_cif_to_jcpds/Convert_CIF_to_JCPDS.py
new file mode 100644
index 0000000..826d6fc
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/Convert_CIF_to_JCPDS.py
@@ -0,0 +1,292 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Convert CIF to JCPDS
+
+# * This notebook shows how to calculate a theoretical diffraction pattern using `pymatgen`.
+# * This also aims to show how to read `CIF` files, convert them to `JCPDS`.
+# * Note that `ds_jcpds` is differernt from that in `PeakPo`, but it produces readable jcpds for PeakPo.
+# * Some `jcpds` files can be downloaded from: https://github.com/SHDShim/JCPDS
+
+# In[1]:
+
+
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# ## What is CIF file
+#
+# https://en.wikipedia.org/wiki/Crystallographic_Information_File
+#
+#
+
+# In[2]:
+
+
+get_ipython().run_line_magic('ls', './cif/*.cif')
+
+
+# In[3]:
+
+
+get_ipython().run_line_magic('cat', './cif/MgSiO3_bm.cif')
+
+
+# ## What is a JCPDS file
+#
+# What is lacking in cif?
+
+# In[4]:
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# ## What is `pymatgen`?
+#
+# https://pymatgen.org
+
+# In[5]:
+
+
+import pymatgen as mg
+from pymatgen import Lattice, Structure
+from pymatgen.analysis.diffraction.xrd import XRDCalculator
+from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
+
+
+# In[6]:
+
+
+mg.__version__
+
+
+# This works with `pymatgen` version `2019.4.11`.
+
+# `ds_jcpds` is written by Dan Shim for making a jcpds file.
+
+# In[7]:
+
+
+get_ipython().run_line_magic('ls', '../../peakpo')
+
+
+# In[8]:
+
+
+import sys
+sys.path.append('../../peakpo/')
+sys.path.append('../local_modules/')
+import ds_jcpds
+import quick_plots as quick
+
+
+# ## Input parameters
+
+# In[9]:
+
+
+get_ipython().run_line_magic('ls', './cif/')
+
+
+# In[10]:
+
+
+fn_cif = "./cif/MgSiO3_bm.cif"
+fn_jcpds = './jcpds/MgSiO3-bm.jcpds'
+comments_jcpds = "Bridgmanite"
+
+
+# Parameters for the equation of state of bridgmanite.
+
+# In[11]:
+
+
+k0 = 260. # 200.
+k0p = 4.00 # 4.
+alpha = 3.16e-5 # 1.e-5
+
+
+# In[12]:
+
+
+wl_xray = 0.3344
+xrange = (0,40)
+
+
+# ## Read CIF
+
+# The `cif` file below was downloaded from American mineralogist crystal structure database.
+
+# In[13]:
+
+
+material = mg.Structure.from_file(fn_cif)
+
+
+# ## Get some parameters in CIF
+
+# In[14]:
+
+
+print('Unit-cell volume = ', material.volume)
+print('Density = ', material.density)
+print('Chemical formula = ', material.formula)
+
+
+# In[15]:
+
+
+lattice = material.lattice
+print('Lattice parameters = ', lattice.a, lattice.b, lattice.c, lattice.alpha, lattice.beta, lattice.gamma)
+crystal_system = SpacegroupAnalyzer(material).get_crystal_system()
+print(crystal_system)
+
+
+# ## Get diffraction pattern
+
+# In[16]:
+
+
+c = XRDCalculator(wavelength=wl_xray)
+
+
+# In[17]:
+
+
+pattern = c.get_pattern(material, two_theta_range = xrange)
+
+
+# ## Extract twotheta, d-sp, int, hkl
+
+# In[18]:
+
+
+pattern.hkls[0][0]['hkl']
+
+
+# In[19]:
+
+
+pattern.hkls.__len__()
+
+
+# In[20]:
+
+
+h = []; k = []; l = []
+for i in range(pattern.hkls.__len__()):
+ h.append(pattern.hkls[i][0]['hkl'][0])
+ k.append(pattern.hkls[i][0]['hkl'][1])
+ l.append(pattern.hkls[i][0]['hkl'][2])
+
+
+# In[21]:
+
+
+d_lines = [pattern.x, pattern.d_hkls, pattern.y, h, k, l ]
+diff_lines = np.transpose(np.asarray(d_lines))
+print(diff_lines[1,:])
+
+
+# ## Table output
+
+# We can make a nice looking table using the `pandas` package. `pandas` is more than looking-good table producer. It is a powerful statistics package popular in data science.
+
+# In[22]:
+
+
+table = pd.DataFrame(data = diff_lines, # values
+ columns=['Two Theta', 'd-spacing', 'intensity', 'h', 'k', 'l']) # 1st row as the column names
+table.head()
+
+
+# ## Plot peak positions generated from pymatgen
+
+# In[23]:
+
+
+f, ax = plt.subplots(figsize=(8,3))
+quick.plot_diffpattern(ax, [0], [0])
+ax.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b');
+
+
+# ## Convert to JCPDS
+
+# Setup an `jcpds` object from a `cif` file
+
+# In[ ]:
+
+
+material_jcpds = ds_jcpds.JCPDS()
+material_jcpds.set_from_cif(fn_cif, k0, k0p, thermal_expansion=alpha,
+ two_theta_range=xrange)
+
+
+# Calculate diffraction pattern at a pressure.
+
+# In[ ]:
+
+
+material_jcpds.cal_dsp(pressure = 100.)
+dl = material_jcpds.get_DiffractionLines()
+tth, inten = material_jcpds.get_tthVSint(wl_xray)
+
+
+# In[ ]:
+
+
+f, ax = plt.subplots(2, 1, figsize=(7,3), sharex=True)
+ax[0].vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')
+ax[1].vlines(tth, 0., inten, color = 'r')
+ax[0].set_xlim(7.5,9)
+
+
+# ## Save to a JCPDS file
+
+# In[ ]:
+
+
+material_jcpds.write_to_file(fn_jcpds, comments=comments_jcpds)
+
+
+# In[ ]:
+
+
+get_ipython().run_line_magic('cat', '{fn_jcpds}')
+
+
+# # Read back the written JCPDS for test
+
+# In[ ]:
+
+
+material_test = ds_jcpds.JCPDS(filename = fn_jcpds)
+
+
+# Calculate a pattern at a pressure
+
+# In[ ]:
+
+
+material_test.cal_dsp(pressure = 100.)
+material_test.get_DiffractionLines()
+tth, inten = material_test.get_tthVSint(wl_xray)
+
+
+# In[ ]:
+
+
+f = plt.figure(figsize=(8,3))
+plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b', label='0 GPa')
+plt.vlines(tth, 0., inten, color = 'r', label='100 GPa')
+plt.legend();
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/1_cif_to_jcpds/JCPDS_slider.html b/jnb-tools/1_cif_to_jcpds/JCPDS_slider.html
new file mode 100644
index 0000000..5b48945
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/JCPDS_slider.html
@@ -0,0 +1,13541 @@
+
+
+JCPDS_slider
+
+
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+This notebook shows how to make jupyter notebook interactive.
+
+
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+
0JCPDSformat.txt * alooh.jcpds
+Al2SiO5.jcpds ar-NoTh.jcpds *
+Ar-hcp-NoTh.jcpds au-Dewaele.jcpds *
+CF_75_Na.jcpds * au-Ye2017.jcpds *
+Ga2O3.jcpds au.jcpds *
+H-phase_Zhang2014.jcpds bm-test.jcpds
+LICENSE casio3-pv.jcpds *
+LiGaO2.jcpds casio_Linbo3.jcpds *
+Linbo3_Megaw (1968).jcpds diamond-NoTh.jcpds *
+Mg2FeAl2Si3O12-gt.jcpds * mg0.8fe0.2sio3-opv.jcpds *
+Mg3Al2Si3O12-pyrope.jcpds mg0.9fe0.1sio3-opv.jcpds *
+MgAl2O4 Liu 1978.jcpds mg2sio4-g.jcpds *
+MgAlSiO3-gt.jcpds * mgal2o4-CF.jcpds *
+MgAlSiO3-ilm.jcpds * mgo-Ye2017.jcpds *
+MgAlSiO3-opv.jcpds * mgo.jcpds *
+MgFe2SiO4-g.jcpds mgsio3-ilm.jcpds *
+MgFeAlSiO3-cgt.jcpds mgsio3-opv.jcpds *
+MgSiO3-bm.jcpds ne-NoTh.jcpds *
+Na0.4Mg0.6Al1.6Si0.4O4-CF.jcpds phaseD.jcpds *
+PhaseX.jcpds phaseH.jcpds
+README.md pt-Ye2017.jcpds *
+SiC.jcpds * pt.jcpds *
+TAPP_Harris1997.jcpds re-NoTh.jcpds *
+TiC-NoTh.jcpds * sio2-stv.jcpds *
+al2o3.jcpds * ver3 /
+
+
+
+
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+
+
+
Read back the written JCPDS for test¶
+
+
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+
+
+
+
+
Can we also change unit-cell parameters?¶
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
Signature:
+bm_high_p. cal_dsp(
+ pressure= 0.0 ,
+ temperature= 300.0 ,
+ b_a= None ,
+ c_a= None ,
+ use_table_for_0GPa= True ,
+)
+Docstring:
+b_a and c_a are newly included for adjusting axial ratios.
+For cubic structure, these two inputs are ignored.
+For tetragonal and hexagonal, only c_a will be used.
+
+recalculate_zero = False: use the table d-spacing value for 0 GPa
+File: ~/Dropbox (ASU)/Python/PeakPo-v7/peakpo/ds_jcpds/jcpds.py
+Type: method
+
+
+
+
+
+
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+
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+
+
+
"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 194,
+ "width": 430
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def f(pressure=0., temperature=300.):\n",
+ " plt.figure(figsize=(7,3))\n",
+ " bm_high_p.cal_dsp(pressure = pressure, temperature=temperature)\n",
+ " bm_high_p.get_DiffractionLines()\n",
+ " tth, inten = bm_high_p.get_tthVSint(wl_xray)\n",
+ " plt.vlines(tth, 0., inten, color = 'r')\n",
+ " plt.ylim(0, 100)\n",
+ " plt.xlim(1,30)\n",
+ " plt.grid(True)\n",
+ " plt.show()\n",
+ "\n",
+ "interactive_plot = interactive(f, \n",
+ " pressure=widgets.FloatSlider(min=0, max=100, step=1, readout_format='.0f'), \n",
+ " temperature=widgets.FloatSlider(min=300, max=3000, step=10, readout_format='.0f'))\n",
+ "output = interactive_plot.children[-1]\n",
+ "#output.layout.height = '300px'\n",
+ "interactive_plot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Can we also change unit-cell parameters?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a_0 = bm_high_p.a0\n",
+ "b_0 = bm_high_p.b0\n",
+ "c_0 = bm_high_p.c0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "v_0 = bm_high_p.v0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[0;31mSignature:\u001b[0m\n",
+ "\u001b[0mbm_high_p\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcal_dsp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m \u001b[0mpressure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m \u001b[0mb_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m \u001b[0mc_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m \u001b[0muse_table_for_0GPa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+ "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mDocstring:\u001b[0m\n",
+ "b_a and c_a are newly included for adjusting axial ratios.\n",
+ "For cubic structure, these two inputs are ignored.\n",
+ "For tetragonal and hexagonal, only c_a will be used.\n",
+ "\n",
+ "recalculate_zero = False: use the table d-spacing value for 0 GPa\n",
+ "\u001b[0;31mFile:\u001b[0m ~/Dropbox (ASU)/Python/PeakPo-v7/peakpo/ds_jcpds/jcpds.py\n",
+ "\u001b[0;31mType:\u001b[0m method\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "?bm_high_p.cal_dsp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 194,
+ "width": 430
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def f(pressure=0., temperature=300., a0=a_0, b0=b_0, c0=c_0):\n",
+ " plt.figure(figsize=(7,3))\n",
+ " bm_high_p.a0 = a0\n",
+ " bm_high_p.b0 = b0\n",
+ " bm_high_p.c0 = c0\n",
+ " bm_high_p.cal_dsp(pressure = pressure, temperature=temperature,\n",
+ " use_table_for_0GPa=False)\n",
+ " bm_high_p.get_DiffractionLines()\n",
+ " tth, inten = bm_high_p.get_tthVSint(wl_xray)\n",
+ " plt.vlines(tth, 0., inten, color = 'r')\n",
+ " plt.ylim(0, 100)\n",
+ " plt.xlim(1,30)\n",
+ " plt.grid(True)\n",
+ " plt.show()\n",
+ "\n",
+ "min_frac=0.9\n",
+ "max_frac=1.1\n",
+ "interactive_plot = interactive(f, \n",
+ " pressure=widgets.FloatSlider(min=0, max=100, step=1, readout_format='.0f'), \n",
+ " temperature=widgets.FloatSlider(min=300, max=3000, step=10, readout_format='.0f'),\n",
+ " a0=widgets.FloatSlider(value=a_0, min=a_0*min_frac, max=a_0*max_frac, step=0.001, readout_format='.3f'),\n",
+ " b0=widgets.FloatSlider(value=b_0, min=b_0*min_frac, max=b_0*max_frac, step=0.001, readout_format='.3f'),\n",
+ " c0=widgets.FloatSlider(value=c_0, min=c_0*min_frac, max=c_0*max_frac, step=0.001, readout_format='.3f'))\n",
+ "output = interactive_plot.children[-1]\n",
+ "#output.layout.height = '300px'\n",
+ "interactive_plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.7.0"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 0,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "nav_menu": {},
+ "toc": {
+ "navigate_menu": true,
+ "number_sections": true,
+ "sideBar": true,
+ "threshold": 6,
+ "toc_cell": false,
+ "toc_section_display": "block",
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/1_cif_to_jcpds/JCPDS_slider.py b/jnb-tools/1_cif_to_jcpds/JCPDS_slider.py
new file mode 100644
index 0000000..5f946cf
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/JCPDS_slider.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Interactive JCPDS
+
+# In[1]:
+
+
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# * This notebook shows how to make jupyter notebook interactive.
+
+# In[2]:
+
+
+get_ipython().run_line_magic('ls', './jcpds/')
+
+
+# In[3]:
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# In[4]:
+
+
+from ipywidgets import interactive
+import ipywidgets as widgets
+
+
+# In[5]:
+
+
+import pymatgen as mg
+from pymatgen import Lattice, Structure
+from pymatgen.analysis.diffraction.xrd import XRDCalculator
+from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
+
+
+# In[7]:
+
+
+import sys
+sys.path.append('../../peakpo/')
+sys.path.append('../local_modules')
+import ds_jcpds
+
+
+# In[8]:
+
+
+fn_jcpds = './jcpds/MgSiO3-bm.jcpds'
+
+
+# In[9]:
+
+
+wl_xray = 0.3344
+xrange = (0,40)
+
+
+# ## Read back the written JCPDS for test
+
+# In[10]:
+
+
+bm_high_p = ds_jcpds.JCPDS(filename = fn_jcpds)
+
+
+# In[11]:
+
+
+def f(pressure=0., temperature=300.):
+ plt.figure(figsize=(7,3))
+ bm_high_p.cal_dsp(pressure = pressure, temperature=temperature)
+ bm_high_p.get_DiffractionLines()
+ tth, inten = bm_high_p.get_tthVSint(wl_xray)
+ plt.vlines(tth, 0., inten, color = 'r')
+ plt.ylim(0, 100)
+ plt.xlim(1,30)
+ plt.grid(True)
+ plt.show()
+
+interactive_plot = interactive(f,
+ pressure=widgets.FloatSlider(min=0, max=100, step=1, readout_format='.0f'),
+ temperature=widgets.FloatSlider(min=300, max=3000, step=10, readout_format='.0f'))
+output = interactive_plot.children[-1]
+#output.layout.height = '300px'
+interactive_plot
+
+
+# ## Can we also change unit-cell parameters?
+
+# In[12]:
+
+
+a_0 = bm_high_p.a0
+b_0 = bm_high_p.b0
+c_0 = bm_high_p.c0
+
+
+# In[13]:
+
+
+v_0 = bm_high_p.v0
+
+
+# In[14]:
+
+
+get_ipython().run_line_magic('pinfo', 'bm_high_p.cal_dsp')
+
+
+# In[15]:
+
+
+def f(pressure=0., temperature=300., a0=a_0, b0=b_0, c0=c_0):
+ plt.figure(figsize=(7,3))
+ bm_high_p.a0 = a0
+ bm_high_p.b0 = b0
+ bm_high_p.c0 = c0
+ bm_high_p.cal_dsp(pressure = pressure, temperature=temperature,
+ use_table_for_0GPa=False)
+ bm_high_p.get_DiffractionLines()
+ tth, inten = bm_high_p.get_tthVSint(wl_xray)
+ plt.vlines(tth, 0., inten, color = 'r')
+ plt.ylim(0, 100)
+ plt.xlim(1,30)
+ plt.grid(True)
+ plt.show()
+
+min_frac=0.9
+max_frac=1.1
+interactive_plot = interactive(f,
+ pressure=widgets.FloatSlider(min=0, max=100, step=1, readout_format='.0f'),
+ temperature=widgets.FloatSlider(min=300, max=3000, step=10, readout_format='.0f'),
+ a0=widgets.FloatSlider(value=a_0, min=a_0*min_frac, max=a_0*max_frac, step=0.001, readout_format='.3f'),
+ b0=widgets.FloatSlider(value=b_0, min=b_0*min_frac, max=b_0*max_frac, step=0.001, readout_format='.3f'),
+ c0=widgets.FloatSlider(value=c_0, min=c_0*min_frac, max=c_0*max_frac, step=0.001, readout_format='.3f'))
+output = interactive_plot.children[-1]
+#output.layout.height = '300px'
+interactive_plot
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/MgSiO3-bm.jcpds b/jnb-tools/1_cif_to_jcpds/MgSiO3-bm.jcpds
similarity index 100%
rename from jnb-tools/MgSiO3-bm.jcpds
rename to jnb-tools/1_cif_to_jcpds/MgSiO3-bm.jcpds
diff --git a/jnb-tools/bm.jcpds b/jnb-tools/1_cif_to_jcpds/bm.jcpds
similarity index 100%
rename from jnb-tools/bm.jcpds
rename to jnb-tools/1_cif_to_jcpds/bm.jcpds
diff --git a/jnb-tools/1_cif_to_jcpds/cif/MgSiO3_bm.cif b/jnb-tools/1_cif_to_jcpds/cif/MgSiO3_bm.cif
new file mode 100644
index 0000000..650563f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/cif/MgSiO3_bm.cif
@@ -0,0 +1,60 @@
+data_global
+_chemical_name_mineral 'Bridgmanite'
+loop_
+_publ_author_name
+'Horiuchi H'
+'Ito E'
+'Weidner D J'
+_journal_name_full 'American Mineralogist'
+_journal_volume 72
+_journal_year 1987
+_journal_page_first 357
+_journal_page_last 360
+_publ_section_title
+;
+ Perovskite-type MgSiO3: Single-crystal X-ray diffraction study
+;
+_database_code_amcsd 0001071
+_chemical_compound_source 'Synthetic'
+_chemical_formula_sum 'Mg Si O3'
+_cell_length_a 4.7754
+_cell_length_b 4.9292
+_cell_length_c 6.8969
+_cell_angle_alpha 90
+_cell_angle_beta 90
+_cell_angle_gamma 90
+_cell_volume 162.345
+_exptl_crystal_density_diffrn 4.107
+_symmetry_space_group_name_H-M 'P b n m'
+loop_
+_space_group_symop_operation_xyz
+ 'x,y,z'
+ 'x,y,1/2-z'
+ '-x,-y,1/2+z'
+ '1/2+x,1/2-y,1/2+z'
+ '1/2-x,1/2+y,1/2-z'
+ '1/2-x,1/2+y,z'
+ '1/2+x,1/2-y,-z'
+ '-x,-y,-z'
+loop_
+_atom_site_label
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+Mg 0.51410 0.55600 0.25000
+Si 0.50000 0.00000 0.50000
+O1 0.10280 0.46600 0.25000
+O2 0.19610 0.20140 0.55310
+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
+Mg 0.00555 0.00565 0.00619 0.00052 0.00000 0.00000
+Si 0.00342 0.00367 0.00241 -0.00005 0.00003 -0.00016
+O1 0.00434 0.00581 0.00217 0.00013 0.00000 0.00000
+O2 0.00430 0.00449 0.00431 0.00083 0.00048 0.00083
+
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/0JCPDSformat.txt b/jnb-tools/1_cif_to_jcpds/jcpds/0JCPDSformat.txt
new file mode 100755
index 0000000..bf09fe9
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/0JCPDSformat.txt
@@ -0,0 +1,22 @@
+JCDPS file format
+
+----------------------------
+line 1: version_number (should be 3)
+line 2: header
+line 3: symmetry_code, K0, K0p
+line 4: a, b, c, alpha, beta, gamma (this line is different depending on symmetry, see examples)
+line 5: (blankline for future use)
+line 6: column description (d I/I0 h k l)
+line 7: d-spacing, I/I0, h, k, l
+.
+.
+.
+-----------------------------
+
+* symmetry code
+1 cubic
+2 hexagonal
+3 tetragonal
+4 orthorhombic
+5 monoclinic
+6 triclinic
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Al2SiO5.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Al2SiO5.jcpds
new file mode 100644
index 0000000..2d9d8e4
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Al2SiO5.jcpds
@@ -0,0 +1,31 @@
+3
+Al2SiO5 (Ahmed_Zaid 1991)
+ 5 185.000 4.00000
+ 8.478 4.471 6.782 90 104.25 90
+(blank for future use)
+d-spacing I/I0 h k l
+3.7231 5 0 1 1
+3.2903 8 0 0 2
+2.9402 14 2 0 -2
+2.6827 6 1 1 -2
+2.5779 5 2 1 1
+2.3382 32 3 1 0
+2.2379 100 0 2 0
+2.1366 7 3 1 -2
+2.0579 11 4 0 0
+2.0144 21 1 2 1
+1.915 64 2 2 -1
+1.9103 5 4 1 -1
+1.743 13 3 2 -1
+1.7217 6 3 2 0
+1.6695 5 2 0 -4
+1.5752 18 4 0 2
+1.4903 7 0 3 0
+1.465 7 1 3 0
+1.368 53 6 0 0
+1.3308 11 2 3 -2
+1.278 5 3 0 4
+1.2316 6 0 3 3
+1.2056 5 4 3 0
+1.1715 21 3 3 2
+1.124 8 7 1 -3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Ar-hcp-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Ar-hcp-NoTh.jcpds
new file mode 100644
index 0000000..a3616d1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Ar-hcp-NoTh.jcpds
@@ -0,0 +1,16 @@
+3
+ar HCP (based on cobalt hcp file, EOS same as fcc)
+ 2 6.5 5.1
+ 3.7067 6.0078
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1694 20. 1.00 0.00 0.00
+ 2.0300 60. 0.00 0.00 2.00
+ 1.9134 100. 1.00 0.00 1.00
+ 1.4822 12. 1.00 0.00 2.00
+ 1.2525 80. 1.00 1.00 0.00
+ 1.1482 80. 1.00 0.00 3.00
+ 1.0847 20. 2.00 0.00 0.00
+ 1.0660 80. 1.00 1.00 2.00
+ 1.0479 60. 2.00 0.00 1.00
+ 1.0150 20. 0.00 0.00 4.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/CF_75_Na.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/CF_75_Na.jcpds
new file mode 100755
index 0000000..6a04960
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/CF_75_Na.jcpds
@@ -0,0 +1,161 @@
+3
+CF phase (from original structure by Decker, EOS from Yamada1983)
+ 4 220.000 4.10000
+ 10.040 8.620 2.750
+(blank for future use)
+d-spacing I/I0 h k l
+6.54019 0.3 1 1 0
+5.02 24 2 0 0
+4.33799 21 2 1 0
+4.31 71.5 0 2 0
+3.96049 1.3 1 2 0
+3.2701 27.9 2 2 0
+3.11979 1.8 3 1 0
+2.76243 8.1 1 3 0
+2.65231 8.4 1 0 1
+2.64335 0.2 3 2 0
+2.53502 4.8 1 1 1
+2.51 68.8 4 0 0
+2.49373 100 2 3 0
+2.40991 3.8 4 1 0
+2.32262 30.9 2 1 1
+2.3183 14.1 0 2 1
+2.25886 0 1 2 1
+2.18006 9.3 3 3 0
+2.169 0 4 2 0
+2.155 2.7 0 4 0
+2.1247 25 3 0 1
+2.10701 28.7 1 4 0
+2.1047 1.8 2 2 1
+2.06296 87 3 1 1
+1.98025 17.4 2 4 0
+1.95564 24 5 1 0
+1.94892 64.9 1 3 1
+1.90572 2.5 3 2 1
+1.89033 7.2 4 3 0
+1.84731 12.9 2 3 1
+1.82015 0.6 5 2 0
+1.81245 24.4 4 1 1
+1.81186 1.6 3 4 0
+1.70837 6.5 3 3 1
+1.70303 45.6 4 2 1
+1.69913 5.3 1 5 0
+1.69623 32.4 0 4 1
+1.67333 0.1 6 0 0
+1.67253 23.4 1 4 1
+1.64591 1 5 3 0
+1.64267 7.8 6 1 0
+1.63505 4 4 4 0
+1.63053 4.2 2 5 0
+1.6217 4.2 5 0 1
+1.60697 13.7 2 4 1
+1.59374 1.1 5 1 1
+1.55989 23.2 6 2 0
+1.55779 0.1 4 3 1
+1.5326 0.2 3 5 0
+1.51781 0.8 5 2 1
+1.51299 2.3 3 4 1
+1.46909 1.7 5 4 0
+1.446 10.2 6 3 0
+1.44548 0.6 1 5 1
+1.43667 14.1 0 6 0
+1.42218 0.7 1 6 0
+1.42108 1.7 4 5 0
+1.41483 1.2 7 1 0
+1.41228 3.4 5 3 1
+1.41023 21.8 6 1 1
+1.4054 43.6 4 4 1
+1.40253 25.1 2 5 1
+1.38122 0.9 2 6 0
+1.375 41.7 0 0 2
+1.36091 0.5 7 2 0
+1.35681 18.3 6 2 1
+1.34558 0 1 1 2
+1.33874 0.6 3 5 1
+1.32615 0.6 2 0 2
+1.32167 1.2 6 4 0
+1.32016 1.6 3 6 0
+1.31073 0.7 2 1 2
+1.30995 2.9 0 2 2
+1.30804 1 5 5 0
+1.29894 0.1 1 2 2
+1.29578 1.7 5 4 1
+1.28329 0 7 3 0
+1.27985 10.9 6 3 1
+1.27337 5.6 0 6 1
+1.27171 1.3 7 0 1
+1.26751 2.6 2 2 2
+1.26325 0.3 1 6 1
+1.26248 0.1 4 5 1
+1.25822 0.2 3 1 2
+1.25809 7.4 7 1 1
+1.255 1 8 0 0
+1.24687 6 4 6 0
+1.24191 5.7 8 1 0
+1.23428 6.2 2 6 1
+1.23094 1.1 1 3 2
+1.22227 0.2 1 7 0
+1.21984 0 3 2 2
+1.21972 2.7 7 2 1
+1.20591 12.3 4 0 2
+1.20496 0 8 2 0
+1.20409 20.9 2 3 2
+1.20074 1.2 6 5 0
+1.19597 5 2 7 0
+1.19428 1.3 4 1 2
+1.19401 0.1 7 4 0
+1.19124 18 6 4 1
+1.19013 1.3 3 6 1
+1.18122 1.6 5 5 1
+1.16841 3 5 6 0
+1.163 1 3 3 2
+1.16131 0.2 4 2 2
+1.15915 0.5 0 4 2
+1.15568 0.9 3 7 0
+1.1515 4.6 1 4 2
+1.15008 2.8 8 3 0
+1.13559 1.9 4 6 1
+1.13184 3.7 8 1 1
+1.12943 3.4 2 4 2
+1.12481 4.6 5 1 2
+1.11692 2 1 7 1
+1.11195 0.9 4 3 2
+1.10633 0.6 9 1 0
+1.10554 0.9 4 7 0
+1.10366 0.2 8 2 1
+1.1026 1.3 7 5 0
+1.10042 3.1 6 5 1
+1.09713 0.1 5 2 2
+1.09674 1.6 2 7 1
+1.09531 1.1 3 4 2
+1.09003 0.3 6 6 0
+1.0845 0.9 8 4 0
+1.07997 1 9 2 0
+1.0775 2.6 0 8 0
+1.07537 2.5 5 6 1
+1.07135 0.2 1 8 0
+1.06886 1.8 1 5 2
+1.06542 6.7 3 7 1
+1.06235 0.2 6 0 2
+1.06103 0 8 3 1
+1.05523 0.5 5 3 2
+1.05437 3.1 6 1 2
+1.05351 0.3 2 8 0
+1.05235 1.3 4 4 2
+1.05114 2.7 2 5 2
+1.04975 0.2 5 7 0
+1.03993 0 9 3 0
+1.03374 1.6 9 0 1
+1.03148 11.1 6 2 2
+1.02638 1 9 1 1
+1.02576 2.4 4 7 1
+1.02565 1.2 3 8 0
+1.02347 7.7 3 5 2
+1.01503 1.4 7 6 0
+1.01463 6.5 8 5 0
+1.01333 1.2 6 6 1
+1.00888 5.2 8 4 1
+1.00523 0.6 9 2 1
+1.004 1.6 0 0 0
+1.00389 1 5 4 2
+1.00324 0.9 0 8 1
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Ga2O3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Ga2O3.jcpds
new file mode 100644
index 0000000..4c1b707
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Ga2O3.jcpds
@@ -0,0 +1,190 @@
+3
+Rh2O3(II) type Ga2O3_Yusa 2008
+ 4 240.000 4.00000
+ 4.732 4.855 6.942 90 90 90
+(blank for future use)
+d-spacing I/I0 h k l
+3.91002 0.1 1 0 1
+3.471 3.8 0 0 2
+3.04523 14.5 1 1 1
+2.4275 21.1 0 2 0
+2.42474 100 1 1 2
+2.366 28.4 2 0 0
+2.29144 1.2 0 2 1
+2.12688 1.9 2 1 0
+2.07876 2.4 1 0 3
+2.06236 1 1 2 1
+2.03358 1.2 2 1 1
+1.98928 0 0 2 2
+1.95501 0.1 2 0 2
+1.91096 4.6 1 1 3
+1.83382 1 1 2 2
+1.8135 0 2 1 2
+1.7355 13.1 0 0 4
+1.69434 21.2 2 2 0
+1.67493 2.7 0 2 3
+1.64602 3.8 2 2 1
+1.57894 0.3 1 2 3
+1.56591 0.1 2 1 3
+1.5447 4 1 1 4
+1.53813 0.4 3 0 1
+1.52262 1.4 2 2 2
+1.49531 11.2 1 3 1
+1.4663 1.3 3 1 1
+1.4118 10.6 0 2 4
+1.40099 15.1 1 3 2
+1.39939 13 2 0 4
+1.37704 23.7 3 1 2
+1.36705 3.7 2 2 3
+1.35287 0.1 1 2 4
+1.34465 0.1 2 1 4
+1.33576 0.1 2 3 0
+1.33224 0.1 1 0 5
+1.31169 0.1 2 3 1
+1.30334 0 3 0 3
+1.29927 0.1 3 2 1
+1.28475 1.2 1 1 5
+1.27698 3.2 1 3 3
+1.25877 0 3 1 3
+1.24663 0.1 2 3 2
+1.23595 0.3 3 2 2
+1.21375 0.7 0 4 0
+1.21237 8.4 2 2 4
+1.2052 0 0 2 5
+1.19561 2 0 4 1
+1.183 2.6 4 0 0
+1.16791 0.1 1 2 5
+1.16261 0.2 2 1 5
+1.15918 0.2 1 4 1
+1.157 0.2 0 0 6
+1.15685 0 2 3 3
+1.14937 0 4 1 0
+1.1483 0 3 2 3
+1.14822 0.4 1 3 4
+1.14572 0.2 0 4 2
+1.13492 0.8 3 1 4
+1.13393 0 4 1 1
+1.11975 0.3 4 0 2
+1.1149 2.1 3 3 1
+1.11355 0 1 4 2
+1.09494 7 1 1 6
+1.09111 0.1 4 1 2
+1.07994 1 2 4 0
+1.07486 2.6 0 4 3
+1.07411 4.3 3 3 2
+1.0739 0.3 2 2 5
+1.0671 3.1 2 4 1
+1.06344 3.4 4 2 0
+1.05853 0.2 2 3 4
+1.05197 0 3 2 4
+1.05118 1.8 4 2 1
+1.04816 0 1 4 3
+1.04443 1 0 2 6
+1.04218 0 3 0 5
+1.03938 1.1 2 0 6
+1.03118 0 2 4 2
+1.02938 0 4 1 3
+1.02855 3.6 1 3 5
+1.01989 0 1 2 6
+1.01896 0.3 3 1 5
+1.01679 0.1 4 2 2
+1.01635 0.1 2 1 6
+1.01508 1.2 3 3 3
+0.99464 0.4 0 4 4
+0.97861 4.5 2 4 3
+0.9775 2.7 4 0 4
+0.97337 0 1 4 4
+0.97063 0 1 0 7
+0.96628 1.8 4 2 3
+0.9626 0.1 2 3 5
+0.95827 0.1 4 1 4
+0.95765 0 3 2 5
+0.95548 0.9 2 2 6
+0.95504 0 4 3 0
+0.95282 0 3 4 1
+0.95179 0.1 1 1 7
+0.9467 0.5 3 3 4
+0.94613 0 4 3 1
+0.94238 3.7 1 5 1
+0.93773 0 5 0 1
+0.92699 0 3 4 2
+0.92312 4 1 3 6
+0.92082 0 4 3 2
+0.92071 0 5 1 1
+0.91806 1.1 0 2 7
+0.91736 1.2 1 5 2
+0.91691 2.5 2 4 4
+0.91617 7 3 1 6
+0.9138 0.4 0 4 5
+0.90675 6.3 4 2 4
+0.90125 0.1 1 2 7
+0.89881 0 2 1 7
+0.89829 0 2 5 0
+0.89734 9.4 5 1 2
+0.89722 0 1 4 5
+0.89087 0.1 2 5 1
+0.88824 0.2 3 4 3
+0.88536 0 4 1 5
+0.88281 0 4 3 3
+0.87976 1.9 1 5 3
+0.87621 4.2 3 3 5
+0.87597 0 5 0 3
+0.87473 0 5 2 1
+0.87454 0 2 3 6
+0.87083 0.1 3 2 6
+0.86964 0 2 5 2
+0.86775 2.1 0 0 8
+0.86205 0.1 5 1 3
+0.85588 2.2 2 2 7
+0.85461 0 5 2 2
+0.85243 1.1 2 4 5
+0.84717 2.4 4 4 0
+0.84425 1.1 4 2 5
+0.84133 0 3 4 4
+0.84093 3.2 4 4 1
+0.84063 4 1 1 8
+0.83956 0.1 3 0 7
+0.83747 0.4 0 4 6
+0.83741 0 2 5 3
+0.83672 0 4 3 4
+0.83412 0 1 5 4
+0.83239 0.8 1 3 7
+0.82728 0 3 1 7
+0.82716 1.5 4 0 6
+0.82465 0 1 4 6
+0.82396 0 5 2 3
+0.82301 0.1 4 4 2
+0.82108 7.6 3 5 1
+0.81898 0.7 5 1 4
+0.81711 2.5 0 2 8
+0.81541 0 4 1 6
+0.81469 3.5 2 0 8
+0.81136 3.7 5 3 1
+0.80917 0 0 6 0
+0.80825 4.9 3 3 6
+0.8052 0.1 1 2 8
+0.80437 0.7 3 5 2
+0.80373 4.1 0 6 1
+0.80345 0.1 2 1 8
+0.79776 0.2 2 5 4
+0.79625 0 2 3 7
+0.79553 8.3 4 4 3
+0.79523 6.3 5 3 2
+0.79345 0 3 2 7
+0.79238 0.1 1 6 1
+0.79069 0.1 3 4 5
+0.78947 1.7 2 4 6
+0.78867 4.1 6 0 0
+0.78804 0 0 6 2
+0.78686 0 4 3 5
+0.78611 0 5 2 4
+0.78469 16.4 1 5 5
+0.78296 4 4 2 6
+0.782 0 5 0 5
+0.77866 6.8 3 5 3
+0.77846 0 6 1 0
+0.77733 0.3 1 6 2
+0.77361 0.3 6 1 1
+0.77235 32.4 2 2 8
+0.77205 5.1 5 1 5
+0.77036 9.3 5 3 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/H-phase_Zhang2014.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/H-phase_Zhang2014.jcpds
new file mode 100644
index 0000000..972d333
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/H-phase_Zhang2014.jcpds
@@ -0,0 +1,44 @@
+4
+Aluminous CaSiO3 pv in LiNbO3 structure (Takafuji 2002)
+ 2 255.000 4.00000
+ 5.148 13.863
+2.3e-5
+d-spacing I/I0 h k l
+3.74965 100 0 1 2
+2.73624 72.6 0 1 -4
+2.574 60.5 1 1 0
+2.3105 8.2 0 0 6
+2.24868 1.1 1 1 3
+2.12211 18 0 2 -2
+1.87483 25.9 0 2 4
+1.71941 30.4 1 1 6
+1.67277 0.1 1 2 -1
+1.63739 18.5 1 2 2
+1.61516 11.5 0 1 8
+1.51545 22.9 1 2 -4
+1.4861 14 0 3 0
+1.43999 0.1 1 2 5
+1.36812 6.9 0 2 -8
+1.32378 7 0 1 -10
+1.32175 0.2 1 1 9
+1.287 6.3 2 2 0
+1.28338 0 1 2 -7
+1.24988 7.4 0 3 6
+1.23981 0.1 2 2 3
+1.23162 0.1 1 3 1
+1.21729 8 1 3 -2
+1.20807 8.7 1 2 8
+1.17722 4.4 0 2 10
+1.16461 8.5 1 3 4
+1.15525 1.1 0 0 12
+1.12929 0 1 3 -5
+1.12434 7.4 2 2 6
+1.10044 3.1 0 4 2
+1.07057 7.3 1 2 -10
+1.06105 3.4 0 4 -4
+1.05396 5.3 1 1 12
+1.04886 0 1 3 7
+1.02003 0 2 3 -1
+1.01185 4.2 2 3 2
+1.00923 0 1 2 11
+1.00653 5.8 1 3 -8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/LICENSE b/jnb-tools/1_cif_to_jcpds/jcpds/LICENSE
new file mode 100644
index 0000000..0fc3afe
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/LICENSE
@@ -0,0 +1,698 @@
+<<<<<<< HEAD
+The MIT License (MIT)
+
+Copyright (c) 2016 S.-H. Dan Shim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+=======
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. .
+>>>>>>> a2140bf4e80e20fb41e3ccf2a39300e125adb0a5
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/LiGaO2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/LiGaO2.jcpds
new file mode 100644
index 0000000..03625c5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/LiGaO2.jcpds
@@ -0,0 +1,29 @@
+4
+LiGaO2
+ 2 255.000 4.00000
+ 2.911 14.466
+1.86e-5
+d-spacing I/I0 h k l
+4.822 100 0 0 3
+2.48382 44.6 0 1 -1
+2.411 6.9 0 0 6
+2.38077 19 0 1 2
+2.06826 69.4 0 1 -4
+1.90079 16.5 0 1 5
+1.60733 2.7 0 0 9
+1.59828 15.4 0 1 -7
+1.4694 19.8 0 1 8
+1.45565 19.9 1 1 0
+1.39354 16.3 1 1 3
+1.25587 4.9 0 2 1
+1.25474 3.6 0 1 -10
+1.24614 6.5 1 1 6
+1.24191 3.1 0 2 -2
+1.2055 3 0 0 12
+1.19038 9.3 0 2 4
+1.16601 5.6 0 1 11
+1.15569 3.2 0 2 -5
+1.07895 4.6 1 1 9
+1.0762 4 0 2 7
+1.03413 6 0 2 -8
+1.01803 1.8 0 1 -13
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Linbo3_Megaw (1968).jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Linbo3_Megaw (1968).jcpds
new file mode 100644
index 0000000..972d333
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Linbo3_Megaw (1968).jcpds
@@ -0,0 +1,44 @@
+4
+Aluminous CaSiO3 pv in LiNbO3 structure (Takafuji 2002)
+ 2 255.000 4.00000
+ 5.148 13.863
+2.3e-5
+d-spacing I/I0 h k l
+3.74965 100 0 1 2
+2.73624 72.6 0 1 -4
+2.574 60.5 1 1 0
+2.3105 8.2 0 0 6
+2.24868 1.1 1 1 3
+2.12211 18 0 2 -2
+1.87483 25.9 0 2 4
+1.71941 30.4 1 1 6
+1.67277 0.1 1 2 -1
+1.63739 18.5 1 2 2
+1.61516 11.5 0 1 8
+1.51545 22.9 1 2 -4
+1.4861 14 0 3 0
+1.43999 0.1 1 2 5
+1.36812 6.9 0 2 -8
+1.32378 7 0 1 -10
+1.32175 0.2 1 1 9
+1.287 6.3 2 2 0
+1.28338 0 1 2 -7
+1.24988 7.4 0 3 6
+1.23981 0.1 2 2 3
+1.23162 0.1 1 3 1
+1.21729 8 1 3 -2
+1.20807 8.7 1 2 8
+1.17722 4.4 0 2 10
+1.16461 8.5 1 3 4
+1.15525 1.1 0 0 12
+1.12929 0 1 3 -5
+1.12434 7.4 2 2 6
+1.10044 3.1 0 4 2
+1.07057 7.3 1 2 -10
+1.06105 3.4 0 4 -4
+1.05396 5.3 1 1 12
+1.04886 0 1 3 7
+1.02003 0 2 3 -1
+1.01185 4.2 2 3 2
+1.00923 0 1 2 11
+1.00653 5.8 1 3 -8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Mg2FeAl2Si3O12-gt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Mg2FeAl2Si3O12-gt.jcpds
new file mode 100755
index 0000000..5721f7c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Mg2FeAl2Si3O12-gt.jcpds
@@ -0,0 +1,40 @@
+4
+pyrope (EOS from Wang 1998 PEPI)
+ 1 171.000 5.00000
+ 11.488
+3.16e-5
+d-spacing I/I0 h k l
+4.71445 7.1 1 1 2
+4.08283 1.7 0 2 2
+3.08633 5.8 1 2 3
+2.887 52.1 0 0 4
+2.58221 100 0 2 4
+2.46204 32.2 2 3 3
+2.35723 21.8 2 2 4
+2.26475 26.7 1 3 4
+2.10837 12.8 1 2 5
+2.04142 2.9 0 4 4
+1.87333 22.8 1 1 6
+1.8259 7.4 0 2 6
+1.7819 0.4 1 4 5
+1.70266 2.1 1 3 6
+1.66681 18.4 4 4 4
+1.63313 0.8 3 4 5
+1.60142 48.4 0 4 6
+1.57148 1.3 1 2 7
+1.54317 79.3 2 4 6
+1.4666 2.8 1 5 6
+1.4435 15.1 0 0 8
+1.42146 3.7 1 4 7
+1.4004 0.8 0 2 8
+1.38025 4.2 3 5 6
+1.36094 0.7 0 6 6
+1.34243 1.1 1 3 8
+1.30755 2 2 5 7
+1.29111 18.2 0 4 8
+1.25999 24.9 2 4 8
+1.24525 4.4 1 2 9
+1.23102 13.4 4 6 6
+1.21727 5.9 1 5 8
+1.19109 0.2 2 3 9
+1.17861 0.5 4 4 8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Mg3Al2Si3O12-pyrope.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Mg3Al2Si3O12-pyrope.jcpds
new file mode 100644
index 0000000..ec81fc7
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Mg3Al2Si3O12-pyrope.jcpds
@@ -0,0 +1,40 @@
+4
+pyrope (EOS from Wang 1998 PEPI)
+ 1 171.000 5.00000
+ 11.452
+3.16e-5
+d-spacing I/I0 h k l
+4.71445 7.1 1 1 2
+4.08283 1.7 0 2 2
+3.08633 5.8 1 2 3
+2.887 52.1 0 0 4
+2.58221 100 0 2 4
+2.46204 32.2 2 3 3
+2.35723 21.8 2 2 4
+2.26475 26.7 1 3 4
+2.10837 12.8 1 2 5
+2.04142 2.9 0 4 4
+1.87333 22.8 1 1 6
+1.8259 7.4 0 2 6
+1.7819 0.4 1 4 5
+1.70266 2.1 1 3 6
+1.66681 18.4 4 4 4
+1.63313 0.8 3 4 5
+1.60142 48.4 0 4 6
+1.57148 1.3 1 2 7
+1.54317 79.3 2 4 6
+1.4666 2.8 1 5 6
+1.4435 15.1 0 0 8
+1.42146 3.7 1 4 7
+1.4004 0.8 0 2 8
+1.38025 4.2 3 5 6
+1.36094 0.7 0 6 6
+1.34243 1.1 1 3 8
+1.30755 2 2 5 7
+1.29111 18.2 0 4 8
+1.25999 24.9 2 4 8
+1.24525 4.4 1 2 9
+1.23102 13.4 4 6 6
+1.21727 5.9 1 5 8
+1.19109 0.2 2 3 9
+1.17861 0.5 4 4 8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgAl2O4 Liu 1978.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgAl2O4 Liu 1978.jcpds
new file mode 100644
index 0000000..bf355b6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgAl2O4 Liu 1978.jcpds
@@ -0,0 +1,26 @@
+3
+ε-MgAl2O4 Liu 1978
+ 4 200.000 3.98000
+ 8.5070 2.7400 9.4070
+(blank for future use)
+d-spacing I/I0 h k l
+4.70000 10.0 0 0 2
+4.26000 5.0 2 0 0
+3.15300 80.0 2 0 2
+2.73700 20.0 0 1 0
+2.64000 5.0 0 1 1
+2.43300 20.0 3 0 2
+2.35500 50.0 0 0 4
+2.10400 100.0 3 0 3
+2.05800 30.0 2 0 4
+1.64000 5.0 4 0 2
+1.77000 5.0 4 0 3
+1.74200 10.0 1 1 4
+1.69800 5.0 5 0 0
+1.66600 30.0 3 1 3
+1.59800 40.0 5 0 2
+1.57700 15.0 4 0 4
+1.51300 5.0 3 1 4
+1.48600 30.0 4 1 3
+1.47100 5.0 2 0 6
+1.41800 10.0 6 0 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-gt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-gt.jcpds
new file mode 100755
index 0000000..4efdcc6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-gt.jcpds
@@ -0,0 +1,40 @@
+4
+pyrope (EOS from Wang 1998 PEPI)
+ 1 171.000 5.00000
+ 11.503
+3.16e-5
+d-spacing I/I0 h k l
+4.71445 7.1 1 1 2
+4.08283 1.7 0 2 2
+3.08633 5.8 1 2 3
+2.887 52.1 0 0 4
+2.58221 100 0 2 4
+2.46204 32.2 2 3 3
+2.35723 21.8 2 2 4
+2.26475 26.7 1 3 4
+2.10837 12.8 1 2 5
+2.04142 2.9 0 4 4
+1.87333 22.8 1 1 6
+1.8259 7.4 0 2 6
+1.7819 0.4 1 4 5
+1.70266 2.1 1 3 6
+1.66681 18.4 4 4 4
+1.63313 0.8 3 4 5
+1.60142 48.4 0 4 6
+1.57148 1.3 1 2 7
+1.54317 79.3 2 4 6
+1.4666 2.8 1 5 6
+1.4435 15.1 0 0 8
+1.42146 3.7 1 4 7
+1.4004 0.8 0 2 8
+1.38025 4.2 3 5 6
+1.36094 0.7 0 6 6
+1.34243 1.1 1 3 8
+1.30755 2 2 5 7
+1.29111 18.2 0 4 8
+1.25999 24.9 2 4 8
+1.24525 4.4 1 2 9
+1.23102 13.4 4 6 6
+1.21727 5.9 1 5 8
+1.19109 0.2 2 3 9
+1.17861 0.5 4 4 8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-ilm.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-ilm.jcpds
new file mode 100755
index 0000000..6fb7f6f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-ilm.jcpds
@@ -0,0 +1,38 @@
+4
+MgSiO3 ilmenite (GSAS calculation, EOS from guess)
+ 2 212 7.5
+4.7066 13.4968
+2.44e-5
+d-spacing I/I0 h k l
+4.5197 26 0 0 3
+3.92005 6 1 0 1
+3.50514 92 1 0 -2
+2.61119 100 1 0 4
+2.3642 43 1 1 0
+2.26098 5 1 0 -5
+2.0949 38 1 1 -3
+2.0949 41 1 1 3
+2.02451 6 2 0 -1
+1.96002 1 2 0 2
+1.75257 32 2 0 -4
+1.751 2 1 0 7
+1.63403 5 2 0 5
+1.6336 44 1 1 -6
+1.6336 20 1 1 6
+1.56605 5 1 0 -8
+1.53775 1 2 1 1
+1.50891 4 2 1 -2
+1.40792 5 2 1 4
+1.40792 13 3 -1 4
+1.36497 23 3 0 0
+1.34421 2 2 1 -5
+1.28718 5 1 0 10
+1.27053 6 1 1 9
+1.1821 3 2 2 0
+1.18033 1 1 0 -11
+1.14363 1 2 2 -3
+1.13049 1 2 0 -10
+1.12012 1 3 1 2
+1.07689 1 4 -1 -4
+1.04745 1 2 2 6
+1.01989 1 2 1 10
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-opv.jcpds
new file mode 100755
index 0000000..82ccfe9
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgAlSiO3-opv.jcpds
@@ -0,0 +1,162 @@
+4
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.7852 4.9393 6.9112
+2.2e-5
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgFe2SiO4-g.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgFe2SiO4-g.jcpds
new file mode 100644
index 0000000..f1ac81b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgFe2SiO4-g.jcpds
@@ -0,0 +1,22 @@
+4
+(Mg,Fe)2SiO4 spinel (GSAS calculation, EOS from Knittle and Jeanloz,1987, don't trust 0GPa peak positions)
+ 1 183. 5.38
+8.08
+1.89e-5
+d-spacing I/I0 h k l
+2.85349 30 2 2 0
+2.43347 100 3 1 1
+2.32987 1 2 2 2
+2.01773 39 4 0 0
+1.85159 2 3 3 1
+1.64747 6 4 2 2
+1.55325 6 3 3 3
+1.55325 9 5 1 1
+1.42675 31 4 4 0
+1.27612 1 6 2 0
+1.2308 3 5 3 3
+1.16493 2 4 4 4
+1.05074 2 7 3 1
+1.05074 1 5 5 3
+1.00886 1 8 0 0
+0.90235 1 8 4 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgFeAlSiO3-cgt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgFeAlSiO3-cgt.jcpds
new file mode 100644
index 0000000..65cce81
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgFeAlSiO3-cgt.jcpds
@@ -0,0 +1,40 @@
+4
+pyrope (EOS from Wang 1998 PEPI)
+ 1 171.000 5.00000
+ 11.53
+3.16e-5
+d-spacing I/I0 h k l
+4.71445 7.1 1 1 2
+4.08283 1.7 0 2 2
+3.08633 5.8 1 2 3
+2.887 52.1 0 0 4
+2.58221 100 0 2 4
+2.46204 32.2 2 3 3
+2.35723 21.8 2 2 4
+2.26475 26.7 1 3 4
+2.10837 12.8 1 2 5
+2.04142 2.9 0 4 4
+1.87333 22.8 1 1 6
+1.8259 7.4 0 2 6
+1.7819 0.4 1 4 5
+1.70266 2.1 1 3 6
+1.66681 18.4 4 4 4
+1.63313 0.8 3 4 5
+1.60142 48.4 0 4 6
+1.57148 1.3 1 2 7
+1.54317 79.3 2 4 6
+1.4666 2.8 1 5 6
+1.4435 15.1 0 0 8
+1.42146 3.7 1 4 7
+1.4004 0.8 0 2 8
+1.38025 4.2 3 5 6
+1.36094 0.7 0 6 6
+1.34243 1.1 1 3 8
+1.30755 2 2 5 7
+1.29111 18.2 0 4 8
+1.25999 24.9 2 4 8
+1.24525 4.4 1 2 9
+1.23102 13.4 4 6 6
+1.21727 5.9 1 5 8
+1.19109 0.2 2 3 9
+1.17861 0.5 4 4 8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/MgSiO3-bm.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/MgSiO3-bm.jcpds
new file mode 100644
index 0000000..2be2653
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/MgSiO3-bm.jcpds
@@ -0,0 +1,728 @@
+4
+Bridgmanite
+4 260.00 4.00
+4.77540 4.92920 6.89690
+3.1600e-05
+d-spacing I/I0 h k l
+3.926131 0.82 1.0 0.0 1.0
+3.448450 8.23 0.0 0.0 2.0
+3.429802 20.92 1.0 1.0 0.0
+3.071022 8.94 1.0 1.0 1.0
+2.464600 24.50 0.0 2.0 0.0
+2.431802 100.00 1.0 1.0 2.0
+2.387700 18.57 2.0 0.0 0.0
+2.320866 0.47 0.0 2.0 1.0
+2.190117 3.77 1.0 2.0 0.0
+2.148865 10.92 2.0 1.0 0.0
+2.087399 10.37 1.0 2.0 1.0
+2.071424 23.71 1.0 0.0 3.0
+2.051591 19.61 2.0 1.0 1.0
+2.005137 13.51 0.0 2.0 2.0
+1.963065 8.00 2.0 0.0 2.0
+1.909655 14.02 1.0 1.0 3.0
+1.848773 10.74 1.0 2.0 2.0
+1.823757 5.02 2.0 1.0 2.0
+1.724225 38.99 0.0 0.0 4.0
+1.714901 65.71 2.0 2.0 0.0
+1.681123 7.67 0.0 2.0 3.0
+1.664226 8.88 2.0 2.0 1.0
+1.585732 3.16 1.0 2.0 3.0
+1.569861 3.41 2.0 1.0 3.0
+1.553674 10.26 1.0 3.0 0.0
+1.551026 1.15 3.0 0.0 1.0
+1.540515 4.15 1.0 1.0 4.0
+1.535511 7.41 2.0 2.0 2.0
+1.515691 17.39 1.0 3.0 1.0
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+1.397856 20.35 2.0 0.0 4.0
+1.386872 54.06 3.0 1.0 2.0
+1.374592 0.37 2.0 2.0 3.0
+1.354762 0.03 1.0 2.0 4.0
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+1.279760 0.10 1.0 1.0 5.0
+1.264888 0.89 3.0 1.0 3.0
+1.259970 1.14 2.0 3.0 2.0
+1.246712 0.10 3.0 2.0 2.0
+1.232300 5.96 0.0 4.0 0.0
+1.215901 27.85 2.0 2.0 4.0
+1.213088 2.46 0.0 4.0 1.0
+1.203683 0.02 0.0 2.0 5.0
+1.193850 5.01 4.0 0.0 0.0
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+1.143267 0.01 3.0 3.0 0.0
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/Na0.4Mg0.6Al1.6Si0.4O4-CF.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/Na0.4Mg0.6Al1.6Si0.4O4-CF.jcpds
new file mode 100644
index 0000000..4ed7763
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/Na0.4Mg0.6Al1.6Si0.4O4-CF.jcpds
@@ -0,0 +1,304 @@
+3
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/PhaseX.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/PhaseX.jcpds
new file mode 100644
index 0000000..870cdef
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/PhaseX.jcpds
@@ -0,0 +1,56 @@
+3
+PHASE X (GSAS, EOS from Yang 1997)
+ 2 134.000 4.30000
+ 5.0756 6.5969
+(blank for future use)
+d-spacing I/I0 h k l
+6.5969 0 0 0 1
+4.3956 6 0 1 0
+3.65796 0.3 0 1 1
+3.29845 1.5 0 0 2
+2.63826 100 0 1 2
+2.5378 87.7 1 1 0
+2.36858 55.7 1 1 1
+2.19897 15.7 0 0 3
+2.1978 0.1 0 2 0
+2.08513 0.1 0 2 1
+2.01136 61.4 1 1 2
+1.96661 0.9 0 1 3
+1.82898 24.4 0 2 2
+1.66188 0.8 1 1 3
+1.66138 0 1 2 0
+1.64922 10.3 0 0 4
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+1.55449 0 0 2 3
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+1.48379 27.4 1 2 2
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+1.00842 0 2 3 0
+1.00568 16.3 2 2 4
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/README.md b/jnb-tools/1_cif_to_jcpds/jcpds/README.md
new file mode 100644
index 0000000..6553a19
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/README.md
@@ -0,0 +1,3 @@
+This is JCPDS file repository by the Shim group. The files are all checked and confirmed to be used for synchrotron X-ray diffraction experiments.
+
+Plrease report any problems to Dan Shim, S.-H. Dan Shim
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/SiC.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/SiC.jcpds
new file mode 100755
index 0000000..80964d4
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/SiC.jcpds
@@ -0,0 +1,13 @@
+4
+SiC
+ 1 250 4.0
+ 4.3596
+4.00e-6
+d-spacing I/I0 h k l
+ 2.5170 100.0 1 1 1
+ 2.1798 18.1 0 0 2
+ 1.5413 46.1 0 2 2
+ 1.3145 34.9 1 1 3
+ 1.2585 5.0 2 2 2
+ 1.0899 7.5 0 0 4
+ 1.0002 17.2 1 3 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/TAPP_Harris1997.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/TAPP_Harris1997.jcpds
new file mode 100644
index 0000000..6577967
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/TAPP_Harris1997.jcpds
@@ -0,0 +1,131 @@
+4
+H-phase (Harris1997)
+ 3 255.000 4.00000
+ 6.526 18.182
+2.3e-5
+6.14233 0 0 1 1
+4.5455 0.6 0 0 4
+4.44094 0.6 0 1 3
+4.11482 15 1 1 2
+3.263 7.7 0 2 0
+3.17654 21 0 1 5
+3.07117 2.3 0 2 2
+2.88163 14.9 1 2 1
+2.65073 100 0 2 4
+2.62952 9.2 1 2 3
+2.533 1.9 1 1 6
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\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/TiC-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/TiC-NoTh.jcpds
new file mode 100755
index 0000000..cab89a3
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/TiC-NoTh.jcpds
@@ -0,0 +1,17 @@
+4
+TiC (EOS from Gu)
+ 1 268.0 4.00
+ 4.326
+0.0
+d-spacing I/I0 h k l
+ 3.2580 13. 1.00 1.00 1.00
+ 2.8201 100. 2.00 0.00 0.00
+ 1.9941 55. 2.00 2.00 0.00
+ 1.7006 2. 3.00 1.00 1.00
+ 1.6282 15. 2.00 2.00 2.00
+ 1.4100 6. 4.00 0.00 0.00
+ 1.2940 1. 3.00 3.00 1.00
+ 1.2612 11. 4.00 2.00 0.00
+ 1.1515 7. 4.00 2.00 2.00
+ 1.0855 1. 5.00 1.00 1.00
+ 0.9969 2. 4.00 4.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/al2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/al2o3.jcpds
new file mode 100755
index 0000000..8cc70a9
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/al2o3.jcpds
@@ -0,0 +1,18 @@
+4
+Al2O3 corundum (JCPDS 46-1212, EOS)
+ 2 254.000 4.30000
+ 4.7587 12.9929
+2.3e-5
+d-spacing I/I0 h k l
+ 3.4797 45. 0.00 1.00 2.00
+ 2.5508 100. 1.00 0.00 4.00
+ 2.3795 21. 1.00 1.00 0.00
+ 2.0853 66. 1.00 1.00 3.00
+ 1.7401 34. 0.00 2.00 4.00
+ 1.6016 89. 1.00 1.00 6.00
+ 1.5110 14. 0.00 1.00 8.00
+ 1.4045 23. 2.00 1.00 4.00
+ 1.3737 27. 3.00 0.00 0.00
+ 1.2392 29. 1.00 0.00 10.00
+ 1.2343 12. 1.00 1.00 9.00
+ 1.0990 9. 0.00 2.00 10.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/alooh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/alooh.jcpds
new file mode 100644
index 0000000..1cab2ea
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/alooh.jcpds
@@ -0,0 +1,47 @@
+4
+alooh
+ 4 252.000 4.00000
+ 4.698 4.206 2.833
+1.86e-5
+d-spacing I/I0 h k l
+4.2241 2.8 0 1 0
+3.1457 100 1 1 0
+2.42785 4.9 1 0 1
+2.3567 8.6 2 0 0
+2.35256 52.5 0 1 1
+2.11205 0.7 0 2 0
+2.10493 64 1 1 1
+2.05806 36.2 2 1 0
+1.9274 2.5 1 2 0
+1.69317 1.6 0 2 1
+1.66497 30.2 2 1 1
+1.59348 65.1 1 2 1
+1.57285 22.1 2 2 0
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+1.41626 17.5 0 0 2
+1.40803 0.9 0 3 0
+1.37508 1.4 2 2 1
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+1.30655 1.3 3 1 1
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+1.13501 1.6 4 1 0
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+1.05603 4.9 0 4 0
+1.05358 6.7 4 1 1
+1.05247 11 2 2 2
+1.04857 2.2 3 3 0
+1.03048 1.2 1 4 0
+1.02903 8.5 4 2 0
+1.02077 7.2 3 1 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ar-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ar-NoTh.jcpds
new file mode 100755
index 0000000..d40f812
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ar-NoTh.jcpds
@@ -0,0 +1,11 @@
+4
+Argon FCC (made by Shim, EOS from Errandonea and Boehler)
+ 1 6.5 5.1
+ 5.22932
+0.0
+d-spacing I/I0 h k l
+ 3.1828 100. 1.00 1.00 1.00
+ 2.7564 40. 2.00 0.00 0.00
+ 1.9491 25. 2.00 2.00 0.00
+ 1.6622 30. 3.00 1.00 1.00
+ 1.5914 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/au-Dewaele.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/au-Dewaele.jcpds
new file mode 100755
index 0000000..47c95da
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/au-Dewaele.jcpds
@@ -0,0 +1,19 @@
+4
+Gold (04-0784, Dewaele)
+ 1 166.70 5.32
+ 4.07860
+4.278e-5
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/au-Ye2017.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/au-Ye2017.jcpds
new file mode 100755
index 0000000..27f7bc5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/au-Ye2017.jcpds
@@ -0,0 +1,19 @@
+4
+Gold (Ye2017, JGR in Vinet EOS, for BM3 converted from Vinet EOS, fit to Pt)
+ 1 167.00 5.598
+ 4.07860
+4.278e-5
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/au.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/au.jcpds
new file mode 100755
index 0000000..4a38e92
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/au.jcpds
@@ -0,0 +1,19 @@
+4
+Gold (04-0784, Tsuchiya 2003, Note that Tsuchiya used Vinet EOS but XPEAKPO uses BM)
+ 1 166.70 6.12
+ 4.07860
+4.278e-5
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/bm-test.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/bm-test.jcpds
new file mode 100644
index 0000000..f2be0dd
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/bm-test.jcpds
@@ -0,0 +1,109 @@
+4
+test bridgmanite
+4 260.00 4.00
+4.77540 4.92920 6.89690
+1.0000e-05
+d-spacing I/I0 h k l
+3.92613 0.8 1.0 0.0 1.0
+3.44845 8.2 0.0 0.0 2.0
+3.42980 20.9 1.0 1.0 0.0
+3.07102 8.9 1.0 1.0 1.0
+2.46460 24.5 0.0 2.0 0.0
+2.43180 100.0 1.0 1.0 2.0
+2.38770 18.6 2.0 0.0 0.0
+2.32087 0.5 0.0 2.0 1.0
+2.19012 3.8 1.0 2.0 0.0
+2.14887 10.9 2.0 1.0 0.0
+2.08740 10.4 1.0 2.0 1.0
+2.07142 23.7 1.0 0.0 3.0
+2.05159 19.6 2.0 1.0 1.0
+2.00514 13.5 0.0 2.0 2.0
+1.96307 8.0 2.0 0.0 2.0
+1.90966 14.0 1.0 1.0 3.0
+1.84877 10.7 1.0 2.0 2.0
+1.82376 5.0 2.0 1.0 2.0
+1.72422 39.0 0.0 0.0 4.0
+1.71490 65.7 2.0 2.0 0.0
+1.68112 7.7 0.0 2.0 3.0
+1.66423 8.9 2.0 2.0 1.0
+1.58573 3.2 1.0 2.0 3.0
+1.56986 3.4 2.0 1.0 3.0
+1.55367 10.3 1.0 3.0 0.0
+1.55103 1.2 3.0 0.0 1.0
+1.54051 4.1 1.0 1.0 4.0
+1.53551 7.4 2.0 2.0 2.0
+1.51569 17.4 1.0 3.0 1.0
+1.51477 0.5 3.0 1.0 0.0
+1.47951 3.7 3.0 1.0 1.0
+1.41654 22.4 1.0 3.0 2.0
+1.41281 22.9 0.0 2.0 4.0
+1.39786 20.4 2.0 0.0 4.0
+1.38687 54.1 3.0 1.0 2.0
+1.37459 0.4 2.0 2.0 3.0
+1.35476 0.0 1.0 2.0 4.0
+1.35355 0.3 2.0 3.0 0.0
+1.34483 0.3 2.0 1.0 4.0
+1.33716 1.8 3.0 2.0 0.0
+1.32822 0.8 2.0 3.0 1.0
+1.32520 2.0 1.0 0.0 5.0
+1.31271 0.0 3.0 2.0 1.0
+1.30871 0.2 3.0 0.0 3.0
+1.28727 6.3 1.0 3.0 3.0
+1.27976 0.1 1.0 1.0 5.0
+1.26489 0.9 3.0 1.0 3.0
+1.25997 1.1 2.0 3.0 2.0
+1.24671 0.1 3.0 2.0 2.0
+1.23230 6.0 0.0 4.0 0.0
+1.21590 27.9 2.0 2.0 4.0
+1.21309 2.5 0.0 4.0 1.0
+1.20368 0.0 0.0 2.0 5.0
+1.19385 5.0 4.0 0.0 0.0
+1.19321 3.6 1.0 4.0 0.0
+1.17575 0.0 1.0 4.0 1.0
+1.16718 4.2 1.0 2.0 5.0
+1.16640 3.6 2.0 3.0 3.0
+1.16080 3.0 2.0 1.0 5.0
+1.16043 2.3 0.0 4.0 2.0
+1.16030 2.2 4.0 1.0 0.0
+1.15586 1.4 3.0 2.0 3.0
+1.15421 5.3 1.0 3.0 4.0
+1.14948 0.5 0.0 0.0 6.0
+1.14422 1.1 4.0 1.0 1.0
+1.14327 0.0 3.0 3.0 0.0
+1.13799 0.1 3.0 1.0 4.0
+1.12816 1.4 4.0 0.0 2.0
+1.12788 1.5 3.0 3.0 1.0
+1.12762 0.5 1.0 4.0 2.0
+1.09972 1.5 4.0 1.0 2.0
+1.09506 2.8 2.0 4.0 0.0
+1.08990 12.7 1.0 1.0 6.0
+1.08611 0.0 0.0 4.0 3.0
+1.08518 6.6 3.0 3.0 2.0
+1.08151 1.6 2.0 4.0 1.0
+1.07483 3.6 2.0 2.0 5.0
+1.07443 3.6 4.0 2.0 0.0
+1.06468 0.0 2.0 3.0 4.0
+1.06163 1.8 4.0 2.0 1.0
+1.05906 0.6 1.0 4.0 3.0
+1.05665 0.6 3.0 2.0 4.0
+1.04370 2.0 2.0 4.0 2.0
+1.04244 0.7 3.0 0.0 5.0
+1.04175 0.2 0.0 2.0 6.0
+1.03585 2.0 4.0 1.0 3.0
+1.03571 0.3 2.0 0.0 6.0
+1.03151 4.9 1.0 3.0 5.0
+1.02580 1.7 4.0 2.0 2.0
+1.02367 3.8 3.0 3.0 3.0
+1.01988 3.2 3.0 1.0 5.0
+1.01781 0.0 1.0 2.0 6.0
+1.01358 0.2 2.0 1.0 6.0
+1.00257 4.3 0.0 4.0 4.0
+0.98863 5.3 2.0 4.0 3.0
+0.98153 4.1 4.0 0.0 4.0
+0.98118 0.6 1.0 4.0 4.0
+0.97443 0.0 3.0 4.0 0.0
+0.97338 1.8 4.0 2.0 3.0
+0.96582 0.2 4.0 3.0 0.0
+0.96548 2.0 1.0 5.0 0.0
+0.96495 0.8 1.0 0.0 7.0
+0.96484 0.2 3.0 4.0 1.0
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/casio3-pv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/casio3-pv.jcpds
new file mode 100755
index 0000000..4108c8f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/casio3-pv.jcpds
@@ -0,0 +1,25 @@
+4
+Ca-perovskite (using aristotype, EOS by Shim et al 1999)
+ 1 236.000 3.90000
+ 3.56660
+2.2e-5
+d-spacing I/I0 h k l
+ 3.5666 4. 1.00 0.00 0.00
+ 2.5220 100. 1.00 1.00 0.00
+ 2.0592 31. 1.00 1.00 1.00
+ 1.7833 56. 2.00 0.00 0.00
+ 1.5950 1. 2.00 1.00 0.00
+ 1.4561 15. 2.00 1.00 1.00
+ 1.2610 9. 2.00 2.00 0.00
+ 1.1889 1. 2.00 2.00 1.00
+ 1.1889 1. 3.00 0.00 0.00
+ 1.1279 3. 3.00 1.00 0.00
+ 1.0754 1. 3.00 1.00 1.00
+ 1.0296 3. 2.00 2.00 2.00
+ 0.9892 1. 3.00 2.00 0.00
+ 0.9532 1. 3.00 2.00 1.00
+ 0.8917 1. 4.00 0.00 0.00
+ 0.8650 1. 3.00 2.00 2.00
+ 0.8650 1. 4.00 1.00 0.00
+ 0.8407 1. 4.00 1.00 1.00
+ 0.8407 1. 3.00 3.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/casio_Linbo3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/casio_Linbo3.jcpds
new file mode 100755
index 0000000..d9ae62f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/casio_Linbo3.jcpds
@@ -0,0 +1,14 @@
+4
+Aluminous CaSiO3 pv in LiNbO3 structure (Takafuji 2002)
+ 2 255.000 4.00000
+ 4.203 12.35
+2.3e-5
+d-spacing I/I0 h k l
+3.14 100 0 1 2
+2.35 47 1 0 4
+2.11 32 1 1 0
+2.05 10 0 0 6
+1.59 14 1 0 7
+1.57 7 0 2 4
+1.41 8 0 1 8
+1.37 10 0 0 9
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/diamond-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/diamond-NoTh.jcpds
new file mode 100755
index 0000000..0f3cb62
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/diamond-NoTh.jcpds
@@ -0,0 +1,11 @@
+4
+diamond (JCPDS 6-0675, dummy EOS)
+ 1 999. 1.000000
+ 3.56670
+0.0
+d-spacing I/I0 h k l
+ 2.0600 100. 1.00 1.00 1.00
+ 1.2610 27. 2.00 2.00 0.00
+ 1.0754 16. 3.00 1.00 1.00
+ 0.8916 7. 4.00 0.00 0.00
+ 0.8182 15. 3.00 3.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mg0.8fe0.2sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mg0.8fe0.2sio3-opv.jcpds
new file mode 100755
index 0000000..e16bcd6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mg0.8fe0.2sio3-opv.jcpds
@@ -0,0 +1,162 @@
+4
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.7963 4.9346 6.9092
+2.2e-5
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mg0.9fe0.1sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mg0.9fe0.1sio3-opv.jcpds
new file mode 100755
index 0000000..dd0d76e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mg0.9fe0.1sio3-opv.jcpds
@@ -0,0 +1,162 @@
+4
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.7859 4.9319 6.9032
+2.2e-5
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mg2sio4-g.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mg2sio4-g.jcpds
new file mode 100755
index 0000000..b36949e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mg2sio4-g.jcpds
@@ -0,0 +1,22 @@
+4
+Mg2SiO4 spinel (GSAS calculation, EOS from Knittle and Jeanloz,1987)
+ 1 183. 5.38
+8.0709
+1.89e-5
+d-spacing I/I0 h k l
+2.85349 30 2 2 0
+2.43347 100 3 1 1
+2.32987 1 2 2 2
+2.01773 39 4 0 0
+1.85159 2 3 3 1
+1.64747 6 4 2 2
+1.55325 6 3 3 3
+1.55325 9 5 1 1
+1.42675 31 4 4 0
+1.27612 1 6 2 0
+1.2308 3 5 3 3
+1.16493 2 4 4 4
+1.05074 2 7 3 1
+1.05074 1 5 5 3
+1.00886 1 8 0 0
+0.90235 1 8 4 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mgal2o4-CF.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mgal2o4-CF.jcpds
new file mode 100755
index 0000000..d61829d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mgal2o4-CF.jcpds
@@ -0,0 +1,304 @@
+3
+CF phase (from original structure by Decker, EOS from Ono2002)
+ 4 253.000 3.60000
+ 8.64 10.115 2.793
+(blank for future use)
+d-spacing I/I0 h k l
+6.58195 0.2 1 1 0
+5.064 2.5 0 2 0
+4.37146 4.7 1 2 0
+4.33 29 2 0 0
+3.9814 0.2 2 1 0
+3.29097 14.1 2 2 0
+3.14544 1.1 1 3 0
+2.77611 2.2 3 1 0
+2.68622 7.1 0 1 1
+2.6624 1 2 3 0
+2.56563 2.6 1 1 1
+2.532 60.5 0 4 0
+2.50783 84.3 3 2 0
+2.43025 0 1 4 0
+2.34943 43.3 1 2 1
+2.34293 25.2 2 0 1
+2.28264 0.9 2 1 1
+2.19398 11.5 3 3 0
+2.18573 1 2 4 0
+2.165 1.1 4 0 0
+2.1488 20.6 0 3 1
+2.12637 2.7 2 2 1
+2.11717 15 4 1 0
+2.08555 100 1 3 1
+1.9907 37.1 4 2 0
+1.97236 16.3 1 5 0
+1.96649 73.3 3 1 1
+1.92481 16.2 2 3 1
+1.90351 2.7 3 4 0
+1.86391 5.2 3 2 1
+1.83476 1.5 2 5 0
+1.83139 21.3 1 4 1
+1.82245 0.5 4 3 0
+1.72367 15.6 3 3 1
+1.71966 71.1 2 4 1
+1.70951 44.2 4 0 1
+1.70722 1.3 5 1 0
+1.688 0 0 6 0
+1.68567 30.4 4 1 1
+1.6581 2.5 3 5 0
+1.65682 3.2 1 6 0
+1.64549 1.2 4 4 0
+1.6388 2.9 5 2 0
+1.63834 6.9 0 5 1
+1.61971 5.3 4 2 1
+1.60978 0.1 1 5 1
+1.57272 34.7 2 6 0
+1.57169 0 3 4 1
+1.54103 0.1 5 3 0
+1.53232 2.7 2 5 1
+1.52512 3.8 4 3 1
+1.47914 0.1 4 5 0
+1.45715 26.1 3 6 0
+1.45565 0.9 5 1 1
+1.44333 23.4 6 0 0
+1.42954 2.6 5 4 0
+1.4289 1 6 1 0
+1.42708 0 1 7 0
+1.42485 1.6 3 5 1
+1.42403 29.2 1 6 1
+1.41682 50.4 4 4 1
+1.41254 31.6 5 2 1
+1.393 52.9 0 0 2
+1.38805 2.7 6 2 0
+1.37227 2.7 2 7 0
+1.36957 25.6 2 6 1
+1.36281 0 1 1 2
+1.34849 2.3 5 3 1
+1.34311 0.1 0 2 2
+1.3312 5.2 4 6 0
+1.32724 0.2 1 2 2
+1.32713 1.1 6 3 0
+1.32607 1.5 2 0 2
+1.31639 0 5 5 0
+1.31485 0 2 1 2
+1.30643 0.2 4 5 1
+1.29348 0.7 3 7 0
+1.29121 7.3 3 6 1
+1.28403 0.2 0 7 1
+1.28281 2 2 2 2
+1.28156 1.2 6 0 1
+1.27369 0.1 1 3 2
+1.27188 0.8 5 4 1
+1.27142 0.1 6 1 1
+1.27014 9.4 1 7 1
+1.266 2.1 0 8 0
+1.25392 21 6 4 0
+1.25269 5.7 1 8 0
+1.24505 0.4 3 1 2
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+1.23427 0.2 2 3 2
+1.23104 0.6 2 7 1
+1.22802 1.4 7 1 0
+1.22049 15.3 0 4 2
+1.21775 25.7 3 2 2
+1.21513 1.1 2 8 0
+1.20885 0.6 5 6 0
+1.20854 0.3 1 4 2
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+1.20113 8.1 4 6 1
+1.19814 0.3 6 3 1
+1.19021 1.3 5 5 1
+1.17599 1.6 3 3 2
+1.17546 3.7 6 5 0
+1.17471 0 2 4 2
+1.1732 2.4 3 7 1
+1.17146 0.3 4 0 2
+1.1637 2.7 4 1 2
+1.1616 0.1 7 3 0
+1.1594 2.6 3 8 0
+1.14344 0.9 6 4 1
+1.14251 1.1 1 8 1
+1.14132 8.8 4 2 2
+1.13783 3.5 1 5 2
+1.12414 0.4 3 4 2
+1.1237 0.7 7 1 1
+1.11595 0.9 1 9 0
+1.1138 2.3 2 8 1
+1.11156 0.8 7 4 0
+1.11039 2 5 7 0
+1.10947 0.4 2 5 2
+1.10896 2.2 5 6 1
+1.10673 0.1 4 3 2
+1.1044 0.2 4 7 1
+1.10351 4.5 7 2 1
+1.09699 0.5 6 6 0
+1.09287 0.3 4 8 0
+1.08915 1.9 2 9 0
+1.08301 7.7 6 5 1
+1.0825 0.4 8 0 0
+1.0793 0.5 5 1 2
+1.07637 0.3 8 1 0
+1.0744 0.1 0 6 2
+1.07215 9.1 7 3 1
+1.07041 0.4 3 8 1
+1.06657 0.9 3 5 2
+1.06622 1.3 1 6 2
+1.06319 0.4 4 4 2
+1.06137 1.6 5 2 2
+1.05858 0.5 8 2 0
+1.0558 0 7 5 0
+1.04848 0 3 9 0
+1.04343 0.3 0 9 1
+1.04278 15 2 6 2
+1.03594 3.5 1 9 1
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+1.03242 1.2 7 4 1
+1.03149 5.7 5 7 1
+1.03081 1.4 8 3 0
+1.02207 4.7 5 8 0
+1.02183 0.2 6 7 0
+1.02072 0.3 6 6 1
+1.01739 2.5 4 8 1
+1.01439 0.5 2 9 1
+1.01409 0 4 5 2
+1.0128 0.5 0 10 0
+1.00901 2.9 8 0 1
+1.00692 14 3 6 2
+1.00594 0.2 1 10 0
+1.00404 2.4 8 1 1
+1.00232 12.5 6 0 2
+0.9985 0.3 4 9 0
+0.99784 0.2 7 6 0
+0.99767 1.6 5 4 2
+0.99745 0.6 6 1 2
+0.99683 0 1 7 2
+0.99535 3.2 8 4 0
+0.98956 0 8 2 1
+0.98728 0.3 7 5 1
+0.98618 7.5 2 10 0
+0.98325 1.3 6 2 2
+0.98129 2.1 3 9 1
+0.97759 1.3 2 7 2
+0.96676 0 8 3 1
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+0.95677 0 5 5 2
+0.95568 1.8 3 10 0
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+0.94028 0.2 7 7 0
+0.93996 0.7 4 9 1
+0.93941 4.2 7 6 1
+0.93733 1.9 8 4 1
+0.93689 1.5 0 8 2
+0.93196 13.5 6 4 2
+0.93145 3.7 1 8 2
+0.92966 3.9 2 10 1
+0.92537 0 9 3 0
+0.92479 0.3 0 1 3
+0.92117 0.8 7 1 2
+0.91956 0.2 1 1 3
+0.91738 1.8 4 10 0
+0.9157 0.8 2 8 2
+0.91557 0.2 1 11 0
+0.913 0.5 5 6 2
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+0.90839 3.1 1 2 3
+0.90802 1.6 2 0 3
+0.90586 0.1 9 1 1
+0.90439 0 2 1 3
+0.90398 0.6 3 10 1
+0.90316 1.4 8 5 1
+0.90065 1.3 2 11 0
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+0.89835 2.4 6 5 2
+0.89541 0.5 0 3 3
+0.89518 3.6 9 2 1
+0.89377 2.4 2 2 3
+0.89213 0.1 7 3 2
+0.89113 1.9 3 8 2
+0.89091 0.2 7 7 1
+0.89066 4 1 3 3
+0.88747 0 6 9 0
+0.88482 0.9 7 8 0
+0.8807 3 3 1 3
+0.87819 0 9 3 1
+0.87719 0 3 11 0
+0.87686 0.6 2 3 3
+0.87429 2 0 11 1
+0.87136 3 4 10 1
+0.87094 1 1 9 2
+0.8698 0.1 1 11 1
+0.86914 0.1 9 5 0
+0.86884 0.6 7 4 2
+0.86829 1.4 5 7 2
+0.86749 1.4 1 4 3
+0.86676 1 8 7 0
+0.86608 2.5 8 6 1
+0.86285 0.1 10 1 0
+0.86184 0.4 6 6 2
+0.85983 0.2 4 8 2
+0.85802 1.4 2 9 2
+0.85693 1.6 2 11 1
+0.85596 0.3 9 4 1
+0.85521 1.1 3 3 3
+0.85475 5.9 2 4 3
+0.85361 0.1 10 2 0
+0.85346 3.6 4 0 3
+0.85173 0.2 8 1 2
+0.85045 2.2 4 1 3
+0.84729 0.5 4 11 0
+0.8456 1.4 6 9 1
+0.84418 0.6 0 5 3
+0.844 0 0 12 0
+0.84331 6.3 7 8 1
+0.84283 0.3 8 2 2
+0.8416 0.5 4 2 3
+0.84143 0 7 5 2
+0.84019 0 1 5 3
+0.84002 1 1 12 0
+0.83884 0.7 10 3 0
+0.83771 0 3 9 2
+0.8367 0.1 3 11 1
+0.83594 3.1 9 6 0
+0.83463 0 3 4 3
+0.83418 2.5 5 10 1
+0.83246 0 7 9 0
+0.82971 0.1 9 5 1
+0.82905 0.2 6 10 0
+0.82861 1.4 2 5 3
+0.82841 1.3 2 12 0
+0.82763 0.3 8 7 1
+0.82743 0.4 4 3 3
+0.82697 5.2 10 0 1
+0.82423 2.8 10 1 1
+0.82405 3.7 5 8 2
+0.82393 0.2 6 7 2
+0.82274 0.3 8 8 0
+0.8194 0.4 10 4 0
+0.81917 0.5 0 10 2
+0.81616 0.4 10 2 1
+0.81578 0.1 5 1 3
+0.81553 0.2 1 10 2
+0.81299 0.1 5 11 0
+0.81155 0.2 4 9 2
+0.81119 0.2 7 6 2
+0.81063 0 4 11 1
+0.81024 0.2 3 5 3
+0.81009 4 1 6 3
+0.80985 2.8 8 4 2
+0.80876 5.8 4 4 3
+0.80796 3.6 5 2 3
+0.80489 6.3 2 10 2
+0.80426 6.9 1 12 1
+0.80322 1.8 10 3 1
+0.80122 0 9 7 0
+0.80068 1.6 9 6 1
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mgo-Ye2017.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mgo-Ye2017.jcpds
new file mode 100755
index 0000000..f1c12cf
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mgo-Ye2017.jcpds
@@ -0,0 +1,16 @@
+4
+MgO (Ye2017, JGR in Vinet EOS, for BM3 converted from Vinet EOS, fit to Pt)
+ 1 160.300 3.927
+ 4.21300
+3.16e-5
+d-spacing I/I0 h k l
+ 2.4324 10. 1.00 1.00 1.00
+ 2.1065 100. 2.00 0.00 0.00
+ 1.4895 52. 2.00 2.00 0.00
+ 1.2700 4. 3.00 1.00 1.00
+ 1.2160 12. 2.00 2.00 2.00
+ 1.0533 5. 4.00 0.00 0.00
+ 0.9665 2. 3.00 3.00 1.00
+ 0.9419 17. 4.00 2.00 0.00
+ 0.8600 15. 4.00 2.00 2.00
+ 0.8109 3. 5.00 1.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mgo.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mgo.jcpds
new file mode 100755
index 0000000..1ee169d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mgo.jcpds
@@ -0,0 +1,16 @@
+4
+MgO (JCPDS 4-0829, EOS from Jackson)
+ 1 160.000 4.00000
+ 4.21300
+3.16e-5
+d-spacing I/I0 h k l
+ 2.4324 10. 1.00 1.00 1.00
+ 2.1065 100. 2.00 0.00 0.00
+ 1.4895 52. 2.00 2.00 0.00
+ 1.2700 4. 3.00 1.00 1.00
+ 1.2160 12. 2.00 2.00 2.00
+ 1.0533 5. 4.00 0.00 0.00
+ 0.9665 2. 3.00 3.00 1.00
+ 0.9419 17. 4.00 2.00 0.00
+ 0.8600 15. 4.00 2.00 2.00
+ 0.8109 3. 5.00 1.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-ilm.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-ilm.jcpds
new file mode 100755
index 0000000..d83a163
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-ilm.jcpds
@@ -0,0 +1,38 @@
+4
+MgSiO3 ilmenite (GSAS calculation, EOS from guess)
+ 2 212 7.5
+4.7284 13.5591
+2.44e-5
+d-spacing I/I0 h k l
+4.5197 26 0 0 3
+3.92005 6 1 0 1
+3.50514 92 1 0 -2
+2.61119 100 1 0 4
+2.3642 43 1 1 0
+2.26098 5 1 0 -5
+2.0949 38 1 1 -3
+2.0949 41 1 1 3
+2.02451 6 2 0 -1
+1.96002 1 2 0 2
+1.75257 32 2 0 -4
+1.751 2 1 0 7
+1.63403 5 2 0 5
+1.6336 44 1 1 -6
+1.6336 20 1 1 6
+1.56605 5 1 0 -8
+1.53775 1 2 1 1
+1.50891 4 2 1 -2
+1.40792 5 2 1 4
+1.40792 13 3 -1 4
+1.36497 23 3 0 0
+1.34421 2 2 1 -5
+1.28718 5 1 0 10
+1.27053 6 1 1 9
+1.1821 3 2 2 0
+1.18033 1 1 0 -11
+1.14363 1 2 2 -3
+1.13049 1 2 0 -10
+1.12012 1 3 1 2
+1.07689 1 4 -1 -4
+1.04745 1 2 2 6
+1.01989 1 2 1 10
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-opv.jcpds
new file mode 100755
index 0000000..fae20c7
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/mgsio3-opv.jcpds
@@ -0,0 +1,162 @@
+4
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.77540 4.92917 6.89711
+2.2e-5
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ne-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ne-NoTh.jcpds
new file mode 100755
index 0000000..92d26b5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ne-NoTh.jcpds
@@ -0,0 +1,11 @@
+4
+Neon FCC (made by Shim, EOS from Hemley et al 1989)
+ 1 7.23 5.21
+ 4.1225
+0.0
+d-spacing I/I0 h k l
+ 2.3802 100. 1.00 1.00 1.00
+ 2.0613 40. 2.00 0.00 0.00
+ 1.4575 25. 2.00 2.00 0.00
+ 1.2430 30. 3.00 1.00 1.00
+ 1.1901 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/phaseD.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/phaseD.jcpds
new file mode 100755
index 0000000..bb2e86e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/phaseD.jcpds
@@ -0,0 +1,24 @@
+3
+PHASE D (GSAS, EOS from Yang 1997)
+ 2 200.000 4.30000
+ 4.7453 4.3450
+(blank for future use)
+d-spacing I/I0 h k l
+4.3450 23 0 0 1
+4.1010 16 1 0 0
+2.9860 100 1 0 1
+2.3730 10 1 1 0
+2.1773 0 0 0 2
+2.0820 82 1 1 1
+2.0820 82 2 -1 1
+2.0550 5 2 0 0
+1.9210 0 1 0 2
+1.8580 26 2 0 1
+1.6020 76 1 1 2
+1.6020 76 2 -1 2
+1.5533 0 2 1 0
+1.4928 1 2 0 2
+1.4626 25 2 1 1
+1.4626 25 3 -1 1
+1.4483 0 0 0 3
+1.3699 29 3 0 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/phaseH.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/phaseH.jcpds
new file mode 100644
index 0000000..7f4329a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/phaseH.jcpds
@@ -0,0 +1,16 @@
+3
+PHASE H (GRL, Ohtani 2014)
+ 4 160.000 4.00000
+ 4.4723 4.0652 2.6975
+(blank for future use)
+d-spacing I/I0 h k l
+3.0116 50 1 1 0
+2.3057 10 1 0 1
+2.247 30 0 1 1
+2.2328 80 2 0 0
+2.0073 80 1 1 1
+1.9599 100 2 1 0
+1.5838 10 2 1 1
+1.5276 10 1 2 1
+1.5057 10 2 2 0
+1.3025 10 3 0 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/pt-Ye2017.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/pt-Ye2017.jcpds
new file mode 100755
index 0000000..788ab3d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/pt-Ye2017.jcpds
@@ -0,0 +1,19 @@
+4
+Platinum (Ye2017, JGR, for BM3 converted from Vinet EOS)
+ 1 277.3 5.038
+ 3.92310
+2.61e-5
+d-spacing I/I0 h k l
+ 2.2650 100. 1.00 1.00 1.00
+ 1.9620 53. 2.00 0.00 0.00
+ 1.3870 31. 2.00 2.00 0.00
+ 1.1826 33. 3.00 1.00 1.00
+ 1.1325 12. 2.00 2.00 2.00
+ 0.9808 6. 4.00 0.00 0.00
+ 0.9000 22. 3.00 3.00 1.00
+ 0.8773 20. 4.00 2.00 0.00
+ 0.8008 29. 4.00 2.00 2.00
+ 0.7550 10. 3. 3. 3.
+ 0.66312 10. 5. 3. 1.
+ 0.65385 10. 4. 4. 2.
+ 0.62030 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/pt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/pt.jcpds
new file mode 100755
index 0000000..66db0f3
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/pt.jcpds
@@ -0,0 +1,19 @@
+4
+Platinum (JCPDS 4-0802, EOS from Holmes)
+ 1 266. 5.81
+ 3.92310
+2.61e-5
+d-spacing I/I0 h k l
+ 2.2650 100. 1.00 1.00 1.00
+ 1.9620 53. 2.00 0.00 0.00
+ 1.3870 31. 2.00 2.00 0.00
+ 1.1826 33. 3.00 1.00 1.00
+ 1.1325 12. 2.00 2.00 2.00
+ 0.9808 6. 4.00 0.00 0.00
+ 0.9000 22. 3.00 3.00 1.00
+ 0.8773 20. 4.00 2.00 0.00
+ 0.8008 29. 4.00 2.00 2.00
+ 0.7550 10. 3. 3. 3.
+ 0.66312 10. 5. 3. 1.
+ 0.65385 10. 4. 4. 2.
+ 0.62030 10. 6. 2. 0.
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/re-NoTh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/re-NoTh.jcpds
new file mode 100755
index 0000000..5459c21
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/re-NoTh.jcpds
@@ -0,0 +1,26 @@
+4
+Rhenium (JCPDS 5-0702, EOS from dcal)
+ 2 433.800 3.46000
+ 2.76000 4.45740
+1.e5
+d-spacing I/I0 h k l
+ 2.3880 32. 1.00 0.00 0.00
+ 2.2260 34. 0.00 0.00 2.00
+ 2.1050 100. 1.00 0.00 1.00
+ 1.6290 11. 1.00 0.00 2.00
+ 1.3800 22. 1.00 1.00 0.00
+ 1.2620 16. 1.00 0.00 3.00
+ 1.1948 3. 2.00 0.00 0.00
+ 1.1730 20. 1.00 1.00 2.00
+ 1.1540 15. 2.00 0.00 1.00
+ 1.1142 2. 0.00 0.00 4.00
+ 1.0530 3. 2.00 0.00 2.00
+ 1.0099 2. 1.00 0.00 4.00
+ 0.9311 7. 2.00 0.00 3.00
+ 0.9033 3. 2.00 1.00 0.00
+ 0.8854 15. 2.00 1.00 1.00
+ 0.8671 8. 1.00 1.00 4.00
+ 0.8373 5. 2.00 1.00 2.00
+ 0.8354 8. 1.00 0.00 5.00
+ 0.8151 2. 2.00 0.00 4.00
+ 0.7968 5. 3.00 0.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/sio2-stv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/sio2-stv.jcpds
new file mode 100755
index 0000000..a7f7515
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/sio2-stv.jcpds
@@ -0,0 +1,36 @@
+4
+stishovite (by Sinclair and Ringwood, EOS from Heaney)
+ 3 298.000 3.98000
+ 4.17720 2.66505
+1.86e-5
+d-spacing I/I0 h k l
+ 2.9537 100. 1.00 1.00 0.00
+ 2.2468 19. 1.00 0.00 1.00
+ 2.0886 1. 2.00 0.00 0.00
+ 1.9787 29. 1.00 1.00 1.00
+ 1.8681 10. 2.00 1.00 0.00
+ 1.5297 34. 2.00 1.00 1.00
+ 1.4769 11. 2.00 2.00 0.00
+ 1.3326 4. 0.00 0.00 2.00
+ 1.3210 3. 3.00 1.00 0.00
+ 1.2918 1. 2.00 2.00 1.00
+ 1.2341 10. 3.00 0.00 1.00
+ 1.2147 4. 1.00 1.00 2.00
+ 1.1835 1. 3.00 1.00 1.00
+ 1.1586 1. 3.00 2.00 0.00
+ 1.1234 2. 2.00 0.00 2.00
+ 1.0848 1. 2.00 1.00 2.00
+ 1.0625 1. 3.00 2.00 1.00
+ 1.0443 1. 4.00 0.00 0.00
+ 1.0131 1. 4.00 1.00 0.00
+ 0.9894 1. 2.00 2.00 2.00
+ 0.9846 1. 3.00 3.00 0.00
+ 0.9470 1. 4.00 1.00 1.00
+ 0.9381 1. 3.00 1.00 2.00
+ 0.9341 1. 4.00 2.00 0.00
+ 0.9236 1. 3.00 3.00 1.00
+ 0.8815 1. 4.00 2.00 1.00
+ 0.8743 1. 3.00 2.00 2.00
+ 0.8689 1. 1.00 0.00 3.00
+ 0.8507 1. 1.00 1.00 3.00
+ 0.8354 1. 4.00 3.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Al2O3-rh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Al2O3-rh.jcpds
new file mode 100755
index 0000000..2b30bf8
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Al2O3-rh.jcpds
@@ -0,0 +1,101 @@
+3
+Al2O3 - orthorhombic rh2o3 structure (GSAS calculation, EOS from Handbook)
+ 4 260.000 4.00000
+ 4.7954 5.0402 6.8546
+(blank for future use)
+d-spacing I/I0 h k l
+4.08112 0 1 0 1
+3.5125 9 0 0 2
+3.21583 11 1 1 1
+2.6115 28 0 2 0
+2.51987 100 1 1 2
+2.507 24 2 0 0
+2.44783 0 0 2 1
+2.26012 4 2 1 0
+2.19969 2 1 2 1
+2.15152 2 2 1 1
+2.12169 6 1 0 3
+2.09573 0 0 2 2
+2.04056 1 2 0 2
+1.96569 4 1 1 3
+1.93362 3 1 2 2
+1.90065 0 2 1 2
+1.80853 23 2 2 0
+1.75625 9 0 0 4
+1.75142 3 2 2 1
+1.74343 1 0 2 3
+1.64672 0 1 2 3
+1.62621 0 2 1 3
+1.62595 0 3 0 1
+1.60791 3 2 2 2
+1.60137 9 1 3 1
+1.57987 8 1 1 4
+1.55246 1 3 1 1
+1.48948 14 1 3 2
+1.45735 8 0 2 4
+1.44988 22 3 1 2
+1.43841 8 2 0 4
+1.43134 3 2 2 3
+1.43 0 2 3 0
+1.40126 0 2 3 1
+1.39943 0 1 2 4
+1.38678 0 2 1 4
+1.38028 0 3 2 1
+1.36037 0 3 0 3
+1.35289 0 1 0 5
+1.34588 1 1 3 3
+1.32444 0 2 3 2
+1.31645 0 3 1 3
+1.30967 1 1 1 5
+1.30669 0 3 2 2
+1.30575 1 0 4 0
+1.28376 1 0 4 1
+1.25993 4 2 2 4
+1.2535 1 4 0 0
+1.24365 0 1 4 1
+1.2373 0 0 2 5
+1.22392 0 0 4 2
+1.22043 0 2 3 3
+1.21889 0 4 1 0
+1.20649 0 3 2 3
+1.20126 0 1 2 5
+1.20095 0 4 1 1
+1.20047 1 1 3 4
+1.19323 0 2 1 5
+1.18901 0 1 4 2
+1.18831 0 3 3 1
+1.18058 0 4 0 2
+1.17945 1 3 1 4
+1.17083 0 0 0 6
+1.15808 2 2 4 0
+1.15153 0 4 1 2
+1.14266 1 2 4 1
+1.14043 1 0 4 3
+1.14038 2 3 3 2
+1.13006 2 4 2 0
+1.11572 1 4 2 1
+1.11393 2 1 1 6
+1.11203 0 1 4 3
+1.10953 0 2 2 5
+1.1089 0 2 3 4
+1.09985 0 2 4 2
+1.09842 0 3 2 4
+1.08119 0 4 1 3
+1.07576 0 4 2 2
+1.07547 0 3 0 5
+1.07194 0 3 3 3
+1.06837 1 0 2 6
+1.06827 1 1 3 5
+1.06084 1 2 0 6
+1.05337 0 3 1 5
+1.04787 0 0 4 4
+1.04491 0 1 2 6
+1.03962 0 2 1 6
+1.03807 1 2 4 3
+1.02571 0 1 4 4
+1.02028 1 4 0 4
+1.01809 0 3 4 1
+1.01775 0 4 2 3
+1.01726 0 4 3 0
+1.01198 1 1 5 1
+1.00676 0 4 3 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2-SrOH2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2-SrOH2.jcpds
new file mode 100755
index 0000000..fe6cd35
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2-SrOH2.jcpds
@@ -0,0 +1,130 @@
+3
+CdOH2 Brucite (From FeOH2, EOS)
+ 4 37.6 10.6
+ 9.3247 3.7056 5.7626
+(blank for future use)
+d-spacing I/I0 h k l
+4.9021 43 1 0 1
+4.6624 43 2 0 0
+3.6246 2 2 0 1
+3.1168 4 0 1 1
+2.956 14 1 1 1
+2.9009 100 2 1 0
+2.8813 6 0 0 2
+2.7529 6 1 0 2
+2.7357 4 3 0 1
+2.5911 0 2 1 1
+2.451 1 2 0 2
+2.3312 12 4 0 0
+2.2098 32 1 1 2
+2.2009 9 3 1 1
+2.161 2 4 0 1
+2.1131 7 3 0 2
+2.0443 10 2 1 2
+1.9732 20 4 1 0
+1.8814 5 1 0 3
+1.8668 6 4 1 1
+1.8528 23 0 2 0
+1.8356 1 3 1 2
+1.8123 2 4 0 2
+1.776 4 2 0 3
+1.7743 0 5 0 1
+1.7331 5 1 2 1
+1.7218 5 2 2 0
+1.7054 3 0 1 3
+1.6775 2 1 1 3
+1.6498 1 2 2 1
+1.634 0 3 0 3
+1.628 1 4 1 2
+1.6016 3 2 1 3
+1.6003 1 5 1 1
+1.5656 0 5 0 2
+1.5584 1 0 2 2
+1.5541 1 6 0 0
+1.5371 1 1 2 2
+1.5341 1 3 2 1
+1.5005 1 6 0 1
+1.4951 4 3 1 3
+1.4824 1 4 0 3
+1.478 0 2 2 2
+1.4505 3 4 2 0
+1.4422 0 5 1 2
+1.4406 3 0 0 4
+1.4332 0 6 1 0
+1.4238 0 1 0 4
+1.4066 1 4 2 1
+1.3931 2 3 2 2
+1.3908 3 6 1 1
+1.3764 1 2 0 4
+1.3764 0 4 1 3
+1.3678 1 6 0 2
+1.338 0 5 0 3
+1.329 1 1 1 4
+1.3201 3 1 2 3
+1.3071 0 3 0 4
+1.2979 0 7 0 1
+1.2956 1 4 2 2
+1.2903 3 2 1 4
+1.2832 0 6 1 2
+1.2821 2 2 2 3
+1.2815 0 5 2 1
+1.2585 1 5 1 3
+1.2326 0 3 1 4
+1.2255 0 4 0 4
+1.2255 0 3 2 3
+1.2249 0 7 1 1
+1.2091 0 7 0 2
+1.2082 0 6 0 3
+1.2078 0 0 3 1
+1.1978 0 1 3 1
+1.1958 0 5 2 2
+1.194 3 2 3 0
+1.1907 1 6 2 0
+1.1692 0 2 3 1
+1.1661 0 6 2 1
+1.1656 0 8 0 0
+1.1635 1 4 1 4
+1.1575 1 4 2 3
+1.1495 0 7 1 2
+1.1487 0 6 1 3
+1.1438 0 1 0 5
+1.1425 1 8 0 1
+1.1401 0 5 0 4
+1.1373 2 0 2 4
+1.1289 0 1 2 4
+1.127 1 1 3 2
+1.1258 1 3 3 1
+1.1188 0 2 0 5
+1.1119 0 8 1 0
+1.1049 1 2 2 4
+1.103 1 2 3 2
+1.1005 0 0 1 5
+1.1004 0 6 2 2
+1.0946 0 7 0 3
+1.0929 0 1 1 5
+1.0917 0 8 1 1
+1.0915 1 4 3 0
+1.0897 0 5 1 4
+1.0848 0 5 2 3
+1.0806 0 3 0 5
+1.0805 0 8 0 2
+1.0724 0 4 3 1
+1.0711 0 2 1 5
+1.0681 0 3 2 4
+1.0664 0 3 3 2
+1.063 0 7 2 1
+1.0565 0 6 0 4
+1.0498 0 7 1 3
+1.0389 0 0 3 3
+1.0374 0 3 1 5
+1.0373 0 8 1 2
+1.0332 0 4 0 5
+1.0325 0 1 3 3
+1.0221 0 4 2 4
+1.0207 0 4 3 2
+1.0197 0 9 0 1
+1.016 0 6 1 4
+1.0141 0 2 3 3
+1.0137 0 5 3 1
+1.0126 0 7 2 2
+1.012 0 6 2 3
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2.jcpds
new file mode 100755
index 0000000..22ad9b5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CdOH2.jcpds
@@ -0,0 +1,22 @@
+3
+CdOH2 Brucite (From FeOH2, EOS)
+ 2 37.6 10.6
+ 3.495 4.706
+(blank for future use)
+d-spacing I/I0 h k l
+ 4.7015 86 0 0 1
+ 3.0273 30 0 1 0
+ 2.5453 100 0 1 1
+ 2.3529 5 0 0 2
+ 1.8567 50 0 1 2
+ 1.7478 28 1 1 0
+ 1.6383 12 1 1 1
+ 1.5686 12 0 0 3
+ 1.5134 24 0 2 0
+ 1.4408 15 0 2 1
+ 1.4029 18 1 1 2
+ 1.3927 15 0 1 3
+ 1.2728 10 0 2 2
+ 1.1765 2 0 0 4
+ 1.1118 4 2 1 1
+ 1.0887 4 2 0 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CeO2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CeO2.jcpds
new file mode 100755
index 0000000..f957b43
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/CeO2.jcpds
@@ -0,0 +1,14 @@
+3
+CeO2 (43-1002, no data for high P)
+1 99. 4.
+ 5.411
+(blank for future use)
+d-spacing I/I0 h k l
+3.1240 100. 1.00 1.00 1.00
+2.7060 27. 2.00 0.00 0.00
+1.9132 46. 2.00 2.00 0.00
+1.6316 34. 3.00 1.00 1.00
+1.5621 6. 2.00 2.00 2.00
+1.3528 6. 4.00 0.00 0.00
+1.2414 12. 3.00 3.00 1.00
+1.2100 7. 4.00 2.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3P.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3P.jcpds
new file mode 100755
index 0000000..e3b1613
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3P.jcpds
@@ -0,0 +1,49 @@
+3
+Fe3P (JCPDS24-0508, EOS from guess)
+ 3 170.000 4.00000
+9.1000 4.4592
+(blank for future use)
+d-spacing I/I0 h k l
+3.01 5 2 1 1
+2.51 5 3 0 1
+2.27 5 4 0 0
+2.23 5 0 0 2
+2.20 100 3 2 1
+2.14 45 3 3 0
+2.11 40 1 1 2
+2.03 80 4 2 0
+1.978 100 4 1 1
+1.832 20 2 2 2
+1.785 35 5 1 0
+1.762 55 3 1 2
+1.685 30 4 3 1
+1.592 5 4 0 2
+1.580 10 5 2 1
+1.561 5 5 3 0
+1.546 5 3 3 2
+1.503 5 4 2 2
+1.439 5 6 2 0
+1.418 15 6 1 1
+1.393 5 5 1 2
+1.354 5 5 4 1
+1.335 15 3 0 3
+1.305 5 4 4 2
+1.298 5 6 3 1
+1.287 30 5 5 0
+1.281 65 3 2 3
+1.262 15 6 4 0
+1.254 5 6 0 2
+1.248 20 7 0 1
+1.233 30 4 1 3
+1.209 20 6 2 2
+1.204 20 7 2 1
+1.151 30 4 3 3
+1.127 65 6 5 1
+1.115 80 7 1 2
+1.103 25 8 2 0
+1.098 55 6 4 2
+1.094 60 8 1 1
+1.058 20 7 5 0
+1.054 30 7 3 2
+1.005 60 1 9 0
+1.002 25 3 6 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3Si.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3Si.jcpds
new file mode 100755
index 0000000..bb65859
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe3Si.jcpds
@@ -0,0 +1,13 @@
+3
+Fe3Si (11-616, JCPDS Ogwa and Matsuzaki 1951)
+ 1 100.600 5.50000
+ 5.64
+(blank for future use)
+d-spacing I/I0 h k l
+3.25 40. 1 1 1
+2.83 40. 2 0 0
+1.99 100. 2 2 0
+1.70 40. 3 1 1
+1.62 20. 2 2 2
+1.41 100 4 0 0
+1.145 100 4 2 2
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe5Si3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe5Si3.jcpds
new file mode 100755
index 0000000..95ff0c6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/Fe5Si3.jcpds
@@ -0,0 +1,20 @@
+3
+Fe3Si (11-615, JCPDS Ogwa and Matsuzaki 1951)
+ 2 100.600 5.50000
+ 6.757 9.440
+(blank for future use)
+d-spacing I/I0 h k l
+2.92 30 2 0 0
+2.74 20 1 1 2
+2.35 50 0 0 4
+2.18 60. 1 0 4
+2.00 100 2 1 2
+1.94 80 3 0 0
+1.92 90 1 1 4
+1.83 30 2 0 4
+1.68 20 2 2 0
+1.62 30 3 1 0
+1.58 40 2 0 5
+1.52 20 3 1 2
+1.46 30 4 0 0
+1.37 60 2 2 4
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/H2O-VII.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/H2O-VII.jcpds
new file mode 100755
index 0000000..7b2bb26
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/H2O-VII.jcpds
@@ -0,0 +1,14 @@
+3
+ice VII (using crystal maker, EOS by Wolanin)
+ 1 14.9 5.4
+ 3.451
+(blank for future use)
+d-spacing I/I0 h k l
+2.333 100. 0.00 1.00 1.00
+ 1.905 1. 1.00 1.00 1.00
+ 1.650 8. 0.00 0.00 2.00
+ 1.347 16. 1.00 1.00 2.00
+ 1.167 5. 0.00 2.00 2.00
+ 1.044 6. 0.00 1.00 3.00
+ 0.953 1. 2.00 2.00 2.00
+ 0.882 12. 1.00 2.00 3.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KBr-B2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KBr-B2.jcpds
new file mode 100755
index 0000000..d104ebc
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KBr-B2.jcpds
@@ -0,0 +1,23 @@
+3
+KCl B2 (GSAS calculation, EOS from Walker 2002 AM)
+ 1 13.8 5.650000
+ 4.045
+1.2e-4
+d-spacing I/I0 h k l
+ 3.4669 6. 1.00 0.00 0.00
+ 2.4515 100. 1.00 1.00 0.00
+ 2.0016 1. 1.00 1.00 1.00
+ 1.7335 8. 2.00 0.00 0.00
+ 1.5504 1. 2.00 1.00 0.00
+ 1.4154 21. 2.00 1.00 1.00
+ 1.2257 4. 2.00 2.00 0.00
+ 1.1556 1. 2.00 2.00 1.00
+ 1.1556 1. 3.00 0.00 0.00
+ 1.0963 7. 3.00 1.00 0.00
+ 1.0453 1. 3.00 1.00 1.00
+ 1.0008 1. 2.00 2.00 2.00
+ 0.9615 1. 3.00 2.00 0.00
+ 0.9266 3. 3.00 2.00 1.00
+ 0.8667 1. 4.00 0.00 0.00
+ 0.8408 1. 4.00 1.00 0.00
+ 0.8408 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KCl-B2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KCl-B2.jcpds
new file mode 100755
index 0000000..a602314
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/KCl-B2.jcpds
@@ -0,0 +1,23 @@
+3
+KCl B2 (GSAS calculation, EOS from Walker 2002 AM)
+ 1 17.2 5.890000
+ 3.7914
+1.2e-4
+d-spacing I/I0 h k l
+ 3.4669 6. 1.00 0.00 0.00
+ 2.4515 100. 1.00 1.00 0.00
+ 2.0016 1. 1.00 1.00 1.00
+ 1.7335 8. 2.00 0.00 0.00
+ 1.5504 1. 2.00 1.00 0.00
+ 1.4154 21. 2.00 1.00 1.00
+ 1.2257 4. 2.00 2.00 0.00
+ 1.1556 1. 2.00 2.00 1.00
+ 1.1556 1. 3.00 0.00 0.00
+ 1.0963 7. 3.00 1.00 0.00
+ 1.0453 1. 3.00 1.00 1.00
+ 1.0008 1. 2.00 2.00 2.00
+ 0.9615 1. 3.00 2.00 0.00
+ 0.9266 3. 3.00 2.00 1.00
+ 0.8667 1. 4.00 0.00 0.00
+ 0.8408 1. 4.00 1.00 0.00
+ 0.8408 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/MgF2-cot.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/MgF2-cot.jcpds
new file mode 100755
index 0000000..a35f8d0
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/MgF2-cot.jcpds
@@ -0,0 +1,195 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 163.000 7.00000
+ 5.1637 6.2387 3.0810
+(blank for future use)
+d-spacing I/I0 h k l
+5.8182 1 1 1 0
+4.515 13 2 0 0
+4.0455 32 1 0 1
+3.8827 76 2 1 0
+3.804 41 0 2 0
+3.5719 100 1 1 1
+3.5056 1 1 2 0
+2.9467 4 2 1 1
+2.9118 8 0 2 1
+2.9091 12 2 2 0
+2.7989 2 3 1 0
+2.7713 53 1 2 1
+2.5062 50 3 0 1
+2.447 1 2 2 1
+2.4415 0 1 3 0
+2.3804 5 3 1 1
+2.3604 3 3 2 0
+2.2625 26 0 0 2
+2.2575 11 4 0 0
+2.2111 30 2 3 0
+2.1642 8 4 1 0
+2.1487 36 1 3 1
+2.1087 0 1 1 2
+2.0928 46 3 2 1
+2.0227 3 2 0 2
+1.9866 2 2 3 1
+1.9548 25 2 1 2
+1.9524 0 4 1 1
+1.9446 14 0 2 2
+1.9414 8 4 2 0
+1.9394 0 3 3 0
+1.902 8 0 4 0
+1.901 0 1 2 2
+1.8612 1 1 4 0
+1.786 5 2 2 2
+1.7841 1 4 2 1
+1.7826 1 3 3 1
+1.7595 1 3 1 2
+1.7572 0 5 1 0
+1.7534 1 0 4 1
+1.7528 1 2 4 0
+1.7213 3 1 4 1
+1.6862 5 4 3 0
+1.6773 2 5 0 1
+1.6595 0 1 3 2
+1.638 17 5 1 1
+1.6345 1 2 4 1
+1.6334 2 3 2 2
+1.6315 0 5 2 0
+1.6079 1 3 4 0
+1.5981 7 4 0 2
+1.5813 19 2 3 2
+1.5801 0 4 3 1
+1.5639 6 4 1 2
+1.5348 0 5 2 1
+1.5151 10 3 4 1
+1.505 3 6 0 0
+1.5004 1 1 5 0
+1.4877 2 1 0 3
+1.4764 2 6 1 0
+1.4733 6 4 2 2
+1.4725 0 3 3 2
+1.4711 0 5 3 0
+1.4601 7 1 1 3
+1.4559 6 0 4 2
+1.4546 4 4 4 0
+1.4419 5 2 5 0
+1.4373 1 1 4 2
+1.4242 11 1 5 1
+1.406 0 2 1 3
+1.4036 0 6 1 1
+1.4021 1 0 2 3
+1.3995 6 6 2 0
+1.399 13 5 3 1
+1.3878 0 5 1 2
+1.3856 0 2 4 2
+1.3855 5 1 2 3
+1.3848 0 4 4 1
+1.3739 1 2 5 1
+1.358 0 3 5 0
+1.352 5 4 3 2
+1.3485 6 3 0 3
+1.339 0 2 2 3
+1.337 0 6 2 1
+1.3278 1 3 1 3
+1.3233 0 5 2 2
+1.3106 1 3 4 2
+1.3097 0 5 4 0
+1.3006 1 3 5 1
+1.2942 1 6 3 0
+1.2832 6 1 3 3
+1.2718 0 7 1 0
+1.271 8 3 2 3
+1.268 1 0 6 0
+1.2617 3 4 5 0
+1.258 0 5 4 1
+1.2557 0 1 6 0
+1.2531 3 6 0 2
+1.2505 1 1 5 2
+1.246 0 2 3 3
+1.2444 0 6 3 1
+1.2406 4 7 0 1
+1.2375 0 4 1 3
+1.2364 2 6 1 2
+1.2333 0 5 3 2
+1.2244 1 7 1 1
+1.2235 4 4 4 2
+1.2217 0 7 2 0
+1.221 1 0 6 1
+1.2208 0 2 6 0
+1.216 5 2 5 2
+1.2154 1 4 5 1
+1.21 2 1 6 1
+1.1911 0 4 2 3
+1.1906 0 3 3 3
+1.1902 6 6 2 2
+1.1818 0 0 4 3
+1.1802 1 6 4 0
+1.1794 3 7 2 1
+1.1786 0 2 6 1
+1.1718 1 1 4 3
+1.1685 0 3 6 0
+1.1643 0 3 5 2
+1.1636 0 5 5 0
+1.1577 0 5 0 3
+1.1498 0 7 3 0
+1.1445 4 5 1 3
+1.1433 0 2 4 3
+1.142 1 6 4 1
+1.1335 0 5 4 2
+1.1314 3 3 6 1
+1.1313 2 0 0 4
+1.1287 0 8 0 0
+1.127 4 5 5 1
+1.1242 0 4 3 3
+1.1234 1 6 3 2
+1.1165 2 8 1 0
+1.1144 2 7 3 1
+1.1105 0 1 1 4
+1.1087 0 7 1 2
+1.1075 0 5 2 3
+1.1061 1 0 6 2
+1.1055 1 4 6 0
+1.102 3 4 5 2
+1.1001 3 3 4 3
+1.0979 0 1 6 2
+1.0973 0 2 0 4
+1.0861 3 2 1 4
+1.0843 1 0 2 4
+1.084 0 8 1 1
+1.0821 0 8 2 0
+1.0791 0 1 7 0
+1.0766 0 1 2 4
+1.075 0 7 2 2
+1.0744 0 2 6 2
+1.074 0 4 6 1
+1.07 0 6 5 0
+1.0676 0 7 4 0
+1.0638 3 1 5 3
+1.0567 1 2 7 0
+1.0551 0 6 1 3
+1.0543 1 2 2 4
+1.0531 4 5 3 3
+1.0524 0 8 2 1
+1.0496 1 1 7 1
+1.0488 0 3 1 4
+1.047 0 4 4 3
+1.0464 1 6 4 2
+1.0423 0 2 5 3
+1.0413 0 6 5 1
+1.0391 3 7 4 1
+1.0382 0 3 6 2
+1.0378 0 5 6 0
+1.0348 0 5 5 2
+1.0312 1 8 3 0
+1.029 1 2 7 1
+1.0264 0 1 3 4
+1.0259 0 6 2 3
+1.025 0 7 3 2
+1.0223 0 3 7 0
+1.0201 0 3 2 4
+1.0115 0 5 6 1
+1.0114 1 4 0 4
+1.01 0 8 0 2
+1.0092 0 3 5 3
+1.0071 3 2 3 4
+1.0054 0 8 3 1
+1.0026 1 4 1 4
+1.0012 2 8 1 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaAlSiO4_Ca-ferrite-type.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaAlSiO4_Ca-ferrite-type.jcpds
new file mode 100755
index 0000000..ce5d1d7
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaAlSiO4_Ca-ferrite-type.jcpds
@@ -0,0 +1,130 @@
+3
+NaAlSiO4 Ca-ferrite-type
+ 4 191. 4.
+ 10.137 8.643 2.749
+(blank for future use)
+d-spacing I/I0 h k l
+6.59108 0.3 1 1 0
+5.0773 22.9 2 0 0
+4.38055 21.4 2 1 0
+4.3321 68.4 0 2 0
+3.98465 1.3 1 2 0
+3.29554 26.2 2 2 0
+3.15281 1.7 3 1 0
+2.7779 7.9 1 3 0
+2.66723 0.2 3 2 0
+2.64404 7.8 1 0 1
+2.53865 69.8 4 0 0
+2.52891 4.5 1 1 1
+2.51036 100 2 3 0
+2.43623 3.7 4 1 0
+2.32209 31.7 2 1 1
+2.31478 14.3 0 2 1
+2.25689 0 1 2 1
+2.19703 9.8 3 3 0
+2.19028 0 4 2 0
+2.16605 2.7 0 4 0
+2.12899 25.9 3 0 1
+2.11839 29.9 1 4 0
+2.10622 1.9 2 2 1
+2.06748 89.5 3 1 1
+1.99232 18.5 2 4 0
+1.97732 26.4 5 1 0
+1.95019 68.1 1 3 1
+1.91072 2.6 3 2 1
+1.90673 7.6 4 3 0
+1.8505 14 2 3 1
+1.83887 0.7 5 2 0
+1.82447 1.8 3 4 0
+1.8202 27.1 4 1 1
+1.71369 6.9 3 3 1
+1.71048 51.9 4 2 1
+1.70815 6.1 1 5 0
+1.69886 37 0 4 1
+1.69243 0.1 6 0 0
+1.67558 26 1 4 1
+1.66128 1.2 5 3 0
+1.66104 9.3 6 1 0
+1.64777 4.6 4 4 0
+1.63996 5.2 2 5 0
+1.63128 4.6 5 0 1
+1.61107 15.9 2 4 1
+1.60311 1.4 5 1 1
+1.5764 27.5 6 2 0
+1.56479 0.1 4 3 1
+1.54246 0.2 3 5 0
+1.52663 0.9 5 2 1
+1.51836 2.8 3 4 1
+1.48155 2.1 5 4 0
+1.46018 12.6 6 3 0
+1.44932 0.8 1 5 1
+1.44403 17.1 0 6 0
+1.43121 2 4 5 0
+1.43074 1.5 7 1 0
+1.42965 0.8 1 6 0
+1.42036 4.1 5 3 1
+1.42021 27.2 6 1 1
+1.41189 54.2 4 4 1
+1.40696 30.6 2 5 1
+1.38895 1.2 2 6 0
+1.37558 0.7 7 2 0
+1.36925 49.7 0 0 2
+1.36621 23.2 6 2 1
+1.34394 0.7 3 5 1
+1.34063 0 1 1 2
+1.33362 1.4 6 4 0
+1.32822 2 3 6 0
+1.32202 0.7 2 0 2
+1.31822 1.2 5 5 0
+1.30689 1 2 1 2
+1.30559 3.8 0 2 2
+1.30307 2 5 4 1
+1.29632 0 7 3 0
+1.29493 0.1 1 2 2
+1.28847 13.8 6 3 1
+1.28191 1.5 7 0 1
+1.27733 6.9 0 6 1
+1.26933 1.3 8 0 0
+1.26843 0.1 4 5 1
+1.2681 9.2 7 1 1
+1.26734 0.3 1 6 1
+1.26445 3.1 2 2 2
+1.25592 7.4 3 1 2
+1.25518 7.6 4 6 0
+1.23873 7.9 2 6 1
+1.22922 3.3 7 2 1
+1.22865 0.2 1 7 0
+1.22816 1.3 1 3 2
+1.21812 0 3 2 2
+1.21076 1.6 6 5 0
+1.20532 0.1 7 4 0
+1.20513 15.1 4 0 2
+1.20253 6.2 2 7 0
+1.20207 25.4 2 3 2
+1.199 22.7 6 4 1
+1.19507 1.6 3 6 1
+1.19364 1.4 4 1 2
+1.18777 1.9 5 5 1
+1.17687 3.7 5 6 0
+1.17167 0 7 3 1
+1.16246 1 3 7 0
+1.16205 5 3 3 2
+1.16104 0.4 4 2 2
+1.15739 0.6 0 4 2
+1.14995 5.4 1 4 2
+1.14159 4.7 8 1 1
+1.14103 2.4 4 6 1
+1.12844 4 2 4 2
+1.1257 5.5 5 1 2
+1.12099 2.4 1 7 1
+1.11884 0.7 9 1 0
+1.11297 0.3 8 2 1
+1.11255 1 4 7 0
+1.11233 1.6 7 5 0
+1.11219 0.9 4 3 2
+1.10736 3.8 6 5 1
+1.10319 0.8 7 4 1
+1.10105 2.1 2 7 1
+1.09851 0.5 6 6 0
+1.09823 0.1 5 2 2
+1.09514 1.4 3 4 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B1.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B1.jcpds
new file mode 100644
index 0000000..0b0a7b4
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B1.jcpds
@@ -0,0 +1,17 @@
+3
+NaCl (JCPDS 5-0628, EOS from dcal)
+ 1 23.8800 5.20000
+ 5.64020
+4.4e-6
+d-spacing I/I0 h k l
+ 3.2580 13. 1.00 1.00 1.00
+ 2.8210 100. 2.00 0.00 0.00
+ 1.9940 55. 2.00 2.00 0.00
+ 1.7010 2. 3.00 1.00 1.00
+ 1.6280 15. 2.00 2.00 2.00
+ 1.4100 6. 4.00 0.00 0.00
+ 1.2940 1. 3.00 3.00 1.00
+ 1.2610 11. 4.00 2.00 0.00
+ 1.1515 7. 4.00 2.00 2.00
+ 1.0855 1. 5.00 1.00 1.00
+ 0.9969 2. 4.00 4.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B2.jcpds
new file mode 100644
index 0000000..19e8110
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaCl-B2.jcpds
@@ -0,0 +1,23 @@
+3
+NaCl B2 (GSAS calculation, EOS from Heinz and Jeanloz, 84)
+ 1 36.2000 4.00000
+ 3.46690
+4.4e-6
+d-spacing I/I0 h k l
+ 3.4669 6. 1.00 0.00 0.00
+ 2.4515 100. 1.00 1.00 0.00
+ 2.0016 1. 1.00 1.00 1.00
+ 1.7335 8. 2.00 0.00 0.00
+ 1.5504 1. 2.00 1.00 0.00
+ 1.4154 21. 2.00 1.00 1.00
+ 1.2257 4. 2.00 2.00 0.00
+ 1.1556 1. 2.00 2.00 1.00
+ 1.1556 1. 3.00 0.00 0.00
+ 1.0963 7. 3.00 1.00 0.00
+ 1.0453 1. 3.00 1.00 1.00
+ 1.0008 1. 2.00 2.00 2.00
+ 0.9615 1. 3.00 2.00 0.00
+ 0.9266 3. 3.00 2.00 1.00
+ 0.8667 1. 4.00 0.00 0.00
+ 0.8408 1. 4.00 1.00 0.00
+ 0.8408 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF-b2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF-b2.jcpds
new file mode 100755
index 0000000..9fae7fb
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF-b2.jcpds
@@ -0,0 +1,23 @@
+3
+NaCl B2 (GSAS calculation, EOS from Heinz and Jeanloz, 84)
+ 1 103.0000 4.00000
+ 2.714
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.4669 6. 1.00 0.00 0.00
+ 2.4515 100. 1.00 1.00 0.00
+ 2.0016 1. 1.00 1.00 1.00
+ 1.7335 8. 2.00 0.00 0.00
+ 1.5504 1. 2.00 1.00 0.00
+ 1.4154 21. 2.00 1.00 1.00
+ 1.2257 4. 2.00 2.00 0.00
+ 1.1556 1. 2.00 2.00 1.00
+ 1.1556 1. 3.00 0.00 0.00
+ 1.0963 7. 3.00 1.00 0.00
+ 1.0453 1. 3.00 1.00 1.00
+ 1.0008 1. 2.00 2.00 2.00
+ 0.9615 1. 3.00 2.00 0.00
+ 0.9266 3. 3.00 2.00 1.00
+ 0.8667 1. 4.00 0.00 0.00
+ 0.8408 1. 4.00 1.00 0.00
+ 0.8408 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF.jcpds
new file mode 100755
index 0000000..74703a0
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaF.jcpds
@@ -0,0 +1,17 @@
+3
+NaF (JCPDS 5-0628, EOS from dcal)
+ 1 46.400 4.90000
+ 4.62
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.2580 13. 1.00 1.00 1.00
+ 2.8201 100. 2.00 0.00 0.00
+ 1.9941 55. 2.00 2.00 0.00
+ 1.7006 2. 3.00 1.00 1.00
+ 1.6282 15. 2.00 2.00 2.00
+ 1.4100 6. 4.00 0.00 0.00
+ 1.2940 1. 3.00 3.00 1.00
+ 1.2612 11. 4.00 2.00 0.00
+ 1.1515 7. 4.00 2.00 2.00
+ 1.0855 1. 5.00 1.00 1.00
+ 0.9969 2. 4.00 4.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-Pv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-Pv.jcpds
new file mode 100755
index 0000000..ad7bc24
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-Pv.jcpds
@@ -0,0 +1,401 @@
+3
+NaMgF3-Perovskite
+ 4 76.000 4.00000
+ 5.3603 5.4884 7.666
+(blank for future use)
+d-spacing I/I0 h k l
+4.39292 2 1 0 1
+3.83479 29 1 1 0
+3.833 12 0 0 2
+3.42962 6 1 1 1
+2.7442 22 0 2 0
+2.71097 76 1 1 2
+2.68015 14 2 0 0
+2.4427 8 1 2 0
+2.40834 16 2 1 0
+2.3274 17 1 2 1
+2.30664 38 1 0 3
+2.29762 30 2 1 1
+2.2313 40 0 2 2
+2.19646 26 2 0 2
+2.12647 14 1 1 3
+2.05996 16 1 2 2
+2.03922 9 2 1 2
+1.9174 100 2 2 0
+1.9165 53 0 0 4
+1.8701 7 0 2 3
+1.8601 7 2 2 1
+1.76573 6 1 2 3
+1.75261 6 2 1 3
+1.74013 4 3 0 1
+1.7314 11 1 3 0
+1.71481 8 2 2 2
+1.71433 6 1 1 4
+1.699 1 3 1 0
+1.68886 16 1 3 1
+1.65875 4 3 1 1
+1.57789 20 1 3 2
+1.57125 22 0 2 4
+1.55894 18 2 0 4
+1.55325 48 3 1 2
+1.51101 1 2 3 0
+1.50781 1 1 2 4
+1.49962 1 2 1 4
+1.49735 3 3 2 0
+1.48248 2 2 3 1
+1.47409 4 1 0 5
+1.46958 1 3 2 1
+1.46431 1 3 0 3
+1.43336 4 1 3 3
+1.41482 2 3 1 3
+1.40572 2 2 3 2
+1.3721 10 0 4 0
+1.35549 35 2 2 4
+1.35064 2 0 4 1
+1.34008 6 4 0 0
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+1.27767 1 0 0 6
+1.27135 1 3 1 4
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+1.07744 1 3 4 1
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+1.06324 1 2 2 6
+1.06173 1 5 0 1
+1.05304 1 1 1 7
+1.05218 1 5 1 0
+1.04687 3 3 4 2
+1.0424 1 5 1 1
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+0.99931 1 1 2 7
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+0.99236 1 4 1 5
+0.99117 3 1 5 3
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+0.98858 1 5 0 3
+0.98189 1 2 5 2
+0.97563 1 2 3 6
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+0.68365 1 2 3 10
+0.68332 1 0 8 1
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+0.67783 1 1 8 1
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+0.67532 1 0 8 2
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+0.67123 1 6 2 7
+0.67107 1 4 5 7
+0.67075 1 3 4 9
+0.67017 1 1 2 11
+0.67004 1 8 0 0
+0.66944 1 2 1 11
+0.66932 1 3 5 8
+0.66906 1 4 3 9
+0.66889 1 5 4 7
+0.66875 1 6 5 3
+0.6677 1 1 5 9
+0.6669 1 5 0 9
+0.66643 1 4 7 2
+0.66614 1 7 4 1
+0.6655 1 5 3 8
+0.6651 1 8 1 0
+0.66467 1 7 2 5
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-ppv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-ppv.jcpds
new file mode 100755
index 0000000..5d11d52
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/NaMgF3-ppv.jcpds
@@ -0,0 +1,103 @@
+3
+NaMgF3 postpv orthorhombic (Xtal Diffract, Martin et al 2006, based on MgSiO3-ppv)
+ 4 137.000 4.00000
+ 2.951845 9.257341 7.31897
+(blank for future use)
+d-spacing I/I0 h k l
+4.3995 13.9 0 2 0
+3.6720 0.1 0 2 1
+3.3333 4.4 0 0 2
+2.6568 100.0 0 2 2
+2.5700 18.9 1 1 0
+2.3980 4.3 1 1 1
+2.1998 5.4 0 4 0
+2.0890 5.4 0 4 1
+2.0353 0.3 1 1 2
+1.9835 39.0 0 2 3
+1.9814 65.8 1 3 0
+1.8992 82.7 1 3 1
+1.8360 2.5 0 4 2
+1.7032 40.0 1 3 2
+1.6809 35.4 1 1 3
+1.6666 26.7 0 0 4
+1.5633 0.7 0 4 3
+1.5585 0.3 0 2 4
+1.4789 6.8 1 3 3
+1.4722 5.3 1 5 0
+1.4665 0.1 0 6 0
+1.4376 7.0 1 5 1
+1.4323 1.5 0 6 1
+1.3983 16.4 1 1 4
+1.3467 25.6 1 5 2
+1.3436 18.0 2 0 0
+1.3423 21.5 0 6 2
+1.3284 11.7 0 4 4
+1.2850 0.2 2 2 0
+1.2760 0.4 0 2 5
+1.2754 5.9 1 3 4
+1.2618 0.2 2 2 1
+1.2462 0.2 2 0 2
+1.2273 0.1 1 5 3
+1.2240 0.6 0 6 3
+1.1990 9.7 2 2 2
+1.1835 0.2 1 1 5
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+1.1386 0.1 1 7 0
+1.1300 0.4 2 4 1
+1.1223 1.1 1 7 1
+1.1124 2.9 2 2 3
+1.1111 0.4 0 0 6
+1.1062 8.2 1 3 5
+1.1034 1.3 1 5 4
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+1.0999 1.8 0 8 0
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+0.9918 0.8 0 4 6
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+0.9857 0.0 0 8 3
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+0.9447 3.5 2 4 4
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+0.9308 0.1 0 2 7
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+0.9188 0.1 1 9 0
+0.9180 0.5 0 8 4
+0.9102 0.8 1 9 1
+0.9048 0.1 2 6 3
+0.8930 0.9 1 1 7
+0.8911 0.3 3 1 0
+0.8869 0.7 1 5 6
+0.8857 0.6 1 9 2
+0.8856 1.1 0 6 6
+0.8833 0.0 3 1 1
+0.8799 0.0 0 10 0
+0.8740 0.0 0 4 7
+0.8723 0.0 0 10 1
+0.8694 0.0 2 4 5
+0.8658 0.0 1 7 5
+0.8609 0.0 3 1 2
+0.8584 0.4 1 3 7
+0.8567 1.4 3 3 0
+0.8562 0.3 2 0 6
+0.8516 0.0 2 6 4
+0.8511 1.1 2 8 0
+0.8508 0.4 0 10 2
+0.8497 0.3 3 3 1
+0.8490 0.2 1 9 3
+0.8484 0.0 0 8 5
+0.8442 0.0 2 8 1
+0.8405 2.0 2 2 6
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/SiC-alpha.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/SiC-alpha.jcpds
new file mode 100644
index 0000000..f7c9ded
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/SiC-alpha.jcpds
@@ -0,0 +1,48 @@
+3
+hexagonal SiC (based on cobalt hcp file, EOS same as fcc)
+ 2 250. 4.
+ 3.073 10.053
+4.e-6
+d-spacing I/I0 h k l
+2.57268 63.1 0.0 1.0 1.0
+2.35198 100 0.0 1.0 2.0
+2.08403 53.4 0.0 1.0 3.0
+1.82723 20.3 0.0 1.0 4.0
+1.6755 24.8 0.0 0.0 6.0
+1.60424 6.7 0.0 1.0 5.0
+1.5365 88.4 1.0 1.0 0.0
+1.31914 8.2 0.0 2.0 1.0
+1.28634 16.7 0.0 2.0 2.0
+1.26386 7.2 0.0 1.0 7.0
+1.23671 12.7 0.0 2.0 3.0
+1.17599 6.3 0.0 2.0 4.0
+1.13632 16.9 0.0 1.0 8.0
+1.13243 44.5 1.0 1.0 6.0
+1.10964 2.2 0.0 2.0 5.0
+1.02996 14.5 0.0 1.0 9.0
+1.00088 6.6 1.0 2.0 1.0
+0.98632 13.8 1.0 2.0 2.0
+0.97608 3 0.0 2.0 7.0
+0.96341 10.8 1.0 2.0 3.0
+0.94044 5.8 0.0 1.0 10.0
+0.93386 5.5 1.0 2.0 4.0
+0.91362 7.9 0.0 2.0 8.0
+0.89958 2.1 1.0 2.0 5.0
+0.8871 14.1 0.0 3.0 0.0
+0.86436 0.9 0.0 1.0 11.0
+0.85553 7.4 0.0 2.0 9.0
+0.83775 1.2 0.0 0.0 12.0
+0.82389 3.2 1.0 2.0 7.0
+0.80212 3.1 0.0 2.0 10.0
+0.78528 8.8 1.0 2.0 8.0
+0.78399 11.2 0.0 3.0 6.0
+0.76825 8 2.0 2.0 0.0
+0.75334 0.5 0.0 2.0 11.0
+0.74747 8.5 1.0 2.0 9.0
+0.74259 0.4 0.0 1.0 13.0
+0.73613 2 1.0 3.0 1.0
+0.73553 3.7 1.0 1.0 12.0
+0.73028 4.1 1.0 3.0 2.0
+0.72083 3.1 1.0 3.0 3.0
+0.71106 3.7 1.0 2.0 10.0
+0.7082 1.5 1.0 3.0 4.0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/TiC.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/TiC.jcpds
new file mode 100755
index 0000000..721c97d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/TiC.jcpds
@@ -0,0 +1,17 @@
+3
+TiC (EOS from Gu)
+ 1 268.0 4.00
+ 4.326
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.2580 13. 1.00 1.00 1.00
+ 2.8201 100. 2.00 0.00 0.00
+ 1.9941 55. 2.00 2.00 0.00
+ 1.7006 2. 3.00 1.00 1.00
+ 1.6282 15. 2.00 2.00 2.00
+ 1.4100 6. 4.00 0.00 0.00
+ 1.2940 1. 3.00 3.00 1.00
+ 1.2612 11. 4.00 2.00 0.00
+ 1.1515 7. 4.00 2.00 2.00
+ 1.0855 1. 5.00 1.00 1.00
+ 0.9969 2. 4.00 4.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ag.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ag.jcpds
new file mode 100755
index 0000000..4272ae2
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ag.jcpds
@@ -0,0 +1,15 @@
+3
+Gold (04-0783, shock wave)
+ 1 120.8 4.84
+ 4.0862
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3590 100. 1.00 1.00 1.00
+ 2.0440 52. 2.00 0.00 0.00
+ 1.4450 32. 2.00 2.00 0.00
+ 1.2310 36. 3.00 1.00 1.00
+ 1.1760 12. 2.00 2.00 2.00
+ 1.0215 6. 4.00 0.00 0.00
+ 0.9375 23. 3.00 3.00 1.00
+ 0.9137 22. 4.00 2.00 0.00
+ 0.8341 23. 4.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/al2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/al2o3.jcpds
new file mode 100755
index 0000000..1638255
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/al2o3.jcpds
@@ -0,0 +1,18 @@
+3
+Al2O3 corundum (JCPDS 46-1212, EOS)
+ 2 254.000 4.30000
+ 4.7587 12.9929
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.4797 45. 0.00 1.00 2.00
+ 2.5508 100. 1.00 0.00 4.00
+ 2.3795 21. 1.00 1.00 0.00
+ 2.0853 66. 1.00 1.00 3.00
+ 1.7401 34. 0.00 2.00 4.00
+ 1.6016 89. 1.00 1.00 6.00
+ 1.5110 14. 0.00 1.00 8.00
+ 1.4045 23. 2.00 1.00 4.00
+ 1.3737 27. 3.00 0.00 0.00
+ 1.2392 29. 1.00 0.00 10.00
+ 1.2343 12. 1.00 1.00 9.00
+ 1.0990 9. 0.00 2.00 10.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-Boehler.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-Boehler.jcpds
new file mode 100755
index 0000000..d1cc14e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-Boehler.jcpds
@@ -0,0 +1,11 @@
+3
+Argon FCC (made by Shim, EOS from Errandonea and Boehler)
+ 1 6.5 5.1
+ 5.22932
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.1828 100. 1.00 1.00 1.00
+ 2.7564 40. 2.00 0.00 0.00
+ 1.9491 25. 2.00 2.00 0.00
+ 1.6622 30. 3.00 1.00 1.00
+ 1.5914 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-hcp.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-hcp.jcpds
new file mode 100755
index 0000000..fd12ce1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar-hcp.jcpds
@@ -0,0 +1,16 @@
+3
+ar HCP (based on cobalt hcp file, EOS same as fcc)
+ 2 1.95 7.07
+ 3.8958 6.3143
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1694 20. 1.00 0.00 0.00
+ 2.0300 60. 0.00 0.00 2.00
+ 1.9134 100. 1.00 0.00 1.00
+ 1.4822 12. 1.00 0.00 2.00
+ 1.2525 80. 1.00 1.00 0.00
+ 1.1482 80. 1.00 0.00 3.00
+ 1.0847 20. 2.00 0.00 0.00
+ 1.0660 80. 1.00 1.00 2.00
+ 1.0479 60. 2.00 0.00 1.00
+ 1.0150 20. 0.00 0.00 4.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar.jcpds
new file mode 100755
index 0000000..8f07af1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ar.jcpds
@@ -0,0 +1,11 @@
+3
+Argon FCC (made by Shim, EOS from Finger et al and Ross et al)
+ 1 1.95 7.07
+ 5.5128
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.1828 100. 1.00 1.00 1.00
+ 2.7564 40. 2.00 0.00 0.00
+ 1.9491 25. 2.00 2.00 0.00
+ 1.6622 30. 3.00 1.00 1.00
+ 1.5914 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-dewaele.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-dewaele.jcpds
new file mode 100755
index 0000000..015406a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-dewaele.jcpds
@@ -0,0 +1,19 @@
+3
+Gold (04-0784, Dewaele 2004, Note that Dewaele used Vinet EOS but XPEAKPO uses BM)
+ 1 167.00 6.0
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-heinz.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-heinz.jcpds
new file mode 100755
index 0000000..dd909a6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-heinz.jcpds
@@ -0,0 +1,19 @@
+3
+Gold (04-0784, Heinz and Jeanloz 1984)
+ 1 166.600 5.50000
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-shim.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-shim.jcpds
new file mode 100755
index 0000000..61d691e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-shim.jcpds
@@ -0,0 +1,19 @@
+3
+Gold (04-0784, Shim et al 2002)
+ 1 167. 5.00000
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-tsuchiya.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-tsuchiya.jcpds
new file mode 100755
index 0000000..910b7c0
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au-tsuchiya.jcpds
@@ -0,0 +1,19 @@
+3
+Gold (04-0784, Tsuchiya 2003, Note that Tsuchiya used Vinet EOS but XPEAKPO uses BM)
+ 1 166.70 6.12
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au.jcpds
new file mode 100755
index 0000000..dd909a6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/au.jcpds
@@ -0,0 +1,19 @@
+3
+Gold (04-0784, Heinz and Jeanloz 1984)
+ 1 166.600 5.50000
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
+ 0.78493 10. 3.00 3.00 3.00
+ 0.68941 10. 5. 3. 1.
+ 0.67977 10. 4.0 4.0 2.0
+ 0.64488 10. 6. 2. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-2B.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-2B.jcpds
new file mode 100755
index 0000000..2038062
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-2B.jcpds
@@ -0,0 +1,12 @@
+3
+calcium (10-348, JCPDS)
+ 1 17.4 3.7.
+ 4.486
+(blank for future use)
+d-spacing I/I0 h k l
+3.15 100 1 1 0
+2.23 20 2 0 0
+1.83 40 2 1 1
+1.58 18 2 2 0
+1.41 12 3 1 0
+1.19 14 3 2 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-bcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-bcc.jcpds
new file mode 100755
index 0000000..5df711b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-bcc.jcpds
@@ -0,0 +1,12 @@
+3
+Ca (bcc) (from Fe, EOS from mp book)
+ 1 9.8 3.7
+ 4.53245
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.2050 100 1 1 0
+ 2.2663 19 2 0 0
+ 1.8504 30 2 1 1
+ 1.6025 9 2 2 0
+ 1.4333 12 3 1 0
+ 1.3084 6 2 2 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-fcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-fcc.jcpds
new file mode 100755
index 0000000..e619240
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ca-fcc.jcpds
@@ -0,0 +1,14 @@
+3
+calcium (23-430, JCPDS, Bernstein and Smith, Acta Cryst 12, 419, 1959)
+ 1 17.4 3.7
+ 5.5886
+(blank for future use)
+d-spacing I/I0 h k l
+3.2269 100. 1 1 1
+2.7945 46. 2 0 0
+1.9754 23. 2 2 0
+1.6851 23. 3 1 1
+1.6133 6. 2 2 2
+1.3871 2. 4 0 0
+1.2320 6. 3 3 1
+1.2498 5. 4 2 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cao.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cao.jcpds
new file mode 100755
index 0000000..8a3f2fc
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cao.jcpds
@@ -0,0 +1,19 @@
+3
+CaO (JCPDS 37-1497, EOS by Richet et al 1988)
+ 1 111.000 4.20000
+ 4.81059
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.7774 36. 1.00 1.00 1.00
+ 2.4059 100. 2.00 0.00 0.00
+ 1.7009 54. 2.00 2.00 0.00
+ 1.4505 16. 3.00 1.00 1.00
+ 1.3888 16. 2.00 2.00 2.00
+ 1.2026 6. 4.00 0.00 0.00
+ 1.1037 6. 3.00 3.00 1.00
+ 1.0758 16. 4.00 2.00 0.00
+ 0.9819 12. 4.00 2.00 2.00
+ 0.9257 6. 5.00 1.00 1.00
+ 0.8504 6. 4.00 4.00 0.00
+ 0.8131 10. 5.00 3.00 1.00
+ 0.8018 16. 6.00 0.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caob2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caob2.jcpds
new file mode 100755
index 0000000..40dc151
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caob2.jcpds
@@ -0,0 +1,18 @@
+3
+CaO-b2 (GSAS, EOS by Richet et al 1988)
+ 1 130.000 3.50000
+ 2.90740
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.9074 29. 1.00 0.00 0.00
+ 2.0558 100. 1.00 1.00 0.00
+ 1.6786 8. 1.00 1.00 1.00
+ 1.4537 13. 2.00 0.00 0.00
+ 1.3002 9. 2.00 1.00 0.00
+ 1.1869 15. 2.00 1.00 1.00
+ 1.0279 2. 2.00 2.00 0.00
+ 0.9691 1. 2.00 2.00 1.00
+ 0.9691 1. 3.00 0.00 0.00
+ 0.9194 2. 3.00 1.00 0.00
+ 0.8766 1. 3.00 1.00 1.00
+ 0.8393 2. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caoh2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caoh2.jcpds
new file mode 100755
index 0000000..2ee5923
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/caoh2.jcpds
@@ -0,0 +1,49 @@
+3
+CaOH2 Brucite (From FeOH2, EOS)
+ 2 50.000 4.00000
+ 3.584 4.896
+(blank for future use)
+d-spacing I/I0 h k l
+4.8962 60.7 0 0 1
+3.10418 16.8 1 -1 0
+2.62168 100 1 0 -1
+2.4481 0.7 0 0 2
+1.92225 45.2 -1 1 2
+1.7922 30.4 2 -1 0
+1.683 16.7 2 -1 1
+1.63207 1.4 0 0 3
+1.55209 2.4 -2 2 0
+1.47953 12.5 -2 0 1
+1.4461 1.3 -2 1 2
+1.44457 13.2 1 0 -3
+1.31084 10.4 -2 0 2
+1.22405 1.9 0 0 4
+1.20669 2.9 -2 1 3
+1.17327 2 -2 3 0
+1.14097 9.4 -3 1 1
+1.13872 2.2 0 -1 4
+1.1247 5.2 2 0 -3
+1.05804 10.4 -3 1 2
+1.03473 3.9 3 -3 0
+1.01237 3.2 -3 3 1
+1.01079 6.8 1 -2 4
+0.97924 0.8 0 0 5
+0.96112 1.7 -2 2 4
+0.95309 0.6 3 0 -2
+0.95265 7.9 -3 1 3
+0.93388 2.6 0 -1 5
+0.8961 3.7 4 -2 0
+0.88146 3.4 -4 2 1
+0.87389 2.2 -3 3 3
+0.86095 1.8 -4 1 0
+0.85933 5.5 1 1 -5
+0.84794 8.4 -4 1 1
+0.84701 4.5 -3 1 4
+0.8415 0.9 4 -2 2
+0.82818 3.7 -2 2 5
+0.81603 0.3 0 0 6
+0.81218 15 -4 1 2
+0.79022 14.8 -3 3 4
+0.78922 10.8 0 -1 6
+0.78549 5.5 -4 2 3
+0.77605 3.5 -4 4 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-i4mmm.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-i4mmm.jcpds
new file mode 100755
index 0000000..a55aeb6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-i4mmm.jcpds
@@ -0,0 +1,108 @@
+3
+Ca-perovskite tetragonal (I4/mmm, EOS by Mao et al 1989)
+ 3 281.000 4.00000
+ 7.879 7.7582
+(blank for future use)
+d-spacing I/I0 h k l
+5.5713 2 1 1 0
+5.5281 3 0 1 1
+3.9395 0 0 2 0
+3.8791 0 0 0 2
+3.2082 2 1 2 1
+3.1835 2 1 1 2
+2.7856 40 2 2 0
+2.764 100 0 2 2
+2.4916 0 1 3 0
+2.4877 3 0 3 1
+2.4571 8 0 1 3
+2.2627 48 2 2 2
+2.1034 8 2 3 1
+2.0964 0 1 3 2
+2.0848 3 1 2 3
+1.9698 59 0 4 0
+1.9396 24 0 0 4
+1.8571 1 3 3 0
+1.8555 0 1 4 1
+1.8427 0 0 3 3
+1.8317 0 1 1 4
+1.7618 1 2 4 0
+1.7563 0 0 4 2
+1.7401 0 0 2 4
+1.675 1 3 3 2
+1.6691 0 2 3 3
+1.6041 42 2 4 2
+1.5917 22 2 2 4
+1.5452 0 1 5 0
+1.5443 1 0 5 1
+1.5443 0 3 4 1
+1.5369 2 1 4 3
+1.5305 0 1 3 4
+1.5224 0 0 1 5
+1.4378 0 2 5 1
+1.4355 1 1 5 2
+1.4201 1 1 2 5
+1.3928 13 4 4 0
+1.382 20 0 4 4
+1.3512 0 3 5 0
+1.3457 1 0 5 3
+1.3457 0 3 4 3
+1.3414 0 3 3 4
+1.3359 1 0 3 5
+1.3132 0 0 6 0
+1.3109 0 4 4 2
+1.3041 0 2 4 4
+1.293 0 0 0 6
+1.2776 0 1 6 1
+1.276 0 3 5 2
+1.2734 1 2 5 3
+1.2651 2 2 3 5
+1.2596 0 1 1 6
+1.2458 4 2 6 0
+1.2438 8 0 6 2
+1.2285 6 0 2 6
+1.2153 0 4 5 1
+1.2086 0 1 5 4
+1.2046 0 1 4 5
+1.1861 3 2 6 2
+1.1728 0 2 2 6
+1.1613 0 3 6 1
+1.1581 0 1 6 3
+1.1477 0 1 3 6
+1.1313 7 4 4 4
+1.1143 0 1 7 0
+1.1143 0 5 5 0
+1.1139 0 0 7 1
+1.1111 1 4 5 3
+1.1087 0 3 5 4
+1.1056 0 3 4 5
+1.1056 0 0 5 5
+1.0975 0 0 1 7
+1.0926 0 4 6 0
+1.0874 0 0 6 4
+1.0809 0 0 4 6
+1.0719 0 2 7 1
+1.071 0 1 7 2
+1.071 0 5 5 2
+1.0694 0 3 6 3
+1.0645 0 2 5 5
+1.0612 0 3 3 6
+1.0572 0 1 2 7
+1.0517 7 4 6 2
+1.0482 5 2 6 4
+1.0424 6 2 4 6
+1.0346 0 3 7 0
+1.0321 0 0 7 3
+1.0211 0 0 3 7
+1.0004 0 5 6 1
+0.9996 0 3 7 2
+0.9984 0 2 7 3
+0.9944 0 1 6 5
+0.9916 0 1 5 6
+0.9885 0 2 3 7
+0.9849 2 0 8 0
+0.9698 1 0 0 8
+0.9696 0 1 8 1
+0.9696 0 4 7 1
+0.9662 0 1 7 4
+0.9662 0 5 5 4
+0.9641 0 4 5 5
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-p4.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-p4.jcpds
new file mode 100755
index 0000000..df81e0b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-p4.jcpds
@@ -0,0 +1,30 @@
+3
+Ca-perovskite tetragonal (P4/mmm, EOS by Shim 2002)
+ 3 255.000 4.00000
+ 3.582 3.567
+(blank for future use)
+d-spacing I/I0 h k l
+3.582 1 1 0 0
+3.567 0 0 0 1
+2.53286 49 1 1 0
+2.52754 100 1 0 1
+2.06517 59 1 1 1
+1.791 70 2 0 0
+1.7835 32 0 0 2
+1.60192 0 2 1 0
+1.60057 0 2 0 1
+1.59655 0 1 0 2
+1.46132 28 2 1 1
+1.45825 13 1 1 2
+1.26643 12 2 2 0
+1.26377 23 2 0 2
+1.194 0 3 0 0
+1.19344 0 2 2 1
+1.19177 0 2 1 2
+1.189 0 0 0 3
+1.13273 4 3 1 0
+1.13225 4 3 0 1
+1.12846 4 1 0 3
+1.0796 2 3 1 1
+1.07631 1 1 1 3
+1.03258 6 2 2 2
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pv.jcpds
new file mode 100755
index 0000000..03472c6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pv.jcpds
@@ -0,0 +1,25 @@
+3
+Ca-perovskite (using aristotype, EOS by Shim et al 1999)
+ 1 236.000 3.90000
+ 3.56660
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.5666 4. 1.00 0.00 0.00
+ 2.5220 100. 1.00 1.00 0.00
+ 2.0592 31. 1.00 1.00 1.00
+ 1.7833 56. 2.00 0.00 0.00
+ 1.5950 1. 2.00 1.00 0.00
+ 1.4561 15. 2.00 1.00 1.00
+ 1.2610 9. 2.00 2.00 0.00
+ 1.1889 1. 2.00 2.00 1.00
+ 1.1889 1. 3.00 0.00 0.00
+ 1.1279 3. 3.00 1.00 0.00
+ 1.0754 1. 3.00 1.00 1.00
+ 1.0296 3. 2.00 2.00 2.00
+ 0.9892 1. 3.00 2.00 0.00
+ 0.9532 1. 3.00 2.00 1.00
+ 0.8917 1. 4.00 0.00 0.00
+ 0.8650 1. 3.00 2.00 2.00
+ 0.8650 1. 4.00 1.00 0.00
+ 0.8407 1. 4.00 1.00 1.00
+ 0.8407 1. 3.00 3.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvi.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvi.jcpds
new file mode 100755
index 0000000..c38c015
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvi.jcpds
@@ -0,0 +1,11 @@
+3
+Ca-perovskite tetragonal (using aristotype (I), EOS by Mao et al 1989)
+ 3 281.000 4.00000
+ 5.056 7.0991
+(blank for future use)
+d-spacing I/I0 h k l
+2.5189 43 1 1 2
+2.1545 10 2 1 1
+2.0591 36 2 0 2
+1.7876 100 2 2 0
+1.7748 31 0 0 4
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvp.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvp.jcpds
new file mode 100755
index 0000000..cbc1b1a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvp.jcpds
@@ -0,0 +1,12 @@
+3
+Ca-perovskite tetragonal (using aristotype (I), EOS by Mao et al 1989)
+ 3 281.000 4.00000
+ 5.0606 3.5431
+(blank for future use)
+d-spacing I/I0 h k l
+2.5177 44 1 1 1
+2.2632 19 2 1 0
+2.0591 37 2 0 1
+1.9073 6 2 1 1
+1.7892 100 2 2 0
+1.7716 33 0 0 2
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvti.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvti.jcpds
new file mode 100755
index 0000000..91c1a20
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/casio3-pvti.jcpds
@@ -0,0 +1,51 @@
+3
+Ca-perovskite tetragonal (using aristotype (I), EOS by Mao et al 1989)
+ 3 281.000 4.00000
+ 5.0064 7.1332
+(blank for future use)
+d-spacing I/I0 h k l
+3.5666 0.1627 0 0 2
+3.54006 0.155 1 1 0
+2.51253 72.3996 1 1 2
+2.5032 41.2529 2 0 0
+2.13618 5.8976 2 1 1
+2.04892 68.2413 2 0 2
+1.7833 54.6273 0 0 4
+1.77003 100 2 2 0
+1.63003 3.17 2 1 3
+1.59264 0.018 1 1 4
+1.58552 0.0441 2 2 2
+1.58316 0.3578 3 1 0
+1.45242 39.1022 2 0 4
+1.44701 79.8819 3 1 2
+1.36294 1.3395 3 2 1
+1.25627 88.5194 2 2 4
+1.2516 40.8244 4 0 0
+1.20315 1.1984 2 1 5
+1.19905 0.8599 3 2 3
+1.19701 4.0286 4 1 1
+1.18887 0.0093 0 0 6
+1.18393 0.3285 3 1 4
+1.18099 0.1482 4 0 2
+1.18002 0.0867 3 3 0
+1.12701 19.9994 1 1 6
+1.1203 15.6419 3 3 2
+1.11947 26.1494 4 2 0
+1.08139 2.749 4 1 3
+1.0739 9.6686 2 0 6
+1.06809 13.9693 4 2 2
+1.02446 43.3921 4 0 4
+0.99504 0.4145 3 2 5
+0.99156 1.403 4 3 1
+0.98691 0.0133 2 2 6
+0.98408 0.1149 3 3 4
+0.98184 0.0195 5 1 0
+0.95066 26.4228 3 1 6
+0.94813 30.7364 4 2 4
+0.94662 21.3498 5 1 2
+0.92748 0.4219 2 1 7
+0.92466 1.4351 4 1 5
+0.9228 1.0344 4 3 3
+0.92187 0.139 5 2 1
+0.89165 7.4399 0 0 8
+0.88501 0 4 4 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_bcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_bcc.jcpds
new file mode 100755
index 0000000..b6750f1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_bcc.jcpds
@@ -0,0 +1,12 @@
+3
+Co (bcc) (based on Fe, EOS from hcp)
+ 1 194.000 5.00000
+ 3.30980
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3404 100. 1.00 1.00 0.00
+ 1.6549 19. 2.00 0.00 0.00
+ 1.3512 30. 2.00 1.00 1.00
+ 1.1702 9. 2.00 2.00 0.00
+ 1.0467 12. 3.00 1.00 0.00
+ 0.9555 6. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp.jcpds
new file mode 100755
index 0000000..cde5ae5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp.jcpds
@@ -0,0 +1,22 @@
+3
+Cobalt DHCP (GSAS calculation, EOS from hcp)
+ 2 194.000 5.00000
+ 2.50500 8.12021
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1694 10. 1.00 0.00 0.00
+ 2.0959 46. 1.00 0.00 1.00
+ 2.0300 37. 0.00 0.00 4.00
+ 1.9134 100. 1.00 0.00 2.00
+ 1.6928 21. 1.00 0.00 3.00
+ 1.4823 4. 1.00 0.00 4.00
+ 1.3001 6. 1.00 0.00 5.00
+ 1.2525 12. 1.00 1.00 0.00
+ 1.1482 7. 1.00 0.00 6.00
+ 1.0752 2. 2.00 0.00 1.00
+ 1.0659 10. 1.00 1.00 4.00
+ 1.0479 5. 2.00 0.00 2.00
+ 1.0229 2. 1.00 0.00 7.00
+ 1.0150 2. 0.00 0.00 8.00
+ 1.0069 1. 2.00 0.00 3.00
+ 0.8464 1. 2.00 0.00 6.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp_yoo.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp_yoo.jcpds
new file mode 100755
index 0000000..f61b3d5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dhcp_yoo.jcpds
@@ -0,0 +1,22 @@
+3
+Cobalt DHCP (GSAS calculation, EOS from hcp)
+ 2 194.000 5.00000
+ 2.50500 7.99095
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1694 10. 1.00 0.00 0.00
+ 2.0959 46. 1.00 0.00 1.00
+ 2.0300 37. 0.00 0.00 4.00
+ 1.9134 100. 1.00 0.00 2.00
+ 1.6928 21. 1.00 0.00 3.00
+ 1.4823 4. 1.00 0.00 4.00
+ 1.3001 6. 1.00 0.00 5.00
+ 1.2525 12. 1.00 1.00 0.00
+ 1.1482 7. 1.00 0.00 6.00
+ 1.0752 2. 2.00 0.00 1.00
+ 1.0659 10. 1.00 1.00 4.00
+ 1.0479 5. 2.00 0.00 2.00
+ 1.0229 2. 1.00 0.00 7.00
+ 1.0150 2. 0.00 0.00 8.00
+ 1.0069 1. 2.00 0.00 3.00
+ 0.8464 1. 2.00 0.00 6.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dtfcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dtfcc.jcpds
new file mode 100755
index 0000000..693e86e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_dtfcc.jcpds
@@ -0,0 +1,78 @@
+3
+Cobalt dtfcc (GSAS calculation, EOS from hcp)
+ 2 194.000 5.00000
+ 5.01300 12.2793
+(blank for future use)
+d-spacing I/I0 h k l
+ 4.0931 1. 1.00 0.00 1.00
+ 4.0931 1. 0.00 0.00 3.00
+ 3.5447 1. 1.00 0.00 -2.00
+ 2.5065 1. 1.00 1.00 0.00
+ 2.5065 1. 1.00 0.00 4.00
+ 2.1376 1. 2.00 0.00 -1.00
+ 2.1376 4. 1.00 1.00 3.00
+ 2.1375 9. 1.00 0.00 -5.00
+ 2.0466 100. 2.00 0.00 2.00
+ 2.0465 28. 0.00 0.00 6.00
+ 1.7724 53. 2.00 0.00 -4.00
+ 1.6264 3. 2.00 1.00 1.00
+ 1.6264 1. 2.00 0.00 5.00
+ 1.6264 2. 1.00 0.00 7.00
+ 1.5853 1. 2.00 1.00 -2.00
+ 1.5852 1. 1.00 1.00 6.00
+ 1.4471 1. 3.00 0.00 0.00
+ 1.4471 1. 2.00 1.00 4.00
+ 1.4471 1. 1.00 0.00 -8.00
+ 1.3644 1. 3.00 0.00 3.00
+ 1.3644 2. 3.00 0.00 -3.00
+ 1.3644 1. 2.00 1.00 -5.00
+ 1.3644 1. 2.00 0.00 -7.00
+ 1.3644 1. 0.00 0.00 9.00
+ 1.2533 11. 2.00 2.00 0.00
+ 1.2532 8. 2.00 0.00 8.00
+ 1.1983 1. 3.00 1.00 -1.00
+ 1.1983 1. 2.00 2.00 3.00
+ 1.1983 3. 2.00 1.00 7.00
+ 1.1983 2. 1.00 1.00 9.00
+ 1.1816 1. 3.00 1.00 2.00
+ 1.1816 1. 3.00 0.00 6.00
+ 1.1816 1. 3.00 0.00 -6.00
+ 1.1816 1. 1.00 0.00 10.00
+ 1.1209 1. 3.00 1.00 -4.00
+ 1.1209 1. 2.00 1.00 -8.00
+ 1.0811 1. 4.00 0.00 1.00
+ 1.0811 1. 3.00 1.00 5.00
+ 1.0811 1. 1.00 0.00 -11.00
+ 1.0688 4. 4.00 0.00 -2.00
+ 1.0688 7. 2.00 2.00 6.00
+ 1.0688 2. 2.00 0.00 -10.00
+ 1.0233 3. 4.00 0.00 4.00
+ 1.0233 1. 0.00 0.00 12.00
+ 0.9927 1. 3.00 2.00 1.00
+ 0.9927 1. 4.00 0.00 -5.00
+ 0.9927 1. 3.00 1.00 -7.00
+ 0.9927 1. 3.00 0.00 9.00
+ 0.9927 1. 3.00 0.00 -9.00
+ 0.9927 1. 2.00 0.00 11.00
+ 0.9831 1. 3.00 2.00 -2.00
+ 0.9831 1. 2.00 1.00 10.00
+ 0.9474 1. 4.00 1.00 0.00
+ 0.9474 1. 3.00 2.00 4.00
+ 0.9474 1. 3.00 1.00 8.00
+ 0.9474 1. 1.00 1.00 12.00
+ 0.9230 1. 4.00 1.00 -3.00
+ 0.9230 1. 4.00 1.00 3.00
+ 0.9230 1. 3.00 2.00 -5.00
+ 0.9230 1. 4.00 0.00 7.00
+ 0.9230 1. 2.00 2.00 9.00
+ 0.9230 1. 2.00 1.00 -11.00
+ 0.9230 1. 1.00 0.00 13.00
+ 0.8862 1. 4.00 0.00 -8.00
+ 0.8661 1. 5.00 0.00 -1.00
+ 0.8661 1. 3.00 2.00 7.00
+ 0.8661 1. 2.00 0.00 -13.00
+ 0.8597 1. 5.00 0.00 2.00
+ 0.8597 1. 4.00 1.00 -6.00
+ 0.8597 1. 4.00 1.00 6.00
+ 0.8597 1. 3.00 1.00 -10.00
+ 0.8597 1. 1.00 0.00 -14.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_fcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_fcc.jcpds
new file mode 100755
index 0000000..2dc3441
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_fcc.jcpds
@@ -0,0 +1,11 @@
+3
+Cobalt FCC (JCPDS 15-806, EOS from ?)
+ 1 177.000 4.00000
+ 3.54470
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0465 100. 1.00 1.00 1.00
+ 1.7724 40. 2.00 0.00 0.00
+ 1.2532 25. 2.00 2.00 0.00
+ 1.0688 30. 3.00 1.00 1.00
+ 1.0233 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_hcp.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_hcp.jcpds
new file mode 100755
index 0000000..c8e99fb
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_hcp.jcpds
@@ -0,0 +1,16 @@
+3
+Cobalt HCP (JCPDS 5-0727, EOS from Lazor and Shen)
+ 2 194.000 5.00000
+ 2.50500 4.06010
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1694 20. 1.00 0.00 0.00
+ 2.0300 60. 0.00 0.00 2.00
+ 1.9134 100. 1.00 0.00 1.00
+ 1.4822 12. 1.00 0.00 2.00
+ 1.2525 80. 1.00 1.00 0.00
+ 1.1482 80. 1.00 0.00 3.00
+ 1.0847 20. 2.00 0.00 0.00
+ 1.0660 80. 1.00 1.00 2.00
+ 1.0479 60. 2.00 0.00 1.00
+ 1.0150 20. 0.00 0.00 4.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_orth.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_orth.jcpds
new file mode 100755
index 0000000..5049db6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/co_orth.jcpds
@@ -0,0 +1,50 @@
+3
+Cobalt orthorhomic (GSAS calculation, EOS from HCP)
+ 4 194.000 5.00000
+ 2.50500 4.44638 4.06061
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.5050 2. 1.00 0.00 0.00
+ 2.2094 38. 0.00 2.00 0.00
+ 2.1792 4. 1.00 1.00 0.00
+ 2.0300 33. 0.00 0.00 2.00
+ 1.9407 11. 0.00 2.00 1.00
+ 1.9201 100. 1.00 1.00 1.00
+ 1.6570 1. 1.00 2.00 0.00
+ 1.5771 1. 1.00 0.00 2.00
+ 1.5341 1. 1.00 2.00 1.00
+ 1.4948 16. 0.00 2.00 2.00
+ 1.4854 2. 1.00 1.00 2.00
+ 1.2837 1. 1.00 2.00 2.00
+ 1.2697 3. 1.00 3.00 0.00
+ 1.2525 3. 2.00 0.00 0.00
+ 1.2118 7. 1.00 3.00 1.00
+ 1.2050 1. 2.00 1.00 0.00
+ 1.1552 1. 2.00 1.00 1.00
+ 1.1540 1. 0.00 2.00 3.00
+ 1.1497 8. 1.00 1.00 3.00
+ 1.1047 1. 0.00 4.00 0.00
+ 1.0896 2. 2.00 2.00 0.00
+ 1.0765 2. 1.00 3.00 2.00
+ 1.0660 1. 0.00 4.00 1.00
+ 1.0659 2. 2.00 0.00 2.00
+ 1.0524 1. 2.00 2.00 1.00
+ 1.0482 1. 1.00 2.00 3.00
+ 1.0362 1. 2.00 1.00 2.00
+ 1.0150 1. 0.00 0.00 4.00
+ 1.0108 1. 1.00 4.00 0.00
+ 0.9808 1. 1.00 4.00 1.00
+ 0.9703 1. 0.00 4.00 2.00
+ 0.9600 2. 2.00 2.00 2.00
+ 0.9542 1. 2.00 3.00 0.00
+ 0.9407 1. 1.00 0.00 4.00
+ 0.9289 1. 2.00 3.00 1.00
+ 0.9260 1. 1.00 3.00 3.00
+ 0.9223 1. 0.00 2.00 4.00
+ 0.9201 1. 1.00 1.00 4.00
+ 0.9048 1. 1.00 4.00 2.00
+ 0.9000 1. 2.00 1.00 3.00
+ 0.8655 1. 1.00 2.00 4.00
+ 0.8635 1. 2.00 3.00 2.00
+ 0.8558 1. 0.00 4.00 3.00
+ 0.8487 1. 2.00 2.00 3.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-i.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-i.jcpds
new file mode 100755
index 0000000..454e04f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-i.jcpds
@@ -0,0 +1,66 @@
+3
+Cr2O3 - orthorhombic structure (GSAS calculation, EOS from Handbook)
+ 4 222.000 4.00000
+ 5.014 5.223 7.025
+(blank for future use)
+d-spacing I/I0 h k l
+3.5125 9 0 0 2
+3.21583 10 1 1 1
+2.6115 27 0 2 0
+2.51987 100 1 1 2
+2.507 23 2 0 0
+2.26012 5 2 1 0
+2.19969 2 1 2 1
+2.15152 3 2 1 1
+2.12169 5 1 0 3
+2.04056 1 2 0 2
+1.96569 4 1 1 3
+1.93362 3 1 2 2
+1.80853 26 2 2 0
+1.75625 10 0 0 4
+1.75142 4 2 2 1
+1.74343 1 0 2 3
+1.60791 3 2 2 2
+1.60137 9 1 3 1
+1.57987 7 1 1 4
+1.55246 1 3 1 1
+1.48948 17 1 3 2
+1.45735 8 0 2 4
+1.44988 22 3 1 2
+1.43841 8 2 0 4
+1.43134 3 2 2 3
+1.34588 1 1 3 3
+1.30967 1 1 1 5
+1.30575 1 0 4 0
+1.28376 1 0 4 1
+1.25993 5 2 2 4
+1.2535 2 4 0 0
+1.24365 1 1 4 1
+1.22392 1 0 4 2
+1.20047 1 1 3 4
+1.17945 1 3 1 4
+1.15808 1 2 4 0
+1.14266 1 2 4 1
+1.14043 1 0 4 3
+1.14038 2 3 3 2
+1.13006 2 4 2 0
+1.11572 1 4 2 1
+1.11393 2 1 1 6
+1.06837 1 0 2 6
+1.06827 1 1 3 5
+1.06084 1 2 0 6
+1.03807 1 2 4 3
+1.02028 1 4 0 4
+1.01198 1 1 5 1
+0.994 1 3 3 4
+0.98285 1 2 2 6
+0.98187 1 1 5 2
+0.96681 1 2 4 4
+0.95382 1 1 3 6
+0.95033 1 4 2 4
+0.94825 1 5 1 2
+0.94318 1 3 1 6
+0.91498 1 3 3 5
+0.87886 1 3 5 1
+0.85334 1 1 1 8
+0.84353 1 5 3 2
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-ppv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-ppv.jcpds
new file mode 100755
index 0000000..fd50b4c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-ppv.jcpds
@@ -0,0 +1,103 @@
+3
+cr2O3_postpv (GSAS calculation, EOS from Mao et al)
+ 4 222.000 4.00000
+ 2.883 9.363 7.014
+(blank for future use)
+d-spacing I/I0 h k l
+4.3995 13.9 0 2 0
+3.6720 0.1 0 2 1
+3.3333 4.4 0 0 2
+2.6568 100.0 0 2 2
+2.5700 18.9 1 1 0
+2.3980 4.3 1 1 1
+2.1998 5.4 0 4 0
+2.0890 5.4 0 4 1
+2.0353 0.3 1 1 2
+1.9835 39.0 0 2 3
+1.9814 65.8 1 3 0
+1.8992 82.7 1 3 1
+1.8360 2.5 0 4 2
+1.7032 40.0 1 3 2
+1.6809 35.4 1 1 3
+1.6666 26.7 0 0 4
+1.5633 0.7 0 4 3
+1.5585 0.3 0 2 4
+1.4789 6.8 1 3 3
+1.4722 5.3 1 5 0
+1.4665 0.1 0 6 0
+1.4376 7.0 1 5 1
+1.4323 1.5 0 6 1
+1.3983 16.4 1 1 4
+1.3467 25.6 1 5 2
+1.3436 18.0 2 0 0
+1.3423 21.5 0 6 2
+1.3284 11.7 0 4 4
+1.2850 0.2 2 2 0
+1.2760 0.4 0 2 5
+1.2754 5.9 1 3 4
+1.2618 0.2 2 2 1
+1.2462 0.2 2 0 2
+1.2273 0.1 1 5 3
+1.2240 0.6 0 6 3
+1.1990 9.7 2 2 2
+1.1835 0.2 1 1 5
+1.1466 1.1 2 4 0
+1.1402 0.1 0 4 5
+1.1386 0.1 1 7 0
+1.1300 0.4 2 4 1
+1.1223 1.1 1 7 1
+1.1124 2.9 2 2 3
+1.1111 0.4 0 0 6
+1.1062 8.2 1 3 5
+1.1034 1.3 1 5 4
+1.1010 0.0 0 6 4
+1.0999 1.8 0 8 0
+1.0852 0.3 0 8 1
+1.0843 0.1 2 4 2
+1.0775 1.5 1 7 2
+1.0773 3.4 0 2 6
+1.0460 4.9 2 0 4
+1.0445 0.9 0 8 2
+1.0199 1.8 1 1 6
+1.0190 0.1 2 4 3
+1.0177 0.2 2 2 4
+1.0133 1.7 1 7 3
+0.9918 0.8 0 4 6
+0.9907 0.0 2 6 0
+0.9883 2.0 1 5 5
+0.9865 0.4 0 6 5
+0.9857 0.0 0 8 3
+0.9799 0.4 2 6 1
+0.9691 0.6 1 3 6
+0.9496 6.4 2 6 2
+0.9447 3.5 2 4 4
+0.9401 0.1 1 7 4
+0.9308 0.1 0 2 7
+0.9252 0.1 2 2 5
+0.9188 0.1 1 9 0
+0.9180 0.5 0 8 4
+0.9102 0.8 1 9 1
+0.9048 0.1 2 6 3
+0.8930 0.9 1 1 7
+0.8911 0.3 3 1 0
+0.8869 0.7 1 5 6
+0.8857 0.6 1 9 2
+0.8856 1.1 0 6 6
+0.8833 0.0 3 1 1
+0.8799 0.0 0 10 0
+0.8740 0.0 0 4 7
+0.8723 0.0 0 10 1
+0.8694 0.0 2 4 5
+0.8658 0.0 1 7 5
+0.8609 0.0 3 1 2
+0.8584 0.4 1 3 7
+0.8567 1.4 3 3 0
+0.8562 0.3 2 0 6
+0.8516 0.0 2 6 4
+0.8511 1.1 2 8 0
+0.8508 0.4 0 10 2
+0.8497 0.3 3 3 1
+0.8490 0.2 1 9 3
+0.8484 0.0 0 8 5
+0.8442 0.0 2 8 1
+0.8405 2.0 2 2 6
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-pv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-pv.jcpds
new file mode 100755
index 0000000..495a192
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-pv.jcpds
@@ -0,0 +1,112 @@
+3
+Cr2O3 - orthorhombic pv structure (GSAS calculation, EOS from Handbook)
+ 4 222.000 4.00000
+ 5.014 5.270 7.167
+(blank for future use)
+d-spacing I/I0 h k l
+4.08112 3 1 0 1
+3.61706 7 1 1 0
+3.5125 1 0 0 2
+3.21583 10 1 1 1
+2.6115 20 0 2 0
+2.51987 100 1 1 2
+2.507 27 2 0 0
+2.44783 4 0 2 1
+2.31617 0 1 2 0
+2.26012 1 2 1 0
+2.19969 1 1 2 1
+2.15152 3 2 1 1
+2.12169 2 1 0 3
+2.09573 3 0 2 2
+2.04056 5 2 0 2
+1.96569 5 1 1 3
+1.93362 1 1 2 2
+1.90065 1 2 1 2
+1.80853 33 2 2 0
+1.75625 15 0 0 4
+1.75142 4 2 2 1
+1.74343 2 0 2 3
+1.64672 1 1 2 3
+1.64467 3 1 3 0
+1.62621 1 2 1 3
+1.62595 0 3 0 1
+1.60791 2 2 2 2
+1.60137 9 1 3 1
+1.59182 0 3 1 0
+1.57987 1 1 1 4
+1.55246 1 3 1 1
+1.48948 7 1 3 2
+1.45735 7 0 2 4
+1.44988 13 3 1 2
+1.43841 8 2 0 4
+1.43134 1 2 2 3
+1.43 0 2 3 0
+1.40772 0 3 2 0
+1.40126 0 2 3 1
+1.39943 0 1 2 4
+1.38678 0 2 1 4
+1.38028 0 3 2 1
+1.36037 0 3 0 3
+1.35289 0 1 0 5
+1.34588 2 1 3 3
+1.32444 0 2 3 2
+1.31645 0 3 1 3
+1.30967 0 1 1 5
+1.30669 0 3 2 2
+1.30575 2 0 4 0
+1.28376 1 0 4 1
+1.2636 0 1 4 0
+1.25993 8 2 2 4
+1.2535 2 4 0 0
+1.24365 0 1 4 1
+1.2373 0 0 2 5
+1.22392 1 0 4 2
+1.22043 0 2 3 3
+1.21889 0 4 1 0
+1.20649 0 3 2 3
+1.20569 0 3 3 0
+1.20126 0 1 2 5
+1.20095 1 4 1 1
+1.20047 1 1 3 4
+1.19323 0 2 1 5
+1.18901 0 1 4 2
+1.18831 1 3 3 1
+1.18058 0 4 0 2
+1.17945 0 3 1 4
+1.17083 0 0 0 6
+1.15808 1 2 4 0
+1.15153 0 4 1 2
+1.14266 1 2 4 1
+1.14043 0 0 4 3
+1.14038 2 3 3 2
+1.13006 1 4 2 0
+1.11572 0 4 2 1
+1.11393 3 1 1 6
+1.11203 0 1 4 3
+1.10953 1 2 2 5
+1.1089 0 2 3 4
+1.09985 0 2 4 2
+1.09842 0 3 2 4
+1.08119 1 4 1 3
+1.07576 0 4 2 2
+1.07547 0 3 0 5
+1.07194 1 3 3 3
+1.06837 0 0 2 6
+1.06827 1 1 3 5
+1.06084 0 2 0 6
+1.05337 0 3 1 5
+1.04787 1 0 4 4
+1.04491 0 1 2 6
+1.03962 0 2 1 6
+1.03807 1 2 4 3
+1.02896 0 3 4 0
+1.02571 0 1 4 4
+1.02264 1 1 5 0
+1.02028 1 4 0 4
+1.01809 0 3 4 1
+1.01775 0 4 2 3
+1.01726 0 4 3 0
+1.01198 1 1 5 1
+1.00676 0 4 3 1
+1.00221 0 2 3 5
+1.00135 0 4 1 4
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-rh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-rh.jcpds
new file mode 100755
index 0000000..0f5e8cc
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3-rh.jcpds
@@ -0,0 +1,101 @@
+3
+Cr2O3 - orthorhombic rh2o3 structure (GSAS calculation, EOS from Handbook)
+ 4 222.000 4.00000
+ 5.014 5.270 7.167
+(blank for future use)
+d-spacing I/I0 h k l
+4.08112 0 1 0 1
+3.5125 9 0 0 2
+3.21583 11 1 1 1
+2.6115 28 0 2 0
+2.51987 100 1 1 2
+2.507 24 2 0 0
+2.44783 0 0 2 1
+2.26012 4 2 1 0
+2.19969 2 1 2 1
+2.15152 2 2 1 1
+2.12169 6 1 0 3
+2.09573 0 0 2 2
+2.04056 1 2 0 2
+1.96569 4 1 1 3
+1.93362 3 1 2 2
+1.90065 0 2 1 2
+1.80853 23 2 2 0
+1.75625 9 0 0 4
+1.75142 3 2 2 1
+1.74343 1 0 2 3
+1.64672 0 1 2 3
+1.62621 0 2 1 3
+1.62595 0 3 0 1
+1.60791 3 2 2 2
+1.60137 9 1 3 1
+1.57987 8 1 1 4
+1.55246 1 3 1 1
+1.48948 14 1 3 2
+1.45735 8 0 2 4
+1.44988 22 3 1 2
+1.43841 8 2 0 4
+1.43134 3 2 2 3
+1.43 0 2 3 0
+1.40126 0 2 3 1
+1.39943 0 1 2 4
+1.38678 0 2 1 4
+1.38028 0 3 2 1
+1.36037 0 3 0 3
+1.35289 0 1 0 5
+1.34588 1 1 3 3
+1.32444 0 2 3 2
+1.31645 0 3 1 3
+1.30967 1 1 1 5
+1.30669 0 3 2 2
+1.30575 1 0 4 0
+1.28376 1 0 4 1
+1.25993 4 2 2 4
+1.2535 1 4 0 0
+1.24365 0 1 4 1
+1.2373 0 0 2 5
+1.22392 0 0 4 2
+1.22043 0 2 3 3
+1.21889 0 4 1 0
+1.20649 0 3 2 3
+1.20126 0 1 2 5
+1.20095 0 4 1 1
+1.20047 1 1 3 4
+1.19323 0 2 1 5
+1.18901 0 1 4 2
+1.18831 0 3 3 1
+1.18058 0 4 0 2
+1.17945 1 3 1 4
+1.17083 0 0 0 6
+1.15808 2 2 4 0
+1.15153 0 4 1 2
+1.14266 1 2 4 1
+1.14043 1 0 4 3
+1.14038 2 3 3 2
+1.13006 2 4 2 0
+1.11572 1 4 2 1
+1.11393 2 1 1 6
+1.11203 0 1 4 3
+1.10953 0 2 2 5
+1.1089 0 2 3 4
+1.09985 0 2 4 2
+1.09842 0 3 2 4
+1.08119 0 4 1 3
+1.07576 0 4 2 2
+1.07547 0 3 0 5
+1.07194 0 3 3 3
+1.06837 1 0 2 6
+1.06827 1 1 3 5
+1.06084 1 2 0 6
+1.05337 0 3 1 5
+1.04787 0 0 4 4
+1.04491 0 1 2 6
+1.03962 0 2 1 6
+1.03807 1 2 4 3
+1.02571 0 1 4 4
+1.02028 1 4 0 4
+1.01809 0 3 4 1
+1.01775 0 4 2 3
+1.01726 0 4 3 0
+1.01198 1 1 5 1
+1.00676 0 4 3 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3.jcpds
new file mode 100755
index 0000000..082609c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cr2o3.jcpds
@@ -0,0 +1,45 @@
+3
+cr2o3 corundum (calculated from GSAS, from Reiki)
+ 2 240. 3.5
+ 4.95876 13.5942
+(blank for future use)
+d-spacing I/I0 h k l
+3.6305 79 1 0 -2
+2.6650 100 1 0 4
+2.4794 100 1 1 0
+2.2657 7 0 0 6
+2.1751 30 1 1 3
+2.0475 6 2 0 2
+1.8153 36 2 0 -4
+1.6725 86 1 1 6
+1.6117 1 2 1 1
+1.5801 1 1 0 -8
+1.5788 6 2 1 -2
+1.4647 26 2 1 4
+1.4315 33 3 0 0
+1.2960 12 1 0 10
+1.2899 2 1 1 9
+1.2397 6 2 2 0
+1.2102 2 3 0 6
+1.2102 2 3 0 -6
+1.1958 1 2 2 3
+1.1732 3 3 1 2
+1.1486 5 2 0 -10
+1.1329 1 0 0 12
+1.1240 5 3 1 -4
+1.0875 10 2 2 6
+1.0605 1 4 0 -2
+1.0422 5 2 1 10
+1.0304 1 1 1 12
+1.0237 1 4 0 4
+0.9471 1 1 0 -14
+0.9463 3 3 2 4
+0.9371 3 4 1 0
+0.8959 3 3 1 -10
+0.8883 1 3 0 12
+0.8883 1 3 0 -12
+0.8848 1 2 0 14
+0.8660 2 4 1 -6
+0.8660 2 4 1 6
+0.8425 1 4 0 10
+0.8335 1 1 0 16
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cscl.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cscl.jcpds
new file mode 100755
index 0000000..35addaa
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/cscl.jcpds
@@ -0,0 +1,23 @@
+3
+CsCl (Yagi 1978) 4.1221
+ 1 18.2 5.1
+ 4.11
+(blank for futre use)
+d-spacing I/I0 h k l
+4.1221 6 1 0 0
+2.9148 100 1 1 0
+2.3799 1 1 1 1
+2.0611 8 2 0 0
+1.8435 1 2 1 0
+1.6828 21 2 1 1
+1.4574 4 2 2 0
+1.3740 1 2 2 1
+1.3740 1 3 0 0
+1.3035 7 3 1 0
+1.2429 1 3 1 1
+1.1899 1 2 2 2
+1.1433 1 3 2 0
+1.1017 3 3 2 1
+1.0305 1 4 0 0
+0.9998 1 4 1 0
+0.9998 1 3 2 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/diamond.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/diamond.jcpds
new file mode 100755
index 0000000..a74fa74
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/diamond.jcpds
@@ -0,0 +1,11 @@
+3
+diamond (JCPDS 6-0675, dummy EOS)
+ 1 999. 1.000000
+ 3.56670
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0600 100. 1.00 1.00 1.00
+ 1.2610 27. 2.00 2.00 0.00
+ 1.0754 16. 3.00 1.00 1.00
+ 0.8916 7. 4.00 0.00 0.00
+ 0.8182 15. 3.00 3.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-bcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-bcc.jcpds
new file mode 100755
index 0000000..93931dd
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-bcc.jcpds
@@ -0,0 +1,12 @@
+3
+Fe (bcc) (JCPDS 6-0696, EOS from ?)
+ 1 999.000 0.000000
+ 2.86640
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0268 100. 1.00 1.00 0.00
+ 1.4320 19. 2.00 0.00 0.00
+ 1.1702 30. 2.00 1.00 1.00
+ 1.0134 9. 2.00 2.00 0.00
+ 0.9064 12. 3.00 1.00 0.00
+ 0.8275 6. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc-dewaele.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc-dewaele.jcpds
new file mode 100644
index 0000000..bac358d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc-dewaele.jcpds
@@ -0,0 +1,15 @@
+3
+FCC iron (JCPDS 4-0829, EOS from Dewaele 2006)
+ 1 163.400 5.38000
+ 3.5531
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0760 100. 1.00 1.00 1.00
+ 1.7950 100. 2.00 0.00 0.00
+ 1.2710 100. 2.00 2.00 0.00
+ 1.0840 100. 3.00 1.00 1.00
+ 1.0380 100. 2.00 2.00 2.00
+ 0.8990 100. 4.00 0.00 0.00
+ 0.8040 100. 4.00 2.00 0.00
+ 0.7340 100. 4.00 2.00 2.00
+ 0.6920 100. 5.00 1.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc.jcpds
new file mode 100755
index 0000000..94dc23b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-fcc.jcpds
@@ -0,0 +1,15 @@
+3
+FCC iron (JCPDS 4-0829, EOS from Mao et al 90)
+ 1 165.000 5.33000
+ 3.59500
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0760 100. 1.00 1.00 1.00
+ 1.7950 100. 2.00 0.00 0.00
+ 1.2710 100. 2.00 2.00 0.00
+ 1.0840 100. 3.00 1.00 1.00
+ 1.0380 100. 2.00 2.00 2.00
+ 0.8990 100. 4.00 0.00 0.00
+ 0.8040 100. 4.00 2.00 0.00
+ 0.7340 100. 4.00 2.00 2.00
+ 0.6920 100. 5.00 1.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp-dewaele.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp-dewaele.jcpds
new file mode 100644
index 0000000..801da99
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp-dewaele.jcpds
@@ -0,0 +1,12 @@
+3
+iron (hcp phase -- 19.2 GPa) (JCPDS 34-529, EOS from Mao et al 90)
+ 2 163.400 5.38000
+ 2.5296 4.0473
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1200 20. 1.00 0.00 0.00
+ 1.9700 80. 0.00 0.00 2.00
+ 1.8400 100. 1.00 0.00 1.00
+ 1.4400 10. 1.00 0.00 2.00
+ 1.2340 10. 1.00 1.00 0.00
+ 1.1130 10. 1.00 0.00 3.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp.jcpds
new file mode 100755
index 0000000..b31c540
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe-hcp.jcpds
@@ -0,0 +1,12 @@
+3
+iron (hcp phase -- 19.2 GPa) (JCPDS 34-529, EOS from Mao et al 90)
+ 2 165.000 5.33000
+ 2.51600 4.02560
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.1200 20. 1.00 0.00 0.00
+ 1.9700 80. 0.00 0.00 2.00
+ 1.8400 100. 1.00 0.00 1.00
+ 1.4400 10. 1.00 0.00 2.00
+ 1.2340 10. 1.00 1.00 0.00
+ 1.1130 10. 1.00 0.00 3.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-ppv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-ppv.jcpds
new file mode 100755
index 0000000..1b8f2c1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-ppv.jcpds
@@ -0,0 +1,103 @@
+3
+Fe2O3_postpv (fit to ono at 68)
+ 4 183.000 4.00000
+ 2.851416 9.231715 6.900015
+(blank for future use)
+d-spacing I/I0 h k l
+4.3995 13.9 0 2 0
+3.6720 0.1 0 2 1
+3.3333 4.4 0 0 2
+2.6568 100.0 0 2 2
+2.5700 18.9 1 1 0
+2.3980 4.3 1 1 1
+2.1998 5.4 0 4 0
+2.0890 5.4 0 4 1
+2.0353 0.3 1 1 2
+1.9835 39.0 0 2 3
+1.9814 65.8 1 3 0
+1.8992 82.7 1 3 1
+1.8360 2.5 0 4 2
+1.7032 40.0 1 3 2
+1.6809 35.4 1 1 3
+1.6666 26.7 0 0 4
+1.5633 0.7 0 4 3
+1.5585 0.3 0 2 4
+1.4789 6.8 1 3 3
+1.4722 5.3 1 5 0
+1.4665 0.1 0 6 0
+1.4376 7.0 1 5 1
+1.4323 1.5 0 6 1
+1.3983 16.4 1 1 4
+1.3467 25.6 1 5 2
+1.3436 18.0 2 0 0
+1.3423 21.5 0 6 2
+1.3284 11.7 0 4 4
+1.2850 0.2 2 2 0
+1.2760 0.4 0 2 5
+1.2754 5.9 1 3 4
+1.2618 0.2 2 2 1
+1.2462 0.2 2 0 2
+1.2273 0.1 1 5 3
+1.2240 0.6 0 6 3
+1.1990 9.7 2 2 2
+1.1835 0.2 1 1 5
+1.1466 1.1 2 4 0
+1.1402 0.1 0 4 5
+1.1386 0.1 1 7 0
+1.1300 0.4 2 4 1
+1.1223 1.1 1 7 1
+1.1124 2.9 2 2 3
+1.1111 0.4 0 0 6
+1.1062 8.2 1 3 5
+1.1034 1.3 1 5 4
+1.1010 0.0 0 6 4
+1.0999 1.8 0 8 0
+1.0852 0.3 0 8 1
+1.0843 0.1 2 4 2
+1.0775 1.5 1 7 2
+1.0773 3.4 0 2 6
+1.0460 4.9 2 0 4
+1.0445 0.9 0 8 2
+1.0199 1.8 1 1 6
+1.0190 0.1 2 4 3
+1.0177 0.2 2 2 4
+1.0133 1.7 1 7 3
+0.9918 0.8 0 4 6
+0.9907 0.0 2 6 0
+0.9883 2.0 1 5 5
+0.9865 0.4 0 6 5
+0.9857 0.0 0 8 3
+0.9799 0.4 2 6 1
+0.9691 0.6 1 3 6
+0.9496 6.4 2 6 2
+0.9447 3.5 2 4 4
+0.9401 0.1 1 7 4
+0.9308 0.1 0 2 7
+0.9252 0.1 2 2 5
+0.9188 0.1 1 9 0
+0.9180 0.5 0 8 4
+0.9102 0.8 1 9 1
+0.9048 0.1 2 6 3
+0.8930 0.9 1 1 7
+0.8911 0.3 3 1 0
+0.8869 0.7 1 5 6
+0.8857 0.6 1 9 2
+0.8856 1.1 0 6 6
+0.8833 0.0 3 1 1
+0.8799 0.0 0 10 0
+0.8740 0.0 0 4 7
+0.8723 0.0 0 10 1
+0.8694 0.0 2 4 5
+0.8658 0.0 1 7 5
+0.8609 0.0 3 1 2
+0.8584 0.4 1 3 7
+0.8567 1.4 3 3 0
+0.8562 0.3 2 0 6
+0.8516 0.0 2 6 4
+0.8511 1.1 2 8 0
+0.8508 0.4 0 10 2
+0.8497 0.3 3 3 1
+0.8490 0.2 1 9 3
+0.8484 0.0 0 8 5
+0.8442 0.0 2 8 1
+0.8405 2.0 2 2 6
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-rh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-rh.jcpds
new file mode 100755
index 0000000..bc82a3f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3-rh.jcpds
@@ -0,0 +1,101 @@
+3
+Fe2O3 - orthorhombic rh2o3 structure (GSAS calculation, EOS from Handbook)
+ 4 200.000 4.00000
+ 5.05937 5.221714 7.408399
+(blank for future use)
+d-spacing I/I0 h k l
+4.08112 0 1 0 1
+3.5125 9 0 0 2
+3.21583 11 1 1 1
+2.6115 28 0 2 0
+2.51987 100 1 1 2
+2.507 24 2 0 0
+2.44783 0 0 2 1
+2.26012 4 2 1 0
+2.19969 2 1 2 1
+2.15152 2 2 1 1
+2.12169 6 1 0 3
+2.09573 0 0 2 2
+2.04056 1 2 0 2
+1.96569 4 1 1 3
+1.93362 3 1 2 2
+1.90065 0 2 1 2
+1.80853 23 2 2 0
+1.75625 9 0 0 4
+1.75142 3 2 2 1
+1.74343 1 0 2 3
+1.64672 0 1 2 3
+1.62621 0 2 1 3
+1.62595 0 3 0 1
+1.60791 3 2 2 2
+1.60137 9 1 3 1
+1.57987 8 1 1 4
+1.55246 1 3 1 1
+1.48948 14 1 3 2
+1.45735 8 0 2 4
+1.44988 22 3 1 2
+1.43841 8 2 0 4
+1.43134 3 2 2 3
+1.43 0 2 3 0
+1.40126 0 2 3 1
+1.39943 0 1 2 4
+1.38678 0 2 1 4
+1.38028 0 3 2 1
+1.36037 0 3 0 3
+1.35289 0 1 0 5
+1.34588 1 1 3 3
+1.32444 0 2 3 2
+1.31645 0 3 1 3
+1.30967 1 1 1 5
+1.30669 0 3 2 2
+1.30575 1 0 4 0
+1.28376 1 0 4 1
+1.25993 4 2 2 4
+1.2535 1 4 0 0
+1.24365 0 1 4 1
+1.2373 0 0 2 5
+1.22392 0 0 4 2
+1.22043 0 2 3 3
+1.21889 0 4 1 0
+1.20649 0 3 2 3
+1.20126 0 1 2 5
+1.20095 0 4 1 1
+1.20047 1 1 3 4
+1.19323 0 2 1 5
+1.18901 0 1 4 2
+1.18831 0 3 3 1
+1.18058 0 4 0 2
+1.17945 1 3 1 4
+1.17083 0 0 0 6
+1.15808 2 2 4 0
+1.15153 0 4 1 2
+1.14266 1 2 4 1
+1.14043 1 0 4 3
+1.14038 2 3 3 2
+1.13006 2 4 2 0
+1.11572 1 4 2 1
+1.11393 2 1 1 6
+1.11203 0 1 4 3
+1.10953 0 2 2 5
+1.1089 0 2 3 4
+1.09985 0 2 4 2
+1.09842 0 3 2 4
+1.08119 0 4 1 3
+1.07576 0 4 2 2
+1.07547 0 3 0 5
+1.07194 0 3 3 3
+1.06837 1 0 2 6
+1.06827 1 1 3 5
+1.06084 1 2 0 6
+1.05337 0 3 1 5
+1.04787 0 0 4 4
+1.04491 0 1 2 6
+1.03962 0 2 1 6
+1.03807 1 2 4 3
+1.02571 0 1 4 4
+1.02028 1 4 0 4
+1.01809 0 3 4 1
+1.01775 0 4 2 3
+1.01726 0 4 3 0
+1.01198 1 1 5 1
+1.00676 0 4 3 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3.jcpds
new file mode 100755
index 0000000..cab5ffc
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe2o3.jcpds
@@ -0,0 +1,71 @@
+3
+Fe2O3 corundum (GSAS, EOS from Handbook)
+ 2 200. 4.
+ 5.038 13.772
+(blank for future use)
+d-spacing I/I0 h k l
+3.6855 31 1 0 -2
+2.7028 100 1 0 4
+2.5190 73 1 1 0
+2.2953 2 0 0 6
+2.2084 20 1 1 3
+2.0797 2 2 0 2
+1.8428 36 2 0 -4
+1.6966 43 1 1 6
+1.6374 1 2 1 1
+1.6037 2 2 1 -2
+1.6014 8 1 0 -8
+1.4873 26 2 1 4
+1.4544 26 3 0 0
+1.4149 0 2 1 -5
+1.3514 2 2 0 8
+1.3133 7 1 0 10
+1.3078 1 1 1 9
+1.2638 0 2 1 7
+1.2595 5 2 2 0
+1.2285 1 3 0 6
+1.2285 1 3 0 -6
+1.2146 1 2 2 3
+1.2054 0 3 1 -1
+1.1918 1 3 1 2
+1.1909 3 2 1 -8
+1.1646 3 2 0 -10
+1.1477 0 0 0 12
+1.1416 5 3 1 -4
+1.1079 0 3 1 5
+1.1042 5 2 2 6
+1.0773 0 4 0 -2
+1.0571 5 2 1 10
+1.0444 0 1 1 12
+1.0398 1 4 0 4
+1.0307 0 3 1 -7
+0.9983 0 3 2 1
+0.9972 0 2 1 -11
+0.9905 0 3 2 -2
+0.9900 1 3 1 8
+0.9725 0 2 2 9
+0.9612 2 3 2 4
+0.9596 1 1 0 -14
+0.9521 2 4 1 0
+0.9408 0 3 2 -5
+0.9323 0 4 1 3
+0.9323 0 4 1 -3
+0.9214 1 4 0 -8
+0.9090 2 3 1 -10
+0.9009 0 3 0 12
+0.9009 0 3 0 -12
+0.8968 1 2 0 14
+0.8921 0 3 2 7
+0.8913 0 2 1 13
+0.8794 1 4 1 -6
+0.8794 1 4 1 6
+0.8701 0 3 1 11
+0.8657 0 5 0 2
+0.8653 0 3 2 -8
+0.8626 0 1 1 15
+0.8551 1 4 0 10
+0.8483 0 2 2 12
+0.8459 1 5 0 -4
+0.8448 1 2 1 -14
+0.8445 0 1 0 16
+0.8397 1 3 3 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe3o4.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe3o4.jcpds
new file mode 100755
index 0000000..6e57944
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe3o4.jcpds
@@ -0,0 +1,45 @@
+3
+Fe3O4 (GSAS, EOS from Mao 1974)
+ 1 183. 4.
+ 8.3958
+(blank for future use)
+d-spacing I/I0 h k l
+ 4.8473 8 1 1 1
+ 2.9684 29 2 2 0
+ 2.5314 100 3 1 1
+ 2.4237 8 2 2 2
+ 2.0990 21 4 0 0
+ 1.9261 1 3 3 1
+ 1.7138 9 4 2 2
+ 1.6158 6 3 3 3
+ 1.6158 22 5 1 1
+ 1.4842 37 4 4 0
+ 1.4192 1 5 3 1
+ 1.3993 0 4 4 2
+ 1.3275 3 6 2 0
+ 1.2804 6 5 3 3
+ 1.2657 3 6 2 2
+ 1.2118 2 4 4 4
+ 1.1757 0 5 5 1
+ 1.1757 0 7 1 1
+ 1.1219 2 6 4 2
+ 1.0930 3 5 5 3
+ 1.0930 5 7 3 1
+ 1.0495 3 8 0 0
+ 1.0257 0 7 3 3
+ 1.0181 0 6 4 4
+ 0.9895 0 6 6 0
+ 0.9895 0 8 2 2
+ 0.9695 0 5 5 5
+ 0.9695 2 7 5 1
+ 0.9631 1 6 6 2
+ 0.9387 1 8 4 0
+ 0.9216 0 7 5 3
+ 0.9216 0 9 1 1
+ 0.9161 0 8 4 2
+ 0.8950 0 6 6 4
+ 0.8801 1 9 3 1
+ 0.8569 2 8 4 4
+ 0.8438 0 9 3 3
+ 0.8438 0 7 5 5
+ 0.8438 0 7 7 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feAl2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feAl2o3.jcpds
new file mode 100755
index 0000000..96741e7
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feAl2o3.jcpds
@@ -0,0 +1,394 @@
+3
+Fe,Al2O3 - orthorhombic structure
+ 4 120.000 4.00000
+4.9839 8.5544 9.2413
+(blank for future use)
+d-spacing I/I0 h k l
+6.27768 26.1 0 1 1
+4.62065 2.7 0 0 2
+4.30634 0.1 1 1 0
+4.2772 0.8 0 2 0
+3.90335 25.3 1 1 1
+3.24579 7.2 1 2 0
+3.1503 23.5 1 1 2
+3.13884 36 0 2 2
+3.06239 5.3 1 2 1
+2.89825 26.1 0 1 3
+2.72471 6.1 0 3 1
+2.65599 100 1 2 2
+2.50542 31.4 1 1 3
+2.49195 11.4 2 0 0
+2.47501 8 1 3 0
+2.40601 9.6 2 0 1
+2.3925 6.9 2 1 0
+2.39076 18.6 1 3 1
+2.31614 3 2 1 1
+2.31032 3 0 0 4
+2.23437 10.8 1 2 3
+2.19332 13.9 2 0 2
+2.18174 29.6 1 3 2
+2.15317 0.1 2 2 0
+2.1386 2 0 4 0
+2.12459 12.6 2 1 2
+2.097 3.2 2 2 1
+2.09256 1 0 3 3
+2.03584 1 1 1 4
+2.03274 2.4 0 2 4
+1.96531 9.4 1 4 0
+1.95167 6.2 2 2 2
+1.9408 13.5 0 4 2
+1.93739 10.2 2 0 3
+1.9294 18.5 1 3 3
+1.92232 1.7 1 4 1
+1.88954 6.7 2 1 3
+1.88221 0.3 1 2 4
+1.87639 3.7 2 3 0
+1.83887 1.5 2 3 1
+1.80852 1.4 1 4 2
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+1.76479 7.3 2 2 3
+1.73851 0.3 2 3 2
+1.69844 0.7 1 1 5
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+1.68229 4.7 0 5 1
+1.66194 1.5 2 1 4
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+1.60602 0.1 3 1 1
+1.6025 0.2 2 3 3
+1.59843 0.3 2 4 1
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+1.55095 2.8 0 3 5
+1.54859 0.1 3 2 0
+1.54022 2.5 0 0 6
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+1.5312 4.5 2 4 2
+1.5273 3.1 1 1 5 2
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+1.46832 1.7 3 2 2
+1.46265 1.4 2 1 5
+1.45652 1.7 2 3 4
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+1.37082 4.8 1 1 6 0
+1.36235 0.2 0 6 2
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+1.31675 1.2 2 3 5
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+1.31196 0.1 3 4 0
+1.31016 0.8 2 0 6
+1.30768 2.2 1 3 6
+1.30474 2.4 0 1 7
+1.30112 0.1 3 3 3
+1.29894 1.4 3 4 1
+1.29506 0.8 2 1 6
+1.28635 0.1 3 2 4
+1.28242 6.7 2 5 3
+1.2622 7.7 1 1 7
+1.26207 0.3 3 4 2
+1.25554 0.5 0 5 5
+1.25271 0.5 2 2 6
+1.25235 0.1 1 6 3
+1.24982 0.3 0 4 6
+1.24598 0.2 4 0 0
+1.23751 1.4 2 6 0
+1.2348 2.9 4 0 1
+1.23297 1.5 4 1 0
+1.22656 0.5 2 6 1
+1.2229 2.5 1 1 2 7
+1.22214 0 4 1 1
+1.2195 0.9 2 4 5
+1.21927 2.1 3 3 4
+1.2175 0.2 1 5 5
+1.2133 0.1 0 6 4
+1.21228 0.5 1 4 6
+1.21151 0.8 0 7 1
+1.20705 0.3 3 4 3
+1.20384 0.4 2 5 4
+1.20301 1.4 4 0 2
+1.19801 0 0 3 7
+1.19625 0.1 4 2 0
+1.19538 2.3 2 6 2
+1.19186 0 3 5 0
+1.19128 3.9 4 1 2
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+1.07658 0 4 4 0
+1.07459 1.7 3 6 1
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+1.05344 0 3 6 2
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+1.0208 0.3 3 6 3
+1.01973 8.8 1 8 2
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+1.00426 0 4 2 5
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+1.00126 0.2 4 5 1
+0.99881 0.1 1 1 1 9
+0.9987 2.8 1 7 5
+0.99587 0.2 1 4 8
+0.99113 1 2 7 4
+0.99008 1.3 1 1 8 3
+0.98445 1.5 1 3 7 0
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+0.9837 0.2 2 3 8
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+0.97714 0.1 2 8 1
+0.97584 0.1 4 4 4
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+0.97135 0.1 4 3 5
+0.97077 0 5 2 0
+0.9704 0 0 8 4
+0.96869 0.6 4 0 6
+0.96811 1.3 5 1 2
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+0.96546 0.6 5 2 1
+0.9647 0.2 2 6 6
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+0.9628 1.1 3 7 2
+0.96254 0.6 4 1 6
+0.96116 0.1 2 8 2
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+0.95003 1.5 5 2 2
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+0.94477 0.1 4 2 6
+0.94358 0.8 1 2 1 9
+0.94264 2.2 2 3 1 8
+0.9411 0.3 2 4 8
+0.94095 1.6 5 3 0
+0.94018 0.1 1 1 5 8
+0.93819 0 4 6 0
+0.93769 0.1 3 7 3
+0.93617 0.7 1 2 8 3
+0.93372 2.6 1 1 9 0
+0.9334 0.5 4 6 1
+0.93059 0.1 3 4 7
+0.93027 0.7 4 4 5
+0.92893 0 1 9 1
+0.92682 0.1 2 2 9
+0.92593 2.2 1 3 2 8
+0.92413 1.8 0 0 10
+0.92327 0.3 4 5 4
+0.92202 0.6 5 3 2
+0.91943 0.8 4 6 2
+0.91721 0.2 4 3 6
+0.91517 0.7 1 9 2
+0.91008 0.6 2 1 4 9
+0.90824 0.1 0 9 3
+0.90614 0 4 0 7
+0.90562 1.1 3 7 4
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+0.89995 4.3 1 3 3 8
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+0.89754 1.8 1 0 6 8
+0.89681 0 0 7 7
+0.89497 1.1 1 3 8 1
+0.89369 2.6 1 2 5 8
+0.89352 0.5 1 9 3
+0.88881 0.5 1 2 10
+0.88808 1.1 2 9 0
+0.88667 0 5 4 2
+0.88646 1.5 4 2 7
+0.88533 0 3 6 6
+0.88467 0 3 5 7
+0.8844 0 4 5 5
+0.88401 0.3 2 9 1
+0.88333 0.3 1 6 8
+0.88264 1.4 1 1 7 7
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+0.88042 0.1 0 5 9
+0.87837 0.1 0 8 6
+0.87275 0.1 5 1 5
+0.87246 0.6 4 7 0
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+0.86504 2.1 1 8 6
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+0.85578 0.1 3 2 9
+0.85544 0.9 0 10 0
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+0.84922 0.1 2 2 10
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+0.84711 0 4 0 8
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+0.84142 0.4 5 4 4
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+0.83514 0.3 3 3 9
+0.83433 1.7 4 4 7
+0.83337 1.4 1 9 5
+0.83285 0.2 5 1 6
+0.83097 0.1 4 2 8
+0.83065 2.2 6 0 0
+0.83013 0.1 2 5 9
+0.82949 0.3 3 1 10 2
+0.82904 1.0 1 2 3 10
+0.82841 0.1 2 8 6
+0.82782 0 1 7 8
+0.82731 0.4 6 0 1
+0.82676 0 6 1 0
+0.825 1.5 3 9 0
+0.82457 0.4 1 1 11
+0.82347 0 6 1 1
+0.82181 0.3 1 1 6 9
+0.82125 0.3 5 2 6
+0.81962 1 1 8 7
+0.81754 0 6 0 2
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+0.81542 0 6 2 0
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+0.81332 0.2 1 2 11
+0.8132 0 1 10 3
+0.81226 0.3 1 3 9 2
+0.81203 0.1 4 3 8
+0.81168 0.4 5 4 5
+0.81145 0 4 8 0
+0.80909 0.8 2 10 0
+0.8086 0.7 1 3 4 9
+0.80834 0 4 8 1
+0.80701 0 5 5 4
+0.80601 0.6 2 10 1
+0.80587 0 0 3 11
+0.80445 0.1 5 6 2
+0.80402 0.2 3 1 10
+0.80306 1.0 2 2 4 10
+0.80249 0 1 5 10
+0.80221 1 0 10 4
+0.802 0 6 0 3
+0.80125 0.5 4 6 6
+0.80076 0.2 4 5 7
+0.80047 0.8 2 9 5
+0.79922 0 4 8 2
+0.7985 0.8 1 1 9 6
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe_bcc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe_bcc.jcpds
new file mode 100755
index 0000000..93931dd
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/fe_bcc.jcpds
@@ -0,0 +1,12 @@
+3
+Fe (bcc) (JCPDS 6-0696, EOS from ?)
+ 1 999.000 0.000000
+ 2.86640
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.0268 100. 1.00 1.00 0.00
+ 1.4320 19. 2.00 0.00 0.00
+ 1.1702 30. 2.00 1.00 1.00
+ 1.0134 9. 2.00 2.00 0.00
+ 0.9064 12. 3.00 1.00 0.00
+ 0.8275 6. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feco3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feco3.jcpds
new file mode 100755
index 0000000..3497188
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feco3.jcpds
@@ -0,0 +1,70 @@
+3
+siderite (GSAS, EOS from MgCO3)
+ 2 117.000 2.30000
+ 4.6916 15.3796
+(blank for future use)
+d-spacing I/I0 h k l
+3.59242 35.7389 1 0 -2
+2.79269 100 1 0 4
+2.56327 0.6212 0 0 6
+2.3458 18.9949 1 1 0
+2.13309 18.8427 1 1 3
+1.96414 19.1478 2 0 2
+1.79621 10.0401 2 0 -4
+1.73775 18.5871 1 0 -8
+1.73051 27.5937 1 1 6
+1.52809 2.4179 2 1 1
+1.50595 10.8618 2 1 -2
+1.43836 2.4719 1 0 10
+1.42614 8.2057 2 1 4
+1.39635 3.1895 2 0 8
+1.38122 1.9361 1 1 9
+1.37397 1.2519 2 1 -5
+1.35435 7.2816 3 0 0
+1.28163 3.0595 0 0 12
+1.2587 0.4222 2 1 7
+1.22621 2.4976 2 0 -10
+1.19986 3.3583 2 1 -8
+1.19747 0.3611 3 0 6
+1.19747 0.3611 3 0 -6
+1.1729 1.155 2 2 0
+1.14336 0.2685 2 2 3
+1.12472 1.8294 1 1 12
+1.12387 0.0295 3 1 -1
+1.11498 0.7752 3 1 2
+1.0867 1.7149 2 1 10
+1.0814 3.1969 3 1 -4
+1.06655 1.9962 2 2 6
+1.06047 0.336 1 0 -14
+1.05811 0.0583 3 1 5
+1.03385 0.2383 2 1 -11
+1.00701 0.1756 4 0 -2
+1.00269 0.1157 3 1 -7
+0.98207 1.5889 4 0 4
+0.97218 1.2884 3 1 8
+0.96703 0.043 2 2 9
+0.96631 0.403 2 0 14
+0.93949 0.2313 1 1 15
+0.93719 0.0818 2 1 13
+0.9354 0.5261 1 0 16
+0.9309 0.7581 3 0 -12
+0.9309 0.7581 3 0 12
+0.93042 0.1136 3 2 1
+0.92535 0.9019 3 2 -2
+0.90899 0.2878 3 1 -10
+0.90589 0.4221 3 2 4
+0.8981 0.4989 4 0 -8
+0.89347 0.5955 2 1 -14
+0.89207 0.1257 3 2 -5
+0.88663 0.7565 4 1 0
+0.87738 0.0463 3 1 11
+0.87366 0.0291 4 1 3
+0.87366 0.0291 4 1 -3
+0.86887 0.1254 2 0 -16
+0.86526 0.2723 2 2 12
+0.85809 0.0757 3 2 7
+0.85442 0.0376 0 0 18
+0.84758 0.0853 4 0 10
+0.83874 0.2282 3 2 -8
+0.83792 0.1905 4 1 6
+0.83792 0.1905 4 1 -6
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feooh.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feooh.jcpds
new file mode 100755
index 0000000..e381e7c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/feooh.jcpds
@@ -0,0 +1,27 @@
+3
+Perovskite_orthorhombic (29-713, EOS from mgpv)
+ 4 261.000 4.00000
+ 4.608 9.956 3.0215
+(blank for future use)
+d-spacing I/I0 h k l
+4.98 12 0 2 0
+4.183 100 1 1 0
+3.383 10 1 2 0
+2.693 35 1 3 0
+2.583 12 0 2 1
+2.527 4 1 0 1
+2.489 10 0 4 0
+2.450 50 1 1 1
+2.303 1 2 0 0
+2.253 14 1 2 1
+2.190 18 1 4 0
+2.089 1 2 2 0
+2.011 2 1 3 1
+1.920 5 0 4 1
+1.802 6 2 1 1
+1.7728 1 1 4 1
+1.7192 20 2 2 1
+1.6906 6 2 4 0
+1.6593 3 0 6 0
+1.6037 4 2 3 1
+1.5637 10 1 5 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/gold.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/gold.jcpds
new file mode 100755
index 0000000..992f600
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/gold.jcpds
@@ -0,0 +1,15 @@
+3
+Gold (04-0784, Heinz and Jeanloz 1984)
+ 1 166.600 5.50000
+ 4.07860
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3550 100. 1.00 1.00 1.00
+ 2.0390 52. 2.00 0.00 0.00
+ 1.4420 32. 2.00 2.00 0.00
+ 1.2300 36. 3.00 1.00 1.00
+ 1.1774 12. 2.00 2.00 2.00
+ 1.0196 6. 4.00 0.00 0.00
+ 0.9358 23. 3.00 3.00 1.00
+ 0.9120 22. 4.00 2.00 0.00
+ 0.8325 23. 4.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.85fe0.15sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.85fe0.15sio3-opv.jcpds
new file mode 100755
index 0000000..30cd4f1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.85fe0.15sio3-opv.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Ar data)
+ 4 252.600 3.28
+4.7923 4.9339 6.9063
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.8fe0.2sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.8fe0.2sio3-opv.jcpds
new file mode 100755
index 0000000..3508b6d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.8fe0.2sio3-opv.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.7963 4.9346 6.9092
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
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+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.92fe0.08sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.92fe0.08sio3-opv.jcpds
new file mode 100755
index 0000000..101eaf8
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.92fe0.08sio3-opv.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Ar for 15%)
+ 4 252.600 3.28
+4.7809 4.9297 6.9028
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
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+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
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+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.9fe0.1sio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.9fe0.1sio3-opv.jcpds
new file mode 100755
index 0000000..3349d8a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg0.9fe0.1sio3-opv.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.7859 4.9319 6.9032
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
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+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
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+ 0.8576 1. 2.00 4.00 5.00
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+ 0.8476 1. 4.00 2.00 5.00
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+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg2sio4-g.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg2sio4-g.jcpds
new file mode 100755
index 0000000..1747d20
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mg2sio4-g.jcpds
@@ -0,0 +1,22 @@
+3
+Mg2SiO4 spinel (GSAS calculation, EOS from Knittle and Jeanloz,1987)
+ 1 183. 5.38
+8.0709
+(blank for future use)
+d-spacing I/I0 h k l
+2.85349 30 2 2 0
+2.43347 100 3 1 1
+2.32987 1 2 2 2
+2.01773 39 4 0 0
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+1.00886 1 8 0 0
+0.90235 1 8 4 0
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgal2o4-CF.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgal2o4-CF.jcpds
new file mode 100755
index 0000000..d61829d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgal2o4-CF.jcpds
@@ -0,0 +1,304 @@
+3
+CF phase (from original structure by Decker, EOS from Ono2002)
+ 4 253.000 3.60000
+ 8.64 10.115 2.793
+(blank for future use)
+d-spacing I/I0 h k l
+6.58195 0.2 1 1 0
+5.064 2.5 0 2 0
+4.37146 4.7 1 2 0
+4.33 29 2 0 0
+3.9814 0.2 2 1 0
+3.29097 14.1 2 2 0
+3.14544 1.1 1 3 0
+2.77611 2.2 3 1 0
+2.68622 7.1 0 1 1
+2.6624 1 2 3 0
+2.56563 2.6 1 1 1
+2.532 60.5 0 4 0
+2.50783 84.3 3 2 0
+2.43025 0 1 4 0
+2.34943 43.3 1 2 1
+2.34293 25.2 2 0 1
+2.28264 0.9 2 1 1
+2.19398 11.5 3 3 0
+2.18573 1 2 4 0
+2.165 1.1 4 0 0
+2.1488 20.6 0 3 1
+2.12637 2.7 2 2 1
+2.11717 15 4 1 0
+2.08555 100 1 3 1
+1.9907 37.1 4 2 0
+1.97236 16.3 1 5 0
+1.96649 73.3 3 1 1
+1.92481 16.2 2 3 1
+1.90351 2.7 3 4 0
+1.86391 5.2 3 2 1
+1.83476 1.5 2 5 0
+1.83139 21.3 1 4 1
+1.82245 0.5 4 3 0
+1.72367 15.6 3 3 1
+1.71966 71.1 2 4 1
+1.70951 44.2 4 0 1
+1.70722 1.3 5 1 0
+1.688 0 0 6 0
+1.68567 30.4 4 1 1
+1.6581 2.5 3 5 0
+1.65682 3.2 1 6 0
+1.64549 1.2 4 4 0
+1.6388 2.9 5 2 0
+1.63834 6.9 0 5 1
+1.61971 5.3 4 2 1
+1.60978 0.1 1 5 1
+1.57272 34.7 2 6 0
+1.57169 0 3 4 1
+1.54103 0.1 5 3 0
+1.53232 2.7 2 5 1
+1.52512 3.8 4 3 1
+1.47914 0.1 4 5 0
+1.45715 26.1 3 6 0
+1.45565 0.9 5 1 1
+1.44333 23.4 6 0 0
+1.42954 2.6 5 4 0
+1.4289 1 6 1 0
+1.42708 0 1 7 0
+1.42485 1.6 3 5 1
+1.42403 29.2 1 6 1
+1.41682 50.4 4 4 1
+1.41254 31.6 5 2 1
+1.393 52.9 0 0 2
+1.38805 2.7 6 2 0
+1.37227 2.7 2 7 0
+1.36957 25.6 2 6 1
+1.36281 0 1 1 2
+1.34849 2.3 5 3 1
+1.34311 0.1 0 2 2
+1.3312 5.2 4 6 0
+1.32724 0.2 1 2 2
+1.32713 1.1 6 3 0
+1.32607 1.5 2 0 2
+1.31639 0 5 5 0
+1.31485 0 2 1 2
+1.30643 0.2 4 5 1
+1.29348 0.7 3 7 0
+1.29121 7.3 3 6 1
+1.28403 0.2 0 7 1
+1.28281 2 2 2 2
+1.28156 1.2 6 0 1
+1.27369 0.1 1 3 2
+1.27188 0.8 5 4 1
+1.27142 0.1 6 1 1
+1.27014 9.4 1 7 1
+1.266 2.1 0 8 0
+1.25392 21 6 4 0
+1.25269 5.7 1 8 0
+1.24505 0.4 3 1 2
+1.24239 5.9 6 2 1
+1.23427 0.2 2 3 2
+1.23104 0.6 2 7 1
+1.22802 1.4 7 1 0
+1.22049 15.3 0 4 2
+1.21775 25.7 3 2 2
+1.21513 1.1 2 8 0
+1.20885 0.6 5 6 0
+1.20854 0.3 1 4 2
+1.20295 0.2 4 7 0
+1.2018 3.8 7 2 0
+1.20113 8.1 4 6 1
+1.19814 0.3 6 3 1
+1.19021 1.3 5 5 1
+1.17599 1.6 3 3 2
+1.17546 3.7 6 5 0
+1.17471 0 2 4 2
+1.1732 2.4 3 7 1
+1.17146 0.3 4 0 2
+1.1637 2.7 4 1 2
+1.1616 0.1 7 3 0
+1.1594 2.6 3 8 0
+1.14344 0.9 6 4 1
+1.14251 1.1 1 8 1
+1.14132 8.8 4 2 2
+1.13783 3.5 1 5 2
+1.12414 0.4 3 4 2
+1.1237 0.7 7 1 1
+1.11595 0.9 1 9 0
+1.1138 2.3 2 8 1
+1.11156 0.8 7 4 0
+1.11039 2 5 7 0
+1.10947 0.4 2 5 2
+1.10896 2.2 5 6 1
+1.10673 0.1 4 3 2
+1.1044 0.2 4 7 1
+1.10351 4.5 7 2 1
+1.09699 0.5 6 6 0
+1.09287 0.3 4 8 0
+1.08915 1.9 2 9 0
+1.08301 7.7 6 5 1
+1.0825 0.4 8 0 0
+1.0793 0.5 5 1 2
+1.07637 0.3 8 1 0
+1.0744 0.1 0 6 2
+1.07215 9.1 7 3 1
+1.07041 0.4 3 8 1
+1.06657 0.9 3 5 2
+1.06622 1.3 1 6 2
+1.06319 0.4 4 4 2
+1.06137 1.6 5 2 2
+1.05858 0.5 8 2 0
+1.0558 0 7 5 0
+1.04848 0 3 9 0
+1.04343 0.3 0 9 1
+1.04278 15 2 6 2
+1.03594 3.5 1 9 1
+1.03338 0.1 5 3 2
+1.03242 1.2 7 4 1
+1.03149 5.7 5 7 1
+1.03081 1.4 8 3 0
+1.02207 4.7 5 8 0
+1.02183 0.2 6 7 0
+1.02072 0.3 6 6 1
+1.01739 2.5 4 8 1
+1.01439 0.5 2 9 1
+1.01409 0 4 5 2
+1.0128 0.5 0 10 0
+1.00901 2.9 8 0 1
+1.00692 14 3 6 2
+1.00594 0.2 1 10 0
+1.00404 2.4 8 1 1
+1.00232 12.5 6 0 2
+0.9985 0.3 4 9 0
+0.99784 0.2 7 6 0
+0.99767 1.6 5 4 2
+0.99745 0.6 6 1 2
+0.99683 0 1 7 2
+0.99535 3.2 8 4 0
+0.98956 0 8 2 1
+0.98728 0.3 7 5 1
+0.98618 7.5 2 10 0
+0.98325 1.3 6 2 2
+0.98129 2.1 3 9 1
+0.97759 1.3 2 7 2
+0.96676 0 8 3 1
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+0.96087 0.7 6 3 2
+0.95954 0.1 5 8 1
+0.95934 0.8 6 7 1
+0.95791 1.1 9 1 0
+0.95677 0 5 5 2
+0.95568 1.8 3 10 0
+0.95472 0.2 8 5 0
+0.95175 1.5 6 8 0
+0.94786 0.4 3 7 2
+0.94616 4.3 1 10 1
+0.94531 5.2 9 2 0
+0.94364 0 5 9 0
+0.94028 0.2 7 7 0
+0.93996 0.7 4 9 1
+0.93941 4.2 7 6 1
+0.93733 1.9 8 4 1
+0.93689 1.5 0 8 2
+0.93196 13.5 6 4 2
+0.93145 3.7 1 8 2
+0.92966 3.9 2 10 1
+0.92537 0 9 3 0
+0.92479 0.3 0 1 3
+0.92117 0.8 7 1 2
+0.91956 0.2 1 1 3
+0.91738 1.8 4 10 0
+0.9157 0.8 2 8 2
+0.91557 0.2 1 11 0
+0.913 0.5 5 6 2
+0.91122 3.4 8 6 0
+0.91045 0.1 4 7 2
+0.90995 2.4 7 2 2
+0.90839 3.1 1 2 3
+0.90802 1.6 2 0 3
+0.90586 0.1 9 1 1
+0.90439 0 2 1 3
+0.90398 0.6 3 10 1
+0.90316 1.4 8 5 1
+0.90065 1.3 2 11 0
+0.89946 0 9 4 0
+0.89835 2.4 6 5 2
+0.89541 0.5 0 3 3
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+0.89377 2.4 2 2 3
+0.89213 0.1 7 3 2
+0.89113 1.9 3 8 2
+0.89091 0.2 7 7 1
+0.89066 4 1 3 3
+0.88747 0 6 9 0
+0.88482 0.9 7 8 0
+0.8807 3 3 1 3
+0.87819 0 9 3 1
+0.87719 0 3 11 0
+0.87686 0.6 2 3 3
+0.87429 2 0 11 1
+0.87136 3 4 10 1
+0.87094 1 1 9 2
+0.8698 0.1 1 11 1
+0.86914 0.1 9 5 0
+0.86884 0.6 7 4 2
+0.86829 1.4 5 7 2
+0.86749 1.4 1 4 3
+0.86676 1 8 7 0
+0.86608 2.5 8 6 1
+0.86285 0.1 10 1 0
+0.86184 0.4 6 6 2
+0.85983 0.2 4 8 2
+0.85802 1.4 2 9 2
+0.85693 1.6 2 11 1
+0.85596 0.3 9 4 1
+0.85521 1.1 3 3 3
+0.85475 5.9 2 4 3
+0.85361 0.1 10 2 0
+0.85346 3.6 4 0 3
+0.85173 0.2 8 1 2
+0.85045 2.2 4 1 3
+0.84729 0.5 4 11 0
+0.8456 1.4 6 9 1
+0.84418 0.6 0 5 3
+0.844 0 0 12 0
+0.84331 6.3 7 8 1
+0.84283 0.3 8 2 2
+0.8416 0.5 4 2 3
+0.84143 0 7 5 2
+0.84019 0 1 5 3
+0.84002 1 1 12 0
+0.83884 0.7 10 3 0
+0.83771 0 3 9 2
+0.8367 0.1 3 11 1
+0.83594 3.1 9 6 0
+0.83463 0 3 4 3
+0.83418 2.5 5 10 1
+0.83246 0 7 9 0
+0.82971 0.1 9 5 1
+0.82905 0.2 6 10 0
+0.82861 1.4 2 5 3
+0.82841 1.3 2 12 0
+0.82763 0.3 8 7 1
+0.82743 0.4 4 3 3
+0.82697 5.2 10 0 1
+0.82423 2.8 10 1 1
+0.82405 3.7 5 8 2
+0.82393 0.2 6 7 2
+0.82274 0.3 8 8 0
+0.8194 0.4 10 4 0
+0.81917 0.5 0 10 2
+0.81616 0.4 10 2 1
+0.81578 0.1 5 1 3
+0.81553 0.2 1 10 2
+0.81299 0.1 5 11 0
+0.81155 0.2 4 9 2
+0.81119 0.2 7 6 2
+0.81063 0 4 11 1
+0.81024 0.2 3 5 3
+0.81009 4 1 6 3
+0.80985 2.8 8 4 2
+0.80876 5.8 4 4 3
+0.80796 3.6 5 2 3
+0.80489 6.3 2 10 2
+0.80426 6.9 1 12 1
+0.80322 1.8 10 3 1
+0.80122 0 9 7 0
+0.80068 1.6 9 6 1
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgco3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgco3.jcpds
new file mode 100755
index 0000000..fa1779f
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgco3.jcpds
@@ -0,0 +1,67 @@
+3
+Magnesite (08-0479, EOS from Ross 1997)
+ 2 117.000 2.30000
+ 4.6339 15.0177
+(blank for future use)
+d-spacing I/I0 h k l
+3.53931 1 1 0 -2
+2.74166 100 1 0 4
+2.50295 13 0 0 6
+2.31695 5 1 1 0
+2.10265 54 1 1 3
+1.93852 14 2 0 2
+1.76966 5 2 0 -4
+1.70038 18 1 0 -8
+1.70029 24 1 1 6
+1.50912 5 2 1 1
+1.48677 8 2 1 -2
+1.40651 0 1 0 10
+1.40636 7 2 1 4
+1.37083 2 2 0 8
+1.35403 6 1 1 9
+1.35395 3 2 1 -5
+1.33769 11 3 0 0
+1.25148 3 0 0 12
+1.23852 1 2 1 7
+1.20232 1 2 0 -10
+1.1798 2 2 1 -8
+1.17977 0 3 0 6
+1.17977 0 3 0 -6
+1.15848 1 2 2 0
+1.12865 1 2 2 3
+1.10998 0 3 1 -1
+1.10112 1 1 1 12
+1.101 0 3 1 2
+1.06719 1 2 1 10
+1.06712 3 3 1 -4
+1.05133 1 2 2 6
+1.04367 0 3 1 5
+1.03631 0 1 0 -14
+1.01474 0 2 1 -11
+0.99443 0 4 0 -2
+0.98798 0 3 1 -7
+0.96926 2 4 0 4
+0.95739 2 3 1 8
+0.95162 0 2 2 9
+0.946 0 2 0 14
+0.91905 0 1 1 15
+0.91902 0 2 1 13
+0.91894 0 3 2 1
+0.91394 0 1 0 16
+0.91389 1 3 0 12
+0.91389 1 3 0 -12
+0.91382 0 3 2 -2
+0.89421 0 3 1 -10
+0.89417 0 3 2 4
+0.88483 1 4 0 -8
+0.88024 0 3 2 -5
+0.87581 0 2 1 -14
+0.87572 0 4 1 0
+0.86267 0 3 1 11
+0.86262 0 4 1 -3
+0.86262 0 4 1 3
+0.85019 0 2 0 -16
+0.85014 0 2 2 12
+0.84605 0 3 2 7
+0.83432 7 0 0 18
+0.83423 10 4 0 10
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt.jcpds
new file mode 100755
index 0000000..3c2f66b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt.jcpds
@@ -0,0 +1,53 @@
+3
+Mg-ferrite (GSAS calculation, EOS from Knittle and Jeanloz,1987)
+ 4 266.000 3.90000
+ 10.4130 8.43453 2.81776
+(blank for future use)
+d-spacing I/I0 h k l
+ 6.5539 100. 1.00 1.00 0.00
+ 5.2065 15. 2.00 0.00 0.00
+ 4.4303 9. 2.00 1.00 0.00
+ 4.2170 13. 0.00 2.00 0.00
+ 3.9086 3. 1.00 2.00 0.00
+ 3.2770 33. 2.00 2.00 0.00
+ 3.2098 6. 3.00 1.00 0.00
+ 2.7201 14. 1.00 0.00 1.00
+ 2.6799 18. 3.00 2.00 0.00
+ 2.6033 39. 4.00 0.00 0.00
+ 2.4737 56. 2.00 3.00 0.00
+ 2.3777 34. 2.00 1.00 1.00
+ 2.3430 33. 0.00 2.00 1.00
+ 2.2859 7. 1.00 2.00 1.00
+ 2.1878 47. 3.00 0.00 1.00
+ 2.1366 4. 2.00 2.00 1.00
+ 2.1177 65. 3.00 1.00 1.00
+ 2.0666 13. 1.00 4.00 0.00
+ 2.0219 8. 5.00 1.00 0.00
+ 1.9549 25. 1.00 3.00 1.00
+ 1.9543 40. 2.00 4.00 0.00
+ 1.8673 9. 5.00 2.00 0.00
+ 1.8649 38. 4.00 1.00 1.00
+ 1.8591 12. 2.00 3.00 1.00
+ 1.7415 25. 4.00 2.00 1.00
+ 1.7266 30. 3.00 3.00 1.00
+ 1.6999 7. 6.00 1.00 0.00
+ 1.6882 6. 0.00 4.00 1.00
+ 1.6748 21. 5.00 0.00 1.00
+ 1.6665 17. 1.00 4.00 1.00
+ 1.6385 3. 4.00 4.00 0.00
+ 1.6059 8. 2.00 4.00 1.00
+ 1.6049 10. 6.00 2.00 0.00
+ 1.6047 14. 2.00 5.00 0.00
+ 1.5566 7. 5.00 2.00 1.00
+ 1.5182 6. 3.00 4.00 1.00
+ 1.4650 3. 7.00 1.00 0.00
+ 1.4556 4. 6.00 1.00 1.00
+ 1.4389 7. 5.00 3.00 1.00
+ 1.4165 8. 4.00 4.00 1.00
+ 1.4090 22. 0.00 0.00 2.00
+ 1.4057 6. 0.00 6.00 0.00
+ 1.3946 12. 6.00 2.00 1.00
+ 1.3944 9. 2.00 5.00 1.00
+ 1.3400 5. 6.00 4.00 0.00
+ 1.2243 4. 2.00 3.00 2.00
+ 1.2101 4. 6.00 4.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt_full.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt_full.jcpds
new file mode 100755
index 0000000..f2ba335
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgfrt_full.jcpds
@@ -0,0 +1,198 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Knittle and Jeanloz,1987)
+ 4 266.000 3.90000
+ 10.4130 8.43453 2.81776
+(blank for future use)
+d-spacing I/I0 h k l
+ 6.5539 100. 1.00 1.00 0.00
+ 5.2065 15. 2.00 0.00 0.00
+ 4.4303 9. 2.00 1.00 0.00
+ 4.2170 13. 0.00 2.00 0.00
+ 3.9086 3. 1.00 2.00 0.00
+ 3.2770 33. 2.00 2.00 0.00
+ 3.2098 6. 3.00 1.00 0.00
+ 2.7201 14. 1.00 0.00 1.00
+ 2.7141 1. 1.00 3.00 0.00
+ 2.6799 18. 3.00 2.00 0.00
+ 2.6033 39. 4.00 0.00 0.00
+ 2.5888 1. 1.00 1.00 1.00
+ 2.4874 1. 4.00 1.00 0.00
+ 2.4737 56. 2.00 3.00 0.00
+ 2.3777 34. 2.00 1.00 1.00
+ 2.3430 33. 0.00 2.00 1.00
+ 2.2859 7. 1.00 2.00 1.00
+ 2.2152 1. 4.00 2.00 0.00
+ 2.1878 47. 3.00 0.00 1.00
+ 2.1846 1. 3.00 3.00 0.00
+ 2.1366 4. 2.00 2.00 1.00
+ 2.1177 65. 3.00 1.00 1.00
+ 2.1085 1. 0.00 4.00 0.00
+ 2.0666 13. 1.00 4.00 0.00
+ 2.0219 8. 5.00 1.00 0.00
+ 1.9549 25. 1.00 3.00 1.00
+ 1.9543 40. 2.00 4.00 0.00
+ 1.9420 1. 3.00 2.00 1.00
+ 1.9101 2. 4.00 3.00 0.00
+ 1.8673 9. 5.00 2.00 0.00
+ 1.8649 38. 4.00 1.00 1.00
+ 1.8591 12. 2.00 3.00 1.00
+ 1.8021 2. 3.00 4.00 0.00
+ 1.7415 25. 4.00 2.00 1.00
+ 1.7355 1. 6.00 0.00 0.00
+ 1.7266 30. 3.00 3.00 1.00
+ 1.6999 7. 6.00 1.00 0.00
+ 1.6882 6. 0.00 4.00 1.00
+ 1.6748 21. 5.00 0.00 1.00
+ 1.6734 1. 5.00 3.00 0.00
+ 1.6665 17. 1.00 4.00 1.00
+ 1.6651 2. 1.00 5.00 0.00
+ 1.6428 1. 5.00 1.00 1.00
+ 1.6385 3. 4.00 4.00 0.00
+ 1.6059 8. 2.00 4.00 1.00
+ 1.6049 10. 6.00 2.00 0.00
+ 1.6047 14. 2.00 5.00 0.00
+ 1.5811 1. 4.00 3.00 1.00
+ 1.5566 7. 5.00 2.00 1.00
+ 1.5182 6. 3.00 4.00 1.00
+ 1.5171 1. 3.00 5.00 0.00
+ 1.4817 2. 5.00 4.00 0.00
+ 1.4768 1. 6.00 3.00 0.00
+ 1.4650 3. 7.00 1.00 0.00
+ 1.4556 4. 6.00 1.00 1.00
+ 1.4389 7. 5.00 3.00 1.00
+ 1.4335 2. 1.00 5.00 1.00
+ 1.4165 8. 4.00 4.00 1.00
+ 1.4156 2. 4.00 5.00 0.00
+ 1.4090 22. 0.00 0.00 2.00
+ 1.4057 6. 0.00 6.00 0.00
+ 1.4029 1. 7.00 2.00 0.00
+ 1.3946 12. 6.00 2.00 1.00
+ 1.3944 9. 2.00 5.00 1.00
+ 1.3930 2. 1.00 6.00 0.00
+ 1.3775 2. 1.00 1.00 2.00
+ 1.3601 1. 2.00 0.00 2.00
+ 1.3571 1. 2.00 6.00 0.00
+ 1.3427 1. 2.00 1.00 2.00
+ 1.3400 5. 6.00 4.00 0.00
+ 1.3364 1. 0.00 2.00 2.00
+ 1.3358 1. 3.00 5.00 1.00
+ 1.3255 1. 1.00 2.00 2.00
+ 1.3155 1. 7.00 0.00 1.00
+ 1.3148 1. 7.00 3.00 0.00
+ 1.3115 1. 5.00 4.00 1.00
+ 1.3108 1. 5.00 5.00 0.00
+ 1.3080 1. 6.00 3.00 1.00
+ 1.3029 1. 3.00 6.00 0.00
+ 1.3016 1. 8.00 0.00 0.00
+ 1.2998 1. 7.00 1.00 1.00
+ 1.2944 1. 2.00 2.00 2.00
+ 1.2902 1. 3.00 1.00 2.00
+ 1.2864 1. 8.00 1.00 0.00
+ 1.2650 1. 4.00 5.00 1.00
+ 1.2579 1. 0.00 6.00 1.00
+ 1.2558 1. 7.00 2.00 1.00
+ 1.2505 1. 1.00 3.00 2.00
+ 1.2488 1. 1.00 6.00 1.00
+ 1.2471 1. 3.00 2.00 2.00
+ 1.2437 1. 8.00 2.00 0.00
+ 1.2391 2. 4.00 0.00 2.00
+ 1.2369 1. 4.00 6.00 0.00
+ 1.2260 1. 4.00 1.00 2.00
+ 1.2243 4. 2.00 3.00 2.00
+ 1.2227 1. 2.00 6.00 1.00
+ 1.2155 1. 7.00 4.00 0.00
+ 1.2101 4. 6.00 4.00 1.00
+ 1.2096 1. 6.00 5.00 0.00
+ 1.1969 1. 1.00 7.00 0.00
+ 1.1915 2. 7.00 3.00 1.00
+ 1.1889 1. 4.00 2.00 2.00
+ 1.1885 1. 5.00 5.00 1.00
+ 1.1841 1. 3.00 3.00 2.00
+ 1.1826 2. 3.00 6.00 1.00
+ 1.1812 1. 8.00 3.00 0.00
+ 1.1738 1. 2.00 7.00 0.00
+ 1.1715 1. 0.00 4.00 2.00
+ 1.1702 1. 8.00 1.00 1.00
+ 1.1651 1. 5.00 6.00 0.00
+ 1.1642 1. 1.00 4.00 2.00
+ 1.1560 1. 5.00 1.00 2.00
+ 1.1463 1. 9.00 1.00 0.00
+ 1.1429 1. 2.00 4.00 2.00
+ 1.1382 1. 3.00 7.00 0.00
+ 1.1378 1. 8.00 2.00 1.00
+ 1.1339 1. 4.00 3.00 2.00
+ 1.1326 1. 4.00 6.00 1.00
+ 1.1247 1. 5.00 2.00 2.00
+ 1.1161 1. 7.00 4.00 1.00
+ 1.1158 2. 9.00 2.00 0.00
+ 1.1157 1. 7.00 5.00 0.00
+ 1.1115 1. 6.00 5.00 1.00
+ 1.1100 1. 3.00 4.00 2.00
+ 1.1076 1. 8.00 4.00 0.00
+ 1.1016 1. 1.00 7.00 1.00
+ 1.0939 1. 6.00 0.00 2.00
+ 1.0934 1. 4.00 7.00 0.00
+ 1.0923 1. 6.00 6.00 0.00
+ 1.0893 1. 8.00 3.00 1.00
+ 1.0848 1. 6.00 1.00 2.00
+ 1.0836 1. 2.00 7.00 1.00
+ 1.0778 1. 5.00 3.00 2.00
+ 1.0767 1. 5.00 6.00 1.00
+ 1.0756 1. 1.00 5.00 2.00
+ 1.0703 1. 9.00 0.00 1.00
+ 1.0699 1. 9.00 3.00 0.00
+ 1.0683 1. 4.00 4.00 2.00
+ 1.0618 1. 9.00 1.00 1.00
+ 1.0588 1. 6.00 2.00 2.00
+ 1.0588 1. 2.00 5.00 2.00
+ 1.0554 1. 3.00 7.00 1.00
+ 1.0543 1. 0.00 8.00 0.00
+ 1.0489 1. 1.00 8.00 0.00
+ 1.0429 1. 5.00 7.00 0.00
+ 1.0413 1. 10.00 0.00 0.00
+ 1.0374 1. 9.00 2.00 1.00
+ 1.0374 1. 7.00 5.00 1.00
+ 1.0335 1. 10.00 1.00 0.00
+ 1.0333 1. 2.00 8.00 0.00
+ 1.0324 1. 3.00 5.00 2.00
+ 1.0308 1. 8.00 4.00 1.00
+ 1.0305 1. 8.00 5.00 0.00
+ 1.0217 1. 7.00 6.00 0.00
+ 1.0210 1. 5.00 4.00 2.00
+ 1.0194 1. 6.00 3.00 2.00
+ 1.0194 2. 4.00 7.00 1.00
+ 1.0185 1. 6.00 6.00 1.00
+ 1.0155 1. 7.00 1.00 2.00
+ 1.0143 1. 9.00 4.00 0.00
+ 1.0109 1. 10.00 2.00 0.00
+ 1.0087 1. 3.00 8.00 0.00
+ 1.0003 1. 9.00 3.00 1.00
+ 0.9986 1. 4.00 5.00 2.00
+ 0.9951 1. 0.00 6.00 2.00
+ 0.9941 1. 7.00 2.00 2.00
+ 0.9906 1. 1.00 6.00 2.00
+ 0.9897 1. 6.00 7.00 0.00
+ 0.9874 1. 0.00 8.00 1.00
+ 0.9830 1. 1.00 8.00 1.00
+ 0.9781 1. 5.00 7.00 1.00
+ 0.9774 1. 2.00 6.00 2.00
+ 0.9772 1. 4.00 8.00 0.00
+ 0.9765 1. 10.00 3.00 0.00
+ 0.9710 1. 6.00 4.00 2.00
+ 0.9703 1. 10.00 1.00 1.00
+ 0.9701 1. 2.00 8.00 1.00
+ 0.9678 1. 8.00 5.00 1.00
+ 0.9613 1. 7.00 3.00 2.00
+ 0.9605 1. 7.00 6.00 1.00
+ 0.9597 1. 5.00 5.00 2.00
+ 0.9566 1. 3.00 6.00 2.00
+ 0.9561 1. 8.00 0.00 2.00
+ 0.9550 1. 8.00 6.00 0.00
+ 0.9544 1. 9.00 4.00 1.00
+ 0.9541 1. 9.00 5.00 0.00
+ 0.9516 1. 10.00 2.00 1.00
+ 0.9500 1. 8.00 1.00 2.00
+ 0.9497 1. 3.00 8.00 1.00
+ 0.9407 1. 11.00 1.00 0.00
+ 0.9406 1. 5.00 8.00 0.00
+ 0.9363 1. 7.00 7.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgo.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgo.jcpds
new file mode 100755
index 0000000..36418b2
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgo.jcpds
@@ -0,0 +1,16 @@
+3
+MgO (JCPDS 4-0829, EOS from Jackson)
+ 1 160.000 4.00000
+ 4.21300
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.4324 10. 1.00 1.00 1.00
+ 2.1065 100. 2.00 0.00 0.00
+ 1.4895 52. 2.00 2.00 0.00
+ 1.2700 4. 3.00 1.00 1.00
+ 1.2160 12. 2.00 2.00 2.00
+ 1.0533 5. 4.00 0.00 0.00
+ 0.9665 2. 3.00 3.00 1.00
+ 0.9419 17. 4.00 2.00 0.00
+ 0.8600 15. 4.00 2.00 2.00
+ 0.8109 3. 5.00 1.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgpv-erase.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgpv-erase.jcpds
new file mode 100755
index 0000000..70ecef9
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgpv-erase.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.77540 5.0062 6.9283
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
+ 1.1664 1. 2.00 3.00 3.00
+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
+ 1.1495 1. 0.00 0.00 6.00
+ 1.1442 1. 4.00 1.00 1.00
+ 1.1433 1. 3.00 3.00 0.00
+ 1.1380 1. 3.00 1.00 4.00
+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
+ 1.1276 1. 1.00 4.00 2.00
+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cmc.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cmc.jcpds
new file mode 100755
index 0000000..f614bd3
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cmc.jcpds
@@ -0,0 +1,22 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 2.7001 8.8396 6.6939
+(blank for future use)
+d-spacing I/I0 h k l
+4.042 10 0 2 0
+3.373 0.8 0 2 1
+3.061 7.2 0 0 2
+2.44 100 0 2 2
+2.361 25.1 1 1 0
+2.203 9.1 1 1 1
+2.021 7.6 0 4 0
+1.919 7.8 0 4 1
+1.87 0.1 1 1 2
+1.821 40.5 0 2 3
+1.82 88.5 1 3 0
+1.745 98.1 1 3 1
+1.686 3.9 0 4 2
+1.564 58.7 1 3 2
+1.544 47.5 1 1 3
+1.53 39 0 0 4
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cpv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cpv.jcpds
new file mode 100755
index 0000000..0af086d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-cpv.jcpds
@@ -0,0 +1,23 @@
+3
+Perovskite_cubic (GSAS calculation, EOS from Mao et al)
+ 1 261.000 4.00000
+ 3.43690
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.4369 57. 1.00 0.00 0.00
+ 2.4303 100. 1.00 1.00 0.00
+ 1.9843 38. 1.00 1.00 1.00
+ 1.7184 68. 2.00 0.00 0.00
+ 1.5370 13. 2.00 1.00 0.00
+ 1.4031 19. 2.00 1.00 1.00
+ 1.2151 11. 2.00 2.00 0.00
+ 1.1456 1. 3.00 0.00 0.00
+ 1.1456 2. 2.00 2.00 1.00
+ 1.0868 2. 3.00 1.00 0.00
+ 1.0363 1. 3.00 1.00 1.00
+ 0.9922 1. 2.00 2.00 2.00
+ 0.9532 1. 3.00 2.00 0.00
+ 0.9186 3. 3.00 2.00 1.00
+ 0.8592 1. 4.00 0.00 0.00
+ 0.8336 1. 4.00 1.00 0.00
+ 0.8336 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-en.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-en.jcpds
new file mode 100755
index 0000000..f283765
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-en.jcpds
@@ -0,0 +1,140 @@
+3
+enstatite (JCPDS card,just guess)
+ 4 125. 5.
+ 18.214 8.818 5.177
+(blank for future use)
+d-spacing I/I0 h k l
+6.33497 10 2 1 0
+4.409 2 0 2 0
+4.3361 8 1 1 1
+4.04591 1 4 1 0
+3.59673 2 3 1 1
+3.35665 1 0 2 1
+3.30106 35 1 2 1
+3.18786 16 4 1 1
+3.16749 100 4 2 0
+3.14953 76 2 2 1
+2.93759 27 3 2 1
+2.87034 45 6 1 0
+2.82245 14 5 1 1
+2.79725 12 2 3 0
+2.70188 14 4 2 1
+2.5885 1 0 0 2
+2.53127 12 1 3 1
+2.51032 1 6 1 1
+2.48988 46 2 0 2
+2.46952 1 4 3 0
+2.46848 13 5 2 1
+2.46098 2 2 3 1
+2.46093 1 1 1 2
+2.39619 1 2 1 2
+2.38112 3 3 0 2
+2.35581 20 3 3 1
+2.27675 4 8 0 0
+2.24805 3 7 1 1
+2.22892 6 4 3 1
+2.21565 3 1 2 2
+2.2045 1 0 4 0
+2.18044 3 4 1 2
+2.11004 20 5 0 2
+2.09511 11 3 2 2
+2.09237 6 5 3 1
+2.05648 3 7 2 1
+2.05211 5 5 1 2
+2.02827 2 0 4 1
+2.02823 2 8 1 1
+2.02295 7 8 2 0
+2.01581 17 1 4 1
+2.00434 1 4 2 2
+1.9842 10 4 4 0
+1.97976 8 2 4 1
+1.96966 1 6 0 2
+1.95526 5 6 3 1
+1.90331 3 5 2 2
+1.88421 2 8 2 1
+1.85277 1 4 4 1
+1.8502 3 3 3 2
+1.8351 3 7 0 2
+1.82344 1 7 3 1
+1.79836 4 6 2 2
+1.7868 1 4 3 2
+1.78375 8 10 1 0
+1.7721 1 5 4 1
+1.70012 5 8 3 1
+1.69421 9 7 2 2
+1.67831 4 8 1 2
+1.67125 4 1 4 2
+1.665 1 2 1 3
+1.65053 2 2 4 2
+1.64203 2 2 5 1
+1.63127 2 3 1 3
+1.60965 1 3 5 1
+1.60696 8 0 2 3
+1.60075 5 1 2 3
+1.59433 3 9 0 2
+1.59393 1 8 2 2
+1.58667 1 9 3 1
+1.56738 1 4 5 1
+1.55248 1 11 1 1
+1.52432 1 5 4 2
+1.51783 3 12 0 0
+1.51762 1 5 5 1
+1.51537 1 4 2 3
+1.51446 4 8 4 1
+1.49932 5 9 2 2
+1.48333 4 10 3 1
+1.48321 8 1 3 3
+1.47896 1 6 1 3
+1.46967 7 0 6 0
+1.46879 1 6 4 2
+1.45283 2 1 5 2
+1.43261 1 9 4 1
+1.41938 1 7 1 3
+1.41453 1 4 3 3
+1.4138 1 0 6 1
+1.41122 1 10 2 2
+1.41039 2 7 4 2
+1.40956 1 1 6 1
+1.40507 1 7 5 1
+1.40145 1 9 3 2
+1.39862 1 4 6 0
+1.39485 5 11 0 2
+1.38971 8 11 3 1
+1.37772 2 11 1 2
+1.35885 1 0 4 3
+1.33623 1 6 3 3
+1.32604 2 3 4 3
+1.30508 5 12 3 1
+1.30211 2 4 4 3
+1.29514 4 12 1 2
+1.29099 2 1 0 4
+1.27738 1 1 1 4
+1.2749 1 1 6 2
+1.26699 3 10 5 0
+1.26581 1 3 0 4
+1.26564 5 2 6 2
+1.25848 1 9 2 3
+1.24494 1 4 0 4
+1.22861 1 13 3 1
+1.2275 1 8 5 2
+1.22226 1 2 5 3
+1.20873 1 3 5 3
+1.19891 1 9 3 3
+1.19809 1 4 2 4
+1.18967 1 14 3 0
+1.18669 1 13 2 2
+1.17543 1 5 2 4
+1.16025 1 5 7 1
+1.15241 1 10 3 3
+1.14893 1 7 1 4
+1.13838 1 16 0 0
+1.1352 1 6 7 1
+1.12814 1 9 4 3
+1.10223 1 16 2 0
+1.09022 1 8 2 4
+1.0806 1 9 6 2
+1.06148 1 3 8 1
+1.05128 1 12 5 2
+1.04695 1 14 5 0
+1.02618 1 14 5 1
+1.01971 1 11 0 4
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-frt.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-frt.jcpds
new file mode 100755
index 0000000..f2ba335
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-frt.jcpds
@@ -0,0 +1,198 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Knittle and Jeanloz,1987)
+ 4 266.000 3.90000
+ 10.4130 8.43453 2.81776
+(blank for future use)
+d-spacing I/I0 h k l
+ 6.5539 100. 1.00 1.00 0.00
+ 5.2065 15. 2.00 0.00 0.00
+ 4.4303 9. 2.00 1.00 0.00
+ 4.2170 13. 0.00 2.00 0.00
+ 3.9086 3. 1.00 2.00 0.00
+ 3.2770 33. 2.00 2.00 0.00
+ 3.2098 6. 3.00 1.00 0.00
+ 2.7201 14. 1.00 0.00 1.00
+ 2.7141 1. 1.00 3.00 0.00
+ 2.6799 18. 3.00 2.00 0.00
+ 2.6033 39. 4.00 0.00 0.00
+ 2.5888 1. 1.00 1.00 1.00
+ 2.4874 1. 4.00 1.00 0.00
+ 2.4737 56. 2.00 3.00 0.00
+ 2.3777 34. 2.00 1.00 1.00
+ 2.3430 33. 0.00 2.00 1.00
+ 2.2859 7. 1.00 2.00 1.00
+ 2.2152 1. 4.00 2.00 0.00
+ 2.1878 47. 3.00 0.00 1.00
+ 2.1846 1. 3.00 3.00 0.00
+ 2.1366 4. 2.00 2.00 1.00
+ 2.1177 65. 3.00 1.00 1.00
+ 2.1085 1. 0.00 4.00 0.00
+ 2.0666 13. 1.00 4.00 0.00
+ 2.0219 8. 5.00 1.00 0.00
+ 1.9549 25. 1.00 3.00 1.00
+ 1.9543 40. 2.00 4.00 0.00
+ 1.9420 1. 3.00 2.00 1.00
+ 1.9101 2. 4.00 3.00 0.00
+ 1.8673 9. 5.00 2.00 0.00
+ 1.8649 38. 4.00 1.00 1.00
+ 1.8591 12. 2.00 3.00 1.00
+ 1.8021 2. 3.00 4.00 0.00
+ 1.7415 25. 4.00 2.00 1.00
+ 1.7355 1. 6.00 0.00 0.00
+ 1.7266 30. 3.00 3.00 1.00
+ 1.6999 7. 6.00 1.00 0.00
+ 1.6882 6. 0.00 4.00 1.00
+ 1.6748 21. 5.00 0.00 1.00
+ 1.6734 1. 5.00 3.00 0.00
+ 1.6665 17. 1.00 4.00 1.00
+ 1.6651 2. 1.00 5.00 0.00
+ 1.6428 1. 5.00 1.00 1.00
+ 1.6385 3. 4.00 4.00 0.00
+ 1.6059 8. 2.00 4.00 1.00
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+ 1.6047 14. 2.00 5.00 0.00
+ 1.5811 1. 4.00 3.00 1.00
+ 1.5566 7. 5.00 2.00 1.00
+ 1.5182 6. 3.00 4.00 1.00
+ 1.5171 1. 3.00 5.00 0.00
+ 1.4817 2. 5.00 4.00 0.00
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+ 1.4335 2. 1.00 5.00 1.00
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+ 1.3080 1. 6.00 3.00 1.00
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+ 1.3016 1. 8.00 0.00 0.00
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+ 1.1115 1. 6.00 5.00 1.00
+ 1.1100 1. 3.00 4.00 2.00
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+ 1.1016 1. 1.00 7.00 1.00
+ 1.0939 1. 6.00 0.00 2.00
+ 1.0934 1. 4.00 7.00 0.00
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+ 1.0836 1. 2.00 7.00 1.00
+ 1.0778 1. 5.00 3.00 2.00
+ 1.0767 1. 5.00 6.00 1.00
+ 1.0756 1. 1.00 5.00 2.00
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+ 1.0588 1. 6.00 2.00 2.00
+ 1.0588 1. 2.00 5.00 2.00
+ 1.0554 1. 3.00 7.00 1.00
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+ 1.0333 1. 2.00 8.00 0.00
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+ 1.0194 1. 6.00 3.00 2.00
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+ 1.0109 1. 10.00 2.00 0.00
+ 1.0087 1. 3.00 8.00 0.00
+ 1.0003 1. 9.00 3.00 1.00
+ 0.9986 1. 4.00 5.00 2.00
+ 0.9951 1. 0.00 6.00 2.00
+ 0.9941 1. 7.00 2.00 2.00
+ 0.9906 1. 1.00 6.00 2.00
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+ 0.9874 1. 0.00 8.00 1.00
+ 0.9830 1. 1.00 8.00 1.00
+ 0.9781 1. 5.00 7.00 1.00
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+ 0.9701 1. 2.00 8.00 1.00
+ 0.9678 1. 8.00 5.00 1.00
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+ 0.9605 1. 7.00 6.00 1.00
+ 0.9597 1. 5.00 5.00 2.00
+ 0.9566 1. 3.00 6.00 2.00
+ 0.9561 1. 8.00 0.00 2.00
+ 0.9550 1. 8.00 6.00 0.00
+ 0.9544 1. 9.00 4.00 1.00
+ 0.9541 1. 9.00 5.00 0.00
+ 0.9516 1. 10.00 2.00 1.00
+ 0.9500 1. 8.00 1.00 2.00
+ 0.9497 1. 3.00 8.00 1.00
+ 0.9407 1. 11.00 1.00 0.00
+ 0.9406 1. 5.00 8.00 0.00
+ 0.9363 1. 7.00 7.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ilm.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ilm.jcpds
new file mode 100755
index 0000000..c0cd12b
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ilm.jcpds
@@ -0,0 +1,38 @@
+3
+MgSiO3 ilmenite (GSAS calculation, EOS from guess)
+ 2 212 7.5
+4.7284 13.5591
+(blank for future use)
+d-spacing I/I0 h k l
+4.5197 26 0 0 3
+3.92005 6 1 0 1
+3.50514 92 1 0 -2
+2.61119 100 1 0 4
+2.3642 43 1 1 0
+2.26098 5 1 0 -5
+2.0949 38 1 1 -3
+2.0949 41 1 1 3
+2.02451 6 2 0 -1
+1.96002 1 2 0 2
+1.75257 32 2 0 -4
+1.751 2 1 0 7
+1.63403 5 2 0 5
+1.6336 44 1 1 -6
+1.6336 20 1 1 6
+1.56605 5 1 0 -8
+1.53775 1 2 1 1
+1.50891 4 2 1 -2
+1.40792 5 2 1 4
+1.40792 13 3 -1 4
+1.36497 23 3 0 0
+1.34421 2 2 1 -5
+1.28718 5 1 0 10
+1.27053 6 1 1 9
+1.1821 3 2 2 0
+1.18033 1 1 0 -11
+1.14363 1 2 2 -3
+1.13049 1 2 0 -10
+1.12012 1 3 1 2
+1.07689 1 4 -1 -4
+1.04745 1 2 2 6
+1.01989 1 2 1 10
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-maj.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-maj.jcpds
new file mode 100755
index 0000000..4735bc6
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-maj.jcpds
@@ -0,0 +1,109 @@
+3
+MgSiO3 Majorite (GSAS calculation, EOS from Yagi et al, 1992)
+ 3 159.800 4.98000
+ 11.5010 11.4800
+2.5e-5
+d-spacing I/I0 h k l
+8.125 2 0 1 1
+4.69383 5 2 1 1
+4.69383 12 1 2 1
+4.68954 9 1 1 2
+4.06622 3 2 2 0
+4.0625 8 0 2 2
+3.63627 2 0 3 1
+3.63096 3 0 1 3
+3.31803 5 2 2 2
+3.07337 8 2 3 1
+3.07337 1 3 2 1
+3.07217 4 3 1 2
+3.07217 3 1 3 2
+3.07016 10 1 2 3
+2.87525 100 0 4 0
+2.87 52 0 0 4
+2.5717 44 4 2 0
+2.5717 25 2 4 0
+2.57076 70 0 4 2
+2.56794 67 0 2 4
+2.45121 23 3 3 2
+2.45019 25 3 2 3
+2.45019 24 2 3 3
+2.34692 29 2 4 2
+2.34692 24 4 2 2
+2.34477 25 2 2 4
+2.25537 6 3 4 1
+2.25537 8 4 3 1
+2.25537 0 0 5 1
+2.2541 6 1 4 3
+2.2541 7 4 1 3
+2.25299 6 3 1 4
+2.25299 8 1 3 4
+2.25157 2 0 1 5
+2.09966 1 2 5 1
+2.09966 4 5 2 1
+2.09927 2 1 5 2
+2.09927 1 5 1 2
+2.09659 4 1 2 5
+1.91528 1 4 2 4
+1.91528 1 2 4 4
+1.86562 4 6 1 1
+1.86562 2 1 6 1
+1.86535 1 3 5 2
+1.86535 1 5 3 2
+1.8649 1 5 2 3
+1.86347 2 3 2 5
+1.86248 3 1 1 6
+1.81847 5 6 2 0
+1.81847 2 2 6 0
+1.81814 6 0 6 2
+1.81548 6 0 2 6
+1.69566 1 3 6 1
+1.69512 1 6 1 3
+1.69331 1 3 1 6
+1.65901 8 4 4 4
+1.5949 4 4 6 0
+1.5949 7 6 4 0
+1.594 11 0 6 4
+1.59288 11 0 4 6
+1.56249 1 1 2 7
+1.53668 16 4 6 2
+1.53668 9 6 4 2
+1.53608 10 2 6 4
+1.53608 17 6 2 4
+1.53508 14 2 4 6
+1.53508 11 4 2 6
+1.46046 1 3 7 2
+1.46024 1 2 7 3
+1.45852 2 3 2 7
+1.43763 7 0 8 0
+1.435 3 0 0 8
+1.3947 1 2 8 0
+1.37431 1 5 6 3
+1.37373 1 3 6 5
+1.37334 1 3 5 6
+1.35527 2 2 8 2
+1.35321 1 2 2 8
+1.28585 2 4 8 0
+1.28585 2 8 4 0
+1.28538 4 0 8 4
+1.28397 3 0 4 8
+1.25475 1 8 4 2
+1.25475 1 4 8 2
+1.25442 1 8 2 4
+1.25442 2 2 8 4
+1.25312 1 4 2 8
+1.25312 1 2 4 8
+1.23889 1 6 1 7
+1.2256 2 6 6 4
+1.22509 2 6 4 6
+1.22509 3 4 6 6
+1.17346 1 4 8 4
+1.04983 1 4 10 2
+1.04983 1 10 4 2
+1.04964 1 2 10 4
+1.04964 1 10 2 4
+1.04829 1 4 2 10
+1.04829 1 2 4 10
+1.01655 1 8 8 0
+1.01563 1 0 8 8
+0.8343 1 9 10 3
+0.83357 2 9 3 10
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-opv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-opv.jcpds
new file mode 100755
index 0000000..64eda3d
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-opv.jcpds
@@ -0,0 +1,162 @@
+3
+Perovskite_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 4.77540 4.92917 6.89711
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.9261 3. 1.00 0.00 1.00
+ 3.4485 12. 0.00 0.00 2.00
+ 3.4298 30. 1.00 1.00 0.00
+ 3.0710 10. 1.00 1.00 1.00
+ 2.4646 23. 0.00 2.00 0.00
+ 2.4318 100. 1.00 1.00 2.00
+ 2.3877 20. 2.00 0.00 0.00
+ 2.3209 1. 0.00 2.00 1.00
+ 2.1901 4. 1.00 2.00 0.00
+ 2.1489 9. 2.00 1.00 0.00
+ 2.0874 9. 1.00 2.00 1.00
+ 2.0714 19. 1.00 0.00 3.00
+ 2.0516 15. 2.00 1.00 1.00
+ 2.0051 9. 0.00 2.00 2.00
+ 1.9631 5. 2.00 0.00 2.00
+ 1.9097 13. 1.00 1.00 3.00
+ 1.8488 8. 1.00 2.00 2.00
+ 1.8238 3. 2.00 1.00 2.00
+ 1.7242 25. 0.00 0.00 4.00
+ 1.7149 40. 2.00 2.00 0.00
+ 1.6811 4. 0.00 2.00 3.00
+ 1.6642 4. 2.00 2.00 1.00
+ 1.5857 3. 1.00 2.00 3.00
+ 1.5699 1. 2.00 1.00 3.00
+ 1.5537 6. 1.00 3.00 0.00
+ 1.5510 1. 3.00 0.00 1.00
+ 1.5405 3. 1.00 1.00 4.00
+ 1.5355 5. 2.00 2.00 2.00
+ 1.5157 8. 1.00 3.00 1.00
+ 1.5148 1. 3.00 1.00 0.00
+ 1.4795 3. 3.00 1.00 1.00
+ 1.4165 9. 1.00 3.00 2.00
+ 1.4128 8. 0.00 2.00 4.00
+ 1.3979 7. 2.00 0.00 4.00
+ 1.3869 19. 3.00 1.00 2.00
+ 1.3746 1. 2.00 2.00 3.00
+ 1.3548 1. 1.00 2.00 4.00
+ 1.3535 1. 2.00 3.00 0.00
+ 1.3448 1. 2.00 1.00 4.00
+ 1.3372 2. 3.00 2.00 0.00
+ 1.3282 1. 2.00 3.00 1.00
+ 1.3252 1. 1.00 0.00 5.00
+ 1.3127 1. 3.00 2.00 1.00
+ 1.3087 1. 3.00 0.00 3.00
+ 1.2873 2. 1.00 3.00 3.00
+ 1.2798 1. 1.00 1.00 5.00
+ 1.2649 1. 3.00 1.00 3.00
+ 1.2600 1. 2.00 3.00 2.00
+ 1.2467 1. 3.00 2.00 2.00
+ 1.2323 3. 0.00 4.00 0.00
+ 1.2159 7. 2.00 2.00 4.00
+ 1.2131 1. 0.00 4.00 1.00
+ 1.2037 1. 0.00 2.00 5.00
+ 1.1939 2. 4.00 0.00 0.00
+ 1.1932 2. 1.00 4.00 0.00
+ 1.1758 1. 1.00 4.00 1.00
+ 1.1672 2. 1.00 2.00 5.00
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+ 1.1608 1. 2.00 1.00 5.00
+ 1.1604 1. 0.00 4.00 2.00
+ 1.1603 1. 4.00 1.00 0.00
+ 1.1559 1. 3.00 2.00 3.00
+ 1.1542 2. 1.00 3.00 4.00
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+ 1.1282 3. 4.00 0.00 2.00
+ 1.1279 1. 3.00 3.00 1.00
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+ 1.0997 1. 4.00 1.00 2.00
+ 1.0951 1. 2.00 4.00 0.00
+ 1.0899 2. 1.00 1.00 6.00
+ 1.0861 1. 0.00 4.00 3.00
+ 1.0852 2. 3.00 3.00 2.00
+ 1.0815 1. 2.00 4.00 1.00
+ 1.0748 1. 2.00 2.00 5.00
+ 1.0744 1. 4.00 2.00 0.00
+ 1.0647 1. 2.00 3.00 4.00
+ 1.0616 1. 4.00 2.00 1.00
+ 1.0591 1. 1.00 4.00 3.00
+ 1.0567 1. 3.00 2.00 4.00
+ 1.0437 1. 2.00 4.00 2.00
+ 1.0424 1. 3.00 0.00 5.00
+ 1.0417 1. 0.00 2.00 6.00
+ 1.0359 1. 4.00 1.00 3.00
+ 1.0357 1. 2.00 0.00 6.00
+ 1.0315 1. 1.00 3.00 5.00
+ 1.0258 1. 4.00 2.00 2.00
+ 1.0237 1. 3.00 3.00 3.00
+ 1.0199 1. 3.00 1.00 5.00
+ 1.0178 1. 1.00 2.00 6.00
+ 1.0136 1. 2.00 1.00 6.00
+ 1.0026 1. 0.00 4.00 4.00
+ 0.9886 1. 2.00 4.00 3.00
+ 0.9815 1. 4.00 0.00 4.00
+ 0.9812 1. 1.00 4.00 4.00
+ 0.9744 1. 3.00 4.00 0.00
+ 0.9734 1. 4.00 2.00 3.00
+ 0.9661 1. 2.00 3.00 5.00
+ 0.9658 1. 4.00 3.00 0.00
+ 0.9655 1. 1.00 5.00 0.00
+ 0.9650 1. 1.00 0.00 7.00
+ 0.9648 1. 3.00 4.00 1.00
+ 0.9626 1. 4.00 1.00 4.00
+ 0.9601 1. 3.00 2.00 5.00
+ 0.9565 1. 4.00 3.00 1.00
+ 0.9562 1. 1.00 5.00 1.00
+ 0.9548 1. 2.00 2.00 6.00
+ 0.9528 1. 3.00 3.00 4.00
+ 0.9470 1. 1.00 1.00 7.00
+ 0.9460 1. 5.00 0.00 1.00
+ 0.9377 1. 3.00 4.00 2.00
+ 0.9376 1. 5.00 1.00 0.00
+ 0.9300 1. 4.00 3.00 2.00
+ 0.9297 1. 1.00 5.00 2.00
+ 0.9291 1. 5.00 1.00 1.00
+ 0.9244 1. 2.00 4.00 4.00
+ 0.9241 1. 1.00 3.00 6.00
+ 0.9190 1. 0.00 4.00 5.00
+ 0.9157 2. 3.00 1.00 6.00
+ 0.9149 1. 0.00 2.00 7.00
+ 0.9119 1. 4.00 2.00 4.00
+ 0.9112 1. 2.00 5.00 0.00
+ 0.9048 2. 5.00 1.00 2.00
+ 0.9034 1. 2.00 5.00 1.00
+ 0.9024 1. 1.00 4.00 5.00
+ 0.8985 1. 1.00 2.00 7.00
+ 0.8972 1. 3.00 4.00 3.00
+ 0.8956 1. 2.00 1.00 7.00
+ 0.8906 1. 5.00 2.00 0.00
+ 0.8904 1. 4.00 3.00 3.00
+ 0.8902 1. 1.00 5.00 3.00
+ 0.8879 1. 4.00 1.00 5.00
+ 0.8832 1. 5.00 2.00 1.00
+ 0.8820 1. 5.00 0.00 3.00
+ 0.8810 1. 2.00 5.00 2.00
+ 0.8802 1. 3.00 3.00 5.00
+ 0.8762 1. 2.00 3.00 6.00
+ 0.8717 1. 3.00 2.00 6.00
+ 0.8682 1. 5.00 1.00 3.00
+ 0.8623 1. 5.00 2.00 2.00
+ 0.8621 1. 0.00 0.00 8.00
+ 0.8576 1. 2.00 4.00 5.00
+ 0.8575 1. 4.00 4.00 0.00
+ 0.8543 1. 2.00 2.00 7.00
+ 0.8509 1. 4.00 4.00 1.00
+ 0.8483 1. 3.00 4.00 4.00
+ 0.8476 1. 4.00 2.00 5.00
+ 0.8471 1. 2.00 5.00 3.00
+ 0.8426 1. 4.00 3.00 4.00
+ 0.8424 1. 1.00 5.00 4.00
+ 0.8406 1. 0.00 4.00 6.00
+ 0.8381 1. 3.00 5.00 0.00
+ 0.8378 1. 3.00 0.00 7.00
+ 0.8361 1. 1.00 1.00 8.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ppv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ppv.jcpds
new file mode 100755
index 0000000..976b13c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-ppv.jcpds
@@ -0,0 +1,103 @@
+3
+postpv_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 261.000 4.00000
+ 2.6872 8.7990 6.6665
+(blank for future use)
+d-spacing I/I0 h k l
+4.3995 13.9 0 2 0
+3.6720 0.1 0 2 1
+3.3333 4.4 0 0 2
+2.6568 100.0 0 2 2
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv1.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv1.jcpds
new file mode 100755
index 0000000..08e3e4c
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv1.jcpds
@@ -0,0 +1,32 @@
+3
+Perovskite_rhombohedral (ideal) (GSAS calculation, EOS from Mao et al)
+ 2 261.000 4.00000
+ 4.889877 11.86968
+(blank for future use)
+d-spacing I/I0 h k l
+3.44719 73 1 0 -2
+2.44495 69 1 1 0
+2.43018 74 1 0 4
+2.07988 7 1 1 3
+1.99427 32 2 0 2
+1.97828 10 0 0 6
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+0.98914 1 0 0 12
+0.91694 1 1 1 12
+0.89473 1 2 1 -11
+0.8618 1 4 0 -8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv2.jcpds
new file mode 100755
index 0000000..3750066
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mgsio3-rpv2.jcpds
@@ -0,0 +1,92 @@
+3
+Perovskite_rhombohedral (doubled) (GSAS calculation, EOS from Mao et al)
+ 2 261.000 4.00000
+9.754914 11.82239
+(blank for future use)
+d-spacing I/I0 h k l
+4.87745 1 1 1 0
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+0.91348 1 2 2 12
+0.85918 1 8 0 8
+0.83491 1 8 2 6
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-ppv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-ppv.jcpds
new file mode 100755
index 0000000..021b795
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-ppv.jcpds
@@ -0,0 +1,85 @@
+3
+mn2o3 bixbyite (JCPDS24-0508, EOS from guess)
+ 4 200.000 4.00000
+2.93 9.61 7.28
+(blank for future use)
+d-spacing I/I0 h k l
+4.805 12.2 0 2 0
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+3.64 5.1 0 0 2
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-syn.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-syn.jcpds
new file mode 100755
index 0000000..782fe48
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3-syn.jcpds
@@ -0,0 +1,442 @@
+3
+mn2o3 bixbyite (JCPDS24-0508, EOS from guess)
+ 4 200.000 4.00000
+9.4161 9.4237 9.4051
+(blank for future use)
+d-spacing I/I0 h k l
+5.4355 0 1 1 1
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+1.0949 0 3 7 4
+1.0946 0 8 3 1
+1.0946 0 7 4 3
+1.0944 0 8 1 3
+1.0944 0 7 3 4
+1.0939 0 3 4 7
+1.0938 0 4 3 7
+1.0936 0 1 3 8
+1.0935 0 3 1 8
+1.0878 0 5 7 1
+1.0875 0 7 5 1
+1.0874 0 1 7 5
+1.0871 0 5 5 5
+1.0868 0 7 1 5
+1.0867 0 1 5 7
+1.0864 0 5 1 7
+1.0804 1 6 6 2
+1.0799 1 2 6 6
+1.0795 1 6 2 6
+1.0737 0 3 8 2
+1.0736 0 2 8 3
+1.0732 0 5 6 4
+1.0731 0 8 3 2
+1.073 0 6 5 4
+1.073 0 4 6 5
+1.0729 0 8 2 3
+1.0728 0 6 4 5
+1.0727 0 4 5 6
+1.0726 0 5 4 6
+1.0721 0 2 3 8
+1.072 0 3 2 8
+1.0666 0 5 7 2
+1.0663 0 7 5 2
+1.0663 0 2 7 5
+1.0658 0 7 2 5
+1.0656 0 2 5 7
+1.0654 0 5 2 7
+1.0534 0 4 8 0
+1.0531 0 0 8 4
+1.0529 0 8 4 0
+1.0525 0 8 0 4
+1.0519 0 0 4 8
+1.0517 0 4 0 8
+1.0468 0 4 8 1
+1.0466 0 1 8 4
+1.0465 0 4 7 4
+1.0464 0 6 6 3
+1.0463 0 8 4 1
+1.0461 0 7 4 4
+1.046 0 3 6 6
+1.046 0 8 1 4
+1.0457 0 6 3 6
+1.0456 0 4 4 7
+1.0454 0 1 4 8
+1.0452 0 4 1 8
+1.0403 0 3 8 3
+1.0397 0 8 3 3
+1.0389 0 3 3 8
+1.0343 0 1 9 1
+1.0339 0 5 7 3
+1.0336 0 3 7 5
+1.0336 0 7 5 3
+1.0335 0 9 1 1
+1.0332 0 7 3 5
+1.033 0 3 5 7
+1.0329 0 5 3 7
+1.0323 0 1 1 9
+1.0279 0 4 8 2
+1.0277 0 2 8 4
+1.0274 0 8 4 2
+1.0271 0 8 2 4
+1.0266 0 2 4 8
+1.0265 0 4 2 8
+1.022 0 0 9 2
+1.0216 0 7 6 0
+1.0213 0 9 2 0
+1.0212 0 0 7 6
+1.0206 0 6 0 7
+1.0201 0 2 0 9
+1.0161 0 2 9 1
+1.016 0 1 9 2
+1.0158 0 6 7 1
+1.0157 0 7 6 1
+1.0153 0 9 2 1
+1.0153 0 5 6 5
+1.0153 0 1 7 6
+1.0153 0 9 1 2
+1.0152 0 6 5 5
+1.0151 0 5 5 6
+1.015 0 1 6 7
+1.0148 0 7 1 6
+1.0147 0 6 1 7
+1.0142 0 1 2 9
+1.0142 0 2 1 9
+1.0038 0 6 6 4
+1.0036 0 4 6 6
+1.0034 0 6 4 6
+0.9987 0 2 9 2
+0.9986 0 5 8 0
+0.9985 0 4 8 3
+0.9985 0 6 7 2
+0.9984 0 3 8 4
+0.9983 0 7 6 2
+0.9981 0 8 4 3
+0.998 0 9 2 2
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3.jcpds
new file mode 100755
index 0000000..acb62a4
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mn2o3.jcpds
@@ -0,0 +1,35 @@
+3
+mn2o3 bixbyite (JCPDS24-0508, EOS from guess)
+ 4 200.000 4.00000
+9.4161 9.4237 9.4051
+(blank for future use)
+d-spacing I/I0 h k l
+4.706 1 2 0 0
+3.844 18 2 1 1
+3.1379 1 1 2 2
+2.7185 100 2 2 2
+2.5157 2 3 1 2
+2.354 11 4 0 0
+2.2192 1 4 1 1
+2.1054 1 4 0 2
+2.0069 9 3 2 3
+1.921 1 2 2 4
+1.8462 10 4 1 3
+1.7191 2 5 2 1
+1.6643 27 0 4 4
+1.6143 2 3 3 4
+1.5686 1 4 2 4
+1.5272 2 5 2 3
+1.4887 1 6 0 2
+1.4524 4 1 4 5
+1.4196 11 6 2 2
+1.3883 3 6 3 1
+1.3589 3 4 4 4
+1.3314 1 5 3 4
+1.3052 1 6 0 4
+1.2814 1 7 2 1
+1.2802 1 1 2 7
+1.2585 1 2 6 4
+1.1986 1 7 2 3
+1.1772 1 8 0 0
+1.1755 1 0 0 8
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mo.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mo.jcpds
new file mode 100755
index 0000000..c7a36ad
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/mo.jcpds
@@ -0,0 +1,13 @@
+3
+Molibdenum (JCPDS 42-1120, EOS from Hixson and Fritz, 1992)
+ 1 267.000 3.86000
+ 3.14720
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.2247 100. 1.00 1.00 0.00
+ 1.5738 16. 2.00 0.00 0.00
+ 1.2847 31. 2.00 1.00 1.00
+ 1.1129 9. 2.00 2.00 0.00
+ 0.9953 14. 3.00 1.00 0.00
+ 0.9085 3. 2.00 2.00 2.00
+ 0.8411 24. 3.00 2.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-epsilon.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-epsilon.jcpds
new file mode 100755
index 0000000..077bda2
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-epsilon.jcpds
@@ -0,0 +1,12 @@
+3
+N2 epsilon (based on Olijnyk, EOS is fitted for BM3)
+ 2 1.26 6.9
+10.0348 14.3498
+(blank for future use)
+d-spacing I/I0 h k l
+3.716743 30 2 0 2
+3.462094 90 1 1 3
+3.316019 60 1 0 4
+3.20185 100 2 1 1
+2.986573 94 1 2 2
+2.76643 48 0 2 4
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-xi.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-xi.jcpds
new file mode 100755
index 0000000..6ef40e5
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/n2-xi.jcpds
@@ -0,0 +1,15 @@
+3
+N2 Gregoryanz (2007)
+ 7 0 0
+ 0
+(blank for future use)
+I/I0 h k l Pr dsp Pr1 dsp1
+ 90. 0.00 1.00 1.00 62.95704 2.49866 116.24458 2.32077
+ 60. 0.00 0.00 3.00 63.35847 2.34087 116.16716 2.22152
+ 60. 1.00 1.00 1.00 63.19083 2.31320 116.62960 2.17600
+ 80. 1.00 0.00 3.00 63.49998 2.22862 116.75675 2.07513
+ 70. 3.00 0.00 1.00 64.00138 2.15219 116.44401 2.00189
+ 10. 1.00 1.00 2.00 63.56406 2.01712 116.56705 1.90428
+ 10. 2.00 1.00 1.00 63.58867 1.99759 116.39531 1.87987
+ 10. 2.00 0.00 3.00 63.86911 1.93579 116.40556 1.87173
+100. 3.00 0.00 2.00 63.34361 1.87067 117.12178 1.78556
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol.jcpds
new file mode 100755
index 0000000..7639b69
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol.jcpds
@@ -0,0 +1,26 @@
+3
+NaAlSi3O8 hollandite (Zhang et al 1993, EOS from jd by Zhao et al 1997)
+ 3 125.000 5.00000
+ 9.30000 2.73048
+(blank for future use)
+d-spacing I/I0 h k l
+ 6.5761 100. 1.00 1.00 0.00
+ 4.6500 89. 0.00 2.00 0.00
+ 3.2880 12. 2.00 2.00 0.00
+ 2.9409 91. 1.00 3.00 0.00
+ 2.9409 30. 3.00 1.00 0.00
+ 2.3250 16. 0.00 4.00 0.00
+ 2.2823 18. 2.00 1.00 1.00
+ 2.2823 26. 1.00 2.00 1.00
+ 2.0795 16. 2.00 4.00 0.00
+ 2.0488 79. 0.00 3.00 1.00
+ 1.8749 5. 3.00 2.00 1.00
+ 1.8239 8. 5.00 1.00 0.00
+ 1.8239 6. 1.00 5.00 0.00
+ 1.7388 21. 4.00 1.00 1.00
+ 1.7388 16. 1.00 4.00 1.00
+ 1.5500 21. 0.00 6.00 0.00
+ 1.4595 8. 5.00 2.00 1.00
+ 1.4595 19. 2.00 5.00 1.00
+ 1.3650 10. 0.00 0.00 2.00
+ 1.2822 16. 5.00 4.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol_f.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol_f.jcpds
new file mode 100755
index 0000000..0f94b98
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/na-hol_f.jcpds
@@ -0,0 +1,101 @@
+3
+NaAlSi3O8 hollandite (Zhang et al 1993, EOS from jd by Zhao et al 1997)
+ 3 125.000 5.00000
+ 9.30000 2.73048
+(blank for future use)
+d-spacing I/I0 h k l
+ 6.5761 100. 1.00 1.00 0.00
+ 4.6500 89. 0.00 2.00 0.00
+ 3.2880 12. 2.00 2.00 0.00
+ 2.9409 91. 1.00 3.00 0.00
+ 2.9409 30. 3.00 1.00 0.00
+ 2.6195 3. 0.00 1.00 1.00
+ 2.3250 16. 0.00 4.00 0.00
+ 2.2823 18. 2.00 1.00 1.00
+ 2.2823 26. 1.00 2.00 1.00
+ 2.1920 1. 3.00 3.00 0.00
+ 2.0795 16. 2.00 4.00 0.00
+ 2.0795 1. 4.00 2.00 0.00
+ 2.0488 79. 0.00 3.00 1.00
+ 1.8749 1. 2.00 3.00 1.00
+ 1.8749 5. 3.00 2.00 1.00
+ 1.8239 8. 5.00 1.00 0.00
+ 1.8239 6. 1.00 5.00 0.00
+ 1.7388 21. 4.00 1.00 1.00
+ 1.7388 16. 1.00 4.00 1.00
+ 1.6440 2. 4.00 4.00 0.00
+ 1.5949 1. 5.00 3.00 0.00
+ 1.5949 1. 3.00 5.00 0.00
+ 1.5500 21. 0.00 6.00 0.00
+ 1.5371 1. 3.00 4.00 1.00
+ 1.5371 1. 0.00 5.00 1.00
+ 1.5371 1. 4.00 3.00 1.00
+ 1.4705 1. 6.00 2.00 0.00
+ 1.4705 1. 2.00 6.00 0.00
+ 1.4595 8. 5.00 2.00 1.00
+ 1.4595 19. 2.00 5.00 1.00
+ 1.3650 10. 0.00 0.00 2.00
+ 1.3365 1. 1.00 1.00 2.00
+ 1.3340 1. 1.00 6.00 1.00
+ 1.3340 1. 6.00 1.00 1.00
+ 1.3152 1. 5.00 5.00 0.00
+ 1.3152 1. 1.00 7.00 0.00
+ 1.3152 1. 7.00 1.00 0.00
+ 1.3097 1. 0.00 2.00 2.00
+ 1.2897 1. 6.00 4.00 0.00
+ 1.2897 1. 4.00 6.00 0.00
+ 1.2822 16. 5.00 4.00 1.00
+ 1.2822 3. 4.00 5.00 1.00
+ 1.2607 1. 2.00 2.00 2.00
+ 1.2381 4. 1.00 3.00 2.00
+ 1.2381 1. 3.00 1.00 2.00
+ 1.2361 1. 3.00 6.00 1.00
+ 1.2361 1. 6.00 3.00 1.00
+ 1.2212 1. 3.00 7.00 0.00
+ 1.2212 2. 7.00 3.00 0.00
+ 1.1946 1. 0.00 7.00 1.00
+ 1.1771 2. 0.00 4.00 2.00
+ 1.1625 1. 0.00 8.00 0.00
+ 1.1587 1. 3.00 3.00 2.00
+ 1.1571 1. 2.00 7.00 1.00
+ 1.1571 1. 7.00 2.00 1.00
+ 1.1411 1. 4.00 2.00 2.00
+ 1.1411 1. 2.00 4.00 2.00
+ 1.1278 1. 8.00 2.00 0.00
+ 1.1278 1. 2.00 8.00 0.00
+ 1.0960 1. 6.00 6.00 0.00
+ 1.0928 1. 1.00 5.00 2.00
+ 1.0928 1. 5.00 1.00 2.00
+ 1.0914 1. 5.00 6.00 1.00
+ 1.0914 1. 6.00 5.00 1.00
+ 1.0811 1. 5.00 7.00 0.00
+ 1.0811 1. 7.00 5.00 0.00
+ 1.0626 1. 7.00 4.00 1.00
+ 1.0626 1. 8.00 1.00 1.00
+ 1.0626 1. 4.00 7.00 1.00
+ 1.0626 1. 1.00 8.00 1.00
+ 1.0502 1. 4.00 4.00 2.00
+ 1.0398 1. 4.00 8.00 0.00
+ 1.0398 1. 8.00 4.00 0.00
+ 1.0371 1. 5.00 3.00 2.00
+ 1.0371 1. 3.00 5.00 2.00
+ 1.0270 1. 1.00 9.00 0.00
+ 1.0270 1. 9.00 1.00 0.00
+ 1.0244 2. 0.00 6.00 2.00
+ 1.0111 1. 8.00 3.00 1.00
+ 1.0111 1. 3.00 8.00 1.00
+ 1.0004 1. 2.00 6.00 2.00
+ 1.0004 1. 6.00 2.00 2.00
+ 0.9803 1. 3.00 9.00 0.00
+ 0.9803 1. 9.00 3.00 0.00
+ 0.9664 1. 0.00 9.00 1.00
+ 0.9471 1. 1.00 7.00 2.00
+ 0.9471 1. 5.00 5.00 2.00
+ 0.9471 1. 7.00 1.00 2.00
+ 0.9462 1. 9.00 2.00 1.00
+ 0.9462 1. 7.00 6.00 1.00
+ 0.9462 1. 2.00 9.00 1.00
+ 0.9462 1. 6.00 7.00 1.00
+ 0.9394 1. 7.00 7.00 0.00
+ 0.9374 1. 6.00 4.00 2.00
+ 0.9374 1. 4.00 6.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/nacl-b2-fei.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/nacl-b2-fei.jcpds
new file mode 100755
index 0000000..13fe4ae
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/nacl-b2-fei.jcpds
@@ -0,0 +1,23 @@
+3
+NaCl B2 (GSAS calculation, EOS from Fei)
+ 1 29.9 4.33
+ 3.458
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.4669 6. 1.00 0.00 0.00
+ 2.4515 100. 1.00 1.00 0.00
+ 2.0016 1. 1.00 1.00 1.00
+ 1.7335 8. 2.00 0.00 0.00
+ 1.5504 1. 2.00 1.00 0.00
+ 1.4154 21. 2.00 1.00 1.00
+ 1.2257 4. 2.00 2.00 0.00
+ 1.1556 1. 2.00 2.00 1.00
+ 1.1556 1. 3.00 0.00 0.00
+ 1.0963 7. 3.00 1.00 0.00
+ 1.0453 1. 3.00 1.00 1.00
+ 1.0008 1. 2.00 2.00 2.00
+ 0.9615 1. 3.00 2.00 0.00
+ 0.9266 3. 3.00 2.00 1.00
+ 0.8667 1. 4.00 0.00 0.00
+ 0.8408 1. 4.00 1.00 0.00
+ 0.8408 1. 3.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ne.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ne.jcpds
new file mode 100755
index 0000000..b9ec251
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ne.jcpds
@@ -0,0 +1,11 @@
+3
+Neon FCC (made by Shim, EOS from Hemley et al 1989)
+ 1 7.23 5.21
+ 4.1225
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.3802 100. 1.00 1.00 1.00
+ 2.0613 40. 2.00 0.00 0.00
+ 1.4575 25. 2.00 2.00 0.00
+ 1.2430 30. 3.00 1.00 1.00
+ 1.1901 12. 2.00 2.00 2.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/phaseD.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/phaseD.jcpds
new file mode 100755
index 0000000..f4bb21a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/phaseD.jcpds
@@ -0,0 +1,71 @@
+3
+PHASE D (GSAS, EOS from Shieh 2000)
+ 2 134.000 4.30000
+ 4.7453 4.3450
+(blank for future use)
+d-spacing I/I0 h k l
+4.3450 33 0 0 1
+4.1096 13 1 0 0
+2.9857 100 1 0 1
+2.3727 11 1 1 0
+2.1725 0 0 0 2
+2.0824 13 1 1 1
+2.0824 52 1 1 -1
+2.0548 3 2 0 0
+1.9206 0 1 0 2
+1.8575 20 2 0 1
+1.6023 38 1 1 -2
+1.6023 21 1 1 2
+1.5533 0 2 1 0
+1.4928 1 2 0 2
+1.4626 3 2 1 1
+1.4626 16 2 1 -1
+1.4483 0 0 0 3
+1.3699 29 3 0 0
+1.3660 7 1 0 3
+1.3065 3 3 0 1
+1.2635 0 2 1 2
+1.2635 1 2 1 -2
+1.2362 5 1 1 3
+1.2362 0 1 1 -3
+1.1863 2 2 2 0
+1.1838 4 2 0 3
+1.1587 1 3 0 2
+1.1444 0 2 2 -1
+1.1444 2 2 2 1
+1.1398 1 3 1 0
+1.1025 4 3 1 1
+1.1025 1 3 1 -1
+1.0863 1 0 0 4
+1.0593 3 2 1 3
+1.0593 1 2 1 -3
+1.0502 0 1 0 4
+1.0412 3 2 2 -2
+1.0412 4 2 2 2
+1.0274 1 4 0 0
+1.0093 0 3 1 2
+1.0093 0 3 1 -2
+0.9998 1 4 0 1
+0.9952 0 3 0 3
+0.9877 0 1 1 -4
+0.9877 1 1 1 4
+0.9603 1 2 0 4
+0.9428 1 3 2 0
+0.9288 0 4 0 2
+0.9214 0 3 2 -1
+0.9214 1 3 2 1
+0.9178 0 2 2 3
+0.9178 1 2 2 -3
+0.8968 0 4 1 0
+0.8957 0 3 1 3
+0.8957 0 3 1 -3
+0.8902 0 2 1 -4
+0.8902 0 2 1 4
+0.8783 0 4 1 1
+0.8783 0 4 1 -1
+0.8690 0 0 0 5
+0.8649 0 3 2 -2
+0.8649 0 3 2 2
+0.8511 1 3 0 4
+0.8502 0 1 0 5
+0.8380 0 4 0 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-ii.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-ii.jcpds
new file mode 100755
index 0000000..09c17b1
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-ii.jcpds
@@ -0,0 +1,66 @@
+3
+Rh2o3 -II phase (GSAS, EOS from guess)
+ 4 40.000 4.00000
+ 5.168 5.381 7.242
+(blank for future use)
+d-spacing I/I0 h k l
+3.6213 6 0 0 2
+3.3145 7 1 1 1
+2.6907 27 0 2 0
+2.5975 100 1 1 2
+2.5843 25 2 0 0
+2.5223 1 0 2 1
+2.3296 1 2 1 0
+2.2668 1 1 2 1
+2.2177 1 2 1 1
+2.1874 1 1 0 3
+2.1598 1 0 2 2
+2.1036 1 2 0 2
+2.0264 2 1 1 3
+1.9928 1 1 2 2
+1.8639 20 2 2 0
+1.8107 8 0 0 4
+1.8050 3 2 2 1
+1.7969 2 0 2 3
+1.6572 2 2 2 2
+1.6501 7 1 3 1
+1.6287 6 1 1 4
+1.6003 1 3 1 1
+1.5349 14 1 3 2
+1.5022 7 0 2 4
+1.4946 19 3 1 2
+1.4829 7 2 0 4
+1.4753 2 2 2 3
+1.3870 1 1 3 3
+1.3502 1 1 1 5
+1.3454 1 0 4 0
+1.3227 1 0 4 1
+1.2987 5 2 2 4
+1.2922 2 4 0 0
+1.2373 1 1 3 4
+1.2247 1 3 3 1
+1.2159 1 3 1 4
+1.1933 1 2 4 0
+1.1775 1 2 4 1
+1.1753 3 3 3 2
+1.1752 1 0 4 3
+1.1648 2 4 2 0
+1.1484 2 1 1 6
+1.1014 1 0 2 6
+1.1011 1 1 3 5
+1.0937 1 2 0 6
+1.0698 1 2 4 3
+1.0518 1 4 0 4
+1.0427 1 1 5 1
+1.0132 1 2 2 6
+1.0117 1 1 5 2
+0.9964 1 2 4 4
+0.9832 1 1 3 6
+0.9796 1 4 2 4
+0.9775 2 5 1 2
+0.9723 1 3 1 6
+0.9431 1 3 3 5
+0.9056 1 3 5 1
+0.8798 1 1 1 8
+0.8695 1 5 3 2
+0.8521 1 1 5 5
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-iii.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-iii.jcpds
new file mode 100755
index 0000000..a1412a0
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3-iii.jcpds
@@ -0,0 +1,87 @@
+3
+Rh2o3 -III phase (GSAS, EOS from guess)
+ 4 20.000 4.00000
+ 5.1477 5.4425 14.6977
+(blank for future use)
+d-spacing I/I0 h k l
+7.3489 3 0 0 2
+3.6744 11 0 0 4
+3.6244 13 1 1 1
+3.3331 2 1 1 2
+2.9727 6 1 1 3
+2.7213 22 0 2 0
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\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3.jcpds
new file mode 100755
index 0000000..8695daf
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/rh2o3.jcpds
@@ -0,0 +1,44 @@
+3
+Rh2O3 corundum phase (GSAS, EOS?)
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+(blank for future use)
+d-spacing I/I0 h k l
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\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2.jcpds
new file mode 100755
index 0000000..d5a828a
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2.jcpds
@@ -0,0 +1,54 @@
+3
+cacl2-type silica (by see notebook, EOS from Stishovite)
+ 4 298.000 3.98000
+ 4.25700 4.05692 2.66488
+(blank for future use)
+d-spacing I/I0 h k l
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2mono.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2mono.jcpds
new file mode 100755
index 0000000..2cf7802
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-cacl2mono.jcpds
@@ -0,0 +1,54 @@
+3
+cacl2-type mono cheating (by see notebook, EOS from Stishovite)
+ 5 298.000 3.98000
+ 4.25700 4.05692 2.66488 90.
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.9170 100. 1.00 1.00 0.00
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diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-coe.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-coe.jcpds
new file mode 100755
index 0000000..cdfcf38
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-coe.jcpds
@@ -0,0 +1,410 @@
+3
+coesite (all data from Levien and Prewitt, 1981)
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+(blank for future use)
+d-spacing I/I0 h k l
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+ 1.0769 0. 5.00 3.00 -6.00
+ 1.0765 0. 3.00 5.00 -6.00
+ 1.0750 0. 1.00 3.00 -6.00
+ 1.0745 0. 3.00 9.00 -4.00
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+ 1.0740 0. 5.00 7.00 -4.00
+ 1.0738 0. 1.00 9.00 -4.00
+ 1.0730 0. 1.00 11.00 -2.00
+ 1.0722 0. 1.00 11.00 1.00
+ 1.0722 0. 1.00 3.00 5.00
+ 1.0721 0. 1.00 7.00 4.00
+ 1.0721 0. 1.00 9.00 3.00
+ 1.0707 0. 3.00 9.00 1.00
+ 1.0702 0. 5.00 7.00 -1.00
+ 1.0697 0. 3.00 5.00 3.00
+ 1.0681 0. 5.00 3.00 1.00
+ 1.0615 0. 0.00 6.00 5.00
+ 1.0610 0. 0.00 10.00 3.00
+ 1.0477 0. 2.00 8.00 -5.00
+ 1.0476 0. 6.00 4.00 -5.00
+ 1.0440 0. 2.00 8.00 3.00
+ 1.0421 0. 6.00 4.00 -1.00
+ 1.0344 0. 6.00 0.00 -6.00
+ 1.0319 0. 0.00 0.00 6.00
+ 1.0308 0. 0.00 12.00 0.00
+ 1.0301 0. 6.00 6.00 -3.00
+ 1.0264 1. 6.00 0.00 0.00
+ 1.0204 0. 4.00 6.00 -6.00
+ 1.0203 0. 6.00 2.00 -6.00
+ 1.0196 0. 2.00 6.00 -6.00
+ 1.0193 0. 4.00 8.00 -5.00
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+ 1.0168 0. 0.00 12.00 1.00
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+ 1.0157 0. 5.00 7.00 -5.00
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+ 1.0104 0. 5.00 7.00 0.00
+ 1.0096 0. 5.00 5.00 1.00
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+ 1.0039 0. 4.00 10.00 -3.00
+ 1.0022 0. 4.00 10.00 -1.00
+ 0.9996 1. 4.00 2.00 3.00
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+ 0.9885 0. 7.00 1.00 -5.00
+ 0.9880 0. 3.00 11.00 -3.00
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+ 0.9871 0. 7.00 3.00 -4.00
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+ 0.9857 0. 7.00 3.00 -3.00
+ 0.9853 0. 3.00 9.00 2.00
+ 0.9849 0. 3.00 7.00 3.00
+ 0.9846 0. 3.00 3.00 4.00
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+ 0.9829 0. 5.00 1.00 2.00
+ 0.9810 0. 2.00 2.00 -7.00
+ 0.9810 0. 6.00 4.00 -6.00
+ 0.9797 0. 6.00 6.00 -5.00
+ 0.9788 0. 0.00 4.00 6.00
+ 0.9782 0. 2.00 12.00 -2.00
+ 0.9780 0. 0.00 12.00 2.00
+ 0.9775 0. 2.00 12.00 0.00
+ 0.9765 0. 2.00 2.00 5.00
+ 0.9751 0. 6.00 6.00 -1.00
+ 0.9741 0. 6.00 4.00 0.00
+ 0.9700 0. 4.00 4.00 -7.00
+ 0.9672 0. 4.00 10.00 -4.00
+ 0.9672 0. 5.00 3.00 -7.00
+ 0.9665 0. 0.00 8.00 5.00
+ 0.9663 0. 0.00 10.00 4.00
+ 0.9643 0. 4.00 10.00 0.00
+ 0.9643 0. 1.00 9.00 -5.00
+ 0.9642 0. 7.00 3.00 -5.00
+ 0.9640 0. 5.00 9.00 -4.00
+ 0.9626 0. 1.00 9.00 4.00
+ 0.9626 0. 4.00 4.00 3.00
+ 0.9613 0. 5.00 9.00 -1.00
+ 0.9604 0. 7.00 3.00 -2.00
+ 0.9590 0. 5.00 3.00 2.00
+ 0.9460 0. 2.00 4.00 -7.00
+ 0.9439 0. 2.00 12.00 -3.00
+ 0.9438 0. 3.00 5.00 -7.00
+ 0.9433 0. 5.00 7.00 -6.00
+ 0.9429 0. 7.00 1.00 -6.00
+ 0.9426 0. 6.00 8.00 -3.00
+ 0.9425 0. 2.00 12.00 1.00
+ 0.9425 0. 1.00 1.00 -7.00
+ 0.9420 0. 1.00 7.00 -6.00
+ 0.9419 0. 2.00 4.00 5.00
+ 0.9417 0. 3.00 11.00 -4.00
+ 0.9413 0. 1.00 11.00 -4.00
+ 0.9405 0. 1.00 13.00 -1.00
+ 0.9403 0. 7.00 5.00 -4.00
+ 0.9403 0. 1.00 13.00 0.00
+ 0.9403 0. 1.00 1.00 6.00
+ 0.9402 0. 1.00 7.00 5.00
+ 0.9401 0. 1.00 11.00 3.00
+ 0.9392 0. 3.00 11.00 1.00
+ 0.9392 0. 7.00 5.00 -3.00
+ 0.9382 0. 3.00 5.00 4.00
+ 0.9374 0. 5.00 7.00 1.00
+ 0.9369 0. 7.00 1.00 -1.00
+ 0.9362 0. 6.00 2.00 -7.00
+ 0.9352 0. 4.00 8.00 -6.00
+ 0.9345 0. 2.00 8.00 -6.00
+ 0.9340 0. 2.00 10.00 -5.00
+ 0.9329 0. 6.00 8.00 -4.00
+ 0.9314 0. 2.00 10.00 3.00
+ 0.9312 0. 2.00 8.00 4.00
+ 0.9309 0. 6.00 8.00 -2.00
+ 0.9299 0. 4.00 8.00 2.00
+ 0.9283 0. 6.00 2.00 1.00
+ 0.9246 0. 6.00 6.00 -6.00
+ 0.9231 0. 5.00 5.00 -7.00
+ 0.9227 0. 0.00 6.00 6.00
+ 0.9221 0. 0.00 12.00 3.00
+ 0.9217 0. 7.00 3.00 -6.00
+ 0.9213 0. 1.00 3.00 -7.00
+ 0.9212 0. 5.00 9.00 -5.00
+ 0.9205 0. 7.00 5.00 -5.00
+ 0.9197 0. 1.00 13.00 -2.00
+ 0.9193 0. 1.00 3.00 6.00
+ 0.9192 0. 1.00 13.00 1.00
+ 0.9188 0. 6.00 6.00 0.00
+ 0.9172 0. 5.00 9.00 0.00
+ 0.9172 0. 7.00 5.00 -2.00
+ 0.9161 0. 7.00 3.00 -1.00
+ 0.9160 0. 5.00 5.00 2.00
+ 0.9153 0. 4.00 6.00 -7.00
+ 0.9137 0. 4.00 10.00 -5.00
+ 0.9100 0. 4.00 10.00 1.00
+ 0.9091 0. 4.00 6.00 3.00
+ 0.9056 0. 6.00 4.00 -7.00
+ 0.9036 0. 6.00 8.00 -5.00
+ 0.9020 0. 3.00 9.00 -6.00
+ 0.9000 0. 6.00 8.00 -1.00
+ 0.8984 0. 6.00 4.00 1.00
+ 0.8980 0. 3.00 9.00 3.00
+ 0.8967 0. 4.00 0.00 -8.00
+ 0.8951 0. 2.00 6.00 -7.00
+ 0.8937 0. 2.00 12.00 -4.00
+ 0.8925 0. 4.00 12.00 -2.00
+ 0.8919 0. 2.00 12.00 2.00
+ 0.8919 0. 8.00 0.00 -4.00
+ 0.8916 0. 2.00 6.00 5.00
+ 0.8897 0. 4.00 0.00 4.00
+ 0.8874 0. 4.00 2.00 -8.00
+ 0.8854 0. 5.00 1.00 -8.00
+ 0.8847 0. 3.00 1.00 -8.00
+ 0.8844 0. 7.00 1.00 -7.00
+ 0.8841 0. 3.00 7.00 -7.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2.jcpds
new file mode 100755
index 0000000..61913f4
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2.jcpds
@@ -0,0 +1,98 @@
+3
+PbO2-type silica (by see notebook, EOS from Stishovite)
+ 4 298.000 3.98000
+ 4.257 4.0569 5.330
+(blank for future use)
+d-spacing I/I0 h k l
+4.257 2 1 0 0
+3.22817 47 0 1 1
+2.93685 0 1 1 0
+2.665 3 0 0 2
+2.57223 100 1 1 1
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+1.53885 0 2 1 2
+1.52015 1 1 1 3
+1.50924 36 1 2 2
+1.46843 16 2 2 0
+1.419 0 3 0 0
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+1.33943 1 3 1 0
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+1.29904 7 3 1 1
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\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2ii.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2ii.jcpds
new file mode 100755
index 0000000..eaf4a59
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-pbo2ii.jcpds
@@ -0,0 +1,98 @@
+3
+PbO2-type silica (from sharp et al, EOS from Stishovite)
+ 4 298.000 3.98000
+ 4.55 4.16 5.11
+(blank for future use)
+d-spacing I/I0 h k l
+4.257 2 1 0 0
+3.22817 47 0 1 1
+2.93685 0 1 1 0
+2.665 3 0 0 2
+2.57223 100 1 1 1
+2.25887 5 1 0 2
+2.1285 5 2 0 0
+2.02845 0 0 2 0
+1.97357 39 1 1 2
+1.88483 12 2 1 0
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+1.28883 0 1 3 0
+1.28611 2 2 2 2
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+1.19677 1 3 1 2
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+1.16027 2 1 3 2
+1.14142 2 2 3 0
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+1.02942 1 4 1 0
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+0.92802 0 4 2 1
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+0.92524 1 1 4 2
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+0.91892 0 3 3 2
+0.91559 1 2 4 0
+0.90238 2 2 4 1
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+0.87609 0 3 2 4
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\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-qtz.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-qtz.jcpds
new file mode 100755
index 0000000..0419788
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-qtz.jcpds
@@ -0,0 +1,16 @@
+3
+quartz (JCPDS 46-1045) EOS from
+ 2 38. 5.4
+ 4.91344 5.40524
+(blank for future use)
+d-spacing I/I0 h k l
+4.25499 16 1 0 0
+3.343471 100 1 0 1
+2.45687 9 1 1 0
+2.28149 8 1 0 2
+2.23613 4 1 1 1
+2.12771 6 2 0 0
+1.81796 13 1 1 2
+1.54153 9 2 1 1
+1.38210 6 2 1 2
+1.37496 7 2 0 3
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-stv.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-stv.jcpds
new file mode 100755
index 0000000..ff00f40
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/sio2-stv.jcpds
@@ -0,0 +1,36 @@
+3
+stishovite (by Sinclair and Ringwood, EOS from Heaney)
+ 3 298.000 3.98000
+ 4.17720 2.66505
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.9537 100. 1.00 1.00 0.00
+ 2.2468 19. 1.00 0.00 1.00
+ 2.0886 1. 2.00 0.00 0.00
+ 1.9787 29. 1.00 1.00 1.00
+ 1.8681 10. 2.00 1.00 0.00
+ 1.5297 34. 2.00 1.00 1.00
+ 1.4769 11. 2.00 2.00 0.00
+ 1.3326 4. 0.00 0.00 2.00
+ 1.3210 3. 3.00 1.00 0.00
+ 1.2918 1. 2.00 2.00 1.00
+ 1.2341 10. 3.00 0.00 1.00
+ 1.2147 4. 1.00 1.00 2.00
+ 1.1835 1. 3.00 1.00 1.00
+ 1.1586 1. 3.00 2.00 0.00
+ 1.1234 2. 2.00 0.00 2.00
+ 1.0848 1. 2.00 1.00 2.00
+ 1.0625 1. 3.00 2.00 1.00
+ 1.0443 1. 4.00 0.00 0.00
+ 1.0131 1. 4.00 1.00 0.00
+ 0.9894 1. 2.00 2.00 2.00
+ 0.9846 1. 3.00 3.00 0.00
+ 0.9470 1. 4.00 1.00 1.00
+ 0.9381 1. 3.00 1.00 2.00
+ 0.9341 1. 4.00 2.00 0.00
+ 0.9236 1. 3.00 3.00 1.00
+ 0.8815 1. 4.00 2.00 1.00
+ 0.8743 1. 3.00 2.00 2.00
+ 0.8689 1. 1.00 0.00 3.00
+ 0.8507 1. 1.00 1.00 3.00
+ 0.8354 1. 4.00 3.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ta.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ta.jcpds
new file mode 100755
index 0000000..78cbf90
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/ta.jcpds
@@ -0,0 +1,14 @@
+3
+Tantalum (4-0788 EOS from Cynn and Yoo)
+ 1 194.7 3.4
+ 3.3058
+(blank for future use)
+d-spacing I/I0 h k l
+2.338 100. 1. 1. 0.
+1.65300 21. 2. 0. 0.
+1.3500 38. 2. 1. 1.
+1.16870 13. 2. 2. 0.
+1.04530 19. 3. 1. 0.
+0.954300 7. 2. 2. 2.
+0.883500 29. 3. 2. 1.
+0.826500 4. 4. 0. 0.
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tib2.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tib2.jcpds
new file mode 100755
index 0000000..d9f1977
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tib2.jcpds
@@ -0,0 +1,20 @@
+3
+TiB2 (JCPDS 35-0741, EOS from Abbate et al, 1991)
+ 2 236.000 2.00000
+ 3.03034 3.22943
+(blank for future use)
+d-spacing I/I0 h k l
+ 3.2295 22. 0.00 0.00 1.00
+ 2.6247 55. 1.00 0.00 0.00
+ 2.0370 100. 1.00 0.00 1.00
+ 1.6145 12. 0.00 0.00 2.00
+ 1.5153 27. 1.00 1.00 0.00
+ 1.3751 16. 1.00 0.00 2.00
+ 1.3717 18. 1.00 1.00 1.00
+ 1.3122 7. 2.00 0.00 0.00
+ 1.2156 16. 2.00 0.00 1.00
+ 1.1049 14. 1.00 1.00 2.00
+ 1.0766 1. 0.00 0.00 3.00
+ 1.0183 5. 2.00 0.00 2.00
+ 0.9959 8. 1.00 0.00 3.00
+ 0.9919 6. 2.00 1.00 0.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tungsten.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tungsten.jcpds
new file mode 100755
index 0000000..f568a26
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/tungsten.jcpds
@@ -0,0 +1,12 @@
+3
+Tungsten (4-0806 EOS from Hixson and Fritz)
+ 1 311.220 3.90000
+ 3.16480
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.2380 100. 1.00 1.00 0.00
+ 1.5820 15. 2.00 0.00 0.00
+ 1.2920 23. 2.00 1.00 1.00
+ 1.1188 8. 2.00 2.00 0.00
+ 1.0008 11. 3.00 1.00 0.00
+ 0.8459 18. 3.00 2.00 1.00
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3-Rh-new.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3-Rh-new.jcpds
new file mode 100755
index 0000000..915f289
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3-Rh-new.jcpds
@@ -0,0 +1,101 @@
+3
+V2O3 - orthorhombic rh2o3 structure (GSAS calculation, EOS from Handbook)
+ 4 195.000 4.00000
+ 5.0612 5.3196 7.2345
+(blank for future use)
+d-spacing I/I0 h k l
+4.08112 0 1 0 1
+3.5125 9 0 0 2
+3.21583 11 1 1 1
+2.6115 28 0 2 0
+2.51987 100 1 1 2
+2.507 24 2 0 0
+2.44783 0 0 2 1
+2.26012 4 2 1 0
+2.19969 2 1 2 1
+2.15152 2 2 1 1
+2.12169 6 1 0 3
+2.09573 0 0 2 2
+2.04056 1 2 0 2
+1.96569 4 1 1 3
+1.93362 3 1 2 2
+1.90065 0 2 1 2
+1.80853 23 2 2 0
+1.75625 9 0 0 4
+1.75142 3 2 2 1
+1.74343 1 0 2 3
+1.64672 0 1 2 3
+1.62621 0 2 1 3
+1.62595 0 3 0 1
+1.60791 3 2 2 2
+1.60137 9 1 3 1
+1.57987 8 1 1 4
+1.55246 1 3 1 1
+1.48948 14 1 3 2
+1.45735 8 0 2 4
+1.44988 22 3 1 2
+1.43841 8 2 0 4
+1.43134 3 2 2 3
+1.43 0 2 3 0
+1.40126 0 2 3 1
+1.39943 0 1 2 4
+1.38678 0 2 1 4
+1.38028 0 3 2 1
+1.36037 0 3 0 3
+1.35289 0 1 0 5
+1.34588 1 1 3 3
+1.32444 0 2 3 2
+1.31645 0 3 1 3
+1.30967 1 1 1 5
+1.30669 0 3 2 2
+1.30575 1 0 4 0
+1.28376 1 0 4 1
+1.25993 4 2 2 4
+1.2535 1 4 0 0
+1.24365 0 1 4 1
+1.2373 0 0 2 5
+1.22392 0 0 4 2
+1.22043 0 2 3 3
+1.21889 0 4 1 0
+1.20649 0 3 2 3
+1.20126 0 1 2 5
+1.20095 0 4 1 1
+1.20047 1 1 3 4
+1.19323 0 2 1 5
+1.18901 0 1 4 2
+1.18831 0 3 3 1
+1.18058 0 4 0 2
+1.17945 1 3 1 4
+1.17083 0 0 0 6
+1.15808 2 2 4 0
+1.15153 0 4 1 2
+1.14266 1 2 4 1
+1.14043 1 0 4 3
+1.14038 2 3 3 2
+1.13006 2 4 2 0
+1.11572 1 4 2 1
+1.11393 2 1 1 6
+1.11203 0 1 4 3
+1.10953 0 2 2 5
+1.1089 0 2 3 4
+1.09985 0 2 4 2
+1.09842 0 3 2 4
+1.08119 0 4 1 3
+1.07576 0 4 2 2
+1.07547 0 3 0 5
+1.07194 0 3 3 3
+1.06837 1 0 2 6
+1.06827 1 1 3 5
+1.06084 1 2 0 6
+1.05337 0 3 1 5
+1.04787 0 0 4 4
+1.04491 0 1 2 6
+1.03962 0 2 1 6
+1.03807 1 2 4 3
+1.02571 0 1 4 4
+1.02028 1 4 0 4
+1.01809 0 3 4 1
+1.01775 0 4 2 3
+1.01726 0 4 3 0
+1.01198 1 1 5 1
+1.00676 0 4 3 1
\ No newline at end of file
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3.jcpds
new file mode 100755
index 0000000..0079a2e
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2O3.jcpds
@@ -0,0 +1,45 @@
+ 3
+V2O3(unit cell from Jade Software, K(GPa) from J. Solid State Chem 21:145, K' estimate)
+ 2 195 4
+4.952 14.0020
+(blank for future use)
+d-spacing I/I0 h k l
+3.7320 25. 0 1 2
+2.7120 50. 1 0 4
+2.5720 60. 1 1 0
+2.2770 8. 0 0 6
+2.2380 35. 1 1 3
+2.1160 12. 2 0 2
+1.8650 35. 0 2 4
+1.7040 100. 1 1 6
+1.6340 12 1 2 2
+1.5920 4. 0 1 8
+1.5100 30. 2 1 4
+1.4830 45. 3 0 0
+1.3060 25. 1 0 10
+1.2849 18. 2 2 0
+1.2432 12. 3 0 6
+1.2365 8. 2 2 3
+1.2146 8. 3 1 2
+1.1643 8. 0 2 10
+1.1613 18. 1 3 4
+1.1386 8. 0 0 12
+1.1192 25. 2 2 6
+1.0982 6. 0 4 2
+1.0603 12. 2 1 10
+1.0578 18. 4 0 4
+1.0407 12. 1 1 12
+1.0098 6. 2 3 2
+0.9805 8. 2 2 9
+0.9783 12. 3 2 4
+0.9713 12. 4 1 0
+0.9533 4. 0 1 14
+0.9502 4. 4 1 3
+0.9158 20. 1 3 10
+0.9030 18. 3 0 12
+0.8934 30. 4 1 6
+0.8626 14. 4 0 10
+0.8612 12. 0 5 4
+0.8566 18. 3 3 0
+0.8520 12. 2 2 12
+0.8439 8. 1 2 14
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2o3-ppv-new.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2o3-ppv-new.jcpds
new file mode 100755
index 0000000..e786bd8
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/v2o3-ppv-new.jcpds
@@ -0,0 +1,103 @@
+3
+V2_O2_postpv_orthorhombic (GSAS calculation, EOS from Mao et al)
+ 4 195.000 4.00000
+ 2.8420 9.3059 7.0505
+(blank for future use)
+d-spacing I/I0 h k l
+4.3995 13.9 0 2 0
+3.6720 0.1 0 2 1
+3.3333 4.4 0 0 2
+2.6568 100.0 0 2 2
+2.5700 18.9 1 1 0
+2.3980 4.3 1 1 1
+2.1998 5.4 0 4 0
+2.0890 5.4 0 4 1
+2.0353 0.3 1 1 2
+1.9835 39.0 0 2 3
+1.9814 65.8 1 3 0
+1.8992 82.7 1 3 1
+1.8360 2.5 0 4 2
+1.7032 40.0 1 3 2
+1.6809 35.4 1 1 3
+1.6666 26.7 0 0 4
+1.5633 0.7 0 4 3
+1.5585 0.3 0 2 4
+1.4789 6.8 1 3 3
+1.4722 5.3 1 5 0
+1.4665 0.1 0 6 0
+1.4376 7.0 1 5 1
+1.4323 1.5 0 6 1
+1.3983 16.4 1 1 4
+1.3467 25.6 1 5 2
+1.3436 18.0 2 0 0
+1.3423 21.5 0 6 2
+1.3284 11.7 0 4 4
+1.2850 0.2 2 2 0
+1.2760 0.4 0 2 5
+1.2754 5.9 1 3 4
+1.2618 0.2 2 2 1
+1.2462 0.2 2 0 2
+1.2273 0.1 1 5 3
+1.2240 0.6 0 6 3
+1.1990 9.7 2 2 2
+1.1835 0.2 1 1 5
+1.1466 1.1 2 4 0
+1.1402 0.1 0 4 5
+1.1386 0.1 1 7 0
+1.1300 0.4 2 4 1
+1.1223 1.1 1 7 1
+1.1124 2.9 2 2 3
+1.1111 0.4 0 0 6
+1.1062 8.2 1 3 5
+1.1034 1.3 1 5 4
+1.1010 0.0 0 6 4
+1.0999 1.8 0 8 0
+1.0852 0.3 0 8 1
+1.0843 0.1 2 4 2
+1.0775 1.5 1 7 2
+1.0773 3.4 0 2 6
+1.0460 4.9 2 0 4
+1.0445 0.9 0 8 2
+1.0199 1.8 1 1 6
+1.0190 0.1 2 4 3
+1.0177 0.2 2 2 4
+1.0133 1.7 1 7 3
+0.9918 0.8 0 4 6
+0.9907 0.0 2 6 0
+0.9883 2.0 1 5 5
+0.9865 0.4 0 6 5
+0.9857 0.0 0 8 3
+0.9799 0.4 2 6 1
+0.9691 0.6 1 3 6
+0.9496 6.4 2 6 2
+0.9447 3.5 2 4 4
+0.9401 0.1 1 7 4
+0.9308 0.1 0 2 7
+0.9252 0.1 2 2 5
+0.9188 0.1 1 9 0
+0.9180 0.5 0 8 4
+0.9102 0.8 1 9 1
+0.9048 0.1 2 6 3
+0.8930 0.9 1 1 7
+0.8911 0.3 3 1 0
+0.8869 0.7 1 5 6
+0.8857 0.6 1 9 2
+0.8856 1.1 0 6 6
+0.8833 0.0 3 1 1
+0.8799 0.0 0 10 0
+0.8740 0.0 0 4 7
+0.8723 0.0 0 10 1
+0.8694 0.0 2 4 5
+0.8658 0.0 1 7 5
+0.8609 0.0 3 1 2
+0.8584 0.4 1 3 7
+0.8567 1.4 3 3 0
+0.8562 0.3 2 0 6
+0.8516 0.0 2 6 4
+0.8511 1.1 2 8 0
+0.8508 0.4 0 10 2
+0.8497 0.3 3 3 1
+0.8490 0.2 1 9 3
+0.8484 0.0 0 8 5
+0.8442 0.0 2 8 1
+0.8405 2.0 2 2 6
diff --git a/jnb-tools/1_cif_to_jcpds/jcpds/ver3/w.jcpds b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/w.jcpds
new file mode 100755
index 0000000..f568a26
--- /dev/null
+++ b/jnb-tools/1_cif_to_jcpds/jcpds/ver3/w.jcpds
@@ -0,0 +1,12 @@
+3
+Tungsten (4-0806 EOS from Hixson and Fritz)
+ 1 311.220 3.90000
+ 3.16480
+(blank for future use)
+d-spacing I/I0 h k l
+ 2.2380 100. 1.00 1.00 0.00
+ 1.5820 15. 2.00 0.00 0.00
+ 1.2920 23. 2.00 1.00 1.00
+ 1.1188 8. 2.00 2.00 0.00
+ 1.0008 11. 3.00 1.00 0.00
+ 0.8459 18. 3.00 2.00 1.00
diff --git a/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.html b/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.html
new file mode 100644
index 0000000..b04bb69
--- /dev/null
+++ b/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.html
@@ -0,0 +1,13748 @@
+
+
+how_to_read_dpp
+
+
+
+
+
+
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+
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+
How to read dpp from PeakPo¶
+
+
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+
+Please check setup_for_notebooks file if you have problem using the notebooks in this folder.
+In this notebook, we will learn how to plot XRD patterns using the information saved in dpp.
+dpp is a project file saved in PeakPo. You may plot, jcpds information and cake as well as many other information.
+
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+
This notebook takes advantage of the PeakPo modules and other local modules. They can be found in ../local_modules folder.
+
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+
Check the versio of pyFAI in your conda environment¶
+
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/Users/DanShim/anaconda/envs/peakpo7721/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
+ from ._conv import register_converters as _register_converters
+WARNING:pyFAI.opencl.common:Unable to import pyOpenCl. Please install it from: http://pypi.python.org/pypi/pyopencl
+
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Out[2]:
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+
Note that the example data files I provided are made with pyFAI version 0.14. If you see version higher than 0.15 here, you will get error when you read the example dpp file. In that case, you either follow the instruction in setup_for_notebooks.ipynb or you may use your own dpp for this note book.
+
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+
Change the following two cells for your own dpp file¶
+
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+
Data files should be in the ../data folder. You need: dpp, chi, tif, and poni files.
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../data/hStv/hSiO2_404_009.dpp
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The cells below show how to look into the data structure of the model_dpp and get values from it.
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Out[7]:
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{'base_ptn': <ds_powdiff.DiffractionPattern.PatternPeakPo at 0x10a4a9da0>,
+ 'waterfall_ptn': [],
+ 'jcpds_lst': [<ds_jcpds.jcpds.JCPDSplt at 0x1111f81d0>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x1111f8f98>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x111261518>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x1112618d0>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x111261ac8>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x1112696a0>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x111271128>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x111271978>],
+ 'ucfit_lst': [],
+ 'diff_img': <ds_cake.DiffractionImage.DiffImg at 0x11127e518>,
+ 'poni': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv/LaB6_37keV_p49_center.poni',
+ 'session': <ds_jcpds.jcpds.Session at 0x1112882e8>,
+ 'jcpds_path': '/Users/DanShim/Python/jcpds',
+ 'chi_path': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv',
+ 'current_section': None,
+ 'section_lst': [],
+ 'saved_pressure': 39.6,
+ 'saved_temperature': 300.0}
+
+
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+
Setup a new PeakPo model and assign info from dpp¶
+
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Make sure to reset the chi folder location using the new_chi_path option.
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+
Some basic model methods¶
+
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Type: PeakPoModel
+String form: <model.model.PeakPoModel object at 0x111288cf8>
+File: ~/Dropbox (ASU)/Python/PeakPo-v7/peakpo/model/model.py
+Docstring:
+session is only for reading/writing/referencing.
+components of the models are not part of session.
+session is a reference object
+
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Out[11]:
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Out[12]:
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../data/hStv/hSiO2_404_009.chi
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SiO2-hhstv
+ar-NoTh
+au
+ne-NoTh
+Dicvol_404_018Mono
+Dicvol_404_003Mono
+SiO2-NiAsBis
+sio2-cacl2
+
+
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SiO2-hhstv
+au
+ne-NoTh
+sio2-cacl2
+
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+
../data/hStv/LaB6_37keV_p49_center.poni
+
+
+
+
+
+
+
+
+
+
+
,\n",
+ " 'waterfall_ptn': [],\n",
+ " 'jcpds_lst': [,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ],\n",
+ " 'ucfit_lst': [],\n",
+ " 'diff_img': ,\n",
+ " 'poni': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv/LaB6_37keV_p49_center.poni',\n",
+ " 'session': ,\n",
+ " 'jcpds_path': '/Users/DanShim/Python/jcpds',\n",
+ " 'chi_path': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv',\n",
+ " 'current_section': None,\n",
+ " 'section_lst': [],\n",
+ " 'saved_pressure': 39.6,\n",
+ " 'saved_temperature': 300.0}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_dpp.__dict__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup a new PeakPo model and assign info from dpp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from model import PeakPoModel\n",
+ "model = PeakPoModel()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make sure to reset the chi folder location using the `new_chi_path` option."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.set_from(model_dpp, new_chi_path='../data/hStv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Some basic model methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[0;31mType:\u001b[0m PeakPoModel\n",
+ "\u001b[0;31mString form:\u001b[0m \n",
+ "\u001b[0;31mFile:\u001b[0m ~/Dropbox (ASU)/Python/PeakPo-v7/peakpo/model/model.py\n",
+ "\u001b[0;31mDocstring:\u001b[0m \n",
+ "session is only for reading/writing/referencing.\n",
+ "components of the models are not part of session.\n",
+ "session is a reference object\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "?model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "39.6"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.get_saved_pressure()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "300.0"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.get_saved_temperature()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "../data/hStv/hSiO2_404_009.chi\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model.base_ptn.fname)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.3344\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model.base_ptn.wavelength)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model.waterfall_ptn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SiO2-hhstv\n",
+ "ar-NoTh\n",
+ "au\n",
+ "ne-NoTh\n",
+ "Dicvol_404_018Mono\n",
+ "Dicvol_404_003Mono\n",
+ "SiO2-NiAsBis\n",
+ "sio2-cacl2\n"
+ ]
+ }
+ ],
+ "source": [
+ "for phase in model.jcpds_lst:\n",
+ " print(phase.name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SiO2-hhstv\n",
+ "au\n",
+ "ne-NoTh\n",
+ "sio2-cacl2\n"
+ ]
+ }
+ ],
+ "source": [
+ "for phase in model.jcpds_lst:\n",
+ " if phase.display:\n",
+ " print(phase.name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "../data/hStv/LaB6_37keV_p49_center.poni\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model.poni)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "peakpo7721",
+ "language": "python",
+ "name": "peakpo7721"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.py b/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.py
new file mode 100644
index 0000000..844f653
--- /dev/null
+++ b/jnb-tools/1_how_to_read_dpp/how_to_read_dpp.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # How to read dpp from PeakPo
+
+# - Please check [setup_for_notebooks](../0_setup/setup_for_notebooks.ipynb) file if you have problem using the notebooks in this folder.
+# - In this notebook, we will learn how to plot XRD patterns using the information saved in `dpp`.
+# - `dpp` is a project file saved in `PeakPo`. You may plot, jcpds information and cake as well as many other information.
+
+# This notebook takes advantage of the `PeakPo` modules and other local modules. They can be found in `../local_modules` folder.
+# The cell below defined the search path for this local module folder.
+
+# In[1]:
+
+
+import sys
+sys.path.append('../../peakpo')
+
+
+# ## Check the versio of pyFAI in your conda environment
+
+# In[2]:
+
+
+import pyFAI
+pyFAI.version
+
+
+# Note that the example data files I provided are made with `pyFAI` version `0.14`. If you see version higher than `0.15` here, you will get error when you read the example `dpp` file. In that case, you either follow the instruction in [setup_for_notebooks.ipynb](./setup_for_notebooks.ipynb) or you may use your own dpp for this note book.
+
+# ## Read dpp
+
+# In[3]:
+
+
+import dill
+import numpy as np
+
+
+# ### Change the following two cells for your own dpp file
+
+# Data files should be in the `../data` folder. You need: `dpp`, `chi`, `tif`, and `poni` files.
+
+# In[4]:
+
+
+get_ipython().run_line_magic('ls', '../data/hStv/*.dpp')
+
+
+# In[5]:
+
+
+filen_dpp = '../data/hStv/hSiO2_404_009.dpp'
+
+
+# In[6]:
+
+
+with open(filen_dpp, 'rb') as f:
+ model_dpp = dill.load(f)
+
+
+# The cells below show how to look into the data structure of the `model_dpp` and get values from it.
+
+# In[7]:
+
+
+model_dpp.__dict__
+
+
+# ## Setup a new PeakPo model and assign info from dpp
+
+# In[8]:
+
+
+from model import PeakPoModel
+model = PeakPoModel()
+
+
+# Make sure to reset the chi folder location using the `new_chi_path` option.
+
+# In[9]:
+
+
+model.set_from(model_dpp, new_chi_path='../data/hStv')
+
+
+# ## Some basic model methods
+
+# In[10]:
+
+
+get_ipython().run_line_magic('pinfo', 'model')
+
+
+# In[11]:
+
+
+model.get_saved_pressure()
+
+
+# In[12]:
+
+
+model.get_saved_temperature()
+
+
+# In[13]:
+
+
+print(model.base_ptn.fname)
+
+
+# In[14]:
+
+
+print(model.base_ptn.wavelength)
+
+
+# In[15]:
+
+
+print(model.waterfall_ptn)
+
+
+# In[16]:
+
+
+for phase in model.jcpds_lst:
+ print(phase.name)
+
+
+# In[17]:
+
+
+for phase in model.jcpds_lst:
+ if phase.display:
+ print(phase.name)
+
+
+# In[18]:
+
+
+print(model.poni)
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.html b/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.html
new file mode 100644
index 0000000..76f149b
--- /dev/null
+++ b/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.html
@@ -0,0 +1,13659 @@
+
+
+1D_xrd_pattern
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Plot XRD patterns from dpp files from PeakPo¶
+
+
+
+
+
+
+
+Please check setup_for_notebooks file if you have problem using the notebooks in this folder.
+In this notebook, we will learn how to plot XRD patterns using the information saved in dpp.
+dpp is a project file saved in PeakPo. You may plot, jcpds information and cake as well as many other information.
+
+
+
+
+
+
+
+
This notebook takes advantage of the PeakPo modules and other local modules. They can be found in ../local_modules folder.
+
+
+
+
+
+
+
+
+
Check the versio of pyFAI in your conda environment¶
+
+
+
+
+
+
+
+
+
+
+
+
+
Out[12]:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Note that the example data files I provided are made with pyFAI version 0.14. If you see version higher than 0.15 here, you will get error when you read the example dpp file. In that case, you either follow the instruction in setup_for_notebooks.ipynb or you may use your own dpp for this note book.
+
+
+
+
+
+
+
+
+
+
Change the following two cells for your own dpp file¶
+
+
+
+
+
+
+
Data files should be in the ../data folder. You need: dpp, chi, tif, and poni files.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
../data/hStv/hSiO2_404_009.dpp
+
+
+
+
+
+
+
+
+
+
+
+
+
+
The cells below show how to look into the data structure of the model_dpp and get values from it.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Out[17]:
+
+
+
+
+
+
{'base_ptn': <ds_powdiff.DiffractionPattern.PatternPeakPo at 0x1101758d0>,
+ 'waterfall_ptn': [],
+ 'jcpds_lst': [<ds_jcpds.jcpds.JCPDSplt at 0x110175c50>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x11020da58>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x11020df98>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x110216390>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x110216588>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x110220160>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x110220ba8>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x110227438>],
+ 'ucfit_lst': [],
+ 'diff_img': <ds_cake.DiffractionImage.DiffImg at 0x10e94b320>,
+ 'poni': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv/LaB6_37keV_p49_center.poni',
+ 'session': <ds_jcpds.jcpds.Session at 0x110235c50>,
+ 'jcpds_path': '/Users/DanShim/Python/jcpds',
+ 'chi_path': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv',
+ 'current_section': None,
+ 'section_lst': [],
+ 'saved_pressure': 39.6,
+ 'saved_temperature': 300.0}
+
+
+
+
+
+
+
+
+
+
+
+
Setup a new PeakPo model and assign info from dpp¶
+
+
+
+
+
+
+
+
Make sure to reset the chi folder location using the new_chi_path option.
+
+
+
+
+
+
+
+
+
+
The following three modules are all in the ../local_modules folder.
+
+
+
+
+
+
+
+
+
+
+
+
Let's make some plots¶
+
+
+
+
+
+
+
Let's plot in regular scale.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Let's do some fancy stuff¶ I wrote similar plot functions with more options in fancy_plots.py in the same folder.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
In the plot below, we plot diffraction pattern in $2\theta$ scale to prevent any distortion in the diffraction pattern. We just plot tickmarks in d-spacing scale.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
,\n",
+ " 'waterfall_ptn': [],\n",
+ " 'jcpds_lst': [,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ],\n",
+ " 'ucfit_lst': [],\n",
+ " 'diff_img': ,\n",
+ " 'poni': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv/LaB6_37keV_p49_center.poni',\n",
+ " 'session': ,\n",
+ " 'jcpds_path': '/Users/DanShim/Python/jcpds',\n",
+ " 'chi_path': '/Users/DanShim/Dropbox (ASU)/Desktop/PMatRes/github-dev/XRD-peakpo/data/hStv',\n",
+ " 'current_section': None,\n",
+ " 'section_lst': [],\n",
+ " 'saved_pressure': 39.6,\n",
+ " 'saved_temperature': 300.0}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_dpp.__dict__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup a new PeakPo model and assign info from dpp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from model import PeakPoModel\n",
+ "model = PeakPoModel()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make sure to reset the chi folder location using the `new_chi_path` option."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.set_from(model_dpp, new_chi_path='../data/hStv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Make XRD plot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following three modules are all in the `../local_modules` folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from xrd_unitconv import * # Make conversios between different x-axis units"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import quick_plots as quick # A function to plot XRD pattern\n",
+ "import fancy_plots as fancy # A function to plot XRD pattern"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let's make some plots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot in regular scale."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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MGjUqDjvssPjmN78Zv//971tUfhLx9+cpnnrqqbj66qvj+OOPjwEDBsTVV18d6XS6wMkBKCYFKAC02SWXXBK5XK7ZuYsuuig6dOhQhES01Lnnnhvdu3dvdu6qq66K1157rQiJ+Ec33XRT3HfffUnHaLfVq1fHD37wgzatnTBhQjz//PNxyimntCtDeXl5fOlLX4rnnnsuhg8f3ur18+fPb/dXmd8ZPPvss3H11Ve3ef306dPj4Ycfjl69euUxVf7ss88+sWjRovj4xz/epvV33313LFiwIM+pti2bzbZp3Y5cgBLx99+r119/fZx66qltWv/AAw9EVVVVnlPBzufb3/52bN26tdXrDj744FiyZEn893//d+y+++55ybL33nvHL3/5y1i4cGEccsghbdqjsrIyfv7zn+clz7Zs3rw5vv71r7dp7XHHHRdLly6Ns846q10lLxH/n737jorqWtsA/tCb2FGs2AAbttgixihWYouoGEXBgl1ji0aNUeM1sWK5ltgbdtBYorErlljAniD2SlBEKYogbb4/srxfijNz9plzZgZ8fmuxVlb2u/d+OCochtnvAT7//HNcvHgRrVq1Ep7722+/ISQkxKD99cnMzMTAgQMl/Wz4T1WrVkVkZCRGjBgBGxsbg3I0adIE586dQ69evYTnPnv2DOPHjzdof1JHdnY2AgMDDW5+0qFDB+zevVu4cRMRERERERERERERERERERERERFpl5mZiU6dOmHRokUm2T82Nhbt2rWTfXbFFNatW4cGDRrg+vXrJs2h0WiwaNEi1K5dGxcvXlR9v4sXL+Ljjz9Gt27dcPr0aUXWfPToEcaOHQtPT0+Eh4crsiaROVi3bh2mTp363o9du3aZOh6R6tgAhYiIZDl8+DAOHz6sty5fvnzo37+/ERKRiIIFC6Jv3756696+fYuvvvrKCInonVu3bmHUqFGmjqGIMWPGyOoEO2jQIOzcuVPRZhllypTBqVOn8MknnwjPHTVqFJKTkxXLkttkZGSgT58+shtojB8/HgsWLICVlZXCyZRVsGBB/Pzzz6hUqZKs+dOmTVM40fvJbYCSlZWlcBLlWVpaYs2aNShZsqTw3Ldv32Lv3r0qpCLKO27cuIGNGzcKz2vRogUiIyNRo0YNFVIBn3zyCS5duoQOHTrImr948WLVvsZ99913sp5E0L59exw7dkzW1zNtChYsiP3796Nbt27Cc6dNm4b79+8rluWflixZgt9//114Xv369XH27FlUrlxZsSx2dnbYsGEDxowZIzx3xYoVOHv2rGJZyHDZ2dkICgrC5s2bDVqnS5cuCA8Ph62trULJiIiIiIiIiIiIiIiIiIiIiIiISKPRoEePHiZ/H3dOTg6+/fZbTJw40aQ5pBg1ahT69OmDN2/emDrK/9y8eRNNmjTB/v37Vdtj6dKlaNiwIc6fP6/K+rGxsejatSu+/PJLs394LJEU69atw3fffffeDzZAoQ8BG6AQEZEsEyZMkFTXt29fRZsYkHKGDh0q6Wn0P/30Ew8CGklmZia6d++O1NRUU0cx2K+//ordu3cLz+vVqxeWLl2qQiLA0dERu3btgqenp9C8p0+fYu7cuapkyg3+85//yDrUDABjx47FjBkzFE6knqJFi2Lfvn1wcnISnnvixAlcuXJFhVR/J7cBSm55Ea9AgQJYuHChrLl79uxROA1R3jJ37lzk5OQIzWnUqBF2796t+v28o6Mjdu7ciY4dOwrPff78OQ4dOqR4pgcPHuDHH38Unte8eXOEhYXBxsZG8UxWVlZYv349GjduLDQvLS0NkydPVjwPAKSkpOD7778Xnufl5YUDBw4gf/78KqQC5syZA39/f6E5Go0G48aNUyUPicvJyUFQUBA2bdpk0Drdu3fH1q1bVfk3SURERERERERERERERERERERE9CGbPn06wsPDJdUWLlwY3bp1w+zZs3H06FHExMQgLi4OqampyMrKwqtXrxAbG4vz589j06ZNGDVqFGrXri2UZ8aMGVi2bJmcT0V1Go0GQ4cOxYIFC4Tnuru7Izg4GKtXr0ZERAQePXqEpKQkZGVlIS0tDQkJCbhx4wb27t2L2bNno127dsLv/X3z5g06duyIDRs2COfT56uvvsLQoUON8lDXRYsWITAwUPg900REZF7YAIWIiITt2rULFy9e1FtnYWGBoUOHGiERyeHh4YFWrVpJqs0NXVDzgkmTJuHSpUumjqGISZMmCc+pWbMmVq5cKakxj1yFCxdGWFgYrK2theYtWrQIycnJKqUyXzdv3sSsWbNkzQ0MDMTs2bMVTqQ+Dw8PWS8qApA9T0RaWpqsebmlAQoA+Pn5wd3dXXjemTNnVEhDlDe8ePECmzdvFppTokQJ7NmzB46Ojiql+jsrKyts2bIFVatWFZ67bds2xfNMmzYNGRkZQnNKlSqFbdu2wc7OTvE879jZ2SEsLEz4F1NbtmzB3bt3Fc+zYMECJCQkCM3Jly8fduzYgUKFCime5x0LCwusXbsW5cqVE5p3+vRpREREqBOKJFOq+UlQUBA2btwIKysrhZIRERERERERERERERERERERERERAFy8eBHfffed3jovLy+sX78esbGx2Lp1K8aOHQsfHx94enrC1dUVjo6OsLKyQr58+VCyZEnUr18fPXr0wLx583Dp0iXcv38fY8eOhbOzs6Rco0ePRkxMjKGfnuJGjBgh9LDg/PnzY8yYMbhy5Qpu3bqFlStXom/fvmjSpAnKlCmDAgUKwMrKCvb29ihSpAgqV66Mdu3aYezYsdi7dy/i4+OxY8cO+Pr6St4zKysLffv2xf79++V8iu81c+ZMhISEKLaeFJs3b+YD8YiIcjk2QCEiIiEajQZTp06VVNuiRQt4eHioG4gMMmzYMEl1J06cwOnTp1VO82E7duwY5s6dK6m2fPnyKqcxTFRUFI4fPy40x8HBAVu2bFH1wPA7Xl5eGD16tNCc5ORkLF68WKVE5mvy5MnIzMwUnvfRRx9h+fLlKiQyjuDgYPj4+AjP27p1K+Lj41VI9P/S09NlzctNDVAsLS0xePBg4XlPnjxBbGysComIcr9t27YJf/1YsWIFihQpolKi93NwcMC6deuEm6GJ3nfo88cffwg3XrCwsEBoaKhRrpmrqytmzJghNCc7OxszZ85UNEdaWpqs+6PFixfLanQlytHRUeiXhe9Mnz5dhTQkVU5ODnr37o2NGzcatE7//v2xdu1aWFry5W8iIiIiIiIiIiIiIiIiIiIiIiKlDRo0SOd71C0sLDBixAhERUUhMDAQ9vb2svYpV64cZs+ejZs3b6Jz585669PS0jBmzBjhPTQajdaP3r17y8r+zpo1a7Bo0SJJtba2tvjmm2/w4MEDzJ07FzVr1pS1p62tLfz8/LB//35cuHABTZo0kTQvOzsb3bp1w9WrV2Xt+1dHjhyR/EDuKlWqYOLEidi/fz/u3buHlJQUZGZm4vnz54iJicH27dsxcOBAFC9eXNJ6ISEh2LlzpyHxiYjIhPgOcCIiErJz507JP8QMHTpU5TRkKF9fX5QuXVpSrdIHJun/vXz5EoGBgcjJydFba2lpidDQUCOkkm/OnDnCc0aNGoUqVaqokOb9Jk+ejEKFCgnNWbJkSa5qImGoq1evIiwsTHhekSJFsHPnTtkvUJqLkJAQ4QOzb9++xU8//aRSoj99CA1QAKB9+/ay5l25ckXhJER5w5YtW4TqW7dujXbt2qmURrd69erBz89PaM7jx4/x8OFDxTIsXLgQGRkZQnO6d++OZs2aKZZBn4EDB6Jy5cpCc0JDQ/Hy5UvFMqxbtw7Pnz8XmuPt7Y2goCDFMujj6+uLVq1aCc05cuQIoqOjVUpEuuTk5KBPnz4G/7wzbNgwLF++XLiZEhEREREREREREREREREREREREem3Z88eREVF6ayZM2cOFixYAFtbW0X2LFGiBMLDwzFt2jS9tfv378e5c+cU2ddQkZGRGDJkiKTa+vXr48qVK5g+fbrweRdd6tWrhxMnTmDJkiWSznm8fv0aHTt2xKtXr2Tv+fr1awQHB0Oj0eisq1atGg4ePIjo6Gh8//338PX1Rfny5eHs7Axra2sULVoUnp6e6Nq1K5YtW4b79+9j4cKFKFy4sN4MgwYNQkJCguzPgYiITIcNUIiISIjUJ42XLFnSZIcmSTorKyv06dNHUu3+/ft5EFAl/fv3R2xsrKTa8ePHw9vbW+VE8sXHxws3gChWrBjGjx+vUqL3c3JywqBBg4TmxMXFYf/+/SolMj/ffvut3heb3mfevHkoW7asComMq1atWujSpYvwPDZAUUalSpVQvnx54XkPHjxQPgxRLpeYmIizZ88KzRk3bpxKaaQZPHiw8BylGiBlZGRgzZo1QnPs7Ozwww8/KLK/VJaWlhg1apTQnLdv32LTpk2KZVi2bJnwnLlz5yq2v1SiT3IAgNWrV6uQhHTJyclB3759sWHDBoPWGTNmDBYtWsTmJ0RERERERERERERERERERERERCpZuHChzvH+/fvLeu+eFN9++62k97muWrVKlf1FpKWloXv37nj79q3e2n79+uHkyZOqPVjYwsICQ4YMwfHjx1G8eHG99Q8fPsTo0aNl7xcSEqL34Yb9+vXDpUuXhB505+DggC+//BJRUVGoWbOmztrnz59j8uTJktcmIiLzwQYoREQk2aFDh3Dx4kVJtb1794aVlZXKidSl0Wjw4sULPHjwADExMbh27RpiYmJw7949xMXFCT8V3lz17dtX0uEwjUaDJUuWGCHRh2XVqlXYuXOnpNqPPvoIU6dOVTeQgdatW4fMzEyhOaNHj4azs7NKibQbPnw4rK2theZ8KIdho6KisHfvXuF5TZs2RWBgoAqJTEPOC3bHjh1DcnKyCmn+lJaWJmteVlaWwknUV61aNeE55twAJT09HbGxsbhz5w5+++03/P7777h9+zYePnyIpKQkU8ejPOzYsWNCTZCqV68OHx8fFRPp16xZMxQsWFBozr179xTZe9euXcLd3vv06QM3NzdF9hcRGBiIIkWKCM1R6l7mwoULuHbtmtCcNm3aoGHDhorsL6JVq1bC31NCQ0OF72lJvpycHPTr1w/r1683aJ2JEyeapMkOERERERERERERERERERERERHRh+KPP/7AiRMntI4XK1YMc+bMUTXD999/j1q1aumsCQsLM/l76KdMmYK7d+/qrZswYQJWrVoFOzs71TM1bNgQERERKFasmN7aVatWISIiQniPN2/eYPHixTpr+vfvj1WrVsHW1lZ4fQAoX748IiIiULlyZZ11K1euxP3792XtQUREpsMGKEREJJnID6B9+vRRMYmyNBoNbty4gZUrV2LYsGHw8fFBmTJlYGdnh6JFi6J8+fKoUqUKatasiSpVqqBixYooWbIk7OzskD9/fnh4eMDX1xfDhg3DsmXLEBkZKak7p7koV64cvL29JdWGhobi1atXKif6cNy6dQsjR46UVOvg4ICNGzfCxsZG5VSGET206ODggODgYJXS6FaiRAnhA9779u3Dy5cvVUpkPvS92PQ+NjY2WLZsmQppTKdBgwaoV6+e0JzMzEzs27dPpUR/NtGQQ6T5gbnQ92Lk+zx9+lSFJGJSUlJw8OBBTJs2Dd26dUONGjVQqFAhODg4oHTp0nB3d4eXlxeqV68ODw8PlCtXDoUKFYKNjQ1KlCiBhg0bIiAgAFOnTsWePXsQGxtr6k+JcrkzZ84I1Xfs2FGlJNJZWlqicePGQnOU+uWEnAYMw4YNU2RvUfb29ujcubPQnKtXr+L69esG7y3nOg0fPtzgfeUKCAgQqn/+/DkOHDigUhr6q5ycHAQHB2PdunUGrTN16lR8//33yoQiIiIiIiIiIiIiIiIiIiIiIiKi9zp06BBycnK0jo8ZMwYFChRQNYO1tTVCQkJ01qSkpCAqKkrVHLpER0dj/vz5euvGjBmDH374wQiJ/p+npycOHToEJycnvbVfffUVNBqN0PobNmzQ+TDC2rVryzqz8k8FChTAnj17dH4eWVlZWLRokcF7ERGRcbEBChERSXLjxg0cOXJEUu3HH3+MSpUqqZzIMBkZGdi7dy/69u2L4sWLo2rVqhgwYACWLFmC48eP48mTJ5Ke+P3q1Svcvn0bBw4cwJIlSzB48GDUr18fBQsWRMuWLTF37lxJ3TpNrVevXpLqXr16hY0bN6qc5sOQmZmJgIAApKamSqqfO3eurGYAxvT7778jOjpaaE737t1RpEgRlRLp161bN6H6rKws/PLLLyqlMQ/JyckICwsTntezZ094enqqkMi0unfvLjzn+PHjKiT504fUAKVo0aLCc968eaNCEv3u3buHOXPmoFGjRihUqBDatGmDKVOmYPv27bh+/TqSkpL0rpGVlYWnT5/i/Pnz2Lx5M7777jt07NgRpUuXRoUKFTBo0CDs2bMHGRkZRviMKC+JjIwUqm/btq1KScSI3vckJiYavGdSUhIOHz4sNMfHxwfVqlUzeG+5RO9lAGDPnj0G7ZmTk4MdO3YIzalYsSJ8fX0N2tcQprhOpJ9Go0FwcDDWrl1r0DozZszAlClTFEpFRERERERERERERERERERERERE2pw+fVrneNeuXY2Sw8fHB15eXjpr9GVV06RJk5CVlaWzxtfXF7NnzzZSor+rWbMmVqxYobcuKipK+D2j4eHhOseXLFkCW1tboTW1cXd3x9ChQ3XWbNq0KVeepyAi+pCxAQoREUki0llRzmFxY3n48CG+/vprlClTBh06dMDatWvx/PlzxfdJT0/HkSNHMHbsWFSqVAkff/wxVq5cKfvguto6deoES0tptwWrV69WOc2H4dtvv5XcTdbX1xdDhgxROZHh9L1I8T4BAQEqJJHu888/l/x3/529e/eqlMY8bNq0SbiJhKWlJb7++muVEpmWv78/LCwshOacO3dOpTRAWlqarHm58QU7Z2dn4TnGbICSk5OD8PBwtGjRApUqVcK4ceNw9uxZnR3N5bp//z6WL1+Ojh07wtXVFYMGDcL169cV34fyJpG/K/ny5et3VUcAACAASURBVEODBg1UTCNd6dKlheqlNpXTZffu3ZKaIP6Vqe9lPv30U+Fmcobey5w6dQrPnj0TmtOjRw/h76dKqlChAmrVqiU05+effxZ+agFJp1Tzk/nz52P8+PEKpSIiIiIiIiIiIiIiIiIiIiIiIiJdbty4oXWsRIkSKF++vNGy+Pv76xy/deuWkZL83cWLF/HTTz/prClWrBg2btwofJ5FST169EDPnj311s2bN0/ymq9fv8apU6e0jnt7e+Pjjz+WvJ4UY8eOhY2Njdbx+Ph4Vc94EBGR8tgAhYiI9EpPT8fmzZsl1VpYWBitW6eI+/fvIzg4GO7u7pg9ezbi4+ONuv+5c+cwYMAAuLm5YcaMGbIPsKvFxcUFjRo1klR78eJFXLlyReVEedvx48cxZ84cSbVFixbFmjVrVE6kjF27dgnVFytWDJ9++qlKaaQpXLgw6tWrJzTnwIEDwoejc5OVK1cKz/Hz84Onp6cKaUyvVKlSejtD/1N0dDRevXqlSp6MjAxZ83JjAxQ7OzvhOcb4t5mTk4PQ0FBUqVIFXbt2xdGjR416QD4xMRHLly9HjRo10KZNG0RGRhptb8p9nj9/juTkZMn1VatWNekvUv7KyclJqF6JBkii9zI2Njbo1KmTwfsawsrKCi1atBCac+HCBeEGJn8lep0A/b9kNIbWrVsL1T99+lRyw0ISo9Fo0L9/f4N+zrGwsMCSJUswcuRIBZMRERERERERERERERERERERERGRLvfv39c6VrJkSSMmAdq0aaNzXFdWNc2fP19STeHChY2QRreQkBAUKlRIZ83Zs2clv2f9xIkTOs889OnTRyifFEWLFtX7AEhdTVlyo6dPn2L58uXo378/6tevj9KlSyN//vywtrZG/vz5Ubp0aTRu3BjBwcHYsGEDnj59aurIAP48D7Nv3z6MGzcObdq0QYUKFVCkSBHY2NjAwcEBLi4uqFGjBrp06YIZM2bg0qVLJnuYYVxcHC5fvoxjx44hPDwcGzduxM6dO3H48GHcuHHDqA/vJfoQWZs6ABERmb/du3cjKSlJUm3dunXh6uqqciLp3rx5gxkzZmDu3LlIT083dRzEx8dj4sSJWLp0KUJCQsziIOA7HTt2xOnTpyXVrl27FgsXLlQ5Ud6UmJiIwMBA5OTkSKpfuXKlWf2b0iY+Ph5Xr14VmtOpUydYWVmplEi61q1b4/z585Lrk5OTcfnyZdSvX1/FVKZx/fp1WQ2Ohg4dqkIa89G8eXNcu3ZNcn1OTg4iIyPh4+OjeBa5jUxyYwMUOc3CHBwcVEjy/86ePYvhw4fj4sWLqu4j1cGDB3Ho0CH06NED8+fPh4uLi6kjkZl58OCBUH316tXVCSKDaEMjXZ3bpcjKysKxY8eE5vj4+Oj9pY8xtG7dGtu2bZNcr9FoEBERIftnkYMHDwrVe3p6msXfrdatW2PWrFlCc44fPy7cLI9002g0GDBgAFavXi17DUtLSyxfvhzBwcEKJiMiIiIiIiIiIiIiIiIiIiIiIiJ9dD2Yz9iNCmrVqgVbW1utDTdevnwpaZ0HDx6gfPnyWsfXrl2L3r17S1rr2bNnCAsL01lTt25d9OjRQ9J6aitWrBjGjRuHCRMm6KwLDQ2V9H5KfecumjdvLpRPqubNm+s8Eyd63slcnT59GtOmTcPRo0e1nkt79eoVXr16hdjYWJw5cwarV6+GlZUV2rdvj6+//hoNGzY0cuo/H+o5Y8YMrFu3DomJie+tycrKQnp6OhISEnD9+nXs2LEDEydOROXKlTFixAgEBwfD2lq9lgiJiYnYvHkzTp48iV9//RVPnjzRWW9hYQFPT094e3ujQ4cOaN26tayHAJ8+fRp37tz51//X1bTmzp07WLdu3XvHKlWqhMaNGwMAli5divj4eK3rVK9eHV26dBELLGDTpk24ffu21vGAgAC4u7urtj/lbmyAQkREem3YsEFybbt27VRMIubq1avw9/fHrVu3TB3lX548eYJu3bph7969WLp0KZydnU0dCR07dsTYsWMl1W7btg3z5s0zi+YVuc2AAQP0/hD0Tr9+/fD555+rnEgZhw8fFn6xqmXLliqlEePj44Np06YJzTlz5kyebICyb98+4TmlS5dGkyZNVEhjPkaOHCn8OVaoUEGVLHIbmWRlZSmcRH2vX78WnuPo6KhCkj+v3+TJkzFz5kyTdRDWRqPRYNOmTThy5AjWr1+P1q1bmzoSmZFnz54J1VetWlWlJOJSUlKE6g1tgHT+/HnhPc3pXkbUmTNnZDVAiY2NxY0bN4TmmMt18vb2hp2dHd6+fSt5zpkzZ1RM9OHRaDQYOHAgVq1aJXsNKysrrFmzBoGBgQomIyIiIiIiIiIiIiIiIiIiIiIiIil0PZz68ePHRkwCWFtbo3LlylqbbqSmpho1DwBs3LhRa0OWdyZNmmSkNNIMHToUs2fP1tqYAgDCw8OxYMECWFpa6lzr5s2bWsdKlCiBcuXKyY2pk5ubm85x0YdKmpsXL15gwIAB2Llzp6z52dnZ2LVrF3bv3o0BAwZg/vz5qj989p3FixdjwoQJss6HAEBMTAwGDx6MZcuWYf369ahZs6ai+WJiYjBv3jxs2rQJb968kTxPo9EgJiYGMTExWL16NQoVKoThw4dj5MiRQg/YXLVqFdavXy+U+cyZM1rf4xwUFPS/Bih37tzB/Pnzta5TokQJdO7cGRYWFkL7S/H27VsMHTpUa9MsR0dHjBkzRvF9Ke9gAxQiItIpPj5e6Anf5tIAZdu2bejdu7fOH6zNwcaNG3Hjxg388ssvcHFxMWkWd3d3lC9fHvfv39db++zZMxw5coSHrAWtXr0a4eHhkmorVqyIBQsWqJxIOceOHROqt7CwQNOmTdUJI6hevXqwsrISaixx5swZjBo1SsVUprF//37hOd27d9f7IlZuV7ZsWZQtW9bUMQDIb2Qit3GKKcl5Abxo0aKK53j16hXatWuHkydPKr62kp49e4a2bdti2bJlCA4ONnUcMhMJCQlC9SVKlFApibi4uDih+uLFixu0n+i9DCCv8Yga3NzcUKJECaFrJrexR26+Tra2tqhduzbOnTsnec6vv/6qYqIPi0ajwaBBg7By5UrZa1hbW2Pjxo3o1q2bgsmIiIiIiIiIiIiIiIiIiIiIiIhIKnt7e61NAp4/f46bN2/C09PTaHn27duntbGCra2t0XK8ExYWpnO8XLly6NChg5HSSOPs7IzAwEAsXLhQa01cXBwuXLiAhg0b6lxLVwOUSpUqyc6oj75zBElJSartrbaYmBj4+voq0sRFo9Fg+fLluHr1Kvbt24fChQsbHlCLzMxM9OvXD6GhoYqsd/XqVTRu3Bjh4eGKnGfMycnBvHnzMGnSJKGHK2qTmJiIadOmYdmyZViyZAm6dOli8JqGCgoK0tkAJS4uDmfOnPlfwxQl/fLLL1qbnwBA586d4ezsrPi+lHfk7ZOKRERksN27d0s+NO3i4oLatWurnEi/bdu2ISAgwOybn7xz8eJFNG3aVOdNnbG0aNFCcu2mTZtUTJL33L59GyNGjJBUa2VlhdDQUOTLl0/lVMo5e/asUH3NmjVRpEgRldKIcXR0hJeXl9CcvHgYNjk5WfjPEfizAQoZj9xGJrmxAYqUhlz/pHRH6FevXqF169Zm3/zknezsbPTv39+gw+WUt4h2ylajiZBcv//+u1B9mTJlDNpP9HtgoUKFFO8gbogGDRoI1V+9elWoS/k7otfJnJreAdD7i7d/SkhIwK1bt1RK8+F41/xkxYoVstewsbHB9u3b2fyEiIiIiIiIiIiIiIiIiIiIiIjIhPSdAzHkfWJylC5dGpUrV37vR4UKFYyaJTY2FhcuXNBZ07NnT1hYWBgpkXR9+vTRW3PixAm9NfHx8VrHlH6v/19ZWVnpHM/MzFRtbzVFR0ejSZMmijQ/+atz587Bz89PteuSlZWFrl27Ktb85J3Xr1/Dz88Ply9fNmidly9fwsfHB2PHjlWk+clfxcfHo2vXrpgwYQI0Go2ia4uqWbMmatSoobNG6oPeRW3dulXnuJSvOfRhYwMUIiLSaffu3ZJrmzRpYvIfws6fP49evXrluoPe0dHR6N69O3Jyckyao3nz5pJrf/rpJ8Vv8vOqzMxMBAQEIDU1VVL9xIkT8fHHH6ucSjlJSUmIiYkRmlO/fn2V0shTr149ofq4uDgkJCSolMY0Dh48iKysLKE5Li4uqFWrlkqJ6H1E/4zeyW3fFwEgKipKeI7SL4r27NlTVmMgUxs6dGiuadpC6hK9VzOXBijZ2dm4ePGi0Jxq1arJ3k+j0eD8+fNCc+rVqwdLS/N5WU30XiYrKwvR0dHC+4h+TXR3d0ehQoWE91GL6HUCgGvXrqmQ5MOh0WgwePBgg36pbWdnh507d6JTp04KJiMiIiIiIiIiIiIiIiIiIiIiIiJRHh4eOscXLVqUK99/rYSjR4/qbXhgrg8Bq1mzpt6GMREREXrX0fXwRjXfTxoXF6dz3MnJSbW91fLo0SO0aNECz58/V2X9iIgIfPfdd6qs3bt3b6EzqSLevHmDrl27Ij09Xdb8pKQkNG3aVNLfZ0PMnDkTY8aMUXUPKYKCgnSO79ixQ/FGLW/evMHPP/+sdbxcuXJm9YBNMk/Wpg5ARETmKzU1FUePHpVc/+mnn6qYRr+kpCR88cUXubYr4y+//ILly5dj8ODBJsvg4+MDCwsLSTeur1+/xvHjx9GmTRsjJMvdJk+ejMjISEm19erVw+TJk1VOpKzIyEjhH3Zq166tUhp5qlevLjzn999/N/nXPSUdO3ZMeE7Tpk1N3vjqQyO3kUlua4ASExMj64U6Jb+2LFiwAHv27FFsPWPKzMxEUFAQoqOj4eDgYOo4ZELNmzfHjz/+KLm+UqVKKqaR7tSpU0hMTBSaU6dOHdn73blzBy9fvhSak1fuZerWrSu5Pi0tDdevXxfaI69cpy5duqiQJu/TaDQYMmQIli9fLnsNBwcH7Nq1C61atVIwGREREREREREREREREREREREREcnRoEEDnWfNMjMz4efnh7CwMDRu3NiIyUzvxIkTOsddXV1lvY/RWFq1aoVly5ZpHb906ZLeNXQ9OFrN97XfuHFD53iJEiVU21sNaWlpaNu2rc7GLmXLlkWLFi3g5uaGYsWKIT09Hc+ePcOdO3dw4MABnc1o3pkzZw769OmDihUrKpb9P//5j87zSY6OjmjWrBm8vLxQvHhx2NvbIz4+HnFxcTh8+DDu3r2rd4+7d+9i7ty5mDRpklA2jUYDf39/4fdDyzV//nxUq1YN/fr1M8p+7xMQEIBx48ZpPVP05MkTnD9/Hg0bNlRszz179uj8WtC7d2+eRSO92ACFiIi0OnTokFA3PFN3Xps6dSoePHhg0BoeHh5o1qwZ6tWrBw8PD5QpUwaFChWCg4MDLC0t8erVK7x+/Rqpqal49OgRbt68iZs3b+LChQuIiooyuOPdxIkT4e/vjyJFihi0jlwuLi7w9PRETEyMpPq9e/eyAYoeERERmD17tqRaR0dHbNy4EdbWuesW7dq1a8JzzO0wbLVq1YTn5LUGKBcvXhSe4+Pjo0IS0iUrK0vWvNzWACUsLEx4TpEiRRRr3vDHH3/gm2++MWgNe3t7+Pj4oGHDhqhVqxbc3NxQsmRJODo6wt7eHmlpaf+7p0hMTMStW7cQExODGzdu4NixY3jx4oVB+z948AAzZszAtGnTDFqHcrdatWqhVq1apo4hbN26dUL1VatWNegXEx/yvYyI6Oho4e8n5nadKleuDGtra6Hvp6LXif6k0WgwdOhQnb8M1cfJyQl79+5Fs2bNFExGREREREREREREREREREREREREcn322Wf44YcfdNY8ffoUTZs2xVdffYXRo0ejWLFiRkpnWufOndM5bu7nLz799FOd7/mLj49HQkICihYtqrVmzpw5yMnJee9Y/fr1Dc6ojb4Hn7q7u6u2txri4+MRHx//r/9vaWmJrl27Yvz48TrfI56eno7t27dj9OjROs8lZGRkYMmSJZg3b54iuQHtD2euWrUqJk2aBD8/P9jZ2Wmdf/nyZYwcORInT57Uuc/ixYvx9ddfw8bGRnK2pUuX4vDhwzprihcvDn9/fzRv3hzVqlWDi4sL8uXLh9TUVCQlJSEhIQFRUVE4d+4cdu/erfchnGPGjIGvry9KliwpOaeSihcvjtatW2P//v1aa8LDwxVtgLJ161atYxYWFggKClJsL8q7ctfpWiIiMipdHTn/KV++fLIO3Snl/v37Qk+3/ysHBwcEBgZi2LBhejtpFipUCIUKFQLw5+G5vz6F+unTp9i3bx9+/PFHWY0EACApKQkrVqzAhAkTZM1XQoMGDYQaoCxZskTlRLlXYmIievXqpfWH93+aN28ePDw8VE6lvN9++014jrl1rZXz9Ss6OlqFJKaRlZUl68+xSZMmKqQhXeQ2MslNDVCysrKEmx8AULRD+JQpU/DmzRtZc2vUqIExY8agc+fOcHJy0lrn5OT0t/GPPvrof/+dnZ2NX3/9FeHh4Vi1apXsLIsXL8b48ePh6Ogoaz6RKTx69AibN28WmtO+fXuD9pTzPdDLy8ugPZVWoUIFODg4IC0tTfIc0XuZvHCdbG1tUbFiRdy8eVPynLx0z2dMw4YNk/0zOgA4Oztj//79H9wTQIiIiIiIiIiIiIiIiIiIiIiIiMyZt7c3qlWrpvfhYtnZ2Zg1axbmz5+Pzz77DF27dsVnn32GggULGimpcWVkZOD27ds6a/76fnFzVLNmTb010dHROs+RDBkyRMlIkpw+fVrvtVeyuYOpVKlSBVu2bJH052Rvb4/AwEC0bNkSrVq10vke4A0bNmDWrFlCjURE2NvbY+7cuRg8eDAsLS311teuXRvHjx/H+PHjMWfOHK11z549w969e+Hn5ycpx+vXrzF16lSdNePHj8e333773vMX+fPnR/78+VG2bFnUqVMHAwYMQGpqKlauXIlvv/0Wr1+/fu+aycnJmDBhAtavX//e8XXr1r33/EzTpk0RERHx3jlBQUFCZ24CAwN1NkDZsWMH5s6dK3k9XVJSUnDgwAGt402bNkW5cuUU2YvyNv1fLYiI6IN1/PhxybW1a9eWdBOqlpCQEGRkZAjP++yzz3Djxg0sW7bM4IYMrq6u6NevH6KiorBnzx7Zh/2WLl0KjUZjUBZDNGjQQHLt48ePcf36dRXT5G4DBw7E48ePJdW2a9cOAwcOVDmROvS9cPVPJUqU0NkUwBRcXV3h4OAgNOfBgwfqhDGB6OhopKenC82xs7ODp6enSolIm6ysLFnzclMDlNDQUNy7d094nqENEN55+vSprAYsBQsWxMqVK3HlyhUEBgYa9HXOysoKn3zyCRYuXIh79+5h9OjRsLYW71+amJgo3EiCyNSGDBmCzMxMoTm9e/c2aE/RexkLCwtUqFDBoD2VZmFhgbJlywrNEb2XEb1OAFCpUiXhOWoTfdE8L93zGcvQoUOxdOlS2fMLFCiAw4cPs/kJERERERERERERERERERERERGRGZo8ebLk2oyMDOzatQsBAQFwcXHBp59+iu+//x7nz5+X/d54cxQTE6P38zG3Bwn/k4eHB+zs7HTWxMbGGimNdGPHjtU5bmlpiebNmxspjToCAgIQFRUlqfnJX5UoUQLh4eFwdnbWWvPixQtERkYaGvG93NzccP78eQwdOlTo3KmlpSVmzZqFNm3a6KzT1WjjnzZv3oyEhASt4wsWLMCMGTOEHj7r5OSEkSNH4tdff9X5Pu4tW7YgLi5O8rpK69ixo87mUw8ePEBUVJQie/300094+/at1vE+ffoosg/lfWyAQkRE7xUfHy/0pOu6deuqmEa39PR0WYd7J0+ejJ9//hlubm6KZ2rfvj0iIyNlHcZ88uQJLl++rHgmqUS7Woo0yvmQrFmzBmFhYZJqXVxcsHr1apUTqUdfp9R/MseDsACEDw1LbW6TG8j5mlO5cmVYWVmpkIZ0kdvIJLc0QHn27BnGjRsnPM/Kygrt2rVTJMP69euFX0yvVKkSzp8/j+DgYFhYWCiS453ixYsjJCQEERERKFKkiPD8PXv2KJqHSE0LFizAvn37hOa0bt0alStXNmhf0XuZ0qVLw97e3qA91SDa2EP0Xkb0OllZWaF8+fJCc4xB9DqlpqYiMTFRnTB50LBhwwxqflK4cGEcPXpUqDEnERERERERERERERERERERERERGY+/vz98fX2F52VlZeHkyZOYNGkSGjZsiEKFCqFNmzaYPn06Tpw4gTdv3qiQ1jju37+vt6ZKlSpGSCKflZUVSpYsqbPm6dOnRkojTUhICM6dO6ezplmzZnB1dTVSIuV17twZGzZsEGrM8Veenp748ssvddacPHlS1tq6FC5cGMePH0eNGjVkzbewsMB///tfneczRHKHh4drHfv4448xYsQIoXx/5eXlhe3bt2tt8pKZmYnQ0FDZ6xvK3t4e/v7+OmuknoHUZ+vWrVrHnJ2d0blzZ0X2obxP/BHSRET0QYiIiBCqN2UDlL179wofSPv222/x3XffqZToT3Z2dlizZg0yMzOxadMmobkHDx5EnTp1VEqmm5eXF2xtbZGRkSGpPiIiQu8PQh+aO3fuCP3gs3r1ahQrVkzFROpJSkpCUlKS0BxzbYBSvnx53Lx5U3J9XmqAcu3aNeE51apVUyEJ6SO3y3VuaICSlpYGf39/nV11tWnbti2KFy+uSI4NGzYI1ZcqVQpHjx4VbqIkqlGjRjhy5Ai8vb2FXtw/duwYMjMzYWNjo2I6IsOtX78eo0ePFp43depUg/eW8kunvzLnexkRKSkpSElJQf78+SXV37t3T2j9smXLmuXXHjlNWR4/foxChQqpkCZvGT58OJYsWSJ7vqWlJY4cOYLatWsrmIqIiIiIiIiIiIiIiIiIiIiIzM7vYg9jIjKKau6mTpCrbNy4EQ0aNMCdO3dkr/H69WscPHgQBw8eBABYW1ujdu3a8Pb2RqNGjeDt7a23IYe5iIuL0zluYWGBEiVKGCmNfKVKldL5vtr4+HgjptFt3759kh6+akhjC1MrXrw4Vq1apbWxhlSBgYH4/vvvtY6r8SD3xYsXG/wgRXd3dzRs2BBnz5597/jNmzeRlpYGBwcHnevk5OTgzJkzWscDAgIMygkADRo0QNeuXbFt27b3jh8/flzWw4KVEhgYiBUrVmgd37FjB2bNmmXQHi9evMCRI0e0jvv7+8tu5EMfHjZAISKi9zp//rxQffXq1VVKot+7H3Slatq0KaZNm6ZSmr+zsLDAihUrcOrUKTx69EjyvKtXr6qYSjdra2t4enri+vXrkupPnjwJjUajs6PihyQrKws9evTA69evJdUPGDAA7du3VzmVekQPwgJQvUmAXKIdXRMTE5GamgonJyeVEhnPgwcPhOdUrVpV+SCkl9xGJubeACUxMRH+/v6yOwcPHjxYkRyxsbGIjo4WmhMaGmq0r2u1atXCrFmzMHz4cMlzUlNTcffuXVSuXFnFZETyZWdnY+LEiZg9e7bw3L59+6Jhw4YG7f/y5UskJycLzckr9zIA8OTJE8nf00UbxeS16yS3A/2H4ssvv8TixYsNWiMnJwf79u1jAxQiIiIiIiIiIiIiIiIiIiKivK56R1MnIPo3jdh7iD90hQsXxokTJ9CyZUvcuHFDkTWzsrIQGRmJyMhILFiwAABQrlw5eHt7w9vbGz4+PvD09FRkL6U9ffpU57iLiwusrc3/OLe+h8WlpaUZKYluu3fvxhdffIGcnByddfXq1UO7du2MlEp548ePR8GCBQ1ex8PDA66urlr/nso5U6RLrVq18MUXXyiy1qeffqq1AQrwZ/YqVaroXOPRo0c6H0JbsWJF2fn+ql+/floboERFRSmyh1ze3t6oVKmS1qZVd+/exeXLlw16D294eLjOhz736dNH9tr04TGs7RMREeVZIjdVlpaW8PDwUDGNbseOHZNca2lpiWXLlqmY5t8cHR0xZcoUoTlK/fAvl5eXl+TahIQEk+c1J5MnT0ZkZKSkWnd3d8ybN0/lROoSaezzTvHixVVIYrhixYoJz3n27JkKSYzvyZMnwnPKlCmjQhLSRaPR5MkGKHv27MFHH32ks9OrLl5eXmjdurUiWY4ePSpUHxAQgGbNmimyt1SDBw9GuXLlhObw+zSZqyNHjqBWrVqymp9UqFABISEhBmf40O9l9P3C7Z2kpCS8evVKaO0P8Tp9qEaMGIFFixYpstZ3330n3JCViIiIiIiIiIiIiIiIiIiIiIiIjK9UqVI4f/48AgICVNvjwYMH2LRpE4YMGYLKlSujTJkyCA4OxuHDh83qPfIpKSk6x4sWLWqkJIZxcHDQOZ6enm6kJO+n0Wjwww8/oHPnznqzWFlZYcmSJbn2gd92dnbo3bu3YuvpemDiw4cPFdsHAAYOHKjYddf3oEcp2ePj43WOiz5MU5tPPvkElpbvb9vw4sULnc1BjKFXr146x8PDww1af+vWrVrH3N3d4e3tbdD69GFhAxQiIvoXjUaDy5cvS64vV64c7O3tVUyk3fPnz4WeRN6uXTuTdPv84osv9P4Q+Fembqog0gAFAC5duqRSktzl5MmTmDVrlqRaa2trbNy4EU5OTiqnUpecw6Curq4qJDGcnMOwSUlJKiQxPjkNUMz1zzEv09edWBdTv1DyPhcuXMCnn36Kjh07Cn0v/6fp06cr9uKY6IHrMWPGKLKvCCsrKwQFBQnNMfV9BdFfvXz5EsuXL0ft2rXRsmVL/Pbbb8Jr5M+fHzt37lSkoznvZaTdy/A65Y17PjWMHDkS//3vfxVbLysrCwEBAXj9+rViaxIREREREREREREREREREREREZE6nJ2dsXHjRhw8eBD16tVTfb8nT55g9erVaNWqFUqVKoUpnf2BvQAAIABJREFUU6bgxYsXqu+rj75mHKY6dyfKzs5O53hmZqaRkvxbbGwsfH198c0330hqfjN16lSj/J1US/PmzRV5r/Q7ZcuW1TqWmJio2D4WFhbw8/NTbD1duQFp2TMyMnSOnzhxQiSSVvb29ti1axfCwsL+9bF9+3ZoNBpF9pErMDBQ59kbQxqgxMXF4eTJk1rHlWzmQx8GNkAhIqJ/uX37tt7Ok39VpUoVFdPoFhMTI1Tfv39/lZLo5ujoKNSlTvTp6kqrVq2aUD0boPx5KLJnz56SGxRMmjQJ9evXVzmV+vR1wXyf4sWLq5DEcC4uLsJzlPwh31Sys7NlNUcw10PNeZkhTUw0Go1BDVSU8vDhQ8yePRu1a9dGgwYNdL7AIUXjxo3RoUMHhdKJ3Vd89NFHqF27tmJ7i2jZsqVQvanvKyhvev78OaKjo5GcnPy3ry8ajQapqal4/vw57t69i6NHj2LFihUYNWoU6tSpAxcXFwwaNAhXrlyRta+zszMOHDiAmjVrKvJ58F5G2r0Mr1Puv+dTw6hRo7Bw4ULF17179y6GDx+u+LpERERERERERERERERERERERESkjlatWuHChQuIiIhAjx494OjoqPqez549w7Rp01C2bFlMmTIFb9++VX1PbfTtnVsaoOhrFKGvQYpa1q5di+rVq+PgwYOS6v39/TFx4kSVU6mrWbNmiq6nq5lKRkaGYudNqlatKuthhdroawKTlpamd438+fPrHN+wYQNu3rwplEub9u3bo0uXLu/9sLGxUWQPucqVK4dPPvlE6/itW7dw/fp1WWuHhYVp/TtkaWmJwMBAWevSh8va1AGIiMj8iD6FvUKFCiol0U/k5tLOzg4+Pj4qptGtevXqOHLkiKTaN2/eqJxGt/LlywvVswEKMHDgQDx+/FhSbcOGDTFp0iSVExmHnMYZRYsWVSGJ4fLlyyc8JykpSYUkxvXHH39I6n77T2yAYnxy/pz+Od/SUt0emBkZGUhJSUFKSgqSk5MRFxeHa9eu4cqVK7h69Spu3rypWNdaW1tbrFixQpG13hG5r/jss88U3VuEaKMyU99XUN509OhRdO/e3ah7urm5YefOnahTp45ia/JeRtq9DK9T7r/nU9ro0aOxYMEC1dZft24dfH194e/vr9oeREREREREREREREREREREREREpKwmTZqgSZMmSE1Nxc8//4zdu3fj0KFDePHihWp7vnnzBtOmTcOWLVsQFham2EP2RFhZWRk0bi7S09N1jjs4OBgpyZ+uX7+OIUOG4PTp05LntG3bFqGhoaqfnVBbrVq1FF1P35/dmzdvZL3H9p9MkVsfd3d3WFlZaT2T8+bNG7Rr1w4///wzPD09ZeXMLYKCgnQ+yDg8PBxeXl7C627dulXrWMuWLVG6dGnhNenDlru/ghMRkSpEO9a5ubmplES/J0+eSK5t1KiRUTqIamOuT0B/H9GmNlevXlUpSe6wdu1abN++XVKtk5MTQkNDc82LF/okJiYKz3F2dlYhieHk5EpOTlYhiXHJfSHRXA8152WGNkCxtbWFhYWFqh92dnZwcXFBxYoVUadOHbRt2xYTJkzAtm3bEBMTo1jzEwCYPHkyqlSpoth6OTk5+OOPPyTXt2jRQrG9RRUsWFCoe7aS153IVD7//HNERUUp2vwE4L2M1HsZXqfcf8+npDFjxmD+/Pmq7yPSZJKIiIiIiIiIiIiIiIiIiIiIiIjMh5OTE7p164bNmzcjPj4ekZGRmD17Ntq3b48iRYqosuft27fh7e2NAwcOqLK+LvqaNLx9+9ZISQyj7/2SxmqAkpSUhJEjR6JOnTpCzU/69euHn376Cba2tpLnPHjwQPFzFSdOnJDxWf+d0o049F2TrKwsRfYxx9wODg5o3Lixzpo7d+7go48+wpw5c/L0A2i7du2q899xeHi48JqPHj3CuXPntI736dNHeE0iNkAhIqJ/yU0NUEQO4indQVCUsbtcGsLR0VGoYUtSUhKeP3+uYiLzdffuXXz55ZeS6xcsWIBKlSqpmMi4Xr16JTzHXA/DyulUqq+7bW6QlpYmPMfGxsbk3XCtra1Vb+Zh6Ie1tbWin7NSLyjlBa1atcKECRMUXTM5OVmoUQjvK4iMw93dHbt378ZPP/2kSvMt3stIu5fJS9fJxsZG6BdbQN6451PKV199hXnz5hllr6SkJPTq1Qs5OTlG2Y+IiIiIiIiIiIiIiIiIiIiIiIiUZ2lpibp162Ls2LHYs2cPEhIScOPGDaxZswbBwcGoWrUqLCwsFNkrNTUVfn5+OHXqlCLrSWVvb69zXM65DVOIi4vTOV6sWDFV98/JycHy5cvh7u6OhQsXSj5DYW9vj2XLlmHVqlWwsbFRNaMxWFhYwNXV1dQxZClZsqSpI7zXoEGD9NakpqZi3LhxKFmyJAYMGIADBw7kuWYozs7O6NSpk9bx6OhoREdHC625detWrWdxChYsiI4dOwqtRwSwAQoREb1HbmqAkpSUJLm2atWqKibRL7d063ynfPnyQvW3bt1SKYn5ysrKQo8ePfD69WtJ9R06dEBwcLDKqYxL9DCshYUFnJycVEpjGEdHR+E5mZmZKiQxLjkvpOl7cY7UkZ2dbeoIZsHNzQ2bNm1SvAmPyD1FqVKlkD9/fkX3F5Xb7iuIRNna2uLHH3/EjRs30KFDB9X2yUuNPdS8l8lL1wkQv1Z54Z5PCRMmTEBISIhR94yIiMDMmTONuicRERERERERERERERERERERERGpq3LlyujTpw9WrlyJ33//HS9fvsT+/fsxadIkNG3aFHZ2drLXTktLg7+/v1Ef9Fy4cGGd4wkJCUZKYhh9DVDUbG4RERGBOnXqYNCgQULXq2bNmoiMjMTAgQNVy2ZsBQoUyLWNXNR44KUS/P390bhxY0m1ycnJWLlyJXx9fVGoUCE0adIE3377LY4cOZInGqIEBgbqHA8PDxdab+vWrVrHunfvzjNoJIuyjyQnIqI84c6dO0L1puzMN23aNIwaNUpSbYUKFVROo5u+HwLNjeif6+3bt+Ht7a1SGvM0ZcoUXLhwQVJt8eLFsWrVKpUTGZ/oYVgnJyfFOvMqzdpa/NY4LxyGldMAxZAXE0k+qd2L8zJXV1ccOXJElRfFSpYsicuXL0uqldNkQEkpKSm5pgs4kVwZGRkYNWoUjh49ip49e+Kzzz5T5YV8OY098uXLp3gOJah5L5OXrhMgfq3ywj2fEnbt2iV7ro2NjezrOHXqVLRs2RL16tWTvT8RERERERERERERERERERERmZHfdps6ARGZmYIFC8LX1xe+vr4A/jzncObMGfzyyy/YuXMnHjx4ILTe06dPMXr0aISGhqqQ9t9KlSqlczw+Ph7Z2dmwsrIySh45/vjjD6SkpOisUeMM4cOHDzF27FiEhYUJzbOzs8PEiRMxYcKEXNssRBsHBwdTR5DNXLNbWlpi8+bN+PjjjxEbGyt5XkZGBk6dOoVTp05h+vTpsLGxQd26ddG0aVM0a9YMn3zySa5r8NGyZUuULFkSf/zxx3vHw8PDMXnyZElr3b59W+c5nD59+sjKSMQGKERE9Ddv374V7irp4uKiUhr93Nzc4ObmZrL9RVy/ft3UEYS4uroK1d+9e1elJObp5MmTQk8iX716tUn/rajl7du3QvXm/KLCh9oAJT09XXgOG6CYRnZ2tqkjmNS75ieVKlVSZX07OzvUqlVLlbWVdu3aNVNHIDKK9PR0hIeHIzw8HOXLl8fMmTPh7++v6B6i9zKA+d7PqHkvk5euE8AGKMbm4+ODhQsXolGjRrKa6WRmZqJHjx64cuUKnJycVEhIREREREREREREREREREREREZVzd3UCYjIzDk4OKBFixZo0aIFQkJCEBUVhRUrVmDTpk148+aNpDU2bdqEcePGwcvLS+W0+huDZGdn49GjRyhfvrzqWeSKjo7WW+Pp6anYfmlpaZg5cybmzJkj/HBQHx8fLF26VNE85sTW1tbUEWQz5+xlypTByZMn4evri1u3bslaIzMzE2fPnsXZs2cxY8YM2Nvbo0mTJmjfvj0+//xzlC5dWuHUyrO0tERAQADmzJnz3vHr16/j1q1b8PDw0LvWli1btI5Vq1aNDz8k2SxNHYCIiMyLts5t2uTPn9+sb0zNxYsXL3D69GlTxxBSvHhxofrHjx+rlMT8JCUloVevXsjJyZFUP3jwYLRt21blVKaRlZUlVC/nYK6xfKgNUERfKAL+/GGXjE/031te0qBBA0RFRaFatWqmjmIWdu/mkw/ow3P//n1069YNjRo10tklWpScr63mej+j5r1MXrpOABugGJOvry/27duH6tWrY8GCBbLXuXPnDr788ksFkxEREREREREREREREREREREREVFuUbduXaxYsQKxsbEYP3487O3t9c7RaDRYtmyZEdIBFStW1Fvz22+/GSGJfBcuXNA5XrJkSRQpUkSRvQ4ePIjq1atj2rRpQmda3NzcsG3bNhw9ejTPNj8hdVWoUAGXLl3C0KFDYWVlZfB66enpOHToEIYPH46yZcuicePGWL16teRGTaYSFBSkczw8PFzSOtu2bdM61rt3b5FIRH/Dk4tERPQ3sbGxQvXFihVTKUneMnHiRFlPTTclV1dXoXrRvzu52aBBg/Do0SNJtZ6enpg7d67KiUwnLzVAkdPUQ4kfdk3NwsJCeE5u+3qWV2RnZ5s6gtFZW1tjzJgxiIiIQKlSpUwdxyzcvXsXS5cuNXUMIpM5e/YsvL29sXXrVkXWy0uNPdS8l8lL1wkQv1Z54Z7PFPz8/LBr167//aK5b9+++Pzzz2Wvt2bNGsm/UCEiIiIiIiIiIiIiIiIiIiIiIqK8p2DBgpgxYwZ+++03uLu7663fvn07NBqN6rnc3NxQoEABnTVKPgBQDWfOnNE5XrNmTYP3eP36NQYMGIA2bdrg3r17kuc5Oztj+vTpiImJgb+/v8E56MPm5OSExYsX47fffkNQUJCkhkpSaDQanDlzBsHBwShTpgxmzpxptuevqlWrhjp16mgdl/J+3evXryM6Ovq9Y9bW1ujZs6fsfERsgEJERH8j2sRCqc6Nedl///tfrFy50tQxhLm4uAjVP3nyRKUk5mXdunU6uxP+lbW1NTZu3AhHR0eVU5mOaEMGcz4Im5mZKTzHxsZGhSTG5eDgIDzHXH8Az+vkHD7PzZo0aYJLly5h7ty5sLOzM3UcsxAbGws/Pz+z7wZMpLa0tDR0794d33zzjcG/lJLTXMpc72fUvJfJS9cJEL9WeeGez9gCAgKwbds22Nra/u3/r1ixAsWLF5e97oABAz6Ynz2JiIiIiIiIiIiIiIiIiIiIiIjo/SpWrIjTp0+jbNmyOusSEhJw9epVo2SqUaOGzvHjx48bJYccb9++xcmTJ3XWNG7c2KA9bty4gbp16wqdsbOyssKAAQNw+/ZtfPPNN4o1qiACgMqVK2PdunV48uQJfvzxRzRr1kyxhya+fPkSEyZMQMOGDXH//n1F1lRaUFCQ1rHLly/j7t27OufreqCpr68vXF1dZWcjYgMUIiL6m4SEBKF6Z2dnlZLkbhqNBmfPnkXHjh0xYsQIo3QLVZron+3Tp09VSmI+7t69iy+//FJy/ZQpU1C3bl0VE5me6MFWS0vzvf2U01wiLxyGZQOU3EPO4fPcxsrKCp06dcKvv/6KiIgIeHl5mTqSWUhKSkJISAjq1KmDa9eumToOkdn44YcfMGXKFIPWkNOkw1zvZ9S8l8lL1wkQv1Z54Z7PmPr164cNGza89++Ni4sLVq1aJXvtxMRE9OrVCzk5OYZEJCIiIiIiIiIiIiIiIiIiIiIiolyuWLFiWLBggd46Y73/ukGDBjrHz549i9TUVKNkEXX06FG8fv1aZ42Pj4/s9X/55RfUr18fN2/elDzH19cX165dw/Llyw168Jo+5cqVg0ajUfSjadOmquUl5RUpUgSDBg3CsWPH8OLFC+zYsQODBg2Ch4eHwWtfuXIFn3zyCR4+fKhAUmX16NFD53ukw8PDdc7X9YD5Pn36yM5FBADm+yhWIiIyieTkZKH6fPnyqZQkd3j58iXu37+PBw8e/O/jzp07uHDhAl6+fGnqeAbJnz+/UH1ycjJycnLM+qCjIbKyshAQEIBXr15Jqm/UqBEmTJigcirTEz0MKudgrrGwAYp0b9++RXZ2tmKdTUkac/73YwhHR0c0btwYnTt3xueff45ixYqZOpJJZGZm4vHjx/+6r7h69Sp+//13HvQms9OmTRtcvnz5b/9Po9EgIyMDGRkZeP36NRISEvD8+XPcu3cPd+7cwdWrVxVvmvef//wHHh4e6Nmzp6z5cr6XZ2VlyWoIojY172XkXidzxQYo6hk+fDgWLlwICwsLrTXt2rVDcHCw7EYoJ06cwOzZszF+/Hi5MYmIiIiIiIiIiIiIiIiIiIiIiEii9PR0dOnSRet4w4YNMWnSJCMm+n+dOnVChQoVcO/ePa01d+7cMUqWli1bYu7cuVrH3759i59//hndunUzSh4R27dv1zleoEAB2Q+I3rx5M4KCgiS/d7N69eqYP38+WrRoIWs/IkMUKFAAfn5+8PPzAwDExcXhxIkT//u4deuW8JqxsbHo2rUrzp07Z1bnLosWLQpfX1/s2bPnvePh4eH4+uuv3zsWGRmJu3fval23Xbt2iuWkD5P5ndYgIiKTSkpKEqp3dnZWKYl5ePny5d8OIf/zQ2ozjNxItAGKRqNBSkoKChYsqFIi05o6dSrOnz8vqdbZ2RmhoaEfRHMI0cOg2dnZKiUxnJxDuvb29iokMS45DVA0Gg2eP38OV1dXFRKRNub870cbCwsLODg4wMHBAY6OjihevDjKli2LsmXLokqVKqhXrx68vLzMspGA0t41ONF2TxEbG8smJ5SrFCxYELVq1RKeFxsbi2PHjuHAgQPYu3evIvfTwcHB8PDwQP369YXnymlskZ2dbZZft9S8l5F7ncyV6LXKC/d8xjBu3DjMmjVLUu38+fNx/Phxrb/80Gfy5Mlo0aKF7F+oEhERERERERERERERERERERERkTQ2NjbYt2+f1vEXL16YrAEKALRt2xaLFi3SOp6YmGiUHE2aNIG9vT3S09O11mzevNnsGqCkpKQgLCxMZ42fn5+s986GhYUhMDBQ0ntKCxQogOnTp2Pw4MEfxHksyh1KlCiB7t27o3v37gD+bIhy5MgRHDlyBL/88gueP38uaZ3IyEisW7cOffv2VTOusKCgIK0NUKKiovDw4UO4ubn9a2zr1q1a1wwICODDJ8lg5ndag4iITCo5OVmoPl++fColMY6srCzcvXsXMTExuHfv3r8OI6ekpJg6osmINkAB/nxRIC82QDl16hRmzpwpuX7hwoWoUKGCionMh62trVC9nIO5xpKZmSk8p0CBAiokMS5HR0dZ8549e2bSBigXL16ERqMx6p5PnjxB+/btjbrnXxn67+fcuXNo0KCBQmnofV68eIGYmBjcunXrvQ1OzLkZAJGxlCpVCr169UKvXr2QlpaGnTt3IiQkBJcvX5a95tu3b9GnTx9cvnxZ+N5EtB748+uxnZ2d8Dy1qXkvI/c6mSvRa5UX7vnUNnXqVEyZMkVyfb58+bBhwwY0adJE1vfHzMxMBAQE4NKlS3BychKeT0RERERERERERERERERERERERNL8H3v3GRbVtbYB+Blm6KAIqIgo1QaiBBVFFMWS2LElKhp7idHP2HNiotHE4zFqNLZEjSWWRKOiEnvXxBorRWxYURRrUOkMfD/OlZwYKbP27D0zwHNflz/Cft+1Hoa2IbPeUavVsLa2Rnp6er7X7927Z+BEr/P39y/0ekG55WZlZYW2bdti69atBdbs2LEDt27dgqenp0Ey6WLNmjVIS0srtCYiIkJ43d9//x3vv/++Ts8RDA8Px7fffgtXV1fhfYgMqVKlSn89Fz43NxfHjx/HunXrsG7duiK/jhYtWmRyA1A6dOgAR0dHPHv2LN/rmzdvxrhx4157W15eXqFDk/r37y9nRCqlOACFiIheIzrwwxQP/hUmNTUVBw4cwKFDh3Dq1ClcvHgRWVlZxo5lkqR8bFNTUxVIYlwpKSk6/8INAF26dMGAAQMUTmU6RA/DGuoPR1K8fPlSuKckHIatUKGCpL5Hjx7JnERM3bp1Db6nsSeQ6js8w5QPohdXly9fxu7du3H8+HGcOnUKSUlJxo5EVKxYW1ujd+/e6N27N6KiojB27FjcvHlT0lrx8fFYsGABxo8fL9QnZbBHenq6SQ5cUPJeRurjZIpycnIKfXWF/JSEez4lzZ49W/hrDwAaN26Mjz/+GDNmzJC077Vr1zB69Gh8//33kvqJiIiIiIiIiIiIiIiIiIiIiIhIN1WqVMG1a9fyvXbv3j08efIEzs7OBk71X+XLly/0upWVlYGSAO+//36hA1Byc3Mxd+5cLFy40GCZCpOdnY05c+YUWlO1alW0aNFCaN2UlBR0794dmZmZhdZZW1tj/vz5GDJkiND6RKbAzMwMTZs2RdOmTTF9+nT0798fu3btKrD+woULuH//PipXrmzAlIWzsLBAz5498e233+Z7Pb8BKMePH0diYmK+9QEBAQgICJA9J5U+ZsYOQEREpkV0GIhGY/qztLKyshAZGYlOnTrByckJnTt3xoIFC/D7779z+EkhpBz0L4mP5wcffIA7d+7oVOvi4oJly5YpnMi0iB4GTUtLQ15enkJp9FOaB6BIOdR8//59BdKYNmMf5NZ3gAkHoMgjISEBkydPhre3N3x9fTFu3Dhs2bKFw0+I9BQeHo5Lly5h8ODBktf4z3/+I/zzXMrPclMd+qfkvQwfp+J/z6cElUqFxYsXSxp+8qepU6ciMDBQcv/y5cuxZcsWyf1ERERERERERERERERERERERERUNG9v70Kvnzp1ykBJ3mRjY1PodTs7OwMlAdq3bw8nJ6dCa5YtWyb5BQPltmrVqiLPTI0ZMwZmZmJH0ceNG1fggIQ/VapUCb/99huHn1CJUL58eWzfvh1vv/12oXVnz541UCLd9e3bt8Brp0+fxr17915724YNGwqsL00vKk/K4gAUIiJ6TXZ2tlC9KQ9AefHiBWbOnAl3d3d0794d27dvL3JyJP0PB6AAa9asKfSm/J9WrVpltIm1xlKuXDmh+ry8PKMPkSiIlMOwDg4OCiQxLJVKJWl6aEHTm0uyjIwMo+6v1Wr16ucAFP0cP34cHTt2RPXq1TF9+nST+aMzUUliZWWF77//Hl999ZWk/mfPnmHVqlVCPaL3MkDJGuyh670MH6fif88nNzMzM6xYsQIffvihXuuYm5tj3bp1er3CxpAhQ0rlcD4iIiIiIiIiIiIiIiIiIiIiIiJDqVevXqHXd+zYYaAkb3r27Fmh193c3AyUBLCwsMDIkSMLrcnKytLrhcfk8vLlS0yZMqXQGgcHB+EXN4yNjS3y+byurq44cuRIkZ9XRPo6d+4c5syZk++/Xbt2ybqXmZkZFixYUGiNri/SbkgNGzZEjRo18r2Wl5eHyMjIv/5bq9Vi8+bN+dZaWFigd+/eimSk0ocDUIiI6DUlYQBKbm4ulixZgmrVquGTTz7Bw4cPjR2pWJLysRX9/DFlN2/eLPKPDn83cuRItGnTRsFEpknKYdiUlBQFkuhPymFYFxcXBZIYXpUqVYR7rl69qkAS02bsg9wcgGIc169fR+fOndGkSRPs2LEDeXl5xo5EVOJNnDhR8v/YWbJkiVA972V0u5fh41Qy7vnkotFo8OOPP8o2pb1WrVqYOXOm5P5nz56hb9++yM3NlSUPERERERERERERERERERERERERva5p06aFXt+4caPRXrT64sWLhV4v6GC/UkaNGgU7O7tCa7Zu3Yqff/7ZQIny99lnnyE5ObnQmk8++aTI9+WfZs+eXejz+ZydnXHkyBFUr15daF0iKa5cuYIJEybk+2/u3Lmy71ejRg14e3sXeF3K85gNoW/fvgVe+/vAk8OHDxf4faNjx45wcnKSPRuVThyAQkRErxEdYKFWqxVKIs3169fRtGlTDB8+HI8ePTJ2nL907doVfn5+xo4hRMoAFDOzknFrkZOTg969e+v8S0XNmjUxa9YshVOZJkdHR+Gep0+fKpBEf6KHdG1tbVG2bFmF0hiWlAEoV65cUSCJaUtKSjLq/voOMClJQ6oMITc3F/PmzUOdOnUQFRVl7Dh/cXFxwdChQ40dg0hxM2bMkDTV/fLly4iNjdW5vjTfywBA5cqVdarj46Tb41QaWFhYYNOmTejZs6es644aNQotW7aU3H/o0CHMmTNHxkRERERERERERERERERERERERET0p2bNmsHBwaHA68+fP8fSpUsNmOh/9uzZU+A1lUqFgIAAA6b573MuP/rooyLrPvzwQyQkJBgg0Zv27t2LhQsXFlrj7e2t0/vxd0+fPsWmTZsKvK5SqbBu3TpUq1ZNaF0iqapWrVrgtbi4OEX2LF++fIHXzM3NFdlTX++//36B50KPHz+OBw8eAAA2bNhQ4Br9+/dXIhqVUiXjlDIREckmLy9PqF7fw9hy2rJlCwIDA3HixAljRwEAlC1bFkOGDEF0dDQiIyPh7Oxs7EhCpHxsTfUmXNTMmTNx6tQpnWrNzc3x448/wtraWuFUpknK57WpHoZ9+PChUH1JOgjr5eUl3HP9+nWkp6crkMZ0GXsAilar1avflH5mm7qUlBR06NABY8eORUZGhrHjAADq16+PpUuX4tatW+jVq5ex4xApztzcHN98842k3p07d+pcW5rvZSwtLXWesl2aHyegZN336cPa2hpRUVHo3Lmz7GurVCr88MMPhf4P8qJ89tlnOH/+vIypiIiIiIiIiIiIiIiIiIiIiIiICPjvcw67d+9eaM0XX3xh8OcOHj58GNHR0QVer1evXqHDCJSeUX6jAAAgAElEQVQyadKkIl+o9tmzZ+jQoYPBH7PLly+jT58+RZ4hnD9/PiwtLYXWXrlyZaHPvx8/fjzeeecdoTWJ9OHp6VngteTkZFy6dEn2PQt7rrIxvh/pokqVKmjevHm+1/Ly8hAZGYns7Gxs3bo13xoXFxe0adNGwYRU2nAAChERvcbKykqo3lQOU8+ZMwfdu3fHq1evjJZBo9Ggfv36+Oijj7Br1y4kJydj2bJlqFOnjtEy6aM0D0A5e/aszrXTpk1DYGCggmlMW1F/kMlPcnKyAkn0J3oY1tXVVaEkhvfWW28J92RnZwt9rZQE9+/fN+r++v7MNZWf2aYuKSkJjRo1wu7du42ao2LFiujSpQvmz5+PGzdu4MyZMxg6dKjwvRpRcdakSRO0aNFCuO/o0aM615bme5lKlSpBpVLpVFuaHye1Wo2KFSsqlKZ4Wb9+vaL/c8LNzQ2LFy+W3J+dnY2IiAikpaXJmIqIiIiIiIiIiIiIiIiIiIiIiIgAYNSoUYU+7/Dp06fo3bs3cnNzDZInPT0dw4cPL7Sma9euBsnyTzY2Njq9EODVq1fRtGlT3L171wCpgISEBLRu3RpPnjwptG7o0KFo37698PqbN28u8JqzszM+++wz4TWJ9OHm5gZvb+8Cr69du1bW/R48eIDExMQCr9eqVUvW/eTUr1+/Aq9t3rwZe/fuxbNnz/K9/v7770Oj0SgVjUohDkAhIqLXiB6qzc7OViiJ7ubMmYMJEyYUOXlSLhqNBh4eHmjVqhVGjhyJRYsW4ejRo0hJScGZM2fwzTffoG3btsJTLk2NlIPyFhYWCiQxbZMmTYJKpTL4PxF37tyRtEf//v2LXNvd3V34Mbt3755wjyGIHob18vJSKInhSR3ic/z4cZmTmLY7d+4YdX+tVqtXPwegFO3Bgwdo1qwZrly5YrA9HRwc8NZbb6Fnz56YOnUqfv75ZyQkJODhw4fYsmULRo0aVaK+3xCJ0uV+5J/Onz+vcy3vZXRTrlw52NvbC61fUh4nd3d3mJnxT6gAUKNGDcX3iIiIQM+ePSX3X716FWPGjJExEREREREREREREREREREREREREQGAv78/unTpUmjN3r17MXDgQL2f+16UzMxMdO3aFVevXi2wxtLSEoMHD1Y0R2G6du2q0/NgL1++jKCgIGzfvl3RPHv27EFQUFCRLwxbo0YNzJ07V3j9x48fF/oiuxMmTECZMmWE1yXSV2HDfJYsWYIHDx7IttfSpUsL/P5na2uLevXq6bROYWcHMzIyJGUrSrdu3WBra5vvtd9++w0LFiwosFfKc/6JCsNxOkRE9Bpra2uhemMfpt6+fTsmTpwo+7qOjo6oVasWfHx84OHhAQ8PD3h6esLDwwNubm5Qq9Wy72lqOACFdFG1alXhnsImWRqT6GFYQxwANRR3d3c4OzsXOcX3n0rbAJSLFy8adX99f+Ya+2e2qcvKykK3bt2QkJAg67oajQZeXl6oVavWX/cUf95XeHp68o+4REXo3Lkz1Gq10P8Ie/ToEZ48eQJnZ+ciax0dHWFnZ4dXr17pvH5pvZepWrUqLl26pHN9aX2cSH/ffvstfvvttyL/J2tBli1bhrZt26Jz584yJyMiIiIiIiIiIiIiIiIiIiIiIird5s6di7179yI1NbXAmtWrVyM5ORk//PADKlasKHuGmzdvokePHoUO2wCAkSNHonz58rLvL2LRokU4efJkoYNaACA5ORmdOnX660U15Xzu4qNHj/DJJ59g1apVRb74uLOzM3bu3FngEITC7NmzB7m5ufleU6lU6N27t/CaRHLo0qVLgcM7UlJS0K1bN+zatQsODg567XPixAnMmDGjwOvh4eEwNzfXaS07O7sCr8XGxgpn04WtrS26deuGNWvWvHEtNzcX+/fvz7cvKCgIvr6+imSi0osDUIiI6DWiA1CUmhiniwcPHuD9998v8pevoqjVaoSEhKBp06YICQlBYGCgIr9gFzdZWVnCPTzEXfo4OzvD1ta20D9e/dPt27eVCyRRbm4u7t69K9RT0g7DBgYGYt++fUI9hw8fRkZGBqysrBRKZTqePHmCpKQko2bQdwo2B6AUbvLkyTh58qTe61SuXBktW7ZESEgIgoODUaNGDQ4II9KDvb096tSpgwsXLgj1JSYm6jQABfjvYI/4+Hid1zbFexkAuHXrllC96L2Mu7u70ACU0vo4kf7KlSuHVatW4Z133pH8+/7gwYMRFBQEV1dXmdMRERERERERERERERERERERERGVXu7u7pgzZw6GDx9eaN2ePXvg7++PKVOmYMiQIbC0tNR774cPH+Kbb77BN998g8zMzEJr3dzc8Pnnn+u9p75sbW2xe/duhIaG4t69e0XWb9iwARs3bkTnzp3x/vvvo127dpKfix4XF4fvvvsOa9as0emFAq2srBAVFQVvb29J+x07dqzAa5UrVy5weIIh1KhRA8HBwUbbn4yrefPmaNiwIU6fPp3v9ZMnTyIsLAybNm2Cj4+PpD1WrVqFkSNHIjs7u8CakSNH6rxeuXLlCrwWHx+PFStWYNCgQUIZddG3b998B6AUZsCAAbLnIOIAFCIieo3ohMYXL14olKRoY8aMQUpKiuT+unXrYsSIEejSpYvOhyNLk5cvXwr36DvpkIqnGjVq4Pz58zrXFzW51hju3r1b5B/A/qmkHYZt0KCB8ACU1NRUHDx4EO3bt1colemIjo42dgS9B5gU9oeU0i4uLg5z586V3G9lZYX+/fujT58+aNy4MVQqlYzpiKhBgwbCA1AePnyoc23NmjWFBqBcu3YNeXl5JvW1npOTIzxwRPRepmbNmti1a5fO9X/88QcePXqEChUqCO2jtOvXrwvVl7R7vuKidevWGDFiBBYtWiSp/+nTp+jXrx/27dtnUl+rRERERERERERERERERERERERExd0HH3yAs2fPYsWKFYXWPX78GP/3f/+H6dOnIyIiAu+99x7q1asHc3NznfdKTk7G3r17ERUVhe3bt+v0nHgrKyts2bIF9vb2Ou+jJE9PTxw6dAihoaE6Pb81NzcXW7ZswZYtW2BnZ4dGjRqhcePG8PX1hYeHB9zc3GBnZwcbGxuoVCq8evUKL1++xO3bt3H16lWcO3cO+/btw82bN3XOaGtriy1btqBx48aS38/Czlzcu3fPqEMShg0bxgEopdznn3+Odu3aFXj94sWLqFWrFvr27YtRo0ahTp06RT7/9OXLl/jll1/w9ddfF/lc906dOgl9Dvr6+hZ6ffDgwdi8eTNCQ0NRvnx5aDQaZGZmws3NTa8zXmFhYahSpQoSExN1qreyskLPnj0l70dUEA5AISKi15QvX16oXsqQDDlER0fj559/ltTr6emJBQsWoEOHDjKnKll0me75d5aWlrJMZKXix9/fX2gAyo0bN6DVaqFWqxVMJebatWtC9dbW1pKnepqqNm3a4N///rdwX1RUVKkYgHL48GFjR4BWq9WrX98BKiXZ5MmTJT0+KpUKw4YNw7Rp00zugD+Vbk+fPhUaelSmTBnY2NgomEg/Xl5ewj0i97L+/v7YsmWLzvVpaWm4d+8eqlSpIpxLKTdv3hT+PlanTh2hen9/f6F64L+D70zp+2NGRobOf5D/k+jjRPKZNWsWDhw4gCtXrkjqP3DgAL7++muMHz9e5mRERERERERERERERERERERERESl25IlS5CSkoLNmzcXWZucnIx58+Zh3rx5sLa2Rr169eDt7Q0PDw84ODjAxsYGGo0GGRkZePnyJe7fv4/bt28jOjoad+/eFcplZWWFDRs2oEGDBlLfNUVUq1YNx48fR6dOnXDp0iWd+169eoUDBw7gwIEDimVzcnLCrl27EBQUJHmN3NxcxMbGypiKSF5t27ZFhw4dsGPHjgJrcnJysHLlSqxcuRJOTk4IDQ2Fl5cXHB0dUa5cOajVarx48QJ37txBTEwMTp8+rdOLcZcrVw6LFy8WyqvLsJQ9e/Zgz549r72tX79+ep3xMjMzQ58+ffCf//xHp/ouXbrAwcFB8n5EBeEAFCIiek2lSpWE6l+8eKFQksLNmjVLUl/37t2xevVqkz7gaSpEB6DwZrX0Ej0UmpWVhWvXrqFWrVoKJRJ3/fp1ofrAwEBoNCXrVjo4OBhOTk54+vSpUN/WrVuxaNEiWFhYKJTMNOzevdvYEfQeYMIBKPm7du0aoqKihPscHBzw888/4+2331YgFZF+2rRpg7Nnz+pcP23aNEyZMkXBRPqRMmgkIyND51opAy7i4uJMagCK6L1M5cqV4erqKtQj9XFq2rSpcJ9Sbty4gdzcXJ3rNRoN3nrrLQUTUWGsra2xbt06BAcHCw11+rtPP/0ULVu25MeRiIiIiIiIiIiIiIiIiIiIiIhIRhqNBuvXr0eZMmWwcuVKnfvS09Nx7NgxHDt2TPZMFStWxMaNGxEaGir72nLw8vLCyZMn0adPH/zyyy/GjgMACAoKwk8//QRvb2+91rl9+zbS0tJkSkWkjNWrVyMwMBB37twpsvbp06fYunWr3nuq1Wr89NNPcHNzE+pr2rQpXF1dkZSUpHcGUf369dN5AEr//v2VDUOllpmxAxARkWkpDgNQXr16JfQK7X/q3bs3Nm7caLThJ3l5eUbZVyrRASjOzs4KJSFT5+/vL9wjcijbEC5evChUb2rTgOWgVqvRpk0b4b4nT55I+p5cnDx69AgXLlwwdgxotVq9+jkAJX9r1qwR/hltZ2eHgwcPGm34SXG7pyDDs7a2Fqo3xh9GRdjZ2Qn3WFpa6lzLexnd+Pr6Qq1WC/UU98epdu3awl9PJK969eph8uTJkvuzsrIQERGB9PR0GVMRERERERERERERERERERERERGRRqPBihUrsHjxYlhZWRk1y3vvvYe4uDiTHX7yJ3t7e0RFRWHFihUoV66c0XKo1Wp8+umnOH78uN7DTwDTfy4yEQA4Ojpi06ZNkp6bLoVarcbq1aslndUyMzPDhAkTFEhVtBo1aiAoKKjIOjc3N7Rq1coAiag04gAUIiJ6jegAlMePHyuUpGDbt28XekV34L+HGleuXAmVSqVQqqIZY1iMPp48eSJUX7lyZYWSkKmrV6+e8NeWqR2G/f3334XqS+IAFADo2LGjpL6lS5fKnMS0REZGmsTACX0HmHAASv42bdok3PP9998jMDBQgTS6KW73FGR4ogP/EhMTFUoiD1tbW0V7fHx8hP8nUmm8l7GyskLt2rWFekrj40TymzRpEho1aiS5/8qVKxg7dqyMiYiIiIiIiIiIiIiIiIiIiIiIiOhPH374IWJjY41yEL5p06Y4duwYfv7552L1ws4DBw5EfHw8Bg4cCHNzc4PuHR4ejpiYGEyfPh0ajUaWNR8+fCjLOkRKa9CgAfbv3w8XFxdF93FwcMCOHTvQu3dvyWuMGDECwcHBMqbSXb9+/XSqMTPjmApSBj+ziIjoNaI3b48ePTL4ofAjR44I9yxevBgWFhbyhxGQkpJi1P1FiQ634QCU0svR0RF+fn5CPb/99ptCacSlpaXh0qVLQj3NmjVTKI1xtWnTBpaWlsJ9R44cQVxcnAKJTIOpDHjRarV69XMAypsePnyIa9euCfW0aNECPXv2VCiRborbPQUZnugwjxs3biiURB5ZWVnCPSIDUFQqFUJCQoTWP378uEkMx/rTmTNnhOqbN28uaZ+mTZsK1cfFxeH58+eS9lKC6AAUqY8TyUutVmPt2rWShiH9acmSJYiKipIxFREREREREREREREREREREREREf3Jx8cH+/fvx8GDBxEWFqboXlZWVujZsydOnjyJX3/9Vfg5oKbCxcUFK1aswPXr1zF8+HDY29srtpelpSV69OiBkydPYtu2bfD19ZV1fQ5AoeKkUaNGiI2NRZ8+fWQf4KFSqdCjRw/Ex8ejTZs2eq1lbm6OPXv2IDw8XKZ0uuvZs2eRZ3H79+9vmDBUKnEAChERvaZChQqwtrbWuT4nJwdPnz5VMNGbTp06JVTfsGFD4YN6ctNqtcXulznRAShubm4KJaHiIDQ0VKg+OjraZA7DnjlzRmiwRK1atUrswJ+yZcvi3XffldQ7bdo0mdOYhlOnTiE6OtrYMQDoPwAlOztbpiQlx8mTJ4V7JkyYoEASMffv3zd2BDJxVapUEaq/deuWSQ9JevXqlXBPhQoVhOpF72WePn2KmJgYoR6l3LlzBw8ePNC5vkyZMggKCpK0l+jjlJubi19//VXSXnLLzMzExYsXda5XqVRo2bKlgolIhI+PD+bMmaPXGoMHDxb6WiEiIiIiIiIiIiIiIiIiIiIiIiIxLVq0wKFDh3D9+nVMnjwZdevWhUql0nvdMmXKoEuXLlixYgUePnyI9evXo1GjRjIkBjw8PJCXl1fgP6UP+bu7u+Pbb79FcnIyNmzYgM6dO8PBwUHvdW1sbNC6dWssWLAASUlJ2LBhg2yP2T+NHDmy0MfQ2P+WLFmiyPutj6lTpxaY9/bt2wbdLy8vT+fPuaK+XuR+8cGi9hs9erSkdZ2dnbF27VrExMRgxIgRcHJy0iunk5MThg8fjpiYGGzYsAGVKlXSa70/lSlTBtu2bcOpU6cwfvx4NGzYEK6urrCysoK5uTkcHR3h4eGBsLAwjB8/HoMHD5ZlX0dHR2RmZhb62Pv4+MiyF1F+NMYOQEREpsXMzAzVqlUTOsyXnJwMZ2dnBVP9T15eHq5duybU0717d4XS6O7q1atIT083dgwhogNQStJAiHnz5mHq1KnGjlGodu3a6Xx4r1KlSti1a5fwHo6OjjrXhoaG4ttvv9W5Pjc3F/v378d7770nnEtuoo9N69atFUpiGj744AOsW7dOuC8yMhIxMTGoU6eOAqmMZ9asWcaO8Bd9hxOY8nADY7ly5YpQvYODA1q1aqVQGt1duHDB2BHIxFWtWlWoPisrC9euXZN9mrtcEhMThepVKhU8PT2FekQHewDA3r17UbduXeE+uYneyzRv3hwajbQ/CUp9nIwxefyfDh06hIyMDJ3r/f39UbFiRQUTkagPPvgA27dvl/S7DQA8efIE/fr1w969e2X5H+pERERERERERERERERERERERESUPx8fH3zxxRf44osv8OjRI5w8eRLR0dGIj4/HvXv3kJSUhOfPnyMjIwOZmZnQaDSwtLRE2bJlUaFCBVSuXBnVq1dHrVq1EBQUhNq1a8PMzMzY75airK2t0aNHD/To0QO5ubmIi4vDiRMncPXqVdy4cQO3bt3C8+fPkZqaitTUVOTl5cHW1ha2trYoU6YM3N3dUa1aNVSrVg2BgYFo2LAhzM3Njf1uEZk0Pz8/LFq0CAsWLMC5c+dw7NgxxMbGIiEhAQ8ePMDjx4+RkZGBrKwsaDQaWFlZwcnJCZUqVUK1atXg7++Ppk2bon79+lCr1YrlbNiwIRo2bKjY+kSmhgNQiIjoDTVr1hQagJKUlAQ/Pz8FE/3Pw4cPhQ6tAUBwcLBCaXR39uxZY0cQlpSUJFRfrVo1hZIYnuiBWWOwsLAQqg0ICFAwDdCqVSuo1WpotVqde7Zt22YSA1B27twpVP/OO+8olMQ0hISEwN/fH7GxsUJ9eXl5+OSTT4QfT1N28uRJbN261dgx/iLy9ZUfDkB5k+hk5Pr160seGiCn4nhfQYYl5b7s3LlzJjsARXQAoouLC6ytrYV66tevD2dnZzx58kTnnm3btmHixIlC+yjBkPcyFStWREBAAC5evKhzT1RUFBYvXmz0gRO85ysZVqxYAX9/f6Gv1b/bv38/5s2bh7Fjx8qcjIiIiIiIiIiIiIiIiIiIiIiIiPJToUIFhIeHm8SLqRUXZmZmqFOnTol7gVoiU2VmZoYGDRqgQYMGxo5CRABK9sgzIiKSpGbNmkL1N2/eVCjJmx4/fizcU716dQWSiImMjDR2BGF37twRqjeFx5mMx8nJCaGhoUI9O3fuRHp6ukKJdHPr1i1cunRJ53oHBwe0atVKwUSm4YMPPpDUt2vXrmL5/S4/eXl5mDBhgrFjvEbfASYcgPIm0fsKU/hZd+HCBdy6dcvYMcjEBQYGCvecOHFCgSTyOHbsmFB97dq1hfdQq9Xo2LGjUM+pU6eQmJgovJecUlNTcejQIZ3rzczM0LVrV7327NKli1B9UlKS0T+/8vLysGPHDqGe7t27K5SG9OHi4oKlS5fqtcakSZMQHR0tUyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIikgsHoBAR0RtMeQBKWlqacI+Dg4MCSXT3/Plz7Nmzx6gZpBA5zGlra4vKlSsrmIaKA9HDsC9evMCGDRsUSqObFStWCNWHh4fDwsJCoTSmo1+/fqhYsaKk3lGjRuHFixcyJzK8b775BsePHzd2jNdotVq9+jkA5U2i9xXGvqcAgPXr1xs7AhUD5cuXh5ubm1DPwYMHFUqjn6SkJMTHxwv1hISESNpL9F4mLy8Py5cvl7SXXNavXy80UK5p06ZwcXHRa0/RxwkAli1bptee+jp48KDQgEcPDw8EBQUpmIj00bVrV/Tr109yf2ZmJiIiIow+jJGIiIiIiIiIiIiIiIiIiIiIiIiIiIiIXscBKERE9AY/Pz+hekMOQMnOzhbuyczMVCCJ7ubMmYOsrCyjZhD1xx9/CA0wqF69OlQqlYKJqDjo0qWL8OeBvq/ero+cnBysXLlSqKdHjx4KpTEttra2mDx5sqTepKQkjBgxQuZEhnXhwgX861//MnaMN+g7wIQDUN4kel9h7HuKp0+fGvX7JhUvTZo0Eaq/fv06EhISFEoj3Y8//oi8vDyhHtH3/U+tW7eGvb29UM+KFSv0HlClD9HvCXLcy/j7+6NatWpCPRs3bsTz58/13lsq0cfpvffeUygJyWXBggVwd3eX3B8fH49x48bJmIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI9MUBKERE9AZfX1/Y2NjoXH/jxg0F07xOJNefHjx4oEAS3Tx8+BDz58832v5SiQ61qV27tkJJqDhxc3NDq1athHpOnz6Ns2fPKpSocBs3bhT6/lC5cmW8/fbbCiYyLUOHDoWPj4+k3nXr1mHBggUyJzKMpKQkdOvWzSQHV+l7wF7KELGSTvS+wpj3FAAwY8YMoQFlVLq1b99euGf9+vUKJJEuJycHS5YsEeqxt7dHSEiIpP2srKzQs2dPoZ779+9j27ZtkvbT14kTJ4Tuo6ytrYXfv4L0799fqD4jI0N48Jxcbt++jaioKKGeAQMGKJSG5FKmTBmsWbMGZmbS/7z93XffYfv27TKmIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJ9cAAKERG9QaPRIDAwUOf6K1euIDc3V8FE/1O2bFnhnuvXryuQpGi5ubno27cvUlNTjbK/Pi5fvixUX79+fYWSUHEzbNgw4Z5JkyYpkKRwmZmZ+PTTT4V6hg4dCrVarVAi02Nubo7p06dL7h83bhwOHTokYyLlPX78GK1atcKtW7eMHSVfOTk5Ru0viUTvK4x1TwEAR48eLZZD1ch42rRpIzwYYPny5Sb1vWLVqlXCg/k6deoEKysryXt+8MEHwj2TJ0/We0iVFOPHjxeq79mzJ8qVKyfL3gMHDoS5ublQz8yZM40yxOmTTz4RGgIWFhaGmjVrKpiI5BIaGoqxY8fqtcagQYPw8OFDmRIRERERERERERERERERERERERERERERkT44AIWIiPLVqFEjnWvT09OFDyZKVbVqVeGDnMZ6RfapU6di//79RtlbX1euXBGq5wAU+lN4eDgqVaok1LN//36Df63MnTsXt2/f1rne3NwcQ4YMUS6QiXrvvfcQEhIiqTcnJwcdO3YsNt8H79y5gxYtWggPgDIkfQ/Xm9JQA1Ph4eEhVH/27Fncu3dPmTCFuH//Pnr16mWUAQtUfDk7O6NVq1ZCPXfv3sW6desUSiQmOTkZ//rXv4T7evToode+gYGBwve2ly9fxsqVK/XaV9T69etx8uRJoZ4PP/xQtv1dXFwQHh4u1PPkyRN89dVXsmXQxYkTJ/Dzzz8L9cj5OJHypk+fDn9/f8n9jx8/Rv/+/ZGXlydjKiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKSggNQiIgoXw0bNhSqj4uLUyjJ6ywsLODu7i7Us2XLFrx8+VKhRPmbMWMGvvzyS4PuKSeRAQBqtRoBAQEKpqHiRKPRYMSIEcJ9o0ePRkZGhgKJ3nTu3DlMnTpVqKdnz57Cg11KApVKhR9++AG2traS+tPS0tCxY0ds3bpV5mTy+u2339CgQQOD/SyTSt8BJhyA8qbq1asL1efl5WHt2rUKpclfYmIimjVrhgcPHhh0XyoZBg8eLNzz6aef4tmzZwqk0Z1Wq0W/fv2Ec3h6eqJdu3Z67z969GjhnsmTJ+Px48d6762Lu3fvCt9vNW3aVPahhVIep3nz5uHq1auy5ijIixcv8P777wsNtvD09ETnzp0VTEVys7S0xLp162BpaSl5jb1792L+/PkypiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKTgAhYiI8tWoUSOh+tjYWIWSvCk4OFio/unTp/jss88USvO6rKwsjB49Gp9++qlB9lOKyBAAf39/2NjYKJiGiptRo0bByclJqCc+Ph5jxoxRKNH/PH/+HD179kRWVpbOPWZmZsX+a1ofPj4+mD17tuT+zMxMdO/eHR9//DGys7NlTKa/nJwcfPnll2jZsqXBDq3rQ6vV6tXPAShvEr2nAP475CwxMVGBNG/6/fff0aRJE9y4ccMg+4ny8PCASqUS/keGEx4ejgoVKgj1JCUlYdiwYQol0s2YMWOwd+9e4b7x48dDrVbrvX+vXr1Qs2ZNoZ7k5GT069dPaNiGFJmZmejVqxeeP38u1DdlyhTZs4SEhKB169ZCPenp6ejRowcyMzNlz/N3eXl5GDRoEG7evLtg5sEAACAASURBVCnUN2nSJGg0GoVSkVLq1Kmj9wDSf/3rX4iJiZEpERERERERERERERERERERERERERERERFJwQEoRESULzc3N/j4+Ohcf/78eQXTvK5FixbCPYsXL8bOnTsVSPM/V65cQePGjYv9K0e/fPkS165d07k+LCxMwTRUHNnb22PcuHHCfUuWLMH69esVSPRfaWlpaN++PRISEoT6+vTpgxo1aiiUqngYPnw43nnnHcn9ubm5mDVrFoKDgxEfHy9jMunOnz+PRo0aYcqUKSY3mKUg+g4w4QCUN1WrVg1ubm5CPa9evUK/fv2Qnp6uUKr/fqxmz56NJk2a4O7du4rtQyWfhYWFpJ/JmzdvxhdffKFAosLl5eVhzJgxWLhwoXCvq6srBgwYIEsOMzMzSQNDdu/ejZkzZ8qSIT9arRa9evXCiRMnhPpCQ0PRqlUrRTJNmzZNuCc6OhofffSRAmn+Z/To0di8ebNQj4+PD/r166dQIlLauHHjEBoaKrk/MzMTERERyMjIkDEVEREREREREREREREREREREREREREREYngABQiIiqQyGH3U6dOKZjkdV26dIG5ublQj1arRdeuXbFt2zbZ8zx58gQjR46Ev78/zp07J9u6f/zxh2xriTh//rzQK9dzAArl5//+7//g6uoq3Ne3b1/hw6q6+OOPP9C+fXucPHlSqM/GxgYzZsyQPU9xtGrVKlSuXFmvNc6dO4c6depg4MCBuHPnjkzJxNy8eRMRERGoX7++8PdsjUaDQYMGKZSsaFqtVq/+4jLoxdDee+894Z7Dhw+jQ4cOSE1NlT3P9u3b4e/vj4kTJ8r2MTPWPQWZhhEjRqB8+fLCfZ9//rlBh6C8evUK3bt3xzfffCOpf86cObC2tpYtT48ePRAQECDcN2nSJMnvQ2EyMzPRu3dvbN26VahPpVJh7ty5suf5U3BwMDp16iTct3TpUowZM0b2PLm5uRg7diwWLFgg3Dtr1izh3zPJdJiZmWHNmjUoU6aM5DUuXbqE8ePHy5iKiIiIiIiIiIiIiIiIiIiIiIiIiIiIiERwAAoRERXo7bff1rn24cOHBjvM7ujoKOmQXVZWFrp164ZRo0bJcmA5NjYWw4YNg7u7OxYvXoycnBy91/y7GzduyLqerkQGAqjVar1eZZtKLjs7OyxatEi4LycnB7169cKyZctky3LlyhUEBwfjyJEjwr2TJk3Se+hHSVGpUiXs2LEDdnZ2eq2j1WqxatUqVK9eHQMHDsSvv/4qNHRJqkOHDqF79+6oUaMG1q9fL7ynSqXCDz/8gJkzZ0KlUimUsnD6/pyR++dUSdG/f39JfYcOHUJAQACOHj2qd4b09HSsXLkSb731Fjp16oQrV67ovebfGeuegkyDra0tpkyZIqn3888/R3h4OJ49eyZzqtcdOnQIdevWxZYtWyT1h4WFoVevXrJmMjMzw/fffw+1Wi3cO2bMGEybNg25ubmyZLl//z5atGiBn3/+Wbh30KBBqFevniw5CrJo0SJJ9wfffPMNhg0bhoyMDFly/PHHH+jWrRvmzZsn3NuqVSt06dJFlhxkPO7u7pg/f75eayxevBg7d+6UKRERERERERERERERERERERERERERERERieAAFCIiKlCLFi2EXgH71KlTCqZ53ccffyypLzc3FwsXLkStWrUwZ84cPH/+XKg/Pj4es2bNQkhICOrUqYNly5YhLS1Np157e3uhvVavXi1ULxeRj2O9evVQtmxZBdNQcdalSxdJB0lzcnIwbNgwdO7cGY8fP5a8f2ZmJmbOnImAgABJwwTq1q2LiRMnSt6/JAoICMDGjRslHQb/p6ysLKxatQrNmjWDt7c3Pv30Uxw4cECWAVUAkJ2djSNHjmDMmDHw8vJCy5YtERkZKWkIiEqlwrJly9C7d284Ozujdu3asmQUpdVq9ernAJT8+fv7o127dpJ6ExISEBYWhnfffRe//fabUG9qaiq2bduGwYMHw83NDYMGDcLFixd17he5r9i3bx8ePnwolI9Klg8//FDyEIxffvkF1apVw9dffy3boIo/XbhwAeHh4WjZsiVu3rwpaQ0HBwesWLFC1lx/ql+/PkaNGiWpd+rUqWjWrBlu3boleX+tVovvv/8efn5+OHHihHC/q6srZs+eLXl/XVWpUgX//ve/JfUuW7YM9evXF/r+l5+tW7fCz88P27ZtE+61tbWVdfgeGVf//v3RtWtXvdYYOHAgkpOTZUpERERERERERERERERERERERERERERERLpSGeLV1omkUqlUlwD4/vPtvr6+uHTpkhESERnApetA7XDcqKlGrxPOhZauL7MS3mpPReM0b94cR48e1al25MiRWLhwoaJ5/q5z586IiorSaw1ra2s0btwYTZs2ha+vL5ycnFCmTBmkpaUhJSUFz58/x5UrVxAbG4uLFy/i3r17kvZxcHDA/v37ERwcrPMBdLVajc8//xy9e/dGlSpVoNVqkZKSgtu3byMnJwchISGSshSlYsWKePTokU61//73vzFp0iRFclDhPDw8cOfOHZ1q3d3dcfv2bWUDFSApKQl+fn74448/JPXb2dlh4MCBGDNmDDw8PHTqefDgAX788UfMnTsXDx48kLSvlZUVTp06hbp160rqL+mWLFmC4cOHK7K2RqNBYGAg6tatCy8vr7/+lStXDra2trCxsYGtrS20Wi3S09ORlpaGJ0+e4O7du7hz5w6uXLmC33//HRcvXkRmZqbeeVQqFZYsWYKhQ4f+9baPPvoICxYsKLJXrVbLOnSkb9++WLt2reT+li1b4sCBA7LlKUnOnTuHoKAg5Obm6rWOt7c3mjVrhuDgYFSqVAlOTk5QqVR48eIFUlJScO/ePcTFxSE2NhbR0dGSP0cHDRqESpUqYfr06Tr31KtXD//5z38QFBQEOzs7vHr1Co8ePcL169fRqFEjODo6CucQ+Vn0d/w7hHGcO3cOjRo10uv7kqOjIyIiIhAREYEGDRpAo9EIr5GYmIidO3di9erVeg9QVKlU2LZtGzp16qTXOoVJTU2Fv7+/5EEmFhYWiIiIwLhx43QeoPX8+XNs2rQJs2fPRkJCgqR9VSoVdu3ahTZt2kjqF5Wbm4uQkBDJH1MzMzN07twZ48aNQ+PGjXXqSU1NxS+//ILZs2fjwoULkvYFgKVLl772c97Ybmpz0Ptl4YM6f7QvBy+1+Nff302dOhXTpk0T6rl8+TJq1qyp176G8OTJE/j7++s1/Ktt27bYuXMnVCqVjMmIiIiIiIiIiIiIiIiIiIiIDCczM7PIF6by8vKCpaWlgRIRERERERlXcbpH9vPzQ3x8fH6X4vPy8vwMnceQOACFTBoHoFCpZGIDUObPn4/Ro0frVFurVq2CfqAq4u7du/D19UVqaqrB9pTCysoKe/fuRWhoKOrUqYPY2Fi91wwPD5f06uZFiY+Ph5+f7vc+cXFxQvUkn+IyAAUAdu/ejQ4dOug1WEClUsHPzw/NmzdHvXr1UL58+b8GC6SkpODBgweIjo7GqVOncOrUKb0P15vaQVhTtGrVKnzwwQfIysoydhTFqNVqrFy5En379n3t7Vu3bkXXrl116pdzAEpERATWr18vub9Zs2Y4cuSIbHlKmlGjRhl0kJtUHTt2xNatWxEVFYVu3brJsuaFCxcQEBAg3McBKMXPnDlzMGHCBFnWsrOzQ3BwMPz8/ODl5YWqVavC3t4eNjY2sLCwQEZGBlJTU/HgwQMkJiYiLi4O58+fx7Vr12TZHwCmTJkiPMBBiosXLyIkJARpaWl6rePj44PmzZujYcOGqFChApydnaHRaPDixQs8evQIMTExOHPmDH799Ve9f3588sknmDFjhl5riEpMTESDBg2QnJys1zpVqlRBs2bN0Lhx47+GSVlbW+PFixd48uQJ4uLicO7cORw6dAgZGRl67dWzZ0+9frYqgQNQ5LFr1y60b99erzXmz5+PUaNGyZSIiIiIiIiIiIiIiIiIiIiIyLCK0+FOIiIiIiJDKE73yKV5AIp+z5QnIqIS791338XYsWN1Glxw+fJlJCUlwdXV1QDJgKpVq2Lx4sXo37+/QfaTwsnJCdu2bUOTJk0AAGFhYbIMQFHK0aNHda718fHh8BPSSdu2bTFr1iyMHz9e8hp5eXmIi4tDXFycjMnyN3z4cA4/0cGAAQNQvXp1dO3aFY8ePTJ2HNlZW1tjw4YN6NSp0xvXmjVrBpVKZfAhDlqtVq9+OYexlEQzZ87E4cOHDfJ9Rqo+ffpgxYoVUKvVaNasGczMzPQaLkWlz7hx4/Drr79i+/bteq/16tUr7N+/H/v375chmbiRI0caZPgJAAQEBGD16tV477339Pren5CQgISEBCxfvlzGdG/q0KEDvvzyS0X3yE+VKlWwdetWhIWFITMzU/I6iYmJWLduHdatWydjujfVr18fK1asUHQPMp527dph2LBhWLp0qeQ1Pv74Y4SFhcHf31/GZERERERERERERERERERERERERERERERUEDNjByAiItPm6uqKpk2b6lx/8OBBBdO8qV+/fhgxYoRB99SVt7c3Tp48+dfwEwAIDw83YqKi7dmzR+farl27KpiESppx48Zh4MCBxo5RpE6dOmHhwoXGjlFshISE4Pfff0eDBg2MHUVWLi4uOHz4cL7DTwDA0dERderUMXAq/QeYZGdny5SkZLKxsUFkZCTKlStn7Cj5mjx5MtauXQsLCwsA/x2yFhISYuRUVNyoVCr8+OOPxf779qBBg7BgwQKD7tm9e3eDDVzRR1BQEDZs2AC1Wm2U/YODg7Fs2TKoVCqj7K8rb29vbN++HTY2NsaOQgr6+uuv4ePjI7k/IyMDERERyMjIkDEVERERERERERERERERERERERERERERERWEA1CIiKhIPXv21LlWZICGXBYsWIBevXoZfN/C9OnTB+fOnUO1atVee3tYWJheB7CUlJmZKTTApm/fvgqmoZLo+++/x9ChQ40do0AdOnTApk2bjHZguLhyd3fHiRMn8MUXX8Dc3NzYcfQWFBSEM2fOoGHDhoXWhYWFGSjR/2i1Wr369R2gUhpUr14de/bsgb29vbGj/MXFxQU7duzAF1988ca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S2tqanB89erSgJADAZPXaa69ldzZsHH4ByubNm5Pzcrkc27dvH/b1AACY+p588snsznALUCIiPvCBu5PzoaGheOKJJ4Z9PQAAAACASlCAAgAAUEG9vb3JeUNDQ0FJKquxsTG748k/wFQx1QpQVmUKUDo6OqK/v7+gNNPbuXPn4hvf+EZyZ/67botZK0b339jcuXOzT5C+sPPcqK4NAG/n4MGDyfns2bNj6dKlxYRJaGtrS86PHTtWUBIAYLLatWtXdmfjhuEXoNx4443ZnW3btg37egAATH25ApTZs2fHnXfeOezrvf/9746ZM2cmdx577LFhXw8AAAAAoBLS37UEAABgRKZqAUpTU1N2p6enp4AkANU1NDQUR48eTe5MtgKUlZkClHK5HEeOHIm1a9cWlGj6+vrXvx4XLlxI7jT+/ENjOuOGG26Ip59++orz87vOjun6APCPHTp0KDlfsWJFlEqlgtJcWa4A5ejRo1EulydEVgBgYsoVoCxevDiWLFkUZ4fZE97Q0BBr1qyJ9vb2K+4oQAF4s/2DB+JX3vgXyZ0vLfh0rK1ZXVAigOIMDg7Gli1bkjt33XVXzJkzZ9jXXLhwfrz73e+O7373u1fc+cY3vhGDg4NRU1Mz7OsCAAAAAIzFjPEOAAAAMFUMDg7GmTNnkjuTtQClsbExu9PdPcxPdgNMYCdOnIjLly8nd6ZaAUpE/uZlKuMrX/lKdqfxwQfGdMYNN9yQnF/u6o/Lp/rHdAYA/NjBgweT81WrVhWSI6e1tTU5P3/+fJw+fbqgNADAZLRz587kfOPGjSO+5ubNm5NzBSgAAPzYjh07sg+luf/++0d83QcffDA57+npieeee27E1wUAAAAAGC0FKAAAABWSKz+JiKivry8gSeXNnTs3Zs2aldzJfdgGYDI4cuRIdmeyFaAsj5lRyuzkbl5m7C5duhSPPvpocuf6m26KWcvbxnROrgAlIuL8zrNjOgMAfixXorZy5cqCkqS1teV/fz127FgBSQCAyWrXrl3JeTUKUHbv3h19fX0jvi4AAFPP1q1bszujKUB54IF8Mf/jjz8+4usCAAAAAIyWAhQAAIAKOXHiRHansbGxgCSVVyqVoqmpKbnT3d1dUBqA6jl8+HB2Z7IVoMyKGdESM5M7uZuXGbstW7Zkb1r62V/8xTGfs2nTppgxI/1t3ws7z435HACImDwFKK2trdmd4bwPBACmp76+vux7hWoUoJTL5dixY8eIrwsAwNSTK0BZtmxZXHvttSO+7rXXXhurVq1K7jz22GMjvi4AAAAAwGgpQAEAAKiQzs7O7E5zc3MBSaojV95y8uTJgpIAVM+RI0eS87lz50ZDQ0NBaSpnZdQm5wcPHiwmyDT293//99md+z70wTGfM3fu3LjmmmuSO+dfPTvmcwDg3LlzcerUqeRO7uaJoixdujRqa9Pvh/bv319QGgBgsnn11VezO6MpQLnxxhuzO9u2bRvxdQEAmFqGhobie9/7XnLnrrvuilKpNOJrl0qleOCBB5I7O3bsiGPHjo342gAAAAAAo6EABQAAoEK6urqyO5O5AGXJkiXJ+XAKYAAmulwByooVK0b14cHxtipTgHLo0KGCkkxP/f398dWvfjW5c91118XqTHHJcN1www3J+fmdClAAGLvhvH9YuXJlAUnyampqsmUsClAAgCvZtWtXdmc0BSiNjY2xevXq5I4CFAAAXn/99eju7k7u3HnnnaO+/oMPPpjdeeKJJ0Z9fQAAAACAkVCAAgAAUCG5ApCGhoaYPXt2QWkqr7W1NTn3xB9gKsgVoCxfvrygJJW1UgHKuHr88cejt7c3ufPhD3+4YuflClAu7j4fQ/1DFTsPgOlpOO8fcqUjRVq7dm1yrgAFALiSnTt3JufNzc3R1NQ0qmvfdNNNybkCFAAAtm7dmt15z3veM+rr33333dnPsihAAQAAAACKogAFAACgQrq6upLz5ubmgpJUhwIUYDqYrgUoR44ciYGBgYLSTD+f/exnszuVLEC5/vrrk/Py5XJc3Hu+YucBMD0dPHgwOZ85c2a0tLQUE2YYFKAAAKO1a9eu9IWPqwAAIABJREFU5Hzjxo2jvvbmzZuT89dffz3Onj076usDADD55QpQGhsb49prrx319efOnRt33313cmfLli0xODg46jMAAAAAAIZLAQoAAECFdHZ2JucT6cav0Vi2bFly3tHREeVyuaA0ANUxVQtQVmUKUAYHBxVZVUl3d3c8+uijyZ1rr712TDdL/bQbbrghu3N+p5unABibQ4cOJefLly+PmpqagtLk5QpQ2tvbY2hoqKA0AMBkMp4FKOVyOXbs2DHq6wMAMLmVy+VsAcqdd94ZM2aM7ZaA+++/Pznv7e2NF154YUxnAAAAAAAMhwIUAACACpnqBSitra3J+blz5+KNN94oKA1A5Q0MDGR/LZ+sBSgrMwUoEREHDhwoIMn088gjj8Tly5eTO7/+678epVKpYme2tbVFQ0NDckcBCgBjdfDgweR81apVheQYrlwByqVLl6Kjo6OgNADAZHH69OlsaeymTZtGff0bb7wxu+NGUwCA6evgwYPZ96Pvec97xnxOrgAlIuKJJ54Y8zkAAAAAADkKUAAAACqkq6srOW9ubi4oSXXkClAiIvvBG4CJrKOjI4aGhpI7k7UAZdUwClDa29sLSDL9fPazn03OS6VSfOQjH6nomaVSKW644YbkzoWd5yp6JgDTz969e5PzlStXFpRkeHIFKBER+/fvLyAJADCZvPrqq9mdjRs3jvr6jY2NsXr16uTOtm3bRn19AAAmt61bt2Z37rzzzjGf8453vCP796AKUAAAAACAIihAAQAAqJDOzs7kvKWlpaAk1bFs2bLsjgIUYDI7cuRIdmeyFqDMiRmxLGYmdw4cOFBQmulj9+7d8dxzzyV33ve+90VbW1vFz84VoJzfdTbK5XLFzwVgeiiXy7Fnz57kzjXXXFNQmuFZs2ZNdkcBCgDw03bu3Jnd2bBhw5jO2Lx5c3KuAAUAYPr63ve+l5zPmzcv3vnOd475nFKpFPfff39y57nnnovTp0+P+SwAAAAAgBQFKAAAABVw6dKl6OnpSe5M9gKU5ubmKJVKyZ2Ojo6C0gBU3lQuQImIWB21yXl7e3tBSaaPz33uc9mdX/u1X6vK2bkClIFTl+Py8f6qnA3A1Hf8+PE4e/ZscmfdunUFpRmeOXPmZIs9FaAAAD9t165dyXlra2vU19eP6YxcAcrrr7+efe8FAMDUtHXr1uT8jjvuiJkz0w9BGK5cAcrg4GB861vfqshZAAAAAABXogAFAACgAo4fP57daW5uLiBJ9dTW1sbSpUuTO8eOHSsoDUDl5QpQ6uvro66urqA0lbcmrkrOFaBUVrlcji9+8YvJnXnz5sWHPvShqpyfK0CJiDj/ipunABidPXv2ZHcmWgFKRMTatWuTcwUoAMBPyxWgbNy4ccxn5ApQyuVy7NixY8znAAAwuRw/fjz27t2b3Lnzzjsrdt773ve+mDEjfWvBE088UbHzAAAAAADejgIUAACACujs7MzutLS0FJCkunJPy1aAAkxmBw4cSM6XL19eUJLqWBO1yXnu35+ROXDgQPbn9MMf/nDVSnU2bNgQNTU1yZ1z2/uqcjYAU99wClCuvvrqApKMjAIUAGCkcgUomzZtGvMZN954Y3Zn27ZtYz4HAIDJ5dlnn83uvOc976nYeQ0NDXHrrbcmd5544okol8sVOxMAAAAA4KcpQAEAAKiArq6u7E5zc3MBSaqrtbU1Oe/o6CgoCUDl5coqVq9eXVCS6sgVoBw/fjzOnTtXUJqpb+vWrdmdj370o1U7f/bs2dmbsBSgADBauQKU5cuXx9y5cwtKM3wKUACAkeju7s5+73/jxo1jPqepqSn7fScFKAAA00+uAKW2tjZuvvnmip55//33J+eHDx+O3bt3V/RMAAAAAIB/TAEKAABABXR2dibns2bNivr6+oLSVE+uAOXYsWMFJQGovKlegLI6rsru5H4OGL6nnnoqOa+rq4u77767qhlyH3o9t/0NT+kDYFRyBSjXXHNNQUlGJleA0tvbG729vQWlAQAmul27dmV3KlGAEhGxefPm5FwBCgDA9JMrQHnnO98Zc+bMqeiZuQKUiIgtW7ZU9Mxq2D94IG7pfW/yn/2D/l4UAAAAACYiBSgAAAAVkHsKZEtLS5RKpYLSVM+yZcuScwUowGRVLpfj0KFDyZ3JXoCyJmqzO+3t7QUkmR5yBSh33HFHzJw5s6oZcgUoAycux+WOS1XNAMDUlCtAWbduXUFJRiZXgBIRsX///gKSAACTwXAKUDZs2FCRs3IFKK+//nqcO3euImcBADDxDQwMxA9/+MPkzm233Vbxc2+++eZoaGhI7kyGAhQAAAAAYPJSgAIAAFABnZ2dyXlzc3NBSaqrtbU1Oe/q6orBwcGC0gBUTldXV1y8eDG5M9kLUFpiZsyKdBnXgQOedFYJR44cyf5c3nXXXVXPcdNNN2V3zm3vq3oOAKaWwcHBbEmIAhQAYCrIFaCsWLEi5s+fX5GzcgUoQ0NDsWPHjoqcBQDAxLdr165sAV41ClBqamrinnvuSe5897vfjcuXL1f8bAAAAACACAUoAAAAFZErQGlpaSkoSXXlClCGhobi+PHjBaUBqJzhFH+sWrWq+kGqaEaUYnXUJnfa29sLSjO1bd26NbtTRAHKddddF7NmzUruKEABYKQOHz4c/f39yZ2JWoDS2NgYCxcuTO4oQAEAfixXgLJp06aKnXXjjTdmd7Zt21ax8wAAmNieffbZ7E41ClAiIu69997kvK+vL5577rmqnA0AAAAAoAAFAACgArq6upLz6VKAEhFx7NixApIAVNZwClBWr15dQJLqUoBSjKeeeio5nzNnTtx0001Vz1FbWxs/8zM/k9w596ICFABGZs+ePdmdiVqAUiqVYu3atckdBSgAwI/t3LkzOd+4cWPFzmpqasqW777wwgsVOw8AgIntBz/4QXK+ZMmSqj284b777svuPPnkk1U5GwAAAABAAQoAAEAFdHZ2JufNzc0FJamuZcuWZXcUoACT0cGDB5PzpqammD9/fjFhqmhNXJWcD6cIhrxcAcrtt98eV12V/t+iUnJFK+d29EW5XC4kCwBTQ64AZebMmVW7+aISFKAAAMNx4sSJOHXqVHKnkgUoERGbN29Ozrdt21bR8wAAmLieffbZ5Py2226LUqlUlbNXr16d/R7ali1bqnI2AAAAAIACFAAAgDEaGhqK48ePJ3daWloKSlNdDQ0NMXv27OROR0dHQWkAKidX/LF69eqCklTXmqhNztvb25VhjFFnZ2f2xvD3vOc9BaWJuPnmm5PzwTMDcenAhYLSADAV5H6fW7NmTdTWpt9zjCcFKADAcOzatSu7U3QByuuvvx7nzp2r6JkAAEw8PT09sXv37uTObbfdVtUM9957b3L+/PPPx+nTp6uaAQAAAACYnhSgAAAAjFFPT09cvnw5udPc3FxQmuoqlUrR2tqa3Dl27FhBaQAqJ1eAsmrVqmKCVNmauCo5v3DhQrbUi7Tvfe972Z277rqrgCQ/kitAiYg4t72vgCQATBW5ApRrrrmmoCSjkytAOXbsWFy8eLGgNADARDWcApRrr722omfmClCGhoZix44dFT0TAICJ5/nnn8/ujHcBytDQUHznO9+pagYAAAAAYHpSgAIAADBGnZ2d2Z2WlpYCkhRj2bJlybkCFGAyyhWgrF69uqAk1bU6arM77e3tBSSZup566qnkfNasWXHrrbcWlCZi/fr1MXfe3OSOAhQARiJXgLJu3bqCkoxOrgClXC7H/v37C0oDAExUuQKUNWvWxLx58yp6Zq4AJSJi27ZtFT0TAICJ59lnn03OZ8yYETfddFNVM9xzzz0xY0b6NoMtW7ZUNQMAAAAAMD0pQAEAABijrq6u7M5UKkBpbW1Nzjs6OgpKAlAZAwMDceTIkeTOdCpAyZXBkLZ169bk/NZbb43Zs2cXlCaipqYmNt64KbmjAAWA4bp48WIcOnQouTPRC1Cuvvrq7M5rr71WQBIAYCLbuXNncr5x48aKn9nU1BSrVq1K7ihAAQCY+nIFKJs2bYr58+dXNUN9fX3ccsstyR0FKAAAAABANShAAQAAGKPOzs7kvFQqxZIlSwpKU325ApRjx44VlASgMo4dOxYDAwPJnalSgLIgamJRQ0Nyp729vaA0U09/f3/2hum77rqroDQ/cf1N1yfn51/uy/5/AAAifvQ+oVwuJ3cmegFKW1tb1NXVJXd27dpVUBoAYCIql8vZ9wPVKECJiNi8eXNyrgAFAGBqGxoaiueeey65c9tttxWS5d57703O9+3b58EKAAAAAEDFKUABAAAYo66uruR88eLFMXPmzILSVN+yZcuScwUowGQznA/m5Z6+O5msaVuenCtAGb329vYYHBxM7tx+++0FpfmJ6266LjkfOjcU33tia0FpAJjM9uzZk92Z6AUopVIpNmzYkNxRgAIA01tXV1f09vYmd8arAOW1116Lc+fOVeVsAADG3+uvvx6nT59O7rzrXe8qJEuuACUiYsuWLQUkAQAAAACmEwUoAAAAY9TZ2Zmct7S0FJSkGK2trcn5mTNnfAAbmFSmWwHK6ta25FwByugN56bwa6+9toAkb3bdTddndx7+1MMFJAFgssv9Xjd37txsaeZEoAAFAEjZuXNndme8ClCGhobipZdeqsrZAACMv2eeeSa7c+uttxaQJOK2226Lurq65I4CFAAAAACg0hSgAAAAjFGuAKW5ubmgJMXIFaBERHR0dBSQBKAyDh48mJy3tLTE7NmziwlTgDVty5Pz4RTC8PZyN4XPnj07li9P//xXw4o1K2LWqvR/w9957Ntx7NixghIBMFm9/PLLyfk111wTM2ZM/L9+zN2wvGfPnujv7y8oDQAw0eTe88yYMSPe8Y53VOXsXAFKRMS2bduqcjYAAOMvV4DS2NgY69evLyRLbW1t3H333cmdb33rWzE4OFhIHgAAAABgepj4n0AEAACY4Lq6upLzlpaWgpIUYzhP83YDNTCZ5Ao/Vq9eXVCSYuQKUI4ePRoXL14sKM3Usnv37uR8vG4KL5VKsegj6fcjQ0ND8elPf7qgRABMVs8991xyXtTNF2OVK0AZGBiIvXv3FpQGAJhocgUo69atizlz5lTl7Kampli5cmVy54UXXqjK2QAAjL9cAcrtt99e6N813Xvvvcl5b29vvPjiiwWlAQAAAACmAwUoAAAAY9TZ2ZmcK0ABmNhyBSirVq0qJkhB1ixPF6CUy+Vob28vKM3UsmfPnuR83bp1BSV5q0W/2hxRk9751Kc+5Sl9AFxRd3d37Nu3L7lz0003FZRmbHIFKBERu3btKiAJADAR5QpQrr/++qqev3nz5uR827ZtVT0fAIDxcfz48ez33+64446C0vzIfffdl93ZsmVLAUkAAAAAgOlCAQoAAMAY5QpQmpubC0pSjNmzZ0dTU1Ny5+DBg8WEAaiAXAHK6tWrC0pSjGtWrMru5Io8eHsTuQDlqpZZUX9/+vfvw4cPx5NPPllQIgAmmx/+8IfZnVtvvbWAJGO3fPnymD9/fnJHAQoATE+XL1+OV199Nbkz3gUor732Wpw9e7aqGQAAKN4PfvCD7M7tt99eQJKfWL9+fbS1tSV3FKAwFR05eik+/ZnO+OSnOuLZ59+Icrk83pEAAGBaGxxojzPH3538Z3DAg/8ApgoFKAAAAGPQ29sbfX19yZ2WlpaC0hRn5cqVyfnevXsLSgIwNpcuXYqOjo7kzlQrQGldujTmzJmT3PHr+Mi98cYb0dXVldwZzwKUiIjFv74su/M3f/M3BSQBYDJ67rnnkvOamprszboTRalUig0bNiR3FKAAwPS0Z8+e6O/vT+5UuwDlpptuSs6HhobihRdeqGoGAACK98wzzyTntbW1cfPNNxeU5kdKpVLce++9yZ1nnnlGQR9Txp695+Nf/M7uuP7mF+J/+Xft8Qd/fCB+9udfiQ/8wivx6qv+Dh0AAACgCApQAAAAxqC9Pd8UnCsLmYxyN3Dv2bOnoCQAY3P48OHs05qmWgHKjBkz4uqrr07u+HV85Ibzc7Z+/foCklzZwnsa46rWWcmdr3/963Hx4sWCEgEwmeQKUDZt2hTz5s0rKM3Y5QpQXn311YKSAAATycsvv5zdGe8ClIiIZ599tqoZAAAoXq4A5cYbb8w+5KAacgUoly9fjq1btxaUBqqjt/dMfOx/2hu33bU9vvLVU/HTHyF49vm+uOnmn49PfOITMTAwMD4hAQAAAKYJBSgAAABjMJwClLVr1xaQpFi5ApS9ez31BJgcDhw4kN1ZtWpV9YMUTJFV5Q3n5yz3815tpZpSLPpoS3Ln0qVLbvgG4C3K5XI8//zzyZ1bb721oDSVsXHjxuR879690d/fX1AaAGCiyBWgLFiwIFasWFHVDI2NjdkSVQUoAABTy8WLF2Pbtm3JnTvuuKOgNG/2vve9L7uzZcuWApJAdbzxxhtxx7s/HF985EQMDV15r7+/P/7oj/4o3vve90Zvb29xAQEAAACmGQUoAAAAY7B///7kfMGCBdHY2FhQmuLkbuA+depU9PT0FJQGYPT27duXnM+YMSOWL19eUJriKECpvN27dyfnjY2N0dTUVFCaK1v0K83ZneE87RqA6WX//v3R3d2d3LnlllsKSlMZuQKUgYEB74kAYBrK/Zn4+uuvj1KpVPUct912W3L+gx/8IMo//UhyAAAmrRdeeCFbxjteBShLliyJd77znckdBShMZn/4h38Ye/bkH5zyY08//XR87GMfq2IiAAAAgOlNAQoAAMAYtLe3J+dr1qwp5MPQRcvdOB/xo6dlA0x0r732WnK+YsWKqK2tLShNcXK/jnd1dUVfX19Bad5s6MJgnPxMRxz+433R8RcH4/yrZ8clx0jlbpDOPbm5KFe1zYqZi9L/TStAAeCnPffcc9mdW2+9tYAklZMrQImI2LVrVwFJAICJZDgFKEXIFaCcOHEiDh48WEgWAACq7/vf/3525/bbby8gydu79957k/Ndu3bF4cOHC0oDldPe3h5/8zd/M+LXPfzww/HCCy9UIREAAAAAClAAAADGYP/+/cn52rVrC0pSrGuuuSa740nZwGSQK0C59tprC0pSrOH8Oj4eRVZnX3wjdr77h3Hwd/fE8f9yNI594mDsuvOF6PqrIxP+qca53/eGUx5WhFKpFHM3zkvuKEAB4KflClDq6uom3fumtra2mD9/fnJHAQoATC89PT1x9OjR5E5RBSjvete7sjvPPvtsAUkAACjCM888k5yvWbMmmpubC0rzVrkClIiIxx9/vIAkUFkf//jHY2BgYFSv/bM/+7MKpwEAAAAgQgEKAADAmLS3tyfna9asKShJserr62PJkiXJHQUowGQwXQtQhlPGUfSv4y88/cPY/cGX4tKBi28elCOO/On+OPmZzkLzjES5XJ40BSgREXM21CXnL7300oQvnAGgWLkClJtvvjlqamoKSlMZpVIpNmzYkNxRgAIA08srr7yS3SmqAGXjxo0xb166wFQBCgDA1FAul+P73/9+cueOO+4oKM3be/e73x1z585N7jz22GMFpYHKeO211+Lzn//8qF//+OOPZ793DgAAAMDIKUABAAAYpf7+/jh8+HByZ+3atQWlKd4111yTnCtAASa6M2fOREdHR3JnqhagLFq0KOrr65M7e/fuLShNxLe+9a34jQf+eQydHbzizuE/3Buv7ni1sEwj0dnZGWfPnk3urF+/vqA0eXM3pm+gOnXqVBw/frygNABMdJcuXYodO3Ykd2699daC0lTWxo0bk3MFKAAwvbz88svZnU2bNhWQJGLmzJlx8803J3d+8IMfFJIFAIDq2rNnT5w6dSq5M94FKLNnz473v//9yZ1vfetbceHChYISwdj96Z/+aQwNDY3pGn/2Z39WoTQAAAAA/JgCFAAAgFE6fPhw9i/C16xZU1Ca4q1bty45V4ACTHSvv/56dmeqFqCUSqUJU2S1bdu2eOihh+LC+fQHIsuXyvFvfuV/jDfeeKOQXCOxe/fu7E7u980izdlYl90Zzk1fAEwPL730UvT39yd3pmoByr59++LixYsFpQEAxlvuz8Jr166N+fPnF5Qm4rbbbkvOt2/f7gZTAIAp4JlnnsnujHcBSkTEgw8+mJxfuHAhvvvd7xYTBsboxRdfjC9/+ctjvs4TTzwR3//+9yuQCAAAAIAfU4ACAAAwSvv378/urF27toAk4yN3I/fevXujXC4XlAZg5F577bXszlQtQImYGEVWAwMD8Vu/9VvDvrH44N6D8Tu/8zsT7veX3M9VqVSKq6++uqA0eXPWz81+Z1gBCgA/9uyzz2Z3pmoByuDgYLz00ksFpQEAxlvuz8LXX399QUl+5F3veldyPjAwENu3by8oDQAA1ZIrT1i4cGFs2LChoDRX9sADD2R3Hn300QKSwNj95V/+ZXbngZ9tjH/2K0uyex//+McrEQkAAACA/58CFAAAgFFqb29PzmtqamL58uUFpSle7sb5c+fORWdnZ0FpAEYuV4CyZMmSaGxsLChN8YZTgFLtopFHH300duzYMaLXPPzww/Htb3+7SolGJ1eAsmLFipgzZ05BafJmzK6J2VfPTe4oQAHgx3JPoG1ra4uWlpaC0lTWcG5ifv755wtIAgCMt8HBwdi5c2dyp+gClOGUzA2nrA4A4P9j777Dorzyt4Hfw9CrXWxBsaASBQtYiChKjBFLVCKJGo1R08wvxd2Ujdloym5iejVNYzQae0OsIPaGXWOhCHZFBVGRzsz7R97dTdFznmlnZuD+XJfXtfHcc+aOawaYeZ7vIccme/+tW7ducHGx/yX/jRs3RlhYmDCzevVqhzvIgOjPbt68iWXLlgkzOh0w+dV78PJLTeDqqhNmN2zYgNOnT1uxIRERERERERFR9Wb/d0OJiIiIiIic1KlTp4TrQUFBcHNzU9RGPdmN84D8hnAiIns6efKkcL1NmzaKmtiH7HW8oKAAeXl5Nu2wYMECsx6n5UQuldLT04XrWr5mquYd6iNc5wAUIiICAKPRiO3btwszXbt2VdTG+ho0aIDGjRsLMxyAQkREVD1kZWWhqKhImFE9AKV+/fpo1qyZMMMBKERERETOLS8vT/qZZVRUlKI2cnFxccL1M2fO4Pjx44raEJln8eLFKC4uFmaGPVQHoW18EHSPJ0Y9Wk+657x586xVj4iIiIiIiIio2uMAFCIiIiIiIjNlZ2cL14ODgxU1sY/mzZtDpxOfcsIBKETkyE6cOCFcr+oDUFq2bCnN2PJ1vLCwEImJiWY9duPGjSgsLLRyI/OUl5dLbzZyxAEoXqG+wvXjx4+jvLxcURsiInJUZ86cwcWLF4WZ++67T1Eb2+jSpYtwnQNQiIiIqoe9e/dKM+3atVPQ5I9kw+Z27twJo9GoqA0RERERWdvOnTulGWcagAIAq1evVtCEyHyzZ8+WZl6Z1OS///tvLzSBm5v4+qiff/6ZP5sREREREREREVkJB6AQERERERGZ6dSpU8L15s2bK2piH15eXrjnnnuEGQ5AISJHVVpaKn0d5wAU276OJyYmSk/WupvS0lIkJydbuZF5UlNTkZeXJ8yEhIQoaqOdd6iPcL28vBzp6emK2hARkaPavn27NOPsA1AiIyOF6xkZGbh+/bqiNkRERGQvsgEo/v7+dnnPXzYA5cKFC9L3uIiIiIjIce3YsUO4rtfrpe9fqdSlSxfUrl1bmElKSlLUhsh0p06dwrZt24SZbl380aql93//uUljDwx4sJbwMenp6di3b59VOhIRERERERERVXccgEJERERERGQGo9GI7OxsYSY4OFhRG/tp1aqVcJ0DUIjIUWVmZsJgMAgzVX0Air+/PwIDA4UZW76Oz58/36LHJyYmWqmJZRYuXCjN9OzZU0ET03iF+kozR44cUdCEiIgcmewGDB8fH4SFhSlqYxtabiCR3RBNREREzi8tLU243rlzZ7i4qL/Mqnv37tLM5s2bbV+EiIiIzHaqMgeR12OEv05V5ti7JtnJzp07hesdOnSAj494qP1//P7vWrdb43C/u9tffp3WWdZXr9ejX79+wsyOHTuQm5tr2RMR2cicOXOkmUeH1/3L7yXE15M+bu7cuWZ1IiIiIiIiIiKiP+IAFCIiIiIiIjNcvXoVhYWFwow9ToNUrWXLlsJ1DkAhIkd14sQJaaaqD0AB5IOsMjMzbfK8+fn5WL9+vUV7JCUlobKy0kqNzFNWVobly5cLM61atUK7du0UNdLOvZEH9AGuwgwHoBAR0fbt24XrXbt2haur+OuJo9NyM7PshmgiIiJybuXl5Th06JAwo2Vomi2Eh4fD399fmOEAFCIiIiLnVFZWJh28q2UgnmpxcXHCdYPBgBUrVihqQ6SdwWCQDkDx9HTB4IF1/vL7fWJqoE6dWsLHzp8/H+Xl5RZ1JCIiIiIiIiIiDkAhIiIiIiIyS3Z2tjQTHBysoIl9yW6cz87ORkVFhaI2RETayQag+Pn5oVGjRora2I+9BlktW7ZMevGXa2034fq1a9ewe/dua9YyWXJyMgoKCoSZhIQE6HQWHqdnAzqdDt6h4hMDOQCFiKh6u379Oo4dOybMREVFKWpjO76+vmjbtq0wwwEoREREVduvv/6KkpISYSYiIkJRmz9ydXVFdHS0MLN582YYjUZFjYiIiIjIWg4cOCD9PtQR33/r16+fdCjy0qVLFbUh0m7r1q04ffq0MDOwfy0E+P/177ebmwsShouH/1y9ehXJycmWVCQiIiIiIiIiInAAChERERERkVlOnTolzXAAym8nZ545c0ZRGyIi7WQDUFq3bu2QQyusTfY6np6ebpNTqubPny8OuADB37aR7pOYmGilRuZZuHChNJOQkKCgiXm8Qn2F6wcPHuQNVERE1diuXbukXwfuu+8+RW1sKzIyUrielpbGr4lERERVmJZhZ/ZNqUQSAAAgAElEQVQagAIAvXr1Eq5fuHBB02cWRERERORYduzYIc044gCUmjVrIjY2VphJTU1FXl6eokZE2vz888/SzKPD6911beTIIVZ5DiIiIiIiIiIiEuMAFCIiIiIiIjNkZ2cL12vXro2AgABFbexHduM8AJw8eVJBEyIi08gGoLRpIx++URWEhIQI10tLS63+On7lyhVs3rxZmPG7rwb8e9eEexMPYc6eA1BKSkqwYsUKYSY0NBShoaGKGpnOu62PcP3y5cv8Ok5EVI1t375duO7i4oKuXbsqamNbsgEoubm5OHfunKI2REREpNrevXuF64GBgWjcuLGiNn8lG4ACQPpeCxERERE5HtkAlKCgIDRq1EhRG9PEx8cL1ysrK7Fy5UpFbYjkSktLsXTpUmGmYQN39OxR467rERHt0bJlS+EeiYmJKC4uNqsjERERERERERH9hgNQiIiIiIiIzCA7TbF58+aKmthXUFAQ3NzchJnDhw8rakNEpE1lZSXS09OFmeoyACU8PFyaOXjwoFWfMyUlBQaDQZipPaQedDodajxYR5g7efIkMjIyrFlPs3Xr1uHWrVvCzPDhwxW1MY93mJ80s379egVNiIjIEcluwAgLC4Ofn/xriTPo0qWLNLNnzx4FTYiIiMge0tLShOsRERHQ6XSK2vxVeHg4/P39hZlNmzYpakNERERE1lBZWYktW7YIM1FRUYramG7w4MHQ6/XCzJIlSxS1IZJbt24dbty4IcwMH1YXev3df/bT6XR47LHHhHsUFRXx5zMiIiIiIiIiIgtxAAoREREREZEZZANQgoODFTWxL1dXV+mQgAMHDihqQ0SkzZkzZ1BSUiLMVJcBKPfccw9q1qwpzNhiAIqIzlWHmoPqAgBq9Kst3W/VqlVW6WUq2QlhAJCQkKCgifm82/lCX9NVmFm3bp2iNkRE5EhKS0ulNwLfd999itrYXmhoKLy8vIQZ2Z8HEREROafbt2/j2LFjwkxkZKSiNnem1+sRHR0tzGzevBlGo1FRIyIiIiKy1L59+5Cfny/MOPIAlDp16qBXr17CTEpKCgoKCtQUIpKYP3++NJMQX0+aGTlypDSTmJioqRMREREREREREd0ZB6AQERERERGZISMjQ7jevHlzRU3sr0OHDsJ1a984T0RkKdlNLUD1GYCi0+mUvo4bjUbpABTfbgFwrekGAPDrXgN6P/HpcfY4QctgMGDDhg3CTFhYGEJCQhQ1Mo9Or0NAL/EAnC1btqC4uFhRIyIichQHDhyQDoxz5BswTOXm5oaOHTsKMxyAQkREVDUdOHAABoNBmImIiFDU5u5iYmKE6xcvXkRWVpaiNkRERERkqfXr10szsgEj9hYfHy9cLy8vt9tBBkS/V1hYKB1K0u7eELRp7S3dKzg4GO3btxdmkpKSOKCSiIiIiIiIiMgCHIBCRERERERkosuXL+PKlSvCTHUagCK7SSw7O5un+hCRQzl06JBw3d3dHcHBwYra2J9sAMqhQ4esdoFWZmYmzp07J8z49/zfQA4XdxcExNYS5nfv3q38ArIjR45IvxcYPny4ojaW8e8t/vMtKSnBtm3bFLUhIiJHkZycLM1UpQEoABAZGSlc37t3r3QoDBERETmfvXv3SjOdO3dW0ERMy82vmzdvtnkPIiIiIrKOdevWCdebNGni8Ac2DBkyBDqdTphZsmSJojZEd5eYmCg98CHhkYGa9xs0aJBw/cKFCzwsioiIiIiIiIjIAhyAQkREREREZCLZjfMApKd9VCWyG+cBbX9mRESqyC42Cg0Nhaurq6I29id7Hb9x4wZycnKs8lwpKSnSzO8HoNzpn/8sLy8PmZmZFvUylZabwgcMGKCgieUCJANQAPlFuEREVPWsWbNGuB4UFITGjRsraqNGly5dhOvFxcXYvn27ojZERESkSlpamnC9efPmqF27tqI2dxcWFoaAgABhZtOmTYraEBEREZElrl+/jj179ggz/fr1kw4Xsbf69eujR48ewsz69etx69YtRY2I7mz+/PnSTMJw7Z/tDhwoH5ayatUqzfsREREREREREdEfcQAKERERERGRiWTDPPR6PUJDQxW1sb+wsDBphiebEJEjkb2OaxnsVJVo+fe11uu4bACK3l8Pn3C/P/yeb4T45h4A2LVrl0W9TLVhwwbhev369dGuXTtFbSzj3sADXm19hJn169crakNERI7g2rVr0huBY2NjFbVRJyoqSprhUDAiIqKqZ+/evcL1iIgIRU3E9Ho9oqOjhZnU1FQYDAZFjYiIiIjIXCkpKdLv2/r166eojWXi4+OF66WlpVi9erWiNkR/lZ+fL/2ss2vXrmjWrInmPTt37ozAwEBhJjExUfN+RERERERERET0RxyAQkREREREZCLZjfNt2rSBp6enojb25+/vj5YtWwozBw4cUNSGiEisoKAAOTk5wkx4eLiiNo4hJCQEXl5ewow1BqBUVlYiNTVVmPGLrgmd/o+n2XmGeEPvrxc+TuUAlOLiYmzbtk2Y6du3r8Ofyvd7Ab1rCdePHz+Oc+fOKWpDRET2tn79ehiNRmGmf//+itqo07hxY+kwUw5AISIiqlpyc3ORnZ0tzDjKABQAiImJEa7n5ubi6NGjitoQERERkblk7zHp9Xr06dNHURvLDB06VJpZsmSJgiZEd7Zo0SKUl5cLM48++qhJe7q4uGDAgAHCzIEDB3DhwgWT9iUiIiIiIiIiot9wAAoREREREZGJZANQqtuN8wDQoUMH4bo1bpwnIrKGw4cPSzPV7XVcr9ejffv2wow1Xsf379+PGzduCDMBPWv+5fd0Ljr4dPIXPk7lAJRt27ahtLRUmLn//vsVtbEO/95//XP/M9nJaEREVHWsWbNGuO7q6orY2FhFbdSSnax77NgxnD9/XlEbIiIisrVNmzZJM5GRkQqaaKPlJtgNGzYoaEJERERE5jIajdIBKN26dUNAQICiRpZp1KgRunfvLsysWbMGt2/fVtSI6H+MRiOmT58uzLi4uGD48OEm7z1w4EBpJikpyeR9iYiIiIiIiIiIA1CIiIiIiIhMcvv2bWRkZAgz1e3GeQDo2LGjcP3EiRMoKipS1IaI6O5kQ6wAICwsTEETx6JikFVKSoo043+HASgA4NtZPADl119/xa1bt8zqZSotNxI5203hfl0D4OXtJczILsYlIqKqobKyUvqa36NHD/j7i782O6sHHnhAmuFQMCIioqpDNgDF3d0dnTp1UtRGrl27dqhfv74ww+9ViIiIiBzbsWPHcPHiRWFGNqTX0QwbNky4XlxcjLVr1ypqQ/Q/27dvx9GjR4WZmJgYBAYGmrx3bGwsPD09hZnExEST9yUiIiIiIiIiIg5AISIiIiIiMsnRo0dhNBqFmeo4AEV247zBYJBeVEBEpIJskEfz5s2r7A29IrLX8UuXLiE3N9ei55ANQGnQpAE8mt95CIdvhPj/E4PBgLS0NLO7meKbb74Rrrdr1w4NGjRQ0sVaXDz16NKzizCzc+dORW2IiMie0tLSkJ+fL8z0799fURv1evToAS8v8VAw3lRMRERUdcgGoHTr1k36vYFKOp0Offv2FWa2bdvGYeREREREDkzLwPmqNgAFAJYuXaqgCdEfffXVV9LMqFGjzNrb29tbeihGSkqKskM8iIiIiIiIiIiqEg5AISIiIiIiMsGhQ4ekGQ5AuTPZ0AEiIhVkr+PV8TUcsP3reFFREXbs2CHMRPWJgk6nu+OaT2f5UJrdu3eb1U0Lo9GIuXPnom7dutKbiGQ3IjmqHn2jheuXLl3CzZs3FbUhIiJ7WbNmjTRTlQegeHp6olevXsJMcnIyKioq1BQiIiIimzl//jwyMzOFmZiYGEVttJO971BWVoatW7cqakNEREREplq9erVwvW7dupo+t3MkQUFBiIiIEGaSkpJQXFysqBERcPHiRSxbtkyYqVWrFhISEsx+joEDBwrXy8rKNL3nTkREREREREREf8QBKERERERERCaQ3TjfpEkT1K5dW1Ebx1G3bl00btxYmDlw4ICiNkREd1ZWVobjx48LM852QaG1tGvXDnq9XpixZADK9u3bUVZWJsx07xN11zXXGm7wbOUtfPyuXbvM6qbFa6+9hsceewzXrl2TZp11AMq9He+VZjIyMhQ0ISIie5JdjB0UFIQ2bdooamMfshN2CwoKsHfvXkVtiIiIyFY2bdokzfTu3VtBE9PIThgHgA0bNihoQmS+U5U5iLweI/x1qjJH+4bHMgFdW/GvY+KBR0RERP9lw68r165dkw6r69u3L1xcnO/y/mHDhgnXCwsL+X0qKfX9999LB1mPGzcOXl5eZj/HwIED73rAx3/IhrAQEREREREREdFfOd87pERERERERHYkG4ASHh6uqInjkQ0NsOTGeSIiazh27BjKy8uFmer6Ou7p6Sm9mdmS1/GUlBRppnvv7sJ13wh/4fru3bthNBpN6qXF4cOH8cEHH2jKenh4oEePHlbvoEKzkGBpJj09XUETIiKyl0uXLkkHV/bv3196Qbeze+CBB6SZdevWKWhCREREtiQbgOLl5YXIyEhFbbQLDAxEWFiYMLN+/XpFbYiIiIjIFKtWrYLBYBBmZMN5HZVsAAoALFq0SEETqiqMRiOuXLmCnJwc6X83f1ZWVobvvvtOmNHpdHjmmWcsqYgGDRqgW7duwsyaNWtQUlJi0fMQERERERERkfUZyspsct05WQcHoBAREREREWlUWVmJI0eOCDPV9cZ5QD4A5ciRI9LBA0REtiQbYgXIX8uqMtm/+86dO81+ozc5OVm43r59e9SpX1eYkQ1AycvLQ2am9U9ynTNnjuZsjx49LDolzJ5q1amFWrVqCTMZGRmK2hARkT0sXrxYmunfv7+CJvbVqlUrNG3aVJhZu3atmjJERERkM6mpqcL1qKgoeHh4KGpjGtnAtuPHj+P8+fOK2hARERGRVsuWLROuu7q6Ii4uTlEb62rRooX0epkVK1agsLBQUSNyRgaDAdu2bcOkSZPQvHlz1K9fH8HBwahduzaeeOIJJCcno6KiQrrP+++/j8uXLwszcXFxaNasmcWdhw4dKlwvLCzUdFgIEREREREREdle2aXLOPev93G4WzTSGgbhQNv2yH5hEopPZdu7Gv0JB6AQERERERFplJmZieLiYmGmOg9A6dixo3C9rKwMR48eVdSGiOivZANQ6tatiwYNGihq43hkA1AuXLiAkydPmrzv1atXpX/2sbGx0n18IwKkmV27dmnupdX27ds1ZwcOHGj151epVatWwvX09HRFTYiIyB7mz58vXPfw8EBMTIyiNvaj0+mkNxXv3bsXly5dUtSIiIiIrC0nJwdnzpwRZnr37q2ojen69u0rzciG0RIRERGRWrdu3ZJ+j9arVy/UrFlTUSPri4+PF64XFRVhxYoVitqQs9mxYwciIiIQHR2NTz/9FDk5Of9dKygowKxZs9C3b180adIEr7/++h/Wf+/IkSN45513pM83ceJEq/QeMmSINCMbfkREREREREREtnX+9Glkjn8aB9t3xIWPPkHxyZNAZSXKL+fiypy5ONL1Pkx94QVcuXLF3lXp/+MAFCIiIiIiIo1kN28D1XsAiuzGeQDYtGmTgiZERHd28OBB4Xp4eDh0Op2iNo6nR48e0syGDRtM3ld2ojKgbQCKZ4g3fP19hZndu3dr7qVFSUmJ9O/Nf9SvXx9jx4616vOrFhISIlznABQioqorOztb+nU0NjYWPj4+ihrZV79+/aSZpKQkBU2IiIjIFrS8T+3Ig9+ioqLg5eUlzKxbt05RGyIiIiLSYu3atSgtLRVmhg4dqqiNbQwfPlyamTdvnoIm5Gy+//579OrVCwcOHJBmL1++jPfeew/NmzdHXFwcDh8+/N+18vJyjBkzBhUVFcI9WrRooWmwpBbBwcEICwsTZlauXCntRERERERERES2sWXLFjwYFo68pctgvMvP58aKCvz89XS0bdsWmzdvVluQ7ogDUIiIiIiIiDSS3QDt7++Ppk2bqinjgJo0aYLAwEBhJiUlRVEbIqI/MhgM0kFWWgY5VWUdOnRArVq1hBlzTg+Wvfa7ublpGr6ic9EhLFI8aCwtLc2kbjL79+9HeXm5NBcUFIQNGzbAz8/Pqs+vmmwASkZGBgwGg6I2RESk0vz586WZESNGKGjiGGJjY+Hu7i7MJCYmKmpDRERE1iYb1urr64tOnTopamM6T09P9OzZU5hZv369pvc0iIiIiEiN5cuXSzODBw9W0MR2WrZsiS5duggzGzZsQG5urqJG5OgqKirw4osv4qmnnjJ5QIjRaMSaNWvQqVMnTJw4ERcuXEBcXJymw62ef/55uLhY7zYa2fCi/Px8bN261WrPR0RERERERETaFBYWYuTIkSi6fVtTPi8vD3Fxcbhw4YKNm5EMB6AQERERERFpJBuAEhYWZtUPyJ2NTqdDnz59hJmtW7dKTzUiIrKFrKws3Lp1S5gJDxcP16jq9Ho9YmNjhZnNmzejrKxM855Go1E6NKVbt27w9fXVtF+HLuIhNUeOHEFxcbHmfjK7du2SZj799FNkZGSgffv2Vntee5ENQCkqKuKb+kREVZDRaJSevOrt7Y1BgwYpamR/vr6+0p9vU1JScFvjh+NERETkOAwGAzZu3CjMREdHw83NTVEj88hOK79x4wa2b9+uqA0RERERiZSWlmL16tXCTNeuXdGwYUNFjWxn1KhRwnWDwYAFCxYoakOOzGg0YuzYsfj8888t2qeyshLTp09H48aNNR3mERoaiieffNKi5/wz2QAUAFi6dKlVn5OIiIiIiIiI5L799luTr3suKirCe++9Z6NGpFX1vTOPiIiIiIjIBBUVFdKboKv7jfMApDfOFxUVYffu3YraEBH9j+xkX4Cv4wBw//33C9dv376taSjIf2RnZ+PMmTPCjOxrx++1jxAPGamoqNB0qpdWO3fuFK4HBwfjxRdfhLu7u9We055kA1AAICMjQ0ETIiJS6ciRIzhx4oQwM3jwYM0Dy6oK2cCXkpISpKSkKGpDRERE1rJz505cvnxZmImJiVHUxnxxcXHSTFJSkoImRERERCSzceNG6UENWgYoOIOEhATo9XphZu7cuYrakCP77rvvlP9dcHFxwaxZs+Dh4WHVfUNDQ9GyZUthZtWqVTAajVZ9XiIiIiIiIiK6u+LiYnz00UdmPXbRokWoqKiwciMyBQegEBERERERaXDw4EEUFhYKM506dVLUxnHJTsgGwBvEiMguZCf7+vv7o1WrVoraOC7ZABQAmk7O+g8tr/mmDEAJiwyTZvbs2aN5PxGj0Sgd9tKtWzerPJejaNGiBXQ6nTCTnp6uqA0REakyf/58aebRRx9V0MSxDBgwQJpZtWqVgiZERH9UdrEUV+dewrk3TyHjkSM4HrsfmSOP4tr8yygtLbV3PSKHt2TJEmmmd+/eCppYplWrVtL3svi9ChEREZFjWLx4sTQzZMgQBU1sr27duujXr58ws2/fPn7eVM3l5+dj8uTJyp/35ZdfRkREhNX31el00iFG586dw5EjR6z+3ERERERERER0ZzNnzkRubq5Zj7169So2bdpk5UZkCg5AISJyEuV5ZShYfw1XfrqI2wdvwmjgJHAiIiKVtmzZIs1ER0craOLYmjRpIr3oWjaEgIjI2iorK5GamirMxMTESE8jqw6CgoKkp1Nt2LBB836yASh+fn4mXWRWu14dNG3aVJhJS0vTvJ/ImTNnpCdCd+/e3SrP5Sg8PDykf768IJWIqGoxGAzSASg1a9bEAw88oKiR42jcuLF00OmqVatgMBgUNSKi6u7yhcvIGnsMh8N24fTz6bj81Tnc2JCP2wduoWBtHnImnkSv5tH497//jYKCAnvXJXJIBoMBS5cuFWbuuecedOjQQVEjy8gGtmVmZiIjI0NRGyIiIiK6k6KiIukQvnvvvRctWrRQ1Mj2Ro0aJc3Mnj1bQRNyVFOnTkV+fr7S52zdujWmTp1qs/0feughaYZDKomIiIiIiIjUKCsrw7Rp0yzaY+HChVZqQ+bgABQiIgdmKDXg2oLLyHj4CA613onMR3/FmUkZON7nANKHHEbuRfMmkBEREZHptm7dKlxv3Lix9Ibh6iI2Nla4npaWhhs3bihqQ0QEHDp0SHoBley1qzrp27evcH3fvn2aLkgrLy+XDr2KiYmBq6urSf0iIyOF69YagLJr1y5pplu3blZ5LkcSEhIiXOcAFCKiqmX16tU4e/asMPPwww/D3d1dUSPHMmjQIOH6lStXrPa9BxGRyNKlSzGgQ39cX3kVqLx77urlq5g8eTJat26NzZs3K+tH5Cz27NmD8+fPCzPx8fHQ6XSKGllm4MCB0gxvsCMiIiKyr+XLl6OwsFCYGTJkiKI2agwaNAi+vr7CzKxZs1BeXq6oETmSY8eOYfr06ZqyISEh6NmzJ4KCgix6Tk9PT8yePRuenp4W7SMSGRmJevXqCTP8+YyIiIiIiIhIjdmzZ0s/F5ZZtmwZysrKrNSITMUBKEREDupAcRGORe9FzrMncWNj/l8uZry1rQAvjniep0sSEREpUFlZiW3btgkzPXv2dJqLom1NNkSgsrISW7ZsUdSGiAjSIRwA0KdPHwVNnMP9998vXDcajUhNTZXuk5qaiuvXrwsz5gyekQ1AOXXqFPLy8kze989kA1B8fHzQrl07i5/H0XAAChFR9fL5559LMyNGjFDQxDHJBqAAwMqVKxU0IaLqymAwYOLEiYiPj0dBfoHmx+Xm5uKhhx7C6dOnbVeOyAktWbJEmomPj1fQxDqioqIQEBAgzCQlJSlqQ0RERER3MmfOHGnm0UcfVdBEHW9vbwwbNkyYuXz5MhITExU1IkdhNBrx0ksvobJSMN0VgE6nw6pVq3Dy5Els3rwZ2dnZ2LhxIxISEqDX6016Tj8/P6xYsUL6GbOlXFxcEBcXJ8ykpaUhN5cHXxIRERERERHZksFgwLRp06Q5t8D6wvXr168jJSXFWrXIRByAQkTkgPJQgafPn0FJZrEwt3f7Xn4IREREpMDRo0dRUCC+waBnz56K2ji+Xr16wcVF/OMm3wggIpVkrzkNGzZE69atFbVxfL169ZJeOKbldKqFCxdKM7YYgAIAe/fuNXnfP9u5c6dwPSIiAq6urhY/j6ORDUA5c+YMSkpKFLUhIiJbOnr0qHRQXKNGjdCjRw9FjRxPWFgYmjRpIszMmzdPesE+EZG5Xn/9dc2nIv/ZjRs38MILL1i5EZHzMhqN0gEojRo1QpcuXRQ1spybmxsefPBBYWbbtm3SAbVEREREZBsXLlyQfk4ZERGBNm3aKGqkzujRo6WZb7/9VkETciRr1qxBcnKyNDdr1iwMGDDgv//s4uKC3r17Y8GCBdi/fz86d+6s6fmioqJw+PBhPPDAA2Z3NsXvO9/N6tWrFTQhIiIiIiIiqr727duHU6dOCTPujRuj9YJ50r20XAtPtsEBKEREDuhd5CFf4wXTM2bMsHEbIiIi2rp1qzTDASj/U7NmTXTq1EmYkd1kR0RkLSUlJdi+fbswExsbC51Op6iR4wsICEDXrl2FmUWLFglvnikrK8Py5cuFe7Rq1cqswTMdO3aUDmjZs2ePyfv+XlFREQ4fPizMdOvWzaLncFSyAShGoxFZWVmK2hARkS198cUX0szTTz8tHXBZlel0OgwaNEiYOXfuHId8EpFN7NmzR9OpRCKJiYlISkqyUiMi57Zv3z6cPXtWmBk2bJjTfe8ju8GusrIS69evV9SGiIiIiH7vl19+gcFgEGa0DApxRjExMWjZsqUwk5KSws+cqpHKykq8+uqr0lxMTIzwv4uwsDDs3r0bX375Je699947Znx8fPDuu+9i8+bNaNasmdmdTdW3b1+4u7sLM1oOGiEiIiIiIiIi88kOwASAhi88B+/27eAV0kqYW7FiBQ+NtBPn+tSeiKgaKC8vx1zc0Jxfu3YtLl68aMNGREREtGXLFuF6/fr1pRduVDexsbHC9ePHjyMzM1NRGyKqznbt2oXi4mJhpk+fPoraOI/7779fuF5SUoKffvrpruvJyckoKCgQ7pGQkGDW4BkfH5+7Xsz2H2lpaSbv+3v79u1DRUWFMNO9e3eLnsNRyQagAEB6erqCJkREZEvXrl3D3LlzhRkPDw889dRTiho5roceekiamTlzpoImRFSdGI1G/P3vf7fKXs8//7z052Ki6mDx4sXSzMMPP6ygiXU9+OCD0qEtS5YsUdSGiIiIiP7DaDRi9uzZwoyrqyseeeQRRY3U0ul0ePLJJ6W5H374QUEbcgRz5szBsWPHhBkXFxd89tln0s+Q9Xo9nnvuORw9ehRZWVnYuHEj5s2bh6+++grz58/H2bNnMXnyZLi6ulrzX0HK19cXMTExwkxycjJvnCIiIiIiIiKyIdkhmq516qDeqBHQ6XSoPWSwMHvz5k0eNmEnHIBCRORgknftwDVUas4bDAbpB2VERERkPqPRiK1btwozPXv2NOsG7qpMNgAFgPRmOyIia0hJSZFmOADlr7Tc8PPNN9/c9dS6RYsWSR8/fPhwk3v9R2RkpHA9LS0NRqPR7P13794tzXTt2tXs/R1Zw4YN4ePjI8xwAAoRkfP7/vvvpRdZjxw5EnXr1lXUyHHFxMSgSZMmwsyKFStw9epVRY2IqDpYuXIltm/fbpW9cnJy8P7771tlLyJnVVxcjFmzZgkzDRo0cMphp7Vq1UJUVJQwk5SUhBs3tB/CQkR2dCwT0LUV/zrGAwaIiJzBwYMHpcMe4uLiUKdOHUWN1Hv88cfh7u4uzMyaNQtlZWWKGpG9FBcX480335TmnnzySbRv396kvZs3b47evXtjxIgRmDhxIh555BHUqlXL3KoWGzBggHD99u3b2Lx5s5oyRERERERERNWQ7BBN/x5RcPH0BADUHkoBPGEAACAASURBVCI/GIsDUOyDA1CIiBzM3KREkx/z448/WnRjFxEREd3d8ePHce3aNWGmZ8+eito4j+7du0tvnp47dy6/hyEim9u4caNwvU2bNmjUqJGiNs6jbdu20q9vmZmZd/zzLSkpwYoVK6T733vvvWb3kw1AuXbtGk6fPm32/rI3v1u2bFllL0jV6XRo1aqVMMMBKEREzq2oqAhffvmlNPfCCy8oaOP49Ho9nnjiCWGmvLycQz6JyGrKy8vx6quvWnXPadOm4cyZM1bdk8iZ/Pzzz9L3+YcOHQoXF+e8jGrQoEHC9dLSUixbtkxRGyIiIiICfvs5TGb06NEKmthPnTp1MGzYMGHm6tWrmD9/vqJGZC9ffvklzp8/L8z4+/vj7bffVtTIdgYOHCjNrFy5UkETIiIiIiIyh9FgwO1fj6EgdRNuHz0KQ3GxvSsRkQmuXr2K7OxsYcavU8f//m+vVi3hHdpWmD9x4oRVupFpnPOTeyKiKurWrVtYkSo/nfzPsrKysG3bNhs0IiIioq1bt0oz0dHRCpo4F09PTwwZMkSYyc7Oxq5duxQ1IqLqKC8vD3v37hVmYmNjFbVxPs8++6w0M3369L/83vr163Hz5k3h4xISEszuBQBdunSRZvbs2WP2/rK/N1qe35mFhIQI148ePaqoCRER2cL06dNx+fJlYSYmJsbkkzarsrFjx0Kn0wkzM2bM4JBPIrKKH374ARkZGdLcPR+0RER+L4Qd6ybNlpaW4pNPPrFGPSKnYzAYNP39j4+PV9DGNoYPHy7NzJs3T0ETIiIiIgKAI0eOYNGiRcJMzZo1ERcXp6iR/Tz11FPSzNtvv43y8nIFbcge8vPz8d5770lzr732GurWraugkW0FBQWhXbt2wsyiRYtQVlamqBEREREREWlRduUKLnzyOQ51iMTRHjE4OSwBR6P7IK1RU8SEtMbzzz+P48eP27smEUnIrv8GAJ+OHf7wzwG9Y4T5rKwsizqReTgAhYjIgSxfvhzFJSVmPfbHH3+0chsiIiICgLVr1wrXa9eujbZtxRM/q6vHHntMmvn5558VNCGi6mrNmjUwGAzCTJ8+fRS1cT4PPfQQAgMDhZnExETk5OT84fdmzpwp3VvLjTkibdu2hY+PjzCzfft2s/bOzc3F2bNnhZnIyEiz9nYWrVu3Fq4fOnQIeXl5itoQEZE13bp1S9Ppsy+88IKCNs4jKChIOjjv+PHjFg1gIyICgKKiIrz11lvSXL0JjVB/fCMAgHsDD9zzfgvpY2bMmMHv46laWr16NdLT04WZNm3aOPWg83vuuQc9evQQZlJTU3Hp0iVFjYiIiIiqtylTpkgzCQkJ8PDwUNDGvqKjo6WfO2VnZ2P27NmKGpFqX331FQoKCoSZBg0aVKn3pAcMGCBcz8/Px5o1axS1ISIiIiIikZLiYpz55xQcDA3HuXf+hdI/XztqNOLsqVP48ssv0a5dO7z++uscaEjkwKTXbun18An746FgXi2aCx9y/vx5FBUVWVqNTMQBKEREDmTu3LlmP3bx4sXSE7aJiIjINPn5+Vi3bp0wEx0dDRcX/mh1J3369JHeOL9w4UKUlpYqakRE1c2qVauE63q9Hr169VJTxgm5u7tj/PjxwozBYMCYMWNQUVEBAFi5cqX0z719+/bSCx1l9Ho9OnXqJMxs2bLFrL21TP+OiIgwa29n0blzZ+G60WhEamqqojZERGRNX375Ja5duybMBAcHSy/Qro5k3xcB2gbBERGJzJgxA1euXBFm9AGuaPRa0z/8Xr0nGsK7na/wcUVFRfjmm28srUjkdD7++GNpZtKkSU7/Pv/IkSOF60ajEQsWLFDUhoiIiKj62r9/P1asWCHM6HQ6TJw4UVEj+9LpdHjmmWekuXfeeYfXjlRBJSUl+Prrr6W5t956C97e3goaqREfHy/NzJkzR0ETIiIiIiISuX79Oob37IlLX30D4/+/BlbEYDDgvffeQ9euXXH8+HEFDYnIVGlpacJ177ZtoP/TexCezYOl+2ZnZ1vUi0zn3J/eExFVIRcvXsTGjRvNfnxRUZH0JjMiIiIyzdKlS1FeXi7M9O7dW1Eb56PX6zFixAhh5vr16zzVhIhsorS0VDrEqmfPnggICFDUyDk9+eST0huAtm3bhnfeeQc3btzAs88+K90zISHBKt1kJxv/+uuv0hu870Q2AMXV1RXh4eEm7+tMoqOj4erqKsykpKQoakNERNZSUFCADz/8UJp74403oNfrFTRyLoMHD0bt2rWFmQULFqCwsFBRIyKqakpLS/HBBx9Icw0m3QPXmm5/+D2dqwvueb+F9LFffPEFiouLze5I5Gz2798vHZBar149jBo1SlEj24mPj5f+LD9v3jxFbYiIiIiqrzfffFOaGT58OO69914FbRzDuHHjUK9ePWHm7Nmz+PHHHxU1IlXmzp0rHfTaunVrjB07VlEjNTp06IDQ0FBhJikpCXl5eYoaERERERHRn928eRP9+vXDsQMHTX7swYMHERERgfXr19ugGRGZy2g0Sgeg+Hbq+Jff82zWTLp3Zmam2b3IPByAQkTkIBYtWgSDwWDRHsuXL7dSGyIiIgLkFwO7uLhg2LBhito4p8cee0ya+fbbb2E0GhW0IaLqZMuWLbh165YwM2jQIEVtnFeTJk00/Tm9/fbbqFGjBi5evCjM6XQ6qw1A6dmzpzSzbds2k/eVvfndvn17eHp6mryvM/Hz80O3bt2EmeTkZEVtiIjIWj7++GMUFBQIMy1bttT0c1x15OHhIb05urCwEIsWLVLUiIiqmtmzZ+PChQvCjHsTD9Sf0OiOa37daqBjt07Cx1+9epUnDFO18s4770gzEydOrBI/59euXRsPPvigMLN//36kp6crakRERERU/WzYsEF6AIyLiwumTp2qppCD8PHxwWuvvSbNvfvuuygqKlLQiFQwGAz4+OOPpbl//etf0mGOzkan02H06NHCTHl5ORYuXKioERERERER/V5hYSH69+8vvVZUpKioCIMHD+Z1lEQO5NSpU8jPzxdm7jQAxa1BIFy8vYWPy8rKsqgbmY4DUIiIHERSUpJwXeeqg2dLL2Fm7dq1/ACIiIjISs6dO4etW7cKM71790aDBg0UNXJOYWFh0pOLNmzYwA/1icjqEhMTpZmBAwcqaOL8XnrpJavtlZCQgObNm1tlr+7du0svhpOd8vxnRqMRe/fuFWYiIiJM2tNZxcbGCtdzcnKQnZ2tqA0REVkqKysLH374oTQ3derUKnexuTWNGzdOmpk5c6aCJkRU1ZSXl+O9996T5hq+3BQunvq7rk94+UnpHh999BEqKytN6kfkjNLS0rBy5UphxtPTE88884yiRrY3cuRIaeaHH35Q0ISIiIio+iksLMSTT8p/Jhs5ciRat26toJFjefrpp9GwYUNh5uLFi/jggw8UNSJbW7t2LU6ePCnMhISE4KGHHlLUSK2RI0dCp9MJMxxSS0RERESkXkVFBYYNG4YdO3ZYvFdpaSkGDRqEjRs3WqEZEVlqz5490sydBqDodDp4NmsqfFxmZqaZrchcHIBCROQACgsLpadSB9xfC3UfF38AVFRUhA0bNlizGhERUbW1cOFCGI1GYWbEiBGK2jgvnU6n6fTw5557DleuXFHQiIiqA6PRKB2AEhoaiuDgYEWNnFt0dDRGjRpl8T41atTAp59+aoVGv/Hx8UHnzp2FGVMHoOTk5CAvL0+YiYyMNGlPZyUbgAIAKSkpCpoQEZGljEYjJk6ciNLSUmGubdu2SEhIUNTKObVr1076vcDOnTtx4sQJRY2IqKqYP38+Tp8+Lcw0vKchaifUF2b6DOiDkJAQYSYrKwtr1641tSKR03njjTekmTFjxqBu3boK2qgxcOBA+Pr6CjM//PADbt26pagRERERUfXxz3/+E2fOnBFm9Ho93nzzTUWNHIuXlxdef/11aW7atGnIyclR0Ihs7eOPP5ZmJk2aBBeXqnk7S6NGjaSft+7Zswfp6emKGhEREREREQC8+OKLVr33sqSkBAMHDpTeF0pEtpeWliZcd/H1gVerlndc8wxuJnxsVlaW2b3IPFXzHSMiIieTmpqKsrIyYabmoLqoOUB+8dXy5cutVYuIiKha++WXX4TrHh4eGDp0qKI2zm3EiBHSCxby8vIwbtw46dAZIiItDh8+jHPnzgkzgwYNUtSmapg+fTpatGhh0R4fffQRAgMDrdToNz179hSuHz58GNevX9e83969e6WZiIgIzfs5s4iICPj5+QkzycnJitoQEZElFi9erOnijbfffht6vV5BI+c2btw4aWbmzJkKmhBRVWE0GvHee+9Jc0+98jRc3MTvsbm4uODvf/+7dK/Zs2dr7kfkjLZs2SL9mVWn0+Gll15S1EgNb29vDBkyRJi5efMmZs2apagRERERUfWwZ88efP7559Lc448/bvHnbc5s/PjxaNKkiTBTUlKCSZMmKWpEtvLrgQPYtGmTMFO3bl1NByo5s9GjR0szP/30k+2LEBERERERgN+ug/3666+tvm9xcTEGDx7Mw3KI7GzPnj3Cdd8OHaC7y7VxnpJDVTMzM83uRebhABQiIgeg5ZS1gN614NHEE97h4hObEhMTUV5ebq1qREREShQVFWH16tX429/+hscffxwJCQmIj4/HSy+9hOXLl+PmzZtK+5w4cQIHDx4UZuLi4hAQEKCokXNr3LixposWkpKSMGbMGOmp5EREMomJidIMB6CYxs/PDwsXLoS7u7tZj4+JicETTzxh5VbyAShGoxHbt2/XvJ9sAIq3tzfatGmjeT9n5ubmhpiYGGEmNTUVlZWVihoREZE5bt68iRdffFGa69Chg/RmWfrNI488Am9vb2Fm9uzZ0qHnRET/sW3bNpw8eVKYadiwIeIff1jTfqNGjUL9+vWFmcTERJOGRRI5E6PRiMmTJ0tzo0aNQkhIiIJGao0fP16a+eKLL/jzPBEREZGVlJaWYvz48dLDXnx9fTF16lQ1pRyUh4cH3nzzTWluxYoVWL9+vYJGZCvfvD9Nmnn22Wfh5eWloI39DBkyBD4+PsLMTz/9xGu+iYiIiIgUSElJwfPPP68p6+LlhaB/v4OmH02Dxz33aHrM9evX0a9fP1y8eNGSmkRkprKyMuk9YL4dO9x1zTO4mfCx58+fR1FRkVndyDwcgEJEZGdGo1E6AMW7gx/c6v52k1nNuLrCbEFBATZv3mytekRERDZVUVGBb7/9FkFBQRgwYAA++eQTzJ49G4sWLcLSpUvx2WefYejQoahduzbuv/9+7Ny5U0mv7777TpoZMWKEgiZVx4cffog6depIcz///DMCAwPxySefID0nG0aILxIichS3b9/GihUrMHXqVCQkJKBTp05o3749JkyYIB2oQNa3fPly4Xq9evUQGRmpqE3V0bFjR3z44YcmP87T0xPfffcddDqd1TtFRUXBxUX8FueWLVs075eWliZc79SpE1xdXTXv5+xiY2OF6/n5+Th06JCiNkREZCqDwYAnnngCly5dkma//vpr6ddU+o2/vz+GDx8uzFy7dg2LFi1S1IiInN0PP/wgzbz88svw8PTQtJ+npycmTpwozJSVlWHhwoWa9iNyNsuXL8eOHTuEGVdX1yp782mPHj3QocPdL94DgFOnTiEpKUlRIyIiIqKqbcqUKfj111+luffffx+NGzdW0MixjR07FuHh4dLc//3f/6G4uFhBI7K24vQMrJd8Xu/p6Ylnn31WUSP78fHxQXx8vDBz+fJlrF69WlEjIiIiIqLq6ejRoxg2bJim4fCewc0Qvn8PGjzzFALHjUVY2g489uwzmp7n7NmziIuLw40bNyytTEQmOnz4sPSwKt/OHe+65tk8WPoc2dnZJvci8/FKRiIiOzt58iTOnDkjzNToU+u//7vmAPmNw7Kb/YiIiBzBxo0b0aFDBzzzzDO4du2aMFtRUYGUlBRERUVh4sSJNj2ZMC8vT3rTgb+/P+Li4mzWoSqqW7cuvvrqK03ZgoIC/O1vf0Prgf3QFFkYj4tYhJvIQ4WNWxKZ7sqVK3jttdcQGBiIIUOG4K233sKiRYtw4MABHD16FDNmzEBkZCQefPBB7Nq1y951q4UDBw5IBzIMGDCAN/ia6f/+7//w0ksvmfSY6dOno2XLljbp4+/vj44d7/6GNADNQ0IrKiqwf/9+YSYiIkJrtSpBNgAFAJKTkxU0ISIic7z77rtYunSpNDdhwgR069ZNQaOqY/z48dLM5MmTeZMGkQ2UXy3DlZkXkPPcSWSNPYbsiSdw5uUM5M64gLKLpfauZ7Lr169jyZIlwkydOnUwYcIEk/YdPXq0NDNnzhyT9iRyBnl5eZpuohs/fjyCg+UXsTkjnU6n6b2bTz/9VEEbIiIioqpt586dmg4P6NatG555RtvNUlWdXq/XdO1IZmYmJk+erKARWduFz76A0Sg+7Gj06NGoV6+eokb29fjjj0szM2bMsH0RIiIiIqJq6vz58+jfvz9u3rwpzbo3aYI2K5bCvUHgf3/PxcMDUz7/HJMmTdL0fIcOHUJsbCzy8/PN7kxEppMdgAkAvoLrzT2bNZM+PjMz06ROZBne6UFEZGdr166VZgJi/zcAxSvEB54tvYT55cuXw2AwWNyNiIjIFjIzMzF48GDExsZqOgXnz6ZPn46RI0dKp3Oa66uvvkJRUZEwM3ToUHh6etrk+auy4cOH46GHHjLpMWdRgZm4gQRcQF1kohNy8CauIpfDUMjOiouL8dprr6Fp06aYNm0aCgsLhfl169ahe/fuePTRR3H9+nVFLaun7777TpoZNGiQgiZVk06nwyeffIIff/xRelFa27ZtkZycjLFjx9q0U8+ePYXrBw8e1DRR/8SJE9LvAarbAJTWrVujUaNGwsyWLVsUtSEiIlMsW7YMU6ZMkebq1KmD9957T0GjqqV79+4ICQkRZs6ePYvPPvtMUSOi6uHXkmIc770fZ17OxLVfLuP6yqvIm5+LKzMv4uwrmTjaNQ2Lflxo75om+eWXX1BSUiLMjBs3Dj4+PibtGxQUhJiYGGFm165dyMjIMGlfIkf34osvIjc3V5jx9PTEG2+8oaiRfSQkJKBBgwbCzJYtW7B7925FjYiIiIiqntu3b2P06NHS6zTd3NwwY8YMHs7wO1FRURg1apQ099lnn2Hbtm0KGpG1lJw5g2uLxUO5XVxc8Pe//11RI/vr2bMnWrRoIcysXbsW58+fV9SIiIiIiKj6uHnzJuLi4jR9v63380PrRb/Ao0njv6zpdDp89NFHeP755zU97759+9CnTx/pIcFEZD179uwRrgc2agT3hnf//NStQSC8vL2Fe2RlZZnVjczDd1OJiOxMNgBFX9MVPp38//B7NQfUFT7m8uXL0i/aREREqlVWVmLKlCkIDQ1FYmKiRXstXLgQQ4YMkd6kbKrbt2/jyy+/lOZMPXGVfqPT6fDNN9+gZs2aZj3eCOAASvAOrqEZsvA18qWnxhDZQm5uLqKiojBt2jSTT3RfsGABwsPDsWPHDhu1q95u3bqFX375RZjx8/PD/fffr6hR1TV27FhkZWXhzTffhPef3vBt2rQpZs+ejSNHjiA2NtbmXWQDUAwGg6aLI7X8HB0ZGam5V1Wg0+mk/x/u37+fX4+JiBzM2rVrNd1AAAAffPABateubeNGVY9Op9P03sC///1v6U3YRKTNaZRh9NkclF0ovWvGUFiJ15/8ByZPnuwU36MajUb88MMP0ty4cePM2n/MmDHSzJw5c8zam8gRJSUlYe7cudLcs88+Kx326ezc3d0xceJEae7VV191itdLIiIiIkf0t7/9DadOnZLmJk+ejLZt2ypo5Fw++OAD+Pr6CjNGoxGPP/649DASchwXP/8KqKwUZoYPH46WLVsqamR/Op0O48ePF2YMBgN+/PFHRY2IiIiIiKqHsrIyDBs2DEeOHJGHXVzQYub38G5994NwdDodPvvsMzzxxBOanv/QoUPo1asXrl69qrUyEVkgLS1NuN5ecgCmTqdDUIvmwkxmZqbJvch8HIBCRGRHhYWF2Lp1qzATEFMLOr3uD79Xc0Ad6d6rVq2yqBsREZE1FRcX4+GHH8bbb7+N8vJyq+y5Zs0aDBs2zGr7AcCPP/6IvLw8YSYqKgrdu3e32nNWN4GBgZg/fz5cXV0t2qcYRjyHXIx4ZRJu3bplpXZEcjk5Objvvvtw8OBBs/c4e/YsoqOj8eGHH/IGAyubN2+e9AK4xx577C8DO8g8fn5+eOutt3Dx4kXMnTsXn3/+OZKTk5GZmYnRo0dDr9cr6dGjRw/odDphZs2aNdJ9ZENSateujWbNmpnUrSro0aOHcP3q1as8kYyIyIEsXLgQgwYN0jSoLzo6WtPN8XRnEyZMQN264mHlhYWFePPNNxU1Iqq6SmHAcFzATcmp2v/x73//G6NHj0ZZWZmNm1lm//79OHz4sDDTq1cvs28KGjp0qPTn359//ll6WjmRM8jMzNQ0LKhGjRp47bXXFDSyv6eeegqenp7CzNatW7F69WpFjYiIiIiqjrmrVuK7776T5sLCwvCPf/xDQSPn06BBA0yZMkWay87OxquvvqqgEVmq9Nx5XJ03X5qrjv9NjBkzRnqd1MyZM1EpGR5DRERERETaGI1GTJgwASkpKZryQe++hZr395HmdDodvv32W/Tr10/TvseOHUP//v15rwORjV2/fh3p6enCTLiGAzCDmrcQrmdlZZnUiyzDAShERHa0adMm6cWXAbG1/vJ73uF+cG/kIXxcYmKiRd2IiIisJS8vD7GxsVi+fLnV9163bh2eeeYZqwwQKCsrw0cffSTN8cIKyz3wwANYu3YtatasafFeC9auRmRkJE6fPm15MSKJo0ePIioqyipvXhkMBrzyyiuYNm2aFZoR8NsHFlouNHzqqacUtKleAgICMHLkSDz//POIjY21eMiVqWrUqIHw8HBhJikpSfr9gmwASpcuXaSDVqqizp07SzP79u1T0ISIiGRmzJiBRx99FBUVFdJsYGAgfvnlF7i48KNCc/n7++Ptt9+W5mbMmIEdO3YoaERUdb2MK9iLEpMeM3fuXDz99NM2amQdM2bMkGZkJwSL+Pn5YdiwYcLM2bNnkZqaavZzEDmCnJwc9O7dG1euXJFmP//8c+kAs6qiTp06eOyxx6S51157jTfZEREREZngKErw5Fv/lObc3NwwZ84cuLu7K2jlnF588UVESE7fBYDp06fz/TUncPatd2GUXA89cOBAtG/fXvOepypzEHk9RvjrVGWOpdVtLjAwEAMHDhRmzp49y4MvbeVYJqBrK/51jKd4ExHZQ1X5Wk+OhX+vCACmTJmCOXPmaMqOeOpJBD79pOa93dzcsHjxYnTs2FFTft++fXjooYdQUmLa5930P3l5eUhNTcX06dPxxRdfYOHChcjJyRFeE8zXgupFyzXMYRoGoDRtIR6Akpmp7mfH//wdzq48rew5HQ2vaiQisqP169dLMwG9/zoARafTocaDtYWPO3bsGE6dOmV2NyIiImvIzc1Fjx49sHPnTps9x8yZM/Gvf/3L4n2mTp2Ks2fPCjNt27ZFXFycxc9FQGxsLNLS0tC2bVuL9zp58iT69OmDa9euWaEZ0Z39+uuviImJwaVLl6y67z/+8Q8sXbrUqntWV3v37sWhQ4eEma5du5p0QRU5jwcffFC4fu7cORw5cuSu6xcuXEBOjvjDjB49epjVzdm1bdsWHh7iIawcgEJEZH+zZ8/GhAkTNA0IdXd3x/Lly9GoUSMFzaq28ePHIzQ0VJgxGAwYPXo0CgsLFbUiqlqW4Ca+xHWzHjtr1iybDGW2hvz8fMydO1eYqVGjBoYOHWrR84wZM0aa+f777y16DiJ7On78OHr37o3z589Lsw8++KCmgSBVyT/+8Q+4ubkJM8eOHcPs2bMVNSIiIiJybjdQiaE4j2INNy29/fbb/FxSwtXVFbNnz5Z+DgUATz75pPSwQbKfW2l7kbd0mTT3+uuvK2jjmCZMmCDNvP7665oGnBMRERER0d198803eOeddzRlBwwYgCmff27ywXi+vr5YvXo1WrZsqSmfmpqKRx55hENQTFBUVISvv/4arVu3Rp06ddCnTx9MnDgRL7zwAh555BEEBwejXr16GDlyJH799Vd71yU727Nnj3Bdp9Ph3k7yoUVBLZoL18+fP4/i4mKTupH5OACFiMiOkpOTheve4b5wq3fnEwBq9Ksj3Z/TwImIyJ7KysowdOhQnDhxwuTHRkZGIiQkRHP+n//8p+YpvXeyfft2TJs2TZp75ZVXeDq2FbVo0QK7du3CxIkTpRdhy2RnZyM+Pp4XvJBNnDhxAn369EFeXp5N9n/sscc4PMAKvv76a2nmqaeeUtCE7EF2YhYg/hl527Zt0sdX1wEobm5uCA8PF2b4GkZEZF+LFy/GE088oTn//fffo2vXrjZsVH24urri448/luays7MxadIkBY2IqpbrqMTTuGzRHlOmTIHBYLBSI+v57rvvcPv2bWFm1KhR8PLysuh5evXqhcaNGwszy5cvR25urkXPQ6RaaWkppk6divDwcJw+fVqa9/f3x/fff2/yBazOrlmzZnj22WeluVdeeQUZGRkKGhERERE5rxIYMATnkYVyabZbt254+eWXFbRyfm3atMG7774rzR0/fhwffvihgkZkKqPBgDOT35TmevfuXa3fl+7bty+aNGkizJw4cQI//vijokZERERERFXPTz8t0fS5CABERERgwYIFcHV1Neu5AgMDsWXLFs2Hwa5cuRJ9+vTB1atXzXq+6uTkyZOIiIjAc889h/T09Lvmrl27hl9++QUdO3bETz/9pK4gOZy0tDThemhoKHz9/KT7NG0hH2p06tQpzb3IMrxzj4jITs6cOSO9iCigd627rvlF1YCPn6/w8YmJ0jzgEAAAIABJREFUiWZ1IyIisoYXX3wRO3fu1Jz38fHBu+++i6KiIuzZswcnT55ESkoKAgICND1+3Lhx2Lhxo8k9b926hdGjR0tvhGjcuDEeffRRk/cnMX9/f3z11Vc4ffo0vv/+e8THx6NGjRpm7bVlyxY899xzmk48J9IqIyMDvXv3xpUrVzQ/xtvb26TnKC4uxsCBA3mTgQUyMjKkJ2cHBARg+PDhihqRapGRkahXr54wY8kAFA8PD3Tu3NmsblWB7N993759/PpLRGQnSUlJGDFihOab+19++WWMGTPGxq2qlwceeAD9+vWT5n744Qe+Z09kondwDXmotGiPo0ePYsWKFVZqZB2lpaX44osvpDktJwPL6PV6PP7448JMRUUFLwgjp2E0GpGYmIj27dvjrbfeQnm5/OZTAPjoo4+kw4CqqjfeeAP+/v7CTF5eHvr168dhSERERER3UQ4jHsYFbEKRNOv3/9i787iatv9/4K/TaVQZGqSUkmQuMifJUKIBJVPm6ZojdJFrvCKZLzJcYzIlkVAZrzIlU8otISpDUdE8nNP+/eF3fT93cPY+dc5pej8fDw9u573Wel/qnL3XXuu91NVx5MgR8Pl8GWRWOyxYsAC9evVijVu7di2Sk5NlkBERR1ZwCPJjH7LGrVq1SvrJVGN8Ph9TpkxhjVuxYgXy8/NlkBEhhBBCCCG1y5mQT5g2fQmn2ObNm+PChQtQVVWt1Ji6urq4ceMGzMzMOMXfuXMH3bt3r9ABw3VFUFAQunbtiufPn3NuU1ZWhkmTJuHnn3+uloejEOliGAb3798XGdOtWzdOfRmatGCNefnyJae+SOVRARRCCKkiV65cYY0RVQBFTkkO1gOtRba/desWcnJyxM6NEEIIqayDBw/C39+fUyyPx8OkSZOQnJwMb2/vv51q2r9/f9y7dw9NmzZl7UcgEMDFxQXx8fGc8ywvL8dPP/2ElJQU1thFixZBUVGRc99EPHp6epg2bRqCgoLw+fNn3L9/H2vnzkdvqECcusr79+/Hpk2bpJYnqVs+f/6MgQMH4uNHbqdNGxoa4vLly8jPzwfDMEhJSYGdnR2nth8/fkTv3r3x9OnTyqRcZ61evZp10nr8+PFiF6chNYecnBwcHBxExsTExPzw55mtAEr37t2hpKRU4fxqOrYCKNnZ2ZxO3CaEECJZwcHBcHFxgUAg4BS/ePFi+Pr6Sjmrumnz5s2c5gxGjhyJ8PBwGWRESM33EqXYiWyJ9LVmzZpqVbDv+PHjrHMNPXv25LxQjs2UKVPA4/FExuzfv58Wg5EKKy4uRkREBJYtW4YZM2bA3d0dY8aMwfLlyznNvXORlpaGAwcOoG/fvhgyZIhYhYRdXV0xdepUieRRE2lpaeHnn39mjUtJSYGDgwPy8vJkkBUhhBBCSM0hBIPxeI8wcCtIcPjwYbRsyX5aLPk/fD4fhw4dYn0WV1JSgsmTJ3OeDyXSJ/j6FW9XrmaNGz58OHr37i2DjKq3OXPmsB4ElpGRgc2bN8soI0IIIYQQQmqH0ItZ+GnOC07PhDU0NHD58mXo6OhIZOzGjRvj+vXr6NChA6f4lJQUWFlZIS4uTiLj1ybr1q3DiBEjKlwUcuPGjXB1dUVRUZGEMyPVWWpqKutBt927d+fUl46eHuteAyrOKztUAIUQQqpIZGSkyNfr8eSg2kX0SUz9nQaIfF0oFOLy5cti50YIIYRURmxsLGbOnMkpVldXF3fv3sXBgwehq6v7nzGtW7fGlStX0LBhQ9b+cnNzMWjQIKSlpbHGMgyDOXPm4MSJE6yxHTp0wIwZM1jjiGTw+Xx069YNy3+ahVswQjZMsRU6aMTxFtbLywtbt26VcpakthMIBBg9ejTnDf19+vTB06dPYW9v/31TkZGRES5fvgw/Pz/Iy7OX8snMzISNjQ3u3r1bmdTrnISEBE7v5T/99JMMsiFVydHRkTXm0qVL//paTk4OawG1ur4gj60ACgA8fMh+shshhBDJCQgIwIgRI1BWVsYp3sfHB76+vqwb4EnFtG3bFmvXrmWNKy4uhrOzM0JCQmSQFSE12xJkgu0dTk6ND6Otpqx9PX36FKGhoZJJrJIYhuFUvHfx4sUSG9PIyAgDBw4UGfPq1SvcuHFDYmOSukEgEMDf3x8GBgawt7fH+vXrsXfvXhw/fhwnTpzAunXrYGpqCh8fH7EK7DAMg8ePH8PPzw8jR46EsbExmjVrhqlTp+KPP/4QK0c7OzscO3aszl8DzZ8/H3p6eqxxDx8+xIABA/D582cZZEUIIYQQUv2VgYE73uMkcjnFL168GC4uLlLOqnZq2bIlli9fzhoXHR3NaR6OyMbbZb+g7IPoIq+KiopUmPv/09TUxNKlS1njNm7cSIdPEEIIIYQQwlHk1WxMmZEEoZA9VklJCaGhoWjVqpVEc9DU1ERERASaN2/OKT47Oxv9+/cX6+Df2m7fvn2c5gXYnDt3DkOGDEFxUbEEsiI1wf3791ljunXrxqkvHo8HExMTkTGyvl8vyyiV6XjVCRVAIYSQKiAUCnH16lWRMd3rqUJOUfTbtM0gG/D5fJEx1WVBJyGEkLohLy8Po0aNQmkp+01W69atcffuXU7VNNu0aYPz589zOk05PT0dPXv2xLNnz34YwzAMvLy84O/vz9qfoqIijh07xnrSDJEedfAxHxqIhzF6QoVTG09PTyqCQirF29ub9Zr9L0OHDkV4ePh/ntQjJyeHRYsWYf/+/Zz6+vLlC+zs7PDkyROx8q3LVq1axVq13dHREe3atZNRRqSq2NnZsV4rXLhw4V9fu337Nuv3kLW1daVyq+lat27NWtU8NjZWRtkQQkjdxjAMduzYgQkTJnDeSLxr1y4sXbq0zm/8lbaFCxdyKppWVlYGNzc37Nu3TwZZEVIzRT18gGDkscYZbmoJ7Ql60F1oyBq7evVqTid+SVt4eDieP38uMsbExATOzs4SHZdLUdC9e/dKdExSu/3xxx/o2LEjZs2aJbJYhkAggLe3N7p164aoqCgIf7D6tLS0FI8ePcLatWvRpk0bWFhYwMvLC6dPn0ZKSkqFcrSxsUFISAiUlZUr1L42qVevHtavX88pNiYmBlZWVkhNTZVyVoQQQiqDYRjk5+cjLS0Nb968wZs3b5CamvrDz1pCiPhKUA43pOMUx+InNjY28PHxkXJWtZuXlxfatm3LGrd27Voq4lkN5IRH4tPxk6xxCxYsgLGxsQwyqhnmzZsHAwMDkTGFhYWYPn16tZjLIoQQQgghpDq7eesLxk1JRFkZ+7Uzj8dDYGAgevXqJZVcdHV1ceXKFejo6HCK//z5M/r378/67LguiIyMxKxZsyTW35UrV/DT0GkQFtJcaV0QExMj8nUVFRW0b9+ec3+GhqLXn3z48IFzX5UlLBBC+EUgs/GqGyqAQgghVeDRo0fIyckRGWOlqsbaT0ONhrCyshIZc/nyZZSUlIiVHyGEEFJRs2fPxqtXr1jj2rRpg+joaNabw/9lbW2Nw4cPc4p99+4devXqhcOHD/9rQ1hycjJsbW05nbQKAL/++ivMzMw450mkRw8KuAgDmIK9EA7wrQjKtm3bpJwVqY2CgoKwceNGTrHDhg3D6dOnWTdSTJw4EUuWLOHUZ35+PpycnGQ6QVZTPXnyBGfOnGGNW7NmjQyyIVVNTU0Nffv2FRkTGRmJ4uK/V3aPiooS2UZOTg49e/asdH41mby8PDp16iQyhgqgEEKI9AkEAsyZMwceHh6cFz5v3bpVoosEyI/x+XwcPXoU6urqrLFCoRA//fQTFi1aRJvTCPkHhmGwaBP7qcBqPRtA0+3b4rEmc/TBVxd9YMDjx49x7tw5ieRYUQzDcNoM5+npyXoAgrgcHBygq6srMiYkJATp6ekSHZfUTlu3bkXfvn2RkJDAuc3Dhw9hbW2N+vXrw9raGh4eHliyZAlmzpyJbt26QV1dHZ07d8aKFSuQlJRU6RytrKxw4cIF1mKedcm4ceMwbtw4TrFJSUkwMzODj48PCgoKpJwZIYQQNjk5OQgLC8PKlSsxaNAgNGvWDCoqKlBXV0ezZs3QvHlzNG/eHIaGhlBXV4e9vT3Onj0LgaDuLowmpLK+QggnpOM88jnFN2/eHCdPnoS8vLyUM6vdFBUVORXnZBgGY8eOxadPn2SQFfkvgi9f8HrBQta4xo0bY9myZTLIqOZQUVHBunXrWOOuXLmCQ4cOySAjQgghhBBCaqbQi1kYPeFPlJRwWz+zd+9euLq6SjWnFi1aIDIyEtra2pziMzMz0aNHD5w4cUKqeVVn8fHxGD58uMTXzty+dhvJY55REZQ64P79+yJf79y5s1hzdmzrKmS5vyP//leZjVUdUQEUQgipApGRkawxXAqgAGA9AS43NxehoaGc+iKEEEIqIzAwEAEBAaxx9evXx7lz56CpqSn2GKNHj8aGDRs4xebl5WHSpEnQ0NCAnZ0dhg0bhtatW8PU1BTXrl3j1Ie1tTU8PT3FzpNITyPwcQH6aMjxdnbBggVUBIWI5enTp5g4cSKnWCcnJ5w8eRIKCgqc4tetWwcXFxdOsenp6RgyZAiKioo4xddVS5cuZY1xdXVlLdxAag8nJyeRrxcWFiIoKOhvX2MrgNKpUydOG5lru86dO4t8PTY2lk4hI4QQKUpLS8PgwYOxe/duzm1+/fVXzJ8/X4pZkX8yMjLCjh07OMdv3rwZrq6uKCwslGJWhNQsV69eRcyzONY4g7UtwOPxAADyDRSg85M+a5tly5ZV6SbQiIgIREdHi4zR1NTEhAkTJD62goICJk+eLDJGIBDAz89P4mOT2oNhGKxatQqenp4Vvv8rLCxEVFQUduzYAV9fX+zZswcPHjxAaWmpRHLk8XiYP38+rly5AjU1bs/b6woej4fff/8dAwYM4BT/9etXeHt7o0WLFti/fz8VbSOEEBlLTk7GunXrYGVlBS0tLTg5OWHNmjUIDw9HWlraDw/DKioqQkREBFxdXWFoaAhfX1961kSImF6npcISb3AF3ArB6enp4erVq5xPeCaiWVlZYfr06axx79+/x7Rp0+jZVBVgGAYpi5ag7GMGa+y6detQv359GWRVs7i7u6Njx46scZ6ennj37p0MMiKEEEIIIaTmYBgGW3akY/yURBQVlbM3ALBjxw5MmzZNypl9Y2Zmhvv376NNmzac4vPy8jBmzBhMmTKlzq0dycnJgZOTE/Ly8qTSf96tL0ge/QzCAnrGVVsJBAI8fPhQZEz37t3F6rM6FUDJu/1FZmNVR1QAhRBCqsCVK1dEvq4PeRgrKnLqi21zFwAcPnyYU1+EEEJIRb169QozZ87kFBsYGAhTU9MKj+Xl5YUZM2Zwjv/69SuuXLmCc+fOiXVypIaGBo4cOSLx01ZJ5ZlCCUHQB9d/GSqCQrjKzMyEs7MzpwnkAQMGICgoCIocr9sBQE5ODseOHcPAgQM5xT948AATJ05EeTm3Cfq65urVqwgPDxcZw+PxsHr1ahllRKoDR0dH1pjt27d/XwxZVFSE2NhYkfG9e/eWSG41XZcuXUS+/vXrV7x69UpG2RBCSN1RUlICHx8ftG7dmnVe+X8tXbqUTtasIhMmTICHhwfn+PPnz8PW1hY5OTlSzIqQmsPX15c1RsOtMdQs/r55RmemPuTURM8WJSYmVtnJueXl5Zzel2fPno169epJJYepU6d+LxrzI/v27UNGBvsGJlL3CAQCeHp6Vut5lg4dOuDu3bvYunUrlJWVqzqdaklRURHBwcEwNzfn3CYjIwPTp09H9+7dcffuXSlmRwghBABSU1Ph7u4OU1NTLF++HLdv367wc6L3799jyZIlaNeuHUJDQ6lIACEcREVFodvo4XgObgX6tLS0cPXqVRgbG0s5s7pl06ZNMDExYY07f/48jhw5IoOMyP8KPnoUWcFnWeMGDBiAKVOmyCCjmkdOTg6bN29mjfv69SsmTJggsaKhhBBCCCGE1HTv3pdg/NQkrPF5y7nNxo0bMXfuXClm9W/NmzfHnTt3YGtry7nNwYMH0b9//zqzdoRhGEyePBlv3rzhFD9ixAhkZ2ejrKwMvr6+nNfv50VREZTaLCkpibUAeLdu3cTqk0sBFFnNtedGUwEUQgghMpSfn487d+6IjLGDKusCxL+0bNkSHTp0EBkTHh4u0+pihBBC6paioiIMHz6cU+XVlStXctqYLAqPx8Nvv/0GBweHSvUjipqaGi5dugQjIyOpjUEqZwBUcQR6kJPjdlu7YMECuLu748uXuj0JQH6stLQUw4cPR2pqKmussbExTp06BSUlJbHHUVFRwfnz5+Hq6sop/vTp09V6Y0lVKS8vx+LFi1njRo0ahXbt2skgI1JdGBoasm7iefjw4fcNO1FRUSgrKxMZTwVQvmErgAKAtZgMIYQQ7srKyrB//36YmprC29tbrFNe1qxZg3Xr1nGeYyaSxePxsHXrVk7Xq3+5c+cOrK2t6TRPUuc9fPgQ165dExnDU5aD/i//3lgm30gBOj81ZR1j5cqVVXJy1tmzZ/H48WORMUpKSpg9e7bUcjAyMoK9vb3ImOLiYmzZskVqOZCa6dGjR+jWrVu1LTLdoEED+Pn5ITY2VuwTvOqi+vXr49KlS2jevLlY7R4+fAhLS0uMHz+e1h4QQogUFBQUwNvbG61atcLx48cl2ndKSgqGDBkCR0dHuu8kRIQLFy7A1tYWWRzXFDRo0AARERGcT3Qm3Kmrq+PUqVOcNjPNmzcPb99y3/hGKicpKQmr57EXf1ZTV8fvv/9Oc9Qi9OvXD5MmTWKNu3btGqZOnUqFzAghhBBCSJ2Wl5cHv0170c3qES5czOLcbvXq1WKt3ZCkhg0b4uLFi5zXiwPAvXv30K9fP3z69EmKmVUPO3fuxLlz51jjeDwejh49ilOnTqFRo0aQl5eHl5cX7t69y1qo4i950V+QOPgx8mNzK5s2qWbi4uJYY7p27SpWn2zfV6WlpcjOzharz4ooyC9A4WP2PXq1GRVAIYQQGbt48SLr5ipbqIrV58SJE0W+Xl5ejoCAALH6JIQQQriaO3cunjx5whrXp08f/PLLLxIZU15eHidPnkSPHj0k0t//UlZWRlhYGC2UrgHc0QBH1vlyLoJy/PhxmJmZISgoCEIhVfEl/4dhGMyZMwdRUVGssSoqKjh79iw0NDQqPJ6SkhJOnjzJaTEL8G0Dq6QXu9Z0x44dY/3skZOTw8qVK2WUEalOpk6dyhqzfft2AEBQUBBrrJWVVaVzqg1MTU2hpqYmMoZOgiaEkMphGAZxcXFYsWIFWrZsienTp3Mq0Pe/Nm/ejF9++YUWllcxHo8HX19fsYoZxsfHo2fPnvR5Suo0X19f1hidaU2hpK/8n681mW2A+g3ri2z/4cOH7/cDsiIQCDjNi86aNQuNGzeWai5Llixhjdm9ezeysrgvHCS116dPn+Dh4YFu3bqxFvCpCvLy8pg3bx5evXqFRYsWcT7pjQB6enqIjo5G+/btxW4bEBAAU1NTbNy4EQUFBVLIjhBC6p6YmBh06tQJPj4+KC4ulto4ly5dgrm5OS5cuCC1MQipqU6fPg0XFxeUlJRwitds2BA3btyAhYWFlDOruywsLODn58cal5eXh4kTJ6K8vFwGWdVtJSUlGD16NAo53Acs2+QHQ0NDGWRVs23ZsoXThr2AgAB4e3vLICNCCCGEEEKql8TERMydOxdNmzbF0qUbUVDI/d5vyZIlEtu7UlEKCgo4fvw4hgwZwrnNkydPYG1tjfj4eClmVrUePnyIRYsWcYrdvHkzxo0b96+vW1hY4NatW9DX1+fUT+GzfPxp9wgpsxNR9IKeb9UWT58+Ffl6gwYNxD4Um8t9uiwOi3h05xEYQd0uhkoFUAghRIYYhmFdvMnj8TBAzAIo7u7u4PP5ImMOHz5MFcAJIYRI3MGDB3HgwAHWuEaNGiEgIID180ocampquHr1KhwdHSXWp4KCAkJCQtCnTx+J9Umka6zTEBw5coRzEZS0tDSMGDECbdu2xa5du5Ceni7lDElNsHv3buzfv59T7IEDB2Bubl7pMeXl5bF//344Oztzip88eTJthPz/ioqKsHz5cta4qVOnolWrVjLIiFQ3EyZMQP36ojc9BgcHIyUlBWfPnhUZ17ZtW6lvQKwp+Hw+60LesLAwmnsghBAxMQyDx48fw9vbG61bt4a5uTnWrl0r9qmlfD4f+/btg6enp5QyJeLi8XhYsWIFtm3bxrkgTVpaGnr37o0NGzbQpg1S57x8+RLBwcEiYxR4POjM/PECJvmGCpi5ZBbrWBs2bJBpgY/ff/8diYmJImPU1NSwdOlSqedibW2N3r17i4zJz8+XeZEYUn1kZWXh8uXL+Pnnn2FsbIwdO3ZUq2LS9erVg4ODA7Zt24bU1FRs374dmpqaVZ1WjaSnp4dbt26hV69eYrfNz8/Hzz//jGbNmuGXX37Bx48fpZAhIYTUfgKBAGvXroWlpSWSk5NlMmZWVhacnZ0xZ84cqRZbIaQmOXLkCEaPHg2BQMApvgn4+ONwIDp16iTlzMjcuXM5PU+/efMmFi5cSM+opMzT05NTccwG/ftixOTJMsio5mvYsCH27NnDKXb9+vU0X0MIIYQQQuoEgUCAc+fOYcCAAWjTpg127tyJvLw8sfrw8PCAj49PtTg8SFFREadPnxZrz0tiYiLMzc0xa9YsfPr0SYrZyd779+/h6uqK0tJS1tjZs2dj/vz5P3zdxMQEN2/ehIGBAefxP5/4iPgeD5Dk+hTnj59DRkYG57ak+mErgGJmZib2+0B1KYBy/497Uh+juqMCKIQQIkMRERGsDwA6tWkLLciL1a+Ojg4cHBxExvz555+IiYkRq19CCCFElNjYWMyePZtT7IEDB8SaWOBKVVUVISEhmDlzZqX70tXVRWRkJOzt7SWQGZGlsWPH4siRI2JNTrx48QJz5syBgYEBzMzM4ObmhoULF2L37t14/Pjx90X9ubm5SE5ORkpKCjIyMvDmzRtcu3ANH7a9ReovL/HOJwUfd6bhU8AHfLmShcLn+Sh9X4JXia9w//593LhxA1evXkVERASuXbuGx48fIzU1lRYUViPXrl2Dh4cHp9iFCxdi9OjREhubz+cjMDAQZmZmrLElJSUYOnQo3rx5I7Hxa6qlS5ciLS1NZIyqqipWr14to4xIdaOuro7JLIvqhEIhXFxckJ2dLTLOxcVFkqnVeD169BD5+uvXr5GQkCCjbAghpGbLysrC5s2b0bp1a1hYWMDHxwcvXryoUF/169fHxYsXMW3aNAlnSSTBw8MD586dg6oqt8LnQqEQS5cuRffu3XHt2jUpZ0dI9bFp0ybWwj8u9RtCsYmSyJhxs8eznvKUm5sLHx8fsXOsiJcvX3I6vWrBggXQ1taWQUbgVFTUz88PSUlJMsiGVCWGYZCQkIDt27fD3d0dJiYm0NLSwuDBg7Fx40bk5+dz7ovP52Pt2rUoLCxEVlaWxAqXa2lpwd3dHdu2bUN0dDSys7MRFhYGDw8PTovPiGiNGjVCZGQk3NzcKtQ+Ozsbv/76K/T19eHk5ITAwEDExsbi48ePVMyNEEJYvH79GtbW1lixYkWVFBvbtWsXevXqhdevX8t8bEKqk927d2PixImcr10MoYBbMEI7k5ZSzowA3woM//7775wOK9i2bRuWLVtGRVCk5MCBA9i9ezdrnLyGBlrs4F4QmgDOzs4YM2YMp9j58+fj4MGDUs6IEEIIIYSQqlFSUoK9e/fC1NQUw4YNq/B6iaVLl2Lr1q3V6r5EUVERwcHBYu15KS8vh7+/P1q1aoXAwMBacb/75csX2NvbczoUytbWltOBQy1atMDNmzfRrFkzsXLJvZGDheM90aRJE3Tq1AleXl4ICwtDQkIC4uLi8OjRI7x//75W/L3XZnFxcSJf57JH4590dHRYv+9kUwDlvtTHqO7E22FPCCGkUrgsphxpPxh4HiZ23xMnTkRoaKjImEOHDqF79+5i900IIYT8U3p6OpydnTkVcfjpp58wbNgwqeUiLy+PXbt2oW3btliyZAkKCgrE7mPKlCnYtGkTGjZsKIUMiSyMHTsWADB+/HixJ5qePXuGZ8+e/evrysrKFS5UMhC2Il/n8Xho164devXqBSsrKwwaNIhOKq0CycnJcHNz47SwdNCgQfD19ZV4Dmpqarhw4QK6devGWkU6MzMTTk5OuH37NurXry/xXGqC8PBwTqcaLV68GE2aNJFBRqS6mjNnDrZv3y7yM+HJkyes/VR0A1Bt5eDggI0bN4qMOX/+PNq3by+jjAghpGZhGAZ37tzBnj17EBQUhJKSkkr32bx5c4SFhaFt27YSyJBIi7OzM6KiouDk5IR3795xahMbG4sBAwagV69ecHV1haOjI0xMTKrVIh1CJOXPP//EgQMHRMbwAEzV1MIylr6UVZSxZs0a1qKIO3fuxLx582BoaChesmIQCAQYO3Ys63xlo0aNsHDhQqnl8U+2trbo2rUrHjx48MOY4uJijB8/Hrdv34a8PC0rqU2EQiFu3bqFkJAQhIWFISUlpdJ9Kioq4uTJk9+fA6ioqCA0NBRXr17Fjh07EBUVha9fv3LuT0dHB/3798eYMWNgZ2cHBQWFSudIfqxevXo4deoUxowZA29vbzx//lzsPoRCIcLCwhAW9n/rHNTU1NClSxd0794dPXv2xIABAzgXhPunsrIyxMTE4Pr160hOTkZJSQm0tLSgra0NfX19mJubo0OHDlBWVq5Q/4QQIksMw+DIkSOYO3euWMXGpOHRo0ewsLDAoUOHpPo8n5DqatOmTVi8eDHneEuoIAT6aExL72VKW1sb+/fvx5AhQ1hjN2zYAGVlZaxcuVIGmdUd9+7dw6xZszjFtti5HYp6VKxSXL/99hvu3LnD6UCcqVOnQlVVFSNHjpR+YoQQQggopvOsAAAgAElEQVQhhMiAQCDAnj17sH79erx//77C/Sgo8LB3jy8mTeZ+ry9LioqK2L17N/r3748pU6ZwfnaWk5ODsWPHIigoCDt37mQ9DKS6ys7OhouLy3/u2finJk2a4NixY5yfUxsbG+PmzZvo27cvp+Iq//TkyRM8efIEfn5+/3pNXV0dpqam0NXVhaamJrS0tNCqVStYWFigffv2UFISfYALkZ7Pnz+zvmeYm5uL3a+CggK0tbWRmZn5wxhpF0DJz8/Hs1jRxV3qApqFJYQQGYmKikJUVJTImPr162P68JHAFvELoDg4OEBLSwufP3/+YcyRI0ewZMkSGBkZid0/IYQQ8peCggI4OztzummzsLDAtm3bpJ4Tj8fDnDlz4OrqCn9/f+zZswefPn1ibWdjYwNvb28MGDBA6jkS6Rs7diwYhsGECRMkUm23osVPuGAYBvHx8YiPj8fevXuhqKiIMWPGwMPDAx07dpTauLXJK2EKRueK3sx0ov5BtOA3/8/Xnj17Bjs7O+Tk5LCO1apVK5w4cQJ8Pr9CubJp1qwZzp07BxsbG9ZNsPHx8RjZQA8XYAB58ID480C72nHCGNu/admnUqRPfMHaT5MmTWS6cYxUTy1atICjoyMuXLhQ4T5atWqFDh06SDCrms/S0hKamprIysr6Ycz58+fh7e0tw6ykp7KfNYQQAny79k9MTMTFixdx9OhRTg/yuRo1ahR+++03aGlpSaxPIj2dOnXCnTt3MHDgQCQmJnJud/v2bdy+fRuenp5o1KgR2rdvj44dO2Lw4MHo378/bQonNR7DMFiwYAEEAoHIOBeoo7kit8VD48ePx+bNm5GQkPDDmNLSUqxYsQJHjhwRK19x+Pj44P599lN5lixZggYNGkgtj3/i8Xj45Zdf4OzsLDIuJiYGvr6+teb6vq6LjY3F0aNHERQUhI8fP0qsXxUVFZw7dw52dnZ/+zqPx4OtrS1sbW3BMAxSUlLw4MED3LlzB9HR0YiLi4NAIICmpia6dOmCrl27okuXLujSpQv09PSo4JeM8Xg8DB06FE5OTggMDIS3tzfS09Mr1Wd+fj5u3ryJmzdvAvhWcHzgwIEYPHgwOnbsCH19fWRlZSEtLQ2ZmZnIyclBdnb2v37Pzs7G+/fvUVhYKHI8Pp//veh479690aNHDzRr1kxqc7qEkFoiIRloz7KhXoLPYYqLizF9+nQEBARUqD2fz4e5uTm6du2Kjh07QkdHBxoaGqhXrx6ys7MRHR2N48eP4/Xr15z7/Pr1K1xcXODp6YkNGzbQPSapExiGwerVq7F69WrObcahAfahCZQhJ8XMyA+fy/QGtNyb4HMg+73MqlWroKSkBLfFEigOIePPierow4cPcHV1RWlpKWtsk+lT0WjQQBlk9Q/tnYFEEYfe1IB/Iw0NDYSHh8PS0hLZ2dkiYxmGgbu7Oz59+oTZs2fT/TMhElDt1wXQ51GNw+l7yvIzWtTwz68aiX6eSHVC348Avq3lnjx5MmJjYyvVj4aGPAIOtMZAR1cJZSYh//Hv7AqgM7QwCEVIBPu91l/Onz+PsLAwODk5Ydq0aRg4cGDlnoH8R26vWvMx+o7oNUjiXheVlZVh7969WLlyJev9DgDIycnh+PHjaNy4MecxgG8HSN28eRM2NjYVKoLyI3l5eXj48OF/viYvLw9tbW1oampCT08PvXr1wtixY2FsbCyx8cmPPX36lDWmIgVQAEBXV1d6BVA4vP/f2buYdQ1NXUAFUAghRAZKS0sxY8YM1rjZs2ejYQVPcldUVIS7u7vI09CLi4vh5eWF06dPV2gMQgghJDs7G6NHj8bjx49ZYxs2bIgzZ87I9MQ9XV1drFmzBsuWLUNoaCju3buHt2/fgsfjgcfjQV1dHa1bt0a7du1gbm5eYyvgkh8bN24cGjRogIkTJ3IqbFFdlJaW4vDhwzh8+DD69esHPz8/WFhYVHVatdb9+/cxaNAgTt8jDRs2RGhoqNQ3IvXo0QOHDh3CmDFjWGPDUYAFyMBvaCLVnKoTRsggZU4ivmawT36vXr0aampqMsiKVHceHh6VKoDi5uZGC8b+QV5eHo6OjiI3iT548ADv37+Hnp6eDDMjhJDqJyEhAUeOHMGZM2eQkpIi0b7btWuHnTt3wsbGRqL9Eulr1qwZoqKi4ODggJiYGLHb5+TkfC+2/ttvv6FRo0YYOnQoxo8fjz59+tC1C6mRwsLCEBERwRr3MzQ598nn87F+/XrWAh8BAQFYuHAhzMzMOPfNVXBwMNasWcMa17RpU8yZM0fi47NxdHSEubk566Kg1atXw97eHp07d5ZRZkSSCgoKcPLkSfj7+/9wYV5lmJqaIjAwEF26dBEZx+PxYGxsDGNj47+dUP1XEWv6/Ko++Hw+xo8fD1dXV2zYsAF+fn6sBZu5Ki4uxvnz53H+/HmJ9PdPQqEQcXFxiIuLg7+/P4BvayiMjY1hZmYGe3t7ODg4iL1glhBCJOXDhw8YNmwYpwJ5/zR06FC4u7tjwIABaNiw4Q/jBg4ciJUrV2Lv3r1Yvnw5vnz5wnmMLVu24O7duzh16hQMDAzEzpGQmoJhGHh5eWHTpk2c2yyDJn6FNnig69aq1GydCXJv5aA0jf36dOnSpchXLAAmySCxWiw/Px+Ojo6cTmCv174dmq1eIYOsaq9WrVohLCwM/fr1Yz2wSSgUYu7cuYiJicGePXtQr149GWVJCCGEEEKIZBQUFGDjxo1Yv349ysrKKtXXMGdN+KxpDt0m3A7TqA6MoIg/YAg7pOIpuD+HEQqFOHfuHM6dOwcDAwNMmTIFw4YNg4mJSbW8L4iIiMCCBQvw559/cm6zYsUK9O3bt0LjGRkZ4Y8//oCDg4PIw1IkRSAQ4MOHD/jw4QPi4+MRGRmJVatWwdbWFtOnT4eDg4NM91LVNXFxcSJf5/F4aNeuXYX61tXVFbmWolIFUDi4EXNPqv3XFFSKmhBCpKysrAyjRo3C8+fPRcYpKytj/vz5lRpr0iT2JzZBQUH4448/KjUOIYSQuun27dvo2LEjIiMjOcUfO3YMzZtXTdV7ZWVljBgxAlu2bEFwcDDOnDmDoKAgHDx4EF5eXnBwcKDiJ7WYs7Mznj17Bltb26pOpUKuX7+OLl26YNasWZwqHRPxXLlyBQMGDOBU/EROTg6nTp2CqampDDIDRo8ejZUrV3KK3YkcHAb3has1GcMweOuVjK9X2H8eOnfujMmTRZ+gQeqOfv36VWoj44gRIySYTe0xZAjLyRMAQkNDZZAJIYRUP/n5+dizZw+6du2K9u3bw8/PT6LFT/T09LBr1y48fvyYip/UYFpaWrh27RoGDx5c6b5ycnJw6NAh9O3bF23btsXWrVvx8OFDiW1UJkTaSkpKsGDBAta4/qiHrlARq29HR0f07t1bZAzDMBgzZoxET4ACvl0Pjxo1CkKhiNMb/7/9+/dXyWI0Ho+Hbdu2scaVlZXByclJ4n9HRHqEQiEiIiIwbtw46OjoYOrUqRIvfiIvLw9vb288ffqUtfiJKH8VLifVj6qqKtauXYvnz59j6NChVZ1OhZWWliIxMRGnT5/G5MmT0aRJE1haWmL9+vWIj4//XoSHEEKkqby8HCdPnkTXrl3FLn7SuXNnREVFISQkBMOHDxdZ/OQv8vLymD17NpKSkjBq1Cixxrt79y7Mzc1x8OBBeo8k35WDwSMUwRefMQBvoY9kNEISWuIleuENJizzQmBgIOtm/eqgvLwcs2fPFqv4yTpoYx0aU/GTaoBfXx4tDrSDsgq3jUPrFv6KD7+l0vtZBQkEAowaNQqPHj1ijeWrq6Plwf2Qo01dldazZ0+cOnUKcnLctvgEBATAysqKU5EaQgghhFQdhmHoupSQ/08gEGDv3r0wMTHBmjVrKlX8xKSFMs6dbodD+1rXqOInf2kMedyAIbqbmVeofVpaGlatWgVzc3OoqqrCwMAAbm5u2L9/f5U/23369CkcHBxgb28vVvETe3t7LF++vFJjGxoaIiYmBrOWzgZPUfbzOQzDIDIyEsOHD4eOjg4mT56MW7du0eeAFLAd9tKyZUuoqqpWqG9dXV2Rr0u7AMrNWPEP1aqNqAAKIaRaYhgGJSUl+PLlC96/f4+0tLQauWD38+fPcHR0REhICGvstGnTKn3akLm5OXr27Mka5+HhwWnRJSGEEAIAGRkZmDlzJvr06YO0tDRObZYvXw4HBwcpZ0bIjzVt2hTh4eH47bff0KBBg6pOR2wMw8Df3x/Gxsbw8vJCampqVadUKwQGBmLw4MHIz8/nFL9582bY2dlJOau/W7ly5d9OwhVlBj7i0XPpV6iuau83vsWnQ+wLdlRUVBAYGAh5eXkZZEVqAh6Ph507d1ZoM1WrVq3Qvn17KWRV89nZ2bFWpZfWac6EEFLdlJWVITExEUFBQZg7dy6aNm2KmTNnIjY2VqLj6OnpYceOHXj16hVmzZoFBQUFifZPZE9NTQ0XLlzAhg0bwOfzJdJnYmIiPD090aVLF6ipqaFz587w8vJCZGQkCgsLJTIGIZLm4+ODV69eiYzhAfCDjth983g8+Pr6ssYlJCSgW7duuHdPMifonD9/Hm5ubhAIBKyxs2bNwqBBgyQybkXY2Nhg3rx5rHEfPnyAvb09Feqt5hiGQUhICNq2bQt7e3scO3YMBQUFEh+nT58+ePjwIX799Vc6sawOMDY2RkhICCIiItC6deuqTqfSGIbB3bt3sWzZMnTo0AGNGzeGlZUVJk+ejG3btiEqKorz3DEhhLBhGAahoaHo1KkTRo8ejXfv3nFuKy8vj19//RX37t2DlZVVhcZv3Lgxjh8/jsOHD4u1yDsnJwdTpkxB//79cfPmTZSXl1dofFLzlZWVYV/QSbTEK3TGGyzBJ1xDId5BgC8ox0uU4Q6KcDT0HMaOHQtDQ0OsWbMGnz59qurU/9P79+/h5OQEf39/zm22QQfLoCXFrIi41LrUx96QfVBS4raxLX3laySPfIayjJq39rgqCYVCzJkzBxcvXuQUb7J3N1Ramkg5q7rD2dkZBw4c4Bz/+PFj9OzZk/WQTkIIIYTIBsMwSEhIgL+/P8aPH4+WLVtCWVkZSkpKMDQ0hKWlJSZMmICjR49W2/snQqShsLAQu3btgqmpKWbMmIGPHz9WuK/OndTgv6Ml7t7sBBtr9oLB1Vkj8HHjYADmzp3LuRDij6Snp+PMmTOYPn06jIyM0KpVK8ydOxcXLlxAXl6ehDL+MYZhcOvWLQwePBgdO3bEpUuXxGrfpUsXBAUFSWQNTb169eC5diHa3+mKBnYale6vonJzc3Ho0CH06dMHffr0QVRUVJXlUhvFxcWJfN3cvGLFhYCqLYCSj3I8iH8mtf5rEtoRQgiRqfLychQUFODLly94/fo1Xr58+bdf7969Q2FhIQoLC/9V2YzH46FZs2YwMTFBu3bt0LlzZ1hYWMDU1BSKiopV9H/0d0lJSYiOjsa7d+9w584dXLlyhdODYHl5eSxatEgiOWzcuJH1RLunT59i27ZtWLhwoUTGJIQQUjtlZ2djx44d2Lx5s1gLPl1cXLB69WopZkYIN3JycpgzZw7GjRsHf39/bN26FZmZmVWdlli+fv0KPz8/bNmyBY6jnFDqXQLFGlilujrYsmWLWNe/kyZNgoeHhxQz+m88Hg+HDh3C27dvWTdflYCBy/w5eNivNzQ1NWWUoWxlHnqH975vOMVu27YNrVq1km5CpMbp3bs3tm7divnz54vVbsSIEXQK9Q+oqqpiwIABCAsL+2HM9evXkZubi/r168swM0IIkazS0lLk5OQgPT0daWlpSE1NRWpq6vc/p6Wl4f3791I7oUNTUxPDhg3D8OHD0a9fPyp6UgvJycnh559/hrW1NUaNGiXRwpcCgQCPHj3Co0eP4OfnB2VlZTg7O8Pd3R329vbV5pkKqdtCQ0OxZs0a1rhpaIhOqFiRhZ49e2LYsGGsBxVkZmbCxsYGhw8fxqhRoyo0VllZGZYvX46NGzdyim/VqhX8/PwqNJYkrV+/HuHh4Xjx4oXIuMTERAwaNAjBwcHQ19eXUXZElNzcXDx69AjPnz9HcnIyQkJCpHqam7m5OTZs2ICBAwfS/XIdZGdnh7i4OJw4cQJ+fn6Ij4+v6pQk4vPnz/j8+TNu3779/Ws8Hg8GBgZo0aIFWrVqBQcHBwwcOJCuxwkhnDEMg2vXrmH58uW4f/++2O1NTU1x7NgxdO3atdK58Hg8TJgwAZaWlhg5ciQeP37Mue2NGzdw48YNGBgYwN3dHTNmzIChoWGlcyLVX3l5OU6ePIkVK1awFqz8X5mZmVi5ciV8fX2xZMkSLF68uFoUzGMYBidOnMCcOXOQk5PDqQ2Px8M+RgdT0UjK2ZGK6DXACsHBwRg2bBink8K/Xs1GfK8HMNrWCo0ctWWQYc2WnZ0Nd3d3hIeHc4rXX+qFRoMGSjmrumfixInIz8/H3LlzOcWnpqaiV69eOHv2LPr27Svl7AghhBDyT0KhEBEREQgODkZkZCTS09P/M+6vNQd3797F0aNHwePx0L17d0ydOhUTJkygg99IrfTq1Sv8/vvvOHDgQKWK/igpKWHkCAdMHPMSFp3UJZhh1VNRVsaOHTswffp0LFiwAFevXpVIvy9evMCLFy+wc+dOyMvLw9LSEnZ2drC2tkaXLl2gIpFRvs0lhYWFYcOGDbh7926F+jAxMcHFixehpqYmoay+UTauB9OTZsh/lIvM/e+QfTYTTJl01nmxiYqKgrW1Nezs7ODl5YV+/frRM9dKKCsrQ0KC6ENkzczMKtx/VRZAuY1CCIVCqfVfk9CVEamR6AdYsvLz8/HgwQPExMQgNTUV5eXlUFBQgLy8/H/+zuPxUFJSgpKSEvD5fBgZGaF169bQ1tZGRkYG3r17h8TExO+LrXJyclBQUID8/PxKnTLIMAzevn2Lt2/f4tq1a9+/LicnB0NDQ7Ro0QL6+vrQ1dXFly9fkJCQgLdv34JhGBgaGqJly5Zo06YN7O3tJXqKc0pKCoKDgxEYGIgnT55UqI8JEyagWbNmEsnHysoKo0aNwsmTJ0XGeXl5wczMDLa2thIZlxBpSklJQXR0ND58+AAVFRXo6elBW1sbJSUlKCgogLKyMlq3bg0jI6OqTpWQWiEjIwN+fn7Ys2eP2CdEWlhY4OjRo5WuQEuIJDVo0ABLliyBh4cHTpw4gbNnz+LatWsoLi6u6tQ4EwqFOB94DvwLfOivagHt8brgydGkFxfl5eXw8vLC5s2bObextbWFv79/lU0sqqio4Ny5c+hu0Rlv34s+EfDt+3dwc3PDpUuXqsUiQknKDv2Et4uSOcUOGTIE06ZNk3JGpKby8PDA27dvsXXrVs5t3NzcpJhRzTdkyBCRBVBKS0uxdu3aarGhkxBC2JSXlyMuLg7Xrl3D9evX8eTJE2RlZaGkRPangmppacHFxQVubm7o06cPbbKsI3r27Ilnz55hw4YN2L59e6Weo/xIcXExTp8+jdOnT0NDQwNubm4YM2YMrKysaA6HVInnz59j7NixrHENIIdfUbnNST4+Pjh//jzrgQUlJSUYPXo0kpKSsGLFCs5zAgzDIDIyEitXruS8wVVeXh7Hjh1DvXr1OMVLU7169XD06FFYWlqy/h3FxMTAzMwM+/fvh6urq4wy5EaYJ0BpegkEuQLI15eHoqEy+PUqfzpYdVFcXIzY2Ni//UpKSpLqmHw+H+3bt0f37t0xbNgw2NnZ0WdGHaegoIDx48dj3LhxCA8Px86dOxEREVHr1u8wDPN9I8KNGzewZ88emJqawsfHBy4uLrQYtQZgwKAYDL5AiC8o//674OZ16Jfmo3379jX+XothGJSWlEBOTq7G/7/UJgUFBTh58iT27t2LBw8eVKiPGTNmYNOmTVBVVZVobi1btsSdO3cwf/587N27V6y2aWlp2LBhA/z8/ODq6op58+bB0tKS3g9rqXv37sHDwwMxMTEV7qOwsBArVqzAoUOHsHnzZgwdOrTKvl/+/PNPzJs3T6yNQ3w+H0d9NmLMz79LMTNSWQ4ODjh16hTc3Nw4XY8KsgV4OT4BWmOboJmPCfhqtH3iv9y9exdjxozBmzdvOMU3chiEpos8pZtUHTZnzhzk5eVh2bJlnOK/fPmC/v37Y+HChVi7dm2tWz9Cqpf8/HycPXsW9+/fx4cPH8AwDMrLy6Gqqgp9fX0YGBjAxMQEvXr1okNTCCG1GsMwiIiIgJeXF549e1ah9vfu3cO9e/fg5+cHX19fODs70z03qbE+f/6M8PBw3Lt3D2/fvsWbN28qXVBdV1cX8+bNw5QpU6DRKA/5WeMllG310759e0RGRuLMmTOYNWsWPn/+LLG+BQIBbt26hVu3bgH49synXQsT6CEVupBHffChDB4KP/PxYUch5JTlIKckB56SHHiKcpBT5IGnJAc5RTnckLuOax+FePv2LZ48eYKHDx8iIyOjwrnp6uoiIiICjRs3ltT/7r+oWdSHmn99GKwyxufTGfh6LRv5976CKZV9MZTIyEhERkbCzMwMc+fOxciRI6GuXruK+shCUlISSktLRcaYm5tXuH+2AigFBQXIy8uTyr/dDUh+3VhNRTN4pEZ68+YNMjIyoKOjU9Wp1Fj5+fk4evQojhw5gtjYWNYFbdVZeXk5UlJSkJKS8sOY1NRUREVFAQAWL16M7t27Y/r06Rg5cqTYD42FQiH++OMPnDt3DhEREawnorHR19fnfBocV76+vjh//jyKiop+GFNeXo6RI0fi/v37aNmypUTHJzUTwzBISEjAzZs38fLlSxQUFKC4uPiHv/h8PoyNjWFpaQlLS0tYWFhU+qEJwzBISUlBQkICUlNT8fr1a1y5coXzhFDLli3RoUMHNGrUCLm5uVBVVUWbNm1gY2MDCwsLqopLCAfBwcGYMmUKvn79KnZbXV1dhIaGSnxBFiGSoqKigsmTJ2Py5MkoKirCzZs38eDBg++nt8fFxeHjx4+c+uIp8KBkrAKUA8JcAQRZZWAE0p8EE+YK8dbzBbKDM2C8pw0Um9KCBVFKS0sxftp4BAYGcm4zZMgQnDx5EkpKSlLMjJ2Ojg7Cdu2F5TBn5EH0/dqNGzcwcuRInDlzptYsds6NzsHr6c8BDj9Wenp6+P333+nBHxFp06ZNSE1NRXBwMGtsp06dJFo4tTZycnICj8cDw/z4h3TTpk3o378/7O3t//Z1oVCI1NRUFL16iQYoQyPwoQIeeKCfYUKI5OTn5yM9PR1fv35Fbm4uysrK0KhRIzRu3Bj5+flISEjA06dP8eDBA8TGxiIvL69K8+3fvz9mzJiBIUOG1JrrOSKe+vXrw8fHB3PnzsWvv/6KAwcOSK0IT3Z2Nvbu3Yu9e/dCSUkJzZo1Q4sWLdCnTx+4u7vDwMBAKuMS8pfo6Gi4u7tzeu9dDW1oV3JJQ+vWrTF16lTs27ePU/yqVasQGxuLLVu2iHx+Vl5ejjNnzsDHxwdPnz4VK6ctW7agS5cuYrWRpu7du8Pb2xtr165ljc3JycHw4cMxYsQIbNq0qUreMzIhwC0U4nJGMf60T0VRUiGEXwX/ilNooohpnadi/PBxcHNzq3Hz1qWlpbhy5QqOHz+O8+fPi12sXFwaGhro168funfvju7du8PCwqLG/Z0R2eDxeBg0aBAGDRqEjx8/4sSJEwgKCsLjx49rVNFxcbx48QLDhw9Hjx49sHHjRvTu3buqUyL/IBQKcRxfcRJfcROFKPyvieU5MwB8e1bUtWtX9O7dGw4ODujWrRv4/OpZNOsdyhCJAjxFMeJQghcoxVeUo9C8zfd1Xnw+HyoqKmjatCl69OiBvn37om/fvhI7hIn8n6KiIjx58gSxsbFITExEQUEBhEIhCgsLkZiYiBcvXkAg+Pc1CReampo4fPgwHB0dJZz1/1FWVsaePXtgZWWFn376Sezim0Kh8HthTSMjI4waNQq2trawsLBAw4YNpZQ1kZXMzEwsWrQIAQEBEuszJSUFLi4u6NGjB3x8fNC3b1+J9c3mw4cP2LhxI3bu3CnWz6WCggJOnTqFYabtASqAUu0NGzYMgYGBGDNmDOf1z5+PfURe1Bfor26BRk5aUs6w5sjJycHSpUuxb98+kc/+/le9Du1h4r8LPCqUKVVLly6FkpISFi9ezOn7nGEYbNq0CRcvXoS/vz/69OkjgyxJXZKUlITdu3fj8OHDyM3NZY1XVFTEwIED4erqCgcHB2hp0XsvIaT2ePbsGebPn4/r169LpL+kpCQMHToUFhYWmDZtGsaMGUNFpEiN8OnTJwQEBCA4OBh3797lfE/BxtDQEEuXLsXEiRO/r+cWCqp2bY8s8Hg8uLm5wcbGBrNnz0ZQUJBUxikrK8OTxD/x5J8vfAawKlNk22mYKrE8+vbti4CAADRt2lRifYqioKME3bnNoDu3GYQFQsx+Mgnx158hMjISCQkJMsnhL3FxcZg2bRrmz58PNzc32Nraolu3bmjRogWth+cgLi6ONUaaBVCAb3OQ0iiAchH5Eu+zpqJdwKRGKikpgY2NDa5fv87pzYR8k5eXh+joaFy+fBlHjhzhNPFUW92/fx/379/H3Llz4ejoCBcXF7Rq1QoGBgbQ0ND414VCUVER7ty5g7CwMJw6dQofPnyQSB6NGzfGlStXoKGhIZH+/tKsWTP8/PPPWLVqlci4nJwc2NnZ4fjx4+jZs6dEcyB/V15ejnfv3uHly5d4+fIlkpOT8fLlS7x//x7y8vJo0qQJdHR0oKCgAIFAgPLycjRs2BBNmjSBtrY25OXlv1fI/uv3goICZGRk4OPHj99/z8zMhEAggJaWFnR0dL7/aty4MVRVVVFSUoLS0tLvv4qKipCRkYEPHz7g0aNHYlddfPr0KUJCQgB8m6ju3LkzLC0tYW5ujjZt2qBp06YoLCxEQUEBCgoKkIQDReQAACAASURBVJ+f//3P//zvN2/e4NatW5X6+UpOTkZycvJ/vqasrIxGjRqhfv360NDQgIGBwfefeXl5ecjLy6Np06awtLSkRf6kThIKhfD29oavr2+F2uvp6SEiIkJmkw+EVJaKisr3Rdp/YRgGycnJiIqK+v55ZGxsDCMjI/D5fBQWFkIoFKJMW4BfdNdDTuH/FnIwQgZln0pR+r4E5UXl8NVZg/YabaGmpgZ5eXnw+XwUFRUhKysL6enpuHfvHm7cuPG9krK48m5/xXPbR2h5ogNUzanq738RfC3DNLcpuH3tNuc248aNw8GDB6tN0bT2LU0RCD04I501NjQ0FBMmTEBAQEC1XazNVcHjXLx0j+dUWVtdXR1hYWG0SIKwkpOTw7Fjx6CkpITjx4//MI7H48HPz48eILDQ0dFBjx49cPfuXZFxgwYNgqOjI8zNzZGRkYG4uDg8e/bsXwVbVcFDayihHZTQFopo9///bFheDlo2SQhhwzAM3r59i9u3b+P27duIjo5GfHy8xBZVSIuGhgYmTZqE6dOnw9TUtKrTIdWErq4udu3ahbVr1+LSpUsIDQ1FeHi41Ir0lJSUfJ9TDQ8Ph7e3N+zt7TF69GhYW1vTpkkiUYWFhfD29sb27ds5vUe3gSJmoZFExt60aRNu377NedFSWFgYwsPDMXv2bPzyyy/Q1NT82+tXr17FkiVL8PDhQ7FzWbt2LebOnSt2O2lbuXIl4uPjvz/zYXP69GlcuHAB48ePh6urK3r16oV69epJLb+CggKEXDiPI0jFNRR829aeA0DEwfRlH0tx4+J13Lh4HR4eHnB0dESLFi2+Px8yMDCAvr4+1NXVIVcNNmwJhULcuHEDERERuHfvHmJjY6VeTMLIyAhubm4YMmQIevToUePnlIjsNWnSBAsWLMCCBQvAMAyys7ORlpaG58+fIyYmBtHR0RV6rxTlr5/fvwqsSmrtBhf37t2DtbU1nJycsGHDBrRt21ZmY5Mfu3XrFjx+moEneM8pvqio6PtJl+vWrYO2tjYGDx4MR0dH2NnZVYuNJQ9QBF9k4Szy/rtG+P/sfRUKhcjPz0dSUhKSkpJw5MgRAECLFi3Qt29f9OvXDwMHDpT4uqSaQij8dhLpu3fv/nbgT0lJCYqLi5H/MgXZ+IQsCJH9P79KwUAZPChDDvkjhuFDTjYyMjKkcsBYhw4dEBoaCiMjI4n3/V/Gjh0LCwsLuLm54fnz5xXq482bN9iwYQM2bNgA4Nv3m6mpKVq2bIl69eqhqKgIZWVlaNy4MTp27IjOnTujadOmNO9eTYWFhWHKlCnIzBS9uaWi7t27h379+qFnz56YOHEiRowY8a+iOcXFxYiJicHTp09RUFAAbW1ttGvXDq1bt0aDBg1Efu+Ul5dDIBDgy5cviImJweXLl3Hw4EGxr6WVlZUREhLyrah7wn+vPSPVz8iRI1FSUoKJEydyng8ueVuMVxMToNqlPmI3PUCLPs2lnGX1VF5ejujoaAQEBCAoKEisg7Ka6OtD72Qg+OpqUsyQ/MXT0xNmZmYYNWoUsrKyOLX5888/YWNjg2HDhmH58uXo2LFjtZj7IDWTUCjEpUuXsHPnTkRGRorVtrS0FBcuXMCFCxfA4/HQtWtX2NnZoUuXLujUqRO0tbUBfCvsqKCgUKevFxMSEhAaGor4+HikpqYiPT0dcnJy0NfXR+/W7dAH+eiFeqhHKygIqXIlJSVYt24d1q9fX+FCqKI8evQIM2fOhKenJ+zt7dGxY0c0btwYWVlZePfuHdLT078fBvPXPIecnBwaNWoEDQ0NaGlpQV9fHwYGBtDW1oaKigpUVFQgFApRXFyMgoICfPr0CZmZmcjKyoJAIIBQKASPx4Oenh7MzMxgZmaGtm3bSm2erLy8HK9evUJKSgpycnLw5csXZGZm4s2bN3jz5g3y8/OhqakJU1NTmJiYQF1dHUpKSlBWVoaWlha0tbXRuHFjNGzYsE5/dlQlhmFw69Yt+Pv74+zZsygrK5NY33JycvD09MTq1aul+uyxutPW1sbp06dx7do1bN++HRcvXpTK3GRVMTY2xqZNmzB06NAq+znmq/LRZ5ANJjtOAvCtmM/bt2/B4/GgoqKC0tJSvHz5EklJSXj37h2ysrLw6dMnPH/+XOy9l6IUFBTg8OHDOHz4MIBv68m6deuGbt26wdLSEjY2NlV+qGt1xHZITcOGDSu1N5RrARRJr/l7hVLEQzoHZtVE1WNHDyEVkJiYiL59+yIkJARt2rSp6nSqnFAoRFxcHP744w88evQInz59Qm5uLvLy8pCXl4fc3Fx8+fKlVl3sSML/Y++8w6Oqtv/9TiaTySSZ9F5IJQmhhQChBQLCpSgoXKUKAl5FELDCVbmK3isqWK8FFRERpSlouBg6GMBA6AklBZKQhPRO6iSZ9vuDX85XpKRNGpz3efYzk5mz914zmTmzz9prfVZ1dbVQnaIehUKBh4cHbm5uaDQaKioqSExMNHi1RTs7Ow4dOkRgYKBBx61n6dKlfPfdd1y7du2ux6WnpxMWFsZrr73G8uXLMTExaRV77jXUajUlJSXC90sikeDh4YGdnR0ajYaEhATOnj3LmTNnOHv2LBcuXGjTildpaWltNlc9dXV1xMTENJgA117U1NSQm5srBMHdzU4/Pz9CQ0NxdHTE1tYWIyMj4fxpZWWFnZ0djo6O9OzZE2dn5zaxX6TjU11dTWVlJSYmJsjlcuRyeafZOCwuLmbGjBlN3qCqZ9y4cWzYsEHYkBIR6axIJBL8/f0bdESkatMwKr/5+y2RSjBxlmPifMPB1MOyB77SmwNlrK2tcXFxoUePHowdO5a33nqLhIQEPv30U7777rsmb4ao8+pIeigWn7VB2IwTxR/+TE1KNckzLlKTomr44P/Pc889xyeffNLhzt0TUPI6dqyg4UCWLVu2YGdnx+eff94GlrUOpbuLuDovAV11w9euJiYm/O9//6NPnz5tYJnIvYCpqSmbNm3i+eefZ9u2bfz+++/ExsYKAZGurq588803jBw5sp0t7Rw8+eSTjbr+i4yMJDIy8q7HVKHnLDWc5ebrZqsh/Rn/8MMsWLCAwYMHi5vWIiIiAGg0Gi5cuEB0dLQgepKdnd3eZjUKGxsbxo4dy4QJE5g4cSIKhaK9TRLpoNja2jJz5kxmzpyJWq0mMTGRixcvcv78eSIjI0lMTGyVeXU6Hbt372b37t0AuLm54eXlhaur6x2bUqkUf6NFGiQ6Opq5c+eSkpLSqONNZDK+V7siwzCfLaVSycGDB3nkkUc4deouihl/QqPRCD6bCRMm8Oijj3Lp0iW2b9/OxYsXm2XHG2+8weuvv96svq2NVCpl06ZN/O1vf+PYscaJyapUKtasWcOaNWswNjYmMDCQ4OBgQkJCGDJkCH369EEmkzXbJp1Ox9GjR/nhhx/Ytm0blZXNr3RUXl5+RzFMiUSCUqnEzs6O3r17M2zYMAYPHoyzszNWVlaYmpoikUhQq9Wkp6dz5cqVm1p2djY6nQ5jY2PkcjnBwcGMGzeOqVOnNrjvqlarOX36NBEREWzatKlNhBwsLS2ZNWsWs2bNIjQ0VDyHixgMiUSCnZ0ddnZ2BAcHM2PGDACysrLYsWMHu3fvJjY2lry8vFv6SqVSbG1tsbW1FQL1/3rr4uLCgAED8PT0vKlvfn4+586dIyYmhqNHj3Ly5MlW35f/7bff2LVrFzNmzGD58uV07dq1VecTuT2nT59mxYoV7Ny5s0XjFBYWsmHDBjZs2IBMJmPkyJEsXLiQBx98sE33C6qqqoiIiGDdZ19wmPQWj5eamkpqairffvstUqmUYcOG8be//Q0/Pz+8vLywtbVFqVQKSQR6vZ7MzEzi4+O5fPkyVVVVmJmZYW5uflOrFxmoT075aysvL0cul2NpaXlL8/LyIjg4mJ49e7YoeUFTrqEmuRptmQZtlRZdtRZdlfbG/Qotr5W8SmVuBenp6aSmplJXV9eyNzOh9Sp/PvLII/z444+tUiXybgQFBXHq1CkWLFjAjz/+2OLx6j9ve/bsueMxcrkca2trrK2t8fPzY/Dgwfj7+2NqakpBQQEVFRUYGRkhlUrR6XSoVCpUKhVSqRQHBwecnZ2xt7dHqVRiYWEh3BdpPnl5eSxfvpy1a9e2yXz1MWULFy6ke/fueHp6IpFIyM7O5sKFC3f8rhoZGWFhYYGNjQ1ubm54eHhQVVVFcnIy6enpBonptLCwIDIykvDw8BaPJdL2PPHEE6jVaubNm9ek+OiqM+VMGz6VfXP38sEHH9wifnovotVqSUxMZOvWrWzcuJGMjIwmj6FUKlm383+87SoWL21LRo0axdmzZ5k0aRKxsbGN7hcREUFERAQ2NjYMHTqU8PBwwsPDCQ4OFkVQOwA1NTWcO3eO06dPC/4liUQiNLhxvWxiYnJLs7a2pkePHnTr1q3V/pepqanCtVJDuQiNQa/Xc+rUqTv6Z+tfl5mZGdXV1VRUVKBWq4VrEaVSiaOjI05OTlhZWaHVatFoNCgUCjw9PfH19UUul1NYWEhpaSkmJiY4OjoKceBKpRKlUomlpSVmZmYdwidWXV3Nb7/9xpdffnnHImpXr169IeDJjcIyM7HiOWwJQkyCFRFpa/Lz89m6dStfffUVly9fbvX5VCqV8FveGIqKigxug52dHT4+PtjZ2Ql+JJ1Oh1arRSaT4ejoiLOzM+bm5jcVSa4voFxbW4tarb6pXb9+nUuXLlFVVdXg/HfzMwAYGxvj4OCAk5MTfn5+BAQE4Ovri85eT6VZOSaucmROJh3inH+voNfriYyM5J133uHkyZMGH79Xr16sXbuW0NBQg4/dWRk5ciQjR44kKyuL9evX8+233xpkbdZeKJVKXn/9dZ5//vkOJ+rh4OBwSz5UcHDwbY/NyckhLi6O3Nxc8vLyiImJYf/+/QYRAyopKWHv3r3s3bsXuLG3O2HCBCZPnsxDDz3UYYq8tjcNCaD06tWrRef/xgqgGJr/0ToFsjor4qddpFNz+fJlQkJCWLlyJYsXL+5wiWqthV6vp6CggKtXrxIdHc2RI0eIjo5ukgK3yJ1RqVRCwFprYWtry4EDB+jRo0erzWFmZsaGDRsYNWoUWq32rsfqdDreeecd9uzZw8aNG+8LUaG6ujoKCwspLi5GpVJRW1tLXV0dZmZm2NjYCE0ul1NUVMS+ffs4dOgQly9fJiMjg5ycnNtWDZBKpQ2+3yIdn5SUlEYHYvft25eJEycyevRoevbsedvElbq6OhISEoiLiyM5OVn4rJmZmWFlZYWTkxNOTk54e3s3apEs0r7U1tYSGxtLTEwMJ0+eJCUlhfT09FsqLMhkMrp168aAAQMYOHAgf/vb31qkINlaxMXFMWnSJNLT05vc19jYmHfffZeXX375vlmHiYgYmqCgINasWcMLL7zA4sWLOXToUJP666p1pMy8hOsST1yXejbc4T6g7GAxqU8noi1rvKDMypUr+ec//9lhNzrewoHT1LCPhjd9vvjiCwYPHsz06dPbwDLDodfrWffJt6T88xK3L2l5MxKJhE2bNjFixIjWN07knqNeHR2gtLRUCO7r1auXuKZpArNnz+azzz5rdvJnYyirqGDTpk1s2rSJPn36sGjRIqZPny4KBoiIdGA0Gg2///47hw4dIjU1VQhw0ev1ODk54e7uTpcuXejfvz8DBw68a1J0WVkZly9fJikpiaSkJOF+cnKyQavHtDZKpZIpU6Ywa9YshgwZIm5EizQZmUwmVNp6/PHHef/994mPj2fjxo2sW7eOwsLCVps7Ozu7QYEhe3t7QkNDGTRoECEhIUJCpSi2LgKQnJzMp59+ypdfftnoSswAXy//D6Fv/GBQW5ydnTl8+DBz5sy5qTBCQ1RUVLB58+Y7imc0ltdee41///vfLRqjtVEoFPzvf/8jLCyMpKSkJvXVaDRcunSJS5cusXHjRmG8AQMGEBYWxpAhQwgJCcHBwaFB/0tKSgo//PADP/74Y7P81k1Fr9dTXl5OeXk5aWlp7Nixo0XjJSYmsmXLFpYtW8YLL7zAuHHjCAgIEJJRysrK2Lx5MxERERw7dozq6mpDvIwGGTBgAE899RTTp0/H3Ny8TeYUEQFwd3dn0aJFLFq0CLgRiJ+QkEBFRQV2dnZ06dIFJyenZidsOTk5MW7cOMaNGwfc2Je9evUqKSkpJCUlsX//fg4fPmzwawidTsfGjRvZsmULM2fOZMmSJa0a/yHyfxw9epQVK1Zw4MABg4+tVquFwOJu3brx8ssvM2XKlFYTWaitreXAgQNs27aNX3/9tUViX3dDq9USFRVFVFRUq4zfVIyMjPD39yc4OJjevXvj5uYmxOhYW1tjY2NDWVmZ4AMoKSmhsrKS4uJiTsWe4urlq3cdfxtNT+Zua+zs7Hjrrbd49tln280nbW5uzg8//MCkSZNYuHBhqwux1dbWkp+fT35+PpcvX2bXrl0tHtPX11fw9w8YMIDg4GDRd9wAdXV1nDlzhh9//JH169cbvCBcY9BoNJw/f77BJIV6dDqdsF5vjlhDQ7i4uLBjxw4xuaqT849//AMvLy9mz57dZLHs9evXExkZyYcffsjMmTPvmb1CvV5PUlISx44dIzo6mri4OJKSklr0vTc1NSUiIgLvXr2gotSA1oo0Bk9PT44ePcrkyZOFRLjGUlpays6dOwXxQEtLS8LCwggPD2f48OGEhISIexetjFarJSMjg7NnzwqiYOfOnWuxWJ9SqaRfv3506dIFFxeX27bGig/W1dVx+vRp9u7dy549ezh79myLbGsqdXV1FBQU3PJ4WVmZkCNjKMGBeoE1S0tLlEolJhYmpJllYKQ0xlhpjGlXBRYDrTEPUWJkYrjfhfLyck6fPs2JEyc4dOgQx44da9JnoAo9a7jOGq4zHDPGYE4YZlhgBEkJ5GddJSMjg+zsbGpqatBoNHds9Xu3PXr0EJroMxS519HpdGTl5nCNakrQUoqWcnSYIkGJ0f9vUpSJCdRVlpCUlERiYqJwm5KSct8VJC8uLr4lP6IjodFohALJcXFxtz1G5mKCcrA1FqGWyL0UyN1NkTmZoDXXgqgH12i0Wi3btm3jvffe48KFCwYf38PDgxUrVvD444+LQn13wN3dnTfeeINly5Zx4MAB1q1bx/79+ykvL29v0xqFRCLhH//4BytWrMDJyam9zWkx9QWD/kxBQQEbNmxg06ZNjfZ7NYby8nIhhtbX15eVK1fy6KOPdtich7aioXNR7969WzS+qakp1tbWXL9+/Y7HtI4ASuvsE3VWRE+FSKenpqaGF154gV9//ZUvv/yS7t27t7dJBker1RIdHU1ERAQHDx4kJSWlXTafRAzDAw88wLp16/Dy8mr1uYYPH84nn3zCc88916jjz507R/fu3Zk2bRpTp06lf//+uLi4dMhFUUlJCQkJCcTHx5OQkEBaWhrp6elcu3aN2tpaQe3ZwsJCaNXV1RQUFFBYWNhqgkGi+Mn9x9mzZzl79ixvvPEGRkZG+Pn5oVQqkcvlVFdXk5+fT0FBQaM/Gx4eHoSFhREaGoqPjw/e3t74+fmJQRrtTHFxMb/99hu//vorBw4caFTlOLVazYULF7hw4QJr165FIpEwbtw4Fi1axJgxYzrEhvmmTZt4+umnUalUTe47evRoPvzwQ3r27NkKlomI3H9069aNAwcOEBERwdtvv31HZ/xt0UPOBxmUHy0l44cMfP29W8/QDoxOpSXz31cp+KbxAU1SqZR169Yxe/bsVrSs5UiRsBk3+pPGVRoO1J83bx59+/bF39+/DaxrOWq1msWLF7NmzZpG91m9ejWPPfZYK1olcr9QH1gv0nRkMhlbt26lX79+zVpPNpXY2Fj+8Y9/sHTpUv7xj3+wYMECvL3vz988EZGOyJkzZ1i3bh3bt29vdGUhKysrxowZw5AhQ3B0dKSmpoaLFy8SGxtLUlJSqyfetDZ9+/Zl4cKFTJkyRQwYFDE43bt357333uOtt97i119/5aeffuLEiRPk5+e3uS1FRUXs3r2b3bt3C48ZGRkREBDA8OHDeeCBBwRBFI1GQ0FBgdDy8/MpKirCxsaGwMBAHn74YZydndv8NYgYlpSUFHbv3s22bduIjo5ucv/Fixczd9KjYGABFLghyLFlyxYCAwP5z3/+Y/Dx7zTn6tWrmTt3bpvM11Ls7OzYt28fo0ePbnFCgUql4vDhwxw+fFh4zNzcXNj7qK8aWFxcTH5+PteuXSMjI4OsrKwWvoqOQVZWFkuWLGHJkiVYWFjg4+NDQUEBeXl5bWaDjY0NixcvZtasWfj5+bXZvCIid8Pe3p5hw4a12vgmJiYEBgYSGBjI+PHjWbJkCeXl5ezfv5+dO3eye/dugwbta7VaoSL26NGjWbp0KSNHjuyQMRadGb1ez759+3jnnXeatb5oDomJiTz11FMsXryYhx9+mMcff5wxY8a0WOhPr9dz8uRJvv32W7Zt29ZpAtQNiU6nE8ROt27d2t7mtCmWlpa8/PLLvPDCC1haWra3OQBMmjSJESNG8Oqrr/LNN980SbiwvUlNTSU1NZUtW7YANwq4+Pv74+joiL29Pe7u7gQFBREUFES3bt2wtbVt1jy1tbVkZWXdVOzIzMwMhULRrLgPdVEdVbEV1GXVoClWoynRoC3TsFi9CEk1VFZWCqLCRkZGODk54erqioeHB3369GHIkCG4ubk1OI9eryctLU2Iazpz5gwxMTEtEuBzQMoozPFCRjk6dlNJWiP2Ljsq06dP5/PPP8fOzq69TRExACNHjuTChQssWLCgScKnAIWFhcyePZvPP/+cjz76qFXXq61Jfn4++/btY9++fRw4cMCgwsmenp5s376dfv36cVXb+GI0IobFwsKCnTt3smDBAtatW9fsccrLy2/y6VpYWDBkyBB69uyJs7OzUNDPyckJpVIpCFHp9XocHBxwcnLCxsbmnrju0aMnBTWJUYe4emAX6enplJWVYW5ujpWVFTY2Nnh7e+Pv70/Xrl0xNTW9ZYy6ujqys7NJS0sTBIJTU1NRqVRotVoqKytJTU1tldyPioqKBoUO60U+pFIpxsbGghhp/XpCpVKRlJTE6dOnGxUXey/wZ4G1u2GkMMLE3RSptTFPOz5FN89AvLy8cHBwQKVSUVVVRWlpKYWFhRQVFd1WfFWr1VJYWEheXh7Z2dkGW28fpprD/Gld99jEFo0nlUoJCQlh6NChODg4CDHvpqamQlMoFJiammJubo6Liwtubm7Y2to2+Vyg1WqFtW7951IqlXaImGqRewO9Xk9cXBynTp0iOTmZlJQUkpOTG38untyy75NIx0KdW0fJLwWU/HKzyFYAXVEqlYIInLGxMV27dmX06NFMnTqVwMDA9jC3w5GSkkJkZCRffvklycnJBh/f1dWVl19+mWefffa26yyRW5FKpYwdO5axY8ei1+vJzc0lJSWFuLg4Dhw4QFRUFFVVDRfbbCtkMhmzZs1i6dKl9/z3ytHRkaVLl7J06VLi4+P55ptv+Prrr1ssevhnUlNTmTx5MoMHD+ajjz5i4MCBBhu7M1FcXNxgXGGvXr1aPI+Li0ubCqAUoSGatime0lkQBVBE7hmOHj1K7969ef7553njjTewtrZub5OaTHl5uVBNLzs7m8TEROLi4jh9+jQlJSXtbZ5IC+natSsffvghEyZMaFOn76JFi7hw4QLffvtto47X6/Vs2bJF2By2sbEhKCgILy8vnJyccHFxoXfv3gwbNgy5XN7gePWBC3FxcWRnZ5OTk0NRURHm5uZ07dqVwMBAHB0dkUgkSCQSjIyMkEgk1NXVkZOTQ2ZmJgUFBVRUVFBRUUFGRgbx8fENBgfW1NR0aLVRkXsTnU7HlStXWjRGZmbmTd9BuJEwEBQURN++fXF0dMTY2BiFQkHXrl0ZMmQIHh4eLTVd5E+oVCpiYmI4fPgw586d4+LFi1y7dq3F4+r1emHj0NfXlwULFjB37txmB9m0lPfee49ly5Y1uV+vXr344IMPGD16dCtYJSJyfyORSPj73//OpEmT+OOPP/jggw+IjIxsdP/Kk+WMCnyAMWPG8PTTTzNhwgRkMlkrWtwx0Gt0lEYWkb0ynZorjXf6mJmZsX37dqEyaEfHFin76MJQ0snj7sJqlZWVTJkyhZiYmA4volZWVsbkyZObVK3zzTffZMGCBa1olYiISGMJCgriv//9L88880ybzVlSUsIHH3zAhx9+yEMPPcSiRYv429/+JgbDiIi0IRqNhuzsbAoLCzl58iQ///wzR48ebfI4ZWVl/Pzzz00ORu+omJub06NHDwYMGMCMGTMIDQ29J4J/RTo2crmc6dOnM336dADy8vI4c+YMUVFRHDx4sFWqLzUGnU5HYmIiiYmJfPXVV43uN3/+fIYNG8ZDDz1EWFgYISEhjdqHEGl/UlNT+eGHH9iyZUuLgt9GjBjBRx99BFfSDWfcXzAyMuLf//43AQEBPPnkk61adCIgIIBt27Z1OhHpLl26cOrUKRYtWsSPP/5o0LGrqqq4ePEiFy9eNOi4HZ3Kyso2PSebmJiwePFi/vWvf4minyIi3Ej2euyxx3jsscfQarXExsaSkJDAlStXSEpKIjY2lqtXr7Z4nv3797N//34GDRrEW2+9xahRo0R/xV/Q6/VkZWVxKuUUZUXFaKt1oNcjc5Jj4i7HxEWORPp/11EajYbt27fz4Ycftnn18XpUKhU//fQTP/30E7a2tkyePJmpU6cydOhQIUGiISoqKjh27BhHjx5l586dxMfHt7LVIh0NhULBc889x9KlSzuk0IK1tTVff/01L774It999x2bN2/ulKJ0Go2GhIQEEhISbvu8k5OTIIhSL4ri5uYm/E+uXr1KamrqTbdXr14lMzPzjomqCoUCCwsLrKyssLS0xN7eXqgAq1Qq4FubQQAAIABJREFUkclk6HQ64jMTSE69iCq+itqM2ycW72H3bR+/3evx8PDA29sbNzc3oTk7O1NYWEhKSgqXLl3i3Llzdw3Qbwr2SFmGHQuwwZT/+23ToWf3Fy+ybM0XnWqNb2dnx1dffcXkyZPb2xQRA2Nra8vWrVuZMGECCxYuoLK8aVVzz5w5Q3h4OBMnTmTVqlX407H8uzU1NWi1WszMzJBIJGi1WtLS0ti/f7/gp28NIasxY8awadOmDvkbdj8ik8lYu3YtQUFBvPbaawZJpqusrBTEc5pih6OjI05OTri7u9O3b1+GDBnCgAEDsLCwaLFNrUkVOqKoYheV7KaSa2hgccMxKBKJBE9PT/z9/VEqlWRmZpKZmUleXl6HFpH7q9DH1atXOX36dDta1HnQqXTUJN+IRYvid6L4vZ0taj20Wi2nT59u8mdDoVAQFBREcHAwfn5+GBkZUVdXx5UrV4iPjyctLQ2NRoNMJkMqlVJdXX3HpGyJRHKTIIpcLsfZ2RkPDw88PDzo0qULHh4e2NraUlVVRWVlJXq9Hmtra6ytrTEyMqKqqgqVSoWdnR19+vTB3t7eEG+PSCegqqqKQ4cOERkZya5du8jJyWlvk4Ab69Nhw4bRu3dvrK2tKSkpISYmhiNHjtxWNEmkbamoqLjp78LCQo4fP85bb72FmZkZ9vb2KBQKoRkbGwuCpcbGxnh7e9O9e3eCgoJwdnbGzs4OR0dHXF1dkUqlBrVVr9dTXV2NTqdDp9Oh1WqF+/VNo9GQm5tLWlraTS0zM5PKykpUKhU1NTXo9XpBeMra2loQwDM1NcXY2Bi1Wk1eXh7Xrl0zSA7N7QgMDGTp0qU8/vjjYjxAC5BIJIIfatiwYTz33HPU1dURExMj7FecPXu2Xdaq5ubmPPPMM7z44ou4u7u3+fztTffu3fn0009ZsmQJb7/9NuvXr0ejMZyY6PHjxxk0aBBTpkxh5cqV910hwcbssRhKACUxMfGOzxtaACWSSnQGHbHzIwqgiNxTaLVaPv74Y9auXcvChQuZP38+Xbp0adcgY61WS05ODunp6eTm5iKRSLCysqKmpobLly9z4sQJEhISyM7OvmXxfL/g7u6On58fvr6++Pr64ujoiEKhEColmJmZUVtby9WrV7ly5Qrnz5/n7NmzlJaWtrfpd0SpVOLv74+NjQ0+Pj6MHz/eIJVgmoNEImH16tUkJiZy7NixJvcvLS3l2LFjt/RVKpWMGjWKoKAgLCwsMDExQS6XY2JiQllZGZmZmaSkpBATE9Oh/1ciIp0BnU4nKMPfji5dujBmzBieffZZgoOD29i6zktubq5Q8SI+Pp7Kykrq6uooKCho1aB3uJEQsGTJEt544w0efvhhxowZw+jRoxtVpael6PV63nzzTd5+++0m9QsNDeVf//oX48ePF4M1RURaGYlEwrBhwxg2bBi//fYbzz77bKODDPV6PXv37mXv3r04OjoyZ84cHnnkEWGdfS8lYGo0Ggq+yybv88w7BgreCTs7O3bt2sWAAQNaybrWwQ8TDuJJOBkUNyCCcv78eRYsWMD69es75P9dr9eza9cuXnzxRVJSUhrdb/q86bz55putaJmIiEhTefrpp9m/fz+//PJLm86r1+uJjIwkMjJSFOkT6bTo9XqKi4u5cuUKV69epaysTKgWVlhYSH5+PkVFRUilUpRKJRYWFjg6OuLj44O3tzfe3t54eXkZVPCsuLhYSBRJSEggIyMDvV4vVIaLj4/n0qVLrX7d3BFRKBRCcF19gF39fV9fXzw9PcXrZZF2x9nZmfHjxzN+/HgA0tLS2LJlC5s2bbpjAlhHQq/Xc+TIEY4cOQLcEHgJDQ3Fx8cHY2Nj0tLSyMnJwdjYGC8vL7y9vXF0dMTMzAxzc3M8PT3p27cvDg4O7fxK7g8qKirYvn0733//fbOEsP7KiBEjiIiIaDMx1xkzZuDt7c3EiRMpKChouEMTMDExYf78+axYsQKlUmnQsdsKS0tLfvjhB8aNG8f8+fMbrIYq0r78eW3Yv39/Jk2ahJOTU3ubJSLSIZFKpfTr149+/frd9HhpaSlxcXGkpKTwxx9/EBERQWVl0xJm64mJiWHMmDFYWloSHByMk5MTxsbGGBsbCxWT/9wsLS3p1q0b/fr1w9zc3BAvs0Oh1WqJiYlhx44d7Nixg9TU1DsfLLlRYdvI1IgQXTDl1zvW709JSQlr1qxhzZo12NnZMX78eLp16yYkY3l4eODq6kplZSU5OTlERUWxY8cOjh49atDgZpHOg1KpZO7cubz22ms4Ozu3tzkNEhAQwKpVq3jvvfc4cuQIn332Gf/73/86dEJvU8jPzyc/P5+oqCiDjalSqVCpVBQWFhpszMZQn3Dd2pjIZLystuQ17FBya/KWERLGDx/B2GeeZM2aNbzxxhsdOk5QIpEI12rtVaxIpPWRSCTMnDkTj0GejJ81gcqYsiaPsWPHDiIjI5k/ZRr/QoNzG6ZbVFVVERMTw5UrV0hPTyctLY309HTS09MpKioCwNTUFHt7e4qKiqipaVqsRFOQy+UsX76cV155xeAJnCItQyKR8NJLLzF69Ghmz57NuXPn2twGtVotFHw9d+4cO3fuBG6ID3ft2pUePXoIrWfPntjb26PT3UjlsrGxua2YYE1NDampqVy5coXk5GRSUlJQqVQ4ODjg7u6Ou7s7Hh4euLu74+LickdBQr1eT35+PgUFBRQVFVEUe5FCSihEywlUHKaaWpq+vtHr9cL3UURE5AYqlYqzZ88aRLRUr9ej0WiE6+eqqipKSkpatL/Vp08fhg0bhqurKw4ODiiVSiHnxc3NjcDAwEaLm4p0PNLT09m1axeRkZFERUV1mBgGCwsLnnvuOSZOnEhISMht11EVFRV8/fXXvPPOO5SVNX29KtL6VFdXNyj+ER8ff9silzKZDC8vL8zNzSktLaWsrAy1Wo1Op0Ov1wuCJXq9HiV67JFihxR3ZPhhgjcyatBTipa0Zf/kUtY1EhMTUalUBn+d5eXlrSZy8ldMTEx49NFHmTdvHuHh4R0yrvlewMTEhPDwcMLDw3nnnXcoKiri0KFD7N+/n6NHjzYpXrs5ODk58eyzz7Jw4UJRRJMbIsbffPMNb775Jl988QVr1qwxqO/s559/ZseOHTz99NMsXboUT09Pg43dkWnM+jAoKKjF87i4uNz1eUMLoPyP+1Nb4G6IK3WRe5KKigpWrlzJypUrsbe3JyQkhLFjx/L444/j6OhosHny8/PJzc0VFERzcnLIyMggPT1duM3KyupUm9gBAQG4uLigVqvRaDQ33dbf1+l0yOVy5HI5RUVF5OXl3TKOhYUFgYGBdO/eHV9fX6ysrDA3N8fCwgILCwvMzc1xdHTE29u7WQH6er2ea9euCQ7OlJQUsrOzycnJIS8vD2NjY3x8fPDx8UEqlXLt2jX27dvX7ACVxmBhYcHo0aOZMWMGDz74YIeqtG5iYkJkZCSTJk3i8OHDBhmzoqKCiIgIIiIiDDKeiIhI87l27Rpr165l7dq1jBs3jscee4wePXpgb2+Pp6dnh9iArKuro6Ki4qZWVVWFqakpTk5OODs7o1QqW82RUVdXx759+zhy5IhQfTYtLa1V5moKf64YBjB48GCeeeYZJk+e3Cq/I3q9nn/+8598+OGHje6jUChYs2YNM2fOFB1NIiLtwIQJEwgPD2fOnDlNXncVFBTw/vvv8/777wM31qthYWE8+uijTJw4sVNXOIiOjuapZ58i42LTq0p7eXmxb98+/P39W8Gy1qc7cvbhwQiuUdGAzu+GDRvw8PBosuhVa5OUlMTzzz/P/v37m9TP9u+OvPX5f8TfIxGRDoZEImHt2rVcuXKl3So8XrhwgTFjxjBmzBg+/PBDevTo0S52iIg0hszMTHbv3s3u3buJjo6mpKSkxWO6uLgISa8+Pj74+voKgtMKhULwC9TU1FBbW0thYSGZmZlkZWWRlZVFZmYmGRkZJCUlGTwBvLPh6OjIyJEjCQsLw8nJCUtLS+zs7OjSpQt2dnbiOkSk0+Ht7c2yZct47bXXOH/+PJs2bWLLli1kZ2e3t2mNora2lj/++IM//vjjlufuJBYNN153//79CQ0NJTQ0lJCQkHsymbg9qKmp4cCBA2zbto1ffvmF6urqFo8pl8t5++23eemll9rclz1o0CDOnz/P0qVL2bhxY4vHs7a25tlnn2Xx4sWdIsG1MUyfPp2hQ4eyZMkSwY8t0r74+PgI4g39+vUjJCQEKyur9jZLRKTTY2Njw4gRIxgxYgRPP/00paWlrFy5kk8//bTZyRPl5eVNEgmTSqX07NkTFxcX4VokICBASBo0ZLxTa1NXV8f+/fvZsWMHO3fubLwwgB501Tp01TrK6VjiJ3+luLiYDRs2tLcZIh2U/v37M2/ePKZNm4aFhUV7m9NkjIyMhHNiSkoKX375JVu2bLltjKDIvcuECRP477PP4zNucYPHGhsbs3DhQqZNm8b777/PZ5991qqiDM0hLCyMTz/9lJCQkPY2RaSNcPdyJ3BnMPlrs8l5Px3t9abFcWs0Gr7YvJFvkDADSxZhQwimSGi6j7iyspKSkhJkMhkKhQJTU1OhQntVVRV5eXmkpKTw66+/snPnzgb9LTU1NY0uotNcRo4cyVdffUXXrl1bdR6RltGjRw9OnDjBqlWrePfdd1slKbap6HQ6Ll++zOXLl+9YQMPY2Bhvb298fX3R6XRUVFSQm5sriPI3BplMRv/+/Rk5ciQeHh5IpVKuXLnC2bNnOXPmDNevXzfkyxK5C66urvTv3x+FQkFBQQHXrl0jNTX1nhHRE+ncxMbGEhsbe8fnFQoFISEh9OnTh8DAQAICAggICMDd3V3cF+4g6PV6yq+XE58XT1ZWFomJiVy6dIkTJ04QHx/f3ubdhJGREfPnz+fNN99s0I+nVCpZunQpTz75JCtXruSbb74RhejvIdRqNcnJjYttvg5cR0cKak5ym+vonTsMa1wbYWxsTFhYGD169KBLly507dqV8PBwbGxs2tu0+w57e3umTp3K1KlTgRu5BTExMcTHx5MXn0Te5h0UoKUGHTXoKTfRk+0oQVerQ1+jQ1enQ19767rO2NgYNzc33N3d6dGjByEhIfTt25fg4OAOkb/W0XBzc+O9997j9ddfZ/Pmzaxfv56YmBiDjF1XV8fq1auFnK9XXnmFwMBAg4zdUWloDeDp6WkQv3xbCqBUo2MfVQYb715BFEARuecpKipi//797N+/n3/+859MmDCBGTNmMGbMmCZV3NJqtSQlJXHy5EmOHDnC4cOH20zlrrXp27cvixcv5uGHH27WYvL69eukpqZSW1uLg4ODkMTemkgkEjw9PfH09GTUqFGN6lNRUcGWLVtYt24dp06dMogdnp6eTJ06lQcffJBBgwZhYmJikHFbA2traw4ePMgHH3zA8uXLUavV7W2SSAfG3t6eXr16YWZmhqmp6U2tXgDp2rVrHDt2rNVEJExMTOjSpQuenp50796dsWPHMmLECORyOeXl5VRUVKBQKDAxMeHMmTPs3r2b2NhYCgsLsbKyQqfTkZ6e3mmC+Q3Fnj172LNnj/C3lZUV4eHhjBgxgj59+hAUFNRqFVD1ej0JCQkcPnyYkydPkpGRQU5ODjk5OY0KhFcoFDg7O+Po6Iharaa8vJzq6mrMzc2xsbHB2toamUyGkZERMpkMd3d3AgICBLErrVZLYWEhGRkZZGZmUlNTg4mJCenp6Z1mU+v48eMcP36cF198kTlz5vDMM88YLEFfpVLx5JNPsnXr1kb38fLyIiIiguDgYIPYICIi0jwsLS3Zvn07zyydz7cfr232OJWVlezdu5e9e/cyf/58HnjgASZPnszEiRM7RXXs+krgX331FT///HOzxhg2bBjbt2/vFK/3bvRFwXpceIyG1zkrVqzAzc2N+fPnt4Fld6euro5Vq1axYsUK6urqmtTX+XkP3N/wER3jIiIdFBsbG6Kioli+fDnr169vt+C6ffv2ceDAAZ588kn+85//NLgBISJSj06no7i4GCMjI2xtbe8azKTX60lLS6O0tBSFQoGfn98dfYI6nY64uDiOHDnCyZMnOXXqVKv4UXJzc8nNzeX48eMGH/textramu7duxMSEkJoaCj9+/fH399fDGYTuSeRSCQEBwcTHBzMqlWrhAqa6enpREVFsXPnTrRabXubaTDS0tJIS0sTrh2NjY0JDQ1lxIgRhIaG4u/vj4+PT4fb06mtraW6uhoTExNkMhkymazdz0m1tbWcOXOG48ePc+zYMQ4ePEhVleGCLgYMGMD333/frgE4zs7O/Pjjj7zwwgu8/PLLHDlypMljmJmZ8dJLL7FkyZJ7UojC3d2drVu38swzz/Dcc8/dVYCoLZEhQT5AiUV/K8x6W2DiborMQYY6r47qS5UUbc2jOq71imO0Jd26dWPGjBlMmzYNPz+/9jZHROS+wMbGhlWrVrFo0SLefPNNNmzYIFQrby20Wi1xcXHExcXd9nlPT08GDRpE//79cXFxESoXy2Qy5HI5Hh4eWFpaNmtunU5Hfn4+JSUlQhGo7OxsMjIyuHbtGqWlpZSXl1NeXo5EIsHc3FzYw3V2dsbFxQV7e3sqKys5c+YMO3fuJD8/vyVvR7MxRcIklDyOJa7IsMYIK6SU7f2Kk6UFHD16lMjISDIzM9vFvuZgBAzHjGBM8ccE5aqlKLp6o9PpqKmp4dq1axw+fJjo6GiDCNSJNEyXLl0IDAxEJpMhlUqxs7OjW7dudO/eneDgYFxdXdvbRIPh5+fHxx9/zAcffMAff/zBTz/9RFRUFFeuXBGTWu9RLCws+Oyzz5gzZw6ShKZVBbazs2PVqlU8//zzvPvuu3z33XftnowfHh7O8uXLGTFiRLtfY4u0PRKpBOf57thPcyL3v9fIX5N128Stu1GHnu8p43vKcELKCMzxQoYtUqwwQrFzB/J4T8rKysjLy7ul5efnG9SX0toEBwfz6quvMmXKFPE700mQyWS8/vrrzJ49m2XLlhlE5Le10Wg0JCcnNzox+Hao1Woh1lKk7TExMWHKlCksWrSI0NDQW84XOTk5RERE8Msvv/DHH390qmLCIvcXKpWKY8eOcezYsZseNzMzE8RQAgICBHEUf39/UXD/DqjVasrKyqioqMDExARHR0dkMlmTxtBcV6NKqKI6oQpVYhWqhEpUiVWElHf8GPZevXqxbt06+vXr16R+dnZ2fPDBB7z11lts376dtWvX3vJ5FBHpTIwePZq5c+cybty4e3LP9F7A0dGRRx55hEceeQTik2HzmZueT/WRMv34zQVW9Xo9+jo9+tobgihrlJ/SzzEEIyOjtjT9nsDc3Jynn36ap59+msTERDZv3kx0dDRnzpyhsrJl+9oajYbvv/+eDRs28Oijj7Js2TL69OljIMs7Fg0JoHTv3t0g87SlAMpBqlAh+tv/iiiAItLRMai0m0ajISIigoiICExMTBg2bBghISF069YNnU5HQUEBBQUF5OfnU1BQQGlpKRqNBrVaTUZGRqdyQt+N+kDX4cOHM3nyZAYOHNgiR7W1tTV9+/Y1oIWtg1KpZN68ecybN08IfI2IiCAuLq5JFXuCgoIYNWoU06ZNa/F719ZIpVJeffVVxo4dy8yZMzuc6un9gkQiwcPDAz8/P/z8/IRgouLiYuDG/0mn01FYWEh+fj5lZWU39ZVIJBgZGWFsbIyDgwNOTk44OzsLtzKZ7KZzWf2tVqtFLpdjYmKCiYmJcN/KygoXFxdcXFzw8fEhPDycXr16NfpiJC8vj5iYGI4fP87JkydJSEgQXsudMDIywsLCQgiMqr/v7u7OoEGDGDp0KL169cLY+PZLFSsrq5suiOsrz/yV+qSk2NhYioqKqKiooKysjOzsbDIzM8nOzqaurg6NRkNBQUG7b7q3BmVlZezcuZOdO3cKjzk4OBAeHs6ECRN48MEHsbe/+QJVp9MJlZ+zsrIoLy+nsrKSiooK4bampkYIwq+uriYzM5OrV682+L+/GyqVSkhOuN8pKSnh448/5uOPP+aBBx5gxowZPPTQQ82uGpqbm8vEiRObJAA2evRoNm/ejJ2dXbPmFBERMSxGRka8+v5rHHA/wrVXktGrW+bg0Gq1HDhwgAMHDrBgwQKGDh3K2LFjCQgIQC6XU11dja2tLUFBQTg5OVFcXExcXBwZGRlcv36d69evU1ZWJtyHG8HXAwcOZNCgQXh5ebV4nazX67l8+bKwzvj999+5evVqs8d76qmnWL16dYdLcGsuj2LJYqr5nNIGj124cCF6vZ4FCxa0gWW3otfrOXjwIC+99FKTk6QkxhI8P+yKwxP3TrBwR2H16tW3rbzq4ODAwoUL28Eikc6OnZ0dq1evZtWqVfz2229s3bqV2NhYcnNzsba2xtPTky5dutCjRw+Cg4Px1Egon/oCRWhJopZ46kiglsvUUdcCR75Op+Pbb79l8+bNPPDAAwQGBqJQKCgpKaG8vJyqqiqqq6uF2+rqaiQSCRYWFiiVShwdHQkKCqJXr16YByjRWmiRmoniS/caWVlZHDlyhOPHjxMbG8uFCxcE36+NjQ19+vTB29sbrVaLWq0WfMQlJSXExsbe5KsxNjbGz88PGxsbjIyMhAZw6dKlFl0nizQdMzMzBg4cyJAhQxgyZAi+vr5YWVkhlUopKiqisLAQiUSCl5cXLi4uncq3KwJfrv4Si5IS4W9x3dI8jIyMCAwMFAQnFi9eTF5eHj/++CN79uzhxIkT95yfVKPR3BIML5VK6dOnDxMmTODhhx+md+/erX5O0Ol0JCUlcebMGfLy8qiurqayspIrV65w4cIFMjIybuljbGyMTCbDzc2NwYMHM3HiRMaMGYOZmVmr2VlWVkZ0dDRbt25lx44dLQ60uR1yuZy3336bl156qcMIXfbt25eoqCgiIyNZunQply9fbrCPtbU1TzzxBK+99lqzfbediREjRnD+/HkOHjzIrl27+P3339tFDKU/pszGigFdbXh2z63vu6mPGcrB1jjNc+eNq0uI232OhIQErl27RmZmJjk5OR1e9Mnb25tHHnmEwYMHM3DgQDw8PNrbJJFGsmXLViaMbm8rRAyJh4cH3333HS+99BLLly8nIiKi3WzJyMggIyPjrsUOQkJCmDBhAhMmTCAkJOS265vS0lJOnTrFyZMnOX36NCkpKaSnp1NTc5sKo50IKTADK1bgQBduTbCxdffAe8wDTJs2jdWrV3Px4kUiIyOJjIzkxIkTHVLIwQQJc7FiCXb48ae9jYcmQPeuNx372muvUVdXx6lTp4iKiuLQoUNER0d3+N+89qJeOEhhYoJNSSW2SLFDii1SbDHCDCNq0KNCh/GM8bh074aLiwvu7u4EBwd3eqH95iCVShk+fDjDhw8HoLy8XPCrpaSkkJycTFZWFhKJBDMzM2pra4mPj2+yIL5I++Lp6cncuXOZO3dui8ZxdXXliy++4N133+WXX35hw4YNzRKbbA7Gxsb07t2bwYMHM23aNAYPHtwm84p0bIytZXi85YvjXFfSX75C+e8N77Hfjny0bKX85geX/dMAFrYvbm5uPP7448yaNYsePXq0tzkizcTDw0MQ+f3iiy/Ys2dPu4kSity7uLu7M3/+fJ566imcnJzueJyrqysLFy5k4cKFlJeXExUVxb59+zh16hQXL14U14h3wdTUlAkTJuDq6kppaSnnzp3rMILU9xPV1dXExsYSGxt7y3MeHh4MGTKEyZMnM27cOBQKRTtYaFg0Gg25ubkUFRVRV1d3SystLSU7O5usrCyKi4spKyu7pf1VkFUikeDo6Iirqyuurq5CAnF9ToJWq0Wn01FWW87FvIuoC+vQlnY+sSRra2teeeUVXn755SYLvvwZc3NzZs+ezezZs0lPT2fHjh1ERERw4sQJ4Zzp5OSEu7u70JydnYVix/XxLMXFxeTk5Ah5MlVVVahUKqF4dn0xZBsbGxwdHXFwcMDMzAypVEpZWRkXLlxol+LDFhYWWFlZ4ebmhpeXF7a2tqSnp5OcnExhYSF1dXXU1tZ2SP+dyI3P7zPPPMPChQvx8fFpb3M6BH+OzXUoKKOzR7VIJBIkcgnIjZACdpZ2oviJAejWrRtvv/02cCOvIzExkVOnTnHq1Cn++OMPEhISmjWuXq9n+/btbN++nXHjxrFs2TLCwsIMaXq701A+dFBQkEHmaUgApbS0lJqaGkxNTVs817a/+ptEAJCIP/4iHRmJRFIDyNvbjs6Gi4sLQ4YMwcnJCaVSiVKpxNLSEqVSibOzM/3798fW1ra9zeww1AtNZGZm3tSKi4sxNTUVEmD8/f0JDw+/ZwIYa2pq+Ne//sXHH3/c3qbc09jb29OvXz/69etHSEgIAQEB+Pj4NGlxo9PpBOGTzkJRUREpKSmoVKrbCp3I5fIO93rqg4EOHz5MYmIiRUVFFBUVUVZWJojOaDQaSkpKhETvewVXV1d8fX3RarUUFBSQlZXV6QPc7lVCQ0OFoMVevXo16nu0f/9+5syZ0yR1yVdffZUVK1Z0mOQDERGRmzl37hzTp0/nypUr7W3KHfH09GTGjBk88cQTTarinJqayi+//MKRI0c4ceIEJX9KbmwuCoWCTz75hHnz5nW49UdLqa2tJSwsjDNnzjR8MDB16lTWrFnTZsrqOp2Offv28fbbbxMTE9Pk/lZWVvzyyy+MHDmyFawT6d69+20d5EFBQaJYpohB0ev1TTr/ajQaEhMT+f777/nuu+86xPVXvQinXC7H2NiYiooKSktLqa2txcnJiT59+tC3b1/8/f1xcnLCyckJb2/vdqn8o9PpOHr0KBs3biQ6Opr09HThmlYqlQrN3NwcX19funXrJiTBBwYG4u7u3il+L0tLS4mPj+fSpUvEx8ej0WiwtbUlLCyMYcOG3fYgGyAdAAAgAElEQVS9T09P58svvyQiIoKUlKZVLhXpuLi4uBAWFiYInvTu3btFgUUiHRNx3dK21NXVce7cOc6fP092djY5OTk3tduJ6N0LODk5MXToUMLCwvD19cXNzQ0rKys0Gg1arRaNRiM0lUpFVVXVTa2mpga5XI65uTlarZarV6+SkpJCUVGR0C8xMZHS0uYl2PwZhULBmDFjmDRpEuPHj2/Unl9lZSXFxcVIJBKkUqmwNtBoNMTExHDw4EFOnDhBWlraTUJfrcGQIUNYu3Yt3bp1a9V5WoJarebbb7/lo48+IjU19abn7O3tCQ8P5/HHH+fBBx9ELr+/t7ILCgo4e/Ys58+fJy4ujpiYGK5du2bwedzc3Jg5cyZPPPFEiwOWtFotubm5wn5weXk5hYWFnDx5kqNHjzYquNfMzAx/f3/8/f3p2rUrSqWSS5cucfDgQfLy8ppll7e3N6NHj2bmzJkMGTKkU6zLRW5FXLfc+5w7d44333yTyMjI9jalQdzc3Bg5ciR+fn4YGRmRnJzMiRMnGiXy1ZmQyWTMnTuXV155pdkB9nl5eXz99dd88cUXHULItH///syePZtp06a1qFhFcXExu3btYu/evcTHx5OWlkZFRUWD/SwsLPD09KS2tlZY71ZWVqLT6YAbSQ3W1ta3bZaWltTV1VFeXi60srIyMjIyWi2BxtjYWIhJMTc3x97eHjc3N9zc3PD09CQgIICuXbtiZ2cnJPqI++BtQ11dHQkJCaSlpXH9+nVKS0u5dOkS0dHRJCcnC8dJpVJsbW2RSCSCaI9CoUChUFBTU0NeXp6QrCXS+rTWuqWwsJDTp09z9uxZ4uLiKCkpQalUCgmR3bt3JywsDCcnJ0pLS0lKSqKwsJCKigrKy8vJzc0lKyuL3NxcpFIpXl5edO3aFVdXV2QyGVKpFEdHR3r27NmqoqEinR+9Xs9PP/3ECy+8cF+LQ1hZWTFx4kRmzZrF8OHDxd/Ge5D6okNHjhwRWk5OTnubJXIHPD09cXBwEBK79Xo9er0erVZ7ixBB/Rq9rTA1NeXvf/87c+bM4YEHHmjx+UKtVpOUlERmZqaw9lOr1ZSXl3P9+nVUKpUQb15foLGiooKVK1fe9nrN1NQUhUIh+N7lcjn29vaoVCqDxJ21FcHBwcyaNYu5c+diY3NzDenY2Fg+/fRTtmzZIorHdDCUSiUPPvggo0aNYvjw4Xh4eDS4Z6DX66mtrUWtVqPVaoVWf81tZ2dn0P3u2tpacnJyyMrKumPLy8sT5hdpGKlUKhSkfvjhh1v1+kOv13P9+nXMzc1bVPhPq9ViZGTUqH2H4uJikpOTSU1NJTU1lfz8fCoqKoQCuvVxR5WVleTl5ZGfn49arRb8MmZmZsJ9U1NToeDun5uzszM9e/YkODgYb2/vOxZN/iuVlZUUFhZSWFhIQUGBUCw6NTWVy5cvk5KSQkFBgfh5biOsra1ZvHgxzz//vFhw9y+Ie0UihuDKlSv89NNP/Pe//23xunbo0KEsW7aMMWPGdPo96KKiogYFytevX8+cOXNaPNfhw4cZMWLEXY9JS0vDy8urRfNUV1fj6OjYnOu8BL1e371Fk3dwRAEUkQ7NnQRQTExMeOaZZ1i9erW4MOWGimh4eLjQ/Pz8Ov2PkUjbERUVxbx588TEj0Ygk8kYOnQogwcPxsvLC3d3d2xtbVEqlVRVVZGamkp6ejpSqRRvb2/69euHh4eH+H28B6murubAgQNERERw4MCBJm0M2dvb4+HhQU1NjeCEEcVGOi+enp4MGjSInj178uWXX7ap6rGHh4cghjJo0CAsLS0ByM3NJTk5mfj4eNatW8e5c+caPaaxsTE//PAD06dPby2zRUREDERlZSXPPfcc69evb29TGiQ0NJTFixczZcqU227CpKen8/PPP/Pzzz9z9uxZg87dr18/Nm7cSEBAgEHH7UhcvXqVfv36NSmJ7sEHH+TRRx9l6tSpBk/OV6vVXL58mT179rBmzZpbEsUai4+PD7t27WqSgI5I0xA3WUQ6A1VVVWzevJnPP/+cixcvtrc5TcbT05Nu3brh6uqKg4MD9vb2ODg44ODggI2NDXK5HLlcjlqtprKyksrKSioqKqisrKSqqgpzc3O8vLzw8fHB3d39tsFkRUVFREdHc/78eS5dusSJEyfIyspqts3m5uYEBAQIYihyuRyZTIaJiYnQrK2tcXBwwNLSkpycHDIyMigsLBT8tH/eczAyMsLMzEwQGLazs8Pe3l5otra2jQqSu3TpEtu2bROqgt3t2ksmkxEaGkrfvn3p0qULtbW1REVF8fvvv4u+5DbGy8uL/v37Y29vj1qtJjs7m6SkJNLS0po8lqOjo/DZrG9BQUF4enqKvrf7AHHd0rGoq6sjLy+P7OxsEhISiImJERJpNZrOV5mtsyORSIQ1h7Ozs5DQqdPp0Gq15OTkEBcX16xzryFRKBQ89thjPPnkk4SHh3eac7der6ekpITq6mohAdPMzKzT2N9eZGZmcuzYMaKjo4mOjubChQvNqsqnUCj4+9//zuzZsw2SXNEY9Ho9BQUFFBYWCpUr689ter0ea2tr/Pz8cHV1ve3nQK1Ws23bNjZt2sTx48cbFHQcOnQoc+bMYdSoUXTp0qVVXpNI2yKuW+4fLly4wCeffMKmTZvEpPx2QqFQMG/ePJYsWYK7u7tBxqyurubHH3/khx9+4Pjx4wYZs7H07NmTxx57jMmTJ7eaUJxer6e0tPSmxBWVSiUU5JHL5fj5+eHh4XFLBc/6BC2pVNrsRKzCwkJBNC0uLk4QXbx+/fod1woKhQJfX1+srKywsLDAzs6O3r17079/f3r27ImlpWWLEoBE2o/r169TXFyMpaUldnZ3rxpbn/BVXym8sLCQs2fPcurUKU6ePElGRkYbWn7vIJPJbvsbJq5bRO4XKioqeP/99/noo49QqVTtbU6rYmNjw5AhQxg4cCA9e/YkKCgIb29vUfTkPkOv15OamiqIoRw+fJjMzMz2Nuu+xNTUlH79+jFo0CChNaU4q16vJyUlhRMnThAXF0d2dja5ublCq6ysbLGN3t7ejB07lnHjxjFixAgsLCxaPGZLacjnUp8s+Gf/rVqtFgSQ60XV6q+Fbne/rKyM2NjYNvGnd+vWjYEDBzJq1ChGjhyJk5NTg32Ki4vZvXs3v//+O4cOHbrtd1gqleLu7i6IaBgbGyOVSjE2Nr6pVVVV8f/Yu/Poqqt77+OfnXlOIDMJJCSBAAnIIENABURRLCjgbWnVVltb7cNQ22qpj/a29va2Tyct1VZrpZPW2usIAtKCQ0FQQUEEEcKQAAkZSEJCAiHj2c8fB87VSk6mk3OS8H6t9VsH8ttn729cy8P3/Pbe3/3RRx916mBFdMzHC/ic/+8dGBgoPz8/1dTUqLKyUo2NjW2+3xij5ORkpaSkyN/f33WQblRUlKKiohQcHKz6+nrV19fLWus6LDskJEQOh0MOh8N1GGpxcbFOnDjhxd++bwkICFB0dLQaGhra3HCcmJioESNGuA4XGjlypCZOnPipQkXoPRwOh6qrq1VQUKAtW7Zo8+bN2rVrl4qLi5nL9pD4+Hh9+9vf1uLFi137V/BJzBXBk2pqavSTn/xEv/71r7tdDG/8+PG67777tGDBArfPY3uzzZs3a/r06W7bbN++XRMnTuz2WPn5+e3uY3jrrbeUl5fXrXGeffZZLVq0qCtvpQAK4EttFUAJDg5WQ0ODdu3apaVLl2rr1q0+iM43MjIytGDBAl1++eVKS0vT4MGDqZSHbmttbdX69eu1YcMG1wPRvrRIZ/DgwRo1apSysrKUnp6ugQMHqr6+/hMbh06fPq3AwEDXRqOEhATXn88/DAoMDHSd4vzxy+FwKC0tTdOmTVNkZKSvf130QuXl5a7NZidPnlRjY6MaGxsVEBCghIQEJSYmKjk5WaNHj/7Uolhrrerq6rR//37XguC9e/fqyJEjbh9wwjf8/f01Y8YMLVy4UPPmzdPgwYNd99p6UDFs2DDdfPPNevzxx3t0suD8xE1Xq/sHBQXp2Wef1Q033ODhyAD0pK1bt2rFihVatWpVr384n5KSoltvvVXDhw+Xn5+fCgoK9Morr2j79u0eHysiIkLLly/Xvffe69ETEXqrLVu26Oqrr+50UbWYmBh99atf1Re+8AXl5uZ2eZHwoUOH9PTTT2vdunX64IMPuv2A9bLLLtOLL77YboVmdA+TLOhLrLXasmWLfvOb3+iFF15wnQR1MQkICNCQIUM0dOhQRUREyOFw6MiRI32yMMzHGWM0YMCATxREiYqKUnR0tPz8/FRdXa0dO3b0u9Op+7OQkBDddtttuuOOOzR27NgLbgw+ePCg1q1bp82bN2v37t0qKChwbXQKDg7WhAkTNGXKFOXm5mrEiBHKzs7WwIEDvf2roBchb+kbWlpaVFRUpIMHD2rr1q167bXXtG3btgt+V/Xz83N99tfU1HDyaD8VFRWla665Rtddd50WLlzIAriLWF1dnQ4fPqyCggIVFBSosLDQ9Xr27FnFxsYqISFBCQkJSk9PV3p6ujIzMzVx4sQ+fXL7+Y1FO3bsUGFhocrKylRWVqaIiAgNHz5c119/PYVf+yHylotPaWmpHnzwQT366KP9fuNsbxEZGaklS5boW9/6lhISEnpsnMLCQj3zzDN6+umnL/j/tSfExsbqS1/6km6//Xbl5PTrNapuORwO11qdmpoaVVdXy8/PT0OHDlVqamqfXYgN7ykvL9f27du1f/9+VVZWqrKyUkVFRdq3b1+3ikX3tI9vTj1/ANf5U7LPX+dPay8oKNDOnTu7fMBSWFiYxo4dqwkTJujSSy/VjBkzNGfOHPIWQFJxcbHuv/9+PfXUU10q3tkbBQYGavr06brmmms0e/Zs5ebm8u8pPsVaqyNHjmjTpk3aunWrDh8+rPLycpWXl6uqqsrX4flEcHCwhg4dqkGDBuns2bOqqanRsWPHOrUe0xijjIwMjR49WkOGDHEVQIiLi1N2drays7M1dOjQHl3LdPr06U8URDlx4oSam5vV0tKis2fPqri4WEVFRaqsrFRgYKBCQ0MVHh6ujIwMTZ06VdOmTfNYkUlP8uYzl6KiIr3zzjsqKipSdXW1qqqqdPz4cRUWFuro0aNqaGhQWFiYq9DF+cNQwsPDLzg3GhUVpeTkZCUlJbkOWI2Oju5WjNZaVVVVqbq6WtZaWWsVHh6upKQkBQQEdLifyspKvf3229q8ebPefvtt10Ek/v7+rkKYDQ0Namho0NmzZzlwBH1OQkKCxowZo6ysLA0bNsz1mpqaqoiICNf/sw6H4xMHF7W0tCg5OZl1Cv1Ia2urysrKVFxc7HoGderUKVfR1V27dmn9+vWqra31dai9UlRUlGbPnq3rr79eN954Y5+ev/MG5orQEwoLC3Xffffp73//e7f7GjFihO69917ddNNNfW6fxWOPPabFixe7bVNXV+eRIo61tbXt5u0vvPCCFi5c2K1x5s+fr9WrV3flrRRAAXypvQIokvPL+0svvaQf//jH2rlzp7dD7FEJCQnKysrSuHHjNG7cOE2ZMkWjRo3iVDH0uIaGBuXn52vv3r3au3evjh49qvLych07dkwHDhzocr/R0dFKS0tTQkKCSktLdfDgwXY3R4aGhio1NVUDBgxwVahNT09XTk6OcnJyNGrUKBbNol9yOBwqLS3Vhx9+qB07dmjnzp0qKSlRU1OTmpqadOjQIRbRecHIkSN1xRVX6JJLLlFubq4uueSSNj9z2ntQ0dzcrFWrVuk3v/mNNm/e3NOhd0pISIhWrVqla665xtehAOiiEydO6C9/+YueeOIJHTx40Nfh+MygQYN011136Y477lBMTIyvw/Gql156Sf/xH//R5YnmoKAgpaenuxY+nf/eaYxRdHS0Bg0apOTkZIWHhyswMNC1uXHfvn0e+y4eHR2tH/zgB1q2bFmnJuPRNUyyoK86fvy4HnvsMa1YsaLLxf8AeJYxRpdcconmz5+vxYsXd7qI2enTp1VcXKyAgAANHjxYwcGfmhLARY68pe9qbm5WeXm5mpqa1NjYKGut4uLiFBsb+4kTZmtqavTyyy9r48aN2rp1q1dOV0TPCAkJ0cKFC3Xrrbdq5syZfW6xDAB0F3nLxau8vFy//OUv9eSTT3Kybg9JSkrS4sWLtXTpUq+esmut1e7du/W3v/1NL7zwgg4fPtzlvowxGjNmjKZPn64rr7xS1157Ld+BgR5WW1urffv2ad++ffroo49cl7vvndHR0crMzFRmZqYyMjJcr3FxcTp79qzq6+t15swZ12tdXZ1OnTql6upqlZaWqqSkxPVduLm5Wa2trYqNjXUddjdixAhNmjRJY8eO7dSGoaamJu3cuVPvv/++ioqKdPz48U9cdXV1CggIUEZGhrKysjR8+HCNHz9eEyZMUHZ29ie+h0vkLcC/27lzp+655x698cYbvg6lS0JDQzVjxgwtWrRIN9xww0W3XgKe1dzcrIqKCpWXl6uxsVGRkZGKjo5Wa2urysvLdeLECVexlMLCQr311ls9VjSwp40fP17XXXedPvOZz2jixImf+vfSWquSkhLl5+crPz9fBw4cUEFBgVpbW5WcnKzBgwd/6mJDcM8gd/E9a62am5tdBWGOHTumPXv26IMPPtD+/ftVX18vyfndNyYmRiNGjFBOTo4SExNdRXhCQ0MVFRXlKjzR2tqqlpYW1+vH/3zq1CkVFRV94iorK5O1VsYY18ExrBvBv7v00ks1d+5czZ07V+PGjaMQHDqssbFR27Zt04EDB1RfX6+zZ89+4mppaVF4eLjCwsJUUVGhvXv3at++faqoqPB16J8SExOjoUOHuq6kpCSFhYUpJCREfn5+am1tVVNTkyvnq6ysdH0GW2sVGxvrWjM8evRo5eXlMe/bCeQt6Enbtm3Tvffeq3/961/d7islJUW33XabvvKVrygjI6P7wXnB0qVL9dvf/rbN+2lpaTpy5IhHxrLWKiIiwpXnXsgjjzyipUuXdnmM6upqJSUlud1fHRIS0lZhbAqgAL7UkQIo51lrtWHDBj388MN69dVXu33itLdkZmYqLy9P48ePV2pqqlJSUpSSkqLk5OQun74N9KRDhw7pxRdf1Jtvvqljx465Fi6fL8rQ2NiogIAApaSkKDU1VUOGDNGll16qadOmKTs7+xMPEFpaWlRWVqbm5mZX9WFrrWvTZlxcnAYMGEDRH+ACmpubtWPHDj388MP6+9//3m9OoPCF4OBgzZgxQ3l5eQoLC1NQUJAGDx6sqVOnKikpqcP9dOZBRX5+vtatW6cNGzZo06ZNXT6lxxPi4+O1atUqTZ061WcxAPAca602b96sP/7xj9qwYYPKysp8HZJXhISE6L777tN3vvMdhYSE+Docn+lIVePeyBijO+64Qz/60Y86vWEaXcckC/q6srIyPfDAA3riiSc4ZQj9RkxMjCIiIhQaGqro6GglJSUpISFBxhjV1tbq5MmTOnLkiI4ePaqWlpYej2fIkCEaNWqUoqOjVVdXp7Nnz2rgwIEaM2aMxo4dq6ysLMXHx2vAgAEUL0OPIm+5+JSUlGjr1q3asmWLtm7dqtLSUllrNWLECGVkZCggIEBFRUUqLi5WfX296uvrVV5ertbWVl+HftGaOnWqbrvtNn3uc5/r9smVANCXkbegtbVVBw8edG1QLy4udm3yaW5u/tSi+dOnT6ukpMTXYXtNenq6brjhBs2fP19jx45VeHi4GhsbdezYMR09elQ1NTWuzQWSNGDAAA0YMEBpaWm94sAoa60+/PBDrV69Wu+++66OHj2qoqIinTx5ss33RERE6Oqrr9YNN9ygz3zmM4qLi/NixADaUl9fr5KSEp08eVJVVVWu070zMzO9WmTJk5qamhQQENDhzX3kLcCnWWu1ceNGPfjgg9qwYYOvw1FGRoaGDh2q9PR01+v5zZYVFRUKDg5WVlaWcnJyNHToUJ7Tw6eqqqq0c+dOffjhh5+43G0Ucyc0NFTDhw9XXFycSktLVVxcrNra2i7HFxUVpbi4OMXFxWnYsGGaNWuWrr32WiUnJ3e5T3gXuQsk59r5pqYmhYaGuvLe5uZmnTp1StZahYeHq7W1VW+++aZeffVV7d69W+Xl5aqoqFBlZSXzSP1UeHi4Zs+erblz52rOnDl8tsPrWlpaVF1drRMnTujo0aMqKChQUVGRrLWKiYlRdHS063PLGCM/Pz/5+fnJ4XCopqZGlZWVKikp0aFDh3Tw4EGdPHlSYWFhGjBggAYOHKihQ4dq9OjRGjFihMLDw13vv9AVHh6utLQ0CiL6GHkLvOGtt97ST37yE61bt84j/c2cOVNf/epXtWDBAoWGhnqkz54wc+ZMt8VfrrvuOo/9N5GkrKwst4Xx77jjDj3++ONd7v8Pf/iDvvrVr7ptk5iYqPLy8gvdogAK4EudKYDycU1NTfroo4+0c+dOPffcc/rnP//plY3hxhglJycrLS1N6enpn3hNS0vTkCFDFBgYqFOnTun06dMaOHAgiwABAN22Z88erVixQhs2bFBxcbGvw+n1Ro0apSuvvFIjR47UqFGjNGnSJI9U3e/qg4ra2lo9/fTTeuyxx7Rnz55ux9EZo0eP1po1a5SWlubVcQF4T21trQoLC7Vt2zY9//zzev311/vdRN6CBQv04IMPaujQob4OpVdYsWKFvv3tb/eZ4mg5OTlauXKlpkyZ4utQLjpMsqC/2Lt3r5YvX65XXnnF16EAnRIWFqZZs2Zpzpw5mjRpkoYNG6aoqKgOvbe1tVXHjx9XYWGh6yooKFBhYaEOHz7c4SJ4QUFBSk1NdV1DhgxxfVceMWKEIiIiuvMrAh5D3oKOqK+v165du7R9+3Zt375d7777rg4dOuTrsPq1sWPHasGCBVq0aJGys7N9HQ4A9ArkLeiKyspKvfPOO9qxY4dOnDih2tpaVVdX68CBAzp06FCfedbblmHDhunmm2/W/PnzNWbMGJ8XMekJZ86cUXFxsYqKilRZWamwsDAlJycrKSlJgwYN+tQp8gDQG5C3AO7t3btXjzzyiF566SWdOHHCK2MGBATokksu0cKFC/X5z3++z5y+DLTF4XCosLBQx44dU2trq/z9/dXQ0KBDhw7pwIEDqqioUEhIiCIjIxUZGakhQ4Zo+PDhGj58uAYNGvSpol61tbU6fvy49uzZo7Vr1+qVV15RVVWVpP/dw5GRkaHx48fr0ksvVW5urhITExUbG6vg4E9tR0EfQ+4CT2htbVVzc7NKS0v13nvv6d1339XevXuVn5+vwsJCDt/pA/z8/JSVlaXc3Fzl5uZq2rRpmj59Op/zAHoV8hZ4065du/TTn/5Uzz77rEfmk2JiYnTHHXdo+fLlio2N9UCEnpWQkKCKioo273/nO9/Rz3/+c4+NN3fuXLcFVcaNG6edO3d2uf9Zs2bp9ddfb/O+MUbDhg3TgQMHLnSbAiiAL3W1AMq/Kyoq0l//+letXr1a27dv7/aH+YABAzR58mTNmDFDV1xxhVJTUxUREaGIiAgFBgZ2q28AALrKWquGhgbV1dWpoqJCW7du1euvv6533nlHR48e9WossbGxmj59ukaMGKFBgwYpMTFRUVFRrsmqyMhIhYeHq66uTmVlZSovL1dZWZnKyspUVVWl4OBgRUVFKSwsTHV1daqurlZtba0cDoccDoeqqqp04MABHTly5FMPnOPj45Wenq6BAweqvr5ep0+fVkJCgsaMGaMvfOELGjt2bI8srOvugwprrd5++2397ne/07PPPqvGxkaPx/hx119/vf76178qMjKyR8cB0LtUVlZq1apVeu655/Taa6/12WIoSUlJ+vKXv6yvfOUrysrK8nU4vc769ev1hS98QadOnfJ1KG0KDQ3Vvffeq3vvvVdBQUG+DueixCQL+ptXX31V3/ve97Rt2zZfhwJcUFJSkqZPn67Jkydr0qRJmjBhgkJCQnpkrLq6Oh0+fFiVlZVqaWlx5XwhISEKDg5WeHi4UlJSFBcX1+ETYQFfIm9BV1VWVmrTpk164403tGXLFu3fv7/Hn7n1Z5mZmcrLy1NeXp7mzJlDIVIAuADyFnhafX29du/erbfffltvvfWW9uzZo7KyMq89+z0/75qSkqKYmBjXvOKZM2dUV1fnmuMtLy9XbW2taz1WfHy8pk+frq9//eu68sor+2XREwDo68hbgI5xOBzas2ePNm7cqHfffde1tq2qqkr19fU6e/asmpubFRQUpKSkJCUmJiopKcl1nf97QkKCrLWqr69XQ0ODAgICFBAQoKCgIMXHxyspKUmxsbE8swc6wVqrmpoa1dbWKikpic3v/Ry5C3paY2OjDh06pPz8fNe1f/9+5efnq6amxtfhXXTS0tI0evRo15WRkaGUlBQlJSUpICDA1+EBgFvkLfCFAwcO6Gc/+5mefPJJtbS0dLu/qKgoffe739Vdd92l8PBwD0TYfRUVFUpISHDb5s9//rNuvfVWj435wAMP6Ic//GGb9/39/VVXV6fQ0NBO911aWqqUlBS3tQ5mzpyp8vLyC36miAIogG95qgDKx5WXl2v9+vWuap179+5VZWWlAgIClJCQ8IkrLi5OoaGhCggIUHBwsDIzM3XppZcqMzOTyXkAQJ9y+vRp7d+/X1u2bNHatWu1adOmDn2pCQgIUHx8vCIiIhQZGel6DQ0NVUtLi5qbmyVJycnJGjJkiIYMGaJx48YpNzfXKxOyTU1Nqq2tlb+/v/z9/RUcHOyziSxPPqiorKzUs88+q5dffllvvPGGmpqaPBWmQkND9dBDD+nOO+8knwEuclVVVfrHP/6htWvXav369R1aMG2MUUJCgmJiYhQTE6Po6GhFR0eroqJC27dvV319fY/EGhMToylTpigvL0+XXXaZLr/8copPtuPAgRF2+8cAACAASURBVAOaP3++9u3b5+tQPiE0NFSLFy/WPffco6SkJF+Hc1FjkgX91YEDB7Rp0yZt3rxZO3bs0MmTJxUZGanY2FgNGDBAERERCg8PV1hYmOvV4XCorq5OVVVVrueFnszB0XuFhIQoLy9PU6ZM0ZAhQ+RwOHTw4EEdOHBATU1NCggIUGBg4Cdek5OTNXbsWCUmJqqpqUn5+fkqKSlRS0uLrLWuwp0Oh0NBQUHKzs7WzJkzNWLECL6DAV1E3gJPcTgcKioq0vvvv6+1a9dqzZo1XjvB99/FxMQoMTFRYWFhio6OVnZ2tmsBp8PhUFNTk5qbm9Xc3KyCggKtXr1aO3bs8HqcOTk5+vznP69FixZp2LBhXh8fAPoa8hZ4S1NTk6qqqtTY2Kjm5mbt379fa9a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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 962,
+ "width": 2208
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5), dpi=300)\n",
+ "quick.plot_diffpattern(ax, model)\n",
+ "quick.plot_jcpds(ax, model)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.05,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test-1.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let's do some fancy stuff\n",
+ "\n",
+ "I wrote similar plot functions with more options in `fancy_plots.py` in the same folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 962,
+ "width": 2223
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5), dpi=300)\n",
+ "fancy.plot_diffpattern(ax, model)\n",
+ "fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.70,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test-2.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the plot below, we plot diffraction pattern in $2\\theta$ scale to prevent any distortion in the diffraction pattern. We just plot tickmarks in d-spacing scale."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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191kL1/p9JEsff3tEVf1Zd39vNTkAgPnhSQlsBCeNiD+8u3eOKddvJblgSPxWVbV9TLnGoqr+LsmFGdxwe0qSv07yqCS3jKYrG8mov/MndPcPx5Gou8/KoFvoMPdeuPh8JhaaIN1wyJCvJ3nJWvN099eT/OmIYfdaax42LMvl0pY60KfxCkyG9dHFVNU+GTQ6WcpZSf5gXPm6+xUZHIwf5ufHlQ9gKVV1UFWdleT8JKcleWOSP0vykMzJidiFYzLDmnRemEEzslU3XblId5+S5E+GDLlPVd1grXkAYBpsR1zGbyW5wpD4s7t7HM01n5Bk2EMD7j6GHAAAsGL2EZKq2i/Drwd5a3e/ahy5uvuTSV44Ytjtx5ELAAAAgNlz/O0SHpmlG6IkyYu7+91rSdDdH8/we8wOSPIba8kBAMwHjVfYCO43JHbKWp8scnHd/c0kbxsyZP8kVx9XvjG5YpZ++jIbxz2HxL6e5F/GnO/vknx7SHx7Zttw5MQR8X/o7t1jyvWCDG7OW8p1qurAMeViY7FcLu24JV7/3FSrgPlhfXRJd8jgAPhS/qa7fzTmnMNuvE+Se1aVpofApG1NcqVZFzFj90py+JD433f3p8eY7y+TnDskrvEWABuF7YgFVbUtya8NGfKlJE8eR67u/loGF9At5eZVdblx5AIAgBWyj5DcNYPrwJbyrDHn+/sRcU2eAQAAADYPx9+SVNX+SR42ZMi5SZ4ypnTPSbJ3SPxhVVVjygUAbFIar7CuVdXBSW48ZMirJ5D2LSPix0wgJ3Osqq6f5MghQ17W3cN2/lasu3cmeceIYbcbZ84VGnVByX+MK1F3n5PkP0cMO3Zc+dgYLJdLW/huXuog4OenWQvMA+ujRd1tRPylE8j59gxv1LZ/kptMIC8Al3T/IbHOoFHK2HT395O8fMiQBzsZCwAbzv0zfD/7Cd09bP9vpV41JLY1jj0DAMCs3GFI7LtJThlnsu7+ZJIzhgxxPRoAAAAAm81JSS4/JP7y7j5rHIm6+0tJPjhkyLFJbjOOXADA5qXxCuvd9UfE3zWBnKePiHv6IOP2EyPiY2syssJ5bzihvMtx9Ij4uJs7fHFE/OAx52P9s1wu7ZpDYhqvwPhZH13WsM/kawsHzsequ89P8qERw9bDOhpgs7vLkNiHu/tzE8g57DvxKkluO4GcAMDk/OqQ2GeTvHbM+Uadxzp8zPkAAIDlGfZAoHd3d08g57Br0lyPBgAAAMBmc98R8b8ec77Xj4iPqgcAmHPbZl0AjHC9IbE9Sb4wgZzfHRHfMYGczLdrD4ntTfLhCeX92oj41SaUdzkOHBH/3pjznT0ifv6Y87H+WS6Xdo0hsUl8L8O8sz66rGGfyQcmmHc9fyYAm15VHZFBo5OlvG9CqUfNe9ck751QbgBgjKrqoCR3HDLk5HHfXNndZ1bVg5Lss8SQz44zHwAAsGzDrkk7bUI5h12T5no0AAAAADaNqtqR5G5Dhnyhuz855rSvT/KsIfGfTPKEMecEADYRjVdY744cEvt6d++aQM59R8R/NIGca/HpJIes4n13GHchrNqwm4e/2d2TavoxqsnQav6uxuXCEfGDM97mK6OeKvrDMeZiY7BcLu24JV7/VnefM9VKYD5YH11MVe2X5KpDhnxxgunX5WcCzJXdSU5ZxfuunOHfJxvFdUfEJ9J8a+Fm6e8nucISQ249ibwAMGbzvh1xkbsm2b5EbFeSV04iaXe/ahLzAgDAGsz1PkJVVQa/y1K+NKHUw65JW2/XowEAAACwenN9/G3BiUkOGhJ/07gTdvfnq+rLSa6+xJDrVtWVunvUNeEAwJzSeIX1btgG9qROOB8zIv79CeVdle7+w9W8r6rG+tRG1uSwIbGvTjDvnhHxpS5An4azRsSPyngbr1x/SGxvkm+PMRcbg+VyaUs1XvncVKuA+WF9dEmHJqkh8a9OMPd6/UyAObHQ5O7Elb6vqn4xyUvHXc8MHD0ifvoEc/8gSzdeuXlVbenuvRPMDwBrYjvif9xrSOx93T3OY84AALBu2UfIAUm2DInP4pq0dXU9GgAAAACr5/hbktEPjH/XhPJ+NEs3XkkGD1pSPRN2AAAgAElEQVR7w4RyAwAb3LATiLAeDGu8ct6Ecp4wJNZJPjuhvMyvA4fEfjDBvEeMiJ87wdyjfH1E/G7jSlRVhye52ZAhp3b3LD8LZsNyubSlGq98fqpVwPywPrqkYZ9HMp+fCcC8uOKI+CRvTBj2/XJQkutOMDcAMD7Djiu/dWpVAAAAszbserRkAtekVdUVkxw7ZMip484JAAAAADN00xHxD04o78dHxG88obwAwCag8QrrWnf/enfXEj+3GXe+qtqa5P5Dhpza3Z4wwrgdMCR2/gTzHj8ifsYEc48yqnPpQ6tqXN9hj0tSQ+LvH1MeNhbL5dKuscTrGq/AZFgfXdKwzyOZz88EYF7sNyL+4wnmvmBEfKnmhADAOlFVhya56pAh751WLQAAwGx19xlDrker7n7HBNI+MMOv1XzfBHICAAAAwKwMa3Dy9e7+3oTyjmq8cr0J5QUANoFtsy4A1pmHJjl6SPzN0yqEufKnSS63ROyzE8x7vxHxL0ww9ygfSPLDJIcsEf+JJL+R5OS1JKmq45P85ohhL1tLDjYsy+Uiqmr/JEcsEf78pcbuSHJikttlcNDs2IX37p/BNui5SX6Q5CtJPpNBx+K3d/dZk6gdNjDro0v6RpJHDYn/9ySSVtVRSW4yYthM19EAc2DPiPj+E8w9qunLMRPMDQCMx7CLunYm+cS0CgEAAObLwrnzxw0ZckFGP6AIAAAAADaEqrpKksOGDJnI9d7LnPs6E8wNAGxwGq/Agqq6epI/HzJkT9bY5AEW090vn3bOqjo2yR1GDPvQNGpZTHfvqqq/SPKUIcOeU1Vf6e63rCZHVV0hyRuS7Dtk2Ce7+wOrmZ+NzXK5pGskqSVin0+Sqjo6yROSPCTJ5YfMdfDCzzEZNGh5ZJI9VXVKkhcmeV13j7q5FjY966NL6u4zkrxgBql/KUuv/y4y63U0wGZ37oj4IUm+PqHch46Ia7wCAOvfjYbEPt/dFy4VrKrrJLlzklsluVYGDfwPSrJPkvOTfD/JV5N8KoOm4m/r7u+Pp2wAAGAT+LMM9iWW8nL7EAAAAABsIsePiH9xgrm/nWRXku1LxI+eYG4AYIPbMusCYD2oqusleWeSKwwZ9nfdPakbeGDa/jRL70Re5O3TKGSIv8hgh3cp+yZ5bVX9dlWt6Pts4UL5DyY5bsiwvUl+cyXzwhpthOVyqWVmd5IzqurZGRwEe2SGN11ZytYkd0ry6iSfrqp7r6pKYK02wvpoaqrqsAwaSg1zand/cxr1AMyxM0fEj5pE0qranuSIEcOcjAWA9e+GQ2KnXfqFqtpeVQ+vqk8sxF+Q5GeTnJDk8CT7Z3As68AMtgVun8Hx5Fck+XZVvamq7jHeXwEAANhIqmpLVT0zyW8PGXZeBo1ZAAAAAGCzuOqI+Jcnlbi7O8OvNbxcVR00qfwAwMam8QpzraoOqaonJflIkmOHDP1ikt+ZTlUwWVV1UpIHjRj2xe7+r2nUs5Tu/lGSkzLoNLqUHUmel+RDVXVSVW0bNmdVHV5Vz0ry0Qx/mlCSPKe737+SmmG1NspymeQaS7x+XgbL1eMyulnDcl0nyRur6pVVtZomLsAqbKD10VRUVSX5mwyeZD7MK6dQDsC8++qI+AkTynu9jN7GvdyEcgMA47PUca3kUhd1LTQDPi3Ji5LcaBW5tiX5ySRvraoPVdWtVzEHAACwgVXVbZO8L8nvjRj6O939pSmUBAAAAADTcpUR8TMmnH/UwzSvPOH8AMAGNfQGddioFp5GfIUk3+3uPQs3TO6XwY0wV09ygyR3SnLvhdeHOSvJ/bv7nAmWDFNRVTdJ8o/LGPr8SdeyHN39vqr6pST/kOHfWSckeVWS71fV+5J8Isn3klyQ5IoZ7BTfNsmNs7ymY/+Y5ImrrxyWb4Mtl8ct8frBCz+T8OAkJ1TVvbv7sxPKAWTDrY+m5ZlJ7jdizPkZNGcBYLL+O8meJFuXiE/qhuY7L2PM/hPKDQCMz5FDYt9M/ufc0p8nedQY894iyXur6vlJfre7d45xbgAAYMqq6tAk53X3uQv/vz2D44NHJrlmBscp751BQ+dRXtjdL55UrQAAAAAwI0eNiJ854fzfGhH3YGAAYFEar7BZHZPkC0ky6LmyaqcmeUB3f34cRcEsVdUtk7w5o28IOz3J306+ouXp7v9bVWckeXmGXxyfDBou3XfhZzX2JHlKdz99le+HFdmAy+VSjVcm7RpJPlBVd+nuj8+oBtjUNuD6aOKq6hlJfncZQ0/u7u9Muh6Aedfd51XVpzJoqLmYO1fVEd097qdhPGgZY0Y19QUAZmihOf+wJ1adUVX7ZNDc+z4TKGFLkt9OcquqesAEtlcAAIDpeV6Sn13j9Wh7kzwliWtTAAAAANiMRt379b0J5z9nRPyQCecHADaoLbMuANapc5L8cZKbarrCZlBVv5TkXRk0Jhnl0d19/oRLWpHufleS62ZwAcsFE0pzWpLba7rCtGzQ5XKljVfOSfLvGTwp+LFJfjXJo5M8I8k/ZeFpwst0+STvqKrjV1gDMMIGXR9NTFUdVFX/muT3lzH8mxnsNwAwHW8aEtuW5FfGmayqbp/k5ssYOqpxGQAwW4cl2T4k/uMk/5rJNF25uFskeU9VXXXCeQAAgPXrQ0lu2d1P6+6edTEAAAAAMAEHjoj/eML5R913dsCE8wMAG9S2WRcA61AneWGSl3T3pBo8wFRU1SFJnp/koct8y99192snWNKqdfePkjy2qt6VwUXw43yi+DlJHtvdHxjjnLCojbpcVtWOJMu9KeS9GTRbeWt37xwx7w0yeOLvQzP8Bphk0BTidVV184V1ArAGG3V9NElVdcskL0tyzWUM35PkZ7v77MlWBcDFvDLJk4bEH19VL+vur601UVXtm+SvljncMVYAWN+OGBH/7ST3HDHm20nemeTLC/+9K4OLsY5Ocv0kt8ryLs46LslbquqW3X3uMsYDAACbx+cyaOj/8VkXAgAAAAATNOp+r0lfez3qftAdE84PAGxQW2ZdAKxDleQJSb5eVa+sqqvNthxYnaq6T5LPZPk3U783yW9OrqK1qaqbVNU7krwx4226kgy6qb61qt690AQCJmKDL5fHZvS243eS/HR33767Xz+q6UqSdPd/d/cvJ7l2Br/vKNfKoKkLsAYbfH00dlW1f1U9K8n7s7ymK0ny6O4+ZYJlAXAp3X1akncNGXJQkr+vqlEN/YZaeP/Lk/zEMt9y/lryAQATd9CI+LCmK29OcuskR3T3z3X3k7v75O7+m+5+Xnc/prvvmuRKSX4mg33tUa6fwQMAAACA+XLtJG9J8tWq+sWqcu0mAAAAAJvRviPiI+8zWaNR1/Ot6fpCAGDzcvIOllZJHpzks1X1K7MuBparqo6pqtcleUOSI5f5to8muW93j+rqOXVVtaOqnpnkw0nuMuF0Jyb5eFU92QUujNMmWS6PGxH/aJIbd/erVzN5d38lyR2T/Nkyhv9yVd16NXlg3m2S9dFYVdX9kpya5PFZ/j7yk7r75MlVBcAQTxkRv1OS11fV/quZvKqOTfIfSU5awdvOW00uAGBqRl3UtZhvJjmxu+/d3R/s7h42uLsv6O5/TnLDJH+yjPl/vqpOXEVdAADAxneVJC9N8p6qOnzWxQAAAADAmI162PauCee/cES8JpwfANig3FQOo+2T5CVV9exZFwLDVNV+VfXkDG4cvt8K3vrmJHfq7h9OprLVq6rLJXlbkt9Lsm1KabcleWqSN6z2Rj24yCZbLoc1Xvlwkjt397fWkqC793T37yX582UMf+pacsG82WTro7GoqmtX1VuSvC7JMct8264kD+/up0+uMgCG6e73JvnXEcPumeSjVfXAqlrWSdKqOqSqfj/Jp5LcboVlabwCAOvbShuvvC+DBsOnrDTRwvGtP0jyxGUMt28JAADz7TYZHMe8zqwLAQAAAIAx2j4s2N27J5x/1P1nk278AgBsUNO6iR2m7dtJfuli/79vkoOSHJjk6CTXXfg5cAVzPq6qzuju546tShiTqvrpJM/O8m8aTpI9SZ6W5GndvXciha1BVR2U5D+T3GgZwzvJuzN4Kvl7k3wjyXeT7ExyhSRXSnLjJLdNcu8kRyxjzp9M8tqquud6/HxY/zbjcplBg5VLOzfJQ7r7x2PM8/gMGr0Maw5xl6o6vrtPG2Ne2JQ26fpo1RYauz05yaMy4sD+pXwjyYO7+/0TKQyAlXhkktsnufKQMccneU2Sz1bVvyd5V5IvJPlekh8mOSDJYUlOSHKXJD+VwbGjpZw5JJ/GKwCwvq2k8cppSe6z1gak3f2nVXVikrsPGXabqrpFdy92zA0AAFif/jrJOxf+e0v+93q0yye5TpLrZXBObrlPzb1Kkjcv7Bt8d8y1AgAAAMAsDG2sUlXbJtx8ReMVAGBVNF5hU+rus5P8w7AxVbUlgycYPzDJzya54jKmfnZVfbC7P7jmImEMquqGSf4yyR1W+NavJPmFhSeFrzsLTyR/WZbXdOUVSf6kuz+zRPyMhZ//TvKyqtqe5MFJnpTkmiPmvlsGN53/wXLqhmTzLpfd/ZcZ/F7TyNVV9Zgk98rwpgg/m+QPp1ETbESbdX20Wgvb/w9L8owMbrRfiVcleXh3f3/shQGwYt393ap6QAYNOEfdSH2dhZ/fXkPKd2awX/nQJeJnr2FuAGDy9lnmuAuT3HutTVcu5nFJ7prBzZhLeXAWb3YMAACsQwvnz4aeQ6uqKye5f5L/k+TEZUx79SQvT3LPtdYHAAAAAOvAqMYm+2REc5Y1GnXPtAetAQCLGnahH2xq3b23u0/p7kdncAL7aUnOH/G2LUn+auGmTZiZqrpSVb0oycezspup9yZ5QZKfWOc3U/96BhehDPPDJCd1988OabpyGd29q7tfnkFTlxct4y1PrKqV3rDOHJqD5XKquvsrSV4yYtjdplELbDTWR5dVVbdN8l8ZrFdW0nTlzCQP6u4HaboCsL5094eSnJTkggmn+nwGDf+OGTLm9AnXAABMx99095fHNVl3fzrJK0cMu9+48gEAAOtDd5/Z3S/u7jtmcK5uOc0W71FV951waQAAAAAwDaOu6Vvuw1NW64AR8R9NOD8AsEFpHgFJuvvH3f3kDJ48+IMRw2+axIluZqKqtlbVbyX5QpKHZ2Xr8Y8nuVV3P6q7z5lIgWNQVZdL8scjhp2d5I7d/ZrV5unu87r7EUl+b1RJSZ662jxsfvOwXM7QqBtTblJV+02lEtgArI8uq6qOqqpXZPDkwZus4K17k7w4yfHd/aqJFAfAmnX3mzN4CuxZE0rxhSR37u7vZNC0dylfnVB+AGA8di5jzJ4kfzqB3H89In5sVV15AnkBAIB1oLvfk+R2Sf7vMoY/bcLlAAAAAMA0jGpsMunGKweNiGu8AgAsSuMVuJjufn+Su2Vwge0wvzyFcuASqurmSf4ryV8mOWQFb/1ukkckOaG7PzKJ2sbs4UkOHRLvJD/d3Z8cR7Lu/rMkLxgx7A5Vdbtx5GNzmaPlclY+lOTHQ+Jbk1xjSrXAumZ9dElVtaWqHp3ktCQ/s8K3vz/Jzbv7N7r7h+OvDoBx6u7/THLjJG8Z89SvSXKz7v5GVR2U5KghY7825twAwHhduIwxH+vub407cXe/N8mXRwxbSaNQAABgg+nuXUkemuR1I4b+RFXddAolAQAAAMAk/WBEfCXXuq/GqMYr355wfgBgg9J4BS6luz+a5EUjht2jqg6YRj1QVdur6mlJPpDBzWTLtTPJc5Ncs7tf1N17J1Lg+P3CiPiru/ttY875xCTfGTHmQWPOyQY2h8vlTHT37iQfHjHs6GnUAuuV9dFlVdWxSU5J8hcZfeD84r6S5MHdfdvu/thEigNgIrr7m939k0nuncF34lp8NMk9u/uk7r6oCeB1k9SQ93x1jTkBgMk6fxlj3j3B/KPmPnaCuQEAgHWguzvJbyU5b8TQk6ZQDgAAAABM0vdGxK884fzDHgZ+YXd/d8L5AYANSuMVWNzTkgy7+XRbPIGQKaiqozK4cfgPk2xdwVtfneT47v6d7v7hRIqbgKo6PsnxI4Y9adx5u/ucJM8cMezO487LxjRvy+U6MKop0kqaKsCmYn10WVV1/ySfTHLbFbztR0mekMFn8i8TKQyAqejuN3f3bZLcMMkzMmjit3PE2/Yk+ViSZyW5cXef0N3/fqkx1xvy/r0ZNO8CANav5Vw09dEJ5v/QiPhVJpgbAABYJ7r79CQvHTHs5tOoBQAAAAAm6Bsj4pNuvDJs/lG1AQBzbNusC4D1qLu/U1UfzfCT2TdJ8t4plcQcqqqbJXlTksNX8LYPJXlsd39wMlVN3C1GxD/d3Z+bUO7XJHnekPjxVXVYd49qAsEmNqfL5aydNSK+fSpVwDpjfXRZVfWkJE9NUst8y64kL07y1O4e1VkdgA2kuz+V5FNJ/rCqdiS5WpJjkhySZN8MmrGcneTrSb7Y3ReMmPKEIbFPd/e5ay4aAJik5RzTneQTrb44In7gBHMDAADry5uTPHJI3IPAAAAAANjovjYiPunGK8Our//8hHMDABuYxiuwtHdkeOOVw6ZVCPOnqu6U5PVZ/gXXX0vyu939L5OraipuNCL+9kkl7u7Tq+q0JMcPGXbVLO8ifTahOV4uZ23UTbDnTaUKWEesjy6pqirJC5I8YgVve2OSx3W3g+cAm1x378zgZOla1vnDjg99aA3zAgDT8Z0kneGNOifZkPP7I+L7TTA3AACwvrw7ye4sfd3mIVW1vbt3TbEmAAAAABinL4+IX3NSiavqyAw/B+/aceD/s3fn8dLf493AP1cWsslqiSCJtZaKprZWNSKNpaHULiRElRJLq08fwaOKouiC1lJKKUoitPZdNY19acSDpsSSEKEIIpE91/PHnHjuJGd+c5aZOefc5/1+vc7Lbb7X73td92Rm7nPOzO/zAxhL8Arr0sLViB89UHJKd394xmN8e8L6HjPuzyZVVXfM6GTgnZZQfkGSv0jywu4+b6aDzcdQqmiS/PeM+38lw8ErV59xf9apTf68XGuTnnc/mcsUsE54PVrUi7P00JWvJnlCd88szA2ArUtV7ZzklgMlglcAYJ3r7gur6owk1x0omxT+uxqTfiZ3QiUAAMzJwnttQxcFen13nz2r/t19flX9MMNX9d0jLgoEAAAAwMb1fyesD30eb7VuMmH9izPsDQBscIJXWK+2SfJ3A+vvTzLr4JUfTFj3/GHqqur6Sd6epZ1M/bEkj+zurSltc/cJ65Oel6s1af+9Ztyfdcjzcs1dc8L6aXOZAtYBr0dXVlV/mOSJSyi9JMkLkzy7u2d5Mh0AW5+Dkmw/sC54BQA2hlMyHLyy5wx7TwryP2eGvQEAgMu7Z5JjBtY/m+TTM57hBxkOXvGZNAAAAAA2rO7+SVWdnmTfMSWzDF65+YT1z8ywNwCwwXmTjnVp4eoelyTZdkzJuG+8p2nSB11/PocZ2ESqarskx2dyuMclSZ6Z5Hndfems55qzSf8uzfoD6GdNWL94xv1ZZzwvk6r6tYz/0NcF3f2+GY9w24G1C5KcPuP+sC54Pbqyqrpdkr9aQunpSQ7v7k/MeCQAtk6HDqyd1t3/NbdJAIDVOCXD/67PMnhl0t5nzLA3AABweedOWN83sw9e8Zk0AAAAALZ2n8n48z93q6obdfepM+h7+4G1s5P4vB8AMJbgFdazMzP+6oP7V9U2Mz6Z9BoT1n84w95sTk9JcusJNT9L8oDu/sAc5lkLkz48Mulk89XadcL6z2bcn/XH8zJ5TJKHj1m7tKp27u7zZ9G4qm6S5NoDJV/o7otm0RvWIa9HW6iq7ZO8LpN/pv14kvt09w9mPhQAM1dVO2Z8SO+F3X3hDNred2DtbTPoBwDMxqeTPH5g/eZJZhUwPOmKWl+fUV8AAODKvjth/YZzmGHoM2kXZXQCCAAAAABsZCcmuf/A+t2TvHQGfe8wsPbv3X3JDHoCAFuJbdZ6ABjwzYG1nZLcbMb9h070Tobng2WpqmslOWZC2dlJ7raVn0w9KdBonxn3nxS4dNqM+7OOeF7+wtDjfpskvzLD3g+YsP7vM+wN64bXo0U9OpN/Hjgho/tE6ArA1uM/MgoaW+zrFdNuVlW3S7L/QMlbp90TAJiZEyes32mGvX99wvrnZtgbAAC4vEmf95p0IYRp2Htg7bQZX4gMAAAAAObh3yas333aDavqRkluNFDywWn3BAC2LoJXWM9OnrB+uxn3n/Qh2y/OuD+by+OS7DKwfn6Su3f3J+c0z1r5xoT1g2bc/8YDaxdl8nxsXTwvRyb9e/zbs2haVdsnOXpC2btm0RvWIa9HW6iqbZL8yYSyTyQ5rLvPncNIAMzPOQNr15tBvz8YWDsjyadm0BMAmIHuPi3DJ1geVFU7TLtvVW2X4d9rf6O7z5x2XwAAYKwvTFif6efRqurWGX7fz+fRAAAAANjwuvtLSb4+UHJIVe0+5bb3GRopydun3A8A2MoIXmE9m3Ti6D1n1biqdk5y6EDJT5N8eVb92VwWwgV+f0LZEzfJydSTPkByyCw+/J4kVXXNJAcOlJzU3efPojfrj+fl5Xx6wvrDq2rbGfR9RJJ9Bta/mVGwAmzVvB4t6rAk+w+s/yDJA7v75/MZB4A5+s7A2tDPc8tWVddIcvhAyRu7u6fZEwCYuX8ZWNstyZEz6HmvJNccWH/nDHoCAABjdPdZSb42ULJvVd1yhiPce8L6x2fYGwAAAADmaeg9+h2TPGxajaqqkjxqoORj3X3GtPoBAFsnwSusZx9IcunA+t2rarcZ9f7DJEPhDu/r7qHZYDl+N8m1B9aP7+5/mNcwa+xjGX7eXy3Jo2fU+35JamD9/TPqy/rkeblg4ZdLJw+U7JfkkdPsWVV7J3nBhLJXOtGVTcLr0ZU9dsL6UX4xDrDVOmVg7epVddsp9npGRm/uLuaiJH83xV4AwHy8ZcL6k2YQMHz0hPVjp9wPAACY7H0T1h88i6YLn3N7zISy98yiNwAAAACsgddOWH/iwkVKp+G+SW48sP6aKfUBALZigldYt7r7R0k+OlCyU0YBKVNVVfskeeqEsjdPuy+b2v0H1i5M8uR5DbLWuvvHGYWvDDmmqnadZt+q2jnJn04oO26aPVn3PC8vb9Lj/wVVdYNpNFpIGn5Vkt0Hys5K8opp9IMNwOvRFha+B7jLQMmHuvu985oHgLn75IT1oStWLFlV/XqGT4A4TsgXAGw83f2ZJCcNlNwsyTHT6ldV907yWwMln+/uT0+rHwAAsGTHT1g/ekYXA3tGkmsMrJ/U3f89g74AAAAAMHfd/V9JThgouWEmBxVPVFU7ZfjCv2fFOWEAwBIIXmG9e/WE9SdX1U2m1ayqrpbkXUl2GSg7Ncm7l7Hn66qqB76+tcqx2cAWrp5514GS13T3t+Y0zopU1VETHuNdVfsvY8tJiab7JPnHFQ+8uL9Mcu2B9Y9091em3JN1yvNyUf+Q5PyB9d2TvLeqrrPKuSvJi5P8zoTS53f32avpBRuB16NF3SXJULL501c1MABTVVXPnPTvwDK3/FiSnw6sP7yqbrGKkbPwPe2xSbYbU3JpRj9DAgAzNIPvIy7zwgnrz6yq26xw719YOEnzJRPKnrfaPgAAsFlM82eE7v5YklMGSnZP8lerHnoLVXVEkidNKHvxNHsCAAAAwFKt4Xv0z62qG65w78u8PKMQl3H+sruHzocBAEgieIX1761Jvj6wvnOSd1bVtVbbqKr2yih05VcnlD63uy9dbT9YcIeMPrAxzhvmNcg68qYkp0+ouV9VvWAhpGFVquq5SR47oew5q+3DhuJ5eQXd/cMkfzeh7JeSfLaqhkIixqqqPTP6d/+JE0q/FB84Y/PwenRlhw2sfXXh6uUAbKW6+8IkbxkouUqSY1d6Ndqq2jfJh5PsO1D29939xZXsDwCsC8cnOXlgffsk719N+EpV7ZjR+037DZR9urv/ZaU9AACAVZt0wsfvV9UfTaNRVT04owsMDX3G5WtJ3jyNfgAAAACwXnT3e5N8eqDkakneVlV7rGT/qnp2kocPlJyZ5G9XsjcAsPmMu3IrrAvdfXFVPSPJPw+U/VKST1XVQ7r7kyvpU1WHJHl9kutMKP1kkn9aSQ8Y444Da+cluWlV/dK8hrmCM7r7Q/Nu2t0XVtXTkrxxQumTk9ygqh7V3T9Zbp+qunaSf0hyjwmlb+3uf1/u/mxonpeLe06SB2X4JNRrJ/lAVb03oyv6fnhSWFlV7ZPk9zK6uteeE2a4MMlR3X3RkqeGjc3r0ZUN3SenV9VR8xpkESd09zfXsD/AZvHiJI/M+EDpX07y71V1n+7+1lI3rapDMwo123ug7AdJnr7UPQGA9ae7L6mqxyT5RMaf9LhXko8s/O55KPTtSqrqOklel+Q3B8ouTvIHy9kXAACYujck+d9JbjZQ86Kqun6SY1ZyRdyq2iXJ3yR51BLKn+h9cAAAAAC2Uk9I8qmM/8zfrTJ6j/53u3vShbyT/OKCKC9O8ugJpX/a3T9f8qQAwKYmeIV1r7vfVFWPTHLIQNn+ST5eVccneWmSj3V3D+1bVVdJcrckf5zk4CWM8tMkR07aF5bpVgNrO2Z0xZu18oEkaxLw0N3/XFX3TXLfCaX3T3LXqnpZklct5aS6qrpRksdlFPSw64Ty7y3Usrl4Xi6iu8+uqsOTfDTJVSaUH7bw9eOq+liSryQ5I8nPM/r+c48k109y24zu73G/QLuiP+7uz69gfNiovB5toap2SnKjgZJDF77WyuFJBK8AzFh3f6WqXptR+Mo4v5LkS1X1wiQv7e6zxhVW1YFJjskoZHCSo7v7x8saGABYd7r7U1X13AwHqu2a5LiqelySZ2cUtnnxuOKqulaSI5M8I6Mrcg15VnefvMyxAQCAKVq4GNhjM3r/e1woY5I8Mcl9qxlSNhQAACAASURBVOqvkryxu380ae+q2i+jsMXHZPTe+CQv6+73L6EOAAAAADac7v5sVf1tkj8aKDswyRer6plJXt3d5yxWVFXbJXlARu/jD32uPEne092vWcHIAMAmJXiFjeIRSU5KsudATSV54MLXD6rqE0m+mtGJ3ucm2X7h+L2S/GqS2yfZYYn9L8kodOXrK5oexvuVtR5gHTsqyQ0y+T7aNclTkzy1qk5LcmKSbyf58cLXThk992+Q5I4ZhT0sxYVJHtzd/7PsydnoPC/H6O5PVNVDkhyXZNslHLJHkt9Z+FqtF3b3y6awD2wkXo8u74AsPagJgK3bk5PcPcl1Bmp2TvKsJE+vqhOSfDbJd5JckGS3JDdJcqckN11iz7/o7reueGIAYL15RkZXtr/fhLqDknw4yU+q6kMZBW5+P8k5Sa6e5FoZfQDsN7K0n1nfm+S5K5wZAACYou4+oar+OsmfTCi9bkZXz/3rqjo5yX9m9LPBT/L/f9+4Z5J9k/xmRhcQW6oTM7poGAAAAABszZ6W0Tldtxmo2S3Ji5I8a4vP/P0gSWf0/vwtk9wlw+eXXub7Gb64GwDAlQheYUPo7tOr6sFJ3p3kKks45BpJ7j2l9pckeUR3v2tK+0GSpKq2T3LjtZ5jverun1XV3ZO8J8mtl3jYfgtfq3VJkod19wlT2IsNxPNysu5+W1XdI8mxSXafU9vndPefzqkXrAtejxZ187UeAID1obvPqqoHJflIkqtOKN8+yaELXyv1jiRPX8XxAMA60929EDB8bJL7LOGQ3TO6atZqfDrJg7q7V7kPAAAwPU9NcquMTtiYZNuMLvb1q1Pq/bkk9+zuC6e0HwAAAACsS919XlXdO8lnMnzBtWR0ge7VXAD4p0nu3t3fX+HxAMAm5WrhbBjd/aEkD0kyzzebz0ly7+5+wxx7snlcK16HBy38kHunJG+ZY9vzk9y/u4+bY0/WD8/LJejuDyQ5IKMTUGfpBxk9H4WusBl5PbqyfdZ6AADWj+7+eJIHZ/a/J3p9Rt+TXjrjPgDAnC2c3PjAJC+fQ7v/SPLb3X3OHHoBAABL1N0XZxTGeOKcW787ycHdffac+wIAAADAmuju7yY5OMm3Ztjmp0nu0d1fmGEPAGAr5UQ+NpTufltG32B/dw7tPp7kwO5+zxx6sTntvdYDbATdfW53PyjJw5P8cMbtTknya9399hn3Yf3yvFyi7v52d/9uknsl+dqUtz8vyUuT3HTh337YjLweXZn7BIDLWfjZ7bczCuybtkuSPDvJUQsnXwAAW6Huvri7H5fkYRl9AGvqLTIKdjm0u388g/0BAIBV6u5zkxya5JVzaHdekj/O6EJg586hHwAAAACsG919apLfyOi8zWn7UpLbLFzUDQBg2QSvsOF09yeT3CzJSzKbqxp/PcmRSQ5a+GYeZsXJw8vQ3a9PcuMkL0gy7Sv+nJ3kaRmFLZ085b3ZWDwvl6m735Xkl5LcLcnbMzpBdaX+M8lTk+zb3U/o7rOmMCJsVF6Prsx9AsCVdPe/JTkgybEZndg8DZ9Pctvu/rPuntaeAMA61t1vSHLzTPd7ipOT3Lm7H9fdF01pTwAAYAa6+8LufkxGASxfnkGLS5K8LsnNuvtF3X3pDHoAAAAAwLrX3d9NclCSJyc5Zwpbnp/k+RldiNu5oADAipVzB9jIqmrvJI9NcnhGgQwr9bMkH0jy2iTv9+Y2rG9VtVuSIxa+bp+kVrjVSUn+Kclru3vaYS6wKS08P++QUQrx7TIKStgryZ5JtsvoCl7nJvlektOTfDXJZ5N8oru/sxYzAwCwdaiqA5M8IckDk+y8zMM7yYeTvCLJO7t7NYGCAMAGVlW3SPKkjL6nuNoyD78oyQeTvDLJu4W4AQDAxlNVleSwJI/K6AIkO6xiuy8nOS7JP3b3GVMYDwAAAAC2GlW1V0bvz/9ekmsv8/D/SfLmJH/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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 973,
+ "width": 2223
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5), dpi=300)\n",
+ "fancy.plot_diffpattern(ax, model, dsp_ticks=True, dsp_step=0.3)\n",
+ "fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.70,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test-2-dsp.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "peakpo7721",
+ "language": "python",
+ "name": "peakpo7721"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.py b/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.py
new file mode 100644
index 0000000..bb90170
--- /dev/null
+++ b/jnb-tools/2_1D_from_dpp/1D_xrd_pattern.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Plot XRD patterns from dpp files from PeakPo
+
+# - Please check [setup_for_notebooks](../0_setup/setup_for_notebooks.ipynb) file if you have problem using the notebooks in this folder.
+# - In this notebook, we will learn how to plot XRD patterns using the information saved in `dpp`.
+# - `dpp` is a project file saved in `PeakPo`. You may plot, jcpds information and cake as well as many other information.
+
+# This notebook takes advantage of the `PeakPo` modules and other local modules. They can be found in `../local_modules` folder.
+# The cell below defined the search path for this local module folder.
+
+# In[11]:
+
+
+import sys
+sys.path.append('../../peakpo')
+sys.path.append('../local_modules')
+
+
+# ## Check the versio of pyFAI in your conda environment
+
+# In[12]:
+
+
+import pyFAI
+pyFAI.version
+
+
+# Note that the example data files I provided are made with `pyFAI` version `0.14`. If you see version higher than `0.15` here, you will get error when you read the example `dpp` file. In that case, you either follow the instruction in [setup_for_notebooks.ipynb](./setup_for_notebooks.ipynb) or you may use your own dpp for this note book.
+
+# ## Read dpp
+
+# In[13]:
+
+
+import dill
+import numpy as np
+
+
+# ### Change the following two cells for your own dpp file
+
+# Data files should be in the `../data` folder. You need: `dpp`, `chi`, `tif`, and `poni` files.
+
+# In[14]:
+
+
+get_ipython().run_line_magic('ls', '../data/hStv/*.dpp')
+
+
+# In[15]:
+
+
+filen_dpp = '../data/hStv/hSiO2_404_009.dpp'
+
+
+# In[16]:
+
+
+with open(filen_dpp, 'rb') as f:
+ model_dpp = dill.load(f)
+
+
+# The cells below show how to look into the data structure of the `model_dpp` and get values from it.
+
+# In[17]:
+
+
+model_dpp.__dict__
+
+
+# ## Setup a new PeakPo model and assign info from dpp
+
+# In[18]:
+
+
+from model import PeakPoModel
+model = PeakPoModel()
+
+
+# Make sure to reset the chi folder location using the `new_chi_path` option.
+
+# In[19]:
+
+
+model.set_from(model_dpp, new_chi_path='../data/hStv')
+
+
+# ## Make XRD plot
+
+# The following three modules are all in the `../local_modules` folder.
+
+# In[22]:
+
+
+from xrd_unitconv import * # Make conversios between different x-axis units
+
+
+# In[23]:
+
+
+import quick_plots as quick # A function to plot XRD pattern
+import fancy_plots as fancy # A function to plot XRD pattern
+
+
+# In[24]:
+
+
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# In[25]:
+
+
+import matplotlib.pyplot as plt
+
+
+# ## Let's make some plots
+
+# Let's plot in regular scale.
+
+# In[26]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5), dpi=300)
+quick.plot_diffpattern(ax, model)
+quick.plot_jcpds(ax, model)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.05,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test-1.pdf', bbox_inches='tight')
+
+
+# ## Let's do some fancy stuff
+#
+# I wrote similar plot functions with more options in `fancy_plots.py` in the same folder.
+
+# In[27]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5), dpi=300)
+fancy.plot_diffpattern(ax, model)
+fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.70,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test-2.pdf', bbox_inches='tight')
+
+
+# In the plot below, we plot diffraction pattern in $2\theta$ scale to prevent any distortion in the diffraction pattern. We just plot tickmarks in d-spacing scale.
+
+# In[28]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5), dpi=300)
+fancy.plot_diffpattern(ax, model, dsp_ticks=True, dsp_step=0.3)
+fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.70,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test-2-dsp.pdf', bbox_inches='tight')
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/2_1D_from_dpp/peakpo_plots.py b/jnb-tools/2_1D_from_dpp/peakpo_plots.py
new file mode 100644
index 0000000..abc2a60
--- /dev/null
+++ b/jnb-tools/2_1D_from_dpp/peakpo_plots.py
@@ -0,0 +1,537 @@
+import os
+import time
+import datetime
+import numpy as np
+import numpy.ma as ma
+from matplotlib.widgets import MultiCursor
+import matplotlib.transforms as transforms
+import matplotlib.patches as patches
+from PyQt5 import QtWidgets
+from PyQt5 import QtCore
+from ds_jcpds import convert_tth
+
+"""
+def get_cake_range(self):
+ if self.widget.checkBox_ShowCake.isChecked():
+ return self.widget.mpl.canvas.ax_cake.get_xlim(),\
+ self.widget.mpl.canvas.ax_cake.get_ylim()
+ else:
+ return None, None
+
+def _read_azilist(self):
+ n_row = self.widget.tableWidget_DiffImgAzi.rowCount()
+ if n_row == 0:
+ return None, None, None
+ azi_list = []
+ tth_list = []
+ note_list = []
+ for i in range(n_row):
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 4).text())
+ tth_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 1).text())
+ tth_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 3).text())
+ note_i = self.widget.tableWidget_DiffImgAzi.item(i, 0).text()
+ tth_list.append([tth_min, tth_max])
+ azi_list.append([azi_min, azi_max])
+ note_list.append(note_i)
+ return tth_list, azi_list, note_list
+
+def zoom_out_graph(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits()
+ self.update(limits=data_limits,
+ cake_ylimits=(-180, 180))
+
+def update_to_gsas_style(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits(y_margin=0.10)
+ self.update(limits=data_limits, gsas_style=True)
+
+def _get_data_limits(self, y_margin=0.):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ return (x.min(), x.max(),
+ y.min() - (y.max() - y.min()) * y_margin,
+ y.max() + (y.max() - y.min()) * y_margin)
+"""
+
+def update(self, limits=None, gsas_style=False, cake_ylimits=None):
+ """Updates the graph"""
+ t_start = time.time()
+ self.widget.setCursor(QtCore.Qt.WaitCursor)
+ if limits is None:
+ limits = self.widget.mpl.canvas.ax_pattern.axis()
+ if cake_ylimits is None:
+ c_limits = self.widget.mpl.canvas.ax_cake.axis()
+ cake_ylimits = c_limits[2:4]
+ if (not self.model.base_ptn_exist()) and \
+ (not self.model.jcpds_exist()):
+ return
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.model.diff_img_exist():
+ self.widget.mpl.canvas.resize_axes(
+ self.widget.horizontalSlider_CakeAxisSize.value())
+ self._plot_cake()
+ else:
+ self.widget.mpl.canvas.resize_axes(1)
+ self._set_nightday_view()
+ if self.model.base_ptn_exist():
+ if self.widget.checkBox_ShortPlotTitle.isChecked():
+ title = os.path.basename(self.model.base_ptn.fname)
+ else:
+ title = self.model.base_ptn.fname
+ self.widget.mpl.canvas.fig.suptitle(
+ title, color=self.obj_color)
+ self._plot_diffpattern(gsas_style)
+ if self.model.waterfall_exist():
+ self._plot_waterfallpatterns()
+ # if self.model.jcpds_exist():
+ # self._plot_jcpds(limits)
+ if self.model.ucfit_exist():
+ self._plot_ucfit()
+ if (self.widget.tabWidget.currentIndex() == 8):
+ if gsas_style:
+ self._plot_peakfit_in_gsas_style()
+ else:
+ self._plot_peakfit()
+ self.widget.mpl.canvas.ax_pattern.set_xlim(limits[0], limits[1])
+ if not self.widget.checkBox_AutoY.isChecked():
+ self.widget.mpl.canvas.ax_pattern.set_ylim(limits[2], limits[3])
+ self.widget.mpl.canvas.ax_cake.set_ylim(cake_ylimits)
+ if self.model.jcpds_exist():
+ self._plot_jcpds(limits)
+ if not self.widget.checkBox_Intensity.isChecked():
+ new_low_limit = -1.1 * limits[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ self.widget.mpl.canvas.ax_pattern.set_ylim(
+ new_low_limit, limits[3])
+ if self.widget.checkBox_ShowLargePnT.isChecked():
+ label_p_t = "{0: 5.1f} GPa\n{1: 4.0f} K".\
+ format(self.widget.doubleSpinBox_Pressure.value(),
+ self.widget.doubleSpinBox_Temperature.value())
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.98, label_p_t, horizontalalignment='left',
+ verticalalignment='top',
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ fontsize=int(
+ self.widget.comboBox_PnTFontSize.currentText()))
+ xlabel = "Two Theta (degrees), {: 6.4f} A".\
+ format(self.widget.doubleSpinBox_SetWavelength.value())
+ self.widget.mpl.canvas.ax_pattern.set_xlabel(xlabel)
+ # if I move the line below to elsewhere I cannot get ylim or axis
+ # self.widget.mpl.canvas.ax_pattern.autoscale(
+ # enable=False, axis=u'both', tight=True)
+ """Removing the lines below for the tick reduce the plot time
+ significantly. So do not turn this on.
+ x_size = limits[1] - limits[0]
+ if x_size <= 50.:
+ majortick_interval = 1
+ minortick_interval = 0.1
+ else:
+ majortick_interval = 10
+ minortick_interval = 1
+ majorLocator = MultipleLocator(majortick_interval)
+ minorLocator = MultipleLocator(minortick_interval)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_major_locator(majorLocator)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_minor_locator(minorLocator)
+ """
+ self.widget.mpl.canvas.ax_pattern.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.ax_cake.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.draw()
+ print("Plot takes {0:.2f}s at".format(time.time() - t_start),
+ str(datetime.datetime.now())[:-7])
+ self.widget.unsetCursor()
+ if self.widget.checkBox_LongCursor.isChecked():
+ self.widget.cursor = MultiCursor(
+ self.widget.mpl.canvas,
+ (self.widget.mpl.canvas.ax_pattern,
+ self.widget.mpl.canvas.ax_cake), color='r',
+ lw=float(
+ self.widget.comboBox_VertCursorThickness.
+ currentText()),
+ ls='--', useblit=False) # useblit not supported for pyqt5 yet
+ """
+ self.widget.cursor_pattern = Cursor(
+ self.widget.mpl.canvas.ax_pattern, useblit=False,
+ lw = 1, ls=':')
+ self.widget.cursor_cake = Cursor(
+ self.widget.mpl.canvas.ax_cake, useblit=False, c= 'r',
+ lw = 1, ls=':')
+ """
+
+def _plot_ucfit(self):
+ i = 0
+ for j in self.model.ucfit_lst:
+ if j.display:
+ i += 1
+ if i == 0:
+ return
+ axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ bar_scale = 1. / 100. * axisrange[3]
+ i = 0
+ for phase in self.model.ucfit_lst:
+ if phase.display:
+ phase.cal_dsp()
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ intensity = inten
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ self.widget.tableWidget_UnitCell.removeCellWidget(i, 3)
+ Item4 = QtWidgets.QTableWidgetItem(
+ "{:.3f}".format(float(phase.v)))
+ Item4.setFlags(
+ QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)
+ self.widget.tableWidget_UnitCell.setItem(i, 3, Item4)
+ if self.widget.checkBox_Intensity.isChecked():
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, intensity * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ else:
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, 100. * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ i += 1
+
+def _plot_cake(self):
+ intensity_cake, tth_cake, chi_cake = self.model.diff_img.get_cake()
+ min_slider_pos = self.widget.horizontalSlider_VMin.value()
+ max_slider_pos = self.widget.horizontalSlider_VMax.value()
+ if (max_slider_pos <= min_slider_pos):
+ self.widget.horizontalSlider_VMin.setValue(1)
+ self.widget.horizontalSlider_VMax.setValue(99)
+ intensity_cake_plot = ma.masked_values(intensity_cake, 0.)
+ prefactor = self.widget.spinBox_MaxCakeScale.value() / \
+ (10. ** self.widget.horizontalSlider_MaxScaleBars.value())
+ # intensity_cake_plot.max() / \
+ climits = np.asarray([
+ self.widget.horizontalSlider_VMin.value(),
+ self.widget.horizontalSlider_VMax.value()]) / \
+ 1000. * prefactor
+ if self.widget.checkBox_WhiteForPeak.isChecked():
+ cmap = 'gray'
+ else:
+ cmap = 'gray_r'
+ mid_angle = self.widget.spinBox_AziShift.value()
+ if mid_angle != 0:
+ int_new = np.array(intensity_cake_plot)
+ int_new[0:mid_angle] = intensity_cake[360 - mid_angle:361]
+ int_new[mid_angle:361] = intensity_cake[0:360 - mid_angle]
+ else:
+ int_new = np.array(intensity_cake_plot)
+ self.widget.mpl.canvas.ax_cake.imshow(
+ int_new, origin="lower",
+ extent=[tth_cake.min(), tth_cake.max(),
+ chi_cake.min(), chi_cake.max()],
+ aspect="auto", cmap=cmap, clim=climits) # gray_r
+ tth_list, azi_list, note_list = self._read_azilist()
+ tth_min = tth_cake.min()
+ tth_max = tth_cake.max()
+ if azi_list is not None:
+ for tth, azi, note in zip(tth_list, azi_list, note_list):
+ rect = patches.Rectangle(
+ (tth_min, azi[0]), (tth_max - tth_min), (azi[1] - azi[0]),
+ linewidth=0, edgecolor='b', facecolor='b', alpha=0.2)
+ rect1 = patches.Rectangle(
+ (tth[0], azi[0]), (tth[1] - tth[0]), (azi[1] - azi[0]),
+ linewidth=1, edgecolor='b', facecolor='None')
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect1)
+ if self.widget.checkBox_ShowCakeLabels.isChecked():
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[1], azi[1], note, color=self.obj_color)
+ rows = self.widget.tableWidget_DiffImgAzi.selectionModel().\
+ selectedRows()
+ if rows != []:
+ for r in rows:
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 4).text())
+ rect = patches.Rectangle(
+ (tth_min, azi_min), (tth_max - tth_min),
+ (azi_max - azi_min),
+ linewidth=0, facecolor='r', alpha=0.2)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+
+def _plot_jcpds(self, axisrange):
+ # t_start = time.time()
+ if (not self.widget.checkBox_JCPDSinPattern.isChecked()) and \
+ (not self.widget.checkBox_JCPDSinCake.isChecked()):
+ return
+ selected_phases = []
+ for phase in self.model.jcpds_lst:
+ if phase.display:
+ selected_phases.append(phase)
+ if selected_phases == []:
+ return
+ n_displayed_jcpds = len(selected_phases)
+ # axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ cakerange = self.widget.mpl.canvas.ax_cake.axis()
+ bar_scale = 1. / 100. * axisrange[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ pressure = self.widget.doubleSpinBox_Pressure.value()
+ for i, phase in enumerate(selected_phases):
+ phase.cal_dsp(pressure,
+ self.widget.doubleSpinBox_Temperature.value())
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ intensity = inten * phase.twk_int
+ if self.widget.checkBox_Intensity.isChecked():
+ bar_min = np.ones_like(tth) * axisrange[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = intensity * bar_scale + bar_min
+ else:
+ data_limits = self._get_data_limits()
+ starting_intensity = np.ones_like(tth) * data_limits[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ bar_min = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ if pressure == 0.:
+ volume = phase.v
+ else:
+ volume = phase.v.item()
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, bar_max, colors=phase.color,
+ label="{0:}, {1:.3f} A^3".format(
+ phase.name, volume),
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # hkl
+ if self.widget.checkBox_ShowMillerIndices.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_pattern.text(
+ tth[j], bar_max[j], hkl, color=phase.color,
+ rotation=90, verticalalignment='bottom',
+ horizontalalignment='center',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # phase.name, phase.v.item()))
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.widget.checkBox_JCPDSinCake.isChecked():
+ self.widget.mpl.canvas.ax_cake.vlines(
+ tth, np.ones_like(tth) * cakerange[2],
+ np.ones_like(tth) * cakerange[3], colors=phase.color,
+ lw=float(
+ self.widget.comboBox_CakeJCPDSBarThickness.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_ShowMillerIndices_Cake.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ trans = transforms.blended_transform_factory(
+ self.widget.mpl.canvas.ax_cake.transData,
+ self.widget.mpl.canvas.ax_cake.transAxes)
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[j], 0.99, hkl, color=phase.color,
+ rotation=90, verticalalignment='top',
+ transform=trans, horizontalalignment='right',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ leg_jcpds = self.widget.mpl.canvas.ax_pattern.legend(
+ loc=1, prop={'size': 10}, framealpha=0., handlelength=1)
+ for line, txt in zip(leg_jcpds.get_lines(), leg_jcpds.get_texts()):
+ txt.set_color(line.get_color())
+ # print("JCPDS update takes {0:.2f}s at".format(time.time() - t_start),
+ # str(datetime.datetime.now())[:-7])
+
+def _plot_waterfallpatterns(self):
+ if not self.widget.checkBox_ShowWaterfall.isChecked():
+ return
+ # t_start = time.time()
+ # count how many are dispaly
+ i = 0
+ for pattern in self.model.waterfall_ptn:
+ if pattern.display:
+ i += 1
+ if i == 0:
+ return
+ n_display = i
+ j = 0 # this is needed for waterfall gaps
+ # get y_max
+ for pattern in self.model.waterfall_ptn[::-1]:
+ if pattern.display:
+ j += 1
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05 + j * 0.05,
+ os.path.basename(pattern.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=pattern.color)
+ """
+ if self.widget.checkBox_BgSub.isChecked():
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_bgsub.max() * float(j) / 100.
+ y_bgsub = pattern.y_bgsub
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = y_bgsub / y_bgsub.max() * \
+ self.model.base_ptn.y_bgsub.max()
+ else:
+ y = y_bgsub
+ x_t = pattern.x_bgsub
+ else:
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_raw.max() * float(j) / 100.
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = pattern.y_raw / pattern.y_raw.max() *\
+ self.model.base_ptn.y_raw.max()
+ else:
+ y = pattern.y_raw
+ x_t = pattern.x_raw
+ if self.widget.checkBox_SetToBasePtnLambda.isChecked():
+ x = convert_tth(x_t, pattern.wavelength,
+ self.model.base_ptn.wavelength)
+ else:
+ x = x_t
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y + ygap, c=pattern.color, lw=float(
+ self.widget.comboBox_WaterfallLineThickness.
+ currentText()))
+ if self.widget.checkBox_ShowWaterfallLabels.isChecked():
+ self.widget.mpl.canvas.ax_pattern.text(
+ (x[-1] - x[0]) * 0.01 + x[0], y[0] + ygap,
+ os.path.basename(pattern.fname),
+ verticalalignment='bottom', horizontalalignment='left',
+ color=pattern.color)
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05,
+ os.path.basename(self.model.base_ptn.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=self.model.base_ptn.color)
+ """
+
+def _plot_diffpattern(self, gsas_style=False):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ x_bg, y_bg = self.model.base_ptn.get_background()
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_bg, y_bg, c=self.model.base_ptn.color, ls='--',
+ lw=float(
+ self.widget.comboBox_BkgnLineThickness.
+ currentText()))
+
+def _plot_peakfit(self):
+ if not self.model.current_section_exist():
+ return
+ if self.model.current_section.peaks_exist():
+ for x_c in self.model.current_section.get_peak_positions():
+ self.widget.mpl.canvas.ax_pattern.axvline(
+ x_c, ls='--', dashes=(10, 5))
+ if self.model.current_section.fitted():
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ x_plot = self.model.current_section.x
+ profiles = self.model.current_section.get_individual_profiles(
+ bgsub=bgsub)
+ for key, value in profiles.items():
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, value, ls='-', c=self.obj_color, lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ total_profile = self.model.current_section.get_fit_profile(
+ bgsub=bgsub)
+ residue = self.model.current_section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ y_range = self.model.current_section.get_yrange(bgsub=bgsub)
+ y_shift = y_range[0] - (y_range[1] - y_range[0]) * 0.05
+ #(y_range[1] - y_range[0]) * 1.05
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, residue + y_shift, facecolor='r')
+ """
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, residue + y_shift, 'r-')
+ self.widget.mpl.canvas.ax_pattern.axhline(
+ self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, c='r', ls='-', lw=0.5)
+ """
+ else:
+ pass
+
+def _plot_peakfit_in_gsas_style(self):
+ # get all the highlights
+ # iteratively run plot
+ rows = self.widget.tableWidget_PkFtSections.selectionModel().\
+ selectedRows()
+ if rows == []:
+ return
+ else:
+ selected_rows = [r.row() for r in rows]
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ data_limits = self._get_data_limits()
+ y_shift = data_limits[2] - (data_limits[3] - data_limits[2]) * 0.05
+ i = 0
+ for section in self.model.section_lst:
+ if i in selected_rows:
+ x_plot = section.x
+ total_profile = section.get_fit_profile(bgsub=bgsub)
+ residue = section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, section.get_fit_residue_baseline(bgsub=bgsub) +
+ y_shift, residue + y_shift, facecolor='r')
+ i += 1
diff --git a/jnb-tools/2_1D_from_dpp/test-1.pdf b/jnb-tools/2_1D_from_dpp/test-1.pdf
new file mode 100644
index 0000000..04805ca
Binary files /dev/null and b/jnb-tools/2_1D_from_dpp/test-1.pdf differ
diff --git a/jnb-tools/2_1D_from_dpp/test-2-dsp.pdf b/jnb-tools/2_1D_from_dpp/test-2-dsp.pdf
new file mode 100644
index 0000000..52b8be0
Binary files /dev/null and b/jnb-tools/2_1D_from_dpp/test-2-dsp.pdf differ
diff --git a/jnb-tools/2_1D_from_dpp/test-2.pdf b/jnb-tools/2_1D_from_dpp/test-2.pdf
new file mode 100644
index 0000000..e7e55aa
Binary files /dev/null and b/jnb-tools/2_1D_from_dpp/test-2.pdf differ
diff --git a/jnb-tools/2_1D_from_dpp/test.pdf b/jnb-tools/2_1D_from_dpp/test.pdf
new file mode 100644
index 0000000..6c5b7e9
Binary files /dev/null and b/jnb-tools/2_1D_from_dpp/test.pdf differ
diff --git a/jnb-tools/3_2D_from_dpp/cake_from_dpp.html b/jnb-tools/3_2D_from_dpp/cake_from_dpp.html
new file mode 100644
index 0000000..ab1520c
--- /dev/null
+++ b/jnb-tools/3_2D_from_dpp/cake_from_dpp.html
@@ -0,0 +1,13819 @@
+
+
+cake_from_dpp
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Plot Caked Patterns from dpp files from PeakPo¶
+
+
+
+
+
+
+
+Please check setup_for_notebooks file if you have problem using the notebooks in this folder.
+In this notebook, we will learn how to plot XRD patterns using the information saved in dpp.
+dpp is a project file saved in PeakPo. You may plot, jcpds information and cake as well as many other information.
+
+
+
+
+
+
+
+
This notebook takes advantage of the PeakPo modules and other local modules. They can be found in ../local_modules folder.
+
+
+
+
+
+
+
+
+
Check the versio of pyFAI in your conda environment¶
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
/Users/DanShim/anaconda/envs/peakpo7721/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
+ from ._conv import register_converters as _register_converters
+WARNING:pyFAI.opencl.common:Unable to import pyOpenCl. Please install it from: http://pypi.python.org/pypi/pyopencl
+
+
+
+
+
+
+
Out[2]:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Note that the example data files I provided are made with pyFAI version 0.14. If you see version higher than 0.15 here, you will get error when you read the example dpp file. In that case, you either follow the instruction in setup_for_notebooks.ipynb or you may use your own dpp for this note book.
+
+
+
+
+
+
+
+
+
+
Change the following two cells for your own dpp file
+
+
+
+
+
+
+
+
Data files should be in the ./data folder. You need: dpp, chi, and tif.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
../data/hStv/hSiO2_404_009.dpp
+
+
+
+
+
+
+
+
+
+
+
+
+
+
The cells below show how to look into the data structure of the model_dpp and get values from it.
+
+
+
+
+
+
+
+
Setup a new PeakPo model and assign info from dpp¶
+
+
+
+
+
+
+
+
Make sure to reset the chi folder location using the new_chi_path option.
+
+
+
+
+
+
+
+
+
+
+
Let's make some plots¶
+
+
+
+
+
+
+
In the plot below, we plot diffraction pattern in $2\theta$ scale to prevent any distortion in the diffraction pattern. We just plot tickmarks in d-spacing scale.
+
+
+
+
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+
+
+
+
Can I add cake to the diffraction pattern?¶
+
+
+
+
+
+
+
+
+
+
+
+
+
Out[12]:
+
+
+
+
+
+
{'base_ptn': <ds_powdiff.DiffractionPattern.PatternPeakPo at 0x10b9a4cf8>,
+ 'waterfall_ptn': [],
+ 'jcpds_lst': [<ds_jcpds.jcpds.JCPDSplt at 0x112704198>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x112704f60>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x11275f4e0>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x11275f898>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x11275fa90>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x112768668>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x1127720f0>,
+ <ds_jcpds.jcpds.JCPDSplt at 0x112772940>],
+ 'ucfit_lst': [],
+ 'diff_img': <ds_cake.DiffractionImage.DiffImg at 0x11277d4e0>,
+ 'poni': '../data/hStv/LaB6_37keV_p49_center.poni',
+ 'session': <ds_jcpds.jcpds.Session at 0x1127852b0>,
+ 'jcpds_path': '/Users/DanShim/Python/jcpds',
+ 'chi_path': '../data/hStv',
+ 'current_section': None,
+ 'section_lst': [],
+ 'saved_pressure': 39.6,
+ 'saved_temperature': 300.0}
+
+
+
+
+
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Out[14]:
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+
[2.60987346e-03 7.82961977e-03 1.30493661e-02 ... 3.03032372e+01
+ 3.03084569e+01 3.03136767e+01]
+
+
+
+
+
+
+
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+
Out[16]:
+
+
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+
+
<matplotlib.image.AxesImage at 0x11a68ef60>
+
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+
+
+
+
+
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+
+
+
+
Modify plot_diffpattern function to plot cake¶
+
+
+
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+
+
+
"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 973,
+ "width": 2223
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5), dpi=300)\n",
+ "fancy.plot_diffpattern(ax, model, dsp_ticks=True, dsp_step=0.3)\n",
+ "fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.70,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Can I add cake to the diffraction pattern?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'base_ptn': ,\n",
+ " 'waterfall_ptn': [],\n",
+ " 'jcpds_lst': [,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ],\n",
+ " 'ucfit_lst': [],\n",
+ " 'diff_img': ,\n",
+ " 'poni': '../data/hStv/LaB6_37keV_p49_center.poni',\n",
+ " 'session': ,\n",
+ " 'jcpds_path': '/Users/DanShim/Python/jcpds',\n",
+ " 'chi_path': '../data/hStv',\n",
+ " 'current_section': None,\n",
+ " 'section_lst': [],\n",
+ " 'saved_pressure': 39.6,\n",
+ " 'saved_temperature': 300.0}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_dpp.__dict__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#model_dpp.__dict__['diff_img'].__dict__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(360, 5808)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "intensity_cake = model_dpp.__dict__['diff_img'].__dict__['intensity_cake']\n",
+ "tth_cake = model_dpp.__dict__['diff_img'].__dict__['tth_cake']\n",
+ "chi_cake = model_dpp.__dict__['diff_img'].__dict__['chi_cake']\n",
+ "intensity_cake.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2.60987346e-03 7.82961977e-03 1.30493661e-02 ... 3.03032372e+01\n",
+ " 3.03084569e+01 3.03136767e+01]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(tth_cake)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 244,
+ "width": 377
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(intensity_cake, origin=\"lower\", \n",
+ " extent=[tth_cake.min(), tth_cake.max(), \n",
+ " chi_cake.min(), chi_cake.max()], \n",
+ " aspect=\"auto\", cmap=\"gray_r\", clim=(1.e2, 7.e3))\n",
+ "#plt.xlim(0,20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Modify plot_diffpattern function to plot cake"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from xrd_unitconv import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[5, 20]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 973,
+ "width": 2208
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5), dpi=300)\n",
+ "fancy.plot_diffcake(ax, model_dpp, dsp_ticks=True, dsp_step=0.3)\n",
+ "fancy.plot_jcpds(ax, model, \n",
+ " show_index=False, in_cake=True, show_legend=True,\n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'],\n",
+ " bar_alpha=0.5, bar_thick=0.5)\n",
+ "#print(ax.axis())\n",
+ "#pressure = model.get_saved_pressure()\n",
+ "#temperature = model.get_saved_temperature()\n",
+ "#ax.text(0.70,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ "# transform = ax.transAxes, fontsize=16)\n",
+ "#plt.savefig('test.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[5, 20]\n"
+ ]
+ },
+ {
+ "data": {
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0bdtBnU3TLNvQtq3Y/+n4xGWk51K/aHO2Zi63rUfTOFiGMjd/LOvbWjbUk2ZP+8hCbX7vYts11CeWl2tLzh8r2Y7SHFD1FuqM/e+SLy7tlc65ZbnF48WYNE0zWH9xGUknTV/puWVvtdqd0nwLdbV9J5zvOnTdeL9Q0kHqIylfbNe0Mrlz4bbOphakc0qsm2WvS8cjplTee7+06Wl+C6Wzr2TTw787rsNkMjxHTadT+Gm7LBPL1nwly7qMy7dtuyyzu7ub9bG0vaeb9sC5N0MuAMC5c+fRzlY65+ytxa7m5rvlzJqzfzVxJ7XuZUwg6LKYg06wlfE8t+1jcZ68HZS+59aRRsm/CzY8hCml8S2NRc4HSeXkxl+qb9HO9ecW30LTNzA23mFhG+dBKcZk8Y1DuVSH1PbEciVC+VhezdqXxrq0NmvP4bXyNdnanqn1rUbf96P8lW3Gm0pySz5YrU6bnn3T+sfoXLsnWc/MmpyxPnlaR06Psf2qyS7FUkId/bGPP8iXzOva2EC6ri37QMq25lluT0zR9nctzhA/r12XMaX+0eaNJV5Wq5PUXycR77LKstRt2dvTumvzW8/fWoxOeg+5ab+W9s/cOTC3b7lZh64bPp/NZpjNMDhrLdKP0B3lz6gplnOBlK61J45txGux67q1GF9In81my2ex7L7vB2e+MLem0yma/Rb37t079psXeQ4O7mHaTcVzVtrmkK75ZjVo+1jJPkjE58pUp+F5ZHFuGBauUrtKr1RHa7zIssa0Np4UVn++1lcs+Tylc/Ym+5Yko3Zsa3yDeL3G9dXUbxmDEmlcrsaHtcY7as/hKZp9L/m7NXGVXF9a2ijpVDrrl8bPsmfn2qbF0qxx3LGU5nFtW6x2T4oB5eLOJUKcSYrjjJF1ElhsfxiPxeOV7l3XoWvq9LOcOVP90jEba7e22YeSDxmSnAv/ueX3VI+SbWl7oGnW43aaf1+7XixnQd8E3YMdW5Ubc5bMocWbas/b0r6YPgvPrXP/pIj3h23OTYtNF57EEop1jInzpna0Vka67zTNer+ldcTzyNrP1phoDSfpTwMh/pr/TUwtuTO59Xeg6brq2uhM2S7GwzUNGr86d7m5XEfpXGnRO03b1nlnzHpIf4eQdmF8/k6R+7v8HnGsrtLvlU6S9B0MYJvP636KW/4XzxvNt5V0CGjnnMXvnYbzZzKZoOnd8Rh6tO3wNyLh3fh0Ol3TX2vXoly/5qe2bYu+l9eKhOldZCPH/nPxUGv9pXO69H3btqzm/F86J47VU45D2stbbVZpjMbGgmviC5Y+svjFeln7nLPGsq32xpK/Ji6Sq2dl39Jn9v2rVG9NO2pthLb24nrHnO9Oyr/app1Iv4tzQvBpw9zOrY2SXx8/rumrTWLykh+S+16K04x5x1ezP9X5F8NzvTbOlthnrt6u65Oxk+tL0zbpqzHndS3dEsdN80k2pGlscbhc7MPqu+Tk1a7xetIzrnzO2va7CqvN1WKk24wFSXlL57TTtP/bYls6p3aoLNe2Z+filvcDk995fPZrDt7C/iuv4O4jT6xlCU04uHoZdx+6iv033hyKqHhfcz/740Gofwxjzn9j2Ja8dN6d1LsGu376e7mTtFfkwWb7b2IfYJxzvx/AJwH8eQBTYO3ilRD5+wUAv897/43e+7/h36EXr8R47w+993/Te/9xAN8F4GePH8VWxx//5wB8BMA/dM79V6erKSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQ+82FL7yIyeFhNs+NZ59Bb/w/rCGEkAeVd9XlKwB+DsBTWF26El+37AH8rwC+2Xv/Pd77v3t/VDx5vPe/7L3/PgDfCOBvAphDvoSlBfBvnr6GhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELuJ03X4dLzn8vm6XZ3cPPpp05Jo9PDA5jv7uLg0kXcfegq7lx7GHcfuoqDS5cw29tbXlRACPnaYHK/FbhPxJeuzAH8TwD+svf++fun0unjvf80gD/unPsPAfy7AP5VAHsAbT0hhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEvNs58+abOPPGm7j30FU1z9uPPYazr34VO2+/fYqabZ/Z3h7uXXsYB5cu4ejiefimUfO6rsPOrbeX/TM5PDxFTQkh2+bdevmKA9AB+B8B/Cfe+xfvsz73Fe/9ywD+LefcX8LiEpZ/DcD+/dVqHE20gXm/foeMc26Q7pzL5pfKWD5LMtL6msxmm5MX0tPnfd9n5eX0ytWX6hx/jz+nugV90jya7FSHUt+meuT6P9arNC+ksUqfaTrl6iiVS3UJdS360SO+E8nDL+sp1Zej1K9N05jbGcpJ+S3jJ82TdK7H8yqHpIs0roF0LebmrFZfrKN1nkjfrTbF8txKrLNWf852pt9zfZ3Wp5UN+UrzKXyW5oWmb834Wte+dZxzebcla8xeUENuXgD63lbq95ydyNminCxtv9T0svT9GB8grkfab3J6aLYip6NW91jSNRsTxjvVues6U/lYTtwv1n30pOf7GNK+kMaz67q1dsTPQv/NZrPB58Pj4MtsNsN8Psd8Pl+W2dnpcffuIm/o7xtv9Zh3i+Pezs4OptMpAGAymSz/C9/jtdu27VKONE/btq223TlyZSSbI2WttbfSOpS+m21v20fpKx/O+fyeKDHGXpawnlMs/ommY41N1PRIfQjNLsRrP6wR5xy6poP3/WCOzGYz9LNV/niuW/aAlLjupmnWvqf5LHNKSk/lBtlN0yLMsSC/adq1/SXkL/lIWrrWrrSNcfu0MtZzq+bDp9NNOztobdHSavfU9CwS2674c2rjpXTpXON9v7Trgdl8hu7Yjuf87dQu5/x2iVSXUqwilhn2DO15yQ/P7fvee3TTfq2O6XSKFnkdczrV6BDrUurLkm2U0nL9E4/zMN+i39tj/0HTK7fHee/hJt1x2mr/atsGbb8+plpbSu3S9tuyXL/8t+TTW+a75oOn61ijNE/7vofLuKWWPWYbPvuYfNr5PWcH4vGt9c3TfUTal3Nnj7i+tm3Vdsc6WuJNVv0tc7qGTeXV9vlJypT28XSMJdm5uLsmV0qvOauX7KbF3mvzymqTrdSWs/jkY2IOqS5SX1n93jhvqc8Wz0JdywemsbLUkWK119I5ZVt2YROk9VJrA2I5FvlaP28yd8fol4tDa99ze0iN7SjJ0/KU4u25dGnuWfq/9gxU8oGkf8cQx8XSOmL5bhknW6UfHNzDQeOWZ6lwhpzNZmjn7XINxLZb0zWOZwbi+LS2R2rytPUX+zDpWTKuJ36WW8v9To+77Wxwfm6aZvEriIKeaXwl7q/43/SzlTE2KKW0HtO4weqII8doLX5vaU8r+R+xjNKeJclYO7tl4le1tmls7LOG0t5b0yelerRxrU2P06T4T42e6TlJ1T9516/pZLXvkg3LldnG+qyhZp/T1pZVhuSr1eiWW3PhuaRLGo9N2xGnb2pn49izxb+plZ/K1toc11lzlpDWYur/x+TOqdq6HtYzTB9zVojrk/ZxSQctLdXdUmaRd92m18Rxx1Cz79TsH6W+t8R74u/SHF19X08P8yNvy6W0sk3YdK8dlh/GhON3Ram8TeMRsU61e4olX8m25tJzz8fOcct8LcW0aurQ7Khm93KyZBn59Zp75xeeWdvb9714huv7Hoje5TyIv+MI1MQRY9L3f4BsS6w2Id1TtDJp2Tiv5LdK5fs2/PYUWDNwSznr75bjmLwU+9HaWvLxSmlyv62fyyz9ZiF9x1Oav4u5MMyzWBv1OmTHbUPbU0vQYfx7h2DT/PI/6/lIl7liff0M07uuA/r83w7k0kuka6/v9XhTOp65Ps21O3delOoqxdek/KW1O8Zn0+Io1npq1rPFx4ifbxJDrdHDage15xY9tTNvjQ9f6uuhvw2s/JZ6v82avom+ab6cXGu8IC5mmRo1c3JVx9j5X6ePpFutvhbGxGLitDF9qKXlzhDp+K7SZD+6XHeQq+u0nu4GZ9RV3uF8s+g81hcac2bctI56G2Erq8XbLPW3/brttpyza+19TSwoh3geU2RtGi+Q6rTWYUXbR3PntZwfrZE7V9TamzE2fl2XUh3rem6bTffd2njgmDrG5E3rHDu+Uvxjm2dRCyXdLfVbffrBe+K1DWfxPyG5cW4Zq7zyuc/j1SuX0Au/Iw5l33r/s7j2T359cWos6GwZLyl+O/Tb6m2ExsGlS7j11HtweOmisYSDb1scXr6Iw8uXcPPZ9+HMG2/i3Mtfxu6tW0u9trGmx5w7amSehN0p1WuZr6el1zbq2Za9OGlbQ/Kc7lv1BwMP4KcBfNB7/8f9u/zilRjv/ave+z8H4GkAPw7g3n1WiRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELIfWRydISLL34xm+fo/Dm8/fhjp6TRdpjv7uL1D38Ir3/ThyouXlnHw+HuQw/hq9/8EbzxDR/EfG9vi1oSQk6Dd9vlK/87gI947/8V7/0L91uZBxXv/eve+x8B8D4Af+1+60MIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5P5x7pWvYOfW7WyeW+99CvOdnVPSaDPuPvwQXvv4x3Bw5dJW5d57+Cpe/fjH8Pajj2xVLiHkZHlXXb7ivf9D3vtP3m893il471/z3v/w/daDEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsj9wwG4/NnnAe/VPP2kxY1n33d6So3k1pNP4M0PfgD9pD0R+b5tcP39z+H6+5+F3luEkAeJd9XlK4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCKln585dnH/5y9k89x5+CPeuXDkljeq59eR7cPPp9y5ukzlh7nzdo7j+9e/nBSyEvAOY3G8FCNkE5xycc8vPAe89/PGtaT66PS3+HOeXvsfl42fx56ZpBjKlejTGlOv7vig3R9Os7ltK2xunaXrE/dr3/VJeTq+4jFafpI80rpsSz5egm6UOaS6lzy1jbsEix8Et25K2aS2vc6Z25taGBW3upOtH68tc38Z1xHPYkj9uu7VdUj5JP2neW9phJW2rRX9rv6ZyNdnSuOZsl7YW0rWe66eQN25/LFcqr+mv6ZrOi9IaGbu+rXY+zKWSjc6lB9nSGtFs9BjbVdo7U/lpPm2N5ojnRS6/dV7E+WvXSLoWJPsm1S3NsTF2MNUr/qytU0s9pXZY5nK8b7dtay4nsa09dRukY1fax9L0rutEmX3fL9fmfD7HfD4HAMxmM8xms2X63bt3AQC3b99efj46OsKdO3fw1ltvLb+fPdvi2z7+EJqmRRjOT3/6DtrJWQDAuXPnsL+/DwA4e/Yszp07N9B1Op0udYvbEnQMYxq3MdibnD8nUTO+qe1ffM/P6ZyPW6ubZrvWyjmPXLStxt5YbEH43DUd+j7WbShrk/GIbVyNH6Wh2RPrmSQ9g6U6AoCbBF9xlTadTtH44d6onfM04n20aZrl93R/lda7xCZ9mdM5Tk99C8s+GstJ64htQCD11aTPcR+V6q49/6U+T1zXGL9Dqz8uk9pCyV5OJhMxTywrnTvOOUynPXZ2DgY67O+fxdHRul7SGGn91jQN2n79edM04rhaSOvfln2Q0n3jj8cuTh9v18fokEsv+Tql+iXfUm9bSLed3fu+F89Qocxq/8jPrZzO8Xdt7af15wg6x7r2fY/5fHPfMN47Ut3if9P09Ltv0vW4sA0TP1xPmg8p5Qn5LLZ6m76W5ZyRI7dXaPYunpfAau/cZuxvDFafwHr+re1bS/1SvKDoo2bO0toZMPZ1NFLfCND9IEsse5M9Jec/bLoH1FLjb2lxKMscG+PPaHWk81aLu2b3amUeWuIiJXzTHNvHVVrTNMX4cFxfCUnnXBwl9e+080xtjNG6DnK2prQOas7icV/k/OMxbLrmamNNWhwsF6NL82pppbkQp5fO+5I+ub7PPRsbj8uVqY1bloj7MLem43buNBNMp8OfNZw9ew7uDHBn52gpzzmH3d09ONjfiUqk/W2xPRra+Gv+oJRP8u0BoJv2uLMzO362SNvd3UPb6LEQiThWKSH1Ze3ZwZpe0iOuf+VfRvuFW/4PnNP3jty4WPZq6ZyVtiXMydoYl5RXshW1a7B2fyz5kDU+mOW8mdahodnNVMYYP187m1l/F5HKW9g54fzUtGjbet9LSrO8WyztdbU+TSp70/0g1jHnr2r1p+Vy+dP605hiLK9m7whyNH1q43BWX0UrM/ZsLz3LrbE4bazdH36XfTerzjX5rH5AnNe6LmrR9p5trK1NqJFjyRvb0dh2SbHEgNQHY35Hk6KVCfH0pllfw9IeLiG9qygR+w7e+7UYWvwspbb9Vt2t5bZ1xsvJ6bquup7aGGnqN2z6+0zrmljZl0Va27Zoe32Mcu2y6Bzn0T5Lem7aHw8SWixIsz0hvWkW544a+aksLa91H03901L+OC28E3JuYUt836PvV+2Xymkx37SNmj9a2sNrfC3vV+uklFdj1c64T/Ux0vabxXvX8Mwv824SN6ihdE6M820Sx6st732P0B/SnJJ+GzkGzW+Ix2CxXmVqfMnQv9Kc1+IM28C5xdwMDOJAXSeeTa39mtvvLPFJXWf57zhOGsnmlPz8YdtC3hBbcoP9OUfpPUKow2Lfa84A2jlNyrMNFmMLDPdAu3zrPriJLMt7lzSfpR8lW1byyWt9UKmMtpfaYlx1MdtczNqyD1jP3dqesCnWvSrOt4pBS3uVhxN009bowr+KZci+5bCf9Ljfar3ppM0txeVy68Dad2OIx16zl3mfBkhjRGF8SvFPrc25vU3q+7ExIEusbduMlxuXs78r0XykbbPtvhzu/au0MW3ZdB/OxcAX/4V8Qz0fNLYdO9wmqR0eo6sX/MIxYyGVWc3HzfZaC1a/Y/hM8buifyVZF196GfeuPYz53p4q68Zzz2D3/7uBRvEHcn28OvMNz2Spdt8qvwAAIABJREFULqXfH6Q+U9u2uHftMdx639Prm23SjmY2w5k3r2PvzeuY3r2L5miGZj5HP5mg293B7OxZHF65jHsPXUXfalc2LGTdffQaJocHuPiFl1R9c2i+3CbzZox92yT2cJKytkHJz8+tKem7Vi6kxclx0QetX95t8PIVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEFGn6HleefwFf/fCH1DzzvV3ceu+TuPT5L5yeYgWOzp3D9Wfel83juh7nv/QlnH/pS+LFMe18jnY+x86duzj3+hvoX/g8bj/+GG49+R74zGUwt558Ers3bmLvxs2N20EIORlO57plQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEvOPZe+sGzn71q9k8t594HEdn909JozweDd587uuzF6RMDg7wyK/+Gi5+4SXx4hWJputw8aWX8civ/Tomh4d6RgfcePYZ+FrFCSGnBi9feQfgnDvvnPuQc+7bnXMfd849db91IoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDy7uTS515EO5vpGZzDW889+0BcOHJ48X2Y759Rn++8/TYe+bXfwPTevVHyd+7cxbVf/020R0dqntnZfdz5ukdHySeEnDyT+60AkXHOPQvgTwP4AwCeFZ7fBfArAH4GwP/svb97uho+GPTRrWHe1229aX6tvFWucy77PcgK6c65gWzn3PJZmh6+N8ptaqEf4ue9cKNampbK09oqyWqaZqlv27ZLXYOcIKvv+0FfSHXEfSLpFsuV+kjTMS1vSZf0y+mc+1yqXyPuv0XZ8N/xc3ho0zLuI0n38D2dY1KenO7p/I311eZvbo1p9WhzXiNte65tko45PUvPJB1ylOrM9UtcxtJGScd4XLSxjPNr8tfnq01/DWm+lD6X5qz2XGt7QLKtcd+NaZ9k24INtchMdY7Tc3ZQIt53JN2kcc/1X0xq+2O50vd0rafrUqsjJzvGsvZzeTU7FvdLzV4jpUtrrsaOlepLicev5ANpz+P9u7TX59I1uaW00h5h9fVicvNK299y9QcdJbnx/J9MJoNyocz+/uJm38lkgr29PQDAwcEBzp07h6tXrwIA5vM5ppMOly5N0TRuWed7nrwK73cAALu7u5hOp0tZcZ3he6qX5pt2XbfWllBesyNj5mjMWLtrneMluTnbP5wXenntu2a70/kW+jaeV27i1tZB0zRmH99iB0L9tWctqa7wrzQv0vpyz2OZg750PWKfOa07lanN0bRcvE/H/Z+Sro2c3mmdqcx4XsRyJ74XbFsP67261j1J84ly5++adWTJrxHmrbW/Ux0s9UvzteQvh3kS+2SafxDPqVCmabBWVzefo+tk+yr9m35umgZ936Pv1+dN3/eDPpTmdap/rv9K8Y8a3yCNK+TMTzoWmu3M1ZHTp8b2WW1l7mwi1RnGr7RkpDWp+ZeyLkDX9UBhaWljb7HZpfQQZ0rn8XQqh6+tdsQSI8npGMtY6BfyD+XUntNK81OSHdcRy41jdKX2xufc2jOkVX/prCvpU6IUx7SW24TYT7b5g04dpzEs9wTBB9d0kBi779bG5XKUzpXbZlvxuZMkZ5PG2tvaOqVnpflVE2MpYR+nVd219dT2WcnPKfmFVtm5eKcmr2maYp3p/q/57SWfJP0u+V2SjJz+krxSG3Jp2nPLeOT0tPg16f5c6zeGchKlvSPe90vt0GyHZYws5ORY6sidxet0WPhocaytnayfoVL50ny35k3l1vSpJMvyTjct65xD3/bHcYGVrez7Hi4qajmPS35bjY9fswZLz1NdcsTrIdHuWMf1/oj1kObiGN8g17+WeTVmDx3rw5RsrFWWZcxL+5tUb5pm8SG1sdTiMpp+uTzx+Uk7+2l6lvSwyMr5WqWzAlA+f2qxsJLtKNnxUpnceOXWjlZXTfwJkN9lajGyWFbNHJCwxtu0+jf1b2ux2oi47yz9IduE9TSt/8fuQxolH0HXeR1pHll/zyTV0TTr467vgcM8afzIos9Jkf72Io4f1diLwNJGT3u07bDPp9MpvPdommG5tm3RdBZfZX0cNB8lnS+lWIpljjbTfk1O27Zoe1s8UdMNGD/22u8GpXi2VZc4fcw5sCZutYktzMV4NyF+dxO6p+s6oDvd+FQJi/2S3leXyo5hG/HF0rzJ/dYpTbb4ebn9vdRP0m9uQx+0bSvGoYt7sHIeqdm707y5d7bSHOn7Xn2nIbFom4f3IZ8/fv+5Xr4Up0htfBxLkOtdZ9Fe/Z2ehdq4WEzNb0FKhLGQvltiihb5rvI33SX5TdOgw/p8j8/+uXHIxWQlpPfsm5wJ0jbHv8NI9wQtnhHYxj5oOZuMiRlY3zVYn8WU4qJpum2PKOuprYmA5b1v6bwspW/z/Dc2/rQopsdGc+8aYlsvxR4kvUp9Y82vlbeWXewbqU/eoO3tsctNkezUGL82HQtLvlr9ap5b539NXaXyafv98Tk7FpvzC2I5qQztuaUv4z0l3XMWsW0f7RUe6Vr0fvz7lm3+DsBaJyDb8XWbGv9ueZWWnkFr9qht20cLNWtq0zleyi/LL69RaZ7k3mvkYrS1bLPvVvk80n1/U7m5cpY25MYpfLWe2WrPGduIo+ae1cjfRJdtxAryZfX3GLEeNTqu5uPJssnZc6x85xza2QwXX/wCrr//OWhr7ujiRdz9ukdx7tXX1Hh8Sog/LPo0PSytr6WS7e+bHRxdeC92b/tBvCTQzGe4+tufRJO5OEWTHdMeHODKJz+D17/pw6oJuv3E4zj7lVeNFkrHepaznmdiNvn7/bg6SzxtE2rfg8h+f4v2+Lf3IY823zfRMbWZQ3GbnQ3J9jgZr/ldjnPu4865v+Cc+1+cc7/knPs9FWVb59yPAfgdAH8OQNht0v/OAvhuAD8J4AvOuT/juJIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJwC5159DXs3bmTz3Hjfe9Ed/x8L3w+OLj4N7+T/U0AAuPqZ5zE5PNxKXXs3b+Lsa6+pz+f7Z3B48eJW6iKEbBfdSpBqnHN/DMAPA/hQSMLiOrJLxvJ7AP42gN+D1RVFueugQp6HAPw4gO93zv1h7/0rlaoTQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC7iOWCyq81//02DlnSrOQqyd+ViN/E12aaYOjZmeQ3k2n6NCKZdrZbFRdpJ7Lz7+AV7/lY/BNIz7vJ1PceOZpXPnUZ05ZM8DDYXbuCfX57s2bOPPm9ewf9Ndy/qUv4c4jj6xuAki49/BV7N28ucUaCSHbgJevbAHn3DcC+EkA3wrVDJr4bwH83uPPsY2WZHohz7cD+MfOuX/ee/+pDfQghBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEnCKv/DPfZsiVuyZCunxlnC6Zu1dQ/jNomU10adsen3j87UH6y5Pz6HtZ6Ht++VfGVUaqmd47wIWXXsbN9z6l5rlz7Rr2v/Ia9m7cOEXNgPn+I/DJpT0xF196eet1Tu/dw+6Nmzi8fFF8fnDlCoDPb71eQshmyNdHETPOue8D8AkML15JL0axyPlDAH4gKeugexwueR7KPAbgHzjn9N2JEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjZAhde/hKmd+5k87z13DPwY2/gGcl8/1H1WTOfYfetk7kMZv+NN9Rn8zN76CaTE6mXEDIersoNcM79QQA/A6A9ToovTam6fAXAj8aik2elK+jSC1iuAfg/nXMf997PKvX4msBVbLxxXi9cwxenSc81+r4X07uuW35umtX9R5ps7/1SR61dbdsOymt1p3XG8pxzSxlpPXGZHLEOoUwsNyano+X5WHL9HJP2TZoW50/bmMpK86akac65ZVrbtmgah6ZZpTWuARqHpmnRtm1WXqpXeJZ+1vL3fW9aT5KsUjvj+RaXj9O1cUnne0nHnF658dHWhIY0llq+GnsS65KTHfdfjayczJBfs0M525EjV1eJtL5cP0tlc3Y/nmNhfeXGVftcg7QGc3NEq0ez1WPsqTaXuq7L7gmWukL5OG/f92L6GFkStXMkZ8eltNK8Lc0Nq365/UbbX7Q5rtXZ972p/XG6Nl9Sm21ZI+kazeUrPdt0XpWokRn3Ud/3mM/ngz08yJrNZjg8PAQAHB4e4ujoCADw5ptv4rXXXgMAvP322zg6OsLdu3eXZfb3G3zndzyOpmkxnS6OeC+8cAs7OxcAAJcvX8a5c+cAAGfPnkXbtkvZ3ntMjoM10+l0oHPoi9jXjPftVP8aGxBkxaSy0/m6+NcNbrlumkYdM8nel9aNRNwu3R74wY3efd/DKTdna7pq81rr177rTetL829K+UrpY6hZ36W2yfNn/Wb19MxV2rvDPNTGWrLDsd8A1O+9cf7cnMZ8dZZcrYth3qZpqsdMs/vSnmCVUcpn2Wv6to/y43h88/5uab2WzmOWz7k5GtvONG/QLbQhtm2x/Qv/7uzuwglzIT5rl9rXNA0m3qFpmsHaaNvhWVJqX852bHLmqDkHda4/Pu9FaV0PPy/bMYufXnsms6D5lyVdymfakG8oN503dT4qEEKKzgFt26Dt12MMQz3y5ym9LnkNpekrcR6AG6ydIMcSf9Dsda2uafm+79H38XiuykhrMzeumr2S7IOkf85njcun8y/uP4sPJclM9UzbEKPN/zS/Ze+M/ftUXvzZ6s9L1MS1tPRSW2rOKNYz61h/rWmatX6tocbnqdWxRidpPdXaJ+tZVCt/moyJwwVOS1/N17P4gEM77+H9cK3X7uta7LjmbLiN+F9aZ/zdembbpD5pDGpst2R3JLtes7eUdJWe52IGWlmN3F4s6ZzWrcXYxvhHMaV3a3F6aU2UbGJpP7Ge4aU+S324OH8uPqnt9W0vn39X785WZ8fUh0xlhc9j9r/cfNtk7KW+1/QbjGvjjvvF9g4s/S6t8VzamDmSyirFpUp1pLLiM4T3PukKl4+zKHVo1MbUrFjs2Nj1WUupr3I2OtVr/bxXvwfFZ7Exe4xlveXInZ9ydi5Xn3NuEZfpF/Jy7drEJ6iJP2i+kuW3LOmzuM/EeF/ybjAlF/fK6T0mXpHbR2MZlvlrXaPW9Nq4Ws5mbzv+pcVEY7Qzejrm6/2a31tTG6PZpE3PSlb9Nca8m9f6M8y/tKmaDxaXjX+bJn236JJSc67QZG1jTsZy576L2rZIn80WP5WM43jAcR/0+b2u6zp436+ldUbXseasqerQ9Gtrous6oJPfTWzqi4yZ50GnXN2bxOissUKN3L6g7VXWs9ZJEc4ApxGnA+rm6lCnlS2JfTVs8J7USmqfLTGiVJdt6RbOnrU2TbPTlrak81rar2KdQlsHsgsxrty82DQmHfSR5njOnvkm3evkmFT4XHsGSPWx2ETp/Yb2/6peqm8baP0xxpaO+Z3Dot+H8RznGtWmbboOa37DkJI7s0r+c9Oszx+LrU6fF9dLu/h9+CApOjda5Gwak8xRGw/JnUVKelrX8KaxnPXzh/0dQU09cfnavk/ju+kZSJvPuZi+RV/dxg51G/u7yVL9pZj4pljHYZhtGHs+LbS4inMOfS/9xrkcNyitZ8u6Lc09qYzlHYxlXm7qjwxjPYMng/7T2pWXvZrr0pyV4h4xi3rienH890PHfl7jgbXfzervEWQdbetwqFOZ2jhY6VkpDiKVt8TB0jPfUH6+vhw5G7WNGOVYynWv96Fl7pd0vR9naXu+su7aHDkNwhg4tz4nberkMsn+lD5PcmtJk+8B6Oc9oGwLa1mIPun1sx6rXddjdaZN++ek5pIlrjPGDktrxcGt/dG6Ra/jBFz+7Av46kc/og7VfP8Mbj35Hlz84ksF6dmas7oM5iGA+ZmHVEm7b91YLEJn+51qDbs3bkbf1jtkfuE8Jidw8Uu6DtP2hBjVpnv/ml/SYnnW3SSGVqo7/i1mHH+y/n2lFOdxmd+b5Bj7u9Cw9obxPvlvxsnpsv1I3rsE59xHAPw0FhevBA+g7JHJsn4fgA9EMmLCCnbKf1r050MA/v1aXQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkBp2b93Cua+8ms1z+8knMDtz5lT06afn4Zup+nz31u0Tq3ty9y6auX5J+mx//8TqJoSMY3K/FXgn4pxrAfx3AM5geDkKsH5dluUylj8hpMWXuXwZwI8C+DkArwN4FMC3AvjXAfxurF/aEr7/iHPur3vv3zToQAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCLlPPPaPPrFReefW/6y5aRr0fS/m9z79s+iyfO+9Wk+g7/tq2SWm0w7f+i3JxR6/+ihms3ar9ZDNuPj5F3Hv6hV0uzvic980eOu5Z3HtN3/rxHXpd85nnzez2YnV7QBM7t3D0flz4vP5mb0Tq5sQMg5evjKOHwTwzdAvXnEAXgbwPwD4JQCqp+OcOwfgezG8tCVcnuIBfBbAd3rvvxo9f+n4v59xzv15AD+G4WUtQdY+gD8F4K/UNpAQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBByerQbXgahXYriTvnyFXcCl6+06LDTHw3TZjP0M7lt5P7QdB0ufe7zePMbPqDmObx8EXceuYazr31VzbMNuql88UngJC9fAYDJvQP18pVuR76chhBy/+DlK+P49zC8aCX+fAjgPwDw17z3c4Os7wWwi+GFKwEP4PuTi1cGeO//c+fcEwD+TCQD0ecfxNfw5SttO/42utRp05y9OG/8XMsrEfJ67zGZDJddrEf4nOpWqivNHzuoqRypDVJf1Di14cZDSf/4s9aOVK9aJF0t+lvGMxwEAKzd7KjVMeZAoNXf9D28BwYi3eJ/tH6LxyHWfyA3M0diOWn5Uttyz7Vnmo7WtLh8bh5p9cRyNx3TtP5UL+t6kOoNY1bSNZTX5JTSJB0l25HOM0mWpMOmusRIa6DU/qZpsjY2HjPLuKfro9Ze16yvNI+kozTX4r3SMgdz+mi2Q7v5tiZvSM/Z2nQedl23lj6W3NjEdcZzxLqOc7Y6lRs/02yKpp9WRzwvSmMVtystr623kj5jdJXKxXbBOudy+QYBPcNYauVzcyeuP7WD8b9B1pkzZ7C7uwsAODo6wuw4gHPhwgU89thjy/Q7d+7g4OAAAHB4eIimOcK1a7voun4p69q1veXNzXFbu65byg3PwvO+75dt7vt+0Ma0LyR7EM8fq5+V9pnEep8Nn0n9LJXXdNDmeJxnTDBZ2kdr7FWxziaWNy4gHcpr9j3VReuL0tqV8sbzqhR0t9iIku7p9zQ9nkdN06hzWPNJgrywN43VMS6f1td6jzDWK3vYARiu47gdObZ1c32qZ0yt7zDY69wqxBHEl86Omt+T0ze3v+bylXzY+Kb+rusG6X3fYz6fL8tMJnMcHByg7/vlHHjllTnm83ZZPjCZTNC27bIPJ5MJptMpgPW527YtdmYdjo7iF0wOh4eHmPlVbKJkC0pzqYYaf2Ix3roMbT1a6gXks0GJnP4xmg+bs5G5M0/8KN67c2fY/PkYWIUQV3O3dN6Mn5X6QjtnaUx6n8w1j7Zt1mJ/JX88V0etz79WvgGaJuxjCx2DnNIZPdU5Tk/XWDwOFttTcx6L0fxhS79q30v7vranp/9PJxo1+1xMbq3l2h7rb91X0tivVL52zNK2Wn3K0nwJ+0vYk7QyWp016y0nU5oXsU419Up5NDucG3tLjGcMOV+jlDf3fEwcMKW2X3M6aOVy+6Z0Tjh+uvzU9z2Q+MupHlLd0prLzYVU39L+OGaulPxaSc9NqF3DNXqkYybNx037UFo7mh9v7bPcXlyrX1z3mHNraR8do49lTdaQG0OLDbL2i9YX/vg8HCcvzlwNYjvhnEPTNGjbYUzB0pc1MUxNlqUvrHHU0jgtnq/voYv2y+/PrXbHsnduyz6l8tL6ataRc0B4hxro+x6u4HpZbdNJ+AZWrPNGY8w+kCsTz+Wcf2CVra39QBrvKOmZYt0ncvFCa5k0PfRV3MZFbGhVR83cyuWN/58qc+8/rOusZPPjekv5xsZyUx1r5kHJLy7ZGMs5ObcOSvO6pKumkzXeYh0/S/2a/JKfD4yPWZTkWp6HZoVsuX1YS4/138QOWeszlFyW3+ZebG1DKa5lHe9Nz6jr5bezR69ipMsUACGmr4+/Zi/G9E0iXawn9vU2nX9peekdS6q7xe6naaW1t425k45D0LPGV5Bsd8neS4wZ7+AjlHzXQOwfxfHV3P5msWlBnlYuXiOSfT0tn1nr43R+5n4fWWNHmyaNfSzWS3r2qnn3mj//rpe32H7p91aDMooesX23jqHl/UL82fI7b73tq/nmnBy/G0vf92gh2+sxdlyzPbG9i9/rb+N3eNtEarOlH1bnH6DvPRDeeXfdfTtLS8slF/OQ4hLSugvnuVy7OqXd+d+pyTHxVP8S1lhG2s5cv1hkWOMOY9/tWdHav+bz9B7ODd9LB/ti0SkXS8qdz7Tytf64RUctvTYWv6p7qEfct5v692MYex6yxycAbS3GsrYRL605aw7n9NBuBH+yJEuyRaEtkl206mc9b2/7DDNGVml8LbHEvl/fI7yX947Qt6moOG94B4LoTwHTuKJzw/1N8xGtfWTpV8sZbJO431g03TeNJWn11MUo9Lot6+R+vofQqNHvtM+G22TMXD6tdsa2YozfIJVPf6dco0dOv7TuMfXcT2r6OC4T/pViJidBbFNq7FAgP5bJ/ga/tmta4gdpHfuvv4E7b17HwdUr0OK5N595GntvXkeb+c1YiZLN7tszqhfgnMObH/nwIL9Fds07FsnvCn3eTacnMmekeFHQSfpbjRTNl8zlzZ0rLXGGmjrjfNoZMNYnxKi89/DCO+j499k5HWO5QYYUS8mx1Hfer80T7/v7Fh8iK7b31xPvEpxz3wbgufAVGFy80gP4Qe/9f+FtF68AwB+QqjmW+3Pe+980yPh3AHzl+HN86gKA55xzzxp1IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCRnP5+c/BZS7k6aY7uPm+956oDn6yd6LyN6E3XFxLCDldePlKPf+SkBYuS/kJ7/3PWAW5xZVE3w396sz/3iLHe38E4L+E/n/l8LutOhFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIWOZHB7i4hdeyua58+ijOLxw4cR08M3kxGRviuflK4Q8cPDylXp+V/Q5vjRlBuA/q5T1MQBXjz+HC1wC9wD8nQpZPx2VTy9z+WilXoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyCjOfenL2Ll9W8/ggLfe/yy8cydSv3cP7gUnvuE1D4Q8aDy41zU9gDjnJgC+GcPLTcKlKT/vvX+lUuR3SdUcy/t73vsDqyDv/SvOud/A4qKV9PKVD1Tq9Y7Be5/9XkrX8uTyu+MN3Hu//BynS7IssnN1hmdOcR609FK+vu/NOkik5YN8iz5N4hSksixYx2yM3NCGvu/Vcba0s3a80/S+7+F9+M8v03rnMJ/PgHm+3en8k8bIOn/SOa/JTdNLY1PSRSqf6lLLGNuh1Rfros1JaVxL+jdNM1j7lvmesxHps5o2p/PIUj4uM2Z9SjqX9B1TlzZHrf2Tq1+y3bm6+r5fs42hfO3aHWNTY5m59W6R3TTNMl/cplR/aY6E/FJ5re4aO2PJo62XnO3R9mrrPM6t3xo/Il07Ut+GdElunJ7atpzfkPMBJBtZS1rWMi9KcqRyNfuLZuOkvszhnFvq0nUdZrMZAODg4AAHBwfLz3fu3AEA3Lx5E9evX8etW7eW3/f3G3z1q48P5H7yk1/G3bsLubu7u7hwfAvwpUuXcPHiRZw/fx4AsLe3hzNnzgAA9vf3MZ1OAQCTyeqoOJlMln0e5mS8RrX9W7MpaV/FfSGVj5+nYpxzou3cNmU/IDwv+wLS/h7kxP1cmovS877v4Xqn7m+W84v0fUy6ZhekemPfqLQOtb20c93xvFyV7fsezss2PbYBTdOo+1WaV0qLy7Ztu2ZrcrLTNMl2hLqcW7UvjHEqL96Dc2x6Fkyx+M3xM23dLs4/43WJ537tPErLxLYu9W3j/ot17vt+UCb+3HUdAGA+n2M2my2fzedzNO4I9+4doeu6pawbNw5wdLSyCaHP2rZF0zRLe933Pebz+fJZsN/t8S3oC5lY2s90X47tTcknkdK0NW71bXK4Sbc2VyaTFq0fvgDZ5Gw45jwgtTGk18QpSnvj6rlHSPY+2Pp+LX/6XTtPAkDfe6ThQ+91/0bCPI7Gs2jTLWIfx6XWdErJnQ1y+2hpvsTrcE12uxqLuP9ybbT6hFYdpb18rC9k6aNN4ydpX0hnA2kvS5/l/EYtPXc2ir9LZ6DAag/W5Ug+XUqanraxdo5Y9t20/eFz6qvkZFv00taiti5KvlAubRtY5Wo2veSnl8amdK4o+fAWauode7bOyS3tq6V5VbKbkt9Sir1pfaLpavEn0zK1sbtauXFaKtvSX2N0kuZzqX/T/tPqr9G5pHft+VWSPWZ/s4yF5dxhSS9REz+o8U20+ay12aJ/yR7kfDxJ7fV0f+wvy3pa7cXYtpRiQTkZkh6ldClWFuRabbT0LB4H7/1yv0jLW/1Qq82ylI/liO1PxKcxH4nSOFj8O6vN1OrX5mM6d2t8Bc2XqXluie+l5S022+KbWPKn7dCoXQ/huWWOazG2uO/ktTvO5sfjIulXM9/H6FAzr0vnOWkdSWOe81NjWdYOd8yjAAAgAElEQVRzkqRjnKa1R6rTKqvkI9eeHSx5NzlH1IzzmLqkOhefT+YcGJqTzpMxfkxNW+P3Z2P2vzgm0TSh3tX6j9/jae8rrO8BrPtQTs8aNj0rrZe3+eEWucG/yImI+6V0Bgrpkn3W3lUv5Hloa8LS57V+JrD+zjeOJZXqKNm5EuHdltZPY+KfNXNT83dDmrbGYuL1ntN3ma+p9xtjxv4myII9HhJ/Xj8Tjd2frONden+7TaT3l4t3gra+att28D489540RurX9FlM+ltDAOgmPdq2OX6+sFNN26I9tjHtZIIp5HVuiQucNPFaieuP06xzRpIVnxlLNnLxr4dzoT6/JmNZl7Ku4/i89b3+abLpunIu/q987tFiaZrftfJPhvGgEAsKnxd51s8a8b4d23f1Nwq+X77jD2UODw5wNGvW2hf2S20/DfVIe44UFwhpaeyypk9zcc+YsTHk1CeylNfsWu6cY/FJtXO/1v5u8F56Qd/r58pSm3L2WitTil1KZUr6WOq39LVcLuRdl1Pry+TOGtb5p8UWJLS4RJZm+PuzRblh+TH7YS4uUbNeg61d6ejQtsNzooY1hm3p41z6pjGDmG3GHwAALaL9fEHTOPVcINqYJh2/1f6XyljYCQBrf4KY1VrYSx2cW8mIfZAxsZxSzLRkq0sxKuv6LvfVon+1uWi1fdp+tTqDA/G41MRwNRttsZVjYxibyBi2eZWmzf2atXeaZ4VtMGZctDHeRrx9tQZkauL/2jl9E52t/tg25vWmjPFP4n9rysTfV75SuXx6psn97arE2Pef2yC1GTUxLgfg8mefx2sf++ixQxO3c1F+dnYft594HBde/lJB0qpMrFuqV0hfxn/XW2TSf/W8PE/i9ZbfQ6N1W+lzSUjzQovv1mAd4/gMHc7aazGcbvU8Jf5bwjEx4aBfrIf0fKlv06ydDbuuA5TyOf8mbmttvKf163GJ3JmWnB68EqmOJwDsHH9OLdevjJD3XZlnvzBC3q8l30ME8XEhLyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQciLsvH0H57/0SjbPraeexHx3d/uV+wfrEtQB76z7qwh5V1C+3pLEPJV59v/WCHKL6zK/A+kVWyvGXL7yvJJ+bYQsQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFkNBe+8EXce/ghzPf2xOe+bfDWc8/i4d/+na3W63yXfX7lM88DXvtT/2MZrnxLimsa+L580YuP6yrUSwg5fXj5Sh0XM8/yV26t880AzmNx+YrD8BKW17z3n6mUBwA3lfQzI2QRQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGjafoel55/AW98+ENqnoOrl3H3oavYf+PNrdXr+ln2+ZnX30DT5S9osVy+0jQN+trLVwghDxzN/VbgHcbZzLMblbK+U0gLl7D8cqWsgLYD7IyURwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEjObM9bew//rr2Tw3nn0GfdturU43P8g+7yeTrdVFCHnnQ4tQR+7KqVuVsqTLVwK/VCkrcFlJn4+U98Cj3fAVpzvnxFvFSjeNbev2sKZZ3XHknFvKDfVL9Xjvq+vX2hO3P5WZftf6Kc4Xf47blkO6ra3v+0F5TVZaf1w+1tdyc5yVNnHMgv65OizjlcujPVvUufgvVL8Y00WfhX5L55WFeF5Y1lJOz5DuvTfJtYxdWl77PBap3lh/6ZlWv1UfSYZWNp3jKTVjLemb2sn0c7r24jza2NaMt8UGb/MWx3iNxHK12yQt9lGaL7V2KZaR2h5Jn1Sv3G2YObuqYbld00IsJ7b32jqK86e3fFp0qtnTreNSS24uxHItayyVE9tV6xoKNE0zKC/taVZbm/oxY2zf2PxB33heh3zpXM/Ns7jd0hqx9qtlLcZ2LP7cHd+GG773fS9+bppmaRem0yn29/eXss+cOYNHH30UADCbzdB1d3DlytlBG97//rM4OI4P7e/v48yZMwCAyWSC6XSKvb09AMDe3h4mx4EirX/ivgvzMDdO6ee4vdI8sviqqzxYy2OZY2PGVtJLWuvxOgvE46ftKdv1ZVdrJIydts5zaQFprUllcnt/zbiU+kJ6vqZXCzjXYHGf6SqvZp+bphHbJuWXdNJsT2xr47Wd6hzmTpqettE5t5xLTefX+sK5ZuBDeO/Xvkuy27YV2yztz6WxTH2iOH+XuQFcm/dB/971qF0aJZsg+VSW81Df9wNbH49zmn50dAQAmM/ng/IhPaTN56tQzc7eBE0zH4zdpYtn0PVTAIs+Wc6DY/uSfgeG8zX8uzPpMJlMBn05mUzQT1blt2WXNP/OcsZK8wY7YrH7pbOVVkf63XKekb6X0qyxF5su+Xq1M8BanzUeYe84TlHlWvb0EnGZnN+7upd63abmkPS1zEFJR0DfB4f9pI9ZzZhb01PZljblYnpaXECziek4WGOCKRb9rfanJq5gjSmle1ruzGDxI+Iyuf1eIj0nW2yXdQ8+bUrzxbLOczJC+U1i1RJj9sWxfn3urL/tdknzpOTnW310S964Ds2HdM7BD/YFmU1jh7E/nmtPbVwprSNdzzmktV6KC256nrTGhUq6Ark9tFw2TS+dY2vjEhrp+Mflx+7XObljZAV5FmrnQ+ksZHkmzdGcHjkbo/kkIWbm/Wo9zedzdJ2L1oK+ZjS5mm9bs6dr5bcb91mx7tOV82lIOgbda+eG5ZwQr4vcGtMwxQHd+rmpREkPLT4Svsd+V2z3cz5iGreX9Inbq53LwjNLPGfMHrYehyr7sLn1sw37v41nOdsvfc7JjWNsgD2eVkOqS+59YiDna2nySzHKXF2SzJrzay6vdVws8acxfmucXtsuq27S+b0U77LEq3KU9vOxbTbZasi2S/J3XeaYUzsvU7s2JrYRyqS/d9LOY7Vnc80G9v36eEl1pm2S3hdp9QDr7dimDzMGae7l51c6b/0gvYZFlYtyTdMs36WO+X2GZkcle+69B9rFux8LtftNzqaPif9t6/clJXnbrkeSn7Y37idtrKxx9FJ6+By/5z2JNlv/H3ctxM1yzonzZWxMa4yO246faaT7iHW8xrQx5/PHutj9fjnOljubSL6Kc26gf23c1OorBIKPsKpmWF869rm+tfgb6W+908+Lc81KF+/ls471TD32XctJY/WzusyfVJTe0UjzzXKe6n2P+bwb2KH5fAZ0k+X8lMa57/XYTdu2on89nfZre8DemTNoJ/qeUcISK47F1ZztUkrx0zGU4gLxs2A7LGev3Jkp9x5BSs+1O/03p38uLXdOqo3Jxd+tMeUxMYdcP+biouFrbswknUs6amcVC9p+lSKNt7QPlfUPditfX0mmlG4d7/U2BH3ic7XttxVS3aU2WdZera2qmTelvPHnnF8f8g7PizjuT/vf0qzkxPUM95b0fZlv/No5L/Yng0+W77r1uV97bpbseG49p/ZOqmdbe8uS5W9Ao6S2Qdu3xTGyxPbStKE/vC5T1VN5VjOPU9J5KI1TTUxTY5F/faw3HUuLfd6G/rl9qxarvT4ppDWXVl0zNtvo37Fs852IpZ7wb+r/S+fnkqz48/DsGmSWy6dlLMRy4z1Bz1/vJ9b0Ry3bmm+XX/g8Di5fRj+ZDOx/8IH6vT3cfuZpXPnci8s6hvvE0LZLe/GSBmi6g4EJXJN3Zg/tfF70q0r9mr4byNmbbcYGtL9p0uov/c17SmmMB2doYU9Pz+LamU+LV8Vx3BrfV+3jthX8jhZt4e95JTZZa85Jf4vUDv42IP6XnB4PZuTuweWtzLM9qxC3WJW/C1I0e8EvVegUc0VJz1/LRQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEnBDtbIZLn38xm+ftx74Oh+fObae+o9vZ57OzZ7dSDyHkawNevlJH7vKVSxVyviXKn14p/pr3/jO1ih3zmJKe05sQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDlRzr76GnZv3lSfezhcf+6ZwR/fj6U9upV9fnh+O5e8EEK+NpjcbwXeYbyRefYBAK8Y5XyfkBYuYfkHtUpFfAeGF7kEma9uIJMQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCNkIB+DKZ1/Aa//Ux+BdI+Y5OncOtx/7uo3rarp7aGb30E/PiM8PL17cuA6NbjLBK9/+T6+10TkHAHjkN34Te7dun1j9hJB6ePlKBd77F51zNwBcBNYuzPp2AH+/JMMtLOIfEcoHfnGMbs65JwA8fSw3XLoSeHGMzHcyYePJ0fd9tTzv/UB2rh7v/VqZruvUfNb0uO60fqlMnOacK8pN6fte7aumaZZy07riMiFfro40X834lCjVL+milZeex+323q/1cfrcQsjXdd1SZigaPq/SZfnS/IjTpLxpnpJ+EtrYxbLT8pJeY+arpmOQX2pb+lzLb7UDOZ3itJo5IukY25uSHKkvpHJN0wzyau202rDS97QtqY5aHTm50nyzzqmx46q1S6vDYsfj9DR/27ZiPZrceB2W9jBpLZdss2Rv43Zq7bPa2ZINk8qkadY1JsmsmRea7Lit2noMOub6u2Tvw7+pjPjZmPVgsX+W9W5JH9vfmo7p/NTmiNUHKfVf3MdSfwfdmqYZrOXYL5pOp8u02WwGYLHunXPL7wcHB2iaHt73gzoODg5xdLRae0dHRwCA/f19AMBkMlmW39nZWeoU0oMPEoh1jG1J3/fLvrX6yuncL80ly36Q5hvrR2h2JpfW9/1aet/3cH2931Vj58agrf3S/mXxiXLU+k6aHXHOFdfoqn/X99pc+602rWQTvfcDe2OxiTn/Oabp5vA+5A319YNzZtM02bkf92XpHGdl7PzU9Azt8c3qDFSq1+IXaGfjpmnEMYvXdm4exXtH3/cD+z6fz5fpwb5679F13dLGO+fQTvza+XoyncJ1k6WsWG6sf3HMZt1aLGI2m2Hu1ueC5vfH9cek+XP7bqAUo7DYbun8UyJ3/rHI0fxcSa8U7SxuOTvEsQCLzvaYA7AKIZby2upK21jy0dPPC99jOJ8X87sx2d5UriUWJMnKnUt75xE3K+6CnO0v1a2l59aHdR5YbLRl7QZfsORH5PTKxRY0feK0MWtXspXBpmlzNJ5XVr9JKi+1wSon1jNXVyrXMpYlHcbMX+uZPKSnazfoao0na3tfWt4a0y7pvWmcOteW3BnGMhbb8t22dd7YRA/pnLj46ESf0GJTpD23xnaEz5uc09Ix2uZ7j5ja/VqzVzVnN8lean0t2cAa21HST6pX81XHzPcx8QXtbFZa65uevyW5ku0t1VF7jpfq1OaAZX9L863271V8ajKZYDJp0LZDO5ue8VKkZ7k9PGVbe6/UX9o4aet16C/Ic9/SDklPTRetndY9zbJeJX20etIzjcMwLSe7dk+Q4kxA2W8bW7fF56qtJ+erh+e5eRi32XKmTZ+NnZtSO+L5U7vXx2W0mE9u3sT7eamPNf+y7+3rMCXEeaT1WPMez7on5PJa1teY2I/VJsT5LXY07dfa/XGMbpayVlsb59Xm/pj9rJRe8iHj9Nyen9vfVv4/krR6u7Hqi/Bdrr/WXgDD33yV1pv07mgz5Jh0XEdOp03mRq3NryHda2vX0WKerPuDi2er95cp0jt77z2axot6WM5y8TtTfV7W25Cx80iqK7UvlnHc7jy2x3xKvyFJYz+5WJCFtKz2TsDi58ZY0uMYphOaUPO7x9Leps197Yyd0rYtmmY4b9LfO1hIfSpNV+uYnlS8JdYFWLe5fb+5LSydE8NnzQctrdEe4V2yA+CXsoLu89kM3fH7SIuvUppjlnhSOt+k+bceI5T1ya3d9DfJvrCUrL8pyvX5JjazdJ7K6ZRiWRNpX6VnBdO6yiz9eKw1/yKe4yHOockKdNP++HdTq+fT6Q5aLMq2PdZ8g6CLxbdf91Fl/6Q2jhOwxY/W67LIrqHkm2vPJJ/GWvY00GLC6/nidSXnzfXRmBirhZr80vzL2XFN53itS/N99dVm3zS/d4xuEmPkbjImx6WOy9r9eWubtDKldbXQZahj08h2NOdfaFjtpSZbK5PTaxNy69Nio50rv1dI8U3sw+T3Yi1fOh7petOfL/L0vYer8IO1vsmN90na8fxZKTyzrXmrjZbqT/s2rHVLjEJCOjtYud/76Kak46DFojdplzVeGDPGJpd02Aa5ObZIXsUKtvmbXst6GdPHNfuKRZ70WZM5dkys5eL4cm0c/rQpnXEs5SWfOt7KnCBTixvU1D29dw/nvvgSbj31pCgXAG48+R4sYhvxmpDndNd1g/pjmzS59zqOJk+u9Iykzc6cwcGF89i7dVttZ/w78RruPnJNvVzG9T123r4jP6uoq+TPBNsj2YJcLCCVK/0deEq8pw/33eG4au89S3F969+qp5+tWN69pL951HQq1Z/+blrTxRvPZWR72E8IJPAJpF784vsPGMv/iwCejsql/MJIvf5Y5tnvjJRJCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRsjQtffBmTu/fU5/2kRX/8f4K5CdO7r2Sf33n0kY3rSOmbBrefeFx9vnfjBpoTvlyYEFIPL1+p5x9GnxfXgS/4BufcH8kVdM5dAPBXozI4/hwuYfkt7/2XahU6lvvDidyYT9TKJIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCto3zHpc/+4L+1/FbYnJ4A83stvr8ziPXcHju3FbrvPXUk5jv7qrPz33lta3WRwjZDrx8pZ6/hZUZj/91AP6Gc+5bpELOuT0APw3g6ZCUZPEA/reROv0EgMuR3HibOQDwf4+USwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEbJW9mzdx9tVXT7ye3ZufV595OLz13DPwLv3T/3Hcu3oFt9/zuPp85+4dnLl+fSt1EUK2Cy9fqcR7/zkA/xdWl6eEfz2ACwB+xTn3o865DzrndpxzDzvn/jCATwD4F7C6qCWUQfT5b9Xq45z76wD+aCI36OUB/Jz3/rBWLiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQclJc/PwX0BzNTrSOyZ1X0Mxuq88Pz53D6x/8+sEf/o/h7sMP4Y0PfgAe+kUuF198KfOUEHI/mdxvBd6h/DiA3x99jy9T2QXwF4//g5InTfcA/t7xxS4mnHPfDeA/AvCthaw/aZX5tYT35e3NkqdUru97VY6W7o5vPnPOLT/H6WN1SuuT6vfeo+/7ory2bQd6pd/TOuI2BZpGvtsplJF0seimtUvTySI3lWnRIyYdR+fcQGbcP0F2brzjsk3TwTlgkN0v/qc07t77wTxLP5faIsmM80n9bimbpsf9kubX1khav0SpLdpcHrum0+9aek4Pi965Z6ldKeUvEa+F2vZo+aU1GtsFSVdprsd5YtlS+632tXZfyK2nNE2aY1JfaONf08ehTNwvpb6stXsaQU68B+TGz0rNvM7VZVlvJXLr11Km67q1tPi7No9DfVL91v6ttReSTQxl+r4X668ZC4sdtrbNOkcs8sL8ldaFpa2arxOnz+fz5VyIPx8eHuLu3bsAgIODAxwcHAAA3njjDXz6058GAMxm68Gk/f0Gb701vBH3pZe+jLt319swnU7x2GOP4dq1awCAs2fPLtt89uzZpZ5nzpxZKxvKa36eNq4pY+2tNCbOOVEfi2+cqyv9bJMrr7F4XTdNI9r/nC6xHc2XsfldUhutOlnJ2X5tHHPfS/5oaFdcLMyNuM+1dbutfRAY7oGp3HQsSzZ9zHjUtEXTNU2P529Mrd+V7nGlPcVNHJpmeMds27Zo+xY5NB20Mbf4+fHaCfpKtif19SaTRdhtMpkMxr5pmkGbd3c9dne7gaxzZ89i3k1EXeLy2jkvfN5pO0ynE8T39Z45cwaTnXXZ0nctLUY7p2tr1yLbOQfv/HJdB1GxyNXat6+V0plZy5uWsciP9ZQ+pz6zRVaOmvPsYnwAIKwF27jnzjlavrjPcn038et2ZjJp0bdtld9qGUfpWS62JNfv4Jxfm4M1PrfmQ+bsUslXkeTXzKVt+UySPMs5J22/5nv1fa/aYalsbDdLusSftTNXrp+Cb68R9C6ti7F+Wc1419j3Tf2lks0LeSzt1nwY6ewdiOXm2iLNqzg2HmPt6236mpuSm8va+V9jW2eHVJa014avwb9P0XQf68OkcrQ9phQ3kBjja9VSa+9iLDY4Jy+3J8ekZ+NN4j/aOUPKBwzPHxb5Y8n5Gbm+lfpS022s35OTI41fTbw1TrfEVTR5uk+k+bUrW5HWZY1X1ZLz22Ksdcf2LTcX1/2V5bcqvXI65nwy6zqpscFjYyGLcsGPPNYdQ937vocrbMXW/s4927Yt35Zc6xk89Fk6D3P+wWnsaZrcnI20nIu996pPYdkjLYR11Pf9ms/Q9R26rvxOs7Q2uq4bFdcpneHDvzV7ksRJ7beWvcfSxpJM7Uyd8yEtY1rrh+X6XjqzhrlX28+W/NYxHeODLp6F5+vxO0lW7N8FwrlyaK9WZWrOnJbf9qRlUn3Kba6xL6t9XzsbnRTW2E8pb0DTXfPVS32UPk/jRkFm0zRoj2ON8RqPYwjx7wBXMWn5t3bS3NdiAEEfa9xokzNSjlKMIvcOWMISM9H2kdRni+tOY0ShnrRfUn3TuJukl/SblqBD7vu2yf3mVMobKJ0bQn9L+3haXzwfc+83S3p3XYeu274fmOqRm5+lOTa2zlL6JtTGcdu2Vctk9WtWcRhtlLR1YdVN6nPt7DAu5mjzNTSbkJa19mPpHURJtzF7WkmWNfYW22dLnECaX7nfToXnvjnWYy1WsfqtRk1MRqsrnVsLkXKdbQ80zXCcJ5MJ+kk7+nfh67GYuvfTgbH+m0VvaX/I6RnPi1IcSDonxvVs8p5B000rXxNvkr4H+l6O0Y6Nr8Z5SmfsVJZml7YZYynZo5ztSotK3aCdk+M5Ursvl9ZL7mxuTU8ZjhGwOpvqZ1zLWFrrzZUvx2XW17G2j2hzS/qNm/RZqiP917oWtT1tG7Ejy3650mPdLpZ0iWPTmg4h3+q/9TUV8q72NyDMufh3j8PnizxN49bOQLVxq1y6JC+OlWjjmpNn8QuHMaJ6JHub1rW+X5VlWRljB06b9fm0moe1tnRMvDC3xkr1jO3f1Zq1zc9tINlRaz/E/m4cB0zz5fbLkm9d8hVrSGVtsg5qfe2mkf0oyZdf5Lf55G4yEc+YufK5tZWm5fwLi67W/rb8TroKh8F5L9QhxQhr9pdA0HfSdbj8+Rfx5gfev6rYIDu3z/R9j67r0Peruvfe+G3APwLv5fVy5/IldB94P64+/zm0R0fLNmu/bxuse+dw88n34NaTTyz0V9q89+ab2HvjDeROfaWxk3Sx/E23RDyu8ec0dlR7vnVuZcdq1+VY+yzNy9T2dms+2PrvgTUd434J/tr4+HvonzhNsB+8pufU4eUrI/De/6Jz7qcA/CDWPbCcR5Z79ldzdTrnPgTg+wB8CMB3AbgWHiVyfZT2j7z3v5BtDCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQch84+9XXcefawzi4cvnE6mgPb+D8Ky/j7cefUvMcXL2Cr1y6hPMvfwnnX38dk8OjrMxuMsHdhx/Crfc8gfnubr7+2QxXPvvCKN0JIacDL18Zzw9hcQnKE1hdeBIuPdGulJIuafEAfsF7/4uF+r4HwF9C/rKXuN4jAP9GsRWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsh94vLzn8OrH/8YfNueWB3nv/gFdDvncO/hq2qevm1x871P4dbT78XOnTvYvXUb7XyG9mgGeI9uZ4p+OsXhuXM4On8OfvBn/zLOe1z91KfRHh0BrpyfEHJ/4OUrI/He33bO/UEAfx/ABQwvYCkWjz7fBvCnaquPPqf1hQtdfsh7/1uVcgkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkFNjcnCAC1/8Im6+730nVocDcPVTn8Yb7Tfg4MrlYv6js2dxdPaseF+K9+tpcp0eVz7zWezduAljEULIfYKXr2yA9/7XnHO/F8DPAngU+UtRlsWi5zMA3++9/+KI6mP5qcw/673/r0fIfMfho53JObf87pMdyx3valJ6SHPJzhfnTctJsiUZFpqmyT7P1R3Xa627FW58k8pK9aZpqe5WXcO/obz3Xi3b971YPh17C9ZxtKTniMcjblvcljh9NputtfPu3bsAgO7W27h9+zaOjjyOjo4AeLz99tvoJy3eeOMNtLPFeIa+3N3dxf7+/qCeQNM0Yr+l80fSPZWn9aX3fk1WqQ+lMlK6Fcs8jPNZ109sL3J5LHrEdfZ9X1yD6fOcviUd4nHPkVuXaT5pzmhzJ/1s7dfYXkg6hHxa+Zz+gXSN1JLbR6xpWv3aHCm1PX2m5Qv9G2xRapM0fa31SuVzdViwjne8V1nqzM0Bbe5a59iY/tLyWObNmPmc9pHFFqR9kbMzpXZq9q6kQ1x/rj9q+iTno+X8k1iX2O/qum6ZZzKZYDJZHMXatsXh4SGAxT6+s7MDADg4OMD169cBAGfOnMFHP/pRAAu/4fr163jllVeWciftBE3TDPaUy5cv48KFKQBgb28PFy5cWKY3TYO9vb3ls+A7TCaTZbun0+nys+Q/WtDsTzqXS/YilFnkGx65asdV089K27aD8XfOwTehDSt5TdOoe7PT8LUAACAASURBVFft2aRGT2kv1uqX0Oykxd5ZKZ1/JKQ+894P9O2aDn0ft9VjPp/Dz+X5EeshnWu0/apkczXfLm1L+jmXluQY5HNuMddq/dnUB47XucX3TNeutC+lad77pS3UiMv0TR/JAoDFmLvetu9ayfmwEuk+N51OMZ0u7G3fr3Te3d1dfu66LusfTyd+bR5OJhPATQZ1AUP7kpKmO+fQ9lizUenYSftoLCtna3M+Wc3ZI/4XWPRl3/vjujFId9GyjM8KaV1a/MDqu+VkamWtsSTL86BrXGVcRJpT1rPRSu74eEqurVZffb1vw3fd79ZiFmN88tL5WY6LhE+rs5NLts9Yx9K+EPJb5plmr0r7jjVWYXku5bPaTk3neE8p6SvZKEmelB6n5fTR5Gh7aKpj2FPH9GWaPmZf24a/JskK7ZLOXCU9Fza9PgYg2fe0rnAOSvW1ko6zRU8pziDtzUEfi/87NkaSq1cbl7iu2AdMdUhjRSU9tPIW+r5fy5/3Godovk34Hucb44+X9jWrTY6xxk5zeoyZ87k5E+qWbFzpXGTxp9N9XNrTa/qy5GtK55ESWozC6s+m+bQ60zi0JX6g+SrbjDemsrXv6dy37r1SujZOaf2L92jd8VxcyDo8PMRhC8znczi3qns2O0I/W9m3EIOz6lVibBwq1Gl5nvZL6l8GumZxbpLESuOivde1noOkMdMoybSkW2Qu0nE8B1K59nGWbF/JpqVnWC2f1pcl0vHW9LRi8ZnH+LAW/92ql/TdUr/mq2tlcr6C9iyMRcn2xvOn73v0fn1uaLEY61yttV1xO7quWysfbIN1Tymf7W26jEUao5wPnI6Z1U/d1GbV1rGJ3Jp1NMZ3lZDOAFIdVhZl4lhwSCvPt03HJBdrTc8qWputZ7rS/JPkpPHBBw3LO8S0Xbn+im2SaS41vWgDpDNM3/covKIYEPo9d5bM7Qvxudo6R8bGUHL1W9lm3ZouOfnxs03jJLmznTRnvfeqXSv5hLn6xpKrU+ub9FxZOk/GZdL1lps7TbPe1rZt0QllcnEzjZr4Uix/098CbXP8rGjzx/I+BBj6p+lvUuP03veYzeZLm+a9h+97eL/+u93YVyy9f9Z0ltDOuHbituu/Ecm9hw6xX6ufos299fJ+oE8pNivppdUj7Wtx/ty8z/mZ1thjILab1vhamH9ui3HvlUIOzjUD/6hpmsE7DGDoP4X1lMot/T5SO1+EeVxzlktJ/cyhCDf4TYDlzKYx5lyjx1/G+vkyJR8ql0+LP1j7aJhv+I7SYvtycdxaHWriazXnOUvMNn0m+bnrvjDQNOvryUppHlnichqWmMk24hLWOVIbb4yf28/5ZXtvfT9i0cW6RrR0be2MRZNbimEv+mRoe7V4XY5hFr+I1Sd79mocFnm8d8v8oc7VfytZwecJslZ9lR+PTeLHkoyc7DFx/dLYrPq+Ps5ck28bMbnSuo77KNdf24hB5uLdqSx5vh37Jl7+W7MafSxzcIwdiZ9b3k9IMXBtP9xkb8j585aYdBovCb7ssRRR7v/P3tvG3JZkd33/qn3Oc+/t1+m2u6cHMzM2xiAbbDzgICdS7CiBDw6OkCwRxZGIlCA7QiGRSCKEiPLi8CGKlXwgJLGJYmyQooASCCYCAoEIR04gYGeE4xG28QtOPPbMeDzucfd9e87ZVfmwnzqndu21qlbts5/n9u3+/0Z37jm162XV26q1Vu17Oqf0gXrOwS3ORI019xiJ3n0s2SvTe/7yuzCt95dPZYahy05LaSm5p/tlHyx2lhYjyMv3xGXOa9AjxqL+Qi2f/b2lf5q3lY+11XaU4nav/dJn8fStt3D90ouZDHodtbaGYYD3y7PTA3jjU/8QX/jNvwkP33zDJKvENIWaXr+Rbwz40p/8R3jwK18AKrH1Wqy7ZK1tvpRxvo+kNVn6w6Wsy7OjvRlavlHL7ug5b+q6qc9/1eLMPcx1WVdRckdss7s+wMQY/28Avw3AX8CkEXJLR/qDmzxfAPCtMca/vqLZso2U9v8B+KYY4/euqJMQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCLlzHIDX/9HP1H4XY5t2YsSX/MRP4rWf+dnVPyTaYv/wEd76sf8HL9z8x5kJIe99+OMrGxBj/OUY4+8D8I0A/hyARzj/EEv55zGAPwXgt8QY/9e1TSL9pOX05xGA7wbw22KM/9cFXSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQu6ce+++i5d/8ZfupK2XP/2LeOtHP4n7b7+9WZ3D8YgP/dzP4yOf/Ae4evhos3oJIbfP7lkL8H4ixvj3APzLzrkdgN8O4KsBvAngCsCvAfgJAD8cY3x8QTMu+/xjAP4nAP9VjPHzF9RJCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIeQDwlf/nU/iq//OJ0/fwz/1cYzfVP6T9VfuVigAr//sz+H1n/05AECM8Vbb2j9+jDd/7Mfx9JVX8M6v/3V4/CWvIzrfVYdDxNW7D/HiZz+Hlz77ObhxvHng6gUJIe8p+OMrt0CM8Qjg79382YqfBvDfAvh/AfwcgL8dY/z0hvW/L3A3h5DLDiPn3OlgzdPLwzbGOCvvhANNStMO7dZhnp5L+WKMs3SpXStaWe/94llqM4SgytXTRqtM2ce8/RZamz1j5f3Z+Elt5mnlsx6kfmvrb7/fL/Leu3cPALB74SW8/PJn8fRewNXVuwCAl19+GWHn8eabb2J33KntlW1a5S3rKveTta60jvPx0/aoRZaaXFZyXaDtca3eXEfkddVk1GS26o2aTtJ0RD7eVqdCmhdA3g+J8cb4L9vo3S/5uGrj11qDPbq5VrYc5zVOmSZrbe5b+r5nrffKXI5xmvP8jMjPpFxXS3Of0tL60NopueScK6mtW0nva7K09HhOeW5b65TSy/Mxp7a/tH1ck01Kl+yDHmprvLa/1uxzy7N8vZbt9+4XTcb8rHPOzdZZ3n5KT/nTPtnv9ydbYBxHHA4HAJMt8MorU3DqrbfewsOHDwEAh8MBH//4x/GVX/mVAICHDx/C4SneeOMKQETq1sc+OuJwHBYyD8OAF198Ebvd2RVMbQ7DcMqXr7dhGDAMw6mefDzzMrVxWkO5n6XzUDu7LfutpZsk1tiFFt1Xs83rMizPznJ913TMbQQga20458y2vsVPKp9N42G3f2v9l+S06PoyLV/Hlv1R6qucPTyGYb4vhmFAGM57vTWnmi6W7JFyj/T6qdo5Y8aX59ukS1MdaY561rHFvszrS22M47iQXbOJpD4Ow1CVc78PJz2b2O33gDvr3hJtLMs15317TdR0ZPm5lKfMp9lQvWfCJLu7KXtO3+93cMFmU7ZsIIt/Wpap9TH/W/Mfy/Zb46Kde5qPaBnn85iWdcv2ft5W/lzbEzVqZ8Bupsfn7bXiRfn50vK7pTpaa0De79M45nqpzF/W16UDO7D6I7XyEuVYafJL61Sq22qfaFjqDSGc5MzrbfkC2nrX2i/LSPT4YZZnOXk/tTq0+ZPS18YdrH6ztY1ajMni62s6wrL3arHhsm6pHckuafU5HxdrTNp7L8qSp7f6m2wOiw1TSy/7l/uYml7K1673fnamlPVNvmZq227vtfbubXCprg9haQvW8pZYYqo1uXIbooxp5OlSnF/7Lsml5V8zR73nxhp7tAet/1KMSCrTihmX+dfava30nrRae1K6tY+qP7zfw/thtlfu37+PeN9hv39c5L3CAF13WX0dSzynRmv8a2n5/rOs3xiBMovUF4s9VrbXK8tStstiTzUfBEg+gNb2Mnab9zP/bI35pf6XOrE2LlabuEzL06UyWp4a2vncqmvt+ar5yDX5tD1S05vS+LV0n7QWyvnJbbw8zziOs3vSlC+EsChzdRVxfX09k+XRo8e4vnYYhmFmB+VrSUuX9nbuA0njIj3L+9nK1+NTtvyFHvm02Jtm42jtlXW34gI9z54Hyjlp2SA18rKa3rx8v8t5pc81HTPVr58ROa0zYM290F3U1SIfF+tca7GURO5/975bZfG38+9rfCvNx77UHrG0mZDuhPL7AWkPttfF+e6vRyfd5Xoryfe4c27x/khKr9GzBvKzuFZ+y3veNZS+9Zbt1+rVzlSpnVp7Z/vEAdmdbG6r5G2k/krzX/oZWnsarVidxLM+02s2kzZHZcxRO8e1eB8AhKsA79NYY1FPjnPy+y7lu8RlPklm7Y68TNNswvnzpZ2rxawSUrw0+qUswzBgCHVZ522HhTw5+Tiltd+Ky/VS3olIWPRNOUbSXs3zJkobzDkgzu72PJzzN2MbzHEsK6nNMk3ImVqcfLjGsdJjL1l0pkRpQ83f883bsN0ztO4jyjjYGnrjsPn3VgzMco9m7YPkG7TiLku52zHb8j66Jq+0Vtb6z1Iey/me57XECEK2Z9P5FEI6p+ZnSV6XZS6t1OIX+fNLYuKmONcw6bOshlPZcux7Zcmfl+OXr5naHjo/Sh+cuObKcjV5S6Q10/LPW2dPa71YdFkrdmWKSfg4nV/KUNjty5v6YrKPljbSaex86l+Sz83eNXEuZPurds7IdmQNyRZpxaGlPGvPFG1dW3Rznm7xia3nZplePspjvmUfamnPip49sHw+74e1Pz0x49bzWoyvzCPtMcvaHDGK+S17oYX0vkrNZ9LqWOqepc+ttZ3oiTlsGZfpbVtqI5VP/m3rvYj8HC714XSH026/Oq7COyxzvS2cRzt38r+lMoDtfY/efw9YyiK9R9Qqf5Zx6cNJa9AS65XyXKLHy3xb2aDSu5+JB+++iwc/8VMYdzs8ee1DuH71VVy/+AKO9+8j7AZEP8CFEX4McOMR+8dPsH/8GFfvPsSDt7+I4ebf59wIvehDTd58LFrvn2vlevNYbUirHTHlW9oTWlzDEj9p+Wy9/a/Fndbcebbe58zz3ua9BdkO/vjKc0KM8QcB/OCzloMQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCHm/MRyPePGXP4+XPv8rzbzvhR/lIoRsx+385zYJIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHkPQ5/fIUQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPKBhD++QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII+UCye9YCEHIJzjn1WYxx9ncr3Xsv1penlc9jjGKZsu48XXtWa/cSyvZSvSGEWXr53fpMakOj1qd8/IdhMNVfk8v75W9LhRBmdY3juGijrNM5d/FcpPLl/FvqTeMy/UkFp/87p5/riTGe+pDas7SjjbFW1rL3Ur68/1p72j7T2qmtudqz1lhY13JeVzn+Wnva3Gsy5eu1Z49JdUvrIK9bqt85J+6j2jPrepPkyOVdS62u3rW8Vo9Lz3rWlSSXZYzW7oneNdbSx2t0hrUNaz2lHi/XautMq7VVWxe1tDW6Jf8u6Zsa+TnQ2265xmtrPqVr+qVWxppeQzpfWvpV03eW9mvtpHUWY5zZF/ncHY9HAHP7A5jm6/r6etHe06dP8c477wDAqWxK/4Vf+AV8/vOfP6W98MDj4cMP3cg4yflLn3kbjx9Pn1955RW89dZbACY768mTJ9jtJlfw1VdfxYMHDxbt7/d7XF1dif0t+5w/k9Z+zZ5aY2vl05XbPvUytnXZWguSrKWNmdJ89Kcy2trL89fal86FeV4HIIr2YcmWtpLl3CvrrNkXa/He47gbMQy7mQy73Q5DHGpFV1Nbd7XzVXsmzXF9Lqc5L9HGN9XbY/fXziRL+RaaD5YYxxEhJHnjKQ2jru979rA0zvl+DSGc0ne73UwX53XFGE96PB9jLT2R69Grq4AnT57M9OuTx49xfTjPZ/KTpbEufWjgfN5cHcbFOfPo0SMcrpYhwWEYTvXna8k5V20/z5eQbAopPX8WQpith3EMOByON3VPaY8fP8FwOOvXMpaTz6W2rsv0NFY1HWc5a8q11KNTNf9Fqsd7r+71HltrXq1b1LuljXtu06IrzmtkHAOCb+/x3Bdt+Tg9vnzp40rz4Zyue2u2tbampDGS6q/ZvRa/sWbDJXKdUDsnWme/hd64VD6W2hzl/WqdFZqtVfMpev0tS37r2NXO+1YeLZ4D2P3lsr6W3JY4WCmbZSykcdXmR0u3xMhrercs17P+14x3CGF27uftpXRrbMrqM5TjnMudxkU6j6W9VJ4zmv94ftZ/1uR470V5Uzs5rZhXLU/OOI6r9KC2HiwxSu0cyMuWsYg8X+v8T3ktuifvh3Uc1sSvLo0j3wXW87n1bE1bgN33713jZXmrvq49s56RqZo8+1S33Xct5an5ADV7XcvbssNa9nrP2eC9z3SlQ20YLXtTm9fesbzN/Vmu1+EUk8vuUOPp/zK5lvZ9zd+z7F9LLKJVTy6L1ZezrsWaXDW0dd9rS6yVx7I+pX3b8q+kc79lN4YQZmdfigvFGGe+0uFwmMWP8vq897i6CtjtrmcyvPTii7i+8gt9Vdoq6bslxlD2uUSb29KOa+m+njYv8Q01e1SzOaV6LPs0f271bSU90jrPeveLVnZNG9r43RblWFjiL845RBcwxafm6/N83s3rsJ5VeZM1G9pKvi9b1PRTel5SG79eJH/RGuMsOcfqznHU9NkiY+mbSXWXlPm1M2V0ATHO8y7jvf2xnBLrXKzx9Q2td50FW5GfSy3funbWlnpAi72We8YylnlcIqe8a7gNWvabNl+nuyG/zKPFoFJfLn335dIzP4SAcRyzPXfWDWMlZNCyp2vt3RbWc7wVy6zpN6munnNImqO0BvI7zLKNxRry5zTvbvZdxWaUYkjS+EjvxeQylunlOmyNwdwu7L+/k/SJ9F5H63tLnqS7QljuZ+n+1tJWkrUWywemMcx9k5xaLC9fh7lOtsT+yvKW+G3P/pJsbTmPHgta1juNkxQ/b8VLWn695UzUdExedvTlno6ns9Rqv6yNTdRk1PKXz0o77RKbq1W/9q6a9rm8pzjP06K1hb0ljYvUliW+KOWxxhMt/bT6Rz2cZPfxZixSej2u0qrPahfU/bz+fxfR6zMvz5+lnkn+as9aqK2hsn1Jfn0Nx+55WRvHkMptse81faWNay3uWNN98zmf57HYJ+fYYSxiDu04byxi1yEEuDC1O7dT5nHjllxr9r2ljNVe62nfft6u8yFyuXpkKquu2VK9dfew1Rl6CXchs7bfcvtZiwtJd/Fa/GJeeVvetXFU7a615w62pz2LP1SuXykeYN3jVrQ9U45D3q7l34NabPZSzw/DgBBu573xRO4TSzaqJt8wDPBjvImD5770IPqQNVu9zCfJ0PJLy/LTZ9zUtciFdI6lcU6s8Sc0tDVqtQGltq3r+VJb+lI9LsVL18pQ6rSa7XRbtPx3oL5GrbqzZutb6pHiTlZ/zIrd59F9PfJs4I+vvEdwzg2Y5mMA8DTGuIxiE0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCNoM/vnIH3Pywym8F8A0AvgbAVwH4CIA3AbwM4CWcfksPAPCvAvizQj3fDuDvxhh/7rZlJoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHk/Q5/fOWWcM7dB/D7AHwbgN8N4EGZRSkaK9X+dwCic+7HAfwpAH86xvjkUlkJIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPkgwh9f2Rjn3MsA/iiAfx3AaylZyV7+0IqWr8zztQD+JIB/3zn3xwF8T4yx9qMt71uck4dMSwcA61ClfHn+suwldbXkavXNe9+sw0oIQX3mnMMwDKoclvbHceyWQepfSZ4nlU8y5G3mskpyS23VxkTCMvbOueraLPNOsoWsnMvqAYBzffkaS2mttizP837VxjHPl6+XGOPpmdaeNnZ52TKvtl9622ih9d9Ka+3V2tTWS23v5c8u1VchBNPera3rLY+mS/V6bZ31ymGZy0vXjlUWbX9p7ec6olXOkp7KS+dETz3AWe9uPV6lPu+d87Vj0tOPEIJYn3MO3vvu9VTKYOlDbVy0NSPpnHKNAcszutVGntarO7X0HhusJpNkZ9RIfc/ze+8xjuPpWQgBu93uVP+jR48AAE+ePMF+vwcA7Pf7U/rTp0/xxhtv4I033jjV7dw1PvQhh7wrH/voK/DDC6fyOQ8ePMCLL754+v7kyfR7jrvd7pTuvcfxeDzlubq6mo1Lkj+3o3rPHus5l/DjiBDy/SCvuVY9p/o6bMDaOol+KYP3XtwvMUaxjd79kWQ9Fzuvdamu1l4o5zVPq43lpXv3EvK+jOOI0R1v5J+no7JdNdtpjW2j6QdtjQL6/tHkmuY35SnT+9bQGvup5qP2npcW3bvb7Yp9GjEMA4Yw2R35+SKt19Z8SfsiL5+3XdP73nvcv39/0e7Tp09x7969RfqjR49wOBwKeZZtvPPuuxjH6Xy4d+/eQs4kn3Nupq+TXZbOll108H5A6ppzDvv9HnF3tt/yuiScc6p+6NURUnrqW6mXx31Y2Jn7/R4DvJjf6ptoY1k+y/ucy9Gr40q58jOhXIel/ba0E6e5cMVZ0joLF2Puy/0RT/KUOsXid1j9itqzEFK78/hHqz6p3vIcPrcRxHO4pjvKZ/PnEeMYgGwP9siZ96+2rjT7xHpeteyAWkzOaiu39G/tLLrEp2/lta6hNWt57fpck1ca79uwt0q9ehdxnZodY4kPS/XV2i7lKM8Z6z65hFq/eue1dr5J9a7x/ctySf5cp1p9nDWxktI+a9ktkryanL1rbK2fqdHrf5ay9MSea+fblut7DXfhP65Fi7vfpsz5+Sjta6v9bZWx5n9a5e3xOaW4SP5dKjcMA3A4THZXA+eWNr6FWkzLYh+0bLncvs/r0eKmh8PBpFvDVcTTp09nfX78+BH89VlflmddHtOXzoeajy/1rYc1azTPW65XL8TkJldi3k7Ks4VtseZ8szxfG7sp27g0nl7L31v3mvj+Fn1ea/u36i1j7In79++fYjpS21f7gP3+8aKuiPZ6lOISUhzawppzoYV0VrX8VOu5U/M5pTK9PmotXSovrYuWjL3voPTa7OX4t9Z2Deu6sur93pjguS/zPk0xsHXx7fN4zNu/1I5bazvX9mpZp9XvWPOek0VntGJn5XctDgesi9FIVG3g3Qjn5v1a6kx363a8RO4/v5fR5NTkTuee1S+v5SvXZO+YrRnbnv2Y6PHNWu/O5DGKhX1/o/d65VuDZf6k/Tpm9/WpirWxvS3tE4s/K91zSfmluqT5Oh6POLq6brHE6GrnZhrD4/GIGKPpPZU8T4wRwSfZM1/vdBejo/nWtXPZuq60taHfdbVtw3XcjOmx7ueV7yiXPo92Lmt3jlK9Zbuaz15+ds6t1g0tG3wd8fSnFvuX2s8JIajr5Gw3FvljOV7zsWrZIJYzSIqTS3k0HWP3X12X7doTy2n5Zr1xPkkWS5qFmqwWvb+F/W+xn2/LxuyZV43edaE9Kx/HKM9Pb0yh1b7Fz1iD9f73nG3SO+MYgExFaO8GpvLaWSLp8pos1rVQ03VW20ND8xN7138r1tq6u7KOqZaWGP3StquNn+gD+BFl9/MxKc9xq/+kPU/2nNZeq3xLBkmWlsyW+FteT0/dgDw/lj5Y4sbzvRtwHtt1dyVSG7fNpfvvknyl7ttqnLR/S9Eqb/GTpPMsYTnLet/xWMQ4NzzTLHOf69Dy36Fobdfif624Xs13scSaJd8plcvLS30fvCA72nNqvauoYbEV6/WlM0vXc5rua52pFnS7Yfo7NTv3y3Nbqd6m1Teo+bxaXimmVlKuHefkf++Rv5O+di3k7bSetc7MXj1ca3eNrW7NZ9LXLi7spTKGkq9xi/98qf+1HDvAYmNdujbs9MUyyO3T/4YJUXHO/SsAfhrTj6+8Dpz+tUBU/qwhlXMAPozpR1j+vnPua9ZLTgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELIBw/++MoGOOfuOed+AMD3A3gDyx9cAc4/xKL96SGv2wH47QB+xDn3+y/qCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghHyB2z1qA5x3n3H0APwjgd+H8oyunx0X2WHzv/dGVslz+4y73AfyAc+4jMcbvXlkvIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEfGPyzFuB9wP8I4Hdj/sMrDssfSCmfrf3hlVSn1I4D8J84577jgroJIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPlAsHvWAjzPOOf+GIB/HvMfQ0nEPOvN3wHApwD8BICfBfBHcP7RFAu/DOCNov70AyzpB14cgP/SOfeTMcb/3dyZ9xkxxtn3EIKYHmNcpCWck6dFS69RK5Paz/M459QyKX/qk/SspwwAeL/8Habe/rfa7kVrZxxHMV3qQ0Lrt/ZcWxe1eVyzLjTy9qfPwFmceEpLn1O+rUhjWY5pbRxrcmhjmcbs0rGr9V3aX2vbbK2j3rp611hv+7Uxtuyvlk7K5d9y/UloOrHV7lZjXGtHq28Yhu66epF0VWtt9zxPdWt6d0uGYViMWavdNXuyZ++37IOeulprqzaPZdn0rGct1c7IFlb9Uevj2nVflo0xds+71vc160fryziO4nqNMeLp06di+he/+EVRhp/6qZ/C5z73uUX6pz/96apsL7zg8U/+E182S/uR2EIBBAAAIABJREFUH/00Hj2S+/nWW2/Nvl9dXQEAPvGJT+DJkyeL/C+99NJC3rRn9/u92MZutxPXS83O1Vijhy22aWsdtOQcxxExRowYEcI4KxNCgAtLnart6Zqs5fra7SY33nuPsrj3vrrntWdb6EeLrailS35aL3HIf/P0jFUHWsag1g/t3KqtUWuZ1Idp/+R1rPNpW3KVeXrnL6HZvFZGNyLGoMrb0vGS/do6kyXfMJHbKrW6UnquH2OMOB6PAICXX355kX+/G3Hv3nw9vPzSS7g+yH3UbE1d9wIp9JTWTSIfR2n+y/SWTtZ0fw2tTPRRtEW0tXmpre2cm42tNDY9sY9WXMWkd7wXdcJa+y6t3dEv9+c4BmCs98WyRspntXL58yl9+axc72vmWRvzlj2+yO9jcfa603zU5OqdL2uMxTIWlvhLKV/vGK+Nl0lyaW1LY9iKd1pt/rzuNXHHu0Bao9YYHLDO/+mJtaZ9WrMvemKGteda3L+HWsymJ34ObHdXUNt3tfiIJpdWn9b35NtodUtnYit2nPcz5S3z5HZuKb90Bkp1XMKWdW1Z56Xxcu0+zKJvrbKskXEL37633Uv0dwhhlW3bwmpPr/FRWzZXDz3rRWq3tnbyebHKeQWP3W443Y1pZZO/Y9mLVhve4nf03LPWSHK/8MIL1fYSx92Ip/fn58SDBy9gt5f1fY01NutWd6Nrz1Nf+IzO1WOP+bpY2+Zt6YCWz9lq12I3XXLPbe33FmuiFUctx0q7w6jFzNbEslJcp6xDS0/yX12F2T2Bcw4PHz3E9fV5z+Xx3pz8zr5sB5hsu16/sEzPZdael2jnTe8as5atyWjBund7bX5JpjTvWl2aDd17p52eW/ecJE/trLT6RWvPWwkpLiTFwOx9LmM4Oj3ra62ek8ZKey9nmS8u4gAhBAThDkqTVfPF0jPN/9PI67sNv6qUo3a3F4Q4Z21v3RXe+1sbm1qbGjVdlsu51oeSdFLvOQz0r6ct4zXAPG6y1h9u+Q+pjej1+8BnTc2vG4YB3l/2Dkliqz26pf+r1Vf6HlNa/a5Get+llrdk0vW6D2PxAZIM57vTeLqjHsIk++F4wOFwjtFJ8fJavGIYBtXW691Hkg8w9eny96Occ8DgivXrsNvtMET5zGnVn/xii12mxWVPsgntt873njuWrWJ8sp85H9P0Z60fAizv5UsZpKJzX7H/rkSyl8q9X7NJNJnz+Mmas7FGK5aQyNdKT7xT8rs1X7x83qrbQuu+2RKz1PbUOAaEkPoypYUQF3pXk6H3PeOeuMpt72OJ2j3sNA5lfifa65odtGZdtGIMlnVriZdr6ySPQ9farult7VnPHYBUr5wnyTxvQ0JqvxUXt8RwLolRpfKW+HxZnyZT7Rx1uxHOLeNwLXs8l1GKY0j9q+ngvE0v3IvWbLAeesqujQNfuq+X+eq2t21f2Ozv0nZwzsP7YRE7sa5x6bNFpl5641mlbKVfNwwD3FGfo177Zk28d834WGyCWtlcztq7qGveP8rTzDHNMS7mxvvlv7FZS69fKj2z3gO0/L61cd88v6brS9+2Z73N9fb8Wam3F3UqsQNZZ+Zjddn7oGvRfXkt//mzJu+WNmvPOwo1ezb/LunzcRxn/97DQvkel9UnS0zxtfZ8b2HrJ6y6csuzar7Wz76B5f0L7Z7XYhvWkGzOsphzcx1jPet6z6R63nNc5VnecxDgvRGxfw5xzn0dgO8Cqj+84gC8C+D7AfweAB+KMX5djPFfjDH+0RXN/joA3wbgR1Bqn/n3PYAfcM49WNEGIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEfCPjjK+v5LwCkn4/Lf/gk3nx/CuA/AvCxGOMfiDH+tRjju5c0GGMcY4x/Kcb4OwH8oZs2Uru5HADwcQD/wSXtEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyfoY/vrIC59y3APgmnH9oBZj/AMo/BPD1Mcb/OMb4xduQIcb4XwP4ZgBvF+2nzw7AH3LOfeg22ieEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5Hln96wFeE75t4vv6cdOIoB/AOCfua0fXZk1GuPfd859G4C/DmBfyAEALwD4TgDffduyPCucc6fPMUbEGMV8Wrr3flaHpcwaanVp7YcQuutq4b3t95acc6pctXRJNuecud2c2pxJ9I6X1o/0rPZ8KzTZ8r6EEE5r+5Q/Tv+Xp/X2s7ZftLFcMybaWKZ+9bTTu79rbFnXWh0ilauN8TAMYv298xVjrJZZM189fZHabq3jNWj6fc0Yr1kv4ziK7Uo6bO16LNtYQ02uxG43NxfTHEpz2avvUx1SX1rjUj6vze2ac6h3b6e+9MhVrjlLWymtrLe257ZYK5cgyda735Ou6dlDa+jZj/lclPsEAI7HI66urhbp19fX2O/3s7Q0RzFGfOmXfumizKc//em28EY+/OEP480335ylvf766wCA+/fvz9LTeJfp+TNtzZfrLuWXbMZUpmV/lrrdueU51LtHa7TkSXvY7Vw2HnJdKa+276W1rZ3XKe/Rj3DOF8+Gpm7JqekOrX0tXaPmc/Xal9W2fekfTt97+7HG5q3p2XJuW+On2WLApFfGMczyTs/mc17bU6090jO/Ft+slKFXZ59coJsP4zgCK4+1GKM6JtqYafIeDoeuNqSzIrdpetbdOI64vr6epT19+hQA8IUvfGGR//4Y8YnPvYv8N3s/9alfw0sffgMAcO/evXn+G53/4MGDRR+ApW0ItG2tHt8z19djCBjHY5IAwDT24XBuz3uvtq/Na29co1ZXkrdnDlv6/TYo19skbi5zPJ2prX3Q6muvHwZM5+MQj/De3bSZZFq2vyYO0tL5PbZ/mdf7yQ6o2ZFbxSBqPntv27U5kcpI69Nqw1lkbdVVo7R9ev2fEIJqq/XGzmr7eKu932uL9dLSqYBu02r7Vatrbawxp3f8WrFqqW+1MV+zjy26JUc6e2ty1eI1PfvjeDwu0mpjt2btSDo9xSFH7xHCfKzGccTQOX5bsOU+vYu6trpDqbH23kWrQ9sXl87tbetMYN1Y9NpXPXO65fxfoq+tWNffZNekb2e5zratHpPpaaOFti5rsUNL+fy5lKbVNQyDEBtaxmHWnMm9dlAva/yvkvm6uFkDN75EXtcW8WmpzJo9slWMrIZ2h1Drs7aGe+M61v71yrImvSZPqStTHTVbRYvrlJT21tU+4OpqHsd54cEL2O3aNoEmf0/8t1ZPbW/0xE5bNol1/mv65bbtvVb7ObV7yiRnWVfLBrzt/vXo7pqdYu3XlvHh22TLWFyvz6+l6/7i/DwbBv0uqLWPpWdbne9b3Lto+8hSJpHGN8UaE8MwYAhejclveafeowekvkp90vRqYgv519wBAvU45G21LZVp3V9b7wr677J0f6B2Z142P90lLfd2rx205lyx9tn75f6p3dMAuu/aK+eaONxWdVv7XTsDe/vbc07V7KtxH7DbTe+nDEOE9wfs9nvsxkme+/cfYBiS3tTje1vZK2UbzfjJ4ES/t/ccT3oqxvl4TXfPrfn32dkSxT5Yx2etT3Vp3Zfq6Loemu7zkujp85oYiOV59Po5eT7j53egx+MRR7fsQyteFot37lIb0jlT0/etfk1py/uVrW3nss4kS895csmdqoWan2oZS23cyruhIbTf0Ygxmu2r3jO5d29o667WtpVW+RCWbYcQ4Yp/5yChrSGrP7Smb3ndefu98SPvlzpMSqvVYz2r1pwNU90uy1OXr/XOrXWsrT5+bS9q9a6xDXt1T+tdkxZ3EZ96nmiNYy12ZqGW1boWtdjJ0m6f64sYl+/O9sa4rP3vvdfbirkOSPfw8juo5WctTw89+qpGCGFWl1p+WMaJet/r1bC8J2Eh91mk9EvQ4iLWete879SzZnru1UuZomBHhxgQQn+MEajfv+RzYZU5hAAfpTsp3f632BsWO6dn3czrXQSsFjpZW5e9duFtIo2R1D9rjCrX4733dGu51Ha5TSzrbhxHxJjWxNl/uaQ/tTssiz9RvqMw+TtlG/Oyl8Z8LPqwtO8t5cndcHtv9b9Pcc79JgD/HLD4oRMAeBvAt8Y7+OGVRIzxhwD8O5B3mAPw++9KFkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCnif44yv9fJuQln6E5d+LMW73n6W38z0AfvLmc/mjMF/jnPvoM5CJEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJD3NPzxlX7+hexzzD7/MoDvu2NZJiFiDAD+OKYfXZH4XXcoDiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghzwX88ZUOnHMewCcw/9EVd/P9L8YYD89EsIm/DCC1H4tnX3vHshBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ8p5n96wFeM74zQDuY/pxk/SjK4m/+UwkuiHG+K5z7v8E8M1Y/vjKb30GIt06zjl4f/79oBACYiy7fs4rEWNUy1jaL+vqpSZv3jdLmVpftP6vke14PIrpmrzANDc96d57DMPQ1Y5W15q+1/qi0dvHZ41zrnu91NakVldtXKQyzrnqfu1p33uv1nUbOkFqY6v2Y4zdazzG2L3+1+gQ7Zkmb218tTVW25O1dWFNX7tPa+NStpO+S221ZO3dXxJpDHvmSyuTvkt6OtVj3Uc1nd5av737awvW6oit9mKtrpru1vRKb180vVarq9eeWcOWcz6Oo/qslDm1W9uLUv93ux1eeOGFWVqq4yu+4ivEsXn69CnefvvtWdpnP/tZUa4WNRuglDftc6kfvToipUvzpT1r9S2dj/kctMpI8q7VJ2kMogem3+kEvJ/K7PdX2LnzGGg2dKKmh0sOh+k3N0eMCOG8Zp1zCGFEZRlfzBpfpKbTe/dvze6R7MuWfrLanem7piNqNpT1vE6ySPM+P1/L34Ktj4tWl4bUfq99nuqQ2tL8PJHhvKfy8kMYqv6/xpp1qI3XbieH047HozrutTNG0kW1uICmY19//fVF3nvHgAefG5Gvnddeew37m7Oo7Ium+9P3njls7XGprnyc/D7A+3me3W6HIZ5lq+lxjdo+0M6pNeun17+S5JrOuXk94zgiKfs1tiEARL/0F73X19xd4xwQoxPHvlfGmj/d72dPf8r6Wz57T5yldnb1rqk167NVzloHIJ+ba/3bnnlPeTU9JvWvNy6wxs/pOgNvqNnOPe3XfIAtZOqVp3YmavTGhGv19JbZMg6w5Zj0sCb2tdWYa3WtqUfTqcMwbBYb0GIoElvG/jX5a7qjd22umVNrG0n+nrPqNmN4iS3vejRqcyTVtaX92rM+ND9ty/vDNVjbn8Yt2WLnuLlkn5V1W3Xyludlrz1XQ4tfxBgxjiNinM/pOI6Ix/57WUsa0J4zax9rd1hr7oSmdRGn/0V3Spv8p3OZtfcONVm23BMlt3m+XfJMi5X31KPpyJbuLtvSYo+12NGWe1STTxsj5xyG3Q67IrZRq0uTs0eHavVs0eeWjbuVjylxF/aE1mZP27335nfRry3aXmvTKxItUqT3hmp37r1o99rPcvz1/MvzfvRyXc9iXyR2u92tnV09fkvKK8dVo3p/7r2HF+KmwzA0Y9lWrHehNVup917+Lthi3reMQ2ho939ry9eovVdzU5vahnX9P4s5l2ysST65n8657rjobb/zU2vztnRq+/53SU+MquabzO6XpfLZmkvvIZSseV+k931Ca7uX1Nm6X669P5CYnud5bs+36L3D6j03tFhDTx299NSj+T1pTefn/PF4RDxO60WKlWjxOOnOP7/zc37eftLRZT8kPdevP+R3mZ6Fz7DVe4636W8C/ff6eV0W0aQzoTfOXe7j3ljSmndZeutq6z1guT7n5XrP+i3WzFbvm/Vym7pzjY68hDV3zj3pt2WrronLaHVolLo+78sW7d81W+n13npvY4x6177Fb5+/j9lnT5Ws6fOl7wlYYuo5PefHFu8VadTeL+1pTzuDrHPWq6u2WNc1/zPdh65t87bOu9ZZ33M+StT8X82eSng/Lu6Jpvct9fexa+mX3nNOxc/peZ75GSLb/2tl6UWLkZV+s3O4iRvMy/faxrd5T2QhH3vrNpHG6NL+9WJdpy0/49L3ImpzXe37AKR/X5OYYvDr7cSetWTp45q5u107MPdbY3XdkbvhvfFG/fPDl1WeferOpND5ZPE9YjqRP/4MZCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5D0Nf3yljw9Vnv3inUmh88tK+it3KgUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIc8B/PGVPq4qzx7emRQ6n1fSX75TKQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIeQ7YPWsBnjPerTz7EIAv3JUgCg+U9NqPxjzXxBhPn51z8N4v0tMzKd17v0gry1jaTvm1umptSM+0/K1nd8EwDOozTTatTJovKzHGZv+lOkMI1bzl8xBCVz2tZ9pa0tJDCPU1gIgYbeNRQxv/3nmpUet7737pJYRQXa+SbDWdUJNLqqs1P726R5sXbe3FGKvrUmu7pfukdrS6JKR+pLxa+1v2RZI3l6l3/W2Zv5S3NRfDMKj19Y5XTx2pzS3asKCtfW18xnFcpK3RK1L9tT1yie5aa4tIdUjzku+xnrrLPGv2Yq/ubJVde3ZIz1v2gYQ2vtK6G4YBx+PRlJ7Oq5deegnvvPPOosxHP/pRPHw4/dZjKvvhD38YAPCZz3xGlVfiyZMneP3112f9TO0Pw4D9fr8oM47jIj2NqfXssJyJrf0+tTV7gt1uh7hburVr9o+GtlZSeoxn2y39PY5H4GjXC5IM0roCzuMUfRT3aYzz9DX6qWbHaPoxyWU9f2v02iOp/cnGXKZf2r5FX0nrZK0OLJ+lPbrbAd7P7VvvPeSVotu7Nfl6/VOgbTPkpHVdyiatn+nzci6ccyY7qKcvrT701CXpsta4OyfbqFJdu90Ou93OvE+uDiNe/cw18rH82Mc+huu97itNMuk2j7XtNfpnNlaCnhsGjyHUZb9EjpbOL0nylXNlGSctdlWmTelxkXZJ7Gh6vmxfkqFnj+d19bKMhcixvNq+7CXpV2n+JJnm52M68wMw1vvcq3t640i1tnp9jfxMt9QDTLpd1m3ympZIc1Frp9cXKfuRytfiEtb1pu0XzefaMtab9n+Pj7MG771YXy3WVqKNgRaTrVE731t7Q9vjViyxAGntbOU/13T+Gn0rUdsXl9TRyl/Tg0v/T0fzndbQ6kc+Lj37IdGKa6zZx71xY6tMNXrj/tJeuWT9WsveRfyy1saaO8Oecamt/TXrUzs7t2CNrZSnz+2y+X1CWXU5jj0xDkv81HIftOauBZDHPISg2+RDhNQ1re2dEENrlWk9uySvhkUvncc86Zfpz/RVfn/A0kaPz5u3UZPTwho/Z81dhWb39tQP4BSHs9KKUeX0xvqdc0191bobtLZdtpv+zsuV+nfYCe8dFDqjZcto8QNpHraIza4td2ksybKXSpnS3Evj0NojUmx9jWwa+bz3+v81eba8y9fsNqt+adl2/fc0EXkMM4SADU39qk2wlGWbM22LuJrF37Hu/bV9q+2X0mbb8n2j26L17lc5duM4YrywW3mblrhwnk97LvWj9123GmtiMBbWxA+s7a99l0A6N7T+9/rvW9hNW8YfepHuLJ3zyG3u2pxaY2TSmbJGn1jP91JfWeI1kh0SwvI8lWzucRxnsuV3gJp+qNm52plc5o8xIgxnn9JhOgvjjezA9P7J8VjfZ7V3MK13EjmaDVumJ39Y0svuFkI++fhZ742AOI2hcNerUd6LXxJHLuMfmk0m2dNrYtgl4xgQQkR0Yb4Ob+bObRBbqvUrke8p72X9bdXF6V0EABiGZblzPGput2qUe0ez0SQdU65zbb57fBvJjusp38sltsPWvuyy7/n3s15PSHFCyQet+RkpXX8fxi5/iTb/WvqaNlI9PdO4lf/fU0fN92mV1WTJbbhUVU+8uMYaf1/WRXmdtrq2iD1emreXNf6rxT5r1dF7N7wd8/WZ7+nanNe4NBa8JRbf7Jx3Xm7rMZ/ry7z/sau9S8fuNuNPidy29d7P3sebbIq5DdTbhiUOKuUFdH93ja1q9yHn94wttDhYqiN/1ltfS8bURqtvPbGmfO4tY1b6H73nbu+4WONdqc7dsByfwQ8I2drqsZt7Yk/yfcU8xt2zznpi15dSs2sWjzrswbWxOYtsvWjr1craOLdlXVntQeu/U9buLPPnpf+3xp+Unvfathb/qVbX1v7qdAamum3lND2wxl+Ylwdwemd6aQuSZ8N7/8brvcWvVp595M6k0HlDSX90p1IQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPIcwB9f6eNnKs++9s6k0HlLSf/CnUpBCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQshzAH98pYMY4y8CeCd9LR7/3jsWR+KbMZfL3Xz/6WcjDiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh7112z1qA55AfBvAtOP/IScT0Iyff4px7Lcb4q89CKOfcVwH4jZk8+Y+w/NizkOm2iTEihHBxHc45ADj9nT/bilobZdraui4hhLBJf1sylc/T9xBC91x672f1DcOg5k3Pevo4jmO1zBbjVavDe3/62zl3+gMA3nnAO3g/LPpd1llrIz0r89TmojXHUnutsdLWhdSW1n6rjbJPMUa1/1r7lj2n6RFJvt493JK3p05Nf9bGRWtDaz8vL8mVP9c+S+1I7a/dpz3zYh0PC2vWsfastl+1Z0m/SHJp+06bw8PhoLYvtbPm3O4tI52t+Xet/5faFNr67y1rZc2arJXp1XG5vpCeWWRL+bSxr41jr46Wnqe1oJ2dmp6U0GyN1EZp69y/fx/X19cAzjYHALz44ot4+vQpAOCdd945pe92O7z22msAgJ//+Z8/lW3x4MEDAMCrr746k+1XfuVX8OVf/uUA5mPz8OFDfMmXfMmpzZxUXrK5tH3Vm67Vn9ou7b9Er33Su+Zs54nNBmut655y5T7M0zQ/QyqT2s7n5VLfqGw/fbb2T5MzRz/P8s91/VazeyRd4L2f7VkrqS6LrRRCmPkCy3rktab1s6YvNblqdkePbqxRtpPXm+QZ3bho73g8Ih7t7VnPxfxMuORcGobhlG8cR7FfwNQPC9I6CSHgeDw2YwNLfzvln2Qbs+WV+l+uBSm9/G61gaT95pxT11UIAWMMN3vuXObp02vsx50ol/de1A2a/DV/oNemqu2Nmp1dwzmHeDr7Ut6o9tMSD2jpvZY/aPWfpTw1GeT2YlXH1dq02vb5fpX6Up6PGADv03OHGIFh8PDjfD56dbKme9fEZ2p2VknLz+zRob1rJ0fTKdIe18pL+7olv5ZXW9d5rDA/L2rzZNGja9D0Sj5GNZ1g2SNanpYtlPtAl2KxW3Msba6xc2t2S04pV89+LOuulS3t01Z6L8kv2WrN3BZaez1nUiLva7nGLbGPNfHZtfSeFzW02J1177XWq1RX6+zriW9fgrW89Uy/rfI5Fpnzei2+a7l2amvp0jFPSGdYfxy/3H/Lc1bbl2v8vZ68vTGq2nfLfp/2WUCedRyPiMdzDE2zD2q21RZsVU9tTPL5dy79ma8Jq60p0brbauUt03tsVSlPHm+zrtnSTs9tXU0PX6qLpX5upUM0tHbyfWCxA/Jxac2X2bc19F2z6Wrtp7xy/HCdfXLpuaXFpNeUrelHa99ac9Ojz/Nxta5n7Szu3Rdb+I+tZ9Lctfz8Ne21y92uLV3GD3rvLbaSIf+7heWupGfv9pxtrbpbust7v+l93G37Wj1Yfe41fuuZuQ4LIcCFedta3Ckv10N5Xkr3ZFtwaX21eJt0RvTcTVqQ7OwWqe7lXew57h/j/I6qNU6XrsO1jOOIGJd+SQi2u6IenWWJk0mU7ef1pHby+zwLNRnW2Py5nVCzoSx9z88KyTYchwDvh5vnYdor3sPHqc3dbocQlzHdmg3dkgewx8RrNuA5LULz87ZAimnX4uPlHpDqslC7l67VX4uv5bq7FauX7ht7xzb6mzKF/t1ijtQ58AEhWOyjswzD0H7PutVuDS1uCkxrpvYuzjm9/H4ex9YdWI+9+F6yqSQ0v156bolraGM23ffm47LcE5L/1zvuVp9VK299nvtvtft1a93LOmdPTnZLns/K1mvQcvbWfPyaL3xb4aw1sTdpXmM8644UF+1ZN5fI1uI2Y4HWuO6aWE6vHOfxdyebTtPZef4yPX0GXFPeVlfK8mvmtEdH9Ja1zp80Vtq/FauNWe4LlGf82bf0xbw5035qxUVySvnyvlwy3hKaLKWtW64T7z3C4ZxnrZ9isXs0yn62bOt870nlKy2d8rdsjySH5Xy1YrsjP59DtfigdPdTo6Ufe+9sa75GLlPrjM7nsddH8V7wI5V3rHt0Vxm3aeU990HWYbX2U56a7WuVe1tKefLP7bg1cHk8pX4m1vdl+zxsr9G8fG/scE3MtqeN3nszbVy3tOfLes8xncvrltpYW0diimnq/xbHare0qJ018/IOabzS2XzJ+39kGzgD/fzN7HO+wl8G8B/esSw531l59kN3JgUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYThDHbpAAAgAElEQVQQQgghhBBCCCGEEEIIIc8J/PGVfv4cgPTT4zH72wH4g86533nXAjnnvgrAv1XIk3gI4G/dtUyEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhLzX4Y+vdBJj/CUAfw3Tj60g+zsC2AP4n51zv+Gu5HHOvQTgz9y0ncvjbmT6CzHGR3clDyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghzwv88ZV1fFfxPf8BljcA/C3n3CduWwjn3MsA/hcA33jTdvrBlUQE8J/dthyEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDyP7J61AM8jMcYfdc799wC+HecfPUk/fBIBfBzA/+Gc+3cBfG+MMWwtg3Pu9wL4EwA+Kj2+keN/iDF+auu230s459qZivypTFk2xogY4yzvmjbKuvI6pe/WZ2vz5/KXfSzrCmG5VL2Xf6Np7bhodQ3D0CVXCEGVTcq/FuecKLfWdqsuDX2cj3AOmBV1p/9rtlHOf69c5Vj2rrn8uzYvIQRRhhgjhmFQ5ZP2q9aONo/ANPbWdZ7XIdVX22M1esokPVbby5Y0aVx7x7olZ0Kb45SvpXvLcd9yj6+Zs1pfLPnK9nvSy2e1Nna7XTVfjNGkI/Ixr41/uY+kNjVdr7UnPauVs5D3pZc1638LtD3caqdXt2jk85HPc+9Y5msub8+yLrT6amk1HZ4/0/qe91Wy51rnTd7fYRgW47Xf70/lrq+vT89eeeUVAMCDBw/wa7/2a6fnH/3oZPZ/2Zd9GX71V38Vjx49OpV58MDhwx++AuAwDJPc3/A7PoInT6f6r66u8Oqrr57qTTKl7yktZ7/fz/RIPi7e+6513mM3jeN46teinnG8GUc3y388rrfbc6z6fSmYg3PzPno/nPqt2a3aGrLKEH3M2o2nfDX9sPUYpXnSzhmJls5I+yjXFxa5pzx5vstsxXz/5s9yndWrh602TJ5Pb2O+7iX/M/VL803bbSzL5Ou6R3+37Nct7buy7vR3r32lnRvjSRedyx6Px0UZAKf08lmM8TSW03oPpzpTW+M4YhzPZZJOjjGedKVUd844jjgeRhwOx1n6kydPgOGs+1PbuW/knJvJlO9JrS8W3SOtKc3PGvfjzTo7p+33ewyotye1K9XfE/fR1kivD1DbB2UbaQ4kXeJu0jXdpZ3VJ9l9CmfOn2n+mKTvNf2i+Xhav8+fpz/z5/r+rdVr8XM0vVOWne1/HxBCOR9x8evmLb0rySbtsdvwP3p8hDX+l6bvL4mzau205NF0i7aea+WlNvP9ZqGU4RJ/ztL/Mr33rLXGytb4UyWtfVrzP/Ky3nvVf23JnOakV2dY8+Sy1PzR1npK8dJc5rJsj22utQG0+9zS9T1+YGlrWVgTny/LlfaXJtvauIxVxp67ji1jTpazMFGz2Wtr5bZ800vZov3cN+qhduZcst5627LU3dvH2v1JTX7LXvFhXfy91lauR616sqdNadwsPnrZVj5HpR8+7gK8n6ft91cnn6lWr5a2NqYsxU8vpWcdafm0Na7ZY2vWcakje31MS3qqt9entY7ZWnut9dziG0pxhh4026plP2o6Ukofx1Fd49pdev48jw8BU7z/+nCOhyTdZLVfa3rTGguRsI59r89qrbsWZ7jUp7TYyJrPpq3PtedzTaa7QPNNEmv0t1U/1pjaXc7JWv95KjtPm+Js699/svjp1thfre3SBrittWK5t8p1Wbn+k+9W6l5pH1vmsVZX/qxcFz3xkxT/vvQ+4rbuM+bI41W2ncfXt+xXvj60uEwZd+o9n2rpGj3x9Zbd3bKBLbEoDUvZUu85d973W8b61sxLvY/tuE3pt1jOmzwOWPbt0rjm2ndfJX/A6sdZ5EqUe6+8g5RksZwr8/Pk9DBLu4lvDwP8aI9199ib1rOzZsdP3y87C1vnUM87094v65LeobH4yz260+oz9fSlbKM3j3MOwwB47xAKm12zY3psTUlu5xwwOHjfuuN0yHWstGda7ybW+l0+K+dS04PaXpHWeVpX9TIyl8RoWmvCojsvPd+19b4mTqT5ks4t32f2fjm35dzl37V7MGu8MZfN6mfX/FOp/CWxz1PZk95LdbjZWPXOdyuOZY2Z5WPWinf2yPisfPUWch8vq9Nyf1Brv7fuHiw6zmpz6H3aXjYpXzqDpDjFMhYWF/t8aSud6x+GAUPQ393rtQtq5UpymWp19rw3K5GGI8b5u3K9a1L7t2hT3cv3skqsd741m1+6o57av/wupYUUS5DGsJRRtceMSD6sNS6z5bg4V3v/ru3ntepOrH1/AZj7fjVdUba9xt6u5bG8r5JjeX+4JqNFp9f+HVp6vmgT8/p6bKy5/XS2fxZtVH2+ed2W91LWoK0L652QlKfmg5frvfXOsCRjPue1mIdW19o7jWWeuc8oIc2x1f6Qnq/xZyx1Jx0n1anZyTUfxBJ/0Mov6vLpXbxl+2vZch855xCFfwM0DL5qY/XY0akdW/p87EMITR1Ibp/1pzv5NwF89uZzeaJGAPcB/EkAP+Wc+wPOuVcvbdA5d8859+3Ouf8NwF8E8DGcNX6p+b8I4A9f2iYhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIe9Xlv8pc2IixvgF59y/BOBvYBrH9AMo6UdQ0vffAOC/AfC9zrlPAvi7AD4H4POV6t90zn0dgA8BeAPA1wP4BgDfCOCVmzzzn5E9k9L/YIzxMxd2kxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQ9y388ZULiDH+kHPuOwH86ZSE5Q+w4Ob7gOkHVH5HUY0T/v5Pb/6U5D+yUv7wSmo7AvjPY4x/vrc/hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR8kOCPr1xIjPHPOOcigO8D4DH/ARZg+SMsblHJEi1PVPLk6d8fY/wjhjbeF8QYm3mcmw9nKhNCMNdRa7O3fF6ulC3hnBPrjTGK6Vo9FhlyvPcAzmNTfq6Rylqp5S/7I8lVKzMMQ3NeanVp41J+zuso+5Oe1fppGWexdDz9XxflutLGKB9L59zpezkv6btWZ20cNcoy+bjU5rS1B8r9VsppWed5mXwsQwiLZxa5pGdaH6VxXbvvW1j7ouVr6ShpLlp98d7P8qS11KN/U941OrRHV1vWorXufI9p68U6BmvOy7xsrgfWrL3y7LXQe67k41XqZ6ldaz80vb8F2rpcY1tI/ZH0dKud2rhoOr61pzU5WrZOrU8aWtu1smvG3zmn2ifaft3v96fPMUYcj0cAwNXV1elZCAGHwwHAZM/cu3cPAHA4HE7px+MRr7766qnd4/GI3W7Ea689mrX50Y+9gsNhADBfx/fu3YP3HldXVwt5d7sdhmE4pedzkafn1PaqtMZr6641X5IOKtMsduPWxBiEdRQQwlmuS+Qpx/i0pzzg/fk3PGNs6+kQgnlfaHtU+lx+r60TS/ulXmnty6T7z/nO546Psh1RlrfaOtoel3Rdmo+Upo2DldImSvVo+zC13bLVpDI50l4u+2LVnTWbTMq/FeXe0HwVy5mZ/M08LenrPL30E7S2Y4zY7Ubsdg8X7bjjOc/19fWpL7nMeVr5ebfbYRfdja6Y+8yHTC+lMiGEk77S9Em+DlOecRzFMi1bpCV/cAHT13wP1/WcxefN27KuM4tdUq6LvP/a+af52/M88zTvfbetfAmlb5KnB2Ed9dhW8rjMP2vrw+JLSmUSVp2sx04AwC3WpBY7kL63ZKyhnVtr/VsrvT6vZf57/GQtT28cVdv/llhHz3rR+niXtqrUfqJlNyby/ZL73N77at0tHSc9k/apptvLcbTox3JdlHX0nFlSW1vukVb7vW0B69ee5Ge12k/2QY7mJ4jzfmNzrImR5JTlc7tFkiUnt4vK+iQdr5W3YI2Dt6jtydJv0c53rbxWf638JeeO9azvxXpnUyPGKK7xNbFa63kjnfXWtJyWv50/z/tYm4uW7tX8sTKOmz8rmWRdpkG4Ltfsckmu8rO2x1vyWeQv27p0v5z7mepun7dr9lRtjeTjdRv7tRajAM5+97npOP3vJNdc5lyv1NaFZS+XdfXsw7ytGmVcQYvxaGt+7Xxbffmyvd42e2JE0jps7beWXFo/pfWy2y1fq0rlxnGs6ovdEGZ2c4rllWk1ypiudAbldZd9aZ0pLZ1Zrr3yc8vP7Tl7W+uvFvex+Ixr16vWjjaWlrpvy9bR2kqk9dPj+7TqtZy1rfJT3svnJE+TYmr58575amGJSdb20W20p7Wd0HwQSbZ0R2ihpnN70efWNn5SPMaF+vtd3tf3Zs13tOSt5V+LNJeajrTORe87VFKZMp6U2tds7kv9f4t9t0VcRPpcYo13amhn+qVjlJPL3+ObDMMA5+b93u12CDtdT0jjrs3FFvvDuhZzWror6YbzXExjUb6jWpM/t++19V/G3so9XdqUKU2bM+89ok91nX2kss3Wew013VeeIRYbo7zz7vWTtThwbe065+B2y3272+0wRPn9gR6fyLrPy/h6bc2sebdAOrN77gd6fboUr0l/pvT0xz6GPXt/9AEhCDHfMffR5vWV61zqi5TWo3ct74tra1yyXVvta3uvPEOs1HwwLX6Z02tr1Ppm9S961rBUdq4T8/S++wBNB0g6UYvrlGk9WNZqSz9q7SedNY+/9ctpjTdZzpQ1cS/rfp6d6UP57wLscecSyx6qyWKlFkdtybU1W/qAQPtuqrf9mq/ZY8uXuiDZKPm5djqbh9IWU2tdyCSR9qe7Od8ku2G3283WrXUt9vqytX9PJb3rCMxtlsW+GnDjc5x1xzAMGMIwz5eVv8Rnck56H7P+3kGZvxU3Sc+TfPk6WXt2t6jFvkY3ArN/7nomn08tDn0J4zh27XWJmp+QkPxCaZ1sMfaX6NOy7FnO+b5Y00bLLurVrT3cZrxncd4p/65Ssxla/c7vfkKIi/nQ6knzNNkMSmcg3e9O5O/tln3QvmvPan3um/cIKP903TLHkt10iR+6xqbSmctvGRfn3Oq4VW/coIdaXMjSrzVjaX2/SXqHvmab35Z92l47dhnLfGv8E12WfB/P419bxoVJH3f3pv77mBjjnwXwewB8AdNKz0/C/IY4Fn/UKpU/ZX0o0v8EgO+4oCuEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHxg4I+vbESM8W8A+AYAfxvLH1sBzj+akv/RkPJqP+LiADwC8B0xxj8c+VNGhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSY4I+vbEiM8edjjP8sgH8NwKch/1jK2h9HKcunH2P5qwC+Nsb4fWvlJoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHkgwh/fOUWiDH+AIDfCODfAPApnH8oRfsxFssfZHUEAH8FwD8dY/zWGOM/vu0+EUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyfmP3rAV4vxJjvAbwPQC+xzn3DQC+DcC3APg6nH+EpYdrAD8M4K8C+PMxxk9vJevzinMO3nvEGE/fNVKesnyrnFQ+5ZfaldrR6pLabcninDPJW8vj/fk3lzR5h2FQn9X62JItxngqH0JQy8UYF+NsqT/lz+sG5n2W0sr8zrlTmvSs/OycwziOYp6y/Ca40/+JWNYhcPl6a7VjmS+pTKo3rRdpDUhzWms/1SvtvxBC95jlY5ProbKe/Lt1T+VrP2+vlF3KZ+1HrX2L/OXaSPNR60uZpqH1RRvj1rNWW6U8Wl9y3VWbZy091wXa+s3lyedca8O6x6xzbK1Ty2MZh1L3Wvdy66yPMc70cE6ph6X1UtN3+Zxbz0DLmi/n24J1vmr5tP7X8mvrr6X7anvFej7W9ouGRfdYzurSpujdL5LNkNeT+jaO42n9eu9x7949AMDhcMDxeAQw2WYJ7z0Oh0NTluvrA54+PZzK73aT6/f06dNT/QCw3+9P9R+Px1NdeZlhGE5jkdZukr9mK+Uy5/XW8uaUY57Xc1PDJNtNnZIdqNWVp9X2qVSm18bx3l+85nPSegl+RAjLfZZkbJ0dtX4Mw6A+z9drrV+WvmiUvoJVD59FPttpkq/RonamWey7GpqtU7PnTnpjPM956lYIAeM4irZhadfke6TUkdp+0NZ8rx9clqvp5RPDch0Nw4AhDFX9XEPz7aV51cZLSkufx3FUy5S2Sm5fXl0FXF9fz+Q6HA8A9gDma1myW9pxEVk3S6T08rnmi9XsKItNsJB0YYfMn4cQ4ZT+WnXcGiz2eOkn5mdnrZ78mbQ3Q4gz/Sb5tjU9qZ1n5996BmKc1qIL7fND68slvsV8P50+IYTJzs/722trW2j5MvO60lhPz4/HEfEYzfqt13/X/BarP6PZ89r8bbmPrDGlXv9jbfv52JW+fdqvuY6zxsXW2BqaTWyxD/Lzo5RZs81q/Vjjcw2Z/b1mnss8PWOZ58nHsSZPzRct+98Ty7XEekq5evxIzdbQ2sjrWGOnSjIk1sS4LTokt5t0ne6Rn1k9aDpFajONrebTJnlvw95YY09b7PTas1o/tHND04tWPdDKW6u3Vk/NN5X6Uu6jsq48/ZK9VFKTsWUvan5o2w+A6XmPn7uVjuipHzjbr7l4IQQcj6GIISS7Vvb5yvHW9tSa+E9N/kv2jt2fOvexPHtafWnFcG7lrtNIbV7OvrKm68/l8jrKeq30+Lat5xYdU85d/bxcfz5Z410W38aqTyy6r9Z+bV1bdZ5kn9Vkqn3X/Lw0tuUYT3cCUdyvGpKc5d8xxplNk8cGNTt5jT9To/RXAMxilzktX3DNfrWsS6tNlKdL9V26xyW5Nbn64hY2fbE2biLJot0TJftZ2lvWuFFZthUXTvnHUyw9IsVGQwhIxfO6W+ecZcyks3TL81NaAlZ9raH5Odr937NEikmf9qLP52gaq577KefcKb4w6c7l/tntdgi7cywkJ8aottc7hrl+KdtJNq7Uxto91itXWW+rjZ7+S35dvhYtc2rRuz1xMGlcyzv7S/bJ6MPinnccR2DU+2EZBylmaMnbE7tKey3Z4ZN+lctp86rdQUlY5rY2F2vnqYy7lufhOI4YO6/DW3ZILf6R65v8TMzPfWm/5mttbsfIsqR3REpK2crYbJmvZd9In6XvEiEEDMJ/d7YWjz7rVtlmL8trTPUuZdZsRul7Hl+36iWtb5rMa84gbexr+iTtjRgDznGJ8+dSXuk7UI/1L/o4jPDezewj7/1Jlw0hxZRTezfvGBTvwFj812GYvxs82Q0eISzXf6+P2mrfEs/R2tnajrvEt6nlU+fY0L5lXMsxKb/XfPgeGSw+d0vP5OdwT6y2hnUuameP9x5RsI/TnmvVm++d3tiPdV2siS+ZxsbL+stiK5WyrJnHNfb8mjP1vcaaGKWWXr279bZ3K2syTvaYZM/P8+v/Hkm2IXpIe7H1jnzrXJDi7lp7Odb9l8vR926CPR6ct99vBwVRp9TLrJszTTbNj1sTx5PKA3aZc188j5fU6rbQim8m1tgxrX8rCAAjlv/Wz3sPjO3xr1HaDi37QCq//J728LKNrcYnUatPs6vK+/SUt+bnWc516zm20PfOXrZ8Vtsju93upq99Nsz0ff6svKtZ+pVRta9yHXepjtJ06dIGLj7H5Tu8tXZqMrX0kjX2WDs/LXLlWcp7B22MtGeWs7y2xq2xhBIpLrP1OWbZh627svNavxubWCIff3keZBlbe0/aU2vvbS6NjZLbgz++cgfEGH8EwI8A+GPOuZcAfD2A3wLgYwB+PYBXADzANB/XAB4D+AKAXwDwjwH8GIAfjzEeFpUTQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEJWwR9fuWNijO8C+OGbP4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkGeEf9YCEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyLNg96wFIOQSnHPwXv4NoRjj6W/nnPpcq9fSdo0QwiJfKUtNhp62ynpq9Vqe1dqrjWXqc/nZSj6X4zgu0pxzM/klWZxzGIZhlu8SWfL2tbFrzU9eR5LFe79KrrJdbQzWIvUxT7OMf55XG7PWsxb52OVtljLleqA2f6lcS1+Uf5ft5mtUmh/rnq/l12SQ5Jf0Td7XMo9FljJ/bV9Ka6ele7W5rSGNfwihuWel8crbl8rn86rtQal8a29Z8rfKW57VxmTNeVnS0mve+1Mbkm7sIa8r6X5AllezFXJq8yGd6RplXyz7o4XWbmu/SHOaj5kkS64v0+de3V+mJzkt85yfj+W8lfq6haSfJF3kvRfltupwbfxKfZJsm/L7OI6zMofDAQBwfX19+pynP378GI8fP561u9sd8fjx1HYat3ffPWIc9wCAq6sr7Ha7LP8O+/35WW73pL2U7zEAp/RS90nfreRjIlGOuR9HhBABnM+kcRwxjme9kvczr0dbO702Y0//0rhadVy5TvL0vP35GuvX9xYsMrfyrLV1vfeLM0I660v9NJ7WB2Zp8WjTF9LnWr7yu+Ws18ak1ENl/XPbQ5ZNsiPSepL6Z7FhJHl711ZuQ5Z6JVH3ueb9CiHABafKoWGd1/LslOyL5G9qZ0oat1K/1GIXV/tw0l+p3OAH5CpBWg+pzTyt9KGvhhG73TDTg/v9HlHwefPyPefgJbar5lsBQAj1ujUfp1VvmWetXabZz0D7jNPalc5Y75e6J+lDi8+r1T+Vk/NY40ya/lzjZ3qf9H3qk1usaQuS/C0bMh+/mm3jvYNz+Z4BdrsBQxxMY5TLINm6pfxpjUnzbFmLUl+kdV8bl55zsYY0J6012zqrWv5rax1rtq5UR80GsMhdxovyumvx1FJHS896ba6az5WfV7XxqtkT2hy04kEpj7RftfG27O2SMia7Zvxq+02Su9y70j7cIk5c+/5eQVpzgKwrz7G9vnhZTk/sTkpLc5fO9XJ+pb2wZuwlHWGR2aJHe8/0Ur9eupbKfdATY5LklfZVGW/RdJTFFxrH8c73z5r9a8nTu1a2aNOC1baTzsTSJx6GAbvd0l4chgFDeO/892e0uajFkQF9j8x8aC+voblfFRbPS6wx7fL7JTq2rNOqB3Ofe4qrhlO8cBwjwhgQsrPjcLgGbmKkef01+0I6t1to/p02li1bW2tDqq+cc0ubmr60+jWS77oF2n5ZM3ZlXWUdrTXa2qPSM8lPH4ewuCsIMSCvvowtAfoYa35Bnrcso8lf23eWuFDqo9SOti60uWudD9azTOqT1mayOaU8Wr+kNlsyWZ5rY9iqax6zOPuIlj73yCM9s+xRKe4t+ZznvZP2D859CXIML7fTSw6HA8bjiMPhGs6d7//ffvttHK52J5nz+q6urmbypTy73a4Zc8nb0KitcdvdlN7+WrR2L/VPE7U+rzkDbc/na/UcV53syP1+j0H47xTqcYXl89r5XubVYjetMybd/5Xnkgv1uHMrZqHRo6/WzmtrDqV1l8evtHOw1As976vk5bWYa0v+3tj12vdipPcttLZb7yOmPqZ4aJ6mUa5FyQaQSHLkbWn1rrHBc7S7La29Ne2skWsNNduqJ64ytwuBpAvz+oD53bxl/KT90rsXrHjvET0W92TDMMAFu60J2O6HLCzzLmNeNVo2npRW22/aWi51Qe2uIbWRk98VVP2/4ea89cNsTbngTmvLeu7L56GOc+ezOq9jGVOWY5OWtkKIC39uHAMM18Az8rHUz5q+Oku0tWXxha31Wp7Z7TbZzr/Uzyrb0s7zaV2U+euxE20vluOd2sn9oZZ+0nzOreItWrt5W2Vasq1GX76HfX5vR5JZiwus0dG18e6JOdwm5Tj2vGf8PNGzT7fiUn1Quw+X/n3BGh1pYR4rXD7T22zLk79DJ72rJtHaO1KsUatT+rdepXx3Ra7HrH7ryTcKyz6U76+mui+VUYuLaaRneSxgy3hLXqbssxZ3LmXrbaeVbrEv1sQ4ol+e48lWlcZUe3dHsrGlNrV5asWcs5wz/036t40SPbZYXp/Vv2zRsvVyyvdptTM15U3ylvo9RP3fjEn1as90dH9IXr/6ml766ec2JL+/lLEmb+981c/2og8zv67PJtD6k4+lZmf3+Nm9tkp59mmxKyuSPd3Ckld7h69W55r7rFp9Wl0WWy24cFrrMebrf15HLWZTa0s6H7QyUhuTDbUcs+QbJrT3LWp2Uk+M8px2N/Yp6eO98+YRIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE3CH88RVCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsgHEv74CiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5APJ7lkL8EHCOfeVAL4GwFcB+AiANwG8DOAlAA8wzccA4LtijH9FKP+RGOMv3Z3E721ijBjH0Zy3N59zrprXOafmyeuJMZ7ylfmdc6e8pYyt9vM8ZV7v57+rVMpTfs7luIRcjmEYmnlqlH0A5mNZpueEELrrL+vN60z1WWUvkeQp08o+pO/H4xEhBIQQTmkhBATnEMKIcZyvw9q6tLQnIdWXp7XWl9ZOqqO171pr1rJ2Y4zimkq01kyPrFLe9L1Hx0jtS+VKHSP1RWrbIovUbrnGtPEv92s+LrXyPeNSW2O5nDm6ZvYAACAASURBVK010lpLpbx5vRZ9o9VVtnmpHq6tE4my7bwv2n5prcXdbmlaWtaXNA7aGbVmzCx6uMS6bkpqOtOqr8rvWp0t/VzWZxk7qb1c/nx+pDnq3RPAcr1J6y+EMEvPZczTS7lKW0mynbQzonWOSWnlXsrHS1sH3vtZmZTPe3/aU9fX17P89+7dO9nBx+MR3ns4J+t/ABjHEU+fPgWAU535GKS03W4njm15vkztyXs4H+OUno+vdk6V9QDA4XCYZzwcb2yicx1Pnz7BdTjrnv1+v2hT00PlvA/D0OxLyTiOcM4L++Zcd9mOtk+0/avZPfPzfblPLefwXWDVfbn9W7NvNR8oDhHe5+tyGnuLXyL5b706W8qf5lqakxo1/1Hy/Wp2riab1qbUfi5/0j3lvpb6WK43TY/k8s30yzDfS85NemqI8znN108uS5nHQqnv8r2X0st5Tf5a2X6enpcrZQwh4HgVcDgcZmP2+MljHA7DSa6UPgzD4ozL9fXC1z2MN22n/LK/W/a5PHvz/Jo/raVr/kca1/zZ8Xg8f8aIEEZMj6c8jx8/xu44H5e8fc1GyPtXs09bNl7Nn9HKr2kPOK+hKc+8zVY7ms9/nt8o1KvrK4uvrtnvNeSxSesmwrl6v1rp5XPrXGj58mZSlnIdSvJoZ3KeX2vfEueptS3pNInW2NbWXM3/19Ks/ow+F3pcS/pcG0OtrMVHtlLOkdZObreUYyrZ1ul76+wr13h+buSkM6ZWl/RdozXHNftJa0+yLXrt29p8turSdLB1TKz5LHW0zh2LXttSFgu1mMFiXgR7cYt+rInnbNVerT7JnpLGq0emWownl612jvfYBqX8wHyf1mJWWlptz+S2phZz6Z3D2rlzV760pqNr+lk6V9bIW8aoWqyxLaY4St2GkOoaxxEhpDrP8a5xdFmcyJ38nZp9VZNZ06FavMiCRe9IOl3ai6WfOcYRx+P8rvxwuMZ4rcdRpb1TW29WW7nWP0td2j7W0s7yu/+fvTeLkS1J7/v+ESczq+oufbvv9DIbOdRIM5yRhoRoUpYl+cGLZHkhLEo2KPhBtuRFhi0TEGDABmSYNkDIBEybHot684NgwtaDBWszIUiwLAkiLMoWSZsjkRRJS9xmeqa7p7vvXlWZ50T4ISsy48SJL+KLk1l1+07/fxeFyowTyxfbF198cSouui789ugWHRDpotXqBHatt6dzcrX6VaTwUh/X0ntfPuecI6M030s+Hs3eUCODpFclnVDa55TyiMNa9++xbZzzBZR8KzGrpZvY+avlCi3/T1YqVy68lEaKm4sTry01H0uIJ+V5TPuqdexp54Rm7qbfW236uF019Q/xW/Ydxuz9tiWbS7Pu5vI+JHyOfkwJ4y8dl/G5dBx+cnKC1WbAycnJVbpt+CuvvILLxby5p30uzYk556cln8xNM2dPNqfOc8j5vva++LH9uNls4DblMWD7YSL7MAyI3NYjSr5a7b5pQpf6G83V2YjNvo9Ry+9Qu3VOvjW/NzCWW+t7k+ZYsJXS9wbSNLVxGdJr/BeS7zBXv1w8b+O9xf5cU9sWtbrEcsydj2ldNefvaXrnXPb9Xo1tUWpLrZ2tqXur7yYQ91drG0vnadLcye31W/ZOgF5/a/ONw2I7ZI69IZ1H7n/G5Q/DAAz186K0/MGO8wrytshs7fbsak+bTVXzdeXW3NoedfZ6k8kj/JbesYjZ+1+w+w3sfTLOORjl3NC0Tz7dPv24Djfjx9NS0xGDcZN5vm0/Xf7GmKyeiJ/nPufCan3Q4gPQUrKb0rIlPVfyZcTxAta6pMzpuXTIIydjyY9Zk7eWJka7h5wTRzNWvPfwGb1Xao/Wfb/W957qibn+hbbzvLqvQbsHPib5OTPtFwlprcz5v3LxUllay8k9n4M2fc4W3a9bW8L8N2b6ztX2eWad6jB6TxHYrp+d6/J7hUz88vo1fhdqOxfr/VNqa8340L5fIqUpIe0nyvaRnF+rTzzVy2MfQtg/YheW1iv93ro/0pwVpeEhTWybpf2pWZPS8FjHxXU2bvqeeHiWhknU2mKOHq/Zwxr7ZPx+4n5Op3Mpnnc5GXN9EX/X1l8iZ8tqdO2hvt7a81o5pT6R9r8tbTnHh5SWVyNfh+nZVLzHSfPOvbvsnIP18t/3tsg7x1aM45XWZmnsGxOHeYTxKc0RTdkp6Xtvx7Sn8jpz748LYa32bKtOT+Ncp88/t8YeS0fE+Uq+onhPO857X4bWhq6Nn5BXbfwU11qb9j/QdRadk3WUhlKa3LqV2qf7sJv/ux8yhpevXCPGmE8B+B4A/zKA3wbgXi0JtqvRR4TnXzHG/BqAvwjgf/De/+yxZCWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5MPGYf9lI8lijPk9xpi/AuAfAfhhAL8bwMvYXq4i/Wj5JgDfB+BLxpi/ZIz5tmPKTgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELIhwVevnJEjDHfaoz5awD+KoDfi237hstVfOWnqairn+8G8FPGmC8aY86OUglCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQj4kLJ63AN8oGGP+MIA/DeAM24tRgOmlKgYyLRewhLgG2z78PgD/ojHmD3jvf64hH0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5EPD+myDy5fW2NzqsTnd/riFg7cernMADIwzMAPQbTosLjssLjqcPF1h9WSJ1bMlTPHqAELIiwYvXzkCxpgfAPAnkL90Jac1feGZqshMPp8F8BPGmN/nvf9bM/N94TBm34Te+9H3GGvtJI730/tucmFSeWk+zjkxn1q+c0jrHpcRy5LGzaGVL24/Y8wunTFm91PLMw6XPqfyt2CtRdd1o7Bcn5faxDnXLEMYY8653ec4v5R0vKbyhO+LxQLWWli7D7PWAtbC2m5SVy1x/+XKDzJKaMZMWoZmXuRkkuZsbVy3xM09z41pqZ2CbN57sb+lsLScHM65Wbqr1MfpOE3jSGlTfVNCI1fpmWaMlMZy3MY50jEWpynVUxteKz99ph3TmvbT6ONUj0u6syRzXEYaHrdlTcfk5nfLHM9xyFoSZEiJ500pf6nO2n7RzsUctXrnxn1KWmY8TnKf4/p2XaeSzVo70mul+RjylYh1ZJxH2haxjo77Mm2LYRjEsmJya78xZhSeWx+GYYD3Hn3f776HMjebDS4uLgAAl5eX2Gw2AICLiws8fvwYAPD06VOs12tcXl7uvhtzid/+na8D2LflT/3U29hstu1269Yt3L59GwBwcnKCs7Oz3ffbt29juVwCAE5PT7FarQAAy+VyF951e5sj9H1sl+TsQ8nuzo29uH/jNIvFYpTvsuvRdRbAvv9WqxOY1WKUNi0zJe6vOK6279N8vHdReSGvHujz9Z6j3ySdNA43V3bjdI1/nmjqe6jOT5G6X9qzpTa8ZJun1PpVo3Nz8khr6vaznE4rVz7f6ed0vgP7+arV0ZoypXVn4zfw3o3iOOdgnFwna624Fmpt2Hgs5HSatXZkK8Z2VLo3yK0DqZ3pvcdyMWCxuBiFn6xOYEx+XMb2QtrH8VrXalPFdU91c7xHlcZYOl5ybR7WwSCr9z6rf733GOCu1vm9vuu6DtZN192cPLU6lsK0zNnn5mz23L7GGAM/0fU6W7m25w9f07GYjm0pb014LZ+pL2u6B/Ne739ofSbp+mLbdvFzve8v1xalcaepc06XBGIbJzeu0nhS+lJ4/Cy1AaX4JfljWvb14VnrWiuNdcmvo91favVJanfU2i21W+J4mraM2yiNq7GBJX+BRvZcHQJhTc3lnabR2OxS+5f0UEzaz7n80n1trQxNORLHsusP9Y/EpH6RMDaOUUaaR7ynPia1OZZ+P8RHkyunNh5jWymUI+0HS2dSJRnivMPvmn1S2qPEz7X76lK9YnJ+nDg8l++hSOtrXGZpTdXkX+qvVM/n0Jan1SOhTM2Y0tg8OXK2VhqWritxvKkPJJ2HAODh/dU4HPJ7E23bSWVJe74cLe2aK1c6wwn+MgCwy+k54WKxxAL5ffqcOaLZ87SUJdkkpXSS7rbWX81H2RdUkiXem8ZxtX6hNJ/aszl7m1I7afcNUnqpL+LwdOy3rM0lPSnVU1orazpXsx+J3xdJ02p0f/Dnp2WlNss0fzeJs+k32GzKek3yAwU5QniqH3P9KtntuXbL1aPk+ymhnc+1tK3+iRTNPAx55Wz8ufZ8S/klNPt4qbzcM60dewy7Sst+jAebYBteOu+Q6hLyGrfbvpxcfi1rUg2tHtfmofV5XDca2z2NV5s7Nd0jlftBI7bFcu+LAWU/MCD5fabnFBI5vZQ750hlKckprYelcjR5ldKU0Og8afzEskjnWcEnWPP7aHSydv86fpdgH2YalhnNPicuJ5U5tivi+OE9hPhcC9i+s3A6eDx+/BjG7P16b775JvytUwDAarUatcdyudztYeK26LpOZSsYY0Rfg8aeLZHrV4mcn2o7ZvR7Bck/XUrb6vfOfd/KHvoyLn//PDdM5+4fW31UcR/H617JBizpkVYfTfAhAGN/tZRP+h4QcPXOjx3XS8qjtk5oZJ67T8/RarvIvpvdp+hHz3X4gWNye2CtbaKZi+ma0Tp/zMLB2pBmeq6jya91f6TZC6R1n1M3aU7UfDW5ONqz1ZKve9//fqcbr2LA+7I9el3jVKvrNW1WSl8qU9evIY7OhzDHBtUQ+lLjR29lOkbntNOWOWNJ0sm58Gke7b67nCyHpj80Xswh86/WV9M23bdrbJeUyh7sAOfGz7Z2xbiM3DlBIPZ7xO9F7eW04t5OQuurTsm1WekceI6/S3uGOJghmuNR2rZXewHIa0dcJ+eGSR2dGzAM8rqX5lub19KeTAqr5Rs/k3xkJVvHdfl9ojEm+y733PcWyn7zNn9cyYbRrD05vR7C47y161eL7FJ50nNjymdeJZ/anD1k7tmhdk/ruM71l+RrSfvImvrfKUnlaPyTafrcWVPNbm/pn1xayS9Vk1/rH4n52mffx1//9Pko7Je/+U28/01PsbjscPft21V5c7LX0tTs+Ro1H1G+XMDD49ndC5zfv8D5y5foV7XFxgMWMB0wrAask+boNh3O3j/FnXdu4ezBSfYiljnzq3UNrz1L52htrMzxsZXKLvdLm42hlbl1nyLFO9R+ltoo9z7MB/Fvgj6M8PKVAzHGfD+A/+zqa5gx6ezLzaRDdvseew+lj/K/C+DHjDH/vPf+/zogf0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5IXFw+Py7gaXd9dA52GG41xwMSwHPHn9KZ68/hSLiwXuvXkHd792B9Z/8C8ZJ4Tk4fU3B2CM+VcA/BcYX4ASa8Q0PPy8C+D/juI0FZukM0nYLQB/0RjzTY35EkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghLzQeHpe313j08Sc4f+UCbuGuraz+tMe7n36AL3/n1/D0/vm1lUMIuV54+cpMjDGvAfgfsb/4JL0ExUdhvwLghwB8N4BXvfeve+//qRnFfjuAPwXg/Cpf6dKXNwD8TzPyJ4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCXkj6VY+3Pv8unn3kAm7h6wmOVe5Jj7c+/3W8/Zn34Oz1XfZCCLkeFs9bgBeY/wrAfewvWQnEl6H8LQA/4L3/m8co0Hv/DwD8cWPMDwL4EQD/elReKDPI808bY/6I9/7PHKPsDzLGbJvf2vxdQuF5wHu/ix8+58g9i8O897uftBzpcy6/OH1JnhA/l58xRqxnCyVZ0+dxmaGsWpuV5Irz7rquSUZN26Xx4rbPIY0n5/LGThwuxUllicvSpBnj4b3bpSu1vdSvtfC0f6X8a3lJfVYjN65axrV27s2Jk4bHbRbGTjrecuk0cqZ51WSpxanprEA8B2LdU+rPtM1ycVv0bqnMnGzacgLOuWLb5tqoVJZmHSjpRI2+LM01731Wd6VpcnM7tGNtrKTpNHO/FF9TRvzdOberY0lvSmNpLtKaUCpT005SPqX5Lo0ZrX5s0b+5cE16KY61VpxLtbWitCZp7LGAZGPEcklzREpTWsfT9T7gnMNyudzFCemNMbtw5xzW6zUAYLPZjPJ59uwZ3nnnHQDAxcUFbt2yWK/vjcp+8uQJnj3b5vvw4UOcnZ0BAF599dVRnfq+x71727Rx+3jvMQwDAGC5XO6edV0Ha+3u+zAMo3RxPTV6XIP3Hs65qr2kmfvp+hbnWZpHubyCThowFOOXbH7N+irJtn0ut21Nd7Xbn4fnf0xdlU8T2nMbFusdYDt+c3NbO261e5s4L0lH5eWfjssY51wyfvbxpbxT3V3To2kczThJx0JJf2tkyeUb2tI5B+PMpMz4e25sxjotLSe3zw+fg7xp28ff071gKKfv+12cYRh24bFOD+UsFgMuLi52dQWAR48fY72e7jNSmZfLpajvuq6D6wf0fY/9/DBYr9fozb5Pcm0mzfGcDpP0Wq6PY19MmJOLxdQ96b3HsHTousWoL7quQ+f2ey7JpoifHboOlUjHdIutFMeXbMBtOBC7G7V71dp8AwyMGceX9vC5PFueSfFj3WBM0Gvh+V6fpzLmaN0zz/GbDXaqh2PZQr7S/kbji8jJdsgY1vrCNPva1rbU2GKlvEpzJM471sPa/U7rWqcl3rOmZUr6Qus7K83pmg8vjRvHz+ngmixpXi1701rcWvp0X5WGpfK1kMs7910jQ2lvWvJ91yjZilpq8ufGcW78xbZ1vC/W+ro1YyWNorWttWVIcTX+K60fb44vRRM/bWONj0Iqr1R+af63hGuRfH/H1NWl/kx18pz61OzlNN9D2izd88ZlafwoufwkYt/VHDT+zpKtku7J9nmO/QDAdn+j8eeWZJFkT5+nbaxJH/dR2l+5/vPej9aO0Z7rZOsb3AZtw589e4pu043OqiS7I44Tc8gc146RFj2uHxc+6ccrX8Cgt1fj8tLnko7V7pM0NlOqQzT7n5LvR+svysmc7tmk8qX0tfKk/q2t37nxmqt/qb019U/ThXkYxl289pZkXy7cxPeyXCzhfV1P5cqIwzX7iBDWEl6iZI+le7hj+WNa9pyaNKV0x7Q54jJrlObBHP+FVK5W32tpsc2PRa5t5vjnNAS/+3WP5Ziy/2WcV8v+u1bmHGJ/ey2/mh+05iOJme4XxrbAuIzp3DrmGAGm7RC+H2t+zaVFn2nHZms5cV9p3vH4oFGTVesLkmwFb6c+5bCXaW2n3L4q169934/GbCjn6dOnOD8/n4Q/ePAAwzBgtVrtng2PHuPdd7c+xeBXfO89j2fvb8t76aWXcHp6umuHy8vL3ffYDxnPxa7rsvvn2jxq8XUHcmOx5BM8FMk2ktZ6SV/OaYusva1YO+b0RWs8YN/uJb//fl0ct5FzDt3V/zur8Q3v2sJO11rJhyCtc93kvL7dD5G2cauffI5N1xonrWPaZgEXvStjzHiItdhRtXij5zaMjWlZ2/4I4zjfrrl9luQnXCyGiT/f2unZesnXp5lL2/A4j3z7SXZv6x5dS+h7TTlSeVpfgGRDxs80NrCUVxoe+9Jw9f8gl/Iura+pjDU5Ss/n2IDS3NXmp+mzbTbx2aqHEfzFcb9p9+259iutSXP2zNq9+XiM5vcTc8rJpZszRo6NZLceA+kMdY691epbad0vS3o3V/bOnjQOafSWcQ8g+w7cuB77cwLpPcxUV6Z+SImWsZqzl0t2Qy2tRrZUvBb9qDnr0lKyx1vmaIsfN/6cszFq61Wof6kdhsUAa8c2Ttd1sG6cptVOLTHHLtSuKZoycuMqtiHj8rT55tbQFv0jrYXeQzUHcvZULs+SrVrKP36mrVfrvGi1Y2s2siZ87lqePsvbTKEdqtvvZD8jxyvN5ZoemTWHs8/91mL2+8da2zy2rUp9p9GJtTJK8Z69fIG3v/Vd9Kuh8FcXACZ/oi+VmQ+fzt395yevP8X61gYf/fmPoLvUvTdWmluPX3+K/iT4ovfleXisnixx+72zRLa9TpDarDSfDvVXx+3ZMnYkeWpxJV9cvo6xbr6y0Qq2ZevY1Niph7cvuS54+coMjDFfAPBHkDvB22qDpwD+mPf+R6+jfO/9WwC+1xjzfQD+u1i0SBYD4AeMMT/qvc97ggkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkBecR288wdd/4wPAPP/LLdZ31njz297Bx770Khbrw650ePL6M5zfuwQwvnwF8Ljz1u3J5SuEkHnw8pV5/McALPaXnMQXr7wL4Pd673/6uoXw3v+IMaYD8MOJDOHzxwD8QQB/9rplIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCbpqHH3uMdz/9oCmNGSzOHq5w9ugUy2dLLM8XsL2FHQy88XALD9c5DKsBl3c2uLy7xuXdNfrVoMq/P+3x1S98HR//0mvo+m5OtQghNwgvX2nEGPMqgH8D+wtO4ktPegD/2k1cvBLw3n/RGPM5AH8U+8tgYv498PIVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHfYDx+7Sne/Q0P1fFPnqzw0ldu49a7p7DOwpj0z/MB4w3sGgA64HyJs4enAAAPj2f3L/D4Y09x/spFtazNWY+3Pv8ePvYPXoXx03IIIR8c7PMW4AXkewCsrj6b6LcH8Ke993/7Ocj0nwJ4/+pzfCmMAfA7jTEvPQeZCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQa+Hi7iXe+cz7gPHVuIuLBd74+Y/gEz/zOu68cwvWtV+1YGBw+70zfOznXsNHf/Y1LC4XdRlfusR736K/HIYQ8nyoz2aS8j3R51gLXwL4kzcsy1YI7x8aY/5rAD+I/aUrQbYFgN8N4M8/D9muG+999rMxZnfLWBwupdXmmysjvs0sfM7dcCbloSGkSWUu1SEnS1qXObJI6UvxcnJpyzbGVMtxzhXLLMW9bqy12TLjcOccrLW7z7H84fs2bD8OtlFMtp9L7RmXoyXk55zL5i2Ny9oczDEnbmmOp+NfM85L8zitW6tcof1THRV/jsvI6R9pTuXkyOXVonck/VFCqlf8XMrrkP6qkSszTavRS6lccV+W8sv1n7SG5L7n+i+nx2vjB9jP/1ybhGdhrErxWpDGUam/pTHbdd3os1a2WOdp+zn+PAyDSmZNvq3tWSonpraW1sJLfROHx2O51caaY3sF4rXyEFrasqSvSvWP51g89sI4SrHWYrVa7T4vl0sAwGKx2IWfnZ1hvV4DANbrNe7fv49PfOITu+8nK4+PfnR5Jde2zN/8m+9hvbaTfE9OTrBcLnffV6vV7nMc3nXd6HOYf9baUV+k7RjXOXvrr8KWyBHaM2d3tK5xOVlD+lZbyTkHdHHIfvzkxmxt3LTqlapsBWp7i5qO6Pu+mLe0H2mxJ9P0ErU4GtsjjSvN5Rotej4uX5pXaRxrxzKnczKWobb3iuNJbZHqwlxbpDo6Nyfn6IOSbZSu7Tkdm+uLWlha15we7/sewzDsvg/DsJsP6/V699k5h4uL7S3mDx48wPn5+S7+5eUlHj16tMvvzp0Ffsu3vgZgr9P+zt95AGPOAIz18NnZGc7OzrBYbF16i8UCp6fbG9RPTk5G+nq1WsH07qovQx2n7ZDT3Wlfx3uZuL00drM0xmoMy2Fk/wHAcrlAFyleyQbXlFfbW9XmcxpfY6tI+6SQX97vBOzvf9a1p2SrTPsszreed0u7tvT5vtxQX4/g8yi1q2a9KO2NamnTZ96mc2na1jWbv3Vd0+ZV0rM1favZp9TmS82GTtdXSf50Pcn5GOYi1THWMzVbLciYm6+5PGL5pf2AhMY20CLZMrV9lqSvavHTNFq/UNrONUpxpHXsmL7hOK/r9jm3ztGYdMzEYzFe92P9nfZFPIdDW6b9GuaS937iE8/1VdUn03UT27frOnSF8awto5XaXCjpyEPnb5y/Rvekuqa0BtZs9GO1X67sklw5fVnaG2jXTSkvKWwux8xf0nPx2VIJ7Z4oyJfqhzm6Z+7+PJd+Ok5yPqGtTRaFbnWVH48RaS+stScPGSOS3RX7G+KwNF7OHxbCsYj3ant5g71Skj+OE5OzIWt+VFG+AtV1ICNLmqZL1or9nmYXMqp/yaYs+UUCwzBU51XLuJI4ZLyV+ltC6iut71BrK2r2Hbn8pe+SXFJeuf12La9jtKW19T5tLSeQG9Na3V8rvyVcWtdb+rtEqo9KNplmbxrr2jlzVItmjB2zvFI5x84/LUPSF6memNveLT7d8Nn2w9WcHPuUnWtri5KPLfc5tTmktazkr0ptlZwMcVjJR3Mokt0koenjQ22rso1hRvvTuP+PUXa2xGuYX8dG2qNrz99b1mDJV9Lq84nTpPFLY6DF/3Do3qW2z5X2IDVZavXTjLlUtpBnOPtPuXv3blaWHKvNgE/9v782ivuFL3wK62Unps/pPkkHpu9QArox03S235A2J29Kbe8O6HVFbR/WYmtldXdOjmT/mSPnq56zT8ylq61j299y+7X4ZcP+ep+3vDZI76RZWz6r0OgWjb+8pV4a3adZx1vtm328nH+mLE8aXtOT+bTTMsP5/ri+Y3tK6+OV7LDUz5Ci0SkhH00aaY5ozk9Lduxc+67mV2vxEWhlzsmSjkXteh/abbymbn93ncUQnYek+Uh7U8mfKYWX0PpCpGdz95y1dk+Td51F57p8AgU1nxMgn5Pmvh+TVW0HFwAAIABJREFU3Vg2Hq37CY1P8Bjk94jy/lUrc+tZtpbryveYaF1Y8bhMbfNWP5gxJvvO31jX7J/7jA2yPTeV52Jp7ki+o9z6INmK0jogrVXavaBUV+vk82dJJ6Zype+gbfP2yXsqgLXd7r28GnPme82XBkz3E5r9RZssOttHomXt167v2vw0cuXDDeJ6a9cXrf14TLzfb9+Cviihte3m2moau0tKX/Kv5sZRya8wlzlnJLW9aC7+3m7a20+prh/XtZ7v3D6bzYy2Po5O2qI9b5L2IwDQdz3e+tZ34eFTVQdsQ3ecPFni4z/zGlbrFWCOY8PdenCKT/4/b+Dtz76HZ/fPs3FCMY8+/hS33zvD2aPTTJyyLGPbNKwjAGBgFXojMMeG0aYbz6P6ni/OP/2sWUtKukTSQ87p96AhbE6b6eoinwOR58fhf8H44eN3YjyagwX2l7337z4fkQAAP1p49h03JgUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEHKNfP0zD9Cvype/GQ/ceu8Ut947g3XHv1rBDhZv/MOP4NZ7Z+WIxuPrv/FBciUMIeSDBC9facAY880AXg5fk8d/9YbFGeG9/yqAv4/0Or4tX7h5iQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkOPy9P45nt4/L8YxHjh77xQnT1bXKovxBm/8w4/g5PFJMd7m1gaP33h6rbIQQubDy1fa+HTh2c/cmBQy/2cmzAD4zE0LQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEHBNnHd79DQ+q8U4fnuDk6fVevBIw3uD1X7wPO5Svb3j4iSfw8DciEyGkjcXzFuAF4+XCs1+5KSEKfC357rG9fKUk9wuLMWb3E4fl4gW8ny5GcZj0OUd4HufvnBPlqOVbSiPlE8vgvc+2hZSvtXaXPs4rlTH+HMrJyZ7Gq9VL20a1fmjNu+s6VTxJHik89H1KKTxOPwzDLs9hGPbPhgHOuaufbZgbBjgD9P0G6Mfj0Fo7qVc8FmJ5NG1b6ss4X6n/S22Xm0PpONbKluYNbNsiJv0e5xPPBW2ZtTTavKS2K5WtKSvVjyFOqqfS8uPwXB/l5Jd0a+v8Ted+SRelYemzEtJYPpR4fmn1JSDXLZde0ikhbTzOpfGaCw9yhfSaeZwrv0UXlPq7VM8aubkewpxz2f6ujdfamqqtazrHavmm6Uv6Js5L0t3p+NCU673f9Yc0duP2S+OkfSnp4lyaOG5pfUs/a8aspGNSeTXjIo0X90Vch2CDhDYNa7/UF3Gd4zjWWiyXy1G/LBbuqiwP57bxVssVFovVruwgy2KxwK1bt7BcLnf5rVbbeMvlcifnYrHYpUll0dBqp+cIdVyv19uATT+yk5xzePr0KTabxa4tQprlcpnVaV3X7eqYq9Mh+kdDOm5Ka2072/obN86r1ebLpZPGqJZc3JotF9D0ybSO43Uvp3vT9Gl4bX8U919qA5Rkk/oj6IRcHvE6FvYEcTrn8uu/ti4l0rkcZJHyad1vlHDWJeXobEyNfV7KJ56X8f4xfA6/Q5+F/VpIGz7H4W+88Qb6vgcA9H2/+wn5LBY9Pv7xYSTX5z73UVxe5u35WF/HsqzXaywWW51orcVms0HfO6zXaxiz1+nn5+fYDJtdfWK9GPo7XjustaP1OdWfkr6v2bY5avpQ2rOkz9I0OTs5N4db9HKtXpJdqNn3hbRD5LsJbHV93j6r5VveV87bx0llSd9jpvbp7lsUJq8XpTEm9aXUFpKuj3Fdrpy6bpX2YDVqa8gcu0Uzx3Lfc3s2aazUdEMuTOOXKdnfUvraOMiVkZMhp0Nq9UvzjfcDGmq2Ui0f51zVXkufH2qTpjK15lfy3UlIz+K8Yrni8Np+Nc47rK9xXl3XqfpT0xdSupwsgXQ/MdfuT5/Ftmcqh1SPmk+npq+07ROvgRoO9fdI8Wvh0lxI1y5NeaW8WmUutXNpbrQilaPZN9X6S/KX5vryUJ2UUlv3rgNNX8S2eko6r6UypP5vtRtKtOqoNO/FYlqPxWKBrgOsHcftug5m0PlG0jmlnectlG1w3ZmIlG+/GLBcXl5934afnd1Ct6nr65pMGllK/hZJxx4ybnL28DYs3ksAYT8BbMdNWMe1lOZUTf7cfJPya/XRSGnTMnJzKfY7afIr5VX6LOV7DF15iK1f8v1pfZRa/5e0VymRizMMg7iOx/1ZsmFr+7BWWyiVN/W9aij1Q81nIuWRyqRN27LO5cbLHP+J1sehkU9izt5Di9YPXvIdtiLZdLW+XHgzaYvYr1nyDWvXNA2l/tTq1e338VqXOze9DlrmTI5jzpOWMq/7rO8QtL6vY5QT8kvbI3cWP0eG3PjInSGl36Xy073JnH1qzoeRzmvNvjpe33J+0WOQnoGFvJd2oX4HMfXh5MKstQetDdN+Ka/X0rmIFmkMtKRL0djjtfCpj8nswtIz3lbd1+IjkcJyddydgXX782VjtjJ753bvlQzDgGGYruNz5NeuG4fqO2vt6Jw0oF0D9m18ZVsP5T6bng9M1+qabyp+ngtPv8fvds+hdZ+Ws7mksRiPeb/YtiPgr34A793VT/5dspIvQbNPy51Pxem2P6PaiLqw9WwqDS/ZeKV+TsO8BaQu07yPqpU5faaZi9I6qklf8s+m7de6fqTpNe0+/R6XNY1X8y/n6h/P3bi/Uh2h3f/u5Ttsfa2VV/fxHU+nt9b9WNyEf1+ao8eub07/GKPzXR6r/Byls4pATTfeBNZaIHOu0Go3p3U1ZrzeaNoifs8zrJ3XtS8M33Ofc9/nlJV+Tv2bQLvfamfTOocOXcYum5Zfk6sUJ+Qb/C5a3/Hc8LjMFMnvW8qrZe+c248aV3/nO0Ub55Ax1rK+S+m3efjRmu+cg/H1/Fp8wsedw/sya3bydfjCjuULLM2lWntry6gxx99l7VRua6c+51iusd2011/5d/qn4zH398ha2VvGaSlfbUu19mvgkLNRKZ/A4zeeoj/tozpM5bj14AzGBd96fl4duiamLM47vPKrL+HdT8sXw2zONri4d4mzh6e7sNrfDY51Qz7eddhe2vM4AFd/8zP1TYc5kdPpuTEinb2W1oR0TojzqrNJ3L0tUEs/d+w8LzuYzOP6Tl2/MbldePboxqSQeUcIv3OjUhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUfEG4+Hn3hSjGMHi4/841dg1FfMHI+7X7uDbj29RDbmyWvPbkgaQkgLbf/NDylpsjMAZU19/UhXJN26USkIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHzDsTnp8ez+OdZ3N1jf2mBYOrjOwVsP4yxsb7C46LB6tsTpwxOcPjhB19ujlP3k1WfoT/pinFe+fBeLTfkClOvCeoN7b97Fe9/yQIxz/vLlDUpECNHCy1faeFx49jEAv3RTggi8LoRTAxNCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIImcWzV87x/jc9xuXd8KfrZhLHdw6uA/qTHhf3LvHoY0+AweDO189w78t3sTpfHiTDk9efFZ93vcVLX70D3/mDyjmEO+/cwnufegiYvAz9SY9+1WOxHl/1cHH3Epuz8cUyxhgMq0Esa3PW4/HrT+G9H6UBgOX5AqePT+ZWg5APHbx8pY1fKTz7NJ7/5SuvCeGPblSKG8RaO1oMpM8SmjhhgUk/5/LKPdeUAQDOOVW8XL7hcy7MGFOVKzyvySrVMc4jF54rK5dvLHNOzvhza3sB2/GSlpuTMSUuK+QhlZ/ri/RZja7b36bXDR7WdrDW7MruugVMZ7FcrrC0C3jvs3mn9UrjSWk0MmvnTmnO5Pq5lkaTT9rPuXhxmTWZcnHjuSDJm+onCWns12hNE9ollJdLn45r7ZiNidtFmvtz8tXK1TKua/pJiheHS7qgpBOlsVPTRSVCn0rjNidPqU9KcpTGfi5PTd5pXl3XNY+fuC9a14g5uieWxTk3Wkvj73G8UJZGHk14zUZJx0SpXTT6Ps1PUxdrbbVvJPvCOTfS6Wm8eIxp2jsNj/ssfF4sFtn4aVhaD6kt4vixvMMw7PIYhgHDMOzirddrAMBms0Hfb50lfd/vPj979gzn5+e770+ePMGzZ+/hwYP7WK/Xu3K+9KVfx5On2zjL5RK3b98GAJycnODWrVt49dVXAQCvvvrqSLbT01MA274LNom1dtcXIbymAw4lLn+5XMJ7j+W6x3K5xN4x53H37l1sVtNtbYttXtO9x6xXmm9uHLbRtp8Rc1HsA0JeWpnjPMMY18iV2nLpmhAT5tE4rzCe9zpZkqsUXosXP091XVyHeP7Hskr2WK7s0H6dG7ejMdO0sRy5NTv3WTsHJP2q3TOn1PZ/sGUZW+VJ49f6OF2/4nEsjZc4XhzfOTcar8vl8kqfbZ+tVh6r1bORXK+8cob1empbp2MqltVaO+p3ay0WcDDGjvYDy+UCZrXapcnp+7T9pH1WXH+pXUs2zCFo8wlrbwvaPUfJTm1dK2OdFXTEkOgXYGsX+L6vtmupDoN18D7Od79PnNM/2vWgJNd0bJusHm+VqWRr1vYZ9T2ayepiqc7H3Bencmrz1fhCNPmle6jaeNf6VKV97Zz2Ko3n1nFeGoup/0fyi8TxS+VoqdUtXhNayij1eXiutS9LvrUWWv1AOeK1MiZeH3O/0zJq/gPNOhjHLeUV+4hz9kltHpbGWomS3p2rk3OkNnOurJb9W8luqfnytUjzqpZnft9S9++Wns9ZUyRbpcVW0rR/yKtF19bmXq6cOKy2Z6mh0X1SGo1eLD1Px6t2nwaM9xm1fLXlt+iOVLeWdETJpzy338Z5AFt/QFh/ZX09x6el6Y85ejjV6y16L4y9VPSSXV9ax0pr/TF8S1q0Y2C75w3ny9j9wI9lcc5hs9mo8y/VXTPfa2ulpNslG/JY9myN6/R/asrR+uvifFr0ZU223L4j3aNLssV2b2oDh3OA4MsPPH32FOt1+Tw75yMN4el4ydlB8ViS2l06r0nTS2j7t7S+a30GObRxD/VdpmGlNW0uc2XMcUybPXCov08zlnxQ4pN0h7dNbp/cdd21tJVU9uH5XF/eErX9T2nvE7huGaek+nw6pjQ+3G3cfLpD65RLr7Gnj1FOINSl5ewuJmfnpn6L9Nwsh3Sulns+h1z61I8nydvqU6n5OFpoSS/5u+IwaR3WlJOeGU05/po4yj1jj+TqI9ktsc7PpU/zCZ/zYye/T5qzvyyR2hqpbKE/0vqGOsbyz7VZWvfoGh9wauvM8QNo4hljRm0RkNYnY8bnpBr5t4R403ON1v2JZA+n5yYlcvFKabR+NKnNY70w2Kuztmg+WGthYXc6ROMT1babtRbeTmVL90laSnGD30EiZ1dI+eTaOu2vNKtc/qU+lvSjFEeixTelyaN1juf2snGb1fwHWvs11+Ypc89YpP2dtMa0+JePZZPWyom/5/yP6Rp4HXv5nDxz46RtPB0L03bd9lUs6/HWq1JcrV7ZptXZJiWk+Tp3/NfSaX2nqTyHlpvKIL13ZdxUB4lztCvPycnfLGXsydjeNsZO5lvcp/u0Jvt8V84BvjagvE+bOy5ay5XOiFPZpLppzoXHOsHBuTgvj2Ho4Vz+/blSvhItdorWrszVp2R3T/OTdH6bHmn1+Zf8zjXbLJdv2g4lW8F7TOZY67ieY19N5Sife9TWXa1cLWOo5bmmnzRIbaHVW7EvKjcuWvVfanM6N83DOQ9T0DGS/sqPG3lNKKVtsYe1umekK4oxx2nWqw3e+ez7uHjpMkkcZKnkYT0ev/4MT187x72v3MUrv/aSKECpXftVj/N7Fxn7ft9Xt9+8Db/xGDBg/87svs+1/ri5drj3Ht2lxerJEpd31mK8y7MNustuFPb4o0/xOLlcJoiRk8Z74PzuJc53l+GM++LuW7cPunxF428IxHv1kKzub6znq+mH3N9SBDR7Q40OadUz5MWEl6+08csANti2WzpDvhvAX7txicZ8V/LdYCvnP34OshBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIeUG5uHuJr3/mAYbF4ZdJe+vx4JseYX17g9d+/j6sb7vg5OlHLqqXxtx969Z8AY/Iy1++i/OXL8Tndrjpy8wJITV4+UoD3vvBGPOTAH4H9pevhP9q4/cZY/64977tv9I9EsaY1wD8NuTv+fr5GxaHEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsgLyuakx1uffxfbP6U/Hs/un+Prn3kfr//i/aZ0pctMAGD1ZIXlxfIQ0Y7G7XfPcPvds+ctBiGkAfu8BXgB+ZvR53il+ASA/+CGZYn5Xuz7M13BfvyGZSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ8gLSLwc8fe0cvvPXkv+T157h8RtP1fE9PC7uXRbj3Hr/9FCxCCEfYnj5Sjv/SybMY3vhyX9pjHnthuWBMeYegO+/kgPRbwBwAP73m5aJEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsiLhe88nr7+DN7KF68szxd46Wu38cqvvoRX/79XcP+XX8a9L9/FrffOYJxRlfPepx5i6Jwq7uZWD1e5COb04UqVFyGE5Fg8bwFeNLz3XzLG/DSA78D+0pWgqe8D+DFjzD/jvT+/QbH+WwCvJfKE33/de/+1G5TlRvHeZz9LcbQYk1/UvfeTZ8cqVyrzWPHTNGnbpfLG33NlpfHTOPF3SdYQXqqL9KzrOlEWKSx9rpExLUtTvnNuVv/ksNbCGCCXXeg3qa5BjvBcM1Zr7aZtMylOyL80Xkryaco7dE5K47I0R3Jt7JzLxpXKy8XLtUsqX6tOMsZM2inXL/EcqfW7VGaI26IvJDT9mtYrll+jJ7RltM4DCW35LUh1lvqtNP5q1ORvnYvxnNFgrRW/d123K8c5N3omlaOZ4yUdEwhjRJqjqdxzKbWXpo0lPVAjN2bSOoZytH0qzaPtGijPN824lsZ4XP+SnSL1feuYstaOxmTXdaL8i8V+izYMwy59sD1u3boFYwwuL7c39XZdh9devYuXX+7Q9z2c26b53OdvY7PZ2yur1dZ5dOfOHZycnGC5XAIAFosF7ty5AwA4OTnZld913a7M2O7J9UPN1kvbQ1ofYkLd4zTOuas09fF6qC12LFuuBe08DPGGYYhskO0z51zRQSmtY7k+KdkhNZlrtkGJ3JyS7Nn4+2AHeO8mz1vbNfddsutq+nbOPicOD7omhC18Ot+mspR0b6xX07VKspU0+4lYxtIYKbVpbWxs5d3qrM53RVk0649mXMS62xiz04XDMIhrfdzGsT0SdK5U9snK4/R0LPcrL7+M9SZvN2jWRGMMrLVYbQacvXkZ7SkNbt26jc1qUZRfKiOn91ttSs2YAhDp/XGY9bI91SrLob6VuKw5OjJui3jtC/prMAbD4BDP9816jWGzUcuW66/BODgXy7z3L4S6tO4DS+2t2Y85l+psj2Fw8N24vzV2RPo8Fze19XI6cVJvO1RtofhzaU3TrhHp3vxQv422/Wp5a5izZ5HKO9Zc1eRfWhOlOJKOlPZ/mrUvl2YuaV3jcVDyq9TyOka8mFbfZRhjmn12HCfIFodp16aav6XVj5nue+J8am2Y1ru2hkvl5OTSxDsmuT70iU8gxAs2WWkvHto19JfG9taQ+pikOBriuajxMdTykgh5p/nOLbPWZukc1frFc+M3dy4T5z1HvoC1duInK6GxsaRy0riltTjVy3PGwzHSzkWaH1pdKY0Z5xyGYYBz42dbP1jYt5mrn6kfINXdks6XdLyk92vrZrxOHeInLY61zsCY0O5bWbb7ZlutS6wf47GXxi3VU7KnDvWBa/yt4/rtfWM5WbruOD75Y/gLg27T2F0ph85rbb9oy6nts8L3FrnisrVzR2Ona/akuXylNkvfBZDGZtd1WC3dyEcEBF+9FdswFya1ZTp/0zYsIdmPWub6XePyD5lXubSS7RP71KX0Gl+IZIPPtZc16Y+5t26ZIy35zbGl8nHKfZK2f25PGGx2a/PzMo57qB3+vNHaXoHaeYY2XEtJB0l7q8P7Ya/XtvmXx1RKGDvbePvw+Mz0OHKOmZtfbr+RI91jaOakpC9CHqH8NK+4nWrv9cX1Tt8v2PZFXqdLaOa0Nry1T449JnJ+rBpau6cma64d474Y79v266tTmJraepXW4xYdlzvD0J4PTsn78Erlx8zxt6V+XGkPkbZXKv/Quei9j20+tuvQXenI5WoFH+nPVvTrfJ28j7DefjUbOISleYUstDbNnrb1JaVUXi2s5mPRzPFc+SW9X5uvxmA3hq5CAZjJGM6lLZVR8p1tHyXrm9v7htM9Wt/36I18rimdeQafVMxm02OzKa+nrWM+1q0haXxmm8tXs8dvOSvIoT03OWa5pbEYf5fGbxpeOhvYt/X0mUaWViRd0aJH5+5Bc2Ncc24ynnPheTlNIPXrlGy7Q86gjnHmnZM/926nMSZ7biH1yzHPe1Kdtf1etnVCXi2+2lxdWvwSc8rMpU8p+eji37W0uz7scOVXH/sLcuM0tq/ivay3+vP+qR29Dw/veXrvJvPNuQHDsH2+fS9y3O/DMNTM1F3dUiTZS2dzUvpjnCOP9o+7tt2e+eTIvVucku5tJDtqOp/kM725ejwt8xBya6FmXRzFsX6kz2OxNOdBMZo6lewuKd6cM5SaTZCrwqF9ItVNe0ae5rXXFdP1eK5ch87X0tmihkNtqDl4xd8YTNKIcwejz6X5lhtnOd2z1zv1fpbWd6n9S+OwZAPtdEs21y3DMn7nGzBX7Wy8xd23buHeV+5iebEQ93euc3j8xlO8/6lHcHave9NlvF/0ePTqY7z05p2RfKkN673HxdllYSx6wBssHy5GNkPOrwWF2RbrJU1bljhc9wDSGinH3+KudI3EnDkr7R1r9k8Jje7S6qOSHh2sxfhvT8z2vWrl3xxq0I6JNOucX4DcPLx8ZR4/CODPYa+pgmVvAHwXgL9gjPle7/2j6xbEGPPfA/i3ka68e37kumUghBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPKNyemjE7z2i/exvKxfT2AHi3tv3sWt907x1W/7OvpVL8Z98tFno8tXJNa38/+BYWBx2cEOx/lPOp4Xr/3Sfbz2S/cn4W9+4W1c3LvMprnz1m289kuvXLdohHwoeLE1yHPCe/+/AvjJ8PXqd3wBy+8B8JPGmG+/LhmMMZ8wxvwFAP9RKl4ky0967//KdclACCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIeQbl3tfvouPfulV1cUrMcuLJV77hfuAN2Kc9e0N+tVQzas/LcdZPmuTjRBCUnj5ynz+HQDhiqzcBSy/CdsLWP6MMeazxyr06tKV7wfw8wD+1aRMH0V1AP7YscolhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPLh4aWv3sb9X7kHA/kClRJnj05w552zYpzLe+tqPkPlgpbFOS9fIYQcBrXITLz3f98Y858A+CLGl57El6AsAPybAP6QMeZnAPwfAP4ugLcBfF1TjjHmIwB+K4DvAvDPXf3Yq3KA/cUrafn/jff+J9tr9uJijMl+TvHei880aaTPqSwlGTSySOlDeCmtVt70WfjuvR+VE8JLMsV5xd8lOb33cM6JdQh0XTcqNydLLGNAk/dNoB1vUj+ZYYBzHs7t6+i8g3MGzg0YBjPqrxzhmWaOpOHSGJnIKeSX65tcmdoxq5lXkhyl8jXxpPw07WDt+K4zbftrytTopFw7xuMiN0ZK+qIUHs/9UhpJl+XGqTFm1Iat46A2R6Q0uc+leHPQjD9NX9TkqI2ZFj2RyyfoXM18D32Z5p/OE0nmUFaLnq/FrekIqV1ybVSa7+lY3On1GWtWaVxLujwNb7VVtDZQaANr7UgXtNYzFz9t37jc+HcpTvpds1YFWXL9F8sUyxyvb8653bNhGOC9xzBsHT593+/irddr9H0PANhsNri8vNyFP336FABwcXGB8/NzXFxc7L6fnho8e3ZnVJcnj58C5hQAcHp6iq7rRrKdnJzsni2Xy11dQh27rhulCZ/TttbqjRb9kmKtHfWBVl9qns2ZF7FcAOCMvK5pSde6mHQuxH1kTNCpAOBhrRXzyaG1tSR7PBc/6Cdpb6EpLyUei5Iu8TaUs98q59pDs1al8ePypXU4F56TVbNmBxnS9MPQT/RRquNrsgbdIxHrAY28oexcX5b2bJq+H8wANDrnnXNHtc9iXRfr/XT/vFhM3Wvpulmq83LpsFw+HYWdnp2hW9Tnc20PsrIDFosF9tG2a4BZdpM0cV6STRPrZOCwPVstrjMOaZTanqnmv4if5eZuyxwvoVmrSjpiKldenjiPMDZzpGM5nSthHsc/ufJS2WJ9VfInlGTKPLnqd4OusxgSvVSj5I/S+jHK62H8bG4d9XFLtkpORwE6X+CcvWirvPGYLI2POf3bImvueanNJFr2hZp8W/RKLq5mDySlT/ctuXhzdJ8kY06GWvy4j3J6CdivVYfY4JqwlJoub6G2x82F19aqdN5Jeq3FVpH6sqRLA8f202vsXmmMS0j7+hySPX3InnNuG2nW2pB3aufHY0lqy5pcNX9tbR05RMeU9s+7vXpB/pI9W0O7Pqbtn6Zv7feWvX7gOsrI5XksXZjuNQJd16FzgLXjcbZYLGBtOv6Mel+bs3k1/t5WO+bmzivH+5ycbPE+fb1oAAAgAElEQVR8l8j5QVtJ9ynHorxWXfnH5oudpWWN0NCqf6RzlDmyiD61GXJIYaVwqcx4bY3Hbs2HpUG7f6jJnOqLruvENTONG/RUHL7oFnALW5yvkly1MGl9bu2vOet0Wpc5+7QcLfpIGuc5G7KFIOswDKKv/CZo2T8csu/X6phavdOzAk2alNI6IunE+ExuW/S2/L7vsUHbXkHikLOlY9GyFuT2iTFSeAmN3SQR231xmlnvhHTpWf5+rsf2o/eYfYa2HVM338c5cvLPXTNb2uI69gFaGdI4UrnxfjDdE+Qo6cmSn2GOfZuVpTOqMXpsH0tOfqktSrZnan9I7wFJlGwQrU+1prNa/Bo52dK2KPnlamjPLdL6p+9i1eqfC3POYbPZwBjAu+350ND36IdtPdeXl9j043dJgPK7T7F9mzsfCrJI/kmp/yVdU7Mlgt5vtWG3+0hZF8RrVfy+T3w2FPpMa/ekewNZNt2ZW9quLTpqjv2Sa6N9G1zty+EB+NF4bj1fScdPvv7TMbltg+l4WywWcIv2vcByOUx8p4tFB++3YSU9U+uLVI/m7MvSeGn109fkLcko9VnrOVsqszaNdEZWG0uls8Dt2NWXl5P/EFrPY455Xlcrv4b35fcHJP93yeeoqWdJ5tI5ao2yTTINy9nec/ZTN0Hsy6iRq0Pr2WBrmVqZgLpPtSRTTvcNdoD30zFqnCnuG2vvIIY85LPcXFg8/uMIbb6e0vgrjdtA6z7n0DVGYizHvoyu62CHsc+g5tfSvn9uB4/wDu4+//x7CHN96TVa9Meh+yBg+05m7l33ks0p+UE1bTRnb1bKd854S+sMXL273+vaXqN7SvNII7P3Hp3b/u1gPP6HocfVnxVUZcrJdsxzulq5tfjXcWZq7ZDVSX2u0aBrm2EYMERn/YH1eoNusxHzzY2zzWYDtxnPt1o/l/pMsrU0/azZVw5u2p7eecSLWLexuPfLd5rfy0m5/dYZHr/2DBDev7y8vcbtqwtapLO0/OUrY1mPRWlvcch58tw56sfdAiDsyTVp6763Fttc0v0a3autv6Zd037Q+EGnNpq/sulctsx0LKZnGi2+w5HPOfr7kEDfb7Ax7bYIOS68fOUAvPd/yhjzeQD/PvbaObzWE1bC8P07sL1EJSWn2n7YGPNFAC9lnofvXvjuAfwNAH+iqTKEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQ58awnF4wob0YwdsPzh/qX8vlK8sB66SOw3LAILTPouef0R/K6YMTGH/4RdUnj1YwHpCyGk7rl7m5rjy+j3n5CiHkwwlXjcP5DwF0AP5dTC9cia8iq/1fSyb6fb8QL3elZVzGTwH4A55XGRFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPLC8OXf8dYkrPQXw9HdK6OLWJ431/FnztZ6uE88HYX9evcOhiFf72/58Y8fXYYPE3YwWJx3x8nLWSwuO2xO++zz/qR++UrtciHb8/IVQshhUIsciN/yRwH8yTgY40tY4rD4R8y28INMviHs7wL4F7z3jw+oEiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQDymLZwsYHO9Cn+5yIT5zS1dNX7t8xbgPzuVDhJAXE16+ciS89/85gN8P4F1ML1wxwo8GKV16Gcv/DOCf9d6/f1BFCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR8aFlcdkfNz/byn9Z7U75YRYOp399CCCFF5CuiSDPe+79sjPkcgB8G8IewvRQlviQF0F26IsVJVw4D4G0A3+e9/3ON4r7weO93PzmMMaPfaXgtb0380rOcXCV503yleJr0aV6tdS6Fh+9puCRzqf27bm94xWlLfRqnD/FyYR8U4nZxzo0+bzYbAMD5+TkuLi4AAMMwYL1e4+233wYAPH3767j3q4+xOQXef3+FzWaNn332LtZuwM/9pZ+BuQRefvnlXRucnp7i5ZdfBrANPz09xXK5BAAsl0ucnJwA2LZ9PEdyn+M6xL9zlNpdSh/3n2aMlsqsjd/c+MyVWZMjpxtK81Wbb8A5pxrP0rzUyJuGl9pFo//S+Jp+kZ6VZJHy1dRRKiPVZ7H8c8bLHFm0fSmVr5mfkmxxmHNukk/LHLHWwnuffX5IH0llBZlLedTapFbXeI2S4kk6xjm3kzOVcRiGolxapPppbY1culpZcT0l3ZfOsVY9UOu3MFattSp9dSx7Kk0X5Ahhkm7JzbMg72Kx3Yp1Xbd7tlwud5+HYUDf9wCAzWaDe/fuAQDW6/XuJzyzdo179xyGwSFsGd746F2s19t2sdbu7IHVaoXlcjnSBaHMkryp3RV+x32R9omko8L8SMuRSNtbIi0nN49T0vw08qRxvZf1UY5Yj80te1vudHtorR21bz6eHJ6zr9O40hjXrmHx2JPW3ViPG2OqbeO9h7OhH8bj2DidrVlaozTla9DYFwAmfbgP72Ctfn+bexZ0TwnJTi/p3dxYSNutpBdzDHaA92MbJbe2tMyd3JhL9WD47JzbrdvDMIzixPFi+WKkfU5ON3rndzo/sL68xHpTvzPZGLMbM7EOiO2RbVFm93n7U19zc/Pi2HZGKe62rXfforC6fZHaoDVfQhy3LlN+HJXSBXLzIs03xHPOwRnAuWEUf73ZwF7ZAZLsqR5J1+2gL2MR+76H7+0oXppvqT6adaD0uXMexkzH/KH7PCmelHdxPbDp3Aec8zDJfCm1nXZvLoVJSHlJOlJaa1I7X7vvyO0z47mXtm/YQ+bKyYUHubI69EDbvvV5aR8vtVdp3ZTK0tpWreO6JGdJd2qQ6lVau+OxOKcuUlgtH01azdia49OcQ8s6HOZLy9zQ+DdzaeY8y2Gtze6PcmNH41eRdJxWlphS29TswZo8ko5u3VtpKNnM8v7DTuZPnE/rHvY6ie3hNAwoy9o6XufM+1z5mvWjlsehaOfKdff1Iflv2246NzQ+oX16vT2R7pMkDtFDtfIPiatJX9I1ab2ksV2yR7X2Ulxm+xjR+6jTstO48bqjHSfaOpbatkarfzWlVQcdul9pOcPS9tchdtjcOkvPSjZtt3CT/l2uVoCxWTla/ZgSkl+gJS9pbEn2vHa+S74b7R7xmHZrrow5ceP9akt6rd0XOMaaXPIHBJk0+l7DMeZrav9rfOrjMWp2/i9rrdpGiGnVmXOQzo3moPWdavfcLWjkz9nuLelTBuuSebUfN85l/BFDeb5vzwI8Sk1RGhOaNV3yEY+/T8O899k9cctZ05w+zukF4Djj9dh7jUPPWiW/pFRG634/ZrAuO0ZLfxQyxw9akv8QpucZe79viWP2ea2s2l5COt/ItbPth0leod4lP3J6Zqrx4+fOjUrpS3aIMQYOLsp/f8YZ+qwfevT9fh3IveuU27vkZNGcH7faTFvZp+e6tT+g0vgbd2uGmz9ugx89Pvso0bI/1YQfwlydMh0bZnf+nOZf82uX/DIln2y61x+dcWbma9/36Bv+cG+ft5vkNQwOwzC/T/JnpWX7rWVPXSozLbvW7nN09pzzu7nnfIfMF+/jcre/h8HBd7q99Jx9aWmtOZbPKR4vJZtVW6+gc3NnNcbNG5fSGXNKyxw7REdKfTG33jmZameRLfPmuvakOX2t8dOUwnK0yJ+zdSSd2Dont+MwFzbdc2l8NCGv1vPZipSTtTS/fxjreO1ZeMkPfpDUB+r0YRgw2GFS177v0Q2H/8F/rp6d27+fGHBuwDDczHsAz4N0DuzGvmIYaP24LVz331UA5Xk/l3SPUzr7P5bfXytu2r/58nR5SUzljb/rz3y2caZy6bsmF9En8sRl7eO3+NnCM+06JMl/6LjL+QeAI+rxZA8eytqN9wsL56Y6Y+6exW+m61tgMNP92OQcoAf8Yrom7+szfcelZuPN6Z9a+5f0gKbvsuPOucz6FfLsd3+v3JJniTntotG9h4zdXF+WvucYzPRvVPq+BwpySfIf8g5i7kyAfDDg5StHxnv/HoA/bIz5IQDfD+D3Y9/Ox5gJYSa+C+CLAL7ovX96YJ6EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQ58gnf+INAPMvuMlx6IVepQvgWy/0y13oo63rcunw278zuRj+p17HRvGfHpJGvIHtj3sxVemOTK+4QLN6IWDDJZyEEJKDl69cE977nwXwB40xrwP4t7C9hOWfBJCu4CVNnq4CzwD8DQB/FsCf995P/5tbQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyAtHt+kAvJiXr9Qux5jEb7x8pQOwSsroNhbuqs3I8bDOwEz+zP35Yip3+3jewUMIORBevnLNeO/fBvBDAH7IGPMKgN8F4LcC+C0AvhnAJwG8BOAM2/5YAzgH8B6ALwP4FQBfAvDTAH6CF64QQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCLkOzPDBungFAGxvAQzic7eo3M5CCCEVePnKDeK9fx/Aj139kAMxxqDr9rfRpbfdSTfdpTfq5W7sS8PCrXyl2/PSZ/GNe9JNfFJ+x7yRUEOuDWqyadotl9ecuoU0mrRBhnhsHFL2XJzbG2mxTEGGrut2Mp6cnGAY9gaf9x6f/OQnAQD2/ALf8rd/DpcnDv/o3lMAwOe+5XW4zuLz5jdh0S9grR21fRiv6W2SsSwSuTaqtZsxRn3zZTx+5tyWWZMvN9cCaTtpymjJu1XWHCXdNWf8luZpbQ7X9F1tXrfUI57jUh9pZZPS5uptrVXlpdWHpf6ryRXyq+nj9HlJJ6dlSXlI+Wj6MHwOOizuy7ljRJIxlyYXL53rmvkqtWsg1ukauQCM9Pp1UGqL9LNGd7bYOGmatAxpXKZtX2vXEmnakLd2fanNiUBcF428tbbMxXPOoe97AMDl5eXu82azwbNnzwAADx48wFtvvQVgO7YuLy/x6NGj3bPbtzr8E9/+CVi7l/Pv/b2voOtuAwBeeukl3Lp1CwCwWq1wdnaGO3fuAADu3LmDxWK7LVwul1itVrt4Ibzrul07LZfL7DwK7VRaZ0LdtfPj0Ju1a+XUbEytvWuMvao7ELIyZtxGcRmpzqyVrVkbc0hzV1OvUv/V5NG2W2qvl+ZY7WZzYwyccQCm9ZDGa639a3NZaou4Hi12gbQ+jcfCvo5hvGn0Xk1Hp3LGcyOuTyxj67qT1ifez4vxFyaaS9M4uTGjtWHjuqV7sfB9GIasbRN+6/qsrBMj6ZTxxmUFOXJ+hrB2OeeuxmuoozzfS7Zpy748R2mfJY253g0Yhv7q2Tbs4uISdp1vV2k+xG2UjkOt/qvFkXRTWkcJrQ0BbNebMNck+zuVLa2zg7sa63udstls4DY2O46l/VRsg2ko7RGGwcH7sS53rmw31NYHqaw0XKrjZJ+Q/HcJ3k/HbUyL76wma42SXm6JX0ojofVxxmtE6jfL6ZhYDx3qE9HuibVlSevoMXw62n1+LX0prLW+MaXxIsmcrpmpDSD1c+t8kfam2vGZUhsrGpkA3d5XsllLaPb7ubA5e/Ga77PUV7FPPEba1+/y363f+7y7rkPXIP9cnXqIvyJHbi0rtZnGLzZHR0jtMQzDta1PWv9VLW1YE6Q0h/RZaf7l8m21fwLaNjz2+Gspq7TnPyat+r6Uzzar6TqnGddzyywRytT2o7RGa/wS+/gesa4Mc+VY9c/5tHKU9lm5vErP4vQlP0sYA6PiPLBvj7EtMHe/p3l2KMea+y32aGyD1eqmrftN6JCAxi9UsnsPtdHU61hmvzT0Pfq+vqcu+Wxqcmh88Nr+kt5FAObpu5K9OmcPKxHk1Mh4U2M3nW9z/PBS/NJ3Sa++CEx8MwWbNo239dvs67v1leZ9eJrzmjTeMcfNMfvl0PVuDi37hbSvbnLtaCGuUov9KvVlTidZa+GcE3wtOt/3WObU31peC0r5afwUtTgfJH0jrQmSn0tat9L4N13H65ovx9qjAfPeralxHb6AUhn5c6bp2hzOSEp2Tu7cIRdPe74s+T5LZ0WmM1m/afg6DAP6fn/2GXSHdJ5Wm0e58PQsU8v+LCzZ9ylwzk3PtjsDa2O7enzWFdK1obNVRnJoclXaQIdQs9E1/vHgl9jaXbj6PP4ptWna9nP85DXCWd6csVdCq+9yuiMtQ3vuoD2/qp29aPPK5Z373CpjTgZpz9uaXtvX2/6L49XfG9D6HDR7C214+rzmCyntOefsq5OSdp+c8zAN+jItW1N+bXzMPd/Ujy9gX+d55zGt58aa8RHShveAYobBYbC6dyUlWs9G5/i0Ws5yc3m1psk92/vVgxzbNjVO39eDHeDc1KaT8pjqnZwdh8pzOb2Edu04FnPPL6fjItR7G1Zq20PtprTttVVonctzx2ya9nr2hX7SD9p6HGqntrbjnLz2dTuu7C35tNk37XNdW+Yh4zBFu3fK2ac5uVJ7TrsmHbsfp2vtPv9hGADh3Y+w98nNkbSttvHGYcMwYLjar8b78vj3c6fQ1HPHaG7M75nuy1J/p7208LflMvrlMMoj10fOud39Ldp9SalPajag9j2AWnnOT+sSl9Vqi86VpTSvW9f0OL9D5desY957DNYm89GM/rYrl37OGUwuTTwWavb9B0YPfAg5vqeKEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghLxzm+HeCHszisis+H06u9z/TJoR848PLVwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPKBpKtcvrK51d+QJISQb1QWz1uAFwljzGcBnBai/IL3/vKm5CGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQr6RWT578S5fefTJp/BwwlODl3799o3KQwgpw8tX2vhxAK8KzzyATwH4ys2JQwDAGDP6nYbnngW89/Dej77n4hxLxlrYMfMPSPWr1XtOWa2EvNLy4zK895Pv2nxz32vyl8ZA+iz+ntbFWgsAcG5sFMXxQpy4jmn8brmCtRbWAtYaAAad7WA6exVuYYzZ5RXjnIMxJitnSqldWvq81j9xW8ZzV9uvmrkk5ZXOd6kM6Xvcxun8ieuVKyPVU5o2LdUj/S7NpRLpWNOSa6PWeXWIXk11Qk6mUjklva+ZK6Vy47aQxvUc3TtH75b0Ztz3mvkj9Z/3fpRXOhdy4aW8JZmDnkvRjL2QPpdGqnupvSRq7XiI7kkJeWnHRam/W8pLP2vihLJzbZimyemxXB+H/pTW9ni8ldYM7VxM61Ora5p2GIbd57g9hmHYPev7fvf58vISl5fbuxwfPnyIR48eAQAePHiAhw8fAgDeeeedaVnwADycG9fl8ePHo985Tk9P8cYbbwAAXn75ZZydnQEA7ty5g9u3tw6ck5MTrFYrAMByucRqtdr1Rdd16Lqt8yqdbyE8fI9/a0jHb9oHwFQXpeWVwlOZcvZUTWbvPRxCP4vRsjqupKtLYzS2G8dz5sr+c1OdkssvZydKujBOW7OtpXkbKOmGVL5aOVOdEeczzi/NP823tN7ViPOJx32aPq1z/CzogTS/GDv0kVz7PHP2kSSf5nk8N7quO9hGism1a2m+5sZTaQ8UrwPTOZK3AyS7JU6f0zWSzojJjfOQb8g77vt0vAzDtJ1KNpC0l07Xx74fMNi8PtT4WOagHRehzbz38B1gjL2qy17+dL0JxHZj2j+lPUPLHCnZkHP9LCFf59yo/s45uMFiGBy2bsdt+Hq9gV2vRVs85Jdri3jdBvZ6M7RriCu1cfost8cvoWsXeS9R0h2lZ4eM32leobx62uCXiZHWudIcr+15NXZDjta9fEn3SD4TKT9pjLXKVEo3xz/Uaifkns3R0bX0OVnm6Jnc51RftuRbiltqh3Qe5Hwp2n3xIbq3NG6lvaW0/8jlmQtr2Zdr5kNpnEosFtNjwZwe18yTOCy0S64NcnWJ5cjND5/xB9X0oYQ036S4Ya2s9beGuD1a85J8/jmkeVGiZX2cs5bm7LGabJIf61BbNMfcvqnFjX1Xmr2stGds1WktSLr2Oto5lFda9zR13erlWqz6nimmtKeT+nluG2n1VG7cl9dbINRbS+te9tA6H3MupzZM58LZ6dVzAMbG/b/V6aZe5WtBY9ccm9Q2D31Zs5dz46zVn5Rjji6TbPKSzorbWmNna+yckq9Qo69DuDVTn/b2fGBqZ2j0snRmJsUvxSnp5Dhe7LvSllMqV7vXas03pdX2OmTt3dmwirmkLafFLz13TW/1fR/KMeybdt0zHnuadfAm0Oib8Nn2w9V43tdl60e8Hj+uVs6W8lpkal0/t/GlNsyFl/ff27VEfl5CM77SOIfsGQPHmstxPsMw3KgNcyjpO3rW2lF71t5XyH2X7Kdj6Vhp7Gr3DsdA419Oy8yd12/DZH+Q5Euq0eLP0aTT7D+lcTEMw+RdjGEYMCTVbs0/h7betf3j7rPd28nW+p3vq7vSh8vFctd/aZ6STk7Lydkkku9Su2cf1z/2CWzP06yv55HasYNxE30wDAMw6Nee7X4gls1hs9lgg6ltf8h8nXsOPbcM59ykzLT9cgzWwTkPb8ZnmWk5Gl+3Zk303mOwDt4fVvc4vxzxe9elPeqcPs7VbduO4/C+7+H7D4btel1ozha1tKaP+zb8zs1lidL7ZXPGRc0G1tgIufLnrsGxLM7pys6hLf+Q/tfuRyR7J23bMBcHuKv3NPZ59P0A3+fXPa2Mtf1fvKaXbIJcvxxiS6bzQXsGWfI95spMdaqm73PvXOTyjqnZ+nmbwOHqFVDVPnubZci3Xv923eCzuqpUhvR9l2NhHrRSmketcqX5Bfvi6gmA7Zg3hb/JktD7imM9sB0Pgx3bQsfwW2rHbAhr3RNpz2PifV0cbU6ZrdTWgZwenJN/2sfbn2lcrU2tGeNzmJ4duMneM36fsJaPRncHWvzWcf6H+tFrsmh1RRw2bcdh5zuV5r8kSxh7wzBM9h3b9/OnZ6ghzXicRfprsi6l68g2XrrmzenXEpq2dc7Hb25uw7zH7t9Vm8TrZi3PXNn7tsut21cfnEPf99l0ge6hLbSJR790uFxeYnGxfT/J2el4cc4jWPfSHrBmW2jpT3u8/+mH4vPFxSJ7+UpunJdsvxb9BtTPkTXhOYLN6v1+vcn1uZT/nL3DIetYmJfOOUi78bm+4Zr+2+uFcn7XszqTEt/Ynpnj8yqu3tsRfqZ/CUkIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJBZLC4WsJVLKy9f3tyQNHVqsiyfTf8zLULI84WXr7RxAcBnfgK8QIgQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCDkiq8flC0vOP3J5Q5LUuXh5XXy+fMLLVwj5oMHLV9r4avTZXP3EvH6DshBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIId/wnL53Unx+cf8SrnM3JI2MNx4Xr5Yvgjl5tLwhaQghWnj5Shu/gOmFKzFv3JQghBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELIh4Gzd8uXr/jO49nrFzckjczF/TXcQr4ExjiDkwerG5SIEKJh8bwFeMH43wD8S4XnvwvAT9+QLOQK5/KLj/ceAGBM6b6c/fMQv4T3fhcv/lzKVwpLn8ffNbKkcpXKjp9r5K/lVwvP5aGJk5NTm1ep/eI65+Lnvpfk1JQ5F2PMKN+u62CthbVAuPspxOm6Dp3vivmV2rg2N6Q8auFSPGlcHqvtamj6S2qf2nwPWKu708x736x70s+a9ivN3ZqOivOujZXafE2fG2NUc3zOeJXSx6T9JPVF69iM16NS/aS1oKS7NbTq0LQfSvlJYXFbOudGddDMhzn9qqE0psIzyX44Bi1rTJCr9L2WTsq/RV9IxOMk1YtaPRbGQtzmpXkg1ctauwvTzqNY3pKsreM9xjk3ehaXFdc91D9uu/B9GIZdvL7vR3kDwDAMu3wXi8UoTisnJyd45ZVXcOfOHQDArVu3cHZ2tvt8crJ1Qq1WK6xWWyfOcrlE121tjrCGhLppbKi0jyRdUeu/hcvPL+36W6O2r5DwncvMYwcgb6eV1uAWuzeeEyG86xZYVOzDQKq307zTuOF5qvs15fz/7L1brC7Ldt/1r+pvfvOy1l5r73OOfS4mdmLr2Ca2hAKRFaygJPIDWARhgsLlBYx4iZB4IVLESxQhHhCxkCPBAxKKiFDygGQggILACBBRLKEowYlNLufY+NjEx8fnYu/LWnOuOb+vq4qHntVfdXVdRnX3nHOvvf8/ae3VX3V11aiqUaNGjerVO0TiK0iZ+2vpPGt8jdp6GZdTomRHUvh5Nz5v6usbINu/LV2Dc2OV0uGUfqXyxfL7a6PN/Vw60fc9XD/cT/VnWEfcf7lxivs/XGtKexnJ2uFlqOXd7XqcnZ1N0i6vrrA7q9s3yX61dC+3T4rXq9qzJaRxGWA6rvpMz8bx8vICultu91tsUC7vEj899ANGHTdmlu5/WzusLVbr8drnu729hT4cZukh4VwIbbePMVjr0Pc9tD7lORzuoA96XF/Ctb42L7TWm+zf4q71/RLWv3YP0brOhPVrHfbFkKfrNLpdfu2V1tc6t0JdkszdVLktfVGLPUhsUc3X89ct/lBJvqX+hSflK0lkyZHb90n2gzXdT93Lra/xc7W1u0Su3lT94f1S/KnmF8fytsbYpXpRmkfx2rQGaUx6iQ5L64nv+zHKjUWurN1uJ8qXIjUPUuuCtRYqSMz50Tm/W2pfS2X5OiV72bjM1rPefnIAACAASURBVP1Aab8lnSMl/7117W71O5fG3kpzt0XOWl4fi5GWK0Xa7pq9LMVScmXF17ny1sRRluzZa/6AdL2Ns536ZZm9ecj4cFxXyW8P0yV+XJh32DOnbWiOVLtjndD3+5CYFjuwRdyhhDHmfk8VjOm4fkzHuRP8/4iWzIuaDm1pW1qQ6nZOr7aktbzSfkaqG5I1Mv69JPYnkccldLKUntt/h9TGN+ejSHw7aZ9I+zhHKEtuvVuiO0vlqdXXsi9cOodKNmitT1XyCT01P6PVV1yyfxmeaVvTSvrry3PuFMMJY28tY1XbZ8bprXpQ0z+tZWOQkmUNa22ClNy+vqV+f3veTbKYwVx/gEEfVZBW74OlfRE/F8Z9wzSVMAdr+r/mw62NHbbKJpnvubXS7+/879x7CSW2iD2nZA4p65EsNrrWJ0rRsv9cUp9EPyR7jbVx79K8zzGd39NYVTheOV9AMg6pOR+Xn/otaYu1FlbbYP0byjDGQJtB1mN/xPE490Viv3KJ3xbP0ZDUc3Gd0za3nQmkSPW1ZN6VUc3zRlJf3GdSn6hFjtjP3DJO4v0utXHspRQLmsbagNC3HN7Zahuj0HcM04ey0rItZahnGx9OSsl2tJ4VheTey5XK0hKPWkMcU/L1tMSlUjxVLOghMcrOxmV4byd4r6MSV9syJhvXJV1rt0Tq/0visKUz0zgtzhcmh/veErm5n4pTt/hnrfFo/8yacw9pjCxl06T1lojf13HO4XA4ojum7YOxFsbEc8mc3oHrTbSPAawN3yHy786EsqZ9xlJ7WuNC8bNLfJ41MZPSvlzSFsnewPdtfL/Fdkj9sdb4mbTvWuMSVofvGw1pxlgg0+R1Yzgvo5Ulsdv53mg+ztY6qGiP8HFcy51b7yNJ4z3S9x4k91veUYnLkOzT/VqltUmMrc36a6Wywufd/buQYfrgA+X//YZxc7/JGAOYab8Ma0iYTzXvWVp8domuTNZ0FeV3DnCndQfIxyqldfv6TutpfS7n9Eu/VuiuNfqr/r6ceZ2vvnSNq69fZOOuNXVZM29GGb5wXSxn//4Z3MHBoK67g86ebPgUN/EhALm92NoGTv2lU5rXnVr8dIm/3Yq5n5OTNGOAFf9WawnqaGCtmfSJMQbmfvswyvgA+2NSZpt/pfbp4ecAvL6/TmnrP/+IshBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIZ8Krr55Ubx/fN7j5rtuH0maRP2XPW4/eyjmufpWuQ2EkKeBH19pwDn3HQA/g/kntfxntv6YUurLjy4YIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyCebZb11A2fif+k/56Puv4ZR7JImmvPq910ChbmUULr91/ogSEUKk8OMr7fx5AH/7/tp/dMWzA/Czjy4RIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyCaY7dNWPl/RXPV593/UjSXTi7uUBN1+4LeZ59o1LaMNPPBDycWT31AK8bTjn7pRSPwXgbwL4Ak4fYPF//6RS6k875/6TJxTzU4O1NpmulIJSarxO4ZxL/p0qK1Wufyb1vLSsEjm5wt+1ttWIn/fP1WSL86TyS8qIkcq9NG88din90VrmsFhrRXml5YXllsZYKQDK66Iey1/S31Ikui0Zj5Yxqz0vmUuluSMpd4m8OZuUqqcmz9J5LKm/ZKNS5aXy1+TL2c5aWS11lex4aJ9bkNizXJ+3pqfuh/Yq12fS8cvZ9FR/x2WWdDmH1nqy9qbW4ZLuSyi1RZIe0nVdU/7WelL3Sn5LKr9Ud2p+Ry1f7V5NryQ6lisHmPdLqt3xeto6ZkvnYdyW1BjG9kbiw2mtRx10zo3t2+/36Pt+vPbpu91p2+bvt3JxcQEAePfdd/HixQtcXV0BAJ4/f4533nlnzHN+PgSfzs/Psd/vx/q9vF3XTcZDKTXxR3K6kRuDuE/DsmL98f7PsPWa5k8R3gvr8empulNjHNqLVFt6baD1NE3rbvacp6T/uWeS/depqP1u7CfJHGnxe0u/JaTWt1x6y55kUod2wTPTMsKypfuwkFpf5eZ7yYcIbUdtTQrtSix+OAdrtPqNNV0K+1Xiw5XWq2QbOtzP+RO73Q6d60T2GcDMXqVkCed+i9+San+uj7XWM5lr63PYrpq/7MsK/bHpfT3RnXAdKumFxHdfsybn9h/TuhzCKoyxgJ3KUlt7pLEYia8kfT4ev5Lt93mMMeM6b63F8XiENWb425pT+ocfQt3dwRgz05FQF/wY73a70Zfoug673Q7WOVhr4Nxp3TXGwBk3+iZhmaHfkurXJb5srK+DPoy/xjytfmCLTkr3cL5Max2snZZvjAXMsnJzSO1nqtyUD1XKW6u7lDeXr2SzlqzDqXulvVXqt3QvGT+7VcwzVWdrX5dkDvtDIvNJp+f92NrmJfdK8Zzcszl9lejY2piktKwl++TW+F/On631YUsfx+mhHc7pcTgWrTYrVaevd0if+m1eHufcZH0K8bod5k2xZG+zxbNb0GKXPbn9JiBrz1L718oS25vaT5T8+aceP6Cs/0vLim1Eyt7UfAVpXTVazsZax8OLP9geILQTp/S6fd3Cb2yltm7mxibVR9ZaWG2TNlAVujQlg7VW1MbcfiCXd819CX7PGKQk91+l/gBO+6FUulyO/L0lsekWlszpJT6nRM5SzEDi/2691kj3F55wrrU+K8XHwXI+Vs2nKvWXNDa/hW5v9XxJRyR65qm1ce1eVOrjLtXh1LlPLXYY3muJV9XqaN2jbIVf18Nia/NBtrdPP5Mb1y3iAK1zp9a++B0if0+6B2utXxJ/CSn5fVL/Yv3zvi+mOp30k9xc3rjNpzKHdGMMelWfb62yp/Bx5WGMT/3f90fYY7qvc+cgcbk1OUN/VBrXqJUPtPn7tbZIfX2pbNJnWmId8e/cmOTmdk2WVD25vV8Nqd8R3u8ScltrYRP/d+HUvrwWO8/FeJaylQ0/7T/jtHIf5uRfE58ulZXSKXtm0XUazgH6/jxdaw19bzN33Q52l46rSP3LJaR0NfX+xqmf6/u8JXJ5u+ufT8kV7utSc9fPgVIsqpUlZxot+XL5W583yg59okLfxQHWwRoDJPbaIa16Fa6TEzmMAezpPSDn7GTOhnZKuu8DAK3sLF5wPBxwSKzJa+aFU25Wz+FwB33Q2bVx7TyU7iFSv0vlrYmv1MreksHGpdLWnQGujYdtZUMAeYxgC1Lj3lJXS7tzddXqk47R1CfxedVof2pxxhpbnU0YYyb7BeeG2IJR+XOgrWjxN2OWvANc6rPUO6D+HbDSc8P6NfUv+r6H6+vnGqO9G8vwzGPTYX5g3l5jDGBO+87UmPq9qF9zQ47H+T4x977sQ9JyHlSaw2Oeft5WYwxcLzvHaEX3Junf+RiDhFb7LdHrJaTekfbEuth6thQ+m6Ok74+hj6W+C8/T/N/xvI/f75w8v6H8JTnDdwTDtB4Pb98fm/Cd3bhP2mJJKZtha1uRKql9h4Ta3Err2cPMlyX96pzDTDqf5jCufeEatqbOYc2ersmnm9Pz29p4XH7tHK+/6w2g8vk++p5rmL3B7rYDEOxhEm2REsoVxxVs5/C7P/jh7H3TEOWAq6+dJ9fIUpuN6Wf2wmOD95Bn9RXOnkrx4SUYHftL6/s7phTLk8YZTkUM19YYqA3mYUsfejnC9hhjwG/yPD0cggU4574O4I8C+LpPAiYfYPnzSql/92mkI4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCPnmcvd7h6usXxTxOOdx+9wF29/AfhHJw+OCHX+F4Vf4S0LN/dInu7pP3gSVCPinw4ysLcc79CoB/BsA/wOnDK8DpAyx/QSn1c0qpzz6RiIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyCeKd37tCvpY/lSC6xzefPHuwT/A8uEPvcabz98V83R3Gu987epB5SCErIMfX1mBc+43APwhAP8thg+u+D/+Ayz/EoBfVUr9R0qp73kyQQkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkE8A3VHj5VeeVfPZncPNl27RX5rNZXDa4f0feYXr77kt5lMA3v2H70Cb7T/t4Dq3eZmEfFrZPbUAbxtKqdiq3QD4V+7//AcAvnyf7j/A8hLAnwHwZ5RSfw/AXwfwmwC+A+Dw0PI65/6rh66DEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgh5LC6/eYG7d3vc/GNvivlc5/DmC3f48Adf4+VXn6M7rv8IyuHlER/80Cscn/cYPimQ59n/d4mL39kvrkubfPnH59t/VIaQTyv8+Eo7R0Eeb8Fc9PtHAfzI5hKV+UR/fEXr0+Lm3PTLXP53mB7nidOUKi9uYX6fN/67VFecJ/eMUiorv38mvC7Vk5Ijdy9XXqoMa63oWUmfpuooyZ2qIx6XmgxKqYn+lPKm5MrpXihLfC+uJ1WXL9ff6yyw23Uwu5O8ne5glM6WUZM9JUuJ8H5LfVsT63xNR+KxWDNf4rLC53LPOOfEupD6Le33uF+kOiyZ72vsaMsczpWfKivV1qXzYEn7cnlSSMdC0qclPQvzxbY5JUeLrS+htS6WXStPWl9uLqXamsPbz/CZeA2I77WUvwUpfZf4EnFa2C5rbbGdIa22rCRnyd6mdCReU1vbHduFVF5vKyV6l3seALquS8qZ00v/21qbtY9KKex2p23Z+fn5eP35z38eAPDOO+/gvffeAwB84QtfwKtXr/Dq1SsAwJs3b6DVsdi+i4sLXF5ejtdaa5ydnSXbG8oZjncun++TuC9q/lmO0tzTxiT72Ln0epArK04PdTD2xWr4fvJdf9IFA7MgdtVie2w3ny9enrgc6bxK+ceptFEGobwte4V16/ppLHxay3qz5F5MyUeX2M7YpoXzbxiL+VzMrb8lXwNIj19pvGNK9jJ1v+bTxs8P8zut47HMS/Z2cRmxLCl5ctTW8fDZuA3Den1KO9lXjd1uamNTdYbXKf3bqx5aT9O0Tu+HS3XkaPUJ4jWl1K/WludwSQdzcZWc7PHv+Lpm7+K64udDXyHM48t1zqHv+1m+ob8A/y1orYHz8z1UoqyQMOahtR7X691uN6RrB6V0YF/UOJd8Xv93qFclGyedc6n55sfr1IxTP1jbVmdtHyl9PpZxuDf8SeVvnS9L8pT2iZL6c2tGzm9bsq/PzZWU7oR9J5m7WutF/qUvK7XGltpSWs+XrPW59JwsNZsqiY/UxrCkv5K2SOqQIN0Px/n9dU1vW3z7lvRaPC5sV0ucr0RJd1vjGC1+39I6UnVKx9loDefm+z9dGWPn3Cw28pQs6ecQyd5uaVlrWRpjiWnZc+bspc9X8lW2otbuNWPU6meV+k66ppdihS1+lbTcFuY+tZfhlGeQRwGQ2//Sutsak5Ceh9TKXXp/2v58HslatkY+KVv4DZ7TfPf+jP8ztQO5mIFEnpQet+pK67yW+gmtfotkv+7TJWWU9tM5UmdKsSwlXZXIFZdVKjslS9d1Ir1omRNdN4/BxHraOsdK+cM4QzwXUs+XfOvaPiF1L/V8aeykdmGtP71kXVoap06Vk7Md0jhBTZal9lVqM3z/tfq2JbnTfTGP70vHLiXzNNYEGGPQY7mPkmt/Sxw8JGVXU3YrbgcA9H2PXrmkXNJ+T9XfMldan5H6lGF7zP1BW23PcCrbTZ4L95RD/PUIG71EH9tHr4v3KWN6SgZpvKTVNtnOJvdCa2xc+L7EEt+o1d8N80vevajZl1rsLFz/UvJI65T4JSmbWPONUu23ygZyD76sMT3cMX+OnSurJC/QvqaXnjfGwNpwnkxtUq6+EjU7lvMpJPuxlJ6U8uTSdT9/TyFst3Stntr2+XV8nhhex8+E7SrNEbt3uLsb/h+hXedgjMHh4AA79Pub2zc4Hk/nkSVfMfw75V/Wno2v47ak2nCyiaEe9nC9/Gx47AtlYUw8jkfoSllhTHOJrZTGQ0s2Opc/V5d0rV3LdN0cUzH4dXV/X9KvIdbae/s5fRnHmB7K7k557Pw82tr8OUQsq7/War4mG2NgjJvsn0pndqW43lh2ZY/nxyyORbXuK5fEFSRzOdd/KVrlDH8v9btTTM/ZhzRjzGQ9i1lqy3K0rsWxjtXifbUxaYnzJn0bUy9zbSxwiX/TGmfN6W92n5Fod4uMqdhVaz855wKf7IQxBiZh4rc8M1kbx6n5Y1Jb4stN+tmC/ZH0fbScnABgtI32nCcdSfV5nB+Y6tRcJjXKVPJxYrlL/4agVNZam5qTL/xbWmfKv/CxkZyOLInFeIa1fdqG4/GIYyWGVJvjSzGCl44lfZorxzkHg7mP0/dpH7ul/liGJfdrz5XGvXSufJqD9dj6QzLEp/rs/S4RB5TYp5b6pWfID03Y3xK9z6H13KZ7nz2mNV79EN0z7E0yMkdTcMn4pOxAy7yzzsFFNtgOBhjWWXj7nNPLcCwl8juXjmkM9+a6IbEFAPDiq1c4Pj/i8HI639ysfQ6vv3CDN5+5xdXXL3D1jQucXbd/auHw8ojr33OL28/fDaWf/hPIebo+/84ez3/l8r5Pl6EOKqujx6ser79wg6vfurivW75OtvplpTVp6v8MMuT2EqnnpXMgtWeU7HmttbBwkZ45HPsjzCH/+YjSPr9G7lljzP18mKZtYRfIOvjxlXZavD3/tpMLfm+3g63DGUUIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5BOHsgqf+Tsv8Lt/4CMcXuQ/eORxO4fr73uD6++7xdl1h/37Zzj7aIfdTYfuVkP1CsoqQA3/41x7YdFfGRxeHnH3uSP6S/kHhPYfnOHdX34Hyq37vMDuOv8/9gSAD3//a9x+9wH7D3bojh3gAKeB3V2Hi9/Zr6qbkE8T/PjKMkofNYmtX/j7MT+G8pgfeSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQh4V3Wt85hflH2DxHJ8ZHJ/JP6bSwv79M3zm776E2qD4sw/PqnnuPnfA3ecOCD8x8OwbF/z4CiEN6KcW4C1GZf4seWbrP4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyCce3Wt89m+/xOU3z59aFFx+4wKf/Tsvoc02/+x//8EO3R0/C0HIQ7N7agEIWYNzLntPKZW8lpabKts5N5ZVq7tWp68jLCe8Tj2vtWxhLMkWy1lLj+Xzv5VSonqW5mkds9K45MrK9b21tqnulF7k+im8H6YbY8Z0fw0A6uYW19c3OPQOh8MBAHB9cw3bdXj//fex6ztorUfd6LpuvI7/jq9DmZfMl1z/tRL2UarunD7mZErpQvxcrpyWdqT6LNePLXNyie1I6Ww8rnE9a9oaly0ZF2n5Xkel9lZyP84j0d3SOCyZOyWdy603uXJTdabKydmbnJw5WyGVK0csl3+utqbV7Ghcf1xPOC9Sc6Rk68N7WuvZb0kZS0n1a4u9DdcQYC5jq72O7VLYfsn6Fted0/c187zFP5MgkTHs15yvFK7p8Zof3/PXYbld140yd12HrusAALvdDvv9Hufn52N5cBZKaTiX/hTu7e0tbm9vAQDvv/8+Li8v8eLFCwDAixcv8OzZMwDAy5cv8fz5cwDAs2fPRrn2+/0o236/h3Muaa+BtB2P+yBnOyVjlMri+2YJsa0I5U/lSeGcbZpbJftXryuUy8A5n9/PAQtr8/5UXHdcX4tdK/kaj0HcFqcBpfRER7puh5076YekfSW/UepzSPZZOb8vt26enhl/ASjbv5SPGtqolC7WfKDafkviz5bqmvS3drM578tfspeN08JnUvdztr5UplRfQjmHPVzbfGrZd5yS2n3V1H2Jn5x6rnXPvda+WGtFc6x0X5K3tJ8I/dZwPvr0WEat9eTefr+H3e9xd3YWjKPD8+fvQO8PcM7N9DS092Gdfr4rpdB1Heze4rYzkzxd10F3eswf3qvteaWE5cb9VWKNDyvdZ4fXeX92VhOUmrerNR5Qi4vkytoy3pnLL/PP5LY/pGYLUuuONE6QKstTWhNCwv5KxUVa6qylp+SrxTNre94cudhgjYf0+yT6L50btTVLGiNcut9P9a+U0jjk4hLhGPp9Uev8iMuP6wjpui4Zr6m1NRVLk8oZ+xBh3OOhdLc0jjn5U3PT/5b2U44lYxo/VytjyfrcKkOuzNx8k7Y79KEkz61tl1TfYyQyStLD8uM44JI1XRKjWWKDt47XhXPJ/933PQ4HB2Om/6eq4/EAc8jHukt9kVv7vZ7Gz5Zir6W9Qnyd27/EpGLgp7RpfFbS/0tscVh36r7UZ127tzj5ar7d92nWwamTbH3fw/XpdU0yX0v1S+zBlvvfNePVspZIxmbJ2hTvR9b6YSl/JYzvtsRZlxDapGLcCXZmr/u+R9/r2V5KuocozbPa2VvsV7bO2bU2Ze26H8oSMosVJ+zl0jpzfVKLrab0osWXXeuztMZ+S/Lk9GrJezU5rLIwZlpe3x9hj2nfvNZ/ujcz+fq+h1Xz8yRpX8dz+bEYzhXnPofvg6eSK5Rlq2fitVNi0+O+CdNP8/BUb1z35P0spQY9ieo7Ho844nHeC9Baw6n5eWgq/payM7lYSNhOqc6k9vIttOjGU+hx7QyoxlKZ0/vv03VJR9fWs3Ttm75L5PXhlGe328F0+bWiVm+8PrbqmzReG9MaK5eWVfJ5a+dG4XsPNfnimFmcPsaq9g5vuuN9+e50HoTy+wOxTY7PicJ8KX80F19r6UsAsMbrkE+/l8suLTvMI/ONQh3tewNrw3k5rB29GvLn9ime8B20OG/tXQ4JkvlTsn+157P7DefX22GttdbBWAOXeGdqLcN6Pk/z4zT8PX/Hy79HI40pZ+0B2t77z52zTs54TGgrT3K2+HuttlP6bxGMMYvflSzVEc/dNec5cXmlZ7z8nRn0NKx/WLPqccOaLDm5HiuuIx2j2rlTmG/oIy/LffwF873VlnO9Rsua3eJf5GI8PvZYqju8F+pMzV9P3Wtf0/xYzefdkphL3GdSvWw9A4zz156XxNwlpHxUay2UrawB0doS2+4wX/psdF7e6LPNynPj+nWyUWmZa9R0d8tzs5Ku5PLFfW60HW30mGYMYPNnPWv2A7Gvmasj93xIzganfIEtabU3krJK/SuNEUvW9zXnIy3jfvIhp36+stvFaaW+QslvScUBjTEwCbeqRT/XzhdJHa3llmSS+Irh/ZQskj11SoZxz6v1bCysMUDBNx/s1zyu6GPc3t6po0nEwk/7ypjW9TV+rgVrDLCL+/N+DXIY7bMxBjDrdcna+Vid/E5ZbKvaLwZ495ef4+x3O3z4w9exC3Vfhq+7VNYyu6qMwotfeYZnX78Y6koJICDWUQWF579xiQ9/8LXk6fHKOjfT09ZYjQR3X8/p2eHvvj9CZ87ul9S7NC44raNtPUldp363kDvT8nbhoXwIUocfX1nO40UICCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQUuTqNy+w+7DD7efv0D97nA9BX3x7j5dfeYbubvn/ZLjE1W9e4M133+Hw7vFByieE8OMrS+HnggghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkI8Z3W2Hi2+ew5wbHN7rYa7s5nUoAOff2eP5r19i/+HZ5uVP6nIK7/3iO/jwR17j9rsPD1oXIZ9W+PGVRpxz+qllIIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBClvDe//MMf+SX3p+kXf/B92B/4qMHqe/dv/8c7/7955M0pdSD1BXS3XW4/O0Odmfx7GuXOH7miONzs6rMs1c7XH5rj4vfPsfutttI0jraaLz3Sy9weHHE7ecPOLzbw1wYuDMHAFBGQfUK3RuNs1c7XHxr/2iyEfJJgB9fIW81SqnJwuqcG9PXluvLKpWbygMA1ta/fhbml5Krr3avVp5zbtK2sF/j9NR1a71xfl/WkjbkkOiAtG0pWcIxrvVLDl+utXZ23fc9AED3Pay1sNYBuB8v62DV1LEL+zCUoeu6SR6JfNJ5lKqzNG658mrjniszVV5N5lz5NX2XEvd/XFZu3kj6Oh6XXJ+H+XJ955xrnmOt/ZHry9DuxGml67WE/aV1+jtqEh0Nf/s+DstuWYeUUtBaJ9uZe742rqXfpTpqc1g6FpJyUjogra80X+L03S7t5tbsQOp+Tmdy6SHhetGqe6F8cVmp+/F16netDimlcYjn/NI5HtdRWrcl60er72StFY19aVx8PmPMmM9ai77vx7W+73vc3d0BAI7HI66vrwEAt7e3eP36NQDg9evXeP/9IZj1wQcf4Pb2dlLX1ZWGtfKgz5s3b/DmzRsAwM3NDT772c9OZPOyXF5eAgAuLi6w3+/H9LOzs9HH2O12Yzu11jBmkCP0QeK5u8Zndw6IH/N9q7VOzrlSPfEzEj++lVi/1tQxnwdtfbhl++J+jedLasxb5KnZCK9rY1nawLmwPAXnLKzN91HJdjy0rwRM2y+pb9grjE+Mz6X2c4+JZE2P/abwXmyP/W+jzWzfZa2FisZU2o9h3aG+xnv5lj2JL6vmQ4XtSum91vNxLBHWF8+FWX8eDfp+mufu7oDD/bqhtZ7sMWr7LKmvIV3Hy/4dMNi5ZX5jqv7avji3n/LrWpie010gb9/idSosq+u6yTrqnIM920HrDuGc3+120FH5qbU1XAfja3tm78f+1D9dt0O3m+pDXGbqd1xfnEcae+k6Da2nCFpClQAAIABJREFUa9tu18F06cOoFj+3tv7l9CX3+z61Wl5t/1ubb6WxkM7F3Bxreb50v4XHiPW1POt1ttRHretrvOeVrEs5najFKkO9yM2/MP9D8lDlr4kRleInEuJ9gpTaM+G6C8hjkV2jLYzlqMUvUvcfYm/Ugk34WLl5tbX/u1XbU+vBkpi2NGYZ55ecc7TW0TKvluxRcrq6ZO/Ykt5ybtKCNI6Zu7dmX1ryOSTjLR2zlnh1S7n5tk9lHOzqVG+07tB19bhtSb/Xng+V9gZLdCm3JhprYEw/iZcdDndQd/n5l9sb+WtJzBtoa1dqn5LzwVO2Mlf28XiEMXbMb6279//D/jeAaevzUh+kzoOGeuprR1yuZJ+yZo1LtSPVz5K4SJwvVWYpT5yvRO2cyRPHQzy+X2t7IWk98XP+WmrjUnGIME3q80rPR3L9ksLnrfXBY8caa6Tk7ftetAdYu08IaenrJayVc6l8Uh8cqMc1auXP7YeDJN4iKX+QGwh9h1xb1vpNcb0S+VrQvY+Tn2QxxsDosoxSJLYnt8eoldfq+8WySNbX4X2qaT5j+mR5W43PVnYktVYZY2C78CzolLdlfgL5Obp07Wtly31Ha/3W2qIfsJSW+Varyxo7+q2+WGstdMP/F7Lkz9TsUos/PjnX6M1szh2PR/RIx65a7UZNr5fWkaqrlJZ5ukmeuC9Tv1OyhP5qSbZwb5WKHfpn7c6O+2al3OiLelm6rsNulz43LbUvR/hejJcxPu+V9LmX0a/pYbW1sz45fg9ZXm/K4+Ym41bzsUP79ND+ZAspm5Hq49k7Gt1gy5yy0zJ8vyb6Nt6T5ajtX3N+3skPRPJ+rb6wzljXnHNQSO/x4vbl6k7t7VJ78dge1JDasrBOaTlLdfUxdTy3JubSS/GP1v360vMEybuHqbwpedLvoKTf4fLPhjY697y19t43ne5HrDXo+2k5sSy192RbfbW1/uiymOw8Le6r1vEvxR1bZEzZOkkZLXv2VrsZ/r32nE2yZ8i99yUvf9pWH0cW75d2YV7fbgPt0uu81X4uBWlB7DocU5+v73v06r5sZWGtmfTH8A5dm01P0XoO+dBYnd7nu16+VwLk+4Tw3W7P8Xgc+95Tejc2Zdukdq7V9pXmV0nGuIzUWqJs+XyrJpuk7rWk9qw1tNYw93Mwbl/OXan5d2tYEhdJ8Zjvc6yx70v7S7pmaZWwGX2Pvk+/R1VbP7z9tp2enDcC937T/b+xSMlj1dzWD++wT/Pm5mDpXfetqI1HKu7T3/+b1hCpXX6o9zCW4tcYL5Y6Alf/7x76H1zAnFuYzxjcPT/CPLMwlxb23MJpB9c5wALKDh8y0XcK3ZsO3Y3G2Ucdzj7YQR+G98EcLI5YvkeQ+PYpdh90eOfDq2q+OC6Ts/tbjIuPGYSU9ucte+1c+pI48rQ/AGMsXOG9/CVxd9lcyKUHMQ9hfJJsBz++QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEkE883Z3G2Td32H/jbHbvqT5IRgh5eto+/0cIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGfEPjxFUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCyKcSfnyFEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyqWT31AIQsoau6+CcG38rpcbrMD13P/wdEt6Lr3Plh2XF5eZkSaWXZC6lS8qKKbUhl5aTpzVPSQ7nXLavU9epcc/1R6tsAKD1/DtVqbRcvak0a62o7t3VEVdXV+jOLc7OegDA1dUV7E7jvffew5mpm/ElupF7LjffUsQ6HrY5nFclHVuio5JylvZJilzZKdlrtiAnV8n2xPlytkhqb1LP18ZaqvdxXVK5cs+upaYXEt1aYiPD+uJ50UKp30tySda0ki6V2u+Rtqu1zZL54utP2elQLq212BbXqPkcNblytlIqY8u8b1kfU3arpj81X6nVLuX6Jsbfy63PMXGfpfowLCs1R1LyhOVorbO6obVG13UABn/W17Xb7XB2dgYAePPmDc7PzwEAFxcXePbsGQDg3Xffxe3tLV6/fg0AOB6P6Loeu90ZAAdf5cXFJfb7oaz9fj+We35+jmfPnuHq6mq8d3FxMd67vLwcZfH1K6Ww3+/HdnVdN7Y1vPZtK/Vr3BdSu6m1vv8zzd91Hex9X/rfUqQ2qLRvCAnnjFJ6kr4lvo+tmsullK7Og61s3xblbumTJUof9xUtfkfs/5T2jKnrUpmlfBI/V2uNMMkXF++fJGW33D/VV/dVW56trUmDjajb9ZLOt7YtXntze07pnjVMi+fIOJczfpMxFn1/8nvivVgoW05fB7vpEHdDuA6t8fla+jf04XybS/6wUgrGWRhjJmUZY6CCrozbX/N7t7DJEn3O/c7tC30f+f6w1g5/dmdR7Mvh4uI8qfexH5JK97/NzqLrNADfd8Pa3zk9K6M0/rn9R47SXLHW68l0Hljdrn+puSB5rkqHmV3yPookZliqO/V8zZ9O1VFKXxOvS8mU0mvpuLTupUv1x9db3c/V60ntQXLtKrUvns8SGyPRpRa/p0bNz8jt89bGfGp+bdzWNTGaVNm5+iX7z3he5OZOqw9dyl9a72r6OrfJdbla9lO59oeyxDIPz8lkkPrj8fVWSP1kyd6idF8qe2r8tt571XyfGq3r1pL4dKnskvwlX61W7kOx5fjV+nXtWErmQ6yjufhZqPu6N+NzUz//5M+Gz9b8s5TsUkp9FO5f1o5brs9C22e0gbXuPs/w9/F4hO5le+nUXspft/ZPq41a2j/hc9rayfy01sE6C+emcd3WuuJ96FYsKXeNHsX1SWLXubhI/HyOh9pzt+Qr2Rt/PxenCJ8P040xk+u+78ffd3d34zPGmEm/d12H83OHDz6YzrHf/uYt7u4Uuq6bxNf9/jJ1nTq3qMV2cv0T2tH42VIsTFpHLZ9k7KS+fc1Wtsgnlbe2Pyql5doYj4WUln1tTr4apdhjqm0t/RWP3zwuZOBMvW/ycvjr0z2p792KVOeW+GJDPoWwb2I71upHS/SlZZ+1hpIdKskzrv/azux93/eDr2S8/3hKt8dp3lkcMfA7T+UdcYTNPpOSS0qqrFybcj7e0r1mnCcXu24ts4Rk3ZFSs29rys6VJZkj0j3SMMZTO2WtBYShImnMYYv02D8a7M7p/nCe8fB745RMpfTauixJD9sex6pC25CLpeV0Zm0cMy43F8sAAAsX7KftOI5+zI7HI/q+G59ZqzPh79A3bm2j93v9mn5qohv8834aI5DUY8c95CnNGANt63toX+7Qd+FYnuaA1KeTxhKlMqV+L6mj1cYppWDu102nTr6KcxbOWvS9ge77ou4smcu2z6+Tzrn7tXy6Hz4eDzi48rtEqfY7Z9H3/STf4XjA4VDWmdZ5hA6BXgXxnWP+rLYUX5f0+dr4bunZ3PiV9hap56V7r5Iu5d/FMHAuvDf0+RE2ij3O6yvJXELqI6QY5G2b18aYotypdobp3oZ7u3nK5yY+S24N3PrdtNa9eJgu9ZtC5j65Q99P4641fzOVvjbuGK5HcbuMMTCfwP8te0nHW/awQNB/s/FVzTGD1BhYO6wbOZ86zB5nSZUX1jmfixjWO7NujxbW0ZqvZpdKNv/jdLYmta8PdW6wJVIZU3EHY3o4wdlSivjsIU5LIVm7a3XFdZbWJOfmc8w5i/CRtXFBSVzF58uRjkv16NX6uS6pf23cZ0u2lCX2A+XPRHbAGuiCr5paS3Jp01hvOray9NygxGPY35DWcZTaZOneNuZk/055DocDdGWPF1Paj6/p45x+Ll2Dtjrvb/HF5/7Z1Aey1sIJ/JeWeM8a+z34V/W8retay34WwEwvfZq3C489d8mJT+AWixBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQurw4yuEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJBPJfz4CiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5FPJ7qkFIGQNzrnsPaXULE+cv/Y7le6vffm5usNnwrwlmVtlkaRLcc4l25ZrR6r9pT6J64jLLqH16TtRqTqW9GlNjlw9Kb2qkSqr67pJGdbaqsype6FMLfVLWDpfcvdz+lPS6XgupfRxre7HdZauW/pSKZXNH7crTG8tqzQuS/qm1t8p2VrSU+Uu1WFfTqsuSuou6W+cnpKjZRxa9Ku2tkjbkyLsj9J8TbVtbR+kZJHcK5Xr7Wq4hqTu18g9v0Qmab1hnV3XJcuWrBspWVrGoiarxBeI5anJFD+f08U4n++n0rqR+i3Nk7oXjlPYV6l5pLXGbrcby3DOoe97AMB+vx+fN8aM6c+ePYMxBgBwOBzG67u7OxhjcDwex7p3ux7f+739xC/48R//AfT9bpRlv9+Psuz3+1H+s7Oz5LXWeuzXrutmehm2UzpOa7DWJnUyp1f+GWBoS/hsPK9T87xlTdZn3X1fOQDDva7r0O30KGMscyhfK/4552yibAtr83uREnE/LJWvhVYdKfmNwx8gLjLe57SuSVKbkENaZ8nPzevjcE9rLZalpf6W51PlLFmHrLWjLg55w/wq67+1ytaaHuqRv/ZzJJwrYXp4z9r5fA2xxo7233O4u8PhmN4Lx/M1peNeL4a653MjZcclvnlYX9wuyd4k7q/Ucz7Nlz+IpII69aQNqfqWzPeUDEvTl/rF4dhqrWF2O3TddLzPzy/Q6S5+tI1OQSk96cOu69DZet+W0kJabfyQf+4LlvxDKS1rWsmXmdul+byf55/a9VwsQBLviJ+X9EW8V5HsGXPjnZvjJX8sJ0t8XVt7SnvOlN315Py8LWJJ4V4tLj+WUdr3JdbGe0L5cnKtjSOFbOFLptocyr/UJ8ihtRY9X4pV1+IjD+ljS2Qv1b92XrTGQUvP+Hsp/yVlS0qyr517W+4hWuMVkvW49kwt/SFp2Uul9u+1cyHJeVTpd6g/LWcNqXlUk7VGqt4l9qIUx2w9W5P6HLFe1/rS72H9dclGzn0ob2OH+8YYGDOPF/X9EfZgsjLX2tGCNJZZq6fkb8ttiveNHJwxVd8h3nt4ef11/DvVplQbS3oYx1CX9Hsoi9Yu6nNAYb5+bEnNd0r5xg8VL12zPi2xMa175BprYjnS5yXlhDoe7u/icQvXp3AvcHFxMZbvY/bAqY/P9w5ddzfR3f3ZHs6p0SaG5xvhdc3v6ft+HqsO4vshqXlc2m+VWBrzaGXJGLc+k5vTEl8tri/ea5XkrLGFrajFcqR7wS3GVbpXSfX78nMUA2vj8obzkxQ5HZLuJyR749KZtb8XXp/uGzjnz4WGeozp0atTHHgtrb6i5P4WSMbfGjsba3+mepqTp3RnynJ3xszqNcagR90OLCG5F1ApH7eH7uVjLd2H59aLpdT0wo9NS/0tPsNSvVyyjqSeb2E4+w7t8r3OW3m8reWMu0Tsm+f27ACws+7enp7WUWsN/NCujX1Jz6tz72qsrTdbXz+3DcMYlvs7jj3kYocpXy/eWyyJ6ymlYI0L1pHT+uplN8ai79tiTnEcteZXlPZ/cfv9tbcXVnubeKqj73toU7aJqflhTdrfqPlu07GzQfrQp74/H3JtlMRES2dVa/ZfsnjP5Ne415me+9frS/mrab/LjfoMTNf42L76Z+KxDn+bII4S5jXGzc7w37y5xeEwfW8h5UOG8a8wX+qZeUwsnbe0F5Pun3Myb0nJJiw9s43T1u5L18Tal5Tl87WeyUn3olv58+U2y3UuxZLzjqwkka3ItUV6hp/PU5bR1/0QsZGa/pko/uvrTvkaS+Owa2VsyStdJ0rlt8Rgh/tznVwzL/26lCtn/n7VdP0axtTrrAqeCe1eesyX9C8giwUs0Z3cfI9tYOtaJJ1rufhXTsahH8N7TWJl69963knbJbH/g188j3040z4uMfG+PydnKM9Sas9O/K1M/KgUJ1q71qXu1WyNtxVhFmMMUtufh/TnYhmXxmtSZW3phwFp/3LYE4iLAJDwW02X0BkLF+h4as8/FOOCZ6Z65pxLxhhS41yb06m9dKotNcJ6cnH63BoUypFizdpaotTe0p5HdQpaT/2A4d971N/FbZWz1ZdZSqkvWuebdH2V1pHTJ4X2dUyS/hB7uBSSelpliW3/KW3+76rJ48KPrzSilPqLAH7fU8uRwQE4ArgJ/vw2gK8B+DUAX3XOfePpxCOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5OMDP77Szo8B+P1PLcRSlFK/BuD/BPDzAP6qc+7wxCIRQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPIk8OMry1AA3P3fbxs/AOD7AfxbAH5XKfVfAvgLzrnfelqxCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgh5XPRTC/CW4oK/38Y/6v7PZwH8aQD/UCn17ymlqA+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5FPD7qkFeMtRTy3AAvwHWDwKwHMAPwPgjyul/mXn3PtPItkCnDs1xVo7/g7Tw2sA0Pr0jRmlVPI6RCk1uReXF//OpaXqz+VzziXlidNzMufI9UuqvvC+vxfmS8lSk6fU3pJ8kjKk9Uquc/Uopcb02jjkyjbGjNfW2vG+vzbGoO/78Znd3RF3d7e4g0Pf9wCAu7s7ONvhzZsbHPvBjHu9UkpNrnNylvQ9l6dV30qk9Csly5Z1xvXm6nzIulK6Li1L+mzrmC9BMl+2oCZ/be6l0sK+BKZrh1SWUp6c7SjJ6O97OxDTIl+un2r9V2pfSf7QJobrgrV2sf6F60wtX87WhmttSJwukSsnR268cnXX7rfqmNY6KYO036XrXnivdV3w8y3Wk7hOiY5JkdrIFp3PlS+Zs2Ge2B/I2fWU3xn3XWi7rLXj79CnOB6Bw8GMzx+PRwDAxcUF+r7HxcXF+Pzl5SUAoO97nJ2djeleX0P/RSkFY8woZ9d1Yv/II/FVUpy5uT2TzJ2wLTUk602so8N4WoRDaYyBSqhITm9qpOS3au7DK6Wrbc3JsFS2pWzpP0znTb6O3D4rLkNK614wJrU3TOlbq+1a0rehzZf6ekt869Qzsa30NsdoA2vD/MOeyPXpvlpKqR0lWx3aMo8xBl3Xjb/9ddyv8e+uM2N5oS6EZcdzO9xPhmuCt/fGGNzd3eG8t3j16iP0vRnzf+Urt7jthvLOz8/Hsp89ezZeX11djfLv9/tRFq315Drsg1y8RbI2pjBnZlyXPBcX59CHdD0huXTp/CjlW2NHcvPLz3X/u+u6yfobP1PbH9b23MM4uJnNXLpnjZHYq9S8iGWy1sIYme8dX9f6oLQmtOQpUfMrwrkb1yuRITdOS9YEiS5JfOEcsQ2L949b1FGrsxTHbF3TJT7p2rhWaZ/QsteL01O25THjZVJK7c+xVs64b2oxnofolzX74VI5uX1mDmkcWRpfb5lj41qk1MwOmL4HMuujlC39/q3GKyZsd+xfSWIpYTn++ZZ9cUqOErkYVSxfTuYwPSwn1w/x8zFSuSVx3BS1fpToQixjOE5rkIxzLF/JD3lscnPK7xNP+8PTeZoxCs75NigotXxPLHmutl9uOS9cooOxjzv88c8Nz56d7aBdWww6Zfcla0GqPX49bRkHiU1L5Y3Pd07xaDVLK5UtlVW6hkjiDJI2b+Gjtfr5a6nNiVC/anOg6itk2iA5YyrtpcK4TjImm7DVpbr2e4urq1eTtOfvPMf+IF/b4nulsffytazjKdbGGmMZUmuddM1qWR9b/Z0Y6Rmlz1vTw1z5EhlqablY45J+W8vS+HGIVQ7GWIRNPR6P6EyXfyggFXcY4kwnGYwxMI+/vZ3U70n5OnE+v9aHflDYP+HaWxuDJWf1S4ifbfVzW3RpOl/nPtq8HdNz65IMcZaafW7ts7pv4suN5WqPlcT1pPYDUrtTKjdVdi3d1x/WuWVfhjqy5dlgK7kYZVy3tfdnzqauo6dnltv60vurpfcgQpsU2iGr2/oyF0uQtql0ZlAqozQXyvXN56C1Tuwb1uxIKkYeznu/98mtJWFavJdz1uF4HN45dfcyG+PQ2UEH+uMRfT9/f2NWTrCmhPEHqZ8aIo1vT/P5fhn6HtUxS+iXHvyNU/nDOzK6n553lvDvhnhZhrQevVq+VtR0Zsk7MiWkPrNoPu7uddWe1k83LGTD88F5tqRcyfxxOpwzOfse/1azdSYsN9wHhuz3Fmdn/STt8vICXde276jFWKYx4PnzEr86Fz9du6aXxi5V59IYTyktpHXtC23XtF02iDX69Sw/l6WySs8et7CXpXrWMFvvtJ31+zC/y+XUdGFNbKo1ZrHkTNgoU1wzavWUWBq/Dhl8g+gMzRiYQLy1upGbP2v2D0vIxRHXxHQlei2J1ZdESOt8qPenOn0bT4+48awj57+eZJC/51drz0Pll+zHJutNYnz6voc27euvRM55/8199FS+1jpbdVYaK14SkwzPlMI0qX3dqo1LabW3p3ly2lMA5fWs5IPn2rnUTwnrmJ57zX3IXEx4jV8iQdK2reIVHqmvqbWdxEeVUuhNP9nn5p4vnX+mYo7hHiclmx+/koqe5lp5DkrIjeGaOZZa31L/3k7iD0qRnBlKygDqscLc+l2rX7rWLtG3uFzJOrCl75Wbr1vsWWq6vuQ8LPW7ll4va253c7LkkPodZbnmss18vW23f0TAupNn4oI/0nxL/rTIUStHBX/CZxWAPwrg/1JKfY+gTkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC3mr48ZV2VOZPitxHT1r+hOXkPsSypByXuAcAXwbwPyqlnpU6gRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQt53dUwvwtuGc+9Hwt1LqywD+GoAf8EmYfiTF//5bAP4GgL8L4KsAfhvAdwDcAugBXAF4BuAL92X9EIA/fP/nha8ep4+m+L8B4GsA/n0AHwK4vC/rXQCfBfAlAL8PwD8O4HvDpiD9ARYF4J8A8BcB/GvijiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5C2DH19ZgVLqDwD4XzB85ASYfnhFAfhNAP8pgL/snPtGpbjr+z/fAvBLQR0dgH8OwE8D+CkAXVCH//v3AvhZAH/COffzBXk/B+AnAPzrAP6FoAz/8ZXwoy5/Uin1l5xz/3NF7idFaw3n3Hgd4tNDlFLJ9Dh/Lo9SKpleIyzPWlvNX5IzVW5Jrpa25MrJpcd9lmpbqb/jcmttju+X8kv6L0coV+46V1eq3jDN66m1dryO7+92u5NeO4WzszPYM4uu0wDuf+80Li4usOt3UEqJxk7SJ0t1vKWOpfWEZUvGopYuKS+k1Mep8kP75OvzZUjsTWt6Sd7WuSbJV5sbkja21L+knx5SH1tkkdYXrmOh/JJ5LLWfrXOnhTXra0hqnpTypX5vud5Lxitc/yTrfKqMJfrTdV3y+RYZUuW23Ivvx+1Y4tPEPl1NhiX+wNq5BMh9uvhZP7/988YYHI9HAMDhcMDhcAAA9H2P6+trAMDxeMRHH300pr969Qp93wMAXr9+jYsL4Md/7D0Y04/1/fIv/w7evBnqvLi4GPXl5cuX6LoOL14M33fc7XZ45513xuurqysAg37t9/sx3aO1numuZH3L6UmYnhr7EG3MrN+NMbBd3Y4vmRdSrDawNvZTLazdZn0B0vI7ZxP+8aneJb7fVv5ibizDdnRdl2yX1nrReGmt72Xzc63t+Zxetq5j0vyhfWyzweHv8joW1y/t19IaW0orpXtS/RP7zSMdoPW0vK7r0NlunjdDqc0tNj8er9R+FpjayxbdOTsz6Lpukm9/fg5VsIs1mwkM7d8fDV588zhJ//KXvxf9+dn427cl9oFS6anfvp/X+pTpPb+bzGdrLZSbztHafqTlnif2YVLXS/Y8OZ3MlWm1vl9jTrbteDzAHI6pYkZi/Uitt84BuSkr8YdrtO7Phz9pWbwf02J7cvorIefzGW3HNd8Xa62DKuw/JXvTUhwqN/9a5tuaPW/u2dhmScjF9XLlLPUJavVL17XaXqbVBjykP9pCaB9SbXwIOaXxJUl66V5LHDpVjsQHytW3NCbUqpe5Z0r7rxwlmUM9qMVf1tQjz1O26VvEIVPk9ispJP6IdH7l6mhtZ1hOyXeUypWr08cnPFvE7mOMMYtka10DpbK0nrvU5Kr50y37zNR4bhH/Lp2xSM5apHoh68+TTRjKDcdj7n/l1oEaa2IAuXhxKFv8rDEmWae18/jPeG/vcHd3N2nf9fUNuqMefehTvGS+L0j5lqmzyxwl27elbc7NuaHPfP+qYF9hp3lM21iWfO41sTNJ7ClOl9qbVtsb6mXurENKaY3x9besqSnW+qg1W7OljUit/6mYtqnopeRMJPbnl7QjXmO3OjONzxE84blWzBZ+3JoYldS3lZ6D1MqV9rVE17aO3ZRI2ciaTavFwad9ism1tVa0N4vnWdfP55616XeaarTYKy9P6rqUL3ffWgtjTr6A/7vvDcIjKEkft+55W+VN5VnS31K9PK0x89ibj6eGPsGYXrETQ39bhD6mta66JynJWLID0jPwUPatKM3nLWNiYb5SXCT2lVvtZymmn4tzxHKt8WFbz+asmutobr/QUn+yrkT7cz6AvN6p7W7fV58hAAAgAElEQVSd76F9l9jDUMYUuXbl8knOyNNpvpx8+UviZd5Hyumtb9fZ2dmYL+VrJ2XWDloPz2utoNR0bV0a4wj7uvbORy4OUssHANa44Rxmtu619/Pw+/TsEEPIz/0tzsokz7b4kFuw5Pw8J4+PWZz6FoNtc/Z+P962fkr6wiq/Tp/qLJGKFZXzT+dXaT8nPcfI7eE8tj+tCf523/fQfeY9ikJdNbk8YVwoJp4H/nd8bpfTpdI5da2+lG//EAw2TG7zWvxcyf5hiW8pqadFFyR+EwBYPZ8HXj9b5ZPg65LEm9YS/juOGqEtyfkRS/fYrfG7oa7yO4NbUFvfU9R8qtr8rtWzlX1I6bU0juz7xY57x3oZzjlYc3rXxKeFc0n3BtZObcPxeMDBDX6ihUXfT+9L5uLHgVb/wvsVzp387lJMLh7LVvuzS673PYySv58omcc52Vr12pchOZvO1dEf+nEtGvYwJ/2t7Q9SaS2+a80vq/ngS/fNLhE/Ksmy1VojLTe9HpzaWdOT0vpUq7eWXorR5AjPAST77xSSWAAAdFrN3j/WKu3r1s6K4ntSNZjW5Z+7Xy+sBaLhG9YPmT62xpfX7iVTPgYa+qJ2ZiJpY2nsW3U5fjZ17p4ao1JdqTolMa54Hree7T4UOX+0tPZKy7WdvL/XsMaWT3Vi+NvHM1riQk1rUjLv1PchHx8+/t72xxSl1I8A+N8AfA6nSK6PzBoAfw7Al51zP+PqH17J4pwzzrm/5pz7kwB+FMBfDeobxQHwJQD/u1LqJwtlfcc59187534KwB8E8LeAyQdjxqz36T+7VG5CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQj7u8OMrC1BKfQbA/wTgXUw/n6wAfATgjznn/kPn3N2W9TrnvuKc+xMA/g0Ab8Jb93+uAPycUurHBGX9IoA/DOCvYPoBlvDDLj9Y+pgLIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFvM/z4yjL+MwC/B6cPlgDDR0uuAfxx59wvPGTlzrm/DOCfBfAqvgXgEsB/o5R6KSjnCOCnAfyvmH6AJeTfWSUsIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEfU3ZPLcDbhlLqnwLwr2L+4RUH4E855/7GY8jhnPsFpdS/CeC/u687/HjKlwD8xwD+lKAco5T6twH8PQDPgzJ8mT+hlLpwzt1u3ITVKKWSfwOAc272O0UuPXy2di+XN1V2rr5anbVnWuTNUZJNkq+1L1va68tWSk3q8WWk6q71zZL+DuvJXaew1hbLk+vFVGbn3KjrqX6Jy861WWudvKeUKvZTrg+MMU35S7LFebQevhmW69NS20t1SvQkfr42frEdyl1L5l6rPSnJkmtH6TpXVsySeZWjpNelOv0zXlfCtJi1/bp1mUvmRcySuZx6riTzmnWgpU01nc3ZgbWk7EULoe6FZQL5PqqtE3E5LTJI7GWOcP0tyVayK+HztXw+Law396y0L9bYpVQbW/UutT4652CMGcs6HA44Ho8AgNvbW/R9DwC4ubnBzc0NAOD6+hofffTReP3BBx/g9evXY3lXVxrX12eTuj/44APc3Mzl/fa3v43dbocvfvGLAICu60bZdrvT9rDrulGXjDGTsQh1LBynnE8Rzwupf5EiflZrnZx3OR7KdsTEupxCa/2g8nRdl0xv9RVLhM+HbZG0K5dH2iexrlhrgz73f+ZrYIv9S9WzJD3VTy1661F9D+em/WOthbVW7E9LfJVWvWjxbVv2UUO5vuxla7Okn0Odk6zVfn7n1oaWNnqUsrNyhrHNP1PaB7ci0eclcYdWedLzVSFMTtn9pXsqaXrJz43jFSl9CtPja5/HGAPn3OgHOOdwPB5h93vc3FzD2lO4znzrW1B3h4ndS8nm+0lrPa4JSinsdjvYvcPd3S2U0mP69fVr6IOe2c5wPYl9gNp1idBv8HXGj/p0yf47fi5Hqy7M83hZhjRrLbTLrzWt8udiKp5Wu9ZaVsu6mfN1WmMspf2o179U39Xa+Vh+30P7dDla/WZPbDu2jh23ItknPkW5kvwp29dqD1viMTHhOtL6bK7+VLokJrrFHEit787hfm1wQVrd345jyJI4Scom5vZUqXpz/bEkxurXgpoOpexwLSb+ECw564mvU+XV4pMS/V2rm1JbUIudtZYZ5yu1NewLaYwxVU4LpfJa+7zkR8z1JfbDDIxRs32yMQaul611y/Zv6Rh8SM5vjvcMISWbk92r7h3udtPyzs/30Gq5z1jzRyVyAdueF+Xq0b2BMad2GHOvF8H/f6jve+i+3B/xeEnalYpLLaV0ntU632q2JzUu4ZjXbHPq+aVxkVpdLeRsZi59iV3I7etysYBTXfN53/cGfW+DPPP4emmtC+sK8y3p77B+aXp4XxJLW4IkDlWaL0tsuqTP4/qlseaWmGk833K+RhxvKMUeHtom5/QwNUbl8ZunObf2HCOc++17Nsm+SzL31tq2VHxc6i9Kyl/iu9b0OtybxbIs2Qvn8vS9ya7L8SNep3Kc5ptC2Ofxc5J9ZTh3l9lZh6n+qqzdXbv32HI/kCozZ8dCUv5E7pnWfW5LHGBpWanyUvZuvjeYtsUYC2dccd9QqlMi61q7pIyb7RmMMTDqVG5tDxvLUqqv9DtXR+n30jXlZAdO+9LaOV6uXk8tfiTtq5ou2y6YT/6vcJyiMSv5FCG52Khvl9T21d7v8z6CvzXOD9Me6zjFG9M2vhRX89fWpueuKaiTNMaXy1uzhy3PxPW11hVfW+sSYwTAyfxgKek6MUnzE9Lfn67dbnY2ItnLaD33BY2xMGY6T8KyfbtTdjjnKziD8b0xfzZ+e3sHfSjb1LDcVj0IbXnL2aAkz9ZnZkvsv4T5XDbFuRzTsh+exwry+zxpXbUY3tJ9YKzztTmX05+1McawH6Vtke7xQnJyWmNxijsH8feNj4SlNjn1PlFqH2St7Dx+SzklSGJXQJvetNZdozS+of32Mlpjk3bERWuEl6H0fs9SUjK37AO3GtcSsa8qeqc06FsvYti3tfpa16Hhfa35OVO8JizZQ5bemw5jwqk8a2IyErmme4CTD7Ok7JYYVYuf2rInqeEWvnuaqnNN/5fOQYd74d7T55WNi3T8JGcorW3c+iy25Xnr5vbVRXG1EIl/rZSC6jS0nu9f/ZxN6Wsunpvec8cSpMcv1LncecYSfz5VDwBoi1m7VWK/IYn3lXRScgbUei+VZyZ3p6C1mvR/y79D2Wr+r6XVz9ui7lyMrTZepapT+yRJ/dL2iGLpWkNF71Z0XQeV0QmpDmzhi0vmGXl42j1A8mcxXYX9idv/4Zz7K48piHPuvwfwlwIZgJOX9dNKqS8Ky/lNAP8FTu0K23cO4I9sIS8hhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIR8n+PGVBpRSnwXwk0j9LyWAP/f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5yPk0JRlaYgUlOeOYSWpLrjne0jqTxjXnn5fOb6X9di7Dcm8exxHjaMQ9NHd+LcWBgvzXPEvk5rrlTCqllfaRkk/REsNpKVuL96Tla/Ol93WXlPouzXHLWOVse66+Ut2ta1Srl/5prRzzH8s459D5+nlG4vi8zc32Bmst0C9fk4z94aqcSnuZ5pfSc/oqxf1KQ5DWpW3z2ntxQKv7l5zZY98tHZ/YrjsXr+WjToVY/VqbGfuApT0hRRt/Oss7l+8YFz8/t43zSWMp6VJgTSzrWueuWoxi7byUzthrkeYmfo8lblfrm6X1S36Qq/iuWvtfO0em6do1ecyXf2axzKut80yuL9oz2CX6mi07Tguf8XAYMcKrno2u1ZE0X+s4O+dgjX0au6NdDHbSPtnIw+EAa6NnNYrnj+k9ScdSXSv9Ls9Zqm/l2K/k24f6Lexsbwj5J6s/M87XQD1mmVLzgS9ZS2vOiaU9TKovldeZoy3zCPvx8fmj9097bjIfl8QaSvlS30Brp079yD6Xk94xcAs9k9ZHyz6Y+jNxNzXxqhwt/lYuzrimzTV+XineoUkr+cD1+k9XWnEv1tlLid8/Aerva8Sy5HQzdx2f0ZbpejR6oT3btLLex0x/L5/1r/HNculav/ycL56rdfNSkqd2/xr6fekeJ/lwUlpqA45rpazXurnxs2vpGd187c3tffxuiDmdD87zG+5772G9nbXn/bn8c9maFi49Z8RIZyVpf6nZ/tZ75zzrzzhr3rX7NFDzR7Q2/hp5tGXS8Q1+a1pWo1cl+1Ait/fmZJ7Xe1lMu8S1860tm1sD2mcvl/pjpbjJeY8PeS6za3L7gPfmdN33PXzmuVnNrpZ8krIMtTIm62Okf7shrZHaGmuNT6fxTs1akOSW/LRLY58te0ZO7lTeXL+uFaeV2mwpp8HCPsUIzm1Ya2Fsfv1dq23t2XZ+Hj/H+9O/8cmhPZtW80zj4t1paydMU6Kf1zFBpIG2p73kk8xvBPBzwn//xrsUihBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQTyr8+Mpnh7+I42fV0v9+87sUihBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSQTyrDuxaAXI2vP/3rozQD4B99B7IQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPKJhx9f+ezwnZn0v+utSvGW6bpOlc97P/sXAIwxs3/Tayktvvbez+rLtZPmKcko5c2Vr9Wb60suves6VVvOOQD6sY8JZaV0Sa6aLLn0NE9tjmv1a9oM9Ya0XDuX9jOtS9LjXHupvLU2JH3WyJSTMa4rp4vpOshdl/olkfbFOTfT4ZxuatZ4ej/Xr5weanVCq1OxjCWblupsidw41GQp9U3T79rcSn3I9avVlpbkS9e5dg6lNku2NNwr2bRUlpwN0+p43EbrOqutP+m6ZHNL/WxFKtf3fbMehnJSeiAda60916DJF+fpug7Ouea9rTTemnnJ+UulNnM6mpJbM2v3hZRYDsm+Su3E6fH+aq0V04PPFXQpJfRxGAYMw/G4tt1usdvtAACvXr3Cfr/H/f09AOBwOOD21mC328G583i/evUKd3cbAEe93WyO17e3t7i7u8N2u13c2263Mx0v7b1x33L2MKdDGhudpk3TBIz2yW+cp09GllO6lmSU9ErrWxljMHUWxoQ6wrroZnJKfUzXq1Zfg54exz7kWdYttVlaQ2vsVWktrT3HaNoV07o4zQDw6Lpucc6J1258ba3Nth/GLdV3Sa9K/Uv1LWcfS3ZznmSyfrVUX02+Wtl0TcX+R2zTantluKe1/Tlq6zSVPdZLY8xJzjQ9LZMS0uKzRUiL9SjWqzhPSJ/dsx6Hw3Ym89e//oiHh+X5JYx9+N33/akv0zTN+uK9x431eP/9B+z3+1P6X/3wq/jITQCO+v2FL3wBALDb7U51bTYb3N3dndJDe13XnfKENRbGLe5/PJbpniftjynLdN3ZtnRGSIntaVom3utiPYjTS/tZWmYtDljYp3Ec0Y3jTHZgaeMle3X63Xt03XxejvNZt7easU3zadJL5PwmjQ8Y2/tL58V1S9tlrYW38/GN5yIdf8nfjPOnPkFO37V7T4pku3O6q6XU35L+5PbhS9aMltK+L5Hue+m9ltiK5D+0nHVzZ+lwL1dvzg8s1Zez1y0xpVQO6Z7k++TkKvmZab2580DNB0up+Xe1NtN5yZXPtVk6i0l1pf0rxSlqc5PmSX3Z1rN4nK+m984YhP0/ZO1MB5PYnBLaOU7r0vjnpXnVtqc5s9bqAZbrWzrzpPVo4lcaG1KTL/Zh0zql8uk5reRTSnKW6r30/BGT7n25dgOlc8XbIsictqt51vc29uZaW0fdOF6fbMLJb5rbivTMFNKkNtIzwby9pV2bpikrX04Xcuf2a+hA7DuWYjSamGpYY2tsm1bvc7TEq2r1pvJ3XQePc1xWKl9aB297reZI9ai0D+XsZUr8zL+1n3H+S+ONKRq7lNPr3Dos7Xu5/TK1FWX7JO+9xhhY60U/XYotpmVrY1HydVr9zxSNTkjyaWQoxSov3XNa5M750ylrZar57bn0VL5cjGqNLLW0kJ6zMbkypecbJZlm8ZIpxE/PaeN4gBvl57m19ropPE8655umCVOktlJME8ifWUrnv1O7K2xiSUeOMRMH70Ns+ZhnHCeMWK639MxY8k9qZ4A4v7bMNe1QiTV1eH+20+mcz3wiZ+Fc6MfxX2snSG5gLl4iIa3fnF8VP08IHA4H9GO/WJ+lZ9uacSrpiJRH8rVybbXGLC/VDW15jT+b2o81drgWZ7KwQqx5gimELXPPAKT2auPfGo8MnP2iuR627lEtNiLnO7Tu0emcamJUyzUS0mWb0hp3rnGpb3T2OwE8PT+P08O1tJYlf1WSS4qX165z+SWsyayVSS5ftMXGCmtFp7+nPNMU5T/HCqb+cju2BundA0AXn62R2/eX5ykAxgOo13vpM4WwT0o2L39ONtFayPszqU041iftfQbe6961f07elr5pfI01PkgLub27Zsfr+2Q4y4c0V11HrfOrfR4itVmzTRrbVRqDmNKzg8lPy7RpgrHLPpXiGM+tJ5o2JcrP8Jb1rfU5r2Ub5vKW21jjK7WU0e4716DFj9D4LRq7UhuL1v1UP7bn5x3x/nVcX7Fv9fSOD/JnMq1s2vTavU8yabyr1I8w9tLf1mnOK5+UZwo5zroe903e0093G+1Dro6STFrKczf36eI2pfhlza7X1q9GP2plUo76t0zToN331+Sr5Qnrq2ZH1/wNaox2fcVzq9Gvso7r5jm21XGW8H5tzDlfOV5ci9PVZNLUnV4bY5vqk+qVfATpzFbbj3N50rpbKJ0na5T8xlr+S87hUp5cvjV/j2Wtzb5L0lpXyjEmD8TryDnd3zZee0/K6dU4jnBu+X62n6aL3rGRyhUZp0Vccb8/YPTzOtyn1Bf7NMOPr1wZY8wNgG8F8AUA27fQ5ADgVwP4PZn7N29BBkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCPnXw4ytXwBjzdwL47QC+H8B3If6s5FsUI/o3/ozRm3cgCyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghn3j48ZULMcb8+wB+BMAt3s1HV3IEWX7xnUpBCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQsgnFH58ZSXGmAHAHwPw/Zh/dMW/G4kALD/+4gH8qXchCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghn3T48ZX1/CEA//LTdfrBlfQjKG+TWJYHAD/6rgR5bowx6Ptelc97f7qW0qXf0nWclmsrravrOrHOWBaJWvupLN77ap05eaXxker13p/645xTyx+3J6WHOnNySOm1uajlK41Vqe7cGKVz0zLXaZvL9j3mS9vD++McOOdgjBHnQ2o3nXOtTOF3S79K7efays19ek+SLVdnKBvrcmw7rLXVPnRdt5AtXu+5sjnbkc5XPC85HZPypnXlxiVNT21hnD83zqX5rq1Nzfzk8pXakmReI6PWxkvt1+ou1RvWVIsu5eQt6XEJzT4a7EwJaY3E5MagtpfUKO3bNb1yzon3cumX0KLjcf4aNX0LeqGxx7m9trSP5vql3XvjNnO6mCuTtrF2DVyLII+1diZb0PGwZ4dr7/1JZmstxnEEAEzThP1+DwC4v7/Hxx9/DAB4fHzEmzdvAAAffvjhKU9gmjrs9+/N0j766CPc35/X2M3NDQDg7u4Oj4+PePHixen3ZrMBABwOh9neF4jHt+/7xd5Ts2O5e6W1ke4Tks557+GcxeFwHsucrxl0rO/7mbzSmSAul6bl/aPUH3QwOI9hbX9fo8PHtudpUp+usV9IrPEFNft2yCONe8m/SusI/+ZsWapTYR1IPl/L2U1qR/qdWzvxmSvcC/X3br42AaDvBwxDn11HrXtPOket/kmp3vTftJ+LerqlXF3Xoes6OOfEs2nuXJaOde5skKL1FVIbD8xtkrX2tP6Otuu8L3jvsdt57Hbzdr7hG27x8mV/knsYhtN1GIvTUAnXwT/bTQ5/x+E14qn89b/6y9gPZRslzU/cfkzLmV3SS8kOH9sBUtH6vofp8nJLddbWQYvvV1of0h6SttG6V7rtFo+Jjm63W3RJvpo/lo6Hcx7ez31xay28za95TVqtbGm99d7CmO60r6ay1VgT/7kmsX2L5c3tfVpdAuT1uCauA5RjCGkbtXNerq3S+VU6AwbbXiNn+0uylGjRjZp8rXHSUuylJr/GN2mh1Ldc7CanR9rYbU7nu66blUtjxrV6cvJLZ7Rc3jVxsVx9kg/bUn6tHKUzTs4Hla41MUqND7pWX4MvsEzTx+FSm1nybUu/NZR8n5azUW0Pz5HbH3J+ft/LZ4j0zKUZs9I5Lay5NXHDtL3WOmK54thTLRYRZM6dy2qyaH3rks5o06V1WJszye7n9GxN7DauX/L9ajGWXJuSTahRkz/2QaRnNq0+cO4MFVMa39h+eu8xTdOiTDxG3nvYvYN97GZlH19PwGPeDsd2PsxR7PeF63jNSDGytM8p8b3aXGh1LfUbu1PcwCA+Q5TklOY8177WT9VS8gm0Y6DZF+LziGZPi/3stEyuDc24rPUHavtCHIOplS9dS+uxZA/j9ZmuxWI7DpimHkBYe8Dj44T9/hyrjteY5HenMe1Ay36k2d+l/KV60znI6YVWvyW7qJUrla3Ux0ufTcakfY7nSbN3S37BNWL4aeywFh+IyfVJ+h2u02fJmv0ylWkaJ1jrZnu+tRZ+0vmkaVqIzR51/lzf0/ZalVOSudZ2KY80jtI5RdrDpHi0xldpiY9qYldaP7U1Ftsyv3Xqz49qMkmxyVq5S869UprvPIClLki6mPpxpboDpThdWnfIl/MHS+217jet81ZaOxp9ldbhNfpRY95WPR6Re9YAtO8ZWt+2ZPuPthVA9C7j8RlYm4+Si1GleVtijKFe6br2u6bbZpqSmOj8HRCtfDlZ4nSpD1odk3xc6yym6agrfXeUeRxHdP545jvsDxin5btF6bXkswJzOxTfq8VPpHbS3957WGPh3PKZUefKNk/qS+obhDStbOe0/Jk/F4sroY2NSVzTt81R8iudcZFdOOcJ55m1Z4ESduNknbBP7xXZuc4Y45/sVLeYoxh5HpZ+mLVuYX8lv6lm45Zth3YMvF/aA7nMsv0W3VuzRqX2W/fwUOZafmBLbL5mVzUxkZKMNR+k1rd03rXnfq1/l5O11Newzs/tLp+BnfI2xPjW6M1aNOOekvYbWO79a33HS6j5F/F7Sem9UF679lrWaM1mXcoae1W3oTobm13XZtnn+jsD+fZSmWJ/8+wX62S+BO3ec00k3UunPI11pfdqcUTJ/tdsc0mvS+fR+NmY5J+/K8rzJ79jW/I3rte2rpzWt6rFZ4/16trXtpUjFx+O72vGVSOv5INqYyNa2Wvt59JK7WnjjZesn1psIVfmbIvm59+uK/uIpf10qcfluIaWXLyrlldifub1Qto5n7QutTqV0/9Y/jiP9BxvKXf5GpDfhdQ+D9bqa9z2pfFjaVxK9WnfhY/Lx+8OtTzf1+TLvWvv3PP5zM3PKgtznyuz5r3Vmr1PYytp2ZMuZFsmzwU/vrICY8wPAPhBzE8wkv76wr1rk8oyAfgd3vuffQttE0IIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyqYMfX2nEGNMB+P1xUpJF+s6Q9iMtKhEU6X8NwO/y3v/ZhnoJIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPlcwY+vtPNbAPwaHD+YkvvwSkgfAewBvEzypOVyH1RJ8UkbPvr3fwTwFwD8ae/9X1LWRwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELI5xZ+fKWd3y2kxR9E+b8A/GcAfsJ7/8sAYIzZAfhNAH4fgO/F8gMqPwbg3xXq7QG8B+DvBfCPAfh+AN+B+UdYAu8B+C+99x+v6tWnEGMMttttNZ/3PnttjMn+LuWLZSjJJ6WF8rn7JUp9iX+XKLUr1ZGrt9aeVGfc/5wsre2EPNr+S/U657L5uq5TyalpJ9eWJHtoc0CHYRhgB4eu6wAA/TDA9D1ubm6xscOsjnSM47q06bFca/UsV590DeDUN0m++Het/ZKMufTNZnO6l+YprVcJSea4rvC77/uqzCWbFNed5tHqtkTf91XbVWtjjW2L5z+uL5feSklvWnW6RS+lMVuzhnLjV7KpsV7lrmt7WMgX62tOrtSelPbXWOZL51iSQUIa99TupP2V7GpuXdZka0Gyvbmx0aytuOxa/dPo8iX7sCZfSd+NMeI+UmpH64+EfM65bL5YL+Ix996LcoW6Yl0L1+M4wloLALDWztbWOI7FPklsNhsAwIsXL3B7ewsA2O12uLm5wd3dHQDg9vb21P5mszldx3tC3/cYhuF03XXdqW/GmFn/Y0r2K6a2x4R6N+gWbWy3O2BztlM5Xcj5Pc459R6X1h/Kus7BueWauMRvaiUMqTHLMZrny+9buT04lAtoykt11AjlS35HieOYL9NjHU3Tc22kerB2LiVdl9ot7ZU5uY71n/uXO3+l5XN+p7RflPzOnC5dqvtauxGf0/q+r9r12J6naTlfqWQf0jJSerwnDMMws/vpGG23DsMwztJ2uw2MWdpaad+L+xX2i2maYK3FjfV4eHg47S/eO/zCL7yBvTnHU0LdNzc3pzhL13UnmYdhmNn9cB3mQfLJNP5fieO6AM7fKz6Pd2fqNiqnozmbIJFbr6mcrXXlkOp2brnHjOOIbpzrS0q8RkSfuVv6KcPQo3PL+dH0ec26z5U5ynhe332f1/e4Ls0Yx3lKupj3v4Guk88+ubNJbvzWxltazvypbKmMtbhCsBs1NPEayY4HnHMq25DmyZ0n4zOs9pxZ20cu8Qdq7cW/teeUa7ImLhLGviVulssbr9/cWs6tqVJMSqPja9H2t2VdpPtTyT/UPEOoPYfIlcnJ3zJmWt1tibHM5Mjsydn8hd9xeml/y+ml1neN03Oxs5xcrbTIpI2r1eRJfb2cvmr6k55LNTE5TXrL/intSeme2HrerY1DbS406z5Xd6ndWptpnPQScj5QzJr48PlMck7rux7doD93APkYeJovJZY5lb92hpDOn9J51DkXnef86do5B7d16MabUDEAwO4fgf15XuOzXay7IcYX0tPreB/N6eAlz21ieWp7YnxvaRcMAH+KC6XxIeccevSzOgCcYp1vm5zfo7ErufWzdh4kfzq14zG5dnKyt8h1LT94GTsrxyta6k3XdPxv6fn/buex2cz3kdvb4XS2jMcvvi6tCw3aWH1O7tz8lfZkjY6U5KjF9lMZa6zyNSt1afy8Ur3p+Ucq37Jnl3wM6UyvsTXW2uJa0silIX0Px+G8lkLy4TCiH892qaUdPzlM0zTzEfb7R4x+s6ir5ttLY1maCw0t+e1oMY7TrMzj4yNGV97LSjZQ2/4lfm8pT6sPX+KYr/ysIlQ7T9PPgdYfv5T5nJ3cuyqtsTwtcX21umPfE8jH+1rsWMy1YtKltlNbrjm/t8YLlsT1Xcfnrq3bS314Yy28Pz/TlSjFBdbEImvXWl8yzdcSC0nPGcDTmUwZow/XOV2WdO8aPn+ubWMMvDvbDu+X/lrq92neWyrFNzR+oFR+wvT0js05v7UWnS3bGKm9CRO8d9FcmkVdVb9H8JVyuqC1Ba0xr7dNUf/EcMoynlBb+5IOnGpLzwPdtEjrug6dP/qX0vve0pqv/dbEr0t2sBYDjHFm+QxK0qvSuaPWhpaWtXvJXlyy3Rpb0no2k+Jz0pi3+FNrfNrWMSuNU4t/dMmZLo29hnYu0cVr+zc1WsZlzRqKbV3reUOjE+l+HPTce8zmJthAqV6Nv9Qq8xobsDYGqBlXTd0l3y7nk5Xai7PE586Y83yFs2uQzc9svTSnpb6kMl/rXJqyJkZZipHVzoChT9JYaGJvOcI8SPLHvkOoqtbXNeN9jTh6jtp5Ik2f/BTp3vH+OI6Anecv7bvXsqElNPqiKTsvs6yjZe23yKCRJ51/55Z7bPwMoubraGzCNe3FGl+nti+vkW/hMz6dDwKl5x1pHfN/6+cKqa6afMe0+e/4WW1L3dr2SvXk6zehQnF/WuNT1NayVsa1eiKdF9OxL+loa/xaEweS2mm51sqiIa4n97d2Elmd6z26zsxsb0nXNdT2zWvsq5eQs2W1GEE4d839S6OKA5Dn5bLT6OcMY8yvAvDdSE8+5+s/CuB7vPd/2D99eAUAvPd77/1PAvgnAfy0UPafA2C89z+f/Pf/eO//ivf+x7z3v9d7/10A/mkA/6dQx/cB+DFjDD+oQwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIAn58pY3vT36HD594AH8BwG/13h9yhb33I4AfEW71AH6LRgDv/U8B+HUA/hssP8DymwD815p6CCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgj5vMOPr7TxfYV7P+y9nxR1/FkA6QdaDIB/RSuE9/7gvf9tAP4Ilh9g+TBRK70AACAASURBVEFjTPqRGEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCSMLwrgX4lPEdOH7kBDh/7AQAfsZ7/+c0FXjvD8aYnwPwa5J6vtMY82Xv/fsN8vxbAL4bwD+U1PWjxpif8t5/1FDX5wJjzCLNe3+6J92Py4W8Et578X6pTE6mOK1WvqWtUn7vPbpu/feYWtuW2tfUF6eHcSrNXQ5jzKmuvu/V5WJ9SeWolQGAYRgWaanueO/hnGuSKafbxphZXdL4pcTjek2dyM1pTY617aRjGs95rv7aWLbIU9LvErG9kWxBqa5UxriMZvxb15FEzg5q5c7ZW824ttqhoBMta3hNOy20rH0tpXWsbU8a99o4hLpLay+e72CLpXpjOaX+SPtATbdz6yUng3RvzZpZ4yusrbc0v2ttVCC1T5fWV2sjrXdNG865rP7k2gljWLMDkmzOudl1qMs5B2vt6be1FtN0/Ibj7e0tXr58CQAYxxGHw/Gbjfv9/nR9OBwwTROstQCAaZqw2Vh86UsvnmzasY/f9m23OByO18MwYLPZAAC22y02m83iN4BZet/3J7+l7/vTOIXrmh926Z7Sdd1pjMK8bTuLYTj7bcYY3N7eot+c0yS9r+1Ba3Ur2C239U9ynfu83e7QP81Fya/VjFNqd8J42I3DMLyZ3dvtduijsQPmdrNmF1p9ljW+cdxefB3k1NrJdO7cxi/8+s1mi77wzVlpvWvlTu1g/Du1NbkxybWf9jeU79yEc1Vmdq/WliRX7l5uLHK6mNKq17m+d5suauMY7uj7Hl3fqduJfY30zBv7NJIM8ZqIr4N9j38Hmxzbe2vtKd1ai3EcZ3mC7XfOYbfzePNmi3jqf/mX9yc7Ho/3MAzoum5mo9N7AI72oO+xmxxubmxkt4Ff9au+EfvhmK/rOnENp7ZeQzy+JbugibEAgNu6p33nnLbbbdGZ+TyuWcstaO1oqZxErS7vPUzfo+/n9ex2O3QN+6zmbAUA1jp4W7a36bxKMbWSXcqdcwFgY9yir5vNADfkYzaa2FFrunQ2iefKudDnc/l4jaZjVFoLtXNWKm9pr4jT18TIanqe25u0LPXNFn9LlPbanP3JnTNzvlLL3lJLi9GeMePfufPLGjS6E2jdx+PfmvPypWfilrO+xg6X+iLZvhZ5W/fReP3WbGmrLDl/Onddst0S14wpzudg2U7LuKZnBqmdOG9K6YwC1Pdy7Vjm5jf1mUp9brVNGt2pjVE8F5JP8LaI28+epRrOaJKvWlp3uf1e0r+a/CVK8kj9bDkrSrYvV1aSN6fD2vYD6Zk+rmuAi9bkOb+BgVS1Noar1ddcH2OZS76uRGpDcmfQ9Mw6y7/zONwcfdPQ9u4bX6If+6xe1uKjubhHmjeVK9Rbs42S35wijY3EMT22P4DpDLou8ge7vrjmU1ku9ZXW2sBau6X7fd+rzmwlUl2W2i/lkXRBa3tbxl9DLeYg6W4tFib5E+EMU9ojjm150T6ErLHf75xr9iFTpPce4r1K8kekNd51XfN8pPVofP/SHvMcMZ7nIhcTqJHTsVr+EiW/sOVsKOVL9WfNdajPWjvTS9ct97txHGEPdVuWpnddB2udUN+EvW/Xq1rfND5wiMG2+u3GGFhrF37aNE0YzTl+UBsbyTfUPHMPtq7mW+bal9q4NMaUYo07xetOaXb+fDc0aa2DceU5OMcK0rS2+NEaLrUJGl9cq4OlmMoa3z5mTVyjtf01rJEld+7Q7GHLuMfRDgYdXdvPNM76nGf0OI5Z6vO13j3UxJBLvl2pjeo4jROcswsbaa1+fDVyrZU/vl7E4rvzuyl+Vubs99moSU08JBdL1O4PafncfnMcj+O5z/u5Px2XqcUyAhOm414Q3RrHEbByGdFm2qMuzOqdJky9ycaIgsw11o5fLf81kc6CwZ6d97FjmouelUuU9L10b/LT4t40TTD2yeeyFt47xHt5GmfJ9SdFWlMhTXvmAfLn1PnaaYtRXeP8LJHrr0amWnrOdtTKxeeBWhxYG19Z7sPLvtbW1Joz6yV2Ief7aeI/tZiuxpbkxC3H7WRZSr8lnjs+IL1HlL5qFuxcLlYr1RVzTRt91tW5Hqc+WevcqHwi6OLtGv2+1P/WytRQ+2wMWuuMdSQuv5Q53wepyZLdkvzwFltWiheW0qV61+hOLi6RvscW37v0PCnJM/dnj/f6vodP5NP4yWl+jd3XnpdKMeycnxOn1+ay6zr0mL+brdGlUnxXQyl/6QwS32+xPcbM9zRNXE9b96UxjvM8hbRy/0syvI2zQakd7bxK6dJ85p5Bdl0HIzzL8JU4RSBn+9YQ5u74b1xH7m+c5na/67rsc5maLdHKrDmvLm1wXi8lSueHVjtS6pd2f0rTpT0mjH0J6QzY7iuseyYj3U/tei3mq12T6ThcZ68Nuh7W+GXn2pZ3ubW2u3Y2XMNaOywVe1s2nchc5+3dzwHGmDsA3yrc8gB+vLG6r0KOlP2Glkq89wcA/7ZQ1zcB+A8bZSKEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5HMFP76i59tw/shJ+rGT/62xrq9m0v/hxnrgvf9fcfz4S/wZMQPgdxtjvtRaHyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghnxf48RU9Xyjc+78b6/q5TPo/0FhP4A8IaTcAfmhlfYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEfObhx1f0vCrc+5XGur4qpBkA39ZYDwDAe/9nAPx8nPRU329fUx8hhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIZ8HhnctwKeI0sdXPm6s66vJ7/CxlG9prCfmxwH8rqe6At9qjPl27/1fv6DeTzR938N7L95zzgEAjDHi/TQ9V09M182/VxSXMcZk6yjVLd3z3p/ki+XM9SXUEeSL64zrStvK1aeRMSWuK21fQypLTuZ0zKX2uq4T243nKB4XjTxSem7uNHXFshhjFvWGuRysP90PWcLvruvQ+fo3tMJaiOm6TkzXkq4FqT1pTuM+X4t07UlrR9umdg1rdLy0djWy5WyK974oj1Rvre/xusi1U6qjts6DvpZk1MiXypX+26pXtfw5O6RljVy1tmo6mrNRufS+76vtxLronMuWSZHGL7U7pbVUs+NSW6HcGhtTsu/S3iGtRYl0LKVx6bpOTA/5a3ufltYytTmp1VfaZ3LjlxuvdE61+1g8d7HfqPGVjDELfc/5HkGWdF6lPTHMaVzGWgsAeHx8xOPjIwDg/v4er1+/BgC8//77p+tpmhb9vLvr8Pr1V2Zpf/Nv/i3c3y/H6ObmBl/+8pfx6tXxePPy5Uu8fPkSAPDixYtFX8N1+N113UxnQ1qunHSdy5+SztfgU7tlsNkM8JvlsbZ1fbaQlpsG+9Snc182mw16lPsppbWcJ7qNX+yvYX5y+65mriTW2J4Wf0lbb65O5xzs4Bb9HoYBvV+OUSgjjVNID2s0zXPJvq71h3PjMXQjhmGu77vdDmYj781xX8PvnC7Wzjml9Pie1u/UMPYTNpv7UBLeA7vdDfrCWaSkd7V9SUqT7Htst9N8zjmxnZIP773HduvxxS8+zO5/y7fcYRyPfU3nCDjbQ2mPjNN3k8V2ew9jzuN2d3eHIdIbyR7k9oG0Le3ZWRubiZkGi83m4amOY9pud4PuqYxzbpUtr8VWtOVr+1ktXlXDew+33eA+WvfeAzc3u+KZXIPbOtxvD7O0V69eojvIe8iafaDWz7Su7WgFG3czs3HX9IHX1OW2DvvdvF+vXr1EP/YXxznW+iWXlpXKa+NdEteI95TauiS2s0aO54ydldZaTa447yXxGqndtXEJYN6vdOy0ZwBNuy12NGVN25fa3DV7YCkuKFGbl8ClMeHYp8n5By11lcq77RbjdjNLu7m9RdcvfRit3rbKW4vDXhKHXiNP3P414+vvijW+1CX6e21abEPrWSSka8/tNVlaxk1zlteW1+bXzP9uchiGAXHyzc0N/I3Bfpj3b3dzg65bxuLCdSva/Vnr/2t0J7Xd2Xxbjw/v7mdp733hDt0hf87X7vu5s+2l1M5TtT0/vr/rPfr+fIZw/TEWZKL5393cAND7N9fw67Vx0JrOXMuvjblW3OZa5+oWWdbYQY2caWxBeoaQ/tbaLgDYbT12u3nM5+WLW2w2+ecfJdlr91r15tPgU2hikoGW+MU1nt9e0x/O7VUtcfwauXNGrUwOTdw/15/l80AgZA15+r5H1xsxVq95pmHMMZaW1lu6jn+v9fMDUuw0d84rzUtvLZyzs7RpGjFCfgYpXUv6VtMFY8xizFv0sebztMQiWsb+qEu558vyOXbZr/P7WSHtmvYyF9M/0Z91+JTU9+hdP/OPWvbGS+S/dC0Ay2foz8ElcUNtDOmS956WlaXl/Gpde5v7uWTb+r6Hyzyz0qCdF63uaPO1yNr3fvacLZRfs7eWyJ0FSz5Ozi6c/NrOo9TV47PN8zlN8yw5bSttM9yX/Btpb8z1z1oLayyc84jPddNkgfnWeJJfkiXgnH/aA4MsT+8ZRM99a3tdv3g2aeD98fmwRqfW+vnXyJ+WqfVVb1c9gHZZSvGqtTHA8Kz+LNeRaZow9ct4hIZUR33mfYDS3luLS6XyHvXKL8pp4heX2qWSLuTqvoavENPyHCwtU8sf15lmq+3DOR9OG0+9ZJyksY/b19SlzS/FQ2oqdenZuMY1/QsJaR+w1kZr/ZiWex8o5tJnGJKNEtedc4t50cgX8rW0r41p1u5JddXS17C+rrwNyPtHUlrb2r70rKaJL9aeOZf6fc2zpCZfsFEhpjPL49v8F817MOcySxtd+rvB9LcUU45t97lf+ZhZDWnfkX6n/cjVFZ6thPLDMKC3ur8PKfE2fNoSOX2P5zk0qf1bPO3zAI0fVtLRo94FPTn72F2X/zus0louj4XMmue8ElqfSpJf8p1DWvo3HXHea53NT3YVgp0XbEUsx5NUT2nn3/K69fC+LV4btxm3e+k6Sm2r5Pcu9TL/dwoaNO9IlNaLhhYfprWua9fxHP60dk/UzIUkoyaeF/tLQd/jPba03jXzn/vb8RKLsej72bslwFMsPsm3xs9vfS+EfDJ5nqcZnz+EUG6Rr0bX8Qr9JmPMzUoZ/udM+j+zsj5CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQj7T8OMrer5euPelloq8978C4Py/b57zrS11RfwfmfR/ZGV9hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR8puHHV/SUPr7y5RX1/Xwm/e9fURcA/JKQZgD8gyvrI4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHkM83wrgX4FCF93CTwPQD+emN9XwXw7QB8kv4dAH6isS4A+Cj57XH8+MpXVtT1qcH78/A555rLd113Ktf3vaquuM30tzFGLBPS07Kt5MqH9PhfTVsluVrKp/2Of+fGRFNvSc7cde2+VFdJ/lK9GtlL9Uh503RjDIzpcEwyUdr5v5pcXSd/ZyuXHrNm/uK1VCLInK63Ul9qa6zWVusa1MjSMue537F82vpa11maJ7Sp1fecfZF0di01GxfqT/U+t5ZyPIeM16Bl/jV1tebJ2cVYrpzdkPRLs8ZLuhPa0uzvNXvWoqPOuaLMkt6VxvsSXTTGZPWi1H6uzVCfFs1aK1HaD0o+XDyfGh0p+QSh7pwNlNDYxXQsY30P19ba2V4RxsNai77vxXt932MYjke0YRhO17vdDu+99x4A4MMPP8Tr168xTVNWzpRXr17hxYsXAICXL1+e/gOA7XaL29vbUzvb7fbUfpBrGIbTeKf/xnY5/JauY7Tjn+61XdeJfltujdTWaYt/UNprus5H62VZVto7W8j1sTaO8fhda+/L7Q+5dG29tTGq2aFhGGAGF+nlMX2z2aBHJ45VuneEPPF6bEGrh/HaidM1c7zZAF3XJ2kb+I0s69p516xp7dmsdC6rnaX7vo/m6Zh3u92iN8c5rfkIsV6lY5zbL+J7sX1O2+v7HtbaUz7JJlpriz503PcgWixjbPPSfaNke9N9tPeIzpTn9JodT+u5xOcv7Qc5nfHew3VOmCuL7imcme4LpbN9bg9fI3eOtN6cXdDYl5MebTaL/JvNFj3K/aj6h12Y07OMfT+gH2S90LSRyi6RW7feewzeLGzzMAyw/XW/Ha714wOxzG5wok+q8etTNDEZabxqe6JUb6jn2udZTUwqRRNHLZWT2iqt61z+XPsae6WNu5Zk6fs+659KvkJN7tqZtWQfW9HGka8V37iGLGvOwaVrrUyS3pZ8Ze0+pikbl0n1TRtDyemcxu9tRTob2OgcGhiGAb3CD03tZ81mXvP8VGu3po8tunaJf3ZNNHGolrhMXP6a8mjQxoQuRRvLW1sfcLmcl9g9SaaSnmjW3+YwYbPZPNV1TLu9vYW5M3jYjKd0Y4C7u1t0gxwLWNufGtrx0pyxJTmyPsrO434Ywy8AwM3NTiXPNXT5mrY/UDrrp/vCbrLYbM6vu/iNx9D3MNEZ4vb2Bp1wptDElDUypFzDNmt95pq+at+d0JxNpPqkOH6Lb77m3Q4Nuf5onzVdIpfk02y3DsNpnR7Z3exgMs9BYmr6pNWx3DMXCY0urT3XtXLtMyxwXXklXavNpaZPJZ/jOfYmqd7cOtA+J27Few+/83jczJ973dzsFnEx7bOH7WhPz9gCu90OZnOO92rrbelf6ttcg42x0Tgc69xud+h2m2L7Wi71G3L3Yj1KdSe1b5r9MytDD3RdGq+TYujH83HvyrHZONZ3jpX3M31qtds5vy8X7/Ld0v/rug5d183WhKRrtX29xc/R7Bmt85XzFZ7zHL62bGvftGeJUH7sJwzDXB93uxsMT3Nci2Nf4idqkXRs2I+LZ5XDMMAr1sjb4jna3yCsv/N4bjYbYLN8/V6zh7c836nlqe2V02DR98NT3mP8eRgGDO645re7Hfqn+dP4eWv3OY0fIfq8g3myzee0zWbAplvug7l607pi+r5H75frLnfdWS/UMV8Da1m7J19yVtCUq9v75b5uYGC6Dt0V42mhHt9joRPb7RYbO8AYgwHdk8772f1uu2m2iX3vFmts2AwwT/qn0RnV/tLP3wMxBthuN9jYuZ5fO95zzRjtc8SirnHOyHF8/2L+flHfD8CFa/maZybJJr+T53E90Cextc1mg22/zcqUUlsH14ox5uTRxKYXcnRmcabcbnfYuvL+U2ONb5b60MYY9Iv9yGOz2aDbnOVb82yxRdY1+8/aZ2RrYq6ltlwn7ec9erP0CXKySHUcfSzZP/Td8p2/OH8fvfd4lP/JzwvvMXZucfaNZZZkXMvber6Qw3Ue6XtuubENXBKv7K1f7Pfb7RZutyu2c8l1rt4W/V673rz38N3yHLHb3ahsXOkZQIs8l77XUSM9U3d9J5zBdwt/q0WWNc+OS7rqvUeP7mn/ic+eW3RP7/XnqD3b0O7XLe9e1erU1KWd81Ispu/HxZ59c3sDe8HcAsDY98v48s0NhsLfbvT9tJTl5hbDlNh/s6z75uYGkxDvjWmZn5hajGkmG5Zr5WZ3I/ZrY5exhDXvQVz6/DDV/5JPPvbT7NkysOzLNfe4S57fPjfXPA/m6jqO91yv7+5enMb7GjKtXReBaeix2cQ21uPu7hbDZMX6NLHh9Lcmjjv0YyIHcHd3t7AL2uf65HpwxJV4738BwOvwM7n9m1dU+dVM+vesqAsA7jLpr1bWRwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELIZxp+fKWNv4z4M83Hj7AYAP+CMab1Iyc/m/wOdf3GlbJ9SyZ9+clFQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIP77SyJ+LruOPsHwJwA831vVXM3V9szHme1sFA/B9mfSvraiLEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJDPPPz4Sht/TEjzOH485YeNMf9iQ11/pXDvdzRJdeSHkt/hgy7/34q6CCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgj5zDO8awE+TXjv/4Yx5i8C+LU4f3TFP/23AfDHjTG/x3v/BxV1vW+M+VkAf99TeUR1/mvGmD/kvf9fNHIZY34QwHcmMoX6fk7bv08jzrnsva6rf1soLl+qK8YYU/yd4r0v3s+Vz6VL9ZVkiPPnrmt9qvWhJmOoL1dPqX5N22meS+VdQ6ijpg81jDHZeZLyhvZa29WOmbVWXEtx22m6ds5C+b7vizJq12aKxgYEcm3k1sKaNachN67pevXez2SW1lhcT2m9r9H/eJ7T6zSftr6SPHGaNFchLa4nJ4v3Pqv/6TjldFzTL6mdtL3SdS5/jpysUnltm1Kd6boq7VXSGi/1PZ1baa67rlPvu2ttR9xWibR+SY+k39K9kr6W6tCuX2lt5NaddN1iTzX11Qhj23Xdonxt/rX70KX7dbrGJTvb9/3pOh7DYRhmOu+9h7UWALDb7XB7ewsAmKYJ+/0eAPDlL3/5lGe/32O/32OaplO+zWDxla90M738nu/+Zkx2OMkyDOfr3W6HzWZzkme73QIANpvNac12XXcqk669uG+pP3LJ2GrmN0Vrby9pN60zXUd938GY7intmG8YevS+r9YfdMM516y73cai7+frc7MZ0GPuW2l97dz6idNzup/z/8N1zp+UrluYyT/E7RyPxn3foXe9KGfal9ZzWVqfVCZtI7ZrmrFf1nvWsThNspc1GSUZamVa00v+Se5+aY7StFivpH0/tkm1PTXYvlz71tpTnrBe47qDjY771XXdKd05N7P78Zr33sNZh2maZulf//oBj49nWVJbG+83YSxi2x3W3jg5jOMB4Ru9XWew3+8xenvKF/oW15va/txa0MZecn5+fC8eJwCYBov9/jAr+9FHH6M7RLaw62YyxPtYSM/1o8XH0eiz9pzQwjF/u30q9c17D/TmtHfF9bXEGHJnS+2ZC5ivXWstnJv7x6n/p7WXaVut+huX7ftoHzHHNTSvoz/ZYqlfuTY1OnON87tzTuxnLOPauNjaeMIaarqZO/9e6vfH9VyrrqDXtf16bYxFqlPjs5TK5+RI96RanbX6SjJoZExlic/cuXQt2jKXjEWufo0tKLVxDV9bU3fJV1xvL8LaP6YF/z7XTnpd8wFz59r0vvRba/dTGS85B6Xla+O61u9Zgza+Xhu3tXtSqc2WfM+xltLy3nuVD3IpLTHKFlnSPSyn0yW/reUMOb83twnntRunz8/gmrol2VrXamm84+v0bJSLMcf3Yju6OEsePA6H/dPvY7lwZpLsWnx+TOM1qZ+a63dtT6ztf7Xy6TPDWswpnv/jz3OanH95XZMhh3avbPUFpDGU6snZkjU+SEme5+LSc4g2/gXMYx8hFiGRS6/Z1JwPd1xny9hdHLNI9/e0vERt7kv1auQO5QPx84A4jySjFBvQ2v3WvSO1mV3XZZ8t1tCev0q2UbNeSrLk/LwWXV9LyWfL5WvZK2txaGMMbG9hTIe4qmEY0LkuOzalNrpuGVNbe7bXnNkvyZ8rL5c5r6s4Jt1KelbQzqckm0YvL41DleSzzmGa5mOx3x9m8flQ5HAYMUzLd0ti2brJLWzJOI444FxOYzO0diEtH2zZPC7sszHXxXgIzypK+1Z8z1o7KyNdl4htR26e47GVzoc5H15zZr+Ua9nbmn7U6pb2z0v63XoWStPye6JcXtrPW+Qv2Zdr74M5eyv15dz2fL1eI96y5syZS5Pq7rr4GeMyXtr3Hbw/P2eU1uO7YO5zzu4070P5vPn3fgq1IM0W2+hLbJPGb5ZsQy6tRbfSutp8g/OeFcbCew/vlnvqNXDGPbVxTrPWorNPck4TvF++gyi9X7qGnE9Z8lPrMRQgjml5DzjnFzKXzo8lOTTnm9y+o2HtXtNyjlgjS7n+1LZchxadv+R9zOeM16RtxPE3qf3WNZHe08TOSv3VxEJadHRe3/K+dh4u9Vtq5/Tc/lhbAy1j1KJna3RyrR5r7Fzt/H8+Byfxusr/1345vvHvfB0aHc/V56P3wSS9qMmsQRsXA67nk+vjhef0kn6v9ZfTvKHaNIbcqmNS+yW/s1X+S2zn8YzQrk+xn9i6fltiJNozc0AbxzrGHdN5zJ/pWmQuyVe6n87jfG7ObQ/DADMs//Q7bSv8bUKrTAHnnMpvXMOa9ag5q5X0OZxzNUh+set7HN+tnO8VpWeIrneQ/pZgYzazfIPz6Lp5PX3fwVeekWptpxapvq6zSH2/czvzNlvjdVr/UEOaX/tsN2v/KvrSMsa1Zxjac8BznJdaubQN3y/7OwzLv3Op0RIXyZF9Btz1yfvABkM/YIDc5rX9/MDGG+FvgjYwT3/v9Tbmm8i82wjpp5P/KvkdPBsPYAvgvzDG/Kwx5vcaY77XGHNXqOu/x3lXOj8xO34U548bY/6emjDGmF8L4A8g3tHn/JlaHYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEfB7hx1fa+W8B/MzT9fJT2MePqHwrgN8H4M8D+MAY8xsydf13ye/4Ayx/N4D/3RjzrxpjFp+pM0d+CMBPAniVlI/504W+EEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDyuWXxUQ9SxnvvjTH/AYAfx/zjK+bpd57y8wAAIABJREFUt49+A8cP3Ijj7L3/S8aYnwbw63D+cEtcz5cA/FEA/7kx5n8C8IsADjh+mOWfAvBNUf74wy0h7ae993/tsh4TQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEPLZhB9fWYH3/k8YY/4wgN+G+cdW4g+gxB9EKfEjAH4S+Q+5GADfCOAHknJxWzn+Y0X7n2r6vp/99v48HM45VR1xmTV5NOWlvFK5kGZMXnXCPe99se2QL66rVG+tnnAtyRjScvdycramt8i8tg5tuXRcpPHWtqOZx1mayrSU69PI6b1H13Wn3/GaqulfTCgX19WSX1NOWu9pWovMKUGn4vWXG8NL11yMtKYk/ZbaSfNodC4eI63ssf6nY6RpJ5YnTiv1OZVNGvNc/1vWW8lOaxDXbzRetbo1e0Y69lKZ1CbndFRjk3N5SjqoXSthrdfGPVxr9/mSDUntWnwd37t0LbeUX2urWveftL8aX0eju2ld2nmSaCmbm+fcPraW2vpJ+x/ad85lx8laC2vtKV+QM03f7/cAgP1+j/v7exwOBwDA4+MjhmHCw8MLOGcRmvnb73+MpyLY7XbY7XYAgNvbW1hrcXd3d+pHaNM5N/OvJb2ordXaPljS1RYbE9/T7IPXJK3bWgfv3SINtr3u2pil6yuVxTkH4+Z7YckOp/u2NG6atVOb16DLpfKlvT6tT+I8D+fjtDQP2jNZ4Bq2IyasodxayZ25jrIBsXjhdsk3LNnumh+g8Z8kYn/+knP5ZOxibR11XCdHzd+M04KcuWtr7cwHie21c0604/EajddXXP+5HYtpmss4TQ7Onc9EQXeGYUDf9yd7nV7HOtZ1HXaTxWazQRyi2u126LbLkGDOHy6dseOxSfsVxsVai2maAACHw+G0h+33ezw8PODx8REAMI4jxnE85XNbjxff/sWZvfrFn3nExh5lv7m5wXa7xXa7PfUr7Hd93z/1+zh+wzAsxtJaO/NDa+cuqY+5sdPGdUp1G2OAk+5EYxzpWSsn/6OT9i4L2LNt0JwVYjTnqXTfjn21wZvEp/OLsUr1bQ259i/x+VNfVBPHkOxCLl8tvTYm0j58zdjFtdH6BBLP1RdNHEZL13Uqvzltv7QvS3WV4g9p3dJ1rv44X0mma/jjUkyxNG4t6yKU19jveE/QnJ1LdZXy1+qv5cm1f027qWFtews/3B59wTj9cBgxPPkUqYyls1TLWijF53JoYl9SmnYuW9GW1+hISV7NXr9GLo0sWhtXQtp/c32UxqHWvsafrFEr3+KDtsqh1QtNnjXrKt0re2sXc+achbVGPjd63XPCVp9Xm08TH0339zReWGvvPEbB1h3T+77HsOlFG1iKP+TkLPm2a9ZhbfxazjrWnuMG3gPOPZ2XojzjOKKbzn3QruEW0jHK6dVzxpxqZ5A1Zw6Jmj2s1bUm/qu5r20/lzcXNyrtFaVYU8w0+VOsI/D4uMfhUH+XI/zuum62poONaPF1noPnbLd0BsqNWRyLS9M/DVzz3PkuWOP3pPNk7dzXf3x8RHfoVLZ74dtOdhEPGccDDr5Piy7q0cREr03xmch0jq0G9vtH7G25LzWkmGhNljhPbV6u4VtpnjP63dnOhluPjw8wxizs7/39G/Tjcdxye6cdrVDuHodNL7Z/jbNJatfsxuFwSGV4QD/O5yy0vbb9a+2/rXlb11Ucw4/bukZ8s+U8H6jJr5XF+afnmBHjOMJP/WLv0sbGauT8ltpeGbfvxwnOBft6bH+aJljdK4FZeTTrvVRey2pdmeJ+H88f+/0eo1/xQoCSljkqYTfnfSQ8XxrHEebpGeT+cY8x2me066KWT+Pn1uoCgMkv9/T9/gBn28dkcpJ/MMFPS13KnrHGuS4ABtM0YkyyX7o3tMb3avcukac019MwwVoH5z2ci57lOYv94QA7TVfdJwFg8tPCb9zvH+Hs8bmw3Y8L3+nx8RHT03ptseNDPy38gof7B0x2bF4TUp6gZ2M/Jbr55MdY/TOENfN9LR1Jy699PqOpO+aS54nDeEjib8f3EyZz+drTll+z98X5WmNca2IrxhiMbkzsHp70U1XdRbJcgvbvFVLCnjv2o3imlNal1NY1+lg+J06iLzkJ3XbOVcfjOWJMn+S4xnF+53tFbn51dRz7ut/v4TN+26Fb6tThcADcMc7fHw5RzC/INML2xx9jN4pzrn2HLpCzPdfwPYD1seaYqZsW/m88VrU6c/YxG7uaptlzBuB4xnENcZfW2EgpprzmfYHWs5E0xtM0oXPPd7Z/G/WWytp+eoofn9OmaYRZsZ89Z7zZjct9d5om2Cn/3n5M7X0ibVmNjmpth2Ytat/1kW3Y8j36499f6N7dy6WPzolzYQrvwk8+PM+ev+Pik/3FHSQ/Y8LU6d9raaHl3aRB0kEr24w160cjg8S11t3oZB8q9gGe41nyNXiu94u0rBmPycu6AyEOE7jkHcQ19m6CEKOdRvipruAt+7Ek2yz2tx8xTWn8a48Jc9l88TMS5DlYd7IkAPDvAPgJYPHBFURpVbz3PwXgTzyViVdAWq9J/pPa89G9P+m9/5P67hBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ8vmCH19Zifd+BPD9AH4/jt9vlT7CouXfBPD/RuUD5/9d1bne9KMrRij3cwD+9UYZCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgj5XMGPr1yA9956738EwHcA+CMA3uD8QRRTKJrW8z6AHwDw9ZAE+SMs6X+I8od8vwjgn/fef9DYHUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCPlcM71qAzwLe+78B4LcaY34ngH8cwPcC+DYAXwHwEsBHijp+2hjzTwD4U0/l0g+wAOcPrqTp4d5fxvHDK7+0ph+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHye4MdXroj3/gHATz39t6b8zxhjvgvAfwTgdwK4CbeSfwPhYyzvA/hPAPxB7/24pu3PAt7Ph6frOgCAc05dJocxZpEWykr3cuW994v8ORlqshljFnXlZInrysmdthd+p2XjvpSQ2sy1sZYgi9Tv2rzk2jbGqOTKjYVzTj3HpTzht3MO3vun309p3sF7g2maYOwxf9B3qT9SvaX0XJ5Sv+J7ab6cbDm96PtezB/nLa3rElr9TfOXrlPW6HWuPkleqf614yHVGcui1V1Jf1I0elGyEdoxbxn/S23Qc9df09d0vNfuJ3Fb0u9cG9pxz927VG9rhHattdU86fWafKXyUl8v8UHW0roPl+x+uveV2pDWcuoTtfhTEpfok3ZOS3ogjUeaP6Q752CtnenoOB5d+HEccTgcAACPj494eHgAAHz88ce4v78HAHzwwQe4v7/Hhx9+CADY7/e4u+vw+vVXZjL9wi/8Ldzfn8fl1atXAIAXL17gG77hG/DixQsAwN3dHW5ujseO29tbbLdbAMAwDBiG4XQd9ue+79F13cmOl/REmpfUh9XM/dwviuua65hmH6ntLbW02PZKuhOSUt3XIpXJjdexjZC+bLc2Lmv9mWvun9fcIywcrD2WC32z1gJ2eQZb27Z0nquR6lxr32ZyjhbOzfe1w2HEwct1tpxZY3L+u1Z2aQ8u6U3Oh5TWUZjTnI2t7XXxupCuw/krXE/TtGgjpKd2PVyHdGvtTK6QJ04PZbZbh4eH3UzWDz7YY78/zl2wvQCw3W4xDAM2mw2Aub2O83VddxzbyWEcp5kePD4+4mCPdj22yzmfXWsv0n6F39M0nfo/juPpOoxjfG+/35/u+c7DWQsT7TtxG9bahV0MdXVdd9pfh2GYrYd0jOL+X9P3WsNCt72DtdNs/9sfDuie/IV0nWvPsK53cC62T4D3DvEyz63L+LqWp0Q63n60sHYCYE772jRNmEz7nqRNr8XRFuNmzntNiNGM4wHTXhefa6Vm0yTfusUHyZ37ajGKWrrGN2uVr1Q+tgtd1z3bWTPdH68Vi3kuu6PV91yZVK/eBjmfoDbWtTN7SXfW6OUaX+laMaJc23H7pbmPy9fOZZJduUbcW5JZKuOm6bSXBx4fH097YCxPrl9aedK4xqW6r42Pl2SQ6tEQ20HJbmlkeRf+0NrnOaFs7AtdEiPVyqWNmZXiBy3tpfcu2WPTMbpmTK30fElbR+7eYbQ4HPaz+x999DEerMF+H9sF4OOP36A7PI9/9i4o6aET/n8t8bnoOWl9lpRSOq9ry4yTO8VSAYOxAw7jCNudZXvzxqAfy88pS+k5u7JGpy7Vw5qtSX+3PD+XyrTEVq8VB20ht1+X/AtNumQjY78xxIjS+ESujd0OePNm7qu8fv2A/d5U4xJxXEby3aQ4Ru2dAW18M9X9a54NSv65xlfR5m+JCcd1AvNxXLtXat7fWOMr59oq+fct9mfN/l6zSZpnQm70OBxGINrb3nwMmEO7fwGke8SRjz9+g/1Q1jGtTQHq5xCtD5srH+rI9eWwkd+r0e4XgVZdk+StzU0p/yXPXADAbT0eH89+ojHA179+vH58fJzl/drX/MJPTGXYTQ4PD/ez+69fv86ONyCvd80c5+LybusE2V+jO9RtZG3vyMkiIdmr1rhhei+1V7nyl9ixNfmDbJfWUZMlnhc72tnaNga4v3+Dfuyz5Z+L0lk6ZTsu4zX3929wGNe/hr7m+etzIo3FdpxmZ08AePPmYxwOw0X7bC1vyYfT1mU35/dRXO+e3k+ZYNyxvo/ffIxxzNu4gNam5Gxiy+84LZY/8Pj4kJW55B9Ogz2+bxs1t9/v4WxZf+Oxng7Tk89y5uOP32Dczutoice3nN1a7OO1zrKleLPdHP0U13Wn9xjGcUTnDO7v7zE8PWu/Jsc253Pw5s396ey/OaTr1eOjjz7CuJ/PkeaMOAx2sSd//YMPME292r/RrBG7Sdsx+PDDDzBMg1hHCxpduoa+PUe+S9ZGjWE/LmzLw8MDRjtPK/nWrXHg9PyvSY8p+VGa9LXPHcZ+Or0381Qz9vsDXPJ+lkQplnDpewal3zW50jalvGM/Lt7PGscRWBl2XTP+pbzD/nB6t+Is3wGTsA2+ree8wDrbcg1qay9l7MN7S/H4tc3v6Mbo3ZFQxwGwsiznNuX8/WlOzzw+PsLCncpPyb663z/CJXW2xNZi1sTRtHW2rtGxOyzGaprG099MtbYfyPWnP6TtmeO7bX3Zvj3H+y+lWPW14ohAXh/9VH/f4Vpr+NL3DFqR+jxNE3Bld7UWl6vpzjAeFvZpmkZMY3ncLx2rlmdzl5TJ5VuzpkL+vp8EfZ7Q8qhUant6ehYU3xsPB/hCxaMoy7jYG/wo+xnpPn7tPVxjU/wo7W+SvAe4cTkWz+V3tMZTgJIPMF/4h8Mebjr35W3FqN6mj9bSbu6+xgdPy07DhGlK/+ZhDzcN2TKXsKauyQ2LfXSaLDBN6n24lkf1XNaGv0mIYlDTiLHT+wPkeeDHVz5heO+/DuDfM8b8pwD+JQD/LIBfD+DLAELE2AL4FQB/HsBPAPgfvPcfvQNxCSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgj51MKPr3xC8d7/EoAfffoP5viZoy8B6AD8bc9PFRFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQchH8+MqnhKePrbz/ruUghBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIeSzAj++Qj7VjOP4bHUbY1T3j9/FwaprbXu5e7m6cum5fMaY5jKl9DRP3E5M13WqdsJ1rt5cuhZpfGt1lMbskvQgS/yvMcDxZ5xmMAwDBjNk69Kmxf0vjbtzTpT/XVCa89b517ahuZcbVy1pmWv1paWumr7k1l2a1tL/2J62lCv1Ka6ndB3r9bX1SKrv0nVUspuX1r3GvgeCTUxtWC5fS91SHZr8mj0up8eX6II0D5o61q6DtP60v5q6tP3NyZaWj3+v0ctr2r41rLGXsW2Mr0P/43TnHKy1p3vWWkzTtLgO/wLAZrPBzc0NgKP/2/c9drsdAOBwOKDrDtjtbuCcO7Vzd/cCw3Ccr2EYcHd3BwC4ublB13Wz9oNPbYyBtfZUZrPZAAD6vkff96frrutOutD3vbj20+uSLmrXiB/tbFwAj4eHRxymvlo+165mv6hhvYO1c12fpgl+6k7t1NaZ5C/XxsVu3OI89ObNPfpxvlekfSn1s+YXxjam1W6XqO2Zuf0vrtsadypjDOD9cX30YyfaJe3eoLGpLX6jtI9pz572MGEcwxo4lr+/v8dh04v1pXr1nGdRqe7aHlzTIbdd6vjDwz26g+4cme5BqY2WdDm2o/G9VN7YLlprMQzDrA6pTKmd3c7j7s7O5P3Gb3yF/X5pX4PdDesinaO4/aNdd/De4dycwTRNGOFO5aW64j0hR+ijtK7SvU/S0TBuof3dbnea82maYDcO25ubWVlrDNyTzXXOYb/fL2QC5vNirZ31McxdjUv2iDXnwZjT2Hmf7H3Afr9HdziI7Wnbdc4t6n14eCiur5q82thMLkbkJodxnBAnPzw8YD/WZdL4qDU08RQ3esEuPaI7lP2c3J42j/3IOnbNs2k6JjVfPfYZS2dRoH1/TbkkhhL7dO/6LNESY9XUFdDGAta23eqvpWna83ktPR6/lrGs2eiWeJvGFpT299pa0ZLz00ttv23ic2GKxsaEOgLpPPqTDTqnO2vhI/8klEn3NEme9ForYyva9aqxsWv9iTX+/SVcavda12RKOo+adXiNWICE1v/QnrNyNm2N3b7kbJfeL+270pk+R+5MI+ad7KLOw+GAcbM8txwOe+Dx/LtUf84OaeUKXCMmqiG1HdansTJgv3+E2V9/rb9NcmtpEcub3Gz+rT2e/6w/5xnHAyZhPNau9xabV/NBcrGUXPlr0Ho20j63UK3jjH0u+bNr/MGcbUr7VfNRc+u467pZX7z3pzi61OZu59H36V5l0HWmaE8BnPQ7jt2E39J1Se4cped5NTS+cyut8l9qb3Njqd3HNDHslFy8TfucRCI9i2j8gFx8WpKj1bfPyVaqx/dL/9w6C2PrMRIpvZscnJuP9TiOGH1+70+p3c/1R2OHmmL1yX4HHP2gg1/GOFuexWliNZfamFxb19zjvPEze2kMYJ/8I+fm7Vpr4SZZppNswng/Pj5i//TML9V1rQ/Zku6mYwxyHic9xiCl9rRxu0CrvYn3vlq8Kk7LnXk1sZuWtIDmDNYaQypROstrnpNb52Dt2Zf3/jjPg/K5d07W3POeUpkWrHVwzs/00zlX9OlyXOKHtMQOW/2T/FlcSmsb09qz+NJ1OsbaM+jxXYZg147lrLOwT3vs4TBiHJf+Scn/jPuuebaYjlPLswdrlvGA47sY+r309OzQW8E/OMAJ/c/WO05RHefY5NM28SznuBrXiklrifs44TimDv5kG5xzgLMYDwe4pz05d/ZZYwdCmzHjOMI/2U8zTYnO+Nkc5d4RkPDOwlo7PytYi6DmpX0lZ0tiv2XepzTuNcJNblE+95w1x6X6camtflt1t649d5gSe2UwjuNJT9I6U5t3afwmdx4r5SnZ+taYeK4didFNi3fixnEErP6dpxYureuSc95srRsvnCccumSfWxMH0faxKKsN7+BE79LYZax4LaW4ZtDF+D2g4A/G71LEayzki9+tCu+gAjjF1mJfNl5vLf5sSfZ4Pzomz/uVW2d5/yLsKeU6vPewsPA+ee4Z5e98GM+4rfTdtuW5PtXB2nteEu/Cfykx9elefvS34hiRdn/TnCG7kz6c7hT1oYQ23rNmzEvxO+ksXkcfJ6qdfda8L3LJsxZNPVIcLM2avs/Yyto+1J4LPNU+a0f7DLpVtkvWf+lcfule19pfqd6Wvknr65gW6nlK6zp0Dc/C11Dre82HXvOOQponzub9eSyeUor1lfbh9He8x6U+RUhP/QkpNhnHR9P3mC711WMusYNrytTG8pL2c2fLljb0tqjsvzzH2NXKxNfWmCTGcHy/HpFfXVp317LJS7/ueB5Z2JrLHsOSFbzdqBshhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR8QuDHVwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIZ9L+PEVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEELI55LhXQtAyFq89/DeZ++tqa8lnzFG/J3LF4h/x22m11L5Nf2Ky60dlzXl0n5L6bk8od34XwBwzs3ux7/jdGOMOLaludDQdZ3Y5rMyTXDOwTl36odzDs4YTNMEY8vFSzqWy6epK0Uz361rDEB2vNeu15xsNVp1JW47vZZ+r03XylAqH99L145GltKa6DrdN95a11WoNy2nXaPp3KT2XLrW6H/JvklzEeqV9DS9Dr+tnS/6a+uMBqnukq6ttZu5edaSGwPNem61kTV9CX1I11sgXiu1vaomfzpeJVskybUWqR2p3kvtsYR2TtO5Kely+lsa11yf4zJxeqjDWgvnHKZpAgBM04THx0cAwDiOePPmDQDg8fERX/va1wAAH3zwwela4u6uw37/apZ2f/8G9/dnuV+/fj27/8UvfhEA8N577+HVq2PZ3W6Hu7s7AMDNzQ02mw0AYLPZoO97AMB2u0XXdRiG4dTPWIdDemxfWyjpj7UW3s/n4jjO5/Zz83qttZ/6oF3XwcLBuWCfj+1M0wQ/lffBlj1VwvVuNh7eA947pNXk+l8bF8k2lfbE1FfPca2zWiq77Wwyhh7TNMIe6nNfsrW5McrpS6svpSkT6J2rroG0Po3tW2uHW+cy53eV8sfZvPdHO2Dza6fWV4mu6055g60D5n6XMWbRTrifs/3pPhCXTf1u5/xiTRpj/n/23ufXkiXJ8zL3iPsr8+WrqaIaupvpnhYj/gIGwY6RZomEYAUbFqyRQEiwAYkNy1nMFokVG/4CEAsk2CCxArFjOwumkZqi3qv3MvOee8J/sIjrcSw83NzNI+Jmvur8fqRXeU6E/zB3Nzc3s4h7apHHWrvcS3uT71HJl7j5kYb4MvO2az5oizSO1L80F3z+eJl0zqS+hmFYzhHvPfk7T+bubjZwr2UePhiyV7uU52fUOI7F+Un9JtLapTLafZ5fb82RlNfp2TvzPG7bzfVF0jVJzqRz+bV0iBz1z7Rn6vb7et/P+RD9nHN6xyD5qWsffiXx/L+Zrcrr8/2Wo8l/1ORtxV8le6Npd49MpfhNin96qOWV9tj8t2CPn1mrU7rnvT+cIz5ybkvk+RBu43ri6lLcpdW/3N6XcjQ1euel5jdKe13T/9HcCfcVSj5Fq//ee3mZPfaiJVtJjhADGSGXUcs3SPaCy1IaQy3XWNM9aVzDMKz2CNcZbZyg8T+050spdyvlNI9ydE/31NFQ8g3PQCtLTX9L+tqav73rVFpvac41Y8v3V+v5Tmnupf55fKFBew681TM/Kf6VxlCzHZw5N3abv2gjxbiOGaZpIjOV2+P99MZ/PWj0pean8nXp9oEibXIJxL5LeQntHPScXT3+UKkMt+nb2KTPn0zXtbF+LksrnyPJyQkhNJ8PHH2WX5NVaruUsyG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AAABs07D/8YzeWmm27z+eYrbQmnrUqq3liaDBR8ZUDQAAAMD+dpAW/55n8S8AwGFTGlfn/LLy1UaXnNdNPgdHT61n17U1m3fblb6nT/yHpMxssGeYxXO/vCtjAADAYVaH59a1NRp3HEMlW2u27r2urQ7XX2sAcHS1xl3AAbbZXeyNAk9Wt+3lHXDhKwAAsE+cvenrxl3CjpSUnLjqFeMuA2DP1Tq7rq3RvGoMlWyt2bp2XdtGk1gAAAAAWPwLAMD+cPLqP8ig/6H0On+V3sKrMxx8Mmuf+bkRASwccbWzrqk0Lt+VrhvNqzJ17N9lcfaXc/6/w6Wf/e5b0+u8Je3Jh+3KWAAAcCjVxfVtZWLv67gdpXFyXVutc2OoBID9SvjKxdlJuMleBaK4ww4AAPtIHdyYrRfJ7E9VpiNwVG00EbQPb6WVcnxdW60LY6gEAAAA4ACw+BcAgH2i2bo2zda1mZz5pvQ6r8viuRelDs9k7dqS8yEQrYmHpD312LHVC+PXTNIfadu9dVgTM09Jd+HVGQ4+mtFn7y7O/mpaEw9NKY1dGw8AAA6VMpXU+TVNdXg2aV49poI2Vg/IPBEA47P//mLkYDlYfzUJAADsAwcpzMQlD3B0lXIstc6uaavDW5LmHcZU0cbqyGRVkqRM7n0hAAAAAAeBxb8AAOwzpZRMTH1ZWhMPydyZZ2TY/3A2erhPo3mPTEw/fiw1wn5QykxqPbumbWkO/0671H8zUye+L/O3PDOrg4+SmmH/I+kuvDKTM0/elbEAAOCwaTROZjhYO/8y6H8gzfY9x1TRxpbCFtfa6CGIABxdoncBAAAAYMRGTxce9N4/hkq2Nux/aF1bKetrBwAAAGBp8e+oQf8DY6hkaxb/AgAcPY3GqRy7/AWrHrRwkB7uA5deaVy+rm3Q/8iujtGefGhak4/I+eCVZCWApTP7kgyHZ3Z1PAAAOCwazc/KaIhor3P9WGrZSr/z16u2ls77G627jascAPah1rgLOKA+Go+ABwAAtqE0rk4dfmZla9WerS4tLKQBGJdG8x4ZDj6R1f8v7nb+PBMzXz2+ojbQ67xp1dbSRFCzdfdxlQMAAACwrzWan5Xh4JNZfc+n17k+E9OPG19RG7D4FwDgaGo075SJqcenu/DKWDMCazVad8tw8E9Z/d9Gv/vWTEx/xa6OM338e3Ou8zdJ+mvaa53N4tlfzMzlz93V8QAA4DBotu+Xfvety1tLAYb9zl9n0PtAmu17jbO02wwHn0l38c8yer3dbN17PAUBsC8JX9mBWutnj7sGAADgYDl59R9k0P9Qep2/Sm/h1asWdy/dXNyYzEeAcWlO3D/97sofuSz9v3rQfXv63b9Pa+LB4yztNoP+P6W3+BdZNxHU/tzxFAQAAACwz1n8CwDAfteefsJy+AqwWrN1bfqdv1zeWrqe63XemOHw1jQal+3aOI3WXTN57F+lM/fynF/XtTLe69NdeEQmpr9818YDAIDDoDXx0HTmfmukdZj5W5+b41f8ekrj2DjKuk2t/czf+rykLmZ0/qU9+S/GUxQA+5LwFQAAgD3SbF2bZuvaTM58U3qd12Xx3ItSh2eyNoDl/KR9a+IhaU89dmz1Ahxl7YmHpZMXr2pZ+v/z/K0/meNXvDiN5lXjKi1JUodzmb/1x5MMMjoR1Jp8xFhqAgAAANjvLP4FAGC/a7U/N6VxKnV4y7hLgX2lNfGQdOZetraxdrI4++uZOflDuzrW5LFvTnfhT1OHN+X8tdnSmoGFcz+XZvteabau2dUxAQDgIGtNPCCN1jUZ9j+y3LJ0/jwc/FPmznx/Zi7/mbGtu63D+czf+twMem/P6NxLadwhzfbnjaUuAPYn4SsAAAB7rJSSiakvS2viIZk784wM+x/O2gCWJY3mPTIx/fix1Ahw1DXb16XZvk8Gvfetai2pw89k7sx3Z+ayn0qzfd1YahsOPp35W56dYf+DOT8RtPQ7pNG8e1rt+4ylLgAAAID9zuJfAAAOgmb7/ul33pTR80I4yprtB6aUy1Lr2eWWpeu53sJr0mndK5MzT961sUqZyvSJ78v8rc/J6gdpJSWp85k788wcO/VLabbuumtjAgDAQTc58w1ZOPvTWXsOXTPovz+zNz81k8eflonpJ6aUvfuz9t7im7I4+0sZDm4c2bNU3+Sxf5NSXHsDcF5j3AUAAAAcVY3GqRy7/AVJmVxuceMOYD+ZPPbUnA/GWvlZMhx8MrOnn56Fc7+S4fDMntVTaz+d+Vdk9uZvyaD//g0+UTJ57Jv3rB4AAACAg2hy5huy9p7P2sW/nfnfT639Pa2pt/imzJ7+lvS7bx7ZY/EvAMBR1Gzde9wlwL5TSivt6Sdko+u5xXMvzMK5X0qtnV0brz31mLQmH7lmnOVKlh/a8r0Z9G7YtfEAAOCgm5h+/HKQ/Opz6KW5jVrPZfHcC3PupidncfbFl/Rcejg8k+78H+bczf8u87c+O8PBJ9fUsvK+0bpXJqafeMnqAOBg2ruIMAAAANZpNO+UianHp7vwyghfAdhf2pOPSGvy0el33pB1T7NKL9353013/vfTnnp02pNfktbkF6SU6V2vY9B7X3qLb0h38Y9Th2eyelHXkqW6mhOfn4npL9/18QEAAAAOk4npx6e78McZ9N6R0acvriz+7cy9LBPTX5n25Ben2b7uktQxHJ5Jf/GN6Sz8YYb9D2Szez4W/wIAHD3N1j3HXQLsS5Mz/zrdhVcldT6j13Pd+f+V3uJfZGL6iWlPfXGarXtc9HjTJ38osze/J3V4OqNrBurwpsyefnqmTnxXJqafnFI8FxkAAGYue3ZmT3/nBufQSVJTh6fTmXt5OnMvT2lckVb7QWm2PyeN1jVpND8rjeYdUm57sO3tq8OzGQ4+mUH/Qxn0P5hB9x0Z9G9YHnd03mWVMp2Zy56TUpoXc7gAHELCVwAAAMasPf2E5fAVAPab6ZP/f+ZOfyTDwUezPoClJumlt/i69BZfl6SZZvveabbunUbr2uWJoDum0TiV0jix5Ti1DlOHp1OHp9dOBPXemTq8deVTyz/XTwSVxhWZOfmju3TUAAAAAIebxb8AAOxnjeadVm15kA+saDSvyNTxb8viuRdmNLhy6VrupnTmXprO3EtTysk0WndLo3mnlDKTZutzMjHzNdsbr3FZZi57XubOfH+SfjZ6aMviuV9Kd/4PMjnzDWlO3H/XjhUAAA6iRvNOOXb5z2fuzPel1nNZe027+hw+qcOb0+u8Pr3O69d2UmbSaJxMykxKmUrSTNJI0k9qLzW91OHs8sMM+yMV1JHt0WvqmqSdY5c/P83WNTs9TAAOMeErAAAAY9Zqf25K41Tq8JZxlwLAiEbjZI6d+vnMnvne1MGN2WoiKOln0HtvBr33btBTScr08h/lNJdf5yeCUhezftInI20bLaysKeVkjp36hTSaV2/v4AAAAACOKIt/AQDYzxrNOy+/22j+EI62yZmnZND7h+UHpIwGsKy8T2q9NYPe2Qx6706StCZu2nb4ytL3HpDpkz+ShbPPW25Z/9CW4eDjWTj3cxGWBAAASbN9zxy74sWZv/U/Ztj/UNafJ6/e3uC6t85lOJjb4LObfH6d0f7PXyuUxhWZueyn0hKcCMAmhK8AAADsA832/dPvvCkm4QH2n0bzjjlxxUsyf+uPp999W7Y9EbTSXudS69wm+zez1e+FmkbzHpm5/AVptu66zX4BAAAAjjaLfwEA2K9K43hmLn9+Us+fVzaadxljRbC/TJ98dmrtp995Q86HoKzY6Fru4tZjTUx/eZJeFs6+IOev31YHsKyMVVe9twYMAICjq9m6a45f8eIszv5GuvO/n6WQ+o3mRbZeI7v5fMuFnm+vnLsnrclHZvrEM9NoXnmB3wXgKBK+MmallEaS48uv6Sz9O2km+USt9dZx1gYAAOydZuvey+ErAOxHpXEyM5f/QroLf5DO7EtT69lsPHlzKRZQjS7MqkkamZj5ukwd//blJysDAAAAsF0W/wIAsF+1Jx8x7hJg3yqlnZnLnpfO3EvTmXt51l/L3fbJXFg45u2bmH5CSuNUFm79T6l1doPxVoewAAAApUxm+sT3ZmL6ienMvSS9xTdk6dw92fj8fTQ88ULmWLYKXFza12zdO5PHvy3tyYdfYOUAHGXCV/ZIKeXOSR6a5AuS3DfJdUnunOSKTb7yrUletkE/P5LkLUneWGsdXJpqAQCAvdZs3XPcJQBwO0opmZx5ciamvjyduf+Z7sKrcz47d6uJoM32b/b5rRZplbQmH5Wp409Ls3XthRcPAAAAwIYs/gUAADh4SimZOv60tCcfk8W5l6Tf+askw5W9qz+5a2O2Jx+e5pX/PQvnfmHVQ7YuxQNaAADg8Gi27paZy34iw+M3p7vwqvQ612fY/9CqT2xnvmXUaKD+SvN02pOPyMT0V6c18fk76BeAo0r4yiVUSrlLkqcleVKSB47u3uKrW8Ud/9Ty/tOllP+a5Bdrrf98UYUCAABj12jeadWWSXmA/aw0TmbqxNMzefxb01v8i/Q616ffeVuSzupPZfv/Px/9/PlbRI3mXdOe+pJMTH91Gs0776xwAAAAADZl8S8AAMDB02zfM8cu/+kM+h9Pb/F16XffkkHvvUkuzXNuG82rcuzyn06/9750538vvcXXJ+ku793JOgEAADgaGs0rM3X8WzN1/FszHNyYXufNGfT+IYPeP2Y4+GjOhynuQJlOs3XPNNsPSGvi89OaeEhKmdi12gE4OkqtW+V8sBOllM/OUkjKU7IUcLPZHbSN/uGX5fZvrbW+bIO+h1l5zPHSz06SX07y3Frr3MXWvh+VUt6d5L6j7fe9733z7ne/ewwVAQDA7qvD2Zz9zOPXtE1MPznTJ58xpooA2I5aO+l3/z6D3rsz6P1jBv33pw5P77C3ZhrNu6XZvu62iaBm67N3s1wAAAAALoDFvwAAAAdPrf0MBx/LsP+x1OGZ1LqY0rwqE1NfuvtjDc+m13lz+t03p9/9u9ThLSOfKLnsjm/c9XEBAOAwqbWT4eATGQ4+kzr8TIaDm5K6mJpOUruptZ9SmknaKWUmpXEipXEqjeYd0mjexUMNgUPrfve7X97znvdstOs9tdb77XU9R0Fr3AUcJqWUkuQ5SX44yWQ2fWzN2q9dwGc2shLAMpXkmUn+dSnl39Va/3wbfQAAAPtEaRzPzOXPT1YFZDaadxljRQBsRymTaU8+PO3Jh9/WVuvC0kTQ4NMZDm9OrQtLk0DpJHWQlGZKWmsnghp3SGneMaW4bQcAAAAwbo3mnTI587VJvjaJxb8AAAAHQSmtNFvXpNm65tKP1TiZienHZWL6cUmS4eCmDPo3ZDj4eOrg5gx3/NAWAAA4OkqZTLN1bZqta8ddCgBHnL/i2CWllCuS/K8kj8n5QJXVYSpl9Du7YKX/kuSzkvyfUspza63PuwRjAQAAl1h78hHjLgGAXVTKdJqtuyetu4+7FAAAAAB2gcW/AAAAbKXRvCqN5lXjLgMAAACAHWiMu4DDYDl45XU5H7xSszYYZXXwSh157XjYrA15qVn69/kTpZRfuYh+AQAAAAAAAAAAAAAAAAAAAOBIEL5ykUop7SR/kuRBy00bha6Mhq2UrA9l2a7NxilJvrOU8tyL6BsAAAAAAAAAAAAAAAAAAAAADr3WuAs4BH4hyRdmbRjKirrq/Ur7Z5L8TZL3JflQkl/N+dCUC/GOJJ830v9KAMtKwEtJ8qOllHfVWn//go8EAAAAAAAAAAAAAAAAAABgj9ThfIbDT6cOz6bW2aR2Ums/SU0pk0mZSilTKWUypXFVGs2rxl0yAIeQ8JWLUEp5VJLvydbBKyXJh5P8VpLfq7W+d6SPX93OmLXWB5dSPi/Jc5J8bdYGrowGsPxaKeX1tdbT2xkDAAAAgItTh7MZDj61PBF0LrWeW5oIyiBJTclkUiaXJ4KmUhpXptG8s8kgAAAAgH2g1kEGvfdmOPxU6vBMUrspjVMpjVNptj8njcapSzh2P/3u369rb08+9JKNCQAAcFi4ngMAgP1v0P+nDLrvTL/3ngwHH86g/09Jnd9mL600mndKo3nnNFv3SnPiQWm1H5DSOHFJagbgaBC+skOllEaSF65uWv65OnTlM0l+MMlv11oHuzV2rfUdSZ5cSnlClkJdrsz6AJYstz8/ydN3a2wAAAAA1qq1m3737zPoLU8E9T+ytIhrR9ppNO+0PBH0wLTaD0qzfa9drRcAAACAjfU6f53uwquX/liuLmzyqZJm695pT31xJmaelFKmdrWGOjyb+VuembXPgCq57I5v2NVxAAAADhPXcwAAsL8N+h9Kd+E16XXemDr41MjeuuF3ttbLcPCxDAcfT7/71mT+fyYpabSuSXvqSzMx9dg0mnfchcoBOEqEr+zc1yd5UM6HnmTk/RuSPKXWevOlKqDW+sellC9Kcn2Sz9qklm8upfxErfWTl6oOAAAAgKOm1pp+983pLrwm/e7fJrWzeu9F9NzNcPDRDAcfS6/z+iRJKcfSnvritKe+Iq2JB15U3QAAAACs1+u8NYuzv5Zh/wPLLVvd36kZ9N+Xwez705n/nUwe++ZMzjz5ElR1MfeYAAAAjgbXcwAAsL/1u+/I4tx/zaD79uWWjc6XywZtF6queT/sfzCd2Q+lM/ubaU08OBMzX5/25MMvon8AjhLhKzv3H0a2V8JOapK/SPKEWmvvUhdRa/1wKeUrk/xlkhMjdSRJO8l3J3nOpa4FAAAA4CjoLvyfdOb+W4aDTyy3jE4EXcwk0IrzfdY6m+7CH6W78EdpNO+SiZl/lYnpr04pbu0BAAAAXIxa+1mc/dV0539vpWX55+3d36lJaurwTBbPvTD97tsyc/I5KY2ZXaxu9fOXAAAAWM31HAAA7G/D4a1ZPPei9BZfu9xye+fs2z1/LiM/V/ez9Op3/y797t+l2X5gpk98f5rt67Y5BgBHTWPcBRxEpZTPT/KFWR90kiT/nOQpexG8sqLW+g9JviMbnyWUJP96r2oBAAAAOKwG/Y9n9vT3ZuHsT2c4+HjOT9CUkVeyevLmwiaERj832ufS/uHgE1k8918ye/M3pdd5824cFgAAAMCRNByezdzp71r+Q73R+zzJ+vs7q+/frL1v0+/8VWbPfGeGwzN7eQgAAABHkus5AADY3wa9GzJ789OWg1c2O2cfNbpu9vZem1m/9nbQe2dmTz89nbmXX+yhAXDICV/Zma/doG3lN/Gzaq237nE9qbW+IsnbVtWx+uzh2lLK5+x1TQAAAACHRa/zN5k7/e0Z9N6V9YEryfqJoAud5Bn9/O3tX7r1Mxx8IvO3/FAWzv5c9jADGAAAAOBQqLWTuTPPyKD/vqy917NVOO5Gf8iX27437H8482eelTqc34tDAAAAOJJczwEAwP7W7703s2e+J3X46Wx9zp5sHpy409eo1dcCvSzOvjjztz4vtQ5252ABOHRa4y7ggPqqVe9X/0b+aJJX7HEtqz0vyR9usu8xSd6/d6UAAAAAHA69zl9n/pZnJ+kvt6ye9CkbtG2klZTJlDKRZCLJMMkgqd3UOr+8vZGNAlnOj9VdeFUGg4/l2OU/k1KmL/SQAAAAAI60xXMvyrB/Q9bf07m9ezwrNvpezaB/QxbOviAzlz9394oFAADgNq7nAABg/xoObsz8mR9M6kLWhq5s9LDDZhqte6TZunearWtSGlel0bw6pXFVSplOKZNJmUgpk8thKYOk9lLrfGqdSx2eyXB4OnVwYwaDj2fYuyGD/gezfq1v1ozdW3xtUvvO/QHYkPCVbSqltJPcL+tj1mqS36+13t7dukvpz5IsJJnK+ruGD9j7cgAAAAAOtkHvA5m/5ceyNBkzGrqyNom/NK5Os3Vdmu17p9m6dmQiaOvbcLV2bpsIGg4+mWH/4xn0b8ig997l9P9kfRDL8iKw7tszd+aHcuzUL6aUxi4cNQAAAMDhNei9L92FV2fjgN2alKlMTD0urYkvSrN9XUrj8iTJcPCZDLpvT3fxzzLovTNr7w/V2973Oq9Pd/7BmZh54p4eFwAAwGHneg4AAPa3+Vt/OrXemvXn7EtrbUvj6rQn/2Vak49Ma+Lzlh9oePtKaSZpLoWx5FiSq5N89rrP1dpNv/v29DtvSHfxz1eFwNzWU1bO/Ttzn5vJY9+wswMF4NASvrJ990nSzuhv/SWvH0tFy2qtnVLKG5J8RdaHr9xvDCUBAAAAHFi19jN/9ieTdLLZRFCzff+0Jx+Z1uQj0mzdfcdjlTKZ0rxTGs07Je37rtk36H80vc716S38UYaDT25Yy6D3jizO/lqmT3zPjmsAAAAAOAoW516etfd4zt/raU99aaZOfH8ajVPrvtds3S3N1t0yMfM16XXenIWzP5M6PJO1y4eW3i/M/kpak1+0dK8HAACAXeF6DgAA9q/u4usy6L09G4WdNJr3yNTxp6c1+YiUMvogwt1TykTak1+U9uQXZer496Qz97J05n83yXBdTYuzv5H21GPSaN75ktUDwMHjUbjbt9VdtPfuWRWbe8/I9spdxc8aQy0AAAAAB1Zv4Y8z7H8wG00EtSYekuNXvDTHr/i1TB77NxcVvHJ7mq27Z+rYU3P8yt/J1IlnJmV6VS3na+rOvyKD/ocuWR0AAAAAB10dnku/85cZva+SlEwee2pmLvuJDf9Qb1R78uE5fsVvpNG6Jhs+v6kuZOHsz+52+QAAAEeW6zkAANjfOnO/PdKyEpT4lTl+5W+lPfXISxq8Mqo0jmXqxHfl2KkXpZSTG3yin8XZ39izegA4GFrjLuAAunyLfZ/esyo2t1kNJ/a0CgAAAIADrjP/P7I2eGVl4dbTMnX8W/a8nlIamZz5/9Ka+ILM3/LMDAefHPlEzeK5X8+xUxaCAQAAAGyk13lLkkHO3/NZut/TmnxUpo5/+7b6ajTvkGOnfjFzp5+e4eDGjD55vd99W7qLr8vE1Jfu4hEAAHAYzJ7+vnGXsDOl5PipF467Co4o13MAALB/DfofzrB/Q0bP19uTX5yZy354jJUlrYkH5tgVL8rs6e9O6vxy69K5f2/x+tQTz0hpbBTOAsBRJHxl+5pb7JvfYt9eOb1Ju/AVAAAAgAvU770vw8EnMjoRNDHz5LEEr6zWbH1Wjp36pcyefnrq8Obl1pVFYH+T4eCmNJpXjbNEAAAAgH1p0Hv3Bq2tTJ/49zvqr9E4lZnLfiqzp78zSS+jT2BfPPcraU/+y5QytbOCAQA4lAa9t2ftQyAOgpVgChgP13MAALB/9Tp/ua6tlGOZOvnMMVSzXrN1baZP/EAWzj4va69t++ktXp+Jma8ZV2kA7DONcRdwAM1use/KPatic8c3aW/vaRUAAAAAB1i/81fr2krjVKaOf+cYqlmv0bxDpk/+cJYWOa5W0+tcP4aKAAAAAPa/Qf+Dq7aWn7o49ag0mnfccZ/N9nWZOvFdOX+f5vz9mjq8KZ25l+24bwAADrt6QF4wfq7nAABg/xr03r9qa+V8/cvTaJwcV0nrTEw/No3WvTJ6ndvv/cN4CgJgXxK+sn03b7HvTntWxebusEn73J5WAQAAAHCADfo3rNpamQh6XEqZHFdJ67QnvyjN9oMyOhE0MBEEAAAAsKE6/FTWPtEwaU184UX3OznzlFX3adY+Lb0z97sZDm686DEAADiMygF5wfiAHTOHAAAgAElEQVS5ngMAgP1r2P9I1p+vP3QstWxlYurxIy01g/4/jqUWAPYn4Svb94Et9l383buLd9dN2j+zp1UAAAAAHGAbTwQ9eCy1bKU99WUjLTWD3la3rwAAAACOruHw1nVtzdY9d6Xv6ZM/mKS9wZ5eFmd/fVfGAADgsGhlKeihjrTXLV5wtLmeAwCA/asOb1nX1mjdfQyVbK05cb9VW0trhOvw9HiKAWBfao27gIOm1vrpUsrpJKfy/9i78zDJsrpO+N8Tey5V1dXN0tqIzdIu3UBLg2wDNAI6OKLoq6KIG4zLiBuDMCC8I+q4O/qCMDiOO8yog4o7bYNssoigwCiLiuy2wNBdXVtmRsZ23z+yisolsroyKzIjs/LzeZ77ZMS5957fL6r7ybznxLm/u3Em+wlJfm33s1rj0VmbVznz/v3jDwcAAHbD6WPfN+0UtqeUzB994bSzANh11ejkhrZa/W5TyOT8Gs3PX/VuZRpo3JdYAAAAACSpljc0ldplE+m63vjstOe+PssLL8u55TorP/vd12Yw+8Q0mtdOJBYAAPvb4bvclEHvHRksvyW97quSaikr145nryPHUYCFA854DgAA9qyqWtjQVsrsFDI5v1rtThvaqtHpKWQCwF6l+Mr2vCHJV+XcLPbZ2bUvLqVcVVXVLdNIqpRyfZKrsnq275x3TiMnAABgxbD/zpytjrx/nB1aABw8VbW4oa2UzhQyOb9Su3xD27jcAQAAAEiSWpLRurbJzYO3574lvaU/TzW6dV2/VbqnXpT5y39xYrEAANi/Sumk2X5Ymu2HpTP/tCwv/X6WT/9mkl7WLgE/VwSi3vy8NFoPmVbKsAcYzwEAwJ5VGkk1XNO0N9eyrh9TJO6XAGA1xVe251VZKb6SrJ3hbif5ySTfPI2kknzvefa9freSAAAAzme/PInIJCJwwJX2mSfMnVNVp5NsrHo/TVXV29hY6rufCAAAAMA+UMpMqurUmrZqdDKp33VC/bfTOfS0LJ34kax/Wvqw/570un+RVuexE4kFAMClodRm05n7pjTbj8zC7c9INfpUNj6DM6k3rk1n/qlTyRH2AuM5AADYu0o5lKpaXtM2Gnw09cbdp5TReKPhJze0ldr8FDIBYK+qTTuBferlSc5eCVSrfpYk31BKefxuJ1RKeUCSp6zL56zbk7xht3MCAADGKftkAzjYSjm8oW00+OAUMjm/0fBfN7SVMjeFTAAAAAD2vlLbOOczHH50ojFancem3rxfzi0lSs7esNc99ZJU6wr+AgBAktQbn525y34q55b3W7sBqxnPAQDA3lWr3yXri4gOen81nWTOY9D721XvVvKtTaigIwCXBsVXtqGqqtuT/F7WzqglK39ta0l+u5TywN3Kp5RyZZL/NSafsyWXf6uqqv5u5QMAAIzTyMrlebWuvTrPBsC0rHyZsvZ3cX/5zdNJ5jwGvbetereyAKxWv2pa6QAAAADsabX63bJ+zme4ZqHtZMwcenrGLcuqRreme+qXJh4PAIBLQ715TZqdx8SaEdjIeA4AAPauevO6Ve9Wbmvudf8io9Ht00ppg6rqpbf0J1lb7LSk3rj3tFICYA9SfGX7fjTJ8Mzr1aWNqyRzSW4upXzZTidRSvmsJK9P8jmr8lg9qzhI8nM7nQcAAHB+h+9yU2Yv++m0Zp6QlE7OXbaf70lF5yvMshsbwMHVaN5n1buV6ZZ+93UZDT8+rZQ2qEan01/6s6z/W1JvXDOdhAAAAAD2uFrjHqvenZ3zef3En15eb16T1szjs3au/cxi46U/yGD5byYaDwCAS0dr5sunnQLsScZzAACwdzWa129srJaydPJndz+ZTXRPvSTV6LYN7Y3WF04hGwD2qsa0E9ivqqp6fynlJUm+N2vvmjx7l+LRJH9USvn5JD9eVdWJScYvpZQk35eVIjDz2Xhn5Nlcfrmqqo9MMjYAALB1pXTSbD8szfbD0pl/WpaXfj/Lp38zSS9rayiefV1Sb35eGq2HTCtlgAOt3npAsvi/1rX2s3jixzJ39IUpZfrTaksnfyZVdSrri6802g+eTkIAAAAAe1yjdUN6i7+9pq2qTmf59EvTOfSdE43Vnv+O9LuvS1WdXtVakoyyePK/ZP7yX0mtfueJxgQAYP+rN++blLmkWpx2KrCnGM8BAMDe1Wg/LKV2NNXo+JmWlXsiBstvytLJn0nn0DNTSm1q+XUXXpre0u9nw4NzSyeN9kOnkhMAe9P07xLZ356T5N8luWfO3h25tgBLLckPJPn2UsqLk7y8qqq/v5iApZSrknxzkqckuVfO/bU/G391EZaPJ3nuxcQDAAAmr9Rm05n7pjTbj8zC7c9INfpUNl7OJ/XGtenMP3UqOQIcdI3WA1PqV6YafvJMy8rv6WH/77N4/LmZPfLDKbXZqeRWVaN0T/18+suvy/ovgko5rAo/AAAAwCYarfsnZSapumdaVuZ8lhd/K/XmtWl2HjGxWLXakbTnvyPdUz+X1YXXk5JqdCwLx5+ZuaMvTK122cRiAgCw/5VST6N5vwx6f5UNN4XBAWY8BwAAe1cpjbRmnpDlhd/I2mvoKr2lP81w8KHMHHpm6s177Wpeo+GtWTr1ggyW/3LdnpX8WjNfmVI6u5oTAHvb9EqFXQKqqlpK8lVJzpY0Xv2o+rPvS5IjWSmC8q5SyidKKX9cSvmVUspPnaf7a0spX1FK+eZSyg+UUl5WSnlfko8k+bEk987aQi+r79QsSQZJvqmqqpMT+bAAAMDE1RufnbnLfirnhmYWzQDsFaWUtGe+NuemW85Nvwx6b83pY09Jf/ktu57XsP/+LNz+Xekt/fG6PWe+CJr7upSi3jIAAADAOKW00+p8SdYWQz/z9PITP5Tlxd+daLz27Fem3rxfxj1TaTT4YBZu//6Mhp+aaEwAAPa/evOaaacAe47xHAAA7G3tuW9MqV955t3aAizD/rtz+thTs3D8eekvvz1VNdrRXIaDj2bp1Ity6rYnnSm8cjaXc0rtsrTnvnFH8wBg/3EnxkWqqurdpZQnJvnDJK2sLmt8rjBKcu4v812SfNm6bsqYn88aE271X/f1s4arX1dJnllV1esu/JMAAADTUG9ek2bnMel3XxXFVwD2ltbsV6fXfWVGgw9m/RdBo+G/ZvH4c1Jr3Dvtma9Ko3NjarXDO5JHVY0y6P1Nekt/mMHym7O2Fu9ZJbX6Z6Q9+7U7kgMAAADApaI1+6T0ll6ZlecaJefmWQbpnnpReks3pT37dWm2H5FSm73oeLNH/t+cuu0pSbWYtXNMKzfsnb7tKZk5/Jw0Ow+/6FgAAFwa6o17TzsF2JOM5wAAYO8qpZ3Zwz+Yhdt/IMkw69fdJqMMlt+YwfIbU8rhNNoPTL35Bak3Pzf1xj1SSmfbsUfDT2Q4+EAGvXdl0PvrjAYfPrNn9e3d1ZqfM4eetWPrfgHYvxRfmYCqqm4upTw+KwVYZrO2AEuytghLcmF3VI47pjrPMav3/UhVVb9wATEAAIA9oDXz5WeKrwCwl5RSz+zh5+X07d+TVEtZv5gqqTIavD9Lp342OfVzqTc/L43mF6Te/JzUGvdMrX5VSmluOe5odDKjwQfPfBH0zgx6f3NmMVeysc7v2bZmZg4/76K+fAIAAAA4COqNq9Kee1KWF16acc87Gg3en6WTP56l1FJv3Cu1xmenVr9rUmZTr99jyzfV1eqfkdnDz8viieetiXN2aVFVncjiieemvnT/tGe/MfXG1RP4lAAA7Ge1+meueudBPnCW8RwAAOxtjdYNmTn83Cyd/C9nWjauu02SqjqRfve16Xdf++lzS+3y1Op3TaldnlIOp9RmU9JJSj1JLckgVdVPqn6q6nSq0bGMRrdlNPzEmTW+WRPjXPyzbefGEJ35/5Bm5xET/OQAXCoUX5mQqqpeU0r5N0lekeSeWXsnzPq7YS6kEMv6QiurjSu6UpKMkjyjqqoXXmjeAADA9NWb903K3Kob6wHYK+rNazJ32U9m4fZnZuXpWWsXU52b6hlm2H9vhv33rjq7pNQuS6kdTakdSSmzKaWdpH5mG6Sqeis/R6dTjW7LaHRs3ZdAyfmnks5U4D/y3DRa95vY5wYAAAC4lLXnnppB/90Z9t6R8873DP4pw8H7P31eo/XQbT3RvNl5RDrD/5Du6V9cF+fc62HvnVnsvTMpF/90dgAA9rdSv/LMq/MtJ4eDyXgOAAD2ttbMF6eUZhZP/kRSdbOxcOJZa8e81ei2DEfHthhts3Hz+rW2Z6/ja+nMf3fac1+3xTgAHBSKr0xQVVV/V0p5QJIXJvnmjC+0cqHlx+/ouPX9/muSp1RV9eoL7B8AANgjSqmn0bxfBr2/iicWAew9jdYNmTv6C1k88Z9TjW7NZpXwN36JU6UaHUs1OpYL+/1+oV8CrTq+dDJ7+Hlpdh51Af0DAAAAkKzMy88d+bEsHP9PGfbfnfPP90zmhtf23DekqrpZXvj1bHbD3krIhU1yAQDgoKjVjqRz6Puz8lzOFfXGNdNLCPYQ4zkAANj7mp1HZb5xdZZO/HiGg3/I+OvjcW3buYbfrJ+144NSu0tmDz8njfYXbiMGAAdFbdoJXGqqqjpRVdW3JnlMkr/N2oIrVS5uFm/9+SUrj1x+SZLrFF4BAID9q960SAZgL2u07pP5K34tjfaNWTs1s1rZZEvWTutstp3v/PWq1Bufl/nLf0nhFQAAAIBtKLVDmTv6gjQ7j8sdz/eM27d1nfmnZubQM5PU1/W5fm4IAICDrj37NWnPPvHTW6N1/2mnBHuG8RwAAOx99cbVmb/ilzNz+DkptTtn81ur198yvdUt2djv6vZWWrNPyqE7/U+FVwC4Q4qv7JCqql5XVdWDknxZkldmpfT4Vu+4Wb9lVR8nkvy3JJ9bVdX3VFV1Yhc+FgAAsEPqjXtPOwUA7kCtdjRzl/1Y5o6+MPXmtTl/jd3V7Vv5AujsuZt/wVRqV6Rz6BmZu/yXUm/c86I+EwAAAMBBVko7s0eel9nLfia1xr2z+bzM5G6ga80+IfOX/4918dygBwAAsBXGcwAAsD+0Zr4sh+708swe+dHUWzdk5Rp63C3TF2Pjbduldqe0556SQ3f+3cwcelpKmbnIGAAcBI1pJ3Cpq6rqpiQ3lVKuTPIVSb40ycOTXLGN7j6S5DVZKebyyqqquhNLFAAAmKpa/TNXvfOlPMBe1mjdkPnL/3uG/X/I8uIrMlh+U6rq1KojLvaLoPWFWFba6s37pDXzFWl2HpNSWhfRPwAAAACrNdsPTbP90Ax670iv++oMlv861ehTOxav3rwmh674tfS7r83y4isy7P+fVXs9LR0AAOBCGc8BAMDeV0o9zc4Xpdn5ooxGJzJYfksGy3+VQf/dF3D9vv4ae7OHJtZTb1yTeuv+abYfnnrzvinF9TkAW6P4yi6pquoTSf7HmS2llLsluS7J3ZPcLcnhJDNZ+W/SS7KU5FiSf0ny4SR/V1XV7bueOAAAsCtK/cozrzabDARgr6k3Py+zR56bqhpm2P/79JffkmH/PRkO/jmpFi+6/1K7UxrN+376i6Ba/c4TyBoAAACAzTRaN6TRuiFJMhr+a4aDj2Q0+Fiq0e2pqqXUGp810XjNzqPT7Dw6w8GH0+++PoPeWzPsvy/JaKJxAAAALnXGcwAAsD/UakfSmvnStGa+NEkyGp3IsP+PGQ0/lmr4qYxGn8podGtSdVNVy0nVSzJIUk9KM6XMppRDKbWjqdXvklr9M1Nr3DP1xj1SSnuqnw2A/a9UlRv72NtKKe9Jcu369muvvTbvec97ppARAADsjOXF38vqL+DrjWvSaN1/egkBsG3DwUczGn4so+GnUo0+ldHwtqRaSpVeUi2nyjAl9SSNlNq4L4LulVrtyLQ/BgAAAAC7rBotZjj4QIaD92c0vCXV8NaMRscyf/mLpp0aAAAA52E8BwAAAEzSddddl/e+973jdr23qqrrdjufg6Ax7QQAAABY0Z79mmmnAMCE1Bt3T71x92mnAQAAAMA+U2qzabTum0brvtNOBQAAgC0wngMAAADY32rTTgAAAAAAAAAAAAAAAAAAAAAAYBoa004AAAAAAAAAAAAAAADYOVW1lNHwU6mqU0lKSplLrX5lSmlPOzUAAADYslO3PXVD2/zRF6TUDk8hGwAuBYqvbEMp5fokTxi3r6qqH93ldD6tlPLwJI8es+sVVVW9e7fzAQAAADgIRsNbMxp9MtXo9qTqpdSOptQuT61+t5RS37G4VTXKaPgvG9rrjbvvWEwAAACAg8KcDwAAl4Lh4F/SX/rT9HtvyWjwkSTVhmNq9c9MvXl9mp1HpdF6SEopu58oTJDxHAAAHAyjwfuTnB3DVklKqgxjVAvAdim+sj1fkOSHM272OZla8ZUk98n4vBpJFF8BAAAAmJDh4IPpLf1pBstvy2j40fEHldk0Ww9Ko3NjWp3HTDyHanQ8p297crLma6KSI3d9w8RjAQAAABwE5nwAANgrRsPbMhr931Sj21NViyllPrXanVNr3POCiqNUo9Ppnv7v6S39SVaWlo9b9n421i0ZDf81/e5NqdU/I+25p6Q187jJfRjYBcZzAAAAAFwsxVcu3uqZsc1npXfH0qrXq/N64G4nAgAAAHApGg4+mO7pX8lg+c25o0WKqRbSX359+suvz/LCS9OZ/8402w/bgaymPSUFAAAAsL+Z8wEAYC8YDT+Z3uIfpN97S0aDD48/qMym2X5IWrNfm0bzurGHDAe3ZPH4szIa/kvWXleer2hLdSaHf83SyZ9Ir3tTZg//UGr1K7bzUWDXGM8BAAAAMCm1aSdwCbiDGbpdtfq/5+q87j2FXAAAAAAuKcuLL8/p274tg+U3JRllZeql3MG2MkUzGnwwi8efk6WTL0hVjSac2dlYAAAAAGyVOR8AAKatqgbpnv61nLrtG7O8+FsZDT6Uc0vB123VQvrd12bh2Hdl6eTPpKq6a/oaDY9l4fbvzWj4sWy8ts0m/Sbrr3eHvXfk9LFvy3DwoZ386HBRjOcAAAAAmCTFVy7eXpoV26y0+JFdzQIAAADgElJVvSwc/8F0T704ST9rF2wlmy58TLJ+EVdv6RVZOP4fNyyCBAAAAGB3mfMBAGAvqKpeFo8/O8sLv5FU3Yy/5hxfPKK39KdZOP7sVNXg0/0tnvyRVKNPZeO1bc7Tb9Ydt9JWjW7Nwu3fl9Hglgl9WpgM4zkAAAAAdoLiK5eWB23SfmhXswAAAAC4RFTVKIvHn3PmSVnrF2ydtdmix2TjIq4qw947s3j8uWsWQQIAAACwe8z5AACwF1TVKAvHn5VB721Ze116tsDKZs4dM+y9M0snfzJJ0l9+U4a9d2TjdesdFaRYW3RldS7V6PiZAi/LF/txYSKM5wAAAADYKY1pJ8BklFK+OMlX5fwz7QAAAABswfLCr2bQe3s2PvFt3FPi1hv3NLmVxVuD3t+ke+rFmTn89InmCwAAAMAdM+cDAMBe0Ft8+bpiKcnGYhKrr03HHVel3311BjNfnuXF3x2z/8zrMpdm+9+k0XpQavUrU2pHk6qX0ehYhv13pd99fUbDj62Kfe780fCj6Z7+lcwc+u4JfXLYPuM5AADYe0588pFTjF6teX3qU0/YwrklR+76hkknBMA+pvhKklLKjUlu3MIpX3Cevn7o4jPakk6S+yb5t0nqWT2Tfs7tu5wTAAAAwL43HHw0ywu/lXFPeDv7utF6cBrtB6XeuCaldlmSZDS8NcP+O9Lvvi6j4S3rzjk3ddNbekUarQek2XnELn8yAAAAgIPLnA8AAHvBaPiJdBd+NZsVgkiSUrs8tfpnpZS5VKNjGQ0/nqo6seq4c5ZO/tdVxVPW9tPsfGk6h747tdqRDXnUkzTbD0p77tvTX/rjLJ3+xaRa3NBPb/H305796tTqV178h4dtMp4DAIC96nxFEHfbXsoFgP1G8ZUVj0ry/Gz9r2oZ8/P5E8ppq9aWGD/XliQf3/10AAAAAPa3lUVbg2ycdqlSb94vM4d/IPXGPTecV2/cI832F6Y9923pLf1Ruqd/MamWs3qB49nXS6d+JvXW/cYudAQAAABg8sz5AACwF/QW/yipuhl/Xfr56cx/dxqt6zecN1j+mywv/nYGvbdl9bLx0fCj2VjApaQ1+zWZOfR9d5hPKSWt2Sek3rw2C8efmWq0/tmfgywv/l5mDn3PFj8pTI7xHAAA7GXljg+ZuHG3hF9oHoq0ALBRbdoJ7DFlC9sk+pjkttlf+irJm7fwbwAAAABw4FVVN/3uq7PZk+Hmjr5w7KKt1Uqppz37/2T+6EtSanfe0E+SVKMT6Z56waTTBwAAAGAMcz4AAOwFVTVMr3tTxl+X/tvMHf2lsYVXkqTRfmDmjv5c2nNPzbniE9Wq1+f6qjXudUGFV1arN6/J7GU/kbW3Gaz02V/681SVm9OYDuM5AADYD6pd3gBgshRfWWsSf5V3++pgdT6bFYX54wv9BwAAAAAgGSy/LUlvVcvKYsWVp2U9O6U0LrivevOazB19Qcqnn4y1+ilcVfrd12Sw/PZJpQ4AAADAJsz5AACwFwz7f5dqdNuqlrPXpddl5vDzUsodP6W7M/+UNDuPyblr0PXnlHTmv21b+TWa16U99+SsXzJfVScz7P/9tvqEi2U8BwAA+8H5xrNlBzYAmCzFVza62L/KO3EFsNWcqlU/31RV1Wu28e8AAAAAcGANxi4aLJk5/MyUUt9yf/XGZ2X2yA9n7VO4zv6ssnTqhamq4bZyBQAAAODCmPMBAGAvGPbfN6a1ZObwsy6o8MpZM4eekZSZsftKOZxG62HbzDBpzX5dUjob2gf9d227T7gYxnMAALB3zR75sZQyn80LhObMvklvZ527ni+1O6XU7noB25UptbtO8p8BgEvAhZf3PTiqOz4kyeYFWC70/J1Wkrw/yddPOxEAAACA/WY0+OdV71a+DGq0H5p64+pt99loPSDtuSdneeFlWfvkrGQ0/Gh6i7+X9tzXbT9pAAAAAM7LnA8AAHvBsP+Pq96duS5tPSD1xj231E+pHU6z85j0l/4055a2n73OfdiWCrmsV6sdTrP98PS7f5HVy+ZH/fdvu0+4GMZzAACwdzU7N6be/LwsnvjRDPt/l7UFWM5evz8ys4efnVI7NLG4Jz75iKy/1Xv+il9NrXZ0YjEAOFhq006AiSjrtuUkL0zy4KqqPj7NxAAAAAD2o9HwE1n/hUyj9eCL7rc999TUPr1ocu2Ts7oLv57R6MRFxwAAAABgPHM+AADsBcPhRze0NdoP31ZfjdYDx7bXm5+zrf7W9nH9upYqw+EtF90vbIfxHAAA7G21+l0zd/RFac99a9Zeu6+8Hiz/ZU7d9q0Z9N45jfQA4II0pp3AHnE8yUe2cPx8kityrrTx6p8bZ8N3VpXkdJLbk7w3yV8l+cOqqk7uch4AAAAAl4zR6PYNbZNYoFhKIzOH/1MWjj0tK9M6q1SLWT79q5k5/IyLjgMAAADARuZ8AADYC6rR6awvIlFvfv62+qo37jW+vX71tvpb2/e9V71bWSpfjW676H5hO4znAABg7yulls78v0+j9cAsnviRVKNP5dz4t0o1+r9ZuP3pac8+Ke35b0spbnEHYG/xlylJVVUvTPLCCz2+lPItSX59k77uMam8AACAS8fiiZ+YdgrbVDJ75AennQTA7quWNzTVymUT6brRvC6tmS9Pb+mPsv7JWb2lP05r9mtSb9x9IrEAAAAAWMWcDwAAe0F1ekNTrXZ0W13VancZ21622d/aPg5vaKtGCxfdL2yL8RwAAOwbjdb1OXTFb2bx5E9lsPyXWbm+PnutPcry4m9l0PvbzBx5fuqNu00xUwBYS/EVAACAXdDv3pT1Ty3a+6okJVF8BWBFaU2sq/b8d6TffV2q6tS6PaN0T704c0d/ZmKxAAAAADgPcz4AAOyyqloc0zjcXmelM765Nru9/lb3Mbbv/kX3CxNjPAcAAHtWqR3K3GU/nuXFP0z31IuT9M7uSVJlOPiHnD72lMzMf19as18+xUwB4JzatBMAAAA4WKp9tAEcXOMWEm5cZLV9tdrhtOefmnO/b88UvEqVQe+t6S+/fWKxAAAAAFhhzgcAgD1hTMGI0ej27XVVNrkdYJOiLFtRjU6MafXsV6bDeA4AAPan9uxXZv6KX06tcY+cu94uK1vVzdKpn83C8edlNDo5xSwBYIXiKxfPHYkAAMAWlH20ARxcpXZoQ9to8LGJxmjNfFVqjXuejbjqZ5XuqRemqgYTjQcAAABw0JnzAQBgLyhldkNbNbp1W31V1XD1u21mNN5odHx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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 1765,
+ "width": 2223
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(2, 1, figsize=(9,7.), dpi=300, sharex=True)\n",
+ "fancy.plot_diffcake(axs[0], model_dpp, dsp_ticks=True, \n",
+ " no_xlabel=True, dsp_step=0.3)\n",
+ "fancy.plot_jcpds(axs[0], model, \n",
+ " show_index=False, in_cake=True, show_legend=True,\n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'],\n",
+ " bar_alpha=0.5, bar_thick=0.5)\n",
+ "fancy.plot_diffpattern(axs[1], model, dsp_ticks=True, dsp_step=0.3)\n",
+ "fancy.plot_jcpds(axs[1], model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "axs[1].text(0.7,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = axs[1].transAxes, fontsize=16)\n",
+ "plt.subplots_adjust(hspace=0)\n",
+ "plt.savefig('test.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "peakpo7721",
+ "language": "python",
+ "name": "peakpo7721"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/3_2D_from_dpp/cake_from_dpp.py b/jnb-tools/3_2D_from_dpp/cake_from_dpp.py
new file mode 100644
index 0000000..f09b80f
--- /dev/null
+++ b/jnb-tools/3_2D_from_dpp/cake_from_dpp.py
@@ -0,0 +1,208 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Plot Caked Patterns from dpp files from PeakPo
+
+# - Please check [setup_for_notebooks](../0_setup/setup_for_notebooks.ipynb) file if you have problem using the notebooks in this folder.
+# - In this notebook, we will learn how to plot XRD patterns using the information saved in `dpp`.
+# - `dpp` is a project file saved in `PeakPo`. You may plot, jcpds information and cake as well as many other information.
+
+# This notebook takes advantage of the `PeakPo` modules and other local modules. They can be found in `../local_modules` folder.
+# The cell below defined the search path for this local module folder.
+
+# In[1]:
+
+
+import sys
+sys.path.append('../../peakpo')
+sys.path.append('../local_modules')
+
+
+# ## Check the versio of pyFAI in your conda environment
+
+# In[2]:
+
+
+import pyFAI
+pyFAI.version
+
+
+# Note that the example data files I provided are made with `pyFAI` version `0.14`. If you see version higher than `0.15` here, you will get error when you read the example `dpp` file. In that case, you either follow the instruction in [setup_for_notebooks.ipynb](./setup_for_notebooks.ipynb) or you may use your own dpp for this note book.
+
+# ## Read dpp
+
+# In[3]:
+
+
+import dill
+import numpy as np
+
+
+# Change the following two cells for your own dpp file
+
+# Data files should be in the `./data` folder. You need: `dpp`, `chi`, and `tif`.
+
+# In[4]:
+
+
+get_ipython().run_line_magic('ls', '../data/hStv/*.dpp')
+
+
+# In[5]:
+
+
+filen_dpp = '../data/hStv/hSiO2_404_009.dpp'
+
+
+# In[6]:
+
+
+with open(filen_dpp, 'rb') as f:
+ model_dpp = dill.load(f)
+
+
+# The cells below show how to look into the data structure of the `model_dpp` and get values from it.
+
+# ## Setup a new PeakPo model and assign info from dpp
+
+# In[7]:
+
+
+from model import PeakPoModel
+model = PeakPoModel()
+
+
+# Make sure to reset the chi folder location using the `new_chi_path` option.
+
+# In[8]:
+
+
+model.set_from(model_dpp, new_chi_path='../data/hStv')
+
+
+# ## Make XRD plot
+
+# In[9]:
+
+
+import matplotlib.pyplot as plt
+get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# ## Let's make some plots
+
+# In the plot below, we plot diffraction pattern in $2\theta$ scale to prevent any distortion in the diffraction pattern. We just plot tickmarks in d-spacing scale.
+
+# In[10]:
+
+
+import fancy_plots as fancy
+
+
+# In[11]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5), dpi=300)
+fancy.plot_diffpattern(ax, model, dsp_ticks=True, dsp_step=0.3)
+fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.70,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test.pdf', bbox_inches='tight')
+
+
+# ## Can I add cake to the diffraction pattern?
+
+# In[12]:
+
+
+model_dpp.__dict__
+
+
+# In[13]:
+
+
+#model_dpp.__dict__['diff_img'].__dict__
+
+
+# In[14]:
+
+
+intensity_cake = model_dpp.__dict__['diff_img'].__dict__['intensity_cake']
+tth_cake = model_dpp.__dict__['diff_img'].__dict__['tth_cake']
+chi_cake = model_dpp.__dict__['diff_img'].__dict__['chi_cake']
+intensity_cake.shape
+
+
+# In[15]:
+
+
+print(tth_cake)
+
+
+# In[16]:
+
+
+plt.imshow(intensity_cake, origin="lower",
+ extent=[tth_cake.min(), tth_cake.max(),
+ chi_cake.min(), chi_cake.max()],
+ aspect="auto", cmap="gray_r", clim=(1.e2, 7.e3))
+#plt.xlim(0,20)
+
+
+# ## Modify plot_diffpattern function to plot cake
+
+# In[17]:
+
+
+from xrd_unitconv import *
+
+
+# In[18]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5), dpi=300)
+fancy.plot_diffcake(ax, model_dpp, dsp_ticks=True, dsp_step=0.3)
+fancy.plot_jcpds(ax, model,
+ show_index=False, in_cake=True, show_legend=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'],
+ bar_alpha=0.5, bar_thick=0.5)
+#print(ax.axis())
+#pressure = model.get_saved_pressure()
+#temperature = model.get_saved_temperature()
+#ax.text(0.70,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+# transform = ax.transAxes, fontsize=16)
+#plt.savefig('test.pdf', bbox_inches='tight')
+
+
+# In[19]:
+
+
+f, axs = plt.subplots(2, 1, figsize=(9,7.), dpi=300, sharex=True)
+fancy.plot_diffcake(axs[0], model_dpp, dsp_ticks=True,
+ no_xlabel=True, dsp_step=0.3)
+fancy.plot_jcpds(axs[0], model,
+ show_index=False, in_cake=True, show_legend=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'],
+ bar_alpha=0.5, bar_thick=0.5)
+fancy.plot_diffpattern(axs[1], model, dsp_ticks=True, dsp_step=0.3)
+fancy.plot_jcpds(axs[1], model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+axs[1].text(0.7,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = axs[1].transAxes, fontsize=16)
+plt.subplots_adjust(hspace=0)
+plt.savefig('test.pdf', bbox_inches='tight')
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/3_2D_from_dpp/peakpo_plots.py b/jnb-tools/3_2D_from_dpp/peakpo_plots.py
new file mode 100644
index 0000000..abc2a60
--- /dev/null
+++ b/jnb-tools/3_2D_from_dpp/peakpo_plots.py
@@ -0,0 +1,537 @@
+import os
+import time
+import datetime
+import numpy as np
+import numpy.ma as ma
+from matplotlib.widgets import MultiCursor
+import matplotlib.transforms as transforms
+import matplotlib.patches as patches
+from PyQt5 import QtWidgets
+from PyQt5 import QtCore
+from ds_jcpds import convert_tth
+
+"""
+def get_cake_range(self):
+ if self.widget.checkBox_ShowCake.isChecked():
+ return self.widget.mpl.canvas.ax_cake.get_xlim(),\
+ self.widget.mpl.canvas.ax_cake.get_ylim()
+ else:
+ return None, None
+
+def _read_azilist(self):
+ n_row = self.widget.tableWidget_DiffImgAzi.rowCount()
+ if n_row == 0:
+ return None, None, None
+ azi_list = []
+ tth_list = []
+ note_list = []
+ for i in range(n_row):
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 4).text())
+ tth_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 1).text())
+ tth_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 3).text())
+ note_i = self.widget.tableWidget_DiffImgAzi.item(i, 0).text()
+ tth_list.append([tth_min, tth_max])
+ azi_list.append([azi_min, azi_max])
+ note_list.append(note_i)
+ return tth_list, azi_list, note_list
+
+def zoom_out_graph(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits()
+ self.update(limits=data_limits,
+ cake_ylimits=(-180, 180))
+
+def update_to_gsas_style(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits(y_margin=0.10)
+ self.update(limits=data_limits, gsas_style=True)
+
+def _get_data_limits(self, y_margin=0.):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ return (x.min(), x.max(),
+ y.min() - (y.max() - y.min()) * y_margin,
+ y.max() + (y.max() - y.min()) * y_margin)
+"""
+
+def update(self, limits=None, gsas_style=False, cake_ylimits=None):
+ """Updates the graph"""
+ t_start = time.time()
+ self.widget.setCursor(QtCore.Qt.WaitCursor)
+ if limits is None:
+ limits = self.widget.mpl.canvas.ax_pattern.axis()
+ if cake_ylimits is None:
+ c_limits = self.widget.mpl.canvas.ax_cake.axis()
+ cake_ylimits = c_limits[2:4]
+ if (not self.model.base_ptn_exist()) and \
+ (not self.model.jcpds_exist()):
+ return
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.model.diff_img_exist():
+ self.widget.mpl.canvas.resize_axes(
+ self.widget.horizontalSlider_CakeAxisSize.value())
+ self._plot_cake()
+ else:
+ self.widget.mpl.canvas.resize_axes(1)
+ self._set_nightday_view()
+ if self.model.base_ptn_exist():
+ if self.widget.checkBox_ShortPlotTitle.isChecked():
+ title = os.path.basename(self.model.base_ptn.fname)
+ else:
+ title = self.model.base_ptn.fname
+ self.widget.mpl.canvas.fig.suptitle(
+ title, color=self.obj_color)
+ self._plot_diffpattern(gsas_style)
+ if self.model.waterfall_exist():
+ self._plot_waterfallpatterns()
+ # if self.model.jcpds_exist():
+ # self._plot_jcpds(limits)
+ if self.model.ucfit_exist():
+ self._plot_ucfit()
+ if (self.widget.tabWidget.currentIndex() == 8):
+ if gsas_style:
+ self._plot_peakfit_in_gsas_style()
+ else:
+ self._plot_peakfit()
+ self.widget.mpl.canvas.ax_pattern.set_xlim(limits[0], limits[1])
+ if not self.widget.checkBox_AutoY.isChecked():
+ self.widget.mpl.canvas.ax_pattern.set_ylim(limits[2], limits[3])
+ self.widget.mpl.canvas.ax_cake.set_ylim(cake_ylimits)
+ if self.model.jcpds_exist():
+ self._plot_jcpds(limits)
+ if not self.widget.checkBox_Intensity.isChecked():
+ new_low_limit = -1.1 * limits[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ self.widget.mpl.canvas.ax_pattern.set_ylim(
+ new_low_limit, limits[3])
+ if self.widget.checkBox_ShowLargePnT.isChecked():
+ label_p_t = "{0: 5.1f} GPa\n{1: 4.0f} K".\
+ format(self.widget.doubleSpinBox_Pressure.value(),
+ self.widget.doubleSpinBox_Temperature.value())
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.98, label_p_t, horizontalalignment='left',
+ verticalalignment='top',
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ fontsize=int(
+ self.widget.comboBox_PnTFontSize.currentText()))
+ xlabel = "Two Theta (degrees), {: 6.4f} A".\
+ format(self.widget.doubleSpinBox_SetWavelength.value())
+ self.widget.mpl.canvas.ax_pattern.set_xlabel(xlabel)
+ # if I move the line below to elsewhere I cannot get ylim or axis
+ # self.widget.mpl.canvas.ax_pattern.autoscale(
+ # enable=False, axis=u'both', tight=True)
+ """Removing the lines below for the tick reduce the plot time
+ significantly. So do not turn this on.
+ x_size = limits[1] - limits[0]
+ if x_size <= 50.:
+ majortick_interval = 1
+ minortick_interval = 0.1
+ else:
+ majortick_interval = 10
+ minortick_interval = 1
+ majorLocator = MultipleLocator(majortick_interval)
+ minorLocator = MultipleLocator(minortick_interval)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_major_locator(majorLocator)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_minor_locator(minorLocator)
+ """
+ self.widget.mpl.canvas.ax_pattern.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.ax_cake.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.draw()
+ print("Plot takes {0:.2f}s at".format(time.time() - t_start),
+ str(datetime.datetime.now())[:-7])
+ self.widget.unsetCursor()
+ if self.widget.checkBox_LongCursor.isChecked():
+ self.widget.cursor = MultiCursor(
+ self.widget.mpl.canvas,
+ (self.widget.mpl.canvas.ax_pattern,
+ self.widget.mpl.canvas.ax_cake), color='r',
+ lw=float(
+ self.widget.comboBox_VertCursorThickness.
+ currentText()),
+ ls='--', useblit=False) # useblit not supported for pyqt5 yet
+ """
+ self.widget.cursor_pattern = Cursor(
+ self.widget.mpl.canvas.ax_pattern, useblit=False,
+ lw = 1, ls=':')
+ self.widget.cursor_cake = Cursor(
+ self.widget.mpl.canvas.ax_cake, useblit=False, c= 'r',
+ lw = 1, ls=':')
+ """
+
+def _plot_ucfit(self):
+ i = 0
+ for j in self.model.ucfit_lst:
+ if j.display:
+ i += 1
+ if i == 0:
+ return
+ axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ bar_scale = 1. / 100. * axisrange[3]
+ i = 0
+ for phase in self.model.ucfit_lst:
+ if phase.display:
+ phase.cal_dsp()
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ intensity = inten
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ self.widget.tableWidget_UnitCell.removeCellWidget(i, 3)
+ Item4 = QtWidgets.QTableWidgetItem(
+ "{:.3f}".format(float(phase.v)))
+ Item4.setFlags(
+ QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)
+ self.widget.tableWidget_UnitCell.setItem(i, 3, Item4)
+ if self.widget.checkBox_Intensity.isChecked():
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, intensity * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ else:
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, 100. * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ i += 1
+
+def _plot_cake(self):
+ intensity_cake, tth_cake, chi_cake = self.model.diff_img.get_cake()
+ min_slider_pos = self.widget.horizontalSlider_VMin.value()
+ max_slider_pos = self.widget.horizontalSlider_VMax.value()
+ if (max_slider_pos <= min_slider_pos):
+ self.widget.horizontalSlider_VMin.setValue(1)
+ self.widget.horizontalSlider_VMax.setValue(99)
+ intensity_cake_plot = ma.masked_values(intensity_cake, 0.)
+ prefactor = self.widget.spinBox_MaxCakeScale.value() / \
+ (10. ** self.widget.horizontalSlider_MaxScaleBars.value())
+ # intensity_cake_plot.max() / \
+ climits = np.asarray([
+ self.widget.horizontalSlider_VMin.value(),
+ self.widget.horizontalSlider_VMax.value()]) / \
+ 1000. * prefactor
+ if self.widget.checkBox_WhiteForPeak.isChecked():
+ cmap = 'gray'
+ else:
+ cmap = 'gray_r'
+ mid_angle = self.widget.spinBox_AziShift.value()
+ if mid_angle != 0:
+ int_new = np.array(intensity_cake_plot)
+ int_new[0:mid_angle] = intensity_cake[360 - mid_angle:361]
+ int_new[mid_angle:361] = intensity_cake[0:360 - mid_angle]
+ else:
+ int_new = np.array(intensity_cake_plot)
+ self.widget.mpl.canvas.ax_cake.imshow(
+ int_new, origin="lower",
+ extent=[tth_cake.min(), tth_cake.max(),
+ chi_cake.min(), chi_cake.max()],
+ aspect="auto", cmap=cmap, clim=climits) # gray_r
+ tth_list, azi_list, note_list = self._read_azilist()
+ tth_min = tth_cake.min()
+ tth_max = tth_cake.max()
+ if azi_list is not None:
+ for tth, azi, note in zip(tth_list, azi_list, note_list):
+ rect = patches.Rectangle(
+ (tth_min, azi[0]), (tth_max - tth_min), (azi[1] - azi[0]),
+ linewidth=0, edgecolor='b', facecolor='b', alpha=0.2)
+ rect1 = patches.Rectangle(
+ (tth[0], azi[0]), (tth[1] - tth[0]), (azi[1] - azi[0]),
+ linewidth=1, edgecolor='b', facecolor='None')
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect1)
+ if self.widget.checkBox_ShowCakeLabels.isChecked():
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[1], azi[1], note, color=self.obj_color)
+ rows = self.widget.tableWidget_DiffImgAzi.selectionModel().\
+ selectedRows()
+ if rows != []:
+ for r in rows:
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 4).text())
+ rect = patches.Rectangle(
+ (tth_min, azi_min), (tth_max - tth_min),
+ (azi_max - azi_min),
+ linewidth=0, facecolor='r', alpha=0.2)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+
+def _plot_jcpds(self, axisrange):
+ # t_start = time.time()
+ if (not self.widget.checkBox_JCPDSinPattern.isChecked()) and \
+ (not self.widget.checkBox_JCPDSinCake.isChecked()):
+ return
+ selected_phases = []
+ for phase in self.model.jcpds_lst:
+ if phase.display:
+ selected_phases.append(phase)
+ if selected_phases == []:
+ return
+ n_displayed_jcpds = len(selected_phases)
+ # axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ cakerange = self.widget.mpl.canvas.ax_cake.axis()
+ bar_scale = 1. / 100. * axisrange[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ pressure = self.widget.doubleSpinBox_Pressure.value()
+ for i, phase in enumerate(selected_phases):
+ phase.cal_dsp(pressure,
+ self.widget.doubleSpinBox_Temperature.value())
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ intensity = inten * phase.twk_int
+ if self.widget.checkBox_Intensity.isChecked():
+ bar_min = np.ones_like(tth) * axisrange[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = intensity * bar_scale + bar_min
+ else:
+ data_limits = self._get_data_limits()
+ starting_intensity = np.ones_like(tth) * data_limits[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ bar_min = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ if pressure == 0.:
+ volume = phase.v
+ else:
+ volume = phase.v.item()
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, bar_max, colors=phase.color,
+ label="{0:}, {1:.3f} A^3".format(
+ phase.name, volume),
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # hkl
+ if self.widget.checkBox_ShowMillerIndices.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_pattern.text(
+ tth[j], bar_max[j], hkl, color=phase.color,
+ rotation=90, verticalalignment='bottom',
+ horizontalalignment='center',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # phase.name, phase.v.item()))
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.widget.checkBox_JCPDSinCake.isChecked():
+ self.widget.mpl.canvas.ax_cake.vlines(
+ tth, np.ones_like(tth) * cakerange[2],
+ np.ones_like(tth) * cakerange[3], colors=phase.color,
+ lw=float(
+ self.widget.comboBox_CakeJCPDSBarThickness.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_ShowMillerIndices_Cake.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ trans = transforms.blended_transform_factory(
+ self.widget.mpl.canvas.ax_cake.transData,
+ self.widget.mpl.canvas.ax_cake.transAxes)
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[j], 0.99, hkl, color=phase.color,
+ rotation=90, verticalalignment='top',
+ transform=trans, horizontalalignment='right',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ leg_jcpds = self.widget.mpl.canvas.ax_pattern.legend(
+ loc=1, prop={'size': 10}, framealpha=0., handlelength=1)
+ for line, txt in zip(leg_jcpds.get_lines(), leg_jcpds.get_texts()):
+ txt.set_color(line.get_color())
+ # print("JCPDS update takes {0:.2f}s at".format(time.time() - t_start),
+ # str(datetime.datetime.now())[:-7])
+
+def _plot_waterfallpatterns(self):
+ if not self.widget.checkBox_ShowWaterfall.isChecked():
+ return
+ # t_start = time.time()
+ # count how many are dispaly
+ i = 0
+ for pattern in self.model.waterfall_ptn:
+ if pattern.display:
+ i += 1
+ if i == 0:
+ return
+ n_display = i
+ j = 0 # this is needed for waterfall gaps
+ # get y_max
+ for pattern in self.model.waterfall_ptn[::-1]:
+ if pattern.display:
+ j += 1
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05 + j * 0.05,
+ os.path.basename(pattern.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=pattern.color)
+ """
+ if self.widget.checkBox_BgSub.isChecked():
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_bgsub.max() * float(j) / 100.
+ y_bgsub = pattern.y_bgsub
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = y_bgsub / y_bgsub.max() * \
+ self.model.base_ptn.y_bgsub.max()
+ else:
+ y = y_bgsub
+ x_t = pattern.x_bgsub
+ else:
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_raw.max() * float(j) / 100.
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = pattern.y_raw / pattern.y_raw.max() *\
+ self.model.base_ptn.y_raw.max()
+ else:
+ y = pattern.y_raw
+ x_t = pattern.x_raw
+ if self.widget.checkBox_SetToBasePtnLambda.isChecked():
+ x = convert_tth(x_t, pattern.wavelength,
+ self.model.base_ptn.wavelength)
+ else:
+ x = x_t
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y + ygap, c=pattern.color, lw=float(
+ self.widget.comboBox_WaterfallLineThickness.
+ currentText()))
+ if self.widget.checkBox_ShowWaterfallLabels.isChecked():
+ self.widget.mpl.canvas.ax_pattern.text(
+ (x[-1] - x[0]) * 0.01 + x[0], y[0] + ygap,
+ os.path.basename(pattern.fname),
+ verticalalignment='bottom', horizontalalignment='left',
+ color=pattern.color)
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05,
+ os.path.basename(self.model.base_ptn.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=self.model.base_ptn.color)
+ """
+
+def _plot_diffpattern(self, gsas_style=False):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ x_bg, y_bg = self.model.base_ptn.get_background()
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_bg, y_bg, c=self.model.base_ptn.color, ls='--',
+ lw=float(
+ self.widget.comboBox_BkgnLineThickness.
+ currentText()))
+
+def _plot_peakfit(self):
+ if not self.model.current_section_exist():
+ return
+ if self.model.current_section.peaks_exist():
+ for x_c in self.model.current_section.get_peak_positions():
+ self.widget.mpl.canvas.ax_pattern.axvline(
+ x_c, ls='--', dashes=(10, 5))
+ if self.model.current_section.fitted():
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ x_plot = self.model.current_section.x
+ profiles = self.model.current_section.get_individual_profiles(
+ bgsub=bgsub)
+ for key, value in profiles.items():
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, value, ls='-', c=self.obj_color, lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ total_profile = self.model.current_section.get_fit_profile(
+ bgsub=bgsub)
+ residue = self.model.current_section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ y_range = self.model.current_section.get_yrange(bgsub=bgsub)
+ y_shift = y_range[0] - (y_range[1] - y_range[0]) * 0.05
+ #(y_range[1] - y_range[0]) * 1.05
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, residue + y_shift, facecolor='r')
+ """
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, residue + y_shift, 'r-')
+ self.widget.mpl.canvas.ax_pattern.axhline(
+ self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, c='r', ls='-', lw=0.5)
+ """
+ else:
+ pass
+
+def _plot_peakfit_in_gsas_style(self):
+ # get all the highlights
+ # iteratively run plot
+ rows = self.widget.tableWidget_PkFtSections.selectionModel().\
+ selectedRows()
+ if rows == []:
+ return
+ else:
+ selected_rows = [r.row() for r in rows]
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ data_limits = self._get_data_limits()
+ y_shift = data_limits[2] - (data_limits[3] - data_limits[2]) * 0.05
+ i = 0
+ for section in self.model.section_lst:
+ if i in selected_rows:
+ x_plot = section.x
+ total_profile = section.get_fit_profile(bgsub=bgsub)
+ residue = section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, section.get_fit_residue_baseline(bgsub=bgsub) +
+ y_shift, residue + y_shift, facecolor='r')
+ i += 1
diff --git a/jnb-tools/3_2D_from_dpp/test.pdf b/jnb-tools/3_2D_from_dpp/test.pdf
new file mode 100644
index 0000000..b72d0d0
Binary files /dev/null and b/jnb-tools/3_2D_from_dpp/test.pdf differ
diff --git a/jnb-tools/4_peakfit_from_dpp/peakpo_plots.py b/jnb-tools/4_peakfit_from_dpp/peakpo_plots.py
new file mode 100644
index 0000000..abc2a60
--- /dev/null
+++ b/jnb-tools/4_peakfit_from_dpp/peakpo_plots.py
@@ -0,0 +1,537 @@
+import os
+import time
+import datetime
+import numpy as np
+import numpy.ma as ma
+from matplotlib.widgets import MultiCursor
+import matplotlib.transforms as transforms
+import matplotlib.patches as patches
+from PyQt5 import QtWidgets
+from PyQt5 import QtCore
+from ds_jcpds import convert_tth
+
+"""
+def get_cake_range(self):
+ if self.widget.checkBox_ShowCake.isChecked():
+ return self.widget.mpl.canvas.ax_cake.get_xlim(),\
+ self.widget.mpl.canvas.ax_cake.get_ylim()
+ else:
+ return None, None
+
+def _read_azilist(self):
+ n_row = self.widget.tableWidget_DiffImgAzi.rowCount()
+ if n_row == 0:
+ return None, None, None
+ azi_list = []
+ tth_list = []
+ note_list = []
+ for i in range(n_row):
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 4).text())
+ tth_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 1).text())
+ tth_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(i, 3).text())
+ note_i = self.widget.tableWidget_DiffImgAzi.item(i, 0).text()
+ tth_list.append([tth_min, tth_max])
+ azi_list.append([azi_min, azi_max])
+ note_list.append(note_i)
+ return tth_list, azi_list, note_list
+
+def zoom_out_graph(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits()
+ self.update(limits=data_limits,
+ cake_ylimits=(-180, 180))
+
+def update_to_gsas_style(self):
+ if not self.model.base_ptn_exist():
+ return
+ data_limits = self._get_data_limits(y_margin=0.10)
+ self.update(limits=data_limits, gsas_style=True)
+
+def _get_data_limits(self, y_margin=0.):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ return (x.min(), x.max(),
+ y.min() - (y.max() - y.min()) * y_margin,
+ y.max() + (y.max() - y.min()) * y_margin)
+"""
+
+def update(self, limits=None, gsas_style=False, cake_ylimits=None):
+ """Updates the graph"""
+ t_start = time.time()
+ self.widget.setCursor(QtCore.Qt.WaitCursor)
+ if limits is None:
+ limits = self.widget.mpl.canvas.ax_pattern.axis()
+ if cake_ylimits is None:
+ c_limits = self.widget.mpl.canvas.ax_cake.axis()
+ cake_ylimits = c_limits[2:4]
+ if (not self.model.base_ptn_exist()) and \
+ (not self.model.jcpds_exist()):
+ return
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.model.diff_img_exist():
+ self.widget.mpl.canvas.resize_axes(
+ self.widget.horizontalSlider_CakeAxisSize.value())
+ self._plot_cake()
+ else:
+ self.widget.mpl.canvas.resize_axes(1)
+ self._set_nightday_view()
+ if self.model.base_ptn_exist():
+ if self.widget.checkBox_ShortPlotTitle.isChecked():
+ title = os.path.basename(self.model.base_ptn.fname)
+ else:
+ title = self.model.base_ptn.fname
+ self.widget.mpl.canvas.fig.suptitle(
+ title, color=self.obj_color)
+ self._plot_diffpattern(gsas_style)
+ if self.model.waterfall_exist():
+ self._plot_waterfallpatterns()
+ # if self.model.jcpds_exist():
+ # self._plot_jcpds(limits)
+ if self.model.ucfit_exist():
+ self._plot_ucfit()
+ if (self.widget.tabWidget.currentIndex() == 8):
+ if gsas_style:
+ self._plot_peakfit_in_gsas_style()
+ else:
+ self._plot_peakfit()
+ self.widget.mpl.canvas.ax_pattern.set_xlim(limits[0], limits[1])
+ if not self.widget.checkBox_AutoY.isChecked():
+ self.widget.mpl.canvas.ax_pattern.set_ylim(limits[2], limits[3])
+ self.widget.mpl.canvas.ax_cake.set_ylim(cake_ylimits)
+ if self.model.jcpds_exist():
+ self._plot_jcpds(limits)
+ if not self.widget.checkBox_Intensity.isChecked():
+ new_low_limit = -1.1 * limits[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ self.widget.mpl.canvas.ax_pattern.set_ylim(
+ new_low_limit, limits[3])
+ if self.widget.checkBox_ShowLargePnT.isChecked():
+ label_p_t = "{0: 5.1f} GPa\n{1: 4.0f} K".\
+ format(self.widget.doubleSpinBox_Pressure.value(),
+ self.widget.doubleSpinBox_Temperature.value())
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.98, label_p_t, horizontalalignment='left',
+ verticalalignment='top',
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ fontsize=int(
+ self.widget.comboBox_PnTFontSize.currentText()))
+ xlabel = "Two Theta (degrees), {: 6.4f} A".\
+ format(self.widget.doubleSpinBox_SetWavelength.value())
+ self.widget.mpl.canvas.ax_pattern.set_xlabel(xlabel)
+ # if I move the line below to elsewhere I cannot get ylim or axis
+ # self.widget.mpl.canvas.ax_pattern.autoscale(
+ # enable=False, axis=u'both', tight=True)
+ """Removing the lines below for the tick reduce the plot time
+ significantly. So do not turn this on.
+ x_size = limits[1] - limits[0]
+ if x_size <= 50.:
+ majortick_interval = 1
+ minortick_interval = 0.1
+ else:
+ majortick_interval = 10
+ minortick_interval = 1
+ majorLocator = MultipleLocator(majortick_interval)
+ minorLocator = MultipleLocator(minortick_interval)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_major_locator(majorLocator)
+ self.widget.mpl.canvas.ax_pattern.xaxis.set_minor_locator(minorLocator)
+ """
+ self.widget.mpl.canvas.ax_pattern.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.ax_cake.format_coord = \
+ lambda x, y: "{0:.2f},{1:.2e},{2:.3f}A,{3:.3f}A-1".\
+ format(x, y,
+ self.widget.doubleSpinBox_SetWavelength.value()
+ / 2. / np.sin(np.radians(x / 2.)),
+ 4. * np.pi / self.widget.doubleSpinBox_SetWavelength.value() *
+ np.sin(np.radians(x / 2.)))
+ self.widget.mpl.canvas.draw()
+ print("Plot takes {0:.2f}s at".format(time.time() - t_start),
+ str(datetime.datetime.now())[:-7])
+ self.widget.unsetCursor()
+ if self.widget.checkBox_LongCursor.isChecked():
+ self.widget.cursor = MultiCursor(
+ self.widget.mpl.canvas,
+ (self.widget.mpl.canvas.ax_pattern,
+ self.widget.mpl.canvas.ax_cake), color='r',
+ lw=float(
+ self.widget.comboBox_VertCursorThickness.
+ currentText()),
+ ls='--', useblit=False) # useblit not supported for pyqt5 yet
+ """
+ self.widget.cursor_pattern = Cursor(
+ self.widget.mpl.canvas.ax_pattern, useblit=False,
+ lw = 1, ls=':')
+ self.widget.cursor_cake = Cursor(
+ self.widget.mpl.canvas.ax_cake, useblit=False, c= 'r',
+ lw = 1, ls=':')
+ """
+
+def _plot_ucfit(self):
+ i = 0
+ for j in self.model.ucfit_lst:
+ if j.display:
+ i += 1
+ if i == 0:
+ return
+ axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ bar_scale = 1. / 100. * axisrange[3]
+ i = 0
+ for phase in self.model.ucfit_lst:
+ if phase.display:
+ phase.cal_dsp()
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ intensity = inten
+ bar_min = np.ones(tth.shape) * axisrange[2]
+ self.widget.tableWidget_UnitCell.removeCellWidget(i, 3)
+ Item4 = QtWidgets.QTableWidgetItem(
+ "{:.3f}".format(float(phase.v)))
+ Item4.setFlags(
+ QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)
+ self.widget.tableWidget_UnitCell.setItem(i, 3, Item4)
+ if self.widget.checkBox_Intensity.isChecked():
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, intensity * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ else:
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, 100. * bar_scale,
+ colors=phase.color,
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()))
+ i += 1
+
+def _plot_cake(self):
+ intensity_cake, tth_cake, chi_cake = self.model.diff_img.get_cake()
+ min_slider_pos = self.widget.horizontalSlider_VMin.value()
+ max_slider_pos = self.widget.horizontalSlider_VMax.value()
+ if (max_slider_pos <= min_slider_pos):
+ self.widget.horizontalSlider_VMin.setValue(1)
+ self.widget.horizontalSlider_VMax.setValue(99)
+ intensity_cake_plot = ma.masked_values(intensity_cake, 0.)
+ prefactor = self.widget.spinBox_MaxCakeScale.value() / \
+ (10. ** self.widget.horizontalSlider_MaxScaleBars.value())
+ # intensity_cake_plot.max() / \
+ climits = np.asarray([
+ self.widget.horizontalSlider_VMin.value(),
+ self.widget.horizontalSlider_VMax.value()]) / \
+ 1000. * prefactor
+ if self.widget.checkBox_WhiteForPeak.isChecked():
+ cmap = 'gray'
+ else:
+ cmap = 'gray_r'
+ mid_angle = self.widget.spinBox_AziShift.value()
+ if mid_angle != 0:
+ int_new = np.array(intensity_cake_plot)
+ int_new[0:mid_angle] = intensity_cake[360 - mid_angle:361]
+ int_new[mid_angle:361] = intensity_cake[0:360 - mid_angle]
+ else:
+ int_new = np.array(intensity_cake_plot)
+ self.widget.mpl.canvas.ax_cake.imshow(
+ int_new, origin="lower",
+ extent=[tth_cake.min(), tth_cake.max(),
+ chi_cake.min(), chi_cake.max()],
+ aspect="auto", cmap=cmap, clim=climits) # gray_r
+ tth_list, azi_list, note_list = self._read_azilist()
+ tth_min = tth_cake.min()
+ tth_max = tth_cake.max()
+ if azi_list is not None:
+ for tth, azi, note in zip(tth_list, azi_list, note_list):
+ rect = patches.Rectangle(
+ (tth_min, azi[0]), (tth_max - tth_min), (azi[1] - azi[0]),
+ linewidth=0, edgecolor='b', facecolor='b', alpha=0.2)
+ rect1 = patches.Rectangle(
+ (tth[0], azi[0]), (tth[1] - tth[0]), (azi[1] - azi[0]),
+ linewidth=1, edgecolor='b', facecolor='None')
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect1)
+ if self.widget.checkBox_ShowCakeLabels.isChecked():
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[1], azi[1], note, color=self.obj_color)
+ rows = self.widget.tableWidget_DiffImgAzi.selectionModel().\
+ selectedRows()
+ if rows != []:
+ for r in rows:
+ azi_min = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 2).text())
+ azi_max = float(
+ self.widget.tableWidget_DiffImgAzi.item(r.row(), 4).text())
+ rect = patches.Rectangle(
+ (tth_min, azi_min), (tth_max - tth_min),
+ (azi_max - azi_min),
+ linewidth=0, facecolor='r', alpha=0.2)
+ self.widget.mpl.canvas.ax_cake.add_patch(rect)
+
+def _plot_jcpds(self, axisrange):
+ # t_start = time.time()
+ if (not self.widget.checkBox_JCPDSinPattern.isChecked()) and \
+ (not self.widget.checkBox_JCPDSinCake.isChecked()):
+ return
+ selected_phases = []
+ for phase in self.model.jcpds_lst:
+ if phase.display:
+ selected_phases.append(phase)
+ if selected_phases == []:
+ return
+ n_displayed_jcpds = len(selected_phases)
+ # axisrange = self.widget.mpl.canvas.ax_pattern.axis()
+ cakerange = self.widget.mpl.canvas.ax_cake.axis()
+ bar_scale = 1. / 100. * axisrange[3] * \
+ self.widget.horizontalSlider_JCPDSBarScale.value() / 100.
+ pressure = self.widget.doubleSpinBox_Pressure.value()
+ for i, phase in enumerate(selected_phases):
+ phase.cal_dsp(pressure,
+ self.widget.doubleSpinBox_Temperature.value())
+ tth, inten = phase.get_tthVSint(
+ self.widget.doubleSpinBox_SetWavelength.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ intensity = inten * phase.twk_int
+ if self.widget.checkBox_Intensity.isChecked():
+ bar_min = np.ones_like(tth) * axisrange[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = intensity * bar_scale + bar_min
+ else:
+ data_limits = self._get_data_limits()
+ starting_intensity = np.ones_like(tth) * data_limits[2] + \
+ self.widget.horizontalSlider_JCPDSBarPosition.\
+ value() / 100. * axisrange[3]
+ bar_max = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ bar_min = starting_intensity - \
+ i * 100. * bar_scale / n_displayed_jcpds
+ if pressure == 0.:
+ volume = phase.v
+ else:
+ volume = phase.v.item()
+ self.widget.mpl.canvas.ax_pattern.vlines(
+ tth, bar_min, bar_max, colors=phase.color,
+ label="{0:}, {1:.3f} A^3".format(
+ phase.name, volume),
+ lw=float(
+ self.widget.comboBox_PtnJCPDSBarThickness.
+ currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # hkl
+ if self.widget.checkBox_ShowMillerIndices.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_pattern.text(
+ tth[j], bar_max[j], hkl, color=phase.color,
+ rotation=90, verticalalignment='bottom',
+ horizontalalignment='center',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_ptn_Alpha.value())
+ # phase.name, phase.v.item()))
+ if self.widget.checkBox_ShowCake.isChecked() and \
+ self.widget.checkBox_JCPDSinCake.isChecked():
+ self.widget.mpl.canvas.ax_cake.vlines(
+ tth, np.ones_like(tth) * cakerange[2],
+ np.ones_like(tth) * cakerange[3], colors=phase.color,
+ lw=float(
+ self.widget.comboBox_CakeJCPDSBarThickness.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_ShowMillerIndices_Cake.isChecked():
+ hkl_list = phase.get_hkl_in_text()
+ trans = transforms.blended_transform_factory(
+ self.widget.mpl.canvas.ax_cake.transData,
+ self.widget.mpl.canvas.ax_cake.transAxes)
+ for j, hkl in enumerate(hkl_list):
+ self.widget.mpl.canvas.ax_cake.text(
+ tth[j], 0.99, hkl, color=phase.color,
+ rotation=90, verticalalignment='top',
+ transform=trans, horizontalalignment='right',
+ fontsize=int(
+ self.widget.comboBox_HKLFontSize.currentText()),
+ alpha=self.widget.doubleSpinBox_JCPDS_cake_Alpha.value())
+ if self.widget.checkBox_JCPDSinPattern.isChecked():
+ leg_jcpds = self.widget.mpl.canvas.ax_pattern.legend(
+ loc=1, prop={'size': 10}, framealpha=0., handlelength=1)
+ for line, txt in zip(leg_jcpds.get_lines(), leg_jcpds.get_texts()):
+ txt.set_color(line.get_color())
+ # print("JCPDS update takes {0:.2f}s at".format(time.time() - t_start),
+ # str(datetime.datetime.now())[:-7])
+
+def _plot_waterfallpatterns(self):
+ if not self.widget.checkBox_ShowWaterfall.isChecked():
+ return
+ # t_start = time.time()
+ # count how many are dispaly
+ i = 0
+ for pattern in self.model.waterfall_ptn:
+ if pattern.display:
+ i += 1
+ if i == 0:
+ return
+ n_display = i
+ j = 0 # this is needed for waterfall gaps
+ # get y_max
+ for pattern in self.model.waterfall_ptn[::-1]:
+ if pattern.display:
+ j += 1
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05 + j * 0.05,
+ os.path.basename(pattern.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=pattern.color)
+ """
+ if self.widget.checkBox_BgSub.isChecked():
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_bgsub.max() * float(j) / 100.
+ y_bgsub = pattern.y_bgsub
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = y_bgsub / y_bgsub.max() * \
+ self.model.base_ptn.y_bgsub.max()
+ else:
+ y = y_bgsub
+ x_t = pattern.x_bgsub
+ else:
+ ygap = self.widget.horizontalSlider_WaterfallGaps.value() * \
+ self.model.base_ptn.y_raw.max() * float(j) / 100.
+ if self.widget.checkBox_IntNorm.isChecked():
+ y = pattern.y_raw / pattern.y_raw.max() *\
+ self.model.base_ptn.y_raw.max()
+ else:
+ y = pattern.y_raw
+ x_t = pattern.x_raw
+ if self.widget.checkBox_SetToBasePtnLambda.isChecked():
+ x = convert_tth(x_t, pattern.wavelength,
+ self.model.base_ptn.wavelength)
+ else:
+ x = x_t
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y + ygap, c=pattern.color, lw=float(
+ self.widget.comboBox_WaterfallLineThickness.
+ currentText()))
+ if self.widget.checkBox_ShowWaterfallLabels.isChecked():
+ self.widget.mpl.canvas.ax_pattern.text(
+ (x[-1] - x[0]) * 0.01 + x[0], y[0] + ygap,
+ os.path.basename(pattern.fname),
+ verticalalignment='bottom', horizontalalignment='left',
+ color=pattern.color)
+ """
+ self.widget.mpl.canvas.ax_pattern.text(
+ 0.01, 0.97 - n_display * 0.05,
+ os.path.basename(self.model.base_ptn.fname),
+ transform=self.widget.mpl.canvas.ax_pattern.transAxes,
+ color=self.model.base_ptn.color)
+ """
+
+def _plot_diffpattern(self, gsas_style=False):
+ if self.widget.checkBox_BgSub.isChecked():
+ x, y = self.model.base_ptn.get_bgsub()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ else:
+ x, y = self.model.base_ptn.get_raw()
+ if gsas_style:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color, marker='o',
+ linestyle='None', ms=3)
+ else:
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x, y, c=self.model.base_ptn.color,
+ lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ x_bg, y_bg = self.model.base_ptn.get_background()
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_bg, y_bg, c=self.model.base_ptn.color, ls='--',
+ lw=float(
+ self.widget.comboBox_BkgnLineThickness.
+ currentText()))
+
+def _plot_peakfit(self):
+ if not self.model.current_section_exist():
+ return
+ if self.model.current_section.peaks_exist():
+ for x_c in self.model.current_section.get_peak_positions():
+ self.widget.mpl.canvas.ax_pattern.axvline(
+ x_c, ls='--', dashes=(10, 5))
+ if self.model.current_section.fitted():
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ x_plot = self.model.current_section.x
+ profiles = self.model.current_section.get_individual_profiles(
+ bgsub=bgsub)
+ for key, value in profiles.items():
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, value, ls='-', c=self.obj_color, lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ total_profile = self.model.current_section.get_fit_profile(
+ bgsub=bgsub)
+ residue = self.model.current_section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ y_range = self.model.current_section.get_yrange(bgsub=bgsub)
+ y_shift = y_range[0] - (y_range[1] - y_range[0]) * 0.05
+ #(y_range[1] - y_range[0]) * 1.05
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, residue + y_shift, facecolor='r')
+ """
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, residue + y_shift, 'r-')
+ self.widget.mpl.canvas.ax_pattern.axhline(
+ self.model.current_section.get_fit_residue_baseline(
+ bgsub=bgsub) + y_shift, c='r', ls='-', lw=0.5)
+ """
+ else:
+ pass
+
+def _plot_peakfit_in_gsas_style(self):
+ # get all the highlights
+ # iteratively run plot
+ rows = self.widget.tableWidget_PkFtSections.selectionModel().\
+ selectedRows()
+ if rows == []:
+ return
+ else:
+ selected_rows = [r.row() for r in rows]
+ bgsub = self.widget.checkBox_BgSub.isChecked()
+ data_limits = self._get_data_limits()
+ y_shift = data_limits[2] - (data_limits[3] - data_limits[2]) * 0.05
+ i = 0
+ for section in self.model.section_lst:
+ if i in selected_rows:
+ x_plot = section.x
+ total_profile = section.get_fit_profile(bgsub=bgsub)
+ residue = section.get_fit_residue(bgsub=bgsub)
+ self.widget.mpl.canvas.ax_pattern.plot(
+ x_plot, total_profile, 'r-', lw=float(
+ self.widget.comboBox_BasePtnLineThickness.
+ currentText()))
+ self.widget.mpl.canvas.ax_pattern.fill_between(
+ x_plot, section.get_fit_residue_baseline(bgsub=bgsub) +
+ y_shift, residue + y_shift, facecolor='r')
+ i += 1
diff --git a/jnb-tools/4_peakfit_from_dpp/test.pdf b/jnb-tools/4_peakfit_from_dpp/test.pdf
new file mode 100644
index 0000000..12ec08e
Binary files /dev/null and b/jnb-tools/4_peakfit_from_dpp/test.pdf differ
diff --git a/jnb-tools/4_peakfit_from_dpp/xrd_pattern-peakfitting.html b/jnb-tools/4_peakfit_from_dpp/xrd_pattern-peakfitting.html
new file mode 100644
index 0000000..a5d0157
--- /dev/null
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+xrd_pattern-peakfitting
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+
Peak fitting for XRD pattern from dpp¶
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+
+Please check setup_for_notebooks file if you have problem using the notebooks in this folder.
+In this notebook, we will learn how to plot XRD patterns using the information saved in dpp.
+dpp is a project file saved in PeakPo. You may plot, jcpds information and cake as well as many other information.
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+
Check the versio of pyFAI in your conda environment¶
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/Users/DanShim/anaconda/envs/peakpo7721/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
+ from ._conv import register_converters as _register_converters
+WARNING:pyFAI.opencl.common:Unable to import pyOpenCl. Please install it from: http://pypi.python.org/pypi/pyopencl
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Out[2]:
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+
Note that the example data files I provided are made with pyFAI version 0.14. If you see version higher than 0.15 here, you will get error when you read the example dpp file. In that case, you either follow the instruction in setup_for_notebooks.ipynb or you may use your own dpp for this note book.
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../data/hStv/hSiO2_404_009.dpp
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Setup a new PeakPo model and assign info from dpp¶
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+
Make sure to reset the chi folder location using the new_chi_path option.
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See xrd_pattern.ipynb file for basic operations.
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The following three modules are all in the ../local_modules folder.
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(6.0005517, 20.994111, -49.297263099930205, 2799.9014723947657)
+(6.0005517, 20.994111, -2334.2171366494, 2799.9014723947657)
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Check by changing the xrange for plot_diffpattern.
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Get background subtracted pattern for masking.
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Out[15]:
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array([ 6.0005517, 6.0109784, 6.021405 , ..., 20.973258 , 20.983684 ,
+ 20.994111 ])
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Out[17]:
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[<matplotlib.lines.Line2D at 0x123d78c50>]
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Make initial peak position array¶
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Out[19]:
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array([ 67, 220, 246, 325, 373, 397, 421])
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Plot to see if the search positions are reasonable.
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Out[20]:
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[<matplotlib.lines.Line2D at 0x11c20e828>]
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Define some functions for peak fitting¶
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Define functions for peakfitting. Here we use LMFIT module.
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First fitting attempt without any restriction¶
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[[Model]]
+ (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))
+[[Fit Statistics]]
+ # function evals = 225
+ # data points = 480
+ # variables = 30
+ chi-square = 85049.827
+ reduced chi-square = 189.000
+ Akaike info crit = 2545.059
+ Bayesian info crit = 2670.273
+[[Variables]]
+ bg_intercept: 11.7490860 +/- 7.314150 (62.25%) (init= 0)
+ bg_slope: -1.92238746 +/- 0.867763 (45.14%) (init= 0)
+ pk0_fraction: 0.99914425 +/- 0.439206 (43.96%) (init= 0)
+ pk0_sigma: 0.09905194 +/- 0.022508 (22.72%) (init= 0.05)
+ pk0_center: 6.67814455 +/- 0.011066 (0.17%) (init= 6.699139)
+ pk0_amplitude: 13.7291342 +/- 3.199312 (23.30%) (init= 1000)
+ pk0_fwhm: 0.19810388 +/- 0.043560 (21.99%) == '2.0000000*pk0_sigma'
+ pk1_fraction: 0.71390507 +/- 1.440882 (201.83%) (init= 0)
+ pk1_sigma: 0.04899342 +/- 0.022649 (46.23%) (init= 0.05)
+ pk1_center: 8.27232961 +/- 0.013928 (0.17%) (init= 8.29442)
+ pk1_amplitude: 1.94815516 +/- 1.650007 (84.70%) (init= 1000)
+ pk1_fwhm: 0.09798684 +/- 0.045272 (46.20%) == '2.0000000*pk1_sigma'
+ pk2_fraction: 0.53578175 +/- 0.013639 (2.55%) (init= 0)
+ pk2_sigma: 0.04131704 +/- 0.000178 (0.43%) (init= 0.05)
+ pk2_center: 8.56763923 +/- 0.000107 (0.00%) (init= 8.565514)
+ pk2_amplitude: 298.762770 +/- 1.505359 (0.50%) (init= 1000)
+ pk2_fwhm: 0.08263408 +/- 0.000352 (0.43%) == '2.0000000*pk2_sigma'
+ pk3_fraction: 0.01241608 +/- 5.77e+05 (4647278978.67%) (init= 0)
+ pk3_sigma: 9.6370e-05 +/- 0.473812 (491658.24%) (init= 0.05)
+ pk3_center: 9.39347570 +/- 0.004071 (0.04%) (init= 9.389221)
+ pk3_amplitude: 22.7444443 +/- 1.28e+07 (56470649.79%) (init= 1000)
+ pk3_fwhm: 0.00019274 +/- 0.947947 (491825.72%) == '2.0000000*pk3_sigma'
+ pk4_fraction: 0.42273441 +/- 0.060602 (14.34%) (init= 0)
+ pk4_sigma: 0.07398460 +/- 0.001004 (1.36%) (init= 0.05)
+ pk4_center: 9.89043232 +/- 0.000654 (0.01%) (init= 9.889702)
+ pk4_amplitude: 114.039270 +/- 2.756552 (2.42%) (init= 1000)
+ pk4_fwhm: 0.14796921 +/- 0.001722 (1.16%) == '2.0000000*pk4_sigma'
+ pk5_fraction: 0.99989658 +/- 0.227448 (22.75%) (init= 0)
+ pk5_sigma: 0.03301947 +/- 0.003775 (11.43%) (init= 0.05)
+ pk5_center: 10.1384658 +/- 0.001881 (0.02%) (init= 10.13994)
+ pk5_amplitude: 16.1121387 +/- 1.941011 (12.05%) (init= 1000)
+ pk5_fwhm: 0.06603895 +/- 0.007729 (11.70%) == '2.0000000*pk5_sigma'
+ pk6_fraction: 0.00057210 +/- 0.209475 (36614.88%) (init= 0)
+ pk6_sigma: 0.08438759 +/- 0.002174 (2.58%) (init= 0.05)
+ pk6_center: 10.3880876 +/- 0.001593 (0.02%) (init= 10.39018)
+ pk6_amplitude: 48.6505860 +/- 3.622713 (7.45%) (init= 1000)
+ pk6_fwhm: 0.16877519 +/- 0.004349 (2.58%) == '2.0000000*pk6_sigma'
+[[Correlations]] (unreported correlations are < 0.500)
+ C(pk3_fraction, pk3_amplitude) = -1.000
+ C(bg_intercept, bg_slope) = -0.986
+ C(pk3_sigma, pk3_center) = -0.962
+ C(pk6_fraction, pk6_amplitude) = 0.954
+ C(pk4_fraction, pk4_amplitude) = 0.900
+ C(pk2_fraction, pk2_amplitude) = 0.830
+ C(pk1_fraction, pk1_amplitude) = 0.821
+ C(pk0_fraction, pk0_amplitude) = 0.701
+ C(bg_intercept, pk0_amplitude) = -0.694
+ C(pk5_fraction, pk5_amplitude) = 0.689
+ C(bg_slope, pk6_amplitude) = -0.616
+ C(bg_slope, pk0_amplitude) = 0.613
+ C(pk5_amplitude, pk6_amplitude) = -0.589
+ C(pk2_fraction, pk2_sigma) = -0.589
+ C(pk4_amplitude, pk5_amplitude) = -0.584
+ C(pk1_fraction, pk1_sigma) = -0.566
+ C(bg_slope, pk6_fraction) = -0.554
+ C(pk5_amplitude, pk6_fraction) = -0.545
+ C(bg_intercept, pk6_amplitude) = 0.544
+ C(pk0_fraction, pk0_sigma) = -0.539
+ C(pk4_fraction, pk5_amplitude) = -0.525
+ C(pk5_fraction, pk5_sigma) = -0.525
+
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Plot the fitting results
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Do better initial guess¶
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Pereform new fitting
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[[Model]]
+ (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))
+[[Fit Statistics]]
+ # function evals = 189
+ # data points = 480
+ # variables = 30
+ chi-square = 60700.902
+ reduced chi-square = 134.891
+ Akaike info crit = 2383.165
+ Bayesian info crit = 2508.379
+[[Variables]]
+ bg_intercept: 29.1722806 +/- 4.995923 (17.13%) (init= 0)
+ bg_slope: -3.83526210 +/- 0.645062 (16.82%) (init= 0)
+ pk0_fraction: 0.51590945 +/- 0.431690 (83.68%) (init= 0)
+ pk0_sigma: 0.03063716 +/- 0.004060 (13.25%) (init= 0.03)
+ pk0_center: 6.69518915 +/- 0.002586 (0.04%) (init= 6.699139)
+ pk0_amplitude: 6.55216766 +/- 1.023892 (15.63%) (init= 100)
+ pk0_fwhm: 0.06127433 +/- 0.008120 (13.25%) == '2.0000000*pk0_sigma'
+ pk1_fraction: 0.59988174 +/- 0.807284 (134.57%) (init= 0)
+ pk1_sigma: 0.06145041 +/- 0.008172 (13.30%) (init= 0.03)
+ pk1_center: 8.27073401 +/- 0.005765 (0.07%) (init= 8.29442)
+ pk1_amplitude: 4.56442997 +/- 1.489646 (32.64%) (init= 100)
+ pk1_fwhm: 0.12290083 +/- 0.016344 (13.30%) == '2.0000000*pk1_sigma'
+ pk2_fraction: 0.55998216 +/- 0.011321 (2.02%) (init= 0)
+ pk2_sigma: 0.04126828 +/- 0.000146 (0.35%) (init= 0.03)
+ pk2_center: 8.56758351 +/- 9.03e-05 (0.00%) (init= 8.565514)
+ pk2_amplitude: 302.091134 +/- 1.270450 (0.42%) (init= 100)
+ pk2_fwhm: 0.08253656 +/- 0.000292 (0.35%) == '2.0000000*pk2_sigma'
+ pk3_fraction: 0.73052766 +/- 0.365205 (49.99%) (init= 0)
+ pk3_sigma: 0.06668190 +/- 0.007879 (11.82%) (init= 0.03)
+ pk3_center: 9.37829233 +/- 0.003947 (0.04%) (init= 9.389221)
+ pk3_amplitude: 6.70558552 +/- 1.220807 (18.21%) (init= 100)
+ pk3_fwhm: 0.13336381 +/- 0.015759 (11.82%) == '2.0000000*pk3_sigma'
+ pk4_fraction: 0.31530220 +/- 0.059561 (18.89%) (init= 0)
+ pk4_sigma: 0.07496412 +/- 0.000848 (1.13%) (init= 0.03)
+ pk4_center: 9.89032617 +/- 0.000578 (0.01%) (init= 9.889702)
+ pk4_amplitude: 110.329860 +/- 2.513495 (2.28%) (init= 100)
+ pk4_fwhm: 0.14992825 +/- 0.001695 (1.13%) == '2.0000000*pk4_sigma'
+ pk5_fraction: 0.98649024 +/- 0.219909 (22.29%) (init= 0)
+ pk5_sigma: 0.04167542 +/- 0.003769 (9.05%) (init= 0.03)
+ pk5_center: 10.1371170 +/- 0.001982 (0.02%) (init= 10.13994)
+ pk5_amplitude: 19.7762331 +/- 2.139238 (10.82%) (init= 100)
+ pk5_fwhm: 0.08335085 +/- 0.007539 (9.05%) == '2.0000000*pk5_sigma'
+ pk6_fraction: 0.02796641 +/- 0.166538 (595.50%) (init= 0)
+ pk6_sigma: 0.08238659 +/- 0.001861 (2.26%) (init= 0.03)
+ pk6_center: 10.3885518 +/- 0.001341 (0.01%) (init= 10.39018)
+ pk6_amplitude: 48.4116664 +/- 3.192891 (6.60%) (init= 100)
+ pk6_fwhm: 0.16477319 +/- 0.003723 (2.26%) == '2.0000000*pk6_sigma'
+[[Correlations]] (unreported correlations are < 0.500)
+ C(bg_intercept, bg_slope) = -0.984
+ C(pk6_fraction, pk6_amplitude) = 0.942
+ C(pk4_fraction, pk4_amplitude) = 0.904
+ C(pk1_fraction, pk1_amplitude) = 0.902
+ C(pk0_fraction, pk0_amplitude) = 0.821
+ C(pk2_fraction, pk2_amplitude) = 0.819
+ C(pk5_fraction, pk5_amplitude) = 0.772
+ C(bg_slope, pk6_amplitude) = -0.659
+ C(pk3_fraction, pk3_sigma) = -0.654
+ C(pk4_amplitude, pk5_amplitude) = -0.638
+ C(pk3_fraction, pk3_amplitude) = 0.616
+ C(bg_intercept, pk6_amplitude) = 0.595
+ C(bg_slope, pk6_fraction) = -0.594
+ C(pk5_amplitude, pk6_amplitude) = -0.581
+ C(pk2_fraction, pk2_sigma) = -0.560
+ C(pk0_fraction, pk0_sigma) = -0.555
+ C(bg_intercept, pk6_fraction) = 0.536
+ C(pk4_fraction, pk5_amplitude) = -0.514
+ C(pk1_fraction, pk1_sigma) = -0.511
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Use the last fitting results for the next fitting¶
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[[Model]]
+ (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))
+[[Fit Statistics]]
+ # function evals = 503
+ # data points = 480
+ # variables = 30
+ chi-square = 47779.818
+ reduced chi-square = 106.177
+ Akaike info crit = 2268.275
+ Bayesian info crit = 2393.488
+[[Variables]]
+ bg_intercept: 28.3283335 +/- 4.486009 (15.84%) (init= 29.17228)
+ bg_slope: -3.99541920 +/- 0.582845 (14.59%) (init=-3.835262)
+ pk0_fraction: 0.74671887 +/- 0.313435 (41.97%) (init= 0.5159095)
+ pk0_sigma: 0.03050200 +/- 0.004038 (13.24%) (init= 0.03063717)
+ pk0_center: 6.69504203 +/- 0.002299 (0.03%) (init= 6.695189)
+ pk0_amplitude: 7.39223915 +/- 0.896851 (12.13%) (init= 6.552168)
+ pk0_fwhm: 0.06100400 +/- 0.008076 (13.24%) == '2.0000000*pk0_sigma'
+ pk1_fraction: 0.00473684 +/- 0.983531 (20763.43%) (init= 0.5998817)
+ pk1_sigma: 0.04023895 +/- 0.007253 (18.03%) (init= 0.06145042)
+ pk1_center: 8.27500294 +/- 0.005252 (0.06%) (init= 8.270734)
+ pk1_amplitude: 3.38135714 +/- 1.077574 (31.87%) (init= 4.56443)
+ pk1_fwhm: 0.08047791 +/- 0.014507 (18.03%) == '2.0000000*pk1_sigma'
+ pk2_fraction: 0.53523776 +/- 0.009813 (1.83%) (init= 0.5599822)
+ pk2_sigma: 0.04133250 +/- 0.000128 (0.31%) (init= 0.04126828)
+ pk2_center: 8.56765135 +/- 7.99e-05 (0.00%) (init= 8.567584)
+ pk2_amplitude: 298.846896 +/- 1.093183 (0.37%) (init= 302.0911)
+ pk2_fwhm: 0.08266501 +/- 0.000256 (0.31%) == '2.0000000*pk2_sigma'
+ pk3_fraction: 1.2819e-09 +/- 0.575693 (44910663219.10%) (init= 0.7305277)
+ pk3_sigma: 0.06052691 +/- 0.005850 (9.67%) (init= 0.06668191)
+ pk3_center: 9.38425335 +/- 0.004238 (0.05%) (init= 9.378292)
+ pk3_amplitude: 7.71940342 +/- 1.508313 (19.54%) (init= 6.705586)
+ pk3_fwhm: 0.12105382 +/- 0.011701 (9.67%) == '2.0000000*pk3_sigma'
+ pk4_fraction: 0.33341226 +/- 0.051429 (15.43%) (init= 0.3153022)
+ pk4_sigma: 0.07516648 +/- 0.000753 (1.00%) (init= 0.07496413)
+ pk4_center: 9.89032149 +/- 0.000512 (0.01%) (init= 9.890326)
+ pk4_amplitude: 111.694162 +/- 2.284480 (2.05%) (init= 110.3299)
+ pk4_fwhm: 0.15033296 +/- 0.001505 (1.00%) == '2.0000000*pk4_sigma'
+ pk5_fraction: 0.96950193 +/- 0.178592 (18.42%) (init= 0.9864902)
+ pk5_sigma: 0.04213893 +/- 0.003350 (7.95%) (init= 0.04167543)
+ pk5_center: 10.1370259 +/- 0.001720 (0.02%) (init= 10.13712)
+ pk5_amplitude: 19.4688965 +/- 1.893864 (9.73%) (init= 19.77623)
+ pk5_fwhm: 0.08427786 +/- 0.006701 (7.95%) == '2.0000000*pk5_sigma'
+ pk6_fraction: 0.08936925 +/- 0.145983 (163.35%) (init= 0.02796641)
+ pk6_sigma: 0.08243764 +/- 0.001599 (1.94%) (init= 0.0823866)
+ pk6_center: 10.3884873 +/- 0.001169 (0.01%) (init= 10.38855)
+ pk6_amplitude: 50.7440009 +/- 2.863534 (5.64%) (init= 48.41167)
+ pk6_fwhm: 0.16487528 +/- 0.003199 (1.94%) == '2.0000000*pk6_sigma'
+[[Correlations]] (unreported correlations are < 0.500)
+ C(bg_intercept, bg_slope) = -0.985
+ C(pk6_fraction, pk6_amplitude) = 0.948
+ C(pk3_fraction, pk3_amplitude) = 0.916
+ C(pk4_fraction, pk4_amplitude) = 0.909
+ C(pk1_fraction, pk1_amplitude) = 0.900
+ C(pk2_fraction, pk2_amplitude) = 0.819
+ C(pk5_fraction, pk5_amplitude) = 0.745
+ C(pk0_fraction, pk0_amplitude) = 0.735
+ C(bg_slope, pk6_amplitude) = -0.670
+ C(pk4_amplitude, pk5_amplitude) = -0.641
+ C(pk0_fraction, pk0_sigma) = -0.609
+ C(bg_intercept, pk6_amplitude) = 0.608
+ C(bg_slope, pk6_fraction) = -0.608
+ C(pk2_fraction, pk2_sigma) = -0.564
+ C(pk5_amplitude, pk6_amplitude) = -0.552
+ C(bg_intercept, pk6_fraction) = 0.552
+ C(pk4_fraction, pk5_amplitude) = -0.522
+ C(pk1_fraction, pk1_sigma) = -0.502
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Fix some parameters for fitting¶
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Out[30]:
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Parameters([('bg_intercept',
+ <Parameter 'bg_intercept', value=28.32833357262428 +/- 4.49, bounds=[-inf:inf]>),
+ ('bg_slope',
+ <Parameter 'bg_slope', value=-3.9954192033102194 +/- 0.583, bounds=[-inf:inf]>),
+ ('pk0_fraction',
+ <Parameter 'pk0_fraction', value=0.746718872944975 +/- 0.313, bounds=[0.0:1.0]>),
+ ('pk0_sigma',
+ <Parameter 'pk0_sigma', value=0.030502000098484028 +/- 0.00404, bounds=[0.0:0.5]>),
+ ('pk0_center',
+ <Parameter 'pk0_center', value=6.6950420336582175 +/- 0.0023, bounds=[6.1991389:7.1991389]>),
+ ('pk0_amplitude',
+ <Parameter 'pk0_amplitude', value=7.392239151294238 +/- 0.897, bounds=[0.0:inf]>),
+ ('pk0_fwhm',
+ <Parameter 'pk0_fwhm', value=0.061004000196968056 +/- 0.00808, bounds=[-inf:inf], expr='2.0000000*pk0_sigma'>),
+ ('pk1_fraction',
+ <Parameter 'pk1_fraction', value=0.0047368437901394445 +/- 0.984, bounds=[0.0:1.0]>),
+ ('pk1_sigma',
+ <Parameter 'pk1_sigma', value=0.040238957993689484 +/- 0.00725, bounds=[0.0:0.5]>),
+ ('pk1_center',
+ <Parameter 'pk1_center', value=8.275002942079906 +/- 0.00525, bounds=[7.7944203000000005:8.7944203]>),
+ ('pk1_amplitude',
+ <Parameter 'pk1_amplitude', value=3.3813571421224795 +/- 1.08, bounds=[0.0:inf]>),
+ ('pk1_fwhm',
+ <Parameter 'pk1_fwhm', value=0.08047791598737897 +/- 0.0145, bounds=[-inf:inf], expr='2.0000000*pk1_sigma'>),
+ ('pk2_fraction',
+ <Parameter 'pk2_fraction', value=0.5352377607277863 +/- 0.00981, bounds=[0.0:1.0]>),
+ ('pk2_sigma',
+ <Parameter 'pk2_sigma', value=0.0413325079572846 +/- 0.000128, bounds=[0.0:0.5]>),
+ ('pk2_center',
+ <Parameter 'pk2_center', value=8.567651349923386 +/- 7.99e-05, bounds=[8.0655138:9.0655138]>),
+ ('pk2_amplitude',
+ <Parameter 'pk2_amplitude', value=298.84689642751647 +/- 1.09, bounds=[0.0:inf]>),
+ ('pk2_fwhm',
+ <Parameter 'pk2_fwhm', value=0.0826650159145692 +/- 0.000256, bounds=[-inf:inf], expr='2.0000000*pk2_sigma'>),
+ ('pk3_fraction',
+ <Parameter 'pk3_fraction', value=1.2818636707656594e-09 +/- 0.576, bounds=[0.0:1.0]>),
+ ('pk3_sigma',
+ <Parameter 'pk3_sigma', value=0.060526913876875005 +/- 0.00585, bounds=[0.0:0.5]>),
+ ('pk3_center',
+ <Parameter 'pk3_center', value=9.384253358157784 +/- 0.00424, bounds=[8.8892212:9.8892212]>),
+ ('pk3_amplitude',
+ <Parameter 'pk3_amplitude', value=7.719403428960279 +/- 1.51, bounds=[0.0:inf]>),
+ ('pk3_fwhm',
+ <Parameter 'pk3_fwhm', value=0.12105382775375001 +/- 0.0117, bounds=[-inf:inf], expr='2.0000000*pk3_sigma'>),
+ ('pk4_fraction',
+ <Parameter 'pk4_fraction', value=0.33341226288747644 +/- 0.0514, bounds=[0.0:1.0]>),
+ ('pk4_sigma',
+ <Parameter 'pk4_sigma', value=0.07516648474708912 +/- 0.000753, bounds=[0.0:0.5]>),
+ ('pk4_center',
+ <Parameter 'pk4_center', value=9.890321491864697 +/- 0.000512, bounds=[9.3897016:10.3897016]>),
+ ('pk4_amplitude',
+ <Parameter 'pk4_amplitude', value=111.69416284692755 +/- 2.28, bounds=[0.0:inf]>),
+ ('pk4_fwhm',
+ <Parameter 'pk4_fwhm', value=0.15033296949417824 +/- 0.00151, bounds=[-inf:inf], expr='2.0000000*pk4_sigma'>),
+ ('pk5_fraction',
+ <Parameter 'pk5_fraction', value=0.969501936318661 +/- 0.179, bounds=[0.0:1.0]>),
+ ('pk5_sigma',
+ <Parameter 'pk5_sigma', value=0.04213893040841052 +/- 0.00335, bounds=[0.0:0.5]>),
+ ('pk5_center',
+ <Parameter 'pk5_center', value=10.137025962124882 +/- 0.00172, bounds=[9.639942:10.639942]>),
+ ('pk5_amplitude',
+ <Parameter 'pk5_amplitude', value=19.46889654226545 +/- 1.89, bounds=[0.0:inf]>),
+ ('pk5_fwhm',
+ <Parameter 'pk5_fwhm', value=0.08427786081682104 +/- 0.0067, bounds=[-inf:inf], expr='2.0000000*pk5_sigma'>),
+ ('pk6_fraction',
+ <Parameter 'pk6_fraction', value=0.089369250814746 +/- 0.146, bounds=[0.0:1.0]>),
+ ('pk6_sigma',
+ <Parameter 'pk6_sigma', value=0.08243764124151592 +/- 0.0016, bounds=[0.0:0.5]>),
+ ('pk6_center',
+ <Parameter 'pk6_center', value=10.388487314912071 +/- 0.00117, bounds=[9.890182:10.890182]>),
+ ('pk6_amplitude',
+ <Parameter 'pk6_amplitude', value=50.74400090246448 +/- 2.86, bounds=[0.0:inf]>),
+ ('pk6_fwhm',
+ <Parameter 'pk6_fwhm', value=0.16487528248303185 +/- 0.0032, bounds=[-inf:inf], expr='2.0000000*pk6_sigma'>)])
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[[Model]]
+ (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))
+[[Fit Statistics]]
+ # function evals = 510
+ # data points = 480
+ # variables = 30
+ chi-square = 50433.257
+ reduced chi-square = 112.074
+ Akaike info crit = 2294.218
+ Bayesian info crit = 2419.431
+[[Variables]]
+ bg_intercept: 23.8937712 +/- 0 (0.00%) (init= 28.32833)
+ bg_slope: -3.43395798 +/- 0 (0.00%) (init=-3.995419)
+ pk0_fraction: 0.81497275 +/- 0 (0.00%) (init= 0.7467189)
+ pk0_sigma: 0.03062433 +/- 0 (0.00%) (init= 0.030502)
+ pk0_center: 6.69497495 +/- 0 (0.00%) (init= 6.695042)
+ pk0_amplitude: 7.71600697 +/- 0 (0.00%) (init= 7.392239)
+ pk0_fwhm: 0.06124866 +/- 0 (0.00%) == '2.0000000*pk0_sigma'
+ pk1_fraction: 0.00415563 +/- 0 (0.00%) (init= 0.004736844)
+ pk1_sigma: 0.04028962 +/- 0 (0.00%) (init= 0.04023896)
+ pk1_center: 8.30000000 +/- 0 (0.00%) (init= 8.3)
+ pk1_amplitude: 2.92514518 +/- 0 (0.00%) (init= 1000)
+ pk1_fwhm: 0.08057924 +/- 0 (0.00%) == '2.0000000*pk1_sigma'
+ pk2_fraction: 0.53241065 +/- 0 (0.00%) (init= 0.5352378)
+ pk2_sigma: 0.04134291 +/- 0 (0.00%) (init= 0.04133251)
+ pk2_center: 8.56765142 +/- 0 (0.00%) (init= 8.567651)
+ pk2_amplitude: 298.492876 +/- 0 (0.00%) (init= 298.8469)
+ pk2_fwhm: 0.08268582 +/- 0 (0.00%) == '2.0000000*pk2_sigma'
+ pk3_fraction: 0.00071570 +/- 0 (0.00%) (init= 1.281864e-09)
+ pk3_sigma: 0.05922553 +/- 0 (0.00%) (init= 0.06052691)
+ pk3_center: 9.38410655 +/- 0 (0.00%) (init= 9.384253)
+ pk3_amplitude: 7.42227692 +/- 0 (0.00%) (init= 7.719403)
+ pk3_fwhm: 0.11845107 +/- 0 (0.00%) == '2.0000000*pk3_sigma'
+ pk4_fraction: 0.37237975 +/- 0 (0.00%) (init= 0.3334123)
+ pk4_sigma: 0.07485621 +/- 0 (0.00%) (init= 0.07516648)
+ pk4_center: 9.89045474 +/- 0 (0.00%) (init= 9.890321)
+ pk4_amplitude: 113.124270 +/- 0 (0.00%) (init= 111.6942)
+ pk4_fwhm: 0.14971242 +/- 0 (0.00%) == '2.0000000*pk4_sigma'
+ pk5_fraction: 0.88984288 +/- 0 (0.00%) (init= 0.9695019)
+ pk5_sigma: 0.04142481 +/- 0 (0.00%) (init= 0.04213893)
+ pk5_center: 10.1375474 +/- 0 (0.00%) (init= 10.13703)
+ pk5_amplitude: 18.2814904 +/- 0 (0.00%) (init= 19.4689)
+ pk5_fwhm: 0.08284962 +/- 0 (0.00%) == '2.0000000*pk5_sigma'
+ pk6_fraction: 0.01367158 +/- 0 (0.00%) (init= 0.08936925)
+ pk6_sigma: 0.08278052 +/- 0 (0.00%) (init= 0.08243764)
+ pk6_center: 10.3882145 +/- 0 (0.00%) (init= 10.38849)
+ pk6_amplitude: 49.3175883 +/- 0 (0.00%) (init= 50.744)
+ pk6_fwhm: 0.16556104 +/- 0 (0.00%) == '2.0000000*pk6_sigma'
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"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 231,
+ "width": 533
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,3.5))\n",
+ "fancy.plot_diffpattern(ax, model)\n",
+ "print(ax.axis())\n",
+ "fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "print(ax.axis())\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.01,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Choose ROI"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check by changing the `xrange` for `plot_diffpattern`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 312,
+ "width": 535
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(9,5))\n",
+ "fancy.plot_diffpattern(ax, model, xrange=[6, 11])\n",
+ "fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5, \n",
+ " show_index=True, \n",
+ " phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)\n",
+ "pressure = model.get_saved_pressure()\n",
+ "temperature = model.get_saved_temperature()\n",
+ "ax.text(0.01,0.9, \"(a) {0:.0f} GPa, {1: .0f} K\".format(pressure, temperature), \n",
+ " transform = ax.transAxes, fontsize=16)\n",
+ "plt.savefig('test.pdf', bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Get background subtracted pattern for masking."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 6.0005517, 6.0109784, 6.021405 , ..., 20.973258 , 20.983684 ,\n",
+ " 20.994111 ])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x, y = model.base_ptn.get_bgsub()\n",
+ "x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Masking for ROI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy.ma as ma\n",
+ "\n",
+ "x_ma = ma.masked_outside(x, 6., 11.)\n",
+ "x_roi = x_ma.compressed()\n",
+ "y_roi = ma.masked_where(np.ma.getmask(x_ma), y).compressed()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Quick plot to check"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 190,
+ "width": 598
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figure = plt.subplots(figsize=(10,3))\n",
+ "plt.plot(x_roi, y_roi)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup fitting model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lmfit.models import PseudoVoigtModel, LinearModel\n",
+ "from lmfit import Parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Make initial peak position array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 67, 220, 246, 325, 373, 397, 421])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from scipy.signal import find_peaks\n",
+ "\n",
+ "peaks, _ = find_peaks(y_roi, height=30.)\n",
+ "peaks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot to see if the search positions are reasonable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 190,
+ "width": 598
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figure = plt.subplots(figsize=(10,3))\n",
+ "plt.plot(x_roi, y_roi)\n",
+ "plt.plot(x_roi[peaks], y_roi[peaks], 'o')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup fitting model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lmfit.models import PseudoVoigtModel, LinearModel\n",
+ "from lmfit import Parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define some functions for peak fitting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define functions for peakfitting. Here we use `LMFIT` module."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from xrd_pkfit import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First fitting attempt without any restriction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[Model]]\n",
+ " (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))\n",
+ "[[Fit Statistics]]\n",
+ " # function evals = 225\n",
+ " # data points = 480\n",
+ " # variables = 30\n",
+ " chi-square = 85049.827\n",
+ " reduced chi-square = 189.000\n",
+ " Akaike info crit = 2545.059\n",
+ " Bayesian info crit = 2670.273\n",
+ "[[Variables]]\n",
+ " bg_intercept: 11.7490860 +/- 7.314150 (62.25%) (init= 0)\n",
+ " bg_slope: -1.92238746 +/- 0.867763 (45.14%) (init= 0)\n",
+ " pk0_fraction: 0.99914425 +/- 0.439206 (43.96%) (init= 0)\n",
+ " pk0_sigma: 0.09905194 +/- 0.022508 (22.72%) (init= 0.05)\n",
+ " pk0_center: 6.67814455 +/- 0.011066 (0.17%) (init= 6.699139)\n",
+ " pk0_amplitude: 13.7291342 +/- 3.199312 (23.30%) (init= 1000)\n",
+ " pk0_fwhm: 0.19810388 +/- 0.043560 (21.99%) == '2.0000000*pk0_sigma'\n",
+ " pk1_fraction: 0.71390507 +/- 1.440882 (201.83%) (init= 0)\n",
+ " pk1_sigma: 0.04899342 +/- 0.022649 (46.23%) (init= 0.05)\n",
+ " pk1_center: 8.27232961 +/- 0.013928 (0.17%) (init= 8.29442)\n",
+ " pk1_amplitude: 1.94815516 +/- 1.650007 (84.70%) (init= 1000)\n",
+ " pk1_fwhm: 0.09798684 +/- 0.045272 (46.20%) == '2.0000000*pk1_sigma'\n",
+ " pk2_fraction: 0.53578175 +/- 0.013639 (2.55%) (init= 0)\n",
+ " pk2_sigma: 0.04131704 +/- 0.000178 (0.43%) (init= 0.05)\n",
+ " pk2_center: 8.56763923 +/- 0.000107 (0.00%) (init= 8.565514)\n",
+ " pk2_amplitude: 298.762770 +/- 1.505359 (0.50%) (init= 1000)\n",
+ " pk2_fwhm: 0.08263408 +/- 0.000352 (0.43%) == '2.0000000*pk2_sigma'\n",
+ " pk3_fraction: 0.01241608 +/- 5.77e+05 (4647278978.67%) (init= 0)\n",
+ " pk3_sigma: 9.6370e-05 +/- 0.473812 (491658.24%) (init= 0.05)\n",
+ " pk3_center: 9.39347570 +/- 0.004071 (0.04%) (init= 9.389221)\n",
+ " pk3_amplitude: 22.7444443 +/- 1.28e+07 (56470649.79%) (init= 1000)\n",
+ " pk3_fwhm: 0.00019274 +/- 0.947947 (491825.72%) == '2.0000000*pk3_sigma'\n",
+ " pk4_fraction: 0.42273441 +/- 0.060602 (14.34%) (init= 0)\n",
+ " pk4_sigma: 0.07398460 +/- 0.001004 (1.36%) (init= 0.05)\n",
+ " pk4_center: 9.89043232 +/- 0.000654 (0.01%) (init= 9.889702)\n",
+ " pk4_amplitude: 114.039270 +/- 2.756552 (2.42%) (init= 1000)\n",
+ " pk4_fwhm: 0.14796921 +/- 0.001722 (1.16%) == '2.0000000*pk4_sigma'\n",
+ " pk5_fraction: 0.99989658 +/- 0.227448 (22.75%) (init= 0)\n",
+ " pk5_sigma: 0.03301947 +/- 0.003775 (11.43%) (init= 0.05)\n",
+ " pk5_center: 10.1384658 +/- 0.001881 (0.02%) (init= 10.13994)\n",
+ " pk5_amplitude: 16.1121387 +/- 1.941011 (12.05%) (init= 1000)\n",
+ " pk5_fwhm: 0.06603895 +/- 0.007729 (11.70%) == '2.0000000*pk5_sigma'\n",
+ " pk6_fraction: 0.00057210 +/- 0.209475 (36614.88%) (init= 0)\n",
+ " pk6_sigma: 0.08438759 +/- 0.002174 (2.58%) (init= 0.05)\n",
+ " pk6_center: 10.3880876 +/- 0.001593 (0.02%) (init= 10.39018)\n",
+ " pk6_amplitude: 48.6505860 +/- 3.622713 (7.45%) (init= 1000)\n",
+ " pk6_fwhm: 0.16877519 +/- 0.004349 (2.58%) == '2.0000000*pk6_sigma'\n",
+ "[[Correlations]] (unreported correlations are < 0.500)\n",
+ " C(pk3_fraction, pk3_amplitude) = -1.000 \n",
+ " C(bg_intercept, bg_slope) = -0.986 \n",
+ " C(pk3_sigma, pk3_center) = -0.962 \n",
+ " C(pk6_fraction, pk6_amplitude) = 0.954 \n",
+ " C(pk4_fraction, pk4_amplitude) = 0.900 \n",
+ " C(pk2_fraction, pk2_amplitude) = 0.830 \n",
+ " C(pk1_fraction, pk1_amplitude) = 0.821 \n",
+ " C(pk0_fraction, pk0_amplitude) = 0.701 \n",
+ " C(bg_intercept, pk0_amplitude) = -0.694 \n",
+ " C(pk5_fraction, pk5_amplitude) = 0.689 \n",
+ " C(bg_slope, pk6_amplitude) = -0.616 \n",
+ " C(bg_slope, pk0_amplitude) = 0.613 \n",
+ " C(pk5_amplitude, pk6_amplitude) = -0.589 \n",
+ " C(pk2_fraction, pk2_sigma) = -0.589 \n",
+ " C(pk4_amplitude, pk5_amplitude) = -0.584 \n",
+ " C(pk1_fraction, pk1_sigma) = -0.566 \n",
+ " C(bg_slope, pk6_fraction) = -0.554 \n",
+ " C(pk5_amplitude, pk6_fraction) = -0.545 \n",
+ " C(bg_intercept, pk6_amplitude) = 0.544 \n",
+ " C(pk0_fraction, pk0_sigma) = -0.539 \n",
+ " C(pk4_fraction, pk5_amplitude) = -0.525 \n",
+ " C(pk5_fraction, pk5_sigma) = -0.525 \n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "mod, pars = make_model(x_roi[peaks])\n",
+ "out = mod.fit(y_roi, pars, x=x_roi, fit_kws={'maxfev': 500})\n",
+ "print(out.fit_report(min_correl=0.5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot the fitting results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 244,
+ "width": 798
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_peaks = len(x_roi[peaks])\n",
+ "figure, ax = plt.subplots(figsize=(12,4))\n",
+ "plot_fitresult(ax, x_roi, y_roi, out, n_peaks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Do better initial guess"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pereform new fitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[Model]]\n",
+ " (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))\n",
+ "[[Fit Statistics]]\n",
+ " # function evals = 189\n",
+ " # data points = 480\n",
+ " # variables = 30\n",
+ " chi-square = 60700.902\n",
+ " reduced chi-square = 134.891\n",
+ " Akaike info crit = 2383.165\n",
+ " Bayesian info crit = 2508.379\n",
+ "[[Variables]]\n",
+ " bg_intercept: 29.1722806 +/- 4.995923 (17.13%) (init= 0)\n",
+ " bg_slope: -3.83526210 +/- 0.645062 (16.82%) (init= 0)\n",
+ " pk0_fraction: 0.51590945 +/- 0.431690 (83.68%) (init= 0)\n",
+ " pk0_sigma: 0.03063716 +/- 0.004060 (13.25%) (init= 0.03)\n",
+ " pk0_center: 6.69518915 +/- 0.002586 (0.04%) (init= 6.699139)\n",
+ " pk0_amplitude: 6.55216766 +/- 1.023892 (15.63%) (init= 100)\n",
+ " pk0_fwhm: 0.06127433 +/- 0.008120 (13.25%) == '2.0000000*pk0_sigma'\n",
+ " pk1_fraction: 0.59988174 +/- 0.807284 (134.57%) (init= 0)\n",
+ " pk1_sigma: 0.06145041 +/- 0.008172 (13.30%) (init= 0.03)\n",
+ " pk1_center: 8.27073401 +/- 0.005765 (0.07%) (init= 8.29442)\n",
+ " pk1_amplitude: 4.56442997 +/- 1.489646 (32.64%) (init= 100)\n",
+ " pk1_fwhm: 0.12290083 +/- 0.016344 (13.30%) == '2.0000000*pk1_sigma'\n",
+ " pk2_fraction: 0.55998216 +/- 0.011321 (2.02%) (init= 0)\n",
+ " pk2_sigma: 0.04126828 +/- 0.000146 (0.35%) (init= 0.03)\n",
+ " pk2_center: 8.56758351 +/- 9.03e-05 (0.00%) (init= 8.565514)\n",
+ " pk2_amplitude: 302.091134 +/- 1.270450 (0.42%) (init= 100)\n",
+ " pk2_fwhm: 0.08253656 +/- 0.000292 (0.35%) == '2.0000000*pk2_sigma'\n",
+ " pk3_fraction: 0.73052766 +/- 0.365205 (49.99%) (init= 0)\n",
+ " pk3_sigma: 0.06668190 +/- 0.007879 (11.82%) (init= 0.03)\n",
+ " pk3_center: 9.37829233 +/- 0.003947 (0.04%) (init= 9.389221)\n",
+ " pk3_amplitude: 6.70558552 +/- 1.220807 (18.21%) (init= 100)\n",
+ " pk3_fwhm: 0.13336381 +/- 0.015759 (11.82%) == '2.0000000*pk3_sigma'\n",
+ " pk4_fraction: 0.31530220 +/- 0.059561 (18.89%) (init= 0)\n",
+ " pk4_sigma: 0.07496412 +/- 0.000848 (1.13%) (init= 0.03)\n",
+ " pk4_center: 9.89032617 +/- 0.000578 (0.01%) (init= 9.889702)\n",
+ " pk4_amplitude: 110.329860 +/- 2.513495 (2.28%) (init= 100)\n",
+ " pk4_fwhm: 0.14992825 +/- 0.001695 (1.13%) == '2.0000000*pk4_sigma'\n",
+ " pk5_fraction: 0.98649024 +/- 0.219909 (22.29%) (init= 0)\n",
+ " pk5_sigma: 0.04167542 +/- 0.003769 (9.05%) (init= 0.03)\n",
+ " pk5_center: 10.1371170 +/- 0.001982 (0.02%) (init= 10.13994)\n",
+ " pk5_amplitude: 19.7762331 +/- 2.139238 (10.82%) (init= 100)\n",
+ " pk5_fwhm: 0.08335085 +/- 0.007539 (9.05%) == '2.0000000*pk5_sigma'\n",
+ " pk6_fraction: 0.02796641 +/- 0.166538 (595.50%) (init= 0)\n",
+ " pk6_sigma: 0.08238659 +/- 0.001861 (2.26%) (init= 0.03)\n",
+ " pk6_center: 10.3885518 +/- 0.001341 (0.01%) (init= 10.39018)\n",
+ " pk6_amplitude: 48.4116664 +/- 3.192891 (6.60%) (init= 100)\n",
+ " pk6_fwhm: 0.16477319 +/- 0.003723 (2.26%) == '2.0000000*pk6_sigma'\n",
+ "[[Correlations]] (unreported correlations are < 0.500)\n",
+ " C(bg_intercept, bg_slope) = -0.984 \n",
+ " C(pk6_fraction, pk6_amplitude) = 0.942 \n",
+ " C(pk4_fraction, pk4_amplitude) = 0.904 \n",
+ " C(pk1_fraction, pk1_amplitude) = 0.902 \n",
+ " C(pk0_fraction, pk0_amplitude) = 0.821 \n",
+ " C(pk2_fraction, pk2_amplitude) = 0.819 \n",
+ " C(pk5_fraction, pk5_amplitude) = 0.772 \n",
+ " C(bg_slope, pk6_amplitude) = -0.659 \n",
+ " C(pk3_fraction, pk3_sigma) = -0.654 \n",
+ " C(pk4_amplitude, pk5_amplitude) = -0.638 \n",
+ " C(pk3_fraction, pk3_amplitude) = 0.616 \n",
+ " C(bg_intercept, pk6_amplitude) = 0.595 \n",
+ " C(bg_slope, pk6_fraction) = -0.594 \n",
+ " C(pk5_amplitude, pk6_amplitude) = -0.581 \n",
+ " C(pk2_fraction, pk2_sigma) = -0.560 \n",
+ " C(pk0_fraction, pk0_sigma) = -0.555 \n",
+ " C(bg_intercept, pk6_fraction) = 0.536 \n",
+ " C(pk4_fraction, pk5_amplitude) = -0.514 \n",
+ " C(pk1_fraction, pk1_sigma) = -0.511 \n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "mod, pars = make_model(x_roi[peaks], fwhm=0.03, amplitude=100.)\n",
+ "out = mod.fit(y_roi, pars, x=x_roi, fit_kws={'maxfev': 500})\n",
+ "print(out.fit_report(min_correl=0.5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 244,
+ "width": 798
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_peaks = len(x_roi[peaks])\n",
+ "figure, ax = plt.subplots(figsize=(12,4))\n",
+ "plot_fitresult(ax, x_roi, y_roi, out, n_peaks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use the last fitting results for the next fitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pars_new = out.params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[Model]]\n",
+ " (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))\n",
+ "[[Fit Statistics]]\n",
+ " # function evals = 503\n",
+ " # data points = 480\n",
+ " # variables = 30\n",
+ " chi-square = 47779.818\n",
+ " reduced chi-square = 106.177\n",
+ " Akaike info crit = 2268.275\n",
+ " Bayesian info crit = 2393.488\n",
+ "[[Variables]]\n",
+ " bg_intercept: 28.3283335 +/- 4.486009 (15.84%) (init= 29.17228)\n",
+ " bg_slope: -3.99541920 +/- 0.582845 (14.59%) (init=-3.835262)\n",
+ " pk0_fraction: 0.74671887 +/- 0.313435 (41.97%) (init= 0.5159095)\n",
+ " pk0_sigma: 0.03050200 +/- 0.004038 (13.24%) (init= 0.03063717)\n",
+ " pk0_center: 6.69504203 +/- 0.002299 (0.03%) (init= 6.695189)\n",
+ " pk0_amplitude: 7.39223915 +/- 0.896851 (12.13%) (init= 6.552168)\n",
+ " pk0_fwhm: 0.06100400 +/- 0.008076 (13.24%) == '2.0000000*pk0_sigma'\n",
+ " pk1_fraction: 0.00473684 +/- 0.983531 (20763.43%) (init= 0.5998817)\n",
+ " pk1_sigma: 0.04023895 +/- 0.007253 (18.03%) (init= 0.06145042)\n",
+ " pk1_center: 8.27500294 +/- 0.005252 (0.06%) (init= 8.270734)\n",
+ " pk1_amplitude: 3.38135714 +/- 1.077574 (31.87%) (init= 4.56443)\n",
+ " pk1_fwhm: 0.08047791 +/- 0.014507 (18.03%) == '2.0000000*pk1_sigma'\n",
+ " pk2_fraction: 0.53523776 +/- 0.009813 (1.83%) (init= 0.5599822)\n",
+ " pk2_sigma: 0.04133250 +/- 0.000128 (0.31%) (init= 0.04126828)\n",
+ " pk2_center: 8.56765135 +/- 7.99e-05 (0.00%) (init= 8.567584)\n",
+ " pk2_amplitude: 298.846896 +/- 1.093183 (0.37%) (init= 302.0911)\n",
+ " pk2_fwhm: 0.08266501 +/- 0.000256 (0.31%) == '2.0000000*pk2_sigma'\n",
+ " pk3_fraction: 1.2819e-09 +/- 0.575693 (44910663219.10%) (init= 0.7305277)\n",
+ " pk3_sigma: 0.06052691 +/- 0.005850 (9.67%) (init= 0.06668191)\n",
+ " pk3_center: 9.38425335 +/- 0.004238 (0.05%) (init= 9.378292)\n",
+ " pk3_amplitude: 7.71940342 +/- 1.508313 (19.54%) (init= 6.705586)\n",
+ " pk3_fwhm: 0.12105382 +/- 0.011701 (9.67%) == '2.0000000*pk3_sigma'\n",
+ " pk4_fraction: 0.33341226 +/- 0.051429 (15.43%) (init= 0.3153022)\n",
+ " pk4_sigma: 0.07516648 +/- 0.000753 (1.00%) (init= 0.07496413)\n",
+ " pk4_center: 9.89032149 +/- 0.000512 (0.01%) (init= 9.890326)\n",
+ " pk4_amplitude: 111.694162 +/- 2.284480 (2.05%) (init= 110.3299)\n",
+ " pk4_fwhm: 0.15033296 +/- 0.001505 (1.00%) == '2.0000000*pk4_sigma'\n",
+ " pk5_fraction: 0.96950193 +/- 0.178592 (18.42%) (init= 0.9864902)\n",
+ " pk5_sigma: 0.04213893 +/- 0.003350 (7.95%) (init= 0.04167543)\n",
+ " pk5_center: 10.1370259 +/- 0.001720 (0.02%) (init= 10.13712)\n",
+ " pk5_amplitude: 19.4688965 +/- 1.893864 (9.73%) (init= 19.77623)\n",
+ " pk5_fwhm: 0.08427786 +/- 0.006701 (7.95%) == '2.0000000*pk5_sigma'\n",
+ " pk6_fraction: 0.08936925 +/- 0.145983 (163.35%) (init= 0.02796641)\n",
+ " pk6_sigma: 0.08243764 +/- 0.001599 (1.94%) (init= 0.0823866)\n",
+ " pk6_center: 10.3884873 +/- 0.001169 (0.01%) (init= 10.38855)\n",
+ " pk6_amplitude: 50.7440009 +/- 2.863534 (5.64%) (init= 48.41167)\n",
+ " pk6_fwhm: 0.16487528 +/- 0.003199 (1.94%) == '2.0000000*pk6_sigma'\n",
+ "[[Correlations]] (unreported correlations are < 0.500)\n",
+ " C(bg_intercept, bg_slope) = -0.985 \n",
+ " C(pk6_fraction, pk6_amplitude) = 0.948 \n",
+ " C(pk3_fraction, pk3_amplitude) = 0.916 \n",
+ " C(pk4_fraction, pk4_amplitude) = 0.909 \n",
+ " C(pk1_fraction, pk1_amplitude) = 0.900 \n",
+ " C(pk2_fraction, pk2_amplitude) = 0.819 \n",
+ " C(pk5_fraction, pk5_amplitude) = 0.745 \n",
+ " C(pk0_fraction, pk0_amplitude) = 0.735 \n",
+ " C(bg_slope, pk6_amplitude) = -0.670 \n",
+ " C(pk4_amplitude, pk5_amplitude) = -0.641 \n",
+ " C(pk0_fraction, pk0_sigma) = -0.609 \n",
+ " C(bg_intercept, pk6_amplitude) = 0.608 \n",
+ " C(bg_slope, pk6_fraction) = -0.608 \n",
+ " C(pk2_fraction, pk2_sigma) = -0.564 \n",
+ " C(pk5_amplitude, pk6_amplitude) = -0.552 \n",
+ " C(bg_intercept, pk6_fraction) = 0.552 \n",
+ " C(pk4_fraction, pk5_amplitude) = -0.522 \n",
+ " C(pk1_fraction, pk1_sigma) = -0.502 \n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "out = mod.fit(y_roi, pars_new, x=x_roi, fit_kws={'maxfev': 500})\n",
+ "print(out.fit_report(min_correl=0.5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 244,
+ "width": 798
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_peaks = len(x_roi[peaks])\n",
+ "figure, ax = plt.subplots(figsize=(12,4))\n",
+ "plot_fitresult(ax, x_roi, y_roi, out, n_peaks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fix some parameters for fitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Parameters([('bg_intercept',\n",
+ " ),\n",
+ " ('bg_slope',\n",
+ " ),\n",
+ " ('pk0_fraction',\n",
+ " ),\n",
+ " ('pk0_sigma',\n",
+ " ),\n",
+ " ('pk0_center',\n",
+ " ),\n",
+ " ('pk0_amplitude',\n",
+ " ),\n",
+ " ('pk0_fwhm',\n",
+ " ),\n",
+ " ('pk1_fraction',\n",
+ " ),\n",
+ " ('pk1_sigma',\n",
+ " ),\n",
+ " ('pk1_center',\n",
+ " ),\n",
+ " ('pk1_amplitude',\n",
+ " ),\n",
+ " ('pk1_fwhm',\n",
+ " ),\n",
+ " ('pk2_fraction',\n",
+ " ),\n",
+ " ('pk2_sigma',\n",
+ " ),\n",
+ " ('pk2_center',\n",
+ " ),\n",
+ " ('pk2_amplitude',\n",
+ " ),\n",
+ " ('pk2_fwhm',\n",
+ " ),\n",
+ " ('pk3_fraction',\n",
+ " ),\n",
+ " ('pk3_sigma',\n",
+ " ),\n",
+ " ('pk3_center',\n",
+ " ),\n",
+ " ('pk3_amplitude',\n",
+ " ),\n",
+ " ('pk3_fwhm',\n",
+ " ),\n",
+ " ('pk4_fraction',\n",
+ " ),\n",
+ " ('pk4_sigma',\n",
+ " ),\n",
+ " ('pk4_center',\n",
+ " ),\n",
+ " ('pk4_amplitude',\n",
+ " ),\n",
+ " ('pk4_fwhm',\n",
+ " ),\n",
+ " ('pk5_fraction',\n",
+ " ),\n",
+ " ('pk5_sigma',\n",
+ " ),\n",
+ " ('pk5_center',\n",
+ " ),\n",
+ " ('pk5_amplitude',\n",
+ " ),\n",
+ " ('pk5_fwhm',\n",
+ " ),\n",
+ " ('pk6_fraction',\n",
+ " ),\n",
+ " ('pk6_sigma',\n",
+ " ),\n",
+ " ('pk6_center',\n",
+ " ),\n",
+ " ('pk6_amplitude',\n",
+ " ),\n",
+ " ('pk6_fwhm',\n",
+ " )])"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pars_new = out.params\n",
+ "pars_new"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pars_new['pk1_center'].set(min=8.3, max=8.4, vary=True)\n",
+ "pars_new['pk1_amplitude'].set(1000., vary=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[Model]]\n",
+ " (((((((Model(linear, prefix='bg_') + Model(pvoigt, prefix='pk0_')) + Model(pvoigt, prefix='pk1_')) + Model(pvoigt, prefix='pk2_')) + Model(pvoigt, prefix='pk3_')) + Model(pvoigt, prefix='pk4_')) + Model(pvoigt, prefix='pk5_')) + Model(pvoigt, prefix='pk6_'))\n",
+ "[[Fit Statistics]]\n",
+ " # function evals = 510\n",
+ " # data points = 480\n",
+ " # variables = 30\n",
+ " chi-square = 50433.257\n",
+ " reduced chi-square = 112.074\n",
+ " Akaike info crit = 2294.218\n",
+ " Bayesian info crit = 2419.431\n",
+ "[[Variables]]\n",
+ " bg_intercept: 23.8937712 +/- 0 (0.00%) (init= 28.32833)\n",
+ " bg_slope: -3.43395798 +/- 0 (0.00%) (init=-3.995419)\n",
+ " pk0_fraction: 0.81497275 +/- 0 (0.00%) (init= 0.7467189)\n",
+ " pk0_sigma: 0.03062433 +/- 0 (0.00%) (init= 0.030502)\n",
+ " pk0_center: 6.69497495 +/- 0 (0.00%) (init= 6.695042)\n",
+ " pk0_amplitude: 7.71600697 +/- 0 (0.00%) (init= 7.392239)\n",
+ " pk0_fwhm: 0.06124866 +/- 0 (0.00%) == '2.0000000*pk0_sigma'\n",
+ " pk1_fraction: 0.00415563 +/- 0 (0.00%) (init= 0.004736844)\n",
+ " pk1_sigma: 0.04028962 +/- 0 (0.00%) (init= 0.04023896)\n",
+ " pk1_center: 8.30000000 +/- 0 (0.00%) (init= 8.3)\n",
+ " pk1_amplitude: 2.92514518 +/- 0 (0.00%) (init= 1000)\n",
+ " pk1_fwhm: 0.08057924 +/- 0 (0.00%) == '2.0000000*pk1_sigma'\n",
+ " pk2_fraction: 0.53241065 +/- 0 (0.00%) (init= 0.5352378)\n",
+ " pk2_sigma: 0.04134291 +/- 0 (0.00%) (init= 0.04133251)\n",
+ " pk2_center: 8.56765142 +/- 0 (0.00%) (init= 8.567651)\n",
+ " pk2_amplitude: 298.492876 +/- 0 (0.00%) (init= 298.8469)\n",
+ " pk2_fwhm: 0.08268582 +/- 0 (0.00%) == '2.0000000*pk2_sigma'\n",
+ " pk3_fraction: 0.00071570 +/- 0 (0.00%) (init= 1.281864e-09)\n",
+ " pk3_sigma: 0.05922553 +/- 0 (0.00%) (init= 0.06052691)\n",
+ " pk3_center: 9.38410655 +/- 0 (0.00%) (init= 9.384253)\n",
+ " pk3_amplitude: 7.42227692 +/- 0 (0.00%) (init= 7.719403)\n",
+ " pk3_fwhm: 0.11845107 +/- 0 (0.00%) == '2.0000000*pk3_sigma'\n",
+ " pk4_fraction: 0.37237975 +/- 0 (0.00%) (init= 0.3334123)\n",
+ " pk4_sigma: 0.07485621 +/- 0 (0.00%) (init= 0.07516648)\n",
+ " pk4_center: 9.89045474 +/- 0 (0.00%) (init= 9.890321)\n",
+ " pk4_amplitude: 113.124270 +/- 0 (0.00%) (init= 111.6942)\n",
+ " pk4_fwhm: 0.14971242 +/- 0 (0.00%) == '2.0000000*pk4_sigma'\n",
+ " pk5_fraction: 0.88984288 +/- 0 (0.00%) (init= 0.9695019)\n",
+ " pk5_sigma: 0.04142481 +/- 0 (0.00%) (init= 0.04213893)\n",
+ " pk5_center: 10.1375474 +/- 0 (0.00%) (init= 10.13703)\n",
+ " pk5_amplitude: 18.2814904 +/- 0 (0.00%) (init= 19.4689)\n",
+ " pk5_fwhm: 0.08284962 +/- 0 (0.00%) == '2.0000000*pk5_sigma'\n",
+ " pk6_fraction: 0.01367158 +/- 0 (0.00%) (init= 0.08936925)\n",
+ " pk6_sigma: 0.08278052 +/- 0 (0.00%) (init= 0.08243764)\n",
+ " pk6_center: 10.3882145 +/- 0 (0.00%) (init= 10.38849)\n",
+ " pk6_amplitude: 49.3175883 +/- 0 (0.00%) (init= 50.744)\n",
+ " pk6_fwhm: 0.16556104 +/- 0 (0.00%) == '2.0000000*pk6_sigma'\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "out = mod.fit(y_roi, pars_new, x=x_roi, fit_kws={'maxfev': 500})\n",
+ "print(out.fit_report(min_correl=0.5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 244,
+ "width": 798
+ },
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_peaks = len(peaks)\n",
+ "figure, ax = plt.subplots(figsize=(12,4))\n",
+ "plot_fitresult(ax, x_roi, y_roi, out, n_peaks)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "peakpo7721",
+ "language": "python",
+ "name": "peakpo7721"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jnb-tools/4_peakfit_from_dpp/xrd_pattern-peakfitting.py b/jnb-tools/4_peakfit_from_dpp/xrd_pattern-peakfitting.py
new file mode 100644
index 0000000..35d867d
--- /dev/null
+++ b/jnb-tools/4_peakfit_from_dpp/xrd_pattern-peakfitting.py
@@ -0,0 +1,319 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Peak fitting for XRD pattern from dpp
+
+# - Please check [setup_for_notebooks](../0_setup/setup_for_notebooks.ipynb) file if you have problem using the notebooks in this folder.
+# - In this notebook, we will learn how to plot XRD patterns using the information saved in `dpp`.
+# - `dpp` is a project file saved in `PeakPo`. You may plot, jcpds information and cake as well as many other information.
+
+# In[1]:
+
+
+import sys
+sys.path.append('../local_modules')
+sys.path.append('../../peakpo')
+
+
+# ## Check the versio of pyFAI in your conda environment
+
+# In[2]:
+
+
+import pyFAI
+pyFAI.version
+
+
+# Note that the example data files I provided are made with `pyFAI` version `0.14`. If you see version higher than `0.15` here, you will get error when you read the example `dpp` file. In that case, you either follow the instruction in [setup_for_notebooks.ipynb](./setup_for_notebooks.ipynb) or you may use your own dpp for this note book.
+
+# ## Read dpp
+
+# In[3]:
+
+
+import dill
+import numpy as np
+
+
+# In[4]:
+
+
+get_ipython().run_line_magic('ls', '../data/hStv/*.dpp')
+
+
+# In[5]:
+
+
+filen_dpp = '../data/hStv/hSiO2_404_009.dpp'
+
+
+# In[6]:
+
+
+with open(filen_dpp, 'rb') as f:
+ model_dpp = dill.load(f)
+
+
+# ## Setup a new PeakPo model and assign info from dpp
+
+# In[7]:
+
+
+from model import PeakPoModel
+model = PeakPoModel()
+
+
+# Make sure to reset the chi folder location using the `new_chi_path` option.
+
+# In[8]:
+
+
+model.set_from(model_dpp, new_chi_path='../data/hStv')
+
+
+# See `xrd_pattern.ipynb` file for basic operations.
+
+# ## Make a simple plot
+
+# The following three modules are all in the `../local_modules` folder.
+
+# In[9]:
+
+
+from xrd_unitconv import * # Make conversios between different x-axis units
+
+
+# In[10]:
+
+
+import quick_plots as quick # A function to plot XRD pattern
+import fancy_plots as fancy # A function to plot XRD pattern
+
+
+# In[11]:
+
+
+get_ipython().run_line_magic('matplotlib', 'inline')
+get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
+
+
+# In[12]:
+
+
+import matplotlib.pyplot as plt
+
+
+# In[13]:
+
+
+f, ax = plt.subplots(figsize=(9,3.5))
+fancy.plot_diffpattern(ax, model)
+print(ax.axis())
+fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+print(ax.axis())
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.01,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test.pdf', bbox_inches='tight')
+
+
+# ## Choose ROI
+
+# Check by changing the `xrange` for `plot_diffpattern`.
+
+# In[14]:
+
+
+f, ax = plt.subplots(figsize=(9,5))
+fancy.plot_diffpattern(ax, model, xrange=[6, 11])
+fancy.plot_jcpds(ax, model, bar_position=0.1, bar_height=5,
+ show_index=True,
+ phase_names = ['hStv', 'Au', 'Ne', 'hCt'], bar_vsep=5.)
+pressure = model.get_saved_pressure()
+temperature = model.get_saved_temperature()
+ax.text(0.01,0.9, "(a) {0:.0f} GPa, {1: .0f} K".format(pressure, temperature),
+ transform = ax.transAxes, fontsize=16)
+plt.savefig('test.pdf', bbox_inches='tight')
+
+
+# Get background subtracted pattern for masking.
+
+# In[15]:
+
+
+x, y = model.base_ptn.get_bgsub()
+x
+
+
+# Masking for ROI
+
+# In[16]:
+
+
+import numpy.ma as ma
+
+x_ma = ma.masked_outside(x, 6., 11.)
+x_roi = x_ma.compressed()
+y_roi = ma.masked_where(np.ma.getmask(x_ma), y).compressed()
+
+
+# Quick plot to check
+
+# In[17]:
+
+
+figure = plt.subplots(figsize=(10,3))
+plt.plot(x_roi, y_roi)
+
+
+# ## Setup fitting model
+
+# In[18]:
+
+
+from lmfit.models import PseudoVoigtModel, LinearModel
+from lmfit import Parameters
+
+
+# ## Make initial peak position array
+
+# In[19]:
+
+
+from scipy.signal import find_peaks
+
+peaks, _ = find_peaks(y_roi, height=30.)
+peaks
+
+
+# Plot to see if the search positions are reasonable.
+
+# In[20]:
+
+
+figure = plt.subplots(figsize=(10,3))
+plt.plot(x_roi, y_roi)
+plt.plot(x_roi[peaks], y_roi[peaks], 'o')
+
+
+# ## Setup fitting model
+
+# In[21]:
+
+
+from lmfit.models import PseudoVoigtModel, LinearModel
+from lmfit import Parameters
+
+
+# ## Define some functions for peak fitting
+
+# Define functions for peakfitting. Here we use `LMFIT` module.
+
+# In[22]:
+
+
+from xrd_pkfit import *
+
+
+# ## First fitting attempt without any restriction
+
+# In[23]:
+
+
+mod, pars = make_model(x_roi[peaks])
+out = mod.fit(y_roi, pars, x=x_roi, fit_kws={'maxfev': 500})
+print(out.fit_report(min_correl=0.5))
+
+
+# Plot the fitting results
+
+# In[24]:
+
+
+n_peaks = len(x_roi[peaks])
+figure, ax = plt.subplots(figsize=(12,4))
+plot_fitresult(ax, x_roi, y_roi, out, n_peaks)
+
+
+# ## Do better initial guess
+
+# Pereform new fitting
+
+# In[25]:
+
+
+mod, pars = make_model(x_roi[peaks], fwhm=0.03, amplitude=100.)
+out = mod.fit(y_roi, pars, x=x_roi, fit_kws={'maxfev': 500})
+print(out.fit_report(min_correl=0.5))
+
+
+# In[26]:
+
+
+n_peaks = len(x_roi[peaks])
+figure, ax = plt.subplots(figsize=(12,4))
+plot_fitresult(ax, x_roi, y_roi, out, n_peaks)
+
+
+# ## Use the last fitting results for the next fitting
+
+# In[27]:
+
+
+pars_new = out.params
+
+
+# In[28]:
+
+
+out = mod.fit(y_roi, pars_new, x=x_roi, fit_kws={'maxfev': 500})
+print(out.fit_report(min_correl=0.5))
+
+
+# In[29]:
+
+
+n_peaks = len(x_roi[peaks])
+figure, ax = plt.subplots(figsize=(12,4))
+plot_fitresult(ax, x_roi, y_roi, out, n_peaks)
+
+
+# ## Fix some parameters for fitting
+
+# In[30]:
+
+
+pars_new = out.params
+pars_new
+
+
+# In[31]:
+
+
+pars_new['pk1_center'].set(min=8.3, max=8.4, vary=True)
+pars_new['pk1_amplitude'].set(1000., vary=True)
+
+
+# In[32]:
+
+
+out = mod.fit(y_roi, pars_new, x=x_roi, fit_kws={'maxfev': 500})
+print(out.fit_report(min_correl=0.5))
+
+
+# In[33]:
+
+
+n_peaks = len(peaks)
+figure, ax = plt.subplots(figsize=(12,4))
+plot_fitresult(ax, x_roi, y_roi, out, n_peaks)
+
+
+# In[ ]:
+
+
+
+
diff --git a/jnb-tools/Convert_CIF_to_JCPDS.ipynb b/jnb-tools/Convert_CIF_to_JCPDS.ipynb
deleted file mode 100644
index e0ea95d..0000000
--- a/jnb-tools/Convert_CIF_to_JCPDS.ipynb
+++ /dev/null
@@ -1,597 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 0. General note"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* `pymatgen` works only with `py35`\n",
- "\n",
- "* This notebook shows how to make an XRD plot using `pymatgen`.\n",
- "\n",
- "* This also aims to show how to read `CIF` files, convert them to `JCPDS`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 1. General setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import pymatgen as mg\n",
- "from pymatgen import Lattice, Structure\n",
- "from pymatgen.analysis.diffraction.xrd import XRDCalculator\n",
- "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Import `ds_jcpds` which is used by `peakpo`. Because of this, if you move this notebook out of its original directory, this notebook needs modification to function properly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.insert(0, '../peakpo')\n",
- "import ds_jcpds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "fn_cif = \"../jcpds_from_cif/CIFs/corundum_Kirfel1990.cif\"\n",
- "fn_jcpds = '../jcpds_from_cif/Al2O3.jcpds'\n",
- "comments_jcpds = \"corundum by Kirfel 1990\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "k0 = 160.\n",
- "k0p = 4.00\n",
- "alpha = 3.16e-5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "wl_xray = 0.3344\n",
- "xrange = (0,40)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 2. Read CIF of Bridgmanite"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `cif` file below was downloaded from American mineralogist crystal structure database."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": true,
- "run_control": {
- "marked": true
- }
- },
- "outputs": [],
- "source": [
- "material = mg.Structure.from_file(fn_cif)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 3. Get some contents from CIF"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Unit-cell volume = 254.524259692\n",
- "Density = 3.991226963516955 g\n",
- "Chemical formula = Al12 O18\n"
- ]
- }
- ],
- "source": [
- "print('Unit-cell volume = ', material.volume)\n",
- "print('Density = ', material.density)\n",
- "print('Chemical formula = ', material.formula)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 4. Get lattice parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Lattice parameters = 4.757 4.757 12.9877 90.0 90.0 120.0\n",
- "trigonal\n"
- ]
- }
- ],
- "source": [
- "lattice = material.lattice\n",
- "print('Lattice parameters = ', lattice.a, lattice.b, lattice.c, \\\n",
- " lattice.alpha, lattice.beta, lattice.gamma)\n",
- "crystal_system = SpacegroupAnalyzer(material).get_crystal_system()\n",
- "print(crystal_system)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 5. Get diffraction pattern"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "c = XRDCalculator(wavelength=wl_xray)\n",
- "pattern = c.get_xrd_data(material, two_theta_range = xrange)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5.1. Extract twotheta, d-sp, int, hkl"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 5.50982446e+00 3.47871214e+00 4.78556021e+01 1.00000000e+00\n",
- " 0.00000000e+00 2.00000000e+00]\n",
- " [ 7.51871844e+00 2.55009722e+00 7.97457632e+01 1.00000000e+00\n",
- " 0.00000000e+00 4.00000000e+00]\n",
- " [ 8.06202386e+00 2.37850000e+00 3.72574125e+01 2.00000000e+00\n",
- " -1.00000000e+00 0.00000000e+00]\n",
- " ..., \n",
- " [ 3.96871028e+01 4.92557143e-01 1.18076820e+00 4.00000000e+00\n",
- " -2.00000000e+00 2.40000000e+01]\n",
- " [ 3.98302664e+01 4.90858219e-01 8.50345008e-03 8.00000000e+00\n",
- " 0.00000000e+00 8.00000000e+00]\n",
- " [ 3.98888823e+01 4.90166221e-01 1.38637524e-01 8.00000000e+00\n",
- " -4.00000000e+00 1.50000000e+01]]\n"
- ]
- }
- ],
- "source": [
- "d_lines = []\n",
- "for values in pattern:\n",
- " hkl_key = values[2].keys()\n",
- " hkl_txt = str(hkl_key)[12:-3].split(\",\")\n",
- " # print(hkl_txt[0], hkl_txt[1], hkl_txt[-1])\n",
- " d_lines.append([values[0], values[3], values[1], \\\n",
- " int(hkl_txt[0]), int(hkl_txt[1]), int(hkl_txt[-1]) ])\n",
- "\n",
- "diff_lines = np.asarray(d_lines)\n",
- "print(diff_lines)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5.2. Table output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "
\n",
- " \n",
- " \n",
- " Two Theta \n",
- " d-spacing \n",
- " intensity \n",
- " h \n",
- " k \n",
- " l \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 5.509824 \n",
- " 3.478712 \n",
- " 47.855602 \n",
- " 1.0 \n",
- " 0.0 \n",
- " 2.0 \n",
- " \n",
- " \n",
- " 1 \n",
- " 7.518718 \n",
- " 2.550097 \n",
- " 79.745763 \n",
- " 1.0 \n",
- " 0.0 \n",
- " 4.0 \n",
- " \n",
- " \n",
- " 2 \n",
- " 8.062024 \n",
- " 2.378500 \n",
- " 37.257413 \n",
- " 2.0 \n",
- " -1.0 \n",
- " 0.0 \n",
- " \n",
- " \n",
- " 3 \n",
- " 8.860143 \n",
- " 2.164617 \n",
- " 0.470187 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 6.0 \n",
- " \n",
- " \n",
- " 4 \n",
- " 9.200940 \n",
- " 2.084604 \n",
- " 88.646843 \n",
- " 2.0 \n",
- " -1.0 \n",
- " 3.0 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "f = plt.figure(figsize=(10,3))\n",
- "plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 6. Convert to JCPDS"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Setup an `jcpds` object from a `cif` file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "material_jcpds = ds_jcpds.JCPDS()\n",
- "material_jcpds.set_from_cif(fn_cif, k0, k0p, \\\n",
- " thermal_expansion=alpha, two_theta_range=xrange)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate diffraction pattern at a pressure."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "no symmetry is given\n"
- ]
- }
- ],
- "source": [
- "material_jcpds.cal_dsp(pressure = 100.)\n",
- "dl = material_jcpds.get_DiffractionLines()\n",
- "tth, inten = material_jcpds.get_tthVSint(wl_xray)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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RlwFPA5/HsXSKBfr0ALAfx9I8mflsZr4REbuBLwOv4niaZ4EefRXflxbzIHAnkHTHzQ/7\nlq3qWJq08PEq8Ep2L171nYg4DPwU8P3xllVLR4H39T3eAHTGVEvd3Z+ZJwEi4qvAr4y5nrGLiHuB\n64E7gB8w/6c6x1JPf58yc29EnO1Ymi8izgfeyMwdEbEZ+Gdgum+VM348LdCj54HzHUvzRcTNdDPA\nt7ufUuEocF7fKqs6liZtBuq3gd8FiIifptu4/xhnQTX2XeD8iNjUm/rdSfenZPWJiB8DvhcR7+89\n9THmv7mfcSLiJmArsDUz9+JYWtBgnxxLi3oQ+Hjv/v/QvWq042m+wR79L46lhWwHroyIfcA1wB8D\nH4qILb3l19L9jOuqmLQZqAeBL0fEfrqnYFqzCV3zZeZMRNxO9/MFJ4BdmfnamMuqncx8JyLuBL4e\nEW8B/wo8Ouayxm0H8EHgqd5PedA9Ze5Ymm+hPjmWTnUP8BcRcRfd7zm/CZyN46nfYI9+g+5pO8dS\nn8z89Oz9iHgM2AW8CTwSETPA/szcs1r1+KdcJEmSCk3aKTxJkqSxM0BJkiQVMkBJkiQVMkBJkiQV\nMkBJkiQVMkBJkiQVMkBJkiQV+n9eeoNV7QGergAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "f = plt.figure(figsize=(10,3))\n",
- "plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')\n",
- "plt.vlines(tth, 0., inten, color = 'r');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 7. Save to a JCPDS file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "material_jcpds.write_to_file(fn_jcpds, comments=comments_jcpds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 8. Read back the written JCPDS for test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "material_test = ds_jcpds.JCPDS(filename = fn_jcpds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate a pattern at a pressure"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "material_test.cal_dsp(pressure = 10.)\n",
- "material_test.get_DiffractionLines()\n",
- "tth, inten = material_test.get_tthVSint(wl_xray)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "f = plt.figure(figsize=(10,3))\n",
- "plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')\n",
- "plt.vlines(tth, 0., inten, color = 'r');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "display_name": "Python [conda env:py35peakpo]",
- "language": "python",
- "name": "conda-env-py35peakpo-py"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.3"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 0,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "nav_menu": {},
- "toc": {
- "navigate_menu": true,
- "number_sections": true,
- "sideBar": true,
- "threshold": 6,
- "toc_cell": false,
- "toc_section_display": "block",
- "toc_window_display": false
- }
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- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/jnb-tools/Convert_CIF_to_JCPDS.py b/jnb-tools/Convert_CIF_to_JCPDS.py
deleted file mode 100644
index 9be8bad..0000000
--- a/jnb-tools/Convert_CIF_to_JCPDS.py
+++ /dev/null
@@ -1,207 +0,0 @@
-
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().magic('matplotlib inline')
-
-
-# # 0. General note
-
-# * `pymatgen` works only with `py35`
-#
-# * This notebook shows how to make an XRD plot using `pymatgen`.
-#
-# * This also aims to show how to read `CIF` files, convert them to `JCPDS`.
-
-# # 1. General setup
-
-# In[2]:
-
-
-import pymatgen as mg
-from pymatgen import Lattice, Structure
-from pymatgen.analysis.diffraction.xrd import XRDCalculator
-from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-
-# Import `ds_jcpds` which is used by `peakpo`. Because of this, if you move this notebook out of its original directory, this notebook needs modification to function properly.
-
-# In[3]:
-
-
-import sys
-sys.path.insert(0, '../peakpo')
-import ds_jcpds
-
-
-# In[4]:
-
-
-fn_cif = "../jcpds_from_cif/CIFs/corundum_Kirfel1990.cif"
-fn_jcpds = '../jcpds_from_cif/Al2O3.jcpds'
-comments_jcpds = "corundum by Kirfel 1990"
-
-
-# In[5]:
-
-
-k0 = 160.
-k0p = 4.00
-alpha = 3.16e-5
-
-
-# In[6]:
-
-
-wl_xray = 0.3344
-xrange = (0,40)
-
-
-# # 2. Read CIF of Bridgmanite
-
-# The `cif` file below was downloaded from American mineralogist crystal structure database.
-
-# In[7]:
-
-
-material = mg.Structure.from_file(fn_cif)
-
-
-# # 3. Get some contents from CIF
-
-# In[8]:
-
-
-print('Unit-cell volume = ', material.volume)
-print('Density = ', material.density)
-print('Chemical formula = ', material.formula)
-
-
-# # 4. Get lattice parameters
-
-# In[9]:
-
-
-lattice = material.lattice
-print('Lattice parameters = ', lattice.a, lattice.b, lattice.c, lattice.alpha, lattice.beta, lattice.gamma)
-crystal_system = SpacegroupAnalyzer(material).get_crystal_system()
-print(crystal_system)
-
-
-# # 5. Get diffraction pattern
-
-# In[10]:
-
-
-c = XRDCalculator(wavelength=wl_xray)
-pattern = c.get_xrd_data(material, two_theta_range = xrange)
-
-
-# ## 5.1. Extract twotheta, d-sp, int, hkl
-
-# In[11]:
-
-
-d_lines = []
-for values in pattern:
- hkl_key = values[2].keys()
- hkl_txt = str(hkl_key)[12:-3].split(",")
- # print(hkl_txt[0], hkl_txt[1], hkl_txt[-1])
- d_lines.append([values[0], values[3], values[1], int(hkl_txt[0]), int(hkl_txt[1]), int(hkl_txt[-1]) ])
-
-diff_lines = np.asarray(d_lines)
-print(diff_lines)
-
-
-# ## 5.2. Table output
-
-# In[12]:
-
-
-table = pd.DataFrame(data = diff_lines, # values
- columns=['Two Theta', 'd-spacing', 'intensity', 'h', 'k', 'l']) # 1st row as the column names
-table.head()
-
-
-# ## 5.3. Plot peak positions generated from pymatgen
-
-# In[13]:
-
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b');
-
-
-# # 6. Convert to JCPDS
-
-# Setup an `jcpds` object from a `cif` file
-
-# In[14]:
-
-
-material_jcpds = ds_jcpds.JCPDS()
-material_jcpds.set_from_cif(fn_cif, k0, k0p, thermal_expansion=alpha, two_theta_range=xrange)
-
-
-# Calculate diffraction pattern at a pressure.
-
-# In[15]:
-
-
-material_jcpds.cal_dsp(pressure = 100.)
-dl = material_jcpds.get_DiffractionLines()
-tth, inten = material_jcpds.get_tthVSint(wl_xray)
-
-
-# In[16]:
-
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')
-plt.vlines(tth, 0., inten, color = 'r');
-
-
-# # 7. Save to a JCPDS file
-
-# In[17]:
-
-
-material_jcpds.write_to_file(fn_jcpds, comments=comments_jcpds)
-
-
-# # 8. Read back the written JCPDS for test
-
-# In[18]:
-
-
-material_test = ds_jcpds.JCPDS(filename = fn_jcpds)
-
-
-# Calculate a pattern at a pressure
-
-# In[19]:
-
-
-material_test.cal_dsp(pressure = 10.)
-material_test.get_DiffractionLines()
-tth, inten = material_test.get_tthVSint(wl_xray)
-
-
-# In[20]:
-
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')
-plt.vlines(tth, 0., inten, color = 'r');
-
-
-# In[ ]:
-
-
-
-
diff --git a/jnb-tools/Convert_CIF_to_JCPDS.txt b/jnb-tools/Convert_CIF_to_JCPDS.txt
deleted file mode 100644
index bd574a7..0000000
--- a/jnb-tools/Convert_CIF_to_JCPDS.txt
+++ /dev/null
@@ -1,84 +0,0 @@
-
-%matplotlib inline
-
-import pymatgen as mg
-from pymatgen import Lattice, Structure
-from pymatgen.analysis.diffraction.xrd import XRDCalculator
-from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-import sys
-sys.path.insert(0, '../')
-import ds_jcpds
-
-fn_cif = "./CIFs/bridgmanite_Horiuchi1987.cif"
-
-material = mg.Structure.from_file(fn_cif)
-
-print('Unit-cell volume = ', material.volume)
-print('Density = ', material.density)
-print('Chemical formula = ', material.formula)
-
-lattice = material.lattice
-print('Lattice parameters = ', lattice.a, lattice.b, lattice.c, \
- lattice.alpha, lattice.beta, lattice.gamma)
-print(SpacegroupAnalyzer(material).get_crystal_system())
-
-wl_xray = 0.3344
-xrange = (0,30)
-
-c = XRDCalculator(wavelength=wl_xray)
-pattern = c.get_xrd_data(material, two_theta_range = xrange)
-
-d_lines = []
-for lines in pattern:
- hklarr = lines[2].keys()
- for line in hklarr:
- d_lines.append([lines[0], lines[3], lines[1]] + list(line))
-
-diff_lines = np.asarray(d_lines)
-
-table = pd.DataFrame(data = diff_lines, # values
- columns=['Two Theta', 'd-spacing', 'intensity', 'h', 'k', 'l']) # 1st row as the column names
-table.head()
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b');
-
-k0 = 260.
-k0p = 4.
-alpha = 1.e-5
-
-material_jcpds = ds_jcpds.JCPDS()
-material_jcpds.set_from_cif(fn_cif, k0, k0p, \
- thermal_expansion=alpha)
-
-material_jcpds.cal_dsp(pressure = 100.)
-dl = material_jcpds.get_DiffractionLines()
-tth, inten = material_jcpds.get_tthVSint(wl_xray)
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')
-plt.vlines(tth, 0., inten, color = 'r');
-
-fn_jcpds = './bm.jcpds'
-comments_jcpds = "test bridgmanite"
-
-material_jcpds.write_to_file(fn_jcpds, comments=comments_jcpds)
-
-material_test = ds_jcpds.JCPDS(filename = fn_jcpds)
-
-material_test.cal_dsp(pressure = 10.)
-dl = material_test.get_DiffractionLines()
-tth, inten = material_test.get_tthVSint(wl_xray)
-print(dl)
-
-f = plt.figure(figsize=(10,3))
-plt.vlines(diff_lines[:,0], 0., diff_lines[:,2], color='b')
-plt.vlines(tth, 0., inten, color = 'r');
-
-
-
-
diff --git a/jnb-tools/data/hStv/LaB6_37keV_p49_center.poni b/jnb-tools/data/hStv/LaB6_37keV_p49_center.poni
new file mode 100755
index 0000000..ec071a5
--- /dev/null
+++ b/jnb-tools/data/hStv/LaB6_37keV_p49_center.poni
@@ -0,0 +1,12 @@
+# Nota: C-Order, 1 refers to the Y axis, 2 to the X axis
+# Calibration done at Sat Feb 21 09:28:00 2015
+PixelSize1: 7.9e-05
+PixelSize2: 7.9e-05
+Distance: 0.196171211837
+Poni1: 0.0815325481193
+Poni2: 0.0820848848084
+Rot1: 0.00490714466113
+Rot2: -0.00207980067919
+Rot3: 1.61249618769e-09
+SplineFile: None
+Wavelength: 3.344e-11
diff --git a/jnb-tools/data/hStv/hSiO2_404_009.chi b/jnb-tools/data/hStv/hSiO2_404_009.chi
new file mode 100755
index 0000000..c7f1f85
--- /dev/null
+++ b/jnb-tools/data/hStv/hSiO2_404_009.chi
@@ -0,0 +1,2159 @@
+T:\dac_user\2015\IDD_2015-1\Shim\Patterns\hSiO2_404_009.chi
+2th_deg
+
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diff --git a/jnb-tools/data/hStv/hSiO2_404_009.dpp b/jnb-tools/data/hStv/hSiO2_404_009.dpp
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diff --git a/jnb-tools/data/hStv/hSiO2_404_009.ppss b/jnb-tools/data/hStv/hSiO2_404_009.ppss
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index 0000000..f9177fd
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diff --git a/jnb-tools/data/hStv/hSiO2_404_009.tif b/jnb-tools/data/hStv/hSiO2_404_009.tif
new file mode 100755
index 0000000..74e2dff
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diff --git a/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.azi.cake.npy b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.azi.cake.npy
new file mode 100644
index 0000000..02467b5
Binary files /dev/null and b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.azi.cake.npy differ
diff --git a/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bg.chi b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bg.chi
new file mode 100644
index 0000000..18940d3
--- /dev/null
+++ b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bg.chi
@@ -0,0 +1,1443 @@
+# BG ROI: 6.00000, 21.00000
+# BG Params: 20, 10, 20
+
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diff --git a/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bgsub.chi b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bgsub.chi
new file mode 100644
index 0000000..9527469
--- /dev/null
+++ b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.bgsub.chi
@@ -0,0 +1,1443 @@
+# BG ROI: 6.00000, 21.00000
+# BG Params: 20, 10, 20
+
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+2.0931551e+01 -4.5855308e+01
+2.0941977e+01 -3.8546212e+01
+2.0952404e+01 -2.8269395e+01
+2.0962831e+01 -1.5293716e+01
+2.0973258e+01 -3.6183134e-01
+2.0983684e+01 1.6504422e+01
+2.0994111e+01 3.7386831e+01
diff --git a/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.int.cake.npy b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.int.cake.npy
new file mode 100644
index 0000000..78ac32e
Binary files /dev/null and b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.int.cake.npy differ
diff --git a/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.tth.cake.npy b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.tth.cake.npy
new file mode 100644
index 0000000..8ac46df
Binary files /dev/null and b/jnb-tools/data/hStv/temporary_pkpo/hSiO2_404_009.tth.cake.npy differ
diff --git a/jnb-tools/local_modules/fancy_plots.py b/jnb-tools/local_modules/fancy_plots.py
new file mode 100644
index 0000000..2138d80
--- /dev/null
+++ b/jnb-tools/local_modules/fancy_plots.py
@@ -0,0 +1,188 @@
+import numpy as np
+from xrd_unitconv import *
+
+def plot_diffcake(ax_cake, model, xrange=[5,20], yrange=None,
+ no_yticks=True, dsp_ticks=False,
+ no_xlabel=False, dsp_step = 0.2):
+ """
+ ax_pattern = axis of diffraction pattern
+ model = PeakPo model
+
+ """
+ wavelength = model.base_ptn.wavelength
+ ax_cake.set_ylabel('Azimuthal angle (degrees)')
+
+ if no_yticks:
+ ax_cake.set_yticks([])
+
+ intensity_cake = model.__dict__['diff_img'].__dict__['intensity_cake']
+ tth_cake = model.__dict__['diff_img'].__dict__['tth_cake']
+ chi_cake = model.__dict__['diff_img'].__dict__['chi_cake']
+
+ """
+ if dsp_ticks and (xrange is not None):
+ xrange[0] = dsp2tth(xrange[0], wavelength)
+ xrange[1] = dsp2tth(xrange[1], wavelength)
+ """
+
+ if xrange is None:
+ xrange = [tth_cake.min(), tth_cake.max()]
+ if yrange is None:
+ yrange = [intensity_cake.min(), intensity_cake.max()]
+ #x = np.ma.masked_where( (tth_cake <= xrange[0]) | (tth_cake >= xrange[1]), tth_cake )
+ #y = np.ma.masked_where( (tth_cake <= xrange[0]) | (tth_cake >= xrange[1]), intensity_cake )
+ x = tth_cake
+ y = intensity_cake
+ ax_cake.set_xlim(xrange)
+ #ax_cake.set_ylim(yrange)
+ ax_cake.imshow(intensity_cake, origin="lower",
+ extent=[tth_cake.min(), tth_cake.max(), chi_cake.min(), chi_cake.max()],
+ aspect="auto", cmap="gray_r", clim=(1.e2, 7.e3))
+
+ #if xrange is not None:
+ # ax_pattern.set_xlim(x.min(),x.max())
+ x_roi = np.ma.masked_outside(x, xrange[0], xrange[1]).compressed()
+ print(xrange)
+
+ if dsp_ticks:
+ ticks = np.arange( np.floor( (tth2dsp(x_roi, wavelength)*10.).max())/10.,
+ np.ceil( (tth2dsp(x_roi, wavelength)*10.).min())/10.,-dsp_step)
+ if ticks.size <= 20.:
+ ticks_in_tth = dsp2tth(ticks, wavelength)
+ ax_cake.set_xticks(ticks_in_tth)
+ ax_cake.set_xticklabels(np.around(ticks, decimals=2))
+ if not no_xlabel:
+ ax_cake.set_xlabel('d-spacing ($\mathdefault{\AA}$)')
+ else:
+ if not no_xlabel:
+ ax_cake.set_xlabel('Two Theta (degrees)')
+
+def plot_diffpattern(ax_pattern, model, xrange=None, yrange=None, bgsub=True,
+ no_yticks=True, dsp_ticks=False, dsp_step = 0.2,
+ no_xlabel=False):
+ """
+ ax_pattern = axis of diffraction pattern
+ model = PeakPo model
+ xrange = always in two theta unit even for dsp_ticks=True
+ """
+ wavelength = model.base_ptn.wavelength
+ ax_pattern.set_ylabel('Intensity (arbitrary unit)')
+
+ if no_yticks:
+ ax_pattern.set_yticks([])
+ if bgsub:
+ x_data, y_data = model.base_ptn.get_bgsub()
+ else:
+ x_data, y_data = model.base_ptn.get_raw()
+ """
+ if dsp_ticks and (xrange is not None):
+ xrange[0] = dsp2tth(xrange[0], wavelength)
+ xrange[1] = dsp2tth(xrange[1], wavelength)
+ """
+ if xrange is None:
+ xrange = [x_data.min(), x_data.max()]
+ if yrange is None:
+ yrange = [y_data.min(), y_data.max()]
+ x = np.ma.masked_where( (x_data <= xrange[0]) | (x_data >= xrange[1]), x_data )
+ y = np.ma.masked_where( (x_data <= xrange[0]) | (x_data >= xrange[1]), y_data )
+ ax_pattern.set_xlim(xrange)
+ ax_pattern.set_ylim(yrange)
+ ax_pattern.plot(x, y, c='k', lw=1.0)
+
+ #if xrange is not None:
+ # ax_pattern.set_xlim(x.min(),x.max())
+
+ if dsp_ticks:
+ ticks = np.arange( np.floor( (tth2dsp(x, wavelength)*10.).max())/10.,
+ np.ceil( (tth2dsp(x, wavelength)*10.).min())/10.,-dsp_step)
+ ticks_in_tth = dsp2tth(ticks, wavelength)
+ ax_pattern.set_xticks(ticks_in_tth)
+ ax_pattern.set_xticklabels(np.around(ticks, decimals=2))
+ if not no_xlabel:
+ ax_pattern.set_xlabel('d-spacing ($\mathdefault{\AA}$)')
+ else:
+ if not no_xlabel:
+ ax_pattern.set_xlabel('Two Theta (degrees)')
+
+def plot_jcpds(ax_pattern, model,
+ in_cake=False,
+ show_index=False, show_legend=False,
+ bar_height=1., bar_position=0., bar_vsep=0.,
+ phase_names=None, bar_alpha=1.0, bar_thick=1.):
+ """
+ bar position: position of the bar base in fraction. negative number will shift
+ bars further down
+ """
+ selected_phases = []
+ for phase in model.jcpds_lst:
+ if phase.display:
+ selected_phases.append(phase)
+ if phase_names is not None:
+ if len(phase_names) != len(selected_phases):
+ return
+ else:
+ phase_names = []
+ for phase in selected_phases:
+ phase_names.append(phase.name)
+
+ n_displayed_jcpds = len(selected_phases)
+ axisrange = ax_pattern.axis()
+ #bar_scale = 1. / 100. * axisrange[3] * bar_factor / 100.
+ pressure = model.get_saved_pressure()
+ temperature = model.get_saved_temperature()
+ wavelength = model.base_ptn.wavelength
+ for i, phase in enumerate(selected_phases):
+ phase.cal_dsp(pressure, temperature)
+ tth, inten = phase.get_tthVSint(wavelength)
+ intensity = inten * phase.twk_int
+ starting_intensity = np.ones_like(tth) * axisrange[2] - \
+ bar_position * (axisrange[3] - axisrange[2])
+ if in_cake:
+ bar_max = axisrange[3] * np.ones_like(tth)
+ bar_min = axisrange[2] * np.ones_like(tth)
+ else:
+ bar_max = starting_intensity - \
+ (i*bar_vsep) * 100. * (bar_height) / n_displayed_jcpds
+ bar_min = starting_intensity - \
+ (i*bar_vsep+1) * 100. * (bar_height) / n_displayed_jcpds
+ if pressure == 0.:
+ volume = phase.v
+ else:
+ volume = phase.v.item()
+ ax_pattern.vlines(
+ tth, bar_min, bar_max, colors=phase.color,
+ label=phase_names[i],
+ lw=bar_thick,
+ alpha=bar_alpha)
+ # hkl
+ if show_index:
+ hkl_list = phase.get_hkl_in_text()
+ for j, hkl in enumerate(hkl_list):
+ if tth[j] >= axisrange[0] and tth[j] <= axisrange[1]:
+ ax_pattern.text(
+ tth[j], bar_max[j], hkl.replace(" ", ""), color=phase.color,
+ rotation=90, verticalalignment='bottom',
+ horizontalalignment='center',
+ fontsize=8.,
+ alpha=1.0)
+ if in_cake:
+ pass
+ else:
+ ax_pattern.text(
+ axisrange[0] + (axisrange[1] - axisrange[0])*0.01,
+ (bar_max[0] + bar_min[0])/2., phase_names[i],
+ color=phase.color,
+ verticalalignment='center',
+ horizontalalignment='left',
+ fontsize=8.,
+ alpha=1.0)
+ ymin = axisrange[2] - bar_position * (axisrange[3] - axisrange[2]) - \
+ ( (n_displayed_jcpds-1) * bar_vsep + 1) * 100. * bar_height / n_displayed_jcpds
+ if not in_cake:
+ ax_pattern.set_ylim((ymin, axisrange[3]))
+
+ if show_legend:
+ leg_jcpds = ax_pattern.legend(
+ loc=0, prop={'size': 10}, framealpha=0., handlelength=1)
+ for line, txt in zip(leg_jcpds.get_lines(), leg_jcpds.get_texts()):
+ txt.set_color(line.get_color())
\ No newline at end of file
diff --git a/jnb-tools/local_modules/quick_plots.py b/jnb-tools/local_modules/quick_plots.py
new file mode 100644
index 0000000..c89f7e0
--- /dev/null
+++ b/jnb-tools/local_modules/quick_plots.py
@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+def plot_diffpattern(ax_pattern, model):
+ """
+ ax_pattern = axis of diffraction pattern
+ model = PeakPo model
+ """
+ wavelength = model.base_ptn.wavelength
+ ax_pattern.set_ylabel('Intensity (arbitrary unit)')
+
+ ax_pattern.set_yticks([])
+ x_data, y_data = model.base_ptn.get_bgsub()
+ xrange = [x_data.min(), x_data.max()]
+ yrange = [y_data.min(), y_data.max()]
+ ax_pattern.set_xlim(xrange)
+ ax_pattern.set_ylim(yrange)
+ ax_pattern.plot(x_data, y_data, c='k', lw=1.0)
+ ax_pattern.set_xlabel('Two Theta (degrees)')
+
+def plot_jcpds(ax_pattern, model):
+ selected_phases = []
+ for phase in model.jcpds_lst:
+ if phase.display:
+ selected_phases.append(phase)
+ n_displayed_jcpds = len(selected_phases)
+ axisrange = ax_pattern.axis()
+ pressure = model.get_saved_pressure()
+ temperature = model.get_saved_temperature()
+ wavelength = model.base_ptn.wavelength
+ bar_min = 0.
+ bar_scale = 1. / 100. * axisrange[3]
+ for i, phase in enumerate(selected_phases):
+ phase.cal_dsp(pressure, temperature)
+ tth, inten = phase.get_tthVSint(wavelength)
+ intensity = inten * phase.twk_int
+ if pressure == 0.:
+ volume = phase.v
+ else:
+ volume = phase.v.item()
+ ax_pattern.vlines(
+ tth, bar_min, intensity*bar_scale, colors=phase.color,
+ label=phase.name, lw=1.0, alpha=1.0)
+ ax_pattern.legend()
\ No newline at end of file
diff --git a/jnb-tools/local_modules/xrd_io.py b/jnb-tools/local_modules/xrd_io.py
new file mode 100644
index 0000000..4ae5255
--- /dev/null
+++ b/jnb-tools/local_modules/xrd_io.py
@@ -0,0 +1,27 @@
+import numpy as np
+
+def write_chi(filen, x, y, preheader = None):
+ """
+ write a chi file
+
+ :filen = string, filename and path
+ :x = two theta array
+ :y = intensity array
+ :preheader = header. Each component of a list represents a line of header.
+ Only first three will be used
+ """
+ if preheader is None:
+ preheader = "\n\n\n"
+ header = str(x.__len__())
+ np.savetxt(filen, np.asarray([x,y]).T, \
+ fmt='%1.7e', header = header, comments = preheader)
+
+def read_chi(fname):
+ """
+ read a chi file and return two arrays: twotheta and intensity
+
+ :fname = string, filename and path
+ """
+ data = np.loadtxt(fname, skiprows = 4)
+ twotheta, intensity = data.T
+ return twotheta, intensity
\ No newline at end of file
diff --git a/jnb-tools/local_modules/xrd_pkfit.py b/jnb-tools/local_modules/xrd_pkfit.py
new file mode 100644
index 0000000..02a00bf
--- /dev/null
+++ b/jnb-tools/local_modules/xrd_pkfit.py
@@ -0,0 +1,44 @@
+from lmfit.models import PseudoVoigtModel, LinearModel
+from lmfit import Parameters
+
+def make_model(peak_positions, fwhm=0.05, max_fwhm=0.5, pos_range=0.5, amplitude=1000.):
+ n_peaks = len(peak_positions)
+ pars = Parameters()
+
+ bg = LinearModel(prefix='bg_')
+ pars.update(bg.make_params(slope=0, intercept=0))
+
+ mod = bg
+ #pars['bg_intercept'].set(vary=True)
+ #pars['bg_slope'].set(vary=True)
+
+ for i in range(n_peaks):
+ prefix = 'pk{}_'.format(i)
+ peak = PseudoVoigtModel(prefix= prefix)
+ # Set this zero
+ pars.update( peak.make_params())
+ pars[prefix+'center'].set(peak_positions[i], min=peak_positions[i]-pos_range,
+ max=peak_positions[i]+pos_range, vary=True)
+ pars[prefix+'sigma'].set(fwhm, min=0., max=max_fwhm, vary=True)
+ pars[prefix+'amplitude'].set(amplitude, min=0., vary=True)
+ pars[prefix+'fraction'].set(0.0, min=0., max=1., vary=True)
+ mod += peak
+ return mod, pars
+
+def plot_fitresult(ax, x, y, out, n_peaks):
+ #plt.title(data_filename)
+ comps = out.eval_components(x=x)
+ ax.plot(x, y, 'k.', label='data')
+ #plt.plot(spr_x, init, 'k--')
+ ax.plot(x, out.best_fit, 'b-', label='total fit')
+ #plt.plot(x, comps['pk_water_'], 'b-', label='indiv. peak')
+ #plt.plot(x, comps['pk1_']+comps['pk2_']+comps['pk3_']+comps['pk4_'], 'g-', label='No H2O')
+ #plt.plot(spr_x_fit, comps['bg_'], 'b-', label='background')
+ ax.plot(x, y - out.best_fit - y.max()*0.2, 'g-', label='fit residue')
+
+ for i in range(n_peaks):
+ prefix = 'pk{}_'.format(i)
+ ax.axvline(out.params[prefix+'center'])
+ ax.plot(x, comps[prefix] - y.max()*0.1, 'r-', label='indiv. peak')
+
+ ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
\ No newline at end of file
diff --git a/jnb-tools/local_modules/xrd_unitconv.py b/jnb-tools/local_modules/xrd_unitconv.py
new file mode 100644
index 0000000..fdacc5e
--- /dev/null
+++ b/jnb-tools/local_modules/xrd_unitconv.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+def dsp2tth(dsp, wavelength):
+ return np.rad2deg( np.arcsin( wavelength / (2. * dsp) ) ) * 2.
+
+def tth2dsp(tth, wavelength):
+ return 0.5 * wavelength / np.sin( np.deg2rad(tth/2.) )
\ No newline at end of file
diff --git a/peakpo.bat b/peakpo.bat
deleted file mode 100755
index d397602..0000000
--- a/peakpo.bat
+++ /dev/null
@@ -1,3 +0,0 @@
-activate py36pkpo
-cd C:\Users\Shim\peakpo-v7\peakpo
-python -m peakpo
diff --git a/peakpo.command b/peakpo.command
deleted file mode 100755
index 631f239..0000000
--- a/peakpo.command
+++ /dev/null
@@ -1,3 +0,0 @@
-conda activate py36pkpo
-cd ~/python/peakpo-v7/peakpo
-python -m peakpo
diff --git a/peakpo/peakpo.py b/peakpo/peakpo.py
index d8b83e8..c780c53 100644
--- a/peakpo/peakpo.py
+++ b/peakpo/peakpo.py
@@ -1,12 +1,15 @@
+import os
import sys
import time
import numpy
import traceback
from io import StringIO
from PyQt5 import QtWidgets
-import qdarkstyle
+from PyQt5 import QtCore
+from PyQt5.QtCore import Qt
from control import MainController
from utils import ErrorMessageBox
+import qdarkstyle
def excepthook(exc_type, exc_value, traceback_obj):
@@ -42,7 +45,11 @@ def excepthook(exc_type, exc_value, traceback_obj):
errorbox.exec_()
+#app = QtWidgets.QApplication(sys.argv)
+QtCore.QCoreApplication.setAttribute(Qt.AA_EnableHighDpiScaling, True)
+os.environ["QT_AUTO_SCREEN_SCALE_FACTOR"] = "1"
app = QtWidgets.QApplication(sys.argv)
+# app.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling)
sys.excepthook = excepthook
app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())
# app.setStyleSheet('fusion')
diff --git a/peakpo/view/qtd.py b/peakpo/view/qtd.py
index 9515b38..b44e458 100644
--- a/peakpo/view/qtd.py
+++ b/peakpo/view/qtd.py
@@ -2,7 +2,7 @@
# Form implementation generated from reading ui file '../ui/peakpo.ui'
#
-# Created by: PyQt5 UI code generator 5.6
+# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
@@ -14,7 +14,7 @@ class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(931, 750)
- MainWindow.setMinimumSize(QtCore.QSize(0, 750))
+ MainWindow.setMinimumSize(QtCore.QSize(931, 750))
MainWindow.setMaximumSize(QtCore.QSize(16777215, 16777215))
font = QtGui.QFont()
font.setFamily("Helvetica")
@@ -983,7 +983,7 @@ def setupUi(self, MainWindow):
self.tab_Cake1 = QtWidgets.QWidget()
self.tab_Cake1.setObjectName("tab_Cake1")
self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.tab_Cake1)
- self.verticalLayout_3.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_3.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.pushButton_ApplyCakeView = QtWidgets.QPushButton(self.tab_Cake1)
sizePolicy = QtWidgets.QSizePolicy(
@@ -1144,7 +1144,7 @@ def setupUi(self, MainWindow):
self.tab_Cake2 = QtWidgets.QWidget()
self.tab_Cake2.setObjectName("tab_Cake2")
self.verticalLayout_19 = QtWidgets.QVBoxLayout(self.tab_Cake2)
- self.verticalLayout_19.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_19.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_19.setObjectName("verticalLayout_19")
self.groupBox_29 = QtWidgets.QGroupBox(self.tab_Cake2)
sizePolicy = QtWidgets.QSizePolicy(
@@ -1334,7 +1334,7 @@ def setupUi(self, MainWindow):
self.tab_JCPDSList1 = QtWidgets.QWidget()
self.tab_JCPDSList1.setObjectName("tab_JCPDSList1")
self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.tab_JCPDSList1)
- self.verticalLayout_4.setContentsMargins(12, 0, 12, 12)
+ self.verticalLayout_4.setContentsMargins(12, -1, 12, 12)
self.verticalLayout_4.setObjectName("verticalLayout_4")
self.pushButton_ForceUpdatePlot = QtWidgets.QPushButton(self.tab_JCPDSList1)
self.pushButton_ForceUpdatePlot.setObjectName("pushButton_ForceUpdatePlot")
@@ -1551,7 +1551,7 @@ def setupUi(self, MainWindow):
self.tab_JCPDSList2 = QtWidgets.QWidget()
self.tab_JCPDSList2.setObjectName("tab_JCPDSList2")
self.verticalLayout_23 = QtWidgets.QVBoxLayout(self.tab_JCPDSList2)
- self.verticalLayout_23.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_23.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_23.setObjectName("verticalLayout_23")
self.groupBox_9 = QtWidgets.QGroupBox(self.tab_JCPDSList2)
sizePolicy = QtWidgets.QSizePolicy(
@@ -1881,7 +1881,7 @@ def setupUi(self, MainWindow):
self.tab_UnitCellFit = QtWidgets.QWidget()
self.tab_UnitCellFit.setObjectName("tab_UnitCellFit")
self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.tab_UnitCellFit)
- self.verticalLayout_5.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_5.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_5.setObjectName("verticalLayout_5")
self.groupBox_15 = QtWidgets.QGroupBox(self.tab_UnitCellFit)
sizePolicy = QtWidgets.QSizePolicy(
@@ -2013,7 +2013,7 @@ def setupUi(self, MainWindow):
self.tab_Output = QtWidgets.QWidget()
self.tab_Output.setObjectName("tab_Output")
self.verticalLayout_9 = QtWidgets.QVBoxLayout(self.tab_Output)
- self.verticalLayout_9.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_9.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_9.setObjectName("verticalLayout_9")
self.groupBox_17 = QtWidgets.QGroupBox(self.tab_Output)
self.groupBox_17.setFlat(True)
@@ -2046,14 +2046,13 @@ def setupUi(self, MainWindow):
self.tab_PkFt = QtWidgets.QWidget()
self.tab_PkFt.setObjectName("tab_PkFt")
self.verticalLayout_14 = QtWidgets.QVBoxLayout(self.tab_PkFt)
- self.verticalLayout_14.setContentsMargins(3, 3, 3, 0)
+ self.verticalLayout_14.setContentsMargins(3, 3, 3, -1)
self.verticalLayout_14.setObjectName("verticalLayout_14")
self.tabWidget_PeakFit = QtWidgets.QTabWidget(self.tab_PkFt)
self.tabWidget_PeakFit.setObjectName("tabWidget_PeakFit")
self.tab_PeakFitSection = QtWidgets.QWidget()
self.tab_PeakFitSection.setObjectName("tab_PeakFitSection")
self.verticalLayout_15 = QtWidgets.QVBoxLayout(self.tab_PeakFitSection)
- self.verticalLayout_15.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_15.setObjectName("verticalLayout_15")
self.frame_28 = QtWidgets.QFrame(self.tab_PeakFitSection)
sizePolicy = QtWidgets.QSizePolicy(
@@ -2099,7 +2098,6 @@ def setupUi(self, MainWindow):
self.tab_PeakFitPeaks = QtWidgets.QWidget()
self.tab_PeakFitPeaks.setObjectName("tab_PeakFitPeaks")
self.verticalLayout_16 = QtWidgets.QVBoxLayout(self.tab_PeakFitPeaks)
- self.verticalLayout_16.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_16.setObjectName("verticalLayout_16")
self.groupBox_35 = QtWidgets.QGroupBox(self.tab_PeakFitPeaks)
sizePolicy = QtWidgets.QSizePolicy(
@@ -2189,7 +2187,6 @@ def setupUi(self, MainWindow):
self.tab_PeakFitConfig = QtWidgets.QWidget()
self.tab_PeakFitConfig.setObjectName("tab_PeakFitConfig")
self.verticalLayout_13 = QtWidgets.QVBoxLayout(self.tab_PeakFitConfig)
- self.verticalLayout_13.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_13.setObjectName("verticalLayout_13")
self.groupBox_25 = QtWidgets.QGroupBox(self.tab_PeakFitConfig)
self.groupBox_25.setFlat(True)
@@ -2250,7 +2247,7 @@ def setupUi(self, MainWindow):
self.tab_Bkgn = QtWidgets.QWidget()
self.tab_Bkgn.setObjectName("tab_Bkgn")
self.verticalLayout_10 = QtWidgets.QVBoxLayout(self.tab_Bkgn)
- self.verticalLayout_10.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_10.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_10.setObjectName("verticalLayout_10")
self.groupBox_7 = QtWidgets.QGroupBox(self.tab_Bkgn)
sizePolicy = QtWidgets.QSizePolicy(
@@ -2452,7 +2449,7 @@ def setupUi(self, MainWindow):
self.tab_Process = QtWidgets.QWidget()
self.tab_Process.setObjectName("tab_Process")
self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.tab_Process)
- self.verticalLayout_7.setContentsMargins(3, 0, 3, 12)
+ self.verticalLayout_7.setContentsMargins(3, -1, 3, 12)
self.verticalLayout_7.setObjectName("verticalLayout_7")
self.groupBox_10 = QtWidgets.QGroupBox(self.tab_Process)
sizePolicy = QtWidgets.QSizePolicy(
diff --git a/shortcuts/peakpo.bat b/shortcuts/peakpo.bat
new file mode 100755
index 0000000..48326b9
--- /dev/null
+++ b/shortcuts/peakpo.bat
@@ -0,0 +1,6 @@
+REM In the line below replace py36pkpo with your conda environment for peakpo
+conda activate py36pkpo
+REM In the line below replace the path with your path to peakpo
+cd C:\Users\Shim\peakpo-v7\peakpo
+REM Do not change the line below
+python -m peakpo
diff --git a/shortcuts/peakpo.command b/shortcuts/peakpo.command
new file mode 100755
index 0000000..4418c5d
--- /dev/null
+++ b/shortcuts/peakpo.command
@@ -0,0 +1,4 @@
+eval "$(conda shell.bash hook)"
+conda activate peakpo2018
+cd ~/Dropbox\ \(ASU\)/python/PeakPo-V7/peakpo
+python -m peakpo
-
-
+
+
+
+
+
+
+
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+
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+