{ "cells": [ { "cell_type": "markdown", "id": "accurate-breakdown", "metadata": {}, "source": [ "# GWAS Tutorial" ] }, { "cell_type": "markdown", "id": "written-product", "metadata": {}, "source": [ "This notebook is an [sgkit](https://sgkit-dev.github.io/sgkit) port of Hail's [GWAS Tutorial](https://nbviewer.jupyter.org/github/tomwhite/sgkit/blob/86753e814c6d56982b6950ec3de727f3b1bfad7d/docs/examples/01-genome-wide-association-study.ipynb), which demonstrates how to run a genome-wide SNP association test. Readers are encouraged to read the Hail tutorial alongside this one for more background, and to see the motivation behind some of the steps.\n", "\n", "_Note that some of the results do not exactly match the output from Hail. Also, since sgkit is still a 0.x release, its API is still subject to non-backwards compatible changes._" ] }, { "cell_type": "code", "execution_count": 1, "id": "valid-pride", "metadata": {}, "outputs": [], "source": [ "import sgkit as sg" ] }, { "cell_type": "markdown", "id": "scheduled-spelling", "metadata": {}, "source": [ "Before using sgkit, we import some standard Python libraries and set the Xarray display options to not show all the attributes in a dataset by default." ] }, { "cell_type": "code", "execution_count": 2, "id": "compound-activity", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "xr.set_options(display_expand_attrs=False, display_expand_data_vars=True);" ] }, { "cell_type": "markdown", "id": "color-trinity", "metadata": {}, "source": [ "## Download public 1000 Genomes data" ] }, { "cell_type": "markdown", "id": "useful-imperial", "metadata": {}, "source": [ "We use the same small (20MB) portion of the public 1000 Genomes data that Hail uses.\n", "\n", "First, download the file locally:" ] }, { "cell_type": "code", "execution_count": 3, "id": "taken-banana", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import requests\n", "\n", "if not Path(\"1kg.vcf.bgz\").exists():\n", " response = requests.get(\"https://storage.googleapis.com/sgkit-data/tutorial/1kg.vcf.bgz\")\n", " with open(\"1kg.vcf.bgz\", \"wb\") as f:\n", " f.write(response.content)\n", "if not Path(\"1kg.vcf.bgz.tbi\").exists():\n", " response = requests.get(\"https://storage.googleapis.com/sgkit-data/tutorial/1kg.vcf.bgz.tbi\")\n", " with open(\"1kg.vcf.bgz.tbi\", \"wb\") as f:\n", " f.write(response.content)" ] }, { "cell_type": "markdown", "id": "headed-restoration", "metadata": {}, "source": [ "## Importing data from VCF" ] }, { "cell_type": "markdown", "id": "elementary-college", "metadata": {}, "source": [ "Next, convert the VCF file to Zarr using the `vcf2zarr` command in [bio2zarr](https://sgkit-dev.github.io/bio2zarr/), stored on the local filesystem in a directory called _1kg.vcz_." ] }, { "cell_type": "code", "execution_count": 4, "id": "78effc1d-b45e-4af5-85ae-e0e7a40ca049", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " Scan: 0%| | 0.00/1.00 [00:00 1kg.schema.json # then edit 1kg.schema.json by hand\n", "vcf2zarr encode --force -s 1kg.schema.json 1kg.icf 1kg.vcz" ] }, { "cell_type": "markdown", "id": "plastic-running", "metadata": {}, "source": [ "We used the `vcf2zarr explode` command to first convert the VCF to an \"intermediate columnar format\" (ICF), then the `vcf2zarr encode` command to convert the ICF to Zarr, which by convention is stored in a directory with a `vcz` extension.\n", "\n", "Note that we specified a JSON schema file that was created with the `vcf2zarr mkschema` command (commented out above), then edited to drop some fields that are not needed for this tutorial (such as `FORMAT/PL`). It was also updated to change the `call_AD` field's third dimension to be `alleles`, which was not set by `vcf2zarr` since the dataset we are using defines `FORMAT/AD` as `.` which means \"unknown\", rather than `R`.\n", "\n", "For more information about using `vcf2zarr`, see the tutorial in the [bio2zarr documentation](https://sgkit-dev.github.io/bio2zarr/).\n", "\n", "Now the data has been written as Zarr, all downstream operations on will be much faster. Note that sgkit uses an [Xarray](http://xarray.pydata.org/en/stable/) dataset to represent the VCF data, where Hail uses MatrixTable." ] }, { "cell_type": "code", "execution_count": 5, "id": "blind-sunset", "metadata": {}, "outputs": [], "source": [ "ds = sg.load_dataset(\"1kg.vcz\")" ] }, { "cell_type": "markdown", "id": "imperial-california", "metadata": {}, "source": [ "## Getting to know our data" ] }, { "cell_type": "markdown", "id": "otherwise-constant", "metadata": {}, "source": [ "To start with we'll look at some summary data from the dataset.\n", "\n", "The simplest thing is to look at the dimensions and data variables in the Xarray dataset." ] }, { "cell_type": "code", "execution_count": 6, "id": "hundred-manitoba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 28MB\n",
       "Dimensions:               (variants: 10879, samples: 284, alleles: 2,\n",
       "                           ploidy: 2, contigs: 84, filters: 1)\n",
       "Dimensions without coordinates: variants, samples, alleles, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/17)\n",
       "    call_AD               (variants, samples, alleles) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig        (variants) int8 11kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_filter        (variants, filters) bool 11kB dask.array<chunksize=(10000, 1), meta=np.ndarray>\n",
       "    variant_id            (variants) object 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_id_mask       (variants) bool 11kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_position      (variants) int32 44kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_quality       (variants) float32 44kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 28MB\n", "Dimensions: (variants: 10879, samples: 284, alleles: 2,\n", " ploidy: 2, contigs: 84, filters: 1)\n", "Dimensions without coordinates: variants, samples, alleles, ploidy, contigs,\n", " filters\n", "Data variables: (12/17)\n", " call_AD (variants, samples, alleles) int8 6MB dask.array\n", " call_DP (variants, samples) int8 3MB dask.array\n", " call_GQ (variants, samples) int8 3MB dask.array\n", " call_genotype (variants, samples, ploidy) int8 6MB dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool 6MB dask.array\n", " call_genotype_phased (variants, samples) bool 3MB dask.array\n", " ... ...\n", " variant_contig (variants) int8 11kB dask.array\n", " variant_filter (variants, filters) bool 11kB dask.array\n", " variant_id (variants) object 87kB dask.array\n", " variant_id_mask (variants) bool 11kB dask.array\n", " variant_position (variants) int32 44kB dask.array\n", " variant_quality (variants) float32 44kB dask.array\n", "Attributes: (3)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "id": "available-officer", "metadata": {}, "source": [ "Next we'll use `display_genotypes` to show the the first and last few variants and samples.\n", "\n", "_Note: sgkit does not store the contig names in an easily accessible form, so we compute a variable `variant_contig_name` in the same dataset storing them for later use, and set an index so we can see the variant name, position, and ID._" ] }, { "cell_type": "code", "execution_count": 7, "id": "friendly-nation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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samplesHG00096HG00099...NA21133NA21143
variants
(1, 904165, .)0/00/0...0/00/0
(1, 909917, .)0/00/0...0/00/0
(1, 986963, .)0/00/0...0/00/0
(1, 1563691, .)./.0/0...0/00/0
(1, 1707740, .)0/10/1...0/10/0
..................
(X, 152660491, .)./.0/0...1/10/0
(X, 153031688, .)0/00/0...0/00/0
(X, 153674876, .)0/00/0...0/00/0
(X, 153706320, .)./.0/0...0/00/0
(X, 154087368, .)0/01/1...1/11/1
\n", "

10879 rows x 284 columns

" ], "text/plain": [ "samples HG00096 HG00099 ... NA21133 NA21143\n", "variants ... \n", "(1, 904165, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 909917, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 986963, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 1563691, .) ./. 0/0 ... 0/0 0/0\n", "(1, 1707740, .) 0/1 0/1 ... 0/1 0/0\n", "... ... ... ... ... ...\n", "(X, 152660491, .) ./. 0/0 ... 1/1 0/0\n", "(X, 153031688, .) 0/0 0/0 ... 0/0 0/0\n", "(X, 153674876, .) 0/0 0/0 ... 0/0 0/0\n", "(X, 153706320, .) ./. 0/0 ... 0/0 0/0\n", "(X, 154087368, .) 0/0 1/1 ... 1/1 1/1\n", "\n", "[10879 rows x 284 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[\"variant_contig_name\"] = ds.contig_id[ds.variant_contig]\n", "ds2 = ds.set_index({\"variants\": (\"variant_contig_name\", \"variant_position\", \"variant_id\")})\n", "sg.display_genotypes(ds2, max_variants=10, max_samples=5)" ] }, { "cell_type": "markdown", "id": "durable-insured", "metadata": {}, "source": [ "We can show the alleles too.\n", "\n", "_Note: this needs work to make it easier to do_" ] }, { "cell_type": "code", "execution_count": 8, "id": "forward-testimony", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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variant_allele
variant_contig_namevariant_positionvariant_id
1904165.[G, A]
909917.[G, A]
986963.[C, T]
1563691.[T, G]
1707740.[T, G]
\n", "
" ], "text/plain": [ " variant_allele\n", "variant_contig_name variant_position variant_id \n", "1 904165 . [G, A]\n", " 909917 . [G, A]\n", " 986963 . [C, T]\n", " 1563691 . [T, G]\n", " 1707740 . [T, G]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_variant = ds[[v for v in ds.data_vars if v.startswith(\"variant_\")]].to_dataframe()\n", "df_variant.groupby([\"variant_contig_name\", \"variant_position\", \"variant_id\"]).agg({\"variant_allele\": lambda x: list(x)}).head(5)" ] }, { "cell_type": "markdown", "id": "systematic-bailey", "metadata": {}, "source": [ "Show the first five sample IDs by referencing the dataset variable directly:" ] }, { "cell_type": "code", "execution_count": 9, "id": "thick-newark", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['HG00096', 'HG00099', 'HG00105', 'HG00118', 'HG00129'],\n", " dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.sample_id[:5].values" ] }, { "cell_type": "markdown", "id": "important-curtis", "metadata": {}, "source": [ "## Adding column fields" ] }, { "cell_type": "markdown", "id": "color-accounting", "metadata": {}, "source": [ "Xarray datasets can have any number of variables added to them, possibly loaded from different sources. Next we'll take a text file (CSV) containing annotations, and use it to annotate the samples in the dataset.\n", "\n", "First we load the annotation data using regular Pandas." ] }, { "cell_type": "code", "execution_count": 10, "id": "enhanced-companion", "metadata": {}, "outputs": [], "source": [ "ANNOTATIONS_FILE = \"https://storage.googleapis.com/sgkit-gwas-tutorial/1kg_annotations.txt\"\n", "df = pd.read_csv(ANNOTATIONS_FILE, sep=\"\\t\", index_col=\"Sample\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "empirical-queens", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 3500 entries, HG00096 to NA21144\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Population 3500 non-null object\n", " 1 SuperPopulation 3500 non-null object\n", " 2 isFemale 3500 non-null bool \n", " 3 PurpleHair 3500 non-null bool \n", " 4 CaffeineConsumption 3500 non-null int64 \n", "dtypes: bool(2), int64(1), object(2)\n", "memory usage: 116.2+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 12, "id": "ideal-typing", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PopulationSuperPopulationisFemalePurpleHairCaffeineConsumption
Sample
HG00096GBREURFalseFalse4
HG00097GBREURTrueTrue4
HG00098GBREURFalseFalse5
HG00099GBREURTrueFalse4
HG00100GBREURTrueFalse5
..................
NA21137GIHSASTrueFalse1
NA21141GIHSASTrueTrue2
NA21142GIHSASTrueTrue2
NA21143GIHSASTrueTrue5
NA21144GIHSASTrueFalse3
\n", "

3500 rows × 5 columns

\n", "
" ], "text/plain": [ " Population SuperPopulation isFemale PurpleHair CaffeineConsumption\n", "Sample \n", "HG00096 GBR EUR False False 4\n", "HG00097 GBR EUR True True 4\n", "HG00098 GBR EUR False False 5\n", "HG00099 GBR EUR True False 4\n", "HG00100 GBR EUR True False 5\n", "... ... ... ... ... ...\n", "NA21137 GIH SAS True False 1\n", "NA21141 GIH SAS True True 2\n", "NA21142 GIH SAS True True 2\n", "NA21143 GIH SAS True True 5\n", "NA21144 GIH SAS True False 3\n", "\n", "[3500 rows x 5 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "suited-split", "metadata": {}, "source": [ "To join the annotation data with the genetic data, we convert it to Xarray, then do a join." ] }, { "cell_type": "code", "execution_count": 13, "id": "hidden-custom", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 28MB\n",
       "Dimensions:               (samples: 284, variants: 10879, alleles: 2,\n",
       "                           ploidy: 2, contigs: 84, filters: 1)\n",
       "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/22)\n",
       "    call_AD               (variants, samples, alleles) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) object 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    Population            (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool 284B False True False ... False True\n",
       "    PurpleHair            (samples) bool 284B False False False ... True True\n",
       "    CaffeineConsumption   (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 28MB\n", "Dimensions: (samples: 284, variants: 10879, alleles: 2,\n", " ploidy: 2, contigs: 84, filters: 1)\n", "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n", " filters\n", "Data variables: (12/22)\n", " call_AD (variants, samples, alleles) int8 6MB dask.array\n", " call_DP (variants, samples) int8 3MB dask.array\n", " call_GQ (variants, samples) int8 3MB dask.array\n", " call_genotype (variants, samples, ploidy) int8 6MB dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool 6MB dask.array\n", " call_genotype_phased (variants, samples) bool 3MB dask.array\n", " ... ...\n", " variant_contig_name (variants) object 87kB dask.array\n", " Population (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 284B False True False ... False True\n", " PurpleHair (samples) bool 284B False False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n", "Attributes: (3)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_annotations = pd.DataFrame.to_xarray(df).rename({\"Sample\":\"samples\"})\n", "ds = ds.set_index({\"samples\": \"sample_id\"})\n", "ds = ds.merge(ds_annotations, join=\"left\")\n", "ds = ds.reset_index(\"samples\").reset_coords(drop=True)\n", "ds" ] }, { "cell_type": "markdown", "id": "tough-concentrate", "metadata": {}, "source": [ "## Query functions" ] }, { "cell_type": "markdown", "id": "israeli-saint", "metadata": {}, "source": [ "We can look at some statistics of the data by converting the relevant dataset variable to a Pandas series, then using its built-in summary functions. Annotation data is usually small enough to load into memory, which is why it's OK using Pandas here.\n", "\n", "Here's the population distribution by continent:" ] }, { "cell_type": "code", "execution_count": 14, "id": "floating-republican", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SuperPopulation\n", "AFR 1018\n", "EUR 669\n", "SAS 661\n", "EAS 617\n", "AMR 535\n", "Name: count, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_annotations.SuperPopulation.to_series().value_counts()" ] }, { "cell_type": "markdown", "id": "second-gregory", "metadata": {}, "source": [ "The distribution of the `CaffeineConsumption` variable:" ] }, { "cell_type": "code", "execution_count": 15, "id": "advanced-leave", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 3500.000000\n", "mean 3.983714\n", "std 1.702349\n", "min -1.000000\n", "25% 3.000000\n", "50% 4.000000\n", "75% 5.000000\n", "max 10.000000\n", "Name: CaffeineConsumption, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_annotations.CaffeineConsumption.to_series().describe()" ] }, { "cell_type": "markdown", "id": "applied-retrieval", "metadata": {}, "source": [ "There are far fewer samples in our dataset than the full 1000 genomes dataset, as we can see from the following queries." ] }, { "cell_type": "code", "execution_count": 16, "id": "considerable-dragon", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3500" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ds_annotations.samples)" ] }, { "cell_type": "code", "execution_count": 17, "id": "gorgeous-fence", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "284" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ds.samples)" ] }, { "cell_type": "code", "execution_count": 18, "id": "missing-border", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SuperPopulation\n", "AFR 76\n", "EAS 72\n", "SAS 55\n", "EUR 47\n", "AMR 34\n", "Name: count, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.SuperPopulation.to_series().value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "id": "crazy-abraham", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 284.000000\n", "mean 4.415493\n", "std 1.580549\n", "min 0.000000\n", "25% 3.000000\n", "50% 4.000000\n", "75% 5.000000\n", "max 9.000000\n", "Name: CaffeineConsumption, dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.CaffeineConsumption.to_series().describe()" ] }, { "cell_type": "markdown", "id": "automated-oxford", "metadata": {}, "source": [ "Here's an example of doing an _ad hoc_ query to uncover a biological insight from the data: calculate the counts of each of the 12 possible unique SNPs (4 choices for the reference base * 3 choices for the alternate base)." ] }, { "cell_type": "code", "execution_count": 20, "id": "wrapped-confusion", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "variant_allele\n", "(C, T) 2418\n", "(G, A) 2367\n", "(A, G) 1929\n", "(T, C) 1864\n", "(C, A) 494\n", "(G, T) 477\n", "(T, G) 466\n", "(A, C) 451\n", "(C, G) 150\n", "(G, C) 111\n", "(T, A) 77\n", "(A, T) 75\n", "Name: count, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_variant.groupby([\"variant_contig_name\", \"variant_position\", \"variant_id\"])[\"variant_allele\"].apply(tuple).value_counts()" ] }, { "cell_type": "markdown", "id": "subject-mistake", "metadata": {}, "source": [ "Often we want to plot the data, to get a feel for how it's distributed. Xarray has some convenience functions for plotting, which we use here to show the distribution of the `DP` field." ] }, { "cell_type": "code", "execution_count": 21, "id": "raising-moderator", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dp = ds.call_DP.where(ds.call_DP >= 0) # filter out missing\n", "dp.attrs[\"long_name\"] = \"DP\"\n", "xr.plot.hist(dp, range=(0, 30), bins=30, size=8, edgecolor=\"black\");" ] }, { "cell_type": "markdown", "id": "capital-roller", "metadata": {}, "source": [ "## Quality control" ] }, { "cell_type": "markdown", "id": "quarterly-surfing", "metadata": {}, "source": [ "QC is the process of filtering out poor quality data before running an analysis. This is usually an iterative process.\n", "\n", "The [`sample_stats`](https://sgkit-dev.github.io/sgkit/latest/generated/sgkit.sample_stats.html) function in sgkit computes a collection of useful metrics for each sample and stores them in new variables. (The Hail equivalent is `sample_qc`.)\n", "\n", "Here's the dataset _before_ running `sample_stats`." ] }, { "cell_type": "code", "execution_count": 22, "id": "becoming-colon", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 28MB\n",
       "Dimensions:               (samples: 284, variants: 10879, alleles: 2,\n",
       "                           ploidy: 2, contigs: 84, filters: 1)\n",
       "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/22)\n",
       "    call_AD               (variants, samples, alleles) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int8 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool 6MB dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool 3MB dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) object 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    Population            (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool 284B False True False ... False True\n",
       "    PurpleHair            (samples) bool 284B False False False ... True True\n",
       "    CaffeineConsumption   (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 28MB\n", "Dimensions: (samples: 284, variants: 10879, alleles: 2,\n", " ploidy: 2, contigs: 84, filters: 1)\n", "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n", " filters\n", "Data variables: (12/22)\n", " call_AD (variants, samples, alleles) int8 6MB dask.array\n", " call_DP (variants, samples) int8 3MB dask.array\n", " call_GQ (variants, samples) int8 3MB dask.array\n", " call_genotype (variants, samples, ploidy) int8 6MB dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool 6MB dask.array\n", " call_genotype_phased (variants, samples) bool 3MB dask.array\n", " ... ...\n", " variant_contig_name (variants) object 87kB dask.array\n", " Population (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 284B False True False ... False True\n", " PurpleHair (samples) bool 284B False False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n", "Attributes: (3)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "id": "cognitive-aquarium", "metadata": {}, "source": [ "We can see the new variables (with names beginning `sample_`) after we run `sample_stats`:" ] }, { "cell_type": "code", "execution_count": 23, "id": "incoming-valuation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 28MB\n",
       "Dimensions:               (samples: 284, variants: 10879, alleles: 2,\n",
       "                           ploidy: 2, contigs: 84, filters: 1)\n",
       "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/28)\n",
       "    sample_n_called       (samples) int64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_call_rate      (samples) float64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_het          (samples) int64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_hom_ref      (samples) int64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_hom_alt      (samples) int64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_non_ref      (samples) int64 2kB dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) object 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    Population            (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool 284B False True False ... False True\n",
       "    PurpleHair            (samples) bool 284B False False False ... True True\n",
       "    CaffeineConsumption   (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 28MB\n", "Dimensions: (samples: 284, variants: 10879, alleles: 2,\n", " ploidy: 2, contigs: 84, filters: 1)\n", "Dimensions without coordinates: samples, variants, alleles, ploidy, contigs,\n", " filters\n", "Data variables: (12/28)\n", " sample_n_called (samples) int64 2kB dask.array\n", " sample_call_rate (samples) float64 2kB dask.array\n", " sample_n_het (samples) int64 2kB dask.array\n", " sample_n_hom_ref (samples) int64 2kB dask.array\n", " sample_n_hom_alt (samples) int64 2kB dask.array\n", " sample_n_non_ref (samples) int64 2kB dask.array\n", " ... ...\n", " variant_contig_name (variants) object 87kB dask.array\n", " Population (samples) object 2kB 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 284B False True False ... False True\n", " PurpleHair (samples) bool 284B False False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 4 2 ... 6 4 6 4 6 5 5\n", "Attributes: (3)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = sg.sample_stats(ds)\n", "ds" ] }, { "cell_type": "markdown", "id": "together-drink", "metadata": {}, "source": [ "We can plot the metrics next." ] }, { "cell_type": "code", "execution_count": 24, "id": "dimensional-merchandise", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.sample_call_rate.attrs[\"long_name\"] = \"Sample call rate\"\n", "xr.plot.hist(ds.sample_call_rate, range=(.88,1), bins=50, size=8, edgecolor=\"black\");" ] }, { "cell_type": "code", "execution_count": 25, "id": "certain-needle", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gq = ds.call_GQ.where(ds.call_GQ >= 0) # filter out missing\n", "sample_gq_mean = gq.mean(dim=\"variants\")\n", "sample_gq_mean.attrs[\"long_name\"] = \"Mean Sample GQ\"\n", "xr.plot.hist(sample_gq_mean, range=(10,70), bins=60, size=8, edgecolor=\"black\");" ] }, { "cell_type": "code", "execution_count": 26, "id": "north-bryan", "metadata": {}, "outputs": [], "source": [ "dp = ds.call_DP.where(ds.call_DP >= 0) # filter out missing\n", "sample_dp_mean = dp.mean(dim=\"variants\")\n", "sample_dp_mean.attrs[\"long_name\"] = \"Mean Sample DP\"\n", "ds[\"sample_dp_mean\"] = sample_dp_mean # add new data array to dataset\n", "# Following does not work with recent versions of xarray, see https://github.com/sgkit-dev/sgkit/issues/934\n", "#ds.plot.scatter(x=\"sample_dp_mean\", y=\"sample_call_rate\", size=8, s=10);" ] }, { "cell_type": "markdown", "id": "voluntary-dietary", "metadata": {}, "source": [ "The following removes outliers using an arbitrary cutoff." ] }, { "cell_type": "code", "execution_count": 27, "id": "handed-reducing", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After filter, 250/284 samples remain.\n" ] } ], "source": [ "ds = ds.sel(samples=((ds.sample_dp_mean >= 4) & (ds.sample_call_rate >= 0.97)).compute())\n", "print(f\"After filter, {len(ds.samples)}/284 samples remain.\")" ] }, { "cell_type": "markdown", "id": "wicked-threshold", "metadata": {}, "source": [ "Genotype QC is more complicated. First we calculate a variable `ab`, which is the fraction of reads that were the alternate allele." ] }, { "cell_type": "code", "execution_count": 28, "id": "selective-interval", "metadata": {}, "outputs": [], "source": [ "# fill rows with nan where no alternate alleles were read or where sum of reads is 0\n", "ad1 = ds.call_AD.sel(dict(alleles=1)).pipe(lambda v: v.where(v >= 0))\n", "adsum = ds.call_AD.sum(dim=\"alleles\").pipe(lambda v: v.where(v != 0))\n", "# compute alternate allele read fraction\n", "ab = ad1 / adsum" ] }, { "cell_type": "markdown", "id": "transsexual-macro", "metadata": {}, "source": [ "Then we can use the `ab` variable in the filter condition, to filter homozygous reference calls with >10% alternate reads, homozygous alternate calls with >10% reference reads, or heterozygote calls without a ref/alt balance near 50%." ] }, { "cell_type": "code", "execution_count": 29, "id": "bearing-jenny", "metadata": {}, "outputs": [], "source": [ "GT = ds.call_genotype\n", "hom_ref = (GT == 0).all(dim=\"ploidy\")\n", "het = GT[..., 0] != GT[..., 1]\n", "hom_alt = ((GT > 0) & (GT[..., 0] == GT)).all(dim=\"ploidy\")\n", "filter_condition_ab = ((hom_ref & (ab <= 0.1)) |\n", " (het & (ab >= 0.25) & (ab <= 0.75)) |\n", " (hom_alt & (ab >= 0.9)))" ] }, { "cell_type": "code", "execution_count": 30, "id": "painted-terminal", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filtering 3.65% entries out of downstream analysis.\n" ] } ], "source": [ "filter_mask = xr.where(ds.call_genotype_mask, True, filter_condition_ab)\n", "fraction_filtered = GT.where(~filter_mask).count().values / GT.size\n", "print(f\"Filtering {fraction_filtered * 100:.2f}% entries out of downstream analysis.\")" ] }, { "cell_type": "markdown", "id": "israeli-harris", "metadata": {}, "source": [ "_Note: genotype QC is filtering out slightly different numbers of entries compared to the Hail tutorial._" ] }, { "cell_type": "markdown", "id": "banner-structure", "metadata": {}, "source": [ "Variant QC is similar. This time we use the [`variant_stats`](https://sgkit-dev.github.io/sgkit/latest/generated/sgkit.variant_stats.html) function, but we won't do any filtering on these variables." ] }, { "cell_type": "code", "execution_count": 31, "id": "specialized-train", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 26MB\n",
       "Dimensions:                   (variants: 10879, alleles: 2, samples: 250,\n",
       "                               ploidy: 2, contigs: 84, filters: 1)\n",
       "Dimensions without coordinates: variants, alleles, samples, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/38)\n",
       "    variant_n_called          (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_call_rate         (variants) float64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_het             (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_ref         (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_alt         (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_non_ref         (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    Population                (samples) object 2kB 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation           (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool 250B False True ... False True\n",
       "    PurpleHair                (samples) bool 250B False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float32 1kB dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 26MB\n", "Dimensions: (variants: 10879, alleles: 2, samples: 250,\n", " ploidy: 2, contigs: 84, filters: 1)\n", "Dimensions without coordinates: variants, alleles, samples, ploidy, contigs,\n", " filters\n", "Data variables: (12/38)\n", " variant_n_called (variants) int64 87kB dask.array\n", " variant_call_rate (variants) float64 87kB dask.array\n", " variant_n_het (variants) int64 87kB dask.array\n", " variant_n_hom_ref (variants) int64 87kB dask.array\n", " variant_n_hom_alt (variants) int64 87kB dask.array\n", " variant_n_non_ref (variants) int64 87kB dask.array\n", " ... ...\n", " Population (samples) object 2kB 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 250B False True ... False True\n", " PurpleHair (samples) bool 250B False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n", " sample_dp_mean (samples) float32 1kB dask.array\n", "Attributes: (3)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = sg.variant_stats(ds)\n", "ds" ] }, { "cell_type": "markdown", "id": "jewish-people", "metadata": {}, "source": [ "## Let's do a GWAS!" ] }, { "cell_type": "markdown", "id": "determined-population", "metadata": {}, "source": [ "First we need to restrict to common variants (1% cutoff), and not far from Hardy-Weinberg equilibrium." ] }, { "cell_type": "code", "execution_count": 32, "id": "delayed-destiny", "metadata": {}, "outputs": [], "source": [ "ds = sg.hardy_weinberg_test(ds)" ] }, { "cell_type": "markdown", "id": "color-detection", "metadata": {}, "source": [ "(The warning is telling us that some variables are being regenerated, and can be safely ignored.)" ] }, { "cell_type": "code", "execution_count": 33, "id": "optical-television", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 26MB\n",
       "Dimensions:                   (variants: 10879, genotypes: 3, alleles: 2,\n",
       "                               samples: 250, ploidy: 2, contigs: 84, filters: 1)\n",
       "Coordinates:\n",
       "  * genotypes                 (genotypes) <U3 36B '0/0' '0/1' '1/1'\n",
       "Dimensions without coordinates: variants, alleles, samples, ploidy, contigs,\n",
       "                                filters\n",
       "Data variables: (12/41)\n",
       "    variant_hwe_p_value       (variants) float64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_genotype_count    (variants, genotypes) uint64 261kB dask.array<chunksize=(10000, 3), meta=np.ndarray>\n",
       "    genotype_id               (genotypes) <U3 36B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "    variant_n_called          (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_call_rate         (variants) float64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_het             (variants) int64 87kB dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    Population                (samples) object 2kB 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation           (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool 250B False True ... False True\n",
       "    PurpleHair                (samples) bool 250B False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float32 1kB dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 26MB\n", "Dimensions: (variants: 10879, genotypes: 3, alleles: 2,\n", " samples: 250, ploidy: 2, contigs: 84, filters: 1)\n", "Coordinates:\n", " * genotypes (genotypes) \n", " variant_genotype_count (variants, genotypes) uint64 261kB dask.array\n", " genotype_id (genotypes) \n", " variant_n_called (variants) int64 87kB dask.array\n", " variant_call_rate (variants) float64 87kB dask.array\n", " variant_n_het (variants) int64 87kB dask.array\n", " ... ...\n", " Population (samples) object 2kB 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 250B False True ... False True\n", " PurpleHair (samples) bool 250B False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n", " sample_dp_mean (samples) float32 1kB dask.array\n", "Attributes: (3)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 34, "id": "minus-airline", "metadata": {}, "outputs": [], "source": [ "ds = ds.sel(variants=((ds.variant_allele_frequency[:,1] > 0.01) & (ds.variant_hwe_p_value > 1e-6)).compute())" ] }, { "cell_type": "markdown", "id": "wireless-italian", "metadata": {}, "source": [ "_Note: again, the number of variants is different to the Hail tutorial, but the final results work out to be very similar._" ] }, { "cell_type": "code", "execution_count": 35, "id": "sexual-charles", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Samples: 250 Variants: 8394\n" ] } ], "source": [ "print(f\"Samples: {len(ds.samples)} Variants: {len(ds.variants)}\")" ] }, { "cell_type": "markdown", "id": "lovely-scratch", "metadata": {}, "source": [ "Run a linear regression of dosage (number of alt alleles) against the `CaffeineConsumption` trait." ] }, { "cell_type": "code", "execution_count": 36, "id": "forty-richardson", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 37MB\n",
       "Dimensions:                   (variants: 8394, traits: 1, genotypes: 3,\n",
       "                               alleles: 2, samples: 250, ploidy: 2,\n",
       "                               contigs: 84, filters: 1)\n",
       "Coordinates:\n",
       "  * genotypes                 (genotypes) <U3 36B '0/0' '0/1' '1/1'\n",
       "Dimensions without coordinates: variants, traits, alleles, samples, ploidy,\n",
       "                                contigs, filters\n",
       "Data variables: (12/45)\n",
       "    variant_linreg_beta       (variants, traits) float64 67kB dask.array<chunksize=(5439, 1), meta=np.ndarray>\n",
       "    variant_linreg_t_value    (variants, traits) float64 67kB dask.array<chunksize=(5439, 1), meta=np.ndarray>\n",
       "    variant_linreg_p_value    (variants, traits) float64 67kB dask.array<chunksize=(5439, 1), meta=np.ndarray>\n",
       "    variant_hwe_p_value       (variants) float64 67kB dask.array<chunksize=(5439,), meta=np.ndarray>\n",
       "    variant_genotype_count    (variants, genotypes) uint64 201kB dask.array<chunksize=(5439, 3), meta=np.ndarray>\n",
       "    genotype_id               (genotypes) <U3 36B dask.array<chunksize=(3,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    SuperPopulation           (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool 250B False True ... False True\n",
       "    PurpleHair                (samples) bool 250B False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float32 1kB dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "    call_dosage               (variants, samples) int64 17MB dask.array<chunksize=(5439, 250), meta=np.ndarray>\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 37MB\n", "Dimensions: (variants: 8394, traits: 1, genotypes: 3,\n", " alleles: 2, samples: 250, ploidy: 2,\n", " contigs: 84, filters: 1)\n", "Coordinates:\n", " * genotypes (genotypes) \n", " variant_linreg_t_value (variants, traits) float64 67kB dask.array\n", " variant_linreg_p_value (variants, traits) float64 67kB dask.array\n", " variant_hwe_p_value (variants) float64 67kB dask.array\n", " variant_genotype_count (variants, genotypes) uint64 201kB dask.array\n", " genotype_id (genotypes) \n", " ... ...\n", " SuperPopulation (samples) object 2kB 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool 250B False True ... False True\n", " PurpleHair (samples) bool 250B False False ... True True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 6 2 2 ... 4 6 4 6 5 5\n", " sample_dp_mean (samples) float32 1kB dask.array\n", " call_dosage (variants, samples) int64 17MB dask.array\n", "Attributes: (3)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[\"call_dosage\"] = ds.call_genotype.sum(dim=\"ploidy\")\n", "ds_lr = sg.gwas_linear_regression(ds, dosage=\"call_dosage\",\n", " add_intercept=True, covariates=[], traits=[\"CaffeineConsumption\"])\n", "ds_lr" ] }, { "cell_type": "markdown", "id": "caring-bikini", "metadata": {}, "source": [ "You can see that new variables have been added for `variant_linreg_p_value`, `variant_linreg_t_value`, and `variant_linreg_beta`.\n", "\n", "Since sgkit doesn't have any plotting utilities, we implement Manhattan plots and QQ plots here using Seaborn." ] }, { "cell_type": "code", "execution_count": 37, "id": "cubic-assessment", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 38, "id": "radio-gnome", "metadata": {}, "outputs": [], "source": [ "def manhattan_plot(ds):\n", " df = ds[[\"variant_contig_name\", \"variant_contig\", \"variant_position\", \"variant_linreg_p_value\"]].to_dataframe()\n", " df[\"variant_linreg_log_p_value\"] = -np.log10(df[\"variant_linreg_p_value\"])\n", " df = df.astype({\"variant_position\": np.int64}) # to avoid overflow in cumulative_pos\n", " \n", " # from https://github.com/mojones/video_notebooks/blob/master/Manhattan%20plots%20in%20Python.ipynb, cell 20\n", " running_pos = 0\n", " cumulative_pos = []\n", " for chrom, group_df in df.groupby(\"variant_contig\"): \n", " cumulative_pos.append(group_df[\"variant_position\"] + running_pos)\n", " running_pos += group_df[\"variant_position\"].max()\n", " df[\"cumulative_pos\"] = pd.concat(cumulative_pos)\n", " \n", " df[\"color group\"] = df[\"variant_contig\"].apply(lambda x : \"A\" if x % 2 == 0 else \"B\")\n", " \n", " g = sns.relplot(\n", " data = df,\n", " x = \"cumulative_pos\",\n", " y = \"variant_linreg_log_p_value\",\n", " hue = \"color group\",\n", " palette = [\"blue\", \"orange\"],\n", " linewidth=0,\n", " s=10,\n", " legend=None,\n", " aspect=3\n", " )\n", " g.ax.set_xlabel(\"Chromosome\")\n", " g.ax.set_xticks(df.groupby(\"variant_contig\")[\"cumulative_pos\"].median())\n", " g.ax.set_xticklabels(df[\"variant_contig_name\"].unique())" ] }, { "cell_type": "code", "execution_count": 39, "id": "inner-egypt", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n", " self._figure.tight_layout(*args, **kwargs)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "manhattan_plot(ds_lr)" ] }, { "cell_type": "code", "execution_count": 40, "id": "static-grove", "metadata": {}, "outputs": [], "source": [ "import math\n", "import matplotlib.pyplot as plt\n", "\n", "def qq_plot(ds):\n", " p = ds[\"variant_linreg_p_value\"].squeeze().values\n", " p.sort()\n", " n = len(p)\n", " expected_p = -np.log10(np.arange(1, n + 1) / n)\n", " observed_p = -np.log10(p)\n", " max_val = math.ceil(max(np.max(expected_p), np.max(observed_p)))\n", "\n", " df = pd.DataFrame({\"Expected -log10(p)\": expected_p, \"Observed -log10(p)\": observed_p})\n", "\n", " fig, ax = plt.subplots(figsize=(12, 12));\n", " g = sns.scatterplot(data=df, x=\"Expected -log10(p)\", y=\"Observed -log10(p)\", ax=ax, linewidth=0)\n", "\n", " x_pred = np.linspace(0, max_val, 50)\n", " sns.lineplot(x=x_pred, y=x_pred, ax=ax)\n", " \n", " g.set(xlim=(0, max_val), ylim=(0, max_val))" ] }, { "cell_type": "code", "execution_count": 41, "id": "brutal-pharmaceutical", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "qq_plot(ds_lr)" ] }, { "cell_type": "markdown", "id": "timely-tsunami", "metadata": {}, "source": [ "## Confounded!" ] }, { "cell_type": "markdown", "id": "young-parallel", "metadata": {}, "source": [ "As explained in the Hail tutorial, the data contains a confounder, so it is necessary to include ancestry as a covariate in the linear regression.\n", "\n", "Rather than just use the reported ancestry, it's better to use principal components from running a PCA on the data." ] }, { "cell_type": "code", "execution_count": 42, "id": "virgin-creativity", "metadata": {}, "outputs": [], "source": [ "ds_pca = sg.stats.pca.count_call_alternate_alleles(ds)" ] }, { "cell_type": "code", "execution_count": 43, "id": "humanitarian-starter", "metadata": {}, "outputs": [], "source": [ "# To run PCA we need to filter out variants with any missing alt allele counts\n", "# Or where the counts are zero for all samples\n", "variant_mask = (((ds_pca.call_alternate_allele_count < 0).any(dim=\"samples\")) | \\\n", " (ds_pca.call_alternate_allele_count.std(dim=\"samples\") <= 0.0)).compute()\n", "ds_pca = ds_pca.sel(variants=~variant_mask)" ] }, { "cell_type": "code", "execution_count": 44, "id": "statistical-bolivia", "metadata": {}, "outputs": [], "source": [ "ds_pca = sg.pca(ds_pca)" ] }, { "cell_type": "code", "execution_count": 45, "id": "furnished-modem", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ -8.453593 , -26.128805 , -11.008166 , ..., -14.8012085,\n", " 24.537292 , -1.0794816],\n", " [ -9.496219 , -26.31961 , -10.1165 , ..., 1.6827679,\n", " 7.681701 , -5.957277 ],\n", " [ -7.8734045, -25.404318 , -9.859189 , ..., 4.3828707,\n", " -9.368455 , 6.384332 ],\n", " ...,\n", " [-10.974407 , -11.576625 , 20.124645 , ..., -4.421063 ,\n", " -0.5393095, 1.0124555],\n", " [-10.754403 , -11.41477 , 15.358788 , ..., 1.7951674,\n", " 3.4263668, -7.985674 ],\n", " [-13.0628805, -11.688103 , 16.351276 , ..., -7.2050505,\n", " -1.7339798, 5.17502 ]], dtype=float32)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_pca.sample_pca_projection.values" ] }, { "cell_type": "code", "execution_count": 46, "id": "faced-dutch", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 17MB\n",
       "Dimensions:                              (samples: 250, components: 10,\n",
       "                                          variants: 3491, genotypes: 3,\n",
       "                                          alleles: 2, ploidy: 2, contigs: 84,\n",
       "                                          filters: 1)\n",
       "Coordinates:\n",
       "  * genotypes                            (genotypes) <U3 36B '0/0' '0/1' '1/1'\n",
       "Dimensions without coordinates: samples, components, variants, alleles, ploidy,\n",
       "                                contigs, filters\n",
       "Data variables: (12/48)\n",
       "    sample_pca_projection                (samples, components) float32 10kB dask.array<chunksize=(250, 10), meta=np.ndarray>\n",
       "    sample_pca_component                 (variants, components) float32 140kB dask.array<chunksize=(3491, 10), meta=np.ndarray>\n",
       "    sample_pca_explained_variance        (components) float32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    sample_pca_explained_variance_ratio  (components) float32 40B dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    sample_pca_loading                   (variants, components) float32 140kB dask.array<chunksize=(3491, 10), meta=np.ndarray>\n",
       "    call_alternate_allele_count          (variants, samples) int16 2MB dask.array<chunksize=(3491, 250), meta=np.ndarray>\n",
       "    ...                                   ...\n",
       "    SuperPopulation                      (samples) object 2kB 'EUR' ... 'SAS'\n",
       "    isFemale                             (samples) bool 250B False True ... True\n",
       "    PurpleHair                           (samples) bool 250B False ... True\n",
       "    CaffeineConsumption                  (samples) int64 2kB 4 4 4 3 ... 4 6 5 5\n",
       "    sample_dp_mean                       (samples) float32 1kB dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "    call_dosage                          (variants, samples) int64 7MB dask.array<chunksize=(3491, 250), meta=np.ndarray>\n",
       "Attributes: (3)
" ], "text/plain": [ " Size: 17MB\n", "Dimensions: (samples: 250, components: 10,\n", " variants: 3491, genotypes: 3,\n", " alleles: 2, ploidy: 2, contigs: 84,\n", " filters: 1)\n", "Coordinates:\n", " * genotypes (genotypes) \n", " sample_pca_component (variants, components) float32 140kB dask.array\n", " sample_pca_explained_variance (components) float32 40B dask.array\n", " sample_pca_explained_variance_ratio (components) float32 40B dask.array\n", " sample_pca_loading (variants, components) float32 140kB dask.array\n", " call_alternate_allele_count (variants, samples) int16 2MB dask.array\n", " ... ...\n", " SuperPopulation (samples) object 2kB 'EUR' ... 'SAS'\n", " isFemale (samples) bool 250B False True ... True\n", " PurpleHair (samples) bool 250B False ... True\n", " CaffeineConsumption (samples) int64 2kB 4 4 4 3 ... 4 6 5 5\n", " sample_dp_mean (samples) float32 1kB dask.array\n", " call_dosage (variants, samples) int64 7MB dask.array\n", "Attributes: (3)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_pca" ] }, { "cell_type": "markdown", "id": "interior-puzzle", "metadata": {}, "source": [ "Let's plot the first two components. Notice how they cluster by ancestry." ] }, { "cell_type": "code", "execution_count": 47, "id": "returning-champagne", "metadata": {}, "outputs": [], "source": [ "ds_pca[\"sample_pca_projection_0\"] = ds_pca.sample_pca_projection[:,0]\n", "ds_pca[\"sample_pca_projection_1\"] = ds_pca.sample_pca_projection[:,1]\n", "ds_pca[\"sample_pca_projection_2\"] = ds_pca.sample_pca_projection[:,2]\n", "# Following does not work with recent versions of xarray, see https://github.com/sgkit-dev/sgkit/issues/934\n", "#ds_pca.plot.scatter(x=\"sample_pca_projection_0\", y=\"sample_pca_projection_1\", hue=\"SuperPopulation\", size=8, s=10);" ] }, { "cell_type": "markdown", "id": "abroad-pilot", "metadata": {}, "source": [ "Now we can rerun our linear regression, controlling for sample sex and the first few principal components." ] }, { "cell_type": "code", "execution_count": 48, "id": "armed-newspaper", "metadata": {}, "outputs": [], "source": [ "# copy pca components back to dataset with full set of variants to run linear regression again\n", "ds[\"sample_pca_projection_0\"] = ds_pca.sample_pca_projection[:,0]\n", "ds[\"sample_pca_projection_1\"] = ds_pca.sample_pca_projection[:,1]\n", "ds[\"sample_pca_projection_2\"] = ds_pca.sample_pca_projection[:,2]\n", "ds_lr = sg.gwas_linear_regression(ds, dosage=\"call_dosage\",\n", " add_intercept=True,\n", " covariates=[\"isFemale\", \"sample_pca_projection_0\", \"sample_pca_projection_1\", \"sample_pca_projection_2\"],\n", " traits=[\"CaffeineConsumption\"])" ] }, { "cell_type": "code", "execution_count": 49, "id": "excellent-buffer", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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Jby9n6gr4G2+8oY8//ljTp09Xu3bttGrVKg0bNkxhYWF6+OGHzRwNAAAAAExzOr5/3X5MVYIqaOrQGMU2q2n2WCgjUwN8+fLluuOOO3TLLbdIkpo0aaI5c+Zo5cqVFzw+Pz9f+fn5Z36dmZlZLnMCAAAAQHnJLXDE9287HPE9bVh3dW8abvZYcAFTt6D37NlTP/30k7Zt2yZJWrdunX777Tf17t37gsePGzdOYWFhZ74iIiLKc1wAAAAAcKvcAptGTE/WbzuOqWpQBU0fTnz7Eoth4gXXdrtdzzzzjN58801VqFBBNptNr732mp5++ukLHn+hFfCIiAhlZGQoNDS0vMYGAAAAAJfLKbBqxLRVWrHr+Jn47taE+PY0mZmZCgsLK1WHmroF/fPPP9esWbM0e/ZstWvXTmvXrtWjjz6qBg0aaMiQIecdHxwcrODgYBMmBQAAAAD3ySmwavi0ZCXuSle14EBNHx6jro2Jb19jaoCPHTtWTz31lOLj4yVJHTp00N69ezVu3LgLBjgAAAAA+JqcAquGTU1W0u7T8d1dXRvXMHssuIGpAZ6Tk6OAgHMvQ69QoYLsdrtJEwEAAABA+TmVb9WwaclauTtdIcGBmj6iu7pEEt++ytQAv+222/Taa68pMjJS7dq105o1a/Tvf/9bw4cPN3MsAAAAAHC77Hyrhk1dqeQ9JxQSHKhPR3RXZ+Lbp5l6E7asrCw999xz+uqrr3TkyBE1aNBA/fv31/PPP6+goKBizy/Lxe8AAAAAYJbsfKuGTlmpVXtPKKRSoGaMiFWniOpmj4USKEuHmhrgZUWAAwAAAPA2WXmFGjo1WSl7Tyi0UqBmjoxVdKPqZo+FEvLau6ADAAAAgD/JyivUkCkrtTr1pEIrBWrWyDh1aBRm9lgoJwQ4AAAAAJSDzKL4XpN6UmGVK2rWyFi1b0h8+xMCHAAAAADcLDOvUIMnr9TatJOqXqWiZo4gvv0RAQ4AAAAAbpSRW6jBU1ZqHfHt9whwAAAAAHCTjNxCDZ6cpHX7MlSjSkXNGhmntg24gbS/IsABAAAAwA0ycgp135QkrSe+UYQABwAAAAAXO5lToEGTk7Rxf6bCqwZp1shYtalPfPs7AhwAAAAAXOhkToEGTkrSHwcyVbNqkGaPilPreiFmjwUPQIADAAAAgIucOOWI700HiW+cjwAHAAAAABc4O75rVXPEd6u6xDf+RIADAAAAQBmlF8X35oOZqlUtWHNGxaol8Y2/IMABAAAAoAyOZ+dr4KQkbTmUpVrVgpUwOlYt6hDfOB8BDgAAAACldHZ81w4J1pxRcWpRp5rZY8FDEeAAAAAAUArHsvM1cGKSth7OUp2QYM0ZHafmtYlvXBwBDgAAAABOOpqVrwETE7X9SLbqhjpWvpsR3ygGAQ4AAAAATjg7vuuFVtKc0XFqWquq2WPBCxDgAAAAAFBCR7LyNGBiknYcyVb9sEqaMypOTYhvlBABDgAAAAAlcCQzT/0nJmrn0VOqH1ZJCaPj1Lgm8Y2SCzB7AAAAAADwdEcy8xRfFN8NiG+UEivgAAAAAHAJhzPz1H9ConYdO6WG1Strzqg4RdasYvZY8EIEOAAAAABcxKEMx7bz3UXxnTA6ThHhxDdKhwAHAAAAgAs4mJGr/hMSted4DvENlyDAAQAAAOAvDpzMVf+Jidp7PEeNajjiu1EN4htlQ4ADAAAAwFn2n3SsfKem5ygi3HHNN/ENVyDAAQAAAKDI/pO5ip+wQmnpuYoMr6I5o+PUsHpls8eCjyDAAQAAAEDSvhM56j8xUWnpuWpcs4rmjIpTA+IbLkSAAwAAAPB7aemO+N53IldNajpWvuuHEd9wLQIcAAAAgF9LS89R/IRE7T+Zq6a1qmrOqDjVC6tk9ljwQQFmDwAAAAAAZjk7vpsR33AzVsABAAAA+KXU4zmKn7BCBzLyHPE9Ok51Q4lvuA8BDgAAAMDv7D1+SvETEnUwI0/NaldVwqg41SG+4WZsQQcAAADgV/Yc+zO+m9euqoTRxDfKByvgAAAAAPzG7mOn1H9Cog5l5qlFnWqaMypOtUOCzR4LfoIABwAAAOAXdh3NVv+JiTqcma+WdappNvGNckaAAwAAAPB5O49mq/+ERB3Jyleruo74rlWN+Eb5IsABAAAA+LQdR7I1YKIjvlvXDdHsUbGqSXzDBAQ4AAAAAJ+140iW+k9M0tGsfEXVC9GskcQ3zEOAAwAAAPBJ2w874vtYtiO+Z4+KU3jVILPHgh8jwAEAAAD4HEd8J+pYdoHa1g/VrJGxqkF8w2QEOAAAAACfsu1wlvpPSNTxUwVq1yBUM0cQ3/AMBDgAAAAAn7H1UJYGTPwzvmeNjFX1KsQ3PAMBDgAAAMAnbDmUqQETk5R+qkAdGoZpxojuxDc8CgEOAAAAwOttOpCpgZMSdSKnUNGNwjRjeKzCqlQ0eyzgHAFmDwAAAAAAZXF2fHdsFKYZI4hveCZWwAEAAAB4rT8OZGjgpCSdzClUx4jq+nR4d4VVJr7hmQhwAAAAAF5p435HfGfkFqpTRHV9OqK7QisR3/BcbEEHAAAA4HXOju/OkcQ3vAMr4AAAAAC8yoZ9GRo4KVGZeVZ1iayu6cO7K4T4hhcgwAEAAAB4jXVpJ3Xf5CRl5lnVtXENTRsWQ3zDaxDgAAAAALzC2qL4zsqzqlvjGpo2vLuqBZM08B78bgUAAADg8dakntDgySuVlW9VTJMamjqM+Ib34SZsAAAAADza6rPiu3uTcE0jvuGl+F0LAAAAwGOl7D2hIVNWKjvfqtim4ZoyNEZViW94KX7nAgAAAPBIKXvTNWRKsrLzrYpr5ojvKkEkDLwXv3sBAAAAeJxVe9I1ZMpKnSqwqUezmpo8tBvxDa/H72AAAAAAHiV5T7qGFsV3z+Y1NXlIjCoHVTB7LKDMCHAAAAAAHmPl7nQNnbpSOQU2Xd6iliYO7kZ8w2cQ4AAAAAA8QtKu4xo2LVk5BTZd0dIR35UqEt/wHQQ4AAAAANMl7jquYVOTlVtIfMN3EeAAAAAATLV85zGNmLZKuYU2Xdmqtibc15X4hk8iwAEAAACYZvmOYxo+PVl5hXZd1aq2PiG+4cMCzB4AAAAAgH/6/az4vqY18Q3fxwo4AAAAgHL32/ZjGjE9WflWu66NqqOPB3VRcCDxDd/GCjgAAACAcrVs29Ez8X0d8Q0/wgo4AAAAgHKzdNtRjfp0lQqsdvVqU1cfDexMfMNvEOAAAAAAysWSrUc0ekaKCqx2Xd+2rj4a0EVBgWzKhf/gdzsAAAAAt/tlyxGN/tQR3zcQ3/BTrIADAAAAcKuftxzW/TNWq8Bm103t6unDAZ1VsQLxDf/D73oAAAAAbvPT5j/ju3d74hv+jRVwAAAAAG6xeNNhPTArRYU2Qzd3qKf344lv+DcCHAAAAIDL/fjHIY2ZvVqFNkO3RNfXe/d2Ir7h9/h/AAAAAACXWnhWfN/WsYHeJ74BSayAAwAAAHChHzYe0oOzV8tqN3R7xwb69z0dFUh8A5IIcAAAAAAusmDDQT00Z42sdkN3dGqgd/oR38DZCHAAAAAAZfZ9UXzb7Ibu6txQb/frqAoBFrPHAjwK/xwFAAAAoEzmr/8zvu8mvoGLYgUcAAAAQKl9u+6AHv1srSO+uzTUW32Jb+BiCHAAAAAApfK/dQf0aMIa2Q2pb9dGeqNPNPENXAIBDgAAAMBp36zdr8c+Wyu7IfUriu8A4hu4JK4BBwAAAOCUr9f8Gd/3dosgvoESIsABAAAAlNhXa/bp8c8d8R0fE6Fxd3cgvoESYgs6AAAAgBKZl7JPT8xdJ8OQ+neP1Gt3tie+ASewAg4AAACgWHPPiu8BscQ3UBoEOAAAAIBL+mJVmsYWxfeguEi9egfxDZQGW9ABAAAAXNTnyWl68sv1MgzpvrjGevmOdrJYiG+gNAhwAAAAABf0WXKqnpy3QZI0pEdjvXg78Q2UBQEOAAAA4DxzVqbq6S8d8T20ZxO9cFtb4hsoIwIcAAAAwDlmJ6Xqma8c8T3ssiZ6/lbiG3AFAhwAAADAGTMT9+rZrzdKkoZf1lTP3dqG+AZchAAHAAAAIEmakbhXzxXF94jLm+rZW4hvwJUIcAAAAAD6dMUePf/NH5KkUVc01TM3E9+Aq5n6HPAmTZrIYrGc9zVmzBgzxwIAmCQtPUdvLdyih+as0VsLtygtPcfskQDAL0xf/md8/+3KZsQ34CamroAnJyfLZrOd+fXGjRt1/fXXq1+/fiZOBQAww9yUfXpy3nrZ7MaZ1z5Zukvj+0Srb9dGJk4GAL5t6u+79dK3myRJf7uqmZ66KYr4BtzE1BXw2rVrq169eme+vvvuOzVv3lxXXXWVmWMBAMpZWnrOefEtSVa7oafmrWclHADcZPJvf8b3A1c3J74BNzM1wM9WUFCgmTNnavjw4Rf9P31+fr4yMzPP+QIAeL+E5NTz4vs0q91QQnJqOU8EAL5v0q+79Mp3jvj++9XN9c8bWxPfgJt5TIB//fXXOnnypIYOHXrRY8aNG6ewsLAzXxEREeU3IADAbVLTcy/5flox7wMAnDPp1116df5mSdKD17TQWOIbKBceE+CTJ09W79691aBBg4se8/TTTysjI+PMV1paWjlOCABwl8jwypd8P6KY9wEAJTdx2Z/x/dC1LfSPG1oR30A58YjHkO3du1eLFy/Wl19+ecnjgoODFRwcXE5TAQDKS3xMpD5ZukvWC2xDDwywKD4m0oSpAMD3fLJ0p8Yt2CJJevi6lnqsV0viGyhHHrECPnXqVNWpU0e33HKL2aMAAEwQEV5F4/tEKzDg3L8EBgZY9EafaEWEVzFpMgDwHR8v+TO+H7mupR6/npVvoLyZvgJut9s1depUDRkyRIGBpo8DADBJ366NFNs0XAnJqUpLz1VEeGXFx0QS3wDgAh/9skNvLdwqSXq0V0s92quVyRMB/sn04l28eLFSU1M1fPhws0cBAJgsIryKxt4YZfYYAOBTzo7vx69vpYeva2nyRID/Mj3Ab7jhBhnGhR89AwAAAKD0Pvxpu95ZtE2S9MQNrfTgtcQ3YCbTAxwAAACA672/eLveXeyI77E3ttaYa1qYPBEAAhwAAADwMe8t3qb3Fm+XJP3zptb6+9XEN+AJCHAAAADARxiGoXcXb9cHPzni+6neUbr/quYmTwXgNAIcAAAA8AGGYejdRdv0wc87JEnP3Byl0VcS34AnIcABAAAAL2cYhv69aJs+LIrvf93cRqOubGbyVAD+igAHAAAAvJhhGHr7x6366JedkqRnb2mjkVcQ34AnIsABAAAAL2UYht5cuFUfL3HE93O3ttWIy5uaPBWAiyHAAQAAAC9kGIbe+GGr/rvUEd8v3NZWwy4jvgFPRoADAAAAXsYwDI1fsEWfLNslSXrp9nYa0rOJuUMBKBYBDgAAAHgRwzA0bsEWTSiK75fvaKfBPZqYOxSAEiHAAQAAAC9hGIZem79Zk37bLUl65Y52uo/4BrwGAQ4AAAB4AcMw9Mp3mzXld0d8v3pnew2Ka2zyVACcQYADAAAAHs4wDL383SZN/X2PJOn1uzpoQGykuUMBcBoBDgAAAHgwwzD00rebNG35HknSuLs7qH934hvwRgQ4AAAA4KEMw9CL//tD01fslcUijburg+KJb8BrEeAAgDJJS89RQnKqUtNzFRleWfExkYoIr2L2WADg9QzD0PPf/KEZiY74fuPuaN0TE2H2WADKgAAHAJTa3JR9enLeetnsxpnXPlm6S+P7RKtv10YmTgYA3s1uN/T8/zZqZmKqLBbpzT7R6teN+Aa8XYDZAwAAvFNaes558S1JVruhp+atV1p6jkmTAYB3s9sNPfvNn/H9Vt+OxDfgIwhwAECpJCSnnhffp1nthhKSU8t5IgDwfna7oX99vUGzkxzx/XbfjuwoAnwIW9ABAKWSmp57yffTinkfAHAuu93QM19tUEJymgIs0jv3dNRdnYlvwJcQ4ACAUokMr3zJ9yOKeR8A8Ce73dBTX67X56v2KcAi/fueTrqzc0OzxwLgYmxBBwCUSnxMpAIDLBd8LzDAovgYHpMDACVhtxt6ct6f8f3uvcQ34KsIcABAqUSEV9H4PtHnRXhggEVv9InmUWQAUAI2u6F/zluvL1Ic8f1efGfd0Yn4BnwVW9ABAKXWt2sjxTYNV0JyqtLScxXBc8ABoMRsdkNj567Tl6v3q0KARe/d20m3dWxg9lgA3IgABwCUSUR4FY29McrsMQDAq9jshsZ+sU5frnHE9wfxnXVLdH2zxwLgZgQ4AAAAUI5sdkP/+Hytvl57QBUCLPqwf2fd3IH4BvwBAQ4AAACUE6vNrn98sU7frD2gwKL47k18A36DAAcAAADKgdVm12Ofr9O36xzx/Z8BXXRT+3pmjwWgHBHgAAAAgJtZbXY9+tlafbf+oAIDLPpoYBfd2I74BvwNAQ4AAAC4UaHNrkcT1mr+hoOqWMGijwZ00Q3EN+CXCHAAAADATQptdj2SsEbfbzikihUs+nhgV/VqW9fssQCYhAAHAAAA3KDQZtfDc9ZowcZDCqoQoI8HddF1bYhvwJ8R4AAAAICLFVgd8f3DH474/u99XXRtFPEN+DsCHAAAAHChAqtdD85erR83HVZQhQB9cl9XXRNVx+yxAHgAAhwAAABwkQKrXWNmr9aiTYcVFBigCfd11dWtiW8ADgQ4AAAA4AL5VpvGzFqtxZuPKCgwQBMHd9NVrWqbPRYAD0KAAwAAAGWUb7Xp7zNX66ctRxRcFN9XEt8A/oIABwAAAMog32rTAzNX6+ei+J40pJuuaEl8AzgfAQ4AAACUUl6hTQ/MTNEvW4+qUsUATR4So8ta1DJ7LAAeigAHAAAASiGv0Ka/zUjR0m2O+J4yJEY9iW8Al0CAAwAAAE7KK7Rp9IwULTsd30Nj1LM58Q3g0ghwAAAAwAl5hTaN+nSVft1+TJUrVtCUoTHq0bym2WMB8AIEOAAAAFBCf43vqcNiFNeM+AZQMgQ4AAAAUAK5BY74/m3HMVUJqqCpQ2MUS3wDcAIBDgAAABQjt8CmEdOTtXzncVUNqqBpw7srpkm42WMB8DIEOAAAAHAJOQVWjZi2Sit2OeJ7+vDu6kZ8AygFAhwAAAC4iJwCq4ZPS1birnRVCw7U9OEx6tqY+AZQOgQ4AAAAcAGn8q0aNi1ZK3efju/u6tq4htljAfBiBDgAAADwF6fyrRo2NVkr96QrJDhQ00d0V5dI4htA2RDgAAAAwFmy860aNnWlkvecUEhwoD4d0V2diW8ALkCAAwAAAEWy860aOmWlVu09oZBKgZoxIladIqqbPRYAH0GAAwAAAJKy8go1dGqyUvaeUGilQM0cGavoRtXNHguADyHAAQAA4Pey8go1ZMpKrU49SXwDcBsCHAAAAH4tsyi+16SeVFjlipo1MlbtG4aZPRYAH0SAAwAAwG9l5hVq8OSVWpt2UtWrVNTMEcQ3APchwAEAAOCXMnILNXjKSq0riu9ZI2PVrgHxDcB9CHAAAAD4nYzcQg2enKR1+zJUo0pFzRoZp7YNQs0eC4CPI8ABAADgVzJyCnXflCSt35eh8KpBmjUyVm3qE98A3I8ABwAAgN84mVOgQZOTtHF/psKrBmn2qFhF1SO+AZQPAhwAAAB+4WROgQZOStIfBzJVs2qQZo+KU+t6IWaPBcCPEOAAAADweSdOOeJ708FM1armiO9WdYlvAOWLAAcAAIBPSy+K780HM1WrWrDmjIpVS+IbgAkIcAAAAPis9FMFGjAxUVsOZalWtWAljI5VizrENwBzEOAAAADwScez8zVwUpK2HMpS7ZBgzRkVpxZ1qpk9FgA/RoADAADA5xzLztfAiUnaejhLdUKCNWd0nJrXJr4BmIsABwAAgE85lp2vARMTte1wNvENwKMQ4AAAAPAZR7Mc8b39SLbqhjq2nTcjvgF4CAIcAAAAPuHs+K4XWklzRsepaa2qZo8FAGcQ4AAAAPB6R7LyNGBiknYcyVb9sEqaMypOTYhvAB6GAAcAAIBXO5KZp/4TE7Xz6Ck1CHOsfDeuSXwD8DwEOAAAALzWkcw8xU9M1K6jp9SwemXNGRWnyJpVzB4LAC6IAAcAAIBXOpyZp/4TErXrmCO+E0bHKSKc+AbguQhwAAAAeJ1DGY5t57uJbwBehAAHAACAVzmYkav+ExK153iOGtVwbDsnvgF4AwIcAAAAXuPAyVz1n5iovcdzFBHuiO9GNYhvAN6BAAcAAIBX2H/SsfKdmu6I74TRPdSwemWzxwKAEiPAAQAA4PH2n8xV/IQVSkvPVWR4FSWMjlMD4huAlyHAAQAA4NH2nchR/4mJSkvPVeOajviuH0Z8A/A+BDgAAAA8Vlq6I773nchVk5pVNIf4BuDFCHAAAAB4pLT0HMVPSNT+k7lqWquq5oyKU72wSmaPBQClFmD2AAAAAMBfnR3fzYhvAD6CFXAAAAB4lNTjOYqfsEIHMvIc8T06TnVDiW8A3o8ABwAAgMfYe/yU+k9IdMR37apKGBWnOsQ3AB9BgAMAAMAj7Dl2Sv0nJupgRp6a13asfNcJIb4B+A4CHAAAAKbbfcyx8n0oM08t6lTTnFFxqh0SbPZYAOBSBDgAAABMtfvYKcVPWKHDmflqWaeaZhPfAHwUAQ4AAADT7Dyarf4TEnUkK1+t6jriu1Y14huAb+IxZAAAADDF2fHdum4I8Q3A57ECDgAAgHK340i2+k9M1NGsfEXVC9GskbGqSXwD8HEEOAAAAMrVjiNZip+QpGPZjviePSpO4VWDzB4LANyOAAcAAEC52X44S/0nJupYdoHa1A/VrJGxxDcAv8E14AAAACgX286K77b1QzWb+AbgZ0wP8P3792vQoEGqWbOmKleurA4dOmjVqlVmjwUAAAAX2nooS/0nOOK7XYNQzR4VqxrENwA/Y+oW9BMnTuiyyy7TNddcowULFqh27dravn27atSoYeZYAAAAcKEthzI1YGKS0k8VqH3DUM0cEavqVYhvAP7H1AB/4403FBERoalTp555rWnTpiZOBAAAAFfafDBTAyc54rtDwzDNHBGrsCoVzR4LAExh6hb0//3vf+rWrZv69eunOnXqqHPnzpo4ceJFj8/Pz1dmZuY5XwAA10lLz9FbC7fooTlr9NbCLUpLzzF7JABebNOBTA2YmKj0UwWKbhSmmSOJbwD+zWIYhmHWN69UqZIk6fHHH1e/fv2UnJysRx55RP/97381ZMiQ845/8cUX9dJLL533ekZGhkJDQ90+LwD4srkp+/TkvPWy2f/8YyEwwKLxfaLVt2sjEycD4I3+OJChgZOSdDKnUB0bhenTEbEKq0x8A/B+mZmZCgsLK1WHmhrgQUFB6tatm5YvX37mtYcffljJyclasWLFecfn5+crPz//zK8zMzMVERFBgANAGaWl5+jqt5ecE9+nBQZY9MsTVysivIoJkwHwRhv3O+I7I7dQnSKq69MR3RVaifgG4BvKEuCmbkGvX7++2rZte85rbdq0UWpq6gWPDw4OVmho6DlfAICyS0hOvWB8S5LVbigh+cI/lwHgr86O786RxDcAnM3Um7Bddtll2rp16zmvbdu2TY0bNzZpIgDwT6npuZd8P62Y9wFAkjbsy9DASYnKzLOqS2R1TR/eXSHENwCcYeoK+GOPPabExES9/vrr2rFjh2bPnq0JEyZozJgxZo4FAH4nMrzyJd+PKOZ9AFi/7+SZ+O7auAbxDQAXYGqAx8TE6KuvvtKcOXPUvn17vfLKK3rvvfc0cOBAM8cCAL8THxOpwADLBd8LDLAoPiaynCcC4E3WpZ3UwElJysyzqhvxDQAXZepN2MqqLBe/AwDONTdln56at17Wv9wF/Y0+0erDXdABXMSa1BMaPHmlsvKtimlSQ1OHdVe1YFOvcgQAtypLh/LTEQAgSerbtZFim4YrITlVaem5igivrPiYSO5+DuCiVqee0JCi+O7eJFxTh8WoKvENABfFT0gAwBkR4VU09sYos8cA4AVS9p7QkCkrlZ1vVWzTcE0ZSnwDQHH4KQkAAACnpOxN15ApycrOtyqumSO+qwTx10oAKA4/KQEAAFBiq/aka8iUlTpVYFOPZjU1eWg34hsASoiflgAAACiR5D3pGloU3z2b19TkITGqHFTB7LEAwGsQ4AAAACjWyt3pGjp1pXIKbLq8RS1NHNyN+AYAJxHgAAAAuKTEXcc1fFqycgpsuqKlI74rVSS+AcBZBDgAAAAuasVOR3znFhLfAFBWBDgAAAAuaPnOYxoxbZVyC226slVtTbivK/ENAGVAgAMAAOA8y3cc0/DpycortOuqVrX1CfENAGUWYPYAAAAA8Cy/7zimYdMc8X1Na+IbAFyFFXAAAACc8dv2YxoxPVn5Vruujaqjjwd1UXAg8Q0ArsAKOAAAACRJy7YdPRPf1xHfAOByrIADAABAS7cd1ahPV6nAalevNnX10cDOxDcAuBgBDgAA4OeWbD2i0TNSVGC16/q2dfXRgC4KCmSjJAC4Gj9ZAQAA/NgvW45o9KeO+L6B+AYAt2IFHAAAwE/9vOWw7p+xWgU2u25qV08fDuisihWIbwBwFwIcAADAD/20+bDun5miQpuh3u3r6YP+xDcAuBs/ZQEAAPzM4k1/xvfNHYhvACgv/KQFAADwIz/+cUgPzHLE9y0d6uv9eOIbAMoLW9ABAAD8xMI/DmnMrNWy2g3dGl1f793bSYHENwCUGwIcAADAD/yw8ZAenO2I79s6NtC793QkvgGgnPFTFwAAwMct2HDwTHzf0Yn4BgCz8JMXAADAh32/4aAenLNGVruhOzs10Dv9iG8AMAtb0AEAAHzU/PUH9XDCGtnshu7q3FBv9+uoCgEWs8cCAL/FP38CAAD4oG/XHTgT33d3Ib4BwBOwAg4AAOBj/rfugB5NWCO7IfXp0khv9o0mvgHAA7ACDgAA4EO+Wbv/THz360p8A4AnYQUcAADAR3y9Zr8e/3yt7IZ0T7dGGn93tAKIbwDwGKyAAwAA+ICv1uw7E9/xMRHENwB4IFbAAQAAvNy8lH16Yu46GYbUv3uEXruzA/ENAB6IFXAAAAAvNves+B4QG0l8A4AHI8ABAAC81Oer0jS2KL4HxUXq1TvaE98A4MHYgg4AAOCFPk9O05NfrpdhSPfFNdbLd7STxUJ8A4AnI8ABAAC8TMLKVD315QZJ0pAejfXi7cQ3AHgDAhwAAMCLzFmZqqeL4ntozyZ64ba2xDcAeAkCHAAAwEvMTkrVM1854nvYZU30/K3ENwB4EwIcAADAC8xM3Ktnv94oSRp+WVM9d2sb4hsAvAwBDgAA4OFmJO7Vc0XxPfLypvrXLcQ3AHgjAhwAAMCDfbpij57/5g9J0qgrmuqZm4lvAPBWBDgAAICHmr58j174nyO+/3ZlMz3VO4r4BgAvRoADAAB4oKm/79ZL326SJN1/VXM9eVNr4hsAvBwBDgAA4GGm/LZbL3/niO+/X91cY28kvgHAFxDgAAAAHmTSr7v06vzNkqQx1zTXEzcQ3wDgKwhwAAAAD3F2fD90bQs9fn0r4hsAfAgBDgAA4AEmLNup17/fIkl6+NoWeoz4BgCfQ4ADAACY7L9Ld2r8Akd8P3JdSz12fSuTJwIAuAMBDgAAYKKPl+zUGz844vvRXi31aC/iGwB8FQEOAABgko9+2aG3Fm6VJD3Wq5Ue6dXS5IkAAO5EgAMAAJjgPz9v19s/bpMk/eP6VnroOuIbAHwdAQ4AAFDOPvxpu95Z5IjvJ25opQevJb4BwB8Q4AAAAOXo/cXb9e5iR3yPvbG1xlzTwuSJAADlhQAHAAAoJ+8t3qb3Fm+XJD15U5QeuLq5yRMBAMoTAQ4AAOBmhmHo3cXb9cFPjvh+qneU7r+K+AYAf0OAAwAAuJFhGHp30TZ98PMOSdIzN0dp9JXENwD4IwIcAADATQzD0L8XbdOHRfH9r5vbaNSVzUyeCgBgFgIcAADADQzD0Ns/btVHv+yUJD17SxuNvIL4BgB/RoADAAC4mGEYenPhVn28xBHfz93aViMub2ryVAAAsxHgAAAALmQYht74Yav+u9QR3y/c1lbDLiO+AQAEOAAAgMsYhqHxC7bok2W7JEkv3tZWQ4lvAEARAhwAAMAFDMPQ699v1sRfd0uSXr6jnQb3aGLuUAAAj0KAAwAAlJFhGHpt/mZN+s0R36/c0U73Ed8AgL8gwAEAAMrAMAy98t1mTfm9KL7vbK/74hqbPBUAwBMR4AAAAKVkGIZe/m6Tpv6+R5L02l3tNTCW+AYAXBgBDgAAUAqGYeilbzdp2vI9kqRxd3dQ/+6R5g4FAPBoBDgAAICTDMPQC//7Q5+u2CtJGn93B8UT3wCAYhDgAAAATjAMQ89/84dmJO6VxSK9cXe07omJMHssAIAXIMABAABKyG439Pz/NmpmYqojvvtE655uxDcAoGQIcAAAgBKw2w09+81GzU5yxPdbfTuqb9dGZo8FAPAiBDgAAEAx7HZD//p6o+asdMT32307qg/xDQBwEgEOAABwCXa7oWe+2qCE5DQFWKR37umouzoT3wAA5xHgAAAAF2G3G3r6yw36bJUjvv99Tyfd2bmh2WMBALwUAQ4AAHABdruhJ+et1xcp+xRgkd69t5Pu6ER8AwBKjwAHAAD4C1tRfM8tiu/34jvr9o4NzB4LAODlCHAAAICz2OyGxs5dpy9X71eFAIveu7eTbiO+AQAuQIADAAAUsdkNjf1inb5c44jvD+I765bo+maPBQDwEQQ4AACAHPH9j8/X6uu1B1QhwKIP+3fWzR2IbwCA6xDgAADA71ltdv3ji3X6Zu0BBRbFd2/iGwDgYgQ4AADwa1abXY9/vk7/W+eI7/8M6KKb2tczeywAgA8iwAEAgN+y2ux67PN1+rYovj8a2EU3tiO+AQDuQYADAAC/ZLXZ9chnazV//UFVrGDRRwO66AbiGwDgRgQ4AADwO4U2ux5NWKv5Gxzx/X8Du+r6tnXNHgsA4OOcDvD8/HwlJSVp7969ysnJUe3atdW5c2c1bdrUHfMBAAC4VKHNrofnrNGCjYcUVCFAHw/qouvaEN8AAPcrcYD//vvvev/99/Xtt9+qsLBQYWFhqly5stLT05Wfn69mzZpp9OjRuv/++xUSEuLOmQEAAEql0GbXQ7PX6Ic/HPH93/u66Noo4hsAUD4CSnLQ7bffrnvvvVdNmjTRjz/+qKysLB0/flz79u1TTk6Otm/frmeffVY//fSTWrVqpUWLFrl7bgAAAKcUWO16cPZqR3wHBuiTwV2JbwBAuSrRCvgtt9yiefPmqWLFihd8v1mzZmrWrJmGDBmiTZs26eDBgy4dEgAAoCwKrHaNmb1aizYdVlBggCbc11VXt65j9lgAAD9jMQzDMHuI0srMzFRYWJgyMjIUGhpq9jgAAMADFVjt+vus1Vq82RHfEwd301Wtaps9FgDAS5WlQ0t9F/RVq1Zp8+bNkqQ2bdqoW7dupf0oAAAAt8i32jRm1mot3nxEwUXxfSXxDQAwidMBvm/fPvXv31+///67qlevLkk6efKkevbsqYSEBDVq1MjVMwIAADgt32rTAzNX6+ctjviePCRGl7esZfZYAAA/VqKbsJ1t5MiRKiws1ObNm5Wenq709HRt3rxZdrtdI0eOdMeMAAAATskrtOn+GSn6ecsRVaoYoClDiW8AgPmcvga8cuXKWr58uTp37nzO6ykpKbriiiuUk5Pj0gEvhWvAAQDAX+UV2nT/zBQt2XrUEd9DYtSzBfENAHCNcr0GPCIiQoWFhee9brPZ1KBBA2c/DgAAwGXyCm0aPSNFy7YdPbPy3bM58Q0A8AxOb0F/66239NBDD2nVqlVnXlu1apUeeeQRvf3220591osvviiLxXLOV1RUlLMjAQAAKK/QplGfrtKybUdVuWIFTR3anfgGAHgUp7eg16hRQzk5ObJarQoMdCygn/7fVatWPefY9PT0S37Wiy++qLlz52rx4sVnXgsMDFStWiX7w5It6AAAQPozvn/dfkxVgipo6tAYxTarafZYAAAfVK5b0N977z1nT7n0AIGBqlevXomOzc/PV35+/plfZ2ZmunQWAADgfXILHPH92w5HfE8b1l3dm4abPRYAAOdxOsCHDBni0gG2b9+uBg0aqFKlSurRo4fGjRunyMjICx47btw4vfTSSy79/gAAwHvlFtg0Ynqylu88rqpBFTRteHfFNCG+AQCeqURb0E+dOnXe9nJXHL9gwQJlZ2erdevWOnjwoF566SXt379fGzduVEhIyHnHX2gFPCIigi3oAAD4oZwCq0ZMW6UVuxzxPX14d3UjvgEAblaWLegluglbixYtNH78eB08ePCixxiGoUWLFql379764IMPSvTNe/furX79+ik6Olo33nijvv/+e508eVKff/75BY8PDg5WaGjoOV8AAMD/5BRYNXxaslbsOq5qwYH6dATxDQDwfCXagr5kyRI988wzevHFF9WxY0d169btzLbxEydOaNOmTVqxYoUCAwP19NNP629/+1uphqlevbpatWqlHTt2lOp8AADg+07lO+I7aXe6qgUHavrw7urauIbZYwEAUKwSBXjr1q01b948paam6osvvtCvv/6q5cuXKzc3V7Vq1VLnzp01ceJE9e7dWxUqVCj1MNnZ2dq5c6fuu+++Un8GAADwXafyrRo2NVkr96QrJDhQ00d0V5dI4hsA4B2cfgyZKz3xxBO67bbb1LhxYx04cEAvvPCC1q5dq02bNql27drFns9jyAAA8B/Z+VYNm7pSyXtOKKRo23ln4hsAUM7K9TFkrrRv3z71799fx48fV+3atXX55ZcrMTGxRPENAAD8R3a+VUOnrNSqvScUUilQM0fEqmNEdbPHAgDAKU4F+KZNm/Sf//xHK1as0KFDhyRJ9erVU48ePfTggw+qbdu2Tn3zhIQEp44HAAD+JyuvUEOnJitl7wmFVgrUzJGxim5U3eyxAABwWokDfMGCBbrzzjvVpUsX3XHHHapbt64k6fDhw1q0aJG6dOmib775RjfeeKPbhgUAAP4lK69QQ6as1OrUkwqrXFEzR8SqQ6Mws8cCAKBUSnwNeMeOHXXHHXfo5ZdfvuD7L774or788kutX7/epQNeCteAAwDguzKL4ntNUXzPGhmr9g2JbwCAudz+HHBJ2rZtmwYOHHjR9/v376/t27c79c0BAAAuJDOvUIMnO+K7ehXiGwDgG0oc4E2aNNH8+fMv+v78+fPVuHFjlwwFAAD8V0Zuoe6bvFJr04hvAIBvKfE14C+//LIGDBigJUuWqFevXudcA/7TTz/phx9+0OzZs902KAAA8H0ZOYW6b0qS1u/LUI0qFTVrZJzaNuAyMwCAbyhxgPfr108NGzbUBx98oHfeeee8u6AvWbJEPXr0cNugAADAt2XkFGrQ5CRt2J+h8KpBmjUyVm3qE98AAN/h1GPIevbsqZ49e7prFgAA4KdO5hRo0OQkbdyfqfCqQZo9KlZR9YhvAIBvcSrAAQAAXO1kToEGTkrSHwcyVbNqkGaPilPreiFmjwUAgMuV+CZsxdm8ebOaNWvmqo8DAAB+4MSpAg2Y6IjvWtWCNGc08Q0A8F0uWwEvKCjQ3r17XfVxAADAx6Wfcqx8bz5YFN+j4tSyLvENAPBdJQ7wxx9//JLvHz16tMzDAAAA/5B+qkADJiZqy6Es1aoWrDmjYolvAIDPK3GAv//+++rUqZNCQy98Q5Ts7GyXDQUAAHzX8ex8DZyUpC2HslQ7JFhzRsWpRZ1qZo8FAIDblTjAW7Rooccee0yDBg264Ptr165V165dXTYYAADwPcey8zVwYpK2Hia+AQD+p8Q3YevWrZtSUlIu+r7FYpFhGC4ZCgAA+J5j2fkaMDFRWw9nqU5IsBJGE98AAP9S4hXwd955R/n5+Rd9v2PHjrLb7S4ZCgAA+JajWfkaOClR2w5nq26oY+W7WW3iGwDgX0oc4PXq1XPnHAAAwEcdycrTgIlJ2nEkW/VCK2nO6Dg1rVXV7LEAACh3LnsMGQAAwF8dycpT/wmJ2nn0lOqFVlLC6Dg1Ib4BAH7K6QCvUaOGLBbLea9bLBZVqlRJLVq00NChQzVs2DCXDAgAALzTkcw89Z/oiO8GYY6V78Y1iW8AgP9yOsCff/55vfbaa+rdu7e6d+8uSVq5cqV++OEHjRkzRrt379YDDzwgq9WqUaNGuXxgAADg+Q5nOla+dx1zxHfC6B6KrFnF7LEAADCV0wH+22+/6dVXX9X9999/zuuffPKJfvzxR82bN0/R0dH64IMPCHAAAPzQ2fHdsHplzRkVR3wDACAnHkN22sKFC9WrV6/zXr/uuuu0cOFCSdLNN9+sXbt2lX06AADgVQ5l5Cn+rPhOGE18AwBwmtMBHh4erm+//fa817/99luFh4dLkk6dOqWQkJCyTwcAALzGwYxcxU9Yod3HTqlRDUd8R4QT3wAAnOb0FvTnnntODzzwgH755Zcz14AnJyfr+++/13//+19J0qJFi3TVVVe5dlIAAOCxDpzMVf+Jidp7POdMfDeqQXwDAHA2i2EYhrMn/f777/rPf/6jrVu3SpJat26thx56SD179nT5gJeSmZmpsLAwZWRkKDQ0tFy/NwAAcNh/Mlf9JyQqNT1HEeGVlTC6hxpWr2z2WAAAuEVZOrRUAe4pCHAAAMy1/6Rj23laeq4iw6soYXScGhDfAAAfVpYOdXoLuiTZbDZ9/fXX2rx5sySpXbt2uv3221WhQoXSfBwAAPBC+07kqP/ERKWl56pxzSqaM4r4BgDgUpwO8B07dujmm2/W/v371bp1a0nSuHHjFBERofnz56t58+YuHxIAAHiWtHRHfO87kasmNatozug41Q8jvgEAuBSn74L+8MMPq3nz5kpLS9Pq1au1evVqpaamqmnTpnr44YfdMSMAAPAgaek5ip/giO+mtaoqYXQP4hsAgBJwegV86dKlSkxMPPPIMUmqWbOmxo8fr8suu8ylwwEAXCMtPUcJyalKTc9VZHhlxcdE8ngolMrp+N5/MlfNalXVnNFxqhtayeyxAADwCk4HeHBwsLKyss57PTs7W0FBQS4ZCgDgOnNT9unJeetls/95z81Plu7S+D7R6tu1kYmTwdukHs9R/IQVOpCRp2a1q2rOKOIbAABnOL0F/dZbb9Xo0aOVlJQkwzBkGIYSExN1//336/bbb3fHjACAUkpLzzkvviXJajf01Lz1SkvPMWkyeJu9x0/p3qL4bl67qhKIbwAAnOZ0gH/wwQdq3ry5evTooUqVKqlSpUq67LLL1KJFC73//vvumBEAUEoJyannxfdpVruhhOTUcp4I3mjPsVOKn5Cogxl5alGnmuaMjlMd4hsAAKc5vQW9evXq+uabb7R9+3Zt2bJFktSmTRu1aNHC5cMBAMomNT33ku+nFfM+sPvYKfWfkKhDmXlqWaeaZo+KU+2QYLPHAgDAK5XqOeCS1LJlS7Vs2dKVswAAXCwy/NJ3po4o5n34t93HTil+wgodzsxXq7rVNGsk8Q0AQFmUKMAff/zxEn/gv//971IPAwBwrfiYSH2ydJesF9iGHhhgUXxMpAlTwRvsPJqt/hMSdSQrX63rhmjWqFjVqkZ8AwBQFiUK8DVr1pTowywWS5mGAQC4VkR4FY3vE62n5q0/J8IDAyx6o080jyLDBe04kq0BEx3xHVUvRLNGxqom8Q0AQJlZDMO48N15vEBmZqbCwsKUkZGh0NBQs8cBAI91+jngaem5iuA54LiEHUey1X9ioo4S3wAAXFBZOrTU14BL0pw5c3T77beratWqZfkYAICbRYRX0dgbo8weAx5ux5EsxU9I0rHsfLWpH6pZI2MVXjXI7LEAAPAZTj+G7Gx/+9vfdPjwYVfNAgAATLL9cJbiJyTqWHa+2tYP1WziGwAAlyvTCrgX714HAABFth3OUv8JiTp+qkDtGoRq5ohY1SC+AQBwuTIFOAAA8G5bD2VpwMQ/43vWyFhVr0J8AwDgDmUK8AULFqhhw4aumgUAAJSjLYcyNWBiktJPFahDwzDNGNGd+AYAwI3KFOCXX365q+YAAADlaNOBTA2clKgTOYWKbhSmGcNjFValotljAQDg08p0EzYAAOB9zo7vjo3CNGME8Q0AQHngGnAAAPzIHwcyNHBSkk7mFKpjRHV9Ory7wioT3wAAlAcCHAAAP7FxvyO+M3IL1Smiuj4d0V2hlYhvAADKC1vQAQDwA2fHd+dI4hsAADOUaAX8gw8+KPEHPvzww6UeBgAAuN6GfRkaOClRmXlWdYmsrunDuyuE+AYAoNxZDMMwijuoadOm5/z66NGjysnJUfXq1SVJJ0+eVJUqVVSnTh3t2rXLLYNeSGZmpsLCwpSRkaHQ0NBy+74AAHiLdWkndd/kJGXmWdW1cQ1NGxZDfAMAUAZl6dASbUHfvXv3ma/XXntNnTp10ubNm5Wenq709HRt3rxZXbp00SuvvFKq/wAAAOB6a9NOalBRfHdrXIOVbwAATFaiFfCzNW/eXHPnzlXnzp3PeT0lJUV9+/bV7t27XTrgpbACDgDAha1JPaHBk1cqK9+qmCY1NHVYd1UL5t6rAACUldtXwM928OBBWa3W81632Ww6fPiwsx8HAABcbPVZ8d29SbimEd8AAHgEpwP8uuuu09/+9jetXr36zGspKSl64IEH1KtXL5cOBwAAnJOy96z4bhquqcNiVJX4BgDAIzgd4FOmTFG9evXUrVs3BQcHKzg4WN27d1fdunU1adIkd8wIAABKIGVvuoZMWansfKtim4ZrGvENAIBHcfpP5dq1a+v777/Xtm3btGXLFklSVFSUWrVq5fLhAABAyaza44jvUwU29WhWU5OHdlOVIOIbAABPUuo/mZs0aSLDMNS8eXMFBvIHPAAAZkkuiu+cApt6Nq+pyUNiVDmogtljAQCAv3B6C3pOTo5GjBihKlWqqF27dkpNTZUkPfTQQxo/frzLBwQAABe3cvef8X1ZC+IbAABP5nSAP/3001q3bp2WLFmiSpUqnXm9V69e+uyzz1w6HAAAuLjEXcc1dKojvi9vUYv4BgDAwzm9d/zrr7/WZ599pri4OFksljOvt2vXTjt37nTpcAAA4MJW7Dyu4dOSlVto0xUta2ni4G6qVJH4BgDAkzkd4EePHlWdOnXOe/3UqVPnBDkAAHCP5TuPacS0VcottOnKVrU14b6uxDcAAF7A6S3o3bp10/z588/8+nR0T5o0ST169HDdZAAA4DzLdxw7s/J9FfENAIBXcXoF/PXXX1fv3r21adMmWa1Wvf/++9q0aZOWL1+upUuXumNGAAAg6fei+M632nVN69r6eBDxDQCAN3F6Bfzyyy/X2rVrZbVa1aFDB/3444+qU6eOVqxYoa5du7pjRgAA/N5v2/+M72uj6ui/rHwDAOB1LIZhGGYPUVqZmZkKCwtTRkaGQkNDzR4HAAC3WLbtqEZ9ukr5Vruui6qj/xvURcGBxDcAAGYoS4c6vQLeq1cvTZs2TZmZmc6eCgAAnLR021GNLIrvXm2IbwAAvJnTAd6uXTs9/fTTqlevnvr166dvvvlGhYWF7pgNAAC/tmTrEY36dJUKrHZd37au/m9gV+IbAAAv5nSAv//++9q/f7++/vprVa1aVYMHD1bdunU1evRobsIGAICL/LL1iEZ/mqICq103tK2rjwZ0UVCg039sAwAAD1Lma8Dz8vL07bff6rXXXtOGDRtks9lcNVuxuAYcAOCLftlyRH+bkaICm103tqur/wzooooViG8AADxBWTrU6ceQne3QoUNKSEjQzJkztX79enXv3r0sHwcAgN/7afNhPTBztQpsdvVuX08f9O9MfAMA4COc/hM9MzNTU6dO1fXXX6+IiAh9/PHHuv3227V9+3YlJia6Y0YAAPzC4k2Hdf9Mx8r3zR2IbwAAfI3TK+B169ZVjRo1dO+992rcuHHq1q2bO+YCAMCvLNp0WH+flaJCm6FbouvrvXs7Ed8AAPgYpwLcMAx98MEHGjhwoKpUqeKumQAA8Cs//nFIY2avVqHN0K1F8R1IfAMA4HOc+tPdMAyNGTNG+/fvd9c8AAD4lR82HtLfZzni+7aODYhvAAB8mFN/wgcEBKhly5Y6fvy4u+YBAMBv/LDxoB6cvVpWu6E7OjXQu/d0JL4BAPBhTv8pP378eI0dO1YbN250xzwAAPiF7zcc1JjZa2S1G7qzUwP9+x5WvgEA8HVOPwe8Ro0aysnJkdVqVVBQkCpXrnzO++np6S4d8FJ4DjgAwBvNX39QDyeskc1u6O7ODfVWv46qEGAxeywAAFAC5foc8Pfee8/ZUwAAQJFv1x3Qo5+tdcR3l4Z6qy/xDQCAv3A6wIcMGeKOOQAA8Hn/W3dAjxXFd58ujfRm32jiGwAAP1Kqi8127typZ599Vv3799eRI0ckSQsWLNAff/zh0uEAAPAV36zdr0eLtp3360p8AwDgj5wO8KVLl6pDhw5KSkrSl19+qezsbEnSunXr9MILL7h8QAAAvN3Xa/brsc/Wym5I93RrpDf6EN8AAPgjpwP8qaee0quvvqpFixYpKCjozOvXXnutEhMTXTocAADe7qs1+/T45474jo+J0Pi7oxVAfAMA4JecDvANGzborrvuOu/1OnXq6NixYy4ZCgAAXzAvZZ8e/3yd7IbUv3uEXr+rA/ENAIAfczrAq1evroMHD573+po1a9SwYUOXDAUAgLebm7JPT8xdJ8OQBsRG6rU7iW8AAPyd0wEeHx+vJ598UocOHZLFYpHdbtfvv/+uJ554QoMHD3bHjAAAeJUvVqVpbFF8D4yN1Kt3tCe+AQCA8wH++uuvKyoqShEREcrOzlbbtm115ZVXqmfPnnr22WfdMSMAAF7j8+Q0/XPeehmGdF9cY716J/ENAAAcLIZhGKU5MS0tTRs2bFB2drY6d+6sli1bunq2YmVmZiosLEwZGRkKDQ0t9+8PAMDZPktO1ZPzNkiSBvdorJdubyeLhfgGAMCXlKVDA0v7TSMiIhQRESGbzaYNGzboxIkTqlGjRmk/DgAArzZnZaqe/tIR30N7NtELt7UlvgEAwDmc3oL+6KOPavLkyZIkm82mq666Sl26dFFERISWLFni6vkAAPB4s5P+jO9hlxHfAADgwpwO8Llz56pjx46SpG+//Va7du3Sli1b9Nhjj+lf//qXywcEAMCTzUzcq2e+csT38Mua6vlbiW8AAHBhTgf4sWPHVK9ePUnS999/r3vuuUetWrXS8OHDtWHDBpcPCACAp5qxYo+e/XqjJGnE5U313K1tiG8AAHBRTgd43bp1tWnTJtlsNv3www+6/vrrJUk5OTmqUKGCywcEAMATzVixR89984ckadQVTfXsLcQ3AAC4NKcDfNiwYbrnnnvUvn17WSwW9erVS5KUlJSkqKioUg8yfvx4WSwWPfroo6X+DAAAysO033efie/RVzbTMzcT3wAAoHhO3wX9xRdfVPv27ZWWlqZ+/fopODhYklShQgU99dRTpRoiOTlZn3zyiaKjo0t1PgAA5WXq77v10rebJEl/u6qZnropivgGAAAlUqrHkPXt2/e814YMGVKqAbKzszVw4EBNnDhRr776aqk+AwCA8jD5t9165TtHfD9wdXP988bWxDcAACgxp7egS9JPP/2kW2+9Vc2bN1fz5s116623avHixaUaYMyYMbrlllvObGW/lPz8fGVmZp7zBQBAeZj0664z8T3mGuIbAAA4z+kA/7//+z/ddNNNCgkJ0SOPPKJHHnlEoaGhuvnmm/XRRx859VkJCQlavXq1xo0bV6Ljx40bp7CwsDNfERERzo4PAIDTJi7bpVfnb5YkPXhNCz1xA/ENAACcZzEMw3DmhEaNGumpp57Sgw8+eM7rH330kV5//XXt37+/RJ+Tlpambt26adGiRWeu/b766qvVqVMnvffeexc8Jz8/X/n5+Wd+nZmZqYiICGVkZCg0NNSZ/wwAAEpkwrKdev37LZKkh69toceub0V8AwDgxzIzMxUWFlaqDnU6wKtVq6a1a9eqRYsW57y+fft2de7cWdnZ2SX6nK+//lp33XXXOY8us9lsslgsCggIUH5+frGPNSvLfzgAAMX579KdGr/AEd+PXNdSj13fyuSJAACA2crSoU5vQb/99tv11Vdfnff6N998o1tvvbXEn3Pddddpw4YNWrt27Zmvbt26aeDAgVq7di3PFAcAmOrjJX/G96O9iG8AAFB2JboL+gcffHDmf7dt21avvfaalixZoh49ekiSEhMT9fvvv+sf//hHib9xSEiI2rdvf85rVatWVc2aNc97HQCA8vTRLzv01sKtkqTHerXSI71amjwRAADwBSXagt60adOSfZjFol27dpV6mOKuAf8rtqADAFztPz9v19s/bpMk/eP6VnroOuIbAAD8qSwdWqIV8N27d5dqMGctWbKkXL4PAAAX8uFP2/XOIkd8P3FDKz14LfENAABcp0QBfiHHjh2TJNWqVctlwwAAYJb3F2/Xu4sd8T32xtYac02LYs4AAABwjlM3YTt58qTGjBmjWrVqqW7duqpbt65q1aqlBx98UCdPnnTTiAAAuNe7i7adie8nb4oivgEAgFuUeAU8PT1dPXr00P79+zVw4EC1adNGkrRp0yZNmzZNP/30k5YvX64aNWq4bVgAAFzJMAy9u3i7PvhpuyTp6d5R+ttVzU2eCgAA+KoSB/jLL7+soKAg7dy5U3Xr1j3vvRtuuEEvv/yy3n33XZcPCQCAqxmGoXcXbdMHP++QJD1zc5RGX0l8AwAA9ynxFvSvv/5ab7/99nnxLUn16tXTm2++ecHngwMA4GkMw9A7P/4Z38/e0ob4BgAAblfiFfCDBw+qXbt2F32/ffv2OnTokEuGAgDAXQzD0Ns/btVHv+yU5IjvkVc0M3kqAADgD0q8Al6rVi3t2bPnou/v3r1b4eHhrpgJAAC3MAxDby78M76fv7Ut8Q0AAMpNiQP8xhtv1L/+9S8VFBSc915+fr6ee+453XTTTS4dDgAAVzEMQ+N/2KKPlzji+8Xb2mr45U1NngoAAPgTi2EYRkkO3Ldvn7p166bg4GCNGTNGUVFRMgxDmzdv1v/93/8pPz9fq1atUkREhLtnPiMzM1NhYWHKyMhQaGhouX1fAIB3MQxD4xds0SfLdkmSXrq9nYb0bGLuUAAAwCuVpUNLfA14o0aNtGLFCv3973/X008/rdPdbrFYdP311+s///lPucY3AAAlYRiGXv9+syb+uluS9PId7TS4RxNzhwIAAH6pxAEuSU2bNtWCBQt04sQJbd/ueGZqixYtuPYbAOCRDMPQa/M3a9Jvjvh+5c72ui+usclTAQAAf+VUgJ9Wo0YNde/e3dWzAADgMoZh6JXvNmvK7474fu2u9hoYS3wDAADzlCrAAQDwZIZh6OXvNmnq73skSa/f1UEDYiPNHQoAAPg9AhwA4FMMw9BL327StOV7JEnj7+6g+O7ENwAAMB8BDgDwGYZh6IX//aFPV+yVxeKI73tjiG8AAOAZCHAAgE8wDEPPf/OHZiQ64vuNu6N1TwxP5wAAAJ6DAAcAeD273dDz/9uomYmpslikN/tEq1834hsAAHgWAhwA4NXsdkPPfrNRs5Mc8f1W347q27WR2WMBAACchwAHAHgtu93Qv77eoDkr02SxSO/066i7uxDfAADAMxHgAACvZLcbeuarDUpITlOARXrnno66qzPxDQAAPBcBDgDwOna7oae+XK/PV+1TgEX69z2ddGfnhmaPBQAAcEkEOADAq9jthp6ct15fpDji+917O+mOTsQ3AADwfAQ4AMBr2Irie25RfL8X31m3d2xg9lgAAAAlQoADALyCzW5o7Nx1+nL1flUIsOi9ezvpNuIbAAB4EQIcAODxbHZDY79Ypy/XOOL7g/jOuiW6vtljAQAAOIUABwB4NJvd0BNfrNNXRfH9Yf/OurkD8Q0AALwPAQ4A8FhWm13/+GKdvll7QIFF8d2b+AYAAF6KAAcAeCSrza7HP1+n/61zxPd/BnTRTe3rmT0WAABAqRHgAACPY7XZ9ehna/Xd+oMKDLDoo4FddGM74hsAAHg3AhwA4FGsNrse+Wyt5q8/qIoVLPpoQBfdQHwDAAAfQIADADxGoc2uRxPWav4GR3x/PLCrerWta/ZYAAAALkGAAwA8QqHNrofnrNGCjYcUVCFAHw/qouvaEN8AAMB3EOAAANMV2ux6aPYa/fCHI77/e18XXRtFfAMAAN9CgAMATFVgteuhOau18I/DCqoQoE/u66prouqYPRYAAIDLEeAAANMUWO0aM3u1Fm06rKDAAE24r6uubk18AwAA30SAAwBMUWC16++zVmvxZkd8TxzcTVe1qm32WAAAAG5DgAMAyl2+1aYxs1Zr8eYjCi6K7yuJbwAA4OMIcABAucq32vTAzNX6eYsjvicN6aYrWhLfAADA9xHgAIByk1do0wMzU/TL1qOqVDFAk4fE6LIWtcweCwAAoFwQ4ACAcpFXaNP9M1O0pCi+pwyJUU/iGwAA+BECHADgdnmFNv1tRoqWbiuK76Ex6tmc+AYAAP6FAAcAuFVeoU2jPl2lX7cfU+WKFTRlaIx6NK9p9lgAAADljgAHALjNX+N76rAYxTUjvgEAgH8iwAEAbpFb4Ijv33YcU5WgCpo6NEaxxDcAAPBjBDgAwOVyC2wa+Wmyft9xXFWDKmja8O6KaRJu9lgAAACmIsABAC6VU2DViGmrtGKXI76nD++ubsQ3AAAAAQ4AcJ2cAquGT0tW4q50VQsO1PThMeramPgGAACQCHAAgIvkFFg1bGqyknafju/u6tq4htljAQAAeAwCHABQZqfyrRo2LVkrd6crJDhQ00d0V5dI4hsAAOBsBDgAoExO5TtWvlfuccT3pyO6qzPxDQAAcB4CHABQatn5Vg2bulLJe04opFKgZoyIVaeI6maPBQAA4JEIcABAqWTlFWro1GSl7HXE98wRsepIfAMAAFwUAQ4AcFpWXqGGTFmp1aknFVopUDNHxiq6UXWzxwIAAPBoBDgAwCmZRfG9JvWkwipX1KyRsWrfMMzssQAAADweAQ4AKLHMvEINnrxSa9OIbwAAAGcR4ACAEsnILdTgKSu1Lu2kqlepqJkjiG8AAABnEOAAgGJl5BZq8OQkrduXoRpVKmrWyDi1bRBq9lgAAABehQAHAFxSRk6h7puSpPX7MhReNUizRsaqTX3iGwAAwFkEOAD4gLT0HCUkpyo1PVeR4ZUVHxOpiPAqZf7ckzkFGjQ5SRv3Zyq8apBmj4pVVD3iGwAAoDQIcADwcnNT9unJeetlsxtnXvtk6S6N7xOtvl0blfpzT+YUaOCkJP1xIFM1qwZp9qg4ta4X4oqRAQAA/FKA2QMAAEovLT3nvPiWJKvd0FPz1istPadUn3viVIEGTHTEd61qQZozmvgGAAAoKwIcALxYQnLqefF9mtVuKCE51enPTD9VoAGTkrTpYFF8j4pTq7rENwAAQFmxBR0AvFhqeu4l308r5v2/Sj9VoAETE7XlUJZqVQvWnFGxakl8AwAAuAQBDgBeLDK88iXfjyjm/bMdz87XwElJ2nIoS7VDgjVnVJxa1KlW1hEBAABQhC3oAODF4mMiFRhgueB7gQEWxcdEluhzjmXna8BE4hsAAMCdCHAA8GIR4VU0vk/0eREeGGDRG32iS/QoMkd8J2rr4SzVCQlWwmjiGwAAwB3Ygg4AXq5v10aKbRquhORUpaXnKsKJ54AfzXLE9/Yj2aob6lj5blab+AYAAHAHAhwAfEBEeBWNvTHKqXOOZOVpwMQk7TiSrXqhlTRndJya1qrqpgkBAABAgAOAl0hLz1FCcqpS03MV6cQq94UcycxT/4mJ2nn0lOqHVdKcUXFqQnwDAAC4FQEOAF5gbso+PTlv/TnP/P5k6S6N7xOtvl0bOfVZRzLzFD8xUbuOnlKDMMfKd+OaxDcAAIC7cRM2APBwaek558W3JFnthp6at15p6Tkl/qzDmXmKn/BnfCeM7kF8AwAAlBMCHAA8XEJy6nnxfZrVbighObVEn3Mooyi+j51Sw+qVlTC6hyJrlm4LOwAAAJzHFnQA8HCp6bmXfD+tmPcl6WBGrvpPSNSe4zlF8R1X6uvHAQAAUDoEOAB4uMjwypd8P6KY9w9m5Cp+QqL2Hs9RoxqVNWcU8Q0AAGAGAhwAPMxf73Z+Zcva+mTpLlkvsA09MMCi+JjIi37WgZO56j/xz/hOGB2nRjWIbwAAADMQ4ADgQS52t/M+XRtpXsq+cyI8MMCiN/pEX3Q1e/9Jx7bz1PQcRYQ7rvluWP3Sq+UAAABwHwIcADzEpe52Pi9ln2aNjNWy7UeVlp6riGKeA77vRI76T0xUWnquIsOrKGF0nBoQ3wAAAKYiwAHAQxR3t/Nl249q7I1RxX5OWrojvvedyFXjmlU0ZxTxDQAA4AkIcADwEK6423laeo7iJyRq/8lcNalZRXNGx6l+GPENAADgCQhwADDR2TdcO3Ay55LHFne387Pju2mtqpozKk71wiq5clwAAACUAQEOACa50A3XLqa4u52nHndsO99/MlfNalXVbOIbAADA4xDgAGCCi91w7UKKu9v53uOn1H9Cog5k5KlZraqaMzpOdUOJbwAAAE9DgAOACS51wzVJ6tq4uhpWr1Ls3c73HDul/hMTdTAjT81qV1XCqDjVIb4BAAA8EgEOACYo7oZrDatX0Qf9O1/ymD3HTil+QqIOZeapeW3HynedEOIbAADAUxHgAFBOXHnDtd3HTil+wgodzsxXizrVNGdUnGqHBLtyXAAAALgYAQ4A5cCVN1zbdTRb8RMSdSQrXy3rVNNs4hsAAMArEOAA4GauvOHazqPZ6l8U363qOuK7VjXiGwAAwBsQ4ADgZq664dqOI9nqPzFRR7Py1bpuiGaPilVN4hsAAMBrEOAA4Canr/n+as2BSx5Xkhuu7TiSpf4Tk3Q0K19R9UI0ayTxDQAA4G0IcABwA2eu+S7uhmvbDzvi+1i2I75nj4pTeNUgV40KAACAckKAA4CLOXvN96VuuLbtcJYGTEzUsewCta0fqlkjY1WD+AYAAPBKBDgAuFhx13yfVtwN17YecsT38VMFatcgVDNHEN8AAADeLMDMb/7xxx8rOjpaoaGhCg0NVY8ePbRgwQIzRwKAMktNz73k+w2rV9aYa5rrlyeuVp+ujS54zF/jm5VvAAAA72fqCnijRo00fvx4tWzZUoZhaPr06brjjju0Zs0atWvXzszRAKDUIou5pvvOzg009saoi76/5VCmBkxMUvqpAnVoGKYZI7qrehXiGwAAwNuZugJ+22236eabb1bLli3VqlUrvfbaa6pWrZoSExPNHAsAyiQ+JlKBAZYLvlfcNd+bDmSq/4REpZ8qUHSjMM0cEUt8AwAA+AhTA/xsNptNCQkJOnXqlHr06HHBY/Lz85WZmXnOFwB4mojwKhrfJ/q8CC/umu9NBzI1cFKiTuQUqmOjMM0YEauwKhXLY2QAAACUA9NvwrZhwwb16NFDeXl5qlatmr766iu1bdv2gseOGzdOL730UjlPCADO69u1kWKbhishOVVp6bmKCK+s+JjIi8b3HwcyNHBSkk7mFKpjRHV9Ory7wioT3wAAAL7EYhhG8bfqdaOCggKlpqYqIyNDc+fO1aRJk7R06dILRnh+fr7y8/PP/DozM1MRERHKyMhQaGhoeY4NAC6zcb8jvjNyC9Uporo+HdFdoZWIbwAAAE+UmZmpsLCwUnWo6QH+V7169VLz5s31ySefFHtsWf7DAcATnB3fnSOra/pw4hsAAMCTlaVDTd+C/ld2u/2cVW4A8FUb9mVo4KREZeZZ1aUovkOIbwAAAJ9laoA//fTT6t27tyIjI5WVlaXZs2dryZIlWrhwoZljAYDbrd93UoMmJSkzz6pujWto2vDuqhbscf8mCgAAABcy9W97R44c0eDBg3Xw4EGFhYUpOjpaCxcu1PXXX2/mWABQKmnpOUpITlVqeq4iL3HTtXVpJzVocpKy8qyKaVJDU4cR3wAAAP7A464BdwbXgAMob3+N7Ctb1tay7Uf1+47jWpd2Umf/QA0MsGh8n2j17drozGtr007qvklJysq3qnuTcE0ZFkN8AwAAeBGfugYcADzV3JR9enLeetnsf2b2R7/svOjxVruhp+atV2zTcEWEV9Ga1BMaPHmlI76bhmvq0BhVJb4BAAD8Bn/zA4BipKXnaMKyXZqZuFfObhmy2g0lJKfq2qi6GjJlpbLzrYptGq4pxDcAAIDf4W9/AHAJF1r1dta6tAxNX75X2flWxTVzxHeVIH78AgAA+Bv+BggAF1CWVe+/Stp9XIU2Qz2a1dSUoTGqHFTBJTMCAADAuxDgAPAXrlj1PluhzVDP5jU1eQjxDQAA4M8IcAAo4spV77Nd3qKWJg7uRnwDAAD4OQIcAOS6Ve8Ai9S8VjXtTj8lq83QFS0d8V2pIvENAADg7whwAH4vLT3H6fi2SBoUF6lboxto2fajSkvPVUR4ZbWuG6In520gvgEAAHAeAhyA30tITnUqvgMDLHqjT7T6dG0kSYptVlOStHznMY2Ytkq5hTZd2aq2JtzXlfgGAADAGQQ4AL+35WBWiY47veo9+srmigivcs57y3cc0/DpycortOuqVrX1CfENAACAvyDAAfi1uSn79PPWI8Ue99dV77P9vuOYRhTF9zWta+vjQcQ3AAAAzkeAA/Bbp6/9Ni6x+/xSq96S9Nt2R3znW+26NqqOPh7URcGBxDcAAADOR4AD8FvFXfttkfR2v44XXPWWpGXbjmrUp6uUb7Xruqg6+j/iGwAAAJcQYPYAAGCGtPQcLfzj8CWPua5NnYvG99JtRzWyKL57tSG+AQAAUDxWwAH4nZI+87t1vZALvr5k6xGNnpGiAqtd17etq48GdFFQIP+eCQAAgEvjb4wA/EpJn/kdGGBRfEzkea//suWIRn/qiO8biG8AAAA4gb81AvArJXnm9+k7nv/1pms/bzmsv81IUYHNrhvb1dV/iG8AAAA4gS3oAPxKanruJd9vWaeapgyNOS++f9p8WA/MXK0Cm12929fTB/07q2IF4hsAAAAlR4AD8CuR4ZUv+f4N7eqeF9+LNx3WA7NSVGgziG8AAACUGn+DBOBX4mMiFRhgueB7F7ru+8c/Dp2J71s61Ce+AQAAUGr8LRKAX4kIr6LxfaLPi/ALXfe98I9DGjN7tQpthm6Nrq/34zsR3wAAACg1tqAD8Ctp6TnafSxbV7WqrZO5BapeOUhR9UMUHxN5Tnz/sPGQHpy9Wla7ods6NtC793RUIPENAACAMiDAAfiNCz3/OzDAot4d6p8T3ws2HNRDc9bIajd0e8cG+jfxDQAAABfgb5QA/ELSruMa+8W68x5BZrUbemreeqWl50iSvt9wUA8WxfednYhvAAAAuA4r4AB83tyUfRo7d50u9vRvq91QQnKq2tYP08MJa2SzG7qrc0O93a+jKlzkhm0AAACAswhwAD4tLT1HT85bL+Ni9V0kcVe6/rt0l2x2Q3d3bqi3iG8AAAC4GAEOwKclJKeet+38QlannpBhSH26NNKbfaOJbwAAALgcFzYC8Gmp6bklOs4wpH5diW8AAAC4DyvgAHxaZHjlEh13T7dGGn93tAKIbwAAALgJK+AAfFp8TKQCi4nq+JgI4hsAAABuR4AD8HlXtaqti6V1/+4Rev2uDsQ3AAAA3I4t6AB8Ulp6jl783x/6ecuRiz5+bEBspF69oz3xDQAAgHJBgAPwOXNT9unJuetlu8Szx+7o1ID4BgAAQLliCzoAn3L6ud+Xim9Jali9EvENAACAckWAA/ApJX3u974TeeUwDQAAAPAnAhyATynpc78jSvh4MgAAAMBVCHAAPqUkz/0ODLAoPiayHKYBAAAA/kSAA/ApxT33u4JFeqNPtCLCq5TjVAAAAAB3QQfgI9LSc5SQnKotB7MUWjlQ6acKz3nfIunaNnX04m3tiG8AAACYggAH4NVK8rzv66Jq68Xb2xPeAAAAMBUBDsBrfbJsp8Z/v+Wi4X3a0m3HymUeAAAA4FIIcABeJS09RxOW7dKCjQd1LLugROdY7YYSklM19sYoN08HAAAAXBwBDsBrzE3Zp3/OXacSPOb7PGklfDwZAAAA4C4EOACvkLTruMZ+sa7Y7eYXw3O/AQAAYDYCHIDHOn1n85+3HNHmg1ml/hye+w0AAABPQIAD8Ciuiu7TAgMsPPcbAAAAHoEAB+Ax5qbs05Pz1stWmou8z9KweiVF1QtVVP0QxcdEEt8AAADwCAQ4AI+Qlp5T5vi2SHrm5jYadWUz1w0GAAAAuAgBDsAjJCSnlim+r2tTRy/e1o7VbgAAAHgsAhyAR0gt5WPC7uzUQP+4oTXhDQAAAI9HgAPwCJGleEzYMzdHafSVzd0wDQAAAOB6AWYPAACSFB8TqQqWkh0bYJH+dXMb4hsAAABehRVwAKY7cwO2Yi4Bb1s/RNdE1eHO5gAAAPBKBDgAU81N2aexc9fJuER8d4msrvfjOxPdAAAA8GoEOADTJO06rrFfrFNx9z7v0bwm8Q0AAACvxzXgAEwxN2Wf4icmFhvfkpRWyjukAwAAAJ6EAAdQ7k5f832pbedniyjFHdIBAAAAT0OAAyh3CcmpstlLVt8WOe6QDgAAAHg7AhxAuUt1Ykv50zdHcf03AAAAfAI3YQNQrtLSc7R6b3qxx1kkPXNzG426spn7hwIAAADKAQEOoFycvu57+c7jxR57Z6cG+scNrVn5BgAAgE8hwAG43SfLdmrc91tKdOx1UXX0XnxnN08EAAAAlD8CHIBbvfnDZv3fkl0lPr5qMD+WAAAA4Ju4CRsAt3E2viUeOQYAAADfRYADcIvSxDePHAMAAIAvI8ABuNyEZTudjm+JR44BAADAt3GxJQCXStp1XK+X8IZrZ/sXjxwDAACAjyPAAbiEM48ZO1vP5uF6o09HVr4BAADg8whwAGXmzGPGTusSGab347sQ3gAAAPAbBDiAUktLz9EDM1dp44Esp84bc3Vzjb0pyk1TAQAAAJ6JAAdQKnNT9mnsF+tkOHke8Q0AAAB/RYADcErSruN67puN2nY42+lzezavSXwDAADAbxHgAErsyXnr9VlyWqnODbBIb/SJdvFEAAAAgPfgOeAASiRp1/FSx7dF0lt9udM5AAAA/Bsr4ABK5M2Fzj/bW+IxYwAAAMBpBDiAYqWl52jzQefudN6uQYj+O6gb4Q0AAAAUIcABXNLclH3659x1sjtxu/PbouvrwwFd3DcUAAAA4IUIcAAXlZaeo39+sU52J875ML6TbuvU0G0zAQAAAN6Km7ABuKgn55Y8vgMs0jv9OhLfAAAAwEWwAg7gPEm7juvhhDU6nJlfouOb1KyiGSNiud4bAAAAuAQCHMA5xsxerfnrDzp1zi3R9YlvAAAAoBhsQQdwxqNz1jgd3xUsUnxMpJsmAgAAAHwHK+AAJEnDp63Uz1uOOn3em315xjcAAABQEgQ4AD371YZSxfdno+MU26ymGyYCAAAAfA9b0AE/N2HZTs1MSnX6vHf6dSS+AQAAACewAg74sbT0HI1bsMXp81j5BgAAAJzHCjjgxxKSU2UYJT/eYmHlGwAAACgtVsABP/bLliMlOq5aUAXd1aWhRl/ZnBuuAQAAAKVEgAN+Ki09R5sOZhV73HVRtTV5aPdymAgAAADwbWxBB/xUQnLxN15rUrMK8Q0AAAC4CAEO+KmvVu8v9phbouuXwyQAAACAfyDAAT+UtOu4DmTkFXtcfExkOUwDAAAA+AdTA3zcuHGKiYlRSEiI6tSpozvvvFNbt241cyTALzw5b32xx7StH8oN1wAAAAAXMjXAly5dqjFjxigxMVGLFi1SYWGhbrjhBp06dcrMsQCflpaeoz3Hc4o97pqo2uUwDQAAAOA/TL0L+g8//HDOr6dNm6Y6deooJSVFV1555XnH5+fnKz8//8yvMzMz3T4j4GsGTFxRouPYfg4AAAC4lkddA56RkSFJCg8Pv+D748aNU1hY2JmviIiI8hwP8Gpp6Tnq/uoipZ0o/trvv1/djO3nAAAAgItZDMMwzB5Ckux2u26//XadPHlSv/322wWPudAKeEREhDIyMhQaGlpeowJeZ27KPj3xxboSHduiTlUtfvxq9w4EAAAAeKnMzEyFhYWVqkNN3YJ+tjFjxmjjxo0XjW9JCg4OVnBwcDlOBXi/b9fuL3F8S9JUnvsNAAAAuIVHBPiDDz6o7777TsuWLVOjRo3MHgfwCWnpORo0OUl7S3DDtdOa1KzC1nMAAADATUwNcMMw9NBDD+mrr77SkiVL1LRpUzPHAXzGJ8t2atz3W5w+740+0W6YBgAAAIBkcoCPGTNGs2fP1jfffKOQkBAdOnRIkhQWFqbKlSubORrgtZ79ar1mJqU5fd51UbUV26ymGyYCAAAAIJl8EzaLxXLB16dOnaqhQ4cWe35ZLn4HfNHwaSv185ajTp/XOaK6vhpzmRsmAgAAAHyL196EzUNuwA74hEfnrClVfPdoVlNzRse5YSIAAAAAZ/OIm7ABKJu7PvpNa9IynD7vzo4N9F7/zm6YCAAAAMBfEeCAF0tLz9EtH/yqzDyr0+feFl2f+AYAAADKEQEOeKnS3uk8JChAk4Z254ZrAAAAQDkjwAEv9OYPm/V/S3Y5fd6g2Ei9elcHN0wEAAAAoDgEOOBlJizbWar4HnN1c429KcoNEwEAAAAoiQCzBwBQcmnpOXq9FNvOiW8AAADAfKyAA17kyXnrnD7nXze30agrm7lhGgAAAADOIMABL5GWnqPlO9NLfHxIcKC+f+QKRYRXceNUAAAAAEqKAAe8xLCpK0t8bJeIMH055nI3TgMAAADAWVwDDniBpF3HtePoqRIde2fHBsQ3AAAA4IEIcMALDJ+eXKLjmtSsovf6d3bzNAAAAABKgwAHPNyQSUk6lW8r0bEzRsS6eRoAAAAApcU14ICHmvrbLr303eYSH//MzVHccA0AAADwYAQ44IHiXl+sQ5n5JT6+fYNQjb6yuRsnAgAAAFBWbEEHPMxVb/7sVHxL0seDurppGgAAAACuwgo44CGSdh3XoMlJKrQZTp3XJbI6W88BAAAAL0CAAyZL2nVcw6cl61RByW609lfvx3PXcwAAAMAbEOCAiZ6ct16fJaeV+vy/X92M1W8AAADAS3ANOGCSpF3HyxTft0XX1z9vauPCiQAAAAC4EyvggAnS0nPUf2Jiqc6tFlxBk4fEKLZZTRdPBQAAAMCdCHCgnH2ybKfGfb+lVOd+GN9Jt3Vq6OKJAAAAAJQHAhwoJ2npORo8OUm7j+eU6vx3+nUkvgEAAAAvRoAD5aAsq96hwYGa/8gV3GwNAAAA8HIEOOBmE8oQ34NiI/XqXR1cPBEAAAAAMxDggBulpefo9VLEd9WgAP3w6FWsegMAAAA+hMeQAW6Slp6jXv9eUqpziW8AAADA97ACDrjB3JR9euKLdaU6951+HYlvAAAAwAcR4ICLfbt2f6niu0rFAC18jJVvAAAAwFcR4IALjZm9WvPXH3T6vBa1q2rxP652/UAAAAAAPAYBDrjIqGnJWrTliNPnfRjfied7AwAAAH6AAAdc4Io3flbaiVynz/v1n9ew5RwAAADwEwQ4UEadXlqok7lWp8/7+9XNiG8AAADAjxDgQBm0fGa+Cu3On3dbdH3986Y2rh8IAAAAgMciwIFSavH0fFkN58/jmm8AAADAPwWYPQDgjdo8t6BU8f1Ov47ENwAAAOCnWAEHnNRz3GLlOrnvvFKgRYsev5prvgEAAAA/RoADTnj2qw06kJHv1DkNwoK1/OlebpoIAAAAgLdgCzpQQkm7jmtmUqpT57SrH0J8AwAAAJDECjhQIp8s26lx329x6pxro+poytAYN00EAAAAwNsQ4EAxnv1qvWYmpTl1zmej4xTbrKabJgIAAADgjQhw4BKGT1upn7ccdeqcX/95DTdbAwAAAHAergEHLuLZrzY4Hd9/v7oZ8Q0AAADggghw4ALe/GGz0zdcq1U1SP+8qY2bJgIAAADg7Qhw4C8mLNup/1uyy+nzvhpzmRumAQAAAOArCHDgLGnpOXrdybudS9I7/Tqy9RwAAADAJXETNuAsV775i9PncNM1AAAAACVBgANyrHxf9eYvMpw4J9Ai7Rh3i9tmAgAAAOBbCHD4vU+W7dQ4J7edB1WQtr1GfAMAAAAoOa4Bh1+bUIr4DrQQ3wAAAACcR4DDb5XmhmsWse0cAAAAQOkQ4PBbV5TihmvL/nmNGyYBAAAA4A8IcPidpF3H1eSp+U6f9/erm3G3cwAAAAClxk3Y4Ffu/Og3rU3LcPq8LhFh+udNbdwwEQAAAAB/QYDDb3R44Qdl5ducPq9N/RB9OeZyN0wEAAAAwJ8Q4PALrf41XwXOt7c+jO+k2zo1dP1AAAAAAPwOAQ6fV5rrvSXp139ewzXfAAAAAFyGm7DBp5U2vt/p15H4BgAAAOBSrIDDZ5U2vj8bHafYZjVdPA0AAAAAf0eAw+f8+8ct+uDnnaU6951+HYlvAAAAAG5BgMOntH1ugXIK7aU6l2u+AQAAALgTAQ6f0eLp+bIazp9XKdCiLa/e7PqBAAAAAOAsBDh8Qmmv964aFKA/Xu7t4mkAAAAA4HzcBR1er0Up47tBWDDxDQAAAKDcEODwau2fXyBrKc67s2MDLX+6l8vnAQAAAICLYQs6vFab575XbqHzF32Pubq5xt4U5YaJAAAAAODiCHB4pavf/LlU8f2vm9to1JXN3DARAAAAAFwaAQ6vM3zaSu1Jz3X6PB4zBgAAAMBMBDi8StKu4/p5y1GnzqlokbaPu8VNEwEAAABAyXATNniVeyckOnV8jcqBxDcAAAAAj0CAw2s4+6zvQIu05oUb3TQNAAAAADiHAIdXcDa+JWkHK98AAAAAPAgBDo9XmvjeM574BgAAAOBZCHB4NOIbAAAAgK8gwOGxShPfL9zaxg2TAAAAAEDZEeDwOEm7jpcqvutUC9Kwy5u5YSIAAAAAKDueAw6P8uS89fosOc3p8+qEBGnlv653w0QAAAAA4BqsgMNjJO06Xqr4blc/hPgGAAAA4PEIcHiMeyckOn1O4/9v7+6joqzz/4+/RnQAEe8DwbjxpvCmVBRBdHfTdEO+Lqt9N3M7VqBltUFJbLRqp8zNm7Jj6aailWG/bvVsqZWbxlKouRiojUrmbaYeNzEDIdHQhuv7Rzo/R7BgBK4Z5vk4Z85xLucz84YrT+c51zXXtPfX2sm/a4BpAAAAAKB+EeBwC6585tu/RTNteOzmBpgGAAAAAOofAQ7TuRLfwYFWffV0YgNMAwAAAAANg4uwwTTT1+zSa/lH6rxuTN9Qzb8jugEmAgAAAICGQ4DDFN2nrdVPVXVf9/j/9NSk3/FVYwAAAAA8DwGORudqfE//Q0++5xsAAACAx+Iz4GhUvZ74l0vx3a1jAPENAAAAwKNxBByNptvUtbIbdV8XFGhV7qND630eAAAAAGhMHAFHo7hummvx7d/CooLHf1//AwEAAABAI+MIOBqcK18zJkkWSV89/T/1OwwAAAAAmIQj4GhQrsZ357Z+OvTMqHqeBgAAAADMwxFwNBhX43vFfYMU17VDPU8DAAAAAOYiwNEguroY399w1BsAAABAE8Up6Kh30TPWy4VvGiO+AQAAADRppgb4xo0blZSUpNDQUFksFq1evdrMcVAPIqesVenZn+q8jvgGAAAA0NSZGuAVFRXq27evFi1aZOYYqCeufuab+AYAAADgDUz9DHhiYqISExPNHAH1hPgGAAAAgF/mURdhq6ysVGVlpeN+eXm5idPgIuIbAAAAAH6dR12Ebc6cOWrTpo3jFhYWZvZIXi/m6RyX1hHfAAAAALyNRwX41KlTVVZW5rgdPXrU7JG82ge2YzpZca7O64hvAAAAAN7Io05B9/X1la+vr9lj4IKH3rHVeQ3xDQAAAMBbedQRcLgPVz73TXwDAAAA8GamHgE/ffq0Dhw44Lh/6NAh2Ww2tW/fXuHh4SZOhl9CfAMAAABA3Zka4Fu3btWwYcMc9zMyMiRJycnJWr58uUlT4Uo+sB3jtHMAAAAAcJGpAT506FAZhmHmCKil25fmq+BQSZ3XEd8AAAAA8DM+A45f9YHtmEvxvemxYb/+IAAAAADwEgQ4fpUrp53PG9tXYe1b1v8wAAAAAOChPOpryND4XLng2qbHhhHfAAAAAHAZjoDjilyJ74ReQcQ3AAAAANSAAEeNXIlvSVp698B6ngQAAAAAmgYCHNW4Et8+Fq54DgAAAAC/hACHE1fi+3+jQ3VwDvENAAAAAL+Ei7DBwZX4fnBoVz02smcDTAMAAAAATQtHwCHJtfjuH9aG+AYAAACAWiLA4VJ8+zaX3kv9TQNMAwAAAABNEwHu5Vy92vnemXzmGwAAAADqggD3Yq7GN1c7BwAAAIC6I8C9lCvxbRHxDQAAAACuIsC9kKtHvg8R3wAAAADgMgLcy3DaOQAAAACYgwD3IsQ3AAAAAJiHAPcSxDcAAAAAmIsA9wLENwAAAACYjwBv4ohvAAAAAHAPBHgTRnwDAAAAgPsgwJso4hsAAAAA3AsB3gQR3wAAAADgfgjwJob4BgAAAAD3RIA3IcQ3AAAAALgvAryJIL4BAAAAwL0R4E0A8Q0AAAAA7o8A93DENwAAAAB4BgLcgxHfAAAAAOA5CHAPRXwDAAAAgGchwD0Q8Q0AAAAAnocA9zDENwAAAAB4JgLcgxDfAAAAAOC5CHAPQXwDAAAAgGcjwD0A8Q0AAAAAno8Ad3PENwAAAAA0DQS4GyO+AQAAAKDpIMDd0NGSM8Q3AAAAADQxzc0eAM7uXV6gf+/5zqW1xDcAAAAAuC8C3I10n7JWP7m4lvgGAAAAAPdGgLuJLlPWynBhnUXSIeIbAAAAANwenwF3A/f/v0KX4lsivgEAAADAU3AE3GR//MdG7fzvDy6t5bRzAAAAAPAcBLiJXL3SuUR8AwAAAICn4RR0kxDfAAAAAOBdCHATuBrfAyPaEt8AAAAA4KE4Bb2RuRrfhDcAAAAAeDaOgDeim5/7xKV1L/65X/0OAgAAAABodAR4I/nTos/09fdn67yuZ6dAJfXr3AATAQAAAAAaE6egNwJXTzsf3LW93rovvp6nAQAAAACYgQBvYK7G94r7Bimua4d6ngYAAAAAYBYCvAFxwTUAAAAAwEV8BryBEN8AAAAAgEsR4A2A+AYAAAAAXI4Ar2fENwAAAACgJgR4PSK+AQAAAABXQoDXE+IbAAAAAPBLCPB6QHwDAAAAAH4NAX6ViG8AAAAAQG0Q4FeB+AYAAAAA1BYB7iLiGwAAAABQFwS4C4hvAAAAAEBdEeB1RHwDAAAAAFxBgNcB8Q0AAAAAcBUBXkvENwAAAADgahDgtUB8AwAAAACuFgH+K4hvAAAAAEB9IMB/AfENAAAAAKgvBPgVEN8AAAAAgPpEgNeA+AYAAAAA1DcC/DLENwAAAACgIRDglyC+AQAAAAANhQC/gPgGAAAAADQkAlzENwAAAACg4Xl9gBPfAAAAAIDG4NUBTnwDAAAAABqL1wY48Q0AAAAAaExeGeDENwAAAACgsXldgBPfAAAAAAAzeFWAE98AAAAAALN4TYAT3wAAAAAAM3lFgBPfAAAAAACzNfkAJ74BAAAAAO6gSQc48Q0AAAAAcBdNNsCJbwAAAACAO2mSAU58AwAAAADcTZMLcOIbAAAAAOCOmlSAE98AAAAAAHfVZAKc+AYAAAAAuLMmEeA3TF/v0jriGwAAAADQWJpEgLuC+AYAAAAANCavDHDiGwAAAADQ2LwuwIlvAAAAAIAZ3CLAFy1apMjISPn5+SkuLk4FBQUN8jrENwAAAADALKYH+IoVK5SRkaHp06dr+/bt6tu3rxISEnTixIl6fR3iGwAAAABgJtMD/Pnnn9ekSZM0YcIE9erVS0uWLFHLli316quv1ttrEN8AAAAAALM1N/PFz507p23btmnq1KmObc2aNdOIESOUn59f7fGVlZWqrKx03C8rK5MkVVWeueJrFM1IUHl5eT1ODQAAAADwVhf70jCMOq81NcBPnjwpu92u4OBgp+3BwcHas2dPtcfPmTNHM2bMqLb9WFbKFV+jzfyrnRIAAAAAAGfff/+92rRpU6c1pgZ4XU2dOlUZGRmO+6dOnVJERISOHDlS5x8cDau8vFxhYWE6evSoWrdubfY4uAz7x32xb9wX+8a9sX/cF/vGfbFv3Bv7x32VlZUpPDxc7du3r/NaUwO8Y8eO8vHxUXFxsdP24uJiderUqdrjfX195evrW217mzZt+I/STbVu3Zp948bYP+6LfeO+2Dfujf3jvtg37ot9497YP+6rWbO6X1LN1IuwWa1WDRgwQLm5uY5tVVVVys3NVXx8vImTAQAAAABQv0w/BT0jI0PJycmKiYlRbGys5s+fr4qKCk2YMMHs0QAAAAAAqDemB/i4ceP03Xff6cknn9Tx48fVr18/rVu3rtqF2Wri6+ur6dOn13haOszFvnFv7B/3xb5xX+wb98b+cV/sG/fFvnFv7B/3dTX7xmK4cu10AAAAAABQJ6Z+BhwAAAAAAG9BgAMAAAAA0AgIcAAAAAAAGgEBDgAAAABAI/DoAF+0aJEiIyPl5+enuLg4FRQUmD0SJG3cuFFJSUkKDQ2VxWLR6tWrzR4JkubMmaOBAwcqMDBQQUFBGjNmjPbu3Wv2WLggKytLffr0UevWrdW6dWvFx8fro48+Mnss1OCZZ56RxWJRenq62aNA0lNPPSWLxeJ069Gjh9lj4YJjx47pzjvvVIcOHeTv768bb7xRW7duNXssrxcZGVnt343FYlFqaqrZo3k9u92uJ554Ql26dJG/v7+6deump59+Wlw323388MMPSk9PV0REhPz9/TV48GAVFhbWer3HBviKFSuUkZGh6dOna/v27erbt68SEhJ04sQJs0fzehUVFerbt68WLVpk9ii4xIYNG5SamqotW7YoJydH58+f1y233KKKigqzR4Oka6+9Vs8884y2bdumrVu36uabb9bo0aP15Zdfmj0aLlFYWKilS5eqT58+Zo+CS/Tu3Vvffvut4/bZZ5+ZPRIklZaWasiQIWrRooU++ugj7d69W/PmzVO7du3MHs3rFRYWOv2bycnJkSSNHTvW5Mnw7LPPKisrSwsXLtRXX32lZ599VnPnztWLL75o9mi44N5771VOTo5ef/117dq1S7fccotGjBihY8eO1Wq9x34NWVxcnAYOHKiFCxdKkqqqqhQWFqaHHnpIU6ZMMXk6XGSxWLRq1SqNGTPG7FFwme+++05BQUHasGGDfve735k9DmrQvn17Pffcc7rnnnvMHgWSTp8+rf79+2vx4sWaOXOm+vXrp/nz55s9ltd76qmntHr1atlsNrNHwWWmTJmizZs3a9OmTWaPgl+Rnp6uDz/8UPv375fFYjF7HK/2hz/8QcHBwVq2bJlj25/+9Cf5+/vrjTfeMHEySNLZs2cVGBioNWvWaNSoUY7tAwYMUGJiombOnPmrz+GRR8DPnTunbdu2acSIEY5tzZo104gRI5Sfn2/iZIDnKCsrk/Rz5MG92O12vfPOO6qoqFB8fLzZ4+CC1NRUjRo1yun/PXAP+/fvV2hoqLp27arx48fryJEjZo8ESe+//75iYmI0duxYBQUFKTo6Wi+//LLZY+Ey586d0xtvvKGJEycS325g8ODBys3N1b59+yRJO3bs0GeffabExESTJ4Mk/fTTT7Lb7fLz83Pa7u/vX+uzr5o3xGAN7eTJk7Lb7QoODnbaHhwcrD179pg0FeA5qqqqlJ6eriFDhuiGG24wexxcsGvXLsXHx+vHH39Uq1attGrVKvXq1cvssSDpnXfe0fbt2+v0GS80jri4OC1fvlxRUVH69ttvNWPGDP32t79VUVGRAgMDzR7Pq3399dfKyspSRkaGpk2bpsLCQj388MOyWq1KTk42ezxcsHr1ap06dUopKSlmjwL9fOZIeXm5evToIR8fH9ntds2aNUvjx483ezRICgwMVHx8vJ5++mn17NlTwcHBevvtt5Wfn6/u3bvX6jk8MsABXJ3U1FQVFRXxOUk3ExUVJZvNprKyMv3zn/9UcnKyNmzYQISb7OjRo5o8ebJycnKqveMN8116VKhPnz6Ki4tTRESEVq5cycc3TFZVVaWYmBjNnj1bkhQdHa2ioiItWbKEAHcjy5YtU2JiokJDQ80eBZJWrlypN998U2+99ZZ69+4tm82m9PR0hYaG8u/GTbz++uuaOHGiOnfuLB8fH/Xv31933HGHtm3bVqv1HhngHTt2lI+Pj4qLi522FxcXq1OnTiZNBXiGtLQ0ffjhh9q4caOuvfZas8fBJaxWq+Pd0wEDBqiwsFALFizQ0qVLTZ7Mu23btk0nTpxQ//79Hdvsdrs2btyohQsXqrKyUj4+PiZOiEu1bdtW119/vQ4cOGD2KF4vJCSk2huIPXv21LvvvmvSRLjc4cOH9e9//1vvvfee2aPggszMTE2ZMkV//vOfJUk33nijDh8+rDlz5hDgbqJbt27asGGDKioqVF5erpCQEI0bN05du3at1XqP/Ay41WrVgAEDlJub69hWVVWl3NxcPi8JXIFhGEpLS9OqVav0ySefqEuXLmaPhF9RVVWlyspKs8fwesOHD9euXbtks9kct5iYGI0fP142m434djOnT5/WwYMHFRISYvYoXm/IkCHVvu5y3759ioiIMGkiXC47O1tBQUFOF5OCuc6cOaNmzZwTzcfHR1VVVSZNhCsJCAhQSEiISktLtX79eo0ePbpW6zzyCLgkZWRkKDk5WTExMYqNjdX8+fNVUVGhCRMmmD2a1zt9+rTTkYdDhw7JZrOpffv2Cg8PN3Ey75aamqq33npLa9asUWBgoI4fPy5JatOmjfz9/U2eDlOnTlViYqLCw8P1ww8/6K233lJeXp7Wr19v9mheLzAwsNq1EgICAtShQweuoeAGHn30USUlJSkiIkL//e9/NX36dPn4+OiOO+4wezSv98gjj2jw4MGaPXu2br/9dhUUFOill17SSy+9ZPZo0M9v8mZnZys5OVnNm3tsEjQ5SUlJmjVrlsLDw9W7d2998cUXev755zVx4kSzR8MF69evl2EYioqK0oEDB5SZmakePXrUvkMND/biiy8a4eHhhtVqNWJjY40tW7aYPRIMw/j0008NSdVuycnJZo/m1WraJ5KM7Oxss0eDYRgTJ040IiIiDKvValxzzTXG8OHDjY8//tjssXAFN910kzF58mSzx4BhGOPGjTNCQkIMq9VqdO7c2Rg3bpxx4MABs8fCBR988IFxww03GL6+vkaPHj2Ml156yeyRcMH69esNScbevXvNHgWXKC8vNyZPnmyEh4cbfn5+RteuXY3HH3/cqKysNHs0XLBixQqja9euhtVqNTp16mSkpqYap06dqvV6j/0ecAAAAAAAPIlHfgYcAAAAAABPQ4ADAAAAANAICHAAAAAAABoBAQ4AAAAAQCMgwAEAAAAAaAQEOAAAAAAAjYAABwAAAACgERDgAAAAAAA0AgIcAAAvMXToUKWnp9dpTUpKisaMGdMg89SH77//XkFBQfrmm29qvWbdunXq16+fqqqqGm4wAABqQIADALxSSkqKLBZLtdvIkSPNHs2JK9Hs7n788UelpKToxhtvVPPmza8Y+Hl5eerfv798fX3VvXt3LV++vNpjZs2apdGjRysyMrLWrz9y5Ei1aNFCb775pms/AAAALiLAAQBea+TIkfr222+dbm+//bbZYzV5drtd/v7+evjhhzVixIgaH3Po0CGNGjVKw4YNk81mU3p6uu69916tX7/e8ZgzZ85o2bJluueee+o8Q0pKiv7xj3+4/DMAAOAKAhwA4LV8fX3VqVMnp1u7du0k/Xz01Wq1atOmTY7Hz507V0FBQSouLpb089HptLQ0paWlqU2bNurYsaOeeOIJGYbhWFNZWalHH31UnTt3VkBAgOLi4pSXl+c0x+bNmzV06FC1bNlS7dq1U0JCgkpLS5WSkqINGzZowYIFjiP0F0+1LioqUmJiolq1aqXg4GDdddddOnnypOM5KyoqdPfdd6tVq1YKCQnRvHnz6uV3VllZqYcfflhBQUHy8/PTb37zGxUWFjo95v3339d1110nPz8/DRs2TK+99posFotOnTolSQoICFBWVpYmTZqkTp061fg6S5YsUZcuXTRv3jz17NlTaWlpuu222/TCCy84HvOvf/1Lvr6+GjRokGNbXl6eLBaL1q5dqz59+sjPz0+DBg1SUVGR0/MnJSVp69atOnjwYL38XgAAqA0CHACAGlw89fuuu+5SWVmZvvjiCz3xxBN65ZVXFBwc7Hjca6+9pubNm6ugoEALFizQ888/r1deecXx92lpacrPz9c777yjnTt3auzYsRo5cqT2798vSbLZbBo+fLh69eql/Px8ffbZZ0pKSpLdbteCBQsUHx+vSZMmOY7Qh4WF6dSpU7r55psVHR2trVu3at26dSouLtbtt9/ueN3MzExt2LBBa9as0ccff6y8vDxt3779qn8vjz32mN5991299tpr2r59u7p3766EhASVlJRI+vnI9W233aYxY8Zox44duv/++/X444/X+XXy8/OrHR1PSEhQfn6+4/6mTZs0YMCAGtdnZmZq3rx5Kiws1DXXXKOkpCSdP3/e8ffh4eEKDg52eoMFAICG1tzsAQAAMMuHH36oVq1aOW2bNm2apk2bJkmaOXOmcnJydN9996moqEjJycn64x//6PT4sLAwvfDCC7JYLIqKitKuXbv0wgsvaNKkSTpy5Iiys7N15MgRhYaGSpIeffRRrVu3TtnZ2Zo9e7bmzp2rmJgYLV682PGcvXv3dvzZarWqZcuWTkeKFy5cqOjoaM2ePdux7dVXX1VYWJj27dun0NBQLVu2TG+88YaGDx8u6ec3Cq699tqr+n1VVFQoKytLy5cvV2JioiTp5ZdfVk5OjpYtW6bMzEwtXbpUUVFReu655yRJUVFRKioq0qxZs+r0WsePH3d6o0OSgoODVV5errNnz8rf31+HDx92/F4vN336dP3+97+X9P9/9lWrVjm9SREaGqrDhw/XaS4AAK4GAQ4A8FrDhg1TVlaW07b27ds7/my1WvXmm2+qT58+ioiIcDr9+aJBgwbJYrE47sfHx2vevHmy2+3atWuX7Ha7rr/+eqc1lZWV6tChg6Sfj4CPHTu2TnPv2LFDn376abU3DyTp4MGDOnv2rM6dO6e4uDinnysqKuqKz3nkyBH16tXLcf/SNyIufe7z589ryJAhjm0tWrRQbGysvvrqK0nS3r17NXDgQKd1sbGxdfr5auvs2bPy8/Or8e/i4+Mdf774s1+c8SJ/f3+dOXOmQWYDAKAmBDgAwGsFBASoe/fuv/iY//znP5KkkpISlZSUKCAgoNbPf/r0afn4+Gjbtm3y8fFx+ruL8ezv71/HqX9+3qSkJD377LPV/i4kJEQHDhyo83OGhobKZrM57l/6RoQZOnXq5Pis/UXFxcVq3bq143fWsWNHlZaWuvwaJSUluuaaa65qTgAA6oLPgAMAcAUHDx7UI488opdffllxcXFKTk6u9t3Rn3/+udP9LVu26LrrrpOPj4+io6Nlt9t14sQJde/e3el28ZTyPn36KDc394ozWK1W2e12p239+/fXl19+qcjIyGrPGxAQoG7duqlFixZOs5WWlmrfvn1XfJ3mzZs7PU9NAd6tWzdZrVZt3rzZse38+fMqLCx0HD2PiorS1q1bndZdfpG22oiPj6/2e8nJyXE6sh0dHa3du3fXuH7Lli2OP1/82Xv27OnY9uOPP+rgwYOKjo6u82wAALiKAAcAeK3KykodP37c6XbxSuJ2u1133nmnEhISNGHCBGVnZ2vnzp3VriZ+5MgRZWRkaO/evXr77bf14osvavLkyZKk66+/XuPHj9fdd9+t9957T4cOHVJBQYHmzJmjtWvXSpKmTp2qwsJCPfjgg9q5c6f27NmjrKwsxxyRkZH6/PPP9c033+jkyZOqqqpSamqqSkpKdMcdd6iwsFAHDx7U+vXrNWHCBNntdrVq1Ur33HOPMjMz9cknn6ioqEgpKSlq1uzq/rcfEBCgv/zlL8rMzNS6deu0e/duTZo0SWfOnHF8Fdj999+vPXv26G9/+5v27dunlStXOr6/+9JT9Xfv3i2bzaaSkhKVlZXJZrM5HYF/4IEH9PXXX+uxxx7Tnj17tHjxYq1cuVKPPPKI4zEJCQn68ssvazwK/ve//125ubmOn71jx45O3ze+ZcsW+fr6OgU9AAANzgAAwAslJycbkqrdoqKiDMMwjBkzZhghISHGyZMnHWveffddw2q1GjabzTAMw7jpppuMBx980HjggQeM1q1bG+3atTOmTZtmVFVVOdacO3fOePLJJ43IyEijRYsWRkhIiHHrrbcaO3fudDwmLy/PGDx4sOHr62u0bdvWSEhIMEpLSw3DMIy9e/cagwYNMvz9/Q1JxqFDhwzDMIx9+/YZt956q9G2bVvD39/f6NGjh5Genu547R9++MG48847jZYtWxrBwcHG3LlzjZtuusmYPHlynX9Po0ePdtw/e/as8dBDDxkdO3Y0fH19jSFDhhgFBQVOa9asWWN0797d8PX1NYYOHWpkZWUZkoyzZ886HhMREVHj7/9Sn376qdGvXz/DarUaXbt2NbKzs6vNFxsbayxZssRpjSTjgw8+MHr37m1YrVYjNjbW2LFjh9O6++67z7j//vvr9LsAAOBqWQzjki8rBQAAtTZ06FD169dP8+fPN3sUtzZr1iwtWbJER48erffnXrt2rTIzM1VUVKRmzZopLy9Pw4YNU2lpqdq2bVvjmpMnTzpOle/SpUu9zwQAwJVwETYAAFCvFi9erIEDB6pDhw7avHmznnvuOaWlpTX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Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/tom/miniconda3/envs/sgkit-dev-3.10/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n", " self._figure.tight_layout(*args, **kwargs)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "manhattan_plot(ds_lr)" ] }, { "cell_type": "markdown", "id": "incorrect-mystery", "metadata": {}, "source": [ "The \"caffeine consumption\" locus in chromosome 8 is clearly apparent, just like in the Hail tutorial." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }