{
"cells": [
{
"cell_type": "markdown",
"id": "1f783f7c-b79c-42dd-945f-e5942b4d43fe",
"metadata": {},
"source": [
"# Usage of multinmrfit package"
]
},
{
"cell_type": "markdown",
"id": "8a875d9c-0886-46a1-a3e5-6dc2f6a9a7c1",
"metadata": {},
"source": [
"## 1. General information\n",
"\n",
"This notebook showcases usage of the Spectrum object of multinmrfit.\n",
"\n",
"### Content\n",
"\n",
"- [Prepare environment](#2.-Prepare-environment)\n",
"- [Load NMR data](#3.-Load-NMR-data)\n",
"- [Peak picking](#4.-Peak-picking)\n",
"- [Spectrum simulation and fitting](#5.-Spectrum-simulation-and-fitting)\n",
" - [Load models of NMR signals](#5.1.-Load-models-of-NMR-signals)\n",
" - [Model construction](#5.2.-Model-construction)\n",
" - [Simulation](#5.3.-Simulation)\n",
" - [Fitting](#5.4.-Fitting)"
]
},
{
"cell_type": "markdown",
"id": "48ef990f-51e5-4d82-957b-551db9a7184f",
"metadata": {},
"source": [
"## 2. Prepare environment\n",
"\n",
"- Download and install Anaconda (>= 3.7) on your computer: https://www.anaconda.com/distribution/\n",
"- Install `multinmrfit`:\n",
" - run `Anaconda prompt` from the start menu\n",
" - install multinmrfit with: `python -m pip install git+https://github.com/NMRTeamTBI/MultiNMRFit.git@branch_name` (where `branch_name` is the git branch you want to install)\n",
"- Start Jupyter from the start menu\n",
"- Open the jupyter notbook\n"
]
},
{
"cell_type": "markdown",
"id": "82e45651-4954-4715-af0c-6e7744f11c43",
"metadata": {},
"source": [
"Import required packages."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "56964584-9076-4298-9831-a03720622e3f",
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import sys\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "6af36e5f-c9ac-44e8-af52-9de7364e487c",
"metadata": {},
"source": [
"Initialize logger."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "17914892-bb8b-41a1-81bb-af4b280f78a7",
"metadata": {},
"outputs": [],
"source": [
"logging.basicConfig(format='%(asctime)s | %(levelname)s : %(message)s', level=logging.DEBUG, stream=sys.stdout)"
]
},
{
"cell_type": "markdown",
"id": "f1f32d21-9b59-4eb0-89a8-ece1dff4fa34",
"metadata": {},
"source": [
"Load multinmrfit."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e2af57f9-5b2c-41b0-8e2a-08e542dd3755",
"metadata": {},
"outputs": [],
"source": [
"import multinmrfit.base.spectrum as spectrum\n",
"import multinmrfit.base.io as io"
]
},
{
"cell_type": "markdown",
"id": "7ad3b339-8e9a-422b-8dbf-f9d0e9aceb40",
"metadata": {},
"source": [
"## 3. Load NMR data"
]
},
{
"cell_type": "markdown",
"id": "0e226f40-31f7-43ff-90ac-472cce42df55",
"metadata": {},
"source": [
"Data can be loaded from a TSV file containing columns 'ppm' and 'intensity'."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5c7fd577-659d-422e-a94e-fa931394c6cf",
"metadata": {},
"outputs": [],
"source": [
"test_synthetic_dataset = pd.read_table(\"./data/data_sim_nmrfit.csv\", sep=\"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "20c51f23-ce0e-4e97-bfb3-65b24382f1c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ppm', 'intensity'], dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_synthetic_dataset.columns"
]
},
{
"cell_type": "markdown",
"id": "a3803ec6-b54d-4d8d-b9a8-8bf6b4f78702",
"metadata": {},
"source": [
"Data can also be loaded from TopSpin files by providing all required information in a dictionnary, assuming one-dimensional spectrum if rowno is None or two-dimensional spectrum if rowno is provided."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "047de85f-106f-45f6-b6d5-86daf0166e04",
"metadata": {},
"outputs": [],
"source": [
"test_topspin_dataset = {\"data_path\":\"C:/Bruker/TopSpin4.0.7/data\",\n",
" \"dataset\":\"CFE_test\",\n",
" \"expno\":\"991\",\n",
" \"procno\":\"1\",\n",
" \"rowno\":\"3\"}"
]
},
{
"cell_type": "markdown",
"id": "8c60b318-b861-41ac-a131-bae396e7901a",
"metadata": {},
"source": [
"The window of interest can be provided as a tuple containing lower and upper boundaries."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "79368034-f40d-407d-8add-f4a8e5735285",
"metadata": {},
"outputs": [],
"source": [
"window = (-0.2, 0.2)"
]
},
{
"cell_type": "markdown",
"id": "7e9793ad-f820-4841-a677-bbcd48a1fa4e",
"metadata": {},
"source": [
"We can then load the data in a `Spectrum` object."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "470cc00b-35d0-41db-a871-e319e941cfbc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:44:20,814 | DEBUG : create Spectrum object\n"
]
}
],
"source": [
"sp = spectrum.Spectrum(data=test_synthetic_dataset, window=window)"
]
},
{
"cell_type": "markdown",
"id": "d4dd004b-f145-4bba-a9b6-57893ec7a018",
"metadata": {},
"source": [
"We can then work directly with this object. For instance, to view the experimental spectrum, use the `plot()` method with `exp=True`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4d51675e-5447-4acc-9431-885e82383baf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:44:25,632 | DEBUG : create plot\n"
]
}
],
"source": [
"fig = sp.plot(exp=True)"
]
},
{
"cell_type": "markdown",
"id": "67ea44a5-1d61-4d9f-9918-b7fa125c98b6",
"metadata": {},
"source": [
"This method returns a `plotly.graph_objects.Figure` object that can be updated (e.g. to change the layout as shown below fot the size) and plotted."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a1f0f818-7230-4490-bca5-43bb04a7c8fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"plotly.graph_objs._figure.Figure"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(fig)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8bdd5bc6-fdd0-4cd3-b2ff-2315762ea010",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#386CB0"
},
"mode": "lines",
"name": "exp. spectrum",
"type": "scatter",
"x": [
-0.1999999999999999,
-0.1968253968253968,
-0.1936507936507936,
-0.1904761904761904,
-0.1873015873015873,
-0.1841269841269841,
-0.1809523809523809,
-0.1777777777777778,
-0.1746031746031746,
-0.1714285714285713,
-0.1682539682539682,
-0.165079365079365,
-0.1619047619047618,
-0.1587301587301587,
-0.1555555555555555,
-0.1523809523809524,
-0.1492063492063492,
-0.146031746031746,
-0.1428571428571429,
-0.1396825396825396,
-0.1365079365079364,
-0.1333333333333333,
-0.1301587301587301,
-0.1269841269841269,
-0.1238095238095238,
-0.1206349206349206,
-0.1174603174603174,
-0.1142857142857143,
-0.1111111111111111,
-0.1079365079365078,
-0.1047619047619047,
-0.1015873015873015,
-0.0984126984126984,
-0.0952380952380952,
-0.092063492063492,
-0.0888888888888889,
-0.0857142857142857,
-0.0825396825396825,
-0.0793650793650794,
-0.0761904761904761,
-0.0730158730158729,
-0.0698412698412698,
-0.0666666666666666,
-0.0634920634920634,
-0.0603174603174603,
-0.0571428571428571,
-0.053968253968254,
-0.0507936507936508,
-0.0476190476190476,
-0.0444444444444444,
-0.0412698412698412,
-0.038095238095238,
-0.0349206349206349,
-0.0317460317460317,
-0.0285714285714285,
-0.0253968253968254,
-0.0222222222222222,
-0.019047619047619,
-0.0158730158730159,
-0.0126984126984126,
-0.0095238095238094,
-0.0063492063492063,
-0.0031746031746031,
0,
0.0031746031746031,
0.0063492063492063,
0.0095238095238094,
0.0126984126984126,
0.0158730158730158,
0.0190476190476189,
0.0222222222222221,
0.0253968253968253,
0.0285714285714284,
0.0317460317460318,
0.034920634920635,
0.0380952380952381,
0.0412698412698413,
0.0444444444444445,
0.0476190476190476,
0.0507936507936508,
0.053968253968254,
0.0571428571428571,
0.0603174603174603,
0.0634920634920634,
0.0666666666666666,
0.0698412698412698,
0.0730158730158729,
0.0761904761904761,
0.0793650793650793,
0.0825396825396824,
0.0857142857142856,
0.0888888888888888,
0.0920634920634919,
0.0952380952380953,
0.0984126984126985,
0.1015873015873016,
0.1047619047619048,
0.107936507936508,
0.1111111111111111,
0.1142857142857143,
0.1174603174603174,
0.1206349206349206,
0.1238095238095238,
0.1269841269841269,
0.1301587301587301,
0.1333333333333333,
0.1365079365079364,
0.1396825396825396,
0.1428571428571428,
0.1460317460317459,
0.1492063492063491,
0.1523809523809522,
0.1555555555555554,
0.1587301587301588,
0.161904761904762,
0.1650793650793651,
0.1682539682539683,
0.1714285714285714,
0.1746031746031746,
0.1777777777777778,
0.1809523809523809,
0.1841269841269841,
0.1873015873015873,
0.1904761904761904,
0.1936507936507936,
0.1968253968253968,
0.1999999999999999
],
"xaxis": "x",
"y": [
3203.8213991064235,
3304.9953804123325,
3411.164836862961,
3522.66404088274,
3639.8556725481512,
3763.133763647805,
3892.927003571289,
4029.702458698749,
4173.969765405678,
4326.285866798944,
4487.260375182877,
4657.561656414764,
4837.9237492355705,
5029.154252960412,
5232.14334133612,
5447.874089860236,
5677.434339583963,
5922.030363873389,
6183.002657652052,
6461.844233673511,
6760.2218904118645,
7080.001015106422,
7423.274608374431,
7792.3973701227005,
8190.025878726982,
8619.16613773078,
9083.23007131418,
9586.102940977684,
10132.224157308034,
10726.684607379988,
11375.344457799236,
12084.97649050273,
12863.441472574465,
13719.903976641106,
14665.09962882495,
15711.668212789427,
16874.57175256134,
18171.623140802843,
19624.15981319587,
21257.90948612848,
23104.112706328684,
25200.992377970106,
27595.697324856184,
30346.901209590505,
33528.319082432376,
37233.52641405797,
41582.65405017458,
46731.827531132905,
52886.68883104754,
60322.09975150958,
69411.38272265853,
80670.56607297751,
94826.70482754818,
112925.56782011809,
136504.71598862327,
167876.05440175824,
210589.5700679826,
270178.87550814607,
355252.18629190105,
478560.307797556,
655560.5845798899,
890960.0766567012,
1135648.3899828095,
1250091.1286377506,
1135648.5389350115,
890960.3745614183,
655561.0314377496,
478560.9036094988,
355252.9310591847,
270179.7692323337,
210590.6127509561,
167877.24604571276,
136506.05659606747,
112927.0573938728,
94828.34337075106,
80672.35358907796,
69413.31921541966,
60324.18522500821,
52888.9232896744,
46734.210979591975,
41585.18649348388,
37236.207857549336,
33531.14953175147,
30349.88067069681,
27598.8258040231,
25204.269881784952,
23107.539241692743,
21261.48506025704,
19627.88443361832,
18175.496815362523,
16878.594489415795,
15715.840020410382,
14669.42051599834,
13724.37395246709,
12868.060546467656,
12089.744672192051,
11380.261757328044,
10731.75103510613,
10137.439723903935,
9591.467657430334,
9088.743948925265,
8624.829188116715,
8195.838113819003,
7798.358802166903,
7429.385249931858,
7086.260879053124,
6766.630989938982,
6468.402582287343,
6189.710269174148,
5928.88725244059,
5684.440519648522,
5455.029576189883,
5239.448149014146,
5036.608397385743,
4845.5272461229,
4665.314521794594,
4495.162625401626,
4334.337518519044,
4182.170835605667,
4038.0529646733567,
3901.4269629315513,
3771.7831943211554,
3648.654592778527,
3531.6124692306844,
3420.262792205728,
3314.242881943995,
3213.2184663379844
],
"yaxis": "y"
}
],
"layout": {
"autosize": false,
"height": 400,
"legend": {
"orientation": "h",
"x": 1,
"xanchor": "right",
"y": 1.02,
"yanchor": "bottom"
},
"plot_bgcolor": "white",
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"width": 900,
"xaxis": {
"anchor": "y",
"autorange": false,
"domain": [
0,
1
],
"linecolor": "black",
"mirror": true,
"range": [
0.1999999999999999,
-0.1999999999999999
],
"showline": true,
"ticks": "outside",
"title": {
"text": "chemical shift (ppm)"
},
"zeroline": false
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"exponentformat": "power",
"linecolor": "black",
"mirror": true,
"showexponent": "last",
"showline": true,
"title": {
"text": "intensity"
},
"zeroline": false
}
}
},
"image/png": "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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig.update_layout(autosize=False, width=900, height=400)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "b56e11aa-32a5-46aa-aaa6-de04deaaae17",
"metadata": {},
"source": [
"## 4. Peak picking"
]
},
{
"cell_type": "markdown",
"id": "7c8659e6-ad50-47cb-a0ae-f64ad58bdb0b",
"metadata": {},
"source": [
"Use the `peak_picking()` method with argument `threshold`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "87e448d5-6445-47d4-bed9-8697067bbffd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:44:33,517 | DEBUG : peak peaking\n",
"2024-12-20 09:44:33,543 | DEBUG : peak table\n",
" ppm intensity cID\n",
"0 0.0 1.250091e+06 \n"
]
}
],
"source": [
"peak_table = sp.peak_picking(1e6)"
]
},
{
"cell_type": "markdown",
"id": "61bb57c6-0c0c-46a9-95cc-623f4eadc6de",
"metadata": {},
"source": [
"To visualize the spectrum and the identified peaks, use the `plot()` method with the `peak_table` provided as argument `pp`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e231fbf2-85a6-4bf2-a582-bd7cf27ad7ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:44:36,253 | DEBUG : create plot\n"
]
}
],
"source": [
"fig = sp.plot(pp=peak_table)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1f211d65-4143-45c6-bf05-6d11c9e36277",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#386CB0"
},
"mode": "lines",
"name": "exp. spectrum",
"type": "scatter",
"x": [
-0.1999999999999999,
-0.1968253968253968,
-0.1936507936507936,
-0.1904761904761904,
-0.1873015873015873,
-0.1841269841269841,
-0.1809523809523809,
-0.1777777777777778,
-0.1746031746031746,
-0.1714285714285713,
-0.1682539682539682,
-0.165079365079365,
-0.1619047619047618,
-0.1587301587301587,
-0.1555555555555555,
-0.1523809523809524,
-0.1492063492063492,
-0.146031746031746,
-0.1428571428571429,
-0.1396825396825396,
-0.1365079365079364,
-0.1333333333333333,
-0.1301587301587301,
-0.1269841269841269,
-0.1238095238095238,
-0.1206349206349206,
-0.1174603174603174,
-0.1142857142857143,
-0.1111111111111111,
-0.1079365079365078,
-0.1047619047619047,
-0.1015873015873015,
-0.0984126984126984,
-0.0952380952380952,
-0.092063492063492,
-0.0888888888888889,
-0.0857142857142857,
-0.0825396825396825,
-0.0793650793650794,
-0.0761904761904761,
-0.0730158730158729,
-0.0698412698412698,
-0.0666666666666666,
-0.0634920634920634,
-0.0603174603174603,
-0.0571428571428571,
-0.053968253968254,
-0.0507936507936508,
-0.0476190476190476,
-0.0444444444444444,
-0.0412698412698412,
-0.038095238095238,
-0.0349206349206349,
-0.0317460317460317,
-0.0285714285714285,
-0.0253968253968254,
-0.0222222222222222,
-0.019047619047619,
-0.0158730158730159,
-0.0126984126984126,
-0.0095238095238094,
-0.0063492063492063,
-0.0031746031746031,
0,
0.0031746031746031,
0.0063492063492063,
0.0095238095238094,
0.0126984126984126,
0.0158730158730158,
0.0190476190476189,
0.0222222222222221,
0.0253968253968253,
0.0285714285714284,
0.0317460317460318,
0.034920634920635,
0.0380952380952381,
0.0412698412698413,
0.0444444444444445,
0.0476190476190476,
0.0507936507936508,
0.053968253968254,
0.0571428571428571,
0.0603174603174603,
0.0634920634920634,
0.0666666666666666,
0.0698412698412698,
0.0730158730158729,
0.0761904761904761,
0.0793650793650793,
0.0825396825396824,
0.0857142857142856,
0.0888888888888888,
0.0920634920634919,
0.0952380952380953,
0.0984126984126985,
0.1015873015873016,
0.1047619047619048,
0.107936507936508,
0.1111111111111111,
0.1142857142857143,
0.1174603174603174,
0.1206349206349206,
0.1238095238095238,
0.1269841269841269,
0.1301587301587301,
0.1333333333333333,
0.1365079365079364,
0.1396825396825396,
0.1428571428571428,
0.1460317460317459,
0.1492063492063491,
0.1523809523809522,
0.1555555555555554,
0.1587301587301588,
0.161904761904762,
0.1650793650793651,
0.1682539682539683,
0.1714285714285714,
0.1746031746031746,
0.1777777777777778,
0.1809523809523809,
0.1841269841269841,
0.1873015873015873,
0.1904761904761904,
0.1936507936507936,
0.1968253968253968,
0.1999999999999999
],
"xaxis": "x",
"y": [
3203.8213991064235,
3304.9953804123325,
3411.164836862961,
3522.66404088274,
3639.8556725481512,
3763.133763647805,
3892.927003571289,
4029.702458698749,
4173.969765405678,
4326.285866798944,
4487.260375182877,
4657.561656414764,
4837.9237492355705,
5029.154252960412,
5232.14334133612,
5447.874089860236,
5677.434339583963,
5922.030363873389,
6183.002657652052,
6461.844233673511,
6760.2218904118645,
7080.001015106422,
7423.274608374431,
7792.3973701227005,
8190.025878726982,
8619.16613773078,
9083.23007131418,
9586.102940977684,
10132.224157308034,
10726.684607379988,
11375.344457799236,
12084.97649050273,
12863.441472574465,
13719.903976641106,
14665.09962882495,
15711.668212789427,
16874.57175256134,
18171.623140802843,
19624.15981319587,
21257.90948612848,
23104.112706328684,
25200.992377970106,
27595.697324856184,
30346.901209590505,
33528.319082432376,
37233.52641405797,
41582.65405017458,
46731.827531132905,
52886.68883104754,
60322.09975150958,
69411.38272265853,
80670.56607297751,
94826.70482754818,
112925.56782011809,
136504.71598862327,
167876.05440175824,
210589.5700679826,
270178.87550814607,
355252.18629190105,
478560.307797556,
655560.5845798899,
890960.0766567012,
1135648.3899828095,
1250091.1286377506,
1135648.5389350115,
890960.3745614183,
655561.0314377496,
478560.9036094988,
355252.9310591847,
270179.7692323337,
210590.6127509561,
167877.24604571276,
136506.05659606747,
112927.0573938728,
94828.34337075106,
80672.35358907796,
69413.31921541966,
60324.18522500821,
52888.9232896744,
46734.210979591975,
41585.18649348388,
37236.207857549336,
33531.14953175147,
30349.88067069681,
27598.8258040231,
25204.269881784952,
23107.539241692743,
21261.48506025704,
19627.88443361832,
18175.496815362523,
16878.594489415795,
15715.840020410382,
14669.42051599834,
13724.37395246709,
12868.060546467656,
12089.744672192051,
11380.261757328044,
10731.75103510613,
10137.439723903935,
9591.467657430334,
9088.743948925265,
8624.829188116715,
8195.838113819003,
7798.358802166903,
7429.385249931858,
7086.260879053124,
6766.630989938982,
6468.402582287343,
6189.710269174148,
5928.88725244059,
5684.440519648522,
5455.029576189883,
5239.448149014146,
5036.608397385743,
4845.5272461229,
4665.314521794594,
4495.162625401626,
4334.337518519044,
4182.170835605667,
4038.0529646733567,
3901.4269629315513,
3771.7831943211554,
3648.654592778527,
3531.6124692306844,
3420.262792205728,
3314.242881943995,
3213.2184663379844
],
"yaxis": "y"
},
{
"marker": {
"color": "#FDC086",
"line": {
"width": 1.2
},
"size": 9,
"symbol": "arrow-down"
},
"mode": "markers",
"name": "peaks detected",
"type": "scatter",
"x": [
0
],
"xaxis": "x",
"y": [
1312595.685069638
],
"yaxis": "y"
}
],
"layout": {
"autosize": false,
"height": 400,
"legend": {
"orientation": "h",
"x": 1,
"xanchor": "right",
"y": 1.02,
"yanchor": "bottom"
},
"plot_bgcolor": "white",
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"width": 900,
"xaxis": {
"anchor": "y",
"autorange": false,
"domain": [
0,
1
],
"linecolor": "black",
"mirror": true,
"range": [
0.1999999999999999,
-0.1999999999999999
],
"showline": true,
"ticks": "outside",
"title": {
"text": "chemical shift (ppm)"
},
"zeroline": false
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"exponentformat": "power",
"linecolor": "black",
"mirror": true,
"showexponent": "last",
"showline": true,
"title": {
"text": "intensity"
},
"zeroline": false
}
}
},
"image/png": "iVBORw0KGgoAAAANSUhEUgAABEAAAAGQCAYAAACu+RXHAAAgAElEQVR4Xu3dC/wNdf7H8Y/7/a4IIaR+pEKlhCIVXVDLpq2sVKLNVlSSVptIuqj+2lhb0m1TbLlUJEVI6UISyi0qopDc7/7znfObY85xfmfOOd855zcz53Uea3+3+d6e3++v32/ev+/MFDhivIQXAggggAACCCCAAAIIIIAAAgggEGCBAgQgAZ5dhoYAAggggAACCCCAAAIIIIAAAqYAAQgLAQEEEEAAAQQQQAABBBBAAAEEAi9AABL4KWaACCCAAAIIIIAAAggggAACCCBAAMIaQAABBBBAAAEEEEAAAQQQQACBwAsQgAR+ihkgAggggAACCCCAAAIIIIAAAggQgLAGEEAAAQQQQAABBBBAAAEEEEAg8AIEIIGfYgaIAAIIIIAAAggggAACCCCAAAIEIKwBBBBAAAEEEEAAAQQQQAABBBAIvAABSOCnmAEigAACCCCAAAIIIIAAAggggAABCGsAAQQQQAABBBBAAAEEEEAAAQQCL0AAEvgpZoAIIIAAAggggAACCCCAAAIIIEAAwhpAAAEEEEAAAQQQQAABBBBAAIHACxCABH6KGSACCCCAAAIIIIAAAggggAACCBCAsAYQQAABBBBAAAEEEEAAAQQQQCDwAgQggZ9iBogAAggggAACCCCAAAIIIIAAAgQgrAEEEEAAAQQQQAABBBBAAAEEEAi8AAFI4KeYASKAAAIIIIAAAggggAACCCCAAAEIawABBBBAAAEEEEAAAQQQQAABBAIvQAAS+ClmgAgggAACCCCAAAIIIIAAAgggQADCGkAAAQQQQAABBBBAAAEEEEAAgcALEIAEfooZIAIIIIAAAggggAACCCCAAAIIEICwBhBAAAEEEEAAAQQQQAABBBBAIPACBCCBn2IGiAACCCCAAAIIIIAAAggggAACBCCsAQQQQAABBBBAAAEEEEAAAQQQCLwAAUjgp5gBIoAAAggggAACCCCAAAIIIIAAAQhrAAEEEEAAAQQQQAABBBBAAAEEAi9AABL4KWaACCCAAAIIIIAAAggggAACCCBAAMIaQAABBBBAAAEEEEAAAQQQQACBwAsQgAR+ihkgAggggAACCCCAAAIIIIAAAggQgLAGEEAAAQQQQAABBBBAAAEEEEAg8AIEIIGfYgaIAAIIIIAAAggggAACCCCAAAIEIKwBBBBAAAEEEEAAAQQQQAABBBAIvAABSOCnmAEigAACCCCAAAIIIIAAAggggAABCGsAAQQQQAABBBBAAAEEEEAAAQQCL0AAEvgpZoAIIIAAAggggAACCCCAAAIIIEAAwhpAAAEEEEAAAQQQQAABBBBAAIHACxCABH6KGSACCCCAAAIIIIAAAggggAACCBCAsAYQQAABBBBAAAEEEEAAAQQQQCDwAgQggZ9iBogAAggggAACCCCAAAIIIIAAAgQgrAEEEEAAAQQQQAABBBBAAAEEEAi8AAFI4KeYASKAAAIIIIAAAggggAACCCCAAAEIawABBBBAAAEEEEAAAQQQQAABBAIvQAAS+ClmgAgggAACCCCAAAIIIIAAAgggQADCGkAAAQQQQAABBBBAAAEEEEAAgcALEIAEfooZIAIIIIAAAggggAACCCCAAAIIEICwBhBAAAEEEEAAAQQQQAABBBBAIPACBCCBn2IGiAACCCCAAAIIIIAAAggggAACBCCsAQQQQAABBBBAAAEEEEAAAQQQCLwAAUjgp5gBIoAAAggggAACCCCAAAIIIIAAAQhrAAEEEEAAAQQQQAABBBBAAAEEAi9AABL4KWaACCCAAAIIIIAAAggggAACCCBAAMIaQAABBBBAAAEEEEAAAQQQQACBwAsQgAR+ihkgAggggAACCCCAAAIIIIAAAggQgLAGEEAAAQQQQAABBBBAAAEEEEAg8AIEIIGfYgaIAAIIIIAAAggggAACCCCAAAIEIKwBBBBAAAEEEEAAAQQQQAABBBAIvAABSOCnmAEigAACCCCAAAIIIIAAAggggAABCGsAAQQQQAABBBBAAAEEEEAAAQQCL0AAEvgpZoAIIIAAAggggAACCCCAAAIIIEAAwhpAAAEEEEAAAQQQQAABBBBAAIHACxCABH6KGSACCCCAAAIIIIAAAggggAACCBCAsAYQQAABBBBAAAEEEEAAAQQQQCDwAgQggZ9iBogAAggggAACCCCAAAIIIIAAAgQgrAEEEEAAAQQQQAABBBBAAAEEEAi8AAFI4KeYASKAAAIIIIAAAggggAACCCCAAAEIawABBBBAAAEEEEAAAQQQQAABBAIvQAAS+ClmgAgggAACCCCAAAIIIIAAAgggQADCGkAAAQQQQAABBBBAAAEEEEAAgcALEIAEfooZIAIIIIAAAggggAACCCCAAAIIEICwBhBAAAEEEEAAAQQQQAABBBBAIPACBCCBn2IGiAACCCCAAAIIIIAAAkEQmDRpkmzfvj3hoXTr1i3hYzkQgWwQIADJhllmjAgggAACCCCAAAIIIBAIgRuu+4sUL7BPqlYoFXM8W3bsldW//CETJkyQsmXLBmLMDAIBtwQIQNySpB4EEEAAAQQQQAABBBBAIM0CahfIxP+Ok5f/1ixmS39/cZE0adFWetzcM809oXoE/CdAAOK/OaPHCCCAAAIIIIAAAgggkMUCahfIVWeWl07nnBihMGfZr/L41O/l9fHj2f2RxeuDoectQADC6kAAAQQQQAABBBBAAAEEfCSQ1y4Qdn/4aBLpar4IEIDkCzuNIoAAAggggAACCCCAAAKpC0TvAvHb7o+GF3aXoffdLJ3atUgdIUbJkS+8JW9OnSVzJ410td5MVeb3/mfKKdV2CEBSlaMcAggggAACCCCAAAII+EKgZac+8ucrW0ufm672RX8T6WT0LhC/7f7wSgDi5tpwoy4CkERWf+rHEICkbkdJBBBAAAEEEEAAAQQQ8IGAGyemXhymtQukYulivrv3BwFI7BVFAJLe7zQCkPT6UjsCCCCAAAIIIIAAAp4UGPDIGJkyY364b+NHDZJGOXXE+vzS2ePCX1Mnqx0uaS7D7u8pKkxocU6jiLLJXMqgTvBGvzIlXHfF8mXClyskUnde/bYqVH21Xr1u6CCLlq6UBQuXhz9Xr3Z1mTxuqKjj1Netvqjx3zbgqYidItEno6pMsyY54fqUyabNv4c/TsbBjUVh7QIpX7Ko609+0Z2LePOsxm4PQJYsXyNdew82bceO6C9OZaPtOnYfKKvWro+5ptQn81ozPfoOj7k24pVJdp0lUpdT/91YK9QREiAAYSUggAACCCCAAAIIIJBmgSsfeD/NLcSufuqQS2N+QZ0Qzvt8STh4iD7RV19ftmKdGRSok8QqlSuY4Yd6qRNj9bLusRArMMlrsJOmz5OBjz4v9nBFnfwN6X+TGb441e3Ub+uk3eqr6rs6oY61A0SdgNvDF2ts9ktl8gpA7CfpVjCk2tqydbtplslXl85/kh07d8mbb77p6pNfdOfCsrcsVH0n16lhzod62QMQK4xSlyg5rZFo22j3WGs53lqPtTbcXGdOdTn1P5NrKRvaIgDJhllmjAgggAACCCCAAAL5KnBen8n50v6nIzvGbFed9PXrdU3EDSijL0mwToDtYUeskCD6ZDbeQK2T27x2SuQVVFjHx+u3aleFK9ZOFns/nOq1n6Q7BSBWX6xdC1Z7+XXpgtoFsnXzr9Lj5lBA5dbLySyRNWTviz1Us6+ZJ0e/EbHrxmmNRI8vet1Gz4NTP2ON08115tS+U//dmk/qCQkQgLASEEAAAQQQQAABBBBIs8DmP/amuYXY1VcuVzzmF+yXidgPsAcT0Sf4eYUE9pPZRJ7oEX05gnXZg6rH6aQ7Xr9V+ejdJcn2Obr9WDtAvBaAqDHu2LFDypQp4+oa05kLax2oOrZu2xHul3X5kbVmrC/YdwSpz8VbI/ZBxlqjseYsFow9VIu+Qa7b6yyv9uvWqmZe+mMP7fIrSHN18Xi4MgIQD08OXUMAAQQQQAABBBBAIB0CidyAUp28Vixf1jiB3R7xSFGnE+Nk+mudwKp7cajLH5zqjtdva+dAtu0AScY7mWN15kK1E305UqwdIGreN2zaHL7cKlb/otdI9DFOOyic1rrTOKPbc2Od2et06n8yc8axzgIEIM5GHIEAAggggAACCCCAQKAE1H0HVq75OSLYUJ+765Yu4Ruhqpt7qvs1qM+rl3XvhugTxui61D091CvWvTDUX7fVCa91jw51nP0E0Klup37ndQ8Q1adKFcuGxxDdrjW50cdF3wcj1o078/sSmHQtTN25iD6xj14X9q/b15jTGokeb/ScqY/toZ3Tmom1NpzKJLPOnOpy6n+65jdb6yUAydaZZ9wIIIAAAggggAACWS0QfZmBdUPQRJ4CY7+sIfpGovECEOuv+XZ4a/eH+lz0JRPRdatj8uq3VWf0U2DsN9ZUx9ifAhN9L5Lo/qkbnNpvoJltAUi8eXaai+gnuSh3ezAWKyBRwcVzw+4yLwvJa43E+qa1z7m6pCo63Iu3ZqwdHfa14TQ29fVE11mydcXqf1b/h8rlwROAuAxKdQgggAACCCCAAAIIBFkg1iUDbo03nXW71cdsqYe5yJaZzq5xEoBk13wzWgQQQAABBBBAAAEEtATSeWKczrq1Bp2FhZmLLJz0LBgyAUgWTDJDRAABBBBAAAEEEEAAAQQQQCDbBQhAsn0FMH4EEEAAAQQQQAABBBBAAAEEskCAACQLJpkhIoAAAggggAACCCCAAAIIIJDtAgQg2b4CGD8CCCCAAAIIIIAAAggggAACWSBAAJIFk8wQEUAAAQQQQAABBBBAAAEEEMh2AQKQbF8BjB8BBBBAAAEEEEAAAQQQQACBLBAgAMmCSWaICCCAAAIIIIAAAggggAACCGS7AAFItq8Axo8AAggggAACCCCAAAIIIIBAFggQgGTBJDNEBBBAAAEEEEAAAQQQQAABBLJdgAAk21cA40cAAQQQQAABBBBAAAEEEEAgCwQIQLJgkhkiAggggAACCCCAAAIIIIAAAtkuQADiwgooUKCAC7VQBQIIIIAAAggggAACCCCAAALxBY4cOQJRigIEICnC2YupAIRF6AIkVSCAAAIIIIAAAggggAACCOQpwLmn3uIgANHzM0uzCF1ApAoEEEAAAQQQQAABBBBAAIG4Apx76i0QAhA9PwIQF/yoAgEEEEAAAQQQQAABBBBAwFmAAMTZKN4RBCB6fgQgLvhRBQIIIIAAAggggAACCCCAgLMAAYizEQGInpFjaRahIxEHIIAAAggggAACCCCAAAIIaApw7qkHyA4QPT+zNIvQBUSqQAABBBBAAAEEEEAAAQQQiCvAuafeAiEA0fMjAHHBjyoQQAABBBBAAAEEEEAAAQScBQhAnI3iHUEAoudHAOKCH1UggAACCARbYNGiRfLAAwPlyOEj5q7JWC/1OPkCBQvKkCFDpHHjxsEGYXQIIIAAAgikKEAAkiJcbjECED0/AhAX/KgCAQQQQCD4Anf8vY+UO7xZupxXK+ZgJ3z6kxwqU0OGDhsefAxGiAACCCCAQIoCBCApwhGA6MHZS7MI3bOkJgQQQACBYAqoXSD33nuvvND7XKlRqWTEIH/esltuGvWZPPbYY+z+COb0MyoEEEAAAZcEOPfUg2QHiJ6fWZpF6AIiVSCAAAIIBF5g4P0DpPKhDXLHZadGjPWZaStlc4Hj2P0R+BXAABFAAAEEdAU499QTJADR8yMAccGPKhBAAAEEskMg1i4Qdn9kx9wzSgQQQAABdwQIQPQcCUD0/AhAXPCjCgQQQACB7BGI3gXC7o/smXtGigACCCCgL0AAomdIAKLnRwDigh9VIIAAAghkj4B9F4gaNff+yJ65Z6QIIIAAAvoCBCB6hgQgen4EIC74UQUCCCCAQHYJWLtAChQsLL9JZe79kV3Tz2gRQAABBDQECEA08IyiBCB6fgQgLvhRBQIIIIBAdglYu0DUqHnyS3bNPaNFAAEEENATIADR9DtivPSqoDSLkDWAAAIIIIBAcgJqF0jRwoXkwcFDkivI0QgggAACCGSxAOeeepPPDhA9P7M0i9AFRKpAAAEEEEAAAQQQQAABBBCIK8C5p94CIQDR8yMAccGPKhBAAAEEEEAAAQQQQAABBJwFCECcjeIdQQCi50cA4oIfVSCAAAIIIIAAAggggAACCDgLEIA4GxGA6Bk5lmYROhJxAAIIIIAAAggggAACCCCAgKYA5556gOwA0fMzS7MIXUCkCgQQQAABBBBAAAEEEEAAgbgCnHvqLRACED0/AhAX/KgCAQQQQAABBBBAAAEEEEDAWYAAxNko3hEEIHp+BCAu+FEFAggggAACCCCAAAIIIICAswABiLMRAYiekWNpFqEjEQcggAACCCCAAAIIIIAAAghoCnDuqQfIDhA9P7M0i9AFRKpAAAEEEMgqgTdmrzbHe82FdbNq3AwWAQQQQAABHQHOPXX0jHP3I8ZLrwpKswhZAwgggAACCCQuMGneWhn+xmKzQP9rzpBOLWonXpgjEUAAAQQQyGIBzj31Jp8ARM/PLM0idAGRKhBAAAEEskag+2Mfy/c/bTPHe8qJ5WXcvRdkzdgZKAIIIIAAAjoCnHvq6LEDRE8vtzSL0BVGKkEAAQQQyAKBT5f9Kn1HfSpVKpQwR7vp9z0yovd5cl6D47Ng9AwRAQQQQAABPQHOPTX9uARGD1CVZhHqG1IDAggggEB2CPQfs0DmLNkot16RY/78HD11mbRqVFWG92yWHQCMEgEEEEAAAQ0Bzj018NS5OwGIHiABiL4fNSCAAAIIZIfAml+2y3WPzDKCD5H3hrU3B33ZgGmi7kb22v2tpc4JZbMDglEigAACCCCQogABSIpwucUIQPT8zNIsQhcQqQIBBBBAIPACIyYukQkfr5FO59eW/l3PMMc7fPximfTJWulyQR3p27lR4A0YIAIIIIAAAjoCnHvq6LEDRE/PSpGMP2XxMB1XKKkEAQQQQCCgArv2HpT2902TA4cOmzc9VTc/VS91M1R1U9QihQrKtEfbS6nihQMqwLAQQAABBBDQFyAA0TNkB4ien1maRegCIlUggAACCARa4LWZq+TZyUulecMq8mSvcyPG2m/0ZzJ/6Sa5vWNDua5tvUA7MDgEEEAAAQR0BDj31NFjB4ieXm5pFqErjFSCAAIIIBBggS6DZ8rPv+2S4bc0k1anV40Y6ZxvfpH+//lcahxXSiYMahtgBYaGAAIIIICAngDnnpp+3ARVD1CVZhHqG1IDAggggEBwBWYuXC//ePFL4yanZYybnbaJOdDrh82S1Ru2y8M3niVtm1QPLgYjQwABBBBAQEOAc08NPHXuTgCiB0gAou9HDQgggAACwRa4/f8+ka9WbpZ+XU6Xzq1OijnYiXN+kCcnfCNNT64sz/79/GCDMDoEEEAAAQRSFCAASREutxgBiJ6fWZpF6AIiVSCAAAIIBFJgyZqt0vOpuVK6RBHj0bftzJudxnqpm6NeNmC67NxzQMbc1VIa1akYSA8GhQACCCCAgI4A5546euwA0dOzUiSeAuOKI5UggAACCARP4OFXF8l7C36U6y6qJ7d3ahh3gM9OWiqvfbhKLmtWU/5xfePgYTAiBBBAAAEENAUIQPQA2QGi52eWZhG6gEgVCCCAAAKBE/h1217p+I/3zXFNfLCtVK9cKu4Y12/eJZ0fmmkeM/nhS+X48sUDZ8KAEEAAAQQQ0BHg3FNHjx0genq5pVmErjBSCQIIIIBAwATGvLNcXnx/hVzctLoM7n5WQqMbNO5L+eCr9XLjpfWl5xU5CZXhIAQQQAABBLJFgHNPvZlmB4ien1maRegCIlUggAACCARO4LL7p8vvO/bJs33Ol6b1Kyc0vq9WbJbbR34iFcoUk/ceaZdQGQ5CAAEEEEAgWwQ499SbaQIQPT8CEBf8qAIBBBBAIHgCaheH2s1xep1K8u+7WiQ1wFufmiffrNli7hpRu0d4IYAAAggggEBIgABEbyUQgOj5sQhd8KMKBBBAAIHgCYx8e6n896NV0vPyHLmxXf2kBvji9BUy5t3l8pc29aTPVfFvnJpUxRyMAAIIIICAzwUIQPQmkABEz48AxAU/qkAAAQQQCJ7A3/7vE1m4crM82ftcad6gSlIDnL9sk/Qb9Zk0Obmy/Ovv5ydVloMRQAABBBAIsgABiN7sEoDo+RGAuOBHFQgggAACwRO46J53Zffeg/KucR+Pisb9PJJ5bTXuG3K5cf+QksULy4ePX55MUY5FAAEEEEAg0AIEIHrTSwCi50cA4oIfVSCAAAIIBEtgzYbtct2wWVKtUkn53z8vTmlwf/rnB7Jhy2557f7WUueEsinVQSEEEEAAAQSCJkAAojejBCB6fgQgLvhRBQIIIIBAsATeW/CjPPzqImlzZjUZetPZKQ1u4AtfyEdfb5B/XN9YLmtWM6U6KIQAAggggEDQBAhA9GaUAETPjwDEBT+qQAABBBAIlsCIiUtkwsdrpHeHBtLt4pNTGtzLH6yUUVOWSZcL6kjfzo1SqoNCCCCAAAIIBE2AAERvRglA9PwIQFzwowoEEEAAgWAJ9BwxV5b8sFWe+VtzOefU41Ia3Off/SZ3/Gu+NDqpoozp2zKlOiiEAAIIIIBA0AQIQPRmlABEz48AxAU/qkAAAQQQCJZAq7umyoGDh+X94e2lbMmiKQ1u++79cmn/aVKkcEGZ89SVKdVBIQQQQAABBIImQACiN6MEIHp+BCAu+FEFAggggEBwBFb8/If8dfhsqVWltIx/4CKtgXUd8qGs27RTXup/odSvUU6rLgojgAACCCAQBAECEL1ZJADR8yMAccGPKhBAAAEEgiMwZf46Gfb613LJWTXkob821RrYgy99JTO+/FkG/OVM6XBeLa26KIwAAggggEAQBAhA9GaRAETPjwDEBT+qQAABBBAIjsBjbyyWt+etlb9fdZpc26au1sBe/2i1/N/b38pVLU6Se685XasuCiOAAAIIIBAEAQIQvVkkANHzIwBxwY8qEEAAAQSCI9DjiTmyfN3v8twdLaRxvUpaA1u0aovc9sw8ObVmeXnxngu06qIwAggggAACQRAgANGbRQIQPT8CEBf8qAIBBBBAIBgChw4dlpZ3TpUjxnA+euJyKVGssNbA9uw7KG3uflcKGLXMffpKKVSooFZ9FEYAAQQQQMDvAgQgejNIAJKE36Tp82Tgo8+bJcaPGiSNcuoQgCThx6EIIIAAAsEWWLr2d7n5yTlS54Qy8tr9bVwZ7HWPfCRrftkhz/drJQ1rV3ClTipBAAEEEEDArwIEIHozRwCSoN+S5WvkgeEvyORxQ48pwSJMEJHDEEAAAQQCLfC/uT/IE29+I5c1O1H+cX0TV8b68KsL5b0FP8ndfz5d/tTyJFfqpBIEEEAAAQT8KsC5p97M+SoAGfnCW/Lm1Fkyd9JIrVHHq6dj94Gyau16s/56tauHAw9VZvQrU475vPoEi1BrOiiMAAIIIBAQgaGvLZJ3PvtR+nZuJF0uCO2S1H1N+HiNjJi4RK44r6YM/Etj3eoojwACCCCAgK8FOPfUmz5fBCD2S08qli+TcgDiVE+PvsNly9bt4dBDhSGVKpaVsSP6y4BHxki1KpWlz01Xi6pnyoxPzM8TgOgtQEojgAACCARHoNujs2Xl+j9kzF0tpVGdiq4MbMmardLzqblycvVy8vJ9F7pSJ5UggAACCCDgVwECEL2Z80UAYg0xr50b6vKU/749U4bd3zNCQwUYsS5Zyauelp36SL9e10indi3MelTQ8eToN8zARZVRLxWAqPae+s8EAhC9tUdpBBBAAIEACew7cEgu7PuOOaKPn7pSihZ254al+w8elgvummrWO3vEFVKsSKEAqTEUBBBAAAEEkhMgAEnOK/roQAQgalAq7GhQv1Y4BIn+2D7wWAGICjW69h4ccXPT6M+pgGTrth1mVdwEVW/hURoBBBBAIFgCi1dvkV5Pz5NTTiwv4+5195G13R/7WL7/aZuMNnaWnOHSzpJg6TMaBBBAAIFsESAA0ZvpwAQg9hBk2Yp1EWFINFGqAUhe1CxCvUVIaQQQQAAB/wu8MXuNPP2/JdKxeS2579ozXR3Qo69/LZPnr5M7rj5Nurau62rdVIYAAggggICfBDj31JutQAUg9hAk+nIYOxMBiN6ioTQCCCCAAALRAoNfWSjTPv9J+nc9QzqdX9tVoEmfrJXh4xdLu7NryIPdmrpaN5UhgAACCCDgJwECEL3ZClQAYl32ksoOEMUY7x4g8ZhZhHqLkNIIIIAAAv4XuHbIh7J2004Ze88FklOzvKsDWv7jNunx+MdSu2oZeX1gG1frpjIEEEAAAQT8JMC5p95sBSYAsT+xRZEkew8QVSbeU2AIQPQWGqURQAABBIIrsGvvQWl7z7tSqGABmft0B+Px8O6O9cgR448Ud06RQ4ePyAePXSalSxRxtwFqQwABBBBAwCcCBCB6E+WLAMT++FpruB0uaR6+4Wn0U1msY6KfAuNUjxWcrFq73qyiXu3qMZ8iE03OItRbhJRGAAEEEPC3wJcrfpM+I+dLw9oV5Pl+rdIymJufnCNL1/4uI/s0l7PqH5eWNqgUAQQQQAABrwtw7qk3Q74IQPSGmP7SLML0G9MCAggggIB3BV6buUqenbxU/tTyJLn7z6enpaNPTvhGJs75QW7v1FCuu6heWtqgUgQQQAABBLwuwLmn3gwRgOj5maVZhC4gUgUCCCCAgG8F/vHilzJz4XoZeF1jueLcmmkZxzuf/ShDX1skbZtUl4dvPCstbVApAggggAACXhfg3FNvhghA9PwIQFzwowoEEEAAAX8LdBk8U37+bZe8MqC11KtWNi2DWbVhu9wwbJbUOK6UTBjUNi1tUCkCCCCAAAJeFyAA0ZshAhA9PwIQF/yoAgEEEEDAvwLbdu6X9gOmSfGihdLjAeQAACAASURBVGTWk1ekdSCt+70je/cfkmnD2kv50kXT2haVI4AAAggg4EUBAhC9WSEA0fMjAHHBjyoQQAABBPwrsGD5r3Lnc5/KmXUryag7W6R1IL2fnidfr94iT992njTLOT6tbVE5AggggAACXhQgANGbFQIQPT8CEBf8qAIBBBBAwL8CL81YIaOnLpdrWteVO68+La0Defqtb+WNWavl1itypPul9dPaFpUjgAACCCDgRQECEL1ZIQDR8yMAccGPKhBAAAEE/Ctw3/Ofy8eLf5EHuzWRdmefmNaBTP/iJ3no5YVywRknyKM3n5PWtqgcAQQQQAABLwoQgOjNCgGInh8BiAt+VIEAAggg4F+BToNmyKbf98jrA9tI7apl0jqQtRt3yLVDP5IqFUrIpMGXpLUtKkcAAQQQQMCLAgQgerNCAKLnRwDigh9VIIAAAgj4U2DL9n1yxcDpUqZEEZnx2GUZGcQl974nO/YckKlDLpXK5YpnpE0aQQABBBBAwCsCBCB6M0EAoudHAOKCH1UggAACCPhTYN6SjXLPmAVy1inHycjbm2dkEH2enS9ffv+bPH5rM2lxWtWMtEkjCCCAAAIIeEWAAERvJghA9PwIQFzwowoEEEAAAX8KPP/ed/LCtO/l+rYny986NsjIIP41eZm8OnOl3NT+FLn5slMz0iaNIIAAAggg4BUBAhC9mSAA0fMjAHHBjyoQQAABBPwpcPe/F8gn326UIT3OlosaV8vIID5ctEEeGPuFnG/s/njC2AXCCwEEEEAAgWwSIADRm20CED0/AhAX/KgCAQQQQMCfApffP1227tgnEx9sK9Url8rIINZv3iWdH5opFcsUk3cfaZeRNmkEAQQQQAABrwgQgOjNBAGInh8BiAt+VIEAAggg4D+Bn3/bJV0GzzRvRKpuSJrJ15UPvC+b/9grEwa1lRrHZSZ4yeT4aAsBBBBAAIG8BAhA9NZG3ACk4YXdpcMlzWXY/T31Wgl4aRZhwCeY4SGAAAIIHCMw46uf5cFxX0nLRlXlsZ6ZvRTlXuPGq3ONG7AO7t5ULm5ag9lBAAEEEEAgawQ499Sb6rgByMgX3pLRr0wJt9CsSY6MHdFfr8UAlmYRBnBSGRICCCCAQFyBZ976VsbPWi09r8iRGy+tn1GtF99fIWPeWS5dW9eVO64+LaNt0xgCCCCAAAL5KcC5p55+UpfAqB0h1osw5Cg8i1BvEVIaAQQQQMB/Ar2emiuL12yVp287T5rlHJ/RASxY/qvc+dynckadijL6rpYZbZvGEEAAAQQQyE8Bzj319BMOQJYsXyNdew+O2Vq92tVl8rihej3xcWkWoY8nj64jgAACCKQk0OquqXLg4GF5f3h7KVuyaEp1pFpo++79cmn/aVKkcEGZ89SVqVZDOQQQQAABBHwnwLmn3pTFDUB69B0uCxYuD7eQ164PtTNk6exxej3xcWkWoY8nj64jgAACCCQt8N1P2+TGxz6WWlVKy/gHLkq6vBsFug75UNZt2ilj77lAcmqWd6NK6kAAAQQQQMDzApx76k0RN0HV8zNLswhdQKQKBBBAAAHfCLw9b6089sZiaXf2ifJgtyb50u/BryyUaZ//JPdec4Zc1aJ2vvSBRhFAAAEEEMi0AOeeeuIJXwKj10ywS7MIgz2/jA4BBBBAIFLgkf8ukqmf/ij9upwunVudlC88Ez5eIyMmLpErz6sl9//lzHzpA40igAACCCCQaQHOPfXEHXeADL3vZunUrkVEK+rpMG9OnSVzJ43Uaz0gpVmEAZlIhoEAAgggkJDA9cNmyeoN2+X5fq2kYe0KCZVx+6Bv126VW56cK3WrlZVXB7R2u3rqQwABBBBAwJMCnHvqTUtKAcik6fNk4KPPZ/V9P+zsLEK9RUhpBBBAAAH/COzae1Da3vOuFCxYQOYaNyBVb/PjdfjwEWlp3IhVvZ35+OVSqnjh/OgGbSKAAAIIIJBRAc499bhTCkAGPDJG5n2+hB0gufYsQr1FSGkEEEAAAf8IfPn9b9Ln2flymrHz4z/GDpD8fKkdIGonyMjbm8tZpxyXn12hbQQQQAABBDIiwLmnHvMxAYi1u8Op2liXxjiVCerXWYRBnVnGhQACCCAQLfDyBytl1JRl0uWCOtK3c6N8BVL3AFH3AundoYF0u/jkfO0LjSOAAAIIIJAJAc499ZRT2gGi12TwSrMIgzenjAgBBBBAILbAgOc/l9mLf5FBNzSR9uecmK9M6ikw6mkwF55xggy7+Zx87QuNI4AAAgggkAkBzj31lHkKjJ6fWZpF6AIiVSCAAAII+EKg06AZsun3PTL+gYukVpXS+drndZt2StchH0qVCiVk0uBL8rUvNI4AAggggEAmBDj31FMmANHzIwBxwY8qEEAAAQT8IaCCDxWAlCtVVKY/2t4Tnb60/3uyffcBeXvwxVK1QklP9IlOIIAAAgggkC4BAhA92ZgBSMMLu0uvGzrI6FemxK196exxeq0HpDSLMCATyTAQQAABBOIKqEtf1CUw5zaoIk/1PtcTWnc+96ksWP6rPHLT2dL6zGqe6BOdQAABBBBAIF0CnHvqybIDRM/PLM0idAGRKhBAAAEEPC+gbn6qboLao90pcsvlp3qiv2PeWS4vvr9Cbmh7stzWsYEn+kQnEEAAAQQQSJcA5556sgQgen4EIC74UQUCCCCAgD8E1ONv1WNwH7+1mbQ4raonOj13yUa5d8wCaVq/sjzb53xP9IlOIIAAAgggkC4BAhA9WQIQPT8CEBf8qAIBBBBAwB8Cbe95T3btPSBTh1wqlcsV90SnN/+xV6584H0pVbyIzHz8Mk/0iU4ggAACCCCQLgECED3ZuAFIj77DZeWan2XupJFmKy079ZGt23aY748fNUga5dTRaz0gpVmEAZlIhoEAAgggkKfA6g3b5fphs6Ra5ZLyvwcv9pRU54c+kPWbd8urA1pL3WplPdU3OoMAAggggICbApx76mnGDUBU4NGv1zXSqV0LGfnCW/Lm1FlmGKLenzn3K5k8bqhe6wEpzSIMyEQyDAQQQACBPAWmfrpOHvnv19K2SXV5+MazPCX1jxe/lJkL18vA6xrLFefW9FTf6AwCCCCAAAJuCnDuqacZNwBRT4MZet/NZgCidoOo19gR/WXS9Hky8NHnhafAhPBZhHqLkNIIIIAAAt4XeOyNxfL2vLXS56qG8pc29TzV4f9+tEpGvr1UrmpRW+695gxP9Y3OIIAAAggg4KYA5556mnEDkI7dB0rblk2lz01Xi/VoXPW+fTeIXvPBKM0iDMY8MgoEEEAAgbwFbnzsY/nup20y6o4Wcma9Sp6i+nrVFun9zDw59cTy8uK9F3iqb3QGAQQQQAABNwU499TTjBuALFm+Rrr2Hmy2UK929fAlLyoMadYkx9wNwosdIKwBBBBAAIFgCxw4eFha3TXVHOTsEVdIsSKFPDXgfQcOyYV93zH7NOepK6VI4YKe6h+dQQABBBBAwC0BAhA9SZ4Co+dnlmYRuoBIFQgggAACnhVYvGar9Hpqrpxi7LAY59EdFt2NHSrfqx0qdxo7VOp6a4eKZyeWjiGAAAII+E6Ac0+9KSMA0fMjAHHBjyoQQAABBLwtMH7WannmrW+l0/m1pX9Xb95jY7hxj5JJxj1K/m7co+Raj92jxNuzS+8QQAABBPwkQACiN1uOAYj90bfRTXET1JAIi1BvEVIaAQQQQMDbAoPGfSkffLVe7v9LY7nyPG8+ZWXK/HUy7PWv5eKm1WVwd289pcbbs0vvEEAAAQT8JMC5p95sOd4EtVLFstzrw8GYRai3CCmNAAIIIOBtgc4PzZT1m3fJKwNaS71qZT3Z2ZXr/5Buj86W6pVLycQH23qyj3QKAQQQQAABXQHOPfUEE34Mrl4zwS7NIgz2/DI6BBBAIJsFtu7YJ5ffP11KFCskHz1xhacp2tz9juzZd0jefaSdVCxTzNN9pXMIIIAAAgikIsC5ZypqR8sQgOj5maVZhC4gUgUCCCCAgCcFPvl2o9z97wXS9OTK8uzfz/dkH61O3f5/n8hXKzfLE7eeK+efVsXTfaVzCCCAAAIIpCLAuWcqagkGIB27D5S2LZtKn5uu1msl4KVZhAGfYIaHAAIIZLHAf977TsZO+16ub1tP/taxoacl/jV5qbw6c5X0aH+K3HLZqZ7uK51DAAEEEEAgFQHOPVNRSzAAmTR9njw5+g2ZO2mkXisBL80iDPgEMzwEEEAgiwX6jvpMPl22SYbedLa0ObOapyU++nqDDHzhCzmvQRUZ0ftcT/eVziGAAAIIIJCKAOeeqaglGIA0vLB73Np5CkyIh0WotwgpjQACCCDgXYH2A6bJtp375e2HLpaqFUt6t6NGzzZu3S1XPfiBlC9dVKYNa+/pvtI5BBBAAAEEUhHg3DMVtQQDEL2qs6c0izB75pqRIoAAAtkk8P1Pf0j3x2YbwUcJIwC5xBdDv+rBGUYQskfG3XuhnHJiOV/0mU4igAACCCCQqADnnolKxT4u7k1Q9arOntIswuyZa0aKAAIIZJPAyx+slFFTlknH5rXlvmvP8MXQH319sUyev1Z6d2gg3S4+2Rd9ppMIIIAAAggkKsC5Z6JSKQYg6kaoq9auN0sPve9m6dSuhahLY5o1yZGxI/rrtR6Q0izCgEwkw0AAAQQQiBDoM/IT+XLFZhnaw7j/R2Nv3//D6vhHi4z7gIz9Qs6qX1lG9vH2U2tYbggggAACCCQrwLlnsmKRx8fdAaLCj0oVy5pBR8tOfaRfr2vMAGTkC2/Jm1NncXPUXEsWod4ipDQCCCCAgPcEdu89KBfd867ZsQ8eu0xKlyjivU7G6NGOPQfkknvfM7/y4eOXS8nihX3RbzqJAAIIIIBAIgKceyailPcxcQMQtdNj/KhB0iinTkQAop4OM/DR54WboIZgWYR6i5DSCCCAAALeE5i9+BcZ8Pzn0rheJXnujhbe62CcHt32zDxZtGqLDLv5HLnwjBN81Xc6iwACCCCAQDwBzj311kfcAETt+nhu2F3HBCDsAIlEZxHqLUJKI4AAAgh4T2D4+MUy6ZO1cusVOdL90vre62CcHr00Y4WMnrpcOp1fW/p39ce9S3wFTGcRQAABBPJNgHNPPfq4AciAR8bIvM+XmJe6WJfA1K1VTbr2HiwdLmkuw+7vqdd6QEqzCAMykQwDAQQQQCAs0PmhmbJ+8y4Ze88FklOzvK9klq/bJj2e+FiqVy4lEx9s66u+01kEEEAAAQTiCXDuqbc+HJ8CY13uYm+m1w0dpM9NV+u1HKDSLMIATSZDQQABBBCQ1Ru2y/XDZknlcsVl6pBLfSly5QPvy+Y/9sqrA1pL3WplfTkGOo0AAggggEC0AOeeemvCMQDRqz47SrMIs2OeGSUCCCCQLQL//WiVjHx7qVzW7ET5x/VNfDnsh19dKO8t+En6XNVQ/tKmni/HQKcRQAABBBAgAHF3DTjeBNV69K29We4BEjkJBCDuLkpqQwABBBDIX4E7n/tUFiz/Vf7ZralcenaN/O1Miq3P+PJnefClr6RZzvHy9G3npVgLxRBAAAEEEPCWAOeeevORUgDCU2AIQPSWHaURQAABBLwqsP/AIWnd7105fOSITBvWXsqXLurVrsbt17ad+6X9gGlSsEABmfXk5VK0SCFfjoNOI4AAAgggYBcgANFbDykFIPabo+o1H4zSLMJgzCOjQAABBBAQmfftRrnn3wuk0UkVZUzflr4m6Tliriz5Yas8fmszaXFaVV+Phc4jgAACCCCgBDj31FsHxwQgsW56GquJWJfG6HXFv6VZhP6dO3qOAAIIIBAp8OSEb2TinB/kpvanyM2XneprnhemfS/Pv/eddG51kvTrcrqvx0LnEUAAAQQQIADRXwMp7QDRbzZYNRCABGs+GQ0CCCCQzQLXDPlQfty009z9oXaB+Pmldn+oXSA1q5SWNx64yM9Doe8IIIAAAgiYApx76i0EngKj58cidMGPKhBAAAEEvCGwzgg+uhoBSPnSxYz7f7TzRqc0e9F+wHTZtnOfjDcCkFpGEMILAQQQQAABPwsQgOjNHgGInh8BiAt+VIEAAggg4A2BCR//ICMmfiOXnFVDHvprU290SrMX6kkw6okwfTufLl0uOEmzNoojgAACCCCQvwIEIHr+jgFIy059ZOu2HTFbWTp7nF7rASnNIgzIRDIMBBBAIMsF7v73Z/LJt5vkgesby+XNagZC470FP8nDry6U80+rIk/cem4gxsQgEEAAAQSyV4BzT725jxuAdOw+UCpVLCtjR/TXayXgpVmEAZ9ghocAAghkgcDhw0eMx9++I/sPHpapQy6VyuWKB2LUm//YK1c+8L4ULVzQeBzuFVKwYIFAjItBIIAAAghkpwDnnnrznpU3QVXBzqq16025erWry+RxQ/NUVI/8nTJj/jFft+9+YRHqLUJKI4AAAgjkv8Bny3+Vu577VHJqVZCxd7fK/w652IMeT8yR5et+l6duO0/OzTnexZqpCgEEEEAAgcwKcO6p5511AUiPvsNly9bt4dDDaZeLCkCWrVgXNyRhEeotQkojgAACCOS/wDNvfSvjZ62Wv15SX3pdmZP/HXKxB6OnLpeXZqyQrq3ryh1Xn+ZizVSFAAIIIIBAZgU499TzdrwEpm3LptLnpqv1WvFQaXVPk369rpFO7VqYvZo0fZ48OfoNmTtpZMxeEoB4aPLoCgIIIIBA2gSue2SWrPlluzx3RwtpXK9S2trJj4oXrdoitz0zT+qcUFZeu791fnSBNhFAAAEEEHBFgABEjzFuAOIUDug1nfnSS5avka69B8v4UYOkUU4dswOxPmfvWfQlMBXLlzkmLGERZn4uaREBBBBAwD2B9Zt3SeeHZkrpEoXlg8cud69iD9V08b3vys49B2Xig22leuVSHuoZXUEAAQQQQCBxAc49E7eKdaTjJTDxqvfbU2BSCUCix68umVEv+31DWIR6i5DSCCCAAAL5K/D2vLXy2BuLpU3j6jK0x1n525k0tT5w7Bfy0aINcu81Z8hVLWqnqRWqRQABBBBAIL0CnHvq+To+Blevem+VdiMAUbtiBj76vHATVG/NLb1BAAEEEEhd4L7nP5ePF/8i9117pnRsXiv1ijxccvL8tfLo64vlgjNOkEdvPsfDPaVrCCCAAAII5C1AAKK3OrIqAFFUyd4DJJqXAERvwVEaAQQQQMB7Am3uflf27Dsobz90iVStWMJ7HXShRxu37pGrHpwhJYoVlo+eCOZlPi4wUQUCCCCAgMcFCED0JihmANLwwu7S64YOMvqVKXFr99slMGowTk+Bib7ERQUm9hukqo9PrlNDxo7oH7ZhEeotQkojgAACCOSfwJff/yZ9np0vJ1cvKy/fF+wbhHZ7dJasXL9dRt7eXM465bj8Q6dlBBBAAAEEUhTg3DNFuNxiWbcDRI1bhRyr1q43CerVrh5xP4/oAMR+rDq+WZOciPBDfY5FqLcIKY0AAgggkH8Cw17/WqbMXyfXXVRPbu/UMP86koGW/zV5mbw6c6V0MC7zGWBc7sMLAQQQQAABvwlw7qk3Y1kZgOiRHVuaRei2KPUhgAACCGRCYNfeg3Jp//fk0OEj8vrANlK7aplMNJtvbazduEOuHfqRFCpYQN4ffpmUKl443/pCwwgggAACCKQiwLlnKmpHyxCA6PmZpVmELiBSBQIIIIBAxgXe/HiNPDVxiZzX4HgZ0fu8jLefHw32HfWpfLrsV7mrcyP58wV18qMLtIkAAggggEDKApx7pkwXOnc/Yrz0qqA0i5A1gAACCCDgR4G/Dp8tK37+Q4bceJZc1KS6H4eQdJ8/XLheHnjxS6lfo5y81P/CpMtTAAEEEEAAgfwU4NxTT58ARM8vlCIVKCDkSC5AUgUCCCCAQMYEvlxh3Px05HypUqGETBp8Scba9UJDnQbNkE2/75GRfYybodbnZqhemBP6gAACCCCQmADnnok55XUUAYieHwGIC35UgQACCCCQeYHBryyUaZ//JD3anSK3XH5q5juQjy3+593vZOz076X92SfKoG5N8rEnNI0AAggggEByAgQgyXlFH00AoudHAOKCH1UggAACCGRWYNvO/dJ+wDSz0f/982KpVqlkZjuQz61t2LJb/vTPD8xeTBvWXsqXLprPPaJ5BBBAAAEEEhMgAEnMKa+jCED0/AhAXPCjCgQQQACBzAq89uEqeXbSUml1+gky/JZzMtu4R1rr/5/PZc43v5iP/lWPAOaFAAIIIICAHwQIQPRmiQBEz48AxAU/qkAAAQQQyKzAX4xHwf5gPBJ2eM9m0qpR1cw27pHW5izZKP3HLJCTjEf//td4BDAvBBBAAAEE/CBAAKI3SwQgen4EIC74UQUCCCCAQOYEPl22SfqO+kyqVy4lEx9sm7mGPdhS54dmyvrNu4xHAJ9rPAq4igd7SJcQQAABBBCIFCAA0VsRBCB6fgQgLvhRBQIIIIBA5gQeGPuFfLhog/S8PEdubFc/cw17sKUXp6+QMe8ul7bGI4AfNh4FzAsBBBBAAAGvCxCA6M0QAYieHwGIC35UgQACCCCQGYFft+2Rjv+YYTY2+eFL5PjyJTLTsEdbwcOjE0O3EEAAAQTyFCAA0VscBCB6fgQgLvhRBQIIIIBAZgRefN/Y8fDOcrmocTUZ0uPszDTq8VbYEePxCaJ7CCCAAAIRAgQgeguCAETPjwDEBT+qQAABBBDIjAD3vDjWmXuiZGbt0QoCCCCAgDsCBCB6jgQgen4EIC74UQUCCCCAQPoFeOpJ3sY8FSf9648WEEAAAQTcESAA0XMkANHzIwBxwY8qEEAAAQTSL9D/P5/LnG9+kds7NZTrLqqX/gZ91MJrH66SZyctlVannyDDbznHRz2nqwgggAAC2SZAAKI34wQgen4EIC74UQUCCCCAQHoF1KNe1eUv6vXeI+2kQpli6W3QZ7X/vmOfXHb/dLPX6tHA6hHBvBBAAAEEEPCiAAGI3qwQgOj5EYC44EcVCCCAAALpFVA3PlU3QG1/zoky6IYm6W3Mp7UPfmWhTPv8J7nx0vrS84ocn46CbiOAAAIIBF2AAERvhglA9PwIQFzwowoEEEAAgfQJbP5jr7n7Y9+BQ/Jsn/Olaf3K6WvMxzV/tWKz3D7yEylWpJC5C6RyueI+Hg1dRwABBBAIqgABiN7MEoDo+RGAuOBHFQgggAAC6RMY9vrXMmX+OmljPPp2KI++jQs9cOwX8tGiDdKheS0ZcO2Z6ZsUakYAAQQQQCBFAQKQFOFyixGA6PkRgLjgRxUIIIAAAukRsHY1qNpfH9hGalctk56GAlLruk07peuQD83RsFsmIJPKMBBAAIGACRCA6E0oAYieHwGIC35UgQACCCCQHoHeT8+Tr1dvkRvbnSI9Lz81PY0ErFbrfiln1q0ko+5sEbDRMRwEEEAAAb8LEIDozSABiJ4fAYgLflSBAAIIIOC+wNvz1spjbyyWapVKygTjnhYFCxRwv5EA1nj4yBHpYtwzZcOW3XLvNWfIVS1qB3CUDAkBBBBAwK8CBCB6M0cAoudHAOKCH1UggAACCLgrsHvfQfPGp+rxruqpL+rpL7wSF5j+xc/y0MtfmY8LVjdELVmscOKFORIBBBBAAIE0ChCA6OESgOj5EYC44EcVCCCAAALuCjzz1rcyftZqObfB8fJU7/PcrTxLarvruU/ls+W/StfWdeWOq0/LklEzTAQQQAABrwsQgOjNEAGInh8BiAt+VIEAAggg4J7A8h+3SY/HPzYrfOHuVtKgVgX3Ks+imuyOY++5QHJqls+i0TNUBBBAAAGvChCA6M0MAYieHwGIC35UgQACCCDgnkC/0Z/J/KWb5M8X1JG7Ojdyr+IsrOnp/30rb8xeLc0bVpEne52bhQIMGQEEEEDAawIEIHozQgCi50cA4oIfVSCAAAIIuCPwvnHvin8a964oX7qoTBjUVkqXKOJOxVlay849B6TL4Jmybed++We3pnLp2TWyVIJhI4AAAgh4RYAARG8mCED0/AhAXPCjCgQQQAABdwTUjU/Xb94ld//5dPlTy5PcqTTLa3nLeJrO48bTdKpXLmXeEJUXAggggAAC+SlAAKKnTwCi50cA4oIfVSCAAAII6Av8573vZOy076XRSRVlTN+W+hVSQ1jg1qfmyTdrtkiP9qfILZedigwCCCCAAAL5JkAAokdPAKLnRwDigh9VIIAAAgjoCSxatUVue2aeWcnI25vLWaccp1chpSMEvvz+N+nz7Hzzc8/d0UIa16uEEAIIIIAAAvkiQACix04AoudHAOKCH1UggAACCKQu8MuW3dLr6Xny67Y90rlVHenXhRufpq6Zd8knJyyRiXPWyPHlS8joO1vICZVKpqMZ6kQAAQQQQCCuAAGI3gIhANHzIwBxwY8qEEAAAQRSF7j1qbnG5Rlb5fzTqsgTt/KkktQlnUve/e/P5JNvN8npdSrKv+/iMiNnMY5AAAEEEHBbgABET5QARM+PAMQFP6pAAAEEEEhNQD3xRT35pW61suauBJ76kppjoqXUU2HUbpvVG7abT4RRT4bhhQACCCCAQCYFCED0tAlA9PwIQFzwowoEEEAAgeQFRk9dLi/NWCGliheR0Xe1kHpGCMIr/QKrjPCjl3FT1F17D8hfL6kvva7MSX+jtIAAAggggECuAAGI3lIgANHzIwBxwY8qEEAAAQSSE5g8f508+vrXZqHHejaTlo2qJlcBR2sJzF2yUe4ds8Cs475rz5SOzWtp1UdhBBBAAAEEEhUgAElUKvZxBCB6fgQgLvhRBQIIIIBA4gILvvtV7vzXp2aBvp0bSZcL6iRemCNdE5jw8RoZMXGJWd/TfztPmp16vGt1UxECCCCAAAJ5CRCA6K0NAhA9PwIQF/yoAgEEEEAgMYEff90p6qan23bul2vb1JW/X3VaYgU5Ki0CjfYwiAAAIABJREFU//f2t/L6R6ulfOmi5k1Rax5fOi3tUCkCCCCAAAKWAAGI3logANHzIwBxwY8qEEAAAQScBXbtPSh9np0vy9f9Lq3PrCaP3HS2cyGOSLvA/S98IbO+3iA5tSrIyNubG/dkKZz2NmkAAQQQQCB7BQhA9OaeAETPjwDEBT+qQAABBBCIL7B+8y4ZNO4rWWaEH6eeWE7+3beVFC1cEDYPCOw/eFhuHTFHvvvpD2lghCCDuzeV6pVLeaBndAEBBBBAIIgCBCB6s0oAoudHAOKCH1UggAACCOQtsHDlZjP82LJ9r5xihB+D/3qW1KzCpRZeWjM/btopg176Ur43QpBKZYvJwzeeLY3rVfJSF+kLAggggEBABAhA9CaSAETPjwDEBT+qQAABBBCILTD9i5/loZe/Mr+onvTykLG7oERRLrHw4nrZs/+g/POlhTLnm1/M7j3YrYm0O/tEL3aVPiGAAAII+FiAAERv8ghA9PwIQFzwowoEEEAAgWMFXpqxQkZPXW5+oXOrk6Rfl9Nh8oHAkxO+kYlzfjB72rtDA+l28ck+6DVdRAABBBDwiwABiN5MEYDo+RGAuOBHFQgggAACkQJPvPmN/G8uJ9F+XRcvf7BSRk1ZRnjl1wmk3wgggICHBQhA9CaHAETPjwDEBT+qQAABBBAICezdf8i438eXMnfJRvNjLqPw78p4/8ufjUtiQpcvtTr9BPnnX9XlS4X8OyB6jgACCCDgCQECEL1pIADR8yMAccGPKhBAAAEERD5atEFGTV0mP/+2SyqXK248TeQsbqTp84WxaNUWM9Da/MdeqXFcKel9ZQNp07iaz0dF9xFAAAEE8lOAAERPnwBEz48AxAU/qkAAAQSyWWDT73vM4ON944an6nVm3UrywPWNeZRqQBaFeoTxkFcXydert5gjuvTsGmYQUqVCiYCMkGEggAACCGRSgABET5sARM+PAMQFP6pAAAEEslVgwsc/yHNTlpqXvhQpXNA8Mb62Td1s5Qj0uF//aLUZdB04eFiKG5fC3NahoXS54KRAj5nBIYAAAgi4L0AAomdKAKLnRwDigh9VIIAAAtkmsHTt7+ZNMr9audkc+kXGZRG3dWwo1SqVzDaKrBrvhi275bnJS+VD43In9Wp6cmXzSTENa1fIKgcGiwACCCCQugABSOp2qiQBiJ4fAYgLflSBAAIIZIvA7zv2yfhZq0U9JUS9qlYsYe4EuLhp9WwhYJyGwAdfrTd3/mzcusf0UI/K7dqmnlQoXRQfBBBAAAEE4goQgOgtEAIQPT8CEBf8qAIBBBAIuoC6senEOT/IhI/XyOEjR8zhXtO6rtx2ZY4ULcKTQYI+/7HGt//AIXlu6nJ5wwjE1KtggQLSudVJxmUxdcwbpvJCAAEEEEAglgABiN66IADR8yMAccGPKhBAAIGgCqz4+Q8z9Hjnsx/DQ2x9ZjXpYpzoNjYuf+CFwCLjMqgJRjg26+vQZTHqdcW5Nc0w5JQTywOEAAIIIIBAhAABiN6CIADR8yMAccGPKhBAAIGgCSxaZZzUGjc4tZ/UXt6spvnX/VNOLBe04TIeFwS+/ykUlr27ICosM9ZM43qVXGiBKhBAAAEEgiBAAKI3iwQgen4EIC74UQUCCCAQBAF1Y9O5SzbKnCW/yA+/7Mj9+SBm6NG5VR05kcsagjDNaR/DT+blUmvMMCT3aik51dgJ0ta4T0zbJtV5fG7aZ4AGEEAAAW8LEIDozQ8BiJ4fAYgLflSBAAII+FVA7fQwQ49vNsr6zbvCw6hQpph0bF7LvIyhUtnifh0e/c5HgS3b95r3jZn0yVrZtnN/uCfnNjjeeGpQKAxRj9PlhQACCCCQXQIEIHrzTQCi50cA4oIfVSCAAAJ+Eliw/Fcz9FD/ft0WeoqHelWpUEJaNqoqrU4/Qc4+5Tg/DYm+elxg9uINMtN4coz1+FzV3SKFC5qPT25prLcm9SpLeZ4g4/FZpHsIIICAOwIEIHqOBCB6fgQgLvhRBQIIIOB1gTnf/GIGHvO+3Rjx1/iax5c2Q48Wxr8z63KfBq/Po9/7t3PPQZm50AhCjH9frvgtYjj1a5Qzb6zbxLhfiFqLZUvxSF2/zzf9RwABBGIJEIDorQsCED0/AhAX/KgCAQQQ8JrAxq27ZfzsNfL7jn1m8LFn38FwF+tVL2eEHlWk5WlVJadWBa91nf5kiYB6tLIKQ9RlWItWbpEDhw5HjFytTSsMUcFIqeKFs0SGYSKAAALBFiAA0ZtfAhA9PwIQF/yoAgEEEMhPgSPGnSaXrtsmS3/YKt8aNzL9du1W2bj16KUtqm85Ncubl7ao3R51q5XNz+7SNgLHCBw21vDXq7YYYYjxz3is7kIjFLFuoGod3OikiubOkMYnG/+MS2a4fwgLCQEEEPCnAAGI3rwRgOj5EYC44EcVCCCAQKYEfvx1p/xk/FNvf/x1l6zdtEPU01sOHIz863nZkkXltJMqSNP6lc2dHical7rwQsAvAgcPHcndGWLsDjFCka9Xbzmm62fUqSj1jafL1DMCvXrVy5rBXrEi3FTVL3NMPxFAIHsFCED05p4ARM+PAMQFP6pAAAEE3BTYaly2cjTo2GV7f6ccOnwkZlN1TihrBh6n1a5o/KsgJ51Qxs0uURcC+Sqw78Ah8zIZ83IZIwxZsmZrzP7UrlLaCEPKmYFIrSplpGrFEnJCxZJSjvuJ5Ov80TgCCCBgFyAA0VsPBCB6fgQgLvhRBQIIIJCsgDqhix1y7JLtu48+MjS63hMqlZSax5U2d3TUPL6U8a+0nGpc3sIJXrIzwPF+Fti996B8Y1zytWr9H8a/7bL6l+3m27xe6v4h6nunaoWSobe5wcgJlUoZnyvBE2j8vBjoOwII+E6AAERvyghA9PwIQFzwowoEEEAgWkDdv2DzH3tl8/a95qNmf9u2V9YZl6uELl/ZJb8YNynN61WmZBEz2LD/swIPtviz1hCILaB2R5mByAYjENmwQ9ZvNr7Ptuw27oezW3bsORCXrVTxIqFQxAxJrLehsET9K88OEpYdAggg4JoAAYgeJQGInh8BiAt+VIEAAsEX2LP/kGzftd/cnbF914HQW+Pfjt0Hcz+333ziym/b95nBx29G6BHvVbBggVDAcVyp3N0cpY0t+6GdHRXLFAs+KCNEIIMC6ns1FIbsMcNH8+2WXbLx9z2ywfj8ToeApGSx3B0kRkhyfPkSUsH4Hq1Yupj51vxXuqj5lp1YGZxUmkIAAd8KEIDoTR0BiJ4fAYgLflSBAAL+EVAnOn/YAgwVauzYY4QY0eGG7fN/GCdP0TcZTWTElcsVF/Nf2dDb0I6Oo4FHInVwDAIIpF9gx+4DZjBi7Rgx3w+HJcYOEuPribxUsKkutyldooj5tpQRnKj3S9o+F/6aOsb8etHQsbZjihQumEhzHIMAAgj4UoAARG/aCED0/AhAXPCjCgQQSI/AfuPJJip42G/cL0O9b/5T7xv/9hnv79l30Ph3SHabbw+Kui/A7v2hz6kTltBuDbVTw3hfvTXCj8N53ETUaQTqkZvqySplja3wZUoUNt+aHxuXq1jvly9dRI4rV8IMO44z/qmTIV4IIOB/AfXfE3UpjQpGfjN2eKndXua/naGdX7/v3Cdbd6gdYXnfvycZhaLG02zMQMS4NMcKRlSIYg9JrCDFDFdsx4XClCJm8FKA/wQlw86xCCCQIQECED1oApAE/Dp2Hyir1q43j6xXu7pMHjc0ohSLMAFEDkEgywT2HzhshAyHjgYQxsf7D1kBRG4YYXzdPM4IJMygwvh4n/GxFVqokCL8vvp8uLwKNI6Yx4fCDeN9863tfePj2M870ZsIdVKgQgt1nw0r0DBDDFu4obaxhz4OfV4dy7039NwpjUA2CKiAVe0y22UEsuZbI5TdZb013rd/TQW21jHhY3OPV/+tdOOlwpESRQsb//0qKMVz3xYzwlwV6Kr/pql/ardJYSOsLVyooPl+Idv7hQsZny9ofF0do95Xxxj/Qu8b/woXyi1bwChrvG8en/v53GOKFMr9vPmx/X12ubgxx9SBgB8FOPfUmzUCEAe/Hn2Hy5at28OhhwpDKlUsK2NH9A+XZBHqLUJK+1dA/bKqNgQcPnw49Na4c6W6kZ66gaX62iHj8+p99Tn1sTohD72fe7x5jO34I1HHm2VtnzMqC7WXeB3HHB/VR9XnUF9D/Tho/OKs3j/27RFbABEZYIR3VtgCCC/MqvpFu6jxi7v6a2hR4xdw81/u++oX9xLG9vESxQqJuj7fer9UsSLm51TQoYKMMirwyA0y1E4N9cs9LwQQQMDLAuq/yfZwxB6WqFBl5579ZshyTLhiC1VUGfXzwasvtTtFhSuFjGDEDGFUsGL899l63/y8GbaEgpljwhijbPjzUeGKGcaYQY4tjDHrDgUwobqPhjHW8eG+qNDH1i/1c0Pt6CtodNrojvl+IdV3460ah/q8mP8LfcwLAQTiC3DuqbdCCEAc/Fp26iP9el0jndq1MI+cNH2ePDn6DZk7aeQxAYj6OWme4uX+vFRv7J87Ynxg/SgNfT50gHWM+XNW/Zc/6nOhn7/mF40vheowy9h+MIc+F6orVG3u+6HqzOKqZERb6qeN9blwvbkdM/pxtK3Q56w2zH7klouu1/wJFtXHUM8ti6N9C1tZbdn8wm65Yw6PyT72CCv7+HOtIkxyx64qNts76mc3tSbV8rPGrH4oh/1slrHmxT5+65cnMxDInXRVTzgQME/wVd9yPxdxgq9O9iNPzo8GDEfrOHqCbxxr1G0/mbcHFGY7xgAj2zaCiHCIkdue6qsROliBQMTxRkBhP17vPz/BLh0KGgoY/4y3VgChwgj1sRlEhMII9Quq+uui+b7x+WLm8aFfTCPeD9dhlQ+9NY9TX1NlzLqOljd/qeSFAAIIIJCSgLo8UAUhamee2mW317hEUL1vvTV37xmh+cFDodD8oBG8HDQDdCMwNwJx8/PqY+t99flDuZ9XX1Oftx9vvK92/R3KrTN23aGdgepnc1BfRwOR0K/F9oDEujRS/XSLFZ6oE8PcPCX0Nvxx7ufVx7lllZ+qL/Sx+vrRsuqTql2rLvWO+XGMsupr6vNWXfHKhoKeyGNV6fC4bF9X/bNCIbtJ5LHHli1gjSnGuCJNQoOxj9NeNvpreY4r91cNaz7MD3MDLXtfrXbML5u+BcOBl2VifS2uSW5Zy/LYskb9RsBmzZX5NjzPIf/w13P7GmlwtGzkGgl9xxW06o4qG2tc9jVxdN5DYGGvCD8jIcxdA5HrO7Lfql77eWBQ/1uQrnERgMSRXbJ8jXTtPVjGjxokjXLqmEfG+pz6D+a5t09K1xxRLwKeFVD/cQ79Zaeg8YMs9MtC+C86uX/hOXpM6BcE9Vef0F+CrL8C5f4FSH3e/MESvw7rmHCb0cfn/kA9+henUJ32Pka2Hfrlweq3+muZtYU5+m0RK9CwBRhFbbss7KGFZyeNjiGAAAII+F7A3LFo/LHCDF1yA5iIwCQczOSGMblBSziMMcMWWxhjC2bMz6udkVFBjgperN2R1vtm2GO2ZeuL0a/Q14/upgz98UT98cb4Q4zxfxE7Rm1/IPLwphvfrxkGEByBz57tRACiMZ0EIC4GIKGUODe2U8lebrpnfc5MlnPbsyfKVjnzj7W5OxTsn1Pv5346lE5b9ZoFQq/Q545uHQy/H9rwELG18OgWw9BOiXAanFuHtV3jaFuhMVltqF0P4Tpyxxweu9pjENVHc1hhi8g0PqItm5+9PtUPc4zRY4+wso8/1GKkydFxKkxrzNH1hj1zLa061N4Ja8wR8xUaQETf7OO3/gIfmeJGn+CH/upgnrCbSbx1wp77sf0EPxww5B5vCxLMk/zcvyJE1hFKq80TfKO3VjAQCiYiw4ij7R/dmhpxfFS4oPHfHooigAACCCCAAALHCBzdMRv6/de+g9a6Ebe1G9r+NWt3b26eEt7ta9/9HL3r17o8N7Tz19opHfrFVdVt1aXeMT9WX8rdfGOVtXb+Hv34aFk1OFXK/jVVPPLj0NfNY3N3XEeOM9S2tRs78thjyx7J3fFr7WKO3NVs34kdatA+TnvZ6K/laZLrYc2V+aHpF7qE2RqX1c7Rj21fzzU5ahDHJLfu8O70Y8oazavLp22e4XHl+oa/Hu7r0bm2l1VfPrpGQmNRu6TNuqPKxhpX7Hk/OtexTI7uurev/dyd+Ebb6oxs/siOBCAa/+0kAImDl8wOELYhaaxCiiKAAAIIIIAAAggggAACCDgKcA8QR6K4BxCAOPglcw8QvamgNAIIIIAAAggggAACCCCAAAJ5CxCA6K0OAhAHP54Co7fAKI0AAggggAACCCCAAAIIIOCOAAGIniMBSAJ+6tG3q9auN4+sV7t6+JG4VlEWYQKIHIIAAggggAACCCCAAAIIIKAlwLmnFp8QgOj5maVZhC4gUgUCCCCAAAIIIIAAAggggEBcAc499RYIAYieHwGIC35UgQACCCCAAAIIIIAAAggg4CxAAOJsFO8IAhA9PwIQF/yoAgEEEEAAAQQQQAABBBBAwFmAAMTZiABEz8ixNIvQkYgDEEAAAQQQQAABBBBAAAEENAU499QDZAeInp9ZmkXoAiJVIIAAAggggAACCCCAAAIIxBXg3FNvgRCA6PkRgLjgRxUIIIAAAggggAACCCCAAALOAgQgzkbxjiAA0fMLByAuVEMVCCCAAAIIIIAAAggggAACCMQVOHLkCEIpChCApAhnL/byyy+bH3br1s2F2qgivwXuvvtuueGGG+SMM87I767QvgsCF198sXzwwQcu1EQV+S2wePFieeWVV+SJJ57I767QvgsC/Ox0AdFDVfCz00OT4UJX+NnpAqJHquBnp0cmwqVu8LNTH5IARN9QWIguIHqoCn6J89BkuNAVfolzAdEjVfBLnEcmwqVu8LPTJUiPVMPPTo9MhEvd4GenS5AeqIafnR6YBBe7wM9OfUwCEH1DAhAXDL1UBb/EeWk29PvCL3H6hl6pgV/ivDIT7vSDX+LccfRKLfzs9MpMuNMPfna64+iFWvjZ6YVZcK8P/OzUtyQA0TckAHHB0EtV8Eucl2ZDvy/8Eqdv6JUa+CXOKzPhTj/4Jc4dR6/Uws9Or8yEO/3gZ6c7jl6ohZ+dXpgF9/rAz059SwIQfUMCEBcMvVQFv8R5aTb0+8IvcfqGXqmBX+K8MhPu9INf4txx9Eot/Oz0yky40w9+drrj6IVa+NnphVlwrw/87NS3JADRN6QGBBBAAAEEEEAAAQQQQAABBBDwuAABiMcniO4hgAACCCCAAAIIIIAAAggggIC+AAGIviE1IIAAAggggAACCCCAAAIIIICAxwUIQDw+QXQPAQQQQAABBBBAAAEEEEAAAQT0BQhA9A2pAQEEEEAAAQQQQAABBBBAAAEEPC5AAJLgBHXsPlBWrV1vHl2vdnWZPG5oniV79B0uCxYuD3/d6fgEu8BhGgLJzJ/VzMgX3pI3p86SuZNGRrQ84JExMmXG/GN6s3T2OI0eUlRHwM351ekHZZMXSHbu4h3P92by/vlVItl5z69+0m6kQLLzxverv1ZQsvOrRpfX70r+Gnkweuvm/PHz1HtrIpn55Vw0/vwRgCSwvtUi2rJ1ezj0UAuwUsWyMnZE/5ilW3bqE3HSrD5ucU4jGXZ/zwRa4xC3BZKdv0nT58nAR583u1GxfJmYAciyFevihmBuj4H68hZwe36xzpxAsnPndLz6hY3vzczNX6otOc1jqvVSLr0Cyc6b0/F8v6Z3vpKt3Wm+outz+l0p2fY5Xk/A7fnj+1NvPtwunez8ci5KAKK9BtUi6tfrGunUroVZl/qP/pOj3zjmxDivhviPiPYUaFWQ6vzF2wHCSZbWlLha2O35dbVzVBZXINm5czqe/9b6Y8E5zaM/RpF9vUx23pyO5/vVW2vIab7y6i07QLwxj27PH9+f3phXqxepzq9VnvmMnE92gDis7yXL10jX3oNl/KhB0iinjnl0rM/Fq0btGGlQvxY7QPLhvyU685foJTCxdonkw1Czssl0zG9WQubDoJOdu0SOj96yy/dmPkxsBn6mem9Uwe9RIt9/doVEjuf71TvrJpH5IgDxznxF9yQd88f3p3fmW2d+rVFwLkoAktSK1l101n9AuD9EUuyuHawzf4n+VUP9R0W94t0XxrUBUVGEQCbmF/L0CCQ7d8ker3rN92Z65k6n1lTmUac9yrojkOy8JXs836/uzFOqtaQyX1Zbif6ulGrfKOcskIn54+ep8zyk6wid+VV94lz02JlhB4jDatVZdOqHwuhXpkTsHknXNwf1xhbQnb9YN0GNbsm6DpaQK/OrMBPzm/lRZUeLyc5dsscrRb43vbeWUplH740ieD2y31wvenTNmuTIXbd0SWo3bCrzzPdr+taV2/Nr7ykBSPrmzarZC/PH92f65jnd88u5KAFISqs3leuuSNtSok5LoVTmT3Uk0R/q/FBIy7QlXGm65zfhjnBg0gLJzl2yx/O9mfSUZKRAsvOYkU7RiKNAsvOW7PF8vzpOQVoPSHa+rM4k+rtSWjtP5ZLu+eP7M38XWSrzy7lo3nPGDpAE1rPTnXejt4WxTSwB1Awekuz8Of1Qj3Vn5ZPr1MjzqUAZHGpWNuX2/GYlYj4NOtm5czqe7818msgkm3WaxySr4/AMCTjNW/TvPk7H8/2aoYlLsBmn+crrd1sCkASB03yY2/PH92eaJyzJ6pOdX85F4wMTgCS4AOM9e9m+yKxtn7GqHXrfzeEnySTYLIe5JJDo/Knm7I92s5rvcEnz8E1so7eqqe3BeT0S2aXuU42DgJvzC3ZmBZKZO9Uzp+NXrV0fHgDfm5mdy2RaizePydTDsZkVcPr+U72x3w/L6Xi+XzM7f06tOc2XfX6dfldyaouvuy/g5vzxu67786NbY6Lzy7moszQBiLMRRyCAAAIIIIAAAggggAACCCCAgM8FCEB8PoF0HwEEEEAAAQQQQAABBBBAAAEEnAUIQJyNOAIBBBBAAAEEEEAAAQQQQAABBHwuQADi8wmk+wgggAACCCCAAAIIIIAAAggg4CxAAOJsxBEIIIAAAggggAACCCCAAAIIIOBzAQIQn08g3UcAAQQQQAABBBBAAAEEEEAAAWcBAhBnI45AAAEEEEAAAQQQQAABBBBAAAGfCxCA+HwC6T4CCCCAAAIIIIAAAggggAACCDgLEIA4G3EEAggggAACCCCAAAIIIIAAAgj4XIAAxOcTSPcRQAABBBBAAAEEEEAAAQQQQMBZgADE2YgjEEAAAQQQQAABBBBAAAEEEEDA5wIEID6fQLqPAAIIIIAAAggggAACCCCAAALOAgQgzkYcgQACCCCAAAIIIIAAAggggAACPhcgAPH5BNJ9BBBAAAEEEEAAAQQQQAABBBBwFiAAcTbiCAQQQAABBBBAAAEEEEAAAQQQ8LkAAYjPJ5DuI4AAAggggAACCCCAAAIIIICAswABiLMRRyCAAAIIIIAAAggggAACCCCAgM8FCEB8PoF0HwEEEEDA3wIduw+UShXLytgR/X03kCXL10jX3oNl/KhB0iinTkr9T2X8qsyqtevN9nrd0EH63HR10m1bdej0PelGYxSwDOvVri6Txw11o0rqQAABBBBAAIE8BAhAWBoIIIAAAgjko0AqAYBudydNnycDH31eht53s3Rq1yLl6jIRgET3dcAjY2Te50tk7qSR4X736DtctmzdnnCAMPKFt2T0K1Nk6exxKY/d7YINL+yecpjjdl+oDwEEEEAAgaAKEIAEdWYZFwIIIICALwTyIwBxCyYTAUh0X5VXg/q1ZNj9PVMOQFp26iMtzmkUUYdbJqnWo0KZN6fOigh2Uq2LcggggAACCCAQW4AAhJWBAAIIIIBABgTULoUFC5eHW7Iu3bACEPUF6+sVy5c55kQ4urz90g3rhF7tjNi6bYfZhqr/xOrHmzs9rJdVJlZwkVf/oj+v6rJ2TiQagKjdDfaX/XIPp/Hb23jqPxMiDJWTCjKmzJgfUX+zJjl5XlKU1+4Pp36oBhI5Jtm5sHecXSAZ+EakCQQQQACBrBYgAMnq6WfwCCCAAAKZEFAhwso1P4dDDXVSr07m1X0/rHtR2O9loU6iT65TI3wSH32JR/RJvDpeBR9WwGF93R6kqHbUS91nIjq4iNc/9bW7bukSvsdHvHpiWcY6qVf9tS5hcRp/dF9j7ZhJ5hIYdax6Rd9zJVY/7GO1AhB17xH7XEUfk+xc2M3y6lsm1ihtIIAAAgggkA0CBCDZMMuMEQEEEEAg3wSsE/i87rcR64Re3edi2Yp1McMKayDqRPvPV7Y2bwAafUlHrJ0Z9ntn2L+u6lM3Mk30fiD2SzWcdoA4fd0KFaJvAhtv/LoBSKxLaPLqh3X/EStYitV29DHJzoV9YSrbmXO/SvheJvm2qGkYAQQQQAABnwoQgPh04ug2AggggIA/BKwT5LxuuOkUgFjlY43W2omQ10m3PdTIK7hYvW6DeZlMvBuCWrsa7H1QxycacFhPbIl1aY/T+N3eAWIPjuzjidWP6PAqkWOSnYvoAIT7gPjj+5peIoAAAgj4U4AAxJ/zRq8RQAABBHwi4FYA4hRQ2G/qGWvXSaoBiLqExX5PDfvlN4kEINY0qV0d9nt1WOMhADm6kLkRqk++qekmAggggIBvBQhAfDt1dBwBBBBAwA8Cbl0CE+8SlWR3HSR6CUys8CbVAMQ+V/b7gmQ6AFHttW3Z1Lx0yP6Kd3lLvLAm2ijZubD3gUtg/PAdTR8RQAABBPwsQADi59mj7wgggAACvhBQJ9dbt23P8yao8e6BoQZoPYnFvgtE7aho1qSBdGrXIs97gCRyCUyjnDrmjVhj9U/d/DT6/iDWE10SuQRGhQMvjp8WcU+LRO6rkew9QPJ6skusxRHvJqjR86DG2uEDjMvwAAAEB0lEQVSS5uHH5cYKSaKP0QlAuAmqL76d6SQCCCCAgI8FCEB8PHl0HQEEEEDAPwLWU0asHlvhhNMOCOv4eI+jTfakO9alK3n1L/rSFXXfkdGvTDHvGZLIJTDRj8BV47E/wtdp/IncA0TVae9/qo/Bte5VYpnbw4/oNvI6Jtm5sK9gHoPrn+9neooAAggg4E8BAhB/zhu9RgABBBBAAIEUBaJDCivciN4BEl19rLAmxS4cU4z7f7glST0IIIAAAgjkLUAAwupAAAEEEEAAgawSiHXJTCLhRiLHpArJ7o9U5SiHAAIIIIBA4gIEIIlbcSQCCCCAAAIIBETAumTGuhwnkXAjkWOS5bEu8alXu3rEvVKSrYfjEUAAAQQQQMBZgADE2YgjEEAAAQQQQAABBBBAAAEEEEDA5wIEID6fQLqPAAIIIIAAAggggAACCCCAAALOAgQgzkYcgQACCCCAAAIIIIAAAggggAACPhcgAPH5BNJ9BBBAAAEEEEAAAQQQQAABBBBwFiAAcTbiCAQQQAABBBBAAAEEEEAAAQQQ8LkAAYjPJ5DuI4AAAggggAACCCCAAAIIIICAswABiLMRRyCAAAIIIIAAAggggAACCCCAgM8FCEB8PoF0HwEEEEAAAQQQQAABBBBAAAEEnAUIQJyNOAIBBBBAAAEEEEAAAQQQQAABBHwuQADi8wmk+wgggAACCCCAAAIIIIAAAggg4CxAAOJsxBEIIIAAAggggAACCCCAAAIIIOBzAQIQn08g3UcAAQQQQAABBBBAAAEEEEAAAWcBAhBnI45AAAEEEEAAAQQQQAABBBBAAAGfCxCA+HwC6T4CCCCAAAIIIIAAAggggAACCDgLEIA4G3EEAggggAACCCCAAAIIIIAAAgj4XIAAxOcTSPcRQAABBBBAAAEEEEAAAQQQQMBZgADE2YgjEEAAAQQQQAABBBBAAAEEEEDA5wIEID6fQLqPAAIIIIAAAggggAACCCCAAALOAgQgzkYcgQACCCCAAAIIIIAAAggggAACPhcgAPH5BNJ9BBBAAAEEEEAAAQQQQAABBBBwFiAAcTbiCAQQQAABBBBAAAEEEEAAAQQQ8LkAAYjPJ5DuI4AAAggggAACCCCAAAIIIICAswABiLMRRyCAAAIIIIAAAggggAACCCCAgM8FCEB8PoF0HwEEEEAAAQQQQAABBBBAAAEEnAUIQJyNOAIBBBBAAAEEEEAAAQQQQAABBHwuQADi8wmk+wgggAACCCCAAAIIIIAAAggg4CxAAOJsxBEIIIAAAggggAACCCCAAAIIIOBzAQIQn08g3UcAAQQQQAABBBBAAAEEEEAAAWcBAhBnI45AAAEEEEAAAQQQQAABBBBAAAGfC/w/H+8CEouBhk0AAAAASUVORK5CYII=",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig.update_layout(autosize=False, width=900, height=400)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "f1e6ca05-915e-4646-a43f-187170fdceab",
"metadata": {},
"source": [
"## 5. Spectrum simulation and fitting"
]
},
{
"cell_type": "markdown",
"id": "b19c4783-1b0a-44df-a762-64e8cc35b1ba",
"metadata": {},
"source": [
"### 5.1. Load models of NMR signals"
]
},
{
"cell_type": "markdown",
"id": "33260e66-ab78-43fc-9582-72cfe6270b61",
"metadata": {},
"source": [
"Load models of signals implemented in multinmrfit."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7edeb66d-b53d-43a5-98db-7b7ece01198b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:44:43,764 | DEBUG : add model from file 'model_acetate.py'\n",
"2024-12-20 09:44:43,785 | DEBUG : model name: 13C-acetate (CH3)\n",
"2024-12-20 09:44:43,787 | DEBUG : add model from file 'model_doublet.py'\n",
"2024-12-20 09:44:43,791 | DEBUG : model name: doublet\n",
"2024-12-20 09:44:43,792 | DEBUG : add model from file 'model_doublet_of_doublet.py'\n",
"2024-12-20 09:44:43,797 | DEBUG : model name: doublet of doublet\n",
"2024-12-20 09:44:43,798 | DEBUG : add model from file 'model_quartet.py'\n",
"2024-12-20 09:44:43,802 | DEBUG : model name: quartet\n",
"2024-12-20 09:44:43,803 | DEBUG : add model from file 'model_singlet.py'\n",
"2024-12-20 09:44:43,807 | DEBUG : model name: singlet\n",
"2024-12-20 09:44:43,808 | DEBUG : add model from file 'model_triplet.py'\n",
"2024-12-20 09:44:43,813 | DEBUG : model name: triplet\n"
]
}
],
"source": [
"available_models = io.IoHandler.get_models()"
]
},
{
"cell_type": "markdown",
"id": "35e5fb0e-0e38-412a-9576-7094e0c732f7",
"metadata": {},
"source": [
"`io.IoHandler.get_models()` returns a dict with model_name-model_object as key-value pairs."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2c4f0a8c-f0a8-4443-8b57-2781abc9a66f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['13C-acetate (CH3)', 'doublet', 'doublet of doublet', 'quartet', 'singlet', 'triplet'])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"available_models.keys()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4d45ba42-ffaa-457b-8700-cf3bb214cfbd",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"multinmrfit.models.model_doublet.SignalModel"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"available_models[\"doublet\"]"
]
},
{
"cell_type": "markdown",
"id": "69193fc4-3503-49f5-b13e-e37028dc9486",
"metadata": {},
"source": [
"### 5.2. Model construction"
]
},
{
"cell_type": "markdown",
"id": "26ec8bf7-313e-4e98-89db-8945209824af",
"metadata": {},
"source": [
"To simulate or fit a spectrum, we need to provide a list of signals containing the type of signal (e.g. singlet or doublet) and the corresponding parameters (chemical shift, coupling constant, linewidth, intensity, etc). Signals must be provided as a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ebfb81b4-9aab-4d08-a7fe-35fc4b441a80",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"signals = {\"singlet_TSP\": {\"model\":\"singlet\", \"par\": {\"x0\": {\"ini\":0.0, \"lb\":-0.05, \"ub\":0.05}}}}\n",
"\n",
"#signals = {\"singlet_TSP\": {\"model\":\"singlet\", \"par\": {\"x0\": {\"ini\":0.0, \"lb\":-0.05, \"ub\":0.05}}},\n",
"# \"doublet_TSP\": {\"model\":\"doublet\", \"par\": {\"x0\": {\"ini\":-0.01, \"lb\":-0.01, \"ub\":0.01}, \"J\": {\"ini\":0.147, \"lb\":0.14, \"ub\":0.15}, \"lw\": {\"ini\":0.001}}}}\n"
]
},
{
"cell_type": "markdown",
"id": "063bc75f-9ad7-4af8-9c67-9edd1174e44d",
"metadata": {},
"source": [
"Then we can build a model of the spectrum with the `build_model()` method."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "65d2b952-0403-4c35-a277-549b4306d7fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-12-20 09:45:01,755 | DEBUG : build Model for signal 'singlet_TSP'\n",
"2024-12-20 09:45:01,762 | DEBUG : parameters\n",
" signal_id model par ini lb ub\n",
"0 singlet_TSP singlet x0 1.000 0.0000 1.000000e+01\n",
"1 singlet_TSP singlet intensity 1000000.000 1.0000 1.000000e+15\n",
"2 singlet_TSP singlet lw 0.001 0.0001 3.000000e-02\n",
"3 singlet_TSP singlet gl 0.500 0.0000 1.000000e+00\n"
]
}
],
"source": [
"sp.build_model(signals=signals, available_models=available_models)"
]
},
{
"cell_type": "markdown",
"id": "d882e8b9-c6a0-4e1b-b7ff-920e09890f2b",
"metadata": {
"tags": []
},
"source": [
"Parameters can be accessed via the `params` attibute."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "24d34cef-5b9a-486c-a413-55c831949d92",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" signal_id | \n",
" model | \n",
" par | \n",
" ini | \n",
" lb | \n",
" ub | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" singlet_TSP | \n",
" singlet | \n",
" x0 | \n",
" 0.000 | \n",
" -0.0500 | \n",
" 5.000000e-02 | \n",
"
\n",
" \n",
" | 1 | \n",
" singlet_TSP | \n",
" singlet | \n",
" intensity | \n",
" 1000000.000 | \n",
" 1.0000 | \n",
" 1.000000e+15 | \n",
"
\n",
" \n",
" | 2 | \n",
" singlet_TSP | \n",
" singlet | \n",
" lw | \n",
" 0.001 | \n",
" 0.0001 | \n",
" 3.000000e-02 | \n",
"
\n",
" \n",
" | 3 | \n",
" singlet_TSP | \n",
" singlet | \n",
" gl | \n",
" 0.500 | \n",
" 0.0000 | \n",
" 1.000000e+00 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" signal_id | \n",
" model | \n",
" par | \n",
" ini | \n",
" lb | \n",
" ub | \n",
" opt | \n",
" opt_sd | \n",
" integral | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" singlet_TSP | \n",
" singlet | \n",
" x0 | \n",
" 0.000 | \n",
" -0.0500 | \n",
" 5.000000e-02 | \n",
" 8.048877e-10 | \n",
" 0.000004 | \n",
" 38655.427821 | \n",
"
\n",
" \n",
" | 1 | \n",
" singlet_TSP | \n",
" singlet | \n",
" intensity | \n",
" 1000000.000 | \n",
" 1.0000 | \n",
" 1.000000e+12 | \n",
" 1.250012e+06 | \n",
" 62.154093 | \n",
" 38655.427821 | \n",
"
\n",
" \n",
" | 2 | \n",
" singlet_TSP | \n",
" singlet | \n",
" lw | \n",
" 0.001 | \n",
" 0.0001 | \n",
" 3.000000e-02 | \n",
" 1.000263e-02 | \n",
" 0.000002 | \n",
" 38655.427821 | \n",
"
\n",
" \n",
" | 3 | \n",
" singlet_TSP | \n",
" singlet | \n",
" gl | \n",
" 0.500 | \n",
" 0.0000 | \n",
" 1.000000e+00 | \n",
" 1.000000e+00 | \n",
" 0.000177 | \n",
" 38655.427821 | \n",
"
\n",
" \n",
"
\n",
"