diff --git a/.gitignore b/.gitignore index 01c7531..8cdc357 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,8 @@ *.pyc *.log *.json +*.sh.o* +*.sh.e* *.png /.metadata/ src/build/* diff --git a/README.md b/README.md index 7b1df9e..6b1b8ff 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,7 @@ * [Run MiXeR](#run-mixer) * [MiXeR options](#mixer-options) * [Visualize MiXeR results](#visualize-mixer-results) +* [AIC and BIC interpretation](#aic-bic-interpretation) ## Introduction @@ -38,20 +39,26 @@ If you encounter an issue, or have further questions, please create a If you use MiXeR software for your research publication, please cite the following paper(s): -* for univariate analysis: D. Holland et al., Beyond SNP Heritability: Polygenicity and Discoverability Estimated for Multiple Phenotypes with a Univariate Gaussian Mixture Model, bioXriv, doi: https://doi.org/10.1101/133132 +* for univariate analysis: D. Holland et al., Beyond SNP Heritability: Polygenicity and Discoverability Estimated for Multiple Phenotypes with a Univariate Gaussian Mixture Model, PLOS Genetics, 2020, https://doi.org/10.1371/journal.pgen.1008612 * for cross-trait analysis: O.Frei et al., Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation, Nature Communications, 2019, https://www.nature.com/articles/s41467-019-10310-0 -The MiXeR software may not be used for commercial purpose or in medical applications. -We encourage all users to familiarize themselves with US patent https://www.google.no/patents/US20150356243 "Systems and methods for identifying polymorphisms". - MiXeR versions: * ``v1.0`` - public release for Matlab * ``v1.1`` - internal release for Matlab and Python * ``v1.2`` - internal release for Python (Matlab support removed) +* ``v1.3`` - internal release for Python (change HapMap3 to twenty random sets of ~600K SNPs each) ## Install MiXeR +### Singularity containers + +Update May 2021: MiXeR v1.3 is now available as a [singularity container](https://github.com/comorment/mixer). +See [mixer_simu.md](https://github.com/comorment/mixer/blob/main/usecases/mixer_simu.md) and [mixer_real.md](https://github.com/comorment/mixer/blob/main/usecases/mixer_real.md) for usage examples. +If you are new to singularity containers, please refer to [hello.md](https://github.com/comorment/containers/blob/main/docs/hello.md) to get started. + +The steps below provide an alternative setup (without singularity containers). + ### Prerequisites * Linux environment (tested on CentOS release 6.9, Ubuntu 18.04). @@ -78,8 +85,9 @@ Not available yet. The exact steps depend on your build environment. * If you work in HPC environment with modules system, you can load some existing combination of modules that include Boost libraries: ``` - module load Boost/1.68.0-intel-2018b-Python-3.6.6 Python/3.6.6-intel-2018b CMake/3.12.1 # SAGA (intel) - module load Boost/1.71.0-GCC-8.3.0 Python/3.7.4-GCCcore-8.3.0 CMake/3.12.1 # SAGA (gcc) + module load CMake/3.15.3-GCCcore-8.3.0 Boost/1.73.0-GCCcore-8.3.0 Python/3.7.4-GCCcore-8.3.0 # TSD (gcc) + module load Boost/1.71.0-GCC-8.3.0 Python/3.7.4-GCCcore-8.3.0 CMake/3.12.1 # SAGA (gcc) + module load Boost/1.68.0-intel-2018b-Python-3.6.6 Python/3.6.6-intel-2018b CMake/3.12.1 # SAGA (intel) ``` * Alternatively, you may download and compile Boost libraries yourself: ``` @@ -116,8 +124,7 @@ The exact steps depend on your build environment. ## Data preparation * Summary statistics (NIRD: ``/projects/NS9114K/MMIL/SUMSTAT/TMP/nomhc/``) - * MiXeR recognizes summary statistics in LDSC format as described [here](https://github.com/bulik/ldsc/wiki/Summary-Statistics-File-Format). In brief, each trait must be represented as a single table containing columns SNP, N, Z, A1, A2. Thus, it is possible to use ``munge_sumstats.py`` script as described [here](https://github.com/bulik/ldsc/wiki/Partitioned-Heritability#step-1-download-the-data). This might be convenient for users who are already familiar with LDSR functionality. - * However, we recommed to use our own scripts to pre-process summary statistcs (clone from [here](https://github.com/precimed/python_convert)): + * We recommed to use our own scripts to pre-process summary statistcs (clone from [here](https://github.com/precimed/python_convert)): ``` python sumstats.py csv --sumstats daner_PGC_SCZ49.sh2_mds10_1000G-frq_2.gz --out PGC_SCZ_2014_EUR.csv --force --auto --head 5 --ncase-val 33640 --ncontrol-val 43456 python sumstats.py zscore --sumstats PGC_SCZ_2014_EUR.csv | \ @@ -130,9 +137,9 @@ The exact steps depend on your build environment. python sumstats.py qc --exclude-ranges 6:26000000-34000000 --out SSGAC_EDU_2018_no23andMe_noMHC.csv --force gzip SSGAC_EDU_2018_no23andMe_noMHC.csv ``` - * We note that for case/control ``munge_sumstats.py`` generate sample size as a sum ``n = ncase + ncontrol``. We recommend to use ``neff = 4 / (1/ncase + 1/ncontrol)`` to account for imbalanced classes. Additionaly, we recommend to keep summary statistics for the entire set of SNPs available in GWAS, without filtering by HapMap3 SNPs). HapMap3 constraint can be applied later during fit procedure. - -* Reference panel (alternatively, download [this](https://1drv.ms/u/s!Ai1YZmdFa9ati40Inztrv_4erqcdWw?e=ixWDUe) or take it from NIRD (``/projects/NS9114K/MMIL/SUMSTAT/LDSR/1000G_EUR_Phase3_plink``). NB! Download size is ``24 GB``. + * [DEPRECATED] If you use MiXeR v1.1 and v1.2, it will recognize summary statistics in LDSC format as described [here](https://github.com/bulik/ldsc/wiki/Summary-Statistics-File-Format). In brief, each trait must be represented as a single table containing columns SNP, N, Z, A1, A2. Thus, it is possible to use ``munge_sumstats.py`` script as described [here](https://github.com/bulik/ldsc/wiki/Partitioned-Heritability#step-1-download-the-data). This might be convenient for users who are already familiar with LDSR functionality. In MiXeR v1.3 the format for GWAS summary statistics files is the same, but now I advice against using HapMap3 to constrain the set of SNPs for the fit procedure. Instead, MiXeR should receive a full set of summary statistics from a GWAS on imputed genotype data. Also, note that for case/control ``munge_sumstats.py`` from LD Score Regression generate sample size as a sum ``n = ncase + ncontrol``. We recommend to use ``neff = 4 / (1/ncase + 1/ncontrol)`` to account for imbalanced classes. + +* Generate ``.ld`` and ``.snps`` files from the reference panel. Note that this step optional if you work with EUR-based summary statistics. To use EUR reference, simply download the files from [here](https://github.com/comorment/containers/tree/main/reference/ldsc/1000G_EUR_Phase3_plink) or take it from NIRD (``/projects/NS9114K/MMIL/SUMSTAT/LDSR/1000G_EUR_Phase3_plink``) or TSD (`` ``) if you have access. NB! Download size is around ``24 GB``. * Run ``python mixer.py ld`` to calculate linkage disequilibrium information in a genotype reference panel. The following command must be run once for each chromosome. ``` python3 /precimed/mixer.py ld \ @@ -148,12 +155,21 @@ The exact steps depend on your build environment. The files DO NOT contain any individual-level information. When you store the resulting ``.ld`` file, it is important to keep it along side with corresponding ``.bim`` file, as information about marker name (SNP rs#), chromosome, position, and alleles (A1/A2) is NOT encoded in ``.ld`` file. - - * Save the list of dbSNP rs# into a separate file called ``w_hm3.justrs``: + + * To generate ``1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.repNN.snps`` files, repeat the following in a loop for ``` - cut -f1 w_hm3.snplist | tail -n +2 > w_hm3.justrs + export REP=1 # repeat for REP in 1..20 + python3 /precimed/mixer.py snps \ + --lib /src/build/lib/libbgmg.so \ + --bim-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ + --ld-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ + --out LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps \ + --maf 0.05 --subset 2000000 --r2 0.8 --seed ${SLURM_ARRAY_TASK_ID} ``` - + This can be done as a job array, see [this script](https://github.com/precimed/mixer/blob/master/scripts/xsede_snps_script.sh) for an example. + Note that in the code above the ``@`` symbol does NOT need to be replace with an actual chromosome. It should stay as ``@`` in your command. + + ## Run MiXeR ### Univariate analysis @@ -162,8 +178,8 @@ Fit the model: ``` python3 /precimed/mixer.py fit1 \ --trait1-file SSGAC_EDU_2018_no23andMe_noMHC.csv.gz \ - --out SSGAC_EDU_2018_no23andMe_noMHC.fit \ - --extract LDSR/w_hm3.justrs \ + --out SSGAC_EDU_2018_no23andMe_noMHC.fit.rep${SLURM_ARRAY_TASK_ID} \ + --extract LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps \ --bim-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ --ld-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ --lib /src/build/lib/libbgmg.so \ @@ -173,8 +189,8 @@ Apply the model to the entire set of SNPs, without constraining to ``LDSR/w_hm3. ``` python3 /precimed/mixer.py test1 \ --trait1-file SSGAC_EDU_2018_no23andMe_noMHC.csv.gz \ - --load-params-file SSGAC_EDU_2018_no23andMe_noMHC.fit.json \ - --out SSGAC_EDU_2018_no23andMe_noMHC.test \ + --load-params-file SSGAC_EDU_2018_no23andMe_noMHC.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out SSGAC_EDU_2018_no23andMe_noMHC.test.rep${SLURM_ARRAY_TASK_ID} \ --bim-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ --ld-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ --lib /src/build/lib/libbgmg.so \ @@ -185,7 +201,8 @@ Repeat the above analysis for the second trait (``PGC_SCZ_2014_EUR_qc_noMHC.csv. To visualize the results: ``` -python precimed/mixer_figures.py one --json .json --out +python precimed/mixer_figures.py combine --json PGC_SCZ_2014_EUR_qc_noMHC.fit.rep@.json --out combined/PGC_SCZ_2014_EUR.fit +python precimed/mixer_figures.py one --json PGC_SCZ_2014_EUR.json --out PGC_SCZ_2014_EUR --statistic mean std ``` @@ -196,10 +213,10 @@ Fit the model: python3 /python/mixer.py fit2 \ --trait1-file PGC_SCZ_2014_EUR_qc_noMHC.csv.gz \ --trait2-file SSGAC_EDU_2018_no23andMe_noMHC.csv.gz \ - --trait1-params-file PGC_SCZ_2014_EUR_qc_noMHC.fit.json \ - --trait2-params-file SSGAC_EDU_2018_no23andMe_noMHC.fit.json \ - --out PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.fit \ - --extract LDSR/w_hm3.justrs \ + --trait1-params-file PGC_SCZ_2014_EUR_qc_noMHC.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --trait2-params-file SSGAC_EDU_2018_no23andMe_noMHC.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.fit.rep${SLURM_ARRAY_TASK_ID} \ + --extract LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps \ --bim-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ --ld-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ --lib /src/build/lib/libbgmg.so \ @@ -210,8 +227,8 @@ Apply the model to the entire set of SNPs, without constraining to ``LDSR/w_hm3. python3 /python/mixer.py test2 \ --trait1-file PGC_SCZ_2014_EUR_qc_noMHC.csv.gz \ --trait2-file SSGAC_EDU_2018_no23andMe_noMHC.csv.gz \ - --load-params-file PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.fit.json \ - --out PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.test \ + --load-params-file PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC.test.rep${SLURM_ARRAY_TASK_ID} \ --bim-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ --ld-file LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ --lib /src/build/lib/libbgmg.so \ @@ -220,9 +237,11 @@ python3 /python/mixer.py test2 \ Note that these parameters point to the results of univariate analysis for both traits, so those must be generated first. The results will be saved ``.json`` file. -To visualize the results: +To visualize the results (where `` is, for example, ``PGC_SCZ_2014_EUR_qc_noMHC_vs_SSGAC_EDU_2018_no23andMe_noMHC``): ``` -python precimed/mixer_figures.py two --json .json --out +python precimed/mixer_figures.py combine --json .fit.rep@.json --out .fit +python precimed/mixer_figures.py combine --json .test.rep@.json --out .test +python precimed/mixer_figures.py two --json-fit .fit.json --json-test .test.json --out --statistic mean std ``` ## MiXeR options @@ -230,6 +249,7 @@ python precimed/mixer_figures.py two --json .json --out Run ``--help`` commands to list available options and their description. ``` python3 mixer.py ld --help +python3 mixer.py snps --help python3 mixer.py fit1 --help python3 mixer.py test1 --help python3 mixer.py fit2 --help @@ -239,12 +259,24 @@ python3 mixer.py perf --help ## Visualize MiXeR results +First step is to average the results across 20 runs with ``mixer_figures.py combine``, which works for univariate (fit1, test1) and bivariate (fit2, test2) runs: +``` +python precimed/mixer_figures.py combine --json .fit.rep@.json --out .fit +``` + The resulting ``.json`` files can be converted to figures and ``.csv`` tables via the following commands (``one`` for univariate, ``two`` for bivariate; each of these commands accept ``.json`` files from ``fit`` and ``test`` steps). + ``` python precimed/mixer_figures.py one --json .json --out python precimed/mixer_figures.py two --json .json --out +python precimed/mixer_figures.py two --json-fit .json --json-test .json --out ``` +For the ``two`` command, instead of ``--json``, it is possible to specify ``--json-fit`` and ``--json-test`` separately. +This allows to combine negative log-likelihood plot (available in fit2 only) and QQ plots (available in test2 only). +Note that all ``--json`` accept wildcards (``*``) or a list of multiple files. This allows to generate ``.csv`` tables +containing results from multiple MiXeR runs. + ### MiXeR results format MiXeR produces the following results, as described in the original publication. @@ -258,4 +290,42 @@ MiXeR produces the following results, as described in the original publication. * Bivariate density plots * Bivariate negative log-likelihood function -TBD - provide more details. +These output is described in the cross-trait MiXeR publication. + +### AIC BIC interpretation + +``.csv`` files generated by ``python precimed/mixer_figures.py`` commands contain AIC ([Akaike Information Criterion](https://en.wikipedia.org/wiki/Akaike_information_criterion)) and BIC ([Bayesian Information Criterion](https://en.wikipedia.org/wiki/Bayesian_information_criterion)) values. To generate AIC / BIC values you should point ``mixer_figures.py`` to json files produced by ``fit1`` or ``fit2`` steps (not those from ``test1`` or ``test2`` steps). + +The idea of model selection criteia (both AIC and BIC) is to find whether the input data (in our case the GWAS summary statistics) have enough statistical power to warrant a more complex model - i.e. a model with additional free parameters that need to be optimized from the data. For example, the LDSR model has two free parameters - the slope and an intercept. The univariate MiXeR model has three parameters (``pi``, ``sig2_beta`` and ``sig2_zero``). Naturally, having an additional free parameters allows MiXeR to fit the GWAS data better compared to LDSR model, however it needs to be *substantially* better to justify an additional complexity. AIC and BIC formalize this trade-off between model's complexity and model's ability to describe input data. + +The difference between AIC and BIC is that BIC is a more conservative model selection criterion. Based on our internal use of MiXeR, it is OK discuss the resulsult if only AIC supports MiXeR model, but it's important point as a limitation that the input signal (GWAS) has borderline power to fit MiXeR model. + +For the univariate model, AIC / BIC values are described in the cross-trait MiXeR paper ([ref](https://www.nature.com/articles/s41467-019-10310-0)). A negative AIC value means that there is not enough power in the input data to justify MiXeR model as compared to LDSR model, and we do do not recommend applying MiXeR in this situation. + +For the bivariate model the resulting table contains two AIC, and two BIC values, named as follows: +``best_vs_min_AIC``, ``best_vs_min_BIC`` , ``best_vs_max_AIC``, and ``best_vs_max_BIC``. They are explained below - but you may need to develop some intuition to interpret these numbers. Consider taking a look at the figure shown below, containing negative log-likelihood plots. Similar plots were presented in [ref](https://www.nature.com/articles/s41467-019-10310-0), supplementary figure 19. We use such likelihood-cost plots to visualise the performance of the ``best`` model vs ``min`` and ``max``. + +First, let's interpret ``best_vs_max_AIC`` value. It uses AIC to compare two models: the ``best`` model with polygenic overlap that is shown in the venn diagram (i.e. fitted by MiXeR), versus the ``max`` model with maximum possible polygenic overlap given trait's genetic architecture (that is, in the ``max`` model the causal variants of the least polygenic trait form a subset of the causal variants in the most polygenic trait). A positive value of ``best_vs_max_AIC`` means that ``best`` model explains the observed GWAS signal better than ``max`` model, despite its additional complexity (here additional complexity comes from the fact that MiXeR has to find an actual value of the polygenic overlap, i.e. estimate the size of the grey area on the venn diagram). + +Similarly, ``best_vs_min_AIC`` compares the ``best`` model versus model with minimal possible polygenic overlap. It is tempting to say that minimal possible polygenic overlap means the same as no shared causal variants, i.e. a case where circles on a venn diagram do not overlap. However, there is a subtle detail in our definition of the minimal possible polygenic overlap in cases where traits show some genetic correlation. Under the assumptions of cross-trait MiXeR model, the presence of genetic correlation imply non-empty polygenic overlap. In our AIC / BIC analysis, we constrain the ``best`` model to a specific value of the genetic correlation, obtained by the same procedure as in LDSR model (i.e. assuming an infinitesimal model, as described in [ref](https://www.nature.com/articles/s41467-019-10310-0). In this setting, minimal possible polygenic overlap corresponds to ``pi12 = rg * sqrt(pi1u * pi2u)``. ``best_vs_min_AIC`` is an important metric - a positive value indicates that the data shows support for existense of the polygenic overlap, beyond the minimal level need to explain observed genetic correlation between traits. + +Finally, ``best_vs_max_BIC`` and ``best_vs_max_BIC`` have the same meaning as the values explained above, but using more stringent ``BIC`` criterion instead of ``AIC``. + +The last panel on the following figure shows negative log-likelihood plot as a function of polygenic overlap. For the general information about maximum likelihood optimization see [here](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation). In our case we plot negative log-likelihood, that's we search for the minimum on the curve (this sometimes is refered to as a cost function, with objective to find parameters that yield the lowest cost). + +On the negative log-likelihood plot, the ``min`` model is represented by the point furthest to the left, ``max`` furthest to the right and ``best`` is the lowest point of the curve. (off note, in some other cases log-likelihood plot can be very noisy - then ``best`` is still the lowest point, but it doesn't make practical sence as it is just a very noisy estimate - and this is exactly what we want to clarify with AIC / BIC). + +The minimum is obtained at ``n=1.3K`` shared causal variants - that's our ``best`` model, scores at about 25 points (lower is better). A model with least possible overlap has ``n=0`` shared causal variants - that our ``min`` model, scored at 33 points. Finally, a model with largest possible overlap has ``n=4K`` shared causal variants - that our ``max`` model, scores at `50` points. We use AIC (and BIC) to compare ``best`` model versus the other models. + +![GIANT_HEIGHT_2018_UKB_vs_PGC_BIP_2016 json](https://user-images.githubusercontent.com/13171449/83339454-469edb00-a2ce-11ea-9e69-99270d94689f.png) + +### Upgrade nodes from MiXeR v1.2 to v1.3 + +* The source code is nearly identical, but I've changed the procedure to run MiXeR which is described in this README file. Still, you need to updated the code by ``git pull``. If you updated MiXeR in early summer 2020 there will be no changes in the native C++ code (hense no need to re-compile the ``libbgmg.so`` binary), but to be safe it's best to execute ``cd /src/build && cmake .. && make`` command (after ``module load`` relevant modules, as described in [this](https://github.com/precimed/mixer/blob/master/README.md#build-from-source---linux) section.) +* If you previously downloaded 1kG reference, you need to download 20 new files called ``1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.repNN.snps``. Otherwise you need to generate them with ``mixer.py snps`` command as described above. +* If you previously generated your input summary statistics constraining them to HapMap3 SNPs, you need to re-generated them without constraining to HapMap3. +* Assuming you have a SLURM script for each MiXeR analysis, you need to turn this script into a job array (``#SBATCH --array=1-20``) which executes 20 times, and use ``--extract 1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps`` flag in ``mixer.py fit1`` and ``mixer.py fit2``, also adjusting input/output file names accordingly. Example scripts are available in [scripts](https://github.com/precimed/mixer/tree/master/scripts) folder. +* To process ``.json`` files produced by MiXeR, you now first need to run a ``mixer_figures.py combine`` step as shown above. This will combine 20 ``.json`` files by averaging individual parameter estimates, and calculating standard errors. +* When you generate figures with ``mixer_figures.py one`` and ``mixer_figures.py two`` commands and use the "combined" ``.json`` files, you'll need to add ``--statistic mean std`` to your commands. +* With MiXeR v1.3 you should expect a slightly lower polygenicity estimate, mainly because of ``--maf 0.05`` constraint on the frequency of genetic variants used in the fit procedure. The rationale for ``--maf 0.05`` filter is to still have a robust selection of SNPs in the fit procedure despite not having HapMap3 filter. +* With MiXeR v1.3 you should expect a 10 fold increase in CPU resources needed per ran (20 times more runs, but each run is ~600K SNPs which is half the size of HapMap3). diff --git a/annotations/readme_annot.txt b/annotations/readme_annot.txt new file mode 100644 index 0000000..3e76246 --- /dev/null +++ b/annotations/readme_annot.txt @@ -0,0 +1,63 @@ +1.1. Download KnownGene table +wget -O /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.txt.gz ftp://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/knownGene.txt.gz + + + +1.2. Download MiRNA target regions and regulatory features from Ensembl BioMart. +MiRNA: +wget -O /mnt/seagate10/projects/annotations/data/test/mirna_targets.txt 'http://www.ensembl.org/biomart/martservice?query=' + +Regulatory features: +wget -O /mnt/seagate10/projects/annotations/data/test/regulatory_features.txt 'http://www.ensembl.org/biomart/martservice?query=' + +http://grch37.ensembl.org/biomart/martview/891d411f1bfeb1cd8d2a0f46fe62cff2?VIRTUALSCHEMANAME=default&ATTRIBUTES=hsapiens_mirna_target_feature.default.mirna_target_feature.chromosome_name|hsapiens_mirna_target_feature.default.mirna_target_feature.chromosome_start|hsapiens_mirna_target_feature.default.mirna_target_feature.chromosome_end|hsapiens_mirna_target_feature.default.mirna_target_feature.accession&FILTERS=&VISIBLEPANEL=attributepanel + + + +2. Process UCSC annotations with knownGene2annot.py. +python knownGene2annot.py /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.txt.gz /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.txt.gz + + + +3.1. Convert gene, mirna and regulatury feature files into bed format (mark features accordingly in the 4-th column of bed file). +zcat /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.txt.gz | awk -F$'\t' 'BEGIN{OFS="\t"} {if($2 ~ "chr[0-9]+") print($2,$6,$7,$5)}' | gzip -c > /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.bed.gz + +awk -F$'\t' 'BEGIN{OFS="\t"} {print("chr"$1,$2-1,$3,"mirna"}' /mnt/seagate10/projects/annotations/data/test/mirna_targets.txt | gzip -c > /mnt/seagate10/projects/annotations/data/test/mirna_targets.bed.gz +awk -F$'\t' 'BEGIN{OFS="\t"} {print("chr"$1,$2-1,$3,"tfbs"}' /mnt/seagate10/projects/annotations/data/test/regulatory_features.txt | gzip -c > /mnt/seagate10/projects/annotations/data/test/regulatory_features.bed.gz + + + +3.2. Merge and sort all bed files for known genes, mirna and tfbs. +zcat /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.bed.gz /mnt/seagate10/projects/annotations/data/test/mirna_targets.bed.gz /mnt/seagate10/projects/annotations/data/test/regulatory_features.bed.gz | sort -k1,1 -k2,2n | gzip -c > /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.complete.sorted.bed.gz + + + +4. Create sorted template bed file. +cat /mnt/seagate10/genotypes/1000genomes503eur9m/chr[0-9]*.bim | awk 'BEGIN{OFS="\t";} {print("chr"$1, $4-1, $4-1+length($5), $2)}' | sort -k1,1 -k2,2n | gzip -c > /mnt/seagate10/projects/annotations/data/test/template.1000genomes503eur9m.sorted.bed.gz + + + +5. Intersect annotations bed file with template bed file using bedtools. +/mnt/seagate10/projects/github/bedtools2/bedtools intersect -a /mnt/seagate10/projects/annotations/data/test/template.1000genomes503eur9m.sorted.bed.gz -b /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.complete.sorted.bed.gz -wa -wb -sorted | gzip -c > /mnt/seagate10/projects/annotations/data/test/template.1000genomes503eur9m.complete_annot_hg19.intersect.txt.gz + + + +6. Create binary annotations (this step is slow). +python annot2annomat.py /mnt/seagate10/projects/annotations/data/test/template.1000genomes503eur9m.complete_annot_hg19.intersect.txt.gz /mnt/seagate10/projects/annotations/data/test/template.1000genomes503eur9m.sorted.bed.gz /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annomat.txt.gz + + + +7. Create unique annotations (many variants belong to multiple annotation categories, this step assigns a single category to each variant, prioritizing categoryes according to "All SNPs ..."). +python uniq_annot.py /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annomat.txt.gz /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annomat.uniq.txt.gz + + + +8. Calculate LD r2 coefficients with parameters from "All SNPs are not created equal" supplement. +for ((i=1;i<23;i++)); do plink --bfile /mnt/seagate10/genotypes/1000genomes503eur9m/chr${i} --r2 inter-chr gz yes-really --ld-window-r2 0.2 --out /mnt/seagate10/genotypes/1000genomes503eur9m/schork/chr${i}.schork.r2; done + + + +9. Create ld-induced categories for the experiment (this step is very slow). +python ld_informed_annot.py +python ld_informed_annot.py /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annomat.uniq.txt.gz /mnt/seagate10/genotypes/1000genomes503eur9m/schork/ /mnt/seagate10/projects/annotations/data/test/knownGene_ucsc_hg19.annot.ld_informed.txt.gz + diff --git a/annotations/src/annot2annomat.py b/annotations/src/annot2annomat.py new file mode 100644 index 0000000..160068d --- /dev/null +++ b/annotations/src/annot2annomat.py @@ -0,0 +1,45 @@ +import sys +import pandas as pd +from collections import defaultdict +import numpy as np +import argparse + + +def parseArgs(args): + parser = argparse.ArgumentParser( + description="Creates binary annotation matrix from the output of knownGene2annot.py") + parser.add_argument("annot_file", help="A file with snp id in column 4 and annotation category name in column 8") + parser.add_argument("template_file", help="Template bed file") + parser.add_argument("out_file", help="Output file name") + return parser.parse_args(args) + + +if __name__ == "__main__": + args = parseArgs(sys.argv[1:]) + print("Processing %s" % args.annot_file) + df = pd.read_csv(args.annot_file, header=None, sep='\t') + dd = defaultdict(set) + + for t in df.itertuples(): + dd[t[4]].add(t[8]) # t[0] = index + + template_snps = pd.read_csv(args.template_file,sep='\t',usecols=[3], + header=None,names=["SNP"],squeeze=True) + # hardcoded list of categories supported by knownGene2annot.py + l = ["100kDown", "10kDown", "1kDown", "100kUp", "10kUp", "1kUp", "3UTR", + "5UTR", "Exon", "Intron", "ProteinCoding", "NoncodingTranscript", + "mirna", "tfbs"] + + data = np.zeros((len(template_snps), len(l)), dtype=int) + for i,s in enumerate(template_snps): + for j, k in enumerate(l): + if k in dd[s]: + data[i,j] = 1 + + dfa = pd.DataFrame(index=template_snps, columns=l, data=data) + print("Writing results to %s" % args.out_file) + # out_file = "../data/9m_template_knownGene_ucsc_hg19_annomat.txt.gz" + dfa.to_csv(args.out_file, sep='\t', + compression='gzip' if args.out_file.endswith('.gz') else None) + print("Done") + diff --git a/annotations/src/knownGene2annot.py b/annotations/src/knownGene2annot.py new file mode 100644 index 0000000..0745426 --- /dev/null +++ b/annotations/src/knownGene2annot.py @@ -0,0 +1,187 @@ +import gzip +import sys +import argparse + +SHOW_WARNINGS = False + +def parseArgs(args): + parser = argparse.ArgumentParser( + description="Create annotation file from UCSC knownGene file.") + parser.add_argument("known_gene_file", help="UCSC knownGene file") + parser.add_argument("out_file", help="Output file name") + parser.add_argument("--show-warns", action="store_true", + help="Suppress warnings") + return parser.parse_args(args) + +def myOpen(f_name, mode='r'): + if f_name.endswith(".gz"): + return gzip.open(f_name, mode) + else: + return open(f_name, mode) + + +def write2file(columnList, f): + line = '\t'.join(columnList) + if columnList[-2] == columnList[-1]: + msg = "Warning generating annotation:\n%s\n" % line + msg += "Empty segment generated. It will be ignored." + if SHOW_WARNINGS: print(msg) + else: + f.write("%s\n" % line) + + +def parseLine(line, outFile): + """ + Parse a line from UCSC knownGene file. Write annotated segments to outFile. + UCSC knownGene file for hg19 can be downloaded from here: + http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/ + knownGene format description is here: + http://genome.ucsc.edu/cgi-bin/hgTables + Supported categories: + 5UTR, 3UTR, Exon, ProteinConding, Intron, 1kUp, 10kUp, 100kUp, 1kDown, + 10kDown, 100kDown + for coding transcripts and a single category NoncodingTranscript for + noncoding transcripts (to be consistent wit classification used in + "All SNPs are not vreated equal" paper by A. Schork). + Output file contains 7 tab-delimited columns: + gene name, chromosome, strand, protein ID, category, start, end + Algorithm (assuming the gene is on "+" strand): + Gene + start end + -------------[--Exon--|----Intron----|--Exon--]------------- + [-1kUp-] [-1kDown-] + [---10kUp---] [---10kUp---] + All Exons are taken as they are reported in the knownGene file, all segments + between consequent exons are taken as Introns. + If cdsStart == cdsEnd: no coding sequence, no further processing + else: + each exon is compared to cds start/end as follows (6 different cases): + s e + -----------|-------Exon-------|----------- + cs ce + 1: [-cds-] (s,e) - 3UTR + 2: [----cds----] (s,ce) - ProtCod + (ce,e) - 3UTR + 3: [----------------cds----------------] (s,e) - ProtCod + 4: [---cds---] (s,cs) - 5UTR + (cs,ce) - ProtCod + (ce,e) - 3UTR + 5: [----------cds----------] (s,cs) - 5UTR + (cs,e) - ProtCod + 6: [-cds-] (s,e) - 5UTR + + additional special sub-cases are when cds limits are equal to exon limits. + """ + spltLine = line.split("\t") + (name, chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, + exonStarts, exonEnds, proteinID, alignID) = spltLine + txStart, txEnd, cdsStart, cdsEnd, exonCount = map(int, spltLine[3:8]) + exonStarts = exonStarts.split(",")[:-1] + exonEnds = exonEnds.split(",")[:-1] + columnList = [name, chrom, strand, proteinID] + if cdsStart == cdsEnd: + # noncoding transcript + s,e = str(txStart), str(txEnd) + write2file(columnList + ["NoncodingTranscript", s, e], outFile) + else: + for s,e in zip(exonEnds[:-1], exonStarts[1:]): + write2file(columnList + ["Intron", s, e], outFile) + for s,e in zip(exonStarts, exonEnds): + write2file(columnList + ["Exon", s, e], outFile) + exonStarts = map(int,exonStarts) + exonEnds = map(int, exonEnds) + if strand == "+": + s, e = max(0, txStart-1000), txStart + write2file(columnList + ["1kUp", "%d" % s, "%d" % e], outFile) + s, e = max(0, txStart-10000), txStart + write2file(columnList + ["10kUp", "%d" % s, "%d" % e], outFile) + s, e = max(0, txStart-100000), txStart + write2file(columnList + ["100kUp", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+1000 + write2file(columnList + ["1kDown", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+10000 + write2file(columnList + ["10kDown", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+100000 + write2file(columnList + ["100kDown", "%d" % s, "%d" % e], outFile) + if cdsStart < cdsEnd: # otherwise this gene is non-coding + # consider 6 different cases described above + for s,e in zip(exonStarts, exonEnds): + if cdsEnd <= s: # case 1 + write2file(columnList + ["3UTR", "%d" % s, "%d" % e], outFile) + elif e <= cdsStart: # case 6 + write2file(columnList + ["5UTR", "%d" % s, "%d" % e], outFile) + elif cdsStart < s and cdsEnd < e: # case 2 + write2file(columnList + ["ProteinCoding", "%d" % s, "%d" % cdsEnd], outFile) + write2file(columnList + ["3UTR", "%d" % cdsEnd, "%d" % e], outFile) + elif cdsStart <= s and cdsEnd >= e: # case 3 + write2file(columnList + ["ProteinCoding", "%d" % s, "%d" % e], outFile) + elif s <= cdsStart and cdsEnd <= e: # case 4 + write2file(columnList + ["ProteinCoding", "%d" % cdsStart, "%d" % cdsEnd], outFile) + if s < cdsStart: + write2file(columnList + ["5UTR", "%d" % s, "%d" % cdsStart], outFile) + if cdsEnd < e: + write2file(columnList + ["3UTR", "%d" % cdsEnd, "%d" % e], outFile) + elif s < cdsStart and cdsEnd > e: # case 5 + write2file(columnList + ["ProteinCoding", "%d" % cdsStart, "%d" % e], outFile) + write2file(columnList + ["5UTR", "%d" % s, "%d" % cdsStart], outFile) + else: + msg = "Error processing line:\n%s\n" % line + msg += "unclassified exon: (start, end) = (%d,%d)" % (s, e) + raise ValueError(msg) + elif strand == "-": + s, e = max(0, txStart-1000), txStart + write2file(columnList + ["1kDown", "%d" % s, "%d" % e], outFile) + s, e = max(0, txStart-10000), txStart + write2file(columnList + ["10kDown", "%d" % s, "%d" % e], outFile) + s, e = max(0, txStart-100000), txStart + write2file(columnList + ["100kDown", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+1000 + write2file(columnList + ["1kUp", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+10000 + write2file(columnList + ["10kUp", "%d" % s, "%d" % e], outFile) + s, e = txEnd, txEnd+100000 + write2file(columnList + ["100kUp", "%d" % s, "%d" % e], outFile) + if cdsStart < cdsEnd: # otherwise this gene is non-coding + # consider 6 different cases described above, but for "-" strand + for s,e in zip(exonStarts, exonEnds): + if cdsEnd <= s: # case 1 + write2file(columnList + ["5UTR", "%d" % s, "%d" % e], outFile) + elif e <= cdsStart: # case 6 + write2file(columnList + ["3UTR", "%d" % s, "%d" % e], outFile) + elif cdsStart < s and cdsEnd < e: # case 2 + write2file(columnList + ["ProteinCoding", "%d" % s, "%d" % cdsEnd], outFile) + write2file(columnList + ["5UTR", "%d" % cdsEnd, "%d" % e], outFile) + elif cdsStart <= s and cdsEnd >= e: # case 3 + write2file(columnList + ["ProteinCoding", "%d" % s, "%d" % e], outFile) + elif s <= cdsStart and cdsEnd <= e: # case 4 + write2file(columnList + ["ProteinCoding", "%d" % cdsStart, "%d" % cdsEnd], outFile) + if s < cdsStart: + write2file(columnList + ["3UTR", "%d" % s, "%d" % cdsStart], outFile) + if cdsEnd < e: + write2file(columnList + ["5UTR", "%d" % cdsEnd, "%d" % e], outFile) + elif s < cdsStart and cdsEnd > e: # case 5 + write2file(columnList + ["ProteinCoding", "%d" % cdsStart, "%d" % e], outFile) + write2file(columnList + ["3UTR", "%d" % s, "%d" % cdsStart], outFile) + else: + msg = "Error processing line:\n%s\n" % line + msg += "unclassified exon: (start, end) = (%d,%d)" % (s, e) + raise ValueError(msg) + else: + msg = "Error processing line:\n%s\n" % line + msg += "unknown strand: '%s'" % strand + raise ValueError(msg) + + + +if __name__ == "__main__": + args = parseArgs(sys.argv[1:]) + SHOW_WARNINGS = args.show_warns + with myOpen(args.known_gene_file, 'rt') as f, myOpen(args.out_file, 'wt') as of: + print("Processing UCSC knownGene file %s" % args.known_gene_file) + print("Writing to %s" % args.out_file) + for i, l in enumerate(f): + parseLine(l, of) + if (i+1)%10000 == 0: + print("%d lines processed" % (i+1)) + print("%d lines processed in total" % (i+1)) + print("Completed") diff --git a/annotations/src/ld_informed_annot.py b/annotations/src/ld_informed_annot.py new file mode 100644 index 0000000..a61ec3d --- /dev/null +++ b/annotations/src/ld_informed_annot.py @@ -0,0 +1,114 @@ +import pandas as pd +import numpy as np +from collections import defaultdict +import os +import sys +import argparse + +""" +Use output of uniq_annot.py and files with r2 coefficients generated by plink +(e.g. chr{i}.height.2m.r2.ld.gz i=1..22) to construct ld-informed annotations. +""" + + +def parseArgs(args): + parser = argparse.ArgumentParser( + description="Creates LD informed annotation matrix from the output of uniq_annot.py") + parser.add_argument("annot_file", help="Non-overlapping annotation file (output of uniq_annot.py)") + parser.add_argument("ld_r2_dir", help="Directory with precalculated LD r2 scores") + parser.add_argument("out_file", help="Output file name") + parser.add_argument("--ld-r2-prefix", default="chr", help="Suffix for LD file: {prefix}{1-22}{suffix}") + parser.add_argument("--ld-r2-suffix", default=".schork.r2.ld.gz", help="Suffix for LD file: {prefix}{1-22}{suffix}") + return parser.parse_args(args) + + +def main(): + #chr_r2_file_dir = "/mnt/seagate10/genotypes/ldsc489eur10m/schork/" + #chr_r2_prefix = "1000g.eur.qc." + #chr_r2_suffix = ".schork.r2.ld.gz" + ## nonoverlapping_annot_file file is created with uniq_annot.py script + #nonoverlapping_annot_file = "/mnt/seagate10/genotypes/ldsc489eur10m/template.ldsc489eur10m.sorted.complete_annot_hg19.annomat.uniq.txt.gz" + #out_f_name = "/mnt/seagate10/genotypes/ldsc489eur10m/template.ldsc489eur10m.sorted.complete_annot_hg19.annomat.uniq.ld_informed.txt.gz" + #annot2use = ["5UTR", "3UTR", "Exon", "Intron", "1kUp", "1kDown", "10kUp", + # "10kDown"] + #auxiliary_annot = ["NoncodingTranscript", "100kUp", "100kDown", "mirna", + # "tfbs"] + + args = parseArgs(sys.argv[1:]) + print("Processing %s" % args.annot_file) + + chr_r2_file_dir = args.ld_r2_dir #"/mnt/seagate10/genotypes/1000genomes503eur9m/schork/" + chr_r2_prefix = args.ld_r2_prefix # "chr" + chr_r2_suffix = args.ld_r2_suffix # ".schork.r2.ld.gz" + # nonoverlapping_annot_file file is created with uniq_annot.py script + nonoverlapping_annot_file = args.annot_file # "/mnt/seagate10/genotypes/1000genomes503eur9m/template.1000genomes503eur9m.sorted.complete_annot_hg19.annomat.uniq.txt.gz" + out_f_name = args.out_file # "/mnt/seagate10/genotypes/1000genomes503eur9m/template.1000genomes503eur9m.sorted.complete_annot_hg19.annomat.uniq.ld_informed.txt.gz" + annot2use = ["5UTR", "3UTR", "Exon", "Intron", "1kUp", "1kDown", "10kUp", + "10kDown"] + auxiliary_annot = ["NoncodingTranscript", "100kUp", "100kDown", "mirna", + "tfbs"] + + + dfa = pd.read_csv(nonoverlapping_annot_file, sep="\t") + df_annot2use = dfa[annot2use] + + ld_annot_data = df_annot2use.values.copy().astype(float) + + # snp_i : annot_i, + # where + # snp_i - index of snp in dfa["SNP"] + # annot_i - index of annotation category in annot2use + snp_annot = dict(zip(*np.where(ld_annot_data))) + + # snp_id : snp_i + snp_ind = dict(zip(dfa.SNP, dfa.index)) + + # snp_i : [snp_i_1, snp_i_2, ...] - ids of snps in LD with the key snp + snp_in_ld_id = defaultdict(list) + # snp_i : [snp_r2_1, snp_r2_2, ...] - r2 of snps in LD with the key snp + snp_in_ld_r2 = defaultdict(list) + for i in range(1,23): + # chr3.schork.r2.ld.gz + f_name = os.path.join(chr_r2_file_dir, f"{chr_r2_prefix}{i}{chr_r2_suffix}") + print("Reading %s" % f_name) + df = pd.read_csv(f_name, usecols=["SNP_A", "SNP_B", "R2"], + delim_whitespace=True, dtype={"R2":np.float32}) + for row in df.itertuples(): + i1 = snp_ind[row.SNP_A] + i2 = snp_ind[row.SNP_B] + snp_in_ld_id[i1].append(i2) + snp_in_ld_id[i2].append(i1) + snp_in_ld_r2[i1].append(row.R2) + snp_in_ld_r2[i2].append(row.R2) + + for snp_i, snp_in_ld_ii in snp_in_ld_id.items(): + if snp_i%100000 == 0: print("%d snp processed" % snp_i) + snp_i_r2 = snp_in_ld_r2[snp_i] + annot_ii = [snp_annot[i] for i in snp_in_ld_ii if i in snp_annot] + annot_r2 = [r2 for i,r2 in zip(snp_in_ld_ii, snp_i_r2) + if i in snp_annot] + annot_ld = np.bincount(annot_ii, annot_r2, len(annot2use)) + ld_annot_data[snp_i] += annot_ld + + i = ld_annot_data<1 + ld_annot_data[i] = 0 + ld_annot_data[~i] = 1 + + out_df = pd.DataFrame(index=dfa.SNP, columns=annot2use, data=ld_annot_data) + # add intergenic column properly based on auxiliary annotation columns from dfa + intergenic = np.zeros(len(out_df)) + i = (out_df.sum(1).values + dfa[auxiliary_annot].sum(1).values) == 0 + intergenic[i] = 1 + out_df["Intergenic"] = intergenic + + out_df = out_df.astype(int) + print(out_df.sum()) + + print("Saving result to %s" % out_f_name) + out_df.to_csv(out_f_name, sep='\t', compression='gzip') + + +if __name__ == "__main__": + print("Start") + main() + print("Done") diff --git a/annotations/src/uniq_annot.py b/annotations/src/uniq_annot.py new file mode 100644 index 0000000..9633a26 --- /dev/null +++ b/annotations/src/uniq_annot.py @@ -0,0 +1,66 @@ +import pandas as pd +import numpy as np +import sys +import argparse + +""" +Contains a logic for making unique binary annotation categories from overlapping +binary annotations produced by annot2annomat.py. + +All annotation categories: +100kDown 10kDown 1kDown 100kUp 10kUp 1kUp 3UTR 5UTR Exon +Intron ProteinCoding NoncodingTranscript mirna tfbs + +Categories used for annotations: +3UTR 5UTR Exon Intron 10kDown 1kDown 10kUp 1kUp + +Only variants in protein coding transcripts are considered for annotation: +NoncodingTranscript == 0 +Priority order is given by the annot2use list below. + +Returns a file where all categories form annot2use are mutually exclusive, +categories from auxiliary_annot are directly copied from the input table. +""" + + + +annot2use = ["5UTR", "3UTR", "Exon", "Intron", "1kUp", "1kDown", "10kUp", "10kDown"] +auxiliary_annot = ["NoncodingTranscript", "100kUp", "100kDown", "mirna", "tfbs"] + + +def parseArgs(args): + parser = argparse.ArgumentParser( + description=("Creates nonoverlapping annotations for 'main' categories " + "(annot2use). Auxiliary categories (auxiliary_annot) may overlap.")) + parser.add_argument("annomat_file", help="An output of annot2annomat.py") + parser.add_argument("out_file", + help="Output file name (will be gzipped if ends with '.gz')") + return parser.parse_args(args) + + +if __name__ == "__main__": + args = parseArgs(sys.argv[1:]) + print("Processing %s" % args.annomat_file) + # annomat_file = "../data/test_1000genomes_chr22/1kgenomes_chr22_complete_annot_hg19_annomat.txt.gz" + # out_file = "../data/test_1000genomes_chr22/1kgenomes_chr22_complete_annot_hg19_nonoverlapping_and_aux_annot.txt.gz" + df = pd.read_table(args.annomat_file, index_col="SNP") + + assert len(set(annot2use) - set(df.columns)) == 0, "Some annot2use categories are absent in the input table" + + df_tmp = df[df["NoncodingTranscript"] == 0].copy() + + ddf = pd.DataFrame(index=df.index, columns=annot2use+auxiliary_annot, + data=np.zeros((len(df), len(annot2use+auxiliary_annot)), dtype=int)) + for c in annot2use: + snps_in_c = list(df_tmp[df_tmp[c] == 1].index) + print("%d variants in %s category" % (len(snps_in_c), c)) + ddf.loc[snps_in_c,c] = 1 + df_tmp.drop(snps_in_c, inplace=True) + + for c in auxiliary_annot: + ddf[c] = df[c] + print("Writing output to %s" % args.out_file) + compression = 'gzip' if args.out_file.endswith(".gz") else None + ddf.to_csv(args.out_file, sep='\t', compression=compression) + + print("Done") \ No newline at end of file diff --git a/figures/GIANT_HEIGHT_2018_UKB_vs_PGC_BIP_2016.png b/figures/GIANT_HEIGHT_2018_UKB_vs_PGC_BIP_2016.png new file mode 100644 index 0000000..445623e Binary files /dev/null and b/figures/GIANT_HEIGHT_2018_UKB_vs_PGC_BIP_2016.png differ diff --git a/misc/mixer2_jupyter.ipynb b/misc/mixer2_jupyter.ipynb deleted file mode 100644 index b074e07..0000000 --- a/misc/mixer2_jupyter.ipynb +++ /dev/null @@ -1,4515 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "folder = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/LDSR_Annot/1000G_EUR_Phase3_baseline/'\n", - "#baseline.10.annot.gz\n", - "annonames = [x.replace('.bedL2', '') for x in pd.read_csv(folder+'baseline.21.l2.ldscore.gz',sep='\\t').columns[3:]]\n", - "\n", - "df=pd.concat([pd.read_csv(folder + 'baseline.{}.annot.gz'.format(chri),sep='\\t') for chri in [1]])\n", - "#df=pd.concat([pd.read_csv(folder + 'baseline.{}.annot.gz'.format(chri),sep='\\t') for chri in range(1, 23)])\n", - "\n", - "del df['CHR']\n", - "del df['BP']\n", - "del df['SNP']\n", - "del df['CM']\n", - "annomat = df.values.astype(np.float32)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.libbgmg\n", - "import precimed.mixer.utils\n", - "import precimed.mixer.cli\n", - "import precimed.mixer.figures\n", - "import numpy as np\n", - "import numpy.matlib\n", - "from precimed.mixer.utils import UnivariateParams\n", - "from precimed.mixer.utils import AnnotUnivariateParams\n", - "from precimed.mixer.utils import _log_exp_converter\n", - "from precimed.mixer.utils import _arctanh_tanh_converter\n", - "from precimed.mixer.utils import _logit_logistic_converter\n", - "import scipy.optimize\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "\n", - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=False)\n", - "\n", - "def perform_fit(bounds_left, bounds_right, parametrization):\n", - " bounds4opt = [(l, r) for l, r in zip(parametrization.params_to_vec(bounds_left), parametrization.params_to_vec(bounds_right))]\n", - " optimize_result = scipy.optimize.differential_evolution(lambda x: parametrization.calc_cost(x), bounds4opt,\n", - " tol=0.01, mutation=(0.5, 1), recombination=0.7, atol=0, updating='immediate', polish=False, workers=1) #, **global_opt_options)\n", - " params = parametrization.vec_to_params(optimize_result.x)\n", - " print(params)\n", - "\n", - " # Step 2. neldermead-fast\n", - " optimize_result = scipy.optimize.minimize(lambda x: parametrization.calc_cost(x), parametrization.params_to_vec(params),\n", - " method='Nelder-Mead', options={'maxiter':240, 'fatol':1e-7, 'xatol':1e-4, 'adaptive':True})\n", - " params = parametrization.vec_to_params(optimize_result.x)\n", - " print(params)\n", - " return params\n", - "\n", - "def do_plots(params, label, ylims=[7.3, 20, 50, 150], strat=True):\n", - " data = {}\n", - " trait_index = 1\n", - " downsample_factor=50\n", - " mask = np.isfinite(libbgmg.zvec1)\n", - " data['qqplot'] = precimed.mixer.cli.calc_qq_plot(libbgmg, params, 1, downsample_factor, mask)\n", - " for ylim in ylims:\n", - " precimed.mixer.figures.make_qq_plot(data['qqplot'], ylim=ylim)\n", - " plt.savefig(figures_folder + label + 'ylim={}.qq.png'.format(ylim) , bbox_inches='tight')\n", - "\n", - " if not strat: return\n", - " mafvec = libbgmg.mafvec[libbgmg.defvec]\n", - " tldvec = libbgmg.ld_tag_r2_sum\n", - " maf_bins = np.concatenate(([-np.inf], np.quantile(mafvec, [1/3, 2/3]), [np.inf]))\n", - " tld_bins = np.concatenate(([-np.inf], np.quantile(tldvec, [1/3, 2/3]), [np.inf]))\n", - " data['qqplot_bins'] = []\n", - " for i in range(0, 3):\n", - " for j in range(0, 3):\n", - " mask = np.isfinite(libbgmg.zvec1) & ((mafvec>=maf_bins[i]) & (mafvec= tld_bins[j]) & (tldvec < tld_bins[j+1]))\n", - " data['qqplot_bins'].append(precimed.mixer.cli.calc_qq_plot(libbgmg, params, trait_index, downsample_factor, mask,\n", - " title='maf \\\\in [{:.3g},{:.3g}); L \\\\in [{:.3g},{:.3g})'.format(maf_bins[i], maf_bins[i+1], tld_bins[j], tld_bins[j+1])))\n", - "\n", - " for ylim in ylims: \n", - " plt.figure(figsize=[12, 12])\n", - " for i in range(0, 3):\n", - " for j in range(0, 3):\n", - " plt.subplot(3,3,i*3+j+1)\n", - " precimed.mixer.figures.make_qq_plot(data['qqplot_bins'][i*3+j], ylim=ylim)\n", - " plt.title(data['qqplot_bins'][i*3+j]['title'].replace(';', '\\n'))\n", - " plt.savefig(figures_folder + label + 'ylim={}.binqq.png'.format(ylim) , bbox_inches='tight') " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=True)\n", - "libbgmg.init_log('/home/oleksanf/github/mixer/src/build/lib/mixer.log')\n", - "\n", - "# ToDo: optimize gaussian cost function to benefit from complete tag indices. \n", - "# In this case we don't need to compute redundant Edelta2 and Edelta4 for undefined tag indices.\n", - "libbgmg.set_option('use_complete_tag_indices', 1)\n", - "\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin0 = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "#trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/SSGAC_EDU_2018_no23andMe.sumstats.gz'\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/GIANT_HEIGHT_2018_UKB.sumstats.gz'\n", - "trait2_file = ''\n", - "extract = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/w_hm3.justrs'\n", - "exclude = ''\n", - "chr2use = [1] # range(1, 23)\n", - "_cost_calculator_sampling = 0\n", - "_cost_calculator_gaussian = 1\n", - "_cost_calculator_convolve = 2\n", - "\n", - "libbgmg.init(bim_file, frq_file, chr2use, trait1_file, trait2_file, exclude, extract)\n", - "libbgmg.set_option('ld_format_version', 0)\n", - "libbgmg.set_option('seed', 123)\n", - "libbgmg.set_option('cubature_rel_error', 1e-5)\n", - "libbgmg.set_option('cubature_max_evals', 1000)\n", - "libbgmg.set_option('cost_calculator', _cost_calculator_gaussian)\n", - "\n", - "for chr_label in chr2use: \n", - " libbgmg.set_ld_r2_coo_from_file(int(chr_label), plink_ld_bin0.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(int(chr_label))\n", - "libbgmg.set_weights_randprune(64, 0.1)\n", - "\n", - "mafvec = libbgmg.mafvec[libbgmg.defvec]\n", - "hetvec = 2 * np.multiply(mafvec, 1-mafvec)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## print('Useful categories:')\n", - "print([(annoname, beta) for annoname, beta in zip(annonames, betavec[1:]) if beta>0])\n", - "\n", - "beta2 = np.multiply(hetvec, np.matmul(annomat, betavec[1:]))\n", - "h2_annot = np.matmul(beta2.reshape((1, len(beta2))), annomat); h2_annot = h2_annot / h2_annot[0][0]\n", - "annot_frac = np.sum(annomat, 0)/np.sum(annomat, 0)[0];\n", - "\n", - "df['NNLS'] = np.divide(h2_annot, annot_frac).flatten()\n", - "\n", - "\n", - "mod_wls = sm.WLS(z2, A1, weights=w)\n", - "res_wls = mod_wls.fit()\n", - "#plt.plot(res_wls.fittedvalues, z2, '.')\n", - "\n", - "beta2 = np.multiply(hetvec, np.matmul(annomat, res_wls.params[1:]))\n", - "h2_annot = np.matmul(beta2.reshape((1, len(beta2))), annomat); h2_annot = h2_annot / h2_annot[0][0]\n", - "annot_frac = np.sum(annomat, 0)/np.sum(annomat, 0)[0];\n", - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "\n", - "df['WLS'] = np.divide(h2_annot, annot_frac).flatten()\n", - "\n", - "def find_sig2_vec(params):\n", - " return np.multiply(np.dot(params._annomat, np.array(params._sig2_annot).astype(np.float32)),\n", - " np.multiply(np.power(np.float32(2.0) * params._mafvec * (1-params._mafvec), np.float32(params._s)),\n", - " np.power(params._tldvec, np.float32(params._l)))) * params._sig2_beta\n", - "\n", - "def find_annot_enrich(params, annomat):\n", - " sig2_vec = find_sig2_vec(params)\n", - " h2_vec = params._pi * np.multiply(hetvec, sig2_vec)\n", - " h2_annot = np.matmul(h2_vec.reshape((1, len(h2_vec))), annomat);\n", - " h2_total = h2_annot[0][0]\n", - "\n", - " snps_annot = np.sum(annomat, 0)\n", - " snps_total = snps_annot[0]\n", - "\n", - " return np.divide(np.divide(h2_annot, h2_total), np.divide(snps_annot, snps_total))\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead.\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - }, - { - "data": { - "text/html": [ - "
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CategoryProp._SNPsProp._h2Enrichment_std_errorEnrichmentNNLS_p7NNLS_p8NNLS_p10NNLS_p11
0baseL2_01.0001.0000.0001.0001.0001.0001.0001.000
1Coding_UCSC.bedL2_00.0140.1562.31610.9247.2537.1570.9567.328
2Coding_UCSC.extend.500.bedL2_00.0640.2310.5743.6313.7673.1010.9833.153
3Conserved_LindbladToh.bedL2_00.0260.3011.47111.70211.94610.8070.97110.934
4Conserved_LindbladToh.extend.500.bedL2_00.3300.6690.1232.0252.0681.8961.0151.900
5CTCF_Hoffman.bedL2_00.0240.0391.0261.6181.8802.0631.0452.039
6CTCF_Hoffman.extend.500.bedL2_00.0710.1080.3971.5301.6721.6631.0431.644
7DGF_ENCODE.bedL2_00.1360.4870.4523.5812.3913.6511.0313.637
8DGF_ENCODE.extend.500.bedL2_00.5380.9180.0631.7051.5061.5681.0271.562
9DHS_peaks_Trynka.bedL2_00.1110.3870.4523.4952.8293.2371.0353.223
10DHS_Trynka.bedL2_00.1660.5230.3663.1462.5762.8101.0352.798
11DHS_Trynka.extend.500.bedL2_00.4960.8060.0861.6241.5711.5671.0301.560
13Enhancer_Andersson.extend.500.bedL2_00.0190.0270.9821.3952.9012.3261.0742.273
14Enhancer_Hoffman.bedL2_00.0420.1710.6694.0844.6153.1601.0493.127
15Enhancer_Hoffman.extend.500.bedL2_00.0900.2830.3583.1453.3532.6121.0472.587
16FetalDHS_Trynka.bedL2_00.0840.2770.5083.2953.3343.3761.0403.361
17FetalDHS_Trynka.extend.500.bedL2_00.2830.5440.1661.9202.0571.9521.0431.938
18H3K27ac_Hnisz.bedL2_00.3890.7580.0751.9491.8061.6871.0461.675
19H3K27ac_Hnisz.extend.500.bedL2_00.4200.7960.0561.8941.7271.6251.0431.615
20H3K27ac_PGC2.bedL2_00.2690.6070.2912.2592.2712.0531.0372.043
21H3K27ac_PGC2.extend.500.bedL2_00.3350.7430.1332.2171.9961.8411.0361.831
22H3K4me1_peaks_Trynka.bedL2_00.1700.4870.4092.8692.6112.3681.0362.353
23H3K4me1_Trynka.bedL2_00.4240.9910.1112.3381.8651.8061.0331.798
24H3K4me1_Trynka.extend.500.bedL2_00.6060.9630.0371.5901.4651.4411.0281.437
25H3K4me3_peaks_Trynka.bedL2_00.0420.1680.8464.0314.2473.4501.0093.451
26H3K4me3_Trynka.bedL2_00.1330.4520.3343.3953.2932.8191.0112.814
27H3K4me3_Trynka.extend.500.bedL2_00.2550.5860.1532.2952.3192.0961.0182.088
28H3K9ac_peaks_Trynka.bedL2_00.0380.2271.1505.9036.0493.8581.0353.844
29H3K9ac_Trynka.bedL2_00.1250.5650.3424.5054.5023.5441.0393.525
30H3K9ac_Trynka.extend.500.bedL2_00.2300.6750.1872.9372.8572.5841.0412.570
31Intron_UCSC.bedL2_00.3870.4500.0641.1611.2431.1631.0011.163
32Intron_UCSC.extend.500.bedL2_00.3970.5560.0491.4011.4001.3481.0001.353
34PromoterFlanking_Hoffman.extend.500.bedL2_00.0330.1210.8433.6533.7122.6261.0042.640
35Promoter_UCSC.bedL2_00.0310.1190.7983.8913.6013.2590.9983.301
36Promoter_UCSC.extend.500.bedL2_00.0380.1070.4672.8083.3443.0251.0003.060
37Repressed_Hoffman.bedL2_00.4610.1370.0880.2980.4070.4741.0000.472
38Repressed_Hoffman.extend.500.bedL2_00.7190.3400.0350.4730.5410.6371.0020.635
39SuperEnhancer_Hnisz.bedL2_00.1670.3870.1072.3162.2792.0171.0691.987
40SuperEnhancer_Hnisz.extend.500.bedL2_00.1700.4010.0972.3552.2652.0051.0681.975
41TFBS_ENCODE.bedL2_00.1310.4300.4123.2782.7402.7061.0352.696
42TFBS_ENCODE.extend.500.bedL2_00.3410.7020.1302.0561.8351.7511.0331.743
43Transcribed_Hoffman.bedL2_00.3460.4910.1301.4191.2721.2380.9791.251
44Transcribed_Hoffman.extend.500.bedL2_00.7620.6390.0570.8380.9460.9510.9930.955
45TSS_Hoffman.bedL2_00.0180.1381.3617.7386.7295.2961.0095.343
46TSS_Hoffman.extend.500.bedL2_00.0340.2030.7205.9215.1313.9781.0233.994
47UTR_3_UCSC.bedL2_00.0110.0972.1038.6886.4723.7000.9823.770
48UTR_3_UCSC.extend.500.bedL2_00.0260.1491.6655.6204.2332.8270.9842.874
50UTR_5_UCSC.extend.500.bedL2_00.0270.0770.5762.8753.8835.3030.9855.376
51WeakEnhancer_Hoffman.bedL2_00.0210.0350.9701.6832.9802.9991.0712.939
52WeakEnhancer_Hoffman.extend.500.bedL2_00.0890.1140.2941.2892.3742.1841.0662.146
\n", - "
" - ], - "text/plain": [ - " Category Prop._SNPs Prop._h2 \\\n", - "0 baseL2_0 1.000 1.000 \n", - "1 Coding_UCSC.bedL2_0 0.014 0.156 \n", - "2 Coding_UCSC.extend.500.bedL2_0 0.064 0.231 \n", - "3 Conserved_LindbladToh.bedL2_0 0.026 0.301 \n", - "4 Conserved_LindbladToh.extend.500.bedL2_0 0.330 0.669 \n", - "5 CTCF_Hoffman.bedL2_0 0.024 0.039 \n", - "6 CTCF_Hoffman.extend.500.bedL2_0 0.071 0.108 \n", - "7 DGF_ENCODE.bedL2_0 0.136 0.487 \n", - "8 DGF_ENCODE.extend.500.bedL2_0 0.538 0.918 \n", - "9 DHS_peaks_Trynka.bedL2_0 0.111 0.387 \n", - "10 DHS_Trynka.bedL2_0 0.166 0.523 \n", - "11 DHS_Trynka.extend.500.bedL2_0 0.496 0.806 \n", - "13 Enhancer_Andersson.extend.500.bedL2_0 0.019 0.027 \n", - "14 Enhancer_Hoffman.bedL2_0 0.042 0.171 \n", - "15 Enhancer_Hoffman.extend.500.bedL2_0 0.090 0.283 \n", - "16 FetalDHS_Trynka.bedL2_0 0.084 0.277 \n", - "17 FetalDHS_Trynka.extend.500.bedL2_0 0.283 0.544 \n", - "18 H3K27ac_Hnisz.bedL2_0 0.389 0.758 \n", - "19 H3K27ac_Hnisz.extend.500.bedL2_0 0.420 0.796 \n", - "20 H3K27ac_PGC2.bedL2_0 0.269 0.607 \n", - "21 H3K27ac_PGC2.extend.500.bedL2_0 0.335 0.743 \n", - "22 H3K4me1_peaks_Trynka.bedL2_0 0.170 0.487 \n", - "23 H3K4me1_Trynka.bedL2_0 0.424 0.991 \n", - "24 H3K4me1_Trynka.extend.500.bedL2_0 0.606 0.963 \n", - "25 H3K4me3_peaks_Trynka.bedL2_0 0.042 0.168 \n", - "26 H3K4me3_Trynka.bedL2_0 0.133 0.452 \n", - "27 H3K4me3_Trynka.extend.500.bedL2_0 0.255 0.586 \n", - "28 H3K9ac_peaks_Trynka.bedL2_0 0.038 0.227 \n", - "29 H3K9ac_Trynka.bedL2_0 0.125 0.565 \n", - "30 H3K9ac_Trynka.extend.500.bedL2_0 0.230 0.675 \n", - "31 Intron_UCSC.bedL2_0 0.387 0.450 \n", - "32 Intron_UCSC.extend.500.bedL2_0 0.397 0.556 \n", - "34 PromoterFlanking_Hoffman.extend.500.bedL2_0 0.033 0.121 \n", - "35 Promoter_UCSC.bedL2_0 0.031 0.119 \n", - "36 Promoter_UCSC.extend.500.bedL2_0 0.038 0.107 \n", - "37 Repressed_Hoffman.bedL2_0 0.461 0.137 \n", - "38 Repressed_Hoffman.extend.500.bedL2_0 0.719 0.340 \n", - "39 SuperEnhancer_Hnisz.bedL2_0 0.167 0.387 \n", - "40 SuperEnhancer_Hnisz.extend.500.bedL2_0 0.170 0.401 \n", - "41 TFBS_ENCODE.bedL2_0 0.131 0.430 \n", - "42 TFBS_ENCODE.extend.500.bedL2_0 0.341 0.702 \n", - "43 Transcribed_Hoffman.bedL2_0 0.346 0.491 \n", - "44 Transcribed_Hoffman.extend.500.bedL2_0 0.762 0.639 \n", - "45 TSS_Hoffman.bedL2_0 0.018 0.138 \n", - "46 TSS_Hoffman.extend.500.bedL2_0 0.034 0.203 \n", - "47 UTR_3_UCSC.bedL2_0 0.011 0.097 \n", - "48 UTR_3_UCSC.extend.500.bedL2_0 0.026 0.149 \n", - "50 UTR_5_UCSC.extend.500.bedL2_0 0.027 0.077 \n", - "51 WeakEnhancer_Hoffman.bedL2_0 0.021 0.035 \n", - "52 WeakEnhancer_Hoffman.extend.500.bedL2_0 0.089 0.114 \n", - "\n", - " Enrichment_std_error Enrichment NNLS_p7 NNLS_p8 NNLS_p10 NNLS_p11 \n", - "0 0.000 1.000 1.000 1.000 1.000 1.000 \n", - "1 2.316 10.924 7.253 7.157 0.956 7.328 \n", - "2 0.574 3.631 3.767 3.101 0.983 3.153 \n", - "3 1.471 11.702 11.946 10.807 0.971 10.934 \n", - "4 0.123 2.025 2.068 1.896 1.015 1.900 \n", - "5 1.026 1.618 1.880 2.063 1.045 2.039 \n", - "6 0.397 1.530 1.672 1.663 1.043 1.644 \n", - "7 0.452 3.581 2.391 3.651 1.031 3.637 \n", - "8 0.063 1.705 1.506 1.568 1.027 1.562 \n", - "9 0.452 3.495 2.829 3.237 1.035 3.223 \n", - "10 0.366 3.146 2.576 2.810 1.035 2.798 \n", - "11 0.086 1.624 1.571 1.567 1.030 1.560 \n", - "13 0.982 1.395 2.901 2.326 1.074 2.273 \n", - "14 0.669 4.084 4.615 3.160 1.049 3.127 \n", - "15 0.358 3.145 3.353 2.612 1.047 2.587 \n", - "16 0.508 3.295 3.334 3.376 1.040 3.361 \n", - "17 0.166 1.920 2.057 1.952 1.043 1.938 \n", - "18 0.075 1.949 1.806 1.687 1.046 1.675 \n", - "19 0.056 1.894 1.727 1.625 1.043 1.615 \n", - "20 0.291 2.259 2.271 2.053 1.037 2.043 \n", - "21 0.133 2.217 1.996 1.841 1.036 1.831 \n", - "22 0.409 2.869 2.611 2.368 1.036 2.353 \n", - "23 0.111 2.338 1.865 1.806 1.033 1.798 \n", - "24 0.037 1.590 1.465 1.441 1.028 1.437 \n", - "25 0.846 4.031 4.247 3.450 1.009 3.451 \n", - "26 0.334 3.395 3.293 2.819 1.011 2.814 \n", - "27 0.153 2.295 2.319 2.096 1.018 2.088 \n", - "28 1.150 5.903 6.049 3.858 1.035 3.844 \n", - "29 0.342 4.505 4.502 3.544 1.039 3.525 \n", - "30 0.187 2.937 2.857 2.584 1.041 2.570 \n", - "31 0.064 1.161 1.243 1.163 1.001 1.163 \n", - "32 0.049 1.401 1.400 1.348 1.000 1.353 \n", - "34 0.843 3.653 3.712 2.626 1.004 2.640 \n", - "35 0.798 3.891 3.601 3.259 0.998 3.301 \n", - "36 0.467 2.808 3.344 3.025 1.000 3.060 \n", - "37 0.088 0.298 0.407 0.474 1.000 0.472 \n", - "38 0.035 0.473 0.541 0.637 1.002 0.635 \n", - "39 0.107 2.316 2.279 2.017 1.069 1.987 \n", - "40 0.097 2.355 2.265 2.005 1.068 1.975 \n", - "41 0.412 3.278 2.740 2.706 1.035 2.696 \n", - "42 0.130 2.056 1.835 1.751 1.033 1.743 \n", - "43 0.130 1.419 1.272 1.238 0.979 1.251 \n", - "44 0.057 0.838 0.946 0.951 0.993 0.955 \n", - "45 1.361 7.738 6.729 5.296 1.009 5.343 \n", - "46 0.720 5.921 5.131 3.978 1.023 3.994 \n", - "47 2.103 8.688 6.472 3.700 0.982 3.770 \n", - "48 1.665 5.620 4.233 2.827 0.984 2.874 \n", - "50 0.576 2.875 3.883 5.303 0.985 5.376 \n", - "51 0.970 1.683 2.980 2.999 1.071 2.939 \n", - "52 0.294 1.289 2.374 2.184 1.066 2.146 " - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "df['NNLS_p7'] = find_annot_enrich(params7, annomat).flatten()\n", - "df['NNLS_p8'] = find_annot_enrich(params8, annomat).flatten()\n", - "df['NNLS_p10'] = find_annot_enrich(params10, annomat).flatten()\n", - "df['NNLS_p11'] = find_annot_enrich(params11, annomat).flatten()\n", - "df[['Category', 'Prop._SNPs', 'Prop._h2', 'Enrichment_std_error', 'Enrichment', 'NNLS_p7', 'NNLS_p8', 'NNLS_p10', 'NNLS_p11']][df['Prop._SNPs']>0.01].round(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['params', 'optimize', 'annot_enrich', 'full_cost', 'qqplot', 'qqplot_bins'])" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['params3'].keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 187, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.all(~np.isfinite(data['params3']['qqplot']['hv_logp']))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.set_option('diag', 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.set_weights_randprune(64, 0.1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "#idx0=(tag_pdf_convolve==0) & (libbgmg.weights>0)\n", - "idx=((tag_pdf_convolve>0) & (libbgmg.weights>0))\n", - "#print(np.sum(idx0), np.sum(idx))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1186.4207\n", - "17336.46220645369\n" - ] - } - ], - "source": [ - "print(np.dot(libbgmg.weights[idx], tag_pdf_convolve[idx]))\n", - "print(cost_gaussian)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.1754944e-38" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[idx].min()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.16240817, 0.16096343, 0.10743059, ..., 0.21106009, 0.2116424 ,\n", - " 0.19055355], dtype=float32)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.16240814, 0.16096346, 0.10743059, ..., 0.21106012, 0.21164232,\n", - " 0.19055364], dtype=float32)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_convolve[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.nonzero(np.abs(np.log(tag_pdf_gaussian)-np.log(tag_pdf_convolve))>60)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "50.11847" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx=((tag_pdf_convolve>0) & (libbgmg.weights>0))\n", - "np.max(np.abs(np.log(tag_pdf_gaussian[idx])-np.log(tag_pdf_convolve[idx])))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0659866e-11" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(tag_pdf_convolve[tag_pdf_convolve>0])" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "16770.205 16770.06\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in less\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - } - ], - "source": [ - "#idx=(libbgmg.weights>0)\n", - "#idx=(tag_pdf_convolve>0) & (libbgmg.weights>0)\n", - "idx=((libbgmg.weights>0) & (np.abs(libbgmg.zvec1) < 10.45))\n", - "a=-np.sum(np.multiply(libbgmg.weights[idx], np.log(tag_pdf_gaussian[idx])))\n", - "b=-np.sum(np.multiply(libbgmg.weights[idx], np.log(tag_pdf_convolve[idx])))\n", - "print(a,b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "matrixproduct() takes at most 3 arguments (4 given)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibbgmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag_pdf_gaussian\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcost_gaussian\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcost_convolve\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: matrixproduct() takes at most 3 arguments (4 given)" - ] - } - ], - "source": [ - "np.dot(libbgmg.weights[idx], tag_pdf_gaussian[idx],cost_gaussian,cost_convolve)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.7355818e-15 7.7725946e-15 3.2065870e-15 6.3927813e-15 1.2732968e-14\n", - " 6.4004287e-15 6.4921624e-15 7.2407172e-15]\n", - "[1.7515170e-15 7.7827589e-15 3.2165242e-15 6.3835359e-15 1.2711976e-14\n", - " 6.4108921e-15 6.4771657e-15 7.2322832e-15]\n", - "[-23.675 -15.184 17.072 23.526 23.251 23.518 23.514 23.463]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in subtract\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in less\n", - " \n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in greater\n", - " \n" - ] - } - ], - "source": [ - "a=np.abs(np.log(tag_pdf_gaussian)-np.log(tag_pdf_convolve))\n", - "idx = (a<0.01) & (a>0.001)\n", - "print(tag_pdf_gaussian[idx])\n", - "print(tag_pdf_convolve[idx])\n", - "print(libbgmg.zvec1[idx])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.1754944e-38" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(tag_pdf_gaussian[idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8.673067e-19" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(tag_pdf_convolve[idx])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([538262]),)\n" - ] - }, - { - "data": { - "text/plain": [ - "50.11847" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.set_option('aux_option', 2)\n", - "libbgmg.set_option('cost_calculator', _cost_calculator_gaussian)\n", - "\n", - "sig2_vec=params3.find_sig2_vec()\n", - "pi_vec = params3._pi * np.ones(shape=(libbgmg.num_snp, 1), dtype=np.float32)\n", - "tag_pdf_gaussian = libbgmg.calc_unified_univariate_Ezvec2(1, pi_vec, sig2_vec, params3._sig2_zeroA, 1, 0)\n", - "cost_gaussian = libbgmg.calc_unified_univariate_cost(1, pi_vec, sig2_vec, params3._sig2_zeroA, 1, 0)\n", - "\n", - "libbgmg.set_option('cost_calculator', _cost_calculator_convolve)\n", - "libbgmg.set_option('cubature_rel_error', 1e-9)\n", - "\n", - "tag_pdf_convolve = libbgmg.calc_unified_univariate_Ezvec2(1, pi_vec, sig2_vec, params3._sig2_zeroA, 1, 0)\n", - "cost_convolve = libbgmg.calc_unified_univariate_cost(1, pi_vec, sig2_vec, params3._sig2_zeroA, 1, 0)\n", - "\n", - "print(np.nonzero((tag_pdf_convolve==0) & (libbgmg.weights>0)))\n", - "\n", - "idx=((tag_pdf_convolve>0) & (libbgmg.weights>0))\n", - "np.max(np.abs(np.log(tag_pdf_gaussian[idx])-np.log(tag_pdf_convolve[idx])))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([27.693], dtype=float32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.zvec1[(tag_pdf_convolve==0) & (libbgmg.weights>0)]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.504482e-10" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_convolve[116049]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.5604272e-18" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[538395]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.1754944e-38" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[tag_pdf_gaussian>0].min()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.785933e-10" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_convolve[tag_pdf_convolve>0].min()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 98499, 115943, 116049, 116074, 405443, 405579, 405582, 422751,\n", - " 422838, 422854, 425860, 499174, 499199, 499473, 499477, 499544,\n", - " 499545, 499612, 499637, 499651, 499733, 499737, 499761, 513194,\n", - " 513318, 538071, 538094, 538179, 538196, 538209, 538254, 538262,\n", - " 538395, 658693, 658721, 658732, 658742, 658770]),)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nonzero((tag_pdf_convolve==0) & (libbgmg.weights>0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.7412007e-05" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx=((tag_pdf_gaussian != 0) | (tag_pdf_convolve != 0))\n", - "np.max(np.abs(tag_pdf_gaussian[idx] - tag_pdf_convolve[idx]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "51.900333" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.ld_tag_r2_sum[98499]" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2372" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.mafvec[98499]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 98499, 115943, 116049, 116074, 405443, 405579, 405582, 422751,\n", - " 422838, 422854, 425860, 499174, 499199, 499473, 499477, 499544,\n", - " 499545, 499612, 499637, 499651, 499733, 499737, 499761, 513194,\n", - " 513318, 538071, 538094, 538179, 538196, 538209, 538254, 538262,\n", - " 538395, 658693, 658721, 658732, 658742, 658770]),)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nonzero(tag_pdf_convolve==1e-100)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "tag_pdf_convolve = tag_pdf_convolve.astype(np.float64)\n", - "tag_pdf_convolve[(tag_pdf_convolve<=0) & idx]=1e-100" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(38,)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([], dtype=float32)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[idx & (tag_pdf_gaussian==0)]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.3648368e-09, 2.7026832e-08, 6.4867961e-10, 1.5176944e-08,\n", - " 1.7327101e-15, 1.5692145e-10, 1.4268707e-10, 1.9460981e-11,\n", - " 8.6939191e-15, 8.0767183e-15, 2.6020112e-14, 9.9690698e-13,\n", - " 8.9799874e-13, 5.3225446e-10, 4.7733278e-10, 2.4014339e-12,\n", - " 2.2727085e-12, 2.7929742e-12, 2.7114335e-12, 2.4634110e-12,\n", - " 2.4037946e-12, 5.9166592e-09, 1.7406645e-08, 1.0126087e-09,\n", - " 4.2284817e-10, 1.6631777e-14, 4.3795572e-09, 1.5734064e-24,\n", - " 1.9755140e-11, 1.0735530e-11, 8.2271723e-10, 1.0075949e-18,\n", - " 2.5387707e-18, 6.3821980e-15, 1.1759822e-12, 1.2904978e-09,\n", - " 6.4813187e-15, 4.2550941e-12], dtype=float32)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tag_pdf_gaussian[idx & (tag_pdf_convolve==0)]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17989.09476996527" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx=libbgmg.weights>0\n", - "-np.sum(np.multiply(libbgmg.weights[idx], np.log(tag_pdf_convolve[idx])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17336.462241622565 17989.09476918166\n" - ] - } - ], - "source": [ - "print(cost_gaussian, cost_convolve)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.5857617479789346e-07, 6.220790236650385e-08)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1*2.5857617479789346e-07, 5.905602509994463e-05*0.001053370968689902" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# +.-T indicate status (fitted, in progress, done, ToBeImplemente)\n", - "# number in parentheses indicate a dependency (e.g. params5 depend on params3)\n", - "\n", - "# params1 + standard causal mixture model, fit using old implementation\n", - "\n", - "# params3 + infinitesimal model (just to find baseline sig2beta)\n", - "# params4 + infinitesimal model, allowing for flexible S and L parameters\n", - "\n", - "# params5 (3) + baseline annotation model, infinitesimal, without accounting for S and L parameters\n", - "# params6 (4) + baseline annotation model, infinitesimal, allowing for flexible S and L parameters\n", - "\n", - "# params7 (5) + causal mixture with annotation, without accounting for S and L parameters\n", - "# params8 (6) + causal mixture with annotation, allowing for flexible S and L parameters\n", - "# (note that here S and L are kept from infinitesimal model - need to re-fit them?) \n", - "\n", - "# params9 - causal mixture without annotations, without accounting for S and L parameters\n", - "# params10 + causal mixture without annotations, allowing for flexible S and L parameters\n", - "\n", - "# params11 (6) + causal mixture with annotations, allowing for flexible S and L parameters,\n", - "# and re-fit S and L in the context of mixture model\n", - "\n", - "\n", - "Take home messages\n", - "1. Modeling annotations affects \"pi\" estimates in mixture model - they increase by a factor of 10\n", - "\n", - "Technical issues\n", - "1. Fast const function is ~150 times faster, and gives high quality results - however I'm not sure if we can trust it 100%\n", - "2. Why for infinitesimal cost function gaussian approximation doesn't give exactly the same answer as convolutions? \n", - "\n", - "TBD\n", - "- increase number of iterations in fminsearch\n", - "- split runs across parameters so that we can run " - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnotUnivariateParams(_pi: 0.015880541721926548, _sig2_beta: 2.2497055296175e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.365544728354278, _l: -0.13260565509183503, _sig2_zeroA: 1.8823111632885878)" - ] - }, - "execution_count": 294, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "params11" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "df['NNLS_p7'] = find_annot_enrich(params7, annomat).flatten()\n", - "df['NNLS_p8'] = find_annot_enrich(params8, annomat).flatten()\n", - "df['NNLS_p10'] = find_annot_enrich(params10, annomat).flatten()\n", - "df['NNLS_p11'] = find_annot_enrich(params11, annomat).flatten()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UnivariateParams(_pi: 0.0014400133519476379, _sig2_beta: 0.0002144974094950297, _sig2_zero: 2.2960585139856797)\n", - "UnivariateParams(_pi: 0.0015684598531926141, _sig2_beta: 0.00020448806343576262, _sig2_zero: 2.23992429765977)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/github/mixer/precimed/mixer/cli.py:459: RuntimeWarning: divide by zero encountered in log10\n", - " hv_logp = -np.log10(2*scipy.stats.norm.cdf(-hv_z))\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:50: RuntimeWarning: invalid value encountered in less\n", - " y2[x2 self.x[-1]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params1 - standard causal mixture model, fit using old implementation\n", - "parametrization = precimed.mixer.utils.UnivariateParametrization_natural_axis(lib=libbgmg, trait=1)\n", - "bounds_left = UnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zero=0.9)\n", - "bounds_right = UnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zero=2.5)\n", - "params1=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params1, '_params1')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 3.326658501249406e-07, _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 2.1516625516482493)\n", - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 3.369167984860813e-07, _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 2.04649596972114)\n" - ] - } - ], - "source": [ - "# params3 - infinitesimal model (just to find baseline sig2beta)\n", - "constraint = AnnotUnivariateParams(pi=1, sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(sig2_beta=5e-8, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params3=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 6.75500989148921e-07, _sig2_annot: [1], _s: -0.011335565551680804, _l: -0.16934948578635015, _sig2_zeroA: 2.054008709175631)\n", - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 7.53163000205981e-07, _sig2_annot: [1], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 2.0661803442547924)\n" - ] - } - ], - "source": [ - "# params4 - infinitesimal model, allowing for flexible s and l parameters\n", - "constraint = AnnotUnivariateParams(pi=1, sig2_annot=[1], annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, sig2_beta=5e-8, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params4=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1.0, _sig2_beta: 2.9443784386506135e-07, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 2.3190660353261126)\n" - ] - } - ], - "source": [ - "# params5 - baseline annotation model, infinitesimal, without accounting for S and L parameters\n", - "trait_index=1\n", - "params5 = AnnotUnivariateParams(pi=1.0, sig2_beta=params3._sig2_beta, sig2_annot=None, annomat=annomat, annonames=annonames, s=0, l=0, sig2_zeroA=0, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "params5.fit_sig2_annot(libbgmg, trait_index)\n", - "params5.drop_zero_annot()\n", - "print(params5)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1.0, _sig2_beta: 7.53163000205981e-07, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.7988296958524468)\n" - ] - } - ], - "source": [ - "#params6 - baseline annotation model, infinitesimal, allowing for flexible S and L parameters\n", - "trait_index=1\n", - "params6 = AnnotUnivariateParams(pi=1.0, sig2_beta=params4._sig2_beta, sig2_annot=None, annomat=annomat, annonames=annonames, s=params4._s, l=params4._l, sig2_zeroA=0, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "params6.fit_sig2_annot(libbgmg, trait_index)\n", - "params6.drop_zero_annot()\n", - "print(params6)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.020112336745176665, _sig2_beta: 1.9934921745404774e-05, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 2.010107269906994)\n", - "AnnotUnivariateParams(_pi: 0.016490772435950134, _sig2_beta: 2.1573172356293674e-05, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 1.9785604206089915)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params7 - causal mixture with annotation, without accounting for S and L parameters\n", - "constraint = AnnotUnivariateParams(s=0, l=0, sig2_annot=params5._sig2_annot, annomat=params5._annomat, annonames=params5._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params7=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params7, '_params7', True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.015785388620568757, _sig2_beta: 4.874428472741427e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.886429562529495)\n", - "AnnotUnivariateParams(_pi: 0.015785512563498437, _sig2_beta: 4.5576351979317775e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.9063891647797888)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params8 (6) - causal mixture with annotation, allowing for flexible S and L parameters\n", - "constraint = AnnotUnivariateParams(s=params6._s, l=params6._l, sig2_annot=params6._sig2_annot, annomat=params6._annomat, annonames=params6._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params8=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params8, '_params8', True)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "if 0:\n", - " # params9 - causal mixture without annotations, without accounting for S and L parameters\n", - " constraint = AnnotUnivariateParams(sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - " parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - " bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - " bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - " params9=perform_fit(bounds_left, bounds_right, parametrization)\n", - " do_plots(params9, '_params9')" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.0019422768605894315, _sig2_beta: 0.0001514049546435859, _sig2_annot: [1], _s: -0.6445122556819146, _l: -0.1664666397153803, _sig2_zeroA: 2.1554019231845993)\n", - "AnnotUnivariateParams(_pi: 0.0017433067614436263, _sig2_beta: 0.00032365469994769143, _sig2_annot: [1], _s: -0.4346094933177381, _l: -0.25205482692795367, _sig2_zeroA: 2.1819290510636007)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params10 - causal mixture without annotations, allowing for flexible S and L parameters\n", - "constraint = AnnotUnivariateParams(sig2_annot=[1], annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params10=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params10, '_params10', True)" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.014591266436314046, _sig2_beta: 2.5377289052973313e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.4796922678191362, _l: -0.15359210045600655, _sig2_zeroA: 1.8451261379413293)\n", - "AnnotUnivariateParams(_pi: 0.015880541721926548, _sig2_beta: 2.2497055296175e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.365544728354278, _l: -0.13260565509183503, _sig2_zeroA: 1.8823111632885878)\n" - ] - } - ], - "source": [ - "# params11 (6) causal mixture with annotations, allowing for flexible S and L parameters,\n", - "# and re-fit S and L in the context of mixture model\n", - "constraint = AnnotUnivariateParams(sig2_annot=params6._sig2_annot, annomat=params6._annomat, annonames=params6._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params11=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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VC0wqVZ3Mn7eIcMrE+FWTrN65mcqMfHI8VQw7bU7c2lUqFk30asAorqhoNz4fCRvSsVEvTR3eF2hqIi0tjWyXK26xvPboQ2C1MmfapLi1qVRHNNGrAcHv97Nt9+52UyuHNw3CabGw29rx1Mpqt5spcezNB70e9tkzcXrrmHLNt+LWrlId0USvBoRlH3xAncfTdnpk2MLCwHjqIiH2OmNum0BFTQ15OTlxrVa57Pc/JeJwcmqubhOojg1N9KrfC4VCvPDmm2RnZLQ5fqpvFC6LlZccu7HE2MLe5/cTjkS48aqr4lqt8uO6MNaAn3nfvTNubSp1JJroVb93oKwMj89H5mGJvjCcgScSpjwt9kYjDT4fJ4wezSkTJsQtlnWP/4FAeibHhT3YU9Pj1q5SR6KJXvV7pR3Mthlm0imXjlfD+puaOPG44+Iay8rNuyAc5oJv6Mbf6tjRRK/6vcqaGj7b1riZhOxkWmzss7o7vtEYRsZxX9gD779NXWYeg71V5Iw9MW7tKtUZTfSq39u4cycOu73NscFNzWPu+22xE70vEMDpdDItjrNtXv/HPwDhvAXnxq1NpbpCE73q94pLS9vtKDU40jwnvs7mjXmPp6GBMcOH43Q64xJD7d7tlLqaa86Pm39pXNpUqqs00at+rayqCrfHQ2pK26mMx4dyqIuEwBaKeV8gEODMqVPjFscrD9+Psdo4+5QT4tamUl3VaaIXkcdFpEJEtrQ6lisiy0Rkd/TPnOhxEZH7RGSPiGwSkfj9pCjVDTv27sVAm60DTdhKoaSxwxJ7kVQgGMRutzN72rS4xBCoq2WfPQunt45p190UlzaVOhpd6dE/ASw47NhtwNvGmPHA29HPAOcD46P/LQEeik+YSnXPx7t2YT2sYuWwQB5WEbbYy2PeU1tXx6Rx4+JWe/71e39CxJHCjMHxK6Gg1NHoNNEbY1YAh3d9FgJPRr9+EvhCq+NPmWargWwR0fqrKmk+LS4m9bDx+dGhLIwxVDtiv4gNBoNMnzw5Ls8Ph0JsCdix+Ro457s/jkubSh2t7o7RDzbGlAJE/xwUPT4cONDquuLoMaWOOb/fT1lVFamHvVAdG8mm1oRiroYNh8OICCeMGROXGN79/U8JpaZzkj2oO0ippIn3y9hY61JiFvkWkSUisk5E1lVWVsY5DKVg065dBJuaSGk1tVKCdoZYnGyxxl4NW11Xx7iRIxlTWBiXGD4sq8cSDHDB934Ul/aU6o7uJvryQ0My0T8roseLgdb7rRUCMatFGWMeMcZMN8ZMLygo6GYYSnVsb1ERWNp+i49qygdgq6Mi1i00NTVx1owZcXn++qcewO/KZkygjpTs3Li0qVR3dDfRvwRcG/36WmBpq+Nfic6+OQOoOzTEo9SxdqCsrF39+UGRdIwxeGz1Me8RESbEadjmvY92QDjERf+lM21UcnU6aCgiTwPnAPkiUgz8GPgV8JyI3AAUAZdFL38NuADYAzQCX01AzEp1SUlFBSkOR5tjwyIu6k045vh8KBRCRBg+aFD7k0dpz5v/pD4zj6H15VruQCVdp4neGHNlB6fmxbjWADf2NCileioUClFeXU1uZmab44NNKqUdFDJzezwcN2JEXFbDvvn6vyGzgAsuu6zzi5VKMF0Zq/ql8upqwqEQ9lYvYk1IcImVUktDzHsCgQCnnNDzlatlG96nMjOfnPoqRsxq1x9S6pjTRK/6pfLq6nbTvdIiaYgIpdbY9W0QYVQcqlW+/NenQCzM/9zsHrelVDxoolf90sHy9qtehwazAKiyedqdC4fDGGMYNbxnyz6qd27mYLR42YSFV/WoLaXiRRO96pd279vXpr4NwLhwLgEToTFGj77R56MgJ4fhgwf36Lkv/elBsNqYO31Sj9pRKp400at+afenn5J+2EvVEREXJTRisbZf11ff0MCUHtaery/aS1FaHun1NZx69Td71JZS8aSJXvU7fr+fare7TQ16E4YssXHQ0sH4vDFMPemkHj33pfvvxdjszDlxVI/aUSreNNGrfmfDtm1EjGkzdDPRNxKLCMXW9uPzHm9z8i8cMqTbz2ysLOUTRzZOr5vTv35zt9tRKhE00at+559vvdVm68BI2HBOqJCqSBNFKWXtrq/zernmkksYkp/f7We+cu8viNgdzCzUUgeq99FEr/oVt9dLcUUFuVlZLcfyg3mkW6yssh9styLW4/WSnpbGvFmzuv3MoNfDTnHhaPAw+79/2O12lEoUTfSqX3l39WpCwWCbGjezAyNoMobdzvb19dweD/Nnz+7RJiOv/vpOwilOpuelaCli1Svpd6XqV95+/30yXJ/t5GQJ2RlHBmulErFG2lxb7/GQk5nJl849t9vPC/oa2NLkwGYamHfHL7vdjlKJpD161W9U1dRQUl1NRqve+Yn+oVhEWOcsbnd9vdfLnNNO61Ftmzd//WNCzjROTTPam1e9ln5nqn7jjVWrED7bCNyE4axQIVU04Xe0rW/jbWzE7nAw74wzuv28oK+BjT4LNho5/ye/6EnoSiWU9uhVv7Hiww/JSk9v+ZzflIvLYmWZY3+7a9319Vzyuc/1aCXs6/fcSciZxpQ0gzUlpdvtKJVomuhVv3CwvJzq+vo2i6RmN40gYCIUp7TdqjIUCiHAGaee2u3nBX0NfOy3YfM1sODWn3e7HaWOBU30ql94Y8UKpNUiKUvQwTgyWB/jJWx5VRUzp07t0b6wr/3qTsLONKa40N686vU00as+r9bt5t0PPyS71SYj5/jGArDhsCmVHq8Xm83Govnzu/28oK+BTYFob/772ptXvZ8metXnvf/xxzT6fC1z4U3Ywskmj23U4Xe0rW3jbmhg0fz5jBw6tNvPe/WXdxB2pjE1w6K9edUn6Kwb1ef9Z/16Ulol3HH+odhEeN9xoM11Pr+fFJuNM6dN6/azgl4Pm4MObJEG5v9Ue/Oqb9AeverTat1u9hQVtdkbdlpwCA2RMLX22rbX1tczfdKkHtW0aV4Fm8q0TO3Nq76jR4leRL4rIltFZIuIPC0iThEZIyJrRGS3iDwrIo54BavU4dZs3kw4EmkpeSAhGyMklQ3WyjZ158PhMJFIhC+ed163n9Xcm0/B7vPy+Vt13rzqO7qd6EVkOPD/gOnGmEmAFVgM3AP8zhgzHqgFbohHoErFsnn3buytVqSOaMpHRNjmaLuVoKexkWGDBvVops2/7r6dcEoq07PtugpW9Sk9HbqxAakiYgPSgFJgLvB89PyTwBd6+AylOrRt925crRZJTQoWEDAR6m3uNtd5GxqY2YN5842VpWwnHUdDPefe8rNut6NUMnQ70RtjDgL/CxTRnODrgPWA2xgTil5WDPRst2WlOrBl1y48DQ24ooukTNjKCWSxU9yI9bNv7UAggNViYc6MGd1+1gu//hkRRwpnjcjV3rzqc3oydJMDLATGAMOAdOD8GJeaDu5fIiLrRGRdZWVlrEuUOqK9RUVtPuc2ZWIV4SN722GbuoYGJowd2+1yB7V7t7M3JYdUTy1n/b87uh2vUsnSk6Gbc4F9xphKY0wQeAGYBWRHh3IACoH2RcABY8wjxpjpxpjpBQUFPQhDDVS7iora1J0fGW7ebKTysNk2waYmJowZ0+3nvPjgHzA2O+dOHtftNpRKpp4k+iLgDBFJExEB5gHbgHeARdFrrgWW9ixEpWL75NNPSW9VYnhsOIf6SAhs4TbXGWDiuO4l6fKP11KUXkBGXTXTrrupJ+EqlTQ9GaNfQ/NL1w3A5mhbjwC3Av8jInuAPOCxOMSpVBtlVVVUuN2fFTELCyNJZ7flsJewjY240tI4qZuJ/sW/PA4WC+fPOa2nISuVND16q2SM+THw48MOfwLoT4VKqPVbt7YpYjYiMAibCBscpW2uc9fXc8m8eW2GeLrq0xWvU5Y5iJy6SiZ+8SdxiVupZNCVsapP2rl3b5vkPS6US8gYauw1ba4TYMakSd16xtIXXgLgkktizTFQqu/QRK/6pD0HDpDWqgTByEgGZcaPtfW0ymAQq83GmOFHP8N324t/pSZrEIPqKxkz9+K4xKxUsmiiV32O3++ntr6e1OiLWBO2kCMOyi1ttwtsaGxkcF5et/aEfXXlhxAJc+lXvxqXmJVKJk30qs9ZuX49TcEgdrsdgKm+0dhE+MhR1nJNOBzG09DAReecc/Tt3/dzGjJzGeOrZvAp+rpJ9X26xE/1Of9ctoyMaNmDcDjCmaGh7BcvNfYaLDQXMqtyu5k8fjznnXnmUbUdDgRYUezGYrOz6BZdHKX6B+3Rqz6lrKqKqpoasjMygOYNwJ0WCx/YS9pUqwyGQsybOfOo23/5F7cRTHNxss1P+mCt3qH6B+3Rqz5l886dbWpqTAoOJmwMB1MqWo4FgkFsViunTJx4VG03lB9kU8iJPeTl4p/9Kk4RK5V82qNXfcqqDRtIiY7NA0yI5FJsGsH6Wfqvrq1l+qRJZLtcR9X287/5ORFHCnMKs3VTEdWvaKJXfYa3sZFd+/aRFU3gmf4ssiw2PrZ/1psPh8OEw2HOmzXrqNou27iafal5pNfXaOEy1e9oold9xvotWwiGwy37w05qGkzEGHY6P1sNW+f1Mnr4cE6ZMOGo2v7nE0+CxcqFZ3W/lLFSvZUmetVnbNu7F4t89sJ1QiSXEuNDrJ8VMfP5fEw/ypWw2/75JJVZBeTXVTDxi1+OW7xK9Raa6FWfEAqFWLtpExlpaUDzsE2uxc4m22d7GQSCQRDhzGnTutxuOBTi5fc3IJEIi667Nu5xK9UbaKJXfcKmXbuob2ggIzo+f3pTIWFj2Jpa3HJNrdvNlIkTGTl0aJfb/fev78SXkcP4QA1DTj0j7nEr1Rvo9ErVJ6zeuBGrpblfYsIRTjQ57KAOsUZargmGw1x49tldbrOxspS1HoPNNHLp7b+Ie8xK9Rbao1d9wvY9e1pqz2cGs3GIsNX+2bCNz+8n3ek8qrrzz/3qp4RTnJw12EVKVk7cY1aqt9BEr3q9Xfv2UVpV1TI+PzswirAxFDmqWq6pqatj5pQpXa47/+mKN9mfno+rrpqzv/ujhMStVG+hiV71em+9/z4WiwWr1YoE7Uwgi41SDbYQ0Nybt1mtLD6/63XjX3jxJUD44gVzExS1Ur2HJnrVq3kbG/ngo4/IyWre+Pt0/2gswH9S97dcU+12c+7MmeRkZ3epzQ8e/jV1WQUUeioYe94XExC1Ur2LJnrVq63bvJlAUxOpKSlEwoZTwwXsp4Emuw+AYDCIxWLhS+ed16X2gr4Gln9SgaUpwBU335bI0JXqNTTRq17t3bVrW+rO5wfzcFmsfGQrbzlfWVvL1IkTu9ybf/7H3yeY5mKKI0BG4ehEhKxUr9OjRC8i2SLyvIjsEJHtIjJTRHJFZJmI7I7+qdMZVLfUut1s37eP3MxMAGYGCmkyhj3RkgfhcJhwJMLCefO61N7BNe+yMyWHVE8tF9zx64TFrVRv09Me/R+AN4wxE4BTgO3AbcDbxpjxwNvRz0odtffWrSMU3UnK1uTkBDLZLNVYopUqq+vqGFtYyIljx3apvWef+QeIhS/OnYW1i7NzlOoPup3oRSQTmAM8BmCMaTLGuIGFwJPRy54EvtDTINXA9M6aNbiiUyqnBUYA8F7qXqC5Nx8IBFh80UVdauvd391FfVYBI70VHH/hFYkJWKleqic9+uOASuAvIvKRiDwqIunAYGNMKUD0z0FxiFMNMEWlpZSUl5PpchEJG04O51Nm/ETsTQB4GhspyMtjahc2F2msLGVlhQ9rwMfiH/w40aEr1ev0JNHbgKnAQ8aYKUADRzFMIyJLRGSdiKyrrKzs/AY1oLy7di3GGKxWK9nBbLIsNlbbS1rOexoaWDB7dpfaeuaXPyGcksqcQemkFXS9Do5S/UVPEn0xUGyMWRP9/DzNib9cRIYCRP+siHWzMeYRY8x0Y8z0goKCHoSh+qMPN23CFS1gNqNpOBFj+CS6XaAvEMBus3H29OmdtrPr1Wcpcg0iq65SV8CqAavbid4YUwYcEJEToofmAduAl4BD9V6vBZb2KEI14KzbsoWDFRVkpKVha3Jysslli7hbVsJW1dYyc8qUTqdUhkMhXlj+AZgIV1yp4/Jq4Orp1INsBaOiAAAeTUlEQVSbgL+JiAP4BPgqzf94PCciNwBFwGU9fIYaYJ555RWcKSlYrVY+7x2PAf6dugsAt8dDtsvFDZde2mk7L/zof/Bn5DIxUM2w0+YkOGqleq8eJXpjzEYg1u/PXZvYrNRhtuzaxf6SEoYWFGBpcjCBLNZJ1WcvYT0evnX11S2zcTpyYNUytlozcXrruPQX/3ssQleq19KVsapXeeGtt7BZrVitVs7zH48BPnDuB5pXwQ4pKODsGUfe1zUcCvHsCy+BCJfOnYk1usesUgOVJnrVa7i9Xrbs2UN+Tg4mJEww2WyVWoIOPwCBQICrLr6401LEr//iNryZeYz1VTP+gsuPRehK9Wq6PFD1Gh9+/DGRSASbzcZxDUOxi7DO0VzuwOvzkeZ0Mq2TefPlH69lfTAFR5OHxT++51iErVSvpz161Wu8sXIlqdFhlpnB4dRGglTZqwFw19dz4ec+h9PpPGIbf3/iSYzFysXTT8Luykh4zEr1BZroVa9wsLycotJSsjMzcTalM9Ti5GNrJRar4AsEsFksnY7Nv/WrH1KXVcAIbwWTL7/+GEWuVO+niV71Cis+/BATiWC1WpnuLyRiDOuim4vU1tUxc+pUhuTnd3h/2cbVfOABm6+BK2//6TGKWqm+QRO9SrpQKMQ7a9c2r4QNW5lmCtiNB2xhQqEQEWP4/KxZHd4fDoX421P/h7HauHjK8aTlaXklpVrTRK+Sbs2mTVTX1pKdkcGMxjHYRViWugeAipoapk6ceMRSxC//9Ht4MvM5rrGSUxZ//ViFrVSfobNuVNL94803SXU6MWELMyND2IMHv6OBQCCAAFdeeGGH93664nU2GhcpDXVc+TNdGKVULNqjV0n13tq1FB08SE5mJp9rOB478KazuTdfXlPD5888kzGFhTHvDQcCPPPSm4Bw2bmzsaemH7vAlepDNNGrpPrX22+TnpqK1aQwxeSzWWrxpXjxeL3kZmZy9cUXd3jvs3d+F58rm5NCbsbN77z2jVIDlSZ6lTT7iospjk6pPNE/DKsI/0kpAppXyZ43e3aH8+Y3P/c4u5z5pNXX8KWf/e5Yhq1Un6Nj9Cpp3li5ErFYsCDMCRVSQQCPrR6/z0+q08nnZ86MeZ/3YBFLN+xArDauWbxI939VqhPao1dJ4W1s5D8ffUR2RgYjA0PJsFh52/EpFqtQXV/PvNNP77De/F9+80tCzjTm5Dq0/LBSXaCJXiXFW6tW4fP5cDqcfL5pDLWRIAdSyvB4vbhSU/ni/Pkx73vt57dQnT2YoXVlfO7mu45t0Er1UZro1THn9/t57b33yHC5KAwMIcdiZ5ljPxarUFtfz/zZs8mObiPY2r7lL/NhIAVHo4fr7vplEiJXqm/SRK+OuZXr11NbX09GWjrzm0ZTFwnxaUoJtXV1DMrP5/IFC9rdE/R6eOb1dzDA5fNmkZKVc+wDV6qP0kSvjim318szr75KhsvFaY3jyLM4eM3xCU2RMJ7GRq684IKY9eafuPMWAumZTLM26lRKpY6SJnp1TD398svUNzRQkFLAnMgwdpg6DqaVU1ldzbkzZ3L2aae1u+fNu3/Awawh5LnLufiu3yYhaqX6Np2Xpo6ZotJS3lmzhvzsbC7yTcAAr6XtoMbtJj83l6984Qvt7tn92nN84LNiD3i54fYfHfugleoHetyjFxGriHwkIq9EP48RkTUisltEnhURR8/DVP3BU0uXAjAiMpyxksG71oM0SSNen4+vLVrUbsNv78Einn3vQ8QYrlpwNmkFQ5MRtlJ9XjyGbr4NbG/1+R7gd8aY8UAtcEMcnqH6uC27drFx2zYKsnNZGBiPOxJkQ+o+yqurmTVlCtMnTWpzfTgU4tHf3EPImcbsbCtj5nZcCkEpdWQ9SvQiUghcCDwa/SzAXOD56CVPAu1/H1cDzr+WL8dmtXJCcCRZFhuvpuwlEPRjtVq5LsaQzT/v+A7u7AJGesqZ9z3dSESpnuhpj/73wPeBSPRzHuA2xoSin4uB4T18hurjDvXm8zLzWRAcTWUkwEFHBVW1tVx0zjnk5+a2uf79P/6KbY5c0jw1XPvLPyQpaqX6j24nehG5CKgwxqxvfTjGpaaD+5eIyDoRWVdZWdndMFQvFwqFeOpf/8Jut3Nu4ERSxcILqTuorqtl2KBBLL7ggjbXf/L2UpaVeLAGA9yw5OtYo5uFK6W6ryc9+jOBS0RkP/AMzUM2vweyReTQbJ5CoCTWzcaYR4wx040x0wsKCnoQhurNlr3/PnuLihifdhzTTT4bpJoKU0FTKMRN11zTZs68e/9u/v7WKhDhsllTyDthchIjV6r/6HaiN8b8wBhTaIwZDSwGlhtjrgbeARZFL7sWWNrjKFWftLeoiKeWLmVY5nC+0jSJehPmLcdWKqqruXzBAo4fM6bl2nAgwKP33UfImcbZuTYmXLw4iZEr1b8kYsHUrcD/iMgemsfsH0vAM1Qv5/Z6+fVjj2EVG18LnYYF4S/OjyitKeP8OXO47LAyB4/f9m28mXkc76vinO/elZygleqn4rJgyhjzLvBu9OtPgPbLG9WA8uhzz1FdW8vVaeeQh4O/2raz372fGZMns+SKK9pc++Id32lZ+XrF/96fpIiV6r+0BIKKu7WbNvHBRx8xOWsCMyhgPVVs9u+iICeHm7785TbXvnPvXXxszSTV62bJXb/QTUSUSgD9qVJx5fZ6+dMzz5CTnsNVgZNwE+QV20eEQyFuvPrqNqtfN/7tYd5zh7E1+VnyzSWkZOceoWWlVHdpj17F1SNPP029p4EbImfiEOH/UjZRXVfNlxcuZNLxx7dct2/5y7y0rQhLOMS1F3+enHETkxi1Uv2bJnoVNy8vX87qTZu42DWTEZY0lto+YVftHs6bPZuLPve5luuqd3zM395aibFY+OKp4xkx+7wkRq1U/6eJXsVFUWkpz7z6KhMzT+CcaPnhld6PGD9qFF+/7LKW6+qL9vLIY08ScjiZOyiVyZdfn8SolRoYNNGrHvM2NnLPI4+QYc3l+tDJ1JsQT4XeZ1BODrfccEPLoqjGylIe+sP9BNIyOM3h46ybbk9y5EoNDJroVY89++qrlFZV8VVmAPCwYw0hAtz6jW+01LEJej388Zd348vI5pRwHRfc8ZtkhqzUgKKJXvXIxzt28MbKlVzoOoPhllRetX1CifsgV110ESOHNtePDwcCPHDnbc0Lohor+eLPf5/kqJUaWHR6peo2v9/PI888w/C0Qs6LjGAPHpZ7NnDWtGktL1/DgQAP3fod6rIHM9pTxlX3PpzkqJUaeLRHr7rtby+/TFlVNddFptFkDE+ZDxien9+y8jUcCPDHW79DVfZghtWV8eV7HkhyxEoNTJroVbfsLSri9VWruNY1j3yLg3/Yd+Lx17UsigoHAjx463epzh7M8LoybvjNA7rqVakk0USvjtrB8nLufvhh5qVNYyp5vE856+o3c/Ull3Di2LEEfQ08cOt3qckeRGFdGddrklcqqfSnTx0Vv9/P7558kqGRIVxkRrMXD3/3vsM1F1/MF849l6DXw4N33IY7exAj68u4/nc6Jq9UsmmPXh2Vp5Yupa6ska+aU6g1IR5o/DfnzprFogULaKws5Q8/+iHu7AJG1Zdz/W81ySvVG2iPXnXZu2vWsOw/a7jDeSEA94dXcsJxI/japZdSu2cbf3r4z/gz8zi+sYKrfvtQkqNVSh2iiV51yd6iIv707LN8M3Ueudh5VDZhc0X43te/Ts2mtfzlny8TTM9giqln4a//mOxwlVKtaKJXnaqqqeHuhx/hCvtsTpBM3uBTii0l/PibN1H1nzd5+t0PCac4mZMhzP3eb5MdrlLqMJro1RF5Gxu5+09/4pzIJKZLAe9Tziq2cOc3v0XJS3/jzQO1YLVy4ahcZtzw3WSHq5SKQRO96pDf7+fXjz3GpLqRfI7hbDY1vGzWcud//RdbHv0dH5GBNRzisllTdDNvpXoxTfQqJr/fz88ffoTJZUM5kyFsM26eZTU/vOF6Vtx3D8VZQ3B66/jaV79C/klTkh2uUuoIup3oRWQE8BQwBIgAjxhj/iAiucCzwGhgP3C5Maa256GqY8Xv93PPnx/ltNIRTJV8NpgqXrZt4AeXLuTVhx+gPmsIue4KvnHXz3X7P6X6gJ7Mow8BNxtjTgTOAG4UkYnAbcDbxpjxwNvRz6qPcHu93PH7+zizeDRTJZ+VlLI8YztfP2kE/3juBeoz8xjrreDG/71Pk7xSfUS3e/TGmFKgNPq1R0S2A8OBhcA50cueBN4Fbu1RlOqYqKqp4ecPPsJVnqkMs6Q2z64pdDOvuobXtvsQm53P5Vg5+yc6fVKpviQuY/QiMhqYAqwBBkf/EcAYUyoig+LxDJVYbq+Xn9z/EFc2TGOoOPk7O8ia6GDIhi2szx6Cw+/h6ovPZdSc85MdqlLqKPU40YuIC/gn8B1jTL2IdPW+JcASgJEjR/Y0DNUDbq+Xn93/MFc3zKDQkso/2M2gzE8p2dxIU/YQ8t3lXH/nT0jL03+zleqLelTrRkTsNCf5vxljXogeLheRodHzQ4GKWPcaYx4xxkw3xkwvKCjoSRiqB6pqavjpvQ/x5dopDBMnT5sd5Ab+w36fEHSmMY16/vv3D2mSV6oP68msGwEeA7YbY1ovh3wJuBb4VfTPpT2KUCXMwfJy7n3grywJzMApFv4vtBZn+BP2ZY8gpaGOxefPZczci5MdplKqh3oydHMm8GVgs4hsjB77Ic0J/jkRuQEoAi7rWYgqEYpKS3nogee4MTQDA7zW+BKhDDv19gJG1ZdxzS/uxZ6anuwwlVJx0JNZN6uAjgbk53W3XZV4H+/YwStPvcc3wlOoNT7eCb5BfV4WNn8j54/MZsYNP0l2iEqpONKVsQPM8vff58BL+7mWiWxiP+scO4ikZjK8royr7/ipjsUr1Q9poh8gQqEQDz7+HFP35DDDms9S3qfS6cPeGOCSU0Zw6tXai1eqv9JEPwAcKC3l1Qf/zQXBQrbY97Pcth8TiXCct4or7ryblKycZIeolEogTfT93F/++hInbk7hJFsaLzj+g88aItNdxWWLvsCI2eclOzyl1DGgib6f2vPpAVb+aRUTTSofpmyhyurB5mtgXmE2Z931YLLDU0odQ5ro+5lQKMQDf3yWqcWpRFKqeN1ajSUYZGKwji/e+Uvsroxkh6iUOsY00fcjL721guBbe8hx+lmeuhtLxDCyrpTLv3cHruFaZkKpgUoTfT+wYfMOdj75Do3OEAdc1VgjQoG7giuWfF03BVFKaaLvy/Z+8invP/hPfKkWSlxubBFhUK2bS669nMKps5IdnlKql9BE3wft/fgj3n3yZerSU6jP8OGIWBlS6+WCJVczUnvwSqnDaKLvQ957/CG2b62gxmWhKTNCdthQWBfk4u9ey+CRY5IdnlKql9JE38u59+/mhfsfpc6ZSZ09iLiE4aEMLAEfF972NQbn5yc7RKVUL6eJvhdqrK7gzQd+S7HXiTtNCGekkhWxM64xg2Cugyu/cx1OpzPZYSqlesDtdlNUVERJSQllZWVUVlYyZswYFi1ahNfr5fLLL8fr9dLQ0IDf78fv93PWWWfxxBNPsHXrVmbOnNnlZ4kxJoH/K10zffp0s27dumSHkVQBdw3vPnQvO6oieNKdhCyGFGNjWDAbSzDEcZedw5mnnZrsMJXqN/x+P8XFxZSUlFBSUkJlZSUWi4Ubb7wRgOuvv579+/fT0NCAz+fD5/MxZMgQVq5cCcCQIUPwer2Ew2HC4TDGGHJzcykvLwfAYrFweH5NSUnB7/cDEGs3vkPn3W43OTntS5Okp6fj9XpZtWoVZ511FsB6Y8z0zv5fNdEn0YFVy3j/1Zc5GEynIS2FsAUcxsbIcB6pfhuecbksvu5C7b2rfikUClFVVcWBAwcoKSmhvLycyspKbr/9dgB+9rOfsWLFChoaGmhsbMTn8xGJRNi9ezcA06ZNY9euXS2JNhKJYLVaWxJpRkYGXq+33XMP5byOtj3tyXmHw0EgEADAZrMRiUQQEUQEi8XCyJEj2bNnDwAFBQWICHa7nZSUFJxOJ7NmzeLRRx8F4LLLLiMnJ4ecnBzy8/MpKCjg5JNPZurUqS3PExFN9L1N7d7tbHjxaXYerMeTmoXP0fyN4jJOhodzSQvYqcpJYcF18xg+bHCSo1UDgdfr5eDBgxQVFVFaWkplZSULFy5k3LhxPP/88zz++OPthg/+/Oc/M3fuXK677jpeeOGFNok2EonwyiuvsGDBAiZOnMj27dvbPXPlypXMnj0bl8tFQ0NDu/O1tbVkZ2fjdDpbkmZrh3KW3W4nFAp1eD4rKwuPx9OSaEUEp9OJx+MBYOLEiVRUVLQk2pSUFIYPH87y5csBuOmmm/B4PGRnZ5OXl0dBQQFjx47lvPOaa0SFQiFstuSOfnc10esYfQKVrFvF5mWvsreiEY8zE5/DCmJBMnMYEsnihEAWErJSNSSVWZefxagRQ5IdsjrGQqEQxcXFFBcXU1ZWRnl5OZMnT2bOnDls3bqVO+64A4/H09Kr9fv9XHPNNdx555088cQTfPvb325JtIeGD6688kqeeuoplixZwp///Od2z7zkkktYunQpp512Gh9++GG782vXruXZZ5/ljjvuYOfOne3OL126lLlz57J8+fKWpNlaUVERAFartaXX27pXm5KSAsDZZ5/N6tWrsdvtOByOll7tIXfddRebN28mKyuLnJwcCgoKGDTos/0SfD7fERNtXV1dh+cAtm3bdsTz999//xHPJzvJH42+E2kvV7ZxNVv//Rr7Smqot2Tgc6YQtAGkYM1MpcBkMj6UiaPJSq3TQu7pY5i/4Iw+9c3SH4VCoTYvxcrLy8nJyeFLX/oSAFdeeSW1tbU0NDS0JNtTTjmFZ599llAoREFBAaFQqE2P9oQTTmDz5s1s3bqVyZMntxunHTx4MGVlZfz973/n6quvbhfToEGDKC8v54UXXuBf//pXu/OPPfYYd955J2vWrKG+vr7d+X379gHN472xEu2YMc1TcS+88EJ2796N3W5vk2yvuOIKAO6++26efvrplkQ7aNAgCgoKuOiiiwD45JNPjvj9u3nz5iP+3b/66qtHPH/bbbcd8bz+7HSdDt0cpYot69mzYhmfFh2kOuDA70gl4LARtH52TVYkjXyTQVYoFUvISm2qlfSThnD+wtmkOh3JC76XOjR8cPDgQYqLi/H7/SxZsgSAW265hW3btuH1elvGaTMyMvjggw8AGD9+PBUVFYRCISKRCOFwGJfLRU1NDdA8ZhoMBts8z2q1tvzKH2uctbPzdrudpqamDs9nZ2dTW1tLWVkZQ4cObTN0ICJMnTqVNWvWsHXrVmbNmoXNZsPhcOBwOHA6nVx66aXcfffd7Nmzh+9///tkZma2GT4466yzOOmkk1pi1IQ3cOkYfTeFAwGK1yynePNGSkvKqG4U/FYnTfYUmmwWgtbP/r5sxkqOSSfbpOMKpWALWfHYhKbCTE4960ROPnl8Ev9Pui7W8MG1116Ly+XikUce4fXXX283fLB+/XpcLhfz589nzZo1bXq1xpiWRDho0CAqKyvbPfPQ953NZiMcDnd4PtbMhdbnYyVai8XS0qbdbm/5+lCizc3NpaKiAmieOdHU1NSmRzt58mRefPFFABYtWoTdbic7O5ucnBzy8vI4+eSTW8ZpvV4vLpfrKP62lYqfpI/Ri8gC4A+AFXjUGPOrRD2rq4K+Bso+Wk35ji2UFpdRXd+EL2IjZHEQtFkJ2gS/1WAO5Q57DpIJ6TjJjDhxGSepfjvWsIUmEeqynOSOG8SMM09iRGHPXp4ePqd25syZnHDCCSxfvpyHHnqoTaJtaGjgrrvuYvHixfzwhz/koYceajd88Lvf/Y4bb7yROXPmsGrVqnbJ8r777uOmm25i8ODBLUmvtcGDB7No0SJuueWWmMMDu3btYurUqaxcuRKfz9fu/KEXVcaYdsMHVutnv/5MmjSJTz/9tE2vtvW0shtvvJHS0lIyMzPJyckhNze3ZegBwOPx4HQ6O+zVHt6bP1xZWdkRzz///PNHPK9JXvUFCUn0ImIFHgTOA4qBD0XkJWPMkd9+dEPAXUPFto8o3bWTkuJKPJ4ATUEIYiNstRK2WAjahCaLockS/iyJkwKuFMQIqThIjdhxBa1YGyDiD+MQO6muDGrtwsqN71EfdFMfcNPoax4+uOCCC7jn9ntYsWIFp54ykVAo1Gb4YP78+bz88svce++93HLLLe0S7YwZM1i7di1XXHEFzz33XLv/rzPOOIMPPviAn/70p7z33nvtzj/11FMsXryYN954A7fb3e78jh07gOaeb+tka7FYEBGysrIAmDt3Lq+99lqbRJuSksKJJ54IwI9+9CNWrFhBRkYGeXl55Obmkp+fz8SJEwEoKSnB6XR2OAU0Vm++tY0bNx7xfGcvxDTRKtW5RPXoTwP2GGM+ARCRZ4CFQOxEbwyfbt/C1tUfUrRzH+5qNxIRBmXnEjEWth8swhcKEjIRgiZMkwmRluFi1HGjCEqE1atX4/f7W5JtKBRi2NBhzJp6OqnGwRNPP0HA7ycYDNIUbCIQbGLyiAl89UtfpdHl5Fu3fYXDBwdcLhcej4dly5bxzbufbBdySUkJ99xzD9u2bWsZD27t0GwFq9Uas1c7cmRzffh58+bx+uuvY7PZsNlsLdO8rrnmGqD5hVR6enrLOO2h4YOFCxcCsGLFCkKhENnZ2TH/ag8t7ujI008/fcTzN998MzfffHOH5zt6rlKq90jIGL2ILAIWGGO+Fv38ZeB0Y8x/x7re4XCYw3/FHj16NNdeey3QPMxQW1vb5vyksSdy86JvYI3At+6/Ha+/AavFgkUsgJCXkcvr76xi1JjB5OVktSTbQ/8dd9xx7Nq1qyVJ2u12bDZby1jt3Llzefzxx3G73VxxxRVkZGS0GT44++yzmT17dssqtvz8fH0pppQ6ppL6MlZELgPmH5boTzPG3NTqmiXAkujHScCWuAeSePlAVbKD6AaN+9jSuI+dvhgzdD/uUcaYgs4uSlQXtBgY0epzIVDS+gJjzCPAIwAisq4r/yr1Nhr3saVxH1t9Me6+GDMkPm5Lgtr9EBgvImNExAEsBl5K0LOUUkodQUJ69MaYkIj8N/AmzdMrHzfGbE3Es5RSSh1Zwt4eGmNeA17r4uWPJCqOBNO4jy2N+9jqi3H3xZghwXH3ipWxSimlEidRY/RKKaV6iaQnehFZICI7RWSPiBy5XF0vISKPi0iFiPSZKaEiMkJE3hGR7SKyVUS+neyYukJEnCKyVkQ+jsb9k2THdDRExCoiH4nIK8mOpatEZL+IbBaRjSLSO4pQdYGIZIvI8yKyI/p93vW99pJERE6I/j0f+q9eRL4T9+ckc+gmWiphF61KJQBXJqJUQjyJyBzACzxljJmU7Hi6QkSGAkONMRtEJANYD3yhD/xdC5BujPGKiB1YBXzbGLM6yaF1iYj8DzAdyDTGXJTseLpCRPYD040xfWo+uog8Caw0xjwane2XZoxpXx+kl4rmw4M0Ly79NJ5tJ7tH31IqwRjTBBwqldCrGWNWAO3rHvRixphSY8yG6NceYDswPLlRdc40O7QfnD36X594sSQihcCFwKPJjqW/E5FMYA7wGIAxpqkvJfmoecDeeCd5SH6iHw4caPW5mD6QfPo6ERkNTAHWJDeSrokOf2wEKoBlxpg+ETfwe+D7QCTZgRwlA7wlIuujK9j7guOASuAv0aGyR0UkPdlBHaXFwJGLT3VTshN9rN13+0Rvra8SERfwT+A7xpj29Yd7IWNM2BhzKs0rrE8TkV4/XCYiFwEVxpj1yY6lG840xkwFzgdujA5V9nY2YCrwkDFmCtAA9Il3fgDRoaZLgH8kov1kJ/pOSyWo+ImOcf8T+Jsx5oVkx3O0or+KvwssSHIoXXEmcEl0vPsZYK6I/F9yQ+oaY0xJ9M8K4EWah1h7u2KguNVve8/TnPj7ivOBDcaY8kQ0nuxEr6USjpHoS83HgO3GmN8mO56uEpECEcmOfp0KnAvsSG5UnTPG/MAYU2iMGU3z9/VyY8w1SQ6rUyKSHn1ZT3To4/P0gYKDxpgy4ICInBA9NI+OyqL3TleSoGEbSPLm4H21VIKIPA2cA+SLSDHwY2PMY8mNqlNnAl8GNkfHuwF+GF3B3JsNBZ6MzkiwAM8ZY/rMVMU+aDDwYnT/BBvwd2PMG8kNqctuAv4W7TR+Anw1yfF0iYik0Tzz8BsJe4aujFVKqf4t2UM3SimlEkwTvVJK9XOa6JVSqp/TRK+UUv2cJnqllOrnNNErpVQ/p4leKaX6OU30SinVz/1/EHbFRxN8eUcAAAAASUVORK5CYII=\n", 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a7lg0TduXzlFNS3w6j7X90QWvpmmapmmaltT0HF5N0zRN0zQtqfVIW7K+ffuq0tLSnth13PH5/QCICLt8EcIRxdCcKBkNmwhlDSLiySVimuRld7ltp5YEtm7dSlVVFcAepVQ/u+NpT7Lkq1IKb2MjTqd1A3dFvcFwtRVxZxDKslYcjZgmmenpuF26I6O2r/r6ejZs2IBSSufrIWj0+1FYx0UAVVeBR4UwCvbtoGeaJh63m/S0tF6OUkt0hmGwdu1agsFgp/K1Rz71S0tLWbHiQKtdJgelFG9/+CF52dk4HA5+vbyKHQ0R/j7ma0Z8chdfn/lHAnkj2FNbyznjxtkdrtaLotEo1157LZ9++ik33XQTv/71r+O2XVOy5GsgFKL8k0/om28tKDTnpS18zJXsPPwqdhw1C4DqujrGfuc79C0osDNULQ69+uqrXHLJJRx33HF89tlnOl8PwfIVK3A6HHjc1hoQua9cjc+RQ3TaI/tsW11Xx5EjRjBk0KDeDlNLYNu3b2fixIlNAxydylc9peEQRCIRotEoDof1a2yaDZ3esAWFg2D2YJRSOKS9Zbi1ZGWaJjNnzuSxxx7j9ttv58EHH7Q7pJRgmmbzgvdhU5Eb2oGDKKGcwa22azoIa1qTF198kenTp3P88cezdOnSjl+gHVAkEmk+LgIMiO5mj7N/u9sqpXDrnNS6YOvWrZSVlbFz507eeuutTr9OF7yHIGQYrb6v9pvkZzjI8G4mlD0I5fQQNgwyMzJsilCzQ2VlJW+//TZ3330399xzT/NlPa1nhVvkY1VjhOGyE4Bg9hDAOrCilM5HrRWlFC+88AKnnHIKS5YsIT92hUA7OKZpEjYMXLGpRWI0ko8Xf8bAdrd3iJCZnt6bIWoJ7sMPP6Smpoa3336b0047rdOv0xPZDoE/GKSpy4VhKrbXG0wYkUVGzWYCOaWAdZm1ZMAAG6PUeothGLhcLoqLi1m5ciV9+vSxO6SUEg6Hm/NxT8uCNzbCGwiFKMjLaz4Qa1o4HMbj8fDMM89gGAZZWVl2h5TwwpFIq+9Vg5WHRlbRPtsqpVD6JFTrpKZ8nTFjBlOmTOnyMbbDEV4RGS0iX7R4NIjIvIOOOIls3bGDNI8HgMXfeglGFCcUeUjzVRDMGUokEiESiTCof/uXcrTkEQqFmD59OvPmWalhV7GbyvkaCIWaL6PuaTQZJjsJegow3dYNo2HDoCAvz84QtTgyf/58TjzxRKqrq/F4PLYVu8mWs0abK59m3Q7rv9n7FrxhwyAnK0vfRKp1aPXq1Rx++OHNU44O5hjbYcGrlPpWKXWsUupYrKUq/cArXX6nJONtbGR3dTXZmZnUBUxeXFnPCcUZnJxbg0NFCOSWUl1fz1EjR5KXk2N3uFoPCgQCXHjhhbz22muMHj3a1lhSOV/9gUDz6O0ef4QRjp2Ec4Y0Px+NRvWBVQPg0UcfZfbs2ZSUlNg+qptsOWu0GeGVBqvgjeYU77NtxDRJ19MZtA58/fXXjB8/Hr/fz8CB7U+N6YyuzuGdBGxQSsXtHay9pbKqCpfTiYjw0VY/oYji8mPzSG+wVjOsSyuiT14eg4v2PavVkoff7+eCCy7gzTffZP78+cyZM8fukFpKqXw1TBNnbIS3xm8y3FFJqE3Bm6FbH6W83/3ud9xwww1MmzaNl19+Od4KroTP2UgkQsvlrNyNlQSVm/TcfUfkTNMkPXaVVNPa8/nnnzN+/HhcLhfLli3jqKOOOuh9dbXgnQE8194TIjJbRFaIyIpY39GktrumhozYB+WKigDFuS4G53vI8FqfU1XOfgwrKdE3LCUxpRQXXngh7777Lk888QSzZs2yO6S2UipfI5EIEit43YaXQuoJZu/t0CAizc9rqWn+/Pn85Cc/Yfr06bz44oukxd8JULs5m0j5GmnRLQUgPbCTCtWP/Ix9r66Y0Wg8/j/Q4sTGjRuZOHEimZmZLFu27JCvoHb6019EPMAFwIvtPa+U+ptSaqxSamy/fnHZr7vbREwTr89HmseDUop11SG+M8AqftO9Wwhn9CfqyqQgN9fmSLWeJCLMmTOHp59+mquvvtrucFpJxXyNRCLNLQBzA9sBCLYY4QX0CWiKO++887j55pt57rnn4q4V1oFyNpHyNRgOt8qznGAl2+lHdtq+5YZpmmTpG9a0/SgtLeVHP/oRy5cvZ+TIkYe8v64Md5wDfKaU2nXI75rgwuEwiCAi1AWjBAxFcZ714ZnRsAl/9lA8Ho9eOSZJ1dbW8q9//QuAadOm8b3vfc/miNqVcvkaMc3mm9byghUArUZ4geYpD1rqUErxz3/+07qBeNAgHnzwQVzxOZc7KXLW29jYqtd1XngXXs+AffrRN3VUydf3uGhtfPDBB2zevBmHw8H9999Pd60s2JVP/++xn8ujqaZlv8/dPmuC/oBsFyiTdO8WvFmD9VLCSaq6uppJkyZx6aWXsmtXXB+XUi5fw4bRfFDtF67AxEEou/XqTXrRidSilOKnP/0p3/ve9/jHP/5hdzgdSYqcrWtoIK0pz0I+cvFh5u67ilooHCY3O7t5aqCmASxdupQpU6Zw/fXXd/u+O1XwikgmMBl4udsjSEDhFpPya/wmAIWZTtJ823GaIeozh+iCNwnt3r2bCRMm8M033/DSSy8xIE77K6divkajUcKGgdPpxDAVxdGd1LoHohwtClyl9ME1hSilmDdvHr/+9a+5/vrr427aUUvJlLOhUKh5BN1fY3VoIGffgjcYDlOoF/nQWnjrrbeYOnUqw4cP5/HHH+/2/Xfquo5Syg8Udvu7J6hwONw8Kb8mYI3w9sl0krlnIwANmUMYphuYJ5WdO3dy5plnsmnTJhYtWsSZZ55pd0j7lYr5GjYMlFKICLUBa9GJuvS9bZAiponb7daLTqSIaDTKnDlz+Otf/8qNN97Ib37zm7iev50sORuNRlHsnSvv8lpz6UOZ+7aSikQi5OjjpBbz+uuvM336dI488kjefvtt+vbt2+3voSe0HYTGQABn7MBZ7TdxOyDb4yCjfgMKwZc9RF86TTIvvPACW7Zs4Y033ojrYjdVtez9WeuPUCqV+LP33rAWjl0+1VLDunXrePrpp7n11lvjvthNJqZptvo+3W+tshbIbL89pz4B1cA6UfrlL3/JMcccw9KlS3uk2AW9tPBB8fn9zQ3st9QaFOW6EREy6jcSyi7GdKTptcGTRNOo4dy5c7ngggsYNmyY3SFp7QgbRnNRE66rJFNCGLl7C95gOEzxITQs1xJDU76OHj2alStXUlpaqovdXmRGo803owFk+HfgVRlE09qZuiBiPbSUppTC4XCwaNEinE4neT24GqYe4T0I9V4vaW43ZlTx7Z4QR/S3ujFkNmzAlzOc7MxMPVcwCWzcuJETTzyRr7/+GhHRxW4cC4XDzQdaT701tYg+e/9/KaX0CG+SMwyDGTNm8MgjjwAwbNgwXez2smg02up3nh3YwVbVH7er/VJD//9JbU899RRTp04lGAzSp0+fHi12QRe8XWYYBuFwGJfLRaU3QiiiGFnowREJkubbTn3GYAb00HC81nvWrl3LuHHj2LRpk9WGTotrgVCoeZpRls9a/EX6DG9+XoFeVjiJhcNhLr30Ul544QWCwaDd4aQsMxpt9X12YAdb1ACyPfuWGgJ4dE6mrAULFnDNNdcQDAb3mQrTU3TB20WBUKj5MswOr9WerDjXTUbDJgRldWjQfQUT2urVqykrKyMcDvPee+9x/PHH2x2S1oFgMNjcY7cgsIVq8jDTW1xGVUr3xU5SwWCQiy66iIULF/Lwww9z00032R1SympVuCiT3HAlW1V/ctP3LTUU6FXWUtSf//xnZs2axZQpU1i0aBFZvXTzoi54u6jpbnCAhqB1NluQ4SSjYQMAvpzSvT0ItYSzbt06ysrKUEpRXl7OMcccY3dIWif4g8HmVkgDwtuocJY0PxeNRnE4HKR7PHaFp/UQ0zT57ne/y7/+9S/+8pe/MHfuXLtDSmktR3g9gSpcKkKFGkDWfkZ4XXohmJTz17/+lTlz5jB16lQWLlxIRi+utKf/2rrINM3mEd6wGZsz6LRuWDOd6fgzBjav9qQlnpKSEs4++2yWLVvGkUceaXc4WicFgkHcTicoRYm5jUrP3hXWTNMkIz1dzxdMQk6nk7PPPpsFCxZw7bXX2h1Oyou06FGf5rN68O5xD9xnlbWmub5xuuKd1oNOOeUUZs6cyUsvvUR6L9/rpCuzLjKjUYiN8BqxgtftFDLqNxDIHQbibJ5LqCWOL774grq6OjIyMnjqqacYPXq03SFpnaSUIhAM4nQ6cQerycFPbcbQ5ucjpqmvuiQZr9fLp59+CsC8efOYOXOmzRFpYHVDaSpt0xqtHrze9H0XnQgbBrnZ2fokNIWUl5cDMGbMGBYsWIDHhituuuDtorBhNI/gGi1GeDMbNhLIG36gl2px6sMPP6SsrIzrrrvO7lC0gxAMh4nGWttUV6wFIJy7t0ODPxikTw/f/av1nrq6OqZMmcLkyZOpr6+3OxythZYtO9MadxDBiStv33aAwXCYgtzc3g5Ps4FSijvvvJMJEyawcOFCW2PR1xO6yDCM5sszIVPhEEgL1+IO1RLIHQGgpzQkkOXLl3PuuecyaNAgHnroIbvD0Q5CTV1d89cVm6yCd9RhhwPWJVanw0FpcXG7r9USS01NDVOmTOGrr77i+eef7/E2RlrXeH2+5kWXXN4KtkX7UpS/72XrSCRCH72scNJTSnHbbbfxwAMPMHPmTM4//3xb49GVWRe1HOENRRRup5BZb92w5o+N8Dp1wZsQli5dytlnn83gwYNZtmwZgwcP7vhFWtyprqsjLXZ5LMu7Ba/k4Mq2VmkNhEL0LyzErac0JLyqqiomTpzIypUrefnll7nwwgvtDklrwYhEqPN6mwveaG0FW9UAjurfuuCNRCI4nU4KdDejpKaU4ic/+QkPPPAAP/rRj5g/f77t0z11ZdZFoXC4uaBdtTtIaYGHzPp1ANRmDCE7M1P3+0wAkUiEG264gREjRlBeXk5RUftLX2rxr87rJc3joTZgMji6jZr0Ic03loYMgwGFhTZHqHWH3/72t3z77be8/vrrTJ061e5wtDZ8fj/EphYB5AR3UOkcyOh+redqNjQ2MqykRJ+EJrkVK1bwhz/8gR//+Mc8+uijcXHlW1dmXaCUorahgcyMDHb5ImypNbjq+Hwy69YSyhxAXcTNMSNK9ET8BOByuVi8eDE5OTk9tm631vNM08QfCNAnL4/K2hAnyHaqcs9ofl6A7F7q8aj1rLvvvpvLLruMY4891u5QtHaEDaO5Q4Mz3ECWaqQhrWif42E0GtXzd1PAiSeeyMcff8wJJ5wQNzWR/SV3AvEHgxiGgcvpZNUuazWf4wZlkFm3Dn/eKESEQj0vKa69/PLLXH/99USjUYYNG6aL3QQXCodBKUSE+to9FIoXI6/1EtBpuv9uwtq2bRtTp06lsrISt9uti904FgqFmgubdJ/VoaExY98rZyLSPO1BSy6maTJ79mwWL14MwNixY+Om2AVd8HZJYyDQfKl0ze4QOWkOBmeGSfdupSFnBDlZWXo1pzj2z3/+k0svvZTPP/+cQCBgdzhaNwgZBiqWk9E9GwFw9bXm0huGQXpamp5ilKA2bdrEuHHjeP/996moqLA7HK0DoXC4eY6mO1bwhrNL2t1Wn4Qmn0gkwpVXXsn8+fP58ssv7Q6nXbrg7QJ/iyKposFgSL6brIaNCIrazFLd+iiOPfXUU1x++eWcdtppvPXWW722lKHWs0Kh0N6+nw2brZ/FRngDoRB9CwrsCUw7JOvXr6esrIz6+nqWLl3K2LFj7Q5J60Cwxf0tqt46QZH81t1RmlY91CO8ySUcDjNjxgyee+45HnjgAW677Ta7Q2qXLni7oGXLlZ0NEQblusmstdog1WaWkpudbWd42n48/vjjXHPNNUyYMKF53q6WHLyNjc03QxQEthCQDIyMfoB117ieK5h41q5dy7hx4wgEArz33nuccMIJdoekdULYMJoLXkd9BbtV/j7HxFA4TG5WVlxd5tYOTTgcZvr06bz00kv87ne/46c//andIe1XpwpeEckXkf8VkTWOtzEnAAAgAElEQVQislpETu3pwOKRN9ZU2xsy8YWjDMpxkVm/FiMtn1B6X7IzM+0OUWtHcXEx06ZN4/XXX0+Jkd1Uytf62ElowIhSEqmgKm1vhwalFDkp8P872eTn53P44YdTXl7OmDFj7A6nVyRDzrZs2ZkZ2MEW1Z8sT+sSw4xGyczIsCM8rYe43W6GDBnCo48+yrx58+wO54A6O7ntYeBNpdR0EfEAKVfZKaXw+f3kZWezrcYAYGCOi8zta/HnHQYiZPbyutDagX3zzTcceeSRnHXWWZx11ll2h9ObUiZfvX4/aW43m+sMznRspzr7ZMDKVxHRB9cEsm7dOkpLS+nfvz/vvvuu3eH0toTP2XA43DxfPjewg4/UEXic+3ZocOk59UnB7/eze/duSktLeeSRR+wOp1M6HOEVkVxgHLAAQCkVVkrVHfhVyceIRJrnH9UFTQAK00wyGjbizR1p3Ryj5yXFjQcffJDvfOc7vPfee3aH0qtSLV/DoRBOhwO/t47+UkcorxSAiGmSkZaGy+ZG51rnrFixgpNPPpmf/OQndofS65IlZ8OGgdPpxBHxk23sYUO0iHRXm4JXKdw6JxOez+fj3HPPZfz48Ql1A3hnpjQMB6qAJ0TkcxF5TERS7jqhaZrNX9cHowAUG9twKJP67GHk6fm7ceOee+7hlltu4bLLLuOMM87o+AXJJWXy1TRNorFG91neTQCEckubn9MnoInh3//+N5MmTSIvLy8lC16SIGdN0yRimjgcDtJiHRq2UETfrNajuZFIhCw99S+h1dfXc9ZZZ/H+++/zq1/9iowEuorWmYLXBRwP/FkpdRzQCNzadiMRmS0iK0RkRVVVVTeHaT8jEmn+ui5gFb8DAusBaMgZoQ+ucUApxR133MGdd97JVVddxdNPP52Kl89SJl/NaLT55pc8n9WSLNxnFGBdOtV3gse/999/n8mTJ9OvXz+WLVvGsGHDOn5R8ukwZ+M9X8ORSPPc+XTfVgB8GcW420xpEBGyEqhA0lqrra1l8uTJfPLJJzz//PPMmDHD7pC6pDMFbwVQoZT6OPb9/2IlZytKqb8ppcYqpcb269evO2OMC2Y02vx1XdAky+Mgp2EdpisTX/oA3eszDpSXl3Pvvfcya9YsnnjiCdvX7bZJyuRry5PQPo0bqVXZOLP7A1a+6l6f8S0YDHLZZZdRXFzM8uXLGTJkiN0h2aXDnI33fPUHAqCsddbSfVZLMpU/uNU2EdPE6XDoEd4Eduutt/Lll1/y8ssvc/HFF9sdTpd1WKUppSpFZJuIjFZKfQtMAr7p+dDiSzgcbl42sT5okp/uIKtuLf78UZhRRYZecMJ2EyZMYNGiRZxzzjlxsW63HVIpXw3DaP66b2ATG2QIbpej+Tk9zSi+paens3DhQoYMGcKAAQPsDsc2yZCz9V5v82eup2EbO1Qf+uW3bv/Y6PdTPGCAnlefwB566CEuv/xyxo0bZ3coB6WzVcF/Ac+IyFfAscD9PRdSfGoMBHDELtlU+Uz6Z0Bm3Toa80ejlCJdd2iwRTQa5ac//SmfffYZAOedd17KFrstpES+No/wqijFxha2uEqbn4uCzsk4tXjxYn77298CcOKJJ6Z0sdtCQudsQ2Nj8xQiZ/1WNkWLGJzXekqRYZoU6oVgEs7OnTuZPXs2fr+f3NzchC12oZNtyZRSXwApvdRNU0IrpdjRYDCxpBJHfZjGPkda7Y/0wbXXRaNRZs+ezYIFC8jJyeH44/e5cp+SUiVfI6YJSpHWuIMMglS4h9F8UVwpXPrEJ+4sXLiQSy+9lGOOOYbrr7+eNH1lDEj8nA0Gg80jt5mN29ikTqK4TcHrAH0lNMFUVFQwceJEduzYwbXXXpvwi8DoI0IneX0+3C4XFfUGgYhirMu6SaY+bzROh0MXvL3MNE2+//3vs2DBAu644w5uv/12u0PSelkgGEQcDjLqrJtH67P33vAkoIupOPPiiy9yySWXcPzxx/POO+/o/z9JJBgO43Q6cYbqyTC9bKKIopzW42lRpfSNpAlky5YtlJWVUVlZyZIlSxK+2AVd8HaKEYnQGAjgdrn4qjIIwBHRdRiefGrIo6h/f30ZvRdFIhGuuOIKnnrqKe6++27uvvtuvVRlCtpTW0t6Whq+Hd9iKiF3kNWhIRAMkp2ZqU9C48gzzzzDjBkzOOWUU1iyZAn5+fl2h6R1o1CsH3a6bxsAtZ4S0lx7j4kR08TjdpOuT3ISwsaNGxk3bhw1NTW88847/Md//IfdIXULXaV1Qm1DA0opEOGttT5GFHro6/uWxoLDCUciDCkqsjvElBKNRvF6vTzwwAPccccddoej2cA0TWobGkj3ePDUrGezGsgJpdb8wMZAgNKSEn0SFEe8Xi/jx4/njTfeIDc31+5wtG4UMU0U4GhR8NZnFLfaJhqN6tadCSQUCpGVlcXSpUs56aST7A6n2+heWp2wc/du0jwevq0KsdMb4ScnZ5Dx5WYq+5/OwH79yNV3g/eKUChEY2Mjffr04dVXX03VtmMaVt/PpkUn+gc38amUUthiREm3JIsPO3fupKioiOuuu44f/vCHOmeTUMtuKem+bURwEspsPQhkRCLkZCXUWhopaefOnQwcOJAjjjiClStXJl2+6hHeDiilqKquJiM9nW92hwA4I2MLgqI6azjF+g7jXhEIBJg2bRqTJ0/GiC1hqaUuwzBAKRyGn35mJVtbdGgQEV3wxoE//vGPjBw5ks8//xxA52ySMiIRmq6lpHu3UaH6kZvZeupCKBwmLydn3xdrceOrr75izJgx/PKXvwSSM191wduBSCRCJBrF5XSyuTZMUY6Lvr41gHXDWq4+a+1xjY2NTJ06lSVLlnD99dfrS2MakUjE6o7SsAGAyvTSVs/rgtdev/nNb5g7dy5TpkzhqKOOsjscrQeFWvSo93i3sSFaREFG62IpqpQe4Y1jn332GRMmTMDj8TB9+nS7w+kxuuDtQNP8JIDGcJTcNAdZNasJZhVjuHJ08dXDvF4v5557LuXl5Tz55JPMnDnT7pC0OGCY1vLeGfVWwVuTORywrsgAeuVDG91///3cdNNNXHLJJbzwwgt49MlHUguFw9YXKkq6r4JNaiAFGa1LCwHS9LEyLn3yySdMmjSJ7Oxsli9fzmGHHWZ3SD1GF7wdaHm5xheOkuVxkF27Gl/BETgcDr1qTA+bM2cOH3zwAc888wxXXnml3eFocaJp3mB63Xq8KgMzayBg3czmdrt11xSbvPrqq/z85z/n8ssv59lnn9UDAinA29iIy+nEHdiDKxpkkypiaMHek5xoNApAth7hjTv19fWcc8459OnTh+XLlzN8+HC7Q+pR+qjQgUhsJAmsEd5iZx2ewG68+YfrUaRecP/99/PKK68wY8YMu0PR4ojP78fpdOKqWc9qNYQhsQNsSC8pbKupU6cyf/58nnzySVz68zElNLXsbOrQsFEVUZy790Sn6YY1PTgUf/Ly8njiiSdYtmwZQ4cOtTucHqcL3g4EQyFQisZwlCqf2bzgRG32SN2doYdUV1dz1113YZomgwcP5vzzz7c7JC3O1DY0kO52kePdyOroEEYWWjfJBINBBvbta3N0qUUpxa9+9SsqKipwOp3MmjUrKW940doXCoVwOp3NBe8uVxFu596WgJFIhKzMTLvC09rxzjvv8MorrwBwwQUXUFJSYnNEvUMXvB2ora/H7XaztiqEAo51rCcqTvaklegDaw/YvXs3EyZM4Je//CVff/213eFoccg0Teq9XnIiNaRF/WxwDGVQ7t7RxHzd57XXRKNR5s6dy2233cZTTz1ldziaDYKxrjnp3q2E8BD0tD4uGqaplxSOI2+++SZTp07lvvvuw2xxBTsV6IK3A02rOa2uCuEUKA1+gz//MKLONH1g7WY7d+5k/PjxrF+/nn/961+MGTPG7pC0ONQYCKCUIrt+nfV97ggcIkRME5fTSVZGhs0RpoZoNMp1113HI488wn//939z22232R2S1suUUlabSIeDDO9mtjqKKcxuPW/b1AVv3Hj99deZNm0aRx55JG+99VbKXYnRBe8BBEMh/MEgHrebr3cFGV0g5NSuob7gKNLT0/WBtRtVVFRQVlbG1q1beeONN5g0aZLdIWlxytvYCIDsXo2hnGQMHA1Ao9/PoAED9AprvcA0TWbOnMn8+fP52c9+xkMPPaR/7ymo6R4XESHdu4W10WL6Zu47d1tPabDfSy+9xEUXXcSxxx7L0qVLKSwstDukXqcL3gNoOrDWBUzW7wlzXuF2HNEwu7MOY3BRkf6A70abNm3C6/WyZMkSysrK7A5Hi2O79uwhPS0NR9Vq1qkSjh+aD1gH30H9+9scXWrw+Xx88cUX3HXXXdx77736szBFNV0Sd0T8pPl3sSYyiNz0fUcNdV9s+3388cecdNJJLFmyhIKCArvDsYW+jfYAqmpqcLtcfLojgAJOT7MuodbkHc7IPn3sDS5J+Hw+srOzOeOMM9i4cSMZetRc60B9YyNpbjf9GtezWB3P4Ngd4Uop3fqohxmGQTQaJS8vj48++kjna4prajmW7t0KwNroIPq49z35ceo2gbZpOsY+8MADBIPBlM5Z/Vd4ALUNDaSnpbGiIkDfTCdDGlcRyCwmraBErxrTDb799luOOOII/v73vwOkdCJqnWOaJqFQiMzwHrKjDWz1jMDlEIxIhPT0dN36qAeFQiEuueQSLrnkEqLRqM5XzVqYSSkyGjYDsF4Vk5PWuqxQAPoKgC0WLFjA6NGj2bRpEyKS8jmrC979UErh8/txOZ2sqQpx9IA0squ/pibvcEoGDLA7vIT3zTffUFZWRigU4oQTTrA7HC1BhA0DBWTXrQWgOttaFSgcDpOnT0J7TDAY5KKLLuLVV19lypQpemEPDbBGeEWEDO9mouJkixpAdlrrk04BfSJqgz/96U/MmjWLY445hoEDB9odTlzQn1r7EQqHiUaj1IcU3lCUsdlVuMN11OQdqfvvHqKvvvqK8ePHIyKUl5dz9NFH2x2SliACoRACZNSsIaIcGAUjAAiGw/RN0XlpPc3v93PBBRfwxhtv8Ne//pUbbrjB7pC0ONF001p6wxbq00uI4CLHs7esMGOdU/QiTb3r97//Pddffz3nn38+CxcuTPmR3Sad+isUkc2AFzCBiFJqbE8GFQ9C4TAOEUKmAmBkaDUA9flHMlQn70GrqqpiwoQJZGRk8O677yb1ut12SeZ8DQSDADj2rGGdKqF/vnXyGVWKTP2h3iOuuuoq3nnnHR5//HGuueYau8NJSomas5FIBIAM7yYq0ksBWk1pMCIRsnWHhl713HPPceONN3LxxRfz7LPP4tE3DDbrSuU2QSm1p8ciiTPBUIioUjQErUn5g/2rMDx5eDOK9eW8Q9CvXz/uvfdezjrrrKRft9tmSZmve2pr8bhc5NR9y/vqOMaWZBAxTdwul+6L3UNuu+02pk+frpf37nkJl7MR00SiBmm+HVT2PwOA7DYFb15Ojl3hpaTzzz+fe++9l1tuuUUv792Grtz2Y3d1NR63m8+2B3AIDA18Q0PBUWRlZur+uwfhgw8+4OOPPwbgRz/6kS52tYPibWwkJ1JLdrSBTe6R5KQ5iUQi5GRm6nmC3aiuro7HH38cgBNOOEEXu1q7gqEQOcGdCFEqXNbytNltpjToRSd6nlKK+fPn4/V6yc7O5uc//7kudtvR2YJXAUtE5FMRmd3eBiIyW0RWiMiKqqqq7ovQJjX19WSkpbGhJsxx+Y1kNW6jOvdwivr31z0nu6i8vJyzzjqLuXPnopSyO5xUkJT5qpSi0e8nz7cBgB3pIwHrRjbdjqz7VFdXM2nSJK677jrWr19vdzip4oA5G6/5GgiFyPVXALDdORgBPM69x0czGtU9eHuYUopbbrmF2bNnM3/+fLvDiWudLXhPU0odD5wDXC8i49puoJT6m1JqrFJqbL9+/bo1yN5mmiaBUAi3281uX4Qy9xoAqvKP1jesddE777zDueeey9ChQ3n11Vf1yULvSMp89QeDKKXwVH9LRDlw97fmf4cMg376hrVuUVVVxcSJE1m1ahULFy5k5MiRdoeUKg6Ys/Gar41+PzmB7SiE7Y5BuJ3S6jNeKZVyy9f2JqUUN954Iw899BBz5sxh3rx5docU1zpV8CqldsT+uxt4BTipJ4OyWzg2ET+qFFWNEU5QXxNxZeHLGqanM3TB4sWLmTp1KqNGjaK8vFy3RuklyZqvtfX11he7rRXWxgzJJxqN4nQ46JOXZ29wSaCyspLx48ezbt06Xn/9dc4991y7Q0oZiZqz/mCQLN9WQllF7Aq6yEvft6TwuN02RJb8otEoc+bM4eGHH+bGG2/kkUce0fcXdaDD346IZIlITtPXwBTg654OzE7hcBgBagMmkSgcHvoab98xKKeLzPR0u8NLGP/4xz846qijePfdd4mnUYlklsz5uru6mnSPhz6+dXzDMEYWemgMBBjQty9ufVA9ZO+//z7btm1j8eLFTJ482e5wUkai5qxpmoQNg0zfVoI5pWyuDTMkv3UeKtCDRD1k165dvPbaa9x666385je/0VdPO6Ezs5oHAK/Efpku4Fml1Js9GpXNmprbV/lM+lNLYXg7Wwqn4Xa59BlUJ0QiEVwuF08++SSBQIA8PfrWm5I2X2sbGshXXnKj9ezJOowhDiFkGAwsLLQ7tITWlK/Tp0+nrKxMn5z2voTM2XAkgkRN0n3bqOp7Its3RygbtncuvWEYZKSlkaEHibqVaZo4HA6Kior44osv6Nu3ry52O6nD6k0ptVEpNSb2OEopdV9vBGansGGglGJ3Y4RTHasAqOlzDOl68n2Hnn32WcaOHcuePXvweDy62O1lyZqvYcPAMAxyGqwV1nz51vxdAd1/9xBs2rSJo48+mrfffhtAF7s2SNScNQyDjGAljmiYLWJ1aBjZd29HhsZAgOIBA3Qx1o0Mw+Dyyy9n3rx5KKXo16+f/v12gR6ubIc/EMDldFLpNTjFsZqIO5u6rKFk6gbaB/Tkk09yxRVXkJ+fT7o+q9e6UdgwQARP1TcYykkgf+/NVPou8IOzbt06xo0bx65du+jTp4/d4WgJJhQOk+3dBMCutFIAMt0tWpJFo3r+bjcKh8NcdtllPP/885SUlOhC9yDogrcdu6urSU9L4/9VBBjnXo2377EEwxEG9u1rd2hxa/78+Xz/+99n0qRJLF68mGzdzULrRk3TjNL2fM0qNZR+edmYponT4dAH1YOwevVqysrKCAaDvPfee5xwwgl2h6QlmFA4TG7jFhTCV0YRAANz9s6SVEqRo9sFdotgMMjFF1/MK6+8wu9//3tuvvlmu0NKSLrgbSNsGDQ0NuKLOAnVVlKsKmnoeywiQl/d+qhdzzzzDLNnz+bss8/m9ddf1yPhWrcLBIM4oiZ9Gr7l8+goRhR6CIXD5Ofm6pGOLtq2bRvjx48nGo1SXl7OmDFj7A5JS0B1Xi+5/q2EsotZU+ugKMdFVmzRiWg0ioiQqwveQ6aU4rLLLmPRokX8+c9/5sc//rHdISUsXfC24fP7QYQttQanOL4BoDr/aPJzcnDrlUvaNXHiRP7rv/6LV155RU9l0HpEg89HXmAbaSrIOvdo8tKdBMNh+uTn2x1awikuLuaaa65h2bJlHHXUUXaHoyWouoYGcnyb8eeNZEN1mJGFe6cWhcJh65ipr74cMhFh5syZPPbYY1x33XV2h5PQdMHbRiAYRICt9WFOd36N4c6lOq2YQn1g3cfChQuJRCIUFRXxhz/8gTS9hKTWQ+q8XgobrVW/6guOBKyRjzw9dabTPv30UzZt2oTD4eCBBx5g9OjRdoekJSjTNAl6a0hv3EFd5jBqAibD+uwteAOhEH31vPBD4vV6eeuttwCYNm0aP/jBD2yOKPHpgreNBp8Pt8vF1towZc6VNAw4kYiC3Jwcu0OLK/fccw8XXnghjz32mN2haEkuYpo0+Hxk166mSuWS07ekeYlqvfJh53z00UdMnDiRWbNm2R2KlgRC4TCZjVsRFFtdQwAoyd07mquUIl8fMw9afX09Z511FtOmTWPHjh12h5M0dMHbRp3Xi8ftRqo30Jc6GgacCECOnpcKWB9kt99+O3feeSdXX301P/zhD+0OSUty/kCAaDRKZvWq2PzdNELhMHk5OfqGtU5Yvnw5U6ZMYcCAAfz973+3OxwtCRiRCDm+zQCsUUMBGNSi4EUp0vUVv4NSU1PDmWeeyYoVK3juuecYNGiQ3SElDV3wthCNRmnweomKk8MDnwNQU3g8HrdbN8/GKnZvueUW7rvvPn74wx/y+OOP63XStR4XCodxGw3kBSr4PDqKwfluguGwnmbUCe+++y7nnHMOJSUllJeXM3jwYLtD0pJAU8FrOtPZGOmLAIVZLY4FIvpk9CDs2bOHSZMm8dVXX/Hyyy9z4YUX2h1SUtEFbwvBUAilFNvqTc5wfEVN+hC8znwKcnL0neBYTer/9Kc/cf311/OXv/xFrzqn9YpAMEh+wzoAVsoo+mQ4iZimnr/bAaUUd911F8OHD6e8vFyPFGndxohEyPZtJpA7jD0BRX6GE5fDOkaaponL4dA3eR+E5557jjVr1vDaa68xdepUu8NJOvovsgV/MAgibN3jZbpjDZUDpxEMhxme4u3IlFKICMOHD+fzzz9n5MiR+gRA6zWNgQCF3rWYOKjKGoWIIOgFJw6kKWcXLlyIaZr01T3EtW7k8/kY6ttMffE4dlZH6J+9t5QIGQZ5ul1glzTl6w033MDZZ5/NqFGj7A4pKekhuhZ8fj8A7sovSReD0KCTgdS+McY0TWbNmsUf/vAHAEaNGqU/yLRe5W1sJL/hWzbIEPJy994Io+cItu/ll1/m/PPPJxgMUlBQoItdrdv5a7bhMRrw5w5na32YIfl7py8EQyEK9JLynbZt2zZOO+00Vq1ahYjoYrcH6YK3hdqGBtI9HkrqV2DgwtvXasielaI3rEUiEa655hoef/xxampq7A5HS0FKKRoa6smpW8P/i4xgYLaLaDSKw+HQI7zteP7557n00kupqakhFArZHY6WpBy7VgFQkzmMxrBiUJsV1nSHhs7ZvHkzZWVlrFq1Cq/Xa3c4SU9PaWih3usFp5tjjS/ZlHUkQeUiK8OZknORDMPgiiuu4IUXXuC+++7jZz/7md0haSkoGA6T1rAZV6SRz8xRjOqbRmMgQP/CQn2loY1//OMfXHPNNZx++uksWrSIHF10aD1AKYW7Zi0A21xDgSAFGa1vXtY3rHVsw4YNTJgwAa/Xy9KlSxk7dqzdISU9PcIbY0QiBEMhaqurOMKxlT2FYwml6EpOSilmzJjBCy+8wEMPPaSLXc02DT4fBfWrAfiSUYwpSicUDlPcv7/NkcWXp59+mquvvprx48ezePFiXexqPSYUDpPt20w4vZC1jdbSwU1TGpr6Y2dmZNgWXyLYtGkT48aNw+/389577+lit5fogjcmEAyilMKz7UMAIkNOJ2wY9E3BgldEKCsr4+GHH+amm26yOxwthXl9PvrUf0ON5KPyh5Dhtj6ycrKybI4svowZM4YZM2awaNEisvTvRutBYcMgp3ELgdzhbKsP43Ls7cEbDIXIz8nBpdtVHtDAgQMpKyujvLycY4891u5wUkbqXavfj5r6ehwOB312fcROCknrP4LGhoaUOrAGAgHWrFnDcccdx9y5c+0OR9NoDAQoqVvF+xzOwBy3nr/bxocffsipp57K0UcfzbPPPmt3OFoKCAcb6ePbwq6ik6n1mhRkOHHGWpIZkQj99JLC+/XNN98waNAg8vPzdb7aQI/wxmzftQszqvhO+As25J6MLxBgYL9+KXNpprGxkfPOO4/x48dTXV1tdziaBoBRtYH04G7eD49mYI6LsGGQl52t5+8Cv/71rznttNN44YUX7A5FSyFq92ocKoI/fzT1wSi5aXtHcyOmqbun7Mdnn33GGWecoZf3tlGnC14RcYrI5yKyqCcDskPENGnw+VA7VpIlIYIlpxGORBiUIvMEvV4v55xzDsuWLePRRx+lsLDQ7pC0Q5QM+aqUwlP5KQCfREdT1FTw6vmp3Hfffdx8881cdtllXHTRRXaHox2iRMrXaMVnADTmH8aOBoOBbTo0ZKdoV6MD+fjjj5k4cSI5OTk8+OCDdoeTsroywvtjYHVPBWKnQDCIiNBn10cElAfX0BMRpchKgdHduro6pkyZwocffsizzz7LFVdcYXdIWvdI+HwNhcPk135NyJnFGjWEIfkejEiEgtxcu0OzjVKKX/ziF9x+++1ceeWVPP3007j1HfHJIGHyVXatJOLKpDatiKpGs1UPXgW64G3j/fffZ/LkyRQWFrJs2TKGDx9ud0gpq1MFr4iUAOcBj/VsOPbwB4OoaJQRDR/ziXyneRpDKlya+f3vf8+nn37Kiy++yGWXXWZ3OFo3SJZ8DYXD9Kn7hg1pRyDioCTPjQLSUiAv92flypXce++9zJw5kyeeeAJXCrZMTDaJlq9pNWtozB3JtnoT2NuhIRqN4tTz61sxTZPrrruOoqIili9fztChQ+0OKaV19tPy98BPgf1eSxSR2cBsgCFDhhx6ZL2oqqaGzMBO+pu7WJR7EaV+PwP69UuJO01vv/12zj33XE466SS7Q9G6T1Lka7B2OwP82/g0o4ySPDdup7WkcCpcedmfY445hg8++ICTTjoJh0PfgpEkEiZflRkhu34Du4edz25fBICiHKvgDRsGuVlZ+u+yBafTyWuvvUZmZiYDBw60O5yU1+FfpohMBXYrpT490HZKqb8ppcYqpcb269ev2wLsDVU1NbgrPgEgfdQZhAyDIUVFNkfVc3bt2sVFF13Ezp07cblcuthNIsmUr9HNHwGwNDCK0gIPkUgEj9udck3to9Eo8+bNY9Eia3rnKaecoouKJJFo+WpUrlMCFOcAACAASURBVMYZDREoGE213xrhLci0BobChkFOdrZtscWTxYsXM3fuXJRSDB8+XBe7caIzn5qnAReIyGbgn8BEEXm6R6PqRcFQiGAoxMDqf/NNdCglxSWQxPN3d+zYwfjx43nrrbdYv3693eFo3S9p8tVZ8W9Mh5sPg6UM6+MmZBjkptgBNRqNcu211/Lwww/z0Ucf2R2O1v0SKl/N7dYNa/78w6ioN+iT4SQz1hvbMAz65OXZGV5cePXVV/nud7/LBx98gM/nszscrYUOC16l1G1KqRKlVCkwA3hXKZU0dzY1BgJ4wnWUBlbzb9dYXJhkpKcn5fzdbdu2UVZWRkVFBW+++SZnnHGG3SFp3SyZ8jWj8lMqsw4njJvSAg+hcJjCFFoIxjRNZs6cyWOPPcbtt9/Ovffea3dIWjdLuHzd+RWmw0MgZygV9QYleS1uWBNJ+fm7L774ItOnT+e4445j6dKlesXDOJPy18XqvV4G7PkEB1HWF5yOPxikfxI2zt68eTPjxo1j9+7dLFmyRBe7WlwL1e8iu2E9q9PGADA034NSKmU6NEQiEa688kqefPJJ7r77bu655x7de1iznWPXSrzZpeBwsaPBoDhv721AkuItyZ577jlmzJjBySefzNtvv01+Cp2cJ4ouFbxKqXKl1NSeCsYO1XV1FO76N1ui/cksGp20K8VkZWUxePBgli5dyqmnnmp3OFovSOR8Da0rR1B8YB5JvywnmW5ryetUWfnQ6XRSWFjIr371K+644w67w9F6QdznazSKq2oV3tyRBIwogYiiMMMqeCOmidvjSbn59S0VFBQwefJk3nzzTXJT5MQ80aR0TxulFN7qnfSv+5zHov+fvfsOj6pKHzj+PclMeu8hIQkloVcBQYEkSC9KERsqi4VFBXVF14Jid13XsqL8VFxAURBRAQGRTgKI0ov0ElogIZ30TDu/P2ZwI0sJMD3n8zx5Hidz5953zLzcd+495z39aB7ljaDSrcYJHj9+nAYNGhAZGUlmZqa6SqS4BHFsHQYPb9aUJ5EU6kW1TkdYcDCebt45paamhtzcXBITE5kyZYrKV8V5lBzHU19OZXAyRVXmCWshvuZ8NBqN+NTT4QyHDx8mOTmZ/v37069fP5WzTqxeD2morqkhLH8TntLAeo8uRPpKAgMC3GYc0p49e+jatSsTJkwAUImouAxt9kaKQ1pxsgwaWcbvhrj5eLiqqiqGDRtG9+7dKS8vV/mqOJec3QBUhjajsMJc8Eb4mwteg8GAn4+Pw0JzlKlTp9K8eXNWrVoFqHOss6vXBW9ldTUxeRspIITqiNbU6HRuM353165dpKen4+Hhwd/+9jdHh6ModVeeh8+5LE4EtEcCSaFajEYjIW58m7CyspJbb72VZcuW8fLLLxPgRneZFDeRsxOT8KQ6pAkFleYevBH+/x3S4I4TvS/n/fffZ/z48QwZMkTNiXER9brgLT9XSEThNlaaOhEV6IXJZCLADcYIbtu2jfT0dHx8fMjMzKR58+aODklR6sxwNBOAXdq2ACSFeiGEcKuhRrWVl5czcOBA1qxZwxdffMFDDz3k6JAU5X/I09spC0jCQ+tLYYURAYRZhjToDQa3zc+L+cc//sHEiRMZOXIk3333Xb1e/dGV1OuCt2b/MjSmGpYYOhMTqMFkMrn8t1SdTseIESMIDg5m3bp1JCcnOzokRbkqpqMZ6DX+bK5OxN9LEOIt8dJq3Wao0YUmTZrEhg0bmD17Nvfff7+jw1GU/2UywZntnAtKwcPDg7PlBkJ8PdF6mm/hC8DPTXvXX2j9+vW88MILjBo1ijlz5qCtxxP1XE29nbRWXVOD//FVlIsAdnq05J5oQVhgsMuPE/Ty8uK7774jOjraaZeMVZTL8TyxgcKQVmSVGEkK9UJvNLp1u6PXX3+dwYMH06dPH0eHoigXV3gEUVNGSWAKAPvyqkmOMH8BNZlMeHh61psOKj169GDBggUMGTLE7SfRupt6e4X3bF4OkfmbWG7oSI/GwQiTjuSkJJcddJ6RkcH7778PQOfOnVWxq7imkpN4njtOYWg7TpTozR0aamrcrlVgYWEhjz32GBUVFQQFBaliV3Fup7cCUBqcQpXeRH6FkaZh5oK3RqcjOCAAjRsXf1JKXn75ZXbs2AHA0KFDVbHrguptwVu192e8jBUsNXYmrZEvPt7eLrss4sqVKxk4cCAzZsygqqrK0eEoyrU7ugaArMBO6IySRqFapJQum5sXk5eXR3p6OtOnT2fXrl2ODkdRruz0Noxaf6qDEskpM09Yiw0y38rXufmS31JKnnjiCV577TW+//57R4ejXId6WfDq9XqCji8zD2fw6kCYl57YyEiXvLq7dOlShgwZQnJyMmvXrsW3noyjUtzU0TXU+ESy3xgPmCesAW7T0D4nJ4e0tDSOHDnCkiVLuOmmmxwdkqJc2eltVIQ0Q6v1JqdUD0CDIPOISL3B4LYrIJpMJsaNG8dHH33EU089pZb3dnH1suAtO1dIVP5vrJBdaB4dgNFodMlbpgsXLmTo0KG0atWKNWvWEBkZ6eiQFOXaGQ3IrAzyQ9tzstSExgOi/AVajcYtJqxlZ2eTmprKyZMn+fnnn+ndu7ejQ1KUK9NXQ+4eigKa4uXlxZlSAwKIDvjvFCB3nLBmNBp58MEHmTZtGs8//zzvvvuuS14UU/6rXha8hn1L0Bir+U7XjVbR5q4MwS54SyY/P58bbriB1atXEx4e7uhwFOX6nNmBqD5HQXhHTpToaRiiRa+rISYyEg8P1/+nqqKiAoDly5eTmprq4GgUpY7O7gGTnmL/Jmg8PTlUUENcsBZvjYd5wpqHh9sWvHl5ebzyyiu8+eabqth1A/WuS4OUEo+98ynxDGWzbMHQYBPREREu1VokPz+fyMhIHn74YcaMGYNGU+/+jIo7OroGiaAgtC3HftdxQ7wver3e5Tun5OfnExERQbNmzdi3b5/KV8W1nN4GQElwM3xNkoMFNXRPNHdkqKqpISw42K0mrOn1esrLywkNDeXHH39U+epGXP+yyVUqOXuS0LxNLNR1pVtCAIEaI01dqKPBF198QaNGjdiyZQuASkbFfRxdTVV4C84aQyitMdEkzAsJLt3u6NChQ3To0IHXXnsNUPmquKDsrRj8IqnxiSCrSEeVXtIiynxntKq6mgZRUQ4O0Hpqamq444476N27NzqdTuWrm6l3BW/Z9nl4SgMLDN24pZGWmMhIl5lhOm3aNMaMGcNNN91Eq1atHB2OolhPVQkyeytng9txqFgC0CrSE39fX5ctePft20dqaio6nY7hw4c7OhxFuSby1CaKA5sR4OfHskNl+GoEHRqY775otVqiXHD+y8VUV1czYsQIFi5cyJgxY/Byg3kDyp/Vq4LXaDTid+QnzogYioOaE+1nIiE21tFh1cnHH3/MX//6VwYOHMiiRYvwc+NG/Eo9dGwdQho5G9KOrGIDob6e+HsaiHHR7im7d+8mLS0NMPfIbtOmjWMDUpRrUZaLKDlBQVAztFotW7Or6JroR4C3B2WVlSTFxbnUcMBLqays5LbbbuOnn37is88+Y/z48Y4OSbGBelXwVuQfJ7xoFwsMXWkb64MAgl1gfODy5cuZMGECt912G/Pnz8fHx8fRISmKdR1egck7iJLg5hwt0tE03Auj0UiQC17draiooF+/fnh5eZGZmUnLli0dHZKiXJtTmwEoCW5BYYWRSr38Y8EJk8nkNu3IJkyYwMqVK5kxYwZjx451dDiKjdSrASqmPfMRmFhouIlbgjwJ8PNB6wJjdHr37s3UqVN5+OGH3eLbtKL8ickEh1dQFXczeulJTpmBmxL9EEgCXWS4UW3+/v58+umntG7dmiZNmjg6HEW5dqc2IT29KA1swoH8GgCSI7yR0jzsyFWHG13o5ZdfZsCAAdx+++2ODkWxoSte4RVC+AghNgshdgkh9gohXrVHYLagPfAjOV6JHJbxJASYiImIcHRIlySl5MMPP+TkyZN4enry6KOPqmJXuSKXzNecnVB+luKYmyisFkgJcYFaEAIfFxpHt3HjRubPnw/AbbfdpopdpU6cOmdPbaIqvCUaL1/yK8wrrDUI0mAwGvH18XHpBWFKSkp48803MRqNJCQkqGK3HqjLkIYaoJeUsh3QHugvhOhq27CsTxYcwb/gd5Z79CAuSEOYryDCSQfbSymZNGkSTz75JNOmTXN0OIprcb18PbQchAe5wW3JqzKP140NEPj5+LjMevWZmZn07duXyZMnYzAYHB2O4lqcM2f1VXBmJyUhrdBqNBRUGvH3EnhrPDAYDPh6ezs6wmtWVFRE7969efXVV9mxY4ejw1Hs5IoFrzQrtzzUWn6kTaOyAd3WL5F48HlpN9rGeOPp6emU4wOllDz99NP84x//4K9//esf7YwUpS5cMl8PLUPGd6HEoOVwoQEfjSDEy0BEaKijI6uT1atXM2DAABISEli5cqVqZaRcFafN2TM7waQnz7cJXlothwtqaGwZv1tVU0N4SIiDA7w2+fn59OrViz179rBgwQI6derk6JAUO6nTpDUhhKcQYieQB6yUUm66yDZjhRBbhRBb8/PzrR3n9TEZ8fz9W474t+e0KZS2kYL4mBinu3pkMpl4/PHHef/995kwYQKffPKJW6wwpdiXS+VraQ7k7MTQpDd6o5GsIj1Nwr2QJqNLnFCXLVvG4MGDadq0KRkZGcS6SNcXxblcKWcdkq+nfgOgIKgZJjw4UaInOdx8VddkMhHmAvl5odzcXNLT0zl48CCLFi1i0KBBjg5JsaM6VVNSSqOUsj0QD3QRQrS+yDbTpJSdpJSdIiMjrR3n9cnKQFNxloWkEebrSUIQRDhhslZUVLBhwwYmTpzIhx9+6JLtmBTHc6l8PbwCgMqEVCp1kqxiHS2ivBFC4OMCt0zXrVtHixYtWLt2LVFu1IBfsa8r5axD8vXUZoyhjdF7BVNUZURKiPC33L1wsfH15x05coSzZ8/y008/0bdvX0eHo9jZVd17k1KWCCEygP7AHptEZAPG7V9h1AQws7gdqU3NnRmcqR2Z0WjEYDAQGBjI+vXr8ff3V8Wuct1cIl8PLYfgBIq1sRwoPIyU0C7GGyF0BDhxr+mqqip8fX158803mTRpEv5OODxKcT1Ok7NSwqlN1CSkIaTkcIG5Q0OTcC8MBgNeGg2+LtQe83y+du/enWPHjhHggt1flOtXly4NkUKIEMt/+wK9gQO2DsxqqorxOPgTOwN6UGnS0iYSGsXHO83sUoPBwP3338+IESMwGo0EBASoYle5Zi6Vr/pqyFoLKf3ILSzkQJHE30sQ7WMgNiLC6YYcnTd37lxSUlI4cuQIQghV7CrXxSlztuAwVBaaJ6xptRwqqMHLU5AYojWP3w0NdZnz1LFjx2jVqhUzZ84EUMVuPVaXIQ2xwFohxG5gC+bxRUtsG5YV7ZmPMOr41phGuJ8niUHCadqR6fV67rnnHubMmcPNN9/stCd4xaW4Tr4e3wD6SnSNb6H43Dn25OlpG+OLwWigQXS0o6O7qFmzZjFq1CgaN25MtJPGqLgc58vZ4+sByAtsjlajIfucnoQQLZ4eAp1OR3R4uEPDq6sjR46QmppKSUmJWu1QufKQBinlbqCDHWKxCdOOrynzS+THwnh6N/EmJDDQKZpl19TUcNddd7Fw4ULee+89nnrqKUeHpLgBl8rXQ8tA60dxWDtOHz1IcZWRNtFeaDwhxAlXcJo+fToPP/wwvXr14scff1RXdhWrcMqcPfELBDagyCMMX42Gokoj8cGWu6JC4O0kd0gv58CBA/Tq1Qu9Xs+aNWto3769o0NSHMy9WwDkHcDjzHZ+9U/HYBK0iYDEBg0cHRUADz/8MAsXLuSjjz5Sxa5S/5hMcHApNE4nv7SCg8XmLkzJodAgKgqNk93tWLhwIQ899BD9+vVj8eLFqthV3JeUcHwDpsSb0On1eHp6UlRpJMzvvznp5eQT1oqKikhLS8NkMrF27VpV7CqAuxe8O2djEp58U9ODcD9PGodpiHSSxSaefPJJpk+fzvjx4x0diqLY35kdUHoaU7NB5OTns7/ARMNgLQEao9MMOaqtT58+vPTSSyxcuBBfX19Hh6MotlN4FMrPoo/rCkKQW26gyiCJD9JiMplcooNKWFgYL774IhkZGbRu/T9NapR6yn0LXoMOufMbcsM68UuBLx1jNcRHReHtwG+m5eXlzJo1C4COHTvywAMPOCwWRXGo/YvAQ8O5uO5UVus5kF9D2xgvvLVaQp1oOMNXX31FWVkZ/v7+vPbaa3g7+YleUa6bZfxuaUR7pJRsP10FQLtYXyqqqoiOiHC6OzDnbd26lU2bzC2Mx48fT/PmzR0ckeJM3LfgPbAEUZnPWp8+GEzQNsqTWAf2ySwtLaV///6MGTOGvXv3OiwORXE4Kc0Fb6OeFOs8OFxswmCClFBBbHS000zefP3117n//vv58MMPHR2KotjP8Q0QEMMZQwDeXl5sO11Nw2At0YEaqnU64p10suZvv/3GLbfcwrhx4zCZTI4OR3FC7lvwbpuJzi+W+RWtCfP1ICVc67CJMCUlJfTt25dNmzYxd+5cWrVq5ZA4FMUp5O2HoixoMYSC4mIOFkm8PAWNgnGK1dWklLz00ktMnjyZ++67j+eff97RISmKfUgJJ35BJnWn4Nw5fLy8yCqqoVmkNyaTCQ8hnKqH/Xnr16+nT58+REZG8uOPP6oVSpWLcs9PRcEROLaOI9F92J1nokOMlrjoaIfchikqKqJ3795s376d77//npEjR9o9BkVxKvsXAQJ9k77kF5ewPUdHq2hvvDQeBDu4R6aUkmeffZY33niDBx98kJkzZzrNFWdFsbmiLCjLwRB/I3q9nuIaQYVO0jhMS7VOR2hQEFrNVa1XZXNr1qyhf//+xMXFsW7dOhISEhwdkuKk3LPg3TYT6aHhZ89U9CZoF+NJAwctn7p+/Xr27dvHwoULue222xwSg6I4lf2LIaEbhQZvDhYYKKw00rWBJ/ExMQ4dYw9QUFDAnDlzeOSRR5g2bZoqdpX65fgGAKpiOgNwrEgHQFKoFzqdjrDgYIeFdinTp0+nUaNGZGZm0sBJujApzsm5vqpZg74ads7mXFxPlmQHEOEnaRnlY/fbMEajEU9PT2677TaysrKIiYmx6/EVxSkVHoWze6DfPziVm8vG00b8vQTNwiDOgWMDz88+j4yMZOvWrURHR7vMSlKKYjXHN0BANJV+ccBBsop0eAhICNFSVlZJkBOtUnb+HDtz5kzKysoId5HFMBTHcb8rvPsXQVUxG3xSOVZipHdjL1o2aWLXKzWnT5/mhhtuYPny5QCq2FWU8/YvAqC6cR+O5BSyPUdHz0RfIoICCHHQ2ECj0cjYsWN5/PHHkVISExOjil2l/pESjmVCUg/yS0rQajRszq4iJcIbrQcIIMhJxu8uWLCALl26UFBQgJeXlyp2lTpxv4J36wwMwUmsrG4JQPsoT0LteBvm5MmTpKamkpWVpZrTK8qF9syH+M6cNfixM9eIScKNsYLkpCSHFJkGg4ExY8Ywffp0wpykR7eiOETePig/i6lxGrn5+ZQZtGSf09MtwY+q6mrCQ0PxdYK2fPPmzWPkyJF4eXmhdYEV3xTn4V4Fb95+OPkr+Y1vZdtZE03DNCRFhdgtSY8dO0ZqaioFBQWsXLmS7t272+W4iuISCg5D7m5oNZzcggL2FpiI8vckJsDDIR1U9Ho99957L1999RVvvPEGr776qrqyq9RfR9cAUBF7Iwajkd1nzeN3O8T5UKXTEe8Edyq//vpr7r77brp168aKFSsIdsIxxYrzcq+Cd+sMpKcXGR5dyC410jHGk8S4OLscOjc3l549e1JaWsrq1au58cYb7XJcRXEZe+YDgqrkgRzPK2ZPno4b472Ijox0yGS10aNH8+233/Kvf/2LSZMm2f34iuJUjq6FiBRKCEQIwY4zVcQGaogJ0CBwfMvA77//nvvvv5/U1FSWLVtGoJMMr1Bch/sUvNWlsPMbapoOJDPXByGgS7w3EaGhdjl8VFQUd999N2vWrOGGG26wyzEVxWVICXu+h8SbKTb5sfm0AYB2kdDQQVeO7r77bqZMmcLTTz/tkOMritPQV8OJjdCkF7kFBZiElt9zq7khzpfK6mrCgoPxcvDwgZtvvpmxY8eyZMkSNVxQuSbuU/Du+gZ0ZZxoeCvbcw00D9fQKinO5j0D9+zZQ1ZWFh4eHrzzzju0a9fOpsdTFJd0di8UHILWw8kvKuJQsYnYQA3xwfZdEKaqqoqVK1cCMGTIECZMmGC3YyuK0zq1CQxV6BN6UFhczM488+qH3ZP8qaiqstud0otZunQpBoOB2NhYPv30U/z8/BwWi+La3KPgNZlg02eY4jqRURJOXoWJTrEaYiIibHrYnTt3kpaWxn333YeU0qbHUhSXtucHEJ7oUwZx+Ew+e/P0tI/WEBsZabcFYSoqKhg8eDCDBg3ixIkTdjmmoriEo2vAQ0NhSCukEGw4XklsoIaGQQIfLy8i7XSn9ELvvfcegwYN4pNPPnHI8RX34h4F79HVUHSU8jb3sfm0Do0HdGnoY9NVm7Zu3UqvXr3w8/Pjyy+/VJNdFOVSpIS986FxKsVGL347VYNJQqdYDxLs1Ci+rKyMAQMGkJGRwYwZM0hMTLTLcRXFJWSthfguZBdXoDNp2JdXQ48kf8orK2kUH++QpXrfeustnn76ae644w7GjRtn9+Mr7sc9Ct5Nn0JADIf8OrDljIHWkRpaJDW0WZL++uuv3HLLLQQHB7Nu3TqaNm1qk+Moils4sx2Kj0PrEZw4fZrNZww0CtWQHB1ol967586do1+/fmzcuJE5c+Zw77332vyYiuIyKgogZzfGRqkUlpRwuNh8t7JjnA8mk4lIO7frk1LyyiuvMGnSJEaNGsXs2bNV+zHFKq5YEQohGgoh1goh9gsh9gohnrBHYHVWcBiOrELfYTQLdudTWiNJT/Ky2apNUkomT55MVFQU69atIykpySbHUZRr4ZT5+vv34KGlunFfdp0o4OQ5I51jNTSOj7fLnZG5c+eydetW5s2bx5133mnz4ynK1XB4zmZlAJKKBt2QUrIrt4ZAbw8aBICvjw8Bdh4ze/LkSd59913+8pe/8OWXX6Kx8Twcpf6oyyfJAEyUUm4XQgQC24QQK6WU+2wcW91sngaeXpxpOIC1v+WSEOxJn7YJNuu9K4Tgu+++o7KyUq3brTgj58pXowF+/w6a9aewWrLhlB6NB3RuoLHblaOxY8fSo0cPWrZsaZfjKcpVcmzOHl4JvqHkauNB5LA7t5p2sT5UVlXRqmlTuw/XS0xMZOvWraSkpDhkKIXivq74aZJS5kgpt1v+uwzYDzhuymZt1edg5xxkq+GsOFLOmTITN8ZpiLfB1d3ly5dz6623UlVVRUhIiCp2FafkdPl6dA1U5CPb3sWerJP8mm2gc5wXzRMb2LT37tmzZ0lPT+f3339HCKGKXcVpOTRnTUY4vAKa9iG/5Bz5VZ6cqzbRPtYHpLTbKqUmk4kJEyYwZcoUAJo3b66KXcXqruoTJYRIAjoAm2wRzFXbMRt05RQ1u5Pv91YQ7C0Y0SGOICtPVluyZAm33norp06dorKy0qr7VhRbcYp83TUHfMMoje3GuqPnqDFKUhtqaGTDNkc5OTmkpaWxefNmCgoKbHYcRbE2u+fs6W1QVYSpaR/KKirYX2Duj906ygtPT0/8fHxsHoLJZGLcuHF8/PHHnDp1yubHU+qvOhe8QogA4AfgSSll6UWeHyuE2CqE2Jqfn2/NGC/OaIDf/g8SunHQEElWsZEbYjUkN4y16mEWLFjA8OHDadu2LatXryY8PNyq+1cUW3CKfK0qgQNLoc3tnC0pY8sZAxF+HrSJD7bZuMDs7GxSU1PJzs7m559/Jj093SbHURRru1zO2ixfDy0H4Ul1Qg8AzpQZCfX1xE9jIiggwOZXWY1GIw888ACff/45L7zwAu+8845Nj6fUb3X6NAshtJgTcbaUcv7FtpFSTpNSdpJSdoqMjLRmjBe3byGcO4Wx63h+3puP3gQdYr2tenV3/vz5jBw5khtuuIFVq1YRZufZqopyLZwmX/ctBGMN+la3s3THcQ4VGUlP1JBso5Zgp0+fpmfPnpw9e5bly5fTs2dPmxxHUaztSjlrs3w9tBwa3ki18EUCx4p0JIRoqdHpbD6cQUrJ6NGj+fLLL3n11Vd54403VHtPxabq0qVBANOB/VLK920fUh1ICRunQHhT8sI7sfZ4DQ0CPejXNsGqK6ulpKRw6623smLFCoLtNJZJUa6HU+XrrrkQ0YwznnGsPlZDsLcHQ9tE2myyWnh4OJ06dWLVqlXcdNNNNjmGolibw3L23Gk4+zuk9KOgpISiKsmJEj2torzRGww2n1QqhKBz58689dZbTJ48WRW7is3V5QrvzcB9QC8hxE7Lz0Abx3V5x9dDzi5kt/H8tOMY2aUm0hK9rNbEfsuWLUgpad26NfPnzyfQDr1CFcVKnCNfi7Lg5K/Q7i52Hc9hT56BrvEamiZYvxXZ4cOHKS4uxsfHh3nz5tG5c2er7l9RbMwxOXt4BQAyuS/ZublsOysRwI3xXvj5+tqsR3ZNTQ27du0C4IknnuD555+3yXEU5UJ16dKwQUoppJRtpZTtLT9L7RHcJW38CPwjKWk8iPl7zhHgJRjRMR4fK7Qi+/TTT+nSpQtfffWVFQJVFPtymnzd9S0gqG4+lFWHziGB7glehAQFWfUw+/bto0ePHtx///1W3a+i2IvDcvbQcghJoMyvIVU1Naw/XkWbGB+8qSGxQQObXHGtrq5m2LBh9OjRg7y8PKvvX1Eux/X6fuTth8MrkJ0f5ufdp9hXYKRPYy9aNEq47l1PmTKFRx55hMGDB3PHHXdYRnKDnQAAIABJREFUIVhFqYdMJtj1DTTqQZ7Bl19O6WgRoaF1QpRVW5Ht3r2btLQ0PDw81GQXRbka+mo4lgnJ/cgrLuZIkYn8CiOpjfyQUhJtg8nZlZWVDBkyhGXLlvHee+8RFRVl9WMoyuW4XsG78WPQ+FLS/E5mb8vHXwtjujfBz9f3unb77rvv8sQTTzBs2DB++OEHfOzQjkVR3NLxdVByAlP7USzZnkVhlaRrnCeJVuxdvX37dtLT0/H29iYzM5MWLVpYbd+K4vaOrQN9JcamvTmenc2WXImfVtA8zETD2NjrPp9eqLy8nEGDBrF69WpmzpzJww8/bNX9K0pduFbBW5YLu7+FDvey7EABe/ON9G3iQ4vE+Ova7YEDB3juuee44447+Pbbb/GyYUN8RXF722eBTwiFsamsPlKOv1bQt1U0YVaa+Cml5MEHHyQwMJDMzEySk5Otsl9FqTcOLAavQIrD2nOuSs/m7GpuSvRDSKNVv5ie9+9//5v169fz9ddfM3r0aKvvX1HqwrUWqd70GUgj5e3GMGfeKfy18ECPJmi12uvabfPmzcnIyKBr165q3W5FuR4VhbB/MXR6gD2n8tl11kCPBC3NEq9/yNF5Qgjmz5+Ph4cHiTZqcaYobstkhAM/QUo/zhSWsCdfojNKusVpiAgJItDf3+qHfO6550hLS6N79+5W37ei1JXrXOGtKYOt06HFEDac9eD3PCO9GnmT0vDavo1KKXnxxRdZtGgRAN27d1fFrqJcr93fglGHrs3dLN2bh8EE6U0CrDLjOzMzk8cffxyTyUSjRo1Usaso1+Lkr1BZiKn5IHILC9lbYCLcz5NYPyOJVlwBsbCwkLvuuovc3Fw0Go0qdhWHc52Cd8t0qD5HTedHmfXbKbQecF/XxGuaBCOlZOLEibz55pusWrXKBsEqSj0kJWz/EuI6kU0kGcd1JIV4kNoy8bpXbFq1ahUDBgxg1apVnDt3zkoBK0o9tH8JeHpTEdcdg9HI3rwa2sb6IISw2sJNeXl59OrVi4ULF7J3716r7FNRrpdrFLz6Kvh1KjROZ1NZGL9l6+mR6EW7pld/hcdkMjFhwgQ++OADHn/8cT788EMbBKwo9VD2Fsg/gKHdKKavO0R+pWRYywAaXOds7KVLlzJ48GCaNm1KRkYGoaGhVgpYUeoZKc1DjpreQrlBcKbUSIXORHKYBn9fX3yt0NozJyeH9PR0Dh8+zOLFi7nlllusELiiXD/XKHi3fwUVeehvepJp64/jAYzt2eSqr+6aTCbGjRvH1KlTefrpp/n3v/+tVndRFGvZ9iV4BZAfl87aY9UkBnswsmuz6xpjv2jRIoYOHUqrVq1Yu3atamWkKNfjzA4ozYYWQygsKeFoifnXCQEmYq2wZPHp06dJS0vjxIkTLF26lD59+lz3PhXFWpy/4DXo4Jd/Q0I3fquKZ2O2ntQkLzo0ufrODEIIfHx8mDRpEu+8844qdhXFWqpKYO98aD2c5fvzOVNmomeiNxHXeTXWx8eHbt26sXr1asJt0BtUUeqV/YtBeGJK7kdufj5bc/Q0DNYS5iOtspSwVqslLCyM5cuXk5aWdv3xKooVOf8srd1zofQ0hoHv8+GKY2g84JG0pld1dddgMJCTk0PDhg3/GMKgil1FsaKdc0BfSWWbe1m8qBg/rWB4pwS8rvHq7rFjx2jUqBF9+/alT58+Kl8V5XpJCfsXQaMelOg9OXNOx+FCPXe2DiDA35/g65hYmp2dTVRUFFFRUWzcuFHlq+KUnPsKr9EAGz6A2PasKEtk6xkdfZv40q5JwzrvQq/Xc/fdd9O1a1dKSkoQQqhkVBRrMplgy38gvgv7asLZmWvgxjgNKQ2vbcb3l19+SUpKCkuXmldXVfmqKFaQtw8Kj0CLIZwtKmJLjhEBtI2CxvHx15xnhw8fplu3bjz22GOAylfFeTl3wbtvIRRloev2OFPXncBPCxMHtEZbx/ZhNTU13H777Xz//fc8/fTThISE2DhgRamHstZC0VFMnR7kh20nMUoY3DoS/2tYrenzzz9nzJgxpKWlqVuiimJNe34A4Ymx2WCOn8nhl1M62sR4E+7rcc3DGfbv309qairV1dWMHz/eygErinU5b8FrMsH69yCyOQtLk9mbb2RoyyCSYuo2sL6qqophw4axaNEiPv74Y/72t7/ZOGBFqae2/Af8IsiN7cmqo9UkBHmQ2irpqnczdepUxo4dS//+/Vm8eDF+fn7Wj1VR6iMpzQVv41QKdBq2ZFdTVGWiZ0Pzkt/X0t5zz549pKWlYTKZyMjIoF27djYIXFGsx3kL3oNLIW8f1V0e49MNpwn2FjzZv22db5e88sorLFu2jGnTpv1xq0VRFCsrPgGHliE7jubLX46RXykZ0SaY8Ku8m7JlyxbGjx/PbbfdxoIFC/Dx8bFRwIpSD53eBsXHofXtZJ06ReZJA7GBGlpGepIUf/UTwA0GA0OHDkWj0ZCZmUmrVq2sH7OiWJlzTlozmSDjbQhrzLyy1mSV5DCuazhRocF13sWLL75I9+7dGTJkiA0DVZR6busMAIpTRrDwyxM0DPJgVPcWVz2Or3PnzsybN4+hQ4de91LhiqJc4PfvwdOb0oR0dv5ykKNFBu5q5UOjuLhr6r2r0WiYPXs2ERERNGnSxAYBK4r1OecV3gOL4ezvVN80kc9+PUukn+Dh9JZXfFlpaSlPPvkkFRUVBAYGqmJXUWxJXw3bZ0Gzgfx4qJqzFZJBzQLqfHVXSsk777zD9u3bARg5cqQqdhXF2kxGc8vA5D7klNaw/pQBb42gcwMNCQ0aXNWufv311z86Hd14442q2FVcivMVvOev7oYnM6OgGafLTIzuEk14cNBlX1ZcXEyfPn2YOnUqmzZtslOwilKP7V0AVUXUtL+feTvzCfIS3HNTSp2u7kopefHFF3n22WeZNWuWHYJVlHrq+AYoP4uh5XD2n8hm6xk9N8ZpSY6PuaqJpevWraNv375MnTqViooKGwasKLbhfAXvvoWQt4+KG59k+qZ8GgZ5MCb18uODCgsLueWWW9i5cyc//PADvXr1slOwilJPSQmbP4PwZH4ujmN/gZH+KX7ERUXU4aWSv//977z11luMHTuW999/3w4BK0o9tecH8AqgKLor647XoDNKborzJCE2ts67WL16NQMGDCA+Pp7MzEz8/f1tGLCi2MYVC14hxAwhRJ4QYo/NozEZzVd3I5vzYXYTCqsk41MT8Pe99ASWvLw80tPT2bdvHz/++CO33nqrzcNUFGdml5w9sRHO7MDQeSz/+fU0/loYm9YMT0/Py75MSsmTTz7Ju+++y/jx4/n000/x8HC+792KYi82zVdDDez7EdlsIHtO5rIiS0fLSA03NIqs80ITy5cvZ/DgwTRu3JiMjAxir6JQVhRnUpczzRdAfxvHYbZnPhQcpOiGx5m9o4gWERqGd0m57EvOnTtHRUUFP/30E/372ydMRXFyX2DrnP11KviGsdajK3vyDPRP9iMpNuqKLzMYDGRlZTFx4kSmTJmimtQrii3z9dAyqC6hMnkIGYeLKddJ+jbSkJKUVOfcO3nyJC1btmTt2rVER0fbJExFsYcrdmmQUq4TQiTZPBKjATLfhqhWvH00gQp9KU/3Tb7kJJaioiJCQ0NJTk7mwIEDarKLoljYPGcLj8LBpZi6P8U3O86i8YBxvVqguczVXaPRSFlZGSEhIcyfPx+NRqOKXUXBxvm6cw4ExnLMK5mVWceJC/SkS6MwggICrvjSoqIiwsLCePjhh/nLX/6izrGKy7PavUQhxFghxFYhxNb8/Pyr38Ge76HwCOVd/8byQ6W0jdaS3jrpopueOHGCzp07M3nyZACViIpyla4rX3/7P/DUUtjsTjaf1tM2SkNSzKXH7hoMBkaPHk16ejpVVVVotVpV7CrKVbimfC07C4dXYmpzJ+sO5ZJTbqJXI22dru5+++23JCUl/TEBXJ1jFXdgtYJXSjlNStlJStkpMrJuq6H9wWgwj92NacvbRxI4VwP33hh/0bF9WVlZ9OzZk8LCQtV2TFGu0TXna2UR7JgNbe7g612llOskg9pEXXK5b71ez6hRo5g9ezYjR47E9xqWG1aU+u6a8nX3tyCNFDUaxOpjOny1gq7xvoQEXb7j0VdffcU999xD+/btadnyyu1AFcVVOMdskZ1fQ/Exslo9yjc7Cuga782wi4zdPXz4MD179qS8vJw1a9bQpUsXBwSrKPXY1hlgqOJc2zHM2ppPQpAHd3VrdtFNdTodd955J/PmzePdd9/lhRdesHOwilJPSQk75yDju7AuR7Ij18DN8RrapTS+7NCjGTNmMHr0aNLS0vj5558JrOPENkVxBY4veHWVsPYfyPgbeeS3cLw94a3bO/zPFaPq6mp69+6NTqdj7dq1dOzY0UEBK0o9ZaiBzZ9D43T+73cTxdWSsTfH4e/nd9HNn3rqKRYsWMCUKVOYOHGinYNVlHrszHbI309Fs+F8u6sETwGDmgcSe5mrw2vXruXBBx+kb9++LFmyRLUeU9xOXdqSfQP8CjQTQmQLIR60agSbPoXyXBaE3M/BQj3jujegcUz4/2zm4+PDhx9+SEZGBm3btrVqCIriTmyWszvnQHkuxW0fZM62fJLDPBlx46W7qJxfVGLChAlWObyiuCOb5OvOOUiNDyt1zdl82kDPRC092ja/bNvAnj178sEHH7Bw4UI19EhxS3Xp0nC3zY5eWQQb/k1VUm8m7QylWYSGR3u3+dMmO3bsICsrixEjRjB06FCbhaIo7sImOWs0wC//hgYdePdgBGW6It5MT8LX5889sisqKpg6dSoTJ06kYcOG3HfffVYPRVHcidXzVVcBu79D13QAX/2uw8sT7uoQSURo6EU3/+yzzxg4cCANGzbkySeftGooiuJMHDukYcP7yJpSXi0bgs4I/xzRHk2toQybN2+mV69ePPvss9TU1DgwUEWp5/YugOLjnG07ju92F9ExRkv/9k3+tElZWRkDBgzg+eef57fffnNQoIpSz+35AWrOsc67J9tzDdzSSEuXFk0vuukbb7zBuHHjmDJlip2DVBT7c1zBW3IKNk3jVMMhzD0dyd0dI2nf6L9NrTdu3Ejv3r0JDQ1l9erVeHt7OyxURanXTCZY/x4ysgUv743GYITnBjTHq1aropKSEvr27cvGjRv55ptvuPnmmx0YsKLUY1tnYAhP4b3DMfhrBQ/cnPQ/q6pJKZk8eTIvvfQS9913H2+//baDglUU+3FcwZvxNhJ47HRfIv08eG5wuz+eWrduHf369SMmJoZ169aRmJjosDAVpd479DPk7+d4yhhWHKmkd7I/nVMa/vF0UVERffr0Ydu2bXz33XfccccdDgxWUeqx09vhzA42BfXlQJFkYLI3bZok/WkTKSXPP/88r7/+Og8++CAzZ8684pLgiuIOHFPw5u2HXXPYEDyY3yvDeGVIcwJ8/3sFd9WqVcTHx5OZmUl8fLxDQlQUBXN7o/XvIUMSeWZfEloPeG5Qmz81rj948CBHjx5l/vz5DBs2zIHBKko9t3U6UuPLKyc7Eu4reCg1BW8vrz9tUllZyfLly3nkkUeYNm2aKnaVeuOKk9ZsYuVkjBo/nsjpS68mAQzq0Agwtx7z8fHh1Vdf5emnnyboCg2yFUWxsawMOL2NPe0msXWTgftvCP+ji8r5fO3WrRvHjh0jODjYsbEqSn1WVYL8/Qe2BfTkcK4PE7oG0jgu9o+nTSYTBoMBf39/MjMzCQwMVCseKvWK/a/wHl4Fh1fwOcPQaYN4+45OACxevJiUlBQOHjyIEEIVu4riaFLCun8hA6J5Yl8LwnwETw0wtwQ8c+YMHTp04PPPPwdQxa6iONquuQhDFf8sSiUlzIMH0tv+0c/eZDIxduxYhg8fjsFgICgoSBW7Sr1j34LXaIDlL1DkHcd7ZX14undjooL9mT9/PsOHDyc2NpaoqCi7hqQoyiVkZcCJX1gbfhdZZR482asRIQF+nDp1itTUVLKzs2nevLmjo1QUxWREbv6M494pbNEl8ZcbYwi1fAk1Go2MGTOG6dOn07FjRzWEQam37FvwbpsJBQd5ofxOOiYEM7pHM+bOncsdd9xBly5dWLFiBaGX6BWoKIodSQlr3sAY0IAnj3akZaSWUTencOzYMXr27El+fj4rV66kR48ejo5UUZRDyxBFWbxb3p8ucVqGdW0JgF6v595772XWrFm8/vrrvPbaa+rKrlJv2W8Mb1Uxcu2bbBet+cWzE6tGmQvcUaNG0b17d5YsWaLW7VYUZ3F4BZzeylch4yk3anl9aBsqKipITU2lvLycVatW0alTJ0dHqSgKYNr4EYUeEayhM98NaY2vpY3no48+yty5c/nnP//J3//+dwdHqSiOZb+CN/MdZFUJL9b8nVeHtyA62I8ePXrwzDPP8NJLL6l1uxXFWViu7lb4xfNGbhfu7BDBDU3Mk1+eeeYZevToQfv27R0cpKIoAJzZgcfJX/lUP4oBzUNokRDzx1OPPPII7du357HHHnNggIriHOwzpKHgMKZN0/jWmEZskzaYTmyntLQUPz8/3n77bVXsKooz2b8YcnfzVsUQYoK8GZHizaZNmwCYMGGCKnYVxYkYf/mYSnz4WZPO0wPaUFNTw5w5cwDo2LGjKnYVxcL2Ba+UGJdMpEJ6Md3zTpKLNjFy5Ejeeecdmx9aUZSrZDTAmjfI0cTzre5m/tpaw4C+fRgzZgxGo9HR0SmKUtu5bMS+BXxjSGdE5ySC/bwZMmQI9957L7t373Z0dIriVGxf8O6dj+fxTN7R30HSuT288OwzDB8+nMmTJ9v80IqiXKUds6DgIC9XjiQtpIgn/nIHPj4+/Pjjj2p2t6I4GeP6DzBJmK8ZwN2dExg4cCBr167liy++oG3bto4OT1Gcim3H8FaXUrPkWQ6Zklh9sJJff5jCXXfdxaxZs9BqtTY9tKIoV6mmDOPqN9khm7OtJIQjUx8nLCyMNWvW0KhRI0dHpyhKbWW5yG2z+N7Qg57tkxg5fDibNm1i9uzZ3HXXXY6OTlGcjk2v8FaseANtdQHvGO7h5G/LuO+++/j6669VsasoTsi04d94VhXwtvEeEgs3ExkZSWZmpip2FcUJVWV8ACYDi/yG0dSjkK1bt/Ltt9+qYldRLsFmV3hNOb/jvW0ac41pPPPIQ/xz/H1ER0er26KK4oxKz2Dc8BGLDF25tf9A7pr8CMXFxURHRzs6MkVRLlRRgMf2mSw0dmPcsFvo2aIhR48eJT4+3tGRKYrTstEVXknuVw8xfrmBGbs1tIkPo0GDBqrYVRQnlf/DMyw/XM24r44woFkIXl5eqthVFCd1duk/KC6r5omvD1J1ch+AKnYV5QrqVPAKIfoLIQ4KIY4IIZ670vY1Jbm88d0OPttUQatGcdcfpaIodXa1+WqqLmPzygUMm1tBVHCAPUJUFKWWq8pZo47q32bS+UtJcV6OupCkKHUkpJSX30AIT+AQ0AfIBrYAd0sp913qNRF+HrKwSvLE3ybywXv/UksZKvWeEGKblNLmS5NdS74mhXvJMyV6mrRowy/rMggLC7N1mIri1OyVr5ZjXVXONo8PlcaqUk5VerF82TJSU1PtEaaiOK265mtdrvB2AY5IKbOklDpgLnDb5V5QWCUZ+8gjqthVFPu76nw9UaQnqVESv25Yp4pdRbG/q8rZY7klnK7wZOWKlarYVZSrUJdJa3HAqVqPs4EbL9xICDEWGGt5WDPtk0/2TPvkk+uP0LlEAAWODsLK3PE9gfO9r0Q7Heea8vXw0eN7QkND7RCeXTnbZ8Ba1PuyPXvlK9QhZy/MV4z6PT179rBTeHbjTH9/a1Lvy/bqlK91KXgvdon2f8ZBSCmnAdMAhBBb7XU7yJ7c8X2543sC931fdaDy1UK9L9firu+rDq6YsypfXZd6X86jLkMasoGGtR7HA2dsE46iKNdJ5auiuBaVs4piB3UpeLcAyUKIRkIIL+AuYJFtw1IU5RqpfFUU16JyVlHs4IpDGqSUBiHEeGA54AnMkFLuvcLLplkjOCfkju/LHd8TuO/7uiyVr3+i3pdrcdf3dVnXkLPu+v9JvS/X4nLv64ptyRRFURRFURTFldlopTVFURRFURRFcQ6q4FUURVEURVHcmlUL3qtd0tQVCCEaCiHWCiH2CyH2CiGecHRM1iSE8BRC7BBCLHF0LNYihAgRQnwvhDhg+bt1c3RMzkjlq+tR+Vq/qZx1Le6Yr+C6OWu1gteyPOJUYADQErhbCNHSWvt3BCGELzAd6ATsAboCj13qfQkhXhBC/Ocaj5UhhKgWQqy75oCvzRPAfjsf09Y+BDZi7mXZGfd7f9fNHfPVQoN54k8DzMu1XjJfwSVz1l3zdRlwftmwow6MxWm5Y87Wg3OsO+YruOg51ppXeK96SVMXcDsQAoRKKUdKKcsw/2HjhBCThBBv1N5YSvmWlPKh6zjeeCllz/MPhBBhQogFQogKIcQJIcQ9l3qhMPunEKLQ8vOOsKzrLISIEEL8Yvl9iRDiVyHEzUKIeGAQsAroIoQoEEL8zyxGIUT5BT9GIcRHl4nlb0KIXCHEOSHEDCGE9yW287J8SzwuhJBCiLQLnvcWQnwqhDgrhCgSQiwWQsTVej5JCLFUCFFsOd7HQohQoCfwAbAWGCOlLLlUrPWYO+YrQHfABwiXUg7Dkq8AbpCzQzHn63+AQCHEcjfI2c8w5+t0KeVZzDl756XirOfcMWfd+Rzrjvnq0udYaxa8F1seMe4S27qKROCQlNIA5j8+0AHYBCzF/GG2pamADogGRgGfCCFaXWLbscBQoB3QFhgM/NXyXDnwABAJhAL/BBZj/pb2d0AP5AAPXmzHUsqA8z+WWKqA7y62rRCiH/AccAuQBDQGXr3Me9wA3AvkXuS5J4BulvfTACgBav8j8H9AHhALtMd8heg5IB+YCfQGXhVC+F/m+PWVO+Yr1MrZC/IVXD9n5wHPAybLzzxcP2d7YT4PzRRC7MC8AMO4yxy7PnPHnHXnc6w75qtrn2OllFb5AUYC/6n1+D7gI2vt/yriOA48A+wGKjDfLokGfgbKMF/NDK21/XeYPwjngHVAK8vvX8WcCHrMH+ZHgW3A8FqvzQYa1Hr8CvC15b+TMF/uHw2cxLzm9KTLxJ0BPFTrsb/l+Cm1fvcV8PYlXr8RGFvr8YPAbxfZzgMYYoltpuV3acASoKn5I3HZ/7+jgSwsLe0u8vwc4K1aj28Bcuvwd8sG0i743SfAO7UeDwIO1nq8HxhY6/G/gB8AA+a16DWWv98Ue38Onf3HWfLVcmxb5ezx2vla63Pmijn7miW2qPP5annO1XN2FuZi4EbL4ymWv1+io3PE2X6cJWdtmK/udI5113x16XOsNa/wOtPyiCOAPkAK5uLuZ+AFIALzh/HxWtv+DCRj/mBuB2YDSClfBt4CvsX8je02YLaUcn6t1y7DPJ7qcroDzTB/KCcLIVrU8T2kAEYp5aFav9sFXOrbZyvL85fcVgixG6jGvIrPLqCPEOI45ltjvYB36xDXaGCWtHz66xhHtBAivA77vtB04GYhRAMhhB/mb+A/13r+Q+AuIYSf5TbMAMyFe7aUcpM0XzU4gfkbrPJnzpSvYN2cnQf8gvkf4dr5Cq6bsy9hLi42Y8lXIcTXdYzLmXO2C1AgpTx/Ff47zFe32l3Dsd2dM+WsOsfWz3x16XOsNQteZ1oe8SMp5Vkp5WlgPbBJSrlDSlkDLMB8ywQAKeUMKWWZ5blXgHZCiOAL9jcd2C+lfP+C39fllsurUsoqKeUuzB/Muv5DHoD5G3Ft54DAOm5/Dgg4P8YIQErZFggC7gE+kFLGSymTMP+t1gBPXy4gIUQC5lsaX15F3Of/+1JxX84hzN/cTwOlQAvM35zPy8Sc/KWYTwZbgS+AU0KIZpZtvDDfklH+zJnyFaybs924eL6Ca+fsY7XzVUp575UCcoGc/Q04WCtfb8F8lTDkGo7t7pwpZ9U5tn7mq0ufY61W8Foq/fPLI+4H5skrL2lqK2dr/XfVRR4HwB8tQ94WQhwVQpRivlUD5m+p50VivnXUSwix0/Iz0PLcSqCnEEJ7mVhqj5upPH/sOijHnDi1BWE+GdRl+yCg/MJviVLKainlN8BzQoirvYpyP7BBSnnsMttcLA64dNyX8wmWCUiYbz/Nx/LtUwjhgfmzNt/yXAT/HTs1AZht+bYdDNT1m3W94WT5CtbL2QSgERfPV1A5W9c4wH45e5T/5mt7zOMIXWISjD05Wc6qc2z9zVeXPcdatQ+vlHKplDJFStlESvmmNfdtI/dgvo3SG/MfLcnye1Frm3wppZBStpVStrf8LAWQ5hmlu4EeNojtEKARQiTX+l074FL/wO3lz99sL7ctgBbzYHeklBlSysF1iOl+Lv/N81JxnJVSFtZh/xdqB3whpSyyXB34CHM3iQggDPPtvY+llDWW/c/EPN5op5SyE9AR8/vceA3HdnsumK9w5Zw9ifm26P/kK7hHzl5FvoJr5GwnKWUny9Wx2zF/Ydl1if3Xay6Ys/X+HOuG+eqy59j6vtJaIFADFAJ+mMcTXa2fgIFX3OoqSSkrMH+zek0I4S+EuBnzPxxfXeIls4CnhBBxQogGwETMtx4QQnQVQnS3tCjxFUI8i3mSwSbL80II4YP51gRCCB9xQZsTIcRNmGcE/8/MUfHndiezgAeFEC0t7UtePB/HxQhzWxQfy0Mvy7HP/2O4BbhfCBFs+Yb/KHBGSlkgpSwAjgGPCCE0QogQzGOfap8ouwDHpZQnLnV8xeWonEXlrOIyVL6i8tVZ1PeCdxbmAdengX2Yx5NdLVu2TnkU8MU8PuYb4JHzt7CEED2EEOW1tv0Mc6ux3zE38P7J8jsAb8ztVwoxv9eBwCAp5fkJD4mYb0Od/7ZaBRwtdWs1AAAgAElEQVS8IJbRwHzLN+4/CHMv33LLcZFSLgPewdyf74Tl5+Va2+8VQoyqtYuDluPFYb59UmWJB8xjiquBw5jboAwEhtV67XCgv+W5I5hnjv6t1vOjgE9R3InKWTOVs4orUPlqpvLVCQh5yYmASl0JIbKAW64w7uZK+1iBebLNVillutWCszEhxL2Y28w87+hYahNCRGEecN9BSlnt6HgU56JyVuWs4jpUvqp8tQZV8FqBEGIE5t51exwdi6IoV6ZyVlFch8pXxRpUwasoiqIoiqK4tfo+hldRFEVRFEVxc6rgVRRFURRFUdyaKnjtTAjxghDiP9f42gwhRLUQYp019m9plTJTCFEshNh8LTFZg6VtygHLIHhFcShb56i7EUJECyH2X9hmSVEcyV3yWAjxqRDipTpu6yuEWCyEOCeE+E4IcasQYq6tY3QVquC9Rpa+eE2vsM0kIcQbtX8npXxLSvnQdRx6vJSy56WevMr9d8e8Hnq8lLLLhU8KIe4SQhy0JE+eEOJLIcSFK9MghEi2/ONwydVWhBBPCiGyhBClQogzQogPhBAaS8w1wAzg2TrGrShX5Cw5KoT4WgiRY/nsHxJCXHTfQoiXLTH3vky8a4UQ+ZZ97RJC3HaZbUMsOZtn+Xml1nMJQojyC36kEGLi5d6YMPcZPSCEyD7/OynlWcwtksZe7rWKci2cKI9bCCHWWM6HR4QQw2o9N+qCXKq0xH3DFeK+4rlTSjlOSvl6HWO+HXP/33Ap5Ugp5SKgtRCibR1f79ZUwWtbtuwfaA2JmJtGV1zi+V+Am6WUwZhXZdMAb1xku6mYG1hfzmKgo5QyCGiNeYWXx2s9PwcYra4SKXZmjxz9B5Bk+ezfCrxx4YlQCNEE88kq5wr7egKItexrLPC1ECL2Ett+gLnZfxLmBvH3CSHGAEgpT0opA87/AG0AE/DDFY7/DOaepReaDfz1Cq9VFFuxaR5bLs78CCzBvALZ+dxLAZBSzr4gnx4FsoDtV9h1Xc6dVyMROCTNy1Cf9w3qyyigCl6bklLuACL/n737Do+6TPc//n4mbdI7IaF3KSIoqBw5BAIISNEDuIqySlF/gq6CrnpQ1y4W9Fh2WQ8WFEXUFdAVFFxqED2KKBa6lEBCei+TTH1+f8zgIlICZPKdcr+uiwtIhskHzc3c83yf7/0o96ksACilHjn6bk4p1d7zLvBGpdRhpVSpUuqBc/majX1+pdR04HVggOcd6aMnyJ/rOW3lKCfwm3faSqlrcZ97v+5UubTW+7XWlUf/GO4X187HfD4PqAAuPcO/shBnrTlqVGu9w3MVA0B7fnQ67mF/w32Fw3aa5/rpmBczjftYzzYnefhY4FmttUVrnQO8AUw7yWNvADZ5HndCSqkOwGTcDfzxvgE6KqXaneBzQnhVM9TxeUAG8ILW2qm1Xo97QeiPJ3n8jcDb+hRjsBr72qmUeuvo6rVSarBSKk8pdbfnqk3B0Texntfwh4BrPK/p0z1PsRHfXnhrNtLwet9qYNRpHjMQ6AYMBR5SSnVv4gy/e36t9RvArcD/ed6VPnyiP6jcxyVWATXABODFYz4XBzyG+4jF01JKXaeUqgZKca/wLjjuIbv47RnhQjQHr9eoUurvSikLsBv3Ku5nx3zuasCmtf7sZH/+uOdaqZRqwN1kbgS2nurhx/2610kedwOw6DRf+q/A/bhPavoNTxO+D6lfYRxv1rE6ycd+V0+eN32DcJ8yd+InO8PXzuO0BOJxn5w2HZivlEr0vIbPBT7wvKa/4Xn8LqC9OsF2xGAjDa/3NeZSy6Na63qt9Y+4z6lu6heNs35+rfVmz5aG1sA8IOeYTz8OvKG1zm3kcy3xXIrtivs4wqLjHlIDJDQ2mxBNxOs1qrWeCcQC/wksB6wASqkY3C9Ss87gucZ4nusK4HOtteskD10N/LdSKtazB3Ia7i0Ov6GU+k/c+/6WnuxrevYrhmqtPzpFNKlfYSRv1vFu3Ft57lFKhSmlLgcyOUE94X7z+MVpToU7o9fO49iBx7TWds+b5FrcTfzJHD2qOOhrUxpe71sDDFJKhZ3iMYXH/NoCxDTmiY/bKL+qqZ//WFrrI7hfQN/3fO0+wDDc+wTP9Ll+wX2m+N+P+1Qs7ks8QjQnr9XosTyXQjfjfvM4w/PhR4F3zvTIVM+L3SpghFJq3Ekedgfu1dhfcO8/fA/IO8HjbgSWaa1rT/QkSqlo4FngT6eJJfUrjOS1OtZa24GrcDfUhbhXZv/BievplFdLzuW106PsuD26p/t7xHp+DvraDDU6QKDTWtcopX7CvbKzvomf+13cN4s0l1D+vfdwMO6bYQ4rpcBdcCFKqR5a6wvP8LmO6g483yRJhWgkb9boSRz7vT8UaK2Umun5fSrwD6XUM1rrZ87wuX5Da10OXH/090qpucBvxg8qpSKBq4H/4uS64K71Lzy1Hg7EK6UKgUu11jmem3o64141E6LZebuOtdY/4V7VBUAp9RXHNbZKqctw7/U96dUSzv2180x1x31zerUXntuvyArvuQlXSpmP+RFyksd9ivvyo8/zbOwf7Pn19co9vkh59iU9yb832L+K+4W2j+fH/+L+e444yfPepDxzdpVSPYA5xzwXSqlWuO9+/dobfy8RtAytUaVUC+Ue7xejlApRSo0AJvHvF+ShuPcBHq2jfNzTDuaf4LnOU0qNUu5Zm2FKqcm49wpmez5/9Mac9p7fd1JKJXu+7ijcd2ofP2Xlv3Cv/Gw4xV9jO+4b445mvAn3dqQ+wNFLshfjflE91Nj/NkKcAcNfa5VSvT1fO0op9WcgHXjruIcdvVpS87sn+LdTvnYeX8dNIBM41RXgoCEN77nZgfuS4dEfU0/yOF8fTwaAUqo17v1AP3s+1AP4yvOxL4E9wM0Anju/C4/+8DymQWtd4nmu/1RKHXuJ9DLgZ6VUHe7/Hp/hvgHmqOuARcfczS5EUzC6RjXu7QtHp5A8B8zSWv8TQGtddlwdOYGKo9sLlHvo/P96nksBj+DeS1iCe0TZNVrro6OP2gCHgCOe31+Eu5ZrcE9WuF5rveO4fCe8m/zY+tVaO47LWA64PL93ev7I9bhfuIXwBqPrGNwTGQpw199QYPixr1dKKTPwB06wnUG5D8FYBad/7eT3dXyuJvH7G8SDkjrF1AzRhJRSB4ChZ7pX77jn+BcwANiqtR7SZOH+/fyTgZ5a6zlN/dyn+boRuC+FDtJan2jGpxBe5w81epqv/SBQorVu1hc3z5WbbKCv1rqhOb+2EMeTOv7Nc40F/qi1/sO5J/N/0vA2E6XUBGCP1nq70VmEEL8nNSqE/5M6FicjDa8QQgghhAhosodXCCGEEEIENGl4hRBCCCFEQPPKHN6UlBTdvn17bzx1s9FaU1pdS2Gti9QoEy0bDkJUEsS3Njqa8BOlpaUcOnSI2NhYampqSrXWqUZnOhFfr9fqyjLiLIcpD08nMTkNz+xKIZpUQ0MDe/fuxeVy4XQ6pV5PpGwfDoeDXY50Es0mUmLCMUdEGJNFBDWtNfv376eqqgqgUfXqlYa3ffv2bN16quPdfV9dfT0LVn3Ny1ss3N/PxS3bJ8Pw++GyO4yOJvzAK6+8wsyZMxkxYgQfffQRUVFRPjuf1NfrdcVbTzE252ne6/oo11w7FZNJLkyJprV9+3aGDRtGYmIi69at4/zzz5d6PQE9/xJynYkMyv8Tf+wdyZT/7EKnNm0MySKCV319PRMmTOD777/nb3/7G7fffnuj6lVeOU7CbrdT2eA+or6lqdz9QVndFY1w+PBhZs+ezZgxY/j444+JjIw0OpLfcjidhFrc4ynDE1pKsyuanNaa2267DZPJRHZ2Nr169TI6ku+qK6HWFA9AVKgmIuxUp/gK4R1vvPEGq1ev5tVXX+W2225r9J+To4VPwuZwUFbvQgFputT9QWl4RSO0bduWjRs3cuGFFxIeHm50HL/mcDiIsJZh1aHEJrYwOo4IQEop3n//ferq6ujcubPRcXyX0wGWcqpj3Q1vdLgiLFRaCNH8Zs6cSZ8+fRg4cOAZ/bnTLpcopboppX445ke1UmrWWSf1EzabjfJ6TWKkiXh7mfuDCW2NDSV82ty5c1m8eDEAl156qSHNbqDVq8PpJNpeRjGJJMbKSrloOt988w0333wzDoeD9PR0w5pdv6nZ+nIUmgriAIgJNxEqDa9oJtXV1Vx77bXk5ORgMpnOuNmFRjS8Wus9Wus+Wus+uI+qtAAfnXlc/1JTV0d5gyY5KoQ4ZzmEREC0rDCJ39Na89BDD/HAAw+wbt06o7MEVL06HA5i7eUUk0Sk3BwjmsjmzZsZPnw4GzZsoKyszNAsflOzde6tReW/NryK0JAQIxOJIFFZWcnw4cNZtmwZP//881k/z5luiBsK7Nda++yG/qZSV19PucVFklkRYy2BhDYg+wfFcbTWzJkzh8cff5xp06bx+uuvGx3pWH5fr06XiwRnGRWmJMyyPUQ0gY0bNzJy5EjS09PJzs4mLS3N6EjH8t2a9TS8pdqzpSFMESINr/CysrIyhg4dyrZt21i6dCljx4496+c60w7uWuC9s/5qfqTWYqG83kWCGSLqCyGhndGRhI/RWnP33XfzzDPPMGPGDF577TVfewHw+3q122wkucqpDE0m0mw2Oo7wc2vXruWKK66gXbt2ZGdn06pVK6MjHc93a7bOfS9LkSuWyDBFiElWeIV3lZSUkJWVxY4dO/jnP//JlVdeeU7P1+iGVykVDowDPjzJ529RSm1VSm0tKSk5p1BG01pTUFmPU0NSpImwmiOyf1f8jlKK6Oho7rzzTubPn+9TEwQCpV4dtaWYsVEXlkyY3BEuzlFkZCR9+/Zl48aNtGzZ0ug4v3GqmvWJevU0vIWOOGIj3P/W+dgbfBFgwsLCSEhIYOXKlYwaNeqcn+9MdpyPAr7XWhed6JNa61eBVwH69eunzzmZgWx2O2X1npFk4Q2YGiogUVZ4hZvL5eLw4cO0b9+exx57DMAXD0MIiHp1VOYBYAlPltUkcdYOHDhAx44dueyyy9i8ebMv1iucomZ9ol7rStAqhGJHNLHhJhRITQqvKCgoID4+noSEBDZu3Nhk9XomS1KT8NVLLU3M6pnQANDW5Hk3LSu8AnA6nUydOpV+/fpRVFSEUspXXzwDol4bynIBsJlTCJcVXnEWPvzwQ7p168b7778P+OSb06N8u2brSnCYE6lzKKLDTZgjInz5v6XwU4cOHWLgwIHceOONQNPWa6MaXqVUFDAcWN5kX9mH2ex2yixOAFqpoyPJ2hsXSPgEh8PBH//4R95++23uvPNOX7vZ5VeBVK8N5fkAuKJkhVecuSVLlnDttddyySWXcMUVVxgd56T8ombrSnGEJ1Br00SHQZQcqCOa2IEDB8jMzKSsrIw///nPTf78jdrSoLW2AMlN/tV9lM1up7xeExOuSHZ5TlmTFd6gZrfbmTRpEsuWLeOZZ57h3nvvNTrSSQVSvTqrC3BphYpOlhVecUYWLVrE1KlTyczMZMWKFcTExBgd6aT8ombrSrCGx1Nr00SFQZTcRCqa0C+//EJWVhYWi4X169dz4YUXNvnX8J27bHyIpb6e8gZNSlQI0dYSCIuC6BSjYwkDzZs3j2XLlvHCCy/4dLMbaELqiiklnoSYSJ+6KVD4tl27djF16lSGDRvGp59+6tPNrt+oK6EhNI56hyY6TBEhYwJFE3G5XEycOBGr1cqGDRu80uyCHC18QvVWK+UNmvQYE1HWYvfqruxVCmqzZ8+mR48eXHXVVUZHCRoOp5NIaykFOom0OFlNEo3XvXt3li9fzsiRIzHLSmTTqCulJup8AKJCkSsuosmYTCbeeecdQkND6dGjh/e+jtee2Y/V1tVRZnGSYFaYLYWynSFIWSwWZs2aRWVlJZGRkdLsNjO7w0GMo4winUhqXJTRcYQf+Otf/8oXX3wBwFVXXSXNblOxWcBWQ5XyHDoRLiu84txt27aNuXPnorWmd+/eXm12QRreEyqqsmBzQnKkIrQmTw6dCEK1tbVcccUVv3kBFc3LbrcT7yij1JRMbJTcICNO7emnn+aOO+5g4cKFRkcJPLXuSWkVKgHwHCscKheIxdn79ttvycrKYsGCBVRUVDTL15SG9zh2h4OiWjsAGeH1mGw1ssIbZKqrqxkxYgSbN29m8eLF53SUoTh7jvoaYnQdlaYkzBERRscRPkprzWOPPcacOXO47rrreO2114yOFHhqiwEoP9rwhinCpOEVZ+mrr75i2LBhJCYmsmnTJpKSkprl60rDexy73U65xX3oRFvlLnI5dCJ4VFRUMHz4cLZs2cIHH3zApEmTjI4UtHSNeyRZVahMaBAnprXmwQcf5OGHH2bKlCm8/fbbsvLoDZ4V3hLcDW90uDS84uxs2rSJyy+/nLS0NDZt2kS7ds3XX0nDexzrMaestTa5j1KUFd7gUVtbS2VlJcuWLWPChAlGxwlquuoIAHVyypo4Ca01Bw4c4JZbbuGNN96Qo269xdPwFmlPwxumpCbFWcnPz6dDhw5kZ2fTunXrZv3a8hbtOEdn8JpDFanas69E9vAGvIqKCuLj42nTpg3bt28nTFYUDacrDgNgNafKapL4Da01FRUVJCUl8c4772AymWRsnTfVFqOViSJHDGEmJ+EhyEq6OCNlZWUkJydz7bXXMmHCBENeY+VfiONYrVbK6l0kR5qIsZdCeCxEJhodS3hRQUEBAwcO5M477wSQZtdHODwNrzM6VbY0iF+5XC5mzJjBgAEDqKqqIjQ0VJpdb6stwhWZTK3dREyEifDwcDlWWDTaihUraN++PevXrweMe42VfyWOU2+1UtGgSYpURNcXQmJ7mcEbwI4cOcLgwYM5dOgQEydONDqOOIajIo9inUBsZLisJgkAnE4nN910EwsWLGDChAnExcUZHSk41BbjMCdTZ9fEhpswy0gy0UjLly9n/PjxdO/enb59+xqaRRre49RZLJTVu0g0K8Jr8iC5o9GRhJccOnSIQYMGUVBQwOeff05mZqbRkcQxTDUFHNEpxEeYZEuDwOFwcOONN/Lmm2/yyCOP8OSTT8oqY3OpLcRuTqLOLjN4ReN98MEH/OEPf6B///6sWbOGxERjr5ZLw3uc8hoLdTZNaqR2z+BNkoY3EDkcDkaOHEl5eTlr167lsssuMzqSOI65vogjOpm0uAhpbAQPPfQQ7777LnPnzuXhhx+W74nmVFuMNTyROpv7WOFIGRMoTuPbb7/luuuu47LLLuPzzz8nPj7e6Ehy09rxcivqAWgXUoZy2aXhDVChoaG8/PLLJCcne+3cbnH2XE4nsfYS8nUfuiZGGx1H+IBZs2bRuXNnpk2bZnSU4OJyuRvelgnU2l1EhYXICq84rX79+vHyyy8zZcoUoqN9499wWeE9ht3hoLTOAUA75R7DIg1vYNm5cyeLFy8GYPjw4dLs+ih7TTHh2kaRSiYlVk5ZC1YNDQ089dRT2Gw2WrRoIc2uERoqwWXHEhpPrU0TFYrcRCpO6s033+SXX35BKcVtt93mM80uSMP7G3a7nSqrBiDDVej+oDS8AePnn39m8ODB3HfffdTW1hodR5yCozwHgKrQVKKjoowNIwxhsVgYN24c999/Pxs3bjQ6TvDyzOCtJBat3ccKywqvOJGXXnqJadOm8dxzzxkd5YSk4T2G3eGg2tPwttAlEBoJMS0NTiWawrZt2xgyZAjh4eFs2LCBmJgYoyOJU3CVu0eS1UWkyH7BIFRXV8eYMWNYu3YtCxcu5PLLLzc6UvDyNLyl2r0HMyZcydQU8Tvz5s1j1qxZTJgwgb/+9a9GxzkhaXiPYXc4qGxwER2miGsocK/uynxHv7dlyxaysrKIjo4mOzubrl27Gh1JnIauzAWgwZxKuKwmBZXq6mpGjhxJdnY277zzDlOnTjU6UnCrLQag2NPwxptlaor4rSeeeIJ7772Xa6+9lvfff99n/82Wbu4YVpuNUouLlCgTUfUFkNTB6EiiCXzxxRckJSWxadMmOnXqZHQc0Qi6Kpd6HU6IOV72CwaZnJwcdu3axfvvv8/1119vdBzhWeEtcLmPFY6LUHI4j/iVzWZj9erV/PGPf2Tx4sU+vfrfqGRKqQTgdaAXoIFpWuv/82YwI9TU1VFs0XSMg/DqPOg51uhI4hw0NDRgNpu5++67ufnmm4NmSH0g1KuzIpd8nUxCpEka3iBxtF579+7NgQMHgqZewcdrtrYIHWqmxBYB2IgPV7LCK9BaY7VaMZvNrF69msjISEJCQoyOdUqNXeF9CVittT4PuADY5b1IxqmoqaPM4qJrZIWMJPNza9eupVOnTvzwww8AQfXiSSDUa/UR96ETcvk0KJSUlHDppZfy/PPPA0FXr+DLNVtThCsqlSobhJkg1hxCqI83NsK7tNbMnj2bkSNH0tDQQExMjM83u9CIhlcpFQcMAt4A0FrbtNaV3g5mhIMlNWigS6h7z5I0vP5p1apVjBkzhuTkZDIyMoyO06wCpV4j6ovI18lkxJvlgIEAV1hYyJAhQ9izZw/nn3++0XGanc/XbG0RzqgUqq2aeHMIkWaz0YmEgVwuF7fddhsvvfQSffr0IcKPbipuzApvR6AEeFMptU0p9bpS6neD1ZRStyiltiqltpaUlDR5UG9zOp0crmgAoD0yksxfffLJJ1x11VX06NGDDRs20KJFC6MjNTf/r1eHlWh7Bfk6hYwEeXENZEeOHGHw4MEcPHiQzz77LFinMZy2Zg2t19piHOZkqhpcJJhNRMgWo6DldDq55ZZbeOWVV7j33nt54YUX/GpBojENbyhwIfCK1rovUAf89/EP0lq/qrXup7Xul5qa2sQxvc9mt1NqcQGQQTGEREBcK4NTiTOxefNmJkyYwAUXXMC6detITk42OpIR/L5eXZV5ABSQTIs4OXQiUDU0NDBkyBCOHDnC6tWrGTJkiNGRjHLamjW0XmuLsEUkUWXVxEUoWeENYvfeey9vvPEGDz74IE8//bRfNbvQuJvW8oA8rfU3nt8v5QQvoP7OarNRYnERFaZIdhS5JzTISDK/0r9/f+655x7uu+8+nzi32yB+X6/OikOYgKqwFsRGScMbqMxmM/fddx89evRgwIABRscxku/WrMMG9eVYwxOotmq6pSBzsYPYTTfdREZGBnfffbfRUc7KaTs6rXUhkKuU6ub50FBgp1dTGcBqs1Fc5x5JFl1fKNsZ/MhHH31EaWkpERERzJ07N5ib3YCoV1fFIQAaIlKIktWkgLNv3z42bNgAwPTp04O92fXtmq1zb5+oNsVRZ9fEhkFUpLwJDSY2m41FixahtaZ79+5+2+xC46c0/Al4Vyn1E9AHmOu9SMaosVgornPRMkoTXpsnDa+feP3115kwYQKPPfaY0VF8iV/Xq6s8BxcKe5QcOhFodu/ezaBBg5gyZQpWq9XoOL7EN2vWM4O3yOE+mTLOLMcKBxOr1crEiROZMmUKX375pdFxzlmj5v1orX8A+nk5i6FKK6spr9eMTq9CVTRIw+sHXnnlFWbOnMnIkSN55plnjI7jM/y9XnVFDoU6ibiocMJlJFnA2L59O8OGDQPg008/9au7u73NZ2u2xn0Dd6HDfQ9dQoTMxQ4W9fX1jB8/ntWrV/P3v/+dgQMHGh3pnMkmVY99RdVo4LyQfPcHUrud8vHCWC+99BIzZ85k7NixfPzxx0TKZbaA4ao4TK5OJdFsktWkAPHjjz8yZMgQTCYTGzdupFevXkZHEo1RUwBAgSsR8BwrLA1vwKurq2Ps2LF8/vnnvP7668yYMcPoSE1CGl7cozZyyt0jyTqa3AVOSlcDE4lTsVgszJ8/n/Hjx7N06VJZKQowIVW55OoWJJrlCNNA8eabbxIZGcmmTZs477zzjI4jGqumAK1MFDjd90XEhyMHwQSBLVu2sHnzZhYtWsT06dONjtNk5DsXaLDZKKp1ooB2FIE5HqJ9a1STcHO5XERFRfHFF1+QlJQkDVGgcVgxW0vJdaWSFhsmJzr5OZfLhclk4vnnn+e+++4jPT3d6EjiTFQXoKNTqbQplILEaKnJQHa0XocMGcL+/ftp1SqwRrPKCi/uCQ0FdS6So0zENeRDSjfws/lygU5rzV/+8hduvPFGnE4naWlp0uwGospcFJpcnUqb5Bij04hz8MUXX9C3b19yc3MJCQmRZtcf1RTgjE7zzOA1ES1bxwJWRUUFAwcOZNmyZQAB1+yCNLyAZyRZrYsWUQpzzSHZzuBjtNbcd999PPHEE5jNctRsIHOVHwSg0NSCjKQ4g9OIs7VhwwZGjhyJ1WolRFYE/VdNAY6oFlQ1aBIiFNFRUUYnEl5QWlpKVlYW3333XUBPxpGGF6iqqaGozkX7qAZCLCWQ0sXoSMJDa83s2bOZN28eM2fOZMGCBZjkQJCA5Sg7AEBNRBpxMbLC64/+9a9/ccUVV9ChQweys7PJyMgwOpI4W9X52MwpVFldxEUomYsdgIqLi8nKymL37t3885//ZOzYsUZH8hrpHICckiqsTugV7pnQICu8PuOee+7hpZdeYtasWfztb3+TZjfA6fKD2AjFFZmEWW5G9DsbN25k7NixdOvWjQ0bNpCWlmZ0JHG27PXQUElDeBLVnmOFpeENLFVVVQwePJh9+/axcuVKRo4caXQkr5Kb1oBfimoA6KI8ExpkJJnPGDNmDJGRkTz22GOylSEI6IpDFOgUEiJDZSSZH+rduzfXXXcdzz//PElJSUbHEefCM5KsNjTx14ZX7psILHFxcfzXf/0XI0aMYNCgQUbH8bqgb3jtDgd5lXYAOoQUgSkMEtoZnCq4OZ1O1q9fz/Dhwxk8eDCDBw82OpJoLpWHOOxKJUlOdPIrGzZsYMCAASQlJfHmm28aHUc0hWp3w1ui49BAfISJMNmPHRAOHTqExWKhe/fuPI3SYAgAACAASURBVPnkk0bHaTZBf324wWqloNZJdJgi1VEIyZ0gJOjfBxjGbrczefJkLr/8crZt22Z0HNHMTNV55OoWJEeHyLxPP/Huu+8ybNgwnnjiCaOjiKbkWeHNPzqDNwJCpSb93v79+xk0aBATJkzA6XQaHadZBf13r7vhdZEWYyKq5jBknG90pKBls9mYNGkSy5cv59lnn6Vv375GRxLNqaGacFsVuTqVjHgZf+QP3nzzTaZPn87gwYOZM2eO0XFEU/I0vLk2982jcsqa/9u7dy9ZWVnU19fz8ccfB90ElaBf4a2rr6eozkWrKCeh1YflhjWDWK1WJk6cyPLly3nxxRe55557jI4kmlvlIQBydQuZwesHFixYwLRp0xg+fDgrV64kOjra6EiiKVUXoMOiKLa7b1SLjzDJoRN+bOfOnWRmZmKz2di4cWNQLigF/QpvcWUN1VZNz/AilHZKw2uQTz/9lBUrVvDKK69w6623Gh1HGEBX5KCAXJ1KO2l4fVp5eTkPPPAAo0ePZunSpZjl7v3AU1OAjmlJtc19s3BSpGwz8mePPvoo4J6k0qNHD4PTGCPov3v3FVUBcJ4p1/2BtOD8RjDa+PHj+emnnzj/fNlSEqycZQcJBSrCWpAYJw2vL0tKSuLLL7+kQ4cOAT2oPqjVFOCKSaOqwUVMuCIuRg6d8GcLFy6kqKiIjh07Gh3FMEG9pUFrTU6pBYDOpgJQIbLC24xqamoYO3YsX3/9NYA0u0HOVX4QC2ZM5jiZ0OCj5s6dy2OPPQZAt27dpNkNZNX57lPWrJr4CJOcsuaHtmzZwujRo6mpqSE6Ojqom10I8obXarNRXOcAoJXzCCR3hlAZdt8cqqqqGDFiBKtWrSI3N9foOMIXlB/kCC1IigqRhtfHaK155JFHeOCBB9i7dy8ul8voSMKbtIaaQhyRqb/O4I2Ug2D8yldffcWwYcPYvXs3lZWVRsfxCUHf8JZY3MUcW3NAtjM0k4qKCoYPH87WrVv5xz/+wdVXX210JOEDVEUO+10tSZQZvD5Fa83999/Po48+ytSpU1m0aJGceBjo6ivAaaU+IvnXY4WjI2Vyir/Izs7m8ssvJz09nezsbNq0aWN0JJ/QqD28SqkcoAZwAg6tdT9vhmouDTYbxXUu2kZaCa3JgxZTjI4U8CoqKsjKymLnzp0sX76cMWPGGB0p4PhlvTodhFbncsB1PilRIXI3uA+57777mDdvHrfeeivz58+XZtcLfK5mq/MBsITGU23VxEfIm1B/sWHDBkaPHk2HDh1Yu3Yt6enpRkfyGWfyL9cQrXUfwwuxCdU3NFBc56JvxBH3B2SF1+tiYmLo2bMnn3zyiTS73uVf9VqVi9IOcnQa6Qlyx78v6dWrF7NmzeLvf/+7NLve5Ts1W1MIQLErFocL4s2KcJnB6xfatm3L4MGD2bBhgzS7xwnqKQ1lVbVUWTU9Qjx7SFtIw+stBQUFKKVo2bIlixcvNjqO8DXlBwDIcbWkf5LMczWay+Xi559/5oILLuCGG27ghhtuMDqSaE417hXePFssAHERJrlB0cf98MMPXHDBBXTq1InPPvvM6Dg+qbFv1zXwL6XUd0qpW070AKXULUqprUqprSUlJU2X0IsOlNQA0Jk8dFg0JLQzOFFgys3NZdCgQVx11VVorY2OEwz8r16PNry6pczgNZjT6WTatGlccskl7Nu3z+g4weKUNdvs9Vqdj0Zx2Op+8xkfgczg9WHLli2jf//+vPjii0ZH8WmNbXgv01pfCIwCblNKDTr+AVrrV7XW/bTW/VJTU5s0pLfklLlHkrVx5qJanAdyua7J5eTkkJmZSXFxMS+88AJKKaMjBQO/q1ddth+riqDclEBGojS8RnE4HNxwww0sWrSIBx54gM6dOxsdKVicsmabvV6rciGmBeU29176BLPsq/dV7733Htdccw0XX3wx06dPNzqOT2tUh6e1zvf8XAx8BFzszVDNwe5wkFdpBTTJ9YdkO4MX7N+/n8zMTCoqKli7di0DBgwwOlJQ8Md6dZXuI1+1JCUqhEg5tcsQdrudSZMmsWTJEp566in+8pe/GB0paPhczVYdQce1psrmviLXMl5q0hctWrSIyZMnM3DgQD7//HPi4uKMjuTTTtvwKqWilVKxR38NXA5s93Ywb7PabOTXuuhiribMVglpPY2OFHBmzJhBXV0dGzZsoH///kbHCQp+W6/lBzik02gRZZK7wQ3y1ltvsXTpUv7nf/6H//7v/zY6TtDwyZqtysMZ05LqBhfmUEWinLLmc/Lz87n11lvJysris88+IyZGroydTmM25aQBH3kuRYcCS7TWq72aqhk0WK0cqXExNOoIWJAVXi9YtGgR5eXl9Owpbyaakf/Vq9OBqeowex09SImWhtco06dPp1OnTmRlZRkdJdj4Vs1qDVV5ONplUl3iHkkWJVddfE5GRgZr167loosuwiz/fxrltCu8WusDWusLPD96aq2fbI5g3lZRXUNhrYveoTKhoSn9+OOP/L//9/9wOBykp6dLs9vM/LJeq/NQLjv7XS1Jjw2Tm2OakcViYcqUKRw8eBCTySTNrgF8rmbrK8BRjz0qjSqrJjZCESWHTviMF198kXfffReAyy67TJrdMxC0d2ntyCvHpaGrOoyObgExxt+44+++//77Xy+vFBYWGh1H+ItjRpK1S5IX1uZSW1vL6NGjefvtt9myZYvRcYSvqHIvAjVEpFJllRVeX/L0008ze/ZsVqxYIROPzkLQNry7C6sBaOc4hEq/wOA0/u+bb74hKyuL2NhYNm3aROvWrY2OJPxF2X4AcnQaHVvITRfNobq6mpEjR/LFF1+wePFirrnmGqMjCV9R5T6IqTY8maoGF/ERJsLk0AlDaa157LHHmDNnDpMmTWLx4sUy8egsBGXD63A6ySm3EqlsxFsOgTS85+TLL79k+PDhJCcnk52dTYcOHYyOJPxJ+UFsKpwSlUjHVGl4va2yspLhw4fzzTff8N5773HdddcZHUn4kqo8AEp0PFannLJmNK01f/nLX3j44Ye54YYbeOeddwiVbV9nJSgb3garlfxaF5dGHUFpJ6T3NjqSXzOZTHTt2pVNmzbRrp0c3iHOjC7bR4GpJUmRJqKj5NKptymlCA8PZ+nSpVx99dVGxxG+pjoPQsLJs7prMS5cyQxeAx1dyb3pppt48803CZH/F2ctKN8mNFitFNa6GBWeA/XICu9ZOnz4MG3btmXAgAF8++23colFnBVd9u+RZJEREUbHCVilpaVERUURHx/Ppk2bpF7FiVXlQVwrimtsAMSbTbLCawCtNXl5ebRp04bHH38cQGr2HAXlCm9lTR0ldS56mXLQ5ng5UvgsrFq1iq5du/LOO+8AUojiLDkdqMoc9jpakhJlkkMnvKSwsJDMzEwmTZoESL2KU6jKQ8e1oqzODkByTBgmOYW0WblcLmbOnMmFF15Ifn4+Simp2SYQlN/Fu46Uo4FOzoPuG9bkG+mMfPLJJ1x11VX07NmTK664wug4wp9V5KBcdnY7M2gZJyPJvOHIkSNkZmZy6NAhZs+ebXQc4euqjuCKa0VFgwuAjHiZnNKcnE4nN998M//7v//LTTfdRHp6utGRAkZQNrx7CqsJxUGaTW5YO1NLly5lwoQJ9O3bl3Xr1pGcnGx0JOHPSvcCsM/VivbJcppTUzt8+DCZmZkUFBTw+eefM3jwYKMjCV/mdEBNPs6YdCrq3aesJcfIVZfm4nA4mDJlCgsXLuThhx9m7ty5srLbhIJuOcXpdJJT3kAXlU+Iyw7pfYyO5Df279/PpEmTuOSSS/jss8/k3G5x7kr3ALBfZ9BJRpI1Ka01V199NaWlpaxZs4ZLLrnE6EjC19UWgnbhiG5JRYMm0ayIjpI3os3lhRdeYPHixTzxxBM88MADRscJOEHX8FptNgpqnVxizgGNrPCegU6dOvHee+8xcuRIObdbNI3SX6gKSaRORdG1ZYLRaQKKUorXX38dm83GRRddZHQc4Q88I8lsUWlUNLhIMCu5kbQZ3X777bRt21bmYntJ0G1paLDZKKx1cWFoDjosGpI6GR3J573xxhtkZ2cDMHHiRGl2RZPRJbs5rDJIiVTEx8hKUlPYtWsXTz31FFprzj//fGl2ReN5Gt4GcyoV9ZpEs9xI6m0NDQ3cc889VFZWEhkZKc2uFwVdw1tdZ6Gw1kUv9qNb9ga5+/SU5s+fz0033cT8+fONjiICjdZQ+gt7Xa1IiwmRF9Ym8PPPP5OZmclLL71ESUmJ0XGEv/E0vBXEU23VJEYqIsLDDQ4VuOrr67nyyit57rnnWLdundFxAl7QdXs/HiolRNtp6ziIqU1/o+P4tBdeeIHbb7+dK6+88tfxY0I0mdoilLWa7bZ00mNMmOWF9Zxs27aNIUOGEBYWRnZ2Ni1atDA6kvA3VXkQEc+hGo0GEuSUNa+pq6tj9OjRrFmzhoULFzJhwgSjIwW8oGt4fz5SSXd1iFDtgNb9jI7js55++mnuuusuJk6cyIcffkiE7OMSTc0zoWGvK4P2KVFyN/I5+Pbbb8nKyiI6OppNmzbRrVs3oyMJf1SVC/Gtya+0AJAUGSINrxfU1NQwatQosrOzefvtt5k6darRkYJCUDW8DqeTX0rq6R+63/2BVtLwnojWmu3bt3Pdddfx3nvvESb/4Alv8DS8+10ZdGkRa3AY/5aTk0NKSgrZ2dl06iT3JYizVHEIEtuRX9UAQMu4CDl0wgsqKyspKChgyZIlTJ482eg4QSOopjTUNzSQW+3khvB9uMxpmOJbGR3Jp2itqa6uJj4+nrfeegullJzbLbynZC9WUySFJNGztcxzPhuVlZUkJCRw9dVXM27cOLkSI86e1lB5GGeHQZQVOwFonRRtcKjAUl1dTUxMDG3atGH79u1Sr80sqN661VosHKl20Yt90Fr27x5La829995L//79KS8vJzQ0VJpd4VW6dC9HTBlEhylaJsgL65lat24dHTp0YM2aNQDy4inOjaUM7HU4YlpR2aCJDFW0iJeJPE2ltLSUzMxM7rjjDkDq1QiNbniVUiFKqW1KqZXeDORNewsqiHJWk+YskhvWjqG1ZtasWTz33HNcfvnlJCTIPFR/5xf1WrKHAzqDtGgTZvnH/4x8/vnnjBkzhtatW9O7d2+j44hz5BP1WnEIAHtMBhX17hm8MXLoRJMoKipiyJAh7N69m7FjxxodJ2idyQrvncAubwVpDtvzKuhj8uzflRvWAHC5XMyYMYOXX36Zu+66i7/+9a+yZysw+Ha9WmtQNfnscmbQIiZERh+dgZUrVzJu3DjOO+88NmzYQFpamtGRxLkzvl4rcwCwRqVT0eAi0azkjWgTyM/PZ/DgwRw4cIBPP/2UESNGGB0paDWqs1FKtQZGA697N4537Sqspa9pH1qZIKOv0XF8wpNPPsmCBQuYM2cOzz33nNwpHwD8ol5LfwFguy2d1vHhsn2mkX766SfGjx9P7969WbduHSkpKUZHEufIZ+q18jAAtREt3McKR5rkjeg5cjqdjBo1iry8PFavXk1WVpbRkYJaY29aexG4FzjprdRKqVuAWwDatm177smamM1u52CFnT+G7ceZfB6h4bJnEODWW28lOTmZGTNmSLMbOHy/Xkv2ALBPt2JCquwTbKzzzz+fZ599lqlTpxIfH290HNE0fKNeKw5BZBJVdhM1Vi0zeJtASEgIzz77LHFxcQwYMMDoOEHvtCu8SqkxQLHW+rtTPU5r/arWup/Wul9qamqTBWwqdRYLh6sc7hPWWgX3UZt2u53nn38em81GamoqM2fOlGY3QPhNvRbvwKHCyNEt6ZAibz5P54MPPmDv3r0opZg1a5Y0uwHCp+q18hAktCWvvBYNJJlNhIUG1SCnJrN//36WLFkCwIgRI6TZ9RGN2dJwGTBOKZUDvA9kKaUWezWVFxworqKFPZ9oXUdou0uMjmMYm83GNddcw5///GdWrVpldBzR9PyjXot3URTeBichdE1PNDqNT1u4cCGTJk3i8ccfNzqKaHq+U6+eGbwFnhm8KdEyqeds7Nmzh0GDBjFr1iyqqqqMjiOOcdqGV2s9R2vdWmvdHrgWWK+19rtJyVsPlnKxaTcAqt1/GJzGGA0NDYwfP56PPvqIl19+mSuvvNLoSKKJ+U29Fu3kAK2Jj1Akx8qd4CfzyiuvMH36dC6//HJeffVVo+OIJuYz9epyuU9ZS2hHYWU9ABmJkc0ew9/t2LGDzMxMHA4H69evlysxPiZobsf/+UgVl5h244hMhqSORsdpdhaLhSuvvJJPP/2UBQsW8Kc//cnoSCJY1VdATT47na1lJNkpvPTSS8ycOZMxY8bw8ccfExkpDYjwktpCcNpwxrehtN596ES7ZDn98Ez8+OOPDB48GJPJRHZ2Nr169TI6kjjOGTW8WuuNWusx3grjLXa7nf1lVi4N2YOz9aUQhPtVDx48yNatW1m4cCG33HKL0XFEM/DZei3aCcA2a2vSZCTZCTkcDpYtW8b48eNZtmwZZrPZ6EjCywyt119n8Lai3OIiKkyRmiAN75lYt24dZrOZ7OxszjvvPKPjiBMIih3pNRYL9qpCWoaU4uww0Og4zcpmsxEeHk7Pnj3Zv3+/HCohjFfsbnh/sLchK9EsN0we52jNfvbZZ0RERBAmd8oLb6t0N7y26HTKG0pIilREyZusRjlar3fddRfTpk2T11gfFhRbGn4pqKCX0z0GKSSIGt7KykoyMzN59tlnAaQQhW8o2oE9LJZCkugkI8l+pbXmoYceYtiwYVgsFmJiYqTZFc3Ds8Lb4Dl0Islsku+9Rti8eTNdunThhx9+AOQ11tcFRcP7reeGNXtoNKT1NDpOsygvL2fYsGF89913dO3a1eg4Qvxb8U6Kze0BRZeWclMHuJvdOXPm8Pjjj9OlSxciZF+zaE6VhyCmJQ0OTXm9i8RIJVuNTmPjxo2MGDGCyMhIfHEUq/i9oGh4tx+p4mLTbhwZ/cAU+GNWSkpKyMrK4ueff+ajjz7iqquuMjqSEG5aQ/EuDtAak0IaXtzN7l133cUzzzzDjBkzeO2112QclGheFTmQ2J6SqlosdkiKNMmhE6ewZs0arrjiCtq3b8/GjRtp1aqV0ZFEIwR8w2u12agoL6OzKR9T+8uMjuN1NpuNoUOHsmfPHlasWMHo0aONjiTEv1XlgbWan+ytaBltIjZKJg88/PDDvPjii9x5553Mnz8fkyng/1kWvqb8ACR3Iq+8DoCUqBA5dOIkvvnmG8aOHUvXrl3ZuHEjLVu2NDqSaKSA/46uq68nrWYXhEBYx/80Oo7XhYeHM2vWLNq3by/ndgvf47lhbWtDazKSTETKpXuuv/56wsPDeeCBB+QGPtH8bHVQUwBJHcj3zOBNT5A3oifTp08f/vSnPzFnzhySkpKMjiPOQMAvJeSUVNNX78CuIjC1DtwjhXNzc9mwYQMA06ZNk2ZX+KaiHYC74W2faA7aS/dOp5MlS5agtaZbt248+OCD0uwKY5QfdP+c1JF8zylrrRPkMJjjffbZZ5SWlhIREcG8efOk2fVDAd/wbsspZaBpO+XJfSA0MFeTcnJyGDRoENdddx319fVGxxHi5Iq2Ux+ZTg1RnNcyOOd8OhwOJk+ezPXXX8+6deuMjiOCXfl+AOzx7SmqdRJqgrYpwVmbJ7NkyRLGjh3Lgw8+aHQUcQ4CvuHNPXyALqYjhHXKNDqKV+zbt49BgwZRVVXFihUr5DQm4dsKfiIvwn3SYe+2wbdCYrPZuPbaa3n//fd55plnGDZsmNGRRLArPwCAPbYNJRYXyZEm4mKiDQ7lOxYtWsTkyZPJzMzkueeeMzqOOAcB3fA6nU5ii78HILzrUIPTNL3du3eTmZmJxWJh/fr19OvXz+hIQpyctRbK9rHT1Y6IEOjYIrgmNFitViZOnMiyZct44YUXuPfee42OJIS74Y1OxWoyU2pxkRplkpFkHq+99hpTp05l2LBhrFy5kpgYmRvuzwK64a21WOhU/xM1KpbIdoG3f/eNN97A4XCwceNG+vTpY3QcIU6taDug2WptTUasKehOcvr2229ZvXo18+fPZ9asWUbHEcKt7AAkdcRqtVJicZEaLQ0vQENDA/PmzWPUqFF88sknREXJvmZ/F9BTGnYfKeMStpMX14fuoYEzU1BrjVKKp59+mjvuuIM2bdoYHUmI0yv4EYAvLO1okx6KOUgmNByt14EDB/LLL7/Qrl07oyMJ8W/lB6BjJoWVdTQ4ICVSERHkM3i11pjNZrKzs0lKSpKDYAJEQK/w7t7+PemqHNVhkNFRmszWrVvp168fhw8fJiQkRJpd4T8KfsJhTibHnkCnlMigmDdbU1PDsGHD+PDDDwGk2RW+xWaBmnxI6sQvRVUApMeFBfWxwk899RQ33ngjTqeT9PR0aXYDSMC+4mit4dCXAKT1HWFwmqbx9ddfM3ToUMrLy3E6nUbHEeLMFPxIaUwXQNGrdaLRabyuqqqKESNGkJ2dLfUqfFPF0ZFkHcgpdR860T4lOPepaq159NFHuf/++3G5XO4eQgSUgG14G6xW2tf+SKFKJbZVT6PjnLPNmzczfPhwUlNTyc7OpkOHDkZHEqLxHFYo2cU+1R6A3m2TDY3jbRUVFQwfPpxvv/2WDz74gGuvvdboSEL8nmdCg07qRG5FPQronBZcN5OCu9l98MEHeeSRR5gyZQqLFi0iVE6aCzgB2/AWlFXS17WDQ7F9/P4b9+uvv2bEiBG0atWK7Oxs2rZta3QkIc5M8S5wOdhma01cOKQnBu6cz7q6OoYOHcqPP/7I8uXLmTBhgtGRhDixMvcMXltsG4prnSSYFcnxgVubJ/PQQw8xd+5cbrnlFt54442gPRAn0Pl3J3gKe7/fSEdlQbcbaHSUc9atWzeuuuoqnn/+eTm3W/gnzw1rX1pa0youhMgAntAQFRXFqFGjePLJJxk1apTRcYQ4ufIDEJWC1WSmxOIiJcoU0LV5MkOHDqW+vp558+bJiYcB7LQrvEops1Jqi1LqR6XUDqXUo80R7Fyp/etxakWri64wOspZ+/LLL2loaCAxMZF3331Xml1xWj5br4U/ocNj+b4uhfaJEYQG4ApKQUEBO3fuRCklza5oNENrtvwAJHXAarO5R5JFmYgMkpu0XC4XGzZsAGDw4ME899xz0uwGuMZsabACWVrrC4A+wEil1KXejXVuHE4nHau+YYepKynp/nlX9EcffcSQIUPkKENxpnyzXo98R01iD2wuE+elB94l07y8PDIzMxk3bhwOh8PoOMK/GFezpXshpSvlNXVUW3XQzOB1Op1Mnz6drKwstmzZYnQc0UxO2/Bqt1rPb8M8P3z69sXqgn100Tnsj+vvl+9W//GPf3D11Vdz0UUXScMrzohP1qu9AQq3kxPeGYDzWwfWkcKHDh0iMzOToqIi3n77bb+/Z0A0L8Nqtr4SaosgpSv7Ct0jyVolmAN+XKDD4eCGG27grbfe4pFHHqF///5GRxLNpFHf2UqpEKXUD0AxsEZr/Y13Y52bsh8+BaC+babBSc7cu+++y6RJkxgwYAD/+te/SEhIMDqS8DM+V69F28Fl5zt7O0wKurcKnJFkBw4cYNCgQZSXl7NmzRr+4z/+w+hIwg8ZUrOlv7h/TunK/pIaADqmBvZIMrvdznXXXceSJUuYO3cuDz/8sGxjCCKNani11k6tdR+gNXCxUqrX8Y9RSt2ilNqqlNpaUlLS1DnPSMiB9eTrJNI6+9dxwtXV1cyePZvMzExWr15NbGzgXfoV3udz9XrkOwDWVbehdayJpNho7369ZvTII49QW1vLunXruPjii42OI/zU6WrWK/Vausf9c2o3civqAegS4CPJVq1axYcffsjzzz/PnDlzjI4jmtkZXbvQWlcCG4GRJ/jcq1rrflrrfqmpqU0U7yw4bKRXfMsXug8XtEsxLsdZiIuLIzs7m5UrVxIdHThNgTCGz9Trke9wRaexpSqOrinhAbVH8JVXXuHLL7/kwgsvNDqKCAAnq1mv1GvpXggJxx7bisIaB9FhirSEwF7hHTduHN999x133XWX0VGEARozpSFVKZXg+XUkMAzY7e1gZ6v+lw1E6gZy4vuTFOcfK6R/+9vfePRR94253bt3JyoqyuBEwl/5ZL0e+Y6KhJ7YnIo+bfx/Bennn3/myiuvpKamhujoaM477zyjIwk/ZljNluyFpE5YHS4Ka120jDFh9sN7Xk7HYrEwceJEvvnGvUtE3pwGr8as8KYDG5RSPwHf4t5ftNK7sc5e0dZ/YtWhJHYf7Beb759//nn+9Kc/8cMPP8jxo6Ip+Fa91ldA2T52q04AXNqphWFRmsK2bdsYMmQI3333HcXFxUbHEYHBmJot3QOpXbHabBTWukiLDryGt66ujjFjxrB8+XL27NljdBxhsNPeTqy1/gno2wxZmkTU4Y184+rOsL5djI5yWk899RT3338/f/jDH1i8eLGc7iLOmc/Va/42ADZbWhMbDl3S/feGtS1btjBixAji4uJYv349nTp1MjqSCACG1KzDChU50GsiBeXV1Ng0reJCAmq7UXV1NaNHj+arr77inXfe4frrrzc6kjCY7y+BngFn0S5a2I/wk/ki2qT49nSDxx9/nPvvv5/Jkyfz7rvvEhYWZnQkIZqe54a11RWt6ZgYQoyfbtf5+uuvGTZsGElJSWzatEmaXeHfyvaDdkFKV3YeqQSgU2p0wEwsqK6u5vLLL+frr7/m/fffl2ZXAAHW8JZs+RCAhvZDfL6B7NChA9OnT+ett96SuZ0icOV9hz2hEwfrI+mVHu23VzFatGhB//79yc7Opl07/zzMRohf/TqhoSt7CqsB6NEqcOZjR0ZG0qFDBz788EOuvvpqo+MIHxFQnZZr10p+cHVkUH/fHEemtWbnzp308lOjdAAAIABJREFU7NmTyZMnM3nyZKMjCeE9WkPeFvIS3bNpL+5o4PSWs7Rjxw66d+9Ox44dWbdundFxhGganhm8rsSO5JR/TXiIf283OqqkpASXy0VaWhrvvfee0XGEjwmYFV5dlUeGZQ9fhfTngvZpRsf5HZfLxR133MGFF17Izp07jY4jhPeV7QNLGd85OxOi4OKO/nXD2urVq+nXrx/z5s0zOooQTatkD8S3xUoYBbVO0qJNREdGGp3qnBQWFjJkyBDGjh2Ly+UyOo7wQQGzwluzbTlxQHlGps9tvHe5XNx666289tpr3H333XTv3t3oSEJ43+H/A2B1dXtax5lIivOfGZ8rVqxg4sSJ9OzZk5tuusnoOEI0rdK9kNqVequVghoXXZNC/HpCw5EjRxg6dCi5ubmsXLnSLyY0ieYXMN8V9T/9k32uDHr27md0lN9wOp1Mnz6d1157jfvvv5958+YFzI0BQpzS4W/QkUlkV6b41YETy5YtY/z48fTp04d169aRnJxsdCQhmo7L6W54U7pRUlVLRYOmVXwY4T5+38vJHD58mMzMTPLz8/n8888ZMmSI0ZGEjwqMhtdSTkr596zR/cjs0croNL+xZMkS3nrrLR599FGeeOIJaXZF8Mj9moqkC7C7FBe184/9gSUlJdxwww1cfPHFrFmzhsRE/8gtRKOVHwBHA6T15Oe8CgDOa+kfhzSdyIwZMygtLWXNmjUMHDjQ6DjChwXElgbH9o8JxcXeuP8gMca3juS9/vrrSUlJYdSoUUZHEaL51JVC2T52tBkKwH909r199SeSmprKqlWruPDCC4mJ8Z8tGEI0WtEO989pPdjxpXsk2QVt/fcqxuuvv05hYSF9+/rO+HHhmwJihbdu24fkuNJo0/1in1hBtVqt3Hrrrezfvx+TySTNrgg+ue5jPDfUdSA+Ajqn+/bIo9dff53FixcDMGjQIGl2ReAq3gnKhE7pxr6SeqLDoEML/zrye/fu3dx22204HA7S09Ol2RWN4v8Nb20JsQVfs8I1gJEXtDE6DQ0NDYwfP54FCxawadMmo+MIYYzDX6NDwllVnk7npDAizWajE53U3//+d26++WY++OADtNZGxxHCu4p2QFInGnQIudUOWseFEOVHExq2b99OZmYmy5YtIy8vz+g4wo/4fcOrd36MCRdbzJfRNcPYyzIWi4Vx48axatUqFixYwNSpUw3NI4RhDn+NNfV8ChrC6NMmwSeuvJzIiy++yG233ca4ceNYunSpz+YUoskU7YC0HtTVN5Bf46JVbAiRfjKh4YcffmDw4MGEhoaSnZ1N+/btjY4k/IjfN7y2Hz5kr6sVaR16EGrgKU61tbWMHj2atWvXsnDhQm655RbDsghhKFsd5G/jgLknAAM6++aBE8888wyzZ89mwoQJfPjhh0T4yYu+EGfNWgsVByGtF78UVmJzQufUSL84AXHr1q1kZWURFRVFdnY23bp1MzqS8DP+3fBW5xOev4UVzgGM6m3sdAaXy4Xdbmfx4sVMmTLF0CxCGCr3G3DZ2WTtQoiCfh1888AJi8XCpEmTeP/99wn3k5FpQpyTkt3un1v04MfDZQD0auUfk0jsdjtt2rRh06ZNdO7c2eg4wg/595SGHR+j0GwMGcCtnVoaEqGyspKwsDDi4uLYtGmTDLwW4uAXaFMon1S0pX1CCPExUUYn+pXWmoKCAjIyMnjkkUfQWkvNiuBxzISGXd8cRgF92vvmFZij8vPzycjIYMCAAWzbtk3qVZw1v/7Ocf60lB26PSmtOxEd1fyb7svLyxk6dChXX321vHAKcVTOFzjSLmB3VRj92sX7zL5YrTX33XcfvXv35vDhwyilpGZFcCnaAWHROOLacLC8gbQYEwk+9Ib0eOvXr6dLly68/fbbAFKv4pz473dPRQ4hBd/xiWMAI3plNPuXLykpYciQIezYsYPbb7/dZ17UhTCUtQaOfM/+qN64NAzq6hvbGbTW/5+9+w6vqsoePv5d6Q2S0LsgvSiIwMCghCK9KaKgI/hjcEQcsdexjXUUx8JrZ0QQEUEQBxRQkBYLg6CCUqRIkdBDSC+37fePc6OICSncmrs+z5OHJPfcfdYJWTnr7LPP3txxxx0899xzjBkzhkaNGvk7JKV87/h2qNOW/MIiDmS5OC8xnPgAnaFhxYoVDB06lPPPP5+BAwf6OxxVBQRvwbv1QwBW0p2BF/j25HX06FF69+7N7t27+fjjjxkyZIhP969UwPrlf2CcrMpvQWQY9Gzpn6FGp3O5XNx8881MmzaNO+64g1deeUV7ilToMQaObYW67dl/PIusIkPrOoH5wNrSpUsZPnw4bdq0Yc2aNdStGxwL16jAFpx/9Y3B9f1cNpk21G/UhORqvltdzRjD2LFjOXDgAMuWLaN///4+27dSAW//F5iwSBalN6J5jQiSfJibpXnttdd44403uO+++3j++ef1bowKTTlHoOAU1G3PN3tPAHBx08BbYe3AgQOMGjWKCy64gFWrVlGrVi1/h6SqiDIfWhORxsBsoB7gAqYbY6Z5O7CzOvQtYRl7+MDxN0Zd3MSnuxYRXnnlFTIzM3XdbhVw/J6v+77AVrcTe/dFcn2bwFhd7YYbbqBatWqMHz9ei10VcHyWs4c3W//W78iPOzIJE7g4AGdQOe+885g9ezYDBw4kKSnJ3+GoKqQ8PbwO4C5jTFugO/B3EWnn3bDKsHkuRUSxNuxPDO7om4J37969PPvssxhj6NChgxa7KlD5L18Ls+DIZnbHXoABLvHj+F273c7DDz/MqVOniImJ4frrr9diVwUq3+TskS2A4Kjdjj0nC2lQLYya1QNnCe25c+f+ujrpmDFjtNhVHldmwWuMOWKM+c79eQ6wA/DfpLeOIlxbP+QzVxc6n1+PuGjvz5+5e/duUlJSmDp1KocPH/b6/pSqLL/m64H1YFysdo/f7d6yvk92eyabzcaYMWN48sknWbp0qV9iUKq8fJazRzZDrVbkO8NIy3bRuHo4MQGy2MrMmTO57rrreP755/0diqrCKjSGV0SaAhcBG0p47UYR2SQim06cOOGZ6EqyczlhhZkscPRiYAfvn1B37NhBSkoKhYWFrF69moYN/bvAhVLl5fN8/XkVJjKOxScb0aJmJAkxvl/MoaioiCuvvJKPPvqIadOmcd111/k8BqUqq7Sc9Ui+HtkCDTpxKCOXrCJDq7oJAXHX48033+Svf/0r/fv35/333/d3OKoKK3fBKyIJwIfA7caY7DNfN8ZMN8Z0McZ0qV3bixNZb3mfU+E12UAHrz8BvnXrVnr37o3L5WLt2rV07NjRq/tTylP8kq97VlHUsDt7cyLoep7vb0cWFBQwcuRIPvnkE15//XVuvfVWn8egVGWdLWfPOV9zjlkPrdXvyHf7rYL5goaJHoj63Lz88svcdNNNDB06lMWLFxMXF7hzAqvgV66CV0QisRLxPWPMIu+GdBa5xzG7V/KhvScXN4qnTpJ3xx/t2rWLmJgY1q1bR/v27b26L6U8xS/5mrEPMn5mc2RHDHBZO98PZ8jIyGD37t3MmDGDm266yef7V6qyvJ6zR7ZY/9bvxMb9pwgT6Nbcv1N9GWP43//+xxVXXMGiRYuIiYnxazyq6ivPLA0CzAB2GGNe8H5IZ/HDB4hxMs9+CX/v4r2H1XJycqhWrRqjRo1i8ODBxAboxNxKnclv+frzKgDmnWxBUoz4dPxuXl4eMTExNGzYkK1bt2q+qqDik5w9Ys3Q4KjTjm1HN9C4ehh1k6t7ZVflUXyOfeeddzDGEBkZ6bdYVOgoTw9vT2Ac0FdENrs/fL/SgjHw7Sx+imjN8ajGDO50nld2s379epo1a8by5csB9OSpgo1/8nXPalyJTVh2LInuTRKIiizzWtojsrKy6N+/PzfffDOg+aqCkvdz9sgWqNmCTFsY+zOdtKkdTXSU78fYG2N49NFHufjii0lPTyciIkKLXeUzZZ6VjDFfAv4f2X7gKzi5m//Yb6J/+2RiojyfJKmpqQwdOpT69evToUMHj7evlLf5JV8dNtiXyoF6A7AdE4Zc6JsHO0+dOsWAAQPYsmULd999t0/2qZSn+SRnD2+GJn/i2/3p2F3QtZnv58g2xvCPf/yDZ555hgkTJpCcnOzzGFRoC56V1jbNpDA8gU+c3bm2+/keb3716tUMHjyYRo0asXbtWho3buzxfShVJaV9A7Yclhe0JTYC+rTzfsGbnp5O3759+eGHH1i0aBGjRo3y+j6VCkq5xyE7DRpcxBe7jgFwaSvfjrE3xnDXXXfxzDPPcNNNN/HWW28F5JLGqmoLjoI37yTsWMInXEq9xFg6e3h1mJ07dzJ06FDOP/981q5dS4MGDTzavlJV2p5VGAnnnaPN6Nwghmpx3n34xBjD0KFD+emnn1iyZAnDhg3z6v6UCmppmwAwDbuw6UAODauF0aSOb2dRee6553jxxRe59dZbee211wgLC47SQ1Utvhlod662zAWnjTeL+nBFr/oenzuwVatWPPnkk1x//fW6brdSFbVnJVk1O3IsLZYpPhjOICI8+eSThIWF0a9fP6/vT6mglrYRwiI4EX8+uzM2MaBlHLE+XnBiwoQJREREcMcddwTE3L8qNAX+ZZb7YbU90e3YL434y59beKzpJUuWsHPnTkSEu+66S4tdpSoqKw2O/siXdCYiDAZd6L2hQGlpacybNw+A/v37a7GrVHmkbYR6F/DFz1k4DfRq6cV58k/jdDp55ZVXsNls1K5dmzvvvFOLXeVXgV/w7v8STu5hel4vep2fQO3EeI80O3/+fEaNGsWDDz7okfaUCkk7rdlMZp5sS7vakdSq7pn8PNP+/fvp1asXkydPJiMjwyv7UKrKcTrg0HfQqCtrfzpGhEBKW+8P2XM4HIwfP54pU6awePFir+9PqfII/IL321kURVRjsaM71/f0TO/unDlzuPbaa/nzn//MzJkzPdKmUiFp53IKqzfj24L6DPbSUt8///wzKSkpnDp1ihUrVlCjhu+fMFcqKJ3YAfY8XA0v5ru0XJrXCKdOsndXWLPb7VxzzTXMnTuXZ555hquuusqr+1OqvAK74C1+WM1cQq3qsVzS+txPqG+//Tbjx4+nd+/eLF++nGrVqnkgUKVCUGE27Evlu+huCDD0Is8vBrNz50569epFXl4ea9asoWvXrh7fh1JVVtpG65/YNhzKcdGlcTUivDg7QlFREaNHj2bhwoW88MIL3HfffV7bl1IVFdgPrX03C5w2Xi/qw1UpDc75yU6Xy8Xs2bMZMGAAH330kU5Sr9S5+HkVuOy8l9mWFjUjaFLL8z1HK1aswOFwsHbtWp0bW6mKStsEcbVYmWad6nu38e5ywj///DOpqam8+uqrvy4Go1SgCNyC12mHb95iW3QnDtobMe4cH1ZzOBxERETwySefEBERoet2K3Wudi7HGZPM8swW3NTTs1MFFufrlClTuOaaa/SBUqUqI20jNOrKF3vSiY+Ers29U/AW52u7du3YvXu35qsKSIE7pGHHEsg5zIu5l9G/dRI1q8dVuql///vf9OnTh7y8PBISErTYVepcOR2w6zN+SvgTLsIY3tlzwxm+++47WrduzXfffQegJ0+lKqPgFKTvwtmgM98fyqdt7Uiqx3v+odLc3Fz69+/P1KlTAc1XFbgCt+D93xucjGrIamcnbu7XptLNPPXUU9xzzz00bNiQKD+sHa5UlXTwf1CYyYe5HWhYPZy2DWt6pNkNGzbQt29fnE6nLj2q1Lk4+A0AeyJbkVVk+HOzZI8v+JCdnc2gQYP44osvdHVSFfACs+BN+xbSvuHNwsvo1CCWdpU4mRpjePTRR3nooYcYN24cc+bMITIy0gvBKhWCdnyCKzyaeZntGNzeM8MZvvzyS/r370+tWrVITU2lWbNmHmlXqZB04CsIi2RZujWM4bL2np2OLDMzk/79+7NhwwbmzZvHNddc49H2lfK0wCx4N7yOLTye92y9mNynVaWamDp1Ko8//jh//etfmTlzJhERgTtcWamg4nLB9sXsqdaNfGIY0+3cC9PNmzczaNAgGjRowLp162jSxPMzPigVUvZ/BQ0vZt3+fOrECS0bem6ogcPhoH///nz//fcsXLiQ0aNHe6xtpbwl8KrA7COYbR/xEQOomViNyzpU7jbJFVdcQVZW1q9LkCqlPCTtG8g5zNzw0bSoEUHL+uc+9KBdu3b87W9/47777qNevXoeCFKpEFaUC4e/x9Z9CttTC0lpGuvR5YQjIiK46aabqF+/PkOGDPFYu0p5U+BVgptmgMvJKwX9+eslTSu0FKHL5WLBggUYY2jVqhVPP/20FrtKedq2j3CFRbEgryOXX9TwnJpavXo1J06cICoqihdffFGLXaU84eAGME5+lNbYnNCrtWeGHR09epTU1FQAJk6cqMWuCiqBVQ3aC2HTTL6J6EJ2VD3G/On8cr/V5XIxadIkrr76apYuXerFIJUKYS4XbPsv2+O7UiCxXNW18sMZFi9ezKBBg7jnnns8GKBSigNfg4SzOL0+YQJ9253bhSnAoUOHSElJYfTo0eTl5XkgSKV8K7AK3h/mQ346L+UP4JouDYiNKt9DZk6nkwkTJvDWW2/x0EMPMXToUC8HqlSIOvg/yD3KnJyL6VQ/hrpJlZvmaMGCBYwePZrOnTvz0ksveThIpULcga8wDTqR+ksRzZLCqV/j3BaFOXDgAL169eLIkSN89NFHxHthejOlvK3MgldE3haR4yKy1auRuJzw1TT2R7bgO2nH3/qUbyoyh8PBuHHjmD17No8//jhPPPFEhYZBKFXVeDVnt32EMyyaJYWdGN2lcg+WzZ07l7Fjx9K9e3dWrFhBUlKSh4NUKnh4PF/tBXDoW3LrduVAppPuzZLOaWjf3r17SUlJ4eTJk3z++ef07NnTI2Eq5WvlyYJZwCAvxwE7PoaMn3kufwhD29eiZkL5Fof49ttvWbhwIc8++ywPP/ywl4NUKijMwhs563LC9sV8H30xJiKGyy9uWuEmbDYbTzzxBCkpKSxfvpzq1at7PEylgswsPJmvaRvBaeM7V0sM0KfNuY2Lnz59Ojk5OaxevZpu3bp5Jkal/KDMWRqMMaki0tSrURgDX71EelRDPivqxorL2pXjLQYR4U9/+hM7duygefPmXg1RqWDhtZw98BXkHmOO42pSWiQRF12xea2NMURFRbFq1SqSkpKIi6v86olKVRUez9e960DCWZLRiKhwJ91b1q9sXIgITz31FJMmTdJ5sVXQ89gYXhG5UUQ2icimEydOVOzN+9bB4e95tWgwFzeuxvl1zt7rU1BQwIgRI/jggw8AtNhVqoIqla9b5mGPSGC542JGXlSx4QyvvPIK48ePx+l00qBBAy12laqACuXrz6sxjbryvyOGVjWjSIit+HRkP/74Iz169ODgwYOEh4drsauqBI8VvMaY6caYLsaYLrVr167Ym798iYKomswtuoRr/9T0rJvm5+czYsQIli5dSk5OTuUDViqEVThfbfmwfTEbYv4M4VH0aVv+XqMXXniBKVOmkJOTg9PpPIeolQpN5c7X/Aw4/D2Z9XpwKMdF16YVHx///fff06dPHw4ePEhBQcE5RK1UYPH/LA2Hv4e9a3jLMZDa1eMYdtF5pW6am5vLkCFDWL16NbNmzWLixIk+DFSpEPbTUrDl8nrmn0hpnkhMVPnWrHnmmWe46667uOqqq1iwYAFRUVFeDlSpELZ3LWBYkd8agKEdG1Xo7Rs3bqRv377Ex8eTmppKq1aVW+lUqUDk/5XWvnwJW3g80/P68vSYNoSHlTzDQmFhIQMHDmTDhg3MmTNH1+1Wype2vE92dD2+LmzNgt7lOwk+88wzPPDAA1x77bW88847ury3Ut7282qISWTmvkQaVBM6N6tb7rd+++23XHbZZdSsWZPVq1fTtGlT78WplB+UZ1qy94H1QGsRSRMRz3WrntiF2bGEOc5+1K+ZyLBOpY8LjI6Opnfv3syfP1+LXaXOwuM5m3MUs3cNC21/pnmNaLqcX75Vm3r06MFNN93E7NmztdhVqhQey1dj4Oc15Db4Mz9lGAa2rV2h6ciaNWvGgAEDWLdunRa7qkoqzywN3qsuU5/DIVG8WjCYF8deUOL8uSdPnuTYsWO0a9eOp556ymuhKFVVeDxnf1yAGBfvFvXkr/2alrVvvvrqKy655BJSUlJISUnxaChKVTUey9f03ZCdxtfJYwAY3bX04YGn27hxIxdccAE1atRgwYIFHglFqUDkvzG86bsxWxfyruMymjSoS682f3wI5vjx4/Tp04chQ4ZQVFTkhyCVCnHGwPfvsSuiFemRDbjqLMt9G2O47bbbuPTSS/niiy98GKRSij2fAzDjSFPOSwynXaNaZb7l008/pVevXtx///3ejk4pv/NfwZv6HHaieNU2lEdGdvzDy0eOHKF3797s2bOHGTNmEB1d8alVlFLn6OA3cGIHMwpSGHlhHWIiS74p5HK5mDx5Mi+//DJ33nknl1xyiY8DVSrE7VpOQWILNmTX5PJO9ctccfTjjz9m5MiRtGnThoceeshHQSrlP/4peNN3Y35cwGxHP9qf34iLzvv9lWhaWhopKSn88ssvLF++nH79+vklTKVC3rezKAyLY5mrB5P6ti1xE6fTyQ033MCbb77JAw88wL///W9d3lspXyo4Bfu/4quwi4kIg7Hdzz43/aJFixg1ahQdO3Zk9erV1KpVdm+wUsHOP0+SpD6HjUjecg1j/qiL/vDyY489xtGjR/nss8903W6l/KXgFGbbIhbZe/KnFnVoXCO+xM1WrlzJzJkzefTRR3n00Ue12FXK1/asAuNkRno7ujWKoX5yQqmb5uXlcfPNN9O1a1eWL19OYmKiDwNVyn98X/C6e3ffsQ9mSJfWnFer2h82mTZtGlOmTOHCCy/0eXhKKbcfPkAchbzn6MczA0pf7nvQoEGsX7+e7t27+zA4pdSvdi6jIKoGG7Kb80qP0sfZA8THx7Nq1SqaNGlCtWp/PP8qVVX5fEiDa83TFJkIPowczj1Dfitod+3axahRo8jOziYuLk6LXaX8yRhcm2byo2lOVL3WXNC45u9eLioqYty4cXz99dcAWuwq5S8OG+z+nC+4iBpxEfTv0LjEzWbMmMFjjz0GQPv27bXYVSHHtwXvkS2EbVvEDMcgbhzcjdjoSAB27NhBSkoKX375JYcOHfJpSEqpEhz8hrATO5jj6MvtZ/TuFhYWcuWVVzJnzhy2bNnipwCVUgD88jUUZfFBbidGdKhFZAkPlr7++uvccMMNrF+/HofD4YcglfI/nxa8RZ8+SqaJ56saoxnV1brt8uOPP/46V+fatWtp27bkB2OUUr7j2vAGOcSxLfHS300ZmJ+fz8iRI1m6dClvvPEGkydP9mOUSim2L8YWFsNXrg6Mv6TlH16eNm0aN998M8OGDeO///2vLgKjQpbvCt59qUQfWMPrzpE8MrYXIsLmzZvp06cPUVFRrFu3jnbtSh8nqJTykaxDsH0x7zv6MLFvx18fQsvPz2fYsGGsXLmSt99+m0mTJvk5UKVCnNOB2b6E1a6LaF2/Gk3rJP3u5X//+9/cfvvtjBo1ig8//JCYmBg/BaqU//mm4DWG3KUPcdjUILvtNbRpkAxAYmIibdu2Zd26dbRq1conoSilzs588x+McbE8ZhAjOv+2WlNUVBR16tRh9uzZTJgwwY8RKqUA2J+K5Kfzka0740t4WK1OnTpce+21zJs3j6ioKD8EqFTg8Mm9Ddf2JSSkb+E5mcQDo7qza9cuWrRoQbNmzUhNTdVpjJQKFLZ87N/M4HNnV67s/2fCw4TMzEwKCwupV68e77//vuarUoFi6yLyJZZvIzvxcuemgLXi4a5du2jdujXjx49n3LhxmrNK4YseXoeNnKUPs9vVkJb9/o/NG/9H586deeqppwA0EZUKIK4t84iyZ7M4aihju59PRkYGl112GYMHD8bpdGq+KhUoHDac25fwmeNi+rdvSFREOMYY7r//fi688EJ+/PFHQM+xShXzeg9vwVdvkJh/gOfj7qdX0WEGjRhB06ZNueGGG7y9a6VURbic5K59if2uZvQeMIxTGSfp378/P/30Ex9++CHh4eH+jlApVeznVYQXZbHU1Z1H+rXFGMMdd9zBtGnTmDx5Mu3bt/d3hEoFFO/28Oalw7pnWOe8kPr1mzN8+HBatGjB2rVrqV+/ftnvV0r5jH3rR1TPO8DC6Cvo1TSePn36sHPnTpYsWcLQoUP9HZ5S6jRFG2eTbqoTef6lNEqO4+9//zvTpk3jtttu49VXXyUszOfT7CsV0LyaEcf/+xCRzgJW1Z3A/bf+jbZt27JmzRrq1Knjzd0qpSrKGLI+/Rd7XA24dMQEptzyd/bt28eyZcsYMGCAv6NTSp0u9wQRez5jkfNSbh3cifnz5/P6669z77338uKLL+owBqVK4LUhDUVpW6i5ex4fyCDu/dt4+nY4n44dO5KcnOytXSqlKilv6yfUyt/DuwlTuOOCJlzw2mvs27ePHj16+Ds0pdQZCr+bSwxOfqoziBsbJNF6zBji4uIYMWKEFrtKlcJrPbyH5t7CzB/D+DGiEwnREfTu3VuLXaUC1MllT/HlySQ27jiGw+GgXr16WuwqFaBy18/kG0dzDn//FXv37iUsLIyRI0dqsavUWZSr4BWRQSKyU0T2iMj9ZW1flH2cL9dv4saPMvnf6hW4XK5zj1QpVS4VzVd73ilsh7Yxck4mny7+kL179/oiTKWUW0Vy1hTlUD13LzcuzmP+rOmsWLHCV2EqFdTEGHP2DUTCgV1AfyAN2AhcY4zZXtp7miRFmLQsJyl9+vDJxx8THx/vyZiVCjoi8q0xposP9lPhfG1dN8Zk5jtxRieyatUqOnbs6O0wlQpovspX974qlLMXgahGAAAgAElEQVTtGyeaxnE2PttVyIsvvsjtt9/uizCVCljlzdfy9PB2A/YYY/YaY2zAPGDk2d5wMMtJ9x5/YtnSpVrsKuVbFc7XvelF2MJjWbt2rRa7SvlehXI27UQ2n+0q5LXXXtNiV6kKKE/B2xA4eNrXae7vlSo+Joo1a9YRGxt7LrEppSquwvkaJkLql+vp0KGDVwNTSpWoQjnrdMHTTz/J5MmTvR6YUlVJeWZpKGkU/B/GQYjIjcCN7i+LYmJitp5LYAGqFpDu7yA8rCoeEwTecZ3no/1UKl8vvKCD5mvw0OPyPl/lK5QjZ8/M13/846Gt//jHQ14PzMcC6f/fk/S4vK9c+VqegjcNaHza142Aw2duZIyZDkwHEJFNvhr/5EtV8biq4jFB1T2uctB8ddPjCi5V9bjKocyc1XwNXnpcgaM8Qxo2Ai1FpJmIRAFjgSXeDUspVUmar0oFF81ZpXygzB5eY4xDRG4BPgPCgbeNMdu8HplSqsI0X5UKLpqzSvlGuVZaM8YsA5ZVoN3plQsn4FXF46qKxwRV97jKpPn6Kz2u4FJVj6tMFczZqvpz0uMKLkF3XGXOw6uUUkoppVQw89rSwkoppZRSSgUCjxa8FV3SNBiISGMRWSMiO0Rkm4jc5u+YPElEwkXkexH5xN+xeIqIJInIQhH5yf3/1sPfMQUizdfgo/ka2jRng0tVzFcI3pz1WMHrXh7xVWAw0A64RkTaeap9P3IAdxlj2gLdgb+XdVwiEisiH4tIlogsKGsHIvIPEXmrMsGJyFoRKRSR1Mq8H7gN2FHJ91aIiCwXkevLuW1dEUkVkRwReV5EbhWRZ8q5q2nAp8aYNkBHfHR8wUTz9TearyXTfA0smrMWzdeS+TBfIVhz1hjjkQ+gB/DZaV8/ADzgqfYD5QNYDPQvY5txwDdARAmvPQg86cF41gI3nPG9GsBHQB5wALi2lPc2Ar4DvgfswP4StukEfAFkYc0X+cgZr1+N9cueA2wHLvfQcT0MLOK3ceYx7v3XKeN91YF9xe/Tj1J/Tpqvv22j+Xrux6X56uUPzdlfX9d8PffjqlS+urcN2pz15JCGCi9pGmxEpClwEbChjE3PA3YZYxwlvLYMGOrZyP7gVcAG1AX+ArwuIu1L2O4l98cySr9CmwukYiV5CjBZREYAiEhDYA5wJ1YS3APMFZE6HjiG84Dtxp1hxphCYDkwvoz3nQ+cAGa6byW9JSLxHoinqtF8/Y3m67nTfPU+zVmL5uu5q2y+QhDnrCcL3nItaRqoRGS/iNwjIj+ISJ6IzHB3+y93d/uvAf4L3G6MyRaRBSJy1H1bJbX4F15EHgMeAcaISK6ITDx9P8aY74HaItLgtH3/U0TmuD9vKiJGRK4XkV9EJF1EHqzAccQDVwIPG2NyjTFfYk1iPu6M7YYBx40xs4GVQH4pTTYF3jPGOI0xPwNfAsXJ3QjINMYsN5alWFe9zUuJba2I3OD+/P9E5EsR+beInBKRfSIy2P3aLOB64F73z/AydxNrKfuPWQTQGXjdGHORO54qMdbNwzRf0XxF8zWYBG3OliNfPxeRRsCHwO3ADM3XgMxXCOKc9WTBW64lTQPclUB/oBUwHOuK5x9APeBC4KgxZpF72+VAS6AO1m2L9wCMMY8CTwPzjTEJxpgZJeznU6xxWGdzCdAa6Ac8IiJty3kMrQCnMWbXad/bwm9JVKwnMEJE9gPzsG6t1CqhvZeA8SISKSKtsW6rfe5+bROwQ0RGiDU4/3KgCPihnLH+Cdjp3u9UrD9yYoz5P6yf51T3z7B4fzuwxgudTRqQZowp7iFYiJWc6vc0X9F8RfM1mAR7zpaWr7WwFtxYi1X8LULzFQIzXyGIc9aTBW9VWB7xZWPMMWPMIaxxNRuAzcDrWL9khcUbGmPeNsbkGGOKgH8CHUUksZz7Kc9tl8eMMQXGmC1YCVWeX0SABKzxQKfLAqqd/g1jzAPGmEbGmKZY/1ebgfQS2vsEGA0UAD8BM4wxG91tOIHZWLdlitz/TjLG5JUz1gPGmP+423kHqI91m6g0OcBZf8bGmKPAQfcfD7D+oG0vZzyhRPNV81XzNbgEe87+IV/dPbI2IB5rTOgLoPnqbiPg8tUdV9DmrMcKXvd4muLlEXcAH5jgWx7x2GmfF7i/7ol1u6IF0E9ENovIMBF5RkR+FpFsYL/7PSVdwZVkJdBLRCLPss3R0z7Px0q08sjFGu9zuupYv8wVIiI1sK6WH8ca1N4YGCgiN7tfvwzryrE3EIU1BuktEelUzl38eozGmOJbPmc7zmr88Y9NSaYA74nID1hX1k+XM56QofkKaL5qvgaRKpCzJeUrWDnbFajjztfNIvKB5mvA5isEac56dB5eY8wyY0wrY0xzY8xTnmzbX4wxXxpjBHgM64q0E5AMjAQuw7oiaurevKQxViW1mYPVA3WpxwOGXUCEiLQ87XsdgVL/MBpj1gIPlfDS+Vi3b2YbYxzGmDSs2zND3K93AlKNMZuMMS73lekGrJ+LN7TFuho/K2PMZmNMF2PMhcaYy40xp7wUT1DTfNV8RfM1qFTVnAX+xm/5+jxwAZqvAZmvELw5qyutVU41rFsMJ4E4Knd1s5TffrE9xn27YxHwuIjEi0hPrJP9uyVtLyJhIhIDRFpfSoz7dhlYyS0icq17u3rAGH5Lio3ApcVXnCJyEdYfmR/cX/cWEU8+VJGCNbZLqYrQfLVovqpgoPlq0Xz1MC14K2c21vx7h7DGrvyvEm14c/qUm4FY4DjwPjC5+NaXiFwqIrmnbdsL6/bSMqCJ+/MVAMaYbGAUcAdwCmsc0lbgKffr67DGVy0UkRysJ2yfNsascLfdGFjviQNy/9EYgjUWSamK0HxF81UFDc1XNF+9oXjSYeUHIrIX6GeM2XcObazAerJzkzGmj8eC8wCxVrdZYIz5zANtTQEaG2PuPffIlKo4zdcKtaX5qvxK87VCbYVEvmrB60ciciWw0xiz1d+xKKXOTvNVqeCh+arOpAWvUkoppZSq0nQMr1JKKaWUqtK04FVKKaWUUlWaFrw+JiL/cA82r8x714pIoYikeqJ9scwUa63tbyoTkyeISLSI/CQidfwVg1LFvJ2jVY2I1BWRHSIS7e9YlCpWVfJYRN4QkYfLuW2siHwsIlkiskCsZYnneTvGYKEFbyWJiBGRFmVs86CIPHn694wxTxtjbjiHXd9ijOlV2osVbP8SrLXNGxljup35ooiMFZGd7uQ5LiLviMiZq8wgIi3dfxzmlLYjEbldRPaKSLaIHBaRF0Ukwh1zEfA2cF8541aqTIGSoyIyR0SOuH/3d4lIiW2LyKPumEudWF5E1ojICXdbW0Rk5Fm2TXLn7HH3xz9Pe62JiOSe8WFE5K6zHZiIRLkvTtOKv2eMOQasAW4823uVqowAyuO2IrLafT7cIyJXnPbaX87IpXx33BeXEXeZ505jzE3GmCfKGfNorOWDaxpjrjLGLAE6iMiF5Xx/laYFr3d5cy5ATzgP2H+Wtbm/AnoaYxKxVoWJAJ4sYbtXsSbJPpuPgc7GmOpAB6zVaW497fW5wPXaS6R8zBc5+i+gqft3fwTw5JknQhFpjnWyOlJGW7cB9d1t3QjMEZH6pWz7ItbE/U2BbsA4EZkAYIz5xRiTUPyBtbKVC2uuz7O5B2v+0TO9B0wq471KeYtX89jdObMY+ASowW+51wrAGPPeGfl0M7AX+K6Mpstz7qyI84Bd7mWoi72PXowCWvB6lTHme6C2iDQo/p6I/LP4ak5EmrqvAq8XkV9EJF1EHjyXfZa3fRGZCLwF9HBfkT5WQvwHjTHpp33LCfzuSltExgKZwKqzxWWM+dkYk1n8NqyTa4vTXk/Dmny7ewUPWalK80WOGmO2ue9iABj3R/MzNnsF6w6HrYy2fjjtZGawVnBqXMrmw4Gpxph8Y8x+YAbw11K2HY+1jOn+0vYtIs2A67AK+DNtAM4XkfPOFr9S3uCDPG4DNABeNMY4jTGrsTqExpWy/fXAbHOWabDKe+4UkVnFvddira6WJiJ3ue/aHCm+iHWfwx8BxrjP6RPdTawlsDvefEYLXu/7FBhcxjaXAK2BfsAjItLWwzH8oX1jzAzgJmC9+6r00ZLeKCKXiEgWkANcCbx02mvVgceBs94GPW37a0UkG0jH6uF984xNdri/r5QveT1HReQ1EckHfsLqxV122mtXATZjzLLS3n9GW5+ISCFWkbkW2HS2zc/4vEMp242n7FWWXgb+gbVa1O+4i/A9aP4q//FmHksp3/tDPrkv+nphrRhXcmMVPHeeoR6QCDQEJgKvikiy+xz+NDDffU6f4d5+B9BUShiOGGq04PW+8txqecwYU2CM2YK1jranTxqVbt8Y86V7SEMj4Dlg/2kvPwHMMMYcLGdbc923YlsBbwDHztgkB0gqb2xKeYjXc9QYczNQDbgUWAQUAYhIAtZJ6vYKtDXM3dYQ4DNjjKuUTT8F7heRau4xkH/FGuLwOyJyKda4v4Wl7dM9XjHCGPPRWULT/FX+5M08/glrKM89IhIpIgOAFErIJ6yLxy/KWOGtQufOM9iBx40xdvdFci5WEV+aHPe/IZ+bWvB630qgl4hEnmWbo6d9ng8klKfhMwbKL/d0+6czxhzCOoHOc++7E3AZ1jjBira1G9gGvHbGS9WwbvEo5Utey9HTuW+Ffol18TjZ/e3HgHcruvyp+2S3HBgoIiNK2exWrN7Y3VjjD98H0krY7nrgQ2NMbkmNiEg8MBWYUkZYmr/Kn7yWx8YYO3A5VkF9FKtn9gNKzqez3i05l3On28kzxuiWdRzV3P+GfG5G+DuAqs4YkyMiP2D17Kz2cNvvYT0s4isR/Db2sDfWwzC/iAhYCRcuIu2MMZ0r2FaxtsDzHolUqXLyZo6W4vTf/X5AIxG52f11beADEXnWGPNsBdv6HWNMBvCX4q9F5Gngd9MPikgscBVwBaVriZXrX7hzPQpIFJGjQHdjzH73Qz0tsHrNlPI5b+exMeYHrF5dAETka84obEWkJ9ZY31LvlnDu586Kaov1cHq2F9oOKtrDe26iRCTmtI/wUrZbinX7MeC5B/b3dn/+F7GmLxL3uKSn+G2A/XSsE20n98cbWMc5sJR2bxD3PLsi0g544LS2EJGGWE+//s8bx6VCll9zVETqiDW9X4KIhIvIQOAafjsh98MaB1icR4exZjt4tYS22ojIYLHm2owUkeuwxgquc79e/GBOU/fXzUWkpnu/g7Ge1D5zlpUrsHp+1pzlMLZiPRhXHOMNWMOROgHFt2S7YZ1UD5T3Z6NUBfj9XCsiF7r3HScidwP1gVlnbFZ8tyTnDw385qznzjPz2ANSgLPdAQ4ZWvCem21YtwyLPyaUsl2gT08GgIg0whoP9KP7W+2Ar93f+wrYCfwNwP3k99HiD/c2hcaYE+62LhWR02+R9gR+FJE8rJ/HMqwHYIpdC7xz2tPsSnmCv3PUYA1fKJ6F5N/A7caYxQDGmJNn5JETOFU8vECsSeffcLclwD+xxhKewJqibIwxpnjqo8bAAeCQ++uLsXI5B2tmhb8YY7adEV+JT5Ofnr/GGMcZMWYALvfXTvdb/oJ14lbKG/ydx2DNyHAEK//6Af1PP1+JSAxwNSUMZxBrEYzlUPa5kz/m8bm6hj8+IB6S5CyzZigPEpG9QL+KjtU7o40VQA9gkzGmj8eC+63964D2xpgHPN12GfuNxroV2ssYU9Icn0p5XTDkaBn7fgg4YYzx6cnNfedmHXCRMabQl/tW6kyax79razgwzhhz9blHFvy04PUREbkS2GmM2ervWJRSf6Q5qlTw0zxWpdGCVymllFJKVWk6hlcppZRSSlVpWvAqpZRSSqkqzSvz8NaqVcs0bdrUG017nNPl4mR2Hifz7LSVA7iqNSCsWl1/h6WqgLy8PHbv3k14eDg2my3dGFPb3zGVJFDy1WkvJPzEDo6F1aZmrXpEROg04cp3XC4Xu3fvJjc3FyC087UgA04d4GBEE3IcETSvFUt0VJR396lUBR05coTDhw9DOfPVK2eUpk2bsmnT2ZZ3DxynsrN57dNNfPn9TyyPfoDcYc+R0GWsv8NSQe7rr79m8ODBNGnShNWrV9OsWbOAnZ80UPI1a8caEudfztMJtzH5xr+TXD3kl35XPpKZmcngwYMpKChg/vz5jBkzJrTzdf1r8NkDjKv+b/bkx7Hwb51pWFc7glRgMMbwz3/+k8cff5y//OUvvPfee+XK15Af0uBwOMgtclFXMgCQ6vX9HJEKdgcPHmTgwIHUrVuXdevWEQi9p8HA5B4DwBadTKT27iofMcYwevRovv32WxYsWMDVV+sMThRkYCSME45Y4iKFiPDS1nlQyvemT5/O448/zoQJE3jnnVJXcf6DkD+rOJ1O8uyGenIKgPCkRn6OSAW7xo0b869//YtRo0bRoEEDf4cTNOxZ1jL3zuhkPcEqnxERHn/8cTIyMhg2bJi/wwkM+RmY6ETyHEJSjOjwIhVQxo4dS1ZWFnfffTdhYeXvtw35Ht4iu53sIkPjiFMYhMikhv4OSQWpFStW8P333wNwyy23aLFbQYWnDgNgYhO1h1d53bFjx5g1axYAf/7zn7XYPV1BBq6YJPLsRnt4VUBwuVy8/PLL5Ofnk5iYyL333luhYhfKUfCKSGsR2XzaR7aI3F7pqANMfkEBOTZoHH4KW1QS4VEx/g5JBaGPP/6Y4cOHc++99/o1jmDOV0f2MTJMAtViownXE6zyosOHD9O7d2/+/ve/c+iQp1ZwrZyAzNn8DBxRieTbDXGRaA+v8iuXy8WkSZO49dZbmTt3bqXbKfO32BizE+gEICLhWOs7f1TpPQaYwqIisooM9eUU9tjaRPs7IBV0Fi1axJgxY7jooov44IMP/BpLMOer5KVzwiRRI0GzUHnPwYMH6du3L0ePHuXTTz+lYUP/3tULyJwtyMARlUyhA+IihfAK9qQp5SlOp5OJEyfyzjvv8OCDDzJx4sRKt1XR3+J+wM/GmIB9grWiCoqKyC4y1CEDV4I+haoqZv78+Vx99dV07dqVlStXkpyc7O+QThdU+RpRlMEJk0iteJ3+SHnH/v37SUlJ4fjx46xYsYJLL73U3yGdKTByNv8U+eHVAKvg1R5e5Q8Oh4Px48fzzjvv8Pjjj/Pkk08iIpVur6IF71jg/ZJeEJEbRWSTiGw6ceJEpQPytYLCQjILXdQwGbgS6vk7HBVEjDHMmTOHnj178tlnn5GYmOjvkM4UVPkaa8vgBEnUrKbDipR3rFu3jszMTD7//HN69Ojh73BKUmLO+jxfCzLIlQQA4rWHV/lJWloaK1eu5JlnnuHhhx8+5/bKfdkmIlHACOCBkl43xkwHpgN06dLFnHNkPmCMIbvAjjiLqObKIau6PmSkysdutxMZGcmCBQtwOp3Ex8f7O6TfCbp8NYYExylOmkRaJmjBqzyrOF+vv/56hg0bRs2aNf0d0h+cLWd9mq/2QrDnkyPW37SEqPBz6lVTqqLsdjsRERE0bdqUHTt2eCxfK3LZNhj4zhhzzCN7DgAOp5PsIhd13VOSUU0LXlW2119/nR49epCZmUlMTEzAFbtuQZWvrsJsok0R2eFJxEbrGF7lOdu2baNNmzakpqYCBGSx6xYYOVtgzUmfaay/a9VjI/0ZjQoxBQUFjBw5kgcffBDwbL5WpOC9hlJujwYrh9NJjs1QD/eiE4la8KqzmzZtGjfffDMNGjQgNjbW3+GcTVDlqyPLmpIsN0ILXuU5W7ZsoXfv3hQUFFCnTh1/h1OWwMjZfOt8eMplFbxJcVrwKt/Iz89nxIgRfPrpp5x//vkeb79cBa+IxAH9gUUej8CP7Ha7VfC6e3jDEnXRCVW65557jttvv51Ro0axcOFCogO0MAvGfHXmWJ1aBZHJREXqCVadu2+//ZY+ffoQExPDunXraNOmjb9DKlVA5ay7h/ekMw6ApDh9iFR5X25uLkOGDGH16tXMmjWLG264weP7KNcYXmNMPhCw94Eqy+5wkGszvy4rHFmjiZ8jUoHqtdde495772Xs2LHMnj2byAAuyoIxX11ZRwAoitJlhdW527VrF/369SMpKYk1a9bQrFkzf4d0VgGVs+4e3nR3D28NHVOvvMzlcjFs2DC+/PJL5syZwzXXXOOV/YT0o5d2u53sIhf15BSOsBgi4wNqSikVQIYNG8Y999zDu+++G9DFbrBy5VjLCtujk7TgVeesefPm3HjjjaSmpgZ8sRtwCooL3gQiw6BabGDeyVJVR1hYGDfddBPz5s3zWrELIV7wFtps5NqgQVgGtthahOnqTuo0xhgWLFiAy+WiSZMmTJ06Veej9BJXzjEcJgyJqa4Fr6q0L774grS0NMLDw5k6dSpNmuhduwpz9/CecCYQFylE6wW+8pKTJ0+ycuVKAMaOHcvo0aO9ur+QLngLCgvJsxsahFmrrClVzBjDnXfeydVXX828efP8HU6VZ8s8ykmqkxAdoRcVqlJWrlzJwIEDmTJlir9DCW4FpzCRsWQ5IomLFKKidAyv8rzjx4/Tp08frrzySjIyMnyyz5AueAuLisi1QV05pYtOqF+5XC5uueUWXnrpJW677Tav3mJRFlfucU6YJJJiI3TOT1Vhy5YtY/jw4bRs2ZLp06f7O5zglp+BiU4i326sZYX1zqfysCNHjtC7d2/27NnDokWLqFGjhk/2G9oFr81GbpGTmiYDowWvwip2J02axGuvvcY999zDiy++qAWYD4Tnp5NuEqmRoL1JqmIWL17M5ZdfTvv27Vm9ejW1a+vdunNScApXbPKvBW+EFrzKg9LS0khJSeGXX35h+fLlXHbZZT7bd0gXvHaHg3BbFpE4QVdZU8D27duZM2cODz30EM8++6wWuz4SXXSSEyaRWvpEuKoAp9PJk08+SefOnVm1alUgLyoRPAoycEUnkWc3xEWiyworj5o9ezZHjx7ls88+IyUlxaf7DunBckU2Gwm2dIgC0YI3pBljEBE6dOjAtm3bvDLptSqFMcTaMzlBEp2qB/RiHiqAGGMIDw9n2bJlREdHU716dX+HVDXkZ+BMauHu4Q3TMfXKI4rPsQ888ABjx471yzk2pC/dcgoc1HGvsmaq1/dzNMpfbDYbV199NW+99RaAFru+VnCKCBycMInUTozzdzQqCMyaNYtRo0Zhs9moXbu2FrueVJCBPbI6hQ6Ii0SHNKhztnv3brp3787u3bsREb+dY0O24HU4nWQXOX9dZU2qN/RzRMofioqKGD16NAsXLiQ3N9ff4YQkZ7a16ERueBLxMTqkQZ3d9OnTmTBhAnl5eTgcDn+HU7W4XFBwirzwBABrDK/28KpzsGPHDnr16sW+ffsoKCjwayyhW/A6HGTbDA0kHSfhhGsPb8gpKCjg8ssv5+OPP+bVV1/l9ttv93dIIcmZbS06kRehi06os3vllVeYNGkSQ4YMYcmSJcTF6R0BjyrKAuMi21irrMVHio7hVZX2448//jpOd+3atVx44YV+jSdkzy5WDy90kHTyo2oSGaWryYQSh8PB8OHDWb16Nf/5z3+8sm63Kh9X1mHAWlZYe5NUaV555RWmTJnCyJEjmT9/PtHR+jfb49yLTmS6C97qMZH64K6qlO3bt9OnTx9iYmJYvXo1rVq18ndIodvDa7PbyShw0UBOUhhbR5eLDTERERH079+fWbNmabHrZ8Y9pKEgpqb28KpSde/enYkTJ7JgwQItdr2lwBril+G0Hh5NitNpAlXlNGnShAEDBrBu3bqAKHYhhHt47XY7GYWGRnISR3xHHZgfIrKysti3bx+dOnXivvvu83c4CiDnMHkmhsjoOM1D9TvGGL744gt69epFly5dfn2wVHlJ/kkA0l1WD29yvBa8qmK+++47WrZsSbVq1Zg7d66/w/mdkO3hLbTZyMq3U0cycCbolGSh4NSpU/Tv358BAwboA2oBxGQf5ahJplpUmPbwql8ZY3jwwQdJSUlh2bJl/g4nNOSlA3DCYY2NTo7XnnRVfqmpqfTq1YvbbrvN36GUKGTPLvkFBYQVZhCBC5Ia+Tsc5WXp6ekMGDCAbdu2sWDBAhISEvwdknJz5RzhmEkmMTZclzFVgFXs3n333bzwwgvceOONDBo0yN8hhQZ3D+8xp/X3sWY1nRdblc+qVasYPnw4TZs25amnnvJ3OCUK2R7evIICYgtPABCefJ6fo1HedPz4cfr27cv27dtZvHgxI0aM8HdI6jRhucc5RjI19fapwlre+9Zbb+WFF17glltu4Y033iBMZwrwjfx0THgUJ+0xRIVDYpxOE6jK9umnnzJs2DBatGjB2rVrqV8/MGe9Ctm/Iqdy86nhsG7fRNTQgrcqe/bZZ9mzZw9Lly7VnqJAYwzRhSc4ZpKpobdPFbB+/XpeeeUV7rrrLv7f//t/OkuAL+WdhLia5DogIVKI0oe5VRlsNhuTJ0+mbdu2rFmzhjp16vg7pFKVa0iDiCQBbwEdAAP81Riz3puBeZMxhsOZhTQQd8Fbs6l/A1Je9a9//Ytx48bRqVMnf4fiE0GVrwWnCDd2jptkuifp7VMFPXv2ZMOGDXTt2jVkit2Aydn8dFyxNcgtcBEfpYtOqLJFRUWxYsUKatWqRXJysr/DOavy9vBOAz41xrQBOgI7vBeS9zkcDk7mO60pycITiIoP7P8kVXEHDhxg5MiRpKenExUVFTLFrlvw5GuONSXZUZNMg+R4Pwej/MXhcDBx4kQ+//xzALp16xYyxa5bYORsnlXw5tkhPkp0TL0q1fz587n77rsxxtCyZcuAL3ahHAWviFQHegEzAIwxNmNMprcD8ya7w0FmoYsGkk5+dG19MryK+fnnn+nVqxepqakcPHjQ3+H4VNDlq7vgPRWWTHKC9vCGIrvdzrXXXsvbb7/N5s2b/R2OzwVUzuan484T6LYAACAASURBVIypQZ7dkBApOk2gKtHs2bO59tpr2bhxI0VFRf4Op9zK08N7PnACmCki34vIWyLyh64YEblRRDaJyKYTJ054PFBPsgpeQ0M5SVFsHS14q5CdO3eSkpJCbm4uq1at4qKLLvJ3SL4WVPlavMpaYXRNvX0agoqKirjqqqtYsGABzz//PHfffbe/Q/KHMnPWZ/madxJnTDK5NveQBi141RlmzJjB//3f/9G7d2+WLVtGTEzwPNhYnoI3AugMvG6MuQjIA+4/cyNjzHRjTBdjTJfatWt7OEzPsjscZBVZq6y5EuqF2q2zKmvHjh307t0bm83G2rVr6dy5s79D8oegyldXtlXw2qOTidSTa0gpKiriyiuvZPHixbz88svceeed/g7JX8rMWZ/kq6MIbDnYoxIpsKM9vOoP3njjDW644QYGDhzIJ598Qnx8cA1DK0/BmwakGWM2uL9eiJWcQctmt1NUkEei5OGq3tDf4SgPSUxMpFWrVqxdu5YLLrjA3+H4S1Dlq8k+TCYJxMdGaw9viImMjKR+/fq88cYb3HLLLf4Ox58CI2fdi05kUQ0DJESFaU6q36lXrx6jRo3iv//9L7GxwTcErczfZmPMURE5KCKtjTE7gX7Adu+H5j35BQXEFFi3hSSpsZ+jUedq9+7dNGvWjAYNGrB27dqQ7rEPtnw12Uc46komKSaMSJ0CKSTk5eWRkZFB48aNmT59ekjnKwRQzuZbBW+GsXrtqsdGhPz/jbL89NNPtGnThssvv5yRI0cG7e9FeWdpmAK8JyI/AJ2Ap70XkvcVFBaSYLcKXp2DN7h98803dOvWjfvvt+4ABmsieljQ5Kszy1plLSlaiNLepCovOzubQYMG0bdvX4qKijRff+P/nHX38KY7rYK3hi4Eo4AnnniCDh06sH69NUteMOdsuc4wxpjNQBcvx+Iz2Xn5JNlPQCRE1Gzm73BUJX399dcMGjSI2rVrM2XKFH+HEzCCKV8l7xjHTFtqxUfqFEhVXGZmJoMGDWLTpk28//77REfrQiPFAiJni5cVtlu3qrXgDW3GGB555BGefPJJxo0bR7du3fwd0jkLyS6VYzmFNJITOIggIrGBv8NRlZCamsqQIUNo0KABq1evplGjRv4OSVWUy0lU4UmOkUzdxOB50ldVXEZGBgMGDOCHH35g4cKFXH755f4OSZ3JXfAetVs9vLWqaU6GKmMM999/P1OnTmXixIm8+eabVaJDIuSWFna5XBzPLqKxHCcrqg6RUdrLEGzy8/O56qqraNKkCevWrdNiN1jlnSDMODlmkqmfFOfvaJQX3XXXXWzdupWPPvpIi91AlZcOEs7RIuucWFsL3pC1bNkypk6dyuTJk5k+fXqVKHYhBHt47Q4Hpwqc9JTj5MXUIzFKb9sEm7i4OP773//SvHnzgF63W5XBvejEcZNM4xoJfg5GedPzzz/PhAkT6NWrl79DUaXJT8fE1SDbZogIg8Q4LXhD1ZAhQ1i8eDHDhw8P6jG7Zwq5Hl6b3U5moaGxnMCRUF/nGQwiS5Ys4dVXXwWgR48eWuwGu5yjAORGJFEtXnt4q5pDhw4xefJkCgsLqVGjhha7gS4vHRNXk1ybIT5SiNbOoJDidDq566672LZtGyLCiBEjqlSxCyFY8NodDmwFuSRJnk5JFkQ+/PBDrrzySt59913sdru/w1EeYLLSACiIrkWUTklWpfzyyy+kpKTw3nvvsXPnTn+Ho8oj/yQmtga5dkNClOg0gSHE4XAwYcIEXnjhBZYtW+bvcLwmJAveqPxj1hfJOiVZMHj//fcZM2YM3bp1Y8WKFfqHuIpwZR7EQTgmpoYWvFXIvn37SElJIT09nRUrVtCxY0d/h6TKIy8dV0wNcm1Wwat3P0OD3W7nuuuu49133+WJJ57gnnvu8XdIXhN6Ba/dTnyhdSuVpKZ+jUWVbfbs2Vx33XX07NmTzz77jOrVq/s7JOUhrsyDHDPJVIsN14K3itizZw+9evUiKyuLVatW0b17d3+HpMorPx1nTDJ5xQWvzotd5dlsNsaOHcv8+fOZOnUqDz30kL9D8qqQK3jzCgpItFk9vJG1m/s5GlWWU6dO0bdvX5YtW0ZCgj7YVJWYzEMcMjVJ1lXWqoz8/Hzi4+NZs2YNF198sb/DUeXldEDBKZwxydrDG0KcTieZmZm89NJLVbpnt1jIXcJl5+ZRw3GcvIgEoqvX9nc4qhTHjh2jbt263Hbbbdxyyy1VZloU9RuTfYgjpgnJsWF6cg1yx44do06dOlx44YVs27ZN8zXYFGQAUBSVSL5dC96qrqCgAJvNRmJiIitWrAiZfA25Ht60U3k05jiZUXX0KdQA9dJLL9GyZUu2bt0KEDLJGFJcLiLyj3HE1KRudZ3+KJht3ryZ9u3b88ILLwCar0HJvaxwtknAANWiw/X/sYrKy8tj+PDhDB06FKfTGVL/zyFV8BpjOJhRQGP3HLwxurRlwHn22We54447GDBgAK1atfJ3OMpb8tMJd9k5bGpQPzHW39GoStq0aRN9+/YlLi6OkSNH+jscVVl5JwBIN9awsRpxOsSoKsrJyWHIkCGsWbOGSZMmhVSxCyFW8NodDk7m2WkkJ7DH1yVSB+UHlCeeeIL777+fsWPHMm/ePKK0B77qck9JdtTUoGFyvJ+DUZWxfv16+vXrR2JiIqmpqbRo0cLfIanKche8xxzugjdBO4OqmqysLAYOHMhXX33Fe++9x7hx4/wdks+FVMFbZLNhyz1JtDiIrHFelZtUOZgtWLCARx55hPHjxzNnzhx9Qriqyz4EwHGpSd0kLXiDTUZGBoMHD6ZOnTqkpqbStGlTf4ekzkXucQAO26wFYGon6DCjqmbixIls3LiR+fPnM3bsWH+H4xchVfDa7HYic60ZGiJqNfNzNOp0V1xxBW+++SYzZ84MudssISnLKnjzImsRE6Mn12BTo0YN3n77bdatW0fjxrqAT9DLOw7/v737Do+qTP8//n4mvSeQAIGEGnoVEEVKCCAIqEgTRECxoCiKK4qoqysrX7trX10WFNFFFARRmvQgFqQoSq8hpEAS0vuU5/dHoj8LQgIzc6bcr+vyWiCTOZ9ZcnPuc85TTH5kVFQ9VYsJl2FGnub5559nxYoVjBo1yugohvGqhre8ooLQ8kwA/GJaGpxGaK159tlnOX36NL6+vkyZMgWTyat+JL2WLjhFJX7ooEhZg9eNfPnll6xatQqAkSNH0rBhQ4MTCbsozoaQGPLLrADUi5Ctvj1BVlYWzzzzDFprWrRowdChQ42OZCiv6i5KysqoY87Egg/+9WS8mZFsNhv33HMPjz32GB988IHRcYST2fLTOEMdIoNM0vC6iZUrV3L99dfz9NNPY7PZjI4j7KkkCx0aQ2G5lQAfCAuSMbzuLjMzk379+jFnzhwOHjxodByX4FUNb25hMbHWDHJ96xEYKFewRrFardx555288847PPLIIzz00ENGRxJOpgvSSLfVJSJAScPrBpYvX87IkSPp1KkTq1evlicxnqb4DDo4hmIzhMgua24vLS2NxMREUlNTWbNmDW3btjU6kkuo0U+1UioFKAKsgEVr3d2RoRzl5NliOqoz5AfE0lhWADCExWJh8uTJfPjhhzz55JM89dRTMnnQztyiXgszSNfNqRPkI6uluLhPPvmE8ePHc/nll7N27VoiIiKMjuRxDK/Z4mxs0W0pztKE+St85ILGbaWkpNC/f3/Onj3LunXruOqqq4yO5DJqc6ZJ0lrnOCyJg2mtSc8t5Vp1miPBHWXTCYMUFhaye/du5syZw+OPP250HE/muvVqs+JTcoZMfTn1w+XRqavbtm0bV111FatWrSIsLMzoOJ7MmJrVGkqysQbVpbjSJnd43dzRo0cpLS1lw4YNXH755UbHcSle81NdaTZTUZRDiKpARTWVu4pOVllZCVTN7t6xYwfBwTKkxGsVn0FpK6d1HdrKGrwuq6SkhJCQEF599VUqKioICpKZ+x6pLA9sZixBdSmq1MSGylMXd/RLvQ4cOJDjx4/LOfYcavrcQgPrlFK7lFJTzvUCpdQUpdROpdTO7Oxs+yW0k0qzGf/iqqWQAuq1MDiNdykvL2fUqFHcfPPNaK2lEB3Pteu1ekmyDF2XeGl4XdI777xDu3btOHXqFCaTSZpdxztvzTq0Xqs3nTAHRFFUoQkLUNLwupn9+/fTqlUrPv74YwA5x/6Fmja8vbTWXYEhwL1Kqb5/fIHWeq7WurvWuntMTIxdQ9qD2WwmuDQDAF9ZksxpysrKGD58OCtXrmTAgAFyZ905XLteC6t2WcvUdYmrK4/IXc3rr7/O1KlT6dixI674b7mHOm/NOrReqzedKPGNwmyD8ACTTEp0Iz/99BP9+vXDZrPRsWNHo+O4tBr9VGutM6r/NwtYDvRwZChHqDSbiajIxIwv/nWbGh3HK5SUlDBs2DDWr1/P/Pnzufvuu42O5BVcvl6rtxXO961LqCx/5FJeeuklpk+fzogRI1i2bJlsCuIkhtZsSVXDe8ZS9bSlTrCsmuIudu/eTVJSEv7+/iQnJ9OuXTujI7m0Cza8SqkQpVTYL78GBgF7HR3M3srKy6lnziTLpz6BwfIY1RluuukmkpOTWbhwIbfddpvRcbyCO9SrzjtJCUGYAsIIkCXJXMbChQt5+OGHGTt2LB9//DH+MrHXKQyv2eo7vJnmqvNi3VD5e3cHGRkZ9O/fn7CwMLZu3UqrVq2MjuTyajJQpz6wvPpRtC+wSGu91qGpHCA9t4jGZJIX0FCWJHOSRx99lAkTJnDjjTcaHcWbuHy96ryTZKoYIoJM0lS5kBtuuIGnn36aWbNmySx95zK2ZouzQPmQXl518SnbCruHhg0b8tRTTzFixAiaNGlidBy3cMF/1bTWx4HOTsjiUMezCrhcZbEnuDuBAfIY1VFyc3NZuXIlkyZNomfPnvTs2dPoSF7FHepV56eSaqtHZIAJf2msDKW1Zt68edx8882Eh4fz97//3ehIXsfwmi3JgtB65BSbAWggDa9LS05OJiIigi5duvDAAw8YHceteMXIdK01+WdSCVBmrGFxMgPVQXJychgwYABTpkwhJSXF6DjCFWmNKjhFijWauiG++Pj4GJ3Ia2mtmTFjBlOmTGH+/PlGxxFGKc6GkBhySqqWjoytIxNJXdWGDRsYMmQI06dPR2ttdBy34xUNb6XZ/OtEmbCGrWSlAAc4c+YMSUlJHDx4kBUrVtC0aVOjIwlXVJaHyVxCmo6hfpg8aTGKzWbjvvvu45VXXuG+++5j2rRpRkcSRinJQofW42yJmWA/ZCKpi1qzZg3XXnstCQkJLFmyRPqYi+AVDW9FZSWhJVUNb1AjmcVob5mZmfTr14/jx4+zatUqBg8ebHQk4aryTwKQpqOJjZRHp0aw2WzcfffdvPXWWzz00EO89tprcvL0ZsXZ6OBoCsq1DDNyUZ9//jk33HAD7du3Z/PmzdSrV8/oSG7JKxre8spK6lacophggqObGh3H42zevJn09HTWrFlD//79jY4jXFl+KgBpOoaGsumEITIyMlixYgWPP/44L7zwgjS73kxrKMnCGlSXggpNeKBsK+xqtNbMnz+fLl26sHHjRurWrWt0JLflFT/ZZeXlxFrSyfCLI1omrNmNxWLB19eX8ePHM3DgQLnqFBf2m4a3cbSMFXQmq9WKyWQiLi6On3/+WepVQHk+WCurG14brUJ8ZY6LC/nlHLt48WLMZjPh4eFGR3JrXnGH90xeIc1IIzcwnkBZBskujh07RseOHUlOTgaQk6eomfxUSlUw5aYQ6stscKcxm82MGzeOWbNmAVKvolrxL9sK16GgXBMRKNsKu4r333+fnj17kp+fT1BQkDS7duAVDe+p9DRiVCHFIfEESMN7yQ4dOkTfvn3Jzs4mIiLC6DjCjej8k5xW9agbbJLlAZ2koqKC0aNHs3TpUho0aGB0HOFKis8AkKsisWqoE+In2wq7gP/+979MnjyZyMhIWavcjrziUq4k4zAAprrNZRmkS7R//3769++P1potW7bQoUMHoyMJN6JzT5KmY6gTpOTi0wnKy8sZOXIka9as4c033+Tee+81OpJwJUWnAcg0hwKV1A+Vi1CjvfXWW0ybNo0hQ4bI9t525vGXcmaLBb+CFAAi4mWFhkuRkpJCv379MJlMJCcnS7MrakdrVEEqKdZoYoJ95NGpg2mtGTVqFGvXrmXu3LnS7Io/K8oE4GRF1QTSBrJyiqHmz5/PtGnTGD58OMuXL5dm1848vuEtr6ggvPQUZQQQ0bCl0XHcWnx8PBMnTiQ5OZk2bdoYHUe4m9JclLmUE9ZoGkTInSRHU0pxyy238N5773HnnXcaHUe4oqLT4BdCanHVSh0NI4MNDuTdBg4cyP3338+SJUsIkCFfducVDW9MZRpppkaEhcqs8IuxY8cOTp06hY+PDy+//DItW8qFg7gIv67BG0N8lJxYHaWwsJANGzYAcOONN3LLLbcYnEi4rKJMCGtAdvW2wnF15RzpbFprPvvsM2w2G02aNOG1117Dz8/P6FgeyeMb3uLSUuJtaWT5xxEkV0y1tm3btl+3CxbikvxmSbKmMXJidYS8vDyuvvpqhg8fTlZWltFxhKsrOg1hseSUVBLkK7usOZvWmr///e+MGDGCDz74wOg4Hs/jG97U9AxiVS5FwfEEyXiYWtmyZQvXXHMNsbGxzJs3z+g4wt39puFtJg2v3Z09e5YBAwbwww8/8NFHH8nSY+LCik9jC61HXpmViEDZZc2ZtNbMnDmTZ555hjvvvJOJEycaHcnjeXzDe/bkPgB0nWYySaYW1q9fz9ChQ2nSpAnJyck0atTI6EjC3eWfpMQUSrkpmPoRMqTBnrKyskhKSmL//v2sWLGC66+/3uhIwtVpDUWnsYXUI7/cRmSAwl8epTuF1poHHniAl156iXvvvZd33nlHloNzAo/+f1hrjTX7KADhcW0NTuM+tNY8+eSTtGzZki1btsjancIudO5xMlV96gYpGV5kZwsXLuTo0aOsXLmSIUOGGB1HuIOKQjCXYgmKoaBCExFokrGjTnLo0CHmzp3L3/72N9544w1pdp3Eo295VprNhJakUK79iIlvbXQct6C1RinFF198gVJK9u0W9pN7ghQdS90QH1mD105+qdcZM2Zw/fXX06pVK6MjCXdRvQavOSiawnJNZKBJnoI62C/12qZNG/bs2UPLli1RShkdy2t49GVFeUUFDcpTSDHFExYeaXQcl7dkyRJGjRpFZWUl0dHR0uwK+7GaIT+Vw+Z6xIb7yQYwdnDy5En69OnD4cOHUUpJsytqp3oN3jwViUVDTKhchDqSxWLh1ltvZf78+QC0atVKml0nq3HDq5TyUUr9oJRa6chA9lReXk5T20ky/JvKI9QLWLRoEePGjSMrK4uKigqj44hL5HL1mp+K0laO2+rTOEomj16q48ePk5iYyN69e8nPzzc6jrhEhtRrUdW2wumWqgmk9cLkHOkoZrOZm2++mYULF3LmzBmj43it2tzhnQ4ccFQQR8hJP0YUReQFNyU4SHaQ+SsLFixgwoQJ9O3bl7Vr1xIWJjPoPYBr1WvuCQBSbPVpLis0XJIjR46QmJhIUVERmzZtokePHkZHEpfO+fVafYf3VPUua/Vl0wmHqKysZOzYsXzyySe8+OKLPPbYY0ZH8lo1aniVUnHAMMCt1qbKOb4bgMqoBJl9+hcWLFjA5MmTGThwIKtWrSI0NNToSOISuWS95lU1vCd1A1o2CDc4jPs6evQoiYmJlJeXs3nzZrp27Wp0JHGJDKvXotPgH0ZGadVj9djIEKce3htYrVZGjRrF8uXLee2113jooYeMjuTVanqH91VgJmBzYBa7s52pumAOb9zR4CSuq1OnTtx00018/vnnBAfLFb6HcL16zT1OhQrkrIqgSbTc4b1YsbGx9O7dmy1bttCpUyej4wj7MKZeq3dZyymqGsIWK3d47c7Hx4devXrx9ttvc//99xsdx+tdsOFVSl0LZGmtd13gdVOUUjuVUjuzs7PtFvBiWaxWQguPka6jaRYfZ3Qcl/Pdd98B0LVrVxYtWkSgbMrhEVy2XnOPc9pUn7pBJkICZaxgbe3du5eioiJCQkL45JNPaN++vdGRhB0YWq9FpyGsAWdLzfgqiAqRurSXkpISfv75ZwBmzZrF3XffbXAiATW7w9sLuF4plQIsBvorpT7844u01nO11t211t1jYmLsHLP2yisqqF+RwglTE+qEyx2l33r22Wfp2bMny5cvNzqKsD+XrFede5wUXZ+YEJMML6qlHTt20KdPHzlpeibj6rUoEx3WgLxSK2EBSpYks5OioiKGDBlCUlISBQUFRscRv3HBhldr/ajWOk5r3RQYB2zSWk9weLJLVFZcQCNbBmcCmsiEtWpaa2bPns1jjz3G+PHjue6664yOJOzMJevVZoW8FI5Y6lE/xEca3lr45ptvGDhwIFFRUTzzzDNGxxF2Zli9ag3FZ37dZS0qUC5E7SE/P59BgwbxzTff8NZbbxEREWF0JPEbHrsOb87x3figKQprLg0vVc3u448/zlNPPcWtt97KwoUL8ZUreuEMhRkoayXHrPWJjwqStSdraOvWrQwaNIj69euzdetWmjRpYnQk4SnK8sBSjiW4HnnlmshAJbusXaLc3FwGDhzIrl27WLJkCWPHjjU6kviDWjW8WustWutrHRXGnrKOVA2JCmzYBl9Z5J5du3bx3HPPMWXKFObPny8L/3sBl6nX3OMApOgGtKgnw4tqwmKxcOeddxIfH09ycjJxcTIPwdM5tV4L0wGoDKq6w1s32EfOk5fo+eef5+eff2bZsmWMGDHC6DjiHDzyFp/WGrIOUqIDaNlGZjIDdO/enW3bttGzZ0+5wyacq3pJslRbPVo2kEd8NeHr68vKlSsJDw+nfv36RscRnqagquEt8IumwmqWTSfs4Omnn2bUqFGyLrYL88ghDaXl5USXHuO4akzj+sZPoDOKzWbj/vvvZ926dQBcddVV0uwK5zt7DIvy5TR1aSabTpzXF198wYwZM9Ba07JlS2l2hWMUpgGQZq66AI2NlGF/FyMjI4PRo0eTk5ODv7+/NLsuziMb3pKSYppZT5AWkECol64ta7Vauf3223njjTf4+uuvjY4jvNnZo2SaYokM8iE0SJa/+yvLli1j5MiRbN26ldLSUqPjCE9WkA4mX46XVt3ZbSRr8NbaqVOnSExM5Msvv+TYsWNGxxE14JEN79njPxJMBfnhCQQGeN+jGovFwi233MKCBQt46qmneOqpp4yOJLyYzjnMMd2QeiEmAv39jY7jkhYvXsyNN97I5ZdfzoYNGwgJkV2vhAMVZkBYLOkFlQA0jpYdNmvjxIkT9O3bl6ysLNatW8cVV1xhdCRRAx45hjfn6PcA+DXs7HWP8C0WC+PHj2fJkiU888wzPProo0ZHEt7MUgm5Jzho7UCDMF+ZCX4OH3zwAbfeeiu9e/dm5cqVhIXJsA/hYIXpEN6I0wXlKGSXtdo4evQo/fv3p7i4mI0bN9K9e3ejI4ka8sg7vD5n9lKsA2ncsoPRUZzOZDIRGRnJSy+9JM2uMF5eCkpbOWSJpXGUjBM8l4iICAYNGsTq1aul2RXOUZAGEY3IKq4kLEDJ7oe1EBwcTFxcHJs2bZJm18143B1ei9VKTPFhDqpmxNePNjqO05SXl5OdnU18fDz/+c9/vO7OtnBROYcBOKYbMlIem/7O0aNHSUhI4Prrr+e6666TmhXOoTUUZqDbDOPsSQtRgUo2naiBEydOEB8fT8OGDfn666+lXt2Qx93hLS0uoIk1hVP+CQQHescEmdLSUoYPH05iYiJlZWVSiMJ1VDe8x3UsrWJlSbJfvPbaa7Rp04bk5GQAqVnhPKVnwVqBJSSWvDJNpOyydkF79uyhR48ePPzww4DUq7vyuIa3MOUH/LGQH9aSEC9YoaGkpIRrr72W9evX88QTTxAku8oJV5JzhALfupQQTPN60vACvPjiizzwwAMMHz6cnj17Gh1HeJuCqiXJzCH1yavedEI2Ivpru3btIikpicDAQO655x6j44hL4HEN75kD3wDg27Cjx+8cU1RUxJAhQ0hOTmbhwoVMnjzZ6EhC/N7ZI5xSDYkKVESEyMXYnDlzmDlzJuPGjWPx4sX4y6oVwtkKMwDI96lLuQUahMv43b+yfft2BgwYQEREBFu3bqVly5ZGRxKXwOMaXjL3kKdDSfCCHdZmzpzJN998w0cffcSECROMjiPE72mNzjnMUWsD6oWYCPDy5m7jxo088cQTTJw4kQ8//FBWrBDGqN5WOLV604k4mUx6TuXl5YwYMYLo6GiSk5Np1qyZ0ZHEJfKoSWtmi4Xo4sPspzktG0QZHcfhnnnmGUaOHMnVV19tdBQh/qwkG1VewH5bLI0i/DGZPO/6ujb69+/PkiVLGDFihDxCFsYpSAOTH0eKqy5A4+vKZNJzCQwM5JNPPqFZs2Y0atTI6DjCDjzqDFRWkE28NZUT/q08doe17Oxs7rvvPsrLy4mKipJmV7iu6glrBywNaVrXM+vxQrTWPPHEE+zduxelFKNHj5ZmVxirMB3CG5KeVwYg233/wbp163j77bcB6N27tzS7HsSjGt6KE99gQpMd3tYjd1g7ffo0SUlJzJs3j59++snoOEKc3y9Lktka0qJ+uMFhnM9ms3HvvfcyZ84cli5danQcIaoUZkB4I9Lzy/BRUD/COy9Gz2XVqlVcd911zJ07l8rKSqPjCDvzqIY3/9DXWLXCr1Fnj3t8mpGRQb9+/Thx4gSrV6+mR48eRkcS4vxyjmA2BZBJHdo1ijQ6jVPZbDamTJnC22+/zcyZM/nHP/5hdCQhqlRvOnG6qJI6QUq2+6722WefMWLECDp27MjGjRtlQqkH8qiuboU1WwAAIABJREFU0C9zN4d0Y9o1b2x0FLs6deoUiYmJpKens3btWpKSkoyOJMSFZR0gwzcefx8TzWO85w6v1Wpl8uTJzJ8/nyeeeILnnntO1u0UrsFmhcJ0bOFx5JRYqBtkkoYXWLJkCWPGjKFr165s2LCBOnXqGB1JOIDHNLzaaqF+yQF+0K1o28izflgLCgrQWrN+/Xr69OljdBwhaibrAIdtjWgYZiLYi9aHNpvNpKen8/TTT/PPf/5Tml3hOooywWbBEtaQ3DJNdIiPrBZC1RPUK6+8knXr1hEZ6V1Po7yJx6zSUJG+hyBdzsnA1oR4yMk1JyeHunXr0qFDBw4ePIivr8f8dQlPV5oLxaf50dafJjEBHr8mNkBlZSVlZWVERESwdu1aqVfhevJTASgJjKWgQnv9Grw5OTlER0czffp07r33XqlZD3fBO7xKqUCl1PdKqT1KqX1KqdnOCFZb5uNfA5AX2c4jGt6DBw/SqVMnnn/+eQApRFEjLlOvWfsB+NkST6t6IYZEcKaKigpGjx7N4MGDsVgsUq+ixpxas3knAUizVj0FjYvy3glrc+fOpUWLFr9OAJea9Xw1GdJQAfTXWncGugDXKKWudGys2is59g1ndCT1GjZ1+2V/9u7dS2JiIjabjeuuu87oOMK9uEa9Zh0A4JAtnnYNPfsRYVlZGTfccANffPEFt9xyi5w4RW05r2ar7/AeLa9ae7eJl67B++abb3LXXXfRp08fWrVqZXQc4SQXbHh1leLq3/pV/6cdmuoiBJ3ZzU5bK7o0jTE6yiX58ccf6devH76+viQnJ9O+fXujIwk34jL1mrWfcp9QzhBFh3jPGlP/WyUlJVx33XV8+eWXzJs3j6lTpxodSbgZp9ZsfiqExXIir2rJraZeuAbvv/71L+677z5uuOEGli1bRmBgoNGRhJPUaNKaUspHKfUjkAWs11pvP8drpiildiqldmZnZ9s75/kVpBFReYbdujXt4tz35FpYWMigQYMIDg4mOTmZ1q1bGx1JuCGXqNesA6T6NCbcX9EwynPvIt19991s3ryZ999/n9tvv93oOMJNXahm7Vav+SchsjFpeWUoIK6O5w83+q0VK1YwY8YMxowZwyeffCJLj3mZGjW8Wmur1roLEAf0UEp1OMdr5mqtu2utu8fEOPcuq+VYMgBHAzsQHuy+43fDw8P597//TXJyMgkJCUbHEW7K8HrVGs7sZ781joZhPgR48Ell9uzZfPLJJ0ycONHoKMKNXahm7Vav+akQ2YTMwgoiAhUhgd41aW3o0KG88cYbLFq0SFan8EK1WpZMa50PbAGucUiai2Q5uoV8HQp1mhHsho8ntm3bxsqVKwEYPXo0zZo1MziR8ASG1WthBlQU8ENFI5rVCXD7MfV/lJeXxwsvvIDNZqN58+aMGjXK6EjCQzi0Zq0WKEjDFhHP6SIr9UNMHn0x+gutNa+88gqnT5/Gz8+PadOmyTh7L1WTVRpilFKR1b8OAgYCBx0drDZsKV/zna0tnePC3e7kunnzZgYPHszjjz+O1Wo1Oo5wcy5Rr9UT1g5Y42gd61kbTuTk5DBgwACeeOIJ9u7da3Qc4QGcVrNFGaCtmENiOV1sJS7C3+3Ol7Wlteahhx7iwQcfZN68eUbHEQaryWVOLPC+UsqHqgb5E631SsfGqoX8VIJL0/nWNpBhrRsYnaZW1q1bx/Dhw2nRogXr1q3z+H98hFMYX6/VS5Id0vHc7UET1rKyshg4cCBHjhxhxYoVdOrUyehIwjM4p2arV2jI9ommzALNoj17STKbzcb06dN58803mTZtGo8//rjRkYTBLtjwaq1/Ai5zQpaLYju+FRNw0K8dDzaMNjpOja1atYqRI0fStm1b1q9fj7PHPQvP5BL1mrWfQt+6FBJKWw9ZkiwzM5MBAwaQkpLCypUrGTBggNGRhIdwWs1WN7xHyiOBUlo1iHD4IY1is9mYOnUqc+fOZcaMGbz44ouy46Fw/62FLce2kKvDMNVtSkiw+1yxbty4kU6dOrFp0yZpdoVnOb2X46bGRAcr6oR5xizwgwcPkpWVxZo1a6TZFe4pPxVQ7CmomtjtyQ1vYWEh27Zt47HHHpNmV/zKvUdua40+sY1vbW3pEh/hFtuXlpWVERQUxEsvvURpaSmhoZ67ZJPwQpYKyD7Aj3oYcRG+bj8p5pd6TUpK4sSJE4SFed+6pcJD5J2E8IYcPVuJn8kz1+C1WCxorYmMjOS7774jNDRUml3xK/e+w5uXQkBpJt/Z2tG7ZX2j01zQhx9+SNu2bUlJScFkMkmzKzxP1n6wWdhR0YSEGPd54nIux44do127dvzvf/8DkGZXuLf8VIhsTGpeOfVClFuuaHQ+ZrOZ8ePHM378eGw2G2FhYdLsit9x64ZXH98CwE++HWgf79rjd9977z0mTZpE8+bNZQiD8FyZVfvS/6yb0b5RlMFhLt6hQ4dITEyksLCQtm3bGh1HiEuXdwJbRGMyiizEhvni70Hr0FZUVDBmzBiWLFlCz549MZncurURDuLWPxXWQ1+SQTS+UY0JC3HdsYL/+c9/uO2227j66qtZuXIlIS6cVYhLkrmHSp8QTukYOsS7Z8O7f/9+EhMTqaysZMuWLXTt2tXoSEJcGnMZFKZTEdaYs6WaxlGec3e3vLyckSNHsmLFCt544w0efPBBoyMJF+W+Da/VjEr5ii2WTnSJd931d5cuXcrdd9/NsGHDWLFiBcFuNLFOiFo7/ROp/s3xMZloHet+DW92djb9+vVDKcWWLVvo2LGj0ZGEuHR5KQCkq/poIKGe5wynmzhxImvWrOE///kP06ZNMzqOcGHu2/Cm7cDHXEyyrRMD2sUaneYv/bKpxLJlywj0sDFTQvyOzQqn97LP2pjYUBOhQe738x4TE8OsWbNITk6mXbt2RscRwj5yjwNwxFw19K91rGcsFwjw4IMPsmDBAqZMmWJ0FOHi3Lbh1UfWY8XEQf+OdGpSz+g4f/LBBx9QUlJCWFgYc+bMwd/NZ6sLcUE5R8BSxvbyJjSvG+hWE0a+//57du/eDVSdQFu1amVwIiHsqLrh/bG4audDd1+SrLCwkEWLFgHQs2dPJk2aZHAi4Q7ctuG1Hl7PblsrWjSsQ6gLDRPQWvPUU08xadIk3nrrLaPjCOE8mXsA2GluQud497mD9PXXXzNw4ECmTp2K1troOELY39ljEFSHA7mKiACIDnffeST5+fkMGjSIW265haNHjxodR7gR92x4i7PwzfqZzdZO9GpR1+g0v9Ja89hjjzF79mxuvfVWZsyYYXQkIZzn9E9YTAEc0w3p3tS1V035xZYtWxg8eDCxsbF8+umnbnVXWogayz0OdZpzKr+SBqE+brs+dm5uLgMHDmT37t0sXbqUhIQEoyMJN+KeDe+xTQAk2zrTr21Dg8NU0VozY8YMnnvuOe666y7mz5/vshPphHCIzD2k+zdFmXzo3Nh1LkT/yoYNGxg6dChNmjRhy5YtxMXFGR1JCMfIPYE1simni63ERwa45bJd2dnZJCUlsXfvXj777DOGDx9udCThZtzvpx6wHdlAHhEUhDSjcYxrPDo9c+YMixYt4r777uPtt992y39QhLhoNhtk/sTPlsbEh/sQHhJkdKILeuedd0hISGDz5s3ExrruxFchLomlAgpOURjYiDILNIt2nSGAtbFx40aOHj3KF198wdChQ42OI9yQ+20tbLPCsY0kWzvSIT4EP4MXz7bZbCilaNCgAT/88AMNGjSQx6LC+5w9AhUFfGVpTtuEYJeuAZvNhslk4sMPP6S0tJQ6deoYHUkIx8lLATSpqmo30pb1ww2NU1u/1Ou4ceNITEyUi1Nx0dzvNmTaDkxluWy0dqFXS2PHCVqtVm677TYefvhhtNbExsa69IleCIdJ2wnATmsC3Zq47nCGpUuX0rNnT3JzcwkMDJRmV3i+6hUaDpVV/ay3caMlyVJTU+nSpQvJyckA0uyKS+J+De/BVVjwZavuQmIb48bvWiwWJk2axPvvv094eLg0usK7pe2gwieU4zqW7s1cc8LaRx99xLhx4/D19cXX1/0ebglxUaob3h9KIvEzQdN67nGH98SJE/Tt25fU1FRZw17Yhdv9q68PrmIXbWlYN5xGdY1ZS9BsNnPzzTezZMkSnn32WWbNmmVIDiFcRvpOjvklEORnom1D19th7f333+e2226jT58+rFy5ktBQz9lpSojzOnsMAiPYn+dDvRAIdoPm8ejRoyQlJVFSUsLGjRvp1q2b0ZGEB3CvO7zZh1G5x1hp7kZiQpQhqyBorX9tdv/1r39JsytEZQmc2c/3lc1pHuXncpusfPTRR0yePJn+/fuzevVqaXaFd8k5jK7bkowiC40i/PB18dWD0tLS6Nu3L+Xl5WzevFmaXWE3F2x4lVLxSqnNSqkDSql9Sqnpzgh2TodWAbDB2o1hXeINiaCU4sYbb+TNN9/kb3/7myEZhPgrhtRrxo+grXxV3pxOca63g1OvXr244447+OKLLwh2oU1qhAAn1GzOYSojW5BTqmlW1/V//mNjYxkzZgxbtmyhc+fORscRHqQmQxoswAyt9W6lVBiwSym1Xmu938HZ/kQfXMVR1QyfsBjaxMU49dilpaVs376dpKQkRo8e7dRjC1ELzq/X9KoJaz/YEvi7C43fXbNmDYMGDaJx48bMnTvX6DhC/BXH1WxZPhSfITsgDg20iHHdpxt79uyhTp06xMfH89prrxkdR3igC97h1Vpnaq13V/+6CDgANHJ0sD8pzoK0naw0d6NHkzD8nDjppLi4mGHDhjFkyBDS09OddlwhasuQek3bSb5/LLmEu8wOa88//zxDhw5l3rx5RkcR4rwcWrM5RwA4oasmeLdo4HpPYAB27txJUlISkydPNjqK8GC1GsOrlGoKXAZsP8fXpiildiqldmZnZ9sn3W8dWo1Cs87ajX6t69v//f9CYWEh11xzDV999RXvvvsujRo5v9cX4mI4rV7TdnLApyXhAYr4aONngD/99NPMmjWLm266idtvv93oOELU2F/V7EXXa84hAPaVV12IJrjgCg3ffvstAwYMICIiQi5QhUPVuOFVSoUCnwIPaK0L//h1rfVcrXV3rXX3mBgHDDfY9xlnfGM5bmpM79YN7P/+55Cfn8/VV1/N9u3bWbx4MePHj3fKcYW4VE6r14J0KMpge0UzmkT6Gbo8n9aaJ554gieffJJJkybxwQcfyPJjwm2cr2Yvul5zDoOPP3uKwwn0hfoRrjWG96uvvmLQoEHUq1ePrVu30rRpU6MjCQ9Wo4ZXKeVHVSH+T2u9zLGRzqEkB31iK1+Yr6Bj/QCiwpwzDmnhwoX88MMPLF26VMbtCrfh1HpN/RaATWUt6dbE2OXIjh8/zssvv8wdd9zBe++9Z8gqLkJcDIfVbPZhqNOCI2fNxIX7EOBCK6horXn00UeJi4sjOTmZ+HhjJqIL73HB2x+q6pbNfOCA1vpfjo90Dgc+R2krSyuvZFT7+k67i3TfffcxYMAA2rdv75TjCXGpnF6vqd9i9glmn27CfS3rOfxw59OiRQt27txJmzZtMJnca8VF4b0cWrM5h7HWa09qhoUBCaEutUGSUorly5djs9moX995wxSF96rJWaEXMBHor5T6sfq/oQ7O9Xv7lpPp24jjpniu79rUoYc6ffo0AwYM4ODBgyilpNkV7sa59XryW44HtEGZfOjZwvkNr81m45577vl1FYZ27dpJsyvcjWNq1lIBeSfICYij0grtG7rGhLWVK1cyZswYKisriYmJkWZXOM0F7/BqrbcBxl0WFmehU7ax3Dqcy+OCqR/luEH36enp9O/fn/T0dLKysmjTpo3DjiWEIzi1XktzIWs/23zG0CLKl9Bg5+7gZLVamTJlCu+++65sACPclsNqNvc4aBuHzFVzXrq6wAoqy5cvZ+zYsXTu3JnS0lKX26RGeDbXvxVy4HOUtvGZ+UpuuCzOYYdJTU0lMTGRzMxMvvzyS/r27euwYwnhEU5tBzTry1rRo1kdpx7aYrFw66238u677/Lkk0/yzDPPOPX4Qri87IMA7CyOwkdBhzjn1ugfffzxx4wZM4Zu3bqxYcMGIiMjDc0jvI/rN7z7PiPDN54UUxyDOjim4U1NTaVv377k5OSwfv16evXq5ZDjCOFRTn6DTfnygy2B3q2cN5xBa82ECRP48MMPmTNnDrNnz3apsYlCuIQz+0GZ+CovikbhPoQFBxkW5ZdVjq666irWrVtHRIRrDK8Q3sW1G96i0+iUbSyrvJweccFEhDpmSZXo6Gi6du3Kxo0bueKKKxxyDCE8Tuq3nAxoic3kT6+WzlkqEKomu1x22WW88MILPP744047rhBuJWs/uk5zDuebaBkdaOhFYcuWLbnhhhtYs2YNYWFhhuUQ3s21F6n8eSkKzXJzTx66sond3/7IkSPUr1+f8PBwli1z/mprQritylLI+JGv9BBaR/sRGhTg8EOWl5dz9OhROnTowCOPPOLw4wnh1s7soziqLSXp0CHOmOEDu3btolu3bnTr1o1PP/3UkAxC/MK17/Du+YgDpgRKguMZ0KGxXd9679699O7dm9tuu82u7yuEV0j9FmxmNlW0oW8rB2w08wdlZWUMHz6cPn36kJub6/DjCeHWKooh7wQnTVXnzS7xzh+/+/rrr9O9e3eWLFni9GMLcS6u2/Ce/hnO7GVRRW+u61AXfz8/u731Dz/8QL9+/fD19eX//u//7Pa+QniNE8lYlS/f29rQv22sQw9VUlLCsGHDWL9+PS+//DJ16hg7+UYIl1c9YW1PRQMU0LlxXace/sUXX2T69OmMGDGC4cOHO/XYQvwV12149yzGgi9rdE9u7tnCbm+7Y8cO+vfvT0hICFu3bqV169Z2e28hvMbxZI76tcY3IIjLmjruDm9RURFDhgwhOTmZhQsXyhMZIWrizD4AthbEEBtmIirMeVsKz5kzh5kzZzJ27Fg+/vhjWXpMuAzXbHitFmw/fcJm22UkNKxLk/r2uaNjs9mYPHkyUVFRJCcn06KF/RppIbxGaS46cw8bytvSOTbIoVv4vvjii3zzzTcsWrSICRMmOOw4QniUM/vQfiF8kx9J63pBTpuw9uOPP/Lkk08yceJEPvzwQ/zs+GRWiEvlmpPWjm3CVJLFEsvNTLiyqd2K1WQysXz5cgIDA2XfbiEuVspXKDSbzO0Z1saxqzP8/e9/Z9CgQfTu3duhxxHCo2TtpzyqFUWpii7xUU47bJcuXdi8eTO9e/d26IWwEBfDNe/w7vmIAhXGvsCuXN3p0ldn2LRpEw899BBaa1q2bCnNrhCX4vgWKk1B7NEtGNC+kd3fPicnh/Hjx5OdnY2/v780u0LUhtZwZh9pflXnzu7NHLvDmtaaRx55hPXr1wOQmJgoza5wSa7X8JblYzu4kuXmngzt2ICASxz/8+WXXzJs2DC+/PJLCgsL7RRSCC92PJkfVVvqhwXQJMa+W32fOXOGfv36sXz5cvbv32/X9xbCKxRlQlkue80N8TVBlyaOa3htNhvTpk3jhRde+LXhFcJVuV7Du/dTTNZKVug+TOrd6pLe6osvvuD666+nTZs2bN68WXZ3EeJS5Z+C3GN8WdGexJb2XS0hIyODfv36ceLECVatWkViYqJd318Ir5DxIwDJRQ1pHOFDSKBj1si22Wzcdddd/Pvf/+bhhx/m+eefd8hxhLAX12p4tca64z0O6CYENWxHfMzFL5a9bNkyRo4cSadOndi4cSPR0Y59rCOEVzi+GYBt1vYM7Wy/rb5PnTpFYmIiaWlprF27lv79+9vtvYXwKpk/opWJTYWNaB/rmF3NrFYrt912G/PmzePxxx/n+eefl+29hctzrYY3Yzc+WT/zP0t/JvVOuKS38vf3p1evXmzYsEHW7RTCXo6s46wpmtP+jemZYL8Ja76+vkRFRbFu3Tr69Oljt/cVwutk/EhZeAsKrQF0a+qYc59SCl9fX/75z38yZ84caXaFW3CtVRp2LaCcAL4N6MM/2l/c3aOTJ0/SpEkTrr32WoYNGyaFKIS9WCrRxzaz3tyDns3D8fG59OvltLQ0GjRoQGxsLNu3b5d6FeJSZf7IyaDLALiyRT27vrXZbCYrK4tGjRrx3//+V+pVuBXXucNbXoj1p6V8ZunJwE6N8fOtfS/+7rvvkpCQwIYNGwCkGIWwp9RvUZXFbLBexrAul77SycGDB7niiiuYPn06IPUqxCUrzITiM+yqiCciQNGywcUPC/yjiooKRo8eTZ8+fSgpKZF6FW7HdRren5fgYynlE92fW/rUfvezt99+m9tvv50BAwbQq1cvBwQUwssdWYcZX3ap9gy8xOXI9u3bR79+/bBYLEydOtVOAYXwcplVE9Y2FsbRtl6g3ZYHKysrY8SIEXz++ec89NBDhISE2OV9hXCmCza8Sql3lVJZSqm9DktRPVltv25CcKOONKxbu6WOXnvtNe655x6uvfZaPvvsM4KCghwUVAjX56ia1UfWsUO3o02jOgT5X/wOSnv27KFfv36YTCaSk5Pp0KGDHVMK4V7sWq8ZVRPWviuP54rmde2QDkpLS7n++utZu3Yt//3vf7nnnnvs8r5COFtN7vAuAK5xaIr0qslqiyz9uaVP7Sarff311zzwwAOMHDmSTz/9lMDAQAeFFMJtLMDeNZt7ApVzmPWWzgzrfPF3dysrKxk+fDiBgYEkJyfTpk0bO4YUwi0twF71mvkjBcFNKSOQngn2Gb87a9YsNm3axIIFC7jjjjvs8p5CGOGCA2W11luVUk0dmuL7uZQSyLeBffhnLSerXXXVVXz00UeMGjVK9u0WAgfV7JF1AGyxdWFap4sfv+vv78///vc/GjZsSLNmzeyVTgi3Zbd61RrSd3HY1Al/H7jMThtOzJ49m0GDBnHttdfa5f2EMIrxY3iLzqD3fsrHlkSGdEvAZLpwJK01zz33HD///DNKKcaNGyfNrhCOdGg1p1QsPnUaUzes9kOGtm3bxttvvw1Ar169pNkVwt7yTkBJNtvKm9E8yo+ASxh2lJeXx4wZMygvLycqKkqaXeER7NbwKqWmKKV2KqV2Zmdn1/wbd72Hspl53zqIoTW4c6S1ZtasWTz66KO8//77l5BYCO9Vq3otzUWf+IrPzZfTJ6H2d422bNnC4MGDef311ykrK7vIxEJ4rxrV66kdAGwoSaBL/MXvKnr27FkGDBjAG2+8wa5duy76fYRwNXZreLXWc7XW3bXW3WNiYmr2TZYK9I75fMVlBEc3pl3c+RfJ1lrz4IMP8sILLzB16lReeOEFOyQXwvvUql4PrUFpK2utPRjauXbDGdavX8/QoUNp2rQpmzdvlgmlQlyEGtVr2vdYfEM4aIujV0INz8F/kJWVRVJSEvv372fFihWy4pHwKMYOadi3HFWSxX8rBzE1qdV5X2qz2Zg2bRqvvvoq06dP56233qrR8AchxCU68AVZKprs4AS6N6v5Hd7Vq1dz3XXX0bJlS7Zs2UKDBvbbmU0I8QenvudkQGtQJnq1jq31t2dmZpKUlMTRo0dZuXIlQ4YMcUBIIYxTk2XJPgK+BVorpdKUUrfb5chao797mxQaciT4MoZ1aXLel5vNZg4fPszMmTN55ZVXZNFrIf6CXWu2ogh9bBMrzd0Z1rFBreru2LFjdOjQgU2bNlHjpz5CeBm71GtFMZzZy7eVzWga6Uud0No/STl79iwlJSWsWbOGgQMH1vr7hXB1NVml4SaHHPnUdlTmj8wzT2bqwBaYTOc+kVqtVkpKSggPD2fVqlX4+flJsyvEedi1Zo+sR1kr+NJ6Oc/3alGjb8nLyyMqKor77ruPu+66C39/f7vFEcLT2KVeM3aDtrGxtAVXdI6q1bfm5eURGRlJhw4dOHz4sNSr8FiGjQnQX79KAaFsDUjkpivPfSI1m81MmDCBgQMHUlFRgb+/vzS7QjiR3v85uYRTFt2BpjEX3hBm0aJFNGvWjN27dwPIyVMIZzi1HYBd1gQGd2hY4287fvw4Xbp04bnnngOkXoVnM6bhzTqIOrSG9yyDmNi3DX6+f97+sLKykptuuonFixczevRoAgICDAgqhBerKMJ2aA2rLZczrueF7+4uWLCACRMmcNlll9Gq1fnH5Ash7Cjla075NcXmH0avVjUbv3v48GH69u1LcXExgwcPdnBAIYxnSMOrv3mNcvz5wvcaJvX684mxoqKCMWPG8Omnn/LKK68wc+ZMA1IK4eUOrsbHWs5a1ZsR3Zqe96Vz585l8uTJDBw4kFWrVhEaGuqcjEJ4O0sl+tR2kita0y0u+Jw3kP7owIEDJCYmUllZyebNm+natasTggphrAuO4bW7gnT0nk9YbOnPmKSOBPj9uTjvv/9+Pv/8c9566y3Zt1sIg5h/XMwZHU2dhMsJCvjrRezXrVvHXXfdxdChQ2V7byGcLX0XylzKV5Z2DOl44W2/S0pKGDBgAFC1Rna7du0cnVAIl+D0hld/+xZa2/jU71qW9m19ztc8+uij9OnThwkTJjg5nRACgOJsfE4k87l1GJP6tjnvS/v378/LL7/MvffeK0OPhHC2lG3YUOzQbXm244XXyQ4JCeHVV1+lS5cuMvRIeBXnDmkoy8Oy4z2+sF7JqP49f3d3t7i4mJdffhmbzUbTpk2l2RXCSPuWY8LK98F96d7s3EuKvfPOO2RmZuLr68uDDz4oza4QRkjZylEa06heHeqE/vXTle+//55Vq1YBcOONN0qzK7yOUxte27f/xs9aytKAG5hwVcKvf15QUMDgwYN55JFHZCtDIVxAya6POGCLp9vlV/3pa1prZs+ezdSpU3njjTcMSCeEAMBSgS11O19Z2jG4/V9PVvvmm28YOHAgDz/8MBaLxYkBhXAdzmt4y/KwfP0Wa6yXc8PgQfj6VB06Ly+Pq69bFUmeAAAI40lEQVS+mu+//56PP/6Yyy+/3GmRhBDnkHOUkKzdfGHrxc1/WJ1Ba83jjz/OU089xa233srTTz9tUEghBKnfYbJW8K2tHdde1vicL0lOTmbQoEE0aNCAdevW4evr/Kk7QrgCpzW8lq/fxN9awrKQsYzq3hSo2tllwIAB7Nmzh08//ZRRo0Y5K44Q4i+Uf/8eFm0iu8mw3z0i1Vrz8MMP8+yzzzJlyhTmz5+Pj8+FZ4QLIRzk6HrM+HAqtNM518neuHEjQ4YMoXHjxiQnJxMXF2dASCFcg3Mu9UpzsX77NuusPZgw4rpfN4/Yt28fJ06cYMWKFVxzzTVOiSKEOA9LJdYf/keyrSsTr+n9uy8VFRWxdu1apk2bxuuvvy6bwAhhsMqD69hhbUNSp2bn/PqqVatISEhgw4YN1KtXz8nphHAtTml4y756gyBrCevrjOeV1g2oqKggICCAvn37cuLECSIjI50RQwhxAdaDawgx5/FN6NXMjq8DgM1mw2q1Eh4eztdff014eLg0u0IYrSAN/9xDbLbdzPgeTX/3pV/OsS+99BLFxcWEh194l0QhPJ3jhzSU5qK2v8Nqaw9uHz2ctLQ0OnfuzAcffAAgza4QLiQ7eS6Zug6XD7wRAKvVyu233864ceOwWq1ERERIsyuEKziyHoDjYd1oXi/i1z/+9NNPadu2LSkpKZhMJml2hajm8IY3Z80z+FvL+KHJbYTZikhMTCQjI4PmzZs7+tBCiNrIT6Ve9tesMiUx5LJmWCwWJk2axIIFC+jUqRMmkzE7kQsh/qx43xrSdDSdOv//id6LFy9m7NixxMbGEhUVZWA6IVyPQ89gOi+FiJ/fYwV9uaZHZ/r27Utubi4bNmygV69ejjy0EKKWMta9jk0rfLpNwGa1MH78eBYtWsQzzzzDP/7xD7mzK4SrqCwhIGULG61dGX1F1UoqCxcu5Oabb6ZXr16sXbuWiIiI87+HEF7GoQ1vypLHsGpFTse7GD50EMXFxWzcuJEePXo48rBCiNqqLCH8wCI20IOxg/pyxx13sGTJEl5++WUeffRRo9MJIX7DemgdfrqCI1G9iasTwueff86tt95Kv379WL16NWFhYUZHFMLlOGzSWknKLpplrGKR3whuHzmUwpN/4+qrr6ZTp06OOqQQ4iKlb5lPI13CmTYTCfb3ZerUqVx55ZVMnTrV6GhCiD84s/1j/HU43ROvBaBfv3489NBDzJ49m6CgIIPTCeGaHNbwZix5iNOnA9CDRmIyKWbMmOGoQwkhLpHa/g7bK5thq6z6/ZVXXsmVV15pbCghxJ9pTVT6ZlbRC/OxnZS0bUB4eDgvvPCC0cmEcGkOGdJQWZxL0dEdDP2giBdmP4HNZnPEYYQQdlBRnEt4WRq3LyvijttuYf/+/UZHEkL8BUtpPkG6nE8OwPhx43jllVeMjiSEW6hRw6uUukYpdUgpdVQpNetCry8/m0bSwjIi68awbNkymd0thBPVtl5tBafp/6GFA0dSWLhwIe3atXNGTCFEtdrUrKU4m5nJJpYsWc64ceN45JFHnBVTCLemtNbnf4FSPsBh4GogDdgB3KS1/svbQD4mpevXr8+3322nSZMm9swrhFtSSu3SWnd3wnFqXa+h/kqX20x89NFixowZ4+iIQrg8Z9Vr9bFqVbOxYSZ9ulgzceJE3nvvPdneW3i9mtZrTW699gCOaq2Pa60rgcXA8PN9g4/JxPbvd0izK4Tz1bpeS82waNFH0uwKYYxa1WxOqWbkiOul2RWilmoyaa0RcOo3v08Drvjji5RSU4Ap1b+taNy48d5Lj+dyooEco0PYmSd+JnC9z+Wsq7+LqtexY8fuHTt2rBPiOZWr/QzYi3wux3Pm3ZoL1uwf63XZ8s/3+vo6bM65UVzp79+e5HM5Xo3qtSYVc67V5v80DkJrPReYC6CU2umsx0HO5ImfyxM/E3ju56oBqddq8rnci6d+rhq4YM1Kvbov+VyuoyZDGtKA+N/8Pg7IcEwcIcQlknoVwr1IzQrhBDVpeHcALZVSzZRS/sA44HPHxhJCXCSpVyHci9SsEE5wwSENWmuLUmoa8CXgA7yrtd53gW+ba49wLsgTP5cnfibw3M91XlKvvyOfy7146uc6r4uoWU/9/0k+l3txu891wWXJhBBCCCGEcGeyI4QQQgghhPBo0vAKIYQQQgiPZteGt7ZbmroDpVS8UmqzUuqAUmqfUmq60ZnsSSnlo5T6QSm10ugs9qKUilRKLVVKHaz+e+tpdCZXJPXqfqRevZvUrHvxxHoF961Zu43hvZgtTd2BUioWiNVa71ZKhQG7gBvc/XP9Qin1INAdCNdaX2t0HntQSr0PfKW1nlc96zlYa51vdC5XIvXqnqRevZfUrPvxxHoF961Ze97hrfWWpu5Aa52ptd5d/esi4ABVO+O4PaVUHDAMmGd0FntRSoUDfYH5AFrrSncoRANIvboZqVevJzXrRjyxXsG9a9aeDe+5tkd0+x/a31JKNQUuA7Ybm8RuXgVmAjajg9hRcyAbeK/6UdI8pVSI0aFckNSr+5F69W5Ss+7FE+sV3Lhm7dnw1mhLU3ellAoFPgUe0FoXGp3nUimlrgWytNa7jM5iZ75AV+BtrfVlQAngEWPd7Ezq1Y1IvQqkZt2GB9cruHHN2rPh9djtEZVSflQV4v+01suMzmMnvYDrlVIpVD0a66+U+tDYSHaRBqRprX+5Q7CUquIUvyf16l6kXoXUrPvw1HoFN65Zeza8Hrk9olJKUTVW5YDW+l9G57EXrfWjWus4rXVTqv6uNmmtJxgc65JprU8Dp5RSrav/aADg9pMfHEDq1Y1IvQqkZt2Gp9YruHfNXnBr4Zq6yC1N3UEvYCLws1Lqx+o/e0xrvdrATOL87gP+V31SOA5MNjiPy5F6FS5E6rUGpGaFC3HLmpWthYUQQgghhEeTndaEEEIIIYRHk4ZXCCGEEEJ4NGl4hRBCCCGER5OGVwghhBBCeDRpeIUQQgghhEeThlcIIYQQQng0aXiFEEIIIYRH+3+e4koxQ+MxDAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
fnamemodelpisig2_betasig2_zeroAslnum_annotfullcostfastcostnitannots
0CARDIOGRAM_CAD_2015params11.0000003.190300e-080.9317290.0000000.0000001142677.858266142259.77094649.0base
1CARDIOGRAM_CAD_2015params21.0000002.214803e-070.903487-0.062309-0.3798191142690.105506142242.908921228.0base
2CARDIOGRAM_CAD_2015params30.0003318.440960e-050.9418920.0000000.0000001142007.980791142020.006935103.0base
3CARDIOGRAM_CAD_2015params40.0004041.995192e-040.936226-0.038527-0.2251061142027.252159142012.683656240.0base
4CARDIOGRAM_CAD_2015params51.0000003.190300e-080.9125150.0000000.00000010142203.815582NaNNaNCoding_UCSC.bed Conserved_LindbladToh.bed Enha...
5CARDIOGRAM_CAD_2015params61.0000002.214803e-070.874831-0.062309-0.37981913142222.787414NaNNaNCoding_UCSC.bed Conserved_LindbladToh.bed Cons...
6CARDIOGRAM_CAD_2015params70.0058125.690202e-060.9111930.0000000.00000010141816.513019141572.363161122.0Coding_UCSC.bed Conserved_LindbladToh.bed Enha...
7CARDIOGRAM_CAD_2015params80.0051073.727530e-050.895732-0.062309-0.37981913141800.863172141799.817518124.0Coding_UCSC.bed Conserved_LindbladToh.bed Cons...
8CARDIOGRAM_CAD_2015params90.0046661.110516e-050.896230-0.296061-0.14177413141803.287370141782.209960240.0Coding_UCSC.bed Conserved_LindbladToh.bed Cons...
0GIANT_BMI_2015_EURparams11.0000006.400079e-080.7643860.0000000.0000001140896.341474139869.07860361.0base
1GIANT_BMI_2015_EURparams21.0000002.166433e-070.710826-0.306324-0.3020841140805.400513139823.936243240.0base
2GIANT_BMI_2015_EURparams30.0019642.978039e-050.7827920.0000000.0000001139685.079835139443.869362128.0base
3GIANT_BMI_2015_EURparams40.0023234.472118e-050.766083-0.200229-0.1591431139491.935943139428.768485240.0base
4GIANT_BMI_2015_EURparams51.0000006.400079e-080.7538770.0000000.0000009139881.537639NaNNaNConserved_LindbladToh.bed DHS_Trynka.extend.50...
5GIANT_BMI_2015_EURparams61.0000002.166433e-070.650127-0.306324-0.30208410140011.696242NaNNaNConserved_LindbladToh.bed DHS_Trynka.extend.50...
6GIANT_BMI_2015_EURparams70.0295102.159477e-060.7523880.0000000.0000009140045.076595139306.762723127.0Conserved_LindbladToh.bed DHS_Trynka.extend.50...
7GIANT_BMI_2015_EURparams80.0253956.818272e-060.710333-0.306324-0.30208410139681.651069139305.309619125.0Conserved_LindbladToh.bed DHS_Trynka.extend.50...
8GIANT_BMI_2015_EURparams90.0211943.498326e-060.726244-0.332022-0.12825110139616.586175139283.592648240.0Conserved_LindbladToh.bed DHS_Trynka.extend.50...
0GIANT_HEIGHT_2018_UKBparams11.0000002.942588e-072.3392590.0000000.0000001232517.983044218930.01930549.0base
1GIANT_HEIGHT_2018_UKBparams21.0000007.525474e-072.066815-0.117146-0.2231021232484.236446218832.186004240.0base
2GIANT_HEIGHT_2018_UKBparams30.0015692.043631e-042.2390040.0000000.0000001209856.249609210106.06233593.0base
3GIANT_HEIGHT_2018_UKBparams40.0017443.235556e-042.181802-0.433655-0.2517231209571.201944209901.539661240.0base
4GIANT_HEIGHT_2018_UKBparams51.0000002.942588e-072.3190690.0000000.00000012221988.550384NaNNaNCoding_UCSC.extend.500.bed Conserved_LindbladT...
5GIANT_HEIGHT_2018_UKBparams61.0000007.525474e-071.572479-0.117146-0.22310213222678.451375NaNNaNCoding_UCSC.extend.500.bed Conserved_LindbladT...
6GIANT_HEIGHT_2018_UKBparams70.0164802.156273e-051.9790940.0000000.00000012207717.127344207931.53747194.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
7GIANT_HEIGHT_2018_UKBparams80.0145614.741435e-051.913284-0.117146-0.22310213207335.832531207993.45133695.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
8GIANT_HEIGHT_2018_UKBparams90.0145412.274375e-051.893070-0.366428-0.12513013207238.355441207926.889297240.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
0IIBDGC_CD_2017params11.0000002.159094e-071.1538930.0000000.0000001180158.346327178834.89926254.0base
1IIBDGC_CD_2017params21.0000001.785049e-061.1273400.193086-0.3761141180107.933187178808.104848233.0base
2IIBDGC_CD_2017params30.0001081.865759e-031.1704920.0000000.0000001177791.617301177787.523352107.0base
.......................................
6PGC_BIP_2016params70.0420004.386598e-061.0780440.0000000.00000010136325.566150136324.796134127.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
7PGC_BIP_2016params80.0316993.127124e-051.030445-0.202889-0.38496011136322.720624136317.453360125.0Coding_UCSC.bed Coding_UCSC.extend.500.bed Con...
8PGC_BIP_2016params90.0251552.099188e-051.052301-0.001233-0.21667511136313.895974136313.690817240.0Coding_UCSC.bed Coding_UCSC.extend.500.bed Con...
0PGC_SCZ_2014_EURparams11.0000002.170895e-071.1727290.0000000.0000001151915.046221151912.67589751.0base
1PGC_SCZ_2014_EURparams21.0000007.138049e-071.124776-0.110148-0.2530081151948.232276151892.892008226.0base
2PGC_SCZ_2014_EURparams30.0042115.172702e-051.1722550.0000000.0000001151822.097961151829.583122141.0base
3PGC_SCZ_2014_EURparams40.0056521.098791e-041.139541-0.033306-0.2129021151805.245435151813.582699240.0base
4PGC_SCZ_2014_EURparams51.0000002.170895e-071.1723830.0000000.00000012151683.624896NaNNaNConserved_LindbladToh.bed Conserved_LindbladTo...
5PGC_SCZ_2014_EURparams61.0000007.138049e-071.101870-0.110148-0.25300810151705.539728NaNNaNConserved_LindbladToh.bed Conserved_LindbladTo...
6PGC_SCZ_2014_EURparams70.0412835.849195e-061.1481010.0000000.00000012151624.012587151630.843593109.0Conserved_LindbladToh.bed Conserved_LindbladTo...
7PGC_SCZ_2014_EURparams80.0389741.812297e-051.106621-0.110148-0.25300810151619.548301151625.223701121.0Conserved_LindbladToh.bed Conserved_LindbladTo...
8PGC_SCZ_2014_EURparams90.0341831.129315e-051.117740-0.166779-0.13841010151613.858653151617.766798240.0Conserved_LindbladToh.bed Conserved_LindbladTo...
0SSGAC_EDU_2018_no23andMeparams11.0000005.906943e-081.2177390.0000000.0000001193888.639439193548.01269144.0base
1SSGAC_EDU_2018_no23andMeparams21.0000001.771621e-071.149724-0.065344-0.2407201193717.163319193484.636336219.0base
2SSGAC_EDU_2018_no23andMeparams30.0057911.013542e-051.2206230.0000000.0000001193051.857260193118.82021593.0base
3SSGAC_EDU_2018_no23andMeparams40.0073562.052341e-051.172434-0.082898-0.2159261193007.617302193071.506200240.0base
4SSGAC_EDU_2018_no23andMeparams51.0000005.906943e-081.2683170.0000000.0000009192959.303909NaNNaNConserved_LindbladToh.bed Conserved_LindbladTo...
5SSGAC_EDU_2018_no23andMeparams61.0000001.771621e-071.129882-0.065344-0.2407209192858.668859NaNNaNConserved_LindbladToh.bed Conserved_LindbladTo...
6SSGAC_EDU_2018_no23andMeparams70.0849288.271419e-071.1943890.0000000.0000009192542.969630192583.295829119.0Conserved_LindbladToh.bed Conserved_LindbladTo...
7SSGAC_EDU_2018_no23andMeparams80.0696492.582101e-061.131807-0.065344-0.2407209192527.324705192553.901700122.0Conserved_LindbladToh.bed Conserved_LindbladTo...
8SSGAC_EDU_2018_no23andMeparams90.0664391.775282e-061.142690-0.104342-0.1575129192487.658757192542.778652240.0Conserved_LindbladToh.bed Conserved_LindbladTo...
0UKB_HEIGHT_2018_irntparams11.0000002.589774e-071.6464350.0000000.0000001230217.064658221697.18713653.0base
1UKB_HEIGHT_2018_irntparams21.0000006.453184e-071.551952-0.050818-0.2032091229416.018778221632.605685237.0base
2UKB_HEIGHT_2018_irntparams30.0012132.231666e-041.6491270.0000000.0000001216492.168670216151.75667390.0base
3UKB_HEIGHT_2018_irntparams40.0013423.511158e-041.612809-0.409276-0.2403541215942.349178215989.173963240.0base
4UKB_HEIGHT_2018_irntparams51.0000002.589774e-071.5891400.0000000.00000014222375.499451NaNNaNCoding_UCSC.extend.500.bed Conserved_LindbladT...
5UKB_HEIGHT_2018_irntparams61.0000006.453184e-071.341628-0.050818-0.20320915222685.073554NaNNaNCoding_UCSC.extend.500.bed Conserved_LindbladT...
6UKB_HEIGHT_2018_irntparams70.0144252.084494e-051.4956260.0000000.00000014213831.605292214380.195265101.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
7UKB_HEIGHT_2018_irntparams80.0129334.744524e-051.464666-0.050818-0.20320915213741.792377214439.76613395.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
8UKB_HEIGHT_2018_irntparams90.0132262.264136e-051.440127-0.366972-0.12752115213900.730947214354.068605240.0Coding_UCSC.extend.500.bed Conserved_LindbladT...
\n", - "

108 rows × 12 columns

\n", - "
" - ], - "text/plain": [ - " fname model pi sig2_beta sig2_zeroA \\\n", - "0 CARDIOGRAM_CAD_2015 params1 1.000000 3.190300e-08 0.931729 \n", - "1 CARDIOGRAM_CAD_2015 params2 1.000000 2.214803e-07 0.903487 \n", - "2 CARDIOGRAM_CAD_2015 params3 0.000331 8.440960e-05 0.941892 \n", - "3 CARDIOGRAM_CAD_2015 params4 0.000404 1.995192e-04 0.936226 \n", - "4 CARDIOGRAM_CAD_2015 params5 1.000000 3.190300e-08 0.912515 \n", - "5 CARDIOGRAM_CAD_2015 params6 1.000000 2.214803e-07 0.874831 \n", - "6 CARDIOGRAM_CAD_2015 params7 0.005812 5.690202e-06 0.911193 \n", - "7 CARDIOGRAM_CAD_2015 params8 0.005107 3.727530e-05 0.895732 \n", - "8 CARDIOGRAM_CAD_2015 params9 0.004666 1.110516e-05 0.896230 \n", - "0 GIANT_BMI_2015_EUR params1 1.000000 6.400079e-08 0.764386 \n", - "1 GIANT_BMI_2015_EUR params2 1.000000 2.166433e-07 0.710826 \n", - "2 GIANT_BMI_2015_EUR params3 0.001964 2.978039e-05 0.782792 \n", - "3 GIANT_BMI_2015_EUR params4 0.002323 4.472118e-05 0.766083 \n", - "4 GIANT_BMI_2015_EUR params5 1.000000 6.400079e-08 0.753877 \n", - "5 GIANT_BMI_2015_EUR params6 1.000000 2.166433e-07 0.650127 \n", - "6 GIANT_BMI_2015_EUR params7 0.029510 2.159477e-06 0.752388 \n", - "7 GIANT_BMI_2015_EUR params8 0.025395 6.818272e-06 0.710333 \n", - "8 GIANT_BMI_2015_EUR params9 0.021194 3.498326e-06 0.726244 \n", - "0 GIANT_HEIGHT_2018_UKB params1 1.000000 2.942588e-07 2.339259 \n", - "1 GIANT_HEIGHT_2018_UKB params2 1.000000 7.525474e-07 2.066815 \n", - "2 GIANT_HEIGHT_2018_UKB params3 0.001569 2.043631e-04 2.239004 \n", - "3 GIANT_HEIGHT_2018_UKB params4 0.001744 3.235556e-04 2.181802 \n", - "4 GIANT_HEIGHT_2018_UKB params5 1.000000 2.942588e-07 2.319069 \n", - "5 GIANT_HEIGHT_2018_UKB params6 1.000000 7.525474e-07 1.572479 \n", - "6 GIANT_HEIGHT_2018_UKB params7 0.016480 2.156273e-05 1.979094 \n", - "7 GIANT_HEIGHT_2018_UKB params8 0.014561 4.741435e-05 1.913284 \n", - "8 GIANT_HEIGHT_2018_UKB params9 0.014541 2.274375e-05 1.893070 \n", - "0 IIBDGC_CD_2017 params1 1.000000 2.159094e-07 1.153893 \n", - "1 IIBDGC_CD_2017 params2 1.000000 1.785049e-06 1.127340 \n", - "2 IIBDGC_CD_2017 params3 0.000108 1.865759e-03 1.170492 \n", - ".. ... ... ... ... ... \n", - "6 PGC_BIP_2016 params7 0.042000 4.386598e-06 1.078044 \n", - "7 PGC_BIP_2016 params8 0.031699 3.127124e-05 1.030445 \n", - "8 PGC_BIP_2016 params9 0.025155 2.099188e-05 1.052301 \n", - "0 PGC_SCZ_2014_EUR params1 1.000000 2.170895e-07 1.172729 \n", - "1 PGC_SCZ_2014_EUR params2 1.000000 7.138049e-07 1.124776 \n", - "2 PGC_SCZ_2014_EUR params3 0.004211 5.172702e-05 1.172255 \n", - "3 PGC_SCZ_2014_EUR params4 0.005652 1.098791e-04 1.139541 \n", - "4 PGC_SCZ_2014_EUR params5 1.000000 2.170895e-07 1.172383 \n", - "5 PGC_SCZ_2014_EUR params6 1.000000 7.138049e-07 1.101870 \n", - "6 PGC_SCZ_2014_EUR params7 0.041283 5.849195e-06 1.148101 \n", - "7 PGC_SCZ_2014_EUR params8 0.038974 1.812297e-05 1.106621 \n", - "8 PGC_SCZ_2014_EUR params9 0.034183 1.129315e-05 1.117740 \n", - "0 SSGAC_EDU_2018_no23andMe params1 1.000000 5.906943e-08 1.217739 \n", - "1 SSGAC_EDU_2018_no23andMe params2 1.000000 1.771621e-07 1.149724 \n", - "2 SSGAC_EDU_2018_no23andMe params3 0.005791 1.013542e-05 1.220623 \n", - "3 SSGAC_EDU_2018_no23andMe params4 0.007356 2.052341e-05 1.172434 \n", - "4 SSGAC_EDU_2018_no23andMe params5 1.000000 5.906943e-08 1.268317 \n", - "5 SSGAC_EDU_2018_no23andMe params6 1.000000 1.771621e-07 1.129882 \n", - "6 SSGAC_EDU_2018_no23andMe params7 0.084928 8.271419e-07 1.194389 \n", - "7 SSGAC_EDU_2018_no23andMe params8 0.069649 2.582101e-06 1.131807 \n", - "8 SSGAC_EDU_2018_no23andMe params9 0.066439 1.775282e-06 1.142690 \n", - "0 UKB_HEIGHT_2018_irnt params1 1.000000 2.589774e-07 1.646435 \n", - "1 UKB_HEIGHT_2018_irnt params2 1.000000 6.453184e-07 1.551952 \n", - "2 UKB_HEIGHT_2018_irnt params3 0.001213 2.231666e-04 1.649127 \n", - "3 UKB_HEIGHT_2018_irnt params4 0.001342 3.511158e-04 1.612809 \n", - "4 UKB_HEIGHT_2018_irnt params5 1.000000 2.589774e-07 1.589140 \n", - "5 UKB_HEIGHT_2018_irnt params6 1.000000 6.453184e-07 1.341628 \n", - "6 UKB_HEIGHT_2018_irnt params7 0.014425 2.084494e-05 1.495626 \n", - "7 UKB_HEIGHT_2018_irnt params8 0.012933 4.744524e-05 1.464666 \n", - "8 UKB_HEIGHT_2018_irnt params9 0.013226 2.264136e-05 1.440127 \n", - "\n", - " s l num_annot fullcost fastcost nit \\\n", - "0 0.000000 0.000000 1 142677.858266 142259.770946 49.0 \n", - "1 -0.062309 -0.379819 1 142690.105506 142242.908921 228.0 \n", - "2 0.000000 0.000000 1 142007.980791 142020.006935 103.0 \n", - "3 -0.038527 -0.225106 1 142027.252159 142012.683656 240.0 \n", - "4 0.000000 0.000000 10 142203.815582 NaN NaN \n", - "5 -0.062309 -0.379819 13 142222.787414 NaN NaN \n", - "6 0.000000 0.000000 10 141816.513019 141572.363161 122.0 \n", - "7 -0.062309 -0.379819 13 141800.863172 141799.817518 124.0 \n", - "8 -0.296061 -0.141774 13 141803.287370 141782.209960 240.0 \n", - "0 0.000000 0.000000 1 140896.341474 139869.078603 61.0 \n", - "1 -0.306324 -0.302084 1 140805.400513 139823.936243 240.0 \n", - "2 0.000000 0.000000 1 139685.079835 139443.869362 128.0 \n", - "3 -0.200229 -0.159143 1 139491.935943 139428.768485 240.0 \n", - "4 0.000000 0.000000 9 139881.537639 NaN NaN \n", - "5 -0.306324 -0.302084 10 140011.696242 NaN NaN \n", - "6 0.000000 0.000000 9 140045.076595 139306.762723 127.0 \n", - "7 -0.306324 -0.302084 10 139681.651069 139305.309619 125.0 \n", - "8 -0.332022 -0.128251 10 139616.586175 139283.592648 240.0 \n", - "0 0.000000 0.000000 1 232517.983044 218930.019305 49.0 \n", - "1 -0.117146 -0.223102 1 232484.236446 218832.186004 240.0 \n", - "2 0.000000 0.000000 1 209856.249609 210106.062335 93.0 \n", - "3 -0.433655 -0.251723 1 209571.201944 209901.539661 240.0 \n", - "4 0.000000 0.000000 12 221988.550384 NaN NaN \n", - "5 -0.117146 -0.223102 13 222678.451375 NaN NaN \n", - "6 0.000000 0.000000 12 207717.127344 207931.537471 94.0 \n", - "7 -0.117146 -0.223102 13 207335.832531 207993.451336 95.0 \n", - "8 -0.366428 -0.125130 13 207238.355441 207926.889297 240.0 \n", - "0 0.000000 0.000000 1 180158.346327 178834.899262 54.0 \n", - "1 0.193086 -0.376114 1 180107.933187 178808.104848 233.0 \n", - "2 0.000000 0.000000 1 177791.617301 177787.523352 107.0 \n", - ".. ... ... ... ... ... ... \n", - "6 0.000000 0.000000 10 136325.566150 136324.796134 127.0 \n", - "7 -0.202889 -0.384960 11 136322.720624 136317.453360 125.0 \n", - "8 -0.001233 -0.216675 11 136313.895974 136313.690817 240.0 \n", - "0 0.000000 0.000000 1 151915.046221 151912.675897 51.0 \n", - "1 -0.110148 -0.253008 1 151948.232276 151892.892008 226.0 \n", - "2 0.000000 0.000000 1 151822.097961 151829.583122 141.0 \n", - "3 -0.033306 -0.212902 1 151805.245435 151813.582699 240.0 \n", - "4 0.000000 0.000000 12 151683.624896 NaN NaN \n", - "5 -0.110148 -0.253008 10 151705.539728 NaN NaN \n", - "6 0.000000 0.000000 12 151624.012587 151630.843593 109.0 \n", - "7 -0.110148 -0.253008 10 151619.548301 151625.223701 121.0 \n", - "8 -0.166779 -0.138410 10 151613.858653 151617.766798 240.0 \n", - "0 0.000000 0.000000 1 193888.639439 193548.012691 44.0 \n", - "1 -0.065344 -0.240720 1 193717.163319 193484.636336 219.0 \n", - "2 0.000000 0.000000 1 193051.857260 193118.820215 93.0 \n", - "3 -0.082898 -0.215926 1 193007.617302 193071.506200 240.0 \n", - "4 0.000000 0.000000 9 192959.303909 NaN NaN \n", - "5 -0.065344 -0.240720 9 192858.668859 NaN NaN \n", - "6 0.000000 0.000000 9 192542.969630 192583.295829 119.0 \n", - "7 -0.065344 -0.240720 9 192527.324705 192553.901700 122.0 \n", - "8 -0.104342 -0.157512 9 192487.658757 192542.778652 240.0 \n", - "0 0.000000 0.000000 1 230217.064658 221697.187136 53.0 \n", - "1 -0.050818 -0.203209 1 229416.018778 221632.605685 237.0 \n", - "2 0.000000 0.000000 1 216492.168670 216151.756673 90.0 \n", - "3 -0.409276 -0.240354 1 215942.349178 215989.173963 240.0 \n", - "4 0.000000 0.000000 14 222375.499451 NaN NaN \n", - "5 -0.050818 -0.203209 15 222685.073554 NaN NaN \n", - "6 0.000000 0.000000 14 213831.605292 214380.195265 101.0 \n", - "7 -0.050818 -0.203209 15 213741.792377 214439.766133 95.0 \n", - "8 -0.366972 -0.127521 15 213900.730947 214354.068605 240.0 \n", - "\n", - " annots \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Coding_UCSC.bed Conserved_LindbladToh.bed Enha... \n", - "5 Coding_UCSC.bed Conserved_LindbladToh.bed Cons... \n", - "6 Coding_UCSC.bed Conserved_LindbladToh.bed Enha... \n", - "7 Coding_UCSC.bed Conserved_LindbladToh.bed Cons... \n", - "8 Coding_UCSC.bed Conserved_LindbladToh.bed Cons... \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Conserved_LindbladToh.bed DHS_Trynka.extend.50... \n", - "5 Conserved_LindbladToh.bed DHS_Trynka.extend.50... \n", - "6 Conserved_LindbladToh.bed DHS_Trynka.extend.50... \n", - "7 Conserved_LindbladToh.bed DHS_Trynka.extend.50... \n", - "8 Conserved_LindbladToh.bed DHS_Trynka.extend.50... \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "5 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "6 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "7 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "8 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "0 base \n", - "1 base \n", - "2 base \n", - ".. ... \n", - "6 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "7 Coding_UCSC.bed Coding_UCSC.extend.500.bed Con... \n", - "8 Coding_UCSC.bed Coding_UCSC.extend.500.bed Con... \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "5 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "6 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "7 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "8 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "5 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "6 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "7 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "8 Conserved_LindbladToh.bed Conserved_LindbladTo... \n", - "0 base \n", - "1 base \n", - "2 base \n", - "3 base \n", - "4 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "5 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "6 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "7 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "8 Coding_UCSC.extend.500.bed Conserved_LindbladT... \n", - "\n", - "[108 rows x 12 columns]" - ] - }, - "execution_count": 327, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def insert_key_to_dictionary_as_list(key, value, df_data):\n", - " if key not in df_data:\n", - " df_data[key] = []\n", - " df_data[key].append(value)\n", - "\n", - "df_final=None\n", - "traits=['CARDIOGRAM_CAD_2015', 'GIANT_BMI_2015_EUR', 'GIANT_HEIGHT_2018_UKB', 'IIBDGC_CD_2017', 'IIBDGC_UC_2017', 'LIPIDS_HDL_2013', 'LIPIDS_LDL_2013', 'LIPIDS_TG_2013', 'PGC_BIP_2016', 'PGC_SCZ_2014_EUR', 'SSGAC_EDU_2018_no23andMe', 'UKB_HEIGHT_2018_irnt' ]\n", - "for trait in traits:\n", - " fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.outtag=run1.fit.json'.format(trait)\n", - " data = json.loads(open(fname).read())\n", - " df_data = {}\n", - " for i in range(1, 10):\n", - " p = data['params{}'.format(i)]['params']\n", - " p['num_annot'] = len(p['annonames'])\n", - " p['model'] = 'params{}'.format(i)\n", - " p['fullcost'] = data['params{}'.format(i)]['full_cost']\n", - " not_has_optimize = not data['params{}'.format(i)]['optimize']\n", - " p['fastcost'] = np.nan if not_has_optimize else data['params{}'.format(i)]['optimize'][1][1]['fun']\n", - " p['nit'] = np.nan if not_has_optimize else data['params{}'.format(i)]['optimize'][1][1]['nit']\n", - " p['annots'] = ' '.join(p['annonames'])\n", - " insert_key_to_dictionary_as_list('fname', fname.split('/')[-1].split('.')[0], df_data) \n", - " for k in ['model', 'pi', 'sig2_beta', 'sig2_zeroA', 's', 'l', 'num_annot', 'fullcost', 'fastcost', 'nit','annots']:\n", - " insert_key_to_dictionary_as_list(k, p[k], df_data) \n", - " df=pd.DataFrame(df_data) \n", - " mincost = 0 # np.min([np.min(df['fullcost'].values), np.nanmin(df['fastcost'].values)])\n", - " df['fullcost'] = df['fullcost'] - mincost\n", - " df['fastcost'] = df['fastcost'] - mincost\n", - " df_final = pd.concat([df_final, df]) if (df_final is not None) else df\n", - "df_final.to_csv('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined.csv',index=False,sep='\\t')\n", - "df_final" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def make_qq_plot(qq, color, ylim=7.3, xlim=7.3):\n", - " hv_logp = np.array(qq['hv_logp']).astype(float)\n", - " data_logpvec = np.array(qq['data_logpvec']).astype(float)\n", - " model_logpvec = np.array(qq['model_logpvec']).astype(float)\n", - " ylim_data = max(hv_logp[np.isfinite(data_logpvec)])\n", - " model_logpvec[hv_logp > ylim_data]=np.nan\n", - " hData = plt.plot(data_logpvec, hv_logp, color=color, linestyle='solid')\n", - " hModel = plt.plot(model_logpvec, hv_logp, color=color, linestyle='dashed')\n", - " hNull = plt.plot(hv_logp, hv_logp, 'k--')\n", - " plt.ylim(0, ylim); plt.xlim(0, xlim)\n", - " return hData\n", - "\n", - "cm = plt.cm.get_cmap('tab10')\n", - "legends_h = []; legends_n = []\n", - "trait_index = 1\n", - "downsample_factor=50\n", - "# nope, that's a bad definition - we need an LD-weighted definitions\n", - "annot_indices = [0, 31] # [0, 1, 3, 31, 47, 49]\n", - "for idx, annot_index in enumerate(annot_indices):\n", - " mask = np.isfinite(libbgmg.zvec1) & (annomat[:, annot_index]>0)\n", - " data_qqplot = precimed.mixer.cli.calc_qq_plot(libbgmg, params11, 1, downsample_factor, mask)\n", - " h=make_qq_plot(data_qqplot, cm.colors[idx], ylim=50)\n", - " legends_h.append(h[0])\n", - " legends_n.append(annonames[annot_index])\n", - "plt.legend(legends_h, legends_n)\n", - "#plt.savefig(figures_folder + label + 'ylim={}.qq.png'.format(ylim) , bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import precimed\n", - "import precimed.mixer\n", - "import logging\n", - "import numpy as np\n", - "#logging.getLogger().setLevel(logging.DEBUG)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1. , 1.41421356, 1.73205081])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.power(np.array([1, 2,3]), 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_snp: 8\n", - "num_tag: 5\n", - "defvec: [1 3 4 6 7]\n", - "mafvec: [0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8]\n", - "chrnumvec: [1 1 1 2 2 2 2 2]\n", - "zvec1: [-1.5 1.5 2.5 -2.5 0.123]\n", - "zvec2: [ 1.5 -1.5 -2.5 2.5 0.123]\n", - "nvec1: [100. 200. 100. 200. 300.]\n", - "nvec2: [1000. 2000. 1000. 2000. 3000.]\n", - "weights: [0.2 0.2 0.2 0.3 0.3]\n" - ] - }, - { - "data": { - "text/plain": [ - "LibBgmg(_lib_name: /home/oleksanf/github/mixer/src/build/lib/libbgmg.so, _context_id: 0, num_snp: 8, num_tag: 5)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "libbgmg.init_log(\"/home/oleksanf/github/mixer/testlog5.log\")\n", - "libbgmg.log_message('Test log message succeeded?')\n", - "libbgmg.dispose()\n", - "libbgmg.defvec=[0, 1, 0, 1, 1, 0, 1, 1]\n", - "libbgmg.mafvec = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n", - "libbgmg.chrnumvec = [1, 1, 1, 2, 2, 2, 2, 2]\n", - "libbgmg.zvec1 = [-1.5, 1.5, 2.5, -2.5, 0.123]\n", - "libbgmg.zvec2 = [1.5, -1.5, -2.5, 2.5, 0.123]\n", - "libbgmg.nvec1 = [100, 200, 100, 200, 300]\n", - "libbgmg.nvec2 = [1000, 2000, 1000, 2000, 3000]\n", - "libbgmg.weights = [0.2, 0.2, 0.2, 0.3, 0.3]\n", - "print('num_snp: {}'.format(libbgmg.num_snp))\n", - "print('num_tag: {}'.format(libbgmg.num_tag))\n", - "print('defvec: {}'.format(libbgmg.defvec))\n", - "print('mafvec: {}'.format(libbgmg.mafvec))\n", - "print('chrnumvec: {}'.format(libbgmg.chrnumvec))\n", - "print('zvec1: {}'.format(libbgmg.zvec1))\n", - "print('zvec2: {}'.format(libbgmg.zvec2))\n", - "print('nvec1: {}'.format(libbgmg.nvec1))\n", - "print('nvec2: {}'.format(libbgmg.nvec2))\n", - "print('weights: {}'.format(libbgmg.weights))\n", - "libbgmg" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LibBgmg(_lib_name: /home/oleksanf/github/mixer/src/build/lib/libbgmg.so, _context_id: 0, num_snp: 9997231, num_tag: 1091550)\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from importlib import reload\n", - "import precimed.mixer\n", - "reload(precimed.mixer)\n", - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "\n", - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "libbgmg.init_log(\"/home/oleksanf/github/mixer/testlog5.log\")\n", - "libbgmg.log_message('Test log message succeeded?')\n", - "libbgmg.dispose()\n", - "\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "chr_labels = list(range(1, 23))\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz'\n", - "trait2_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_BIP_2016.sumstats.gz'\n", - "exclude = ''; extract = ''\n", - "libbgmg.init(bim_file, frq_file, chr_labels, trait1_file, trait2_file, exclude, extract);\n", - "print(libbgmg)\n", - "\n", - "options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg.num_snp), ('num_components', 3), \n", - " ('cache_tag_r2sum', False), ('threads', 6), ('seed', None), ('z1max', None)]\n", - "for opt, val in options: libbgmg.set_option(opt, val)\n", - "\n", - "for chr_label in chr_labels: \n", - " libbgmg.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(chr_label);\n", - "\n", - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n", - "\n", - "libbgmg.set_option('diag', 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(libbgmg.weights,bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(libbgmg.ld_tag_r2_sum, range=(0, 600), bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(libbgmg.ld_tag_r4_sum, range=(0, 600), bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 [(3450, 0.050812542)]\n", - "2 [(1627, 0.067566946), (1635, 0.065918975)]\n", - "4 [(2125, 0.05070573), (2128, 0.053543907), (4458, 0.05900664), (4465, 0.05159075)]\n", - "1 [(975, 0.06755169)]\n", - "1 [(975, 0.069092855)]\n", - "1 [(975, 0.069092855)]\n", - "12 [(1969, 0.05009537), (1971, 0.052048523), (3543, 0.051438164), (3579, 0.051819637), (3582, 0.051819637), (3585, 0.051544975), (3586, 0.0520943), (3589, 0.05168231), (3590, 0.05195697), (3591, 0.051422905), (3592, 0.052643627), (3594, 0.052109558)]\n" - ] - } - ], - "source": [ - "# it's possible to look at LD of a given SNP, and of the entire chromosome\n", - "for i in range(1,100):\n", - " tag, r2 = libbgmg.get_ld_r2_snp(i)\n", - " if len(tag>0):\n", - " print(len(tag), list(zip(tag,r2)))\n", - "snp, tag, r2 = libbgmg.get_ld_r2_chr(21)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1,100): libbgmg.calc_univariate_cost(1, 0.003, 1.2, 1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "svec=libbgmg.calc_univariate_power(1, 0.003, 1.2, 1e-4, 5.45, [1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8])\n", - "plt.plot(svec)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibbgmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_univariate_delta_posterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.003\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/github/mixer/precimed/mixer/libbgmg.py\u001b[0m in \u001b[0;36mcalc_univariate_delta_posterior\u001b[0;34m(self, trait, pi_vec, sig2_zero, sig2_beta)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalc_univariate_delta_posterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrait\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpi_vec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig2_zero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig2_beta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0;32mraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0mc0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mc1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin" - ] - } - ], - "source": [ - "c0,c1,c2=libbgmg.calc_univariate_delta_posterior(1, 0.003, 1.2, 1e-4)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "out_file = '/home/oleksanf/github/mixer/results'\n", - "lib_name = '/home/oleksanf/github/mixer/src/build/lib/libbgmg.so'\n", - "log_file = out_file + '.log'\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "chr_labels = list(range(1, 23))\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz'\n", - "trait2_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_BIP_2016.sumstats.gz'\n", - "exclude = ''; extract = ''\n", - " options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg.num_snp), ('num_components', 3), \n", - " ('cache_tag_r2sum', False), ('threads', 6), ('seed', 123), ('z1max', None), ('z2max', None)]\n", - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "\n", - "def setub_libbgmg(lib_name, log_file, bim_file, frq_file, plink_ld_bin, chr_labels,\n", - " trait1_file, trait2_file, exclude, extract,\n", - " options, randprune_n, randprune_r2):\n", - "\n", - " libbgmg = precimed.mixer.LibBgmg(lib_name)\n", - " libbgmg.init_log(log_file)\n", - " libbgmg.dispose()\n", - "\n", - " libbgmg.init(bim_file, frq_file, chr_labels, trait1_file, trait2_file, exclude, extract);\n", - "\n", - " for opt, val in options:\n", - " libbgmg.set_option(opt, val)\n", - "\n", - " for chr_label in chr_labels: \n", - " libbgmg.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(chr_label);\n", - "\n", - " libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n", - "\n", - " libbgmg.set_option('diag', 0)\n", - " return libbgmg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def run_mixer(lib):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 4.930380657631324e-32\n", - " nfev: 9\n", - " nit: 5\n", - " success: True\n", - " x: 1.0000000000000002\n", - "\n" - ] - } - ], - "source": [ - "x=scipy.optimize.minimize_scalar(lambda x:(x-1)*(x-1), method='brent', bracket=[-10, 10])\n", - "print(x)\n", - "print(type(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "143039.9667054704\n", - "136117.94951642904\n", - "279813.56074795313\n", - "BivariateParams(_pi: [0.0012466754854858091, 0.0003354573743336747, 0.0028265604933306715], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8945159908412117, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n" - ] - } - ], - "source": [ - "from importlib import reload\n", - "import precimed.mixer\n", - "reload(precimed.mixer)\n", - "from precimed.mixer.utils import *\n", - "from precimed.mixer.utils import UnivariateParams\n", - "from precimed.mixer.utils import BivariateParams\n", - "from precimed.mixer.utils import _log_exp_converter\n", - "from precimed.mixer.utils import _logit_logistic_converter\n", - "from precimed.mixer.utils import _arctanh_tanh_converter\n", - "from precimed.mixer.utils import UnivariateParametrization_constPI\n", - "from precimed.mixer.utils import UnivariateParametrization_constH2_constSIG2ZERO\n", - "from precimed.mixer.utils import UnivariateParametrization_constPI_constSIG2BETA\n", - "from precimed.mixer.utils import UnivariateParametrization\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO_boundedPI\n", - "from precimed.mixer.utils import BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_natural_axis\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRHOBETA_constPI\n", - "from precimed.mixer.utils import _hessian_robust\n", - "from precimed.mixer.utils import _max_rg\n", - "from precimed.mixer.utils import _calculate_univariate_uncertainty\n", - "from precimed.mixer.utils import _calculate_bivariate_uncertainty\n", - "\n", - "print(UnivariateParams(0.001, 1e-4, 1.23).cost(libbgmg, 1))\n", - "print(UnivariateParams(0.001, 1e-4, 1.23).cost(libbgmg, 2))\n", - "print(BivariateParams([0.001, 0.002, 0.004], [1e-4, 3e-4], 0.8, [1.23, 1.06], 0.4).cost(libbgmg))\n", - "\n", - "#scalar_optimizer = scipy.optimize.fminbound\n", - "params12_fitted, _ = BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO_boundedPI(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_rg=params12._rg(),\n", - " const_rho_zero=params12._rho_zero,\n", - " lib=libbgmg).fit(scalar_optimizer)\n", - "print(params12_fitted)" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UnivariateParams(_pi: 0.004073235978816481, _sig2_beta: 5.288619957821589e-05, _sig2_zero: 1.1755915719572747)\n", - "UnivariateParams(_pi: 0.0031620178676643462, _sig2_beta: 5.231128193651721e-05, _sig2_zero: 1.084084668490775)\n", - "BivariateParams(_pi: [0.0012449932750385182, 0.00033377516388638376, 0.0028282427037779625], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8939839416846624, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n", - "\n", - "Univariate (trait1):\n", - "pi: pe=0.00407, mean=0.0041, median=0.0041, std=0.000453, ci=[0.00329, 0.00506]\n", - "nc: pe=4.07e+04, mean=4.1e+04, median=4.1e+04, std=4.53e+03, ci=[3.29e+04, 5.06e+04]\n", - "nc@p9: pe=9.2e+03, mean=9.26e+03, median=9.26e+03, std=1.02e+03, ci=[7.43e+03, 1.14e+04]\n", - "sig2_beta: pe=5.29e-05, mean=5.32e-05, median=5.32e-05, std=5.34e-06, ci=[4.34e-05, 6.44e-05]\n", - "sig2_zero: pe=1.18, mean=1.18, median=1.18, std=0.00889, ci=[1.16, 1.19]\n", - "h2: pe=0.447, mean=0.447, median=0.447, std=0.0155, ci=[0.418, 0.478]\n", - "\n", - "Univariate (trait2):\n", - "pi: pe=0.00316, mean=0.00321, median=0.00321, std=0.000588, ci=[0.00222, 0.00447]\n", - "nc: pe=3.16e+04, mean=3.21e+04, median=3.21e+04, std=5.87e+03, ci=[2.22e+04, 4.47e+04]\n", - "nc@p9: pe=7.14e+03, mean=7.25e+03, median=7.25e+03, std=1.33e+03, ci=[5.01e+03, 1.01e+04]\n", - "sig2_beta: pe=5.23e-05, mean=5.32e-05, median=5.32e-05, std=8.94e-06, ci=[3.79e-05, 7.26e-05]\n", - "sig2_zero: pe=1.08, mean=1.08, median=1.08, std=0.00764, ci=[1.07, 1.1]\n", - "h2: pe=0.343, mean=0.344, median=0.344, std=0.0178, ci=[0.31, 0.38]\n", - "\n", - "Bivariate:\n", - "sig2_zero_T1: pe=1.18, mean=1.18, median=1.18, std=0.00889, ci=[1.16, 1.19]\n", - "sig2_zero_T2: pe=1.08, mean=1.08, median=1.08, std=0.00764, ci=[1.07, 1.1]\n", - "sig2_beta_T1: pe=5.29e-05, mean=5.32e-05, median=5.32e-05, std=5.34e-06, ci=[4.34e-05, 6.44e-05]\n", - "sig2_beta_T2: pe=5.23e-05, mean=5.32e-05, median=5.32e-05, std=8.94e-06, ci=[3.79e-05, 7.26e-05]\n", - "h2_T1: pe=0.447, mean=0.447, median=0.447, std=0.0155, ci=[0.418, 0.478]\n", - "h2_T2: pe=0.343, mean=0.344, median=0.344, std=0.0178, ci=[0.31, 0.38]\n", - "rho_zero: pe=0.269, mean=0.269, median=0.269, std=0.00418, ci=[0.261, 0.277]\n", - "rho_beta: pe=0.894, mean=0.874, median=0.874, std=0.0897, ci=[0.659, 0.991]\n", - "rg: pe=0.705, mean=0.662, median=0.662, std=0.077, ci=[0.5, 0.793]\n", - "pi1: pe=0.00124, mean=0.00133, median=0.00133, std=0.000679, ci=[0.000153, 0.00273]\n", - "pi2: pe=0.000334, mean=0.000444, median=0.000444, std=0.00036, ci=[3.32e-05, 0.00135]\n", - "pi12: pe=0.00283, mean=0.00276, median=0.00276, std=0.000538, ci=[0.00176, 0.00384]\n", - "pi1u: pe=0.00407, mean=0.0041, median=0.0041, std=0.000453, ci=[0.00329, 0.00506]\n", - "pi2u: pe=0.00316, mean=0.00321, median=0.00321, std=0.000588, ci=[0.00222, 0.00447]\n", - "nc1: pe=1.24e+04, mean=1.33e+04, median=1.33e+04, std=6.79e+03, ci=[1.53e+03, 2.73e+04]\n", - "nc2: pe=3.34e+03, mean=4.44e+03, median=4.44e+03, std=3.6e+03, ci=[332, 1.35e+04]\n", - "nc12: pe=2.83e+04, mean=2.76e+04, median=2.76e+04, std=5.38e+03, ci=[1.76e+04, 3.84e+04]\n", - "nc1u: pe=4.07e+04, mean=4.1e+04, median=4.1e+04, std=4.53e+03, ci=[3.29e+04, 5.06e+04]\n", - "nc2u: pe=3.16e+04, mean=3.21e+04, median=3.21e+04, std=5.87e+03, ci=[2.22e+04, 4.47e+04]\n", - "nc1@p9: pe=2.81e+03, mean=3.01e+03, median=3.01e+03, std=1.53e+03, ci=[347, 6.16e+03]\n", - "nc2@p9: pe=754, mean=1e+03, median=1e+03, std=813, ci=[75, 3.05e+03]\n", - "nc12@p9: pe=6.39e+03, mean=6.25e+03, median=6.25e+03, std=1.22e+03, ci=[3.98e+03, 8.67e+03]\n", - "nc1u@p9: pe=9.2e+03, mean=9.26e+03, median=9.26e+03, std=1.02e+03, ci=[7.43e+03, 1.14e+04]\n", - "nc2u@p9: pe=7.14e+03, mean=7.25e+03, median=7.25e+03, std=1.33e+03, ci=[5.01e+03, 1.01e+04]\n", - "totalpi: pe=0.00441, mean=0.00454, median=0.00454, std=0.000547, ci=[0.00359, 0.00576]\n", - "totalnc: pe=4.41e+04, mean=4.54e+04, median=4.54e+04, std=5.46e+03, ci=[3.59e+04, 5.75e+04]\n", - "totalnc@p9: pe=9.96e+03, mean=1.03e+04, median=1.03e+04, std=1.24e+03, ci=[8.11e+03, 1.3e+04]\n", - "pi1_over_totalpi: pe=0.283, mean=0.288, median=0.288, std=0.131, ci=[0.0366, 0.528]\n", - "pi2_over_totalpi: pe=0.0757, mean=0.094, median=0.094, std=0.0682, ci=[0.00777, 0.262]\n", - "pi12_over_totalpi: pe=0.642, mean=0.618, median=0.618, std=0.141, ci=[0.352, 0.894]\n", - "pi1_over_pi1u: pe=0.306, mean=0.318, median=0.318, std=0.145, ci=[0.0408, 0.591]\n", - "pi2_over_pi2u: pe=0.106, mean=0.135, median=0.135, std=0.0973, ci=[0.011, 0.376]\n", - "pi12_over_pi1u: pe=0.694, mean=0.682, median=0.682, std=0.145, ci=[0.409, 0.959]\n", - "pi12_over_pi2u: pe=0.894, mean=0.865, median=0.865, std=0.0973, ci=[0.624, 0.989]\n", - "pi1u_over_pi2u: pe=1.29, mean=1.32, median=1.32, std=0.283, ci=[0.854, 1.96]\n", - "pi2u_over_pi1u: pe=0.776, mean=0.793, median=0.793, std=0.17, ci=[0.511, 1.17]\n" - ] - } - ], - "source": [ - "import scipy.optimize\n", - "\n", - "libbgmg.set_option('fast_cost', 1);\n", - " \n", - "optimizer = lambda func, x0: scipy.optimize.minimize(func, x0, method='Nelder-Mead')\n", - "\n", - "params = []\n", - "\n", - "for trait in [1, 2]:\n", - " params0, details = UnivariateParametrization_constPI(1.0, 1.5, 1e-4, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params0)\n", - "\n", - " params1, details = UnivariateParametrization_constH2_constSIG2ZERO(0.01, params0, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params1)\n", - "\n", - " params2, details = UnivariateParametrization_constPI_constSIG2BETA(1.0, params1, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params2)\n", - "\n", - " params3, details = UnivariateParametrization(params2, libbgmg, trait=trait).fit(optimizer)\n", - " print(params3)\n", - " \n", - " params.append(params3)\n", - "\n", - "alpha = 0.05\n", - "totalhet = 2.0 * np.dot(libbgmg.mafvec, 1.0 - libbgmg.mafvec) \n", - "num_samples = 10000\n", - "\n", - "# That's the most appropriate initialization for the bivariate model\n", - "# BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG - not used\n", - "# BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO - used to fit the full model\n", - "zcorr = np.corrcoef(libbgmg.zvec1, libbgmg.zvec2)[0, 1]\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.1,\n", - " init_rho_beta=zcorr,\n", - " init_rho_zero=zcorr,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "ci1, ci_sample1 = _calculate_univariate_uncertainty(UnivariateParametrization(params[0], libbgmg, trait=1), alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "ci2, ci_sample2 = _calculate_univariate_uncertainty(UnivariateParametrization(params[1], libbgmg, trait=2), alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "ci12, ci_sample12 = _calculate_bivariate_uncertainty(BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=params12._pi[2],\n", - " init_rho_beta=params12._rho_beta,\n", - " init_rho_zero=params12._rho_zero,\n", - " lib=libbgmg), [ci_sample1, ci_sample2], alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "\n", - "print('\\nUnivariate (trait1):')\n", - "for k, v in ci1.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))\n", - "print('\\nUnivariate (trait2):')\n", - "for k, v in ci2.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))\n", - "print('\\nBivariate:')\n", - "for k, v in ci12.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0.0012449932750385182, 0.00033377516388638376, 0.0028282427037779625], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8939839416846624, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n" - ] - } - ], - "source": [ - "# That's the most appropriate initialization for the bivariate model\n", - "# BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG - not used\n", - "# BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO - used to fit the full model\n", - "zcorr = np.corrcoef(libbgmg.zvec1, libbgmg.zvec2)[0, 1]\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.1,\n", - " init_rho_beta=zcorr,\n", - " init_rho_zero=zcorr,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0, 0, 1], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8810739768325555, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.8528486201057031, rg: 0.8810739768325555)\n", - "BivariateParams(_pi: [0.0015524944994159688, 0.0006412763882638344, 0.002520741479400512], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 1.0, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.27, rg: 0.7023868342220096)\n", - "BivariateParams(_pi: [0.0015403841166866214, 0.0006291660055344869, 0.0025328518621298593], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.9999999995949107, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.2690626563807834, rg: 0.7057613066680437)\n", - "BivariateParams(_pi: [0.0015403841166866214, 0.0006291660055344869, 0.0025328518621298593], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.9999999995949107, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26899243023364927, rg: 0.7057613066680437)\n" - ] - } - ], - "source": [ - "params12, details = BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG(\n", - " const_sig2_beta=[p._sig2_beta for p in params],\n", - " const_sig2_zero=[p._sig2_zero for p in params],\n", - " max_rg=_max_rg(params[0]._pi, params[1]._pi),\n", - " init_rho_beta=0.5, init_rho_zero=0.1, lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_rg=params12._rho_beta,\n", - " const_rho_zero=params12._rho_zero,\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.95,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.5,\n", - " init_rho_beta=0,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE_constRHOBETA_constPI(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_pi12=params12._pi[2],\n", - " const_rho_beta=params12._rho_beta,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0.0012389994058291652, 0.0003277812946770307, 0.0028342365729873155], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8928580429674069, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26906143314603836, rg: 0.7051257384792755)\n" - ] - } - ], - "source": [ - "params12, details = BivariateParametrization_constUNIVARIATE_natural_axis(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.5,\n", - " init_rho_beta=0,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/misc/mixer_cli.ipynb b/misc/mixer_cli.ipynb deleted file mode 100644 index 70ae92a..0000000 --- a/misc/mixer_cli.ipynb +++ /dev/null @@ -1,565 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import precimed\n", - "import precimed.mixer\n", - "import numpy as np\n", - "import scipy.stats\n", - "from scipy.interpolate import interp1d\n", - "import matplotlib.pyplot as plt\n", - "from precimed.mixer.cli import parse_args\n", - "from precimed.mixer.cli import log_header\n", - "from precimed.mixer.libbgmg import LibBgmg\n", - "from precimed.mixer.cli import load_univariate_params_file\n", - "from precimed.mixer.cli import load_bivariate_params_file\n", - "from precimed.mixer.cli import calc_qq_plot\n", - "from precimed.mixer.cli import calc_qq_data\n", - "from precimed.mixer.cli import calc_qq_model\n", - "from precimed.mixer.figures import make_venn_plot\n", - "from precimed.mixer.figures import make_qq_plot\n", - "from precimed.mixer.figures import make_strat_qq_plots\n", - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.figures\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/numdifftools/limits.py:182: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", - " f_del = np.vstack(list(np.ravel(r)) for r in sequence)\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/numdifftools/limits.py:184: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", - " for step in steps)\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/numdifftools/limits.py:182: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", - " f_del = np.vstack(list(np.ravel(r)) for r in sequence)\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/numdifftools/limits.py:184: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", - " for step in steps)\n", - "/home/oleksanf/github/mixer/precimed/mixer/cli.py:437: RuntimeWarning: divide by zero encountered in log10\n", - " data_x=-np.log10(np.flip(np.cumsum(np.flip(data_weights[si])))) # step 2\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/scipy/interpolate/interpolate.py:610: RuntimeWarning: invalid value encountered in subtract\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" - ] - } - ], - "source": [ - "basic_args=\"\"\"\n", - " --bim-file /home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim\n", - " --frq-file /home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq\n", - " --plink-ld-bin0 /home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin\n", - " --lib /home/oleksanf/github/mixer/src/build/lib/libbgmg.so\n", - " --chr2use 1\n", - " --ci-alpha 0.05\n", - " --kmax 100\n", - "\"\"\"\n", - "\n", - "fit_args1=\"\"\"\n", - " --extract /home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/w_hm3.justrs\n", - " --fit-sequence diffevo-fast neldermead-fast\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz\n", - " --out PGC_SCZ_2014_EUR.fit\n", - "\"\"\"\n", - "fit_args2=\"\"\"\n", - " --extract /home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/w_hm3.justrs\n", - " --fit-sequence diffevo-fast neldermead-fast\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_BIP_2016.sumstats.gz\n", - " --out PGC_BIP_2016.fit\n", - "\"\"\"\n", - "fit_args12=\"\"\"\n", - " --fit-sequence diffevo-fast neldermead-fast\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_SCZ_2014_EUR.sumstats.gz\n", - " --trait2-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_BIP_2016.sumstats.gz\n", - " --trait1-params-file PGC_SCZ_2014_EUR.fit.json\n", - " --trait2-params-file PGC_BIP_2016.fit.json\n", - " --out PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit\n", - " --qq-plots\n", - "\"\"\"\n", - "\n", - "test_args1=\"\"\"\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_SCZ_2014_EUR.sumstats.gz\n", - " --fit-sequence load inflation\n", - " --load-params-file PGC_SCZ_2014_EUR.fit.json\n", - " --out PGC_SCZ_2014_EUR.test\n", - " --power-curve\n", - " --qq-plots\n", - "\"\"\"\n", - "\n", - "test_args2=\"\"\"\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_BIP_2016.sumstats.gz\n", - " --fit-sequence load inflation\n", - " --load-params-file PGC_BIP_2016.fit.json\n", - " --out PGC_BIP_2016.test\n", - " --power-curve\n", - " --qq-plots\n", - "\"\"\"\n", - "\n", - "test_args12=\"\"\"\n", - " --fit-sequence load inflation\n", - " --trait1-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_SCZ_2014_EUR.sumstats.gz\n", - " --trait2-file /home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_BIP_2016.sumstats.gz\n", - " --trait1-params-file PGC_SCZ_2014_EUR.fit.json\n", - " --trait2-params-file PGC_BIP_2016.fit.json\n", - " --load-params-file PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit.json\n", - " --out PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.test\n", - " --qq-plots\n", - "\"\"\"\n", - "\n", - "#args = parse_args((\"fit \" + basic_args + fit_args1).split()); libbgmg = LibBgmg(args.lib, init_log=args.out + '.log', dispose=True); args.func(args)\n", - "#args = parse_args((\"fit \" + basic_args + fit_args2).split()); libbgmg = LibBgmg(args.lib, init_log=args.out + '.log', dispose=True); args.func(args)\n", - "#args = parse_args((\"fit \" + basic_args + fit_args12).split()); libbgmg = LibBgmg(args.lib, init_log=args.out + '.log', dispose=True); args.func(args)\n", - "#args = parse_args((\"fit \" + basic_args + test_args1).split()); libbgmg = LibBgmg(args.lib, init_log=args.out + '.log', dispose=True); args.func(args)\n", - "args = parse_args((\"fit \" + basic_args + test_args2).split()); libbgmg = LibBgmg(args.lib, init_log=args.out + '.log', dispose=True); args.func(args)\n", - "\n", - "#params = load_univariate_params_file('mixer.test.json')\n", - "#params12, params1, params2 = load_bivariate_params_file('PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:42: RuntimeWarning: invalid value encountered in sqrt\n", - " q = 10**-data_logpvec; dq= 1.96*np.sqrt(q*(1-q)/qq['sum_data_weights']);\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:48: RuntimeWarning: invalid value encountered in less\n", - " y2[x2 self.x[-1]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import json\n", - "data = json.loads(open('PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.test.json').read())\n", - "plt.figure()\n", - "make_qq_plot(data['qqplot'][0], ci=True)\n", - "\n", - "plt.figure(figsize=[12, 3])\n", - "plt.subplot(1,3,1)\n", - "make_venn_plot(data, flip=False, traits=['SCZ', 'BIP'])\n", - "plt.subplot(1,3,2)\n", - "make_strat_qq_plots(data, flip=False, traits=['SCZ', 'BIP'], do_legend=False)\n", - "plt.subplot(1,3,3)\n", - "make_strat_qq_plots(data, flip=True, traits=['BIP', 'SCZ'], do_legend=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "fname1 = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_SCZ_2014_EUR.sumstats.gz'\n", - "fname2 = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/nomhc/PGC_BIP_2016.sumstats.gz'\n", - "df1 = pd.read_table(fname1, delim_whitespace=True, usecols=['SNP', 'A1', 'A2', 'Z'])\n", - "df2 = pd.read_table(fname2, delim_whitespace=True, usecols=['SNP', 'A1', 'A2', 'Z'])\n", - "df = precimed.mixer.figures.merge_z_vs_z(df1, df2)\n", - "\n", - "plt.figure()\n", - "precimed.mixer.figures.plot_causal_density(data)\n", - "plt.figure()\n", - "precimed.mixer.figures.plot_z_vs_z_data(df, plot_limits=10)\n", - "plt.figure()\n", - "precimed.mixer.figures.plot_predicted_zscore(data, len(df), plot_limits=10, flip=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "data1 = json.loads(open('PGC_SCZ_2014_EUR.test.json').read())\n", - "data2 = json.loads(open('PGC_BIP_2016.test.json').read())\n", - "precimed.mixer.figures.make_power_plot([data1, data2])" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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J3szxqaCP/wEjgU1a6wnVta9B/zcC/bTWs33ddzXnbQ38CgzWWpvr8txClGkMOVrNuVcAH2utf6jj8w4AXtdaj6zL8wpRkSaQx48C2VrrWhep7nn/f9daX1P7yBo/KXjriFLqSmC/1npXfccihDiV5KgQjZ/ksaiMFLxCCCGEEKJJkzm8QgghhBCiSfPLKg3x8fG6S5cu/uha+JnZYsFqsxEYGFiz4+1OjhbZaRtlIDyo4V5P2R0OYiK9WEpVa8g7CJYSaNEVQmM8Ouzw4cNkZ2cD5GitW9UoWD+TfBXiRJs3b5Z8FaKR8DRf/VLwdunShU2bNvmja+FnW/fsobCkhIiwsBod/2NSMa9vyGfZpe1pFdlwV73Lyc9n8pgxuNcFrprDDh/fDPsOwGXvwqDrqz3E6XRy++23s3nzZmbNmsXzzz/fYJdbk3wV4kRKKclXIRoJT/O14Q7BiXphNJsx1HB0F+BIoZ0Qg6JlRM37aFCcTvj6btj3DUx+1qNi1+FwMGXKFFasWMGjjz7KokWL6iBQIYQQQlRGCl5xArPFUsuC10ZCdBABnoycNnRaww9zYNt7MH42jJju0WEZGRn8+OOPLFiwgCeeeMKzUWQhhBBC+E3Dvecs6pzD4cBqsxHtzdzWk6QX2ejbOsSHUdWj1c/A+uUwYgaMe6ja5jabDYPBQEJCAjt37qRFixZ1EKQQQgghqlPtCK9SqrdSalu5V5FS6p66CE7ULZvdXqvjjTYnuUYHHWOCqm/c0P3xCvz6DAy+Ec57CqoZpbVYLFx11VXcc48rNeqr2JV8FaJxkZwVom5UO8Krtd6Pa0tJlFKBuLa7+9zPcYl6YLXZanX7Pb3QBkBCYy94t7zjmsrQ91K4eGm1xa7JZOLKK6/ku+++45VXXqmjICsm+SpE4yI5K0Td8HZKw0TgoNa6wT7BKmrObrdTm41I0otcBW+Hxlzw7v7c9ZBaj0lwxQoIqHo+s9Fo5NJLL+Xnn3/mjTfe4NZbb62jQD0i+SpE4yI5K4SfePvQ2nXABxW9oZSappTapJTa5F53VDQyNru9ViO8RwptGAKgTQNejqxKST/Cp7dBxzPhmnfBEFxlc601l19+OatWreJf//pXQyt2QfJViMamwpyVfBWi9jwueJVSwcAlwMcVva+1fl1rPUxrPaxVqwa5XreohsliqdXxRwrttIsKIjCgEa5KkLIWPrwRWveBGz6E4PBqD1FKMWPGDN577z1uvvnmOgjSc5KvQjQuVeWs5KsQtefNUNz5wBatdaa/ghH1y2Q2E2So+ehsepGNrnFVj4o2SEe3wn+uhdhO8PfPq91FLT8/n3Xr1nHhhRdy6aWX1lGQXpN8FaJxkZwVwo+8mdJwPZXcHhVNg9FsrvGWwlaHJrPETkJMI5vOkLUP3r0CwuLg719ARHyVzXNzc5k4cSLXXHMNmZkN+nNJ8lWIxkVyVgg/8qjgVUqFA+cAn/k3HFGfarPpxLEiG1pDh+hG9MBafgq8exkEBsFNX0BMQpXNs7KymDBhAnv27OHTTz+lTZs2dROnlyRfhWhcJGeF8D+PhuO01kagpZ9jEfXMZDIRGRFRo2OPFDauFRpCLLnw7p1gM8E/VkLL7lW2P3bsGJMmTeLQoUN88803TJo0qY4i9Z7kqxCNi+SsEP7XyO4/C39xOBzYHY4aT2k4UmRDKWjXCEZ4Ay2FnLFtHtjy4KavoE2/ao/56KOPSE1N5bvvvmPcuHF1EKUQQgghfMXbZclEE2W129G12nTCTptIA8GBDXuFhgCbkV5rZxFuOgbX/Qc6DK2yfdm6xHfddRc7d+6UYlcIIYRohKTgFQDYbDZqU6oeKbSR0MBHd5XDQs91DxJRkMi2/g9D17FVtk9OTuaMM85g165dKKXo2rVrHUUqhBBCCF+SKQ0CcG06UVMOp+ZosY0hCaE+jMjHnHa6r59HVM52ks+YS1bUGVU2T0xM5Oyzz8ZkMmG1WusoSCGEEEL4g4zwCsA1wlvTbYUzSuw4nA34gTWt6bJlEXHH1pI66F7yOp1bZfO9e/cybtw4rFYrv/zyC0OGDKmjQIUQQgjhDzLCKwAwW60EBNTs+ifdvUJDQ53S0GHXclqlriS9zz/I7n5FlW2TkpIYN24cAQEBrF69mr59+9ZRlEIIIYTwFxnhFYBr04marsGb3oCXJGu7/13aJf6HzG6Xc7TPlGrbd+jQgcmTJ/Prr79KsSuEEEI0ETLCKwDXtsI1LXjTCm20DA8kLKhhXT+1Sv6CjrteI7fjuRwedC9UsQrFtm3b6NKlC7Gxsbzzzjt1GKUQQggh/K1hVSii3phqsa1wepG9wU1naJn6PZ23/pOCtmdxaNgcUJX/U1+3bh3jxo1j+vTpdRihEEIIIeqKFLwCALPZTJDB+wF/p9akF9ka1HSGuCOr6LrpKYpaDeHAiCfQAZX/XL/99hvnnnsubdq04bnnnqvDKIUQQghRV6TgFdgdDmwOR40eWsstdWCxazrENIzZMTHH1tJtw3xKWvbjwFnPoANDKm37888/M3nyZDp27Mivv/5Kx44d6zBSIYQQQtQVKXgFNrsdVcNd1o4UNZwH1qIzN9Ljz7mYYnuSNOo5nIawSts67HbuvPNOunfvzurVq2nXrl0dRiqEEEKIutQwhuVEvbLZbDU+9kgDWZIsIncXPf6YjTmyI/tHL8YRFFll+0CDgW+//Zbo6Gji4+PrKEohhBBC1AcZ4RW12mUtvdBGVEgA0aE1e+DNF8IKkui1dha20Hj2j1mMIzi60ra//PQTixYuxOl00rVrVyl2hRBCiGZACl6B1WaDGu6ydqTIXq/TGUKKD9P79/twGMLZP+ZF7KEtK237v+++Y84DD7B/3z6sFksdRimEEEKI+iQFr8BssaBq8MCa1pojhTY61NN0hmBjBr3X3ANoEse8iDWibaVtv/3qK+bNns3pgwax9NVXCQ2rfH6vEEIIIZoWKXhFjXdZKzQ7KbU662WE12DOo/eaewi0G9k/+gXMUZ0qbfvV55+zYO5chp5xBi++8goRERF1GKkQQggh6ptHBa9SKlYp9YlSap9Saq9SaqS/AxN1x2gyYfByDd7MYjuf7CoEIKGOlyQLtBbT+/f7CDLlkDTqOUyxPats36p1a8ZOmMA/X3qJsPDwOoqy/ki+CtG4SM4K4X+eVipLgO+11lcppYKBpl81NBO5+fnk5ucTE135g14ne3ldLr8eKgVgSPtQ+rYO9Vd4pwiwm+i57kFCi1JIGrWIkpYDKm2bfPAg3bp3Z+SoUYwcNarOYmwAJF+FaFwkZ4Xws2pHeJVS0cBY4E0ArbVVa13g78CE/1msVrbu20dEeLjHUxp2ZZj59VAp5/SI5OVL2zN7QmuCAmu2hq+3lMNKjz8fITJ3N8nDH6OozfBK277z1ltcf8UVbNqwoU5iaygkX4VoXCRnhagbnkxp6AZkA/9SSm1VSq1QSskkyCYgMSUFh8NBaEjlu5GVp7Xmg+0FtAwP5JZhcbSJrMOpDE473TYuICZzAylDHiS/w4RKm7752mu8/OKLnHPeeQwaMqTuYmwYJF+FaFwkZ4WoA54UvAZgCLBcaz0YKAUePrmRUmqaUmqTUmpTdna2j8MUvmaxWknPzCTOi6kM+7ItJOZYubxfNMF1NKoLgHbSdcuztEhfzeHT7yKn60UVN9OaV19+mddeeYULLr6Y+U8/7fXc5CZA8lWIxqXanJV8FaL2PCl4jwBHtNbr3V9/gis5T6C1fl1rPUxrPaxVq1a+jFH4gcPhAPBqS+GyXdWGdajDJb20ptP2JcSnfseRvreS2fOaSptu3riRt15/nUuvuIJ5TzxBYA1WnmgCJF+FaFyqzVnJVyFqr9rhL611hlIqTSnVW2u9H5gI7PF/aMKf7O6C1xtmu2tzirCgOlrNTms67FpOm4Ofcqzn9Rw77eYqmw8bPpzFL7/MWaNHE1CDdYWbAslXIRoXyVkh6oan93vvBN53Pz2aDPzDfyGJuuB0Or3eXc1sc7UPqaPpDO33/Zt2if8hq9tlHBkwAyoYjXY6nbz84oucO3kyp/Xty+ixY+sktgZO8lWIxkVyVgg/86jg1VpvA4b5ORZRh5xOZ4UFZFVMdifBgYrAAP8XvG2SPiJhz5tkd76A1EH3VVrsPjV/Pl99/jkRERGc1rev3+NqDCRfhWhcJGeF8L9m90SPcHE4nV4fY7FrQg3+L3bjU76h046l5CWMJ2XoQ6BOnZ7gcDh4Yt48Vn79NVNvv50p06b5PS4hhBBCNE5S8DZTjhrN4XUSGuTfgjfuyCq6bF5EYZvhJJ8xD9SpD57Z7XYenzOH/33/PbffcQdTb7/drzEJIYQQonGTgreZcjidaG/n8No1oQb/PQwWk/EH3TYsoKRlPw6MWIgODK6wndaaUqORmffcw01TpvgtHiGEEEI0DVLwNlM2u93rlQzMNv9NaYjK3kKPPx7BFNONpFHP4TScuvSZ1WrFZDIRExPD80uWNNdlx4QQQgjhpea5dpPAbrcT4OVDa2a70y8jvBG5u+i59iEsEe1JHL0YR1Dkqec2m5l1113cOW0adptNil0hhBBCeEwK3maqRiO8du3zObxhBUn0WjsLW2gL9o95EXtI7CltTEYj982cyfo//uCq667DEBTk0xiEEEII0bTJlIZmyma3e7XLGpSN8Pqu4A0tSqH37/fiMISzf8wSbGHxp7QpLS3lvpkz2b51K489+SQXXHyxz84vhBBCiOZBCt5myma3E1ijOby+uSkQUpJO7zX3AAHsH/Mi1oi2FbZbtHAhO7ZtY8HTT3Pu+ef75NxCCCGEaF6k4G2m7DWY0mDy0Tq8QcYseq+5hwCnlX1jX8IS1anStjPuuotJ557LmPHja31eIYQQQjRPMoe3mbJ7OaXBYndic2iiQmr3sJjBnEfvNXdjsBayf/RiTDHdT2lTUFDAG8uX43A4aNO2rRS7QgghhKgVKXibKavD4dUIb5HFtTNbVEjN/8kEWovo/fu9BJuySBz1HMa4005pk5eby4ypU3n7zTc5eOBAjc8lhBBCCFFGCt5mymGzeTWHt9hd8EbXsOANsJXS6/f7CS0+zIGRz1ASP/CUNjnZ2fzf1KmkpaWx+OWX6dW7d43OJYQQQghRnszhbYa01tidTq+mNBRbXFsR12RKg3JY6LnuISIKEjkwYiFFbc44pU1mRgYzbruNnKwslixbxpBhw7w+jxBCCCFERaTgbYYcTido7VXBW2R2T2kI9W6EVznt9PhzLlE520kePo+C9qMrbHc0PR1jaSkvvfYapw8a5NU5hBBCCCGqIlMamiGn0wlersFb5B7h9WpKg3bQbeMCYjPWkTr4fvI6nnNKE6PRCMDgoUP5fOVKKXaF8IG0PCOPfbkLm8P51zfNhfDtLNefNWSz2bBYLD6IUAgh6paM8DZDDocD7eUxxRYnSkFEsIcFr3bSZfOztDiyisMD7iC722WnNEk9dIg7pk1j+syZXHTppYSGhnoZlRCNS3pmJseys/3Wv8Op+T6xmA+2FQDQN9ZC95Yh4LTTc+0DRGdvQQ24CjqN8Lpvi8XCtddei9Pp5IsvvvB6WUMhhKhPUvA2Qw6nE29X0y2yOIkKDiDAk5Fhrem0fQmtUleS3mcKmb2uP6VJ8sGDzLj1VrTTyWl9+ngZjRCNz7GsLLbt3UtEeLjXuxx6Ir3IzpubiziYZ+f0NsHcMiSKluFOSk0meux6iZjMDRweNptONSh2zWYzV155JStXruSll16SYlcI0ehIwdsMORwOr48ptjg8W5JMazrsepU2Bz/lWM/rOdrnH6c0SUpMZOZttxEQGMiyt96iW/dT1+IVoinJLypi2759xEVHExQU5NO+bQ7NF3uK+HRXIeFBAdx5VkvGdPmrqG518DMSUr8kretV5Ha7hMq3eamY0Wjksssu46effuK1115j2rRpPo1fCCHqgkcFr1IqBSgGHIBday2P0Ddi9hoUvEVmJ9EerNDQbv87tEt8n6yul3FkwIxT5grn5+Xxf1OnEhoSwisrVtC5SxevYxFVk3xtWEqMRjbt3ElkeLjPi92kHAvL/8wjrdDGqM7h/GNYHDGhf+VpdOZGOm9fQkHbszjYeyqU753DAAAgAElEQVQxNTjHTTfdxE8//cRbb73FLbfc4rPYxV8kZ4XwP29GeCdorXP8FomoM1abzetjii1O2kVX/c+lTdJHdNj9BjmdziN18H0VPhgX16IF02fOZOSoUSR06OB1HMJjkq8NgMVqZfOuXRgMBkJDQnzWr9nu5L/bC1m5v5i40EAeGhfPsA7hJ7QJLUql+/q5mKK7cHD44+B92gMwe/ZsrrrqKq677rraBy6qIjkrhB/JlIZmqNRk8noOXpHFQa+Q4Erfjz/0DZ12LCUvYTyHhs4GdWL/27duJTAwkP6nn85V115bo7iFaExsdjtb9+7FarcTGxXls353Z5pZ9mcuWSUOzukZyd8GxZ7yMKnBUkDPdQ+iA4JJGvkszqBwsBk9PkdBQQGfffYZU6ZMYejQoQwdOtRn8QshRH3wtOrRwP+UUpuVUhVO4FJKTVNKbVJKbcr241PIovZMJhOGQM83kHBqTbGl8ikNcUd+ocuWRRS0OZPk4Y9BwInXUZs3buSu6dP55zPPoLW360OIGpB8bQD2HDhAYVGRT4vdvVlm5v+cRYBSPD6pNdOGtzil2FUOKz3+fIRgUzZJI5/CGtHWq3Pk5uYyceJEpk+fzgHZ3ruuVJmzkq9C1J6nBe8orfUQ4HzgDqXU2JMbaK1f11oP01oPa9WqlU+DFL5VajIRZPB8cL/I7MSpIS7s1II35tgfdNswn5KW/Tg4YiE64MQ5ihv+/JN77riDtu3b89zSpX55Ol2cQvK1npksFo5mZREXU5NZs5X7PrGE8KAAFp3fln5tKljGT2u6bH2OqJztHBo2h9KW/b3qPzs7m7PPPpvdu3fzxRdf0KNHDx9FLqpRZc5KvgpRex4VvFrro+4/s4DPgeH+DEr4l8li8WqEN9/kesitRfiJx0RlbabHn49giulG0qjncBpO/ABeu2YN982cScdOnXj1zTeJj4+vffCiWpKv9a+gqAjApxd4xRYHG9KMjO0aTlhQxb+62ya+T3zqd6T3mUJex0le9Z+RkcH48eNJSkri66+/5oILLvBF2MIDkrNC+F+1Ba9SKkIpFVX2d+BcYJe/AxP+4XQ6sVitBHpR8OYa7QC0KDfCG5m7k57rHsYcmUDi6MU4giJPOe67r7+mW/fuLFuxgrgWLWofvKiW5GvDkJGTQ0hw5XPea2LNISN2J0zsfmquAcSl/0rHXa+S23FShcsBVuf3338nLS2NlStXcs45p+6KKPxDclaIuuHJfe02wOfukQoD8B+t9fd+jUr4jdVmQ2vt1cjTulQjQYGKDjGu6Qrh+fvp+fssbGEt2T/mRewhsSe0t9vtGAwGHlu4EIvZTKQP5zCKakm+1jOtNXkFBYSHhfm0z1UHS+jeIpjOcacW0uH5++i6cQElLfq6Hxr1PL/L8vWqq65i3LhxyC3zOic5K0QdqHaEV2udrLUe6H7101ovrIvAhH94uyTZxjQja1KMXNg7irCgAEKLkun1+304gqPYP2YJ9tCWJ7T//ttvufn66ynIzycoKEiK3Tom+Vr/zBYLVrvdq2lD1UnOs5JaYOPs7hGnvBdkyqbnuoexh8SSNPJpdKDny58dOnSIAQMG8OOPPwJIsVsPJGcbr2KzjT+Tc9m36j0ozvT7+ex2uzz4XQuyLFkzorXmWFaWR0uSme1O3ttSwA9JJXSODeKK/tGEFKfRe8296AAD+8cswRre5oRjvvnyS56YN4/BQ4cS7MM1R4VoTIqNRvDxh9LPB0sJDlSM6nJiwRtgN9Jz3UME2kvZO375KRegVUlKSuLss8+mtLSUFjLlSDRyTqeTopISrHY7DocDm82GrQabLFWm1OrgQLaJxGwTSVkm9mcbOVJgpb9K5vOQx6H4erj0ZZ+d72RWq5XrrruOHj16sGjRIr+dpymTgrcZOZiWRtLhw7SKi6vwfbtTk1ViJynHysc7C8kssXPRaVFcPyiWSHMmvdfcjXI62DfuJSyRCScc+8Unn/D0E09wxpln8vySJYT68HauEI1JQVGRT0d3i8wOfk8pZUSnsBOXINMOum1YQHjBAZLOehZTjOcrKuzdu5eJEydis9n45ZdfGDhwoM/iFaIulRiNZOTkcPjo0VPuYHq73nxF1qRa+DHZQmaJk7LL2LhQRedYA2d2d7Cg8A0CVGs4Z0Gtz1UZs9nM1VdfzTfffMOSJUv8dp6mTgreJs5ssVBqMpGVm0tyWhrxsbEn/BLILLbz9pZ8DhfYyC6143RndMeYIB6b2Jr+bUMJMuXQe809BNqN7B+7FHN01xPO8f233/LUggWcNXo0z77wAiEyuiuasaycHMJCK1gyrIb+vTkfq0Nzad/oE77fcccy4o79TurAeyhsN9Lj/tLS0hg/fjxKKVavXk2/fv18FqsQdamgqIg/t29HAVEREURFnDrlp7a+P5hOkVlz7ekxdGsRTLeWwce37277xwLCSw6jbvkGwv1zl0RrzbXXXss333zD8uXLmT59ul/O0xxIwduEbdq1i+y8PMB1pRsfF3fKFe9HOwvYnG7izI7hjOocTrtoA+2igujRMpjAAIXBUkDvNfcQZMll/+gXMMb2OuU8w4YP55obbuCu++4j2MdPpgvRmBSXllJUWkorH00R2JxuYk2KkasHRNMp9q/can3gU9oe+JCMHleT1eMqr/pMSEjglltuYcqUKfTu3dsncQpRH+wOB1prWlZy19JXRnQO58oBJ66pHXfkFzoe/R+Os+4lsMtov51bKcWUKVO45JJLmDp1qt/O0xxIwdtEORwOsvPyiK/mF8GOY2ZGdgrnntGnrpEbaC2m1+/3EVJ6lMTRz5+yiP3qVasYPXYs8a1aMevhh30avxCNUUZ2tldL/lXFaHPy+vo8OsYEcXm/vz5sY47+TqftS8hvN4a002d63N/+fftIaNeOwX378uyzz/okRiHqW31sZhRszKTLlmcpiO5FxNgH8d0Epr8UFxezbt06zjvvPC699FI/nKH5qf0EF9EgWazWan8RaK0pMDtpF33qdU+A3UivtQ8QVphM0sinKG415IT333ztNR685x6+/Owzn8YtRGPlcDhIOXqUaB/dVn1vSwH5Zgf/N6IFQYGuXA7P30f3DY9jjOtF8vB5oDz7qN2xfTv333EHC+bN80lsQjQE9bJigXbQdeMTKO1gR79ZEBhU/TFeKiwsPF7oHj161Of9N1dS8DZRFg+WH7M7XX8GBZxYGCuHhZ7rHiYify8Hz5xPUdsRx9/TWrP8pZd47ZVXuPCSS7jsyit9GrcQjVVeYSE295q2tbU708yPB0q4sHcUPeNdc+KDjRn0XPcQ9pBYEs96FqfBswdDt2zaxF23305cixbMXygrXommQ7tfdand/veIztlG6qD7MIa393n/eXl5TJo0iU2bNvHBBx/Qvr3vz9FcScHbRFkslmrb2NxPqJWNHgEop40efz5KVPZWkoc9QkHCuOPvaa156YUX+Ncbb3DZlVcyd8ECn92+FaKxS0lPJ8wHD2xa7E6W/5lHm0gD1w10TWUItJXQc+2DBDgsJI5a5PHyYxvXr+eeGTNo3aYNLyxbRtt27WodnxANhdaaupzQEJG3m4Q9b5HbYSK5nSb7vP+cnBwmTpzIjh07+Oyzz7j88st9fo7mTAreJqrEZKp2SRabw1XwBpcVvE473TbMJzbjD1KGPEBep3NPaH80PZ1PP/yQq6+7jofnzvXJki9CNAVGk4mc/HwifLAc3+rkUjJL7Nx+ZgtCDAEop53uf84ltDiVAyOexBzdzaN+tNasePVVEjp0YPlbbxEvm0qIJqiuRngDbKV02zAfa1grUgfP8mo3Q0998MEH7Nu3j6+++oqLLrrI5/03d/LQWhNVYjQSVM2tVYu9XMGrnXTd/DQt0ldz+PS7yOl6yfF2ZVsRJ3TowLsffUTHTp3q5UEBIRqqopISlFI+yYs/DhtJiDbQv00IaE3nrf8kJmsjyUPnUNx6mEd9lOXsc0uW4HQ4iI2Lo9RorHVsQjQoWvt8k5fKdN62mJDSDPaNexlHsG93EC3L15kzZzJ58mR69uzp0/6FiwzRNVGlpaXVFrwFZtcuNDEhAXTeupj4wz9wpN9tZPa85ngbh8PBk48/zofvvw9Ap86dpdgV4iQFxcU+md5TbHGwJ8vC8I7hKKVot/89WqV8zdHTbia3ywUe9fHLTz9x3513YrFYiI6OJtbPSzYJUV/KCkV/G2H8lfjDP3C0z82UxJ/u077T0tIYNWoUu3fvRiklxa4fScHbBGmtKTGZqi14800OQDP+6Bu0PvQFR3v/nWOn3Xz8fbvdzoK5c/n6888pLCz0c9RCNF5FJSUEB9X+ae3tx8xoDcM6hNEi7Sc67H6N3I7nkN73Vo+O//H775nzwAMUFRZis1prHY8QDZ6fR3jb6yxuLnyV4pYDOFru89EXUlJSGDduHLt376a4uNinfYtTyZSGJshqs+F0OqudY1tgcnBX4Of0TvuEzO5Xkt5v2vH37DYb8+bM4acffuD/7ryTf9x2m7/DFqJR0lpTVFxMpA+WI9ucbiI6JICBeh9dNz1FcfxADg2d7dF8wZVff82CuXMZOHgwi19+mQg/7DolREPi91UanHaecL4ECpLPmAcBviuZDh48yIQJEyguLubnn39m2DDPpiuJmpOCtwnyZA1egL7pn3BF0CdkdTqfwwPvPv6hqrXmkQcf5Jeff+au++7jxltu8XPEQjReZqsVm8NR6ykNDqdm21Ez57cpoPefc7CGtyZpxFPowOp3L/zum2+Y/+ijDD3jDP65dClh4eG1ikWIxsDfUxra73ubBBJZHnM/wyJ8t8LJoUOHGDt2LBaLhV9++YVBgwb5rG9ROSl4myCL1Vrtgtwtk7/kjLw3+d0wkpChD4H6azRYKcWQYcMYMmwY1/7tb/4OV4hGrdRorPUT26n5Vl5dn0eQtYBHihYAisRRz+MIian2WICevXpxzuTJPDp/PqGhobWKRYjGxF+bT0Rlb6X93rf5Vo3hz/Cx+HL8tW3btowbN445c+bQv3//6g8QPiEFbxNkMpsJqOIDuGXq93Td+jy/OAaydeAjjHHfpjGbzaQeOkTvPn2k0BXCA6UmEzsSE2u8/q7VoflkZyFf7SmiRbCdb1suJdqUw76xS7FEdqj2+B3btjFg4EB69OrFk7JdsGhmtNZ+mdIQaCmk28YFWCLas8g0lS4+6nfPnj20b9+e2NhY/vOf//ioV+EpeWitCSoqLa10t6fYtFV02fQU65x9WdbyYUZ3d40gmYxG7r3jDqZPnUpBQUFdhitEo2Qym9mwcydoXaP1d3dnmpn17TE+313E2C5hfNP+LdqV7iV5+DxKW1Y/6vPev//NrTfdxE8//FCT8IVoEnw+oUFrum5+GoM5n4Nnzseoar+2NsCWLVsYM2YMt97q2QOowvc8LniVUoFKqa1KqW/8GZConaKSEtKOHSO8gg/gmGNr6bZhPpudPXit9SPcOTYBpRSlpaXcPWMGWzdv5sE5c4iNja2HyIUvSb76l8liYcOOHTgdDqK8fDisxOJk+Z+5PP5TFk4N8ya25qmoj2lzbDVpA2aQnzC+2j7eev11li5ezDmTJzNh4sQa/hSioZB8rRl/zOFtffAz4o79zpEBMzDG9fZJn+vXr+fss88mKiqKRYsW+aRP4T1vpjTcDewFov0Ui/CBg2lpBAcFYTjpAZqorC30+HMu++jMk5GPMGdsRwKUorioiLtnzGDv7t088cwznDPZ99slinoh+eonZouFTTt3Ync4iI6M9Pw4u5OfD5Tw6a4iSq1OLu0bzdUDoumQ+iXtEv9DZrcryOx5XZV9aK15Y9kyVrz2GudfdBFzFyyo9G6OaFQkX2vA6XT6tL+wgiQ67nyFgrZnkdnjap/0+fvvv3PBBRfQqlUrVq1aRefOnX3Sr/CeRyO8SqkOwIXACv+GI2rK6XSSk5/PsaysU0ac8pK30nnNgxx0tOYG80OM6d3m+BzfD957j3179vDU889LsdtESL76j8VqZcOOHVhsNq+KXYAVG/P59+YCOsUG8ez5bblxcCyts/+g87YXyG83isOD7q724bcDSUm89cYbXHz55cx74gkpdpsAydea8+X83QC7ke7rH8MeHMWhYZ4tBVgdh8PB9OnTadeuHb/99psUu/XM09+WLwIPApXup6eUmgZMA+jUqVPtIxMeKzWZ2LhzJyazmYiwsBNu8RQc3sXILQ+RrWJ4rf2TTOnQnhEd/5ruMGXaNEaNGUO/AQPqI3ThH5KvflJUUkKpyUR8DXYvS8mzMrh9KHMmtAYgPH8f3dc/hjG2J8nDHwdV/bJmPXv14o2336bfgAHVrrMtGg3J1xry5ZSGzlsXE1qSxv4xS7CH+GZ3wsDAQL766ivCw8Np27atT/oUNVftb0yl1EVAltZ6c1XttNava62Haa2HtWrVymcBiurtP3QIh8NBfFzcCXN3QwqTGbn5AYoJJ3XCUq49qwcjO4WTl5fHg/feS052NgaDoUkVu06nE7vDgc1ux2qzYbFaMVssmMxmjCYTpSYTJUZjfYfpN5Kv/lVYiy2E80wO4iNcYwzBpcfoue4h7CGxJI5ahNNQ+YMxTqeTxc8+y5pffwVgwMCBUuw2EZKvteNwOn1S8LZMWUn84e852ucWilsPqXV/K1eu5K677kJrTbdu3aTYbSA8GeEdBVyilLoACAWilVLvaa1v9G9owhMlRiNZubnERZ849Suk+DAdfr6LUmcgz7RcwE0tEgDIzsrijttuI+PYMdIOHya+Dn55mi0WnE6n6/aTexmZsrUTy6+heHyJmUraOLVGQYW/4Mqu9A0BAQQEBqICAgh0vwyBgQQGBqKUcn0vMJCwkJA62YO9Hki++lFuQQGhwdVvBHGy1HwrxRYnLcMCCbQW0WvtLAIcFvaOeRF7aMtKj3M6nTy9YAFffvYZoWFhjBk3rjbhi4ZH8rUW7HZ7rX+PhxYdovO2xRS1GsLRPrfUOqYvv/ySq6++mgEDBlBSUkJUVKUD96KOVVvwaq1nA7MBlFLjgVmSjA2D0+lk8+7dGAyGE0Z8DMVH6bTqLuxOJ8/FL+SqUa4ljjIzMphx663k5uSwZPlyBg8d6peYbHY7docDh8OB2WolJiKCyIiI48WqCgggQCmUUq4/AwJQQEC575/cJiAgwFXQBgZiMBiOF7GBgYEEBgQQ4C5um2gR6zHJV/8xmc3kFxYSF+PZZhBlfj5Qwhsb8ogKCeDM9oH0XPcgIaVH2T/6BczRXSs9zuFw8ORjj/HtV18xZdo0br/jjtr+CKKBkXytHYfTWeWa89UJsJvpvv4xnIYw19bBHkwrqsrHH3/MDTfcwJAhQ/jhhx+k2G1g5ImHRspms7E3ORmjez6h1pp1qUb2JqfxaN4cFEaeb/EkV48bRIBSHE1P5/+mTqWoqIilr73G6QMHen1Op9OJw+HA4Z424HA4sDscrhFZ9y+dAKWIDA8nKiKCsNBQoiMiSGjTptkXoqLxS0xJIfCki8vqaK35eGch3VoG8/DYlgzePp+o3B0cOHM+Ja0q307Ubrfz+COP8L/vvuP2O+5g6u23++JHEKJJsdvtNZ/eozWdtz5PWNEhEkcvxhYWX6tYPvjgA2688UZGjhzJypUriY6WBTcaGq8KXq31amC1XyIRHrHZbCSmpJCWkYECWsbGciDXwme7ikg+ksEnoU8QH1DM132e5bLeg49f/YaFhdGmbVue/uc/6duvX6X9O51OikpLK7xVZAgIIDgkhJCgIKIiIwkNDiY0JITQ4GCCg4IICw0lJDhY5hc2EJKvvmOz28nIySHWyxGbA7lWco0OruwXTb/EZbRIX83hATPJ71D12rmBgYHExMZyxz33cPOUKbUJXTQSkq/es9ViSkN8yjfEH/6e9D5TKGpzRq1jiYuL45xzzuGTTz4h0ssVXETdkBHeRsRksbBp1y5KSktpERNDQEAAWmteW59Hfn4+30Y9SztnLomjF9M13jWCezQ9nVatWxPXogWv/etfFf5yMJpMGM3m4+8ltG5N21atCA0J+Wv6gHvagBDNUV5hIU6n0+Mc0Frz2e4iPtxRSKhBcYXta9oe+JiMHteQ2avytXatViu5OTm0a9+eWQ8/LHdGhKiCs4ZTGsIKkui87QUKWw/naJ+baxVDUlISffv2ZfLkyZx33nmSsw2YFLyNyJFjxyg1Go8viWRzaN7dmk9OfiHfxz5Pe2s6SWctosRd7B5MSuKOadMYN2ECs+fNqzARTWYzRrOZwX36EBsdTZDBUOOn0IVoivIKC9m6Zw+R4eHVts03OfjtUCmrDpZwtMjOWZ3DeaDtFk7bvoy8hPGknT6z0mPNZjMP33cfB5KS+OjLLwn34HxCNGe2GkxpCLQW0+PPR7GHxJI8fG6t5u1+/dlnvLp0KT/88AOTJk2SYreBk4K3kcjIziYxNZUW7nlBDqfmyVVZJGcV8VnkYtpbDnJgxMLjt2YS9+9n5rRpGAwGrv/730/oy2K1Umw0gtYYAgMZ1r8/rVq0qPOfSYiGzGqzcejIEQ4ePkxURAShISFVtt+SbuKZX7PRGnq3CmHGiGgujEqkz9qnKW55OslnzAVV8Yez2WRi1t13s3H9embPmyfFrhAesDscBHpT8GpN181PEWzMYN+4V2q13u7777zDshdf5OKLL2bMmDE17kfUHSl4GwGrzcbOxETioqKO76yUmGPhQFYJX7d4iV7GfSQPf4zC9qMB2LtnD3dOm0ZoWBjLVqygU7ndXfIKCwkNDqZPt260iY8nNDhYrkqFqMCRjAySUlNpFRfn0SjS1qMmtIYlF7ejfXQQoUXJ9F49B0tEAklnPYMOrLhgNhqN3DdzJtu2bGHeE09w4SWX+PpHEaJJcjidBHlxR7JN0n+JO7qGw6ffRWnL/jU+779XrGDZ0qWMmTCB//73v4RUczEsGgYpeBuBIxkZrsQOCgLAYnfy0ppMlgW/RG/jFg4NnU1eR9dDMDabjYfvu4+IyEiWrVhBQocOgGuuU2FJCQFKMXLwYILdfQkhKlZUWkpEWJjHt0zTi2x0jQuifXQQQaZsev0+C2dgCImjnscRXPkT28uXLmX71q0sePppzj3/fF+FL0ST57DZCPCw2IzM2U7HXa+SlzCezB5X1/icWzdvZtnSpUy+8EJm3n//8c9l0fBJwdvAWd2rMsSUe+pzfWoxjzleYlLgJlIH3UtOlwuPvxcUFMTTzz9Pi5Ytaduu3fHv5xYU0LFdO7okJEixK4QHSkpLCTJ49isyu9ROSr6NYQlhBNhK6bV2FgZbMfvGvYI1oupdlm6fOZPR48Zx5siRvghbiGbD053WDOY8uq+fhyWiHYeGzoZa3NUcPHQoi158kTHjxlFQXFzjfkTdk8fuG7is3Fy01hgMBgrNDj7bmU/XzU9zUeCfpPSbQVb3KwHYvHEj77/zDgB9+/c/Xuw6nU5y8vOJioykR+fOREVE1NvPIkRjUVRSQonR6FHB+2NSCXd8cZRii5MhbQPp8cccQotSODBiIcbYXhUeU1BQwKKFCzEZjURGRkqxK4SXynbvrLbg1Q66b5iPwVrMgRFP4gzy/jNQa83rr7zC/r17ARh/9tnycHcjJCO8DZTd4eDQkSMkpqQQGxVFar6VuT9mMFev4ArD7+zp9g9KT7sBgPV//MGsu+8mISGBK6+5BpSi1GRyTYMwGGjXujW9u3YlTOYZCVEtk9nMuq1bCQ4KqnY6Q7HFwfvbCmgfbWD2+FaM2PcMMdmbSR72CEVthld4TF5uLjOnTeNwaiqTL7yQ0wdVvgGFEKJiDqfTo3YJe94iOnszyUPnYIrp4fV5tNZs/PAl9v3yOXaHg959+njdh2gYpOBtgLTW7D1wgMPHjtEyNpadmVYW/ZbNowFvc4NhFUd73Uhpf9di9GvXrOGhe++lU5cuLF2+HKPFggZ6d+lCZEQELWJi5EpUCC/kFhQAeHQ35Nt9xZRancyf1JohqW8Rf/gHjvS9ldzOFc/FzcnO5o7bbuPo0aMsfvllKXaFqCGnBwVvTMYftN/3NtldLiK3ywU1Okfq1y+Rs+k7brjpJv7vzjtrEqpoIKTgbWCcTidJKSkcPnaM+Lg40grtPLU6izlBH3JzwA9k9LiW9P63g1KsXrWKObNm0aNnTxYuXoxDKTq2bk2n9u2Jlp1ehPCazW4n9dgxwkJDK21TaHbw9d4iNqebOVJo44wOYQzLX0n7/e+S1fUSjp1W8UL2mRkZzLjtNnKysliybBlDhg3z148hRJPn1LrK94NLM+i28QmMMT1IHXSv1/07HA4WPv44OZu+o//kG7j7/vtlRaNGTgreBia3oICkw4eJj40lICCA7/YXc4/hM24L+Iqsbpe5Fq53J11BXh6n9e3Lk88/T0xMDINOO43oyEjZEU2IGkg7doy9yclorWkRE1NhG4vdySM/ZJJdaqd/m1AmdIvgmshtdN64mIK2Z5E66L5KH4gxm0wALH31VQYOHuy3n0OI5sDpdEIlRa9yWOm+fi44HRwY8WSlSwJW139+Xh7tJ9zI4MumSLHbBEjB24BorUk9epTw0FCcBLAqqZieqf/lbsOnZHe+4PiHaX5eHnEtWnDZVVdx7oUXYjSbGdqvnzyQJkQNFZeWsjMp6YS1rk9mc2gW/ZpDVomdh8e3YkhCGBG5u+i9Zj6lcb05eOZ8CDj12Py8PGLj4ujctSsffv55pf0LITzncDgqvbjsuPMVIvP3kjRiIZbIDl71a7fZMJpMREdH89ySJdz1TaYUu02EDAU2EHaHg8RDh8jMySE0NIwnV2Vh3fwBDxs+IDNhEilDHwIVwDdffsll55/P1i1byCkowGSxMESKXSFqzOl0sufAAUKCgqosRhf+ksWODDP/GBbHkIQwQorT6LnuYWyh8SSdtQinIeyUY1JTUvj7Ndew4tVXAb4reqEAACAASURBVKTYFcJHdCWjuy3SfqbNwU/J6HkdBQnjvOrTarUy+4EHmHnbbdhsNsnXJkb+bzYQB1JTOeCeyvDMrzkMzF3J40HvkNd+LGnDHwUVyOeffMLTCxYweNgw2nfqxJA+fYiOjKxyvqEQomo5BQXkFhRUub12Sr6VfdkWTmsVwvm9ozCY8+m1dhYAiaP/iT301C1Kkw8e5I7bbsPpcDBh4kS/xS9Ec+RwL0tWXmhRKl22PENxywEc6T/dq/4sFgsP338/a3/7jVmzZ8uGEk2QFLwNQF5hIclpabSKi2NPlpWumT+yMPgtCtqMIPnM+egAAx998AHPP/00Z4wYwQsvv8zAPn2IDA+v79CFaNTsDgep6emEBAdX2ubTnYV8vKuQ8KAA7jyrJQF2Ez3XPUiQOYf9Y5dWeMs0KTGRmbfdRkBgIMvfeotu3bv788cQotlxOp2Un2gQYDfRff2jOANDOHjmAnQF04sqYzaZeODee1m/bh2z583j8quu8n3Aot5JwVvPnE4nu5OSjm9hmr5pJc8HvUpR/BAOjFyIDgjij7Vref7ppxk1diyPLVzIkP79ZU1dIWqpbPm/7Ly8Skd3399awBd7ikiINvDIhNa0Cofuf8wjIn8/B0Y+RWmLfqccYzIauWv6dIKCgnhlxQo6d+ni559EiObnhFUatKbz1ucJK0ohcfRibGGtvOrruWeeYcMffzB3wQIuvuwyH0cqGgopeOtZidFIidFIfFwcBbt/5iHzElLC+lIw6pnjT5YOHzGCB+bMYczEiVLsCuEj2Xl5HP5/9u47PorifeD4Z9JJL/Teu/QOUq3YxYaKDUQExUoRpFko/uxYERQsoNixoPIVxAIinQAJISEBQgrp9S7J3c3vjz0whIQETHKXy/N+ve5F7nZ2d5bck312Z3YmMbHUZFdrzZ9x+XxzMJsRrf14oH8o7gpa7lpCcNJW4no+SWbjIaVut46vLzPnzKFN27Y0bdasqg9DiFrJZrWe7sdbL3bd6TGwsxv0Pe9t3T9pEoMGD2bUZZdVdjWFEyn3oTWllI9S6h+l1F6l1AGl1ILqqFhtYLPZiDl2DA8PDwpj/mJYxLMcUq1JHvkiVncfPv34Y5ISE7FpzdBLL6Vl06ZlDpckBEi8VlRGVhbhUVEE+vuX+gT2T1G5vL4ljTBfd+7qFYK7m6JxxPvUi/ueEx3vIaX12XeB9u3Zw6b//Q+AYSNGSLIrKkRi9sKcmmnNLz2C5ntfJatBPxI73lXh9XOys3l/2TKsVisNGzWSZLcWqMgd3gJgpNY6VynlCfyplFqvtf67iuvm8o4mJJCYkoJn6iF673uaaJoQN+wl6vv48/brr7NyxQrS0tIYc8cddGrdmtbNmsnwKKI8Eq/lsNlshEdFoZTCp5TWkuQcC5/szqRdmBcLLm2Ap7uibuw6mkR8QEqL0SR0Hn/WOrt27OCxKVNo1LgxFw8fLk93i/MhMXsBrFYrXkXZtNkxmyKfusT0nQeqYgNPZWVl8fADDxAdFcWAwYPp3OXsrknC9ZT7V1kbbQa59ree9te5pzgR5Uo4eZKD0dH4Zh2h/76nOU4Dwge+TKuwEF576SVWf/ghV19/PTffeScXtW9Ps0aNJNkV5ZJ4LV9mdja5+fmldmUITzLzwY4MCqyayQPD8HRXBCf8QctdL5LZYABHe00/a+zPf/7+myemTqVx48a8sWyZJLvivEjMXpiiokJ6HHgBz4JMIoa/jdW7Yq2fGenpPPTAAxyNjeWFV1+VZLcWqdBfZqWUO7ATaAu8qbXeVkqZicBEgObNm1dmHV1OVk4OeyMj8cs9Tt/ds0gmlMghr9Kyfj1eXLyYz9es4fqbbuKRadPo07WrjMYgzovEa9lSMzLYHRGBb4mh/FLzLKzdl8WmI3n4ebkxZWAoTYM88U/dR5tt84yJJQY8e9aT31v//JPpjz1Gs+bNeWPZMkLDwqrzcISLKC9ma2u8nkvIjtcITd9LbO+Z5Id0qNA6qampPHT//ZyIj+fF119nwKBBVVxL4UwqlPBqra1AD6VUMPC1Uqqr1np/iTLLgGUAffr0kavTMuSbTOyJiMAt6zg9d84gXQewvssL9G3YkLy8PPbs2sWNt97KlEceYUD37qU2uQpxLhKvZ7PZbBxLTORgdDQBfn5nxNWhlALm/y8ZreGaTgGM6RqEn5cbPtmxtNsyg0Lf+hweXPrEErt37qRlq1YsXbaM4ODgC66f1hqrzYbFYsFqtWLTGq316X91sfduFWzp0Vqf0Spks9kIO8dYw8JxyovZ2hav5Tq4jtDw9znR9EpSW15d4dXijx0jPT2dl994g779+1dhBYUzOq+2N611plLqN+AKYH85xUUJJrOZ3RERHDoUyU3Rs8jHm/BBr9KrQRMKCwvBzY3nX3mF3l260KxRIxn4WvwnEq//ijtxgoMxMYQFB+Ph7n76c1ORjaVb0gj1dWf+qAbU8zf+JHrmn6T9n09gc/fi0JCXsXifObGE2WzGx8eHB6dO5d7776fOebbCWKxWCgoKMBUUAKCUwsvTkzo+PgT4++Pp7o6bmxse7u4oNzc83Nxwc3PD3f65m1IoNzcU4ObmhlLKeNm3dSrRVUoZy+0/S3cL5yYxWwEpUfDNg+SHdSGq82T8K7DKqXjt0asX36xfj6+0mtZK5f71U0rVA4rsgVgHuARYUuU1czEms5ktu3dzKDqWm6LnYMWdyCGvE1K3MfNmzSIjI4PFL7/M0H79aCDNouICSbyezWK1cuT4cUKDgs5IdgFW78nkZK6F+ZfUP53suhdm0/6vJ/EoyiVy2BsU+jU+Y51f1q/n9Zdf5u0VK2jWvHmFkl2tNeaCAsyFhVisVrw8PAgNCqJD69aEBAbi5emJm5vM9F4bScyeB3M2fHYHePhwZOBClK38WUZPxMcz5f77Gf/AA1xz/fXnlexmmKxkmm3/pcbCiVTkcr8RsMrex8gNWKu1/r5qq+VaioqK+GffPrJSErg++mm83WzsH7IUn5AmzJkxg183bGDq448zsGdPmSZY/FcSryUkp6ZSaLEQVOLuZmx6IT9F5XJxS186NzDiTlkLaLf1KXxyjhE15EXyg9ufsc4P69bx7Ny5dO/Zs0L9dU1mMwWFheSbTNQPC6Nx/fqEBAURHBCAe4nkW9RaErMVYbPB1w9AWgzc9Q2m3BDczOZzrnL82DEmT5iAKT+fNu3aVWg3VptmT4KZX2Ny2XnChE1Dp3rStdAVVGSUhn1Az2qoi8uKiosjPy2egbtn4ouZg4Nfg+BmPDVtGps3bmTmrFksev55R1dTuACJ17PFHD+Of52z+9/+EZcHwO097H1vtZXW/zxDQOpeYvrNJ6d+nzPKf/vVVyxcsIA+/frx4muvlXtnNzMnB6UULZo0ISggQFpuRKkkZito82I49CP6isVkhHYnM2E/vqXE9SlxsbFMnjABi8XCW8uX075jx1LLRaUWEHGygORcC6l5FmLTC8k02wjycePqjgGMbONPkyDpXugKpENXFUtJT2f/gXAG7Z2Fvy2HdR2X0L5BR+bNmsXmjRt5fMYMFj73nKOrKYRLOj2TYYkHyiJPFvB9RA6DWvhS188DtKb5nlcJTdjMsW5TSW92yRnlf9u4kefnz2fg4MEseeUVfMpoiSkoLCTPZMJqtRIcGMhF7dsT4OdXZccnRK1wcB1sXkJehxvZrnph2rsXXx8fvMp4ziUrK4tJ990HwNvLl5d6d1drzRf7s1m7LwsAfy836vm707mBD4Nb+NKrSR083GQoUFciCW8V0VqTmJLC0m//4N7jcwlT6fzSZTHtO/UiLz+fy6+9lkFDhjDzySdlfF0hqsixhATjwa8SMfZ7bB6e7oqJ/YxRCxod+pAGR74msd1YktvdctZ2+g8YwPgHHuDe++/Hy8vrrOU2m43MnBzc3dxo3bQpDevVk0RXiMqQfBC+noSpblc21x9LoIcHdctpXQkKCuK+iRPpN2AALVu1KrXMrgQza/dlMbSVL/f0DiHAW7oYuTpJeKuAzWZj36FDfPJnJHcff4aWbifZ128JAUEd+HTNGm6+9VbuvPlmQoOCJNkVoopYrVaOJyURHBBwxuc2rdlxwkSvJj74eblRN+4Hmh54j9TmlxN/0YNnlP3xu+8YNnIkfn5+PDBlSqn70VqTkpFB80aNaNu8+TmbWYUQ5yE/HT4di/b2559OMwgJq3/Wg6fFHTxwAJvVStdu3bhl7NhSy6TkWth2PJ/Nsf92aZJkt3aQx4Irmc1mI/LoMV746RC3H3+ODm7xxA5aSJpfGx6eNIlXlyzB392dsOBgSXaFqEJ5JhM2m+2s0Q8iThaQYbLSp4kvQYlbaLnrBbLq9yOu98wzpiZd8e67zJ89m08/+eSc+0nNzKRxvXp0adtWkl0hKovVAl+Oh+wETgx/iXzP4HMmu+F79zLl/vtZ/Nxz2Gylj6xgttiYsyGZVbsyyTLbuKZTAKF1JNmtLeQObyXSWvP3/kiW/nqYRzMW0c0tlpgBzxKpWjJzyhSiDx1izZo1dO3a1dFVFcLlpWdmnnVRGZ5kZtFvKQR6uzHcN4Y2f88hP6gt0QOeQ7sZ/QG11rz75pu8v2wZV159NfeMH3/WtouKijAVFFBYVIS/ry/dO3aUUReEqEy/zoeYjWQOf549uQHUDQ4ss+junTt5bMoUQsPCePG11866yLVpzf6kAt7YmkaGyUrPxj7MGlG/ig9AOBtJeCuJ1pq9UdEs+OEwc0wv0NctiiP95rHH2pI5j00lNiaGL774guuuu87RVRXC5aWkp3MgOpqQoCDAOOH9dCiXj3ZnEFLHnVcGmemxbSZFPnWJGvwiNk+jT6DWmqWvvMLHK1dy7Q038NTcuWcksvkmE3lmM14eHjSqX5/QoCBCAgMl2RWiMu1bC1uWktvldra6dSPU37/MGNu+bRtPPPwwDRo25K3ly6lX30hkrTbNwZMF/HU0j10nzGSYrHh7KEa08eO+PiGlbku4Nkl4K8mR+Hg++usw0/NfYbD7AWL7zOKwfy9O7NvH8aNH+eabbxg9erSjqymEy0tKSWFPZCSB/v54enhgthizqf1z3ESXBt5M763p+/d0tHInasjLWHz+PfllZmTwy48/MuaWW5g2axYAWTk5WKxWtNYEBwTQpnlz6oeFlfmEuBDiwhUe3Y7ntw+RFXoRf9e7mSB//3POOrru669p3KQJby5fTlhYGFuP5bNiezpZxSaMCPB248EBoQxu4Yu3h/TkrK0k4a0EJrOZ77ZHcUnsy4xy301sz2nEhAzG19OTqQ8+yNibbqJhw4aOrqYQLi/x5El2HTxIsH32srxCG29sTWNnvIm7ewVzbWvo9PtDeBRmEzn0DQr8mwBG33ulFCGhoaz89FOCgoPJyM4GoHH9+jSuXx+/OnXw8faWvvdCVJH81GOoNWOxegQQ2XseYYH1yixrtVpxd3dn7rPPkpeXR3BwMMk5Ft7emoaXh+KmiwLx83SjWyMfmgefPbKKqH3kUuc/KrJY2LxrDx32vciV7tuJ6jKVf1Q3Jt97LxmJibi7u0uyK0Q1yMrJYe+hQ6en6i2yap7deJKdJ0zc1SuYa9p7037rTHxyj3N44CLyQzoAxolz4YIFvLh4MYWFhSh3dzKzs2nbogXD+/WjW4cO1A0JoY6PjyS7QlQVSyHq83vwKszmyOAluAeWfd7c9Ouv3Hv77WRmZODp6UmwfZztV/5MxWTRTOwXyq3dgrm6U6Aku+I0SXj/o/jEBNw2LuJKtrCn9QQO+A3kscmTSU5Koq7MrCREtbBYrew8cIA63t6nmz+/3J9FTFohjw2py9UdfGmzbR7+aeEc6Tvn9CxqFouFZ+bMYd3XX+Pr60t2Xh6d27ZlaN++tG3eHB9vmVJUiOpgXT+DOsk7iek5/fTFaGk2/Pwzs558Eg9PTzyKTRdeZNVkma0MaelLv2bnHqdX1E7SpeE/yMnNJf6zJ7nM8hub691BWthIHhw/HnN+Phs2bKB///6OrqIQtUJCcjIFRUWnZ1SLSi3gy/3Z9G9Wh4HN6tBy12JCEv8krsfjZDQdCYClqIh5s2ez4aefuO+BBxh71130vegigkqM2yuEqFrWHR/gvvN9jjS/kayWl5dZbv3337Pg6ae5qHt3/m/pm/yRoNl6LJn4rCJyCow+u94e0gojSicJ7wUqKCgg/P3JXJz7M9/53gDtrueBO+/EUlTExo0b6d27t6OrKEStkG8yERkbS6B9ZrO4jEKWbkkjpI47d/cOocmBZdSL+4ETne4lpc2Np9eb//TTRrI7aRJ33nMP/bp1w0/G0RWi2thsNtLDfyH0hydJCe1Jco8pZSYlv/7yC/Nnz6Zxhx4Ej5nP/d+lA8YDaQOa+RLi605IHXd6N5EYFqWThPcCWKxWDq56hEHp3/KDz1WEjppKenYWN9x4I5MnTaJHjx6OrqIQtYKpoIDII0dAa4q0Oyu3pfO/6Fw83RWzRtSja8JXND70ESdbXUtCp/tOr2exWBg0dCit2rbloYcfpmPr1ucc1F4IUXm01hxNSOBo5A76b3uEAt8GHB/8PB6epXch2p9kZrulBSG9rsBt2Hjq1PHlqiZedKrnTb9mdaRvvagQSXgvwLEvZtEzYQ3fqFGkt7yexEORDB8yhGveecfRVROi1kjLzGR3RARWq5Ug/wCe/y2Fg8kFXN7en1u6BdEi+Vea73ud9MbDONrzCVCKjIwMtm/fTr+BA7l97FjCgoMJDgg4a6B6IUTVyDOZCI+KIiM1mSH7F+FpM3Nw0GtYvc6cWEJrze4EM2+v3UBG3YtQbnW49L4nGdzCjwHNpY+uOH+S8J6n9J8X0TriHdbpiznacgxzJ02kTZs23HvrrY6umhC1hrmggF0HDuDj7Y23nx9v/p1OeFIBkweEMqKNP4HJ/9Bqx/Nk1+vJkX5zQbmTePIk82fOZP/evURGRtK6RQtHH4YQtYbWmuOJiRyMicHD3Z2+R5cTkBHB4QELMQe2PqPcT1G5bDuez9bv1pCx6X0uu+dRpkwYR6NAGftaXDhJeM9D/h9vErp1MT9Y+/NX2A28PfVBfP38WLN6tcy0JEQ10Vqz//BhAHy8vdkUk8vmI3mM6RrIiDb++KUfpO3W2ZgDWxE9cBE2Ny+OnTjBc7NmEb53L6tWraJ169bl7EUIUVksVivhhw6RkJJCaGAgTWPWUvfYz8R3nkBmk6Gny2WZrXyyJ5NNMXnk/L2WjM0fMvKyy5j/8Dg8ZKIX8R9JwltB6b8vI3TjLDZYe/Olx/V89vQjBAUFsWnTJjq0b+/o6glRa2RkZ3MyLY16oaFkma18ujeL1qFe3NotCJ/sONr9NY0i72AODXkJq6c/8fHxPDtrFgfCw1m9ejW3SmuMENWmqKiIA9HRJKWmUi8khOCkrTTd/zbpTUeS2PFuAAqtmn2JJl78IxWLVRN04HOObv6QK666irnPPnvG8GNCXKhyO64ppZoppTYppSKUUgeUUo9UR8WcScaWVQRvnM7vtm6E95zL7u/eJywsjM2bN0uyK5yKq8erxWolKi7u9Pi4H+zIIKfAyj29g/E2naTDn4+Dcifq4lex+ISRnpnJX7//TsSBA6xdu1aSXeF0XDlm80wmIo4c4URyMmHBwdTJOUqbf+aTH9yO2N6zQCkKLDZm/ZzEks2paA33drAQvfFzrr7uOuY995wku6LSVOSbZAGe0FrvUkoFADuVUhu01geruG5OoWDPFwT+8ij/2DoR0edZOgQplq1YQeO6dWnSpImjqydESS4drynp6aRnZlI3JIQfI3P462g+Y7oGclGQmfabH8etKI/IYcaUwZk5OQQFBrJowQLGjxtH586dHV19IUrjkjFrsVrZffAgOXl51A0JwaMwm3ZbZmBz9za6Gnn4kJZvYd6GkyTnWrihSyA3dAmkjqcbXT79lOYtWsjDpKJSlftt0lonaq132X/OASKAWpHpWSN+xP2biey2teU9y7W8v2Q2zerXp0+3bpLsCqfkyvGalZPD/qgoAvz8+CEyhw92ZnBRQ29u6uhJu7+m4Z2XyOFBS8gJaM3hmBimT52KKizEy8tLkl3htFwxZrXWRB45cjrZdcdG221P42U6SfTAhRT6NiA938LM9Ukk51p4oF8wJ9a/zbq1awBo2aqVJLui0p3XN0op1RLoCWyriso4Ex29Edtn4zhga8FLudfw4YvzyM7IoHG9ejLmn6gRXClerVYreyIi8PT0JLPQjU/3ZXFRQ2/mDAum8z9P45cZRUz/Z8gO68bhmBjmTptGVEQE+bm5jq66EBXmKjGbcPIkcSdOEBoUBFrTfM/LBKbsJq7XDHLDLgJg+fYM8oo00y4O5Z/Vr/D5mjUkJyU5uObClVU44VVK+QNfAo9qrbNLWT5RKbVDKbUjJSWlMutY/eL+xLL6NqJtjZmfNpovlj5Lhw4d+OP336lbt66jaydEuVwtXk8kJ5NnMuFXpw4rd2aggMn9g2m783mCTm4nttcMMhoNJjIqirlPPklyUhLr169nxIgRjq66EBVyrpitSfFqs9mIOXbs9PjW9WO+oH7sOhI73ElaiysACE8ysz3exJgufqxftphvvvySeyZM4OHHH3dw7YUrq1DCq5TyxAjET7TWX5VWRmu9TGvdR2vdp169epVZx+p1/B8sH91MnKUuU+KvYN07i+nStasku6LGcLV4tVitxBw/TqC/P/uTzOw8YeaqDv70PryU0PiNHLtoCmktRxMdE8Osxx4jIz2dn3/+maFDh5a/cSGcQHkxW5PiNTs3lzyTCW8vLwKT/qb53qVkNL6Y+C4TT5fZFJNLHQ/Y/ckL/LBuHRMnT+bBhx+W1lNRpcp9aE0Z38AVQITW+uWqr5IDJeyhcNUNJBQF8KTX01w7uBDb0eF88+WXBAcHO7p2QpTLFeM1KSUFU0EBbp4+LPsniQb+Hjzi8SX1D31DYvs7SG4/lnyzmbCwMAb078/MmTPp27evo6stRIW4Wszm5OWhlMInO4422+ZhCmrNkb5zQBn313YnmPgjLp9hrfyoW9SVtm3bcs+ECQ6utagNKjJKw2BgHBCulNpj/2yW1vrHqquWAyQfoPCDazlZ5MOk9Du5//pGtGvWmMl33YGXDHgtag6XildTQQERMTEE+fvzxt/pnMy18EnHv2h+aCUpLa8mvuskoqKi8Pb15ZLBg7n8yy8dXWUhzpfLxKy5oIDI2FiCPYpo9+cMtLs3hwctxuZhTAUck1bA/21KxC/nBGO6DqDRoDsdXGNRm5Sb8Gqt/wRcu50hJYqC968hvdCdG/ZezI5v53Fxs8VcP+JJmUFN1CiuFK9aaw4dOYLWmu0nCtl6LJ/FrXYzIPZNMhpfTFzPJ4mIiODRyZPp168fN115paOrLMR5c5WYtVqtHI6LQ1sK6Bz+DF6mFCKHvk6hb0O01qzZm8VXe1NJ/3Yh1oQIvG/+HghzdLVFLSLjfqQfoeD9q8g2W7hm50B2fLuKoSNG8OhDD0myK4QDZWRlkXDyJOkWH97+O507gvdzS9LLZNfrSUy/+URGRjF10iS8vLx45eUa3xIsRI2ltWbfoUMcT0qi+5EVBKbsJrb3TPLCugLwc1QuX+45SdY3z5IXs5PHnnyC0DBJdkX1qt1TmGQep3DFVeTn53P53wPZ/csaRl16Kd+vW4ePj4+jaydErWWxWomIjaUAL5ZsTmWQVzTPFL6EKagNhwcuZn/EYaZOmoS/nx+bN2+mXbt2jq6yELVWWmYmCSkpdE3bQIO470joMI705pdh05ofI3P44O9Ecr59juzYfcx55hmuvu46R1dZ1EK1N+HNTqTw/asw52YyLm0Ce/+3kMuvvJJvv/4ab/u0pUKI6qe15lBsLKmZ2SzdXkiToqO86/MCRd71iBr8IibtybNz5xIYGMjm336jTZs2jq6yELVWZnY2eyIjaZG1i+b73iC9yXBiOoznl4PZrIvIJstswzfyB04c3c+ChQu54qqrqq1uWuszX8U+s9lspX5+aj2bvQxag1Jozu53UuP7odQytTPhzU2h8IOrKcpOZjKzGDO6G7cO6cItN9wgya4QDnY8MZGY48dZfVBTmHGCdf5LwN2LQxe/Qp6bPzl5eaxes4aG9erRokULR1dXiFqpoLCQPRERpGVm0qDgGJ32LiIrqCNL/R/hq3VJZJltdG3gzfg+AfS55WEO3DqcHr16/ef9aq2xWq1YrFZMBQXYbDaU/XOKDWumtUYphZubG+5ubiilcHdzM967u+Pt5XX6ZzeljLL28qc+9/XxOb0M+78KjH/tZT3lofYao/YlvPnpFK28Fmv6UQb82oOhI3Lo3aYxXdoOlT67QjhYVk4OB6Kj2ZbkQfTx4/wcuAQfXUDkkLf4fW8s/9uwjPfefZf60v9PCIcoslhISU8nMiYGq81GE+9COm+ZS5ZbMJcmTyUt2cRFDX0Y3ULz9bKXaNdjOp6edSuc7GqtMRcUUGSxYLPZsNrvxAKnx+n18vTE18eH4IAA6oWG4lunDu7u7rifSm7d3E4nr0KcUrsSXnMWRauux5oSRZ+fO3Fw2690bdmQLm3bSrIrhIMVWSzsi4ricKZi3d4kvvX7P0KsaURd/Cob9sbz1BNP0KJlSzzlJCaEw0TExHA8KYkgf398VSHtfpuGzVLIzflP0bZZQ566KIgAWw4PPfAAx+LiuO7GGys8aVNOXh6mggJCAgMJCwnBy8MDby8v6vj44OPtjbeXFx7u7pLIigtSexLeghwsH47BlrSf3j+05+DOvxg7bhwfffCBJLtCOIGouDji03JYuSOLVb6v0NJ2lOiBi/lubwpPT59Ou7Zt2bRpEyEhIY6uqhC1Ukp6OscTE6kXGoqbttLmr6fxzjnG3YUzKQxsyZSBYeRlpvHgxIkknDjBS0uX0m/AgHNu02azkZ2XR1FREUH+CqbfWwAAIABJREFU/vTo1InQoKBqOiJRm9SOhLcwH8vHt6BP7KDnujZE7NnGuHvvZcWyZZLsCuEEUjMy2Bl9nJW7zSziDXrZDhDTdy5fH8jj6enT6dylCxt//ZUw6coghEPk5OWxJyKCQH9/FNBi94sEndzBE4WTSAzuyYKR9chJT2HyhAmknDzJq2++Se8yZjzUWmMym8k3m1FK0bRhQ5o3aoS/r6/cvRVVxvUT3iIz1jW343Z8K48VPUhg/YNMeKAvb73xBp4ern/4Qji77Nxc/tyzn7e2m3m86F0ud/uHo90fIb7exVgi/6BP376s//FHmd5bCAcpLCpie3g4nh4e+Hh70/DQR9SL+56l1hs40vAy5g2pSx1PN4o8PAgMCmLOM8/QvWfPUreVbzKRm59PWEgIbZo3p0HdunIuFtXCtb9llkJsa+9Gx2xkcsZYGg0YzrK776FzmzZ4SIAJ4XCFRUXsOHCA1QcKuKtgNbd4bCKh491sVb0IKypiyv33M+vxx08/rCKEqH7HEhMpLCoiLDiY0OO/0mz/u2xwG8JybuGlgWFkp53EIyyM0LAwVnz0UZnxmpWbC8DAHj0IlQtYUc1ct+3AasH25QSskeu56PMGrP74S0a2CaJL27aS7ArhJA7FxvJdRA7dE79kssc6Tra6jjcjQ7nzppvISEwkODBQkl0hHMhUUEDMsWME+fvjnxZOqx3Pc8SnM1Py72dS/zCykuMZP24cLzz/PECp8aq1JiU9HT8fH0l2hcO4ZuZns2L75kGKwr+hx9owIg9H89i0aQzu21f67ArhJBJOnuTHPcfwOPwzsz1Xk9Z0JC8dbsb/LXyGi4cO5erRox1dRSFqveMJCaA1fuZk2m55imyveozJeoQhbYIJK0xi0v33Y7FYuHns2FLXLywqIiM7m6YNGtCtQwfpoyscxvUSXpsN23ePUrDrM3p+FsShmDimzZrF8wsWSLIrhJPIzc9n4+4IEvf/yUue75FRvx+LDrflpRcWMnLUKH74/nuZ3lsIB0vPzOTw0aM08HOn3eYnsWkbt+U9SWhIGEOD0njwvomgFG+vWEGbtm3PWFdrTUZWFsrNjV6dO9OoXj1prREO5VoJr9bY1k/HbfeHXLOpOYeOHGT2/PnMf/ppPCTZFcIpWK1WtoZHsnXbNl5RS8kK6sw6v9t46YUJXHb55az79luZ8VAIBysqKuJAdDQBPp603zYLr7wk7rHOJk43ZFH/IKaOG4+7hwdvvfceLVq1OmNdk9lMdl4eTerXp2u7djIbmXAKrpPwao3+ZS5u29/jPctomt18I/93WyKPTJ4sya4QTuRgzBG+/20LS6wvkefXjOjBS6iXb+X1t97igfHj8fLycnQVhajVLFYrB2NiyMvPp8/hpQSm7uExy0Psce/I4isa0CTIk2cWLSI4JISmzZqdXq+gsJCcvDy8vLzo3aUL9UNDpWVVOA2XSXj1poXk/PYaN//agEZjb+LhyzrSveONkuwK4USSU1P5aP3vzMtfRKFXEAuO9aPVngiuufJKrho+3NHVE6LWMxcUsCcykoysLLrEf0nd47/wf0W3ENdgJPfUOc4f3/3GbXfeSddu3U6vk2cykWcy4evjQ/tWrWjWsCFecldXOBmX6D2u/3iZzJ+X0OtDdzbsjKVfQAbdO3aUZFcIJ5KTl8eH32/gsfTncHP3YHpUL15/8z12b91K66ZNHV09IWq9gsJCtu/fT05eHk1Tt9A8aiVrLcMw97iHKwKOMffRKXz+2WeY8vNPl09JT8fT05P+3boxtE8f2jRrJsmucEo1/g6v3vom6d/No99HbsSl5rLk5Ze4/557JNkVwolYrVZ++v1PbjoyH383Mw9F9OfDTz9jzC238P7y5fIwixAOVlBYyI79+zGbzdiSDtIx/AW22rqQNmA6oYkHeXTqVBo2asRby5fj5e1NamYm3h4edOvQgSYNGkgMC6dX7h1epdT7SqmTSqn91VGh86G3v0/KVzPpswqOppp48dVXeXTyZJm1RdRqzhizf+7cxUV/T6OBSuf+vd348NNvuWXsWFZ//LH08RO1mjPEa0FhIdvDw8k3m8lJTWLAvnnE05DkYYsoit/PEw8/TJOmTXlrxQo8fXzIzMmhbbNmXNy3L00bNpRkV9QIFenSsBK4oorrcd707k9QPzzGj/mdSLP588obS3lo4kR5GlQIJ4vZ8OgYAn+cQhvi2dFtPnEpeYy96y4+WrVKmj6FcHC82mw2wqOijJEVcvLpu2smVuXBiREv0aR+KMmJibRq3Zo3li3DphTBgYEM69ePdi1bys0lUaOU+23VWv+ulGpZ9VWpOB3+JWmfTuaAZ1d+7zidH9c3YUDPntKNQQicK2aPJiSS+8m99LYe4tc203Fr0J1FL/ZmYM+ekuwKgePjNebYMZLT0qjj7UO7rTOppzLYPfB1zO4BeAPX33QTl44eTb7ZTPsWLWjdrJm0yogaqdIeWlNKTVRK7VBK7UhJSamszZ5FR3xH3Ir76PFeAY9sb8LEoS0l2RXiPFVHvKZlZnFkxb30te7hmo0tmPL8Svx9fRnSuzc+Ms6uEBVWVfGalZND1NGjBAcE4PG/eXTV0ezoMptN+05w/RVXsH/fPswFBeSZzfTu2pV2LVtKsitqrEpLeLXWy7TWfbTWferVq1dZmz3T4Q1EvzOOQSvNpJjdeejOa+jdpYsku0Kcp6qO13yTmZ1vj2dI4RYuW9+IX7aEc8Xo0fTt1k26HQlxnqoiXi1WK3siIvCrU4f8zW8w1LKVP5pO4NfDJubMnEm7Dh1o3LQp+WYzA7p3p0FYWKXsVwhHqTnDkh3ZTMQbtzLkg3wyirx4b+UH3Hv77dKHSAgnU2SxsPmtBxme/wuj1oWyaUcUj02bxmsvvyzxKoSTSEhOJt9sJn3XOi7N+pLNflfwv4QAFjz9NL369OH/Xn8dK9C3a1fCgoMdXV0h/rOacfY5upWcD25m1KpcsnUd3v/wfW69/nppWhHCyWit2fTeDK7M+YoxGxvwx75ops2axcJnnpGWGCGcRFZODgdjYkiL2cNtiW+y17sXB4JH8tz0SQwYNIj5Cxdisdno3aULoZLsChdRbsKrlFoDDAfqKqXigXla6xVVXbHT4ndS+OEYTqpQuowZx4RrhnLztdfg5lZzbk4LUZ0cGbPb1y7isuTlbPEewg2P3cNFo6OYPWOGJLtClKG64zXfZGL7/v2cOBbHLXELifdoRsFlC+nlUYdHp01j5GWXUcfPjx4dOxLo719V1RCi2lVklIax1VGRUiXuZdvCK9md4s6uQXNZdEt3enXuJMmuEOfgqJjdue4tOu1ZwkM763L5jCdoERTIzddeJ90YhDiH6oxXc0EB28PDORKfzNVR8yhwq8M7OSMYkpFLaF0fLhk9mmaNGtGpdWvpay9cjvOeiU5G8Mczl3PNyjS0X12+n9FBkl0hnNSunz+k9ZZZDPtUs/d4HF1ui+PSO+7A28vL0VUTQvDveLuHEzMZGD6fELdcHj86knc+eJ2jSRmMGz+e7h070rRhQ0dXVYgq4ZwJb2o0m+aM4tqVJ1H+dflo9ccM7tVTkl0hnNC+zV/TZOOjjFhdRHiCmWcWLWLCuHFyh0gIJ3IkPp7I48m0372QTiqWyZGDee+zz7h89GjuuPde+nTtSn0ZiUG4MOdLeDPi+GnWCG5alYRHYH0++WwNo0cMl6kLhXBCEf/8j5Af7ufST0xEnLSw6MUXeezhh6UbgxBOJDM7m38ORhO0/Q0u1ru4b083Vq5bz1XXXceDjz7KwB495OE04fKc65ZpVjzZ717JxshMvIIbsObztZLsCuGk4vb/TeMf7mJnWh1isz144dVXeeyhhyTZFcKJmAoK+G1XOKZtH3Ot3sT2hrexJSqNa2+8kYefeIKBPXtKsitqBec5M+UkkbT0cnyLMuC6Jawb2o/BfXpLsiuEEzoWtRePT2/G7O6N9ZrFfDehOUP69ZPRGIRwIkVFRWzZE07sX1/ziO0LDtS9DDX4IZa0uYmQkBB6d+mCv6+vo6spRLVwjju8eamsmTqIvv8XyXTzBO685lJJdoVwUkePHCLt3WsZ+W4S85KG07xNZwb36SPJrhBOxGazse9wDH9tWs9DBe8x9ucAHvg8meTUVBo1aMCgnj0l2RW1iuMT3vx0Vk3uz10fxmILasq4m2+gW/t2kuwK4YTijx8jYeloblp5guO57vQfegm9OneWB9SEcDKx8fF8vf4Xnsh7ldt/8GTttgSatWpN88aN6d21q3Q9ErWOY7/xpgzem9iPB1cfoXGLFny89gsG9e7l0CoJIUp3LCGBwy9ezvgP40gp9Oa9VSu5/aabZPQUIZxMcmoqH33/K1MzFjNuXRFfh+dw5333MXv2bNq3bCkxK2olxyW8pkw+e2QQk1bH0LxVK1Z//iUDe/V0WHWEEGWLS0zhxNJruHflITKsPqz8ZDVjrr1WTpxCOBmT2czH639jQvJzTFufzdfhedwzcSLz5s6lZZMmjq6eEA7jmITXnE3SW1dxed0khgwfxguvvE7/7t0cUhUhxLklpGaQvOwGBnjFcPU1V3L57VO49sorpduREE7GZrOx9tctjD68gFC3XK6Y+DS+BxOZPm2aJLui1qv2hFebs3lvYn/GNE5ieb0nefu9sXRu07q6qyGEqICM7Dx+XXAl7U3h/DbwEaZPnCInTiGc1Ibt4bT48yl2RMViuu8lAv068MSVrSVmhaCaH1rTBbk8c1svHvgokrE7u3HNmDsl2RXCSeWaCvh69hU8/v4/3PKDD81GTZQTpxBO6mDcCUzrnuS5z3Zy99f5bEnyoHvHjrRt3lxaY4SgGu/w6sI8Zo3pzuIfjtD9ok68/NZyOrRsUV27F0KcB3NhEZ9Mu5KnVvyJu48f733yOe1aSLwK4YxSM3PYtfxhln+yiT+O2Xh0xgyuv+oqmjZs6OiqCeE0qiXh1YX5PH5dN1796Qh9enbh4y/W0aG13NkVwhkVWawsf+JqZi/fhK+fP598u54RgwfLXSIhnFBBkYVvX5vK+x+uY9sJG0/Mns3UKVMk2RWihCpPeHWRiV0vXMUXW2IZ2OciPv7ye1o3b17VuxVCXABts7Hl7QfZ8ccm/P39WbPuZ4YOGuToagkhyvDtu3OpH/kZ2xM0M+bNY+rkKTSoW9fR1RLC6VRpwmsrNHHo9RvobdnFE09M4YbxM2khfQCFcFp/LX+SYWmfYb7lWiZeMp2Bffs6ukpCiDKs//gVbkp5i/09BrH8+glcOmKkJLtClKHKEl5roZl7LulMoOkEg+54jJtue0yaWIRwYq9Ou51PVn/GrHsupe3Y5+javoN0YxDCSX27+m2emzGN45e0pcm4JxneuRuN6td3dLWEcFoVGqVBKXWFUuqQUipaKTWzvPJaa24f0ZGP/4gj1u8ihkmyK0S1Od94BXhp+t3MeGUNmTY/Ai+fRZd27SXZFaKanG/M/vztamY+8jD7T9pI6TyOnt160bxx4+qoqhA1ltJan7uAUu5AFHApEA9sB8ZqrQ+WtU6Iv7fOzCvk+lF9eOOT72nSoEFl1lmIGkcptVNr3aca9nPe8dq0YV19MiWNNg0DeOfznxg6cKAku6JWq654te/rvGK2Y/t22pIWS2KOjbnPPcuEiVMICw6ujqoK4ZQqGq8VucPbD4jWWh/RWhcCnwLXnWuFzLxCbr28H2+t/kGSXSGq13nH64nkNDo0DuDdz3+UZFeI6ndeMRt7JJrkXBsLnn+WByc/IsmuEBVUkT68TYDjxd7HA/1LFlJKTQQm2t8Wrd2wI+KzBg2K/nsVnUpdINXRlahkrnhM4HzHVV2D2F5QvB5MMsUMG3xxXjXUrzo523egsshxVb3qHHS63JgtGa9WN4+j06Y/nT1t+tPnbqKtWZzp91+Z5LiqXoXitSIJb2m3e84KMq31MmAZgFJqh81qrZbmoOqklNpRXc1c1cUVjwlc97gq4ILi1VpU5HL/V676HZDjcjnlxmzJeLUUFrrc/5Or/v7luJxHRbo0xAPNir1vCiRUTXWEEP+RxKsQNYvErBDVoCIJ73agnVKqlVLKC7gNWFe11RJCXCCJVyFqFolZIapBuV0atNYWpdRDwM+AO/C+1vpAOastq4zKOSFXPC5XPCZw3eM6J4nXM8hx1SyuelzndAEx66r/T3JcNUuNO65yhyUTQgghhBCiJqvQxBNCCCGEEELUVJLwCiGEEEIIl1apCe+FTGnq7JRSzZRSm5RSEUqpA0qpRxxdp8qklHJXSu1WSn3v6LpUFqVUsFLqC6VUpP33NtDRdXJGEq81j8Rr7SYxW7O4YrxCzY3ZSkt47dMjvglcCXQGxiqlOlfW9h3IAjyhte4EDAeeV0rlKKU+L29FpdQspdTyC9mpUuo3pZRZKfX7hax/Hh4BIqp4H1VOKeVtD776wGvAT1rrjkB3XOD4KlstidcBwEP2k2mWi8SsS8Qr/BuzwLtIvJarlsTscFzrHOty8Vqjz7Fa60p5AQOBn4u9fwp4qrK27wwvYByQAVxeyrLZwHOVuK/fgAklPgsFvgbygKPA7edYXwFLgDT76wX+fUixLvCX/ViKgAPAX8XWvRvYCWRjjBH5AuBRbHluiZcVWHqOujwGJAFZwPuAdxnlvIAvgDiMgdeHl1geDKwCTtpf80ssnw4sBWJPHau8yvyduHy82o9rJxBZ/PtbbFlNi9nt9njNtcfu4GLr1tSYnWPfh8Rr+d8vl49ZXOsc64rxWqPPsZXZpaG06RGbVOL2nUF3jC/M1lKW/QhcVcX7fxMoBBoAdwBvK6W6lFF2InA9Rp27AVcDD9iX5QL3Ab9i3AVbA/RVSp0aps4XeBQjaPsDo4AnT21Ya+1/6mWviwko9WpcKXU5MNO+jZZAa2DBOY7xT+BOjOAt6RV73VpizD8/Til1b7Hlq+3rpgIf2JuSliul/M6xv9rK5eNVKdUSaAPs1VpbSilS02I2DSPpuRqIBr5zgZjdZl++SuK1XC4fs7jWOdYV47Vmn2Mr8WrpZmB5iSu1Mq9IquuFcSUzDdiHcdW2AuMLtB7IAf4HhBQr/zn/Xin9DnSxf74QsGE0v+QC40vZVzzQuNj7+cDH9p9bYlxR3Q0cw/jCzD5HvX+j2NUn4IcRiO2LffYRsLiM9bcAE4u9Hw/8Xez91cBbGN1anrLXrX4Z23oc+K6MZXcDRyjjag8jQBYWez8KSKrA7y2es68+U4G+xd7PAv4oUeaY/XfU3/7+NeBZR38Pne3lrPFqr8t/jlnAH2O2qiL+vctSY2P2VLzafx4B/OMKMQv0sR/HJPt7idey/3+dMmYrI17tn7vMOdZV49X+WY09x1bmHV5nnh5xDHAp0B64BiMQZ2FcXbkBU4uVXQ+0A+oDu4BPlFKeQG9gA/CpNq68VpSyn58w+ledyxCgA8aXcq5SqlMFj6E9YNVaRxX7bC/Gyb00XezLyyo7GJiA0VSy0P7vy2VsayhGt4fS3A18qO3f/ArWo4FSKqyM8uVRJX7uWmJ5BJCltd5mf/8F0OsC9+XKnDle4T/GLPAl8CLwPPCZC8TsYOBapVQhsBHoC0RrrU+Wsa2aErPxGHevvOzvJV7L5swxK+fY2hGvUIPPsZWZ8Drz9IhLtdbJWusTwB/ANq31bq11AUZ/nZ6nCmqt39da59iXzcdorvgQ45dcWjNLcRVpclmgtTZprfdifDG7V/AY/DGuiIvLAgIqWD4L8FdKKQCt9VNaay+gDvAssF9rfWfJjdibM/pgJA8llzUHhmH0+alovU/9XFa9z+UnYKZSKkAp1RajW4ZviTKpQK5SqoP9/Sjg4AXsy9U5c7zCf4/ZGK11WRdwxdWImLXHa1N7zF4O7AaeK20jNSlmtdZJGHfz2tg/kngtmzPHrJxja0G82tXYc2ylJbza6CN3anrECGCtLn9K0+qSXOxnUynv/eH0ECKLlVIxSqlsjKYaMP6wjAQmAaOVUqPL2M8GYKj9arUsxfvN5J/adwXkAoElPgvEaDKqSPlAILfkVaLW2oxxBdpGKXXGHwal1PXAYuBKrXVqKfu4C/hTax17HvU+9XNZ9T6XqRi/r8PAtxh9j+NLlAkAPsW4a7AP6IFxB1sU4+TxChces6f6OI5USu3BiNnG59hPjYtZjGbXBIwTkyvE7EHgBonXc3PymJVzbO2J1xp7jq3UcXi11j9qrdtrrdtorZ+vzG1Xk9uB64BLgCCMPkEA7bTW3YB3gB+11j+WtrLWOgejH9PFVVC3KMBDKdWu2GfdKbsZ5ABnXtmWWVZr/RvGH6jWpz5TSl0BvAdco7UOL2Mfd3HuK8+y6pGstU4rZ73S6pmutb5Da91Qa90F4/v7T4linTB+R3201t201tdrrTPOd1+1gQvEK5wds6eafK/WWvfAiNkym31rYsxqrX/TWl8NeOIaMdsEGCfxWj4XiNlad451wXitsedYmWntTAFAAcbTlb5c2FXLD0BZV6cXTGudB3wFPKOU8lNKDcb4w/FRGat8CDyulGqilGoMPAGsBFBKDVBKDVFKeSml6iilZmA8ZLDNvnwkRj/IMVrrkl927GUGYZyoznpyVCmllVLDi9VjvFKqs1IqBHj6VD3K2K63UsrH/tZLKeVzqhuGUqqNUirMfpfgSoynZJ8rtm4TjGFl/i5r+8LlSMwiMStqDIlXJF4dRRLeM32IMfbeCYxmtgv5pVbl0CmTMfrcnsRoanjwVJOWUupipVRusbLvAt8B4cB+jD8S79qXeWMMv5KGcayjgau01qfuhM3BuPr+USmVa3+tL1GXu4Gv7FfcpymlmmI0sYQDaK1/whhjcBPG/+1RYF6x8geUUncU28QhjCaVJhhNdyaghX1Zb/t2c4BFwB0lmvRuB1bZ+4aJ2kFi1iAxK2oCiVeDxKsDnBokWVQipdQRYFQ5/W7K28YvGGP47dBaj6i0ylUxpdSdGMPMPFXN+/XGeEBhqC77SVghSiUxKzErag6JV4nXCyEJbxVQSo0BDmmt9zu6LkKI8knMClFzSLyKCyEJrxBCCCGEcGnSh1cIIYQQQrg0SXiFEEIIIYRLk4S3mimlZimlll/gur8ppcxKqd8rY/vK8IFSKkMpVerQKNVBKdVAKRVh7xQvhENVdYy6GqVUN6XUFkfXQ4jiXCWOlVLvKKXmVLBsHaXUd0qpLKXU50qpa5VSn1Z1HWsKSXgvkH0cvLbllJmtlDpjOkGt9UKt9YT/sOuHtNZDy1p4ntsfgjH/eVOtdb+SC5VStymlDtmD56RSapVSquRMNCil2tn/OHxc3g7t4xJGKqVOz96itU7GGFJlYgXrLUS5nCVGlVIfK6USlVLZSqkopVSp21ZKzbPX+ZLydqCUGmYvW+p0pfYyTZRS3yql0pVS8UqpSWWUu9u+rbLq5a2UWqGUOqqUylFK7baP0QmA1nofkKmUuqa8egtxvpwojjsppTbaz4fRSqkbyqjLOeNYKVVfKbVGKZVg39ZfSqn+ZVVCaz1Ja/1sBet8E8Z4v2Fa65u11uuArkqpbhVc36VJwlu1qnK8wMrQAoizD7hdmr+AwVrrIIwZYjwofT7wNzHmea+IaRhjHJb0CfBABbchRGWpjhhdBLTUWgcC1wLPKaV6Fy+glGqDcbJKLG9jyphW9TXsg9ifw8dALMYJ8CpgoVLqjOGXlDFQ/VOUPZsUGHF/HBiGMXboHGCtUqplsTISv8KRqjSOlVIeGFPtfo8x8cJE4GOlVPsS5SoSx/4Y58ve9m2tAn5QSlV0CuRzaQFE2aehPmUNcjMJkIS3SmmtdwP1lDELCwBKqfmn7oQqpVrarwTvVkodU0qlKqVm/5d9VnT7SqnxwHJgoDIGvV5QSv2Pl5jf2wqccaWtlLoNyAR+rUDdWgF3YiQAJW0DWiulWpSyTIgqUR0xqrU+UGygdm1/tSlR7A1gBlBYgU0+AfwCRJZVwH7yHA48r7Uu0lrvBb4A7itRdBHwOpBKGbTWeVrr+VrrOK21TWv9PUYiXTxp/w0YpaRbknCAaojjjkBj4BWttVVrvRHjhtC4EuXKjWOt9RGt9cta60T7tpYBXkCH0sorpVaeunutlBpub615wt7qmqiUute+bAEwF7jVfk4fb9/Ebzj3jbdqIwlv1fsJuLKcMkMwvuyjgLlKqU6VXIeztq+1XgFMArZqrf211vNKW1EZ0yNmYcy8MgZ4tdiyQOAZjBNwRSwFZmHM7HIG+xVpNGfOCS5EdajyGFVKvaWUysdIUhMx7kidWnYzUKi1/rGs9YuVbYGRtD5TXtES/576uWuxbfUD+gDvVOQYiq3XAGhPsbvCWusTQBFlnLSFqAZVGceqjM+Kx1OF4/iMjSjVAyPhja7gKg0xWlqaAOOBN5VSIfZz+ELgM/s5fYW9fATQUpXSHbG2kYS36lWkqWWB1tpkvwuzl8pP+i54+1rrP+1dGpoC/wfEFVv8LLBCa328vO3Y+zt5aK2/PkexHCC4onUTopJUeYxqrScDAcDFwFdAAZy+E7sQeLSCm3odmKO1zj1XIft0pH8Bc5RSPkqpXhgXrL72/boDbwEPa61tFT0Oe3eKTzCmFy15h1niVzhSVcZxJEZXvGlKKU+l1GUYXXxOxdP5xjH29QKBj+z1yqrgakXAM/aWmx8xphk+14XmqamJa31sSsJb9TYAQ+0nirIkFfs5H6OPT7mUUneosufh/s/bL85+B+cn4FP7vnsAlwCvVKCefhhzfT9cTtEAjO4RQlSnKovR4uzNl39iXDw+aP94AfBRRaZItT8UFqC1/qyCu7wDaIXR//ZtjET11MOik4F9WuutFa2/UsoN4+RcCDxUShGJX+FIVRbHWusi4HqMhDoJo1VzLf/GU4Xj+BSlVB3gO+BvrXVp3fzKklaij255xxFg/7fWx6aHoyvg6rTWOUqpfRh3djYL6LT+AAAgAElEQVRW8rY/wTiJVRcP/u17OBxoCRxTSoERcO5Kqc5a614l1mtnL/uHvawXEKSUSgIGaK3j7A8FtMW46hai2lRljJaheByNApoqpSbb39fDeCBsidZ6SYn1RgF97HEDRrOmVSl1kdb6upI70VofBa4+9V4ptRo4NfzgKGCYUmq0/X0o0FMp1UNrfVYyq4zAXYHxANxoewJQfHljjLg+VP7hC1H5qjqO7aORDDv1XhlD8a2yvz2fOMbe1/0b4ARV/7BnJ4yH07OreD9OTxLe/8ZLKeVT7H2R1tpaSrkfgNFUz8n0P1FKaWCE1vo3pdQdwB8Yd4iaA8/z78Npy7Df7bV7EiOpfZCz7QeaFXs/CKNzfy8gxf5ZP4ygPFo5RyIE4OAYVUrVB0ZiPN1twmgVGQvcbi8yCih+R2o78DhQWovNHGBxsfevAQkYXYtQSg0HNmmtlf19J4w7UAXALcBlGCc/gHuA4v8vX2E81LaC0r1tX/cSrfVZffAxLoA3Fns4T4jK5PBzrTKG9orCaBmfDDQCVtoXVziO7Xegv8D4e3BXyS5Fyhj9JBZopbWOq4SqDyutHrWRdGn4bw5gfGlPve4to5yzD08GgFKqKUZ/oHD7R52BLfbP/sK4e3M/gNY6X2uddOplL2PWWqfYt3WxUirXXtZSomw6YLO/P/VH6w7O8+EZISrA0TGqMS4C44EM4EXgUa31twBa67QSsWEFMk710VXGoPPv2MvmlChrAvK01un2fTUDindRuBw4Yt/vJOCKU/Gptc4ssa1CIPtUP0JlDNq/3v5zC4y7UD2ApGLdqO4oti+JX1GVHB3HYIzIkIjRl3cUcOmpC7zziWOMGz5XY1yAZhaLp4vty5sBRzHu/laGscC7lbStGk1prR1dh/9n777jo6rSx49/7iSTZNIrCQkJJXSRJigqSlVBxMb61d21N1zEVde1666ryOqyrm3tBV1dy8+2lnVRJIBdRIr0EtJ7m97u3Ht+fwSQkDaTTDIp5/16+YJNZu49WXLmPvfc5zxPv6AoygFgTiA5Pi0c43PgRGCjEGJWe6/vwPEvBo4RQtwZ7GO3c94BwHpgkhDC3Z3nlqRDesMcbefcLwLvCCE+6+bzHgs8L4Q4sTvPK0kt6QPz+B6gRgjR6SD1YN7/JUKI/+v8yHo/GfB2E0VRFgF7hBDbQz0WSZKak3NUkno/OY+l1siAV5IkSZIkSerTZA6vJEmSJEmS1KfJgFeSJEmSJEnq07qkLFlqaqoYMmRIVxxaknqln376qVYIkRbqcbREzldJakrO19DThcDmcBAeFubX6z0+QZlVJSMunGhjcNfyTNYChBKGOy6nw8fQNI342ID71UitEEKQn5+PxWIB8Gu+dknAO2TIEDZu3NgVh5akXklRlB5bX1jOV0lqSs7X0HO6XHy5cSMpif51xN1X6+Guz6q4a2Yak7JMQRtHlPUAx66+lKKJN1Odu6jDx6lraOC0k08mzM8AXmqdy+Vi0aJFbNq0iX/+858sXbrUr/kqUxokSZIkSepResp2+uTStQgU6rNmhnoo0kEvvfQSq1at4vnnn+f666/3+32y05okSZIkST1KT6kglVy6FlvqBHxRKaEeinTQkiVLmDhxItOnTw/ofe2u8CqKMkpRlC1H/GdVFOWmDo9UkqQuI+erJPUucs62TNf1kAe9UdYDmGyF1A+aHdJxSGC1WrnooosoLCzEYDAEHOyCHyu8Qog9NLaURFGUMBrb3X0Q8JkkSepycr5KUu8i52zrDIoS0vMfSmdoyJoR0nH0d2azmTPOOINNmzbx29/+lo5u2gw0pWEOkC+E6LEJ/ZIkHSbnqyT1LnLOHiSECG0erxCN6QxpE2U6QwjV1dVx+umns23bNt59910WLlzY4WMFumntIuDNDp9NkqTuJOerJPUucs4eFOp0BpO1oDGdIWtWSMfRn9XU1DB79mx27NjBhx9+yDnnnNOp4/kd8CqKEgGcDbzTyvevVRRlo6IoG2tqajo1KEmSOkfOV0nqXdqas/1xvuohDniTymQ6Q6gZjUYSExP55JNPmD9/fqePF8gK73xgkxCiqqVvCiGeF0JMEUJMSUvrkfW6Jak/kfNVknqXVudsv5yvQoRulVcIkkvzZDpDiFRUVOB0OklMTGTdunXMnTs3KMcNJOD9NfJRiyT1FnK+SlLvIufsEQSghGjTWmM6Q5FMZwiBoqIipk+fzmWXXQYE93fAr4BXUZRo4DTg/aCdWZKkLiHnqyT1LnLONhfo6m6tUwMgOqLz/bQa0xkMMp2hmx04cIAZM2ZQV1fHH//4x6Af368qDUIIJyDX9SWpF5DzVZJ6Fzlnmws04LV7GgPetJhOtu49nM4gm010p3379jF79mycTid5eXlMnjw56OeQrYUlSZIkSepRAt20ph98eWcfgf+SziCbTXQXXdf51a9+hcfjYe3atV0S7IJsLSxJkiRJUk8T4Ka1fXVewg0QH9m5dbyk0jyZztDNDAYDr732GuHh4YwdO7brztNlR5YkSZIkSeoAh8uFweBfiFLj8LH+gIMTsqMJM3RihVcIksvWHkxnSO74cSS/bN68meXLlyOEYPz48V0a7IIMeCVJkiRJ6kF0XaeovJwYk8mv16/eZwfgnLHxnTqvyXpApjN0kx9//JHZs2fz3HPP0dDQ0C3nlAGvJEmSJEk9htvjwe31EhkR0e5rzS6NVXtsTM6MYmhy+69vS1KprM7QHb799lvmzp1LUlISX375JcnJ3bOaLgNeSZIkSZJ6jAarFfzM3/1snw23Jrj0uKTOnbRJswmZztBVvvzyS04//XTS09P58ssvGTx4cLedWwa8kiRJkiT1GE6Xi/Aw/8qLObw60UaFrHhjp85psh7AZC+WzSa6WHl5OUOHDmX9+vUMGjSoW88tA15JkiRJknoMp9tNeHj3FpGS6Qxdq66uDoCLLrqITZs2MXDgwG4fgwx4JUmSJEnqMVxut98rvEEh0xm61Mcff8yQIUPIy8sDwGjs3Gp8R8mAV5IkSZKkHsOrqoT5WZIswP4ULZLpDF3n/fff5/zzz2fMmDFMmjQppGORAa8UNF6fzs5yK2ant+0XFn4dnE8pSZIkqc/xaRqKnwFvtd1Hkqlz6Q/JstlEl3j77bf5v//7P6ZOncrq1atJSurkxsJOkp3W+hmvqmJzOPD5fJ06jtmpsq/G2fhfdeOfBXUufLrgvjNzmT82tcX3GUu/I/k/F2Od+QCucb9p+eCKwoDk5E63iGyLz+fjV7/6Fc8880xIcokkSZKklvk0DYOfn/+aAFN455pNJJWulekMQfbjjz/ym9/8hunTp/PJJ58QFxcX6iHJgLc/cHs8NFitlFVVUdvQcLhdo78BpcMr2FPno9iiUWLVKbVqWDy/rNAmRCoMig9jzlAjg+LDMHmq2LyrptlxFF3l5B/uwGnK4HvGou/a1eL5hK4za9o0oiIjO/DT+ic8PJzrr7+e6upqGfBKkiT1EEIINE3zu8uaTxcYOtFdzWTNx2QvpmrE/3X4GFJzU6ZM4YknnuDyyy8nJiYm1MMBZMDbZ+m6TmVtLcXl5ZhtNoQQmCIjSYqP9/uDxKsJPtxh5YMdVlQdwhQYlGBkQqaJIUkRDE6MYHCSkYQo/zYXDNz1CrHOUvac/HeSUtJbfV2d2ezX8Tpi586dbNq0iYsvvpjTTjuty84jSZIkBU7XdQIJXx1enZTojm9wSz5UnSFTpjMEw8qVK5k+fTojRozg+uuvD/VwmpABbx+k6zo79u+nuLyc2JgYkhMSAk4P+LnCzYs/1lNh83HS4GjOHBXHsOQIjGEdu5OOtJeRuftf1GfNxJoxrUPH6Kxt27YxZ84cjEYj5557LrGxsSEZhyRJktQyXdcDe73A7/SHZoQgueQLbGmT8EWFNr+0L3j88ce56aabuPbaa3nuuedCPZxmZMDbx6iqyu6CAoorKkgLIA9W1QSlFpVSi8oPJU5+KHGRHhvOPbPTmDDQv37mrRKCnK2PIQxhFE+4sXPH6qDNmzdz2mmnERUVRV5engx2JUmSeiBdCAgggG0MeDt2rtj67UQ5yigffVnHDiAdtmLFCm677TYWLVrEk08+GerhtEgGvH1IdV0d2/buRfX5SEtKCmhV97FvatlQ4gIgJsLAr46N57xjEojo4IrukZLKvySx8juKx9+Aakrr9PECtWHDBs444wzi4+PJy8sjNze328cgSZIktU/XdQKp4aML0eEV3pSiz9DCImnImtmh90uNli1bxr333stFF13Ea6+91u1NQ/zVM0cldcjmXbuIjooivgOrlxVWH6PSIrlmahJZCUbCO7EJ4EgGn5OcrY/jTBhOVe6ioBwzUF999RXJycnk5eV1a99uSZIkKTC6EAHl8HZ0hVfRvCSXrsGceQq6MTrwA0gAeL1eVq1axSWXXMLKlSsJ686GIQHya/eSoiiJiqK8qyjKbkVRdimKcmJXD0wKjKZpCF3vcGUDp6ozMC6cwUkRQQt2AbJ2vkSEq5rCSX8EQ/feX7ndbgBuueUWNm/e3G+CXTlfJal3kXP2F5qmHa4k5I/GFd7Az5NQ+R3hqo26nDMCf7OEEAK3201ERASrVq3q8cEu+N944nFglRBiNDABaLmelBQyqs8X0GOgozm9OtHG4PYhMZn3kb7/XaqHno0jZVxQj92eL774gtzcXLZs2QJAfHx8t54/xOR87Wt8Xnj7Eij+vlOHcasam4ob+Nd3hdz6zlZ+KqoPzvg6qaamhsrKylAPI5TknD1I13W/0/Fcqo7FpRPVgWtXavFnqJHJWAZMDfi9/Z0Qgptvvpl58+bhdruJjY3t8cEu+JHSoChKPHAqcDmAEMILtNNKS+puPk3rcKMGTRe4fIKYiCA2ehAaQzb9DV9EHKXjrgvecf3wv//9j/POO4+RI0eSmZnZrecONTlfexefptFgsVBWXY3Vbm/1dRnFnzJy10dsizuRhmr/npT4dEFBg0pBg4/CBpXCBh/lNh/6wTvjuAiF1DArjprAN6UmJyQwbsSIgN/XksrKSubOnUtUVBQbNmzwu2xiXyHnbFN6AKu7P1e68WiCCQOjAjpHmNdKQsW3VOee3+1PHns7XddZunQpzzzzDDfeeCORXVgvP9j8+ZceBtQAKxVFmQD8BNwohHAc+SJFUa4FrgXIyckJ9jildnRmhdfta3xnMFd40w58SGzDLg5MvRctovtWVz/66CMuuOACjjnmGFavXk1KSkq3nbuHkPO1hxNCYLXbKa+uprSyElXTiIqIIDIiosXcRUX3kbP/DWyJo7Gln0B4Gze2VXYfP1d6+LnSw45q7+G5nRBlYGiSkSlZUQxNNjIsyUiyydChm2RV06i3WAJ+X0vKysqYM2cOJSUlfPLJJ/0u2D2o3Tnbn+ZrIGXJdlS5iQhTmBhgwJtcmodB+GQ6Q4A0TWPx4sW89NJL3HbbbTz00ENd2hE12PwJeMOBycANQogfFEV5HLgDuPfIFwkhngeeB5gyZUpnnq5LHeDz+QJK9D+S09v4ARMdEZyLjdFVy6Dtz2FJO4667NODckx/fP311yxatIhJkybx2Wefhbxvd4jI+dqDWe129hYWUl1XhzE8nNiYGMLbeRSYUvQFUc4KiifehDEiotn3hRCs3mdn9X47hQ0qAANiwzhlaAwTMqIYnhpBsiksqBcmX4C1UlvidruZNWsWFRUVrFq1ilNOOSUII+uV2p2z/Wm+aprm92vLLD5yEo1Ehgd27Uop/gxX3BCciSMDHV6/dtttt/HSSy9xzz33cP/99/eqYBf8C3hLgVIhxA8H//e7NE5GqQdRfb4Ov7fW2fjeRD87prVJCAZvfgSDrlI06ZaA6il21tSpU7n11lu5/fbbSUhI6Lbz9jByvvZQJRUVbNu7l6jISNKSk/17k+4jc/erOBJHYsk4qcWXvL/DyltbLeQmR3D5cYlMzjSRERfe4y9GUVFR3H777RxzzDFMmxaaZjQ9hJyzR/B4/c/mcKp6wKUzI+1lxNVto2Tc4m69PvUFV199NZmZmdxyyy2hHkqHtHtbJISoBEoURRl18EtzgJ1dOiopYB6vt8MXuGJz46pQTqKx0+NILv2CpIqvKDvmajxx3fPo7YMPPqC2tpbIyEiWL1/en4NdOV97KJfHw878fJLi44kLoK98cmkeUfZSykdf3uTiLISgxKLy1lYzb221cOrQaJbPS2fB6HgGxht7dLC7f/9+1q5dC8BVV13V34NdOWeP4vZ6/d4AlV/vJTel+VOPtqQUf4ZAob4bnz72Zl6vl1dffRUhBGPGjOm1wS74X4f3BuDfiqJEAAeAK7puSFJHuD2edh+NtqbIrBJtVDrVjxwg3F3P4C2PYk8+hsoRF3bqWP568cUXufbaa1m6dClPPPFEt5yzF5DztQdxuFxs2bWLsLCwwAqyC43M3a/iTMjFnDkdgK0VLr4qdLKtwk29q/HR7/HZJn43LaXj7VW70e7du5k9ezZGo5G9e/f2qg0vXUzO2YMCuZYJAVHhAfzeC0FK8WfY0ibhjU7v4Aj7D4/HwwUXXMDHH39Mbm4u06dPD/WQOsWvT18hxBZgShePReoEt8dDWAc3fBSbVXISIzq3KiQEQzb/HYPPTcFxd4LS9SVKnnnmGZYsWcK8efN4+OGHu/x8vYWcrz3Htn37KK2oICIigoQAG8Ikl67DZCtiz9T7KTT7KDarPPltHQAn5kQzfmAU4zOiGBDbO3aZb9++nblz5wLw3//+Vwa7R5Bz9hcut9uva5mmB57KHFO/gyhHGRWjL+3I0PoVl8vF+eefz6pVq3j66ad7fbALstNan+EJ4DHQ0artPiZmBrbL9WjJpXkklX9JybjrcMcP6dSx/PH4449z0003sXDhQt555x158ZR6FKvdTmVtLUVlZQxITg78ZlLoZOx6hZrIHM77Phen2lij1qDA0hNTOGWo/2kRPcHWrVuZO3cuRqORvLw8Ro8eHeohST2U2+PB6MeTkO+KnQAMjPM/FS+1eBW6IYJ62Uq4TQ6Hg3POOYe8vDxefPFFrrrqqlAPKShkwNtHuLxeIo0dy8G1e3XiOlGhIdzdQM6Wf2BPGkPliIs6fBx/OZ1OnnrqKc4//3zefPNNIlrYuS5JoVJnNvPjtm0oikJyfHzAwa6qCcp++oyptgLu8C4lKymSBaPjyEk0MjA+eG2/u9PKlSsxmUzk5eUxfPjwUA9H6qG8qorL48EU1f4CzBf77SREGZiW419bYEVXSS7NoyHzFHRj77ph7G4bNmzg66+/5tVXX+WSSy4J9XCCRga8fYAQAq/HQ3QHVjk9Ph1VE8RGdjzgHbzlH4T5nBRMubPLi3jruk50dDRfffUVycnJGDsY5EtSV9hXWMi+oiLiYmI61Oa7yu7jmW9r+LvlNYrCMkmdeAbLRif2ivzclui6jsFg4JFHHuH2229n4MCBoR6S1IPZnU4E+HWTWGpRmTLIhNHPKg0JFd8R7rVSlzOvk6Psuw7N11mzZpGfn09WVlaohxRU/bLKd1/j07TGfuIdyOG1H6zBGxvRsXSIpNI8ksvWUj7mStzxwzp0DH8IIbj33nu57LLL0DSN9PR0GexKPUp1XR17i4pISUzsULAL8HWhg8y67xhjKEabchVnjknqtcHuV199xaRJkygpKSEsLEwGu1K77A6HX/Xk8+u8WD06SSb/r1spxZ+hRiZhSZethFvS0NDA9OnTee+99wD6XLALcoW3T+hMW2HH4YA38GA53NPA4C3/wJE4ioqRv+7Q+f0hhOD2229nxYoVXH311T265JLUP/k0jZ35+cTHxPh94+nVBD+WOKm2+6hxatQ6fGyvcvNB+Pu4YwZRN2hOF4+666xdu5azzjqL7OzsDu8tkPofm8PhV/7ue9stxEcaOGu0f108w7xWEiu/pXrYubKVcAtqa2s57bTT2LlzZ59OEZT/8n2AqqqIAPqPH8nuORjwdiClIWfLY4R57RSc8niXfYgIIbj55pt5/PHHWbJkCU8++WR/bT8q9UBmq5XiigrqzGbcXi+piYltvl4IQZFZZf0BBz+VuaiwNTZ9iYs0kBoTxpUpOxhnLaRgdNenB3WVzz//nHPOOYfc3FzWrFlDeros/yT5x+ZwEOHHkzunqpMZbyTGz4Wa5NI8DLoqWwm3oLq6mrlz57Jv3z4+/PBD5s3ruykfvfMTVWqiMyu89g6u8CaVrSeldA2lY6/GlZDboXP749Zbbz1ckeEf//iHXN2VegSX283+oiKKKyuJiojAFBlJbHTbm2dqHT7+uq6GYrOKQYHBiUaunJLErNwYosINIARj1r6PJ3pgr70wr1u3joULFzJmzBhWr15NWlpaqIck9SJ2h4NYPxqzVNp8DA6gUdIvrYRHtf/ifsRisTBz5kwKCwv55JNPmDOn9z5V8ocMePsA1efr8ArvoZQGf++UAcI8FgZv/juOxJFUjrq4Q+f111lnnYXJZOqVfbulvsnt8fDN5s3ouk5qYqLfTxxe22ymwubjyilJnJBtIjm66cdvQtX3xDbsonDSrYheuro7fvx4fvOb3/DII4+Q7G/7ZEmisbSmqmntpsBU233UOTXmj4rz67iHWgmXHiNbCR8tPj6e8847jzPOOINTTz011MPpcr3zU1Vqwufzdfi9h1d4A0hpGLz1McK8VgpOebRLLsyaprHmiy9YsGABM2fOZObMmUE/hyR1VJ3ZjKqqpCYl+f2eeqePb4ucnDU6ruULtRBk7XwJT/RAaoecGcTRdo+1a9dy4oknkpyczMqVK0M9HKkXcrrdfgWkBfVeAI7N8K92fErxZwDU5ZzW8cH1MUVFRTidTsaMGcODDz4Y6uF0G5kM2QcE0nv8aDaPjkEBk5/tGRPLviSlZDUVoy/DlRD8epo+VWXFsmWcddZZbN68OejHl6TOKq+u9qtO6JGe+q4egLEDWq7ekFjxNTENuykfcznC0Luqj/z73/9m7ty5LFu2LNRDkXoxr6o29gpuR7WjcYEn2Z8KDQdbCVvTJuGNzujsEPuE/Px8Tj31VBYtWoSmaaEeTreSAW8f4PazFWNL7F6d2AiDX+kCYV4rQzb/HWfCcCpGB78Ytaqq3H3bbaxfs4YHly9n0qRJQT+HJHWGpmnUm82YAig7VtjgZXeNh6mDTEzNbiHPV+hk7XwRd+wgantZ7u7KlSu55JJLmDFjBnfeeWeohyP1Yi6Xy6/0oPw6LynRYST6EfAeaiXcW3Pig23v3r3MmDEDu93Ov//9735XQUUGvH2A2+slvIO/uA6v7nf+bs7WxwnzWjgw5e6gr0J5vV7u+MMfWLtmDYtvuIE//OEPQT2+JAVDdX092sHi7P7QdMH9a6pRNcGMVtoBJ5WtI9qST9mYK3tVZYbnnnuOK6+8ktNOO41PPvmEGD82G0lSa+otlna7haqaYHeNh+Ep/pXO+qWV8KxgDLFX27lzJzNmzMDr9bJu3bp+uaDUez5dpVa5PZ4O3am5fTolZtWv/N2E8q9JLf6MsjFX4Eoc0ZFhtumbL7/kq/Xruf2ee5h5+ulBP74kBcP+g13U/PVTmQubR2fJtGROaKkFqtDI2vkyrrgh1Gf3nh3SVouFu+++mwULFvDuu+8SFWCKhyQdyauqNNhsbXYL3VXt5m/ra7F7dX49of12wormla2Ej/CXv/wFaKykMnbs2BCPJjRkwNvLCSFwe71EB3jBKbOorPiyhnKrj2tPaHs3dWMqwwqc8blUjL60M8Nt1ay5c3nj3XcZPnIkdWZzl5xDkjrDarfjcDpJaWezmqYL9tZ6+GK/nS8LnCgKHJPe8vxMKV6NyVbI/hPuB6X3PF6MT0jgm2++YejQoX26UL3UPTbt2IHq9RLexs3kzmoPdq/OnTPTmJxlaveYiRVfN7YSHtx368oG4uWXX6aqqophw7quI2pPJ1Maerni8nLcHk9AKQ0uVefuz6uweXTunTOAucNj23x9zs9PYvSYKZhyZ1BTGRwOB39YupRtW7cCMHzkyKAdW5KCRVVVSisr+X7LljbbWQshePtnM1e9V8afVlfzTZGTU4ZE8/C8DAbENl9bUHSVzJ0v4UgcSUPWzC78CYJn5Qsv8PLzzwMwatQoGexKQWF3OEiMb71rmhCCn0pdRIQpTMr0b3EnreBjPNHpWNKPD9Ywe50NGzawYMECbDYbMTEx/TrYBbnC26v5NI3dBQUkt/FB0ZJSi4rDq/PHU1PbLe2SUP41qUX/o3z0pTiTRndmuE3YbTZuXLKEndu3c+bZZ3Ns0I4sScHj9nj4YetWnB4P8TExbXaBWrXXzrvbrEwZZOLUoTGMz4hqMz8+teATopwV7J30B1B69tqDEIIXnnmGF599ltPnz0fX9VAPSeojhBD42smLd6qCfXVeThse69cG6whHOQnVPzbmxfeiJyfB9O233zJv3jzS0tIwm83ExflXt7gvkwFvL6aqKroQhPvRe/xI5dbGsi6D4tterTW6ahn6019xJIygfPTlHR1mM1arld9fdx17d+9m+YoVzJo7N2jHlqRgKq2q8qtlMMCqvTbGDIjktlNT270oG3xuMne/gi1lPJb0acEabpcQQvD0E0/w6ksvsfDcc7n1rrsQsoC/FCTqwTry/gSymfH+XetSCz9FoPTKmtbBsH79ehYsWEBWVhZr1qxh0KBBoR5Sj+DXb4+iKIWADdAAnxBiSlcOSvKP6vN1KCel3NrY2rSlx6yHCY1hPz6AQfNw4Pj7EGHBeXRptVpZcvXVFOTn8/Cjj3LKjBlBOa70Czlfg6eqtpYYU/v5gntqPJRbfcwYGuPXhXvAgfeJcNeRf8L9Pb7705OPPsrrr7zC+RdcwG13342mafjkCm9Q9ec561XVdl+jH6zP69dU0X2kFf0XS/rx/bL27tq1a1mwYAFDhw7liy++YODAgaEeUo8RyJzjRAIAACAASURBVNLgLCFEbZeNRAqYV1XpSEPhEovKwLhwjGGtf3pk7H2D+JqfKJh8B+74wR0f5FGiTSaG5eZy/Y03cuLJJwftuFIzcr52kq7r7eYWHrKnxgPA7Ny28+EBDKqDgXtex5x+AvbUCZ0eZ1fLHT6ciy6+mJtvvRVFUfpdsfpu1C/nrD/XMau78QYrPrL99ISEqg1EuGoonnBTEEbX++Tk5DBz5kxeeeUVBgwYEOrh9CgypaEXU30+hB+daY5W2OBlRGrr5V9i6rYzaMeL1A+aTe2QBZ0Z4mG1NTWgKKSmpnL/X/8alGNKUldyuFwIIdqtubupzMXHu6woQLQfNa0z9r1FuNdK2THXBGmkwafrOvv37WPkqFEsOPtsFpx9dqiHJPVRHq8XpZ3rWLGlcRW4zaeSB6UVfowamYR54ElBGV9vsWXLFiZMmEBubi6ffvppqIfTI/n7RFwAnyuK8pOiKNe29AJFUa5VFGWjoigba2pqgjdCqVUut9vvAviHOLw6NQ6NwYkt5++GqXaGbfgLXlMahZNuDcrj1qrKShZfcQW33XhjhwJ0KWByvnaSV1X5ec+edvPjC+q9/P2rWkxGA9dNSyaijacmAOHuBjL2vUV91sygbgINJk3TeOBPf+LK3/6WkuLiUA+nv2hzzvbl+epwOjG0U2Vob40Hg0K7DSeMrloSK76ldvD8XteiuzPee+89pk6dymOPPRbqofRo/kZLJwshJgPzgesVRTn16BcIIZ4XQkwRQkxJS0sL6iClljlcLowBblgrNnsBGJzUwgeHEAze/HciXdUcOP7PaBGd39VZXlbG4iuuoL6+nptuu82v/Eap0+R87aSK6mqsdjsJ7exs/niXlTAFHjg93a90hsxdL2PQvJSN7Zmruz6fj/vuvpv/fvQRV1xzDdk5OaEeUn/R5pzty/PVare3Wf1E1QRbKtxkJxgJM7R9/Ugp/h+K0KgZclawh9ljvfnmm1x44YUcf/zxXHXVVaEeTo/mV8ArhCg/+Gc18AHQfwvb9SAOpzPggLeoofHR0JCk5h8wKcWrSCn5grIxV2BP6XyhsNKSEq678kpsVitPPf884yf0/HzFvkDO184RQlBUUUFsdNvdnBpcGt8WO5mVG0NCVPu5hVHWQgYUfET10HOCmhcfLD5V5Z7bb+ezTz/l+htv5KrFi0M9pH6jv85ZIQSWdgLe3TUeSi1q+zeUQpBW8AnW1Il44vrHjdqrr77KxRdfzPTp0/nss8+ID7BEaX/TbsCrKEqMoihxh/4OnA5s7+qBSe1zuFwBlyQrNHuJiTCQbGp6gY60FTN48z+wpk6kYvQlQRnfw8uW4XK5eOallxg7blxQjim1Tc7XzrM5HDicTqLaaHMK8MpPDegC5o/070lI9ran0cKiKB97RTCGGXSffPgheatXc9Ott3KZXCnqNv15zjrdbjweT5sLN1Z34ybJYzPano9xNZuJcpRR209Wd8vLy7nuuuuYPXs2n376KbGx7T9h6u/8iZbSgQ8OPooOB94QQqzq0lFJ7VJ9PryqSnyAv+TFDSpDkoxNUgsUXSV3w32IMCMHpv4paIW6/7xsGRaLhdzhw4NyPMkvcr52Up3Z3G5ufIVV5ftiJ2eMiGVgO/WsAeKqfyKx8ltKxl2HL7Lt1sShcvb555OVnc3UE04I9VD6m347Z30+X7v7ROzexgoNce1UaEgr/BifMZb6QbOCNr6eLDMzky+++ILjjjuOqCj/us/1d+0GvEKIA4B8Ft3DuNzugN9TZfdRaFY5fUTTIHnQ9ueIMe9l37TlqNGdK2Oyd88e3nv7bW696y5S09JI7WP5Zj2dnK+dI4SgoqYGUzuru+9ss2AMU1g0LsGPg+pk//xPPNEZVA2/IEgjDQ63y8XDDz7I1dddR9agQTLYDYH+PGc1XW+3JFmV3YfRAHGRrd+EhnmtJJWtp2bIWYiwtudub/fYY4+RlpbGb3/7W06WpT0D0rP7WUqtsjkcAW0A82qCR76qwWho+gg2vvIHMva9RfWwczFnNdvbFJDdO3ey5Oqr+earr6ir7XflJKU+wOZwYLHZMLWxYlLU4OXrQifTsqNJNLX/NCSl+DNiLPsoHbe4R12MnU4nNy9dyqcff8zO7f3iCbrUw6g+H7RTuafC5iMjru0NaynFn2PQvdQMXRjsIfYoDz30EDfffDMff/yxrHjUATLg7YV8msbewsJ2N9UcIoTg5R/rKahXueGkFNLjGhf2w931DNu4DGf8UIrH39CpMW3/+WeWXHMNMTExPLdyJekZ/a/DjdT7VdXWtpnOoAvBsz/UE26AX09sf3XX4HMzaMfz2JPGUD9oTjCH2il2u50bf/c7tmzaxF+WL+e0efNCPSSpH3K53e0u3FRYVTLi2uoKKkgr+BhH0mhciSOCPMKeQQjB/fffz5133smvf/1rXn/9dVnxqANkwNsLlVZU4PJ4iIxov93vnhoPt/y3kjX5Ds47Jp4pgw4GyUJn2MZlhKkODhz/l06tPG3dvJkbFi8mISGBZ19+mSzZt1vqhbyqSn5JCfExMa2+psLmY3+dlwvHJ5IS3f4WiPR9bxHhqqFk/FJQesbHrc1q5YZrr2X7tm088PDDzFsQnOYykhQol9vd5oY1VRNUO7Q2G07E1v1MtDWfmiF9c3VXCMG9997Ln//8Zy699FJee+21gDerS416xiew5Leyqiq279tHUjv1QQ957od6rB6NpSemcNGEX1ak0vf9PxKqNlA8/gZcCcM6NSbFYCB78GCeW7mSgZmZnTqWJIWK29PYHriti0mpubGs37iM9jeJhLvrGLjn39RnzuhRLYQVRSHcaOShRx5h7umnh3o4Uj/mdLsJa6PpRL1LQ9VEq42SAAbkv4/PGEtdTt/8XT60knv11VezcuXKNv//ktombxN6mZKKCuJiY/26w6uy+SixqFx+XCIzhv2yahXdsJtB25+lIfMUaoad2+GxVFZUkDFwIOMnTODVN9+Uj1ikXs2rqu2+5vsSJ+EGGJTQ/vzL2vEiiu6l9NjfBWN4nWZuaCAqKorYuDief+UVOV+lkHN7PIS3EcDVOnwAJEe3/Jpwdx1JZeupzj0fPdzUJWMMFSEEpaWlZGdn88ADDwDIOdtJcoW3F/GqKmartd0d5IdsLHMCcFzWLx8EBp+T3A334YtKomDyHR1uHfztV1/xq4UL+fTjjwE5EaXer7ahgbB2ypH9XOFmyiATkeFtv85kOUBa4X+pzj0fT2zoU3xqa2tZfOWV3HP77YCcr1LP4HK72wx4Sy2NN6FpMS3fYKYVfIxB+Kgedl6XjC9UdF1nyZIlTJ48mfLychRFkXM2CGTA24s4XC7A/4vVxlIXgxKMZMT98jho8JZHibSXcWDqn9Ai/Sip1IIv167l1ptuYlhuLiedckqHjiFJPU29xdJmswlVE1g9OjkJ7efOD9r2FJoxmvIxoW8yUV1VxXVXXEFleTm/viQ4TWUkqbM8Xi+qqrb5iP7zfXYGJxkZ2NKmNd3HgIIPsQw4Hk9cdheOtHtpmsY111zDs88+y9VXX83AgQNDPaQ+Qwa8vYjd4fB7RVbTBXtqvYw/ItcwtfC/pBb9j/LRl2FLm9ShMaz5/HNuv+UWRo0ezVMvvEBiYmKHjiNJPU17j1e3lDfecKa3tWMciK/aQGLVD5SPvhwtIrStPisrKrjuyiupq63liWef5bipU0M6Hkk6xGq3t3k9c6k6xWaVE7OjW1zkSar4mghXDdW553flMLuVz+fj8ssv5+WXX+bPf/4zy5cvlyu7QSQD3l7Eare3uaP1SJV2H6omGJLUuLprsuSTs+UfWNMmd7i1aWlJCffccQfjjj2WJ557jjjZt1vqI+xOJ26Pp9XVJlUTPL+hnpxEIyfmtFEOUGhk//wU7pjMkF+IhRDcecstmM1mnnzuOSZM6thNriR1Ba+qthnMlRxMZ8hpZcPagPz38URnYB54YpeMLxQeffRRXn/9dZYtW8Z9990ng90gk5vWehGn2+3XZrUyq8oHO6wADEmKwKA6yf3+XvTwGPKP/3OHWwcPys5m2UMPceL06UT7WQNYknqD6ro6wgyGVi8wZpeG2a3zf+MTMYa1fhFKLfyUaGs++0+4HxHWfupDV1IUhbvvuw/V52PM2LEhHYskHa3ebG5zAefbIidhCuSmNJ9HUdYC4ms2UTJucYevZz3R0qVLycnJ4cILLwz1UPokucLbC/g0jdLKSmrq64kwtny3a/NofLbXxl2rKrnp4wrWH3AwYWAUOQnhDNn8N6LspeQffx++qJSAz//h+++zaeNGAOacfroMdqU+x+P1tnkz6dYauxqZjK0Huwafk6ydL2JLOZaGrFlBH6O/Cg4c4JUXX0QIwfCRI2WwK/U4Qgiq6+uJbqWjocenszbfzuQsE8kt1LsekP8BusFI7ZCzunqoXc7tdnPrrbdiNpsxmUwy2O1CcoW3B9N1neq6OnYVFOB2u4mPjW2WY/h9sZNP99jYV+vBpzc+/rl0ciInD44mOTqctPwPSCn5gtJjrsE2YHLAY3jnrbdYsXw5c04/nclTpgTrR5OkHsXldrdZoeGzPTYAshNarweasfdNItx17J/2YIern3TW/r17uf6aa1AMBs4+7zySUwK/wZWkrmZ3OvGqKvGxsS1+//tiJ05VMG9k83rzBtVJavEq6gfNxheZ1NVD7VIul4tzzz2Xzz//nGnTprFo0aJQD6lPkwFvD+XyePhu82bcHg/xsbHEJrU8sT/caaXG4WP+qDhOGRLDkCTj4ceyMfW7yPn5Cczp06gYFfju7Ddee43HVqzg1Fmz+Mvy5Z36eSSpp/JpGjUNDSS0cvEVQrCxzMXUQSYGJ7WcpmB01ZCx9w3qB83GkTKuK4fbqj27drF08WIiIiJ4+oUXZLAr9Vhmq7XN/NRvipwkRBkYm968akpK8SrCfM6Q58h3lsPhYOHChaxbt46XX35ZBrvdQAa8PZTVZsPt9ZKWnNzk65ouaHBpVNl9FDZ4ya/3smBUHJdObhoQh3mt5P5wL2pUMgem3htwW9NXX3qJpx5/nNmnncayhx4ivJVUCknq7dweD0KIVjesmd06dU6Ns8e03l0ta8cLKEKnZNx1XTXMNu3cvp0bFi8mJjaWp198kUHZfadMk9T3VNTUYGolnUHVBNsr3cweHku44aigWAjS89/HkTgKR1LvTdWx2WwsWLCAb775hn/9619cfPHFoR5SvyAD3h5ECIHVbsfldrO/qKhZg4m8fDsvbKjHp//ytRGpEZx3zFHVEoTOsB+XYXTVsnvm0wHX2xVCkL9/P2eceSZ/XrZM9u2W+jSv14sQotXv761tbDmcFtNyQGwy7yO16H9UjrgIb0xoWmuXl5eTmJTEk889R2ZWVkjGIEn+8GkaVpuNmJiYFr//4U4rqk6TkpqHxNVuxmQrpOC4O0OWNhQMZrOZiooK3njjDZmz241kJNOD1FssfLtpE+Hh4ZgiI4k9anPY/jovYQaFK6ckMiA2nAGx4aTHhmM4auJn7nyZxMpvKZp4M45k/++ChRA47HZi4+L40wMPoCiK7Nst9XkWu73ZHDrSugMOjAaYkNlC61IhyP75n2gRcVSM7v6mDjarlbj4eOaefjqnzpxJRERoK0NIUnssNhten4+EVq4tG0td5CZHcHx2883RA/LfxxcRT1323K4eZpewWq0kJCSQnZ3N9u3bifSza6oUHLJKQw9SbzYTGRlJalISMUcFu6omKDarxEcaOG1EHBMGmhgYZ2x2oU4qW0/W7leoGbKA6mH+5zgJIXjyH//gst/8BovFQnh4uAx2pX6hqra21cerANsq3UwfGkNEC+XIEiq/I6HmJ8rGXNHtTSY2fP89586fzw/ffQcgg12pV6g3m1u9tpRYVPLrvZwytHmwa3TVkFT+FTWDFyDCel+gaDGbmT17Nr///e8BZLAbAn4HvIqihCmKsllRlE+6ckD9ldvjoaSyssUyLdsq3dz3RRV7ajycM7b1i6rJcoChPy7DnjyWool/8PuRjxCCfzz8MK+/+irTTjyRuLjmO2Ol3kXOV/9ZHQ4iWwkWVU3g8QkGxDR/GKZoXnJ+fgJ37CBqhp3b1cNs4rtvvuGWG25gQHo6w0eO7NZzS8HXn+ZrndncajmyfQfTh0alNQ8G0wo+AqF3+1wLhrq6Ou646SZ2797NwoULQz2cfiuQFd4bgV1dNZD+zGq388PWrWi63uzCW+/08WBeNTUOjd+flMIZLZRpgcZNasO/uxPNGM3+aQ/6fQes6zoPLVvG22+8wW8uvZQ/3nknhjbKM0m9hpyvfvBpGrqut/o7X2xu7PaUEt18RSpj39tE2UspmnATwtB9mzq/Wr+eP/7+9wweOpRnXnqJFFmNoS/oF/PV4/VisdtbrCdfYVV58ccGhiYZGXpUNRRFV0kr+AhLxjQ8sb0rR72muprfXXklFRUVfPTRR5xxxhmhHlK/5VdkoyjKIGAB8GLXDqd/Kq2qwu31ktjCymp+vRdNwC2npHLK0JaT/BEauRvuI8JZRf60ZaimNL/PvfKFF/jgnXe4/KqruPGWW2Qrwz5Azlf/OV2uNr//TZGDMANMHdT0EWuEs5KBu1+hIfNUrBnTunKITezbu5fbb76Z4SNH8tQLL5DYSrlCqffoT/O1rKoKIUSLN5i7ajyomuDaE5IJMxydqreOCHddQGl6oaSqKjaHg+q6OpYuXkxVZSWPP/MMc+bMCfXQ+jV/N609BtwGtPqsW1GUa4FrAXJycjo/sn6kwWJp9RFPjV0DYGBc6/9Ug7Y/R0LVBgom34Y95diAzn3+BReQkJDAogsvlMFu3yHnq59sDge0UqFB1QTrDjgYnRZJbGTTC3T2z/8EoHj877t8jEcaPmIEN9x8MwvPPZdYmXrUV/Sb+Wq2WolqJXd1Z5WH2AgDw5KPSi8Sgoy9b+GKzcGScUI3jLJ9Pk1DVVW8qorq84GicOTVMyoykrTkZJITEvjb3/7GgLQ0Tj3llJCNV2rU7gqvoihnAdVCiJ/aep0Q4nkhxBQhxJS0NP9XGPs7n6Zhs9tbzSEstzU+UjUZW/6nSi75goF736B62LnUDj3bv3OqKv9+9VVUVSUpOZlfXXSRDHb7CDlfA1NSUYHJ1EL1BeDnSjc2j87CMU3z5uOrNpBcto6K0ZfijcnojmGyetUqigoLURSFX19yiQx2+4j+NF+FEI0bs1u41ulCsLncxcTMqGYbseNqtxBj3kPViAsDricfDLqu43S5MNts1DY0UNvQgNPtJioykkEZGYwfNYoTxo9n+nHHMeuEEzj95JPJTklh28aNZKWn86vzz5fBbg/hzwrvycDZiqKcCUQB8YqivC6EkJWSg8DhdCKgWcCp6YIfSpx8vs/OsRlRGFvYIW4y72PIT3/FljKe4gk3+nU+VVW5+7bbWLdmDYNycpgxa1Ywfgyg8YPBp2n4fD40XUfTNDRdRwjRePerKI0/Kxyue9pSoK0oigzAO07OVz+pPh9mm43khJbrVP9Q7CQmQmlSD1TRvORseRR3zCAqR/y6W8b50Qcf8OB99zFvwQLZ8bDv6Tfz1e3x4NN1wluo0LC72oPVozMlq/nNZ8beN1EjE6kdPK87hokQAq+q4vF68aoqYWFhJMXHkxEbS2x0NCmJia2uUgPs2bOH2bNno6oqCxYsIKGVzxep+7Ub8Aoh7gTuBFAUZSbwx744GUPB5XZTUFraJJ9JCMEnu218vMtGg0sj2RTGDSc135QS7jEz4rs70SLi2T9tmV+bZjweD3fccgvffPklt9xxBzNmzULXdTRdb/zziABV13X0g38qwKGHvsqhoFWIxgBWiMPBqcFgwBQZSUx0NKbISCIjIoiKjMRgMKAoSuOd+8FgVuGXwFY56msGg6HVFW+pbXK++s/j9QIt33QBFFtUshMimtxspu9/G5O9hD0n/x0R1vW/o+++/TZ/e/BBpp10Enf+6U9dfj6pe/Wn+WpzOltt8LKxrDGXftxRzSairEUkVn5L2ZgrurwUmU/TcHs82J1OEuPiyEhLO5yW0FKQ3pIdO3YwZ84chBDk5eXJYLeHkY0nQqS8upqf9+w5fPd4yA8lLv61ycyxGZFcOSWJ47JMzVd3dR+5P/wJo7ue3TOewheVTHvcLhe33nQTP3z3HUtvuYW5Z55JbUMDYWFhRBqNGI1GoiIjiTQaCQ8PxxgefvjPMIMBQ1gYhoNBq2IwoMDhQDbMYMBoNGKUHdmkXsTpdrd6Af6hxEl+nZczR/2SOhDhrCRz16s0ZJ7SLRvV3nr9df7xt78x/dRT+esjj8i6nVKvVm82t9q1c1OZizEDIkmIahpYpu9/G90Q0aWb1dweDxabDZPJRFx0NLnZ2eRkBt4xcevWrcydOxej0UheXh6jR4/ugtFKnRFQhCKEWAes65KR9DNF5eVER0U1K3hfYmnM2b1r1oDmfcQPyt72FPE1mzgw5W4cyWP8Ol9ZWRk7d+zgvgcfZOmSJcSYTI3BrGwu0WfJ+dq2vQUFrTac+HS3jfTYcH4z8ZcVmsaNaqJbNqr5fD7yvviCWXPmsOxvf8PYQhmn9gghDq9aeVW1SXDf4qr2wac2h97b2sq3EILkxMSAxyO1ra/P15r6ekwt3LTtqfFQZvUxOze2ydfD3Q2kFq2idvA8fFHBrUbi0zRcbjcer5fIyEimHnss6ampnTrmmjVriIqKIi8vjxEjRgRppFIwySW5EPF4vS3WItQPXpRaSNkFIKXof2Tsf4fK4RdQN3h+u+dRVRWj0Uju8OG8/OabzD7pJHmxkvq9Q2WDUlso6+Xw6hyo93Ly4GgiwxvTjeKrfiS5bB2lY6/GGzOwy8dmNBp57OmniTAaCW8j2D2UfnQoLenQ3+1OJ8bwcKKiooiKjCQrPZ0YkwmDwXD4KU3YwSc0R6cUHQp0D/3dcOhrR7xG3ihLgbDa7S3ON10IVm5sINkUxmkjmga8Aw68j0H3UjX8wqCMQdd1LHY7mqZhDA8nLTmZ1KQkBiQnd+iG8hCv10tERAR/+MMfuPLKK0mU19ceSwa8IeJV1RbvdkvMKrERhhZXV2LqdzFk0wqsacdRcuz17Z7DZrVy45IlzJw9m4svv5wok0leqCQJsLdRf/ftn824fYKThzTWvVZ09eBGtSwqR3bdRjUhBM8//TQ//fgjTzzzDNFHtBc/VAbpUG69w+VCABEH046MRiMRB9OQIoxGhmVnkzlgQIs31ZLU3cqqqggPD292XdtY6iK/3svSE5ObVCJSNA8D8j/AnHES7vjBnTq3zeHA4/WiKAqDMzPJSk8nNjo6KA2Wvv76a37729/y4YcfMnHiRBns9nAy4A0Bh8uFT9OaTTi7R2dDqYs5Rz3aAQh31zP8+7tQo5LJP+EvYGj7n85isXDD4sXs37uXCy++mOq6OoYPHkxsdPMe5ZLUnwgh2FtQQEQLGyMdXp3/7bEzcWAUx6Q33pBm7H0Tk72YvSet6LKNM0IInnr8cf718svMO+sszHY7Rre7McVACIwREcSaTEQYjUQczLlPio8nJTFRdkaUejSfplFcUdGssZKmC97aaiEjLpzpQ5o2VUot+h9Gr7lTN5gutxub00lqYiITRo8mIS7O781n/li3bh0LFiwgOzub3loqrr+RAW8I1NTXN6s1CPDJbitCwIk5TYNSRVcZ/v09hHmt7Jr5LL7Itu8iG+rrWbp4MYUHDvCXhx5i0tSpTBk3jrTk9je3SVJfp/p8NFitpLSwGlNQ31i54cxRcRgUhUh7GZm7XqE+ayaWgSd2yXiEEDy6YgVvvf46C849lzvuuYfRw4YRHRVFWFgY4WFh8smM1GtV19WhtbDAs7HURYlF5aaTU5p2VhM66fvexpE4ClvqxIDP53C5cLpcJMTHM37kSAYOGBDUQBdg9erVnHPOOQwdOpQ1a9aQkdE99bilzpEBbzcTQlBcUdFspXVbpftwV6djM5quIuVseZy4up/JP/4+XIltJ8Orqsr111xDSXEx9yxbxvwzz2RwZmartUYlqb9xud2tfu+L/XbCFBiZFglCkLPlUYQhrEs3qj37z3/y1uuvc86iRdy/fDljhg7tVE6hJPUkxeXlxLTwZPHzfXbSYsKYdtQCT0LFt5jsJeQf/+fDmyj9IYSg3mIhxmTixEmTmlQ/CqYffviBhQsXMnr0aFavXi1Xd3sRGfB2I03TKKmsxOZwMODgamtRg5envqujoEElKlzhmuPjm+Q5pR34kAEF/6Fi5G+oz57b4nEPNXtweTxomsZZ553HgIwMzj37bMbk5nbLzyZJvUVNfX2LOfI+XfB9sZNTh8UQE2EgqTSPxKrvKR7/e9ToAUEfh83hwOPxcOrs2USbTDy4bBkJsoOa1Ie01tzFpersqHZz1ui4pqu7QMa+t/BEp9OQ5X9TJCEEdWYzGampHDtqVNBXdI80ceJEbrjhBu68806S5VPTXkUGvN1E9fn4YetWbA7H4TtPTRc88309lXYfF09MZN6o2MO7wgFi67aRs+VRLOnHUzpucbNjutxu7E4nkZGR2M1m6mtrOevMMzl58mSiIiNlXVxJakFtQ0Or5ZE0AZMyTYR5LAze8iiOxFFU5Qa/Bmi92czaL77gjltuIXHGDC6/6KKgn0OSQs15cHPo0TeYP1e40fTGuXak6IbdxNduofjYpYh29qkcqcFi6fJg99NPP+X4448nNTWVFStWdMk5pK4ldzt0k6ra2sNlWQ4FooUNXvLrvVw2OYlzjolvEuwaXTXkfn8P3uh08o+/D5Smk9jmcKD6fJw4aRK5GRnc9LvfcdettxJrMhEfG0uE0Sjb80rSUYQQWB2OFjv57a9tzN8dOyCSnJ+fIMxrpeC4O9rdIBoos8XCww88wMP338/GDRuCemxJ6knqLZZm1yEhBGsPOIg2KoxKa3rjmbH3dQKOLQAAIABJREFUTXzhMdQMXRjQOeJiYxmTm9tlwe4bb7zBwoULueeee7rk+FL3kAFvN2mwWJpdZLeUN+YSHt1OUdE8DP/uLsJUJ/tO+itaRNNcJCEEbo+HKePGUVddzamnnorFYuHjjz/GZGrei1ySpEbug2k/LVU2+KHUyaAEIzkNG0gt/oyKUZe0mzMfCJ+mUVFVxf333sva1at5+OGHmTu35TQlSerthBCUVFQQc9Q1qdis8lOZi/mj4po0V4pwVJJcto6aoWejG2OOPlwzPk2jur6e+JgYJo8dS1QXdSJ89dVXufjii5kxYwZ///vfu+QcUveQz7y7gerzUVlXR+wRE7+owct72y1MHWQiPfaIfwYhGLz5EWIbdrF/2oO444c1OZZP06i3WMjOyKC8tJS5c+fi8XjIy8tj4sTAd7RKUn/SYLW2+PV9tR721Xq59BgjQzavwBU3hIrRlwblnLquY3U4cDmdPPbQQ3y9bh2PPvooN910U1COL0k9kc3hwOFyNWs28dleOxFhCvNGNs1Xz9j3FgKoHv6rdo+tqioNVisjhw5leE5Olz3NfOGFF1i8eDFz587lP//5T5Pa2FLvIwPeblBaWYlP05r0Ef/vHhvhBoXFJzRNes/Y+wZpRZ9SNuYKGrJmNPme0+3G4XQyZtgwhmRlcfvtt+Pz+Vi3bh3jxo3rlp9FknqzwrKyZu2EhRC8tslMQpSBxeprGF217J/1DCKsedpDoKx2O16vl9TkZLwWC19/+SVPPfUUS5Ys6fSxJaknaymdwacLvi9xMmFgFImmX9IPwt31pBV8RF3OGXij09s8rqZp1JnNTD7mGLLS235tZ7jdblasWMH8+fN57733iGqlDbnUe8iAtxuUV1URd9Sd4e5qD2PTo0iI+mXSJ5Z9yaDtz1I3aA7lY65sdhyH08lx48YxIDkZRVF46KGH+P3vf092dnaX/wyS1NvZHA7MVmuzetSbytzsqvHwwJgSsgo+onLEhTiSj+n0uTxeL8kJCYw59ljiY2Ph2GPZt28fgwd3rnOUJPUGlTU1RB8VJO6q9mDz6Jw6tGnKQsa+t1B0HxWjLm7zmIeqMYwcOpTMAcGvnHLkeaKioli/fj3JyclEdlG6hNS9ZA5vF1NVFZvTebjFp8en89KP9VTYfOQm/7KCFG3ey7Af78eRNJqCKXc1qz/o9niINpko3L+fKVOmUFxcTFhYmAx2JclPxeXlzSqX2D06/9rcwJBYjQuqn8Adk0XZ2Gs6fA63x0NVXR0xJhMnTZrEmKFDOe+cc3jnnXcAZLAr9Qs+TcNstTbLq91R5UZRONzFECDMY2FA/gfUZ8/GE5fT5nHtTicpiYldmsbw17/+lcsuuwxN0xg4cKAMdvsQGfB2IU3T+HH7doQQhyfnqr12Vu21M2FgFHNHNLYQNrpqGfHtHWgR8ew/8a9N2pda7HbqGhpweTxYa2o4/fTTqa+vR9O0kPxMktQb2RwOiisqGldaj/DP7+qosPp4LPU/RDnKKDzudvTwwB9dWmw2ahsa8Koqk8aMYcq4caDrzJs3j/Xr18v5KvUrTpcLQdNyZEI01rkeOyCSuMhfnmym73+HMM1FeTs5815VxeP1MiY3t0s6Dwoh+Mtf/sJdd92FrusIIYJ+Dim0ZEpDFzLbbE0eobp9Oh/ttDJhYBT3zG58HGPwuRn+3Z2EeW3smvk0qin18PtdbjcKcMrUqWzauJFFF11Eeno6eXl55OS0fScsSVIjIQR7Cwsxhoc3qc6w/oCdn8pc3DKiggklH1A99BxsaZMDOrbH68Vit5OSkMCIIUOIi4khwmikoaGBM844g82bN/P222+zaNGiYP9YktRjNVitzVZg99d5KbP6WDD6l6pDYaqd9Px3qc+c0WyD9pF8Ph/1FguTx47tkuYsQgjuueceli9fzuWXX86LL74o23n3QTLg7UIllZVNHun8Z4cVq0fngmMPdp0ROkM3PkhMw272n/jXJiWQPF4vNqeTkyZNYtvWrcybN4/s7GzWrFlDVlZWd/8oktRrNVgsVNXWNtkt7vDqvL7ZzIhEuMr8JKoplZJj/d9IJoTAbLVijIhg0pgxDEhJOVwD1OFwMGfOHHbs2MH777/PwoX+1xSVpL6gpLy8WTmytfkOIsMVpg/5ZT/LgPz3CVftbVZEEUJQZ7EwNje3y/J2//SnP7F8+XKuvfZannnmmRbLFkq9nwx4u4jFZqO8qoqUxESgsezR+9utHJcVdbjYdtbOF0kuW0vJuCWYM6cffq/D6cStqhw3dixJ8fGMGjWKc889l0ceeYSMjIyQ/DyS1FsVlJURGRHRZMXp2yIHZrfOS1n/JbqskL0nrfCr9uchtWYzaUlJjBs5slnXtujoaObPn8+DDz7I/Pnzg/ZzSFJvoPp82JzOJjeYqib4/+zdd3hURffA8e+khxQCJJTQSyhJ6EWxUAULgq+IhSKoqC/2gv7sir3w2hG7CNIUxIKKgiAgTaR3CARCKum7m7bZMr8/dokJJGYDSTblfJ6HB3b33tlzw57cs3Pnzuw9VUC3MF/8vR3FpIc1n2YxX5PdfCB5jbqU2Z4pN5fmoaG0a9myysbtDh8+nPz8fGbOnCkLNtVh5Ra8Sik/YD3g69x+qdb6uaoOrDaz2mwcjI3F18cHDw8PDqeZeWF1Ko38PZl2QRMAmpz4hfBD80hrN5qUzuMBx3ydhpwctNZc2KMH+/fuJSQoiEaNGrFgwQJ3HpKoJSRfSyowm0nLzCxazhsgwWDhq53ZjAhJonfSYtLbXI6hxUCX2rNYrRhMJsIaNaJvVFSJnqDk5GSysrKIjIzk5ZdfrvRjEXVTXcvZTIOBM0e//nDASIrJyrjohkXPhcX+gHehgaSuU8psK9tkwmaz0alNm0ofYmC321m3bh1Dhw5lyJAhDBkypFLbFzWPK/32ZmCY1ron0Au4Qil1YdWGVXtprTl47BhZBgPBgYEYC2y8vCYVTw94elgYIf6eBKXtoN2ONzCE9SWu93RQigKzmdSMDMIaNeLSvn35Y/Vqhg4dKksZioqSfC0mJi4OTw+PosLUrjUfbsnAR9mZ6fMJNp9gTva436W2jLm55OTl0a1jR/pERpYodhMSEhg8eDBjxozBarVWybGIOqtO5ezREydKLLIEsOFELlHNfBncwXEVRdnMNI9ZhDGsL7lNSp9D3mAyERwQwCV9+xISHFzqNufKZrMxdepUhg0bxlZZ3rveKLeHVztuVcxxPvR2/pHbF8uQkZ1NXFISYY0akZVvY862LPKtmlcub0brEB/8TCfptPkpzIGtOHbhS2gPLwotFvIKCujfvTvNw8L45ptvmDBhAv3795eCV1SI5Os/TLm5xKek0KThP71Kvx7O4Uh6IXM7rCYk6QhHL3gBm2/Df2nF0ROUYTA4ljDt2ZMGZ5zM4+LiGDZsGOnp6axYsaLEAjNClKcu5ezp4QyNi+WcyWwj0WhlcLG5d8NO/IRPQQax/UvvyNZaU2ix0K1jRwIreXUzq9XKlClTWLhwITNmzKB///6V2r6ouVz6zayU8gS2A52AD7TWf1VpVLVUttHIniNHCA4I4HiWhedXp1Jg0YzuFkREqC9eBVlEbJiO9vAk5qLXMdm9ycvOBq3p3a0bzcPCWLBgAZMnT+aiiy7il19+IagK7kgVdZvkq6MH52hcHN6enkU9setic5mzPYurmmZwacp8ssIHkdVyaJltaK0x5eZSYDbTrlUrurZvf9Zl1djYWIYOHYrRaGTVqlUMGDCgSo9L1E11JWfzCwpKTMMJjuEMAFHNHdP9KbuF5ocXYGrSHVNY71LbMeXm0iQk5KxpBM+XxWJh4sSJLFmyhFdeeYUnnniiUtsXNZtLBa/W2gb0UkqFAN8ppaK11vuKb6OUuhO4E6iXU2bl5uezZfdu/Hx82Jpk5aud2fh4Kl69ojktg73xsBYQsen/8DZncnjQe6TqYHyAnl260Cg4GH8/P4xGIw899BCDBw9m+fLlBAS4fhONEKdJvkJSaipJaWmEOW+csWvN9weMtAzy4DXvT7F7+hDX6+GzFngpLiM7m2ahobRv1YqQoKBSb2aZMWMGOTk5rF69mj59KjalmRCnlZeztSVfY+PjS1zhiEk388MBE8M7BtA51HFzZ5O43/DNT+VEn/87K/9Oz8gQ4O9PVEQElW3FihUsWbKEN998k4cffrjS2xc1W4WuvWmts5VSa4ErgH1nvPYJ8AlAv379auXlmHOVaTCwY/9+PDw9WbS/gN+O5BDawJO7BzahZbA3aBsd/n6egKxD7O71NIm0IMjbm77R0SWmbgkODmbdunW0bduWBpV8GUfUP/U1X7XWxKekENigQVGR+tNBEwkGC1902kBwwh5i+z5ZYs7rM2WbTAQGBBAdEVG0SmJpPvzwQ+Lj4+natWulH4eof8rK2dqQr8acHJJSU0vMzrBkr4FG/p5M6u18zm6lxeGvyA3pgrHZBSX2t9vtpGdn07p5cyI7dSqa5q8yjRkzhu3bt8uX03qq3JvWlFJhzm+dKKX8gcuAQ1UdWG1hMJnYumcP3t7e7E5T/HYkhys6B/LO6BZ0d17Cab1nFo2S/mR/x6nozldxSZ8+DOrfv6jYnTVrFs8//zwA3bp1k2JXnDPJV8e8u9lGIw38HPlntWt+OGBkcBMDg5PnYGg2gIy2ZU8XlmUw4O/rS7/o6FKL3b1793LNNddgMpkICAiQYlecl7qSs6kZGXh6ehZ9yTRb7ew7ZWZgmwYE+jpKjSYJq/HLTXTMzHBG764pN5dWzZrRvXPnSi128/LyGDduHH/95RglIsVu/eXKLA0tgD+UUnuAv4FVWuufqjas2kFrXTT9WJbZk3k7smnd0Jvb+jXC18vxo20W8w3Njy4httVoAoc+RK9u3UqsFPPmm29y3333sWvXLll+VFSGep+vSWlp+Pr4FD1efTQHo9nGS56fgqLUS6mnmQsLUR4eDOjR46z5dQF27tzJ0KFD2b59O6mpqVV2DKJeqRM5m5qZWfQlE+BgqhmLTdMr/PTYXSvhB74gr2HHEvPOg6N3t9BiqfS5dnNzc7n66qtZtmwZhw8frrR2Re3kyiwNe4DSR5bXc3FJSWRkZaF8G/LimlN4KHhkUGhRwoYkrqP1nvdJDr2QnIufJDo8vEQyv/rqqzz55JPccMMNzJ8/X5YyFOetvudrfkEBCSkphDi/VOZb7CzbZ+TehhtobdzBiV4PU9ig9MVbCsxmcvLy6N+9e6k9u1u3buXyyy8nODiYNWvW0LFjxyo9FlE/1IWcLbRYMObklJidYVdyAd6eisimp8fu/oJfbiJHLnodVMm+tmyTiXYtW1bqssFGo5FRo0axadMmvvrqKyZOnFhpbYvaSebPOUfpWVkciInB4hnIEz8nU2jTvHx5c8KDHSfKgIx9dNj6PNlBEVjHzKZ76w4lit0XX3yRZ599lkmTJjFnzhyZykiISpCYmoqCoi+Pvx4x0aAgmfs95mIM7UVah/+Uul+hxYIpJ4d+3buXGIN42pYtWxg5ciRhYWGsWbOGtm3bVuVhCFGr5ObnQ7HZGbTW7EjMJ7KpL75eHiibmZYH55DTOApD84tK7Ku1xm63065ly0qLx2g0MnLkSLZv387ixYu5/vrrK61tUXvJgtHnICcvj2379+PfoAGztmSBgmeGN6VjE8dlVN+cRDpt/D8K/ZpgGfcVrdt0POsyTfv27Zk6dSpffvmlFLtCVAK73c6JhISiqYyy8m2sPGTkI/+P8FCK4/2ePqtnCRwn3GyTie5dutC0SZNS227atCn9+/cvuqlUCPEPg8mEKrYQS0xGIckmKwPbOO5HaRr7PT75aSRE/fes4UTG3FyaNmly1vzW58Pf35/27duzZMkSKXZFEam0KkhrzYGjR/Hy8GDVsUKOZhRy54DGRDVzjFPyNBvo9OfDKDRq0rc0De9act8DB4iKimLSpElMmjTJXYchRJ2TeOoUhVYrDb280Frz0ZYMxtu+J5pDHOv7LIUBZw9lsNvtpGVl0bJpU8KbNj3r9f3799OtWzc6dOjA6tWrq+MwhKh1klJTS4zf3XAiF28PGNDaHw9LHi0Of4WhaT9MTUveMGaxWLDabHTt0KFS4khLS8Nut9OsWTMWLVpUKW2KukN6eCso8dQp0jIz+fmYjYW7DfRq4cdlnZzz5VoL6LDhUXwLUjGPnYtfeFTRfna7nfvvv58+ffpw4MABN0UvRN2UX1DAwWPHisbu/hGbiyX5AA96LiWj1XAyW484ax9zYSHpWVl07dCBXt26nTWG/tdff6Vfv37MnDmzWo5BiNrIYrVizMkpcaPokbRCOjbxJcjXk2ZHv8HbnE1i1J1n7WvKy6Nj69Ylpuc8VykpKQwdOpTRo0djt9vPuz1R90gPbwVkZmez69AhDDZ/fjyQTv9W/ky/1HmTmrbTesvzhGQfIPfqjwjsNrxoP7vdzrRp0/j000+ZPn063bp1c+NRCFH3xCcnowFvLy8y8qx8vS2ZH/1nY/VtQlzv6WddRrXZbGSbTPSJjCy1Z3f58uWMGzeOqKgobr/99mo6CiFqn/yCAoCiYXv5FjuxmYWM6x6MZ6GR5kcWkdXiUnIbR561r81up0VY2HnHkJiYyPDhw4mPj+enn34qWl1RiOLkU+Eii8XCvpgY/Hz9+WirgWA/T+66sDGeHo4kD9v5Ps1P/Yll6LME9BtftJ/NZmPq1Kl8+umnPPnkk8ycObNSp10Ror7LLyggNiGBhoGB2OyaL7ZlMV0toJU9ieP9nsLmE1xi+9MT3Hft0KHUk+23337L2LFj6dWrF6tXr6ZJGeN6hRCQl59f4vHBVDMa6NTElxZHFuJpzSMx6uwvjQVmM0ENGhB4nvPOnzx5ksGDB5OUlMRvv/3G0KFlLxcu6jcpeF0Ul5xMbn4+y2PMxBssTLugMUG+jkugIYe/od3xJVh6T8F7UMnlChcuXMiXX37J888/z0svvSTFrhCVSGvNkRMn8PTwwNPTk+UHTQQkbmKi5++kRNyEqWnfEttbrFbSsrLo2Lo1ndq0OSsf09LSmDx5MgMGDGDVqlU0KmXGBiHEP1IzMvB23nittea7/UaCfD3oE5JL06NLyGx9GfkNz57Cz5CTQ4fWrc/7/e+66y7S09NZtWoVl1xySfk7iHpLhjS4IL+ggKNxcaQU+PLjgXSGdwqgT0vHmKPAxD/ptG8Whe2H4XP1W2ddOp04cSKhoaFceWXZKzsJIc5NelYWCSkphDVuzImsQlbtiWOl7yfkBXUk4Ywxg1arlUyDgeiICNqGh5faXlhYGCtWrKBPnz4EOmd7EEKULt9sJiktjUbBjqsoJ7IsHEozM6VPCG1iPsfDbiUxcupZ+2UYDGXeKFpRn332GSkpKfTuXaunMhbVQHp4y6G15lBsLAkmzTubMmkW5MWUPo5eH/+MA0RsnUFhaDd8bvoKPB3fH8xmM9OmTePYsWN4eHhIsStEFbDabOyLiSE4MJBCm+bdDem87vMZQSqf2AHPoj19SmybkZ1Nj86dS13N6bPPPmP+/PkADBo0SIpdIVxwejjD6TGz2xLyUcBlzUyExf5AWrtRmANbldhHaw1aExURcc5XPA8dOsQ999yD1WqlRYsWUuwKl0jBW46jcXHEJp3i0x15eHko/m9QKP7eHnhmnSBi46No/8b4TP4WfB0nyIKCAsaOHcvHH3/M+vXr3Ry9EHXXyaQk8s1m/Hx9WbjLwCW5KxnKdhKjp5W4hHq62I3u3Jk2pfTszp49mzvuuIOvv/7acTIWQrgky2gsUbT+nZBH51AfusbOBeVBUtdbztrHmJtLs9DQUlczdMW+ffsYPHgw3377LQkJCecauqiHpOD9F8acHGLi4vjxGKTl2rj/oia0DvHBIz+DLpsfwctT4X3rclRwCwDy8vIYM2YMK1as4OOPP+bWW2918xEIUTfl5edz5MQJGgUHE5tZyMGYIzzv45jr81SncUXb2U4XuxERpa7k9M4773DPPfcwZswYli5dKmPshaiAU2lpRVOKpeVaOZ5l4cqwDELjfiW1w7VYGpQcsqC1xmKxEHGOi7fs2rWLIUOG4OXlxbp162jXrt35HoKoR6TgLYPdbufgsWPsTIV1x/MY1TWIyGZ+eFjz6PTndPwLs/GYuBRCIwDIyclh1KhR/P7773zxxRfceefZcw4KISrHsfh4PD090Xjw6ZZTvOczGw8vH473e6rEamqZBgNd2rcvdczu66+/zkMPPcR1113HkiVL8PX1rc5DEKJWMxcWYsrNLeqp3ZbgGN5wY8487F6+JHc5e2ElY04OzUJDz2lmhm3btjFs2DAaNGjAunXr6NKly/kdgKh3pOAtw/HERPYnZPLlzhyimvkyvmcIym6h7Z+PEZxzHPu4L1Ct+xdtb7fbsVgszJ8/n1tuucV9gQtRx+Xl55N46hTBAQEs2JXNaNMSunOMuD6PYvH/Z5qxbJOJJiEhdGzdutSe27y8PMaPH8/ixYvxKTZpvhCifBnZ2dhxzL9r15rfj+YwJugwLdI2ktxlMla/kjOcWK1WrFYrXdq3P6f3s1gstG7dmvXr19OpU6dKOAJR38gsDaUw5uSwL+YYCw9Y8PXy4MGLQ/H2gPDNLxKauRPb6Pfw6jYKgOzsbLy9vQkODmb9+vUy4bUQVexgbCxenp7sSjaTevhv3vf9nvQ2l5PValjRNsacHPx8fenRpUuJnNRak5ycTHh4ODNmzEBrLTkrRAVprYk9eZJA53CGTXF5JGSbWdpoPmbvZqRE3HDW9plGIz06d67wqmpJSUmEh4czcOBAdu7cKfkqzpl8cs5gtdnYcySGL3YXcjTDwp0XNCbE35OmO98nPHkNlkGP49l3CgCZmZkMHz6c66+/Xk6cQlSD1IwMUtLTsXr4M29zHLP8PsAc0IK4XtOLtjHl5qKAPpGR+Pv5FT2vteaxxx6jR48enDx5EqWU5KwQ58BgMmHMzS3Krx8PGrk9cBNN84+RED0N7VlyeFC20Uh4WBitmjev0PusWbOGiIgI5s2bByD5Ks6LfHqKsdvtHDl+nLnb0tl9ysKEXg0Z2KYBDfcvoO3xb7D0moL30McBxwT1Q4cOZf/+/dx7771ys4sQVazAbGb3oUMEBQQwa1M6M/RHhGEg9oLnsXs7xgQaTCa8PD0Z0LNniZ4krTUPPfQQM2fO5MYbb6RVq1ZlvY0QohynMjLwci42cTjNTEqmiftYTE7jSDJbXVZiW6vVCkpVeBqylStXMmrUKDp06MDll19eqfGL+kkK3mKS09JYtTeO32MLGd4pgGujGhIQ+ysRhz7C2vkqvMe8DUqRkpLCkCFDiImJYfny5Vx11VXuDl2IOu/AsWNo4LuD+fTK+JkRHttIiJ5GXqOugGO6Ix8fH/pFR5e4KcZut3P33Xfz7rvv8tBDDzFr1izpKRLiHFmsVk4mJRHYoAFaaxbtzuYBv58JsmYS3+O+sxZfOn3jaEWmIfv5558ZPXo0Xbt25Y8//qBZs2aVfRiiHpIxvE4FZjPbDh5h4X4LYYGeTOnTCP/kv+m66zVsLfvjdf0X4OGJ1pqbbrqJuLg4fvnlF4YMGeLu0IWo8xJSUkhOTWV3pi+HDuzmR9/5ZDe7kFMRN6C1JtvZs9s/OrrEMAZwzLP70Ucf8dhjj/Hqq6/K1RghzkNaZiZWmw1vLy92J+eTcSqJqf4/kdFyODlNupfYNtNgoEXTprRp0cLl9uPi4hg7dizdu3dn5cqVNG7cuLIPQdRT5Ra8SqnWwDygOWAHPtFav1vVgVW34wkJLNqXz6kcG89f1pRGucfo8tdTWBu2xWfSN+DtuDyqlGLWrFlkZ2fLut2ixqmL+WrMyWHP4cOYPQJYsj2eH/1nob2DiqYgyzIYaBISQnREBH6lTC12++23ExQUxOTJk6XYFTVObcpZu93OkRMnCAoIcPTu7jLwtP8SPJUmIXpaiW1z8/Lw9/MjuoJDGdq2bcu8efO4/PLLCQkJqexDEPWYK9f1rMB0rXU34ELgHqVUZNWGVb3SMjOZvzmWLQkWrosOpmdgFhF/Tkf7BOA15Xvwb0RsbCyvv/46Wmuio6Ol2BU1VZ3KV601h48fx9PLmw82Z/GC1+e0sicTO2AGVr9G5OTl4ePjc1axa7FYeOaZZ8jKysLPz48pU6ZIsStqqlqTs5kGA/kFBfj6+LDlZD4Nsg5xtV7PqU7XUxjwTy+u1WYjz2ymV9euLg9lWLhwYdHqpDfeeKMUu6LSlVvwaq2TtdY7nP82AQeBs5csqqUsFgtf/7mHpQctRDfzZUIX6LT+Iby0BY/J3+HRqA0xMTEMHjyYN954g6SkJHeHLESZ6lq+xicnk5qZya+xNvoZVzJabSAx8jZMTfuQl5+P1WajX1RUiWK3sLCQG2+8kZdeeomff/7ZjdELUb7akrNaa2Lj4/H39cVi03y1I5MX/RZg8QkhuevNJbbLzM4msmNHggMDXWp7zpw5TJo0iTfffLOqwheiYjetKaXaAb2Bv0p57U6l1Dal1La0tLTKia6K2e12ftm6jw+35REa4MlDFwbSccOjNCg4he2G+Xi16M7BgwcZPHgwBQUFrFmzhpalLE8qRE1U2/PVXFjIodhYMi1+HDq4jxe952Jo2o/krjdjtVrJN5u5oEcPggIC/tnHbOa6667ju+++491332XSpLNXexKipiorZ2tCvhpzckjPyqKBvz+b4nLpU/AXvfRBEqNux+b9T2FryMkhvALjdj/++GNuu+02RowYwaJFi6oqfCFcL3iVUoHAt8CDWmvjma9rrT/RWvfTWvcLCws7u4Eaxm6389O2wzy+IglPD8Vjlzamx45naWg4hOU/H+MbMYR9+/YxZMgQ7HY7a9eupWfPnu4OWwiX1IV8PZ6QgMVm56utKXzo8y7aN4jY/s9h14oMg4GoTp2QTt5pAAAgAElEQVRK9CDl5+dzzTXX8NNPP/Hhhx9y//33uzF6ISrm33K2JuRr4qlTRVOR/Xowk2d8FpIX1I60dlcXbWOxWrHb7XTt0MGlmVDef/99pk2bxqhRo/jhhx9ocA5LDgvhKpcKXqWUN45EXKC1Xla1IVWPwyfiePm34wT6evLq5c3ofWgmYWl/YRn5Kr49rgPgyJEj+Pn5sW7dOqKiotwcsRCuqQv5aszJITY+nlUnNPfkf0QbTnH8Ase43UyDgY6tW581iX1mZiYxMTF8/vnnTJs2rYyWhah5anrOWqxWEpzLee87ZWaw6RdacYr4HveCxz/3vmebTHRt3/6smVJKo7Vmy5YtXHvttSxbtgw/F/YR4ny4MkuDAj4HDmqt36r6kKpebn4+r/8Ww6lczeNDGhN1/AtaJ/6G7eKH8bnoLkwmE0FBQYwdO5Yrr7wS/wouhSiEu9SVfD0UG8txo6LBke+5xnsTCVF3YArrjTE3l0bBwUS0a1d0E1pubi5+fn60bNmSffv2Sb6KWqU25Gx6VhY2mw2UB99vi2O+93dkNR2AsfmFRdvk5uUR1KABLV1YTe30OXbu3LlorfGuwBy9QpwrV3p4LwZuBoYppXY5/9TalRasNhsf/raDtXEWruwSyPC8FbSL+Qpz1A14XvYsmzdvpn379qxYsQJATp6itqn1+ZqakcGJlAw27DjAc97zyAzrT3KXmykwm7FarXTv0gUvT08ADAYDI0aM4O677wYkX0WtVKNz1mazERMXR2CDBqw5lsuE3K8IUAUk9Ly3aBuLc0x9j2K5WRqtNc899xx9+/YlPT0dLy8vKXZFtSm3h1drvQGoM/P5bDxwnE//zqZLmC8PNN1Np7/fwdx2CL5jZ7P+zz8ZNWoULVq0IDo62t2hClFhtT1fC8xm9h45worDJl61vYXNL5i4C57FbLFiysvjgh49ipYMzsrKYuTIkezevZtHHnnEzZELcW5qes6mZ2WRk5dHSHAIsfs38aTXWpI7jacguEPRNllGIz27dKFhUFCZ7WitefLJJ3nttde49dZbadSoUXWEL0SRerXSWlJ6Fo//EIO3pwcvdDtJxLYXKAiLxn/iItas+5PRo0fTpk0bVq9eTXh4uLvDFaJe0Vpz4Ngx/orP57rUD2jtmcaRge9T4BmEwWCgf/fuNHHOzZmens6IESM4cOAAy5Yt4+qrry6ndSFERZ2eiqyBnx8/7s/iYctn5PiFkRR5a9E2BpOJ0JAQWv7L8r9aa6ZPn87bb7/NtGnT+OCDD2R5b1Ht6s0nzlxYyAOLtpOSY+fF3tn02f4UhYGt8Lv1Bw4fj2fUqFF06NCBtWvXSrErhBucSk/nUMIpPPd/x9WeW0iMvANj4+5kGo1079yZpk2aAI6T56hRozh06BA//vijFLtCVJG0zEwyDQby7N4EHPqWSI84Unrfj93LMZtCfkEBHh4e9Oza9V8Xdpk5cyZvv/02999/P7Nnz5ZiV7hFvejhtdvtvPDd3/ydaOa+qHyuOvg4dp9APKf8gGrQmM6dG/HSSy8xZcoUQkND3R2uEPVOocXCvpgYDuzdwwzmkxR2Cae6TiIzO5u24eG0Ljanp1KKl156CQ8PD4YPH+7GqIWou+x2O4ePHycoIICv/4rldY9vSAvtT1bLIYBjbG9OXh4De/cudUnv4m699Va8vLx46KGHZMVD4Tb14mvWoo2HWLgzmytbmbkr6Sk8tB2Pyd/z68bdHD58GKUU06dPl2JXCDeJjY9n19EEHjD+j2zfFiQPfMYxI0PDhnTt0AGlFAkJCSxevBiAESNGSLErRBXKyM7GlJvLSZNixKnP8FMWkvs8DM6CNcNgoHP79jQKDi51f5vNxqxZsygsLCQsLIyHH35Yil3hVnW+4N10KJ4Xfj1OdKNCXi94Ed/CbGw3LWbZ+n2MHTuWp556yt0hClGv5eTlsePwMS479gaBqpCkQa9ixgerzUb3zp3x8vTkxIkTDBo0iLvuuovMzEx3hyxEnaa15vDx4wQ0aMDB7Rv4j+cmEjtPxBzUGnDMkx0aEkL7MlYetVqtTJ48mfvuu48ffvihOkMXokx1uuA9nmrgvq/3EuJtZY7v/wjMPUnhdXNZuvk4EyZM4KKLLmLOnDnuDlOIektrzb6YozTc/jG9VAz7ezxOflA7Mo1GoiMiCPD359ixYwwePJisrCxWrlxJ48aN3R22EHVatsmEKTeXFJOVW00fk+ndnLTIyYBjqEOhxUJURASepUxBZrFYGD9+PAsXLuS1117j+uuvr+7whShVnS14j54ycN2HmyiwWFnSaDZNsveTd+W7LNqSxOTJkxkyZAgrVqwg6F+mURFCVK1T6ekc+WMRo6yr2BJ2PTriMsdKam3a0LJZMw4fPsygQYPIzc3ljz/+oH///u4OWYg6L+HUKbw8PTFt+YpOHkkk9nkI7ekYp5tlNNK+VSsCS1kG2Gw2M27cOJYuXcpbb73FY489Vt2hC1GmOnnTWlJmDhM/3UyB1c6PLebRJm0LucNfpkG/Ccz7v2GMHDmS7777TiapF8KNcvPz+eWX75mY9Sn7fXvgcfHdZGZn0yQkhIi2bQFYuXIlVquVtWvXytzYQlQDU24u8cnJnEzJZLJ5KUcaXkRBq4sBR876+/rSoXXrUvc9duwY69ev54MPPihaDEaImqLOFbx5ZguTPttMdr6NJW2+pVPyKgouegTfgdPw8PDgp59+wsvLS9btFsKNrDYbazZuZGTsaxg8GpIz/CWMufk0DAqid7duoDUA9913H+PHj5cbSoWoJkfj4iiweRBxYBYeSpN70UOAczW1ggIu7tMHnzNWR7NarXh5eREZGUlMTIzkq6iR6tSQBq010+ZuITazkC/araJH8rcU9r6VWbv9GDp0KLm5uQQGBkqxK4SbHTh6lOYbZ9CUbI5e+BK52hc/Pz/6REWxd+9eunTpwo4dOwDk5ClENcnMziYxNZV92zYynL+J6TAZa0AL7HY7mQYD3Tt3JjgwsMQ+OTk5jBgxgjfeeAOQfBU1V50qeN9duZ/1sUbebrOBixPnUNB5DDP3hvLo//0fLVu2xMfHx90hClHvZRoMxP3wKv31XjZ2uA9bkwhsNht9IyPZuWMHw4YNw2azydKjQlQji9XKrkOH2JdcwNScj0n1bYu550TAMUVZl/btz1pNzWg0csUVV/Dnn3/SuoxhDkLUFHWm4P3zSArvr43jgSZb+U/qh+S1GcKrB9vy9DPPcPPNNzN//ny8z7gMI4SoXlabjQ3ffcTovO/YEHg5/j3/gyknh75RUezYvp0RI0YQGhrK+vXrad++vbvDFaLeOJGQQLLRTMuDX9BCZZJ24eNoD28ys7NpERZGpzZtSsyjm52dzYgRI/jrr79YvHgx48ePd2P0QpSvThS8R08ZuXv+Tq7x38kDee+T17QP76X044WXXua2225jzpw5eHnVueHKQtQ6O7f8zrDjb3PQoxMeQx4lPTubrh07cvLECa644grCw8NZt24dbdq0cXeoQtQb+WYzR0+eZO/efUxUK4lr8x/yQruTl5+Pv78/0Z07lyh2rVYrI0aMYOfOnSxdupRx48a5MXohXFPrq0BDfiG3fPEXffV+3tDvkh8Sgd8t3zI2IQ1jTn7REqRCCPdKSogjbNWDmPEm/pKXseXl0zY8nPatWmFp1ow77riDxx57jObNm7s7VCHqlRMJCexOLuBWw2xMPk3I7HUXVpuNnPx8Lu7dG+8zOoy8vLyYNm0aLVq04KqrrnJT1EJUTK0ueG12zZ1fbiHUdJBP/GZibtCCH0Nu5yb/EDp3bsQrr7zi7hCFEIDBZCRu7u0M0Mn8EvkGXl4NaBYaSkpcHM0bNSIsLIy3337b3WEKUe8Yc3LYfzyeoIPf0MUjgUP9XsPm5U9GZibRERGEFFs6OCUlhSNHjjBo0CCmTp3qxqiFqLha2/Vpt2seXLSNjJMHWej3BhbvYO7fHcWEW+7k559/dnd4QginQouFTZ89wkDLVlY1n4pXi640Dw0l7vBhRo0axaOPPuruEIWol2w2G3uPHGHbgVhu199xsulQTC0vIcNgoG14OG3Dw4u2TUxMZPDgwYwbN47c3Fw3Ri3Euam1Pbwv/7SPnfv28VOD10B5cueOSBYuXczTTz/NqFGj3B2eEALHVIGrv36PKw1fs6HBMDy6X0vDoCAO793LzZMm0bdvX9555x13hylEvZR46hR/x6YzKf1dCr0CyBjwMNkmE42Dg4ns2LFo3G5cXBzDhg0jLS2NFStWEBAQ4ObIhai4cnt4lVJfKKVSlVL7qiMgV3y99QQ/btrNt/6v4ms3c8vf3Vi49EdeeOEFXnzxxRKD64Wob2pSzv61/meGxLzGfs8umC9+CG8fHw7u2sXECRO48MILWblyJSEhIe4OUwi3cVe+mgsL2R0Ti//+b4j2OEFCv0fJVQGgNT26dMHT0xOA2NhYBg8eTEZGBr///jsXX3xxdYYpRKVxZUjDl8AVVRyHy3bEZTLz+y0s8X+VJsrI5m7P8P2KNbz++us888wz7g5PiJrgS2pAzh47eoh2a+7DoIJIuugF7HjSIyKC1197jcGDB7NixQqCi40PFKKe+hI35Gt8Sgrb9+znNvsyjocNx9RqMAaTie6dO+NfbHGmTz75BJPJxJo1axgwYEB1hylEpSl3SIPWer1Sql3Vh1K+9BwzD85dz5c+b9BKp2AcPYdhvcZwsO9IOnbs6O7whKgRakLO5uYYKVgwiRbk8mffd7B5N+DC6GgaN2zI6tWrCQkJoUGDBu4MUYgawR35mpufz6rth7gp7X3yvILJunA6WQYD7Vq1oplzpTStNUopXn75Zf773//KvNii1qu0m9aUUncqpbYppbalpaVVVrNFjAUWbv1kHa9Z36C9JZYrV7Xm9yMFAFLsClFBVZqvWrP3o1uI0jH80elRrEHh7Ny0iQfuvRebzUZ4eLgUu0JUQGXmq9aa/Udj8duzkK4e8cT3ewyj1Qt/Pz86t2uHUoq9e/cycOBA4uPj8fT0lGJX1AmVVvBqrT/RWvfTWvcLCwurrGYBKLDYuP3zjdyX9Rq9bPu4YkUzVm/eg8lkqtT3EaK+qMp83TLvaS7MWc1PIRNQbQawYfVqnn7iCUwmEzabrVLfS4j6oDLzNSM7m1Vr13Gz/QcOh43E2OJCCgoL6R0ZibeXFzt37mTo0KHEx8eTn59fSUcghPvV+FkaLDY7d83byviUmVxk28bw5U3YeuAoX375JZMnT3Z3eEKIYg6uXcyA2A/403sg9LqJlcuX8+6bb3L99dezYMECWd5bCDey2mx8t3E3N6TNwujdiJwLHiTLYKBn164EBQTw999/M3LkSIKDg1mzZo1cPRV1So0ueO12zUOLdzDk+JtcyZ9cuiyInUfjmT9/vqzbLUQNk3p0J63XPshh1Y6sgY/w63fL+PC995gwYQJz586V5b2FcLM9x+Lw3vYpER6J7B3wP1Lz7bQJD6dls2Zs376dyy67jCZNmrBmzRratWvn7nCFqFSuTEu2CNgMdFFKJSilqm15lTd+PUjng+8zxWsV2ZETGP6fm/n666+l2BXiX7gjZ82GNKwLbyJf+7C3z3M0ahjCtaNHM23aNObNmyfFrhBlqK58zcnLY8mPPzFZ/8yxFqNIDY6mYWAg3Zzz7bZv356RI0eybt06KXZFneTKLA1uqS6/25GAbeP7TLQsY4P/EC68YRavTZCTphDlqfactVk4+dE42tjSWdzxVTITUxg14goC/P0Zcdll1RqKELVNdeSrzWbjw583c4fxAww+YSR3vxOAXl27snPHDrp3707jxo1ZsmRJVYcihNvUyKWFNx5NZ9uyt7mjcD6XLlRM+mSP3OwiRA21//NpROTvYmHju1n2w288fPfd7Ni2zd1hCSGcth6Oo9mej+ngkUJC/8fIsXnSv3t31q9bx6BBg3j88cfdHaIQVa7GdZkeSjHy7bz3mJ7/KZcusBNv1Cxf/gW+vr7uDk0IcYZ9379JdNJSvvUezTerd/Pr8uU8/PDDXHLJJe4OTQiBYyjDih8X84LnbxxvM5aTfhH0jIhg3R9/MG7cOCIjI3n66afdHaYQVa5G9fCmGAr46NMPuc/0HoPmWUjMgRUrVjB8+HB3hyaEOEPMph/osvNlNtCL2X+m8+vy5TzxxBP873//k+W9hagBtNZ88uMf3J//Ial+7YjpMIk2LVrw18aNjB07lp49e7JmzRpCnYtNCFGX1ZgeXlOBhZmffsGr1v9x50ZPUgu8+O23X2XdbiFqoMTD22m+8r+cUOH84jGcNb89x3PPOf5IsStEzfD3kZP0PfA6QR4F7Oz7OA2CQmjdrBkjBg2if//+rFixgoYNG7o7TCGqRY0oeAutdl7/YjEzTC+Q5xvKi18t5FGzJz169HB3aEKIM2SmnMRz0Q3ka1+293yGm3v2Y/SIEQwcONDdoQkhnMyFFrYtfZO7PXazr9sDZPu14tLISAL8/Vm9ejVt2rQhKCjI3WEKUW3cPqTBbte8Mf9HRh94hAlLTKRf+THtuvSWYleIGig/x0j6p9fiYzEydnUL7DYPunboIMWuEDXMp4uXMLVwATHBFxLXdCj7tm3jf2+8AUBUVJQUu6LecWvBq7Xm7W9Xc+nWe7hmbjqbT/lg96vcZU6FEJXDZrVy8IMbaFlwlMuWN2Hjxr/ISk2VIQxC1DCrth/kiqMvkucZxPHoe/hr/XoeffhhNm/ejNVqdXd4QriFWwvez1b8ReTvUxk3NwW7b0PWrd9At27d3BmSEKIUWmu2fDSNrsZNXPxtCDv3Hmb27Nncc8897g5NCFFMcpYJw/Kn6OCRzIHo6fy+dgsvzpjB1Vdfzffffy+LwIh6y22f/B8276fJ95O4eX483oGNWPvnJrp06eKucIQQ/2LDwlfom/wNFy32Z8/xeD799FNuv/12d4clhCjGbrezcM77TGc1e1pcx+INx5n19tuMHTuWRYsW4ePj4+4QhXAbtxS8Ww/H0XrFFAL902jXsROLv/uFTp06uSMUIUQ5tv62gIuOzGSzdx+adPTmi2cmcsstt7g7LCHEGb5esZo7DO8R7xdBauRkenodY8KECXz55Zd4e3u7Ozwh3KraC96YhFROzvoPYxofZ+/gZ9j85oN4yyUWIWqkfdvW02rNA2y2tcR8zQyWP34hDfz83B2WEOIMh+KS6bH1EUDxe+gEro7qwWWXDmHqbbfJOHshqOYxvCkZBlY9M4JbPt7FvQf60n30A1LsClFDHT16CO9vJnLlVyamfJdP/979pNgVogYyW20c/up+IjnOf3d24b/3PkFKYiKAFLtCOFVbtWnIyeObR4bx+Pw9NG0ayuMzP8dHLrEIUSMlppwi4+NrueOrFA5lwoJFMwkODHR3WEKIUnw/9y1usPzG+PWt+HrtOqZNm0ZUVJS7wxKiRqmWHt68AjOf3TeUx+bvoEWzUP7YtI0O7dpVx1sLISrIkJPL3reu4ba5RzicCV9/s4Trr7vO3WEJIUqx5a+NXBX3BhN/9efrtQe59957mT17Nh4ebp9mX4gapcozwmyxsPa163lh0VZatQhj9cZttG/btqrfVghxDixWK/s+mMgny7dzzODJN98u49r//MfdYQkhSpGamUHoiv+yeL+dRVtTeeDBB3nvvfdkGIMQpajSIQ1Wq40t79/KVazjuTuv5rpHPqBtmzZV+ZZCiHOktWbTh3czOP8P4iaMZ2q/8Yy68kp3hyWEKIXVauPwx7dwsU7gwJin+HxMU26dPFmKXSHKUGUFr91m54XbL6OTYQtrB9/ElAfeokmjRlX1dkKI8/TNmw+waclcLNeOYeDEl2nfurW7QxJClGHdl0+z/IeVHB0xlosHjaNz+/buDkmIGs2lIQ1KqSuUUoeVUkeVUo+7ss/Tt13Oy/PW8s7eYLpMeF2KXSGqybnk67cfvcCjL85izl47hb1uo12rVlUdphDCqaI5u2vN18x+63+8+1chMSqCTjJMUIhyKa31v2+glCdwBBgBJAB/A+O11gfK2qdFaEN9KsNIn05hzPv5TyI7ywpqon5TSm3XWverhvepcL5GtG+jc9MTKLB7MmvOfMZff4NcFhX1WnXlq/O9KpSzPaIjdStLLCuOmPnvPXfz3tvvyIxHol5zNV9d6eEdABzVWsdqrQuBxcA1/7ZDSoaRAZ3D+PLHtVLsClG9KpyvJ07GY9GezJz9CTeNu16KXSGqV4Vy9vjRw6w4YuaO/97GmzP/J8WuEC5ypeBtCcQXe5zgfK5MgX5efLLsD6K7RZ5PbEKIiqtwvnoqxavvz+aWSZNlKiMhql+Fclbb7dw99Sbeeut9Avz9qzw4IeoKV25aK62756xxEEqpO4E7nQ8Le0ZH7z2fwGqoUCDd3UFUsrp4TFDzjqu6BtmdU77ecdude++47c4zN6vtatpnoLLIcVW96hwUW27OnpGv5tmfLz4w+/PF9iqPrHrVpP//yiTHVfVcyldXCt4EoPjt2q2ApDM30lp/AnwCoJTaVl3jn6pTXTyuunhMUHePywWSr05yXLVLXT0uF5Sbs5KvtZccV83hyvXLv4EIpVR7pZQPcBPwY9WGJYQ4R5KvQtQukrNCVINye3i11lal1L3Ab4An8IXWen+VRyaEqDDJVyFqF8lZIaqHSwtPaK1/AX6pQLufnFs4NV5dPK66eExQd4+rXJKvReS4ape6elzlqmDO1tWfkxxX7VLrjqvceXiFEEIIIYSozWQOIiGEEEIIUadVasF7Lkua1nRKqdZKqT+UUgeVUvuVUg+4O6bKpJTyVErtVEr95O5YKotSKkQptVQpdcj5/zbQ3THVRJKvtY/ka/0mOVu71MV8hdqbs5VW8DqXR/wAuBKIBMYrperCyhNWYLrWuhtwIXBPRY5LKeWvlFqulDIopZa4sP2TSqnPziVQpdRapVSBUmp9BXZ7ADh4Lu9XTiwfKaWecXHbEj8jpdQYpdTi83j7d4FftdZdgZ5UwfHVdpKvZavhOVsl+eqMxV05K/nqAsnZ0km+urStnGMBtNaV8gcYCPxW7PETwBOV1X5N+QP8AIyowPY3A1sBr1Jeewp4qRJjWwvcfsZzjYHvgFwgDphQ7LVWwGpgGPATMBT4AzAAJ0pp/yLnsZiAPcAllRT3WT8jYB/Q4xzaCgaO4xyfLn/K/DlJvpa9T43M2TPz1flcrc5ZydcK/awkZ0vfXvL1HH5G9fEcW5lDGiq8pGlto5RqB/QG/qrAbm2BI1praymv/QKMOv/I/tUHQCHQDJgIfKiUinK+9g7wf8DpFXtygS+AR89sRCnVGMfckDOBEOANYLlSqlElxFjaz2gR/6wsVBEdgDRgjvNS0mdKqYBKiLGukXwtW03N2TPzFWp/zkq+uk5ytnSSr+WTcyyVO4bXpSVNawOl1Aml1KNKqT1KqVyl1OdKqfbAbiAMWFb8Q+i8RJDivFyw/nRBqZR6HngWuFEplaOUmlr8fbTWO4EwpVR4sbZmKKXmO//dTimllVJTlFInlVLpSqmnKnAcAcB1wDNa6xyt9QYcCXWzUupqIFVrvb1YPFu11l8BsaU0dxFwSmu9RGtt01rPx/GhH1vGe3+plHrJ+e8hSqkEpdR0pVSqUipZKXVrOT+jtZzbLyovoA/woda6N45fMHVirFslqzP5CqXm7FwcPUXpQKJS6vdanrMzOCNfnfHU9pyVfHVdncnZenCOnUHdzFeoxTlbmQWvS0ua1iLXASOAzsBoHIn4GY5vXh7A/cW2XQFEAE2BHcACAK31c8ArwNda60Ct9eelvM+vOMZk/ZtLgC7AcOBZpVQ3F4+hM2DTWh8p9txuIAq4GBijlDoBLAaGnf4lUAbF2b9wFRDtYizNgYY4eiSmAh8opRr9y8/oINBOKRXsYvunJQAJWuvTPQRLcSSnKKmu5Sv8k7OROFaraoLjsxZK7c/ZXlQsX6F25Kzkq+vqWs7W5XNsXc1XqMU5W5kFb11bHvF9rfUpHL9Q8nGMt5mutTbjGK/T+/SGWusvtNYm52szgJ5KqYYuvo8rl1ye11rna61340imni62HYhjrFBxBiBIa/2E1rqV1rodjv+rNVrrSf/S1iYgXCk1XinlrZSaAnQEGrgYiwV4QWtt0Y5J1nNw/IIpi8n5d4iL7QOgtU4B4pVSp9seDhyoSBv1RF3LV4D3gVTgReAksFxrvbOO5GxiBfMVakHOSr5WSF3L2bp8jq2T+Qq1O2crreB1jg05vTziQeAbXbuXRzzl/PtioA3QTCm1Sym1C+iE44N+etqR15RSx5RSRuCEc79QF99nFTBIKeX9L9ukFPt33un3dkEOjgHmxQXzzwfdZVrrDOAa4GEcP5srgN9xfNtzRcYZ44fKO44g59/ZFQwV4D5ggVJqD45v2q+cQxt1Wh3MV3B8Li/GcYNGKHCtM2evwnFClZytmTkr+eqCOpizco4tphblK9TSnHVpaWFX6YovaVrjaa03KKUWAEe11jMAlFK34xgWADABx4f0MhyJ2BDIovTxVqW1b3J+aC4F1lRq8HAE8FJKRWitY5zP9QRK/JLUWq/FMZ7nX2mt1wH9AZRSXsAx4M1KjLe4bji+8RsruqPWehfQr/JDqlvqar4Cynn58MycPa1W56yr+erctsbnrOSr6+pqztblc2xdy1eovTkrK62dvyDADGTguPRwLt90fgauqsygALTWucAy4AWlVIBS6mIcvzi+Km17pZSHUsoP8HY8VH7OS2enX+/tvNQSDPwPxzie35yvnR78366Swh+MY9yWEJVNchbJWVFrSL4i+VoZpOA9f/NwzL2XiGMcy5ZzaKMqp065G/DHMbZxEXDX6ctgSqlLlVI5xbYdhOPS7y84LjHlAyuLvf5/OO56jwdaANcWe601//wcKsN44ONKakuI4iRnHSRnRZzF4cIAACAASURBVG0g+eog+XqelNa1claTOkcpFQsM11ofP482VuKYnHyb1npopQXn2ns/DaRprc87gZRSo4GbtdY3nH9kQlQNydkSbUnOihpN8rVEW/UyX6XgrSGUUtcBh7XW+9wdixCifJKzQtQekq9CCl4hhBBCCFGnyRheIYQQQghRp0nBK4QQQggh6jQpeKuZUupJpdRn57jvWqVUgVJqfWW0rxzmKKWylFJbzyWmyqCUaqaUOqiU8nVXDEKcVtU5WtcopXoopTa5Ow4hiqsreayU+kgp9YyL2/orpZYrpQxKqSVKqTFKqcVVHWNtIQXvOXLOh9epnG2eUkq9VPw5rfUrWuvby9rHBfdqrQeV9WIF278Ex1rmrbTWA858USl1k1LqsDN5UpVSc1Up624rpSKcvxzKWyscpZSPUuqQUqpo9Rjn8pJ/AHe6GLcQ5aopOaqUmq+USlZKGZVSR1TJRTCKx/KcM+bLynsDpdRg57Yv/cs2LZVSPyilMpVSCUqpaWVsN8XZVllx+SqlPldKxSmlTEqpnUqpK0+/rrXeA2Q77/wWolLVoDzuppRa4zwfHlVKXVvaTuXlsVKqqVJqkVIqydnWRqXUBWUFobWeprV+0cWYxwHNgCZa6+u11j8C0UqpHi7uX6dJwVu1qnLuv8rQFsdKK7llvL4RuFhr3RDogGNlvtJOsB/gWOfdFY/imK/wTAuA/7rYhhCVpTpy9FWgndY6GBgDvKSU6lt8A6VURxwnq+TyGlOOJVLfBf4qZ9P5wHEcJ8BRwCtKqRJTKSmlGgFPcMbqi2fwwjEv6GAcq1w9A3yjSk6AL/kr3KlK81g5Vj37AfgJaIyjc2a+UqrzGdu5kseBOM6XfZ1tzQV+Vkq5upzxv2kLHDljieFFSGcSIAVvldJa7wTClFLhp59TSs043ROq/lk5ZYpS6qRSKl0p9dT5vKer7SulpgKfAQOVUjlKqedLiT9ea51e7CkbjjXOi7/fTTjW4l7tQmztgUk4CoAz/QV0UEq1de1IhTh/1ZGjWuv9Wmvz6YfOPx3P2GwW8BhQ6EKT03FMVn+orA2cJ88hwMtaa4vWejewFLjtjE1fBd7DMdl9WfHnaq1naK1PaK3tWuufcBTSxYv2tcBwJcOShBtUQx53BcKBt7XWNq31GhwdQjefsV25eay1jtVav6W1Tna29QngA3QpbXul1Jene6+VUkOcV2umO6+6JiulbnW+9jzwLHCj85w+1dnEWmp2x1u1kYK36v0KXFnONpfg+LAPB55VSnWr5BjOal9r/TkwDdistQ7UWj9X2o5KqUuUUgbABFwHvFPstWDgBRwnYFe8DzyJY3WZEpzfSI/iWIdciOpU5TmqlJqtlMrDUaQm4+iROv3a9UCh1vqXsvYvtm1bHEXrC+Vtesbfp/8dXaytAUA/4CNXjqHYfs2AzhTrFdZaJwIWyjhpC1ENqjKPVRnPFc8nl/O4RCNK9cJR8B51cZfmOK60tASmAh8opRo5z+GvAF87z+mfO7c/CLRTpQxHrG+k4K16rlxqeV5rne/shdlN5Rd959y+1nqDc0hDK2AmcKLYyy8Cn2ut48trxzneyUtr/d2/bGYCQlyNTYhKUuU5qrW+GwgCLgWWAWYo6ol9BXjQxabeA57RWuf820ZaaxOOHqhnlFJ+Sqk+OL6wNnC+rycwG7hPa2139TicwykWAHO11mf2MEv+Cneqyjw+hGMo3qNKKW+l1EgcQ3xO51NF8xjnfsHAV864DC7uZgFecF65+QXI4d+/aJqcf9f73JSCt+qtAgY5TxRlSSn27zwcY3zKpZSa6Lx0kaOUWlHZ7Rfn7MH5FVjsfO9ewGXA2y7EGQC8AdxXzqZBOIZHCFGdqixHi3NevtyA48vjXc6nnwe+cmW5U+dNYUFa669dfMuJQHsc428/xFGonr5Z9G5gj9Z6s6vxK6U8cJycC4F7S9lE8le4U5XlsdbaAvwHR0GdguOq5jf8k08u5/FpSil/YDmwRWtd2jC/smScMUa3vOMIcv5d73PTy90B1HVaa5NSag+Onp01ldz2AhwnserixT9jD4cA7YCTSilwJJynUipSa93njP0inNv+6dzWB2iolEoBLtRan3DeFNAJx7duIapNVeZoGYrn0XCglVLqbufjMBw3hL2utX79jP2GA/2ceQOOy5o2pVR3rfU1Z76J1joOuPr0Y6XUQuD09IPDgcFKqaucjxsDvZVSvbTWZxWzypG4n+O4Ae4qZwFQ/PVwHHl9uPzDF6LyVXUeO2cjGXz6sXJMxTfX+bAieYxzrPv3QCJVf7NnNxw3pxur+H1qPCl4z4+PUsqv2GOL1tpWynY/A1dRPSfT86KU0sBQrfVapdRE4E8cPURtgJf55+a0T3D29jo9gqOovYuz7QNaF3t8EY7B/X2ANOdzA3AkZVzlHIkQgJtzVCnVFBiG4+7ufBxXRcYDE5ybDAeK90j9DTwMlHbF5hngtWKP3wWScAwtQik1BPhDa62cj7vh6IEyAzcAI3Gc/ABuAYr/XJbhuKntc0r3oXPfy7TWZ43Bx/EFeE2xm/OEqExuP9cqx9ReR3BcGb8baAF86XzZ5Tx29kAvxfH7YPKZQ4qUY/aT/2fvvuOjqtLHj3/ulPTeEwgdpHcQCyIICsiKWFFXVwTbimUV21q+rmV/q6t+1/bVZe2uroplURaVJqCiIL2ItBCSkD7J9H7v+f0RYAlpk2Qmk3Ler1dehMmdex80z8wz5z7nnMNAbyFEfhBCn1RfHF2RbGlonT3U/NIe/5rXwHHtfXkyABRF6U5NP9CuYw8NBjYce+wHakZvbgQQQjiFEKXHv44d4xZCVBw710RFUezHjvWfcmwVoB37+/EXrWto5uQZSQpAuHNUUPMhsAioBp4F7hJCLAUQQphOyQ0VqD7eo6vULDr/2rFjbacc6wIcQoiqY9fKBU5uUbgAyDt23VuA6cfzUwhhPuVcXsB6vI9QqVm0/6tj3/ekZhRqJFB6UhvVNSddS+avFErhzmOoWZGhhJpe3vOAacc/4DUnj6kZ8JlFzQdQ80n5NPHYz3OBI9SM/gbDVcDfg3SuDk0RQoQ7hi5BUZQ84Lzm9PjUc44VwBnAZiHE5KaOb8H5fwsMEUI8GOxzN3HdDGAdMEoI4W7La0vScR0hR5u49uvAEiHEN2183WHAYiHEGW15XUmqTyfI44eBCiFEq4vUY33/1wohrmh9ZB2fLHjbiKIolwL7hBC7wx2LJEl1yRyVpI5P5rHUEFnwSpIkSZIkSZ2a7OGVJEmSJEmSOjVZ8EqSJEmSJEmdWkiWJUtLSxO9evUKxaklqUPasmVLpRAiPdxx1Efma2homobd6USv1zd5bLQ1D6EYccfnNnqcqgmOmH2kxehJiGr6vMGmqiqRERFERkS0+bVDTdM0Dhw4gN1uB5D52s74/H6cLhcGQ/1li051E23NxxObgz+iZhfd/GovcRE60mLb/wqsqqqSENfs/Wy6tJKSEoqLiyHAfA3Jb0GvXr3YvHlzKE4tSR2Soijtdn1hma+hUWU2s3HnTtKSkxs9Lsp6hGErr+HIyD9Q3vfSRo/dVerm8dXl3DcpjXHdY4IZbkDMViu9unWjfycruMxmMzNmzMDlcvHRRx9x5ZVXynxtZw4WFHCooICUxMR6f55x8BN67vgbO2a8hTcmk+3FLp76toK7z07jjJ5tnyvNIYSg2mLhgokTmz5YQgjBY489xuOPP84111zD+++/H1C+ypYGSZKkEHB5PBzbWbBRSSXrATBnn93ksVuOujDqYFhWVJPHSoERQnDZZZexZcsWlixZwhVXyBWc2qMqs5moyMgGfx5v2oknJhNvTCYAGwtdRBsVxnaPbqsQpTayePFiHn/8cebNm8c777zT9BOOaf/j/JIkSR2Qw+UKqJ0hufg7HMkDT7xRN2brUReDM6OIMsiximBRFIXHH3+cqqoqZs2a1fQTpLBwuN1EGo31/1AI4ip3YksfdeKhveVuBqVHYtQ3/aFT6ljmzp2LxWJh0aJF6HSBvxbKV01JkqQQsDkcGBvoNzzO6KokruoXqnOavpVZYvVRYvMzppscsQqGsrIy3n77bQDOPPNMWey2c6rPh76B4ibSUUyEuxJb6vATj5ndGhlxckyvs9A0jZdeegmn00liYiL33Xdfs4pdCKDgVRTlNEVRtp/0ZVUU5a4WRy1JUsjIfG0/HC5XkwVvUvF3AAEVvLvLajYhHJUj2xlaq7i4mHPPPZfbbruNo0eDtYNry8icbZoQAr+mNdgiFGfaCYA9bUStxwPoKJI6AE3TuPnmm7njjjv44IMPWnyeJj/+CCH2UbOHOoqi6KnZ3/nzFl9RkqSQkfnaPgghcLpcJCckNHpcytFvccX1wB3fu8lzunw1mwQlhmF1hs6ksLCQKVOmUFpaytdff023bt3CGo/M2aapmoYQouGCt3InfmMcroRebRuYFHKqqjJ//nzeeecdHnroIebPn9/iczW3peE84JAQot3OYJUk6QSZr2Hi9noRQjR6y83griK+YjtV3ac0ORTl8WtsK3ah14FBJ4etWio/P59JkyZRXl7OihUrmNj+ZsXLnK2H1sjoLkC8aRf21OGg1OSbJgR+Te4i29H5/X6uu+463nnnHR5//HGefPLJgCYCN6S5Be9c4F/1/UBRlJsURdmsKMrmioqKFgckSVLQyHwNk5Lycpp6WU4+ug4Fjeruk5s833vbzOwp93DjuBQ5CacV1q1bh9lsZtWqVZxxxhnhDqc+9eZsV89Xv9/f4M8MHjPRtnxsacNOPGZyqnj8gm4JDUxykzqEoqIiVq5cyV/+8hceeeSRVp8v4IJXUZQI4CJgSX0/F0IsFkKMFUKMTU9vl+t1S1KXIfM1fFxuNweOHCExPr7R4463M7gS+jR4zPZiFy/+UMkP+U56JRk5r59cmL4lfD4fAL/73e84cOAA48aNC3NEdTWWs109X91eLw2N18aZdgHUjPAeU+VUAUiIlO0/HZHP50MIQa9evdi7dy/3339/UM7bnBHeGcBWIURZUK4sSVIoyXwNAyEEv+blodfpGtwRCv7bzlDdfXKD7QxWt8pf11eyo8RN/7QIZg9uvB9Yqt+ePXsYOHAg69fXrHecmpoa5ogaJHO2AT6/H0T9JW9c5U40XQSO5EEnHjs+wXN4tpzg2dG4XC5mz57NQw89BAQ3X5uzZsdVNHB7VJKkdkfmaxiYbTZKKytJTUpq9Ljj7QxV3ac0eMzyfTa8quDpGVl0T5S3Zltix44dTJ06FaPRSEZGRrjDaYrM2QYcH6GvT7xpJ47kgQj9f7e7trhV9DqIjZArr3YkTqeT2bNns3r1ai655JKgnz+g3wZFUWKAacBnQY9AkqSgkvkaPpXV1eh0uiYnVjTVzvBzoZNPd1sZkR0li90W2rJlC5MnTyYqKop169YxcODAcIfUIJmzjXO63fXeMdH53cRU/4o9bXitx8tsfnLiZd50JHa7nZkzZ7JmzRrefvttFixYEPRrBDTCK4RwAu32PpAkSf8l8zU8HC4XhwoLSYiNbfQ4g7ua+IrtlAy8FgE4vRo2j4rNo1Fq8/PpbgtHrX7iI3Xcdob839gS+/fv57zzziMpKYlvv/2W3r2bXvYtnGTONs5stda7y1ps9S/ohFprwwm7R2N3mYdxckvhDkPTNGbNmsX333/PP//5T6666qqQXEduQyJJktRCVrsds9VKmcmEyWzGaDA0udmELm8NChrv2cbw4efFVLvUOseMyoni8mGJJEfLSTct0bdvX2666SYWLlxIjx49wh2O1ApCCCw2GwlxdSdsxlXuRKBgTx164rFtxS68qmDmwMYnjUrth06n45ZbbmHhwoVcdtllIbuOLHglSZKayVRdTUFJCSUVFej1eqIiIkiMj8egb7xA3VniZvCebzhENovzUumfZmDWwHgSonTER+pJiNSRHK0nLVa+NLfEd999R+/evenevTvPPPNMuMORgsDt9aJqWr1rWsebduJK6IMa8d8JnTtK3cRH6uiXGlHneKl9MZlMbN26lWnTpjF37tyQX0++qkqSJDWDzeFg065dREZEkJqUFPB+7qomeHP9QdYb9rKz+9X8c1wPuYlEEK1cuZLZs2dzwQUX8PnncqOyzsLldte/JJlQiTPtxtTj/FoPW90qGXEGdHJf4XatvLycqVOnkp+fT35+PikpKSG/ppzCKEmS1AxHy8rQ6XTEx8YGXOwC/Frh4VyxCT0C48BpstgNouXLl/Ob3/yG/v37s3jx4nCHIwWRx+tFqWdJshjLIfR+Z63+XVUTFJh9JETK0qY9Kykp4dxzz+XgwYN89tlnbVLsgix4JUmSAub1+ThcVERSE5tKnKrU5uNPq8uZpf8Jc1R3XAl9QxRh17N06VIuvvhihgwZwpo1a+iKGzN0ZnaHA109rUJxlTtrfn7SCg0bjjgxOVUm95EbtLRXRUVFTJo0iYKCAr766iumTp3aZteWBa8kSVKA7A4HKEqzRnYBKh0qKcLC6bpfcfY6r8HNJqTmUVWVJ598ktGjR7N69er2vKmE1AJCCIrKyoiNrrviQrxpJ56YTLwxWSeOfW+bmd7JRsblyhUa2qt3332X0tJSvvnmGyZNmtSm15Y9vJIkSQFSNY2WlKoeVTBd/zM6NKq6Tw56XF2REAK9Xs/y5cuJjIwkIUHuRNfZ+Px+PF4vcTExtX8gBHGVO7GljzrxkFcVVLtULhgQJ9uF2iEhBIqi8OCDDzJ37lz69Gl4S/VQkSO8kiRJAdI0rf4JNE3w+DVm6jZii5HtDMHw9ttvc8kll+D1eklPT5fFbifl9njqfTzSUUyEu7JW/+5Rqx+AnAS54UR7c+DAASZMmMCBAwdQFCUsxS7IgleSJClgmhBQzwSappgqKpig+4Xq7pNlO0MrLV68mHnz5uFwOPD7/eEORwohVwMFb3zlNoBaI7yHTDXH9pA7E7Yre/fu5ZxzzuHw4cO4XK6wxiILXkmSpAB5vN5m9+8C9LN8j14RWHOnhCCqruPll1/m5ptvZubMmXzxxRfEnHqrW+pULDYb+vrW363Yhi8yGXd8zxOPfbPfTnqsnqx42anZXuzatetEn+7atWsZPnx4E88ILVnwSpIkBcjv97dofc+xrg3kk40rsV8IouoaXn75ZW6//XZmz57NZ599RlRUVLhDkkLM7nBgPHVLYSFIqNhWM7p7Ui46fRqDMiLRy/7dduGXX35h8uTJREREsG7dOgYPHhzukGTBK0mSFCif39/sEV6Du5qBnt2sYoJsZ2iFCRMmMH/+fJYsWUJkZGS4w5HagNfnqzPCG+koJsJVjjXtv+0MQtRMWIuPlFtxtxc9evTg/PPPZ926dQwYMCDc4QCy4JUkSQqYX1VRmlm0JhevQ4fGamVCiKLqvIQQrF+/HoCxY8fy+uuv1x3xkzott9eL/pQ1eOMr6vbvFtv8+DXIlu0MYbd161ZsNhtxcXF88MEH9O3bfibpyoJXkiQpQC1paUgpXEWxvhsHlZ5NHyydIITgoYceYtKkSSxfvjzc4UhtzOfz4XK7MZxa8FbW7d/9cq8VnQKju8n1d8Np/fr1nHPOOdx5553hDqVesuCVJEkKkF9VUZrR0hDhLCOhcjsbos9p0WS3rkoIwaJFi/h//+//cdNNNzF9+vRwhyS1MZvTCVD7jkoD/bsmh0puopH0WDnCGy6rV69m+vTp9OjRg6eeeirc4dRLvgJLkiQFyK+qzRrhTSlcDcBK3dlE6GX/biA0TeOOO+7g+eefZ+HChbz22mvyw0IXZLZa6/x/r69/d2+5mx0lbgZnyr7ucPn666+ZNWsW/fr1Y+3atWRnZ4c7pHrJVxFJkqQA+Zs5aS2lcCWV8QP5pjyZcd3l7dZA/Pjjj7z88svcc889vPjii83umZY6B5PZTNQpkxPr69/9scCJQa9w1YikNo1PquH1ern11lsZNGgQ3377LRkZGeEOqUEBjf8ripIEvA4MBQRwgxDix1AGJklSy8h8DQ2fz4fD5SI5wF29oqyHibUc4NP4BQBcPETuBhaIs846i40bNzJu3LguU+zKnK1NCIHFaiUuNrbW46f27woh2HrUTU6CgWijHL8Lh4iICFasWEFaWhrJycnhDqdRgf6GvAB8LYQYCIwA9oYuJEmSWknmawjYnE4EBDzCm1q4Eg0dL1WMZmKvGPmG3Ai/38/8+fNZtWoVAOPHj+8yxe4xMmdPYrbZ8Klq7RUahCChYmut/t01hxyU2f1cNEh+mGxrH330EYsWLUIIQf/+/dt9sQsBFLyKoiQA5wBvAAghvEIIc6gDkySp+WS+ho7FZgu8f1cIUgpWsjdyOBUkcfmwxNAG14H5fD6uvvpq3nzzTbZv3x7ucNqczNm6TGZzPf27R4lwVWBNH33isW/zHPRKNnJ2L7njXlt69913ufrqq/n555/xNLD9c3sUyJBDH6ACeEtRlG2KoryuKErsqQcpinKToiibFUXZXFFREfRAJUkKiMzXEHC53RwsKCA2OrA+3GjTHqKcJbxlP50LBsTJ7U4b4PF4uPzyy1myZAnPPfccixYtCndI4dBkzna1fK22WBru300bCdS0MxRZvPRPi2zR7odSy7zxxhtcf/31nHvuuSxfvrxD7XgYSMFrAEYDrwohRgEO4IFTDxJCLBZCjBVCjE1PTw9ymJIkBUjma5AJIdhz8CAKEBkR0eTxbr/GkZ+W4hFGkodOY8G4lK52ez4gHo+HSy+9lKVLl/LSSy9x9913hzukcGkyZ7tSvh7v3408ZYORhPLNeKNST/Tv7ix14/AKBqQ1nZNScLz22mssWLCACy64gGXLlhEbW2cspV0LpOAtAoqEEBuP/f0TapJTkqT2R+ZrkB0tLaXMZCIxPj6g41fus3C6+wf2xo1n2pDMEEfXcRmNRrKzs3nttddYuHBhuMMJJ5mzJ3G63fg0rYH+3TGgKHj8Gp/vsaJXYHSOXP2krWRlZXHJJZfw73//m+gA73a1J03eZxNClCqKUqgoymlCiH3AecAvoQ9NkqTmkvkafEeKi0loxkiGL38jaYqV6mEz8cmR3TocDgdVVVXk5uayePHiLj/6LXO2trzCQvSn9O9GW/MweqqxZowBYNmvNvaUefj9hBQSovT1nUYKol9//ZWBAwdy8cUXM3v27A6bs4FOG74deF9RlJ3ASODPoQtJkqRWkvkaJJqmYXc6A2plAFA1wenOdTiVWCxZZ4Q4uo7HarUyffp0pkyZgsfj6bBvnCEgcxbweL0cLSsj6ZS7KQnlWwCwZoxBCMHqg3YGpUcyuW9cOMLsUp544gmGDh3Kjz/WrJLXkXM2oJkUQojtwNgQxyJJUhDIfA0ej8+HJkRAL/J+TfDGj8X8TfmZgynnIvSyt/BkZrOZ6dOns3nzZv71r38RGSl3xjpO5mwNu9OJqCffEso3447tjjcmi1KbjwqHypwhcuWTUBJC8Oijj/Lkk09y7bXXMn78+HCH1GpyYUhJkqQG7MvLq3N7tSGbCp1EFvxAnOLGMGh6iCPrWKqqqpg6dSpbt27lk08+4fLLLw93SFI7ZHM46hS7iuYnvnLbiXaGgmofADkJcuWTUBFC8MADD/Dkk08yf/583nrrrdo91R2ULHglSZLq4fZ4KKmoICnAndW+y3dyacQGvFGpODJGNf2ELuSee+5h9+7dfP7551x88cXhDkdqp8pMJiJOWZ0htnover8La8YYLG6Vd7dVkxCp47R0eYcgVJYvX84zzzzDrbfeyuLFiztFsQuy4JUkSaqXw+UCAutZs7hVDhyt4By2U5U7FZTO8QYRLM899xwrVqzgwgsvDHcoUjtVZbFQWllZp+CNP9a/a0sfzf9+X0mFXeW8fnEYdB23l7S9mzlzJkuXLuWVV14JeGfJjqDz/EskSZKCyOfzEehbqsOrMUO3CQN+TLnTQhpXR3H06FFuvfVW3G43KSkpnHPOOeEOSWrHjpaWEhcdXWeCaEL5ZhxJA3AaEvmlzMPFQxK4emRSmKLsvFRV5Z577mHPnj0oisJFF13UoSeo1UcWvJIkSfXw+nwQ4Au+zaMxW7eB6qjuOJNOC3Fk7V9BQQGTJk3i/fffZ9++feEOR+oArA5HnWJX53cTV7UHa/oYbB4VAaTHyrsnweb3+5k3bx7PP/88y5cvD3c4ISMLXkmSpHpY7HaMhsAmxuw+mM/pur1U95gWcJHcWR0+fJhJkyZRWVnJihUrGDFiRLhDkto5IQQOl6tOvsWZdqLTfFgzxpB/bLJaRpycrBZMPp+P3/72t7z33ns88cQT3HvvveEOKWTkb44kSVI9KquriW5in3ghBO9vt9CnaAU6ncDWZ2YbRdc+HTx4kMmTJ+NwOFi9ejVjxowJd0hSB+D1+VBVtU6/aEL5ZjTFgD1tBD9vcRJjVBic0XhOSoHzer1cddVVfPbZZzzzzDOdutgFWfBKkiTVomkauw8cwOFyERcT0+ix/7OynL0VbtZGrqM8aQTe2Ow2irJ9cjqdxMbGsmzZMjmyKwXM6XbX2y+fUL4FR+oQNEM0h6ss9E2NwKjv2ndQgklVVcxmM3/729+48847wx1OyMmCt6twmSEqsd3cbrV7/MRFyl8/qf2pqK7mSHExmampTR6bX+3lmqwCeplLOdz3+pDH1l6VlZWRkZHB8OHD2bNnT6dZxkhqG06Xq857k95rJca8n+JB8/BrggKzlxmnxTdwBqk5XC4XXq+XxMREVqxY0WXyVVYcXcXH14ExGq7+CJ/fj8frbfMQKu0eVv9aycq9FWwtsLDmD2eSmRRbp2/r1VdfZeLEiQwdOrTNY5Qks9VKVGRkkzOUNxY6cfkFM9V1qPpIqrpNbqMI25ft27czdepUHnzwQe65554u8+YpBY/d6azze5NQvgUFgTVjDIervPg16J0sdy9sLYfDxZl5iwAAIABJREFUwezZs3G73axbt65L5asseLuCkp1weB1i6p8oLitjb14efr+/TS5d7dLYVupjW6mfg1U1s2wzY3VM6WXguy1bOX1wP/rk5p44/umnn+aBBx7g5ptv5rXXXmuTGCXpOL+qUlJRQXQT295a3Sr/+10l/RIF413fY86ZhGaMbaMo24/Nmzdz/vnnExcXx+zZs8MdjtRB2RwOIk4Z+Egs24TfEIsjZQgbd9jRKzC6W3SYIuwcbDYbs2bN4vvvv+ftt9/uUsUuyIK3a/jxZYQxli2RYyn79VeS4uOJiIsL2eXK7X42Fjr5scDJgcqakeTcRCOXD4vj9B7R5CYaURQFs9WKpmknnvfEE0/w6KOPMnfuXF5++eWQxSdJDbE7HLjcbtKSkxs9bl+lB1XAw732Ytxnp7LnjDaKsP348ccfmT59OikpKXz77bf06tUr3CFJHZAQArPVSuzJ/fJCkFC2EVvGGITOwJ4yN/3SIomNkAtLtZTFYmHGjBls2rSJ999/n7lz54Y7pDYnC95OzmfKx7DrU/K7z8SqGshISQzJdYqtPjYWOPmp0EVeVU2R2zvZyFUjEjm9RwzdEoyNPn/JkiU8+uijXHfddbz55ptd7pOn1D40NHnmVLtLPRj1CiPMq/BGp2PNGB3y2NqTqqoqZsyYQUZGBmvWrCH3pLs0ktQcXp8Pv6piOOk1P8pWQKSrnJKB1+H0aeRVebl4cGBbfEv1mz9/Pj///DMfffQRl156abjDCQtZ8HZSQgjKKivx/OdxctEwD7qG+Njg3HIVQlDhUNlb7mZvuYdfyj2U2GpaJPqnRvDbUUlMyI0hMz7wX685c+bw97//nQULFnSqrQyljqXaasVobPzDmd2jse6wnQmpTpLKf6Z0wFVdbivhlJQU3nzzTSZMmEBOTk64w5E6MIfTiTjlscSyjQBYMsezuciFJmB4tlyOrDWefvppbrjhBmbO7LpLJ8qCt5Px+f1UVFWRV1CAw1LBeUVfU91tMv74bq06rxCCDUecbD7qYm+5B5NTBSA2QmFgeiQXDIjj9NwY0mID/5USQvDaK6+w6O67ycrK4qabbmpVjJLUGj6/n+Ly8kaXIlM1wTPrK3B6Bbcn/4RiVans0XXaGTZv3MjhpCT633ADl1xySbjDkToBs82G7pQJogllm3DHdccbm8MhUzWRBoXBGY331Ut1lZeX8/rrr/Pggw/St29f+vbtG+6QwkoWvJ2IqbqaLb/8gqqqxEZHM8j8PQa/o2YEqpWOmH387QcTUQaF0d2iGZQeyaCMSHKTjHVerAKhaRovPfccy/79bzLT0zv9gtdS+1dQUoLf7691a/VUR60+9pZ7uHZkIsOKvsaWOhx3Qs82jDJ8vlu3jv954AEGDxnCguuvl3dipKA4dYc1RfUQX7mNyl6zAKhw+EmJ1je5aopUW0lJCeeddx75+fnMmTOHQYMGhTuksJMFbydSXFGBXqcjOSEBRfOTeWgJ1rSROJMHtvrcDm/N5LL7JqUzLKt1t5ZUVeXPjz/Osn//m5tuuYVFixa1Oj5Jaq1qi6XJndWO58Hphv1E2wvJG3hdW4QWdt+uXs1D995Ln379+Mdbb8liVwoah9NZq+CNr9yJXvVgyRyPqgn2V3rk7mrNVFRUxJQpUyguLuarr76Sxe4xARW8iqLkAzZABfxCiLGhDEpqGZPZfGI5peSj3xLpLOPIiD8E5dyVjpoWhtZ+xvb7/Tzx6KN8tWwZv503j0X33y8/uQeZzNfmU1UVk9lMUnzjC9sfrvIBMKTyK/yGWKq7ndsG0YXXym++4dEHHmDQkCE88cwzJCUlhTukTqer5qxfVbHY7bXyLqFsE5piwJY+ig1HnFjcGmf1anzHQ+m/8vPzmTJlCiaTiRUrVnDmmWeGO6R2ozkjvJOFEJUhi0RqFZfbjdvjITY6GoQga/+/cMX1wJLdul92h1fj/e1mVh6wkx6rp1crF/52OBz8uncvtyxcyCVz58piN3RkvjaDzeFA07RGRy73lLl5d1s149L99KhYh6nHDDRD518XdMe2bQwbMYL/feUVfKoa7nA6sy6Xs9UWC+opeZdYtgl72nA0QwxLdhWTm2hkXPfOn2fBcvDgQZxOJ6tWrWLcuHHhDqddkS0NnYDb42HLnj0neg/jK7cRa95P/qh7QWnZrUeLW2VXqZt3t5oxu1UuHBjPlcMTiTa27Hw+X83IWGJiIu988AFR0dGYrdYWnUuSgq2sshJ9I8WuVxU8910lCZF6nuq1Gf0uDxW9Z7VhhG3P5XQSHRPD3ffdh9frJSoqSuasFFTVVivGk3rmja5KYqyHKBx6C5oQlNn9zB6c0KJ5Il2Nw+EgNjaWqVOnkpeXR0wjk2+7qkCrFwGsUBRli6Io9U6lVxTlJkVRNiuKsrmioiJ4EUoNEkJgtdvZtGsXbq+XxGO3hbL2f4gvMonKntMDOo/Hr7H+sIN3t1bz+OoyFnxaxIJPj/LCDyYSonT8+YJMrh+T3OJi1+PxcP8f/sAjDzyAEIKoaPlpPcRkvjaDz+fj8NGjJDSyGcuuEjc2j8bvRifRrXAZjsT+OJNOa8Mo29anH3/MlXPmUFZaik6nI6qJ3map1RrN2c6ar2arlYiI/941TCjbBIAl83SOVPvQBHRLkONyTfnll18YMGAAH330EYAsdhsQ6G/SWUKIYkVRMoCViqL8KoRYf/IBQojFwGKAsWPHnrqsnhRk1VYrW/fswef3ExkRQeKxN+sY836SSjdQNHgBQh/YMi4rDth5d6sZo16hR6KR0d2i6ZlkpEdSBIMyIjHoWv7p2u12c+9dd7Fxwwbuf/hh2cLQNmS+NoPFbkcTosHNToQQfLrbQoxR4ZzYAmLN+zky8g/QSX+XP3r/fZ57+mnOOucckprYcU4KmkZztrPmq9Vur7XDWmLZRnyRKbgS+/LLPjsAQ1s5Sbqz27lzJ1OnTkWv1zNs2LBwh9OuBVTwCiGKj/1ZrijK58B4YH3jz5JC6UB+PjqdjtRTJpDk7H0LvzGO8r5N76QihKDI6qfQXNNu8Pbl3YnQB+9N3OV0cvftt7N182Ye/tOfuGjOnKCdW2qYzNfmqaiqqjVL/FT51T4OmLxcOjSB7kUfoekiMOVOa8MI284/336bF59/nnPPO4+nnnmmyU04pODoijnr8njwnbwMoFBJKN+MJWsCJpfG0l9s5CQYSInuWpu6NMfWrVuZNm0a0dHRrFmzhgEDBoQ7pHatyYJXUZRYQCeEsB37/nzg8ZBHJjWozGSisrqatFNGX6LNB0gu/o6jg+ahRjQ+2xxg8aZqVh2s+RQdF6HDEOSVhh6+/362bdnCY089xYxZnbvfsb2Q+do8mqbVbDbRSJvNigN2IvQKF/YzkrJqJVXdJ6NGdL5tTv/zxRe8+PzzTJs+nT899RQGWey2ia6as4cLC2tNVosxH8DotWDJGMebm6uxezUenpLZ4e8KCiHQNK3mq77vj/3Z3H9ncXExU6ZMISkpiTVr1tCnT58Q/Qs6j0BGeDOBz4/9zzAAHwghvg5pVFKDHC4X2/bsIT42tk6C5Pz6DqohhrJ+lzd5no0FTlYdtDO1XxyTesfSLdEQ9IkBv1uwgOmzZjHtgguCel6pUTJfm8HudOL1+Rrs3/Wpgo2FTsZ0i6ZX5XoMfseJBfE7m0lTpnDzwoX87oYbMDQy4i0FXZfLWa/PR2Fpaa3lyI5vJ7zKO4xNhS5+OyqJHkmtWxUomPyqit/vRz1WsB7/s1axKkStVichBIqiYNDrMRoMGAwGoqOiMOj1GAwGjAYDRqMRo17f7D75nJwcHnvsMebMmUPPnl1j85vWavJVTQiRB4xog1ikJvhVlV3792M0GomKrN2fG23JI+XoWooHXndi9EkTgsNVPnaXuTG7VJw+DYdXw+rROGjy0iclghvGJmMMYhuDxWLh+3XruPCiixg+YgSMkL86bUnma/PYnc5Ge3G3HHVh82hM7hNL+sFluOO6Y0sb2YYRhpYQgqWffsr0Cy8kLi6O+XJ77zbXFXP2cFERQojay5GVbsSR2J9Xdwr6pkYwa2DTdylDwevz4XS7Uf1+UBQEoAiB0WgkJjqaGKORyIgIIozGE196vR6DXo9epzvxve7Y93qdLqij1OvWrSMxMZGRI0dy1113Be28XYH8GN+O+Xw+ykwmSioqcLnduNxuUBRSEhPrHJv96zuohmgO5F7GlsMOthW72FHixuqp2RkqUq8QE6EjxqgQF6Fjcp9YLh6SENRi11xdzcKbbyY/L49RY8aQ061b0M4tSaFQWFJCTGT9kzu9quDDHWZSY/SMjysnvnIHhUNv7TST1YQQ/O3ZZ/nXe+/h8Xq58uqrwx2S1AVomkb+0aO1Rnf1Xitxpt3s7XkV5jKNuSPi0LdisnRLCCFqlkkzGMjNyiIxPp6oyEiiIiIwGo2NbjneVlatWsVFF13EuHHjWLt2bYdv92hrsuBth1xuN0VlZRwuKsKvqsRGRWE0GEhKSKh3Yfwoaz4pRWv4OOJiHvjSjsBOfKSOkdlRjMyJZkR2FIlRoU1Wk8nEwhtvpKiwkL++8IIsdqV2z2KzYaquJi0lpd6fL91j5ajVz+1nppJZ8A6aog94qb/2TtM0nvvLX1jy4YdccfXVXHHVVeEOSeoinG43mqbVWhUlsWwjChpfuEZg1CttttGEEAKHy4Xb4wEgIzWVIf361bmD2h589dVXzJkzhwEDBrBkyRJZ7LaALHjbGavdzoZt21AUhYTY2IB66XJ+fQcPETzvmM6lwxIY0y2aPikRbbZYd2VFBb9fsIDSkhKef/llxp1+eptcV5JaI6+oiMjIyHrfOKpdKp/stnBWzxgm9YwgbflXWLLPwh+VGoZIg0vTNP7yxBP8+9NP+e3vfsftd98t3zylNmOx2epsUZ9U8iO+iET+WdKNEdlRJIR4gEbTNBwuF3ank8zUVAb360diXFy7LHQBvvjiCy6//HKGDh3KihUrSE3t+K9D4SAL3nbG5nAA1Nu2UJ8oWwEphav5p3IhA3IzuXJ42+9zv3nTJirKy/nb//0fo8d2iS3gpQ7O4/VSVlFBSlLdfBFC8K/tZoSAi4ckkFSyAaOnmopevwlDpMFXUV7O+m+/Zd6NN3LLwoWy2JXaVP7Ro8ScvCqKUEks+4l9ceOxWxVmDAhd764QArvTicfrpVtGBoP79iUtJaVdtCs0RAjBG2+8wciRI/nmm29Iquc1SwqMLHjbGavd3qzky/71XTS9kZecMzkjpm3/d/r9fgwGA9MvvJDxEyaQIj91Sh1EcXl5zWSUeoq9tXkOvs1zMHtwPL2SI0jb8yXe6HQsWePbPtAgUlUVnU5HZlYWH3z6qcxXqc05XC4sdjvpJy2pGWfag8Fr5X3zUHokGRmSGfxRVp/fj83hQNM0UpOSGDt0aKM7K7YXx99jP/zwQ3w+HwkJnW85xLYU5JVXpday2GxERgS2FEukvYjUwpX8nDSTcpHIaWltt4RLUWEhV196KVs3bwaQb55Sh+HxetmXn09SA28eX+610Tc1gqtHJhHhLCOxbCMVPS8Epf2OAjXF7/Px0H338fLf/gbIfJXCo6SiAv0p81CSSjegomeZcwjXjEwK6mQ1TdOoslhwOJ307dGDSePGMX748A5R7L7zzjucccYZmM1moqOjZbEbBLLgbUd8fj9mm42IABd8z/71XYSi5x/ahaTH6hnbRo3+Rw4f5uZ586iuriauA7xwSNLJnG43Qoh676T4VEGRxceonCh0ikLakeUAVPa6sK3DDBqv18sD99zDmpUrSU1LC3c4UhdWWFJC/ElbCQMklvzIXsMgjDEJjMoJ3jbCQghMFgs5GRlMHDuWfj161G6laMf+8Y9/MG/ePJKSkogIcABMaposeNsRs9UKUO9KDKeKcBSTVvANW1NmsKEilqFZUW3Si5d36BA333ADqt/Pa2+8wYCBA0N+TUkKJrfHgyJEvT8rtPgQQLcEIwiVtPxlWDPG4o3Nbtsgg8Tj8XDfXXexfu1a7v3jH7n62mubfQ5N0/CrKl6fD5+qhiBKqStweTy4PZ5a21VHOEuJsR5iqXsEZ/eKCdp7mM/vp6Kqiqy0NAb17dtuJ6PV55VXXuGmm25i+vTpfPnll8Sc8gFBajnZw9tOeLxeDhUWNjm6K4Sg0OJjwPY38QmF24qmkRCnZ3oIG/2PKz56lFtvuAG9wcD/vf46vXr3Dvk1JSnYqi0W9A2sfrJsrxW9AkMzo0gq+ZFIZxmFw29v4wgDc/JuT8f/PL6NqRACVdN49L772LxxI3fcey+Tp0+nsrq61q5QyrGF9U92fHeo4xRFObFLVEpCQoe4HSy1P0UlJXXWsI4q/AGAzYYx3Ni/de9hqqpiczpRVRVFURg5aBDdMjNbdc629sYbb7Bw4UJmz57NRx99RGQHKtQ7AlnwhplfVSkuK+PAkSP4VZXkJvp0XvjBRP6RI6yNXMnnuqlcd84AxnWPbpPR3cysLGb85jdccvnl9JBbGUodkMfrpaC0lMR6ira95W6+y3dy6dAEkqL1ZGz+DG90OtXZZ4ch0pqC1uvz4fP78fp8NQ8e27pUCIFRr69ZEN9gICYq6sQWpQaDAb1Oh0GvZ968eVx99dVcddVVKDodOkWpWa7w2J/H7ybpjv1MOfalO7Y7lF6nC+iOkyQ1xu50crCgoM7qQ0r+9+RrmcydMoL0uJaXIx6vF6vDQe9u3chOTycuNrZdr7zQkKlTp3LHHXfw7LPP1hoJl4JDFrxhdqS4mD0HD5KamNjo6K7Lp/Hm5mp+LHCyOOHf6Hw6+ky7kd7xob/d8cvu3aSmpZGZlcVdixaF/HqSFCpWux2hafW+Ge6v8AIw87R4Im0FJJZvomjwAtC1/cukpmmUVlaSnJBAekoK8bGxJMbHE2E01oy2Htu6tD5Wq5VNmzYxdepUbpVbBUvtQLXFcuKD1Ak+Fz3t21lmnEqf1JaPZLo9Hmx2O2OGDSOzA07GFEKwdOlSLrroInr27MkLL7wQ7pA6LfnRPYzKKivZl5dHSkJCk60Maw7ZWZvn4Jru5ZznXUt5v8sQ8Vkhj3H71q3cduON/PlPfwr5tSQp1Cx2e4OF4o5SF1nxBhKi9GTk/RtNMVDRu+3W3hVCYHU4MJnNWOx2BvTqxVmjRzNswAB6detGckICsdHRRBiNDf4bqqurmTZtGrNnz6a8vLzNYpekxhSUlBB7yoSxqv0/EYkP+kxs8Xmdx3ZJmzByZIctdh9++GHmzJnDe++9F+5wOj05whtGR4qLiY2ODmgZsr3lHtJj9SzSfYBqjKNkYPMnnzTXlp9/5u6FC0nPyOChxx4L+fUkKdQqqqqIrqcvzuHV2F3qYdageHR+F2lHllPdbVKb7axmMpsRQpCRmkqf3FziW3BL1mQyMW3aNHbv3s0nn3xCRkZGiKKVpMC53G6sdjtpJ629CxBV9AN2EUXOwJbtzOnz+XB7vZwxcmSH7CsXQnDffffx7LPPcuONN3JtCyaUSs0jC94w0TQNs81Wby/hqYQQ/Frh4erkX0ks20TBsNtQI0K7Jt/GH39k0Z13ktOtG6/84x+kyeWMpA7Or6pYrNZ6d1dbf9iBAM7qGUtK4TcYfHbK+14S0ng8Xi8utxuvz0dmWhqD+/Zt8bJJ5eXlTJ06lf3797N06VJmzJgR5GglqWXsTmedxzRN4zT7z+yOHIEhovntDEIIqqxWhvbv32GL3bvuuosXX3yR2267jRdffFH2yrcBWfCGidPtRju281FTNhe5sLr9XO96D09MNuV9Lw1pbEII/v7KK+T26MErixeTnJIS0utJUluw2mz17q4mhOCb/Tb6pUbQN8VIxrbPcCb2xZ46POgxaJqG1eHA5/MREx1Nz27dSE9JaXKyalPeffddDh48yLJly5g6dWqQopWk1quyWNCfcrei4MAvnI6JnVnXkdzA8xoihKDSbCY7PZ2eOTnBC7QN7du3j8WLF/OHP/yB5557Tm7v3UZkwRsmlmNvvo0ptvr4cIeFn4uc3BD3E5nuQxwa9yhCH7qFqI8vSfT8Sy+Bosh9u6VOo6yqCkM9y5HtLfdw1Opn4RkpxFXtJtZygPxR99ZZQqk1hBBY7XbcHg+5OTnkZmWRFB/f6lGd4/l6zz33cNFFFzFgwIAgRSxJradpWs1mE7GxJx5TNYH7wLdoKKQMObfJ98FTVVksZKamMmLgwA5XKB7P14EDB7Jjxw769+/f4f4NHZkcQw+DcpOJbXv31noRqM/6ww5+LHAyPlvPIsPHOJJOoyo3dKM3q1as4P6778bn85GUnCyLXanTEEJQWlFRZ+IMwIHKmtUZRneLJuPgJ/iNcZh6nB/U61dWV5MQF8eZo0YxfMAAUhITW13sHjlyhIkTJ7J//34URZHFrtTuVFZX4/X7a/Wjbyx0Msb1E0UxgxExgffIa5pGRVUVSQkJDBswoMMtO+b3+7n++ut54403ABgwYIAsdttYwK+4iqLoFUXZpijKslAG1BXkFRURHxPT6GQ1j19jzSEHg9IjeTp7LdHucgqH3QZKaD6jfP2f//DwffdRXVWF1+sNyTWktiPztTavz1ezy1M9I7wHTB6So/WkaFUkH11LZa8L0QzB24LU5nCQGB/PqMGDSUlKCsqbXF5eHpMmTWL37t2YzeYgRCmFU2fN14KSEmJOmSS6Z38eg3VHEH3Pbda5qiwW+vXsyfhhw5pc1ai98fl8XHPNNbz77ruUlZWFO5wuqznV053A3lAF0lU4XC6qLJYmJ6fsKfNQ7VKZexpk73sPc9aZ2DJGhySmZUuX8j9//COjxozhhVdfJbaJkWepQ5D5ehKn2019ZabLp7GjxM3oblFkHPoMRQjKgtgjX2k2oygKIwYOrLfYbokDBw4wadIkbDYba9asYfz48UE5rxRWnS5fNU2jymIhOirqxGMmp5/cqprd1Sw55wR8LrfHQ3RUFH1yczvc5C6v18uVV17Jxx9/zF//+lf++Mc/hjukLiug3xxFUboDFwKvhzaczs/t8dTsdNSESocfgEmVH6L3OSkaektI4lm2dCmPP/II4ydM4H9fflnu290JyHytq8JkQqnnjfLFDSbcfsH5vYykH/4Cc85ZeGNbNxHG4/VittmoqKoiPTmZiWPHEhekvDp48CCTJk3C7Xbz7bffMnp0aD4ES22ns+arzeFAPWVi9vrDDs7XbcYS1wdPXLeAzuNXVax2O4P79u1wbQyqqnLppZfy+eef88ILL7BIbtwUVoF+VPobcB+ghTCWLsHv9yNE0236eys8nBZRQY8jn1PZayauxD4hiadf//6cP2MGz774IlEtXBJJandkvp7E6/ORV1REUnx8rcddPo3NRS6m9Y9jvHM9Rq+Fsn6Xt+paZqsVv6rSPTOTM0aNYsyQIUF9k87Ozubss89m7dq1DB8e/FUkpLDolPlaZjLVKnaFEOw4VMw43T7suZMCOoemaZjMZgb360dGB9xYQq/Xc9ZZZ/Hqq69yxx13hDucLq/Je2yKoswCyoUQWxRFObeR424CbgLo0aNH0ALsbMqrqprsP9p61MX3+U4+SfkE4dFzdPCCoMexa8cOho0YwcDBg3ny6aeDfn4pPGS+1mW12xFC1Hnz/XqfDYAzc6PJ3L0EZ2JfbGmjWnwdm8OBAkwYMYKoeja3aI3du3fTs2dP4uPj+fjjj4N6bil8Omu+appGQXExCSe1x5Xa/AxxbkJnFFQH2M5QZbHQNzeXXt0CGw1uLxwOB3l5eQwbNowHHngg3OFIxwQywnsWcJGiKPnAh8AURVH+eepBQojFQoixQoix6enpQQ6zc/D5fBwtK2vy9ubqQ3amR//CWOd3lA64Gl90cDd9ePv115l/7bV8u3p1UM8rtQsyX0/iV1XyCgvrTBD9Zr+dD3ZYGJAWwQTdHmKshyjrd0WzlyJzud2YzGYqzWZ0Oh0TRo0KerH7888/M3HiRG65JTRtTVJYdcp8dbrd+P3+WssArjpo5wLdZpxRWbgS+zV5Dq/Ph06no1/Pnh1qNQObzcaMGTOYPHkyFosl3OFIJ2my4BVCPCiE6C6E6AXMBdYIIX4b8sg6IZfHA9Bg072qCb7Pd3Co3M7DvIk7thslpwXvP7UQgn+8+ir/9+KLXDBzJhMnBXZbSeo4ZL7WVlZZSXlVVa0Pmaom+M8+G31SInh8WibZBz/CF5mEqZlL/lntdlQhGNKvHxNGjOCsUaOC1qt73IYNG5g6dSrJycn8+c9/Duq5pfDrrPlaWllZ68Njhd3P+n3lnKPfhbX7pCY/WHq8Xiw2GyMHDQraZM+2YDabOf/889mwYQOvvPIKiYmJ4Q5JOknH+U3q4FweDwePHGl0ke1txS5e+MHE7fqldDcWs2/kcwh9cEaLhBC8+uKLvP3GG8yaPZuHHnuszu43ktSZCCEoKi0lLiam1gjRmkN2Sm1+7p6YRpwtj6TSHykavCDgXNM0jWqrlajISMYPG9bi7YCbsn79embOnElOTg5r1qyhe/fuIbmOJAWTz+8nr7CQxJN65r/ab2OibgdG/FTnTGz0+X6/H6vdzpihQ8nsQH27VVVVnH/++ezcuZMlS5YwZ86ccIcknaJZBa8QYi2wNiSRdHIHCwoorawktZHNHHaXeeiplHJX5BdU5UzBmnV60K6/95dfeOfNN5lz2WXc//DDHW5pF6n5unq+2hwOqiyWOjn3zQE7uYlGJuRGk7X5A1R9NOV9LwnonMe3Ne2Rnc1pvXuHbD1Qv9/PjTfeSG5uLmvWrCE7Ozsk15Haj86Sr1a7HVXTTkzW1ITg+3wnL8Zuw0cS9rRhjT6/2mplcL9+HarYBXj66afZtWsXn332GbNmzQp3OFI95AhvG3C6XBSVlJCalNRgoenxa3yz38pn8e+CZqBgeHBndA4eMoR/vPMOw0aM6FD9UJLwZhhPAAAgAElEQVTUUmarFUVRav2+O7waBWYfcwYnEOksI7VwFWV9L0ONSAjonNVWK92zshga4i1BDQYDy5YtIyEhgczMzJBdR5KCrbyqCv1J73M/F7qwuzyMV7Zg7jEFlIbvLNqdTqKjouieldUWoQbVE088waWXXirXxW7H5DBfGzhSXIxep2uw2N1Y4OT/ra3gAjYy3LudoiE3BWWimqZpPPuXv/DThg0ADB85Uha7UpdhtlrrjMCuy3MgBAzNiiLrwIcIoKz/FQGdz+fz4fP76ZWTE7I8+vLLL7nnnnsQQtC/f39Z7EodiqZpFJWUEH/S6gyrD9m5IGovkZqz0dUZNE3D5XYzdujQDrPebnFxMZdddhmVlZVERETIYredkyO8IWZzODhSXExyQt0RJE0I/rnNzJd7beREuXkv8j1sCQMo79v63h9VVXnqscdYtnQpCfHxTDjzzFafU5I6Cr+qUlJZSUJc3InHNCFYe9hOeqyekUlu0n5aRlWP8/HGBFZUVlutDB8woFZvYjB99tlnXHnllYwcORKn0yl3PJQ6HLvTiV9VTxSsJqef7SVu7kvfhuqMxpoxpsHnmm02+uTm1iqW27PCwkKmTJlCaWkphw4dIi0tuKspScEnR3hDyOvz8fPu3URFRNQ7uvtTgZMv99qY1i+OT/suJ1mYKRx9b6O3fALh9/v508MPs2zpUm689VZu/P3vW3U+SepozFZrrT5CgEKzj8NVPqb0jSMz7zP0qpuSAVc3eS6/qlJuMpGTkUGPnNbtwtaQDz/8kCuuuIJx48axatUqWexKHdKpm02sPeRAERqj3BuxZE5ocGKoy+3GqNfTJze3rUJtlcOHD3POOedQXl7OihUrOP304M23kUJHFrwhVFZZidfrJbaBpYo+222lW4KB2/uXk3XoM8r7XIwjZVCrrun3+3nkgQf4+j//4fd33MGNt94q2xikLqfKYsF4ym3RveU1ywKe010h49CnVGefhTuhd5PncrpcdMvMZNTgwSGJ9b333uOaa67hrLPO4ptvvpFLGUkd0vHNJo6P0AohWH3IzpWpB4nyVlPVfXK9zxNCYHc6GTl4cMgmgQbT8e29LRYLq1ev5owzzgh3SFKAZMEbQmUmE9ENLEKvaoIiq49x3SLps/05/JFJHB1yU6uvqdPpiI+P58577uH6BcHfoU2SOgKLzVbnzXPLURdZ8QaGmlZg9FooHXBNk+fRNA2P10vPEO70lJiYyPnnn8/y5cuJD1G7hCSFmsPlwuf3n1g3t9Dio8KhcnnkJlR9FJas+tvqzDYbudnZpHSQD3oxMTF0796dNWvWMHbs2HCHIzWD7OENIbvTSUQDi2bvq/CganCRfyVx1Xs5NP5/UCNa/mbn8XgwV1eTmZXFg48+Kkd1pS7Lr6pUWSwknVQ85ld72VHi5pLBMWQd+BBb6jDsacObPFeVxUJudna9PfitdfDgQfr168dFF13Eb37zG5mzUodmsdlq/X39YQcRispQ+w+Ys89CM0TVeY4QAlVV6d0B1pg+fPgwubm55OTk8MMPP8h87YDkCG+IOFwunG43xnpu0XhVwTtbzWTqzEwseRtL+hiqujdvl6eTuV0uFt15JzfPm4fb7ZaJKHVplVVVqKpaq5dw1UE7Bh3MT9hMpLOUkgBGd01mM+kpKQzo1SvoMb7wwgsMHDiQdevWAciclTq8MpOJqGNbeKuaYP1hJ9ekHSTCa6Gq+5R6n2O22cjJyAj6DoXBtmPHDsaPH8+9994LyHztqGTBGyIFxcUYGliGbNleK3lVXt7MXIJe83Bk1D1NbrXYEJfTyR8WLmTTjz8y/+abiYqq+ylakrqSkooKok/KA4tbZW2egzN7xNDr8L9wxffCkt34qiVujwejwcCI004j8tibeLD89a9/5a677mL27Nmy/0/qFDxeLxUm04m8213mptqlclnkRlRDNJasCXWe4/P5UCAkHyiDacuWLUyePJmoqCh+LyeAd2iypSEEvD5fg0uRbSqsWZnh8sRfGVL9LUcHzcMT36NF13E4HPzhttvYuX07jz31FDPaaHcXTdNOfC9EzWbJouYv9X5//BhO+d7n94c4UqmrcbpclJlMtXLv1Z9MaJrgpqxfidl+iLyxD4HS8Gd9q92OqqqMHTas3js0rfHkk0/yyCOPMHfuXN59992gn1+SwsFqtyPgxF2VHSVuonR+Blh+wJw9sd7VGawOBwP79Kn14bS92bhxIxdccAHJycmsWbOG3r2bnuQqtV+y4A0yVVXZfeAAQJ2lyAotPv66vpLsaJX/0b+JO7YbJaf9tsXXeun559m1YwdPPP000y64oFnP9Xi9OJxOjpefAlCOfWlC1NyyEaL2yLMQNS9qioKi0504XtHpTtziUXS6E7cNFJ0O3fHHT9rxSnfs+9SkpA6z5qLUMRwqKKi1ycshk4ctR91cMSyRYUUf4o3OoCq34fYhs81GbHQ0w087Lei3WVevXs0jjzzCtddey1tvvYW+gyyuL0lNqTSbTywB6PFrfHvIwbVp+zFabZjqaWfw+f0oikJ2enpbhxowt9vNnDlzSEtLY82aNfTo0bKBKan9kAVvkFkdDkrKy0lPSan1uFcVvLW5CoD3+q4m7mAR+85+vsF1CQPx+zvvZPLUqZzexG3R4xMD/KqK2+PBr6rERkczoHdv4mNja4pSRUGnKCcKBd2xIlZ3rGg9/jPdScWtJLUnbo+Ho+XlJ0Z3/Zrg75uqiIvQcWVmIQkHtlMw/HaErv5RVbvTiV6nY8TAgcRGRwc9vilTprBkyRLmzJkji12p0/D6fBQWFxN/bJOXHwuc2L0aV0Ztwu+Kw5pZe/cxIQRVFgujBg0KertQMEVFRfHxxx/Tu3dvuoVwlRap7ciCN4g0TWNfXh5RkZF1isKv9tnYVerh/kGV9Dn0HqbcqXVeCAJRXVXF63//O3fcfTcJCQn1Frtenw+704mmaSfiiDAaiYqMpHt2NllpaSTFx8vCVepUCktKUPjvnZUvfrFyuMrHb0cm0Sfv//Ab46no/Zt6n2ux2/H7/UwYMSKoxa4QgkcffZQrr7ySoUOHctlllwXt3JLUHljt9lqbvKw8YKdnvKBX9QbMORMR+tpFrd3pJCstjW7tdNvsFStWcOjQIW699VbOPvvscIcjBZEseIPIZDZTZbWSnpxc6/GVB+x8sN3MuCyF6yufwx+ZzJGRdzf7/JWVlSy88UaOFhUxc9YshgwbVmv01uf34/Z4iIqIoG+PHmSkpBAVGYnRYJDFrdSpuT0eDhUWnhjddXg1/v2LldE5UVyRW03Syu8oGXgdmqFum4LFZiM6KorRo0c3uG52S2iaxsKFC3n11VfR6/UMHTo0aOeWpPai3GTCcGz5zSPVXvZXenmq/34MhfY6qzNomobL7Q7ZJi6t9Z///IdLLrmEwYMHM3/+fCLa8Qi01Hyy4A0Sr8/Htr17iatndGjFARupMXqeTfyQ6CNH2Hf2/6JGNG9dz4rycn6/YAFlpaX87yuvMGTYMBz/v737jq6i2h44/p2bm94bECAJIYWOVBudICgo+BQsWJ6igvhAfGLBLqI/FZ/liVgQFBRR6QiINEmwINJ7C4SEhPR2a26d3x/EPLFAgJvc3GR/1nItIHNn9kg2s3PmnH3MZsyVlfj5+eHn40NYcDDRERFEhIV5xI41QrhKTkEB8L/R3V+yTZhtKiM7hdL8yEc4vXwoSPzz6KrRbEar1dKtfXuXF7tjx45lzpw5PPHEE7zwwgsuO7cQ9YXNbienoICQqrUYyw7o8NUqpDp/xu4djK5Jz7OOL9friWvevFb6Wl+q5cuXc8stt9C5c2fWrVsnxW4DJAWvi5grK1Gdzj+tOLU6VPL0dsY2OUx81jLyk25B17Tn35zlrxXk5/PQ/fdTUlzMfz/4gK7du1dfs3ObNvX21ZAQdcHpdJKVm0tI1RzCikoHS/ZXEBOspaNPHpHZ68lPvhW739lvXlRVxWA00rt7d5euFHc4HIwZM4bPPvuM5557jqlTp8obFtEglZSV4XA48PLywmBx8sspE9cn+RJ9+idKWw5E1fyvxKi0WPD28qqXbcgWLVrE6NGj6d69O9999x1hYWHuDknUAunD6yI6gwHn71puwZnXO4+tziPAXsGDuhmYQhLI6Tjugs9t0OtRVZV3P/qIdh07ojMYKC4vx+l0ElALi2uE8CT5xcVUWq3VW5ou3ldBscnBPd3DaXF4Lk4vX/LbjD7rM3a7naLSUmJjYghz8WiTzWYjNzeXadOm8dJLL0mxKxqsgpKS6oVn23JMOJwwMngfXnYTpS1TzzpWbzLRNjGxXi5UO336NFdeeSXr1q2TYrcBkxHeS6SqKrkFBew9cqT6wWmwOJmzvZSfskwE+Sgsj/kM/wo9B3u+eUFdGUpLSvD19yeiaVPenzsXb60WjUZDk4gImkRFERwQIAWvaPQyc3KqX6naHCo/nDTSOz6Aq4PziDi1kfw2d2D3PXt0t1yvp11iIq1jY10Wh9VqxWw2ExoaynfffVc9r1GIhsjhcFBUWlq9yPOnLBPRgV50LN2A1S8SXXTX6mNtNhveXl5E/WF9i7sVFxcTFRXFpEmT+Ne//iU528Cdd4RXURQ/RVF+VRRlj6IoBxRFmVoXgXmKAxkZ7Dl8mPDQ0Op5sysP6fjxpInr2waz8LJdJJb9TG6HBzCHJdfonBV6Pbv27GH0yJEs/vJLWsfGcnnnzgy48kp6detGpzZtaBoZKcWu+JPGlq8WqxW9wYBf1fzbXafNGK0qfRICaXHwU5xaP/KSbz/rM1abDa1WS1zz5q6Lw2Jh5MiRDBkyBLvdLg9OUWOemrMVBgO2qu/1PJ2NPXmVDGvlIDT/F0pjr4HfTWco0+tpk5BQr9aWzJo1i8TERPbu3QsgOdsI1GRKgwUYqKrqZUAX4FpFUf68T2AjZK6s5FReHtEREdWvUw0WJ5szjcSFefNAiomUgzPQRXcjP/m2Gp3TbreTefw4z06ejLdWy/gHHiApLo6mUVH18lWQqHcaVb6WVlSg/m7KwMYMA6F+Gq70zyEidxMFSbfg8A096zMVej3tExOr2yhdKrPZzI033sjKlSv55z//KQ9OcaE8Mmdz8vKqC9i1xwx4aeBW31/RqHaK46+tPs5oNhMWHFyv1pq89957jBs3jj59+pCSkuLucEQdOW/Bq55hqPqtd9V/6jk+0mjk5OeftYOY3uLg3Z+LKTY5GNE2kNbbpqEqXmSeZytT+F8z7h07d/LEpEl4a7Wkp6fToUOHurgV0UA0tnzNyc/Hv+oHwTKzg52nKxmcHETs4U+xeweRn3zrWcfrDAYiwsJctsOT0WjkhhtuYO3atcyePZvx48e75Lyi8fDEnHU4HOQVFxMUEIBTVfnppJEeLfyJzVuLKTQRc2hS9bEms5m2rVvXm81W3nrrLSZOnMiNN97I0qVL8avHWxsL16rRojVFUbwURdkNFALrVVXd+hfHjFUUZbuiKNuLiopcHWe9k19UxNGsLMKCg4EzcwffSC9m1+lKrm8bzK3WpQSV7ier62SsAX//k63FaqW4vJyisjICfX156amnCA4KIj09nTZt2tTV7YgGpLHkq81mo6S8vHpqz+YTRgCuCc0h/PRmCpJvPav9n9PppNJioW1Cwp+2/b5YDz74IJs2bWLevHncd999LjmnaHzOl7P1LV91RiNOpxONRsOBAgvllU6ujS4hqOwQxXH/G901V1YSGhJCRGjoOc5Wd1asWMHkyZMZNWoUCxculNZjjUyN/tVXVdWhqmoXoCVwuaIof+qgrqrqLFVVe6iq2iO6Hu+P7QoGk4k9R44QGhSEl5cXJSY7D39zmkNFFv7ZLYyHEvJocehTSmKvOTOX6S9UWiyU6XQYTCY6p6TQu1s3ru7enffff5/09HSSkpL+8nNCnE9jydfSigpUVUVRFEpNdlYd1pEU6cPlOZ9h9w6mIGnUWceX6XS0jIkhtOqHVFeYOnUqCxcu5K677nLZOUXjc76crW/5WlJeXv1D49qjeoJ8NKTa01HRnPXMM5hMJLRoUW86lQwdOpQZM2awYMECvOvRfGJRNy5omENV1XIgDbj2PIc2WGaLhS27duGt1VbPqV26X0eZ2cGU/tHckKSl9baXsPpF/u1uaiVVD+qEFi248rLLyDx2jB/S09FoNIwcOZKEhIS6vCXRQDXkfLXabBw+cYIAf3/sTpVXNhVhtKk81baAsPyfyU+5HYf3mb68qqpSUl5OoL8/KfHxl/zwLSsrY/r06TidTlq3bs3NN9/silsSwiNyVlVVsk6fJjgwkGKjnV9zzAxK9Kdpzjp0TXtg848CzkxlCA0JoVlUlNvjffvtt8nPz8fb25sJEybIPPtGqiZdGqIVRQmr+rU/MAg4XNuB1VcFRUU4nE6Cq9og7c+vZN0xAwOTgujewp+4fTPxNeSS2fNZHD5njyT9Nk/X39eXq7p0IblVK3bt2MGQIUN45plncDgc7rgl0YA0lnw9fuoUZouFAD8/Vh3Sk11u45GrI+iZ9TE23/CzdlUrLiujWVQUl3fufMkbTBQXF5Oamspzzz3H/v37L/U2hPC4nC3T6bBW9b3ekm1CVeGWyBP4mgqqpzOoqorBbKZNq1Yumz50MVRV5bHHHuPRRx9l9uzZbotD1A81+TEnBpinKIoXZwrkhaqqrqrdsOonp9PJsays6h2dbA6VtEwj3hr4Z7cwQk//SJPMFeSljEYf3e1Pny/X64kKC6N9cjLe3t6sW7eOESNGkJiYyLp16+rNpH7h0Rp8vprMZk7m5BAeEoLB4mTZgQq6t/DnGu0uQop3c7LLZJzeAcCZ1kkRYWF0SEq65FeYhYWFDBo0iGPHjrFixQo6d+7sitsRwqNytrSiorqI3XW6kpah3iQXb8Ch9ae8eV/gzJS9yNBQt/bddTqdTJo0iffee48JEybwzDPPuC0WUT+ct+BVVXUv0PV8xzV0Nrud/KIi7A4H3lotaScMzN5WhsWukpoUSKC9nISdr2EKTSK3/f3Vn3M4HJgtFux2Ow6Hg6T4ePx9fVm9ejU33XQT7dq1Y/369dSHeVnC8zWGfD1dVIRGo8HLy4tle8ow21RGdwoidseHmINiKU64ATizqM3ucHBZmzaXXOzm5eWRmprKyZMnWbVqFampqef/kBA14Gk5W1xair+vL2abk0OFlQxP9iEiZxOlLQbg1J55g2I0m0lo2dJtMTqdTsaPH8+sWbOYPHkyb7zxRr2ZRyzcRyay1IDVZmPH/v2U6fUEBwTw/XEDH/xSSrtoXwanBHFFS38Stk7By2bicN/nUb3OzO2t0OuxOxw0jYoi0N+fyNDQ6gUzGzdupHPnzqxdu5aIiAh33p4QHsNmt5N56hQhgYGUmOxsyDBweaw/3XTr8def5NiVr6BWNbwv0+vp1r79JU9jADh8+DCFhYWsWbOGfv36XfL5hPBEdoeDcr2e8JAQdp22YHfCDb678LKbKPnddAaAKDc+13Q6HT/++CNPP/00L7/8shS7ApCC97xUVWX/sWPoDAaiw8PZkWPmg19K6dzMjyn9o/H2Umh67GvC8n8mu/PDVIa0xmK1UqHX0zQqinaJidVbL8KZJvX+/v785z//wWQyEVQ1PUIIcX75RUXYHA60Wi0Lfi3G4lC5o6MvLbbMQR/ZqfqVamlFBc2joy95wcxv+TpgwAAyMzMJdmGHByE8jc5gAFVFo9Gw67QZX61Cl4qNWPyboI/uAoDeaKRZdDT+Vbsf1iW73Y6qqoSFhfHLL78QFBQkxa6o5r7Z5B6isKSEvMJCIsLCcDhVvthdTnSgF09WFbtBxXtpue99ypr3pSBpFDa7nQqDga7t29O9Q4ezit358+fTrl07Tp48iUajkWJXiAtgdzg4lpVFaFAQBwoq2ZxpYkS7ELoVLMWnsoScTg+BoqA3GvHz8aFjcvIlPeyOHz9O+/bt+eKLLwCk2BWNXrlOh6LR4FRVtuea6dPETFjhNkrihlRvrmS12YiPianz2Gw2G6NHj2b06NE4nU6Cg4Ol2BVnkYL3HJxOJ4dPnKhepJZ2wsipCht3dAnDx0tBW1lG4tbnsQbEkNnjaVTO9CfslJxM8yZNzkq2Tz/9lLvvvpvWrVvLfF0hLkJeYSGVFgsaLy1ztpURHejFrckOYo5+QWnzfhgiO2GxWrHZ7XTv2PGS5u0eOXKEfv36odPpaNeunQvvQgjPVVhSgr+vL0eLrJSYHNzl/zOK6jhT8HJmypG3VktIHf9waLFYGDVqFIsWLeKqq65ya2cIUX/Jd8U5FJWWYjSb8fP1pcRkZ96OMlqGenNVfACoDhJ/fRGtVUfGldNweAdRVFpKbLNmxP7hp9uPPvqIMWPGcM0117Bq1SoCq1qaCSFq5re+u2HBwaw7auBUhY17uofT6ug8NA4rOR3H4XA40BkMdG3XjqCAgIu+1sGDB+nXrx9Wq5W0tDS6dftzxxUhGhuL1Uq5Toefry8/Zxvx1qj0LP8OfWRnKkNaAWemPCTExqKtw45DlZWV3HTTTaxYsYIZM2bw6KN/3f9eCCl4z+FYVlb1g3NLlgmzXeWRXpFoFIUWB2YTUrSDk10fwxyWTLleT3RkJB2Sks4a2V28eDEPPvggw4YNY8WKFQRcwoNYiMYqr7AQu8OB3qawaF8F7Zv40ie0iOjMFRQmDMcSHEepTkdyfDxNIiMv+jpFRUX0798fRVFIS0ujU6dOLrwLITxXuV6PE1CBX7LN3BF9ggBjDkUJw4EzHYkURSG2WbM6jeuuu+5izZo1fPTRR0yYMKFOry08ixS8f8NcWYneaMTfz498vY0VB3UkRvoQH+5DaN5PND/yOUWtbqCk1VCcTidWq5W2CQl/eo3626YSS5cuxc8Fq8WFaGysNhtHT54kJCiIr/ZWUGl3MvbyCFoe/BjVy4fT7e6lXKcjKiyMhNjYS7pWdHQ0U6ZMIT09nfbt27voDoTwfPlFRfj5+HCs2EqZ2cFtmo3YvYMobTkAODO626pFC3zqeMveRx99lLlz5zJ27Ng6va7wPFLw/gVVVTl4/DheVfOA5u8qx2xTGXd5BD7G07TeNg1jWApZXR7BarNRUlZGcnx89e5rAJ9//jlGo5Hg4GBefvllfKq2IRZCXJis3FwcTid5BpW040auTQkmxX6EiNw08lLuQEcAXl5edG7T5qJfpf7666/s3LkTOPMATUlJceUtCOHRnE4nhaWlBPj5sfWUiUhFT0rFT5TEXYvqdaYbg93hIKaO1qfodDoWLFgAwFVXXcXdd99dJ9cVnk0K3r+gNxopKC4mPDSUtBMGtp4yMzApkNahKkm/PAvA8Sum4VC8KS0vJzkhgZSEBBRFQVVVXnzxRe6++25mzpzp5jsRwrPZbDYyc3MJDQri670VBPgo3NQhiLi972H1iyQvcRQGo5Eubdvid5FtkH766ScGDRrE+PHjq3uICiH+x2AyYbfbQdHw40kTE8J/QeO0UVS1yYu5spKQoKDqBd61qby8nMGDB/PPf/6TjIyMWr+eaDik4P0Dh8PBnsOH8fP15WixhZlbzmwwcUeXMOL2vEtg+VEyezyLyb8ZxeXlJLdqVb2jjKqqPP3000ydOpV77rmHyZMnu/luhPBsxeXlOJxO9DaFHblmBiUF0Sp/PUGl+8np+CBllQ7imjcnIizsos6flpbGkCFDiImJYcmSJdLGSIi/UFBSgkajYX9BJWVmOyOcGzFEdMAcmgiA3mQiOT6+1uMoLS1l0KBB7Ny5k8WLF5OUlFTr1xQNhxS8f5CRnY3eZCLAP4D3t5QQHejFpN6RNM9dR5PMFeSl3EFRkyspKisjJT6eNgkJaL28UFWVyZMn89prrzFu3DjmzJmDVx2uVBWiIfpt3mDacQNOFQbHQez+99FHdqIkbggOh4P45s0v6twbNmxg6NChxMfHk5aWRks3boUqRH2lqiq5BQUEBwSQfsJIH59jRFRmV4/uVlosBAcEXNJi0ZooKipiwIAB7N+/n+XLlzNixIhavZ5oeKTg/R2L1UpmTg6RoaEsP6gjV2fnzq5htLRmEr/rDXTRXcnp8ABlOh2dUlJIbtWqekSooKCABQsWMHHiRD744APpAyjEJbJYrRSUlODn48vG40baN/Glx6l5aC06sro8SrnBSHRExFlz5y/Ehx9+SFJSEps2bSLGDY3yhfAEBpMJc2UlipeWHblmHgxMx6ENoLRlavXXk+Lja/3tyMaNG8nIyGDlypUMHTq0Vq8lGibZWriKw+HgcGYmGkVh/u4KVh7S0ys+gD5NLCSnTcHhE8LR7s9RVK4jOjycFk2bAmcm8yuKQrNmzdi1axfNmjWT16JCuEBBcTGqqrIp00yBwc4jbUtosn8pha1vRB/cGrtOR9vWrS8435xOJxqNhvnz52MymYiIiKilOxDC8+kMBhRF4XChBa1NzxWVP53pTqT1x+Fw4OXlRXQt5tBv+XrbbbfRr18/+eFUXDQZhuR/XRlyCwpIy4GVh/QMaB3IhMuDSd76DFpLOQe6v0R+pRfJrVrRtX17vLVaHA4HY8aM4fHHH0dVVWJiYqTYFcIFHA4HGVlZBAYEsvxgBYkR3lyf9wF2nxByO9xPuU5HcqtWF7zBxOLFi7nqqqsoLS3Fz89Pil0hzqOwpARfHx+255q5WfsTWtVKUeszvXeNZjNNIyPx1tbO2Fl2djZdunQhPT0dQIpdcUmk4AVO5eWRmZvLimNOvtpTQY+W/jzQM5ykvW8SXLKPI50fp9g/np4dO5IcH4+3Vovdbufuu+9m3rx5hISESKErhAuVVlRQabWSftJCocHBlGbbCC7ZS07HBzGqvvj5+l7w3N0vv/yS2267Da1Wi7aWHtBCNCQOh4PC0lJ8fXz56aSB+3w3YAhvhynsTNs+i9X6p51FXSUzM5O+ffuSnZ0tPVFR1hYAACAASURBVOyFSzT6f/VP5uay/+hRVh6H9RlG+rcO5J7u4bTMXER01rcca3UbBc360qNdO8JDQoAzrZLuuOMOFi1axKuvvsqUKVPcfBdCNBxOp5OjWVnoHT58vquMq5s56Jc7G0NEewpih6DT6enZseMFjSrNmzePMWPG0KdPH1atWkVQHbRPEsLTlev1OJ1Ofs2ppKN1Ly19cjmReC8ApqpWZGHBwS6/bkZGBgMGDMBoNLJx40a6d+/u8muIxqdRF7yl5eUczMggt9Kf9RklXNcmiHu7hxNW8Auxe2eSF3UVpism0a9N2+pFaKqqVhe7b731Fv/+97/dfBdCNCzZeXlU6HQsPGDHSwP/F7YC7/Iyjl39OsUVZ+btXsiK8C+//JJ7772X1NRU2d5biAtQUFyM1suLVYf1vOC7DptPOKUtBwJgNJno1r69yxdo5+Tk0LdvX2w2G5s2beKyyy5z6flF43Xe71RFUWIVRdmkKMohRVEOKIoyqS4Cq21Op5NDmZkE+Pnx2a4KYoK13NElDH99Fq23vog+qBWlqdO5rN3ZCa0oCrfccgvvvfeeFLui3vH0fLVVbSNcYPFlR24lDyaWEp+1jKKE4eRqWxDbrBmJF7h9cK9evbj//vtZuXKlFLui3qmvOWt3OMgtKOC0WYul9BS91Z0UJQxH9fLBYrXi5+tLVC3MgY+JiWHUqFGkpaVJsStcqiYjvHZgsqqqOxVFCQZ2KIqyXlXVg7UcW60q0+mo0On4OV9Lrs7Ow1dHEuA0kPzzFJwab/Z1n8rlye2rjzeZTGzdupUBAwYwcuRIN0YuxDl5dL7mFxdjtTv4Yo+JMD+Fe/SzcHgHcrLtGJw2lZTftQI8nzVr1jB48GDi4uKYNWtWLUcuxEWrlzlbWlGB3eHg2yMWxvhsQFU0FLa+ETjTuaFLu3YXvZX3X9mzZw8RERHExsby3//+12XnFeI35x3hVVU1T1XVnVW/1gOHgBa1HVhtstnt7D1yhO0FCgt2l3NlnD994nxI/OU5fMwF7Oj0NO2796+eI2gwGBg2bBjXXXcdubm5bo5eiL/nyflqsVo5cvIkadkqGSVWXk3YTWjJHrLbj6WoUqFDYiL+NVy88vrrrzN06FBmz55dy1ELcWnqa85m5eais2vZl1PGrV5plLfoh80/GnNlJYH+/jR14UYT27dvZ8CAAdx7770uO6cQf3RBk28URWkFdAW2/sXXxiqKsl1RlO1FRUWuia4W2Gw29hw+zLZTJj7ZqadjUz8eujKSuL0zCC3awf62E0i8emT1AjWdTse1117LDz/8wCeffEKLFm7/d0iIGvG0fD2SmcmJUitLDxq4Jk4lNW82xvC2HA7rRXJcHC2bNavReaZNm8aUKVO4/fbbue+++2o5aiFc5+9ytq7zVWcwUFxWRnqWjX9ofiLAaaQg8WZUVUVvMtEpJcVlO4lu2bKF1NRUQkND5QdUUatqXPAqihIELAEeUVVV98evq6o6S1XVHqqq9oiOjnZljC6VU1BATmExSw5biQnW8tSAaOJOfUPTE0vJThhJ0g1PVi+IKS8v55prrmHr1q189dVXjB492s3RC1EznpavZTodmbl5fLa3kjA/L170X4h3ZRn7kh8kOjK6Rjs5qarKc889x/PPP8/dd9/N559/Lu3HhMc4V87Wdb6eysvD4tTw/XEDD/qvxxiajCGyMxUGAy2bNiUiLMwl1/nhhx8YPHgwTZo0YfPmzbRq1col5xXir9So4FUUxZszifiFqqpLazek2mO12TiWlcUv+RoKDHbu6xlOePEu4na/TUFEd/yGvkqAv3/18Z999hm7du1i8eLFMm9XeAxPy1en08n+o0dZm+kkp8LOi+1zaZG1gpyEm6iM6kDnNm1qtBL8xIkTvPnmm9x///18+umnLhuBEqK21aectdls5BQUsD0fujoPEGvPpjDpZpyqit1uJyk+3iXXUVWVp556ipYtW5Kenk7sBS5GFeJCnXf4QzkzrDIHOKSq6lu1H1LtyTp9mj35FhbuM3NFrD89g0pISnsWY0Bziga+QcfoJmcdP3HiRFJTU+nQoYObIhbiwnhivhaUlLAjW8eaY2auS/Thmqy3qQyI4UDLUVyekoKvj0+NzpOYmMj27dtp27aty1slCVFb6lvOFpWVYXM4WXPUxIcB32LzCqck9hoqDAZatWhB4O8GhS6FoigsW7YMp9NJ06ZNXXJOIc6lJk+FXsBdwEBFUXZX/Te0luNyuYLiYr7bmcGHO8w0DdIysYcfKVuexOlUye73Fm3bdQEgPz+f1NRUDh8+jKIoUuwKT+NR+Wq2WPj1wBE+32+habCWpwJW4G84xe6kB0lJ6XDeV6dOp5OHHnqougtD+1roCypELas3OauqKpk5ORwsUYgyZ9LTvouCpJHY0eJ0OmnVsuUlX2PVqlWMGjUKq9VKdHS0FLuizpx3hFdV1R8Bj943V2cwsGbbfubssRIVoOWlQVF03PUMvoYcsgbOJKXbQLReXuTm5jJw4EByc3MpLCykbdu27g5diAviSfmqqioHjh3jq/1mSs1O3rtaR8udC8hpNpDQLsNJOM/D1eFwMHbsWD755BPZ7VB4rPqUsxV6PRV6Pd9n2njY71scGn8KW/+Dcr2epLg4/H19L+n8y5Yt49Zbb+Wyyy7DZDLhU8O3N0K4QoMfCrHabPywez9zdlfiUOGJflF0PD6bsPwtnO7+GPG9bsVbqyU7O5t+/fqRl5fH2rVr6du3r7tDF6JBKykvZ+2BfLbkWLm5fSD9T7yF3TuY010nkXKeRWp2u5177rmHTz75hOeff57/+7//q8PIhWiY8oqKKKlUqCg6zbXqTxQl3IDFKxCNRkN88+aXdO6vv/6aUaNG0b17dzZs2ECYixa+CVFTDbrgdTgc7Dl6nFc3l5GjczD+igi6lK0n5ugCChNvpvnQKWg0GrKzs+nbty/FxcWsX7+eXr16uTt0IRo0m93OL/sOsviQjfhwbyb4ryOw7DBH2o6n02VXnnPBmaqq3HnnncyfP5+XX36ZqVOn1ngzCiHEX7M7HOQUFJB+ysn93mtQFChIvoUKvZ6EFi3w9va+6HP/1uXo6quvZt26dYSGhrowciFqpsEWvKqqsu94JlNWZZKrc/J43yj6+R2j1a43MDS7nPBbZlbP9YuKiqJbt25s3LiRK664ws2RC9HwncrLY/5eIxUWJ493NBB38GMKInvSfMC4824uoSgKXbt2Zfr06TzzzDN1FLEQDVtJWRllRiu7Mgu5Q/s9pbGDMPlE4aXREHeJo7vJycnceOONrFmzhuDgYBdFLMSFabBNKnPy8/nvxuMcL3Py8NWRXBVWQeL3z+AIiiHw7q9RfP05duwYTZs2JSQkhKVL6333JiEaBIPJxNdbjvFLjo1RHQLpnzENu5cvFQP/j5Tw8L/9XGVlJRkZGXTs2JEnn3yyDiMWouHLzM1lRwHcygb81EoyUm6nXK+nbevWNe6U8kc7duyge/fudO/enSVLlrg4YiEuTIMc4TVXVrJwy2E2ZdlJTQqkfwsHST8+ijd2vO9ajBIQwf79++nduzdjxoxxd7hCNBpOp5Pvth1g7h4ziZE+TPT+hqCyQ5zq/iSJbbv/7efMZjMjRoygT58+lJaW1mHEQjR8JrOZkvJyfjyh436ftZQ3vQJ9UAIajYYWF9lF4d1336VHjx4sWrTIxdEKcXEaXMFrdzjYuPMgn+wyExvqzZgugbT64QkCzflw2wKUJm3ZtWsX/fv3R6vV8sorr7g7ZCEajYxTp3j3p2L8vDVMu6yElkfmUtg8ldjUB/923q7RaGTYsGGsX7+eN998k4iIiDqOWoiGLb+4mL2FTvpY0olQy8lPGU2FXk9iXBw+FzF394033mDSpEn84x//YMSIEbUQsRAXrsEVvAcyTjA9rQAn8HifCBK3TSWifD/O4TPxSuzHtm3bGDhwIIGBgWzevJk2bdq4O2QhGgWj2cy8H46RrXMypksgXfb9H1afMIJvef9vH6p6vZ7rrruO9PR0PvvsM3kjI4SL2R0OMnNySM+sZLz3agxhbSgJ64SXlxdxMTEXfL6XX36ZJ554gltvvZWvv/5aWo+JeqNBFbyFJSW8vv4E2TonE6+KpEvG+zQr+AHbwBfQdrkVp9PJvffeS3h4OOnp6SQmJro7ZCEaBVVV2bT7CIsOWejQ1JdRhvkE6LPQD5qOf1izv/3cG2+8wc8//8yCBQu488476zBiIRqHkrIyjhRVklT+E/Hkkd9mNBVGI+1at77g0d3du3fz/PPPc9dddzF//vxL6uwghKs1mEVrlRYL763by885dv7RIYQhhuXEnVyCrft9ePf5NwAajYZly5bh5+cn+3YLUYdOFxXzRloePl4anm9ziphfF1KSfDNRPW4+5+eeffZZBg8eTO/evesoUiEal+y8PNKzrLzivRRjcAKFTfvgU2mhWXT0BZ+rS5cubNq0id69e5+ztaAQ7tAgRnhVVWX19sMs2GeiUzM/xoVtp/XBD7AmDsF72Bt8v2kTjz32GKqqkpycLMWuEHXIYrUyffVesiqc/Ku7H533vYolMIbQm975y/65xcXFjB49mqKiInx8fKTYFaKWmMxm9mQX0zRvM0lKLnnt76XCYCQpPh5tDQtWVVV58sknWb9+PQD9+vWTYlfUSw2i4N1/4hSvrM8l1M+L59qcInnnK1iadsHntnmsXb+BYcOGsXbtWnQ6nbtDFaLR2bAng5VHK+kVH8BNJR/jZy5CvfEjtP4hfzq2oKCA/v37s2zZMg4ePOiGaIVoPLLz8lifUckk76UYghPIi7oKPz+/GndmcDqdTJgwgenTp1cXvELUVx5f8JZW6Hhy2UF0VpUXuxnosuMZHCEt8PvnUlZ+t4Hhw4fTtm1bNm3aJLu7CFHHSioqeHNjFkE+Gp6IPUTT7NUYuz6Af/Kft+4+ffo0/fv3JzMzk9WrV9OvXz83RCxE42C12diZcYrmhZtJVE6T334MepOZ9omJeGvPP9vR6XQybtw43n//fR5//HFef/31OohaiIvn0QWv3eFg6rIdHCx2MPEylUH7n0LR+qK9ezlLv0vjpptuonPnzmzcuJGoqCh3hytEo+J0Onlv3T5OlDt5oLOGtnunYw5LImjYtD8de+rUKfr160dOTg7fffcdAwcOdEPEQjQehSUlbMo0M9FrGbrgRHIjryA4MJDoc2z+8huHw8GYMWOYPXs2zzzzDK+//rps7y3qPY8ueOdt2sOKwyaua6Vwb87z+Nj0cMdCNJEJ+Pj40KtXLzZs2CB9O4Vwg61Hslmwu4LOTX25rfg9vG16vEbOBq3vn47VarWEh4ezbt06+vTp44ZohWg8VFXlQGY24TnptNbkUdhhDAZzJW1bt0ajOX9ZoCgKWq2Wl156iZdfflmKXeERPLZLw86MXP6TlkdSmMI0238IMmTiuOULTjujiQeuv/56hg0bJokohBsYzZW8sOowXhqFaS1/IfrAZky9phDQsutZx+Xk5NCsWTNiYmLYunWr5KsQdaBcr2fT0TIeVJZSFphIbngPwnx8iTrP6K7NZqOwsJAWLVrw8ccfS74Kj+KRI7ylehMPL9yLRoE5YZ8SVbIDy+A3+HxLPklJSWzYsAFAklEIN5m1cR9HSxw80l5Ph0PvYmp+JQGpT551zOHDh7niiiuYNGkSIPkqRF05lZ+P38k0WmvyKe40BmOlhZT4+HPmoMViYeTIkfTp0wej0Sj5KjyOx43wOp1OHv1yG7k6J4sSVxGfuw5dz4f5YreNhx56iCFDhtCrVy93hylEo5VTVMacrYV0jITbT7+GqvXD95Y58LtXpQcOHCA1NRVVVRk/frwboxWicbHZbKzfc5J7HEsoDEwkP/IKAlWVyLCwv/2M2Wzm5ptvZs2aNcycOZPAwMA6jFgI1zjvCK+iKJ8oilKoKMr+ugjofGas20vaCQPT436mR+4CKpL/wafHInnooYe4/vrrWb58Of7+/u4OUwi3cWfOqqrKC8v3YLTBW2GLCNafwHb9DLzCWlYfs2fPHvr3749GoyE9PZ2OHTvWdZhC1Bt1na/5xcVoj6ymlaaAki5j0ZtMtE1M/Nu5uyaTieHDh/Pdd9/x8ccf89BDD9VFmEK4XE2mNMwFrq3lOGrkl2N5vJuey/1R+7m56H10MVezr+U9PPLvf3PTTTexZMkS/Pz83B2mEO42Fzfl7OqdJ9h43MhT8YdIyV1GeYe7COg8ovrrVquVESNG4OfnR3p6Om3btnVHmELUJ3Opo3x1OBx88/Ne7rAtJSvoMgpCuxAaEnLOzgxTpkzh+++/Z+7cudx///11EaYQteK8UxpUVd2sKEqr2g/l3Ap1Zv69cA9X+mYyxfQmppAk/O/6kl7+oXz55ZfcfPPNsm+3ELgvZ8uNZqauPkqnwDLuKf0vxtBkgodPP+sYHx8fvvjiC5o3b05CQkJdhyhEvVOX+Xq6uAS/fV8SpejI7fYQpspKrm7f/pydGaZOncrgwYO5/vrr6yJEIWqNRyxaszucjPlkC/7G08zWTsfiE8pbpakcPn4KRVG47bbbpNgVws2mrdhDmcnK7ICZaJw2lFvm4uUbAMCPP/7IBx98AECvXr2k2BWijjmdThZt+InR6mqORfQn36clCXFxhIX8ecfDsrIyJk+eTGVlJeHh4VLsigbBZQWvoihjFUXZrijK9qKiIledFoApi7ZzOr+AxUFvoMHJsyd788LLrzNv3jyXXkeIxsLV+brpYA5L95bwXtQymuoPUt5vGgEtzszNTUtLY8iQIbz77ruYzeZLvpYQjY0r8vVUYQnxR+ehVZzouj6AAiS0aPGn40pKSkhNTWXGjBns2LHjEiMXov5wWcGrquosVVV7qKraIzo62lWn5b0NB1m1O4fFIW8RYivikSOX885Hcxk/fjzTp08//wmEEH/iynw1WWw8vWw/I/x3ca1hGUWJ/yCyz5m5fuvXr2fo0KG0atWKTZs2yYJSIS7Cpear0+nkq1XfcSNpZLQYToEzmJSEBPx8z94EprCwkAEDBnDw4EFWrFghHY9Eg1KvpzSs35/Lfzcc4/PgmcRbjvLgvq58OH8ZkyZNYubMmTXaEUYIUbte+WYPGn0+r2k+QB+cSOjIGSiKwrfffssNN9xAcnIyaWlpNGvWzN2hCtEoHczOo0f2J1g0/hS3uY2I0FBi/5CPeXl5DBgwgIyMDFatWsV1113npmiFqB01aUv2JbAFaKMoSo6iKPfVflhQUGHmsUW7edf/Y3ratnO6xxSyKuCJJ57g7bfflqbXQvyNuszZLccKWLQjl3nBM/HCge2mOfj4BwNw/PhxOnbsyPfff48r3/oI0ZDUdr46nU7Wr15CqmYnmYmj0at+tE9KwsvL66zjSkpKMBqNrFmzhkGDBrkyBCHqhZp0abi9LgL5PavdyQNzf+ER5+cMVjdzJOkeUq6fwuohk/H29pZiV4hzqKucNVpsTF60h2l+80myHSWv/xvEJHSlrKyM8PBwJk6cyLhx4/Dx8amLcITwSLWdr4eycrmucBZFXtHkxQ0jLqY5IUFB1V8vKysjLCyMjh07cvToUclX0WDVuzkBqqry7NLd9C5cwJ18y03rorhzxhasVis+Pj5S7ApRTzyzZDdXGr7nVtaR23oU0X3uY8GCBSQkJLBz504AeXgK4Wbblr1LW+UUJzv+CxtaElr+bxOYEydO0KVLF1577TVA8lU0bPWu4J2x8SjaPfN5RPM1/1gdzDdbMxk1ahS+f5hcL4Rwn2U7szm271de851DaXgnQm96i/mff86dd95J165dSUlJcXeIQjR6P+zYy40VX3DMrzPFkd1IiosjsGrh6NGjR+nbty8Gg4EhQ4a4OVIhat95pzTUpeU7T3H4+/m8qZnD8BW+rN13mrfffptHHnnE3aEJIapklxiZvuwXlvi9g8M7GMdNc1iw4CvGjRvHNddcw/LlywkICHB3mEI0ana7nZJvpxGkmNnXZTwhwcEkxMYCcOjQIQYOHIjD4WDTpk107tzZzdEKUfvqTcG7/WQpy5d8wcc+M7l/rR9r9xUyc+ZM2bdbiHrEanfy0LwtTFdm0JRScgfP5fChLMaNG8fQoUNle28h6ok1677lBvt6dkddjyGgJb3btEHr5YXRaCQ1NRU40yO7ffv2bo5UiLpRLwrerBIj78z9glneb2EMiOXhd97jmmNZ3Hnnne4OTQjxO6+s2s/g0s/po93Lya6PE9dtGM2dTt58803+9a9/ydQjIeoBU6WF+F9fQqcEUdr+dpLi4ggODAQgMDCQd955hy5dusjUI9GouH0Ob6G+kqc/XMhrlv/jrV9VdDfOpfsVfaTYFaKe2XAwn9xfl/OwdjmnW17LquwwCgoK0Gq1PProo1LsClFPpC94nc4cY2frsWgCIohv0YJff/2V1atXA3DLLbdIsSsaHbcWvCarnSc+XskLuhcZtaCUF9eVUVBhd2dIQoi/UKCr5N2F3/KOz/uUBSYyIyOeiRMnMmPGDHeHJoT4nczMY/TK+oB92o5UxvahY0oK27dtY9CgQTz++OPY7fKMFY2T2wpep1PlhS++Z1Le04z+Io/tuQ6++uorevbs6a6QhBB/weFUeWz+j/xHfRONxovnMy7jtdff4J577mHatGnuDk8IUUVVVU599Sh+WMnu9C9axsRwaN8+Bg8eTLNmzVi3bh1abb2YyShEnXNbwfvmqu0MP/BvxszPZF+hyuIlSxg5cqS7whFC/I2ZG49wR95rJJLDwwcv471Zcxk7dixz5sz5025NQgj3+WHNAvpaNvNj1Cj8myWSl5XF0KFDiYuLIz09nZa/68ErRGPjloL3212Z9N72MPaSbE4YfFm6bDkjRoxwRyhCiHPYkVUK6a9xrdc29iWMYcu+LCZMmMCHH36IRuP2JQBCiCp6fQWtf32RbCUGc4eb6ZySwrq1a0lKSiItLY2YmBh3hyiEW9X5u41DuaWoi+7jKp+DHBj+DBnT7yM6OrquwxBCnEe5ycqyz97lJa+lHIsaRNKol/n5Hy8SEhIiOx4KUc/s/GwK/ShkZfI0mjeJISo8nP/85z8YDAZCQkLcHZ4QblenQzSny4z8+uZtTJ6Vzkune9Hq2klS7ApRD6mqyruffc1T1hncvtqXf39nwd/Pj9DQUCl2hahnju/eTO/CL9nsN4DtJw2MvOEGTp48iUajkWJXiCp1VvDqzVa+feU2Xpq7gVNGL3qOfKR6i0MhRP2ycNM27s19jnu+sbJwRzE9e14uUxiEqIecVjPe3zxEEeGsKE/mleefp3nz5oSHh7s7NCHqlTqZ0mC1OfjqpTt5efZqyqzeLF+5mmsGDaqLSwshLtDhU4UkbhzHI8vzWXrQwiuvvMLTTz/t7rCEEH/h8JdTaO88xQPZ1/LJZ2/Ru3dvVq1aRXBwsLtDE6JeqfWCV1VVVvx3IlNnLkbv8GbxN1LsClFf6cxWcubex+erD7D0oI3XXn+dJ594wt1hCSH+QsGBzbQ5MY/nMtox58tF9Ovfn1UrVxJYtauaEOJ/ar3g3fzZS4wyfMHmPm25btLbDJZiV4h6yelUSfvwEYY7NnP8mmF0vrsPkx991N1hCSH+gsNiwr70QfKJJPb6SUyI3MLrr7+Ov0wVFOIv1WrB+/Frj9LtxCy2xl/FQ++8R7vkNrV5OSHEJfjmk1ex/DSXrVcO4YbxH9K8aVN3hySE+Bv75z7M0b0nKbh6CkOuuoqxt9/u7pCEqNdqbRXK7P88xZSX3uH2leB/41u0TZJ9u4Wor9Z/8znvvPwCo5eaKe00VopdIeqxEz98zdrF87llsZlDJ0tIjItzd0hC1Hs1KngVRblWUZQjiqJkKIoy5XzHFxec5vHnXsfXx5u3P11Ip/YdpZWREHXkQvPVaNDzwoT7+CHbztRpU7l28LV1EaYQosqF5KzDbmXO8w/w5AYLfQcOZPqrr0oHFSFqQFFV9dwHKIoXcBS4BsgBtgG3q6p68O8+46VR1BZhPsyY9xXDr79Ril3R6CmKskNV1R51cJ0LztdAH41qdag888yTPP/i/8nDUzR6dZWvVde6oJxtEuKjFult9OrTi2+//Y6QoKC6CFOIequm+VqTJ9vlQIaqqidUVbUCXwHn3AdYq1GYMW+BFLtC1L0LzlezTeWpJybx7AuvSLErRN27oJwtM9oYePVlfPPNKil2hbgANVm01gI49bvf5wBX/PEgRVHGAmOrfmu9cfjIfZceXr0TBRS7OwgXa4j3BPXvvuLr6DoXla/TXvvv/mmv/ffcr3s8T337HnAVua/aV1f5CjXI2T/m66Ytew9Fhofb6yi+ulKf/v5dSe6r9tUoX2tS8P7VEO2fHoyqqs4CZgEoirK9rl4H1aWGeF8N8Z6g4d5XDUi+VpH78iwN9b5q4Lw5+8d8dTqdDe7/U0P9+5f7qj9q8v4yB4j93e9bAqdrJxwhxCWSfBXCs0jOClEHalLwbgOSFUVJUBTFB7gN+KZ2wxJCXCTJVyE8i+SsEHXgvFMaVFW1K4oyAVgLeAGfqKp64Dwfm+WK4OqhhnhfDfGeoOHe1zlJvp5F7suzNNT7OqeLyNmG+v9J7suzeNx9nbctmRBCCCGEEJ5MehAJIYQQQogGTQpeIYQQQgjRoLm04L3QLU09gaIosYqibFIU5ZCiKAcURZnk7phcSVEUL0VRdimKssrdsbiKoihhiqIsVhTlcNXf21Xujqk+knz1PJKvjZvkrGdpiPkKnpuzLpvDezFbmnoCRVFigBhVVXcqihIM7ABu9PT7+o2iKI8CPYAQVVWvd3c8rqAoyjzgB1VVZ1eteg5QVbXc3XHVJ5KvnknytfGSnPU8DTFfwXNz1pUjvBe8paknUFU1T1XVnVW/1gOHOLMzjsdTFKUlMAyY7e5YXEVRlBCgLzAHQFVVqyck9u/BeQAAAcFJREFUohtIvnoYyddGT3LWgzTEfAXPzllXFrx/tT2ix3/T/p6iKK2ArsBW90biMu8ATwBOdwfiQq2BIuDTqldJsxVFCXR3UPWQ5KvnkXxt3CRnPUtDzFfw4Jx1ZcFboy1NPZWiKEHAEuARVVV17o7nUimKcj1QqKrqDnfH4mJaoBvwgaqqXQEj0CDmurmY5KsHkXwVSM56jAacr+DBOevKgrfBbo+oKIo3ZxLxC1VVl7o7HhfpBQxXFOUkZ16NDVQUZb57Q3KJHCBHVdXfRggWcyY5xdkkXz2L5KuQnPUcDTVfwYNz1pUFb4PcHlFRFIUzc1UOqar6lrvjcRVVVZ9SVbWlqqqtOPN39b2qqne6OaxLpqpqPnBKUZQ2VX+UCnj84odaIPnqQSRfBZKzHqOh5it4ds6ed2vhmrrILU09QS/gLmCfoii7q/7saVVVv3VjTOLcJgJfVD0UTgD3ujmeekfyVdQjkq81IDkr6hGPzFnZWlgIIYQQQjRostOaEEIIIYRo0KTgFUIIIYQQDZoUvEIIIYQQokGTglcIIYQQQjRoUvAKIYQQQogGTQpeIYQQQgjRoEnBK4QQQgghGrT/Bw8cp82vVjfaAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[12, 12])\n", - "for i in range(0, 3):\n", - " for j in range(0, 3):\n", - " plt.subplot(3,3,i*3+j+1)\n", - " make_qq_plot(data['qqplot_bins'][i*3+j])\n", - " plt.title(data['qqplot_bins'][i*3+j]['title'].replace(';', '\\n'))" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import argparse\n", - "import json\n", - "import os\n", - "import itertools\n", - "\n", - "import numpy as np\n", - "from numpy import ma\n", - "import glob\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as patches\n", - "import matplotlib.colors\n", - "\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - "from matplotlib_venn import venn2\n", - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.figures\n", - "\n", - "from scipy.interpolate import interp1d\n", - "from scipy.stats import multivariate_normal\n", - "files=glob.glob('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/*run3.test.json')\n", - "if 0:\n", - " for fname in files:\n", - " data = json.loads(open(fname).read())\n", - " trait1, trait2 = tuple([a.split('_')[1] for a in fname.split('/')[-1].split('.')[0].split('_vs_')])\n", - " plt.figure()\n", - " plt.figure(figsize=[12, 3])\n", - " plt.subplot(1,3,1)\n", - " precimed.mixer.figures.make_venn_plot(data, flip=False, traits=[trait1, trait2])\n", - " plt.subplot(1,3,2)\n", - " precimed.mixer.figures.make_strat_qq_plots(data, flip=False, traits=[trait1, trait2], do_legend=False)\n", - " plt.subplot(1,3,3)\n", - " precimed.mixer.figures.make_strat_qq_plots(data, flip=True, traits=[trait2, trait1], do_legend=True)\n", - " plt.savefig(fname.replace('.json', '.png'), bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6806531134392712 0.42850214416053833 0.8472322651218427 0.7636646048958523\n", - "0.35640014990263613 0.025383745360758683 0.40505187708029555 0.0302617418172466\n", - "0.28642575563948214 0.05557123859668319 0.29020433101820853 0.039759294443758066\n", - "0.16950420378139075 0.038498831531886414 0.2453691329610761 0.04614878749000128\n", - "0.005741824374982982 0.024271881242068288 0.005872570385093034 0.025238025386615338\n", - "-0.019741900094057577 0.005868601870015196 -0.09927190132618824 0.03128144536610123\n" - ] - }, - { - "data": { - "text/plain": [ - "
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def make_venn_plot(data, flip=False, factor='K', traits=['Trait1', 'Trait2'], colors=[0, 1], max_size=None):\n", - " cm = plt.cm.get_cmap('tab10')\n", - "\n", - " if factor=='K': scale_factor=1000\n", - " elif factor=='': scale_factor=1\n", - " else: raise(ValueError('Unknow factor: {}'.format(factor)))\n", - "\n", - " n1 = data['ci']['nc1@p9']['point_estimate']/scale_factor; n1_se = data['ci']['nc1@p9']['std']/scale_factor\n", - " n2 = data['ci']['nc2@p9']['point_estimate']/scale_factor; n2_se = data['ci']['nc2@p9']['std']/scale_factor\n", - " n12 = data['ci']['nc12@p9']['point_estimate']/scale_factor; n12_se = data['ci']['nc12@p9']['std']/scale_factor\n", - " rg = data['ci']['rg']['point_estimate']; rg_se=data['ci']['rg']['std']\n", - " rho_beta = data['ci']['rho_beta']['point_estimate']; rho_beta_se=data['ci']['rho_beta']['std']\n", - "\n", - " print(rg, rg_se, rho_beta, rho_beta_se)\n", - " rg_sig = (np.abs(rg / rg_se) > 2.8653) # norminv(0.05/2/12)\n", - " \n", - " if max_size is None: max_size = n1+n2+n12\n", - " if flip: n1, n2 = n2, n1; n1_se, n2_se = n2_se, n1_se\n", - " f = lambda x: x if x < 7 else x+1\n", - "\n", - " v = venn2(subsets = (n1, n2, n12), normalize_to=(n1+n2+n12)/max_size, set_labels = (\"\", \"\"))\n", - " v.get_patch_by_id('100').set_color(cm.colors[f(colors[0])])\n", - " v.get_patch_by_id('010').set_color(cm.colors[f(colors[1])])\n", - " v.get_patch_by_id('110').set_color(cm.colors[7]) \n", - " formatter = '{:.2f}\\n({:.2f})' if ((n1+n12+n2) < 1) else '{:.1f}\\n({:.1f})' \n", - " v.get_label_by_id('100').set_text(formatter.format(n1, n1_se))\n", - " v.get_label_by_id('010').set_text(formatter.format(n2, n2_se))\n", - " v.get_label_by_id('110').set_text(formatter.format(n12, n12_se))\n", - "\n", - " plt.xlim([-0.75, 0.75]), plt.ylim([-0.7, 0.6])\n", - " newline=''\n", - " plt.title(traits[0] +' & ' + newline + traits[1], y=-0.13)\n", - "\n", - " clr = plt.cm.get_cmap('seismic')((rg+1)/2)\n", - " plt.gca().add_patch(patches.Rectangle(((-abs(0.7*rg) if (rg < 0) else 0) , -0.7), abs(0.7 * rg), 0.15, fill=True, clip_on=False, color=clr))\n", - " plt.gca().add_patch(patches.Rectangle((-0.70, -0.7), 1.4, 0.15, fill=False, clip_on=False))\n", - " plt.gca().add_patch(patches.Rectangle((0, -0.7), 0, 0.15, fill=False, clip_on=False, linewidth=3))\n", - " plt.gca().text(-0.35 if (rg>0) else 0.35, -0.7+0.15/2, '$r_g$={:.2f}{}'.format(rg, ' (*)' if rg_sig else ''), fontsize=11, horizontalalignment='center', verticalalignment='center')\n", - "\n", - "plt.figure()\n", - "plt.figure(figsize=[10, 7])\n", - "traits=[\n", - " 'PGC_BIP_2016',\n", - " 'PGC_MDD_2018_Howard_no23andMe',\n", - " 'PGC_ASD_2017_iPSYCH',\n", - " 'PGC_ADHD_2017_EUR',\n", - " #'CTG_COG_2018',\n", - " 'SSGAC_EDU_2018_no23andMe',\n", - " #'CTG_NEUR_2018_no23andMe',\n", - " #'UKB_LONELY_2018_Loneliness',\n", - " #'ICC_CANNABIS_2018_UKB',\n", - " #'GSCAN_SMOKE_2019_CigarettesPerDay', \n", - " #'GSCAN_DRINK_2019_DrinksPerWeek',\n", - " 'GIANT_HEIGHT_2018_UKB',\n", - "]\n", - "\n", - "do_flip = False # lucky coincidence - CLOZUK is lowest alphabetically, no need to swap left and right on venn diagram\n", - "fname_pattern = '/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/CLOZUK_SCZ_2018_withPGC_vs_{}.outtag=run3.test.json'\n", - "fname_fit_pattern = '/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/CLOZUK_SCZ_2018_withPGC_vs_{}.outtag=run3.fit.json'\n", - "if 1:\n", - " for trait_index, trait in enumerate(traits): # sorted(files):\n", - " fname=fname_pattern.format(trait)\n", - " trait1, trait2 = tuple([a.split('_')[1] for a in fname.split('/')[-1].split('.')[0].split('_vs_')])\n", - " data = json.loads(open(fname).read())\n", - " data_fit = json.loads(open(fname_fit_pattern.format(trait)).read())\n", - " data['ci'] = data_fit['ci']\n", - " plt.subplot(2,3,trait_index+1)\n", - " \n", - " trait2=trait2.replace('MDD', 'DEP').replace('COG', 'INT').replace('SMOKE', 'TOBACCO').replace('DRINK', 'ALCOHOL')\n", - " make_venn_plot(data, flip=do_flip, max_size=18, traits=[trait1, trait2], colors=[0,1+(trait_index%8)])\n", - " plt.tight_layout()\n", - " #plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC.png', bbox_inches='tight')\n", - " #plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC.svg', bbox_inches='tight') \n", - "\n", - "if 0:\n", - " for fig_index, traits in enumerate([traits[:6], traits[6:]]):\n", - " plt.figure(figsize=[12, 3*12])\n", - " for trait_index, trait in enumerate(traits): # sorted(files):\n", - " fname=fname_pattern.format(trait)\n", - " trait1, trait2 = tuple([a.split('_')[1] for a in fname.split('/')[-1].split('.')[0].split('_vs_')])\n", - " data = json.loads(open(fname).read())\n", - " plt.subplot(12,3,1+trait_index*3)\n", - " precimed.mixer.figures.make_venn_plot(data, flip=False, traits=[trait1, trait2])\n", - " plt.subplot(12,3,2+trait_index*3)\n", - " precimed.mixer.figures.make_strat_qq_plots(data, flip=False, traits=[trait1, trait2], do_legend=False)\n", - " plt.subplot(12,3,3+trait_index*3)\n", - " precimed.mixer.figures.make_strat_qq_plots(data, flip=True, traits=[trait2, trait1], do_legend=True)\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC.qq.{}.png'.format(fig_index), bbox_inches='tight')\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC.qq.{}.svg'.format(fig_index), bbox_inches='tight')\n", - "\n", - "if 1:\n", - " #'PGC_SCZ_0418b PGC_SCZ_0518_EUR PGC_SCZ_2014_EUR'\n", - " fname='/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/{}.outtag=run1.testR3.json'\n", - " traits1 = 'CLOZUK_SCZ_2018_withPGC PGC_BIP_2016 PGC_MDD_2018_Howard_no23andMe PGC_ASD_2017_iPSYCH PGC_ADHD_2017_EUR SSGAC_EDU_2018_no23andMe GIANT_HEIGHT_2018_UKB'\n", - " traits1_names = 'SCZ BIP DEP ASD ADHD EDU HEIGHT'\n", - " traits1_colors = [0,1,2,3,4,5,6]\n", - "\n", - " #traits1 = 'CLOZUK_SCZ_2018_withPGC PGC_BIP_2016 PGC_MDD_2018_Howard_no23andMe PGC_ASD_2017_iPSYCH PGC_ADHD_2017_EUR CTG_COG_2018 SSGAC_EDU_2018_no23andMe'\n", - " #traits1_names = 'SCZ BIP DEP ASD ADHD INT EDU'\n", - " #traits1_colors = [0,1,2,3,4,5,6]\n", - " #traits2 = 'CLOZUK_SCZ_2018_withPGC CTG_NEUR_2018_no23andMe UKB_LONELY_2018_Loneliness ICC_CANNABIS_2018_UKB GSCAN_SMOKE_2019_CigarettesPerDay GSCAN_DRINK_2019_DrinksPerWeek GIANT_HEIGHT_2018_UKB'\n", - " #traits2_names = 'SCZ NEUR LONELY CANNABIS TOBACCO ALCOHOL HEIGHT'\n", - " #traits2_colors = [0,8,9,1,2,3,4,5]\n", - " #for traits, traits_names, out, color in zip([traits1, traits2], [traits1_names, traits2_names], ['1', '2'], [traits1_colors, traits2_colors]):\n", - " for traits, traits_names, out, color in zip([traits1], [traits1_names], ['3'], [traits1_colors]):\n", - " data = [json.loads(open(fname.format(x)).read()) for x in traits.split()]\n", - " plt.figure()\n", - " precimed.mixer.figures.make_power_plot(data,traits=traits_names.split(),colors=color)\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC_traits{}.power.png'.format(out), bbox_inches='tight')\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/png_NCp319/CLOZUK_SCZ_2018_withPGC.traits{}.power.svg'.format(out), bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fname_pattern = '/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_aug2019/CLOZUK_SCZ_2018_withPGC_vs_{}.outtag=run3.fit.json'" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/misc/mixer_juputer.ipynb b/misc/mixer_juputer.ipynb deleted file mode 100644 index 10abeee..0000000 --- a/misc/mixer_juputer.ipynb +++ /dev/null @@ -1,4509 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mixer.run1 - constrain QQ plots to HM3\n", - "mixer.run2 - use all SNPs but did not adjust sig2_zeroA\n", - "mixer.run3 - use all SNPs and adjust sig2_zeroA to the full template\n", - "mixer.run4 - fit and test" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "data1 = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.run1.json').read())\n", - "data2 = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.run2.json').read())\n", - "data3 = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.run3.json').read())\n", - "data4 = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.run4.json').read())" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "data1['m01']['qqplot'].keys()\n", - "plt.figure(figsize=[12, 12])\n", - "precimed.mixer.figures.make_qq_plot(data1['m01']['qqplot'], ylim=17, ci=False)\n", - "#precimed.mixer.figures.make_qq_plot(data2['m01']['qqplot'], ylim=7, ci=False)\n", - "precimed.mixer.figures.make_qq_plot(data3['m01']['qqplot'], ylim=17, ci=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'df' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ldsr.baseline.sorted.complete_annot_hg19.annomat.uniq.8categories.txt.gz'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#df=pd.read_csv(fname,sep='\\t')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'base'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CHR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_ldsc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CHR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_ldsc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" - ] - } - ], - "source": [ - "folder = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/LDSR_Annot/1000G_EUR_Phase3_baseline/'\n", - "#df_ldsc=pd.concat([pd.read_csv(folder + 'baseline.{}.annot.gz'.format(chri),sep='\\t') for chri in range(1, 23)])\n", - "\n", - "fname='ldsr.baseline.sorted.complete_annot_hg19.annomat.uniq.8categories.txt.gz'\n", - "#df=pd.read_csv(fname,sep='\\t')\n", - "df['base']=1\n", - "df['CHR']=df_ldsc['CHR'].values\n", - "df['BP']=df_ldsc['BP'].values\n", - "df['CM']=df_ldsc['CM'].values\n", - "first_columns = ['CHR', 'BP', 'SNP', 'CM', 'base']\n", - "for chri in range(1, 23):\n", - " df[['CHR', 'BP', 'SNP', 'CM', 'base'] + [x for x in df.columns.values if x not in first_columns]][df['CHR']==chri].to_csv(fname.replace('.txt.gz', '.chr'+str(chri) + '.txt'), index=False, sep='\\t')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "folder = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/LDSR_Annot/1000G_EUR_Phase3_baseline/'\n", - "#baseline.10.annot.gz\n", - "annonames = [x.replace('.bedL2', '') for x in pd.read_csv(folder+'baseline.21.l2.ldscore.gz',sep='\\t').columns[3:]]\n", - "\n", - "df=pd.concat([pd.read_csv(folder + 'baseline.{}.annot.gz'.format(chri),sep='\\t') for chri in [1]])\n", - "#df=pd.concat([pd.read_csv(folder + 'baseline.{}.annot.gz'.format(chri),sep='\\t') for chri in range(1, 23)])\n", - "\n", - "del df['CHR']\n", - "del df['BP']\n", - "del df['SNP']\n", - "del df['CM']\n", - "annomat = df.values.astype(np.float32)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "figures_folder = 'png_oct6/'\n", - "\n", - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.libbgmg\n", - "import precimed.mixer.utils\n", - "import precimed.mixer.cli\n", - "import precimed.mixer.figures\n", - "import numpy as np\n", - "import numpy.matlib\n", - "from precimed.mixer.utils import UnivariateParams\n", - "from precimed.mixer.utils import AnnotUnivariateParams\n", - "from precimed.mixer.utils import _log_exp_converter\n", - "from precimed.mixer.utils import _arctanh_tanh_converter\n", - "from precimed.mixer.utils import _logit_logistic_converter\n", - "import scipy.optimize\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "figures_folder = 'png_oct6/'\n", - "\n", - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.libbgmg\n", - "import precimed.mixer.utils\n", - "import precimed.mixer.cli\n", - "import precimed.mixer.figures\n", - "import numpy as np\n", - "import numpy.matlib\n", - "from precimed.mixer.utils import UnivariateParams\n", - "from precimed.mixer.utils import AnnotUnivariateParams\n", - "from precimed.mixer.utils import _log_exp_converter\n", - "from precimed.mixer.utils import _arctanh_tanh_converter\n", - "from precimed.mixer.utils import _logit_logistic_converter\n", - "import scipy.optimize\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "\n", - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=False)\n", - "\n", - "def perform_fit(bounds_left, bounds_right, parametrization):\n", - " libbgmg.set_option('cost_calculator', _cost_calculator_gaussian)\n", - "\n", - " bounds4opt = [(l, r) for l, r in zip(parametrization.params_to_vec(bounds_left), parametrization.params_to_vec(bounds_right))]\n", - " optimize_result = scipy.optimize.differential_evolution(lambda x: parametrization.calc_cost(x), bounds4opt,\n", - " tol=0.01, mutation=(0.5, 1), recombination=0.7, atol=0, updating='immediate', polish=False, workers=1) #, **global_opt_options)\n", - " params = parametrization.vec_to_params(optimize_result.x)\n", - " print(params)\n", - "\n", - " # Step 2. neldermead-fast\n", - " optimize_result = scipy.optimize.minimize(lambda x: parametrization.calc_cost(x), parametrization.params_to_vec(params),\n", - " method='Nelder-Mead', options={'maxiter':240, 'fatol':1e-7, 'xatol':1e-4, 'adaptive':True})\n", - " params = parametrization.vec_to_params(optimize_result.x)\n", - " print(params)\n", - " \n", - " if 0:\n", - " libbgmg.set_option('cost_calculator', _cost_calculator_sampling)\n", - " # Step 3. neldermead (sampling)\n", - " optimize_result = scipy.optimize.minimize(lambda x: parametrization.calc_cost(x), parametrization.params_to_vec(params),\n", - " method='Nelder-Mead', options={'maxiter':240, 'fatol':1e-7, 'xatol':1e-4, 'adaptive':True})\n", - " params = parametrization.vec_to_params(optimize_result.x)\n", - " print(params)\n", - " \n", - " return params\n", - "\n", - "if 0:\n", - " class UnifiedUnivariateParams(object):\n", - " def __init__(self, pi, sig2_beta, sig2_zeroA, s, l, sig2_annomat=None):\n", - " self._pi = pi\n", - " self._sig2_beta = sig2_beta\n", - " self._sig2_zeroA = sig2_zeroA\n", - " self._s = s;\n", - " self._l = l;\n", - " self._sig2_annomat = sig2_annomat\n", - "\n", - " def __str__(self):\n", - " description = []\n", - " for attr_name in '_pi', '_sig2_beta', '_sig2_zeroA', '_s', '_l':\n", - " try:\n", - " attr_value = getattr(self, attr_name)\n", - " description.append('{}: {}'.format(attr_name, attr_value))\n", - " except RuntimeError:\n", - " pass\n", - " return 'UnifiedUnivariateParams({})'.format(', '.join(description))\n", - " __repr__ = __str__\n", - "\n", - " def as_dict(self):\n", - " return {'pi': self._pi, 'sig2_beta': self._sig2_beta, 'sig2_zeroA': self._sig2_zeroA, 's': self._s, 'l': self._l}\n", - "\n", - " def cost(self, lib, trait):\n", - " pi_vec = self._pi * np.ones(shape=(lib.num_snp, 1), dtype=np.float32)\n", - " mafvec = libbgmg.mafvec;\n", - " tldvec = libbgmg.ld_tag_r2_sum;\n", - " if self._sig2_annomat is not None:\n", - " sig2_vec = np.multiply(np.dot(self._sig2_annomat, np.array(self._sig2_beta).astype(np.float32)),\n", - " np.multiply(np.power(np.float32(2.0) * mafvec * (1-mafvec), np.float32(self._s)),\n", - " np.power(tldvec, np.float32(self._l))))\n", - " else:\n", - " sig2_vec = self._sig2_beta * np.multiply(np.power(2 * mafvec * (1-mafvec), self._s), np.power(tldvec, self._l))\n", - "\n", - " sig2_zeroL = 1 # self._sig2_beta * self._pi\n", - " sig2_zeroC = 1\n", - " value = lib.calc_unified_univariate_cost(trait, pi_vec, sig2_vec, self._sig2_zeroA, sig2_zeroC, sig2_zeroL)\n", - " return value if np.isfinite(value) else 1e100\n", - "\n", - " def pdf(self, lib, trait, zgrid):\n", - " pi_vec = self._pi * np.ones(shape=(lib.num_snp, 1), dtype=np.float32)\n", - " mafvec = libbgmg.mafvec;\n", - " tldvec = libbgmg.ld_tag_r2_sum;\n", - " if self._sig2_annomat is not None:\n", - " sig2_vec = np.multiply(np.dot(self._sig2_annomat, np.array(self._sig2_beta).astype(np.float32)),\n", - " np.multiply(np.power(2 * mafvec * (1-mafvec), self._s),\n", - " np.power(tldvec, self._l)))\n", - " else:\n", - " sig2_vec = self._sig2_beta * np.multiply(np.power(2 * mafvec * (1-mafvec), self._s), np.power(tldvec, self._l))\n", - " sig2_zeroL = 1 # self._sig2_beta * self._pi\n", - " sig2_zeroC = 1\n", - " return libbgmg.calc_unified_univariate_pdf(trait, pi_vec, sig2_vec, self._sig2_zeroA, sig2_zeroC, sig2_zeroL, zgrid)\n", - "\n", - " class UnifiedUnivariateParametrization_natural_axis(object):\n", - " def __init__(self, lib, trait, sig2_annomat):\n", - " self._lib = lib\n", - " self._trait = trait\n", - " self._sig2_annomat = sig2_annomat\n", - "\n", - " def params_to_vec(self, params):\n", - " return [_log_exp_converter(params._sig2_zeroA, invflag=False),\n", - " #_logit_logistic_converter(params._pi, invflag=False),\n", - " #params._s,\n", - " #params._l,\n", - " ] + [_log_exp_converter(x, invflag=False) for x in params._sig2_beta]\n", - "\n", - " def vec_to_params(self, vec):\n", - " index = 0;\n", - " sig2_zeroA=_log_exp_converter(vec[index], invflag=True); index=index+1\n", - " pi = 1 #_logit_logistic_converter(vec[index], invflag=True); index=index+1\n", - " s = 0 #vec[index]; index=index+1\n", - " l = 0 #vec[index]; index=index+1\n", - " sig2_beta=[_log_exp_converter(x, invflag=True) for x in vec[index:]]\n", - "\n", - " return UnifiedUnivariateParams(pi=pi, sig2_beta=sig2_beta, sig2_zeroA=sig2_zeroA, s=s, l=l, sig2_annomat=self._sig2_annomat)\n", - "\n", - " def calc_cost(self, vec):\n", - " return self.vec_to_params(vec).cost(self._lib, self._trait)\n", - "\n", - " #print(UnivariateParams(pi=0.004, sig2_beta=5.842e-5, sig2_zero=1.185).cost(libbgmg, 1))\n", - " #print(UnifiedUnivariateParams(pi=0.004, sig2_beta=5.842e-5, sig2_zeroA=1.185, s=0.0).cost(libbgmg, 1))\n", - " \n", - "def do_plots(params, label, ylims=[7.3, 20, 50, 150], strat=True):\n", - " data = {}\n", - " trait_index = 1\n", - " downsample_factor=50\n", - " mask = np.isfinite(libbgmg.zvec1)\n", - " data['qqplot'] = precimed.mixer.cli.calc_qq_plot(libbgmg, params, 1, downsample_factor, mask)\n", - " for ylim in ylims:\n", - " precimed.mixer.figures.make_qq_plot(data['qqplot'], ylim=ylim)\n", - " plt.savefig(figures_folder + label + 'ylim={}.qq.png'.format(ylim) , bbox_inches='tight')\n", - "\n", - " if not strat: return\n", - " mafvec = libbgmg.mafvec[libbgmg.defvec]\n", - " tldvec = libbgmg.ld_tag_r2_sum\n", - " maf_bins = np.concatenate(([-np.inf], np.quantile(mafvec, [1/3, 2/3]), [np.inf]))\n", - " tld_bins = np.concatenate(([-np.inf], np.quantile(tldvec, [1/3, 2/3]), [np.inf]))\n", - " data['qqplot_bins'] = []\n", - " for i in range(0, 3):\n", - " for j in range(0, 3):\n", - " mask = np.isfinite(libbgmg.zvec1) & ((mafvec>=maf_bins[i]) & (mafvec= tld_bins[j]) & (tldvec < tld_bins[j+1]))\n", - " data['qqplot_bins'].append(precimed.mixer.cli.calc_qq_plot(libbgmg, params, trait_index, downsample_factor, mask,\n", - " title='maf \\\\in [{:.3g},{:.3g}); L \\\\in [{:.3g},{:.3g})'.format(maf_bins[i], maf_bins[i+1], tld_bins[j], tld_bins[j+1])))\n", - "\n", - " for ylim in ylims: \n", - " plt.figure(figsize=[12, 12])\n", - " for i in range(0, 3):\n", - " for j in range(0, 3):\n", - " plt.subplot(3,3,i*3+j+1)\n", - " precimed.mixer.figures.make_qq_plot(data['qqplot_bins'][i*3+j], ylim=ylim)\n", - " plt.title(data['qqplot_bins'][i*3+j]['title'].replace(';', '\\n'))\n", - " plt.savefig(figures_folder + label + 'ylim={}.binqq.png'.format(ylim) , bbox_inches='tight') " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=True)\n", - "libbgmg.init_log('/home/oleksanf/github/mixer/src/build/lib/mixer.log')\n", - "\n", - "# ToDo: optimize gaussian cost function to benefit from complete tag indices. \n", - "# In this case we don't need to compute redundant Edelta2 and Edelta4 for undefined tag indices.\n", - "libbgmg.set_option('use_complete_tag_indices', 1)\n", - "\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin0 = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/SSGAC_EDU_2018_no23andMe.sumstats.gz'\n", - "#trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/GIANT_HEIGHT_2018_UKB.sumstats.gz'\n", - "trait2_file = ''\n", - "extract = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/LDSR/w_hm3.justrs'\n", - "exclude = ''\n", - "chr2use = [1] # range(1, 23)\n", - "_cost_calculator_sampling = 0\n", - "_cost_calculator_gaussian = 1\n", - "_cost_calculator_convolve = 2\n", - "\n", - "libbgmg.init(bim_file, frq_file, chr2use, trait1_file, trait2_file, exclude, extract)\n", - "libbgmg.set_option('ld_format_version', 0)\n", - "libbgmg.set_option('seed', 123)\n", - "libbgmg.set_option('cubature_rel_error', 1e-5)\n", - "libbgmg.set_option('cubature_max_evals', 1000)\n", - "libbgmg.set_option('cost_calculator', _cost_calculator_gaussian)\n", - "\n", - "for chr_label in chr2use: \n", - " libbgmg.set_ld_r2_coo_from_file(int(chr_label), plink_ld_bin0.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(int(chr_label))\n", - "libbgmg.set_weights_randprune(64, 0.1)\n", - "\n", - "mafvec = libbgmg.mafvec[libbgmg.defvec]\n", - "hetvec = 2 * np.multiply(mafvec, 1-mafvec)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " [pi, sig2_beta, sig2_zeroA, s, l] \n", - "or\n", - " [annots], [pi, sig2_beta, sig2_zeroA, s, l] \n", - "or\n", - "[s, l], [annots], [pi, sig2_beta, sig2_zeroA, s, l] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## print('Useful categories:')\n", - "print([(annoname, beta) for annoname, beta in zip(annonames, betavec[1:]) if beta>0])\n", - "\n", - "beta2 = np.multiply(hetvec, np.matmul(annomat, betavec[1:]))\n", - "h2_annot = np.matmul(beta2.reshape((1, len(beta2))), annomat); h2_annot = h2_annot / h2_annot[0][0]\n", - "annot_frac = np.sum(annomat, 0)/np.sum(annomat, 0)[0];\n", - "\n", - "df['NNLS'] = np.divide(h2_annot, annot_frac).flatten()\n", - "\n", - "\n", - "mod_wls = sm.WLS(z2, A1, weights=w)\n", - "res_wls = mod_wls.fit()\n", - "#plt.plot(res_wls.fittedvalues, z2, '.')\n", - "\n", - "beta2 = np.multiply(hetvec, np.matmul(annomat, res_wls.params[1:]))\n", - "h2_annot = np.matmul(beta2.reshape((1, len(beta2))), annomat); h2_annot = h2_annot / h2_annot[0][0]\n", - "annot_frac = np.sum(annomat, 0)/np.sum(annomat, 0)[0];\n", - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "\n", - "df['WLS'] = np.divide(h2_annot, annot_frac).flatten()\n", - "\n", - "def find_sig2_vec(params):\n", - " return np.multiply(np.dot(params._annomat, np.array(params._sig2_annot).astype(np.float32)),\n", - " np.multiply(np.power(np.float32(2.0) * params._mafvec * (1-params._mafvec), np.float32(params._s)),\n", - " np.power(params._tldvec, np.float32(params._l)))) * params._sig2_beta\n", - "\n", - "def find_annot_enrich(params, annomat):\n", - " sig2_vec = find_sig2_vec(params)\n", - " h2_vec = params._pi * np.multiply(hetvec, sig2_vec)\n", - " h2_annot = np.matmul(h2_vec.reshape((1, len(h2_vec))), annomat);\n", - " h2_total = h2_annot[0][0]\n", - "\n", - " snps_annot = np.sum(annomat, 0)\n", - " snps_total = snps_annot[0]\n", - "\n", - " return np.divide(np.divide(h2_annot, h2_total), np.divide(snps_annot, snps_total))\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead.\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - }, - { - "data": { - "text/html": [ - "
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CategoryProp._SNPsProp._h2Enrichment_std_errorEnrichmentNNLS_p7NNLS_p8NNLS_p10NNLS_p11
0baseL2_01.0001.0000.0001.0001.0001.0001.0001.000
1Coding_UCSC.bedL2_00.0140.1562.31610.9247.2537.1570.9567.328
2Coding_UCSC.extend.500.bedL2_00.0640.2310.5743.6313.7673.1010.9833.153
3Conserved_LindbladToh.bedL2_00.0260.3011.47111.70211.94610.8070.97110.934
4Conserved_LindbladToh.extend.500.bedL2_00.3300.6690.1232.0252.0681.8961.0151.900
5CTCF_Hoffman.bedL2_00.0240.0391.0261.6181.8802.0631.0452.039
6CTCF_Hoffman.extend.500.bedL2_00.0710.1080.3971.5301.6721.6631.0431.644
7DGF_ENCODE.bedL2_00.1360.4870.4523.5812.3913.6511.0313.637
8DGF_ENCODE.extend.500.bedL2_00.5380.9180.0631.7051.5061.5681.0271.562
9DHS_peaks_Trynka.bedL2_00.1110.3870.4523.4952.8293.2371.0353.223
10DHS_Trynka.bedL2_00.1660.5230.3663.1462.5762.8101.0352.798
11DHS_Trynka.extend.500.bedL2_00.4960.8060.0861.6241.5711.5671.0301.560
13Enhancer_Andersson.extend.500.bedL2_00.0190.0270.9821.3952.9012.3261.0742.273
14Enhancer_Hoffman.bedL2_00.0420.1710.6694.0844.6153.1601.0493.127
15Enhancer_Hoffman.extend.500.bedL2_00.0900.2830.3583.1453.3532.6121.0472.587
16FetalDHS_Trynka.bedL2_00.0840.2770.5083.2953.3343.3761.0403.361
17FetalDHS_Trynka.extend.500.bedL2_00.2830.5440.1661.9202.0571.9521.0431.938
18H3K27ac_Hnisz.bedL2_00.3890.7580.0751.9491.8061.6871.0461.675
19H3K27ac_Hnisz.extend.500.bedL2_00.4200.7960.0561.8941.7271.6251.0431.615
20H3K27ac_PGC2.bedL2_00.2690.6070.2912.2592.2712.0531.0372.043
21H3K27ac_PGC2.extend.500.bedL2_00.3350.7430.1332.2171.9961.8411.0361.831
22H3K4me1_peaks_Trynka.bedL2_00.1700.4870.4092.8692.6112.3681.0362.353
23H3K4me1_Trynka.bedL2_00.4240.9910.1112.3381.8651.8061.0331.798
24H3K4me1_Trynka.extend.500.bedL2_00.6060.9630.0371.5901.4651.4411.0281.437
25H3K4me3_peaks_Trynka.bedL2_00.0420.1680.8464.0314.2473.4501.0093.451
26H3K4me3_Trynka.bedL2_00.1330.4520.3343.3953.2932.8191.0112.814
27H3K4me3_Trynka.extend.500.bedL2_00.2550.5860.1532.2952.3192.0961.0182.088
28H3K9ac_peaks_Trynka.bedL2_00.0380.2271.1505.9036.0493.8581.0353.844
29H3K9ac_Trynka.bedL2_00.1250.5650.3424.5054.5023.5441.0393.525
30H3K9ac_Trynka.extend.500.bedL2_00.2300.6750.1872.9372.8572.5841.0412.570
31Intron_UCSC.bedL2_00.3870.4500.0641.1611.2431.1631.0011.163
32Intron_UCSC.extend.500.bedL2_00.3970.5560.0491.4011.4001.3481.0001.353
34PromoterFlanking_Hoffman.extend.500.bedL2_00.0330.1210.8433.6533.7122.6261.0042.640
35Promoter_UCSC.bedL2_00.0310.1190.7983.8913.6013.2590.9983.301
36Promoter_UCSC.extend.500.bedL2_00.0380.1070.4672.8083.3443.0251.0003.060
37Repressed_Hoffman.bedL2_00.4610.1370.0880.2980.4070.4741.0000.472
38Repressed_Hoffman.extend.500.bedL2_00.7190.3400.0350.4730.5410.6371.0020.635
39SuperEnhancer_Hnisz.bedL2_00.1670.3870.1072.3162.2792.0171.0691.987
40SuperEnhancer_Hnisz.extend.500.bedL2_00.1700.4010.0972.3552.2652.0051.0681.975
41TFBS_ENCODE.bedL2_00.1310.4300.4123.2782.7402.7061.0352.696
42TFBS_ENCODE.extend.500.bedL2_00.3410.7020.1302.0561.8351.7511.0331.743
43Transcribed_Hoffman.bedL2_00.3460.4910.1301.4191.2721.2380.9791.251
44Transcribed_Hoffman.extend.500.bedL2_00.7620.6390.0570.8380.9460.9510.9930.955
45TSS_Hoffman.bedL2_00.0180.1381.3617.7386.7295.2961.0095.343
46TSS_Hoffman.extend.500.bedL2_00.0340.2030.7205.9215.1313.9781.0233.994
47UTR_3_UCSC.bedL2_00.0110.0972.1038.6886.4723.7000.9823.770
48UTR_3_UCSC.extend.500.bedL2_00.0260.1491.6655.6204.2332.8270.9842.874
50UTR_5_UCSC.extend.500.bedL2_00.0270.0770.5762.8753.8835.3030.9855.376
51WeakEnhancer_Hoffman.bedL2_00.0210.0350.9701.6832.9802.9991.0712.939
52WeakEnhancer_Hoffman.extend.500.bedL2_00.0890.1140.2941.2892.3742.1841.0662.146
\n", - "
" - ], - "text/plain": [ - " Category Prop._SNPs Prop._h2 \\\n", - "0 baseL2_0 1.000 1.000 \n", - "1 Coding_UCSC.bedL2_0 0.014 0.156 \n", - "2 Coding_UCSC.extend.500.bedL2_0 0.064 0.231 \n", - "3 Conserved_LindbladToh.bedL2_0 0.026 0.301 \n", - "4 Conserved_LindbladToh.extend.500.bedL2_0 0.330 0.669 \n", - "5 CTCF_Hoffman.bedL2_0 0.024 0.039 \n", - "6 CTCF_Hoffman.extend.500.bedL2_0 0.071 0.108 \n", - "7 DGF_ENCODE.bedL2_0 0.136 0.487 \n", - "8 DGF_ENCODE.extend.500.bedL2_0 0.538 0.918 \n", - "9 DHS_peaks_Trynka.bedL2_0 0.111 0.387 \n", - "10 DHS_Trynka.bedL2_0 0.166 0.523 \n", - "11 DHS_Trynka.extend.500.bedL2_0 0.496 0.806 \n", - "13 Enhancer_Andersson.extend.500.bedL2_0 0.019 0.027 \n", - "14 Enhancer_Hoffman.bedL2_0 0.042 0.171 \n", - "15 Enhancer_Hoffman.extend.500.bedL2_0 0.090 0.283 \n", - "16 FetalDHS_Trynka.bedL2_0 0.084 0.277 \n", - "17 FetalDHS_Trynka.extend.500.bedL2_0 0.283 0.544 \n", - "18 H3K27ac_Hnisz.bedL2_0 0.389 0.758 \n", - "19 H3K27ac_Hnisz.extend.500.bedL2_0 0.420 0.796 \n", - "20 H3K27ac_PGC2.bedL2_0 0.269 0.607 \n", - "21 H3K27ac_PGC2.extend.500.bedL2_0 0.335 0.743 \n", - "22 H3K4me1_peaks_Trynka.bedL2_0 0.170 0.487 \n", - "23 H3K4me1_Trynka.bedL2_0 0.424 0.991 \n", - "24 H3K4me1_Trynka.extend.500.bedL2_0 0.606 0.963 \n", - "25 H3K4me3_peaks_Trynka.bedL2_0 0.042 0.168 \n", - "26 H3K4me3_Trynka.bedL2_0 0.133 0.452 \n", - "27 H3K4me3_Trynka.extend.500.bedL2_0 0.255 0.586 \n", - "28 H3K9ac_peaks_Trynka.bedL2_0 0.038 0.227 \n", - "29 H3K9ac_Trynka.bedL2_0 0.125 0.565 \n", - "30 H3K9ac_Trynka.extend.500.bedL2_0 0.230 0.675 \n", - "31 Intron_UCSC.bedL2_0 0.387 0.450 \n", - "32 Intron_UCSC.extend.500.bedL2_0 0.397 0.556 \n", - "34 PromoterFlanking_Hoffman.extend.500.bedL2_0 0.033 0.121 \n", - "35 Promoter_UCSC.bedL2_0 0.031 0.119 \n", - "36 Promoter_UCSC.extend.500.bedL2_0 0.038 0.107 \n", - "37 Repressed_Hoffman.bedL2_0 0.461 0.137 \n", - "38 Repressed_Hoffman.extend.500.bedL2_0 0.719 0.340 \n", - "39 SuperEnhancer_Hnisz.bedL2_0 0.167 0.387 \n", - "40 SuperEnhancer_Hnisz.extend.500.bedL2_0 0.170 0.401 \n", - "41 TFBS_ENCODE.bedL2_0 0.131 0.430 \n", - "42 TFBS_ENCODE.extend.500.bedL2_0 0.341 0.702 \n", - "43 Transcribed_Hoffman.bedL2_0 0.346 0.491 \n", - "44 Transcribed_Hoffman.extend.500.bedL2_0 0.762 0.639 \n", - "45 TSS_Hoffman.bedL2_0 0.018 0.138 \n", - "46 TSS_Hoffman.extend.500.bedL2_0 0.034 0.203 \n", - "47 UTR_3_UCSC.bedL2_0 0.011 0.097 \n", - "48 UTR_3_UCSC.extend.500.bedL2_0 0.026 0.149 \n", - "50 UTR_5_UCSC.extend.500.bedL2_0 0.027 0.077 \n", - "51 WeakEnhancer_Hoffman.bedL2_0 0.021 0.035 \n", - "52 WeakEnhancer_Hoffman.extend.500.bedL2_0 0.089 0.114 \n", - "\n", - " Enrichment_std_error Enrichment NNLS_p7 NNLS_p8 NNLS_p10 NNLS_p11 \n", - "0 0.000 1.000 1.000 1.000 1.000 1.000 \n", - "1 2.316 10.924 7.253 7.157 0.956 7.328 \n", - "2 0.574 3.631 3.767 3.101 0.983 3.153 \n", - "3 1.471 11.702 11.946 10.807 0.971 10.934 \n", - "4 0.123 2.025 2.068 1.896 1.015 1.900 \n", - "5 1.026 1.618 1.880 2.063 1.045 2.039 \n", - "6 0.397 1.530 1.672 1.663 1.043 1.644 \n", - "7 0.452 3.581 2.391 3.651 1.031 3.637 \n", - "8 0.063 1.705 1.506 1.568 1.027 1.562 \n", - "9 0.452 3.495 2.829 3.237 1.035 3.223 \n", - "10 0.366 3.146 2.576 2.810 1.035 2.798 \n", - "11 0.086 1.624 1.571 1.567 1.030 1.560 \n", - "13 0.982 1.395 2.901 2.326 1.074 2.273 \n", - "14 0.669 4.084 4.615 3.160 1.049 3.127 \n", - "15 0.358 3.145 3.353 2.612 1.047 2.587 \n", - "16 0.508 3.295 3.334 3.376 1.040 3.361 \n", - "17 0.166 1.920 2.057 1.952 1.043 1.938 \n", - "18 0.075 1.949 1.806 1.687 1.046 1.675 \n", - "19 0.056 1.894 1.727 1.625 1.043 1.615 \n", - "20 0.291 2.259 2.271 2.053 1.037 2.043 \n", - "21 0.133 2.217 1.996 1.841 1.036 1.831 \n", - "22 0.409 2.869 2.611 2.368 1.036 2.353 \n", - "23 0.111 2.338 1.865 1.806 1.033 1.798 \n", - "24 0.037 1.590 1.465 1.441 1.028 1.437 \n", - "25 0.846 4.031 4.247 3.450 1.009 3.451 \n", - "26 0.334 3.395 3.293 2.819 1.011 2.814 \n", - "27 0.153 2.295 2.319 2.096 1.018 2.088 \n", - "28 1.150 5.903 6.049 3.858 1.035 3.844 \n", - "29 0.342 4.505 4.502 3.544 1.039 3.525 \n", - "30 0.187 2.937 2.857 2.584 1.041 2.570 \n", - "31 0.064 1.161 1.243 1.163 1.001 1.163 \n", - "32 0.049 1.401 1.400 1.348 1.000 1.353 \n", - "34 0.843 3.653 3.712 2.626 1.004 2.640 \n", - "35 0.798 3.891 3.601 3.259 0.998 3.301 \n", - "36 0.467 2.808 3.344 3.025 1.000 3.060 \n", - "37 0.088 0.298 0.407 0.474 1.000 0.472 \n", - "38 0.035 0.473 0.541 0.637 1.002 0.635 \n", - "39 0.107 2.316 2.279 2.017 1.069 1.987 \n", - "40 0.097 2.355 2.265 2.005 1.068 1.975 \n", - "41 0.412 3.278 2.740 2.706 1.035 2.696 \n", - "42 0.130 2.056 1.835 1.751 1.033 1.743 \n", - "43 0.130 1.419 1.272 1.238 0.979 1.251 \n", - "44 0.057 0.838 0.946 0.951 0.993 0.955 \n", - "45 1.361 7.738 6.729 5.296 1.009 5.343 \n", - "46 0.720 5.921 5.131 3.978 1.023 3.994 \n", - "47 2.103 8.688 6.472 3.700 0.982 3.770 \n", - "48 1.665 5.620 4.233 2.827 0.984 2.874 \n", - "50 0.576 2.875 3.883 5.303 0.985 5.376 \n", - "51 0.970 1.683 2.980 2.999 1.071 2.939 \n", - "52 0.294 1.289 2.374 2.184 1.066 2.146 " - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "df['NNLS_p7'] = find_annot_enrich(params7, annomat).flatten()\n", - "df['NNLS_p8'] = find_annot_enrich(params8, annomat).flatten()\n", - "df['NNLS_p10'] = find_annot_enrich(params10, annomat).flatten()\n", - "df['NNLS_p11'] = find_annot_enrich(params11, annomat).flatten()\n", - "df[['Category', 'Prop._SNPs', 'Prop._h2', 'Enrichment_std_error', 'Enrichment', 'NNLS_p7', 'NNLS_p8', 'NNLS_p10', 'NNLS_p11']][df['Prop._SNPs']>0.01].round(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.set_weights_randprune(64, 0.1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# +.-T indicate status (fitted, in progress, done, ToBeImplemente)\n", - "# number in parentheses indicate a dependency (e.g. params5 depend on params3)\n", - "\n", - "# params1 + standard causal mixture model, fit using old implementation\n", - "\n", - "# params3 + infinitesimal model (just to find baseline sig2beta)\n", - "# params4 + infinitesimal model, allowing for flexible S and L parameters\n", - "\n", - "# params5 (3) + baseline annotation model, infinitesimal, without accounting for S and L parameters\n", - "# params6 (4) + baseline annotation model, infinitesimal, allowing for flexible S and L parameters\n", - "\n", - "# params7 (5) + causal mixture with annotation, without accounting for S and L parameters\n", - "# params8 (6) + causal mixture with annotation, allowing for flexible S and L parameters\n", - "# (note that here S and L are kept from infinitesimal model - need to re-fit them?) \n", - "\n", - "# params9 - causal mixture without annotations, without accounting for S and L parameters\n", - "# params10 + causal mixture without annotations, allowing for flexible S and L parameters\n", - "\n", - "# params11 (6) + causal mixture with annotations, allowing for flexible S and L parameters,\n", - "# and re-fit S and L in the context of mixture model\n", - "\n", - "\n", - "Let's limit ourselves to 10, 0P and PP.\n", - "\n", - "\n", - "\n", - "[fixed] TBD - fit separate models for S and L\n", - "[fixed] TBD - enable S,L,A models for 2- and 3- component causal mixtures\n", - "[fixed] TBD - fit sigma2_beta and sigma2_zeroA in models 5,6 (after NNLS has fitted annotation enrichments)\n", - "\n", - "compare power plots (looks good)\n", - "\n", - "Take home messages\n", - "1. Modeling annotations affects \"pi\" estimates in mixture model - they increase by a factor of 10\n", - " however, this is because \"base\" annotation is not included in the model, therefore annotations kindof work as a mixture by itself\n", - "2. Judging from the loglikelihood cost function, pi and annotations clearly represent an important signal,\n", - " while S and L are more subtle effects\n", - "3. Modelin S and L affects sig2zero estimate, i.e. important for inflation control\n", - "4. For a total heritability estimates, modeling polygenicity and annotations doesn't make big difference\n", - " modeling S and L does, however, make a big difference\n", - "5. Gaussian approximation is not suited for a more complex causal mixture (3+ components)\n", - " \n", - "Side notes on modeling:\n", - "1. For annotations I've used an additive model, same as sLDSC. But, fit with non-negative least squares - well defined beta's\n", - "\n", - "Technical issues\n", - "1. [Solved] Fast const function is ~150 times faster, and gives high quality results - however I'm not sure if we can trust it 100%\n", - " Hei, great progress on this one - differences are in the tails, we'll save full results (tag_pdf) and\n", - " use infinitesimal model to find for which SNPs convolution didn't work\n", - "2. [Solved] Why for infinitesimal cost function gaussian approximation doesn't give exactly the same answer as convolutions? \n", - "3 [solved] increase number of iterations in fminsearch \n", - "4 [solved] Ability to make QQ plots on the entire set of SNPs ?\n", - " !!!!! Ability to calculate sampling cost function for a specific set of SNPs, with zmax1 (right-censoring)\n", - "\n", - "\n", - " (!!!!!) Try 1M random SNPs instead of HapMap\n", - " \n", - " \n", - "TBD\n", - "[done] find Schork's annotations from Alexey on 9.99M LDSC template , and run with those annotations.\n", - "[done] add a second gaussian ?\n", - "[done] re-fit params5 and params6 in a standard way, in the context of an infinitesimal model (better sig2_beta and sig2_zero estimate)\n", - "[done] show QQ plots, overall and stratified by MAF and TLD\n", - "[done] Implement sampling\n", - "\n", - "[Other questions]\n", - "[done] further decompose S and L dependency?\n", - "- what if we don't constrain to hapmap3 SNPs, and if we use another reference?\n", - "- random 1,000,000 SNPs from LDSR\n", - "- Can we fit annotations in the context of a mixture model? <- difficult math question\n", - "- what if we use full cost function, not fast cost function?\n", - "\n", - "Code improvements\n", - "[done] split runs across parameters so that we can run full cost functino\n", - "- save LD structure in a different way (transposed] )\n", - "\n", - "\n", - "[all four implemented] mixture models\n", - "- infinitesimal, 1 * N(0, s1^2)\n", - "- causal mixture, pi0 + pi1*N(0, s2^2)\n", - "- infinitesimal causal pi0 N(0, s1^2) + pi1 N(0, s2^2)\n", - "- causal M2 pi0 + pi1 N(0, s2^2) + pi2 N(0, s2^2)\n", - "\n", - "[all fixed!] need to apply hapmap3 constraint only for fit.\n", - " In the same run, qq plots needs to be from full GWAS.\n", - " Sigma0 parameter can be adjusted.\n", - "\n", - "=================================================\n", - "\n", - "[Oct 25th] Remaining ToDo(s)\n", - "- still not sure if I can trust convolve function\n", - " very low p-values -- needs to be re-generated via sampling\n", - " large z-scores -- need to be models via right-censoring\n", - "- transpose LD matrix in the code ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df=pd.read_table('GIANT_HEIGHT_2018_UKB.partitioned_h2.results',sep='\\t')\n", - "df['NNLS_p7'] = find_annot_enrich(params7, annomat).flatten()\n", - "df['NNLS_p8'] = find_annot_enrich(params8, annomat).flatten()\n", - "df['NNLS_p10'] = find_annot_enrich(params10, annomat).flatten()\n", - "df['NNLS_p11'] = find_annot_enrich(params11, annomat).flatten()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UnivariateParams(_pi: 0.0014400133519476379, _sig2_beta: 0.0002144974094950297, _sig2_zero: 2.2960585139856797)\n", - "UnivariateParams(_pi: 0.0015684598531926141, _sig2_beta: 0.00020448806343576262, _sig2_zero: 2.23992429765977)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/github/mixer/precimed/mixer/cli.py:459: RuntimeWarning: divide by zero encountered in log10\n", - " hv_logp = -np.log10(2*scipy.stats.norm.cdf(-hv_z))\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:50: RuntimeWarning: invalid value encountered in less\n", - " y2[x2 self.x[-1]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params1 - standard causal mixture model, fit using old implementation\n", - "parametrization = precimed.mixer.utils.UnivariateParametrization_natural_axis(lib=libbgmg, trait=1)\n", - "bounds_left = UnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zero=0.9)\n", - "bounds_right = UnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zero=2.5)\n", - "params1=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params1, '_params1')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: [1], _sig2_beta: [6.534330016229654e-08], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 1.2382474823918246)\n", - "AnnotUnivariateParams(_pi: [1], _sig2_beta: [6.280174165299782e-08], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 1.2231983439793324)\n" - ] - } - ], - "source": [ - "# params3 - infinitesimal model (just to find baseline sig2beta)\n", - "constraint = AnnotUnivariateParams(pi=1, sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(sig2_beta=5e-8, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params3=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/github/mixer/precimed/mixer/utils.py:815: RuntimeWarning: divide by zero encountered in log10\n", - " data_x=-np.log10(np.flip(np.cumsum(np.flip(data_weights[si])))) # step 2\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/scipy/interpolate/interpolate.py:689: RuntimeWarning: invalid value encountered in greater\n", - " above_bounds = x_new > self.x[-1]\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/scipy/interpolate/interpolate.py:610: RuntimeWarning: invalid value encountered in subtract\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:44: RuntimeWarning: invalid value encountered in sqrt\n", - " q = 10**-data_logpvec; dq= 1.96*np.sqrt(q*(1-q)/qq['sum_data_weights']);\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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+DMaZNdvAeTUrLcPNrE+3MDMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZhotkMzMzM7MMF8lmZmZmZhkuks3MzMzMMlwkm5mZmZllDFkkS7pI0ipJD/cz7rOSQtIW6WtJ+h9JT0laJul1Y9FpMxuYM2tWOpxXs4lrOHuSLwEOyw6UtA1wCPBs3uDDgR3Tx6nAj0ffRTPbTJfgzJqViktwXs0mpCGL5Ii4HVjXz6jvAv8BRN6wo4CfReJuYKakrQrSUzMbFmfWrHQ4r2YT14jOSZZ0JPB8RDyYGTUfeC7v9cp0WH/zOFXSUklLV69ePZJumNkwObNmpcN5NZsYNrtIllQDfAn4Sn+j+xkW/QwjIs6PiMURsXju3Lmb2w0zGyZn1qx0OK9mE0fFCKbZHtgWeFASwALgfkn7kGzVbpPXdgHwwmg7aWaj4syalQ7n1WyC2Ow9yRHxUERsGRGLImIRSWhfFxEvAdcBJ6TfwN0XaIiIFwvbZTPbHM6sWelwXs0mjuFcAu5K4K/AzpJWSjp5kOa/B5YDTwH/C3ykIL00s2FzZs1Kh/NqNnENebpFRLx3iPGL8p4H8NHRd8vMRsqZNSsdzqvZxOU77pmZmZmZZbhINjMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZhotkMzMzM7MMF8lmZmZmZhkuks3MzMzMMlwkm5mZmZlluEg2MzMzM8twkWxmZmZmluEi2czMzMwsw0WymZmZmVmGi2QzMzMzswwXyWZmZmZmGS6SzczMzMwyXCSbmZmZmWUMWSRLukjSKkkP5w37lqTHJC2TdI2kmXnjviDpKUmPSzp0rDpuZv1zZs1Kh/NqNnENZ0/yJcBhmWG3AK+JiNcCTwBfAJC0G3AcsHs6zY8klRest2Y2HJfgzJqViktwXs0mpCGL5Ii4HViXGXZzRHSlL+8GFqTPjwJ+ERHtEfE08BSwTwH7a2ZDcGbNSofzajZxFeKc5A8CN6bP5wPP5Y1bmQ7bhKRTJS2VtHT16tUF6IaZDZMza1Y6nFezIhlVkSzpS0AXcEXvoH6aRX/TRsT5EbE4IhbPnTt3NN0ws2FyZs1Kh/NqVlwVI51Q0onAO4AlEdEb0pXANnnNFgAvjLx7ZlYozqxZ6XBezYpvRHuSJR0GfB44MiJa80ZdBxwnqUrStsCOwL2j76aZjYYza1Y6nFeziWHIPcmSrgQOAraQtBI4k+SbtlXALZIA7o6I0yLiEUlXAf8gOUT00YjoHqvOm9mmnFmz0uG8mk1c2nAUp3gWL14cS5cuLXY3zCYMSfdFxOJi92MgzqzZBs6rWWkZbmZ9xz0zMzMzswwXyWZmZmZmGS6SzczMzMwyXCSbmZmZmWW4SDYzMzMzy3CRbGZmZmaW4SLZzMzMzCzDRbKZmZmZWYaLZDMzMzOzDBfJZmZmZmYZLpLNzMzMzDJcJJuZmZmZZbhINjMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZxpBFsqSLJK2S9HDesNmSbpH0ZPpzVjpckv5H0lOSlkl63Vh23sw25cyalQ7n1WziGs6e5EuAwzLDzgD+FBE7An9KXwMcDuyYPk4FflyYbprZZrgEZ9asVFyC82o2IQ1ZJEfE7cC6zOCjgEvT55cCR+cN/1kk7gZmStqqUJ01s6E5s2alw3k1m7hGek7yvIh4ESD9uWU6fD7wXF67lemwTUg6VdJSSUtXr149wm6Y2TA5s2alw3k1mwAK/cU99TMs+msYEedHxOKIWDx37twCd8PMhsmZNSsdzqvZOBppkfxy7yGe9OeqdPhKYJu8dguAF0bePTMrEGfWrHQ4r2YTwEiL5OuAE9PnJwLX5g0/If0G7r5AQ+8hIzMrKmfWrHQ4r2YTQMVQDSRdCRwEbCFpJXAm8HXgKkknA88C706b/x44AngKaAVOGoM+m9kgnFmz0uG8mk1cQxbJEfHeAUYt6adtAB8dbafMbOScWbPS4byaTVy+456ZmZmZWYaLZDMzMzOzDBfJZmZmZmYZLpLNzMzMzDJcJJuZmZmZZbhINjMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZhotkMzMzM7MMF8lmZmZmZhkuks3MzMzMMlwkm5mZmZlluEg2MzMzM8twkWxmZmZmluEi2czMzMwsw0WymZmZmVnGqIpkSZ+S9IikhyVdKWmqpG0l3SPpSUm/lDSlUJ01s9FxZs1Kh/NqVlwjLpIlzQc+DiyOiNcA5cBxwDeA70bEjkA9cHIhOmpmo+PMmpUO59Ws+EZ7ukUFUC2pAqgBXgTeCvw6HX8pcPQol2FmhePMmpUO59WsiEZcJEfE88C3gWdJgtsA3Aesj4iutNlKYH5/00s6VdJSSUtXr1490m6Y2TA5s2alw3k1K77RnG4xCzgK2BbYGqgFDu+nafQ3fUScHxGLI2Lx3LlzR9oNMxsmZ9asdDivZsU3mtMtDgaejojVEdEJXA28EZiZHhoCWAC8MMo+mllhOLNmpcN5NSuy0RTJzwL7SqqRJGAJ8A/gz8C70jYnAteOrotmViDOrFnpcF7Nimw05yTfQ/LlgfuBh9J5nQ98Hvi0pKeAOcCFBeinmY2SM2tWOpxXs+KrGLrJwCLiTODMzODlwD6jma+ZjQ1n1qx0OK9mxeU77pmZmZmZZbhINjMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZhotkMzMzM7MMF8lmZmZmZhkuks3MzMzMMlwkm5mZmZlluEg2MzMzM8twkWxmZmZmluEi2czMzMwsw0WymZmZmVmGi2QzMzMzswwXyWZmZmZmGS6SzczMzMwyRlUkS5op6deSHpP0qKT9JM2WdIukJ9OfswrVWTMbHWfWrHQ4r2bFNdo9yd8DboqIXYA9gUeBM4A/RcSOwJ/S12Y2MTizZqXDeTUrohEXyZKmAwcCFwJEREdErAeOAi5Nm10KHD3aTprZ6DmzZqXDeTUrvtHsSd4OWA1cLOkBSRdIqgXmRcSLAOnPLQvQTzMbPWfWrHQ4r2ZFNpoiuQJ4HfDjiNgbaGEzDvtIOlXSUklLV69ePYpumNkwObNmpcN5NSuy0RTJK4GVEXFP+vrXJIF+WdJWAOnPVf1NHBHnR8TiiFg8d+7cUXTDzIbJmTUrHc6rWZGNuEiOiJeA5yTtnA5aAvwDuA44MR12InDtqHpoZgXhzJqVDufVrPgqRjn9x4ArJE0BlgMnkRTeV0k6GXgWePcol2FmhePMmpUO59WsiEZVJEfE34HF/YxaMpr5mtnYcGbNSofzalZcvuOemZmZmVmGi2QzMzMzswwXyWZmZmZmGS6SzczMzMwyXCSbmZmZmWW4SDYzMzMzy3CRbGZmZmaW4SLZzMzMzCzDRbKZmZmZWYaLZDMzMzOzDBfJZmZmZmYZLpLNzMzMzDJcJJuZmZmZZbhINjMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZxqiLZEnlkh6Q9Lv09baS7pH0pKRfSpoy+m6aWSE4r2alxZk1K55C7En+BPBo3utvAN+NiB2BeuDkAizDzArDeTUrLc6sWZGMqkiWtAB4O3BB+lrAW4Ffp00uBY4ezTLMrDCcV7PS4syaFddo9yT/N/AfQE/6eg6wPiK60tcrgfn9TSjpVElLJS1dvXr1KLthZsMw4ryCM2tWBF7HmhXRiItkSe8AVkXEffmD+2ka/U0fEedHxOKIWDx37tyRdsPMhmG0eQVn1mw8eR1rVnwVo5j2TcCRko4ApgLTSbZ6Z0qqSLd0FwAvjL6bZjZKzqtZaXFmzYpsxHuSI+ILEbEgIhYBxwG3RsTxwJ+Bd6XNTgSuHXUvzWxUnFez0uLMmhXfWFwn+fPApyU9RXL+1IVjsAwzKwzn1ay0OLNm42Q0p1v0iYjbgNvS58uBfQoxXzMrPOfVrLQ4s2bF4TvumZmZmZlluEg2MzMzM8twkWxmZmZmluEi2czMzMwsw0WymZmZmVmGi2QzMzMzswwXyWZmZmZmGS6SzczMzMwyXCSbmZmZmWW4SDYzMzMzy3CRbGZmZmaW4SLZzMzMzCzDRbKZmZmZWYaLZDMzMzOzDBfJZmZmZmYZLpLNzMzMzDJGXCRL2kbSnyU9KukRSZ9Ih8+WdIukJ9OfswrXXTMbKWfWrHQ4r2bFN5o9yV3AZyJiV2Bf4KOSdgPOAP4UETsCf0pfm1nxObNmpcN5NSuyERfJEfFiRNyfPm8CHgXmA0cBl6bNLgWOHm0nzWz0nFmz0uG8mhVfQc5JlrQI2Bu4B5gXES9CEnJgy0Isw8wKx5k1Kx3Oq1lxjLpIllQH/Ab4ZEQ0bsZ0p0paKmnp6tWrR9sNMxsmZ9asdDivZsUzqiJZUiVJeK+IiKvTwS9L2iodvxWwqr9pI+L8iFgcEYvnzp07mm6Y2TA5s2alw3k1K67RXN1CwIXAoxHxnbxR1wEnps9PBK4deffMrFCcWbPS4byaFV/FKKZ9E/AB4CFJf0+HfRH4OnCVpJOBZ4F3j66LZlYgzqxZ6XBezYpsxEVyRNwBaIDRS0Y6XzMbG86sWelwXs2Kz3fcMzMzMzPLcJFsZmZmZpbhItnMzMzMLMNFspmZmZlZhotkMzMzM7MMF8lmZmZmZhkuks3MzMzMMlwkm5mZmZlluEg2MzMzM8twkWxmZmZmluEi2czMzMwsw0Wy9enq7qEh1zn+C46AjlbobBv/ZQNdXV10d3cXZdlmZmY2MVUUuwM2MhFBrq2Njs7NK2o7u3t4oaGd5+pzPLcux7P1bTxbn+O5+jZeWN/GQTvO5tvH7rZ5nelqRx1NqL0JtTdQTQcVXS3Q1ghtDdDemHme97OtMXne0wWHfQP2PW3w/nd20tHRQW1tLQB///vfqa+vp7m5maamJpqbm1mwYAFHHHEEAJ/+9KdZs2ZN37jm5mYOO+wwzjzzTCKC2tpacrkct912G29+85s3732bmZnZpOUiuUR0d3fTksvR1NLCmvp61tTX09HVhfpp29UTrGntYXVLD6syP9fmgp7Y0LamvJtta9rYvbqNQ+fn2E5PsvyPt1PR1UplVwsVnS1UdLdseJ0+Krta+56X9wxcqDe0BQ3tQXPU0kwNTT3VlE2t4S17bgtb7MTP736BJ1flaO6A5uV/ovmHdzJ//ny+/vWvA3DMMcdw//339xW4HR0dHHzwwdxyyy0AHHvssSxfvnyjZR555JF9RfLNN99Ma2srdXV11NXVMW3aNKZPnw6AJD796U9TVVXFNttsM4q/jpmZmU02LpInqPaODlpaW2lobmb1unXUNzbS2dVNezd0q4Iom0JHVzntuSYaGxpobmqktaWJjtZGor2ZOlqZRivbq5XFZTnmVuaYXdHGzOkt1JGjJlqY0t1KRXeO6Ag6ctDcEUmx2hHsNrcMSTzwYjcPrS2nsXsKTV2VNHWV0xYVfOlde9NdWcsPbn6KW5e9QHN7N63tXTS2tjO9ro4n/nojTJ3OB97/Ia6/4Sague+9bb/99jz1zb8BcNGPD+ZPf7qNmpqavkJ277337mu7yy67MGPGjL5xdXV17Ljjjn3jL7roIoCNxs+YMaNv/MMPPzzo7/mcc84pxJ/LbGMR0N6UHi1pgLb1sOVuUDN7k6ad3T20tnfT3NFFa3sXze1d9ETw+oWbtu3V1dVFS0tL38bjvHnzmDlz5li+I7PS1dkGuXXQuo7OpjW0NrxMW8Ma2htX0928BlrX8cScg3hi1lvo7O6hozuSn1096eseOruDN2w7m/fvu3CzFx8RtLa29h3xrKysZOHCZD7XXntt39HQ3scee+zBv/7rvxb6t2Aj4CK5CNo7u6hvbqO+oZ6W9Wtob66no2U9nc31tDetIde0jo7WRuhoYWpPK9XRSk1PK7NoZVq0MKWnhdllOWaUtbO2tZun1vX0Fbe9he779qhkxlRx09Pw60d7aOosp6lTNHVAcwdc9bmDmTZzFt/67UP8zzX30tXds1Ef7/3ztZTXzeab3/kRv/zNlRuNq62r47hvnoUkVv3tpzRXtlMzo4Y5NTWUV1Sw3aJF8KrXAPDhj36co455V99e3Lq6uo1W5tdffz1TpkyhvLy839/VeeedN+jv0qeU+AxUAAAgAElEQVRI2Fjp6WjjxZdfYn39arpb64nWBrpz68k1rKazpZ7KjkamdDVR1dXE1O4mpnY3U93dTHVPMzU9LZSRZKqrJ+jugW/NOZu7Yg9WrXiC1tYWci2ttOVa6GjLMeVVO1D1qh3obm1g/R1XUNnTzhvmV9GaFsKnfuhDvOPII/nHI4/wzre/nfa2jc/f/+/vf593vfvdw35vZWVlvGqLLZD6OxZlVnyPvdTI359dz/rWDjq7umnr7KKtvQPa1lOeW0tlZyNVnQ1M7WyguruJ6u5GanqaqOlqpK6nmbpoYkY0MZ0matmQl0pgRvoAaIwa1sU0/vLsllzePZ+IHsp7OphSVU1FGXSte57IrYfONp6s62LZDT1U19Rw0JIlAPz80ktZ+eyz5HI5cq2t5FpbWbTddnzqjDMAOOX443n6n/8kYsMh3DcecADnfPvbAJz0wQ9Sv25d37iysjLedsQRbLnttrxqzhx23X77Mf092+BcJI9EBHS1bTifNt1bFG2NtDetpblhLc0N62hcv5Zc0zqa1q+jo6WRrrZmetpb6enIsc+8LnadBk/X9/Crf3T2FbdN7UFzZ/DF/avYdd4UrnuqjE/9oYmmjqClo4fWjh66e4LL/+MIdtlhIVff9U/OveTmTbo4630/YJsdd+OWq6/lt7+/kOqaGqqrq6muraZ6i2oe2f5kps+cybz97+HdNTtRXV3N1OrqpE1NDWs0g/J2cdS//iuHHnkkU6urqampoWrqVCRR39gIwDHHHccxxx3Xt9zWXI5/2WOPvteHH374oL/K6urqAv1RzDbW1t5B4/okjx3N9fTk6olcAz2t64jcerpb6qFtPWXtjZR3NDClq4nq7hY6Whrp7mimq6Ozb+NzdrXY61XJhtz37m6noR3Wt4t1HRWs7yhjj4WzOOR1C2mILfjoT/9KrqOHXEcX7R2ddHV18/oDDmLrwxfQ3rme+77/0U36+sZjTuCgQ3ano7GcH19yJ2U1NTzWtCGTK1ev5sHHH6e+sZF3HH103/CpaV6nz53Lg48/PuzfTURw8H77UTVlSsF+32ZD6e7u5oVVq1j+8jrq1zfS0VJPd249yjWgtvWUdTRS2dFId+t6oq2ROTSzg5qZRRMz1MJMmqnQxjt0WjuD9W1BYwes6pjKqo6pPNU9lb123oaXy17N/3uqmcdebKG5U7R0QmsnlFdO5QMf+ywV1TP49YU/4MF776Kt9XLacueTy+WYv2ABV1x3HQAfP+1slt57LwBPAH8Att9hBw477DAAlt1/P8+sWNG37qyurmZaXR1TKysBOPztb6e1tbVvfE1NDVttvXXf+B/+7/8yZcqUvnFTpkxBEu2dnTQ0NY3PH8YGpPytm4LOWDoM+B5QDlwQEV8fqO3ixYtj6dKlY9KPfnW2QXsTHa3r6Wiup7N1PZ3N62mqX03DulU0r19DrTqYU9VJW9M6bn9wBW2tzbTlcrS1tdHe1sbhO5Sx/6sreLahh4/c0EZzR9CUd7rCNw6eypF7zODPz5Vx9MUvbNKFs056Kwfsswd/e+JlzvjuL5BE9dSqJEjV1XzqPz7Dbnst5tF//IPfXnXVhpViGqS3HX4487baitUvvcQzK1ZQW1tLdXV18rOmhhkzZ1JRUYGkvvOWy8vLkUSZRFl5OWVS32uVlSGSrdiydO+S0uFDUdq219zZs6mrqSnAH+qVS9J9EbF4HJc37LzCOGU2guhopq2pnuaGNbQ0rKWtsZ725rV0ttSTW7+a1oY1tDXVo/YmtpvRxdTuZh5cvpr6xha6Ozr6cjmvtowP/0tSEH7khhxPrO2hsR0aO0VzB+y5TR1fOXY3cmW1vOvbf2Ft08Z7at/4L6/lk588DU2Zxr/922m0tLRQNXVqX6F60MEHc8LJJyOJr33lK1RVVW1UxO72mtew1+tfT1dXF/fde2/fuJqaGqZWV1M3bRpTp07d6L2T2csbsNF3ECJiWHuC+/uMr6ys5M2LF1OZrqhtdF7Ree3upLtlHc3rV9Ncv4rW+pfINa6mo2kNal2H2uopa28gWuqJ3HrU3kR5RzM7z+qmqkI8ubab+1/s6TsS2tQB9R1lfPItr2L6zFn8clmOX9y3lub2oLmjh5b2blrau/jzZedSXrcFX/vRlfz8V9dv1KWysjJu/9vfkMTX/+u/uP6aa6isrOwrYmfNns35l14KwC8uv5wnHnusb1xNTQ2z58zhqGOPJYBHli0jl8v1rX9720xLv9vSazi11ObUW51dXcyaPp1999pr2NPY8A03s2NSJEsqJ9noOgRYCfwNeG9E/KO/9sMOcE833W2NNDWso3Hdauhopb2lnocfeoiG+rW0NNbT2tRAa3MjC2aUse/CGio6m/j6dY/R1tZBe0cHuY4uWjt6+D+7VPCJfato7gi2/r9NNHckK6FeZxxYw8cP2pLlTZXs/90ns++PYw99Awfsvy8vNHZx2RVXU1lVTeXUGqbU1FJdO403LDmcA96wN5Fr4I7bbqOuro7q9Lzbmpoatt9+e2bPmUN5upKrq62lsrKSyooKysvLqaqspLKyMilUJcrLyqioqKC8rMyHSF8BxnOlu7l5heFlNro7yTXV09qwlvp1L6OOFjpb1rPymad54fmVtDbVk2tqoK2lge72HO/ZeyZTupr57X3P8+CzTbR3dNLakaw8p00Rvz0u2fA69qpWrnu8i668HUo7zK3iNx/dk9ayWv79wr/z8DP1G/Vltx0XcfaXP0XPlOl874cXUl+/npraadTV1lBXV8vOu+7KiSedRGVFBddecw0RQV1tLbVpXufNm8f26WHPXC5HbW0tFekpQn15zNvoLJP6XvdufObntr8M9zssO653A3aQaYajTHKBXEAln9cIor2J1sZ1NDesoXHty7Q3r6Un10jjmpd4+umnaW2qp61pPR2tDXS2NnPodmXsPL2Np15q4n/v66C5c+NT/s5/x1R2mjuFnz4oPntDPe1dG9cal37leLZZuJBf/3kZP7r8dxuNq5wyhZ9edhlbbbUVN15/PX+44Ya+HUi9O4xOPu00qmtreWTZMp5dsSLZ2Kytpbq2ltqaGnbZfXfKy8pob2ujrLycKVVVlOft+MnfgZT5/QIbMpvdkdQ3Xf7OpPT1Rhkf+LfdN9+hTK+tZasttxyynW2+YhfJ+wFnRcSh6esvAEREvyeYbr3lrDjygD1oa2kml2ulLZdj0ewKPnrAFlT1tHLKZf/k6bUdtKQrzJYOOGbXCn7znmSlOeebTazLbfw+jn1tHV87ZhE51XDwt/9OWVkZU6sqqaqaQlVVFW/Ye1eWHPRGOsqq+fk1tzClZhpVNdOZOm0G1dNmstNuu7PH7rtTV1XB2heeZYtZM5g+fTozZs6kprqa8rIySIvXsrKyjX7mh6W/FaTZUMZ5pbtZeQXYcvb0eMd+O9PW2kJbrpW2tjbOPWobZpXnuOyuF7j8vsa+vDZ3QFcPdH15GuVl4kPX5zj//o2viFJVIe790utpLavhnOuXc9dja9K8VlE1tYpZs2bxydNPpnzqNP7fnUtZu76RmbPmMGf2bKZPn86WW27J4UccQZnEMytWUF5ezvRp05iWPirToyplwzgyYra5Jnpe582ZHkfvvzu51ibaWltpb8vx7r1m8NaFYsVL9Zz6m9W05B0JbeuCX76rmvfsXskfl3dxyGWtm8zza+9fzJ67bsedT9bzg1/dQVVVFVOnTqWquoapNTUcf+qH2WHn3Xh++RP89S+3J1/OTjc8a2trectb38qs2bNZv24dDQ0NTJs+nenpuN7vqVSUl6N0varMRmdZWRmVFRVUlJd7HWubrdhF8ruAwyLilPT1B4A3RMTpeW1OBU5NX+5RVlGxYhjzFUBAKLOhFskb6SGiJyCI6EkH9qTHOHrf6NicX9K/LYA147i88eL3NfYWRsTc8VjQcPKaDs/P7GvLKiqeTkckPwbfedInkmz2bJTN3tymDXBeC8nva+xN9LzuUVZRsUIbV5KbPpeSU30SQbJq7SaiJyK6yctobGiT/EyGjHduJ9L/QCH5fY29YWV2rL6419/KcqPQRMT5wPkAkpZ2d3aO2/lc40XS0vE8T228+H1NOkPmFSZ/Zifr39/va9JxXlOT9X/A72viGKtjjyuB/LszLAA2/faamU0EzqtZ6XBezcbJWBXJfwN2lLStpCnAccB1Y7QsMxsd59WsdDivZuNkTE63iIguSaeTXFKwHLgoIh4ZZJLzx6IfE4DfV2mZrO9rUCPIK0zO39VkfE/g9zWpOK8b8fsqLSX3vsbsOslmZmZmZqXK10MyMzMzM8twkWxmZmZmllH0IlnSYZIel/SUpDOK3Z9CkLSNpD9LelTSI5I+Uew+FYqkckkPSPrd0K1Lh6SZkn4t6bH077Zfsfs0ETmvpWcyZtZ5HR7ntfQ4rxNLUYvk9PaaPwQOB3YD3itpt2L2abQkVQMXAouBh4F9gY8O9L4kfVHSBSNc1m2S2iTdPuIOb75PAI+O4/LGy/dI/l7rgD2ZnO9xVJzXkswrTM7MOq9DmIx5TVWQfGFxa5Lbcw+YVyjJzE7WvN4EvAdYTwm9v2LvSd4HeCoilkdEB/AL4Kgi92m03gXMBGZFxLsjoonkH2K+pC9JOie/cUSc23vnpBE6PSIO7H0habakayS1SHpG0vsGmlCJb0hamz6+2XtHJkk7SbpW0mpJ6yT9QdKBwNuBC4AySd+V9IKkekk/klSZN+/mzKNb0vcH6cunJL0kqUHSRZKqBmg3Jd0iXSEpJB2UGV8l6SeSXk77fb2k+XnjL5f0oqRGSU9IOkXSdOBA4GySAB8aEesH/7W/IjmvpZXXnSUtIMnspcBuzusrymTMK8D+wFRgTkQcQ5pXgEmQ2cm4jj2dJK8XRsQyoB44YPBf+cRR7CJ5PvBc3uuV6bBSthB4IiK6ACQtAvYG7gF+TxKAsfRDoAOYBxwP/FjS7gO0PRU4mmRPzGuBdwAfSsfNJLn25s7pvO4Ffgf8B9ADbE+y9+01wE7A64D/7J1xRNT1PtLpc8Cv+uuEpEOBM4AlwCJgO5IV4EDuAN4PvNTPuE8A+6XvZ2uSlWj+B8d5wKKImA4cCZxD8jdZDVxMssflh5JqB1n+K5XzWnhjmddrgf8myexxwAyc11eSyZhXyMtsJq9Q+pmdjOvYs4AW4GJJDwDVwEcGWfbEEhFFewDvBi7Ie/0B4PtF6McK4HPAMpI/5oUk/3Q3Ak3AH0n2NPW2/xXJP08DcDuwezr8bJLwdALNJP8I9wH/J2/alcDWea/PAi5Pny8iub3oicCzJPc4/9Ig/b4NOCXvdW26/J3yhl0GfH2A6e8CTs17fTJw9wBtj0v7Ngc4iCQY784b/z7guQGmPRFYTnrJwX7G/xw4N+/1EuClYfzdVgIHZYb9GPhm3uu3A48PMP3OwIskHx5dwBtIViJd+f3xo+/35byWTl5np327MH39OHBf3njndZI/Jkpe02WPVWZX5Oc17/+sFDM7Wdexa4Bu4A3psAvSv19VsTMynEex9yRPpNtrHgscQrLF9k6S8H4R2IJkj/vH89reCOwIbAncD1wBEBFnAucCvwRmkRzauiIirs6b9iaSc8QGsz/JP9cS4CuSdh3me9gJ6I6IJ/KGPQgMtJW7ezp+OG3fRbJ1ex/JYbvpwMfyxgtYIGlGP9OeCPws0oQMsx/zJM0ZoP1gLgTeJGlrSTUkW/o35jdID1u1Ao+RrHSvAlZGxD0R8TzQTvI3sI05r/2biHk9kKQYOVTSCpI9R6+RdHk63nmd/CZSXqGwmb0KuBP4n0xeoXQzO1nXsS+Q5jUdfWn6c+cRLHvcFbtInki31/x+RLycfuj+BbgnIh6IiHbgGpJDOgBExEUR0ZSOOwvYs59/3AuBRyPiO5nhwzkcdHZE5CLiQZJ/5j2H+R7qSLa88zUA04bZvgGo6z1nqpeS8xr3A94fEYtI/k7/BMolzZX0KjZ8wNVkpn018GY2BGO4/WCQfg/mCZI9BM8DjcCuwFfzG0TER9J5HwBcTXJI8jlJvaHtTqe3jTmv/ZuIef0h8O8RsSDN7C9IiuZPOa+vGBMpr1DYzO5H/3mF0s3sZF3HXsXGeV0CtJGcbjLhFbVIjuQ8wN7baz4KXBVD315zrLyc9zzXz+s66Ls8y9cl/VNSI8nhHki2hnvNJTm09VZJf08fR6TjbgEOzD8Bvx/55wG19i57GJpJtj7zTSc5nDWc9tOB5vytUUlzgZuBH0XElXltnwQeAP5OckjptySHUFZllnECcEdEPL0Z/e59PlC/B/Nj0i91kBwau5rMVi5ARHRHxB0ke1c+TLLFfoWkZSTnTP1gBMue1JzXAZVCXi8jWTE6r68QEyyvULjMvhrYlv7zCpMns5NpHXsnG/K6F8lpJSXxZdti70kmIn4fETtFxPYR8bVi92cY3kdyWPZgki/CLEqH528Zro4IRcRrI2Kv9PF7gEi+Pb+Msfl25xNAhaQd84btCQz0wfgIG29Bb9RW0iyS8F6X/7eJiNsi4oiIOD0i5kfEdsBaknMeuzPLOIHBt3AH6sfLEbF2iOn6sydwSUSsS/dCfB/YR9IWA7SvALaPiL9HxGLgMJI9U0tHsOxJz3ktqHHJK0BE3BwR2zqvrywlmFcYOrPPkpwWtUleYXJkdhKuY6dGxOKIeC3Jdz+mkHxPYsIrepFcgqaRnAO3luSwx7kjmMcNwBFDttpMEdFCslX3VUm1kt5E8mFz2QCT/Az4tKT5krYGPgNcAqDkMkt/AO6MiE0uQt87jRL7Al8Gzsy0eSPJF2s2+catNr60zM+AkyXtln5o/GdvP/qj5BI0U9OXUyRNzTt89TfgBEkz0j0JHwFeiIg1kraUdJykunRvxaHAe4Fb82Z/EHBrGn4rfc4rzquVFGcWZ3aicJG8+X4GPENyPs4/gLtHMI+xvEzNR0gOP64CrgQ+3HuITdIBkprz2v4UuB54iOTC/DekwwCOAf4FOEkbX4vx1en47UkOAbWQbMWeERE3Z/pyInB1umXfR8n5V83pcomIm4BvAn8m+d0+Q96HgZK7Kh2fN4vHSQ7PzSf5kMmRXBYI4LMk5zs9SXKZqCPS9wLJIZ4Pk3yhpR74NvDJiLg2b97HAz/BJgvnNeG8WqlwZhPO7ASgGPDLkDaWJC0HlgxxHtFQ87iZ5GT/pRHxloJ1boxJej/JJX2+UOy+5JO0B3B+RJTMLTNtfDivzquVFmfWmS0EF8lFIulYkmsLPlzsvpjZ4JxXs9LizFohuEg2MzMzM8vwOclmZmZmZhkuks3MzMzMMlwkT3CSvijpghFOe5ukNkm3F2L+6aVoLpZUL+nekfSpENLL0zwmacti9cGs11hndLJxfm0imiw5Hs06XdJrJd011n0sJS6Sx0l6zcIdhmjzJUnn5A+LiHMj4pRRLPr0iDhwoJGbOf/9gUOABRGxT3Zken3ExyU1SFol6dL0WpC9K8YLJT0jqUnSA5IOH2hBkv5NUnfm0jgHpX1uBy4CPj/MfpsNaaJkVNLlkl6U1CjpCUmn5I3bV9ItktZJWi3pV5K2GqS/vSvv3gwNeAF/STPTzK5KH2fljdtS0pWSXkjzfaekNwz2piS9TtLt6XJflvQJcH5tbE2gHO8q6dY0L09JOiZv3GblOJ3mOEmPSmpRcjfCfm+WMpp1ekQsA9ZLeucwp5/0XCRPLGN5bcdCWAisSC+o3p87gTdFxAxgO5I77fR+EFUAz5HcY34GyYXRr5K0aJDl/TUi6vIet+WN+zlwoqSqkb4ZsxEYj4yeByyKiOnAkcA5kl6fjpsFnE9yF7KFJLeVvXiI+Z2el6GdB2n3XZKbNywC9gE+IOmkdFwdyU0EXg/MJrlu6w2S+r2dr5K7b91Eck3YOcAOJHcW6+X8WjGNaY4lVQDXAr8jycupwOWSdkqbbFaOJR0CfAM4ieRmKwcCywvQ1f7W6VcAHyrAvCcFF8kTSEQ8AMxVcmceACSdJeny9PmidCv5REnPSloj6UujWeZw5y/pZOACYL90z9DZ/fT/uYhYkzeom2TlSES0RMRZEbEiInoi4nfA0yQr3c0WEb0XK993JNObjcR4ZDQiHsm7G1Wkj+3TcTdGxK8iojEiWoEfAG8qxHsD3gl8MyJaI2IFcCHwwXS5yyPiOxHxYkR0R8T5JLeWHajo/jTwh4i4IiLaI6IpIh7Ne4/OrxXNOOR4F2Br4LtpXm4l2Yn0gXT5m5vjs4GvRsTd6frz+Yh4vr+GBVin3wYs8QZswkXyxHMTMOBpCKn9SVZOS4CvSNq1wH3YZP4RcSFwGhv27p7Z34SS9pfUQLJlfCzw3wO0mwfsxMD3vAfYOw31E5K+nG6d53uUje9HbzYexjyjkn4kqRV4DHiRZM9Xfw5k8AwBnJfm6E5tuE3tgIvOPH/NAP3bi6RIfmqA+ewLrJN0V3rqxvXacCexXs6vFdNY5lgDDOs3TwySY0nlwGKSov4pSSsl/UBS9TD7ApuxTk+L704G3gB+RXGRPPEM5zDQ2RGRi4gHgQcp/IpmxPOPiDvS0y0WAN8CVmTbKLnf+xXApRHx2ACzup3kA2VLkmL7vcDnMm2agJnD7ZtZgYx5RiPiIySHVQ8Argbas20kvRb4CpvmIt/nSU59mk9yePd6SdsP0PYm4AxJ09JzOj9IcvpFdrnTgctI3mPDAPNaQHLL3E8AryY5anRlpo3za8U0ljl+jOS21Z+TVCnpbSSnGvaXp6FyPA+oBN5F8nmwF7A38J/D7MtI3oezmXKRPPHcAhyYFpIDeSnveSvJ+YJDknS8NnyB58ZCzz9fujV6E/CLTB/KSFawHcDpg0y/PCKeTg8tPQR8leRDIt80YP3m9s1slMYso/nSw7R3kBScH84flxaxNwKfiIi/DDKPe9JTHdoj4lKSQ75HDND840AOeJLkfMorgZWZ5VYD1wN3R8R5g3Q/B1wTEX+LiDaSw8VvlDQjr43za8U0ZjmOiE7gaJIi/CXgM8BVbJqn4eQ4l/78fnq60xrgOwyc4/5s7vtwNlMukieYiGgClpFsMRZ63lfkfYFnqMNMhVBBei4lJJebITnPcR5wbPpBMlzBpoewdiXZKjYbN2OZ0QFkc7QQ+CPwXxFx2WbOq78cJSMi1kXE8RHxqojYnWT90Hepx/Qcxd8CzzP0F3uWpcvKXy6ZZTu/VjRjneOIWBYRb46IORFxKMkRnfw8DSvHEVFPUlyPy+2R0/O0pwADXgnnlcRF8viaImlq3qN8gHY3sHlbiUWTfingoPT58ZJercRC4GvAn/Ka/5hkxfjOiMhtOreN5nt4et4yknYhuRrGtXnj55N8a/juQr4fe8UrakaVXGrtOEl1ksolHUpyqtGt6fj56fMfRsRPhpjXTEmHpu+jQtLxJOc+/iEd3/ulnkXp6+0lzUmXezjJN/LPScdVAr8m2at1QkT0DPFWLgaOkbRXOu2XgTsiYn3e+3B+bawUfV2r5JrDUyXVSPossBVwSTpu2DlOXQx8LP18mAV8kuTKGb3L6lsPF8BBwK15Xx5+RXORPL4eIVnJ9D5OGqDdRL8UHACSFgDNwEPpoN2Au9Jhd5Jsif572nYhyd6nvYCX8k77OD4d/+r0de+Xe5YAyyS1kPw+rgbOzVv8+0jOaXaQrZCKndEgObWi9+oP3wY+GRG9G4inkOyROjMvQ829Eyu5kUDvqVSVJEXuamAN8DHg6Ijo3UO0DfAMyZ5hSK408xDJ+YjnAcdHRO+Xid4IvAN4G8l1VHuXfUC63APy+5F+m/+LJEXI/2fvzuPkqOv8j78+fc309NxH7hNIIEEOMbCcKoTTA1BwRUVRQX7uysrq7s8DWNBdXXZdD9x1dRcBwQsWDxb0x6IsiopchhuSQA5yJ5O5+76/vz+qM5npmSSTmZ7MTPJ+Ph796Omq6upvTfKe+vS3vlW1E+8qN+8fsJ3Kr4ynic4xeFey2I73/385cM6A/+/7k2OAf8C7BONreCe8PofXCTXcfnisPgCMpHA/JJhzB6QHX/aTma0HljvnXh/DOn4NnAKscM6dWbHG7V7/5cDRzrnPV3rd+/jcKrzDtG92zu08kJ8tsstUyOg+PvsGoMM5958H+HOVX5k0DoIcV2w/bGbHALc6504Ze8sODiqSJykzuwR41Tn38kS3RUSGUkZFpj7lWPZGRbKIiIiISBmNSRYRERERKaMiWURERESkTPltfidEa2urW7BgwUQ3Q2TSeOaZZzqdc20T3Y49UWZFdlNeRaaWkWZ2UhTJCxYsYMWKFRPdDJFJw8w2TnQb9kaZFdlNeRWZWkaaWQ23EBEREREpoyJZRERERKSMimQRERERkTIqkkVEREREyqhIFhEREREpoyJZRERERKSMimQRERERkTL7LJLN7A4z22lmLw8z72/NzJlZa+m1mdm/mtlaM3vRzE4Yj0aLyJ4psyJTh/IqMnmNpCf5TuD88olmNhc4B9g0YPIFwKLS42rgO2NvoojspztRZkWmijtRXkUmpX0Wyc653wPdw8z6BvAZwA2YdhHwfed5Emg0s5kVaamIjIgyKzJ1KK8ik9eoxiSb2YXAVufcC2WzZgObB7zeUpomIhNImRWZOpRXkckhsL9vMLMa4Hrg3OFmDzPNDTMNM7sa73AR8+bN299miMgIKbMiU4fyKjJ5jKYn+XBgIfCCmW0A5gDPmtkMvG+1cwcsOwfYNtxKnHO3OueWOeeWtbW1jaIZIjJCyqzI1KG8ikwS+10kO+decs5Nc84tcM4twAvtCc65HcADwIdKZ+CeDPQ557ZXtskisj+UWZGpQ3kVmTxGcgm4u4EngCPNbIuZXbmXxR8E1gNrge8Cf1mRVorIiCmzIlOH8ioyee1zTLJz7n37mL9gwM8O+MTYmyUio6XMikwdyqvI5KU77omIiIiIlFGRLCIiIiJSRntGfOUAACAASURBVEWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlNlnkWxmd5jZTjN7ecC0fzGz1Wb2opndZ2aNA+Z93szWmtmrZnbeeDVcRIanzIpMHcqryOQ1kp7kO4Hzy6Y9DLzBOXcs8BrweQAzWwpcBhxdes+3zcxfsdaKyEjciTIrMlXcifIqMints0h2zv0e6C6b9mvnXL708klgTunni4B7nHMZ59zrwFrgpAq2V0T2QZkVmTqUV5HJqxJjkj8K/E/p59nA5gHztpSmDWFmV5vZCjNb0dHRUYFmiMgIKbMiU4fyKjJBxlQkm9n1QB740a5Jwyzmhnuvc+5W59wy59yytra2sTRDREZImRWZOpRXkYkVGO0bzewK4B3AcufcrpBuAeYOWGwOsG30zRORSlFmRaYO5VVk4o2qJ9nMzgc+C1zonEsOmPUAcJmZVZnZQmAR8PTYmykiY6HMikwdyqvI5LDPnmQzuxt4K9BqZluAm/DOtK0CHjYzgCedcx93zr1iZvcCK/EOEX3COVcYr8aLyFDKrMjUobyKTF62+yjOxFm2bJlbsWLFRDdDZNIws2ecc8smuh17osyK7Ka8ikwtI82s7rgnIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiU2WeRbGZ3mNlOM3t5wLRmM3vYzNaUnptK083M/tXM1prZi2Z2wng2XkSGUmZFpg7lVWTyGklP8p3A+WXTPgc84pxbBDxSeg1wAbCo9Lga+E5lmiki++FOlFmRqeJOlFeRSWmfRbJz7vdAd9nki4C7Sj/fBVw8YPr3nedJoNHMZlaqsSKyb8qsyNShvIpMXqMdkzzdObcdoPQ8rTR9NrB5wHJbStNEZGIpsyJTh/IqMglU+sQ9G2aaG3ZBs6vNbIWZrejo6KhwM0RkhJRZkalDeRU5gEZbJLfvOsRTet5Zmr4FmDtguTnAtuFW4Jy71Tm3zDm3rK2tbZTNEJERUmZFpg7lVWQSGG2R/ABwRennK4D7B0z/UOkM3JOBvl2HjERkQimzIlOH8ioyCQT2tYCZ3Q28FWg1sy3ATcA/Afea2ZXAJuA9pcUfBN4GrAWSwEfGoc0ishfKrMjUobyKTF77LJKdc+/bw6zlwyzrgE+MtVEiMnrKrMjUobyKTF66456IiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISBkVySIiIiIiZVQki4iIiIiUUZEsIiIiIlJGRbKIiIiISJkxFclm9ikze8XMXjazu82s2swWmtlTZrbGzP7LzEKVaqyIjI0yKzJ1KK8iE2vURbKZzQY+CSxzzr0B8AOXAf8MfMM5twjoAa6sRENFZGyUWZGpQ3kVmXhjHW4RAMJmFgBqgO3AWcBPS/PvAi4e42eISOUosyJTh/IqMoFGXSQ757YCXwU24QW3D3gG6HXO5UuLbQFmD/d+M7vazFaY2YqOjo7RNkNERkiZFZk6lFeRiTeW4RZNwEXAQmAWEAEuGGZRN9z7nXO3OueWOeeWtbW1jbYZIjJCyqzI1KG8iky8sQy3OBt43TnX4ZzLAT8HTgUaS4eGAOYA28bYRhGpDGVWZOpQXkUm2FiK5E3AyWZWY2YGLAdWAr8FLi0tcwVw/9iaKCIVosyKTB3Kq8gEG8uY5KfwTh54FniptK5bgc8CnzaztUALcHsF2ikiY6TMikwdyqvIxAvse5E9c87dBNxUNnk9cNJY1isi40OZFZk6lFeRiaU77omIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlFGRLCIiIiJSRkWyiIiIiEgZFckiIiIiImVUJIuIiIiIlBlTkWxmjWb2UzNbbWarzOwUM2s2s4fNbE3pualSjRWRsVFmRaYO5VVkYo21J/mbwEPOuaOA44BVwOeAR5xzi4BHSq9FZHJQZkWmDuVVZAKNukg2s3rgzcDtAM65rHOuF7gIuKu02F3AxWNtpIiMnTIrMnUoryITbyw9yYcBHcD3zOw5M7vNzCLAdOfcdoDS87Th3mxmV5vZCjNb0dHRMYZmiMgIKbMiU4fyKjLBxlIkB4ATgO84594IJNiPwz7OuVudc8ucc8va2trG0AwRGSFlVmTqUF5FJthYiuQtwBbn3FOl1z/FC3S7mc0EKD3vHFsTRaRClFmRqUN5FZlgoy6SnXM7gM1mdmRp0nJgJfAAcEVp2hXA/WNqoYhUhDIrMnUoryITLzDG9/8V8CMzCwHrgY/gFd73mtmVwCbgPWP8DBGpHGVWZOpQXkUm0JiKZOfc88CyYWYtH8t6RWR8KLMiU4fyKjKxdMc9EREREZEyKpJFRERERMqoSBYRERERKaMiWURERESkjIpkEREREZEyKpJFRERERMqoSBYRERERKaMiWURERESkjIpkEREREZEyKpJFRERERMqoSBYRERERKaMiWURERESkjIpkEREREZEyKpJFRERERMqoSBYRERERKaMiWURERESkzJiLZDPzm9lzZvbL0uuFZvaUma0xs/8ys9DYmykilaC8ikwtyqzIxKlET/K1wKoBr/8Z+IZzbhHQA1xZgc8QkcpQXkWmFmVWZIKMqUg2sznA24HbSq8NOAv4aWmRu4CLx/IZIlIZyqvI1KLMikyssfYk3wJ8BiiWXrcAvc65fOn1FmD2cG80s6vNbIWZrejo6BhjM0RkBEadV1BmRSaA9rEiE2jURbKZvQPY6Zx7ZuDkYRZ1w73fOXerc26Zc25ZW1vbaJshIiMw1ryCMityIGkfKzLxAmN472nAhWb2NqAaqMf71ttoZoHSN905wLaxN1NExkh5FZlalFmRCTbqnmTn3Oedc3OccwuAy4DfOOc+APwWuLS02BXA/WNupYiMifIqMrUosyITbzyuk/xZ4NNmthZv/NTt4/AZIlIZyqvI1KLMihwgYxlu0c859yjwaOnn9cBJlViviFSe8ioytSizIhNDd9wTERERESmjIllEREREpIyKZBERERGRMiqSRURERETKqEgWERERESmjIllEREREpIyKZBERERGRMiqSRURERETKqEgWERERESmjIllEREREpIyKZBERERGRMiqSRURERETKqEgWERERESmjIllEREREpIyKZBERERGRMiqSRURERETKqEgWERERESkz6iLZzOaa2W/NbJWZvWJm15amN5vZw2a2pvTcVLnmishoKbMiU4fyKjLxAmN4bx74G+fcs2ZWBzxjZg8DHwYecc79k5l9Dvgc8NmxN1VExkiZneKccxSLxf1+XyZXIJbJE0vniaZyxDJ5oqk8sfSAnzM5Yuk8sQE//+flJzCrMTwOW1J5Pp8PM5voZlSS8ioywUZdJDvntgPbSz/HzGwVMBu4CHhrabG7gEdRgEUmnDI7+aUzGTLZLLl8nmwuRzKdJpVOk0ylSGYyZDIZcG6v63DOsbanwKMbc6zpKpDMO/L7qKsNCAegOmjUBIxw0AgH4PcrVtASnvyj8hyw9PDDmT979kQ3pWKUV5GJN5ae5H5mtgB4I/AUML0Ubpxz281s2h7eczVwNcC8efMq0QwRGSFldnLJ5XKsfv11tuzYgc+MonOYGT6fj4DfT8DvpyoYJFJdvcfe0lSuyGMbEjz0WpxNvTkiIePEOTU0VPuIBH3UhHzU9D8bkQGvqwOGbwr3wvZEo+Ty+YluxrhRXkUmxpiLZDOrBX4G/LVzLjrSw13OuVuBWwGWLVu2964REakYZXZy6YlGeX71anLZLC2Njfs9ZKBQdNz9Qh8Pr4mRzDkWNAX5+J81c/qCGqoCk78XWPZOeT14fPLu5zhyRh2fOPOIiW1INgEbH4d1v4Ftz8GHHwSf/lYMZ0xFspkF8cL7I+fcz0uT281sZukb7kxg51gbKSKVocxOLlu2b+fFV1+lNhKhtqFhVOt4fGOS+1dGOXlemLcfWc+RbaGDbWzuIUt5PfC27NjBynXrKBQKFV3v1miBB15I8s5kiP/xb6vouocTCYc59YQTCPj9UCxC+0teUbzuN7DpSShkIVAN80+FVDdEWse9TVPRqItk8/4K3w6scs59fcCsB4ArgH8qPd8/phaKSEUos5PP+i1bqK+royoUGtX7nXPcvyrK7PoAnzq9dUoPmZDBlNeJsXHbNsJVVaPO5J7cvaqLKr/xrmNbqa3yV3Tdw0m0r4PnVsHG38O630Ky05sx/Q3wZ/8HDj8L5p0CwalxYu5EGUtP8mnAB4GXzOz50rTr8IJ7r5ldCWwC3jO2JopIhSizk0gqkyGRStHaNPoreL24I83Gnhx/eXKzCuSDj/J6gGVzOWLxOM2jGPa0Nx2JPH/ckOS8xXXUVVfkVLAhfLkkdZ0vUL9zBfU7n6Ym+ro3IzINjljuFcWHvRXqZozL5x+sxnJ1i8fwTooezvLRrldExocyO7nEEokxr+P+lTGawn5OXxCpQItkMlFeD7zu3l4cjKhAzuSLbOjJsS2aI5N3rO/J4jPI5N3uR8GRyRfpSxdxwDuW1FWsrb58itqul6jveJa6jmeJ9LyKuQJFX4hY63GsnnYmh7/1CoKzjwN9gR618flKIyIie9XR3U0oGBz1+9d3Z3lpR5rLj28k6NdO8IAr5CDeDtHxH18qleOco6u3l95YjFgiQSKZJFcokM1kyBeL1Ef2/YXzlfY0//Z4F13JoeOWp9cGqA4YoYBRHTBqQwFm1xtHT6+iLTL6kssKmQFF8XNEulficwWK5ifRvJTtR36AaNsJxFuOwfmr6Ort5fDpR6tAHiMVySIiB5hzjh0dHdRUV496Hb9bnyDkN85eVFvBlgkA2STEtkN0K0R3PW8bPC3ejneFZplK1m3ezKvr1xMIBAgFgwQDAaqDQWqqqvANc4WH7mSeh9fG6UsX2diTpehgXVeWGXUBPnV6C22RAG2RAJGQr6JfVv3ZKLWdL1LX9SJ1nS9S07Man8vjzE+icTHtiy7ziuLWYygGair2uTKYimQRkQMsn8+Ty+UI1I6+wE3kitRX+YiEdOmmEXMOUj2lYnfbgMfWwdPSvUPfW90A9bOhbqZ38lP9LO9RNwu+eN6B3xYZsa7eXlavX08qnSadzdLa2IjfP7KT5/718S5eac/gN2gt9QRftLSed7+hnnCwQtlzjlCq3SuKO1+ktuuF/jHFRQuQaF5C+6L3Ems9jljrcRSDGl51oKhIln3K5ous2Rnj6Fmju0SViAyWLxRwYzgM6pwjm3caZjGA5dPQvR5iO7yCd9Dzjt09wPlU+TuhdppX/DYthPmnQf3M3QVx/WzvdUiFyVTknGPVunVksllqa2poqNv7uOBC0fFye5qXdqTZ0JPjlfYM7zuugXe/oXL7P18uSaRnNZGeV6jtWkmkZyWhdJf3+YEaYi3H0D33bGItx5FoXoLzV1Xss2X/qEg+BBSLRXqiUYrFfdybdrj3OseNv1zDo2t6+NnHjmd63cERVr/PR3Nj40Q3Qw5BxWKRaDy+xzOyhuOc48nNKZ7ZkmJNV4auRIFMwbGwefRjmqcKK+YJpjsJpjoJpbsIpjoJpjsJlaYF050Ekx0E8/Ghbw5Ue2fz182EmcfBkW8b3ANcP8ub7z/4f4+HmmKxyMZt29ja3k5fPM605uYhyzjn6E4V6E0VSWSLvNKe5vFNSXbE8gR8MKs+yHmLannnkvrRN8QVCEc3EuleSW33K0S6VxKOvo7h7Y/TtXOITltGonkp8ZZjSDYcDjb+l4iTkVGRfJBLZzK8vGYN7V1d+PfzjjrOOe5dmeG3G7K866gqtmxay5ZxaueBZsDZp5464kNuFeWcd8ejVE/p0e09J7t3T5ODjnOO9Zs3s37zZnKFAqER/t/bGs3xH092s7ojQ12Vj6PaqjhhVpjGsJ9jZox+TPOEcwUCmV5CqU6CqQ6v6E13ea/7i+JOgpmheSian1x1C7lwK5nauXQ2HE2kbSEt85buLorrZkB1o05cOoSk0mniyST5QoEtO3aws6uLhro6WofpEHlsQ4I7n+mhLz2488jvg4+d2MRbDovs/x0rnSOUbCfS+yo1Paup7V5JpGcV/nwSgHywjnjzUnpmv5l489EkmpdSCI2hAJdxpyL5IJbJZnnqhRfI5vPDfosulys4NvRkea0zw5pO77kjUeDtR9XxvhMqe93IidbVU6FCNJscXOgOKnZ3Fb89QwviQnbP6wzqJIyDjXOOV19/nbWbNtHS2OjdBWsEtkZzXP+rdnwGV5/UzPIjIpP/esjO4c9G+3t6vedSEZzq2t0LnO7G3OCrAziMXHVzqQBuI9G8hFx1K9lwK7nqttJzK/mqBrDdBUxPNMphc+bQMn/+gd5amST6YjGeeOEFXOmIaVUoRFtzc/9+K5Et8nJ7mk09OTb35XhiU5JFLSHec0yEprCf2pCP2iofcxqCI8uYc1QltlLT+yqRnte8597XCGSjgPdFLtVwBJ3zzvd6iZuXkqmdqy9tU4yK5INMvlCgLxajvbOTre3tmBmNpTFYRed4vTvLhp4ciWyReOmRyBbpSOTZ0J0lV/pS3VLjZ3FrFe86uorlR9QeVAXysHLpPffqDprWO7ggzqf3vM5ANYSbIdzkPVqPKP1cmlYzYN7A5YLVcMNB/vs+xOzs6mLdpk20NTUNewb9+u4sW/pypHJFUjlHMlekM5HnsY1Jqv3GzW+bwcy6CR4S4By+fLK/p7e/CE51eD2/A3qBfcXckLfnQg3kqlvJhVvoq19ILtxKtrq1NK2VbLiNXFUT+LRbkpHJ5fN09fSwvbOTnr4+QoEAdaVLuBWKjkfWJXilPc2m3hxb+3IUnHcUsSns57T5NXzspOYRnfhqhQzh6OvU9K6lpm8t4T7vOZDzhvgULUCq4TC6Z72FZNORJBsXk2w4XGOJDwL6azSF7foD0RONksvnyWSzdPf1USwWCQQC1EYi9GUcv1kX54Xt3okIsczuQ0s+g0jIR23IR2PYz/lH1rG4tYpFrSFaaqbmfw0r5ghk+vDnogSyMQKZPgK5KIFMlEAuij8bJZCNcliiG9/LBe8s9mT3MCfzDOAPDS5imw+DcOPei92aZt3u8xDW2dNDT18fuUKBVDrNjs5O6mtrBxXI8UyRLX05/rAhwa/XDB5P6zOoCfo46/AIbz+qflwLZCvmCKR7CGa6Caa7CKYHPA+a1o2/MDQn+UCEXKmHN9Z6rNcL3N/7Wyp+q5tVMEhF7OoIWv3660RjMcDrNQ4Fg1SFQmzsyfKvj3exNZqjUITmsJ8FTUHeNDvM8TOrOaK1itCeTnh1jlBqJ+G+daVi2Huujm3uH0Nc8Ie9gnjO2SRKBXGq4TCcT+PaD0ZTsxI6xCVSKTZt387m7dspFAoEAwF8Ph9+n4/GujoyBdjcl+Oh9TF+/nIUh/fN+YTZYY6bUc3i1irqq31UB2zS9hBbMd9f0AayfQSysdLrvtK03Q9/NtpfCA+3E9+laH4KoQbyoXpSvhponA81x+8ucPdY7NZU5BBZLpcjkUhQVVVFOBymr6+PZ599lng8Pujxjne8Y8yfJQdeNpcjGo/T2dPDuk2bCAWD/blsqKvnDxvTbOpNsKUvx5a+PD0pb6iBAW87so7zFtdSE/QRDhoh/xiz6YoEstHdBW9ZsRvon9ZNMNs37CrywbpSwdvsDXuoaiEXbvF6f3f1AodbD9prtBaLRZLJJPl8nsbSmNYnnniC3t7eQXlduHDhBLf00JHOZHji+edJZzKEq6uH3NJ9bVeGf/xtBz7zMrWotYqT54aHZqlUDFdHXycc3UA45j1XRzcQyO++E2a6ZiaphiPonnMWqYYjSDYcTiYya9BQH5kcnHNkMhni8TgtLS2YGevWrWP9+vWD8ppOp/nUpz414vWqSJ4iCoUCqUyGVDrNS6++2n9nIL/fz/ZYjl+vjrM1mmNbtIed8Xz/Je5Pmhvmvcc2MLchODEFcTFPIBfzenWzfQMK390FsL80z3tdWq50osNwnPnJh+rIB+vJVzWQC7eRajicfKjBmx5qoBCqJz/o0UAxEO4vdrt6ejjntNP2euJesVhk69atxGIbB4XsiCOOYOnSpfT19fHNb36TWCw2aP6HP/xhLrroIl577TXOPffc/umZTAaAO+64g4985COsWrWKs846a8jnzp49e4y/dDkQcvk8hUKBrt5eXnl9M+t2xuhKOXYmHcm8j75Mhr50gd50gZ3x3WNvD28JcdzMauY2BJnTEGRhc4im8MjGKPvySYLpntLwhlKRm9ld/O4qiAPpbnxu6N3Air4Q2eoW8tUtZGrnEm89ziuEq5r7C+JcdTO5qmacP1Sx39WB0tnZSXd396A8BoNBzjnnHMDL3po1a4YUuV/5ylcAOPfcc3nxxReJx+MkSrcNv+CCC3jwwQcBeM973sPWrVsHfeall156ALfw0FUoFHh+9WoK+fyQ4nhHLMczW1P86Pk+is7xxbOnc2RbFbjiMMWw9/PAfUyuqolU/UK65p1Hqn4hqYbDSTUcRiGoG/WMp0QiQXt7+5COonPOOYe6ujqeeOIJHnzwwUHzYrEYP/zhD2lsbOSWW27hq1/9av+8QqHQv96amhq+9a1vccsttwz6TDPj2muvHXEbVSRPcruu8bhp+3bvqghmhIJBmup3nxF7/ytRfrM+wbzGIIc1h3jLYREWNAaZ3xSiLeKvTHHsCviz8SHFbH9P7oDe3EAuij/T5xXHuWEuy7RrlfjIh+oolArbXHULqfqF5EP1ZUWu1/tbCNaRr2qgEKgZ9E1+Vw9tKpkkmUxSFapizty5ADz80EPEYjGSiQTJZJJUMsmRS5Zw4qmn4pxj+fLlQwJ61VVX8c///M8kk0nmzZs3pN033HAD//AP/0A6neamm24iHA5TW1tLXV0dtbW19PV5PXMNDQ285S1vGTSvtraWk046CYClS5fy6KOP9k8f+JDJJZPNEkskiCcS7Ozp4cXNPbzWmWVrrMia7jw7E4PvvFZX5aOx2k9j2Mfi1ipOmRdgVn2Atx429MQ7K+YJpDoG9fTuLnwHT/MPMyzI4SNb1Ui2qpl0VRN9kflkQ01kqprIVjWTCTaSqfJeF3zhQUdFBrbaudKrHJBLAand0yqoUCiQTqVIpVKkS48jjjwSgFWvvMKm11/3slqaVywWufIv/gKAH991F888/fSg99fW1fGvt90GwAc/+EEeeuihQZ931FFHsWrVKgDuvPNOnnjiCerq6vozWVOzuyf8xBNPZOHChYMyu2jRov75P/nJT/D5fEPy+tOf/rTivyfxOOfo6etj686ddPb0ML2lpX/ec1tT/OTlPnZ09rLQtvO+cDvvn9/HtPVbqXphM9XxzfgLu88b2VUMd86/gFTdAlL1C0nXLyBfpcuB7olzjmQyOWgfOW/ePJqamti2bRsPP/zwkI6ij3/84xx11FH87ne/48Ybbxyyj33kkUdYtmwZ99xzD1ddddWQz3z55Zc5+uij+dOf/sSXv/zlIfvQVCpFY2MjCxYs4Lzzzhsyf9ewtr/8y7/k0ksvHZLX/amJbDz+CO6vZcuWuRUrVkx0MyalLTt28MLq1bQ2NVHEyOQd2YIjnS/SmSiwI5bjB8/1sqApxN+fM33vK3MOfz7h9dzmYqUe3Cj+XT29w0zb/RzH9nALVodRCNYO6rkt9Be3df1Fbi5YR4owCaqpaZlFIVjL2jVr6e7u7i9gk8kktbW1nPe2twFw67e/zaaNG/vnpVIpFi1ezPVf+AIAl7zjHWzetGlQe966fDlf+cY3ADj3LW+hd8CVLKqrq3n7hRdy1Sc+wTmnnca5555LMBgcFKCzzjqLd7/73RSLRe64445B4aurq2P27NlMnz4d5xzFYnFcLiNnZs8455ZVfMUVcihktqMvzosbdvDq1i5e74zRmXTsTBTZGivSW7psVDhoLJ1WxaLWKuY1BpkWCTC9LkDY513eLJjpKY337SGQ6dk9zjfV5Q15SHcTyg0/3CEXiJAJNZEJNZGtKj2Hmrzxv+EWCuEWijVtUNOMz++dkW+l4R27ft61K/D7/YOuyzxwJ2EDxknvKuDNW4h8Pk8qmSQSiRAIBGhvb2f9unUkEwkSySSJRIJkIsF73/c+amtreeThh/nlL35Bcte80vN//+IXRCIRbv7yl/n2t741ZFs3bNmC3+/n85/9LD/8/vf7p4dCIZqam1nx3HMA3PL1r/PUk08SiUSoiUSIRCK0trbyt5/5DA11dTz+2GN0dHQMymtjYyOLFy8GvDsdBgKV7xtSXsdHJptl1bp1bG1vJ5F1ZKMdFHs2EYhupjaxmen5rRxm22mz3Rly+MhEZpKum0e6di7punmk6hZUpBh2zvV/cSwUi/33Htg1vVgs4ga+Lj0758hmswQDgWGH7u1a596KN+fcnueXOtCKxSKZdBozI1JbyynHHcfzzz03pEg95ZRTeNOb3sS2bdu44YYbhsy//vrrueSSS3j66ac5+eSTh3xZvueee3jve9/LI488wtlnn90/3efzUVdXx7333su5dkVGTwAAIABJREFU557LH/7wB/7u7/5uSJH6yU9+ksMOO4w1a9bw+OOPD8prbW0tixcvprq6mkKhgM/nG5ej4CPNrHqSJ1ihUCBfKJDOFXh1R4xX2+Os3hFl9Y4Y23rTJDI5cgXIFBIUHYAjQpoGEjRYgkaLc5YvydvrHTNey5aKXK+49Wej/QWxN+Qh3n/ywXCK5icfrPN6bEO1ZIIN9Pim00GAqL+aaCFINBcgWQxx+hmnkg/W8chTL7HytQ2kUmlSqRTJZJJQMMjnb/osAF+7+Wae+OMfSaVSpJJJCoUCc+bO5Yc/+xlksnzl5pt5/plnBrXjiMWLOaM0DOGlF19ky+bNhMNhr8e2ro66hgaSaa934MJLLiGTThOuqelfZsasWf3zv3PHHYRKY4Crqqv7C9pkyuuRe+SRR/b4+/D5fMN+y93FzCbmOstSMc45uuJpXtvew/qOGBs64rzWHmNVe4qO5O6sGEWmBxIcGUlwalOCYxtSHF4Tpz7fiz/dTaC3h2B7D8FsH6FsL8EB4xoHKviCZELNZKuaSEdmk55xAq6mjUJNK0SmUd0yD1c7DRdpwxf0xlJW+XzUh0L4RvD/LZvNDnv48sQTT2TGjBm89tpr3HPPPYMOXcbjcb785S+zdOlS7rvvPj796U/3z9s1ROiFF17g2GOP5Rf33cc111wz5HM/dPnlzJs1i0QsxrPPPNO/02tqbGTunDlMb2mhsbGRi975Tqa3tQ3aYdbV1TFnxgz8fj9f+5d/4R+/9CVqa2uJRCKEQoOHfHy1NCxiT84999y9zh+PAlkqJ5/qo2/jS/RuWcnOTa/iejZRm97B8fl2phU7CdruIUS9Vk+0di6F1tPYXD+/vyDO1M4echKdc45CsUg2nSaXy+0ucM0wBhegeypGnXP9Xz59gD8YJOT3e69Ly/v9foLBIH6fj4DfT8Dvx+f3e+8Dbx9VNfgE1vb2duKxGPFEgkQ8TiKZpK2tjTe+8Y0AfG3AcIJdw4DOWr6cj3zkI6TTaU4+6aRB8wA++9nPctMXv0i0t5czzjhjyLbcfPPNvOlNbyKXy/Hwww8PyuPs2bOJlK4QMnfuXG644YYheT3xxBMBOOWUU1i/fn3/vOrq6kG/uzPOOINHH310j//eixYtGnSkptxk2L+qJ/kASecKbOhM8MLmbjr6kvT1dRPrbifR10U2FcMyMepIUE+CFl+CmaEkbf4E9ZakziWJuDg1xRjhQgI/ZdcWdY5UHuJZRyxr9BbDLJzRgD/cwMpuPyu2ZIjlA8TyfqJZI56Fa664mFBdCz//zTP84uHHSKZz/YVuKpnk/l//mlAoxDe+8hV+fu+9gz7PHwjwh6efxufz8eUvfIFf3n8/NTU1XqFaU0Nrayv/+b3vAXD3D37A2jVrCNfUUFN6NLe0cOG73gXAqpUryaTT/e+viUSIlH4eT1WhEG9YtGjynrionqmKyRWKbOtNsak7ycbOBOs7YmzsjNPR3U2mbyeRfC8tFvUeRJkViLKgKs40f5RG10dtoY/qXHTYL5gOIxdqoBBupljdTDHcgtW24WpaIdKGr7YNaqfhq51GoH46Fm7E5/ORz+cH7fhaWlqYPn06sViMBx54YMgYvIsvvpjTTz+dtWvX8vGPf3xIEfzv//7vvPe97+XRRx/lzDPPHNLO++67j4svvpgHH3yQt7/97f1DhHY9brvtNk466SQef/xx/uM//mPI4cv3v//9TJ8+nc2bN7Nu3bohPUP19fXDXtruUKG8jpBzEN8JPa9D9+sUutaT2P4q2Y51VMe3UFvoHbR4j6tlq02jOziTQt1sqtoWUj1tITTOoxCqp1gski0VvdlcjmKh4B3vNOvvXTW8IyPBYJCa6mpqIxEi4TChYJBAqcgN+Hz9PZZm1n/Cra9U7JYPOQD6j0489NBDbN26dVBe586dyyc+8QkArrjiCtatWzdo/vLly/nxj38MQFtbG52dnYO2+wMf+AA//OEPAYhEImSz2UF5vPzyy7nuuusoFotcfvnlQ/J66qmnctppp5HL5fjd7343bF7Lv4AeatSTPN4KechESUe76OvtINHbRayvi3hPB5l4N4VEDy7diz8bpSofp9bFqSPJWcQJ5BJEAgVCfqM37XhlZ4F4dleR64hl4dijG5jZ2sBT23x85eleolkjkYV4ropk1vH1v7mMJUuWcvf/Pstn/+nWssMhMVaufJQlS5bw+Ne+xt9++W/75+waT3fzrT9j1qxZ/GkL1D21kpmzBn9TPPWNb6S6uhrftdfyzgsuGBLCY489FjPjZ/feSyAQ2GOxeeI+en5OPOaYSvxryCEsls6xrTfNju5eol07iHZuI9G9nVx0J8V4B6FMd6kA7uNYi3JWqSCuIgd+vMcAOX8NWX8DuVAjuarZdASPphhuJdIyE3/ddJ7f1EeyWE3aFyad9xNPJlm8eDFnnXUW+Xyea665hnh8HbHY8/07xve///1ce+21dHd3M2PGDHK5wdcR/tKXvsT1119PT08Pl19++aB5NTU1HHHEEZx++un4fD5SqRQNDQ3Mnj27P5cLFiwAYMmSJXz3u98dcvjyiCOOAOC8884jn8/vsYfm1FNP5dRTT93j73ru3LnMLY33FxmiWIT4DujdDL2boG8TrmcT6c4NFHs3URXfRqC4e4ywOSNKC5uK09jkjqczOINMZA6+pjk0TpvLtOZ6aoO7T5JNA1GARAGSvfh8PiLhMDXhMPWRCLWRCJSuctDS3EzA72fjhg3s2LGDeF8fO0t5dM7xsY99DIDvfOc7PPXUU4PG1ba0tPDAAw8AcP755/OrX/1q0GYef/zxPFcaAnTjjTfypz/9qX9eKBTizDPP7C+Ss9ksVVVVtLS09GfyhBNO6F/+G6WhgQPzOmPGjP75PT09eyxofT5ff7E9nGAwOGg4hOy/cetJNrPzgW/i7YJuc879056WnZBvuc6RT8eJ93WRinaTinaSifeQS3iPYrIHy/RhqV5iPV3kEr3k0zHyqTiFTJJjW/Ic1uSjI1HktmdzpQLXK3TjWceVJ1TzZ4c38NQOP//nJ+0kckWS2SKpbAHn4N8++TbOPmMZj72ynY994btDmvezn/+cc887j1899BCf/Ku/8oYZDPgm+C//8i8sWbKEFStWcP/99w/ZKZ599tnU19fT3d1NX1/fHg+HyOR0oHum9ievMP6Zzeey9HTtINrZTqK3nVTfTnLRTgqJTkh6V28IZnqoyffS4GI0WYxa83a+PSlHX8b1Z7En44eqWk5YNINCVSM/fraPbXEjUQiQzPtJ5o0jjzqSz/zfv8VfN40L3nExGzds6N9h5vN53ve+9/XvjOrr64mVrs+6y5VXXsltt92Gc47Zs2dTU1MzqOfmz//8z/noRz9KNpvlpptuGpLXY489lqOOOop8Ps/69esHnVQ2GQ45yt4dMnnNxCG2HaJbyXRvJrlzA7nujVjfZqriW4mkt+N3+UFv6XZ1bHGtbHFtbHWtbHVtdAWnkwzPpFA7nfogNFuCpkAacmnvpMxkkpNOPpn6+npWvvQSL7/wAtlMhkx699HOu+++m3A4zM0338x3v/vdQZf4CgQCZLNZzIyPfvSjfK90ZHOXhoYGenu9XuuPfexjQ4YczJ8/n9tvvx3wroiyefPmQXmdPn06y5cvB2Djxo39nU/DDRGSyWlCe5LNzA/8O3AOsAX4k5k94JxbWanPcMUiyXgfiWgPyWgXyWgXfZ076OvaSay3g6piimk1YJk+fvun1WRTcbKpBLl0imw2w+mzi1xylJ9QzvG+e5PEMrsL3HgWPnVyiE+c1sBr0SrOvGXzkM//0NtP4oKzTmFrMct1v/kOPp+PcLiaSCRCJFLLGSf/DUdefDH1W7fyps6veAGrraW+oYGG+nrOvugijjrqKNrO7GL+qe8Z0lPb2NhIIBDgkne/m0ve/e49/h6WLVvGsmV7/ndubm6meQS3pJZD13jnNZ1OEe3uIN7XQbKvk2y0g1y0g1RvO+nedjLRDoqJHo5uzlOT72P1pk62dSf6j67Es46Q37jhzd5Yvs/8Js9jm4rEct7QoWTOMb25lu9+7nJCDdO48ovf57lV6we14aSTlvDYdx4jEAjwg+OP58UXXxy0U2yZv5TGOUsAeOPxx7N40aJB848ZcMTjvvvuIxQKDcprfelqM2bGtm3b9vi7CIVC3HzzzXucHwgE+g/jigxnXPLqHKR6KPRtJdGxiVTXZrLdWyj2bcUX305Vsp2q1A6K6UR/JufU+2iqMp7treV/NlaxLRtmZ3YB3fkQ0XyQxae/DZt+JDvXvsJrv/055LeSz6wjn/GuSvK9H/2Io48+mh/edRdfLJ2IPdDmzZuZM2cOv33wQf7tlluorq4elMl8Po+ZMXfuXM4444whQwp2jS3+9Kc/zQc/+MFBeR14BaHvfndoJ9VAH/3oR/c6f75uhX5QG5eeZDM7BfiCc+680uvPAzjnht07LDp8obvur68i1tNJvK+beLSXhip469I2fJkYt/7yGTp6YqQzGTKZHKlsjtPn+Pjqud5Oc/bXY2yLDd6ODx8f5D8vrCVOhBlf2tJ/u2UAv8+46IylfOTdZ5H2h/n8V39EdU2EcE0Nkdp66hoaOfu8C7jwwnfiikV+du+91NfXU19f33/poMMOO4zZs2bhnCOXyxEKhdRDKxVzIHum9jevAAvnz3F/c/VlxHo7SfZ1k4r1cfGyWYSLMR57fj1/XN1OJpMhm82RyhZI5BxPXxXBzPirB1N8e0WudCKqpybo4w83nEIyUM+NP3mF3z6/+4olZsbMGdP41f+7j4bWmdz8z1/lxeef3/3Fsq6OhQsWcOONNwLw3//93/T09AzaKba2tnLUUUcBkE6nCYVCh/QYWqmsyZ7XwxfMdZ/7+GXEe9pJ9XaSjnVz0pwgRzdl6epo5z9/304qW+gvgGNZx9+/tZrjD2/l/vVBrrl7PbnC4H3shz75f1l88tlseOlP3HbzDYPmhcNh7nvgAU495RQe/tWv+OpXvzqkI+i6665j9uzZvPTSSzz55JNDithjjjmGqqoqstmsNzZYJ11KBY00s+NVJF8KnO+cu6r0+oPAnznnhp4WDdQEzaUGH6HhzfP9/OKKNhJWwwW3bWdnvEC4Kkh1VZDqqiresGgOl739dIrBOu5+6EmKFqSmtp5IfSONLW0cueRoTjn1NKqrq9n4+us0lHpwGxoaqAkPcwcekUnkAO909yuvpWWG/OFY+bezqa5t4Bt/iHLP0x2Eq4NUV4Woqq6mOhzmuk9+iKraZp54YS0btuykqXUazc3eVQ+ampp4z6WXEvD72bFjB7lcrn9nGVZeZZKbinm96W0zueCkw3mhw8dn7nqa6qoqQtVhQuEIoZo63nbZhznjzW/Gkj386oH7vI6iujpvX9rQwFlnnsnMmTOJRqPs2LGjP6+R0k2uRCaziS6S3wOcVxbik5xzfzVgmauBq0svj/EFAhvwrm3vcK7oSs845xwUS2em7fn6ZZNTK9C5z6WmHm3X+JvvnGs7EB80kryWpg/M7LG+QGA9pWw654pA0XkXBS2yK8tTy2T6968kbdf4m+x53bWPLeJccUBeiwPyOhVNpv8DlaTtGn8jyux4Hb/YAgw8BXoOMGignnPuVuBWADNbUcjlJu3lc0bLzFZM5ssCjZa266Czz7zCwZ/Zg/XfX9t10FFeSw7W/wParsljvAbl/QlYZGYLzSwEXAY8ME6fJSJjo7yKTB3Kq8gBMi49yc65vJldA/wK7xI1dzjnXhmPzxKRsVFeRaYO5VXkwBm300Wdcw8CD45w8VvHqx0TTNs1tRys27VP+5lXODh/VwfjNoG266CjvPbTdk0tU267JsVtqUVEREREJhNdKFREREREpMyEF8lmdr6ZvWpma83scxPdnkows7lm9lszW2Vmr5jZtRPdpkoxM7+ZPWdmv5zotlSSmTWa2U/NbHXp3+2UiW7TZKS8Tj0HY2aV15FRXqce5XVymdAiecDtNS8AlgLvM7OlE9mmCskDf+OcWwKcDHxib9tlZmEz+4WZ9ZnZT/a1cjO7zsxuG03DzOxRM0ub2e9H837gWmDVKN+7X0p/AN86wmWPLP1hiZnZJ83s62b28f34uG8CDznnjgKO4wBt41SivO6mzA7vAGZWed0H5XU35XV4yusIeNf+n5gHcArwqwGvPw98fiLbNE7beT9wzl7mfxB4GggMM+964EsVbMujwFVl05qB+4AEsBF4/x7eOwd4FngOyAEbhlnmeOAPQB/e9TxvHDBvKbAC6Ck9/hdYWqHtuh34xoDXM4HNQGgE760HXqc0Rl+PPf6elNfdyyizY9+uUWVWeR3x71d53b2M8jr27Tok8zrRwy1m4/2Sd9lSmnbQMLMFwBuBp/ay2HzgNedcfph5DwJvr3zLBvl3IAtMBz4AfMfMjh5muVtKjwfZ8zfBHwO/x/uj8BbgL8zswtK8bcClpXmteNf2vKdC2zAf6L8MknNuO7AauHCP79jtMKAD+F7pm/JtZhapULsOJsrrbsrs2I02s8rryCivuymvY3dI5nWii2QbZtqUuNyGmW0ws/9rZi+aWcLMbjez6Wb2P6XDEf9rZnOAnwF/DdxuZjtKh3t+vysgZvZF4EbgvWYWN7MrB36Oc+45oM3MZg347C+Y2Q9LPy8wM2dmV5jZJjPrNLPr92M7IsAlwN855+LOucfwgvXBsuXeAex0zn0feBhI7mGVC4AfOecKzrl1wGPA0aVt6XXObXDe10sDCsARe2nbBjM7e8A232tm3y/9fl8xs2Wleb8BzgS+VfodLi6t4lFG9scvAJwAfMc590a8b/sHxfi9CpuyeYURZfa3wH8Df+2ci5rZT5TZSZlZ5XVklFeUV5TXMZnoInlEt9ecxC4BzgEWA+8E/ge4Du8bnB/vP9CPnHM/L81bBEzDO5zyIwDn3E3APwL/5Zyrdc7dPsznPIQ3rmxvTgeOBJYDN5rZkhFuw2Kg4Jx7bcC0FyiFboDTgAvNbAPeN9Pj8baz3C3Ah8wsaGZH4h3y+9+BC5hZL5AG/g1v20fqwtJnN+L9kfkWgHPuLLzDT9eUfoe7tmUV3vinfdkCbHHO7eqN+CleqGWwqZ5X2HNmZwDHAjtKeQVltt8ky6zyOjLKK8oryuuYTHSRPNVvr/lvzrl259xWvP9AT5W+lWaBCN4YnK8DOOfucM7FnHMZ4AvAcWbWMMLPGcnhoC8651LOuRfwAjiS4hCgFm9s00B9QN3ACc65zzvn5jjnFuD9Oz0PdA6zvl/iHe5J4R2Kud0596eydTUCDcA1eGOvRuox59yDzrkC8AP2vY0xvLDvlXNuB7C59AcHvD+CK/ejXYeKqZ5XGCazeP+XvwO8iLdjAZTZsnVNmswqryOmvCqvyusYjdsd90bCTf3ba7YP+Dk14PVpwIlA3MyeL017DXgT0AYUS9NaGRqe4TwM3GpmQedcbg/L7BjwcxIvmCMRxxtYP1A93n/+/WJmzXjfyK/BGzc1A/ipmbU75749cFnnXMLM/gPoMLMlzrmdI/iI8m2sNrPAHsaZgfdHqHeEzf8r4Eelncl64CMjfN8h4yDIKwyf2dPwDn1uARpLmb0Br+foPSizwKTLrPK6D8oroLwqr2M00T3JlL61LHbOHe6c+/JEt6cSSmOOPobXs3w88DXgGOBsvG93C0qLDjdmbLj1xfC+NZ9R8cZ6xXvAzBYNmHYcAwboD9OeR/H+KJU7DO+w0vedc3nn3Ba8Qzdv28OqfEAN43cyyRK8b/z75Jx73jm3zDl3rHPuYudczzi1aUo7WPPqnDPgi+zObBNwEcpsuUmRWeV1ZJRX5RXldUwmvEg+RNQBGaAL7z/s/owR2uX/secgjJpzLgH8HPh7M4uY2Wl4f2x+MNzyZuYzs2og6L206tK3Q/D+GJiZvb+03AzgvZRCZGbnmNkbzbtYej3wdbzL1Kwqzf9waTxWpbwFb5yayP5SZlFmZcpQXlFex4OK5APj+3jXRtyKNxbnyVGsYzwvU/OXQBjYCdwN/MWuw3JmdoaZxQcs+2a8w14PAvNKP/8awDkXBd4NfAovmM8DLwO7ejAaS+vvA9bhnXV7vnNu17iyucAfK7FBZjYT75qR/12J9ckhR5n1KLMyFSivHuW1wsy5KXNFmEOema0HljvnXh/DOn6NdzbsCufcmRVrXAWU2natc27Md+Mxs68B68rHaYkcSMrsfq1LmZUJpbzu17oOibyqSJ5CzOwS4FXn3MsT3RYR2TdlVmTqUF6lnIpkEREREZEyGpMsIiIiIlJGRbKIiIiISBkVyZOcmV1nZreN8r2PmlnazH5fifWb53tm1mNmT4+mTZVgZlVmttrMpk1UG0R2Ge+MHmyUX5mMDpYcj2WfbmbHmtnj493GqURF8gFiZs7MjtjHMteb2ZcGTnPO/aNz7qoxfPQ1zrk372nmfq7/dOAcYI5z7qTymWZ2mZm9amZ9ZrbTzO4qXatx147xdjPbaGYxM3vOzC7Y0weVrudYMLP4gMdbS23OAHcAnx1hu0X2abJk1Mx+aGbbzSxqZq+Z2VUD5p1sZg+bWbeZdZjZT0qXYtpTe3ftvHdl6NW9LNtYyuzO0uMLA+ZNM7O7zWxbKd9/NLM/29tGmdkJZvb70ue2m9m1oPzK+JpEOV5iZr8p5WWtmb1rwLz9ynHpPZeZ2SozS5jZOjMb9sYnY9mnO+deBHrN7J0jfP9BT0Xy5DKe12mshPnAhtLF0YfzR+A051wD3p2BAsCuP0QBYDPexccbgL8D7jWzBXv5vCecc7UDHo8OmPdj4AozqxrtxoiMwoHI6M3AAudcPXAh8CUze1NpXhNwK94dxebj3dr2e/tY3zUDMnTkXpb7Bt6NGBYAJwEftP/P3p2HyVXW+d9/f2vtpXrvztLZgYQlIBAiIItsgsiuAuKgMIg/UFREHEdlxgHlmWcEfzNuo4yMoqAOyIA86DCKCAKySgAJgQAJWTvpfa99u58/qrrTXd2ddNLdqe7weV1XXVV1zqk696nk0+db97nrHLOBy8eGgBeAo4Ba4E7gITMb9dK8ZlZP7vK5PwLqyJ2v9Q9DFlF+pZimNMdm5gMeBP6HXF6uAn5hZsvyi+xWjs3sdOAWcpdzriB3LuUNk9DU0fbpvwSunoT33ieoSJ5GnHMvAw1m1jgwzcxuMrNf5B8vzn9LvtzMtphZh5n9w0TWOd73N7MrgR8D78n3DH19lPZvdc51DJmUIbdzxDkXcc7d5Jzb5JzLOuf+B9hIbqe72/KX4+wGjt2T14vsib2RUefca/neVgCXv+2fn/c759x/O+f6nHNR4N+B4ydj24BzgVudc1Hn3CbgJ8An8uvd4Jz7N+dcs3Mu45y7HQgAYxXd1wMPO+d+6ZxLOOf6h56bVfmVYtoLOT4IaAS+nc/LY+Q6kT6eX//u5vjrwDecc8/l95/bnHPbRltwEvbpjwOn6Qtsjork6ef3wJjDEPJOILdzOg34JzM7eJLbMOL9nXM/AT7Fjt7dG0d7oZmdYGa95L4Zfxj4zhjLzQaWsZPr1wNH5kP9lpl9Lf/tfKi1wOG7tWUiEzflGTWzH5pZFHgDaCbX8zWa97LzDAH8Sz5HT1t+yNLOVl3w+NAx2ncEuSJ5/RjvcyzQZWbP5Idu/NbMFhYso/xKMU1ljm2MaaPmiZ3k2My8wEpyRf16M2sys383s9JxtgV2Y5+eL75TjP0F+B1FRfL0M57DQF93zsWcc6+Qu2b7ZO9o9vj9nXNP5YdbzAe+BWwqXMbM/OQO6dzpnHtjjLd6ktwflFnkiu2PAl8qWKaf3GU4RfamKc+oc+4acodVTwR+DSQKlzGzdwH/xMhcDPVlckOf5pE7vPtbM9t/jGV/D3zFzCryYzo/QW74ReF6K4Gfk9vG3jHeaz5wOfB5cpfW3UjucrlDKb9STFOZ4zfIXYL6S2bmN7MzyA01HC1Pu8rxbMAPXEju78ERwJHAP46zLXuyHcpmnork6ecR4L35QnIsLUMeR8mNF9wlM7vUdvyA53eT/f5D5b+N/h64p6ANHnI72CTw2Z28foNzbmP+0NKrwDfI/ZEYqgLo2d22iUzQlGV0qPxh2qfIFZyfHjovX8T+jtwlZv+8k/d4Pj/UIeGcu5PcId+zxlj8WiAGrCM3nvJuoKlgvaXAb4HnnHP/spPmx4AHnHMvOOfi5A4XH2dmVUOWUX6lmKYsx865FHABuSK8BfgicC8j8zSeHMfy99/PD3fqAP6NsXM8mt3dDmUzT0XyNOOc6wdWk/vGONnv/cshP+DZ1WGmyeAjP5YScqebITfOcTbw4fwfkvFyjDyEdTC5b8Uie81UZnQMhTlaBPwRuNk59/PdfK/RcpSb4VyXc+5S59wc59xycvuHwVM95sco/n/ANnb9w57V+XUNXS8F61Z+pWimOsfOudXOuZOcc3XOufeTO6IzNE/jyrFzrptccb1XLo+cH6cdAMY8E847iYrkvStgZiVDbt4xlnuI3fuWWDT5HwWcnH98qZkttJxFwD8Djw5Z/DZyO8ZznXOxke827H0/kB+3jJkdRO5sGA8OmT+P3K+Gn5vM7ZF3vKJm1HKnWrvEzEJm5jWz95MbavRYfv68/OMfOOf+YxfvVW1m789vh8/MLiU39vHh/PyBH/Uszj/f38zq8uv9ALlf5P8/+Xl+4D5yvVqXOeeyu9iUnwIfNLMj8q/9GvCUc65nyHYovzJVir6vtdw5h0vMrMzM/g6YC/wsP2/cOc77KfC5/N+HGuA6cmfOGFjX4H54EpwMPDbkx8PvaCqS967XyO1kBm5XjLHcdD8VHABmNh8IA6/mJx0CPJOf9jS5b6L/J7/sInK9T0cALUOGfVyan78w/3zgxz2nAavNLELu8/g18P8OWf3fkBvTrCDLZCp2Rh25oRUDZ3/4v8B1zrmBL4hbuhraAAAgAElEQVSfJNcjdeOQDIUHXmy5CwkMDKXykyty24EO4HPABc65gR6iBcBmcj3DkDvTzKvkxiP+C3Cpc27gx0THAecAZ5A7j+rAuk/Mr/fEoe3I/5r/BnJFSBu5s9z8zZDtVH5lKhU7x5A7k0Uzuf//pwGnD/n/vjs5BriZ3CkY3yL3g9eXyXVCjbYfnqhLgfEU7u8I5txe6cGX3WRmG4DTnHMbJ/AefwDeA6xyzp0yaY3b8f4fA5Y757462e+9i/UGyR2mfa9zrm1vrltkwEzI6C7W/Y9Au3PuR3t5vcqvTBv7QI4nbT9sZocBtzvn3jPxlu0bVCRPU2b2YeBN59yaYrdFREZSRkVmPuVYdkZFsoiIiIhIAY1JFhEREREpoCJZRERERKRA4WV+i6K+vt4tXry42M0QmTZefPHFDudcQ7HbMRZlVmQH5VVkZhlvZqdFkbx48WJWrVpV7GaITBtmtrnYbdgZZVZkB+VVZGYZb2Y13EJEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQK7LJINrM7zKzNzNaMMu/vzMyZWX3+uZnZ98xsvZmtNrMVU9FoERmbMisycyivItPXeHqSfwacWTjRzBYApwNbhkz+ALA0f7sKuG3iTRSR3fQzlFmRmeJnKK8i09Iui2Tn3JNA1yizvg38PeCGTDsfuMvlPAdUm9ncSWmpiIyLMisycyivItPXHo1JNrPzgG3OuVcKZs0Dtg553pSfJiJFpMyKzBzKq8j04NvdF5hZGfAPwBmjzR5lmhtlGmZ2FbnDRSxcuHB3myEi46TMiswcyqvI9LEnPcn7A0uAV8xsEzAfeMnM5pD7VrtgyLLzge2jvYlz7nbn3Ern3MqGhoY9aIaIjJMyKzJzKK8i08RuF8nOuVedc7Occ4udc4vJhXaFc64F+A1wWf4XuMcCvc655sltsojsDmVWZOZQXkWmj/GcAu5u4FngQDNrMrMrd7L4/wIbgPXAfwLXTEorRWTclFmRmUN5FZm+djkm2Tn30V3MXzzksQM+M/FmicieUmZFZg7lVWT60hX3REREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQK7LJINrM7zKzNzNYMmfYtM3vDzFab2QNmVj1k3lfNbL2ZvWlm75+qhovI6JRZkZlDeRWZvsbTk/wz4MyCaY8Ahzrn3gW8BXwVwMwOAS4Bludf80Mz805aa0VkPH6GMisyU/wM5VVkWtplkeycexLoKpj2B+dcOv/0OWB+/vH5wD3OuYRzbiOwHjh6EtsrIrugzIrMHMqryPQ1GWOSPwH8Lv94HrB1yLym/LQRzOwqM1tlZqva29snoRkiMk7KrMjMobyKFMmEimQz+wcgDfxyYNIoi7nRXuucu905t9I5t7KhoWEizRCRcVJmRWYO5VWkuHx7+kIzuxw4BzjNOTcQ0iZgwZDF5gPb97x5IjJZlFmRmUN5FSm+PepJNrMzgS8D5znnokNm/Qa4xMyCZrYEWAr8ZeLNFJGJUGZFZg7lVWR62GVPspndDZwM1JtZE3AjuV/aBoFHzAzgOefcp5xzr5nZvcDr5A4RfcY5l5mqxovISMqsyMyhvIpMX7bjKE7xrFy50q1atarYzRCZNszsRefcymK3YyzKrMgOyqvIzDLezOqKeyIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBXZZJJvZHWbWZmZrhkyrNbNHzGxd/r4mP93M7Htmtt7MVpvZiqlsvIiMpMyKzBzKq8j0NZ6e5J8BZxZM+wrwqHNuKfBo/jnAB4Cl+dtVwG2T00wR2Q0/Q5kVmSl+hvIqMi3tskh2zj0JdBVMPh+4M//4TuCCIdPvcjnPAdVmNneyGisiu6bMiswcyqvI9LWnY5JnO+eaAfL3s/LT5wFbhyzXlJ82gpldZWarzGxVe3v7HjZDRMZJmRWZOZRXkWlgsn+4Z6NMc6Mt6Jy73Tm30jm3sqGhYZKbISLjpMyKzBzKq8hetKdFcuvAIZ78fVt+ehOwYMhy84Hte948EZkkyqzIzKG8ikwDe1ok/wa4PP/4cuDBIdMvy/8C91igd+CQkYgUlTIrMnMoryLTgG9XC5jZ3cDJQL2ZNQE3At8E7jWzK4EtwEX5xf8XOAtYD0SBK6agzSKyE8qsyMyhvIpMX7sskp1zHx1j1mmjLOuAz0y0USKy55RZkZlDeRWZvnTFPRERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpMCEimQz+4KZvWZma8zsbjMrMbMlZva8ma0zs1+ZWWCyGisiE6PMiswcyqtIce1xkWxm84BrgZXOuUMBL3AJcAvwbefcUqAbuHIyGioiE6PMiswcyqtI8U10uIUPKDUzH1AGNAOnAvfl598JXDDBdYjI5FFmRWYO5VWkiPa4SHbObQP+L7CFXHB7gReBHudcOr9YEzBvtNeb2VVmtsrMVrW3t+9pM0RknJRZkZlDeRUpvokMt6gBzgeWAI1AOfCBURZ1o73eOXe7c26lc25lQ0PDnjZDRMZJmRWZOZRXkeKbyHCL9wEbnXPtzrkU8GvgOKA6f2gIYD6wfYJtFJHJocyKzBzKq0iRTaRI3gIca2ZlZmbAacDrwJ+AC/PLXA48OLEmisgkUWZFZg7lVaTIJjIm+XlyPx54CXg1/163A18Grjez9UAd8JNJaKeITJAyKzJzKK8ixefb9SJjc87dCNxYMHkDcPRE3ldEpoYyKzJzKK8ixaUr7omIiIiIFFCRLCIiIiJSQEWyiIiIiEgBFckiIiIiIgVUJIuIiIiIFFCRLCIiIiJSQEWyiIiIiEgBFckiIiIiIgVUJIuIiIiIFFCRLCIiIiJSQEWyiIiIiEgBFckiIiIiIgVUJIuIiIiIFFCRLCIiIiJSQEWyiIiIiEgBFckiIiIiIgVUJIuIiIiIFJhQkWxm1WZ2n5m9YWZrzew9ZlZrZo+Y2br8fc1kNVZEJkaZFZk5lFeR4ppoT/J3gd875w4CDgfWAl8BHnXOLQUezT8XkelBmRWZOZRXkSLa4yLZzCqB9wI/AXDOJZ1zPcD5wJ35xe4ELphoI0Vk4pRZkZlDeRUpvon0JO8HtAM/NbOXzezHZlYOzHbONQPk72eN9mIzu8rMVpnZqvb29gk0Q0TGSZkVmTmUV5Eim0iR7ANWALc5544EIuzGYR/n3O3OuZXOuZUNDQ0TaIaIjJMyKzJzKK8iRTaRIrkJaHLOPZ9/fh+5QLea2VyA/H3bxJooIpNEmRWZOZRXkSLb4yLZOdcCbDWzA/OTTgNeB34DXJ6fdjnw4IRaKCKTQpkVmTmUV5Hi803w9Z8DfmlmAWADcAW5wvteM7sS2AJcNMF1iMjkUWZFZg7lVaSIJlQkO+f+CqwcZdZpE3lfEZkayqzIzKG8ihSXrrgnIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUUJEsIiIiIlJARbKIiIiISAEVySIiIiIiBVQki4iIiIgUmHCRbGZeM3vZzP4n/3yJmT1vZuvM7FdmFph4M0VkMiivIjOLMitSPJPRk/x5YO2Q57cA33bOLQW6gSsnYR0iMjmUV5GZRZkVKZIJFclmNh84G/hx/rkBpwL35Re5E7hgIusQkcmhvIrMLMqsSHFNtCf5O8DfA9n88zqgxzmXzj9vAuZNcB0iMjmUV5GZRZkVKaI9LpLN7BygzTn34tDJoyzqxnj9VWa2ysxWtbe372kzRGQcJprX/HsosyJ7ifaxIsU3kZ7k44HzzGwTcA+5Q0DfAarNzJdfZj6wfbQXO+dud86tdM6tbGhomEAzRGQcJpRXUGZF9jLtY0WKbI+LZOfcV51z851zi4FLgMecc5cCfwIuzC92OfDghFspIhOivIrMLMqsSPFNxXmSvwxcb2bryY2f+skUrENEJofyKjKzKLMie4lv14vsmnPuceDx/OMNwNGT8b4iMvmUV5GZRZkVKQ5dcU9EREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllEREREpICKZBERERGRAiqSRUREREQKqEgWERERESmgIllE5B0mnckWuwkiItOer9gNEBGR4ZxzpNLpSX/Pp9/u5PY/b2LZrBD/ePZB43qd3+fDzCa1LSIiM4GKZBGRaSSeSPDqW2/R2dMzKe+XyTpeaknzh7cTbO3LUhU0FpdGeey5Xb9/Kp3mqOXLaZw1a1LaIiITk806+uIpunp6sdd+Tb8rZX39qfTGUvTF0ly0cj6N1aXFbuY+Q0WyiMg00d3Xx1/feIN0Ok1ddfVuvz6ZcTz8Vj/b+9LEUlni6SxbelK0RzI0Vvr49LHVnLi4HL93fD3DnT09ZLMamiFSKJ5IkEgmSaRSxONxIrEY8WQSnJuU99/UneSRt/rpjKXpj2foS2TpS2RoTG7mEs+jfMj7FJUW5feZd3N9qm7wdbWePg6dM3aR7Pf7OWT//fF4NNp2PFQki4gUUSKZ5I0NG+gLh+kPhykvL6e6omK332ddR4IfPNvJtr40VSUeSv0eSn3GvEo/lx9Vw7vnl+LRsAmRPZZKp9nW2srW5mbC0ShmhgOM3LAkr8cD48xY1jlawxl64tncLZbl8U1xAOIpR28i9+W0scJLQyDB3/ie5fTsYyzlTdLm443K43lz9ln01hzKN4Meyv0eSv2Gz5OhNxwec73ReJwDlyxRkTxOe1wkm9kC4C5gDpAFbnfOfdfMaoFfAYuBTcDFzrnuiTdVRCZCmZ1+IrEYL7z6KslUivLSUupqanZ755XOOu57tZcHXuujptTLP5zSwBGNOtw60ymvxZfOZOjq7SUcidDW2Ulvfz9Z56goL6e+pmaXr886x7qOJM39KeIpx+ttCbweiKcdq5piI5b3GMwK+Vi5oIRSr3FK+QbeG/0jtU2P4k3HiFUsYstBn6Fz0QdIB6tZuAfbFE8k9uBV71wT6UlOA190zr1kZhXAi2b2CPC3wKPOuW+a2VeArwBfnnhTRWSClNlpJJFMsurVV8lms9RUVo66jHOOWNqRTDtSGUcym7tPZRzhRJauWIaH1/WzsSvFSUvKuWJlDeUB9RDtI5TXvWygp7i3v59wJEI0HieVTuP1eikJBqmurNytL7HffLydl7fHh03zemB+lZ9l9QFmh3ycsn+I2lIv1aVeyvyGP9FD3Zbf07DpIUo3byLjLaVrwal0LD6HcO2h4+6plsmxx0Wyc64ZaM4/7jeztcA84Hzg5PxidwKPowCLFJ0yWxyZTIZwNEomm8U5RzqTIZlMsqWlhWQ6PebQiv/8Sxd/XB8mu4shjpVBD3/33nqOWVA2Ba2XYlFe965UKsWbmzaxqamJ8rIy/D4fFeXleL3eMV8TTmRp7k/RE8/QE8uwrS/Nlp4kHZEMfYkMkaRjboWPG05poMSXGw4R9I1SZLsMVS3PU7/pf6hufgqPyxCuXc7GFV+ha/6pZP3KdrFMyphkM1sMHAk8D8zOhxvnXLOZjfqzaDO7CrgKYOHCPTloICJ7Spnde7a2tLD6zTcJ+P1ArncYoDQYHLNATmcdT26MsKw+yNELSgl4jYDX8A/cPEZ5wENtqZeaMi8+j3qX9mXK69To7OmhPxymqaWFcCxGJpulobZ2XL3FvfEM1/22mXByxw9bvR5YWO1ncU2A6lIPFQEvxy4qY06Ff9T3KOnbTN2W31O/5WECsTZSgWraDriQ9sXnEK9cMmnbKXtuwkWymYWA+4HrnHN94z2fpnPuduB2gJUrV07Oz0FFZJeU2b2rpb2d6ooKSktKxv2atzuTxNOOsw6q4D0L1Yv0Tqa8To0NW7ey9u238fl8lJWUjPtsMs45NnWn+O3aPiLJLJetqOaQWUEqS7xUl3h3eeYYX6KH2q2PUrfl94S61+Lw0Dv7aLYcfi09c4/HeUYvqKU4JlQkm5mfXHh/6Zz7dX5yq5nNzX/DnQu0TbSRIjI5lNm9K5VO09PXR+1uns7t1ZY4Bhw6Ozg1DZOJyaSgtwm6N+VuU0R5nXzOOXr6+1m3eTO11dX4djKcovB1j2+I8OvX+mjpz13o5wMHhjj34NF/TzCUZRJUNz9D3ZaHqWp5Fo/LEK06gC2HfZauBe8jVVo/oW2SqTORs1sY8BNgrXPu34bM+g1wOfDN/P2DE2qhiEwKZXbvi8XjYLbbV6x7pTnOfrUBKoLj24HLFIh17yiCuzbueNy9KVcgu8yUrl55nXzZbJYNW7fy5saNlJWWjqtATmcdj60P879v9rOtL1ccX3NsLUtqAyyq3kmvbzZNZftL1G79IzXbn8SXCpMsqaP1gIvpXPR+YlUHTNZmyRSaSE/y8cDHgVfN7K/5aTeQC+69ZnYlsAW4aGJNFJFJoszuZZFYjPEe585kHRu7k7zWkuCN9gQfXL7rHiqZgMLe4O6CQjjeO3z5snqoWQzz3w2HXZR7PHD7+pSM+VVeJ1EkFmNN/kqWhT3IbeE023pTtEfSdEQzdMdyt65ohu39KTJZCAU8fGJlDScsLhv7y6vLUNGxmtqtj1Kz7XH8yR7SvnJ6Gk+kc+EZ9M06CkxffGeSiZzd4ily59AezWl7+r4iMjWU2b0jm82STKXo6u3lzY0bKQ2OPWTiT2+HeWJjhO19aXrjmcEzWTRW+jh1//K91OJ9VDYD/S3Qs2XIbXPufrTeYG8AqhflC+GjhxfBNYsguPsXeJkI5XXPRWIxevr66OjuJhyN5s4uk8kQCARoqK0FcsMnfrW6l5e2xdjYnRp8rcegptRLTamXORU+DpkdZPnsEo6eX4p3tB/IOkd512vUNj1KbdOfCMQ7yHhL6Jl7PF3zT6N3zjE4r4ZNzVS64p6IyCTpC4f5y+rVpNK5w7JlJSWUlY5+YQ/nHD9+oZuqEg9HzC2hptTL3EofRzaWUlWi3qZd2lkR3LMlVwRnU8NfE5oD1QtgwdHwrouHF8IVjaCrkM14b27cyIatWwEIBgL4fT6qKypGnLHiL00x7l/TR9BrfOzIag5qCFJT6qWuzDt6MTyUy1Le9To125+ktulPBKPNZD1+euccy9b576Nn7nFkfbqgz75ARbKIyCQJR6MkU6nB3qqxZF3uFG/JjOMDB1aM68c/7zh7XAQvhHlHwfIP5h5XL8z1EFfNB//4zzAiM088kWBjUxO1VVWjnsYtmXG8sDXKX5piPLM5CsBPLpw3+rmLC1g2TUX7y9Rsf4Lq7X8mEO8ka176Zq1k2yGfoKfxRDL+0KRvkxSXimQRkUkQSyTY1tqK37/rUzg9sKaPe1b3UlPqZcU79RLSKoJlksTicTY2NdHU2orX4xm1QH6lOca3n+okksxS5jeOW1TGeQdX7LRA9qSjVLa+QM32J6lufgZfqp+Mt4TeOcfS3fheeue8h0xg7w7Dkb1LRbKIyCTYvG0brZ2dzNpFL/If14e5Z3UvRzaW8OWTGnZ9aHem2qMieHa+CF4Byy/IFb8qgmUnYvE4z61eTSqZJFRejt+3o6yJp7O8tC3GH9aFea01gdfgH09t4LA5JXjGOONMMLyNqpZnqW5+hoqOl/FkU6QDlfQ0npArjGcfrTHG7yAqkkVEJqi3v5/1W7ZQV1W109O9bepO8qPnu6gIevjYkdUzu0CelCK4sCf4HdqrLrstlkjQ1tnJ2rffxu/zUVNVBUAq43irI8EDr/WxujmOA6pLPHxweSWnLw3RUD687LFsmvLONVS3PEN18zOU9m/KvX9oIW37f5ieOccRrn8XzqNy6Z1I/+oiIhOQSqVYu2EDpcEgPt/of1Kdc9z2fBePvx2hPGB866w51JVN8z+/LkMw3kGguRdaIyqCZVroj0To7O7mjY0bcc5RFQrh9flY35ng3tW9rGlNkMo4fB4466AKDp9Twrvmluz4Quocwcg2Klv/QlXrC1S2v4g3HSVrPvobjqB9yXn0zD2ORGh+cTdUpoVp/ldaZrr+eIo3WvpZ29yHz+Phb46ZkvOJTlw2C/EeiHYOv0U6cjv3d11c7BbKNOSc4y+vvkokGh3syRrN620J/vR2hPcdEOLCwyqnR4HsMvhjnQSjzQSjLQQiBffRVjwuPfw1KoKlSKKxGE2trazbtAmvx0NFKMSm3iy/XxPm+a0xWvrTGHDcojLes6iM/WoDg73G3mQ/le0vUdn6Fypb/0JJtBmARNlcOhe8j77ZR9M7691k/eM/7WI2myXrHNlslnQmg8s/dy53Hkfn3LDnAwqfjzbPzMA5MMPl1+UdMs7aOTd4xCqdyeAvuCjKaGsYeI3X693tixu9k02Dv9RSDIlkMnceyZ4e4onEhN8v6xxt4TSbu5Ns6k6yqTvFpu4kbeEdO9ml9QEODPUPPt9vwQJqKqfgV/3OQTIypNjtgmjHKAXwkMexLnDZ0d9v2ZkqkmVU4WiU/kiEup1cdro7luHB1/vwGFy2oppS/146zdgeFMHJkjqSZXOI1BxE9/xT6KKKWQccScOSI1QES1H0hcO8vXUrLe3tOGBztISN3Sn+ur2TdZ1JvAaHzA5ywSGVHD63hPpyH55UlIrOF6jY8DIV7S9T3v0mRpaMr4y+hqNoWfZR+mYfTaJ8HgwpGDOZDPFkkkQyOayYHVqUDjz3mOHz+fD7/ZSVlODz+fB5vbkiFPB4PHg9nsHnQ9djZsNOgG35q3IOu+WnD1yx02Wz+Hy+Ha/Lv59zDt9uFL5er3fcl+IWFcnvKPFEgq7eXra1ttLZ3Y0zI5AP9s6ks4542hFLOWJpRzzliKWzdEWzbO1Ls6U3zdbeDPF0/lswMCfkZXGVj/cuDLCgysfCKh81pR7CkQgAveEwc+rrx1ckp5O5IjZSWOiOVvzml8uMUfh7fFBWt+M266D84/oh02uhPP+8tBYCZbvzMcs7gHOOaDzOtrY2sgU9Q73xDI9viLCxK/eFceBStmcuC01qgexJRQhGWwnEWglE2wjE8rdoG4FxFsGJsjkkyuaSLJ9Lomz2iB8kdfb0ULVgKdTPmbR2i+xKNpulqaWF1s5O2rq6CPr9lJRV8r1nOnl5ez8GzK/y88l313DC4nIqLE5F5ys7iuKetzCXIWs+IrWHsP2gy+ibfTSd5fsTSaRymU2B9fYO9tgC+LxeqisrWTB3LgGfj0D+PMv+/H7S5/XiyZ89Q72x7wwqkvchA4d9MpkMqXQ6dwjIOcLRKK9u3M7Gjn4iSUfWfGTwEks7Yqk00VSSWCpLLOWI5u9jqezg42Rm7MNDpX5jUXWAk/YrYXFN7lr2C6v9Y59Wx2XxJvuxRAuB5jh0ux3DGgYL34GCtyP3PNE39kaXVO8obivnw9zDhxfBgwVwbe5xSdWwb/TFlkwmCYfDw277779/sZslY0ilUrlDvps357KWhc1hP49v7aUtkjuSsrknRdZBQ7mXhdUBTtqvnCMbS1lUvetTww2wTIJAtHVH0Rtr2/E8P82Xjgx7jcNDqrSOZOmsXBE872QS5XNJlM0hWTaXRPkc/Sp/gjKZDJFIZFheQyGdG3cyZbNZ1m3ezLrNmwmVlREKVfLX5gT/9ddWOiJprjyqmjMbI9T2vkaocw2hp9ZQ1rNuWFHcfODH6Gs4kkjtoSTx0ReJkM5kqPD4OHDJfMrLygZPFef3+/F5vQT8fhW++xjnHLFYbFheo9Eoxx577LjfQ0XyDJJKpYjEYsQSCSKxBNFEkkg8QX8sQVd/lN5Yku5Yls5YlvZIlo6ooz2ae54eYySB16DU76HUb5T5PZT6PVSVeJhb4cs/3zG98HFV0MPskiT+ZB++ZBu+RA/+SA++7l58iR58yV78+Xtfohdfsgdfog9jjMb4y3b05JbVQe1+o/fuDtxKa8A7/sJjIpxzgwWt3++nsrKSeDzO008/TX9//7AQnnTSSbz73e9m69atfOUrXxlRBH/jG9/gwx/+ME8//TQnnHDCiHXdd999e2WbZNfSmQx9/f109/fT3NrKxo4IzeEMnUk/L21LsKErycB3yNpSL/OqfHxweSXvWVjGoprAqO9p2RT+WPuQ3t8hPcH5e3+yd8TrkoFqEqUNREvm0FXzLuIlDcSD9cSC9cRLGkgEashYwdjEgV7uLNAfA2IjxkSaWe5wcu5Jbjxj/vByOBqdyMdXVOl0mkgkQjKZpKGhAYBVq1bR0tIyLI91dXV8/OMfB+DLX/4y69evHzb/6KOP5j//8z8BWLBgAc3NzcPW85GPfGTvbtg+LByNsn7LFrY2N9OdKePuFyO80tTH0uxGLgq+zYWNm2jcsJbAa+0AZLwlRGoOovnAS+lrWEG4Zjlx5yWZSpFKp3HhOF6Ph0WNjdTX1Ix5kREpPucc0Wh0MJM+n48tW7bw+uuvD8tjf38/1157LeXl5dx///3ce0LSzTEAABkrSURBVO+9I/axq1evJhgMct111/G9731v2Hq8Xi+pVGqMVoykInmaSSSTxOK509ZEE2le3trDlo4+trT30tqfoCfuaItk2dY/RqGZV+ozZlf4WFwb5JiQjzkVPmaFfIQCXsr8NljoBrw2+O3ZsqkhxeyOe3+yF1+8B1/vjoLXn8wVwp5sctT1O/OSDlSRClaRDlQRq1xMOlBNOv+8K+1n/n7LqW/cf0fROwXDGjZv3kx/f/+wQraxsZFjjjkG5xw33njjiICdffbZXH311fT397N8+fLB16bzlxq+8cYbuemmm+jq6uJ973vfiHXeeuutvPvd7yadTvP8888TCoUIhULU1NSwYMECqvI/8FqyZAn//M//PDh/4LZixYpJ/xxk/GKJBH3hMB09vfx5bROvtSbY0pdlS2+W7vhA7mLMDvk49+DK3Fj7hmDuUtIugz/eRSD6FoGm4b3A/lgbwWgr/kQ3VvDTmpSvnHhJA5FgPZ0N+xMPNhAvqSfsqyFb0UgmNBfzl+D1eAgGAoO9Xh4zQh4P1fkxiR6PJ3f+1zHGPw4sM/B42P2QxwwZF1m5F3tKOzs7aW9vH5bXVCrFhz70IQDuvfdeVq1aNaIn96677gLgsssu43e/+x3hcJh4PA7A8uXLWbNmDQCf//zneeaZZ4at8+ijjx4sktesWcPmzZsHs9jQ0MDixYsHl/3qV79KKpUaltdFixbxq1/9aqo/mn1aV08Pb2zcyJvbe3h7WxvptrdYEl/LF73rOcy/AT+5oiYRm0N//eGEaw8jXLecSMV+hBMpEsncfsgiCcpKSmiorSVUVkZNZSWh8nKNwZ0isViMbdu2jegoOvHEE5kzZw5r1qzhv/7rv0bsY7/97W9z4IEHcs899/CFL3yBcDhMJBIZ/AK/du1aDjroIO6//36uv/76Eev92Mc+Rnl5Odu3b+eVV14ZzOLcuXMJhUKk02mCwSDnnHMO8+bNG7GP3R0qkosolUrR2tlJd28v3eEoTT0xntoYoT2apSfu2N6fJZLasTMt9Ru1pV5mVQQ4ar6fyhIvXoOgzwj6PJT4jNoyL7PKfVT6UvhT/fke3b5ccRvrw9fbiy/RlyuAB6Yn8kVxweHbodL+UL7IrSZZNoto9VLSwWrSgSrSwWpS+fuB5xl/aKfDGjq6u2lYcCBu1izi8Tjh3jDhcCvhcBiPx8Py5csBePDBB9m2bdvgN8hwOMzixYv5/Oc/D+R6cTZs2DBs/plnnsk999wDwIoVK+jq6hq27ssuu4xjjjkGM+Nb3/oWPp9vWID6+3M/LiwrK+O0006joqJicF5FRQXHHHMMAA0NDTz55JPDXltRUUFZWa7YX7JkCevXrx/zM2hsbOSGG24Yc77sHc45esNhXt/SxosbWljfHuXNrgxvd2VIZXPFY2Olj4NnBTm6Ps6yYA/zvZ1UpTpyBfD2VgLr2/DHWgnEO/G4zLD3T3tLiAfrSZTOIjr7WKhsxFXOw1Xk7qmaB4EQ3vwh36AZ1fli1uv1UhqcXkMkUqnUiJ3e4YcfTiAQ4JVXXuH5558flseBnWJJSQm33XYbd91117B5kUiESCSCx+Phhhtu4Pbbbx+2vrKyssEi+Te/+Q3333//sMwtWLBgcNmjjjpqRB7nzp07OP+HP/whyWRycF4oFKK8fMcZDR566KGdbvvnPve5yfgIhdz/o/Ytr9Py2p/Zvu5lasIbOCf7Ng2WO4qS9vuJVC+js/5DhOsOo6/6EMLeSqLx+GAx5YnEmF1fz6zaWipDIcpLSzVkokA2mx0xRGjOnDnMnTuXnp4efv3rXw/rpQ2Hw3zkIx/hhBNO4PXXXx/sMBr6+jvvvJMPf/jDPPXUU5xxxhkj1vnQQw9x1llnsX79em699dZh+9BQKDT4BXbBggWce+65w/I68OUU4OKLL+bYY48dNi8UChHM/0383Oc+t9NMnn766Zx++ukT+vxUJO8lqVSKaCLF6qZu/vR6E1u7orT1p2iPpOlNMFgMm+XGMtaV+Vgxz8uxC0pZUp6k3hshlO3Hmxzoxe3Fm+zFF+/Fm+jNFbvJPnzJPvypPryZ+JhtSXtLSPkrSforiPsrSYaWkqypJBmoyk/fcUsFqkj5K3AeH9lslmQySTwepzr/a/6tmzfTtrmVWLSDWGwrsWgUM+Pc/E7tgXvv5Y3XXyceixGLxYhFo1TX1PD1W27B4/Fwyimn8MQTTwxr31FHHcWqVasAuPnmm3nxxRfzn40RCoU49dRTB4vkQCDArFmz2G+//Ubtib3tttvweDzDAjp79uzB+QM759F4vV5++tOfjvk5+v1+TjzxxDHny/STzTq29cRYvbWT15u6eaOll/VtEdrCSSozvcyxLmZbN2eU9XFwfR9L/N3MpoPSeDuBjjY8bcMP02XNRzxYT7yknljtu6BqPlY9H6uaT6B+Cb7aRfjL6yjz+QgV4TDvwBChsrIySkpK6Ojo4JVXXhmxU7zkkkuYP38+Tz/9NLfddtuI+Q888ADLli3j3//930fdKW3cuJHFixfzu9/9jq9+9auD00tLSwmFQtx8882U5M8AUFlZSWNj47AdYyaTwePxcMUVV3DKKaeMKHQH3HXXXfziF78Yc3sH/i6M5fDDD9+DT1EmzDno2062+RXim1+ge91zlHetpTHbTSNwuDO2eeezrXolHY2H4GYvpze0H4msEcsXVJ60h6oSPwvnziVUXk5JMEhJMLhP9RIPjHl3zg0ecXziiSdG5PGwww7jtNNOI5FIcPXVV4+Yf+WVV3LttdfS3NxMY2PjiPXceuutfOlLX6Kjo4Mrr7xycLrP56OiooKjjjqKE044Ab/fTyAQYNGiRcPyuN9++wFw2GGH8fOf/3xEXhctWgTA+eefTyqVGvOLy/HHH8/xxx8/5ucxb9485s2bt8ef52SwnZ2zb29ZuXKlGyiKZjLnHF2RJBvb+9jSGebtlh42dfTzZluMjmiGcDxNFWGqLcxsb5hFJVHmB6PM8YWp84apdv00eCOEsvliN9mLL9U/omdqcH0YKX+IdKAy14MbqBp8nAlWkSmppi9bSmfMQzgbIOYCJAiSzcK5551HMBjkiccf5/HHHycSDhPO9+hEwmHuu/9+vF4vN3/jG/z0pz8dDKFzjmAwSF++t/XKT3xixE6rtraWlpYWAK655hqeeOKJYd8EFy5cyPe+/318Xi8///nPaW5uHlHEDozV3b59+2BPb+k7qJfAzF50zq0sdjvGMt0zm806mrpjvNHcw4bNm2lr3kSsowkLt1HncoXwbOuh0dvFHOuhxvXiKRgrnzUPiUBdrgAO1pMun4O3ZgFWNR9/3WI81QvwVc6mrLQMv39yxsan0+lRD18eeuihHHDAATQ3N/Mf//EfI3pyr7vuOk455RSee+45Lr744mFDFSDXA3vuuefy29/+lvPOO2/Eeh999FFOPfVUHnzwQa6//voRPTe33HIL+++/P6tWreLhhx8ecfjy5JNPpry8nJ6eHqLR6GAPrXcfKmB2RnkdIhWH9jegdQ3p5tUktq7G1/E6wVSuhzjrjA1uLq+6/WgrX4rNOoTqBQcxu7qUeCJBNj8+viQYpLK8nMpQiFl1dVSUl0/Lv/8tLS309PQMy2NFRQUnnXQSAN/73vcGj4YOFLJHHnkkX/va14Bcp9DWrVsJh8PEYjEgd7TzzjvvBKCkpIREwWlaP/3pT/PDH/6QTCYz2EE0NK8XXXQRl156KbFYjFtvvXVEXg877DD2228/UqnUsP1vIDD67yj2RePNrHqSxymeytAdTdIVSdIdTtLd20VbazM9nW1EulvJRLqweA+hbD81FqbG+llJP2dYmHpvrjAuLykYzpDO3bLOEU756HbldLgQb2dLWThvHhWzV7Al7OG5t/uJZHxE0h7CKSOagr/74hdZctC7ePih/+XWW2/NB7BjMIgvv/wyBxxwAN/61rf4+7//uxHbs23bNmqrq/nL88/zb//6ryN7bpyjJBjkkEMO4eyzzx4RQr/Ph5lxww03cM011wx7fXl5+WDRMPCDl7FcdtllO50/2rdgEYBkLExL0wY6mzfT27aFZFcThJsJxtooT3Ywy3VxkvVwhg3p/c3/xYt5K0iU1JEqqSMaXEqkfBauYi7+mvn4ahbgq1mANzQLXyBIicdDpc83WPA550gkEoTDYbo6u9gS3kJlZSWLFi0ik8kMjsEbWuiedNJJnH/++fT29nLBBReMKHJvuOEGvvSlL9HU1MSSJUtGbOt3v/tdrr32Wjo7O/nGN75BeXn5sLyGw2EA6urqOO2000YcvjzkkEMAOO6440YMERo65OD888/n/PPPH/MzX7lyJStXjr1fqa6uHjzKJPu4bAa6N0H7m7i2tcS3vUq2+VVK+zbiIdexk3IB1rkFvJ5dwXpbSG9ofxLV+zO3poyD630s8tvgOYdLg0EWzJlDdUUF5WVlBCexYCscIpRMJgePKjz++OOsW7duWF7Ly8u58cYbAbj++ut59tlnh71+2bJlPP300wCcc845g0c7B5x44ok8+eSTQO5o5saNG4flcaCnFeCEE04YMQTosMMOG5z/yCOPUFJSMuq4Wq/Xy+bNm8fc7tLS0sHtGI3f72fhwml6ga9p4h1ZJKcyWbqjuWK3t6eTSG8Hsd4Okv0dpCNduGg3xLvxxbsJpHopzfRT4fqpJkKd62d2KkyZN0OZ34gkHS82ZwgnHf0JCCcdPSkfy5fVsGxBA+t6/Fz/SJxwOkAkGSCScsSSWb7+hU9w6mmn8thf1nLh5Z/Ot6x7sI1/+MMfOP300/nTffdxzTUXDU73er1UVFTwt5/6PEs83sFT2CxYsGBEIQtw1llnDQ5mH3obGPPzta99jRtvvHHMb+iXXnopl1566Zif5cEHHzzBfw2RIZzDxXqIdm2np72JSMc24r3NZPpasXAbwVgr5ckOajIdVBBlITD0T3yMIJ1WQ7+/hs7gMtpL60gFa3Dl9VTMXoK3eh7rtvURT2WIRaMk4nES8Tjz6ufxofNyQ4Suv/56tm/fPmyneOqpp3LrrbcCuUKwr2/4aQmvuuoqfvSjH2FmXH755cOunDVQ0J5//vkEAgEymQwNDQ0sWbJkMK9HHHEEALNmzeKOO+4YMYZvYNzt8uXLB4cmjGbp0qU7HSJUV1enIUKye9IJ6HwbOt6E9jdJtb5BumUtgd4NePM/3Dag09WzNruQte5ctvoW0l26CKucy/zqIIuqfKwMeSgNBqmuqKCmspKysjKC+cP5Qb9/8P/04FmEqqvxeDxs2bKF9evXjzi6ct111+H1ern77rt56KGHRhTBL730EgBXXnkld9xxx7BNqq2tpbOzE4Af/OAHw84oVFJSwrJlywaLSzMbHNs+kMuhX2RvuummwR+QDtzq6uoG569Zs2anR1S++93v7vTjV16La8qKZDM7E/gu4AV+7Jz75lSsJ5N19EZi9HR3EO7pINLbQaKvk1S4g2yki2y0m5627STDXWSivWRj/ZAMs7QqxfGzk6QzGf7vM0n6E45w0hFO5QrdCw/2c85hId6OlHL2T1qIJB2RZIZ4KndI9vqPnsTVF5/Olq4Up//L10e06/vnfJOVn/oUJWvW8NzPzyMUyu0oK+tDNIZCzD78dGoOOoEjAwv4p3/6pxE7xYFvuWeeeSabNm0adjhkaEF79tlnc/bZZ4/5+SxfvnzwR3CjeaccDpWdm+q8umSU/q5m+tqbiHZuJ9HTQqavBYu04ou1U5LooCLVSXW2m0gsRVcsn8ekI5yEpPOw8oB6Oq2Wn7zmeLOjhnCmnnjGSzxjzJkzl5tuvpmyqgY+/9nPsmrVKqKRtYTDYbLZLMcdd9xgz89Flx/Ka6+9Nqx9p59++uCPw5599lm6urqGDf8Z+FIJudOEFY5zX7p0KZC7ytb69esHC+PS0tJhBW1paelgD9NoysrKuOKKK8acP3A1Lnlnm/S8ZlJku7fQt/0toi1vkWp/G2/PRsr6N1EV34Y33zOcdcZ218A6N4/17gw2Mo9O32x6s2W5H5b7ktQEstR7HBeccDiNc+fy9ltv8cgffs9f4/Fh56z9wQ9+wKz587njjjv4+te/PqzAhdzRzsbGxsH5hT75yU9SVVXF+vXrefbZZwezWFVVRSgUIpvN4vF4OPfcc1myZMmwvFYNuYT897//fb7zne8MHlHx+YaXRf/6r/+604/unHPO2el87WNntikZk2xmXuAt4HSgCXgB+Khz7vXRll+5cqV7/LFHaNu+lfaWJrrbm8kmwjRWB0lHu3n8qRfo6uogFu4nEQ0Tj8VYVmdcfoSfkIty2b2dtIaH71QvOsTHD84uzV2y8eZ+sgWbefEJB3DtJSeT9JRy6jXfJ+D3UVZWSqi8jPJQBZ/4xCf4P5/+DNFIhC9efz0VoRCVlZVUVFRQUVHBySefzFFHHUU0GuW5554b0VNbUVExaeMU5Z1nb45x3N28Ahy1YoW7/+6f0r59C10tW+np2M5+9UG88S7efHMd6zdtJRnpJxWLkElESCZTfOfMIGbGt59N8MAbacJJR2/SQ38SMA93f/kMIr4avnnPczz713XD1ldXX8/L+XNfXnbppTzyhz8M+2J58MEH88ADDwC5H3tu3rx52PzFixcPns/2ueeeI5vNjshraakuuSx7ZrrndcWKFe6+X/yYlk1v0LV9A/1tm2nw9NPo6SDbtYlVbzYTS2YH96FdCS8nHzaPsjmLeLq1hP/64xqiaUiksqSTcTKJOF/8+j9z4dmn8/QTT/CZT31qxDqfeuopjj/+eO68807+9m//dtgQoVAoxH//93+zdOlSHn74Ye6+++4RHUVXXHEFlZWVbNiwgaamplH3sfrCKHtqvJmdqiL5PcBNzrn3559/FcA59y+jLV8eMBctOLfzKYu9PHZ5bpzcAd/r5+3uHe0M+oyTDpnFjZceS8pXwZfv+DOpLJSWllFWVkZ5RSVHH72Siy+5BF+ojgcf+E2uF7eigsqqKqoqK5k/fz7z5s3DOUc6nVZBK9PKXt7p7lZe88uM+MPR+fcVVJZ4uf6PGb7/TP/gdI/HKA0G+PEtX8BbMYvfPvo8L69eS2V1DTXVuTzW1tRwyy234Pf5+POf/8y2bduG7RArKysHj4ikUil8+THxItPBTMzrv51dxQdWLubJ1jKu/o9nh80LlpZy3Ve/xkcu/CDbt2zm5ptuGuwgqqyooLKykquvvprly5ezefNmHnvssRFF7LJlyygvLyeTyQw7P7fIdFDsIvlC4Ezn3Cfzzz8OHOOc++yQZa4Crso/Pczj820a5a2yOOdc7n7oYwdjXbZtWqkHOordiCmg7Zp6i5xzDbtebOLGk9f89KGZfZfH59s4ZLbDueyQrGaHZLX4p9AZn+n07z+ZtF1Tb6blNeucy+Dc4D0zJ6dDTaf/A5NJ2zX1xpXZqRqTPFoXz7AAOuduB24HMLNVmVRq2p4+Z0+Z2arpfFqgPaXt2ufsMq+w72d2X/3313btc5TXvH31/4C2a/qYquMfTcCCIc/nA9unaF0iMjHKq8jMobyK7CVTVSS/ACw1syVmFgAuAX4zResSkYlRXkVmDuVVZC+ZkuEWzrm0mX0WeJjcKWrucM69tpOX3D4V7ZgGtF0zy766XTu1B3mFffOz2he3CbRd+xTldRht18wy47ZrWlyWWkRERERkOtE5WURERERECqhIFhEREREpUPQi2czONLM3zWy9mX2l2O2ZDGa2wMz+ZGZrzew1M/t8sds0WczMa2Yvm9n/FLstk8nMqs3sPjN7I//v9p5it2k6Ul5nnn0xs8rr+CivM4/yOr0UdUzynlxecyYws7nAXOfcS2ZWAbwIXDDTtwvAzK4HVgKVzrmdX7R+BjGzO4E/O+d+nP/FeJlzrqfY7ZpOlNeZaV/MrPK6a8rrzKS8Ti/F7kk+GljvnNvgnEsC9wDnF7lNE+aca3bOvZR/3A+sBeYVt1UTZ2bzgbOBHxe7LZPJzCqB9wI/AXDOJWdKgPcy5XWG2Rczq7yOm/I6wyiv00+xi+R5wNYhz5vYR/6zDzCzxcCRwPPFbcmk+A7w98yMS4Lvjv2AduCn+cNcPzaz8mI3ahpSXmeefTGzyuv4KK8zj/I6zRS7SB7X5TVnKjMLAfcD1znn+ordnokws3OANufci8VuyxTwASvg/2/vbnEaCqIwDH9HsAsEDo2uo54VgGABLIAuAtsNUNcgkayBBIEjJFSwgeoPMbeCEXBDhsxP3kdedZLmTU9upxmtbZ9J2ksa4vxeYfTakYGbpdd56LUj9Nqm2kvysNdrRsSRUsAb2w+15ylgIekiIt6VfrY7j4j7uiMVs5O0s314G7FVihrf0WtfRm2WXueh177Qa4NqL8lDXq8ZEaF0/ubV9l3teUqwfWv72PaJ0uf0ZPuy8lhF2P6U9BERp9OjpaQh/gRSGL12ZNRm6XU2eu0IvbbpX66lnuuP12v2YCHpStJLRDxPz1a2HyvOhJ/dSNpMXyZvkq4rz9McekVD6PUX9IqGdNsr11IDAAAAmdrHLQAAAIDmsCQDAAAAGZZkAAAAIMOSDAAAAGRYkgEAAIAMSzIAAACQYUkGAAAAMl+1p7OtYaw1OgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "do_plots(params3, '_params3')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 6.75500989148921e-07, _sig2_annot: [1], _s: -0.011335565551680804, _l: -0.16934948578635015, _sig2_zeroA: 2.054008709175631)\n", - "AnnotUnivariateParams(_pi: 1, _sig2_beta: 7.53163000205981e-07, _sig2_annot: [1], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 2.0661803442547924)\n" - ] - } - ], - "source": [ - "# params4 - infinitesimal model, allowing for flexible s and l parameters\n", - "constraint = AnnotUnivariateParams(pi=1, sig2_annot=[1], annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, sig2_beta=5e-8, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params4=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1.0, _sig2_beta: 2.9443784386506135e-07, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 2.3190660353261126)\n" - ] - } - ], - "source": [ - "# params5 - baseline annotation model, infinitesimal, without accounting for S and L parameters\n", - "trait_index=1\n", - "params5 = AnnotUnivariateParams(pi=1.0, sig2_beta=params3._sig2_beta, sig2_annot=None, annomat=annomat, annonames=annonames, s=0, l=0, sig2_zeroA=0, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "params5.fit_sig2_annot(libbgmg, trait_index)\n", - "params5.drop_zero_annot()\n", - "print(params5)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 1.0, _sig2_beta: 7.53163000205981e-07, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.7988296958524468)\n" - ] - } - ], - "source": [ - "#params6 - baseline annotation model, infinitesimal, allowing for flexible S and L parameters\n", - "trait_index=1\n", - "params6 = AnnotUnivariateParams(pi=1.0, sig2_beta=params4._sig2_beta, sig2_annot=None, annomat=annomat, annonames=annonames, s=params4._s, l=params4._l, sig2_zeroA=0, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "params6.fit_sig2_annot(libbgmg, trait_index)\n", - "params6.drop_zero_annot()\n", - "print(params6)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.020112336745176665, _sig2_beta: 1.9934921745404774e-05, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 2.010107269906994)\n", - "AnnotUnivariateParams(_pi: 0.016490772435950134, _sig2_beta: 2.1573172356293674e-05, _sig2_annot: [ 0.77784255 11.55259889 1.36064189 0.04819482 0.05711198 1.86221487\n", - " 2.24970066 0.64362227 0.14398438 0.22548616 2.03233305 3.50915398], _s: 0, _l: 0, _sig2_zeroA: 1.9785604206089915)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params7 - causal mixture with annotation, without accounting for S and L parameters\n", - "constraint = AnnotUnivariateParams(s=0, l=0, sig2_annot=params5._sig2_annot, annomat=params5._annomat, annonames=params5._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params7=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params7, '_params7', True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.015785388620568757, _sig2_beta: 4.874428472741427e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.886429562529495)\n", - "AnnotUnivariateParams(_pi: 0.015785512563498437, _sig2_beta: 4.5576351979317775e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.11721239089931484, _l: -0.22317196090908997, _sig2_zeroA: 1.9063891647797888)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params8 (6) - causal mixture with annotation, allowing for flexible S and L parameters\n", - "constraint = AnnotUnivariateParams(s=params6._s, l=params6._l, sig2_annot=params6._sig2_annot, annomat=params6._annomat, annonames=params6._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params8=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params8, '_params8', True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=False)\n", - "c0u,c1u,c2u = libbgmg.calc_unified_univariate_delta_posterior(1, pi_mat, sig2_mat, params9._sig2_zeroA, sig2_zeroC=1, sig2_zeroL=0)\n", - "c0,c1,c2 = libbgmg.calc_univariate_delta_posterior(1, params9._pi[0], params9._sig2_zeroA, params9._sig2_beta[0])\n", - "\n", - "plt.plot(c2, c2u, '.')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9275.8125" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg.weights.sum()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=False)\n", - "power_nvec, power_svec1 = precimed.mixer.utils.calc_power_curve(libbgmg, params9, trait_index=1, downsample=1)\n", - "\n", - "params9_old = UnivariateParams(pi=params9._pi[0], sig2_beta=params9._sig2_beta[0], sig2_zero=params9._sig2_zeroA)\n", - "_, power_svec2 = precimed.mixer.utils.calc_power_curve(libbgmg, params9_old, trait_index=1, downsample=1)\n", - "\n", - "plt.plot(np.log10(power_nvec), power_svec1)\n", - "plt.plot(np.log10(power_nvec), power_svec2, '--')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: [0.0029543806646680246], _sig2_beta: [1.6388661486125145e-05], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 1.1975626969086972)\n", - "AnnotUnivariateParams(_pi: [0.004300714548333941], _sig2_beta: [1.4122377022132362e-05], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 1.2408333573346944)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/github/mixer/precimed/mixer/utils.py:823: RuntimeWarning: divide by zero encountered in log10\n", - " data_x=-np.log10(np.flip(np.cumsum(np.flip(data_weights[si])))) # step 2\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:50: RuntimeWarning: invalid value encountered in less\n", - " y2[x2 self.x[-1]\n", - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/scipy/interpolate/interpolate.py:610: RuntimeWarning: invalid value encountered in subtract\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", - "/home/oleksanf/github/mixer/precimed/mixer/figures.py:44: RuntimeWarning: invalid value encountered in sqrt\n", - " q = 10**-data_logpvec; dq= 1.96*np.sqrt(q*(1-q)/qq['sum_data_weights']);\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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vTcPjb+TD0Zs/iQw0kVYys6uBje6+rqvbIt2bYrZrKEalLRSv3YviWJrSkoR5ADDA3VebWSawivBoz88CRe7+czO7Hejp7t9ucGwvYCXhOQM9cuyZjQW9iHQMxaxI7FC8isSGZvswR6YtWR15Xkq4v8wg4ArCy6ES+XllI4dfCjwfGW19iPC0LbM7ouEi0jjFrEjsULyKxIaGK6VFFRk9PRF4C8hx970QDngz69fIIYOA3fVe50W2NXbumwmPwiY9Pf3MMWPGtKZpIse1VatWHXD3vq09rrNiVvEq0jTFq0jsaGm8tjhhNrMM4G+ER2iXmDU6dedHDmtkW6N9QNz9fsIrTzF58mRfuXJlS5smctwzs51tOKbTYlbxKtI0xatI7GhpvLZoWrnIvJl/Axa4+9ORzfsjfa/q+mA1Nro5j/BURXUGA3taUqeItJ1iViR2KF5Fur9mE2azI6uxfeDuv6q361ngxsjzG6m3iEQ9S4BLzKxnZHLtSyLbRKSTKGZFYofiVSQ2tOQK8/mEJ9yeaWbvRh5zgZ8Ds8xsMzAr8hozm2xm/wfhpVX5cILtt4G7IttEpPMoZkVih+JVJAZ0y4VL1MdK5GhmtsrdJ3d1OxqjeBU5muJVJHa0NF61NLaIiIiISBRKmEVEREREolDCLCIiIiIShRJmEREREZEolDCLiIiIiEShhFlEREREJAolzCIiIiIiUShhFhERERGJQgmziIiIiEgUSphFRERERKJQwiwiIiIiEoUSZhERERGRKJQwi4iIiIhEoYRZRERERCQKJcwiIiIiIlEkNFfAzB4CLgcK3P20yLa/AqMjRbKBw+4+oZFjdwClQBAIuPvkDmq3iDRBMSsSOxSvIrGh2YQZeBi4F3i0boO7f7LuuZn9EiiOcvwMdz/Q1gaKSKs9jGJWJFY8jOJVpNtrNmF291fMbHhj+8zMgGuBmR3bLBFpK8WsSOxQvIrEhvb2YZ4K7Hf3zU3sd2Cpma0ys5ujncjMbjazlWa2srCwsJ3NEpEmdEjMKl5FjgnFq0g30d6E+Xrg8Sj7z3f3ScAc4N/NbFpTBd39fnef7O6T+/bt285miUgTOiRmFa8ix4TiVaSbaHPCbGYJwCeAvzZVxt33RH4WAM8AZ7e1PhFpH8WsSOxQvIp0L+25wnwxsMHd8xrbaWbpZpZZ9xy4BFjXjvpEpH0UsyKxQ/Eq0o00mzCb2ePAcmC0meWZ2U2RXdfR4FaRmQ00s4WRlznAa2a2BlgB5Lr74o5ruog0RjErEjsUryKxoSWzZFzfxPbPNrJtDzA38nwbML6d7RORVlLMisQOxatIbNBKfyIiIiIiUShhFhERERGJQgmziIiIiEgUSphFRERERKJQwiwiIiIiEoUSZhERERGRKJQwi4iIiIhEoYRZRERERCQKJcwiIiIiIlEoYRYRERERiUIJs4iIiIhIFEqYRURERESiUMIsIiIiIhKFEmYRERERkSiUMIuIiIiIRNFswmxmD5lZgZmtq7ftR2aWb2bvRh5zmzh2tpltNLMtZnZ7RzZcRBqnmBWJHYpXkdjQkivMDwOzG9n+a3efEHksbLjTzOKB3wNzgFOB683s1PY0VkRa5GEUsyKx4mEUryLdXrMJs7u/AhS14dxnA1vcfZu71wB/Aa5ow3lEpBUUsyKxQ/EqEhva04f5FjNbG7md1LOR/YOA3fVe50W2NcrMbjazlWa2srCwsB3NEpEmdFjMKl5FOp3iVaQbaWvC/L/AKcAEYC/wy0bKWCPbvKkTuvv97j7Z3Sf37du3jc0SkSZ0aMwqXkU6leJVpJtpU8Ls7vvdPejuIeABwreGGsoDhtR7PRjY05b6RKR9FLMisUPxKtL9tClhNrMB9V5eBaxrpNjbwEgzO8nMkoDrgGfbUp+ItI9iViR2KF5Fup+E5gqY2ePAdKCPmeUBPwSmm9kEwrd/dgBfipQdCPyfu89194CZ3QIsAeKBh9x9fae8CxE5QjErEjsUryKxwdyb7FbcZSZPnuwrV67s6maIdBtmtsrdJ3d1OxqjeBU5muJVJHa0NF610p+IiIiISBRKmEVEREREolDCLCIiIiIShRJmEREREZEolDCLiIiIiEShhFlEREREJAolzCIiIiIiUShhFhERERGJQgmziIiIiEgUSphFRERERKJQwiwiIiIiEoUSZhERERGRKJQwi4iIiIhEoYRZRERERCQKJcwiIiIiIlE0mzCb2UNmVmBm6+pt+4WZbTCztWb2jJllN3HsDjN7z8zeNbOVHdlwEWmcYlYkdiheRWJDS64wPwzMbrDteeA0dz8D2AR8J8rxM9x9grtPblsTRaSVHkYxKxIrHkbxKtLtNZswu/srQFGDbUvdPRB5+SYwuBPaJiJtoJgViR2KV5HY0BF9mD8PLGpinwNLzWyVmd0c7SRmdrOZrTSzlYWFhR3QLBFpQrtjVvEqcswoXkW6gXYlzGb2PSAALGiiyPnuPgmYA/y7mU1r6lzufr+7T3b3yX379m1Ps0SkCR0Vs4pXkc6neBXpPtqcMJvZjcDlwDx398bKuPueyM8C4Bng7LbWJyLto5gViR2KV5HupU0Js5nNBr4NfNzdK5ook25mmXXPgUuAdY2VFZHOpZgViR2KV5HupyXTyj0OLAdGm1memd0E3AtkAs9HprO5L1J2oJktjByaA7xmZmuAFUCuuy/ulHchIkcoZkVih+JVJDYkNFfA3a9vZPODTZTdA8yNPN8GjG9X60Sk1RSzIrFD8SoSG7TSn4iIiIhIFEqYRURERESiUMIsIiIiIhKFEmYRERERkSiUMIuIiIiIRKGEWUREREQkCiXMIiIiIiJRKGEWEREREYlCCbOIiIiISBRKmEVEREREolDCLCIiIiIShRJmEREREZEolDCLiIiIiEShhFlEREREJAolzCIiIiIiUbQoYTazh8yswMzW1dvWy8yeN7PNkZ89mzj2xkiZzWZ2Y0c1XEQap3gViR2KV5HY0NIrzA8Dsxtsux140d1HAi9GXh/FzHoBPwTOAc4GfthU4ItIh3kYxatIrHgYxatIt9eihNndXwGKGmy+Angk8vwR4MpGDr0UeN7di9z9EPA8H/3DICIdSPEqEjsUryKxoT19mHPcfS9A5Ge/RsoMAnbXe50X2fYRZnazma00s5WFhYXtaJaINELxKhI7FK8i3UxnD/qzRrZ5YwXd/X53n+zuk/v27dvJzRKRRiheRWKH4lXkGGpPwrzfzAYARH4WNFImDxhS7/VgYE876hSRtlG8isQOxatIN9OehPlZoG5U7o3APxopswS4xMx6RgYjXBLZJiLHluJVJHYoXkW6mZZOK/c4sBwYbWZ5ZnYT8HNglpltBmZFXmNmk83s/wDcvQj4MfB25HFXZJuIdBLFq0jsULyKxAZzb7TLU5eaPHmyr1y5squbIdJtmNkqd5/c1e1ojOJV5GiKV5HY0dJ41Up/IiIiIiJRKGEWEREREYlCCbOIiIiISBRKmEVEREREolDCLCIiIiIShRJmEREREZEolDCLiIiIiEShhFlEREREJAolzCIiIiIiUShhFhERERGJQgmziIiIiEgUSphFRERERKJQwiwiIiIiEoUSZhERERGRKJQwi4iIiIhE0eaE2cxGm9m79R4lZvaNBmWmm1lxvTI/aH+TRaQtFLMisUPxKtK9JLT1QHffCEwAMLN4IB94ppGir7r75W2tR0Q6hmJWJHYoXkW6l47qknERsNXdd3bQ+USkcylmRWKH4lWki3VUwnwd8HgT+6aY2RozW2Rm45o6gZndbGYrzWxlYWFhBzVLRJrQrphVvIocU4pXkS7W7oTZzJKAjwNPNrJ7NTDM3ccDvwP+3tR53P1+d5/s7pP79u3b3maJSBM6ImYVryLHhuJVpHvoiCvMc4DV7r6/4Q53L3H3ssjzhUCimfXpgDpFpO0UsyKxQ/Eq0g10RMJ8PU3cKjKz/mZmkednR+o72AF1ikjbKWZFYofiVaQbaPMsGQBmlgbMAr5Ub9uXAdz9PuAa4CtmFgAqgevc3dtTp4i0nWJWJHYoXkW6j3YlzO5eAfRusO2+es/vBe5tTx0i0nEUsyKxQ/Eq0n1opT8RERERkSiUMIuIiIiIRKGEWUREREQkCiXMIiIiIiJRKGEWEREREYlCCbOIiIiISBRKmEVEREREolDCLCIiIiIShRJmadSqnUV8dv4KSqtqO6+S2kpY/gd45iudV0crVFVV8dRTT5Gbm9vVTREREZFupF0r/Un3UVldzep166iqqWn3uXYVB/jv10rpkWw8v3wFWSlt/17VOzubCWPHHr2xthJWPQyv/RrK9sNJ08LbElPb1/A22rFjB3fddRd/+9vfKCkpYe7cuVx22WVd0hYRERHpfpQwHye27NxJWWUlPTIy2nWevSW1/Hr5YdKS4vj+zD70SW/7r0gwGKS4tPTDDQ0T5eFT4eoH4aSp7Wpza7k777zzDqFQiMmTJ5OYmMjTTz/NVVddxbx585g5c+YxbY+IiIh0b0qYjwPFpaXs3rOH3j17EhfX9qvBheUBfrrsIAB3XNSP/j0S29UudycYDEJNBayaD6/f82GifM1DMPyCdp2/tbZt28Zjjz3GggUL2LBhA3PnziU3N5dBgwZRUFBAUlLSMW2PiIiIxAYlzDHO3dmwbRspKSntSpYPVwb58YsFVNaG+NHFOQxqZ7IMEBeoYPC2Z+DlT0F5YbjrRRckygCf//znmT9/PgBTp07lG9/4Btdcc82R/UqWRUREpClKmGNcQVERBw4fpl+vXm0+R1l1iJ+8VEBRRZA7LurHSb3alzzG1ZaTs/VpcjY/TmJNCZwyE6b9Pxg2pV3nbany8nKeffZZnnrqKR599FHS09OZPn06o0aN4vrrr2fYsGHHpB0iIiJyfFDCHMMCwSAfbN1Kj/T0Np+jsjbEz5YVkF9Sy+3T+zK6b3KbzxVfU0LOlifJ2fIkCbVlHMo5l+0nX8+kSz/b5nO2VCAQ4IUXXmDBggU888wzlJeXM2TIELZs2cL48eO54YYbOr0NIiIicnxqd8JsZjuAUiAIBNx9coP9BtwDzAUqgM+6++r21iuQv28fFVVV9O3Zs03H1wSdX7xSyJaDNXxrah/GD2jbLBUJVUX03/wX+m17hvhAJYcGTmXPmBspzhxBbW3nTUvn7pSXl5ORkcG6deuYM2cO2dnZfOpTn2LevHlMnTq1Xd1UjkeKV5HYoXgV6T466grzDHc/0MS+OcDIyOMc4H8jP6Udqqqr2bRjB9mZmW06PhhyfvPaAd7bV82/T+nFOUPSWn2OpIr99N/0OH23P4uFAhQNmcne0TdQmXVyuEAg0Ka2NWfjxo0sWLCAxx57jKlTpzJ//nzGjx/PokWLmDFjBsnJbb9KfoJQvIrEDsWrSDdwLLpkXAE86u4OvGlm2WY2wN33HoO6j1vb8/IIuZOY0Pr/wpA7f3jzIG/nVfL5yT2ZfnLrpqJLLstnwMY/03vnIsA5OGw2e0d9murMIa1uS2vMnz+f3//+96xatQozY+bMmVx66aUAmBmzZ8/u1PpPEIpXkdiheBU5RjoiYXZgqZk58Ed3v7/B/kHA7nqv8yLbjgpoM7sZuBlg6NChHdCs41dJWRnb8/LonZ3d6mPdnYdWHuKV7RVcd0YWc0a3/Ap1Ssl2Bm74E712v4DHJVB40sfZN+pT1KT3b3U7WqKkpITnnnuO66+/nri4ONasWYO788tf/pLrrruOgQMHdkq9xznFq0jsULyKdBMdkTCf7+57zKwf8LyZbXD3V+rtt0aO8Y9sCP8huB9g8uTJH9kvYe7OB1u3kpKc3Kb+uY+vKWbJpjI+NjaTT5zWo0XHpB3ayIANj9Jrz8sE41PZN/KT7B/5SWpT+7S6/ubU1NSwePFi/vznP/Pcc89RVVXF0KFDmTp1Kr/4xS9ITGz/dHcnOMWrSOxQvIp0E+1OmN19T+RngZk9A5wN1A/oPKD+vfrBwJ721nuiKjh4kAOHDtGvd+9WH/uP9SU8s76Ei0ak85mJ2YTHizQt4+B7DNjwKNn7lhNIzCB/zGfZP+LfCCZntbX5Ua1fv55p06ZRVFREnz59uOmmm5g3bx7nnnsugJLlDqB4FYm5QNulAAAgAElEQVQdileR7qNdCbOZpQNx7l4aeX4JcFeDYs8Ct5jZXwgPRihW/6q2qQ0EWL9lC1ltGOj3/OZS/vzuYc4blsYXz+rVdLLsTmbhKgZueJQehaupTcomb9yXKDjlKoKJ7Vt2u6F169axYMECcnJy+MY3vsGoUaP4xCc+wVVXXcWsWbOUIHcwxatI7FC8inQv7b3CnAM8E0m+EoDH3H2xmX0ZwN3vAxYSnvJmC+Fpbz7XzjpPWDvy86mprSWzlfMuv7ajnAdWHGLSwBS+dl5v4uMaSZbdydr3BgM3PEpG0XpqUnqz64yvUXjSxwkltG26ucbs3r2bxx9/nAULFrB27Vri4+P5/Oc/D4SvID/wwAMdVpd8hOJVJHYoXkW6kXYlzO6+DRjfyPb76j134N/bU49AeWUlW3ftavU0cqvyKrn3jYOM7ZfMf0ztQ0LDZNmD9Mx/mQEb/kR68Waq0wawY+JtHBg2B4/vmOnZSkpK6NEj3F/6tttu44knnuCcc87ht7/9LZ/85Cfp169fh9Qj0SleRWKH4lWke9FKfzGgbqBfQnw88fHxLT5u3b4qfvlqIcN7JvHt6X1JTvhwkKCFAvTa/TwDNv6Z1NKdVGYMZdvk71E0ZBYe1/5fi+rqal7+179YnJvLiuXLWbt2LaNHj+bOO+/kpz/9KSNGjGh3HSIiIiLHghLmGFBQVETBwYP0acWKfpsPVPNfLxeSk5nI92b2JS0xnCxbsJq+2/9J/82PkVyxn4qsEWw9+06KBk8Ha3ky3mRb9+/nj3/4A/964QXKSkvp1bs3X/3qV0lNDXfrGDNmTLvrEBER6VDu0MxAeDmxKWHu5moDAdZv3kxmenqzs1rU2XW4hp/+q5CslHjumNmXzOR44mvL6Lf1GXK2PEFi9SFKe5/Ozgm3Udz/3Hb9kXB3Nm3YQGVlJRMmTSI1NZXXli3jwhkzuHj2bM6YMIGZU6a0+fwiIiKtFe6t0gLVJfDCXRAXB3P+u8lzFZRWs2FvKR/sK2HDvlL2Flfxly+e0+LP5baqrq5m0aJFTJkyhf79O2fNA2kZJczd3I68vFYN9NtXWsuPXywgKd6446J+9Isrpv+6J+m77e8k1JZxOOcc9o65gbI+H+ka1yr5eXksWbiQJQsXsn3bNsZPnMgDjzxCZo8e5L74IgkJCdQGAtTW1rarHhERkZaoqq7mUEkJewoKOFBURKiZpLlf4VuM2/S/JFcXsWPIFWx45RVqQrCnNER+aZD80lD4URKivPbDc/VMMQZlxvHcy6+SFN/xCXMoFGL92rX86/nneW3ZMsrKyrjlP/6DX/zsZ6Qkd8zYImk9JczdWGl5OVt27aJnj5YtMHKwIsBdLxYQdPif82qZuOnX9Nm5CAsFODR4OntHzaOi5+h2t+sXd9/Nk48/DsCESZO4/Y47mDlr1pH9CW1YrltERKS1KiorOXj4MPn793O4tBQHUpKSyO7Ro8nFvRKqDjLs3d/QK/9fVPQ4hQ/Ou5u81NE88OZB3tlTdWTll+R4Y2h2IucOS2FYdhJDsxMZmp1IZnL7uy82paqykk9edRV79+whNTWV6RddxOy5czllzBhCoVCn1SvNU2bTTYVCId7fsoXkpKQWDfQrrgry4xcLGFK9lV8PWMrQN17B4+I5MGwO+0ZeT3XmkGbP0ZjKigpeWbaMpYsWccddd5HdsyeTzzqLvn37cuncuQzQ8tQi0dVWwYo/Qk05zPhuiw8rKK3i5Y2FLNtYyLdnj2Fo77Rmj3F33njjDfLz87n22mvb02qRbq+ouJgVa9eCO6mpqfTKyoreRcKdPjtzGbL2XuKCNeSNu5l9oz7Fyj21/P6lvVQHnI+f2oORvZMY2jORnIwE4jq5y8W+vXtZsnAhhYWF3Hb77aSkpnLp3LmcMmIE06ZPJzUtHPcHDx/u1HZI85Qwd1N7Cgo4ePgwfXv1ilpuX2ktb+2s4NCWt/h57d+ZmrCW4KE09o26nv0j/q1Ny1cHAgHefvNNFi9cyLIXX6SyspJ+OTns2rWL7J49mXHxxW19WyLHtbKKCnbk51N0+DAeCpCT/xLDNz1MSlUBB3LOZ33aW02OGQi5s/1QgDX7ali7r4bthwMAZKfE8c+sCsb1S2qy3h3btvHCkiW8sGQJ+/buJad/f/oNG9bi/pXjRo5s1aBika5WUVnJqnXrSE9NbVE3heTS3Qx/5xf0KFxNSZ8J7Jz0/yhJG8KfVx9i0cYyhmUncusFfRiS1fkLZhUXF/Pi0qUszs3l3dWrgfDd2kAgQEJCAl/9+tc7vQ3SekqYu5nqmhry9+9n4/btTc65nF9Sy1u7KnhrZxkjS5bzlYTnmBC3lYrknuwe/SUKT7qSYFLr5mt2d8rLysjIzGT/vn3c+tWvkpmZySVz5jD7ssuYeOaZTd7eEjnRlZSVsSM/n/z9+0mIj2dQyRqGffAA6SXbKMsezbpJ36Gk70Sa+ih+fks5f1tXSkl1CDMY2SuRa0/PZMKAFIZlJzSa+JaVlpIR+Rtx9113sXnjRiaffTZf+MpXmDZjBklJTSfY9R0qLqa6pqatb13kmKutrWX1++8THx/fbLJsoQA5mx9n0PvzCcUnsX3S/+PA8MvZXRzgN4v3setwLZeNyeRTE7I7pT9ynaqqKuLj40lMTOTvTz3F7++5h+EnncSXbrmF2XPnMmjw4E6rWzqGEuZuoqKykt379rEjPx93Jzsz86i+wPnFtbyxs4LluyrYX1zBFfGv80jSPxmStIfy1IHsGNO2xUZ27tjBktxclixaxMkjRvCL3/yGQYMH8/sHHmD8xIkt/tAVOVEdOHSIt997j8SEBIaG8hmy9j56HHiXqvRBkSkbZ4DFfSRZrqwN8frOCl7aWsbmAzWMy0nmolMymDAwpck+kqUlJbz0wgsszs3l/XXryH3xRTIyMvj+j35E77596dOn9XeU9EVYYkkoFGLd5s1UVFbSMysratm0Qxs4adXPSSveQuGAC1k7+usctJ68v7GMBe8Wk5ZofHd6XyYO6rjVbOsLBoOsXLGCxbm5LHvxRb7zgx9wyZw5XH7llZwzZQqjx47t9Fk2pOMoYe5i5ZWVbNu1i7z9+4mLiyMrI+MjfZZX7K7gf149QLpX8o2sl7k2I5cegYOU9xjJ1tF3UjToQmjlYiOLc3P5y4IFvL9uHWbG5LPPZma9rhZnnXNOh7w/keNVdU0NW3buZOeePeRQxEnrH6ZX/jJqk3uyY8J/cOCkj+FxR6fJ7s6WgzW8uKWM13ZWUB1wBmclcsOkbGaPyiSxiStcWzdv5v4//IHXX32Vmpoahgwbxqc/+9kjg4BGjx3b6e9XpKtUVldTW1tLIBBg/8GD7C0sjNqFKC5QSZ+1DzB4+1MUkcW3ar/Jou1nwfZqYB8A4wekcMuU3mSndvwAvurqan5/zz08v3gxBw8cID0jg5mXXMKw4cMB6N27N7179+7weqVzKWHuQsFgkNXvv09lVRW9srIavdKTX1LLgje28pOM57nWl5JYXUZJ3zPZOPp7lPQ7q8VzKJeVlfHySy8xa/ZskpKS2LVzJ6FgkFtvu41LZs+mr5anFmmRUChEYVERazdtoqpoL2N3P8HJ+5cSiEvm1ZzP8ErPKzlcnkzFO2VU1ISoqA1RUetU1IYoqQpSWB4kOd44b1gaF43IYFSfpI9cZQqFQqxeuZIeWVmMGj0azFjz7rt84t/+jdmXXcbYceN0ZUpOCJt37GDLrl2YGe6OmZHdI4vDVSGKq4IUVwUprwnHWHlNiMGHV3JNwb30CRawIHgRLw/4PKkZWXw6OY6MpDgykuPITolnZJ+kDh3Ql7d7N9u2bDnSHWr1ypWcPn48s+fO5fxp00jWdHAxTwlzF9pbWEhpWVmTA/vii3dj/3qIl+KXkVwb4NCgC9k3ah7lvVp2Nam2tpblr73G4oULeXXZMqqrq+mRlcXUCy/kpi99iZu/+tWOfDsix4Xa2lqKioupDQSoqq6muraWmtpaaiM/yysqiKs6yPAdzzA075+AMT94KX8IfJyDO7NgZzVQTUqCkZYYR1pSHKmJRkZSHP3SE7ji1GQuGJ5OetLRX5Ddnc0bN7I4N5elixZRUFDAZR//OD/8yU84ZcQIcl94oUUz5ojEmsrqaiorK6mqqTkq1qpqathbWEjfnj1ZvquSJZvLyC+upbS6hIYzLPekhO8n/pmr419jmw/kuwk/4qTx5/LFERmd1u6igwd5fskSluTmsu6998jMzGTJsmUkJCbyyOOPK16PM0qYu0htINDowL7K2hBVee8xcsdfGFb0GuM8ga39LiYw8TNUZw5t8fn35Odzw3XXUVJcTHbPnnzsyiuZfdllnD4+vGCJAlmkcXsLC1n1/vukJicTFxdHfOQRFxdHYqCc03Y/Rc7WJ7FgDU+ELqRy4k0kZvXnO4lxRxLklAQjPq51V69u/cpXePONN4hPSOC888/n1ttuY+qFFx7Zr5iV400oFGL1++9TWFQEZtRFTHxcHPHx8cTFxdEnO5u9ZUF++8ZBBmQmcPaQNLJTw1eJs1LiyUo2xhxaxoRN95IQKCN/1I0Ujb2BL7ZyPE9r/e2JJ/ifu+8mGAwycvRovvbNb3LJnDkkJIa7YSlejz9KmI+hQDBIWXk5ldXVFB0+THVtLT0yMsgvruWJNYfoX7SCa2v/wbS4Dyj2NP4Q/Dj5w6/mE+ec3Oy5t27ezOKFC0lOTuYLX/4yAwYOZPZll3Hueedx7pQpR4JYRBrn7pSUlbFl9276ZGcfNfo+LlBJ8J3HGLvrr2RSznPBKfwmeA3njR/Lx0e2bGGh+g4fPswLS5bw5uuv8/Nf/YqEhARmXHwx02bM4OJLLiFbU7zJCeBQSQn7Dx6kXzPTp/7tvUMkxxt3zsohK+XDRDSpfB/D3vkF2fvfoqzXqWya9G0qs07p8HYGamt5c/lyFufm8m/XXcf4iRM5/Ywz+PRnPxteVGTkyA6vU7ofJczHSNHhw6xcvz48SMcdi4ujV48eHC6v4p0Xn+bO0LOMtDwOJ/Xl1YFfZveguQxKz+TsHk3/F+3ft48lCxeyeOFCtmzaRHx8PLNmzwbAzLjt9ttb1cZQKBRezjoQIBAIEIwMKHIIf/N3xyNXAer6kh3FHer1M3N3emdnt6oNIl0hFArxzgcfsK+w8Kh5XS1YTd/tzzJgw59Iqi7iheBEXup3A5XZI/nWyAz6prf8T2hVZSWvLFvG4txclr/xBsFAgJNOOYXCggIGDBzIVddc01lvT6RbKiwqIqmZiznBkLN6TxXnDE07kixbsIacLU8w8INHANg5/lYKTvkEWMdd1XV33luzhsW5ubywdCmHDx2iR1YWF0ybxviJExk1ZgyjxozpsPqk+2tzwmxmQ4BHgf5ACLjf3e9pUGY68A9ge2TT0+5+V1vrjFWHS0pY8d57R30Qb8g7wOEVDzG9+DlmWRGHM05m26l3UDT4IlLiEmjq+2ppSQkZmZmYGQ898ADPPPkkp51+Ot+6/XZmXXopvZoYeevuuDuhUIhgKEQgGKS6poaQeziBNyMuLo701FR6ZmWRnpJCakoKiYmJJCYkkBAfT0J8PBYXd2SgRF3CfFTa3GBfZ6+SJC2nmG3a1l272H/gAP0i8WOhWvrsyGXghkdJqizg/cRxfL/660y/4DyuGtr8int1AoEAVVVVZGRksHbNGr7/7W/Tr18/rp83j9mXXcbI0aM1eE8adSLE66GSElKambp05+FaymtCnN4/BdzJ2vs6Q9feS0p5HocGXMCu8d+gJr1/h7WppKSEHj16EAwEuO3WW6msqGDq9OnMnjuXKRdcQKLu1p6w2nOFOQB8y91Xm1kmsMrMnnf39xuUe9XdL29HPTGrpKyMTTt2sP/gQTLT0khJTiZYso+9r/yJK6uWkmmVrEs+nXWj/pMeoy5ocsaL6upqXn/lFRYvXMjrr7zCH+fP57QzzuCGz32OT994I/0HDKCqpobqmprw8pmRK71HuONAYnw88YmJJMbHk5GWxpABA8hISyM1OZmU5GQSExpfIEGOG4rZBmoDAfYUFLBp5076ZGdDKECfXUsY+MHDJFfsZUfKGH4Q+CKvVJ3K1OHpTGlBsuzuvL9uHYtzc3l+yRLmXHYZt952G2eedRb/++CDTJg0Sf0bpSWO63gNBIMUl5TQq5m7kOv3VwFwTvo+Rr3+e7L2r6AycxgbL/gVJTlnd0hbCgsKWLpoEYtzcykpKeHvixaRkJjIr373O4affDIZGZ03cFBiR5sTZnffC+yNPC81sw+AQUDDYD4h1a1xn5SYSJ/sbNJLt5Hz3uP03PUCuPN+1lR80meo6T2GpnpAHioq4t577uFfL7xAWWkpvfv04epPfpKevXpRW1tLcno6ADWBAH179iS7Rw9SU1JIiI8nPnJFuG7gRHxcnJLhE5xi9mjVNTWsXLeO4tJSemam0yfvBQZ98BApZXlsSziFX9vtPHf4dM4fls73Tk5nXE5Ks+f80/z5/P3pp9m9cyeJiYlcMG0aZ0+ZAoQHAZ151lmd/bbkOHG8x2tlVVV4oF8zn0tb9xzk7rS/cN5riwklpLLrjK9TcMon8FauPdCYVW+/zUP338/KFStwd8aOG8d18+YRCARITEzktDPOaHcdcvzokD7MZjYcmAi81cjuKWa2BtgD3Obu65s4x83AzQBDh7Z8NojuqLqmhnWbNpGanExO6XoGvPMYWftXUEEyfwpcTPHY6zn/jBEfOc7d2bRhA8XFxZx97rmkpaezYvlyLpwxg9mXXcb4SZMAKKuooKK6mlNPOYX+ffuSrNX4pJXaG7OxHK8lZWV8sHUrh0pKMJxRFe8wYOV80kt3sJmh/HfNf7CCsxjeM5kvnJbKpaOaXmb+4MGDLH/tNS6/4goAtm7ZQr9+/bjhc59j5sUXk9mj+QGBdd2ljjwa2xZ5BEMhQqEQoUj3qrpko26cgUWe1z93U2MN6n4G651HuqfjMV6rqqtxbzg5XD0eJHHDc/z20P30tFIODL+c/HE3E0hp+4DYmpoa3njtNUaPGcOAgQMpKytj75493HTzzVw6dy7DTjqpzeeW459F/YVtyQnMMoCXgZ+6+9MN9vUAQu5eZmZzgXvcvdnhpJMnT/aVK1e2q11dpTYQ4M1Vb5Od9y9O3v0M6Yc3UZvci7/abO4pncEnJg/holPSj/qAys/LY8nChSxZuJDt27ZxyogRPP50+J8yGAwSDIU4XFJCSkoKqcnJDOzXj0E5OSQmaMzmicLMVrn75A46V4fGbKzEayAY5IOtW9m1dy+pSYkMKXqLAR88QkbZdrYxiF/WXM3+/tOYMCidi0dkNDktXHl5OS+/9BKLc3N5+623CAaDPPmPfzDspJMIBoNHdbdwdwKBALXBIMFgkNrIYNq4yKBY4Mj4gbjIz7optczsI8+TEhNJiowrSExMPLI/Pj7+yPF1rOFYg3p/c+q/MzMjOSlJS2R3IMVrdO7O2o0bKSwqIqvB1KruDnvWMPjd3zCwagurfDSV532L5IGntqmuUCjEu6tXszg3lxeXLqW0tJQv3XILN91885EvnbHwhfHg4cNMmzyZtNTOWcb7RNbSeG1XxmVmicDfgAUNAxnA3UvqPV9oZn8wsz7ufqA99XZbVSVUvfq/TF55P6nVB6jMGMr2if/Jw+Xn88jaCj41PouLG0yifu9vfsOjDz0EwIRJk7j9jjuYOWvWkf2BYJCS8nImn346OVpKU9rpRIzZQDBIcWkpm3fuZPOeA/QpfJtxeQvIqdnF5tAg7gl8jVWp5/PlmX04tZluF++sWsXXv/IVqquqGDBwIJ/53OeYeckl9M7Jobi09MjV4VAoFL7S6056aippkUG0qSkpZGdmkpmeHh5EGyMf1tI1jrd4rVsMaHteHnn794fHDQBl1SF2Hq5h7ZadzNjzEB+Le5293otfZnyD0edfQU5m2wbaBQIBPnnllezetYvU1FQunDmT2XPncva55wLoS6K0SntmyTDgQeADd/9VE2X6A/vd3c3sbCAOONjWOrut4jx46z585cNk1pRyuNcZ7J50GysSzuSRd4rZfKCC84alcfHwxCNXkm/7zncYOGgQk88+m4yMDC6dO5cBAwcC4SA/XFJCIBgkOSmJM089VcmytNuJFLO1gQAFBw9SUVnJ3sJC8otKKFj3KnOKn2BUXD6bQ4P4Tfyt1I6cyRl907guJ5nkhI+uvPfu6tX889lnOWnECGZffjm9+/dn1uzZXHjxxZx2+ulgRmZaGmlpaaRErtImJCSQkpREakoKKUlJuiIkbXI8xWtFZSWHS0vZtGMHVdXVxJnRt2dPzIznPijhiXcKuCluIT9J+AcJcSFe6XM9u0Z+iqkDslv1hXLf3r0sWbiQHdu388Of/ISEhAQ+duWV9B8wgAtnzCA1reUz3HSVujtPH7n7387eANJ+7bnCfD7wGeA9M3s3su27wFAAd78PuAb4ipkFgErgOm9vH5DuZO8aeONeWP807s6+fuez55RrCfQfz7p91fz21QNU1wSYkbiFfbkvcvm3XqKyspJ+OTnk5+UxcNCg8MIi550HQEl5OTW1tSQlJDAoJ4cB/fqRlZGhb8HSUU6ImC2rqGD1+vXsOlDKewUBRpW8xYyDf+Fk8tiTPIQ3Rt5BcMRMPtHE9FBbt2zhub//nReXLmX/vn2kpKRw85e/zMSxY7G4OKadffaRrhDJSUlHLXAi0oGOi3itrqnh9XfeIRgIkJKSctTc/G/vLmf/mudZlvoY/UP72ddvKvsnfY3U9IGMbuH5i4uLeXHpUhbn5vLu6tUAnDFhAlVVVaSkpPDZL3yhw95L3VoFgUgXq7oxBfDhOIK6cQMfGU9Ava5QkbEK9Vm9blpxZljkc7/u0z8tLU2z63Sx9syS8RoNpuBtpMy9wL1traNbCoVgywuw/Hew/RU8KZ3y0z/DyvSplKYNYeHmSl5+NY/SkhL69srm1rNS+Nq//SdpaWlcMmcOsy+7jIlnnklcXBy1tbXUBgJUVlcDkJaSwlmnnUZmerpu00qHO55jNhQKsS0vj9KyMgoPHWLdvmoK1jzPF/k7I+L2sN0G8/Lo75N26iUkWhx1qXIwGORwaemRuVcB/uunP2XtO+9w4fTp3H333Vxz9dVkZjY98E+kMxwP8RoMBtmen08gEKBPvdUriyoCvPDWWq4o/CNfTVxPadpwNkz8DaX9Wtbtu6qqCtxJSU3lxaVL+fmPf8zwk07iS7fcwuy5cxk0eHCjx9UNng0Eg9TW1lITCBAMBsP/yGZHJ7WNiIuLIz0lhR7p6aSmpJCUlERSQgIJCQlHxh/UjSWIi8xMFRcXFx6QG9lXl0xT73ld1yx97ndvGjXWUrVV8N4TsPz3ULgBzxzIgbO+xYKySWw6mExFQTyvr11D4bv/ombjywwc0J97/zSfxHjjvoceYuy4cSQlJVFdU0NRSQkeCpGUlERWRgYnDxlCVmYmGWlpupos0kp1A4jy9u8nvyRE/OalXFPyFEPjCjmccTJbTr2TQ4NnkGZxRx2zOz+fV5ct481XX2X1ypWs/+ADBgwYwPwHH6R/Tg79+3fcYggiJ5qa2lpWr1/PweJiemVlHdledOgQh/71e+4OLaUmMZUtp97KoZFXQTPTxAWDQVauWMHi3FyWvfgit3zzm1x97bXMuvRSTh03jtFjx2JmhEIhqmtqqKisPLIwV93V3LpkNjk5mawePchMTyctJeVIItswiT2SBMfFkZyUpLUKTnBKmJtTfgDefhDefgDKC6npPYbNE+7gvqIJLHmjmpogVGx4ifK3n6ZizybMjDPPOotL5syhrKyEkDuDTzqJ0ooKvKKClKQkTh85kh4ZGaSlppKgWywibeLu7D9wgPe2bOelDQUM3vsCn6j+BwOsiC2JI3hn/DcJDJt6ZBGfmtpaSsvL2b1rF4888ABvL19OTU0NI0aM4Lvf/S7ZPXqQkZbGhPHju/idicS+97dsobi0lH69egHh1TP7bX2GMevmkxYqY9ugyymb9CUCydEXLgkGg/zu179m6aJFHCgsJD0jg5mzZjH21PCsGZk9ejDm1FOprqmhuKyMhLg4MtLTGT54MH2ys0lOTiY5KUlrEUi7KWFuSuHG8NXkNX+BYDXlQy7kmd4zWVI5ljdfK+HQhoVMmTadKyb0571gkCUb45j1yW8yZdo0snv2JCE+npMGD6ZXdjaJdbdr4uNJSUpS0Iq0056CArbs2kXxwf2UrFnIt8qfpY+VsD3tNN6b8D2qBpwNZlRUVlJeWcm6NWvI+P/s3Xd8XNWVwPHfUW+WZVVL7tjGxgYbg0OJgRBMdSCNEMBASIAQ0jbZTTZ1N5Aeskk2CamE3gMJhA42YYFQjQ3YGNyrZKt3jUaa8s7+8UYg7FGbopmRzvfz0ceamffevWPNmXfffefeW1DA6aeeysyKCr739a9z1VVXcdFFF/G+973PYtKYGOr1+ahtbHTzlVUprnmaqo1/Jrd7Py84C1lVcQVnHz/wIj411dVsevttTjvjDNLT09n89tssPOIIzlyxgmUnnUROjjubjarS6/PR2d1NZno6Ry9YQHlJid2pNXFhDeb+VGHXs+5Avu2r0fRsdleexc3+U7lr6wQ6drxG99s/pmf7GoL+XpYuK2PWhDIqP7SCsz58DgJUlpczvbKSQhusZ0zMqSot7e1sXP8KOdse46T6B5koHt7OPYrGYy+nq9TtHfb5/axfv56nV63ixeeeo3b/fj72sY/xhcsuA2Dfvn0Wn8bEQbfXyxubN5OelkZh0+tMffOPFLRuYrvM4Ie+b+KfehyfO+7gWZ9ampt5KjR4b+OGDWRnZ7PsxBPJy8vjDzfc4I77CQTo6u6my+t9Z1BdYUEBhx1yCFMqKsgaYCCvMbFgDWaAQC9s/Lvbo1y/EV92CY8UXsxPGk+gaXsh6m2n7sZL8TzDMqMAACAASURBVHk6mFhUxFkfOosPnHYaixYtoqK0lPKSErIzM8nLySHTAtaYuPD7/ezc8jqe5/7ACQ2Pk0cPz6YtpWvxpyk7ZDEiQq/PR0dXFz+9+mqef/ZZMjIyOOuss7joV7/inHPOeedY1lg2JrZUlU6Ph9fefpusth0s3XkbxXUvUkcJV/uu4h96At/8QAVLphw8zeJjDz/MD7/3PYLBIHMOPZQvffWrnHL66aSlp9Pd00O314sCWRkZzKiqeuecm5aWZrPUmFEzvhvM3S2w9kaCr1xPuqeBvRkz+ZNzFXdur6Lt7eeZnPcA//7VrzJjwkTuaj6HBQsXsuJDH6K8pOSdBQhsWWpj4q+rbhutj/+IQ/Y+Rrrj53E9jppDL2HxEUfg62jnvnvu4Z+rV/PDa6/liPnzuezTn2bl+edz3nnnUVpamujqGzOmebxetu3eTcvet5i3914q963GQw4/9V/I4zkf4ujDivjVIflUFWYS8Pt5+aWXeOLRRznr7LNZduKJHL5oERdccgknL1/OtBkzCDrOOytapqenU1FSQlV5uc0gZRJqfDaYm7YRfPH3sP5u0oM9PBdczHWtn2DNljo8m/5G276dpKWlUXrCCSwoVqZOruS63/zmnalkjDGjI9i4jd5nfknOW/dRicMDgRO4kY/wyWXzad74El/785945aWX3J6puXOZWVHB9KoqPvPpTye66saMCzW1tWx961Vm77mPI2oex1Hl5sAZ/CP/PE45ego/nppLdrrw5oYN3PHIIzy1ahVtra0UTpzIkqVLaWxtJa+wkM998YsUFxWRl5ND6aRJFOTlWePYJJXx02B2HNjxNN3/uo68vc8QIJPbOo/hwZyzmTXnMHoe+wv7nrqbwxYu5ItXXM3FK1cya+bMdwbsGWNGUe16uv95LTnbHyeDNO4OnMzqCR9lVlU5X5tXxtY3X+e73/gGZeXlXPqZz3DxJZdw0rJlNrG/MaOk2+ulZs9W9IXfcuK+h0lz/NwXOInrAh9n6YJZfHtREZ6ONnIy8lFVvvetb9HU1MTxJ5zAycuXs3jpUgonTKCqrIzpVVXk24qYJsmN/Qazz4O+cTc9L/yB3PYdNPkn8t+bl7JqcyeNW5/iv358CsfNySD7K1/gB9/5T45avNgayMYkgiq6+1+0PnktxXXPE9Rc/hRYwX1tC0nbt4HX//VV3nf88Rz3jW9w4rJlPPHkkyw/5RQyMsb+15gxycLj9VK7bzfdz13H7Op/kK8eHgoez80Zn2TegkP5bL6HN55/mMt/9ih1tbXc9+ij+AMBvn3NNVROmcKMadMonTSJvNzcd5bHNiYVjN0zTdteWHM9gbW3kuHrYG33DD6/aiZbt+7A732aScXFrLz4IlaccjJLFi+2wXrGJIrj0LvxIbr++T+UtG8kqBP5hXMB921Sdr30OI377yIjM5NlJ57IRRdeyMnHHENuTg6L5s9PdM2NGTd6fT527NpGzdN/4ZiGv1OkHTwVPIr/K7+EGYcezpm1m7jxt1/n52vWoKrMnTePCz/1KXIyM5kzfTrHLl7MhPx8Mu0C16SosfXJVYW9L+G89EfY9DCv1QV5sG0Ou+d/jamHL6Dtns9y2qmnsHLlSj589tlMKChIdI2NGbfU72XXP28gb92fmOyv4c2OYi7bdgyHf+LrHDU1h8mbriNQNZlvfO3fueTii6koL090lY0Zd3p9Prbs3EHNs7eytPavLKCFV+Vwni5dSWtHD0fPKKWiWHh5awc1NTVcctllnHvuuSw7/ngKCwqsgWzGjLHxSQ5NC+f91++p276BG98UbnwzjbqmLgpLarn+8iOYUVHKV9atpaK01G4BGZNAvo5Gtj/6v1RuvYOynjZ+t7mI2zeVsWXbLhxnNx8+/1KWHPoBlt90E0WFhYmurjHjkj8QoLGpgbce/wvzdt/FIqnjDZ3D7wMfZf3mav65+tt0dXZy7oUX8m//8R+sPO88vvTZz5JnA+PNGJXaDebOerpfvB5ZdxO5vha+9UwWv322C4Dp8xbyxUsu5xPnfYLFCxcyyU68xiRUT902djx8LYfse5AF+Lihdg5fuLkOv7+aispKPvf5z3PZZz7D0qOPTnRVjRnXfD3dvP3wryh/+zZO03q2yQwemXsNX/3+H9mx7dfk5ORw4skns3LlSlasWEFZcbF1RJkxLyUbzL7q19j76C94efVD3PNmLx8+eTFvT7uKuuPSuXD2Vi771EUsWbyYogkTbNS8MQm2641n6PrnL2jb+Ax3bAwQLJnD8Rd9jYoPzOT83ltZeeGFnH7qqRarxiRawEfjs38m7aXrKG6u5X82FPBM8zR+9ZdbKHD8nHLGTj65ciUXr1zJvNmzLWbNuJIyDeZAr5fN/7yN1/7+G1av3c4DmwJ4/MqEoknMnfpJrrzw40wuK6OwoIAMC2JjEirg9/H66ruoe+LXvPLGJm5/M8jetiCZWVl87LwjOeWDpzG1ooJzPvjBRFfVGBPope2FG2ld9QtWra/l+jfTeW2vB+jisMNLaajfT1VlJT/74Q8pLipKdG2NSYioGswicibwGyAduEFVf3bA69nAbcDRQDNwvqruHkkZLTXbeOP+X7Ck5TFm+to55vYuJD2LRSeeyuWXXsxHzz6bEpuaxphhiXfMNjbUsuav13J088O8jyY+9FSQxzf7OfKoo/jihRdy0cqVTKmsjN0bMmYMi/s5tqed/auvQ9f8hSnpbfxpczHffrSHyVOnc/Fl53P6mWeyeNEipk6ezMSCAutRNuNaxA1mEUkHfg+cBtQAr4rIQ6r6dr/NLgdaVXWOiFwAXAucP5zjP3z7dTx2+295at1O8jPhui98kOo5K/jjbVWcuXw5JcXF1pNszAjEM2Y3vPw0f/3Nd3jx5dd4bo+f+798JL7DL+Oynx/Bz+ccyvy5c+1ka8wIxDNevU17ufeX/8Hqxx/loU09rFw2nWnnfI/Siw/nN2fu5QMnnkhleTnFEyfaedaYEFHVyHYUOR64RlXPCD3+NoCq/rTfNk+GtnlJRDKAOqBMhyg0LytNvX5FgCPmVHLCio/z7W//FxUlJTZfshmXRGSdqi6N8hhxidnC3Ez1+QP0BqGqOJ+TTj2Nr3/nGhbMn09udnY0VTYmJSVzvJYW5mqm9lLXpeRlZ3Do4qV85IJL+OCypVRVVFBVXm6r7plxZbjxGk1KxhSgut/jGuDYgbZR1YCItAMlQNOBBxORK4ErQw/96ZmZu1U1uGFHnXfDb3/v/OG3v4+iqkmjlDDvPcWNxfcEyfe+ZsTgGDGL2TDxujctjWBta7f3nnv/Ebjn3n/EoLoJl2yfgVix9xV/yRyvvelZWfvSs6An6PjWr13je2PNy4HvQ2S9Z8kjmf7+sWTvK/6GFa/RNJjDJQ0fGHDD2cZ9UvV64HoAEVkb8PmiujpPRiKyNtpeh2QzFt8TjNn3FbOYtXhNXfa+Ukb84rW3dyz9PwFj8u8P2PtKJmlR7FsDTOv3eCqwf6BtQreLJgItUZRpjImcxawxqcPi1ZgkEk2D+VVgrojMEpEs4ALgoQO2eQi4NPT7J4Cnh8pfNsbEjcWsManD4tWYJBJxSkYoX+pLwJO4U97cpKpvicgPgLWq+hBwI3C7iGzHveq9YJiHvz7SeiW5sfi+xuJ7gjH4vuIYs2Pu/yrE3ldqGVPvy+J1xOx9pZaUe18Rz5JhjDHGGGPMeBBNSoYxxhhjjDFjnjWYjTHGGGOMGURSNZhF5EwR2SIi20XkW4muTyyIyDQR+T8R2SQib4nIVxJdp1gSkXQReV1EHkl0XWJFRIpE5G8isjn0dzs+0XVKRhavqcfidXyzmE0tYzFeIXVjNmkazP2WAT0LWABcKCILElur6IhILu6gjKXARuA44IsDvS8R+Y6I3BBhWc+ISI+IPBdxhSPzFWDTKJcZb7/B/Xu1AIsZe+8vamMxXkMycAdYVQFbGSReISVjdqzG6xPAJ4E2xt77i4mxGLPj4Bw7FuMVUvQcmzQNZuAYYLuq7lRVH3AP8JEE1ylanwCKgEmqep6qduJ+MKaIyHdF5Ef9N1bVn6jqFVGU9yVVPanvgYgUi8gDIuIRkT0isnKgHcV1rYg0h35+LiISeu1QEXlQRBpFpEVEnhSReSIyFfgQcCuwQET2i0iriPxBRDL7HbvrgJ+giFw3SF3+XUTqRKRdRG4SkbDrK4tIVugqdbeIqIicfMDr2SLyJxGpD9X7YRGZ0u/1O0SkVkQ6RGSriFwhIoXAScD3cU++Z6hq2+D/7ePSWIxXgBOAHKBEVT9GKF4BxkDMnoQbrzcAaSLyv2MgZr+EG683quoGoBU4cfD/8nFrLMbsWD7HjsV4TelzbDI1mMMtAzplgG1TxQxgq6oGAERkJrAEeAV4DDcY4un3gA+oAC4C/igiCwfY9krgo7hXe4uAs4HPhV4rwp3vc17oWGuAB4FfA9/AncpoInA4cChwFPBffQdW1YK+n9D+XuC+cJUQkTOAbwHLgZnAIbiBNZDngYuBujCvfQU4PvR+qnCDs/+XyE+BmapaCHwY+BHu36QRuBm3F+b3IpI/SPnj1ViMV+gXswfEK6R+zD6CG68OMBu3Vy7VY/YawAPcLCKvA7nAFwYpezwbizE7ls+xYzFeU/scq6pJ8QOcB9zQ7/ElwHUJqMdu4D+BDbhfxDfifgAfBzqBp3CvZvu2vw/3g9QOPAcsDD3/fdxA8gNduF/i64CP99u3Bqjq9/ga4I7Q7zNxlzi9FNiLu+b6dwep9zPAFf0e54fKP7Tfc7cDPxtg/xeBK/s9vhx4eYBti0N1uzH0eAuwrt/rK4HqAfa9FNhJaErDMK/fBfyk3+PlQN0w/m41wMkHPPdH4Of9Hn8I2DLA/vOAWtwvkgBwLO7JJNC/Pvbzzv9XUsRrqOx4xezu/vHa73OWijF7QahuJcDJuCe28/q9nqox2wQEgWNDz90Q+vtlJzpGku0nWWI2jvE6ls6xYzVeU/ocm0w9zMNZBnS0nAuchnsldw5uIH8HKMXtlf+3fts+DswFyoHXgDsBVPVq4CfAX4FJuLe+7lTV+/vt+wRuPtlgTsD9oC0Hvicihw3zPRwKBFV1a7/n1gMDXf0uDL0+nG1Pwv2iO0NEduNeoR4uIneEXhdgqohMDLPvpcBtGoqgYdajQkRKBth+MDcCy0SkSkTycHsAHu+/QejWVjewGTeY7wVqVPUVVd0H9OL+Dcx7JVO8Qmxj9l7gBeC3B8QrpG7MfgK3p2od7q34QuDL/V5P1ZjdTyheQy/fGvp3XgRlj3XJFLN2jh2f8ZrS59hkajAPZxnQ0XKdqtaH/pj/Al5R1ddVtRd4APeWDwCqepOqdoZeuwZYHOZDfCOwSVV/dcDzw7ll9H1V9arqetwP9uJhvocC3Cvy/tqBCcPcvh0o6Mux6iNu3vLvgc+q6lRVnYkb0B7g30VkMu9+2eUdsO904AO8e1Ibbj0YpN6D2Yrbc7AP6AAOA37QfwNV/ULo2CcC9+PesqwWkb4TbjC0v3mvZIpXiG3MHk/4eIXUjdnjgYtD8XoBsANIF5GyFI/Ze3lvvC4HenBvcZv3SqaYtXPs+IzXlD7HJk2DWd0cpL5lQDcB96rqWwmqTn2/371hHhfAO1O+/ExEdohIB+6tJnCvkvuU4d76OkVE3gj9rAi9tho4qX/yfhj984a6+8oehi7cq9L+CnFveQ1n+0Kgq/9VqoiUAauAP6jq3f22vR036N7Ave30D9zbZA0HlPEp4HlV3TWCevf9PlC9B/NHQgO4cG+f3c8BV78AqhpU1edxe1w+j3slf6eIbMDNifxdBGWPaUkWrxC7mJ0OzCJ8vMLYidltwOuMjZh9gXfj9UjcW9kpMYhoNCVZzNo5dvzGa8qeY5OmwQygqo+p6qGqOltVf5zo+gzDStzbQKfiDnqbGXq+/xVjo6qKqi5S1SNDP48BqDuidwPxGdW9FcgQkbn9nlsMDPQF+RbvvbJ+z7YiMgk3kB868G+jqqtUdZaqTlHVQ4Bm3Jzm4AFlfIrBr3wHqke9qjYPsV84i4FbVLUl1DtxHXCMiJQOsH0GMFtV31DVpcCZuFe/ayMoe8xLwXiFoWN2L+5t3YPiFcZGzKrqM6q6QlW/NEZiNkdVl6rqItw81izccRXmACkYs+P+HDsG4zVlz7FJ1WBOQRNw82+acW+N/CSCYzwKrBhyqxFSVQ/u1d4PRCRfRJbhfvHcPsAutwH/ISJTRKQK+BpwC0BoGpgngRdU9aDJ7vv2EddxwH8DVx+wzftxE/wPGrkr752u5jbgchFZEPoC+a++eoQj7rQ2OaGHWSKS0+8W16vAp0RkYqiH4QvAflVtEpFyEblARApCvRhnABcCT/c7/MnA06EvAjM2WMxiMWtShsUrFq/JwhrM0bkN2IObf/M28HIEx4jn1DdfwL3d0QDcDXy+7xaciJwoIl39tv0z8DDwJu6E4o+GngP4GPA+4DPy3rkep4den417m8iDe3X7LVVddUBdLgXuD13xv0PcfK2uULmo6hPAz4H/w/2/3UO/LwZxV3K6qN8htuDewpuC+4XjxZ1qCODruPmM23CnsVkRei/g3rb9PO5AmFbgF8BXVfXBfse+CPgTZiyxmHVZzJpUYPHqsnhNAqIDDqQ0o0VEdgLLh8g7GuoYq3AHCqxV1Q/GrHJxJiIX404T9O1E16U/ETkCuF5VU2LJTjO6LGYtZk3qsHi1eI0FazAnARE5F3fuwo2JrosxZmgWs8akDotXEwvWYDbGGGOMMWYQlsNsjDHGGGPMIKzBbIwxxhhjzCCswZxiROQ7InJDhPs+IyI9IvJcLI4fmuLmZhFpFZE1kdQpFkLT3mwWkfJE1cGYPvGO0bHG4tcko7ESx9Gc00VkkYi8GO86pgprMCdIaF7EOUNs810R+VH/51T1J6p6RRRFf0lVTxroxREe/wTgNGCqqh5z4IuhORi3iEi7iDSIyK2h+Sb7TpI3isgeEekUkddF5KyBChKRT4tI8IApd04O1bkXuAn45jDrbcyQkiVGReQOEakVkQ4R2SoiV/R77TgRWS0iLSLSKCL3iUjlIPXtO5H3xdCAC3yISFEoZhtCP9f0e61cRO4Wkf2h+H5BRI4d7E2JyFEi8lyo3HoR+QpY/Jr4SqI4PkxEng7Fy3YR+Vi/10YUx6F9LhCRTSLiEXclxLCLs0RzTlfVDUCbiJwzzP3HNGswJ7d4zh8ZCzOA3aEJ3MN5AVimqhOBQ3BX+en7UsrAXVP+A7grOP03cK+IzBykvJdUtaDfzzP9XrsLuFREsiN9M8ZEYDRi9KfATFUtBD4M/EhEjg69Ngm4HncFtBm4y9vePMTxvtQvhuYNst3/4i4WMRM4BrhERD4Teq0Ad9GCo4Fi3LlhHxWRsMsKi7vy1xO4886WAHNwVzXrY/FrEimucSwiGcCDwCO48XIlcIeIHBraZERxLCKnAdcCn8Fd3OUkYGcMqhrunH4n8LkYHDvlWYM5ianq60CZuKsCASAi14jIHaHfZ4auni8Vkb0i0iQi342mzOEeX0QuB24Ajg/1GH0/TP2rVbWp31NB3BMlqupR1WtUdbeqOqr6CLAL9wQ8YqraNzn6cZHsb0wkRiNGVfWtfithaehndui1x1X1PlXtUNVu4HfAsli8N+Ac4Oeq2q2qu4EbgctC5e5U1V+paq2qBlX1etwlqQdqgP8H8KSq3qmqvaraqaqb+r1Hi1+TMKMQx/OBKuB/Q/HyNG6H0iWh8kcax98HfqCqL4fOn/tUdV+4DWNwTn8GWG4Xs9ZgTgVPAAOmKoScgHuiWg58T0QOi3EdDjq+qt4IXMW7vb5Xh9tRRE4QkXbcK+ZzgV8PsF0FcCjuOvcDWRIK8K0i8t+hq/b+NuGubW/MaIp7jIrIH0SkG9gM1OL2iIVzEoPHEMBPQ3H0gry7XO6ARR/w++ED1O9I3Abz9gGOcxzQIiIvhtI7HpZ3VzHrY/FrEimecSwDPBc2nhgkjkUkHViK28DfLiI1IvI7EckdZl1gBOf0UEPcz8AXw+OGNZiT33BuFX1fVb2quh5YT+xPOhEfX1WfD6VkTAX+B9h94DbirkF/J3Crqm4e4FDP4X65lOM2vC8E/vOAbTqBouHWzZgYiXuMquoXcG+9ngjcD/QeuI2ILAK+x8Fx0d83cdOjpuDeAn5YRGYPsO0TwLdEZEIoB/Qy3BSNA8stBG7HfY/tAxxrKu7SvV8BpuPeTbr7gG0sfk0ixTOON+Mun/2fIpIpIqfjpiOGi6eh4rgCyAQ+gft9cCSwBPivYdYlkvdhsYk1mFPBauCkUKNyIHX9fu/GzS8ckohcJO8O/nk81sfvL3SV+gRwzwF1SMM92fqALw2y/05V3RW6/fQm8APcL4z+JgBtI62bMVGKW4z2F7qV+zxu4/Pz/V8LNWgfB76iqv8a5BivhNIhelX1VtzbwisG2PzfAC+wDTf/8m6g5oByc4GHgZdV9aeDVN8LPKCqr6pqD+4t5feLyMR+21j8mkSKWxyrqh/4KG6DvA74GnAvB8fTcOLYG/r3ulBKVBPwKwaO43BG+j4sNrEGc9JT1U5gA+6VZKyPfWe/wT9D3YqKhQxCuZfgTmGDmxdZAZwb+lIZLuXg21yH4V4tGzNq4hmjAzgwjmYATwE/VNXbR3iscHHkvqDaoqoXqepkVV2Ie754Z/rIUE7jP4B9DD0oaEOorP7lckDZFr8mYeIdx6q6QVU/oKolqnoG7p2e/vE0rDhW1VbchvaoLNMcyuvOAgacUWe8sAZzYmWJSE6/n/QBtnuUkV09JkxoQMHJod8vEpHp4poB/Bj4Z7/N/4h7kjxHVb0HH+09xz0rlOeMiMzHnVXjwX6vT8EdffxyLN+PGfcSGqPiTt92gYgUiEi6iJyBm470dOj1KaHff6+qfxriWEUickbofWSIyEW4uZJPhl7vGxA0M/R4toiUhMo9C3dk/49Cr2UCf8Pt7fqUqjpDvJWbgY+JyJGhff8beF5V2/q9D4tfEy8JP9eKO6dxjojkicjXgUrgltBrw47jkJuBL4e+HyYBX8WdgaOvrHfOwzFwMvB0v4HH45Y1mBPrLdwTTt/PZwbYLtmnlwNARKYCXcCboacWAC+GnnsB9wr1s6FtZ+D2Sh0J1PVLDbko9Pr00OO+gUHLgQ0i4sH9/7gf+Em/4lfi5kCP+6A2MZXoGFXc9Iu+WSR+AXxVVfsuFq/A7am6ul8MdfXtLO6iBX3pVpm4Dd5GoAn4MvBRVe3rOZoG7MHtMQZ3xpo3cfMXfwpcpKp9A5HeD5wNnI47T2tf2SeGyj2xfz1CswJ8B7dB0oA7W87Kfu/T4tfEU6LjGNwZMWpxP//LgdP6fd5HEscAP8Sd1nEr7mDZ13E7pMKdh6N1ETCcRvyYJ6qj0qtvoiQiO4HlqrorimOsAo4H1qrqB2NWuXePfzGwUFW/HetjD1FuNu6t3JNUtWE0yzamTyrE6BBl/xfQqKp/HuVyLX5N0hgDcRyz87CIHAFcr6rHR1+z1Ddkg1lEpgG3AZMBB/c/7zciUgz8FXei7d3AJ0O5NQfufynvjt78UWigiRkhETkX2KKqGxNdF5PcLGYTw2LURMLiNblYHJuBDKfBXAlUquprIjIBWIc72vPTQIuq/kxEvgVMUtVvHrBvMbAWd85ADe17dLigN8bEhsWsManD4tWY1DBkDnNo2pLXQr934ubLTAE+grscKqF/Pxpm9zOA1aHR1q2407acGYuKG2PCs5g1JnVYvBqTGkY06C80enoJ8ApQoaq14AY87oISB5oCVPd7XBN6zhgzCixmjUkdFq/GJK8DlxYekIgUAH/HHaHd4U6hO/RuYZ4LmwMiIlfiTltEfn7+0fPnzx9u1YwZ89atW9ekqmUj2SeeMWvxGl+qSqfHQ3r6QLNfjcz+Dj+OwtSJg63JkBpEg+S17yCQOYHe/Mqw2wSDQQoLRrw2TMxYvBqTOoYbr8NqMIfmzfw7cKeq3h96ul5EKlW1NpSDFW50cw3uHH59pgLPhCtDVa/HXaqVpUuX6tq1a4dTNWPGBRHZM8Lt4xqzFq/x1drRwSvr11NSFP1qtG3eIFfev4/zjpjIeYsmDr1DkqvcdAtT376BjafehHfiwat6O45De1cXpy9bloDauSxejUkdw43XIVMyRN5ZjW2Tqv6q30sPAZeGfr+UfotI9PMkcLqITApNrn166DljTJxYzKa+bu+g6/iMyNp9XhQ4ZlpuzI6ZKBLspWLH32irOC5sYzkVWbwakxqGk8O8DHfC7VNE5I3QzwrgZ8BpIrINOC30GBFZKiI3gLu0Ku9OsP0q8IPQc8aY+LGYTXEt7e1kZcYmfWJNdTcVBRlML0r9dIzSPY+T2dtG3byLEl2VWLJ4NSYFDJmSoarPEz5PCtzVag7cfi3uqjV9j28Cboq0gsaYkbGYTX2tHR1kZ2VFfRyPz2FDXQ8r5k1gmDmxyUuDTN56N12TDqOz9MhE1yZmLF6NSQ22NLYxxiSRQDCIp7ubzIxhj8ke0Ov7vQQdOHZaXgxqlliT9j1LjmcfdfMuhlRv/BtjUo41mI0xJon05S/Hokd4TbWXopw05pZG31udUKpUbrmTnoKptFadkOjaGGPGIWswG2NMEvH29MTkOL0Bh9f2e3nftDzSUrxHdkLja+S3baH20JUgsZlqzxhjRsIazMYYk0TaOjvJiEE6xpt1PfQGlGPHwOwYlVvvxJ9dTPP0MxJdFWPMOGUNZmOMSSIt7e3kxGDA35pqL3mZwoLynBjUKnFy27YxsX4N9XPOQ9OzE10dY8w4ZQ1mY4xJEo7j0NHZGfWUckFHWbvPy9FTcslMT+10jMqtdxHMyKXhkI8muirGmHHMGszGGJMkvL29qCppadF9NW9q6KWz10n52TGyPPsprnmaxlkfIZg1IdHVMcaMY9ZgWexcKQAAIABJREFUNsaYJNHt9cZkyrQ1Nd1kpguLq1I7HaNyyx2opFE39/xEV8UYM85Zg9kYY5JEh8cT9XRyqsqaai9HVuaQk5G6X/FZ3fWU7n6Mppkfwp9blujqGJM4QX+ia2AYxkp/xhhjRkdrDAb87Wjx0dwd5MLFqT07xuStdwNK7aEXJ7oqZhxzHIdef+wbrLe+tJdbXtzDk19ZRtYQF7ZZ912CZubi/+j1EZeXnZkZdarXeGcNZmOMSQKqSlt7OwX5+VEdZ021lzSBo6akboM5o6eZsl0P0TzjLHz5kxNdHTNOqSrrN2+mrqkp5qtLvr6th6YuHy+se3XQu0qZvnZO2fEUe6Z9hC1r1kRUluM4HL1gAZPL7E5NNKzBbIwxSaDH58MfDJKeHt3CHGtrvCysyGZCduou8FG59W7ECVA7z3qXTeJ4vF7qmpooKSqKycqb/TlpLeRnBSibNGnQ7cp2/B9pGqR77jmUFhVFVFZzWxsa0Z6mP+ufN8aYJODz+WKSv1zb6WfWpNRdCjujt42ynf+gefpp9BZMTXR1zDjmOA5pIjFvLAN0+xzyModugpVUr6K7cBbeiXNiXgczMtZgNsaYJBB0nKiP0RNQAg4U5qRu73LFtr+SFuyldt4lia6KGeccx4lbz6zH55CXNUTusqeWCc1v0jLt9JinhJiRswazMcYkAdXoT83dfrfRPZyeq2SU7uugYsffaZ1yMj2FMxNdHTPOOTG4iB2Ix++QP0ScllSvBqB52mlxq4cZviFzmEXkJuBsoEFVDw8991dgXmiTIqBNVY8Ms+9uoBMIAgFVXRqjehtjBmAxm5pi0cPc7Xcb3XmZqdkbVbHj76QHutl/2KWJrsqosXhNXo5qTC5kw+n2OZTmDdIEU6Vk7yo6SxbZwNckMZxBf7cAvwNu63tCVd+ZRV5Efgm0D7L/B1W1KdIKGmNG7BYsZlOOEwxGfYxuX+r2MKf5PVRsu5fWyhPHW77mLVi8JiVHNS75y+Be3OZlDXzsvPZt5HbuZveSr8elfDNyQzaYVfU5EZkZ7jVxP0mfBE6JbbWMMZGymE1NsejNeiclY4jcyGRUvvN+Mvyd46p3GSxek5nGMyVjiEF/JXtX4Ug6LVM+GLc6mJGJ9lv1RKBeVbcN8LoCq0RknYhcGWVZxpjoWcwmqUAgEHVvlvedHObUSslIC3iZvPWvtFUcS/ek+YmuTjKxeE2gWKRJheMPKr6gkj/Qha0GKa5+ivbJxxHMnhiXOpiRi3Ye5guBuwd5fZmq7heRcmC1iGxW1efCbRgK9isBpk+fHmW1jDEDiEnMWrzGXtBxom4w9+Uw56ZYSkbZrofI9LVRe9inE12VZGPxmkCO48Qlh7nvTtBAg/4mNL5BVk8Te6d/OeZlm8hF/K0qIhnAx4G/DrSNqu4P/dsAPAAcM8i216vqUlVdWmar0RgTc7GMWYvX2AsEAqRF22D2pV5KhgR7mbz1LjrKjqKr5IhEVydpWLwmXiAYjMty0n1xOlAPc0n1KoIZebRVnhDzsk3kovkknApsVtWacC+KSL6ITOj7HTgd2BhFecaY6FjMJrFAMBiDHmYHAXIyUiclo3T3o2T1NLN//qcTXZVkY/GaYLG46xOOZ5CxBhLsZdK+Z2md8gE0PTvmZZvIDdlgFpG7gZeAeSJSIyKXh166gANuFYlIlYg8FnpYATwvIuuBNcCjqvpE7KpujAnHYjY1OY4TdW9Wt98hN1Oi7qkeLeL4qdxyB50lR9BZtiTR1UkIi9fkFYzBRWw4Ht/AYw2Kal8gw99F87TTY16uic5wZsm4cIDnPx3muf3AitDvO4HFUdbPGDNCFrOpKVY5zKmUv1yy5wmyvQ3sPuob43YlM4vX5BUIBuNy8dntc/Oiw6VklO55Al9uGR3lR8W8XBOd1PlmNcaYMSwWvVle/+BTVSUVJ0DlltvxTJpPR8Wxia6NMQeJWw+zP3wOc0ZPCxPrX3F7lyV1l7cfq1Lkm9UYY8a2mPQw+5yUGfBXUv0UOZ797J9/6bjtXTbJLeg4cephDr/AUEn1U4gGaZpxZszLNNFLjW9WY4wZ42IyS4ZfU2MOZg1Sufk2uifOtpkATNKKxdzo4Xh8Dmly8ODckr1P4Jk0n57CWTEv00TPGszGGJMEYpPDnBopGcU1z5Dbtdd6l01Si2dKRl5m2nuOndu+g/y2rTRNt97lZJX836zGGDMOxOL2b0rkMDsBqt6+ke7CWbRO+UCia2PMgIIxmLkmnG6fc1D+csmeJ9ylsKedGvPyTGwk+TerMcaMD7HKYc5N8pSMkurV5HbtZd+CK2xgk0lqTrzmYfY55GX1O64ToKR6Fe2T308guyjm5ZnYsAazMcYkASfK27/+oOJ3knuVP3H8THn7JjxF82irOinR1TFmULFYTCgcj1/fsyx2YcNasnqaabbBfkkteb9ZjTFmHIm2h7nbH37kfTIp3f0o2d211Cz8rOUum6QXrxzmA1MySvc+QSCrkLbJx8e8LBM7yfvNaowx44SqRr3SX7I3mCXYS9WmW+gsOcLmXTYpIW7Tyvnfnf4x3dfBpH3P0Tz1VDQ9K+ZlmdhJzm9WY4wZRxzHAdWojtG3eliyTitXvvMfZPU0sW/hlda7bJKe4zgEg8G4DPrz+N4dnFtSvZo0x0fTrLNjXo6JLWswG2NMgjmqUTcivX09zEmYw5wW6KZyy+20ly+ls2xJoqtjzJDilY4RdJSegL6TklG6+1E8RYfSXXRozMsysZV836zGGDPOaJS9y5DcKRkV2/9GZm8b+xZ+NtFVMWZYAo4Tl+P2xWl+Zhp5bVvduZdnfiguZZnYSr5vVmOMGWeCjkO0Tea+E3GyTSuX7utk8ta7aK1chqd4YaKrY8ywBAKBuBy3L3UqP0so3fUITloWzdNOi0tZJraswWyMMQmmjkO0zVyvvy+HObm+1idvu4cMf5c777IxKSIYDEY9riAcT+jCdkKGn5LqVbRO+QDBrMKYl2NiL7m+WY0xZhxyYpGS4evrYU6er/WM3lYqtt9Ly9RT8BbNTXR1jBm2oOPEfHCqP6i8vt8LwPz2F8nwd9E40wb7pYohv1lF5CYRaRCRjf2eu0ZE9onIG6GfFQPse6aIbBGR7SLyrVhW3BgTnsVs6nFikC/p8TtkpguZ6cmTklG5+Q7SAr3sO+zyRFclaVm8Jh/HcWjr6Ij+OKrs7/Dz3C4Pf1nTwuce2Mc969upKsxgUcuT9ORV2iDYFJIxjG1uAX4H3HbA8/+rqr8YaCcRSQd+D5wG1ACvishDqvp2hHU1xgzPLVjMppRYNJi7/U5STSmX5amlfOf9NM1cQU/hjERXJ5ndgsVrUggEg7S0t7Ntzx7aOzooKowsVaKhK8ANr7awtakXTyhnOTtdOLIqh1NmF3DMhGYmrX6NmgVXgCTPHSEzuCEbzKr6nIjMjODYxwDbVXUngIjcA3wEsGA2Jo4sZlNPLFIyvH5NqvzlKW/fAAj7Drss0VVJahavyaGjq4t1b72Ft6eHvNxcyoqLIzpOe0+QXz/fxLZmH8vn5DO3JJs5JVlMnZhJepp7QVvx1mMoaTTNCHvjwCSpaL5dvyQiG0K3kyaFeX0KUN3vcU3oOWNMYljMJinHcaKeWq7b5yTNHMy5bdso2buK+jnn4c8rT3R1UpXF6yjp9fnYWV1NIBikrLiY/NzciI7zo6cbuOLv+9jW7GPaxEyuOraE5XMKmDEp653GMhqkdM/jtFccY7GRYiL9dv0jMBs4EqgFfhlmm3D3Bgc8I4jIlSKyVkTWNjY2RlgtY8wAYhqzFq+x5ahGvUhCMqVkTN34Z4KZBdTOuyjRVUlVFq+j6PVNm9jf0EBhfn5E+wcd5ZFNHayv7aGiIIOfnFHBz1dMDrvtxLo1ZHkbaZp1TjRVNgkQUYNZVetVNaiqDvAX3FtDB6oBpvV7PBXYP8gxr1fVpaq6tKysLJJqGWMGEOuYtXiNLY1BDnOypGRMaHiNovqXqZ1/iU2XFSGL19HT3tlJc1sbJUVFES+D/ewuD7e+1sbc0iy+t7ycuaXZZKSFv3gt3f0I/uwi2irfH021TQJE9OkQkcp+Dz8GbAyz2avAXBGZJSJZwAXAQ5GUZ4yJjsVscgvGbNBfghvMqkzb+Ad6c8upn31uYuuSwixeR8/W3bvJy8mJuLEMsL3ZR36W8OPTKygvGHhoWEZPK0W1z9M8/Uw0LTPi8kxiDDnoT0TuBk4GSkWkBrgaOFlEjsS9/bMb+Fxo2yrgBlVdoaoBEfkS8CSQDtykqm/F5V0YY95hMZt6YjJLhs9J+BzMk/b9H/mtm9l59HfQ9OyE1iVVWLwmVqfHQ252dJ/V3a0+ZhRlDZlWVbL3CdI0aHMvp6jhzJJxYZinbxxg2/3Ain6PHwMei7h2xpgRs5hNPYFgMKocZkcVb0DJy0pcDrM4AaZuvJ7uwlk0zzgjYfVINRavieM4Dj6/n4K8vIiPEXSUvW1+Tpk9RP6zKmW7H6Wr+HB6CmdGXJ5JnMQnvBljzDgXCASiajAnw7LYpbseJsdTQ83hV4GkJ6wexgyXz+8HiCr26rsC9AaUWZOyBt0uv+Utcjt3W+9yCrMGszHGJFjQcUiL4qTd7XdTOhLVYE4LdDNl0810li6mfbINZjKpoa/BHI3drT4AZg7RYC7b/QjB9Fxapp4SdZkmMazBbIwxCRZ0nKh6ubp9iW0wT956N5m9LVQf/nmIcno8Y0ZLbBrMftIFpk4ceBBfur+L4uqnaJm2HCcz8vQPk1jWYDbGmATTKBvMvUE3JSMnY/Qbq5ndDUzeehctU0/BU3L4qJdvTKRi1cM8ZWImmekDx17JnidJD/bQcMhHoy7PJI41mI0xJsGiXRq7b/dEdO5Ofet6RB2qD79q9As3JgrdXi/pUUwnB24P84yiQaaIU6V85wN0TTqM7knzoyrLJJY1mI0xJtFStMGc17qZ0r1PUD/nPHz5VaNbuDFR8ni9ZGQMOVnYgLp6HVq9QWYMkr9c0LTeHex3yEciLsckh8g/KcYYY2JCiW6kvoZfETm+VJm+4Tr82UXUzv/U6JdvTJS6urvJSB/5jC4BR3mztodbXmsFBs9fLt/5AIHMAlqmnhpxPU1ysAazMcYkmKNKLDqHJSZHGZ5J+59lQtN6di/5OsHMglEr15hoBYJBdlVX09rRQXlx8Yj2re3w85NnGqnrDADwofkTWFKVE3bbjJ4WJu17lobZH8PJCL+NSR3WYDbGmATTFEvJkKCPqW/+ge7CWTavrEkpqsq2XbvYtncvZcXFI76z873V9bT1OEwvyuS7HyyjOG/gZlTZ7kdJ0wCNs2yw31hgDWZjjEk01ahau6OdkFG+4+/kePazZdkvIc1OIyZ19Pp87Kiupry4mPQI0jF6A8oZhxZwxfuG6JnWIGW7HqSjbAk9hTMirK1JJjbozxhjEix2KRnxl9HbStXmW2mrOI6OyceOQonGxE7QcchIT4+osVzX6ccbUEryht53Yt0rZHfX2VRyY4g1mI0xJsFSKSWj6u2bSQ94qV70xfgXZkyMBYPBiANlV4s7b/OSqtwhty3f+Q/82cW0VZ0UUVkm+ViD2RhjkkEKpGTkdOyifNeDNMz6MD2Fs0apVGNiJxAMRhwvfUvQ52cN3nTK8tQyse4lGmedjaYNMkezSSnWYDbGmATTVEjJUGX6G78mmJHL/gWXxbMkY+ImGAxGPO95V6/bYJ6QPXjTqXznA4DQOOvDEZVjkpM1mI0xJsEcx4lq/9FIyZi0/1kmNq5j34LPEsieFL+CjImjYDAY8b6dviAZaZA9yDLYaYEeynY9TOuUk/DlTY64LJN8hmwwi8hNItIgIhv7Pfc/IrJZRDaIyAMiUjTAvrtF5E0ReUNE1say4saY8CxmU1MyL1ySFuhh2obr6C6cTYOtWBZTFq+jKxBNg7nXYUJ2+qCxWly9mgx/Jw2zz424HJOchtPDfAtw5gHPrQYOV9VFwFbg24Ps/0FVPVJVl0ZWRWPMCN2CxWxKiXbQX594LVwyeeudZHfXs/fIr9o0crF3Cxavo8bn95OWFtnN9S6fQ8Fg+cuqVOz4G92Fs+ksPTLCGppkNeSnRlWfA1oOeG6VqgZCD18GpsahbsaYCFjMph4niWfJyPLsp3LLnTRPXU5n2ZLYFzDOWbyOLn8gQHqEDWa3h3ngfQua1pPXvoP6OZ8YvVWEzKiJRQ7zZcDjA7ymwCoRWSciV8agLGNM9Cxmk4yqRpmSET/TN1yHSjrVR9g0cgli8RpDUfUw9zoUDNJgrtjxNwKZE2iZdlqk1TNJLKp7ayLyXSAA3DnAJstUdb+IlAOrRWRz6Go63LGuBK4EmD59ejTVMsYMIFYxa/EaY9GmZMSph7mw7hUm7f8X1Yd/Dn9eeWwPboZk8Rp7/igazJ29QQ7Nygr7WlZ3PZP2/4u6uefjZOREU0WTpCLuYRaRS4GzgYt0gAQ8Vd0f+rcBeAA4ZqDjqer1qrpUVZeWlZVFWi1jzABiGbMWr7GlRDvozxXL9rI4fqav/zU9BVOpn3N+DI9shsPiNT4iTclQVTeHOTv8Kn9lO/8BqjQc8rFoq2iSVEQNZhE5E/gm8GFV7R5gm3wRmdD3O3A6sDHctsaY+LKYTW7RLo0dj5SMim33kdtVzd5FX0HTw/eqmfiweI2fQCAQ0cVpW49DwAk/B7MEeynb9TBtVcvw5VfGopomCQ1nWrm7gZeAeSJSIyKXA78DJuDeAnpDRP4U2rZKRB4L7VoBPC8i64E1wKOq+kRc3oUx5h0Ws6lHHSe6fIoYp2Rkepuo2nwzbZPfT3vl8bE5qAnL4nX0BIJBenw+0tPD9xIPpLrNx9Wr6xGB+WXZB71esvdJMn1t1M85L1ZVNUloyBxmVb0wzNM3DrDtfmBF6PedwOKoameMGTGL2dQT3bIl787DHKuUjGlv/gFxAuxd/G8xOqIZiMXr6FBV3t6+HX8gQMYIGsz1nQG+u6qe7HTh6uXlzDuwwawOk7feg6doHp2lNovMWGYr/RljTKLFLCUj+ibzhIZ1lFSvou7QlfQW2GxmZmzo6u5mX309xRMnjmi/p7Z30RtQfnh6BQsrDh7MN7H2RXK79lJ36AU2ldwYZw1mMyL+oMOtL+5mR2NXoqvyrg33QtO2RNfCmIipalKkZEjQx4zXf0FP/hT2z/9UdAczJom0tLUhIiPKXw46ynO7PCyuzGHyhMyw20zedg+9eRW0TvlgrKpqkpQt2TQONTQ309zWFtG+dR0+rn5oJ189aTJnzAu7Wuuoym7ZzKzHP0/HjNPZf8KPIjqGiDCtspL83NwY186Y4dEoe5j9TmxSMiq33kluVzVblv0STT84V9OYVNXe1UVWZvhG70B2tvho8Qa5eEn4c11+yyYKm95g76Ivo7YC5phnf+FxaGd1NW2dnWRmjPzPv7nRD0B6wMP+Bl+sqzYiacFejnnpW/RmTuSNmZ8m0NAQ0XG6vF4mTZxoDWaTEI7jEAgGIxq5X9vp5+G3O3lul4f0NJiYE/lNw+zOaio3307z1OV0TD424uMYk4w6PZ4RN5i3NbnnuMMnh59XuWLbPQQy8mmceXbU9TPJzxrM44yq0t7VxaTCwogmb/fUu6kYM0sLKCwY2ZdPrE1//XryPXvZcsL/kldcFfFxfIHA0BsZEwfBYJB1b72F4zgjbjD3Bhx++M8G2nscjp+ex5nzCijOi/ArXZUZb/wKJy2T6kVfjuwYxiQpVaWru5uiCRNGtJ/H5w7HDXchmuWppXjfM+5CJZn5MamnSW7WYB5nenp7cVQjXumoyRNEgJJIT8wxUrTvWSp23k/dnPPpqHhfQutiTKQaWlpoam2lrLh4xPs+srmTRk+Q7y0v54gBesCGq7jmn0xseJU9i/8df25pVMcyJtn4/H40gvNeT8AhM11IC3MxW7H9PhSon/2JGNXSJDsb9DfOeHt6olqGt9EToCg3ncz0xI0GzvLUMmvdT/FMmk/NEVclrB7GRENV2bFnDxPyR9471dwd4IGNHRw7LTfqxnK6r5NpG67DM2k+DbM/GtWxjElGvb6Rpw/WtPt5ZqeH0ryDp6BL93VQtuthWqadakvGjyPWYB5nurq7o1qCt8EToCx/ZJO+x5I4AWavuQZU2XHM99G0xKaFGBOpru5uOru7ycke+eC6+zd24KhyyVGToq7HlLf+QmZPK7uXfB0kcbFtTLw0tLSMeEDs41s66ex1+M+TDr7jUrbrIdKDXurmXhCbCpqUYA3mcaats3PEAx/6a/IEKctPXDrGlLeup6DlLXYf/Q16C6YkrB7GRKuruzviWS22NfeyoCKHioLoYjG/5S3Kdz5Aw+yP0z1pflTHMiZZNba0kDfMQd2t3iAv7Pbwwh4Ph5RkMa3ovcvCi+OnYvvfaC87Gm/R3HhU1yQpy2EeZ9o6OsjOyhp6wzAcVZq6Axw/PS/GtRqewrqXqdx6Fw2zPkLr1OUJqYMxseLxekkb4RK94Mbh/vYAC+ZGl4ohjp+Z667Fn1tGzcLPRnUsY5JRIBhkX309LW1tlE4a/G6MqnL/xg7+uqEdBaYUZvAfJxzcu1xc/RRZPU3sOvpbcaq1SVbWYB5H/IEAHq+XkqLI5k9u9QYJOiQkJSPT28Qhr/6I7sLZtlyvGRO6vd4RLdHbp9kTpDeoTCmMLh1p8pY7yevYydb3X2uj/M2YEwgGeXXDBlo7O4c1K9Sjmzu5Z0M7J8zM4/S5BcwuySbrwLE6qkzeeg/dhYfQUWFTL4431mAeR7w9PQAR5zA3eYIAo5+SoUEOefX7pAV72HHsD2xBBTMmdHV3RzQXek2HOxf6lMLI4zCnYw9Vm2+leepy2iuXRXwcY5JVS3s7rR0dw5qBRlV5clsXh5Vl82/vLxnwHFnYsJa8jh3sOvrbtgz2OGQ5zOOIt7c3qv0butz5isuizJscqapNt1LY+Dp7lnyNnsIZo1q2MfHSHWGDeVNDL2kCM4sjS61CHWa+di1Oeg57F38lsmMYk+TqGxuHnX7Y6AlS1xlg2cy8QTuUJm+9G19OCc3TTotVNU0KsQbzONLV3R3x/MsATR63wVw6iikZExpfo2rTLTRNP4PmGWeNWrnGxJM/EMAfDJIeQUrG+toe5pZmk5cZWSyX7XqICc0b2LvoywRyRj7/szGpoKOra9gN5vpQZ9BgaU65bduY2LCGhtnnoukRXqyalGYN5nGk2+uNqEerT6MnyITsNHIyRudjk9HbyiFrvk9PwRT2LPnaqJRpzGjo6e2NKDWqzRtkZ4uPJVWRDfjL9DYy9c0/0l52tF2AmjGtx+cb9hiBvgbzYLPOVG2+jUBGPg2HfCwm9TOpZ1gtHxG5SUQaRGRjv+eKRWS1iGwL/Rt2CKqIXBraZpuIXBqripuR6+ntjWiQUZ9GT2D08pfVYdarPybD18mOY3+Ak5GYmTlSkcVr8uvp7UUjWEBoZ4u7AMOC8gjy+FWZ+drPEQ2w56hvWA5mkrB4jQ9/IDDsO6r1nQHS06A4zCIl4Ob8T9r3DA2zP04wa2TLa5uxY7hdhbcAZx7w3LeAf6rqXOCfocfvISLFwNXAscAxwNUDBb6JP29PTwwazKOTjjF56z0U1b/M3kVftrkuR+4WLF6TmrenJ6Ie5rpOtyesKoIZMkr2PkFR3UvUHH6VzWGeXG7B4jWmgsEgqA47xho9AUrzMkhPC7995dY7cNKzqJ/7yVhW06SYYTWYVfU5oOWApz8C3Br6/VYg3JqqZwCrVbVFVVuB1Rz8xWBGgari7emJKGeyb//GUVq0JL95I1Pe+jMtU06m8RBbqnekLF6TX6fHE1F6VG2nn9xMoTB7ZGlRmd5Gpq//DZ2li2mYfe6IyzXxY/Eae0HHGdEdFK/fIS8z/PZZnlpK9q6icdaHCWTb9ch4Fk0yaoWq1gKE/g23oPoUoLrf45rQc2aUBQIBFCIe9NfcHcQX1Lg3mNN9Hcxecw3+3HJ2H/VNu20cOxavSaTH54vo4rWuM8DkgoyR9U6rMvO1/0Ecf2g6LBu6kgIsXqMQDAZHtL3H55A9wNicyVvvQhHq5l4Yi6qZFBbvb85w3+phE/dE5EoRWSsiaxsbG+NcrfHHHwhABDmTfe58o430NFhcGd3qYoNSZea6a8n0NrLj2O9brtjos3gdJT6/n/QILl5bvUFKRnjR6qZivMi+hZ+jt2DqiMs0ScvidQD+QGDY23b2Btna5GN2ycEzX2R6myjb/ShNM87CnxfumsWMJ9E0mOtFpBIg9G9DmG1qgGn9Hk8F9oc7mKper6pLVXVpWVlZFNUy4XR0dYX/Jh2GdTVent/dzbkLJzJlYnSriw2mfOf9FO9/lprDr8JTvCBu5YxTFq9JxBdhD3N7T5CJI0jHyPQ2uakYJYuon/OJEZdnEsbiNQq9Pt+wB9U2eYIocFjZwQNpK7b9FXEC1M27KMY1NKkomgbzQ0DfqNxLgQfDbPMkcLqITAoNRjg99JwZZTtrasjLzR3xft1+h+vXtDBtYiYfXVgYh5q5ctu2MW3D72ib/H7q554ft3LGMYvXJNIbQQ+zo0pHr8PEnGE2tPtmxbBUjFRk8RqF4U7buK7Gyx9faQagYsJ779xk9LRQvvN+mqefZndmDDD8aeXuBl4C5olIjYhcDvwMOE1EtgGnhR4jIktF5AYAVW0Bfgi8Gvr5Qeg5M4o6urpo6+wkL2fk6RR3vNZGa0+Qzx9XTGZ6fPKJ0/we5rzy3wSyi9i19Dt2Yo+SxWtyCwaDOI4z4vEEHp+Dowy7wVyy5/FQKsaV9E6YNvQOJiEsXmNrX309b27dSv4QHUQv7enmZ8820uZ1uHhJETMnvTclo3LLnaQ5AfbP/3Qca2tSybCS4VR1oGz35WG2XQtc0e/xTcBNEdXOxER1bS2ZEdz+fau+h9XbuzjnsAnMLY1g3tfhUIdDXv20NZ0gAAAgAElEQVQh2Z5aNp/4GwLZRfEpZxyxeE1ugREOSOrT1uMAMDFn6IZ2lqeOGet/TUfpkdTPOS+i8szosHiNnWAwyNvbt1M0YQKZmYOnD/5rt4einDSu+3DlQQP+Mr1NlO98gKbpZ9jFpnmHdeWNcT6/n5r6egoLCka0X2/A4U+vtFBRkMH5iybGqXZQtelmJtU+z95FX6ar7Mi4lWNMshjpCP4+HT3ufoVD9TCrw6x1PwaUXUu/a3dszLjR1tlJIBgcsrH80p5uXq3xcuy0vLCzY1RuuQPRILXzbS0Y8y77Jh3j6hobI7r9e++Gduo6A1x1bPGA0+1Ea9K+Z5my6WYaZ6ywuWHNuBFJD7OjyoNvd5AmUDlh8BuDFdv/RmHj6+xd/BV8+ZWRVtOYlLOzupqc7KHvhv7j7Q6mTczk0qMPnlc5s7uBsl0P0jRjhS3wY97DGsxjmOM47KypYUJ+/oj229Hcy8ObO1k+J5/DJ8dnGrnc9p3MevVHdBUvYM+Sr9t8y2bcaGhpCTsf2GB2tvh4fX8PFy8ponSQaeVyOnYxdeOfaK1cRtOMD0VXUWNSiD8QoLmtbcixOntafexs8XHyIflhx+VUbbkNVNk//1PxqqpJUdZgHsNaOzro7ukhO+vg+SUH4g8qf3i5haKcdC5ZEp9VjdJ9Hcz5f/bOOzyqMvvjnzslvfdeSEjoIE1AEAUsIAoqAjLYd127AvYCFvC3sOviKq5ldXdVBhQRKSsWQF1AinQINQSSkN4zmV7u+/sjIVKSEEgP9/M8eTLMvfO+Z8icuee+7znfs/V5XFovjg95E6FuvH0KCh2dkrKyi1asya5wADAwpv7XSS47XXa8gUvjpTT9UbjsKKusRAjR4G7qzhwzr6wrxNtNxfAEr/OOu5kKCDn5X0oSxiu7MwrnoQTMnZiTOTl4XESw7JIFyw9Ukl3h4I+DA/F2a4GPh+wkafsc3CzFHB8yD4dnSPPPoaDQjrHYbGgusgg3q9yBm1oirIHV5Zi0D/CuOMbJAc/j9AhqqpkKCh2GKpOJPYcO4eN1fhB8GrtL8O6WUkK8NCwYG0GQ1/m+FHnkU5Ak8rvd1ZLmKnRQWrbPsUKbUW4wUFRaSkhgw6vEJrvM/gIru3It7M61UGWTuSrei0Ex9X/xNIXYtA/wL9rByQHPYwru1SJzKCi0V2x2Oza7/YKSVwAFVQ5251rZnWfhQIGVXuEeqFV1rxr7528h4vgyCpMmURk1vLnNVlBo1xQUF6OSpAbzl1cdNGB2CJ4dFEiYz/mhj7sxl5CstRR3mYjdK7wlzVXooCgBcyejpLyc49nZlNXkcjUk3n6q0sFL3xdgcQq83VT0j/JgUIxXg9u+TSE4+wci0r+gMOl2ShLGt8gcCgrtEZvdTkl5ORnZ2WhUqgb98kCBlX/+VkZ+VXV73yg/DTd18+X2XnWr1WgtJSTufBOzfzKnej/cIvYrKLRXhBBk5ubiewElqH0FVlJC3OgZXneOc/Xqspr8VGV1WaFulIC5E2G2WNhx4ACeHh6EBAZesNPR1wcqEcBrY8JIDXWvd/WqOfAqP0LCrvkYQq/gVJ/HW2weBYX2hsliYcuePTidTny8vPD3a7hj5objRgw2mfsGBHJFtAeRvg1IZAkXXXa8gcplJWPwawh1C+mlKyi0UxxOJy5ZvmCaU5HRSd/IuoNld2MOIdk/UJh0u5ImqFAvSsDcicir2ZZqzHZvrsHBliwzt/Two0c9d9zNhcZaRtetL+LwCCLjytcRKuVjp3D5YDSZcDqdF0yPOs2JMjs9w90Z1833gudGHFuCX/EuTvZ/HqtffFNNVVDocFhtNoQQDZ5TYXFRbnER61/3zWfU4f8gqzTkp+pawkSFToJS9NdJEEKQU1CATyMl5L5JM6BVS9zc/cIX5aYgyQ6St72E2l5J+tD/w+neMsobCgrtlSqzGXUji/xMdpn8Kiddgi5crOtdmkbMwY8pjRlNSYIiIadweWKx2RrcTS2ocvDXjcWoJehTxwqzhyGT4OwfKe5yK06P4JY0VaGDowTMnYQqkwmL1YrbBTocQfUXyKZME9d19cH/Ql3Dmkjc3rfxLT1A5oAXsQR0bdG5FBTaIyazGa2mcbsqmeV2ALoENhwwqx1Gkn57DbtnGFn9n1Ek5BQuSyxWKwfT0/FsoNhvwf9KyKxw8ORVISTU4VcxaR/g0niSnzq9JU1V6AQoAXMnoaS8HFUjL5rfHDSglmBCj4ZzKZtK6ImVhJ1cRX6KjrLY0S06l4JCe8VksTRaRu5EWU3AHNxAwCwE8bv/gpuliIzBc3BpL67tvYJCZyGnoACH01mvrrksBHkGB+NSfRkaf77yk0/JPgLzN1OQOh2ne0BLm6vQwVEC5k6AEIJT+fmNSscoNjr53wkTo5N9CPRsudVln5J9xO1dSEX4EHJ6Pdhi8ygotHfMF7HCfKLMTrCXusGdn5CsbwnO2UBujz8o0owKlzVWu71B36qyybgEBNTlT0IQe+A97J6hFCbf0YJWKnQWlIC5E1BlMmFuZDrGykMGaOHVZXdjDslbX8TuHcmJwXNAatm0DwWF9ord4cDhcjUqh9nhEqSX2ElsIH/Zw5BF3N63MYQOID91WnOaqqDQ4bDZ7agb6OxXYXEB1Lk4FJj7Cz5lh8jt8QCypmUL3xU6B0rA3AkobmQ6RqnZyU8ZRq7t4kNIAx3DmoLGVkHK5qcBOHbVX3G5tWxRoYJCeya3sPCC8o5Q3WVz9rpCCo1OBtWjgy65bCT9NgdZ7cGJQa8oN6IKlz12h6PBm9HymoA5wPPsUEeSncSkfYjZL5GS+LEtaqNC5+GSA2ZJklIlSdp7xo9BkqSnzjnnGkmSKs84Z3bTTVY4k4tJx1h1yIAQcGvPllldllw2kre+gJuliOPD/ozNJ6ZF5lG4NBSfbV1cLhcZWVn4XaChgsHqYt7PRRwvtTOtrz+jkuo+P/bA+3hVHufkwBcVrdjLAMVfL4zN4UDVwApzeT0rzKEnV+FhyiGn18PKjadCo7nkZUYhxFGgH4AkSWogF/imjlM3CSGUtm4thNFsxmK14t0IjdfNmWaGxHnV2Ra0yQiZLjvn4Vt6gONXvo4xuHfzz6HQJBSfbV0OZWTgcLkumL+8PK2Sg4U2JvTwZXz3um9mA/I2E56xnILkO6iMHNYS5iq0MxR/vTAOhwOvBhQyCo3V3TLPDJhVDhNRh/+NIfQKKiOGtriNCp2H5krJGA1kCCGymmk8hUZitdkadZ7JLlNlkxul73opxKR9SFDOT5zq9QjlMaNaZA6FZkXx2RbEYrORW1hIkH/d7axPk1Fq54djRq5N8mb6FYFo1eenb7gbc0jcORdTQEr1ipjC5Yjir+dgdzhwuVwNrjBvyzaTGKTFXfP7ORHHlqK1VZDT6xFFjlHhomiugHkqsLSeY0MlSdonSdJ3kiT1bKb5FGqwOxyNOq+o5k47tAVWl0NPrCTymJ6ixIkUpNzZ7OMrtAiKz7YgOfn5SJLU4MX8QIGVuT8V4e+hZnq/uneIVE4rydteAknF8SHzEOqWueFVaPco/noORpPpgueUW1x0C/19BVprKSEi/QtKY0ZjCurekuYpdEKaHDBLkuQG3AJ8Vcfh3UC8EKIv8C6wsoFxHpQkaackSTuLi4ubatZlg7mRGq/FpuqAOayZi/0Ccv9H/J6/URExjKx+Tyl37M2MLMv88ssvzTpmc/is4q/1YzAaSc/Kwv8Cuctfp1XicAmeHRmCj3sdX8VCEL/nL3hWniBj0Bzs3pEtZLFCc5KRkdGs4yn+WjcOl+uC1xunDBrV7+fEHPwQSbjI7alInSpUYzAYGn1uc6wwjwV2CyEKzz0ghDAIIYw1j9cCWkmS6qxWEUJ8JIQYKIQYGBoa2gxmXR5YbDY0jdB4Pb3CHObTfAUOPiX7SPrtNUxB3cm48nVQtYzyxuXK3LlziYuL49prr23uoZvss4q/1k9hSQlqtbrB6v1DhVYOFtq4IcWH5OC6czDDTqwgJPsHcns8gCHiypYyV6GZWLduHUOGDCE5Obm5h1b8tQ6cTicI0fA5sqgNmL3LDhOS9R2FyZOx+US3hokK7ZjCwkKmTJlCeHh4o1/THAHzndSzVSRJUoRUo6kkSdLgmvlKm2FOhRqsNluDOpSnKTI58dRK+Lg1TxaOZ2UGXbc8j80rgvRh8xUdy2YgMzOTRYsWIWouAnl5efTv358vvviiuadSfLaFsNntZOfn411P57HTfLyjnGAvNTem1C276FN6gNh971ARMYz8bne3hKkKTcRoNLJ48WLS09OBalUUq9XKggULmnsqxV/rwH4BhQyzQ0YW4K6WQAji9v0du0cwed3uaUUrFdoLsiyzceNG1qxZA0BgYCD79+/nD3/4Q6PHaFL0JEmSF3AdsOKM5x6SJOmhmn9OAtIkSdoHvANMFeICt4QKF4XZar3gCnOl1cXBQhth3ppGacJeCPeqbFI3zUDWeHBs+FtKS9EmUFpayvvvv8/w4cNJTEzk8ccf5/DhwwC89957rF69milTpjTbfIrPtixmqxW704m7W8O5xka7TL8ojzprCjTWMpK2vYLdK7xGb1mRy28vOBwO1q5dy7Rp0wgPD+euu+7iyy+/BOCGG25g7969PPPMM802n+Kv9XOhpiV78ywA9Ah3J+jUOnzK0sjt+SCy9vwW2Qqdl7S0NJ5//nkSExMZOXIkL7/8MgBubm4cOnSId999t9FjNWkPXQhhBoLPee6DMx4vAhY1ZQ6F+qkwGDCazYQHB9d7zpEiG29tKsZkl3lkaP3nNRY3cwGpm54CZI4Of1fJq2wCmzZtYtSoUTidTnr06MG8efOYNm0aCQkJAM1yc3Muis+2LLIsc6G/WqXVhcHqwkNz/sVekh0kb3sZjd3AoWs/VBr/tCNcLhddu3YlKyuLoKAg7r77bnQ6HcOGVcv8Kf7aulhstgbTnnbmWPB1V9EtQCZ2/fuYAlKVJiWXGTNnzmThwoWo1WpuuOEG/u///o8JEybUHr9Yn1WSTjsoVSYTv+3fj18DDUuOFtuY+3MRQZ5qXh4VRnxg0yrsNdZSUjc+hdpp5sjV72L1i2/SeJcTTqeTDRs2oNfrGTBgAE8++SQDBw5k1qxZTJ06lb59+7bIBVehdSmrrKz372h1ymzNMrPqkAG1SuKaLuf4rhDE7/kbvqX7yRj8KpaArq1gsUJ9HD58GL1ez6FDh1ixYgVqtZpZs2YRHx/PjTfeiNsFdhEUWpaKqqoGd3JKzC5i/bVEH1+Km6WYjMGvKbs1nZiysjKWL1+OXq/nk08+ITk5mQkTJpCUlMTkyZNpjtx9JWDugLhcLvYfO4ZWq8XTo+7c4d25Fv62uYQgTzWvXRd+Xqeji0VtN5C6aQZaaylHRyxULuaNZOfOnXz++ed8+eWXFBYW4u/vT9eu1f93np6e/PnPf25jCxWaA4vVSmZeHhnZ2YQEnJ+idKDAygfbSykyugj1VvP8NaEknHMDG5bxNaGZa8hLvYuy2DGtZbrCGeTn57NkyRL0ej179uxBpVIxevRozGYzXl5ePP74421tokINF2paYnbI9PQoJfKontLYMRhD+rSidQqtgc1mY/Xq1ej1etauXYvD4SA1NZW8vDySk5MZOXIkI0eObLb5lIC5A3IsMxOD0VjnhRkg3+Bg4eYSovw0vHBNWJODZZXDRMrmWXgYczh21V8wBfdq0nidndzcXKKjq6uw58yZw/r16xk/fjw6nY5x48bhUc9NjkLHxGSxsH3vXpwuFyEBAWdtE7tkwSc7y1mXbiTSV8Ps0WH0DHdHdc4qtF/hDuL2v0t55Ahye/6xtd/CZU1lZSUqlQpfX1++//57nn76aQYOHMjChQuZOnUqERERbW2iwjkIIZBlueGiP5uLP8ofI1RqTvV6pBWtU2hJXC4XRUVFREZGYjQamTZtGqGhoTz22GPodDr69+/fYru1SsDcwTCazZzMySG4nmDZJQve/rUUtUri2atDmx4sO6103fIcXhXHOD5kHlVhA5o0XmelsLCQL7/8ksWLF7Nz506ysrKIjY3lnXfeITg4mIB6/l4KHRub3c5vBw6AJBFYR1e/r9MqWZduZHw3X6b29T+r49hp3KtOkbR9NhbfOKXIr5Ww2WysXbsWvV7Pf//7X+bPn8+TTz7JHXfcwVVXXUVKSkpbm6jQAC5ZvsBxwUDbdq6Qd5Ld+zEcXmGtZJlCSyCEYM+ePej1epYuXUpSUhKbNm0iODiYHTt20Lt37wbz2ZsLJWDuYJgsFlQqVb131tkVDk6U2XlwcBAhTWxSIskOkra9jG/JPk4MnkNl1PAmjdcZOXz4MDNmzGD9+vW4XC769evHggUL8K7JLU9KSmpjCxVakuz8fOw2G0F13BD9cKyKrw4YuDrRi7v7B9S56qF2GOm69QWEpOL40D8rFfwtjCzLPPLII3z55ZdUVFQQGhrKgw8+WKt17uPjowTLHQC73V7vMSEEGw4X8YrmUwo9EilMntSKlik0N59//jlvvvkmR44cQavVMm7cOKZPn157vF+/fq1mixIwdzAcDgcNqQY55OpjwV5Nu9uSXHaSts8hoHAbJ/s/r+RU1uBwOPjhhx/w9fVl5MiRBAYGcvz4cZ577jl0Oh09evRoaxMVWonTHf2C/PzOO7Yzx8zHO8pJCnbjvgFBdW8RChddfnsdd+Mpjo1YqDRTaCH279/Pzp07uf/++1GpVOTm5tamSI0ZM6ZRjZ8U2hdZ+fn1Xgf3F1gJSfsXUZoyfrviDSSloVaHori4mGXLljFt2jQCAwMxGo2EhYUxY8YMJk2aRFBQUJvZpnySOhgWq7XBVtiumoD5zHagF4vkspG87WUCCraS2W8mJYnjL3mszoAQgi1btqDX61m2bBmlpaVMnDiRkSNHEhERQXp6uqJwcZnhcDrZd/QoHm5u520F7s2z8LfNpcT6a3l+ZGi9ba/j9v6dgIItZPabRVVo/1ayvHqV1SXLtXmgQojaH/mM32ceO/PzLaBWOk8647na42ecf+5rm0KjxhUCJAmTwcCf//xn9Ho9aWlpeHp6cscdd+Dr68vq1asVf+3AWKxWMnNyCKojBQog/+RhXlB/R0H8zUhRfVvZOoVLwWQysWrVKvR6PT/88AMul4vg4GCmTp3KQw89xMMPP9zWJgJKwNzhuFCjEmdNapf6EtMgJZeNrltewK9oByf7P0tJ4i2XNlAnYuLEiaxevRpPT08mTJiATqfj+uuvrz2uXHwvP06cOoXZbD4vFaPS6uLvv5YS4aNhzpgw/D3qvrmNOLaU8BMrKOg6leKkW5vVNlmWcbpcOJ1OrHZ7tTa0JNUGk5Ik1Qb6arUatUpV+1ijUqHWaNCe/rdajUajQV2TBlb7c8Znvr7Pv1TPOWc9rud8GnFOfeN89vnn/OGBBwAYOnQo7733Xm2w3JC9Ch0Do9mMJEl1piWWmezcmLsIk9qHvD7tI8hSaJiioiK6dOmCyWQiNjaWWbNmodPp6NOnWtWkPfmrEjB3MFpyhVnltNB1y/P4Fu/m5IAXKE0Yd8l2dlRyc3NZunQpK1asqE29uPfee7n99tu59dZbay+6CpcvZouFE6dO1bnCteqQAaNd5tUGguWgU+uJTfsHZTGjONW7ear3XS4XBpMJuaYYytvTE28vL6IjIgj080Or0dQGv1pN83T8bA9YLBbWrFmDXq/nnnvu4bbbbmPM6NG88cYbTJs2jS5durS1iQrNTGZuLm5abZ3HTHtXc4PqGNtSnkHtdn6qlELbIoRg+/bt6PV6XC4X//jHPwgLC+P5559nxIgRjBgxokHlk7ZGCZg7GBa7Hfd6viwAnLUB88WNq3KY6brlWXxL9nNi0CuUxV1/4Rd1EswmE0sWL2bNqlX88ssvCCEYNGgQubm5dOvWjVtvbd4VQIWOTV5xcZ2Ft4VVTr47WsW1XbzrbRLkW7ybxJ3zMIT048TAl5qkiGG12TCazQCo1WriIiOJCAnBx9u7wZvqjo4QgvXr16PX61mxYgVVVVVERkbW+ml8fHxt+1uFzoUsy5RVVNSpSKO2VXJ90SfsEN1Q91R2RtsT6enpfP755yxZsoSMjAzc3d2ZPHlybWpVR/FXJWDuQAghsNlseDeg4+uqTclo/AqSxlZB11+fwbviGBlXzqE8ZnRTTW332O12qqqqCA4OprCggJlPPUVycjKzZ89Gp9PVNhdRUDgTh9PJyZwcfOvosPn9sSoEcEefunMrPQwnSN76IjbvaI4PfROhrr/pQn3IskyZwQCAj6cnvVNSCAkMxN3NrdOsGteFEIKcnBxiY2MBeOyxxygoKGDSpEnodDquueaaVpGVUmhb7A4Hcj3567EH3sNDNvMv3z9xfyf2hY5CQUEBwcHBaLVaPv30U958801GjRrFSy+9xG233YZ/PTno7RklYO5AOJxOoOGcntMrzI0NmN3MhaRsnom7KZ/0IfM6tXScLMvs3rmT77/9lp/Wr2fEyJG89uabJCYlsf7nnxk1cmSnDjoUmk6V0YjT6UR7Th3B9lNmfj5hJDHQjdA65By1lhJSNj+NrHbn2FV/xXWR28WyLONwOjEYjcRERJAcF4enh0en/7xmZGSg1+tZsmQJBQUFFBQU4OHhwapVq4iPj8fT07OtTVRoRcxWa52fef+CrYRmrWWRcwKBMR1DFlAIgd3hwOF01ip+nKn8IYSoLqat+X3uOUKI6tz9OgphJc4uxLU7HLTGN4XBYGDFihXo9Xp++uknVq9ezU033cTjjz/OI488QlRUVCtY0XIoAXMHwuF0VhfuNICr5rCmEd7hYcgiZfMM1A4TR4f/DWNo6+kZtjaf/etfLFuyhKKiIry8vBg5ahTjJ0yoPd6rd+9OH3woNA2rzcahjIzz8idLTE7e2lhCtL+W+wcGnvc6lcNEyq9Po3FUcWTke9i9L9w5TpZlKqqqai+aKknCx8uLuKgokuPi8GigJXBnYOPGjTz33HNs27YNgJEjRzJr1qzaYKFbt25taZ5CG1FUWnre97TaYSRh9wIK3eN5x3obHyT7tJF1jUeWZUrKy/H38SHQ3x9VTTGuSpKQVCokqC2ulc54TjrjPI1Gg0qSUKnV1b/Pee2Z50qShLtb3WlizUFFRQV/+tOfWL16NVarlcTERF588UV69uwJQHh4eIvN3ZooAXMHwuFwnFU9XhfORhb9eZUfIWXz0yBJHB35LuaAjnFX3ljy8/L4af16pup0qNVqjFVVpHTrxhOzZnH1NdfgoaxMKVwkhSUlVJlMhASeHRTvyrUggLv7B9A15OxAVpKdJG97GU/DSY4NW9BoP6swGIiJiCAmIgI3Nzfctdp2XQzTVIxGI6tWraJ379706dMHNzc3zGYz8+fP584776xNxVC4vLHa7efdsMbufxettQx91Is4KrV4u7V/P7E7HPj7+jJ8QMfsnCvLMr/++iv5+flMnjwZPz8/MjIyeOCBB9DpdAwZMqRTLkApAXMHwl6TktEQrtqUjPrP8c//laTtr+J09+fo8IXYfDvHxaiiooINP/7I999+y749ewDo268fvfr04eEnnuiUDqzQOsiyTHZ+Pj5eZ3fiyyq38+9d5YR4qUkKOmcFR8gk7Poz/kU7ODngBQwRVzY4h9PlwmA0VsvCOZ2EBAXh34lVWRwOB+vWrUOv17Ny5UrMZjPPPPMMCxYs4Morr2Tfvn1tbaJCO8N6jkqUX8E2QjO/JS/1Lr7PjSUxiLMkD9sDQgjMFgsWm602VUKSJKI74KprWlpabYpUdnY2iYmJ3HHHHahUKnbs2NHpr7FNDpglScoEqgAX4BRCDDznuAT8HRgHmIF7hRC7mzrv5ciFuvzBmTrMdX9ww44vJ27fO5gDupI+bD4Oz5DmNrNNOHr4MPfpdDidThKTknj48ce5Ydw4oqKru6d1dkduLIq/XhoVBgMGk4mwc7pMrT9uRAXMHxuB35kyckIQt+8dQrK/J6fHHyhJuKnB8a02GyaLhaS4OIL9/fH08OjUaRdCCPr06cORI0cICgrirrvuQqfTcdVVVwGKv55G8dffcTidVBgMtQoZaoeRxN0LsPgmsC1Sx7F9ZdzWs+2l5FwuF06XC6PJVLsjHBwQQFJcHIH+/ri7uaFRqzvcZ3z27Nm88cYbqNVqrr/+et58800mTJjwe950B3s/l0JzrTBfK4QoqefYWKBrzc+VwPs1vxUuEovVesFK8HpTMoSLuH3vEp6xnPLIEZwYPBtZ0zHTEpxOJzu2b+f7b78lNi6OPzz0EEldu3LPAw9w7ejRdE1NvSyctwko/nqRVFRVnSfVZnXKbM02c0W059nBMhB96GPCM5ZT0HUK+d3uaXBsIQQGo5EBPXsSERra7La3Bw4fPoxer2fr1q2sX78eSZJ45plnCAkJ4cYbb8StBfMrOwGKvwKVVVXIQtSmJsXuX4TWWsrhq+eyeL8ZjQpuSGn9/GUhBFUmEza7HUmS0KrVeHh4EBUeTkJ0NF6enh1O5rG8vJyvvvoKvV7P3/72NwYMGMDNN99MWFgYkydPJiwsrK1NbBNaIyVjAvCZqF4a3SZJUoAkSZFCiPxWmLtTYbHZLuh4dtf5Oswqp5kuv71GYP6vFCRP4VSfR0DqWA4McPjQIdauXs26H36grLQUH19fJk2ZAoBGo+FPjz7axhZ2ChR/PQeb3U5mbi7eZ+S9n6qw85eNJVRaZW5MOTttIuKonqgjn1KccDOnej9Wb92B1WbDbLXicrmIi4oiLDi4Rd9Ha1NYWIher2fx4sXs2bMHlUrF6NGjKS8vJygoiPvvv7+tTewMXBb+6nS5yMjOri1cq07F+C95qXfxWW4Uu/MMTOvnT5BXy4U0pwPjM2uJTitTBPr50a97dwL9/DpsrYHdbq9tT7127VocDgepqamUlZUBMGjQIAYNGtTGVrYtzbjJS0IAACAASURBVPHpEsCPkiQJ4EMhxEfnHI8GTp3x75ya5zqVQ7cGF+ryB9XNEwI8VLjXRMxaSwldtzyLV8VxsvrNoCjp9tYwtdnIy82tTatY8tln/LRuHcOvvpobb7qJYSNG4N6Jt61bCMVfLwJZltl98CAuWa69WJeYnLz9aymVVhdzRofRK+J3XfTQEyuJTXuf0pjRZPZ/us5g2WSxYLZa8fPyIjk+npCAgE6Tq1xZWYnL5SIoKIitW7cya9YsBg4cyMKFC5kyZQqRkZFtbWJHQ/FXoLyyktKKCkKDgs5Kxfhf2DTW/lLB4FhPbu3ZMrq+TpcLi9WK2WolMjSUqLAw3LRatBoN7m5uuGm1HXZH0+VykZeXR2xsLE6nk/vvvx9fX18ee+wxdDod/fv377DvrSVojoD5KiFEniRJYcA6SZKOCCE2nnG8rv/t8xJxJUl6EHgQIC4urhnM6nxYrNbz9F9PI4TgcJGNI8U2ov2rq4i9S9NI3vYyaqeJ9GHzqYwc2prmXjKlpaWs//57vvv2Ww6lpbF0xQqSkpN57KmnePbFF/H1a/s8tQ6M4q+NRAhBemYm5VVVhNYoY1gcMq+uL6LC4uKxYcHnBcsJe/5KRcQwTg565axdHCEEFVVVyC4Xblotw/r1I6CTfI5tNhvfffcder2eNWvW8MILLzBnzhzGjh3LkSNHSE1NbWsTOzKXvb9abTaOnDiBV80OT+y+d9BaSvjpind5ZUM5HhoV9/Q/X87xUnHW6J3LNfVCWrWasOBgwkNCCA0K6rAryKcRQrBnzx70ej1Lly4lKCiIAwcO4OXlxfbt20lNTVWaANVDkwNmIUReze8iSZK+AQYDZzp0DnCmDEMMkFfHOB8BHwEMHDiw4cq2yxCXy4XVZquzEEgIwed7KlhzuAq1Cm7s6kNoxjfE7fs7dq8wjg3/Kxb/5Daw+uLIzclh/ty57Ni+HZfLVS0DN3MmwTVb1eERF9avVWgYxV8bT1llJenZ2bXBcqnZyVubSigyOpkzJoye4b8Hy2EZXxO/dyEVEcM4PmQuQvX7V6vD4aDMYCAmPJz46Gh8vLzqvfHtSAgheOKJJ1i8eDEVFRWEhoby4IMPMqFG39zd3V0JlpvI5e6vQgiOZWZiNJsJCQwkMGcDoVlrOZl8F8/sDsbbDeaMCSfMp2n+5HK5qDKbcbpcSEByXBy+Pj54urvj4+XV4YPk0yxbtow5c+Zw5MgRtFot48aNQ6fT1aaW9OjRo61NbNc06VMmSZI3oBJCVNU8vh54/ZzTVgOPSZL0BdXFCJWdLb+qpRFCcCgjA9cZBQ+nccmCj34r46cME6OTvbmvrxepaQsJPbSWivAhnBg8+6K7irUWToeDrVu2oFapGDZiBP4BARQWFHDXffdx40030SUpqa1N7FQo/tp4Kquq2JmWhp+3NyqVimKjk79sLOZUpYNHhwadFSyHpy8jbv87lEeOIOPK1xDq6tQNm91OhcGAl6cnfVJSiImI6PAX3v3797Nx40Yee+wxJEmioqKC8ePHo9PpGDNmDJpOcCPQXlD8tfqm9VR+PiGBgbiZ8knY/ReMQT2ZY5iA0W5n3vXhxPprLzxQPciyTJXJhMVmIyE6muiwMDw9PfHsJKl+xcXFLFu2jIkTJxIdHY3L5SIsLIwZM2YwadIkgs5R/VFomKZ+u4UD39TkuGiAJUKI7yVJeghACPEBsJZqyZvjVMve3NfEOS87jmdnk52fX7vSBVBkdLIu3ciBQisZpXZu7+XHXUlmuv76DN4Vx8jtfh953e8DqX1doIUQ7N+7l++//Zb1P/5IZUUFg668kmEjRuDj48OXK1e2tYmdGcVfG4HZYmHP4cO4abV4enhgdcq8tqGIKpuLWSNCGBjzuxZz+LEviDuwiLKokZy48lWESovZasVkNuPu5sbAXr0IDwnp0HmA2dnZLFmyBL1eT1paGhqNhjvuuIPw8HA+//zztjavM3PZ+2tmbi6eHh6okEn67TUQgmMDZ7PzOwdXJ3if1yjoYimpqCAuMpKosDCCAwKayeq2xWQy1Rbv/fjjjzidTtzc3PjjH//InXfeyZ133tnWJnZYmhQwCyFOAH3reP6DMx4LQJEvaCSyLONwOpFlGZPFwtGTJzmcV8HaE4L8qnzsLoHVKTDbZVQSxARoeejKIG71PUyXn19FEi6ODf0zlVHD6xxfCIEsy7/3qa9+8qxe9dTxWJZlXDWvk4WofU6SpPN62p8735nBwvzXX+eX9etxd3fnyquuYtT119N/0CBKKyrq/g85bcc5Y5+278yxa587dwio/1xJQoIOJ/tzKSj+emFO7+Y4nc7aQrzPdlVQaHTy8qhQ+kb+rpQRcVRPbNr7lEVfy4nBcxAqDQajEQkY3KcPgX5+HT4X8Ouvv2bSpEkADB06lEWLFjF58mRCO6n8XXvicvdXWZYprajAz9ubqMP/xqcsjYzBr7KpLACzo5QhcU2TRS2tqCA0MJCeyckdfufnNEajkejoaAwGA7GxscycOROdTkefPn3a2rROgbJ/1o4wGI38duAATqezOsgTAlnlxif7HFRaXfSN9MRDK+GulgjwVHNNF29CPCDq0CdE7luMxS+B40PePK9znxCCyqqq6vwsSUKr0aBSq6t708N5ferhnD72Na9x02rRaDSoVSo0Gg1ajQaNWo1arUajVlcfqxkXIDc3l2XLlrF8+XJWrFhBREQEziefZPqddzJhwgR8fX3rXXmr89n6zq3j+frW8+o894z3rXB5U1ZZSWFJCaE1W5W7cy2sO27kumSfs4LlyCOfE3PwQ0pjRnNi0Cug0lBWUYGHhwcDevY8ryNgR8BisbBmzRqWLFnChAkTuO+++xg5ciRvvPEG06ZNo0uXLm1tosJlRLnBgNPlIqBsf7VMY/w4ToWP4t9r8oj01XBF1KUFzDa7nUqjkbCgIPp269Zhg2UhBNu3b0ev11NWVoZer8fHx4dXX32V/v37M2LEiA773torSsDcTnC6XBw4dgy1SoX/GVtDb28uocjkZM7oMLqHeZz1GjdTHkn/ew2fsoMUx48ju99TyJrfL9SyLNeKvUeHh5MUG4unh0eLBodGo5Fly5axePFifvnlF4QQDBo0iIrycuLj4hg/fnyLza2g0BSEEBzOyMDby6vWR749UkWQp5p7Bvzuk5GH/0PMoY8pjb2eEwNfBJWGiqoqfH18uKJ7dzw9POqbol2yYcMGFi9ezNdff01VVRWRkZHceOONAISEhPDyyy+3sYUKlxuyLHP05En8VHa67HgDq08MJ/s8ycLNJVTaZJ4ZGVpvN9v6OK1UA9A3NZXI0NAOuQOUkZHBp59+ypIlS8jIyMDd3Z2JEyficrlQq9XMmDGjrU3stCgBczuhsKQEg9FIyBl5yocKrfyaZWZyH//zguWgU+uI3/1XADIGv0pZ7JizjjudTsoMBuKjokiKi2vRIgabzUZJSQnR0dGUlZXxwAMPkJyczOzZs5k2bRopKSktNreCQnNRZTJRZTLV+uDWLDP7C6zc3suvWtdcCKIOfUL0kf9QEncjJwe+AJIas8WCS5bp161bhwiWhRBkZmaSmJgIwEsvvcThw4eZNGkSOp2Oa665pkMGEgqdh7yiIsorKxl27G001jLSr/2Q1cdd7Mu3ckdvP1IuMnfZYDRitdmIDg8nNTGxQ/jpmRQUFODn54eXlxfffPMNc+fOZdSoUbz00kvcdttt+Pu3jAa1wtkoAXM7QJZljmdlnbWNK4Tgi32VBHqquaX7700NVE4zcXvfJjRrLcagXmQMno3dO+qs8SxWKyaLpbYyvyVWlGVZZuPGjej1epYvX87w4cNZs2YNcXFxpKWl0aNHDyXNQaHDIMsyhzIycNNWV9wfLLTyzpYSuga7Mam3P8hO4ve8RVjmGooTxpPZ/xmQ1FRWVaGSJK7s06dWJ7a9kpGRUVu8d/LkSQoLCwkICECv1xMVFYVnO7df4fLAYrNxOCODXoXfEpi3iew+T7CiKIrFe8oZEO3BHb0bHxxabTYMJhOBfn4M6t0bX2/vDnNdMhgMfPPNN+j1ejZs2MCnn37K9OnTeeCBB5g2bRpRUVEXHkShWVEC5nZAhcGA2WqtXdkSQrBkbyWHi238YVBgbdc+3+I9JOx8E3dzAXnd7iG3+32gOvtPaLPbMVutDOnXj8AWaozwzjvv8Je//IWcnBy8vb257bbbuPvuu2uP9+zZs0XmVVBoKbLz8ymr6SRmsst8vqcCtUriuWtCcZOtJG2fQ0DBFvK63U1ujz+CJFFWWYm/jw+9U1PPapvd3ti+fTtPPfUU27ZtA2DkyJHMmjULbc3NQZIi36jQThBCsP/IEYLK9hJ3+GNKY0ZzNPo2Pl2VR89wd2YMb5zizOmCQR8vL/qkpBAVFtZhdk3MZjP33Xcfq1evxmq1kpiYyIsvvsiwYcMACAwMJDCw+Rq1KDQeJWBuY2x2O4cyMmrb7gL8mG5k5SEDY5J9GJPsg8ppJfrgh0Qc/wqrdzRHRi7CGHJ28bTJYsFsseCm0TCwV69mDZazsrL44osveOKJJ/D09MRqtdK3b18WLFjALbfcgre3d7PNpaDQ2lSZTKRnZuLn40Op2cnsdYWUmlw8PCSIYMlA143P4l1+lMwrnqa4y0QcDgeVRiNenp706969zmZCbYnJZGLlypUkJSUxZMgQ/P39MZlMzJ8/nzvvvJPY2NgLD6Kg0AZUmUyYC9IZfmA+Fr8EMgc8z4YjJpwy/GFQUO3i0YU4LRfXIymp3QfKsizz66+/kpGRwb333ouXlxfFxcU88MAD6HQ6hgwZ0mFWxTs7SsDchgghOJmTg9FsrtWA3Jpl5j+7ykkI1PLg4EB8y9JI3DkPD2MOhUm3k9PrIWTN76tZLpeLKpMJSZKaVcqqtLSUr776Cr1ez+bNmwEYMGAAY8aM4dlnn+XZZ59t8hwKCm2Nw+lkZ422sJtWy+e/lVJkdPHyqFAG+5aR8vMs3CxFHB86j4qoEZgtFixWK726diUyLKzdyBE6HA7WrVuHXq9n5cqVmM1mHnroIYYMGUK3bt3Yv39/W5uooHBBCvNP0T/tTSTh4viQeaRXqvhqfyV9Iz2IaUSDEpfLRUVVFQE+Pu0+WE5LS0Ov17NkyRKys7MJDw9n+vTpaDQafvrpp7Y2T6EOlIC5DckrKuJ4dnZtsHyw0MqHv5US7a/l5av9iT3wDyLSv8DuFcGREe9QFdb/rNefvnhHR0QQHxWFn49Ps9iVnp5Oz549cTgcdO/enblz5zJt2rTaIiEFhc6AEILM3FysNhvePv68tamE7acsjO/myzD3TLr+8gwIwZGr36EqsAeVlZXYnU4G9+lDWDvrkHXVVVexY8cOAgMDmT59OjqdjuHD69ZiV1BojxiqqvDc8BL+VRkcGzYfq08Mn28owstNxVNXhVzw9S6Xi5LycpLi4kiKi2vXwfKCBQt47rnnUKvVXHfddcybN4+JEycqnTLbOcpfp40wGI0cPH4cfx8fNGo16SU2XttQRISPhnk9chjw6yw8jdkUJU7gVO9HkbW/FwSeLmTwcHdnYO/eZylrXCxOp5Off/4ZvV5PcHAwb731Vq3CxU033US/fv2U7SCFTocsyxw5cYKTOTnYVN7M+6GQXIODu64IYHpgGskbZ+NwDyR9+FuUaUIxlZcTFx1NXGRks92YXipHjhxBr9ezbt06Nm3ahFarZebMmXh6ejJ27FjczkjvUlDoKBg3LiKmYAO53e+jMvIqfjxWxcFCG/cPDMTHveFUDJfLRUlFBd2SkkiOi2slixtHeXk5y5cvR6/XM3v2bEaNGsX48ePx9PRkypQphIWFtbWJCo1ECZjbAKvNxoFjx9BqNGzLdfDDsXKOl9oJVRlYHraS6F3fY/WK5OjwhRjCBwG/dwB0uVwYLRb69+hBeHDwJQuT7927l08//ZSlS5dSWFiIv78/f/jDH4DqRh6K9qpCZ0UIwcHjxzmVn49B+LDg5xIcLsEro8IYZfqO+K1vY/ZP4tiwBeRZVAR4armyb1+C27DQpri4mMWLF7N48WJ2796NSqVi9OjRFBcXExUVxdSpU9vMNgWFpmI6soGInX+lPHwIed3v40iRjX/vKueKKA+u79rwDerpVvTdEhNJaif5+U6nk5UrV6LX61m7di12u53U1FRMJhMAPXr0oEePHm1spcLFogTMrYgsy+w7coSCkhKEEGzMVfPF/kqCPFS8GbuD28o/wS3PRF7qXeR3uwdZ44HD6aS8shKNWo2Plxdenp4kxMQQeQmtaTMyMkhMTESlUvHPf/6Tjz/+mPHjx6PT6Rg3bhweHUybUkHhUjiRk0N2fj65Fg/+sqkIP3cVr44K5OqsRYSdXElFxDDS+j5PpVmQEBNN9y5d2qRjVmVlJTabjbCwMA4ePMjMmTMZOHAgCxcuZMqUKURGRra6TQoKzY2r6ChuX9+LxSuCk4NnY3LAW5uK8daqeHxYcIMNSsorK5EkiSH9+hHUxlrELpeLrKys2o6Yjz76KCqVikcffRSdTkf//v2V3doOjhIwtxKyLHM0M5O84mJCAwP5+YSJL/aXcVN4GfO0/yKgeDdVwb05fsUzWPy7IMsy5RUVAHRLSiIxOvqSLtqFhYV8+eWX6PV6fvvtNzZt2sTw4cN55ZVXmDdvHgFndBVUUOjsZObmcjgjg3SDG4u2lRDlq2XOMC2DDzyHX8le8lJ07I+ZjFZyZ0i/bgT5+7fqRc5ms/Hdd9+h1+tZs2YNDz30EG+//TZXX301R48eVZoAKXQuTCWIxZOQkcgY/hZOrS+fbCml0iYz7/pwfN3rzkMWQlBuMODj5cUV3bu3mQa6EII9e/ag1+v54osvUKvVZGZmotFo2Lx5M126dGnXudQKF4cSMLcSFVVVnMjOrs03/jW9mNc8l3NX1VpktXu1ZFXiLSBVB8XlBgNRYWGkJCRckmxVbm4uDzzwAOvXr8flctXKwHXt2hWAiIiI5ntzCgodgKzcXNKOHePnHDVfHywnNcSNef0r6b39JbTWMo70e4GMgCHEhkfSvUuXWp3i1uLpp5/mk08+oaKigtDQUP74xz9y1113AaBSqZRgWaFz4bDiWjIVyVjA0WELsflEsz7dyKZMM1P6+NO1nm5+sixTXF5OTHg4PZKTa5sNtTarVq3i+eef58iRI2i1WsaNG4dOp0OWZVQqVe21VqHzoATMrYDL5SI7Lw83NzcqrTLb13/NP+2fEy5VUBI3llO9HsLpEQxUfxlYbDaEECRERzc6WHY4HPzwww/YbDZuv/12QkNDKS4u5tlnn0Wn0ynNRBQua07m5JCWns7qDIl1x6sYmejFK+Fb6LL5bZxufvw2YD7GoO4MTEkhLCioVVaV9+/fz/fff88zzzyDJEnYbDZuuukmdDodY8aMafWAXUGh1ZBlWPkQ6twd7Ov9Avbwvpwos/OfXeX0ifDgtl519xFw1ihhpCQkkNzKShjFxcUsW7aM66+/nq5du+Lm5kZoaCgzZsxg0qRJBLUz5RyF5kcJmFsYp8vFwePHOZFbQE52JiOy3uMVKZ1THimkDf0zluBeteearVYqq6oICw6me5cuF6zGF0KwZcsW9Ho9y5Yto7S0lMGDB3P77bfj5ubGrl27WvrtKSi0e7Lz8jiQfpwVx+CXkyZuT9XyvPiA0L3fUxHSnx2pTxES3ZUrkpPPaiDUIrZkZ9e2p05LS0OtVjNp0iS6dOnCu+++26JzKyi0G356HQ5+w+Gke7EmjyWrzM7rGwpx10g8ODgIVR03rFabjUqjkZSEBFISElrlptZkMrFq1Sr0ej0//vgjTqeTt956i5kzZzJ27FjGjh3b4jYotB8uOWCWJCkW+AyIAGTgIyHE38855xpgFXCy5qkVQojXL3XOjkZZRQUHjh3jVF4eXns/5THxMwaVH7u6PYPc/WaQVDhdLgxGI06nkwB/f67s27fRK1wPP/wwH374IZ6entxyyy3odDpuuOGGVnhnCh2Ry9Fni0pL2X3kGF8ekdmabeGhFANPlC/A03CSrK53cSByIr1SuhEfHd3iF+Aff/yx1j+HDh3KokWLmDx5MqGXUMCr0PnptP6642PYvJCcmHEUp+ooNjh546ciPDUqXrsunDCfs8OS0/nKkiQxqHdvQgMDWyVYttvtJCQkUFJSQkxMDDNnzkSn09GnT58Wn1uhfdKUFWYnMEsIsVuSJF9glyRJ64QQh845b5MQYnwT5umQ5BYWsi9tH10KfqR/uh4PbByOmYRtwAPI2uqVY4vVisliITEmhoiQEAIaaGedm5vLF198gV6v56uvviIpKYnp06czbNgwbr31Vnx9fVvrrSl0XC4rny2tqGD97oN8sNNGdqWTuYn7uTPv78gqN3b3f4PiwH4MqpFnbG4sFgtr1qxhyZIljBw5khkzZjB8+HDmzp3LnXfeWVtJr6DQAJ3PX/d/hfj2acrDh3Ks+6NU2jW8vqEQjUpi9piw84Jlu8NBucFATHg4qV264NlCbeiFEGzfvh29Xs+JEyf49ttvcXNzY968eaSmpjJixIg2UcpRaF9ccsAshMgH8mseV0mSdBiIBs515ssOq9VC2aaPGZH+Kb62Qn5x9aVs4JPEdvm9aMditWK2Whnar1+9gbLZbK4Nkn/++WeEEAwaNIiSkhKSkpIYPny40s1LodFcTj6bX1zMf7cd4B+7bDgcdtYkrKB3/koMgT3Z0W0mIfG9uCo2Fl9v72ad96effuKzzz5jxYoVVFVVERkZybXXXguAl5cXL730UrPOp9B56XT+evQ7xDd/whjSj109nsGh8ub1HwsBmDM6jEjfs3P2DUYjDpeLK7p3JzI0tEUC1pMnT/Kf//wHvV5PRkYG7u7u3HzzzdhsNtzd3XnwwQebfU6Fjkuz5DBLkpQAXAFsr+PwUEmS9gF5wNNCiIPNMWd7xXDwR/jxZXpXHuWQHM/bqpfoMfhqrkqovjCbLBbMVivuWi2DevU6L1i22Wzk5+eTkJCAxWLhoYceIj4+ntmzZzNt2jSlUl6hWejMPptXVMS/f97P0oN2UlW5fBL4PgEFGZxKuJ1DiXfRI6UbMRERzbKtK4QgPT291i8XLFjA1q1bmTRpEjqdjmuuuUaRlVJoMh3eX09uQiy7B2tQKlu6P8cxow8fbC9AFoK514cT7f97sOx0uSivrCTAz4/BKSnNflNbUFCAl5cXfn5+bNiwgTfeeINRo0bx0ksvcdttt+HfxnrOCu2XJgfMkiT5AF8DTwkhDOcc3g3ECyGMkiSNA1YCdWqtSJL0IPAgQFw7a23ZKIqOYFn7In6ZGyiRgphlf4jcqDE8MiwMT60KWZYprazEx9OTK7p3JyQwEG1N33hZltm4cSN6vZ7ly5fTq1cvNm3aRHBwMAcPHiQ5OVkRPFdoNprDZ9urv2ZkZ/PPX46y7JCVmb7redilR7Z7s6vPKxhjrmZojx7N0to6IyOjtnjv2LFjnDp1iujoaD766CNCQ0PxbCNdWIXOR4f316wtiCVTsHlHsSn1BQ6bA3j71xKSg914bFgw0X7VwbIQgsqqKlxCkJKYSFxkZLNJxhkMBr755hv0ej0bNmzg73//O4899hhTp05l3LhxREVFNcs8Cp0bSQhx6S+WJC3wX+AHIcTfGnF+JjBQCFHS0HkDBw4UO3fuvGS7WpWKbJwb5qFOW4YFd96z38xS1TjG9gxjbKoPKuHCYDIhhKjVVfY8o6Pehx9+yNy5c8nJycHb25tbb72V6dOnK8V7CmchSdIuIcTAZhin2X22PfirEILM3FzmrD7I0ewiPvT5iH7O/ZSFD2FX0sPEJPUmJSGhyau9e/fu5eGHH2bbtm0AjBw5Ep1Ox9SpU5U6AoVaFH+tIXMz6Cfj8Arjfz3nYPaO46Ufiwjz0fDGdeFo1b8vBJVVVhIcEEDPrl2bLVfZ6XQyffp0Vq1ahdVqJTExkWnTpnHvvfeSnJzcLHModHwa669NUcmQgE+Aw/U5siRJEUChEEJIkjQYUAGllzpnu8JUgtj4V8SOjxEyfCZu5G3bLVzVLZq/9PTDSyOoMBpw12pJjo8nJjwcD3d3srKyWLp0KX/6058IrKn2Pd1U5JZbbsG7mbefFBRO05l9Nj37FK+uOUxg3mZ+8voXHsLJkZ5Pciryevp060Z4SMgljWsymVi5ciURERGMHj2asLAwLBYL8+fP58477yQ2NraZ34mCQjUd3l9PboQlU5D9otnUfTY5chh/X18MwKNDgmqDZVmWqTAY8PLyom9qapP0x2VZ5tdff+XAgQM88sgjaDQa7HY7999/PzqdjqFDhyq7tQqXTFNSMq4C7gIOSJK0t+a5F4E4ACHEB8Ak4GFJkpyABZgqmrKk3R6wVsLWfyBveRccFpa7rmah43bCIqN5pJsvSf4yVosBtFoSoqPpGh9PZUUF//n3v9Hr9WzevBmAbt26MXHiRB588EGlsEChteiUPrsnPYvXv97GPab/MNFtC0b/7mzrPhOLTwxD+va96BxIh8PBunXr0Ov1rFy5ErPZjE6nY/To0URFRbF3794LD6Kg0HQ6rr+e+AWWTMXpH8uG7nNYctyT/2WWEuyl5tUxYcQGuCGEwGA0YrPbiY+OJiUh4ZKD5bS0NPR6PUuXLiUrK4uAgADuu+8+PD09WbFiRfO+N4XLlqaoZGwGGrxVE0IsAhZd6hztCmslrq3v49qyCDdHFd+7BrPQdQcxiSk8muRFmLsDh9OCr3cQKQkJRISGolGrKSgoIC4uDofDQffu3Zk7dy7Tpk0jMTGxrd+RwmVGZ/NZh8PJez/sJWfHKj7mPwSoTWSl3MPBiFuIjIiif1LSJW3t3nDDDfz8888EBgYyffp0dDqdokaj0Op0WH89vgHxxTTsPjF8Evo8//xVTYXVys3dfZncxx8PjQqj2YzZaiUqNJSEmBgCXbsXdwAAIABJREFUfH0veeX3/fff55FHHkGtVnPdddcxd+5cJk6cqNQRKDQ7Sqe/C2GtpGjd2/ju/QhPl5H1roF8orqN0C49uTdaRZAHaNQOosPDiY2IYNvWrbz31luo1Wo++eQTIiIimD9/Ptdccw39+vVTtoMUFJqB7en5vLViIw8YP+Ip9U5KfbqypcfjOAJTGJCS0mht5aNHj6LX61m1ahVbtmzB29ubp556iieffJIbb7wR9xbSfVVQ6JTs/wqx8mGqvOL4o/05tu/1INpPxbMjQ0kOdsdstVJkMBPs70+/7t0JbKD3QF2Ul5fz9ddfo9freeKJJ7j11lsZO3Ys77zzDlOmTCEsLKyF3piCghIw14+1kqpf3kH92weEyUZ+cA3kO//JRMancHeoCk93NyJDQ4kICeHkiRP88733WLp0KYWFhfj5+TF9+vTaoWbMmNGGb0RBofNgczj5v1W7sOxZzscaPZ4aB0e63MephNtITUomKjwczQUK+0pLS/nss8/Q6/Xs2rULlUrFqFGjKCoqIjExkVtuuaWV3o2CQidiyyL48SWyPHtyR8VTmCQf7u7vz/VdfZCddorKygj082Ngr16N7mYL1XnJpxUuvv32W+x2OykpKTidTgASEhJ4/PHHW/KdKSgASsB8PtZKLJsWwbZ/4OuqDpR3RevolpLMDRqIi4wkOjyc4sJCEuLj0Wq1vPX11/zjH//gpptuQqfTcdNNN+FxhhKGgoJC09l9spj3lq3h/9m78/C6qnLx49/3nMxz06RTmrZAJ8rQQgsIBQShTIKACIIVUERUxKtyr+KACOLAvf5E7xUVuUwKFZUZmefLXCi0lKEtpQNt2rSZTubkDHu/vz/2CYT2JDljxvfzPHmSs4e1107ynvPutdde66KOP3NI9loay/Zl5dxLqJq3mKOmTPlwmMZYWltbaWtro6qqiq1bt3LppZeycOFCrr32Ws4++2wmT548iGdizCjiuugTVyAv/55Xsj/B+YGvs0dlMVcdNp7yfKGptYXCvDwOnT+f8rKyuIp0HIcNGzYwe/ZsRITLLruMjo4OLr74YpYuXcrChQvtbq0ZdJYw9+huofXZ35P92p/IjybKj5Z9ngXz5rCgAKZNmUxJXh7333cfy5YtY/ny5Tz44IN8+tOf5rvf/S7/8R//wbhx44b6LIwZdUIRh1/c/QoT37qe6/0PEcku4M2Z36JjzuksmDWb0j6GcwuFQjzyyCMsW7aMf/3rX3z+85/n1ltvZf78+axfv96GlTImVZEQ7n0X43v7Tv7Jcfyw/TxO36+Mz+1bSld3F40t3cyeMYM9pk4d8M6PqrJq1aoPH97r6uqitraW3NxcnnjiCaqrq8nq56LYmEyz/77uFhqf+m/yX/8zJdGuF4+XncncOXM4oSybPadOJdvn4xtf+xpPPPEEjuN8OAzcwoULAahIcsgqY0z/3t/Zwt/+ej0Xtv0v1Vn1bJ54DDX7fYOZcxYwYfz4PqfLvfzyy/njH/9IIBCgsrKSCy+8kPPOOw8AEbFk2ZhUdTYR+fsXydryIr+JnMXtWafzk2MqmV3upzHQRHkC/ZQfffRRLr30UtasWUN2djYnnngiS5cu/bAV2R6SN8PB2E2Yu1vY+fjvKFp1A+OjifJz489g9py9OWVcPpvXr2dbXZjjDz8c13Xp6Ojg+9//PkuXLmWfffYZ6tobM6q5rnLno08w6ZVfcIVvFfV51bw87xcUzTuOQ/bcc7fuF6tXr+bee+/l8ssvx+/3IyKcdNJJLF26lGOPPTalsV2NMbvY+S6h286E9p18N/QNtkw+jv86pAw32EFHt5/9Zs9m6qRJfV7Q1tfX889//pPFixezYMECSkpKqKio4Prrr+fMM8+kvLx8kE/ImIGNuYRZ23aw9ZFrqVhzGxO1kyfdRbxY8VnmzNmbmfXb+b+7bueB+++nsbGRvffemy9/+cv4fD6ee+65oa66MWPChg9qWP23H/HZ7gcJ+XJYMf0COvb+HPvMmcf4srIPW522bNny4fTUb7/9Nn6/n9NOO4358+dz9dVXD/FZGDM66dqHcO68kOZIDhdHfsKBiw7m1CofnZ2tzJw+nRlTp8ac0rqjo4P777+fZcuW8fjjjxOJRLjyyitZsGABhx12mH3GmmFvzCTMbuMmtj38n0zccBdVGuEJDmHlhNPYe96+fGHPafz+2mv5/f/8D3l5eZx66qksXbqU448/3h4sMGaQBEMhnvn7tSza8AdOpY2VZcfQst/57Ln3QhZMmvSxPpAvv/wyhx12GACHHnoo1113HWeddRaVlZVDVX1jRjfXxX3u18izv+Jddw9+mPU9zjt6FqX+bny+PA498MA+u1+4rsu8efPYsmULU6dO5dJLL2Xp0qXsv//+g3wSxiRv1CfMoe1vsf3BX1K9/VEmqI8H5QhezT+cpm1beOmeP3LjjTcya/p0vnT++SxauJDTTz+d4j4eIjLGpJ/rKq888wDjX7iSE3Qja7LmsGLeFUzcezGH7rknqHLvPfewbNky5s+fz1VXXcVBBx3ENddcw5lnnsmee+451KdgzOjW0Yh791fxbXyKe53F/KXsYr592CRCwXaqJ09l1vTpH7Yqqyqvvvoqy5Yt44033uD555/H5/Pxi1/8gurqao444og+u2oYM5yN2oS54/0XaHjkV0xvfIEKzeM25zgeqCnhg3ffZOWK76OqHHTQQYSCQQAOPPBADjzwwCGutTFjy4pXX8R5/KccFnmNnZRz/5RvM/7Az7Bor714a9UqLv71r7n77rtpa2tj8uTJH7YqZ2Vlcdlllw1x7Y0ZA7a8gvOP83E6Grky/BU2T/k0X5+fjxPuZOE++zA5eldn8+bN3HLLLSxbtowNGzaQm5vLKaecQltb225zExgzEo2uhFmV5jcfou3J/6K6/U1aw4X8Z+dx5B18NvtNm8TjJ5zA5EmTuOKKK/jCF77A7Nmzh7rGxoxJG95/j233/YTFbY/RKXk8Mu4cig85lzIHDjvwQHw+H3/+8595+OGH+dznPsfSpUs56qij8A8wNJUxJk1cB33xf9Cnr2abW8ElkatYOH9/lk5wKCsuZJ+ZM+loa6OxsZHx48ezYsUKrr76aj71qU/x4x//mM9+9rOUlpYO9VkYkzajI2GOBKl76XbcF3/PhO6NPLG5kKXvTuX1NR9QVbWaN37xF4oKCnhr9WpmzJhh/ZKNGSI1tTt4966rOaLhn1Tj8GzRCdRNPYE333qP+770FdatW8eaNWuYO3cuv/3tb7nlllvIz88f6mobM7YEPqD7zq+St305DzsHc0PRxZyx/zimlmUxpWISr7zwAld873s89dRTXHXVVVx++eWccsopbN26laqqqqGuvTEZMaITZrejiQ8e/z3j3r6VCU4Tv1pZxK//DwItOygoaOW0007j3HPPpTD6gWtjORozNFqaA7xx5zUcUHMbx0kHL+cdxjvjj+dPN9zOG69/GYAjjzySSy+99MNZ92z2PWMGmSrhN27HffgyIhGXy5yv4597PF+tEvaaVs3Pr7iCe+65h+7ubvbYYw9++MMfctZZZwGQm5trybIZ1UZkwty9cwMfPPxrePNO7nmrg7n778+WPS8mcEA3B3W/wHnnncdpp51GYWHhUFfVmDGtq6ONN+7+NXtvvJmDQ638dn0V7uyz+Oo3f8wcv58//+8yrrnmGs455xymTZs21NU1Zuxq2UbTnf9Gec2TLHfncmPx15nka6Xxtfs5/DNXU1ZSQm5uLhdccAFLly7l0EMPtbu1ZkwZOQmzKvVrXuD9+69h5YtP87fVEV7cGgHgqmPP4Dtf/gYlRUVDXEljDECou5O37v8dVW9fT8vGBpa+XcQz64IEg2s55ZRZXBF9UOi1114b4poaM8a5Dm0v/JnsZ68m34nw/foTeHWbsvbVH7Fzxw4KCwv5xZVXAnDzzTcPbV2NGUIpJcwicgLw34AfuFFVr9llfS7wV2Ah0Ah8XlU3J3IMDXez8dnb8a+4gXEta/nUb9rojsCUqVP59+9/gYsuvJDZs2alchrGjBmZjtlgVxur7/8909f+Lwtp4ui7/Dy7tovS0hzOPe98zv3iFzn88MPTd0LGjGIZj9dtb9H8j28wsfUtnnf34z83781Dt92I3+/n2GOP5Yu//jWnnXYaRdYYZUzyCbOI+IE/AEuAGuA1EXlAVd/ttdlXgICqzhSRs4H/BD4fT/mtOzbxj99exjOPPUwwFOS/zpzJw5O/xgXfcjnr9NM4YvFiG8vRmARkMmY7mht49M8/4pWH/86D73Zw/QXzeW7fS7ngJ+O5JDeXk08+mdzc3EycljGjUibjNdJWz6q//AevP34nt612mLxwCSd++VKuPGM8xx64D+eccw4TJ07MxGkZM2Kl0sJ8MPC+qm4EEJG/A6cCvYP5VODK6M93AdeJiKiq9lfwpnVvM3vmXuzsUApz/Rx0yEE0nnkTn509k3NyclKosjFjWkZiduvGtRwxbzIrayP4BPbZZy7Op3/H6YsX2zBwxiQvI/G6s2Yjpx9czePrg4QcmDh5Cp89cglfWLKYvNxcFs2fn5mzMWaESyVhrgK29npdAxzS1zaqGhGRFmA80NBfwYH2IIfuU83ST3+er3/9YvacNs0+eI1JXUZiti7QwbiqUi748sn826U/YL958+zujzGpy0i8NjUFeLkjhyOXHM83v3kJJx13HDnWEGXMgFJJmGM9HrvrVW0823gbilwEXBR9GXrpna3vvPTO/3Ov/a//l0IVh50KBrhYGIFG4znB8Duv6WkoI20xu2u8rtveumbdLcsiN9+yLMUqDivD7X8gXey8Mm84x2uwy4m89+Sjj4WefPSxFKs4rAynv3862XllXlzxmkrCXANU93o9FdjexzY1IpIFlAJNsQpT1RuAGwBEZIWqLkqhbsPSaDyv0XhOMGrPK20xu1u8uu5o+12N1v8BO6+RI2Px6jrOaPo9AaPy7w/YeQ0nqdw3fQ2YJSJ7iEgOcDbwwC7bPACcH/35c8DTA/VfNsZkjMWsMSOHxasxw0jSLczR/lKXAI/hDXlzs6q+IyI/A1ao6gPATcBtIvI+3lXv2emotDEmcRazxowcFq/GDC8pjcOsqg8DD++y7IpeP3cDZyZR9A2p1GsYG43nNRrPCUbpeWUoZkfl7wo7r5Fm1J2XxWtC7LxGlhF3XmJ3b4wxxhhjjOmbjf1kjDHGGGNMP4ZVwiwiJ4jIOhF5X0R+MNT1SQcRqRaRZ0RkjYi8IyLfHuo6pZOI+EVkpYg8ONR1SRcRKRORu0RkbfTvduhQ12k4sngdeSxexzaL2ZFlNMYrjNyYHTYJc69pQE8E5gHniMi8oa1VWkSAf1fVvYFPAN8c6LxEJF9E/iUiLSJy50AHEJEficiNyVRORJ4VkW4ReS6Z/YFvA2uS3Dch0TfDo+Lcdk70jaZNRP5NRK4Vka/Heaj/Bh5V1bnAfAbp/EYSi9ePWLzGZvE6vFjMeixeYxvEeIWRGrOqOiy+gEOBx3q9/iHww6GuVwbO835gyQDbnAu8CmTFWPdj4OdprM+zwIW7LCsH7gU6gA+AL/Sx71TgDWAlEAY2x9hmAfA80II3ZugVvdbNA1YAgejXk8C8NJ3XTcBve72ejDcjVs4A+5UAm4j277evPn9PFq8fbWPxmvp5Wbxm+Mti9sP1Fq+pn1dS8RrddsTG7LBpYSb2NKBVQ1SXjBCRGcABwPIBNp0OvKeqkRjrHgY+nd6a7eYPQAiYCCwF/iQi+8TY7nfRr4fp+wrxb8BzeG8SnwS+ISKfia7bjjd2aDnerD8PAH9P0zlMB97peaGqtcBa4DN97uHZE6gHboleQd8oIoVpqtNoYvH6EYvX1Fm8Zp7FrMfiNXXJxiuM4JgdTglz3NNoD0cisllEviciq0WkQ0RuEpGJIvJI9LbFM8B9wHdUtVVE7hSRHdHbQs/1BIyIXAVcAXxeRNpF5Cu9j6OqK4FKEZnS69hXisjt0Z9niIiKyPkiskVEGkTkxwmcRyFwBvATVW1X1RfwAu3cXbY7GahT1b8CTwCdfRQ5A1imqo6qbgBeAPaJnkuzqm5W77JTAAeY2U/dNovIsb3O+Z8i8tfo7/cdEVkUXfc0cDRwXfR3ODtaxLMM/GaYBRwI/ElVD8BrBRgVff3SzOIVi1csXkeSERuzccTrkyIyFbgb+A5wk8XrsIxXGMExO5wS5nimAR3uzgCWALOBU4BHgB8Bk4D9gR2qek9020eAWcAEvNsuywBU9afAL4F/qGqRqt4U4ziP4vVD68/hwBzgGOAKEdk7znOYDTiq+l6vZW8SDcJeFgOfEZHNeFetC/CuYnf1O+A8EckWkTl4twWf7L2BiDQD3cDv8c49Xp+JHrsM703nOgBV/RTebapLor/DnnNZg9dfqj81QI2q9rRQ3IUX3ObjLF6xeMXidSQZ6THbV7xW4E3s8ixe8ngPFq8fGmbxCiM4ZodTwhzPNKDD3e9VdaeqbsP7h1oOrAL+BKzG+6cFQFVvVtU2VQ0CVwLzRaQ0zuPEc9voKlXtUtU38QIynn9kgCK8/lC9tQDFvReo6g9VdaqqzsD7W60CGmKU9yDebaEuvFs2N6nqa7uUVQaUApfg9deK1wuq+rCqOsBtDHyObXjB3ydV3QFsjb75gPeG+G4CdRorLF4tXi1eR5aRHrO7xWu0RTgEFOL1ib0WLF53KWvYxGu0PiM2ZlOa6S+dtI9pQIe4Wona2evnrujrxXi3W2qAMhFZBVyOd4V6JlAJuNF9Ktg9mGJ5ArhBRLJVNdzHNjt6/dyJF6jxaMfrlN9bCV4wJEREyvGu1i/B62s1CbhLRHaq6h97b6uqHSJyPVAvInural0ch9j1HPNEJKuPvmngvSk1x1Hut4Bl0Q+VjcCX49hnTLF4BSxeLV5HkFEQs7HiFbyYPQhoj8YrwHvAQixegWEXrzBCY3Y4tTATvZqZrap7qeovhro+6aCqL6iqAFfhXREvAMYBpwLH4l35zYhuHquPWawy2/BawI5Ie4W9N5osEZnVa9l8enXwj1GfZ/GSil3tiXf76a+qGlHVGrxbPCf1UZQPKCBzD6Lsjdca0C9VXaWqi1R1f1U9TVUDGarPiGbxavGKxeuIMlpjFvgqH8Xrb4D9sHjd1bCIVxi5MTusEuYxpBgIAo14/8CJ9Cvq8RB9B0bSVLUDuAf4mYgUishivGThtljbi4hPRPKAbO+l5EWvGsF7cxAR+UJ0u0nA54kGlYgsEZEDxBucvQS4Fm/4mzXR9V+K9uFKl0/i9W0zJhEWr1i8mhHD4hWL10ywhHlo/BVv/MVteH13XkmijEwOf3MxkA/UAXcA3+i5dSciR4hIe69tj8S7PfYwMC368+MAqtoKfBb4Ll6grgLeBnpaNsqi5bcAG/Ce4D1BVXv6jlYDL6bjhERkMt64lPelozwzpli8eixezUhg8eqxeE0zUR0Ro8qYGERkI3CMqm5KoYzH8Z6sXaGqR6etcmkQrdu3VTXlWYBE5DfAhl37dhkzWCxeEyrL4tUMKYvXhMoaE/FqCfMIJiJnAOtU9e2hrosxpn8Wr8aMHBavZleWMBtjjDHGGNMP68NsjDHGGGNMPyxhNsYYY4wxph+WMI8wIvIjEbkxyX2fFZFuEXkuHeWL5xYRCYjIq8nUKR1EJFdE1orIhKGqgzE9Mh2jo43FrxmORkscp/KZLiL7i8hLma7jSGEJ8xARERWRmQNs82MR+XnvZar6S1W9MIVDX6KqR/a1MsHyDweWAFNV9eBdV4rI2SKyTkRaRKRORP4SHQ+y50PyJhH5QETaRGSliJzY14GiY0Y6ItLe6+uoaJ2DwM3AZXHW25gBDZcYFZHbRaRWRFpF5D0RubDXuk+IyBMi0iQi9SJyZ3SIp77q2/NB3hND6/rZtiwas3XRryt7rZsgIneIyPZofL8oIof0d1IicqCIPBc97k4R+TZY/JrMGkZxvLeIPB2Nl/dF5PRe6xKK4+g+Z4vIGhHpEJENIhJzopVUPtNVdTXQLCKnxLn/qGYJ8/CWybEg02E6sDk6GHssLwKLVbUUb1aiLKDnTSkL2Io32Hkp8BPgnyIyo5/jvayqRb2+nu217m/A+SKSm+zJGJOEwYjRXwEzVLUE+AzwcxFZGF03DrgBbzaz6XhT7N4yQHmX9IqhOf1s91u8iR9mAAcD54pIzxS2RcBreNMPlwN/AR4SkZhTBItIBd40vn8GxuONCft4r00sfs1Qymgci0gWcD/wIF68XATcLiKzo5skFMcisgT4T7wppYvxxmvemIaqxvpMXwZ8LQ1lj3iWMA9jqroSqBSRKT3LRORKEbk9+vOM6NXz+SKyRUQaROTHqRwz3vJF5CvAjcCh0Rajq2LUf6uqNvRa5OB9UKKqHap6papuVlVXVR8ENuF9ACcsOi1oAPhEMvsbk4zBiFFVfSfaCgug0a+9ouseUdU7VbVVVTuB64DF6Tg34BTgv1S1U1U3AzcBF0SPu1FVr1XVWlV1VPUGIAfoKwG/FHhMVZepalBV23qP/2rxa4bSIMTxXGAK8NtovDyN16B0bvT4icbxVcDPVPWV6OfnNlXdFmvDNHymPwscYxezljCPBI8CfXZViDoc74PqGOAKEdk7zXXYrXxVvQn4Oh+1+v401o4icriItOBdMZ8B/K6P7SYCs4F3+qnHAdEAf09EfhK9au9tDTA/oTMzJnUZj1ER+aOIdAJrgVq8FrFYjqT/GAL4VTSOXpRot6b+Dr3Lz/v2Ub8FeAnz+32U8wmgSUReinbv+JeITNtlG4tfM5QyGcfSx7KY8UQ/cSwifmARXoL/vojUiMh1IpIfZ10ggc/0aCIepu+L4THDEubhL55bRVepapeqvok3j3y6P3SSLl9VX4h2yZgK/BrYvOs2IpKNd9vnL6q6to+insN7c5mAl3ifA3xvl23a8KYDNWYwZTxGVfVivFuvRwD3AMFdtxGR/YEr2D0uersMr3tUFd4t4H+JyF59bPso8AMRKY72Ab0Ar4vGrsctAW7DO8eWPsqaCpwPfBtvit9NeNP29mbxa4ZSJuN4Ld5U2N8TkWwROQ6vO2KseBoojicC2cDn8N4PFgAHAJfHWZdkzsNiE0uYR4IngCOjSWVfdvT6uROvf+GARGSpfPTwzyPpLr+36FXqo8Dfd6mDD+/DNgRc0s/+G1V1U/T201vAz/DeMHorBpoTrZsxKcpYjPYWvZX7Al7y+Y3e66IJ7SN4U90+308Zy6PdIYKq+he828In9bH5vwFdwHq8/pd3ADW7HDcf+Bfwiqr+qp/qdwH3quprqtqNd0v5MBEp7bWNxa8ZShmLY1UNA6fhJeQ7gH8H/snu8RRPHHdFv/8+2iWqAbiWvuM4lkTPw2ITS5iHPVVtA1bjXUmmu+xlvR7+GehWVDpkEe17Cd4QNnj9IicCZ0TfVOKl7H6ba2+8q2VjBk0mY7QPu8bRdOBJ4GpVvS3BsmLFkbdCtUlVl6rqJFXdB+/z4sPhI6N9Gu8DtjHwQ0Gro8fqfVx2ObbFrxkymY5jVV2tqp9U1fGqejzenZ7e8RRXHKtqAC/RHpRpmqP9unOAPkfUGSssYR5aOSKS1+vL38d2D5HY1eOQiT5QcFT056UiMk0804FfAE/12vxPeB+Sp6hq1+6lfazcE6P9nBGRuXijatzfa30V3tPHr6TzfMyYN6QxKt7wbWeLSJGI+EXkeLzuSE9H11dFf/6Dql4/QFllInJ89DyyRGQpXl/Jx6Lrex4ImhF9vZeIjI8e90S8J/t/Hl2XDdyF19p1nqq6A5zKLcDpIrIguu9PgBdUtbnXeVj8mkwZ8s9a8cY0zhORAhH5D2AycGt0XdxxHHUL8K3o+8M44Dt4I3D0HOvDz+E0OAp4uteDx2OWJcxD6x28D5yery/3sd1wH14OABGZCrQDb0UXzQNeii57Ee8K9avRbafjtUotAHb06hqyNLp+WvR1z4NBxwCrRaQD7/dxD/DLXof/Al4f6DEf1CathjpGFa/7Rc8oEv8P+I6q9lwsXojXUvXTXjHU3rOzeJMW9HS3ysZLeOuBBuBbwGmq2tNyVA18gNdiDN6INW/h9V/8FbBUVXseRDoMOBk4Dm+c1p5jHxE97hG96xEdFeBHeAlJHd5oOV/odZ4WvyaThjqOwRsRoxbv//8YYEmv//dE4hjgarxhHd/De1h2JV6DVKzP4VQtBeJJ4kc9UR2UVn2TIhHZCByjqptSKONx4FBghaoenbbKfVT+F4F9VPWH6S57gOPm4t3KPVJV6wbz2Mb0GAkxOsCxLwfqVfXPg3xci18zbIyCOE7b57CI7AfcoKqHpl6zkc8S5hFCRM4A1qnq20NdF2PM7ixGjRn5LI5NXwbskiEi1SLyjHhTML4j0elMRaRcvKkc10e/j+tj//Oj26wXkfPTfQJjharebQFs4mExOzQsRk0yLF6HF4tj05cBW5jFm898sqq+ISLFwOt4w6N8CWhS1WtE5AfAOFW9bJd9y4EVeINsa3TfhdGnPI0xGWAxa8zIYfFqzMgwYAtzdJy/N6I/t+F1MK8CTgX+Et3sL3gBvqvjgSeiwxMF8MY5PCEdFTfGxGYxa8zIYfFqzMiw69TC/YoON3QAsByYqKq14AW8iEyIsUsVsLXX65roslhlX4Q3bBGFhYUL586dm0jVjBnVXn/99QZVrUx0v0zFrMVrclzXpb2zE7+/r1GtYgt0OQS6HPYsz8lQzXalFLRswMkqIFg4xVuiiqpSXFg4SHUYuSxeR66Ori5c18XnS2wQsYKW93GyCgkWTibiKjUtYQqyfUwoSijNGlSO45Cfl0d21vCt42CIN17j/i2JSBFwN96QRq0iMce63223GMti9gFR1Rvwpmpl0aJFumLFinirZsyoJyIfJLFPxmLW4jU5re3tvLRyJePLEpu0FE4TAAAgAElEQVRl9q9vBHjsvXb+enZ1hmr2cSU7X2XOC5ey/hM/p7nqSADCkQjhcJhPHnzwoNRhJLN4HZk6urp4fsUKyktLifP3D0Be6yb2e+JcVsy9lN80LeaVrZ1MAf79iAoOqt5t9uthozEQYMHeezOpMuFru1El3niN6xIqOtD83cAyVb0nunhntO9VTx+sWMMB1eCN7dljKrA9nmMaY5JnMTs8ue5A83vEFoooOf74P8BTVb71SSJZhbRMOmTQjjmWWbwOvdb2dl564w1ysrMTSpYBiutXAvDdN6ewansXn9m7hD+cNmVYJ8smcfGMktEzffEaVb2216oHgJ4ncs+n16xrvTwGHCci46JP+B4XXWaMyRCL2eHLVU1qPtuQM3gJszghxm1/juaqI1F/7qAccyyzeB0eGpubcVw3qS5HJfUrafRX0Jg1kT+eVsUXDyhjfMHY7uYwGsXTwrwYb4aaT4nIqujXScA1wBIRWQ8sib5GRBaJyI0AqtrERzPSvAb8LLrMGJM5FrPDlKrGvIc+kJCj5GQNTsJcunM5WeF2GqceOyjHMxavw0GgpYW8nCSeEVCluGEl72bvS1Gun6Jcm0B5tBrwEkhVXyB2PynwpnfcdfsVeNM89ry+Gbg52QoaYxJjMTt8Jd0lYxBbmMu3Pkk4p4y2CQsH5XhjncXr8NDS3p5UwpzXtpnsYDOv5s2jstBalUczuxQyxphBMty7ZPgiXZTVvkig6ijUZx/+ZmwIhcMEg0Gykhgtoqf/8v8F51Jh3TBGNUuYjTFmkLiuiwwwWVQsg5Uwl9W+iN/ppql6t4ZNY0at7mAwqQtZgJL6NwjmT+Dt7vGUFyQ2XKQZWSxhNsaYQeK6Lsl0yhishHn8lscJ5U+grWJ+xo9lzHARcZyER8YAQB1K6l6nqWIRrgrF1n95VLO/rjHGDBLHcfAl8cEcimT+ob+s7gClO5fTWL0ExD4azNjhOA6axJ2fgub1ZIXb2Fl2IACFORY3o5n9dY0xZpA4rptUS9ZgtDCX1zyFqEPjtOMzehxjhhvHcZIavaak7jUAPihZAEBhtqVUo5n9dY0xZpAk3cI8CAnz+C2P0VE6i67SPTN6HGOGm3AkAknEZUnd63SW7EVAvJk7rYV5dLO/rjHGDJJk+0qGHCU7gwlzXtsWigJraJx2XMaOYcxwFQyF8PkSS4fECVLcsJrWCQtpD3lPJljCPLrZX9cYYwaJk0LCnMkW5vFbHkPx0VS9JGPHMGa4CkUi+BNMmIsa38LnhmidsIgOS5jHBPvrGmPMIHFcN+GWLFUlnMmEWV3Gb3mc1gmLCOdXZOYYxgxjwVAo4YS5tG4Frvhpq1hgCfMYYX9dY4wZJMm0MIcc7+n93AyNklHU+Ba5nbXWHcOMWaEkumSU1K2go3wf3OwCOkIuIpCfPTizcZqhYQmzMcYMkmRGyehJmDPVwjx+y2M4/jwCVUdmpHxjhrtwJILfH/+kI/5QKwWBdbROWMi2ljDv7gxSkO1L6oFeM3LYPI7GGDNIkhkl46OEOf3tG+KEKK95muYpR+JmFaS9fGOGO1WlOxiksCD+//+S+pUISsP4hfz48Z10hl2Om1WUwVqa4cASZmOMGSSOm/g8f6FI5rpklO14maxwOw029rIZowKtrYTCYUoTaGEuqXsNJyufV0N70REK8IOjKllYlZ/BWprhwLpkGGPMIFHVYdUlY/yWxwjnltM6YWHayzZmJNhSW0tuTk5C+5TUraCt4gBW1UXI9sG+E3MzVDsznFjCbIwxg2Q4Jcz+UCultS95U2H77GajGXtUlbrGRooS6I6R07mDvPYamisPZOX2LuZOyCM3y1KpsWDAd0kRuRk4GahT1X2jy/4BzIluUgY0q+qCGPtuBtoAB4io6qI01dsY0weL2eHLcRyys7MT2ieYoS4Z5TVP49MIjdOtO8ZQsngdOuFIBDfBoR5L6l4HYE3uAra3RjhhdnGmqmeGmXiaFW4FrgP+2rNAVT/f87OI/AZo6Wf/o1W1IdkKGmMSdisWs8OSCySa9maqhXn8lsfoKp5BZ+mstJZrEnYrFq9DIhQOJ7xPSd0KwrnjaMibAdQxpcTuzowVA15WqepzQFOsdeLdWzwLuCPN9TLGJMlidvhyHQeGQZeM3PZtFDe+RcP04xOuj0kvi9eh4zhOYjuoUlL3Oq0TFqHRsLGh5MaOVDveHAHsVNX1faxX4HEReV1ELuqvIBG5SERWiMiK+vr6FKtljOlDWmLW4jU56roJf8D2dMnISWOXjPKtTwDYVNjDn8VrBjmOg6rGvX1+6yayg020TljI+oYQAOUF8Y+uYUa2VBPmc+j/ynexqh4InAh8U0T6HBlfVW9Q1UWquqiysjLFahlj+pCWmLV4TY6TwIdzj7S3MKtSseVRWisPIFQwKT1lmkyxeM2g1vb2hC5gS+pWABCoWMTj69uZW5lLVUlizySYkSvphFlEsoDPAv/oaxtV3R79XgfcCxyc7PGMMamxmB16blIz/XljN6crYS4MrCGvvYbGanvYbzizeM28nY2N5OXlxb19yc5X6S6ayqvNpexsj3DiHJusZCxJpYX5WGCtqtbEWikihSJS3PMzcBzwdgrHM8akxmJ2CPUMKZdwwvzhKBnpGbpq/JbHcH05BKYelZbyTMZYvGZQxHEItLSQF+cYzOIEKW5YScvET/DIe22U5/s5uNpmxxxLBnwHFpE7gJeBOSJSIyJfia46m11uFYnIFBF5OPpyIvCCiLwJvAo8pKqPpq/qxphYLGaHJ9d1E+ov2SPoKAJkpyFfFjdC+danaJ5yOE62tY4NBxavQ6OzqwuFuIeUK254E78TpLHyYN6s7eaIPQrJ8tkDf2PJgOOhqOo5fSz/Uoxl24GToj9vBOanWD9jTIIsZocnhYRbl8Hrw5ztT7xlOpaSncvJDjXTUH1cymWZ9LB4HRpdwWBC25fuXI7ry6GpfD7QSGGOJctjjU1PY4wxg8B13aT2C0U0bZOWVGx5jHBOKa2TPpGW8owZqbq6uhKasKR0x3LaKvbnpe3eXaKZ42067LHGEmZjjBkETrIJs6NpeeDPH2qlbPsLNE09BrWpsM0YVx8IxN1/OadzJ/ltm2mZeAiv1nRRWehn34mWMI81ljAbY8wgSLqFOU0J8/itT+BzQzTscXLKZRkzkqkqgdZW8nLjS3pLdr4KQE3ZIt6s7WbR1Py0dJEyI4slzMYYMwgSnlUsKuhoWiYtqdj8EB2ls+gsm51yWcaMZJ3d3biOE3eXjNKdywnlV7I6VEXYUQ6dZqNjjEWWMBtjzCBwXJfEx8jw+jCn2sKc37yewub3aJhhrcvGBEMhNN4WYjdCSd0KWiYewvKaLnL8wrSy+LpymNHFOrIZY8wgcF2XZNLedHTJqNz8IK4vh8ZpNhW2Gdtc16WuqSnuGf6Kmt4lK9zOb7bM5JnODo6fXURhjrU1jkWWMBtjzCBI5qG/YMRlZ3uE2RXJt2iJE2T8lscJTDkCJ6ck6XKMGekcx+G1t9+msbmZ8pKBY2FNXTfFbzzLbBU2Fh7ABfPG8am9CgehpmY4soTZGGMGQTgcTnifh9a2EehyOHFOcdLHHbf9ebLCbdYdw4xpXcEgazdsoKm5mQnl5QNu/8qWTn7zfAP35axgY+5sLj12T/w2UcmYZvcVjDEmw1SVzdu3k5+XF/c+6xuC3P12KwdNzWefifHvt6uKzQ8RLJhE64SFSZdhzEi3YcsWttfXM76sbMBtt7WGufX1ADPy2pnv20TBrMMtWTaWMBtjTKYFWltpaW2lIIGE+f53W8nxCxceNC7p4+Z07KCkbgUN008Csbd7MzY1t7aytbaWirKyAUfGeH5TB9/5Vy3N3Q4/3msTgtIy8ZBBqqkZzuwd1BhjMqy5tRW/3x/39p1hl1XbuzlsegHlBcn3nKv44CEAL2E2ZgwKh8O8+tZbFOTlDZgsu6rc+VYLM8Zl88dTp3BAZBWRnBI6xs0ZpNqa4cwSZmOMybCGQCDuSRI+CIT40yuNBB1N7QEjdan44GFaJywiVDgp+XKMGcHaOjtxXJeC/PwBt125vZvatginzSuhPN9H6Y5XaJlwEEj8F7tm9LKH/owxJkMcx2HTtm3UNTXF9aDR399s5u63W8nywen7lLDX+OSn3y2pe53czp3U7Htx0mUYM9K1trfH3TK4ti6IT+Dg6gIKA2vJDgZonrw4o/UzI4clzMYYkyE7GhpYs2EDFePGDTiVbku3w33vtnJIdT4XHVxOSV5qrVoVmx8kkl1MYMrhKZVjzEjWEAiQmzPwsIzbWsM88X47cypzyfYLpTteQvFZ/2XzIeuSYYwxGbK1tpaSoiKy4ui//PymDhwXPj+/LOVk2R9qZdz252mcdjzqT76V2piRrKOri/qmprhGp/nn6hZAufgT3p2gstqXaB+/D05uaYZraUaKARNmEblZROpE5O1ey64UkW0isir6FfOJEhE5QUTWicj7IvKDdFbcGBObxezw4DgOzW1t5MfZd7muI0J+tlBdmp3yscdveRyfG6J+xqdTLstklsVr5nR2dSEiAz7sB94kQROKsphUnE12VwOFzetomXTYINTSjBTxtDDfCpwQY/lvVXVB9OvhXVeKiB/4A3AiMA84R0TmpVJZY0xcbsVidsgp3vjLA3XF6OEq+OPcdiAVmx+io2w2XWWz0lKeyahbsXjNiHAkEve2qh/9XLrjFQCaJx+a7iqZEWzAhFlVnwOakij7YOB9Vd2oqiHg78CpSZRjjEmAxezwkEiyDOC6kI65EQoC6yhsWU+DtS6PCBavmRNJIGHuDCt5WV5KVLbjJYL5E+gq2StTVTMjUCp9mC8RkdXR20mxRtavArb2el0TXRaTiFwkIitEZEV9fX0K1TLG9CFtMWvxmn6ualpmE6vY/BCuL4fG6iVpqJUZQhavKQo7TlzdMQDqOyJUFPgRJ0TJzte87hhpuuNjRodkE+Y/AXsBC4Ba4Dcxton1n6YxlnkrVG9Q1UWquqiysjLJahlj+pDWmLV4jYP2+XYXk6uptzCLE2T81scJVH0SJ6cktcLMULJ4TQPHceK6y9MWdGjsdJhelkNxw5v4nS7rjmF2k1TCrKo7VdVRVRf4X7xbQ7uqAap7vZ4KbE/meMaY1FjMDr7E0mVwVFNOmMu3PUtWuJ36GSenVpAZUhav6eE4Dr44EuaI630vyPGGk3N9ObRVLsxw7cxIk1TCLCKTe708HXg7xmavAbNEZA8RyQHOBh5I5njGmNRYzA5/Xgtzahlz5cb76Cqqpq3ywDTVygwFi9f0cF03oecIUKWs9iVaJyzEzRp4KDoztgw4cYmI3AEcBVSISA3wU+AoEVmA14iyGfhadNspwI2qepKqRkTkEuAxwA/crKrvZOQsjDEfspgdHnSQu2Tkt2yguPEttux3ifW9HEEsXjPHSTBhLg9uI69jGztmnZ3BWpmRasCEWVXPibH4pj623Q6c1Ov1w8Buw+EYYzLHYnZ4UNWEkmbHTe2hv8qN9+H6cmiYfmLSZZjBZ/GaOfH2YXajcTqz7VUAWqz/sonBZvozxpgMSWhYuRRamH2RTiq2PEbT1KNtZjJjouJtYXajfZhntb1KZ8mehAomZbhmZiSyhNkYYzIg0Yf+UkmYy7c+iT/SSf2epyVXgDGjUFd3d1zT0neGXYrpZGrH27RMttn9TGyWMBtjTCaoJpQ0u6rJzfSnyoSN99FZshft5fsmvr8xo5Cq0hlnwnzr6wE+6V+NXx2aJ1l3DBObJczGGJMBriqSQB/miKtkJfGOXBhYS2Hze9Tteao97GdMVFNLC5E4Ji5p6XZ4e2eQr5SvJpxTRvt4u+g0sVnCbIwxGeA4TkItzJ0hJT878bfkyo334vjzaZx2fML7GjNaNbe1xdW6vK0lTDYR5nW+RvOUxSAD72PGJkuYjTEmA1zXjTkVW186wy4FOYm9JftDrZTXPEXjtCW42YWJVdCYUSzQ0kJeTk6/22xvDXPbymYO8a0h1+kgMPmIQaqdGYksYTbGmAxwXDfuFub1DUF2tEeYWDTgSJ8fM37LY/idoD3sZ0wvgdZWdjY2kjtAwvzXNwJsbQnz3cmrcfx5tE48aJBqaEYiS5iNMSYD6pqayMqKLwFetqqZklwfp+1TEv8BVJmw8X7ax+1NZ9nsJGtpzOizo76e3Oxs/P10yeiOuKytD7F4Wh77dSynZeIhqD93EGtpRhpLmI0xJs0cx6GmtpaigoIBt20LOryzM8gJs4spSKAPc1HDm+S3bbbWZWN20RAIkJfbf/L7fxs76Ai5nDFhGzndDTRPse4Ypn+WMBtjTJpFHAfHdeN66Kg95M2aUFmY2MNGEzbdRyS7iKapxyRVR2NGo65gkPbOzgG7Y6ypC1KW5+OAruWo+Gm28ZfNACxhNsaYNHMT6L/cGfK2TOSBv6zuAONqnqVx2gm4WXlJ1NCY0ak7GBzwYVtV5e2d3cypzGXc9udpq1iAk5NAdygzJlnCbIwxaeYkMEJGZ9hrYU6kO0bl5gfxacQbe9kY86HuYHDAbdpDLi3dLoeV1JPftpnAlCMHoWZmpLOE2Rhj0sx13bi37Yx2ySiMs4VZ3AgTNt5LS+VCukv2SKp+xoxWHZ2d+AboCuVGb//s2/EKAM1TDs90tcwoYAmzMcakWSIJc0eCLcxl258jp6uOuplnJlU3Y0azeKbD7kmY57S+TEfZHEIFEwehZmaks4TZGGPSLJMtzBM33E13wWSaJx+aVN2MGc264kiYHVepJMDUzjUEbHQME6cB36FF5GYRqRORt3st+7WIrBWR1SJyr4iU9bHvZhF5S0RWiciKdFbcGBObxezQczX+SbF7Wpjzsgbu9VzQ/B7FDW9St9dnbQrfUcLiNb26g8EBE+amLocl/jcAaLb+yyZO8TRp3AqcsMuyJ4B9VXV/4D3gh/3sf7SqLlDVRclV0RiToFuxmB1SjuuicSbNHSGX/GzB7xs4YZ7w/l04/jwaZnw61Sqa4eNWLF7TQlXpDgb7nbAE4I5VzRznW0Fb/hS67DkAE6cBE2ZVfQ5o2mXZ46oaib58BZiagboZY5JgMTv0EuqSEda4+i9nBZsZv/VJGqedYENgjSIWr+kTcRxcVXy+vuOptdthW32Aw/3v0DH1SJB4x7MxY106+jBfADzSxzoFHheR10Xkov4KEZGLRGSFiKyor69PQ7WMMX1IOWYtXvvnOE7c23aG3Lj6L1du+hc+N8TOmWekUjUz8li8xinQ2jrg+Od3vNnC4awii4j1XzYJSSlhFpEfAxFgWR+bLFbVA4ETgW+KSJ+dhVT1BlVdpKqLKisrU6mWMaYP6YpZi9f+hSORflu5enSGXdbVBwduYbah5MYki9fE1NTWktfPDH9hR3l5SwdLi14nnFtO+/h9B7F2ZqRLOmEWkfOBk4Gl2kdnPVXdHv1eB9wLHJzs8YwxqbGYHTzNra3440iY73qrhdagy9zK3H63G7f9eRtKboyxeE1cU3MzBXl9z3y5rTWMG+piYfgNmqo+aQ/OmoQklTCLyAnAZcBnVLWzj20KRaS452fgOODtWNsaYzLLYnbwuK5LbX09xYWFA27bGnQpzfOx9ICYgyB8aOKGu2wouTHE4jVx4UiEiOv2+8Bfa7fLp3yryNYggalHD2LtzGgQz7BydwAvA3NEpEZEvgJcBxQDT0SHs7k+uu0UEXk4uutE4AUReRN4FXhIVR/NyFkYYz5kMTu0HMfB5/PF1SWjO+xSnNt/K5cNJTe6WbymRygchgFGpnl2YzunZC0nlFtOW8X8QaqZGS2yBtpAVc+JsfimPrbdDpwU/XkjYP+Rxgwyi9mh5SQwQkZ3RMkfYPxlG0pudLN4TY9ASwvaz4gXjZ0RVm5p4qbclTRXnWQXnyZhNtOfMcakkeO6A7Z09egOu+Rl9/0hn9UdiA4ld7wNJWdMP7bU1lLYR//lsKP86ZUmjpZV5GiIpqpPDXLtzGhgCbMxxqSR4zhxj+3aFVHysvp+G56w8Z7oUHJnpat6xow6dY2NBFpbye8jYX7xgw7erO3ma+PfIJw7jrZKa5g3ibOE2Rhj0shx3QHHgu3RFXb7nBJbnCATNtxL86TD6C6Znr4KGjPK1NbXk5/b90gz7zeEGJcdYu+OFQSm2OgYJjmWMBtjTBo5jhNXlwzHVVqDLkW5sd+GKz54lOxQMztmn53uKhozqrR3dJCTnd3n+sYuh5NzV+N3ummy0TFMkixhNsaYNIpEIkgcXTJagy7BiFJVEuODXl0mrv8HHWWzaas4IAO1NGZ0cF2X9s5OsrP6HsOgtjXMib7lhHPLbHQMkzRLmI0xJo3au7riGlKuI+SNphFrlr/SHS+T376FHbPPibs/tDFjUTAUwoU+Y05VaevoZGE42h3DN+DgYMbEZP85xhiTRm3t7eT009rVY119EIDqst1bmCe9dwfB/AkEquz2sTH96ezu7nNdR8jldy80cLi+Qa4GrTuGSYm1MBtjTBq1DdCfsseKbV1UFPiZvkvCXBBYS0nDKnbOPBO11jBj+uQ4Dms3bOgz3m59PcCq2m6+Me5Vb7KSSuveZJJnCbMxxqSJ67p0dneTNUALc1vQYXVtNwun5u/W33nS+r/jZBXQsMcpmayqMSNeS3s7rR0dlMSYhr4t6PD8pg6OnOywT9frNFUfa6NjmJRYwmyMMWnS3tmJqg740N8LmzsJOcpxs4o+tjyncwflNc9Qv8dncLKL+tjbGANQs2NHn32XH3i3DUfhovGr8LlhmqqXDHLtzGhjCbMxxqRJVz/9KXtr6IiQ7YPq0o/fSp74/l0A7Jz5ubTXzZjRxHVdauvqKCsujrl+Q1OIvcpz2L/lWboLp9Ixbu4g19CMNpYwG2NMmnR1d8c1QkZr0KUkz/+xlmhfuIOKTf+iaerRhAomZbKaxox44UgEpe/RMYIRlyn+AMX1b9A4bYmNNmNSZk+UGGNMmoQjkfgS5m6Hkl0mLJmw8T6yIh3smGUTlRgzkMgAEwTVtIS5cPwrCDrqumOoKqqK67q4Pd9dl65gsN99du0q5gISx/uV8VjCbIwxaRJ2HHxxTlpSkvfRA0jiBJm0/h+0TDiYTrt1bMyAgsFgn63Gm5pCdIaVxd3P0VE2h+7iaYNcu/RwXZdwJELEcYhEvwN0h0IU5uWRlZ1Ntt9PdlYWWVlZlJaUUF5aSmG+9zCxiOCLfhcRfD4fgpck9yzvb8IX83H2mzLGmDSJxNvCHHSYXPzR22/F5ofIDjZRO/fcTFbPmFGjobk55nLHVf7npUb28tVS1b2eLbMuGeSa7U5VCYZCdHZ1ea3DPYl+Twu5CLFSf5/PR2FeHiWFhRTk51NUUEBOTg55OTmUFNlDwYMtroRZRG4GTgbqVHXf6LJy4B/ADGAzcJaqBmLsez5wefTlz1X1L6lX2xjTF4vXoROOROJqYe4IKQU5XmItboTJ7/2NtvH70VaxINNVNMOMxWviQuEwG7ZsYVxJyW7rVtV2U9MS5m97rEJrhabqYwa1bh8mx93daK8uI8WFhczZYw9yc3O91t1oK2/vn3tagf0+H1lZWdb6O8zE+9e4FbgO+GuvZT8AnlLVa0TkB9HXl/XeKRr0PwUWAQq8LiIPxAp8Y0za3IrF65CIt4U57Cg5fi+xLt/6BLmdO/hgwb/bg0lj061YvCYk0NICIvj9u4+rvGp7Fzl+OKDtWdoqDyCcXzlo9YpEIjS2tDCupISZ06czrqSE3JwccrKzLfkdBeLq7a2qzwFNuyw+Fei5mv0LcFqMXY8HnlDVpmgQPwGckGRdjTFxsHgdOhHHGXAMZoCwq2T7BNRl8rrb6SydScukTwxCDc1wY/GauJb2drJiXJgGuhye39zBqRXbyG/fSuMgPOwXiUTo6OykIRCgrbOT/WfP5rADDmCv6uoP+xNbsjw6pPJ45ERVrQWIfp8QY5sqYGuv1zXRZbsRkYtEZIWIrKivr0+hWiatGjf0+yRyMlSVTQ0daS3TDMjidRDEM2mJ4yqqkOMXxm1/jvy2D9g+91xrXTa9Wbz2IeI4bNm+ncL8/N3WPfV+Ox0h5aLSV3Eli0DVJzNWj9b2dhoCATqDQUqKithv9myOPvhgpk2ZEtdFsxl5Mn3ZE+u/Jmb2pao3ADcALFq0KL0ZmomLqtLS1oYbTZB9rdsovfUoug79Lt0HXZy249z35g5++egGbvri/uxX5Q06n5OdTVFBQdqOYZJi8Zoi13XJinGbuLeQ4/26snwwee1f6S6aSqDqqEGonRllxmS8trS1EY5EyM7++KQ/riqPvtfGgZOymV73DC2TDsXJ2b2Pc6raOjoIhkKUl5Yyb+ZMimNMy21Gp1QS5p0iMllVa0VkMlAXY5sa4Kher6cCz6ZwTJNB2+vqWLVmDb7oB/7+7/yGYnVZ7s6me/XqtByjts3hmhc7mFnup71hI8sbBXVdSouLWXzggWk5honJ4nUQuKoDthRHXC9fmdu1ksLm99i08Acg/SfZZsyxeO1DU3NzzL7LG5tCtHS7LK3YQM77DWyZdlxajxsOh2lub6coP58DFiygrLjYWpLHmFQS5geA84Frot/vj7HNY8AvRWRc9PVxwA9TOKbJkK5gkHfWr2dcSQnZ2dkUBNZSteMZamd/gaLJs0nHADYhR7nmxR3kZvm49MhJjMv33vTC4TAR103DEUw/LF4HgRK72a+3nhbmIxv+QSh/Ao3Tjs94vcyIY/Hah9r6+pjdMR5d145PYHHnU0Syi2mevDhtxwyGQrS0tbHf7NlMmThxwLtIZnSKqw+ziNwBvAzMEZEaEfkKXiAvEZH1wJLoa0RkkYjcCKCqTcDVwGvRr59Fl5lhpqa2FgXvNpcq1auvI5xTRu3c89J2jNtXBvigOcwlh47/MFk26WfxOnTUdQduYXZgkaxlWsdb1M4+B/Vl94hsIisAACAASURBVLu9Gd0sXuPX3tlJe1cXOTG6Y7yypZOTZigTdrxAY/US1J+T8vFc16WusRFXlQPmzWPalCmWLI9hcbUwq+o5fazabYBDVV0BXNjr9c3AzUnVzgyKiOOweft2SqJ9scpqX6CkYRWbF1yKk52ewdFX1HTyyLp2Pj23mAOrdm8dMOlj8Tp0VHXAFubOsMvFWQ/QlVVKw4xTBqVeZviyeI1fZ3d3zHHO69sdgo7yaf9yfG6IhhknpeV4zW1t7DVtGnP33DMt5ZmRzSYRNzQ2NxOJRMjKykLcMNVv/YGu4unU7/GZ9JTfGeGPrzSxx7hsli4oS0uZxgw3qkokEhmwX6O/cR2f8q9i7dQzcLPyBql2xox83d3dMZe/tdNbflDrk3QVz6CzbE7Kx4o4Duq6TK+KOfCIGYMsYTZsqqkhP8/74K7ceB957TVs3e+b4Et9EBXHVX7/UiMhR/nO4RVk++0hCTM6ua6Lqg44ccm+W++gVfPZNv30QaqZMaPDzsZGcnM+3tXCVeW+d1o5vLSeitZ3aZh+UkpDNLquS1NLCy1tbewzaxb5ubmpVtuMEpYwj3HtnZ00tbRQkJeHP9RK1ZpbaKlcSMukQ9NS/n3vtvLOziAXHjSOKSXWV9OMXm4cD67mtW5kduB5bnOWkFuY/iGvjBmtuoJBGgKB3R74e21rFzvbI3yr7GUUH40pjI4RDodpaG5mUmUlnzzoIKonT0612mYUselnxrjtdXVk+XyICFPW/gV/qI2t+1+SlkkU1tUH+efqFg6fUcAn97CxKs3o5sTxwF/Vu7cQlDz+N/Jp/pRr7RXGxKu2rg4R+ViXp3X1Qf60vJHqYh8LWp+mZdIhhPMrEi474ji0dXSgquw3axZVEyfGHLrOjG2WMI9h4UiEzdu2UVxURF7bFia8fxcNM06iq2xWymV3hFz++8UGKguz+OrB5TZepRn1uoJBtJ9ZMfNb3qd82zNcFzmN6kkVZPksJoyJR0tbG2s3bWJccfHHli9b2Uxelo9fz99K7oo6tu7/zYTKdRyHppYWsrKyqJ48mepJk2wCLdMnS5jHsIZAAMdxyPL7qV79e1x/HjX7fC3lclWVPy9voqnT4erjJlKQbS1pZvTbXldHdlbfb6lV795M0FfADZGT+N5+9vCrMfGqa2wky+8nq1d8qSqbm0McOaOQWTsfI5JdRPPkwxMqt7mtjZnTprHXtGnWomwGZJnMGKWqbKypoSA/n9Lalynb8TLb9/4SkbzylMt+ekMHL2/p5Jz5ZcyqsAcmzOjnui6NgQB5fTwgVBBYx7jtz3F96ETC2cVMLbX+/MbEq6W9ndxdxl5u6nLoCiuzi7oYt+1ZGqcdj/rj/7zpeeZgj6lTLVk2cbGEeYxqbW+npbWVwhw/1av/h66iaupmfi7lcmtawty8IsD+k/I4ZV7xwDsYMwrsaGigtaNjtyf4e0x+92Y6pJD/z959x0dVpY8f/9wp6b0S0khIIAUSahBBRVQQkAWzCprBH8t3XXbXFQuLuta14q64uljQxbrKJSAsTQUbFhQQkGYgEAwkBNLrlMxMptz7+wOIlKCUhLTzfr14Jcwk955AnpnnnnvO87zpGsf9V4bjJ9YvC8I5M1utx5pqneSo0QnAFfav0SjO8y6DajSb6RkRccZxBeFsxKt2N3WkogK9TkdE4XK8LUc4knHXRXccc7hV/v1dDV46iTsvD22xwLwgdDUOp5NDR46cde2jvnovIRUbWeCYwJUpkfTrIWovC8K5UhSFpqamM5Y7HWlwAipp1WuxhKRjC+x9Xsd1KwrxPXu24kiFrk4kzN1Qk8PB0YoKQnQOeu57l4YewzFGXXwZufd3iNbXQvez/9AhGm22M8pdnRC4603qVT90g27hd4ODL/HoBKFzc7pcLT5+1OTkSs+f8LMcPu/ZZbfbjVarFRv8hPMiEuZuqLKmBoC4fW+icds5kjHroo+57aiVTw5YuCHFn4Gi9bXQTThdLsoqKwkOaLmmsm/tHhJM2/ivNJHLk8Mv8egEofNzud3QQvWZBpsbg+5LXDpf6mJGn9cxrXY7PUJDxdpl4byIhLmbURSFQ0ePEuk4Sljxx1Ql3YzdP+6ijllrdbFgcx2JIR7kiNbXQjfSaLXCabVhTxa1901qVX8qE7PFEiVBuAAul6vF+uYWYwNXuzdTF3cdiu78JmkcTidBZ7nIFYSzEWXlupl6kwmrzcbg/FdxeQZRlvq7Cz5Wo0PhUJ2DpT8acSoq94wIFa2vhW7F1tTU4uwXgH/VDoKrf+AZVw6RweLNWRAuhM1uP6O++eF6B8NtX+Ohd1KVMOm8jmc/Xi89JEhM7gjnRyTM3YSiKBgtFg4UFZFQswH/2jyKBv0Nt97vnI+hqiolDU42Hray9YiVUtOxtWUaCe4cHkqUaH0tdDNGsxltS7WXVZWYPa9h1ofxnn0M94mqGIJwQarr6vA4rZJFmcnJrdovqfXve96NtixWK5kpKWL9snDeRMLcxamqSlVdHfmFhdibmvBSbSQdeBNLSDo1vcaf0/cfMTr5vsTKpsPHkmRJgn6RnlyR4EtSqAe9QzxFmSyh21FVldqGBrxaKCUXXPo1fvX7eEr7Z0L8fUgJF/XIBeFCWO32MxJm75o99NUcZV+v+877eBIQFiw23wrn74ITZkmS+gJLT3ooEXhMVdV/n/Q1o4DVQNHxh1aoqvrkhZ5TOD8mi4V9Bw9S09BAoJ8ffj4+xO98C12TiYKRL4J09iS30aHw4T4Tmw5bKTe7kIDUCE/G9fXnsjgfAr3EZonORsRs62owmzGazWe++SouovcupEwfyzvmETw0OhhPnbigFM6PiNdjrHb7GfXN06rW0qh60Zhw7fkdy2bDx9v7jARcEM7FBSfMqqoWAAMAJEnSAqXAyha+9FtVVW+40PMI58/tdlNYUsLBkhK8PD2JCDnWvc+3bh/hh1ZT1fu3v3gbq6C6iX9/V0OtzU2/SC9uSPFnaKyPKBXXyYmYbV1FR4/i6eFxxoa/8OKP8bYc4S7HbK7u7U9mlKgaI5w/Ea/HqtDYm5pOWT6hdZgZbP2W9R6jiND7ntfxrHY7Q/v3b+1hCt1Eay3JuAY4qKrq4VY6nnCBVFXlQHExh44eJSwoCI3m+MyW6iZ+179weoVQmn77Gd/nUlTyyu1sPGzl2+JGwn11PDMmUrS27rpEzF6Eiupqyqqqmi9GT9C47ITteYsflD5URYzgwayLbzUvCHTTeHU6nWdckPodWocXDnaGXM/Y8ziWoiioqoq/7/kl2YJwQmslzLcAuWd5brgkSbuBMmCOqqp7W/oiSZJmAjMB4uIursxZd9Vos3GwpIQjFRWnJstA+KE1+Nbv52DW35s3+rkVlfyqJjYebmRLiQ2LQ8FHL3FNkh+GAUH4eojbyB2ByWRqi8NeVMx253h1ud3kHzxIoJ/fGW/mvvuW4ues412ve7j3inC0GlE1prtxu91tcdhuGa9Wu/3UB1SFiMLl7FCS6JmUcV7HajCZSIiOPmv7ekH4NRedMEuS5AH8Bniwhad3APGqqlokSRoPrAJaXAugqupCYCHAkCFDWq7TJLTIarNRdPQoJeXl6HS6M5Jlnb2emL3/wRQ+iLqYa6m0uPhwn4nvS6wY7QqeOomhMd6MiPchM8pblIbrID7++GP++9//smbNmlY9bmvEbHeO13qjkSaH44yZKq3DRHzhYj53D2LUyJGd5oJTVVUURUE5/vHE311uNy63G0VRkCQJRVEIFaW4zqqgoICFCxeyZMmSVj1ud47XOqPxlPeywIotBDWV8b5yJzefx0Zah9OJRqslITa2LYYpdEJms5l33nkHWZbP+XtaY4Z5HLBDVdXK059QVdV00udrJUlaIElSmKqqNa1wXoFj6ygLiorQaDSEBAae8uJyQtyP89G47BweMJsaq5u/f16JqUlhcLQXl8f7Mqinl9iU1AEoisLu3bsZOHAgAAsXLmTz5s3MnDmTl19+uTVPJWL2Arndbg6WlLQ4S+Xxw1t4uq3k+k7jT0Htt6nI7XbjdLlocjhwulxnbapyggRoNRq0ej0eWi06nQ69Xo+3lxfenp54eXqi02rR63Rnbf/dXRUVFREYGEhISAhbt27l5ZdfZvz48axevbo1T9Nt47WuoQFvz58T44jCZVQTzIGgK85rYsdksZCamIiXp1hi2J2ZzWbKysro27cvbreb++67j/T09HP+/tZImG/lLLeKJEnqAVSqqqpKkpTFsc6Cta1wToFjt5j2FRYSEhR01hafgeWbCD3yBaWp/0e1ZyxPf1aJ1akwd2wkvYLFramOIC8vD1mWWbx4MUeOHKGoqIhevXqxcOFCQkND0el0rZ0wi5i9QEcrKqgzGgk/be2y1HCYvmWrWKO5mt+NGfKrSWpraXI4sDU14Xa7jzV3kCT0Gg1+vr6EBQcT6O+PVqNB0mjQSBIaSWr+XJIk9Dodnh4eLV5oCy2rqanhgw8+QJZlNm3axPPPP89f//pXbrrpJiZMmEBISEhr//93y3i1NTXRYDY3t533Mh0mqGorzztv5urkwHM+ToPZjKdeT2S4aE3fHTmdTj799FNkWWb16tVkZmayefNmgoKCOHToENHR0eccrxeVMEuS5ANcB/zxpMf+BKCq6uvATcCfJUlyATbgFvX0lj3CBXG53eQdOICPj89Zk2WNs5FeO5/HGpBAUZKBZ7+spsri4uHRESJZ7gB27tzJ9OnTycvLQ6vVMnbsWP7xj38QEREBQGRkZKufU8TshXO73Rw4fLjFlrpBP7xKE3oq+v2BaH3bJ58ulwuL1YrT5SIxNhZfb2+8PD3x8/HBy9PzkiXs3Ynb7SY7O5u1a9ficrno168fzz77LFOmTAHA29sb71aege/O8VpTV4eqqs0XcxEH/4cTHZ94XMfchHPbuGc7XpJuaP/+p8xUC93DSy+9xJNPPkltbS2hoaHMmDEDg8HQ/Hx0dPR5He+iEmZVVa1A6GmPvX7S568Ar1zMOYSWHSkvx2y1Ev4LBdhj9ryO3lZNQdaTPP+dkcI6B3OuCCM90usSjlQ4oa6ujmXLlhETE8OECROIiYkhMDCQV155hSlTphB+CWZARMxeOKPFgsvlQn9aZz//qh0kGTfxH90tDEiKafsxOJ3oPTyIjYoiKjycQH//Nj1nd+Vyufj888/58ccfeeCBB9BqtQQHBzN79mwMBgMZGee36exCdNd4VVWVytra5iUUWqeF4OK1rHZfTmbfnud0QehwOjE3NjI4PV0ky93E3r17kWWZ2bNnExYWRmBgINdddx3Tpk1jzJgx6C+y/rbo9NcJ2ex2CoqLCf6FN0q/mt1EHlpJee+bea6gB7vKrfxpWAhZsaId6KVks9n48MMPkWWZdevW4XQ6+X//7/8xYcIEwsPD+fbbb9t7iMI5On0DEgCqm9gfX6ZUDSMv6rcMasOqGI1WK556PcMyMvDz8RGzyG1AVVW2bNmCLMssXbqU6upqwsLCmDVrFj4+Prz77rvtPcRuod5koqq2trkpUFjxWjwUO+u8xnN7/19fjuFwOjGZzQxMSyMyLKythyu0o6NHj5Kbm4ssy+zevRuNRsNll13Gb37zG6ZPn8706dNb7VwiYe6ECktK0EgSOl3L/32Su4le2/9Jk08U85w3822xlVszA7kmye8Sj7R7UlW1OZmZMGECX331FVFRUcyaNQuDwdC8qU/oXCpravDxOvXuTGjxOnyNP/E355306XHu6yrPlaIoOJxOGm02XG43l2VmijqybeBEzL722mv85S9/wdPTk4kTJ2IwGBg3bhyeYobykqozGtFqtcdeR1U34YXL+UHpg29MOrpzuCg1WSxkpKTQ8/jyNqFrORGv5eXlxMfHoygKWVlZzJ8/n6lTp7bJckYQCXOn02AycaS8/Mx2vCfpuf+/eFtKeDf2aVYccDGurx83pp+57lJoPaqqsmPHDmRZZtWqVezcuZPAwEAeeughHn74YUaNGnXWteZCx1dvMtFgMp2y2U/jshK1ZyE7lCQ0fccwPL717t44XS4aTCa0Gg0B/v6kJiYSFhIiqlS0ovLycpYsWdJ8CzcnJ4fJkyfj7e1NdnY2gYGtfwEknJu6hga8jleiCaz4Hm9rGe+47mJYzK///judTrQajSiB2MXY7XbWrl2LLMt4eHiQm5tLVFQUr732GldffTXJyWfvXtxaRMLciSiKwt7CQny8vM56O9bbWEiPApkf/K/h8Z8SGR7nw+8GB4vbt22ksrKShQsXIssyBQUF6PV6JkyYQH19PYGBgVx77bXtPUThIimKQnELbbB1P7yFt6OOedzD71Iu/oK0ueKFy4WiqvRLTiamRw9RwaIVKYrC+++/jyzLrF+/HkVRGDRoED7HWy/37NmTGTNmtPMou7cmh4M6o5GQ4xcskYXLqdOEskmfxfRzqL1cbzaT2aePKCHXRWzZsoU33niD5cuXYzQaiYyMPGWZxcyZMy/ZWETC3Ek4nU6KSksxms1nlLT68qCF3eV2LFY7z1meoE7x5ffVUxkc7c1fhoegEclyq6qqqqKxsZGEhATq6ur4+9//zpVXXtlcWir4F2b/hc7nYEkJ5dXVp9zVMZUf4oqjy/lYOwrDmCsJ8bmwl1JFUTBaLLjdbnx9fIjt0YNAf3/8fHzE0otW4nA42Lt3LwMHDkSSJObNm4fVauWhhx4iJyeH1NTU9h6icJIT3f0kScLLdIjAqm28qEylT6Tvr078NDkceOp09GyjW/JC21NVld27d5Oamoqnpyfr1q1j6dKlZGdnYzAYGD169FmXo7Y1kTB3YE6Xi1379lFvMqEoCm5Fab7qPqHa4uK17+vw1Eo87rucFLWI18IfYlJEDGOS/URDklZisVhYvXo1sizz2WefcfPNN5Obm0tqaiqlpaVERUW19xCFNmCz2zl45AghgYE/v1mrKj13/hs7HnheeReB/hf2MupwOmkwmYiOjCQxNhZ/319PCIRzoygKGzduRJZlli1bhtPppLKyEm9vb7744gsiIyPFv3UHVWc0Nn8edSAXl8aT9+xXM6PXr19AmhsbyUxJEXdlOqHi4mIWL16MLMvk5+ezcuVKJk+ezL333sv999/ffBeoPYmEuQOrN5moqqs7o9X1Caqq8kOpDYB/D6ni+t0rqY4fz5Ah4y/1ULu0++67jwULFmC1WomNjWXOnDlMmzat+XmRLHdddUYjqqqesv5cW/wNqbYdvOX7f2SEXtimIqfTSb3JRL/kZHqdZy1Q4Zd9/PHH3HHHHZSUlODj48PkyZMxGAzNJaV69OjRziMUfsmJ7n4e1kpCSj5ja/AE6q0BpPzKcgyXy4VOqz3jDqzQsVVXV3PjjTeyceNGAEaMGMGCBQu44oorADrUXgKRMHdQiqJQU1eHXqdrMVneX9XEol0NFFQ30SfAxajC53D4RFKSeXc7jLbrUFWV77//nhUrVvDss8+i0+kICQlh2rRpGAwGRo4cKWYvuglFUSg8fBi/k2c2XHaidr7EASWGkGG3nPcx3W43RosFCRiYmip28beCI0eOkJuby6hRo8jKyiIqKoq0tDTmzp3LpEmT8PMT1YE6E5PFgo+3N5F7PkAC5luvJzZQT7D3L2+aNlutxPfseUaddKFjsVqtrFmzBpPJxMyZMwkLCyMgIIBnnnmGnJwcevXq1d5DPCvxm9UBWW029hYWUl1fT8hpXcUqzE7e3d7A9lIbwd5aZmYF83vTa3gVl7H/qldQ9GLd44XYv39/c3vqQ4cO4eXlhcFgYMCAATz44IPtPTyhHZRVVdFot5/SHKhx45tEqVV8lvgP0kLO7xahvakJo8VCQnQ0iXFxopnCRaivr2f58uXIssyGDRtQVZWnnnqKrKwsBg0axLp169p7iMIFMFksNDmdBHsohBet4VDYKDYfCeL2ob980dPkcKC43USJ9tcdksvlYv369ciyzMqVK7FYLAwYMIA//OEPSJLE2rVr23uI50QkzB1MTX092/fuPXZr6bTNYzanwj+/qaHa4iInM5DxKf5EVG8m8sc1lPfJwRKW2U6j7pxO1HL8/vvvGT58OBqNhtGjR/Poo4+SnZ1NQAstkIXuwel0kn/wIEEnzU56mQ4xsGYZX+hHkTJoxHkdr95oxMvLi2EZGeKW8QU6Ea+qqpKRkcHRo0fp06cPjz/+ODk5OSQlJbX3EIWLdKSiAp1WS8TBFWjdNp5sGIe3TmLkL6xfdjid1DU0MCwzkwBxN6HDONGhXZIk7r//fl588UUCAwOZOnUqBoOBK6+8stPtIxAJcwficrvJLyzE29MT79MaJDQ6FOZtqKbM5ORvV4UzMNobnb2OhO3/wBrQm9K029tp1J2L0WhkxYoVyLLMwIEDmTdvHkOHDuWVV14hOztbrEcWgGO3dxVF+bmVqqoQteWfmFRvfkj4I9ecxwu90+U61n0qMxOPi2zN2t243W6++eYbFi1axPbt29m5cycajYb58+cTFxfH4MGDO92brtAym91OSVkZoX5eRB5czuGgLL6p6MmMwUH4epx9GVyDycTAtDQiQkPP+jXCpfPTTz81361dtGgRWVlZzJgxg5EjRzJ+/Hi8TsttOhORMHcg+w4epNFma7EpyScHzOytbGLW5aEMjPYGVSFx29NonY0UXPFvVK1HO4y48/jkk094++23WbNmDU1NTSQmJnLDDTcAoNVq+ctf/tLOIxQ6ClVVqayp4eQ0LKhwNWGmvTws3cGIxHO/qDI3NmJvaiIpPl4ky+ehsLCQ119/ndzcXMrKyvD39yc7OxuLxUJAQADZ2dntPUShlZVVV4MkEVGyDn1TAy9rbiDUR8tViWefXa41GgkLCSFaVD1pV1arlTfffBNZltm6dSuSJDFq1CjcbjcA/fv3p3///u08yosndi91EKqqcrSi4qzdiaobXQR6abgy4diLR48DMoFVWynJvAtbYO9LOdROQVEUtmzZ0nxbKDc3l6+//po//OEPbN68mcLCQu655552HqXQERnNZg4dPUrQ8SU5elsNsXte4zt3On1H3ki477nNMzSYzWi1Wi7LzCQpLq4th9wlFBUVUVFRAcC+fft46aWXGDJkCEuXLqWyspJ3331XLJPqwsoqK/H38qTHgVyMQemsaOjNlQm+Z51ddrlcSMDg9HSxEbsdmM1m9uzZA4BGo+Gxxx7D4XAwb948SkpK+PLLLxk+fHg7j7J1iRnmDsLlciFJ0lmvkmsa3YQdf6P2q/mRmL1vUhczmuqESZdymB1eXl4esiyTm5tLSUkJu3btIjMzkxdeeIE333zz51vsgnAW5TU1eJxUnSZ297+RFCeLgv/M7yPPrTW1ubERL09PhqSnn7G8SvhZTU0NH3zwAbIss2nTJh555BGeeuoprr/+esrLywkVt9m7hSaHg0abjSTTNrys5XwUfjtKhcTg6LPHm9Fspm9CAjrtL1fPEFqP0+nk008/RZZlVq9eTXx8PPn5+Xh5eVFQUEBkF28YIxLmDsLpcsHx2dDT2ZwKJQ1O+oR5oLPX0Xvr32nyjqB40P0gbkMBx2akpk6dSl5eHlqtlrFjx/Lss882bwQSb7zCuXC53ZRXVeF7vJRcYNl3hJZ+zXPOKaQkJ5/TMRptNhxOJ0P69RPJ8lmoqsqUKVNYtWoVLpeLfv368eyzz5KTkwOAXq8XMduNVNfVoSpueha8R6NfPI8eTKFPmAfJYS0vNbRYrWh1OnqIqhiXzMKFC3nooYeora0lNDSUGTNmNMcr0OWTZWiFhFmSpGLADLgBl6qqQ057XgLmA+MBK/A7VVV3XOx5uxK32029ycTp6XKp0Ym8q4EdZTbcCgzsoaf3lkfQNRnZd/XruPVttyNYVVXcioKqKCiqinryn+PPn/4YJz3HaR9VQOLYjllUFSSp+TFFUc57d3NdXR3Lly8nKCiIKVOmEBcXR3h4OK+88gpTpkwhXLyQtkjE6y8rOnKEJocDf19fNE4rcTtf4IASw489b+au+F8vI9dgMuHt5cXQfv3Ejv2TuFwuvvjiCzZv3swTTzyBJElERUUxe/ZsDAYDGRkZ7T3EDqk7xKvb7eZAcTEJpu34mIqQe9yPrUbi9qEhaFqYEHI4nTQ5nYwYOFBckLah/Px8ZFlm5syZxMfHExERwXXXXce0adMYM2ZMt7xb21ozzFerqlpzlufGAcnH/wwDXjv+UeDYbNS2vDysdjsBvqdubvjXtzVUWFyM7+tPVowP15W/TkDNLg4NfRRrUJ9WOb/b7abBbAZ+LtvE8Y96vR6dTodWo0Gn1aLRaNBoNGiPf9RoNGi1WiSOrWHSHF9SIp30uUaS0Gg0zctNJEn6OXE+6e+eHr++adFms/HRRx8hyzJr167F6XSSnZ3NlClT8PX1Zf369a3yb9INiHhtgaqqHK2sJNDfH4DYvFfwsFfzN+ffyekf+qubipwuFwDDMjK65ZvJ6VRVZevWrciyzNKlS6mqqiI4OJi77rqL0NBQXnrppfYeYmfRpePV1NhIU5ON+APvYfPvxYqmocQFQUJIy+8JJouFjL598fcVPQdaW2lpKbm5uciyzK5du9BoNPTr14/4+HgmT57M5MmT23uI7epSLMmYBLynHptu/F6SpCBJkqJUVS2/BOfu8Cpramhqajqj5rKlSeGI0YlhQBCT0wMILfmUHoXLqEi6mdq4sa1ybkVRqDUaSYqLIzw4GL1ej4dej06rPaUVcHtqTuKBW265hTVr1hAVFcWsWbMwGAwMHDiwnUfY5XTbeK2pr8dmt+Pn40NAxRYiitbwuusGfOIHEB989gs6VVWx2mw02u0MSk3t9snyiZiVZZnbbrsNT09PJk6ciMFgYNy4cXiKhi2tqdPHq9VmI6pqE97mYg5mPUHxDwppES3/jiiKAkDIWTbHC+fvRLwajUYSExNxOBxkZWUxf/58pk6dNC1hvwAAIABJREFU2i2WWpyr1kiYVeAzSZJU4D+qqi487flo4MhJfz96/LFTAlqSpJnATIC4brSjvM5oxKuFN5BysxOASH8dPg0H6LX9n5jCBnC0f+uVP2swm+kdG0ufXr06VEkeVVXZsWMHsizzwQcfsG3bNqKiorj//vu56667GDVqVIdJ6DshEa8tcDqd7Nq3D39fX7QOM/Hb/8FBNZoPA3N46LJfXktbZzQS6OdHZmoqwd20ikNFRQVLlixBlmVmzJjBHXfcwYQJE3j77bfJzs4mMDCwvYfYWXX5eDWZjCQXL8Hm34uCoCuotVbQ6ywXqA6nkwA/P9El8yLZ7XbWrl2LLMtYrVbWrVtHYGAgb731FsOGDSP5HPdrdDetkTCPUFW1TJKkCOBzSZL2q6q64aTnW8rEztjddvyFYCHAkCFDWt791sUoioK5sRHP02akGh0Kb/9Qj7deor+/haTND+HyDOLgsKdQNRf3X6aqKramJqw2GxpJomdERIdJlmtqanjttdeQZZmCggL0ej0TJkzAbDYTFRXFiBHn111NaJGI1xYcLi/H5Xbj5elJ9Lbn0dtr+avrCWYM64GHtuX4UFWVOqMRf19fBqWnn9Oyoq5EVVXef/99Fi1axPr161EUhUGDBhEWFgZAcHAwM2bMaOdRdnpdPl6l/FX4NpZQmPUE/91pRJJg+Fn2C5itVvomJFziEXYd27Zt4z//+Q/Lly/HaDQSGRlJTk4OiqKg0WiYNm1aew+xQ7vohFlV1bLjH6skSVoJZAEnB/RRIPakv8cAZRd73q6gpLy8+RYwQLXFxap8E1uOWLE4FO4f7sfQnQ+ga6pn/1Wv4vI6s6HJr1EUBbfbjdlqbd6EF+TvT+/YWCLDwtDr2rdQSnV1NXV1dfTt25empiaeeOIJRo4cyV//+lduuukmglto4iJcOBGvZzJZLBwoLiYkIIDg0q+JLFnHy67JDBsy9KxLMdxuNzUNDURHRNAvObnbLMNwOBzs2rWLrKwsJEli4cKFlJWV8dBDD5GTk0Nqamp7D7FL6erxWlldRXTBf7EGJPCl5jK2HKnl5v4BLdY6rzMaCQkIIE50Yz1nqqqye/duevfujb+/Pxs3bmTp0qVkZ2djMBgYPXo0unbOATqTi/qXkiTJF9Coqmo+/vkY4MnTvmwNcKckSUs4thnB2JnWV7UFi9XK7v37aTCbCTl+q7Lc5OTpr6qobnSTFePNmCRfbjz8DL71+ykcPhdrcMqvHldV1WPVNo5XrpAkCZ1Gg4eHB72iowkNCsLPx6fFJSCXUmNjI6tWrUKWZT777DOuu+461q1bR3R0NOXl5aLCRRsR8dqyIxUVeOh0eDXV0mv7PynQJLHCdyrPJZ29ykWD2UxSfDx9O9hypragKAobN25ElmWWLVuG2WymoqKCkJAQVq5cSVhYWJf/N2gPXT1eFUWh/ru3iLQe5aesJ1mWZybMR8uN6Wcu3zFZLPj5+DA4Pb3dJ3k6g+LiYhYvXowsy+Tn5/POO+/wu9/9jttvv52ZM2fi4/PrFX+EM13sb14ksPL4i6UOWKyq6ieSJP0JQFXV14G1HCt5U8ixsjfd+h6dvamJH/bsQVEUIkJCAKi3uXlyfRU2l8qDV4UzsKcXsXmvElL2DSUZd9HQ84pfPKbVbsdqs6EC0ZGR9OrZEy9PTzz0+g73Rvboo4/ywgsvYLVaiY2NZc6cORgMhubnRbLcpkS8nsbpdFJTV4eXh47E75/C5XIy034HgxP8z/o9jTYbiqIQ26NHh4uv1rZ+/Xr+7//+j5KSEnx8fJg0aRIGgwH/45VERLy2qS4drw31NcQVvE1jUB++kobxU20tM7NC0J+2BMrtduN0ubh84MBucyfnQlksFq6//no2btwIwIgRI1iwYAETJ04EwE+UurwoF5Uwq6p6CMhs4fHXT/pcBVpvp1onpqoq+QcP4nS5CDr+hnO43sHLm2oxOxSevC6SxBAPova/R4+fllDZ+7dUJt181uM5nU4aLBYCfH0ZlJZGgL8/Xh4eHeZNXFVVvv/+e5YuXcrcuXPx8fEhKiqKadOmYTAYGDlypGhpegmJeD2V2+1m/6FDWG02kso+IqBmJ/c5Z5KY2JupmWfOcrlcLkwWC56engzt3x9f73Pr+teZHDlyhCVLljB48GBGjx5NfHw8aWlpPPPMM0yePFm84V5CXT1ebd++Soi9ioIhD7I6z0Koj5ZRiWeWiqszGumbmChqLrfAarWyZs0aysrKmD17Nn5+fsTExPDMM8+Qk5NDr1692nuIXYq4t3EJFZaUUF5d3VxCrtbq4vEvqpAkmHNFGIkhHkQU/o+YvQupiRtLSebdLXbyMzc20uR0otNqyejTh54RER0q8dy/fz+yLLN48WIOHTqEl5cXN910EyNHjuSOO+5o7+EJAk6nk+35+dQbjehNR0goeIuP3cPwyZjEH1MCTmmY4Ha7aTCZ0Op0xEdHEx8d3aV26dfX17N8+XJkWWbDhg2oqsoDDzzA6NGjSUpKYt26de09RKGLsRurCd/7FsaIIWzTZLC3spJpA4LOmF22WK0EBQSQEB3dTiPteFwuF+vXr0eWZVauXInFYqFPnz7cfffdaLValixZ0t5D7LJEwnyJ1JtMHCguJjQwsHkGeGPxsc19/74hiuhAPaGH1xG/+0Xqe15B0eAHQTozCa5taCAoIIABqan4+/h0mFtUJ3bZ5ufnk56ejkajYfTo0Tz66KNkZ2cT0E3LbQkdj6IobM/Px2Q2E+GjoedXj1NFMNYrHuSGqFNnlm12O402G4mxsSTExODRQeLtYp2IV4CRI0eSn59Pnz59ePzxx8nJyWluKS8IbaHpq3kEOs381O9PfLzHjK+HhrF9T717oSgKNrudQWlpHWpCqD2c2LAvSRJPPfUUTz75JIGBgUydOhWDwcCVV14pSq1eAiJhvkSMJtMpDUHsLoXvDjcS6qMlOlBPcOnXJPzwLMaIoRzMegJaKB+nKAqSJDG0X78OERwmk4kVK1YgyzK9evXijTfeIDU1lTfeeIMJEyYQJXYzCx1QdX09dUYj4UGB6NfOJlSpY3GfF0iPCjvl65ocDsyNjQzLzCSsC1RrcbvdfPPNN8iyzNdff82+ffvw8PDg+eefJzw8nMGDB3eY5VxCF2auwO/Hd6nqeTXl3slsPVrK2GR/vHSnJsVGs5m4nj2bO292R4WFhciyjCzLvPLKK4wZM4bbbruNzMxMxo8fj5dYpnJJiYT5EmhyOCguK2uuTlFudvLa5jqK653cd0UYgeUbSdzyOJbQdAqHz0XVnlrKSlVVjBYLTpeLpLi4dk+WP//8c9544w0+/PBD7HY7iYmJXHfddcCxK+Dbb7+9XccnCGfjdDrZvX8/Ab6+qNveIcP+A2/7z6Rf/0GnfJ3L7cZosZDZt2+nT5aLiop49dVXyc3NpaysDH9/f7KzszEajYSHhzNu3Lj2HqLQjTg+fwKd6qKi/5/47IAFtwLXJp86u+x2u1EUhcSYmHYaZftpamriP//5D7Iss3XrViRJYtSoUc0dMpOSksQdoHYiEuY2ZrPbyT94kCaHg5DAQLYdtTL/u1o0EswaHsoYaTOJm5/EFpTMT5c/h6L7eSOR0+nE1NiIoiiEh4TQNyGBgHbYdKMoCps2beLyyy9Ho9Hw8ccf89VXX/H73/8eg8HAZZddJmamhA5PVVX2FxUdW45QtpPBJe/yuXYEiaMMp/z+2ux2LFYr/ZKTiemkd0mKiorQarXExcVRWlrKSy+9xLhx4zAYDEycOBHvLrhhUegEynej/zGXktjfUOcRxccFpQyN8SY28NSlTnUmE33i4/HpJr+nZrOZAwcOMHjwYHQ6Hf/85z+JiIhg3rx53HLLLcR0wwuHjkgkzG3I4XSy5ccfaXI4CA4IQFVV3t5WT6ivlkdHR5BS/Qm9tjyHJaw/By5/DkX/8w7hBpMJt6KQ3KsXwQEBBAcEXPKkNC8vD1mWyc3NpaSkhK+//pqrrrqKJ554gnnz5nWY9dOCcC7qjUZKysqI9rCRsOMJiugJ1zyMr+fPd2xcLheNNhtZGRmdbma5pqaGDz74AFmW2bRpE3feeScvv/wyl19+OeXl5YSG/nKLb0FoU6qKuu4BnHp/Kvv/gdX5JhodKtn9Tt3fYj3ezKtXF08SnU4nn376KbIss3r1aoKDgykpKUGr1bJ79+7mjplCxyES5jZUbzRitdubq2LsLrdRY3Vz5/AQ0stWEPfjSxgjsyi8bC6K7ue1SE6nExUYlZXVLk1GioqKmDRpEnl5eWi1WsaMGcPcuXMZPHgwAIGBZ5bcEoSOTFVVDhQXE6BXid7wN1BcfNb3cYaetBlVVVVqjUb6JSd3umT5tttuY8mSJbhcLtLT05k7dy45OTkAaDQakSwL7W/vSqSSzRT0/Qv7zV6s3FvFqERfkkJ/fo9zOJ00Wq0My8hA1wH26bSV9957j9mzZ1NbW0tISAjTp0/HYDA0b24UyXLHJBLmNqIoCkcrK/HyOLYeOa/CzvMbagjykrjJupS4gneo63kVh7L+fsqaZXtTEw0mExkpKZcsWT5RVkqn0zFjxgxiYmKIjo5m5syZTJkyhYiIiEsyDkFoK1W1tdQ21JOZ9xzBthIe9XmEyf1+7p55IlmO7dGjw7fedblcfPHFF3z++ec8//zzSJJEfHw89957LwaDgYyMDLFESuhYHFaUTx/G4pfI4bjJvPhFLZF+On43+OcLU6fLRYPJxOD0dEI72QXrr8nPz0eWZQwGA2lpacTExHDttddiMBgYO3YsHh4ev34Qod2JhLkNuN1ufjp8mPLqasKCgpF3NrAq30SMH+RG/JfYgk+piR9H0aAHmqthnNiRr9PpGNq/P+HHuwC2FZvNxkcffYQsy6xduxan08nYsWOZMWMGer1e1F4Vugyj2cyPBQXE/LSEmNpNzNP8juEjr26utexwOqk3mYiOiKBfcnKHLGGlqipbt25FlmWWLl1KVVUVQUFB3H333cTFxfH000+39xAF4azc3/0brbmMgiHzeO7bBuxOhUdGR+LrcSzWmhwOTGYzmSkp9Ogi3SNLS0vJzc1FlmV27dqFRqMhISGBtLQ0Ro8ezejRo9t7iMJ5EglzK3O53ezev5/K2lpCA4OY920N20vtTExUecrxPEFlOylN/T/KUmeAJDVfVXt5etK/Tx/CgoPbbGb55Nqrf/zjH3n//feJiopi1qxZGAwGBg4c2CbnFYT24nA62bZnDzEV60ktXcL/GE3m9b8j2OfYS5/T5cJoNjMoLY0eYWEdLlk+EbMffvghkyZNwtPTk4kTJ2IwGBg3blzzznlB6LBqDyJ99yJlEVew2pJOSUMD918VRlzQsVlVx/HN7YP79yeyky8dOhGvdrudlJQULBYLWVlZzJ8/n6lTpxIZGdneQxQugkiYW9mBoiIqa2sJDw5mX5Wd7aV2/pRs5O6Gf+BpKeXQkEeojb8eOPZC0WAykdq7N/E9e7ZJuThVVdmxYweyLLNkyRK++uor+vbtyz333MP06dMZNWpUu5epE4S2crSigqDyTfTJe5EN7v4cHfpXLj+eLKuqSr3JRP/kZHp2oGVHFRUVLFmyBFmWmTx5Mg8//DDXXXcdb7/9NtnZ2WIPgdB5qCquNXejSjr2pt7Fsi+MDI3xZmiMD3AswWwwmxmcltZpk2W73c7atWuRZZmysjI2b96Ml5cX7777LhkZGSQnJ7f3EIVWIhLmVuR0uSitrETn4cuCzbXsKLMxQbuVOWX/QdV6cuCKFzCHH6v3qigKDSYTKb17kxgb2+pjqa+v55VXXkGWZQoKCtDr9YwfPx6HwwHAoEGDfuUIgtC5VdbWcnTHJwzL+yf5Shwrej1MTq9jm/ycTif1ZjPxPXsS06NHO4/0mMWLF/Puu++yfv16FEVh4MCBxMfHA+Dt7c2MGTPaeYSCcJ5+/ADd4W/Zl/oXFu7VAS5mnLRuuc5oJDEmplMuw9i5cyevvvoqy5cvx2g0EhkZyS233ILT6USv1/Pb3/62vYcotDKRMLeSoqNHOVBcTI1N4fUfaqk02/lX4HImshpLQCoHL3sah08k5sZGmhwOtFot0ZGRxLTiLZrq6moqKiro378/kiQxd+5csrKymD17NjfddBMhbbwuWhA6ipr6enZs+ozhu56k0h3Ayt5PkTMwGkmSMDc24nA6GZCSQnQ73iJ1OBxs2bKFK664AjiWMBcWFvLggw9iMBhITU1tt7EJwkWz1qF88jeM/n34yu8Gtu2sxzAgiHC/Y2mHyWIhwM+PpLi4dh7ouVFVld27dxMbG0toaCg//vgjS5cuJTs7G4PBwOjRo9HpRErVlYn/3YukqiplVVXkHzxISaMnL2ysI8hdzxehrxHXmEdl4o0cyZiFzaVirq/HQ6dj5ODB+Pn4tMpO9sbGRlatWoUsy3z22WcMHTqUzZs3ExQURGlpqUiShW6nsraWbZvXM3j7IyiqwscpcxnXPwGXy0WD2Yyfjw9D+/fH39f31w/WyhRFYePGjciyzLJly6irq6OkpITY2Fjef/99goKCRIULoWv47FEku5E9Q5/mne0mYgL1TEg51uba6XLhdLkYlpnZ4ev5FxcXs3jxYmRZJj8/nxdffJF77rmHqVOncvPNN+Pj49PeQxQuEZEwX6TCkhIKioqodXjyr+9qGedXyDxpPnq7pXm9cl1DAzqdjow+fQgJDGy17kXPPvssTz/9NFarldjYWObMmYPBYGh+XiTLQndT29DAdxu+4PIfH8ZbtfHtoH8xJDEVl8tFndFIyvH9Au1R43XTpk3k5ORw+PBhfHx8mDRpEgaDgR7Hl4QEd7FSWkI3duBT2LWIQ3G/5X81sVQ3mnni2lD02mMXg/UmE+lJSfh14GTT4XBw7bXX8u233wIwYsQIFixYwM033wyAl5fXL3270AVdcMIsSVIs8B7QA1CAhaqqzj/ta0YBq4Gi4w+tUFX1yQs9Z0dTWVvLgaIiihu9WLC5mtkea/iT8384fKLYd8W/sPglUFdfT0hgIAMusq6yqqps2bIFWZZ5/PHHCQ0NJTY2lmnTpmEwGBg5cmSH2+EvdCxdPWaNZjPbvv+KYbsfxVdt5LP+84hP7IfVZqPRZqNfnz7E9+x5ycZz5MgRlixZQkpKChMnTqR3796kpaXx9NNPM3nyZPzaoc290Hl02ni11sGaWdgCe/NtpIEPN5q5KsGXtMhjCabRbCYsKIjYDrJ34ASr1cqaNWs4cOAAjz32GB4eHqSkpHD99deTk5NDr1692nuIQju7mBlmF/BXVVV3SJLkD2yXJOlzVVXzT/u6b1VVveEiztMhVdXWsi0vjw1HJXbm/8gKr/+QohRSG30Nhwfdh1X1oKGhgcSYGJLi4/G4wNtOBQUFyLLM4sWLOXjwIF5eXkycOJExY8Ywbdo0pk2b1so/mdCFddmYtTU1seOHb0nf8iBBqomvBzxHfFImjTYbdoeDywYMIOQSVJc40QRIlmU2bNiAqqrceeedTJw4kcjISNauXdvmYxC6jM4Zr2vnoFrr+DL9Aeb/4CTIS8ttg4IAaLRakSSJjL59O0R1JpfLxZdffsmiRYtYuXIlFouF+Ph4HnjgATw9PVm4cGF7D1HoQC44YVZVtRwoP/65WZKkfUA0cHowdzmllZXs2LePD/KdJJeu5iOPpUhaLwqHPkFN1CjqTSb0WoXBaWlEXUC5KrfbjVar5ejRo6SkpKDRaBg9ejSPPPII2dnZBJzUzlcQzlVXjVmny0Xe9g0kfTuHEKWONX2fJSlpIObGRhRVZXhmJoH+/m12/hPxCjB+/Hi+//57+vTpw+OPP05OTg5JSUltdm6h6+qU8bpnBez5H3nx0/j73p7YnQpPjYkk0EuLyWJBArIyMvBux+UMqqqiqioajYb58+czZ84cAgMDmTp1KgaDgSuvvLJDJPNCx9Mqa5glSeoFDAS2tPD0cEmSdgNlwBxVVfee5RgzgZkAcR1016yqqpRXVyNvyGP7T+XcY1vAcH0+DT2GUzzoAcwafxrq60nu1YvEmJjzmlU2mUysWLECWZYJCgpi2bJlxMTEsGjRIkaPHk1UB2/XK3QuFxuzHSVeFUVh3/Yvif/8z3grjaxIeoaU/lkYzWZ0Oh1Z/fq1yTpJt9vNN998gyzLrFu3joKCAvz9/Xn22Wfx8/Nj8ODBYvOe0Go6RbyaK1A/mo0poC/3VU3A7FB54toI4oM9aDCZ8PH2ZmBqaqvt4TlfhYWFyLKMLMs89dRTTJ06lVtuuYWEhATGjx8v1iQLv+qiE2ZJkvyA/wH3qKpqOu3pHUC8qqoWSZLGA6uAFqt4q6q6EFgIMGTIEPVix9XanE4nO/YXsGhLGd5Fn/GWfhEeeiga+DdKo67DaLEQ6AdZ/fsTGRZ2zsf98ssvef311/nwww+x2+0kJiaeUm/15E18gtAaWiNmO0K8qqrKge8/Ju7zO3ApsDr1efqmZVBTV0dIUBAZffu2+kxWSUkJL730Erm5uZSVleHv7092djZmsxl/f39GjRrVqucThE4Rr24X6v9+j+K08azvHRRUqdw9IpTeoZ6YLBa8vLzI6t//klfEcLlcLFiwAFmW2bp1K5IkcdVVVzVviI+OjiY7O/uSjknovC4qYZYkSc+xQJZVVV1x+vMnB7eqqmslSVogSVKYqqo1F3PeS63BbCH32z1s3vEjf3G8xTD9fhrCBnJg4P1UKf5ompoYlJ5Oj7CwX51VOlFWKisrC09PTzZs2MBXX33F73//ewwGA5dddpmYmRLaTFeJWVVVOfDtMmK+vIsG1Zc1fZ9lYN9kquvriY6IID0pqdXenIuKinC5XCQnJ2MymXjppZcYN24cBoOBiRMn4t1OM2ZC19dp4vWbfyAVf8fy8DtZciScSWn+jOzli7mxEa1Wy5D09EuWLJvNZvbs2cPw4cPRarUsWLAALy8vnnvuOW699VZiYmIuyTiErudiqmRIwFvAPlVVXzjL1/QAKlVVVSVJygI0QO2FnvNSszc1seT7Qt79pgCDYxlvaz/B6eFHQepsisKuAreG5IReREdE/GoFjLy8PGRZJjc3l5KSElatWsWkSZOYM2cODz/8cIevRSl0fl0lZhVFYeuyeQzO/yeH6cHmgc/RLzqSeqOx1drM19TU8MEHHyDLMps2bcJgMLBo0SL69etHZWWlKAEntLlOE6+F61E3PE9+yLU8dPRyBvb04tbMIGx2O263m2GZmW2+ZtnpdPLpp58iyzKrV69Gr9dTWVmJl5cXmzdvFvEqtIqLmWEeAdwG5EmStOv4Yw8BcQCqqr4O3AT8WZIkF2ADblFVtcMttzidw6UgbzrAexuLSDNvYpnH+4Tr6imNncDe+FvxDY2hX1QUwYGB+P7K7FJFRQVjxowhLy8PrVbLmDFjmDt3Ltdccw2AKC0lXEqdPmabmuz88OZdjKheyjZNf0pHPE2Epxa3qh6rhBEUdNHnmDlzJu+88w4ul4v09HTmzp3Lrbfe2vy8ePMVLpGOH6+mctQVMzH7xjO9Zho9/HXcPSIMp9OB1W7nsszMX32PvFjLli3jz3/+M7W1tYSEhDB9+nQMBgMeHh6AiFeh9VxMlYzvgF9cO6Cq6ivAKxd6jkvN7XazfEsh/1pfhJ/1CP/0+i9ZHnkY/RLZ3PchrOEZ9O/d+xeXXpwoK2W325k1axaRkZH06dOHmTNnMmXKFCIuoGqGILSGzh6ztbU1/LRwGiOaNvOZ53V4XH0fOruN2KgokuPjL+gujcvlYv369axevZqXX34ZrVZLcnIy9957LwaDgYyMDLFESmgXHT5enXbUpQYURyN/5EFsePLYVeFoVSeNNhvDMjMJaoOKTvv27UOWZX7zm9+QlZVFQkIC1157LQaDgbFjxzYnyoLQ2kSnv+MOVtTxwLKd7Ck1cr/3GqZ7fYii9SQ/8Y94XP5n0sPC8fX2bvFWr81m46OPPkKWZdauXYvT6WTEiBHMmjULSZJYvnx5O/xEgtB17Ny+Gb8PZ5KlHuHjiNvRZd6Iw+VkQEoK0ZGR55XUqqrKtm3bkGWZJUuWUFVVRVBQELNmzSI1NZX77ruvDX8SQegCVBU+vAupdDvP+fyV7+ujePjqMEK9wWSxMiwjg+BWTJZLS0tZsmQJsiyzc+dONBoNYWFhZGVlMWTIEJYsWdJq5xKEs+n2CbOiKCz7voDH1xYynk286beMIFc1pT2upmnUYyQmpLe4PtntdqPRaJAkifvuu49XX32VHj16cOedd2IwGBg0aFA7/DSC0LXYHC6WL1rApMPPokgaPkt9CqIyCA8JITUx8bzWRp6ol7xhwwZGjRqFp6cnN9xwAwaDgfHjx+N5EZ04BaFb2fQS/LiUjwJu4T9Vg5k+KIi0cD31RiND+vVrlaVRJ+LV7XYzaNAgqqqqGDp0KP/+97+ZOnVqc0t5QbhUunXC3NBo5+nVOynb8y2rPBfTVy3C6JXIT0Mep+ew356x9kpVVXbu3Nm8eW/VqlVkZWVxxx13MHnyZK6++mpR8FwQWkn+kSq2v3s/t7lXUuTRm4KBD+IRHENK795EhYef06xyRUUFS5cuRZZlrrrqKubNm8fIkSN59913mTRpEkGt8MYuCN3K3pWon/+dfQEjubNqIqMSfRmb7EO90UhmSsp5lVU9XVNTE2vXrkWWZfLz89m7dy9arZa3336b5ORk+vTp04o/iCCcn26ZMKuqyvsbC1nzxVf8xb2Iqz12Y/UIZ3fSX/EbNp2k2LhT3ozNZjPz589HlmX279+PXq9n/Pjx6HTH/vnS0tJIS0trrx9HELqc1V9/T8SXs7lNs5c9YWM5mno7vXv3oVd09DmtVf7ggw946623+OKLL1AUhYEDB5KamgqAVqtl+vTpbf0jCELXU/wd6oqZHPZK5caq2xkQ5c2Mgf7UG43079OHmAsGU9MmAAANiElEQVSc9c3Ly+Oll15i+fLlNDQ0EBERwS233ILVasXX15cJEya08g8iCOev2yXMRVUmHnv/U26oX8RS3Te4PHzYnzADsmaSHJvQ3IWourqaw4cPM2TIEPR6Pf/617/IyMjg3nvv5aabbmoufC4IQuupa3Sw7L2XmVLxAl4aJ1uS7qYxaTzD0tJ+cU2kw+Fgw4YNXHvttQCsWbOGn376iQcffBCDwdCcLAuCcIEq81Fzb6VcimRSwz1clhDE9AG+WG2NDEpLI+o8NrSrqsru3buJiIigZ8+eHDx4kCVLlnDjjTdiMBi45pprmiekBKGj6Da/kVUmO29//gOBu95goWYter1CcfRE7Fl3Ep/UHz8fHxobG5tbZ3722WckJSWxb98+vLy8KC4uJjAwsL1/DEHoklRV5aNtB3CufYA/8hUlnr3ZkTmbiOQsBvTqhWcLO99PNAGSZZlly5ZRV1dHXl4e/fr1Y8GCBfj7+4sKF4LQGmoKUd6bRL1Dx022+7gmPYrrE7WoipvLBgw45w1+xcXFLF68uHnJxWOPPcYTTzzBhAkTqKysxKcN2tgLQmvp8gmzW1GRv96F8etXuUP6mACtlaLQK6jO/CO90i+nd2goAC+++CKPPPIIVquV2NhY5syZg8FgaH7DFcmyILSNKrOdNxYt4tbyefTSVLKzx2+xDp7JwOSUs24e2rVrF5MnT+bw4cN4e3szefJkDAYDffv2BSCgDcpZCUK3VHcI97s3YLLaucX+COOHJjE43E2Anx8DUlJ+tWkXHLu4HTt2LF988QUAI0aMYMGCBdx8880A6PV60bxL6PC6dMK8dX8xe1c8x41NqwjSNHI4aBg/Zd5OXOa1lO/fz1OPP86cOXOIj4+nd+/eTJs2DYPBwMiRI9FoNO09fEHo8j7Z/hM1ax7lQT6hXh/GD5lz6ZmVTc+IiFNi8OjRo+Tm5hIdHU1OTg69e/cmIyODp59+msmTJ4sGQILQFuqLcb49gUaLBYPzESZe3o9EfydxPWPom5CA7iyb3K1WKx9++CHbt2/nueeeQ6PR/P/27j6o6jqL4/j7IOZDKoolKojmE+UDZlqZ+QiVrhYm5ioPaWuaszs1Ndluu+nuks5Ymlmt0+ysuYWtKDZoK5Wu1VDWjKmpsauAmZgloIAJpoQol7N/cHNct2UJLv3u73Jef3GvPy/ncPnMPdz7/f2+DBkyhPHjx5OYmEivXr1+2j6M8YGAHJiPnyhl18alxJa9wS1yjs/bDePzwQ9Q1b4P727bRvq8p8jPz6d169aMHz+enj17EhcXR1xcnNOlG9MsfHOuitS0tUwvXE5kUCkHr51EzegnGBw1iDbed6zKy8vJyMggLS2NHTt2oKrMmTOHxMRE2rdvT2ZmpsNdGBPAinOpSo3j/Hff8aAuIn7UQHq0V6L7D6DrD1ylprq6mqysLNLS0ti8eTPnzp0jPDychQsXEhISwooVKxxqxBjfCKiB+asTpXy2+XlGl6QxXb7lQOsb+ecNyVx38ySua9mS7t27o6rExMSwaNEi4uPj7aNbY35i73/6LyrfWcgCPqKkZVf2DH2OniPi6dK5Mx6P59JxM2fOZPv27fTv35+UlBQSExPp27evg5Ub0zzo17upev0+zlwM4rEWKdx7axS9Ordh6IABtLtsnbGq4vF4CA4OJjU1lXnz5hESEsKMGTNISkpizJgxdqlVEzACYmA+XVbGvs0vcOPXqdwrZ9jpuYHN5TF8kp1Pq/c2kJU1D4CNGzcycuRIunXr5nDFxjQ/JWcqyFq3jJ+VvEJbqWJ/2DSujl1AdGQfdu7cyVNpaWzZsoWcnBzCwsJISUlhyZIlDB8+3E7eM+Yn8l3ONlpkzOaEpyPLQn7PtCERREV24/revWnpvXLFkSNHLp0gv2DBAubPn098fDydOnVi8uTJtP4RGwoZ4xauHphLS0+S+/cVRBekc6ec5bWCCFLzrmH3/myqqnbTu3dvkpOTUVVEhGnTpjldsjHNzvmLHt7e9g7X7/sjM+Uoea0Gcnbkr+nQfSCvrF7Nhg0bKCoqol27dsTHx1NZWQnAiBEjHK7cmGZElRPvvkCXT5ZwqKYH6ZGLiL++O9FRUZc2I1m1ahXr1q1jz549iAhjx44lIiICgNDQUHuNNQHNlQNz4fEvOZq5nCHFm5DjFWSH30TF4ESOUkxu5svMnTuXpKQkRowYYe9MGeMQVeXDPdlUvvs08dUfUh4UQnr7OXQecjcxo8dRWFjIqlWrmDBhAitXruSee+6xy0oZ4wCtruLwq/OJKnqTLB1OzqBHmTqoLz3Cwsg5cICu48YBsH79eqqqqli+fDkJCQmXhmVjmgNXDcxHDx/kxNZnaX3obd46cJ7puUJx+Xle/NPP+dW0ecRevMgfFi60y9MY47DcLwvJ27SESWc3cbrSw5PHonj/iyqys19k8uQvuDM2lsjISEpLS+08AmMcVFaUz6m1yURV5bIheAoMS6bDqWKeW7KELVu24PF4KC4upmPHjmzfvt3yapotVwzMh/Z9wJmsl+h36gOmpFZwsMRDUFAQ42NieG7WLKZOnUrL4OBL66uMMc4oPVPBx2+8wOiC1QyQMyRmdeKNTwrwePYwcOBAli5dSkJCwqXj7cXXGOfkZm0g/KMn6KYeXgt9hFK9hmXJCZw+fZrQ0FBmz55NUlLSpZxaXk1z1qgJU0QmAi8BLYA1qvrsFf/eCngdGAZ8A8xQ1WP1eeya6mp2vbWG99cuJ/hsEY+M6sjuLnGEDz1JcuxdPDBrFmFhYY0p35hmp6kye77qAjsyXqZ46/N8lFdCv7hoDt+8mGEhZUTcdoKkpCSio6NtiZQxP0KT5bXiDDlrH6dtzkaePHA1oWPnMGfqQ1ysqGD/rl0kJSUxYcIErvqBHTaNaa4aPDCLSAvgZeBOoAD4VEQyVTX3ssMeBMpUta+IzASWATP+32OfOH6USYM6kvVFBRdroE9EF8Y98yYjB9/IPxa0aWjJxjRrTZXZU8WF/HJsV7YeLKekQmnXtjXTRz3LXbGx3D7ZBmRjGqKp8nq2/BTPTO1D5oEysk/WEBRUyaKYVvSNjAQgPT29Sfoxxu0as53dLcARVT2qqheAdGDKFcdMAdZ6v84AYqUebzEVlZSxr/ACE+8YRfqmTeTmH2fkLbfRto0Ny8Y0QpNk9quCk6zfW06/AQP5yyurKTl1mgl33GHvJhvTOE2T12Nfsfi9bzjbtgcpixdTUFDA0ykpvq7dmIAjqtqw/yhyHzBRVed6b98P3KqqD192zEHvMQXe2/neY079wOM9BDzkvRktQUF5WlNzsUHF+a9rgP/q3eUCsSfwv756quq1jXkAX2b2irwOlqCgw1pTU9WY+vyQv/0O+Ir11fT8Oq9BLVrk13g8lUDDBgD/5E/Pvy9ZX02vXnltzBrmH/or9srw1eeY2jtVVwOrAURkb43HM7wRtfklEdmrqgHVVyD2BAHbl88ya3l1L+vLNZosr57q6kD6OQEB+fwD1pc/acySjAKgx2W3I4Ci/3WMiAQDIcDpRnxPY0zDWWaNcQ/LqzF+pDED86dAPxG5TkSuAmYCmVcckwnM9n59H5ClDV0DYoxpLMusMe5heTXGjzR4SYaqVovIw8B2ai9586qq5ojIYmCvqmYCfwX+JiJHqP2rd2Y9H351Q+vyc4HYVyD2BAHYVxNmNuB+Vl7Wl7sEVF+W1x/N+nIX1/XV4JP+jDHGGGOMaQ4asyTDGGOMMcaYgGcDszHGGGOMMXXwq4FZRCaKyOcickREfut0Pb4gIj1E5AMRyRORHBF51OmafElEWojIZyLyttO1+IqIdBSRDBE55H3ebnO6Jn9keXUfy2vzZpl1l0DMK7g3s36zhtm7DehhLtsGFEi4YhtQ1xGRbkA3Vd0vIu2BfcC9bu/reyLyODAc6KCqdztdjy+IyFrgY1Vd4z07va2qljtdlz+xvLqT5bX5ssy6TyDmFdybWX96h7k+24C6jqqeUNX93q/PAnlAuLNV+YaIRACTgTVO1+IrItIBGEPt2eeo6gU3BNkBlleXsbw2e5ZZFwnEvIK7M+tPA3M4cPyy2wUEwC/95USkFzAU2O1sJT7zIvAboMbpQnyoN1AKvOb9KGyNiFztdFF+yPLqPpbX5s0y6y6BmFdwcWb9aWCu9zbabiQi7YBNwGOq+q3T9TSWiNwNlKjqPqdr8bFg4Cbgz6o6FKgAAmKtn49ZXl3E8mqwzLpGAOcVXJxZfxqY67MNqCuJSEtqg5ymqpudrsdHbgfiROQYtR/txYjIOmdL8okCoEBVv3+HIoPacJv/ZHl1F8urscy6R6DmFVycWX8amOuzDajriIhQu1YnT1VXOl2Pr6jq71Q1QlV7UftcZalqssNlNZqqngSOi0iU965YwPUnjzQBy6uLWF4NllnXCNS8grsz2+CtsX3tf20D6nBZvnA7cD9wQESyvfc9papbHazJ1O0RIM37onIU+IXD9fgdy6vxI5bXerDMGj/iysz6zWXljDHGGGOM8Uf+tCTDGGOMMcYYv2MDszHGGGOMMXWwgdkYY4wxxpg62MBsjDHGGGNMHWxgNsYYY4wxpg42MBtjjDHGGFMHG5iNMcYYY4ypw78BbzlxrTeetl0AAAAASUVORK5CYII=\n", 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lVIaaMjv0lGM5lv4Uw8s97kYLZvYNoNrd7zazzwDj3F233RQZZsqryOiizIoMr+MZM/weglt1En69+vi7IyJDRHkVGV2UWZETpK97hosJTvJw4Hvufq+ZHXT3nLg2Ne4+rpt5byE4W5mxY8eePn/+/EHrvMho9+qrrx5w90mDuUzlVWRoDEVeYeCZVV5FetafvHa97W1PznX3PWY2GXjazHq6m9hbeHBv+3sBli9f7mvWrOnrrCInPTMrGYLFKq8iQ2CI8goDzKzyKtKz/uS1T8Mk3H1P+LUCeAw4E9hvZtPCFU4juKyIiAwz5VVkdFFmRYZXr8WwmY01s8yO7wmuP7keeILgnvSEXx/vfgkicqIoryKjizIrMvz6MkxiCvCYmXW0/5m7P2VmrwCPmNnNwG7gfUPXTRHpI+VVZHRRZkWGWa/FsLvvBJZ283oVwd1WRGSEUF5FRhdlVmT46XbMIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMLqczFsZslm9rqZ/SZ8PsvMXjazbWb2czNLGbpuikh/KK8io4fyKjK8+rNn+BPAprjnXwe+6e6FQA1w82B2TESOi/IqMnooryLDqE/FsJnlAe8CfhA+N+AS4NGwyf3A1UPRQRHpH+VVZPRQXkWGX1/3DP878E9ALHw+ATjo7m3h8zIgt7sZzewWM1tjZmsqKyuPq7Mi0ifKq8joobyKDLNei2EzezdQ4e6vxr/cTVPvbn53v9fdl7v78kmTJg2wmyLSF8qryOihvIqMDJE+tDkXuMrM3gmMAbIItmRzzCwSbr3mAXuGrpsi0kfKq8joobyKjAC97hl298+6e567zwRWAn909xuAZ4Brw2Y3AY8PWS9FpE+UV5HRQ3kVGRmO5zrD/w/4lJltJxjjdN/gdElEhoDyKjJ6KK8iJ1Bfhkl0cvdngWfD73cCZw5+l0RkMCivIqOH8ioyfHQHOhERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWCqGRURERCRhqRgWERERkYSlYlhEREREEpaKYRERERFJWL0Ww2Y2xsxWm9mbZrbBzO4MX59lZi+b2TYz+7mZpQx9d0XkWJRXkdFFmRUZfn3ZM9wMXOLuS4FlwOVmdjbwdeCb7l4I1AA3D103RaSPlFeR0UWZFRlmvRbDHqgLn0bDhwOXAI+Gr98PXD0kPRSRPlNeRUYXZVZk+PVpzLCZJZvZG0AF8DSwAzjo7m1hkzIgt4d5bzGzNWa2prKycjD6LCLHoLyKjC4DzazyKjI4+lQMu3u7uy8D8oAzgQXdNeth3nvdfbm7L580adLAeyoifaK8iowuA82s8ioyOPp1NQl3Pwg8C5wN5JhZJJyUB+wZ3K6JyPFQXkVGF2VWZHj05WoSk8wsJ/w+DXg7sAl4Brg2bHYT8PhQdVJE+kZ5FRldlFmR4RfpvQnTgPvNLJmgeH7E3X9jZhuBh83sK8DrwH1D2E8R6RvlVWR0UWZFhlmvxbC7rwVO7eb1nQRjm0RkhFBeRUYXZVZk+OkOdCIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsHoths0s38yeMbNNZrbBzD4Rvj7ezJ42s23h13FD310RORblVWR0UWZFhl9f9gy3AZ929wXA2cDHzGwh8BngD+5eCPwhfC4iw0t5FRldlFmRYdZrMezue939tfD7WmATkAu8B7g/bHY/cPVQdVJE+kZ5FRldlFmR4devMcNmNhM4FXgZmOLueyEIMzB5sDsnIgOnvIqMLsqsyPDoczFsZhnAL4FPuvvhfsx3i5mtMbM1lZWVA+mjiPST8ioyugwks8qryODoUzFsZlGCkP7U3X8VvrzfzKaF06cBFd3N6+73uvtyd18+adKkweiziByD8ioyugw0s8qryODoy9UkDLgP2OTu98RNegK4Kfz+JuDxwe+eiPSH8ioyuiizIsMv0oc25wLvB9aZ2Rvha7cDdwOPmNnNwG7gfUPTRRHpB+VVZHRRZkWGWa/FsLs/D1gPk1cMbndE5HgoryKjizIrMvx0BzoRERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYakYFhEREZGEpWJYRERERBKWimERERERSVgqhkVEREQkYfVaDJvZD82swszWx7023syeNrNt4ddxQ9tNEekrZVZk9FBeRYZfX/YM/xi4vMtrnwH+4O6FwB/C5yIyMvwYZVZktPgxyqvIsOq1GHb354DqLi+/B7g//P5+4OpB7peIDJAyKzJ6KK8iw2+gY4anuPtegPDr5J4amtktZrbGzNZUVlYOcHUicpz6lFnlVWREUF5FTqAhP4HO3e919+XuvnzSpElDvToROQ7Kq8joobyKDI6BFsP7zWwaQPi1YvC6JCJDQJkVGT2UV5ETaKDF8BPATeH3NwGPD053RGSIKLMio4fyKnIC9eXSag8BLwLzzKzMzG4G7gYuNbNtwKXhcxEZAZRZkdFDeRUZfpHeGrj7X/UwacUg90VEBoEyKzJ6KK8iw093oBMRERGRhKViWEREREQSlophEREREUlYKoZFREREJGGpGBYRERGRhKViWEREREQSlophEREREUlYKoZFREREJGGpGE4wzW3t/M/r5dQ3tw3dSmLtULkF1j4C7a1Dt54+amtrY+fOnaxevXq4uyIiIiIjTK93oJPh1d7ezr4DB2hqaTmu5Ryob+XX66t4ckM1NY1t3HZJHpcvGD/g5SUnJZE/dSrJ3g6Vm2HfWtj7ZvDYtw5aG4KGkxfA1FOOq+990dbWRklJCdu2baO+vp5rrrkGgKuuuoqnnnqK1tZWcnNzKSsrG/K+iIiIyOihYngEa2hsZO3WrVQfPEhycnK/53d3dtS088yuFl7b24o7LJ4c4f2npDM77TDbS2r7tbyk9mYy63aRWbuDzEPbMN8PlZugPSzUUzJg6hI47QMwbWnwmDiv3/3uSWtrKyUlJZSWlnLxxRcDcMcdd/DQQw9RXFxMW1uwtzs3N7ezGD7//PNZtGgRhYWFFBYWDlpfRERE5OSgYngEcnf2VlaybutWIsnJTBrfvz24zW0xXihpYNWWWoprWkmPGu+cl8llRRlMzYz2aRlJrQ2kH9rG2JotpB/cSvrBbaTV7sK8HYCWSAaedyqc9ZGw8F0G42dD0vGNvGltbWXXrl3MmTOHpKQkHn74Ye6//362b99OcXEx7e3tJCUl0djYSEpKCpmZmSxbtoxrr72WuXPnUlhYyNy5czuXd9tttx1Xf0REROTkpmJ4hGluaWHzzp2U7d/PuMxMotG+Fa8AlfVt/H5rHX/YUUdtc4z87Ci3nDmO82eNZUyk5yI1ueUw6Qe3MrZmK+kHg+J3TF0ZhgPQmjqe+nHzODj9POpzimjIKWJvyxguOussklNT+/0eW1paSEpKIhKJsHr1an7yk5+wbds2tm/fzq5du2hvb6ekpISCggKqqqqoqKjgtNNO4y//8i87C96ksOj+x3/8x36vX0RERKSDiuER5EBNDWs3b6YtFmPSuHGYWa/zuDsbK5pZtaWW1WWNAJyRl8YVRZksmpL6lmVEmqo6i96xB7eSfnArqQ37Oqc3p0+lIaeIqoLLaAgL39a0iW9dcevBPr2n0tJSHn30UbZv38727dvZtm0bJSUlPPfcc5x77rns2LGDBx54gMLCQpYvX87KlSspLCwkKysLgI997GN87GMf69O6RERERPpLxfAI0NrayraSEorLysjKyCCzD3tbm9pi/Km4nlVb6ig91EpGShJXLcjissIMJmVEwJ2Uhv1xRe820g9uIaWp6sgyMvKoG7+IitnvpSGniPqcItpTs/vV97q6Op555pnOQrej6L377ru57rrrKCkp4VOf+hTZ2dkUFhZy9tlnc+ONNzJlyhQArrvuOlauXNmnwl9ERERksKkYHmbVBw/y5tatNDc3M3HcuM7D/z3ZX9vGU1treWZnHfUtzsxxUT56VjYrxleRU/8K6cXbgiEPB7cSaTkMgJNEY9ZMDk8+Iyh6x82jIXsusejYXvsXi8XYVVxM6e7dlO3eTWn4eNuFF3LRmWdSVVXFVVddBUBOTk5nwdtR7J5xxhlUVlYyYcKEbgvegZwYKCIi0qu2FjiwFaYu7r1pe4yDja0cbGglNyeNtJQT89lUV1dHenp6r5/9MrRUDA+T+L3BmWPHMiEnp8e27s7afU2s2lLLtvJqFiTt5rYJezg3vZxpzcWkr9tJUiy4okMsKUpj1myqp18YDHMYV0Rj1hxikTE9Lr+pqYnysjLKSkspDa/WUFhYyLUrV9Le3s7111xDLBYDICsri/yCAjrK2ry8PF566SXmzp3LhAkT3rLs1NRUUgcwrlhk1IjFjvvEURHpWSwWo6W1tfMRc++hYTuRAxtJKX2BaNmLpJSvBm/nlWtfZM2eFg7UtXC4qY3DjW0camrjUGMbh8Ov9S3tnYu59/pFLMvLGrT+V1dXU7xzZ/AoLmbnzp3c+vGPs2jRIn756KO8/ZJLKIw78VtOPBXDw6Cyupp1W7fS2tr6lr3BzW0xKurb2Xe4habqcsbUbCe1Ziv5rbv4VvJu8sZUBA1robU5h4bsOVTM+QsasufSkDOXpswZeNJbT7pramqivLSU0tJSynbvJi09nWuuuw6Aa979biorKjrbZufkdBaw0WiUr/3bvzF58mTyCgrIzg6GUVQdDMYMJycnc9ZZZw3Jz0lkJInFYrS0tMCh3STteR3b+zpJe18n6cBWmv5+HSR3f7Jrc2s7JdWN7DxQT/GBeooPNLDnYBN7Dzdx3em5fPiCWW+Zp729nbLSUnbs2MGOnTvZsX07X/ryl0lJSelXn6ORCBEdfZFRpqm5mfKKCmoOHaK+oYGm5mbiy9/O793JqC9hQs06xte8yYSadUTb6gGoTMnj9cgFrGpazG/u30ArEQxIjxpjU4z0qJERNfIyjHnjkhmbEmFs1BgbNWoqSnj1UN83cN2dw4cOsbe8nD3l5ewpK+P0M89kweLFbFi7lttuvbWzrZkxacoUTjv7bJqAMdnZtLQN4U2wpE9UDA+xWCxGY3MzDY2N1DU0cKCmhopEFXhDAAAgAElEQVSqKrIyMkhJTeOZnQ3sqjxM6qFiJjTspKC1mAVJu3m7lZBlwQlxMYyD6bnEJi6mLKcwLHwLaR0zAeKGHjQ1NlK2PRjSUF9fz7vf8x4APnXrrTz/3HNH9WvZaad1FsMfufVWUlJSyC8oIK+goPPktQ4Xr1gxlD8ikRGpvb2d6r07adr5ApS/xpgD68g+vI2U1mD4UXtSlMMZs6kZfy4bn3+OOhtLZUOMfXUx9tcFX/fVt1PV4Ed9kI9PMyakJZGb5pRvfZV71j7J3vJyzr/4YrJzcnjqN7/hO9/8Jm2tR+7emJKSwmlvextTpk3rc/9j7uRNmcKSeYN3rW+RodTY1MTuvXspLivDzBiTkkI0EiFtzJhgmJ07qfXlNOx4mewDr5Ff9yYZbcGOmcrIVF5MeRvPJy/ifw4XUtE0jvSoccrUMXxwWhpLpo5hUkYySQM8P8XdOVhT0zlUMC8/n6WnnsqBykpWXn01tbVHrtuflJTE1MmTOf+881i2dCmfvO028vPzyZ8xg+m5uUdt1BowcWI3J6nLCaVieAi4OxVVVewsK+NwbS3uwYdhUlISmV7HrOZSKrZspGnvFv4itos5todkCz4um6NpHEifzb7sS9kzoZDYxHk0Zs3uHObQ2NBAWVkZ+7as5/yLLgLge9/5Dk889thRe3czMjN511VXYWace8EFLDrllM5iNz8/n8y4gvfKq68+YT8bkZEgFosRcw+y6R48b6qlvfx16ne+xJ5NLzG9YQtTvTJoj1FMHi9wOuuYzevtc9jUkkdDQwQqgB0O1HUuP5psTM+KMHd8CqdmVpFcu49li+axePY0NrzxGl/70pcoLyvrvFEMwIIFC5g9cyZLlyzhr268sfPDMy8/n0mTJ/d7TGFDYyOt2uMkI0Rn1tzxWIyGpib2V1WxdX8tz+08zKHGNqrrmmhqA7dkWtud1vYGJrbvZ3H7Rk5t38ByX880C04C3+fj+H1sMS/GFvJibBGVbZNJazMmZ0S4cPEYlk5LY+6EFJKT+l78ujs11dWUlZYSiURYuHgxsViMm2+8kV27dlFfdyTj7732Wpaeeirjxo/n8ne9i9z8fPILCsgvKDiq4M3Ozub6979/cH+YMuhUDA8Cd6etrY229nbaYzF2lZezY3cJ9VX7qSrbxpTGncxo28Wc2C4m2iEAFgL7bSLNEwvZN/ntNOYU0pBdSPPYaTQ0NlG2ezezZ80hEo3y+1Wr+OUjj1BWWnpUwfvMiy8yduxYJk2ezJlnn90ZxPyCAvLy8ztPWOvYAyySqNrb2zlYW8ubmzfT3NKCxdrIrC8hu3Y7yZWbGVOzlYJYKck4mUCbT2RD8lyeTL2C4mghJdE5xKLpRJKNlCRjcjLkJRuRJCNCO/XV+xmbnsac/KlE6iu579/vZtfu3bxQXt5Z8P7zl7/M6UXvIWfcOObMnctFK1aQF/cBOnHSJAAWL1nC4iVLhvGnJXJ83J3isjL2VlbS0tZGS3MzMfejTqJ2d3Ydcv79pTqa22Fs1MhMgQWRPZzGZpbENrK4bRMTvBqA2qRMtqSewrNpS6mecBozZs4lKyWJK5ONayPW5z2+7k5VVRV1tbXMnBUMUfr6V77ChnXrKC0t7Sx4zz3/fL75ne+QlJTEjFmzWHjKKUdtoE7PzQWCoYK33X77YP74ZBioGB6gtvZ2mpqbqaiqonTXFiJVWzlYvp3UQzvJbSnhci8l1YLDnK1EKIsUsDltORVps6lMm0NbZi5L50wnNTWV9WvX8tj3HmV3yY8oKy2l6sABAB55/HFmzppFS0sLsViMM84+m7y8vCCI+flgRlNzM1dceSWXX3klhFve7bEYrbFYMK7XHTejT/8mujkpwaHbq0CkRqM6+1VGvEO1tWwvKaG+fANZh7Yws3YnOYe3knV4GxEPTjqt9gzWxubwSsbZlKXN41DWPGbnTWXB5FSWmLEEaGttpampiYzMTBobGvjOf/wHpSUllJWWsmfPHtrb2vjwrbdy6lm3UF01lgOVlcwtKuLit7+9c+O0sKgIgNlz5nD3PfcM409FZGjtP3CAjTt2kJOZyZholPTU1Ld8Xhxqauf7fyjlvDG7ua2wlCmH1pFZtZZIczAMqWXMRGqnnMquiUupm7iUxqxZYEn05TQzd+fw4cOd57j88pFHeOXllykLz5lpaGhgblERP3v0UQBqa2sZN348pyxbRkF4BLWjUAb44le/Ojg/GBmxVAz3g8di1JZvpGLj/8HetWTWFTO5tpjZzUf21laTRWl0Fi+MvZLmnDlMnb2YssZUfvvkqnCs0R8p3f1jqquq+Pb3vsdZ55xDVVUVLzz/PHn5+Sw/6yymTp9Obl4eFolQdfAgZ19wAeddfDHJyckkJSWRlJREshmWlESSWedrZkZycjIp0SipKSmkRqMkJycHj4754r5aOK+ZdRa8PRXNXQvi+HlERoJYLEZdfT1tNbup2fYC9cVrSK/ZzLyG7WR4sLen0VNY57N4M/Z2NtocqjLnM2FqAVcuzGJmShKzwr/pRx9+mCeLizsvJ7h3zx6uvPpqbr/jDlLHjOH3q1YxZcoUiubPZ8U73kF+QQFLli4FYPyECTz4yCNA8KEc/zUWi3UOmwonBN+Hh4872na0cfcjbTpn6eFM+i6aW1rIGNv75RNFBlMsFmNLcTHZGRmkdjnhM9baRGv5OtrLXiO78k3+xBbGtjfDZmgam0fNtPOonbiMuolLaR47/ahzYrryuD3Nr7z8MqtfeqlzPG/Z7t1Eo1H+9/nnAVj7xhts27KF/IICTj3tNPIKCpg1e3bnsr7y9a8PwU9CRhMVwz1pOgwVG2H/Bti/gdi+9cT2rSerrZ4sIEYSZUnTebl1Dutbz2fz/laa2pJoOFQTXrHhCT76iU9w1elzOLhvE//97W8zafJk8vLzOff885mWm8vY7GwO1NSweNkyHn78cdJSUxmfk8O4rCzSxowJzgQPzwbXXliRI1pag/GF1WWb2LvpZZpLX2Ny/TbmxIoZTy3jgTZPYqvn81vOZE/6PA5kziM6cQ7pY6LUbH8T9hfTtP1JXtq9m1/s3s3M2bP51299CwcevP9+DtbUkJufz5yiIi645BJOWbqUg7W1xGIxfvrYYyQlJQVHTggLVDOqamrA7KiCNckMwo3HJMCSkoLn4fdGcD5B54ZtuFFrFhz6TUpODtrGFQYG3RYK1uUo0Ljs/t1ER2QgWlpbaWxqoqW1lfL9+6ltaOBg+1jWbtvExMMbmdW8mfltW5nHLlKsnZgbOyyf7ZMvI2PW6dROXNr9nU5Du0tKeP3VVzsL3d27d1NeWsqqZ54hPT2dl154gZ/95Cfk5uaSX1DA6cuXk1dQQHt7O8nJyXzxq1/Vzhs5JhXD7W1QtR0qNoSF70Z8/3rsUGlnkwbSWVUxnhcOFPBaRTLbq1o5UH2YqcvO470rb+SMia187p3BFRc6C94LLmDy1KnUNzQwYcoUfvW735GWlgbuRJKTyQivLTxt8mQy0tOH692LjHiNzc1UHapj6+a17N/8IpGKDeS27GSx7WKqNbAQaPVkSpILeH3MWexJncOO5nGUHW6nrqqC+gPl7C1/lqSk57jz61/HvZV/+sl3Wf/mm6SPHUtuXh5zi4pYsHgxdfX1JCUn84MHH2TihAmkpqR0FqYGRJKTiUajRCMRUlNSjjrKEo1EiEajJIWFrz585WTW2NzM9pIS9lZW0t7eTlJ7CzWlW6BiM5PqNnOGb+VGC6700EwKJWMK+XP6e6nOWUxK/jKmTAxuxFQN1B4+zNb1r3Re576j6L37nnsomDGDF//8Z/7t7ruJRCLk5uWRV1DA8jPPpDW84sqHPvxhPnLrrSQnJxOLxWgPj8C0tLZCa+tRR1bij8Y44cm04Qm1sVisM7feZYxzTwzo07Gajg3kLstsj8WOuQdcTozEKYbdoW4/7F8P+4M9vrH9G+DAVpLamznY5GyugpcPZrO6cgybqqaQNHEm0bNuZI/nUPr99+FtwRjDzPGTmJ2fxzsW53DpDAci/NePfsS06dMZk5YG0DlUYWx6OtOnTGFieO3elPADU0Teyt1pbmpk16Y1lG58iYbdb5DXvJ15lHCJNQHQQpTi6AyeajiVjbUZlNVHqDhYT2VlFV/46sfJcef/7rqLZ55+GgiurDJjxgyK5s9n/qxZZGZk8NMHH2RcTg6506frqItIPxyqrWXTjh00Ve5k3KHNFNVuJ1qxnsmNO4gSnCy6P2kK1dmncjh3CS2TT6E+czYVB6qDInfnbnY/+wBlpaW8/6//mlOWLuXVNWv4p09+EgiubZ+Xn09efj5tbW00t7Rw5rnn8sOHH2by5MlEIhFiBEVomzsHamoASGppwZKSiEajpCQnE4lEOocCwpEjKx1HXDqGGUYiEaJh+2gkEgxHjD+a07FxG8x81NGX7jZ649fT9bXO511+pmN0Y6phd3IWwy31ULGZ5j3raNj9Jla5kbTqzdQfrmF7dYxtVTHeqE7hcPI4Tj3nUjbHCvj+vffRUFMJBNcKzBw/iSUzsrjh3AKyUqB48l1MmTKFwrlzmThhAmPT0oiGYTMzTl+8mLTUVFKiUSLhOF0R6UVrI7H9m6jZ/jIHtr2C7V9HQWsx862VmW3OxpoILx+ayI9r89h12Pjoxz5C0qQ5fP973+eXDz3UuZisrCxmzZ7N/Jkzyc7O5l++9jVav/QlCgsLGT9+/Fs+jI51x0cROVpbcwONxS9Ts+lZ6ne+zLyGrYxrDy5x1ugprPXZrOJyDqUVMCZjImWVhyh9rYRz0vM5c958Nm/cyE0rV3YuLxqNkpuXR3V1NbX19cyaO5e77rmHabm5TJo8mUhy8pG9tu6cungxWZmZRJKTiSQnk5KSQnLcOTA6CiPHa3QXw7F2qNlFQ9laana+TmzfBpr3rKdybzk7q2PUNDl/c0YmWzyfDz3cwmvbjlwUG2ti6pw8pv7NTSzJifLJ9Ewy0lKZNXs2hYWFTJ00iZTwBLRIcjKpF7/tLScDiEg/1FVSv/sNakteo6X0DcZUbyS9toSSmja2V8dYV5XMqYsL2THtCp7cWMd/PfB4OPY22POTlZ3NLZn5nL5gEX/3t3/LVVdcwbyiom5vBT7h9NOH4Q2KnATa22jfv4n9m/5E7Y6XSa9az7TmYjJpJz3mrD44jkerp7D28CxS85cwfvmVLJsIH7v2iuAOjaGUlBTSMzOZPW8emTk53PrpTzM9L4/publMmzaNSCRCclIS2VlZ5E+dytvOOIPUlBQi4bCkaCTS+RksMtRGTzFcX4XvX8+hXW9QtukVdm16g8o9u/nrUyDdjLufaeY/VrdyqCnWOUs0JYXK6x9gctYYzov9Hxe1NTK/qIh58+YxY+ZMsjMzSQnH/0XfdfEwvjmRk0isnbq9W6jYuoamsjdpK3+T2tJNVByoYdnUZGaNS+J/SsZyy6+qOXD46Nusfv6Sv+SiCy7knNllkF3AnLlzWbxoEacuWXLUXZqmT5584t+XyMkmFuNQ+Wb2rP8TzbtfJbN6HVMat1N5qInt1TFaklLJKZjPH5Lfxe3f/wMVFZW0t9UCuwG49IpUblz5F8RiMd5z7bVMnT6dvLw85s2bx8yZM5mQk8OY1FSikQgrzj2X1JQUUqJR7cmVEee4imEzuxz4FpAM/MDd7z7uHjUdoq50HTvefIGta1ezNKOaKc3FPPXmfv7lhWa2V8c41Hyk+b5FHyVp2iJ2Lt3FWTnbOWVBEQsXLGDB/PnMnTuX7KwsUqJRuOqi4+6ayGg32JltOlxF+dZX2bflFUo3vMTUllLOythDzaFG/uaxRrZXxyivPVLuXvO+93LWGTfSnt/CaRUPUlg4lzlz5zJ/3jzmFxUxbdq04MPy7LO54dprj+/Nioxyg5pXd2I1JZRvfIF9G/9MzfbVpB0q5vxprWQDN/+6hT+WQNnBFtrag51KRacU8fW/vpupyXDG+kbS09OZPWsWRUVFzJ83j4KCAlLCWyZf8sMfkqLrz8soNeBi2MySge8AlwJlwCtm9oS7b+zL/E11NWx85f9oq9pJysEdbF+7mp8+s4nSqsajCt6ffaiIsbmLeTZ1PoezdjJ/wSzyZs3ljFPmcfrSUzhl8WIyxo4lvePe5SLSrePJbEtTI9vWvsjeLWvIbCghuXIT3/zVK5RXN7KtOsaesOD94HnTqH/nJeyZNp3y6BMUnj2XqxfOZ+GCBRQVFrJgwQImjB9PakoKn7l5ZS9rFUlcx/UZ29ZCY/k61q/+Ixl1u0ipXM/j//cGf9zWwLbqGMU1MVpjMH3CWO6541YaxxVSOv0Jpmcc5LzZsykqLKSwsJCFCxcye9YsUqNRLr/ggiF+xyLD53j2DJ8JbHf3nQBm9jDwHqDHoO7atonLlkxh74GDlFS3cLgZHvvLNC6fl87rB7NZvceZNDGX0+flMalgHnMWLmHW5SuYk5/HRSkppKWmdp4hKiL91q/Mlu7cytVnzWDv/gOUVTeyp9a54ZQo9703kxKbznO7nYysyRQtzWPF7HksO/1M3nb2WSxcuJC01FQ+dNu/KasiA9evvFaU7+L6i+axd+8+yqvq2FUTIzkJDnx2AtttBs9UTWJt3WEmzszj8kuKmDtnFgvmz+e0Cy8kJyuL6665vvOygSKJxvp6N6O3zGh2LXC5u38ofP5+4Cx3v7VLu1uAW8KnS5IikWKL+4R0cI/FWonF2tw9BsQYfSYCB4a7E0NA72vozXD3SSdiRX3JbJe8npIcjZZ0TAq/eiwWa1FeRyS9r6E3kvO6ODka3U2Q1eAyR8EHfMyDvAaZHX25HUm//8Gk9zX0+pzX49kE7G6Xz1sqa3e/F7gXwMzWtLe2Lj+OdY5IZrbG3fW+RomT9X31Qa+Z7ZrXtpaWk+7ndLL+/vW+TjrKKyfv71/va2Q5npHuZUB+3PM8YM/xdUdEhpAyKzJ6KK8iJ8jxFMOvAIVmNsvMUoCVwBOD0y0RGQLKrMjoobyKnCADHibh7m1mdivwO4LLvvzQ3Tf0Mtu9A13fCKf3NbqcrO/rmAaQ2ZP156T3NbqcrO/rmJTXTnpfo8uofF8DPoFORERERGS009WxRURERCRhqRgWERERkYR1QophM7vczLaY2XYz+8yJWOdQM7N8M3vGzDaZ2QYz+8Rw92kwmVmymb1uZr8Z7r4MFjPLMbNHzWxz+Hs7Z7j7NFIps6PLyZhXUGb7SnkdfU7GzI7mvA55MRx3S8krgIXAX5nZwqFe7wkQITipYTqwFfjYsd6Xmd1uZj8YyIrM7FkzazKz5wbW1QH5BLDpBK7vRPgW8BRwHXCQk+/9DYqTMbNmlgbcBywH1gNnc4zMKq8jxrcIfl/VwFJOzvd4XJTXUZlXODkzO2rzeiL2DHfeUtLdW4COW0qOducBY4AJ7v5egl96LoCZfc7MvhLf2N3v6riT0ADd6u6dN4c3s/Fm9piZ1ZtZiZld39OMFvi6mVWFj2903AXQzIrM7HEzqzSzajP7nZldALwL+AGQZGbfNLM9ZlZjZv9lZtG4Zdd1ebSb2beP0Zd/MLN9ZnbIzH5oZqk9tEsJtzB3mZmb2UVdpqea2XfNbH/Y71+bWW7c9AfNbK+ZHTazrRaclX0BcJ+7rwVqgPOP/SNPWCdjZq8FcoBx7v4+d68lzKzyOiLz+iEzyyLI7J0EG6+XufvBY//YE5LyOrryOs/M8ggyez+wUHkdfieiGM4FSuOel4WvjXYzgK3h5W9mAqcCL4fTfkvwhz6UvgO0AFOAG4D/NrNFPbS9BbiaYEttCfBu4MPhtByCa1fOC5e1GvgN8E8Et+ycQ7B1vhgoAk4DPt+xYHfP6HiE8zcCv+iuE2Z2GfAZYAUwE5hNEJyePA/cCOzrZtongHPC9zOdIHzx/yS+Bsx09yzgKuCLQD3wIzN7HUgDPnqMdSeykzGznXkF6JJZ5bUbw5zXrxD8TiqBHxHs8fyOmY09xvoTlfI6+IYyr48D/06Q2ZVANsrr8HP3IX0A7wN+EPf8/cC3h3q9PfRlF3AbsJagMLqP4A9sFVAL/C/BlmhH+18Q/KEcAp4DFoWv30kQlFagLlzuX3RZVxkwPe75F4EHw+9nEtxW8yZgN8F9vD93jH4/C3wo7vnYcP1Fca/9BLi7h/lfAG6Je34z8FIPbVeGfZsAXEQQgvfFTb8eKO1h3puAnYSX7Otm+s+Au+KerwD29eH3VgZc1OW1/wa+Eff8XcCWHuafF/6M24Gzwtd+EP7+Uofjb3EkP0ZKZocwrx8FXo3PrPLa7fThzOtegg/2NuAsguKuLb4/enT+vJTX0ZPX8WHf7gufbwFejZuuvA7T40TsGR5pt5S8BriUYCvsSoKg3g5MJNhT/vG4tquAQmAy8BrwUwB3vwO4C3gE+DPwH+7+qy7reYpgDNexnEfwh7QC+GczW9DH91AEtLv71rjX3gR62nJdFE7vS9trCfYwvUpwuC0L+Pu46QbkmVl2N/PeBDzgYUL62I8pZjahh/bHch9wrplNN7N0gq33VfENwkNODcBmgr+5Mnfv2Ht/f/h13gDWfbIbSZkdzLz+HBhHcAj5p10yq7z2rR8nKq97Cf6/lrn7y+5eDjQT/A7kaMpr90ZiXi8g2FC4zMx2Eey9XWxmD4bTlddhciKK4ZF2S8lvu/v+8Jf1J+Bld3/d3ZuBxwgOxQDg7j9099pw2heBpV3+SM8BNrn7Pd2spy+Hcu5090Z3f5PgD3dpH99DBsHWdLxDQGYf2x8CMjrGNXUIxzGdA9zo7jMJflc7gGQzm2RmUznyzyy9y7wFwIUcKTL72g+O0e9j2Uqw1V8OHAYWAF+Kb+DuHw2XfT5BUEvNrKP4XQE0ERzGkqONpMwOZl4h+CffXWaV1771g2P0+1j6m9dfERz6j89sezi/HE157d5IzOt3gL9197wwsw8TFMf/oLwOryEvhj0Y89NxS8lNwCPe+22bh9L+uO8bu3meAZ2XPbnbzHaY2WGCQ0AQbOECFACzgEvM7I3w8c64ZT0NXBA/GL4b8WN1GjrW3Qd1BHuA4mURHIrqS/ssoC5+C9PMJgG/B/7L3R+Ka7sNeB14g+Bw0P8QHL6q6LKODwDPu3txP/rd8X1P/T6W/yY8gZHgsNav6LLlCuDu7e7+PMHekj8DPzWztcAygsNVo2aA/4kywjI7WHkFmERwCLm7zCqvfesHx+j3sQwkr39HsJe7I7NpwH8OYN0nNeW1R6Mhrz8hKFqV12F2Qq4z7O6/dfcid5/j7l89EescBNcTHJ55O8EA95nh6x1be7sJDt0scfdl4eO3HTN7cPbrWobmigVbgYiZFca9thTo6R/gBo7eKj6qrZmNIwjqE/G/H3d/1t3f6e63unuuu88GqgjGOLV3WccHOPZWa0/92O/uVb3M152lwI/dvTrcs/Bt4Ewzm9hD+wgwxt2Xu/sSgnFoKQRjtqSLUZjZ3vIKUOnu1l1mldc+9+NE5nWOu7/h7suBywn2NK0ZwLpPesrroDoheQVw99+7+yzldfjpDnQ9yyQY81JFcMjirgEs40ngnb226id3ryfYUvuSmY01s3MJ/rH8pIdZHgA+ZWa5ZjYd+DTwY4Dwcii/A/7s7m+5WHvHPBY4G/gCcEeXNm8jGDD/lrNc7ejLtjwA3GxmC8N/EJ/v6Ed3LLi8y5jwaYqZjYk79PQK8AEzyw73DnwU2OPuB8xsspmtNLOMcA/EZcBfAX+MW/xFwB/DoMvop7yivMqoobyivI4kKoZ79gBQQjDmZSPw0gCWMZSXgPkowWGICuAh4O86Do2Z2flmVhfX9nvAr4F1BBfEfjJ8DeC9wBnAB+3o6xkWhNPnEBy+qSfYMv2Mu/++S19uAn4Vbq13smCMVF24Xtz9KeAbwDMEP9sS4oJvwV2GbohbxBaCQ2u5BP9QGgkuuQPwjwRjfrcRXM7lneF7gWD4w98RnFhSA/wr8El3fzxu2TcA30VOFsprQHmV0UB5DSivI4R5jycmymAws53Ail7G+vS2jN8TnCizxt0vHrTODTEzu5HgcjmfHe6+xDOzU4B73X3U3CpS/j97dx4fV13vf/z1ncm+r83adEtaS9lKSym0LApelp8oigurXq8KXFcuoqCCiMAVFZfLdQPkKsiiiHi5oKKo4MLSBVq6t0nTJE2afV8ms5z5/v6YSUhK0mabLJ338/HII8nMWb6nzWfO53zP93y+00PxqniVuUPxqnidKkqGI8wYcymh+nw7ZrotInJkileRuUPxKlNFybCIiIiIRC2NGRYRERGRqKVkWERERESilpLhWcIY82VjzE8nuO6Lxph+Y8zfp2L74TIvPzPGtBtjNk6kTVMhXPpljzFm3ky1QWRApGP0WKP4ldnoWInjyZzTjTEnGmNejnQb5xIlw1MsXPev9CjLfMUYc+fQ16y1/2mt/fgkdv1pa+1Zo705zu2vJzS/fLG1ds3hb4ZrDO41xnQaY5qMMQ+F6ykOnAAfNMZUG2O6jTFbjDGjziFvjPlXY4xzWNmZc8Jt9gL/A9w0xnaLHNVsiVFjzCPGmHpjTJcxZp8x5uND3ltrjHneGNNmjGk2xvzaGFNwhPYOnKQHYmjUyWSMMRnhmG0Kf31tyHvzjDGPG2MOheP7JWPMaUc6KGPMKcaYv4f322iM+RwofiWyZlEcLzfG/DUcLxXGmPcOeW9ccRxe5zJjzG5jTK8JzdA34sQikzmnW2u3AR3GmIvHuP4xT8nwzIhkfcSpsACoChcfH8lLwDprbTqwmNDsMwMfODGE5ig/m9DMQrcCTxhjFh5hf69Ya1OGfL045L3HgI8YY+InejAiEzAdMfoNYKG1Ng14N3CnMWZV+L1M4H5CM3MtIDSl6s+Osr1PD4mhZUdY7nuEJjpYCKwBrjbGfDT8XgqhgvurgCxCtU9/Z4wZcSpbE5qR6jlCdVWzgVJCs2Hxn6YAACAASURBVG0NUPzKTIpoHBtjYoCngWcJxcs1wCPGmKXhRcYVx8aYdwLfBD5KaGKSs4DKKWjqSOf0R4Frp2DbxwQlwzPAWrsFyDWh2WoAMMZ8zRjzSPjnheGr3o8YY2qMMS3GmK9MZp9j3b4x5mPAT4HTwz09t4/Q/oPW2pYhLzmEToJYa3uttV+z1lZZa4PW2meBA4ROruNmrR0o7L12IuuLTMR0xKi1dueQGZps+GtJ+L0/WGt/ba3tstb2AT8A1k3FsQEXA9+y1vZZa6uAB4F/C++30lr7XWttvbXWsdbeT2ja8tGS6xuAP1prH7XWeq213dba3UOOUfErM2Ya4vhtQCHwvXC8/JVQZ9HV4f2PN45vB75urX01fP6ss9bWjbTgFJzTXwTO1YVqiJLhmfMcMOrwgbD1hE5C5wJfNcYsn+I2vGX71toHget4s7f2tpFWNMasN8Z0ErrSvRT4/ijL5QFLGX1ed4CV4eDdZ4y5NXy1PdRuhs+5LjIdIh6jxpgfGWP6gD1APaGerJGcxZFjCOAb4Th6ybw5Reuouz7s5+NHad/JhJLhilG2sxZoM8a8HB5y8Yx5c3atAYpfmUmRjGMzymsjxhNHiGNjjBtYTSh5rzDG1BpjfmCMSRxjW2Ac5/Rwku1n9AvdqKJkeOaM5fbN7dZaj7X2DeANpv6EMuHtW2v/GR4mUQx8G6g6fBkTmtP8UeAha+2eUTb1d0IfHPMIJdWXA184bJluIGOsbROZIhGPUWvtJwndDj0TeArwHr6MMeZE4Ku8NS6GuonQkKUiQrdlnzHGLBll2eeAm40xqeExl/9GaNjE4ftNA35B6Bg7R9lWMaHpYj8HlBC6C/T4YcsofmUmRTKO9xCasvkLxphYY8y/EBoiOFI8HS2O84BY4P2EPg9OBlYCt4yxLRM5DsVmmJLhmfM8cFY4YRxNw5Cf+wiN5zsqY8yV5s0Haf4w1dsfKnx1+Rzwy8Pa4CJ0IvUBnz7C+pXW2gPhW0Lbga8T+jAYKhXoGG/bRCYpYjE6VPj26j8JJZb/PvS9cLL6B+Bz1tp/HGEbG8JDFLzW2ocI3aq9aJTFPwt4gHJC4x0fB2oP228i8AzwqrX2G0dovgf4rbV2k7W2n9Bt3jOMMelDllH8ykyKWBxba/3AJYSS7Qbg88ATvDWexhLHnvD3/w4PU2oBvsvocTyS8R6HYjNMyfAMsdZ2A9sIXQFO9bYfHfIgzdFuD02FGMJjHSFUxoXQOMQ84NLwB8ZYWd5662k5oatckWkTyRgdxeFxtAD4M3CHtfYX49zWSHEUesPaNmvtldbafGvtCkLngcESiuExhP8L1HH0B2y2hfc1dL8ctm/Fr8yYSMextXabtfZsa222tfZ8QndohsbTmOLYWttOKImelmmBw+Oo44BRK89EEyXDkRFnjEkY8uUeZbnfMb6rvhkTHpx/TvjnK40xJSZkAXAX8Jchi/+Y0AnwYmut561bG7bdC8PjijHGvI1Q9Ymnh7xfROgp3Ven8ngk6s1ojJpQCbPLjDEpxhi3MeZ8QkOE/hp+vyj88w+ttT85yrYyjDHnh48jxhhzJaGxiX8Mvz/wcM3C8O9LjDHZ4f1eSOgJ+DvD78UCTxLqpfqwtTZ4lEP5GfBeY8zJ4XVvBf5pre0YchyKX4mUGT/XmlDN3gRjTJIx5kagAPh5+L0xx3HYz4DPhD8fMoHrCVWqGNjX4Hl4CpwD/HXIQ7xRTclwZOwkdDIZ+ProKMvN9hJrABhjioEeYHv4peOAl8OvvUToyvIT4WUXEOpNOhloGDJc48rw+yXh3wcesjkX2GaM6SX07/EU8J9Ddn8FoTHHCliZSjMdo5bQkIiBagv3ANdbawcuBD9OqIfptiEx1DOwsgkV3B8YAhVLKJltBlqAzwCXWGsHenzmA9WEenohVNllO6Hxgt8ArrTWDjzUcwbwLuBfCNUhHdj3meH9njm0HeGn579MKNloIlRV5oohx6n4lUia6TiGUOWIekJ//+cC7xzy9z6eOAa4g1Bpw32EHjzdQqizaaTz8GRdCYwlQY8Kxtpp6ZGXURhjKoFzrbUHJrGNPwGnA5uttW+fssa9uf2rgBXW2i9N9baPst94QrdXz7LWNk3nvkUGzIUYPcq+bwGarbX3TfN+Fb8yaxwDcTxl52FjzAnA/dba0yffsmPDmJJhY0wVoV4EBwhYa1cbY7KAXxEqJl0FfDA85kXGwRhzKbDXWrtjptsixwbF69RSjEqkKWYjT3EsRzKeZHi1HTLRgjHmW0CbtfZuY8zNQKa1VtNuiswwxavI3KKYFZlZkxkz/B5CU3US/n7J5JsjIhGieBWZWxSzItNkrMmwBf5kjHnNGHNN+LU8a209QPj7vEg0UETGTfEqMrcoZkVm0OHT3o5mnbX2kDFmHvC8MWa02cTeIhzY1wAkJyevetvb3jaBZoocm1577bUWa23uFG9W8SoSARGKV5hgzCpeRUY3nngdUzJsrT0U/t5kjPktsAZoNMYUWGvrjTEFhMqKjLTu/YSmB2X16tV28+bNY9mlSFQwxlRP9TYVryKREYl4hYnHrOJVZHTjidejDpMwxiQbY1IHfiZUf3IH8H+E5qQn/P3pkbcgItNF8SoytyhmRWbeWHqG84DfGmMGln/MWvucMWYT8IQx5mNADfCByDVTRMZI8SoytyhmRWbYUZNha20lcNIIr7cSmm1FRGYJxavI3KKYFZl5mo5ZRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpKhkVEREQkaikZFhEREZGopWRYRERERKKWkmERERERiVpjToaNMW5jzBZjzLPh3xcZYzYYY8qNMb8yxsRFrpkiMh6KV5G5Q/EqMrPG0zP8OWD3kN+/CXzPWlsGtAMfm8qGicikKF5F5g7Fq8gMGlMybIwpBv4f8NPw7wZ4B/BkeJGHgEsi0UARGR/Fq8jcoXgVmXlj7Rn+PvBFIBj+PRvosNYGwr/XAkUjrWiMucYYs9kYs7m5uXlSjRWRMVG8iswdileRGXbUZNgY8y6gyVr72tCXR1jUjrS+tfZ+a+1qa+3q3NzcCTZTRMZC8SoydyheRWaHmDEssw54tzHmIiABSCN0JZthjIkJX70WA4ci10wRGSPFq8jcoXgVmQWO2jNsrf2StbbYWrsQuAz4q7X2SuAF4P3hxT4CPB2xVorImCheReYOxavI7DCZOsM3ATcYYyoIjXF6cGqaJCIRoHgVmTsUryLTaCzDJAZZa18EXgz/XAmsmfomichUULyKzB2KV5GZoxnoRERERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqKRkWERERkailZFhEREREopaSYRERERGJWkqGRURERCRqHTUZNsYkGGM2GmPeMMbsNMbcHn59kTFmgzGm3BjzK2NMXOSbKyJHongVmVsUsyIzbyw9w17gHdbak4CTgQuMMWuBbwLfs9aWAe3AxyLXTBEZI8WryNyimBWZYUdNhm1IT/jX2PCXBd4BPBl+/SHgkoi0UETGTPEqMrcoZkVm3pjGDBtj3MaYrUAT8DywH+iw1gbCi9QCRaOse40xZrMxZnNzc/NUtFlEjkDxKjK3TDRmFa8iU2NMybC11rHWngwUA2uA5SMtNsq691trV1trV+fm5k68pSIyJopXkbllojGreBWZGuOqJmGt7QBeBNYCGcaYmPBbxcChqW2aiEyG4lVkblHMisyMsVSTyDXGZIR/TgTOA3YDLwDvDy/2EeDpSDVSRMZG8SoytyhmRWZezNEXoQB4yBjjJpQ8P2GtfdYYswv4pTHmTmAL8GAE2ykiY6N4FZlbFLMiM+yoybC1dhuwcoTXKwmNbRKRWULxKjK3KGZFZp5moBMRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRERGRqKVkWERERESilpJhEREREYlaSoZFREREJGopGRYRiVLBoKWjzzfTzRhdTxP4PTPdChE5xsXMdANERORNjuPQ6/EQDAandLtd/QEqmnrZ19RLeVMv5c2h78flp/Dg1SdN6b6mgqv9AMlPXYWTv5K+//cDAIwxpKWkYIyZ4daJyLHkqMmwMWY+8DCQDwSB+621/2WMyQJ+BSwEqoAPWmvbI9dUETkaxevcFHAcurq7aWhpoa6piUAgwETTPSdoaeoLUtcVpLY7SF23Q11XkLZ+O7hMUiwUp7pZW+RmUbqPV7dunZoDmSJpXeWseuN2HCyvpZ5F50D7jOHM1atJTkyc2QZOIcWsyMwbS89wAPi8tfZ1Y0wq8Jox5nngX4G/WGvvNsbcDNwM3BS5porIGChe5wi/3097dzcNTU3Ut7QQtJYYt5uUpCRi3O4xb6emw8f2hn6q2/1Udfio7fDjD3cquw0UpsVyXH48CzLiWJARS0lmLFmJ7lnbu5pe/zJLtnyVQHwme9Z/h5jUErLD77V2dGCtPeL6c5BiVmSGHTUZttbWA/Xhn7uNMbuBIuA9wDnhxR4CXkSBKjKjFK+zX09fH/uqqmhqbcVaS1xsLOkpKbjHkQAPqGj18tU/NeIPQnqCiwUZcVywLCGU+GbGUpQWS6x7dia9I8mt/F8WbPkufRmllJ/xLfyJOTPdpIhTzIrMvHGNGTbGLARWAhuAvHAQY62tN8bMG2Wda4BrAEpKSibTVhEZB8Xr7OM4Dq/v3Ik/ECAzLQ2Xa+LPMHf2O9zz9xYyEt3cfl4euSlz+BEQG6R4x30U7HuUjvzT2X/a7QRjkma6VdNuvDGreBWZGmP+JDbGpAC/Aa631naNdT1r7f3W2tXW2tW5ubkTaaOIjJPidXZqaW+nx+MhLSVlUomwE7R8758tdHmDfOGs3DmdCBvHx+KNt1Ow71GaFr2H8tO/Ea2J8LhjVvEqMjXG9GlsjIklFKSPWmufCr/caIwpCL9fADRFpokiMh6K19nJWkt5dTUpU/Dw1y+2dLCz0cu1a7JYlBU3Ba2bGW5fF0v/eQPZtX/h4PHXUb3yRnDN3cR+ohSzIjPrqMmwCT1l8SCw21r73SFv/R/wkfDPHwGenvrmich4KF5nr7bOTrp6ekhMSJjUdv5xoJff7enmwmUpnL04eYpaN/3ie2pZ/uK/k9K2k/1rbqNh2VUwSx/qiyTFrMjMG8sl+DrgamC7MWag/s6XgbuBJ4wxHwNqgA9EpokiMg6K11looFc4aZKJ8IE2Hz/Z0Mby3Hg+fErmFLVu+qU2vU7phlsA2Lf+u3TnrpzhFs0oxWw08/dD7OQ+F2TyxlJN4p8wasnLc6e2OSIyGYrX2amju5u2jg5ys7ImvI1ur8M9/2gmJc7FDWfmEOOam72ouZX/S8nW7+FNmU/5GXfjTSme6SbNKMXszLDW4vP7p3y7exq6ue7RrXzrfStYs+jI8e7e+BNcbzyO/8rfQtLEPhti3O4JVaKR4aJvcJaIyDTbX1NDQnz8pLbx6JYO2vocvv7OPDIS5+DJLxigZNsPyNv/JB15a6k87Ws4sSkz3SqJUtv27qW+uXnK61ZvOuSnvrOfPeW76WkcPU7zG//Byp3foj53HVvf2ANm/A/UWmtZUFjIirKyyTRZUDIsIhJRAcehua2N7IyMSW1nX4uPkwoSKcuZXFI9E9y+bpZsuI30po00lH6Igyd+EswcTOjlmOA4DvXNzWSlp0/55DM9Bzsx9LO8KIv4mJET3JTmrSzb/T26s0+kbt3XyXFPLKZ7+/rwO85kmithSoZFRCLIHwhgjJnUSTdoLQ09AU4unHtjC+O7D1L28k3E99Zx4JSbaVn0rplukkS5QDiBjMQsjHVdfnKS3aMmwgldByh75Wa8yYWUn3E3doKJsEwtJcMiIhHkn4Jxia19Dn7HUpA6tz6yU5s2U/rqrVjjYt+Z34/2B+VklvAHAhHb9qEuP0VpsSO+F9fXwLJ/fp6gO5596+7BiUuLWDtkfObWJ6uIyBwzFSfehu7QNvJTRz7JzjrWMm//U5Rsu5f+lPmUn/FNvClFM90qEQACEUqGg9ZyqCvA8nlvvYMT421n6T9uwBXoY8/ZP8CXXBCRNsjEKBkWEYmgqTjxNnSHepfz50DPsCvQz4It3yan5o905J/B/jW3EYydu/WQ5dgTqZ7htj4Hr2MpShsepy5/H0tf+gLxfQ3sPfN7eNJLI7J/mbjZ/8kqIjKHeX2+SW+jvjtArNuQnTS7HzqL76mj9NUvk9hZSe1xH6f+bR+e0FPyIpEUqWS4rit00Vo4ZJiEcXyUvvplkjrKKV97Fz05J0Vk3zI5SoZFRCLI6/NNug5ofXeA/JQYXLN4hrb0+pdYvOkOwLBv3bfpyl87000SGZHX54vIw3OHukJJdlF6OBm2Dos33UF602YqV3+FzsL1U75PmRpKhkVEIqjf68XtmlzvaEN3YPY+PGcdCnf/nKLdP6M3YykVa+/El1w4060SGZXH6yUmAhNVHOrykxhjyEhwgbUs2PJdsupeoOaET9G64MIp359MnVn66Soicmzo9/kmdeINWktjt5+Vs7CsmtvXxeKNt5PRuIHmBRdRvfLzKhUls16fxxORZLiuK0BheiwGmL/tB8w78DT1y66icenlU74vmVpKhkVEIsjj9U5qmERrr4M/CAWzrJJEUvselrx6K3GeZqpW3kjzovfALB7GITKgq6eHxEnOCHk4byBIXaefFXnxFO16gPyKX9G45P3Urrh2SvcjkaFkWEQkgnxeL6kpE592uD5cVm3WDJOwlryKX1O8/UcEErLYc84P6c1aMdOtEjkqay09fX34/H5Sk8df4cRay6ZaD3Vdfjo8Qdo9Dh39Dh0eh8aeAEELHw48ReGeh2laeDE1J31OF4hzxCz5dBUROfY4jkPAcXBNYszwQFm1grSZ/7iO8XawaPN/ktHwMu0FZ3Jg9Zc0cYDMan6/n5qGBto6Oujq6cEXCBA7jjs1Hn+Q5/Z2s6vJy/aGfhwbej0xxpCR6CYj0c3CzDjOWJDEe33PsL7mYVpKzqf6lBuVCM8hM//pKiJyjPIHAthJbmOgrFpm4syWVUtp3sqSTbcT4+2g+qTraVpyqU72Mms5jsOB2lrKq6vBWpISE0lKTCRtHIlwc0+AL/6hgR5fEIBVRYnkJrv54InppMYP305u5f+ycMt9tBWdw4FVXwIzu8sgynBKhkVEImQq6pnOeFk161C452EKd/0Mb0ohu99+H30ZS2emLSJjVHXoEHsqK8nOyCAmZmKpzjN7uvAEgtywPocT8hNIiR/5Dk9O1bMs3HIPHflnULnmNnAptZpr9D8mIhIh/kBg0r2nDd3+N+uWTrNYTwuLN91OWvOW0K3fkz9PMDZpRtoiMh71jY1kpKVNOBHu6nf4S0UvZy5M5vQFo//N51Y+zcIt36Yzbw0Va+/AumbXg64yNkqGRUQiJOA4TLY/t7nXYWVh4pS0ZzzSG15h0aa7cDn9VK7+iuqkypzh8Xrp7usjJzNzwtt4bl83PsfynuNGHxOfu/+3LNz6HTryT6di7Z0qKziHKRkWEYkQGwxOehtO0BLrnr4hEsbxUrzjPvIrnqAvfQn713yd/rQF07Z/kcnq6Ooa9x0Zjz/I5loPu5q8bDnkobXPYWVhAsWj3JWZV/EkC974Pu0F69h/2h1Yd9xUNF1miJJhEZEICdrJPj4HQQuuacqFk9t2s2jznSR2V9O45FIOnvBJ9XbJnNHW2UlFdTUt7e2kJI1vOM8PX2llw0EPADlJbk4pTODDq0buWc4rf4KSbffSXngm+0/7uoZGHAOUDIuIREgwGJxUQmytxRL5ZNgEAxTseYjCPQ/jT8hm7/rv0ZV3amR3KjLFXt+5E2MM2RkZ4ypn6PEH2VznYV6Km6+/M4/spFFSI2sp2PsLinfeT1vROVSu+RpWD8sdE/S/KCISIcFgcFJVIILhPNpEsJJEQtcBFm+6k+SOvbSUnE/NSdfjxKVGbH8ikeD3+wk4DtkZGeNet7bTjxOED5+SecREeP72H5Jf/ktaSs6natWXlAgfQ/Q/KSISIU4wOKlEdqBPOSI9wzYYmklux304MUlUrL2L9qKzI7AjkcjzeL3YCd6FqWoPTWyzMHOUcb/BAAu3fJvcqt/RuOT91Jz0WTATn0hHZh8lwyIiEeJM8gG6gZ7hqT7txvXWs2jzXaS1bKW9YD1Vp3yRQELWFO9FZPp4fb4JX3hWt/tIjDXMS37rRBnG8bF44+1kHfobdcs/yqHl/6bJZo5BSoZFRCIkOMmpmAd6uqZswg1ryan+HSVv3AtA5aovh0qm6eQuc5ynv3/86/iDvF7n4fVDHhZkxL0lmXYF+ih9+UukN79GzYmfpbHsg1PVXJlllAzLoPpOD/NSE3BP16PrR+MEoKsOMlXWSeamgONM0Zjhybclrq+BBVu+Q0bDK3TlruTAqq/gS86f/IZFZlhLezsHamuJjxt7ebODnX6+9nwjXd4gWYluLlkxvJ6w29fF0pduJLl9r+psRwElw8eQgOPQ3ds74fWvuH8zi3KS+M6lx01hqybAWmKqXiTp73dB0E/Xh/8M7omVrkmMjychXqWhZGYEJzhm2BsIsq2hn4oWHzDJMcPWIa/iNxTtfAAg1MNV+n6NeZRjQldPD5u2bycpMZGUxLFNTuMNBPnGC024DNx27jyWz4sf1gkU19dI2Us3ktBTR8XaO+koPDNSzZdZQsnwMeTAwYPsraoixv3WcU9H09jrcKDVw6l5QTa88UYEWjc2qd2VLKv4HzLbttKbWMC+0n+lcfvOCXWNBRyHxcXFHFdaGoGWiozOWkt9czONra3j7hmuaPXy3y+3cqgrgAGK0mJYljuxC7rEzv0sfO1uUtp305F/OtUnf169wXJMqW9uxu12k5SQMOZ1Nhz00NzrcOOZORyfP3y9xI5ylr70BVwBD/vW3UP3vFOmuskyCykZPkb4/H4qa2vJzsiYUDL8cn0XAGeVZZOdMv1/FnF9jRTtfIDsmj/ixKVSc+JnaVryXqwrluwJbrO7t3fCTxeLTJS1lvKqKvZVV5OSlDSuk/SWQx7ufrGZzEQ3Xzw7hxPyE0iIGX8PrnG8FO7+Ofn7HsOJS2X/mttoKz5PY4PlmOLz+zl46BCpY5hgo8PjUN/t59UaD7/f201avIvj8oZfZKY1vErphltxYlPZc86P8KQviVTTZZZRMnyMqG9qIhgMTigRBthc66EkI5a8aU6EXf5eCvY+Qn75rwBoWHo59cuuVp1TmbO6e3upqKkhNzNz3A/PPb2ri+wkN9++qIDkuIkNY0htfp2Fr3+LhJ5amhdcxMETPoUTnz6hbYnMRj6/n47ubmrr6wkEg8TEjH7equ/286NX2tjT7B18rSA1hjv+JY/U+DfPlzkHnmHhlnvoS1tM+bpv40/MiegxyOxy1MzHGPM/wLuAJmvt8eHXsoBfAQuBKuCD1tr2yDVTjsRxHPYfPEhaSsqE1u/2Ouxp9nLJcWlHX3iKmGCA3ANPU7jrZ8T6Omid/y/UrrhGt3CngGJ2ZrV2dOB2u8edCO9s7Gdno5crTk6fUCLs9nUxf/uPya16hv7kIvac+X26560e93Zkeilex6fP4+HVrVvx+v3ExMSQlX7kC73v/aOFlj6Hy05KpzQ7jnnJMeSnxrw5lt8GKdr5AIV7f0FH3mnsP+0OgrHjm8pZ5r6xfOL+HLjgsNduBv5irS0D/hL+XWZIR3c3Xp+PuNiJPWS25VA/QQuri8f28MGk2CCZtX9lxfNXs2Dr9/CkL2LnO35K5ZqvKhGeOj9HMTsjrLXUNTSMa2gEgBO0/PCVVvJSYrhw2TjvitggOVW/44Q/XUFO9e+pX3olO897SInw3PFzFK9jVtvYiN9xyMnMJCM19YgPqB7s8HGg3c8HTkjn0uPTOakgkYK02MF1XIE+lrx6K4V7f0HTwoupOOObSoSj1FF7hq21fzfGLDzs5fcA54R/fgh4EbhpCtsl49AW7omaiKC1vFTVS0aCiyXZYy9LM27Wkt7wMkU7f0pyZzme1IXsO/1uOgvWaRzjFFPMzpzu3l66+/rIycwc13o7Gvtp7nX4j/XZ4xojnNS+lwVbv0dK2w66s0+g+uQb8GSUjbfZMoMUr+PT2d095gpBWw6Fag+vmf/Wjp74njpKX/kyiV0HqDnxMzSWflDnoig20QGiedbaegBrbb0xZt5oCxpjrgGuASgpKZng7mQ01loaW1rG3RMFoVl37t/Yxr4WH5cclzZ1hf0Pk9q0meKdD5DSCCp+OgAAIABJREFUtpP+5EIqT72V1vnngZlYAi8TMqaYVbxOTmd394TWe63OQ7zbsLp4bL1Sbl8XxTvvJ7fyaQLxGaE6qCUX6GR+7FC8jsBaS1dvL0lHSYadoOVgp5/f7+0mN9lNVuLwc01q02ZKN3wVrGXf+u/QlXdqJJstc0DEn5ay1t4P3A+wevVqPdo/hay1VFRX09nTw7zssddc8AaCPLm9i2d2d5EU5+LTp2dx1qLkKW9fSut2inY+QFrz6/gS53HglC/SuuAirEvPbc5WitfJ6ejqGlfh/wH7WnyU5sQR5z5KMhseElG84z5ifF00LbmUuuM+pgdOo1Q0xavf76extRWv10ta8ujnq4pWL9/5e2icMMB1p2UNGR9smbf/SUq2/YD+lPmUn3E33pTi6Wi+zHITzUoajTEF4SvWAqBpKhslR2etZe+BA4NPrY/V1kMeHtjURlOPw9sXJ3PVygzSEqa2hzapfS9FOx8go/FV/PFZVJ/0OZoXvRvr1uQXM0gxOw3aJ5AM/62yl6o2HxcvP/IDrCkt25i/7b9Jad9Nd/aJVK+8AU+6amgfoxSvQ/j8fl7bsYO2rq5RE2FrLS9W9vLjDW2kxLn41OlZlGbHU5weepbGOD4WbLmH3Orf015wJpWn3qrxwTJoosnw/wEfAe4Of396ylokRxUMBtlTWUlVXd2Yyjf5HEtdp5+nd3XxUnUfhWkxfO28eazIG//QiiNJ6KqkaOeDZB36G4HYVA4efx1NSy4lGDMND+bJ0ShmI6y9q4uu3l7mZWWNaXknaKlq9/GjV1spzY7joreN3Lsb31NH8Y6fkFX3Ar6EnPAwo3/RkIhjm+I1zB8IsG3v3iPG1gv7e/jtzi7quwOsyIvnhvU5wzp5Yj0tlL76FVLadlK3/KMcWv5RzcAow4yltNrjhAby5xhjaoHbCAXoE8aYjwE1wAci2UgJXfV6fT76vV6q6+upa2ggOyNjxETY4w/y6JYO9rV6ae116PIGAYhxwQdPSOeSFWnEHu127Dgkte+hYO8jZNb9jWBMInXLP0pj2YdwYidW6k0mRzE7vYLBIF6fj6raWhLj40d9ur3HG+RX2zo42OmnqSdAa59D0Ibi8rPrcsg8bFyj29dNwd5fkFfxa6xxU3fcx2gou0wXl8cYxevoBqZa9jsO2aOUUPv7gV5+9GobpdlxXLMmi7MWJRE/5CHU1OYtLN74Ndz+PirW3kl70TnT1HqZS8ZSTeLyUd46d4rbIqPw+f28vmsX7Z2dGMDldpOTmTniSbexJ8C3/tbMwU4/J+UnsCQrnpwkN/mpMbxtXjzZSVM0XtdaUlu2ULDnEdKbNhKITaF+2VU0lF2mAv8zTDE7PRzHYcvu3bS2txMMv5aVNvpQhyd3dPLH8h7KsuNYlhtPbnIMeSmhqZaHTXYTDJB74P8o2vU/xPg6aVlwIXUrrtEkAMcoxevodpaXY4wZMRH2BoJsPdTPr7Z1UJAaw53/kofbNeScaIMU7H2Eop0/pT+liH3rv6sZ5WRUepJplnMch21799LZ3X3Uck07G/v5zj9aCFrLV96ey0kFEehBskEy6l+mYO8vSGnbiT8+i4PHX0fz4kvUEyxRpb2ri8bWVnJHuTAdqsPj8Hx5D2ctTObTZ4zysKu1ZBz6O8U7HyCxu4qu3JUcPPEz9GUsjUDrRWavgONQfegQbZ2doz4c/tNN7bxY2UtynIvPnJ41LBGO8XawaNMdZDRuoHX+eVSt/KLGB8sRKRmexay17Kuupqmt7YgPyVlr+VN5Dz/b3E5+agw3nZ1LQdrEJuAYVTBAdu1fyN/7CEldB/AmFVB18udpWXiRHoyTqNTQ0kJ8bOwRE+GgtWxv6Oc3O7rwBy3vO37knuPUps0U77iPlPbdeFJKKD/9bjpUg1uiVGtHBzvLy0ftALLWsq2+n9XFidywPmfYsL/U5i0s3nQHMd52qlbeSPOi9yiO5KiUDM9itQ0N7B9DtYind3Xz6NYOTilM4LPrciY0letojOMlp+r35O97jIS+evrSFoUe4Ck+F1QiTaLQQK9VVW3tEe/WvFHv4cevttHa55AYY/jIKRkUHnaRmty2i6Id95He/BrexHkcWHUzLSUXKLYkqnX39JAQH09szMhxUNnmo83j8L6ChMFE2AQDFO56kIK9j9CfUkz5OT+hL3PZdDZb5jB94s5SXp+Pnfv3k52efsRqEUFr+cPebk7IT+CLZ+cOHzM1CTHeDnIP/B95FU8S622jJ+s4yk/6HB0FZ+gp3Cly6NChmW6CjFO/18vWPXto6+ggOzNzxJkfD7T5+OErrVR3+ClMi+ETp2aybmHysIvUhK5Kinc+QOahf+CPy6DmxM/StPg9ussyi/X39890E6KCtZbGtrYjlih8dk83bhesLg4NBYzvqWXxxttJad9N88KLqTnps3rQVMZFyfAs1dXTgw0GiRnlynjArkYvbR6HK07OmJJEOLGzkryKX5Nd80dcQR+d89ZQv+wqunNX6lbTOHm9XioqKqioqKC8vHzw55/85CeUlpby1FNPzXQTZRy6enrYuH07WEvuKCWe/nGgl/s2thHjgg+fksH5S1OHTaSR0FVF4Z6HyTr4Z5yYRGqP+ziNpR/UeMZZwFpLTU3NW+L1oosu4pprrqGzs3OmmxgVPP39dB3lGZnmngBLsuLITnSTU/U7St74PtbEqFpElGlvb2ffvn3D4hXgkUceGfe2lAzPQgHHoamtbcReJ4Bur8NLVX38vaqX8hYfSbGGVUWTuAq2Dun1r5BX8WvSm1/DccfTsuACGkvfT3/a4olvNwr09fWxf//+YUnvNddcw+rVq3nuuee45JJLBpfNzs6mtLSU7vCUve973/v4zGc+M1NNl3FwHIeN27fjdrlISRqeuPody4F2H9Xtfp7Z3YU3YPnmxQUUDRkSkdhZScGeh8iq/StBdzwNSy+jfumVqrwyzRzHGUx4B+K1tLSUT37yk1hrWbZsGV6vF4CEhASWLFmCx+MBYN68EWdElinW199/xHH4m2r72Nvi42PLHcpevomMhpfpyl3JgdW34EvKm8aWSqRZa2lpaRkWr3V1dTz44IMAfOpTn+Lxxx8HwBhDSUkJJ5100oT2pWR4FnEch/rmZvZUVuIPBMg8rExTu8fh19s7+ev+HpwglGTEcsXJ6axbkExK/PiHLsR6WsipepbcqmeJ72vAlziPg8dfS/PCd+skPcRAwjtw9blu3TrWrVvHtm3b3hJ4ubm5nH/++axevZq1a9fy2GOPUVpaSmlpKZmH9XQUFhZO52HIBHj6++no7qa5rQ2vzzdY9N8JWv5+oJeXq/vY3ezFGwjNhJuZ6OYLZ+UMJsJJHfso2P0QWYf+hhOTSP2yq2gs+yCB+LHPGinjM5DwDsRrTEwM11xzDQArVqxg7969g8smJiZyxRVXAOByuXjooYeYN28eZWVlFBYWDhuidrSKITI1mlpbcR9haOD2eg+Xxr7Ml2ofwgR91Jz4WRpL36/he3PUQMI7tHf3pptuIjk5mVtvvZW77rprcFmXy8WCBQvo6ekhJSWFT3/601x++eWUlZWxaNEi4uMnPsxMyfAs4TgOG7Zto6Ori4zUVGJj31oN4o6/NHGoy8+5pSmcV5rCoqzxTfsKgA2S1riJeQeeJqP+JYx16MxdxcETPklH4VnYKH1wp6enZ7CHNz8/n3Xr1tHZ2clxxx33lrG9t99+O+vWrWPRokXccccdlJaWUlZWxpIlS8jIyBhcLi8vj8svH62EqMxmXp+PPZWVHGoKzYIbFxtLTkYGHR6Hv+zv4fnyHlr7HApSYzhncTLH5yWwKCuO3GQ3LmNIbttFwZ6HyKx/iUBsSmgimtIP4MQdecplGZtAIEB1dTUVFRW0tLRw5ZVXAnD11Vfzq1/9Cr/fP7jsCSecMJgM33jjjQCUlZVRWlpKYWHhsCT3Qx/60DQehYykq6dnxPHCQWt5bksV7zlwLxe4N9GTejyVq7+MN7VkBlop42GtpampabB398ILLyQvL49f/vKXXHvttXR1dQ0u63K5+NCHPsSKFSu48MILycnJGYzXRYsWETfkb+OMM86YsjZGZ+YzC3X39tLZ3T3qWERrLXVdfi5ensZVKzNGXOZIYj0t5FT/ntwDzxDfV48/PoOGsg/RvPBivKnzJ9v8OaGnp4eKigocx2HVqlUAXHDBBWzbto36+vrB5a666irWrVtHWloa73rXuygpKRns3S0tLSU9XAA+NTWVW265ZUaORSLDcRzqGhvZW1UF1pKdkYExBr9jeeyNDn6/pxt/EE4qSODjp2ayqijxzWTKBklveIX8fY+R1rKVQGwqtcd9nKYll+LEjTzV8kRZawk4DsFgEGvt4Fcw/H1gmYHvdsh6I70P4Z5Pa4fvJ/x9pF7RgfVGfZZg6HYPf2vIzyOtba3FCQRoamyk/tAhTjn1VIwx/Pqxx/j0v/0b1VVVBAIBIDSc4fLLL8flcrFu3TqKi4sHL1BLS0spKCgY3O7HP/7xkdsqs8L28nKa29reUlvYcYLseuEJru/4HxLdfraXXkv/iVeAGXkooUw/ay2NjY1UVFSwaNEiioqK2LRpE9deey0VFRWDwwMBnnnmGd71rndRWlrKhz/84WHxunDhwsGEd+Au7HRQMjxLdHZ3H/E2nM+xBC0kx439Vp0JBkhr3EBO1e/JqP8nLuvQlbuK2uOvo73wTKx7Aj3Ls1xvby/tbW0Uzw8l+P/13e+yZ+dOysvLaWhoAOCcc87hhRdeAELjAM8///zBQBz4DqGT+H333TczByLTbmCmx7aODtJTU4kL351p7g3w7b81c6Ddz9mLknnv8WnDxgMbx0f2wefJ3/c4id1V+BLnUXPCp2le9O4JPRgXCATw+f34AgGCwSBBa4cljNZajDGDpafc7lBvdExMDLExMRhjcBmDcbkG13O5XKHXwq8P/Ox2uXC5XKHXjRkxMR08ziMNEzjKEILR1vX7/RysqWH+/PnExcXxx+ee4+c/+xmVlZUcrKkZTHj3lJeTnZ3NrmXL6Gxu5oMf+MCweB3Y/nXXXXfEdsjsdbC+npq6OvJycob9vSR015Dx8t2s7dnGnvgV+M7+Mt60BTPY0uhlraWhoQGXy0VeXh6HDh3i+uuvHxzi0NPTA8CPf/xjrrvuOtLT08nLy2P9+vVvSXgBVq9ezerVq2fwiN6kZHiWaGprI/EI4108/lBfSmLMUcZF2SAprdvJrnmezLoXiPV14o/LoLH0gzQvuviYuKXk6esjMfwQ03O/+x0bX32VgzU1HKypoa21leL583nqd78DoCHc43vRRRcNBuPy5csHt/Xwww9P/wHIrOPxenl9xw76+vsH785Ya3lwUzt/LO/BANevy2bdwuTBddy+rnD5wV8T199KX/oSKk+9lbbic4843CgYDA726gaDQZzwd5/fj8vlIi42lrSUFPKTk4mPiyMxPn4w8Y2JicHlcuEOJ7Bzgc/nAyAuLo5t27bxwAMPDN4uraqqwnEcXn/9dVauXImxlrbWVk5dvZrLL7tsMGYXzp9PfHw8N1x/PTdcf/0MH5FMtYDjUFFTQ0Za2uDftXG8FOx7jPzdD9MTjOVO93Wce9HlxIzyYPlsEQgE6Pf5cBwHGHIXZMhdl6F3Ruxhd2OGvjbiXRtjQq8bQ7/XS/oRpoCfCGstHo+HpKQkfD4ft91227AKK729vXz5y1/mrrvuIjk5ma1bt1JWVsZZZ501mOyuXLkSgKVLl/KHP/xhStsXKUqGZwHHcWjr7CQjdfRbqZ5AEIDE2JGT4cTOCrJrnier9s/E9zXiuBPoKFxP6/x30pW3Buua4hnppsn2N95g44YN1IaT3dqaGnp7e/nbhg24XC62vPYaG155heL581l31lmUlJRQEr7qBPjGt7/NirKymTsAmfXaOjvZvGMHBsgIn1g8/iB3/LWJ8hYfJ+YncNlJ6ZTlhC5Wk9r3Mq/yKbJrng+XH1zNgdVfoWveqSP2kA4kuv5AgH6vl6C1pKekDCa38bGxxMTEkJuVRdZR6orPdo2NjTz++OPDTp7V1dU89dRTvPvd76ahoYGHHnqIsrIyVq1axWXhhLe4uBiAK6+8cnD8r0SPlrY2ejwe8pOSwFrS61+iZNu9JPQeYlPimfx7+xV84pzSWZUID1zUBgIBAo4zeFHr6e9nYVERCfHxb951GbLeYLI/JM5dQz43Bu7sDLtjM7DOkPUHPmuSEydXT/nhhx9m165dwx5gu/TSS3n44YeJjY3lgQceGKyEdM4551BaWsr69esBSE9PZ9++fZPa/2yhZHgW6OnrIxgMHvEk2OcL9wzHvhk0cb31ZB/8M1kHnyepqxJr3HTmraF2xbV0FK4nGDP7a5c2NjTwxpYtgz27Awnv4089RVZ2NhteeYX7f/Qj5s2bx/wFCzjznHOYX1JCIBAgLi6Om265hS/N4eRBZlZ3by+btm8nKSGBhPh4DnX5eW5fNy9X99HtDfLhUzK4YGkqcfjJrPkTefufIqVtB447gZYFF9K05L140kvfst2BE2Wvx0MgECAzPX3wwdjczEyyjzKr5GzV39/Pn//852EnzvLycm666SauvfZaWltb+Y//+A/S09MpKytjzZo1XHHFFSxZsgSA8847j87OzjnTqy2R5/X5KK+uJjUpifjuGkre+C8yGjfQkVjCF9y38kz7ct6xJJmVhQkRbYfjOAStHRyHPxDDPr9/2N+r4zihIUZuN4kJCSQnJZGcmEhsbCwxbjdxsbEU5ObOmr/xDRs2sG3btmEXqCUlJTz77LMA3H333ezfv5/FixdTWlrKueeeO5jsGmNoamqa0xfoY6VkeBbo6ukZNXCaewNsrvXw+72hwefHxTWRv/dlMg/9nZS2HQB0Z59A1ck30F789llXsqnf42F/RUUoyT14cDDhvfnWW1m6bBkbX32VO776VQDm5eUxv6SEs9/xjsFbTJdddRVXfeQjJIxy9RsNQSpTz9PfPzilckxsHNubHf64r4k36vtDM1sVJXL+0lRWpXWSu+eX5B54hlhvO/0pxdSc+FlaFlw4+FCc1+cb7PkNBoODPUGJCQkU5+VRUlj4ltrEs5W1lt27d79lspjzzz+fG2+8EZ/Px8UXXwwwmPCuXbuWkpLQ8KulS5fS3NxMdnb2iJ9pilcZYK2lvbOTLXv2YLzdLK97irzyJ/C74rjX/a/c2/4O0pPi+eTadM5ZnDzp5NJaG7ozEx6aNDBUifAQhLhwMut2u4mNiSE2Npa42FgyUlNJTEgYHHfvcrlITEgYdaro6dbQ0MCuXbuGxavP5+N34aGCX/3qV/nTn/5EXFwcixcvpqysjNNOO21w/b/97W9kZWWNOq9BtMTs7PjfjFLBYJDt1Q28ursSbwBMVx/egKWz36G2009Vu4+qdi/HmyquTXydi9JeI++f1QD0ZiyldsW1tM4/D19ywVH2FFmdnZ2DwxgGkt73vO99nLJ6NdveeINPh8saGWPIy8+neP58AuHSR2eefTaP/+Y3FM2fT0LCW6/8U1JSpvVY5Njm8Xo5WF9PZW0tjmN54SD8qbydLm+QrEQ3HzoxnfMWJ7CwcyM5B35PRv3LYIN0FJxB05JLacs6GV/AwdPnhb4OLJAUH09uVhZJiYlkp6eTlJg4+CDbbNTf309lZeWw3t0FCxZw8803A6FyRQOzrWVlZVFaWjr4dHdaWhobN25k8eLFZGVlveUYY2JiyMnJmd4DkjnH5/ezs7ychoY6ylr+yoL9jxHr7WBT2jv596b3YZOyuGpVKu9YkjLq0MAjCTgOvX19+AOBYUMM0lNTSUhIIC42lqTwWPy42FiSk5KOOP3zTAoGg9TW1g7G68BY+yeeeAKXy8Wtt97KT3/6UwDi4+NZsmQJy5cvH3zQ9t577yUhIYHi4uIRE97c3NzpPqRZScnwNAgGg7R2dODz+7HW4g8E2F3bwgOvNrGlwf+W5VPp44KEnVwRu5W1yVtJddqxQRfdaSdSU/ge2gvPwpecj7UWn99Pf3c3zpASSxAaoH/4qXjoicsOPKEeHow/2onbEKrv2NXZyaHaWurr6qirreW4E05g1Zo11NbU8Imrrhq2j9x58zhp1SoWlJYyr7CQr951F4XFxRQUFBA3JOFt7egAY8jIzaW3v5/e/v432zdCO8Zi6HrBMUxnLdHBcRwqa2vZX1ODMYYOfxz/9XI79d0BVhcn8vbFyaxPqSOv5jdkv/AnYr0d+OOzaCj7EIfmv4sWk04wGCTBHyA1OZkFhYWkpaSQEB9PYkLCrEt8PR7PYN3s8vJyjDGDNXZPPfVUduzYMbhsVlbW4EyJxhgee+wxcnJyKC0tJWuEUo+nnnrq9ByEHJMcx2F3RTmunU/yjgOPEd9XT2PGSr4ZexlPNc3nrEVJXHdaNrHu8cVUMBik3+vF4/UScBwWFhWRnpJCemoqifHxoaors7SX03EcDh48OKx395ZbbiEzM5M777yT2267bXDZgZkROzo6yMrK4lOf+hSXX3754Nj7w49x2bJl0304c5IyhQjz9Pezdfdu2rq6cBmDz4GXDvp5ak8/bpfhgyemsyw7jjx/DSXtGyhs20BWxw5c1iFACp0Fp1GZfzqd+acRiM/E6/PR6/EQbG/HGENyYiLF+fkkxMWNGOyHn6QP/91lDIRv67a3t1O5fz9VBw4wLy+Pc889F5/Px7KyMjo6OoZt46abb+as1avxnXgiLXffzZLFi1kSLplyeA/v/3vHO47YhjGbwHqz5VaWzJyBXqj65mb6TRL/u6uHl6o6SYl3ccf6WM72vUD23j+Q3FlO0BVLR8E6WhZcSEP6SXR7vMS741hYUEBBbi5ps+hOxdCpwBsbGwfLil111VU8+uijw5Y9/vjjB5PhW265BcdxBieKOTzhveiii6bnACTq9PT2UvvPR1i0/cek9RygLaWUb6bexv80LCUxxsW/rkrngqWpuF1H/6wPBoP09ffTH54+2+VykZWezoKiIvKys0kc4U7jTBo6FXh5eTkXX3wx8+fP58knn+TKK68crLoCoYT36quvJjMzk4svvpj8/PzBSg1FRUXDzvMnn3zyTBzOMUeZQoRYa2nv6uKNvXsJOg6xiek8t7ebP5b30OsLcta8Pm5cWElh5xbStm4mztMMQF96KQ1Lr6Azfy09WSsI2FD5FF+/H6e3jeTERJaUlJCbmUlKUtKo43xGa9PAPN+BQIAzzzwTgHe+851s2rRp8NYowPve9z4ufe97AfjEJz5BQUHB4KQTixcvHpz2MCkxkZtvummq/tlEpozjOLS0t7N1z162N3h56ZBly6Em4vFxY/FeLnX/g5zXXsFlHXoz30bVSddTl3smnU6o8kpmXAInliwkPydnxBkhp8PQqcAvueQSXC4X99xzD9///vepq6sbXM7tdvOxj32M2NhYzj//fJYtWzZsopihU4FrljWZbtZa6jb+lsRXvsvbOrbTn1zIr4u+yJcPnEhyXAzvXZHCJSvSSDrKkAivz0dXby+G0N98TkYGSxcuJDkxkZTk5BmvNjF0KvCBc+WWLVu4/PLLqaysHDYzYk5ODvPnz2f58uV87nOfG1brfuhU4CtXrhwsVSaRo2Q4Arp6ethzoJqtVY30BGJ4qTZA1aEqVpu9fCutnDNSdpDZVQXbwB+XTve8VXTOW01n3lo88dl4fT48Xi+mK/Rg3bzsbNJTUshISyNzSB3GkQxMe9jU1MQJJ5wAwM0338yf//xnKioqBhPeU089lY0bNwKwfPnywZPn4UWxAb71rW9F7N9KZKoFHIe2jg6e31rOX8u72Fzv0O/t5/z4HdySs5kTPRuJbenDl5BN/eL3U5P3dtriC3EZQ056Jm/LyGBedva0PfQ2MBV4WVkZSUlJPPvss9xzzz2Ul5cPmwq8urqakpISioqKOO+884bFa2lp6WDCfvXVV09Lu0WOylqcir/S//ydFDe9jj8+kw2LP83Xm9azc3+QE/LjuX5dDmkJoyexAcehq6eHYDBIbEwMx5eWkpudfcS6/JE0MBV4XFwc8+fPp7m5mY9+9KOUl5dz4MCBwYT3nnvu4fOf/zy5ubkcf/zxXHLJJcPitbCwEIAVK1boHDsLKBmeIt29vXT39tLY1sEPXqyitqGFMt8u1rj28A3XbpbG1wIQ9MXRnXMSNYsvpDHtBNoS5xO0oTGxjidIvNNHVno6i4qKyExPJzE+/i29UgM9vAMD33/xi1/wzDPPDD4M093dTX5+/uAUw729veTk5LB27drBYBw6jujee++dnn8kkQjyeL1s3V/LbzZX8/JBL63d/bzd/QY/THmNNe7N/5+9O4+Ps6z3///6TCb7nnTfl6QbSwuUpbIKIqBQRT0qcBT9injcl+OKgoqIHkXAHfjBUeR4ROUICIqCArKUFkr3vWmbtmn2fU9muX5/zCSkbdJMkpkkk3k/H488mszcc9/XnfQ99+e+57qvi5RgO76uHGqmnc+RSauoLziNlLR0pk2axLycnN67xmOhtbU1NBRTejpbt27l7rvv7u0b2JPTl156iXPPPTc0pJPfz6WXXnrU1aJp06YBcM0113DNNdfEpJ0iUREMQskz+J/7Pt6KDSSlFLCp+BPcVn0e63dAXhp84pyCAUeJ8Pl8tISHHE1KSmLezJlMnzyZzPT0IX0aOlx+v5+Wlhby8/MJBoN8/vOf783rgQMH8Pv9fPazn+Xuu+8mJyeHI0eOcMopp/Cud72rN68nnXQSALNmzeKRRx6JeZtlZFQMj5DP7+fVbTvZtXs7VrmNgsbtfD2wgwWeSkgBX1I6TQWnUDblbbRMWkFdxnyaOrpJSkoiPyeHRYWFpCQnk+TxkJSURGFeHh6P56iZZ9asWcOTTz55VOf69vZ22tvbSU1NZfPmzWzcuJHi4mLOO++83gNoz41xP/3pT8eA6MkIAAAgAElEQVT4tyQSG8FgkNKqev746j7+vque2oYWzvds5baMDZyb8TqpwQ58wRyqpp1HxeRzaShcwZQp05hZUMApubmkpqRE7ea3nrxVV1dz//33H5XXyspKHn74Yd73vvfR3NzMX//6V4qLi7n88suPO0FdvXo1q1evjkqbREaVrwO2/B73ys+x2j10p01h/YJPck/bRfxrq4+MFOO6FblcvjiLtGNmU/UHArS0tYXGkE9OZu6MGUydNImczMyYFMB9bxy///772bJlS29eS0tLecc73sEjjzyCx+PhqaeeIisrixUrVvBv4anAe24kTU1NZePGjVFvn4wuFcPD0NXWRPuBdex9/Vk6D77O4sAezrXQDWYtZFKdfwoHZ7+L+ryTaEifR3fwjVEe0pyHM046iSmFhb19gkpLS/nbP/951FBHJSUlbNu2jXnz5rFmzRp++MMfMn/+/N5pD4uKinrH4r3jjju44447xuaXITIGmto7efS1/Ty2oYyOmgNcaBu5I2Uzy9N2kUSQbsulctoFVE05j+CcNzF16nQW5eSQnZk54jvKu7q6eOyxx44qdvfu3cuXvvQlvvjFL9LR0cHXv/51pk+fTnFxce9U4D3dls4999zeq8EiE0JrNbx2P+61+7H2Olqyi/jb1M/wQMsqdu9wpCb5Wb0sm9VLc47qEtHV3U17ZyeBQACPGXPCN7/lZWdHdeSHF198kQ0bNhyV12nTpvHyyy8DcO+997J7926Kioo4/fTTee9733vUWLwTZZY1GZiK4cE4B40HcYdfpWH3y/hK11LYtpd8ApwFHGYqZdkr2DPpJJJmrsCXM4f2rm7MjNzsbKZkZpLs8bBn1y6OlJVxYP9+fhYudu+55x5WrVrFunXruOGGG/B6vb2zwFx00UW9w4J98pOf5HOf+5yGCZOE1u0P8PTWwzz+Wgnu0Kucx0Z+5t3ErJRqAJoz51Fa+G5qJp1FyvxVzJw+g+U5OaQM4+a3DRs2sGfPnqMmn7jkkku49dZbMTOuvfZagsEgM2bMoKioiKuuuoqTTz4ZgNmzZ9PS0qIxsmVicw4OrYXXf4Xb/igEfBzMO5tfez/N/9YV011jzMr18sHTM7lgfia5aUkEg0Fa29t7R4DISEtj3syZTMrLIzszc9g3qh46dOi4Wdaam5t55ZVXALjrrrt49NFHyc7Opri4mDPPPPOoURieffZZsrKyxt0QiTJ6VF0dq6MRKjbRXrqezv2vkFa9gYzuegxIc6nsDi7kL0lX0px/Et7pyyiekY/f56Outpaa3VXU1+6grrqaqooKPvaxj7Hq8stZs2YNq6+8EoDk5GQWLlxIUVFR75nv5Zdfzr59+5gzZ06/BW/6COceF4lXrV1+nt9ZzsYNr+FKX2KV28SPPdvJ8Hbh86RSX7CcbYXvoW3W+RTOPZnJ+fnMCU96cSKNjY3HzbI2c+ZMbr/9dgCuvPLK3qu3M2fOpLi4uHcyiZSUFLZu3crcuXPJzMw8bt0ej0eFsExc7fWw+WF4/ddQuxufN5NX0i/hl52X8krlNNK9xvnzM7hkYRZFhSn4/X6a21qo6wzdG1OQl8fi+fPJz8mJuI9+V1cXBw4cOCqvJSUlPPHEE6SkpHDHHXf0dgfsmRmxuLiYQCBAUlISd911F/fccw+TB5gmOTs7O4q/IIlHCV0Mu45Gmg+8Tsv+9bjyjWTWbaWgK3SjWwZQGZzGRreMnZ5iWnIW0+HNIbmtiu76cqr2bOb0lcnMP+tdNDc2cmWfsXR7pj1saGgA4NRTT+Xpp5+muLiY2bNnH9f/KTc3l9zc3FHbb5HxyjnHzooW1m3ZRsv2Z5jRuJ5Vnu1caXXggabkKVRNvoS6yWeRtPBCpk6bxcIBDqoNDQ1HfSzq9/v5zne+A8Bll13WO5oKhG5yueyyy3p/fvjhhykoKGDBggVk9DOqxLJly2Kw9yLjVDAApS/Bpt/itj+GBbrYm7yYXwU+xqOdZ9PZlkZRYQofWZLJuXNScAEfPn8HdY3tJCclsXj+fCbl55OZnj5g94eemRH7Fry33HIL06dP5yc/+Qlf/vKXe5fNy8ujuLiY+vp6pk2bxic+8QmuvfZaioqK+p0KfO7cuTH99Uj8S5xiuLOZjsMbqd2zjs6D68mq3850fxm5QC5Q5iaxNjCP9W3L2duSTlWHh+kzZvHWt1zE+fnpvO3ii2lva+tdXWpqKosXLmTRvHkEg0F++ctf9g6ZcmzBm5WVxaWXXjr6+ywSBxrbu1m3bS9lm54mq+IVzghs5cOe0JBiLd4sKnNOYcvUFbROXUnO7FOZXFjIrJwckpOTqa+vZ2v4xpcjR470HjA/+MEP8tBDD/Vuw8w45ZRTeovhm2++Gb/fT1FREQsXLjzu05cLLrhglPZeZJxyDso3EtzyR/xbHiGlo5pWMvg//wU8HLiYSuaxfFYaH56UxMJ8IyPZMAJ4CDJp0iTyc3LIysg4qp/+sVOBr169muLiYh577DHe9a539d5bA6GZET/84Q8zffp0rrzyyt6x7ouLi4+bCnzJkiWj/uuRiWVCFsOuq4XqPa/RUPIqlG8ir3EbU3xlpOOYEXS81pTL4w2TqElaxbwzLsQ/aQn3/uC77N3+0lGzwFx+xRW8//vfICkpia/fdBOFhYW9d373nfbQ4/H0zv4kIifW5fOza9cOyjf/Azu8loUd27jME5pAooNUDmYs5fUpbyV5wXmkz1tJwOenvrycQwcP8r7z5pKamsqdd97Jbbfd1vvpC4Ry+MlPfpLMzEyuvvpqli9f3pvXBQsWHDUz4pXhbksicoy6fXRtfJjujX8gu60Uv/PyXHAFjweu4XDemSydnsl7Cz1MzTRSvF4mFRYyKS+PjLQ0MjMyCPj97N+/n1fXrGHJkiUsXbqUbdu28ba3vY2ysrKjCt6pU6dSXFzMKaecwje/+c2jxuHtOzPi0qVLWbp06Vj8NiRBxHcx7BydDeVU7nmNltKNWNVW8pt3M6W7jI6WII2tQebOnMJum88Nz3pYv6eS2roGAv4WoIxFixfz2k8fITM9nertG+h6y8W9QSwuLj5q2sObbrppbPdVJE51dXezb8fr1O14Hju0loUdW1hu9SwHWshgX+oi/pp2DvUps1l+8buZOXMOL/7rX9z2pdsoKSk5airwM888k6VLl1JUVMT73//+o/I6f/783oL36vDsiSIyiGAQyjfStvXP+Lb9mby2/SQ74/XgUv5uH+VQ/jnMn57PxZO85KZ7mZSfT3Z6OrXV1RTk57No0SIaGxu5+uqrKSkpoaysrHfVt956KzfffDPTpk3jwgsvPGrc7L4zIy5cuJBvfvObY/UbEImfYjjo91F5YBs1Ja/jO7KZlJptBKt2syKvjXnAg5u6eWiHh70NUN7QiT8QJD0jg2fX/oWiKYUsbruDpIK9LCouZtGiRb0d7HPCN7r0fHwqIsPnnKPs0AEqdryE7+Cr5NRtZn73XpbSTk27o9Wbz8GcZfy5fRqPvLSXyroWDh3cRHPzCwA8/fR5nHLSqWRlZZGfn88111zTm9Wewhc0Fq/IiPi78O97noYNj5G+/2myfLWkOg+bg0tYk/RByiatYuakXBZn+HnPkiVMLijgGzfdREm4e0PPVOA33ngj9957Lzk5OQQCAd785jcfldeesbMnTZp0VLclkfFmRMWwmV0O/BhIAu53zn0/Go1qqKuhbM/rNO5/HW/1Dgrb9nB43x6e2NlJSX2QPfVBShscvqDj17d/nKTpJ/HUgdcoS1rH8jct4j3FxSxetIjFixdz1klL8Hg8/OiHP4xG00TiWjQz65yjsraeQ9vW0H5gHenVG5nRtoM5SfWktQW5c62fLQ1p7G2EI3XdtHd28YM7vsVHP/pRvHv38oP/fS/FxcVcGB43u7i4mDPOOAOASy65hEsuuSQ6Oy0Sp6KWV+fwV2yjevPfCJY8x+T69aS6LpL8qfzLlrPe+17+tbsWX105LbUvU1n+R6qqqrj88st56qmnANi2dSter/eoqcB7hifzeDy88MIL0dlpkTEw7GLYzJKAnwOXAmXAa2b2Z+fcjkjX0dnRTtneTdQf2MyhrWtY/+qr1FZVUl7fTkl9kAONQV64cTJtM4p5rGkx92/axLTpM5i7vIhLly1j6ZIlXHXttRTk5/PvHxrunogkhpFmtqaujkM71tF0YANWsZlX1qylpb6GffUBSuqD7K13vP/8hbzr6v9H4+zZ/PCuLzNnznSKlhVx2eLFFBUV8da3vpW8nBxWnnEG+/bti+XuisS1EeXVOVor91C2+TmC+55net1aNu6rYV1ZgNfr0tjekMyRhiAZeZn86uHbuW7WVP5+zTXs37ePoqIirrjiit4JKHqsXbs2VrsqMuZGcmX4LKDEObcfwMweBt4BDBjU6ooyPn/tW6g4tJ+q6hrK6tr55dvTeMsCL2W7AvzXE22kpyQxY0oB04tmsHLhIjqv+wyLTj6Z76ek8JP0dA2KLTJ8Q8psXU0lN92wmvIDu6mtrOBIfRsXzvVy9+Vp1LlsrvhrBf6gY9qUQuYtWMj7rjqV1atXc/nb3w7Aez746WEPoi8iQ8trS1M9//X591O+ZzO15Yc5VN9JTZvjxU9MZ733FG7elsW6DdvJL8ymqGghV51fxPLly7niTaFphZ9/7jlSUlJGa99ExpWRFMMzgcN9fi4Dzh5gWQAOl1dx9++qyErxMLMwk5lz5rJp7mUEz76E2e9cwNZb0li4YAFpqakqekWib0iZrThyhO89cASvB2bkZzBt6ix8S1ay9tKPM2P+MjZd18r8uaHRHfqjQlhkRIaU1+aKUr569wEACrNTmTJlBtNPXcD2K+/g1IXzefj6VgoLCgacYEKFsCSykRTD/VWr7riFzG4Ebgz/2O1JStrX5nf+3RUt/t0VLcF/bboHuGcEzRgXJgG1Y92IGNB+xd5ojgY/aGaPy6vXuy8QDPoP1bX7DtUdcq/uOMQvHvxTzBsaY+Pp7x9N2q/YG8957fIkJe13zgXqWrt9dS2Hgzv3Heb5s8+MeUNjbDz9/aNJ+xV7Eed1JMVwGTC7z8+zgPJjF3LO3QfcB2Bm6wN+/8oRbHNcMrP1zjntV5yYqPsVgUEze1xefb4J93uaqH9/7deEM/S86vgaN7Rf40v/8yJG5jWg2Mzmm1kK8H7gz9FplojEgDIrEj+UV5FRMuwrw845v5l9Cvg7oWFf/ts5tz1qLRORqFJmReKH8ioyekY0zrBz7q/AX4fwkvtGsr1xTPsVXybqfg1qiJmdqL8n7Vd8maj7NSjlFdB+xZu43C/rO0+4iIiIiEgiGUmfYRERERGRuDYqxbCZXW5mu82sxMy+OhrbjDUzm21mz5nZTjPbbmafHes2RZOZJZnZRjN7cqzbEi1mlmdmj5jZrvDfbdVYt2m8Umbjy0TMKyizkVJe489EzGw85zXmxXCfKSWvAJYB15jZslhvdxT4gf90zi0FzgE+Odh+mVm6mT1hZk1m9sfBNmBmN5nZ/cNpnJk9b2adZjbcCeM/C+wc5muHJPxGd1GEyy4Ov4G0mNlnzOxOM/uPCDf1Y+BvzrklwHJGaf/ijTIborz2bxTzCsrsoJTXNyiz/dMxNgLOuZh+AauAv/f5+WvA12K93dH+Ah4HLh1kmQ8ArwLefp77OnBbFNvzPHDDMY8VAI8CbcBB4NoBXjsL2ABsBHxAaT/LrABeBJoIjYd5S5/nlgHrgYbw1z+AZVHarweAu/r8PJ3QLE0pg7wuBzhAuJ+8vk74u1JmnfIapf0aVl7Dyyqzkf2Oldc3llFmR75fCXmMHY1uEv1NKTlzFLY7asxsHnAasG6QRecCe5xz/n6e+yvw9ui27Dg/B7qBqcB1wC/N7KR+lrs7/PVXBj6z+1/gBULhvxD4uJmtDj9XDrwn/NwkQmNjPhylfZgL9A4v5JyrAHYBqwd8RcgCoAb4Vfis934zy4xSmyYaZTZEeR254eYVlNlIKa9vUGZHLiGPsaNRDEc0bfN4ZWalZvYlM9tiZm1m9oCZTTWzp8IfIzwHPAZ8zjnXbGZ/NLPK8Mc0L/QEwcy+DdwCvM/MWs3sI32345zbCEw2sxl9tv0tM/uf8PfzzMyZ2fVmdsjMas3s60PYj0zg3cDNzrlW59xLhAL0gWOWuxKods79BngGaB9glfOA3zrnAs65fcBLwEnhfWl0zpW60OmiAQGg6ARtKzWzt/TZ5z+Y2W/Cv9/tZrYy/NyzwJuBn4V/h4vCq3iewd/kvMDpwC+dc6cROnOfEH3rYiBuMxtBXv9hZrOA/wM+BzygvI7LvIIyG6m4zSvoGMvEyWxc53U0iuGIpm0e594NXAosAq4CngJuAqYBpwKVzrk/hZd9CigGphD6GOS3AM65bwK3A793zmU55x7oZzt/I9Tv60TOAxYDlwC3mNnSCPdhERBwzu3p89hmwuHq41xgtZmVEjrTXEHozPNYdwMfNLNkM1tM6KO6f/RdwMwagU7gp4T2PVKrw9vOI/Rm8jMA59zFhD42+lT4d9izLzsJ9U86kTKgzDnXc2XhEULBlePFe2YHyuskQpMXPE/oIPMnlNde4yyvoMxGKt7zCjrGToTMxnVeR6MYnghTSv7UOVflnDtC6D/KOmAT8EtgC6H/jAA45/7bOdfinOsCvgUsN7PcCLcTycc433bOdTjnNhMKWiQHFYAsQn2P+moCsvs+4Jz7mnNulnNuHqG/1Sagtp/1PUnoY5oOQh+hPOCce+2YdeUBucCnCPWNitRLzrm/OucCwEMMvo8thEI9IOdcJXA4/KYCoTe6HUNoUyKJ98wel9fwVaFuIJNQn7Y7QXk9Zl3jJq/h9iizkYn3vIKOsXGf2XjP64hmoIuEmxhTSlb1+b4j/PO5hD7+KAPyzGwT8A1CZ5X/BkwGguHXTOL4kPTnGeA+M0t2zvkGWKayz/fthAIYiVZCHdz7yiH0n3xIzKyA0Bn2pwj1a5oGPGJmVc65X/Rd1jnXZmb3ADVmttQ5Vx3BJo7dxzQz8w7QDwxCbzaNEaz308BvwweM/cCHI3hNwpkAme0vrxDK7JlAazivAHuAM1BegXGXV1BmBzUB8go6xh4ljjMbt3kdlXGGw2cgi5xzC51z3x2Nbcaac+4l55wB3yZ05WkFkA+8A3gLobO1eeHF++vT1d86WwidBZ8f9QaHDvpeMyvu89hy+nSU76c9zxN68znWAkIfB/3GOed3zpUR+sjlbQOsygNkELubOpYSOoM/IefcJufcSufcqc65dzrnGmLUnrg3UTMLfJQ38voj4BSU12ONi7yCMhupiZpXHWPjK7PxnFfNQBdd2UAXUEfoP+ZQ+vD0+AsD/4cfNudcG/An4FYzyzSzcwm9qTzU3/Jm5jGzNCA59KOlhc/2IBR6M7Nrw8tNA95HOCxmdqmZnWahQcVzgDsJDf+yM/z8h8L9paLlQkL9yESGQnlFeZW4osyizMaCiuHo+g2hsQWPEOors3YY64jl8C+fANKBauB3wMd7Pk4zs/PNrLXPshcQ+rjqr8Cc8PdPAzjnmoF3AZ8nFMBNwDag54pEXnj9TcA+Qne5Xu6c6+n3NRt4ORo7ZGbTCY25+Fg01icJRXkNUV4lXiizIcpslJlzcTMCS8Iws/3AJc65AyNYx9OE7j5d75x7c9QaFwXhtn3WOTfi2WnM7EfAvmP7UYmMFuV1SOtSXmXMKbNDWldCZFbF8DhkZu8Gdjvnto11W0TkxJRXkfiizMqxVAyLiIiISMJSn2ERERERSVgqhkVEREQkYakYHifM7CYzu3+Yr33ezDrN7IVorN9CfmVmDWb26nDaFA1mlmpmu8xsyli1QaRHrDM60Si/Mh5NlByP5JhuZqea2ZpYtzGeqBiOMjNzZlY0yDJfN7Pb+j7mnLvdOXfDCDb9KefcBQM9OcT1n0donvhZzrmzjn3SzN5vZrvNrMnMqs3swfBYhz0HwAfM7KCZtZjZRjMbcC748HiIATNr7fN1UbjNXcB/A1+JsN0igxovGTWz/zGzCjNrNrM9ZnZDn+fOMbNnzKzezGrM7I/hIY4Gam/PQbonQ7tPsGxeOLPV4a9v9Xluipn9zszKw/l+2czOPtFOmdnpZvZCeLtVZvZZUH4ltsZRjpea2bPhvJSY2dV9nhtSjsOveb+Z7TSzNjPbZ2b9ThAykmO6c24L0GhmV0X4+glPxfDYiOU4h9EwFygNDyLen5eBc51zuYRmyvECPW84XuAwoUG6c4GbgT+Y2bwTbO8V51xWn6/n+zz3v8D1ZpY63J0RGYbRyOj3gHnOuRxgNXCbmZ0Rfi4fuI/QDFtzCU3p+qtB1vepPhlafILl7iI0YcE84CzgA2bWM21qFvAaoSmqC4AHgb+YWb9T0prZJELTxt4LFBIa7/TpPosovzKWYppjM/MCjwNPEsrLjcD/mNmi8CJDyrGZXQr8F6FpjLMJjUW8PwpN7e+Y/lvgY1FY94SgYngMOOc2ApPNbEbPY2b2LTP7n/D388Jnvdeb2SEzqzWzr49km5Gu38w+AtwPrApf6fl2P+0/7Jyr7fNQgNBBEOdcm3PuW865Uudc0Dn3JHCA0MF1yMLTUDYA5wzn9SLDMRoZdc5tD189BXDhr4Xh555yzv3ROdfsnGsHfgacG419A64CfuCca3fOlQIPAP8vvN39zrk7nXMVzrmAc+4+IAUYqLj+AvB359xvnXNdzrmWvmObKr8ylkYhx0uAGcBd4bw8S+hi0QfC2x9qjr8N3OqcWxs+fh5xzh3pb8EoHNOfBy7RiWqIiuGx8zdgwO4DYecROghdAtxiZkuj3Ibj1u+cewD4D964WvvN/l5oZueZWROhM913A3cPsNxUYBEnmJ8dOC0c3j1mdnP4bLuvnYTmeBcZTTHPqJn9wszagV1ABaErWf25gBNnCOB74Ry9bOGuRifa9DHfnzxA+1YQKoZLBljPOUC9ma0Jd7l4wszmHLOM8itjKZY5tgEe6zdPnCDHZpYErCRUvJeYWZmZ/czM0iNsCwzhmB4usn0MfKKbUFQMj51IPr75tnOuwzm3mdCc5NE+oAx7/c65l8LdJGYBPwRKj13GzJIJfRTzoHNu1wCreoHQG8cUQkX1NcCXjlmmhdD0kyKjKeYZdc59gtDHoecDfwK6jl3GzE4FbuH4XPT1FUJdlmYS+lj2CTNbOMCyfwO+ambZ4T6X/49Qt4ljt5sDPERoH5sGWNcs4Hrgs4SmlD1AaJrYvpRfGUuxzPEuQlMvf8nMks3srYS6CPaXp8FyPBVIBt5D6P1gBXAa8I0I2zKc/VA2w1QMj51ngAvCBeNAKvt8306oP9+gzOw6e+NGmqeivf6+wmeXfwMePqYNHkIH0m7gUyd4/X7n3IHwR0JbgVsJvRn0lQ00DrVtIiMUs4z2Ff549SVCheXH+z4XLlafIjS16osnWMe6cBeFLufcg4Q+qn3bAIt/BugA9hLq7/g7oOyY7aYDTwBrnXPfO0HzO4BHnXOvOec6CX3M+yYzy+2zjPIrYylmOXbO+YB3Eiq2K4H/BP7A8XmKJMcd4X9/Gu6mVAvcycA57s9Q90PZDFMxPEaccy3AFkJngNFe92/73Egz2MdD0eAl3NcRQsO4EOqHOBV4d/gNI1KO4z96WkroLFdk1MQyowM4NkdzgX8A33HOPTTEdfWXo9ATztU7565zzk1zzp1E6DjQO4RiuA/hY8ARBr/BZkt4W323yzHbVn5lzMQ6x865Lc65C51zhc65ywh9QtM3TxHl2DnXQKiIHpVpgcP9qFOAAUeeSSQqhmMjxczS+nwlDbDcXxjaWd+YCXfOvyj8/XVmNsdC5gLfBf7ZZ/FfEjoAXuWc6zh+bUet94pwv2LMbAmh0Sce7/P8TEJ36a6N5v5IwhvTjFpoCLP3m1mWmSWZ2WWEugg9G35+Zvj7nzvn7hlkXXlmdll4P7xmdh2hvol/Dz/fc3PNvPDPC82sMLzdKwjdAX9b+Llk4BFCV6k+6JwLDrIrvwKuNrMV4dfeDLzknGvssx/Kr8TKmB9rLTRmb5qZZZjZF4HpwK/Dz0Wc47BfAZ8Ovz/kA58jNFJFz7Z6j8NRcBHwbJ+beBOaiuHY2E7oYNLz9eEBlhvvQ6wBYGazgFZga/ihZcCa8GMvEzqz/Gh42bmEriatACr7dNe4Lvz8nPDPPTfZXAJsMbM2Qr+PPwG399n8tYT6HCuwEk1jnVFHqEtEz2gLdwCfc871nAjeQOgK0zf7ZKi158UWGnC/pwtUMqFitgaoBT4NvNM513PFZzZwkNCVXgiN7LKVUH/B7wHXOed6bup5E3Al8FZC45D2bPv88HbP79uO8N3zNxEqNqoJjSpzbZ/9VH4llsY6xxAaOaKC0P//S4BL+/x/H0qOAb5DaGjDPYRuPN1I6GJTf8fhkboOiKRATwjm3KhckZcBmNl+4BLn3IERrONpYBWw3jn35qg17o31/ztwknPua9Fe9yDbTSX08eoFzrnq0dy2SI94yOgg2/4GUOOcu3eUt6v8yrgxAXIcteOwmZ0C3OecWzXylk0MKobHmJm9G9jtnNs21m0RkeMpoyLxTzmWE4moGDazUkIfqQUAv3NupZkVAL8nNLNKKfDecAdwERlDyqtIfFFmRcbWUPoMv9k5t8I5tzL881eBfzrnigndPPXVqLdORIZLeRWJL8qsyBgZyQ107yA0bz3hf9858uaISIworyLxRZkVGSWRdpM4QOiOZwfc65y7z8wanXN5fZZpcM7l9/PaGwkN3UNmZuYZS5YsiVrjReLd66+/XuucmxzNdSqvIrERi7zC8DOrvIoMbCh59Ua4znOdc+VmNgV4xswGmlr3OM65+whND8rKlSvd+vXrI32pyIRnZgdjsFrlVSQGYpRXGGZmlVeRgQ0lrxF1k3DOlYf/rQYeBc4CqsxseniD0wmNsSciY2pjNyQAACAASURBVEx5FYkvyqzI2Bq0GDazTDPL7vme0GDs24A/A9eHF7uePrOGicjYUF5F4osyKzL2IukmMRV41Mx6lv9f59zfzOw14A9m9hHgEPBvsWumiERIeRWJL8qsyBgbtBh2zu0HlvfzeB2hqQdFZJxQXkXiizIrMvZGMrSaiIiIiEhcUzEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCirgYNrMkM9toZk+Gf55vZuvMbK+Z/d7MUmLXTBEZCuVVJH4oryJjayhXhj8L7Ozz838BdznnioEG4CPRbJiIjIjyKhI/lFeRMRRRMWxms4C3A/eHfzbgYuCR8CIPAu+MRQNFZGiUV5H4obyKjL1IrwzfDXwZCIZ/LgQanXP+8M9lwMz+XmhmN5rZejNbX1NTM6LGikhElFeR+KG8ioyxQYthM7sSqHbOvd734X4Wdf293jl3n3NupXNu5eTJk4fZTBGJhPIqEj+UV5HxwRvBMucCq83sbUAakEPoTDbPzLzhs9dZQHnsmikiEVJeReKH8ioyDgx6Zdg59zXn3Czn3Dzg/cCzzrnrgOeA94QXux54PGatFJGIKK8i8UN5FRkfRjLO8FeAL5hZCaE+Tg9Ep0kiEgPKq0j8UF5FRlEk3SR6OeeeB54Pf78fOCv6TRKRaFBeReKH8ioydjQDnYiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkLBXDIiIiIpKwVAyLiIiISMJSMSwiIiIiCUvFsIiIiIgkrEGLYTNLM7NXzWyzmW03s2+HH59vZuvMbK+Z/d7MUmLfXBE5EeVVJL4osyJjL5Irw13Axc655cAK4HIzOwf4L+Au51wx0AB8JHbNFJEIKa8i8UWZFRljgxbDLqQ1/GNy+MsBFwOPhB9/EHhnTFooIhFTXkXiizIrMvYi6jNsZklmtgmoBp4B9gGNzjl/eJEyYGZsmigiQ6G8isQXZVZkbEVUDDvnAs65FcAs4CxgaX+L9fdaM7vRzNab2fqamprht1REIqK8isSX4WZWeRWJjiGNJuGcawSeB84B8szMG35qFlA+wGvuc86tdM6tnDx58kjaKiJDoLyKxJehZlZ5FYmOSEaTmGxmeeHv04G3ADuB54D3hBe7Hng8Vo0UkcgoryLxRZkVGXvewRdhOvCgmSURKp7/4Jx70sx2AA+b2W3ARuCBGLZTRCKjvIrEF2VWZIwNWgw757YAp/Xz+H5CfZtEZJxQXkXiizIrMvY0A52IiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiIiKSsFQMi4iIiEjCUjEsIiIiIglLxbCIiIiIJCwVwyIiE0xDWzcfeGAdOyuao7vigA8evg5K/hHd9YqIjCHvWDdARCQROefYtHMnDc3RLVidc/zi1VY2VXazdvMWKg4O/jbv9XpZtWIFyd5Blv3Ht2DXk7DsHdFprIjIOKBiWERkDDS2tFBZW0tednZU1/vPfW28XtHNtctzWDI1M6LX1Dc3EwgETlwMb38UXvkZnPlROPW9UWqtiMjYUzEsIjIGDpWXk5KcjHewq7FDcKTZx0MbmzllWirvOCkXj1lEr0vyDNJjrmY3PP4pmHUmXHZ7FFoqIjJ+qBgWERllnV1dVFRXk5+bG7V1+gKOH79cS4rX+OSqwogL4UF1tcDvPwDeNPi3B8GbEp31ikwUB9fA3mfggi9BSkZMNtHY3s328ma2lzfR6QvymUuKY7KdRKViWERklFXW1oIZnsGuyA7Bw5sbOVDv48sXTKIwI0pv7cEgPPofUFcCH3gUcmdGZ70icSYYDNLY3ExlbS3Nra1vPOECnPLcp/H6WticdxlBb9qIt9XhC7KrppsDDd2UNvg40OCjpi3Q+/ycXC9nFbadcB0zpk5lzvTpI25LolAxLCIyinx+PyWHDpGTGVl/3sEEgo5NFZ38eWcLlxZlcebsKF6ZevGO0A1zl30PFlwYvfWKxInW9nYqqqs5VFFBl89HitdLasobn45MO/gEmc0l7Dz9Frrwgt8/5G34Ao6Seh87qrvZVtXN/gYfQRdef1YSC/K9XLwgnbl5XublJZOd6sF3gu20d3aSkZ6uYngIVAyLiIySbp+PwxUV+Px+krOyhr2eI80+7llbT1Wrn8bOAM7BzBwv15+RF73G7n4KnrsdTn0fnPPx6K1XJA4Eg0F2l5ZSWlZGksdDVmYmOcdkNqm7mfm7/5vmyafROu9S0obRNek3Gxr4+55WugMOMygqSOGdy3I4eVoaCwtTyEge+qdH/kBg8IXkKCqGRURGQWNzM+u2bCEQDJI/whEkfvN6AwcbuzlnTgYF6UkUpCdx5uwMUr1R6nZRuxf+dCNMPxWu+jFEq/+xSBwIBAJsLymhrLKSwry8AbszzdzxAEndrRxa/tlhZ+SJnS3MzPFy3Wl5LJuSRmaKpn8YCyqGRURGwaHycrxJSRSM8Ka53TVdbCjv5NrluVx9cvRuwOvV2QS/uwaSUuB9v4Xk9OhvQ2QcK6us5HBlJZPz87EBitz0pn1M2fco1QveSUduUcTrbukK8JddLTR3BWnvDgKwam4GZ86KzY13EhkVwyIiMdbZ1UV5lEaP+N3mRnLTPFyxJLrjEwOhG+b+9DGo3w8ffBzyZkd/GyLjWH1jI7tLS8nPzh6wEMY55my+m0BKFkdOuiGi9TrnKG3w8f+9Ws/eum4Apmd7OXlqKmdHs5+/DIuKYRGRGKuK0ugRu6q72F7VxfWn55EWrS4Rff3rv2DPU3DFD2D++dFfv8g45fP7KS0rY09pKZkZGSQnJw+4bMHhZ8ip2UjpaV8kkJJzwvWuO9TOusPtbKvqoqEjQJIHVs3J4DPnFuL1qPvReKFiWEQkxg5XVJCZPvLuBn/a3kR2qoe3FA//5ruBeHY+Dv/6Pqy4Ds66MerrFxmvqmpr2bJ7N/5AgEn5+Sc8aU3qbmHOlp/Rmr+UmvlX9buMc45Ov6Omzc8dL9aSnerh1GlpnDItjbNmp5OdmhSrXZFhUjEsIhJDHV1dtLS3Myk/f0Tr2V/fzcbyTq5Znhv1q8I5zXtJ/tfXYPY5cOVdumFOEkIwGKSptZVte/eSnpZ21JBpA5m54368XY3sOfcHYG8UtU2dAWrbAqw73M7f97TQ7nO9z33srALOnqOuEOOZimERkRhqbG6Oynr+tK2JjGTjskXR7Suc3FHDKVu+A5mT4X3/A97UqK5fZLwJBAJU1NSw79Ah2js7SU5OjqgQzmjYFbppbuHVtOcv6X38uX2t3LuunsAb9S/XrcgjO9VDYUYSp04f+UQcElsqhkVEYqiytjaiA+2JlDX5ePVwB1eflBPVoZc8/k6K13wVr7+D7vf8H6lZk6O2bpHxav/hw+wuLSUvOzvyT2xcgLkbf4Q/NY8jy964aa6yxccv1tazeFIKq5flkJXiYVq2l4JozQIpo0J/LRGRGAkEAlTX1ZE3gnGFnXM8srWJlCTj7dEcQcIFmb/+u2Q07mHDqd/gpCknRW/dIuNYWVUVBbm5JHsjL4EmH3iCrIad7DvzFgIpb+SwuSs0PNrVJ+dyxkwNQxivBv2fYGazgd8A04AgcJ9z7sdmVgD8HpgHlALvdc41xK6pIjIY5XX8CAaDVNbWEggETnhDTtA5Kpr9VLb66fQF6fQ72rqDlDf7ONTko6zRR4ffsXppNjlp0bvxZsbOX1Fw5DkOn/wJaiafHbX1ytAos6PL5/PR1dVFVkbkfXi9nQ3M2nYvzZNPo372pf0uo4Eh4lskp0V+4D+dcxvMLBt43cyeAT4E/NM5930z+yrwVeArsWuqiERAeR0jbR0d1Dc20tnVRXtnJzX19XT5fOSeYNrldl+Qzz9RQX3H8dOnZqd6mJOXzIULMplfkML58zKj1taCw/9g5s5fUTP3bVQuugaamqK2bhkyZXYUtXV0DPkG0dlbf47H387BFf8JZlS2+Hj5YDtbKzspa/IBkKSbTuPaoMWwc64CqAh/32JmO4GZwDuAi8KLPQg8j4IqMqaU17GzcccOmlpaSElJIcnjITMjg9ykE1/JrWzxU98R4Kql2ayak0F6soc0r5Ge7InZtKxZtZuZv/67tBSeysHTvqiRI8aYMjs6AoEA+w8fZn9ZGd4hjPedU/Uakw79jfLFH6AxYw5/397M77Y0EgjC/IJkVsxIZ25eMkum6MbTeDakPsNmNg84DVgHTA2HGOdchZlNGeA1NwI3AsyZM2ckbRWRIVBeR1d7Z+egY5Qeq6kzdEX47NkZFE+K/cE0reUQxa98ja6Maexd9T1c0shu7JPoGmpmldfI+Px+Dhw+zN6DBynIy8M7yElqD4+/gzkbfkCVdyYfOnQ5JVvKCDooSE/iu5dNZVKmbruaKCL+S5pZFvB/wOecc80DTlN4DOfcfcB9ACtXrnSDLC4iUaC8jq5AIEBwkL7B/WkOF8O5abG5CtyXt7OB4pe/iDMPe8+9g0DqyKeGlugZTmaV18G1trezduNGfBFMqHGsmTseIL29guu7bqYtO5l3Lstg0aRUlkxJjdknNzI2IiqGzSyZUEh/65z7U/jhKjObHj5jnQ5Ux6qRIhI55XX0+fx+hlOJNHaG7kTPjeKNcf3x+DspfuUrJHfWsfuCn9CVNTOm25OhUWZj51B5Oc5syJPeZDTsYureP/B8xmVsDSzjv98+XdMnT2CDntpY6PT0AWCnc+7OPk/9Gbg+/P31wOPRb56IDIXyOjb8gQCRXn3vq6kzQHKSkeaN4UHWBVjw2q1k1u9k/5nfpK1AQ6iNJ8ps7ASDQQ5XVJCTObSbTy3oZ/7r38eXls8PAtewZHKqCuEJLpLr/OcCHwAuNrNN4a+3Ad8HLjWzvcCl4Z9FZGwpr2NguFeGmzuD5KZ6hlVIR2r2lp+TX/4Ch0/9NI0zL4jZdmTYlNkY6ejqwjk35O5LU/f+joymEg4s/wJ7W1KYnZscoxbKeBHJaBIvAQO9U18S3eaIyEgor2PD7/cP+Es/kcbOQEy7SEzd+3umlfyByqJ/o6r4vTHbjgyfMhs7HZ2dQx4tJa3lEDN3/Ir6GReyO/dN+IIVzFIxPOHpVkgRkRHy+f3Del1TZ4CC9NgUw4WH/s6cLT+lfuZFHD71UzHZhsh41t7RMbSTVBdg/vrbCHrTeHzKf/DQS3UAKoYTgG6HFBEZoe7u7mH2GQ6SF4NiOKdyLfPW307z5NPZf+YtYLG9QU9kvOn2+ThUUUFqauRDFk7b8zBZ9TtYM/9T3Paqo8MX5K3FWcwv0BCEE52uDIuIjJDP7x9yv8SgczTFoJtEZv12itZ+g47chRpLWBLW7v37aevooCA3siEE05oPMHPH/dTPuJAv7DmVrBT43uXTNIRagtBfWURkhLp9PjxDvDLc0hUk6CAvisVwWvNBil/+Mr60QvacewfB5OhN4SwSL5xzlNfUkJedHdkLgn7mr7+dgDeTlxd8htqOIBcvzFIhnED0lxYRGSFfIEDSEK8M98w+l5cenbfh5PZqFr38BbAk9px3J/60gqisVyTeNDQ34x/CJDjT9/yOrIadrCv6DF99wUdhRhKXFGXFuJUynqibhIjICPl8PmzIxXD0JtzwdjWw+KUv4O1uYdeFP9OkGpKwgsEg2/fuJSsjI6LlMxr3MGPHA9TNfDNfKTkZCHDrpdOYkqXyKJHoyrCIyAh1d3cP+cpwY0fPleGRFcNJ3c0sevELpLRXsOdNP6A9b9GI1icSzxqbm2lpayMjLW3QZT3+Tha8eiv+1DzuTvkoR5r9XH1SrgrhBKRiWERkBJxztHV2kuwd2gG0saebxAiuDHt87Sx6+Yukt5RScs7ttE5eMex1iUwEe0pLSY+gEAaYtvnnpLeU8tvJn+OhnXDu3AxWL42wn7FMKDr9EREZAb/fTzAYHNJoElWtftYcbCc1ychIHt7scx5/J8Vrvkxmw25KzrmN5mlnD2s9IvHOOUdDczM1dXXUNTYypbBwwGVbugI8tKGR2Q3ruLXjUR7wX8F39i5g8aQUbjy7IKazQcr4pWJYRGQEjlRXEwgGI17+9bIOfvhCDd4kG/bB1wLdFK29iezazew/6xYaZ5w/5HWIxDvnHLUNDWzZs4fOri5SvF7yBxhKrazJx73r6tlV00UhTfww9eccTp6Ld9Wn+UVOBoWZSUMeEUYmDhXDIiLDVNvQwPa9ewcdy7Si2ccTu1rYW9tFaYOP+fnJfOWiyRRmDP0t2IJ+Fr76TXKrXuXAGV+lfvalw22+SNxyzvH69u1U1dWRk5lJTubAwwgebuzm5meq8XrgqiVZfKX5pxQ0drDjop+wLDdnFFst45WKYRGRIWrv6GDTrl00NDeTnZmJ9wT9hRs6Atz8TBWdPsfiyam8++R03r4km+zUYfQVdgHmr/8u+eUvcnD556idd+UI9kIkflXU1FBVV8eUghMPIbilopPvP1+NmXHrpVM5o/6vzCp9hUOnfoaO3IWj1FoZ71QMi4gM0e7SUlrb2wc9EAPsrumiqTPIt98yhWVTI7uxp1/OMW/DHRQefobDJ3+M6qL3DH9dInGsq7ubLbt3RzSpxoGGbnxBuPvKaRRxmNlbfkrTlJVUKT/Sh0aTEBEZoo7OTtJTUyNa1hdwAOSPZAg1F2Tuph8xufQJypd8kMrFHxj+ukTiXEdnJzhHSnLyoMvuqOokN83DrAwfC9fdQtCbyf4zbwZT+SNv0JVhEZEhcsFgxDe+dYeL4eSkYd6c44LM3XgnUw48RsWi6ziy7KPDW4/IBOHz+3ERLlvR4mfplFTmbb6LtJaD7D7/LvxpA482IYlJxbCIyBAFnMMb4VBqvpEUwy7I3I0/YsqBx6lY/O+UnfQx0B3vkuD8gUDkywYdb+56jknVT3FkyYdombIyhi2TeKViWERkiALBIMlJkXV76LkynDLUYtgFmbvxDqYc+DPliz/AkZNuVCEsArS0tpIUQf7+d1MjOe2H+FDwXponraB82YdHoXUSj1QMi4gMkd/nwxNhn+EjzT6SPZDqHUIh6wLM2/BDJpc+qUJYpI/m1lb2l5WRk5U14DJNnQF+v6WJdftqeSL9J5g3nf1nfQtsZFOfy8SlYlhEZAha29vxBwIRzTj3+y2NPLuvjbNnp0c8oL8Ffcx/7TYKy/7JkSUfonzZR1QIiwBtHR1s2LEDb1LSgNOfd/qDfO+5GvbVd/FQzgPM6T7CnrN+hC990ii3VuKJimERkSHYUVIy4IH4WP8saWP59DQ+d15kB2ILdFG09mbyKtdw+ORPULn42pE0VWRC2X3gAD6//4RDqj22vZl99d08WPQC55e9zOGTP0bz1DNHsZUSjzS2iIjIEDS3tJB9gtmuerT7gjR0BDhpaipez+BXdj2+dha9/CVyK1+h9LQvqhAW6aOto4PK2tpBxxZ+9XAH7y0o4YIj99Mw4wIqF/37KLVQ4pmuDIuIRMgfCOCLsItERbMPgBk5g4+FmtTdzKKXv0hmw24OnPkN6uZcNuK2ikwkjc3Ng169q2v3091UxdczfkRn5kz2r/y6uhhJRFQMi4hEyOfzRTy+cHmzHxi8GPZ21rP4pc+T1nKIkrO/Q+PMC0bcTpGJpqahgZSUlAGfr2jxcddzR/hlyt2k08XuVd8lmDz4JzgioG4SIiIR6+jsJJJS+HBjNz9ZU4cB07IGvuaQ0l7Fkn99ktTWI+x90w9UCIv0o6a+niOVlWSkDTyd+S/X1PG57ns53VPCoTO/TmfO/FFsocQ7XRkWEYnQwfLyiK4M76rpAuADp+cNONlGasshFr/0BZK6W9hz3o9onbQ8qm0VmShKDh4kMyOj37GFA0HHn7Y3c1HDI7wj+UXKlt1Aw6w3j0ErJZ7pyrCISIQaI7x5rro1QJLB2xb3f7NPZt02lj7/cTyBTnZf8GMVwiL9cM5xqLycuqamfq8Kt3UH+fyTFTRt+wdfSX6YmpmXULHk+jFoqcQ7FcMiIhHwBwJ0dXXhjWBYtYON3UzK9JLUzygSeeUvsuSFzxBIzmbnRffQnr8kFs0ViXuVtbVs2bOHwtzcfj+ReX5/KwWtJfw07Re05i/l4Jk36YY5GRZ1kxARiUB7RwcuguVq2/xsr+ri/PkZxz03ef9jzN14J235i9l77g/wp+ZHv6EiE0RpWRnZmZnHnYC2dQd5dHsza3Yc4sm0H+FScylZ9T1cUmSzQoocS8WwiEgEKmpq+u2z2FenP8j3/1WDGVy9LPeNJ5xj5vb7mLH7IRqnvYl9Z3+boDc9xi0WiU/BYJBte/dS19TE1MLCo55r7Ajwn3+poKurkz9n3kWBa2PPm36hGeZkRAbtJmFm/21m1Wa2rc9jBWb2jJntDf+ryxsi44QyG31d3d0cPHJk0P7CWys7Odjg48azCpiaHbrWYEE/89d/lxm7H6J63lXsXXW7CmHppbweb3dpKYcrKphSUHDcc//c10prl58/T7mXRYESDp51M+15i8aglTKRRNJn+NfA5cc89lXgn865YuCf4Z9FZHz4NcpsVLW2txMIBvEOcmV4X103HoOzZ4eK3aTuZopf/iKTDv2NsmU3cPD0L4NHH8jJUX6N8torGAxSXlVFQT/9hF8v6+CJnU3cnfkgi5tf4dDyz9Aw88IxamlknHM45wgGgwSDQfx+Pz6fj67ubjo6O2nr6KClrY3m1lZqGhqobWigrrGR2sZG6sJfVXV1VNfX9/7c+xVetu9XbUMDHZ2dEU8ZLyGD/raccy+Y2bxjHn4HcFH4+weB54GvRLFdIjJMymz0dUc42cbhJh/Ts72kej2kthyieM1XSG2rYP8ZN1E3722j0FKJN8rrG5xzbN+7l87ubnKyso56bnNFBz94oYavpT/G6sAzVCz+d6qL/m3U2ubz+Wjv7MQfCITuHXCu9z3B9XzvHA6OehzAY4Z5PHgAT1ISXq8Xb1ISKcnJJCUlkez14vV6SU5KIi01lWSvF/N4MDMsvD6PWe+kIz3rtz7fE1625/nBTtzlaMM9dZjqnKsAcM5VmNmUgRY0sxuBGwHmzJkzzM1JVAUDsOYnkDkZTovevO0VTR3c+fQebjh/AYunnXj+eBl1EWVWee1fe2dnRFMwt3QGyU1LIqfqNRauuxlnXg2dJsORkHktr67mUGUlk/OP7hXyUmkbP11Txw3pz/PR4B+onXM5ZSd9LOrbd87R7fPRES56+xa1aampzJw6lYz0dNJSU0nyePB4PCR7vb1FLbxRoPYWrBrdIi7E/Dq6c+4+4D6AlStXRnIztkRZU0sLe0pL6fb5SOmoZtGG75Fbt4mq2ZdT4paNeP3tviBP7mrlqT1tBB3ke1q4MHwnfUpyMqctW6az1DihvPavu7sbTwQHtZbuIB9OfoZFLz9AR/Zc9r7p+3RnzhiFFkoimkh5bWppYfPu3eRnZx9VQG4s7+AXa+v5YN4WvtZxP41Tz6H0jK9GbQi1ru5uWtvbCQaDJHk8ZGVkMG/mTLIyM3uv2qanppKakqLCdgIbbjFcZWbTw2es04HqaDZKoqe8uprNu3aRmpLC9JqXKd5yJxb0s2fFl6medRm44b9/+oOOZ/e38+j2Vlq6HW+ak8Z7Ts5iSqa39+Oh2oYGAoGAiuGxp8yOQDAYHPTKsAv4+ETn/8e1XU+HRow461sEk48fXk0kAgmV10AgwKZdu8hITSU5Obn38ZauAPeuq+f/b+/O46OszoaP/+6ZSWaSycxk3xOyzEDYNxUBiSyKiEpVVJRXrXax1WrFvrVWfepG69bHFbuo6PNiVapSa7VStS4PCMgOAoGEmex7QvZMJrPd5/0jyUgELEu2Sc738+FjEu/MnJPkmvua+z7nuuYa7PzG3VWSsPDclYgzXHfv9flobm1FURTCDQbGZmdjjojA3J0ASyPP6f5FvQ98H3i8+7//6LMRSX2mqq6OPQcPEhMeQnbec8SVrqc9aixF5zyIOyKV063IKIRgW7mLN/c2U93mY3yCnhumRpIdc+wjtnd0nNkkpL4iY/YMuNxutN+RDGs9raRt/g3nKLvYEnsVIbPuAEWeVKXTNqLitebIETpcLmK7l0cIIdhf42b1jkZS3Xb+YHgcb3g89tlPnnYlFq/PR5vTiaqqKIrCpDFjSIyLkxdqJOAkkmFFUdbStZA/VlGUCuBBugL0bUVRfgiUAQO3il06KZW1tew9dIg0fyWjN/wOvbOaypybqB570xm9qy6od/OX3U0UHPGQagnh13PjmJZskLePhhAZs33L7/fT3tFB6Al2Z4c128nach8hrjru9t7C2ROvJVMmwtJJGsnxqqoqZdXV5DkcRJm+2WfyXl4rb37dwnR9OW+GPYEItVAw57lTalLT3tGB2+Ppeh4hCNPrGZWcTExkJCajEYNeNuiQvnEy1SSuO8H/7XbiewAAIABJREFUWtDHY5H6SHl1NfsPHWBS7QekHn4dT1gc+eevOqNNPNWtXt7Y28y2cheRBg0/mRHNvCzjcdvNSoNLxmzfUVWVovJynB0dGL9V/B8g9PB6xhz4bxrUCH7qeZBZM84hMzp0EEYqBauRHK91DQ0csNuJtlgCpcC8fsH6gjYuiq1nlecx0BrIn/MsnvCEk3pMVVWpb2oiPjoa26hRGPR6wgwGjGGytrd0YrIQ3TBTWllJ2e6POd/+AsbWQo6kX0TZ5BX4Q0+vukNLp591+1v4t70dnVbhmkkWLs0xERZyMiWqJSl4tXd0cLikhKq6OmIiI3v9P0X1Yt7+HKMr3+Mr/zieMf2Cy+dkMD7BMEijlaTgU1FbS0RYWK+auHuqXJjd1Twd8lsUjYb8Oc/hjkg56cdsam0lNTGRCTabXAIhnTSZDA8TQgiKS4vxffEEs8vewaePxD7zcZqTzzup7/f6BUecPho6/DS6/DR2+Klr97GpxInbL1hgjeDqiRaiwuSLizT8+f1+dufl4fF6iY+O7rUMKNRZQ9a2BzA1HeQvXIrpwhXcHSWTYEk6Faqq0tjSguVb9YQP2+28FbqSUMVPwZxVdJpOvmScs6MDvV7PeKtVJsLSKZHJcJASQuB0uWh3OmlzOnGX7yZt2yNY2otpSFtI6ZQV+EPNx3xfm9tPfp2b8hYvte0+att91LT5aOzoLiR+FGOowsQkA9dNjiTVEnLMY0nScHWkqYn2jg7ivtUONrLqS9K3/w6v38+tnjsZdfbFLJCJsCSdsvqmJrw+X68qLbWlBdzfeD/hOkHBnGdxWbJO+vGaWlvRh4YyafRo2X1NOmXyLyaIeH0+6hsbqamvp6G5Gb/fD8KPrfQdxhX/FV+oBfvMx2hOnkNLp5+K2k4qWrxUtnqpaPFS0eKjyeUPPJ7FoCExQse4eD2JJh3xETpiwnVEh2mJDtfKpRD9TJxBWTupfwghqKqrY//hw5iMxsDXFdWL7qtV2GreZZ+ayW+0K7hg9ljOTZel0yTpdFTV1RF21CY2Q7OdObvuwo2G/NxViMiTT4TbnE50Wi3nTJxImEG+OZVOnUyGhzC3xxO4+tvQ0kJ9YyN+VSUsNBST0Yip1UHG7scxttipTbmQv5h/zMZDOoq3VNDmVgOPY9AppFpCmJRkIM0cwui4UDKiQmWyOwCEEDQ0NGC323E4HDgcDm699VYSExN57rnnBnt40lH8fj/7CgqorKsj2mwO1Dv1Hikh5auHGeWx8z++iygZfxu/GhtNqHZobh4VQiCEwK+qgY973nj1fKwe9XW1u+uW1KWjo4PCwsJAvE6ePJmFCxdSXl4+2EMbNlydndTU1xNtsQAQ3pRP1oa7aFJDeCPrceZGn9oVYQWYMXmyTIRHIL/fT1lZWSBeOzs7ueuuu075cWQyPAS5OjvJLyqitqGhq885oA8NDRQE13idpO5fRXzhu3hCLbwcfx/PlE2kw+sjxQznpIWRZgkh1RJCijmEmHCtLH3Wj4QQ1NfX43A4sNvt5ObmkpmZyccff8yyZctoaWkJHKvRaFiwYAGJiYnMmzdvEEctHa21vZ2v8/Npd7lI6KkaIQT6/HeZcPCPuIWOpyy/YvzsxUwI79+XTVVVcXV20unxBJLYE8avEIFOXD3HahQFjUaDLiSEEG1X7Gs0GjSK0vX6odF0ddbq/lin1WLQ6wkNGTlLoZxOJ4WFhdjtdiwWCxdccAF+v5/s7GxKS0t7HbtixQoWLlxIcrLsJNgX/H4/BSUlgXbGxiNfk/Xlr6j3hXNP+EPcPnnsST1Oa3s7bo+H+JgYJthsslTaMObz+SgrK8Nut1NRUcEPf/hDAG6//XZeeuklvEe9mU9JSZHJcLATQlBRU0NeYSE6jYZoi6X3SVAIoss/I23f84R0NvJJ2EXc3byU9jYjM9PDWJxjYnSsfEHoD0II6urqcDgcJCUlkZWVxeHDh7nuuutwOBy0trYGjn311VfJzMwkMzOT66+/HqvVis1mw2q1kpGRgb77RXvy5NMvdSf1HZfbzfb9+9FpNMR2V43QdTYQ/9WjpDRuY5M6ibKz7yU3I61fnt/v99Pc1gZ0/Z3ptFoiwsMZlZJCaEgIWo0GnU5HiE6HVqtFq9Wi02hQNBoUvkmUFUWRb3qP0t7eTmFhIR0dHcycOROAq6++ms2bN1NdXR04bvHixVxwwQVotVqWLl1KVFRUIF6zs7OJ7P6bkJ3JzpyqqhwuLaWipoa4qCgiKzeSse0hyvwxrLQ8xG3zxxLyHXdchBC0tLfj8/mwmExMzskhymyWv5thwOfzUVpaisPhYP78+YSEhPDiiy/y1FNPUVxcjM/nCxx77bXXYjQamTFjBhEREYF4tVqtp/2mVSbDQ4Srs5M8h4PahgaizGZCdDpcXpWyZg8ur4q+vYI5JavIbNvNYSWLX7pXUKhauWCsiUWjI4g1yl/lmRJCUFtbi9/vJyUlhba2Nn70ox8Flji0dScsDz/8MA888ABRUVHEx8cza9asQDDabDYyMjIAGD16NC+88MIgzkj6T9weD/sLClBVFWP3rva2g58xPf8p9KqL34rvY5h2LednnF5pwh5+vx9VVfH5/bg6O4/ZrJqWlERGd/IrN/+cvLa2NqqqqhgzZgwAjz76KB999BEOhyOQ8E6cOJF9+/YBkJiYyEUXXdQrXrOzswOP99RTTw38JEaQ6vp6CsvKiIuKIqHkfdL3PM3XahZ/in+AW3Mz0XzHmzmfz0djSwspCQmMSknBEhHxH1ukS0OL1+ulpKSE5ORkjEYjn332GU899RQOh6NXwpufn8+YMWOIjIxk8uTJXHXVVb0uKoWHd+3VuOGGG/psbPJVd5A0trTg6uzE6/PR6XZTUlmJTqslPjoal1clv6aTVVsacLuc/FT3PjdqP8SDjgd93+cL48XMH2fml9kRct3vKRJC4HQ6iehOfB544AEOHToUWG/U3t7Oj3/8Y1566SWMRiN5eXmkp6dz3nnnBYKx54puXFwc//rXvwZzOtIZKKms5HBJCUIIosxmcLeh+fIZ5rd8wiGRwd9T7+bcKROwGE7tqpMQgtb2drx+P4oQCCBEqyVUr0cfGkp6cjIWkwmdVktoSAihISHyiu53aG9vD8Tru+++ywcffBB4g1pbW0tkZCSNjY0oikJNTQ0AixYtCpw4exJlgFWrVg3KHCTodLs5YLcTZTKRdugVkvPXsIFpPKJfwSPnfXci3Ol20+Z0MjY7m1HJyfJK8BDm9Xrx+/0YDAYOHz7MqlWrAvFaUlKC3+/n008/ZcGCBbjdbmpqapgyZQpXX3114OpuenpXOb1ly5axbNmyARm3TIYHmKuzk8aWFvbm56Ppvq2p1Who9ev53O6ioL6akmYvCJWrtF/yG+M7mP2NlCXMpyDnNuZExHGJXiNPnifp7bffZs+ePb02sM2ZMyeQxL7zzjv4/X5sNhu5ubnYbDbOOeccoGt974EDBwZz+FI/UFWVkspKDhYWEhMZiU6rxZX/GePyniVKNPO38KUkXnAbF4ae/JKjTrebjs5OVFVFARJiY0lOSMAYFoZBr5c1T0/Snj17WL9+fSBe7XY7dXV1tLa2YjKZ2L59Ox9//DE2m41LLrkkkPAKIVAUheeff36wpyCdQFlVFYrqY/T+p4gr+ZAPtfO5q+NmHpmb/J2bUTtcLpwuF2dPnHhMqUNpcDU1NfHaa6/1itfS0lJWr17NTTfdRGtrK2vWrMFmszF9+nSuvfZarFYr48aNA7qWKC1evHiQZ9FFJsMDQAjBkaYmDhUV4ezoAEXB3N0b3elReXbTEb6ubkanVRgTE8KvMwu5suU14px22i3jODjpUZwxE4j8z0814uzZs4ddu3b1CsawsDC2bdsGwJ/+9Cc2b95MVlYWVquVefPmcfbZZwe+Py8vT95qG0FUVWX3wYPUNTQQExmJwdNE2Ob/5uyWTRSIdD4dv5KsnMkn9WZTCEFzayt+VcUUHk52ejqRJhMmoxF9qGzJfDzV1dVs2LAhEKs9cbthwwbGjh3Lli1b+K//+i9SUlKwWq1cdtll2Gw2VLWrOs6jjz7K448/PsizkE5VYXk5JY79zCh4BsuR3bygXsmL4mpW5MaQHXP8N53tHR04XS4iIyKYMWkSMVFRAzxqyefzBZYdHR2vP/rRj7jvvvvweDysWLECs9kcuJC0fPlyJk2aBMD06dNpaWkJiot3MhkeAPnFxRSWlmIxmYg9KqCFEGwsdrK3upOLbBH8OLWM0fbVmKv24g5PoPDsB2hMuwCUkZus1dXVkZeXFwhGh8NBTU0NmzdvRlEUnn76aV5//XVCQkLIysrCZrMxYcKEwPevW7cOi8WC7gTrMGUiPHJ4vV6+LiigrqGBuKhIIhwfkr7/BXSqh5dDl2OZdRPZsd9dN1gI0dXkxutFoyjERkXJnexHcblcHDhwoFe82u12nnjiCXJzc9m+fTvXXXcd0LXr22az8b3vfY/Q7jcP3//+97npppswHlXj+WgyXoNPaWUl5fu+IDfvMUJc9Tyk3MqHIXP5/cIEYo5TmUVVVeqbmrCYTJw7eTLRFov8vfej/Pz8Xomu3W7n3HPPZeXKlSiKwtKlS/F4PFgsFmw2GzNmzCAnJweA+Ph46urqiI2NPW7CGwxJcA+ZDPejns0y1XV1XbdjuxOy/TWdvL2vhfIWL06PyuzwMp7wvEfkV1vx6qMpnbyC+swlCG3fXF0SQqAeXXMUetUe7TlGfPNJr48DHx517PGe43h6ruiciKqqVFZW9gpEh8PBmjVrMJvNPPvsszz22GMAhIaGkp2djc1mo7Ozk7CwMB555BFWrlxJWlracdeRxfSUyZJGNJfbTZ7dzpHGRkYpdSR8+mtiWw+yTc3hw+Sfs3jmRLSaE79we7xe2trbUYUgPiaGjJSUEbuLvampqdeyI7vdzrJly7j00ks5dOhQYJkRQFpaWq8ru3PnzmX//v1kZWUFNsEcLeJbrXml4Fbb0ED91jeZc/Ap3JowrnbfT5lhLHfnxh2TCKuqGqgUYU1PZ3RGhkyC+0BnZydFRUW9zq/R0dH87ne/A7qWKhQXFwMEKqmEhYUBXRVUtm7dSlpaGjExMcckt4qiEBcXN7AT6icyGe4n7R0dbN27F5/fj0ZR0BmN+FVBeYuX5zcfQRVwbWIVV3X+jbGtm/E1miif8FPqspei6sLO6LlVVaW5rS1wAtIoCjqdDo1Wi7a75ujRtUeVb/2Drj/yng0NSvcL0rfLOPX8tyc8lKNeuHq+ptPpUCBQMqUnGG+//XYyMjJ48cUXue222wLfp9fryc7O5siRI5jNZm688UYWLFiA1WolNTX1mOQjMzPzjH5W0vDX2t7OAbudzsZKxhW/SWrFehqEiV+rP8V63hVclnz8q5CqqtLmdOLx+dBpNIyzWok0mzEZjUF1xeNUCSFobGzsFa+TJ0/miiuuoL6+nvj4+MCxiqKQlpZGbm4uADk5Obz33ntYrVaysrICJ9UeFosFS3ejBWl4c3V20rL+IaYX/oU2y2h+4vkFjhALzy1OIkJ/bJLb0NxMUnw8tlGjiDjOGyXpxFwuF0VFRYF4dTqdPPjgg0DXZtINGzYEjo2Ojmb+/PmBz19++eVAebLo46zJnjp1av9PYAiQyXAfU1UVl9uNo7QUVQjCjWa+LHayaVstRU0e3D6VBSH7eSTqI1Lq9uLTGanMuYna0dfiD/nuqyI9V3iP7h7l9/vxdf9ThUDp7iiVlphIdno6Br1+QN5d+/1+ysvLA1eL5s6dS05ODp9//jkTx4zB7XYHjjUYDCxatIiMjAzmz5/Pn//858BGmNTU1F7jzcnJCdySkaRToaoqZdXV5NsPk1nzEaPsr6P1uXjVfzEHM2/kkklJmI9TKaLN6aTT4wEgLSGBuOhooiyWYbUOuKczYk+8ms1mlixZghCC1NRUqqqqAscqisIdd9zBFVdcQWxsLM8880xgDX5WVhaGo7p+hYeH873vfW8wpiQNId62erx//QGjKzdij5nPdUduosGt42czo45JhN0eD63t7USEhzPBag10fpR6c7lcgUYxJSUlrFixIhCb3y7hmZaWxgMPPICiKPziF7/glltuCVRq+HbCu2DBgoGcxpAlk+E+4vZ4KKuqoqSyEp/fT0WbYHu1YHNpJR1eQUaEyn0JO7i44wPiXUV4PLGUT7yNuszvoYYce2XK6/Xicrvxdtfd69ktrdNqCdHpUDQatBoNxvBwjGFhhOn1hBkMhIaGEtZdwqmvr171tD202+2kpqYybtw4iouLWbx4MUVFRXi6EwiAF154gZycHGw2Gz//+c971fVMTk4OJLxjxozpVfpIkvqC1+vFUVZG8cY3mV75Jgm+Kr70T+AP+h9w46JpTI449hat0+Wiw+UiJjKScdnZQZ8ACyE4cuQIdrsdp9PJhRdeCMDSpUv5/PPPaW5uDhw7d+5clixZgqIo/OQnP8FkMgXiNTMzM9AoRlEUVqxYMSjzkYKDt2Qr/rduJKKzgY8Tb+GnpeczKjKUXy+IYVTUN/Hk8/tpam0l3GBg0pgxJMTEjPhEuKczosPhYNGiRYSHh/PSSy+xcuVKKioqeh174403EhMTw4IFC4iPj+9VNzvqqL1JS5YsGehpBCWZDJ8hVVUpqqiguLwcv6qi1RvZXOHijT3NhGoVLk1s5MeGz7HWfYKuoQ2XKYPi6ffSkL4Qofkm8N0eD21OZ+DzMIOBhNhYoszmQKvUiPDwfr8929P2UAhBdnY2nZ2dXHXVVdjtdoqLiwNtD++55x4ef/xx4uLiGDduHEuWLDluF5i0tDSefPLJfh2zJPVobm2luLKS6j0fY3WsYbFaxGE1ldWmX6Ozns/tGUb0Ok1gfWLPWnqdVktsVBQ5WVnERUcHTeOLnlbgFRUVTJs2DYCVK1fy3nvv9eqMmJ2djcPhALrutiQmJvaK16OXGz3wwAMDPxEp+Kkqvi2r0H72CD59NH9Of5InCxKZnmJgxXmxGHTfXBFuczpxezyMzsggIyVlRJUedDqdOBwOMjIysFgsfPHFFzz00EM4HI5ed2T27NnDlClTSElJYf78+b3i1Wq1BjojXn755Vx++eWDNZ1hIzhe8Ycol9tNWVUVh0tKcAojf93fxv7aVnSqh+sj9rHC8r/ENOxGVbQ0p5xPXdbltMVOBUXpWtrgdtPpduPz+1E0GiaOHo3FZCJEp+vX3ek+n4+WlpbA5rJ7772Xffv2BbrAeL1eli9fzhtvvIFer6e5uZkJEyZwxRVXBN599tQJjIiI4G9/+1u/jVWSTobX66WoooKK3R+ReHgti91fUyliWZf8C2KnX8ZCfdcbz063myNtHQCMSk4mPiaGiPDwIV0NoqcVeHx8PIqisG7dOt55553AEofW1lb0ej0dHR1oNBo8Hg9xcXHMnDkzEK82my3weD0bZySpzzSXo/79p+hKN1ERPYOHtXfw7wIN52WE87OZMei6N6f6u68GW0wmzp44EdMJqoYEu/b2dqDr/OhwOHjssccCa/B7OiP+4x//YMmSJWi1Wnw+HxdeeGGvLms9ywMvueQSLrnkkkGby0ghk+HT4PP72VdQQE19PdVOQX6jln8druds5RCvR29lumsLoT4nblcCFeNvoT7jEnyGGIQQNLW24vf70YeGYtDrSUtKIspiwRQeTnjYmW2cO5qqqoGlCG+88Qbbt28PLK4vLi5m5syZbNy4EYANGzbgcrmYNGkSS5cuxWq1BhbNK4rCpk2b+mxcktTXmlpa2P/p6yQefoMF7jyOCDNvRf6QmHOXMSqiayNOzxIIc0QEY7OzSYyNHVIJcE81FkVR2LVrF++++26vig1tbW3U1tYSHx+P3W5n9+7dWK1WZs+eHbhS1BPzK1euHOTZSCOGELDvbcT6/4vw+1gbfSsP1c5BReHaSWaumGBGoyioqkqr04nX58OWnk5mWlrQXw3uibeWlhb++Mc/9orX6upqXnjhBX72s5/h9XpZv349VquVRYsWBeK1p+pKbm4umzdvHuTZSDIZPgVuj4eG5maKKyr47HATHxd6iW63c6l2K1+EfEWcaMDfGUZTSi4NaQtpiJlCp8eHp9OL2tHVLjQxNpYxmZmEGQx9tuRh27ZtbN26tVepI41Gw+HDh4GuZPjLL7/EZrMxZcoUrrrqKqZMmRL4/i1btvTJOCRpILW1tVKx6Q0Me14l13OYGhHFK8YfYDnrKrLizHS63TQ0N6MKgcVk4hybjZjIyEGvBFFVVcVHH310TDnBjRs3Mm3aNPbt28eTTz5JZmYmVquV8847D5vNFqjFe++993LvvfcO6hwkidYq1A/vRlPwTyrCx3Kb+yfsr4pnZno410+NJD5CR6fbTXtHBwqQkpDAqJQUzEFWPk9VVdatW3dMvN5888089thjaDQa7rvvPpKSkgIJr81mY9asWQCMHTs2cDVYGrpkMnwSvF4v+0uq+GBXITvKO0hsy2OOuoO/h+wmXt+AqmhpTZiBPe1CGuLOxa2E0OZ0YnB7iYuKItpiITwsDLPReFobBKqqqtizZ0+vQCwtLeXAgQNotVpeffVVXnrpJUwmEzabjbPOOosxY8YENt298847hA/AemNJGgj1ddXkf/gCtrK3GCvqKVPjeNn8U1JmXsl4ox6ny0V9YyPGsDDGW61YurvCDdTff0dHBzt27OgVrz23Si+++GIOHjzID3/4Q3Q6XaBRzNy5czGZTAAsX76c66+/fsRvJpKGKNUPO15BfPYwwufh9bDlPNC4mLQoPQ+dF8X4BANuj4e6xkZMRiOTxozp6vY4hO7EfNvevXspKCjoFa/Tpk3j+eefR1EUbrnlFlpaWkhOTsZms3HppZcyY8YMAEwmE+3t7SdsFCMFB5kMn4DP76e2oZF/7Cphc14J0Y17ma/dw13avZg1TrxaPS0JZ3Mo7lwqI6fSqY0gNCQEPTr0ISHYxo0jKS7upE7Abreb4uLiY7o2vf7668THx/PKK68ENrX0tD2cPHkyTqcTs9nMgw8+yMqVK4k7wfPJIJWCnRCCvO2f0rTpVaa2fsEcxcUecvgo6ceE2+aQHuJHqB5anT4SY2JIjI8n2mLpl1uxQggqKyuPiddly5axbNkyysrKmDt3LkCgM6LVag2UIJs1axaFhYWkp6cftzOifggnDdIIV70PPrgTqnZzOGwyv3DfTH5zPFdPsnDFeDNCqBxpbsYQGsqUnByS4uKGROOM5ubmY+LVZDLxhz/8AYDrr7+evLw8gEAr8KSkJKBr6dKOHTtITk4+4blUnmODn0yGv6W+xckX+4o4tPNzkpp3M1vs46dKMZpQgTvETFPiHIpiZlBrmYDQhREXHU1OTAwGvf47O1K53e5jusDceeedjBkzhrVr13LzzTcHju1pe9jU1ER8fDzXX389F1xwAVar9bhtD3sqN0jScONtb2Lfv/6MJf9tJviL6BQhbDfMpn3sVWhjM0lQVQx6LZkp6VjMZsxGY590hevpjHh0vE6cOJEbbrgBp9NJWlpa4NjQ0FCysrJoaWkBICsri08++QSr1Up6evox4wkPDycrK+uMxyhJA6atBr54FLHnL3RozTwibuetppmMTzDw26mRjLJoaWlrRQiBbdSoQakQ0dTU1Ctem5ubefrppwG47rrr+OijjwLHpqamBpYxAKxevRqj0UhWVtZxE9ujN6BKw5Nyoja6/eGss84SO3fuHLDnOxlOt4/dRbWU5H2F27GJUc59zNTkEaF04kdDjXEsrsRp1EVOptlsxaDvSoATY2MxR0T0qkPa2dlJcXFxIBjnzZvH1KlT2bRpE7m5ub1aFkdGRrJ27VoWLVpEaWkpGzduDOwkPV7bQ2l4UhRllxDirMEex/EMWrz6vZTt+ICmbW8yumkjYbgpYBQHYy9GN3ouOoMRnVZLenIySXFxp70EQlVVKioqArdFIyIiWL58OdB1dejoMkehoaHccsstrFq1CoBXX3010Gr4RK3ApeFnxMWrxwlbXkBsfhbV62atuJAn3VeSFh/D1RPNpEX48Xi9hIaEkJ6URGpiImFHNWHpaz2dEXv+3X///Wi1Wu68806ef/75XsdmZmZit9vRarV8+umntLW1YbPZTtgKXBp+TiVeR1Qy7HT7+Lq8mYPldbQWbsVYs5Mx7gNM1xzGpLgAqNclUG2ZiitxGq0xk9CbYomJiiLSZCLKbAYhAglveno606ZNo7KyklmzZlFeXt4r4X366ae56667qKur449//GOvOoE9Zc2kkW3EnVxPwOvzUbD933Ts+iujGz4jkjaahZGvQmdSm7aI2PTRhIboSIqPJ6m7/vbJJKB+vz+Q8La1tQXqcV555ZWsX7++V2fEmTNnBjaTPvvss4SFhQXi9XitwKWRZ8TEq9cFu19D/fIpNO21fCJm8DvPMoxx6Vw1wUxquBe/qpIYG8uolBQiTaY+WQ7R0wq854LSZZddhsViYfXq1dxzzz00NjYGjlUUhbKyMlJTU/nkk0/Yv39/r0Yx324FLo08pxKvw3qZhMensqOkgQ37imi1byal7WvO0eRzg1KIXulqHlGpT8VuysUdNwFd+jno4zLRKAreI0eINhiYPm0afr+fhQsXYrfbKS8vDzz+HXfcwbRp04iPjyc3Nzdw4uxJenvaHsbHx/PQQw8Nxo9Akoas+qYW8rf8E5H/T8a1bmaC0oJLhLJNN53i2PMxZp1FXKSZc2JiSOi+E3O8Zhg9rcDLysrIzc0FuhpPrF27lsLCwkBnxMTExEAyPGPGDLKzs3vFa2pqauAxZZc1aUTyOGHnq6ibn0fjrGMvY/it+1Y64ybwf8YaSTX60Gl9pCclk56UdFrlQI9uBd6z9G/Dhg3cfffd2O32Xp0RN23axOzZs8nOzuaaa67pFa9HtwJfuHAhCxcu7LMfgzTyDKtk2OPYxbQAAAAK1klEQVRT2V/RhD1vN62OLZgb9jEBB79SytApKn6dhlpDJqWxl+KOn4ozajxZ46eRpdPxyMMPs+flJ3A4HIG2h5dffjl///vf0Wq1GI1G5s6d2+vq7ujRo4GuTTJ/+ctfBnPqkjTk+fwqhxwOKnauJ6LkE6Z6djFH6aRdhPF1yGQ2xp1LSPZsEhOSuCA2liiLhYju25k9nRFHjRqFVqtl3bp1rFmzBofDEWgFrtFo6OjoQK/XYzAYyMnJ4bLLLuv1JrXHPffcM1g/BkkaetpqEDtfxb/tZXSdjWxVx/Oc9ye0xk7isukGMsyCCGMo1nTrSXVo7OmMqNPpiI6OpqioiPvvvz+wvKEn4V27di3XXnstRqORyMhIli9f3itee9bWz5s3j3nz5vX7j0EauYI6GW7t9PJ1/mHqD25GU7WLhLYDjKeQ6d1LHppUA03m0RTGXcN7+X72lbVRVllNUeG/qapaw/Tp0+m5rXS4oACPx8P8+fMDgThhwoTAc73//vuDMkdJClZCCIprG7Hv+BTV/ikZLduZqJQwEWggkl3hs3EmzyJuwlzSEpIZYzBg6l6Hv3PnTp564onAhpiezojFxcVkZGRQU1NDWVnZMa3Ae27V3n333YM7eUkKBhW7aNv4AuH299EKHxv8U3lR/TkhSeO4IFNPmllLQmwso7qbQx29Nl8IgcfjQa/X097ezuOPP95rA1trayu///3v+eUvf4lWq2Xbtm3YbDaWL18eiNeexhNnnXUWn3zyyWD9FCQpeNYM+1VBUWkxVXlf0Vm2C2NjHpleBynKEQA2lKl8WB5BQZuBilZB5ZF2VEVDSUkpYQYD11xzTa9NajabjfHjx8ue3tKgGm5rEBvaXOzfs5W2g58SU7uZqWoeYYoHH1oO68ZQEzmFkKxZEJXNli83UVFRQVFhYaAz4meffcacOXN46623+MEPftArXq1WK1deeSVRUVH9NGNJ+m7DIl7b62na9jr+3W8Q67TTJsJ4x38+X4RdSGJyKuemh5ORGENyd3lCfWgoQgjWrFnTqw6v3W7n5ptv5rnnnsPj8WAymUhNTe0Vr/Pnz2fixIn9P3lJOo6gXzMshKC6opjqQ9twle3CUL8fag9SXNmAo1HF3qhysFFHUbPKm4/+iMScc3l97ees/vR/SExMxGq1cvGMrpOoQa9HURTefPNNWcRekvpYS7uTgt0baS3YiKl2Ozneg8xVOnB6BO+WW3jHmU6ZK5wGl0J5eSUrf5vL8sXXsXnzZn59zz1ERERgtVoDrcATEhIAWLp0Kddcc42sqiJJfcDvaqHkq/dQ971NZvNXROFnr5rNayE3U5Mwh+lZcSxqquVIjZ3tn1TwVvcm8YkTJ/Laa6+hKAr33XcfdXV1gc6Is2fPZsGCBUBXtZX29nZ5jpWC1hklw4qiLAKeA7TAaiHE46f6GF6PmwrHPirzvsK+ZxP1xQdprq2guqkDR6PKmsvD0USn8rTdxKr1XWt54+JiybbauHiujanXPkxMTAyPT7mYp595LtDF6dtkkErSmcWsEIKa6grK9m+io/Ar2g5vQq1zUNrowdGosr8plJlTx3DRpUsgfgI3XnwVUEFERAQ2m43p06eT2J3snnXWWVRXV5OQkHDchPd4zSgkaaQ5k3htrq+maPM76O0fYnPuJKLdw5YjRv67ZQwFrihcHS4SY0r5f2sewWw0MmvWLLZv345OpwskvEcvFdyxYwfx8fEnPJfKc6wUzE77jKMoihb4A3AhUAHsUBTlfSHEwRN9j8fdyduvPIVj71YqivKprarknrP9nJMMO/O8/GCdK3BstMVIcnIK++Y/xpzz53NnUxM3Nzdjs9mIOE5vc1mqTJK+26nGrM/n5Z9rXyR/5/9S49hPQ3UF58S5ufXsUFw+MK1qxa92HWs0hnc1mZj3I+beeCuqqvLll19is9mIj48/JuHV6/UkJib263wlKZidarwKVWXj+2s4tOkDGu07aa6rob5D5ZEl6XwStoiH/3mQnbv2ArWEhIR0JbxpyV0lQ4GXX34Zo9FIenr6cRPblJSUfpurJA22014zrCjKTOAhIcRF3Z/fCyCEeOw7vqfXk8WbQ/nZ1XOYOnMubaFxNLb7mTJ5ChPGjyfSYjmtcUlSMBnINYinGrN6nSI8/m8+N+p1LDh3PD+742dkTDqPrdt2kTlqFDk5OcftjChJw81QjteECI2oc35zitVpNSQkJPDPf39KRmoqB/bto6OjI9AoRt59kYa7gVoznAKUH/V5BTDjOIO5Bbil+1OvRqcrBvxCVX11rR7fg698pvLKZ2cwjCEhFjgy2IPoB3Je/W/UAD7Xf4zZb8WrR6PTldAVr16n2+d7f8PX4v0NtxDkhtLvvy/JefW/oRyvbk1ISClCqMLvd/v8qq+yqpqp48cP0HD7zVD6/fclOa/+d9LxeibJ8PEuAx1zmVkI8RLwEoCiKDv9Xu+Q3Il7JhRF2TlUdxifCTmvYec/xqyM1+Al5zXsnHq8ejzD7uc0XH//cl5Dy5n0T6wA0o76PBWoOrPhSJLUj2TMSlLwkPEqSQPkTJLhHYBNUZRMRVFCgWsB2ZlCkoYuGbOSFDxkvErSADntZRJCCJ+iKLcDH9NV9uVVIUTef/i2l073+YY4Oa/gMlzn9Z1OI2aH689Jziu4DNd5fScZrwFyXsElKOc1oB3oJEmSJEmSJGkoOZNlEpIkSZIkSZIU1GQyLEmSJEmSJI1YA5IMK4qySFGUAkVRHIqi/HognrO/KYqSpijKF4qiHFIUJU9RlDsHe0x9SVEUraIoexRF+edgj6WvKIoSqSjKOkVR8rt/bzMHe0xDlYzZ4DIc4xVkzJ4sGa/BZzjGbDDHa7+vGe5uKXmYo1pKAtd9V9vmYKAoShKQJITYrSiKCdgFXB7s8+qhKMovgLMAsxDi0sEeT19QFGUN8KUQYnX37uxwIUTzYI9rqJExG3yGY7yCjNmTIeM1OA3HmA3meB2IK8PnAA4hRJEQwgP8FfjeADxvvxJCVAshdnd/3AYcoqtjUNBTFCUVuARYPdhj6SuKopiBXOAVACGEJ1iCdBDImA0iwzFeQcbsKZDxGmSGY8wGe7wORDJ8vJaSw+IPuoeiKBnAVGDb4I6kzzwL/ApQB3sgfSgLqAf+p/vW1GpFUYyDPaghSsZscBmO8QoyZk+WjNfgMxxjNqjjdSCS4ZNq2xysFEWJAP4GrBBCtA72eM6UoiiXAnVCiF2DPZY+pgOmAX8SQkwFnMCwWFvXD2TMBolhHK8gY/ZkyXgNIsM4ZoM6XgciGR62LSUVRQmhK0jfEEK8O9jj6SOzgSWKopTQdbttvqIorw/ukPpEBVAhhOi5srCOrsCVjiVjNngM13gFGbMnS8ZrcBmuMRvU8ToQyfCwbCmpKIpC19qYQ0KIpwd7PH1FCHGvECJVCJFB1+/qcyHE9YM8rDMmhKgByhVFGdP9pQXAsNiI0Q9kzAaJ4RqvIGP2FMh4DSLDNWaDPV5Pux3zyTrNts3BYDZwA7BfUZS93V+7TwixfhDHJH23O4A3uk8YRcDNgzyeIUnGrDSEyJj9D2S8SkNI0MarbMcsSZIkSZIkjViyA50kSZIkSZI0YslkWJIkSZIkSRqxZDIsSZIkSZIkjVgyGZYkSZIkSZJGLJkMS5IkSZIkSSOWTIYlSZIkSZKkEUsmw5IkSZIkSdKI9f8BAjiA+tSKOmIAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# params9 - causal mixture without annotations, without accounting for S and L parameters\n", - "constraint = AnnotUnivariateParams(sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params9=perform_fit(bounds_left, bounds_right, parametrization)\n", - "\n", - "\n", - "do_plots(params9, '_params9')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg.set_option('cost_calculator', _cost_calculator_gaussian)\n", - "tag_pdf_gaussian=params9.tag_pdf(libbgmg, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg.set_option('cost_calculator', _cost_calculator_sampling)\n", - "tag_pdf_sampling=params9.tag_pdf(libbgmg, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(-np.log(tag_pdf_sampling[libbgmg.weights>0]), -np.log(tag_pdf_convolve[libbgmg.weights>0]), '.')\n", - "#plt.plot(-np.log(tag_pdf_gaussian[libbgmg.weights>0]), -np.log(tag_pdf_convolve[libbgmg.weights>0]), '.')\n", - "#plt.plot(tag_pdf_gaussian[libbgmg.weights>0], tag_pdf_convolve[libbgmg.weights>0], '.')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 16792.106170893097\n", - "2 16791.890650355457\n", - "3 16791.813032662463\n", - "4 16792.353774611693\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mlibbgmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'seed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams9\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibbgmg\u001b[0m\u001b[0;34m,\u001b[0m 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"execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z=np.abs(libbgmg.zvec1)\n", - "z=z[np.isfinite(z)]\n", - "z.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg.set_option('cost_calculator', _cost_calculator_convolve)\n", - "#print(seed, params9.cost(libbgmg, 1))\n", - "tag_pdf_convolve=params9.tag_pdf(libbgmg, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.0019422768605894315, _sig2_beta: 0.0001514049546435859, _sig2_annot: [1], _s: -0.6445122556819146, _l: -0.1664666397153803, _sig2_zeroA: 2.1554019231845993)\n", - "AnnotUnivariateParams(_pi: 0.0017433067614436263, _sig2_beta: 0.00032365469994769143, _sig2_annot: [1], _s: -0.4346094933177381, _l: -0.25205482692795367, _sig2_zeroA: 2.1819290510636007)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# params10 - causal mixture without annotations, allowing for flexible S and L parameters\n", - "constraint = AnnotUnivariateParams(sig2_annot=[1], annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params10=perform_fit(bounds_left, bounds_right, parametrization)\n", - "do_plots(params10, '_params10', True)" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: 0.014591266436314046, _sig2_beta: 2.5377289052973313e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.4796922678191362, _l: -0.15359210045600655, _sig2_zeroA: 1.8451261379413293)\n", - "AnnotUnivariateParams(_pi: 0.015880541721926548, _sig2_beta: 2.2497055296175e-05, _sig2_annot: [ 0.80677047 10.9351042 2.24750116 1.36860802 0.52322939 0.04378\n", - " 0.29449296 0.78957198 14.05955733 0.28672715], _s: -0.365544728354278, _l: -0.13260565509183503, _sig2_zeroA: 1.8823111632885878)\n" - ] - } - ], - "source": [ - "# params11 (6) causal mixture with annotations, allowing for flexible S and L parameters,\n", - "# and re-fit S and L in the context of mixture model\n", - "constraint = AnnotUnivariateParams(sig2_annot=params6._sig2_annot, annomat=params6._annomat, annonames=params6._annonames, mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(s=-1.0, l=-1.0, pi=5e-5, sig2_beta=5e-6, sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(s=0.25, l=0.25, pi=5e-1, sig2_beta=5e-2, sig2_zeroA=2.5)\n", - "params11=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "do_plots(params11, '_params11')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# params21 - infinitesimal model (just to find baseline sig2beta)\n", - "constraint = AnnotUnivariateParams(pi=[None, None], sig2_beta=[None, None], sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=[5e-5, 5e-5], sig2_beta=[5e-8, 5e-8], sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=[5e-1, 5e-1], sig2_beta=[5e-2, 5e-2], sig2_zeroA=2.5)\n", - "params21=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 573, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnnotUnivariateParams(_pi: [1, 7.402682974759894e-05], _sig2_beta: [2.7914417333806207e-07, 0.0009516113053639361], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 2.016873888212911)\n", - "AnnotUnivariateParams(_pi: [1, 5.905602713600562e-05], _sig2_beta: [2.5857617302651813e-07, 0.001053370941930103], _sig2_annot: [1], _s: 0, _l: 0, _sig2_zeroA: 2.2396722377742737)\n" - ] - } - ], - "source": [ - "# params20 - infinitesimal model (just to find baseline sig2beta)\n", - "constraint = AnnotUnivariateParams(pi=[1, None], sig2_beta=[None, None], sig2_annot=[1], s=0, l=0, annomat=annomat[:, 0].reshape(-1, 1), annonames=[annonames[0]], mafvec=libbgmg.mafvec, tldvec=libbgmg.ld_tag_r2_sum)\n", - "parametrization = precimed.mixer.utils.AnnotUnivariateParametrization(lib=libbgmg, trait=1, constraint=constraint)\n", - "bounds_left = AnnotUnivariateParams(pi=[None, 5e-5], sig2_beta=[5e-8, 5e-8], sig2_zeroA=0.9)\n", - "bounds_right = AnnotUnivariateParams(pi=[None, 5e-1], sig2_beta=[5e-2, 5e-2], sig2_zeroA=2.5)\n", - "params20=perform_fit(bounds_left, bounds_right, parametrization)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import json\n", - "fname = '/home/oleksanf/github/mixer/precimed/mixer.tmp.json'\n", - "pname = 'params3'\n", - "data = json.loads(open(fname).read())\n", - "#plt.plot(np.log10(data['params1']['power']['nvec']), data['params1']['power']['svec'])\n", - "s=data[pname]['sampling_tag_pdf']\n", - "g=data[pname]['gaussian_tag_pdf']\n", - "c=data[pname]['convolve_tag_pdf']\n", - "plt.plot(np.log10(s), np.log10(c), '.')" - ] - }, - { - "cell_type": "code", - "execution_count": 662, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 662, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a=np.divide(full_err, full)\n", - "(~np.isfinite(full)).sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 349, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12270.056865402554" - ] - }, - "execution_count": 349, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['params1']['optimize'][1][1]['fun']" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12270.056880216127" - ] - }, - "execution_count": 342, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "-np.sum(w*np.log(fast))" - ] - }, - { - "cell_type": "code", - "execution_count": 350, - "metadata": {}, - "outputs": [], - "source": [ - "fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/UKB_HEIGHT_2018_irnt.outtag=run2.fit.json'\n", - "data = json.loads(open(fname).read())" - ] - }, - { - "cell_type": "code", - "execution_count": 353, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1040631" - ] - }, - "execution_count": 353, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(data['weights'])" - ] - }, - { - "cell_type": "code", - "execution_count": 438, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n", - " import sys\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1685\n", - "2431\n", - "2694\n", - "2846\n", - "2846\n", - "2846\n", - "2846\n", - "2846\n", - "2846\n", - "3.406898595921861 214683.36484244416 214423.72249304948\n", - "4.417584384172022 214526.17906430885 214307.73439024464\n", - "3.6948307848314403 212945.93877034655 212376.76623557456\n", - "4.413854023448712 212950.60080635376 212368.9989489987\n", - "4.410832583741891 212878.78043164103 212319.15092463273\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: divide by zero encountered in log\n" - ] - } - ], - "source": [ - "weights = np.array(data['weights'])\n", - "\n", - "mask = weights>0\n", - "for params in ['params1', 'params2', 'params5', 'params6', 'params50']:\n", - " full = np.array(data[params]['full_tag_pdf'])\n", - " fast = np.array(data[params]['fast_tag_pdf'])\n", - " diff=(np.abs(np.log(full)-np.log(fast)))\n", - " mask[~np.isfinite(full) | (diff>1e-5)] = False\n", - " print(np.sum(mask==False))\n", - "for params in ['params3', 'params4', 'params7', 'params8', 'params9', 'params51', 'params52']:\n", - " full = np.array(data[params]['full_tag_pdf'])\n", - " fast = np.array(data[params]['fast_tag_pdf'])\n", - " mask[~np.isfinite(full)] = False\n", - " print(np.sum(mask==False))\n", - "for params in ['params3', 'params4', 'params7', 'params8', 'params9', 'params51', 'params52']:\n", - " full = np.array(data[params]['full_tag_pdf'])\n", - " fast = np.array(data[params]['fast_tag_pdf'])\n", - " diff=(np.abs(np.log(full)-np.log(fast)))\n", - " print(np.max(diff[mask]), -np.dot(weights[mask], np.log(fast[mask])), -np.dot(weights[mask], np.log(full[mask])))\n", - " \n", - "if 0:\n", - "\n", - " print(-np.dot(weights, np.log(fast)), data['params1']['optimize'][1][1]['fun'])\n", - " diff=(np.abs(np.log(full)-np.log(fast)))\n", - " print(np.sum(diff>0.001))\n", - "\n", - " full2 = np.array(data['params2']['full_tag_pdf'])\n", - " fast2 = np.array(data['params2']['fast_tag_pdf'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 388, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "570" - ] - }, - "execution_count": 388, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(diff>0.0001)" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "49.21477645403603" - ] - }, - "execution_count": 398, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.max(diff2[np.isfinite(diff2)])" - ] - }, - { - "cell_type": "code", - "execution_count": 414, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - }, - { - "data": { - "text/plain": [ - "11.90894581763056" - ] - }, - "execution_count": 414, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff2=(np.abs(np.log(np.array(data['params6']['full_tag_pdf']))-np.log(np.array(data['params6']['fast_tag_pdf']))))\n", - "np.max(diff2[np.isfinite(diff2) & (diff<1e-7)])" - ] - }, - { - "cell_type": "code", - "execution_count": 420, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "570" - ] - }, - "execution_count": 420, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(diff>1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "trait='PGC_SCZ_2014_EUR'\n", - "#df=pd.read_table('/home/oleksanf/vmshare/data/MMIL/ANALYSIS/{}.partitioned_h2.results'.format(trait),sep='\\t')\n", - "df=pd.DataFrame({'annonames':data['options']['annonames']})\n", - "fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.outtag=run4.fit.json'.format(trait)\n", - "data = json.loads(open(fname).read())\n", - " \n", - "for i in list(range(1, 10)) + [50, 51, 52]:\n", - " if 'params{}'.format(i) not in data: continue\n", - " df['params{}'.format(i)] = np.array(data['params{}'.format(i)]['annot_enrich'])\n", - "df.to_csv('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/annot_enrich_ASchork_{}.csv'.format(trait),index=False,sep='\\t')" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plsa_folder = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/'\n", - "#fname='GIANT_HEIGHT_2018_UKB'; data=json.loads(open(plsa_folder + fname + '.models=3.outtag=run7.fit.json').read())\n", - "fname='PGC_SCZ_2014_EUR'; data=json.loads(open(plsa_folder + fname+'.models=3.outtag=run8.fit.json').read())\n", - "#fname='PGC_BIP_2016'; data=json.loads(open(plsa_folder + fname +'.models=4.outtag=run7.fit.json').read())" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['params', 'optimize', 'annot_enrich', 'annot_h2', 'convolve_tag_pdf', 'convolve_tag_pdf_err', 'sampling_tag_pdf', 'gaussian_tag_pdf', 'qqplot', 'qqplot_bins'])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['params3'].keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "HEIGHT 3,7,8\n", - "BIP 3,4,7,8\n", - "SCZ 3,4,7,8\n", - "EDU 3,7,8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'pi': [0.004201028586457496], 'sig2_beta': [5.18894008601531e-05], 'sig2_zeroA': 1.172034736090368, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "{'pi': [0.003792662009069121], 'sig2_beta': [5.777405767346033e-05], 'sig2_zeroA': 1.1705578313883305, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "{'pi': [0.004214757372085943], 'sig2_beta': [5.170773994399179e-05], 'sig2_zeroA': 1.1720225315059574, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "{'pi': [0.003791127922836069], 'sig2_beta': [5.779377158997732e-05], 'sig2_zeroA': 1.1705648868404, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fname='PGC_SCZ_2014_EUR'; data=json.loads(open(plsa_folder + fname+'.models=3.outtag=run7.fit.json').read())\n", - "\n", - "plt.figure()\n", - "p0=data['params3']['sampling_tag_pdf']\n", - "p1=data['params3']['gaussian_tag_pdf']\n", - "p2=data['params3']['convolve_tag_pdf']\n", - "plt.subplot(1,2,1);plt.plot(-np.log10(p0), -np.log10(p1), '.');plt.plot(-np.log10(p0), -np.log10(p2), '.')\n", - "plt.subplot(1,2,2);plt.plot(p0, p1, '.');plt.plot(p0, p2, '.');plt.suptitle(fname);\n", - "print(data['params3']['optimize'][1][1]['params'])\n", - "print(data['params3']['optimize'][2][1]['params'])\n", - "\n", - "plt.figure()\n", - "fname='PGC_SCZ_2014_EUR'; data=json.loads(open(plsa_folder + fname + '.models=3.outtag=run8.fit.json').read())\n", - "p0=data['params3']['sampling_tag_pdf']\n", - "p1=data['params3']['gaussian_tag_pdf']\n", - "p2=data['params3']['convolve_tag_pdf']\n", - "plt.subplot(1,2,1);plt.plot(-np.log10(p0), -np.log10(p1), '.');plt.plot(-np.log10(p0), -np.log10(p2), '.')\n", - "plt.subplot(1,2,2);plt.plot(p0, p1, '.');plt.plot(p0, p2, '.');plt.suptitle(fname);\n", - "print(data['params3']['optimize'][1][1]['params'])\n", - "print(data['params3']['optimize'][2][1]['params'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig=plt.figure(figsize=[40, 30])\n", - "legends=[]\n", - "for index, i in enumerate(list(range(1, 10)) + [50, 51,52]):\n", - " kind='params{}'.format(i)\n", - " if kind not in data: continue\n", - " plt.plot(np.log10(data[kind]['power']['nvec']), data[kind]['power']['svec'], linestyle='solid' if index<6 else 'dashed')\n", - " legends.append(kind)\n", - "plt.legend(legends)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[['diffevo-fast',\n", - " {'x': [-15.642787878756575, 0.1151485786444818],\n", - " 'fun': 15173.58464507732,\n", - " 'nfev': 210,\n", - " 'nit': 6,\n", - " 'message': 'Optimization terminated successfully.',\n", - " 'success': True,\n", - " 'cost_n': 9653.984375,\n", - " 'cost_df': 2,\n", - " 'cost': 15173.58464507732,\n", - " 'BIC': 30365.51954230613,\n", - " 'AIC': 30351.16929015464,\n", - " 'cost_fast': 15173.584645077322,\n", - " 'params': {'pi': [1],\n", - " 'sig2_beta': [1.608509175722143e-07],\n", - " 'sig2_zeroA': 1.1220401363902084,\n", - " 's': -0.5,\n", - " 'l': 0,\n", - " 'sig2_annot': [1],\n", - " 'annonames': ['base']}}],\n", - " ['nedlermead-fast',\n", - " {'fun': 15173.497917256009,\n", - " 'nit': 24,\n", - " 'nfev': 51,\n", - " 'status': 0,\n", - " 'success': True,\n", - " 'message': 'Optimization terminated successfully.',\n", - " 'x': [-15.681217553594209, 0.12210286423160534],\n", - " 'final_simplex': [[[-15.681217553594209, 0.12210286423160534],\n", - " [-15.681160139299744, 0.12210898495432682],\n", - " [-15.681120319633523, 0.12209453035846861]],\n", - " [15173.497917256009, 15173.497922948804, 15173.497931283691]],\n", - " 'cost_n': 9653.984375,\n", - " 'cost_df': 2,\n", - " 'cost': 15173.497917256009,\n", - " 'BIC': 30365.346086663507,\n", - " 'AIC': 30350.995834512018,\n", - " 'cost_fast': 15173.49791725601,\n", - " 'params': {'pi': [1],\n", - " 'sig2_beta': [1.5478673764418276e-07],\n", - " 'sig2_zeroA': 1.1298703190450987,\n", - " 's': -0.5,\n", - " 'l': 0,\n", - " 'sig2_annot': [1],\n", - " 'annonames': ['base']}}],\n", - " ['nedlermead-fast',\n", - " {'fun': 15166.941780816924,\n", - " 'nit': 74,\n", - " 'nfev': 149,\n", - " 'status': 0,\n", - " 'success': True,\n", - " 'message': 'Optimization terminated successfully.',\n", - " 'x': [-14.867026769433139, 0.29059829988386215, 0.14087632579219514],\n", - " 'final_simplex': [[[-14.867026769433139,\n", - " 0.29059829988386215,\n", - " 0.14087632579219514],\n", - " [-14.867100464272006, 0.29056926634921754, 0.14088736787991948],\n", - " [-14.867071412089624, 0.2905020692131098, 0.1408714952871056],\n", - " [-14.86711470383311, 0.29052120424809263, 0.14087155607270185]],\n", - " [15166.941780816924,\n", - " 15166.94179020822,\n", - " 15166.941796829851,\n", - " 15166.941797846135]],\n", - " 'cost_n': 9653.984375,\n", - " 'cost_df': 3,\n", - " 'cost': 15166.941780816924,\n", - " 'BIC': 30361.40893986108,\n", - " 'AIC': 30339.883561633847,\n", - " 'cost_fast': 15166.941780816926,\n", - " 'params': {'pi': [1],\n", - " 'sig2_beta': [3.494075715148091e-07],\n", - " 'sig2_zeroA': 1.1512822552588928,\n", - " 's': 0.29059829988386215,\n", - " 'l': 0,\n", - " 'sig2_annot': [1],\n", - " 'annonames': ['base']}}]]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_tmp = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.json').read())\n", - "data_tmp['m03']['optimize_s']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'pi': [0.22928301143666854, 0.004196627817437162],\n", - " 'sig2_beta': [3.2684861279713417e-09, 4.7015866287594755e-05],\n", - " 'sig2_zeroA': 1.1723446770374535,\n", - " 's': -0.06559127240374887,\n", - " 'l': 0.0062005329298866095,\n", - " 'sig2_annot': [0.9267094362721238],\n", - " 'annonames': ['base'],\n", - " 'C1frac': 0.0037837917763347845,\n", - " 'h2': 0.42323243618011475,\n", - " 'num_annot': 1,\n", - " 'model': 'm32_PPLSA',\n", - " 'fastcost': 151829.3706658403,\n", - " 'fullcost': nan,\n", - " 'nit': 510,\n", - " 'annots': 'base'}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['params']" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'m32_PPLSA'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['spec']" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:80: RuntimeWarning: divide by zero encountered in log\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=52.outtag=run8.fit.json - exclude 807 SNPs from cost function, mean(|z|)=1.0093280566797893, #SNP(|z|>20)=0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import json\n", - "import os\n", - "\n", - "def insert_key_to_dictionary_as_list(key, value, df_data):\n", - " if key not in df_data:\n", - " df_data[key] = []\n", - " df_data[key].append(value)\n", - "\n", - "def make_qq_plot(qq, color, ylim=7.3, xlim=7.3):\n", - " hv_logp = np.array(qq['hv_logp']).astype(float)\n", - " data_logpvec = np.array(qq['data_logpvec']).astype(float)\n", - " model_logpvec = np.array(qq['model_logpvec']).astype(float)\n", - " ylim_data = max(hv_logp[np.isfinite(data_logpvec)])\n", - " model_logpvec[hv_logp > ylim_data]=np.nan\n", - " hData = plt.plot(data_logpvec, hv_logp, color=color, linestyle='solid')\n", - " hModel = plt.plot(model_logpvec, hv_logp, color=color, linestyle='dashed')\n", - " hNull = plt.plot(hv_logp, hv_logp, 'k--')\n", - " plt.ylim(0, ylim); plt.xlim(0, xlim)\n", - " return hData[0]\n", - "\n", - "cm = plt.cm.get_cmap('tab10')\n", - " \n", - "plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 20 })\n", - "\n", - " \n", - "df_final=None\n", - "#traits=['CARDIOGRAM_CAD_2015', 'GIANT_BMI_2015_EUR', 'GIANT_HEIGHT_2018_UKB', 'IIBDGC_CD_2017', 'IIBDGC_UC_2017', 'LIPIDS_HDL_2013', 'LIPIDS_LDL_2013', 'LIPIDS_TG_2013', 'PGC_BIP_2016', 'PGC_SCZ_2014_EUR', 'SSGAC_EDU_2018_no23andMe', 'UKB_HEIGHT_2018_irnt' ]\n", - "#traits=[ 'GIANT_HEIGHT_2018_UKB','PGC_BIP_2016', 'PGC_SCZ_2014_EUR', 'SSGAC_EDU_2018_no23andMe', ]\n", - "traits=[ 'PGC_SCZ_2014_EUR' ]\n", - "for trait in traits:\n", - " data = {}\n", - " #fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.outtag=run4.fit.json'.format(trait)\n", - " #fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.outtag=run2.fit.json'.format(trait)\n", - " \n", - " for i in list(range(1, 10)) + [50, 51, 52]:\n", - " fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run8.fit.json'.format(trait, i)\n", - " if (not os.path.exists(fname)) or (os.path.getsize(fname) == 0): continue\n", - " data_tmp = json.loads(open(fname).read())\n", - " if len(data) == 0: data=data_tmp\n", - " else: data['params{}'.format(i)] = data_tmp['params{}'.format(i)]\n", - "\n", - " trait = fname.split('/')[-1].split('.')[0]\n", - " has_zvec = 'zvec1' in data\n", - " \n", - " for ylim in []: #[7.3, 20, 50, 150]:\n", - " fig=plt.figure(figsize=[40, 30])\n", - " \n", - " fig.suptitle(trait)\n", - " for index, i in enumerate(list(range(1, 10)) + [50, 51,52]):\n", - " kind='params{}'.format(i)\n", - " if kind not in data: continue\n", - " plt.subplot(3,4,index+1)\n", - " plt.title(kind)\n", - " h1=make_qq_plot(data[kind]['qqplot'], color=cm.colors[0], ylim=ylim)\n", - " h2=make_qq_plot(data[kind]['qqplot_bins'][0], color=cm.colors[1], ylim=ylim)\n", - " t1='low maf\\nlow LD'#data[kind]['qqplot_bins'][0]['title'].replace('\\\\in', '').replace(';', '\\n')\n", - " h3=make_qq_plot(data[kind]['qqplot_bins'][8], color=cm.colors[2], ylim=ylim)\n", - " t2='high maf\\nhigh LD' # data[kind]['qqplot_bins'][8]['title'].replace('\\\\in', '').replace(';', '\\n')\n", - " plt.legend([h1, h2, h3], ['all', t1, t2])\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/figures/' + trait + '.model={}'.format(i) + '.ylim={}.qq.png'.format(ylim) , bbox_inches='tight')\n", - "\n", - " if 1:\n", - " fig=plt.figure(figsize=[40, 30])\n", - " legends=[]\n", - " for index, i in enumerate(list(range(1, 10)) + [50, 51,52]):\n", - " kind='params{}'.format(i)\n", - " if kind not in data: continue\n", - " plt.plot(np.log10(data[kind]['power']['nvec']), data[kind]['power']['svec'], linestyle='solid' if index<6 else 'dashed')\n", - " legends.append(kind)\n", - " plt.legend(legends)\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/figures/' + trait + '.power.png'.format(ylim) , bbox_inches='tight')\n", - " \n", - " # detect which SNPs lead to unreliable convolution cost function\n", - " weights = np.array(data['weights'])\n", - " mask = weights>0\n", - " for params in ['params1', 'params2', 'params5', 'params6', 'params50']:\n", - " if params not in data: continue\n", - " full = np.array(data[params]['convolve_tag_pdf'])\n", - " fast = np.array(data[params]['gaussian_tag_pdf'])\n", - " diff=(np.abs(np.log(full)-np.log(fast)))\n", - " mask[~np.isfinite(full) | (diff>1e-5)] = False\n", - " #print(np.sum(mask==False))\n", - " for params in ['params3', 'params4', 'params7', 'params8', 'params9', 'params51', 'params52']:\n", - " if params not in data: continue\n", - " full = np.array(data[params]['convolve_tag_pdf'])\n", - " fast = np.array(data[params]['gaussian_tag_pdf'])\n", - " mask[~np.isfinite(full)] = False\n", - " if has_zvec:\n", - " print('{} - exclude {} SNPs from cost function, mean(|z|)={}, #SNP(|z|>20)={}'.format(\n", - " fname, np.sum(mask==False),\n", - " np.mean(np.abs(np.array(data['zvec1'])[mask])),\n", - " np.sum(np.abs(np.array(data['zvec1']))>20)))\n", - "\n", - " df_data = {}\n", - " for i in list(range(1, 10)) + [50, 51, 52]:\n", - " if 'params{}'.format(i) not in data: continue\n", - " p = data['params{}'.format(i)]['params']\n", - " full = np.array(data['params{}'.format(i)]['convolve_tag_pdf'])\n", - " fast = np.array(data['params{}'.format(i)]['gaussian_tag_pdf'])\n", - "\n", - " p['C1frac'] = 1 if (isinstance(p['pi'], (int, float)) or (len(p['pi']) == 1)) else p['pi'][0]*p['sig2_beta'][0]/np.dot(p['pi'], p['sig2_beta'])\n", - " \n", - " p['h2'] = data['params{}'.format(i)]['annot_h2'][0]\n", - " p['num_annot'] = len(p['annonames'])\n", - " p['model'] = 'params{}'.format(i)\n", - " p['fullcost'] = -np.dot(weights[mask], np.log(full[mask])) #data['params{}'.format(i)]['full_cost']\n", - " not_has_optimize = not data['params{}'.format(i)]['optimize']\n", - " p['fastcost'] = -np.dot(weights[mask], np.log(fast[mask])) # np.nan if not_has_optimize else data['params{}'.format(i)]['optimize'][1][1]['fun']\n", - " p['nit'] = np.nan if not_has_optimize else data['params{}'.format(i)]['optimize'][1][1]['nit']\n", - " p['annots'] = ' '.join(p['annonames'])\n", - " insert_key_to_dictionary_as_list('fname', fname.split('/')[-1].split('.')[0], df_data) \n", - " for k in ['model', 'pi', 'sig2_beta', 'sig2_zeroA', 's', 'l', 'h2', 'C1frac', 'num_annot', 'fullcost', 'fastcost', 'nit','annots']:\n", - " insert_key_to_dictionary_as_list(k, p[k], df_data) \n", - " df=pd.DataFrame(df_data) \n", - " mincost = np.min([np.min(df['fullcost'].values), np.nanmin(df['fastcost'].values)])\n", - " df['fullcost'] = df['fullcost'] - np.min(df['fullcost'].values)\n", - " df['fastcost'] = df['fastcost'] - np.nanmin(df['fastcost'].values)\n", - " df_final = pd.concat([df_final, df]) if (df_final is not None) else df\n", - "df_final.to_csv('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined.csv',index=False,sep='\\t')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['params', 'optimize', 'annot_enrich', 'annot_h2', 'convolve_tag_pdf', 'convolve_tag_pdf_err', 'sampling_tag_pdf', 'gaussian_tag_pdf', 'qqplot', 'qqplot_bins', 'power', 'spec'])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['gaussian_tag_pdf']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['options', 'analysis', 'weights', 'zvec1', 'm32'])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_all.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=7.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=8.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=15.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=16.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=23.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=24.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=31.outtag=run10.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=32.outtag=run10.fit.json\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'m01'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_indices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0mmodel_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'm{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_index\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel_index\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'm0{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0mfull\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gaussian_tag_pdf'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mbefore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'm01'" - ] - } - ], - "source": [ - "import json\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "def insert_key_to_dictionary_as_list(key, value, df_data):\n", - " if key not in df_data:\n", - " df_data[key] = []\n", - " df_data[key].append(value)\n", - "\n", - "def make_qq_plot(qq, color, ylim=7.3, xlim=7.3):\n", - " hv_logp = np.array(qq['hv_logp']).astype(float)\n", - " data_logpvec = np.array(qq['data_logpvec']).astype(float)\n", - " model_logpvec = np.array(qq['model_logpvec']).astype(float)\n", - " ylim_data = max(hv_logp[np.isfinite(data_logpvec)])\n", - " model_logpvec[hv_logp > ylim_data]=np.nan\n", - " hData = plt.plot(data_logpvec, hv_logp, color=color, linestyle='solid')\n", - " hModel = plt.plot(model_logpvec, hv_logp, color=color, linestyle='dashed')\n", - " hNull = plt.plot(hv_logp, hv_logp, 'k--')\n", - " plt.ylim(0, ylim); plt.xlim(0, xlim)\n", - " return hData[0]\n", - "\n", - "cm = plt.cm.get_cmap('tab10')\n", - " \n", - "plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 20 })\n", - "\n", - " \n", - "df_final=None\n", - "#traits=['CARDIOGRAM_CAD_2015', 'GIANT_BMI_2015_EUR', 'GIANT_HEIGHT_2018_UKB', 'IIBDGC_CD_2017', 'IIBDGC_UC_2017', 'LIPIDS_HDL_2013', 'LIPIDS_LDL_2013', 'LIPIDS_TG_2013', 'PGC_BIP_2016', 'PGC_SCZ_2014_EUR', 'SSGAC_EDU_2018_no23andMe', 'UKB_HEIGHT_2018_irnt' ]\n", - "#traits=[ 'GIANT_HEIGHT_2018_UKB','PGC_BIP_2016', 'PGC_SCZ_2014_EUR', 'SSGAC_EDU_2018_no23andMe', ]\n", - "traits=[ 'PGC_SCZ_2014_EUR', 'GIANT_HEIGHT_2018_UKB' ]\n", - "for trait in traits:\n", - " #pattern = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run11.fit.json'\n", - " model_indices =list(range(1, 33))\n", - " \n", - " pattern = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run10.fit.json'\n", - " #model_indices = [7,8,15,16,23,24,31,32]\n", - "\n", - " if 1:\n", - " data_all = None\n", - " for model_index in model_indices:\n", - " fname = pattern.format(trait, model_index)\n", - " if (not os.path.exists(fname)) or (os.path.getsize(fname) == 0): continue\n", - " print(fname)\n", - " data_tmp = json.loads(open(fname).read())\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " if data_all is None: data_all = data_tmp\n", - " else: data_all[model_key] = data_tmp[model_key]\n", - "\n", - " weights = np.array(data_all['weights'])\n", - " mask = weights>0\n", - "\n", - " for model_index in model_indices:\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " data = data_all[model_key]\n", - " full = np.array(data['gaussian_tag_pdf'])\n", - " before = np.sum(mask)\n", - " mask[~np.isfinite(np.log(full))] = 0\n", - " after = np.sum(mask)\n", - " if (after < before): print('exclude {} due to {}'.format(before-after, model_key))\n", - "\n", - " df_data = {}\n", - " for model_index in model_indices:\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " data = data_all[model_key]\n", - "\n", - " fname = pattern.format(trait, model_index)\n", - " p = data['params']\n", - " full = np.array(data['gaussian_tag_pdf'])\n", - " \n", - " p['C1frac'] = 1 if (isinstance(p['pi'], (int, float)) or (len(p['pi']) == 1)) else p['pi'][0]*p['sig2_beta'][0]/np.dot(p['pi'], p['sig2_beta'])\n", - " p['h2'] = data['annot_h2'][0]\n", - " p['num_annot'] = len(p['annonames'])\n", - " p['model'] = data['spec']\n", - " p['S'] = 'S' if ('S' in data['spec']) else '0'\n", - " p['L'] = 'L' if ('L' in data['spec']) else '0'\n", - " p['A'] = 'A' if ('A' in data['spec']) else '0'\n", - " p['P'] = data['spec'][4:6]\n", - " p['fastcost'] = data['optimize'][-1][1]['fun']\n", - " p['fullcost'] = -np.dot(weights[mask], np.log(full[mask])) #data['params{}'.format(i)]['full_cost']\n", - " p['nit'] = data['optimize'][-1][1]['nit']\n", - " p['annots'] = ' '.join(p['annonames'])\n", - " insert_key_to_dictionary_as_list('fname', fname.split('/')[-1].split('.')[0], df_data) \n", - " for k in ['model', 'P', 'L', 'S', 'A', 'pi', 'sig2_beta', 'sig2_zeroA', 's', 'l', 'h2', 'C1frac', 'num_annot', 'fullcost', 'fastcost', 'nit','annots']:\n", - " insert_key_to_dictionary_as_list(k, p[k], df_data) \n", - " df=pd.DataFrame(df_data) \n", - " df['fastcost'] = df['fastcost'] - np.nanmin(df['fastcost'].values)\n", - " df['fullcost'] = df['fullcost'] - np.nanmin(df['fullcost'].values)\n", - " df_final = pd.concat([df_final, df]) if (df_final is not None) else df\n", - "df_final.to_csv('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined.csv',index=False,sep='\\t')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['options', 'analysis', 'weights', 'zvec1', 'm07', 'm08', 'm15', 'm16', 'm23', 'm24', 'm31', 'm32'])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_all.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "df_final.to_excel('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined_run12_abc.xlsx',index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=1.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=2.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=3.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=4.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=5.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=6.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=7.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=8.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=9.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=10.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=11.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=12.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=13.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=14.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=15.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=16.outtag=run12.fit.json\n", - "exclude 1 due to m06\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=1.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=2.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=3.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=4.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=5.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=6.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=7.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=8.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=9.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=10.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=11.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=12.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=13.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=14.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=15.outtag=run12.fit.json\n", - "/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=16.outtag=run12.fit.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:68: RuntimeWarning: divide by zero encountered in log\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "exclude 34 due to m02\n", - "exclude 11 due to m08\n" - ] - } - ], - "source": [ - "import json\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "\n", - "cm = plt.cm.get_cmap('tab10')\n", - " \n", - "plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 20 })\n", - "\n", - " \n", - "df_final=None\n", - "traits=[ 'PGC_SCZ_2014_EUR', 'GIANT_HEIGHT_2018_UKB' ]\n", - "#traits=[ 'PGC_SCZ_2014_EUR' ]\n", - "#traits=[ 'GIANT_HEIGHT_2018_UKB' ]\n", - "for trait in traits:\n", - " \n", - " pattern = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run12.fit.json'\n", - " #model_indices = [1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20,21,26,29,30,31]\n", - " #model_indices = [1,2,3,4,5,6,7]\n", - " model_indices=list(range(1, 17))\n", - " \n", - "\n", - " #pattern = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run12.fit.json'\n", - " #model_indices = list(range(1,33))\n", - " #model_indices = list(range(1,9)) #+ [11, 16]\n", - " #model_indices = list(range(1,17)) #+ [11, 16]\n", - "\n", - " if 1:\n", - " data_all = None\n", - " for model_index in model_indices:\n", - " fname = pattern.format(trait, model_index)\n", - " if (not os.path.exists(fname)) or (os.path.getsize(fname) == 0): continue\n", - " print(fname)\n", - " data_tmp = json.loads(open(fname).read())\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " if data_all is None: data_all = data_tmp\n", - " else: data_all[model_key] = data_tmp[model_key]\n", - "\n", - " weights = np.array(data_all['weights'])\n", - " mask = weights>0\n", - "\n", - " for ylim in []: #[20, 50, 150]: #[7.3, 20, 50, 150]:\n", - " #for ylim in []: #[7.3, 20, 50, 150]: \n", - " fig=plt.figure(figsize=[40, 30])\n", - " \n", - " fig.suptitle(trait)\n", - " for model_index in model_indices:\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " data = data_all[model_key]\n", - " plt.subplot(4,8,model_index)\n", - " plt.title(data['spec'])\n", - " h1=make_qq_plot(data['qqplot'], color=cm.colors[0], ylim=ylim)\n", - " h2=make_qq_plot(data['qqplot_bins'][0], color=cm.colors[1], ylim=ylim)\n", - " t1='low maf\\nlow LD'#data['qqplot_bins'][0]['title'].replace('\\\\in', '').replace(';', '\\n')\n", - " h3=make_qq_plot(data['qqplot_bins'][8], color=cm.colors[2], ylim=ylim)\n", - " t2='high maf\\nhigh LD' # data['qqplot_bins'][8]['title'].replace('\\\\in', '').replace(';', '\\n')\n", - " if model_index==1: plt.legend([h1, h2, h3], ['all', t1, t2])\n", - " plt.savefig('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/figures_run12/' + trait + '.ylim={}.qq.png'.format(ylim) , bbox_inches='tight')\n", - "\n", - " for model_index in model_indices:\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " data = data_all[model_key]\n", - " full = np.array(data['sampling_tag_pdf'])\n", - " fast = np.array(data['gaussian_tag_pdf'])\n", - " before = np.sum(mask)\n", - " mask[~np.isfinite(np.log(full)+np.log(fast))] = 0\n", - " after = np.sum(mask)\n", - " if (after < before): print('exclude {} due to {}'.format(before-after, model_key))\n", - "\n", - " df_data = {}\n", - " for model_index in model_indices:\n", - " model_key = 'm{}'.format(model_index) if (model_index > 9) else 'm0{}'.format(model_index)\n", - " data = data_all[model_key]\n", - "\n", - " fname = pattern.format(trait, model_index)\n", - " p = data['params']\n", - " full = np.array(data['sampling_tag_pdf'])\n", - " fast = np.array(data['gaussian_tag_pdf'])\n", - " \n", - " p['C1frac'] = 1 if (isinstance(p['pi'], (int, float)) or (len(p['pi']) == 1)) else p['pi'][0]*p['sig2_beta'][0]/np.dot(p['pi'], p['sig2_beta'])\n", - " p['h2'] = data['annot_h2'][0]\n", - " p['num_annot'] = len(p['annonames'])\n", - " p['model'] = data['spec']\n", - " p['S'] = 'S' if ('S' in data['spec']) else '0'\n", - " p['L'] = 'L' if ('L' in data['spec']) else '0'\n", - " p['A'] = 'A' if ('A' in data['spec']) else '0'\n", - " p['P'] = data['spec'][4:6]\n", - " #p['fastcost'] = data['optimize'][-1][1]['fun']\n", - " p['fastcost'] = -np.dot(weights[mask], np.log(fast[mask])) #data['params{}'.format(i)]['full_cost']\n", - " p['fullcost'] = -np.dot(weights[mask], np.log(full[mask])) #data['params{}'.format(i)]['full_cost']\n", - " p['nit'] = data['optimize'][-1][1]['nit']\n", - " p['annots'] = ' '.join(p['annonames'])\n", - " insert_key_to_dictionary_as_list('fname', fname.split('/')[-1].split('.')[0], df_data) \n", - " for k in ['model', 'P', 'L', 'S', 'A', 'pi', 'sig2_beta', 'sig2_zeroA', 's', 'l', 'h2', 'C1frac', 'num_annot', 'fullcost', 'fastcost', 'nit','annots']:\n", - " insert_key_to_dictionary_as_list(k, p[k], df_data) \n", - " df=pd.DataFrame(df_data) \n", - " offset = np.nanmin(df['fullcost'].values)\n", - " df['fastcost'] = df['fastcost'] - offset\n", - " df['fullcost'] = df['fullcost'] - offset\n", - " df_final = pd.concat([df_final, df]) if (df_final is not None) else df\n", - "df_final.to_csv('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined.csv',index=False,sep='\\t')\n", - "df_final.to_excel('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/combined_run12_abc.xlsx',index=False)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'20191025 04:16:02.646458\\t=AnnotUnivariateParams(_pi: [1, 0.007275616767434327], _sig2_beta: [1.964291607723344e-06, 0.0001260204190927956], _s: 0, _l: 0, _sig2_zeroA: 0.9135216023949413)\\n'" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lines[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import re\n", - "def parse(line):\n", - " vals = [x for x in re.split(' |,|:|]|=|\\\\(|\\\\)|\\\\[',line) if x]\n", - " active = \"\"\n", - " params = {}\n", - " for val in vals:\n", - " if val in ['_pi', \"_sig2_beta\", \"_s\", \"_l\", \"_sig2_zeroA\"]:\n", - " active = val\n", - " continue\n", - " if not active: continue\n", - " try:\n", - " params[active] = float(val)\n", - " active = active + \"2\"\n", - " except:\n", - " pass\n", - " return params\n", - "\n", - "def groupby(vals, token):\n", - " groups = []\n", - " group = []\n", - " for val in vals:\n", - " if token in val:\n", - " groups.append(group)\n", - " group = []\n", - " else:\n", - " group.append(val)\n", - " return groups\n", - " \n", - " \n", - "parse(lines[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['params', 'optimize', 'annot_enrich', 'annot_h2', 'convolve_tag_pdf', 'convolve_tag_pdf_err', 'sampling_tag_pdf', 'gaussian_tag_pdf', 'qqplot_fit', 'qqplot_bins_fit', 'power_fit', 'optimize_test', 'qqplot_test', 'qqplot_bins_test', 'power_test', 'spec'])" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = json.loads(open('/home/oleksanf/github/mixer/precimed/mixer.json').read())\n", - "data['m01'][.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "diffevo-fast - 210167.85535529253\n", - "\t{'pi': [1, 0.0008145231895221258], 'sig2_beta': [9.833544209174823e-08, 0.0003301796543817552], 'sig2_zeroA': 2.1666312961670764, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "\n", - "nedlermead-fast - 210106.0630066026\n", - "\t{'pi': [1, 0.0008778310017981898], 'sig2_beta': [8.103846031693112e-08, 0.00027310698073620965], 'sig2_zeroA': 2.2386018316576797, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "\n", - "nedlermead - 209359.17660183084\n", - "\t{'pi': [1, 0.001043057180706619], 'sig2_beta': [2.89306293522831e-08, 0.0003087633183910141], 'sig2_zeroA': 1.9552045742852584, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n" - ] - } - ], - "source": [ - "import json\n", - "data = json.loads(open('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=17.outtag=run12.fit.json').read())\n", - "print('\\n\\n'.join([\"{} - {}\\n\\t{}\".format(x[0], x[1]['cost'], x[1]['params']) for x in data['m17']['optimize']]))" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "diffevo-fast - 218932.05727683892\n", - "\t{'pi': [1], 'sig2_beta': [3.000970957240736e-07], 'sig2_zeroA': 2.3396661180639513, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "\n", - "nedlermead-fast - 218930.01926150513\n", - "\t{'pi': [1], 'sig2_beta': [2.9421025154479586e-07], 'sig2_zeroA': 2.33952444409647, 's': 0, 'l': 0, 'sig2_annot': [1], 'annonames': ['base']}\n", - "\n", - "diffevo-fast - 211322.51087338387\n", - "\t{'pi': [1, 0.003745606540663753], 'sig2_beta': [5.301418112277961e-08, 7.017369170836056e-05], 'sig2_zeroA': 2.473912540831676, 's': 0, 'l': 0, 'sig2_annot': [0.7784503999211747, 11.561417679311713, 1.3616985745635197, 0.04822668405881403, 0.05718080927717492, 1.8636657527489464, 2.251434514554363, 0.6441248873558078, 0.14409360931521906, 0.225662256926699, 2.033888134223576, 3.5118620212578233], 'annonames': ['Coding_UCSC.extend.500.bed', 'Conserved_LindbladToh.bed', 'Enhancer_Hoffman.bed', 'H3K27ac_Hnisz.bed', 'H3K4me1_peaks_Trynka.bed', 'H3K9ac_peaks_Trynka.bed', 'H3K9ac_Trynka.bed', 'PromoterFlanking_Hoffman.extend.500.bed', 'SuperEnhancer_Hnisz.extend.500.bed', 'Transcribed_Hoffman.bed', 'TSS_Hoffman.bed', 'UTR_3_UCSC.bed']}\n", - "\n", - "nedlermead-fast - 211283.16901788706\n", - "\t{'pi': [1, 0.006230794617944276], 'sig2_beta': [3.479395304599311e-11, 5.403464437986436e-05], 'sig2_zeroA': 2.47116213651773, 's': 0, 'l': 0, 'sig2_annot': [0.7784503999211747, 11.561417679311713, 1.3616985745635197, 0.04822668405881403, 0.05718080927717492, 1.8636657527489464, 2.251434514554363, 0.6441248873558078, 0.14409360931521906, 0.225662256926699, 2.033888134223576, 3.5118620212578233], 'annonames': ['Coding_UCSC.extend.500.bed', 'Conserved_LindbladToh.bed', 'Enhancer_Hoffman.bed', 'H3K27ac_Hnisz.bed', 'H3K4me1_peaks_Trynka.bed', 'H3K9ac_peaks_Trynka.bed', 'H3K9ac_Trynka.bed', 'PromoterFlanking_Hoffman.extend.500.bed', 'SuperEnhancer_Hnisz.extend.500.bed', 'Transcribed_Hoffman.bed', 'TSS_Hoffman.bed', 'UTR_3_UCSC.bed']}\n", - "\n", - "nedlermead - 210363.69463555168\n", - "\t{'pi': [1, 0.0034131332925773587], 'sig2_beta': [2.2429522617727485e-11, 0.00011784929976650242], 'sig2_zeroA': 2.20673631809004, 's': 0, 'l': 0, 'sig2_annot': [0.7784503999211747, 11.561417679311713, 1.3616985745635197, 0.04822668405881403, 0.05718080927717492, 1.8636657527489464, 2.251434514554363, 0.6441248873558078, 0.14409360931521906, 0.225662256926699, 2.033888134223576, 3.5118620212578233], 'annonames': ['Coding_UCSC.extend.500.bed', 'Conserved_LindbladToh.bed', 'Enhancer_Hoffman.bed', 'H3K27ac_Hnisz.bed', 'H3K4me1_peaks_Trynka.bed', 'H3K9ac_peaks_Trynka.bed', 'H3K9ac_Trynka.bed', 'PromoterFlanking_Hoffman.extend.500.bed', 'SuperEnhancer_Hnisz.extend.500.bed', 'Transcribed_Hoffman.bed', 'TSS_Hoffman.bed', 'UTR_3_UCSC.bed']}\n" - ] - } - ], - "source": [ - "import json\n", - "data = json.loads(open('/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=18.outtag=run12.fit.json').read())\n", - "print('\\n\\n'.join([\"{} - {}\\n\\t{}\".format(x[0], x[1]['cost'], x[1]['params']) for x in data['m18']['optimize'] if ('cost' in x[1])]))" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'20191025 05:11:50.284072\\t=AnnotUnivariateParams(_pi: [1, 0.006230794617944276], _sig2_beta: [3.479395304599311e-11, 5.403464437986436e-05], _s: 0, _l: 0, _sig2_zeroA: 2.47116213651773)\\n'" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "groups[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'20191025 04:55:57.464582\\t=AnnotUnivariateParams(_pi: [1, 0.0008778310017981898], _sig2_beta: [8.103846031693112e-08, 0.00027310698073620965], _s: 0, _l: 0, _sig2_zeroA: 2.2386018316576797)\\n'" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "groups[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[210, 98, 900, 598, 481]" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[len(x) for x in groups]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['20191119 15:39:57.344917\\t>calc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.42919, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:39:58.715099\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.08815, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:01.427291\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.40673, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:04.134428\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.09445, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:06.948396\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.18031, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:09.629414\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.41088, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:12.314227\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.7156, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:14.983024\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.29953, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:17.670197\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.11577, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:20.584522\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.24843, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:23.278694\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.1224, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:25.917692\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.31132, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:28.560446\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.22833, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:31.259581\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.18257, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:33.947673\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.24109, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:36.671396\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.28225, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:39.370220\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.44241, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:42.061556\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.14799, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:44.745807\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.74599, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:47.443535\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.23931, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:50.137362\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.30542, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:52.834865\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.11862, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:55.474197\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.34349, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:40:58.164142\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.37685, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:00.799705\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.3945, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:03.492262\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.23931, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:06.182153\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.08615, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:08.817731\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.00448, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:11.510922\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.76631, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:14.199481\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=2.35537, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:16.885244\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, sig2_zeroA=1.96151, sig2_zeroC=1, sig2_zeroL=0)\\n',\n", - " '20191119 15:41:19.573449\\tcalc_unified_univariate_cost_gaussian(trait_index=1, num_components=1, num_snp=9997231, 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#fname = \"/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=3.outtag=run14.fit.log\"\n", - "fname = \"/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/GIANT_HEIGHT_2018_UKB.models=3.outtag=run15.fit.log\"\n", - "#fname = \"/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=3.outtag=run13.fit.log\"\n", - "lines = [x for x in open(fname).readlines() if (\"=AnnotUnivariateParams\" in x) or (\"retrieve_weights\" in x)]\n", - "groups = [g for g in groupby(lines, \"retrieve_weights\") if g]\n", - "\n", - "#groups = [groups[1]]\n", - "\n", - "plt.figure(figsize=(40, 20))\n", - "paramvec = '_pi _pi2 _sig2_beta _sig2_beta2 _s _l _sig2_zeroA'.split()\n", - "for pindex, param in enumerate(paramvec):\n", - " for gindex, lines in enumerate(groups):\n", - " if param not in parse(lines[0]): continue\n", - " valvec = [parse(line)[param] for line in lines]\n", - " if not valvec: continue\n", - " plt.subplot(len(paramvec),len(groups), 1+gindex + pindex*len(groups))\n", - " plt.plot(valvec)\n", - " if gindex==0: plt.ylabel(param)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import precimed\n", - "import precimed.mixer\n", - "import logging\n", - "import numpy as np\n", - "#logging.getLogger().setLevel(logging.DEBUG)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1. , 1.41421356, 1.73205081])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.power(np.array([1, 2,3]), 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_snp: 8\n", - "num_tag: 5\n", - "defvec: [1 3 4 6 7]\n", - "mafvec: [0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8]\n", - "chrnumvec: [1 1 1 2 2 2 2 2]\n", - "zvec1: [-1.5 1.5 2.5 -2.5 0.123]\n", - "zvec2: [ 1.5 -1.5 -2.5 2.5 0.123]\n", - "nvec1: [100. 200. 100. 200. 300.]\n", - "nvec2: [1000. 2000. 1000. 2000. 3000.]\n", - "weights: [0.2 0.2 0.2 0.3 0.3]\n" - ] - }, - { - "data": { - "text/plain": [ - "LibBgmg(_lib_name: /home/oleksanf/github/mixer/src/build/lib/libbgmg.so, _context_id: 0, num_snp: 8, num_tag: 5)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "libbgmg.init_log(\"/home/oleksanf/github/mixer/testlog5.log\")\n", - "libbgmg.log_message('Test log message succeeded?')\n", - "libbgmg.dispose()\n", - "libbgmg.defvec=[0, 1, 0, 1, 1, 0, 1, 1]\n", - "libbgmg.mafvec = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n", - "libbgmg.chrnumvec = [1, 1, 1, 2, 2, 2, 2, 2]\n", - "libbgmg.zvec1 = [-1.5, 1.5, 2.5, -2.5, 0.123]\n", - "libbgmg.zvec2 = [1.5, -1.5, -2.5, 2.5, 0.123]\n", - "libbgmg.nvec1 = [100, 200, 100, 200, 300]\n", - "libbgmg.nvec2 = [1000, 2000, 1000, 2000, 3000]\n", - "libbgmg.weights = [0.2, 0.2, 0.2, 0.3, 0.3]\n", - "print('num_snp: {}'.format(libbgmg.num_snp))\n", - "print('num_tag: {}'.format(libbgmg.num_tag))\n", - "print('defvec: {}'.format(libbgmg.defvec))\n", - "print('mafvec: {}'.format(libbgmg.mafvec))\n", - "print('chrnumvec: {}'.format(libbgmg.chrnumvec))\n", - "print('zvec1: {}'.format(libbgmg.zvec1))\n", - "print('zvec2: {}'.format(libbgmg.zvec2))\n", - "print('nvec1: {}'.format(libbgmg.nvec1))\n", - "print('nvec2: {}'.format(libbgmg.nvec2))\n", - "print('weights: {}'.format(libbgmg.weights))\n", - "libbgmg" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'precimed.mixer' has no attribute 'LibBgmg'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprecimed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmixer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecimed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmixer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlibbgmg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecimed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmixer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLibBgmg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/oleksanf/github/mixer/src/build/lib/libbgmg.so'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mlibbgmg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecimed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmixer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLibBgmg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/oleksanf/github/mixer/src/build/lib/libbgmg.so'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'precimed.mixer' has no attribute 'LibBgmg'" - ] - } - ], - "source": [ - "from importlib import reload\n", - "import precimed.mixer\n", - "reload(precimed.mixer)\n", - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "\n", - "libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so')\n", - "libbgmg.init_log(\"/home/oleksanf/github/mixer/testlog5.log\")\n", - "libbgmg.log_message('Test log message succeeded?')\n", - "libbgmg.dispose()\n", - "\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "chr_labels = list(range(1, 23))\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz'\n", - "trait2_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_BIP_2016.sumstats.gz'\n", - "exclude = ''; extract = ''\n", - "libbgmg.init(bim_file, frq_file, chr_labels, trait1_file, trait2_file, exclude, extract);\n", - "print(libbgmg)\n", - "\n", - "options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg.num_snp), ('num_components', 3), \n", - " ('cache_tag_r2sum', False), ('threads', 6), ('seed', None), ('z1max', None)]\n", - "for opt, val in options: libbgmg.set_option(opt, val)\n", - "\n", - "for chr_label in chr_labels: \n", - " libbgmg.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(chr_label);\n", - "\n", - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n", - "\n", - "libbgmg.set_option('diag', 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(libbgmg.weights,bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(libbgmg.ld_tag_r2_sum, range=(0, 600), bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(libbgmg.ld_tag_r4_sum, range=(0, 600), bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 [(3450, 0.050812542)]\n", - "2 [(1627, 0.067566946), (1635, 0.065918975)]\n", - "4 [(2125, 0.05070573), (2128, 0.053543907), (4458, 0.05900664), (4465, 0.05159075)]\n", - "1 [(975, 0.06755169)]\n", - "1 [(975, 0.069092855)]\n", - "1 [(975, 0.069092855)]\n", - "12 [(1969, 0.05009537), (1971, 0.052048523), (3543, 0.051438164), (3579, 0.051819637), (3582, 0.051819637), (3585, 0.051544975), (3586, 0.0520943), (3589, 0.05168231), (3590, 0.05195697), (3591, 0.051422905), (3592, 0.052643627), (3594, 0.052109558)]\n" - ] - } - ], - "source": [ - "# it's possible to look at LD of a given SNP, and of the entire chromosome\n", - "for i in range(1,100):\n", - " tag, r2 = libbgmg.get_ld_r2_snp(i)\n", - " if len(tag>0):\n", - " print(len(tag), list(zip(tag,r2)))\n", - "snp, tag, r2 = libbgmg.get_ld_r2_chr(21)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1,100): libbgmg.calc_univariate_cost(1, 0.003, 1.2, 1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "svec=libbgmg.calc_univariate_power(1, 0.003, 1.2, 1e-4, 5.45, [1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8])\n", - "plt.plot(svec)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibbgmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_univariate_delta_posterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.003\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/github/mixer/precimed/mixer/libbgmg.py\u001b[0m in \u001b[0;36mcalc_univariate_delta_posterior\u001b[0;34m(self, trait, pi_vec, sig2_zero, sig2_beta)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalc_univariate_delta_posterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrait\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpi_vec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig2_zero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig2_beta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0;32mraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0mc0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mc1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Disable calc_univariate_delta_posterior - for some reason it crashes in native c++ plugin" - ] - } - ], - "source": [ - "c0,c1,c2=libbgmg.calc_univariate_delta_posterior(1, 0.003, 1.2, 1e-4)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "out_file = '/home/oleksanf/github/mixer/results'\n", - "lib_name = '/home/oleksanf/github/mixer/src/build/lib/libbgmg.so'\n", - "log_file = out_file + '.log'\n", - "bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim'\n", - "frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq'\n", - "plink_ld_bin = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin'\n", - "chr_labels = list(range(1, 23))\n", - "trait1_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz'\n", - "trait2_file = '/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_BIP_2016.sumstats.gz'\n", - "exclude = ''; extract = ''\n", - " options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg.num_snp), ('num_components', 3), \n", - " ('cache_tag_r2sum', False), ('threads', 6), ('seed', 123), ('z1max', None), ('z2max', None)]\n", - "randprune_n = 64\n", - "randprune_r2 = 0.1\n", - "\n", - "def setub_libbgmg(lib_name, log_file, bim_file, frq_file, plink_ld_bin, chr_labels,\n", - " trait1_file, trait2_file, exclude, extract,\n", - " options, randprune_n, randprune_r2):\n", - "\n", - " libbgmg = precimed.mixer.LibBgmg(lib_name)\n", - " libbgmg.init_log(log_file)\n", - " libbgmg.dispose()\n", - "\n", - " libbgmg.init(bim_file, frq_file, chr_labels, trait1_file, trait2_file, exclude, extract);\n", - "\n", - " for opt, val in options:\n", - " libbgmg.set_option(opt, val)\n", - "\n", - " for chr_label in chr_labels: \n", - " libbgmg.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(chr_label);\n", - "\n", - " libbgmg.set_weights_randprune(randprune_n, randprune_r2);\n", - "\n", - " libbgmg.set_option('diag', 0)\n", - " return libbgmg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def run_mixer(lib):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 4.930380657631324e-32\n", - " nfev: 9\n", - " nit: 5\n", - " success: True\n", - " x: 1.0000000000000002\n", - "\n" - ] - } - ], - "source": [ - "x=scipy.optimize.minimize_scalar(lambda x:(x-1)*(x-1), method='brent', bracket=[-10, 10])\n", - "print(x)\n", - "print(type(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "143039.9667054704\n", - "136117.94951642904\n", - "279813.56074795313\n", - "BivariateParams(_pi: [0.0012466754854858091, 0.0003354573743336747, 0.0028265604933306715], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8945159908412117, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n" - ] - } - ], - "source": [ - "from importlib import reload\n", - "import precimed.mixer\n", - "reload(precimed.mixer)\n", - "from precimed.mixer.utils import *\n", - "from precimed.mixer.utils import UnivariateParams\n", - "from precimed.mixer.utils import BivariateParams\n", - "from precimed.mixer.utils import _log_exp_converter\n", - "from precimed.mixer.utils import _logit_logistic_converter\n", - "from precimed.mixer.utils import _arctanh_tanh_converter\n", - "from precimed.mixer.utils import UnivariateParametrization_constPI\n", - "from precimed.mixer.utils import UnivariateParametrization_constH2_constSIG2ZERO\n", - "from precimed.mixer.utils import UnivariateParametrization_constPI_constSIG2BETA\n", - "from precimed.mixer.utils import UnivariateParametrization\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO_boundedPI\n", - "from precimed.mixer.utils import BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_natural_axis\n", - "from precimed.mixer.utils import BivariateParametrization_constUNIVARIATE_constRHOBETA_constPI\n", - "from precimed.mixer.utils import _hessian_robust\n", - "from precimed.mixer.utils import _max_rg\n", - "from precimed.mixer.utils import _calculate_univariate_uncertainty\n", - "from precimed.mixer.utils import _calculate_bivariate_uncertainty\n", - "\n", - "print(UnivariateParams(0.001, 1e-4, 1.23).cost(libbgmg, 1))\n", - "print(UnivariateParams(0.001, 1e-4, 1.23).cost(libbgmg, 2))\n", - "print(BivariateParams([0.001, 0.002, 0.004], [1e-4, 3e-4], 0.8, [1.23, 1.06], 0.4).cost(libbgmg))\n", - "\n", - "#scalar_optimizer = scipy.optimize.fminbound\n", - "params12_fitted, _ = BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO_boundedPI(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_rg=params12._rg(),\n", - " const_rho_zero=params12._rho_zero,\n", - " lib=libbgmg).fit(scalar_optimizer)\n", - "print(params12_fitted)" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UnivariateParams(_pi: 0.004073235978816481, _sig2_beta: 5.288619957821589e-05, _sig2_zero: 1.1755915719572747)\n", - "UnivariateParams(_pi: 0.0031620178676643462, _sig2_beta: 5.231128193651721e-05, _sig2_zero: 1.084084668490775)\n", - "BivariateParams(_pi: [0.0012449932750385182, 0.00033377516388638376, 0.0028282427037779625], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8939839416846624, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n", - "\n", - "Univariate (trait1):\n", - "pi: pe=0.00407, mean=0.0041, median=0.0041, std=0.000453, ci=[0.00329, 0.00506]\n", - "nc: pe=4.07e+04, mean=4.1e+04, median=4.1e+04, std=4.53e+03, ci=[3.29e+04, 5.06e+04]\n", - "nc@p9: pe=9.2e+03, mean=9.26e+03, median=9.26e+03, std=1.02e+03, ci=[7.43e+03, 1.14e+04]\n", - "sig2_beta: pe=5.29e-05, mean=5.32e-05, median=5.32e-05, std=5.34e-06, ci=[4.34e-05, 6.44e-05]\n", - "sig2_zero: pe=1.18, mean=1.18, median=1.18, std=0.00889, ci=[1.16, 1.19]\n", - "h2: pe=0.447, mean=0.447, median=0.447, std=0.0155, ci=[0.418, 0.478]\n", - "\n", - "Univariate (trait2):\n", - "pi: pe=0.00316, mean=0.00321, median=0.00321, std=0.000588, ci=[0.00222, 0.00447]\n", - "nc: pe=3.16e+04, mean=3.21e+04, median=3.21e+04, std=5.87e+03, ci=[2.22e+04, 4.47e+04]\n", - "nc@p9: pe=7.14e+03, mean=7.25e+03, median=7.25e+03, std=1.33e+03, ci=[5.01e+03, 1.01e+04]\n", - "sig2_beta: pe=5.23e-05, mean=5.32e-05, median=5.32e-05, std=8.94e-06, ci=[3.79e-05, 7.26e-05]\n", - "sig2_zero: pe=1.08, mean=1.08, median=1.08, std=0.00764, ci=[1.07, 1.1]\n", - "h2: pe=0.343, mean=0.344, median=0.344, std=0.0178, ci=[0.31, 0.38]\n", - "\n", - "Bivariate:\n", - "sig2_zero_T1: pe=1.18, mean=1.18, median=1.18, std=0.00889, ci=[1.16, 1.19]\n", - "sig2_zero_T2: pe=1.08, mean=1.08, median=1.08, std=0.00764, ci=[1.07, 1.1]\n", - "sig2_beta_T1: pe=5.29e-05, mean=5.32e-05, median=5.32e-05, std=5.34e-06, ci=[4.34e-05, 6.44e-05]\n", - "sig2_beta_T2: pe=5.23e-05, mean=5.32e-05, median=5.32e-05, std=8.94e-06, ci=[3.79e-05, 7.26e-05]\n", - "h2_T1: pe=0.447, mean=0.447, median=0.447, std=0.0155, ci=[0.418, 0.478]\n", - "h2_T2: pe=0.343, mean=0.344, median=0.344, std=0.0178, ci=[0.31, 0.38]\n", - "rho_zero: pe=0.269, mean=0.269, median=0.269, std=0.00418, ci=[0.261, 0.277]\n", - "rho_beta: pe=0.894, mean=0.874, median=0.874, std=0.0897, ci=[0.659, 0.991]\n", - "rg: pe=0.705, mean=0.662, median=0.662, std=0.077, ci=[0.5, 0.793]\n", - "pi1: pe=0.00124, mean=0.00133, median=0.00133, std=0.000679, ci=[0.000153, 0.00273]\n", - "pi2: pe=0.000334, mean=0.000444, median=0.000444, std=0.00036, ci=[3.32e-05, 0.00135]\n", - "pi12: pe=0.00283, mean=0.00276, median=0.00276, std=0.000538, ci=[0.00176, 0.00384]\n", - "pi1u: pe=0.00407, mean=0.0041, median=0.0041, std=0.000453, ci=[0.00329, 0.00506]\n", - "pi2u: pe=0.00316, mean=0.00321, median=0.00321, std=0.000588, ci=[0.00222, 0.00447]\n", - "nc1: pe=1.24e+04, mean=1.33e+04, median=1.33e+04, std=6.79e+03, ci=[1.53e+03, 2.73e+04]\n", - "nc2: pe=3.34e+03, mean=4.44e+03, median=4.44e+03, std=3.6e+03, ci=[332, 1.35e+04]\n", - "nc12: pe=2.83e+04, mean=2.76e+04, median=2.76e+04, std=5.38e+03, ci=[1.76e+04, 3.84e+04]\n", - "nc1u: pe=4.07e+04, mean=4.1e+04, median=4.1e+04, std=4.53e+03, ci=[3.29e+04, 5.06e+04]\n", - "nc2u: pe=3.16e+04, mean=3.21e+04, median=3.21e+04, std=5.87e+03, ci=[2.22e+04, 4.47e+04]\n", - "nc1@p9: pe=2.81e+03, mean=3.01e+03, median=3.01e+03, std=1.53e+03, ci=[347, 6.16e+03]\n", - "nc2@p9: pe=754, mean=1e+03, median=1e+03, std=813, ci=[75, 3.05e+03]\n", - "nc12@p9: pe=6.39e+03, mean=6.25e+03, median=6.25e+03, std=1.22e+03, ci=[3.98e+03, 8.67e+03]\n", - "nc1u@p9: pe=9.2e+03, mean=9.26e+03, median=9.26e+03, std=1.02e+03, ci=[7.43e+03, 1.14e+04]\n", - "nc2u@p9: pe=7.14e+03, mean=7.25e+03, median=7.25e+03, std=1.33e+03, ci=[5.01e+03, 1.01e+04]\n", - "totalpi: pe=0.00441, mean=0.00454, median=0.00454, std=0.000547, ci=[0.00359, 0.00576]\n", - "totalnc: pe=4.41e+04, mean=4.54e+04, median=4.54e+04, std=5.46e+03, ci=[3.59e+04, 5.75e+04]\n", - "totalnc@p9: pe=9.96e+03, mean=1.03e+04, median=1.03e+04, std=1.24e+03, ci=[8.11e+03, 1.3e+04]\n", - "pi1_over_totalpi: pe=0.283, mean=0.288, median=0.288, std=0.131, ci=[0.0366, 0.528]\n", - "pi2_over_totalpi: pe=0.0757, mean=0.094, median=0.094, std=0.0682, ci=[0.00777, 0.262]\n", - "pi12_over_totalpi: pe=0.642, mean=0.618, median=0.618, std=0.141, ci=[0.352, 0.894]\n", - "pi1_over_pi1u: pe=0.306, mean=0.318, median=0.318, std=0.145, ci=[0.0408, 0.591]\n", - "pi2_over_pi2u: pe=0.106, mean=0.135, median=0.135, std=0.0973, ci=[0.011, 0.376]\n", - "pi12_over_pi1u: pe=0.694, mean=0.682, median=0.682, std=0.145, ci=[0.409, 0.959]\n", - "pi12_over_pi2u: pe=0.894, mean=0.865, median=0.865, std=0.0973, ci=[0.624, 0.989]\n", - "pi1u_over_pi2u: pe=1.29, mean=1.32, median=1.32, std=0.283, ci=[0.854, 1.96]\n", - "pi2u_over_pi1u: pe=0.776, mean=0.793, median=0.793, std=0.17, ci=[0.511, 1.17]\n" - ] - } - ], - "source": [ - "import scipy.optimize\n", - "\n", - "libbgmg.set_option('fast_cost', 1);\n", - " \n", - "optimizer = lambda func, x0: scipy.optimize.minimize(func, x0, method='Nelder-Mead')\n", - "\n", - "params = []\n", - "\n", - "for trait in [1, 2]:\n", - " params0, details = UnivariateParametrization_constPI(1.0, 1.5, 1e-4, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params0)\n", - "\n", - " params1, details = UnivariateParametrization_constH2_constSIG2ZERO(0.01, params0, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params1)\n", - "\n", - " params2, details = UnivariateParametrization_constPI_constSIG2BETA(1.0, params1, libbgmg, trait=trait).fit(optimizer)\n", - " #print(params2)\n", - "\n", - " params3, details = UnivariateParametrization(params2, libbgmg, trait=trait).fit(optimizer)\n", - " print(params3)\n", - " \n", - " params.append(params3)\n", - "\n", - "alpha = 0.05\n", - "totalhet = 2.0 * np.dot(libbgmg.mafvec, 1.0 - libbgmg.mafvec) \n", - "num_samples = 10000\n", - "\n", - "# That's the most appropriate initialization for the bivariate model\n", - "# BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG - not used\n", - "# BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO - used to fit the full model\n", - "zcorr = np.corrcoef(libbgmg.zvec1, libbgmg.zvec2)[0, 1]\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.1,\n", - " init_rho_beta=zcorr,\n", - " init_rho_zero=zcorr,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "ci1, ci_sample1 = _calculate_univariate_uncertainty(UnivariateParametrization(params[0], libbgmg, trait=1), alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "ci2, ci_sample2 = _calculate_univariate_uncertainty(UnivariateParametrization(params[1], libbgmg, trait=2), alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "ci12, ci_sample12 = _calculate_bivariate_uncertainty(BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=params12._pi[2],\n", - " init_rho_beta=params12._rho_beta,\n", - " init_rho_zero=params12._rho_zero,\n", - " lib=libbgmg), [ci_sample1, ci_sample2], alpha, totalhet, libbgmg.num_snp, num_samples)\n", - "\n", - "print('\\nUnivariate (trait1):')\n", - "for k, v in ci1.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))\n", - "print('\\nUnivariate (trait2):')\n", - "for k, v in ci2.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))\n", - "print('\\nBivariate:')\n", - "for k, v in ci12.items():\n", - " print('{}: pe={:.3g}, mean={:.3g}, median={:.3g}, std={:.3g}, ci=[{:.3g}, {:.3g}]'.format(k, v['point_estimate'], v['mean'], v['median'], v['std'], v['lower'], v['upper']))" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0.0012449932750385182, 0.00033377516388638376, 0.0028282427037779625], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8939839416846624, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26921714348165393, rg: 0.7045218190412361)\n" - ] - } - ], - "source": [ - "# That's the most appropriate initialization for the bivariate model\n", - "# BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG - not used\n", - "# BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO - used to fit the full model\n", - "zcorr = np.corrcoef(libbgmg.zvec1, libbgmg.zvec2)[0, 1]\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.1,\n", - " init_rho_beta=zcorr,\n", - " init_rho_zero=zcorr,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0, 0, 1], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8810739768325555, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.8528486201057031, rg: 0.8810739768325555)\n", - "BivariateParams(_pi: [0.0015524944994159688, 0.0006412763882638344, 0.002520741479400512], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 1.0, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.27, rg: 0.7023868342220096)\n", - "BivariateParams(_pi: [0.0015403841166866214, 0.0006291660055344869, 0.0025328518621298593], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.9999999995949107, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.2690626563807834, rg: 0.7057613066680437)\n", - "BivariateParams(_pi: [0.0015403841166866214, 0.0006291660055344869, 0.0025328518621298593], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.9999999995949107, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26899243023364927, rg: 0.7057613066680437)\n" - ] - } - ], - "source": [ - "params12, details = BivariateParametrization_constSIG2BETA_constSIG2ZERO_infPI_maxRG(\n", - " const_sig2_beta=[p._sig2_beta for p in params],\n", - " const_sig2_zero=[p._sig2_zero for p in params],\n", - " max_rg=_max_rg(params[0]._pi, params[1]._pi),\n", - " init_rho_beta=0.5, init_rho_zero=0.1, lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE_constRG_constRHOZERO(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_rg=params12._rho_beta,\n", - " const_rho_zero=params12._rho_zero,\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.95,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.5,\n", - " init_rho_beta=0,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)\n", - "\n", - "params12, details = BivariateParametrization_constUNIVARIATE_constRHOBETA_constPI(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " const_pi12=params12._pi[2],\n", - " const_rho_beta=params12._rho_beta,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BivariateParams(_pi: [0.0012389994058291652, 0.0003277812946770307, 0.0028342365729873155], _sig2_beta: [5.288619957821589e-05, 5.231128193651721e-05], _rho_beta: 0.8928580429674069, _sig2_zero: [1.1755915719572747, 1.084084668490775], _rho_zero: 0.26906143314603836, rg: 0.7051257384792755)\n" - ] - } - ], - "source": [ - "params12, details = BivariateParametrization_constUNIVARIATE_natural_axis(\n", - " const_params1=params[0],\n", - " const_params2=params[1],\n", - " init_pi12=min(params[0]._pi, params[1]._pi)*0.5,\n", - " init_rho_beta=0,\n", - " init_rho_zero=0,\n", - " lib=libbgmg).fit(optimizer)\n", - "print(params12)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/misc/plsa_mixer_simulations.ipynb b/misc/plsa_mixer_simulations.ipynb deleted file mode 100644 index 39cf5a9..0000000 --- a/misc/plsa_mixer_simulations.ipynb +++ /dev/null @@ -1,2101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3692: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", - " warnings.warn(msg, UserWarning)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import json\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "pattern = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/{}.models={}.outtag=run12.fit.json'\n", - "traits=[ 'PGC_SCZ_2014_EUR'] #, 'GIANT_HEIGHT_2018_UKB' ]\n", - "model_index = 16 # all features combined\n", - "for trait in traits:\n", - " fname = pattern.format(trait, model_index)\n", - " data_tmp = json.loads(open(fname).read())\n", - " \n", - "\n", - " df=pd.DataFrame({'annot_enrich':data_tmp['m16']['annot_enrich'], 'annonames':data_tmp['options']['annonames']})\n", - " df=df[~df['annonames'].str.contains('extend')].copy()\n", - " df['annonames'] = [x.replace('.bed', '') for x in df['annonames'].values]\n", - " #plt.plot([10, 10])\n", - " #f, ax = plt.subplots(figsize=(20, 5))\n", - " g=sns.catplot(x=\"annonames\", y=\"annot_enrich\", kind='bar', data=df, size=3, aspect=4)\n", - " g.set_xticklabels(rotation=90);\n" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3692: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", - " warnings.warn(msg, UserWarning)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot from simulated data after fitting m16 model\n", - "g=sns.catplot(x=\"annonames\", y=\"annot_enrich\", kind='bar', data=df, size=3, aspect=4)\n", - "g.set_xticklabels(rotation=90);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "bim_file='/home/oleksanf/vmshare/data/bfile_merged/chr@.bim'\n", - "chr2use = list(range(1, 23))\n", - "ref=pd.concat([pd.read_csv(bim_file.replace('@', str(chr_label)), header=None, names='CHR SNP GP BP A1 A2'.split(), sep='\\t') for chr_label in chr2use])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "for rep in range(1, 11):\n", - " ref[['SNP']].sample(1000000).to_csv('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run3/defvec_rand1M_rep={}.snps'.format(rep), index=False, header=None, sep='\\t')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'pd' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mlibbgmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights_randprune\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbim_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'@'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchr_label\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CHR SNP GP BP A1 A2'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchr_label\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchr2use\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" - ] - } - ], - "source": [ - "import precimed\n", - "import precimed.mixer\n", - "import precimed.mixer.libbgmg\n", - "import precimed.mixer.utils\n", - "import precimed.mixer.cli\n", - "import precimed.mixer.figures\n", - "import pandas as pd\n", - "chr2use = range(1, 23)\n", - "libbgmg = precimed.mixer.libbgmg.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', dispose=True)\n", - "trait1_file=''; trait2_file=''; exclude=''; extract='';\n", - "libbgmg.set_option('ld_format_version', 0)\n", - "\n", - "\n", - "frq_file='/home/oleksanf/vmshare/data/bfile_merged/chr@.frq'\n", - "plink_ld_bin0='/home/oleksanf/vmshare/data/hapgen_ldmat2_plink/bfile_merged_ldmat_p01_SNPwind50k_chr@.ld.bin'\n", - "\n", - "libbgmg.init(bim_file, frq_file, chr2use, trait1_file, trait2_file, exclude, extract)\n", - "for chr_label in chr2use: \n", - " libbgmg.set_ld_r2_coo_from_file(int(chr_label), plink_ld_bin0.replace('@', str(chr_label)))\n", - " libbgmg.set_ld_r2_csr(int(chr_label))\n", - "libbgmg.set_weights_randprune(1, 0.1)\n", - "\n", - "ref=pd.concat([pd.read_csv(bim_file.replace('@', str(chr_label)), header=None, names='CHR SNP GP BP A1 A2'.split(), sep='\\t') for chr_label in chr2use])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "for rep in range(2, 10):\n", - " libbgmg.set_option('seed', 6643+rep)\n", - " libbgmg.set_weights_randprune(1, 0.1)\n", - " ref[libbgmg.weights>0]['SNP'].to_csv('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run2/defvec_hardprune_p1_rep={}.snps'.format(rep), index=False, header=None, sep='\\t')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oleksanf/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "\n", - "rep=1\n", - "\n", - "ref[libbgmg.weights>0]['SNP'].to_csv('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run2/defvec_hardprune_p1_rep={}.snps'.format(rep), index=False, header=None, sep='\\t')" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "# annotate HAPGEN reference to basedline annotation model using https://github.com/mkanai/eas_partitioned_ldscore\n", - "# see ~/vmshare/data/SIMU_PLSA_MIXER/baseline_hapgen.sh script (note: \"conda activate bx\" with bioconductor or biopython)\n", - "\n", - "fname = '/home/oleksanf/vmshare/data/mixer_analysis/plsa_mixer/PGC_SCZ_2014_EUR.models=16.outtag=run12.fit.json'\n", - "data_tmp = json.loads(open(fname).read())\n", - "\n", - "annot_file='/home/oleksanf/vmshare/data/SIMU_PLSA_MIXER/bfile_merged/baseline.chr@.annot.gz'\n", - "df_annot = pd.concat([pd.read_csv(annot_file.replace('@', str(chr_label)), sep='\\t') for chr_label in chr2use])\n", - "snps_sig2_annot = np.dot(df_annot[[x.replace('.bed', '') for x in data_tmp['m16']['params']['annonames']]].values.astype(np.float32),\n", - " np.array(data_tmp['m16']['params']['sig2_annot']).astype(np.float32))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame({'SNP':ref['SNP'].values,\n", - " 'CHR':ref['CHR'].values,\n", - " 'BP':ref['BP'].values,\n", - " 'MAF':libbgmg.mafvec,\n", - " 'TLD':libbgmg.ld_tag_r2_sum,\n", - " 'ANNOT':snps_sig2_annot}).to_csv('/home/oleksanf/vmshare/data/SIMU_PLSA_MIXER/snps_info.csv', sep='\\t',index=False)" - 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"simu_h2=nan_pi=1e-2_annot=SCZ_s=0.25_l=0.25_rep=9\n" - ] - } - ], - "source": [ - "if 0:\n", - " snp_info = pd.read_csv('/home/oleksanf/vmshare/data/SIMU_PLSA_MIXER/snps_info.csv',sep='\\t')\n", - " snp_info['HET'] = 2 * np.multiply(snp_info['MAF'].values, 1-snp_info['MAF'].values)\n", - "h2_vals='0.1 0.4 0.7'.split()\n", - "pi_vals='1e-4 1e-3 1e-2'.split()\n", - "annot_vals=['SCZ']\n", - "s_vals='-0.50 -0.25 0.00 0.25'.split()\n", - "l_vals='-0.50 -0.25 0.00 0.25'.split()\n", - "rep_vals='0 1 2 3 4 5 6 7 8 9'.split()\n", - "\n", - "folder = '/home/oleksanf/vmshare/data/SIMU_PLSA_MIXER/run1'\n", - "i=0\n", - "#for h2 in h2_vals:\n", - "for pi in pi_vals:\n", - " for annot in annot_vals:\n", - " for s in s_vals:\n", - " for l in l_vals:\n", - " for rep in rep_vals:\n", - " pref = 'simu_h2=nan_pi={}_annot={}_s={}_l={}_rep={}'.format(pi, annot, s, l, rep)\n", - " print(pref)\n", - " df = snp_info.sample(frac=float(pi), random_state=int(rep)).copy()\n", - " \n", - " sig2_beta_vec = np.multiply(df['ANNOT'].values,\n", - " np.multiply(np.power(df['HET'].values, float(s)),\n", - " np.power(df['TLD'].values, float(l))))\n", - " np.random.seed(int(rep))\n", - " df['BETA'] = np.random.normal(0, np.sqrt(sig2_beta_vec))\n", - " df[['SNP', 'BETA']].to_csv('{}/{}.beta'.format(folder, pref), index=False, header=False, sep='\\t')\n", - " \n", - "\n", - "#len(h2_vals) * len(pi_vals) * len(annot_vals) * len(s_vals) * len(l_vals) * len(rep_vals)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/simu_h2=0.7_pi=1e-3_annot=NONE_s=-0.50_l=-0.25_rep=8_tag=run1_model=16.json',\n", - " '/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/simu_h2=0.7_pi=1e-3_annot=NONE_s=-0.50_l=-0.25_rep=4_tag=run1_model=16.json',\n", - " '/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/simu_h2=0.7_pi=1e-3_annot=NONE_s=-0.50_l=-0.25_rep=0_tag=run1_model=16.json',\n", - " '/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/simu_h2=0.7_pi=1e-3_annot=NONE_s=-0.50_l=-0.25_rep=3_tag=run1_model=16.json',\n", - 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" - ], - "text/plain": [ - " pi s \\\n", - " count mean std mean \n", - "true_tag true_pi true_annot true_model \n", - "run1 0.0001 NONE 15 5 0.000160 0.000005 -0.566294 \n", - "run2 0.0001 NONE 15 5 0.000093 0.000004 -0.719884 \n", - "\n", - " l \\\n", - " std mean std \n", - "true_tag true_pi true_annot true_model \n", - "run1 0.0001 NONE 15 0.052555 -0.270728 0.095386 \n", - "run2 0.0001 NONE 15 0.036641 -0.092556 0.075271 \n", - "\n", - " h2 sig2_zeroA \\\n", - " mean std mean \n", - "true_tag true_pi true_annot true_model \n", - "run1 0.0001 NONE 15 0.891892 0.015436 1.044177 \n", - "run2 0.0001 NONE 15 0.676219 0.009865 1.021803 \n", - "\n", - " sig2_zeroL \n", - " std mean std \n", - "true_tag true_pi true_annot true_model \n", - "run1 0.0001 NONE 15 0.002406 NaN NaN \n", - "run2 0.0001 NONE 15 0.002627 1.304248e-07 9.275452e-09 " - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "import glob\n", - "import pandas as pd\n", - "\n", - "def insert_key_to_dictionary_as_list(key, value, df_data):\n", - " if key not in df_data:\n", - " df_data[key] = []\n", - " df_data[key].append(value)\n", - "\n", - "run_tag = 'run2'\n", - "df_data = {}; dfs=[];\n", - "files = glob.glob('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/{}/*json'.format(run_tag))\n", - "#files = glob.glob('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/*pi=1e-3*NONE*model=16*json')\n", - "#files = glob.glob('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/*pi=1e-3*SCZ*model=16*json')\n", - "for fname in files:\n", - " #for rep in range(10):\n", - " #fname = '/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/run1/simu_h2=0.7_pi=1e-3_annot=NONE_s=-0.50_l=-0.25_rep={}_tag=run1_model=15.json'.format(rep)\n", - " try:\n", - " data = json.loads(open(fname).read())\n", - " except:\n", - " continue\n", - " \n", - " model = [k for k in data.keys() if k[0]=='m'][0]\n", - " params = data[model]['params']\n", - " insert_key_to_dictionary_as_list('fname', fname.split('/')[-1], df_data)\n", - " for x in fname.split('/')[-1].replace('.json', '').replace('simu_', '').split('_'):\n", - " insert_key_to_dictionary_as_list('true_' + x.split('=')[0], x.split('=')[1], df_data)\n", - " for what in 'pi sig2_beta sig2_zeroA sig2_zeroL s l'.split():\n", - " if what not in params: params[what] = \"\"\n", - " insert_key_to_dictionary_as_list(what, str(params[what]).replace('[', '').replace(']', ''), df_data)\n", - " insert_key_to_dictionary_as_list('h2', data[model]['annot_h2'][0], df_data) \n", - " insert_key_to_dictionary_as_list('enrich', str(data[model]['annot_enrich']), df_data) \n", - " insert_key_to_dictionary_as_list('sum_weights', str(data['options']['sum_weights']), df_data)\n", - "\n", - " df=pd.DataFrame({'annot_enrich':data[model]['annot_enrich'], 'annonames':data['options']['annonames']})\n", - " df=df[~df['annonames'].str.contains('extend')].copy()\n", - " df['annonames'] = [x.replace('.bed', '') for x in df['annonames'].values]\n", - " dfs.append(df)\n", - "\n", - "#for k in df_data: print(k, len(df_data[k]))\n", - "df=pd.DataFrame(df_data)\n", - "for c in df.columns: df[c]=pd.to_numeric(df[c], errors='ignore')\n", - " \n", - "df.to_csv('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/{}.csv'.format(run_tag),sep='\\t', index=False)\n", - "df.to_excel('/home/oleksanf/vmshare/data/saga/SIMU_PLSA_MIXER/{}.xlsx'.format(run_tag),index=False)\n", - "\n", - "if 0:\n", - " df=pd.concat(dfs)\n", - " g=sns.catplot(x=\"annonames\", y=\"annot_enrich\", kind='bar', data=df, size=3, aspect=4)\n", - " g.set_xticklabels(rotation=90);\n", - "\n", - "df=pd.DataFrame(df_data)\n", - "for c in df.columns: df[c]=pd.to_numeric(df[c], errors='ignore')\n", - "df[['true_tag', 'true_pi', 'true_annot', 'true_model', 's', 'l', 'pi', 'h2', 'sig2_zeroA', 'sig2_zeroL']].groupby(['true_tag', 'true_pi', 'true_annot', 'true_model']).agg({'pi':['count','mean', 'std'], 's':['mean', 'std'], 'l':['mean', 'std'], 'h2':['mean', 'std'], 'sig2_zeroA':['mean', 'std'], 'sig2_zeroL':['mean', 'std']})" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " fname true_h2 true_pi \\\n", - "12 simu_h2=0.7_pi=1e-4_annot=NONE_s=0.00_l=0.00_r... 0.7 0.0001 \n", - "25 simu_h2=0.7_pi=1e-4_annot=NONE_s=0.00_l=0.00_r... 0.7 0.0001 \n", - "27 simu_h2=0.7_pi=1e-4_annot=NONE_s=0.00_l=0.00_r... 0.7 0.0001 \n", - "\n", - " true_annot true_s true_l true_rep true_tag true_model pi \\\n", - "12 NONE 0.0 0.0 2 run5 9 0.000087 \n", - "25 NONE 0.0 0.0 3 run5 9 0.000093 \n", - "27 NONE 0.0 0.0 0 run5 9 0.000095 \n", - "\n", - " sig2_beta sig2_zeroA sig2_zeroL s l h2 \\\n", - "12 0.003317 1.023332 1.404110e-07 0 0 0.691120 \n", - "25 0.003143 1.037525 1.221579e-07 0 0 0.699847 \n", - "27 0.003193 1.030057 1.341945e-07 0 0 0.723696 \n", - "\n", - " enrich sum_weights \n", - "12 [1.0, 0.8860788941383362, 0.9464211463928223, ... 41320.675781 \n", - "25 [1.0, 0.8860801458358765, 0.9464206099510193, ... 41320.199219 \n", - "27 [1.0, 0.8860812783241272, 0.9464226961135864, ... 41320.199219 " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df['true_pi']==0.0001) & (df['true_tag']=='run5')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00014452, 0.00013599, 0.00013666, 0.00013551, 0.00013893,\n", - " 0.00012954, 0.00015371, 0.00014962, 0.00011988, 0.00013593,\n", - " 0.00012701, 0.00012983, 0.00012731, 0.00011871, 0.00012708,\n", - " 0.00013974, 0.00013922, 0.0001304 , 0.00012282, 0.00011971])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "#np.std(df[df['true_pi']==0.0001]['pi'].values)/1e-4\n", - "df[df['true_pi']==0.0001]['pi'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "unexpected indent (, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m .groupby(['true_pi1u', 'true_h2', 'spow'])\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" - ] - } - ], - "source": [ - "df_agg = df[['true_pi1u', 'true_h2', 'spow', 'pi_vec', 'pi_vec_se', 'h2', 'h2_se']]\n", - " .groupby(['true_pi1u', 'true_h2', 'spow'])\n", - " .agg({'pi_vec':['mean', 'std'], 'pi_vec_se':['mean'], 'h2':['mean', 'std'], 'h2_se':['mean']})\n", - " df_agg.columns = ['_'.join(col).strip() for col in df_agg.columns.values]\n", - " df_agg = df_agg.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/misc/power_plot_investigation.ipynb b/misc/power_plot_investigation.ipynb deleted file mode 100644 index 893a27a..0000000 --- a/misc/power_plot_investigation.ipynb +++ /dev/null @@ -1,528 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.interpolate import interp1d\n", - "from scipy.stats import multivariate_normal" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fname = '/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_wcpg/CLOZUK_SCZ_2018_withPGC.outtag=run2.testR7.json'\n", - "data_tmp = json.loads(open(fname).read())\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['options', 'analysis', 'params', 'optimize', 'inft_params', 'inft_optimize', 'ci', 'power'])" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_tmp.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.007815866265445948" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmedian(data_tmp['power']['snp_svec_clump'][:100])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.008978402707725763" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmedian(data_tmp['power']['snp_svec_total'][:100])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7727971076965332" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmax(data_tmp['power']['snp_svec_total'])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7727971076965332" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nanmax(data_tmp['power']['snp_svec_clump'])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ nan nan nan nan nan nan\n", - " 0.00016641 nan nan nan]\n", - "[ nan nan 2.20708134e-05 4.36931215e-02\n", - " 2.02915794e-03 nan 1.66411279e-04 2.01635715e-02\n", - " 1.34246016e-03 3.97507139e-02]\n" - ] - } - ], - "source": [ - "c = np.array(data_tmp['power']['snp_svec_clump'])\n", - "t = np.array(data_tmp['power']['snp_svec_total'])\n", - "idx = range(1000, 1010)\n", - "print(c[idx])\n", - "print(t[idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(np.cumsum(sorted(t[np.isfinite(t)])))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 3))\n", - "b=10000\n", - "a=4672908\n", - "plt.plot(t[(a-b):(a+b)], '.', c[(a-b):(a+b)], '.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## plt.plot(c[:1000], '.')" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'data' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" - ] - } - ], - "source": [ - "data.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([nan, nan, nan, ..., nan, nan, nan])" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PGC_SCZ_2014_EUR.outtag=run2.testR9.json\n", - "[0.5, 899108.2921599138, 36344.886902924176, 0.025904028167012188, 0.0019518859222300386]\n", - "[0.9, 7561243.152657058, 300209.51242455206, 0.025904028167012188, 0.0019518859222300386]\n", - "PGC_SCZ_0518_EUR.outtag=run2.testR9.json\n", - "[0.5, 958490.8338560858, 29393.120349607518, 0.057718504397493146, 0.002934993417400897]\n", - "[0.9, 7615567.966919883, 227555.60431995892, 0.057718504397493146, 0.002934993417400897]\n", - "CLOZUK_SCZ_2018_withPGC.outtag=run2.testR9.json\n", - "[0.5, 863585.4909425201, 27162.774734093196, 0.04645155111591874, 0.0024638437461587523]\n", - "[0.9, 6824759.8661072245, 221242.4488872194, 0.04645155111591874, 0.0024638437461587523]\n" - ] - } - ], - "source": [ - "for trait in ['PGC_SCZ_2014_EUR', 'PGC_SCZ_0518_EUR', 'CLOZUK_SCZ_2018_withPGC']:\n", - " fname = '/home/oleksanf/vmshare/data/mixer_analysis/python_mixer_wcpg/{}.outtag=run2.testR9.json'.format(trait)\n", - " data = json.loads(open(fname).read())\n", - " print(fname.split('/')[-1])\n", - " for threshold in [0.5, 0.9]:\n", - " a=[np.power(10, float(interp1d(data_power['svec'], np.log10(data_power['nvec']))(threshold))) for data_power in data['power_ci']]\n", - " b=[float(interp1d(np.log10(data_power['nvec']),data_power['svec'])(np.log10(data['options']['trait1_nval']))) for data_power in data['power_ci']]\n", - " print([threshold, np.mean(a), np.std(a), np.mean(b), np.std(b)])" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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this koef gives proportion of causal variants that explain 90% of heritability. - # it is specific to BGMG with single gaussian, with MAF specific model -DO_GCTA = False - -#figures_folder = r'H:\Dropbox\analysis\2018_09_24_BGMG_manuscript\figures_and_tables_7pheno.model=full.run3b' -drive_letter = 'C'; # 'H' - -figures_folder = '/home/oleksanf/vmshare/analysis/2019_02_11_MiXeR_display_items/figs' -if DO_GCTA: figures_folder = drive_letter + r':\Users\oleksanf\Dropbox\analysis\2018_09_24_BGMG_manuscript\figures_and_tables_laptop_2018_10_31_gcta' - -tables_folder = figures_folder; -if not os.path.exists(tables_folder): os.makedirs(tables_folder) - -data_root = '/home/oleksanf/vmshare/data' - -format_string_UGMG_fit = data_root + r"/LDSR/BGMG_result/{}" +r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run1.fit.json" -format_string_UGMG_test = data_root + r"/LDSR/BGMG_result/{}" +r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run1.test.json" -format_string_BGMG_fit = data_root + r"/LDSR/BGMG_result/{}_vs_{}"+r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run3bRGconst.fit.short.json" -format_string_BGMG_test = data_root + r"/LDSR/BGMG_result/{}_vs_{}"+r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run3bRGconst.test.json" - -format_string_UGMG_fit_censored = data_root + r"/LDSR/BGMG_result/{}" +r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run2.fit.json" -format_string_UGMG_test_censored = data_root + r"/LDSR/BGMG_result/{}" +r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run2.test.json" -format_string_BGMG_fit_censored = data_root + r"/LDSR/BGMG_result/{}_vs_{}"+r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run2RGconst.fit.short.json" -format_string_BGMG_test_censored = data_root + r"/LDSR/BGMG_result/{}_vs_{}"+r".model=full.r2min=p05.randprune=n64p05.kmax=20000.run2RGconst.test.json" - -format_string_LDSR_phase3_h2 = data_root + r"/MMIL/SUMSTAT/ANALYSIS/{}.h2.DEPRECATED.log" -format_string_LDSR_phase3_rg = data_root +r"/MMIL/SUMSTAT/ANALYSIS/ldsr_rg.csv" -format_string_LDSR_h2 = data_root + r"/MMIL/SUMSTAT/ANALYSIS/{}.h2.log" -format_string_LDSR_rg = data_root +r"/MMIL/SUMSTAT/ANALYSIS/ldsr_rg.DEPRECATED.csv" - -folder_simu_bgmg_paper_examples = data_root + r'/run_simu_bgmg_paper_examples/final' - -format_string_sumstats_noMHC = data_root + r"/MMIL/SUMSTAT/TMP/nomhc/{}.sumstats.gz" -format_string_sumstats_ldsr = data_root + r"/MMIL/SUMSTAT/TMP/ldsr/{}.sumstats.gz" - -# TBD: there are some plots that hardcode the path - those must be configurable here... -globpat_SIMU_BGMG_11pifrac = data_root + r'/SIMU_BGMG_11pifrac' + r'/*run3.bgmg.fit.short.json' -globpat_SIMU_BGMG_11pifrac_wave2 = data_root + r'/SIMU_BGMG_11pifrac_wave2' + r'/*run3.bgmg.fit.short.json' -globpat_SIMU_BGMG_spow2 = data_root + r'/SIMU_BGMG_spow2' + r'/*run3.bgmg.fit.short.json' - -folder_SIMU_BGMG_GCTA = drive_letter + r":\work\SIMU_BGMG_11pifrac_gctaSigma" -folder_SIMU_BGMG_annotenrich = drive_letter + r':\work\SIMU_BGMG_annotenrich' -folder_SIMU_BGMG_subref = drive_letter + r':\work\SIMU_BGMG_subref' - -nsnps_HAPGEN = 11015833; -nsnps_LDSR = 9997231; - -if True: - traits4_ordered = ['PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'SSGAC_EDU_2018_no23andMe', 'GIANT_HEIGHT_2018_UKB'] - traits4 = {'GIANT_HEIGHT_2018_UKB':'Height', 'PGC_BIP_2016':'Bipolar Disorder', 'PGC_SCZ_2014_EUR':'Schizophrenia', 'SSGAC_EDU_2018_no23andMe': 'Educational attainment'} - traits4_short = {'GIANT_HEIGHT_2018_UKB':'HEIGHT', 'PGC_BIP_2016':'BIP', 'PGC_SCZ_2014_EUR':'SCZ', 'SSGAC_EDU_2018_no23andMe': 'EDU'} - trait4_index_map = {trait:index for index, trait in enumerate([trait for trait in traits4_ordered])} - -if True: - traits_ordered = ['PGC_SCZ_2014_EUR', 'PGC_BIP_2016','PGC_MDD_2018_no23andMe', - 'PGC_ASD_2017_iPSYCH','PGC_ADHD_2017_EUR','SSGAC_EDU_2018_no23andMe', - 'GIANT_HEIGHT_2018_UKB'] - traits_short = {'PGC_BIP_2016':'BIP','PGC_SCZ_2014_EUR':'SCZ','PGC_MDD_2018_no23andMe':'MDD', - 'PGC_ASD_2017_iPSYCH':'ASD','PGC_ADHD_2017_EUR':'ADHD','SSGAC_EDU_2018_no23andMe':'EDU', - 'GIANT_HEIGHT_2018_UKB':'HEIGHT'} - traits = {'GIANT_HEIGHT_2018_UKB':'Height', - 'PGC_BIP_2016':'Bipolar Disorder', - 'PGC_SCZ_2014_EUR':'Schizophrenia', - 'SSGAC_EDU_2018_no23andMe':'Educational attainment', - 'PGC_MDD_2018_no23andMe':'Major Depressive Disorder', - 'PGC_ASD_2017_iPSYCH':'Autism Spectrum Disorder', - 'PGC_ADHD_2017_EUR':'ADHD'} # Attention deficit hyperactivity disorder - traitPS_index_map = {trait:index for index, trait in enumerate([trait for trait in traits_ordered])} - -traits_immuno = [('OKADA_RA_2014_EUR','Rheumatoid Arthritis','RA'), - ('IIBDGC_IBD_2017','Inflam. Bowel Disease','IBD'), - ('IIBDGC_CD_2017','Crohn Disease','CD'), - ('IIBDGC_UC_2017','Ulcerative Colitis','UC')] - -traits_antro = [('EGG_BIRTHWEIGHT_2016','Birth Weight','BirthWeight'), - ('GIANT_WHR_2015_EUR','Waist Hip Ratio','WHR'), - ('GIANT_BMI_2015_EUR','Body Mass Index','BMI'), - ('GIANT_HEIGHT_2018_UKB','Height','HEIGHT')] - -traits4_psych = [('PGC_SCZ_2014_EUR','Schizophrenia','SCZ'), - ('PGC_BIP_2016','Bipolar Disorder','BIP'), - ('PGC_MDD_2018_no23andMe', 'Major Depressive Disorder', 'MDD'), - ('PGC_ASD_2017_iPSYCH', 'Autism Spectrum Disorder', 'ASD'), - ('PGC_ADHD_2017_EUR', 'ADHD', 'ADHD'), - ('SSGAC_EDU_2018_no23andMe','Educational attainment','EDU'), - ('GIANT_HEIGHT_2018_UKB','Height','HEIGHT')] - -def create_traits_lists(full_list): - traits_ordered = [idx for idx, full, short in full_list] - traits = {idx:full for idx, full, short in full_list} - traits_short = {idx:short for idx, full, short in full_list} - trait_index_map = {trait:index for index, trait in enumerate([trait for trait in traits_ordered])} - return traits_ordered, traits, traits_short, trait_index_map - -traits14_ordered, traits14, traits14_short, trait14_index_map = create_traits_lists(traits4_psych + [(x,y,z) for x,y,z in traits_antro if z != 'HEIGHT'] + traits_immuno) -traitsIM_ordered, traitsIM, traitsIM_short, traitIM_index_map = create_traits_lists(traits_immuno) -traitsAM_ordered, traitsAM, traitsAM_short, traitAM_index_map = create_traits_lists(traits_antro) - -plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 20 }) - -# Figures (#enabled means I made these plots on the new Ubuntu laptop) -DO_QQ_MODEL_DATA_vs_NULL = False # enabled -DO_QQ_MODEL_vs_DATA = False -DO_QQ_BINS_MODEL_DATA_vs_NULL = False # enabled -DO_POWER_PLOT = False # enabled -DO_ADJ_TRAJECTORY = False -DO_ADJ_TRAJECTORY_BGMG = False -DO_VENN_DIAGRAMS = False # enabled, works only after DO_BGMG_TABLE -DO_VENN_DIAGRAMS_SUPPL = False # enabled -DO_STRATIFIED_QQ=False # enabled - -DO_SIMU_QQ=False -DO_SIMU_QQ_BINS=False -DO_SIMU_STRATIFIED_QQ=False - -DO_SIMU_UGMG=False # enabled -DO_SIMU_BGMG=False # enabled (this does both "spow=0 & h2=0.1, 0.4, 0.7" and "h2=0.4 & spow=-0.25, -0.5, -0.75") -DO_SIMU_UGMG_ANNOTENRICH=False - -# Tables -DO_UGMG_TABLE = False # enabled -DO_BGMG_TABLE = False # enabled -DO_GWAS_DATA_TABLE = False; -DO_SIMU_UGMG_TABLE = True -DO_SIMU_BGMG_TABLE = True -DO_SIMU_BGMG_ANNOTENRICH_TABLE=False -DO_SIMU_UGMG_SUBREF = False -table_index = 0; tables_writer = None - -def concat_se(df, zeros_as_nan=True): - df = df.copy() - se_cols = [col[:-3] for col in df if col.endswith('_se')] - for se_col in se_cols: - df[se_col + '_se'] = ['{} ({})'.format(val, 'nan' if ((float(val_se)==0) and zeros_as_nan) else val_se) for val, val_se in zip(df[se_col].values, df[se_col + '_se'].values)] - del df[se_col] - df.columns = [(x.replace('_se', ' (se)') if x.endswith('_se') else x) for x in df.columns] - - return df - -def concat_se_mean_std(df): - df = df.copy() - se_cols = [col[:-8] for col in df if col.endswith('_se_mean')] - for se_col in se_cols: - df[se_col] = ['{} ({} / {})'.format(val, 'nan' if (float(val_se_mean)==0) else val_se_mean, 'nan' if (float(val_std)==0) else val_std) for val, val_se_mean, val_std in zip(df[se_col + '_mean'].values, df[se_col + '_se_mean'], df[se_col + '_std'].values)] - del df[se_col+'_mean'] - del df[se_col+'_std'] - del df[se_col+'_se_mean'] - df.columns = [(x.replace('_se', ' (se/std)') if x.endswith('_se') else x) for x in df.columns] - return df - -def savefig(folder, filename, exts=['svg', 'png']): - for ext in exts: - plt.savefig(os.path.join(folder, ext, filename + '.' + ext), bbox_inches='tight') - plt.savefig(os.path.join(folder, ext, filename + '.' + ext), bbox_inches='tight') - -def json_loads(fname): - data = json.loads(open(fname).read()) - if 'result' not in data: data={'result': data} - return data - -def make_qq_plot(qq, ci=True): - hv_logp = np.array(qq['hv_logp']).astype(float) - data_logpvec = np.array(qq['data_logpvec']).astype(float) - model_logpvec = np.array(qq['model_logpvec']).astype(float) - ylim_data = max(hv_logp[np.isfinite(data_logpvec)]) - model_logpvec[hv_logp > ylim_data]=np.nan - if ci: - q = 10**-data_logpvec; dq= 1.96*np.sqrt(q*(1-q)/qq['qq_options']['sum_data_weights']); - y1=hv_logp - x1=ma.filled(-ma.log10(q+dq), np.nan) #left CI bound - x2=ma.filled(-ma.log10(q-dq), np.nan) #right CI bound - if True: - y2 = np.empty(hv_logp.shape); y2[:]=np.nan; - y2[x2i2: - plt.subplot(len(trait_index_map)-1, len(trait_index_map)-1, 1+(i1-1)+(len(trait_index_map)-1)*i2) - v = venn2(subsets = (n1, n2, n12), set_labels = (traits14_short[t1], traits14_short[t2])) - v.get_patch_by_id('100').set_color(cm.colors[f(trait_index_map[t1])]) - v.get_patch_by_id('010').set_color(cm.colors[f(trait_index_map[t2])]) - v.get_patch_by_id('110').set_color(cm.colors[7]) - format_numers = '{:.1f}\n({:.1f})' if trait_group != 'immuno' else '{:.2f}\n({:.2f})' - v.get_label_by_id('100').set_text(format_numers.format(n1, n1_se)) - v.get_label_by_id('010').set_text(format_numers.format(n2, n2_se)) - v.get_label_by_id('110').set_text(format_numers.format(n12, n12_se)) - i1, i2 = i2, i1 - t1, t2 = t2, t1 - n1, n2 = n2, n1; n1_se, n2_se = n2_se, n1_se - - fig.subplots_adjust(hspace=0.10, wspace=0.10) - plt.tight_layout() - savefig(figures_folder, 'VENN_DIAGRAMS_{}_sym'.format(trait_group) +(' (censored)' if censored else '')) - -if DO_VENN_DIAGRAMS_SUPPL: - cm = plt.cm.get_cmap('tab10') - for censored in [True, False]: - bgmg_table_df = bgmg_table_df_censored[censored] - - venn_diag_suppl_tasks = [] - for trait1 in traits_ordered: - venn_diag_suppl_tasks.append((traits_short[trait1], 17, traitPS_index_map, ([(trait1, trait2) for trait2 in traits_ordered if trait2 != trait1]))) - venn_diag_suppl_tasks.append(('immuno', 0.5, traitIM_index_map, list(itertools.combinations(traitsIM_ordered, 2)))) - venn_diag_suppl_tasks.append(('antro', 6, traitAM_index_map, list(itertools.combinations(traitsAM_ordered, 2)))) - - for task_name, max_size, traitXX_index_map, venn_diag_suppl_sub_tasks in venn_diag_suppl_tasks: - fig=plt.figure(figsize=(16, 28), dpi=80) - for index, (trait1_orig, trait2_orig) in enumerate(venn_diag_suppl_sub_tasks): - trait1 = trait1_orig; trait2=trait2_orig - row = bgmg_table_df[bgmg_table_df.trait1_ID.isin([trait1, trait2]) & bgmg_table_df.trait2_ID.isin([trait1, trait2])].reset_index(drop=True).iloc[0] - flip_data = (row.trait1_ID != trait1) - - n1 = row['nc1@p9']/1000; n1_se = row['nc1@p9_se']/1000 - n2 = row['nc2@p9']/1000; n2_se = row['nc2@p9_se']/1000; - n12 = row['nc12@p9']/1000; n12_se = row['nc12@p9_se']/1000 - t1 = row.trait1_ID; t2 = row.trait2_ID - if flip_data: t1, t2 = t2, t1;n1, n2 = n2, n1; n1_se, n2_se = n2_se, n1_se - f = lambda x: x if x < 7 else x+1 - - plt.subplot(len(traitPS_index_map)-1, 3, 3*index + 1) - v = venn2(subsets = (n1, n2, n12), normalize_to=(n1+n2+n12)/max_size, set_labels = ("", "")) - v.get_patch_by_id('100').set_color(cm.colors[f(traitXX_index_map[t1])]) - v.get_patch_by_id('010').set_color(cm.colors[f(traitXX_index_map[t2])]) - v.get_patch_by_id('110').set_color(cm.colors[7]) - formatter = '{:.1f}\n({:.1f})' if (task_name != 'immuno') else '{:.2f}\n({:.2f})' - v.get_label_by_id('100').set_text(formatter.format(n1, n1_se)) - v.get_label_by_id('010').set_text(formatter.format(n2, n2_se)) - v.get_label_by_id('110').set_text(formatter.format(n12, n12_se)) - - plt.xlim([-0.75, 0.75]), plt.ylim([-0.6, 0.6]) - if flip_data: plt.title(traits14_short[row.trait2_ID] +' & ' + traits14_short[row.trait1_ID]) - else: plt.title(traits14_short[row.trait1_ID] +' & ' + traits14_short[row.trait2_ID]) - - fname1 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait1, trait2) - fname2 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait2, trait1) - if os.path.exists(fname2): - data = json_loads(fname2) - flip_data = True - elif os.path.exists(fname1): - data = json_loads(fname1) - flip_data = False - else: - print('missing: {} vs {}'.format(trait1, trait2)) - continue - - for repeat in range(0,2): - plt.subplot(len(traitPS_index_map)-1, 3, 3*index + 2 + ((1-repeat) if flip_data else repeat)) - sqq = data['result']['bivariate']['stratified_qq_plot_fit_data'] - for index2, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[index2], linestyle='solid') - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - plt.legend(['All SNPs'] + '$P{0}\leq0.1$ $P{0}\leq0.01$ $P{0}\leq0.001$'.format('_{' + traits14_short[trait2]+'}').split(), loc='lower right', fontsize=14, borderpad=0.2, frameon=False, borderaxespad=0.2, labelspacing=0.1) - for index2, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[index2], linestyle='dashed') - plt.ylim(0, 7.3); plt.xlim(0, 7.3) - if flip_data: plt.title('{} | {}'.format(traits14_short[trait2], traits14_short[trait1])) - else: plt.title('{} | {}'.format(traits14_short[trait1], traits14_short[trait2])) - trait1, trait2 = trait2, trait1 # swap traits - fig.subplots_adjust(hspace=0.25, wspace=0.35) - - savefig(figures_folder, 'SF_VENN_and_stratQQ_{}'.format(task_name) +(' (censored)' if censored else '') ) - - -if DO_ADJ_TRAJECTORY_BGMG: - fig = plt.figure(figsize=(2*16, 3*16), dpi=80) - for index, (trait1, trait2) in enumerate(list(itertools.combinations(traits_ordered, 2))): - fname1 = format_string_BGMG_fit.format(trait1, trait2) - fname2 = format_string_BGMG_fit.format(trait2, trait1) - if os.path.exists(fname2): - data = json.loads(open(fname2).read()) - trait1, trait2 = trait2, trait1 - elif os.path.exists(fname1): - data = json.loads(open(fname1).read()) - else: - print('missing: {} vs {}'.format(trait1, trait2)) - continue - plt.subplot(6, 4, 1+index) - - lat = data['result']['bivariate']['loglike_adj_trajectory'] - fmtr = mpl.ticker.ScalarFormatter(useOffset=math.floor(min(lat['cost']))) - plt.gca().get_yaxis().set_major_formatter(fmtr) - - pi12vector = [x[2] for x in lat['pivec']] - plt.plot(pi12vector, lat['cost'], '.-') - plt.xlabel('$\pi_{12}$') - plt.ylabel('log likelihood') - plt.locator_params(axis='x', nbins=6) - - params_pi12 = data['result']['bivariate']['params']['pi_vec'][2] - params_cost = interp1d(pi12vector,lat['cost'])(max(pi12vector[0], min(pi12vector[-1], params_pi12))) - plt.plot([params_pi12],[params_cost], '*k', markersize=8) - - plt.title('{} vs {}'.format(traits_short[trait1], traits_short[trait2]), loc='right') - fig.subplots_adjust(hspace=0.35, wspace=0.25) - savefig(figures_folder, 'ADJ_TRAJECTORY_BGMG') - -if DO_STRATIFIED_QQ: - fig = plt.figure(figsize=(18, 18), dpi=80) - cm = plt.cm.get_cmap('tab10') - censored = False - - for index, trait in enumerate(traits4_ordered): - plt.subplot(4,4,1+index+4*index) - fname = format_string_UGMG_test.format(trait) - data = json_loads(fname) - qq = data['result']['univariate'][0]['qq_plot_data'] - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[0], linestyle='solid') - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[0], linestyle='dashed') - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - plt.ylim(0, 7.3); plt.xlim(0, 7.3) - plt.title(traits4_short[trait]) - plt.legend(['data', 'model', 'null'], loc='lower right', fontsize=14, borderpad=0.2, frameon=False, borderaxespad=0.2, labelspacing=0.1) - - for index, (trait1, trait2) in enumerate(list(itertools.combinations(traits4_ordered, 2))): - fname1 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait1, trait2) - fname2 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait2, trait1) - if os.path.exists(fname2): - data = json_loads(fname2) - trait1, trait2 = trait2, trait1 - elif os.path.exists(fname1): - data = json_loads(fname1) - else: - print('missing: {} vs {}'.format(trait1, trait2)) - continue - - for repeat in range(0,2): - ti1 = trait4_index_map[trait1] - ti2 = trait4_index_map[trait2] - plt.subplot(4,4,1 + ti1 + 4*ti2) - - sqq = data['result']['bivariate']['stratified_qq_plot_fit_data'] - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[index], linestyle='solid') - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - plt.legend(['All SNPs'] + '$P{0}\leq0.1$ $P{0}\leq0.01$ $P{0}\leq0.001$'.format('_{' + traits4_short[trait2]+'}').split(), loc='lower right', fontsize=14, borderpad=0.2, frameon=False, borderaxespad=0.2, labelspacing=0.1) - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[index], linestyle='dashed') - plt.ylim(0, 7.3); plt.xlim(0, 7.3) - plt.title('{} | {}'.format(traits4_short[trait1], traits4_short[trait2])) - trait1, trait2 = trait2, trait1 # swap traits - - fig.subplots_adjust(hspace=0.35) - fig.text(0.5, 0.075, 'Expected(-$log_{10}$ p-value)', ha='center') - fig.text(0.075, 0.5, 'Observed(-$log_{10}$ p-value)', va='center', rotation='vertical') - - savefig(figures_folder, 'STRATIFIED_QQ') - -if DO_SIMU_QQ: - fig = plt.figure(figsize=(24, 32), dpi=80) - cm = plt.cm.get_cmap('tab10') - - for h2_index, h2 in enumerate(['0.1', '0.4', '0.7']): - for pi_index, pi in enumerate(['3.0000e-03', '3.0000e-04']): - ylim_value = 0 - plt.subplot(3,2,1+2*h2_index+ pi_index) - for repi in range(1, 5): - for po_index, polyOverlap in enumerate([False, True]): - pi12=(pi if polyOverlap else '0.0000e+00') - fname2=r'C:\work\SIMU_BGMG_11pifrac_simple2\simu_h2={h2}_rg=0.0_pi1u={pi}_pi2u={pi}_pi12={pi12}_rep={rep}_tag1=customPolygenicOverlapAt{po}_tag2=evenPolygenicity.bgmg.test.json'.format(h2=h2,pi=pi,pi12=pi12, rep=repi, po=('1p0' if polyOverlap else '0p0')) - if not os.path.isfile(fname2): continue - data = json.loads(open(fname2).read()) - - for repeat in range(0,2): # T1|T2, T2|T1 - sqq = data['result']['bivariate']['stratified_qq_plot_fit_data'] - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[index], linestyle='solid') - ylim_value = max(ylim_value, np.max([y for x, y in zip(qq['data_logpvec'], qq['hv_logp']) if np.isfinite(x)])) - break - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[index+1], linestyle='solid') - break - plt.xlim(0, 7.3);#plt.ylim(0, 20); - plt.ylim(0, ylim_value*1.05 + 1); - if h2_index==2: plt.xlabel('Expected(-$log_{10}$ p-value)') - if pi_index==0: plt.ylabel('Observed(-$log_{10}$ p-value)') - plt.title('h2={}, pi1u={}'.format(h2, pi.replace('.0000e-0', 'e-'))) - fig.subplots_adjust(hspace=0.25, wspace=0.25) - savefig(figures_folder, 'SIMU_QQ') - -if DO_SIMU_QQ_BINS: - for pi_index, pi in enumerate(['3.0000e-03', '3.0000e-04']): - fig = plt.figure(figsize=(18, 18), dpi=80) - cm = plt.cm.get_cmap('tab10') - - for repi in range(1, 11): - fname2=r'C:\work\SIMU_BGMG_11pifrac_simple2\simu_h2=0.4_rg=0.0_pi1u={pi}_pi2u={pi}_pi12=0.0000e+00_rep={rep}_tag1=customPolygenicOverlapAt0p0_tag2=evenPolygenicity.trait1.test.json'.format(pi=pi, rep=repi) - if not os.path.isfile(fname2): continue - data = json.loads(open(fname2).read()) - for subplot_index in range(0,9): - axis = plt.subplot(3,3,1+subplot_index) - qq = data['result']['univariate'][0]['qq_plot_bins_data'][subplot_index] - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[0]) - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[1]) - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - - #plt.title(str(subplot_index) + qq["options"]["title"].replace('$$', '$')) #.split('$$ $$') - qq["options"]["title"] = qq["options"]["title"].replace('L \\in', 'LDscore \\in$\n$').replace('maf', 'MAF').replace(',', ', ').replace('Infinity', 'Inf') - - if True: - label_indices = {'y':[2, 5, 8], 'x':[0, 1, 2]} - axis_indices = {'y':[0, 3, 6], 'x':[6, 7, 8]} - else: - label_indices = {'y':[0, 3, 6], 'x':[6, 7, 8]} - axis_indices = {'y':[2, 5, 8], 'x':[0, 1, 2]} - - if subplot_index in label_indices['y']: axis.yaxis.set_label_position('right'), plt.ylabel(qq["options"]["title"].split('$ $')[1].replace('$$', '$')) - if subplot_index in axis_indices['y']: plt.ylabel('Observed(-$log_{10}$ p-value)') - if subplot_index in label_indices['x']: plt.gca().xaxis.set_label_position('top'), plt.xlabel(qq["options"]["title"].split('$ $')[0].replace('$$', '$')) - if subplot_index in axis_indices['x']: plt.xlabel('Expected(-$log_{10}$ p-value)') - plt.ylim(0, 80 if (pi_index==1) else 30); - plt.xlim(0, 7.3) - - fig.subplots_adjust(hspace=0.25, wspace=0.25) - savefig(figures_folder, 'SIMU_QQ_BINS_{}'.format(pi)) - - -if DO_SIMU_STRATIFIED_QQ: - fig = plt.figure(figsize=(24, 32), dpi=80) - cm = plt.cm.get_cmap('tab10') - - for repi in range(6, 7): - for h2_index, h2 in enumerate(['0.1', '0.4', '0.7']): - for pi_index, pi in enumerate(['3.0000e-03', '3.0000e-04']): - for po_index, polyOverlap in enumerate([True, False]): - pi12=(pi if polyOverlap else '0.0000e+00') - fname2=r'C:\work\SIMU_BGMG_11pifrac_simple2\simu_h2={h2}_rg=0.0_pi1u={pi}_pi2u={pi}_pi12={pi12}_rep={rep}_tag1=customPolygenicOverlapAt{po}_tag2=evenPolygenicity.bgmg.test.json'.format(h2=h2,pi=pi,pi12=pi12, rep=repi, po=('1p0' if polyOverlap else '0p0')) - if not os.path.isfile(fname2): continue - data = json.loads(open(fname2).read()) - - for repeat in range(0,1): # T1|T2, T2|T1 - plt.subplot(4,3,1+h2_index+ 3*(pi_index + 2*(1-polyOverlap))) - sqq = data['result']['bivariate']['stratified_qq_plot_fit_data'] - ylim_value = 0 - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hData = plt.plot(qq['data_logpvec'], qq['hv_logp'], color=cm.colors[index], linestyle='solid') - ylim_value = max(ylim_value, np.max([y for x, y in zip(qq['data_logpvec'], qq['hv_logp']) if np.isfinite(x)])) - hNull = plt.plot(qq['hv_logp'], qq['hv_logp'], 'k--') - for index, qq in enumerate(sqq['trait2' if repeat==0 else 'trait1']): - hModel = plt.plot(qq['model_logpvec'], qq['hv_logp'], color=cm.colors[index], linestyle='dashed') - plt.xlim(0, 7.3);plt.ylim(0, 20); - if (pi_index==1) and (po_index==1): plt.xlabel('Expected(-$log_{10}$ p-value)') - if h2_index==0: plt.ylabel('Observed(-$log_{10}$ p-value)') - #plt.ylim(0, ylim_value + 1); - plt.title('h2={}, pi1u={}, pi12={}'.format(h2, pi.replace('.0000e-0', 'e-'), pi12.replace('0.0000e+00', '0').replace('.0000e-0', 'e-'))) - fig.subplots_adjust(hspace=0.25, wspace=0.25) - savefig(figures_folder, 'SIMU_STRATIFIED_QQ') - -if DO_SIMU_UGMG: - for DO_SPOW, figtag in [(False, ''), (True, '.spow')]: - - df_data = {} - colnames = {'h2':'true_h2', 'rg':'true_rg', 'pi1u':'true_pi1u', 'pi2u':'true_pi2u', 'pi12':'true_pi12'} - - def insert_key_to_dictionary_as_list(key, value): - if key not in df_data: - df_data[key] = [] - df_data[key].append(value) - - if DO_SPOW: - files = glob.glob(globpat_SIMU_BGMG_spow2) - figure_group_field = 'spow' - figure_group_values = [-0.25, -0.5, -0.75] - else: - files = glob.glob(globpat_SIMU_BGMG_11pifrac) - figure_group_field = 'true_h2' - figure_group_values = [0.1, 0.4, 0.7] - - #if DO_GCTA: files = glob.glob(folder_SIMU_BGMG_GCTA+r'/*.trait*.fit.short.json') - for fname in files: - if 'h2=0.8' in fname: continue - data = json_loads(fname) - for trait in ['1', '2']: - # general info about the run ['h2', 'rg', 'pi1u', 'pi2u', 'pi12', 'rep', 'tag1', 'tag2', 'outtag'] - rep = {'.bgmg':'', '.short':'', '.json':'', 'outtag=run1_outtag':'outtag'} - for k in rep: fname = fname.replace(k, rep[k]) - - for key, value in [tuple(x.split('=')) for x in os.path.basename(fname).split('_') if ('=' in x)]: - insert_key_to_dictionary_as_list(colnames[key] if key in key in colnames else key, value) - - if 'spow' not in fname: - insert_key_to_dictionary_as_list('spow', '0.0') - - # polygenicity and heritability estimates - ci = data['result']['bivariate']['ci'] - insert_key_to_dictionary_as_list('h2', ci['h2_T{}'.format(trait)]['point_estimate']) - insert_key_to_dictionary_as_list('h2_se', ci['h2_T{}'.format(trait)]['se']) - insert_key_to_dictionary_as_list('pi_vec', ci['pi{}u'.format(trait)]['point_estimate']) - insert_key_to_dictionary_as_list('pi_vec_se', ci['pi{}u'.format(trait)]['se']) - - df = pd.DataFrame(df_data) - colorder = ['tag1', 'tag2', 'outtag', 'true_h2', 'true_rg', 'true_pi1u', 'true_pi2u', 'true_pi12', 'rep'] - df = df[[c for c in colorder if c in df] + [x for x in df if x not in colorder]] - df = df[df['pi_vec']<0.95].copy() # drop 1 outlier - - df_agg = df[['true_pi1u', 'true_h2', 'spow', 'pi_vec', 'pi_vec_se', 'h2', 'h2_se']].groupby(['true_pi1u', 'true_h2', 'spow']).agg({'pi_vec':['mean', 'std'], 'pi_vec_se':['mean'], 'h2':['mean', 'std'], 'h2_se':['mean']}) - df_agg.columns = ['_'.join(col).strip() for col in df_agg.columns.values] - df_agg = df_agg.reset_index() - - # https://python-graph-gallery.com/8-add-confidence-interval-on-barplot/ - # width of the bars - barWidth = 0.3 - - fig = plt.figure(figsize=(12, 16), dpi=80) - - if DO_SPOW: - figure_group_vec = [('-0.25', '3.0000e-03'), ('-0.5', '3.0000e-03'), ('-0.75', '3.0000e-03'), ('-0.25', '3.0000e-04'), ('-0.5', '3.0000e-04'), ('-0.75', '3.0000e-04')] - else: - figure_group_vec = [('0.1', '3.0000e-03'), ('0.4', '3.0000e-03'), ('0.7', '3.0000e-03'), ('0.1', '3.0000e-04'), ('0.4', '3.0000e-04'), ('0.7', '3.0000e-04')] - - for index in range(0, 2): - plt.subplot(2,1,1+index) - bars1 = df_agg['h2_mean' if index==0 else 'pi_vec_mean'].values - bars2 = df_agg['true_h2' if index==0 else 'true_pi1u'].astype(float).values - yer1 = df_agg['h2_se_mean' if index==0 else 'pi_vec_se_mean'].values - yer2 = df_agg['h2_std' if index==0 else 'pi_vec_std'] - - r1 = np.arange(len(bars1)); r2 = [x + barWidth for x in r1] # # The x position of bars - - plt.bar(r1, bars1, width = barWidth, color = 'blue', edgecolor = 'black', yerr=yer2, capsize=7, label='estimated') - plt.bar(r2, bars2, width = barWidth, color = 'cyan', edgecolor = 'black', capsize=7, label='true') - if index==1: plt.gca().set_yscale('log') - - plt.xticks([r + barWidth for r in range(len(bars1))], ['{}={}\n$\pi_1$={:.0e}'.format('S' if DO_SPOW else 'h2', h2_or_spow, float(pi)).replace('e-0', 'e-') for h2_or_spow, pi in figure_group_vec]) - plt.ylabel('h2' if index==0 else '$\pi_1$' ) - plt.legend() - if index==1: plt.ylim([1e-4, 1e-2]) - - fig.subplots_adjust(hspace=0.35) - savefig(figures_folder, 'SIMU_UGMG_pi_and_h2' + ('.spow' if DO_SPOW else '')) - -table_index += 1 -if DO_SIMU_UGMG and DO_SIMU_UGMG_TABLE: - df_agg['true_h2'] = df_agg['true_h2'].astype(float) - df_agg['true_pi1u'] = df_agg['true_pi1u'].astype(float) - df_agg.to_csv(os.path.join(tables_folder, 'SIMU_UGMG_TABLE.csv'), sep='\t',index=False) - - if tables_writer is None: tables_writer = pd.ExcelWriter(os.path.join(tables_folder, 'all_tables.xlsx')) - df=df_agg.copy() - for c in [a for a in df.columns if 'pi_vec' in a]: df[c] = ['{:.2e}'.format(x) for x in df[c]] - for c in [a for a in df.columns if 'h2' in a]: df[c] = ['{:.3f}'.format(float(x)) for x in df[c]] - concat_se_mean_std(df).to_excel(tables_writer,'table{}'.format(table_index), index=False) - -plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 16 }) - -table_index += 3 -if DO_SIMU_BGMG or DO_SIMU_BGMG_TABLE: - for DO_SPOW, figtag in [(False, 'h2'), (True, 'spow')]: - df_data = {} - colnames = {'h2':'true_h2', 'rg':'true_rg', 'pi1u':'true_pi1u', 'pi2u':'true_pi2u', 'pi12':'true_pi12'} - - def insert_key_to_dictionary_as_list(key, value): - if key not in df_data: - df_data[key] = [] - df_data[key].append(value) - - if DO_SPOW: - files = glob.glob(globpat_SIMU_BGMG_spow2) - figure_group_field = 'spow' - figure_group_values = [-0.25, -0.5, -0.75] - else: - files = glob.glob(globpat_SIMU_BGMG_11pifrac) + glob.glob(globpat_SIMU_BGMG_11pifrac_wave2) - figure_group_field = 'true_h2' - figure_group_values = [0.1, 0.4, 0.7] - #if DO_GCTA: files = glob.glob(folder_SIMU_BGMG_GCTA + r'\*.bgmg.fit.short.json') - - for fname in files: - if 'h2=0.8' in fname: continue - data = json_loads(fname) - - # general info about the run ['h2', 'rg', 'pi1u', 'pi2u', 'pi12', 'rep', 'tag1', 'tag2', 'outtag'] - rep = {'.bgmg':'', '.short':'', '.json':'', 'outtag=run1_outtag':'outtag'} - for k in rep: fname = fname.replace(k, rep[k]) - for key, value in [tuple(x.split('=')) for x in os.path.basename(fname).split('_') if ('=' in x)]: - insert_key_to_dictionary_as_list(colnames[key] if key in key in colnames else key, value) - if 'spow' not in fname: - insert_key_to_dictionary_as_list('spow', '0.0') - - # polygenicity and heritability estimates - ci = data['result']['bivariate']['ci'] - extract = [('pi12', 'pi_vec_C3'), ('pi1u', 'pi1u'), ('pi2u', 'pi2u'), ('rho12', 'rho_beta'), ('rg', 'rg')] - for a,b in extract: - insert_key_to_dictionary_as_list(a+'', ci[b]['point_estimate']) - insert_key_to_dictionary_as_list(a+'_se', ci[b]['se']) - - df = pd.DataFrame(df_data) - colorder = ['tag1', 'tag2', 'outtag', 'true_h2', 'spow', 'true_rg', 'true_pi1u', 'true_pi2u', 'true_pi12', 'rep'] - df = df[[c for c in colorder if c in df] + [x for x in df if x not in colorder]] - df = df[(df['pi1u']<0.95) & (df['pi2u']<0.95)].copy() # drop 1 outlier - - df_agg = df.groupby(['true_pi1u', 'true_pi2u', 'true_pi12' , 'true_h2', 'true_rg', 'spow']).agg({'pi12':['mean', 'std'], 'pi12_se':['mean'], 'rg':['mean', 'std'], 'rg_se':['mean'], 'rho12':['mean', 'std'], 'rho12_se':['mean']}) - df_agg.columns = ['_'.join(col).strip() for col in df_agg.columns.values] - df_agg = df_agg.reset_index() - for col in ['true_pi1u','true_pi2u','true_pi12','true_h2', 'spow']: df_agg[col] = df_agg[col].astype(float) - - if DO_SIMU_BGMG and not DO_SPOW: - fig = plt.figure(figsize=(24, 24), dpi=80) - for true_rg_index, true_rg in enumerate(['0.0', '0.5']): - for index, (pi1u, pi2u) in enumerate([(3e-03, 3e-03), (3e-4, 3e-04), (3e-03, 3e-04)]): - scale_coef, scale_text = (1e4, '10^{-4}') if pi2u==3e-4 else (1e3, '10^{-3}') - plt.subplot(3, 2, 2*index+1+true_rg_index) - for h2_index in [0.1, 0.4, 0.7]: - df_plot = df_agg[(df_agg.true_rg==true_rg) & (df_agg.true_pi1u == pi1u) & (df_agg.true_pi2u == pi2u) & (df_agg.true_h2 == h2_index)].sort_values('true_pi12') - plt.plot(df_plot.true_pi12.values*scale_coef, df_plot.pi12_mean.values*scale_coef, '.-') - plt.plot(df_plot.true_pi12.values*scale_coef, df_plot.true_pi12.values*scale_coef, 'k--') - plt.ylabel('estimated $\pi_{12}\;(\\times ' + scale_text + '$)' ) # @10k - plt.xlabel( 'true $\pi_{12}\;(\\times ' + scale_text + '$)' ) - plt.legend(['h2=0.1', 'h2=0.4', 'h2=0.7', 'true']) - plt.title('$\pi_1$ = {:.1e}, $\pi_2$ = {:.1e}'.format(pi1u, pi2u) + ', $\\rho_{12}$=' + true_rg) - plt.locator_params(axis='x', nbins=7) - plt.ticklabel_format(style='sci', axis='both') - fig.subplots_adjust(hspace=0.30, wspace=0.50) - savefig(figures_folder, 'SIMU_BGMG_{}_rg={}_old_style_pi12'.format(figtag, true_rg)) - - for true_rg in ['0.0', '0.5']: - if not DO_SIMU_BGMG: break - fig = plt.figure(figsize=(18, 16), dpi=80) - for index, (pi1u, pi2u) in enumerate([(3e-03, 3e-03), (3e-4, 3e-04), (3e-03, 3e-04)]): - if DO_SPOW and (pi1u!=pi2u): continue - for jndex, h2_index in enumerate(figure_group_values): - df_plot = df_agg[(df_agg.true_rg==true_rg) & (df_agg.true_pi1u == pi1u) & (df_agg.true_pi2u == pi2u) & (df_agg[figure_group_field] == h2_index)].sort_values('true_pi12') - if df_plot.empty: continue - ax = plt.subplot(3,3,index+3*jndex+1) - plot_simu_bgmg_pi12(df_plot) - - fig.subplots_adjust(hspace=0.50, wspace=0.25) - savefig(figures_folder, '{}_{}_pi12_rg={}'.format('SIMU_BGMG', figtag, true_rg)) - - for do_rg in [True, False]: # rg or rho_beta? - if not DO_SIMU_BGMG: break - for true_rg in ['0.0', '0.5']: - fig = plt.figure(figsize=(18, 16), dpi=80) - for index, (pi1u, pi2u) in enumerate([(3e-03, 3e-03), (3e-4, 3e-04), (3e-03, 3e-04)]): - if DO_SPOW and (pi1u!=pi2u): continue - for jndex, h2_index in enumerate(figure_group_values): - df_plot = df_agg[(df_agg.true_rg==true_rg) & (df_agg.true_pi1u == pi1u) & (df_agg.true_pi2u == pi2u) & (df_agg[figure_group_field] == h2_index)].sort_values('true_pi12') - if df_plot.empty: continue - plt.subplot(3,3,index+3*jndex+1) - plot_simu_bgmg_rg_or_rho12(df_plot, do_rg) - - fig.subplots_adjust(hspace=0.30, wspace=0.50) - savefig(figures_folder, '{}_{}_{}_rg={}.'.format('SIMU_BGMG', figtag, 'rg' if do_rg else 'rho12', true_rg)) - - if not DO_SPOW: - # aggregate together selected simulations for the main text figure - true_rg = '0.5'; pi1u=3e-04; pi2u=3e-04; h2=0.4; - df_plot = df_agg[(df_agg.true_rg==true_rg) & (df_agg.true_pi1u == pi1u) & (df_agg.true_pi2u == pi2u) & (df_agg.true_h2 == h2_index)].sort_values('true_pi12') - - fig = plt.figure(figsize=(18, 4.5), dpi=80) - ax = plt.subplot(1,3,1) - plot_simu_bgmg_pi12(df_plot, do_title=False) - plt.title('A', loc='left') - - ax = plt.subplot(1,3,2) - plot_simu_bgmg_rg_or_rho12(df_plot, do_rg=False, do_title=False) - plt.title('B', loc='left') - - ax = plt.subplot(1,3,3) - plot_simu_bgmg_rg_or_rho12(df_plot, do_rg=True, do_title=False) - plt.title('C', loc='left') - - fig.subplots_adjust(hspace=0.30, wspace=0.50) - savefig(figures_folder, 'SIMU_BGMG_main_figure2') - - if DO_SIMU_BGMG_TABLE: - df_agg.to_csv(os.path.join(tables_folder, 'SIMU_BGMG_TABLE_{}.csv'.format(figtag)), sep='\t',index=False) - if tables_writer is None: tables_writer = pd.ExcelWriter(os.path.join(tables_folder, 'all_tables.xlsx')) - df=df_agg.copy() - for c in [a for a in df.columns if 'pi' in a]: df[c] = ['{:.2e}'.format(x) for x in df[c]] - for c in [a for a in df.columns if 'rg' in a]: df[c] = ['{:.3f}'.format(float(x)) for x in df[c]] - for c in [a for a in df.columns if 'rho12' in a]: df[c] = ['{:.3f}'.format(float(x)) for x in df[c]] - df = concat_se_mean_std(df) - df[(df['true_pi1u'] == '3.00e-03') & (df['true_pi2u'] == '3.00e-03')].to_excel(tables_writer,'table{}'.format(table_index-2), index=False) - df[(df['true_pi1u'] == '3.00e-04') & (df['true_pi2u'] == '3.00e-04')].to_excel(tables_writer,'table{}'.format(table_index-1), index=False) - df[(df['true_pi1u'] == '3.00e-03') & (df['true_pi2u'] == '3.00e-04')].to_excel(tables_writer,'table{}'.format(table_index-0), index=False) - - table_index += 1 - if DO_GWAS_DATA_TABLE: - if tables_writer is None: tables_writer = pd.ExcelWriter(os.path.join(tables_folder, 'all_tables.xlsx')) - df_gwas_data = pd.read_table('gwas_data.csv',sep='\t') - del df_gwas_data['ID'] - df_gwas_data.to_excel(tables_writer,'table{}'.format(table_index), index=False) - -if DO_SIMU_UGMG_ANNOTENRICH: - for mafSuffix in ['bgmgMAF']: # [''] - df_data = {} - colnames = {'h2':'true_h2', 'rg':'true_rg', 'pi1u':'true_pi1u', 'pi2u':'true_pi2u', 'pi12':'true_pi12'} - - def insert_key_to_dictionary_as_list(key, value): - if key not in df_data: - df_data[key] = [] - df_data[key].append(value) - - files = glob.glob(folder_SIMU_BGMG_annotenrich + '\\*' + mafSuffix + '*bgmg.short.json') - for fname in files: - if (mafSuffix=='bgmgMAF') and ('300K' in fname): continue - data = json.loads(open(fname).read()) - - for trait_index in range(0, 2): - # general info about the run ['h2', 'rg', 'pi1u', 'pi2u', 'pi12', 'rep', 'tag1', 'tag2', 'outtag'] - rep = {'.bgmg':'', '.short':'', '.json':''} - for k in rep: fname = fname.replace(k, rep[k]) - for key, value in [tuple(x.split('=')) for x in os.path.basename(fname).split('_') if ('=' in x)]: - insert_key_to_dictionary_as_list(colnames[key] if key in key in colnames else key, value) - - # polygenicity and heritability estimates - ci = data['result']['univariate'][trait_index]['ci'] - insert_key_to_dictionary_as_list('h2', ci['h2']['point_estimate']) - insert_key_to_dictionary_as_list('h2_se', ci['h2']['se']) - insert_key_to_dictionary_as_list('pi_vec', ci['pi_vec']['point_estimate']) - insert_key_to_dictionary_as_list('pi_vec_se', ci['pi_vec']['se']) - - df = pd.DataFrame(df_data) - colorder = ['tag1', 'tag2', 'outtag', 'true_h2', 'true_rg', 'true_pi1u', 'true_pi2u', 'true_pi12', 'rep'] - df = df[[c for c in colorder if c in df] + [x for x in df if x not in colorder]] - df = df[df['pi_vec']<0.95].copy() # drop 1 outlier - - - df_agg = df[['piTag', 'true_h2', 'pi_vec', 'pi_vec_se', 'h2', 'h2_se']].groupby(['piTag', 'true_h2']).agg({'pi_vec':['mean', 'std'], 'pi_vec_se':['mean'], 'h2':['mean', 'std'], 'h2_se':['mean']}) - df_agg.columns = ['_'.join(col).strip() for col in df_agg.columns.values] - df_agg = df_agg.reset_index() - df_agg['true_pi1u'] = df_agg['piTag'].str.replace('K', '').astype(float)*1e3/11015833 - # https://python-graph-gallery.com/8-add-confidence-interval-on-barplot/ - # width of the bars - barWidth = 0.3 - - fig = plt.figure(figsize=(18, 16), dpi=80) - - h2_pi_vec = [('0.1', '300K'), ('0.4', '300K'), ('0.7', '300K'), ('0.1', '30K'), ('0.4', '30K'), ('0.7', '30K'), ('0.1', '3K'), ('0.4', '3K'), ('0.7', '3K')] - if mafSuffix=='bgmgMAF': h2_pi_vec = [('0.1', '30K'), ('0.4', '30K'), ('0.7', '30K'), ('0.1', '3K'), ('0.4', '3K'), ('0.7', '3K')] - for index in range(0, 2): - plt.subplot(2,1,1+index) - bars1 = df_agg['h2_mean' if index==0 else 'pi_vec_mean'].values - bars2 = df_agg['true_h2' if index==0 else 'true_pi1u'].astype(float).values - yer1 = df_agg['h2_se_mean' if index==0 else 'pi_vec_se_mean'].values - yer2 = df_agg['h2_std' if index==0 else 'pi_vec_std'] - - r1 = np.arange(len(bars1)); r2 = [x + barWidth for x in r1] # # The x position of bars - - plt.bar(r1, bars1, width = barWidth, color = 'blue', edgecolor = 'black', yerr=yer2, capsize=7, label='estimated') - plt.bar(r2, bars2, width = barWidth, color = 'cyan', edgecolor = 'black', capsize=7, label='true') - if index==1: plt.gca().set_yscale('log') - - plt.xticks([r + barWidth for r in range(len(bars1))], ['h2={}\n$nc_1$={}'.format(h2, piTag).replace('e-0', 'e-') for h2, piTag in h2_pi_vec]) - plt.ylabel('h2' if index==0 else '$\pi_1$' ) - if (index==1) and (mafSuffix=='bgmgMAF'): plt.ylim([1e-4, 1e-2]) - - plt.legend() - - fig.subplots_adjust(hspace=0.35) - savefig(figures_folder, 'SIMU_UGMG_ANNOTENRICH{}_pi_and_h2'.format(mafSuffix)) - -for mafSuffix in ['_bgmgMAF', '']: - table_index += 1 - if DO_SIMU_BGMG_ANNOTENRICH_TABLE: - df_data = {} - colnames = {'h2':'true_h2', 'rg':'true_rg', 'pi1u':'true_pi1u', 'pi2u':'true_pi2u', 'pi12':'true_pi12'} - - df={'rep':[], 'piTag':[], 'true_n12':[]} - for rep in range(1, 21): - for poly in ['3K', '30K', '300K']: - if (mafSuffix == '_bgmgMAF') and (poly=='300K'): continue - data = [open('C:\work\SIMU_BGMG_annotenrich\simu{}_{}_enriched.rep={}.{}.snps'.format(mafSuffix, poly, rep, trait), 'r').read().split() for trait in ['trait1', 'trait2']] - df['true_n12'].append(len(set(data[0]).intersection(set(data[1])))) - df['piTag'].append(poly) - df['rep'].append(rep) - df_annotenrich_true_n12 = pd.DataFrame(df) - - def insert_key_to_dictionary_as_list(key, value): - if key not in df_data: - df_data[key] = [] - df_data[key].append(value) - - files = glob.glob(folder_SIMU_BGMG_annotenrich + '\\*' +mafSuffix+'*bgmg.short.json') - for fname in files: - if (mafSuffix=='_bgmgMAF') and ('300K' in fname): continue - data = json.loads(open(fname).read()) - - # general info about the run ['h2', 'rg', 'pi1u', 'pi2u', 'pi12', 'rep', 'tag1', 'tag2', 'outtag'] - rep = {'.bgmg':'', '.short':'', '.json':''} - for k in rep: fname = fname.replace(k, rep[k]) - repi = None; piTag = None; - for key, value in [tuple(x.split('=')) for x in os.path.basename(fname).split('_') if ('=' in x)]: - insert_key_to_dictionary_as_list(colnames[key] if key in key in colnames else key, value) - - # polygenicity and heritability estimates - ci = data['result']['bivariate']['ci'] - insert_key_to_dictionary_as_list('pi12', ci['pi_vec_C3']['point_estimate']) - insert_key_to_dictionary_as_list('pi12_se', ci['pi_vec_C3']['se']) - insert_key_to_dictionary_as_list('pi1u', ci['pi1u']['point_estimate']) - insert_key_to_dictionary_as_list('pi1u_se', ci['pi1u']['se']) - insert_key_to_dictionary_as_list('pi2u', ci['pi2u']['point_estimate']) - insert_key_to_dictionary_as_list('pi2u_se', ci['pi2u']['se']) - insert_key_to_dictionary_as_list('pifrac', ci['pi12_over_pi1u']['point_estimate']) - insert_key_to_dictionary_as_list('pifrac_se', ci['pi12_over_pi1u']['se']) - insert_key_to_dictionary_as_list('h2', ci['h2_T1']['point_estimate']) - insert_key_to_dictionary_as_list('h2_se', ci['h2_T1']['se']) - - df = pd.DataFrame(df_data) - colorder = ['tag1', 'tag2', 'outtag', 'true_h2', 'true_rg', 'true_pi1u', 'true_pi2u', 'true_pi12', 'rep'] - df = df[[c for c in colorder if c in df] + [x for x in df if x not in colorder]] - df = df[(df['pi1u']<0.95) & (df['pi2u']<0.95)].copy() # drop 1 outlier - - df_annotenrich_true_n12['rep']=df_annotenrich_true_n12['rep'].astype(str) - df = pd.merge(df, df_annotenrich_true_n12, how='left',on=['piTag', 'rep']) - - df_agg = df[['piTag', 'true_h2', 'pi12', 'pi12_se', 'pi1u', 'pi1u_se', 'pifrac', 'pifrac_se', 'h2', 'h2_se', 'true_n12']].groupby(['piTag', 'true_h2']).agg({'pi12':['mean', 'std'], 'pi12_se':['mean'], 'pi1u':['mean', 'std'], 'pi1u_se':['mean'], 'pifrac':['mean', 'std'], 'pifrac_se':['mean'], 'h2':['mean', 'std'], 'h2_se':['mean'], 'true_n12':['mean','std']}) - df_agg.columns = ['_'.join(col).strip() for col in df_agg.columns.values] - df_agg = df_agg.reset_index() - df_agg['true_pi1u'] = df_agg['piTag'].str.replace('K', '').astype(float)*1e3/11015833 - - # b Standard errors (the first number is the mean of theoretical standard errors derived from variance formulas and the second one is the empirical standard errors across 100 replications). - koef=1e-03; # if change koef one must also change the logic that calculates expected true_n1U. Currently it is taken from piTag. - df_agg['N12 (x1000)'] = ['{:.2f} ({:.2f}/{:.2f})'.format(v*koef*nsnps_HAPGEN,se*koef*nsnps_HAPGEN,sd*koef*nsnps_HAPGEN) for v,se,sd in zip(df_agg['pi12_mean'],df_agg['pi12_se_mean'],df_agg['pi12_std'])] - df_agg['N1u (x1000)'] = ['{:.2f} ({:.2f}/{:.2f})'.format(v*koef*nsnps_HAPGEN,se*koef*nsnps_HAPGEN,sd*koef*nsnps_HAPGEN) for v,se,sd in zip(df_agg['pi1u_mean'],df_agg['pi1u_se_mean'],df_agg['pi1u_std'])] - df_agg['h2'] = ['{:.2f} ({:.2f}/{:.2f})'.format(v,se,sd) for v,se,sd in zip(df_agg['h2_mean'],df_agg['h2_se_mean'],df_agg['h2_std'])] - df_agg['N12/N1u'] = ['{:.2f} ({:.2f}/{:.2f})'.format(v,se,sd) for v,se,sd in zip(df_agg['pifrac_mean'],df_agg['pifrac_se_mean'],df_agg['pifrac_std'])] - df_agg['true_n12'] = ['{:.2f} ({:.2f})'.format(v*koef,se*koef) for v,se in zip(df_agg['true_n12_mean'],df_agg['true_n12_std'])] - df_agg['true_N1u (x1000)'] = [x.replace('K','') for x in df_agg['piTag']] - df_agg = df_agg[['true_N1u (x1000)', 'true_h2', 'true_n12', 'N1u (x1000)', 'N12 (x1000)', 'N12/N1u','h2']].copy() - - df_agg.to_csv(os.path.join(tables_folder, 'SIMU_BGMG_ANNOTENRICH{}_TABLE.csv'.format(mafSuffix)), sep='\t',index=False) - - if tables_writer is None: tables_writer = pd.ExcelWriter(os.path.join(tables_folder, 'all_tables.xlsx')) - df_agg.to_excel(tables_writer,'table{}'.format(table_index), index=False) - -plt.rcParams.update({'mathtext.default': 'regular', 'font.size': 20 }) - -table_index += 1 -if DO_SIMU_UGMG_SUBREF: - df_data = {} - colnames = {'h2':'true_h2', 'rg':'true_rg', 'pi1u':'true_pi1u', 'pi2u':'true_pi2u', 'pi12':'true_pi12'} - - def insert_key_to_dictionary_as_list(key, value): - if key not in df_data: - df_data[key] = [] - df_data[key].append(value) - - files = glob.glob(folder_SIMU_BGMG_subref + r'\*run2.fit.short.json') - for fname in files: - data = json.loads(open(fname).read()) - # general info about the run ['h2', 'rg', 'pi1u', 'pi2u', 'pi12', 'rep', 'tag1', 'tag2', 'outtag'] - rep = {'.bgmg':'', '.short':'', '.json':''} - for k in rep: fname = fname.replace(k, rep[k]) - for key, value in [tuple(x.split('=')) for x in os.path.basename(fname).split('_') if ('=' in x)]: - insert_key_to_dictionary_as_list(colnames[key] if key in key in colnames else key, value) - - # polygenicity and heritability estimates - ci = data['result']['univariate'][0]['ci'] - insert_key_to_dictionary_as_list('h2', ci['h2']['point_estimate']) - insert_key_to_dictionary_as_list('h2_se', ci['h2']['se']) - insert_key_to_dictionary_as_list('pi_vec', ci['pi_vec']['point_estimate']) - insert_key_to_dictionary_as_list('pi_vec_se', ci['pi_vec']['se']) - insert_key_to_dictionary_as_list('sig2_beta', ci['sig2_beta']['point_estimate']) - insert_key_to_dictionary_as_list('sig2_beta_se', ci['sig2_beta']['se']) - - df = pd.DataFrame(df_data) - for col in df.columns: df[col] = pd.to_numeric(df[col],errors='ignore') - df['hat_ncausal'] = nsnps_HAPGEN*df['frac'].values*df['pi_vec'].values - df['hat_ncausal_se'] = nsnps_HAPGEN*df['frac'].values*df['pi_vec_se'].values - df['true_ncausal'] = nsnps_HAPGEN*df['true_pi1u'] - for col in ['h2', 'h2_se']: df[col] = ['{:.3f}'.format(x) for x in df[col].values] - for col in ['pi_vec', 'pi_vec_se']: df[col] = ['{:.3e}'.format(x) for x in df[col].values] - for col in ['sig2_beta', 'sig2_beta_se']: df[col] = ['{:.3e}'.format(x) for x in df[col].values] - for col in ['hat_ncausal', 'hat_ncausal_se', 'true_ncausal']: df[col] = ['{:.1f}'.format(x/1000) for x in df[col].values] - df_agg = concat_se(df)[['true_h2', 'true_ncausal','frac', 'h2 (se)', 'hat_ncausal (se)', 'sig2_beta (se)', 'pi_vec (se)']].copy() - - df_agg.to_csv(os.path.join(tables_folder, 'SIMU_UGMG_SUBREF_TABLE.csv'), sep='\t',index=False) - if tables_writer is None: tables_writer = pd.ExcelWriter(os.path.vjoin(tables_folder, 'all_tables.xlsx')) - df_agg.to_excel(tables_writer,'table{}'.format(table_index), index=False) - -if tables_writer: tables_writer.save() - - diff --git a/misc/vis_density.py b/misc/vis_density.py deleted file mode 100644 index a15c6d4..0000000 --- a/misc/vis_density.py +++ /dev/null @@ -1,417 +0,0 @@ -DO_PREPARE_READ_DATA_NOMHC=False -DO_BGMG_DENSITY=False -DO_BGMG_CAUSAL_DENSITY=False -BGMG_SIMU_DENSITY_WITH_CAUSAL_DENSITY = False -DO_BGMG_CAUSAL_DENSITY_SUPPL=True - -def merge_z_vs_z(df1, df2): - _N_CHR = 22 - # complementary bases - COMPLEMENT = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} - # bases - BASES = COMPLEMENT.keys() - # true iff strand ambiguous - STRAND_AMBIGUOUS = {''.join(x): x[0] == COMPLEMENT[x[1]] - for x in itertools.product(BASES, BASES) - if x[0] != x[1]} - # SNPS we want to keep (pairs of alleles) - VALID_SNPS = {x for x in map(lambda y: ''.join(y), itertools.product(BASES, BASES)) - if x[0] != x[1] and not STRAND_AMBIGUOUS[x]} - # T iff SNP 1 has the same alleles as SNP 2 (allowing for strand or ref allele flip). - MATCH_ALLELES = {x for x in map(lambda y: ''.join(y), itertools.product(VALID_SNPS, VALID_SNPS)) - # strand and ref match - if ((x[0] == x[2]) and (x[1] == x[3])) or - # ref match, strand flip - ((x[0] == COMPLEMENT[x[2]]) and (x[1] == COMPLEMENT[x[3]])) or - # ref flip, strand match - ((x[0] == x[3]) and (x[1] == x[2])) or - ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]]))} # strand and ref flip - # T iff SNP 1 has the same alleles as SNP 2 w/ ref allele flip. - FLIP_ALLELES = {''.join(x): - ((x[0] == x[3]) and (x[1] == x[2])) or # strand match - # strand flip - ((x[0] == COMPLEMENT[x[3]]) and (x[1] == COMPLEMENT[x[2]])) - for x in MATCH_ALLELES} - - df1 = df1[['SNP', 'A1', 'A2', 'Z']].rename(columns={'Z': 'Z1'}).copy() - df2 = df2[['SNP', 'A1', 'A2', 'Z']].rename(columns={'Z': 'Z2', 'A1': 'A1x', 'A2': 'A2x'}).copy() - df = pd.merge(df1, df2, how='inner', on='SNP') - df = df.dropna(how='any') - alleles = df.A1 + df.A2 + df.A1x + df.A2x - df = df[alleles.apply(lambda y: y in MATCH_ALLELES)] - alleles = df.A1 + df.A2 + df.A1x + df.A2x - flip_status = alleles.apply(lambda y: FLIP_ALLELES[y]) - df.Z2 *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) - df = df.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) - return df - -def load_data(trait1, trait2, censored=False): - fname1 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait1, trait2) - fname2 = (format_string_BGMG_test_censored if censored else format_string_BGMG_test).format(trait2, trait1) - if os.path.exists(fname2): - data = json_loads(fname2) - flip_data = False - elif os.path.exists(fname1): - data = json_loads(fname1) - flip_data = True - else: - raise ValueError('missing: {} vs {}'.format(trait1, trait2)) - return data, flip_data - -def plot_causal_density(data, flip_data, vmax=1e3, plot_limits=0.025): - params = data['result']['params'] - sb1, sb2 = params['sig2_beta'][0][2], params['sig2_beta'][1][2] - pi1, pi2, pi12 = tuple(params['pi_vec']) - rho =max(min(params['rho_beta'][2], 0.98), -0.98) - cov = rho * np.sqrt(sb1*sb2) - factor = 1; sb_null = 1e-7 - #print(sb1, sb2, cov, sb_null, pi1, pi2, pi12) - rv1 = multivariate_normal([0, 0], [[sb1, 0], [0, sb_null]]) - rv2 = multivariate_normal([0, 0], [[sb_null, 0], [0, sb2]]) - rv12 = multivariate_normal([0, 0], [[sb1, cov], [cov, sb2]]) - grid_step=plot_limits/50 - x, y = np.mgrid[-plot_limits:plot_limits:grid_step, -plot_limits:plot_limits:grid_step] - pos = np.empty(x.shape + (2,)) - pos[:, :, 0] = x; pos[:, :, 1] = y - z=factor*1e7*grid_step*grid_step*(pi1*rv1.pdf(pos)+pi2*rv2.pdf(pos)+pi12*rv12.pdf(pos)) - plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - im=plt.imshow(np.maximum(1,z if flip_data else z.T),interpolation='none', origin='lower', cmap='magma', norm=LogNorm(), vmin=1, vmax=vmax,extent=plot_extent) - return im - -def plot_predicted_zscore(data, flip_data, num_snps): - density=data['result']['bivariate']['stratified_qq_plot_fit_data']['trait1'][0] - plot_limits = max(density['pdf_zgrid']) - plot_step = density['pdf_zgrid'][1]-density['pdf_zgrid'][0] - plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - nbins_to_pdfsize_scale = 10 # zDATA histogram is 100x100 grid, while zBGMG histogram is 1000x1000 grid. Therefore counts in zBGMG are 10 times smaller, and we need to adjust for it. - zBGMG = (nbins_to_pdfsize_scale*nbins_to_pdfsize_scale) * np.array(density['pdf']) * plot_step * plot_step * num_snps - im=plt.imshow(np.maximum(1, zBGMG.T if flip_data else zBGMG), interpolation='none', origin='lower', cmap='hot', norm=LogNorm(), vmin=1, vmax=1e4,extent=plot_extent) - plot_limits=15; plt.axis([-plot_limits, plot_limits, -plot_limits, plot_limits]) - return im, zBGMG - -if DO_BGMG_DENSITY or DO_BGMG_CAUSAL_DENSITY: - fig = plt.figure(figsize=(18, 18), dpi=80) - -if DO_PREPARE_READ_DATA_NOMHC: - #read_data_noMHC=None - if ('read_data_noMHC' not in locals()) or (read_data_noMHC is None): - read_data_noMHC = {} - for trait in traits14_ordered: - fname = format_string_sumstats_noMHC.format(trait) - print('reading {}...'.format(fname)) - read_data_noMHC[trait] = pd.read_table(fname, delim_whitespace=True, usecols=['SNP', 'A1', 'A2', 'Z']) - -t1vo = 'PGC_SCZ_2014_EUR' # trait1 vs others -if DO_BGMG_DENSITY: - if ('read_data_noMHC_pairs' not in locals()) or (read_data_noMHC_pairs is None): - #for trait1, trait2 in list(itertools.combinations(traits4_ordered, 2)): - read_data_noMHC_pairs = {} - for trait1, trait2 in [(t1vo, x) for x in traits4_ordered if (x != t1vo)]: - print('processing {} vs {}'.format(trait1, trait2)) - df1 = read_data_noMHC[trait1] - df2 = read_data_noMHC[trait2] - df = merge_z_vs_z(df1, df2) - - if False: # filter hm3 snps - hm3_snps = pd.read_table(data_root + r"/MMIL/SUMSTAT/misc/w_hm3.snplist", delim_whitespace=True) - df = pd.merge(df, hm3_snps, how='inner', on='SNP') - use_hm3_snps = True - else: - use_hm3_snps = False - - read_data_noMHC_pairs[(trait1, trait2)] = df - - for trait1, trait2 in list(read_data_noMHC_pairs.keys()): - read_data_noMHC_pairs[(trait2, trait1)] = read_data_noMHC_pairs[(trait1, trait2)].copy().rename(columns={'Z1': 'Z2', 'Z2':'Z1'}) - - for index, (trait1, trait2) in enumerate([(t1vo, x) for x in traits4_ordered if (x != t1vo)]): - # density of Z scores - ti1 = trait4_index_map[trait1] - ti2 = trait4_index_map[trait2] - plot_limits=15 - plt.subplot(3,3,1 + index) - df = read_data_noMHC_pairs[(trait1, trait2)] - plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - zDATA, _, _ = np.histogram2d(df['Z2'], df['Z1'], bins=100, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) - im=plt.imshow(np.maximum(1,zDATA.T),interpolation='none', origin='lower', cmap='hot', norm=LogNorm(), vmin=1, vmax=1e4,extent=plot_extent) - if True: plt.xlabel('$z_{'+traits4_short[trait2]+'}$') - if index==0: plt.ylabel('$z_{'+traits4_short[trait1]+'}$') - if index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - if index!=2: plt.gca().set_visible(False) - -if DO_BGMG_CAUSAL_DENSITY: - for index, (trait1, trait2) in enumerate([(t1vo, x) for x in traits4_ordered if (x != t1vo)]): - ti1 = trait4_index_map[trait1] - ti2 = trait4_index_map[trait2] - num_snps_data = len(read_data_noMHC_pairs[(trait1, trait2)]) - plt.subplot(3,3,7 + index) - - data, flip_data = load_data(trait1, trait2, censored=False) - im = plot_causal_density(data, flip_data) - if True: plt.xlabel('$\\beta_{'+traits4_short[trait2]+'}$') - if index==0: plt.ylabel('$\\beta_{'+traits4_short[trait1]+'}$') - if index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - if index!=2: plt.gca().set_visible(False) - - # BGMG-estimated density of Z scores - plt.subplot(3,3,4 + index) - im, zBGMG = plot_predicted_zscore(data, flip_data, num_snps_data) - plot_limits=15; plt.axis([-plot_limits, plot_limits, -plot_limits, plot_limits]) - if True: plt.xlabel('$\\hat z_{'+traits4_short[trait2]+'}$') - if index==0: plt.ylabel('$\\hat z_{'+traits4_short[trait1]+'}$') - if index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - if index!=2: plt.gca().set_visible(False) - -if DO_BGMG_DENSITY and DO_BGMG_CAUSAL_DENSITY: - figure_name = 'BGMG_DENSITY{}_WITH_CAUSAL_DENSITY'.format('_hm3' if use_hm3_snps else '') -elif DO_BGMG_DENSITY: - figure_name = 'BGMG_DENSITY{}'.format('_hm3' if use_hm3_snps else '') -else: - figure_name = 'BGMG_CAUSAL_DENSITY' - -if DO_BGMG_DENSITY or DO_BGMG_CAUSAL_DENSITY: - fig.subplots_adjust(hspace=0.20, wspace=0.20) - #plt.tight_layout() - savefig(figures_folder, '{}'.format(figure_name)) - -if BGMG_SIMU_DENSITY_WITH_CAUSAL_DENSITY: - #for file in glob.glob(r'H:\work\run_simu_bgmg_paper_examples\*.pheno'): - fig = plt.figure(figsize=(18, 12), dpi=80) - phenos = [folder_simu_bgmg_paper_examples + r'/simu_h2=0.4_rg=0.0_pi1u=1.0000e-04_pi2u=1.0000e-04_pi12=0.0000e+00_rep=1_tag1=customPolygenicOverlapAt0p0_tag2=evenPolygenicity.pheno', - folder_simu_bgmg_paper_examples + r'/simu_h2=0.4_rg=0.99_pi1u=1.0000e-04_pi2u=1.0000e-04_pi12=5.0000e-05_rep=1_tag1=customPolygenicOverlapAt0p5_tag2=evenPolygenicity.pheno', - folder_simu_bgmg_paper_examples + r'/simu_h2=0.4_rg=0.0_pi1u=1.0000e-04_pi2u=1.0000e-04_pi12=5.0000e-05_rep=1_tag1=customPolygenicOverlapAt0p5_tag2=evenPolygenicity.pheno'] - for file_index, file in enumerate(phenos): - print('loading {}'.format(file)) - file = os.path.splitext(file)[0] - fname = os.path.basename(file) - causals = file + '.{}.causals' - qassoc = file + '.pheno.trait{}.chr{}.qassoc.gz' - - df_causals = [pd.read_table(causals.format(trait_index), sep='\t') for trait_index in range(1, 3)] - df_qassoc = [pd.concat([pd.read_table(qassoc.format(trait_index, chr_index), delim_whitespace=True) for chr_index in range(1, 23)]) for trait_index in range(1, 3)] - - if (df_causals[1].SNP!=df_causals[0].SNP).any(): raise("SNPs are different - need pd.merge") - - for trait_index in range(0,2): - BETA_cols = [c for c in df_causals[trait_index] if c.startswith('BETA_')] - if np.max(np.array([[df_causals[trait_index][c] != 0] for c in BETA_cols]).sum(axis=0)) > 1: raise('error') - df_causals[trait_index]['BETA'] = np.squeeze(np.array([[df_causals[trait_index][c]] for c in BETA_cols]).sum(axis=0)) - - for trait_index in range(0, 2): - df_qassoc[trait_index]['Z'] = np.divide(df_qassoc[trait_index]['BETA'].values, df_qassoc[trait_index]['SE'].values) - - # plot z-score vs z-score - plt.subplot(2,3,4+file_index) - plot_limits=15 - plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - z, _, _ = np.histogram2d(df_qassoc[0].Z, df_qassoc[1].Z, bins=100, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) - im=plt.imshow(1+z.T,interpolation='none', origin='lower', cmap='hot', norm=LogNorm(), vmin=1, vmax=1e5,extent=plot_extent) - plt.axis(plot_extent) - plt.xlabel('$z_{trait1}$') - if file_index==0: plt.ylabel('$z_{trait2}$') - if file_index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - if file_index!=2: plt.gca().set_visible(False) - - # plot beta vs beta - plt.subplot(2,3,1+file_index) - plot_limits=0.15 - plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits]; - sb_null = 1e-7; x1, y1 = np.random.multivariate_normal([0, 0], [[sb_null, 0], [0, sb_null]], len(df_causals[0])).T - z, _, _ = np.histogram2d(df_causals[0]['BETA'].values+x1, df_causals[1]['BETA'].values+y1, bins=100, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) - im=plt.imshow(1+z.T,interpolation='none', origin='lower', cmap='magma', norm=LogNorm(), vmin=1, vmax=30,extent=plot_extent) - plt.axis(plot_extent) - plt.title(['(A) Independent traits', '(B) Polygenic overlap\nwith genetic correlation', '(C) Polygenic overlap\nw/o genetic correlation'][file_index]) - plt.xlabel('$\\beta_{trait1}$') - if file_index==0: plt.ylabel('$\\beta_{trait2}$') - if file_index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - if file_index!=2: plt.gca().set_visible(False) - - fig.subplots_adjust(hspace=0.15, wspace=0.15) - #plt.tight_layout() - - savefig(figures_folder, 'BGMG_SIMU_DENSITY_WITH_CAUSAL_DENSITY') - -if DO_BGMG_CAUSAL_DENSITY_SUPPL: - - causal_density_suppl_tasks = [] - for trait1 in traits_ordered: - causal_density_suppl_tasks.append((traits14_short[trait1], 1e3, 0.025, ([(trait1, trait2) for trait2 in traits_ordered if trait2 != trait1]))) - causal_density_suppl_tasks.append(('immuno', 1e2, 0.1, list(itertools.combinations(traitsIM_ordered, 2)))) - causal_density_suppl_tasks.append(('antro', 1e2, 0.025, list(itertools.combinations(traitsAM_ordered, 2)))) - censored=False - - for task_name, vmax, plot_limits_causal, causal_density_suppl_sub_tasks in causal_density_suppl_tasks: - fig=plt.figure(figsize=(24, 42), dpi=80) - for index, (trait1_orig, trait2_orig) in enumerate(causal_density_suppl_sub_tasks): - trait1 = trait1_orig; trait2=trait2_orig - print('{} vs {}...'.format(trait1_orig, trait2_orig)) - plt.subplot(len(causal_density_suppl_sub_tasks), 3, 3*index + 3) - - data, flip_data = load_data(trait1, trait2, censored) - im = plot_causal_density(data, flip_data, vmax, plot_limits_causal) - if True: plt.xlabel('$\\beta_{'+traits14_short[trait2]+'}$') - if True: plt.ylabel('$\\beta_{'+traits14_short[trait1]+'}$') - #if index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - #if index!=2: plt.gca().set_visible(False) - - df = merge_z_vs_z(read_data_noMHC[trait1], read_data_noMHC[trait2]) - plt.subplot(len(causal_density_suppl_sub_tasks), 3, 3*index + 1) - plot_limits=15; plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - z, _, _ = np.histogram2d(df['Z2'], df['Z1'], bins=100, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) - im=plt.imshow(np.maximum(1,z.T),interpolation='none', origin='lower', cmap='hot', norm=LogNorm(), vmin=1, vmax=1e4,extent=plot_extent) - plt.xlabel('$z_{'+traits14_short[trait2]+'}$') - plt.ylabel('$z_{'+traits14_short[trait1]+'}$') - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - - # BGMG-estimated density of Z scores - plt.subplot(len(causal_density_suppl_sub_tasks), 3, 3*index + 2) - im, zBGMG = plot_predicted_zscore(data, flip_data, len(df)) - if True: plt.xlabel('$\\hat z_{'+traits14_short[trait2]+'}$') - if True: plt.ylabel('$\\hat z_{'+traits14_short[trait1]+'}$') - #if index!=0: plt.gca().get_yaxis().set_visible(False) - plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) - #if index!=2: plt.gca().set_visible(False) - - fig.subplots_adjust(hspace=0.35, wspace=0.35) - savefig(figures_folder, 'BGMG_CAUSAL_DENSITY_SUPPL_{}'.format(task_name) + (' (censored)' if censored else '') ) - -EVALUATE_HAPGEN = False -if EVALUATE_HAPGEN: - if 0: - df_joint=pd.read_table('/home/oleksanf/vmshare/data/bfile_merged_relcheck/1kG(x)_and_hapgen(y)_r2.csv',sep='\t') - df=pd.read_table('/home/oleksanf/vmshare/data/bfile_merged_relcheck/1kG(x)_and_hapgen(y)_ld.csv', delim_whitespace=True) - df_pca=pd.read_table('/home/oleksanf/vmshare/data/bfile_merged_relcheck/10k_indep_pca.eigenvec',header=None, delim_whitespace=True,names=['id1', 'id2'] + ['pca{}'.format(i) for i in range(1, 21)]) - - fig = plt.figure(figsize=(24, 24), dpi=80) - plt.subplot(2,2,1) - n=plt.hist([df['TLD_x'].values, df['TLD_y'].values*nsnps_LDSR/snps_HAPGEN], np.linspace(0, 25, 25), label=['1kG EUR', 'HapGen']) - plt.legend(loc='upper right') - - plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0)) - plt.xlabel('LD score') - plt.ylabel('#SNPs') - plt.title('A', loc='right') - - plt.subplot(2,2,2) - plt.hist([df['TLD_x'].values, df['TLD_y'].values*nsnps_LDSR/snps_HAPGEN], np.linspace(0, 500, 25), label=['1kG EUR', 'HapGen']) - plt.legend(loc='upper right') - plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0)) - plt.xlabel('LD score') - plt.ylabel('#SNPs') - plt.title('B', loc='right') - - d1=df_joint[df_joint['R2_x'].isnull()] - d2=df_joint[df_joint['R2_y'].isnull()] - d3=df_joint[(~df_joint['R2_y'].isnull()) &(~df_joint['R2_x'].isnull())] - plt.subplot(2,2,3) - plt.scatter(d1.SNP.values, d1.TAG.values, s=2, c='r') - plt.scatter(d2.SNP.values, d2.TAG.values, s=1, c='silver') - plt.scatter(d3.SNP.values, d3.TAG.values, s=1, c='k') - plt.ylabel('SNP index') - plt.xlabel('SNP index') - plt.title('C', loc='right') - - plt.subplot(2,2,4) - plt.scatter(df_pca['pca1'].values, df_pca['pca2'].values,s=1 ) - plt.ylabel('PCA component #1') - plt.xlabel('PCA component #2') - plt.title('D', loc='right') - - savefig(figures_folder, 'evaluate_hapgen', ['png']) - -if 0: # data prep - import precimed - import precimed.mixer - libbgmg = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', context_id=1) - libbgmg.init_log("/home/oleksanf/github/mixer/testlog5.log") - libbgmg.log_message('Test log message succeeded?') - libbgmg.dispose() - - bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim' - frq_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink_freq/1000G.EUR.QC.@.frq' - plink_ld_bin = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin' - chr_labels = [1] # list(range(1, 23)) - trait1_file = ''; #'/home/oleksanf/vmshare/data/MMIL/SUMSTAT/TMP/ldsr/PGC_SCZ_2014_EUR.sumstats.gz' - exclude = ''; extract = '' - libbgmg.init(bim_file, frq_file, chr_labels, trait1_file, '', exclude, extract); - print(libbgmg) - - options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg.num_snp), ('num_components', 1), - ('cache_tag_r2sum', False), ('threads', 6), ('seed', None), ('z1max', None)] - for opt, val in options: libbgmg.set_option(opt, val) - - for chr_label in chr_labels: - libbgmg.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label))) - libbgmg.set_ld_r2_csr(chr_label); - - libbgmg.set_option('diag', 0) - libbgmg_hapgen = precimed.mixer.LibBgmg('/home/oleksanf/github/mixer/src/build/lib/libbgmg.so', context_id=2) - libbgmg_hapgen.dispose() - - bim_file = '/home/oleksanf/vmshare/data/bfile_merged/chr@.bim' - frq_file = '/home/oleksanf/vmshare/data/bfile_merged/chr@.frq' - plink_ld_bin = '/home/oleksanf/vmshare/data/hapgen_ldmat2_plink/bfile_merged_ldmat_p01_SNPwind50k_chr@.ld.bin' - chr_labels = [1] # list(range(1, 23)) - trait1_file = '' - exclude = ''; extract = '' - libbgmg_hapgen.init(bim_file, frq_file, chr_labels, trait1_file, '', exclude, extract); - print(libbgmg_hapgen) - - options=[('r2min', 0.05), ('kmax', 100), ('max_causals', 0.03*libbgmg_hapgen.num_snp), ('num_components', 1), - ('cache_tag_r2sum', False), ('threads', 6), ('seed', None), ('z1max', None)] - for opt, val in options: libbgmg_hapgen.set_option(opt, val) - - for chr_label in chr_labels: - libbgmg_hapgen.set_ld_r2_coo_from_file(plink_ld_bin.replace('@', str(chr_label))) - libbgmg_hapgen.set_ld_r2_csr(chr_label); - - libbgmg_hapgen.set_option('diag', 0) - - import pandas as pd - - chrlist = [1] - - df_hapgen = pd.concat([pd.read_table('/home/oleksanf/vmshare/data/bfile_merged/chr{}.bim'.format(chri),delim_whitespace=True,header=None, names='CHR SNP GP BP A1 A2'.split()) for chri in chrlist]) - df_hapgen.reset_index(drop=True, inplace=True) - df_hapgen['index']=df_hapgen.index - df_hapgen['TLD'] = libbgmg_hapgen.ld_tag_r2_sum - - df = pd.concat([pd.read_table('/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.{}.bim'.format(chri),delim_whitespace=True,header=None, names='CHR SNP GP BP A1 A2'.split()) for chri in chrlist]) - df.reset_index(drop=True, inplace=True) - df['index']=df.index - df['TLD'] = libbgmg.ld_tag_r2_sum - - df_merge = pd.merge(df, df_hapgen, on='SNP', how='inner') - - df.to_csv('/home/oleksanf/vmshare/data/bfile_merged_relcheck/1kG_EUR_ld.csv',sep='\t',index=False) - df_hapgen.to_csv('/home/oleksanf/vmshare/data/bfile_merged_relcheck/hapgen_ld.csv',sep='\t',index=False) - df_merge.to_csv('/home/oleksanf/vmshare/data/bfile_merged_relcheck/1kG(x)_and_hapgen(y)_ld.csv',sep='\t',index=False) - - index_y = df_merge[(df_merge.CHR_x==1) & (df_merge.BP_x<2e6)]['index_y'].values - index_x = df_merge[(df_merge.CHR_x==1) & (df_merge.BP_x<2e6)]['index_x'].values - - [hSNP, hTAG, hR2] = libbgmg_hapgen.get_ld_r2_chr(chr_label=1) - [kgSNP, kgTAG, kgR2] = libbgmg.get_ld_r2_chr(chr_label=1) - df_hR2 = pd.DataFrame({'SNP':hSNP, 'TAG':hTAG, 'R2':hR2}) - df_kgR2 = pd.DataFrame({'SNP':kgSNP, 'TAG':kgTAG, 'R2':kgR2}) - - r2thresh = 0.2 - - df_kgR2=df_kgR2[(df_kgR2.SNP <= max(index_x)) & (df_kgR2.TAG <= max(index_x)) & (df_kgR2.R2 >= r2thresh)] - df_hR2=df_hR2[(df_hR2.SNP <= max(index_y)) & (df_hR2.TAG <= max(index_y)) & (df_hR2.R2 >= r2thresh)] - - df_hR2 = df_hR2[df_hR2.SNP.isin(index_y) & df_hR2.TAG.isin(index_y)] - df_kgR2 = df_kgR2[df_kgR2.SNP.isin(index_x) & df_kgR2.TAG.isin(index_x)] - - for col in ['SNP', 'TAG']: - df_kgR2[col] = df_kgR2[col].map({v:i for (i,v) in enumerate(index_x)}) - df_hR2[col] = df_hR2[col].map({v:i for (i,v) in enumerate(index_y)}) - - df_joint = pd.merge(df_kgR2, df_hR2,how='outer',on=['SNP', 'TAG']) - df_joint.to_csv('/home/oleksanf/vmshare/data/bfile_merged_relcheck/1kG(x)_and_hapgen(y)_r2.csv',sep='\t',index=False) \ No newline at end of file diff --git a/precimed/Untitled.ipynb b/precimed/Untitled.ipynb deleted file mode 100644 index 3034949..0000000 --- a/precimed/Untitled.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/precimed/mixer/cli.py b/precimed/mixer/cli.py index 50561db..b1566cb 100644 --- a/precimed/mixer/cli.py +++ b/precimed/mixer/cli.py @@ -14,6 +14,7 @@ import logging import json import scipy.stats +import random from scipy.interpolate import interp1d import time @@ -110,13 +111,13 @@ def fix_and_validate_args(args): def convert_args_to_libbgmg_options(args, num_snp): libbgmg_options = { - 'r2min': args.r2min, - 'kmax': args.kmax[0], - 'threads': args.threads[0], - 'seed': args.seed, - 'cubature_rel_error': args.cubature_rel_error, - 'cubature_max_evals': args.cubature_max_evals, - 'z1max': args.z1max, + 'r2min': args.r2min if ('r2min' in args) else None, + 'kmax': args.kmax[0] if ('kmax' in args) else None, + 'threads': args.threads[0] if ('threads' in args) else None, + 'seed': args.seed if ('seed' in args) else None, + 'cubature_rel_error': args.cubature_rel_error if ('cubature_rel_error' in args) else None, + 'cubature_max_evals': args.cubature_max_evals if ('cubature_max_evals' in args) else None, + 'z1max': args.z1max if ('z1max' in args) else None, 'z2max': args.z2max if ('z2max' in args) else None, } return [(k, v) for k, v in libbgmg_options.items() if v is not None ] @@ -220,6 +221,22 @@ def parser_ld_add_arguments(args, func, parser): parser.add_argument('--ld-window', type=int, default=0, help="limit window similar to --ld-window in 'plink r2'; 0 will disable this constraint") parser.set_defaults(func=func) +def parser_snps_add_arguments(args, func, parser): + parser.add_argument("--bim-file", type=str, default=None, help="Plink bim file. " + "Defines the reference set of SNPs used for the analysis. " + "Marker names must not have duplicated entries. " + "May contain simbol '@', which will be replaced with the actual chromosome label. ") + parser.add_argument("--ld-file", type=str, default=None, help="File with linkage disequilibrium information, " + "generated via 'mixer.py ld' command. " + "May contain simbol '@', similarly to --bim-file argument. ") + parser.add_argument("--chr2use", type=str, default="1-22", help="Chromosome ids to use " + "(e.g. 1,2,3 or 1-4,12,16-20). Chromosome must be labeled by integer, i.e. X and Y are not acceptable. ") + parser.add_argument('--r2', type=float, default=0.8, help="r2 threshold for random prunning") + parser.add_argument('--maf', type=float, default=0.05, help="maf threshold") + parser.add_argument('--subset', type=int, default=2000000, help="number of SNPs to randomly select") + parser.add_argument('--seed', type=int, default=123, help="Random seed") + parser.set_defaults(func=func) + def parser_perf_add_arguments(args, func, parser): parser_add_common_arguments(parser, num_traits=2) parser.add_argument('--kmax', type=int, default=[20000, 2000, 200], nargs='+', help="Number of sampling iterations") @@ -243,6 +260,7 @@ def parse_args(args): parser_ld_add_arguments(args=args, func=execute_ld_parser, parser=subparsers.add_parser("ld", parents=[parent_parser], help='prepare files with linkage disequilibrium information')) parser_perf_add_arguments(args=args, func=execute_perf_parser, parser=subparsers.add_parser("perf", parents=[parent_parser], help='run performance evaluation of the MiXeR')) + parser_snps_add_arguments(args=args, func=execute_snps_parser, parser=subparsers.add_parser("snps", parents=[parent_parser], help='generate random sets of SNPs')) return parser.parse_args(args) @@ -531,7 +549,11 @@ def execute_ld_parser(args): def initialize_mixer_plugin(args): libbgmg = LibBgmg(args.lib) - libbgmg.init(args.bim_file, "", args.chr2use, args.trait1_file, args.trait2_file if ('trait2_file' in args) else "", args.exclude, args.extract) + libbgmg.init(args.bim_file, "", args.chr2use, + args.trait1_file if ('trait1_file' in args) else "", + args.trait2_file if ('trait2_file' in args) else "", + args.exclude if ('exclude' in args) else "", + args.extract if ('extract' in args) else "") for opt, val in convert_args_to_libbgmg_options(args, libbgmg.num_snp): libbgmg.set_option(opt, val) @@ -540,7 +562,9 @@ def initialize_mixer_plugin(args): libbgmg.set_ld_r2_coo_from_file(chr_label, args.ld_file.replace('@', str(chr_label))) libbgmg.set_ld_r2_csr(chr_label) - libbgmg.set_weights_randprune(args.randprune_n, args.randprune_r2, exclude="", extract="") + if ('randprune_n' in args) and ('randprune_r2' in args): + libbgmg.set_weights_randprune(args.randprune_n, args.randprune_r2, exclude="", extract="") + libbgmg.set_option('diag', 0) return libbgmg @@ -572,6 +596,40 @@ def execute_perf_parser(args): pd.DataFrame(perf_data, columns=['threads', 'kmax', 'costcalc', 'time_sec', 'cost']).to_csv(args.out + '.csv', sep='\t', index=False) libbgmg.log_message('Done') +def execute_snps_parser(args): + fix_and_validate_args(args) + if args.seed is not None: np.random.seed(args.seed) + libbgmg = initialize_mixer_plugin(args) + mafvec = np.minimum(libbgmg.mafvec, 1-libbgmg.mafvec) + + libbgmg.log_message('Load {}...'.format(args.bim_file)) + ref=pd.concat([pd.read_csv(args.bim_file.replace('@', str(chr_label)), sep='\t', header=None, names='CHR SNP GP BP A1 A2'.split()) for chr_label in args.chr2use]) + libbgmg.log_message('{} SNPs in total'.format(len(ref))) + + # step0 - generate random values for clumping (this is a way to implement random pruning) + buf = np.random.rand(libbgmg.num_tag, 1) + + # step1 - filter SNPs below MAF threshold + buf[mafvec 1) + n1 = data['ci']['nc1@p9'][statistic[0]]/scale_factor; n1_se = data['ci']['nc1@p9'][statistic[1]]/scale_factor if with_std else None + n2 = data['ci']['nc2@p9'][statistic[0]]/scale_factor; n2_se = data['ci']['nc2@p9'][statistic[1]]/scale_factor if with_std else None + n12 = data['ci']['nc12@p9'][statistic[0]]/scale_factor; n12_se = data['ci']['nc12@p9'][statistic[1]]/scale_factor if with_std else None + rg = data['ci']['rg'][statistic[0]] if max_size is None: max_size = n1+n2+n12 if flip: n1, n2 = n2, n1; n1_se, n2_se = n2_se, n1_se @@ -83,6 +86,7 @@ def make_venn_plot(data, flip=False, factor='K', traits=['Trait1', 'Trait2'], co v.get_patch_by_id('100').set_color(cm.colors[f(colors[0])]) v.get_patch_by_id('010').set_color(cm.colors[f(colors[1])]) v.get_patch_by_id('110').set_color(cm.colors[7]) + c=venn2_circles(subsets = (n1, n2, n12), normalize_to=(n1+n2+n12)/max_size, linewidth=1.5, color="white") if formatter==None: if (n1_se is not None) and (n2_se is not None) and (n12_se is not None): formatter1 = '{:.2f}\n({:.2f})' if ((n1+n12+n2) < 1) else '{:.1f}\n({:.1f})' @@ -95,24 +99,29 @@ def make_venn_plot(data, flip=False, factor='K', traits=['Trait1', 'Trait2'], co plt.xlim([-0.75, 0.75]), plt.ylim([-0.7, 0.6]) newline='' - plt.title(traits[0] +' & ' + newline + traits[1], y=-0.18) + plt.title(traits[0] +' & ' + newline + traits[1], y=(-0.18 if plot_rg else 0)) - clr = plt.cm.get_cmap('seismic')((rg+1)/2) - plt.gca().add_patch(patches.Rectangle(((-abs(0.7*rg) if (rg < 0) else 0) , -0.7), abs(0.7 * rg), 0.15, fill=True, clip_on=False, color=clr)) - plt.gca().add_patch(patches.Rectangle((-0.70, -0.7), 1.4, 0.15, fill=False, clip_on=False)) - plt.gca().add_patch(patches.Rectangle((0, -0.7), 0, 0.15, fill=False, clip_on=False, linewidth=3)) - plt.gca().text(-0.35 if (rg>0) else 0.35, -0.7+0.15/2, '$r_g$={:.2f}'.format(rg), fontsize=11, horizontalalignment='center', verticalalignment='center') + if plot_rg: + clr = plt.cm.get_cmap('seismic')((rg+1)/2) + plt.gca().add_patch(patches.Rectangle(((-abs(0.7*rg) if (rg < 0) else 0) , -0.7), abs(0.7 * rg), 0.15, fill=True, clip_on=False, color=clr)) + plt.gca().add_patch(patches.Rectangle((-0.70, -0.7), 1.4, 0.15, fill=False, clip_on=False)) + plt.gca().add_patch(patches.Rectangle((0, -0.7), 0, 0.15, fill=False, clip_on=False, linewidth=3)) + plt.gca().text(-0.35 if (rg>0) else 0.35, -0.7+0.13/2, '$r_g$={:.2f}'.format(rg), fontsize=11, horizontalalignment='center', verticalalignment='center') def make_strat_qq_plots(data, flip=False, traits=['Trait1', 'Trait2'], do_legend=True): cm = plt.cm.get_cmap('tab10') for i in np.array(range(0, 4)) + (4 if flip else 0): hData = plt.plot(data['qqplot'][i]['data_logpvec'], data['qqplot'][i]['hv_logp'], color=cm.colors[i % 4], linestyle='solid') hNull = plt.plot(data['qqplot'][i]['hv_logp'], data['qqplot'][i]['hv_logp'], 'k--') - if do_legend: plt.legend(['All SNPs'] + '$P{0}\leq0.1$ $P{0}\leq0.01$ $P{0}\leq0.001$'.format('_{' + traits[1]+'}').split(), loc='lower right', fontsize=14, borderpad=0.2, frameon=False, borderaxespad=0.2, labelspacing=0.1) + if do_legend: plt.legend(['All SNPs'] + '$P{0}\leq0.1$ $P{0}\leq0.01$ $P{0}\leq0.001$'.format('_{' + traits[1]+'}').split(), loc='lower right', fontsize=10, borderpad=0.2, frameon=False, borderaxespad=0.2, labelspacing=0.1) for i in np.array(range(0, 4)) + (4 if flip else 0): hModel = plt.plot(data['qqplot'][i]['model_logpvec'], data['qqplot'][i]['hv_logp'], color=cm.colors[i % 4], linestyle='dashed') plt.ylim(0, 7.3); plt.xlim(0, 7.3); - plt.title('{} | {}'.format(traits[0], traits[1])) + plt.title('{} | {}'.format(traits[0], traits[1]), fontsize=12) + plt.xticks(np.arange(0, 7.3, step=1),fontsize=10) + plt.yticks(np.arange(0, 7.3, step=1),fontsize=10) + plt.xlabel('Expected -$log_{{10}}(p_{{{}}})$'.format(traits[0]), fontsize=12) + plt.ylabel('Observed -$log_{{10}}(p_{{{}}})$'.format(traits[0]), fontsize=12) def merge_z_vs_z(df1, df2): import pandas as pd @@ -157,7 +166,7 @@ def merge_z_vs_z(df1, df2): df = df.drop(['A1', 'A1x', 'A2', 'A2x'], axis=1) return df -def plot_z_vs_z_data(df, traits=['Trait1', 'Trait2'], plot_limits=15, bins=100): +def plot_z_vs_z_data(df, flip=False, traits=['Trait1', 'Trait2'], plot_limits=15, bins=100): ''' # input can be generated as follows: import pandas as pd @@ -166,10 +175,11 @@ def plot_z_vs_z_data(df, traits=['Trait1', 'Trait2'], plot_limits=15, bins=100): df = precimed.mixer.figures.merge_z_vs_z(df1, df2) ''' plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] - z, _, _ = np.histogram2d(df['Z2'], df['Z1'], bins=bins, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) + z1name, z2name = ('Z2', 'Z1') if flip else ('Z1', 'Z2') + z, _, _ = np.histogram2d(df[z2name], df[z1name], bins=bins, range=[[-plot_limits, plot_limits], [-plot_limits, plot_limits]]) im=plt.imshow(np.maximum(1,z),interpolation='none', origin='lower', cmap='hot', norm=matplotlib.colors.LogNorm(), vmin=1, vmax=1e4,extent=plot_extent) - plt.xlabel('$z_{'+traits[0]+'}$') - plt.ylabel('$z_{'+traits[1]+'}$') + plt.xlabel('$z_{'+traits[0]+'}$', fontsize=12) + plt.ylabel('$z_{'+traits[1]+'}$', fontsize=12, labelpad=-0.1) plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) def plot_predicted_zscore(data, num_snps, flip=False, traits=['Trait1', 'Trait2'], plot_limits=15, bins=100): @@ -192,15 +202,19 @@ def plot_predicted_zscore(data, num_snps, flip=False, traits=['Trait1', 'Trait2' vmin=1, vmax=1e4, extent=data_extent) plt.axis([-plot_limits, plot_limits, -plot_limits, plot_limits]) - plt.xlabel('$\\hat z_{'+traits[0]+'}$') - plt.ylabel('$\\hat z_{'+traits[1]+'}$') + plt.xlabel('$\\hat z_{'+traits[0]+'}$', fontsize=12) + plt.ylabel('$\\hat z_{'+traits[1]+'}$', fontsize=12, labelpad=-0.1) plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) -def plot_causal_density(data, flip=False, traits=['Trait1', 'Trait2'], vmax=1e3, plot_limits=0.025): - params = data['params'] - sb1, sb2 = params['sig2_beta'][0], params['sig2_beta'][1] - pi1, pi2, pi12 = tuple(params['pi']) - rho = max(min(params['rho_beta'], 0.98), -0.98) +def plot_causal_density(data, flip=False, traits=['Trait1', 'Trait2'], vmax=1e3, plot_limits=0.025, statistic=['point_estimate']): + if statistic[0] not in ['point_estimate', 'mean', 'median']: + print('Unable to make plot_causal_density() for statistic=={}'.format(statistic[0])) + sb1 = data['ci']['sig2_beta_T1'][statistic[0]] + sb2 = data['ci']['sig2_beta_T2'][statistic[0]] + pi1 = data['ci']['pi1'][statistic[0]] + pi2 = data['ci']['pi2'][statistic[0]] + pi12 = data['ci']['pi12'][statistic[0]] + rho = max(min(data['ci']['rho_beta'][statistic[0]], 0.98), -0.98) cov = rho * np.sqrt(sb1*sb2) factor = 1; sb_null = 1e-7 rv1 = multivariate_normal([0, 0], [[sb1, 0], [0, sb_null]]) @@ -213,8 +227,8 @@ def plot_causal_density(data, flip=False, traits=['Trait1', 'Trait2'], vmax=1e3, z=factor*1e7*grid_step*grid_step*(pi1*rv1.pdf(pos)+pi2*rv2.pdf(pos)+pi12*rv12.pdf(pos)) plot_extent = [-plot_limits, plot_limits, -plot_limits, plot_limits] im=plt.imshow(np.maximum(1,z if flip else z.T),interpolation='none', origin='lower', cmap='magma', norm=matplotlib.colors.LogNorm(), vmin=1, vmax=vmax,extent=plot_extent) - plt.xlabel('$\\beta_{'+traits[0]+'}$') - plt.ylabel('$\\beta_{'+traits[1]+'}$') + plt.xlabel('$\\beta_{'+traits[0]+'}$', fontsize=12) + plt.ylabel('$\\beta_{'+traits[1]+'}$', fontsize=12, labelpad=0.1) plt.colorbar(im, cax=make_axes_locatable(plt.gca()).append_axes("right", size="5%", pad=0.05)) def extract_brute1_results(data): @@ -237,12 +251,33 @@ def extract_likelihood_function(data): return [dict(funcs)['nc12@p9'](parametrization.vec_to_params([x])) for x in brute1_results['grid']], brute1_results['Jout'] def plot_likelihood(data): - like_x, like_y = extract_likelihood_function(data) - if (not like_x) or (not like_y): - print('--json argument does not contain brute1 optimization results, skip likelihood plot generation') - return - plt.plot(np.array(like_x)/1000, like_y) - #plt.title('cost={}'.format(data['optimize'][-1][1]['fun'])) + if 'likelihood' in data: + cm = plt.cm.get_cmap('tab10') + like_x = np.array([like_x for like_x, like_y in data['likelihood']]) + like_y = np.array([like_y for like_x, like_y in data['likelihood']]) + like_x_min = np.min(like_x) + like_x_max = np.max(like_x) + plot_x = np.arange(like_x_min, like_x_max, (like_x_max-like_x_min) / 100) + plot_y = np.zeros((like_y.shape[0], len(plot_x))) + for i in range(like_x.shape[0]): + plot_y[i, :] = interp1d(like_x[i, :], like_y[i, :], bounds_error=False)(plot_x) + plot_y[i, :] = plot_y[i, :] - np.nanmin(plot_y[i, :]) + plot_y_def = np.sum(np.isfinite(plot_y), 0) + plot_y = np.nanmean(plot_y, 0) + plot_y[plot_y_def < 3] = np.nan + plt.plot(plot_x / 1000, plot_y) + for like_x, like_y in data['likelihood']: + plt.plot(np.array(like_x)/1000, like_y - np.min(like_y), linestyle='dotted', color=cm.colors[0], alpha=0.3) + else: + like_x, like_y = extract_likelihood_function(data) + if (not like_x) or (not like_y): + print('--json argument does not contain brute1 optimization results, skip likelihood plot generation') + return + plt.plot(np.array(like_x)/1000, like_y - np.min(like_y)) + plt.title('-log(L) + const') + plt.xlabel('Shared variant number [k]',fontsize=12) + plt.ylabel('-log(L) + const',fontsize=12) + plt.title('Log-likelihood',fontsize=13) def make_power_plot(data_vec, colors=None, traits=None, power_thresh=None): if colors is None: colors = list(range(0, len(data_vec))) @@ -268,6 +303,7 @@ def make_power_plot(data_vec, colors=None, traits=None, power_thresh=None): display_n = lambda x: '{}'.format(int(float('{:0.1e}'.format(x)))) display_auto = lambda x: '{}K'.format(display_n(x/1000)) if (x < 1e6) else '{:0.1f}M'.format(x/1e6) + print('HAS POWER?', 'power_ci' in data) if 'power_ci' in data: if power_thresh is not None: future_n_ci = [np.power(10, float(interp1d(data_power['svec'], np.log10(data_power['nvec']))(power_thresh))) for data_power in data['power_ci'] if data_power] @@ -294,12 +330,14 @@ def make_power_plot(data_vec, colors=None, traits=None, power_thresh=None): leg_labels.append('N at {}%'.format(int(100*float(power_thresh)))) plt.legend(leg_labels, loc='lower right',frameon=False, numpoints=1) - plt.xlabel('Sample size') - plt.ylabel('Estimated percent variance explained\nby genome-wide significant SNPs') + plt.xlabel('Sample size (N)', fontsize=11) + plt.ylabel('Estimated variance (%) explained\nby genome-wide significant SNPs', fontsize=11) plt.xlim([4, 8]) - plt.ylim([0, 1]) + plt.ylim([-0.017, 1]) plt.locator_params(axis='x', nbins=5) plt.gca().set_xticklabels(labels=['10K', '100K', '1M', '10M', '100M']) + plt.yticks(np.arange(0, 1.01, step=0.2)) + plt.axes().set_yticklabels(labels=['0', '20', '40', '60', '80', '100']) # https://stackoverflow.com/questions/27433316/how-to-get-argparse-to-read-arguments-from-a-file-with-an-option-rather-than-pre class LoadFromFile (argparse.Action): @@ -313,18 +351,28 @@ def __call__ (self, parser, namespace, values, option_string=None): setattr(namespace, k, v) def parser_one_add_arguments(args, func, parser): - parser.add_argument('--json', type=str, default=[""], nargs='+', help="json file from univariate analysis") + parser.add_argument('--json', type=str, default=[""], nargs='+', help="json file from a univariate analysis. This argument does support wildcards (*) or a list with multiple space-separated arguments to process more than one .json file. This allows to generate a combined .csv table across many traits.") parser.add_argument('--trait1', type=str, default=[], nargs='+', help="name of the first trait") parser.add_argument('--power-thresh', type=str, default=None, help="threshold for power analysis, e.g. 0.9 or 0.5, to estimate corresponding N") parser.add_argument('--power-figsize', type=float, nargs='+', default=[], help="figure size for power plots") parser.set_defaults(func=func) def parser_two_add_arguments(args, func, parser): - parser.add_argument('--json', type=str, default=[""], nargs='+', help="json file from cross-trait analysis") + parser.add_argument('--json', type=str, default=[], nargs='*', help="json file from a bivariate analysis, i.e. either 'mixer.py fit2' or 'mixer.py test2' step. This argument does support wildcards (*) to process multiple .json files (this allows to generate a combined .csv table across many cross-trait combinations, but it doesn't generate figures; to generate figures, use --json on a single file, or alternatively use --json-fit and --json-test. ") + parser.add_argument('--json-fit', type=str, default="", help="json file from a bivariate analysis with 'mixer.py fit2' step. This argument does NOT support wildcards. Using --json-fit in conjunction with --json-test produces figures that contain both log-likelihood plots (based on fit2 results) and QQ plot (based on test2 results). When use --json-fit and --json-test, there is no need to specify --json argument. ") + parser.add_argument('--json-test', type=str, default="", help="json file from a bivariate analysis with 'mixer.py test2' step). This argument does NOT support wildcards.") parser.add_argument('--trait1', type=str, default="trait1", help="name of the first trait") parser.add_argument('--trait2', type=str, default="trait2", help="name of the second trait") - parser.add_argument('--trait1-file', type=str, default=None, help="summary statistics file for the first trait") - parser.add_argument('--trait2-file', type=str, default=None, help="summary statistics file for the second trait") + parser.add_argument('--trait1-file', type=str, default=None, help="summary statistics file for the first trait (optional parameter; use it only if you need to generate bivariate z-vs-z density plots)") + parser.add_argument('--trait2-file', type=str, default=None, help="summary statistics file for the second trait (optional parameter; see comment for --trait1-file") + parser.add_argument('--trait1-color', type=int, default=0, choices=list(range(9)), help="color for the venn diagram (first trait); 0-8, encoded as tab10 color palette (https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html) excluding grey code which is reserved the polygenic overlap") + parser.add_argument('--trait2-color', type=int, default=1, choices=list(range(9)), help="color for the venn diagram (second trait)") + parser.add_argument('--flip', default=False, action="store_true", help="flip venn diagram and stratified QQ plots. Note that this arguments does not apply to --trait1 and --trait2 arguments, not to --trait1-color and --trait2-color.") + parser.set_defaults(func=func) + +def parser_combine_add_arguments(args, func, parser): + parser.add_argument('--json', type=str, default=None, help="Path to json files from mixer runs. Must not contain wildcards. Must contain '@' sign, indicating the location of the repeat index.") + parser.add_argument('--rep2use', type=str, default='1-20', help="Repeat indices to use, e.g. 1,2,3 or 1-4,12,16-20") parser.set_defaults(func=func) def parse_args(args): @@ -335,27 +383,39 @@ def parse_args(args): parent_parser.add_argument("--out", type=str, default="mixer", help="prefix for the output files") parent_parser.add_argument('--ext', type=str, default=['png'], nargs='+', choices=['png', 'svg'], help="output extentions") parent_parser.add_argument('--zmax', type=float, default=10, help="limit for z-vs-z density plots") + parent_parser.add_argument('--statistic', type=str, nargs='+', default=["point_estimate"], choices=["point_estimate", "mean", "median", "std", "min", "max"], help="Which statistic to show in the tables and on the Venn diagrams. Can have multiple values. In the case of venn diagram, the first value (typically 'point_estimate' or 'mean') indicate the size of the venn diagram; the second value (optional, typically 'std') allow to include error bars on the Venn diagramm.") subparsers = parser.add_subparsers() parser_one_add_arguments(args=args, func=execute_one_parser, parser=subparsers.add_parser("one", parents=[parent_parser], help='produce figures for univariate analysis')) parser_two_add_arguments(args=args, func=execute_two_parser, parser=subparsers.add_parser("two", parents=[parent_parser], help='produce figures for cross-trait analysis')) + parser_combine_add_arguments(args=args, func=execute_combine_parser, parser=subparsers.add_parser("combine", parents=[parent_parser], help='combine .json files MiXeR runs (e.g. with different --extract setting)')) return parser.parse_args(args) def execute_two_parser(args): df_data = {} + + if len(args.json) == 0: args.json = [x for x in [args.json_fit, args.json_test] if x] + files = glob.glob(args.json[0]) if (len(args.json) == 1) else args.json if len(files) == 0: raise(ValueError('no files detected, check --json {}'.format(args.json))) print('generate {}.csv from {} json files...'.format(args.out, len(files))) for fname in files: - keys = 'dice pi1 pi2 pi12 nc1@p9 nc2@p9 nc12@p9 rho_zero rho_beta rg'.split() + keys = 'dice pi1 pi2 pi12 nc1@p9 nc2@p9 nc12@p9 rho_zero rho_beta rg fraction_concordant_within_shared'.split() try: data = json.loads(open(fname).read()) + if 'dice' not in data['ci']: + data['ci']['dice'] = {'point_estimate' : 2 * data['ci']['pi12']['point_estimate'] / (data['ci']['pi1u']['point_estimate'] + data['ci']['pi2u']['point_estimate'])} + if 'fraction_concordant_within_shared' not in data['ci']: + rho_beta = data['ci']['rho_beta']['point_estimate'] + data['ci']['fraction_concordant_within_shared'] = {'point_estimate' : 2 * multivariate_normal([0, 0], [[1, rho_beta], [rho_beta, 1]]).cdf([0, 0])} + trait1 = os.path.basename(data['options']['trait1_file']).replace('.sumstats.gz', '') trait2 = os.path.basename(data['options']['trait2_file']).replace('.sumstats.gz', '') for k in keys: # test that all keys are available - val = data['ci'][k]['point_estimate'] + for stat in args.statistic: + val = data['ci'][k][stat] except: print('error reading from {}, skip'.format(fname)) continue @@ -363,57 +423,65 @@ def execute_two_parser(args): insert_key_to_dictionary_as_list(df_data, 'fname', fname) insert_key_to_dictionary_as_list(df_data, 'trait1', trait1) insert_key_to_dictionary_as_list(df_data, 'trait2', trait2) - for k in keys: insert_key_to_dictionary_as_list(df_data, k, data['ci'][k]['point_estimate']) + for k in keys: + for stat in args.statistic: + insert_key_to_dictionary_as_list(df_data, k if (stat=="point_estimate") else "{} ({})".format(k, stat), data['ci'][k][stat]) brute1_results = extract_brute1_results(data) - if brute1_results: + mskeys = 'best_vs_min_AIC best_vs_min_BIC best_vs_max_AIC best_vs_max_BIC'.split() + if 'modelselection' in data: + for mskey in mskeys: insert_key_to_dictionary_as_list(df_data, mskey, data['modelselection'][mskey]) + elif brute1_results: min_overlap = brute1_results['Jout'][0] max_overlap = brute1_results['Jout'][-1] best_cost = data['optimize'][-1][1]['fun'] cost_n = data['options']['sum_weights'] df_diff = -1 # fitting polygenic overlap require 1 extra parameter - insert_key_to_dictionary_as_list(df_data, 'best_vs_min_BIC', np.log(cost_n) * df_diff + 2 * (min_overlap - best_cost)) insert_key_to_dictionary_as_list(df_data, 'best_vs_min_AIC', 2 * df_diff + 2 * (min_overlap - best_cost)) - insert_key_to_dictionary_as_list(df_data, 'best_vs_max_BIC', np.log(cost_n) * df_diff + 2 * (max_overlap - best_cost)) + insert_key_to_dictionary_as_list(df_data, 'best_vs_min_BIC', np.log(cost_n) * df_diff + 2 * (min_overlap - best_cost)) insert_key_to_dictionary_as_list(df_data, 'best_vs_max_AIC', 2 * df_diff + 2 * (max_overlap - best_cost)) + insert_key_to_dictionary_as_list(df_data, 'best_vs_max_BIC', np.log(cost_n) * df_diff + 2 * (max_overlap - best_cost)) else: - insert_key_to_dictionary_as_list(df_data, 'best_vs_min_BIC', None) - insert_key_to_dictionary_as_list(df_data, 'best_vs_min_AIC', None) - insert_key_to_dictionary_as_list(df_data, 'best_vs_max_BIC', None) - insert_key_to_dictionary_as_list(df_data, 'best_vs_max_AIC', None) + for mskey in mskeys: insert_key_to_dictionary_as_list(df_data, mskey, None) pd.DataFrame(df_data).to_csv(args.out+'.csv', index=False, sep='\t') print('Done.') - if len(files) > 1: - print('--json argument lists multiple files is a wild-card (contains *), skip figures generation') + if args.json_fit and args.json_test: + data_fit = json.loads(open(args.json_fit).read()) + data_test = json.loads(open(args.json_test).read()) + elif len(files) == 1: + data_fit = json.loads(open(files[0]).read()) + data_test = data_fit + else: + print('--json argument lists multiple files or is a wild-card (contains *), skip figures generation') return - data = json.loads(open(args.json[0]).read()) - if 'qqplot' not in data: - print('Skip generating stratified QQ plots, data not available. Did you include --qq-plots in your "python mixer.py fit" command?') + if 'qqplot' not in data_test: + print('Skip generating stratified QQ plots, data not available.') if args.trait1_file and args.trait2_file: + plt.figure(figsize=[12, 5.5]) + plt.subplot(2,4,1); make_venn_plot(data_fit, flip=args.flip, traits=[args.trait1, args.trait2], colors=[args.trait1_color, args.trait2_color], statistic=args.statistic) + if 'qqplot' in data_test: + plt.subplot(2,4,2); make_strat_qq_plots(data_test, flip=args.flip, traits=[args.trait1, args.trait2], do_legend=True) + plt.subplot(2,4,3); make_strat_qq_plots(data_test, flip=(not args.flip), traits=[args.trait2, args.trait1], do_legend=True) + plt.subplot(2,4,4); plot_likelihood(data_fit) + plt.subplot(2,4,6); plot_causal_density(data_test, flip=args.flip, traits=[args.trait1, args.trait2], statistic=args.statistic) df1 = pd.read_table(args.trait1_file, delim_whitespace=True, usecols=['SNP', 'A1', 'A2', 'Z']) df2 = pd.read_table(args.trait2_file, delim_whitespace=True, usecols=['SNP', 'A1', 'A2', 'Z']) df = merge_z_vs_z(df1, df2) - - plt.figure(figsize=[12, 6]) - plt.subplot(2,4,1); make_venn_plot(data, flip=False, traits=[args.trait1, args.trait2]) - if 'qqplot' in data: - plt.subplot(2,4,2); make_strat_qq_plots(data, flip=False, traits=[args.trait1, args.trait2], do_legend=False) - plt.subplot(2,4,3); make_strat_qq_plots(data, flip=True, traits=[args.trait2, args.trait1], do_legend=True) - plt.subplot(2,4,4); plot_likelihood(data) - plt.subplot(2,4,5); plot_causal_density(data, traits=[args.trait1, args.trait2]) - plt.subplot(2,4,6); plot_z_vs_z_data(df, plot_limits=args.zmax, traits=[args.trait1, args.trait2]) - plt.subplot(2,4,7); plot_predicted_zscore(data, len(df), plot_limits=args.zmax, flip=False, traits=[args.trait1, args.trait2]) + plt.subplot(2,4,7); plot_z_vs_z_data(df, flip=args.flip, plot_limits=args.zmax, traits=[args.trait1, args.trait2]) + plt.subplot(2,4,8); plot_predicted_zscore(data_test, len(df), flip=args.flip, plot_limits=args.zmax, traits=[args.trait1, args.trait2]) + plt.tight_layout(pad=1.5) else: - plt.figure(figsize=[12, 3]) - plt.subplot(1,4,1); make_venn_plot(data, flip=False, traits=[args.trait1, args.trait2]) - if 'qqplot' in data: - plt.subplot(1,4,2); make_strat_qq_plots(data, flip=False, traits=[args.trait1, args.trait2], do_legend=False) - plt.subplot(1,4,3); make_strat_qq_plots(data, flip=True, traits=[args.trait2, args.trait1], do_legend=True) - plt.subplot(1,4,4); plot_likelihood(data) + plt.figure(figsize=[12, 3.5]) + plt.subplot(1,4,1); make_venn_plot(data_fit, flip=args.flip, traits=[args.trait1, args.trait2], colors=[args.trait1_color, args.trait2_color], statistic=args.statistic) + if 'qqplot' in data_test: + plt.subplot(1,4,2); make_strat_qq_plots(data_test, flip=args.flip, traits=[args.trait1, args.trait2], do_legend=True) + plt.subplot(1,4,3); make_strat_qq_plots(data_test, flip=(not args.flip), traits=[args.trait2, args.trait1], do_legend=True) + plt.subplot(1,4,4); plot_likelihood(data_fit) + plt.tight_layout(pad=1.5) for ext in args.ext: plt.savefig(args.out + '.' + ext, bbox_inches='tight') @@ -424,6 +492,145 @@ def insert_key_to_dictionary_as_list(df_data, key, value): df_data[key] = [] df_data[key].append(value) +def parse_val2use(val2use_arg): + val2use = [] + for a in val2use_arg.split(","): + if "-" in a: + start, end = [int(x) for x in a.split("-")] + val2use += [str(x) for x in range(start, end+1)] + else: + val2use.append(a.strip()) + if np.any([not x.isdigit() for x in val2use]): raise ValueError('Value labels must be integer: {}'.format(val2use_arg)) + return val2use + +''' +python ~/github/mixer/precimed/mixer_figures.py combine --json PGC_SCZ_2014_EUR.fit.rep@.json --out combined/PGC_SCZ_2014_EUR.fit +python ~/github/mixer/precimed/mixer_figures.py one --json combined/PGC_SCZ_2014_EUR.fit.json --out combined/PGC_SCZ_2014_EUR.fit + +python ~/github/mixer/precimed/mixer_figures.py combine --json PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit.rep@.json --out combined/PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit +python ~/github/mixer/precimed/mixer_figures.py combine --json PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.test.rep@.json --out combined/PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.test +python ~/github/mixer/precimed/mixer_figures.py two --json-fit combined/PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.fit.json --json-test combined/PGC_SCZ_2014_EUR_vs_PGC_BIP_2016.test.json --out combined/PGC_SCZ_2014_EUR_vs_PGC_BIP_2016 --statistic mean std +''' + +def execute_combine_parser(args): + args.rep2use = parse_val2use(args.rep2use) + failed_indices = [] + data_vec = [] + for rep in args.rep2use: + try: + data = json.loads(open(args.json.replace('@', str(rep)), 'r').read()) + if data: + data_vec.append(data) + except: + failed_indices.append(rep) + if failed_indices: print('WARNING: {}: results for {} runs are missing (rep {})'.format(args.out, len(failed_indices), ' '.join(failed_indices))) + + results = {'ci':{}, 'options':{}} + + for key in ['totalhet', 'num_snp']: + values = [data['options'][key] for data in data_vec] + results['options'][key] = np.mean(values) + + for key in ['trait1_file', 'trait2_file']: + values = [data['options'][key] for data in data_vec if (key in data['options'])] + if not values: continue + if len(set(values)) > 1: raise(ValueError('Input files have distinct value in "{}" field: {}'.format(key, ' '.join(set(values))))) + results['options'][key] = values[0] + + for key in ['trait1_nval', 'trait2_nval']: + values = [data['options'][key] for data in data_vec if (key in data['options'])] + if not values: continue + results['options'][key] = np.mean(values) + + values = [data['analysis'] for data in data_vec] + if len(set(values)) > 1: raise(ValueError('Input files have distinct value in "analysis" field: {}'.format(' '.join(set(values))))) + results['analysis'] = values[0] + + univariate_keys = ['pi', 'nc', 'nc@p9', 'sig2_beta', 'sig2_zero', 'h2'] + bivariate_keys = ['sig2_zero_T1', 'sig2_zero_T2', 'sig2_beta_T1', 'sig2_beta_T2', 'h2_T1', 'h2_T2', 'rho_zero', 'rho_beta', 'rg', 'pi1', 'pi2', 'pi12', 'pi1u', 'pi2u', 'dice', 'nc1', 'nc2', 'nc12', 'nc1u', 'nc2u', 'nc1@p9', 'nc2@p9', 'nc12@p9', 'nc1u@p9', 'nc2u@p9', 'totalpi', 'totalnc', 'totalnc@p9', 'pi1_over_totalpi', 'pi2_over_totalpi', 'pi12_over_totalpi', 'pi1_over_pi1u', 'pi2_over_pi2u', 'pi12_over_pi1u', 'pi12_over_pi2u', 'pi1u_over_pi2u', 'pi2u_over_pi1u'] + for key in (univariate_keys + bivariate_keys): + values = [data['ci'][key]['point_estimate'] for data in data_vec if (key in data['ci'])] + if values: results['ci'][key] = {'mean': np.mean(values), 'median':np.median(values), 'std': np.std(values), 'min': np.min(values), 'max': np.max(values)} + if values and (key=='rho_beta'): + values = [2 * multivariate_normal([0, 0], [[1, rho_beta], [rho_beta, 1]]).cdf([0, 0]) for rho_beta in values] + results['ci']['fraction_concordant_within_shared'] = {'mean': np.mean(values), 'median':np.median(values), 'std': np.std(values), 'min': np.min(values), 'max': np.max(values)} + + if results['analysis'] == 'bivariate': + for data in data_vec: + like_x, like_y = extract_likelihood_function(data) + if (not like_x) or (not like_y): continue + if 'likelihood' not in results: results['likelihood'] = [] + results['likelihood'].append((like_x, like_y)) + + combine_qqplots = lambda qqplots: { + 'hv_logp':np.mean(np.array([np.array(qq['hv_logp']).astype(float) for qq in qqplots]), 0), + 'data_logpvec':np.mean(np.array([np.array(qq['data_logpvec']).astype(float) for qq in qqplots]), 0), + 'model_logpvec':np.mean(np.array([np.array(qq['model_logpvec']).astype(float) for qq in qqplots]), 0), + 'sum_data_weights':np.mean(np.array([np.array(qq['sum_data_weights']).astype(float) for qq in qqplots]), 0) + } + + qqplots = [data['qqplot'] for data in data_vec if ('qqplot' in data)] + if qqplots: + if results['analysis'] == 'univariate': + results['qqplot'] = combine_qqplots(qqplots) + elif results['analysis'] == 'bivariate': + num_plots = len(qqplots[0]) + results['qqplot'] = [] + for index in range(num_plots): + results['qqplot'].append(combine_qqplots([qq[index] for qq in qqplots])) + + if results['analysis'] == 'bivariate': + best_vs_min_AIC = []; best_vs_min_BIC = [] + best_vs_max_AIC = []; best_vs_max_BIC = [] + for data in data_vec: + brute1_results = extract_brute1_results(data) + if not brute1_results: continue + min_overlap = brute1_results['Jout'][0] + max_overlap = brute1_results['Jout'][-1] + best_cost = data['optimize'][-1][1]['fun'] + cost_n = data['options']['sum_weights'] + df_diff = -1 # fitting polygenic overlap require 1 extra parameter + best_vs_min_AIC.append( 2 * df_diff + 2 * (min_overlap - best_cost)) + best_vs_min_BIC.append( np.log(cost_n) * df_diff + 2 * (min_overlap - best_cost)) + best_vs_max_AIC.append( 2 * df_diff + 2 * (max_overlap - best_cost)) + best_vs_max_BIC.append( np.log(cost_n) * df_diff + 2 * (max_overlap - best_cost)) + results['modelselection'] = { + 'best_vs_min_AIC' : np.mean(best_vs_min_AIC) if best_vs_min_AIC else None, + 'best_vs_min_BIC' : np.mean(best_vs_min_BIC) if best_vs_min_BIC else None, + 'best_vs_max_AIC' : np.mean(best_vs_max_AIC) if best_vs_max_AIC else None, + 'best_vs_max_BIC' : np.mean(best_vs_max_BIC) if best_vs_max_BIC else None + } + + if results['analysis'] == 'univariate': + AIC = []; BIC = [] + for data in data_vec: + try: + aic_diff = data['inft_optimize'][-1][1]['AIC'] - data['optimize'][-1][1]['AIC'] + bic_diff = data['inft_optimize'][-1][1]['BIC'] - data['optimize'][-1][1]['BIC'] + except: + continue + AIC.append(aic_diff) + BIC.append(bic_diff) + results['modelselection'] = { + 'mixture_vs_inft_AIC' : np.mean(AIC) if AIC else None, + 'mixture_vs_inft_BIC' : np.mean(BIC) if BIC else None, + } + + data_pdf_vec = [np.array(data['pdf']) for data in data_vec if ('pdf' in data)] + if data_pdf_vec: results['pdf'] = np.mean(np.array(data_pdf_vec), 0) + data_pdf_zgrid_vec = [np.array(data['pdf_zgrid']) for data in data_vec if ('pdf_zgrid' in data)] + if data_pdf_zgrid_vec: results['pdf_zgrid'] = np.mean(np.array(data_pdf_zgrid_vec), 0) + + data_power = [data['power'] for data in data_vec if ('power' in data)] + if data_power: + results['power_ci'] = data_power + results['power'] = {} + results['power']['svec'] = np.mean(np.array([np.array(power['svec']).astype(float) for power in data_power]), 0) + results['power']['nvec'] = np.mean(np.array([np.array(power['nvec']).astype(float) for power in data_power]), 0) + + with open(args.out + '.json', 'w') as outfile: + json.dump(results, outfile, cls=NumpyEncoder) + def execute_one_parser(args): df_data = {} files = glob.glob(args.json[0]) if (len(args.json) == 1) else args.json @@ -434,21 +641,27 @@ def execute_one_parser(args): try: data = json.loads(open(fname).read()) for k in keys: # test that all keys are available - val = data['ci'][k]['point_estimate'] - val = data['inft_optimize'][-1][1]['AIC'] - val = data['optimize'][-1][1]['AIC'] - val = data['inft_optimize'][-1][1]['BIC'] - val = data['optimize'][-1][1]['BIC'] + for stat in args.statistic: + val = data['ci'][k][stat] except: print('error reading from {}, skip'.format(fname)) continue insert_key_to_dictionary_as_list(df_data, 'fname', fname) for k in keys: - insert_key_to_dictionary_as_list(df_data, k, data['ci'][k]['point_estimate']) + for stat in args.statistic: + insert_key_to_dictionary_as_list(df_data, k if (stat=="point_estimate") else "{} ({})".format(k, stat), data['ci'][k][stat]) - aic_diff = data['inft_optimize'][-1][1]['AIC'] - data['optimize'][-1][1]['AIC'] - bic_diff = data['inft_optimize'][-1][1]['BIC'] - data['optimize'][-1][1]['BIC'] + aic_diff = None; bic_diff = None + if 'modelselection' in data: + aic_diff = data['modelselection']['mixture_vs_inft_AIC'] + bic_diff = data['modelselection']['mixture_vs_inft_BIC'] + else: + try: + aic_diff = data['inft_optimize'][-1][1]['AIC'] - data['optimize'][-1][1]['AIC'] + bic_diff = data['inft_optimize'][-1][1]['BIC'] - data['optimize'][-1][1]['BIC'] + except: + pass insert_key_to_dictionary_as_list(df_data, 'AIC', aic_diff) insert_key_to_dictionary_as_list(df_data, 'BIC', bic_diff) @@ -473,6 +686,7 @@ def execute_one_parser(args): for ext in args.ext: plt.savefig(args.out + '.power.' + ext, bbox_inches='tight') print('Generated ' + args.out + '.power.' + ext) + pd.concat([pd.DataFrame({'trait':[trait for i in data['power']['nvec']], 'nvec':data['power']['nvec'], 'svec':data['power']['svec']}) for data, trait in zip(data_list, traits_list)]).to_csv(args.out + '.power.csv', sep='\t', index=False) else: print('Skip generating power plots, data not available. Did you include --power-curve in your "python mixer.py fit" command?') diff --git a/precimed/mixer/make_extract_snps_files.py b/precimed/mixer/make_extract_snps_files.py new file mode 100644 index 0000000..32a5317 --- /dev/null +++ b/precimed/mixer/make_extract_snps_files.py @@ -0,0 +1,80 @@ +import sys +sys.path.append('/home/oleksanf/github/mixer') + +import precimed +import precimed.mixer +import precimed.mixer.libbgmg +import precimed.mixer.utils + +from precimed.mixer.utils import AnnotUnivariateParams +from precimed.mixer.utils import AnnotUnivariateParametrization + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import time +import random + +libbgmg_so = '/home/oleksanf/github/mixer/src/build/lib/libbgmg.so' +logfile = '/home/oleksanf/github/mixer/mixer.log' +bim_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim' +ld_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld' +#bim_file = '/home/oleksanf/vmshare/data/bfile_merged/chr@.bim' +#ld_file = '/home/oleksanf/vmshare/data/bfile_merged/chr@.run4.ld' + +trait1_file = '' +trait2_file = '' +extract = '' +exclude = '' +#annot_file = '/home/oleksanf/vmshare/data/bfile_merged/baseline.chr@.annot.gz' + +libbgmg = precimed.mixer.libbgmg.LibBgmg(libbgmg_so, dispose=True) +libbgmg.init_log(logfile) + +chr_labels= list(range(1, 23)) +libbgmg.init(bim_file, "", chr_labels, trait1_file, trait2_file, exclude, extract) + +for chr_label in chr_labels: + libbgmg.set_ld_r2_coo_from_file(int(chr_label), ld_file.replace('@', str(chr_label))) + libbgmg.set_ld_r2_csr(int(chr_label)) + +mafvec = np.minimum(libbgmg.mafvec, 1-libbgmg.mafvec) +ref=pd.concat([pd.read_csv(bim_file.replace('@', str(chr_label)), sep='\t', header=None, names='CHR SNP GP BP A1 A2'.split()) for chr_label in chr_labels]) + +snps_goodMAF = np.sum(mafvec>=maf_thresh) +maf_thresh = 0.05 +r2_thresh = 0.8 +subset = 2000000 # int(snps_goodMAF/5) +seed = 123 +out_file = '/home/oleksanf/vmshare/data/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep@.snps' +repeats = 20 + +if seed is not None: np.random.seed(seed) + +sets = [] +for repeat in range(repeats): + print(repeat) + # step0 - generate random values for clumping (this is a way to implement random pruning) + buf = np.random.rand(libbgmg.num_tag, 1) + + # step1 - filter SNPs below MAF threshold + buf[mafvec0] - results['convolve_tag_pdf_err'] = params.tag_pdf_err(lib, trait_index)[lib.weights>0] - - lib.set_option('cost_calculator', _cost_calculator_sampling) - results['sampling_tag_pdf'] = params.tag_pdf(lib, trait_index)[lib.weights>0] - - lib.set_option('cost_calculator', _cost_calculator_gaussian) - results['gaussian_tag_pdf'] = params.tag_pdf(lib, trait_index)[lib.weights>0] - lib.set_option('kmax', args.kmax) - - # produce QQ plots and power plots with (=>'fit') and without (=>'test') --extract/--exclude flags. - for suffix in ['fit', 'test']: - if (suffix=='test') and (test_weights is None): continue - - if (suffix=='test') and (test_weights is not None) and (args.qq_plots or args.power_curve): - lib.weights = test_weights - - mafvec = lib.mafvec - tldvec = lib.ld_sum_r2 - - lib.set_option('kmax', args.kmax_fit) - lib.set_option('cost_calculator', _cost_calculator_convolve if ((not args.fit_fast) and (mixture_model != '10')) else _cost_calculator_gaussian) - constraint = AnnotUnivariateParams(pi=params._pi, s=params._s, l=params._l, sig2_beta=params._sig2_beta, sig2_zeroA=None, sig2_zeroL=params._sig2_zeroL, - sig2_annot=params._sig2_annot, annomat=params._annomat, annonames=params._annonames, mafvec=mafvec, tldvec=tldvec) - parametrization = AnnotUnivariateParametrization(lib=lib, trait=trait_index, constraint=constraint) - params, optimize_result = apply_nedlermead(args, lib, trait_index, parametrization, params) - results['optimize_test'] = optimize_result - lib.set_option('kmax', args.kmax) - - if args.qq_plots: - # for QQ plots - but here it makes no difference as we use complete tag indices - mafvec_tag = lib.mafvec[lib.defvec] - tldvec_tag = lib.ld_sum_r2[lib.defvec] - - defvec = np.isfinite(lib.get_zvec(trait_index)) & (lib.weights > 0) - results['qqplot_{}'.format(suffix)] = calc_qq_plot(lib, params, trait_index, args.downsample_factor, defvec, - title='maf \\in [{:.3g},{:.3g}); L \\in [{:.3g},{:.3g})'.format(-np.inf,np.inf,-np.inf,np.inf)) - - maf_bins = np.concatenate(([-np.inf], np.quantile(mafvec_tag, [1/3, 2/3]), [np.inf])) - tld_bins = np.concatenate(([-np.inf], np.quantile(tldvec_tag, [1/3, 2/3]), [np.inf])) - results['qqplot_bins_{}'.format(suffix)] = [] - for i in range(0, 3): - for j in range(0, 3): - mask = (defvec & (mafvec_tag>=maf_bins[i]) & (mafvec_tag= tld_bins[j]) & (tldvec_tag < tld_bins[j+1])) - results['qqplot_bins_{}'.format(suffix)].append(calc_qq_plot(lib, params, trait_index, args.downsample_factor, mask, - title='maf \\in [{:.3g},{:.3g}); L \\in [{:.3g},{:.3g})'.format(maf_bins[i], maf_bins[i+1], tld_bins[j], tld_bins[j+1]))) - - if args.power_curve: - power_nvec, power_svec = calc_power_curve(lib, params, trait_index=trait_index, downsample=args.downsample_factor) - results['power_{}'.format(suffix)] = {'nvec': power_nvec, 'svec': power_svec} - - return results - -def execute_ld_parser(args): - libbgmg = LibBgmg(args.lib) - libbgmg.calc_ld_matrix(args.bfile, args.out, args.r2min, args.ldscore_r2min, args.ld_window, args.ld_window_kb) - libbgmg.log_message('Done') - -def execute_fit_parser(args): - libbgmg = LibBgmg(args.lib) - - fix_and_validate_args(libbgmg, args) - - libbgmg.set_option('disable_snp_to_tag_map', 1) - libbgmg.set_option('cost_calculator', _cost_calculator_gaussian) - libbgmg.init(args.bim_file, args.frq_file, ' '.join([str(x) for x in args.chr2use]), args.trait1_file, "", "", "") - - # Load annotations - if args.annot_file != None: - libbgmg.log_message('Loading annotations from {}...'.format(args.annot_file)) - df = pd.concat([pd.read_csv(args.annot_file.replace('@', str(chr_label)), sep='\t') for chr_label in args.chr2use]) - for col in ['CHR', 'BP', 'SNP', 'CM']: - if col in df.columns: - del df[col] - - annomat = df.values.astype(np.float32) - annonames = df.columns.values - del df - libbgmg.log_message('Done, {} annotations available'.format(len(annonames))) - else: - annomat = np.ones(shape=(libbgmg.num_snp, 1), dtype=np.float32) - annonames = ['base'] - - for opt, val in convert_args_to_libbgmg_options(args, libbgmg.num_snp): - libbgmg.set_option(opt, val) - - if args.plink_ld_bin0 is not None: - libbgmg.set_option('ld_format_version', 0) - args.ld_file = args.plink_ld_bin0 - args.plink_ld_bin0 = None - - for chr_label in args.chr2use: - libbgmg.set_ld_r2_coo_from_file(chr_label, args.ld_file.replace('@', str(chr_label))) - libbgmg.set_ld_r2_csr(chr_label) - - libbgmg.set_weights_randprune(args.randprune_n, args.randprune_r2, exclude=args.exclude, extract=args.extract) - fit_weights = libbgmg.weights; test_weights = None - - if ((args.exclude != "") or (args.extract != "")) and (args.power_curve or args.qq_plots): - libbgmg.log_message('Calculate weights ignoring --extract and --exclude flags') - libbgmg.set_weights_randprune(args.randprune_n, args.randprune_r2, exclude="", extract="") - test_weights = libbgmg.weights - libbgmg.weights = fit_weights - - libbgmg.set_option('diag', 0) - - totalhet = float(2.0 * np.dot(libbgmg.mafvec, 1.0 - libbgmg.mafvec)) - trait_index = 1 - - results = {} - results['options'] = vars(args).copy() - results['options']['totalhet'] = totalhet - results['options']['num_snp'] = float(libbgmg.num_snp) - results['options']['num_tag'] = float(libbgmg.num_tag) - results['options']['sum_weights'] = float(np.sum(libbgmg.weights)) - results['options']['trait1_nval'] = float(np.nanmedian(libbgmg.get_nvec(trait=1))) - results['options']['annonames'] = annonames - results['options']['time_started'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S') - results['analysis'] = 'mixer-plsa' - results['weights'] = libbgmg.weights[libbgmg.weights>0] - results['zvec1'] = libbgmg.zvec1[libbgmg.weights>0] - - # overview of the models - # params1 - m01_10000 - basic infinitesimal model - # params2 - m07_10LS0 - infinitesimal model with flexible s and l parameters - # params3 - m09_0P000 - basic causal mixture model - # params4 - m15_0PLS0 - causal mixture model with flexible s and l parameters - # params5 - m02_1000A - infinitesimal model with annotations - # params6 - m08_10LSA - infinitesimal model with annotations and flexible s and l parameters - # params7 - m10_0P00A - causal mixture model with annotations - # params8 - m16_0PLSA - causal mixture model with annotations and flexible s and l parameters - s and l re-fitted in the context of a causal mixture - - # params50 - basic infinitesimal model with s=-1 (LDSC assumptions) - # params51 - m17_1P000 - a model with infinitesimal and causal mixture - # params52 - m25_PP000 - a model with two causal components (M3) - - # PPLSA, 2^5 = 32 models, PP can be 10, 0P, 1P, PP, for S and L one can set custom value for the constraint. - specs = """m01_10000 m02_1000A m03_100S0 m04_100SA m05_10L00 m06_10L0A m07_10LS0 m08_10LSA - m09_0P000 m10_0P00A m11_0P0S0 m12_0P0SA m13_0PL00 m14_0PL0A m15_0PLS0 m16_0PLSA - m17_1P000 m18_1P00A m19_1P0S0 m20_1P0SA m21_1PL00 m22_1PL0A m23_1PLS0 m24_1PLSA - m25_PP000 m26_PP00A m27_PP0S0 m28_PP0SA m29_PPL00 m30_PPL0A m31_PPLS0 m32_PPLSA""".split() - - common_args = {'args':args, 'lib':libbgmg, 'trait_index':trait_index, 'annomat':annomat, 'annonames':annonames} - misc_args = {'fit_weights': fit_weights, 'test_weights':test_weights} - for model_spec in specs: - model_name=model_spec.split('_')[0] - spec=model_spec.split('_')[1] - spec_args = {'mixture_model': spec[:2], - 'annot_model': ('A' in spec), - 's_model': None if ('S' in spec) else 0, - 'l_model': None if ('L' in spec) else 0} - if int(model_name[1:]) not in args.models: continue - libbgmg.log_message('fitting {}: {}'.format(model_spec, spec_args)) - results[model_name] = perform_fit(**spec_args, **common_args, **misc_args) # may modify libbgmg.weights - results[model_name]['spec'] = model_spec - - results['options']['time_finished'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S') - - with open(args.out + '.json', 'w') as outfile: - json.dump(results, outfile, cls=NumpyEncoder) - - libbgmg.set_option('diag', 0) - libbgmg.log_message('Done') - diff --git a/precimed/mixer_plsa.py b/precimed/mixer_plsa.py deleted file mode 100644 index da7853e..0000000 --- a/precimed/mixer_plsa.py +++ /dev/null @@ -1,28 +0,0 @@ -#!/usr/bin/env python -''' -(c) 2016-2020 Oleksandr Frei, Alexey A. Shadrin, Dominic Holland -MiXeR software: Univariate and Bivariate Causal Mixture for GWAS -NB! PLSA does not refer to Probabilistic latent semantic analysis, rather - P <- causal mixture model (pi) - L <- LD-dependent architectures - S <- MAF-dependent architectures - A <- annotation-informed -''' - -import logging -import sys - -from mixer.plsa import parse_args -from mixer.plsa import log_header -from mixer.libbgmg import LibBgmg - -if __name__ == "__main__": - logging.getLogger().setLevel(logging.INFO) - args = parse_args(sys.argv[1:]) - - libbgmg = LibBgmg(args.lib) - libbgmg.init_log(args.log if args.log else args.out + '.log') - log_header(args, sys.argv[1], libbgmg) - libbgmg.dispose() - - args.func(args) diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000..3ad7f92 --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,33 @@ +MiXeR on TSD +============ + +- ``module load CMake/3.15.3-GCCcore-8.3.0 Boost/1.73.0-GCCcore-8.3.0 Python/3.7.4-GCCcore-8.3.0`` +- ``cd ~ && git clone --recurse-submodules -j8 https://github.com/precimed/mixer.git && tar -czvf mixer_master.tar.gz mixer ``, then import to TSD, place on /cluster and extract. Note that "Download" button on github won't work correctly, because it does not include submodules (i.e. zlib) in the package. +- ``mkdir /src/build && cd /src/build && cmake .. && make -j8`` +- adjust ``/scripts/tsd_ugmg_script.sh`` and ``/scripts/tsd_bgmg_script.sh`` (not fully tested, minor changes may be needed here). +- Submit with "sbatch " as usual, it will internally trigger a job array with 20 runs. +- When results are ready, create figures as described in the main README.md file (see "visualize the results" section) +- first-time configuration of your Python environment: + ``` + ssh p33-appn-norment01 + module load Python/3.7.4-GCCcore-8.3.0 + + # clean install jupyter in a new environment + python3 -m venv /cluster/projects/p33/users//py3 + source /cluster/projects/p33/users//py3/bin/activate # best to have this on /cluster + + pip3 install --index-url=file:///shared/pypi/mirror/web/simple numpy + pip3 install --index-url=file:///shared/pypi/mirror/web/simple pandas + pip3 install --index-url=file:///shared/pypi/mirror/web/simple scipy==1.2.0rc1 + pip3 install --index-url=file:///shared/pypi/mirror/web/simple matplotlib + pip3 install --index-url=file:///shared/pypi/mirror/web/simple statsmodels + pip3 install --index-url=file:///shared/pypi/mirror/web/simple numdifftools + pip3 install --index-url=file:///shared/pypi/mirror/web/simple matplotlib_venn + pip3 install --index-url=file:///shared/pypi/mirror/web/simple jupyter + ``` + Then use this Python environment as follows: + ``` + module load Python/3.7.4-GCCcore-8.3.0 + source /cluster/projects/p33/users//py3/bin/activate + ``` + diff --git a/scripts/mixer_real.py b/scripts/mixer_real.py deleted file mode 100644 index cbc3e32..0000000 --- a/scripts/mixer_real.py +++ /dev/null @@ -1,138 +0,0 @@ -import os.path -import sys -import glob -import itertools - -dry_run=True -num_submit = int(sys.argv[1]) - -out_prefix = '/plsa_mixer/real_run1/mixer' -ugmg_pattern = out_prefix+'_{}.fit.json' -bgmg_pattern = out_prefix+'_{}_vs_{}.fit.json' - -g0 = ['SSGAC_EDU_2018_no23andMe', 'PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'GIANT_HEIGHT_2018_UKB'] # 'PGC_MDD_2018_with23andMe', 'PGC_ADHD_2017_EUR', 'PGC_ASD_2017_iPSYCH', -g1 = ['CTG_COG_2018', 'SSGAC_EDU_2018_no23andMe'] -g2 = ['PGC_SCZ_2014_EUR', 'PGC_SCZ_0518_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_Howard_no23andMe', 'PGC_ADHD_2017_EUR', 'PGC_ASD_2017_iPSYCH'] -g3 = ['GSCAN_DRINK_2019_DrinksPerWeek', 'CTG_NEUR_2018_no23andMe', 'ICC_CANNABIS_2018_UKB', 'UKB_LONELY_2018_Loneliness', 'SSGAC_RISK_2019', 'UKB_CHRONOTYPE_2018', 'UKB_SLEEP_2018', 'CTG_INSOMNIA_2018'] -g3r =['GSCAN_DRINK_2019_DrinksPerWeek', 'ICC_CANNABIS_2018_UKB', 'UKB_LONELY_2018_Loneliness', 'SSGAC_RISK_2019'] -g4 = ['UKB_HS_2019', 'GIANT_HEIGHT_2018_UKB', 'UKB_HEIGHT_2018_irnt', 'GIANT_BMI_2018_UKB_v2', 'EGG_BIRTHWEIGHT_2016', 'GIANT_BMI_2015_EUR', 'GIANT_HEIGHT_2014', 'GIANT_WHR_2015_EUR'] -g4r= [ 'GIANT_BMI_2018_UKB_v2', 'GIANT_BMI_2015_EUR', 'GIANT_WHR_2015_EUR'] -g5 = ['PGC_SCZ_2014_EUR', 'PGC_SCZ_0518_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_with23andMe', 'PGC_MDD_2018_Howard_with23andMe'] -g5r= ['PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_Howard_with23andMe'] -g6 = ['UKB_CHRONOTYPE_2018', 'UKB_SLEEP_2018', 'CTG_INSOMNIA_2018'] -g7 = ['UKB_HEIGHT_2018_irnt'] -g8 = ['IHGC_MIG_2016_with23andMe', 'PGC_SCZ_0518_EUR', 'CLOZUK_SCZ_2018_withPGC', 'PGC_BIP_2016','PGC_MDD_2018_Howard_with23andMe', 'PGC_MDD_2018_with23andMe', 'PGC_ADHD_2017_EUR'] -g9 = ['PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_Howard_no23andMe', 'PGC_ASD_2017_iPSYCH', 'PGC_ADHD_2017_EUR', 'CTG_COG_2018', 'SSGAC_EDU_2018_no23andMe', - 'CTG_NEUR_2018_no23andMe', 'UKB_LONELY_2018_Loneliness', - 'ICC_CANNABIS_2018_UKB', 'GSCAN_SMOKE_2019_CigarettesPerDay', 'GSCAN_DRINK_2019_DrinksPerWeek', 'EGG_BIRTHWEIGHT_2016', 'GIANT_HEIGHT_2018_UKB', - 'CARDIOGRAM_CAD_2015', 'GIANT_BMI_2018_UKB_v2', 'DIAGRAM_T2D_2017'] -g9b=[ 'PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_Howard_no23andMe', 'PGC_ASD_2017_iPSYCH', 'PGC_ADHD_2017_EUR', 'CTG_COG_2018', 'SSGAC_EDU_2018_no23andMe', ] -g9r=[ 'CTG_NEUR_2018_no23andMe', 'UKB_LONELY_2018_Loneliness', - 'ICC_CANNABIS_2018_UKB', 'GSCAN_SMOKE_2019_CigarettesPerDay', 'GSCAN_DRINK_2019_DrinksPerWeek', 'EGG_BIRTHWEIGHT_2016', 'GIANT_HEIGHT_2018_UKB', - 'CARDIOGRAM_CAD_2015', 'GIANT_BMI_2018_UKB_v2', 'DIAGRAM_T2D_2017'] -g10=['PGC_SCZ_0518_EUR', 'PGC_SCZ_0418b', 'PGC_SCZ_2014_EUR'] -g11=['CLOZUK_SCZ_2018_withPGC'] -g12=['PGC_MDD_2018_no23andMe', 'SSGAC_EDU_2018_no23andMe', 'PGC_SCZ_2014_EUR', 'PGC_BIP_2016', 'PGC_MDD_2018_Howard_no23andMe', 'PGC_ASD_2017_iPSYCH', 'PGC_ADHD_2017_EUR'] - -ugmg_traits = g10 -bgmg_traits = list(itertools.combinations(g9b + ['PGC_SCZ_0518_EUR'], 2)) -#bgmg_traits = list(itertools.product(g11, g12+g9r+g9b)) - - -###################################################################### -template_head=''' -#!/bin/bash -#$ -cwd -#$ -l h_vmem=120G -#$ -l h_rt=36:00:00 - -set MIXER_ROOT=/home/oleksandr/github/mixer -set SUMSTAT= -set PYTHON=/home/oleksandr/miniconda3/bin/python3 -''' - -template_ugmg=''' -set OUT_DIR={out} -set TRAIT={trait} - -$PYTHON $MIXER_ROOT/precimed/mixer.py fit \ - --trait1-file $SUMSTAT/TMP/ldsr/$TRAIT.sumstats.gz \ - --out $OUT_DIR/$TRAIT.fit \ - --extract $SUMSTAT/LDSR/w_hm3.justrs --ci-alpha 0.05 \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ - -$PYTHON $MIXER_ROOT/precimed/mixer.py fit \ - --trait1-file $SUMSTAT/TMP/nomhc/$TRAIT.sumstats.gz \ - --load-params-file $OUT_DIR/$TRAIT.fit.json \ - --out $OUT_DIR/$TRAIT.test \ - --fit-sequence load inflation --power-curve --qq-plots --kmax 100 \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ -''' - -template_bgmg=''' -set OUT_DIR={out} -set TRAIT1={trait1} -set TRAIT2={trait2} - -$PYTHON $MIXER_ROOT/precimed/mixer.py fit \ - --trait1-file $SUMSTAT/TMP/ldsr/$TRAIT1.sumstats.gz \ - --trait2-file $SUMSTAT/TMP/ldsr/$TRAIT2.sumstats.gz \ - --trait1-params-file $OUT_DIR/$TRAIT1.fit.json \ - --trait2-params-file $OUT_DIR/$TRAIT2.fit.json \ - --out $OUT_DIR/${{TRAIT1}}_vs_${{TRAIT2}}.fit \ - --extract $SUMSTAT/LDSR/w_hm3.justrs --ci-alpha 0.05 \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ - -$PYTHON $MIXER_ROOT/precimed/mixer.py fit \ - --trait1-file $SUMSTAT/TMP/nomhc/$TRAIT1.sumstats.gz \ - --trait2-file $SUMSTAT/TMP/nomhc/$TRAIT2.sumstats.gz \ - --load-params-file $OUT_DIR/${{TRAIT1}}_vs_${{TRAIT2}}.fit.json \ - --out $OUT_DIR/${{TRAIT1}}_vs_${{TRAIT2}}.test \ - --fit-sequence load inflation --qq-plots --kmax 100 \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ -''' -########################################################## - -skipped = 0; duplicated = 0; processed = set() # exclude duplicates - -cmd_list = [] -touch_list = [] - -for trait in ugmg_traits: - out_name = ugmg_pattern.format(trait) - if os.path.exists(out_name): skipped+=1; continue - if out_name in processed: duplicated+=1; continue - processed.add(out_name) - - cmd = template_head + template_ugmg.format(out=out_prefix, trait=trait) - cmd_list.append(cmd) - touch_list.append(out_name) - -for trait1, trait2 in bgmg_traits: - out_name = bgmg_pattern.format(trait1, trait2) - if os.path.exists(out_name): skipped+=1; continue - if out_name in processed: duplicated+=1; continue - processed.add(out_name) - - cmd = template_head + template_bgmg.format(out=out_prefix, trait1=trait1, trait2=trait2) - cmd_list.append(cmd) - touch_list.append(out_name) - -for cmd, touch in zip(cmd_list, touch_list): - with open('run_script.sh', 'w') as f: f.write(cmd) - if num_submit > 0: - if not dry_run: os.system('qsub run_script.sh') - if not dry_run: os.system('touch {}'.format(touch)) - print('qsub {}'.format(cmd)) - num_submit -= 1 - if num_submit <= 0: break - -print('skipped: {}, duplicated: {}'.format(skipped, duplicated)) diff --git a/scripts/saga_bgmg_script.sh b/scripts/saga_bgmg_script.sh index 9378b9e..68eddd6 100755 --- a/scripts/saga_bgmg_script.sh +++ b/scripts/saga_bgmg_script.sh @@ -1,11 +1,7 @@ #!/bin/bash -# ************************************************************** -# STEP 1: ABEL specifications for job submission -# ************************************************************** - # Job name: -#SBATCH --job-name=ofrebgmg +#SBATCH --job-name=mixer # # Project: #SBATCH --account=nn9114k @@ -18,8 +14,8 @@ # Max memory usage: #SBATCH --mem-per-cpu=4600M -# Email about job status -#SBATCH --mail-type=ALL +# Job array specification +#SBATCH --array=1-20 ## Set up job environment: source /cluster/bin/jobsetup @@ -29,36 +25,30 @@ set -o errexit # exit on errors module load Anaconda3/2019.03 source activate /cluster/home/oleksanf/py3 -export MIXER_ROOT=/cluster/projects/nn9114k/oleksanf/github/mixer_plsa -export SUMSTAT=/cluster/projects/nn9114k/oleksanf/SUMSTAT -export OUT_DIR=/cluster/projects/nn9114k/oleksanf/saga/mixer_test/saga2 +export MIXER_ROOT=/cluster/projects/nn9114k/oleksanf/github/mixer +export OUTDIR=/cluster/projects/nn9114k/oleksanf/saga/mixer_results/ # must end with a forward slash, / +export SUMSTATnomhc=/cluster/projects/nn9114k/oleksanf/SUMSTAT/TMP/nomhc/ +export LDFILE=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld +export BIMFILE=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim +export EXTRACT=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps export PYTHON=python3 export TRAIT1=PGC_SCZ_2014_EUR -export TRAIT2=SSGAC_EDU_2018_no23andMe - -$PYTHON $MIXER_ROOT/precimed/mixer.py fit \ +export TRAIT2=PGC_BIP_2016 + +$PYTHON $MIXER_ROOT/precimed/mixer.py fit2 \ + --trait1-file $SUMSTATnomhc/$TRAIT1.sumstats.gz \ + --trait2-file $SUMSTATnomhc/$TRAIT2.sumstats.gz \ + --trait1-params-file ${OUT}$TRAIT1.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --trait2-params-file ${OUT}$TRAIT2.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUT}${TRAIT1}_vs_${TRAIT2}.fit.rep${SLURM_ARRAY_TASK_ID} \ + --extract $EXTRACT \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ + +$PYTHON $MIXER_ROOT/precimed/mixer.py test2 \ --trait1-file $SUMSTAT/TMP/nomhc/$TRAIT1.sumstats.gz \ --trait2-file $SUMSTAT/TMP/nomhc/$TRAIT2.sumstats.gz \ - --trait1-params-file $OUT_DIR/$TRAIT1.fit.json \ - --trait2-params-file $OUT_DIR/$TRAIT2.fit.json \ - --out $OUT_DIR/${TRAIT1}_vs_${TRAIT2}.fit \ - --extract $SUMSTAT/LDSR/w_hm3.justrs \ # --ci-alpha 0.05 \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ - -# --chr2use 1 --diffevo-fast-repeats 2 --fit-sequence diffevo-fast neldermead-fast - -$PYTHON $MIXER_ROOT/precimed/mixer.py test \ - --trait1-file $SUMSTAT/TMP/nomhc/$TRAIT1.sumstats.gz \ - --trait2-file $SUMSTAT/TMP/nomhc/$TRAIT2.sumstats.gz \ - --load-params-file $OUT_DIR/${TRAIT1}_vs_${TRAIT2}.fit.json \ - --out $OUT_DIR/${TRAIT1}_vs_${TRAIT2}.test \ - --bim-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim \ - --ld-file $SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.p05_SNPwind50k.ld.bin \ - --lib $MIXER_ROOT/src/build/lib/libbgmg.so \ - - #--fit-sequence load inflation --qq-plots --kmax 100 \ - -# --chr2use 1 - + --load-params-file ${OUT}${TRAIT1}_vs_${TRAIT2}.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUT}${TRAIT1}_vs_${TRAIT2}.test.rep${SLURM_ARRAY_TASK_ID} \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ diff --git a/scripts/saga_ugmg_script.sh b/scripts/saga_ugmg_script.sh new file mode 100755 index 0000000..ff91363 --- /dev/null +++ b/scripts/saga_ugmg_script.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +# Job name: +#SBATCH --job-name=mixer +# +# Project: +#SBATCH --account=nn9114k +# +# Wall clock limit: +#SBATCH --time=1-00:00:00 +# +#SBATCH --cpus-per-task=20 + +# Max memory usage: +#SBATCH --mem-per-cpu=4600M + +# Job array specification +#SBATCH --array=1-20 + +## Set up job environment: +source /cluster/bin/jobsetup +module purge # clear any inherited modules +set -o errexit # exit on errors + +module load Anaconda3/2019.03 +source activate /cluster/home/oleksanf/py3 + +export MIXER_ROOT=/cluster/projects/nn9114k/oleksanf/github/mixer +export OUTDIR=/cluster/projects/nn9114k/oleksanf/saga/mixer_results/ # must end with a forward slash, / +export SUMSTATnomhc=/cluster/projects/nn9114k/oleksanf/SUMSTAT/TMP/nomhc/ +export LDFILE=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld +export BIMFILE=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim +export EXTRACT=/cluster/projects/nn9114k/oleksanf/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps +export PYTHON=python3 +export TRAIT=PGC_SCZ_2014_EUR + +$PYTHON $MIXER_ROOT/precimed/mixer.py fit1 \ + --trait1-file $SUMSTATnomhc/$TRAIT.sumstats.gz \ + --out ${OUTDIR}$TRAIT.fit.rep${SLURM_ARRAY_TASK_ID} \ + --bim-file $BIMFILE --ld-file $LDFILE \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --extract $EXTRACT \ + +$PYTHON $MIXER_ROOT/precimed/mixer.py test1 \ + --trait1-file $SUMSTATnomhc/$TRAIT.sumstats.gz \ + --load-params-file $OUT/$TRAIT.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUTDIR}$TRAIT.test.rep${SLURM_ARRAY_TASK_ID} \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ + diff --git a/scripts/tsd_bgmg_script.sh b/scripts/tsd_bgmg_script.sh new file mode 100755 index 0000000..5b16781 --- /dev/null +++ b/scripts/tsd_bgmg_script.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +# Job name: +#SBATCH --job-name=mixer +# +# Project: +#SBATCH --account=p33_norment +# +# Wall clock limit: +#SBATCH --time=16:00:00 +# +#SBATCH --cpus-per-task=8 + +# Max memory usage: +#SBATCH --mem-per-cpu=7600M + +# Job array specification +#SBATCH --array=1-20 + +## Set up job environment: +source /cluster/bin/jobsetup +set -o errexit + +#module init +module load CMake/3.15.3-GCCcore-8.3.0 +module load Boost/1.73.0-GCCcore-8.3.0 +module load Python/3.7.4-GCCcore-8.3.0 +source /cluster/projects/p33/users/ofrei/py3/bin/activate + +export MIXER_ROOT=/cluster/projects/p33/users/ofrei/github/mixer +export OUTDIR=/cluster/projects/p33/users/ofrei/mixer_results/ # must end with a forward slash, / +export SUMSTATnomhc=/cluster/projects/p33/users/ofrei/SUMSTAT/TMP/nomhc/ +export LDFILE=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld +export BIMFILE=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim +export EXTRACT=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps +export PYTHON=python3 +export TRAIT1=PGC_SCZ_2014_EUR +export TRAIT2=PGC_BIP_2016 + +$PYTHON $MIXER_ROOT/precimed/mixer.py fit2 \ + --trait1-file $SUMSTATnomhc/$TRAIT1.sumstats.gz \ + --trait2-file $SUMSTATnomhc/$TRAIT2.sumstats.gz \ + --trait1-params-file ${OUT}$TRAIT1.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --trait2-params-file ${OUT}$TRAIT2.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUT}${TRAIT1}_vs_${TRAIT2}.fit.rep${SLURM_ARRAY_TASK_ID} \ + --extract $EXTRACT \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ + +$PYTHON $MIXER_ROOT/precimed/mixer.py test2 \ + --trait1-file $SUMSTATnomhc/$TRAIT1.sumstats.gz \ + --trait2-file $SUMSTATnomhc/$TRAIT2.sumstats.gz \ + --load-params-file ${OUT}${TRAIT1}_vs_${TRAIT2}.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUT}${TRAIT1}_vs_${TRAIT2}.test.rep${SLURM_ARRAY_TASK_ID} \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ diff --git a/scripts/tsd_ugmg_script.sh b/scripts/tsd_ugmg_script.sh new file mode 100755 index 0000000..d87fd06 --- /dev/null +++ b/scripts/tsd_ugmg_script.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# Job name: +#SBATCH --job-name=mixer +# +# Project: +#SBATCH --account=p33_norment +# +# Wall clock limit: +#SBATCH --time=16:00:00 +# +#SBATCH --cpus-per-task=8 + +# Max memory usage: +#SBATCH --mem-per-cpu=7600M + +# Job array specification +#SBATCH --array=1-20 + +## Set up job environment: +source /cluster/bin/jobsetup +set -o errexit + +#module init +module load CMake/3.15.3-GCCcore-8.3.0 +module load Boost/1.73.0-GCCcore-8.3.0 +module load Python/3.7.4-GCCcore-8.3.0 +source /cluster/projects/p33/users/ofrei/py3/bin/activate + +export MIXER_ROOT=/cluster/projects/p33/users/ofrei/github/mixer +export OUTDIR=/cluster/projects/p33/users/ofrei/mixer_results/ # must end with a forward slash, / +export SUMSTATnomhc=/cluster/projects/p33/users/ofrei/SUMSTAT/TMP/nomhc/ +export LDFILE=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.run4.ld +export BIMFILE=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.@.bim +export EXTRACT=/cluster/projects/p33/users/ofrei/SUMSTAT/LDSR/1000G_EUR_Phase3_plink/1000G.EUR.QC.prune_maf0p05_rand2M_r2p8.rep${SLURM_ARRAY_TASK_ID}.snps +export PYTHON=python3 + +export TRAIT=PGC_SCZ_2014_EUR + +$PYTHON $MIXER_ROOT/precimed/mixer.py fit1 \ + --trait1-file $SUMSTATnomhc/$TRAIT.sumstats.gz \ + --out ${OUTDIR}$TRAIT.fit.rep${SLURM_ARRAY_TASK_ID} \ + --bim-file $BIMFILE --ld-file $LDFILE \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --extract $EXTRACT \ + +$PYTHON $MIXER_ROOT/precimed/mixer.py test1 \ + --trait1-file $SUMSTATnomhc/$TRAIT.sumstats.gz \ + --load-params-file $OUT/$TRAIT.fit.rep${SLURM_ARRAY_TASK_ID}.json \ + --out ${OUTDIR}$TRAIT.test.rep${SLURM_ARRAY_TASK_ID} \ + --lib $MIXER_ROOT/src/build/lib/libbgmg.so --threads 20 \ + --bim-file $BIMFILE --ld-file $LDFILE \ + diff --git a/scripts/xsede_snps_script.sh b/scripts/xsede_snps_script.sh new file mode 100644 index 0000000..0a38f19 --- /dev/null +++ b/scripts/xsede_snps_script.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# Job name: +#SBATCH --job-name=plsareal +#SBATCH --account=csd635 +#SBATCH --time=2:00:00 + +#SBATCH --partition=shared +#SBATCH --cpus-per-task=20 +#SBATCH --mem-per-cpu=5000M +#SBATCH --nodes=1 +#SBATCH --array=1-20 + +# +## Set up job environment: +source /cluster/bin/jobsetup +module purge # clear any inherited modules +module load plink +set -o errexit # exit on errors + +export MODULEPATH=$MODULEPATH:/share/apps/compute/modulefiles && module purge && module load gnu/7.2.0 cmake/3.12.1 && /home/oleksanf/miniconda3/bin/python3 /oasis/projects/nsf/csd635/oleksanf/github/mixer_private/precimed/mixer.py snps \ + --bim-file /oasis/projects/nsf/csd635/oleksanf/UKBDATA/projects/plsa_mixer/ukb_genetics_qc/ukb_bed/ukb_imp_chr@_v3_qc.bim \ + --ld-file /oasis/projects/nsf/csd635/oleksanf/UKBDATA/projects/plsa_mixer/ukb_genetics_qc/ukb_bed/ukb_imp_chr@_v3_qc.run1.ld \ + --lib /oasis/projects/nsf/csd635/oleksanf/github/mixer_private/src/build/lib/libbgmg.so \ + --out /oasis/projects/nsf/csd635/oleksanf/UKBDATA/projects/plsa_mixer/ukb_genetics_qc/ukb_bed/ukb_imp_chr@_qc.prune_maf0p05_rand3M_r2p8_rep${SLURM_ARRAY_TASK_ID}.snps \ + --chr2use 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22 \ + --seed ${SLURM_ARRAY_TASK_ID} --r2 0.8 --maf 0.05 --subset 3000000 \ diff --git a/src/README.md b/src/README.md index a56aced..113e957 100644 --- a/src/README.md +++ b/src/README.md @@ -186,6 +186,15 @@ cd /cluster/projects/p33/users/ofrei/no-backup/software/boost_1_69_0 ./bootstrap.sh --with-libraries=program_options,filesystem,system,date_time && ./b2 --clean && ./b2 --j12 -a ``` +**Build on TSD (MoBa)** +``` +# download Boost, build it from source +module load CMake/3.15.3-GCCcore-8.3.0 +module load Python/3.7.4-GCCcore-8.3.0 +cd /cluster/p/p697/cluster/ofrei/no-backup/software/boost_1_73_0 +./bootstrap.sh --with-libraries=program_options,filesystem,system,date_time && ./b2 --clean && ./b2 --j12 -a +``` + **Build on Saga** ``` @@ -234,4 +243,4 @@ git clone --recurse-submodules https://github.com/precimed/mixer.git mkdir mixer/src/build && cd mixer/src/build cmake .. make -j16 bgmg -``` \ No newline at end of file +```