{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "![dandelion_logo](img/dandelion_logo_illustration.png)\n",
    "\n",
    "## Foreword\n",
    "\n",
    "***dandelion*** is written in `python=3.7.6` and is primarily a single-cell BCR-seq analysis package. It makes use of some tools from the fantastic [*immcantation suite*](https://immcantation.readthedocs.io/), implementing a workflow to streamline the pre-processing and exploratory stages for analyzing single-cell BCR-seq data from 10X Genomics. Post-processed data from ***dandelion*** can be smoothly transferred to [*scanpy*](https://scanpy.readthedocs.io/)/AnnData object for integration and exploration of BCR-seq data and RNA-seq data. I hope to be able to introduce some new single-cell BCR-seq exploratory tools down the road through *dandelion*. \n",
    "\n",
    "This notebook will cover the initial pre-processing of files after 10X's `cellranger vdj` immune profiling data analysis pipeline. We will download the 10x data sets to process for this tutorial:\n",
    "```bash\n",
    "# create sub-folders\n",
    "mkdir -p dandelion_tutorial/vdj_nextgem_hs_pbmc3\n",
    "mkdir -p dandelion_tutorial/vdj_v1_hs_pbmc3\n",
    "mkdir -p dandelion_tutorial/sc5p_v2_hs_PBMC_10k\n",
    "mkdir -p dandelion_tutorial/sc5p_v2_hs_PBMC_1k\n",
    "\n",
    "# change into each directory and download the necessary files\n",
    "cd dandelion_tutorial/vdj_v1_hs_pbmc3;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_v1_hs_pbmc3/vdj_v1_hs_pbmc3_filtered_feature_bc_matrix.h5;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_v1_hs_pbmc3/vdj_v1_hs_pbmc3_b_filtered_contig_annotations.csv;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_v1_hs_pbmc3/vdj_v1_hs_pbmc3_b_filtered_contig.fasta;\n",
    "\n",
    "cd ../vdj_nextgem_hs_pbmc3\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_nextgem_hs_pbmc3/vdj_nextgem_hs_pbmc3_filtered_feature_bc_matrix.h5;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_nextgem_hs_pbmc3/vdj_nextgem_hs_pbmc3_b_filtered_contig.fasta;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/3.1.0/vdj_nextgem_hs_pbmc3/vdj_nextgem_hs_pbmc3_b_filtered_contig_annotations.csv;\n",
    "\n",
    "cd ../sc5p_v2_hs_PBMC_10k;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_10k/sc5p_v2_hs_PBMC_10k_filtered_feature_bc_matrix.h5;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_10k/sc5p_v2_hs_PBMC_10k_b_filtered_contig_annotations.csv;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_10k/sc5p_v2_hs_PBMC_10k_b_filtered_contig.fasta;\n",
    "\n",
    "cd ../sc5p_v2_hs_PBMC_1k;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_1k/sc5p_v2_hs_PBMC_1k_filtered_feature_bc_matrix.h5;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_1k/sc5p_v2_hs_PBMC_1k_b_filtered_contig_annotations.csv;\n",
    "wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_1k/sc5p_v2_hs_PBMC_1k_b_filtered_contig.fasta;\n",
    "```\n",
    "\n",
    "***dandelion*** requires the cellranger fasta files and annotation files to start, particularly either *all_contig.fasta* or *filtered_contig.fasta* and corresponding *all_contig_annotations.csv* and *filtered_contig_annotations.csv*.\n",
    "\n",
    "In this notebook, I'm running everything with the *filtered_contig* files as a standard analysis set up. I'm using a standard laptop for the analysis here: entry level 2017 Macbook Pro with 2.3 GHz Intel Core i5 processor and 16 GB 2133 MHz LPDDR3 ram.\n",
    "\n",
    "If you followed the installation instructions, you should have the requisite auxillary softwares installed already. Otherwise, you can download them manually: [blast+](https://ftp.ncbi.nih.gov/blast/executables/igblast/release/LATEST/) and [igblast](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/). For tigger-genotype, you can download it [here](https://bitbucket.org/kleinstein/immcantation/src/default/pipelines/). Just note that I made some minor modifications to this file, hence there is a version that comes with this package.\n",
    "\n",
    "For convenience, in ***shell***, export the path to the database folders like as follows:\n",
    "```bash\n",
    "# bash/shell\n",
    "echo \"export GERMLINE=/Users/kt16/Documents/Github/dandelion/database/germlines/\" >> ~/.bash_profile\n",
    "echo \"export IGDATA=/Users/kt16/Documents/Github/dandelion/database/igblast/\" >> ~/.bash_profile\n",
    "echo \"export BLASTDB=/Users/kt16/Documents/Github/dandelion/database/blast/\" >> ~/.bash_profile\n",
    "echo \"export PATH=/Users/kt16/Documents/Github/dandelion/bin:$PATH\" >> ~/.bash_profile\n",
    "# reload\n",
    "source ~/.bash_profile\n",
    "```\n",
    "The databases for igblast are basically setup using [changeo's instructions](https://changeo.readthedocs.io/en/stable/examples/igblast.html). \n",
    "\n",
    "For reannotation of constant genes, reference fasta files were downloaded from IMGT and only sequences corresponding to *CH1* region for each constant gene/allele were retained. The headers were trimmed to only keep the gene and allele information. Links to find the sequences can be found here : [***human***](http://www.imgt.org/genedb/GENElect?query=7.2+IGHC&species=Homo+sapiens) and [***mouse***](http://www.imgt.org/genedb/GENElect?query=7.2+IGHC&species=Mus).\n",
    "\n",
    "The utility function `utl.makeblastdb` is a wrapper for:\n",
    "\n",
    "```bash\n",
    "# bash/shell\n",
    "makeblastdb -dbtype nucl -parse_seqids -in $BLASTDB/human/human_BCR_C.fasta\n",
    "```\n",
    "\n",
    "So effectively, this does the same thing:\n",
    "```python\n",
    "# python\n",
    "ddl.utl.makeblastdb('/Users/kt16/Documents/Github/dandelion/database/blast/human/human_BCR_C.fasta')\n",
    "```\n",
    "\n",
    "If you have cloned the directory from dandelion's github, you should have all the databases ready to go and would not need to run makeblastdb.\n",
    "\n",
    "This notebook will now demonstrate how I batch process multiple samples/files from the same donor, as it will become important later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dandelion==0.1.0 pandas==1.1.4 numpy==1.19.4 matplotlib==3.3.3 networkx==2.5 scipy==1.5.3 skbio==0.5.6\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "import sys\n",
    "import os\n",
    "import dandelion as ddl\n",
    "ddl.logging.print_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/kt16/Downloads/dandelion_tutorial'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change directory to somewhere more workable\n",
    "os.chdir(os.path.expanduser('/Users/kt16/Downloads/dandelion_tutorial/'))\n",
    "# print current working directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Formatting the headers of the cellranger fasta file\n",
    "Here, I'm adding a prefix to the headers of each contig in the fasta files, via the function `pp.format_fastas`. The prefix is basically just the folder name, so in this case it's:\n",
    "`sc5p_v2_hs_PBMC_1k`, `sc5p_v2_hs_PBMC_10k`, `vdj_v1_hs_pbmc3` and `vdj_nextgem_hs_pbmc3`.\n",
    "\n",
    "The function will also create sub-folders where a new fasta file and all subsequent files will be located. The function will also add the prefix to the corresponding annotation file automatically and create a copy in the same folder as the formated fasta file. \n",
    "\n",
    "This is to ensure that the barcodes are consistent throughout so as not to interfere with subsequent integration with the gene expression data, which will be covered in notebooks 2 and 3. \n",
    "\n",
    "The file structure should look something like this later on if the settings are left as default. The tmp directory can be deleted once the initial preprocessing has completed.\n",
    "```console\n",
    "# bash/shell\n",
    "(dandelion) mib113557i:dandelion_tutorial kt16$ tree sc5p_v2_hs_PBMC_1k\n",
    "sc5p_v2_hs_PBMC_1k\n",
    "├── dandelion\n",
    "│   └── data\n",
    "│       ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig.fasta\n",
    "│       ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_annotations.csv\n",
    "│       ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast_db-pass_genotyped.tsv\n",
    "│       └── tmp\n",
    "│           ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast.fmt7\n",
    "│           ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast.tsv\n",
    "│           ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast_db-pass.blastsummary.txt\n",
    "│           ├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast_db-pass.tsv\n",
    "│           └── sc5p_v2_hs_PBMC_1k_b_filtered_contig_igblast_db-pass.xml\n",
    "├── sc5p_v2_hs_PBMC_1k_b_filtered_contig.fasta\n",
    "├── sc5p_v2_hs_PBMC_1k_b_filtered_contig_annotations.csv\n",
    "└── sc5p_v2_hs_PBMC_1k_filtered_feature_bc_matrix.h5\n",
    "```\n",
    "\n",
    "The first option of `pp.format_fastas` accepts a list of the fasta file paths to reformat, or list of names of folders containing the fasta files; each folder should only contain 1 fasta file, and 1 contig_annotation.csv. Make sure there's no hidden files and delete those if present.\n",
    "\n",
    "You can provide `prefixes` and/or `suffixes` to add the the cell/contig barcodes as a list and they will be formatted accordingly. The prefixes/suffixes will be separated by an underscore (`_`) if left as default but that can be adjusted with the `sep` option. \n",
    "\n",
    "If you choose not to provide a prefix/suffix, then the function will simply make a copy of the original files and place it in the `dandelion/data` sub-folders.\n",
    "\n",
    "For more complex experimental setups, such as with data from multiplexed experiments, please contact me (kt16@sanger.ac.uk) and I can walk you through a slightly more advanced set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Formating fasta(s) : 100%|██████████| 4/4 [00:02<00:00,  1.66it/s]\n"
     ]
    }
   ],
   "source": [
    "# the first option is a list of fasta files to format and the second option is the list of prefix to add to each file.\n",
    "samples = ['sc5p_v2_hs_PBMC_1k', 'sc5p_v2_hs_PBMC_10k', 'vdj_v1_hs_pbmc3', 'vdj_nextgem_hs_pbmc3']\n",
    "ddl.pp.format_fastas(samples, prefix = samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Reannotate the V/D/J genes with *igblastn*\n",
    "\n",
    "Like immcantation, we will reannotate the V(D)J genes with igblastn using the latest IMGT reference databases. `pp.reannotate_genes` uses [*changeo*](https://changeo.readthedocs.io/en/stable/examples/10x.html)'s `AssignGenes.py` and `MakeDB.py` to call *igblastn* to reannotate the fasta files. With the recent update to changeo v1.0.0, all the column headers are now adhereing to the [*AIRR*](http://docs.airr-community.org/) standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes : 100%|██████████| 4/4 [06:17<00:00, 94.40s/it]\n"
     ]
    }
   ],
   "source": [
    "ddl.pp.reannotate_genes(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you did not set a path to the igblast or germline paths in the environment above, you need to specify the path to the folders containing the fasta files directly.\n",
    "\n",
    "```python\n",
    "ddl.pp.reannotate_genes(samples, igblast_db = \"database/igblast/\", germline = \"database/germlines/imgt/human/vdj/\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 : Reassigning heavy chain V gene alleles *(optional but recommended)*\n",
    "*Please note that prior to v0.0.25, this was step 4. The order is now switched to run this first.*\n",
    "\n",
    "Next, we use *immcantation's TIgGER* method to reassign allelic calls for heavy chain V genes with `pp.reassign_alleles`. As stated in TIgGER's [website](https://tigger.readthedocs.io/en/stable/) and [manuscript](https://pubmed.ncbi.nlm.nih.gov/25675496/), *'TIgGER is a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual (including novel alleles) from V(D)J-rearrange sequences. TIgGER can then infer a subject’s genotype from these sequences, and use this genotype to correct the initial V(D)J allele assignments.'*\n",
    "\n",
    "This impacts on how contigs are chosen for finding clones later. It is also important when considering to do mutational analysis. For convenience, germline sequences are reconstructed at this step using the corrected V-gene alleles. Therefore, it is highly recommended to run it. \n",
    "\n",
    "However, this will only work properly if there is sufficient contigs. An ideal scenario would be to run it on multiple samples from the same subject to allow for more information to be used to confidently assign a genotyped *v_call*. In this tutorial, I'm assuming the four samples can be split into two sets where sets of two corresponds to a different/single individual. So while important, this step can be skipped if you don't have enough data to do this. \n",
    "\n",
    "`pp.reassign_alleles` requires the `combined_folder` option to be specified so that a merged/concatenated file can be produced for running TIgGER."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) : 100%|██████████| 2/2 [00:01<00:00,  1.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenating objects\n",
      "      Running tigger-genotype with novel allele discovery.\n",
      "      Reassigning alleles\n",
      "            Reconstructing heavy chain dmask germline sequences with v_call_genotyped.\n",
      "            Reconstructing light chain dmask germline sequences with v_call.\n",
      "      For convenience, entries for light chain `v_call` are copied to `v_call_genotyped`.\n",
      "Returning summary plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (348121353)>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing out to individual folders : 100%|██████████| 2/2 [00:00<00:00, 10.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# reassigning alleles on the first set of samples\n",
    "ddl.pp.reassign_alleles(samples[:2], combined_folder = 'tutorial_scgp1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) : 100%|██████████| 2/2 [00:01<00:00,  1.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenating objects\n",
      "      Running tigger-genotype with novel allele discovery.\n",
      "      Reassigning alleles\n",
      "            Reconstructing heavy chain dmask germline sequences with v_call_genotyped.\n",
      "            Reconstructing light chain dmask germline sequences with v_call.\n",
      "      For convenience, entries for light chain `v_call` are copied to `v_call_genotyped`.\n",
      "Returning summary plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (351177137)>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing out to individual folders : 100%|██████████| 2/2 [00:00<00:00,  5.86it/s]\n"
     ]
    }
   ],
   "source": [
    "# reassigning alleles on the second set of samples\n",
    "ddl.pp.reassign_alleles(samples[2:], combined_folder = 'tutorial_scgp2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the original ambiguous V calls have now been corrected and only a few remain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to above, if you you can specify the path to the folder containing the fasta files accordingly:\n",
    "\n",
    "```python\n",
    "ddl.pp.reassign_alleles(samples[2:], combined_folder = 'tutorial_scgp2', germline = \"database/germlines/imgt/human/vdj\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Assigning constant region calls\n",
    "\n",
    "10X's annotation files provides a *c_gene* column, but rather than simply relying on 10x's annotation, [hk6](https://twitter.com/hamish_king) recommended using [*immcantation-presto*'s *MaskPrimers.py*](https://presto.readthedocs.io/en/version-0.5.3---license-change/tools/MaskPrimers.html) with his custom primer list. \n",
    "\n",
    "As an alternative, `dandelion` includes a pre-processing function, `pp.assign_isotypes`, to use *blast* to annotate constant region calls for all contigs and retrieves the call, merging it with the tsv files. This function will overwrite the output from previous steps and add a *c_call* column at the end, or replace the existing column if it already exists. The 10x calls are returned as `c_call_10x`.\n",
    "\n",
    "Further, to deal with incorrect constant gene calls due to insufficient length, a pairwise alignment will be run against [hk6](https://twitter.com/hamish_king)'s curated sequences that were deemed to be highly specific in distinguishing IGHA1-2, IGHG1-4. I have also curated sets of sequences that should help deal with IGLC3/6/7 as these are problematic too. If there is insufficient info, the `c_call` will be returned as a combination of the most aligned sets of sequences. Because of how similar the lambda light chains are, extremely ambiguous calls (only able to map to a common sequence across the light chains) will be returned as `IGLC`. This typically occurs when the constant sequence is very short. Those that have equal alignment scores between `IGLC3/6/7` sequences and the common sequence will be returned as a concatenated call; for example, a contig initially annotated as `IGLC3` will be returned as `IGLC,IGLC3`. \n",
    "\n",
    "The curated sequences can be updated/replaced with a dict-of-dict-of-dict style dictionary via the option `correction_dict`. The provided dictionary should be a nested dictionary like the following:\n",
    "```python\n",
    "primer_dict = {\n",
    "    'IGHG':{\n",
    "        'IGHG1':'GCCTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCACCCTCCTCCAAGAGCACCTCTGGGGGCACAGCGGCCCTGGGC',\n",
    "        'IGHG2':'GCCTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCGGCCCTGGGC',\n",
    "        'IGHG3':'GCTTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCTGGGGGCACAGCGGCCCTGGGC',\n",
    "        'IGHG4':'GCTTCCACCAAGGGCCCATCCGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCCGCCCTGGGC'}}\n",
    "```\n",
    "\n",
    "The key for the first level of the dictionary is used for searching whether the string pattern exists in the `c_call`, and the second level holds the dictionary for the the reference sequences to align to. The keys in the second level are used for replacing the existing `c_call` annotation if it is returned with the highest alignment score. The function currently only accepts 2-4 reference sequences for the pairwise alignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving contant region calls, parallelizing with 3 cpus : 100%|██████████| 202/202 [00:00<00:00, 322.42it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (346976021)>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving contant region calls, parallelizing with 3 cpus : 100%|██████████| 2601/2601 [00:25<00:00, 103.89it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAFwCAYAAAC1lgFqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAA9hAAAPYQGoP6dpAABRI0lEQVR4nO3de1wU9f4/8NcCK4LLJggKauuieMEQXRZvGYWknkOJZpqYWWmYqNkhKi0ovBSQx0u3U9qa5l02FU2zzHNE0ko0zoKgiaSGecwl7kKCMnv5/eHP+UagwnJzl9fz8fDxcGY+85n3Z3p0zqvPfGZHYjabzSAiIiKyIXatXQARERFRU2PAISIiIpvDgENEREQ2hwGHiIiIbA4DDhEREdkcBhwiIiKyOQw4REREZHMYcGxYZWUlMjIyUFlZ2dqlEBERtSgGHBt25swZqNVqnDlzprVLISIialEMOERERGRzGHCIiIjI5ji0dgF0d6p4z6/ebV2iTzVjJURERA3HGRwiIiKyOQw4REREZHMYcBooNjYW+/fvBwBUVVVh/fr1mDVrFp544gnMmDEDb731FjIzM+tsf9OlS5cwbty4Wn3v2bMH48aNQ3p6eo39giBg6dKlmDlzJsaNGwedTtcMIyMiIrIdXINjIUEQEBcXB0dHR8TExEChUMBoNCI7Oxvp6elQqVQN7jMlJQUymQwpKSkYPHhwjWO+vr4ICwvDypUrm2oIRERENosBx0KpqakoKCiARqOBk5MTAMDe3h6BgYEIDAxscH/nz5/HxYsX8eqrr+Ldd99FeXk55HI5AEAqlWL8+PEAADu7lpl0+8h/eb3bxjRjHURERJbgIyoLZWVlQaVSieGmsQ4ePAh/f3+MGDECbm5uOHz4cJP0S0RE1BZxBsdC5eXl8PHxEbfz8/MRHR0Ns9kMQRCQnJwsHlu3bh02bdokbptMphp9CYKAI0eOICIiAhKJBMHBwTh48CDCwsIaXJder4derwcA5OTkNPh8IiIiW8CAYyG5XI6SkhJx29PTE0lJSbh06RLmzp1bo21ERARCQ0PF7b+2OX78OKqrqzF8+HAAQHBwMLZv345ffvkFPXv2bFBdGo0GS5YssWRIRERENoMBx0L+/v7YunUrKisr4ezs3Ki+UlJSYDAYEBkZKe6TSCRISUlpcMCJjIwU39DKycnBtGnTGlUbERGRNWLAsVBISAgOHDiAxMREREREQKFQwGQyITc3t0H9FBcXIzMzE7Gxsejdu7e4//vvv4dWq8X06dMhlUohCALMZjPMZjOMRiOqq6vh4OBQa9Gxl5cXvLy8Gj2+in3f1L/xw6F3bkNERNSCGHAsJJVKER8fD61Wi4SEBJSVlUEul0OpVDboEVFqaiq6deuGIUOG1Ng/ZswYaLVapKen4/7778ecOXNQUFAAAIiPjwcAJCQkYMCAAU03KCIiIhshMZvN5tYugppHRkYG1Go1dDodAgICGnRubHRUvdsmvvdBQ0sjIiJqVnxNnIiIiGwOAw4RERHZHAYcIiIisjlcZEx14roaIiKyZpzBISIiIpvDgENEREQ2hwGHiIiIbA4DDhEREdmcuybgxMbGYv/+/QCAqqoqrF+/HrNmzcITTzyBGTNm4K233kJmZmad7W+6dOmS+B2mP9uzZw/GjRuH9PT0GvsFQcDSpUsxc+ZMjBs3Djqd7rY1btu2DcuXL6+xb//+/YiKisKkSZPw9NNPY/78+di/fz9u/n5iXecAwDPPPIOTJ0/W2PfLL79g3LhxWL9+fY39ZWVlWLFiBaZPn47w8HC8/PLLNe4FERER1XTXvUUlCALi4uLg6OiImJgYKBQKGI1GZGdnIz09HSqVqsF9pqSkQCaTISUlBYMHD65xzNfXF2FhYVi5cmWD+/3000/x448/Yvbs2fDz84NUKsX58+fxxRdfYNSoUZBKpRbVmZqaimeeeQb29vYAgGvXrqFXr16YMWMGXF1dcezYMbzzzjv46KOP0Llz5wbXTUREZOvumhmcm1JTU1FQUIA333wT3t7esLe3R7t27RAYGIhZs2Y1uL/z58/j4sWLmDt3Ln788UeUl5eLx6RSKcaPH4/77ruv1kcr7+Ty5cv46quvMH/+fKjVajg6OsLOzg69e/fG/PnzGxxuBEHAt99+i4iICFy/fr3GbJKnpycmTJiATp06wc7ODvfffz88PDxw7ty5Bl2DiIiorbjrAk5WVhZUKhWcnJyapL+DBw/C398fI0aMgJubGw4fPtwk/WZlZaFTp07o06dPk/SXnp4Oo9GIoKAgDBs2DAcPHrxl2+LiYuj1eigUiia5NhERka256x5RlZeXw8fHR9zOz89HdHQ0zGYzBEFAcnKyeGzdunXYtGmTuG0ymWr0JQgCjhw5goiICEgkEgQHB+PgwYMICwtrkjpdXV1r7HvhhRdQUlICQRCwePFi+Pn5AQDS0tLw5JNP1mhbWVlZY/vgwYMYPnw42rVrh5EjR+Ktt97ClStXcM8999RoV11djWXLlmHMmDHo3r17rbr0ej30ej0AICcnp9HjJCIiskZ3XcCRy+UoKSkRtz09PZGUlIRLly5h7ty5NdpGREQgNDRU3P5rm+PHj6O6uhrDhw8HAAQHB2P79u345Zdf0LNnzyatEwA+/vhjADcWEP/5I+3Dhw/H/Pnza7R95plnxL+XlJQgIyMDS5YsAQD4+/vjnnvuwbfffovx48eL7W4uiu7YsSOef/75OuvSaDRiP0RERG3VXRdw/P39sXXrVlRWVsLZ2blRfaWkpMBgMCAyMlLcJ5FIkJKS0uiA4+/vD41Gg59//rnRj6lSU1NhMpmwYsUKSCQSAMDVq1eRkpIiBhxBEPDPf/4TdnZ2mD9/vrgA+a8iIyPFN8lycnIwbdq0RtVGRERkje66gBMSEoIDBw4gMTERERERUCgUMJlMyM3NbVA/xcXFyMzMRGxsLHr37i3u//7776HVajF9+nRIpVIIggCz2Qyz2Qyj0Yjq6mo4ODjAzs4OJ0+exBtvvIG9e/fW6r9bt24IDQ3F8uXLa7xFlZeXh+vXrzeo1kOHDmHixIk1XnEvKirCq6++ivPnz6NHjx5YtmwZBEHAm2++CQeHW/9j8/LygpeXV4OuT0REZGvuuoAjlUoRHx8PrVaLhIQElJWVQS6XQ6lUNujRS2pqKrp164YhQ4bU2D9mzBhotVqkp6fj/vvvx5w5c1BQUAAAiI+PBwAkJCRgwIABKCoqQr9+/W55jVmzZqF79+7YuHEjLl++DCcnJ3h6eiIiIgK+vr71qvPMmTO4fPkyxo4dW2NNj6urK1QqFQ4ePIgRI0bg+PHjaNeuHZ566imxzdy5cxEcHFzfW0JERNRmSMx/XixCNfzrX//CiBEjEBAQ0NqlWCQjIwNqtRo6nc5qx0BERGSJu24G527y4osvtnYJREREZIG77ndwiIiIiBqLAYeIiIhsDgMOERER2RwGHCIiIrI5DDhUp4r3/FDxnl9rl0FERGQRBhwiIiKyOQw4REREZHP4OzgNFBsbi6CgIISGhqKqqgparRZpaWkoLS2FTCaDt7c3wsLCoFKparW/6eZHQf/6CYg9e/Zg3bp1iIuLw+DBg8X9Z86cQVJSEs6dOwcA6Nu3L2bOnImuXbu2wIiJiIisDwOOhQRBQFxcHBwdHRETEwOFQgGj0Yjs7Gykp6eLAachUlJSIJPJkJKSUiPgXL16FaNGjcKCBQvQrl07bN26FfHx8Vi1alVTDomIiMhmMOBYKDU1FQUFBdBoNHBycgIA2NvbIzAwEIGBgQ3u7/z587h48SJeffVVvPvuuygvL4dcLgcAqNXqGm0fe+wx7Nq1q0abpvaR//Ibf0nZj5iHQ2/fmIiI6C7DNTgWysrKgkqlEsNNYx08eBD+/v4YMWIE3NzccPjw4Vu2PXXqFFxdXZst3BAREVk7zuBYqLy8HD4+PuJ2fn4+oqOjYTabIQgCkpOTxWPr1q3Dpk2bxG2TyVSjL0EQcOTIEUREREAikSA4OBgHDx5EWFhYrevm5+dDo9EgMjKyzrr0ej30ej0AICcnp1FjJCIislYMOBaSy+UoKSkRtz09PZGUlCQuIP6ziIiIOhcZ33T8+HFUV1dj+PDhAIDg4GBs374dv/zyC3r27Cm2KyoqwsKFCzFp0iQ88MADddal0WiwZMmSJhkjERGRtWLAsZC/vz+2bt2KyspKODs7N6qvlJQUGAyGGrMyEokEKSkpYsApLi7GG2+8gTFjxmD8+PG37CsyMhLjxo0DcGMGZ9q0aY2qjYiIyBox4FgoJCQEBw4cQGJiIiIiIqBQKGAymZCbm9ugfoqLi5GZmYnY2Fj07t1b3P/9999Dq9Vi+vTpKC8vR2xsLIKDgzFp0qTb9ufl5QUvLy+LxvRnFfu+AQAkvvdBo/siIiJqaQw4FpJKpYiPj4dWq0VCQgLKysogl8uhVCob9IgoNTUV3bp1w5AhQ2rsHzNmDLRaLdLT0/Hrr79Cr9dj9+7d2L17t9jm448/hoeHR5ONiYiIyFZIzGazubWLoOaRkZEBtVoNnU6HgICABp0bGx0FgDM4RERknfiaOBEREdkcBhwiIiKyOQw4REREZHO4yJjqxLU3RERkzRhwqE4V7/k1ug+X6FNNUAkREVHD8REVERER2RwGHCIiIrI5DDhERERkc7gGp4nExsYiKCgIoaGhqKqqglarRVpaGkpLSyGTyeDt7Y2wsDCoVKpa7W+6+RHOvXv3AgC2bduGHTt2oF27dpBIJJDL5RgwYAAmTpyIrl27tso4iYiIrAEDThMTBAFxcXFwdHRETEwMFAoFjEYjsrOzkZ6eLgac+rr//vsxf/58AEBhYSH27NmDl19+GcuWLYNCoWiOIRAREVk9BpwmlpqaioKCAmg0Gjg5OQEA7O3tERgYiMDAwEb17eHhgZkzZ+K3337Dtm3b8PrrrzdFyXX6yH95o/uIaYI6iIiILMGA08SysrKgUqnEcNMchg8fjs2bN9d5TK/XQ6/XAwBycnKarQYiIqK7GQNOEysvL4ePj4+4nZ+fj+joaJjNZgiCgOTkZPHYunXrsGnTJnHbZDLV6xqdOnXCH3/8UecxjUbToK+ZExER2SIGnCYml8tRUlIibnt6eiIpKUlcQPxnERERdS4yvpPi4mLIZLI6j0VGRmLcuHEAbszgTJs2zZJhEBERWTUGnCbm7++PrVu3orKyEs7Ozs1yjbS0NNx33311HvPy8oKXl1ezXJeIiMhaMOA0sZCQEBw4cACJiYmIiIiAQqGAyWRCbm5uo/suLCzEvn37cPr0aSxbtqwJqr21in3fNL6Th0Pv3IaIiKgZMOA0MalUivj4eGi1WiQkJKCsrAxyuRxKpdKitTFHjx7F5MmTIZFI4OLiggEDBuDdd99Ft27dmqF6IiIi2yAxm83m1i6CmkdGRgbUajV0Oh0CAgIadG5sdFSjr88vkhMRUWvhpxqIiIjI5jDgEBERkc1hwCEiIiKbw0XGVCeunyEiImvGgEN1qnjPr95tXaJPNWMlREREDcdHVERERGRzGHCIiIjI5jDgEBERkc3hGpwGiI2NRVBQEEJDQ1FVVQWtVou0tDSUlpZCJpPB29sbYWFhUKlUtdrfdPODmnv37hX3nT9/HklJSTh9+jSMRiNcXV2hVqsxYcIEuLu7QxAErFy5EufOnUNBQQEWLVoEtVrd4uMnIiKyFgw4FhAEAXFxcXB0dERMTAwUCgWMRiOys7ORnp4uBpz6OHPmDBYtWoQJEyZg7ty5cHNzQ0lJCQ4fPozTp0/jwQcfBAD4+voiLCwMK1eubK5hERER2QwGHAukpqaioKAAGo0GTk5OAAB7e3sEBgYiMDCwQX1t2LABI0eOxJQpU8R9bm5umDBhgrgtlUoxfvx4AICdXcs8VfzIf3m928Y0Yx1ERESWYMCxQFZWFlQqlRhuLHXt2jWcOXMGU6dObaLKAL1eD71eDwDIyclpsn6JiIisCQOOBcrLy+Hj4yNu5+fnIzo6GmazGYIgIDk5WTy2bt06bNq0Sdw2mUzi3//44w+YTCa4ubmJ+7Zv347du3fDaDTiwQcfxLx58xpUm0ajseir5URERLaEAccCcrkcJSUl4ranpyeSkpLEBcR/FhERUeciYwCQyWSws7NDcXExunfvDgCYPHkyJk+ejI0bN6K0tLTBtUVGRmLcuHEAbszgTJs2rcF9EBERWTsGHAv4+/tj69atqKyshLOzs8X9tG/fHn369EFaWhoGDhzYJLV5eXnBy8urSfoiIiKyVgw4FggJCcGBAweQmJiIiIgIKBQKmEwm5ObmNrivGTNmYOHChejYsSP+9re/wdXVFWVlZbh06RI6dOggthMEAWazGWazGUajEdXV1XBwcGi2RccV+76pf+OHQ+/choiIqAUx4FhAKpUiPj4eWq0WCQkJKCsrg1wuh1KpbPD6F19fXyQmJkKr1WLPnj3impyAgIAab1LNmTMHBQUFAID4+HgAQEJCAgYMGNB0AyMiIrIRErPZbG7tIqh5ZGRkQK1WQ6fTISAgoEHnxkZH1bstvzxORER3G36qgYiIiGwOAw4RERHZHK7BoTrxsRMREVkzzuAQERGRzeEMDtWp4j2/Wvtcok+1QiVEREQNxxkcIiIisjkMOERERGRzGHCIiIjI5nANTgPExsYiKCgIoaGhqKqqglarRVpaGkpLSyGTyeDt7Y2wsDCoVKpa7W+6+bHNvXv3ivvOnz+PpKQknD59GkajEa6urlCr1ZgwYQLc3d1x5swZJCUl4dy5cwCAvn37YubMmejatWvL3gAiIiIrwYBjAUEQEBcXB0dHR8TExEChUMBoNCI7Oxvp6eliwKmPM2fOYNGiRZgwYQLmzp0LNzc3lJSU4PDhwzh9+jQefPBBXL16FaNGjcKCBQvQrl07bN26FfHx8Vi1alUzjpKIiMh6MeBYIDU1FQUFBdBoNHBycgIA2NvbIzAwEIGBgQ3qa8OGDRg5ciSmTJki7nNzc6vxHSq1Wl3jnMceewy7du1CeXk55HJ5I0Zyax/5L6+1L6ZZrkRERNT0GHAskJWVBZVKJYYbS127dg1nzpzB1KlTG3TeqVOn4OrqWme40ev10Ov1AICcnJxG1UdERGStGHAsUF5eDh8fH3E7Pz8f0dHRMJvNEAQBycnJ4rF169Zh06ZN4rbJZBL//scff4hfD79p+/bt2L17N4xGIx588EHMmzevxrXz8/Oh0WgQGRlZZ20ajabBXzQnIiKyNQw4FpDL5SgpKRG3PT09kZSUJC4g/rOIiIg6FxkDgEwmg52dHYqLi9G9e3cAwOTJkzF58mRs3LgRpaWlNfoqKirCwoULMWnSJDzwwAN11hYZGYlx48YBuDGDM23atMYPmIiIyMow4FjA398fW7duRWVlJZydnS3up3379ujTpw/S0tIwcODA27YtLi7GG2+8gTFjxmD8+PG3bOfl5QUvLy+LayIiIrIF/B0cC4SEhMDd3R2JiYnIy8uD0WiEIAjIzc1tcF8zZsxASkoKtFqtOGNTVlaGS5cuiW2Ki4sRGxuL4OBgTJo0qcnGcTsV+76p9YeIiMhacAbHAlKpFPHx8dBqtUhISEBZWRnkcjmUSmWD17/4+voiMTERWq0We/bsEdfkBAQEiG9S/fvf/4Zer8fu3buxe/du8dyPP/4YHh4eTTo2IiIiWyAxm83m1i6CmkdGRgbUajV0Oh0CAgIadG5sdFStfYnvfdBUpRERETUrPqIiIiIim8OAQ0RERDaHa3CoTnwcRURE1owzOERERGRzGHCoThXv+aHiPb/WLoOIiMgiDDhERERkcxhwiIiIyOYw4BAREZHN4VtUDRAbG4ugoCCEhoaiqqoKWq0WaWlpKC0thUwmg7e3N8LCwqBSqWq1v+nmxzb37t0r7jt//jySkpJw+vRpGI1GuLq6Qq1WY8KECXB3d0d+fj5WrFiBy5cvw2w2495778X06dPRv3//Fr8HRERE1oABxwKCICAuLg6Ojo6IiYmBQqGA0WhEdnY20tPTxYBTH2fOnMGiRYswYcIEzJ07F25ubigpKcHhw4dx+vRpPPjgg5DL5Xj55Zfh6ekJiUSCtLQ0vP3229i8eTMcHPiPkIiI6K/4/44WSE1NRUFBATQaDZycnAAA9vb2CAwMRGBgYIP62rBhA0aOHIkpU6aI+9zc3MTvUAGAs7Oz+NVyk8kEOzs7XL16FVeuXEGnTp2aYES1feS/HAAQ0yy9ExFZh7o+W9MU+FtjzY8BxwJZWVlQqVRiuLHUtWvXcObMGUydOrVe7SMiIlBSUgKj0YiHH364znCj1+uh1+sBADk5OY2qj4iIyFox4FigvLwcPj4+4nZ+fj6io6NhNpshCAKSk5PFY+vWrcOmTZvEbZPJJP79jz/+EL8eftP27duxe/duGI1GPPjgg5g3b16Nvqqrq/Hdd9/dsjaNRtPgL5oTERHZGgYcC8jlcpSUlIjbnp6eSEpKEhcQ/1lERESdi4wBQCaTwc7ODsXFxejevTsAYPLkyZg8eTI2btyI0tLSWtdu164dHn74YcyaNQs9e/aEt7d3jeORkZEYN24cgBszONOmTWuaQRMREVkRviZuAX9/f2RmZqKysrJR/bRv3x59+vRBWlpag881mUzIz8+vtd/LywsBAQEICAiAr69vo+ojIiKyVgw4FggJCYG7uzsSExORl5cHo9EIQRCQm5vb4L5mzJiBlJQUaLVaccamrKwMly5dEtucPHkSZ8+ehdFoxPXr16HValFRUYE+ffo02Zj+qmLfN6jY902z9U9ERNSc+IjKAlKpFPHx8dBqtUhISEBZWRnkcjmUSmWD17/4+voiMTERWq0We/bsEdfkBAQEiG9SVVVV4ZNPPkFhYSGkUimUSiUWLVrUbG9QERERWTuJ2Ww2t3YR1DwyMjKgVquh0+kQEBDQoHNvvhrJVxmJqC1rydfEP/roI2zYsAEnT57EhAkToNVqxWOnTp3CzJkzkZ2djZ49e2L16tUICgoCAHzyySd49913kZWVJb7du3PnTvzjH//A6dOn0bFjx2YZw92Oj6iIiIjuAl27dsWbb76J559/vsZ+QRAwbtw4TJgwAaWlpXj99dcxfvx4cVlDZGQk7r33XrzxxhsAgKKiIsydOxeffPJJmw03AAMOERHRXeHxxx/HY489Bnd39xr7v/32W1RWVmL+/PlwdHTEtGnT4O3tjV27dgEAJBIJ1q1bh3Xr1uHo0aN44YUXMGbMGPGN2raKa3CoTnw0RUR0dzh16hQGDBgAO7v/m5MYNGgQTp06JW4rlUokJiZi/PjxsLe3x08//dQapd5VOINDRER0F/vjjz9wzz331NjXsWNHVFRU1NgXFBSEoqIiPPLII3wJBZzBoVuoeM+vtUsgIhvmEn3qzo0IwI0fhS0vL6+x78qVK3BxcRG3DQYDnnvuOcyePRtbt27F7NmzMWTIkJYu9a7CGRwiIqK7mJ+fH06ePFnjUz8nTpyAn9///YfoP//5TwA33sRavHgxnnvuOVRXV7d4rXcTBhwiIqK7gMFgwLVr12AwGGAymXDt2jUIgoDg4GC0b98eK1euxPXr17Ft2zb88ssv4m+l/fTTT1i6dCk+++wz2Nvb46WXXoJMJkNCQkIrj6h1MeAQERHdBeLj4+Hk5ISEhATs2LEDTk5OeP755yGVSrF3717s3LkTHTt2REJCAr744gu4ubnBaDRi+vTpeOWVV+Dv7w8AsLOzw2effYZ3330X2dnZrTyq1sM1OE0gNjYWQUFBCA0NRVVVFbRaLdLS0lBaWgqZTAZvb2+EhYVBpVLVan/TzY9w7t27FwCwbds2aLVaTJs2DZMnTxbbpaWl4Z133kFQUBDmz5/fsgMlIqJms3jxYixevLjOYwMGDMDx48dr7be3t0d6enqt/f3796+1CLmtYcBpQoIgIC4uDo6OjoiJiYFCoYDRaER2djbS09PFgFNf3bp1w6FDh2oEnJSUFHTr1q2pS6/lI//lzX4NImq7Ylq7ALJ59Q44N39QqL4ef/zxBhdj7VJTU1FQUACNRiP+XLa9vT0CAwMRGBjY4P569uyJ//3vf8jJyYGvry+uXLmCM2fOYPTo0SgoKGjq8omIiGxGvQPOpEmT6t2pRCKB0Wi0qCBrlpWVBZVKJYabphASEoJDhw7B19cX3377LYYPHw6pVHrL9nq9Hnq9HgCQk5PTZHUQERFZk3oHnLy8vOaswyaUl5fDx8dH3M7Pz0d0dDTMZjMEQUBycrJ4bN26ddi0aZO4/efX//4sODgY8+bNw/PPP49Dhw5hzpw5yMjIuGUNGo2mwV80JyIisjX1Djg9evRozjpsglwuR0lJibjt6emJpKQkcQHxn0VERNS5yPivOnbsiH79+iEpKQnV1dXo16/fbQNOZGSk+P2RnJwcTJs2rbHDIiIisjpcZNyE/P39sXXrVlRWVsLZ2bnJ+g0JCcE///nPeoUVLy8veHl5Ndm1iYjaMn6Xz3rVO+C4uLhAIpHUq61EIsGVK1csLspahYSE4MCBA0hMTERERAQUCgVMJhNyc3Mb1e+QIUOwZMmSGo+/mlvFvm9a7FpE1PbE7vuG4YGaVb0DziuvvFLvgNNWSaVSxMfHQ6vVIiEhAWVlZZDL5VAqlY1aF+Pg4IBBgwY1XaFEREQ2TmI2m82tXQQ1j4yMDKjVauh0OgQEBDTo3NjoqGaqiojoBmuYwXknZX+z9BvzcOidG1Gj8FMNREREZHMsXmRcVlaGnTt34ueff8a1a9dqHf/www8bVRgRERGRpSwKOGfPnsX999+P69ev4+rVq/Dw8EBJSQkMBgNcXV1xzz33MOBYOWuYOiYiIroVix5Rvfzyyxg6dCh+//13mM1mfP3116iqqsKWLVvg4uKCHTt2NHWdRERERPVm0QzOjz/+iHXr1sHR0REAUF1dDXt7e0ydOhXFxcX4xz/+gR9++KFJC6WWVfGeX2uXQGRzXKJPtXYJRG2GRTM4169fh1wuh52dHdzc3HD58mXx2H333YcTJ040VX1ERERtglKpxDff1P4NsgsXLkAikdS53pVuzaKA06dPH/z6668AAJVKhVWrVqGiogJVVVXQaDTo2rVrkxZJREREjXOrAGWrLHpENWXKFJw4cQJPP/003n77bfztb3+Dq6srJBIJzGYzNmzY0MRlEhEREdWfxYuMV65cCQAYNmwYTp06hdWrV2PlypU4ceJEm//AY2xsLPbvv/HjUFVVVVi/fj1mzZqFJ554AjNmzMBbb72FzMzMOtsDwPnz5/H000/jyy+/BADo9XqsWLECTz/9NMLDwzF37lxs27aN05VERDYmMzMTAwYMwD333IOJEyeirKysVptNmzbhvvvug4uLC3r27ImPP/5YPFZUVISxY8fC1dUVbm5uGDFiBK5fv46nn34aFy9exIQJEyCTyfDGG2+04KhaR5N8bPPee+/F888/3xRd2RRBEBAXFwdHR0fExMRAoVDAaDQiOzsb6enpUKlUtc45ffo04uPjMWPGDIwePRr5+fl49dVX8eCDD+K9996Du7s7fv/9d+zevRt6vR7e3t7NUvtH/subpV+itiymtQugu96GDRuwf/9+eHh44Mknn0RUVFStT/24u7tjz5496NWrF44cOYLQ0FAMGTIEgwcPxsqVK9G9e3cUFBQAuPFSkL29PTZv3ozvvvsOn3zyCf7+97+3xtBanEUzOJ9//jmWL6/7/wBXrFjB18T/v9TUVBQUFODNN9+Et7c37O3t0a5dOwQGBmLWrFm12mdmZuLtt9/GnDlzMHr0aABAUlISevfujcjISLi7uwMAunTpgtmzZzdbuCEiotYxb9489OzZEy4uLkhISIBWq4XJZKrR5pFHHoGPjw8kEgkeeughjBkzBkeOHAFw45uIer0eFy5cgFQqxYgRI+Dg0CRzGVbHooCzdOlS8RXxv3JycsLSpUsbVZStyMrKgkqlgpOT0x3b6nQ6LFu2DNHR0QgKChL3Z2ZmYsSIEfW+pl6vR0ZGBjIyMpCTk2NR3URE1DoUCoX49x49eqC6uhqFhYU12uzfvx/Dhg2Dm5sbOnbsiK+//hpFRUUAgPnz58PHxwd///vf4e3tjbfffhtt9ZOTFsW6n3/+GX5+df9OSv/+/fHzzz83qihbUV5eDh8fH3E7Pz8f0dHRMJvNEAQBycnJ4rGTJ0/C09Oz1n2tqKiAm5tbva+p0Wga9eVyIiJqPRcvXqzxd6lUKs7eAzd+pmXixIn47LPPMHHiREilUkyYMEEMMS4uLli5ciVWrlyJ06dPIyQkBAEBAXj00UchkUhafDytyaIZnPbt2+P333+v85her2+z02F/JZfLUVJSIm57enoiKSkJK1asgCAINdpOnToVLi4uWLJkCaqqqsT9Li4uNfq4k8jISOh0Ouh0OmzZsqXxgyAiohazatUq5OXloaKiAm+++SbCw8Nhb28vHq+ursb169fh4eEBBwcH/Pvf/8a///1v8fi+fftw7tw5mM1myOVy2Nvbi+d36dIF58+fb/ExtRaLAs5DDz2EpUuX4urVqzX2X716FcuWLUNwcHBT1Gb1/P39kZmZicrKyju2bdeuHeLi4uDg4IAlS5aIb0ipVCocPXq03tf08vJCQEAAAgIC4Ovra3HtRETU8p555hmMGzcO3bt3h729PT74oOZ3AV1cXPDhhx9i6tSpcHV1xebNmxEWFiYeP3v2LEaPHg0XFxcMHToUERER4qLimJgYLFu2DB07dkRcXFyLjqs1SMwWPJw7c+YMhg8fDkdHR0yaNAldu3bF5cuXsXPnTlRXV+Po0aPo169fc9RrFWJjYxEUFIRRo0bhtddeg7OzMyIiIqBQKGAymXDkyBF88MEH2Lt3b432oaGhuHbtGpYsWQKJRIJFixahtLQUr7zyCoKDg/H444+jU6dOKCwsxBdffIFRo0bddqFxRkYG1Go1dDodAgICGjaG6KhG3QMiqo0fsbU+76Tsv3MjC8Q8HNos/dL/sehZUr9+/ZCeno5FixYhOTkZxcXF6NSpE0aPHo1FixbVWHfSlkmlUsTHx0Or1SIhIQFlZWWQy+VQKpW3XCfTvn17LFy4EEuWLMFbb72FhQsXYvny5di6dSuioqIgCAI6deqEBx98kL8YTUREdAsWzeA01JEjR6BWq9GhQ4fmvhT9CWdwiO4unMGxPpzBsV4WrcFpCKPRiJEjRyI3N7e5L0VEREQEoAUCDoA2+w4+ERERtQ6+z0114lQ6ERFZMwYcIiKiW+BaGevFgEN1qniv7l+qpqbhEn2qtUsgIrJpLbIGh4iIiKglcQaHiIjoFpprNpuzuM3PooCTkpKCixcvYsaMGbWObdiwAT169MDIkSMBAHZ2dnj22WdrfCysLfjzrxNXVVVBq9UiLS0NpaWlkMlk8Pb2RlhYGFQqVa32f2UwGLBr1y6kpqaiqKgILi4u6Nu3L8LDw6FUKlt4ZERERHc/iwLOm2++ifHjx9d5rLCwEJ9++il++OEHAIBEIsH69estr9DKCYKAuLg4ODo6IiYmBgqFAkajEdnZ2UhPTxcDzu0sW7YM+fn5iIqKgo+PDwwGA44ePYr09HQGHCIiojpYFHB++uknvP3223UeCwgIQEJCQqOKsiWpqakoKCiARqOBk5MTAMDe3h6BgYEIDAy84/lZWVnQ6XRYvXo1OnfuDABwcHBASEhIs9ZNRERkzSxaZCyRSHDlypU6j5WWlsJoNDaqKFuSlZUFlUolhpuGOnHiBHr37i2GGyIiIroziwLO0KFD8fHHH9f6hWKz2YxVq1Zh6NChTVKcLSgvL4ebm5u4nZ+fjyeffBJTpkzBxIkT73h+RUUFOnXqVO/r6fV6ZGRkICMjAzk5ORbVTERE1FSuXbsGiUSCCxcutOh1LXpEtWTJEowcORL+/v6YPn06vLy8cPnyZWzatAk///wzvv322yYu03rJ5XKUlJSI256enkhKSsKlS5cwd+7cO57v4uKCS5cu1ft6Go3mll8qJyIiaqjg4GBMmTIFs2fPbu1SGsSiGZzhw4cjJSUFcrkcr732GqZNm4bXX38d99xzD1JSUjBs2LCmrtNq+fv7IzMzE5WVlRadP2jQIJw9exaFhYX1ah8ZGQmdTgedToctW7ZYdE0iIrJOZrMZBoOhxj6j0QiTydRKFbUei3/ob8SIEfjhhx9QUVGBS5cuoby8HN999x1GjBjRlPVZvZCQELi7uyMxMRF5eXkwGo0QBKHOr6sbjUZUV1eLfwRBwMCBA6FWq5GQkIDc3FwYDAZcv34dhw8fxs6dO2v14eXlhYCAAAQEBMDX17clhkhERE3k8uXLmDJlCjw9PeHm5obHHnsMAPD111/D398f99xzD4YNG4Yff/xRPCc4OBgxMTF46KGH0KFDB/z3v/+FUqnE0qVLoVKp0KFDBxQUFODs2bMIDQ2Fu7s7evXqhVWrVol9mEwmrFy5En369IGLiwvuu+8+ZGRk4I033sB3332Hl156CTKZDE8//TSAG8stwsPD0aVLF9x7771YvHixGKJMJhNiYmLg4eEBhUKBrVu3ttwN/JNG/9Cfk5OTxQto2wKpVIr4+HhotVokJCSgrKwMcrkcSqWy1qOkNWvWYM2aNeK2n58fEhMTsWDBAiQnJ+P9999HUVERZDIZ+vfvj/Dw8JYeDhERNROj0YiwsDAMHToUP//8M9q3b4+0tDScPXsWkyZNwo4dO/C3v/0N69evR2hoKM6dOwdXV1cAwMaNG/H1119jwIAB4gzOli1bsHfvXnTv3h2CIGDo0KF4/fXXsXfvXvzyyy8YPXo0evfujdGjR+PDDz/Ep59+il27duG+++7D2bNn4ejoiISEBPzwww81HlGZTCaMGzcOo0ePxsaNG1FSUoJHH30U3bp1w/PPP49169Zhx44dOH78ODw8PMRQ1NIk5r+uFCabkZGRAbVaDZ1Oh4CAgAady29RNS/+iimRdWjJXzI+duwYHnnkEeTn56Ndu3bi/vj4eOh0OuzevVvcN2jQILz66quYNm0agoODMWzYMCxdulQ8rlQq8frrr4uhZPv27Vi5ciWOHz8utnn77beRl5eHzz77DP369UN8fDwmTZpUq66/rsH58ccfMX78eFy+fBkSiQQAsHnzZqxfvx6HDh1CSEgIJkyYgBdffBHAjbeJBw0ahLy8vBb97TZ+qoGIiOgucPHiRSgUihrhBgB+++23WsFAqVTit99+E7cVCkWt/v6878KFC8jMzETHjh3FfUajEUFBQeK1fXx86lXnhQsXUFhYKM4eATdmde69914ANx6z/fnaPXr0qFe/TY0Bh4iI6C6gUChw8eJFCIIAqVQq7u/WrRsyMjJqtL1w4UKN2RY7u9pLav+8T6FQ4P7777/lW84KhQLnzp3DoEGDah27OUvz57bdu3e/5WvfXbt2xcWLF8XtP/+9JfFr4kRERHeBwYMHo0ePHnj55ZdRUVEBQRBw+PBhTJ48GQcOHMCBAwdgMBjw2Wef4eLFi3jkkUfq3ffYsWNx4cIFrFu3DtevX4fBYMDJkyeRnp4OAJg1axYWLlyI06dPw2w24+zZs/j1118BAF26dMH58+dr1Onh4YG3334bV69ehclkwtmzZ3H48GEAQHh4OD788EPk5eWhoqICixcvbrqb1ACcwaE6cY0IEVHLsre3x5dffomoqCh4e3vDbDYjODgYycnJ0Gq1ePXVV3Hx4kX07dsXX331VY0fkb0TmUyG//znP3j11VcRExMDQRDEdTcAEBUVherqaoSFhaGgoABKpRKbNm1Cjx49EBUVhenTp2Pt2rUYN24cNm7ciC+//BLz589H7969cfXqVfTs2ROvvfYaAGDmzJk4f/48hgwZgvbt22Px4sU11g+1FC4ytmGNWWRMREQtu8iYmhYfUREREZHNYcAhIiIim8OAQ0RERDbHahcZx8bGIigoCKGhoaiqqoJWq0VaWhpKS0shk8ng7e2NsLAwqFSqWu3/ymAwYNeuXUhNTUVRURFcXFzQt29fhIeH1/mjRO+//z5cXV3x7LPP1jj/22+/RUFBATp06IDu3bvj73//u/gbA38956++/vprfPPNN9Dr9XB2dkavXr0wadIk9O/fHwBQUFCATz/9FNnZ2bC3t8fgwYMRHR3dFLeSiIjI5lhtwLlJEATExcXB0dERMTExUCgUMBqNyM7ORnp6uhhwbmfZsmXIz89HVFQUfHx8YDAYcPToUaSnp9frVxeXLVuG33//HfPmzUPv3r1hZ2eHnJwc/Oc//xEDzu2sXbsWx44dwwsvvID+/fvDzs4OOp0Ox44dQ//+/WEwGLBw4UKMHj0aL7/8MhwcHFrtdwWIiIisgdUHnNTUVBQUFECj0YjfxLK3t0dgYCACAwPveH5WVhZ0Oh1Wr16Nzp07AwAcHBwQEhJSr+tnZWUhIyMDq1evhoeHh7jfz88Pfn53Xn1/+fJl7Nu3D0uXLkW/fv3E/cOGDRO/yn7o0CHI5XJMnDhRPN6rV6961UdERNQWWX3AycrKgkqlsviDnydOnEDv3r3FcGPp+X8ONw2RlZUFNze3GuHmr86cOYMuXbpg8eLFOHv2LLp27YoZM2aIj6+IiKh58HVu62X1Aae8vLzG9zPy8/MRHR0Ns9kMQRCQnJx82/MrKirQqVOnRl3/zz+2ZDQaMW3aNABAdXV1jZmhW13/Tj/WVFRUhOzsbMTGxiIgIACHDh1CfHw8NBoNXFxcarTV6/XQ6/UAgJycHEuHRUREZNWs/i0quVyOkpIScdvT0xNJSUlYsWIFBEG44/kuLi4oLi5u1PX/fL69vT2SkpKwceNGCIKAO/2OoouLS4366+Lo6Ih+/fphyJAhcHBwwJgxY+Di4oIzZ87UaqvRaKBWq6FWq8WgRURE1NZYfcDx9/dHZmYmKisrLTp/0KBBOHv2LAoLCy2+/rlz5yw+f+DAgSgpKUFubu4t2zTk8/KRkZHQ6XTQ6XTYsmWLRTURERFZO6sPOCEhIXB3d0diYiLy8vJgNBohCEKdgcFoNKK6ulr8IwgCBg4cCLVajYSEBOTm5sJgMOD69es4fPgwdu7cCQBISUnBzJkz67y+SqXCoEGDEB8fj5ycHAiCAKPRWOfjIZPJVOv6Xbt2xdixY7FixQqcOHFC3J+eno7169cDAEaOHIlz585Bp9PBaDQiJSUFV69eha+vb61reHl5ISAgAAEBAXUeJyIiagusfg2OVCpFfHw8tFotEhISUFZWBrlcDqVSiSVLltRou2bNGqxZs0bc9vPzQ2JiIhYsWIDk5GS8//77KCoqgkwmQ//+/REeHg4AKCwsvG1YeO2117Br1y58+OGHKCwshEwmQ7du3fDaa6/VWHy8e/fuGh8c69y5M9auXYuIiAh07doVn332GS5fvgxnZ2f4+PjgiSeeAHDj0/Pz58/H2rVrUVJSgnvvvRdvvvkmZDJZk9xDIiIiW8OPbdbDwoUL8fzzz+Pee+9t7VIahB/bJCKitsrqZ3BawltvvdXaJRAREVEDWP0aHCIiIqK/YsAhIiIim8OAQ0RERDaHAYeIiIhsDhcZU50q3rvzh0JbE78PQ0REt8MZHCIiIrI5DDhERERkc/iIykKxsbEICgpCaGgoqqqqoNVqkZaWhtLSUshkMnh7eyMsLAwqlapW+78yGAzYtWsXUlNTUVRUBBcXF/Tt2xfh4eFQKpXIzs7G559/jvPnz6Ndu3bYtGlTSw+XiIjIqjDgNJIgCIiLi4OjoyNiYmKgUChgNBqRnZ2N9PR0MeDczrJly5Cfn4+oqCj4+PjAYDDg6NGjSE9Ph1KpRPv27TFq1Cg89NBD/IAmERFRPTDgNFJqaioKCgqg0Wjg5OQEALC3t0dgYCACAwPveH5WVhZ0Oh1Wr16Nzp07AwAcHBwQEhIitunTpw/69OmDkydPNs8g6vCR//IWu5YlYlq7ACIiuqtxDU4jZWVlQaVSieGmoU6cOIHevXuL4YaIiIgajzM4jVReXg4fHx9xOz8/H9HR0TCbzRAEAcnJybc9v6KiAp06dWqyevR6PfR6PQAgJyenyfolIiKyJgw4jSSXy1FSUiJue3p6IikpCZcuXcLcuXPveL6LiwsuXbrUZPVoNBosWbKkyfojIiKyRnxE1Uj+/v7IzMxEZWWlRecPGjQIZ8+eRWFhYZPUExkZCZ1OB51OxwXJRETUZnEGp5FCQkJw4MABJCYmIiIiAgqFAiaTCbm5ubXaGo1GVFdXi9sSiQQDBw6EWq1GQkIC5syZg169esFoNOLYsWMoLCzEpEmTYDKZYDAYYDAYAADV1dWQSCSQSqW1ruHl5QUvL69Gj6ti3zeN7qNZPVz7dXsiIqKbGHAaSSqVIj4+HlqtFgkJCSgrK4NcLodSqaz1qGjNmjVYs2aNuO3n54fExEQsWLAAycnJeP/991FUVASZTIb+/fsjPDwcAPDTTz/hjTfeEM+bNGkSOnfujLVr17bMIImIiKyMxGw2m1u7CGoeGRkZUKvV0Ol0CAgIaNC5sdFRzVRV00h874PWLoGIiO5iXINDRERENocBh4iIiGwOAw4RERHZHC4ypjpxjQsREVkzzuAQERGRzeEMDtXpbn+LytpxhoyIqHlxBoeIiIhsDgMOERER2Rw+orJQbGwsgoKCEBoaiqqqKmi1WqSlpaG0tBQymQze3t4ICwuDSqWq1f6vDAYDdu3ahdTUVBQVFcHFxQV9+/ZFeHg4lEqleKygoAAdOnTAyJEjMXXqVNjb27f0sImIiKwCA04jCYKAuLg4ODo6IiYmBgqFAkajEdnZ2UhPTxcDzu0sW7YM+fn5iIqKgo+PDwwGA44ePYr09HQolUqYzWb84x//gLe3N0pKShAfHw9nZ2dMnDixBUZIRERkfRhwGunmzIpGo4GTkxMAwN7eHoGBgQgMDLzj+VlZWdDpdFi9ejU6d+4MAHBwcEBISIjY5s9BpnPnznjooYdw+vRpBhwiIqJb4BqcRsrKyoJKpRLDTUOdOHECvXv3FsNNffz0009QKBQWXY+IiKgt4AxOI5WXl8PHx0fczs/PR3R0NMxmMwRBQHJy8m3Pr6ioQKdOnep9vX379uHChQt46aWX6jyu1+uh1+sBADk5OfXul4iIyJYw4DSSXC5HSUmJuO3p6YmkpCRcunQJc+fOveP5Li4uuHTpUr2ulZqaip07dyI+Ph5yubzONhqNBkuWLKlf8URERDaKj6gayd/fH5mZmaisrLTo/EGDBuHs2bMoLCy8bbvDhw9j/fr1WLJkCbp3737LdpGRkdDpdNDpdNiyZYtFNREREVk7BpxGCgkJgbu7OxITE5GXlwej0QhBEJCbm1urrdFoRHV1tfhHEAQMHDgQarUaCQkJyM3NhcFgwPXr13H48GHs3LkTwI1w8+mnn2LRokXo0aPHbevx8vJCQEAAAgIC4Ovr2yxjJiIiutvxEVUjSaVSxMfHQ6vVIiEhAWVlZZDL5VAqlbUeFa1ZswZr1qwRt/38/JCYmIgFCxYgOTkZ77//PoqKiiCTydC/f3+Eh4cDADZv3oyrV68iJiZGPLd///5YvHhxi4yRiIjI2kjMZrO5tYug5pGRkQG1Wg2dToeAgIAGnctvUTUvfouKiKh58REVERER2RwGHCIiIrI5DDhERERkc7jImOrENSJERGTNGHCoThXv+bXKdV2iT7XKdYmIyLbwERURERHZHAYcIiIisjkMOERERGRzuAbHQrGxsQgKCkJoaCiqqqqg1WqRlpaG0tJSyGQyeHt7IywsDCqVqlb7vzIYDNi1axdSU1NRVFQEFxcX9O3bF+Hh4VAqlfjmm2+wa9culJeXQyqVQq1WY9asWXB2dm7pYRMREVkFBpxGEgQBcXFxcHR0RExMDBQKBYxGI7Kzs5Geni4GnNtZtmwZ8vPzERUVBR8fHxgMBhw9ehTp6elQKpUYNGgQRowYARcXF1RWVmLVqlXYtGkTZs+e3QIjJCIisj4MOI2UmpqKgoICaDQaODk5AQDs7e0RGBiIwMDAO56flZUFnU6H1atXo3PnzgAABwcHhISEiG08PT1rnCORSHD58uUmHEVtH/kvb9b+byllf+tct42Iebj2DCIRkS1iwGmkrKwsqFQqMdw01IkTJ9C7d28x3NxKeno6Vq5cicrKSjg6OuL111+36HpERERtAQNOI5WXl8PHx0fczs/PR3R0NMxmMwRBQHJy8m3Pr6ioQKdOne54ncGDB0Or1aKwsBAHDhyoNatzk16vh16vBwDk5OQ0YCRERES2g29RNZJcLkdJSYm47enpiaSkJKxYsQKCINzxfBcXFxQXF9f7eh4eHlCpVFixYkWdxzUaDdRqNdRqNaZNm1bvfomIiGwJA04j+fv7IzMzE5WVlRadP2jQIJw9exaFhYX1PsdoNIqzNH8VGRkJnU4HnU6HLVu2WFQTERGRteMjqkYKCQnBgQMHkJiYiIiICCgUCphMJuTm5tZqazQaUV1dLW5LJBIMHDgQarUaCQkJmDNnDnr16gWj0Yhjx46hsLAQkyZNwsGDBxEQEAA3Nzfk5+djy5YtGDhwYJ31eHl5wcvLq9Hjqtj3TaP7sAS/gUVERE2BAaeRpFIp4uPjodVqkZCQgLKyMsjlciiVSixZsqRG2zVr1mDNmjXitp+fHxITE7FgwQIkJyfj/fffR1FREWQyGfr374/w8HAAwLlz57B582ZUVlbCxcUFgYGBePrpp1t0nERERNZEYjabza1dBDWPjIwMqNVq6HQ6BAQENOjc2OioZqrq9jiDQ0RETYFrcIiIiMjmMOAQERGRzWHAISIiIpvDRcZUJ66FISIia8YZHCIiIrI5DDhERERkcxhwiIiIyOYw4BAREZHN4SJjC8XGxiIoKAihoaGoqqqCVqtFWloaSktLIZPJ4O3tjbCwMKhUqlrt/8pgMGDXrl1ITU1FUVERXFxc0LdvX4SHh0OpVAIA8vLysHbtWpw9exbt2rXDmDFj8Mwzz7TkkImIiKwGA04jCYKAuLg4ODo6IiYmBgqFAkajEdnZ2UhPTxcDzu0sW7YM+fn5iIqKgo+PDwwGA44ePYr09HQolUpUVFRg4cKFmD59OhYtWgSz2YzLly+3wOiIiIisEwNOI6WmpqKgoAAajQZOTk4AAHt7ewQGBiIwMPCO52dlZUGn02H16tXo3LkzAMDBwQEhISFimz179mDQoEF4+OGHxX3e3t5NPBIiIiLbwYDTSFlZWVCpVGK4aagTJ06gd+/eYripy5kzZ6BQKLBgwQL89ttv6NmzJ55//nkoFIpabfV6PfR6PQAgJyfHopqIiIisHQNOI5WXl8PHx0fczs/PR3R0NMxmMwRBQHJy8m3Pr6ioQKdOnW7bpqioCGfPnsWSJUvQq1cv7NixAwkJCfj444/h4FDzH6FGo6n1FXMiIqK2hm9RNZJcLkdJSYm47enpiaSkJKxYsQKCINzxfBcXFxQXF9+2jaOjI4YOHYp+/fpBKpUiPDwcRUVFda7DiYyMhE6ng06nw5YtWxo+ICIiIhvAGZxG8vf3x9atW1FZWQlnZ+cGnz9o0CDs3bsXhYWF8PDwqLPNzTep6sPLywteXl4NroOIiMiWcAankUJCQuDu7o7ExETk5eXBaDRCEATk5ubWams0GlFdXS3+EQQBAwcOhFqtRkJCAnJzc2EwGHD9+nUcPnwYO3fuBACMHj0ax48fx9mzZ2E0GrFjxw507twZ3bp1a+nhEhERWQXO4DSSVCpFfHw8tFotEhISUFZWBrlcDqVSWWstzJo1a7BmzRpx28/PD4mJiViwYAGSk5Px/vvvo6ioCDKZDP3790d4eLjY7rnnnsM777yDyspK+Pj44I033oC9vX2LjpWIiMhaSMxms7m1i6DmkZGRAbVaDZ1Oh4CAgNYuh4iIqMXwERURERHZHAYcIiIisjkMOERERGRzGHCIiIjI5jDgEBERkc1hwCEiIiKbw4BDRERENocBh4iIiGwOf8nYQrGxsQgKCkJoaCiqqqqg1WqRlpaG0tJSyGQyeHt7IywsDCqVqlb7vzIYDNi1axdSU1NRVFQEFxcX9O3bF+Hh4VAqlVi8eDFOnz5do71UKsXnn3/eYuMlIiKyJgw4jSQIAuLi4uDo6IiYmBgoFAoYjUZkZ2cjPT1dDDi3s2zZMuTn5yMqKgo+Pj4wGAw4evQo0tPTxYDz1/ZOTk7NNCIiIiLrx4DTSKmpqSgoKIBGoxFDh729PQIDAxEYGHjH87OysqDT6bB69Wp07twZAODg4ICQkJA621dUVOD48eOIj49vukEQERHZGAacRsrKyoJKpbJ4RuXEiRPo3bu3GG7u5PDhw+jcuTN8fX3rPK7X66HX6wEAOTk5FtVERERk7RhwGqm8vBw+Pj7idn5+PqKjo2E2myEIApKTk297fkVFBTp16lTv6x08eBCjRo265XGNRlPrK+ZERERtDd+iaiS5XI6SkhJx29PTE0lJSVixYgUEQbjj+S4uLiguLq7XtfLy8nDhwgWMHDnylm0iIyOh0+mg0+mwZcuWevVLRERkaziD00j+/v7YunUrKisr4ezs3ODzBw0ahL1796KwsBAeHh63bXvw4EEEBATAzc3tlm28vLzg5eXV4DqIiIhsCWdwGikkJATu7u5ITExEXl4ejEYjBEFAbm5urbZGoxHV1dXiH0EQMHDgQKjVaiQkJCA3NxcGgwHXr1/H4cOHsXPnTvFcQRBw+PDh2z6eIiIiohs4g9NIUqkU8fHx0Gq1SEhIQFlZGeRyOZRKZa21MGvWrMGaNWvEbT8/PyQmJmLBggVITk7G+++/j6KiIshkMvTv3x/h4eFi2/T0dADAkCFDWmZgREREVkxiNpvNrV0ENY+MjAyo1WrodDoEBAS0djlEREQtho+oiIiIyOYw4BAREZHNYcAhIiIim8OAQ0RERDaHb1FRnSre86ux7RJ9qpUqISIiajjO4BAREZHNYcAhIiIim8OAQ0RERDaHAaeJxMbGYsKECbh8+bK479KlSxg3blwrVkVERNQ2MeA0IWdnZ2zbtq21yyAiImrz+BZVE3r00UexZ88e/PLLL+jZs2eNY5WVlfj000/x3//+Fw4ODggKCsLTTz8NqVSKkydPYvny5Zg8eTJ27NgBk8mECRMm4PHHHwcAmM1mfPHFFzhw4ADKy8vRt29fvPDCC3B3d2+2sXzkv7zGdkyzXYmIiKjpcQanCbm6umLs2LHYsmVLrWNr1qxBSUkJVq9ejRUrViA7O7vG18LLy8tRXFyMtWvXIi4uDlu2bBEfd3311Vc4cuQIlixZgk2bNqFXr15YtmxZnTXo9XpkZGQgIyMDOTk5zTNQIiKiuxwDThN7/PHHkZubi59++kncZzKZcOTIEUyfPh0ymQydOnXClClTkJqaKraxs7PDU089BalUij59+qBbt27Iy8sDAOzfvx/Tpk1Dly5d4ODggCeffBLnzp1DYWFhretrNBqo1Wqo1WpMmzat+QdMRER0F+IjqibWoUMHPP7449i0aRNefPFFAMCVK1dgMBjQuXNnsV3nzp1RUlIibstkMjg4/N8/DkdHR1y7dg0A8Pvvv2PZsmWws/u/PGpnZ4eioiJ4eHjUuH5kZKS4sDknJ4chh4iI2iQGnGYwduxYfPnll0hPTwcAyOVyODg4oKCgAN7e3gCAgoICuLm51as/Dw8PzJ07FwMGDLhjWy8vL3h5eVlePBERkQ3gI6pm4OjoiClTpohrbOzt7REUFIQtW7bg6tWrKCkpwfbt2xEcHFyv/kJDQ7F582bo9XoAwB9//IHvv/++ucoHAFTs+0b8E/NwaLNei4iIqKlxBqeZjB49Grt370ZFRQUAYNasWfj0008xe/ZsMfA88cQT9epr7NixkEgkePvtt1FcXIwOHTpg0KBBeOCBB5pzCERERFZLYjabza1dBDWPjIwMqNVq6HQ6BAQENOjc2Ogo8e+J733Q1KURERE1Kz6iIiIiIpvDgENEREQ2h2twqE58LEVERNaMMzhERERkcziDY8OqqqoAgJ9sICKb069fPzg7O7d2GXQXY8CxYRcuXAAA/poxEdkcS94OpbaFr4nbsKKiIhw4cABKpRJOTk71Pu/mJx62bNkCX1/fZqzQ+vDe1I335dZ4b26tMfeGMzh0J5zBsWHu7u546qmnLD7f19eX/4V0C7w3deN9uTXem1vjvaHmwEXGREREZHMYcKgWLy8vLFq0iB/trAPvTd14X26N9+bWeG+oOXENDhEREdkczuAQERGRzWHAISIiIpvDgENEREQ2h6+JUw1//PEHPv74Y2RkZMDJyQmTJ0/GI4880tpltbh9+/bh0KFDuHDhAoYPH4758+eLx3799Vf861//woULF+Dp6Yk5c+bgvvvua8VqW5YgCPjkk0+QlZWFiooKuLu744knnkBwcDCAtn1/PvroI/z3v/9FVVUVXFxcMGbMGEyePBlA274vf1ZeXo45c+bAy8sLK1asAMB7Q82DMzhUg0ajgdFoxPr16xEXF4etW7ciOzu7tctqcW5ubpg8eTLGjBlTY7/BYEB8fDyGDx+OpKQkTJw4EQkJCfjjjz9aqdKWZzQa4ebmhvj4eCQlJeGFF17AJ598gjNnzrT5+zNu3DhoNBp8/vnneOedd3D48GF8//33bf6+/Nlnn30GpVIpbvPeUHNhwCHRtWvX8MMPP2DatGlwdnZGr169EBISgoMHD7Z2aS3u/vvvx7BhwyCXy2vsP3nyJK5fv44JEyZAKpVi5MiR6NKlC44ePdpKlba89u3b46mnnoKnpyfs7OzQv39/+Pr6Iicnp83fH4VCAUdHR3FbIpHg8uXLbf6+3HTy5Enk5+dj5MiRNfbx3lBzYMAh0W+//Qbgxv9I39SzZ0/8+uuvrVXSXefixYvo0aMH7Oz+718db29vXLx4sRWral3Xrl3DuXPn0KNHD94fABs3bsQTTzyBiIgIXLt2DSNHjuR9wY1HmxqNBrNnz4ZEIhH3895Qc+EaHBJdu3at1jerOnToIH6VnG58ob1Dhw419nXo0AGVlZWtVFHrMpvN+OCDD9C7d2+oVCr8/PPPbf7+PPvss3jmmWdw7tw5HD9+XPx3qK3flx07dkClUkGpVOL8+fPift4bai6cwSFR+/bta4WZq1evNuhDnbbOycmp1v/wVlZWtsl7ZDabsWrVKhQXF2PBggWQSCS8P/+fRCJB79694eDggKSkpDZ/Xy5fvoxvv/0WU6dOrXWsrd8baj4MOCTq1q0bAOB///ufuC8vLw89evRorZLuOgqFAr/++itMJpO4Ly8vr8ZjvbbAbDbjk08+wS+//ILFixejffv2AHh//spkMkGv17f5+5KTk4Pi4mLMnDkTTz31FNasWYPz58/jqaeeQpcuXdr0vaHmw4BDovbt22PEiBHYunUrKisrkZeXh5SUFDz88MOtXVqLMxqNqK6uhslkgslkQnV1NQwGAwYMGACpVIovvvgCgiDg8OHDyM/Px/Dhw1u75Bal0WiQm5uLJUuWwNnZWdzflu/P1atXkZqaisrKSphMJpw+fRr79+/HoEGD2vR9AYAHHngAGo0GH3zwAT744ANMnToVPXr0wAcffIDAwMA2fW+o+fBbVFTDH3/8gY8++ggZGRlwdnZus7+Ds23bNmi12hr7QkJC8NJLL+HChQv46KOPcOHCBXTp0gVz5syBn59fK1Xa8goKCjBz5kxIpVLY29uL+ydNmoTJkye32ftTWVmJxMREnD9/HiaTCW5ubhg1ahQef/xxSCSSNntf6pKSkoL9+/eLv4PDe0PNgQGHiIiIbA4fUREREZHNYcAhIiIim8OAQ0RERDaHAYeIiIhsDgMOERER2RwGHCIiIrI5DDhERERkcxhwiIiIyOYw4BBZmZ9//hkjRoyAXC7Ho48+ioKCghrHz549Czc3N1y6dKmVKqy/4OBgjB07VtzesGEDJBIJioqKWrEqIrIFDDhEVubZZ5+FUqnEjh078L///Q8vv/xyjeMvvfQSXn31VXTv3r2VKiQian0OrV0AEdXf1atXcezYMezduxceHh4oKyvDiy++KB7/6quvkJubi127drVilURErY8zOERW5Pr16wAAJycnAICzs7O4r7q6GtHR0Xj33Xfh6OjYoD7ffPNN9OzZE46OjujevTtmzJhxx/O++uorjBgxAs7OznB1dUVwcDAyMzMB3Ahi8+bNQ9++feHs7AylUonZs2fjypUrDR0yli5dCh8fH7Rv3x6dO3fGqFGjkJeX1+B+iKht4QwOkRVxc3NDz5498a9//QuRkZFYs2YNBg8eDAB499130bNnT4wbN65BfU6cOBGHDh1CbGwshg0bhsLCQiQnJ9/2nM8//xxPPvkkxo8fj23btqFdu3b44Ycf8Ntvv0GlUqGyshJGoxEJCQnw8PDA//73PyQkJGDChAk4dOhQvWvbtGkT4uLi8NZbb2H48OG4cuUKvvvuO5SXlzdojETU9vBr4kRW5t///jeeeOIJlJeXo2vXrti/fz/c3d0xYMAA/PDDD+jXr1+9+/rPf/6DMWPGYNu2bXjyySfrdY7ZbIZCocB9992Hb775pl7nGAwGHD9+HA888AByc3PRp08fADcWGctkMuzbtw/AjUXGM2bMQGFhIdzd3TFv3jykpaVBp9PVe0xERAAfURFZnTFjxiA/Px9nzpzBhQsX4O/vjwULFmDGjBno168f1q1bhx49eqBTp06IioqCwWC4ZV8pKSlwdnbGlClT6n393NxcXLp0Cc8999xt223evBkqlQoymQxSqRQPPPAAgBtvgdVXQEAAMjMz8fLLL+P777+HIAj1PpeI2jYGHCIr5OTkhL59+0IqleLo0aNISUnBwoULcfLkScyZMwebNm1CZmYm9uzZg7Vr196yn+LiYnh5eUEikdT72sXFxQCArl273rLN7t278cwzz2DIkCHYvn07jh07ht27dwMArl27Vu9rTZ8+He+99x4OHDiAoKAgeHh4ICoqClVVVfXug4jaJgYcIitmMpnw4osv4p133oFcLkdqair8/f3x0EMPQaFQYNKkSfjPf/5zy/M7deoEvV6Phjyp7tSpEwDg8uXLt2yzY8cODBo0CBqNBo888giGDh0KV1fX+g/s/7Ozs0NUVBR++uknXLp0CXFxcVi1ahVWrFjR4L6IqG1hwCGyYmvXroWDgwOeffZZcV9lZaX496tXr972/FGjRqGyshLbt2+v9zX79u2L7t27Y/369bdsU1VVhXbt2tXYt3Xr1npfoy7dunXDK6+8An9/f+Tk5DSqLyKyfXyLishKlZWVIS4uDl9++aX4iCk4OBgvvfQSli9fDqVSiaSkJCQmJt6yj1GjRuGRRx7Bc889h/Pnz2Po0KEoKSlBcnIytFptnedIJBKsWLECTz75JCZOnIhnnnkGjo6OSEtLw+DBgzF27FiMHj0aL7zwAt566y3cf//92L9/P1JSUho8xsjISLi6umLYsGFwdXXFDz/8gKysLMydO7fBfRFRG2MmIqv0j3/8wzxjxoxa+zUajfnee+81u7m5mefNm2cWBOG2/VRVVZlff/11s0KhMEulUnP37t3Nzz333B2vv3fvXvPQoUPN7du3N3fs2NEcEhJizszMNJvNZrPBYDC/8sorZg8PD7OLi4t50qRJ5mPHjpkBmHfs2CH28dBDD5kfffRRcXv9+vVmAObCwkKz2Ww2b9iwwTxixAizm5ubuX379ub+/fubP/zww/rcHiJq4/iaOBEREdkcrsEhIiIim8OAQ0RERDaHAYeIiIhsDgMOERER2RwGHCIiIrI5DDhERERkcxhwiIiIyOYw4BAREZHNYcAhIiIim8OAQ0RERDaHAYeIiIhszv8D4yZFnLgZtiUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (345615521)>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving contant region calls, parallelizing with 3 cpus : 100%|██████████| 2059/2059 [00:15<00:00, 130.87it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (347305873)>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving contant region calls, parallelizing with 3 cpus : 100%|██████████| 3222/3222 [00:38<00:00, 84.48it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (354112573)>\n"
     ]
    }
   ],
   "source": [
    "ddl.pp.assign_isotypes(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should you want to use a different reference fasta file for this step, run with the following option:\n",
    "\n",
    "```python\n",
    "ddl.pp.assign_isotypes(samples, blastdb = \"path/to/custome_BCR_constant.fasta\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This may take a while when dealing with large files; the number of cpus to size of file isn't exactly linear. Nevertheless, I have enabled parallelization as default because there were noticeable improvements in processing speeds with the smaller files. It should work faster with more cpus. The default option will return a summary plot that can be disabled with `plot = False`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, it's worthwhile to manually check the the sequences for constant calls returned as IGHA1-2, IGHG1-4 and the light chains and manually correct them if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Quantify mutations *(optional)*.\n",
    "\n",
    "At this stage, with all the necessary columns in the files, you can quantify the basic mutational load with `pp.quantify_mutations` before subsequent analyses. This will be covered again in notebook-3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Basic mutational load analysis : 100%|██████████| 4/4 [00:28<00:00,  7.03s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "# quantify mutations\n",
    "for s in tqdm(samples, desc = 'Basic mutational load analysis '):\n",
    "    filePath = s+'/dandelion/data/'+ s + '_b_filtered_contig_igblast_db-pass_genotyped.tsv'\n",
    "    ddl.pp.quantify_mutations(filePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (dandelion)",
   "language": "python",
   "name": "dandelion"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
