diff --git a/.dockstore.yml b/.dockstore.yml index 39e09c32d0..273d9ae4d9 100644 --- a/.dockstore.yml +++ b/.dockstore.yml @@ -71,7 +71,6 @@ workflows: subclass: WDL primaryDescriptorPath: /pipelines/wdl/reprocessing/exome/ExomeReprocessing.wdl - - name: FilterMTAndExportToVCF subclass: WDL primaryDescriptorPath: /all_of_us/rna_seq/MTtoVCF/FilterMTAndExportToVCF.wdl @@ -176,6 +175,18 @@ workflows: subclass: WDL primaryDescriptorPath: /all_of_us/mitochondria/merge/mito_post_processing.wdl + - name: MMIDAS_Analyze + subclass: WDL + primaryDescriptorPath: /pipelines/wdl/mmidas/MMIDAS_Analyze.wdl + + - name: MMIDAS_DataPrep + subclass: WDL + primaryDescriptorPath: /pipelines/wdl/mmidas/MMIDAS_DataPrep.wdl + + - name: MMIDAS_Train + subclass: WDL + primaryDescriptorPath: /pipelines/wdl/mmidas/MMIDAS_Train.wdl + - name: Multiome subclass: WDL primaryDescriptorPath: /pipelines/wdl/multiome/Multiome.wdl diff --git a/.gitignore b/.gitignore index e1285cfcee..592c5d395c 100644 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,6 @@ scanvi_gpu_test/ # Personal devcontainer (references local NAS mount + GPU; not shared) .devcontainer/devcontainer.json + +# Jupyter notebook checkpoints +.ipynb_checkpoints/ diff --git a/AGENTS.md b/AGENTS.md index ecce72c108..3608c738c3 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -128,7 +128,7 @@ Test JSON inputs live at two locations per pipeline: When adding a new test input for a regression case, also wire it into the relevant GitHub Actions workflow under `.github/workflows/` so CI picks it up. -**Stale inputs break silently** — they are not validated by womtool. When renaming a workflow or removing/renaming inputs, audit `example_inputs/*.json` and `test_inputs/**/*.json`. +**Stale inputs break silently** — they are not validated by womtool. When **adding**, renaming, or removing/renaming inputs, audit `example_inputs/*.json` and `test_inputs/**/*.json` **and** thread the change through the `Test.wdl` wrapper (see [Registering a CI test](#registering-a-ci-test-for-a-pipeline-plumbing--scientific) step 2 — *forward every testable input*). Mind the framework's `UpdateTestInputs.py` key rewrite: a test-JSON key with more than two dotted parts (e.g. `Pipeline.Sub.input`, reaching into a sub-workflow) is rewritten to `TestPipeline.Pipeline.Sub.input`, which the wrapper rejects as an extra input. Prefer exposing the input at the pipeline's **top level** (`Pipeline.input`) and setting it that way — a two-part key rewrites cleanly to `TestPipeline.input`. ### Registering a CI test for a pipeline (Plumbing / Scientific) diff --git a/all_of_us/phasing/README.md b/all_of_us/phasing/README.md index 70dc350735..42a5b607b0 100644 --- a/all_of_us/phasing/README.md +++ b/all_of_us/phasing/README.md @@ -53,5 +53,41 @@ Runtime Parameters: - `String aou_vcf_header` – URL for single chromosome output VCF header - `String report` – URL for the report file +## Beagle5Phasing +#### Background + +This WDL workflow phases genomic variants using Beagle5. It is designed for use in the All of Us Local Ancestry +pipeline and operates on a single VCF at a time. A user-supplied shell script drives the Beagle5 execution, +allowing flexibility in phasing parameters. + +Key characteristics: +- Each VCF is processed independently; outputs are not merged across chromosomes. +- Phasing is performed with a sliding-window approach controlled by the `window_markers` parameter. +- Designed for large-scale WGS datasets (e.g., AoU cohort). +- Input Data: Accepts a GCS-hosted VCF and a genetic map file. +#### Inputs +Analysis Parameters: +- `String vcf_gs` – GCS URL of the input VCF to phase +- `String map_gs` – GCS URL of the genetic map file +- `String out_prefix` – Prefix for output filenames +- `String out_dir_gs` – GCS directory for storing outputs +- `Int window_markers` – Window size in markers for phasing (default: 3500000) +- `String java_xmx` – Java max heap size for Beagle5 (default: "80g") +- `File run_beagle_sh` – Shell script executed to invoke Beagle5; defines phasing steps + +Runtime Parameters: +- `Int runtime_cpu` – Number of CPUs (default: 32) +- `Int mem_gb` – Memory in GB (default: 120) +- `Int runtime_disk_gb` – Disk size in GB (default: 2000) +- `String runtime_disk_type` – Disk type, "HDD" or "SSD" (default: "HDD") + +#### Step 1. PhaseWithBeagle +- Script Execution: Runs the user-supplied shell script with the provided phasing parameters. +- Phasing: Invokes Beagle5 to phase the input VCF using the supplied genetic map and window size. +- Output Generation: Saves the phased VCF and log file to the specified GCS output directory. + +#### Outputs +- `String phased_vcf_gs` – GCS URL for the phased output VCF +- `String phased_log_gs` – GCS URL for the Beagle5 phasing log file diff --git a/all_of_us/rna_seq/AggregateSusieWorkflow.changelog.md b/all_of_us/rna_seq/AggregateSusieWorkflow.changelog.md index 50a0501ecb..8691c1cd68 100644 --- a/all_of_us/rna_seq/AggregateSusieWorkflow.changelog.md +++ b/all_of_us/rna_seq/AggregateSusieWorkflow.changelog.md @@ -1,6 +1,11 @@ -# aou_9.0.2 +# aou_9.0.3 +2026-05-11 (Date of Last Commit) + +* Removed unnecessary file list from aggregate task +# aou_9.0.2 2026-01-29 (Date of Last Commit) + * Added set euo pipefail to tasks # aou_9.0.1 diff --git a/all_of_us/rna_seq/AggregateSusieWorkflow.wdl b/all_of_us/rna_seq/AggregateSusieWorkflow.wdl index cd60b5be18..d7f64ea242 100644 --- a/all_of_us/rna_seq/AggregateSusieWorkflow.wdl +++ b/all_of_us/rna_seq/AggregateSusieWorkflow.wdl @@ -19,8 +19,6 @@ task AggregateSusie{ mkdir -p localized gsutil -m cp -I localized/ < file_paths.txt - # Write the new local file paths into filelist.txt - ls -1 "$(pwd)/localized/*" > filelist.txt Rscript /tmp/merge_susie.R --FilePaths file_paths.txt --OutputPrefix ~{OutputPrefix} >>> @@ -108,7 +106,7 @@ workflow AggregateSusieWorkflow { } - String pipeline_version = "aou_9.0.2" + String pipeline_version = "aou_9.0.3" call AggregateSusie { input: diff --git a/all_of_us/rna_seq/susieR_workflow.changelog.md b/all_of_us/rna_seq/susieR_workflow.changelog.md index bd8b939e04..a2eebe641d 100644 --- a/all_of_us/rna_seq/susieR_workflow.changelog.md +++ b/all_of_us/rna_seq/susieR_workflow.changelog.md @@ -1,3 +1,10 @@ +# aou_9.1.0 +2026-08-25 (Date of Last Commit) + +* Added a new tsv input for all mapping inputs per ancestry, including qtls, phenotype beds, covariates, and genotype dosages +* Added a new task to the worfklow to dynamically set file inputs from a new input tsv +* Removed the individual mapping file inputs because they were now set dynamically by the pipeline + # aou_9.0.1 2026-01-29 (Date of Last Commit) diff --git a/all_of_us/rna_seq/susieR_workflow.wdl b/all_of_us/rna_seq/susieR_workflow.wdl index 207ecd1de4..da7a3c9fd0 100644 --- a/all_of_us/rna_seq/susieR_workflow.wdl +++ b/all_of_us/rna_seq/susieR_workflow.wdl @@ -2,28 +2,53 @@ version 1.0 # Docker fixed to quay.io/biocontainers/htslib@sha256:ff9d466929dc2d587128afc213fc4516d -#task splitPhenotypeBed { -# input { -# File TensorQTLPermutations -# } +# InputMappingTsv: tab-separated, one row per ancestry, with a header row containing at least: +# mapping_inputs_id (ancestry code: AFR, AMR, COMB, EUR, SAS, EAS, MID), GenotypeDosage, +# GenotypeDosagei, PhenotypePCsOut, PlinkAF, QtlCovariates, Sample_list, VCF, cis_qtl, +# genotype_pcs, pgen, phenotype_bed, psam, pvar. Only the columns consumed below are read; +# each cell (other than mapping_inputs_id) must be a gs:// path to the corresponding file. +task ParseInputMapping { + input { + File InputMappingTsv + String Ancestry + Int NumPrempt + } + command <<< + set -euo pipefail + python3 <>> - #String baseName = basename(PhenotypeBed, ".gz") + runtime { + docker: "python:3.11-slim" + disks: "local-disk 10 SSD" + preemptible: "${NumPrempt}" + memory: "2GB" + cpu: "1" + } -# command <<< -# zcat ~{TensorQTLPermutations} | awk '$18 < 0.05' | head -n 100 > significant_qtls.txt -# awk 'NR==1 {header=$0; next} {out=$1".txt"; print header > out; print >> out}' significant_qtls.txt -# >>> -# -# output { -# Array[File] splitFiles = glob("*.txt") -# } -# runtime { -# docker: "quay.io/biocontainers/htslib:1.22.1--h566b1c6_0" -# disks: "local-disk 500 SSD" -# memory: "2GB" -# cpu: "1" -# } -#} + output { + String GenotypeDosagePath = read_string("GenotypeDosage.txt") + String GenotypeDosageIndexPath = read_string("GenotypeDosagei.txt") + String QTLCovariatesPath = read_string("QtlCovariates.txt") + String SampleListPath = read_string("Sample_list.txt") + String PhenotypeBedPath = read_string("phenotype_bed.txt") + String TensorQTLPermutationsPath = read_string("cis_qtl.txt") + } +} task PrepInputs { input { @@ -168,12 +193,8 @@ task susieR { workflow susieR_workflow { input { - File GenotypeDosages - File GenotypeDosageIndex - File QTLCovariates - File TensorQTLPermutations - File SampleList - File PhenotypeBed + File InputMappingTsv + String Ancestry Int CisDistance File susie_rscript Int memory @@ -181,15 +202,21 @@ workflow susieR_workflow { String OutputPrefix String PhenotypeID } - String pipeline_version = "aou_9.0.0" + String pipeline_version = "aou_9.1.0" + + call ParseInputMapping { + input: + InputMappingTsv = InputMappingTsv, + Ancestry = Ancestry + } call PrepInputs { input: - TensorQTLPermutations = TensorQTLPermutations, + TensorQTLPermutations = ParseInputMapping.TensorQTLPermutationsPath, PhenotypeID = PhenotypeID, - GenotypeDosages = GenotypeDosages, - GenotypeDosageIndex = GenotypeDosageIndex, - PhenotypeBed = PhenotypeBed, + GenotypeDosages = ParseInputMapping.GenotypeDosagePath, + GenotypeDosageIndex = ParseInputMapping.GenotypeDosageIndexPath, + PhenotypeBed = ParseInputMapping.PhenotypeBedPath, NumPrempt = NumPrempt } @@ -197,10 +224,10 @@ workflow susieR_workflow { input: GenotypeDosages = PrepInputs.SubsetDosages, GenotypeDosageIndex = PrepInputs.SubsetDosagesIndex, - QTLCovariates = QTLCovariates, + QTLCovariates = ParseInputMapping.QTLCovariatesPath, TensorQTLPermutations = PrepInputs.SubsetPermutationPvals, - SampleList = SampleList, - PhenotypeBed = PhenotypeBed , + SampleList = ParseInputMapping.SampleListPath, + PhenotypeBed = ParseInputMapping.PhenotypeBedPath, CisDistance = CisDistance, OutputPrefix = PhenotypeID, susie_rscript = susie_rscript, @@ -209,12 +236,7 @@ workflow susieR_workflow { } - #call MergeSusie { - # input: - # SusieOutput = susieR.SusieParquet, - # OutputPrefix = OutputPrefix - # - #} + output { File SusieParquet = susieR.SusieParquet File SusielbfParquet = susieR.lbfParquet diff --git a/pipeline_versions.txt b/pipeline_versions.txt index 7c0851aab1..0b84b9f58e 100644 --- a/pipeline_versions.txt +++ b/pipeline_versions.txt @@ -9,21 +9,24 @@ Glimpse2LowPassImputation 1.1.0 2026-08-26 Glimpse2LowPassImputationBatch 1.1.0 2026-08-26 Glimpse2LowPassImputationQC 1.1.0 2026-08-26 Glimpse2LowPassImputationQuotaConsumed 1.0.1 2026-08-24 -Glimpse2SVImputation 0.0.26 2026-08-28 -Glimpse2SVImputationBatch 0.0.19 2026-08-28 -Glimpse2SVImputationQC 0.0.2 2026-08-26 -Glimpse2SVImputationQuotaConsumed 0.0.2 2026-08-26 +Glimpse2SVImputation 1.0.0 2026-09-09 +Glimpse2SVImputationBatch 1.0.0 2026-09-09 +Glimpse2SVImputationQC 1.0.0 2026-09-09 +Glimpse2SVImputationQuotaConsumed 1.0.0 2026-09-09 IlluminaGenotypingArray 1.12.27 2026-01-21 Imputation 1.1.23 2025-10-03 ImputationBeagle 3.0.1 2026-02-23 JointGenotyping 1.7.3 2025-08-11 +MMIDAS_Analyze 1.1.2 2026-08-21 +MMIDAS_DataPrep 1.0.2 2026-08-11 +MMIDAS_Train 1.3.0 2026-08-27 MultiSampleSmartSeq2SingleNucleus 2.2.8 2026-07-10 -MultilevelHierarchicallyPasteVcfsStreaming 0.0.10 2026-08-28 +MultilevelHierarchicallyPasteVcfsStreaming 1.0.0 2026-09-09 Multiome 7.0.2 2026-07-10 Optimus 9.2.0 2026-07-10 PairedTag 3.0.2 2026-07-10 PeakCalling 1.0.1 2025-08-11 -PreprocessPLsGVCF 0.0.15 2026-08-28 +PreprocessPLsGVCF 1.0.0 2026-09-09 RNAWithUMIsPipeline 1.0.20 2026-01-21 ReblockGVCF 2.4.4 2026-01-29 SlideSeq 3.6.8 2026-07-10 diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.changelog.md b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.changelog.md index 9fe4aff71a..88c7ac2b3f 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.changelog.md @@ -1,3 +1,25 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + +# 0.0.29 +2026-09-08 (Date of Last Commit) + +* Update linked `input_qc_version` to 0.0.3. + +# 0.0.28 +2026-09-03 (Date of Last Commit) + +* Rename outputs `imputed_vcf` to `imputed_vcfs` and `imputed_vcf_index` to `imputed_vcf_indexes` + +# 0.0.27 +2026-08-31 (Date of Last Commit) + +* Update `ConvertInputArraysToManifest` output wiring to use `output_gvcf_manifest`. +* Switch `PreProcessGVCFsBatch` outputs consumed by `Glimpse2SVImputationBatch` from `preprocessed_pls_vcf` to `preprocessed_pls_bcf` naming. +* Update linked `preprocess_pls_gvcf_pipeline_version` to 0.0.16 and `batch_pipeline_version` to 0.0.20. + # 0.0.26 2026-08-28 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.wdl b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.wdl index c6b89ec9b9..041855ae30 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.wdl @@ -5,11 +5,11 @@ import "./Glimpse2SVImputationBatch.wdl" as Glimpse2SVImputationBatch import "../../../../tasks/wdl/Glimpse2SVImputationTasks.wdl" as Glimpse2SVImputationTasks workflow Glimpse2SVImputation { - String pipeline_version = "0.0.26" - String preprocess_pls_gvcf_pipeline_version = "0.0.15" - String batch_pipeline_version = "0.0.19" - String quota_consumed_version = "0.0.2" - String input_qc_version = "0.0.2" + String pipeline_version = "1.0.0" + String preprocess_pls_gvcf_pipeline_version = "1.0.0" + String batch_pipeline_version = "1.0.0" + String quota_consumed_version = "1.0.0" + String input_qc_version = "1.0.0" input { # if both array inputs and gvcf_manifest are provided, array inputs take precedence @@ -62,7 +62,7 @@ workflow Glimpse2SVImputation { } # if neither the full array input set nor gvcf_manifest is provided the workflow will fail at runtime - File gvcf_manifest_to_use = select_first([ConvertInputArraysToManifest.output_manifest, gvcf_manifest]) + File gvcf_manifest_to_use = select_first([ConvertInputArraysToManifest.output_gvcf_manifest, gvcf_manifest]) call Glimpse2SVImputationTasks.SplitVcfManifestIntoBatches as SplitIntoSampleBatches { input: @@ -82,8 +82,8 @@ workflow Glimpse2SVImputation { call Glimpse2SVImputationBatch.Glimpse2SVImputationBatch as RunBatch { input: - input_preprocessed_joint_vcf = PreProcessGVCFsBatch.preprocessed_pls_vcf, - input_preprocessed_joint_vcf_idx = PreProcessGVCFsBatch.preprocessed_pls_vcf_idx, + input_preprocessed_joint_vcf_or_bcf = PreProcessGVCFsBatch.preprocessed_pls_bcf, + input_preprocessed_joint_vcf_or_bcf_idx = PreProcessGVCFsBatch.preprocessed_pls_bcf_idx, chromosomes = chromosomes, genetic_maps_tsv = genetic_maps_tsv, ref_dict = ref_dict, @@ -105,8 +105,8 @@ workflow Glimpse2SVImputation { scatter (batch_annot_idx in range(length(popped_bcfs_for_contig))) { call Glimpse2SVImputationTasks.ExtractAnnotations as ExtractPoppedAnnotations { input: - imputed_vcf = popped_bcfs_for_contig[batch_annot_idx], - imputed_vcf_index = popped_bcf_idxs_for_contig[batch_annot_idx], + imputed_vcf_or_bcf = popped_bcfs_for_contig[batch_annot_idx], + imputed_vcf_or_bcf_index = popped_bcf_idxs_for_contig[batch_annot_idx], batch_index = batch_annot_idx, docker_extract_annotations = gatk_docker } @@ -114,13 +114,13 @@ workflow Glimpse2SVImputation { call Glimpse2SVImputationTasks.MergeSampleChunksVcfsWithPaste as MergePoppedContigVcfs { input: - input_vcfs = popped_bcfs_for_contig, + input_vcfs_or_bcfs = popped_bcfs_for_contig, output_vcf_basename = output_basename + "." + chromosomes[contig_idx] + ".glimpse2.popped.merged" } call Glimpse2SVImputationTasks.RecomputeAndAnnotate as RecomputePoppedAfInfo { input: - merged_vcf = MergePoppedContigVcfs.output_vcf, + merged_vcf_or_bcf = MergePoppedContigVcfs.output_vcf, annotations = ExtractPoppedAnnotations.annotations, num_samples = PreProcessGVCFsBatch.num_samples, output_basename = output_basename + "." + chromosomes[contig_idx] + ".glimpse2.popped.merged.reannotated", @@ -133,7 +133,7 @@ workflow Glimpse2SVImputation { if (info_filter_for_inclusion > 0.0) { call Glimpse2SVImputationTasks.FilterVcfByInfo as FilterPoppedContigByInfo { input: - vcf = final_popped_contig_vcf, + vcf_or_bcf = final_popped_contig_vcf, info_threshold = info_filter_for_inclusion, output_basename = output_basename + "." + chromosomes[contig_idx] + ".glimpse2.popped.info_filtered" } @@ -143,7 +143,7 @@ workflow Glimpse2SVImputation { call Glimpse2SVImputationTasks.CreateVcfIndexAndMd5 as IndexFinalPoppedContig { input: - vcf_input = final_filtered_popped_contig_vcf, + vcf_input_or_bcf = final_filtered_popped_contig_vcf, output_basename = output_basename + "." + chromosomes[contig_idx], gatk_docker = gatk_docker, preemptible = 0 @@ -151,8 +151,8 @@ workflow Glimpse2SVImputation { } output { - Array[File] imputed_vcf = IndexFinalPoppedContig.output_vcf - Array[File] imputed_vcf_index = IndexFinalPoppedContig.output_vcf_index + Array[File] imputed_vcfs = IndexFinalPoppedContig.output_vcf + Array[File] imputed_vcf_indexes = IndexFinalPoppedContig.output_vcf_index } } diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.changelog.md b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.changelog.md index 0f549d079c..02ba5a0aff 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.changelog.md @@ -1,3 +1,15 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + +# 0.0.20 +2026-08-31 (Date of Last Commit) + +* Rename local task outputs and workflow wiring from `*_vcf` to `*_bcf` for phase, ligate, pop, and concat steps. +* Rename `UpdateHeader` inputs to `source_header_vcf` and `to_be_reheadered_bcf` and propagate the new names through command usage. +* update variable names to vcf_or_bcf for any variable that can be either + # 0.0.19 2026-08-28 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.wdl b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.wdl index 833c4f41f3..56e1f25098 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationBatch.wdl @@ -4,11 +4,11 @@ import "../../../../tasks/wdl/Glimpse2SVImputationTasks.wdl" as Glimpse2SVImputa workflow Glimpse2SVImputationBatch { # if this changes, update the batch_pipeline_version value in Glimpse2SVImputation.wdl - String pipeline_version = "0.0.19" + String pipeline_version = "1.0.0" input { - File input_preprocessed_joint_vcf - File input_preprocessed_joint_vcf_idx + File input_preprocessed_joint_vcf_or_bcf + File input_preprocessed_joint_vcf_or_bcf_idx Array[String] chromosomes File genetic_maps_tsv @@ -59,8 +59,8 @@ workflow Glimpse2SVImputationBatch { call GLIMPSE2Phase as ChunkedGLIMPSE2Phase { input: - input_vcf = input_preprocessed_joint_vcf, - input_vcf_idx = input_preprocessed_joint_vcf_idx, + input_vcf_or_bcf = input_preprocessed_joint_vcf_or_bcf, + input_vcf_or_bcf_idx = input_preprocessed_joint_vcf_or_bcf_idx, panel_split_chunk_bin = panel_split_chunk_bins[k], input_region = input_regions[k], output_region = output_regions[k], @@ -75,8 +75,8 @@ workflow Glimpse2SVImputationBatch { call GLIMPSE2Ligate { input: - phased_vcfs = ChunkedGLIMPSE2Phase.phased_vcf, - phased_vcf_idxs = ChunkedGLIMPSE2Phase.phased_vcf_idx, + phased_vcfs_or_bcfs = ChunkedGLIMPSE2Phase.phased_bcf, + phased_vcf_or_bcf_idxs = ChunkedGLIMPSE2Phase.phased_bcf_idx, output_basename = output_basename + ".glimpse2.bubble", docker = glimpse2_docker } @@ -84,8 +84,8 @@ workflow Glimpse2SVImputationBatch { # Update VCF header with reference dictionary call UpdateHeader { input: - bcf_to_reheader = GLIMPSE2Ligate.ligated_vcf, - bcf_to_get_header_from = input_preprocessed_joint_vcf, + to_be_reheadered_bcf = GLIMPSE2Ligate.ligated_bcf, + source_header_vcf_or_bcf = input_preprocessed_joint_vcf_or_bcf, ref_dict = ref_dict, output_basename = output_basename + "." + chromosome + ".glimpse2.bubble.updated_header", docker = glimpse2_docker, @@ -95,8 +95,8 @@ workflow Glimpse2SVImputationBatch { scatter (k in range(length(pop_regions))) { call PopAndMarginalizeCollisions { input: - posteriors_vcf = UpdateHeader.output_bcf, - posteriors_vcf_idx = UpdateHeader.output_bcf_index, + posteriors_vcf_or_bcf = UpdateHeader.output_bcf, + posteriors_vcf_or_bcf_idx = UpdateHeader.output_bcf_index, panel_bubble_split_sites_only_vcf = panel_bubble_split_sites_only_vcf, panel_bubble_split_sites_only_vcf_idx = panel_bubble_split_sites_only_vcf_idx, panel_id_split_vcf_gz = panel_id_split_vcf_gz, @@ -108,8 +108,8 @@ workflow Glimpse2SVImputationBatch { call Glimpse2SVImputationTasks.ConcatBcfs as ConcatPopAndMarginalizeCollisions { input: - bcfs = PopAndMarginalizeCollisions.popped_vcf, - bcf_idxs = PopAndMarginalizeCollisions.popped_vcf_idx, + bcfs = PopAndMarginalizeCollisions.popped_bcf, + bcf_idxs = PopAndMarginalizeCollisions.popped_bcf_idx, output_basename = output_basename + "." + chromosome + ".glimpse2.popped", extra_args = "--naive", } @@ -152,8 +152,8 @@ struct PopAndMarginalizePanelResourcesChromosome { # checkpoint implementation borrowed from https://github.com/broadinstitute/palantir-workflows/blob/main/GlimpseImputationPipeline/Glimpse2Imputation.wdl task GLIMPSE2Phase { input { - File input_vcf - File input_vcf_idx + File input_vcf_or_bcf + File input_vcf_or_bcf_idx File panel_split_chunk_bin String input_region String output_region @@ -170,15 +170,15 @@ task GLIMPSE2Phase { } parameter_meta { - input_vcf: { + input_vcf_or_bcf: { localization_optional: true } - input_vcf_idx: { + input_vcf_or_bcf_idx: { localization_optional: true } } - Int disk_size_gb = 2*ceil(size(input_vcf, "GiB") + size(panel_split_chunk_bin, "GiB") + size(genetic_map, "GiB") + 30) + Int disk_size_gb = 2*ceil(size(input_vcf_or_bcf, "GiB") + size(panel_split_chunk_bin, "GiB") + size(genetic_map, "GiB") + 30) command <<< set -euxo pipefail @@ -186,7 +186,7 @@ task GLIMPSE2Phase { export GCS_OAUTH_TOKEN=$(/google-cloud-sdk/bin/gcloud auth application-default print-access-token) cmd="/bin/GLIMPSE2_phase \ - --input-gl ~{input_vcf} \ + --input-gl ~{input_vcf_or_bcf} \ -R ~{panel_split_chunk_bin} \ ~{extra_phase_args} \ --output ~{output_basename}.bcf \ @@ -213,8 +213,8 @@ task GLIMPSE2Phase { >>> output { - File phased_vcf = "~{output_basename}.bcf" - File phased_vcf_idx = "~{output_basename}.bcf.csi" + File phased_bcf = "~{output_basename}.bcf" + File phased_bcf_idx = "~{output_basename}.bcf.csi" } ######################### @@ -242,8 +242,8 @@ task GLIMPSE2Phase { task GLIMPSE2Ligate { input { - Array[File] phased_vcfs - Array[File] phased_vcf_idxs + Array[File] phased_vcfs_or_bcfs + Array[File] phased_vcf_or_bcf_idxs String output_basename Int cpu = 2 @@ -252,20 +252,20 @@ task GLIMPSE2Ligate { RuntimeAttr? runtime_attr_override } - Int disk_size_gb = ceil(2.1*size(phased_vcfs, "GB")) + 10 + Int disk_size_gb = ceil(2.1*size(phased_vcfs_or_bcfs, "GB")) + 10 command <<< set -euox pipefail - /bin/GLIMPSE2_ligate --input ~{write_lines(phased_vcfs)} --output ~{output_basename}.bcf --threads ~{cpu} + /bin/GLIMPSE2_ligate --input ~{write_lines(phased_vcfs_or_bcfs)} --output ~{output_basename}.bcf --threads ~{cpu} # the index generated by ligate appears to be corrupt for both bcf and vcf.gz output (possibly due to https://github.com/samtools/htslib/issues/1740), so we regenerate with bcftools bcftools index -f ~{output_basename}.bcf >>> output { - File ligated_vcf = "~{output_basename}.bcf" - File ligated_vcf_idx = "~{output_basename}.bcf.csi" + File ligated_bcf = "~{output_basename}.bcf" + File ligated_bcf_idx = "~{output_basename}.bcf.csi" } ######################### @@ -293,8 +293,8 @@ task GLIMPSE2Ligate { task PopAndMarginalizeCollisions { input { # all VCFs should be split to biallelic - File posteriors_vcf - File posteriors_vcf_idx + File posteriors_vcf_or_bcf + File posteriors_vcf_or_bcf_idx File panel_bubble_split_sites_only_vcf # for annotation of INFO fields File panel_bubble_split_sites_only_vcf_idx File panel_id_split_vcf_gz # panel popping script currently requires vcf.gz, so we also use that here @@ -306,7 +306,7 @@ task PopAndMarginalizeCollisions { RuntimeAttr? runtime_attr_override } - Int disk_gb = ceil(3*size(posteriors_vcf, "GB")) + ceil(size([panel_bubble_split_sites_only_vcf, panel_id_split_vcf_gz], "GB")) + 10 + Int disk_gb = ceil(3*size(posteriors_vcf_or_bcf, "GB")) + ceil(size([panel_bubble_split_sites_only_vcf, panel_id_split_vcf_gz], "GB")) + 10 command <<< set -euox pipefail @@ -314,14 +314,14 @@ task PopAndMarginalizeCollisions { # this now only works for pop-glimpse2-joint-opt.rs; # the sort may also be extraneous, but we keep it in to guard against getting out of sync with the popped panel bcftools view -r ~{region} --regions-overlap 0 ~{panel_bubble_split_sites_only_vcf} -Oz -o panel.bubble.split.sites.shard.vcf.gz - bcftools view -r ~{region} --regions-overlap 0 ~{posteriors_vcf} | \ + bcftools view -r ~{region} --regions-overlap 0 ~{posteriors_vcf_or_bcf} | \ /usr/local/bin/pop-glimpse2 ~{panel_id_split_vcf_gz} panel.bubble.split.sites.shard.vcf.gz | \ bcftools sort --max-mem=2G -W -Ob -o ~{output_basename}.bcf >>> output { - File popped_vcf = "~{output_basename}.bcf" - File popped_vcf_idx = "~{output_basename}.bcf.csi" + File popped_bcf = "~{output_basename}.bcf" + File popped_bcf_idx = "~{output_basename}.bcf.csi" } ######################### @@ -348,21 +348,21 @@ task PopAndMarginalizeCollisions { task UpdateHeader { input { - File bcf_to_reheader - File bcf_to_get_header_from + File source_header_vcf_or_bcf + File to_be_reheadered_bcf File ref_dict String output_basename String? pipeline_header_line Int mem_gb = 2 Int cpu = 1 - Int disk_size_gb = ceil(2.1 * size(bcf_to_reheader, "GiB")) + 10 + Int disk_size_gb = ceil(2.1 * size(to_be_reheadered_bcf, "GiB")) + 10 Int max_retries = 1 String docker } parameter_meta { - bcf_to_get_header_from : { + source_header_vcf_or_bcf : { localization_optional : true } } @@ -375,9 +375,9 @@ task UpdateHeader { # Set correct reference dictionary # take input VCF header and add GLIMPSE INFO and FORMAT lines (GLIMPSE header only contains a single chromosome and breaks bcftools concat --naive) - bcftools view --no-version -h ~{bcf_to_get_header_from} | grep '^##' > input.header.txt - bcftools view --no-version -h ~{bcf_to_reheader} | grep -E '^##INFO|^##FORMAT|^##NMAIN|^##FPLOIDY' > glimpse2.header.txt - bcftools view --no-version -h ~{bcf_to_reheader} | grep '^#CHROM' > glimpse2.columns.txt + bcftools view --no-version -h ~{source_header_vcf_or_bcf} | grep '^##' > input.header.txt + bcftools view --no-version -h ~{to_be_reheadered_bcf} | grep -E '^##INFO|^##FORMAT|^##NMAIN|^##FPLOIDY' > glimpse2.header.txt + bcftools view --no-version -h ~{to_be_reheadered_bcf} | grep '^#CHROM' > glimpse2.columns.txt cat input.header.txt glimpse2.header.txt glimpse2.columns.txt > header.vcf # Add pipeline_header_line if provided @@ -389,7 +389,7 @@ task UpdateHeader { java -jar /picard.jar UpdateVcfSequenceDictionary -I header.vcf --SD ~{ref_dict} -O updated_header.vcf - bcftools reheader -h updated_header.vcf ~{bcf_to_reheader} -o ~{output_basename}.bcf + bcftools reheader -h updated_header.vcf ~{to_be_reheadered_bcf} -o ~{output_basename}.bcf bcftools index ~{output_basename}.bcf >>> diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.changelog.md b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.changelog.md index 82a2121d1c..e0c5fd7d72 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.changelog.md @@ -1,3 +1,8 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + # 0.0.2 2026-08-26 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.wdl b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.wdl index b3bedea4f8..b13e7d1577 100644 --- a/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputationQuotaConsumed.wdl @@ -2,7 +2,7 @@ version 1.0 workflow QuotaConsumed { # if this changes, update the quota_consumed_version value in Glimpse2SVImputation.wdl - String pipeline_version = "0.0.2" + String pipeline_version = "1.0.0" input { # service expects only gvcf_manifest even though main wdl can alternatively take input arrays diff --git a/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.changelog.md b/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.changelog.md index 69ad75eaf4..c5e7a7ab25 100644 --- a/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.changelog.md @@ -1,3 +1,15 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + +# 0.0.11 +2026-08-31 (Date of Last Commit) + +* Rename merge outputs from `merged_vcf`/`merged_vcf_idx` to `merged_bcf`/`merged_bcf_idx`. +* Propagate BCF naming through all hierarchical merge levels and final concat wiring. +* update variable names to vcf_or_bcf for any variable that can be either + # 0.0.10 2026-08-28 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.wdl b/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.wdl index 85d55265c8..c663fd0cbf 100644 --- a/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/MultilevelHierarchicallyPasteVcfsStreaming.wdl @@ -6,13 +6,13 @@ import "../../../../tasks/wdl/Glimpse2SVImputationTasks.wdl" as Glimpse2SVImputa workflow MultilevelHierarchicallyMergeVcfs { # if this changes, update the multi_level_paste_pipeline_version value in PreprocessPLsGVCF.wdl - String pipeline_version = "0.0.10" + String pipeline_version = "1.0.0" input { - Array[String]? vcfs_array - Array[String]? vcf_idxs_array - File? vcfs_fofn - File? vcf_idxs_fofn + Array[String]? vcfs_or_bcfs_array + Array[String]? vcf_or_bcf_idxs_array + File? vcfs_or_bcfs_fofn + File? vcf_or_bcf_idxs_fofn Array[String] regions # bcftools regions, e.g. ["chr1,chr2,chr3", "chr4,chr5,chr6", ...] Array[Int] batch_sizes # Parameterizable hierarchical levels, e.g., [100, 50] Array[Boolean] do_localization # Whether to localize at each corresponding level @@ -24,13 +24,13 @@ workflow MultilevelHierarchicallyMergeVcfs { String extra_concat_args = "--naive" } - Array[String] vcfs_in = if defined(vcfs_array) then select_first([vcfs_array]) else read_lines(select_first([vcfs_fofn])) - Array[String] vcf_idxs_in = if defined(vcf_idxs_array) then select_first([vcf_idxs_array]) else read_lines(select_first([vcf_idxs_fofn])) + Array[String] vcfs_in = if defined(vcfs_or_bcfs_array) then select_first([vcfs_or_bcfs_array]) else read_lines(select_first([vcfs_or_bcfs_fofn])) + Array[String] vcf_idxs_in = if defined(vcf_or_bcf_idxs_array) then select_first([vcf_or_bcf_idxs_array]) else read_lines(select_first([vcfs_or_bcfs_fofn])) call CreateBatches as L0_Batches { input: - vcfs = vcfs_in, - vcf_idxs = vcf_idxs_in, + vcfs_or_bcfs = vcfs_in, + vcf_or_bcf_idxs = vcf_idxs_in, batch_size = batch_sizes[0] } @@ -42,13 +42,13 @@ workflow MultilevelHierarchicallyMergeVcfs { # ========================================== # LEVEL 0 # ========================================== - scatter (i in range(length(L0_Batches.vcf_batch_fofns))) { + scatter (i in range(length(L0_Batches.vcf_or_bcf_batch_fofns))) { call MergeVcfs as L0_Merge { input: - vcfs_localize = if do_localization[0] then read_lines(L0_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_localize = if do_localization[0] then read_lines(L0_Batches.vcf_idx_batch_fofns[i]) else [], - vcfs_stream = if !do_localization[0] then read_lines(L0_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_stream = if !do_localization[0] then read_lines(L0_Batches.vcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_localize = if do_localization[0] then read_lines(L0_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_localize = if do_localization[0] then read_lines(L0_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_stream = if !do_localization[0] then read_lines(L0_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_stream = if !do_localization[0] then read_lines(L0_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], timeout_min = timeouts_min[0], region = region, output_basename = region_prefix + ".L0-" + i, @@ -56,8 +56,8 @@ workflow MultilevelHierarchicallyMergeVcfs { } } - Array[File] l0_vcfs = L0_Merge.merged_vcf - Array[File] l0_idxs = L0_Merge.merged_vcf_idx + Array[File] l0_bcfs = L0_Merge.merged_bcf + Array[File] l0_idxs = L0_Merge.merged_bcf_idx # ========================================== # LEVEL 1 @@ -65,18 +65,18 @@ workflow MultilevelHierarchicallyMergeVcfs { if (length(batch_sizes) > 1) { call CreateBatches as L1_Batches { input: - vcfs = l0_vcfs, - vcf_idxs = l0_idxs, + vcfs_or_bcfs = l0_bcfs, + vcf_or_bcf_idxs = l0_idxs, batch_size = batch_sizes[1] } - scatter (i in range(length(L1_Batches.vcf_batch_fofns))) { + scatter (i in range(length(L1_Batches.vcf_or_bcf_batch_fofns))) { call MergeVcfs as L1_Merge { input: - vcfs_localize = if do_localization[1] then read_lines(L1_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_localize = if do_localization[1] then read_lines(L1_Batches.vcf_idx_batch_fofns[i]) else [], - vcfs_stream = if !do_localization[1] then read_lines(L1_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_stream = if !do_localization[1] then read_lines(L1_Batches.vcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_localize = if do_localization[1] then read_lines(L1_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_localize = if do_localization[1] then read_lines(L1_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_stream = if !do_localization[1] then read_lines(L1_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_stream = if !do_localization[1] then read_lines(L1_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], timeout_min = timeouts_min[1], region = region, output_basename = region_prefix + ".L1-" + i, @@ -85,8 +85,8 @@ workflow MultilevelHierarchicallyMergeVcfs { } } - Array[File] l1_vcfs = select_first([L1_Merge.merged_vcf, l0_vcfs]) - Array[File] l1_idxs = select_first([L1_Merge.merged_vcf_idx, l0_idxs]) + Array[File] l1_bcfs = select_first([L1_Merge.merged_bcf, l0_bcfs]) + Array[File] l1_idxs = select_first([L1_Merge.merged_bcf_idx, l0_idxs]) # ========================================== # LEVEL 2 @@ -94,18 +94,18 @@ workflow MultilevelHierarchicallyMergeVcfs { if (length(batch_sizes) > 2) { call CreateBatches as L2_Batches { input: - vcfs = l1_vcfs, - vcf_idxs = l1_idxs, + vcfs_or_bcfs = l1_bcfs, + vcf_or_bcf_idxs = l1_idxs, batch_size = batch_sizes[2] } - scatter (i in range(length(L2_Batches.vcf_batch_fofns))) { + scatter (i in range(length(L2_Batches.vcf_or_bcf_batch_fofns))) { call MergeVcfs as L2_Merge { input: - vcfs_localize = if do_localization[2] then read_lines(L2_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_localize = if do_localization[2] then read_lines(L2_Batches.vcf_idx_batch_fofns[i]) else [], - vcfs_stream = if !do_localization[2] then read_lines(L2_Batches.vcf_batch_fofns[i]) else [], - vcf_idxs_stream = if !do_localization[2] then read_lines(L2_Batches.vcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_localize = if do_localization[2] then read_lines(L2_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_localize = if do_localization[2] then read_lines(L2_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], + vcfs_or_bcfs_stream = if !do_localization[2] then read_lines(L2_Batches.vcf_or_bcf_batch_fofns[i]) else [], + vcf_or_bcf_idxs_stream = if !do_localization[2] then read_lines(L2_Batches.vcf_or_bcf_idx_batch_fofns[i]) else [], timeout_min = timeouts_min[2], region = region, output_basename = region_prefix + ".L2-" + i, @@ -114,18 +114,18 @@ workflow MultilevelHierarchicallyMergeVcfs { } } - Array[File] l2_vcfs = select_first([L2_Merge.merged_vcf, l1_vcfs]) - Array[File] l2_idxs = select_first([L2_Merge.merged_vcf_idx, l1_idxs]) + Array[File] l2_bcfs = select_first([L2_Merge.merged_bcf, l1_bcfs]) + Array[File] l2_idxs = select_first([L2_Merge.merged_bcf_idx, l1_idxs]) # ========================================== # FINAL REGION COLLAPSE # ========================================== # Safety Net: Defaults to localizing workspace intermediate files, no timeout applied (0). - if (length(l2_vcfs) > 1) { + if (length(l2_bcfs) > 1) { call MergeVcfs as FinalRegionMerge { input: - vcfs_localize = l2_vcfs, - vcf_idxs_localize = l2_idxs, + vcfs_or_bcfs_localize = l2_bcfs, + vcf_or_bcf_idxs_localize = l2_idxs, timeout_min = 0, region = region, output_basename = region_prefix + ".final", @@ -134,22 +134,22 @@ workflow MultilevelHierarchicallyMergeVcfs { } # Select exactly 1 file for this region to pass to the final concat step - File final_region_vcf = select_first([FinalRegionMerge.merged_vcf, l2_vcfs[0]]) - File final_region_idx = select_first([FinalRegionMerge.merged_vcf_idx, l2_idxs[0]]) + File final_region_bcf = select_first([FinalRegionMerge.merged_bcf, l2_bcfs[0]]) + File final_region_idx = select_first([FinalRegionMerge.merged_bcf_idx, l2_idxs[0]]) } # concatenate all regions together call Glimpse2SVImputationTasks.ConcatBcfs { input: - bcfs = final_region_vcf, + bcfs = final_region_bcf, bcf_idxs = final_region_idx, output_basename = output_basename, extra_args = extra_concat_args } output { - File merged_vcf = ConcatBcfs.concatenated_bcf - File merged_vcf_idx = ConcatBcfs.concatenated_bcf_idx + File merged_bcf = ConcatBcfs.concatenated_bcf + File merged_bcf_idx = ConcatBcfs.concatenated_bcf_idx } } @@ -166,8 +166,8 @@ struct RuntimeAttr { task CreateBatches { input { - Array[String] vcfs - Array[String] vcf_idxs + Array[String] vcfs_or_bcfs + Array[String] vcf_or_bcf_idxs Int batch_size RuntimeAttr? runtime_attr_override @@ -177,13 +177,13 @@ task CreateBatches { set -euox pipefail # Split with -a 4 guarantees strict alphanumeric ordering up to 456,976 batches - cat ~{write_lines(vcfs)} | split -a 4 -l ~{batch_size} - vcf_batch_ - cat ~{write_lines(vcf_idxs)} | split -a 4 -l ~{batch_size} - vcf_idx_batch_ + cat ~{write_lines(vcfs_or_bcfs)} | split -a 4 -l ~{batch_size} - vcf_batch_ + cat ~{write_lines(vcf_or_bcf_idxs)} | split -a 4 -l ~{batch_size} - vcf_idx_batch_ >>> output { - Array[File] vcf_batch_fofns = glob("vcf_batch_*") - Array[File] vcf_idx_batch_fofns = glob("vcf_idx_batch_*") + Array[File] vcf_or_bcf_batch_fofns = glob("vcf_batch_*") + Array[File] vcf_or_bcf_idx_batch_fofns = glob("vcf_idx_batch_*") } ######################### @@ -210,10 +210,10 @@ task CreateBatches { task MergeVcfs { input { - Array[File] vcfs_localize = [] - Array[File] vcf_idxs_localize = [] - Array[String] vcfs_stream = [] - Array[String] vcf_idxs_stream = [] + Array[File] vcfs_or_bcfs_localize = [] + Array[File] vcf_or_bcf_idxs_localize = [] + Array[String] vcfs_or_bcfs_stream = [] + Array[String] vcf_or_bcf_idxs_stream = [] Int timeout_min String? region @@ -225,14 +225,14 @@ task MergeVcfs { } # Dynamically sizes disk if localizing, defaults to 50GB if streaming - Int disk_gb = if length(vcfs_localize) > 0 then ceil(2.1*size(vcfs_localize, "GiB")) + 10 else ceil(1.1*size(vcfs_localize, "GiB")) + 10 + Int disk_gb = if length(vcfs_or_bcfs_localize) > 0 then ceil(2.1*size(vcfs_or_bcfs_localize, "GiB")) + 10 else ceil(1.1*size(vcfs_or_bcfs_stream, "GiB")) + 10 command <<< set -euox pipefail - if [ ~{length(vcfs_localize)} -gt 0 ]; then + if [ ~{length(vcfs_or_bcfs_localize)} -gt 0 ]; then echo "Localizing files natively via Cromwell..." - cat ~{write_lines(vcfs_localize)} > merge_list.txt + cat ~{write_lines(vcfs_or_bcfs_localize)} > merge_list.txt else echo "Slice-and-downloading regions via bcftools view..." export GCS_OAUTH_TOKEN=$(gcloud auth application-default print-access-token) @@ -241,8 +241,8 @@ task MergeVcfs { # Stitch the VCF and IDX strings together using htslib's explicit index syntax paste \ - ~{write_lines(vcfs_stream)} \ - ~{write_lines(vcf_idxs_stream)} \ + ~{write_lines(vcfs_or_bcfs_stream)} \ + ~{write_lines(vcf_or_bcf_idxs_stream)} \ | awk '{print $1"##idx##"$2}' > remote_list.txt # Prepend the line number (NR) using awk, separated by a pipe, and pass to xargs @@ -330,8 +330,8 @@ task MergeVcfs { >>> output { - File merged_vcf = "~{output_basename}.bcf" - File merged_vcf_idx = "~{output_basename}.bcf.csi" + File merged_bcf = "~{output_basename}.bcf" + File merged_bcf_idx = "~{output_basename}.bcf.csi" } ######################### diff --git a/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.changelog.md b/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.changelog.md index 401a551b68..de060081eb 100644 --- a/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.changelog.md @@ -1,3 +1,15 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + +# 0.0.16 +2026-08-31 (Date of Last Commit) + +* Rename `PreprocessPLs` outputs from `preprocessed_pls_vcf` to `preprocessed_pls_bcf` and update downstream workflow wiring. +* Switch top-level outputs from `preprocessed_pls_vcf`/`preprocessed_pls_vcf_idx` to `preprocessed_pls_bcf`/`preprocessed_pls_bcf_idx`. +* update variable names to vcf_or_bcf for any variable that can be either + # 0.0.15 2026-08-28 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.wdl b/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.wdl index 9c6b68205b..f9b70bb7c2 100644 --- a/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/PreprocessPLsGVCF.wdl @@ -5,8 +5,8 @@ import "../../../../tasks/wdl/Glimpse2SVImputationTasks.wdl" as Glimpse2SVImputa workflow PreprocessPLsGVCF { # if this changes, update the preprocessing_pls_gvcf_pipeline_version value in Glimpse2SVImputation.wdl - String pipeline_version = "0.0.15" - String multi_level_paste_pipeline_version = "0.0.10" + String pipeline_version = "1.0.0" + String multi_level_paste_pipeline_version = "1.0.0" input { File input_gvcf_manifest @@ -26,8 +26,8 @@ workflow PreprocessPLsGVCF { scatter (j in range(length(ParseInputManifest.input_gvcfs))) { call PreprocessPLs as PreprocessPLsGVCF { input: - input_vcf = ParseInputManifest.input_gvcfs[j], - input_vcf_idx = ParseInputManifest.input_gvcf_idxs[j], + input_vcf_or_bcf = ParseInputManifest.input_gvcfs[j], + input_vcf_or_bcf_idx = ParseInputManifest.input_gvcf_idxs[j], mode = "gvcf", panel_bubble_split_sites_only_vcf = preprocess_panel_bubble_split_sites_only_vcf, panel_bubble_split_sites_only_vcf_idx = preprocess_panel_bubble_split_sites_only_vcf_idx, @@ -39,8 +39,8 @@ workflow PreprocessPLsGVCF { # two-level localized hierarchical merge over entire chromosome call MultilevelHierarchicallyPasteVcfsStreaming.MultilevelHierarchicallyMergeVcfs as PastePreprocessPLsGVCFs { input: - vcfs_array = PreprocessPLsGVCF.preprocessed_pls_vcf, - vcf_idxs_array = PreprocessPLsGVCF.preprocessed_pls_vcf_idx, + vcfs_or_bcfs_array = PreprocessPLsGVCF.preprocessed_pls_bcf, + vcf_or_bcf_idxs_array = PreprocessPLsGVCF.preprocessed_pls_bcf_idx, regions = paste_regions, batch_sizes = [50, 50], do_localization = [true, true], @@ -51,8 +51,8 @@ workflow PreprocessPLsGVCF { } output { - File preprocessed_pls_vcf = PastePreprocessPLsGVCFs.merged_vcf - File preprocessed_pls_vcf_idx = PastePreprocessPLsGVCFs.merged_vcf_idx + File preprocessed_pls_bcf = PastePreprocessPLsGVCFs.merged_bcf + File preprocessed_pls_bcf_idx = PastePreprocessPLsGVCFs.merged_bcf_idx Int num_samples = length(ParseInputManifest.input_gvcfs) } } @@ -70,8 +70,8 @@ struct RuntimeAttr { task PreprocessPLs { input { - File input_vcf - File input_vcf_idx + File input_vcf_or_bcf + File input_vcf_or_bcf_idx String mode # joint or gvcf File panel_bubble_split_sites_only_vcf File panel_bubble_split_sites_only_vcf_idx @@ -84,17 +84,17 @@ task PreprocessPLs { RuntimeAttr? runtime_attr_override } - Int disk_size_gb = ceil(2*size([input_vcf, panel_bubble_split_sites_only_vcf], "GB")) + 10 + Int disk_size_gb = ceil(2*size([input_vcf_or_bcf, panel_bubble_split_sites_only_vcf], "GB")) + 10 command <<< set -euxo pipefail # Extract sample name from GVCF header and write to file for extract-bubble-PLs tool - bcftools query -l ~{input_vcf} > sample_name.txt + bcftools query -l ~{input_vcf_or_bcf} > sample_name.txt /usr/local/bin/extract-bubble-PLs ~{mode} \ ~{panel_bubble_split_sites_only_vcf}##idx##~{panel_bubble_split_sites_only_vcf_idx} \ - ~{input_vcf}##idx##~{input_vcf_idx} \ + ~{input_vcf_or_bcf}##idx##~{input_vcf_or_bcf_idx} \ ~{output_basename}.bcf \ ~{"--region " + output_region} \ --samples sample_name.txt \ @@ -108,8 +108,8 @@ task PreprocessPLs { >>> output { - File preprocessed_pls_vcf = "~{output_basename}.bcf" - File preprocessed_pls_vcf_idx = "~{output_basename}.bcf.csi" + File preprocessed_pls_bcf = "~{output_basename}.bcf" + File preprocessed_pls_bcf_idx = "~{output_basename}.bcf.csi" } ######################### diff --git a/pipelines/wdl/glimpse/sv_imputation/README.md b/pipelines/wdl/glimpse/sv_imputation/README.md new file mode 100644 index 0000000000..5a341b9b43 --- /dev/null +++ b/pipelines/wdl/glimpse/sv_imputation/README.md @@ -0,0 +1,12 @@ +## GLIMPSE2 SV Imputation Summary + +The `Glimpse2SVImputation` workflow is a WDL-based pipeline for structural variant imputation using [GLIMPSE2](https://odelaneau.github.io/GLIMPSE/). + +## Documentation + +The full documentation for the GLIMPSE2 SV Imputation pipeline can be found at the [WARP documentation site](https://broadinstitute.github.io/warp/docs/Pipelines/Glimpse2SVImputation_Pipeline/README) or plain [README](../../../../website/docs/Pipelines/Glimpse2SVImputation_Pipeline/README.md). + +## Versioning + +See [Glimpse2SVImputation.changelog.md](Glimpse2SVImputation.changelog.md) for the full release history. + diff --git a/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.changelog.md b/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.changelog.md index 183b60b925..855bed4ffd 100644 --- a/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.changelog.md +++ b/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.changelog.md @@ -1,3 +1,13 @@ +# 1.0.0 +2026-09-09 (Date of Last Commit) + +* Initial version for teaspoons prod release + +# 0.0.3 +2026-09-08 (Date of Last Commit) + +* Add GVCF header fileformat validation in `ValidateGvcfInput` and fail the task when any input header is not VCFv4.x. + # 0.0.2 2026-08-26 (Date of Last Commit) diff --git a/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.wdl b/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.wdl index d724761e65..bf975538cd 100644 --- a/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.wdl +++ b/pipelines/wdl/glimpse/sv_imputation/input_qc/Glimpse2SVImputationQC.wdl @@ -2,7 +2,7 @@ version 1.0 workflow InputQC { # if this changes, update the input_qc_version value in Glimpse2SVImputation.wdl - String pipeline_version = "0.0.2" + String pipeline_version = "1.0.0" input { # service expects only gvcf_manifest even though main wdl can alternatively take input arrays @@ -308,6 +308,7 @@ task ValidateGvcfInput { local worker_id="$2" local gvcfs_with_incompatible_contigs=() + local gvcfs_with_invalid_vcf_version=() local gvcfs_with_missing_format_fields=() local gvcfs_with_multiple_samples=() local gvcf_sample_ids=() @@ -318,6 +319,15 @@ task ValidateGvcfInput { bcftools view -Ov -h "$gvcf" > "header_${worker_id}.vcf" + # Ensure the header declares a VCFv4.x fileformat. + fileformat_line=$(grep -m1 '^##fileformat=' "header_${worker_id}.vcf" || true) + if ! echo "$fileformat_line" | grep -Eq '^##fileformat=VCFv4(\.[0-9]+)?$'; then + echo "[worker $worker_id] GVCF file $gvcf has unsupported fileformat header '${fileformat_line:-}' (expected VCFv4.x)." + gvcfs_with_invalid_vcf_version+=("$gvcf") + else + echo "[worker $worker_id] GVCF file $gvcf declares supported fileformat header: $fileformat_line" + fi + # check that the GVCF contains data for exactly one sample, and record its sample # ID so we can check for sample IDs duplicated across GVCFs once all workers finish mapfile -t sample_ids_in_gvcf < <(bcftools query -l "header_${worker_id}.vcf") @@ -363,7 +373,7 @@ task ValidateGvcfInput { fi # stop early once this worker's own chunk already has enough issues to fill a truncated message - total_issue_count=$(( ${#gvcfs_with_incompatible_contigs[@]} + ${#gvcfs_with_missing_format_fields[@]} + ${#gvcfs_with_multiple_samples[@]} )) + total_issue_count=$(( ${#gvcfs_with_incompatible_contigs[@]} + ${#gvcfs_with_invalid_vcf_version[@]} + ${#gvcfs_with_missing_format_fields[@]} + ${#gvcfs_with_multiple_samples[@]} )) if [ "$total_issue_count" -gt "$MAX_ITEMS_IN_ERROR_MESSAGES" ]; then echo "[worker $worker_id] found more than $MAX_ITEMS_IN_ERROR_MESSAGES GVCF files with issues in this chunk; skipping the rest of this worker's chunk" break @@ -378,6 +388,11 @@ task ValidateGvcfInput { else : > "results/${worker_id}_incompatible_contigs.txt" fi + if [ ${#gvcfs_with_invalid_vcf_version[@]} -gt 0 ]; then + printf '%s\n' "${gvcfs_with_invalid_vcf_version[@]}" > "results/${worker_id}_invalid_vcf_version.txt" + else + : > "results/${worker_id}_invalid_vcf_version.txt" + fi if [ ${#gvcfs_with_missing_format_fields[@]} -gt 0 ]; then printf '%s\n' "${gvcfs_with_missing_format_fields[@]}" > "results/${worker_id}_missing_format.txt" else @@ -405,6 +420,7 @@ task ValidateGvcfInput { # Merge every worker's partial results back into single lists before applying the final, # truncated aggregate message mapfile -t gvcfs_with_incompatible_contigs < <(cat results/*_incompatible_contigs.txt 2>/dev/null) + mapfile -t gvcfs_with_invalid_vcf_version < <(cat results/*_invalid_vcf_version.txt 2>/dev/null) mapfile -t gvcfs_with_missing_format_fields < <(cat results/*_missing_format.txt 2>/dev/null) mapfile -t gvcfs_with_multiple_samples < <(cat results/*_multi_sample.txt 2>/dev/null) mapfile -t all_gvcf_sample_ids < <(cat results/*_sample_ids.txt 2>/dev/null) @@ -433,6 +449,10 @@ task ValidateGvcfInput { "All checked GVCF files have contigs compatible with the expected reference dictionary." \ "${gvcfs_with_incompatible_contigs[@]}" + report_check_result "GVCF file" "with a VCF header version other than 4.x" \ + "All checked GVCF files declare a supported VCFv4.x header version." \ + "${gvcfs_with_invalid_vcf_version[@]}" + report_check_result "GVCF file" "missing the required PL and/or GT FORMAT/ID annotation(s) in its header" \ "All checked GVCF files declare the expected PL and GT FORMAT/ID annotations in their headers." \ "${gvcfs_with_missing_format_fields[@]}" @@ -445,7 +465,7 @@ task ValidateGvcfInput { "All GVCF sample IDs are unique across the provided GVCFs." \ "${duplicate_sample_ids[@]}" - # passes_qc is true if qc_messages is empty + # passes_qc is true only when qc_messages is empty. if [ ! -s qc_messages.txt ]; then echo "true" > passes_qc.txt else diff --git a/pipelines/wdl/glimpse/sv_imputation/input_qc/test_inputs/Plumbing/fail_gvcf_version_3_7.json b/pipelines/wdl/glimpse/sv_imputation/input_qc/test_inputs/Plumbing/fail_gvcf_version_3_7.json new file mode 100644 index 0000000000..fe903c3b9d --- /dev/null +++ b/pipelines/wdl/glimpse/sv_imputation/input_qc/test_inputs/Plumbing/fail_gvcf_version_3_7.json @@ -0,0 +1,13 @@ +{ + "Glimpse2SVImputationQC.output_basename": "fail_gvcf_version_3_7", + "Glimpse2SVImputationQC.gvcf_manifest": "gs://pd-test-storage-public/Glimpse2SVImputationQC/input/plumbing/manifests/gvcfManfiestVersion_3_7.tsv", + "Glimpse2SVImputationQC.preprocess_panel_bubble_split_sites_only_vcf": "gs://pd-test-storage-public/Glimpse2SVImputation/ref_panels/hgsvc_hprc_50/auxiliary_files/hgsvc-hprc-50.panel.reduced_panel_bubble_split_simple_sites.sorted.bcf", + "Glimpse2SVImputationQC.preprocess_panel_bubble_split_sites_only_vcf_idx": "gs://pd-test-storage-public/Glimpse2SVImputation/ref_panels/hgsvc_hprc_50/auxiliary_files/hgsvc-hprc-50.panel.reduced_panel_bubble_split_simple_sites.sorted.bcf.csi", + "Glimpse2SVImputationQC.paste_regions": ["chr19,chr20", "chr21,chr22"], + "Glimpse2SVImputationQC.chromosomes": ["chr19", "chr20", "chr21", "chr22"], + "Glimpse2SVImputationQC.genetic_maps_tsv": "gs://pd-test-storage-public/Glimpse2SVImputation/inputs/b38_genetic_map.tsv", + "Glimpse2SVImputationQC.ref_dict": "gs://gcp-public-data--broad-references/hg38/v0/Homo_sapiens_assembly38.dict", + "Glimpse2SVImputationQC.chunked_panel_json": "gs://pd-test-storage-public/Glimpse2SVImputation/ref_panels/hgsvc_hprc_50/json/hgsvc-hprc-50.chunked_panel.json", + "Glimpse2SVImputationQC.pop_glimpse2_panel_resources_json": "gs://pd-test-storage-public/Glimpse2SVImputation/ref_panels/hgsvc_hprc_50/json/hgsvc-hprc-50.panel.pop_panel_resources.json", + "Glimpse2SVImputationQC.billing_project_for_rp": "terra-f8e3de20" +} diff --git a/pipelines/wdl/mmidas/MMIDAS_Analyze.changelog.md b/pipelines/wdl/mmidas/MMIDAS_Analyze.changelog.md new file mode 100644 index 0000000000..d760113c4d --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_Analyze.changelog.md @@ -0,0 +1,30 @@ +# 1.1.2 +2026-08-21 (Date of Last Commit) + +* Fixed the confusion-matrix heatmaps (conf_Ttype_*.png, conf_ConsType_*.png) in 04_clusterability.py. The classification pickles store conf_mat as raw integer counts (0 to 860, rows summing to 164-546) but the heatmap colour scale is fixed to [0, 1], so every non-zero cell saturated to the darkest blue: a single misassigned cell rendered identically to a correct diagonal entry of 860. The figures were pure black-and-white noise and carried no information. Rows are now normalised to fractions before plotting, matching notebooks/3_evaluation.ipynb, which divides each row by its sum. On the K=89 run this turns 550 uniformly-dark cells into a diagonal averaging 0.94 against an off-diagonal averaging 0.0007. +* Widened the accuracy bar chart (classAcc_RF_K_*.png). At the previous width the three group labels ran together as "t-typesT CategoriesT Categories". +* Category annotations in 05_state_traversal.py are no longer ambiguous. _label_for_cat truncated the dominant t-type at its first space, so "L6 IT Car3" and "L6 CT Nxph2" both became "L6" and a ten-panel figure could be labelled L6, Pvalb, L6, L6, L4, Vip, L6, L6, Pvalb, Pvalb with no way to tell the categories apart. Labels now carry the category number, e.g. "c34 L6". +* State-scatter highlight colours no longer collide with the background. _default_palette used tab10, whose 8th entry is grey (#7f7f7f) against a #dbdbde background, so the 8th selected category's panel looked empty. Switched to husl, which is evenly spaced around the hue circle and returns no neutrals. +* All four are figure-rendering changes only. No model, no numeric output, and no upstream mmidas change: the MMIDAS package stays pinned at warp-v2. Re-running MMIDAS_Analyze is sufficient; MMIDAS_DataPrep and MMIDAS_Train outputs are unaffected. + +# 1.1.1 +2026-08-11 (Date of Last Commit) + +* Documented the two unavoidable divergences from the reference notebooks in dashboard.md: 5_state_traversal.ipynb hardcodes a hand-picked selected_c list that a generic workflow cannot reproduce, and the reference train/test split is unseeded and therefore unrecoverable. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739 (MMIDAS pinned at warp-v2, which reverts the min_con pruning stop to upstream behaviour). + +# 1.1.0 +2026-08-06 (Date of Last Commit) + +* Fixed KEGG pathway mapping, which silently produced zero pathways whenever a kegg_toml was supplied. mmidas.utils.data_tools.load_data read gene identifiers from adata.var.values (the column block, empty for an .h5ad written with no var columns) instead of the var index, so 03c_traversal_prep.py received an empty gene list, mapped no pathways, and exited 0. All pathway box plots in stage 05 were therefore missing without any error. load_data now reads adata.var.index.values and warns when the gene-identifier count does not match the number of expression columns. +* 05_state_traversal.py now selects categories by assigned-cell count, largest first, instead of taking the first n_selected_cats by category index. Surviving pruning is not the same as having cells assigned: on a collapsed model the index-ordered selection landed on ten categories that were all empty, and the ten resulting figures were pixel-identical apart from their titles. Empty categories are now excluded with a warning, and the task exits non-zero if no category has any cells at all. +* state_traversal_manifest.json gains selected_c_n_cells, n_active_categories, and n_populated_categories so a validation step can distinguish a real traversal from figures drawn over empty categories. Note n_selected_cats is now capped at the number of populated categories, so it can be below the requested n_selected_cats input. +* Fixed the silhouette figure (SC_K_*.png) in 04_clusterability.py. The sc_T arrays are shape (1, n_category); the code sorted them as 1-D and took len(), which is 1, so every point was plotted at x=1 as a separate broadcast line and each inherited the same legend label. The result was a 1507x9406 px figure with 134 legend entries and an x-axis spanning 0.96-1.04. The arrays are now flattened before sorting. +* Accuracy bar chart x-axis labels are no longer rotated and overlapping when n_arm > 1. +* The Docker image now installs the mmidas package from a pinned revision of the fork rather than a copy of a local directory, so any published image can be rebuilt from source. See "Docker image provenance" in dashboard.md. The load_data and train fixes above are candidates for upstreaming to AllenInstitute/MMIDAS. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1786046379 + +# 1.0.0 +2026-06-24 (Date of Last Commit) + +* Initial release. Consumes the confirmed evaluation_results.json from MMIDAS_Train and runs RF classification + silhouette analysis (03b) and state traversal preparation (03c) in parallel, followed by clusterability figures (04) and state traversal figures (05). diff --git a/pipelines/wdl/mmidas/MMIDAS_Analyze.wdl b/pipelines/wdl/mmidas/MMIDAS_Analyze.wdl new file mode 100644 index 0000000000..04171fdcc0 --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_Analyze.wdl @@ -0,0 +1,547 @@ +version 1.0 + +workflow MMIDAS_Analyze { + + meta { + description: "Stage 3b-5 of the MMIDAS pipeline. Takes the confirmed evaluation_results.json from MMIDAS_Train (after human review of model_order), runs RF classification and silhouette analysis (03b) and state traversal preparation (03c) in parallel, then generates clusterability figures (04) and state traversal figures (05)." + allowNestedInputs: true + } + + String pipeline_version = "1.1.2" + + input { + # ── Inputs from MMIDAS_Train (after human review) ───────────────────────── + File preprocessed_h5ad # from MMIDAS_DataPrep + File checkpoints_manifest # from MMIDAS_Train: TrainMixVAE output + File model_tar # from MMIDAS_Train: TrainMixVAE output + File evaluation_results_json # from MMIDAS_Train: Evaluate output — REVIEW THIS + + # ── Optional reference files ────────────────────────────────────────────── + File? kegg_toml # KEGG/KEGG.toml — enables pathway box plots in 05 + File? htree_file # hierarchical taxonomy tree CSV — enables tree ordering in 03c + + # ── 03b: Classification parameters ─────────────────────────────────────── + Int n_pca = 100 + Int k_fold = 10 + String date_tag = "" # defaults to today's date inside the script + + # ── 03c: Traversal parameters ───────────────────────────────────────────── + Int n_traversal_steps = 50 + Float traversal_std_range = 3.0 + + # ── 05: State traversal figure parameters ───────────────────────────────── + Int traversal_arm = 0 + Int n_selected_cats = 10 # 0 = all active categories + + # ── Shared ──────────────────────────────────────────────────────────────── + Int batch_size = 5000 + Int seed = 0 + + # ── Runtime ────────────────────────────────────────────────────────────── + String docker = "us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739" + Int analyze_disk_size = 200 + Int analyze_mem_size = 64 + Int analyze_cpu = 8 + Int fig_disk_size = 100 + Int fig_mem_size = 32 + Int fig_cpu = 4 + } + + # ── Restore model checkpoints (shared setup for 03b and 03c) ───────────── + # Both 03b and 03c need the model/ directory. We untar once here so the + # localized directory is available to both parallel tasks via their inputs. + call RestoreModel { + input: + checkpoints_manifest = checkpoints_manifest, + model_tar = model_tar, + evaluation_results_json = evaluation_results_json, + docker = docker, + disk_size = analyze_disk_size, + mem_size = 16, + cpu = 2 + } + + # ── 03b and 03c run in parallel ──────────────────────────────────────────── + call Classify { + input: + anndata_h5ad = preprocessed_h5ad, + checkpoints_manifest = RestoreModel.patched_manifest, + model_dir_tar = RestoreModel.model_dir_tar, + evaluation_results_json = RestoreModel.patched_evaluation_results, + n_pca = n_pca, + k_fold = k_fold, + batch_size = batch_size, + seed = seed, + date_tag = date_tag, + docker = docker, + disk_size = analyze_disk_size, + mem_size = analyze_mem_size, + cpu = analyze_cpu + } + + call TraversalPrep { + input: + anndata_h5ad = preprocessed_h5ad, + checkpoints_manifest = RestoreModel.patched_manifest, + model_dir_tar = RestoreModel.model_dir_tar, + evaluation_results_json = RestoreModel.patched_evaluation_results, + kegg_toml = kegg_toml, + htree_file = htree_file, + n_traversal_steps = n_traversal_steps, + traversal_std_range = traversal_std_range, + batch_size = batch_size, + seed = seed, + docker = docker, + disk_size = analyze_disk_size, + mem_size = analyze_mem_size, + cpu = analyze_cpu + } + + # ── 04: Clusterability figures (depends on 03b) ──────────────────────────── + call Clusterability { + input: + classify_manifest = Classify.classify_manifest, + clustering_tar = Classify.clustering_tar, + docker = docker, + disk_size = fig_disk_size, + mem_size = fig_mem_size, + cpu = fig_cpu + } + + # ── 05: State traversal figures (depends on 03c) ────────────────────────── + call StateTraversal { + input: + anndata_h5ad = preprocessed_h5ad, + checkpoints_manifest = RestoreModel.patched_manifest, + model_dir_tar = RestoreModel.model_dir_tar, + evaluation_results_json = RestoreModel.patched_evaluation_results, + traversal_manifest = TraversalPrep.traversal_manifest, + traversal_tar = TraversalPrep.traversal_tar, + arm = traversal_arm, + n_selected_cats = n_selected_cats, + batch_size = batch_size, + seed = seed, + docker = docker, + disk_size = fig_disk_size, + mem_size = fig_mem_size, + cpu = fig_cpu + } + + output { + # Clusterability (04) + Array[File] clusterability_figures = Clusterability.figures + File clusterability_manifest = Clusterability.clusterability_manifest + + # State traversal (05) + Array[File] state_traversal_figures = StateTraversal.figures + File state_traversal_manifest = StateTraversal.state_traversal_manifest + + String pipeline_version_out = pipeline_version + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: RestoreModel +# +# Unpacks model_tar and rewrites absolute paths in checkpoints_manifest.json +# to point to the current working directory. The patched manifest and a +# re-tarred model directory are passed to Classify, TraversalPrep, and +# StateTraversal so each task can reconstruct the model locally. +# ────────────────────────────────────────────────────────────────────────────── +task RestoreModel { + input { + File checkpoints_manifest + File model_tar + File evaluation_results_json + String docker + Int disk_size + Int mem_size + Int cpu + } + + command <<< + set -euo pipefail + mkdir -p out/model + + tar -xzf "~{model_tar}" -C out/ + + ABS_OUT=$(realpath out) + + # Normalize checkpoints_manifest.json to the local execution directory. + # Be resilient to legacy/new schema differences (e.g. missing output_dir). + python3 - <>> + + output { + File patched_manifest = "out/checkpoints_manifest.json" + File patched_evaluation_results = "out/evaluation_results.json" + File model_dir_tar = "model_dir.tar.gz" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 3 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: Classify (03b) +# +# RF classification + silhouette analysis across label sets and embeddings. +# Outputs clustering pickles (tarred) and classify_manifest.json. +# ────────────────────────────────────────────────────────────────────────────── +task Classify { + input { + File anndata_h5ad + File checkpoints_manifest + File model_dir_tar + File evaluation_results_json + Int n_pca + Int k_fold + Int batch_size + Int seed + String date_tag + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file." + checkpoints_manifest: "Patched checkpoints_manifest.json from RestoreModel." + model_dir_tar: "Tarball of the model/ checkpoint directory from RestoreModel." + evaluation_results_json: "evaluation_results.json from MMIDAS_Train Evaluate task." + n_pca: "Number of PCA components for the linear embedding baseline." + k_fold: "Number of folds for k-fold cross-validation." + batch_size: "Batch size for model inference." + seed: "Random seed (must match training)." + date_tag: "Date string embedded in pickle filenames (empty = today)." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + mkdir -p out + + tar -xzf "~{model_dir_tar}" -C out/ + + python3 /usr/local/03b_classify.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --checkpoints_manifest "~{checkpoints_manifest}" \ + --evaluation_results_json "~{evaluation_results_json}" \ + --output_dir out \ + --n_pca ~{n_pca} \ + --k_fold ~{k_fold} \ + --batch_size ~{batch_size} \ + --seed ~{seed} \ + ~{if date_tag != "" then "--date_tag \"" + date_tag + "\"" else ""} + + tar -czf clustering.tar.gz -C out clustering/ + >>> + + output { + File classify_manifest = "out/classify_manifest.json" + File clustering_tar = "clustering.tar.gz" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 1 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: TraversalPrep (03c) +# +# Pre-computes state traversal data: per-gene variation scores, KEGG pathway +# gene-index mapping, taxonomy ordering, and state-space traversal paths. +# Outputs traversal pickles (tarred) and traversal_manifest.json. +# ────────────────────────────────────────────────────────────────────────────── +task TraversalPrep { + input { + File anndata_h5ad + File checkpoints_manifest + File model_dir_tar + File evaluation_results_json + File? kegg_toml + File? htree_file + Int n_traversal_steps + Float traversal_std_range + Int batch_size + Int seed + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file." + checkpoints_manifest: "Patched checkpoints_manifest.json from RestoreModel." + model_dir_tar: "Tarball of the model/ checkpoint directory from RestoreModel." + evaluation_results_json: "evaluation_results.json from MMIDAS_Train Evaluate task." + kegg_toml: "Optional KEGG.toml for pathway gene-set mapping." + htree_file: "Optional hierarchical taxonomy tree CSV for category ordering." + n_traversal_steps: "Number of points along the state traversal path." + traversal_std_range: "Traversal spans ± (this value) × std along PC1." + batch_size: "Batch size for model inference." + seed: "Random seed." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + mkdir -p out/state + + tar -xzf "~{model_dir_tar}" -C out/ + + python3 /usr/local/03c_traversal_prep.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --checkpoints_manifest "~{checkpoints_manifest}" \ + --evaluation_results_json "~{evaluation_results_json}" \ + --output_dir out \ + ~{if defined(kegg_toml) then "--kegg_toml \"" + select_first([kegg_toml]) + "\"" else ""} \ + ~{if defined(htree_file) then "--htree_file \"" + select_first([htree_file]) + "\"" else ""} \ + --n_traversal_steps ~{n_traversal_steps} \ + --traversal_std_range ~{traversal_std_range} \ + --batch_size ~{batch_size} \ + --seed ~{seed} + + tar -czf traversal.tar.gz -C out state/ + cp out/taxonomy_order_K_*.npy . + >>> + + output { + File traversal_manifest = "out/traversal_manifest.json" + File traversal_tar = "traversal.tar.gz" + Array[File] taxonomy_order = glob("taxonomy_order_K_*.npy") + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 1 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: Clusterability (04) +# +# Generates classification accuracy bar chart, silhouette score curves, and +# confusion matrix heatmaps from the 03b clustering pickles. +# ────────────────────────────────────────────────────────────────────────────── +task Clusterability { + input { + File classify_manifest + File clustering_tar + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + classify_manifest: "classify_manifest.json from Classify task." + clustering_tar: "Tarball of the clustering/ pickles directory from Classify task." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + mkdir -p out + + # Restore clustering pickles and rewrite the manifest path + tar -xzf "~{clustering_tar}" -C out/ + + cp "~{classify_manifest}" out/classify_manifest.json + ORIG_CLUST=$(python3 -c "import json; print(json.load(open('out/classify_manifest.json'))['clustering_dir'])") + ABS_OUT=$(realpath out) + sed -i "s|${ORIG_CLUST}|${ABS_OUT}/clustering|g" out/classify_manifest.json + sed -i "s|\"output_dir\": \"[^\"]*\"|\"output_dir\": \"${ABS_OUT}\"|" out/classify_manifest.json + + python3 /usr/local/04_clusterability.py \ + --classify_manifest "out/classify_manifest.json" \ + --output_dir out + >>> + + output { + Array[File] figures = glob("out/*.png") + File clusterability_manifest = "out/clusterability_manifest.json" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 3 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: StateTraversal (05) +# +# Generates per-category state-space scatter plots and, when KEGG pathways +# were provided to TraversalPrep, per-pathway and per-category box plots. +# ────────────────────────────────────────────────────────────────────────────── +task StateTraversal { + input { + File anndata_h5ad + File checkpoints_manifest + File model_dir_tar + File evaluation_results_json + File traversal_manifest + File traversal_tar + Int arm + Int n_selected_cats + Int batch_size + Int seed + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file." + checkpoints_manifest: "Patched checkpoints_manifest.json from RestoreModel." + model_dir_tar: "Tarball of the model/ checkpoint directory from RestoreModel." + evaluation_results_json: "evaluation_results.json from MMIDAS_Train Evaluate task." + traversal_manifest: "traversal_manifest.json from TraversalPrep task." + traversal_tar: "Tarball of the state/ traversal pickle directory from TraversalPrep." + arm: "Which encoder arm to use for state-space scatter plots." + n_selected_cats: "Number of active categories to generate figures for (0 = all)." + batch_size: "Batch size for model inference." + seed: "Random seed." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + mkdir -p out/state + + tar -xzf "~{model_dir_tar}" -C out/ + tar -xzf "~{traversal_tar}" -C out/ + + # Rewrite absolute paths in both manifests + cp "~{checkpoints_manifest}" out/checkpoints_manifest.json + cp "~{traversal_manifest}" out/traversal_manifest.json + ABS_OUT=$(realpath out) + + ORIG_CKPT=$(python3 -c "import json; print(json.load(open('out/checkpoints_manifest.json'))['output_dir'])") + sed -i "s|${ORIG_CKPT}|${ABS_OUT}|g" out/checkpoints_manifest.json + + ORIG_TRAV=$(python3 -c "import json; print(json.load(open('out/traversal_manifest.json'))['output_dir'])") + sed -i "s|${ORIG_TRAV}|${ABS_OUT}|g" out/traversal_manifest.json + + python3 /usr/local/05_state_traversal.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --checkpoints_manifest "out/checkpoints_manifest.json" \ + --evaluation_results_json "~{evaluation_results_json}" \ + --traversal_manifest "out/traversal_manifest.json" \ + --arm ~{arm} \ + --n_selected_cats ~{n_selected_cats} \ + --batch_size ~{batch_size} \ + --seed ~{seed} + + # Collect all figures into the task root for glob output + find out/state -name "*.png" -exec cp {} . \; + >>> + + output { + Array[File] figures = glob("*.png") + File state_traversal_manifest = "out/state_traversal_manifest.json" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 3 + } +} diff --git a/pipelines/wdl/mmidas/MMIDAS_DataPrep.changelog.md b/pipelines/wdl/mmidas/MMIDAS_DataPrep.changelog.md new file mode 100644 index 0000000000..ec537b39d4 --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_DataPrep.changelog.md @@ -0,0 +1,18 @@ +# 1.0.2 +2026-08-11 (Date of Last Commit) + +* Confirmed stage 1 is a faithful port of notebooks/1_data_prep.ipynb: same filters, same normalize-then-subset order, the same two t-type renames, gene symbols on var_names, and the same final shape of (22365, 5032) with 115 t-types. No code change. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1786468785 (MMIDAS pinned at warp-v2). + +# 1.0.1 +2026-08-06 (Date of Last Commit) + +* Fixed a misleading normalization sanity check in 01_data_prep.py. It printed per-cell sums of log1p values against an expectation of "~log(1e6+1) ~ 13.8", which is not what that sum equals, so a correct run reported values in the thousands and looked broken. It now checks the invariant that actually holds -- inverting log1p recovers CPM, so each non-empty cell sums back to 1e6 -- warns if that deviates by more than 0.1%, and reports the maximum single log-CPM value against its log1p(1e6) bound plus the all-zero cell count. No change to pipeline outputs. +* Corrected the selected_genes gene count in the WDL comments and parameter_meta from ~1,252 to 5,032, matching genes_SS_ALM-VISp.csv. +* The Docker image now installs the mmidas package from a pinned revision of the fork rather than a copy of a local directory, so any published image can be rebuilt from source. See "Docker image provenance" in dashboard.md. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1786046379 + +# 1.0.0 +2026-06-24 (Date of Last Commit) + +* Initial release. Wraps 01_data_prep.py to load raw Mouse Smart-seq ALM/VISp count matrices from the Allen Brain Atlas, filter to neuronal cells, normalize to log-CPM, and write a single AnnData .h5ad file for downstream MMIDAS training. diff --git a/pipelines/wdl/mmidas/MMIDAS_DataPrep.wdl b/pipelines/wdl/mmidas/MMIDAS_DataPrep.wdl new file mode 100644 index 0000000000..c5abe8689a --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_DataPrep.wdl @@ -0,0 +1,129 @@ +version 1.0 + +workflow MMIDAS_DataPrep { + + meta { + description: "Stage 1 of the MMIDAS pipeline. Loads raw Mouse Smart-seq ALM/VISp count matrices from the Allen Brain Atlas, filters to neuronal cells, normalizes to log-CPM, and writes a single AnnData .h5ad file for use by MMIDAS_Train." + allowNestedInputs: true + } + + String pipeline_version = "1.0.2" + + input { + # ── Raw Allen Brain Atlas Smart-seq files ──────────────────────────────── + File visp_exon_matrix # mouse_VISp_2018-06-14_exon-matrix.csv + File visp_samples # mouse_VISp_2018-06-14_samples-columns.csv + File alm_exon_matrix # mouse_ALM_2018-06-14_exon-matrix.csv + File alm_samples # mouse_ALM_2018-06-14_samples-columns.csv + File genes_rows # mouse_ALM_2018-06-14_genes-rows.csv (full gene list) + File selected_genes # genes_SS_ALM-VISp.csv (selected gene subset, 5032 genes) + + # ── Output filename ────────────────────────────────────────────────────── + String output_basename = "Mouse_ALM-VISp_cpm" + + # ── Optional filters ───────────────────────────────────────────────────── + String remove_clusters = "Low Quality,CR Lhx5,Meis2 Adamts19" + String neuronal_classes = "GABAergic,Glutamatergic" + + # ── Runtime ────────────────────────────────────────────────────────────── + String docker = "us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739" + Int disk_size = 100 + Int mem_size = 48 + Int cpu = 4 + } + + call DataPrep { + input: + visp_exon_matrix = visp_exon_matrix, + visp_samples = visp_samples, + alm_exon_matrix = alm_exon_matrix, + alm_samples = alm_samples, + genes_rows = genes_rows, + selected_genes = selected_genes, + output_basename = output_basename, + remove_clusters = remove_clusters, + neuronal_classes = neuronal_classes, + docker = docker, + disk_size = disk_size, + mem_size = mem_size, + cpu = cpu + } + + output { + File preprocessed_h5ad = DataPrep.preprocessed_h5ad + String pipeline_version_out = pipeline_version + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: DataPrep +# +# Runs 01_data_prep.py which: +# - Loads VISp and ALM count matrices +# - Filters to neuronal classes (GABAergic + Glutamatergic by default) +# - Removes low-quality / rare clusters +# - Normalizes: log1p(counts / sum * 1e6) (log-CPM) +# - Subsets to the selected gene list +# - Writes output as AnnData .h5ad +# ────────────────────────────────────────────────────────────────────────────── +task DataPrep { + input { + File visp_exon_matrix + File visp_samples + File alm_exon_matrix + File alm_samples + File genes_rows + File selected_genes + String output_basename + String remove_clusters + String neuronal_classes + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + visp_exon_matrix: "Raw VISp exon count matrix CSV (genes × cells)." + visp_samples: "VISp cell metadata CSV (samples-columns)." + alm_exon_matrix: "Raw ALM exon count matrix CSV (genes × cells)." + alm_samples: "ALM cell metadata CSV (samples-columns)." + genes_rows: "Full gene list CSV (genes-rows) matching the count matrices." + selected_genes: "Selected gene subset CSV (5032 genes for Smart-seq ALM/VISp)." + output_basename: "Basename for the output .h5ad file (no extension)." + remove_clusters: "Comma-separated cluster names to exclude (e.g. 'Low Quality,CR Lhx5')." + neuronal_classes: "Comma-separated cell classes to retain (default: GABAergic,Glutamatergic)." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + + python3 /usr/local/01_data_prep.py \ + --visp_exon_matrix "~{visp_exon_matrix}" \ + --visp_samples "~{visp_samples}" \ + --alm_exon_matrix "~{alm_exon_matrix}" \ + --alm_samples "~{alm_samples}" \ + --genes_rows "~{genes_rows}" \ + --selected_genes "~{selected_genes}" \ + --output_h5ad "~{output_basename}.h5ad" \ + --remove_clusters "~{remove_clusters}" \ + --neuronal_classes "~{neuronal_classes}" + >>> + + output { + File preprocessed_h5ad = "~{output_basename}.h5ad" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 3 + } +} diff --git a/pipelines/wdl/mmidas/MMIDAS_Train.changelog.md b/pipelines/wdl/mmidas/MMIDAS_Train.changelog.md new file mode 100644 index 0000000000..ea2ec07531 --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_Train.changelog.md @@ -0,0 +1,36 @@ +# 1.3.0 +2026-08-27 (Date of Last Commit) + +* n_epoch_p default 10000 -> 1000. Total epochs are n_epoch + max_prun_it * n_epoch_p, so this is the parameter that sets the cost of a run: 52,000 epochs (~13.5 h, ~$13 on an nvidia-tesla-t4) against 430,000 (~4.5 days, ~$110). The published analysis used 10000; 1000 is now the default because it is the configuration validated in the Terra workspace, and the example outputs and validation-notebook results distributed with it come from that configuration. On the example data it reached avg_consensus 0.969 and model_order 89 against the published 92. max_prun_it stays at 42, so the published model_order remains inside the reachable search space. +* To run the published configuration, set n_epoch_p to 10000 or use example_inputs/MMIDAS_Train.json, which retains that value. example_inputs/MMIDAS_Train.staged_validation.json matches the new default. +* No script or Docker change: the image stays at us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739 with the mmidas package pinned at warp-v2. + +# 1.2.0 +2026-08-11 (Date of Last Commit) + +* Corrected training defaults that prevented the workflow from reproducing the published MMIDAS analysis. The goal of these workflows is a faithful port of the authors' notebooks, and several defaults had been carried over from tutorials/train_mixvae.py, whose own defaults are tuned for that script's n_categories of 15 rather than the 120 used for Mouse ALM/VISp. +* max_prun_it 14 -> 42. Pruning removes one category per round, so max_prun_it caps the smallest reachable model_order at n_categories - max_prun_it. The reference analysis reports model_order 92, which is pruning round 28 of the 42 it ran; at 14 the floor was 106 and the published answer was outside the search space entirely. No amount of retraining could have reproduced it. +* tau 0.1 -> 0.005. cpl_mixVAE.init_model documents tau as "usually equals to 1/n_categories" (about 0.0083 at K=120) and defaults to 0.005, which the reference notebooks use by omission. The previous 0.1 is roughly 1/K for the tutorial script's K=15; at K=120 it leaves the categorical softmax far too soft. +* x_drop 0.25 -> 0.0, matching init_model's default. The reference notebooks do not pass it; 0.25 was tutorials/train_mixvae.py's p_drop. +* n_epoch_p 1000 -> 10000, matching tutorials/train_mixvae.py. Each pruning round previously trained for a tenth as long as the reference. +* Reverted the min_con pruning stop condition introduced in 1.1.0. It is commented out upstream and every reference invocation runs with it disabled, so enabling it diverged from all of them -- and with min_con 0.99 it would halt pruning as soon as one category became reproducible, stopping short of the depth the published model_order relies on. min_con is now reporting-only, as upstream intends. The per-round consensus logging and the maximum-entropy reference beside the per-epoch Entropy are retained; both are stdout only. +* NOTE ON RUNTIME: 42 pruning rounds at 10000 epochs each is roughly 4.5 days on an nvidia-tesla-t4 (~$110 at 0.90 s/epoch and $1.00/hr), against about 6 hours for the previous defaults. TrainMixVAE runs with preemptible 0 and has no resume path, so a late failure is costly. +* Added example_inputs/MMIDAS_Train.staged_validation.json: the same configuration with n_epoch_p 1000 instead of 10000, giving a 13 hour / ~$13 run. It keeps max_prun_it 42, so the reference model_order of 92 stays inside the search space, and is intended as a cheap check on whether the categorical posterior commits under the corrected tau before committing to the full-length run. See "Validate cheaply before paying for the full run" in dashboard.md, which also notes that the per-epoch Entropy column answers that question ~2.5 hours in, before any pruning begins. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1786468785 + +# 1.1.0 +2026-08-06 (Date of Last Commit) + +* Fixed checkpoint selection in 03a_evaluate.py. When K_selection found no checkpoint meeting k_select_thr, the fallback computed the correct model_order but failed to load the matching checkpoint: model_order == n_categories maps to pruning round 0, which is the before_pruning file rather than a nonexistent after_pruning_0, and the secondary fallback sorted checkpoint paths as strings so round 9 sorted after round 14. evaluation_results.json reported a model_order read off an arbitrarily chosen checkpoint. Checkpoint lookup is now keyed on the pruning round numerically and covered by a regression test. +* evaluation_results.json gains fields the human-review step needs and previously could not see: k_selection_met_threshold, k_selection_suggested_model_order, n_populated_categories, n_populated_categories_per_arm, and collapse_warning. model_order counts categories that survived pruning, which stays high even when the model assigns every cell to a handful of them; n_populated_categories is the number that reflects whether the run is usable. +* Evaluate now delocalizes summary_performance_K_*.p as the summary_performance output. evaluation_results.json named this file in its summary_pickle field but it was never a task output, so the reference dangled and the per-checkpoint consensus values behind the K-selection decision were lost with the VM. +* checkpoints_manifest.json now lists checkpoints in pruning-round order rather than lexicographic order, which had after_pruning_10..14 preceding after_pruning_1..9. +* Restored the consensus half of the pruning stop condition in mmidas.cpl_mixvae.train, which was commented out. min_con had no effect and pruning always ran exactly max_prun_it rounds. Each round now logs the min/mean/max inter-arm consensus over surviving categories, and the per-epoch log prints the maximum-entropy reference value next to Entropy so a collapsed categorical posterior is visible during training rather than three workflows later. +* Fixed a latent crash in mmidas.utils.cluster_analysis.K_selection. Its guard is `if thr > max(consensus)`, so the selection branch runs whenever max(consensus) >= thr, but the selection itself used a strict `>`. When max(consensus) == thr exactly, the candidate index array came back empty and the next line raised "ValueError: max() arg is an empty sequence". The comparison now uses >= so it agrees with the guard and the empty case is unreachable. Behaviour when no checkpoint reaches thr is unchanged: K_selection still returns None, and 03a_evaluate.py records that as k_selection_met_threshold: false. +* The Docker image now installs the mmidas package from a pinned revision of the fork rather than a copy of a local directory, so any published image can be rebuilt from source. See "Docker image provenance" in dashboard.md. +* Updated the Docker image to us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1786046379 + +# 1.0.0 +2026-06-24 (Date of Last Commit) + +* Initial release. Runs optional UDAGAN VAE-GAN augmenter training (02a), cpl-mixVAE model training with iterative category pruning (02b), and checkpoint evaluation with K-selection (03a). Outputs evaluation_results.json and consensus figures for human review before running MMIDAS_Analyze. diff --git a/pipelines/wdl/mmidas/MMIDAS_Train.wdl b/pipelines/wdl/mmidas/MMIDAS_Train.wdl new file mode 100644 index 0000000000..0e86f7c847 --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_Train.wdl @@ -0,0 +1,486 @@ +version 1.0 + +workflow MMIDAS_Train { + + meta { + description: "Stage 2+3a of the MMIDAS pipeline. Optionally trains a UDAGAN VAE-GAN augmenter (02a), trains the core cpl-mixVAE model with iterative pruning (02b), and evaluates all checkpoints to select the optimal model order (03a). Outputs evaluation figures and evaluation_results.json for human review before running MMIDAS_Analyze." + allowNestedInputs: true + } + + String pipeline_version = "1.3.0" + + input { + # ── Input data ─────────────────────────────────────────────────────────── + File preprocessed_h5ad # output of MMIDAS_DataPrep + + # ── Augmenter (optional) ───────────────────────────────────────────────── + Boolean run_augmenter = false + String augmenter_tag = "mmidas" + Int augmenter_z_dim = 10 + Int augmenter_noise_dim = 50 + Int augmenter_fc_dim = 500 + Int augmenter_n_epoch = 500 + Int augmenter_batch_size = 512 + + # ── cpl-mixVAE architecture ─────────────────────────────────────────────── + # + # Defaults track the reference analysis in the MMIDAS repo. The reference + # notebooks (notebooks/2_train.ipynb, 3_evaluation.ipynb) pass only + # n_categories, state_dim, n_arm and latent_dim to cpl_mixVAE.init_model() + # and let everything else fall to that function's own defaults, so the values + # below are those defaults unless noted. + Int n_categories = 120 + Int state_dim = 2 + Int n_arm = 2 + Int latent_dim = 10 + Int fc_dim = 100 + Float x_drop = 0.0 # init_model default. Was 0.25, which + # is tutorials/train_mixvae.py's p_drop. + Float s_drop = 0.0 + Float temp = 1.0 + # tau is the categorical softmax temperature. init_model documents it as + # "usually equals to 1/n_categories" and defaults to 0.005; at n_categories + # 120 that is ~0.0083. Was 0.1, carried over from + # tutorials/train_mixvae.py, whose n_categories default is 15 (1/15 ~ 0.067). + # At K=120 a tau of 0.1 leaves the categorical posterior far too soft. + Float tau = 0.005 + Float beta = 1.0 + Float lam = 1.0 + Float lam_pc = 1.0 + String training_mode = "MSE" # MSE or ZINB + + # ── cpl-mixVAE training ─────────────────────────────────────────────────── + Int n_epoch = 10000 + # n_epoch_p is epochs per pruning round, and it sets the cost of the run: + # total epochs are n_epoch + max_prun_it * n_epoch_p. + # + # 1000 (default here) 52,000 epochs, ~13.5 h, ~$13 on an nvidia-tesla-t4 + # 10000 (published) 430,000 epochs, ~4.5 days, ~$110 + # + # The published analysis used 10000. 1000 is the default because it is the + # configuration validated in the Terra workspace: on the example data it + # reached avg_consensus 0.969 and model_order 89 against the published 92. + # To run the published configuration use example_inputs/MMIDAS_Train.json. + Int n_epoch_p = 1000 + # min_con is reporting-only in this implementation; pruning runs the full + # max_prun_it regardless of the value set here. + Float min_con = 0.99 + # The reference model directory holds pruning checkpoints 1-42, and its + # published model_order of 92 is round 28 (n_categories - model_order). + # Pruning removes one category per round, so max_prun_it caps the smallest + # reachable model_order at n_categories - max_prun_it. At the previous value + # of 14 the floor was 106 and 92 was outside the search space entirely. + Int max_prun_it = 42 + Float lr = 0.001 + Int batch_size = 5000 + Int n_aug_smp = 0 + Float train_size = 0.9 + # The reference calls get_loaders without a seed, so its train/test split is + # unrecoverable. Seeded here for reproducibility -- a deliberate divergence. + Int seed = 0 + + # ── Evaluation ──────────────────────────────────────────────────────────── + Float k_select_thr = 0.95 + + # ── Runtime ────────────────────────────────────────────────────────────── + String docker = "us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739" + Int train_disk_size = 200 + Int train_mem_size = 64 + Int train_cpu = 8 + Int eval_disk_size = 100 + Int eval_mem_size = 32 + Int eval_cpu = 4 + # Set train_gpu to 1 to attach a GPU to the training task. That is the only + # thing you need to set: the TrainMixVAE runtime block below already + # declares gpuCount and gpuType ("nvidia-tesla-t4"), and passes --cuda to + # the training script. There is nothing to configure on the Terra side. + # + # If a GPU task fails to schedule, the cause is GCP GPU quota for the region + # on the project behind your Terra billing project, not a workflow setting. + Int train_gpu = 0 + } + + # ── 02a: Optional augmenter training ────────────────────────────────────── + if (run_augmenter) { + call TrainAugmenter { + input: + anndata_h5ad = preprocessed_h5ad, + tag = augmenter_tag, + z_dim = augmenter_z_dim, + noise_dim = augmenter_noise_dim, + fc_dim = augmenter_fc_dim, + n_epoch = augmenter_n_epoch, + batch_size = augmenter_batch_size, + use_gpu = train_gpu > 0, + docker = docker, + disk_size = train_disk_size, + mem_size = train_mem_size, + cpu = train_cpu, + gpu = train_gpu + } + } + + # ── 02b: Core cpl-mixVAE training + pruning ──────────────────────────────── + call TrainMixVAE { + input: + anndata_h5ad = preprocessed_h5ad, + augmenter_checkpoint = TrainAugmenter.augmenter_checkpoint, + n_categories = n_categories, + state_dim = state_dim, + n_arm = n_arm, + latent_dim = latent_dim, + fc_dim = fc_dim, + x_drop = x_drop, + s_drop = s_drop, + temp = temp, + tau = tau, + beta = beta, + lam = lam, + lam_pc = lam_pc, + training_mode = training_mode, + n_epoch = n_epoch, + n_epoch_p = n_epoch_p, + min_con = min_con, + max_prun_it = max_prun_it, + lr = lr, + batch_size = batch_size, + n_aug_smp = n_aug_smp, + train_size = train_size, + seed = seed, + use_gpu = train_gpu > 0, + docker = docker, + disk_size = train_disk_size, + mem_size = train_mem_size, + cpu = train_cpu, + gpu = train_gpu + } + + # ── 03a: Evaluate checkpoints, select model_order ───────────────────────── + call Evaluate { + input: + anndata_h5ad = preprocessed_h5ad, + checkpoints_manifest = TrainMixVAE.checkpoints_manifest, + model_tar = TrainMixVAE.model_tar, + k_select_thr = k_select_thr, + batch_size = batch_size, + seed = seed, + docker = docker, + disk_size = eval_disk_size, + mem_size = eval_mem_size, + cpu = eval_cpu + } + + output { + # ── Human-review outputs ───────────────────────────────────────────────── + # Download evaluation_results.json, inspect the consensus heatmap and + # K-selection curve, then supply evaluation_results_json to MMIDAS_Analyze. + # + # Before continuing, check these three fields in evaluation_results.json: + # k_selection_met_threshold — false means no checkpoint reached + # k_select_thr and model_order came from a fallback, not a selection. + # n_populated_categories — categories with at least one cell assigned. + # model_order counts categories that survived pruning, which can stay + # high while the model routes every cell into a handful of them. + # collapse_warning — non-null when the two disagree badly. + # Do not launch MMIDAS_Analyze on a model that fails these. + File evaluation_results_json = Evaluate.evaluation_results_json + File checkpoints_manifest = TrainMixVAE.checkpoints_manifest + File model_tar = TrainMixVAE.model_tar + Array[File] evaluation_figures = Evaluate.evaluation_figures + File summary_performance = Evaluate.summary_performance + + # ── Optional augmenter ─────────────────────────────────────────────────── + File? augmenter_checkpoint = TrainAugmenter.augmenter_checkpoint + + String pipeline_version_out = pipeline_version + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: TrainAugmenter (02a — optional) +# +# Trains the UDAGAN VAE-GAN augmenter and writes: +# RNA_augmenter__.pth — model checkpoint +# loss_curve.png — training loss +# augmenter_manifest.json +# ────────────────────────────────────────────────────────────────────────────── +task TrainAugmenter { + input { + File anndata_h5ad + String tag + Int z_dim + Int noise_dim + Int fc_dim + Int n_epoch + Int batch_size + Boolean use_gpu + String docker + Int disk_size + Int mem_size + Int cpu + Int gpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file (output of MMIDAS_DataPrep)." + tag: "Short label embedded in the checkpoint filename." + z_dim: "Latent space dimension of the augmenter encoder." + noise_dim: "Additive noise dimension." + fc_dim: "Hidden layer width." + n_epoch: "Number of training epochs." + batch_size: "Mini-batch size." + use_gpu: "Whether to pass --cuda to the script." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + gpu: "Number of GPUs (0 = CPU only)." + } + + command <<< + set -euo pipefail + mkdir -p out + + python3 /usr/local/02a_train_augmenter.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --output_dir out \ + --tag "~{tag}" \ + --z_dim ~{z_dim} \ + --noise_dim ~{noise_dim} \ + --fc_dim ~{fc_dim} \ + --n_epoch ~{n_epoch} \ + --batch_size ~{batch_size} \ + ~{if use_gpu then "--cuda" else ""} + >>> + + output { + File augmenter_checkpoint = glob("out/RNA_augmenter_*.pth")[0] + File augmenter_manifest = "out/augmenter_manifest.json" + File loss_curve = "out/loss_curve.png" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + gpuCount: gpu + gpuType: "nvidia-tesla-t4" + preemptible: 1 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: TrainMixVAE (02b) +# +# Trains the cpl-mixVAE with iterative pruning and writes: +# model/cpl_mixVAE_model_before_pruning_.pth +# model/cpl_mixVAE_model_after_pruning__.pth (one per pruning round) +# checkpoints_manifest.json +# +# The model/ directory is tarred so all checkpoints travel as a single File +# to the Evaluate task, which needs them all to build the summary dict. +# ────────────────────────────────────────────────────────────────────────────── +task TrainMixVAE { + input { + File anndata_h5ad + File? augmenter_checkpoint + Int n_categories + Int state_dim + Int n_arm + Int latent_dim + Int fc_dim + Float x_drop + Float s_drop + Float temp + Float tau + Float beta + Float lam + Float lam_pc + String training_mode + Int n_epoch + Int n_epoch_p + Float min_con + Int max_prun_it + Float lr + Int batch_size + Int n_aug_smp + Float train_size + Int seed + Boolean use_gpu + String docker + Int disk_size + Int mem_size + Int cpu + Int gpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file." + augmenter_checkpoint: "Optional UDAGAN augmenter .pth (output of TrainAugmenter)." + n_categories: "Upper-bound number of cell-type categories." + state_dim: "Continuous state latent variable dimension." + n_arm: "Number of coupled encoder arms." + latent_dim: "Low-dimensional embedding dimension." + fc_dim: "Hidden layer width." + x_drop: "Input-layer dropout probability." + s_drop: "State-variable dropout probability." + temp: "Gumbel-softmax temperature." + tau: "Softmax temperature for categorical variable." + beta: "KL divergence regularization coefficient." + lam: "Coupling factor between arms." + lam_pc: "Coupling factor for the reference arm." + training_mode: "Reconstruction loss: MSE or ZINB." + n_epoch: "Training epochs before pruning." + n_epoch_p: "Training epochs per pruning iteration." + min_con: "Minimum inter-arm consensus to retain a category." + max_prun_it: "Maximum number of pruning iterations." + lr: "Adam optimizer learning rate." + batch_size: "Mini-batch size." + n_aug_smp: "Number of augmented samples per real sample (0 = disabled)." + train_size: "Fraction of cells used for training (rest = validation)." + seed: "Random seed." + use_gpu: "Whether to pass --cuda." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + gpu: "Number of GPUs (0 = CPU only)." + } + + command <<< + set -euo pipefail + mkdir -p out + + python3 /usr/local/02b_train_mixvae.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --output_dir out \ + ~{if defined(augmenter_checkpoint) then "--augmenter_checkpoint \"" + select_first([augmenter_checkpoint]) + "\"" else ""} \ + --n_categories ~{n_categories} \ + --state_dim ~{state_dim} \ + --n_arm ~{n_arm} \ + --latent_dim ~{latent_dim} \ + --fc_dim ~{fc_dim} \ + --x_drop ~{x_drop} \ + --s_drop ~{s_drop} \ + --temp ~{temp} \ + --tau ~{tau} \ + --beta ~{beta} \ + --lam ~{lam} \ + --lam_pc ~{lam_pc} \ + --training_mode ~{training_mode} \ + --n_epoch ~{n_epoch} \ + --n_epoch_p ~{n_epoch_p} \ + --min_con ~{min_con} \ + --max_prun_it ~{max_prun_it} \ + --lr ~{lr} \ + --batch_size ~{batch_size} \ + --n_aug_smp ~{n_aug_smp} \ + --train_size ~{train_size} \ + --seed ~{seed} \ + ~{if use_gpu then "--cuda" else ""} + + # Tar the model directory so all checkpoints travel as one File + tar -czf model.tar.gz -C out model/ + >>> + + output { + File checkpoints_manifest = "out/checkpoints_manifest.json" + File model_tar = "model.tar.gz" + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + gpuCount: gpu + gpuType: "nvidia-tesla-t4" + preemptible: 0 + } +} + + +# ────────────────────────────────────────────────────────────────────────────── +# Task: Evaluate (03a) +# +# Evaluates all checkpoints, runs K_selection to choose model_order, and writes: +# evaluation_results.json ← KEY HAND-OFF FILE for human review +# consensus_T1_vs_T2_K_.png +# norm_consensus_T1_vs_T2_K_.png +# state_mu_K__arm_.png +# summary_performance_K__narm_.p +# +# Human review step: inspect evaluation_results.json and figures to confirm +# model_order is biologically sensible before submitting MMIDAS_Analyze. +# ────────────────────────────────────────────────────────────────────────────── +task Evaluate { + input { + File anndata_h5ad + File checkpoints_manifest + File model_tar + Float k_select_thr + Int batch_size + Int seed + String docker + Int disk_size + Int mem_size + Int cpu + } + + parameter_meta { + anndata_h5ad: "Preprocessed AnnData .h5ad file." + checkpoints_manifest: "checkpoints_manifest.json from TrainMixVAE." + model_tar: "Tarball of the model/ checkpoint directory from TrainMixVAE." + k_select_thr: "Minimum consensus threshold for K_selection(). Default: 0.95." + batch_size: "Batch size for inference." + seed: "Random seed (must match 02b)." + docker: "MMIDAS Docker image URI." + disk_size: "Boot disk size in GB." + mem_size: "Memory in GB." + cpu: "Number of CPU cores." + } + + command <<< + set -euo pipefail + mkdir -p out + + # Restore the model checkpoints into the same relative path the manifest expects + tar -xzf "~{model_tar}" -C out/ + + # The manifest contains absolute paths from the TrainMixVAE task's working + # directory. Rewrite them to point to our local out/ directory. + MANIFEST_LOCAL="out/checkpoints_manifest.json" + cp "~{checkpoints_manifest}" "$MANIFEST_LOCAL" + TRAIN_OUTPUT_DIR=$(python3 -c "import json; print(json.load(open('$MANIFEST_LOCAL'))['output_dir'])") + sed -i "s|${TRAIN_OUTPUT_DIR}|$(pwd)/out|g" "$MANIFEST_LOCAL" + + python3 /usr/local/03a_evaluate.py \ + --anndata_h5ad "~{anndata_h5ad}" \ + --checkpoints_manifest "$MANIFEST_LOCAL" \ + --output_dir out \ + --k_select_thr ~{k_select_thr} \ + --batch_size ~{batch_size} \ + --seed ~{seed} + >>> + + output { + File evaluation_results_json = "out/evaluation_results.json" + Array[File] evaluation_figures = glob("out/*.png") + # evaluation_results.json names this pickle in its "summary_pickle" field. + # Without delocalizing it the reference dangles, and the per-checkpoint + # consensus/reconstruction values behind the K-selection decision are lost + # when the VM is torn down. + File summary_performance = glob("out/summary_performance_K_*.p")[0] + } + + runtime { + docker: docker + disks: "local-disk ~{disk_size} HDD" + memory: "~{mem_size} GiB" + cpu: cpu + preemptible: 3 + } +} diff --git a/pipelines/wdl/mmidas/MMIDAS_output_validation.ipynb b/pipelines/wdl/mmidas/MMIDAS_output_validation.ipynb new file mode 100644 index 0000000000..4eb549db7a --- /dev/null +++ b/pipelines/wdl/mmidas/MMIDAS_output_validation.ipynb @@ -0,0 +1,2571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "487767f3", + "metadata": {}, + "source": [ + "# MMIDAS Pipeline Output Validation\n", + "\n", + "Sanity-checks the outputs of `MMIDAS_DataPrep` -> `MMIDAS_Train` -> `MMIDAS_Analyze`.\n", + "\n", + "Fill in the `gs://` paths in the **Config** cell below with the actual outputs from your run,\n", + "then run all cells top to bottom.\n", + "\n", + "Each check prints one of:\n", + "- `[PASS]` / `[FAIL]` - an automated, threshold-based check\n", + "- `[REVIEW]` - something (usually a figure) that needs a human eyeball rather than a hard threshold\n", + "\n", + "Requires `gsutil` on PATH and authenticated (`gcloud auth login` / `gcloud auth application-default login`),\n", + "plus the packages imported below (`anndata`, `matplotlib`, `numpy`).\n", + "\n", + "---\n", + "\n", + "**What this notebook can and cannot tell you.** Most checks here verify *plumbing*: that each\n", + "task ran, wrote the files it declared, and that the manifests agree with each other. A pipeline\n", + "can pass all of those while producing a scientifically useless model, so the checks below are\n", + "deliberately written to separate the two:\n", + "\n", + "- **Plumbing** — files exist, shapes line up, manifests are mutually consistent, stages consumed\n", + " the same inputs (`Stage 0 -- Lineage`).\n", + "- **Model quality** — `n_populated_categories` vs `model_order`, `avg_consensus` vs\n", + " `k_select_thr`, and t-type classification accuracy against the PCA baseline.\n", + "\n", + "Two traps this notebook is written to avoid, both of which previously produced a green result on\n", + "a run whose model had collapsed to ~10 usable categories out of a reported 111:\n", + "\n", + "1. **`model_order` is not the number of cell types the model found.** It counts categories that\n", + " survived pruning. Pruning removes at most one category per round, so with\n", + " `n_categories = 120` and `max_prun_it = 14` it can only ever land in 106-120 — a\n", + " \"`model_order` < ceiling\" check cannot meaningfully fail. Check `n_populated_categories`.\n", + "2. **Counting output files is not checking them.** Ten state-traversal figures were produced for\n", + " ten categories that had no cells assigned; all ten were byte-identical apart from the title.\n", + " The checks below compare figure contents, not just counts." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3134bb7f", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install anndata" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4cbeeecb", + "metadata": {}, + "outputs": [], + "source": [ + "import hashlib\n", + "import io\n", + "import json\n", + "import os\n", + "import pickle\n", + "import re\n", + "import subprocess\n", + "import tarfile\n", + "\n", + "import anndata as ad\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from IPython.display import Image, display" + ] + }, + { + "cell_type": "markdown", + "id": "86fdfd89", + "metadata": {}, + "source": [ + "## Config\n", + "\n", + "Fill in the actual `gs://` output paths from your run." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b8746984", + "metadata": {}, + "outputs": [], + "source": [ + "# Outputs of the run that reproduced the published analysis:\n", + "# MMIDAS_Train a8080266 -> model_order 89, avg_consensus 0.9692,\n", + "# 89/89 categories populated, K_selection met thr\n", + "# MMIDAS_Analyze c63363a3 -> 21 KEGG pathways mapped\n", + "#\n", + "# Figure entries are given as a gs:// *prefix*, not a list of URIs: resolve_paths\n", + "# globs \"/**/*.png\" (see gcs_list in the Helpers cell). That keeps this\n", + "# cell short and survives a change in figure count -- StateTraversal alone emits\n", + "# 41 figures once KEGG mapping is working.\n", + "DATAPREP_RUN = (\"gs://fc-1f55735a-9fc4-4ef2-8bbe-60a7ea8bac60/submissions/1578c9e2-d627-4ef8-bde6-cae0f3fe3cb2/MMIDAS_DataPrep/9a82aa18-07ca-46b0-bd96-e4bd83b79874\")\n", + "TRAIN_RUN = (\"gs://fc-1f55735a-9fc4-4ef2-8bbe-60a7ea8bac60/submissions/1bf141b9-23dd-4aba-b623-81e459ca9bf8/MMIDAS_Train/e131ff3f-6242-4228-8caf-86f9f570bee6\")\n", + "ANALYZE_RUN = (\"gs://fc-1f55735a-9fc4-4ef2-8bbe-60a7ea8bac60/submissions/580a5fe7-6984-4a96-a96a-94f0c0bdd869/MMIDAS_Analyze/414eb7bf-fab7-4fd7-8606-44bc0c9a3df6\")\n", + "\n", + "CONFIG = {\n", + " \"dataprep\": {\n", + " \"preprocessed_h5ad\": f\"{DATAPREP_RUN}/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad\",\n", + " },\n", + "\n", + " \"train\": {\n", + " \"evaluation_results_json\": f\"{TRAIN_RUN}/call-Evaluate/out/evaluation_results.json\",\n", + " \"checkpoints_manifest\": f\"{TRAIN_RUN}/call-TrainMixVAE/out/checkpoints_manifest.json\",\n", + " \"model_tar\": f\"{TRAIN_RUN}/call-TrainMixVAE/model.tar.gz\",\n", + " # 4 figures: consensus / norm_consensus bubble plots + state_mu per arm.\n", + " \"evaluation_figures\": f\"{TRAIN_RUN}/call-Evaluate\",\n", + " },\n", + "\n", + " \"analyze\": {\n", + " \"clusterability_manifest\": f\"{ANALYZE_RUN}/call-Clusterability/out/clusterability_manifest.json\",\n", + " # 9 figures: classAcc_RF, SC_K, and the confusion-matrix heatmaps.\n", + " \"clusterability_figures\": f\"{ANALYZE_RUN}/call-Clusterability\",\n", + " \"state_traversal_manifest\": f\"{ANALYZE_RUN}/call-StateTraversal/out/state_traversal_manifest.json\",\n", + " # 41 figures in three families -- 10 state_mu_arm_* scatters,\n", + " # 21 s_pc_path_* pathway plots, 10 pathway_summary_c_* summaries.\n", + " # The review cell groups them by family; do not treat them as one set.\n", + " \"state_traversal_figures\": f\"{ANALYZE_RUN}/call-StateTraversal\",\n", + " },\n", + "\n", + " # Optional. classify_manifest.json / clustering_tar are intermediate outputs of\n", + " # the Classify (03b) task -- NOT part of MMIDAS_Analyze's final workflow\n", + " # outputs, so they do not appear in the Terra outputs table and have to be\n", + " # pulled from the execution directory by hand.\n", + " #\n", + " # NOTE the attempt-2 segment: Classify was retried on this run and the\n", + " # first-attempt directory holds no outputs. Check which attempt-N actually\n", + " # contains out/ before copying these paths for a different submission.\n", + " \"classify_optional\": {\n", + " # The Classify call directory. The attempt-N subdirectory that actually\n", + " # holds the outputs is discovered at read time by gcs_find_under() --\n", + " # it was attempt-2 on one submission and attempt-3 on the next, so it\n", + " # must not be hardcoded.\n", + " \"classify_call_dir\": f\"{ANALYZE_RUN}/call-Classify\",\n", + " },\n", + "\n", + " # ── Reference analysis ───────────────────────────────────────────────────\n", + " # The published results for this dataset, read from the saved outputs of the\n", + " # MMIDAS repo's own notebooks. These are the numbers a faithful port should\n", + " # land on; the fidelity checks below compare against them.\n", + " #\n", + " # notebooks/1_data_prep.ipynb -> \"final shape of normalized gene\n", + " # expresion matix: (22365, 5032)\"\n", + " # notebooks/3_evaluation.ipynb -> \"Selected number of clusters: 92 with\n", + " # consensus 0.954441573926868\"\n", + " # \"Average consensus on test cells:\n", + " # 0.9390906545871837\"\n", + " # -> model dir holds after_pruning_1..42\n", + " # notebooks/4_clusterability.ipynb, 5_state_traversal.ipynb\n", + " # -> model_order = 92 hardcoded\n", + " \"reference\": {\n", + " \"shape\": [22365, 5032],\n", + " \"n_ttype\": 115,\n", + " \"model_order\": 92,\n", + " # Published: 0.9390 on test cells, 0.9544 from K_selection. A rerun will\n", + " # not hit either exactly -- the reference train/test split was unseeded\n", + " # -- so this is a floor, not a range.\n", + " #\n", + " # Deliberately one-sided: consensus measures how well the two arms agree\n", + " # on their category assignments, so exceeding the reference means the\n", + " # categories are *more* reproducible than published. That is not a\n", + " # fidelity defect and must not fail. An earlier two-sided range capped at\n", + " # 0.960 wrongly failed a run that reproduced the analysis at 0.9692.\n", + " #\n", + " # CALIBRATION. Training is stochastic -- the reference split was unseeded\n", + " # and GPU reductions are non-associative -- so this floor has to admit\n", + " # the algorithm's run-to-run spread, not just the best run seen. Two\n", + " # full-length runs of the identical configuration gave 0.9075 and\n", + " # 0.9692, bracketing the published 0.9390, with model_order 96 and 89\n", + " # against the published 92. The floor was previously 0.930, set from the\n", + " # 0.9692 run alone, and rejected the 0.9075 run even though it completed\n", + " # all 42 pruning rounds with every category populated and\n", + " # k_selection_met_threshold true.\n", + " #\n", + " # 0.900 sits just below the lower of the two observations. It is\n", + " # calibrated on n=2 and is worth revisiting as more runs accumulate; it\n", + " # is not a tight bound. It remains far above a collapsed model, which is\n", + " # the failure this check exists to catch -- a collapsed run measured\n", + " # 0.026.\n", + " \"avg_consensus_min\": 0.900,\n", + " \"pruning_rounds\": 42,\n", + " # How far model_order may sit from the reference and still count as a\n", + " # match. Pruning is one category per round, so this is a tolerance in\n", + " # pruning rounds.\n", + " \"model_order_tol\": 5,\n", + "\n", + " # Classification accuracies from the \"Accuracy (%)\" bar chart saved in\n", + " # notebooks/4_clusterability.ipynb. That notebook prints no numbers, so\n", + " # these were READ OFF THE FIGURE BY EYE -- treat them as +/- 1-2 points,\n", + " # not as published values.\n", + " #\n", + " # group Linear (PCA-100) Non-Linear (MMIDAS lowD)\n", + " # t-types ~84.5% ~73.5%\n", + " # Merged t-types ~85.5% ~77%\n", + " # T Categories ~88.5% ~98%\n", + " #\n", + " # Only the t-types row is comparable here: the workflow does not compute\n", + " # the \"Merged t-types\" group, and \"T Categories\" is the near-circular\n", + " # ConsType comparison.\n", + " \"ttype_acc_pca\": 0.845,\n", + " \"ttype_acc_lowd\": 0.735,\n", + " \"ttype_acc_gap\": 0.110,\n", + " # How much wider than the reference's gap this run's may be before it is\n", + " # worth a look. Advisory only -- a downstream classifier metric with\n", + " # ~0.05 fold-to-fold spread, compared against eyeballed figures, is not\n", + " # a sound gate on whether the port is faithful.\n", + " \"ttype_acc_gap_tol\": 0.06,\n", + " },\n", + "\n", + " # Expected values -- should match the inputs used for this run.\n", + " \"expected\": {\n", + " # Row count of the selected_genes CSV passed to MMIDAS_DataPrep, minus\n", + " # its header. genes_SS_ALM-VISp.csv is 5033 lines => 5032 genes.\n", + " # Confirm for your own run with:\n", + " # gsutil cat | wc -l\n", + " \"n_selected_genes\": 5032,\n", + " \"neuronal_classes\": [\"GABAergic\", \"Glutamatergic\"],\n", + " \"remove_clusters\": [\"Low Quality\", \"CR Lhx5\", \"Meis2 Adamts19\"],\n", + " \"n_categories\": 120, # MMIDAS_Train.n_categories input (pruning ceiling)\n", + " \"k_select_thr\": 0.95, # MMIDAS_Train.k_select_thr input\n", + " \"n_selected_cats\": 10, # MMIDAS_Analyze.n_selected_cats input\n", + " \"kegg_toml_supplied\": True,\n", + "\n", + " # min_retained_cpm_frac: DataPrep computes log-CPM over the whole\n", + " # transcriptome and only then subsets to selected_genes, so undoing\n", + " # log1p on the .h5ad recovers the share of each cell's CPM mass that\n", + " # falls inside the selected panel -- not 1e6. A loose floor to catch a\n", + " # wrong or truncated gene list, not a tight expectation: the value\n", + " # depends on how much of the transcriptome the panel covers.\n", + " # genes_SS_ALM-VISp.csv gives 0.70-0.89 (Malat1 alone is a large\n", + " # share). A full-transcriptome .h5ad would give ~1.0.\n", + " \"min_retained_cpm_frac\": 0.10, # was MMIDAS_Analyze.kegg_toml provided?\n", + "\n", + " # Model-quality thresholds.\n", + " #\n", + " # min_populated_frac: fraction of model_order that must actually have\n", + " # cells assigned. model_order counts categories that survived pruning,\n", + " # which stays high even when the discrete latent has collapsed and\n", + " # every cell lands in a handful of categories.\n", + " \"min_populated_frac\": 0.5,\n", + " },\n", + "}\n", + "\n", + "SCRATCH_DIR = \"/tmp/mmidas_validation\"" + ] + }, + { + "cell_type": "markdown", + "id": "4a70f365", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fdb01be8", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(SCRATCH_DIR, exist_ok=True)\n", + "\n", + "CHECKS = []\n", + "REVIEW_ITEMS = []\n", + "\n", + "\n", + "# Every check answers exactly one of three different questions. Conflating them\n", + "# is how a run gets called \"broken\" when it merely produced a weak model, or\n", + "# \"fine\" when it silently diverged from the reference.\n", + "#\n", + "# plumbing Did the workflow execute correctly? Files exist, shapes agree,\n", + "# manifests are mutually consistent, stages consumed the same\n", + "# inputs. A failure here means the WDL port is wrong.\n", + "# fidelity Does this run match the published reference analysis in the MMIDAS\n", + "# repo? A failure here means the port runs, but not the way the\n", + "# authors ran it. This is the project's actual acceptance test.\n", + "# advisory Is the resulting model any good? Reported, never fatal. These\n", + "# thresholds are this notebook's own opinion, not the authors'.\n", + "CHECK_KINDS = (\"plumbing\", \"fidelity\", \"advisory\")\n", + "\n", + "\n", + "def check(name, passed, detail=\"\", kind=\"plumbing\"):\n", + " \"\"\"Record an automated pass/fail check. See CHECK_KINDS.\"\"\"\n", + " assert kind in CHECK_KINDS, f\"unknown check kind: {kind}\"\n", + " CHECKS.append({\"name\": name, \"passed\": bool(passed),\n", + " \"detail\": detail, \"kind\": kind})\n", + " status = \"PASS\" if passed else \"FAIL\"\n", + " tag = \"\" if kind == \"plumbing\" else f\" [{kind}]\"\n", + " print(f\"[{status}]{tag} {name}\" + (f\" -- {detail}\" if detail else \"\"))\n", + "\n", + "\n", + "def review(name, detail=\"\"):\n", + " \"\"\"Flag something that needs a human eyeball rather than a hard threshold.\"\"\"\n", + " REVIEW_ITEMS.append({\"name\": name, \"detail\": detail})\n", + " print(f\"[REVIEW] {name}\" + (f\" -- {detail}\" if detail else \"\"))\n", + "\n", + "\n", + "# Defaults for thresholds added after the first version of this notebook.\n", + "# CONFIG is normally carried forward by hand when repointing at a new run, so a\n", + "# CONFIG cell can be older than the check cells that read it. Reading through\n", + "# expected() degrades to a documented default instead of raising KeyError\n", + "# part-way through a stage.\n", + "EXPECTED_DEFAULTS = {\n", + " \"min_retained_cpm_frac\": 0.10,\n", + " \"min_populated_frac\": 0.5,\n", + " \"n_selected_cats\": 0, # 0 = \"not specified\", check becomes a no-op\n", + " \"kegg_toml_supplied\": False, # conservative: do not assert pathways exist\n", + "}\n", + "\n", + "\n", + "def expected(key):\n", + " \"\"\"Read CONFIG[\"expected\"][key], falling back to EXPECTED_DEFAULTS.\"\"\"\n", + " exp = CONFIG.get(\"expected\", {})\n", + " if key in exp:\n", + " return exp[key]\n", + " if key in EXPECTED_DEFAULTS:\n", + " default = EXPECTED_DEFAULTS[key]\n", + " print(f\" (CONFIG['expected']['{key}'] not set -- using default {default!r})\")\n", + " return default\n", + " raise KeyError(\n", + " f\"CONFIG['expected']['{key}'] is required and has no default. \"\n", + " f\"Add it to the Config cell.\"\n", + " )\n", + "\n", + "\n", + "def _clean_uri(gs_path):\n", + " \"\"\"Strip stray whitespace from a gs:// URI and fail with a clear message.\n", + "\n", + " Paths are pasted in by hand, and a leading tab or newline reaches gsutil as\n", + " part of the scheme: `InvalidUrlError: Unrecognized scheme \"\\tgs\"`, raised\n", + " from inside subprocess, which is a confusing way to learn about a typo.\n", + " \"\"\"\n", + " cleaned = str(gs_path).strip().strip('\"').strip(\"'\")\n", + " if not cleaned.startswith(\"gs://\"):\n", + " raise ValueError(\n", + " f\"expected a gs:// URI, got {gs_path!r}. Check the Config cell for \"\n", + " f\"stray whitespace, quotes, or a truncated paste.\"\n", + " )\n", + " return cleaned\n", + "\n", + "\n", + "# Cromwell does not always write a task's outputs at call-/. Two\n", + "# segments get interposed, and both are invisible in the Terra outputs table and\n", + "# in the workflow inputs you copy paths from:\n", + "#\n", + "# attempt-N/ the task was retried; the last attempt is the one that succeeded\n", + "# cacheCopy/ the call was satisfied from the call cache, so Cromwell copied a\n", + "# previous run's output in rather than recomputing it\n", + "#\n", + "# So a hand-written CONFIG path routinely names a file that does not exist at\n", + "# that exact URI while a byte-identical one sits a directory deeper. Keep the\n", + "# knowledge of these segments here, in one place, rather than in each caller.\n", + "CROMWELL_MAX_ATTEMPT = 9\n", + "\n", + "\n", + "def _interposed_variants(dir_uri, leaf, attempts_first=False):\n", + " \"\"\"Candidate URIs for `leaf` under a Cromwell call directory.\"\"\"\n", + " dir_uri = dir_uri.rstrip(\"/\")\n", + " plain = [f\"{dir_uri}/{leaf}\", f\"{dir_uri}/cacheCopy/{leaf}\"]\n", + " attempts = []\n", + " for n in range(CROMWELL_MAX_ATTEMPT, 1, -1):\n", + " attempts += [\n", + " f\"{dir_uri}/attempt-{n}/{leaf}\",\n", + " f\"{dir_uri}/attempt-{n}/cacheCopy/{leaf}\",\n", + " ]\n", + " return attempts + plain if attempts_first else plain + attempts\n", + "\n", + "\n", + "# gsutil exits non-zero for \"the object is not there\" and for \"I could not\n", + "# find out\" alike -- expired credentials, a revoked bucket grant, a network\n", + "# blip. Collapsing those to \"absent\" turns an auth problem into a bogus report\n", + "# that the file is missing and the Config cell is wrong, sending you to debug\n", + "# the wrong thing. These substrings mark answers that are not evidence of\n", + "# absence.\n", + "_UNKNOWN_HINTS = (\n", + " \"reauthentication required\", \"login required\", \"credentials\",\n", + " \"accessdenied\", \"403\", \"401\", \"does not have storage.objects\",\n", + " \"anonymous caller\", \"serviceexception\", \"timed out\",\n", + ")\n", + "\n", + "\n", + "def gcs_stat(gs_path):\n", + " \"\"\"Return (exists, uncertainty). uncertainty is None only for a real answer.\"\"\"\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"stat\", gs_path], capture_output=True, text=True,\n", + " )\n", + " if result.returncode == 0:\n", + " return True, None\n", + " blob = f\"{result.stderr or ''}\\n{result.stdout or ''}\".lower()\n", + " for hint in _UNKNOWN_HINTS:\n", + " if hint in blob:\n", + " first = next(\n", + " (ln.strip() for ln in (result.stderr or \"\").splitlines()\n", + " if ln.strip()),\n", + " f\"gsutil exit {result.returncode}\",\n", + " )\n", + " return False, first\n", + " return False, None\n", + "\n", + "\n", + "def gcs_exists(gs_path):\n", + " \"\"\"True if a gs:// object exists. Raises if existence cannot be determined.\"\"\"\n", + " exists, uncertain = gcs_stat(gs_path)\n", + " if uncertain:\n", + " raise RuntimeError(\n", + " f\"could not determine whether {gs_path} exists: {uncertain}. \"\n", + " f\"This is an access problem, not a missing file -- re-run \"\n", + " f\"`gcloud auth login` (and check you are a member of the \"\n", + " f\"workspace) before trusting any check in this notebook.\"\n", + " )\n", + " return exists\n", + "\n", + "\n", + "def gcs_resolve(gs_path, quiet=False):\n", + " \"\"\"Return an existing URI for `gs_path`, allowing for cacheCopy/attempt-N.\n", + "\n", + " Resolution is announced rather than silent. A path that quietly reads from\n", + " somewhere other than what CONFIG names is how stale or mismatched data gets\n", + " validated without anyone noticing, which is worse than the failure it\n", + " replaces.\n", + " \"\"\"\n", + " gs_path = _clean_uri(gs_path)\n", + " if gcs_exists(gs_path): # raises on auth failure rather than reporting absent\n", + " return gs_path\n", + " if \"/\" not in gs_path[len(\"gs://\"):]:\n", + " raise FileNotFoundError(f\"{gs_path} does not exist.\")\n", + " head, leaf = gs_path.rsplit(\"/\", 1)\n", + " for cand in _interposed_variants(head, leaf):\n", + " if cand != gs_path and gcs_exists(cand):\n", + " if not quiet:\n", + " where = cand[len(head) + 1:-len(leaf) - 1] or \".\"\n", + " print(f\" (resolved {leaf} under {where}/ -- Cromwell wrote it \"\n", + " f\"there, not at the configured path)\")\n", + " return cand\n", + " raise FileNotFoundError(\n", + " f\"{gs_path} does not exist, and nor does it under cacheCopy/ or \"\n", + " f\"attempt-2..{CROMWELL_MAX_ATTEMPT}/. Check the Config cell: the \"\n", + " f\"submission/workflow IDs are the usual culprit.\"\n", + " )\n", + "\n", + "\n", + "def gcs_download(gs_path, dest_dir=SCRATCH_DIR):\n", + " \"\"\"Download a single gs:// object and return its local path.\n", + "\n", + " The local cache is namespaced by a digest of the *full* gs:// URI, not by\n", + " basename. Every MMIDAS submission writes files with identical names --\n", + " evaluation_results.json, clusterability_manifest.json,\n", + " Mouse_ALM-VISp_cpm.h5ad, state_mu_arm_0_c_*.png -- so a basename-keyed cache\n", + " silently serves a previous run's file after you repoint CONFIG at a new\n", + " submission, and every check downstream then validates stale data. The\n", + " symptom is subtle: cells that read through this helper show the old run\n", + " while cells using `gsutil cat` directly (e.g. the lineage checks) show the\n", + " new one.\n", + " \"\"\"\n", + " # Resolve before hashing so the cache key is the URI actually fetched.\n", + " gs_path = gcs_resolve(gs_path)\n", + " digest = hashlib.md5(gs_path.encode()).hexdigest()[:10]\n", + " local_dir = os.path.join(dest_dir, digest)\n", + " local_path = os.path.join(local_dir, os.path.basename(gs_path))\n", + " if not os.path.exists(local_path):\n", + " os.makedirs(local_dir, exist_ok=True)\n", + " subprocess.run([\"gsutil\", \"-q\", \"cp\", gs_path, local_path], check=True)\n", + " return local_path\n", + "\n", + "\n", + "def gcs_list(prefix, pattern=\"*.png\"):\n", + " \"\"\"List objects under a gs:// prefix (recursive) matching a glob pattern.\"\"\"\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"ls\", os.path.join(prefix.rstrip(\"/\"), \"**\", pattern)],\n", + " capture_output=True, text=True,\n", + " )\n", + " # Surface auth/permission/typo failures instead of silently returning [],\n", + " # which reads downstream as \"this stage produced no figures\".\n", + " if result.returncode != 0:\n", + " raise RuntimeError(\n", + " f\"gsutil ls failed for {prefix} (exit {result.returncode}): \"\n", + " f\"{result.stderr.strip()}\"\n", + " )\n", + " return [line for line in result.stdout.splitlines() if line.strip()]\n", + "\n", + "\n", + "def resolve_paths(value, pattern=\"*.png\"):\n", + " \"\"\"CONFIG figure entries may be a list of gs:// URIs or a single prefix to glob.\"\"\"\n", + " if value is None:\n", + " return []\n", + " if isinstance(value, (list, tuple)):\n", + " return [_clean_uri(v) for v in value]\n", + " return gcs_list(_clean_uri(value), pattern=pattern)\n", + "\n", + "\n", + "def load_json_gcs(gs_path):\n", + " with open(gcs_download(gs_path)) as fh:\n", + " return json.load(fh)\n", + "\n", + "\n", + "def load_h5ad_gcs(gs_path):\n", + " return ad.read_h5ad(gcs_download(gs_path))\n", + "\n", + "\n", + "def extract_tar_gcs(gs_path, subdir):\n", + " local = gcs_download(gs_path)\n", + " dest = os.path.join(SCRATCH_DIR, subdir)\n", + " os.makedirs(dest, exist_ok=True)\n", + " with tarfile.open(local) as tf:\n", + " tf.extractall(dest)\n", + " return dest\n", + "\n", + "\n", + "def gcs_find_under(call_dir, leaf):\n", + " \"\"\"Locate `leaf` under a Cromwell call directory, allowing for attempt-N.\n", + "\n", + " Cromwell writes a retried task's outputs into call-/attempt-N/ rather\n", + " than call-/. The attempt number is not stable between submissions --\n", + " Classify landed in attempt-2 on one run and attempt-3 on the next -- so\n", + " hardcoding it silently breaks every time the task happens to retry. Probe\n", + " the plain path first, then attempt-N from highest to lowest (the last\n", + " attempt is the one that succeeded).\n", + " \"\"\"\n", + " call_dir = _clean_uri(call_dir).rstrip(\"/\")\n", + " for cand in _interposed_variants(call_dir, leaf):\n", + " if gcs_exists(cand):\n", + " return cand\n", + " raise FileNotFoundError(\n", + " f\"could not find {leaf!r} under {call_dir} (tried the call directory, \"\n", + " f\"cacheCopy/, and attempt-2..{CROMWELL_MAX_ATTEMPT}/, each with and \"\n", + " f\"without cacheCopy/). Check the task actually produced outputs.\"\n", + " )\n", + "\n", + "\n", + "def gcs_md5(gs_path):\n", + " \"\"\"Return the MD5 that GCS holds for an object, for lineage comparison.\n", + "\n", + " Composite/multipart uploads have no MD5; those report a crc32c instead, so\n", + " fall back to that rather than failing the lineage check outright.\n", + " \"\"\"\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"stat\", gs_path], capture_output=True, text=True,\n", + " )\n", + " if result.returncode != 0:\n", + " return None\n", + " for line in result.stdout.splitlines():\n", + " if \"Hash (md5):\" in line or \"Hash (crc32c):\" in line:\n", + " return line.split(\":\", 1)[1].strip()\n", + " return None\n", + "\n", + "\n", + "def show_image(gs_or_local_path, title=None):\n", + " \"\"\"Display a PNG at its native resolution.\n", + "\n", + " Round-tripping through plt.imread -> imshow -> inline PNG resamples the\n", + " figure down to the Matplotlib canvas size, which shrank 726 px source\n", + " figures to under 200 px and made the [REVIEW] items unreadable. IPython's\n", + " Image embeds the original bytes.\n", + " \"\"\"\n", + " path = (\n", + " gcs_download(gs_or_local_path)\n", + " if str(gs_or_local_path).startswith(\"gs://\")\n", + " else gs_or_local_path\n", + " )\n", + " if title:\n", + " print(f\" --- {title}\")\n", + " display(Image(filename=path))\n", + "\n", + "\n", + "def image_content_hash(gs_or_local_path, ignore_top_frac=0.14):\n", + " \"\"\"Hash a figure's pixels below the title band.\n", + "\n", + " Two per-category figures that differ only in their title are two renderings\n", + " of the same data -- which is what the state-traversal step produced when it\n", + " selected categories with no cells assigned. Comparing file bytes would not\n", + " catch it (the titles differ); comparing pixels below the title does.\n", + " \"\"\"\n", + " path = (\n", + " gcs_download(gs_or_local_path)\n", + " if str(gs_or_local_path).startswith(\"gs://\")\n", + " else gs_or_local_path\n", + " )\n", + " img = plt.imread(path)\n", + " top = int(img.shape[0] * ignore_top_frac)\n", + " body = np.ascontiguousarray(img[top:])\n", + " return hashlib.md5(body.tobytes()).hexdigest()" + ] + }, + { + "cell_type": "markdown", + "id": "b75b83a1", + "metadata": {}, + "source": [ + "## Stage 0 -- Lineage\n", + "\n", + "The three workflows are three separate Terra submissions, so nothing structurally guarantees that\n", + "`MMIDAS_Analyze` consumed the `.h5ad` that `MMIDAS_DataPrep` produced, or the checkpoint that\n", + "`MMIDAS_Train` selected. Matching `n_gene` and `model_order` only shows two JSON files agree on a\n", + "number — two different runs of the same config agree on those too.\n", + "\n", + "This cell reads the `.h5ad` path and checkpoint filename each stage actually logged to `stdout`\n", + "and confirms they are the same object. It needs the Cromwell execution directory for each stage,\n", + "which is the parent of the `call-*` directories in the paths configured above." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ed6f42ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h5ad loaded by each stage (bucket-relative):\n", + " submissions/1578c9e2-d627-4ef8-bde6-cae0f3fe3cb2/MMIDAS_DataPrep/9a82aa18-07ca-46b0-bd96-e4bd83b79874/call-DataPrep/cacheCopy/Mouse_ALM-VISp_cpm.h5ad\n", + " <- Classify, Evaluate, StateTraversal, TrainMixVAE, TraversalPrep\n", + "\n", + "checkpoint used for inference by each Analyze task:\n", + " cpl_mixVAE_model_after_pruning_24_2026-08-27-18-22-38.pth\n", + " <- Classify, StateTraversal, TraversalPrep\n", + "[PASS] all stages loaded the same preprocessed .h5ad -- 1 distinct path(s): submissions/1578c9e2-d627-4ef8-bde6-cae0f3fe3cb2/MMIDAS_DataPrep/9a82aa18-07ca-46b0-bd96-e4bd83b79874/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad <- ['Classify', 'Evaluate', 'StateTraversal', 'TrainMixVAE', 'TraversalPrep']\n", + "[PASS] the .h5ad the stages loaded is the one configured above -- configured submissions/1578c9e2-d627-4ef8-bde6-cae0f3fe3cb2/MMIDAS_DataPrep/9a82aa18-07ca-46b0-bd96-e4bd83b79874/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad, stages loaded ['submissions/1578c9e2-d627-4ef8-bde6-cae0f3fe3cb2/MMIDAS_DataPrep/9a82aa18-07ca-46b0-bd96-e4bd83b79874/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad']\n", + "[PASS] every Analyze task ran inference with the same checkpoint -- 1 distinct checkpoint(s): cpl_mixVAE_model_after_pruning_24_2026-08-27-18-22-38.pth <- ['Classify', 'StateTraversal', 'TraversalPrep']\n", + "[PASS] lineage stdout was readable for every consuming task\n" + ] + } + ], + "source": [ + "def _workflow_root(gs_path):\n", + " \"\"\"Strip everything from the /call-/ component onwards.\"\"\"\n", + " idx = gs_path.find(\"/call-\")\n", + " return gs_path[:idx] if idx != -1 else None\n", + "\n", + "\n", + "def _read_stdout(workflow_root, task):\n", + " \"\"\"Fetch /call-/**/stdout, tolerating attempt-N dirs.\n", + "\n", + " Highest attempt first, plain path last -- the opposite order to\n", + " gcs_find_under(), because the last attempt is the one that succeeded.\n", + "\n", + " Returns None rather than raising, so the caller can report which tasks were\n", + " unreadable instead of aborting the whole lineage check.\n", + " \"\"\"\n", + " # attempts_first: a retried task can leave a stdout at the plain path from an\n", + " # earlier *failed* attempt. Also covers cacheCopy/, where Cromwell puts the\n", + " # logs of a call it satisfied from the call cache.\n", + " candidates = _interposed_variants(\n", + " f\"{workflow_root}/call-{task}\", \"stdout\", attempts_first=True\n", + " )\n", + " for candidate in candidates:\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"cat\", candidate], capture_output=True, text=True,\n", + " )\n", + " if result.returncode == 0:\n", + " return result.stdout\n", + " return None\n", + "\n", + "\n", + "def _bucket_relative(path):\n", + " \"\"\"Normalize a gs:// URI and a localized container path to the same key.\n", + "\n", + " Tasks log the *localized* path, e.g.\n", + " /mnt/disks/cromwell_root//submissions/.../file.h5ad\n", + " for what the config names as\n", + " gs:///submissions/.../file.h5ad\n", + " Comparing the two verbatim always fails, so reduce both to the part after\n", + " the bucket name.\n", + " \"\"\"\n", + " for prefix in (\"gs://\", \"/mnt/disks/cromwell_root/\", \"/cromwell_root/\"):\n", + " if path.startswith(prefix):\n", + " rest = path[len(prefix):]\n", + " return rest.split(\"/\", 1)[1] if \"/\" in rest else rest\n", + " return path.lstrip(\"/\")\n", + "\n", + "\n", + "def _lineage_key(path):\n", + " \"\"\"Bucket-relative path with Cromwell's cacheCopy/attempt-N segments dropped.\n", + "\n", + " call-DataPrep/cacheCopy/x.h5ad and call-DataPrep/x.h5ad are the same output\n", + " of the same call, so comparing them verbatim reports a lineage break that\n", + " did not happen. Normalizing keeps the check answering the question it is\n", + " for -- did every stage consume the same file -- rather than whether Cromwell\n", + " happened to satisfy a call from its cache.\n", + " \"\"\"\n", + " parts = [\n", + " p for p in _bucket_relative(path).split(\"/\")\n", + " if p != \"cacheCopy\" and not re.fullmatch(r\"attempt-\\d+\", p)\n", + " ]\n", + " return \"/\".join(parts)\n", + "\n", + "\n", + "TRAIN_ROOT = _workflow_root(CONFIG[\"train\"][\"evaluation_results_json\"])\n", + "ANALYZE_ROOT = _workflow_root(CONFIG[\"analyze\"][\"clusterability_manifest\"])\n", + "\n", + "# Every task that loads the .h5ad directly.\n", + "H5AD_CONSUMERS = [\n", + " (TRAIN_ROOT, \"TrainMixVAE\"),\n", + " (TRAIN_ROOT, \"Evaluate\"),\n", + " (ANALYZE_ROOT, \"Classify\"),\n", + " (ANALYZE_ROOT, \"TraversalPrep\"),\n", + " (ANALYZE_ROOT, \"StateTraversal\"),\n", + "]\n", + "# Analyze tasks that run inference. Each logs exactly one \"Model \" line,\n", + "# so these are safe to compare against Evaluate's selected checkpoint.\n", + "# TrainMixVAE and Evaluate are excluded: they enumerate every checkpoint.\n", + "CKPT_CONSUMERS = [\n", + " (ANALYZE_ROOT, \"Classify\"),\n", + " (ANALYZE_ROOT, \"TraversalPrep\"),\n", + " (ANALYZE_ROOT, \"StateTraversal\"),\n", + "]\n", + "\n", + "stdouts, unread = {}, []\n", + "for root, task in set(H5AD_CONSUMERS) | set(CKPT_CONSUMERS):\n", + " out = _read_stdout(root, task) if root else None\n", + " if out is None:\n", + " unread.append(task)\n", + " else:\n", + " stdouts[task] = out\n", + "\n", + "h5ad_seen = {}\n", + "for _, task in H5AD_CONSUMERS:\n", + " for line in stdouts.get(task, \"\").splitlines():\n", + " for tok in line.split():\n", + " if tok.endswith(\".h5ad\"):\n", + " h5ad_seen.setdefault(_bucket_relative(tok), []).append(task)\n", + " break\n", + "\n", + "ckpt_seen = {}\n", + "for _, task in CKPT_CONSUMERS:\n", + " for line in stdouts.get(task, \"\").splitlines():\n", + " if not line.startswith(\"Model \"):\n", + " continue\n", + " for tok in line.split():\n", + " if tok.endswith(\".pth\"):\n", + " ckpt_seen.setdefault(os.path.basename(tok), []).append(task)\n", + " break\n", + "\n", + "print(\"h5ad loaded by each stage (bucket-relative):\")\n", + "for path, tasks in h5ad_seen.items():\n", + " print(f\" {path}\\n <- {', '.join(sorted(set(tasks)))}\")\n", + "print(\"\\ncheckpoint used for inference by each Analyze task:\")\n", + "for name, tasks in ckpt_seen.items():\n", + " print(f\" {name}\\n <- {', '.join(sorted(set(tasks)))}\")\n", + "if unread:\n", + " print(f\"\\n(could not read stdout for: {', '.join(sorted(set(unread)))})\")\n", + "\n", + "# Collapse cacheCopy/attempt-N so the comparison is between calls, not dirs.\n", + "h5ad_keys = {}\n", + "for _path, _tasks in h5ad_seen.items():\n", + " h5ad_keys.setdefault(_lineage_key(_path), []).extend(_tasks)\n", + "\n", + "_normalized_note = (\n", + " f\" [{len(h5ad_seen)} raw path(s) collapsed to {len(h5ad_keys)} after \"\n", + " f\"dropping Cromwell cacheCopy/attempt dirs]\"\n", + " if len(h5ad_seen) != len(h5ad_keys) else \"\"\n", + ")\n", + "\n", + "check(\n", + " \"all stages loaded the same preprocessed .h5ad\",\n", + " len(h5ad_keys) == 1,\n", + " f\"{len(h5ad_keys)} distinct path(s): \"\n", + " + \"; \".join(f\"{p} <- {sorted(set(t))}\" for p, t in h5ad_keys.items())\n", + " + _normalized_note,\n", + ")\n", + "\n", + "configured_h5ad = _bucket_relative(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"])\n", + "configured_key = _lineage_key(configured_h5ad)\n", + "_matched = configured_key in h5ad_keys\n", + "_detail = f\"configured {configured_key}, stages loaded {list(h5ad_keys)}\"\n", + "\n", + "# Still different after normalizing? The file may nonetheless be the same one --\n", + "# e.g. copied between workspaces, as when a run is reproduced in a clean\n", + "# workspace. Compare content before calling it a lineage break, so this check\n", + "# fails only when the stages really did read different data.\n", + "if not _matched and len(h5ad_keys) == 1:\n", + " _bucket = _clean_uri(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"]).split(\"/\")[2]\n", + " _cfg_md5 = gcs_md5(gcs_resolve(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"],\n", + " quiet=True))\n", + " _got_md5 = gcs_md5(f\"gs://{_bucket}/{next(iter(h5ad_seen))}\")\n", + " if _cfg_md5 and _got_md5 and _cfg_md5 == _got_md5:\n", + " _matched = True\n", + " _detail = (f\"different path, identical content (md5 {_cfg_md5}): \"\n", + " f\"configured {configured_key}, loaded \"\n", + " f\"{next(iter(h5ad_keys))}\")\n", + "\n", + "check(\n", + " \"the .h5ad the stages loaded is the one configured above\",\n", + " _matched,\n", + " _detail,\n", + ")\n", + "\n", + "check(\n", + " \"every Analyze task ran inference with the same checkpoint\",\n", + " len(ckpt_seen) == 1,\n", + " f\"{len(ckpt_seen)} distinct checkpoint(s): \"\n", + " + \"; \".join(f\"{n} <- {sorted(set(t))}\" for n, t in ckpt_seen.items()),\n", + ")\n", + "\n", + "check(\n", + " \"lineage stdout was readable for every consuming task\",\n", + " not unread,\n", + " f\"unreadable: {sorted(set(unread))}\" if unread else \"\",\n", + ")\n", + "\n", + "# Stash for the Train stage, which compares this against evaluation_results.json.\n", + "ANALYZE_CHECKPOINTS = set(ckpt_seen)" + ] + }, + { + "cell_type": "markdown", + "id": "baf95a9e", + "metadata": {}, + "source": [ + "## Stage 1 -- DataPrep (`preprocessed_h5ad`)\n", + "\n", + "Checks: gene count matches the selected gene list, `var_names` carry gene symbols (MMIDAS reads\n", + "gene identifiers from the var index — numeric or missing names silently disable KEGG pathway\n", + "mapping downstream), no NaN/Inf/negative values, the matrix carries signal and satisfies the\n", + "log-CPM bounds, cluster count is plausible, excluded clusters are absent, and `class` only\n", + "contains the configured neuronal classes.\n", + "\n", + "On the CPM row sums: DataPrep normalizes across the whole transcriptome and *then* subsets to\n", + "`selected_genes`, so undoing `log1p` on this matrix recovers the share of each cell's CPM mass\n", + "inside the selected panel — bounded above by 1e6, never equal to it. The checks assert that\n", + "upper bound (a real invariant) and that the retained share clears\n", + "`min_retained_cpm_frac`, which is what would catch a wrong or truncated gene list. The\n", + "pre-subset equality against 1e6 is checked inside `01_data_prep.py`, where the full matrix is\n", + "still available — look for `CPM row sums after inverting log1p` in the DataPrep task log.\n", + "\n", + "Note `n_selected_genes` in the config is the row count of the `selected_genes` CSV minus its\n", + "header, not a fixed number — confirm it for your run rather than trusting the default." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9e8565f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (resolved Mouse_ALM-VISp_cpm.h5ad under cacheCopy/ -- Cromwell wrote it there, not at the configured path)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jupyter/.local/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 22365 × 5032\n", + " obs: 'sample_name', 'sample_id', 'seq_batch', 'sex', 'brain_hemisphere', 'brain_region', 'brain_subregion', 'class', 'subclass', 'cluster', 'confusion_score'\n", + "\n", + "22365 cells x 5032 genes\n", + "obs columns: ['sample_name', 'sample_id', 'seq_batch', 'sex', 'brain_hemisphere', 'brain_region', 'brain_subregion', 'class', 'subclass', 'cluster', 'confusion_score']\n", + "[PASS] gene count matches selected_genes -- got 5032, expected 5032\n", + "first var_names: ['Malat1', 'Vip', 'Npy', 'Ptgds', 'Sst']\n", + "[PASS] var_names look like gene symbols, not positional indices -- first five: ['Malat1', 'Vip', 'Npy', 'Ptgds', 'Sst']\n", + "[PASS] no NaN/Inf values in X\n", + "[PASS] no negative values in X (log-CPM) -- min=0.0000\n", + "\n", + "X: min=0.0000, max=11.7854, mean=3.5911, 78.7% non-zero\n", + "[PASS] X carries signal (1%-90% of entries non-zero) -- 78.72% of entries non-zero\n", + "[PASS] X max is within the log1p(CPM) bound (<= log1p(1e6) ~ 13.82) -- max=11.7854\n", + "[PASS] CPM row sums do not exceed 1e6, and non-empty cells are positive -- max row sum=890063 (limit 1e6); 0 all-zero cell(s)\n", + "CPM mass retained by the 5032 selected genes: min=70.2%, median=83.9%, max=89.0%\n", + "[PASS] retained CPM mass is within [10%, 100%] -- min retained=70.2%, floor=10%\n", + "\n", + "115 unique clusters\n", + "[PASS] cluster count in a plausible range (5-200) -- got 115\n", + "[PASS] excluded clusters are absent from output\n", + "[PASS] obs['class'] only contains expected neuronal classes -- found: {'GABAergic', 'Glutamatergic'}\n", + "\n", + "Cells per class:\n", + "class\n", + "Glutamatergic 11887\n", + "GABAergic 10478\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "adata = load_h5ad_gcs(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"])\n", + "print(adata)\n", + "\n", + "n_cells, n_genes = adata.shape\n", + "print(f\"\\n{n_cells} cells x {n_genes} genes\")\n", + "print(f\"obs columns: {list(adata.obs.columns)}\")\n", + "\n", + "check(\n", + " \"gene count matches selected_genes\",\n", + " n_genes == CONFIG[\"expected\"][\"n_selected_genes\"],\n", + " f\"got {n_genes}, expected {CONFIG['expected']['n_selected_genes']}\",\n", + ")\n", + "\n", + "# var_names must carry gene symbols: MMIDAS's loader reads gene identifiers from\n", + "# the var index, and KEGG pathway mapping in 03c silently maps zero pathways if\n", + "# they are absent or numeric.\n", + "var_names = list(adata.var_names[:5])\n", + "print(f\"first var_names: {var_names}\")\n", + "check(\n", + " \"var_names look like gene symbols, not positional indices\",\n", + " not all(str(v).isdigit() for v in adata.var_names[:50]),\n", + " f\"first five: {var_names}\",\n", + ")\n", + "\n", + "X = adata.X.toarray() if not isinstance(adata.X, np.ndarray) else adata.X\n", + "check(\"no NaN/Inf values in X\", bool(np.isfinite(X).all()))\n", + "check(\"no negative values in X (log-CPM)\", bool(X.min() >= 0), f\"min={X.min():.4f}\")\n", + "\n", + "# min >= 0 also passes for an all-zero matrix, so check that X carries signal and\n", + "# sits in the range log1p(CPM) implies (no single value above log1p(1e6)).\n", + "frac_nonzero = float((X > 0).mean())\n", + "print(f\"\\nX: min={X.min():.4f}, max={X.max():.4f}, mean={X.mean():.4f}, \"\n", + " f\"{frac_nonzero:.1%} non-zero\")\n", + "check(\n", + " \"X carries signal (1%-90% of entries non-zero)\",\n", + " 0.01 <= frac_nonzero <= 0.90,\n", + " f\"{frac_nonzero:.2%} of entries non-zero\",\n", + ")\n", + "check(\n", + " \"X max is within the log1p(CPM) bound (<= log1p(1e6) ~ 13.82)\",\n", + " float(X.max()) <= np.log1p(1e6) + 1e-3,\n", + " f\"max={X.max():.4f}\",\n", + ")\n", + "# Undoing log1p recovers CPM -- but only a *share* of it.\n", + "#\n", + "# DataPrep normalizes to log-CPM across the whole transcriptome (~45.8k genes)\n", + "# and only then subsets to selected_genes, so these row sums measure how much of\n", + "# each cell's CPM mass lands inside the selected panel. They are bounded above by\n", + "# 1e6 and will not equal it. Do not \"fix\" this back to an equality test against\n", + "# 1e6 -- that asserts a whole-transcriptome invariant against a subsetted matrix\n", + "# and fails on a healthy run. The pre-subset equality is checked inside\n", + "# 01_data_prep.py, where the full matrix is still in hand.\n", + "#\n", + "# The upper bound below is a genuine invariant: a subset of a CPM vector cannot\n", + "# exceed the total. It holds whether or not the matrix was subsetted, so it also\n", + "# suits a full-transcriptome bring-your-own .h5ad (fraction ~1.0).\n", + "cpm_row_sums = np.expm1(X.astype(np.float64)).sum(axis=1)\n", + "nonzero_rows = cpm_row_sums > 0\n", + "retained_frac = cpm_row_sums / 1e6\n", + "\n", + "check(\n", + " \"CPM row sums do not exceed 1e6, and non-empty cells are positive\",\n", + " bool(nonzero_rows.any())\n", + " and bool((cpm_row_sums <= 1e6 * (1 + 1e-3)).all()),\n", + " f\"max row sum={cpm_row_sums.max():.6g} (limit 1e6); \"\n", + " f\"{int((~nonzero_rows).sum())} all-zero cell(s)\",\n", + ")\n", + "\n", + "# The fraction is the signal the equality test was reaching for: a wrong or\n", + "# truncated selected_genes list shows up here as a collapse in retained mass.\n", + "if nonzero_rows.any():\n", + " frac_lo = expected(\"min_retained_cpm_frac\")\n", + " print(f\"CPM mass retained by the {n_genes} selected genes: \"\n", + " f\"min={retained_frac[nonzero_rows].min():.1%}, \"\n", + " f\"median={np.median(retained_frac[nonzero_rows]):.1%}, \"\n", + " f\"max={retained_frac[nonzero_rows].max():.1%}\")\n", + " check(\n", + " f\"retained CPM mass is within [{frac_lo:.0%}, 100%]\",\n", + " bool((retained_frac[nonzero_rows] >= frac_lo).all()),\n", + " f\"min retained={retained_frac[nonzero_rows].min():.1%}, \"\n", + " f\"floor={frac_lo:.0%}\",\n", + " )\n", + "\n", + "if \"cluster\" in adata.obs:\n", + " n_clusters = adata.obs[\"cluster\"].nunique()\n", + " print(f\"\\n{n_clusters} unique clusters\")\n", + " check(\"cluster count in a plausible range (5-200)\", 5 <= n_clusters <= 200, f\"got {n_clusters}\")\n", + "\n", + " removed = set(CONFIG[\"expected\"][\"remove_clusters\"]) & set(adata.obs[\"cluster\"].unique())\n", + " check(\"excluded clusters are absent from output\", len(removed) == 0, f\"found: {removed}\" if removed else \"\")\n", + "\n", + "if \"class\" in adata.obs:\n", + " classes = set(adata.obs[\"class\"].unique())\n", + " expected_classes = set(CONFIG[\"expected\"][\"neuronal_classes\"])\n", + " check(\n", + " \"obs['class'] only contains expected neuronal classes\",\n", + " classes <= expected_classes,\n", + " f\"found: {classes}\",\n", + " )\n", + " print(\"\\nCells per class:\")\n", + " print(adata.obs[\"class\"].value_counts())\n", + "\n", + "# Stash for cross-stage consistency checks below.\n", + "DATAPREP_N_GENES = n_genes\n", + "DATAPREP_N_CLUSTERS = adata.obs[\"cluster\"].nunique() if \"cluster\" in adata.obs else None" + ] + }, + { + "cell_type": "markdown", + "id": "5583997b", + "metadata": {}, + "source": [ + "## Stage 2 -- Train (`evaluation_results.json`, `checkpoints_manifest.json`, evaluation figures)\n", + "\n", + "Checks that the gene count matches DataPrep's output, that K-selection actually accepted a model\n", + "rather than falling back, that the categories the model kept are categories it *uses*, and that\n", + "`avg_consensus` meets `k_select_thr`. Figures are shown inline for manual review.\n", + "\n", + "**On `model_order`.** The obvious check — `model_order < n_categories` — is not worth making.\n", + "Pruning removes at most one category per round, so `model_order` is confined to\n", + "`[n_categories - max_prun_it, n_categories]` by construction and the check passes almost by\n", + "definition. It is also insensitive to the failure that matters: a model whose discrete latent has\n", + "collapsed keeps a high `model_order` while assigning every cell to a handful of categories. The\n", + "checks below use `n_populated_categories` instead, which `03a_evaluate.py` computes from the\n", + "per-cell assignments.\n", + "\n", + "If `n_populated_categories` is missing from `evaluation_results.json`, the run predates that field\n", + "and the notebook falls back to deriving it from the Classify pickles further down." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c68d747f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"model_order\": 96,\n", + " \"n_categories\": 120,\n", + " \"n_arm\": 2,\n", + " \"state_dim\": 2,\n", + " \"latent_dim\": 10,\n", + " \"n_gene\": 5032,\n", + " \"selected_model\": \"out/model/cpl_mixVAE_model_after_pruning_24_2026-08-27-18-22-38.pth\",\n", + " \"summary_pickle\": \"out/summary_performance_K_120_narm_2.p\",\n", + " \"avg_consensus\": 0.9074613236264698,\n", + " \"k_select_thr\": 0.95,\n", + " \"k_selection_met_threshold\": true,\n", + " \"k_selection_suggested_model_order\": 96,\n", + " \"n_populated_categories\": 96,\n", + " \"n_populated_categories_per_arm\": [\n", + " 96,\n", + " 96\n", + " ],\n", + " \"collapse_warning\": null,\n", + " \"figures\": [\n", + " \"out/consensus_T1_vs_T2_K_96.png\",\n", + " \"out/norm_consensus_T1_vs_T2_K_96.png\",\n", + " \"out/state_mu_K_96_arm_0.png\",\n", + " \"out/state_mu_K_96_arm_1.png\"\n", + " ]\n", + "}\n", + "[PASS] n_gene in evaluation_results matches DataPrep gene count -- train n_gene=5032, dataprep n_genes=5032\n", + "[PASS] model_order is at least 2 -- got 96\n", + "[PASS] n_categories matches the configured pruning ceiling -- got 120, expected 120\n", + "[PASS] [fidelity] K_selection found a model meeting k_select_thr (not a fallback) -- k_selection_met_threshold=True, suggested model_order=96\n", + "[PASS] [advisory] evaluation reported no collapse warning\n", + "[PASS] [advisory] populated categories are at least 50% of model_order -- n_populated_categories=96 of model_order=96 (100.0%); per arm=[96, 96]\n", + "\n", + "avg_consensus (test cells) = 0.9075\n", + "k_select_thr (K_selection curve) = 0.95\n", + " these measure different quantities -- see Stage 6 for the comparison against the published value\n", + "[PASS] avg_consensus is a valid consensus value in (0, 1] -- avg_consensus=0.9075\n", + "[PASS] k_select_thr matches the configured input -- got 0.95, expected 0.95\n", + "[PASS] Analyze ran inference with the checkpoint Evaluate selected -- evaluate selected cpl_mixVAE_model_after_pruning_24_2026-08-27-18-22-38.pth, analyze used ['cpl_mixVAE_model_after_pruning_24_2026-08-27-18-22-38.pth']\n" + ] + } + ], + "source": [ + "eval_results = load_json_gcs(CONFIG[\"train\"][\"evaluation_results_json\"])\n", + "print(json.dumps(eval_results, indent=2))\n", + "\n", + "model_order = eval_results[\"model_order\"]\n", + "n_categories = eval_results[\"n_categories\"]\n", + "avg_consensus = eval_results[\"avg_consensus\"]\n", + "k_select_thr = eval_results[\"k_select_thr\"]\n", + "n_gene = eval_results[\"n_gene\"]\n", + "\n", + "check(\n", + " \"n_gene in evaluation_results matches DataPrep gene count\",\n", + " n_gene == DATAPREP_N_GENES,\n", + " f\"train n_gene={n_gene}, dataprep n_genes={DATAPREP_N_GENES}\",\n", + ")\n", + "\n", + "check(\"model_order is at least 2\", model_order >= 2, f\"got {model_order}\")\n", + "\n", + "check(\n", + " \"n_categories matches the configured pruning ceiling\",\n", + " n_categories == CONFIG[\"expected\"][\"n_categories\"],\n", + " f\"got {n_categories}, expected {CONFIG['expected']['n_categories']}\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Did K_selection accept a model, or fall back?\n", + "#\n", + "# When no checkpoint reaches k_select_thr, 03a_evaluate.py warns on stdout and\n", + "# selects a fallback checkpoint anyway. Nobody reviewing only the JSON sees\n", + "# that, which is why the field below exists.\n", + "# ---------------------------------------------------------------------------\n", + "if \"k_selection_met_threshold\" in eval_results:\n", + " check(\n", + " \"K_selection found a model meeting k_select_thr (not a fallback)\",\n", + " bool(eval_results[\"k_selection_met_threshold\"]),\n", + " f\"k_selection_met_threshold={eval_results['k_selection_met_threshold']}, \"\n", + " f\"suggested model_order=\"\n", + " f\"{eval_results.get('k_selection_suggested_model_order')}\",\n", + " kind=\"fidelity\",\n", + ")\n", + " check(\n", + " \"evaluation reported no collapse warning\",\n", + " eval_results.get(\"collapse_warning\") in (None, \"\"),\n", + " str(eval_results.get(\"collapse_warning\") or \"\"),\n", + " kind=\"advisory\",\n", + ")\n", + "else:\n", + " review(\n", + " \"k_selection_met_threshold absent from evaluation_results.json\",\n", + " \"run predates this field -- check the Evaluate task stdout for \"\n", + " \"'K_selection could not find a model meeting thr'\",\n", + " )\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# How many categories does the model actually use?\n", + "# ---------------------------------------------------------------------------\n", + "n_populated = eval_results.get(\"n_populated_categories\")\n", + "if n_populated is None:\n", + " review(\n", + " \"n_populated_categories absent from evaluation_results.json\",\n", + " \"run predates this field -- the Classify-pickle cell below derives it \"\n", + " \"from the ConsType confusion matrices instead\",\n", + " )\n", + "else:\n", + " min_frac = expected(\"min_populated_frac\")\n", + " check(\n", + " f\"populated categories are at least {min_frac:.0%} of model_order\",\n", + " n_populated >= min_frac * model_order,\n", + " f\"n_populated_categories={n_populated} of model_order={model_order} \"\n", + " f\"({n_populated / model_order:.1%}); per arm=\"\n", + " f\"{eval_results.get('n_populated_categories_per_arm')}\",\n", + " kind=\"advisory\",\n", + ")\n", + "\n", + "# avg_consensus is NOT comparable to k_select_thr, and gating one on the other\n", + "# is wrong: they are measured on different things. avg_consensus is the\n", + "# inter-arm consensus over the held-out test cells, while k_select_thr is the\n", + "# threshold K_selection applies to its own per-checkpoint consensus curve. The\n", + "# reference reports both and they differ -- 0.939 on test cells against 0.954\n", + "# from K_selection -- so the published run's own test-cell value (0.939) is\n", + "# below its k_select_thr (0.95) and would FAIL such a check. A fidelity gate the\n", + "# reference cannot pass is wrong by construction.\n", + "#\n", + "# The threshold question is already answered correctly, and as fidelity, by\n", + "# k_selection_met_threshold above. What is left for avg_consensus here is a\n", + "# range sanity check; its agreement with the published value is Stage 6's job.\n", + "print(f\"\\navg_consensus (test cells) = {avg_consensus:.4f}\")\n", + "print(f\"k_select_thr (K_selection curve) = {k_select_thr}\")\n", + "print(\" these measure different quantities -- see Stage 6 for the comparison \"\n", + " \"against the published value\")\n", + "\n", + "check(\n", + " \"avg_consensus is a valid consensus value in (0, 1]\",\n", + " 0.0 < avg_consensus <= 1.0,\n", + " f\"avg_consensus={avg_consensus:.4f}\",\n", + ")\n", + "\n", + "check(\n", + " \"k_select_thr matches the configured input\",\n", + " k_select_thr == CONFIG[\"expected\"][\"k_select_thr\"],\n", + " f\"got {k_select_thr}, expected {CONFIG['expected']['k_select_thr']}\",\n", + ")\n", + "\n", + "# The checkpoint Evaluate selected must be the one Analyze ran inference with.\n", + "selected_ckpt = os.path.basename(eval_results.get(\"selected_model\", \"\"))\n", + "if ANALYZE_CHECKPOINTS:\n", + " check(\n", + " \"Analyze ran inference with the checkpoint Evaluate selected\",\n", + " ANALYZE_CHECKPOINTS == {selected_ckpt},\n", + " f\"evaluate selected {selected_ckpt}, analyze used \"\n", + " f\"{sorted(ANALYZE_CHECKPOINTS)}\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "96307fe6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PASS] checkpoints_manifest n_categories matches evaluation_results -- ckpt=120, eval=120\n", + "checkpoints in manifest: 43\n" + ] + } + ], + "source": [ + "ckpt_manifest = load_json_gcs(CONFIG[\"train\"][\"checkpoints_manifest\"])\n", + "\n", + "check(\n", + " \"checkpoints_manifest n_categories matches evaluation_results\",\n", + " ckpt_manifest.get(\"n_categories\") == n_categories,\n", + " f\"ckpt={ckpt_manifest.get('n_categories')}, eval={n_categories}\",\n", + ")\n", + "print(f\"checkpoints in manifest: {len(ckpt_manifest.get('checkpoints', []))}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ff0bac03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4 evaluation figures\n", + "[REVIEW] consensus bubble plot -- expect a strong diagonal of bubbles spanning the full category range. A handful of scattered points means the arms almost never co-assign and only those few categories carry cells\n", + " --- consensus_T1_vs_T2_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] normalized consensus plot -- expect a bright diagonal; average consensus should be >= 0.95. An almost entirely dark matrix means no reproducible categories were found\n", + " --- norm_consensus_T1_vs_T2_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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L399fAQEBcnNz07Vr13Tp0iUlJCTYPSYhIUFPPvmktm7dqmbNmuVjtbl38uRJPfDAAzp//nyW+xUrVkxBQUEqVaqUkpKSdOnSJV25csXu/pMnT5aHh4c++OADh+r67LPPNH78+Cz38fDwUFBQkMqVK6ekpCRFRUXp2rVrNvuNGzdONWrUcKgOe6ZPn65XXnkly33c3d1VtmxZlS9fXsnJybp48aLi4+Pt7n/ixAl16tRJmzZtMp1B74iEhAT17dtX586ds9l26/kLCgqSh4eH/ve//+ncuXMyDMOSc+fEG2+8YXfdh1vc3NwUHBysUqVKydfXVzExMYqKilJKSorp/ufOnVPnzp0VHh6uKlWq5LiWbdu2aciQIdk2SIOCglSmTBkVK1ZMqampiouL07Vr17J8P7jS7t27FR0dbRPP7axwR/n6+qpUqVLy9/eXn5+fEhMTFRsbq8jIyCxfaytWrNDQoUO1ZMkSS+tZs2aNXnjhBdM/51ufc6VLl9bNmzd19uxZ08+U7IwfP15ff/21TdzLy0vlypVT2bJllZSUpIsXL5quK3HLCy+8oJYtW95eZ2LKlCn68MMPbfZzd3dXcHCwgoKClJ6erosXL2b5enzrrbcUGhqqpk2b5vqxFXZlypRR/fr1dfDgwUzxlStX6pNPPnFRVQAAFE403AEA97zff//dNF6uXDnVq1cvn6txjLe3tx544AF1795djRs3VqNGjeTv72+zX0ZGho4fP661a9fqiy++ML0zNyUlRf369dPu3btVvHjxLM8bFBSkkJCQ2/8fFxdn88u8JNWqVUtly5Z14JGZu3r1qkJDQ+0224ODgzVixAh169ZNTZo0sVlc8ejRo/rvf/+rqVOnmt7hOmnSJHXs2FEPP/xwruo6evSoRo8ebXd769atNXz4cD322GM2fz5HjhzRkiVLNH369Nt3rxqGoeeff16tWrXKVR32rFq1SmPHjrW7vUGDBho5cqSeeOIJBQYGZtq2b98+LV68WDNnzlRiYqLNsZGRkerZs6e2bdumYsWKOV3r3//+dx05ciRTrFu3bhoyZIhCQ0NVokSJTNtiY2O1dOlSeXrm/T9fP/nkE7vNdjc3N3Xv3l1DhgxRx44dbRarTEpK0vr16zVr1izTu7ejoqL0t7/9TRs2bMjRoqAZGRl68cUXTZuwXl5eGjBggPr27at27drZfT9fvnxZBw4c0I4dO/TLL78oPDzcobubrbZmzRrTeLt27fLkfFWrVlXPnj0VEhKiRo0aqWbNmqZ/BnFxcdqxY4cWLVqkJUuWKCkpyWafH374QbNmzdLw4cMtqS02NlaDBw9Wenr67VixYsU0fPhw9enTRy1btrSp9fDhw1qwYEGOvw2wdu1amwVqQ0ND9dJLL6lLly6Z3tcZGRnaunWrPvjgA9PX8c2bNzV69GitXr1aGzZssLkI2bJlS40ePVpdu3a1+azZs2ePPv74Y33zzTc2edPT0/Xiiy9qx44d99Ri3/mlffv2Nn9HR0RE6NSpU5ZdLAUAADng0oE2AIC7juzMXi3IM9zr1KljWvMTTzzh6tKyJMlo0KCBMX/+fOPatWsO5fjiiy+MEiVKmD7+t956K9f5croYn7O6d+9ueh53d3fjnXfeMZKTk3OU5+rVq0afPn1Mc5UrV864fPlyjmvKyMgwQkJCTHN5enoa//73v3M02zkqKsp45JFHcjTTODeuXLlilClTxjSPm5ubMWHCBCMlJSXbPBEREUbbtm3t1jR8+PBc1WVvtvGdP6VLl3Z6zrBVM9y3bt1qeHh4mOaqV6+esWfPnhzn+uWXX+y+/z744IMc5diwYYPp8dWrVzcOHjyYq8d2S0xMjDFx4kSjQoUKLp3h/thjj5k+ttjYWEvyv/POO4aPj4/xwgsvGLt373Yox9mzZ43Q0FDTOv38/Iy4uLhc5zSb4f7Xn65duxoXLlxwqGZ7n9N3/pQoUSLHi/m+9957dvNs3brVqFGjxu3/9/b2NubOnWtkZGRkm3fevHl2F1b9/fffHXrsrlIQZrgbhmHMnj3btI5Fixblax0AABR2NNwBALli75fugtpwv3nzpt2F4ewtvlhQrFy50pI827dvN236BQUF5XphwvxouH/99dem5yhSpIhDTdn09HRj6NChpjnHjBmT4zwLFiwwzeHu7m4sXrw4VzWlpKQYjz76aLZNsdwYMWKE3TwzZszIVa7ExES7i/a6u7vnqnmZXcM9KCjI4cZxTs6Tm4Z7SkqKUbduXdM83bp1MxISEnJd1969ew1/f3/T13N0dHS2x48ZM8bmWE9PT0ues9TUVOPGjRtO53GU2QKwFStWtCz/rl27cvQcZyc9Pd14+umnTV8X06ZNy3W+7BruvXv3ztHFMXuya7iXLFky1xcg7F24vHNR2iJFihhr167NVV6z17ckY+jQobnK42oFpeG+detWp/+uAwAAzmPRVADAPe3ChQt2RyfUqlUrn6vJnW7dulmSp2XLlpo2bZpNPDo62vIZxM5KSUnRG2+8Ybrtyy+/VNeuXXOd093dXbNmzVKTJk1s2eE7AAAAJDFJREFUts2bNy/Hix/OmDHDND5u3Dj1798/VzV5eXnp+++/V9WqVXN1nD1RUVGaO3eu6baRI0dqxIgRucpXtGhRrVixQsHBwTbbMjIy9P777ztUp5mvvvpK9evXtyyfM7788kubMTeS1KRJEy1ZssShUTqNGjXS559/bhO/efOmZs+ene3xZiOcunTpYslz5unpqSJFijidxxGRkZGmC8A2aNDAsnM0a9bMklFX7u7umj9/vmrWrGmzbebMmU7nv1P16tW1aNEieXl5WZr3TrNnzzb9PMzK5MmTTUe83Dmy64MPPtBDDz2Uq7zvvPOO6fvqp59+ylUe/Mne+2fnzp35XAkAAIUbDXcAwD3twoULdreZNRPvVUOGDNF9991nE1+7dq0LqrFv8eLFpotoPvHEExowYIDDeYsUKWLaMI+Pj9f8+fOzPX737t3atWuXTbxixYp66623HKrJ19dX06dPd+jYv/ryyy918+ZNm3hwcLDDi8MGBgZqypQpptuWLVumyMhIh/LeqV+/fgoNDXU6jxUMwzB9vO7u7lqwYIFTc+v79u2rLl262MRnz55td4HVWy5dumQTu//++x2upaAwu7AhSdWqVcvnSnLG29tbEyZMsIlHRETozJkzlp1n1qxZ8vX1tSzfX3Xr1k1PPfVUro+rWbOmWrZsaXd7gwYNslw/wp6AgAB1797dJh4bG6vTp0/nOl9h5+fnZ7OuhGT//QYAAPIGDXcAwD3t1uKUZsx+Kb1Xubm5qVevXjbxbdu25X8xWTC7S9vd3V2TJk1yOnfbtm3Vpk0bm/h///vfbI/9+uuvTeOjRo1yqhHbs2dP1alTx+Hjb/nrQoi3vPbaazaLj+bGwIEDTZu76enpds+ZG1ktQJvf1q9fb9rgGzhwoBo2bOh0/r///e82sejoaG3dujXL49LS0mxi2TXp7wb2mtQVKlTI30JyoUePHqZ3nlv1OVqnTp08vwA1ZswYh4/NapHpl19+2eFFTu095j179jiUr7Azew9dvXo1x9/mAgAAzqPhDgC4p924ccPutry8i7AgMvuq+fHjx3Xt2rX8L8aEveZj+/btTUc5OKJnz542sZ07d5o2Ne9kVpe7u7uefvppp2saOHCgU8efP39ehw8ftol7eno6XZ+bm5ueeeYZ022rV692Knft2rXVunVrp3JYyd6FlyFDhliS/8EHH5Sfn59NPLuGe6lSpWxia9eutTsq625x9uxZ03jFihXzuZKcK1GihCpXrmwT3759uyX5Bw0aZEkee0qVKmX6TYucqlevnmnc3d1dffv2tTzvyZMnHc5ZmNl7D9l7zwEAAOvRcAcA3NPMxmzckpczcguioKAgm5hhGAXml/C1a9fKMAybeJ8+fSw7R7t27WxiSUlJ2rt3r91jUlJStG/fPpt4w4YNLWkOPvroo04dv3nzZtN4586dVaZMGadyS7I7n37Lli1ONX07duzo8LF5Yc2aNTax0qVLq0OHDpbk9/DwML3AkF3D3exC2eHDhx0eZVRQXLx40TRuxcz1vGT2OWrVSJm8fk+0adPG4bvQJdldc6J+/fpOfZPG3hihuLg4h3MWZmavUcn+ew4AAFjP09UFAACQl7y9ve1uS01NzcdKrHP58mXt379f+/fv14kTJxQfH6/4+Hhdv349y1ET9poXBeUOd7MZ6ZLUtGlTy85RpUoV0/j+/fvVvHlzu9vMLtw0btzYkprq1q0rLy8vh1+P+/fvN423atXKmbJuq1ixosqXL28zsz0pKUkRERGmawPkRLNmzawozxKxsbGmd9M2atRI7u7W3Z9i9vqz9+d3S2hoqObMmWMTnzhxorZv364333xTnTp1sqrEfJOQkGAaz49vHqWlpenYsWPav3+/Dhw4oOjoaF2/fl3Xr19XYmJilheSzBaxteIz1MPDQ40aNXI6T1aczW/2DQ1JTo9cspeXhrtj7L2H7L3nAACA9Wi4AwDuaVk1b7IaN1PQREdH67vvvtOiRYu0c+dOS3MXlIa7WSNLkiUzzm8xG88hSVeuXLF7jL2Fd62Y6y39eVGoTp06OnDggEPHR0REmMabNGniTFk2ucwWST158qTDDXerxgRZ4dChQ6ZxK197kvnrL6vXnvTn3PBatWrpxIkTNtvWr1+v9evXq1q1aurVq5cefvhhtWvXzm4DsyBJSkoyjedVw90wDIWFhWnRokX673//a+nnnhW5ypcvr6JFizpfTBYCAwOdOt7eehV5lTcxMdGpvIWVvfcQzycAAPmHhjsA4J4WEBBgd9vly5fzrxAH3bx5Ux999JEmTpxot0HlrPj4+DzJm1vnz583jVsxFiU7WS2ua6+ZZmVdzuSKiooyjVevXt3hnH9lrzlu1oTPKX9/f4ePtZq9197MmTM1c+bMPD13UlKSUlJS7H4bx8PDQ59//rm6dOlid62B06dPa9q0aZo2bZo8PT3VsGFDtW7dWh06dFCnTp3sjphwJXufZz4+Ppafa8+ePRoxYoTCw8Mtzy1Z8xmaH+8HZ89hbxxNXuW929cpcBV7Dfe8+jcEAACwxQx3AMA9LasZ25cuXcrHSnIvKipKzZo105tvvpmnvyinp6fnWe7ciImJcdm5r169anebvYa7MzOLrcxl72JBftTnzJ29VtbnLFe+9qSsX3+S1KlTJy1YsEBFihTJNldaWpp2796tWbNmqV+/fgoODtb999+vCRMmmN4l7yr21tDIbgHj3Jo2bZpatGiRZ812yZrP0Px4P3h4eNxVeeEYe+PJCtu6NQAAuBINdwDAPa1ixYp2ZzAXpObTX0VGRqpTp052R13ci1x5911W44Xszb21NwbBEc6MALG3MLCVd8zay+XMWKaC1Pxx9Z2fOXkeBwwYoC1btjg0m//QoUN69913dd999yk0NFTbt293pExL2Xv/JCcnW3aOf/3rXxo7dmyBuaiYlYL0fsDdzd57yMq/swAAQNZouAMA7mlFihRRrVq1TLcV5Gb2888/r+PHj9vdfv/99+vll1/WvHnztGHDBh0/flyXL19WcnKy0tLSZBiGzU9YWFg+PoLcs/rOVqvYa4Zb2aTNi9m6hmEUyFwFUUF97f1Vs2bNtG3bNi1btkyPPvqoPD1zPx1yzZo1at26tUaMGGH3Yk1+sDev3KqG+/bt2/V///d/drf7+fmpZ8+emjRpkn766Sft3r1bFy5cUFxcnFJSUpSRkWH6OdqxY0dL6gPyCg13AABcjxnuAIB7XtOmTXXs2DGb+JYtW1xQTfZ++eUXrVy50nRb586dNXXqVDVu3DjXee19zbyg8PHxMW1ih4SE5Pm5s1r40946AFbOvo+Li3P4WHtjRuLj4+0uEptb9h5rXszbdgV7j6NSpUqqXLmyy85vT48ePdSjRw9dvnxZ69evV1hYmH7//XcdO3YsxxdHZs2apSNHjujXX3+1Oz8+L9l7bTrzXrjFMAyNHDnS9LkoWrSoJk2apOeee86hRUoL+ucoYO895OzitgAAIOdouAMA7nkdOnTQt99+axOPjIzUkSNHVLduXRdUZd+MGTNM40OGDNEXX3zh8Lzc7OZEu5q/v79pw33VqlUqXry4Cyr6k72G+//+9z/LzuHMAr4lS5Y0jVt5QcBeLnvnvtvYG5nz5JNPaurUqflcTc6VLl1aTz31lJ566ilJ0pUrV7R582Zt2rRJGzZs0O7du7NswIeFhWns2LF2P3PyUpUqVUzjFy9edDr3tm3btGvXLpt4iRIltHHjRjVq1Mjh3AX9cxSw9x6qWrVq/hYCAEAhxkgZAMA975FHHrG7bcmSJflYSfauX7+uDRs22MRr1KihmTNnOrU4XUFvFFWqVMk0fuXKlXyuJLMKFSqYxvfv329J/tTUVB09etTh48uXL28aP3XqlMM5/yoiIsI0Xq5cOcvO4UoF9bWXW6VKlVLPnj310UcfadeuXbpw4YL+/e9/q169enaP+eyzz7IcX5VX7DX/Lly44HTuZcuWmcY/+ugjp5rtUsH/HAXM3kPe3t73zOc1AAB3AxruAIB7XqVKldSyZUvTbV999VWBWlAvPDxcKSkpNvHBgwfL19fXqdwHDhxw6vi8Vrt2bdO4FQ04ZzRs2NB05MbevXstyX/48GHTP/Ocqlmzpml8z549Duf8K3uP1d76CHebgvrac1b58uU1atQoHTx4ULNmzTKd+Z6Wlqavv/4632urXr26adyK5/z333+3ifn5+WngwIFO5Y2JiVFMTIxTOYC8ZBiGIiMjbeJVqlSxu4A8AACwHn/rAgAKhRdeeME0furUqQJ1l3tUVJRpvH379k7n3rp1q9M58pK9iyIbN27M50oyK1KkiOldsfv27bNk/MWKFSucOr5hw4am8R07djiV95aLFy+aPk4/Pz/VqFHDknO4WvXq1U1nim/btu2emNnt5uamYcOGafLkyabb169fn88VSfXq1TNdf+Dw4cNO5zb7HG3evLnTFy0L+mcoEBERYXoBt0mTJi6oBgCAwouGOwCgUOjfv7/KlCljum38+PG6ceNGPldkzt7dk8HBwU7lPXPmjCV3uNtboNOZO7Rveeihh0zj69atczq3s9q2bWsTy8jI0OLFi53O/c033zh1vL2LMb/99pslI1G+++4703hISIjc3Nyczl9QmL3+EhMTFR4e7oJq8sbw4cNVrFgxm/jp06fzvRYvLy/Ti0VHjhxRWlqaU7nNPked/QyV/lzQGijI7I06a9GiRT5XAgBA4UbDHQBQKBQtWlTjx4833Xb69Gm9/vrr+VJHcnKyjh07Zne7vQUOnb0g8OmnnyojI8OpHNKfiw6aSUxMdDp3nTp1TGdNb9iwwbLxLY6yN4pixowZpgu95tQvv/yiI0eOOHy89OeM+fvvv98mnpqaardZnhv2xo107drV6dwFyeOPP24anzZtWj5Xknd8fHxUp04dm7ir5pKbNQFTUlKcWtNAMv8cdfYz9H//+58lF9iAvETDHQCAgoGGOwCg0Bg2bJhps0mSpk+f7vSdxtmJiYnRAw88kOUds6VLlzaNZ9Wkz87Zs2f1xRdfOHz8new13M+fP29J/pdeeskmZhiG3nrrLUvyO6p58+Zq2rSpTfzcuXP65z//6VDOGzduaMyYMU5W9qenn37aND5x4kQlJCQ4nPe7777Tvn37bOIeHh7q16+fw3kLol69epneBb106VLt3LnTBRXlDbMLbwEBAflfiKROnTqZxnft2uVUXrPPUWc+QyXp/fffLzDfhALs+eOPP2xiPj4+dke2AQCAvEHDHQBQaBQpUkRfffWVPDw8TLcPGTIkz+5gXLdunRo1aqTt27dnuV/lypVN447Omc/IyNAzzzyj69evO3T8X5UvX950DrKzDbJbhg4datr0XL58uT788ENLzuGoESNGmManTJmi77//Ple50tLS1K9fP506dcqK0jRkyBD5+PjYxCMjIzVhwgSHcsbFxWncuHGm2/r06WPJiI6CxMvLS3//+99t4oZh6Mknn1R0dLQLqrJWQkKCaeO5QoUKLqhG6tKli+nn8YYNG5zKa/Y5euTIER06dMihfOvWrdOMGTOcqgnIa+np6dq0aZNNvFOnTk6vXwAAAHKHhjsAoFBp2bKlpkyZYrotLS1NAwYM0Lhx4yy7kzE6OlpDhgzRww8/rEuXLmW7f0hIiOmc9B9//NH0F+mspKWl6ZlnnrF00VEPDw/Tuctbt251ejSKJBUrVkyTJk0y3TZ+/HhNnz7d6XPcEhsbq8mTJ2vz5s052n/w4MFq06aNTTw9PV0DBw7UrFmz7I4EulNMTIwef/xxLV26NNc12xMcHKwXX3zRdNvUqVM1d+7cXOW7ceOGevTooQsXLthsc3d315tvvulQnQXdqFGjVKtWLZv42bNn1a1bN505c8ayc23cuNHumKs7z9u9e3f99ttvlpzz448/Nh2B1LFjR0vy51ZAQIDpeyosLMypvJ07dzaNjx07NtejtcLDw/Xkk0/m6L0NuNIff/yh+Ph4m3j37t1dUA0AAIUbDXcAQKEzduxYDRs2zO72jz76SA0aNNA333zj8OJ9Z86c0SuvvKIaNWpowYIFOW7W+Pr6mi7eaBiGevXqleMFHM+ePasePXpo0aJFt2NWLXBpNgYiPT1d3bp105IlS5ScnOxU/sGDB+uxxx6ziRuGoVdeeUU9e/ZURESEQ7kzMjK0efNmjRgxQpUqVdJrr72my5cv5+hYd3d3zZ07V97e3jbbUlNTNWLECIWEhGjRokWmTY+jR4/q/fffV926dbVs2bLb8dKlS+uRRx5x6PHcacKECQoKCjLd9tJLL2nixIk5ej2fOXNGoaGhdi/wjBw50vSiy73A29tb33zzjTw9PW227dmzR02aNNH8+fOVmprqUP6YmBh98cUXat68uTp27KiVK1dmub9hGFq1apUefPBBNWvWTLNnz3boTnvDMDR9+nS99957ptvtjSTKD2bv9XPnzunkyZMO5+zRo4dpfM2aNXr22Wd18+bNbHMYhqH58+erS5cuunbt2u34vbRQMO4t9i7MWfH3CwAAyB3b3yYAACgEZs6cKTc3N82aNct0e0REhP72t79p3LhxevLJJ9WlSxe1atVKZcuWNd0/KSlJ+/fv18aNG7Vs2TJt3brV4Tsix48frxUrVtjEr169qvbt2+u5557TCy+8oMaNG8vd/f9fO8/IyNC2bdv0ww8/aM6cOTaN75dfflmffPKJQzXd6dlnn9WUKVNsHt/Zs2fVt29feXh4qFq1agoICDC9W19StneVL1y4UO3bt9eBAwdsti1btkzLly/XY489pj59+qhdu3aqVq2aaZ7k5GQdOnRIe/fuVXh4uJYvX66YmJgcPlJb9erV08cff6yRI0eabg8PD1d4eLg8PT0VHBysoKAgJScn69KlS6YLU7q5uenzzz+35G73gIAAffXVV+revbvNXbzp6el64403tGTJEo0cOVJ9+vRRyZIlM+1z8OBBLVq0SDNmzLA7971hw4Z2v4Fwr2jZsqXmzJmj5557zmbbtWvXNHToUL399tt67rnn1KlTJ7Vs2dLuuIbz589r37592rNnj1avXq3w8HCHFy/evXu3hg8frpEjR6pDhw7q0qWLmjdvrmbNmqlUqVKmx5w7d06//vqrZs2aZTqLX/pzPJArF1Ts37+/XnvtNZvn5eeff9arr77qUM4WLVqoS5cuWrt2rc22BQsWaPv27frHP/6hnj172rwPYmJitHz5cs2ZM8dmdn/z5s1VpEgRbdmyxaG6cPeZN2+e5s2bl+1+586dM42vWrVK7dq1y/b4Jk2a6NNPP811fXf6+eefbWJt27ZV9erVncoLAAByj4Y7AKBQcnNz08yZM1W1alWNHz/e7p2/ly5d0qeffnr7F+HixYurYsWKKlasmDw9PZWYmKgrV64oKioqxw12Ly+vLLe3bdtW/fv317fffmuzLT09XZ999pk+++wzlShRQpUrV1bRokVv12A2LkL6c1RGr169LGm416pVS4MGDdKCBQtMt6enpzt8B/ot/v7+Wr16tUJDQ02b7hkZGfr5559vNxj8/f1VqlQpBQYGyjAMxcXFKS4uTlevXlV6erpTtfzViBEjdPXqVb399tt290lLS9OFCxdMR7LcacqUKerdu7dpwz2714mZ0NBQTZs2TaNHjzbdvnfv3tsXbIKCglSuXDnduHFDkZGRme7iNVOhQgX9/PPPKlq0aK7ruts8++yzSkpK0ujRo03f1xcuXLg9G9/T01Nly5ZVyZIlVbx4cSUkJOjatWuKjY1VYmKi5bVlZGRow4YNmeacBwQEKDAwUCVLlpSHh4cSExN1/vx5029a3Ck4OFizZ8+2vMbcqFixoh544AGtX78+U/yHH35wuOEuSf/617/UunVr0/FgR44c0ZAhQ+Tm5qaqVauqVKlSSk1N1aVLlxQTE2P6Zx4YGKjFixfr+eefd7gm3H3OnTvn1AWWmJiYHF3kNftWTW6cP39eO3bssIkPGjTIqbwAAMAxNNwBAIXauHHjFBISomeffVZHjx7Ndv/r1687PKs8KChIkyZNytH4hrlz5+r48eP6448/7O4THx+vgwcPZpurf//+mj59uqWz3GfOnKnDhw+b/oJvlXLlymnTpk165plnMo1gMXOrwW7VIqTZeeutt1SlShUNHz7coaaqj4+PPv3009t3UZs1Rs0WQc2Jl19+We7u7hozZozdiw0ZGRmKiopSVFRUjnLWqlVLK1assPtNgnvRqFGjVLFiRQ0dOjTLixFpaWmKjIxUZGRk/hX3F9euXcv2gslfVatWTWvWrLH7rZ38NGjQIJuG+/bt23XhwgVVrFjRoZyNGjXS/Pnz1b9/f7v7GIah06dP6/Tp01nm8vf314oVK0zn+wMFwY8//mhzocjHx0d9+/Z1UUUAABRuzHAHABR6bdu21f79+/Xxxx+rfPnyluevVKmSpk6dqlOnTt2+qzI7RYsW1Zo1a9S1a1eHz+vh4aG33npLixYtyjR6xgpFixbV5s2b9fbbb9uMZLCSv7+/li5dqi+//NLufHJHeXt7q0+fPg7PI3/mmWd04MAB9e/fXx4eHjk6xs3NTT169Lh9p/ktZs1SZ57XkSNHat26dZaMEhgwYIB27NhRKJuNvXv31r59+9SnTx/Lc9esWVPPPvtslvt4eXnZHcvkjH79+ik8PFw1a9a0PLcjnnzySZUpUyZTzDAMLV682Km8/fr103/+8x/5+fk5nKNu3boKDw9X69atnaoFyEt3rtdyS//+/RUQEJD/xQAAABruAABIfza2XnnlFZ0+fVpff/21unbt6tBIj1tKlCih/v37a+nSpTp9+rTGjh2b61EcgYGBWrFihWbMmKFKlSrl+Dg3Nzd169ZNO3fu1HvvvZdni/x5eXnp3XffVVRUlH788Ue9+uqr6tKli2rUqKEyZco4fIe2maFDhyoiIkKffPKJ6tWr53Ce4sWL65FHHtGsWbNu1+1MU7patWpavHixzpw5o6lTp6pHjx6qUaOGihYtKg8PDxUvXlzVq1fXo48+qsmTJ+v48eNatmyZateunSmP2ciB4OBgh+uS/lzc9uDBg/r4449VuXLlXB3r7u6u0NBQbdy4Ud98802hbtpUrlxZP/74o3bs2KEBAwbYndeeE3Xr1tWoUaO0efNmnThxQqNGjcpy/woVKujKlSv6+eef9fzzzzv1Wi1SpIh69+6tjRs36ttvv7X8ApYzfHx8NHz4cJv4F1984fBaGLf07dtXu3bt0uOPP56rC4/BwcH68MMPtXfvXtWtW9epGoC8tGfPHu3atcsmPnbsWBdUAwAAJMnNcPZfsQCAQuXW3OK/Gjx4sKpWrZqvteS1uLg4bdmyRdu3b9fBgwd19uxZXbx4UQkJCUpOTpabm5t8fX0VGBioChUqqFatWmrQoIFCQkLUvHlzp2ey3ik1NVXLly/X+vXrFR4erkuXLunKlSvKyMiQn5+fKlSooDp16qh9+/Z69NFHTRtzKSkppgt3+vv7O9VEdIWjR49q7dq12rlzp44dO6YLFy7o2rVrunHjhooUKaISJUrcnrdft25d1alTR82aNVOrVq0s/XOxQkJCgvz9/W0Wjezfv7/Td/jeYhiGNm/erLVr12rHjh2KiIhQTEyMkpKSbl8YqFSpkurVq6eQkBA99thjDo/yuNclJSVp3bp12rp1q/bu3aszZ84oOjpaiYmJMgxDfn5+Kl68uAIDA3XfffepTp06ql+/vjp06KBy5co5ff6YmBiFh4drz549OnHihCIiIhQVFaXr168rISFBHh4eKlGihPz9/VWlShU1adJETZs2Vbdu3Qr0hZOYmBhVqVLFZub6unXr9OCDD1pyjhMnTmjZsmUKCwvT0aNHdfXqVcXHx8vHx0clS5ZUzZo11bhxY3Xp0kUPPfSQvL29bXJcvXpVKSkpmWIeHh42d+gD+eWll17SZ599likWGhqqX3/91UUVAQAAGu4AAAAu9Ntvv5k2FD/44AONHz/eBRUBrjFs2DDNmTMnU+zxxx/XDz/84KKKgIItPj5eFStW1PXr1zPF169fr86dO7uoKgAAwEgZAAAAF/rqq69M4yEhIflcCeBab775ps0oqp9++kknTpxwUUVAwTZ79mybZvsDDzxAsx0AABej4Q4AAOAiUVFR+u6772zixYsXZ5FGFDoVKlTQsGHDMsUyMjI0ZcoUF1UEFFw3btzQtGnTbOITJ050QTUAAOBONNwBAABcwDAMDRkyxGYetCT16tVLRYoUcUFVgGuNHz9exYsXzxRbuHChLl686KKKgIJp3rx5io6OzhTr0aMHF2sBACgAaLgDAADkQlRUlGmTPDcyMjL06quvavXq1abbR44c6VR+4G5VunRpvfnmm5liKSkp+uc//+miioCC58aNG/rwww8zxby9vfXRRx+5qCIAAHAnGu4AAAC5sHTpUlWvXl1Tp05VbGxsro8/d+6cHnvsMdNRAJLUrVs3tWzZ0tkygbvWK6+8orp162aKzZ07V8eOHXNRRUDBMn36dJ0/fz5TbOzYsbrvvvtcVBEAALiTm2EYhquLAAAAuFvMmTPn9pxpLy8vPfTQQ+rVq5datmyp+vXry8vLy+aYK1euaPPmzfrpp5+0ePFipaammub28/PT3r17VaNGjTx9DEBB99tvv+nBBx/MFOvVq5d++uknF1UEFAxXrlxRjRo1FBcXdztWqVIlHT16VEWLFnVhZQAA4BZPVxcAAABwt0pNTdWqVau0atUqSX9+pT8oKEgBAQEqWrSo4uLiFBsbq5iYGGV3j4O7u7sWLFhAsx2Q1LlzZ82ZM0dRUVG3Y25ubkpISJCfn58LKwNc6+TJkxozZkym2IMPPkizHQCAAoQ73AEAAHLhzjvcreLt7a158+ZpwIABluYFAAAAAOQv7nAHAABwoXr16mnBggVq0aKFq0sBAAAAADiJhjsAAEAu9O7dWwkJCfrxxx+1ffv2bEfF2NOwYUO9/PLLGjRokDw9+ScZAAAAANwLGCkDAADgoNjYWG3btk3btm3T8ePHdebMGV28eFHXr19XUlKS0tLS5OPjI39/f1WsWFG1a9dWixYtFBoaqtq1a7u6fAAAAACAxWi4AwAAAAAAAABgAXdXFwAAAAAAAAAAwL2AhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFqDhDgAAAAAAAACABWi4AwAAAAAAAABgARruAAAAAAAAAABYgIY7AAAAAAAAAAAWoOEOAAAAAAAAAIAFaLgDAAAAAAAAAGABGu4AAAAAAAAAAFiAhjsAAAAAAAAAABag4Q4AAAAAAAAAgAVouAMAAAAAAAAAYAEa7gAAAAAAAAAAWICGOwAAAAAAAAAAFvh/62rz3GIawwoAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state-space scatter -- expect visually separated clusters, not one undifferentiated blob. Count the distinct groups: if there are far fewer than model_order (96), the discrete latent has collapsed\n", + " --- state_mu_K_96_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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3u9H2999/v3JyciRVf4ZeeOGFNq+xvLw8djs7O7vdhue88MIL9YaRGGO0YcMGPfvss5o+fbpOPfVUZWRk6Hvf+54WLFjQLnU1xuVyxYal+P1+/fe//221fT/yyCOx20899ZSSkpKabP/LX/4y9jvlxRdfbPKk2ebU/B6RpE6dOjX5+Twcl156aez3yAcffNCibUaOHHnIQ4CGDh0aG/rSkIOn1b3wwgt15plntqh9vH+/tVT37t1jt1tycj06Dsa4A0eooqKizv2mAvDhqD1+vnYobUz37t119tln66233pIkLViwQN/+9rcbbZ+YmNjsBViGDx8eux3vPxKXXXZZk+ttNpuGDh0amw1l27ZtOumkk9q8rtphc9iwYXVes4Z4PB5dccUV+vOf/xzbvvbY87aQk5OjxMRE+f1+rV27Vp9++mmbnI9xsJSUFP3rX//SO++8o8cee0wffvhhgwHU6/XqpZde0ksvvaQLLrhAc+fOVWZmZpvUVFpaqqVLl2rdunUqKipSZWVlnZpqxs9L1WOqW+MiRXv27NHq1aslSYMGDdLQoUOb3SYxMVGjR4/Wu+++q7KyMq1du1ZDhgw5rMevuUqz1PDY+5ZYs2aNPv/8c23btk3l5eWxcwZq1BwMfvnll4pGo83OEf+9733vkGu49NJLm1zft29feTweeb3eFrU/8cQTY7drH9wczWoO+iUd8cxPsBaCO3CEUlNT69yvrKxs1f1//vnnsdstPQFp7NixseC+atWqJtsOHDiw2Qt41P4jX7vXNh5aEsLjUe/hvk81wb2596k1uN1uTZkyRf/85z8VDod15pln6rvf/a4uvfRSnX766a3+bdHBzj33XJ177rkqKCjQwoULtXjxYq1cuVKff/55vZ+bN998U+PGjdOSJUvq/YwdiZ07d+qXv/yl5s2bVy90NqawsLBVHrtmGkapegrZn/70py3arvaFqHbs2HHYwb0myErV84EfimeeeUb333+/Nm7c2KL2oVBIZWVlzR54nXzyyYdUh1Q3aDcmIyMj9nwbump1bVlZWbHb8f791lK137/a7ys6PoI7cITS0tLkdDpjw2VqLu3eWmqucCi1fB7s2nNHNxc60tPTm91f7WBfe1hQPBxqvY3NP9/a2vp9ai2PPvqoVq5cqU2bNikYDOrZZ5/Vs88+K7vdrsGDB2vcuHGaNGmSzjnnHCUkJLRJDbm5ubrsssti356Ew2EtXbpUc+bM0T/+8Y/YZ2zdunW6884761wj4Uh8/vnnOuussw55bvSDv1U7XLt3747d3rp1a51ZmFqqteZ1N8a0uN0111yjOXPmHPJjVFRUNBvcc3NzD3m/Lfkd4HT+L940175223j/fmuplr5/6HgY4w60gtpBrSUX1jkUtXsiPR5Pi7ap3a650NFe45xby9Fab1u/T62lS5cuWrFihe666y517tw5tjwajerLL7/Uk08+qYsuukhdu3bVAw88oEgk0uY1OZ1OnXbaaZo9e7YWLVpUZ7jZ3/72t1a5OE4gENAll1wSC765ubm66667tGDBAu3YsUNer1fRaFSmetKGOkH1SMaV11ZWVnbE+ziSYFn789bS1/Rvf/tbndfi7LPP1jPPPKMvv/xSJSUlCgQCsdfMGFPnd2FLXrfmxvg35FB/BxytvzOORO33r6W/b9AxENyBVnDaaafFbn/22Wetuu/aIaalX4nWbteawwyOZc2FkKPhfWppwExLS9O9996rXbt2aenSpXrwwQc1ZcqUOuNmS0pK9Ktf/UqXXHJJu/bujRkzRnfccUfsvt/v1/Lly494v6+++mps/HL37t31xRdf6N5779WECRPUo0cPJScn1wl4bXEgVTtgXXDBBXUCb0v/TZ069bAfv/b5MS39huehhx6K3f7tb3+rd999V1dddZVOPPFEZWRk1DvBtb0OQI91tb/ha+68J3QsBHegFdSesSA/P1+LFy9utX3X/iq5pZcUr30Cae0whv851OE/zfWWtsX71No1HszhcOjUU0/Vrbfeqtdee0379u3TJ598ogsuuCDW5o033tCrr756SPs9UgfPGrRnz54j3ueHH34Yuz1jxgx17dq1yfb5+flH/JgHq/0NRzxOKKx9MbKCgoJmL7C1Y8cObdq0SVL1mPFf/epXTbYvLy9vtaE8aNquXbtit2sPuUPHR3AHWsFll11WJ3jVnvLtSNWenaSlBwS1240YMaLVaulIal/BtaioqNn2X375ZZPr2+J9au0am2O323Xaaafp9ddf16RJk2LL33zzzSPa76FKTEysc7+hsfaHOvyh9vjylpzgXHtqz8Ycag2nnnpq7Pbq1avb/aTCzp07q1OnTpKqx0g3d6Jp7dfs+OOPb/Yk9v/+97+MvW4ntae5bcnsROg4CO5AK0hKStLNN98cu//qq68eVi+l1+utF/pq9+a/88472r9/f5P72L17t959990Gt29rtQNXe50Uerhq91LVTNHXmBUrVjQ7TVzt1/nzzz/XmjVrmmxfVVWlf/7znw1uX6NXr16xcPjNN980O2PRyy+/3OT6lrLZbHWmP6w9jWB7+OKLL+rc79mzZ702h/pZqz0tYVVVVZNtV65c2aLhOYdaQ9++fXXCCSdIkoLBoGbPnt3sNq1t5MiRsdsHv84HO5TXTJL+8pe/HH5hOCS137va7yk6PoI70Epuv/32Or2mP/zhD2NTMrbE2rVrNWrUKL3//vt1lk+ePDn2FXcgENCMGTMa3YcxRj/72c9iISIvL08TJ048hGdxZGpPw1j7q9yjUe3ez2eeeabRduFwWNOnT292f8cff7xOP/302P2f/vSnTYa5u+66K3YQlpaWpiuvvLJem7S0NB1//PGxOp5//vlG9/f555/rb3/7W5M1VlRUNDs8osaOHTtit2t6aQ/HI4880uKL8UjVAfH++++P3e/cubOGDRtWr92hftb69u0bu93UNwhVVVW6/vrrW1Tr4Xzef/GLX8Ru33XXXYf0LUlrDK+p/U1KcxeW6tOnT+zAce3atdqyZUujbV966SW9/fbbR1wfmldYWBi7zkB6ejrB/RhDcAdaSUJCgl555ZVYyPH5fJoyZYquuuqqRq/eaYzR8uXLdfXVV2vo0KFau3ZtvTZ2u10PPPBA7P6LL76o6667rl7va0VFhaZNm6Z//etfsWV//OMfm70ASmuqPb/yK6+80m6Pezi++93vxl6bJUuW6Je//GW9GVR27typ8847T4sXL27R1Ii///3v5XA4JEmffPKJLrnkknrfkASDQf3qV7/So48+Glv2m9/8ptELd9UO9L/85S8bDFvvvvuuJk+e3OzQjZUrV6p37966++67G539KBKJ6KWXXorNLy9J55xzTpP7bcqyZcs0adIknXLKKXryySeb7L3/7LPPNH78+Dph9he/+EWDn+Han7U33nij2QOS2t8gPPPMM3r44Yfrvd/ffPONJk+erFWrVrVopo4+ffrE5tPOz89vUS/9D37wg9i3KxUVFTrttNP09NNPN1p/eXm5nn/+eU2YMEE/+9nPmt1/c84777zY7doXd2tITk6ORo0aJan6xOdLL71UGzZsqNMmGo3qiSee0A9/+EM5HI56w5zQ+hYsWBAbkvTtb3+7znSW6PhshgFpQKvatm2bzj///HohvHfv3hoyZIhycnIUiUS0d+9erV69ul6Qeeihh/Tzn/+83n5/+tOf1pn3OTU1VWeccYY6d+6s/fv368MPP6wT5mfMmFEnHNa2cOFCnXHGGZKk8ePHa+HChc0+r9qhsLFfG3/7299ivZU2m00TJkzQ4MGD64TeO++8s87czi3Zb+/evWMnC27durXZk7GmTp0a60WfM2dOozNx3HTTTXryySdj93v27KnTTz9diYmJ2rx5sz799FMFg0FNnDhRXbp00XPPPdfsPh966CHddtttsfsJCQk644wzdNxxx6mkpEQLFiyoM179oosu0quvvtpo6C4rK9OgQYNi441tNpvGjh2r448/Xn6/XytWrIj1vs2dO7dOXQe/nrXfd6l6Nophw4apS5cucjqd2rdvn1auXFlnbPO4ceO0cOHCwz4A/N73vqeXXnqpzrK8vDwNHjxYOTk5cjqdKigo0OrVq+sNR7rooov08ssvNxhMysrK1LVr19i0eH379tWECROUkZERey0nT56syZMnx7YZP358nbHrffr00YgRI5Senq5NmzZp8eLFikQi6t69u6ZPn67bb79dknT11Vdr7ty5DT6/73//+3rhhRckVV8U5+yzz1bPnj1jB3BZWVl1ZsmRqs9XmDRpUp2LdqWlpWn06NHq3r27HA6HSkpKtGHDBn311VexE5MvueQSzZs3r5FXuuXGjBkTuxjU+vXrY8N3GvLhhx9q8uTJsRmLXC6Xxo4dq759+6qyslKffPJJ7OTh3/3ud/rrX//a7M/qof48S9KECRO0aNEiSdXBdcKECU22P9THaMnvoUPx5ptv6te//nW95bWHuOTl5dU7YL/gggt0zz33NLnvK6+8Ui+++KKk6oPW2ieT4xhgALS6iooKc88995iMjAwjqUX/hg4dal577bUm93vvvfeahISEJveTmJho7r///ib3s2DBglj78ePHt+g51X6MxgSDQXP66ac3Wd/WrVsPeb+9evVqdPuGXH311bH2c+bMabSdz+cz5557bpP1nnfeeaakpKTF+zTGmL///e8mLS2tyf06HA4zffp0Ew6Hm30+K1euNDk5OY3uy+12myeeeMIY0/TruXTpUuN0Olv8mbz00ktNeXl5s/U15a9//avp06dPix9TkklKSjL33HOPCYVCTe77L3/5i7HZbI3u5ze/+U2d9nv37jUjRoxo8rEHDRpk1q1bZ+bMmRNbdvXVVzdaw7Zt20yXLl0a3V+vXr0a3K6qqsrccMMNLX4/kpKSmv25bqm///3vjb5GDfnLX/7SZJ12u938+te/NtFotEU/q4f682yMMePHj49ts2DBgmbbH+pjtOT30KGo/fk5lH9NfdaMqf7c1Pxu6dKliwkGg61SL6yD4A60odLSUvPCCy+YadOmmSFDhpguXboYt9ttUlJSTM+ePc3kyZPNzJkzzcqVK1u8z23btpm77rrLnHLKKSYnJ8c4nU6Tk5NjRo4caWbOnGny8/Ob3UdbBXdjqsP7X/7yFzNx4sTY8z1ag7sxxkSjUfP888+byZMnm5ycHONyuUz37t3Nueeea15++WUTjUYPeZ/GGFNQUGDuv/9+M27cONO5c2fjcrlMVlaWGTp0qPn5z39u1q1b1+w+aissLDR33HGHOemkk0xKSopJTk42AwYMMDfddFOdfTX3ehYXF5uXX37Z3HzzzWbcuHGmW7duJiEhwTidTpOVlWVOOeUU87Of/cx89tlnh1Rfc7788kvz+OOPmx/84AfmW9/6lsnNzTVutzv2ugwaNMh873vfM08//bQpLi5u8X7/+9//mh/+8IdmwIABxuPx1AnyDYVSv99vHn/8cXPaaaeZjIwM43a7TY8ePcxZZ51l/vrXvxqv12uMMS0O7sYYs2/fPjNz5kxz6qmnmszMzDoht7HgXmPr1q3m3nvvNWeccYbp3r27SUxMNG632+Tm5ppTTz3VXH/99eall14yZWVlLX5NmuPz+Uxubq6RZI477jgTiUSa3WbNmjVm2rRppnfv3sbtdpv09HQzaNAg89Of/tSsWrUq1o7gXq2tgvvcuXNjbe+9995WqRXWwlAZAACOMffff7/uvPNOSdJrr72mKVOmxLcgtMipp56qZcuWKTU1VVu3bq1zgjSODZycCgDAMebmm2+OXTTsD3/4Q5yrQUssXLhQy5YtkyTdcssthPZjFMEdAIBjTEpKSuwkyKVLl+o///lPnCtCc2rer65duzY4gQGODQyVAQDgGBSNRnXKKado1apVGjp0qFatWtWu08ei5f7zn//o7LPPliQ999xz+v73vx/nihAvBHcAAADAAji0BgAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABzngXgEPXpUsXeb1e9ezZM96lAAAAoJbt27fL4/Fo7969rb5vetwtyOv1KhQKxbsMAAAAHCQUCsnr9bbJvulxt6CanvZ169bFuRIAAADUNnjw4DbbNz3uAAAAgAUQ3AEAAAALILi3QEFBgW699VYNHDhQSUlJysrK0ogRI3Tbbbc12P6tt97S+PHjlZaWprS0NE2YMEH//ve/27lqAAAAdCQE92asXLlSJ5xwgh5++GG5XC5deOGFGjVqlIqLi/Xoo4/Wa//YY4/pggsu0OLFizV27FideeaZWrZsmc477zw9/vjjcXgGAAAA6Ag4ObUJBQUFOvvss+Xz+fTGG2/oggsuqLN+2bJlde5v2LBBt956qxISErRgwQKNHj1akrRx40aNGTNG//d//6ezzz5b/fr1a7fnAAAAgI6BHvcm/OY3v1FhYaEefPDBeqFdkkaOHFnn/qxZsxSJRHTDDTfEQrskDRgwQHfeeafC4bBmzZrV5nUDAACg4yG4N8Ln8+m5556Tx+PRtGnTWrRNzTj2Sy+9tN66mmVvvfVW6xUJAACAYwZDZRqxYsUKVVRU6LTTTlNSUpLeffddzZ8/X36/XwMGDNDll1+ubt26xdqXlpZq+/btkqThw4fX299xxx2nnJwc5efnq7y8XGlpae32XAAAAGB9BPdGrF+/XpLUqVMnTZkyRW+88Uad9XfccYdmz56tK664QpJioT0zM1Mej6fBffbo0UOFhYXKz8/XSSed1IbVAwAAoKMhuDeipKREkvTmm2/K4XDoiSee0GWXXaaqqio9/vjjeuihh3T11VfrhBNO0LBhw1RZWSlJSk5ObnSfNYG+oqKiRTU0duWtzZs3Ky8v71CeDgAAACyOMe6NiEajkqRwOKx77rlHP/nJT5Sbm6tevXrpwQcf1GWXXaZQKKQHH3wwzpUCAADgWECPeyNSUlJitxs6OXXatGl65ZVXtGjRojrtq6qqGt2n1+uVJKWmpraohnXr1jW4vLGeeAAAAHRc9Lg3olevXpKqh77k5ubWW9+7d29J0v79+yVJPXv2lFQ9xKYmoB9s586ddfYNAAAAtBTBvRE1M8P4fD4FAoF664uLiyX9r6c9IyMjFt4///zzeu137NihwsJC9erVixllAAAAcMgI7o3o2bOnhg4dKmNMbDhMbTXLak/9+J3vfEeSNG/evHrta5adf/75bVEuAAAAOjiCexNuv/12SdKtt96qPXv2xJavXr1aDz/8sCTphhtuiC2fPn26HA6HnnrqKS1dujS2fNOmTfrd734np9Op6dOnt1P1AAAA6Eg4ObUJV155pd5//30988wzGjRokMaMGSOfz6fFixcrEAjouuuu02WXXRZrP3DgQD344IO65ZZbNG7cOE2aNElut1vvv/++fD6f/vSnP6lfv35xfEYAAACwKoJ7M+bMmaOxY8fq6aef1sKFC2Wz2TRixAj9+Mc/1tVXX12v/f/93/+pX79+evDBB/XJJ59Ikr71rW/p9ttv13nnndfe5QMAAKCDsBljTLyLwKGpmQ6ysekiAQAAEB9tmdMY4w4AAABYAMEdAAAAsADGuAMAAKBDKqoMqKQqqMxkt7JTEuJdzhEjuAMAAKDDWba1WIs27Jc3GJbH7dT4gZ00sk9WvMs6IgR3AAAAdChFlQEt2rBfa3aVyWGzKXJgLpa8XI+le94Z4w4AAIAOpaQqKG8wLIfNpqHHZchus8kbDKukKhjv0o4IwR0AAAAdSmayWx63UxFjtHpHqaLGyON2KjPZHe/SjghDZQAAANChZKckaPzATpJUZ4y7lYfJSAR3AAAAdEAj+2QpL9fDrDIAAADA0S47JaFDBPYajHEHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwb0JEyZMkM1ma/Tfe++91+B2c+fO1ciRI5WSkqKsrCyde+65Wrx4cTtXDwAAgI7EGe8CrOCSSy5RSkpKveXdu3evt2zGjBmaNWuWkpKSNHnyZPn9fs2fP1/vv/++5s2bpylTprRDxQAAAOhoCO4t8NBDD6l3797Ntvvggw80a9YsZWdna8mSJerfv78kacmSJZowYYKmTZumCRMmKCMjo20LBgAAQIfDUJlW9Mgjj0iS7rrrrlhol6TRo0frhhtuUGlpqWbPnh2v8gAAAGBhBPdW4vP59NFHH0mSLr300nrra5a99dZb7VoXAAAAOgaGyrTA7NmzVVRUJLvdrgEDBmjKlCnq2bNnnTYbNmxQIBBQbm6uevToUW8fI0aMkCStWbOmXWoGAABAx0Jwb4H77ruvzv1bb71VM2fO1MyZM2PLtm/fLkkNhnZJ8ng8ysjIUElJiSoqKpSamtp2BQMAAKDDIbg34fTTT9e1116rMWPGqGvXrtqxY4fmzZun++67T7/+9a+Vlpam6dOnS5IqKyslScnJyY3uz+PxqLS0tMXBffDgwQ0u37x5s/Ly8g7jGQEAAMCqGOPehHvuuUc/+MEP1LdvXyUlJWnAgAG644479Prrr0uS7r77bvl8vvgWCQAAgGMCPe6HYfLkyfrWt76lFStW6LPPPtOECRNi87xXVVU1up3X65WkFg+TWbduXYPLG+uJBwAAQMdFj/thqpnucc+ePZIUO1l1586dDbb3er0qLS1VZmYm49sBAABwyAjuh6mkpERS9bh1SRo4cKASEhJUUFCgXbt21Wu/atUqSdKQIUPar0gAAAB0GAT3w1BQUKBPPvlE0v+meUxKStKZZ54pSXrllVfqbTNv3jxJ0vnnn99OVQIAAKAjIbg3YvHixXr99dcViUTqLN+2bZsuuugieb1eXXDBBXWmf7zlllskVU8fuWnTptjyJUuW6Omnn1ZGRoauueaa9nkCAAAA6FA4ObURGzdu1LRp09SlSxeNGDFCGRkZys/P18qVK+X3+zV48GD97W9/q7PNxIkTNX36dM2aNUvDhg3TpEmTFAwGNX/+fBljNGfOHGVkZMTnCQEAAMDSCO6NOPXUU3XjjTfqs88+0/Lly1VSUiKPx6Nhw4bpsssu04033qikpKR62z322GMaNmyYHn/8cc2fP19ut1sTJ07UzJkzNWbMmDg8EwAAAHQENmOMiXcRODQ100E2Nl0kAAAA4qMtcxpj3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCOwAAAGABBHcAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyA4A4AAABYAMEdAAAAsACCewsVFRWpU6dOstls6tevX5Nt586dq5EjRyolJUVZWVk699xztXjx4naqFAAAAB0Rwb2Ffv7zn6uwsLDZdjNmzNC0adO0du1aTZw4USNHjtT8+fN1+umn6/XXX2/7QgEAANAhEdxb4MMPP9Qzzzyj6667rsl2H3zwgWbNmqXs7Gx98cUXev311/Xee+/p448/lsPh0LRp01RaWto+RQMAAKBDIbg3w+fz6cc//rEGDRqkW2+9tcm2jzzyiCTprrvuUv/+/WPLR48erRtuuEGlpaWaPXt2m9YLAACAjong3ozf/va32rJli5566im5XK5G2/l8Pn300UeSpEsvvbTe+pplb731VtsUCgAAgA6N4N6ENWvW6OGHH9a0adM0bty4Jttu2LBBgUBAubm56tGjR731I0aMiO0TAAAAOFQE90ZEo1Fde+21ysjI0B//+Mdm22/fvl2SGgztkuTxeJSRkaGSkhJVVFS0aq0AAADo+JzxLuBo9ec//1nLly/XnDlzlJ2d3Wz7yspKSVJycnKjbTwej0pLS1VRUaHU1NRm9zl48OAGl2/evFl5eXnNbg8AAICOgx73Bmzfvl133XWXxo8fr6lTp8a7HAAAAIAe94bcdNNNCgaDeuqpp1q8TUpKiiSpqqqq0TZer1eSWtTbLknr1q1rcHljPfEAAADouAjuDXj77beVkZGhG264oc5yv98vSdq1a5cmTJggSfrnP/+pLl26qGfPnpKknTt3NrhPr9er0tJSZWZmtji4AwAAADUI7o0oLS3VokWLGlzn9/tj62rC/MCBA5WQkKCCggLt2rVL3bt3r7PNqlWrJElDhgxpw6oBAADQUTHGvQHGmAb/bd26VZKUl5cXW9a7d29JUlJSks4880xJ0iuvvFJvn/PmzZMknX/++e3zJAAAANChENxb0S233CJJuu+++7Rp06bY8iVLlujpp59WRkaGrrnmmniVBwAAAAsjuLeiiRMnavr06SoqKtKwYcM0ZcoUnXvuuTr99NMVDoc1Z84cZWRkxLtMAAAAWBDBvZU99thjmjNnjk444QTNnz9fS5Ys0cSJE/Xxxx9rypQp8S4PAAAAFmUzxph4F4FDUzMdZGPTRQIAACA+2jKn0eMOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENyb8Mgjj+jiiy9W//79lZ6eroSEBPXq1UtXXXWVvvzyy0a3mzt3rkaOHKmUlBRlZWXp3HPP1eLFi9uxcgAAAHQ0NmOMiXcRR6ucnBx5vV4NGTJE3bt3lyStW7dOGzdulMvl0r/+9S+dd955dbaZMWOGZs2apaSkJE2ePFl+v18ffvihjDGaN2+epkyZcsR1DR48OFYLAAAAjh5tmdMI7k349NNPdfLJJysxMbHO8ieffFI33XSTOnfurJ07d8rpdEqSPvjgA02aNEnZ2dlasmSJ+vfvL0lasmSJJkyYoOTkZG3dulUZGRlHVBfBHQAA4OjUljmNoTJNGDt2bL3QLkk/+clPlJeXp3379mn9+vWx5Y888ogk6a677oqFdkkaPXq0brjhBpWWlmr27NltXzgAAAA6HMsF940bN+p73/ueBg0apDPOOEPPPvtsg+3eeOMN9e3bt83qcLlckiS32y1J8vl8+uijjyRJl156ab32NcveeuutNqsJAAAAHZcz3gUcir1792rMmDEqLi6WJH399df6+OOP9cYbb+i5556r0zteWVmp/Pz8Nqnj2Wef1YYNG9S/f/9Yz/qGDRsUCASUm5urHj161NtmxIgRkqQ1a9a0SU0AAADo2CzV4/773/9eZWVlevrpp1VWVqZ169bpoosuip0k6vf72+RxH3zwQU2dOlWXXXaZTjzxRF111VXq2rWrXnzxRTkcDknS9u3bJanB0C5JHo9HGRkZKikpUUVFRZvUCQAAgI7LUj3uH3zwga6++mpdd911kqQTTjhB8+bN0/3336+77rpLF154od56663Y8JXW8p///Ecffvhh7H6vXr30j3/8QyeffHJsWWVlpSQpOTm50f14PB6VlpaqoqJCqampzT5uzckNB9u8ebPy8vJaWj4AAAA6AEv1uOfn52vUqFH1lt9xxx169NFHNX/+fF188cUKhUKt+rgffPCBjDEqKSnRxx9/rP79+2v8+PH63e9+16qPAwAAADTGUj3uqamp8vl8Da6bPn26IpGIbr31Vl122WWtMl/6wTIyMjRu3Di98847Gj16tGbOnKnJkyfrlFNOUUpKiiSpqqqq0e29Xm/sebREY9MINdYTDwAAgI7LUj3ueXl5Wrp0aaPrb7nlFv3+97/Xm2++qTvuuKPN6nC5XPrud78rY0xslpiePXtKknbu3NngNl6vV6WlpcrMzGxxcAcAAABqWCq4T5w4UW+99ZbKy8sbbfOLX/xC9913n/bu3dumteTk5EiSCgoKJEkDBw5UQkKCCgoKtGvXrnrtV61aJUkaMmRIm9YFAACAjinuwX3q1Kl6/PHH1ZILuP7gBz/QTTfdpG+++abJdnfccYf+/Oc/66qrrmqtMutZtGiRJMVOEk1KStKZZ54pSXrllVfqtZ83b54k6fzzz2+zmgAAANBx2UxLEnMbstvtstvtKi8vb3JGlvb26aefqqKiQpMnT5bd/r/jm1AopKeeekozZsxQQkKCNmzYoOOOO05S9UmskyZNUnZ2tpYsWRKb433JkiU644wzlJSUpK1btyojI+OIamvLS+kCAADg8LVlTjsqTk6N87FDgzZt2qRp06YpJydHJ598srKzs1VYWKgvv/xSe/bsUWJioubOnRsL7VL1UJ7p06dr1qxZGjZsmCZNmqRgMKj58+fLGKM5c+YccWgHAADAsemoCO5Ho/Hjx+uOO+7QokWLtGbNGhUWFsrtdqt379669NJLdfPNN6tfv371tnvsscc0bNgwPf7445o/f77cbrcmTpyomTNnasyYMXF4JgAAAOgIjoqhMjabTRUVFUfVUJmjGUNlAAAAjk5tmdPifnIqAAAAgOYR3AEAAAALOGqC++zZs7VkyZLY1UUBAAAA/M9Rc3LqjBkzJEk2m029e/fW0KFD6/zr3bt3XOsDAAAA4umoCe4158gaY7RlyxZt3bpVr7/+emx9amqqTjrppDph/qSTTlJSUlKcKgYAAADaz1ET3D/55BN98803+uKLL/TFF19ozZo1Kioqiq0vLy/X4sWLtXjx4tgym82mvLy8WJAfMmQIVyYFAABAh3RUTwe5e/fuWJCvCfMbN25UJBKp085ms8X+Hw6H2632eGE6SAAAgKNTh79yamO6deumbt266ZxzzoktCwQCWrt2bZ0wv2bNGpWUlMSxUgAAAKBtHdXBvSEJCQk6+eSTdfLJJ9dZvmPHDn3xxRdxqgoAAABoW5YL7o057rjjdNxxx8W7DAAAAKBNHDXzuAMAAABoXNx73GfMmKEVK1bIbucYAgAAAGhM3IP7I488Eu8SAAAAgKNeuwT3559/XsuXL9eIESN06qmnauDAge3xsAAAAECH0S7B/cknn9TSpUslSTfffLMeffTRZrfJz8+X0+lUly5d5HA42rpEAAAA4KjW5gPLfT6fli9frprrPM2YMaPJ9s8//7y6deumvn37qmfPnkpNTdUll1yilStXtnWpAAAAwFGrzYP76tWrFQ6HZbPZNHz4cPXq1avRtv/5z3901VVXae/evTLGyBgjv9+v119/XaNGjdIf//jHti4XAAAAOCq1eXDfvHlz7PaoUaOabHvrrbfKGCObzSabzRZbboxRJBLRr371Kz355JNtVisAAABwtGrz4L5///7Y7QEDBjTa7uOPP9a6detigf2EE07QSy+9pLlz5+rUU0+VVB3gb7vttjoHAwAAAMCxoF3GuNfIyMhotN2LL74oqTqcp6Wlaf78+brssst01VVX6dNPP9V3vvMdSZLf79eDDz7YpjUDAAAAR5s2D+7Jycmx26FQqNF2b775piTJZrPppptuUteuXWPr7Ha7/v73vyspKUnGGL3wwgsKh8NtVzQAAABwlGnz4J6dnR27vWfPngbbfPbZZ3XWTZ06tV6bzp0769xzz5Ukeb1erVq1qnULBQAAAI5ibR7chwwZEru9bNmyBtvMmzdPUnVv+8CBA9WvX78G240cOTJ2e926da1YJQAAAHB0a/PgfuKJJyojI0PGGH3wwQfau3dvnfWBQEAvvPBC7P5ZZ53V6L46deoUu11cXNz6xQIAAABHqTYP7k6nU9///vclVYf06667rs5Y95kzZ9YZJlMzHKYhfr8/djsQCLRBtQAAAMDRydkeD/KrX/1Kzz77rCoqKvTOO+9oyJAhOvfcc/XVV1/pP//5j2w2m4wx6ty5syZNmtTofvbt2xe7nZKS0h6lAwAAAEeFdgnu3bp109NPP63vf//7MsZo48aN2rhxo6Tq6R+l6vHtt912m5zOxkuqfUJq7VlnAAAAgI6uzYfK1Pjud7+rl156SdnZ2TLGxP7VOOecczRjxoxGt49EIvr0009j9xs7gRUAAADoiNqlx73GJZdcookTJ+qll17SwoULtWfPHqWkpOi8887TtddeG7tqakM++OADFRUVSZLcbrdOPPHE9iobAAAAiLt2De6SlJ6eruuvv17XX3/9IW3317/+VVL1kJoRI0bI5XK1RXkAAADAUandhsociS1btuiNN96QVD0mvqkTWAEAAICOqN173A9H3759tW/fPq1evVpffPGFvvOd78S7JAAAAKBdWSK4S1J2drbOOuusJi/QBAAAAHRUlhgqAwAAABzrCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHBvRFVVlV5//XVdc801GjhwoBITE+XxeDR06FDdc889qqysbHTbuXPnauTIkUpJSVFWVpbOPfdcLV68uB2rBwAAQEdjM8aYeBdxNPr73/+u6667TpJ0wgkn6MQTT1R5ebkWL16siooKHX/88Vq0aJE6depUZ7sZM2Zo1qxZSkpK0uTJk+X3+/Xhhx/KGKN58+ZpypQpR1zb4MGDJUnr1q074n0BAACg9bRlTqPHvREul0vXX3+91q9fr/Xr1+vll1/We++9pw0bNmj48OH6+uuvNWPGjDrbfPDBB5o1a5ays7P1xRdf6PXXX9d7772njz/+WA6HQ9OmTVNpaWlcng8AAACsjeDeiKuvvlpPP/20TjjhhDrLu3btqieeeEKS9K9//UvBYDC27pFHHpEk3XXXXerfv39s+ejRo3XDDTeotLRUs2fPbofqAQAA0NEQ3A/D0KFDJUmBQEBFRUWSJJ/Pp48++kiSdOmll9bbpmbZW2+91U5VAgAAoCMhuB+GLVu2SKoeTpOVlSVJ2rBhgwKBgHJzc9WjR49624wYMUKStGbNmvYrFAAAAB0Gwf0wzJo1S5J09tlnKyEhQZK0fft2SWowtEuSx+NRRkaGSkpKVFFR0T6FAgAAoMNwxrsAq3nnnXc0e/ZsuVwu3XvvvbHlNdNDJicnN7qtx+NRaWmpKioqlJqa2uxj1ZyVfLDNmzcrLy/vECsHAACAldHjfgi+/vpr/eAHP5AxRg8++GBsrDsAAADQ1uhxb6Fdu3bp7LPPVklJiW655RZNnz69zvqUlBRJ1RduaozX65WkFvW2S43P/9lYTzwAAAA6LnrcW6C4uFiTJ09Wfn6+pk2bpoceeqhem549e0qSdu7c2eA+vF6vSktLlZmZ2eLgDgAAANQguDejsrJS55xzjtavX6+LL75Yf/vb32Sz2eq1GzhwoBISElRQUKBdu3bVW79q1SpJ0pAhQ9q8ZgAAAHQ8BPcmBAIBXXjhhVq2bJm+/e1v68UXX5TD4WiwbVJSks4880xJ0iuvvFJv/bx58yRJ559/ftsVDAAAgA6L4N6ISCSiK664Qh999JHGjRunf/3rX3K73U1uc8stt0iS7rvvPm3atCm2fMmSJXr66aeVkZGha665pk3rBgAAQMfEyamNePzxx/Xaa69JknJycvSTn/ykwXYPPfSQcnJyJEkTJ07U9OnTNWvWLA0bNkyTJk1SMBjU/PnzZYzRnDlzlJGR0V5PAQAAAB0Iwb0RJSUlsds1Ab4hd999dyy4S9Jjjz2mYcOG6fHHH9f8+fPldrs1ceJEzZw5U2PGjGnTmgEAANBx2YwxJt5F4NDUTAfZ2HSRAAAAiI+2zGmMcQcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7k1YuXKlHnjgAV188cXq0aOHbDabbDZbs9vNnTtXI0eOVEpKirKysnTuuedq8eLF7VAxAAAAOipnvAs4mt1777164403DmmbGTNmaNasWUpKStLkyZPl9/s1f/58vf/++5o3b56mTJnSNsUCAACgQyO4N2H06NEaMmSITjnlFJ1yyinq3bu3AoFAo+0/+OADzZo1S9nZ2VqyZIn69+8vSVqyZIkmTJigadOmacKECcrIyGinZwAAAICOguDehF/84heH1P6RRx6RJN11112x0C5VHwDccMMN+tOf/qTZs2fr5z//eavWCQAAgI6PMe6txOfz6aOPPpIkXXrppfXW1yx766232rUuAAAAdAwE91ayYcMGBQIB5ebmqkePHvXWjxgxQpK0Zs2a9i4NAAAAHQDBvZVs375dkhoM7ZLk8XiUkZGhkpISVVRUtGdpAAAA6AAY495KKisrJUnJycmNtvF4PCotLVVFRYVSU1Ob3efgwYMbXL5582bl5eUdXqEAAACwJHrcAQAAAAugx72VpKSkSJKqqqoabeP1eiWpRb3tkrRu3boGlzfWEw8AAICOix73VtKzZ09J0s6dOxtc7/V6VVpaqszMzBYHdwAAAKAGwb2VDBw4UAkJCSooKNCuXbvqrV+1apUkaciQIe1dGgAAADoAgnsrSUpK0plnnilJeuWVV+qtnzdvniTp/PPPb9e6AAAA0DEQ3FvRLbfcIkm67777tGnTptjyJUuW6Omnn1ZGRoauueaaeJUHAAAAC+Pk1Cb8+9//1r333hu7HwwGJUmjRo2KLZs5c6a+853vSJImTpyo6dOna9asWRo2bJgmTZqkYDCo+fPnyxijOXPmKCMjo12fAwAAADoGgnsTCgoK9Nlnn9VbXntZQUFBnXWPPfaYhg0bpscff1zz58+X2+3WxIkTNXPmTI0ZM6bNawYAAEDHZDPGmHgXgUNTMx1kY9NFAgAAID7aMqcxxh0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALMAZ7wIAwCqKKgMqqQoqM9mt7JSEeJcDADjGENwBWE7tAF3iDSq/uEq9spIlSfnFVZIxks2mXlnJ6tc5tVUC97KtxVq0Yb+8wbA8bqfGD+ykkX2yWvNp1VNUGdDWwkpJNvXJ8XCwAADHOII7gMMSr97n2gH66z3l2lPml9tpV5kvpHDEyBcKKxg28rgd6tc5VcOOy1CSy3FEgbuoMqBFG/Zrza4yOWw2RYyRJOXltl2Y/mD9Pr24LF+7S31yOuzql5uiK07t1eYHCwCAoxfBHcAhi0fvs1Q3QBeU+/VNQaUiUck00LY8ENGq7aXaVeJTj6wkpSa4Dilw1+nVrwrKGwzLYbNp6HEZWr2jVN5gWCVVwTYJ7h+s36s/fbhJ3+yvVDhq5HLYVVoVUobH3aYHCwCAoxvBHcAhiUfvc42aAB2JGFX4QwpHm9+m2BtQp7QEDe3X8sB98IHJ8J6Z8ridihij1TtKFTVGHrdTmcnuVnx21YoqA/rw6/3aXuyTPxyVTZIUra7bG2yzgwUAwNGP4A7gkLR373NtmcluedxOVQZDKvYGW7RNKCpFoy0P3I0dmAzvmSFJdb5laIvnW1IVlC8YUThaHdojRopGjPzBiJJcjkM6WOBkWgDoWAjuAA5JZrJbdtlUEQhp2dZiOR22Vu19bipsZqckaPzATtpT6lN+YZV84XCz+0t02tQrx6NOqQn1AndDj9XQgUmRNyCbzaaLhneXbGrTIJyZ7FaS26EEp12BUFR2myQZ5aYmaGSfrBY/bryGMwEA2g7BHcAh2VzgVUlVUMFwVMFwVD0yk9WvU0qr7LslYXNknyxlJbtU4A3o8+2lCoQiikSlxkbNDOicqkuGd68zy0xjj5WX61FpVUh22WLDYgoq/Npf4ZcvGFGPzGSNH9hJ/Tqltuj5NHYQ0tzByVnHd9KGvRXaVeKTMUapiS6NH5Cr0wfktvhx4zWcCQDQdgjuAFqsJhAWeoNKTXSpqDKgXSVV+mxrkb7ZX3lEvbpNhU1JdYJuv86p+tmZAzT3063atL9STruU5HbI6w9re0mVAiEjt9OmnlnJ+lbvLK3aXj2cZ9WBgJ6V7NJrn+/U1kKvEp0ORYzR7lKfEl0O+UIRBcMR5XjcKvOHVFQZVDASUVFlQAnOUm3YVy678vStPtlNPp/GDkJacnAycVAXSTZ9+NU+GRllexIOaWhOPIczAQDaDsEdQIvVDoQndEvVe2v3qtwf1ub9Xu1y+yXV79Vt6TjrxoaovLxih/ZXBGQOjE+vCboj+2QpL9ejrYVeSUZ9cqp7/VfvKNGmvZXyJDp0Qpc0LdhQUOdgYHepT7JJX+0pV1UgopF9s5Vf5NXy/GIFQlG5HXbZ7DYN7JJaPb1kMKKqUFjRA736e8p82rSvUj86ra+uHtO7wefS2EFIVrKr0eUHD8GZOKizhvfMOKwx6jXnArTHybQAgPZDcAfQYrUD4ec7SqunKrTbNKxnhjbuq6zXq1vTu1zkDcgmm846obMmDurc7L5X7yhVSVVQxd6AvthRqspARNkpCUp02VVcFVRWskuZnuppGvvk/K9HflthlT4/0LvuCTjlSfDFZqHpkpmgL3eWaVdJlZJcDlUFIir3h/Xxxv0KR4wqA2HJGGWmJCgYiGjDnnJJNlWFwnVmrwlHpd1lfr26cocGd01VhsddJ1gXVQa0ekepCisDdQ5CCisD+uDrfdpSUKlIxGhEn0yt3lGqzQVezf7vViW47PV64LNTEg6rh7zmXACp7U+mBQC0H4I7EEdWm/WjdiAsrAwoEjXyhSL6fHt1r24g9L9J1Wt6nT/5plCl3qACkahWbi/Wpv0VuvxbxzV64qkkFXkDqvCHVBmIyOsPqyoUkcftUEFFWKVVQf2+PKC0RKfSk10qqwpJktxOuzYdmPc8JcGpQDii4qqgSqtC2rCvXCvzixWOGjnsNqUluTS4W7q+2VehMn9IwXBUkahR1EgV/pCcdrt8NWPnD5okvvpkUamgMqD/9+k2pSY6ZbfbdNbxnZSW5I4dqNTUUnMQsqe0Squ2l6j0QL3eYFhJbocq/CGVVAWV5HK06lj0mm8krPT5AgA0jeAOxElNb3RhZUB2u00je2XppOPS4xKyDj6AqO41rh5yIruRLWpTQVVAMkY5KYnq39mjEb0ylZJQqGXbirWnzKeqYFgVgbACCyM696Su6p3jUZE3oFJvUP5wVCVVQRVVBvTXjzfry52lmjq2b72x3Xm5HtltudpZ4pMx0o5in9ISXdq0r0I7S32SpFA4qhJvSG6nXV3SE7WnzC+bpJyUBO0r9yscNeqWnqj9lQEVe0PyBcMq9YUUOdBrHokYlVUF9c3+CtntNgXDEYUjks0uGSMFw0aJiTa5nQ5VBkL1ru4UNZItahSOGH2zv0L+UFSBcFQrthWrZ5ZHlYGwotHqHnyn3aZkl10F4agKK4OKRI0iRrLJKBiOqG+uR/5QRL5gtMGx6Ed6YHe4PfYAgKMTwR2Ig4N7o73BsD76ar8Gd0vXCV1TD+kkzyMJdx+u36vXVu/S9iKvKgNhlXqDCkWiCoaNAk1c3Mhll1ITnEpOcMobDKvSXz2cpLSqUtsLvVqZX6JrT+srm2wKRKIqrQoqHDEykir8Ya3aXqrO6Xvq9CzXPmnTLpsq/GF5g2GV+0Ky2arnMpekcDSqCn9YSW6nBnVN09bCSkk2HZeVpP0VflX6Q9oUCCtijELhqCINXFY1HJWCoYgcdtv/rrwalWySkt12ndgtXfnFXvkCkYZnqzFSTqpbXn9E/nBEpVUhFVcFtbPEVz3kxeVUSVVQ6Ukuuex2FXkDqgpF5LLbleSyy0jKSU3QeSd106rtJVqzq6zeWHSmcwQAHIzgDsRBSVVQhZXVvdERY+QNRBQ1YX21p0xVoeq5yVsyXGLZ1mK99vlO7S/3q1Naoi4a3qPZcFdUGdA7a3brofc3qMwfOaz6Q1Gp2BdWsa/+POqBiNGOYp9mfbhRJ3ZLl01S6EBol6pDc7kvpL1lvjo9y4s27NeSLUUKhqOxwF4ZCCt8IHlHTXXArgpVR+mQP6yPvt5X3QNuk/77TaFkpKgxikaiqjVqpx4jVfeIO+yxYG9UPQymT06KjstO1he7ShVuZAd2u+SyO+QNBlTmCylqqusLRYx8oahKVf26+EMBlftDCoSjikQlv6KqDEhJbrvKqkIyksYP7KSqUEQl3qAyPe7YcCGmcwQAHIzgDrRQa45Hz0x2y263KVwzpMJhUyhilOVxKxI1LZq6r6gyoD9/uDHWU2u32bSzuEqPfW94gxcYkqSPNxboD+9+rb0VgSOqvzlRSYXekBZuKmxwfVUoqi93lmlboVf9OqVqa2GlPtqwT/mFXtlkkzfURHd/LbFmRir2huRy2BQ9MBylOb6wkcJ1D1zsNskXjOidL3ervIGDktqPu6vEq6pQpM6JqweLSvIGq6+AardVXwXVSLLLJtls+nx7iYb3zJTNSIFwRKXeoMp9IaZzBAA0iOCODq21wnZrD1s4+CI7VcGwXHa7KvxhpSa5mpy675t9FVq7u0zrdpVpza4yVQUjcjnsCkQj+npvhVbvKNVZJ3TWsq3Feu/LPSquCip4IKB+vKlAlU2NgWlHO0v9+vUb67R0c5EWbtivbwqrDqxpQepuRKglib0Z+yv88gbDzVZR6gu3uFSjuie5dklPVIrboV2lPm0t8uqb/ZUq94UUiRh9va9CV55yHNM5AgDqIbijw1q2tVivrdqp/RV+dUpN1EUjehzWLBtNzcld6gtKsqlPjqfBXu6D5zOvPef4xEFdVOEPaf5X+1VeFVJVKKJOqYnKy/XEhkus2FaknSU+7S33q7A8oLV7yrR5v1cV/pCCYRMbf22LVI/lrvCH9JeF3+i1Vdu1fk+lirwBOWx2BQ50C3uDR0dor7G7zK+/f7ot3mXE2CQFI1HZZZNNpslcXn2SacvV7Muu6qFCDrstNja+qDIg2WyKRI3KAyG9uHyHzhvSVUO6pzOdIwAghuCODql6GMkmrd5Roogxcths+mJHiY7vkia3yx67EmXtIC8pFqwzktzVqcxI+cVVKvIGFIkY9e7k0ZqdpVq6pVBLthSpsDKgQCiizmmJmja2t7pnevTu2j2x8crnnNg1drXMFz/L1zcFlZKkfrkpGtYzU/vL/XI7qqcn/PbgLhrdL1uZyW5tLvBqxj8/18r8YlWFmu/WrRmL7Q8brcgvPWjt4Y1jPxaFolLoEA5uDqd/PyqpKhhRaqJR1G5U4g0cGPJTvbdQxKigIqAdJT5dM7ZPvQszAQCOXQR3dEirt5fq673l8oWictpt8ocj2rCvUnvKA0pNcCrD466ejcRmkzFGNtm0r9ynPWV++UIRuZ12ZSUnSDJKS3Rpe3GVSrwBfba1qMHx0/sqgrp93hplJFePUTeSwpGoPly/T6f0yZQvGNWXu8qqL/IjaW+ZX8u3FSscicp3YA7xDfsqlJLg1OkDc3X3m2u1fk9Fu75maD0HjvkaFTVGdruqZ9o5qKFNUsRUX7FVNqlfp9Q2rBQAYCUEd3RIFYGQIsbIyMhhtysqyWakYDiiiNuh/MJKfbOvXFFjk8Mm+SPR2DzfNfaVByU1H8JqhKJSQWXwoDp82l5SPf94otOmnJREVQbCKqwM1ttnZYFXv3lrrcZ/lU1ot7Bkl13Hd0lVZTCsncW+2Cw4NZx2KcHlUDhqVOYL6uDzcKOmOtBvL67StsKqWHBv6fkaVruoFwCg5QjuOOodPDNK7XHiNVMJbi30ameJV5X+iLqmJ6lHRpKSXU6VekPyBv83VMTtdBy4SE/NEtNsMD/y0x2r+cNGu0p9Te6v3B/RW2v3t9Ijor3UfIYSHDaNzsvR+UO7SLJpy/5Kvbxih/ZXVB+oGelAD7tRWoJT/lBEdls4NtWlJDnsUoLTLn8ocmDWmQxtLvA2eHL0wSG95iTqXaVVCoaNJg3qrItG9KhXb1uFew4aAKBtEdxxVKoJANsKvfp8e/VUeGVVIRVWBg+cECodl5mkvrmp2lJQoRX5JSrxBhWJSi6nTdmeBIUikToh2UgqOXC5eR20vL2052Oh/RhJTpuUm5Ygm016cuFmGSOlJrp0xgmdtLPEp/W7K1ThDypqJG8got1lflUdOKi02fS/ITNGikSjKqwMaHOBV1sLK7VoQ0G9k6PLfcHYz4bH7dTwnhn6fHup3lu3V/vL/YpEjVbml2hPmU8/OaN/rNbmZkg6lPBdu21jBxcAgNZDcMdRpyZYFHkD2rivUuGoUYLDrvyi6nmzbTYpHDbatK9Cn35TJF8oomCtgeeBsNHuMn8cnwGORU6HTVWBsBZu2K9wVHLYJLfTrnAkquln9derq3Zp6ZYiVfirr+pa4g3GTipOcEiBA18MhY1UXBWW0yZtL6pUWVW43pzuRd6APvx6v7YUeBWNGtntNhVXBVVaFdD+cr+CEaNINKoib0AvrdihgZ1T1SvHo/xCr15dtUt7y/1KS3TVubCTJC3aWKC1O8sUlZFdNvXO8WhQt9TYydq1w/zBV7otqQqq0BvkglEA0IYI7jiq1J560R+MaE+ZX5FIVFket8oPTIFo0//mSfGHG79IDtCe/GFT5/MYMZIvFNU3+yv1z+U7tL88oFA0KrvtwDj2Wl+/BBqY+CdspCJvULvKqurN6W6TTfvK/NpT6lNqolNF3qBcDpvsqp5SMhSpvmxsVNL2Ip9ueeULZXvcKvNVX8XVJmlEryzZbTZ5g2H9e80efb6jRF/vKVdFIKJEp11lvupvpxKcdjnsduV18qh3lkcn9kjXkO7pdaZIrQiEFAxHlZro0ojemVwwCgDaCMEdR5XaV4wc2CVV24q88gbCKveH5G/s+vPAUawqFNUHX1Wft3Con+Byf1gfbyjQyb2zlJvilsthV05Kgvp1StGzS/NV5A1qb3n1t0ub9lXqW73SJdW92JORVOYLq+zAlWDtByafX7mtWAO6pGp/eUAr80u098CMStFoVIkupyr8oeoLRx04H2R/uV9fJJRpeX6x+ndKkS8UiX0LsGxrsYLhqALhCBeMAoA2RHDHUSUz2S2P26niqqA27qs4MAwmGgsPgBUdziGn3Va93fJtJdpS6FWC065uGUka1TdbaUkuhSNRBcNRRY1R1EjF3oD++01xdW97M/uNGlX3/kuqDIS0t8x/YF+SL2TkD1WHdrtN0oH2wUhUId+Bnn2bTQ67TS6HPRbUO6UmqlNqghJcdi4YBQBthOCOo0p2SoKG98zU/PV7Ve4PKRSJ1poBBjh2JDjtCkeMvOGwwtGo3E6HCiuDKqgIqHtmkrYUehWq1bUeikpl/uaHjoWj1bPg2G1SIBJRWVlYvlD1WJ1AKFK9zl7d0157tpuIqZ5SNcnlkMftUFqSW5kHrh5c4Q/J5bAryeXQiT3SNX5ALqEdANqAPd4FAAfLSHbJYa/+aNLTjmOVPxRVKFrdm26zVY9frwyEtavUp/W7y6sv0HQYbJLcDpuMbNpe7FNBhV/RqJH/QHhPODAPfUayS26HTXZb9R8Km6067FefXFt94uklI7or0eVQJGpU7gtpU0GlNu+vbL0XAQBQB8G9Dfh8Pv3617/WgAEDlJiYqG7duulHP/qRdu3aFe/SLMLI6bApGjUKRxnXjmNTrdkh5Q1EVFwVVCAUlT8UVfjA1XlbyiYpwWlTsqv6gmOhiFEgFFW5L6zKQFhJboccNpvsdsnlsKvMF1ZOilt5nVPULT1ROalu9c5KVqbHrZxUt/rmeDR+YCftKPHpm/2VKqwIKBiJqtwXip2UCgBofQyVaWV+v19nnnmmli5dqq5du+rCCy/Utm3bNGfOHL399ttaunSp+vbtG+8yj25GCoQjCoSjzHsO6H8h3i4pHDUqrQrK1sSVw+y26nnlc1IT5ElwyhsIq2t6ksJRo/V7ymUOTJ9qVD10xhsIK2yM7Kqe1rLcH5I/VN3zrqiRNxRVTopbndMT1SfHo2vG9lGmx6131+5RaVVIVaGINhdUypPglF02TkoFgDZCj3sru++++7R06VKNHj1aGzdu1EsvvaTPPvtMDz/8sAoKCvSjH/0o3iXGVVFlQN/sr1BRZaDB9c8s3qafv/KFvtnvVYTUDsSyuU1ScqJDjgOzwrhrbjTAbpPs9uqhNeW+kCoCYX29t1z7y6uHxcROPD0gFDaKRA+Mk68KqdIfVkUgrC2FXu0pD6jSH1JlIKyuaYm6aHgP9eucqpKqoIwxyk5JUL/cFCW5HcpMdunEHumMbweANkKPeysKBoN6/PHHJUlPPPGEUlJSYutuueUWPfPMM1q0aJFWrlypk08+OV5lxs3BV2zs1ylFaUku9cpKVr/OqfpmX4X+sXib8ot98S4VOOoYSb5gRJEDJ5faGs/t1Sd0G6NwNCpvIBI7CPYG/3fAXPvAuPZo+ZrlkVBU+w5MNem021TpdirD445drKlmBqjURKcikai6pidqSI8MjR+Qe8TPFQDQMIJ7K/r0009VVlamvLw8DR8+vN76Sy+9VGvWrNFbb711TAX3osqAthZW6rVVu7Rxf4USnA7tK/ercmVYaYlOpSa5dOGw7iqq9GtLoTfe5QJHrZoZlkzsP020NZLMkX1tVfN40ahRYWVA+YVVsYsqZackaPzATpIUOxhnCkgAaFsE91b0xRdfSJJGjBjR4Pqa5WvWrGm3muKtppd9zc5Sfb23QkZSl9QE7Sn1KXxgJouCyqAe/2iTyn1BxrQDTXDa6l5xtb1EJQVCUW0uqFCp938nno7sk6W8XI9KqoLKTHYT2gGgjRHcW9H27dslST169Ghwfc3y/Pz8dqspnooqA1q0Yb9W5JdoZ3GVyn0hyWZTVSCsYCQqh92mXtnJ+qagUmU+TkQFmmJTfEJ7jagkXyiiVz/fpahsGtknS5Jive8AgLZHcG9FlZXV8xcnJyc3uN7jqR4bWlFR0aL9DR48uMHlmzdvVl5e3mFU2L5KqoLyBsOKRo1yUhNU5g8p2eVQOFod0qNG+mpPRZ2LyABomNMhuew2+UKHNhXk4agZPn/w4zjsdm0t9GrRhv3Ky/UQ2AGgnTGrDNpMzclrdrtNxd6gnHa7XA67jsvy6ISuaYpEo4R2oIXcDruyUhKV7Ha0+WMluuxKTXQqyWlTakL1TDZJLrtO6p6uBKeDudoBIE7ocW9FNbPIVFVVNbje660+8TI1NbVF+1u3bl2DyxvriT/a1D55bYvHpX3lAWUkuZWR5NSGveWxE98ANC8qqdwXlC90eFdMbSm7TeqZlaQyX1jG5VBqklPlVSE5HXZV+kNyOOzyuJ3M1Q4AcUBwb0U9e/aUJO3cubPB9TXLe/Xq1W41xVvtk9e+3FGmf3+5R6t3lmprYcMHNwAaFghGFbRXDzE7mE3VgTtiqr9GdTltCkdMnSkfbZKc9upbbqddgXBExlRPK1n7INpukyoDEWV53Ep0OdS/U4qCBxqkJ7uYPQYA4ojg3oqGDh0qSVq1alWD62uWDxkypN1qOhrU/IFfs7tMG/ZVqKAywImowCGKSrI38IPjsElJbofCkajsUaOu6UmqCoVVFYjI6bApGIkqGjHKTUtUl7RE7a8IKBiJyn5gSveoMVI4KpfdLrtdSnY71SfHo6E9MjS8Z6Z65yTHeteZPQYA4ovg3orGjh2r9PR0bd68WatXr9awYcPqrJ83b54k6fzzz49DdYevqDJwxH+wS6qCKvEGFYpE5WjqyjEA6nHZq69qenBvu01SSqJTGclulXqDCkeN7HbJ7XAoYI/KZrMpweFQUpJDp/fP1VkndNKnm4tU4g0qEI7KJmlroVclVUFlJLnkSXRqQOdUXTKih/rk1D/5lMAOAPFFcG9FbrdbP/3pT/W73/1ON910k95///3YTDKPPPKI1qxZo/Hjx1vq4ksHX+10/MBOsWngDkVmsluZnupeu1CEwe3AwRw2KS3RqZF9srWl0KuoiarcF5I/FFWy26niqqCiUSNjqmd7SXTaZbNVn7Sa4nYoyZWkcl9IqYkuHZeZpAp/RKW+6hNI++Wm6KIRPTSyT5aG98yMHYhL0qKNBVq7s0xRmdjP+Ld6H/rPOACg7RHcW9ldd92lDz74QIsXL1b//v01btw45efn67PPPlNubq7+3//7f/EuscVq5mFfs6tMDptNkQNXYTycaeCyUxJ0zoldta/MryVbihTxheqMvwWOdU67TeGo0Vd7ytUtM0kndE3TvjKf1uwqr55SNSVBPbOSFY5EtKPYJ7u9+purZLdD6clundI7S8u3FqlTWqJ+MKqX+uR4tLXQK8moT05K7Gf24HnXLx7RQ+MH5DIMBgAsgODeyhITE7VgwQL9/ve/1wsvvKDXX39dWVlZmjp1qu69995GL850NKqZh91hs2nocRlavaM0Ng3c4fxxrzlR9ZUV2/Xqql3aXVolb5D0DkhSxJjqb6NsUp8cj845sask6b0v96i4KqisZLfOPql62btr96jEG1SS2yF/MKJCb1Crd5TK4bCre0ZSbJhLS39OuYgSAFgDwb0NJCUl6Z577tE999wT71KOSM087BFjtHpHqaLGHPE0cNkpCbphQn/165Smd9bs1kcb9qvUF27FqgFrCkclm4wSnXZdMry7vnVgSFrNrEy1e8NrL9tc4K03nI0QDgAdE8Edjao9D3trh4KJgzpreM8MjRuQqzmfbtGm/ZUKh40cdpscDpscdpsq/BFmn8Exwabqix457DZ1SktUhud/B8cN9YbXXpadktBguAcAdDwEdzSp9jzsrR0KslMSdNGIHjp9QK5Wby/VnjKfuqYnqVd2skp9Qe0s9mnt7jKVVgXVLT1Rb3+5R1sKfa32+MDRwmmTXA67UhKc6t859ZC/1WKoCwAcGwjuaFZbh4LslASdNahzveXf6i1NGfG/cwJOG9BZTy3arOVbi+QNRMTcNOgoEtwOdc9I1PFd0nTOiV0J4QCABhHcYRk1vf9bCyu1s8SntTvL9N66vSqqDMgXbt1BNTaJYTrNsEvyJNgVDEcVihy4eueB+cZbwiYpNdGhcn+kDas8+tlt0uBuabpiZE+N659LaAcANIrgDkup6f3/Vm9pyvAeGpWXow+/2idfKKwtBZXyBiLqleNRWVVIX+4qU7CFc07aVR2g3E6bMpITdM7gLpJNemvNbpVVBRWJSi5ndUhty2ks7ZIlvkmwSTouK0m/Pm+wPvhqnxZs2C9fKKJwJKpQsGXPwEjyHuOhXZISnHb1zUkhtAMAmkVwh6XVnORa+4IyNbc/3lCg11bvUjAcVUqCQ26nXTuKq7S/IqCoMYpEjTqnJ+r4zmka2CVFXdITJdl0Yrd09eucKkk658Qu+svHW7SnzCe33a78Yq8qfGG1cgd/jBVCuyTZbNXzh+8p88kXiignJUFJLod2lfnkdIRVFQjLmAM98JHGv704lmJ7Q9/iOOxS57QE9cxOjkdJAACLIbjD8g4eg19z+6KTe+j0gbn1Qr2M/peibGrypNsMj1vHZSYpEjHq3zlFRd5g9eXkwxGV+cINBlKHqgN4Rx5q43E7lJOSoHW7y7W5oFJlvpAkqWt6ogZ0SlWZL6gN+yqUkuBSmS+kfWW+2HAmm6qDf80VQDu6miFBQ7pnqtwf0p5Sn0Imqmi0ehYlY6R3vtyjjXsrdMWpvQ7rysQAgGMDwR0dWmOhvqVqz2W/cV+lMj1uOe029e+UomAkqqLKgL7YWarKA1NXOmyS02GTL9QxImmK2yG7TfIGIzKm+oAkLdGps46vniZ0Z6lPDput+hjIJg3pkRG7cFDN3OJ22fTN/kpt3F+hQCgqI6OoMYoaKRiOtng4k9XYJXVKTdCAzqm6aHh3nXRcumSk/GKvNu2t1PL8Yq3fU6Fyf1jl/rBKq0LK8LgP68rEAIBjA8EdaMLBc9mf6E7T8J6Z6p2TrMxkt7YWVmrm62u1PeJTossufygqm01y2CLNjoW3SUpy2xWJRBWISC6HTaF2DrFO24EvH2xSJFq3B9xpl7plJGpwt3QFwhEVe4PK8rg1cVAX9cxK0ttr9qikKqTxAztp2dZidU5L0HdO6qJv9a574aDF3xRqeX6xHHabemUnqzIQlsthk81uU2FFQN5ARJKRLxg9oqFCDpvkckr+0BHspJUkOqRhPbN03bi+6pWdLNmkbYVefb69+urDgVBUgbCRXVJakksyUigaVYk3eNhXJgYAdHwEd6AZTc1lv7XQq0A4qnDUKBCOKhSNyu2wKyXRKa+/8bHwTrvUv1OKxuTlaMmWIuUXexUKGzls1eOeg+0w+DvZaVNqkkt2m03eYFjBcFQ2SRFTfaLugM6pGtc/V+MHdqr3/IsqA3Wuqut02NQ9I0l9clJi+695nbYVVqmkKqSqYET5xVVKSXBqQOd0dUlL1O4yv3aXVinJ7dD24iqVVYUUihiFog2/cC6H5LDZFIka2VQdeocfl6mdpVUqrAzK7bDLH46qtCrY4IFT7XHmbTlzUK9sj26ZPFCS9Nrnu1TkDWjjvkqFo0ZZyW75QhFVBauHWpUfGGaUkuBUpsd9RFcmBgB0bAR3oAUam8s+I8klp90mSQpForLJprREl9KTnNpvD8oXDCsUiSoSrQ7DyW6HBnRO1VmDOmvSCZ3Vr3Oqnlm8TW+s3qXCyoACoag6pyWo2BvUnlJ/gydvOiQ5nTYFDuEMWaddih4I5DW12Ow2VfjDks2mSCQq2aTeWcmS3abe2cm67Fs9Ney4jDpX6Kz9erTkqrolVUFFZZTjcVcH9VKfMpJdmjSos04fkKtFGwv0yaYClftCGtQ1/cBpB0Z7S33aWxGUPxRWKGKU6LKrf6cUpSe7VewNqmdmkvKLfTqha6quHddX2wq9+vDr/aoKhLWjxKfURKdKvAH5ghFleNxKS3Spf+cU9euUIo/LKZvN6IXlO7Sz2Fcn4Dc3q4/twP+be+V7ZCUrK9ml1z7fpTW7yuQPRrSnzC+X3aZRfbK0YV+lsjwudUpL1P5yvySpX24Kc7gDAJpEcAeOhE3K65QibzCilASnKv1hDTkuXaP75uirPeXyhcKy2Wwa0DlFAzqnKT3JpT45dccwXz2mt8bmZSu/uErpiU7JZtOc/25RqS8kXzCi6IGTOB326gOF9CSXPAlOhaNGFb6w/KGwqg500dvtNiW57EpLdKlTaqLyOnnUIzNZ+UVV2ri/QuW+sIoqA7Havf6wXE6b7AemuiyqCqlndrLyclPrhPaGtOSqujXnCKQmuRSOGHXNSNTQ7hk6fUCuJOmTjQVanl8iEzWy2W06qXu6Lj25hz7dVKh1e8oVCEUUjEY1sHOqvj+ypxZsKNCaXWUq80eUluRStidBmclu9RuUquE9M7V6R6neX7dXO0p8yk1J0DcFlXLa7UpPdqlvToqmjumj7JQEffjVPrkcdjkdNiU57KoMVL9+TocUidSf7cZ+4JuQHE+Ckt0ObS2sajTgO+3V/88vrpI3GJbDZtOwnhnaW+5XOGr0+Y5SJboc6puToouGd1epLyjJVu9zAQDAwQjuwBHITHarb06Kir0hRaJGGQfuf2dIV31nSNcmQ21t/TqnxqaglKTSqqA27a/U7lK/ojJyORwa0j1d08/qV30WqIz65KSoxBusFfilsqqw0pOd6pOTUucxl20t1qIN+7Wr1KfNBZUypvok2q/3VCgYjspptynBaVdOiltDu2c02HvekOauqttUz/yKbUX6pqBS3kBYaUkulftC2llSpQp/SFEZJbkcGtU3W6t3lCo9yaUMj7vJXv7slAQNOy5Dq/JLtLvMr6qoUUqCUxnJrnrPqVdWstISXXLZ7QpFonLaJZfDrt45yaoKHJgxyERVdmCeeZfdpvQkl0b1zdZ5Q7rqnn+vV36Rr8HnnOB0qFNqonplJWtVAyc2983xKOfA61L7PQcAoDkEd+AINDdk5HB7UCcO6iLJpn9/uVv+UERd0pN0zold9a2DpgrMTkloUfj731VnvXpnzR5tKqhUhT+stESnjKScFLd656Ro8oEhLK3Z89t4z7ztf41qjT1JTXDVGT8fNUYet7O6Z71TapO9/Ae/HzabTUO6p9d7Tv06p+rCYd31xupdKveH5HLYddbxuZowsJP+/eVerd9dLn8oorKqYPVJtTke9cr26JwTu2pknyztLPXrbx9vPtCLXr1Ph716nPqQ7um6aEQP9euc2uSJzfSuAwAOFcEdOEItGTJyOA6+uNSR7remdzxqpKQDUzUO75mhPjkeDeqaWq+XvjU11DPfJ8ejfrkpKq0KKRSNKiXBqX65KRrWM0OpSS5JjR8MHekQHqnuEKVeWcmxA6CosSnZ5Yg9dkNhu2bbpVuKVBkMKcXlkrFVz8Iz7LjMWLu2+mwAAI5NNmNMx5xEuQMbPHiwJGndunVxrgRWVVQZOCrC5LKtxXp37R6VeIPK9LhjPdrxrvFoeX0AANbTljmNHnfgGNRcr3V7aapHOp41Hi2vDwAAtRHcAcQVIRkAgJaxx7sAAAAAAM0juAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFgAwR0AAACwAII7AAAAYAEEdwAAAMACCO4AAACABRDcAQAAAAsguAMAAAAWQHAHAAAALIDgDgAAAFiAzRhj4l0EDk1qaqpCoZDy8vLiXQoAAABq2bx5s1wulyoqKlp93/S4W5DH45HL5Yp3Ga1m8+bN2rx5c7zLOCbwWrcfXuv2wevcfnit2wevc/tpq9fa5XLJ4/G0+n4letxxFBg8eLAkad26dXGupOPjtW4/vNbtg9e5/fBatw9e5/ZjxdeaHncAAADAAgjuAAAAgAUQ3AEAAAALILgDAAAAFkBwBwAAACyAWWUAAAAAC6DHHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcMdR5+uvv9Yf/vAHnXHGGcrJyZHL5VKXLl108cUX65NPPol3eZbk8/n061//WgMGDFBiYqK6deumH/3oR9q1a1e8S+sQqqqq9Prrr+uaa67RwIEDlZiYKI/Ho6FDh+qee+5RZWVlvEvssIqKitSpUyfZbDb169cv3uV0SAUFBbr11ls1cOBAJSUlKSsrSyNGjNBtt90W79I6lOXLl+vyyy9Xt27d5HK5lJGRoXHjxmnOnDli5u5Ds3LlSj3wwAO6+OKL1aNHD9lsNtlstma3mzt3rkaOHKmUlBRlZWXp3HPP1eLFi9uh4pZjHnccdXr06KFdu3YpJSVFo0aNUlZWltavX6+1a9fKZrPpkUce0YwZM+JdpmX4/X6dccYZWrp0qbp27apx48Zp27ZtWrZsmXJzc7V06VL17ds33mVa2t///nddd911kqQTTjhBJ554osrLy7V48WJVVFTo+OOP16JFi9SpU6c4V9rxTJ06Vf/4xz9kjFFeXp6++eabeJfUoaxcuVLf/va3VVRUpMGDB8c+2+vXr9fOnTsVDofjXWKH8Oqrr+q73/2uIpGIRowYoX79+qmgoECffPKJwuGwrrzySj3//PPxLtMypkyZojfeeKPe8qYi74wZMzRr1iwlJSVp8uTJ8vv9+vDDD2WM0bx58zRlypQ2rPgQGOAoc9ZZZ5l//OMfxufz1Vn+1FNPGUnG4XCYdevWxak667nzzjuNJDN69GhTUVERW/7www8bSWb8+PHxK66DmDt3rrn++uvN+vXr6yzfvXu3GT58uJFkrrjiijhV13F98MEHRpK5/vrrjSSTl5cX75I6lP3795ucnByTnJxs3njjjXrrP/vsszhU1fGEQiHTqVMnI8k8//zzddatX7/eZGVlGUnmo48+ilOF1vPAAw+YmTNnmjfffNPs2bPHJCQkmKYi7/z5840kk52dbTZu3BhbvnjxYuN2u01GRoYpKSlph8qbR3CHpUyePNlIMnfffXe8S7GEQCBg0tPTjSSzatWqeuuHDBliJJkVK1bEobpjw+LFi40kk5CQYAKBQLzL6TCqqqpMXl6eGTRokNm4cSPBvQ3ceOONRpJ54okn4l1Kh/bll18aSWbgwIENrr/55puNJPOHP/yhnSvrOJoL7uecc46RZB599NF662pe/4ceeqgNK2w5xrjDUoYOHSpJ2r17d5wrsYZPP/1UZWVlysvL0/Dhw+utv/TSSyVJb731VnuXdsyo+cwGAgEVFRXFuZqO47e//a22bNmip556Si6XK97ldDg+n0/PPfecPB6Ppk2bFu9yOrSEhIQWtcvOzm7jSo5NPp9PH330kaT//U2s7Wj7O0lwh6Vs2bJFktSlS5c4V2INX3zxhSRpxIgRDa6vWb5mzZp2q+lYU/OZdblcysrKinM1HcOaNWv08MMPa9q0aRo3bly8y+mQVqxYoYqKCg0fPlxJSUl69913dcstt+gnP/mJHnvsMTpPWlHfvn2Vl5enDRs26IUXXqiz7quvvtJzzz2nzMxMXXTRRXGqsGPbsGGDAoGAcnNz1aNHj3rrj7a/k854FwC01ObNm/X2229Lki644II4V2MN27dvl6QGfxnVXp6fn99uNR1rZs2aJUk6++yzW9yzhsZFo1Fde+21ysjI0B//+Md4l9NhrV+/XpLUqVOnBk/0u+OOOzR79mxdccUV8SivQ3E4HHrmmWd03nnn6fvf/74efvhh9e/fX/v379cnn3yiQYMGae7cuRz4t5Hm/k56PB5lZGSopKREFRUVSk1Nbc/y6qHHHZYQDoc1depUBQIBffe739XJJ58c75IsoWYawuTk5AbXezweSVJFRUW71XQseeeddzR79my5XC7de++98S6nQ/jzn/+s5cuX68EHH2ToQBsqKSmRJL355pt677339MQTT2j//v3atm2bbr31Vvl8Pl199dVavXp1fAvtIMaOHatFixapb9++WrVqlV566SUtWLBAdrtdkyZNYuavNtTc30np6PpbSY87Wt1FF12kr7766pC2+cc//qGRI0c2uv7mm2/Wf//7X/Xt21dPPvnkkZYItLmvv/5aP/jBD2SM0YMPPhgb647Dt337dt11110aP368pk6dGu9yOrRoNCqputPkd7/7nX7yk5/E1j344IPKz8/XK6+8ogcffJBpClvBiy++qGnTpmnUqFF68cUXNXjwYO3evVsPPfSQHn74YS1YsECLFy/mWzsQ3NH6tm7dqg0bNhzSNlVVVY2u+93vfqe//OUv6ty5s/7zn//wdeEhSElJkdT46+v1eiUp7l/9dTS7du3S2WefrZKSEt1yyy2aPn16vEvqEG666SYFg0E99dRT8S6lw6v53SGpwZNTp02bpldeeUWLFi1qz7I6pE2bNunqq69Wp06d9Pbbb8de+/79++vpp5/W7t279fbbb+v//b//pxtvvDHO1XY8zf2dlI6uv5UEd7S61vzq9KmnntJdd92l9PR0vffee1wZ8RD17NlTkrRz584G19cs79WrV7vV1NEVFxdr8uTJys/P17Rp0/TQQw/Fu6QO4+2331ZGRoZuuOGGOsv9fr+k6gOmCRMmSJL++c9/chL7Eaj5nZCcnKzc3Nx663v37i1J2r9/f3uW1SH985//VCgU0tlnn13ngKnG5Zdfrrffflsff/wxwb0NNPd30uv1qrS0VJmZmQR3oCn//Oc/ddNNNyk5OVn//ve/NWzYsHiXZDk1wzNWrVrV4Pqa5UOGDGm3mjqyyspKnXPOOVq/fr0uvvhi/e1vf2vRZbbRcqWlpY328vr9/ti6mjCPw1MzfazP51MgEKg3RKO4uFiSGgyaODQ1gTE9Pb3B9TXLa847QOsaOHCgEhISVFBQoF27dql79+511h9tfyc5ORVHpXfeeUdXXXWVnE6nXnvtNY0dOzbeJVnS2LFjlZ6ers2bNzf4Tci8efMkSeeff347V9bxBAIBXXjhhVq2bJm+/e1v68UXX5TD4Yh3WR2Kqb5oYL1/W7dulSTl5eXFltX0COPw9OzZU0OHDpUxpsEDpZplDV0fAoem5puhFStWNLh++fLlksRnuo0kJSXpzDPPlCS98sor9dYfdX8n43XlJ6Ax//3vf01SUpJxOp3mtddei3c5lnfnnXcaSWbMmDGmsrIytvzhhx82ksz48ePjV1wHEQ6HzUUXXWQkmXHjxhmv1xvvko4pW7du5cqpbeD55583ksxJJ51kdu/eHVv++eefm6ysLCPJvPzyy3GssGNYuXKlkWQkmSeffLLOuiVLlhiPx2Mkmfnz58epQutr7sqp8+fPN5JMdna22bhxY2z54sWLTUJCgsnIyDAlJSXtUGnzbMYYE6djBqBBmZmZKi0tVZ8+fXT66ac32Oa0007Ttdde286VWZPf79eECRP02WefqWvXrho3bpzy8/P12WefKTc3V0uXLmWqsSM0a9YszZgxQ1L1rEppaWkNtnvooYeUk5PTjpUdG7Zt26Y+ffooLy9P33zzTbzL6VCmTp2qZ555RhkZGRozZox8Pp8WL16sQCCg6667Tn/961/jXWKHcNttt8XOhxk8eLAGDRqk3bt3a8mSJYpGo7r++uv19NNPx7lK6/j3v/9dZwreZcuWyRijU089NbZs5syZ+s53vhO7P2PGDM2aNUvJycmaNGmSgsGg5s+fL2OM5s2bpylTprTnU2hcfI8bgPp0oOehqX9XX311vMu0lKqqKjNz5kyTl5dn3G636dKli5k6darZsWNHvEvrEH7zm9+06HO7devWeJfaIdHj3nai0aj561//ak4++WSTnJxsPB6PGT16tJk7d268S+tw/vWvf5nJkyeb7Oxs43Q6TWZmpjnjjDPMCy+8EO/SLGfOnDnN/j6eM2dOg9vVfNYzMjLM2WefbT799NP2fwJNoMcdAAAAsABOTgUAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFENwBAAAACyC4AwAAABZAcAcAAAAsgOAOAAAAWADBHQAAALAAgjsAAABgAQR3AAAAwAII7gAAAIAFOONdAACgYwiFQlq7dq3WrFmjoqIiVVVVKSkpSZ06dVJeXp6GDRum5OTkeJcJAJZFcAcAHJFdu3bpgQce0HPPPafS0tJG2zmdTp1yyim68cYb9cMf/rD9CgSADoKhMgCAw/bSSy9p0KBBevzxx5sM7ZIUDoe1ZMkSLV++vH2KA4AOhh53AMBhefXVV3XllVcqGo1KkhISEjRx4kSNHDlSXbt2lcvlUmlpqTZu3KilS5fq888/lySNGDEinmUDgGUR3AEAh6y0tFQ33HBDLLSfc845+vvf/65u3bo1us3WrVv12GOP6dRTT22vMgGgQ7EZY0y8iwAAWMvTTz+tG264QZI0YMAArVmzRgkJCXGuCgA6Nsa4AwAO2cKFC2O3zz33/7dzNy9RtX8Ah7+jlpYGUQuLViYZvWjk4CoiaRVSEu3ctGoVtOkNoiAQiQihVUS7wD9AsCijRbSrFmZhBaKBQtmbRCFloflsfg715MvYYz+967pgYGbOfc7cs/LjmfucBtEO8H8g3AGYs6GhodzzZ8+eLeBMAP4ewh2AOSsrK8s9v3XrVpw4cSJGR0fndIyjR49GJpP55cfBgwfn+2sBLGrCHYA527t37w+vW1tbY/369XH8+PG4d+9e5HP51H+9LWQ2m/1P+wOkxsWpAMzZ2NhYNDY2xs2bN6fcvnbt2mhsbIympqbYtWvXlGM+ffqUuyvN97q7u2Pnzp0REXHx4sU4dOjQlPsvW7YsCgsLf/EbAKTHGXcA5qyoqCiuXbsWra2tUV5e/tP2oaGhuHLlStTX18fWrVujs7PzpzHLly+PsrKynx59fX25MXV1dVOOKSsrE+3AX0e4A/BLCgsL49ixYzE4OBjXr1+Pw4cPx8aNG38a9+TJk2hoaIi2tra8jvvo0aOIiMhkMlFTUzOvcwZImaUyAMyr3t7euHr1aly6dCk+fvyYe7+8vDxevnwZBQUznzPavXt33LlzJyoqKuL58+e/e7oAyXDGHYB5VVVVFefOnYunT5/G5s2bc++/fv06BgYGZt3/8ePHERGxbdu23zZHgBQJdwB+i3Xr1sXZs2d/eK+oqGjGfV68eBHDw8MRIdwB/k24A/DbfP78Ofe8uLg41qxZM+P4yfXtEcId4N+EOwB56+/vz3vs+Ph4XL58Ofd6z549sWTJkhn3Ee4A0xPuAOTl3bt3UVVVFQcOHIjOzs4YGxubduyrV69i//79cf/+/YiIKCgoiNOnT8/6GZPhvmLFiqioqJifiQP8IWZebAgA/9Pd3R3fvn2L9vb2aG9vj9WrV8eOHTuitrY2ysvLo6SkJN68eRMPHjyIGzdu/LBMpqWlJerq6mb9jMkLU6urqyOTycw6/vz58/Hw4cPo6uqK/v7+KCgomPEfCoCUCXcA8vL9MpaIiOHh4ejo6IiOjo5p9ykpKYkLFy7EkSNHZj3+6Oho9Pb2RkT+y2ROnToVK1eujO3bt8fIyEi8ffs2r/0AUiTcAchLQ0NDfPjwIW7fvh1dXV3x9evXaceWlpZGU1NTnDx5MjZs2JDX8Xt6emJ8fDwi8g/3vr6+qKysjIiI+vp64Q780YQ7AHnZtGlTNDc3R3Nzc3z58iV6enqir68vhoaGYmRkJIqLi2PVqlWxZcuWqK2tjaVLl87p+L9yYepktAP8DYQ7AHNWXFwc2Ww2stnsvB1zMtwzmUxUV1fP23EB/hTuKgPAojB5YWplZWWUlpYu8GwAFh/hDsCiMBnu7t8OMDXhDsCCGxwcjPfv30eEcAeYjnAHYMF9f2FqTU3NAs4EYPFycSoAC27fvn0xMTGx0NMAWNSccQcAgAQ44w5Astra2mJgYCAiIgYGBmJiYiJaWlpy28+cObNQUwOYd5kJv00CkKj6+vq4e/futNv9iQP+JMIdAAASYI07AAAkQLgDAEAChDsAACRAuAMAQAKEOwAAJEC4AwBAAoQ7AAAkQLgDAEAChDsAACRAuAMAQAKEOwAAJEC4AwBAAoQ7AAAkQLgDAEAChDsAACRAuAMAQAKEOwAAJEC4AwBAAoQ7AAAkQLgDAEAChDsAACRAuAMAQAKEOwAAJEC4AwBAAoQ7AAAkQLgDAEAC/gFCQIKwij5M2AAAAABJRU5ErkJggg==", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state-space scatter -- expect visually separated clusters, not one undifferentiated blob. Count the distinct groups: if there are far fewer than model_order (96), the discrete latent has collapsed\n", + " --- state_mu_K_96_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eval_figure_paths = resolve_paths(CONFIG[\"train\"][\"evaluation_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(eval_figure_paths)} evaluation figures\")\n", + "\n", + "for p in eval_figure_paths:\n", + " name = os.path.basename(p)\n", + " if name.startswith(\"consensus_T1_vs_T2\"):\n", + " review(\n", + " \"consensus bubble plot\",\n", + " \"expect a strong diagonal of bubbles spanning the full category \"\n", + " \"range. A handful of scattered points means the arms almost never \"\n", + " \"co-assign and only those few categories carry cells\",\n", + " )\n", + " elif name.startswith(\"norm_consensus\"):\n", + " review(\n", + " \"normalized consensus plot\",\n", + " f\"expect a bright diagonal; average consensus should be >= \"\n", + " f\"{k_select_thr}. An almost entirely dark matrix means no \"\n", + " f\"reproducible categories were found\",\n", + " )\n", + " elif name.startswith(\"state_mu\"):\n", + " review(\n", + " \"state-space scatter\",\n", + " \"expect visually separated clusters, not one undifferentiated blob. \"\n", + " \"Count the distinct groups: if there are far fewer than \"\n", + " f\"model_order ({model_order}), the discrete latent has collapsed\",\n", + " )\n", + " show_image(p, title=name)" + ] + }, + { + "cell_type": "markdown", + "id": "0e4aa85a", + "metadata": {}, + "source": [ + "## Stage 3 -- Analyze: Clusterability (`clusterability_manifest.json` + figures)\n", + "\n", + "Checks `model_order` is consistent with Train's `evaluation_results.json`, then shows the\n", + "classification-accuracy bar chart, silhouette curve, and confusion-matrix heatmaps for review." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cb84bad5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"output_dir\": \"/mnt/disks/cromwell_root/out\",\n", + " \"model_order\": 96,\n", + " \"n_ttype\": 115,\n", + " \"n_arm\": 2,\n", + " \"figures\": [\n", + " \"out/classAcc_RF_K_96.png\",\n", + " \"out/SC_K_96_20260828.png\",\n", + " \"out/conf_Ttype_pc.png\",\n", + " \"out/conf_Ttype_lowD_arm_0.png\",\n", + " \"out/conf_ConsType_pc_arm_0.png\",\n", + " \"out/conf_ConsType_lowD_arm_0.png\",\n", + " \"out/conf_Ttype_lowD_arm_1.png\",\n", + " \"out/conf_ConsType_pc_arm_1.png\",\n", + " \"out/conf_ConsType_lowD_arm_1.png\"\n", + " ]\n", + "}\n", + "[PASS] clusterability model_order matches Train's evaluation_results -- clusterability=96, train=96\n", + "[PASS] clusterability n_ttype matches DataPrep cluster count -- clusterability n_ttype=115, dataprep clusters=115\n", + "[PASS] clusterability n_arm matches Train's evaluation_results -- clusterability=2, train=2\n" + ] + } + ], + "source": [ + "clust_manifest = load_json_gcs(CONFIG[\"analyze\"][\"clusterability_manifest\"])\n", + "print(json.dumps(clust_manifest, indent=2))\n", + "\n", + "check(\n", + " \"clusterability model_order matches Train's evaluation_results\",\n", + " clust_manifest[\"model_order\"] == model_order,\n", + " f\"clusterability={clust_manifest['model_order']}, train={model_order}\",\n", + ")\n", + "\n", + "# n_ttype is the reference cell-type count read from the .h5ad, so it must match\n", + "# what DataPrep wrote -- a mismatch means Analyze ran against different data.\n", + "if DATAPREP_N_CLUSTERS is not None:\n", + " check(\n", + " \"clusterability n_ttype matches DataPrep cluster count\",\n", + " clust_manifest.get(\"n_ttype\") == DATAPREP_N_CLUSTERS,\n", + " f\"clusterability n_ttype={clust_manifest.get('n_ttype')}, \"\n", + " f\"dataprep clusters={DATAPREP_N_CLUSTERS}\",\n", + " )\n", + "\n", + "check(\n", + " \"clusterability n_arm matches Train's evaluation_results\",\n", + " clust_manifest.get(\"n_arm\") == eval_results.get(\"n_arm\"),\n", + " f\"clusterability={clust_manifest.get('n_arm')}, \"\n", + " f\"train={eval_results.get('n_arm')}\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5ea79ec6", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 9 clusterability figures\n", + "[REVIEW] silhouette score curve -- most categories should have positive silhouette scores, and the MMIDAS curves should sit near the t-type reference curve. If the x-axis spans a single value or the legend has one entry per category, the figure is broken rather than the model\n", + " --- SC_K_96_20260828.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] RF classification accuracy bar chart -- read the 't-types' group only: that is the MMIDAS lowD embedding vs the PCA baseline at recovering the reference labels. The 'T Categories' groups classify the model's own labels, so ~99% there is near-circular and not evidence of anything\n", + " --- classAcc_RF_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_lowD_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_lowD_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_lowD_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_lowD_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_pc_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_pc_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_pc_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_pc_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_lowD_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_lowD_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_lowD_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_lowD_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_pc.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_pc.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clust_figure_paths = resolve_paths(CONFIG[\"analyze\"][\"clusterability_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(clust_figure_paths)} clusterability figures\")\n", + "\n", + "for p in clust_figure_paths:\n", + " name = os.path.basename(p)\n", + " if name.startswith(\"classAcc_RF\"):\n", + " review(\n", + " \"RF classification accuracy bar chart\",\n", + " \"read the 't-types' group only: that is the MMIDAS lowD embedding vs \"\n", + " \"the PCA baseline at recovering the reference labels. The \"\n", + " \"'T Categories' groups classify the model's own labels, so ~99% \"\n", + " \"there is near-circular and not evidence of anything\",\n", + " )\n", + " elif name.startswith(\"SC_K_\"):\n", + " review(\n", + " \"silhouette score curve\",\n", + " \"most categories should have positive silhouette scores, and the \"\n", + " \"MMIDAS curves should sit near the t-type reference curve. If the \"\n", + " \"x-axis spans a single value or the legend has one entry per \"\n", + " \"category, the figure is broken rather than the model\",\n", + " )\n", + " elif name.startswith(\"conf_\"):\n", + " review(\n", + " \"confusion matrix heatmap\",\n", + " f\"expect a tight diagonal ({name}). Large square blocks along the \"\n", + " f\"diagonal mean many reference types are collapsing into one \"\n", + " f\"predicted category\",\n", + " )\n", + " show_image(p, title=name)" + ] + }, + { + "cell_type": "markdown", + "id": "2ae4b614", + "metadata": {}, + "source": [ + "### Optional: numeric accuracy/silhouette check\n", + "\n", + "Only runs if you supplied `classify_manifest` / `clustering_tar` in the config above (these are\n", + "intermediate outputs of the 03b Classify task, not part of `MMIDAS_Analyze`'s final outputs).\n", + "\n", + "This is the most informative cell in the notebook, because it reads the numbers the\n", + "`classAcc_RF` / `SC_K_*` figures are drawn from. Two things come out of it:\n", + "\n", + "1. **The t-type accuracy gap.** How much worse the MMIDAS low-D embedding is than the PCA\n", + " baseline at recovering the reference t-types. This is the only non-circular accuracy\n", + " comparison available — the `ConsType` rows classify the model's own labels.\n", + "2. **The populated-category count**, from the side length of the `ConsType` confusion matrices.\n", + " For a healthy run this is close to `model_order`; a 9×9 matrix under a reported\n", + " `model_order` of 111 means 102 categories are empty." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ad5f55d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classify outputs resolved to: gs://fc-1f55735a-9fc4-4ef2-8bbe-60a7ea8bac60/submissions/580a5fe7-6984-4a96-a96a-94f0c0bdd869/MMIDAS_Analyze/414eb7bf-fab7-4fd7-8606-44bc0c9a3df6/call-Classify/attempt-2/...\n", + "Ttype_classification_K_115_nFeature_100_20260828.p: mean acc=0.909 (+/-0.006), pct categories w/ positive silhouette=95.7%, conf_mat=115x115\n", + "Ttype_classification_K_115_nFeature_10_arm_0_20260828.p: mean acc=0.616 (+/-0.045), pct categories w/ positive silhouette=14.8%, conf_mat=115x115\n", + "ConsType_classification_K_96_nFeature_100_arm_0_20260828.p: mean acc=0.559 (+/-0.017), pct categories w/ positive silhouette=30.2%, conf_mat=96x96\n", + "ConsType_classification_K_96_nFeature_10_arm_0_20260828.p: mean acc=0.926 (+/-0.050), pct categories w/ positive silhouette=89.6%, conf_mat=96x96\n", + "Ttype_classification_K_115_nFeature_10_arm_1_20260828.p: mean acc=0.621 (+/-0.049), pct categories w/ positive silhouette=14.8%, conf_mat=115x115\n", + "ConsType_classification_K_96_nFeature_100_arm_1_20260828.p: mean acc=0.557 (+/-0.014), pct categories w/ positive silhouette=32.3%, conf_mat=96x96\n", + "ConsType_classification_K_96_nFeature_10_arm_1_20260828.p: mean acc=0.927 (+/-0.047), pct categories w/ positive silhouette=91.7%, conf_mat=96x96\n", + "\n", + "t-type recovery\n", + " this run : PCA-100=0.909 MMIDAS-10=0.621 gap=0.288 (Ttype_classification_K_115_nFeature_10_arm_1_20260828.p)\n", + " reference: PCA-100~0.845 MMIDAS-10~0.735 gap~0.110 (eyeballed from 4_clusterability.ipynb)\n", + "[FAIL] [advisory] t-type accuracy gap is within 0.06 of the reference gap (~0.11) -- this run gap=0.288 (PCA=0.909, MMIDAS=0.621); reference gap ~0.110 (PCA ~0.845, MMIDAS ~0.735); allowance 0.110+0.06=0.170\n", + "\n", + "populated categories implied by ConsType confusion matrices: [96] -> using 96\n", + "[PASS] [advisory] ConsType confusion matrices span at least 50% of model_order -- 96 populated of model_order=96 (100.0%)\n", + "[PASS] evaluation_results n_populated_categories agrees with the ConsType confusion matrices -- evaluation_results=96, ConsType=96\n" + ] + } + ], + "source": [ + "classify_cfg = CONFIG[\"classify_optional\"]\n", + "\n", + "if classify_cfg.get(\"classify_call_dir\"):\n", + " call_dir = classify_cfg[\"classify_call_dir\"]\n", + " manifest_uri = gcs_find_under(call_dir, \"out/classify_manifest.json\")\n", + " tar_uri = gcs_find_under(call_dir, \"clustering.tar.gz\")\n", + " print(f\"Classify outputs resolved to: {manifest_uri.rsplit('/', 2)[-3]}/...\")\n", + " classify_manifest = load_json_gcs(manifest_uri)\n", + " clustering_root = extract_tar_gcs(tar_uri, \"clustering\")\n", + "\n", + " # name -> (mean accuracy, pct positive silhouette, confusion-matrix size)\n", + " metrics = {}\n", + " for pickle_path in classify_manifest[\"pickles\"]:\n", + " local_pickle = os.path.join(clustering_root, \"clustering\", os.path.basename(pickle_path))\n", + " if not os.path.exists(local_pickle):\n", + " print(f\" (skipping, not found locally: {os.path.basename(pickle_path)})\")\n", + " continue\n", + " with open(local_pickle, \"rb\") as fh:\n", + " data = pickle.load(fh)\n", + " acc = data[\"acc_T_adj\"]\n", + " sc_flat = np.concatenate([np.atleast_1d(s) for s in data[\"sc_T\"]])\n", + " conf = np.asarray(data[\"conf_mat\"])\n", + " name = os.path.basename(pickle_path)\n", + " metrics[name] = {\n", + " \"acc\": float(acc.mean()),\n", + " \"acc_sd\": float(acc.std()),\n", + " \"pct_pos_sc\": float(100 * (sc_flat > 0).mean()),\n", + " \"n_cat\": int(conf.shape[0]),\n", + " }\n", + " print(\n", + " f\"{name}: mean acc={acc.mean():.3f} (+/-{acc.std():.3f}), \"\n", + " f\"pct categories w/ positive silhouette={100 * (sc_flat > 0).mean():.1f}%, \"\n", + " f\"conf_mat={conf.shape[0]}x{conf.shape[1]}\"\n", + " )\n", + "\n", + " # ----------------------------------------------------------------------\n", + " # The ConsType rows classify the model's own labels, so their ~99% accuracy\n", + " # is near-circular and says nothing about model quality. The one meaningful\n", + " # comparison is how well each embedding recovers the *reference* t-types.\n", + " # ----------------------------------------------------------------------\n", + " def _pick(prefix, contains=None):\n", + " for name, m in metrics.items():\n", + " if name.startswith(prefix) and (contains is None or contains in name):\n", + " return name, m\n", + " return None, None\n", + "\n", + " pca_name, pca = _pick(\"Ttype_classification\", \"nFeature_100\")\n", + " lowd = {n: m for n, m in metrics.items()\n", + " if n.startswith(\"Ttype_classification\") and \"nFeature_100\" not in n}\n", + "\n", + " if pca and lowd:\n", + " # Compare this run's PCA-vs-MMIDAS gap against the *reference's* gap\n", + " # rather than against a threshold of this notebook's own invention.\n", + " # The reference expects a gap here by design: a 10-dimensional MMIDAS\n", + " # embedding is not trying to beat a 100-component PCA basis at recovering\n", + " # 115 reference t-types. What matters is whether our gap resembles\n", + " # theirs.\n", + " #\n", + " # Kept advisory, not fidelity: fold-to-fold spread on this metric is\n", + " # ~0.05, the reference values are read off a bar chart by eye, and the\n", + " # reference train/test split was unseeded. Too soft to gate a verdict\n", + " # on -- but worth reporting, since a gap far wider than the reference's\n", + " # says the embedding is carrying less t-type structure than published.\n", + " ref_gap = CONFIG[\"reference\"][\"ttype_acc_gap\"]\n", + " ref_pca = CONFIG[\"reference\"][\"ttype_acc_pca\"]\n", + " ref_lowd = CONFIG[\"reference\"][\"ttype_acc_lowd\"]\n", + " tol = CONFIG[\"reference\"][\"ttype_acc_gap_tol\"]\n", + "\n", + " best_lowd_name = max(lowd, key=lambda n: lowd[n][\"acc\"])\n", + " best_lowd = lowd[best_lowd_name]\n", + " gap = pca[\"acc\"] - best_lowd[\"acc\"]\n", + " print(\n", + " f\"\\nt-type recovery\"\n", + " f\"\\n this run : PCA-100={pca['acc']:.3f} MMIDAS-10={best_lowd['acc']:.3f}\"\n", + " f\" gap={gap:.3f} ({best_lowd_name})\"\n", + " f\"\\n reference: PCA-100~{ref_pca:.3f} MMIDAS-10~{ref_lowd:.3f}\"\n", + " f\" gap~{ref_gap:.3f} (eyeballed from 4_clusterability.ipynb)\"\n", + " )\n", + " check(\n", + " f\"t-type accuracy gap is within {tol:.2f} of the reference gap \"\n", + " f\"(~{ref_gap:.2f})\",\n", + " gap <= ref_gap + tol,\n", + " f\"this run gap={gap:.3f} (PCA={pca['acc']:.3f}, \"\n", + " f\"MMIDAS={best_lowd['acc']:.3f}); reference gap ~{ref_gap:.3f} \"\n", + " f\"(PCA ~{ref_pca:.3f}, MMIDAS ~{ref_lowd:.3f}); \"\n", + " f\"allowance {ref_gap:.3f}+{tol:.2f}={ref_gap + tol:.3f}\",\n", + " kind=\"advisory\",\n", + " )\n", + " else:\n", + " review(\n", + " \"could not locate both Ttype PCA and Ttype lowD pickles\",\n", + " \"skipping the t-type accuracy comparison\",\n", + " )\n", + "\n", + " # ----------------------------------------------------------------------\n", + " # Derive the populated-category count from the ConsType confusion matrices.\n", + " # Their side length is the number of categories cells were actually assigned\n", + " # to, which is the ground truth for the collapse check above (and the only\n", + " # source of it for runs predating n_populated_categories).\n", + " # ----------------------------------------------------------------------\n", + " cons_sizes = {n: m[\"n_cat\"] for n, m in metrics.items()\n", + " if n.startswith(\"ConsType_classification\")}\n", + " if cons_sizes:\n", + " derived_populated = max(cons_sizes.values())\n", + " print(f\"\\npopulated categories implied by ConsType confusion matrices: \"\n", + " f\"{sorted(set(cons_sizes.values()))} -> using {derived_populated}\")\n", + " min_frac = expected(\"min_populated_frac\")\n", + " check(\n", + " f\"ConsType confusion matrices span at least {min_frac:.0%} of model_order\",\n", + " derived_populated >= min_frac * model_order,\n", + " f\"{derived_populated} populated of model_order={model_order} \"\n", + " f\"({derived_populated / model_order:.1%})\",\n", + " kind=\"advisory\",\n", + ")\n", + " if n_populated is not None:\n", + " check(\n", + " \"evaluation_results n_populated_categories agrees with the \"\n", + " \"ConsType confusion matrices\",\n", + " derived_populated == n_populated,\n", + " f\"evaluation_results={n_populated}, ConsType={derived_populated}\",\n", + " )\n", + "else:\n", + " print(\n", + " \"classify_manifest / clustering_tar not provided -- skipping numeric accuracy/silhouette \"\n", + " \"checks. Relying on the figures above for a visual review instead.\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "64177684", + "metadata": {}, + "source": [ + "## Stage 3 -- Analyze: State Traversal (`state_traversal_manifest.json` + figures)\n", + "\n", + "Checks `model_order` consistency, that the categories the traversal was run for actually have\n", + "cells assigned to them (and were picked largest-first rather than by index), that KEGG pathway\n", + "mapping produced something when a `kegg_toml` was supplied, and that the per-category figures\n", + "differ from one another rather than being one plot rendered ten times under different titles." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bd5dfbb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"output_dir\": \"/mnt/disks/cromwell_root/out\",\n", + " \"model_order\": 96,\n", + " \"arm\": 0,\n", + " \"n_selected_cats\": 10,\n", + " \"selected_c\": [\n", + " 100,\n", + " 111,\n", + " 116,\n", + " 48,\n", + " 104,\n", + " 39,\n", + " 42,\n", + " 113,\n", + " 82,\n", + " 90\n", + " ],\n", + " \"selected_c_n_cells\": [\n", + " 874,\n", + " 808,\n", + " 783,\n", + " 670,\n", + " 572,\n", + " 559,\n", + " 517,\n", + " 493,\n", + " 467,\n", + " 437\n", + " ],\n", + " \"n_active_categories\": 96,\n", + " \"n_populated_categories\": 96,\n", + " \"n_pathways\": 21,\n", + " \"scatter_figs\": [\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_100.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_111.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_116.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_48.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_104.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_39.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_42.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_113.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_82.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/s_mu/state_mu_arm_0_c_90.png\"\n", + " ],\n", + " \"pathway_figs\": [\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_1_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_2_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_3_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_4_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_5_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_6_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_7_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_8_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_9_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_10_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_11_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_12_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_13_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_14_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_15_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_16_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_17_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_18_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_19_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_20_K_96.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/signaling_pathways/s_pc_path_21_K_96.png\"\n", + " ],\n", + " \"summary_figs\": [\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_100.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_111.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_116.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_48.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_104.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_39.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_42.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_113.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_82.png\",\n", + " \"/mnt/disks/cromwell_root/out/state/pathway_summary/pathway_summary_c_90.png\"\n", + " ]\n", + "}\n", + "[PASS] state_traversal model_order matches Train's evaluation_results -- state_traversal=96, train=96\n", + "[PASS] state_traversal ran for the requested number of categories -- selected_c has 10 entries, n_selected_cats=10\n", + "[PASS] n_selected_cats matches the configured input -- got 10, configured 10\n", + "\n", + "cells per selected category: 100=874, 111=808, 116=783, 48=670, 104=572, 39=559, 42=517, 113=493, 82=467, 90=437\n", + "[PASS] every selected category has cells assigned to it -- empty categories: []\n", + "[PASS] selected categories were ranked by size (largest first) -- counts in listed order: [874, 808, 783, 670, 572, 559, 517, 493, 467, 437]\n", + "[PASS] [advisory] state_traversal sees at least 50% of active categories populated -- 96 populated of 96 active\n", + "[PASS] KEGG pathways were mapped (kegg_toml was supplied) -- n_pathways=21 -- check the TraversalPrep log for '0 genes' or 'KEGG: 0 pathways'\n", + "[PASS] pathway figures were produced -- 21 pathway figures\n" + ] + } + ], + "source": [ + "state_manifest = load_json_gcs(CONFIG[\"analyze\"][\"state_traversal_manifest\"])\n", + "print(json.dumps(state_manifest, indent=2))\n", + "\n", + "check(\n", + " \"state_traversal model_order matches Train's evaluation_results\",\n", + " state_manifest[\"model_order\"] == model_order,\n", + " f\"state_traversal={state_manifest['model_order']}, train={model_order}\",\n", + ")\n", + "\n", + "n_selected_cats = state_manifest[\"n_selected_cats\"]\n", + "selected_c = state_manifest[\"selected_c\"]\n", + "\n", + "# Counting entries in selected_c only confirms the list has the requested length.\n", + "# It passed on a run where all ten selected categories were empty, so the checks\n", + "# below look at what those categories contain.\n", + "check(\n", + " \"state_traversal ran for the requested number of categories\",\n", + " len(selected_c) == n_selected_cats or n_selected_cats == 0,\n", + " f\"selected_c has {len(selected_c)} entries, n_selected_cats={n_selected_cats}\",\n", + ")\n", + "\n", + "check(\n", + " \"n_selected_cats matches the configured input\",\n", + " n_selected_cats == expected(\"n_selected_cats\")\n", + " or expected(\"n_selected_cats\") == 0,\n", + " f\"got {n_selected_cats}, configured \"\n", + " f\"{expected('n_selected_cats')}\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Are the selected categories actually populated?\n", + "# ---------------------------------------------------------------------------\n", + "sel_counts = state_manifest.get(\"selected_c_n_cells\")\n", + "if sel_counts is None:\n", + " review(\n", + " \"selected_c_n_cells absent from state_traversal_manifest.json\",\n", + " \"run predates this field -- the figure-content check below is the only \"\n", + " \"guard that the selected categories were not empty\",\n", + " )\n", + "else:\n", + " print(f\"\\ncells per selected category: \"\n", + " + \", \".join(f\"{c}={n}\" for c, n in zip(selected_c, sel_counts)))\n", + " check(\n", + " \"every selected category has cells assigned to it\",\n", + " all(n > 0 for n in sel_counts),\n", + " f\"empty categories: \"\n", + " f\"{[c for c, n in zip(selected_c, sel_counts) if n == 0]}\",\n", + " )\n", + " check(\n", + " \"selected categories were ranked by size (largest first)\",\n", + " list(sel_counts) == sorted(sel_counts, reverse=True),\n", + " f\"counts in listed order: {list(sel_counts)}\",\n", + " )\n", + "\n", + "n_pop_state = state_manifest.get(\"n_populated_categories\")\n", + "if n_pop_state is not None:\n", + " min_frac = expected(\"min_populated_frac\")\n", + " check(\n", + " f\"state_traversal sees at least {min_frac:.0%} of active categories populated\",\n", + " n_pop_state >= min_frac * state_manifest.get(\"n_active_categories\", model_order),\n", + " f\"{n_pop_state} populated of \"\n", + " f\"{state_manifest.get('n_active_categories', model_order)} active\",\n", + " kind=\"advisory\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# KEGG pathway figures\n", + "#\n", + "# n_pathways == 0 with a kegg_toml supplied means gene-name lookup failed, not\n", + "# that the pathways were empty -- MMIDAS's loader read gene identifiers from the\n", + "# wrong AnnData attribute and silently mapped nothing.\n", + "# ---------------------------------------------------------------------------\n", + "n_pathways = state_manifest.get(\"n_pathways\", 0)\n", + "if expected(\"kegg_toml_supplied\"):\n", + " check(\n", + " \"KEGG pathways were mapped (kegg_toml was supplied)\",\n", + " n_pathways > 0,\n", + " f\"n_pathways={n_pathways} -- check the TraversalPrep log for \"\n", + " f\"'0 genes' or 'KEGG: 0 pathways'\",\n", + " )\n", + " check(\n", + " \"pathway figures were produced\",\n", + " len(state_manifest.get(\"pathway_figs\", [])) > 0,\n", + " f\"{len(state_manifest.get('pathway_figs', []))} pathway figures\",\n", + " )\n", + "else:\n", + " print(f\"\\nkegg_toml not supplied -- n_pathways={n_pathways} as expected.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f122bb53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 41 state traversal figures\n", + "\n", + " per-category state-space scatter 10 figure(s)\n", + " per-pathway plot 21 figure(s)\n", + " per-category pathway summary 10 figure(s)\n", + "\n", + "per-category state-space scatter: 10 distinct content(s) across 10 figure(s)\n", + "[PASS] per-category state-space scatter figures are not duplicates of each other\n", + "[REVIEW] per-category state-space scatter -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- state_mu_arm_0_c_100.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-category state-space scatter -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- state_mu_arm_0_c_104.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-category state-space scatter -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- state_mu_arm_0_c_111.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... and 7 more per-category state-space scatter(s) not shown (raise N_TO_SHOW_PER_FAMILY to see more)\n", + "\n", + "per-pathway plot: 21 distinct content(s) across 21 figure(s)\n", + "[PASS] per-pathway plot figures are not duplicates of each other\n", + "[REVIEW] per-pathway plot -- pathway score should trend along the traversal path rather than look like noise\n", + " --- s_pc_path_10_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-pathway plot -- pathway score should trend along the traversal path rather than look like noise\n", + " --- s_pc_path_11_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-pathway plot -- pathway score should trend along the traversal path rather than look like noise\n", + " --- s_pc_path_12_K_96.png\n" + ] + }, + { + "data": { + "image/png": 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97ne/O+qaTl/4whf0s5/9rGLfN7/5zVEf9kvS008/rQcffFBvfetb1dbWNurY7RKJhD75yU/q/vvvH9zX0dGhP/7xjzIMY9Rjf/3rX+uqq64a3D766KP105/+dNRjJmJNu+3OOussfeELXxjx93Dr1q265JJLKv58NTY26sEHHxw1AM1mszrrrLMqQmS/36//+Z//GfX34Pe//70+9rGPDTuTcirXtNvRe9/7Xn34wx+WZVnDvv6nP/1JV1xxRUWQ39DQoN/+9rdqaWkZ9bxf/vKX9YY3vEFHHnlkVXV6nqef//zn+vKXv1zRwvGWW24Z8z12ZV3J4ex8/9jRZZddpssvv1x+v3/Y119++WVdeuml2rZt2+C+sdZUe+ihh3TZZZcNbs+dO1d/+tOfRhz/xBNP6KKLLhrcjkajgyFZMBjU448/rmAwOOyxvb29OuGEEwa3fT6fli9fPuJ6qVN5v/jyl7+sH//4x4Pbhx9+uH7xi19UdU5JWrdunV7/+tdXzGz82c9+pqOOOqrq9wAAANiT0R4TAAAA2MkPfvCDIYFdLBbT+eefX9Xxb3nLW3TeeedVHdhJ0sKFC/WTn/ykov3mPffcU9ECbGfPPvtsRWDX2dlZ1cyh7WKxmN797ndPeGAnqWKGmCRdccUVVQd2knTkkUeOGtjtir6+viEB2EknnaRrr712xLBHkmbNmqUf/vCHVT/MrkZjY6N+9rOfjRrYSeWZZAceeGDFvv/7v/8b8/2XLl2qD3zgA+OqOR6P6zvf+Y4OPfTQwX3d3d2Ds8b2VKeddpq++tWvjvp72Nraqq985SsV+/r7+/W3v/1t1Pe+8847h8z6vPbaa8cMTd/4xjcOOzuvVi655BJdfvnlIwZ2kvT6179+yK/RwMCAbrzxxjHf/5Of/GTVgZ0kGYahiy++WJ/85Ccr9v/kJz+p+j0my7vf/W59/OMfHzGwk6T9999fn/rUpyr2vfTSS1qxYsWIxxx55JEVAfHatWsHZ/UNZ+f2ljsGfoVCQU8++WTVxy5ZsmTEwE6a2vvFBRdcULH95JNPatWqVVWf9/bbb68I7ObPn09gBwAA9imEdgAAAJj2MpmMVq9erdtvv13nn3/+sC0Q/+u//kvxeHxS62hra6sIBguFwohrGkmqmOUhlVvfjTUjaqrs3H7y4IMPrlEl/3LXXXdVhJyhUEif+9znRg0ytovH47ryyisnrJaPf/zjmjVr1pjjfD6fLr744op9Tz/99ITVsTPLsvT+97+/Yt+OM2n2NNFoVF/84heruu4PPfTQioBBkp566qlRj9m5DeNJJ52kN73pTVXVdtZZZ+nVr351VWMn03777af//M//rGrsGWecode97nUV+3b+czORLrzwwopZfA8++OBgm+BamDt3rq644oqqxp5++ulDQq7RrqdQKDTk+lu2bNmI43dsbzlnzpwhrTdHO3Y869ntjl25X8yfP1/HHHNMxb6dPyQzklKppDvuuKNiX7UfpgEAANhbENoBAABg2li4cOGw/x1++OF64xvfqKuvvnrIQ9dAIKAvf/nLOuOMM6akxp0f6v7zn/8ccezOM0F6enomoaJds3NtO4d4tbB9HbntTj311DHXDNzRcA/pd8WMGTN01llnVT1+xzZ3Uvn3ebQZmLtrvMFWLZ1zzjkVs1PHcvzxx1dsj9ZWsbu7u2LdNUl65zvfOa763vGOd4xr/GS46KKLRp2FuLOd14fr7+/XQw89NNFlSSqvobbjTN9sNjvk13wqvf3tbx91ht2OTNPUscceW7FvrDadO4dnI30oI5fLVdz7jz/+eDU3N1fMuq028BvuvBNpV+4XF154YcX23XffXdX6qg888EDF33OBQGDc6+EBAADs6Xy1LgAAAADYE4VCIb3hDW/QBz/4QXV0dOz2+yWTSa1cuVJdXV1KJpPKZDLDPqTctGlTxfbq1atHfM8FCxZUbD/++OP6y1/+MmSmTC0sWLCg4uH7t771Ld14440KhUI1qadUKun555+v2DdWi8OdmaapN7zhDWOu1TaWY489dtR11HY2e/ZsBQKBiuulr69v3DM/i8WiXnrpJb300kvq7+9XOp1WoVDQWMucj6d13VQb70y2nf/M9PX1jTh25/ChoaFh3OHH8ccfr1gspmQyOa7jJtKZZ545rvGHH3642tvbK1o3PvXUUyOuJzgcz/O0fv16rVy5Ulu2bFE6nVY2mx12Ft3O7UdXrVqlxYsXj6vmiTKZ15NUDs+++c1vDm4/+uij8jxvyEzRxx9/vGI9xO3X3XHHHTd4X33++ec1MDCghoaGimO7u7srfu/C4fCQYK0ak3m/OPXUU9XS0jIYwPX39+vee+8dcxbrr371q4rt17/+9WpqahrzfAAAAHsTQjsAAABgGPF4XK9+9at3K7DLZrO6/fbb9Zvf/GaXWxoODAyM+NqCBQu0YMGCwYeknufpAx/4gM4880ydd955OvLII6tq/TgZ3vCGN+j3v//94Pbjjz+uM888U+94xzt0+umnq7W1dUrrWbVqlfL5fMW+XWnZ+apXvWq3axlrHbudGYahaDRa0Q41lUpVdaznefrjH/+ou+66Sw899FBFEFAt27aVy+XGNVtrqoz31zIajVZsj/bruHPIu3jxYpnm+JrVWJalRYsW6bHHHhvXcROlubl5XLNJt1uyZElF8FPt/WvVqlX65S9/qd/97ne7PLt2tHveZAoEApo/f/64jhnP9ST9a225dDotqdzieOXKlUOu4x1n4BmGMTij77jjjtOPf/xjSZLrulq+fLne8IY3jHisJB1xxBFVf0hgqu4Xfr9f5557rq6//vrBfbfeeuuood2mTZv04IMPVuyjNSYAANgXEdoBAABg2ti57ZtUXjdu27Zt+uc//1nxkHrjxo362Mc+ppUrV+pjH/vYuM/117/+VZ/97GeHzJwbr7EeAn/iE5/Qe9/7XrmuK6n8IPc3v/mNfvOb3ygWi+mII47Q4YcfrsMPP1xLly4d1wyv3XH66afrpz/9qZ588snBfevXr9eXvvQlffnLX9aBBx6oI488UocddpiOOOKIqtZ32x07t5MMBALab7/9xv0+EzHrcueZMdXYeYZiNWuMrVmzRldddZX+8Y9/jPt8O0smk3tkaDfeX8udfx1Ha8m385qRu/p739nZWbPQbueZYNU64IAD9Ic//GFwe+vWraOOt21bX/va1/STn/xEpVJpl865XbWB9ESbiD+XY7V49Pl8OuqooyrWfXvkkUeGhHY7trdcvHjx4Gyyo446Sj6fb/DXeNmyZWOGdju38BzJVN8vzj//fN1www2Df3c99thj6urqUmdn57Dj77jjjsGxUvnPVbXfGwAAwN6E0A4AAADTxkc/+tFRX3/kkUf0uc99Tl1dXYP7brzxRrW0tIxrLavf/e53+uhHPzpsK7jxGmumw8knn6wvf/nL+uxnPzskyEkmk7r//vsHHxAHg0EdccQROv300/XGN75xXGuBjZdpmvrOd76jD33oQ/r73/9e8ZrneVq5cqVWrlypW265RVK5BeRrXvManXXWWbvUym0sO8/eqa+v36X32Xlmza6ods2s3fHyyy/r0ksvHRI87apdmXEzFSYzhN65peWuXjO7etxE2JUgShp6nY/W3tO2bV1++eX605/+tEvnGu79amGqPtBw3HHHDQnt3vWudw1u9/X1acWKFRXjt6uvr9fSpUsHPwyxc0DneZ6WL19esW/ndRyHU4v7RXt7u0466SQ98MADg/t+9atf6eMf//iQsa7r6o477qjYd9555+12nQAAAHsiQjsAAADgFccdd5x+8Ytf6OKLL65YS+6rX/2qjjrqqKpaI65bt05XXnnlkMDuoIMO0ute9zotXbpUs2bNUktLi4LBoEKhUMV6RsuXL9ell146rrrPPvtsHXHEEfrOd76j3//+9yPOwioUClq2bJmWLVumr3zlK3rnO9+p9773vZP2sDoej+snP/mJ7rzzTt10002jrs+3fv16/exnP9PPfvYzHX744brqqqu0dOnSCatl5xkwuxqcTdWD/d1RKpX0//7f/xvyAL6trU2nn366Dj/8cM2ZM0dtbW0Kh8MKhUJD2qguXLhwKkveI+0L18yuriEZiUQqtjOZzIhjb7rppiGBnd/v1+te9zodc8wxWrhwoWbOnKmGhgYFg8Ehvx5XXnml7rzzzl2qc2+087qI29ev2359LV++vGLduJ1Dt+OOO24wtOvq6tKmTZsGZw2/8MILFevqNTY2jrk+YC3vFxdeeGFFaHfXXXfp8ssvH/Jn7cEHH9TGjRsHt/1+v84+++xdOicAAMCejtAOAAAA2EE8Hte3v/1tveUtbxl8aF8qlfTpT39ad9xxR0XANpxvfOMbFaFZJBLRtddeq9NOO62q8++87lq15s6dq6985Sv61Kc+pb/97W9avny5/v73v2v16tUVD4C3y2Qy+s53vqO//vWv+tGPfjQhM8iGY1mW3vrWt+qtb32rnn32WT388MN67LHH9M9//nNwXaedPfnkk7rooov0hS98YcIezO78/Y0WQoxmpJr3JL/+9a/18ssvV+x73/vepw984ANVBU+5XG6ySturTNQ1s6vHTYRdvZ9ks9mK7bq6umHHDQwM6Pvf/37FviVLlui6666ruv3sdLveDjzwQDU3Nw+u+ZfNZvXUU0/pyCOPlFRuebldIBDQEUccUXH88ccfr+985zuD24888ojOOeecIcdK0jHHHDPmOoy1vF+cdNJJam9vH2xNvW3bNt1333164xvfWDHuV7/6VcX2KaecohkzZuzyeQEAAPZk41tFGwAAAJgGFixYoPe+970V+5577jndfffdox5XLBb1l7/8pWLflVdeWXVgJw1t4zhe0WhUZ555pj7/+c/rd7/7nR599FF973vf06WXXqrZs2cPGf/ss8/qk5/85G6ds1oHH3yw/uM//kM33XSTHn/8cd1999369Kc/rZNPPnnIw2HbtvWpT31Kzz///ISce7gAZlcCjYlqHzeZdlyLTJLe9KY36SMf+UjVM8VGa4U4nex8zey8LmK1annN7Or9ZOd15UZqpfu3v/2tIuCLxWK6/vrrx7Ve5HS83nZei23HNpc7rmd36KGHDlkb7pBDDqmYCbljULcr69nV8n5hmuaQNpe33XZbxXZPT0/FbDxJetvb3rbL5wQAANjTEdoBAAAAw7jsssvU0tJSse873/mOSqXSiMesWLGi4gF2JBIZ90yxVatWja/QMTQ2Nup1r3udrr76at1333264YYbhoR39957r1auXDmh5x2LaZpatGiR3v72t+uGG27QX//6V7397W+vGFMqlXT99ddPyPk6Ojoqtj3P04svvjju93nhhRcmpJ7J9MQTT1RsX3zxxeM6fudZN9PVztfMrv7e77g+2VTb1fvJSy+9VLHd2to67Lid16s8/fTT1dzcPK5zTcfrbecWmduDt40bN6q7u3tw/3Dr0fn9/sFZedK/Qj7btof82d/5PMOp9f3irW99a0VAuGzZMq1bt25w+9e//nXF37tz5syp6vsCAADYWxHaAQAAAMMIhUJ63/veV7Fv7dq1+r//+78Rj+np6anY7uzsHPd6Vjs/QJ1IhmHo5JNP1o9//OMha1099NBDk3beasyYMUOf/vSndcEFF1Tsf+ihh4Zt7zleLS0tmjVrVsW+HWe0VGv58uW7XctkSqVSQ2YQHnjggeN6j8m8BvcmO6+puGbNGm3ZsmVc77Fly5aKEGaq9fb2DrYeHI9nnnmmYnuk9SW3t3jc7oADDhjXedatW6etW7eO65ixWhTvDXYOnZ555hllMpkhM+WGC+12Pr6np0cvvfSSnnrqqYoPjey3336aN2/eqHXsCfeLlpYWnXLKKYPbnufp9ttvH/x655l355133j5xDQAAAIyE0A4AAAAYwVvf+tYhs+2uv/56OY4z7Pid1/aptr3Ydl1dXUNmrkyG2bNn66ijjqrYt3Hjxkk/bzW2r820XSaTUX9//4S8946zUyTp7rvvHlcguGXLliEP1fc0O69FJo3vOnQcR3feeedElrTXWrJkiYLB4OC267pjtsjd2d133y3XdSe6tHH5/e9/P67x//jHP4YEfYcccsiwY3f3nrc9nBmP4T4IsX390b1Fe3u75s6dO7ht27Yef/zxilaX0WhUBx988LDH7xzmLVu2bMh6dtXMRttT7hcXXnhhxfYdd9yhUqmkRx55pGLWnc/nG/J3BAAAwL6G0A4AAAAYQTAY1GWXXVaxr7u7W7/5zW+GHd/Q0DBk7Hge2H/rW9+akFll1airq5uS84zXZNZ17rnnVmy//PLLuuOOO6o+/qtf/eqIge2eorGxcci+NWvWVH38bbfdtscEuLVWV1enN77xjRX7brrppqrXtkskErrxxhsno7RxueWWW8a1fuNNN91Usd3Y2KgTTzxx2LE73/PGc61t3bpVv/jFL6oev119ff2QfX19feN+n1obrkXmjjN5jz76aFmWNeyxCxcuVDweH9x+5JFHdmk9uz3lfnHsscdWzArcvo7dr371q4pxr3vd64Z8kAYAAGBfQ2gHAAAAjOKCCy6oeDgqlWfbDRfG7b///hXb/f39+tOf/lTVeX7961/rt7/97bhq27Bhwy7N4imVSnrqqacq9u23337jfp+R2LatzZs379KxO880jEQiampqmoiydOyxxw5p/XbNNdcMaQU4nF/+8pcjhrV7kmAwOGTNwp3by43kxRdf1Fe+8pXJKGuvtfP6Xv39/br88stVKBRGPa5QKOgjH/mIBgYGJrO8qmzcuFFf+9rXqhr7xz/+ccg96y1veUvFjMMdLViwoGL7d7/73bCzt3ZWLBZ11VVX7dKvTzgcHnJPeO6558b9PrW2c6h21113VbRYHqk1plRuEbrj8cuXLx9yH6smtNuT7hc7t0a+8cYbdd9991XsO++88ybsfAAAAHsqQjsAAABgFOFwWO985zsr9q1Zs2bYgG3WrFlDgrvPfe5zevHFF0d8f8/z9NOf/lRXX331uGv70Y9+pNNPP10//elPq579UyqV9LnPfU6bNm2q2P/a17523OcfSS6X0ymnnKL//M//1N///veqg8Xnn39eX//61yv2nXzyyRNWlyR99rOfrVgPKZ1O653vfKd++ctfqlQqDRmfTCb1pS99SZ/73OckDT8zZU9z0kknVWz//Oc/1z333DPqMcuXL9cll1xSVeAynSxdulTnn39+xb5HH31UF1988YhB0bPPPquLLrpocNbUnnDN/PjHP9Z111036p/F++67Tx/72Mcq9jU0NOg973nPiMfsfK319PTo4x//+KihZiKR0Pvf//7dWkfzVa96VcX2DTfcoHQ6vcvvVwvHHntsxb1o5wBzrPaWO4Z62WxWtm0Pbi9YsEBtbW1V1bGn3C/OPvvsirVW//GPf1R8T+3t7SPO+AQAANiX+GpdAAAAALCnu/jii3XTTTdVPFT93ve+pzPPPFOmWfk5uMsuu0xXXXXV4HYikdB5552niy66SKeddpo6OjpkWZZ6enr02GOP6bbbbqt4+H/uueeOq2Vjd3e3vvjFL+qaa67REUccoWOOOUYHHXSQOjs71djYqFAopHw+r/Xr1+uJJ57Qr371K61evbriPU499dQhM2Z2V6lU0j333KN77rlHLS0tes1rXqMlS5Zo8eLFam5uViwWk1T+9XnxxRd133336Z577ql4SOvz+Ya0J91dRx55pN7znvfoBz/4weC+dDqtz372s/r617+uY445Rm1tbSoWi1q/fr2WL18+WJNlWbryyit15ZVXTmhNE+2d73ynbr311sFWnq7r6j//8z9133336ZxzztHixYsVDofV19en5557Tvfcc0/F7KrxXoP7uk984hN67LHH1NXVNbjvmWee0TnnnKMDDzxQBx98sBoaGtTf36/nnnuuIqQ/8MAD9ZrXvEY33HBDDSqXjjnmGD3zzDPKZrP69re/rfvvv18XXXSRjjzySDU3NyuZTOqFF17QnXfeqXvvvXfI8Z/4xCdGbUe4dOlSHX300XrssccG9/3pT3/SW97yFr3rXe/S0UcfrebmZmWzWa1bt07333+/br31ViWTSUlSc3OzDj74YD3wwAPj+r5OP/30itDvH//4h1772tfqmGOO0X777TdkZuDxxx8/6sy1WojH41q0aJFWrFgx5LW2trYx78mjzaQbz/e6p9wvGhoa9MY3vnHENfLe+ta3Dvn7FgAAYF9EaAcAAACMob6+Xpdccom+/e1vD+5btWqV/vCHP+iMM86oGPuWt7xFv/vd7/Tggw8O7svn87r55pt18803j3qeiy++WKeddtouPQB1HEePPfZYxcPzasybN0+f//znx32+8ejp6dFtt91Wddu17T72sY9pyZIlE17PRz/6UWUyGf385z+v2N/f368//vGPwx5jmqY+97nP6cgjj6zY7/f7J7y+3dXR0aEPf/jDQ2Yt/v73v9fvf//7UY9dunSpPvWpTxHa7aC+vl4/+clPdMkll6i7u7vitRdffHHEmbSzZs3Sd7/73SEhxFReM7NmzdIFF1ygK664Qp7n6bnnnqt6Vu9ll102ZB3I4Xzuc5/T+eefXzHTbfXq1fr0pz896nHBYFDf/OY3dfvtt1dVz47OOuss/fCHP9SqVasG9yWTyRHbEQeDwT0utJPKs+mGC+3GmmUnSXPmzNGcOXO0bt26Ia9V0xpzuz3pfnHhhRcOG9pZllXVtQgAALAv4GNKAAAAQBUuvfRS1dXVVez77ne/K8/zKvaZpqlvfOMbOuGEE6p+b8Mw9B//8R9jPuTe2e4+/H/Na16jX/ziF5oxY8Zuvc/OTNOUZVm7fHxjY6O++tWvDmlLOpE++9nP6otf/GJVrQtbW1v1ve99T+eff74ymUzFa/X19ZNU4e5573vfq3//938f1zGvfvWrdfPNNysSiUxSVXuvtrY23XrrrTrnnHMqWhqO5KSTTtKtt96qOXPm1PyaOeOMM3TNNddUfb/w+Xz60Ic+pI9//ONVjV+wYIG+//3vD1n7czQzZszQzTffPCQEr1YoFNL1118/pE3m3makcK2a0G6kcZZl6ZhjjhlXHXvK/eKQQw7R4sWLh+w/+eSTq273CQAAsLcjtAMAAACq0NDQoLe//e0V+1566aVhZ2bV19frxhtv1Gc+8xm1t7eP+J6WZek1r3mNbr31Vl1xxRVVhQE7+tjHPqbbbrtNH/zgB3X00UdX9fA0GAzqDW94g26++WZ9//vfV1NT07jOWY36+notW7ZMX/3qV/XmN79Zc+fOreq4zs5OfehDH9K9996rf/u3f5vwunZ23nnn6d5779XnP/95vfrVr9bs2bMVCoUUCAQ0a9YsnXLKKfrSl76k++67T695zWskabCt33bRaHTS69xVH/3oR3XTTTfp0EMPHXXcQQcdpP/93//VjTfeuEd/P7XW1NSka665Rnfffbf+4z/+QwcffLBaWlrk8/lUV1enhQsX6sILL9TPf/5z/eAHP1Bra6ukPeOaOfvss3XXXXfp9a9/vXy+4Rvu+Hw+nXzyybr99tv1wQ9+cFzvf+SRR+quu+7S+eefr0AgMOK4xsZGXXbZZfr973+/y4HddnPnztVtt92mG264Qeeff76WLFmiGTNmVKyLtqc76qijhg1Tdye0e9WrXrVL19iecr+44IILhux729veNuHnAQAA2FMZ3s4fDQYAAAAwYTzP08qVK/Xcc88pkUjIcRxFo1HNnTtXhxxyyODabhOhVCqpu7tb3d3d2rx5s9LptBzHUSQSUUNDg/bff38dcMABQ9Z7mgp9fX1avXq11q1bp/7+fmWzWfn9ftXX12vWrFlatGjRXjGT4pZbbqloJ3rEEUcMabO5J9q0aZOefPJJ9fT0KJvNKhKJaNasWVqyZIn222+/Wpe3Tzv33HP17LPPDm5/8Ytf1HnnnTcp57ruuusq2vieffbZuvbaayvGDAwM6B/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... and 18 more per-pathway plot(s) not shown (raise N_TO_SHOW_PER_FAMILY to see more)\n", + "\n", + "per-category pathway summary: 10 distinct content(s) across 10 figure(s)\n", + "[PASS] per-category pathway summary figures are not duplicates of each other\n", + "[REVIEW] per-category pathway summary -- expect differing pathway profiles between categories, not the same profile repeated\n", + " --- pathway_summary_c_100.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-category pathway summary -- expect differing pathway profiles between categories, not the same profile repeated\n", + " --- pathway_summary_c_104.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] per-category pathway summary -- expect differing pathway profiles between categories, not the same profile repeated\n", + " --- pathway_summary_c_111.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... and 7 more per-category pathway summary(s) not shown (raise N_TO_SHOW_PER_FAMILY to see more)\n" + ] + } + ], + "source": [ + "state_figure_paths = resolve_paths(CONFIG[\"analyze\"][\"state_traversal_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(state_figure_paths)} state traversal figures\")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# StateTraversal emits three unrelated figure families. Group them before\n", + "# checking or displaying anything.\n", + "#\n", + "# state_mu_arm_* per-category state-space scatters\n", + "# s_pc_path_* per-pathway plots (only when KEGG mapping worked)\n", + "# pathway_summary_* per-category pathway summaries\n", + "#\n", + "# Treating them as one set is wrong in two ways: alphabetically\n", + "# pathway_summary_* sorts first, so a naive head-of-list display shows those\n", + "# while labelling them with scatter-specific guidance; and a duplicate check\n", + "# across families compares a scatter against a pathway plot, which is\n", + "# meaningless and could report a coincidental match as the empty-category\n", + "# failure mode the check exists to catch.\n", + "# ---------------------------------------------------------------------------\n", + "FAMILIES = [\n", + " (\"state_mu_arm_\", \"per-category state-space scatter\",\n", + " \"the highlighted category should be a visible cluster of coloured points, \"\n", + " \"and the black traversal path should trace a curve through it rather than \"\n", + " \"collapse to a single dot\"),\n", + " (\"s_pc_path_\", \"per-pathway plot\",\n", + " \"pathway score should trend along the traversal path rather than look like \"\n", + " \"noise\"),\n", + " (\"pathway_summary_\", \"per-category pathway summary\",\n", + " \"expect differing pathway profiles between categories, not the same \"\n", + " \"profile repeated\"),\n", + "]\n", + "\n", + "grouped, unmatched = {}, []\n", + "for p in state_figure_paths:\n", + " name = os.path.basename(p)\n", + " for prefix, _, _ in FAMILIES:\n", + " if name.startswith(prefix):\n", + " grouped.setdefault(prefix, []).append(p)\n", + " break\n", + " else:\n", + " unmatched.append(p)\n", + "\n", + "print()\n", + "for prefix, label, _ in FAMILIES:\n", + " print(f\" {label:<34} {len(grouped.get(prefix, [])):>3} figure(s)\")\n", + "if unmatched:\n", + " print(f\" (unrecognised: {[os.path.basename(p) for p in unmatched]})\")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Within each family, per-category figures must actually differ.\n", + "#\n", + "# A previous run emitted ten scatters for ten categories with no cells\n", + "# assigned. Every one was the same background scatter with a degenerate\n", + "# traversal path collapsed to a single point, identical below the title band.\n", + "# File bytes differ (the titles differ), so only a pixel comparison catches it.\n", + "# ---------------------------------------------------------------------------\n", + "N_TO_SHOW_PER_FAMILY = 3\n", + "\n", + "for prefix, label, guidance in FAMILIES:\n", + " paths = sorted(grouped.get(prefix, []))\n", + " if not paths:\n", + " continue\n", + " hashes = {}\n", + " for p in paths:\n", + " hashes.setdefault(image_content_hash(p), []).append(os.path.basename(p))\n", + " dups = {h: n for h, n in hashes.items() if len(n) > 1}\n", + " print(f\"\\n{label}: {len(hashes)} distinct content(s) across {len(paths)} figure(s)\")\n", + " for names in dups.values():\n", + " print(f\" identical below the title: {', '.join(names)}\")\n", + " check(\n", + " f\"{label} figures are not duplicates of each other\",\n", + " not dups,\n", + " f\"{len(paths) - len(hashes)} duplicate figure(s); groups: {list(dups.values())}\"\n", + " if dups else \"\",\n", + " )\n", + " for p in paths[:N_TO_SHOW_PER_FAMILY]:\n", + " review(label, guidance)\n", + " show_image(p, title=os.path.basename(p))\n", + " if len(paths) > N_TO_SHOW_PER_FAMILY:\n", + " print(f\"... and {len(paths) - N_TO_SHOW_PER_FAMILY} more {label}(s) not shown \"\n", + " f\"(raise N_TO_SHOW_PER_FAMILY to see more)\")\n", + "\n", + "if unmatched:\n", + " check(\n", + " \"every state traversal figure belongs to a known family\",\n", + " False,\n", + " f\"unrecognised filenames: {[os.path.basename(p) for p in unmatched]} -- \"\n", + " f\"add the prefix to FAMILIES so they are checked\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "refcmp-md", + "metadata": {}, + "source": [ + "## Stage 6 -- Reference comparison (the fidelity check)\n", + "\n", + "Everything above asks whether the workflow *ran*. This section asks the question the port actually\n", + "has to answer: **does it reproduce the published analysis?**\n", + "\n", + "The MMIDAS repo's own notebooks ship with their outputs saved, so the authors' results for this\n", + "dataset are recorded rather than inferred:\n", + "\n", + "| Quantity | Reference | Source |\n", + "| --- | --- | --- |\n", + "| matrix shape | 22365 x 5032 | `1_data_prep.ipynb` -- *\"final shape of normalized gene expresion matix\"* |\n", + "| reference t-types | 115 | `2_train.ipynb` data summary |\n", + "| pruning rounds | 42 | `3_evaluation.ipynb` -- checkpoints `after_pruning_1..42` |\n", + "| `model_order` | **92** | `3_evaluation.ipynb` -- *\"Selected number of clusters: 92 with consensus 0.954\"*; hardcoded in notebooks 4 and 5 |\n", + "| `avg_consensus` | 0.939 (test) / 0.954 (K-selection) | `3_evaluation.ipynb` |\n", + "\n", + "Two things this comparison cannot do, by construction:\n", + "\n", + "- **It cannot expect an exact match.** `2_train.ipynb` calls `get_loaders` without a seed, so the\n", + " reference train/test split is unrecoverable. `model_order` is compared within a tolerance of a few\n", + " pruning rounds, and `avg_consensus` against a floor. The floor is one-sided on purpose:\n", + " exceeding the published consensus means the two arms agree *more* strongly than in the\n", + " reference, which is not a fidelity defect.\n", + "- **It cannot detect an unreachable target on its own.** If `max_prun_it` is below\n", + " `n_categories - reference model_order` (28 here), the reference answer lies outside the search\n", + " space and `model_order` cannot match however well training goes. That is checked explicitly below,\n", + " because it is a configuration error rather than a result." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "refcmp-code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference analysis (MMIDAS repo notebooks) vs this run\n", + "======================================================================\n", + "[PASS] [fidelity] matrix shape matches the reference (22365, 5032) -- got (22365, 5032), reference (22365, 5032)\n", + "[PASS] [fidelity] reference t-type count matches -- got 115, reference 115\n", + "\n", + "pruning rounds this run: 42 (reference 42)\n", + "reachable model_order range: [78, 120]\n", + "reference model_order 92 needs pruning round 28\n", + "[PASS] [fidelity] pruning went deep enough to reach the reference model_order (92) -- ran 42 pruning round(s); reaching model_order 92 needs 28. Raise max_prun_it -- with 42 the smallest reachable model_order is 78.\n", + "[PASS] [fidelity] pruning round count matches the reference (42) -- ran 42, reference 42\n", + "[PASS] [fidelity] model_order is within 5 of the reference (92) -- got 96, reference 92 (difference +4, tolerance +/-5)\n", + "[PASS] [fidelity] avg_consensus is at least the reference floor (0.9) -- got 0.9075, floor 0.9 (published: 0.939 on test cells, 0.954 from K_selection)\n", + "\n", + "======================================================================\n", + "Note: an exact match is not expected -- the reference train/test split\n", + "was unseeded (2_train.ipynb calls get_loaders without a seed), so the\n", + "comparison is deliberately a tolerance and a floor, not equality.\n" + ] + } + ], + "source": [ + "REF = CONFIG[\"reference\"]\n", + "\n", + "print(\"Reference analysis (MMIDAS repo notebooks) vs this run\")\n", + "print(\"=\" * 70)\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Stage 1: the preprocessed matrix\n", + "# ---------------------------------------------------------------------------\n", + "ref_shape = tuple(REF[\"shape\"])\n", + "check(\n", + " f\"matrix shape matches the reference {ref_shape}\",\n", + " (n_cells, n_genes) == ref_shape,\n", + " f\"got ({n_cells}, {n_genes}), reference {ref_shape}\",\n", + " kind=\"fidelity\",\n", + ")\n", + "check(\n", + " \"reference t-type count matches\",\n", + " DATAPREP_N_CLUSTERS == REF[\"n_ttype\"],\n", + " f\"got {DATAPREP_N_CLUSTERS}, reference {REF['n_ttype']}\",\n", + " kind=\"fidelity\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Is the reference model_order even reachable?\n", + "#\n", + "# Pruning removes one category per round, so the smallest model_order a run can\n", + "# produce is n_categories - max_prun_it. If that floor sits above the reference\n", + "# value, the run cannot match it and nothing downstream is informative. Read\n", + "# max_prun_it from the checkpoint manifest -- it is the count of after_pruning\n", + "# checkpoints, which is what the run actually did.\n", + "# ---------------------------------------------------------------------------\n", + "ckpt_names = [os.path.basename(p) for p in ckpt_manifest.get(\"checkpoints\", [])]\n", + "rounds_run = sorted(\n", + " int(m.group(1))\n", + " for m in (re.search(r\"after_pruning_(\\d+)_\", n) for n in ckpt_names)\n", + " if m\n", + ")\n", + "n_rounds = len(rounds_run)\n", + "floor = n_categories - n_rounds\n", + "needed = n_categories - REF[\"model_order\"]\n", + "print(f\"\\npruning rounds this run: {n_rounds} (reference {REF['pruning_rounds']})\")\n", + "print(f\"reachable model_order range: [{floor}, {n_categories}]\")\n", + "print(f\"reference model_order {REF['model_order']} needs pruning round {needed}\")\n", + "\n", + "check(\n", + " f\"pruning went deep enough to reach the reference model_order \"\n", + " f\"({REF['model_order']})\",\n", + " n_rounds >= needed,\n", + " f\"ran {n_rounds} pruning round(s); reaching model_order \"\n", + " f\"{REF['model_order']} needs {needed}. Raise max_prun_it -- with \"\n", + " f\"{n_rounds} the smallest reachable model_order is {floor}.\",\n", + " kind=\"fidelity\",\n", + ")\n", + "\n", + "check(\n", + " f\"pruning round count matches the reference ({REF['pruning_rounds']})\",\n", + " n_rounds == REF[\"pruning_rounds\"],\n", + " f\"ran {n_rounds}, reference {REF['pruning_rounds']}\",\n", + " kind=\"fidelity\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# The headline numbers\n", + "# ---------------------------------------------------------------------------\n", + "tol = REF[\"model_order_tol\"]\n", + "check(\n", + " f\"model_order is within {tol} of the reference ({REF['model_order']})\",\n", + " abs(model_order - REF[\"model_order\"]) <= tol,\n", + " f\"got {model_order}, reference {REF['model_order']} \"\n", + " f\"(difference {model_order - REF['model_order']:+d}, tolerance +/-{tol})\",\n", + " kind=\"fidelity\",\n", + ")\n", + "\n", + "floor = REF[\"avg_consensus_min\"]\n", + "check(\n", + " f\"avg_consensus is at least the reference floor ({floor})\",\n", + " avg_consensus >= floor,\n", + " f\"got {avg_consensus:.4f}, floor {floor} \"\n", + " f\"(published: 0.939 on test cells, 0.954 from K_selection)\"\n", + " + (\" -- above the published value, which is fine: the arms agree more \"\n", + " \"strongly than in the reference\" if avg_consensus > 0.954 else \"\"),\n", + " kind=\"fidelity\",\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"Note: an exact match is not expected -- the reference train/test split\")\n", + "print(\"was unseeded (2_train.ipynb calls get_loaders without a seed), so the\")\n", + "print(\"comparison is deliberately a tolerance and a floor, not equality.\")" + ] + }, + { + "cell_type": "markdown", + "id": "08c90de8", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "56e7813e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Automated checks: 47/48 passed\n", + " plumbing 36/36\n", + " fidelity 7/7\n", + " advisory 4/5\n", + "\n", + "ADVISORY (not failures) (1) -- soft metrics: wide run-to-run spread, or compared against reference values read off a figure. Reported, never fatal:\n", + " - t-type accuracy gap is within 0.06 of the reference gap (~0.11): this run gap=0.288 (PCA=0.909, MMIDAS=0.621); reference gap ~0.110 (PCA ~0.845, MMIDAS ~0.735); allowance 0.110+0.06=0.170\n", + "\n", + "22 figures/items flagged for manual review (see inline images above).\n", + "\n", + "======================================================================\n", + "VERDICT: the workflow executed correctly and matches the reference\n", + "analysis within tolerance. The port is faithful.\n", + "\n", + "1 advisory item(s) noted above. Those are\n", + "model-quality observations, not port fidelity, and do not affect\n", + "this verdict.\n", + "The [REVIEW] figures above still need a human eyeball.\n" + ] + } + ], + "source": [ + "by_kind = {k: [c for c in CHECKS if c[\"kind\"] == k] for k in CHECK_KINDS}\n", + "fails = {k: [c for c in v if not c[\"passed\"]] for k, v in by_kind.items()}\n", + "\n", + "print(\"=\" * 70)\n", + "print(f\"Automated checks: {sum(c['passed'] for c in CHECKS)}/{len(CHECKS)} passed\")\n", + "for k in CHECK_KINDS:\n", + " n_pass = sum(c[\"passed\"] for c in by_kind[k])\n", + " print(f\" {k:<9} {n_pass}/{len(by_kind[k])}\")\n", + "\n", + "LABELS = {\n", + " \"plumbing\": (\"PLUMBING FAILURES\",\n", + " \"the WDL port did not execute correctly\"),\n", + " \"fidelity\": (\"FIDELITY FAILURES\",\n", + " \"the port ran, but does not reproduce the reference analysis\"),\n", + " \"advisory\": (\"ADVISORY (not failures)\",\n", + " \"soft metrics: wide run-to-run spread, or compared against \"\n", + " \"reference values read off a figure. Reported, never fatal\"),\n", + "}\n", + "for k in CHECK_KINDS:\n", + " if not fails[k]:\n", + " continue\n", + " title, gloss = LABELS[k]\n", + " print(f\"\\n{title} ({len(fails[k])}) -- {gloss}:\")\n", + " for c in fails[k]:\n", + " print(f\" - {c['name']}: {c['detail']}\")\n", + "\n", + "print(f\"\\n{len(REVIEW_ITEMS)} figures/items flagged for manual review \"\n", + " f\"(see inline images above).\")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Verdict. Plumbing and fidelity are the two questions that matter for a port;\n", + "# advisory items never decide it. A weak model that faithfully reproduces the\n", + "# reference is a success for this project, and a strong model that does not is\n", + "# not.\n", + "# ---------------------------------------------------------------------------\n", + "print(\"\\n\" + \"=\" * 70)\n", + "if fails[\"plumbing\"]:\n", + " print(\"VERDICT: the workflow did not execute correctly. Fix the plumbing\")\n", + " print(\"failures above before drawing any conclusion about the outputs.\")\n", + "elif fails[\"fidelity\"]:\n", + " print(\"VERDICT: the workflow executed correctly but does not reproduce the\")\n", + " print(\"reference analysis. See the fidelity failures above -- check first\")\n", + " print(\"whether the reference model_order was reachable at all (max_prun_it),\")\n", + " print(\"then compare the Train inputs against the reference notebooks.\")\n", + "else:\n", + " print(\"VERDICT: the workflow executed correctly and matches the reference\")\n", + " print(\"analysis within tolerance. The port is faithful.\")\n", + " if fails[\"advisory\"]:\n", + " print()\n", + " print(f\"{len(fails['advisory'])} advisory item(s) noted above. Those are\")\n", + " print(\"model-quality observations, not port fidelity, and do not affect\")\n", + " print(\"this verdict.\")\n", + "print(\"The [REVIEW] figures above still need a human eyeball.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3800231", + "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": "3.10.16" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pipelines/wdl/mmidas/dashboard.md b/pipelines/wdl/mmidas/dashboard.md new file mode 100644 index 0000000000..e57b5f3c65 --- /dev/null +++ b/pipelines/wdl/mmidas/dashboard.md @@ -0,0 +1,846 @@ +### MMIDAS: Mixture Model Inference with Discrete-coupled AutoencoderS + +This workspace contains a set of example workflows that run **MMIDAS**, an unsupervised method for discovering reproducible cell types (and their continuous within-type variation) from single-cell/single-nucleus datasets. MMIDAS combines a generalized mixture model with a multi-armed deep neural network to *jointly* infer a discrete cell-type category and a continuous, type-specific variability ("state") for each cell. In this implementation, coupled mixture variational autoencoders (cpl-mixVAE) keep only the categories that independent encoder "arms" agree on, then iteratively prune categories until the surviving set is highly reproducible. The result is a set of discrete, consensus cell-type categories plus a continuous state that captures variation *within* each type. + +MMIDAS was developed and published by **Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, and Uygar Sümbül** (Allen Institute for Brain Science). In their paper they show that modeling variability as continuous latent factors followed by a separate clustering step — or clustering the data directly — can make qualitative mistakes when the number of cell types is very large (hundreds to thousands), and they demonstrate MMIDAS on four brain single-cell datasets spanning different technologies, species, and conditions, in both unimodal and multimodal settings. The method and all scientific credit belong to those authors; please see [Citation and Credit](#citation-and-credit) and cite their work if you use these workflows. + +The workflows are provided as a worked, end-to-end example on a public reference dataset. They are designed to run in the order presented, but each workflow can be launched independently using the sample inputs provided in this workspace. + +The WDL workflow wrappers and this workspace were developed by the Data Sciences Platform at the Broad Institute; the underlying MMIDAS method and algorithms are the work of the original authors cited below. + +> **Read this first — the data-prep workflow is an example, not a general-purpose tool.** +> `MMIDAS_DataPrep` is a *reference ingest* written specifically for the Allen Brain Atlas Smart-seq Mouse ALM/VISp files used here. **You will not be able to run your own dataset through it as-is.** The two model workflows (`MMIDAS_Train` and `MMIDAS_Analyze`) *are* general: they run on any AnnData `.h5ad` file that follows the simple contract described in [Bringing your own data](#bringing-your-own-data-replacing-mmidas_dataprep). Treat `MMIDAS_DataPrep` as a template you replace, and the other two workflows as the reusable engine. + +--- + +## Workflows Overview + + + +This workspace has three example workflows: + +1. **MMIDAS_DataPrep** *(example ingest — dataset-specific, not general)*: converts the raw Allen Brain Atlas Smart-seq exon-count CSVs into a single normalized, filtered AnnData `.h5ad` file ready for training. This is the only dataset-specific stage; see [Bringing your own data](#bringing-your-own-data-replacing-mmidas_dataprep). + +2. **MMIDAS_Train**: optionally trains a data augmenter, trains the core cpl-mixVAE model with iterative category pruning, and evaluates all checkpoints to recommend the optimal number of categories (`model_order`). It stops at a **human-review checkpoint**: you inspect the evaluation results and figures before continuing. + +3. **MMIDAS_Analyze**: takes the reviewed model from `MMIDAS_Train` and produces the downstream biology — classification/clusterability analysis (how separable the discovered categories are) and state-traversal figures (what varies continuously within each category). + +``` +raw CSVs ──► MMIDAS_DataPrep ──► .h5ad ──► MMIDAS_Train ──► (human review) ──► MMIDAS_Analyze ──► figures + (example only) model + eval classification + + checkpoints state traversal + │ │ + └────────────┬───────────────────────┘ + ▼ + MMIDAS_output_validation.ipynb + (checks the whole chain end to end) +``` + +These workflows cover MMIDAS's **transcriptomic** analysis, matching the authors' reference +notebooks. MMIDAS also supports coupled multimodal (expression + electrophysiology) analysis, which +is not implemented here — see +[Multimodal analysis](#multimodal-analysis-transcriptomics--electrophysiology). + +A notebook, `MMIDAS_output_validation.ipynb`, validates a completed set of runs — see +[Validating a run](#validating-a-run--mmidas_output_validationipynb). Before your first run on your +own data, read +[Tuning for your own data](#tuning-for-your-own-data--read-this-before-your-first-run): three of the +training defaults are specific to the example dataset and will silently produce a useless model if +carried over unchanged. + +--- + +## Sample Data + +The example data is the **Allen Brain Atlas 2018 Mouse Smart-seq** dataset covering two cortical regions — primary visual cortex (**VISp**) and anterior lateral motor cortex (**ALM**). It consists of full-length Smart-seq exon-count matrices plus per-cell metadata (including reference cell-type "cluster" labels curated by the Allen Institute). + +The raw files expected by `MMIDAS_DataPrep` are: + +| File | Description | +| --- | --- | +| `mouse_VISp_2018-06-14_exon-matrix.csv` | VISp raw exon counts (genes × cells) | +| `mouse_VISp_2018-06-14_samples-columns.csv` | VISp per-cell metadata (one row per cell, same order as matrix columns) | +| `mouse_ALM_2018-06-14_exon-matrix.csv` | ALM raw exon counts (genes × cells) | +| `mouse_ALM_2018-06-14_samples-columns.csv` | ALM per-cell metadata | +| `mouse_ALM_2018-06-14_genes-rows.csv` | Full gene list (must contain a `gene_symbol` column) | +| `genes_SS_ALM-VISp.csv` | Selected 5,032-gene subset used for training (must contain a `genes` column) | + +Optional reference files used by `MMIDAS_Analyze`: + +| File | Used for | +| --- | --- | +| `tree_Mouse_ALM-VISp_2018.csv` | Hierarchical taxonomy tree — orders categories by their dominant reference cell type (optional) | +| `KEGG.toml` | KEGG pathway gene sets — enables per-pathway box plots in the state-traversal step (optional) | + +These files are provided in the workspace bucket and referenced by the example input JSONs. + +--- + +## Workflows + +### 1. MMIDAS_DataPrep *(example ingest — dataset-specific)* + +**What does it do?** + +`MMIDAS_DataPrep` runs `01_data_prep.py`, which turns the raw Allen Smart-seq CSVs into one analysis-ready AnnData `.h5ad` file. In detail it: + +1. Loads the VISp and ALM exon-count matrices, reading **only** neuronal-cell columns to keep memory low. +2. Retains only the requested neuronal classes (default `GABAergic` and `Glutamatergic`). +3. Concatenates the two regions into a single matrix. +4. Normalizes counts to **log-CPM**: `log1p(counts / rowsum × 1e6)`. +5. Subsets to the selected gene list. +6. Removes low-quality / rare clusters (default `Low Quality,CR Lhx5,Meis2 Adamts19`). +7. Applies two dataset-specific cell-type renames to match the reference taxonomy. +8. Writes the result as a `.h5ad` with the expression matrix in `X`, cell metadata in `obs` (including the reference `cluster` labels), and gene symbols as `var_names`. + +> **Why this is an example and not a general tool.** Almost every step above encodes assumptions that are specific to this dataset: exactly two regions concatenated together; a fixed CSV layout with *positional* alignment between the count matrix columns and the metadata rows; hard-coded column names (`class`, `cluster`, `gene_symbol`, `genes`); a fixed log-CPM normalization that assumes raw-count input; and hard-coded cluster-removal and rename lists. **Your own data will almost certainly have a different raw format, different metadata columns, and different QC choices, so it cannot flow through this workflow unchanged.** This is expected — data ingest is inherently dataset-specific. See [Bringing your own data](#bringing-your-own-data-replacing-mmidas_dataprep) for the output contract you need to reproduce. + +**What does it require as input?** + +| Input | Type | Description | +| --- | --- | --- | +| `visp_exon_matrix` | File | VISp raw exon count matrix CSV (genes × cells) | +| `visp_samples` | File | VISp per-cell metadata CSV | +| `alm_exon_matrix` | File | ALM raw exon count matrix CSV | +| `alm_samples` | File | ALM per-cell metadata CSV | +| `genes_rows` | File | Full gene list CSV (with `gene_symbol` column) | +| `selected_genes` | File | Selected gene subset CSV (with `genes` column) | +| `output_basename` | String | Basename for the output `.h5ad` (default `Mouse_ALM-VISp_cpm`) | +| `remove_clusters` | String | Comma-separated clusters to exclude (default `Low Quality,CR Lhx5,Meis2 Adamts19`) | +| `neuronal_classes` | String | Comma-separated cell classes to keep (default `GABAergic,Glutamatergic`) | + +**What does it return as output?** + +| Output | Type | Description | +| --- | --- | --- | +| `preprocessed_h5ad` | File | The normalized, filtered AnnData `.h5ad` — the input to `MMIDAS_Train` | +| `pipeline_version_out` | String | Pipeline version string | + +--- + +### 2. MMIDAS_Train + +**What does it do?** + +> **This workspace is configured for a shorter run than the published study.** +> `n_epoch_p` (epochs per pruning round) is set to **1,000** here, giving 52,000 total epochs — +> **~13.5 hours, ~$13**. The published analysis used **10,000**, which is 430,000 epochs — +> **~4.5 days, ~$110**. The example outputs in this workspace, and the validation-notebook results +> reported below, all come from the 1,000-epoch configuration; on the example data it reached +> `avg_consensus 0.969` and `model_order 89` against the published 92. +> +> **To run the published configuration**, set `n_epoch_p = 10000` in the workflow inputs (or use +> `example_inputs/MMIDAS_Train.json`, which keeps that value) and plan for the longer runtime and +> cost. `max_prun_it` is 42 in both, so the published `model_order` is reachable either way. + +`MMIDAS_Train` is the core modeling stage. It runs three steps and ends at a human-review checkpoint: + +- **(Optional) Augmenter training** — trains a UDAGAN VAE-GAN augmenter that can generate realistic synthetic cells to stabilize training. Off by default (`run_augmenter = false`). +- **cpl-mixVAE training with pruning** — trains two coupled encoder arms starting from an upper bound of `n_categories` categories, then iteratively prunes the least-reproducible category (lowest inter-arm consensus) for up to `max_prun_it` rounds. Each round writes a model checkpoint. +- **Evaluation** — scores every checkpoint, runs K-selection to recommend the optimal number of categories (`model_order`), and writes `evaluation_results.json` plus consensus/K-selection figures. + +**Human-review checkpoint:** after this workflow finishes, download `evaluation_results.json` and the evaluation figures and confirm the recommended `model_order` is biologically sensible **before** launching `MMIDAS_Analyze`. The evaluation JSON and the model tarball are the hand-off files to the next stage. + +Three fields in `evaluation_results.json` decide whether the run is usable at all, and none of them is `model_order`: + +| Field | Reject the run if | +| --- | --- | +| `k_selection_met_threshold` | `false` — no checkpoint reached `k_select_thr`, so `model_order` came from a fallback rather than a selection | +| `n_populated_categories` | far below `model_order` — `model_order` counts categories that survived pruning, which stays high even when the model routes every cell into a handful of them | +| `collapse_warning` | non-null — the two above disagree badly enough that downstream Analyze figures will be dominated by empty categories | + +`avg_consensus` should also be at or above `k_select_thr`. A run with high `model_order` and near-zero `avg_consensus` has not found reproducible categories; it has failed to train its discrete latent. In the training log, watch the per-epoch `Entropy` against the `uniform=` value printed next to it — an `Entropy` that stays pinned at `uniform` while the reconstruction loss falls means the categorical variable never committed and no amount of pruning will fix it. The usual cause is `tau` being too large for your `n_categories`; see [Tuning for your own data](#tuning-for-your-own-data--read-this-before-your-first-run). + +After `MMIDAS_Analyze` completes, run `MMIDAS_output_validation.ipynb` to check the whole chain automatically rather than eyeballing the JSON — see [Validating a run](#validating-a-run--mmidas_output_validationipynb). + +**Detail for the detail-inclined.** The model is a *coupled* mixture VAE: two (or more) arms encode the same cell independently, and the training loss penalizes disagreement between the arms' categorical assignments (`lam`/`lam_pc` coupling factors). Only categories the arms agree on survive pruning, which is what makes the discovered categories reproducible rather than an artifact of a single run — this consensus-across-arms idea is the core contribution of the MMIDAS method (Marghi et al., 2024; see [Citation and Credit](#citation-and-credit)). Each cell also gets a low-dimensional continuous **state** variable (`state_dim`) that captures within-type variation. Reconstruction can use `MSE` or `ZINB` loss (`training_mode`). + +**What does it require as input?** + +Key inputs (all hyperparameters have production defaults): + +| Input | Type | Default | Published value | Description | +| --- | --- | --- | --- | --- | +| `preprocessed_h5ad` | File | — | — | Output of `MMIDAS_DataPrep`, or your own contract-compliant `.h5ad` | +| `run_augmenter` | Boolean | `false` | `false` | Whether to train the optional data augmenter first | +| `n_categories` | Int | `120` | `120` | Upper-bound number of categories before pruning | +| `n_arm` | Int | `2` | `2` | Number of coupled encoder arms | +| `state_dim` | Int | `2` | `2` | Continuous within-type state dimension | +| `latent_dim` | Int | `10` | `10` | Low-dimensional embedding dimension | +| `training_mode` | String | `MSE` | `MSE` | Reconstruction loss (`MSE` or `ZINB`) | +| `n_epoch` | Int | `10000` | `10000` | Epochs before pruning begins | +| `n_epoch_p` | Int | **`1000`** | **`10000`** | Epochs per pruning round. **The one default that differs from the published study** — see the note above | +| `max_prun_it` | Int | `42` | `42` | Maximum pruning iterations | +| `min_con` | Float | `0.99` | `0.99` | Reporting only in this implementation; pruning runs the full `max_prun_it` regardless | +| `tau` | Float | `0.005` | `0.005` | Categorical softmax temperature. **Rescale this if you change `n_categories`** — see [Tuning for your own data](#tuning-for-your-own-data--read-this-before-your-first-run) | +| `k_select_thr` | Float | `0.95` | `0.95` | Consensus threshold used to recommend `model_order` | +| `batch_size` | Int | `5000` | `5000` | Mini-batch size | +| `seed` | Int | `0` | unseeded | Random seed; the reference train/test split was unseeded | +| `train_gpu` | Int | `0` | — | Set to `1` to attach a GPU (see note below) | + +> **GPU note.** Training is much faster on a GPU, and every figure in [Time and Cost Estimates](#time-and-cost-estimates) assumes one. Set `train_gpu = 1` and that is all: the `TrainMixVAE` task's runtime block already declares `gpuCount` and `gpuType: "nvidia-tesla-t4"` and passes `--cuda` to the training script. There is **no GPU setting to enable in Terra** for workflow submissions — Terra's Cloud Environment has GPU options, but those apply to interactive notebooks and RStudio, not to Cromwell workflow tasks. +> +> The one thing that can block a GPU task is **GCP quota**: the Google project behind your Terra billing project needs available GPU quota in the execution region, or the task will fail to schedule rather than fall back to CPU. If that happens, raise it with Terra support — billing-project quotas are not adjustable from the Terra UI. + +**What does it return as output?** + +| Output | Type | Description | +| --- | --- | --- | +| `evaluation_results_json` | File | **Review this.** Recommended `model_order`, selected model, and metrics | +| `evaluation_figures` | Array[File] | Consensus heatmaps and K-selection curves for review | +| `summary_performance` | File | Per-checkpoint consensus / reconstruction pickle behind the K-selection decision | +| `checkpoints_manifest` | File | Manifest of all model checkpoints and architecture settings | +| `model_tar` | File | Tarball of all trained checkpoints | +| `augmenter_checkpoint` | File? | Augmenter model (only if `run_augmenter = true`) | +| `pipeline_version_out` | String | Pipeline version string | + +--- + +### 3. MMIDAS_Analyze + +**What does it do?** + +`MMIDAS_Analyze` takes the reviewed model from `MMIDAS_Train` and produces the downstream biological analyses. It first restores the model checkpoints, then runs two analyses in parallel and turns each into figures: + +- **Clusterability (steps 03b → 04)** — trains a random-forest classifier and computes silhouette scores to quantify how separable the MMIDAS categories are, compared against a PCA baseline and the reference cluster labels. Produces classification-accuracy bar charts, silhouette curves, and confusion-matrix heatmaps. +- **State traversal (steps 03c → 05)** — walks along each category's continuous state axis and visualizes how gene expression changes, producing per-category state-space scatter plots and (if a KEGG file is supplied) per-pathway box plots. + +**Detail for the detail-inclined.** The "clusterability" analysis answers *"are these categories real and separable?"* by asking how well a classifier can recover them and how tight/separated they are in embedding space. The "state traversal" answers *"what varies continuously within a type?"* by holding the categorical assignment fixed and moving along the state latent, then decoding the resulting expression profiles. Category ordering can optionally follow the Allen hierarchical taxonomy tree (`htree_file`). + +**What does it require as input?** + +| Input | Type | Description | +| --- | --- | --- | +| `preprocessed_h5ad` | File | Same `.h5ad` used for training | +| `checkpoints_manifest` | File | From `MMIDAS_Train` | +| `model_tar` | File | From `MMIDAS_Train` | +| `evaluation_results_json` | File | From `MMIDAS_Train` — **after** you have reviewed it | +| `kegg_toml` | File? | Optional KEGG pathway file (enables pathway box plots) | +| `htree_file` | File? | Optional taxonomy tree (enables taxonomy-ordered categories) | +| `n_pca` | Int | PCA components for the linear baseline (default `100`) | +| `k_fold` | Int | Cross-validation folds for classification (default `10`) | +| `n_traversal_steps` | Int | Points along each state traversal (default `50`) | +| `traversal_arm` | Int | Which encoder arm to visualize (default `0`) | +| `n_selected_cats` | Int | Number of categories to plot, `0` = all (default `10`) | +| `batch_size` / `seed` | Int | Must match training (defaults `5000` / `0`) | + +**What does it return as output?** + +| Output | Type | Description | +| --- | --- | --- | +| `clusterability_figures` | Array[File] | Classification accuracy, silhouette, and confusion-matrix figures | +| `clusterability_manifest` | File | Manifest for the clusterability outputs | +| `state_traversal_figures` | Array[File] | Per-category state-space and (optional) pathway figures | +| `state_traversal_manifest` | File | Manifest for the state-traversal outputs | +| `pipeline_version_out` | String | Pipeline version string | + +--- + +## Bringing Your Own Data (replacing MMIDAS_DataPrep) + +You cannot run your own dataset through `MMIDAS_DataPrep` — it is hard-wired to the Allen ALM/VISp CSV format. Instead, produce your own AnnData `.h5ad` with any tool you like (e.g. Scanpy) that satisfies the small contract below, then feed it straight into `MMIDAS_Train` (and pass the same file to `MMIDAS_Analyze`). + +**The `.h5ad` contract expected by MMIDAS_Train / MMIDAS_Analyze:** + +| Where | Requirement | +| --- | --- | +| `adata.X` | Log-normalized expression matrix, cells × genes (the example uses log-CPM: `log1p(counts / rowsum × 1e6)`). A sparse `float32` matrix is recommended. | +| `adata.var_names` | Gene symbols/identifiers, one per column of `X`. | +| `adata.obs['cluster']` | A **reference cell-type label per cell** (string). This is used as the ground-truth label for evaluation and classification. This column must exist. | +| `adata.obs['subclass']`, `adata.obs['class']` | Optional additional label columns used for some ordering/plots. | + +Notes: + +- The model itself is dataset-agnostic. Everything that would differ per dataset — number of genes, number of categories (`n_categories`), embedding sizes, epochs — is a workflow parameter, so you tune those to your data rather than editing code. +- The reference `cluster` labels are used to *evaluate* and *order* the discovered categories; they are not required for the model to learn, but the evaluation, classification, and taxonomy-ordering steps expect them. +- `01_data_prep.py` in the [warp-tools](https://github.com/broadinstitute/warp-tools) repo is a good worked example of how to build a contract-compliant `.h5ad`; copy and adapt it for your own raw format. +- `MMIDAS_output_validation.ipynb` validates against the authors' published Mouse ALM/VISp results, so its reference comparisons will not apply to your data. Adapt it as a starting point for your own validation — see [Using it on your own data](#using-it-on-your-own-data). + +--- + +## Multimodal analysis (transcriptomics + electrophysiology) + +MMIDAS as published is not limited to gene expression. The authors describe it as applying to "both, +uni-modal and multi-modal datasets," and the paper demonstrates coupled analysis of **transcriptomic +and electrophysiological** measurements from the same cells — Patch-seq data, where each neuron is +both patched for its electrical properties and sequenced. The appeal is that consensus is then +required *across modalities*: a cell type is kept only if it is recoverable from both what a neuron +does electrically and what it expresses, which is a stronger claim than either modality alone +supports. + +> **These three workflows implement the transcriptomic path only.** They correspond to the authors' +> reference notebooks (`1_data_prep` … `5_state_traversal`), which are single-modality. Nothing in +> this workspace has been run against electrophysiology data, and none of it is tested for that. The +> section below is a pointer for anyone who wants to go that direction, not a supported path. + +**Why it is not just a matter of different inputs.** The multimodal model is a different model, not +the same one with an extra file: + +- The `.h5ad` contract above describes a single expression matrix. A multimodal run needs two feature + matrices plus the pairing that says which row of each belongs to the same cell. +- `MMIDAS_Train` calls `cpl_mixVAE.init_model()`, whose signature takes `n_arm` — the number of + encoder arms over one modality — and has no modality parameter. The network class it builds is + `RNA_RNA_mixVAE`, i.e. arms coupled across one data type. +- The multimodal model instead needs per-modality architecture and coupling: separate arm counts and + state dimensions for each modality, and a cross-modal coupling factor in addition to the + within-modality ones. + +**Where to start.** The authors' repository includes `tutorials/datasets_training/train_patchseq.py`, +which exposes exactly those parameters — `--n_modal`, `--n_arm_T` / `--n_arm_E`, `--state_dim_T` / +`--state_dim_E`, and `--lam_T` / `--lam_E` / `--lam_TE` for transcriptomic, electrophysiological, and +cross-modal coupling. Read it as the specification for what a multimodal workflow would need to +provide. Note that it imports helper modules (`utils.training`, `utils.helpers`, including a +`load_patchseq` loader) that are not part of the revision pinned in this workspace's Docker image, so +those pieces would need to be sourced before it will run. + +**Data and feature extraction.** The authors use the +[Allen Institute Patch-seq dataset](https://dandiarchive.org/dandiset/000020/) and note that the +electrophysiological features were computed following the approach in their companion `cplAE_MET` +repository. Deriving those features from raw traces is a substantial preprocessing step in its own +right, and is the analogue of `MMIDAS_DataPrep` for the electrophysiology side. + +**Scope of the work required.** Realistically this is a fourth workflow rather than an option on the +existing one: an ingest stage producing paired transcriptomic and electrophysiological matrices, a +training stage wrapping the multimodal model, and evaluation and analysis stages that report +consensus per modality as well as across them. `MMIDAS_Analyze` would need corresponding changes, +since its classification and state-traversal steps assume a single feature space. + +--- + +## Tuning for your own data — read this before your first run + +The defaults in these workflows are tuned for the example dataset (Mouse ALM/VISp, 22,365 cells, +5,032 genes, 115 reference t-types, `n_categories = 120`). Three of them are **not** safe to carry +over to a different dataset or a different `n_categories`, and getting them wrong produces a run +that completes successfully and reports plausible-looking numbers while being useless. + +### 1. `tau` must be rescaled whenever you change `n_categories` + +This is the single most important parameter to get right. `tau` is the categorical softmax +temperature, and `cpl_mixVAE.init_model` documents it as *"usually equals to 1/n_categories"*. + +| `n_categories` | Appropriate `tau` | +| --- | --- | +| 15 | ~0.067 | +| 50 | ~0.020 | +| **120 (this workspace)** | **~0.008 — default is 0.005** | +| 250 | ~0.004 | + +If `tau` is too large for your `n_categories`, the categorical posterior stays nearly flat and the +model reconstructs entirely through the continuous state variable rather than the discrete categories. +The effect is large: on the example data at `n_categories = 120`, a `tau` sized for +`n_categories = 15` gave `avg_consensus` **0.026** and **11** populated categories, where the correctly +scaled `tau = 0.005` gave `avg_consensus` **0.969** and 89 of 89 populated. The failure is silent — +nothing errors, and `model_order` still comes back a plausible number. + +**How to tell within the first few hours.** The `TrainMixVAE` log prints, every epoch: + +``` +Entropy: -0.8030 (uniform=-9.5750) +``` + +`uniform` is the value `Entropy` takes when both arms' categorical posteriors are completely flat — +i.e. the discrete latent carries no information. Watch the gap: + +- `Entropy` **moving decisively away from `uniform`** → the categorical variable is committing. Good. +- `Entropy` **pinned near `uniform`** while the reconstruction loss falls → collapse. Kill the run + and lower `tau`. This is visible at the end of the pre-pruning phase, roughly 2.5 hours in, long + before pruning starts. + +For reference, the healthy run moved from −9.57 to −0.80 (about 1.5 effective categories per cell); +the collapsed run only reached −9.02 (about 91 of 120 — essentially no commitment). + +This is the cheapest possible check on a long run: it is readable at the end of the pre-pruning phase +(the first `n_epoch = 10000` epochs, ~2.5 h, ~$2.50), before any pruning starts. If `Entropy` is +still pinned near `uniform` there, lower `tau` and restart rather than paying for 42 pruning rounds. + +### 2. `max_prun_it` bounds which answers are even reachable + +Pruning removes **one** category per round, so the smallest `model_order` a run can produce is: + +``` +n_categories - max_prun_it +``` + +If the number of cell types in your data falls below that floor, no amount of training will find it — +the answer is outside the search space. With the defaults (`n_categories = 120`, +`max_prun_it = 42`) the reachable range is **78–120**. If you expect ~30 types from +`n_categories = 120`, you need `max_prun_it` ≥ 90. + +Set `n_categories` generously above your expected type count and `max_prun_it` large enough that +your plausible range sits comfortably inside the reachable window. + +### 3. Runtime and cost scale with `max_prun_it × n_epoch_p` + +Total epochs are `n_epoch + max_prun_it × n_epoch_p`, and training dominates everything else in the +pipeline. Both parameters therefore multiply your bill: raising `max_prun_it` to widen the search +space (above) also raises the cost proportionally. See +[Time and Cost Estimates](#mmidas_train) for the measured figures. + +Two things worth knowing before you raise `n_epoch_p` from its default of 1,000 to the published +10,000: on the example data the 1,000-epoch configuration already reached `avg_consensus 0.969` and +`model_order 89` against the published 92, and round-by-round consensus in a full-length run +plateaued by round 2 and then moved only within noise for 17 more rounds. + +### 4. There is no resume — protect against losing a long run + +`TrainMixVAE` writes `model.tar.gz` only when the task **completes**. If it is aborted (a cost cap, +a timeout, a manual cancel), the intermediate checkpoints are lost with the VM and the run must +start over. `preemptible` is already `0` so the VM will not be reclaimed mid-run, but: + +- **Set Terra cost caps above the expected spend** — ≥ ~$25 at the default `n_epoch_p = 1000`, + ≥ ~$150 at the published 10,000. A cap hit mid-run discards the whole run, not just the remainder. +- 4–5 days is within the usual 7-day GCP task ceiling, but only just. Confirm your project does not + impose a shorter limit before launching at `n_epoch_p = 10000`. + +### 5. Smaller things worth knowing + +| Parameter / behaviour | What to know | +| --- | --- | +| `min_con` | **Reporting only** in this implementation: pruning runs the full `max_prun_it` regardless of the value set. Do not expect `min_con` to halt anything. | +| `k_select_thr` | If no checkpoint reaches it, `K_selection` returns nothing and `Evaluate` falls back to the un-pruned checkpoint. Check `k_selection_met_threshold` in `evaluation_results.json` — `false` means `model_order` came from a fallback, not a selection. | +| `kegg_toml` | Optional. Omit it and `n_pathways` is 0 with no pathway figures — expected, not a failure. Supply it and confirm `n_pathways > 0`; zero pathways *with* a `kegg_toml` means gene-name lookup failed. | +| `htree_file` | Optional; enables taxonomy ordering in stage 03c. | +| `n_selected_cats` | Capped at the number of *populated* categories, so the manifest may report fewer than you asked for. | +| Run-to-run variation | Training is **not** bit-reproducible even with a fixed `seed`. Two runs of the identical configuration gave `model_order` 96 and 89 with `avg_consensus` 0.9075 and 0.9692. Do not build a conclusion on one run — see [Run-to-run variation](#run-to-run-variation). | +| `Classify` retries | This task has retried on three consecutive runs (10-fold random forest over the full cell set). It succeeds on retry, but its outputs land in `call-Classify/attempt-N/` rather than `call-Classify/`. | + +--- + +## Running the Workflows + +The workflows are pre-configured with the example inputs in this workspace (see the `example_inputs/` JSON files). For each workflow: + +1. Select the workflow from the **Workflows** tab. +2. Provide inputs — either use the provided example JSON or edit the input fields. +3. (For `MMIDAS_Train`) enable a GPU by setting `train_gpu = 1` for a much faster run. +4. Launch the workflow. + +Recommended order: + +1. Run **MMIDAS_Train** on the provided example `.h5ad` (or your own contract-compliant `.h5ad`). +2. **Review** `evaluation_results.json` and the evaluation figures; check `k_selection_met_threshold`, `n_populated_categories` and `collapse_warning`, then confirm `model_order`. +3. Run **MMIDAS_Analyze**, passing the `checkpoints_manifest`, `model_tar`, and reviewed `evaluation_results_json` from step 1. + +If you want to reproduce the example end-to-end from the raw Allen CSVs, run **MMIDAS_DataPrep** first to produce the `.h5ad` — but remember this step only works for the Allen ALM/VISp files. + +--- + +## Time and Cost Estimates + +Measured on Terra with the example dataset (22,365 cells × 5,032 genes). These are the figures every +other section of this document refers back to. Training dominates; the other two stages are minor by +comparison. + +| Stage | Configuration | Time | Cost | +| --- | --- | --- | --- | +| `MMIDAS_DataPrep` | — | ~9 min | < $1 | +| **`MMIDAS_Train`** | **`n_epoch_p = 1000`** (workspace default, 52,000 epochs) | **~13.5 h** | **~$13** | +| `MMIDAS_Train` | `n_epoch_p = 10000` (published, 430,000 epochs) | ~4.5 days | ~$110 | +| `MMIDAS_Analyze` | — | ~1.4 h | < $2 | +| | **End-to-end at the default** | **~15 h** | **~$15** | + +### MMIDAS_Train + +Training cost is set almost entirely by total epochs, `n_epoch + max_prun_it × n_epoch_p`, at +**~0.9 s/epoch** on an `nvidia-tesla-t4` at **~$1.00/hr**. Those two constants are what the table +above is derived from, so you can price any configuration from them. + +The workspace default (`n_epoch_p = 1000`) and the published configuration (`10000`) differ in that +one field; `max_prun_it` is 42 in both. CPU-only training was not benchmarked and is impractical at +these epoch counts. + +### MMIDAS_DataPrep + +Needs `mem_size = 48` GiB: it holds the full 22,439 × 45,768 count matrix in memory before subsetting +to the selected genes. + +### MMIDAS_Analyze + +CPU only, no GPU; the `Classify` task accounts for most of the 1.4 hours. Cheap enough to re-run +freely, which matters because it is the stage you re-run when figures or downstream analysis change +without retraining. + +For more information about controlling Cloud costs, see [this article](https://support.terra.bio/hc/en-us/articles/360029748111). + +--- + +## Fidelity to the original MMIDAS analysis + +These workflows are a port of the original MMIDAS code, recreating the authors' notebook analysis as +WDL workflows. The [MMIDAS repo](https://github.com/AllenInstitute/MMIDAS) ships its notebooks with +outputs saved, so the authors' results for the example dataset are recorded and can be compared +against directly. + +**Reference values for Mouse ALM/VISp:** + +| Quantity | Reference | Source | +| --- | --- | --- | +| matrix shape | 22,365 × 5,032 | `1_data_prep.ipynb` | +| reference t-types | 115 | `2_train.ipynb` data summary | +| pruning rounds | 42 | `3_evaluation.ipynb` (checkpoints `after_pruning_1..42`) | +| `model_order` | **92** | `3_evaluation.ipynb`; hardcoded in notebooks 4 and 5 | +| `avg_consensus` | 0.939 (test cells) / 0.954 (K-selection) | `3_evaluation.ipynb` | + +**Measured results in this workspace.** Two independent runs of the *identical* configuration are +shown, because the difference between them is itself a result you need to know about — see +[Run-to-run variation](#run-to-run-variation) below. + +| Quantity | Reference | Run A | Run B | | +| --- | --- | --- | --- | --- | +| matrix shape | 22,365 × 5,032 | 22,365 × 5,032 | 22,365 × 5,032 | match | +| reference t-types | 115 | 115 | 115 | match | +| pruning rounds | 42 | 42 | 42 | match | +| `model_order` | 92 | **96** | **89** | within tolerance either way | +| `avg_consensus` | 0.939 / 0.954 | **0.9075** | **0.9692** | reference falls between them | +| populated categories | 92 (all) | **96 of 96** | **89 of 89** | all populated, both arms | +| K-selection met `k_select_thr` | yes | **yes** | **yes** | no fallback | + +Run A is the run distributed with this workspace. Run B was produced earlier in a development +workspace and is included here only as the second sample. + +`MMIDAS_output_validation.ipynb` checks a run against these under **Stage 6 — Reference +comparison**; see [Validating a run](#validating-a-run--mmidas_output_validationipynb) for how to +point it at your own submissions and what the check labels mean. Where the results *do* differ, see +[Where the results differ from the published analysis](#where-the-results-differ-from-the-published-analysis). + +### Run-to-run variation + +**Two runs of the same configuration on the same input will not give the same answer.** Plan for +this before you build any conclusion on a single run. + +| | `model_order` | `avg_consensus` | +| --- | --- | --- | +| Run A | 96 | 0.9075 | +| Run B | 89 | 0.9692 | +| Published | 92 | 0.939 / 0.954 | +| **Observed spread** | **7 categories** | **0.062** | + +Both runs completed all 42 pruning rounds with every surviving category populated in both arms and +`k_selection_met_threshold` true, so both are valid runs — the spread is the algorithm's, not a +defect. The published values fall between the two, which is the reassuring part: the runs bracket the +reference rather than sitting to one side of it. + +Two causes, neither removable: + +- **The train/test split.** The reference calls `get_loaders` without a seed. These workflows seed it + (`seed = 0`) so a given workflow run is at least self-consistent, but that does not recover the + reference's split. +- **GPU non-determinism.** Floating-point reductions on a GPU are not associative, so identical + inputs and an identical seed still diverge. In one pair of runs the two encoder arms swapped which + one converged better. + +Practical consequences: + +- Do not treat a single `model_order` as *the* number of cell types. Report it as an estimate, and + run the configuration more than once if the exact count matters to your conclusion. +- Thresholds in the validation notebook are set to admit this spread. `model_order_tol` is 5 pruning + rounds and `avg_consensus_min` is 0.900, the latter calibrated from these two runs plus the + published value. A collapsed model measures around 0.03, so the floor is nowhere near loose enough + to pass a failed run. +- If two of your own runs differ by *much* more than the above — tens of categories, or consensus + moving by several tenths — that is no longer ordinary variation. Check `tau` against your + `n_categories` first. + +### Where the training defaults come from + +The reference notebooks pass only `n_categories`, `state_dim`, `n_arm` and `latent_dim` to +`cpl_mixVAE.init_model()` and let everything else fall to that function's defaults, so `MMIDAS_Train` +follows those defaults. The training-loop values (`n_epoch`, `n_epoch_p`, `max_prun_it`) come from +`tutorials/train_mixvae.py`, since `2_train.ipynb`'s `n_epoch = 10` is a walkthrough demo rather than +the configuration behind the published model. + +Two are worth calling out: + +- **`max_prun_it` bounds which answers are reachable.** Pruning removes one category per round, so + the smallest `model_order` a run can produce is `n_categories - max_prun_it`. Reaching the + reference `model_order` of 92 from `n_categories = 120` requires 28 rounds; the default is 42, the + number the reference used. +- **`tau` scales with `n_categories`.** `init_model` documents it as "usually equals to + 1/n_categories" — about 0.0083 at `n_categories = 120`, and the default here is the library's + 0.005. If you change `n_categories`, rescale `tau` to match; see + [Tuning for your own data](#tuning-for-your-own-data--read-this-before-your-first-run). + +`n_epoch_p` is the one default that deliberately differs from the published configuration — 1,000 +here against 10,000 — because it is what the workspace's example outputs were produced with. See the +note in [MMIDAS_Train](#2-mmidas_train). + +`min_con` is **reporting only** in this implementation: pruning runs the full `max_prun_it` regardless +of its value, which is how every reference invocation behaves. + +### Known divergences from the notebooks + +Two differences are unavoidable in a generic workflow and are deliberate: + +| Divergence | Why | +| --- | --- | +| **State-traversal category selection.** `5_state_traversal.ipynb` hardcodes `selected_c = [80, 119, 1, 13, 92, 25, 55, 110, 62, 69, 31]`. The workflow instead selects the `n_selected_cats` most-populated categories. | A workflow cannot reproduce a hand-picked list chosen by inspection. Selecting by population at least guarantees the figures show categories the model actually uses. | +| **Train/test split seeding.** `2_train.ipynb` calls `get_loaders` without a `seed`, so its split is random and unrecoverable. The workflow seeds it (`seed = 0`). | An unseeded split makes a workflow non-reproducible run to run. This is why an exact numerical match to the reference is not expected, and why the validation notebook compares `model_order` within a tolerance and `avg_consensus` against a range. | + +### Where the results differ from the published analysis + +The validated run reproduces the reference within tolerance. Three things do not match exactly, and +all three are worth knowing before you present results. + +**1. `model_order` 96 against the published 92** — four pruning rounds apart, and 89 on the second +run. This is ordinary stochastic variation rather than a divergence in the port; see +[Run-to-run variation](#run-to-run-variation) for the spread and its causes. Exact agreement is not +achievable. + +**2. t-type classification accuracy sits further below the PCA baseline than in the reference.** + +| | PCA-100 | MMIDAS-10 | gap | +| --- | --- | --- | --- | +| Reference (`4_clusterability.ipynb`) | ~84.5% | ~73.5% | ~0.11 | +| Run A | 90.9% | 62.1% | 0.288 | +| Run B | 90.9% | 66.2% | 0.247 | + +Every comparison points the same direction as the reference — PCA ahead on the 115 reference t-types, +the 10-dimensional MMIDAS embedding well ahead on MMIDAS's own categories (94.7% vs 64.8%) — so this +is a difference in magnitude on a downstream metric, not in behaviour. Some gap here is expected by +design: a 10-dimensional embedding is not attempting to beat a 100-component PCA basis at recovering +115 reference labels. Three caveats on the comparison itself: the metric has ~0.05 fold-to-fold +spread, it tracks `avg_consensus` across runs (the weaker-consensus run shows the wider gap), and +`4_clusterability.ipynb` reports these as a figure rather than numbers, so the reference column is +approximate. The validation notebook therefore reports this as *advisory*. + +**3. The accuracy bar chart has fewer groups than the reference's.** The reference plots three label +sets — t-types, *Merged t-types*, and MMIDAS T Categories. The workflow plots t-types and the per-arm +MMIDAS categories, omitting the merged-t-type group. The two groups that do appear match the +reference's layout and direction. + +--- + +## Validating a run — `MMIDAS_output_validation.ipynb` + +The workspace includes a notebook that checks a completed set of runs end to end. Point it at your +three submissions, run it top to bottom, and it reports what passed, what failed, and which figures +still need a human eye. + +**It validates against the authors' published results for the example dataset.** Its reference values +— matrix shape, 115 t-types, `model_order 92`, consensus, 42 pruning rounds, the accuracy figures — +are all read from the reference notebooks for Mouse ALM/VISp. That makes it a direct answer to "did +this run reproduce the published analysis?" on the example data, and it is the fastest way to know a +run is trustworthy before building analysis on top of it. On any other dataset those comparisons do +not apply; see [Using it on your own data](#using-it-on-your-own-data) below. + +**Pointing it at your run.** The Config cell holds three constants — `DATAPREP_RUN`, `TRAIN_RUN`, +`ANALYZE_RUN` — each a Terra submission/workflow path. Everything else is derived, so repointing at +a new run is three lines. Figure lists are given as `gs://` prefixes and globbed, not enumerated. + +**Two traps when copying paths from Terra:** + +- A Terra output path contains **two** UUIDs — the submission ID and the workflow ID — and the bucket + name contains a third (`fc-`). Pasting the bucket's UUID where the workflow ID belongs + produces a path that looks right and fails with an opaque `gsutil` error. If Stage 0 reports tasks + as unreadable, check this first. +- `Classify` output lives under `call-Classify/attempt-N/` when that task retries, which it has done + on every run so far. The notebook discovers the attempt automatically; you only supply the call + directory. + +**Checks are labelled by what they answer**, and only the first two decide the verdict: + +| Kind | Question | Dataset-specific? | On failure | +| --- | --- | --- | --- | +| `plumbing` | Did the workflow execute correctly? Files present, shapes consistent, manifests mutually agreeing, all stages consuming the same inputs. | No — applies to any dataset | The port is broken. Fix before interpreting anything. | +| `fidelity` | Does this run reproduce the published analysis? Compared against values recorded in the reference notebooks. | **Yes — Mouse ALM/VISp only** | The run does not match how the authors ran it. | +| `advisory` | Is the model any good? Soft metrics with wide run-to-run spread, or comparisons against figures rather than published numbers. | Partly | Informational. Never fatal. | + +On the example dataset, a run that reproduces the reference is a success even if advisory items +complain — and a run with better-looking numbers that does *not* reproduce the reference is not. + +**Result for the run distributed with this workspace:** 47/48 — `plumbing 36/36`, `fidelity 7/7`, +`advisory 4/5`, verdict *"the workflow executed correctly and matches the reference analysis within +tolerance. The port is faithful."* The single advisory failure is the t-type accuracy gap described +above. + +**What it cannot tell you.** The checks confirm figures exist, are distinct from one another, and are +not drawn over empty categories. They cannot tell you a figure is drawn on the wrong scale — a +mis-scaled colour map passes all three. That is what the `[REVIEW]` items are for, and they are worth +actually looking at. + +**Things to check by eye in the `[REVIEW]` figures:** + +| Figure | Healthy | Suspicious | +| --- | --- | --- | +| `consensus_T1_vs_T2` | strong diagonal spanning the full category range | a handful of scattered points — the arms are not agreeing | +| `norm_consensus_T1_vs_T2` | bright diagonal on a dark field | a mostly dark matrix — no reproducible categories | +| `state_mu_K_*_arm_*` | a broad, filled cloud roughly ±3 in both axes. **Do not expect separated clusters** — this is the *continuous* within-type state, not the discrete categories | axes far wider than ±3 (an outlier rescaling the plot — cosmetic, see below), or a cloud collapsed to a point | +| `SC_K_*` | most categories above zero, MMIDAS curves near or above the t-type reference | curves hugging zero; or an x-axis spanning a single value, which means the figure is broken rather than the model | +| `classAcc_RF_K_*` | read the **t-types** group — that is MMIDAS vs PCA on reference labels | the `T Categories` groups classify the model's own labels, so ~95% there is near-circular and proves little | +| `conf_*` | tight diagonal with faint off-diagonal detail | large square blocks (reference types collapsing together), or pure black-and-white with no intermediate shades (a plotting-scale bug, not a model result) | +| `state_mu_arm_0_c_*` | highlighted category is a coherent coloured cluster with the traversal path running through it | a single dot, or an invisible highlight | + +### Known figure quirks in the distributed run + +The figures shipped with this workspace were reviewed by eye. Three things look worse than they are, +and are documented here so you can tell them apart from a genuine problem in your own run. + +**1. `state_mu_K_96_arm_0` is unreadable, and this is cosmetic.** A single cell sits at roughly +(9.8, 58) in state space. Matplotlib autoscales to include it, so the y-axis runs to 60 while the +other 22,364 cells occupy about ±3 — they compress into a flat smear along the bottom. The same +figure for arm 1 has no such outlier and shows the expected cloud, and the two arms are otherwise +comparable, which is how you can confirm the smear is a plotting artifact rather than a degenerate +state variable. + +To check this on your own run: compare the two arms, and read the axis limits. If one arm's axes are +an order of magnitude wider than the other's, you are looking at an outlier, not a model failure. The +underlying values are in `model.tar.gz` if you want to re-plot with clipped limits. + +Worth stressing, because the natural reading is the wrong one: a diffuse cloud here is the **correct** +result. The continuous state captures within-type variation, so it is not supposed to separate into +discrete groups. The discrete structure lives in the categorical variable, and you inspect it in +`consensus_T1_vs_T2` and `norm_consensus_T1_vs_T2` — both of which are clean for this run, showing a +sharp diagonal across all 96 categories with negligible off-diagonal mass. + +**2. The `conf_*` heatmaps look mostly white.** Rows are normalised to fractions, so a strong +diagonal leaves everything else pale by construction. That is the intended appearance. What matters +is that intermediate shades are present and the diagonal is continuous; blocks of mid-tone off the +diagonal are real biology — closely related reference t-types being confused with one another. A +`conf_*` figure with *no* intermediate shades at all, only white and full-saturation cells, would +indicate a plotting-scale problem instead. + +**3. `classAcc_RF_K_96` shows MMIDAS well below PCA on the t-types group** (about 62% against 91%). +The figure is drawn correctly; this is the run's actual result and the one advisory check that does +not pass. See +[Where the results differ from the published analysis](#where-the-results-differ-from-the-published-analysis). +Note the error bars on the MMIDAS bars are wide (roughly ±5 points), which is the fold-to-fold spread +that makes this metric advisory rather than a fidelity gate. + +For reference, the figures that need no such caveat in this run are `consensus_T1_vs_T2_K_96`, +`norm_consensus_T1_vs_T2_K_96`, `state_mu_K_96_arm_1`, and `SC_K_96` — the last showing both arms' +silhouette curves above the t-type PCA baseline across almost the whole range. + +### Using it on your own data + +On a different dataset there is no published result to compare against, so **treat this notebook as a +starting point for your own validation rather than a pass/fail gate.** Most of it still applies; the +reference comparison does not. + +**Keep as-is — none of this depends on the dataset.** All 35 `plumbing` checks: that every expected +file exists, that shapes agree between the `.h5ad`, the manifests and the model, that all three +stages consumed the same inputs (Stage 0 lineage), that manifests agree with one another, and that +figures are distinct rather than redrawn over empty categories. This is the part that tells you the +workflow ran correctly, and it is the same question on any dataset. + +**Update to describe your run.** `CONFIG["expected"]` mirrors the inputs you actually passed, so it +has to be re-stated: `n_selected_genes`, `neuronal_classes`, `remove_clusters`, `n_categories`, +`k_select_thr`, `n_selected_cats`, and `kegg_toml_supplied`. The two model-quality floors, +`min_populated_frac` and `min_retained_cpm_frac`, are generic and can stay. Leaving stale ALM/VISp +values here produces failures that say nothing about your run. + +**Retire or replace.** `CONFIG["reference"]` and **Stage 6 — Reference comparison** are entirely +Mouse ALM/VISp: `shape`, `n_ttype`, `model_order 92`, `avg_consensus_min`, `pruning_rounds`, and the +three `ttype_acc_*` values all come from the authors' notebooks. Expect the `fidelity` checks to fail +or be meaningless, and disregard the three-way verdict, which is written around them. The +`ttype_acc_*` checks additionally assume curated reference cell-type labels exist to classify +against; if your data has none, they cannot be computed at all. + +**What to put in their place.** Substitutes for a published reference, roughly in order of effort: + +- **Reproducibility across runs.** Run the same configuration twice and compare `model_order`, + `avg_consensus` and populated-category counts. Bit-identical results are not expected on a GPU, but + a stable `model_order` and consensus is the strongest evidence available without a reference. +- **Internal consistency.** `avg_consensus` at or above `k_select_thr`, + `k_selection_met_threshold` true, and `n_populated_categories` close to `model_order`. These are + already checked and are meaningful on any dataset. +- **Known biology.** If you have curated labels for even a subset of cells, populate the accuracy + checks with your own baseline instead of the reference figures. If you have none, the `[REVIEW]` + figure table above still applies — the healthy-versus-suspicious patterns are properties of the + model, not of this dataset. + +--- + +## Citation and Credit + +MMIDAS is the work of its original authors. If you use these workflows in your research, please cite the original publication: + +> Marghi, Y., Gala, R., Baftizadeh, F. & Sümbül, U. Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. *Nature Computational Science* **4**, 706–722 (2024). https://doi.org/10.1038/s43588-024-00683-8 + +**Authors:** Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, and Uygar Sümbül (Allen Institute for Brain Science). + +**Original code:** These workflows are built directly on the authors' reference implementation, released by the Allen Institute at **https://github.com/AllenInstitute/MMIDAS**. The `mmidas` Python package and the underlying training, evaluation, clusterability, and state-traversal routines that these WDLs call are the work of the repository's authors and contributors — **Yeganeh Marghi ([@ymarghi](https://github.com/ymarghi))** and **Rohan Gala ([@rhngla](https://github.com/rhngla))**. The five workflow steps here mirror the sequence of the original repository's tutorial notebooks (data preparation → training → evaluation → clusterability → state-traversal analysis). + +The scientific method, algorithms, and original software are the intellectual work of these authors and are described in full in the paper and repository above. This workspace only provides WDL wrappers and an example workflow around their published tool; it does not reproduce the publication. Please consult the paper itself for the complete methodology and results, and refer to the [original repository's LICENSE](https://github.com/AllenInstitute/MMIDAS/blob/main/LICENSE) for the terms governing reuse of the MMIDAS software. + +## Additional Resources + +- **Original publication:** Marghi, Y., Gala, R., Baftizadeh, F. & Sümbül, U. *Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.* [Nature Computational Science 4, 706–722 (2024)](https://doi.org/10.1038/s43588-024-00683-8). +- **Original MMIDAS code (Allen Institute):** [github.com/AllenInstitute/MMIDAS](https://github.com/AllenInstitute/MMIDAS) — the reference implementation these workflows are built upon. +- MMIDAS source scripts and Docker image: [warp-tools](https://github.com/broadinstitute/warp-tools) (`3rd-party-tools/mmidas/`). +- WARP repository: [broadinstitute/warp](https://github.com/broadinstitute/warp). +- For Terra-specific documentation and support, see [Terra Support](https://support.terra.bio/hc/en-us). + +### Docker image provenance + +The `mmidas` image bundles two independently-maintained pieces of code: + +1. The **pipeline scripts** (`01_data_prep.py` … `05_state_traversal.py`), which live in + `warp-tools/3rd-party-tools/mmidas/` and are version-controlled and reviewed there. +2. The **`mmidas` Python package** — a fork of + [AllenInstitute/MMIDAS](https://github.com/AllenInstitute/MMIDAS) containing the model itself + (`cpl_mixvae.py`, `eval.py`, `utils/`). Several pipeline behaviours are implemented only here: + the pruning loop and its `min_con` stop condition, `K_selection`, and the `.h5ad` loader that + supplies gene identifiers for KEGG pathway mapping. + +The second piece is installed from a **pinned revision** of +[jessicaway/MMIDAS](https://github.com/jessicaway/MMIDAS), so any published image can be rebuilt +from source by anyone. `docker_build.sh` resolves the tag in `MMIDAS_GIT_REF` (currently `warp-v2`) +to the immutable commit it points at, fetches that commit's release tarball, and records both: + +| Where | What | +| --- | --- | +| Image labels | `MMIDAS_GIT_URL`, `MMIDAS_GIT_REF`, `MMIDAS_SHA` | +| `docker_versions.tsv` | a second column, `@`, next to each image tag | + +To see exactly which model code an image contains: + +```bash +docker inspect --format '{{json .Config.Labels}}' us.gcr.io/broad-gotc-prod/mmidas: +``` + +**To pick up new MMIDAS changes:** commit and push them to the fork, move or add a tag, then +rebuild with `./docker_build.sh --mmidas-ref ` (a full 40-character commit SHA also works). +The build fails before doing any work if the ref does not resolve or the tarball is not fetchable. + +The image the workflows currently point at is +`us.gcr.io/broad-gotc-prod/mmidas:1.0.0-0.1.0-1787578739`, built at `warp-v2`. Images listed in +`docker_versions.tsv` with the revision `unrecorded` predate this pinning and cannot be rebuilt from +source. + +The fork carries four changes on top of upstream `0963ca7`: packaging so the subpackages install, a +headless-plotting fix and a threshold-comparison fix in `K_selection`, the gene-identifier fix in +`load_data` that KEGG pathway mapping depends on, and added per-epoch entropy and per-round consensus +logging (stdout only). The `load_data` fix is a candidate for upstreaming to AllenInstitute/MMIDAS. + +--- + +## Contact Information + +- For workspace questions and feedback, email the Broad pipelines team at warp-pipelines-help@broadinstitute.org. +- You can also contact the Terra team from the Terra main menu. When submitting a request, it is helpful to include your Project ID, workspace name, Bucket ID, Submission ID, and Workflow ID, and any relevant log information. + +--- + +## License + +**Copyright Broad Institute, 2026 | BSD-3** +All code provided in this workspace is released under the WDL open source code license (BSD-3) (full license text at https://github.com/broadinstitute/warp/blob/develop/LICENSE). Note however that the programs called by the scripts may be subject to different licenses. Users are responsible for checking that they are authorized to run all programs before running these tools. + +--- + +## Workspace Change Log + +| Date | Change | +| --- | --- | +| 2026-09-03 | Reviewed the distributed run's figures by eye and documented three that look worse than they are, most importantly that a diffuse `state_mu` cloud is the correct result rather than a sign of collapse. | +| 2026-09-02 | Documented run-to-run variation with two independent runs of the same configuration (`model_order` 96 and 89, `avg_consensus` 0.9075 and 0.9692, bracketing the published 92 and 0.939). Added a section on MMIDAS's multimodal (transcriptomic + electrophysiology) capability, which these workflows do not implement, and what porting it would involve. | +| 2026-08-28 | Clarified that `MMIDAS_output_validation.ipynb` validates against the authors' published Mouse ALM/VISp results, and documented which parts transfer to a different dataset and what to use in place of the reference comparison. | +| 2026-08-27 | `MMIDAS_Train` `1.3.0`: `n_epoch_p` default is now **1,000**, the configuration this workspace's example outputs were produced with (~13.5 h, ~$13). The published analysis used 10,000 (~4.5 days, ~$110); set it explicitly to reproduce that. Corrected the inputs table, which had understated `max_prun_it` and described `min_con` as halting pruning. Time and cost figures are now measured and stated once. | +| 2026-08-26 | Documented dataset-specific tuning (`tau` scaling with `n_categories`, `max_prun_it` bounding reachable `model_order`, runtime/cost scaling, no-resume exposure). Recorded the validated result against the published analysis and where it differs. Added the validation-notebook section. | +| 2026-08-21 | Figure fixes in `MMIDAS_Analyze` (`1.1.2`): confusion matrices row-normalised before plotting, accuracy bar chart widened, state-traversal category labels made unique, highlight palette no longer collides with the background. | +| 2026-08-11 | Corrected training defaults (`tau`, `x_drop`, `max_prun_it`) so the published `model_order` is reachable and the categorical posterior commits at `n_categories = 120`. Pinned the Docker build to a tagged MMIDAS revision so images are reproducible. | +| 2026-08-06 | Fixed KEGG pathway mapping, checkpoint selection in `03a_evaluate.py`, state-traversal category selection, and the silhouette figure. Added review fields to `evaluation_results.json`. | +| 2026-06-24 | Initial MMIDAS example workspace documentation. | diff --git a/pipelines/wdl/mmidas/example_inputs/MMIDAS_Analyze.json b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Analyze.json new file mode 100644 index 0000000000..edec8f6c23 --- /dev/null +++ b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Analyze.json @@ -0,0 +1,17 @@ +{ + "MMIDAS_Analyze.preprocessed_h5ad": "gs://your-bucket/Mouse_ALM-VISp_cpm.h5ad", + "MMIDAS_Analyze.checkpoints_manifest": "gs://your-bucket/results/checkpoints_manifest.json", + "MMIDAS_Analyze.model_tar": "gs://your-bucket/results/model.tar.gz", + "MMIDAS_Analyze.evaluation_results_json": "gs://your-bucket/results/evaluation_results.json", + + "MMIDAS_Analyze.kegg_toml": "gs://your-bucket/KEGG/KEGG.toml", + "MMIDAS_Analyze.htree_file": "gs://your-bucket/data/tree_Mouse_ALM-VISp_2018.csv", + + "MMIDAS_Analyze.n_pca": 100, + "MMIDAS_Analyze.k_fold": 10, + "MMIDAS_Analyze.n_traversal_steps": 50, + "MMIDAS_Analyze.traversal_arm": 0, + "MMIDAS_Analyze.n_selected_cats": 10, + "MMIDAS_Analyze.batch_size": 5000, + "MMIDAS_Analyze.seed": 0 +} diff --git a/pipelines/wdl/mmidas/example_inputs/MMIDAS_DataPrep.json b/pipelines/wdl/mmidas/example_inputs/MMIDAS_DataPrep.json new file mode 100644 index 0000000000..9c3faea77e --- /dev/null +++ b/pipelines/wdl/mmidas/example_inputs/MMIDAS_DataPrep.json @@ -0,0 +1,9 @@ +{ + "MMIDAS_DataPrep.visp_exon_matrix": "gs://your-bucket/mouse_VISp_2018-06-14_exon-matrix.csv", + "MMIDAS_DataPrep.visp_samples": "gs://your-bucket/mouse_VISp_2018-06-14_samples-columns.csv", + "MMIDAS_DataPrep.alm_exon_matrix": "gs://your-bucket/mouse_ALM_2018-06-14_exon-matrix.csv", + "MMIDAS_DataPrep.alm_samples": "gs://your-bucket/mouse_ALM_2018-06-14_samples-columns.csv", + "MMIDAS_DataPrep.genes_rows": "gs://your-bucket/mouse_ALM_2018-06-14_genes-rows.csv", + "MMIDAS_DataPrep.selected_genes": "gs://your-bucket/genes_SS_ALM-VISp.csv", + "MMIDAS_DataPrep.output_basename": "Mouse_ALM-VISp_cpm" +} diff --git a/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.json b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.json new file mode 100644 index 0000000000..fb92c786e1 --- /dev/null +++ b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.json @@ -0,0 +1,23 @@ +{ + "MMIDAS_Train.preprocessed_h5ad": "gs://your-bucket/Mouse_ALM-VISp_cpm.h5ad", + + "MMIDAS_Train.n_categories": 120, + "MMIDAS_Train.state_dim": 2, + "MMIDAS_Train.n_arm": 2, + "MMIDAS_Train.latent_dim": 10, + "MMIDAS_Train.fc_dim": 100, + "MMIDAS_Train.n_epoch": 10000, + "MMIDAS_Train.n_epoch_p": 10000, + "MMIDAS_Train.max_prun_it": 42, + "MMIDAS_Train.min_con": 0.99, + "MMIDAS_Train.tau": 0.005, + "MMIDAS_Train.x_drop": 0.0, + "MMIDAS_Train.batch_size": 5000, + "MMIDAS_Train.seed": 0, + + "MMIDAS_Train.k_select_thr": 0.95, + + "MMIDAS_Train.run_augmenter": false, + + "MMIDAS_Train.train_gpu": 1 +} diff --git a/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.staged_validation.json b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.staged_validation.json new file mode 100644 index 0000000000..c7817dc525 --- /dev/null +++ b/pipelines/wdl/mmidas/example_inputs/MMIDAS_Train.staged_validation.json @@ -0,0 +1,23 @@ +{ + "MMIDAS_Train.preprocessed_h5ad": "gs://your-bucket/Mouse_ALM-VISp_cpm.h5ad", + + "MMIDAS_Train.n_categories": 120, + "MMIDAS_Train.state_dim": 2, + "MMIDAS_Train.n_arm": 2, + "MMIDAS_Train.latent_dim": 10, + "MMIDAS_Train.fc_dim": 100, + "MMIDAS_Train.n_epoch": 10000, + "MMIDAS_Train.n_epoch_p": 1000, + "MMIDAS_Train.max_prun_it": 42, + "MMIDAS_Train.min_con": 0.99, + "MMIDAS_Train.tau": 0.005, + "MMIDAS_Train.x_drop": 0.0, + "MMIDAS_Train.batch_size": 5000, + "MMIDAS_Train.seed": 0, + + "MMIDAS_Train.k_select_thr": 0.95, + + "MMIDAS_Train.run_augmenter": false, + + "MMIDAS_Train.train_gpu": 1 +} diff --git a/pipelines/wdl/mmidas/round_2_validation.ipynb b/pipelines/wdl/mmidas/round_2_validation.ipynb new file mode 100644 index 0000000000..8119860a7f --- /dev/null +++ b/pipelines/wdl/mmidas/round_2_validation.ipynb @@ -0,0 +1,1882 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "487767f3", + "metadata": {}, + "source": [ + "# MMIDAS Pipeline Output Validation\n", + "\n", + "Sanity-checks the outputs of `MMIDAS_DataPrep` -> `MMIDAS_Train` -> `MMIDAS_Analyze`.\n", + "\n", + "Fill in the `gs://` paths in the **Config** cell below with the actual outputs from your run,\n", + "then run all cells top to bottom.\n", + "\n", + "Each check prints one of:\n", + "- `[PASS]` / `[FAIL]` - an automated, threshold-based check\n", + "- `[REVIEW]` - something (usually a figure) that needs a human eyeball rather than a hard threshold\n", + "\n", + "Requires `gsutil` on PATH and authenticated (`gcloud auth login` / `gcloud auth application-default login`),\n", + "plus the packages imported below (`anndata`, `matplotlib`, `numpy`).\n", + "\n", + "---\n", + "\n", + "**What this notebook can and cannot tell you.** Most checks here verify *plumbing*: that each\n", + "task ran, wrote the files it declared, and that the manifests agree with each other. A pipeline\n", + "can pass all of those while producing a scientifically useless model, so the checks below are\n", + "deliberately written to separate the two:\n", + "\n", + "- **Plumbing** — files exist, shapes line up, manifests are mutually consistent, stages consumed\n", + " the same inputs (`Stage 0 -- Lineage`).\n", + "- **Model quality** — `n_populated_categories` vs `model_order`, `avg_consensus` vs\n", + " `k_select_thr`, and t-type classification accuracy against the PCA baseline.\n", + "\n", + "Two traps this notebook is written to avoid, both of which previously produced a green result on\n", + "a run whose model had collapsed to ~10 usable categories out of a reported 111:\n", + "\n", + "1. **`model_order` is not the number of cell types the model found.** It counts categories that\n", + " survived pruning. Pruning removes at most one category per round, so with\n", + " `n_categories = 120` and `max_prun_it = 14` it can only ever land in 106-120 — a\n", + " \"`model_order` < ceiling\" check cannot meaningfully fail. Check `n_populated_categories`.\n", + "2. **Counting output files is not checking them.** Ten state-traversal figures were produced for\n", + " ten categories that had no cells assigned; all ten were byte-identical apart from the title.\n", + " The checks below compare figure contents, not just counts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3134bb7f", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install anndata" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4cbeeecb", + "metadata": {}, + "outputs": [], + "source": [ + "import hashlib\n", + "import io\n", + "import json\n", + "import os\n", + "import pickle\n", + "import subprocess\n", + "import tarfile\n", + "\n", + "import anndata as ad\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from IPython.display import Image, display" + ] + }, + { + "cell_type": "markdown", + "id": "86fdfd89", + "metadata": {}, + "source": [ + "## Config\n", + "\n", + "Fill in the actual `gs://` output paths from your run." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b8746984", + "metadata": {}, + "outputs": [], + "source": [ + "CONFIG = {\n", + " \"dataprep\": {\n", + " \"preprocessed_h5ad\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/87367fdc-36c5-4f9f-bbec-13e350c6cb67/MMIDAS_DataPrep/ba0faecd-69d1-4d94-b316-56486912cd31/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad\",\n", + " },\n", + "\n", + " \"train\": {\n", + " \"evaluation_results_json\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-Evaluate/out/evaluation_results.json\",\n", + " \"checkpoints_manifest\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-TrainMixVAE/out/checkpoints_manifest.json\",\n", + " \"model_tar\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-TrainMixVAE/model.tar.gz\",\n", + " # Array[File] output of MMIDAS_Train.evaluation_figures. Either paste the\n", + " # explicit list of gs:// URIs (from Terra's data table / Cromwell metadata),\n", + " # or give a single gs:// prefix and we'll glob it for *.png.\n", + " \"evaluation_figures\": [ \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-Evaluate/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/consensus_T1_vs_T2_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-Evaluate/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/norm_consensus_T1_vs_T2_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-Evaluate/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/state_mu_K_120_arm_0.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/1a43adb0-85ca-47e6-82ac-5a91990f38b0/MMIDAS_Train/e6ccf407-c4b4-4263-b036-61d0d72cea27/call-Evaluate/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/state_mu_K_120_arm_1.png\" ],\n", + " },\n", + "\n", + " \"analyze\": {\n", + " \"clusterability_manifest\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/out/clusterability_manifest.json\",\n", + " \"clusterability_figures\": [ \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/SC_K_120_20260810.png\", 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\"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/conf_ConsType_pc_arm_1.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/conf_Ttype_lowD_arm_0.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/conf_Ttype_lowD_arm_1.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Clusterability/glob-2cd2b33e6e0f1b5a03d43a48fbb65c23/conf_Ttype_pc.png\" ],\n", + " \"state_traversal_manifest\": 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\"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_20_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_21_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_2_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_3_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_4_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_5_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_6_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_7_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_8_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/s_pc_path_9_K_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_101.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_120.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_24.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_44.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_52.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_86.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_93.png\", \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/glob-fc36854b6867c1581ab159b09dd7e2f4/state_mu_arm_0_c_98.png\" ]\n", + " },\n", + "\n", + " # Optional. classify_manifest.json / clustering_tar are intermediate outputs of\n", + " # the Classify (03b) task -- NOT part of MMIDAS_Analyze's final workflow outputs.\n", + " # If you pull them out of the Cromwell execution directory, this notebook can\n", + " # compute real accuracy/silhouette numbers instead of just eyeballing the\n", + " # classAcc_RF / SC_K_* figures.\n", + " \"classify_optional\": {\n", + " \"classify_manifest\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Classify/attempt-2/out/classify_manifest.json\",\n", + " \"clustering_tar\": \"gs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-Classify/attempt-2/clustering.tar.gz\"\n", + " },\n", + "\n", + " # Expected values -- should match the inputs used for this run.\n", + " \"expected\": {\n", + " # Row count of the selected_genes CSV passed to MMIDAS_DataPrep, minus\n", + " # its header. genes_SS_ALM-VISp.csv is 5033 lines => 5032 genes.\n", + " # Confirm for your own run with:\n", + " # gsutil cat | wc -l\n", + " \"n_selected_genes\": 5032,\n", + " \"neuronal_classes\": [\"GABAergic\", \"Glutamatergic\"],\n", + " \"remove_clusters\": [\"Low Quality\", \"CR Lhx5\", \"Meis2 Adamts19\"],\n", + " \"n_categories\": 120, # MMIDAS_Train.n_categories input (pruning ceiling)\n", + " \"k_select_thr\": 0.95, # MMIDAS_Train.k_select_thr input\n", + " \"n_selected_cats\": 10, # MMIDAS_Analyze.n_selected_cats input\n", + " \"kegg_toml_supplied\": True, # was MMIDAS_Analyze.kegg_toml provided?\n", + "\n", + " # Model-quality thresholds.\n", + " #\n", + " # min_populated_frac: fraction of model_order that must actually have\n", + " # cells assigned. model_order counts categories that survived pruning,\n", + " # which stays high even when the discrete latent has collapsed and\n", + " # every cell lands in a handful of categories.\n", + " \"min_populated_frac\": 0.5,\n", + " # max_ttype_acc_gap: how far the MMIDAS low-D embedding may fall short of\n", + " # the PCA baseline at recovering reference t-types, in accuracy points.\n", + " # This is the one classification comparison that is not near-circular\n", + " # (the ConsType rows classify the model's own labels).\n", + " \"max_ttype_acc_gap\": 0.15,\n", + " \"min_retained_cpm_frac\": 0.10, \n", + " \"min_populated_frac\": 0.5, \n", + " \"max_ttype_acc_gap\": 0.15, \n", + " \"n_selected_cats\": 10, \n", + " \"kegg_toml_supplied\": True\n", + " },\n", + "}\n", + "\n", + "SCRATCH_DIR = \"/tmp/mmidas_validation\"" + ] + }, + { + "cell_type": "markdown", + "id": "4a70f365", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fdb01be8", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(SCRATCH_DIR, exist_ok=True)\n", + "\n", + "CHECKS = []\n", + "REVIEW_ITEMS = []\n", + "\n", + "\n", + "def check(name, passed, detail=\"\"):\n", + " \"\"\"Record an automated pass/fail check.\"\"\"\n", + " CHECKS.append({\"name\": name, \"passed\": bool(passed), \"detail\": detail})\n", + " status = \"PASS\" if passed else \"FAIL\"\n", + " print(f\"[{status}] {name}\" + (f\" -- {detail}\" if detail else \"\"))\n", + "\n", + "\n", + "def review(name, detail=\"\"):\n", + " \"\"\"Flag something that needs a human eyeball rather than a hard threshold.\"\"\"\n", + " REVIEW_ITEMS.append({\"name\": name, \"detail\": detail})\n", + " print(f\"[REVIEW] {name}\" + (f\" -- {detail}\" if detail else \"\"))\n", + "\n", + "\n", + "def gcs_download(gs_path, dest_dir=SCRATCH_DIR):\n", + " \"\"\"Download a single gs:// object and return its local path.\n", + "\n", + " The local cache is namespaced by a digest of the *full* gs:// URI, not by\n", + " basename. Every MMIDAS submission writes files with identical names --\n", + " evaluation_results.json, clusterability_manifest.json,\n", + " Mouse_ALM-VISp_cpm.h5ad, state_mu_arm_0_c_*.png -- so a basename-keyed cache\n", + " silently serves a previous run's file after you repoint CONFIG at a new\n", + " submission, and every check downstream then validates stale data. The\n", + " symptom is subtle: cells that read through this helper show the old run\n", + " while cells using `gsutil cat` directly (e.g. the lineage checks) show the\n", + " new one.\n", + " \"\"\"\n", + " digest = hashlib.md5(gs_path.encode()).hexdigest()[:10]\n", + " local_dir = os.path.join(dest_dir, digest)\n", + " local_path = os.path.join(local_dir, os.path.basename(gs_path))\n", + " if not os.path.exists(local_path):\n", + " os.makedirs(local_dir, exist_ok=True)\n", + " subprocess.run([\"gsutil\", \"-q\", \"cp\", gs_path, local_path], check=True)\n", + " return local_path\n", + "\n", + "\n", + "def gcs_list(prefix, pattern=\"*.png\"):\n", + " \"\"\"List objects under a gs:// prefix (recursive) matching a glob pattern.\"\"\"\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"ls\", os.path.join(prefix.rstrip(\"/\"), \"**\", pattern)],\n", + " capture_output=True, text=True,\n", + " )\n", + " # Surface auth/permission/typo failures instead of silently returning [],\n", + " # which reads downstream as \"this stage produced no figures\".\n", + " if result.returncode != 0:\n", + " raise RuntimeError(\n", + " f\"gsutil ls failed for {prefix} (exit {result.returncode}): \"\n", + " f\"{result.stderr.strip()}\"\n", + " )\n", + " return [line for line in result.stdout.splitlines() if line.strip()]\n", + "\n", + "\n", + "def resolve_paths(value, pattern=\"*.png\"):\n", + " \"\"\"CONFIG figure entries may be a list of gs:// URIs or a single prefix to glob.\"\"\"\n", + " if value is None:\n", + " return []\n", + " if isinstance(value, (list, tuple)):\n", + " return list(value)\n", + " return gcs_list(value, pattern=pattern)\n", + "\n", + "\n", + "def load_json_gcs(gs_path):\n", + " with open(gcs_download(gs_path)) as fh:\n", + " return json.load(fh)\n", + "\n", + "\n", + "def load_h5ad_gcs(gs_path):\n", + " return ad.read_h5ad(gcs_download(gs_path))\n", + "\n", + "\n", + "def extract_tar_gcs(gs_path, subdir):\n", + " local = gcs_download(gs_path)\n", + " dest = os.path.join(SCRATCH_DIR, subdir)\n", + " os.makedirs(dest, exist_ok=True)\n", + " with tarfile.open(local) as tf:\n", + " tf.extractall(dest)\n", + " return dest\n", + "\n", + "\n", + "def gcs_md5(gs_path):\n", + " \"\"\"Return the MD5 that GCS holds for an object, for lineage comparison.\n", + "\n", + " Composite/multipart uploads have no MD5; those report a crc32c instead, so\n", + " fall back to that rather than failing the lineage check outright.\n", + " \"\"\"\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"stat\", gs_path], capture_output=True, text=True,\n", + " )\n", + " if result.returncode != 0:\n", + " return None\n", + " for line in result.stdout.splitlines():\n", + " if \"Hash (md5):\" in line or \"Hash (crc32c):\" in line:\n", + " return line.split(\":\", 1)[1].strip()\n", + " return None\n", + "\n", + "\n", + "def show_image(gs_or_local_path, title=None):\n", + " \"\"\"Display a PNG at its native resolution.\n", + "\n", + " Round-tripping through plt.imread -> imshow -> inline PNG resamples the\n", + " figure down to the Matplotlib canvas size, which shrank 726 px source\n", + " figures to under 200 px and made the [REVIEW] items unreadable. IPython's\n", + " Image embeds the original bytes.\n", + " \"\"\"\n", + " path = (\n", + " gcs_download(gs_or_local_path)\n", + " if str(gs_or_local_path).startswith(\"gs://\")\n", + " else gs_or_local_path\n", + " )\n", + " if title:\n", + " print(f\" --- {title}\")\n", + " display(Image(filename=path))\n", + "\n", + "\n", + "def image_content_hash(gs_or_local_path, ignore_top_frac=0.14):\n", + " \"\"\"Hash a figure's pixels below the title band.\n", + "\n", + " Two per-category figures that differ only in their title are two renderings\n", + " of the same data -- which is what the state-traversal step produced when it\n", + " selected categories with no cells assigned. Comparing file bytes would not\n", + " catch it (the titles differ); comparing pixels below the title does.\n", + " \"\"\"\n", + " path = (\n", + " gcs_download(gs_or_local_path)\n", + " if str(gs_or_local_path).startswith(\"gs://\")\n", + " else gs_or_local_path\n", + " )\n", + " img = plt.imread(path)\n", + " top = int(img.shape[0] * ignore_top_frac)\n", + " body = np.ascontiguousarray(img[top:])\n", + " return hashlib.md5(body.tobytes()).hexdigest()" + ] + }, + { + "cell_type": "markdown", + "id": "b75b83a1", + "metadata": {}, + "source": [ + "## Stage 0 -- Lineage\n", + "\n", + "The three workflows are three separate Terra submissions, so nothing structurally guarantees that\n", + "`MMIDAS_Analyze` consumed the `.h5ad` that `MMIDAS_DataPrep` produced, or the checkpoint that\n", + "`MMIDAS_Train` selected. Matching `n_gene` and `model_order` only shows two JSON files agree on a\n", + "number — two different runs of the same config agree on those too.\n", + "\n", + "This cell reads the `.h5ad` path and checkpoint filename each stage actually logged to `stdout`\n", + "and confirms they are the same object. It needs the Cromwell execution directory for each stage,\n", + "which is the parent of the `call-*` directories in the paths configured above." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ed6f42ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h5ad loaded by each stage (bucket-relative):\n", + " submissions/87367fdc-36c5-4f9f-bbec-13e350c6cb67/MMIDAS_DataPrep/ba0faecd-69d1-4d94-b316-56486912cd31/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad\n", + " <- Evaluate, StateTraversal, TrainMixVAE, TraversalPrep\n", + "\n", + "checkpoint used for inference by each Analyze task:\n", + " cpl_mixVAE_model_before_pruning_2026-08-07-20-30-34.pth\n", + " <- StateTraversal, TraversalPrep\n", + "[PASS] all stages loaded the same preprocessed .h5ad -- 1 distinct path(s): submissions/87367fdc-36c5-4f9f-bbec-13e350c6cb67/MMIDAS_DataPrep/ba0faecd-69d1-4d94-b316-56486912cd31/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad <- ['Evaluate', 'StateTraversal', 'TrainMixVAE', 'TraversalPrep']\n", + "[PASS] the .h5ad the stages loaded is the one configured above -- configured submissions/87367fdc-36c5-4f9f-bbec-13e350c6cb67/MMIDAS_DataPrep/ba0faecd-69d1-4d94-b316-56486912cd31/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad, stages loaded ['submissions/87367fdc-36c5-4f9f-bbec-13e350c6cb67/MMIDAS_DataPrep/ba0faecd-69d1-4d94-b316-56486912cd31/call-DataPrep/Mouse_ALM-VISp_cpm.h5ad']\n", + "[PASS] every Analyze task ran inference with the same checkpoint -- 1 distinct checkpoint(s): cpl_mixVAE_model_before_pruning_2026-08-07-20-30-34.pth <- ['StateTraversal', 'TraversalPrep']\n", + "[PASS] lineage stdout was readable for every consuming task\n" + ] + } + ], + "source": [ + "def _workflow_root(gs_path):\n", + " \"\"\"Strip everything from the /call-/ component onwards.\"\"\"\n", + " idx = gs_path.find(\"/call-\")\n", + " return gs_path[:idx] if idx != -1 else None\n", + "\n", + "\n", + "def _read_stdout(workflow_root, task):\n", + " \"\"\"Fetch /call-/**/stdout, tolerating attempt-N dirs.\"\"\"\n", + " for candidate in (\n", + " f\"{workflow_root}/call-{task}/stdout\",\n", + " f\"{workflow_root}/call-{task}/attempt-2/stdout\",\n", + " f\"{workflow_root}/call-{task}/attempt-3/stdout\",\n", + " ):\n", + " result = subprocess.run(\n", + " [\"gsutil\", \"cat\", candidate], capture_output=True, text=True,\n", + " )\n", + " if result.returncode == 0:\n", + " return result.stdout\n", + " return None\n", + "\n", + "\n", + "def _bucket_relative(path):\n", + " \"\"\"Normalize a gs:// URI and a localized container path to the same key.\n", + "\n", + " Tasks log the *localized* path, e.g.\n", + " /mnt/disks/cromwell_root//submissions/.../file.h5ad\n", + " for what the config names as\n", + " gs:///submissions/.../file.h5ad\n", + " Comparing the two verbatim always fails, so reduce both to the part after\n", + " the bucket name.\n", + " \"\"\"\n", + " for prefix in (\"gs://\", \"/mnt/disks/cromwell_root/\", \"/cromwell_root/\"):\n", + " if path.startswith(prefix):\n", + " rest = path[len(prefix):]\n", + " return rest.split(\"/\", 1)[1] if \"/\" in rest else rest\n", + " return path.lstrip(\"/\")\n", + "\n", + "\n", + "TRAIN_ROOT = _workflow_root(CONFIG[\"train\"][\"evaluation_results_json\"])\n", + "ANALYZE_ROOT = _workflow_root(CONFIG[\"analyze\"][\"clusterability_manifest\"])\n", + "\n", + "# Every task that loads the .h5ad directly.\n", + "H5AD_CONSUMERS = [\n", + " (TRAIN_ROOT, \"TrainMixVAE\"),\n", + " (TRAIN_ROOT, \"Evaluate\"),\n", + " (ANALYZE_ROOT, \"Classify\"),\n", + " (ANALYZE_ROOT, \"TraversalPrep\"),\n", + " (ANALYZE_ROOT, \"StateTraversal\"),\n", + "]\n", + "# Analyze tasks that run inference. Each logs exactly one \"Model \" line,\n", + "# so these are safe to compare against Evaluate's selected checkpoint.\n", + "# TrainMixVAE and Evaluate are excluded: they enumerate every checkpoint.\n", + "CKPT_CONSUMERS = [\n", + " (ANALYZE_ROOT, \"Classify\"),\n", + " (ANALYZE_ROOT, \"TraversalPrep\"),\n", + " (ANALYZE_ROOT, \"StateTraversal\"),\n", + "]\n", + "\n", + "stdouts, unread = {}, []\n", + "for root, task in set(H5AD_CONSUMERS) | set(CKPT_CONSUMERS):\n", + " out = _read_stdout(root, task) if root else None\n", + " if out is None:\n", + " unread.append(task)\n", + " else:\n", + " stdouts[task] = out\n", + "\n", + "h5ad_seen = {}\n", + "for _, task in H5AD_CONSUMERS:\n", + " for line in stdouts.get(task, \"\").splitlines():\n", + " for tok in line.split():\n", + " if tok.endswith(\".h5ad\"):\n", + " h5ad_seen.setdefault(_bucket_relative(tok), []).append(task)\n", + " break\n", + "\n", + "ckpt_seen = {}\n", + "for _, task in CKPT_CONSUMERS:\n", + " for line in stdouts.get(task, \"\").splitlines():\n", + " if not line.startswith(\"Model \"):\n", + " continue\n", + " for tok in line.split():\n", + " if tok.endswith(\".pth\"):\n", + " ckpt_seen.setdefault(os.path.basename(tok), []).append(task)\n", + " break\n", + "\n", + "print(\"h5ad loaded by each stage (bucket-relative):\")\n", + "for path, tasks in h5ad_seen.items():\n", + " print(f\" {path}\\n <- {', '.join(sorted(set(tasks)))}\")\n", + "print(\"\\ncheckpoint used for inference by each Analyze task:\")\n", + "for name, tasks in ckpt_seen.items():\n", + " print(f\" {name}\\n <- {', '.join(sorted(set(tasks)))}\")\n", + "if unread:\n", + " print(f\"\\n(could not read stdout for: {', '.join(sorted(set(unread)))})\")\n", + "\n", + "configured_h5ad = _bucket_relative(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"])\n", + "\n", + "check(\n", + " \"all stages loaded the same preprocessed .h5ad\",\n", + " len(h5ad_seen) == 1,\n", + " f\"{len(h5ad_seen)} distinct path(s): \"\n", + " + \"; \".join(f\"{p} <- {sorted(set(t))}\" for p, t in h5ad_seen.items()),\n", + ")\n", + "\n", + "check(\n", + " \"the .h5ad the stages loaded is the one configured above\",\n", + " configured_h5ad in h5ad_seen,\n", + " f\"configured {configured_h5ad}, stages loaded {list(h5ad_seen)}\",\n", + ")\n", + "\n", + "check(\n", + " \"every Analyze task ran inference with the same checkpoint\",\n", + " len(ckpt_seen) == 1,\n", + " f\"{len(ckpt_seen)} distinct checkpoint(s): \"\n", + " + \"; \".join(f\"{n} <- {sorted(set(t))}\" for n, t in ckpt_seen.items()),\n", + ")\n", + "\n", + "check(\n", + " \"lineage stdout was readable for every consuming task\",\n", + " not unread,\n", + " f\"unreadable: {sorted(set(unread))}\" if unread else \"\",\n", + ")\n", + "\n", + "# Stash for the Train stage, which compares this against evaluation_results.json.\n", + "ANALYZE_CHECKPOINTS = set(ckpt_seen)" + ] + }, + { + "cell_type": "markdown", + "id": "baf95a9e", + "metadata": {}, + "source": [ + "## Stage 1 -- DataPrep (`preprocessed_h5ad`)\n", + "\n", + "Checks: gene count matches the selected gene list, `var_names` carry gene symbols (MMIDAS reads\n", + "gene identifiers from the var index — numeric or missing names silently disable KEGG pathway\n", + "mapping downstream), no NaN/Inf/negative values, the matrix carries signal and satisfies the\n", + "log-CPM bounds, cluster count is plausible, excluded clusters are absent, and `class` only\n", + "contains the configured neuronal classes.\n", + "\n", + "On the CPM row sums: DataPrep normalizes across the whole transcriptome and *then* subsets to\n", + "`selected_genes`, so undoing `log1p` on this matrix recovers the share of each cell's CPM mass\n", + "inside the selected panel — bounded above by 1e6, never equal to it. The checks assert that\n", + "upper bound (a real invariant) and that the retained share clears\n", + "`min_retained_cpm_frac`, which is what would catch a wrong or truncated gene list. The\n", + "pre-subset equality against 1e6 is checked inside `01_data_prep.py`, where the full matrix is\n", + "still available — look for `CPM row sums after inverting log1p` in the DataPrep task log.\n", + "\n", + "Note `n_selected_genes` in the config is the row count of the `selected_genes` CSV minus its\n", + "header, not a fixed number — confirm it for your run rather than trusting the default." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9e8565f7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jupyter/.local/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 22365 × 5032\n", + " obs: 'sample_name', 'sample_id', 'seq_batch', 'sex', 'brain_hemisphere', 'brain_region', 'brain_subregion', 'class', 'subclass', 'cluster', 'confusion_score'\n", + "\n", + "22365 cells x 5032 genes\n", + "obs columns: ['sample_name', 'sample_id', 'seq_batch', 'sex', 'brain_hemisphere', 'brain_region', 'brain_subregion', 'class', 'subclass', 'cluster', 'confusion_score']\n", + "[PASS] gene count matches selected_genes -- got 5032, expected 5032\n", + "first var_names: ['Malat1', 'Vip', 'Npy', 'Ptgds', 'Sst']\n", + "[PASS] var_names look like gene symbols, not positional indices -- first five: ['Malat1', 'Vip', 'Npy', 'Ptgds', 'Sst']\n", + "[PASS] no NaN/Inf values in X\n", + "[PASS] no negative values in X (log-CPM) -- min=0.0000\n", + "\n", + "X: min=0.0000, max=11.7854, mean=3.5911, 78.7% non-zero\n", + "[PASS] X carries signal (1%-90% of entries non-zero) -- 78.72% of entries non-zero\n", + "[PASS] X max is within the log1p(CPM) bound (<= log1p(1e6) ~ 13.82) -- max=11.7854\n", + "[PASS] CPM row sums do not exceed 1e6, and non-empty cells are positive -- max row sum=890063 (limit 1e6); 0 all-zero cell(s)\n", + "CPM mass retained by the 5032 selected genes: min=70.2%, median=83.9%, max=89.0%\n", + "[PASS] retained CPM mass is within [10%, 100%] -- min retained=70.2%, floor=10%\n", + "\n", + "115 unique clusters\n", + "[PASS] cluster count in a plausible range (5-200) -- got 115\n", + "[PASS] excluded clusters are absent from output\n", + "[PASS] obs['class'] only contains expected neuronal classes -- found: {'Glutamatergic', 'GABAergic'}\n", + "\n", + "Cells per class:\n", + "class\n", + "Glutamatergic 11887\n", + "GABAergic 10478\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "adata = load_h5ad_gcs(CONFIG[\"dataprep\"][\"preprocessed_h5ad\"])\n", + "print(adata)\n", + "\n", + "n_cells, n_genes = adata.shape\n", + "print(f\"\\n{n_cells} cells x {n_genes} genes\")\n", + "print(f\"obs columns: {list(adata.obs.columns)}\")\n", + "\n", + "check(\n", + " \"gene count matches selected_genes\",\n", + " n_genes == CONFIG[\"expected\"][\"n_selected_genes\"],\n", + " f\"got {n_genes}, expected {CONFIG['expected']['n_selected_genes']}\",\n", + ")\n", + "\n", + "# var_names must carry gene symbols: MMIDAS's loader reads gene identifiers from\n", + "# the var index, and KEGG pathway mapping in 03c silently maps zero pathways if\n", + "# they are absent or numeric.\n", + "var_names = list(adata.var_names[:5])\n", + "print(f\"first var_names: {var_names}\")\n", + "check(\n", + " \"var_names look like gene symbols, not positional indices\",\n", + " not all(str(v).isdigit() for v in adata.var_names[:50]),\n", + " f\"first five: {var_names}\",\n", + ")\n", + "\n", + "X = adata.X.toarray() if not isinstance(adata.X, np.ndarray) else adata.X\n", + "check(\"no NaN/Inf values in X\", bool(np.isfinite(X).all()))\n", + "check(\"no negative values in X (log-CPM)\", bool(X.min() >= 0), f\"min={X.min():.4f}\")\n", + "\n", + "# min >= 0 also passes for an all-zero matrix, so check that X carries signal and\n", + "# sits in the range log1p(CPM) implies (no single value above log1p(1e6)).\n", + "frac_nonzero = float((X > 0).mean())\n", + "print(f\"\\nX: min={X.min():.4f}, max={X.max():.4f}, mean={X.mean():.4f}, \"\n", + " f\"{frac_nonzero:.1%} non-zero\")\n", + "check(\n", + " \"X carries signal (1%-90% of entries non-zero)\",\n", + " 0.01 <= frac_nonzero <= 0.90,\n", + " f\"{frac_nonzero:.2%} of entries non-zero\",\n", + ")\n", + "check(\n", + " \"X max is within the log1p(CPM) bound (<= log1p(1e6) ~ 13.82)\",\n", + " float(X.max()) <= np.log1p(1e6) + 1e-3,\n", + " f\"max={X.max():.4f}\",\n", + ")\n", + "# Undoing log1p recovers CPM -- but only a *share* of it.\n", + "#\n", + "# DataPrep normalizes to log-CPM across the whole transcriptome (~45.8k genes)\n", + "# and only then subsets to selected_genes, so these row sums measure how much of\n", + "# each cell's CPM mass lands inside the selected panel. They are bounded above by\n", + "# 1e6 and will not equal it. Do not \"fix\" this back to an equality test against\n", + "# 1e6 -- that asserts a whole-transcriptome invariant against a subsetted matrix\n", + "# and fails on a healthy run. The pre-subset equality is checked inside\n", + "# 01_data_prep.py, where the full matrix is still in hand.\n", + "#\n", + "# The upper bound below is a genuine invariant: a subset of a CPM vector cannot\n", + "# exceed the total. It holds whether or not the matrix was subsetted, so it also\n", + "# suits a full-transcriptome bring-your-own .h5ad (fraction ~1.0).\n", + "cpm_row_sums = np.expm1(X.astype(np.float64)).sum(axis=1)\n", + "nonzero_rows = cpm_row_sums > 0\n", + "retained_frac = cpm_row_sums / 1e6\n", + "\n", + "check(\n", + " \"CPM row sums do not exceed 1e6, and non-empty cells are positive\",\n", + " bool(nonzero_rows.any())\n", + " and bool((cpm_row_sums <= 1e6 * (1 + 1e-3)).all()),\n", + " f\"max row sum={cpm_row_sums.max():.6g} (limit 1e6); \"\n", + " f\"{int((~nonzero_rows).sum())} all-zero cell(s)\",\n", + ")\n", + "\n", + "# The fraction is the signal the equality test was reaching for: a wrong or\n", + "# truncated selected_genes list shows up here as a collapse in retained mass.\n", + "if nonzero_rows.any():\n", + " frac_lo = CONFIG[\"expected\"][\"min_retained_cpm_frac\"]\n", + " print(f\"CPM mass retained by the {n_genes} selected genes: \"\n", + " f\"min={retained_frac[nonzero_rows].min():.1%}, \"\n", + " f\"median={np.median(retained_frac[nonzero_rows]):.1%}, \"\n", + " f\"max={retained_frac[nonzero_rows].max():.1%}\")\n", + " check(\n", + " f\"retained CPM mass is within [{frac_lo:.0%}, 100%]\",\n", + " bool((retained_frac[nonzero_rows] >= frac_lo).all()),\n", + " f\"min retained={retained_frac[nonzero_rows].min():.1%}, \"\n", + " f\"floor={frac_lo:.0%}\",\n", + " )\n", + "\n", + "if \"cluster\" in adata.obs:\n", + " n_clusters = adata.obs[\"cluster\"].nunique()\n", + " print(f\"\\n{n_clusters} unique clusters\")\n", + " check(\"cluster count in a plausible range (5-200)\", 5 <= n_clusters <= 200, f\"got {n_clusters}\")\n", + "\n", + " removed = set(CONFIG[\"expected\"][\"remove_clusters\"]) & set(adata.obs[\"cluster\"].unique())\n", + " check(\"excluded clusters are absent from output\", len(removed) == 0, f\"found: {removed}\" if removed else \"\")\n", + "\n", + "if \"class\" in adata.obs:\n", + " classes = set(adata.obs[\"class\"].unique())\n", + " expected_classes = set(CONFIG[\"expected\"][\"neuronal_classes\"])\n", + " check(\n", + " \"obs['class'] only contains expected neuronal classes\",\n", + " classes <= expected_classes,\n", + " f\"found: {classes}\",\n", + " )\n", + " print(\"\\nCells per class:\")\n", + " print(adata.obs[\"class\"].value_counts())\n", + "\n", + "# Stash for cross-stage consistency checks below.\n", + "DATAPREP_N_GENES = n_genes\n", + "DATAPREP_N_CLUSTERS = adata.obs[\"cluster\"].nunique() if \"cluster\" in adata.obs else None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b3ad394", + "metadata": {}, + "outputs": [], + "source": [ + "# look at examples of the adata obs" + ] + }, + { + "cell_type": "markdown", + "id": "5583997b", + "metadata": {}, + "source": [ + "## Stage 2 -- Train (`evaluation_results.json`, `checkpoints_manifest.json`, evaluation figures)\n", + "\n", + "Checks that the gene count matches DataPrep's output, that K-selection actually accepted a model\n", + "rather than falling back, that the categories the model kept are categories it *uses*, and that\n", + "`avg_consensus` meets `k_select_thr`. Figures are shown inline for manual review.\n", + "\n", + "**On `model_order`.** The obvious check — `model_order < n_categories` — is not worth making.\n", + "Pruning removes at most one category per round, so `model_order` is confined to\n", + "`[n_categories - max_prun_it, n_categories]` by construction and the check passes almost by\n", + "definition. It is also insensitive to the failure that matters: a model whose discrete latent has\n", + "collapsed keeps a high `model_order` while assigning every cell to a handful of categories. The\n", + "checks below use `n_populated_categories` instead, which `03a_evaluate.py` computes from the\n", + "per-cell assignments.\n", + "\n", + "If `n_populated_categories` is missing from `evaluation_results.json`, the run predates that field\n", + "and the notebook falls back to deriving it from the Classify pickles further down." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c68d747f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"model_order\": 120,\n", + " \"n_categories\": 120,\n", + " \"n_arm\": 2,\n", + " \"state_dim\": 2,\n", + " \"latent_dim\": 10,\n", + " \"n_gene\": 5032,\n", + " \"selected_model\": \"out/model/cpl_mixVAE_model_before_pruning_2026-08-07-20-30-34.pth\",\n", + " \"summary_pickle\": \"out/summary_performance_K_120_narm_2.p\",\n", + " \"avg_consensus\": 0.02590336823809513,\n", + " \"k_select_thr\": 0.95,\n", + " \"k_selection_met_threshold\": false,\n", + " \"k_selection_suggested_model_order\": null,\n", + " \"n_populated_categories\": 11,\n", + " \"n_populated_categories_per_arm\": [\n", + " 8,\n", + " 11\n", + " ],\n", + " \"collapse_warning\": \"Only 11 of 120 retained categories are populated (arms: [8, 11]). The discrete latent has likely collapsed -- model_order is not a usable estimate of the number of cell types and downstream Analyze results will be dominated by empty categories.\",\n", + " \"figures\": [\n", + " \"out/consensus_T1_vs_T2_K_120.png\",\n", + " \"out/norm_consensus_T1_vs_T2_K_120.png\",\n", + " \"out/state_mu_K_120_arm_0.png\",\n", + " \"out/state_mu_K_120_arm_1.png\"\n", + " ]\n", + "}\n", + "[PASS] n_gene in evaluation_results matches DataPrep gene count -- train n_gene=5032, dataprep n_genes=5032\n", + "[PASS] model_order is at least 2 -- got 120\n", + "[PASS] n_categories matches the configured pruning ceiling -- got 120, expected 120\n", + "[FAIL] K_selection found a model meeting k_select_thr (not a fallback) -- k_selection_met_threshold=False, suggested model_order=None\n", + "[FAIL] evaluation reported no collapse warning -- Only 11 of 120 retained categories are populated (arms: [8, 11]). The discrete latent has likely collapsed -- model_order is not a usable estimate of the number of cell types and downstream Analyze results will be dominated by empty categories.\n", + "[FAIL] populated categories are at least 50% of model_order -- n_populated_categories=11 of model_order=120 (9.2%); per arm=[8, 11]\n", + "[FAIL] avg_consensus meets k_select_thr -- avg_consensus=0.0259, k_select_thr=0.95\n", + "[PASS] k_select_thr matches the configured input -- got 0.95, expected 0.95\n", + "[PASS] Analyze ran inference with the checkpoint Evaluate selected -- evaluate selected cpl_mixVAE_model_before_pruning_2026-08-07-20-30-34.pth, analyze used ['cpl_mixVAE_model_before_pruning_2026-08-07-20-30-34.pth']\n" + ] + } + ], + "source": [ + "eval_results = load_json_gcs(CONFIG[\"train\"][\"evaluation_results_json\"])\n", + "print(json.dumps(eval_results, indent=2))\n", + "\n", + "model_order = eval_results[\"model_order\"]\n", + "n_categories = eval_results[\"n_categories\"]\n", + "avg_consensus = eval_results[\"avg_consensus\"]\n", + "k_select_thr = eval_results[\"k_select_thr\"]\n", + "n_gene = eval_results[\"n_gene\"]\n", + "\n", + "check(\n", + " \"n_gene in evaluation_results matches DataPrep gene count\",\n", + " n_gene == DATAPREP_N_GENES,\n", + " f\"train n_gene={n_gene}, dataprep n_genes={DATAPREP_N_GENES}\",\n", + ")\n", + "\n", + "check(\"model_order is at least 2\", model_order >= 2, f\"got {model_order}\")\n", + "\n", + "check(\n", + " \"n_categories matches the configured pruning ceiling\",\n", + " n_categories == CONFIG[\"expected\"][\"n_categories\"],\n", + " f\"got {n_categories}, expected {CONFIG['expected']['n_categories']}\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Did K_selection accept a model, or fall back?\n", + "#\n", + "# When no checkpoint reaches k_select_thr, 03a_evaluate.py warns on stdout and\n", + "# selects a fallback checkpoint anyway. Nobody reviewing only the JSON sees\n", + "# that, which is why the field below exists.\n", + "# ---------------------------------------------------------------------------\n", + "if \"k_selection_met_threshold\" in eval_results:\n", + " check(\n", + " \"K_selection found a model meeting k_select_thr (not a fallback)\",\n", + " bool(eval_results[\"k_selection_met_threshold\"]),\n", + " f\"k_selection_met_threshold={eval_results['k_selection_met_threshold']}, \"\n", + " f\"suggested model_order=\"\n", + " f\"{eval_results.get('k_selection_suggested_model_order')}\",\n", + " )\n", + " check(\n", + " \"evaluation reported no collapse warning\",\n", + " eval_results.get(\"collapse_warning\") in (None, \"\"),\n", + " str(eval_results.get(\"collapse_warning\") or \"\"),\n", + " )\n", + "else:\n", + " review(\n", + " \"k_selection_met_threshold absent from evaluation_results.json\",\n", + " \"run predates this field -- check the Evaluate task stdout for \"\n", + " \"'K_selection could not find a model meeting thr'\",\n", + " )\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# How many categories does the model actually use?\n", + "# ---------------------------------------------------------------------------\n", + "n_populated = eval_results.get(\"n_populated_categories\")\n", + "if n_populated is None:\n", + " review(\n", + " \"n_populated_categories absent from evaluation_results.json\",\n", + " \"run predates this field -- the Classify-pickle cell below derives it \"\n", + " \"from the ConsType confusion matrices instead\",\n", + " )\n", + "else:\n", + " min_frac = CONFIG[\"expected\"][\"min_populated_frac\"]\n", + " check(\n", + " f\"populated categories are at least {min_frac:.0%} of model_order\",\n", + " n_populated >= min_frac * model_order,\n", + " f\"n_populated_categories={n_populated} of model_order={model_order} \"\n", + " f\"({n_populated / model_order:.1%}); per arm=\"\n", + " f\"{eval_results.get('n_populated_categories_per_arm')}\",\n", + " )\n", + "\n", + "check(\n", + " \"avg_consensus meets k_select_thr\",\n", + " avg_consensus >= k_select_thr,\n", + " f\"avg_consensus={avg_consensus:.4f}, k_select_thr={k_select_thr}\",\n", + ")\n", + "\n", + "check(\n", + " \"k_select_thr matches the configured input\",\n", + " k_select_thr == CONFIG[\"expected\"][\"k_select_thr\"],\n", + " f\"got {k_select_thr}, expected {CONFIG['expected']['k_select_thr']}\",\n", + ")\n", + "\n", + "# The checkpoint Evaluate selected must be the one Analyze ran inference with.\n", + "selected_ckpt = os.path.basename(eval_results.get(\"selected_model\", \"\"))\n", + "if ANALYZE_CHECKPOINTS:\n", + " check(\n", + " \"Analyze ran inference with the checkpoint Evaluate selected\",\n", + " ANALYZE_CHECKPOINTS == {selected_ckpt},\n", + " f\"evaluate selected {selected_ckpt}, analyze used \"\n", + " f\"{sorted(ANALYZE_CHECKPOINTS)}\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "96307fe6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PASS] checkpoints_manifest n_categories matches evaluation_results -- ckpt=120, eval=120\n", + "checkpoints in manifest: 15\n" + ] + } + ], + "source": [ + "ckpt_manifest = load_json_gcs(CONFIG[\"train\"][\"checkpoints_manifest\"])\n", + "\n", + "check(\n", + " \"checkpoints_manifest n_categories matches evaluation_results\",\n", + " ckpt_manifest.get(\"n_categories\") == n_categories,\n", + " f\"ckpt={ckpt_manifest.get('n_categories')}, eval={n_categories}\",\n", + ")\n", + "print(f\"checkpoints in manifest: {len(ckpt_manifest.get('checkpoints', []))}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ff0bac03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4 evaluation figures\n", + "[REVIEW] consensus bubble plot -- expect a strong diagonal of bubbles spanning the full category range. A handful of scattered points means the arms almost never co-assign and only those few categories carry cells\n", + " --- consensus_T1_vs_T2_K_120.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] normalized consensus plot -- expect a bright diagonal; average consensus should be >= 0.95. An almost entirely dark matrix means no reproducible categories were found\n", + " --- norm_consensus_T1_vs_T2_K_120.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state-space scatter -- expect visually separated clusters, not one undifferentiated blob. Count the distinct groups: if there are far fewer than model_order (120), the discrete latent has collapsed\n", + " --- state_mu_K_120_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state-space scatter -- expect visually separated clusters, not one undifferentiated blob. Count the distinct groups: if there are far fewer than model_order (120), the discrete latent has collapsed\n", + " --- state_mu_K_120_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eval_figure_paths = resolve_paths(CONFIG[\"train\"][\"evaluation_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(eval_figure_paths)} evaluation figures\")\n", + "\n", + "for p in eval_figure_paths:\n", + " name = os.path.basename(p)\n", + " if name.startswith(\"consensus_T1_vs_T2\"):\n", + " review(\n", + " \"consensus bubble plot\",\n", + " \"expect a strong diagonal of bubbles spanning the full category \"\n", + " \"range. A handful of scattered points means the arms almost never \"\n", + " \"co-assign and only those few categories carry cells\",\n", + " )\n", + " elif name.startswith(\"norm_consensus\"):\n", + " review(\n", + " \"normalized consensus plot\",\n", + " f\"expect a bright diagonal; average consensus should be >= \"\n", + " f\"{k_select_thr}. An almost entirely dark matrix means no \"\n", + " f\"reproducible categories were found\",\n", + " )\n", + " elif name.startswith(\"state_mu\"):\n", + " review(\n", + " \"state-space scatter\",\n", + " \"expect visually separated clusters, not one undifferentiated blob. \"\n", + " \"Count the distinct groups: if there are far fewer than \"\n", + " f\"model_order ({model_order}), the discrete latent has collapsed\",\n", + " )\n", + " show_image(p, title=name)" + ] + }, + { + "cell_type": "markdown", + "id": "0e4aa85a", + "metadata": {}, + "source": [ + "## Stage 3 -- Analyze: Clusterability (`clusterability_manifest.json` + figures)\n", + "\n", + "Checks `model_order` is consistent with Train's `evaluation_results.json`, then shows the\n", + "classification-accuracy bar chart, silhouette curve, and confusion-matrix heatmaps for review." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cb84bad5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"output_dir\": \"/mnt/disks/cromwell_root/out\",\n", + " \"model_order\": 120,\n", + " \"n_ttype\": 115,\n", + " \"n_arm\": 2,\n", + " \"figures\": [\n", + " \"out/classAcc_RF_K_120.png\",\n", + " \"out/SC_K_120_20260810.png\",\n", + " \"out/conf_Ttype_pc.png\",\n", + " \"out/conf_Ttype_lowD_arm_0.png\",\n", + " \"out/conf_ConsType_pc_arm_0.png\",\n", + " \"out/conf_ConsType_lowD_arm_0.png\",\n", + " \"out/conf_Ttype_lowD_arm_1.png\",\n", + " \"out/conf_ConsType_pc_arm_1.png\",\n", + " \"out/conf_ConsType_lowD_arm_1.png\"\n", + " ]\n", + "}\n", + "[PASS] clusterability model_order matches Train's evaluation_results -- clusterability=120, train=120\n", + "[PASS] clusterability n_ttype matches DataPrep cluster count -- clusterability n_ttype=115, dataprep clusters=115\n", + "[PASS] clusterability n_arm matches Train's evaluation_results -- clusterability=2, train=2\n" + ] + } + ], + "source": [ + "clust_manifest = load_json_gcs(CONFIG[\"analyze\"][\"clusterability_manifest\"])\n", + "print(json.dumps(clust_manifest, indent=2))\n", + "\n", + "check(\n", + " \"clusterability model_order matches Train's evaluation_results\",\n", + " clust_manifest[\"model_order\"] == model_order,\n", + " f\"clusterability={clust_manifest['model_order']}, train={model_order}\",\n", + ")\n", + "\n", + "# n_ttype is the reference cell-type count read from the .h5ad, so it must match\n", + "# what DataPrep wrote -- a mismatch means Analyze ran against different data.\n", + "if DATAPREP_N_CLUSTERS is not None:\n", + " check(\n", + " \"clusterability n_ttype matches DataPrep cluster count\",\n", + " clust_manifest.get(\"n_ttype\") == DATAPREP_N_CLUSTERS,\n", + " f\"clusterability n_ttype={clust_manifest.get('n_ttype')}, \"\n", + " f\"dataprep clusters={DATAPREP_N_CLUSTERS}\",\n", + " )\n", + "\n", + "check(\n", + " \"clusterability n_arm matches Train's evaluation_results\",\n", + " clust_manifest.get(\"n_arm\") == eval_results.get(\"n_arm\"),\n", + " f\"clusterability={clust_manifest.get('n_arm')}, \"\n", + " f\"train={eval_results.get('n_arm')}\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5ea79ec6", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 9 clusterability figures\n", + "[REVIEW] silhouette score curve -- most categories should have positive silhouette scores, and the MMIDAS curves should sit near the t-type reference curve. If the x-axis spans a single value or the legend has one entry per category, the figure is broken rather than the model\n", + " --- SC_K_120_20260810.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] RF classification accuracy bar chart -- read the 't-types' group only: that is the MMIDAS lowD embedding vs the PCA baseline at recovering the reference labels. The 'T Categories' groups classify the model's own labels, so ~99% there is near-circular and not evidence of anything\n", + " --- classAcc_RF_K_120.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_lowD_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_lowD_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_lowD_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_lowD_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_pc_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_pc_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_ConsType_pc_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_ConsType_pc_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_lowD_arm_0.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_lowD_arm_0.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_lowD_arm_1.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_lowD_arm_1.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] confusion matrix heatmap -- expect a tight diagonal (conf_Ttype_pc.png). Large square blocks along the diagonal mean many reference types are collapsing into one predicted category\n", + " --- conf_Ttype_pc.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clust_figure_paths = resolve_paths(CONFIG[\"analyze\"][\"clusterability_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(clust_figure_paths)} clusterability figures\")\n", + "\n", + "for p in clust_figure_paths:\n", + " name = os.path.basename(p)\n", + " if name.startswith(\"classAcc_RF\"):\n", + " review(\n", + " \"RF classification accuracy bar chart\",\n", + " \"read the 't-types' group only: that is the MMIDAS lowD embedding vs \"\n", + " \"the PCA baseline at recovering the reference labels. The \"\n", + " \"'T Categories' groups classify the model's own labels, so ~99% \"\n", + " \"there is near-circular and not evidence of anything\",\n", + " )\n", + " elif name.startswith(\"SC_K_\"):\n", + " review(\n", + " \"silhouette score curve\",\n", + " \"most categories should have positive silhouette scores, and the \"\n", + " \"MMIDAS curves should sit near the t-type reference curve. If the \"\n", + " \"x-axis spans a single value or the legend has one entry per \"\n", + " \"category, the figure is broken rather than the model\",\n", + " )\n", + " elif name.startswith(\"conf_\"):\n", + " review(\n", + " \"confusion matrix heatmap\",\n", + " f\"expect a tight diagonal ({name}). Large square blocks along the \"\n", + " f\"diagonal mean many reference types are collapsing into one \"\n", + " f\"predicted category\",\n", + " )\n", + " show_image(p, title=name)" + ] + }, + { + "cell_type": "markdown", + "id": "2ae4b614", + "metadata": {}, + "source": [ + "### Optional: numeric accuracy/silhouette check\n", + "\n", + "Only runs if you supplied `classify_manifest` / `clustering_tar` in the config above (these are\n", + "intermediate outputs of the 03b Classify task, not part of `MMIDAS_Analyze`'s final outputs).\n", + "\n", + "This is the most informative cell in the notebook, because it reads the numbers the\n", + "`classAcc_RF` / `SC_K_*` figures are drawn from. Two things come out of it:\n", + "\n", + "1. **The t-type accuracy gap.** How much worse the MMIDAS low-D embedding is than the PCA\n", + " baseline at recovering the reference t-types. This is the only non-circular accuracy\n", + " comparison available — the `ConsType` rows classify the model's own labels.\n", + "2. **The populated-category count**, from the side length of the `ConsType` confusion matrices.\n", + " For a healthy run this is close to `model_order`; a 9×9 matrix under a reported\n", + " `model_order` of 111 means 102 categories are empty." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ad5f55d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ttype_classification_K_115_nFeature_100_20260810.p: mean acc=0.909 (+/-0.006), pct categories w/ positive silhouette=95.7%, conf_mat=115x115\n", + "Ttype_classification_K_115_nFeature_10_arm_0_20260810.p: mean acc=0.314 (+/-0.008), pct categories w/ positive silhouette=3.5%, conf_mat=115x115\n", + "ConsType_classification_K_120_nFeature_100_arm_0_20260810.p: mean acc=0.949 (+/-0.007), pct categories w/ positive silhouette=87.5%, conf_mat=8x8\n", + "ConsType_classification_K_120_nFeature_10_arm_0_20260810.p: mean acc=0.989 (+/-0.002), pct categories w/ positive silhouette=100.0%, conf_mat=8x8\n", + "Ttype_classification_K_115_nFeature_10_arm_1_20260810.p: mean acc=0.431 (+/-0.009), pct categories w/ positive silhouette=6.1%, conf_mat=115x115\n", + "ConsType_classification_K_120_nFeature_100_arm_1_20260810.p: mean acc=0.953 (+/-0.004), pct categories w/ positive silhouette=54.5%, conf_mat=11x11\n", + "ConsType_classification_K_120_nFeature_10_arm_1_20260810.p: mean acc=0.995 (+/-0.002), pct categories w/ positive silhouette=90.9%, conf_mat=11x11\n", + "\n", + "t-type recovery: PCA baseline=0.909, best MMIDAS lowD=0.431 (Ttype_classification_K_115_nFeature_10_arm_1_20260810.p), gap=0.479\n", + "[FAIL] MMIDAS lowD recovers t-types within 0.15 of the PCA baseline -- PCA=0.909, MMIDAS=0.431, gap=0.479 (limit 0.15)\n", + "\n", + "populated categories implied by ConsType confusion matrices: [8, 11] -> using 11\n", + "[FAIL] ConsType confusion matrices span at least 50% of model_order -- 11 populated of model_order=120 (9.2%)\n", + "[PASS] evaluation_results n_populated_categories agrees with the ConsType confusion matrices -- evaluation_results=11, ConsType=11\n" + ] + } + ], + "source": [ + "classify_cfg = CONFIG[\"classify_optional\"]\n", + "\n", + "if classify_cfg[\"classify_manifest\"] and classify_cfg[\"clustering_tar\"]:\n", + " classify_manifest = load_json_gcs(classify_cfg[\"classify_manifest\"])\n", + " clustering_root = extract_tar_gcs(classify_cfg[\"clustering_tar\"], \"clustering\")\n", + "\n", + " # name -> (mean accuracy, pct positive silhouette, confusion-matrix size)\n", + " metrics = {}\n", + " for pickle_path in classify_manifest[\"pickles\"]:\n", + " local_pickle = os.path.join(clustering_root, \"clustering\", os.path.basename(pickle_path))\n", + " if not os.path.exists(local_pickle):\n", + " print(f\" (skipping, not found locally: {os.path.basename(pickle_path)})\")\n", + " continue\n", + " with open(local_pickle, \"rb\") as fh:\n", + " data = pickle.load(fh)\n", + " acc = data[\"acc_T_adj\"]\n", + " sc_flat = np.concatenate([np.atleast_1d(s) for s in data[\"sc_T\"]])\n", + " conf = np.asarray(data[\"conf_mat\"])\n", + " name = os.path.basename(pickle_path)\n", + " metrics[name] = {\n", + " \"acc\": float(acc.mean()),\n", + " \"acc_sd\": float(acc.std()),\n", + " \"pct_pos_sc\": float(100 * (sc_flat > 0).mean()),\n", + " \"n_cat\": int(conf.shape[0]),\n", + " }\n", + " print(\n", + " f\"{name}: mean acc={acc.mean():.3f} (+/-{acc.std():.3f}), \"\n", + " f\"pct categories w/ positive silhouette={100 * (sc_flat > 0).mean():.1f}%, \"\n", + " f\"conf_mat={conf.shape[0]}x{conf.shape[1]}\"\n", + " )\n", + "\n", + " # ----------------------------------------------------------------------\n", + " # The ConsType rows classify the model's own labels, so their ~99% accuracy\n", + " # is near-circular and says nothing about model quality. The one meaningful\n", + " # comparison is how well each embedding recovers the *reference* t-types.\n", + " # ----------------------------------------------------------------------\n", + " def _pick(prefix, contains=None):\n", + " for name, m in metrics.items():\n", + " if name.startswith(prefix) and (contains is None or contains in name):\n", + " return name, m\n", + " return None, None\n", + "\n", + " pca_name, pca = _pick(\"Ttype_classification\", \"nFeature_100\")\n", + " lowd = {n: m for n, m in metrics.items()\n", + " if n.startswith(\"Ttype_classification\") and \"nFeature_100\" not in n}\n", + "\n", + " if pca and lowd:\n", + " gap_limit = CONFIG[\"expected\"][\"max_ttype_acc_gap\"]\n", + " best_lowd_name = max(lowd, key=lambda n: lowd[n][\"acc\"])\n", + " best_lowd = lowd[best_lowd_name]\n", + " gap = pca[\"acc\"] - best_lowd[\"acc\"]\n", + " print(\n", + " f\"\\nt-type recovery: PCA baseline={pca['acc']:.3f}, \"\n", + " f\"best MMIDAS lowD={best_lowd['acc']:.3f} ({best_lowd_name}), \"\n", + " f\"gap={gap:.3f}\"\n", + " )\n", + " check(\n", + " f\"MMIDAS lowD recovers t-types within {gap_limit:.2f} of the PCA baseline\",\n", + " gap <= gap_limit,\n", + " f\"PCA={pca['acc']:.3f}, MMIDAS={best_lowd['acc']:.3f}, gap={gap:.3f} \"\n", + " f\"(limit {gap_limit:.2f})\",\n", + " )\n", + " else:\n", + " review(\n", + " \"could not locate both Ttype PCA and Ttype lowD pickles\",\n", + " \"skipping the t-type accuracy comparison\",\n", + " )\n", + "\n", + " # ----------------------------------------------------------------------\n", + " # Derive the populated-category count from the ConsType confusion matrices.\n", + " # Their side length is the number of categories cells were actually assigned\n", + " # to, which is the ground truth for the collapse check above (and the only\n", + " # source of it for runs predating n_populated_categories).\n", + " # ----------------------------------------------------------------------\n", + " cons_sizes = {n: m[\"n_cat\"] for n, m in metrics.items()\n", + " if n.startswith(\"ConsType_classification\")}\n", + " if cons_sizes:\n", + " derived_populated = max(cons_sizes.values())\n", + " print(f\"\\npopulated categories implied by ConsType confusion matrices: \"\n", + " f\"{sorted(set(cons_sizes.values()))} -> using {derived_populated}\")\n", + " min_frac = CONFIG[\"expected\"][\"min_populated_frac\"]\n", + " check(\n", + " f\"ConsType confusion matrices span at least {min_frac:.0%} of model_order\",\n", + " derived_populated >= min_frac * model_order,\n", + " f\"{derived_populated} populated of model_order={model_order} \"\n", + " f\"({derived_populated / model_order:.1%})\",\n", + " )\n", + " if n_populated is not None:\n", + " check(\n", + " \"evaluation_results n_populated_categories agrees with the \"\n", + " \"ConsType confusion matrices\",\n", + " derived_populated == n_populated,\n", + " f\"evaluation_results={n_populated}, ConsType={derived_populated}\",\n", + " )\n", + "else:\n", + " print(\n", + " \"classify_manifest / clustering_tar not provided -- skipping numeric accuracy/silhouette \"\n", + " \"checks. Relying on the figures above for a visual review instead.\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "64177684", + "metadata": {}, + "source": [ + "## Stage 3 -- Analyze: State Traversal (`state_traversal_manifest.json` + figures)\n", + "\n", + "Checks `model_order` consistency, that the categories the traversal was run for actually have\n", + "cells assigned to them (and were picked largest-first rather than by index), that KEGG pathway\n", + "mapping produced something when a `kegg_toml` was supplied, and that the per-category figures\n", + "differ from one another rather than being one plot rendered ten times under different titles." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "bd5dfbb5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "InvalidUrlError: Unrecognized scheme \"\tgs\".\n" + ] + }, + { + "ename": "CalledProcessError", + "evalue": "Command '['gsutil', '-q', 'cp', '\\tgs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/out/state_traversal_manifest.json', '/tmp/mmidas_validation/710b01fbe6/state_traversal_manifest.json']' returned non-zero exit status 1.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m state_manifest \u001b[38;5;241m=\u001b[39m \u001b[43mload_json_gcs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mCONFIG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43manalyze\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstate_traversal_manifest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(json\u001b[38;5;241m.\u001b[39mdumps(state_manifest, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m 4\u001b[0m check(\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_traversal model_order matches Train\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms evaluation_results\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m state_manifest[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_order\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m model_order,\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_traversal=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstate_manifest[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_order\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, train=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_order\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 8\u001b[0m )\n", + "Cell \u001b[0;32mIn[14], line 68\u001b[0m, in \u001b[0;36mload_json_gcs\u001b[0;34m(gs_path)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_json_gcs\u001b[39m(gs_path):\n\u001b[0;32m---> 68\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[43mgcs_download\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgs_path\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;28;01mas\u001b[39;00m fh:\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m json\u001b[38;5;241m.\u001b[39mload(fh)\n", + "Cell \u001b[0;32mIn[14], line 38\u001b[0m, in \u001b[0;36mgcs_download\u001b[0;34m(gs_path, dest_dir)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(local_path):\n\u001b[1;32m 37\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(local_dir, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 38\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgsutil\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-q\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgs_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m local_path\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:526\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 524\u001b[0m retcode \u001b[38;5;241m=\u001b[39m process\u001b[38;5;241m.\u001b[39mpoll()\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m retcode:\n\u001b[0;32m--> 526\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(retcode, process\u001b[38;5;241m.\u001b[39margs,\n\u001b[1;32m 527\u001b[0m output\u001b[38;5;241m=\u001b[39mstdout, stderr\u001b[38;5;241m=\u001b[39mstderr)\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m CompletedProcess(process\u001b[38;5;241m.\u001b[39margs, retcode, stdout, stderr)\n", + "\u001b[0;31mCalledProcessError\u001b[0m: Command '['gsutil', '-q', 'cp', '\\tgs://fc-a4e31a4b-8622-422a-94fc-fc9899e4546c/submissions/4a93ff38-fbd7-4c34-8073-9bda9770a2e5/MMIDAS_Analyze/eb571efa-2470-4a43-8a65-fc62644084ed/call-StateTraversal/out/state_traversal_manifest.json', '/tmp/mmidas_validation/710b01fbe6/state_traversal_manifest.json']' returned non-zero exit status 1." + ] + } + ], + "source": [ + "state_manifest = load_json_gcs(CONFIG[\"analyze\"][\"state_traversal_manifest\"])\n", + "print(json.dumps(state_manifest, indent=2))\n", + "\n", + "check(\n", + " \"state_traversal model_order matches Train's evaluation_results\",\n", + " state_manifest[\"model_order\"] == model_order,\n", + " f\"state_traversal={state_manifest['model_order']}, train={model_order}\",\n", + ")\n", + "\n", + "n_selected_cats = state_manifest[\"n_selected_cats\"]\n", + "selected_c = state_manifest[\"selected_c\"]\n", + "\n", + "# Counting entries in selected_c only confirms the list has the requested length.\n", + "# It passed on a run where all ten selected categories were empty, so the checks\n", + "# below look at what those categories contain.\n", + "check(\n", + " \"state_traversal ran for the requested number of categories\",\n", + " len(selected_c) == n_selected_cats or n_selected_cats == 0,\n", + " f\"selected_c has {len(selected_c)} entries, n_selected_cats={n_selected_cats}\",\n", + ")\n", + "\n", + "check(\n", + " \"n_selected_cats matches the configured input\",\n", + " n_selected_cats == CONFIG[\"expected\"][\"n_selected_cats\"]\n", + " or CONFIG[\"expected\"][\"n_selected_cats\"] == 0,\n", + " f\"got {n_selected_cats}, configured \"\n", + " f\"{CONFIG['expected']['n_selected_cats']}\",\n", + ")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Are the selected categories actually populated?\n", + "# ---------------------------------------------------------------------------\n", + "sel_counts = state_manifest.get(\"selected_c_n_cells\")\n", + "if sel_counts is None:\n", + " review(\n", + " \"selected_c_n_cells absent from state_traversal_manifest.json\",\n", + " \"run predates this field -- the figure-content check below is the only \"\n", + " \"guard that the selected categories were not empty\",\n", + " )\n", + "else:\n", + " print(f\"\\ncells per selected category: \"\n", + " + \", \".join(f\"{c}={n}\" for c, n in zip(selected_c, sel_counts)))\n", + " check(\n", + " \"every selected category has cells assigned to it\",\n", + " all(n > 0 for n in sel_counts),\n", + " f\"empty categories: \"\n", + " f\"{[c for c, n in zip(selected_c, sel_counts) if n == 0]}\",\n", + " )\n", + " check(\n", + " \"selected categories were ranked by size (largest first)\",\n", + " list(sel_counts) == sorted(sel_counts, reverse=True),\n", + " f\"counts in listed order: {list(sel_counts)}\",\n", + " )\n", + "\n", + "n_pop_state = state_manifest.get(\"n_populated_categories\")\n", + "if n_pop_state is not None:\n", + " min_frac = CONFIG[\"expected\"][\"min_populated_frac\"]\n", + " check(\n", + " f\"state_traversal sees at least {min_frac:.0%} of active categories populated\",\n", + " n_pop_state >= min_frac * state_manifest.get(\"n_active_categories\", model_order),\n", + " f\"{n_pop_state} populated of \"\n", + " f\"{state_manifest.get('n_active_categories', model_order)} active\",\n", + " )\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# KEGG pathway figures\n", + "#\n", + "# n_pathways == 0 with a kegg_toml supplied means gene-name lookup failed, not\n", + "# that the pathways were empty -- MMIDAS's loader read gene identifiers from the\n", + "# wrong AnnData attribute and silently mapped nothing.\n", + "# ---------------------------------------------------------------------------\n", + "n_pathways = state_manifest.get(\"n_pathways\", 0)\n", + "if CONFIG[\"expected\"][\"kegg_toml_supplied\"]:\n", + " check(\n", + " \"KEGG pathways were mapped (kegg_toml was supplied)\",\n", + " n_pathways > 0,\n", + " f\"n_pathways={n_pathways} -- check the TraversalPrep log for \"\n", + " f\"'0 genes' or 'KEGG: 0 pathways'\",\n", + " )\n", + " check(\n", + " \"pathway figures were produced\",\n", + " len(state_manifest.get(\"pathway_figs\", [])) > 0,\n", + " f\"{len(state_manifest.get('pathway_figs', []))} pathway figures\",\n", + " )\n", + "else:\n", + " print(f\"\\nkegg_toml not supplied -- n_pathways={n_pathways} as expected.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f122bb53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 37 state traversal figures\n", + "37 distinct figure contents across 37 figures\n", + "[PASS] state traversal figures are not duplicates of each other\n", + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_101.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_120.png\n" + ] + }, + { + "data": { + "image/png": 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hTMCd/aJRKqFSlkK8c5+oiiBAJpfDysYWljY2sLC04ri1lkRRhKq4GBqVEhqNGgIEiKIIrUYDUae9dX2AW8cVOSwsrSC3sIAgl0PUiVCWKKBVqfTtyttCuLWfCDJY2tjA2s4O1nb27JN7UN3YRmYhh7JIAVVpMbRqDXS6sn1EEATILSxhYWUFK1s7WNna8vf9AMr3DbVKCXVpKbQatf76rVqCAAjQ70cyuRxyuQWs7Oz0+w+PTbVT6ZygUkKn1QGiDmW/ZAEyuQwWlla3jllqiDqxbCx7x/FIkMn0v3v2wf2pdDyCAI1GDVGng7b83AFRv4+Uj63kFhawsLaGjV0DWFhZcVz6gG7vB7Wy/NikgSAAok4su44G/rteuDWHAUA/brWwsoKllTWsGzSEjP1RrduPQerSEv35tuyYIodMJoMgKzvWizodZHI5ZBaWwK0xlE6nhajVlk9r3BoXySC3tID8VjsAFY5XMrmc54paEnU6FBcWQKlQQKNWlZ2b72U1+VvjWrmFBWwbNrp1PS03XMAm5r95JDW0ajXUKiVUxcU1j5Gqc+s8bW1rh0bNmnGOtRZEUYSqpASqkmL9HFLZvLWu5nsLVbltXsPSyhoW1tZl/atScV7vPul0OpQUFqCkoABajfrux6Zbk0gyufzW3LcVbOztYWnDa7n7IYoiivPzUFJUCJ1Gox8H3b0fBMgtLGBtZ89r6Vqqcm5VFKEqLYVWqwZ093hAunX/08LaGhYWlrC0sTXZfnjgBI7Ro0fjmWeewcyZM9G4ceO6iKla+fn52LBhAzZv3owLFy4Y9LMI0Gm1uHH2FBKvXERKVASKC/KhUZaWXQTqdIBOBwgyyOQyCHI5LC3LLrIbNWsO9y7d0N5vIBzd3GFpZS31j1Kv6bRaxIWfQ1ZiAnLTUmFhaQXbho1QlJONlKhrKMzJhkZZes/vK8jkaNDUAe38+qJdn35w9e4MmYwXGbWhVimRk5IMRV5O2SSHTIbivDxo1WoUF+Yh7+ZN5Geko6SoEEpFEWRyednNi/JJQp0IQS4DRB10oogGTRzh6t0Rw2bMgoWlldQ/3kPn9n0k52YKlIoi6LRaKPJzoSwohOpWkpNMbgELays0bt4Cjq5ucG7jifb9B8Laxk7/PqlREchJTYaiIA8lBQUQdToIMhlsGzWCfaMmcGjlCpcOHbmv3Ob231t+dgaSr1xGQXYmSgsLodVpIWo0tXsjQYCFtQ3k8rJJLNtGTdDQwRFyK0u06dYLXYaP4vniLpKuXsaVfw8gKeLqfxNQ2v8SKu/GwtoaLTt0hH2jJrBt0LBsUp1//9Wq7vysyMtFUW4Ocm8mQ5FfAE1p8X8XenchvzXRIZPJIIgirOzt4dnbD4Ofeo4TgbVU3i83b1xH8rUrKMzJQlFeLnQqVe3fRCaDpbUNmjR3hkuHjnDv3A3t+vRjH9xFSsRVpERdQ+7NFDR2bomM2BvIz0xHcWEBtCo1xPKEPp0OMrkM8ls3FXRaDZSKYmjVKv1kSFlSX1lyn8xCDkEQYGlddsPUpb03ugwbBfcu3SX+ieu/6sY2AFCUl42UyIiycdMd52pBJocgK0vysG3UEE2cW6JNt15o2tIFjZo5w6GVK8/JtaRWKRFx/AiSIy8j7cYNFGZnQqNU3tN7yCwtIYiAbaPGcGrtAfvGjdG0pQuc3FrDo0dvHptqUOE6ITX51pyFCkV52dAolfoHUXS6W4kbIgDdHcl+5TdLrSxv3ZCwgW2DBmjX2w/NPTzZB7VU3fFIkZcLZbECqpIS2DVsBI1GjeL8PBQX5EOrUkEUddDpyh5UsbK1Q3MPT7Tr5QtHN3eOS+/Dnf2gyMlGcuQ1FOfn3UoUKLt2kFlaQaf+L9lMkMv0rxdFEXK5HHJLKzRxbgHntu3Qof9guHbknNKdbj8GJUdeRWlREbRqFewbN4HMwhKlRYVQ5OXqkyvVKiWUxcUQtVroxFvzqjIZZDI5ZBZyyG7dkNbptGXXC3I5rG3tYG3fEE5u7nBo2Qo6nQ7F+XnQqFU8V9yFRqVCyLYtuB4agsKsTP0No/shyOWwadAIzh6eaN2tO3o8Og4WFpzfq075sSgrORFpsdEoyEiHqliBrJQkaO/luq0KFja28OrTD4/Mms051hpoVCqc27MDsRfOIfdmMjQqVdncdW0eeqiOIMDSxgaNmzVH66494eDiitKiIuTcTIaLlzc6Dh7Ga4ha0Gm1SLx6ERf270FyxBWoSopr/VpBJoOVrR0sLK1g16QpWni2g6ePL88D90in1SLy1HGcCvoNhZnp0GlFCIJYIVnybuRW1nBq5QpH99boPmocmrVuw7//KlQ1t2pla4e48HPITU0pS166T3IrK9g2bIwGTZvCoZUbuo8ag2atPUyqHx44gaOkpARr167F5s2b8cQTT+Cpp56Cu7t7XcSml5iYiC1btmDbtm0oKSmp0/emqum0WoQf3Iu4ixdwMzoCGqWq6kxAUQedRgdoNNAqlYAgg1KhQF76TWTGx6NdHz84uvLCuyapURFlB7CbKXBwcYOVnR2ykxKQnZpUNgGlvr+BrajTojA7E5f/OYjMuHh49vZF1xGPwtrWro5/AtNR1aSTRqlETmoyVCXFKCksAEQRgoUFShVFUJeWll1w3+7WBKBMJkBuaQlAQH5mGkRRh7O7guE3aQoHVPeofB/JSU0GUJbBn52cCI1KCa1WB+hEiNBBVVoMdWlJ2THoZipyUpKRERcDL7/+cOnQEQmXLlQYLNg1bgK5hQW0Gg0UOTnIT09DSVEBlMUKDnxvKR9kZcbHISniCrKSElFcmA/czwWfKEJTWgINyi44lCUlKCnM11cesLCyQuehI3iuqIZaWYrYsDNIjY6CUlEEtVJ5z5NPGqUSSZfCYdu4Mdy79EAzt9b8+69BdefnkoJ85GemQ6NWQ60svad+0KqU0Kr+u7GnLClBzNkzEDVaDHr6OU4C1kLS1Uu4dOgvZCUnQa0sRUl+3r2/iU4HdUkxMhPjkZ+ZgbQb15EWcx1+/k/qk/6oIrWyFDmpychNS0XDZs5IunIROWmpKC0qLCu+dOtJLlEUy6rTaMqOOcqiwirfT9TpIEIHaAGtWoCljTU0t5IM0mKiYd/EAS3be5vUhXddu30iJDslCarSEiiLi5GXcRNFWVk1HptEnRaiDtBpNCjMLEVRTg6yEhNg36QJ7Bo3gYOLK5rfkQhLFZVfN2QmxiEmPAw3I69CdZ9zFTp12aSVIj8XNtkN0cLTC7mpKQAAazt7uHbsUmdxm5rbz9VyuQVUxcXITU+FVqXSVw+9K1GEKGqhKdVCU1oKmVwBnUaD6NBTZU/6sg/uqqqJWZsGDVCUl4us5AQUZWffetpXhE4nQoAICLKyJ0lv3djWabUoLihA8rUryE9PQwtPL6TH3UDXYY/AinMYtVJVPxRm30pmUpYly2jLnzAtvWMMe8euorlVHSUrKREF2ZkozM6GslgBz16+vE64TfkxKDs5ERaWVhBkAho3d4aFlXVZolJhPpQlxVAWK6q/aarTQafTQVfNDQyNUgWNSgVRp4W6tBTuXbqjQVOHspumqUkAeK6oilqlxMnfNyPy5FEUFxY82E1rAKJWi5L8XCRdu4yCzHRkxMdi5POvwsqGFQfuVH4syoiLxY2L56EqVUKjUkKRm43S4hpuVJcfk+7yBLWyqBCXTxxBXl4OJr01D7YNGtVh9KZBp9Xi3J4diDp1AhmpybcS7bUQ70xivVMt+kCpVqNUqYZObgGLBg1hYWkNuYUlFAV5yElJhrOHZx3+JKZHp9Ui5nwoLh7Yi5ToSGhK7+2hXVGng7K4GLAXUJSThXhFIUqLipCdnITW3XoyEb8Goiii+NYxKCXiKs7s2YGsm6ll9zwF4dazceLtLyj7/+r2B40GSbE3kJudjay0NLTr3Reu7bzQyrsT57Zvc/v1WtOWrtDJ5YgKOYasmzehKr3L9XMt+qBEqUR+bg6yMzKQlXYTXr37opUJ9cMDJ3BMnz4dv/32G4qLi/HTTz/h559/Ru/evTF27FiMHDkSTk5O9/W+WVlZOHToEPbt24ezZ88CKNvJLCwsMG3atAcNu4LTp0/jxx9/xJkzZwAAly5dwjfffIOXXnoJdnbmd6Go02oRffYUEi6FI/naZf1TEbUi6qDT6qAsKkJy1FWolSVo092HN4SqcftkuIOLG6zt7XEzIQ65qSnISE1BabFCP6FXpdoMrDQaxF67hNzsDGSkpKDroCEmcwCrS3dOdsjlZTf2c1KTUZCZjpLCgrKSl0DlpI3b3ZoA1OoArVZb9nsWgPyMdESdPgHbRo3QeehIDqZqQa1SIiMuFhEhR5ERewM6rRalCkXZ5IeoK7v4KC99fNu+INdpobOyRm5qMkoLC1GUl4OrR/+BVqdFcV4eHF3d4dDSpawc6S2cAKlaalQEMuJiEXX6JPIz0+75wqI6ok4HUacrK50pilDHxcLavgHsGjfmuaIaGfGxSLkeiaKcnLKn2B9ASX4+bpw9BUtrazi3bce//yrceX62adAAuTdTUJSTjcKcLKhLlSjKyb7vJ7jKadUq5GWkIe5iGOSWlugfOA0WVkziqE5BViYO//oTspKToFEp7z4RW6sJqHwUFhYgJysTedk56D5sJNw6d+U46Q45qSkoystBaVERCjIzkJmUgFJF0a1Slw+2HwBl5TQFlRqwt4dYrEB2aiIy4mPRqn3HugjfJKVGRSDtRjTiwsNQXJCP4oK8+36iUdRqUVKQD1VJMRR5echJSUFO6n+JsK6dunCfuM3tCa4xYWeQER9beVmz+yBqtchOSYR9VGO4deqG3JupsG3QCM6eXrx2qMLt5+pGTs5IvHoRGYnx0KpVD3SjTqfVQJGbDZWyFNfPnmYf1EJK5DUkXY9CXloqGrdwQWlRIW5evYzcmykoKSqq/DBQDednpUqFEmUpcjIzcDMhDmnJSfDy8YNnj16Qyx94CtMklVcQjb94Hsk3oqHIz4VDKzfYN2mKlNgbyM/NgyjqoFaWVKwaV8ubpcXKUpRcvQKdTA4rWzu0ZoUsABWPQTb2DaEqKSkr521ljaLcHBRmZ6KksLDsyeoHuGbQqlUoLQaUxQoocssqAbp37oYGDo5wcHFDzs1kHqduo9NqkRJ5DfERV3Dp+BEU5WTd/UW13BcAABoNlKnJ0KjVOLJpA0Y+/wrnL+6QGhWBzIR4zHh3HqISkw33QX/8Ce/1m/DXnj/hxrFqBYlXLiLuwjl8uuk3xGVkG/CTVgIAunVojxX/exeKvBwATOC4052JA9dOHEPi9esoLVZUfX6ozTxGngqChQUs5JYoKghDXnrZzfBGjs1YXbcKoihi4MCBCAkJMejndPPugODNP6Ftzz48N6DiWKlpS1e8/OEnCL92zeCfa0r98MBXPx988AECAwOxdOlSHD9+HKIo4uzZszh79iw+/fRTtG3bFj4+PujQoQPatm2LFi1aoEmTJrC1tYUoiigtLUVubi7S09MRGxuLqKgohIWFITY2Vv8Z5RcYgwcPxty5c+Hl5fWgYett3rwZixYtgiiKaNiwIQBAqVRizZo1OHjwIH799Vc0adKkzj6vvhJFEfl5uchJTUHSlYtIiriKlKgIaFU13Ky7y8lEWVSEpBs3oFCUoK2yFFoI8OrVxyTXIrpfOakpUBTkwcLSCtb29nj2rXdw8VqkQT+zk6cHdvz6C9r5+D30B7C6dHs2YBNnF2TeTEFmUgLybqZCWVpcVuWhukSmGvcFtf57quQkhB8+hOLiEri178BEmmqUX3Anx0Qj/vIFZCcmQFVaeqvihqb6NUtv64dilQoWVtZQKEqQk5UJuaUFIIqwb+oIbWoy8rOzYdekCZzdW0N+qw9sGjTgBMht1MpSZCTE4fzhA8jPyqxd1Y17mfgAAI0GtgIAQYashHg4tnJl8kAVdFotrh37FxnxMbV7krQWtCoVrh45BEVeLjx6+PDv/w63n59tGjSARq1CVloaMhLjUJJfgFJF1VUF9O5xX0hLToZocQENHBzRa8zjDxi96Sk/L4wcOxbXDTkJiF/RydMDu37/1SQu9OpSYU4W0mNuQFmiQHZSAvLz82t/M6LW+4MGSo0aVtZ2yM/IQHpMNBM4qqEsViD6zElEhYaULal1H8stVkWrVkMpKmBt3wCZiQkoKSiEVquBWlnKBMvbpEZFIDM+DnGXLiAlLg4aVS2XTKnNvqDRIPF6FCwbN4EAAUX5uXyasRq3n6tzbyYjMyEOmrs9xXUP1MUK5KfdRMLVS2jeth1atmtfZ+9tSlSlJXh8ypMGn4zt1b0bQs+FQW7BJI5yFZe6zMQLc9/H9aQUA35iELqs/wlhl6/AytrGgJ/zcLj9GCRChLq0BJY2tsjNyoQiLxeF2TlQq+sm4RgaDSCToUSpguLWQxAOLVvBvklTPvl+G51Wi9gLZzH56WdxOeq6QT/Lo5kDFtjYIC48DJ4+vgb9rIdJ+c267JQkCDKZwT9PVVKCa8f+hYZjVT21shQJl8ORn5n+wA+c1JogQHdrOU2qyFiJA+1aNsenVlZo2rKVWVTXvT0pprbta7v08YNQlZYiKToKOkGGVt6d79rezs7uob5Xerd+SI+PRVZGGtQ6ETq5BXR3qwJUR0ypH+rkyqd9+/ZYt24dwsLCsG7dOhw9elS/Q8TGxlZIxqit8tcLgoChQ4di1qxZ6NmzZ12Eq3flyhUsXrwYALBw4UKoVCp8/vnn6N69O9RqNa5evYqPPvoI3333XZ1+bn2j1WjQt08fnAsPN/hn9e/fDydOnKy3O0Rdu9tBLPNmCnIyM2FjZ4/iklJoVPe/5lNtKYsVuHL8CHSCDG6dutbqNfX5IFYX7swGfH7e+7h6I8aAn/iNSWUC1iVjXnADQOd2nti86lvI5f8lcVhYWnECBEBmYgKemvWKgScBgTZOTfHm6GFQq5TITkpk8sAddFotroeewtVTx1FS2woo95A8cP38OajUanTo049//7dR5OWgpKAAdo2bQBRFPPvmu7gSfcPgn9sp+E+cvTgEdg0bG/yzHhblT7qnRF6rsypANVGXlCA9LgY29g2YTHabrIR4FBfkoyA7E8t2/434rByDfp6nczP82KevQT/jYaXTanHx0AFcCz1VVpWpNmvG3uOTpaVqNWzsG0JWmI+UyGuwb9KUCZa3lF83JEddw4f/9yNi0zMN80GbgwAAXbw8sfP3X836nFwdRV4OivPzoYGA6+Hny5a7rI172B+UhYVIioqEc9uLZpXAcS+T4mlxMdA8wNrVtVWcn4/r58/CvXO3Wr/GlOcx7qwgqiwuNsrPWlJYgNTrkWjTtYfBP6u+u/16oTg/D1qdDh/9sBFRCUkG/+y2zk74dObTUCtLIbe0hFxuYdJPvtf2mJQScRUp16Ogrm1i5QPQiSJyMjJw5cRROLZtV6v5i4f5mFTbPii/WacoUmBO4ARkJCdDVaK469IdKo0WX+w7AgB4f+xQWFnUYp5UEGBjY4PEqGuwbtQYWgh3vVH3MPcBULt+SI+PRUrsDRQUFODFwX5Q1rKC6331AYBW3p3h3rEzVEoVlGo1FArFXV9jDv1we1tjJA5otTqUlCrRQKvDzbhYlJSW3nWfeFj7wVhJMfcjMj4Bw6c+V+v2/fr1w8mTD+e9UvaDcdRp6rqPjw98fHyQkJCA4OBg7NmzB6mpqff1Xi4uLnjssccwefJktG7dui7D1Pv++++h0+kwceJEPPHEE/jll18AAFZWVliyZAnGjBmDgwcPIjIyEt7e3gaJQWrlF313XW+ojmiUD1b2/WFSXw9iMTcz4D/ng3t6zYABA3D8+PF6eRC7m9oObsuzATUQoa6jpxhrUlJUiMSoSJPIBKyNe73gVhbffcBfFzRqFdIS49G0RSv9NsHaBrmZWci8mYIGzVtUaP+w98O9SLh0/r5Lsd8rrUYNnUaNwuwsJg/cIenaZUx88inDlh7dugdd2nnil++/g13jJigtKDTpCUDg7sekwsJCFJeUQGZtA6Gk1CiTgACgLC3BlZMn0HnQ0Fq1f9iPSbU5N6REXEVqdBSuh57GS8P6oaSkdhMl9zsJ2NzFFfERVyG3sUeDFi53nYw1hz5QK0uRlZGGnMwMaNT3sLTiA9BqtVAUKWo1AQg8/P1wL1Iir+GZ19807HnhlrbNnfDelAmIDw+DfRMHk0+wrO11Q3pqMvKyc6DVGv4pQ61Wi8LCQrPZF+5lQrygoACzP/8SkbHxhg0KQMeDR3Fy0PBaVx14mPuhvs5jRMYnoJNf/3t6zcM8jwHUvD+Uj4/ybj2EokE25j89BVqZDNa29gCArOQE5KXdhK6KY9X93qizsrLCv7/8iIAPPqvVgygP874A1NwHt18vlKrVUKnVdbKcVm3odCLUoojszAxY2dhAoVDAsdCr2nPFw9wP9fWYlJCVi3c3bQM2bQPemler1zysxyRj90H5senerKtVq4e1DwDj9sM99cHuf+75/dkPdS8uIwsvLFt1T6+pzzeta1JcXFzvfv/369SpUyguLoa9vb3Uodwz9oNxCKKBU8CioqIQEhKCixcvIioqCqmpqVAqK06CW1tbo1WrVujQoQO6deuG/v37o0OHDoYMCwqFAn379oVKpcKvv/4KHx8f/PLLL/jss8/g6+uLzZs3Y8aMGQgJCcErr7yCt956y6Dx1LV7uVmaGh2J3JspsHVohoKcTCRcDIcir+b10e79Yk+AfZOmGDXjJXj16VfLn+LhvshQKBRo0KCB1GHUmaKionp5EKtJfR1U3Q8ObuuHh7kfgNqfG9TKUhz8v1W4cf4MVLVM4rjfSUBLuRwyuRxW1nZo3KIFWni2Q5vuveDR3afa1zzM5wZRFJGTk4PSWlQS0KiUOLXjD/zvi2VINPDT7h4tnPH90i+gKS1FUX4eWnl3Rtse1feBg4MDbG1tDRqToZjSMelhveAGTKcf2Af1w8PcD0Dtzw0alRIRJ4/i5XfmIiHTkOtZl3F3csCsUUNgbWeLFu06oPOQEWjm3qba9jw3SO9h3hdMpQ+Ah7sfOI9RP5jK/vAw7wum0gfAw90PPCZJj31QP7Af6gf2g/SKiorQsGFDqcOoMw9jHwDsB2MxeAJHVQoLC/U3kOzs7CTp6HPnzmHatGmwsrLC+fPnYWlpWSmBY9WqVfjuu+/Qv39//Pjjj0aP8X7xIqN+uP2EHh4eDjs7u0ptMpMSkRRxCSV5+bC2s0fClQvIT8+AqKvd01w6XdkTkLLarisok6OJszPkFpaQySzg6dMbnYeMrLZ5cXExevToAaD+HsRqwkFV/cB+qB94bpCeKfWBm5sbEhISHro+AHhMqi9MqR/YB/XDw9oPPDfUD6a0Pzys+4Ip9QHAfqgv2A/SYx/UD+yH+uFh7Af2Qf3AfqgfeNNaejqdTr8U+sPuYb5+Zj8YR50uoVJbDRs2lPxAFxcXB6BsqRZLS8sq27i7u1do+7Bg+Zr64fbcqPIkiPpno9QBGJQE+WlUBfZD/aBQKHhukJgp9UFSUtJD2QdUf/DcID32Qf3Ac0P9wP1BeuyD+oH9UD+wH6THPqgf2A/SYx/UD+yH+qGqB3QfVm5ubg/lzyOTyaDVapGTk1NppYeaZCTEIXTfn8hJTkSpQgGlIr/atiqNFiv+PgkAeHvUgBqrTAtyC9jYN4CjqxtcPLwgl8uhLCmGa6fOaN2lR40xOTg41MukgdqoTT9oVEpcP30KGfHRaNy8JVSlpUiJiUJq9HWIWnWN738vfQBZ2ffkcgs0aekCz649TaYfJEngqA/y88t20KSkJPj5+QGAvkz8+fPn4efnB7W67I8oKytLmiDvk42NDc6dOyd1GHXGxqZ268zWN/b29ibTD1ZWVg/lCZ19UD+wH+oH9oP02Af1A8dJ9YOp7A8P875gKn0AsB/qC/aD9NgH9QP7oX5gP0iPfVA/sB/qh4e1H9gH9QP7oX6QyWTQ6XTQarX6yuh3ExMTU6vlrB+EtaUlnB2bAqIIuaUlZDI5dDodrGxsYVXNEpcWFhb19qb13chkMjg5OdW6vSiKKMjPR+9JU2r9mmlf3HtctjY2cG3eDDILC1jb2cPS2vre3+QhUpt+aNmiJdTKEmhUaqRkZqJ134G1fv/76QPAtPpBkiVU6oPVq1dj5cqVtWorCAIiIyMNHBERERERERERERERERERERGZq3pTgePcuXO4dOkSBg4ciPbt2xv886xvZd10794dW7durbLN0aNH8dJLL8G2miw1IiIiIiIiIiIiIiIiIiIiorpQbxI4tm/fjh07dsDR0bFSAse+ffsQEhICR0dHTJw4ER4eHg/8eY0bNwbw31IqVSn/XnlbIiIiIiIiIiIiIiIiIiIiIkOoNwkcly5dgoWFBUaPHl1h+44dOzB//nwAZWsV/fjjj1iyZAnGjBnzQJ/Xpk0bAEBqairUajUsLS0rtUlMTKzQloiIiIiIiIiIiIiIiIiIiMgQZFIHUC4zMxPOzs76pU3KrVu3DgAwaNAgTJgwARqNBu+//z6SkpIe6PM6duwIS0tLqFQqXLp0qco2YWFhAIAePXo80GcRERERERERERERERERERER1aTeJHAoFAo4ODhU2BYTE4PY2Fh4eXlh7dq1WLJkCebNm4fS0lL88ssvD/R5DRo0wMCBAwEAW7durfT9+Ph4nD59GgAqVQUhIiIiIiIiIiIiIiIiIiIiqkv1JoGjUaNGyM3NrbDtn3/+gSAI8Pf312+bOnUq7O3tcfLkyQf+zFdffRWCIGDXrl34448/IIoiACAjIwPvvPMOdDodRo4cCW9v7wf+LCIiIiIiIiIiIiIiIiIiIqLq1JsEjvbt2yMlJQWpqan6bQcOHAAADBkyRL/NysoKbm5uSElJeeDP7NatG9577z0AwIIFCzBs2DBMmjQJI0aMwNWrV+Hh4YHPPvvsgT+HiIiIiIiIiIiIiIiIiIiIqCb1JoFj3Lhx0Ol0+Oijj5CUlITffvsNV69ehbu7O9q0aVOhrUwm01fLeFDPPfccfvzxRwwePBglJSW4ceMGXFxc8PLLLyM4OLjSsi5EREREREREREREREREREREdU0Q6yoT4gHpdDpMnToV4eHhEARBv/1///sfnnvuuQpt/fz8YG9vj8OHDxs5SiIiIiIiIiIiIiIiIiIiIqK6VycVOH744QeoVKoHC0Qmw/r16zF16lQ4OjrCzs4OgYGBeOaZZyq0i4yMRH5+PlxdXR/o84iIiIiIiIiIiIiIiIiIiIjqizqpwOHt7Q0XFxe88847GD9+fF3EVa0lS5bgxx9/xKuvvoo33njDoJ9FREREREREREREREREREREZAx1UoEDAG7evIm5c+fiiSeewIULF+rqbSvp2LEjpkyZgmHDhhnsM4iIiIiIiIiIiIiIiIiIiIiMqU4qcHTr1g0qlQqCIEAURQiCgEcffRRz5szhUidEREREREREREREREREREREd1EnFTgOHDiAxx57TP+1KIo4cOAAxo4di6VLl6KoqKguPoaIiIiIiIiIiIiIiIiIiIjIJNVJBY5y165dw1dffYXTp0//9wGCgKZNm2L27Nl48sknIZPV2aotRERERERERERERERERERERCahThM4yh09ehTLli1DdHR0hWVVPD09MXfuXAwZMqSuP5KIiIiIiIiIiIiIiIiIiIjooWWQBA6gbBmV4OBgrFy5EhkZGRUSOfr374/33nsPXl5ehvhoIiIiIiIiIiIiIiIiIiIiooeKwRI4ypWWlmLjxo3YsGEDFAoFBEEAAMhkMvj7++PNN9+Eo6OjIUMgIiIiIiIiIiIiIiIiIiIiqtcMnsBRLicnB9999x22bdsGjUZT9uGCADs7O7z00kuYMWMGrKysjBEKERERERERERERERERERERUb1itASOcvHx8Vi2bBkOHTr0XxCCgJYtW+Kdd97B+PHjjRkOERERERERERERERERERERkeRkxv7ANm3aYNWqVdiyZQu6d+8OABBFEampqZg7dy6eeOIJXLhwwdhh0X3S6XTIycmROgwig1uwYAEuX74sdRhmr2PHjnj66adr1Xb69Ono1KmTgSMyTyqVSuoQzF56errUIRAR6Y0YMQJvv/12rdq+8847GDlypIEjIpLG2rVrkZmZKXUYZo9jVSKqT3hMqv+0Wi3CwsKwf/9+JCYmSh0OERERESyk+mAfHx/88ccf+Ouvv7BixQokJCRAFEVcunQJU6dOxaOPPoo5c+bA1dVVqhAJQGxsLE6cOIFOnTqhd+/e+u0qlQpLlixBUFAQVCoVWrZsiYULF2LgwIESRktkOFu3bsW2bdvg5eWFgIAAPP7442jSpInUYZkdURRxL4WjjFxkymwMHDgQ48ePh7+/Pzp37ix1OGZp+PDhGDhwIAICAjBs2DBYWEg2pDNrY8aMQUBAACZOnAhHR0epwzF7CoUCp0+fRlJSEhQKRbXnAEEQ8Nprrxk5OtOWkpKCFi1a1KptZmYmUlJSDBwRhYeHIzQ0FGlpaSgtLcXixYv138vIyIBGo4GLi4uEEZqm5cuXY+XKlRg0aBACAgIwdOhQyOVyqcMyOxyr1i9FRUU4c+bMXc/PADB79mwjRmbeUlJSsHv3bmRkZKBLly6YNGkSZDKjP+dnFnhMqh9OnDiB33//HaNHj65QATwjIwMvv/wyIiIiAJRdK7z66qs8HhERmZn8/HwUFxfXOFblNXTdKyoqQnJyMpo2bQpnZ+cK3ztw4AB+//13/Xj1rbfeQsuWLSWK1PiMvoRKVTQaDX799VesWbMGubm5AMoGS5aWlpg+fTpeeeUVNGjQQOIozdPnn3+OLVu2YM2aNRg6dKh++4oVK/B///d/FdpaW1tj+/bt8PT0NHKURIb3+eefY8+ePcjLy9Mfn4YPH46AgAAmLhmRt7c3fHx8sGXLlru2DQgIQGRkJK5cuWKEyMyLt7c3BEHQ/3dAQADGjx+Pxo0bSxyZ+ejSpQs0Gg0EQUDTpk0xYcIE+Pv7o127dlKHZlbK9wW5XI6hQ4ciICAAgwcP5uS3BH788UesXLkSpaWl+m13XuYIggBRFCEIgn6ClurGvZyfn3jiCVy9epXnZwMpr2x5/vx5AKjyb/7DDz9EcHAwfv31V/Ts2VOqUE3SK6+8guPHj+vP0Y6OjvpzdNu2baUOz2xwrFp/rF27FmvWrKlwfq4Kz8+G8dtvv2HFihV47bXX8Oyzz+q3X7x4ETNnztTfpBAEAf369cP69es5jjUAHpPqh3nz5uHPP//E1q1b0bVrV/32d955B/v27YONjQ2aN2+OpKQkAMD69esxYMAAqcJ96L3//vsP/B6CIFRIQiYyRadOncKRI0eQmJhYY/KAIAj4+eefjRyd6YuPj8eqVatw7NgxFBYW1thWEARcu3bNSJGZj1WrVmH16tX47LPPEBAQoN++c+dOvP/++/p9QhAEtGzZEjt37kSjRo2kCteo6kUCR7mioiL83//9HzZt2gSlUgmgrFMcHBxw8uRJiaMzTxMnTkR8fDzCwsL0Tw6pVCoMGDAAJSUlWLZsGXr27InVq1dj69atmDJlChYuXChx1ESGoVarcejQIQQHByMkJAQ6nQ6CIKBFixaYPHkyJk2axKpBBlbbG0SxsbGYNGkSHB0dcfjwYSNFZz4iIiIQFBSEPXv2ID8/X5/UNGrUKPj7+6N///5Sh2jycnJysHPnTmzfvh03btzQTwh2794d/v7+GDt2LOzt7SWO0vT9/fffCA4OxokTJ/Q365ycnDBp0iRMnjwZbdq0kTpEs7B9+3bMnz8fQNl5olu3bnBycqrxBgSfqKtbtT0/5+TkYNSoUbCzs8Px48eNFJ35yM/Px+TJk/UVUfr374+QkBCkp6dXuCl67tw5PP3003j++ecxd+5cCSM2TdnZ2dixYwe2b9+O2NhY/Tm6Z8+eCAgIwJgxY2BraytxlKaNY9X6YcuWLfjss88AAM2bN0eHDh3g6Oio3yeq8sUXXxgrPLMwa9YsHDt2DP/880+FJ0afeuopXLhwAR07dkTnzp3xzz//IC8vD59++immTJkiYcSmicek+mH06NHIzs7G2bNn9dsKCgrQv39/2NnZYdeuXWjZsiWCg4PxwQcfYOTIkVi1apWEET/cvL29H/g9mNhnXJmZmfrqfX369JE6HJOnUqnw5ptv4siRIwDuXkWa+0Pdi4iIwPTp0+9aIe52kZGRBo7K/EydOhWXLl3C6dOnKxRyGDFiBFJTU/Hkk0+iZ8+e+OWXX3D58mW88soreOONNySM2HjqVQIHAJSUlODkyZP4+OOPkZOTwyx8iQ0YMAANGjTAgQMH9NvOnDmDZ599Fo888ghWrlwJACgtLUW/fv3QrFkzHDx4UKpwzUZMTAx+/vlnhIaGIj09HUqlskL2X1BQENLS0jBjxgzewDOQ9PR0bN++HTt37kRCQgIEQYAgCPD19UVgYCBGjRoFKysrqcN86P3888/YtGmT/uuUlBRYW1vDycmp2tcolUpkZ2cDAAIDA5lUZkAqlUqf1HTq1Cl9UlPLli3h7++PyZMnm1VZM6lcunQJQUFB2LdvH4qKiiAIAmxsbDBmzBhMnjy5whJoZBhZWVnYvn07tm/fjvj4eP2Nid69e8Pf3x+jR4+GjY2NxFGarkmTJiEyMhLz5s3DjBkzpA7HLOzYsQM7duzQfx0aGoqGDRuiY8eO1b5GqVTixo0bKC4uxtixY/H1118bI1Sz8vXXX2PdunUYMWIEli1bBltbW0ydOhUXLlyocD2t0+nQq1cvtG3bFtu3b5cwYtMXHh6OoKAg7N+/HwqFAoIgwM7ODmPGjIG/vz8roBgYx6rSGjNmDOLj4zF79my8/PLLXE5IAiNGjEBpaWmFh+JSU1MxfPhwuLu7Y9++fbCwsMDFixfxxBNPoE+fPti8ebOEEZs2HpOk5evrC2dnZ/z555/6bYcOHcLs2bPxxBNP4NNPPwVQdhN1wIABkMvlTDh+ALdfKzyISZMm1cn7UPWCgoKwfv16JCQkAKhcZeCrr77ClStXsHTp0krLG9D9K69wb2tri4CAAPTo0QOOjo41Poji6+trxAhN3/PPP4+TJ0+ic+fOePPNN9G5c2cujyyBIUOGQCaT4d9//9Vvi4iIwKRJk9CzZ0/89ttvAMruxw0fPhzt27evs3NMfSfZgukKhQIxMTG4ceMGbty4gZiYGERHRyMtLa3W2U5kePn5+ZXWdQoLC4MgCBg0aJB+m42NDVq3bo3Y2Fhjh2h2tm/fjk8++QRqtbpC+aDbFRQUYPXq1Wjbti3Gjh0rRZgmz9nZGa+88gpeeeUVnD17FsHBwThw4ADOnDmDM2fOoFGjRnjssccQEBBQJ1nn5qqwsBApKSn6rwVBgFKprLCtOgMGDMDbb79tyPDMnpWVFcaOHYuxY8ciLS0NwcHB2LlzJ5KSkvDdd99h9erV6NevHwICAjBy5EhYWlpKHbJJ6tatG7p164YPPvgA+/fvx/bt23H27Fls374dO3bsQOvWrREQEICJEyfWmPxE98/JyQkvvfQSXnrpJZw/fx5BQUH466+/cPbsWZw7dw6ff/45xo4di4CAAHTr1k3qcE1ObGwsHB0dmbxhRCkpKQgNDdV/LQgCCgsLK2yrjoeHB8/PBnLo0CFYWlpi0aJFNVZ4kMlkcHNz05cIJ8Pp0aMHevTogQ8//BB//fUXgoODce7cOQQHByM4OBgeHh4IDAzEhAkT4ODgIHW4JodjVWmlpKTAyckJr732mtShmK2cnBx4eHhU2HbmzBkAZQk2FhZl08Ldu3dHq1atcP36daPHaE54TJJWcXFxpQetzp8/r19CqJwgCHBxceFT1g+IiRcPh/nz52PHjh0QRREWFhYQBAEajaZCmw4dOmDjxo04dOgQpk2bJlGkpmfv3r2QyWRYt24dH7ySyPnz52Fra4uNGzdyWTMJ5ebmVrp/du7cOQDAqFGj9NucnZ3Rpk0bfbKZOTB4AkdRURFu3LiB6OhofcJGTEwM0tLSqmzP5I36xdbWFllZWRW2le88Pj4+FbZbWFjwiQoDu3TpEj766CMAwLPPPouRI0fiiy++qLT21ujRo/HVV1/hn3/+YQKHEfTp0wdeXl5wc3PDmjVroNFokJ+fj19++QVbtmxBr1698M4771TaZ+juJk2apM8uFkURzz77LNq3b48PP/ywyvaCIMDa2hpubm5o2rSpMUM1ey1atMBrr72G1157DaGhoQgODsbevXsREhKCkJAQNG7cGBMmTMCTTz5ZaRKR6oa1tTUmTpyIiRMnIjk5GVu3bsXGjRuRkJCAr7/+Gt988w2GDh2KadOmVZigorrVq1cv9OrVCx999FGFm3Xbtm3Dtm3b0K5dOwQGBmLixIlms2ajodna2qJFixZSh2FWRo4ciVatWgEoOz/Pnz8fbdq0waxZs6psX35+dnd3R6dOnWosn0/3LzU1FW3atEGTJk3u2tbe3h4lJSWGD4oAlD3wMHHiRIwfPx6bNm3C8uXLodVqERsbi6+++grLly/HmDFjMHv2bLi7u0sdrkniWNX4HBwcmDwsMY1GA7VaXWHbhQsX9NVDb+fk5IT09HRjhmfWeEwyvsaNGyMlJUVf7RsATp8+DaDyHLdWq4WdnZ3RYyQypj179mD79u1o1qwZFi5ciMGDB2P69Om4cOFChXbDhw+HIAg4fPgwEzjqUHp6OlxdXZm8ISFLS0u4uroyeUNiMpkMhYWFFbaVFxG4c/9o0KBBpSQzU1ZnCRwFBQUVkjTK/2VmZlbZvqZEjebNm8PLy6vCP5KGp6cnLl68iLCwMPj4+OiftnNyckLbtm0rtE1LS2OJIQNbv349dDodPvnkEzzxxBMAym7Y3cnFxQVOTk64dOmSsUM0K6Io4vjx4wgODsbhw4eh0WggiiI8PDzg7++PrKws7Nq1C2FhYZg+fTpWrlyJkSNHSh32Q6VVq1b6G0RAWbJMhw4dWDKuHsvPz0dUVBSuX7+uH1DJZDLk5eXh559/xi+//AJ/f398+OGHXGbIQG7cuIGgoCDs3r1b3weNGzeGQqHAoUOH8M8//6B///5Yvnw5L1IMyNraGo0bN0ajRo0gl8uh1WoBANHR0fjiiy/w7bff4qWXXqr2hjfVXs+ePREWFgaNRqN/kpQMy9vbu8ITEqtWrYK3tzeftJOYpaUlVCpVrdpmZ2dXWF+WDCsmJgbBwcHYvXs3srOzIYoiGjVqhHHjxiErKwv//vsvdu/ejYMHD2L9+vWcyDUgjlWNZ/Dgwfjzzz+hUCi4tKtEmjVrhuTkZBQXF+tvRh8/fhxyubzSEk5FRUW8NpAAj0nG07VrVxw9ehS///47nnrqKZw4cQLXrl2Dl5cXmjVrVqFtYmIil4ogk/fHH39AEASsWLGixrFnw4YNWaXJABwcHHg9JrGOHTsiLi5O6jDMnpubG2JjY5Geng5nZ2eUlJTgxIkTsLW1RefOnSu0zc7ONqvKlXUywzlw4EBkZ2dX2n63ahpNmzatlKjh5eXFpxHrkQkTJiA8PBwvv/wy+vbti4sXL0Kr1WLixIkV2iUmJiIrKwuDBw+WJlAzcf78eTRq1EifvFETZ2dn3LhxwwhRmZ+kpCQEBQVh165dSE9PhyiKsLGxwZgxYzBlypQKg9533nkHa9euxapVq7B69WomcDygjh07Qi6XQ6VScfKinjlx4gSCg4Pxzz//6Jd4cnNzQ2BgICZPnozs7Gxs3boV27dvx7Zt22Bvb4///e9/UodtMoqKirB3714EBwfj8uXL+qeK/Pz8MGXKFIwaNQoKhQI7duzAjz/+iJCQECxZsgSLFy+WOnSTExcXh+DgYOzatQtZWVkQRRH29vYYN24cAgICkJWVha1bt+Lo0aP45ptvAIBJHA/otddew1NPPYUffvgBs2fPljocs3T48GGpQyAAbdq0QWRkJHJycmqc1EhMTERSUhL8/PyMGJ35USgU2LdvH4KDg3Hx4kX9/IiPjw8CAwMxZswYfTJ+VlYWVqxYgeDgYHz99df6dX6p7nCsanyvvfYa/v33X3z00UdYvHgxbGxspA7J7Pj6+mLXrl347LPP8Nxzz+Gvv/7CzZs34efnV6G6gEqlQkJCAh+gMyIek4xv2rRpOHLkCBYuXIhvvvkGhYWFEAShUkWB8PBwKBQKdOzYUaJIzUN2djYiIiKQl5dX49PUd96DoLoTGRmJ5s2b1ypx2MHBoVIFcHowQ4cOxfbt25GXl1erCopU955//nnMmjULu3fvxuOPPy51OGZr2LBhiI6Oxssvv4zJkyfjyJEjUCgUGDduXIUVH/Ly8pCSklIpCdmU1UkCR1ZWFgRBqDZho2HDhmjXrl2lRA1Wa6j/nnjiCZw9exb79u3D33//DaBswunll1+u0G737t0AwJLsBpaXl4f27dvXqi1LU9ctpVKJ/fv3Izg4GGFhYRBFEaIookOHDpgyZQoef/xxNGzYsNLrrKysMHv2bOzbtw8xMTESRG5atmzZgrZt23Liop5ITk7G9u3bsWPHDqSlpUEURf26vgEBARXOCU5OTvjoo48wdepUTJ48GXv37mU/1oHycrsHDx5EaWkpRFGEk5MTJk+ejICAgApl2K2srDBz5kw8/vjjePTRR3HkyBHpAjcxJSUl2LdvH4KCghAeHg6gLJG5R48eCAgIwLhx42Bra6tvP3z4cISGhmLGjBnYunUrEzgekIODA+bPn4/Fixfj8uXLmDJlCtq0aVPhd34nFxcXI0ZIZByjRo3ClStXsHTpUnzxxRdVttFqtfjss88gCAJGjx5t5AjNw7lz5xAcHIy//vpLf25u2rQpJk6ciMDAwEqVLIGycdKiRYtw+vRpREZGShC1aeJYVVrOzs7YvHkz5s6di1GjRmH8+PFwc3OrcVkC3qirW7NmzcKBAwewc+dO7Ny5E0BZdYdXXnmlQrvjx49Do9GY1YS4FHhMktagQYPw8ccf45tvvkF+fj6sra3x3HPP4cknn6zQLjg4GADQv39/KcI0eUlJSfj0009x8uTJGtuVP5TC84LhKJVKuLq61qotH6Sre2+88Qb+/fdfzJ8/H8uWLeOyTRIYPHgw5s+fj48//hhXrlzRz6My6di4XnjhBRw4cAARERFYvHgxRFFE48aN8eabb1Zod/DgQYiiaFaV2eusxrAoirC1tYWnp2elRA2uSf3wkslkWL58OV588UXExcWhZcuW6NGjR6XkAHd3d7z//vucBDSwJk2a1HpN0qSkJCZJ1aEBAwZAoVBAFEXY2dlh3LhxCAwMRLdu3Wr1ekdHR5bkqgNOTk6wtLSUOgyzt3v3bgQHB+Ps2bP6ZCZPT08EBgZi4sSJNWaOe3p6omPHjrh48aLxAjZBa9aswY4dO5CUlARRFCGTyTBo0CBMmTIFw4YNq5ChfCcnJye0b99en2hA9y8sLEx/k66kpER/kfH4449jypQpNT7F6Ovri44dOyIiIsKIEZumESNG6P/72LFjOHbsWI3tBUHg00MGolAocPr0aSQlJenHTVURBAGvvfaakaMzfdOnT0dQUBB27tyJmzdvIiAgAMXFxQCAq1ev4vr169i8ebO+XLi/v7/EEZueRx99FImJifqbDv369UNgYCBGjhxZqzGsi4sLUlNTjRCpaeNYtf6IiIhAVlYWsrKy8NNPP921PW/U1S0PDw9s3rwZq1evRnx8PFxcXDBz5kz07du3Qrs9e/agYcOGGDRokESRmjYek+qPp556Ck888YS+WplMJqvU5rnnnsO0adPQpk0b4wdo4jIzM/HUU08hOzsbPXv2REJCAnJycvD4448jLy8PV65cQXZ2NmxsbPDII4/UOK9BD65Zs2ZISkq6a7vS0lLExsaidevWRojKfBw/fhxPPvkkvv/+ezzyyCMYN24cWrduzURXIxs/fjxCQkKwefNmbN68uca2nEsyjEaNGiE4OBhBQUGIi4uDi4sL/P394eTkVKFdSkoKRowYgUceeUSiSI2vThI4vv/+e3h5ecHNza0u3o7qoY4dO9ZYOo4lhoyja9euOHLkCMLCwuDj41Ntu0OHDiE/P59L2tShoqIidOvWDYGBgRg3btw9Z8V+8MEHKCgoMFB05qN///7Yu3cv8vPzuT6vhObNmwcAsLW1xejRoxEYGIhevXrV+vUtW7ZERkaGocIzC99++y2Ashs95dU27iVhtkuXLrCwqLM8XrM1bdo0fRU6X19fBAYG4tFHH631kym2trbQarUGjtL03W3ZxgdtT7Xz448/YuXKlSgtLdVvu/N3Xb6/MIHDMOzs7LB+/Xq88sorOH36NM6cOaP/XkBAAADobxr98MMPfIrOABISEtC8eXP9ubm2TzWWe+mllzB58mQDRWc+OFatHw4dOoR33nkHoijC2toarq6uZrVmdX3RpUsXrFmzpsY2K1asMFI05onHpPpFJpNVuil0O09PTyNGY17Wr1+PrKwszJ49G7Nnz8bUqVORk5ODJUuWACirFLdjxw4sWrQI2dnZWLt2rcQRmzZfX1/s3LkTO3bswKRJk6ptt2XLFqhUKlalqWPvvfee/vo4KysLmzZtuutrmMBRt1JSUjB9+nTcvHmzVvNEnEsynAYNGuC5556rsc3bb79tnGDqEUHkXx3dA1EUkZubi9LSUpaelsCRI0fw8ssvw8PDA99//z08PDwwdepUXLhwQf8E75UrVzBr1izk5OTgl19+qTHRg2ovKioKHTp0kDoMs5eSkoJJkybBx8cHy5cvr7E8PhnOpEmTMGXKFDz22GNo0KCB1OGYpddffx2BgYEYNGgQl8yS0IABAzBp0iQEBgbyaRQya9u3b8f8+fMBAN7e3ujWrRucnJyqfKqx3OzZs40VntlRqVQICgrCwYMHERUVhcLCQtjZ2cHLywujR4/GlClTYG1tLXWYJunw4cMYOnRojX/7ZHgcq9YPAQEBuHr1KgIDAzF37twqlxwlMgc8JhGVGTNmDNLS0hASEgJbW9tKc9rl/vrrL7z11luYM2cOXnjhBYmiNX3R0dGYNGkSrKyssGDBAowfPx7PPPOMvk9UKhW2bNmCr7/+GhYWFtizZ889JydT9aZPn37Pr7lbhQi6N++++y727t0LNzc3PP/88+jUqRMcHBxqnGdt1aqVESMkc8cEDqqVU6dOYf369Th//jxKS0srlQtau3Yt4uLi8L///a/G0n/04N5//33s2LED1tbW6N27N65fv46srCxMnToV169fR1hYGHQ6HZ5++ml8+OGHUodLVKd27tyJ2NhYbNiwAU2bNsXo0aPRtm1blpcjIkloNBpWMiFC2Y2JyMhIzJs3DzNmzJA6HCIiqgd69uwJGxsbhISEMOG4HggPD0doaCjS0tJQWlqKxYsX67+XkZEBjUbDB7XIZKxatQoA0LRpU0ybNq3Cttpixbi616NHD7i6umLPnj0AgKeffhphYWG4dOlSpaXmhgwZgiZNmmDXrl1ShGo2tm7dik8++QSiKMLGxgZA2ZIp7dq1Q1JSEpRKJWQyGb744gtWYCeT079/fxQVFeHAgQNo2bKl1OEQVcIZZ7qrVatWYfXq1TWWCGrYsCF27tyJPn36sOSrgS1evBitWrXChg0bcPLkSf32LVu2AACsra3x4osv8qlGMkl3lpcr/7uvCRM4iMhQmLxBVCY2NhaOjo5M3iAiIj1bW1u4uLgweUNiqampmDt3Ls6fPw8A+qXMbk/gWLlyJYKDg/Hrr7+iZ8+eUoVKVGdWrVoFQRDg4eFRIYGjfD6pJlzyz3DkcnmFKnDlFWmys7MrLQnr5OSE2NhYo8ZnjqZMmQJ3d3d8/fXXuHz5sn57dHQ0AKBTp06YN28e+vbtK1WIRAZTUlKCtm3bMnnDiDp27AgAaNu2Lfbu3VthW23dWVzAlHHWmWp0/PhxrFq1Cvb29njrrbcwcuRIvPPOOwgPD6/Q7pFHHsGnn36KQ4cOMYHDwARBwOzZs/H000/j6NGjlcoiDxs2DI6OjlKHabLCw8Oxa9cuREREIDc3FxqNpsp2giDg0KFDRo7O9PXp00fqEOgOoigiPj4eeXl51e4PAPvOkLKzsxEREXHXPmAyk+GpVKq79gOfbDSs1NRUnDx5ErGxsVAoFLC3t0fbtm0xYMAA/u4NyNbWttKkK5E5S0tLw549e/TnZ7VaXWU7QRDw888/Gzk688KxqnT69OmDkJAQqFQqWFlZSR2OWcrPz8f06dORkpKCFi1aoH///ggJCUF6enqFdhMnTkRQUBAOHTrEBA4D4zHJOMofamvatGmlbSSdFi1aICsrS/+1u7s7AODChQsYM2aMfrtKpUJiYiITAI2kb9++2LZtG9LT0xEZGYmCggLY2dmhffv2cHNzkzo8IoNp164d8vLypA7DrJQnUep0ukrb7vU9zAETOKhGmzdvhiAI+PLLLzFq1CgAqHLw5OjoiJYtWyIyMtLYIZqtJk2aYMKECVKHYVaWLl2KjRs31uokwYsMw+Baf/VHQUEBvv76a/z5558oKSmpsa05ZcYaU1JSEj799NMK1ZiqUv70EBM4DEOj0WDjxo3YtWsX4uLiajxHcF8wHIVCgc8//xy7d+/WXwiW/+0DgEwmw4QJE/DBBx/A3t5eylBNUs+ePREWFsZlhYzo/ffff+D3uPMJbKobW7ZswZdffgm1Wq0/Bt1+brh9G68ZDIdjVem98cYbOHbsGJYtW4b58+dLHY5ZWr9+PVJSUjBixAgsW7YMtra2mDp1aqUEjl69esHGxganTp2SKFLTx2OScVWVrMEEDul5e3vjwIED+kT7IUOGYNOmTfjmm2/Qvn17eHp6QqlUYuHChSgsLGTVByNzdnaGs7Oz1GGYHbVajX379uH48eOVHkQZPHgwxowZU2mJIaob06ZNw3vvvYeTJ09iwIABUodjFqq6f8x7ytXj7BrV6NKlS3BwcNAnb9TEyckJUVFRRoiKyPgOHjyIDRs2wMPDAwsWLMDXX3+Nq1ev4uDBg8jLy8PFixexefNmpKen44MPPkD//v2lDpnIYIqKivDEE08gPj4ezs7OkMlkUCgU8PHxQV5eHuLj46HRaGBjY4OuXbtKHa5JyszMxFNPPYXs7Gz07NkTCQkJyMnJweOPP468vDxcuXIF2dnZsLGxwSOPPAK5XC51yCZJpVJhxowZOH/+PORyOSwsLKBSqdCyZUvk5+ejuLgYAGBlZQUnJyeJozVdarUaL7zwAsLDwyGKIjw8PODl5YVmzZohMzMT0dHRiIuLw44dOxAfH4+ff/6ZEyB17LXXXsNTTz2FH374gZPjRrJjx44Hfg8mcNS906dP4/PPP4eDgwPeeustbNq0CTdu3MBPP/2kv2bYvn07lEol5s6dCy8vL6lDNkkcq9YPOTk5mD17NlasWIFz585h8uTJcHNzg52dXbWvYdWBunXo0CFYWlpi0aJFsLW1rbadTCaDm5sbkpKSjBid+eAxiajM8OHDsXfvXhw7dgxjxozBgAED4OfnhzNnzmD8+PFo3LgxioqKoNVqYWFhgVdffVXqkIkMKjo6Gm+88Qbi4+MrPQx07do17N27Fz/88ANWrlyJdu3aSRSl6Zo4cSJu3LiBt956C7Nnz4a/v79+aSei+oAJHFQjhUJR60klrVYLmUxm4IjoduU3h2p62pflwuvGH3/8AUEQsHz5cnTs2FFfAtbNzQ1ubm7o2rUrpkyZgtdffx2fffYZtm7dKnHERIazceNGxMXF4cknn8Qnn3yCqVOn4sKFC/jll18AlD1d9OOPP2Lt2rVwd3fHokWLJI7Y9Kxfvx5ZWVmYPXs2Zs+ejalTpyInJwdLliwBUHZO3rFjBxYtWoTs7GysXbtW4ohN05YtWxAWFoZRo0Zh6dKlmDlzJi5cuIB///0XAHD9+nWsX78ef/75JwICAjgBZSC//fYbLly4gObNm2PhwoUYOnRopTZHjx7Fxx9/jAsXLuD333/H9OnTjR+oCXNwcMD8+fOxePFiXL58GVOmTEGbNm1qvFnEMeqD+eKLL6QOgaqwadMmAMDy5cvh5+enT7Qpf4J09OjRePHFFzFr1ix888032L59u2SxmjKOVeuH6dOnQxAEiKKIiIiIu/6eWXWg7qWmpqJNmzZo0qTJXdva29vftTIE3R8ekx4O6enpSE9Ph6enJyv2GcjIkSOxZcsWtGzZUr/t+++/x5dffom9e/fqlzLo0KED5syZA19fX4kiNT+RkZFISkqCQqGosR0ru9adnJwczJgxA1lZWbC1tcVjjz2GDh06wMnJCVlZWbh+/Tp2796N2NhYzJgxA7t27YKDg4PUYZuUESNGAABKSkrw5Zdf4ssvv0TTpk2rnccQBAGHDh0yZohk5pjAQTVycHBASkrKXdtpNBp9JjkZVnx8PFatWoVjx46hsLCwxracAKk7V69ehbOzMzp27Fhh++2lj62srLB48WIMGTIEa9aswbfffitFqGaB5eWk9c8//8DKygpvv/12ld9v1KgR3nzzTTg5OeHzzz9Hjx49EBgYaOQoTduxY8dga2uL559/vsrvy+VyBAQEoEGDBnjrrbfw448/4oUXXjBylKZv3759sLCwwIcffggbG5tK32/fvj2++uoruLi44LvvvoOXl1etqprRvdmzZw8EQcCaNWvQuXPnKtsMGTIEq1evhr+/P/78808mcNSx8okPoOz4dOzYsRrbc4z64CZNmiR1CFSFS5cuwdHREX5+ftW2cXBwwPLly/Hoo4/i+++/ZzKOAXCsWj8wUU96lpaWUKlUtWqbnZ3Np04NhMek+uHSpUvYu3cv+vXrVyHhu6ioCHPmzMHRo0cBADY2Nvjwww/h7+8vUaSmy9raGj4+PhW22dvb47PPPsMnn3yCnJwc2Nra8lhkRHv37sXSpUsrLa1VHSZw1J3yB7P69OmDb7/9tsrkjLfeegtvvvkmzp49iw0bNmDu3LkSRGq6qrrvmZOTU217Ln9pGFqtFiUlJbC0tIS1tXWF7126dAlbt25FRkYGunTpgpkzZ5rVOYIJHFSjXr164a+//sLhw4cxfPjwatvt2rULxcXFNU5U0YOLiIjA9OnToVAoaqy6Ua42bah2ioqK4Obmpv+6/GSiUCgqnDQcHR3Rvn17hIWFGT1Gc8HyctJLTEyEi4sLGjduDAD66ksajQYWFv8NLaZOnYrVq1dj27ZtnICqYzdv3oSrq6s+K7y8D9RqdYXkpdGjR8PZ2Rl//vknEzgMIDY2Fi4uLvoE1vKLOa1WW2HZmtdeew1btmzB5s2bmcBhADExMfDw8Kg2eaNc586d0bZtW8TExBgpMvNxr2NOjlHJVOXl5aFDhw76r8vPycXFxRWWjXBzc0O7du1w6tQpo8doDjhWrR8OHz4sdQhmr02bNoiMjEROTk6NT+0mJiYiKSmJc3oGwmNS/RAUFIRt27ZVmt9eunQpjhw5AqDswaySkhJ89NFH8PLyQrdu3SSI1DzJ5XI0a9ZM6jDMyr59+zBnzhyIoghra2u0atUKjo6OUodlNv79919YWlrim2++qfYc3bRpUyxfvhxDhw7F4cOHmcBRx8qrJ5K0Nm7ciOXLl+P999/HM888o99+9OhRvPbaa9BqtRBFEcePH8fhw4fxxx9/VEr0MFVM4KAaTZ8+Hfv378eCBQvQsGHDKtcjPXjwIBYtWgS5XI6nn35agijNx7Jly1BUVITOnTvjzTffROfOnTmwMhJHR8cKZeTKB1bx8fHo0qVLhbbFxcXIz883anzmguXl6o+GDRvq/7s8iSA3N7fCBbcgCHBxcUFsbKzR4zN1crm8wmC1PJEsOzsbLVq0qNDWycmJfWAgGo2mQknq8n0hPz+/wrHH0tISrVu3RlRUlLFDNAvla4bXho2NDTQajYEjMj+RkZFSh0AoS+LLzMyEra0tmjZtWm273NxclJSUoHnz5hVuHNGDa9KkSYWn3cv7ITk5Ge3bt6/QVqfTITs726jxmROOVYmAUaNG4cqVK1i6dGm11X60Wi0+++wzCIKA0aNHGzlC88FjkvTOnz8PGxubColKJSUl2LVrF2xtbbFp0yZ07twZa9aswXfffYdNmzZh2bJlEkZMZFjr1q0DAAQEBGDevHlo1KiRxBGZl9TUVHh5ed313o6TkxPat2/PB1EMgMs01Q8nT56EIAgYP358he3Lli2DRqPB0KFD0b17d+zYsQNRUVH45Zdfqq1IbWo4W0M16tWrF1599VV8//33eOaZZ+Du7q5fj+7ll19GdHQ0UlNTIYoi5syZU2lSiurW+fPnYWtri40bN+oz98k4WrVqhejoaP3XXbt2xd69e7Fz584KCRyXL19GQkJChfUcqe6wvFz90Lx58wo3HFxdXQGUVUAZMmSIfrtOp0NqaipvlhpAixYtkJWVpf/a3d0dAHDhwgWMGTNGv12lUiExMZFl/gykefPmyM3N1X9dfuy/fv06+vbtW6FtRkYG1xU3EBcXF0RHR9/16dKcnBxER0ejVatWRoyOyHiCgoKwcOFCzJs3DzNmzKi23c6dO/HVV19h4cKFfMK3jrVs2RKJiYn6rzt27IgDBw7g77//rnCtHB8fj/j4+BoTbej+caxKVGb69OkICgrCzp07cfPmTQQEBKC4uBhA2TKx169fx+bNm3Ht2jV4eXlxyQgD4TGpfsjKyqo0V3f27FmUlpZiwoQJ6Nq1KwBg1qxZ+Pnnn3H+/HkpwjQLRUVFOHPmDJKSku5aZXr27NlGjMy8xMTEoEmTJvokPjIuCwsLlJaW1qqtUqlk4j2ZrISEBDg5OVWYz4uNjUV0dDQ6dOiAH374AUBZlemxY8fi4MGDZpPAIZM6AKr/3njjDXzxxRdo3rw5EhISkJ+fD1EUceTIEaSkpMDR0RFffvklS7MbgaWlJTw8PJi8IYH+/fujqKhI/4Tp+PHjYWtriy1btuDtt9/Gli1bsGLFCv3J4/YbqFR37qW8nFwuZ9leA2nfvj0yMzP1T5j2798foihi5cqVFarPfPfdd8jJyalQSpzqhre3N7Kzs/WVgYYMGQJRFPHNN9/os/KVSiU+/fRTFBYWsvSrgXh4eCAzMxNarRYA0Lt3b4iiiHXr1lV4Art8vUYPDw+pQjVpQ4YMgVqtxpw5c1BQUFBlm4KCAsyZMwcajQbDhg0zcoRExnHw4EHIZDJMmjSpxnYTJ06EIAj466+/jBSZ+fDz80NBQQESEhIAAOPGjYNcLsf333+PZcuW4ciRI9i2bRuef/55aLXaGpcppfvHsSpRGTs7O6xfvx4eHh44ffo05s6dq68IFxAQgPnz5+PatWvw9PTEDz/8ACsrK4kjNk08JtUPRUVFFZYzA8oelBMEAQMGDNBvs7CwgKurKzIzM40dollYu3YtBg0ahNmzZ2PJkiVYtWoVVq9eXelf+XYynEaNGqFVq1ZM3pBI27ZtERcXd9dqlpGRkYiJiUHbtm2NFBkBQGFhIXbv3o3169dz2UsDy83N1S9NXe7s2bMAUKE6nIeHB9zd3c2qGg3TtqhWJk2ahMceewzh4eGIiopCYWEh7Ozs4OXlBR8fH17kGUnHjh0RFxcndRhm6dFHH0VYWBgSExPh7e0NJycnLFq0CP/73/+wf/9+/PXXX/qM8d69e+P111+XOGLTxPJy9cOQIUPw999/IyQkBEOHDsXw4cPRoUMHXL16FUOHDkXbtm2RnZ2N9PR0CILABD8DGD58OPbu3Ytjx45hzJgxGDBgAPz8/HDmzBmMHz8ejRs3RlFREbRaLSwsLPDqq69KHbJJGjJkCI4dO4bQ0FD069cPo0ePxrfffouQkBCMHj0aXbp0QWZmJsLDwyEIAqZNmyZ1yCbpxRdfxO7du3Hq1CkMGzYMEyZMgJeXl355rejoaOzatQvFxcVwdHTEiy++KHXIJi08PByhoaFIS0tDaWkpFi9erP9eRkYGNBoNXFxcJIzQdMXGxsLZ2bnC0k5Vadq0KVq0aMES7QbwyCOP4ODBg7hw4QJat24NV1dXzJkzB19++SU2bNiADRs2AABEUYSHhwfeeustaQM2URyr1h8FBQXYuHEjjh49isTERH31h6oIgoBr164ZMTrz0Lp1a+zcuRNBQUE4ePBgpTm90aNHY8qUKWazlrgUeEyqH+zt7ZGWllZh25kzZwAAPj4+ldpzrrvubdmyBcuXLwdQVpmmQ4cOcHR0ZAKBRPz8/HDs2DGoVCr+vUtg3LhxuHz5Ml599VUsWrQI/fr1q9QmJCQEH374YZXLS9CD27dvH9atW4epU6dWqEwZGxuLmTNnIj09Xb9t4sSJ1S5HRw9Gp9NVqlhcnmDZu3fvCtubNGmClJQUY4YnKUGsqUYVEdUrx44dw6xZs7BkyRI8/vjjUodDKFvPet++fUhOToatrS369OmD4cOHQyZjgSND8PHxgbOzM/bt23fXtuPGjUNaWhrCwsKMEJl5KSwsxL///gtvb299OfCMjAy89957CAkJ0bdr0qQJ3n33XZZnNwClUokrV66gZcuW+huhCoUCX375Jfbu3aufHO/QoQPmzJmDQYMGSRmuycrKysK2bdvg5+eHXr16AQCio6PxxhtvVEi4tLCwwPPPP4+3335bqlBNXmRkJF5//XUkJSVVOQEoiiLc3d2xcuVKeHt7SxCh6UtNTcXcuXP15aZFUYQgCIiIiNC3+fDDDxEcHIxff/0VPXv2lCpUk9W1a1d07NgRW7duvWvbwMBAREVF4dKlS0aIjC5cuICdO3fqrxl69+6NKVOmVHoSmOoGx6r1Q1paGqZOnYqbN2/WWB7/dnd7CpXoYcRjUv3w7LPPIjQ0FN999x1GjhyJiIgITJ48Ga6urvj7778rtPXz80OTJk1w4MABiaI1TWPGjEF8fDxmz56Nl19+GXK5XOqQzFpMTAyeeOIJTJ48GfPnz5c6HLOjUqkwbdo0XL58GYIgoF27dmjXrh0cHR2RnZ2N6OhoxMTEQBRFdO/eHb/88gssLS2lDtukvPHGG/j777+xZ88eeHp66re/+OKLOH78OJo3bw5PT0+cP38eKpUKX3/9NcaOHSthxKbp0UcfRWpqKk6cOIHGjRtDo9Fg8ODBUCgUOHv2bIUEs9GjR0OhUOD48eMSRmw8TOAgeshs3rwZy5cvR2BgIAICAuDu7g4bGxupwyIyisDAQFy5cgU7duyo8QZcZGQkJk6ciK5du2Lbtm1GjJAyMzORkpICGxsbtGvXjms0SkCr1SInJwe2trZo0KCB1OGYJZ1Oh8uXLyM5ORk2Njbo0aPHXSsH0YNTqVTYt28fjh07hri4OCgUCtjb28PDwwODBw/G2LFj+WSRgeTn52Py5MlISUlBixYt0L9/f4SEhCA9Pb1CAse5c+fw9NNP4/nnn8fcuXMljNg09e/fH3K5vFaTGYMGDYJarcbp06eNEBlR/cGxqvHMmzcPu3fvhqenJ95++210794dTk5OfNLaiM6ePYuGDRvWKnk1MjIShYWF6NOnjxEio3I8JhnPnj17MGfOHFhYWKB9+/aIi4tDaWkp3n77bbz00kv6dlFRUZgwYQJGjRqF7777TsKITU+3bt3QuHFjs7nx9jA4f/485syZg8aNG8Pf3x9ubm41JhjzHFG3CgsL8emnn2Lfvn3Q6XQAyiqSld+ylclkGDduHBYsWICGDRtKGapJeuSRR5Cfn6+vxgSUPag1ePBgODo6Yv/+/WjQoAGOHDmCl19+GQMHDsT69esljNg0ffbZZ9iyZQsGDBiAadOm4e+//8aOHTswbNgwrFmzRt9OoVDA19cXnTp1Mpv7PRwVkl5qamqdvA9LIhvW+PHjERISgs2bN2Pz5s01tmUJUjI1LC9X/zVr1gzNmjWTOgyzJpfL2QcSk8lk6N69O7p37y51KGbFysoKEydOxMSJE6UOxeysX78eKSkpGDFiBJYtWwZbW1tMnTq1QslRAOjVqxdsbGy4hqyBdOzYESEhITh16lSVY6Ryp06dQmZmZo1tiEwVx6rGc+LECVhaWmLDhg1o0aKF1OGYpenTp6N379745Zdf7tp20aJFCAsL4xySkfGYZDzjx49HVFQUNm7cqP87Hz9+PGbOnFmh3c6dOwEAffv2NXaIJs/BwQFOTk5Sh0F3aNKkCSIiIrBo0aIa2/E+Q91r2LAhli1bhrfeegsnTpyo9CDKwIED4erqKnWYJisnJ6fS7zc0NBQ6nQ7jxo3TPxQ3dOhQNG/enH//BvLSSy9h//79OHnyJEJCQiCKIqysrPD6669XaPfvv/9Cq9VWWlbFlDGBg/SGDx/+wE9C8ERuWCkpKZg+fXqtS5CywE7dGTlyJPz8/NCnTx/07duXE1ASmTp1Kvbu3YvLly9j5syZdy0vN3XqVKlDNknnzp1D9+7dWbpPQjt37oSfnx9atmwpdShm7ebNm+wDIgCHDh2CpaUlFi1aBFtb22rbyWQyuLm5ISkpyYjRmY+JEyfi5MmTeO+99/B///d/VT5xHRkZif/9738QBAETJkyQIErT9uyzz8LPzw++vr4cK0mIY9X6oaioCB4eHrx2lti9zAtxDskweEyqP959913MnDkTiYmJaNmyJZo3b16pzeDBg+Hj42NWN4iMZfDgwfjzzz/1N6hJWufOncPzzz8PlUoFURRhY2MDBwcHqcMyS66urnjyySelDsPslJaWVtp2/vx5CIIAX1/fCtudnZ0rVBeluuPs7Izg4GBs2LAB8fHxcHFxwfTp0+Hl5VWhXWhoKLy9vTFs2DCJIjU+JnCQHitn1H/Lly9Hamoq3Nzc8Pzzz6NTp05wcHBgCVIjSE5ORnJyMrZv3w6gbGDl6+sLPz8/+Pn5wdnZWeIIzYOVlRU2btyoLy8XHR2N6OjoSuXlxo8fjwULFnCCxECefvpp2NjYoHv37rxJIZH33nsPgiCgVatW+j7w8/PjBLmRDR8+HK6urujTp4/+fMA+IHOUmpqKNm3aoEmTJndta29vj5KSEsMHZYbGjx+PHTt2ICQkBP7+/ujfvz969OiBhg0borCwEOHh4QgJCYFWq0X//v2ZwGEAZ86cQWhoKADA2toaPXr00J+ju3fvztL4RsKxav3g6uoKtVotdRhUS3l5ebC2tpY6DJPEY1L90rRpUzRt2rTa77NCmeG89tpr+Pfff/HRRx9h8eLFXBJcYt9++y2USiWGDh2KefPmoW3btlKHRGRUDg4OSE5Ohlqt1p+TT5w4AUEQ4OPjU6GtUqnkMtUG1LJlS3z44Yc1tlm4cKGRoqk/BJHp1UQPjf79+6OoqAgHDhzgE79GFhkZidOnTyM0NBRhYWHIz88HAH3yjJubm/5C3NfXlwkdRpCcnMzychKZMGECrl+/DlEU9fuAjY2N/iZF+YQUb1IYzquvvlrtsai8D5hcZni+vr4oKCgA8F8fuLq6VkiqYR/UrWeeeQYA0KpVK3zxxRcVttWWIAj4+eef6zw2c9a7d284OjriwIED+m1Tp07FhQsXKj2lMmrUKBQWFuL06dPGDtMsFBcX44MPPsD+/fsBoEKid/ml/7hx47Bw4UI++WgAhw4d0idxVDdWKj9HdOvWjWMlA+FYtX74v//7P3zzzTf4888/0a5dO6nDMQtFRUX6sSlQlmzctWtXfPvtt9W+prS0FGfPnsXHH3+Mdu3aYc+ePcYI1azwmFQ/LFiwAIGBgejatavUoZi1+Ph4zJ07F2lpaRg/fjzc3NxgZ2dXbXsuj2k4Pj4+kMlkOHnyJKysrKQOh8jo3nzzTRw8eBAvvfQSXnzxRezbtw8LFixAt27dsHXrVn07rVYLHx8fuLm54c8//5QwYjI3TOAgeoj07NkTrVu31q/HSNIQRREREREIDQ3F6dOnERYWhsLCQgD/TZK7u7tXuIlBZGry8/Nx9uxZnDlzBmfOnEF0dHSlCamePXvqJ6R69eolccSmRxRFREZG6vsgLCysUjKBu7u7vg8ee+wxKcM1Sbf3QWhoKM6dO1djHzDB78GVLwnRtm1b7Nu3r8K22hIEgaUv61hAQAAiIyNx7NgxfdndqhI4EhMT8cgjj8DPz49JNAYWERGBgwcPIiYmBkVFRWjQoAG8vLwwatSoe95n6P7k5eVVGCvduHGj0lipV69e2LBhg8SRmiaOVaWn0WjwwgsvIC0tDV999RW6desmdUgmb9WqVVi9erX+69v/5u9GFEW89dZbePnllw0VnlnjMUl63t7eEAQBXl5eCAgIwOOPP16r6nFUt/bv34+vvvoKaWlptWrP6zbD8fPzg5ubG4KCgqQOxeR17NgRQNk8xt69eytsqy1BEHDt2rU6j82cXbx4EdOmTYNWq62wfcWKFRg9erT+61OnTmHGjBmYPHkyFi9ebOwwzYZGo8GBAwdw5swZpKeno7S0tMK80ZUrV1BSUqJPPjMHTOAgeogEBgYiLy8Pf//9t9Sh0G1EUcTly5exfv16/P333/qLcF5kkDnJy8tDaGio/kb2jRs39N/jRYZxlCeX3Z7QUVhYCEEQ2AdGUlMfANwX6kL50gQ2Njb6G0Hl2+7FneuZ0oP5v//7P6xYsQKTJk3SV0a5M4FDq9Xi5ZdfxokTJ7BgwQI89dRTUoZMZHS5ubk4e/Ys9uzZw2sGCXCsaljvv/9+lds1Gg32798PrVYLb29vtG7dGra2tlW2FQSBk+IP6LvvvquQwHH7UqPVsbGxgZubG8aPH48XXngBcrnc0GESeEySwueff449e/YgLy8PgiDA0tISw4cPR0BAAAYOHCh1eGbh0KFDeP311yGKIqytreHq6qpP/q7O5s2bjRSd+XnxxRdx9epVnDx5ksuzG1h5Ar2Hh4e+UuL9JNVHRkbWaVwEHDlyBMuXL0d8fDxatmyJF154AYGBgRXavPXWW/jrr7+wbNkyjB8/XqJITdu1a9fw5ptvIjk5WT92vfNa+YsvvsCmTZuwceNGs1nujAkcRA+RnTt34r333sOGDRswYMAAqcMxa6Io4urVqxVu0hUXF+tPMF5eXiypZUAqlQr79+/HsWPHKi2hMmjQIIwdO5bl/ySSnZ2NM2fO4K+//uLNCQnodDpcuXIFZ86cQUhICE6fPs0+kIhOp0N4eDh++ukn7gtk8oqLizFhwgQkJyfDz88PAQEBWL9+PaKiohAUFITr169j8+bNuHbtGry8vBAcHMzztAGkp6ezyk89lJOTo79Jd+bMGcTFxemvGRo2bIizZ89KHKF54VjVMMqfbH+QKUb2Q93z9vaGj48PtmzZInUoVA0ek4xLrVbj0KFDCA4ORkhICHQ6HQRBQIsWLTB58mRMmjSJS/IaUEBAAK5evYrAwEDMnTsXDRs2lDoks3b+/Hk888wzePvtt/H8889LHQ5RvVVUVARRFGFvb282lR+MKT09HRMmTEBeXh66dOmCYcOGYffu3UhMTKwwHrp69Sr8/f0xbdo0fPTRRxJGbDxM4KAajRgx4p7aC4KAQ4cOGSgaAoBly5bhjz/+wOzZs+Hv748GDRpIHZLZiIyMxOnTp3HmzBmcO3dOf/IGykqgla9n7efnd9cMcrp/V65cwTvvvIOkpKQqJwgFQYCbmxuWLVvGMr1GcPuTQ2fOnEFMTAwA6Ae2vXv3hp+fH2bOnClxpKbr2rVr+t//uXPnoFAo9PuGp6cn/Pz84Ofnh0cffVTiSE2bKIqV+uL2xL527drBz8/PbC4yjCk1NRXW1tZwdHS8a9vs7GwolUq4uLgYITLzkpCQgFdeeQWxsbFVPsEliiI8PT2xdu1atGrVSoIITV/nzp0xcOBABAQEYNiwYbCwsJA6JLNUXia//Lqh/Knq8rGRj4+P/pqhc+fOnAQ0MI5VjWPVqlV18j6zZ8+uk/ehMqtWrULLli3h7+8vdSh0C49J9Ud6ejq2b9+OnTt3IiEhQV+50tfXF4GBgRg1ahQTjutYz549YWNjg5CQEFZ8qAdSU1Pxzz//4KuvvsLgwYMREBAAd3f3aitlAeB1NBHVuYULF+LXX3/FlClT8Omnn0IQhCqX5AUAHx8fuLi4mM2D00zgoBrVtpRT+ZMWzBI3rPKEmvT0dP3aXE2bNq2xBCkTauqGn58fCgoKAJRdWLdp00Z/U9TX1xdOTk4SR2geEhMTMWnSJCgUCjRo0AATJkyAp6cnnJyckJWVhdjYWOzcuRNFRUWwt7fH9u3b0bp1a6nDNjn//PNPpbV7RVGEnZ1dhZsSXbp04U0JA9m0aZM+SaCgoECfJODh4VEhmaw2N7Tp/kVGRlZI2CgsLKyQ2FfeD0zsMyxvb2/07t0bv/zyy13bTp8+HWFhYSxLbSAqlQpBQUE4ePAgoqKiUFhYCDs7O3h5eWH06NGYMmUKrK2tpQ7TZHXp0gUajQaCIKBp06aYMGEC/P390a5dO6lDMxuTJk1CVFSUfmxka2urHxv17dsXnTt35vIERsCxKhHVJzwm1X9nz55FcHAwDhw4gNLSUgBAo0aN8NhjjyEgIOC+ljqgyvr37w8XFxcEBQVJHQoB6Nix4z215/JOdWvnzp1wdHTEoEGD7tr2xIkTyMrKwsSJEw0fGJGRjRw5EllZWTh9+jRsbGwAVF6St9zEiRORlJSEsLAwKUI1OiZwUI1SUlKq/V5JSQni4+Px66+/4uzZs/joo48wYMAAPlFnQPd6wcCEmrpTXhLW2dkZs2bNwvjx41nqTwLvvvsu9u7di6FDh2LZsmVVVqApKirCnDlzcOTIEYwbNw5ff/21BJGatvL9wdbWFj179tTfoO7SpQtvShhJeR80b94cQ4YM0SdtNGvWTOrQzEp5PwCAu7s7/Pz80LdvXyb2Gdm9lAefPn06zp07x/ERmaScnBzs3LkT27dvx40bN/THp+7du8Pf3x9jx46Fvb29xFGatjuvGQICAmBpaSl1WGaHY1WiylJTU3Hy5EnExsbqlyBt27YtBgwYwCeqDYzHpIdDXl4etmzZgjVr1kCj0ei3C4KAXr164Z133oGPj4+EET783nzzTYSEhODkyZOsblIP3E9iUmRkpAEiMU/3+iAK5zEezPvvvw8AaN68Od5+++0K22pLEAQsXry4zmMzd127doWnpyd27typ31ZdAscTTzyBq1ev4sqVK0aOUhqsqUo1ulsyRrt27TBy5EgsX74cixYtwh9//GGkyMzTpk2bpA7BbPXt2xfh4eFIS0vDwoUL8fnnn8Pb21t/4d27d29OiBvBqVOnYGNjg6VLl1a7fFCDBg305f9CQkKMHKH5EEURlpaWsLW11f/j5JNxiaKIzMxMXL58GXZ2dvqSu1xay7hEUUSLFi0wdOhQ+Pn5oU+fPkzwq8cUCgVvppLJcnBwwMyZMzFz5kxcunQJQUFB2LdvH8LDw3Hx4kUsXrwYY8aMweTJk9G7d2+pwzVJbm5uSEpK0l8zfPvttxWqMbEaivFwrCq9ESNGoFu3blixYsVd277zzju4dOkSK4gagEKhwOeff47du3dDp9MBgL6CLgDIZDJMmDABH3zwAec0DIjHpPpJFEUcP34cwcHBOHz4MDQaDURRhIeHB/z9/ZGVlYVdu3YhLCwM06dPx8qVKzFy5Eipw35ovfHGGzh27BiWLVuG+fPnSx2O2WMyhvT4bL3x7NixA0BZtdzyBI7ybbXFBA7DsLOzQ2FhYa3aZmRkoHHjxgaOqP5gBQ6qEyqVCv369UO/fv3qbO1TovpGrVbj4sWL+vWsL168CJVKBUEQIJfL0alTJ/3krI+PT41rBtL96d69O7y8vGpVbtHf3x8xMTEIDw83fGBmZvfu3foSsMnJyfrJvyZNmlS4SeHp6SlxpKYrLCxM3wfh4eFQKpUQBAEymQwdO3askFxmZ2cndbgm6/vvv0doaCguXLhQoQ+Y4GdctanAoVKpEBoailmzZsHV1RUHDhwwYoRE0lEqldi/fz+2b9+Os2fP6m/atW7dGgEBAZg4cSIrBtWxtLQ0/fVCaGiovqqlIAhwcHCoMFby8PCQOFrTxLFq/cAKWdJTq9V45plnEB4err8p7eXlhWbNmiEzMxPR0dGIi4uDIAjo2bMnfv75Zya6GgCPSfVPUlISgoKCsGvXLqSnp0MURdjY2OCRRx7BlClTKiS6qlQqrF27FqtWrULHjh3v+YYf/efs2bO4dOkSVqxYgfbt22Py5Mlwc3Orcc6iT58+RoyQyHjuZZw0evRopKen48KFC0aIzDSVH7sbNmyoT8S7n+P5pEmT6jQuKqu2cfHiRfz999/6qnBVVeCIjIzExIkTMXjwYKxdu1aqcI2KCRxUZwICApCcnIzTp09LHQqRUahUKpw/fx5nz57VJ3SUl1m0sLDA5cuXJY7Q9IwbNw4qlQp///33XduOGjUK1tbW2LNnjxEiM1+pqakIDQ3F6dOnERoaitTUVABlNykcHR31k1FTpkyROFLTpVKpKiSXXbp0qUJyWZcuXeDn56fPMKe6V94H5ROzdyb4de7cWb/MzcCBA6UO96G3atUqrF69Wv/17U+R1sZzzz2H//3vf4YIzeyFhITg6NGjSExMRHFxcbVPFAmCgJ9//tnI0VFycjK2bt2KjRs3QqvVAgDkcjmGDh2KadOmoV+/fhJHaJpSUlL046TQ0FDcvHlTf8xq1qwZjh07JnGEpo1jVency40JcyuHbCybNm3C4sWL0bx5cyxcuBBDhw6t1Obo0aP4+OOPkZ6ejvnz52P69OnGD9SM8JgknfKk1uDgYISFhUEURYiiiA4dOmDKlCl4/PHHa6ykOHbsWCQnJ+PSpUtGjNq0lC8nVNvrN0EQcO3aNSNERmR4qamp+sRuoCx5tX379vjoo4+qfU1paSnOnj2LtWvXolOnTti+fbsxQiUyqi1btuCzzz7DkCFD8N1338HKyqpSAkdRURFmzJiBK1eu4KuvvsJjjz0mcdTGwQQOqjNDhgxBbm4uB7JGlJCQUGn90tatW0sdltmJi4vD6dOncfToURw9elR/IcKnh+re2rVrsWLFCqxfvx4DBgyott3Jkyfx/PPP491338WLL75oxAgpOTkZZ86cweHDh3H48GEAvOg2NpVKhbCwMPz222/4+++/eUySgEqlwoULF/QJHefPnwfAfaGurFq1qkLFt/JJwLuxt7fHuHHjMH/+fNjY2BgyRLNTUlKCN954AydOnABw91KwPCYZ340bNxAUFITdu3cjJycHANC4cWMoFApoNBoIgoD+/ftj+fLlZlWS1JhKSkpw9uxZBAUF8fwsIY5Vjae2CRw5OTkYNWoU7OzscPz4cSNFZx6mTJmCy5cvIygoCJ07d6623dWrV+Hv749u3bph69atRoyQeEwynt69e0OhUEAURdjZ2WHcuHEIDAxEt27davV6Vgp6cMOHD7/n15TvF2RYqampOHnyZKX7DAMGDNA/EU8P5kEeRBFFER9//DGeeuopQ4VHJBm1Wo0nn3wS165dg6enJx577DHs2rULcXFx+Pbbb3H9+nUEBQUhLS0Nffr0waZNm+7pIa6HmYXUAZBp2LdvH9LT0+Hl5SV1KGZhx44dWLNmDZKSkip9z93dHa+88gomTpxo/MDMREJCQoUnJrKysgD8d7PC2dkZvr6+UoZosp5//nmEh4fjjTfewOuvv47AwMAKSxMUFxdj69atWLVqFUaMGIHnn39ewmjNS0lJCcLCwhAaGoozZ87g6tWrALieozHFx8fry7WfOXMG2dnZ/P1LJCUlBfHx8YiPj0dCQgIA7gt16dlnn9WXrRRFESNHjkTXrl3xzTffVNleEATY2NjAwcHBiFGalxUrVuD48eOwsLDAqFGj0KlTJzg6OprNRXV9VVRUhL179yI4OBiXL1/WTxKWP9k7atQoKBQK7NixAz/++CNCQkKwZMkSrutbR0pLS3H+/Hl9Mt+VK1f0lU9EUYSFhQW6d+8ucZTmg2NVw9uxY0elUtTXr1/HM888U+1rlEolbty4geLi4iqrQ9CDiYmJgYeHR43JGwDQuXNntG3bFjExMUaKjHhMMr6ioiJ069YNgYGBGDdu3D0vNfrBBx+goKDAQNGZByZj1D8KhQKff/45du/eDZ1OB6BiYoFMJsOECRPwwQcfcGnYB9SwYUO0bNlS//XNmzdhaWlZ7XKW5fMYbm5uGD9+PMaPH2+sUImMytLSEuvWrcNbb72F0NDQCnN7b775JoCy45Kfnx++/fZbs5pnYgUOqtHOnTur/Z4oisjKykJ4eDj+/fdfiKKI999/v8aLc3pwn3/+ObZs2aK/qGvSpIl+/dK8vDwAZSf4adOm4cMPP5QwUtMSFBSkv7DOyMgA8N+FdfPmzdGnTx/07dsXvr6+rIJiQM888wxEUcSFCxeg1WphYWGBli1bwsHBATk5OUhLS4NarYaFhQV69OhR5QmdZdvrhlKpxPnz5/WJTJcvX4ZWq9XvFxYWFujatSt8fX3Rt29flmU3gKSkJP2yKaGhocjMzARQ8dh0+5rK7u7uUoZr0hITE/U36KrqC2dnZ/Tp0wd+fn4IDAyUMlST9P7778PDwwMvvfSS1KGYrcGDByMrKwsbNmzg8b4eCA0NRXBwMA4ePIjS0lKIoggnJydMnjwZAQEBVZ4PsrKy8Oijj8La2hohISESRG0aTp06pb9muHz5MjQaTYWxUfmyZn5+fujVqxerARkQx6rGd78VsgDAw8MD69atg6urq6HCM0vdu3eHp6dnrUquT548GTExMbh48aIRIjM/PCZJLyoqCh06dJA6DKJ6Q61W45lnnkF4eDhEUYSHhwe8vLz09xmio6MRFxcHQRDQs2dP/Pzzz7C0tJQ6bJNxL0vN0YOr6T7nveBD04Z17NgxHDhwAFFRUSgsLISdnR28vLwwevTo+6ri9LBjAgfVqHxtupqUZ2VOnz4d8+fPN1Jk5unw4cN49dVXYWFhgenTp2PmzJlo1qyZ/vuZmZn48ccfsWnTJmi1Wnz//fcYNmyYhBGbDm9vb/1/Ozk5wc/PT39jtE2bNtIFZmZu74f7xVLVD27atGm4fPky1Gp1lTclfH190atXL9ja2kocqekaNmwY0tLSAPyXJNCsWTP9ccnX15fHJiOYN28eQkNDkZ6eDqDqvvDz82NiH5m87t27o2XLlvjrr7+kDsWsrVmzBjt27EBSUhJEUYRMJsPAgQMxZcoUDBs2DHK5vMbXP/XUUwgPD+c46QHcvrZ7+dio/HzAsZHxcKwqjcjISP3xQxRFzJ8/H23atMGsWbOqbC8IAqytreHu7o5OnTqZ1dN0xjJmzBgkJyfj6NGjNVYiy8nJwZAhQ9CqVSueyw2AxyQiqo82bdqExYsXo3nz5li4cGGVlbCOHj2Kjz/+GOnp6Zg/fz6mT59u/EBN1I4dO+Do6IjBgwdLHYpZqM19ztrgtTIZE5dQoRpNnDixxgObnZ0dWrdujaFDh/LJXiP47bffIAgCFi1ahAkTJlT6frNmzTBv3jx4e3tj3rx5+PXXX5nAUUfGjh0LX19f+Pr6om3btlKHY7a++OILqUMgAGFhYbCwsEC3bt30E04+Pj6ccDKimzdvwsnJqULChoeHh9RhmZ3du3cDqJjYx74gc9SqVau7JgeQ4X377bcAABcXF321jRYtWtT69V26dIGFBacIHsSdY6N7Lc9OdYNjVWl4e3tXSLhftWoVvL299cuekfENGTIEP/30E+bMmYNvvvkGjRo1qtSmoKAAc+bMgUaj4fyRgfCYRET10Z49eyAIAtasWVPtUltDhgzB6tWr4e/vjz///JMJHHWI4yPjutt9TqL6iBU4iB4iffv2hbW1NY4ePXrXtkOGDIFSqcTp06eNEBkRmZPjx4/zpoTEYmNjmUxWD/z+++9M7KtHSktL8e+//yIiIgJ5eXlQq9VVthMEAYsXLzZydKbt+++/x+rVq/HXX3/Bzc1N6nDM1uuvv47AwEAMGjSIk1Nk1jhWJSqTnZ2Nxx57DLm5ubCzs8OECRPg5eUFJycnZGVlITo6Grt27UJxcTEcHR2xe/fuGit10P3hMcn4RowY8cDvIQgCDh06VAfRmKfyPmjdujU2btxYYVttsQ8My8fHB87Ozti3b99d244dOxbp6ekICwszQmRERAQwgYPoodK1a1d4e3tj27Ztd20bGBiIyMhIXL582QiREREREUnnn3/+wfz581FQUKDfVn6Zc/uN7PKl/1j2sm6pVCo8++yzKCoqwpIlS9CpUyepQyIiIoktWLAAgYGB6Nq1q9ShmLXIyEi8/vrrSEpKqjK5TxRFuLu7Y+XKlXWyZClRfcDld6VX3gdt27bVJwjca7+wDwyre/fu8PT0xPbt2+/advLkyYiJicHFixeNEJl5CQ8Px65duxAREYHc3FxoNJoq2zGhiUyZRqPBjh07cPToUSQmJqK4uBjVpS6Y077A+qhEDxEHBwckJCRArVbD0tKy2nZqtRoJCQl8csJACgsLkZSUVOOJBAD69OljxKhIq9UiPDwcGRkZ6Ny5M5d1IiIyE9euXcObb74JS0tLzJo1C/v370diYiIWLVqEvLw8XLx4EYcPH4aFhQVeffVVNGvWTOqQTY6VlRV+/vlnvPPOOwgICIC3tzfc3d2rLQ3OKiiGoVarkZmZCVtbWzRt2rTadrm5uSgpKUHz5s25ZIqB5efn3/WawcXFxYgRERnP1q1bsW3bNnh5eSEgIACPP/44mjRpInVYZsfb2xt79+7Fvn37cOzYMcTFxUGhUMDe3h4eHh4YPHgwxo4dCysrK6lDJaoz//zzj9QhmL3yPrh9rMl+qV9cXFwQHR2NnJycGu8h5OTkIDo6Gq1atTJidOZh6dKl2LhxY43XCuVYYZFMVUFBAZ577jlERERwX7gDZ2uoRjt37sT777+P1157DbNnz6623apVq7B69WosXboU48ePN2KE5qV3797Yt28fVqxYgXnz5lXbbsWKFSgoKMCQIUOMGJ3pu3DhApYuXYoLFy7cta0gCLh27ZoRojIvJ06cwO+//47Ro0dXONZkZGTg5Zdf1mfmC4KAV199tcbjFj24hISEWmfG8kadYZw6dQpHjhypVR/8/PPPRo7OfCgUCpw+fRpJSUlQKBQ19sNrr71m5OhM34YNG6DVarF8+XI88sgjCA0NRWJiIvz9/fVtYmJi8Morr+C3335DcHCwhNGaJlEUsWTJEvz777/Q6XS4du1ajeMgnhcMIygoCAsXLsS8efMwY8aMatvt3LkTX331FRYuXIjAwEAjRmgekpKSsHLlShw7dqxCVaCq8JrBsDhWldbTTz+NPXv24Pr16/jiiy+wbNkyDB8+HAEBARg4cKDU4ZkVKysrTJw4ERMnTpQ6FLPGY5Lx8Eaz9KrqA/ZL/TJkyBD89NNPmDNnDr755hs0atSoUpuCggLMmTMHGo0Gw4YNkyBK03Xw4EFs2LABHh4eWLBgAb7++mtcvXoVBw8e1D+IsnnzZqSnp+OD/2fvzsNqzN8/gL+fFtqEihYt0qIMIWFElJ0sRYWIrMPY92WMMfZ9TdYyIcy02rIUIjFarYnSSkpFe2l7fn/4db7OtMjonKc5535d1/e6vuc5n7re43Se9f7cn19+gbm5OdeRRdbHjx9x7tw5hISEICEhgVfo2q5dO/Tr1w/jxo2rc4IE+T779u1DTEwMWrVqhenTp6NLly5QVlaGhIQE19E4R0uokDrNnj0bd+7cQXBwMFRVVWsdl56eDisrK1hZWcHV1VWICcVLbGws7OzsUFFRga5du2Ly5MnV1i/18PDAo0ePICkpCW9vb2qB2UCioqLg7OyM0tJSSEtLo02bNlBRUamz4u/06dNCTCgeVqxYgUuXLuGvv/7ia8W7ZMkSBAQEQEZGBq1bt0ZqaioA4MSJE+jduzdXcUUWy7LYvHkzzp49C5Zlv1odS20vG15paSkWLlyI4OBgAKDPgEMnT57EgQMHUFJSwtv2z8+DYRhaukOALC0tUVxcjIcPHwIAHB0dER0dXe3fOjY2FjY2Npg0aRLWrl3LRVSRdezYMezZswfA51a8HTp0gJKSUp3nSVRk2fCmTp2KsLAwhIaG1jnL/ePHj+jduzd69eoFNzc34QUUA/Hx8XB0dER+fj5YlkWTJk2grKxc53fh1q1bQkwoHuhctfEoKytDUFAQfHx8cP/+fVRWVoJhGKipqWHMmDGwtbWFpqYm1zFFVmlpKXXWaARon0QIaYyys7MxcuRIfPz4EXJychg9enS15wwXLlxAUVERlJWVcfHiRer23YCmT5+O+/fvw9fXF8bGxjXexygtLcX8+fPx4MED/PXXX/ScRwDCwsKwaNEifPz4scbjM8MwUFJSwt69e9GjRw8OEoq+fv36ITs7G35+fjAwMOA6TqNCHThInV69egUVFZU6izcAQE1NDSoqKnj16pWQkoknIyMjbNq0Cb/++iuioqJq7ATBsiykpaWxceNGOqg3oIMHD6K0tBRDhgzBunXroKyszHUksfTkyRMoKCjwFW/k5eXhxo0bUFRUxIULF6Curg4fHx/88ssvOHfuHBVwCIC7uzvOnDkDhmFgZWVFlbEcOHToEG7fvg1ZWVnY2dnRZ8ARX19fbN++HcDnY7SJiQlUVFTocxCy7Oxsvou8qja9JSUlkJGR4W03MjKCrq4ugoODqYCjgfn4+IBhGGzfvh2jRo3iOo7YSkhIgKqq6leXKGjZsiXU1NSQkJAgnGBipKoTopmZGX755RcYGxtzHUks0blq4yEtLY1hw4Zh2LBhyMjIgK+vL/z9/ZGcnAxXV1ccPnwYPXr0gL29PQYNGkTFBg2sT58+GDFiBMaOHYsffviB6zhii/ZJjU9xcTGio6OrzbQ2NTXlu34gRJQpKyvD3d0d8+fPR2pqKs6dO1dtDMuy0NbWxoEDB6h4o4E9f/4cqqqq1a4Xqib/AJ87aG3ZsgX9+vXD4cOHsX//fi6iiqy3b99izpw5KCwshKqqKiZOnAhDQ0NeEdOrV69w9uxZpKenY86cObh48SJ1EhKADx8+QEdHh4o3akAFHKROmZmZ9S4CUFNTowIOIbCxsUHHjh3h5uaGkJAQZGVl8d5TUVGBhYUFpk+fDn19fQ5Tip6qwoGdO3fSTSUOffjwAWpqanzbwsLCUF5ejmHDhkFdXR0AMGbMGOzevRuPHz/mIqbIq3pQt3fvXgwdOpTrOGLpypUrkJCQwPHjx2FmZsZ1HLF1+vRpMAzz1eUKiGApKCigoqKC97p58+YAgLS0NLRr145vbJMmTfD27Vuh5hMH7969g7q6OhVvcOzDhw/1LhhQVlbGy5cvBZxI/EREREBGRgaurq41tqEmwkHnqo2Tqqoq5syZgzlz5iA8PBw+Pj64fv06Hj58iIcPH0JRUREjR46EnZ0dTUZpIHl5eTh37hzOnTsHIyMj2NnZYcSIEbxzJSIctE9qPCorK+Hq6goPDw8UFBRUe19BQQHOzs6YPXs2JCUlOUgoHmgp2MbDyMgIV65cQUBAAO7evYvExEReUZOuri769u2L4cOH071wASgoKICWlhbvddOmTQF8XqJXQUGBt11ZWRmGhoaIjIwUekZRd+zYMRQWFmLo0KHYsWNHtb9zS0tLTJ06FStWrMDVq1dx4sQJ/PbbbxylFV2qqqpU1FoLKuAgdZKTk0N2dna9xn748IF3oCGCpa+vj61btwL4fLCvOrH68uBOGhbLsmjbti2dsHKsqKio2mcQFRUFhmHQq1cv3jaGYaChoYHY2FhhRxQLb968gZqaGt184lBGRgY0NTWpeINjCQkJUFZWpuINjqmpqSEjI4P32sDAAEFBQQgNDeUr4MjMzERiYiLk5eW5iCnSVFRUvtr1gQhes2bN8O7du3qNTU9Ph5ycnIATiZ9Pnz6hXbt2VLzBMTpXbfy6d+8OAwMDaGlp4fDhwygvL0dubi7OnDkDT09PmJqaYsmSJejWrRvXUf/T/Pz84O3tjcuXL+PFixfYtGkTtm/fjkGDBmHs2LEwNzfnOqJYoH1S48CyLBYuXIigoCCwLItmzZpBW1sbrVq1QmZmJlJSUpCfnw8XFxfExsbi4MGDXEcWOf9mKVgieE2aNIGNjQ1sbGy4jiJWlJWVUVhYyHtd1eEkKSkJHTt25BtbVFSE3NxcoeYTB6GhoZCVlcXmzZtrfeZT1ek+ODgYISEhQk4oHoYMGYI//viDNzGI/A8VcJA6GRoaIiIiAs+fP6+z3eLz58/x9u1bmJqaCjEdAT5Xh1PhhuAZGhrW+4Y4EZzmzZvj7du3fO3k/v77bwCodnOvoqKCHkwIiKKiIi0jxDElJSXa9zcCsrKy1boCEeHr1q0bPD09kZGRAVVVVQwdOhSHDx/G7t27ISUlBTMzM2RmZmLPnj0oKyujhxUCMGDAAJw/fx7Z2dl0fOCQsbEx7t+/jwcPHvAVtv7TgwcPkJmZWecY8u/o6uoiLy+P6xhij85VGy+WZRESEgIfHx/cunUL5eXlYFkWurq6GDt2LLKysnDhwgVERkbCyckJBw4cwMCBA7mO/Z9lbGyMX3/9FStXrkRQUBB8fHzw4MED3mxrdXV1jB07FmPGjKEb5gJE+6TG4cKFCwgMDIS8vDyWLFkCe3t7vgd2paWl8PLywt69exEUFAR/f396oN3AaClYQv6nTZs2iIuL473u1KkTrly5An9/f74CjqdPnyI5OZmO0wLw/v17GBoafnWSj4KCAvT09Gj1AQGZO3cu7t69i8WLF2Pfvn10n/ULVMBB6jRs2DCEh4dj5cqVcHNzg6qqarUx79+/x8qVK8EwDIYPH85BSgIA+fn5uH37Nt6/f48ffviBbsg2sMmTJ2PJkiUICgqiG0gc6tSpE+7cuYPz589jwoQJuHfvHmJiYmBgYIBWrVrxjU1JSalxn0W+X8+ePXHr1i0UFxdDVlaW6zhiydLSEr6+vsjJyaFZ7xzq2rUrIiMjUV5eDikpOq3mSv/+/XHu3DkEBwdj3LhxMDQ0hLOzM06ePIkNGzbwxrEsC2VlZSxZsoTDtKJpwYIFuHfvHhYvXoxdu3ahdevWXEcSSzY2NggNDcWqVatw9OjRGpcgiI2N5V27jR49moOUos3BwQG///47IiMjqXMAh+hctfFJTU2Ft7c3Lly4gIyMDLAsCxkZGQwbNgwODg58XeWWLFmCY8eOwcXFBYcOHaLr7wbQpEkTDB8+HMOHD0d6ejp8fHzg7++P1NRUHDx4EIcOHUKvXr1gZ2eHgQMHQlpamuvIIoX2SY1D1VI2Bw4cQO/evau936RJE0ycOBFt27bF9OnT4evrSwUcDYyWgm1cKioqUFxcDGlp6Wpd1Z88eYK//voL79+/R8eOHTFt2jSaRNTAzM3NER0djdjYWBgZGWHEiBHYv38/PD09kZ2dDTMzM7x//x7nzp0D8Pk5HWlYMjIy9e5skpubCxkZGQEnEk/u7u4wNzeHp6cnhgwZAgsLC+jo6NR5zjRv3jwhJuQOw36tVxURa2VlZXBwcMCLFy+goKCAkSNHonPnzmjWrBny8/Px6NEjXL58GQUFBejQoQP+/PNPutAToICAABw/fhyOjo6wt7fnbU9ISMC0adP42ofb2NjwllkhDWP//v3w8PDA3LlzMW7cODpx5UBISAhmzpwJhmGgqKiI/Px8sCyL3377DePHj+eNe/ToEcaPHw9ra2vs3r2bw8SiKSUlBWPGjMHw4cPx+++/U1tLDmRnZ8PW1hYdO3bErl27qNsMR549e4YJEybgp59+EpuLh/+SS5cu4cKFC3jz5g1kZWVhZmaGGTNmUHGfALi4uCAvLw9nz56FtLQ0LCwsoKWlRRfcQsayLKZPn4779+9DUlIS5ubm6NKlC9+12/3791FRUQFzc3O4ubnRMVwAVq5cidDQUKxdu5ba5XOEzlUbh0+fPuHq1avw8fFBZGQkWJYFy7Jo3749HBwcMGrUKDRr1qzWnx8+fDjevHmDJ0+eCDG1eAkLC4OPjw+uXLmCiooKAJ+7Xo4ePRrjx4+Hrq4uxwlFA+2TGocePXqgefPmCAwM/OrYQYMGITc3F2FhYUJIJj46deoEdXV13Lhxg+soBMDx48exZ88erF69GpMnT+Ztv3PnDubOnYuKigpeB+T27dvjzz//rFboQf69uLg4bNmyBRMmTMDgwYMBfH72s3LlSpSVlYFhGN4yQ2ZmZjhx4gQVEDSwSZMmITIyEidPnsSPP/5Y67i///4bzs7O6N69O06fPi3EhOLByMiI7+8dqH0Jrap90osXL4QVj1NUwEG+KjMzE/Pnz8ejR4+qfXGq/nxMTU2xf//+ajPgScNasGABAgMDcfnyZejp6fG2z5w5EyEhIWjdujX09PQQFRWF0tJS7N69m7qi/AsDBgyo9b2MjAzejY2WLVvW+mCCYRgEBQUJJJ+4O3fuHPbt24fc3Fw0bdoUzs7OWLx4Md+YX3/9FV5eXti8eTPGjh3LUVLREB4eXuP258+fY9euXWjXrh3s7Oygo6NTZxFB9+7dBRVR5Pn7+9e4PS0tDa6urmjRogWsra2/+hnQ7KHvk5aWVuP2O3fuYMuWLTA3N4eDgwPatm1b50NrDQ0NQUUkhDN0wd14FBUV4ZdffsHVq1cB8H8OVZ+PtbU1NmzY8NVWsaRuX97o/qeoqChUVFRAUVER2tradV4zeHh4CCqiWKBz1cbLzMwMhYWFYFkWcnJysLa2hr29PUxMTOr1805OToiIiKDjhYDk5ubi4sWL8PX15f0bS0hIoLKyEgAgKSmJsWPHYu3atbWuC0+qo31S42ViYgJDQ0N4e3t/daydnR1evXpFBWQNrF+/flBWVoavry/XUQgAZ2dnhIWF4d69e1BSUuJtHzlyJOLi4mBpaYnOnTvDz88PqampWLZsGaZPn85hYvHw5s0bBAQE8CaidO/eHf3796elhgTAx8cHv/zyCxQVFbF8+XLY2tryddctLy+Hn58fdu3ahby8PGzevBljxozhMLFoWrVq1TcXt4rLxHUq4CD1wrIsbt68iRs3biA+Ph6FhYWQl5eHoaEhBg0aVOcDb9JwBg8ejNzcXDx8+JC3LSsrC3379oWysjKuXr0KBQUFBAcHY/bs2ejTpw9OnDjBYeL/ppraTX8rejAhWJWVlfjw4QOUlJRqPIF9/fo1ysrK0LZtW6pO/k5VD+W+B8MwiImJaaBE4qeuz6DqNK4+nxHtk76PsbHxd/8O+i4QUUUX3I3PixcvcOPGDbx+/RoFBQVQUFCAgYEBBg0a1CDnuoSuGRoLOldtvIyMjGBiYgJ7e3tYW1t/c8e42NhY5OXloUePHgJKKJ7u3bsHHx8f3Lx5E2VlZWBZFlpaWrC3t8eYMWOQnZ2Nv/76C76+vigpKYGzszNWrlzJdez/DNonNV4DBw5EdnY27t27V2cRa2FhIfr06QMlJSXcvHlTiAlF32+//QZfX1+EhITQUrCNgJWVFSoqKnD37l3etoSEBAwfPhzt27fHhQsXAACJiYkYPnw4TExM8Oeff3IVl5AGx7Is5syZg+DgYF63bx0dHSgrKyM7OxvJycnIy8sDy7KwsrKCq6srddEiQkWLdZN6YRgGAwcOpLVHOfbhwwdoamrybQsLC0NlZSWsra15S3pYWlqidevWdMH3L506dYrrCOQrJCQkoKKiUuv7X3aoId+HugVwj2ZfNQ4NUfNMddNEVG3bto3rCOQfjI2NG6TwjNSOipAaBzpXbbwuXLiA9u3b/+ufp2KzhvPmzRv4+vrCz88P6enpYFkWTZo0wfDhw2FnZ4devXrxxqqoqODXX3+Fo6MjxowZgytXrlABxzegfVLjZW5uDi8vL6xfvx7btm2DpKRktTEVFRVYv349SkpK0Lt3bw5SirYFCxbg9u3bWLNmDS0F2wh8/PgRBgYGfNuqugh9uQygrq4utLW18fr1a6HmI0TQGIbBwYMH4eLigtOnTyM3N7da5yV5eXk4OTlh7ty5VLxBhI4KOAj5DykpKam2LSoqCgzDVJuVoqqqSrO5/iWa4UPI/9y6dYvrCGKP1ldsHGJjY7mOQACsXr36u38HwzDYsmVLA6QhhIgzW1tbriMQ0LlqY/Y9xRukYVy8eBE+Pj4IDw8Hy7JgWRZ6enqwt7eHjY1NnTPg9fT0YGxsjMePHwsvsAigfVLjNXPmTFy6dAmXL19GZGQkJk6cCAMDA6ioqCArKwtxcXHw9PTEu3fvICsri5kzZ3IdWeSEhIRg/PjxcHV1xeDBg2kpWI5VVlaiuLiYb1vVcwYzMzO+7S1atMDbt2+FGY8QoZCWlsbixYvx008/ITIyEomJibzVB3R1ddGtWzcqNiOcoSVUSL09evQIYWFhSE9PR3FxMd+Mo/fv36O8vJwqzQWsb9++KCoqwoMHDyAtLQ3gc0VsSkoKHjx4gObNm/PGjho1Cu/fv8fff//NVVyRkpaWhqZNm0JZWfmrY7Ozs/Hp0yf6PghQcnIy7ty5g5SUFBQVFdU6q50e0hFCiGihJQsIIYQQQuqn6rxJVlYWQ4cOhb29PUxNTev984sXL8bjx4+pKIGIjPv372Px4sXIzc2tcSY1y7Jo3rw59u7dC3Nzcw4SiraqJYZoKdjGYciQIUhLS8O9e/fQvHlzlJeXo2/fvigsLER4eDiaNGnCGzt06FAUFhYiJCSEw8T/XQMGDPju38EwDIKCghogDSGN0+vXr+Hh4YGwsDBkZGTg06dPfCsMeHt7Iz09HVOnTq1zKTRRQh04yFelpaVh+fLliIqKAvD5ZJZhGL4CjgMHDsDHxwdnz55F165duYoq8rp27YobN27AxcUFM2fOREBAAJKSkmBiYsJXvFFRUYGUlBRoaWlxmFa09O/fH2ZmZjhz5sxXxy5atAiRkZG0hI0AsCyLzZs34+zZs7wZRHWhAg7BcHFxgYaGBsaMGfPVsf7+/njz5g3mzZsnhGTiY/Xq1dDV1cWsWbO+OvbYsWNITEykVu8C4O/vD2VlZVhYWHx17L1795CVlUUziL4T/R03fvn5+UhNTa2zwBKg5aGIaDI2Nka3bt3qdc3g5ORE1wwCQueqjUdeXh7c3d35iu9rwzAMfR8aWIcOHeDg4IARI0bwltz9Fnv37hVAKvFD+6TGw9zcHFevXsXZs2cREhJSbaa1hYUFHB0doaSkxHVUkUTn/41Lnz594OnpiSVLlmDixIkIDAzEhw8fYGVlxVe8UVhYiNTUVHTo0IHDtP9tDdG9hJbvIKLM19cX69evR1lZWa1Ffnl5eTh06BDatWuH4cOHcxFT6KiAg9QpNzcXTk5OePv2LdTU1GBubo779+8jIyODb5yNjQ28vb0RFBREBRwCNG3aNNy8eRPHjh3DsWPHAHzekU2bNo1vXFhYGEpKStCpUycuYoqsb2lYRM2NBMPd3R1nzpwBwzCwsrJCly5doKysDAkJCa6jiRUXFxd069atXjegfHx8EBERQTegGpifnx+6detWrwKOkJAQRERE0INvAVi1ahXMzMzqVcBx9OhRREREUAHHd6IlCxqv6Oho7Ny5E9HR0V8dSw/piKiqT4HxP8eThkfnqo1Deno6HB0d8e7du3r9rdP3oeH9/PPPYBiG70EcET7aJzUuSkpKmDdvHv0bc4CWh21cZs2ahatXryI0NBT3798Hy7Jo0qQJ5s+fzzfu9u3bqKioqLasCqm/mzdv1rj9xo0b2L17N3R0dDBp0iTo6+tDWVkZ2dnZiI+Ph6enJ5KTk7F06VIMGjRIyKnFx4cPH3D+/HncvXu3WmFf3759MX78eCrsE6AnT57g119/BQBMmTIFAwcOxNatW6vdMxo6dCh27NiBmzdvUgEHIQBw4sQJvH37FgMGDMCuXbsgKysLR0fHagUcpqamkJGRwYMHDzhKKh46d+4MFxcX7NmzB0lJSVBXV8eMGTMwdOhQvnF//vknAFC7P44UFhbylrghDcvHxwcMw2Dv3r3V/u5J41TVtYlwp6Kigj4DAaKHDYR8XivZ2dkZpaWlkJaWRps2baCiokL7HkLqUFxcDElJSa5jiD06VxWcPXv2IC0tDXp6eli8eDE6d+5MxwYhmz9/PjQ0NGp9cEQaH9onEUKERVVVFT4+PnBzc0NSUhI0NDTg5OQEAwMDvnFhYWEwMjKClZUVR0n/+9q0aVNtW3h4OHbv3g17e3v89ttvfO+1a9cO3bt3x4QJE7Bhwwbs3LkTHTt2rPH3kO8THByMlStXIi8vj+/+Xm5uLh49eoTHjx/Dw8MDW7duRf/+/TlMKrpOnDiByspKrF+/HuPGjQMANG3atNo4DQ0NqKio4MmTJ8KOyBkq4CB1CgoKgrS0NDZv3gxZWdlax0lISEBLSwupqalCTCeeLC0tYWlpWeeYTZs2YePGjWKzFlRjUVpairCwMLx8+RKamppcxxFJb968gZqaGhVv/IekpaVBTk6O6xhiq6KiAqmpqWjWrBnXUcReZmYmZGRkuI4hktatWwd7e3vqPMahgwcPorS0FEOGDMG6deugrKzMdSRCGrWEhATExcWhdevWXEcRe3SuKjj37t2DtLQ03NzcoKamxnUcsdSiRQuaMfofQ/sk4cjIyEBERATS09NRXFxM3TiI2FJXV8fatWvrHLNhwwYhpREvR44cgZycHFavXl3nuFWrVuHSpUs4cuQITpw4IaR04uH58+eYN28eysvLoaWlhQkTJkBPTw8qKirIyspCQkICzp07h5SUFCxYsADnz59Hx44duY4tcqKioqCoqMgr3qiLqqoq4uPjhZCqcaACDlKntLQ0tG3bFi1atPjqWHl5eRQXFws+FPmqf7O2KeHn4uKCQ4cO8W2LioqCsbFxvX6eKjIFQ1FRkR4KcSA2NhaxsbF827Kzs+Hv71/rz5SUlCA8PBxpaWm0tFYDCA8Px8OHD/m2vXv3Di4uLrX+zKdPnxAdHY2srCz8+OOPgo4oFtLS0qqtXZqfn4/w8PBaf6bqu5CUlERrxgrIX3/9BS8vLxgYGMDOzg6jRo2q17kraThPnjyBgoICdu7cSW3aiVjx8PDAqVOn+LY9e/YMAwYMqPVnPn36hOzsbACf1x4n34/OVRungoIC6OrqUvEGhzp16oTHjx+joqKCOv4IEe2TGq/8/Hxs3LgRV65cQWVlJW/7lwUcCxcuRGBgIHx9fWFkZMRFTEKIGHj27Bl0dHS+ev3cpEkTtG3bFk+fPhVSMvFx8OBBlJeXw87ODhs2bKi2RHu/fv3g7OyMdevWwcvLC4cOHcLhw4c5Siu6cnJyYGhoWK+x4taljAo4SJ2kpaVRWlpar7HZ2dlUOCBExcXFiI6ORkJCAm9drnbt2vGWsyHf78u2WQzD1KtNvry8PKytrbFw4UJBRhNbPXv2xK1bt1BcXFxnVyDSsIKCgqoVNCUnJ3+1Sryq/euUKVMEGU8sPHz4EC4uLnwnqu/evav2ufxT1RqmP/30k6AjigVfX99q/+ZxcXGYPHlyvX7e3t5eELHE3qRJk3D58mW8evUKW7duxa5du9C/f3/Y2dnRw1EhYVkWbdu2peINInby8/P5CvsYhsGnT5+qFfvVpHfv3li8eLEg44kNOldtnDQ1NVFWVsZ1DLE2Y8YMODs7w9XVFfPnz+c6jtigfVLjVFJSgilTpuDFixeQlZVFp06dEBcXh48fP/KNs7e3x/Xr1xEUFEQFHAISEBAAf39/xMTEICcnBxUVFTWOYxgGMTExQk4nniIiIhASEoLExETecwZdXV307dsX3bp14zqeSCotLUVGRka9xmZkZNT7GR2pv+joaCgoKGDdunXVijeqMAyDtWvX4urVq4iKihJyQvHQokWLen8XUlNTxWpyLxVwkDq1bdsWsbGx+PDhQ51tF1NSUpCamoqePXsKMZ14qqyshKurKzw8PFBQUFDtfQUFBTg7O2P27Nk0w+I7TJkyBba2tgA+X0QPHDgQnTp1wr59+2oczzAMZGRkqD2pgC1cuBB37tzB1q1b8fvvv4td1SVX2rRpAzMzM97r8PBwKCgo1Hozo+r7oK2tjeHDh8PU1FRYUUWWkZERb58EAH5+flBWVoaFhUWtP1P1GQwcOBBaWlrCiCnymjVrBnV1dd7rd+/eQVpaGioqKjWOr/ouaGlpYcSIERgxYoSwooqVtWvXYuXKlQgKCoKPjw/u37+Pa9eu4fr161BTU8OYMWNga2tLy5sJkKGhId69e8d1DPIFag0uHLa2tujRoweAz9cMU6ZMgaGhYa2tqBmGQdOmTaGlpYWWLVsKM6pIo3PVxmn06NHYt28f4uPjoa+vz3UcsaSpqYlFixbhwIEDiImJga2tLfT09OqcDKGhoSHEhKKJ9kmN06lTpxATEwNTU1Ps27cPrVu3hqOjY7UCjp49e0JaWhr37t2j86cGxrIslixZgmvXrtVrklx9xpDvk5GRgeXLl/O6iv5zMuOxY8fQo0cP7NixA6qqqlzFFEkGBgZ4+vQpvLy86pzs4+Xlhffv38PExESI6cRDaWkp9PX1vzoRpWnTptDV1RWrpTuEqVOnTggODkZkZGSdBWNBQUHIzc1F3759hZiOWwxLR0JSh6NHj2Lv3r2wtbXF1q1bAQCOjo6Ijo7GixcvAAAVFRWYPXs27t27h3Xr1mHChAlcRhZpLMtiwYIFCAoKAsuyaNasGbS1tdGqVStkZmYiJSUF+fn5YBgGAwcOxMGDB7mOLDJWr14NXV1dzJo1i+soYi08PBzPnz/Hrl270K5dO9jZ2UFHR6fONWK7d+8uxITiwcjICN26dYOnpyfXUcQWfQaNA30OjVNGRgZ8fX3h7++P5ORkMAwDhmHQo0cP2NvbY9CgQdQpooEFBARgyZIlcHFxwcCBA7mOI9Zqaw1ede0GUGtwQXJyckL79u2/upY4ESw6PjcO5eXlmDFjBtLT07Fjxw568MCB+i4BW4VmuwsG7ZMaBxsbG8THx+PGjRu8QqV/3uOuYm1tjaysrGrLmJLv4+XlhV9//RWmpqbYtm0bVq1ahejoaMTExODjx494/Pgx3Nzc8OzZM6xfvx42NjZcRxZphYWFsLOzQ2JiIhiGgbm5OQwNDXnPGeLi4hAaGgqWZdGuXTt4e3vXef+VfJvLly9j2bJlkJSUxOjRozFp0iReMUFpaSni4+Ph6ekJf39/VFZWYufOnTQpqIGNGTMG2dnZuHPnzlfH9uvXD8rKyvD19RVCMvESHByM2bNnQ1dXF66urtDV1a12fH727Bl++uknfPjwAWfOnBGbzkDUgYPUycnJCd7e3vD398e7d+9gZ2eHoqIiAMDz58/x6tUrnD59GjExMTAwMMDYsWM5TizaLly4gMDAQMjLy2PJkiWwt7fnewBRWloKLy8v7N27F0FBQfD396eT3QZSVcBEuOXk5MTruhEXF/fVz4VuQAnGqVOn0KxZM65jiLWbN2+iadOmXMcQe1u3bhWr1n3/FaqqqpgzZw7mzJmD8PBw+Pj44Pr163j48CEePnwIRUVFjBw5EnZ2dvTwuoEMHz4ccXFxWLFiBebOnYtx48bR0oocoNbg3Dt9+jTXEQjoXJULtS0N0apVK0RERGDcuHEwMjKCjo5Ord0fGIbBli1bBBlT7HzrnD2a4ycYtE9qHJKTk6GpqVmvLjPNmjVDcnKyEFKJF39/fzAMg61bt0JbW5u3nWEYKCkpwcrKClZWVlizZg3WrFkDDQ0NXqcz0vD++OMPJCYmwsDAALt374ahoWG1MXFxcViyZAni4+Ph4eGBOXPmcJBUNI0YMQKxsbE4ceIE/Pz84OfnBwCQk5PjPX8DPh+bp0+fTsUbAuDg4ID169fjwoULGD16dK3jLly4gIyMDMydO1eI6cSHpaUlbG1t4efnBxsbG5iZmSE1NRUAsHHjRrx69QqRkZGorKzEpEmTxKZ4A6AOHKQekpOTMWfOHCQkJNS4XAHLstDT08OxY8fQpk0bDhKKDycnJ0RERODEiRPo3bt3reNCQ0Mxffp09OjRA6dOnRJiQkIEq3///t/8M7du3RJAEkIIIf8VOTk58PT0xOHDh1FeXs7bzjAMTE1NsWTJErG6ABSEAQMGAPjc/aRqDeuWLVvW+ZAuKChIaPnExbFjx7Bnz55qrcH/ObO0rKwMpqam+OGHH3D+/HkOExNCRIWRkREYhvmuAgCGYarNgieEkIbStWtXaGho4MqVK7xttXXgGDZsGD58+EAdOBpYjx49oKioyLsOmDhxIqKiovD8+XNISEjwxhUWFqJPnz7o0aMHjh49ylVckWdjY4O4uDhcvXqVr6Dmn1JSUjB06FAYGBjgwoULQkwoHkJCQnDs2DFERUXxrqUBQFJSEqamppg5c6ZYLRkhbJs2bcL58+cxfvx4TJo0CW3btuW9l5SUBE9PT977v/zyC3dBRRzLsjh06BDc3NxQXFxc7f2mTZti5syZYre0GXXgIF+lo6MDf39/eHt748aNG3j58iXy8/MhJycHAwMDDB06FA4ODjQTWAhevnwJTU3NOos3AKB3797Q0tJCbGyskJKJj5KSEty+fRsvXrxATk4OysrKahxHs4cEg4oxCKlZaWkpcnJy+B5O/xOtZ03ECcuyCAkJgY+PD27duoXy8nKwLAtdXV2MHTsWWVlZuHDhAiIjI+Hk5IQDBw7Q0h/f4e3bt9W2ffjwodbxNRWFk+8XEBAAKSkp7Nq1C61bt651nLS0NLS1tZGYmCjEdOLnyZMn9bpmoJlcRBSI281UQsh/j6amJpKTk1FUVFTnMhCZmZlITk6mZZ8EoLi4mO/hqIyMDIDPSwA2b96ct11eXh7t2rXDkydPhB1RrCQnJ0NfX7/O4g0A0NbWhr6+PlJSUoSUTLxYWFjAwsICxcXFSE5ORmFhIeTl5evsWka+XdWkk9p4enrC09MTUlJSaNGiBd89VklJSdy6dQu3b9+miSgCwjAM5s2bh0mTJuHOnTvVnkFbWVmJZQdkKuAg9dKkSRM4OjrC0dGR6yhiraSk5KsnVVWaN2+OjIwMAScSLzdv3sSaNWuQl5fH21Y1w+jLBxEsy1IBBxELAQEB8Pf3R0xMDHJycvgqxb9ES9kIRnl5Odzd3XHhwgUkJibWOeORPgPBevToES5cuIAXL17g48ePtRbSUNcBwUtNTYW3tzevxSXLspCRkcGwYcPg4OAAMzMz3tglS5bg2LFjcHFxwaFDh6iA4ztQx7fGgVqDNw5PnjzBqlWr+Apkqq4PvlS1jQo4BIfOVYWHCjgI+TraJ3HLysoKx48fh6urK5YtW1bruN27d4Nl2a8+7CPfrlWrVsjNzeV7DQAJCQno2rUr39jc3FwUFBQINR+pHRXgC56srCwtbylANU06qUlZWRkyMzP5tpWXl+Pt27f0PRCCFi1a1LmcjbihAg5C/kNat26N169f8yoxa1NYWIjXr1/zToTJ94uJicHChQshLS2Nn376CVevXkVKSgo2b96MnJwcPH78GLdu3YKUlBR+/vln+rcnIo1lWSxZsgTXrl2rV5tkWq2t4ZWWlmLq1KmIioqCpKQkpKSkUFpaCnV1deTm5vLWy2zSpAlUVFQ4Tivadu7cCXd393r9ndPFnmB8+vQJV69ehY+PDyIjI8GyLFiWRfv27eHg4IBRo0bVuO54kyZNMG/ePAQEBOD169ccJBcdtDZ14yEpKVmvcbm5uXVeT5B/JyUlBVOnTkVJSQlGjBiBiIgIpKen4+eff0ZOTg4ePXqEmJgYyMjIYMKECfQZCAidqzYO8+bNg5SUFHbs2IEmTZpwHYcQztA+qXGYOnUqvLy84ObmhuzsbDg4OPCKaHJycvDq1SucPHkSt2/fhrq6OiZMmMBxYtGjpaXF11Wja9eu8Pf3x5kzZ/gKOO7cuYM3b97wdesgDU9bWxvx8fF48+YNNDU1ax2XmpqKuLg46OvrCzEdIQ2LJp2Q/yIq4CDkP8Tc3BxeXl5Yv349tm3bVuMN2oqKCqxfvx4lJSVfXWqF1J+bmxsqKiqwZ88eDB48GGFhYUhJScHYsWN5Y16/fo05c+bg3Llz8PHx4TAtIYLl7e2Nq1evwtTUFNu2bcOqVasQHR2NmJgYfPz4EY8fP4abmxuePXuG9evXw8bGhuvIIsfT0xORkZEYNGgQdu7ciWnTpiE6Ohq3b98GALx69QonTpzApUuXYGdnh59//pnjxKLpxo0bcHNzg66uLtatW4fdu3fj+fPnuHHjBq+47/Tp08jIyMAvv/wCc3NzriOLpN69e6OwsBAsy0JOTg7W1tawt7evd9tjZWVlWkqCiARqDc6948ePo6ioCL/99hvGjx8PR0dHpKenY8GCBbwxDx48wNKlS/HgwQOcO3eOw7Sii85VG4fg4GAYGBhQ8UYjkJ6ejsuXL9drWScPDw8hpxN9tE9qHFq2bImjR49izpw58PPzg7+/P++9Xr16AfhcPKOiooLDhw9DQUGBo6Siy8LCAmFhYXj06BG6dOmC4cOHY+/evQgICMDbt2/RtWtXZGZm4tq1a2AYBmPGjOE6skgbNGgQXr58iQULFmDPnj01FswkJiZiyZIlYFkWgwcPFn5IMZGdnc07Rte1NDIdH/49mnRC/ouogIPwrF69+rt/By0bIVgzZ87EpUuXcPnyZURGRmLixIkwMDCAiooKsrKyEBcXB09PT7x79w6ysrKYOXMm15FFRmRkJBQVFes8WdXT08OBAwdgY2MDV1dXrF27VogJxQstV8Atf39/MAyDrVu38i3rxDAMlJSUYGVlBSsrK6xZswZr1qyBhoYGnSg3sICAAEhJSWHt2rW8dWO/ZGhoiB07dkBDQwMHDx6EgYEBBg0axEFS0fbnn3+CYRjs2bMHxsbGvAcUWlpa0NLSQqdOneDg4ID58+dj48aN+OuvvzhOLJoKCgpgYmICe3t7WFtb1/nguia//PIL3/JohPxXUWtw7j148ADy8vKws7OrdUyvXr2wd+9eTJkyBUeOHMHixYuFmFA80Llq46CqqlrrEhFEeDw9PbFt2zaUlZXxusF92eHhy23ULU4waJ/UeJiYmODSpUtwc3PDjRs3kJqayntPTU0NQ4cOxcyZM6GsrMxhStE1dOhQJCUlIT8/H8DnJf3279+PhQsX4tGjR3j06BFv7MiRIzFjxgyOkooHZ2dnXLp0CTExMbC2tkafPn2qPWe4d+8eKioqoKOjg6lTp3IdWeSkpqbi999/R2hoaJ3jqo7RVMBBRJGxsXG9x0pISEBBQQFt2rSBmZkZHBwcRLo7EMNSXzby/xpijS2GYfDixYsGSENqc//+fSxevBi5ubk1XlyzLIvmzZtj7969NNO3AXXq1AkGBgbw9fUFAEyePBnh4eGIjo6u9vB02LBhKCsro8IBAfnW5Qpon9TwevToAUVFRd7f+MSJExEVFYXnz59DQkKCN66wsBB9+vRBjx49cPToUa7iiqRu3bpBWVkZN27cAPC/z+DZs2d83ZnKyspgbm4OY2NjahcoAD/++CNkZGQQHBwMAHB0dOTNpvvyGJ2dnY1+/fphwIAB2L9/P0dpRdfLly/Rvn17rmOIDRcXFwCfZzFOnDiRb1t9MQyDuXPnNng2cffx40cMHz4cOTk5sLGxgYODA7Zt24YnT57gwYMH1VqDX7p0iWaXNjATExO0bdsWFy9eBAA4OTkhIiICjx8/rtaFYODAgZCWlsbVq1e5iCrS6Fy1cdi0aRPOnTuHmzdvQk1Njes4Yunvv//G1KlToaSkhEWLFuHUqVOIj4/HyZMned3ifH198enTJyxfvhwGBgZUOCAAtE9qvIqLi5GXlwd5eXk6J+JQQUEB7t69izdv3kBGRgbdu3f/pgd65N979+4dlixZgujoaAD8y75W3Xft1q0bdu3aBXV1dU4yiqrMzEzY2toiOzsbXbt2RXJyMj58+IBRo0YhJycHz549Q3Z2NmRkZDB48GBISkpi69atXMcmpMF9z3NpKSkprFy5Ek5OTg2YqPGgDhyEhw4A/w3m5ua4evUqzp49i5CQECQmJqKwsBDy8vLQ1dWFhYUFHB0doaSkxHVUkaKgoMA3e6h58+YAgLS0NLRr145vbJMmTfD27Vuh5hMXtFxB41BcXMzXWrGqiCk/P5/33QAAeXl5tGvXjm+NU9IwysvL0aJFC95rWVlZAEBubi7f/l9aWho6Ojp4+fKlsCOKhYKCAmhpafFeN23aFMDnm69f3gBUVlaGoaEhIiMjhZ5RHEyaNAnNmjXDtWvXqE27ELi4uIBhGOjq6vIVcDAM89XiyqoxVMAhGNQanHuysrKQkvrfbZZmzZoBADIyMviOFwCgqKiIhIQEoeYTF3Su2jjMmzcPt27dwqJFi3DgwAG0bt2a60hip6qAe8+ePejZsyf8/PwAfC5CBsDrNvDTTz9h3759vAkrpGHRPqlxSEtLQ9OmTfm6a8jKyvKupb+UnZ2NT58+QUNDQ5gRxZaCggKGDx/OdQyxpK6ujnPnziE8PBx3796t9pyhb9++6N69O9cxRdKJEyeQlZWFefPmYd68eXB0dMSHDx+wfft2AEBFRQX8/PywefNmZGdn49ixYxwnFm1Pnjyp11JzdB+j4cXGxuLMmTPYvn07hgwZAnt7exgbG0NeXh6FhYWIjY2Fl5cXrl27hhUrVsDOzg6vX7+Gt7c3/vzzT2zZsgXGxsYwMzPj+j+lwVEBB+GxtbXlOgKpJyUlJd7BnQiHmpoaMjIyeK8NDAwQFBSE0NBQvgKOzMxMJCYmQl5enouYIo+WK2gcWrVqhdzcXL7XAJCQkICuXbvyjc3NzUVBQYFQ84mD1q1b4+PHj7zXVTMhXr16xbshW+X9+/coLi4Waj5xoaysjMLCQt7rquKZpKQkdOzYkW9sUVER3/eGNJzy8nIoKytT8YaQVJ1/tmzZsto2wj1qDc6t1q1b4/3797zX7dq1w+3btxEeHs5XwJGfn4/ExETabwkInas2DmfOnIGlpSX+/PNPDBo0CL169YKenl6ND0ur0PGkYT158gTKysro2bNnrWOUlJSwZ88eDBkyBK6urjS5SwBon9Q49O/fH2ZmZjhz5sxXxy5atAiRkZGIiYkRQjJCuNe9e3cq1BCyu3fvQlZWFtOnT6/xfUlJSdjZ2UFBQQGLFi3CyZMnaVkhAXjy5AlWrVqFxMRE3raalpWjiSiCc+vWLWzevBnLly/HtGnT+N5TVFREjx490KNHD/zwww/YsmULNDQ0MGDAAHTs2BEaGhrYs2cPTp8+TQUchBAirrp16wZPT09kZGRAVVUVQ4cOxeHDh7F7925ISUnBzMwMmZmZ2LNnD2/JAtLwnj9/DlVV1WqtFL88sWrSpAm2bNmCfv364fDhw7RcgQBoaWnxzQrq2rUr/P39cebMGb4bUHfu3MGbN2/4ZhuRhqGrq4uwsDBUVFRAUlISZmZm8PLywvHjx2Fqasp7IPTXX3/h/fv3tLyEgLRp0wZxcXG81506dcKVK1fg7+/PV8Dx9OlTJCcnU8tRAdHW1kZOTg7XMcRGTQ/X6IFb46KkpITly5dj+fLl1BpcyExMTODn54ePHz+iZcuWGDhwIE6cOIFdu3ZBRUWFd82wZcsWlJSUVCu6JA2DzlUbhy+7M1VUVCA4OBh37typcWzV9RwdTxpWTk4O33WAtLQ0gM+FxXJycrztWlpa0NfXx4MHD4SeURzQPqnx+JaV5GnVeUKIIL179w6ampq8wtaqJbXKysp4x2vgc7csVVVVXLp0iQo4GlhKSgqmTp2KkpISjBgxAhEREUhPT8fPP/+MnJwcPHr0CDExMZCRkcGECRNowq6AuLu7Q1lZuVrxxj9NmTIFx48fx8mTJzFgwAAAgLOzMw4fPsxbBkrUUAEH+SafPn1CcnIyr5WWjo4Or104Ea7i4mJERUVVa21mampa54wW8u/0798f586dQ3BwMMaNGwdDQ0M4Ozvj5MmT2LBhA28cy7JQVlbGkiVLOEwrumi5gsbBwsICYWFhePToEbp06YLhw4dj7969CAgIwNu3b9G1a1dkZmbi2rVrYBgGY8aM4TqyyOnXrx/u3r2LsLAw9OrVC0OHDsX+/ftx//59DB06FB07dkRmZiYePXoEhmF4yxyQhmVubo7o6GjExsbCyMgII0aMwP79++Hp6Yns7GyYmZnh/fv3OHfuHABg2LBhHCcWTaNGjcKuXbsQFRUFU1NTruMQ0qjU1hqcCEb//v3h4+OD4OBg2NraokuXLrC2tsaVK1fw008/8caxLAtZWVksXLiQw7Sii85VGwcbG5tqsxeJcLVo0QKlpaW811Xds968eQNDQ0O+sZWVlcjOzhZqPnFB+6T/nsLCQr4HqISIooKCArx58wYtW7aEqqoq33vXr1/H+fPn8f79e3Ts2BGLFi2iCSkNTFJSku+5WtV97ezsbKipqfGNVVFRoaUXBeD48eMoKirCb7/9hvHjx8PR0RHp6elYsGABb8yDBw+wdOlSPHjwgHdvjzSs2NhYvg73tWEYBm3atEFsbCxvW5MmTaCrq8s3uU6UUAEHqZeHDx/i8OHDiIiIQEVFBW+7pKQkevTogTlz5lCbLSEpLS2Fq6srzpw5w9e2vYqcnBwmTZqEuXPnUkveBmRubo7nz5/zbVu5ciU6dOiACxcu4M2bN5CVlYWZmRlmzJhR7cSXNAxarqBxGDp0KJKSkpCfnw/g8/ru+/fvx8KFC/Ho0SM8evSIN3bkyJFUIS4AQ4YMQV5eHu9ir2nTpjh69CgWLFiAxMREpKWlAQCkpKQwffp0ODg4cBlXZA0ZMgSRkZFISUmBkZERVFRUsHnzZqxcuRJXr17FtWvXeDO3zMzMMH/+fI4TiyZnZ2eEh4dj3rx5+P333zFw4EB6YEQI4YSVlRXu3LnDNztr+/btMDAwqHbNsGDBAhgZGXGYVnTRuWrjsG3bNq4jiD11dXWkpKTwXhsbG+P69esIDAzkK+BISkpCUlIS3/JopOHQPum/o7S0FGFhYXj58iU0NTW5jkOIQP3xxx84dOgQNm7cCDs7O952f39/rF69mncvIyEhAeHh4fD394eioiJXcUWOmpoasrKyeK+1tbUBANHR0XyTf0pLS5GSkkL3OATgwYMHkJeX5/v7/6devXph7969mDJlCo4cOYLFixcLMaF4YFkWb968qXHpmi9VVlbyxn2JYRjIyMgIOiYnGJb6gZGvcHd3x65du1BZWcnbJisri+LiYt5rCQkJLFu27Kttbsj3KS0txcyZMxEWFgaWZdG8eXNoa2tDWVkZ2dnZSElJQW5uLhiGgZmZGdzd3alinIgUR0dHxMXFITw8HMDni41t27Zh0qRJWLt2LW/c06dP4eDgAHV1ddy6dYuruGKnoKAAd+/exZs3byAjI4Pu3btXW+6GCFZlZSWePn3K+wy6dOkCZWVlrmOJnTdv3iAgIID3oK579+7o378/ryUmaViTJ08Gy7KIiopCZWUlFBQU0LZt21q7DjAMAw8PDyGnFB8syyIpKQk5OTkoLy+vdRwVf38fFxeX7/4dtIYvETd0rkrEza5du+Dm5oZr165BR0cHb968wdChQ8GyLKZOncpb1unIkSNIS0uDg4MDfv/9d65jiw3aJwmWi4sLDh06xHv9tQdD/+Ts7IyVK1cKIhohjYKjoyOePHmCv//+m6+r8YABA5CWlobx48eja9euOHPmDJ4+fYo5c+bwdSYg32fp0qW4fv06Hj58CHl5eYSGhmL69OnQ0dGBq6sr9PT08OnTJ2zYsAE+Pj748ccf8ccff3AdW6SYmJigbdu2uHjxIgDAyckJERERePz4cbWJ0QMHDoS0tDSuXr3KRVSRVvXvvmjRIr6ulf907Ngx7NmzB927d8fp06cBfD62d+/eHSoqKrh27ZqwIgsNdeAgdYqIiMCOHTsAAIMHD8a0adNgaGgIOTk5FBUVIS4uDu7u7rh+/Tp27tyJzp07o1u3bhynFl3u7u54+PAhWrRogZUrV2LEiBF8BRrl5eW4dOkSdu7ciYiICLi5uWH27NkcJiakYdFyBY2bgoIChg8fznUMsSYhIYHOnTujc+fOXEcRa5qampg1axbXMcRGWFgY3+v8/Hw8ffq01vE0c0Uw8vLysHv3bly6dImv0LsmDMMgJiZGSMlEk4uLCxiG+Vfrs1f9HBVwEHFD56pE3AwePBg3btxAdHQ0dHR0oKmpiWXLlmHbtm1wc3ODm5sbgM83v3V1dbFo0SJuA4sZ2icJ3pfnSfU9b5KXl4e1tTUtc0ZE3tu3b9GqVSu+4o0XL17wlnb67bffAAA9e/ZE//79cfv2bSrgaED9+/fHlStXcPfuXQwbNgy9e/dGz5498fDhQ4wYMQLNmzdHQUEBKioqICUlhZ9//pnryCJHVlYWUlL/e0TerFkzAEBGRgbfEu4AoKioSMvYCMiMGTMQHh6Offv24fnz5xg7diyMjIwgLy+PwsJCvHz5Ej4+Prhx4wYYhsHMmTN5P/vgwQMUFBRg0KBBHP4XCA4VcJA6eXh4gGEYLF26tFoLPzk5OXTu3Bn79++Hm5sbdu7cCQ8PDyrgEKALFy6AYRgcOXIEXbp0qfa+lJQUbG1t0bZtW0yYMAH+/v5UwCEAxcXFiIqKQmJiIgoLCyEvLw9dXV2YmprSGuMCRssVEEII+aetW7dyHUHsFRQUYNy4cUhKSoKqqiokJCRQWFiIbt26IScnB0lJSSgvL4eMjAw6derEdVyRMG/ePK4jkHpISkqqds3Qtm1brmMRQsSEiYkJbty4wbfN2dkZnTt3hr+/P9+yTg4ODpCTk+MoKSENb8qUKbC1tQXwuZBj4MCB6NSpE/bt21fj+KoW7FVL9RIi6j5+/FhtOb+IiAgA4HsYqqqqirZt2yI5OVmo+UTdwIED4enpCXV1dd42V1dXbNu2DVeuXEFOTg4AoH379li2bBl69OjBUVLR1bp1a7x//573ul27drh9+zbCw8P5Cjjy8/ORmJhYrSsHaRj9+vXD6tWrsWPHDgQGBiIwMLDaGJZlISkpiRUrVqBv37687ZmZmZg4cSJGjhwpzMhCQ0uokDr16dMHlZWVCA0NrXO2Isuy6N27NyQkJHDv3j0hJhQvJiYm0NDQqFc7oGHDhuHt27d48uSJEJKJh9LSUri6uuLMmTMoLCys9r6cnBwmTZqEuXPn0gFdyGi5Am6Ulpbi6tWruHv3brWHExYWFhg+fDh9FwTsw4cPOH/+fI2fQd++fTF+/Hi6ASUkERERCAkJqfG7YGZmxnU8QgTqwIEDcHV1xfjx47F+/Xo4OjoiOjoaL168APC5O8fJkydx7Ngx2NjYYPPmzRwnJkSwvLy8cPz4caSmplZ7T0tLCzNnzoS9vT0HycQLnasS8m2+dYkJ8m1on8St1atXQ1dXlzolEvL/unTpAnV1db4lIRYtWoTr16/jzz//hImJCW/7uHHj8OLFC3rOICQVFRX48OEDZGVl+TqkkIb1yy+/wM/PD6GhoWjZsiUePXrEu4+6bds23lJzW7Zswd27d2FpaYnDhw9zHVtkxcbGwt3dHaGhocjOzuZtV1ZWRp8+fTB16tRqRWeijgo4SJ06duwIY2NjeHl5fXWsvb09Xrx4gWfPngkhmXjq06cPWrduDV9f36+OHTNmDDIzMxESEiKEZKKvtLQUM2fORFhYGFiWRfPmzaGtrQ1lZWVkZ2cjJSUFubm5YBgGZmZmcHd351vehhBR8+zZMyxZsgSpqak1tiFlGAZaWlrYtWsX30UfaTjBwcFYuXIl8vLyav0MFBUVsXXrVvTv35+DhOLhzZs3WLFiBaKjowFUb9MLAF27dsX27durtWAkRFSMHj0aiYmJCAkJQfPmzasVcFTx9PTEpk2bsGHDBnp4TUQSy7JYvnw5rly5wjseKCkp8a4ZPnz4AODz8WH48OHYvXs3l3FFGp2rEgIEBATUe4mOyspKrFq1ireMMmlYtE8ipHabNm1CXFwcPDw8uI4iVkaOHImEhATcunULqqqqKC4uhoWFBSorKxEeHg5JSUne2IEDB6K8vBzBwcHcBRYxp06dAsMwGDduHBXvceTmzZuYO3cutm7dyuvYtHTpUly5coWvoJVlWcjKyuLcuXNiV0DAlfz8fBQVFUFOTo63tI04oiVUSJ1atGiBtLS0r45jWRZpaWlo0aKF4EOJsR9//BHXr19HdnY2lJWVax2XlZWFuLg4DBs2TIjpRJu7uzsePnyIFi1aYOXKlRgxYgRfgUZ5eTkuXbqEnTt3IiIiAm5ubrR8jQB0794dzZo1w7Vr1+jklkMpKSmYMmUKCgsLoaCggNGjR0NPTw8qKirIyspCQkIC/P39kZKSgqlTp8LX1xc6OjpcxxYpz58/x7x581BeXg4tLS1MmDCh2mdw7tw5pKSkYMGCBTh//jw6duzIdWyRk5WVBUdHR7x//x4SEhLo27cv3+fw+vVrhISEICoqChMnToSvry9UVFS4ji3yPn36hOTkZN6sRh0dHTRt2pTrWCItJSUFGhoaaN68OQDwOmCVl5fzrSnr6OiIQ4cOwcvLiwo4iEg6d+4cLl++jKZNm2LmzJlwdHTk64T18eNHeHp64sSJEwgICEC3bt3g6OjIYWLRROeqhHy2cuVKtGjRAubm5nWOY1kWK1aswJUrV6iAQwBon9T4FBcXIzo6GgkJCbxrhnbt2sHU1BQyMjJcxxM7MTExvAkRRHisrKwQFxeH2bNnY8yYMQgODkZhYSGsra35ijdycnLw9u1bdO3alcO0omfbtm3Q0tKCk5MT11HElpWVFe7cuQN5eXnetu3bt8PAwAAXLlzgW2puwYIFVLwhRM2aNRPrwo0qVMBB6tS5c2fcunULf/zxB5ydnWsdd+rUKWRnZ/Otj0Ya3qJFixASEoJFixZh7969NT4EysrKwuLFiyEnJ4eFCxdykFI0XbhwAQzD4MiRI+jSpUu196WkpGBra4u2bdtiwoQJ8Pf3pwIOASgvL4eysjIVb3Bs//79KCwshKWlJXbt2lVjO79FixZh2bJlCA4OxoEDB2iGaQM7ePAgysvLYWdnhw0bNlRbLqhfv35wdnbGunXr4OXlhUOHDlGbPwE4cOAA3r9/DxMTE+zevbvGDhupqalYunQpnj59igMHDmDDhg0cJBUPDx8+xOHDhxEREYGKigredklJSfTo0QNz5sxB9+7dOUwo2r68uJaVlQXw+WF1q1ateNsZhoGGhgYSEhKEnk8c+Pv7Y/Xq1Zg7dy7mzZtX6zgXFxccOnQIO3fuxIgRI4SYUPT99ddfYBgGe/bswYABA6q937JlS8ybNw8dOnTAzz//jL/++osKOASAzlUJ+Z/58+fj5MmTtXZ1qCreuHz5MjQ0NIScTjzQPqnxqKyshKurKzw8PFBQUFDtfQUFBTg7O2P27Nl8D7AJEUUzZszA9evX8eLFC2zZsoXXcfqfzxNu3LgBlmXRo0cPjpKKJiUlJXpAzTEJCQmoqqrybZOSksLs2bPpuQ6HkpOTqxVYimthKy2hQuoUFhaGyZMnQ0JCAtbW1pg8eTIMDQ3RtGlTfPr0Ca9evYKHhwcCAgLAsixOnTpFN8YbiL+/f43bk5KScOLECUhISGDQoEHQ19fnteSNj49HYGAgWJbFjBkzoKOjAxsbG6HmFlUmJibQ0NDAtWvXvjp22LBhePv2La0LKACjR49GUVERAgMDuY4i1szNzVFUVISQkJA6Lzby8vLQt29fyMrK4sGDB0JMKPp69uyJiooK3L9/v86Cpk+fPqF3796QlJTEw4cPhZhQPPTt2xcfPnzAzZs3q130fSkjIwMDBgxAy5YtaWkzAXF3d8euXbtQWVnJ2yYrK4vi4mLeawkJCSxbtgzTpk3jIqJIGzJkCMrKynDr1i0AwO+//47z58/jyJEj6NevH29cZWUl+vTpg6KiIjx69IijtKJr9uzZuHPnDoKDg+vcJ6Wnp8PKygpWVlZwdXUVYkLR17lzZ6ioqODmzZtfHTtgwABkZWXh8ePHQkgmXuhclZDPrl+/jsWLF6N58+bw9PREu3bt+N6vWvapqnjDw8ODlvwTANonNQ4sy2LBggUICgoCy7Jo1qwZtLW10apVK2RmZiIlJQX5+flgGAYDBw7EwYMHuY4sNmpbfpEIXkFBAby9vZGYmAgNDQ2MHTu22oTRvXv3Ij4+HnPnzkWHDh04Sip6Fi5ciLt37+LBgwfU+YcjaWlpaNq0aZ2d7qtkZ2fj06dPVOwqQH5+fjh8+DBSU1OrvaetrY05c+aI3bNO6sBB6tSjRw8sXrwYe/fuxeXLl3H58mUAgJycHIqKigD8b633JUuWUPFGA1q1ahXfWltfqvo3DwgIqPW9I0eOAIDY7dQERVFREXJycvUaKysry2shThrWqFGjsGvXLkRFRcHU1JTrOGKrsLAQBgYGX60UV1RUhJ6eHl6/fi2kZOKjtLQU+vr6X+1G07RpU+jq6iI+Pl5IycRLTk4ODA0N63xQCgCqqqowNDSkz0FAIiIieO2+Bw8ejGnTpsHQ0JB3vhoXFwd3d3dcv34dO3fuROfOndGtWzeOU4sWQ0NDBAcHo7S0FE2aNIG5uTnOnTuHAwcOoEuXLrzzooMHD+LDhw/o3Lkzx4lF06tXr6CiovLVfZKamhpUVFTw6tUrISUTH3JycmjZsmW9xrZs2ZJ3TU0aFp2rEvLZkCFD8Ntvv+G3337D9OnTcf78ed4xgoo3hIf2SY3DhQsXEBgYCHl5eSxZsgT29vZ819OlpaXw8vLC3r17ERQUBH9/f7qnSkReVdeZuixevFg4YcTMnDlzcPv2bWzevBkbNmyo9TkQEZz+/fvDzMwMZ86c+erYRYsWITIyEjExMUJIJn42bdoET09P3rPNFi1a8Aosc3JykJycjNWrV+PZs2dYu3Ytx2mFhwo4yFf99NNP6NixI44cOYKoqChUVFSgsLAQwOeW1N26dcPs2bO/uqYm+TZUDNO4/Pjjj7h+/Tqys7PrrMrMyspCXFwchg0bJsR04sPZ2Rnh4eGYN28efv/9dwwcOJBOcDmgqamJ3Nzceo3Ny8uDpqamgBOJH11dXbx//75eY9+/fw9dXV0BJxJPampqKCkpqdfYT58+QU1NTcCJxJOHhwcYhsHSpUsxY8YMvvfk5OTQuXNn7N+/H25ubti5cyc8PDyogKOB9evXD4GBgbh//z4sLS3Rv39/tG/fHs+fP4elpSXatWuH7OxsZGRkgGGYap8TaRiZmZn1XpdXTU2NCjgEwNTUFPfv30dBQUGNLfKrFBQU4PXr1+jTp48Q04kPOlcl5H/GjRuHDx8+YP/+/Zg2bRo8PT2hqKjIWzZFXV2dijcEjPZJjYOPjw8YhsGBAwfQu3fvau83adIEEydORNu2bTF9+nT4+vpSAYeQUIN4bjx69KjGZcKJcOTn5+Onn36Cq6srnj17hlGjRqFdu3Z1TiCl50UN71v2P7SvEoxbt27hzJkzkJKSgpOTE6ZNm8a3FG9mZiZOnjyJU6dOwdPTE71794aVlRWHiYWHCjhIvfTu3Ru9e/dGcXExkpOTeesPaWtr17srAfk2p0+f5joC+cKiRYsQEhKCRYsWYe/evdXayQGfizcWL14MOTm5ausFkoYxdepUsCyL3NxcLFiwAAoKCmjbti1kZWVrHM8wDDw8PIScUvSNHj0ae/fuRWhoaI03PqqEhoYiNTUVS5cuFWI68eDg4ID169fjwoULGD16dK3jLly4gIyMDMydO1eI6cTH4MGD4ebmhufPn+OHH36oddzz58/x+vVrzJo1S4jpxEd0dDRatmyJ6dOn1zlu2rRpcHNzQ1RUlJCSiY8hQ4agSZMmvHaikpKSOH78OFatWoX79+/j+fPnAD7Poli6dCkGDRrEZVyRJScnh+zs7HqN/fDhA5o2bSrgROJn3rx5uHfvHn755Rfs3Lmzxk5ZpaWlWLt2LSorK+n4LCB0rtp45OXlwd3dHXfu3EFKSkqdXWcYhqFZjQIyZ84cZGdn48yZM5g5cya0tLQQEBAAdXV1nDp1ioo3BIz2SY3Dy5cvoampWednAHy+B66lpYXY2FghJSPr1q1Dfn4+1zHEzvjx46Gvrw87OzuMGjUKSkpKXEcSK05OTmAYBizLIjY29qv7HDpP4lZhYSGkpaW5jiGSzp07B4ZhsHnz5hrvcbdq1QorVqyAkZERVqxYgbNnz4pNAQfDUtkQIYR8lb+/P5KSknDixAlISEhg0KBB0NfXh7KyMrKzsxEfH4/AwECwLIsZM2ZAR0enxt9D1fvfp76zSqswDENraApARUUF5s+fj4cPH2L+/Pmwt7eHvLw87/2ioiL89ddfcHFxQc+ePXHw4EFISEhwmFg0bdq0CefPn8f48eMxadIktG3blvdeUlISPD09ee//8ssv3AUVYcXFxZg8eTLS09Oxbt26Gh9KBwUFYePGjWjdujVOnz5Na5sKQMeOHWFsbAwvL6+vjrW3t8eLFy/w7NkzISQjwOfZEm/fvoWMjAz09fUhJUVzCATFyckJERER8Pb2/mpR2dixY2FqaoqzZ88KMaHoCw8PR1RUFA4ePIjmzZvDzs4Oenp6vGuG169fw8fHB7m5uZg/fz66du1a4++h2XXfh85VG4f09HQ4Ojri3bt39Z6xSA9MBWvp0qW4cuUKGIaBqqoqTp06BW1tba5jiTzaJzUOJiYmMDQ0hLe391fH2tnZ4dWrV3jy5IkQkhHCjR9//BE5OTlgGAZSUlKwsrLC2LFj0bdvX+p2LAT9+/f/5p+5deuWAJKILyMjI3Tr1g2enp61jiktLUVYWBh++uknaGpq4vr160JMKB5+/PFHNG3aFHfu3Pnq2H79+uHTp0/4+++/hZCMe1TAQQgh9WBkZMSrigVQ44lsXe9VoWKC7+Pn5/fNP2NrayuAJOJt8uTJYFkW0dHRqKiogJSUFNTV1aGkpIQPHz4gPT0dZWVlkJKSQpcuXWr8TlB3lO8zYMAAAEBGRgYqKioAAFJSUmjRogVycnJQXl4O4PMs+Kp1rv+JYRgEBQUJJ7CIWr16NUpLS3H9+nVUVFSgdevWaNeuHe+7kJiYiIyMDEhJSWHw4ME1zsRmGAZbtmzhIL3o6NOnD1iWRWhoaJ3jWJZFnz59wDAM7t27J6R04uHmzZsAAAsLixr/zolwnD17Fhs2bIC+vj7c3Nxq3P+/f/8e06ZNw+vXr/HLL79g0qRJHCQVXVXXDMDnfU5t1wx1XS/Q7LrvR+eqjcOKFStw8eJF6OnpYfHixejcuTNUVFTooZCApKWlfXVMWVkZlixZgtTUVOzfv7/GiSdV3bRIw6F9UuMwcOBAZGdn4969e3wFNP9UWFiIPn36QElJiXeOS4goKi8vx82bN+Hj44PQ0FBUVFSAYRi0bt0atra2GDt2LHVoIiLFxcUFhw4d4r3+2nXZPzk7O2PlypWCiCbWOnXqBCMjo3pPyoqNjcXTp0+FkIx7VMBB6qWgoAAPHz5EamoqCgsL65w9MW/ePCEmI0Q4nJycGuT30NI4RBR8ayeUmlB3lO9Dn0Hj8M/ivn+DPofvN3fuXNy6dQsrV66Es7NzreM8PDywdetWDBo0CAcPHhReQDFgbGwMdXV1mhHEsbKyMjg4OODFixdQUFDAyJEj0blzZzRr1gz5+fl49OgRLl++jIKCAnTo0AF//vkntYFtYP9mJl1N6Lv0feg8qXEwNzdHfn4+AgMDoaamxnUckWdsbPzdv4MKyASD9kmNw7p16+Dl5YURI0Zg27ZtkJSUrDamoqICq1atwuXLl2Fvb48NGzZwkJQQ4Xv//j38/Pzg6+uL5ORk3kPtHj16YOzYsRgyZAgtv0j+81xcXODi4sJ7Xd/7efLy8rC2tsaaNWuoq64A9OvXD8XFxQgNDa3z/kRZWRl69+4NWVnZenXrEAVUwEG+6tixYzh8+DBKSkrqHFdVsUYXFA2j6uK7Xbt2uHLlCt+2+qKLb0KIIPybTig1oe4o/15YWFiD/J4ePXo0yO8RV19e+H0PKn79PmFhYZg8eTIkJCRgbW2NyZMnw9DQEE2bNsWnT5/w6tUreHh4ICAgACzL4tSpU7Q8QQPr1asXNDU16zVjgghWZmYm5s+fj0ePHlWbTVR16W9qaor9+/ejVatWXEQkRODoXLVxMDExQdu2bXHx4kWuo4iFhigSAGgZG0GgfVLjkJqailGjRqGkpATq6uqYOHEiDAwMoKKigqysLMTFxcHT0xPv3r2DrKwsLly4QN0HiFiKiIiAj48Prl+/jqKiIjAMAwUFBYwYMQJjx45Fx44duY5IyL+Sn5+PvLw8AJ+vjQcOHIhOnTph3759NY5nGAYyMjJQUlISYkrxs3TpUgQEBGDq1KlYsWJFreN27NgBd3d3jBw5Ejt37hRiQu5QAQepk6enJzZu3AgAaN26Ndq3bw9lZeU6Wwtt3bpVWPFEWtXFt66uLq5evcq37VvQxTcRJcbGxujWrRvOnDnz1bFOTk6IjIykIiZCCBEDR48exd69e/nOUeXk5FBUVATgfw+ulyxZglmzZnGSUZTNmjULjx8/xv3792uczUiEi2VZ3Lx5Ezdu3EB8fDwKCwshLy8PQ0NDDBo0iLcMFyGECNLw4cPBsizvfgYhhHDt/v37WLx4MXJzc2td5qx58+bYu3cvzM3NOUhISONRVFSEM2fO4MCBA7yle4HPzycmTpwIGxsbSElJcZiQkO+zevVq6Orq0j0ijsXGxsLOzg4VFRXo2rUrJk+eXK3A0sPDA48ePYKkpCS8vb0brHC5saMCDlKnYcOGISkpCfPmzcPs2bPphiwhhFNGRkbo1q0bPD09vzrWyckJERER1BWIEELERGhoKI4cOYKoqCi+G0ySkpLo1q0bZs+eTTdiBSQsLAzOzs6YM2cO5s+fz3UcQgghjcDRo0exb98+XLp0Cfr6+lzHIYQQAMCHDx9w9uxZhISEIDExkVfoqqurCwsLCzg6OtJsayLWWJbF3bt34e3tjeDgYJSVlQEA9PT0kJOTg+zsbDAMA319fRw7dgzq6uocJ/5v+paieklJSSgoKKBNmzYwMzPDyJEjaT9FRIq/vz9+/fVXlJWV1VpgKS0tjY0bN8LGxkb4ATlCBRykTiYmJmjevDlCQkK4jkL+heLiYsjKynIdg5AG8y0FHHZ2doiNjcWzZ8+EkIwQQkhjUVxcjOTkZN7NWG1tbcjJyXEdS6SlpaXh8uXLOHDgACwsLGBraws9Pb06z0M1NDSEmJAQQoiwlZeXY8aMGUhPT8eOHTtgYmLCdSRCiBir6lCsr69PXQMIqUFycjJ8fHzg7++PzMxMsCwLWVlZDBs2DA4ODujSpQvKy8tx8+ZNuLi4IC4uDoMHD8aBAwe4jv6f9G87CFQtabNlyxYMGjSogVOJr4yMDERERCA9PR3FxcW01DEH4uPj4ebmhpCQEGRlZfG2q6iowMLCAtOnTxe7onAq4CB1srS0hIqKCry9vbmOQr5BYWEhTp8+jVOnTuH+/ftcxyGkwdS3gCMhIQG2trZQVlbGrVu3hJSOEEIIEU/GxsbfNJ5hGFrijBBCRNzq1atRXl6Oq1evoqKiAkZGRtDR0am1uI9hGGzZskXIKQkh4sLIyAitW7fG3bt3uY5CSKNRUlKCq1evwsfHB5GRkQA+z3T/4Ycf4ODgAGtraygoKFT7ucLCQgwYMAAsy+Lhw4fCji0S3r59i8DAQOzatQudOnWCvb09jI2NIS8vj8LCQsTGxsLb2xtPnjzB0qVL0adPH7x+/Rre3t4ICQmBtLQ0vL290b59e67/U/7T8vPzsXHjRly5cgWVlZW87V929F64cCECAwPh6+srNkt3cK2goIA3KaumfZC4oHJTUqe+ffvi0qVLvC8LadwKCgrg4eGBU6dOIS8vj+s4hHy3qr/nLz179qzONnOfPn1CdnY2AKBPnz4CzUcIIYSQzzf5BDmeVDd58mQAQJs2bbB161a+bfXFMAw8PDwaPBshhACAn58fGIbh7fNfvHhR5/KWVMDR8Oi4QMj/NG/eHGpqalzHEGvHjh2Dra0tWrVqxXUUAmDt2rW4evUqioqKwLIsmjVrhhEjRsDBwaFagf6mTZtQVFTEO07Ly8tDX1+fV/RBvl16ejp27dqFiRMnYvXq1dXeNzY2hq2tLbZt24Zdu3ahY8eOGDJkCIYMGYItW7bg1KlTOHnyJLZt28ZBetFQUlKCKVOm4MWLF5CVlUWnTp0QFxeHjx8/8o2zt7fH9evXERQURAUcQqKgoCDWhRtVqICD1Gnu3Lm4ffs2fv31V2zZsgUyMjJcRxI7nz59wvHjx3Ht2jW8efMGMjIy+OGHHzBr1iz07NkTAFBRUYGTJ0/i2LFjyM/PB8uyaNWqFaZPn85xekK+T35+Pt6+fct7zTAMPn36xLetNr1798bixYsFGY8QQkgjUlhYiL///hupqakoLCystUiAYRjMnTtXyOlEW1VLaiI8YWFhAIB27dpV21ZfNa0tSwghDYVaT3OvPseFqmMBy7J0XCAizdDQEAkJCVzHEGt79uzhLbloZ2cHS0tLSEpKch1LbFV1XDc1NYWDgwOGDh1a67Ofy5cvIzc3l6/Qsm/fvtDS0hJKVlF0+PBhyMvLY/ny5XWOW7p0Kfz8/HDkyBGcOHECwOeOEOfPn//m6z/C79SpU4iJiYGpqSn27duH1q1bw9HRsVoBR8+ePSEtLY179+7R+a2AlZaWIiYmBunp6SgpKYGNjQ3XkThFS6iQr0pKSsLy5cuRnp6OESNGQEtLq851xMX9S9WQysvL4eTkhEePHlV7CCElJYWDBw/CxMQEP/30E54/fw6WZaGhoYEZM2bAzs4OTZo04Sg5IQ3j7du3vGINlmUxZcoUGBoaYu3atTWOZxgGTZs2hZaWFlq2bCnMqIQQQjh08uRJHDhwACUlJbxt/zx3qpoFzDBMnTOACfkvqLpZJyMjAxMTE75t36JHjx4NmosQQkjjUddxobi4GImJifjrr7+QmpqKFStWoH379nRcICIrMDAQ8+fPx/r16zF+/Hiu44ilOXPmICQkBOXl5WAYBsrKyhg9ejTGjh3LV5RMhGPbtm2wt7eHnp7eV8f++OOPyM3NpevoBtSzZ09oa2vDy8vrq2Pt7e2RkpLCt1yNjY0NEhIS8OTJE0HGFGk2NjaIj4/HjRs3oKGhAQBwdHREdHR0tb91a2trZGVl0ZJBAlJeXo5Dhw7hzJkzKCgo4G3/8nP49ddfcf/+fbi7u0NHR4eLmEJHHTjIV7148QJZWVnIysrCH3/88dXxVMDRcP766y9ER0eDYRhYW1ujc+fOKCkpQXBwMKKiorBt2zaoqKjg2bNnUFVVxfz582FjYwMpKfpqE9HQpk0btGnThve6e/fudFOJEEIIH19fX2zfvh3A57WtTUxMoKKiAgkJCY6TiQ9/f38oKyvDwsLiq2Pv3buHrKwsumb4TjWdC9H5ESGEkC997bjQr18/ODk5Ye3atTh48CB8fX2FlIwQ4Rs0aBCWLVuGLVu2ID4+Hra2ttDT06Nu00J0+PBhZGdnw8/PD76+vkhISIC7uzvc3d3RtWtX2NnZYdiwYZCVleU6qlhYtWoV1xHEWmlpKd6/f1+vse/fv0dpaSnfNmlpaUhLSwsimthITk6GpqYmr3ijLs2aNUNycrIQUomfiooKzJ49G6GhoQA+Pw/6+PEjioqK+Mb17dsXXl5eCAwMxIwZM7iIKnT0lJfUKSgoCEuWLAHLsmjatCk0NTWhpKTEdSyxcfXqVTAMgw0bNsDe3p63fdasWVi+fDkuXbqElJQU9OnTB/v27aN1oYjIO336NNcRCCGENDKnT58GwzBYsWIFpk6dynUcsbRq1SqYmZnVq4Dj6NGjiIiIoAIOQgghpBGQlJTEL7/8goCAABw4cAA7duzgOhIhAmFsbMz7/56envD09KxzPMMwiImJEXQssaOsrIwZM2ZgxowZePToEby9vXH16lVERUUhOjoamzdvxrBhwzB27Fh07dqV67iECIyenh6eP38OPz8/2Nra1jrO398fGRkZ6NSpE9/21NRUek7XAOq7jFNubi7k5eUFnEY8/fnnn7h37x7atWuHPXv2wMjIiNcJ5UsWFhaQlJTEnTt3qICDEAA4cuQIAMDBwQHLly9Hs2bNOE4kXuLi4qCoqMhXvFFl1qxZuHTpEqSlpbF9+3Yq3iCEEEKIWEpISICysjIVb3CMVuZs3PLz83H79m28f/8eP/zwA3r16sV1JEKIGGFZFklJScjJyUF5eXmt47p37y7EVKSKgoIC9PT0cP/+fa6jECIw33quSue2gtelSxd06dIFa9euxbVr1+Dj44OIiAj4+PjAx8cHurq6sLe3x+jRo+lBNRE5kyZNwqpVq/Drr7/i1atXsLOz41vO5vXr1/Dx8cGpU6fAMAwmTZrEe+/x48fIycmBubk5F9FFhqamJpKTk1FUVAQ5Oblax2VmZiI5OZm3dClpWP7+/pCQkMDevXvRvn37WsfJyMhAS0sLr1+/FmI6blEBB6nT69ev0aJFC/z+++9gGIbrOGInPz+fr0L8S1XrPOno6EBZWVmYscTS5MmT6z1WUlISCgoKaNOmDczMzGBpaUnL2hCRkpaWVu+xkpKSkJeXpyKzBhYeHl7vsRISErx9En0ORBTJyspCTU2N6xiknjIzM6lVtYAEBATg+PHjcHR05CsAT0hIwLRp05CRkcHbZmNjg61bt3IRU6StXr263mP/ec1Q23Uf+XZ0rtp45OXlYffu3bh06RKKi4vrHEuz3bmVl5eHvLw8rmOIJNonNQ43b97kOgKphYyMDGxsbDBixAicOnUKe/bsQUVFBRISErBjxw7s2bMHw4YNw7x586Ctrc11XEIahI2NDWJiYnDq1Cn88ccf+OOPPyAlJQU5OTkUFxejrKwMwOdisilTpmD06NG8n3327BksLS2pq+V3srKywvHjx+Hq6oply5bVOm737t1gWRYDBgwQYjrx8fr1a2hoaNRZvFGlefPmePPmjRBSNQ70RJHUSVZWFhoaGlS8wZGKigo0bdq0xveaNGkCAFBUVBRmJLEVFhYGALzvQk2V+DW95+HhAQ0NDezatYta/xGR0b9//28+LigqKsLMzAyOjo7o3bu3gJKJDycnp391bG7fvj0mTpxYY2cn8u2+5WHbl4U0ZmZmcHBwgL6+vgDTiY+uXbsiMjIS5eXlVDApJGlpaXj79i3ftvz8/DqLy0pKShAeHo6kpCR06NBB0BHF0rVr1xAbGwtTU1O+7Vu3bkV6ejpat24NPT09REVFwd/fHxYWFhg+fDhHaUWTn58fgG+7Zqh63aVLF2zZsgW6urrCiCrS6Fy1cSgoKMC4ceOQlJQEVVVVSEhIoLCwEN26dUNOTg6SkpJQXl4OGRmZam3BiXA9fPgQb9++pQejAkL7pMahTZs2XEcgtajqNHDx4kVkZ2eDZVkoKirC2toaWVlZuH37Ni5evIgbN27gxIkTMDMz4zoyIQ1izZo16NWrF06cOIHo6GiUlZUhNzcXwOd7SKamppgxYwYsLS35fm7ixImYOHEiB4lFy9SpU+Hl5QU3NzdkZ2fDwcEBFRUVAICcnBy8evUKJ0+exO3bt6Guro4JEyZwnFg0VVRU1Ht5mqKiIrGaEER3OEmdunfvjvv376O0tJRXMECIODp16hQeP36M/fv3Q01NDaNHj4axsTHk5eVRWFiI2NhYXLx4Ee/evcOCBQtgaGiI+Ph4+Pv7Iy4uDjNnzoS/vz80NTW5/k8h5LtpaGgAAN6/f89rgaygoMD7PhQUFAAApKSk0Lp1axQXF+Pjx4+4efMmbt26BWdnZ6xcuZKz/KKgqr10dHQ0ysvL0bRpU7Rt25b3GSQlJeHTp0+QlpZGly5dUFxcjOTkZMTGxmLdunW4d+8e9u/fz/F/xX/ft7TVraioQG5uLnJzcxETE4OzZ89i5cqVcHJyEmBC8TB37lxMmDABR44cwbx587iOIxZ8fX1x6NAhvm1xcXH17lhGRWSCERsbC0VFRb7Wu1lZWQgNDUWrVq0QEBAABQUFBAcHY/bs2fD19aUCjga2detWvHnzBkePHoWMjAwGDhwIIyMj3vH55cuXCAoKQklJCWbNmgUlJSW8fv0aN27cQHR0NKZMmQJ/f39qE/6d6Fy1cXB3d0diYiLGjx+P9evX89ayPnPmDIDPHR9OnjyJY8eOQVtbG5s3b+Y4seipq7CSZVlkZWXh0aNH8Pb2BgCMHDlSWNHECu2TCKmusLAQAQEB8PHxwePHj3nX1t26dYO9vT2GDRvGm9SYlZWFvXv3wsfHB7t378a5c+e4jP6f5eLi8q9/tqSkpAGTkC9ZWVnBysoKRUVFSElJQWFhIeTl5aGtrV3nsh7k+7Vs2RJHjx7FnDlz4OfnB39/f957VUuOsiwLFRUVHD58mLpjCYiamhpSUlK+OikrLy8PiYmJMDQ0FGI6bjEsLehG6vD69WvY2dnB3t4ea9as4TqO2DEyMoKGhgbGjBlT4/suLi51vg+AHmQ0kLi4ODg4OMDKygrbtm2rsaCprKwMq1atwq1bt3D+/Hm0b98eFRUVWL58OQICAuDo6Ih169ZxkJ6Qhrd9+3acPn0a06dPh729PV9x0tu3b3kVzJMmTcLKlSvx8eNHeHt7w8XFBaWlpTh06BD69+/P4X/Bf1tlZSUWLVqE0NBQrFq1CqNGjeLr2FRaWooLFy5gx44d6NWrF/bv3w+WZXHt2jX8/vvvyMvLw9atW6ndYgM4c+YMtm/fjiFDhsDe3r5acZ+XlxeuXbuGFStWwM7ODq9fv4a3tzf+/PNPAMDp06dpBtE3qK0F9Z07d7BlyxaYm5vDwcEBbdu2haysbK2/p+pGOvl3PDw84OHhwXv97t07SEtLQ0VFpcbxDMPw1isdMWIERowYIayoYsXMzAyampp8N54CAgKwZMkSODs7Y9WqVbztffv2RXl5Oe7fv89BUtGVnp4OW1tbGBoaYt++fWjZsmW1MTk5OVi4cCFevXoFHx8faGhooLCwED///DPCwsIwffr0Olv4kvqhc1XujR49GomJiQgJCUHz5s15BRwvXrzgG+fp6YlNmzZhw4YNVODXwIyMjL7a+aHqtvCAAQOwb98+SEtLCyOa2KF9UuNSXFyMqKgoJCYm8h6W6urqwtTUtM5rCPL9IiIi4OPjg2vXrqGkpAQsy6Jly5awsbGBvb092rVrV+vPDhgwAB8+fEB0dLQQE4uO+hwTasOyLBiGqXYMJ0QUfPjwAW5ubrhx4wZSU1N529XU1DB06FDMnDkTysrKHCYUbRs3bsTZs2exYsUKTJ06FQBqvG7YuXMn3N3dMXv2bCxcuJCruEJFBRykTuHh4Xjy5An27t0LQ0NDjBkzBlpaWnVW/1XNCibf72snVv9su1sTOrFqGAsWLMC9e/dw7969Ov/+i4qK0Lt3b1hYWODAgQMAPp8EWFhYQFNTE9evXxdWZEIExtfXF7/88gt27txZ50O4K1euYNmyZdi4cSPs7OwAAN7e3li7di0sLS1x5MgRYUUWOSdPnsSOHTvg7u7OqwqvyYMHDzB16lSsWLEC06ZNA/B57d+5c+eiZ8+efA9gybe7desW5s6di+XLl/P+fWvyxx9/YPv27XBxceGtmXns2DHs2bMHQ4YMoW4o3+Bblq2pDcMwiImJaYA0pIqRkRG6desGT09PrqOItY4dO0JfX5+vgGPTpk3w9PSs9uDH3t4eL168wLNnzzhIKrpWr16NK1euIDg4uM4uGtnZ2bC0tIS1tTW2bdsG4PMDvIEDB0JPTw+XL18WVmSRROeqjUPXrl2hqqqKa9euAQAmTZqEyMhIPH36lG92Hcuy6N27NzQ1NfHXX39xFVck1dXpjWEYyMnJQUdHB5aWlnVeU5DvQ/ukxqO0tBSurq44c+YMCgsLq70vJyeHSZMmYe7cudSJWgCGDBmClJQUXjFAr169YG9vj4EDB9areMzJyQkRERF0r/tfaojun6dPn26AJKQmmZmZSE9PR0lJCT1f41BxcTHy8vIgLy9PHTeEJC0tDdbW1igrK8Ps2bPh4OCARYsW8Qo43r59i5MnT+LMmTNo3rw5rl69KjYdK6mAg9SpqoCg6sTqa+iGeMOiE6vGw9zcHG3atIGXl9dXx9rZ2SEtLY1vRuPIkSORmpqKR48eCTCl6Lt58yYAwMLCgi6mOTR27Fh8+PABt2/f/upYKysrKCkpwcfHB8DnzhE//vgjpKSkaNbvdxg5ciQ+ffqEGzdufHXs4MGD0bRpU1y6dIm3rXfv3igvL8fDhw8FGVPkTZo0CUlJSbh3716d41iWRZ8+faCrq8trG15aWoqePXuiWbNmuHv3rjDiigQjI6MG+T2xsbEN8nvIZ35+flBWVkbfvn25jiLW+vbti6KiIjx48IB3E3zo0KFISUnBgwcP0Lx5c97YUaNG4f379/j777+5iiuSLCws0Lp1a955T13GjBmDzMxMhISE8LYNGzYM6enpNLP0O9G5auPQtWtX6Ovr866hZ86ciXv37uHu3bto1aoV31g7OzskJSUhIiKCi6iECBTtkxqH0tJSzJw5E2FhYWBZFs2bN4e2tjaUlZWRnZ2NlJQU5ObmgmEYmJmZwd3dnTrSNDAjIyO0bt0aY8aMgZ2d3TcvMx0SEoKsrCzY2toKKCEhwuft7Y0TJ04gOTkZQPXnazt27MCzZ8+wc+dOqKqqchWTEIG6ffs2lixZwluuSUJCApWVlZCRkeF1a5KVlYWrq6tYFR3XvqAMIaD20lyj4ovGo7CwEB8/fqzX2JycnGqV/LKysv+6TR35n3nz5kFdXR23bt3iOopYS0hIgL6+fr3GqqioID4+nvdaQkIC2tra9PD0O6Wmptb7M1BUVOT7DACgTZs2VHDZAGJjY+ts8VqFYRi0adOG7+++SZMm0NXVRVxcnCAjihzadzROdBO1cejatStu3LgBFxcXzJw5EwEBAUhKSoKJiQlf8UZFRQVSUlKgpaXFYVrRlJubi2bNmtVrbElJCXJzc/m2KSoq4t27d4KIJlboXLVxaN26NbKzs3mvqx7UxcTEoF+/frztlZWVSEtLQ3l5udAzEiIMtE9qHNzd3fHw4UO0aNECK1euxIgRI/gKNMrLy3Hp0iXs3LkTERERcHNzw+zZszlMLHpcXV1haWkJCQmJf/XzFhYWDZyIEG6tWbMGfn5+YFkWUlJSYBim2vlQ+/bt4e7ujqCgIEycOJGjpIQIlpWVFby9vXHw4EEEBwfzCjmKi4shLS0NS0tLLFq0CHp6ehwnFS4q4CB1ooekhHymra2N+Ph4hISE1HnBEBISgjdv3sDQ0JBv+7t372pcA5t8mxYtWtCac42AtLQ0kpKSUFpaWmcnlNLSUiQlJVWbtVJRUQF5eXlBxxRpsrKyeP36NQoKCups6VdQUIDXr19XW8e3pKSk3g+YSO1YlsWbN2++2qmssrKSN+5LDMNARkZG0DEJIWJi2rRpuHnzJo4dO4Zjx44B+Lyf+ecST2FhYSgpKUGnTp24iCnS1NXVkZiYiGfPnqFjx461jnv69CkSEhKgo6PDtz0zMxMtWrQQcErRR+eqjYOhoSGCg4N5n4O5uTnOnTuHAwcOoEuXLrzCsoMHD+LDhw/o3Lkzx4kJEQzaJzUOFy5cAMMwOHLkCLp06VLtfSkpKdja2qJt27aYMGEC/P39qYCjgX25nB8h4u7y5cvw9fVFq1atsGHDBvTt2xdOTk7VOvH1798fDMPg1q1bVMAhIElJSbh79y5SUlJQVFRU7d5dFYZhsGXLFiGnEx96enrYt28fysrKkJycjLy8PMjJyaFt27Zie+/035U7EkKImLG3twfLsli4cCE8PT2rddgoKiqCp6cnFi1aBIZhYG9vz3svLi4OmZmZDdb2XZx16tQJKSkpqKio4DqKWOvSpQsKCgp4a7bXZvv27cjPz0fXrl1528rLy5GUlFStbTL5Nj169EBJSQnWrFmD0tLSGseUlpZizZo1KCkpwY8//sjbXlJSgsTERKipqQkrrsjq0KEDPn78yHtQWpsTJ07gw4cP6NChA28by7JITk6mojQiMvLy8rBv3z7Y2tqiW7duMDY2rvV/X34XSMPp3LkzXFxcYGBgAGlpaWhra2PDhg0YOnQo37g///wTwOclAknDGjlyJFiWxezZsxEcHFzjmDt37uDnn38GwzAYOXIkb/ubN2+QlpYmdrOKBIHOVRuHfv36oaysjLfsQ//+/dG+fXs8f/4clpaWGDt2LCwtLXHkyBEwDIMZM2ZwnFh0vX79GuvWrcPQoUPRtWvXasdhb29vuLi4VLvPQRoG7ZMah7dv30JHR6fG4o0vde3aFbq6ukhLSxNOMEKIWPrzzz/BMAz27t0LKysrSEpK1jiuWbNmaNOmDV69eiXkhKKvoqICv/32G4YNG4atW7fizJkz8PX1hZ+fX63/I4InLS0NfX19mJqawsjISGyLNwDqwEEIIfUyadIkhIeHIzAwEJs2bcLWrVvRpk0byMvLo7CwEG/fvkVFRQVYlsXgwYMxadIk3s/evn0bBgYGGD58OIf/BaJhxowZcHZ2hqurK+bPn891HLH1888/IzQ0FOfOncPTp09ha2sLIyMj3vfh5cuX8PPzw9OnTyElJYWff/6Z97O3b99GcXExunfvzuF/wX/fwoULcffuXQQGBmLw4MGwtrau9hkEBATg3bt3kJGR4fu+XL16FWVlZXxFHeTfmTFjBsLDw7Fv3z48f/4cY8eOrfY5+Pj44MaNG2AYBjNnzuT97IMHD1BQUIBBgwZx+F8getLS0nDv3j0kJiaisLAQ8vLy0NXVRe/evdGmTRuu44ms9PR0ODo64t27d7XOVvlSfcaQf8fS0hKWlpZ1jtm0aRM2btxIs3kFYNasWbh37x4ePXqEOXPmQFlZGYaGhrzjwqtXr5CdnQ2WZWFqaopZs2bxftbHxweysrKwsrLi8L9ANNC5auMwZMgQNGnShLc0r6SkJI4fP45Vq1bh/v37eP78OYDPXRaXLl1K50QC4uvri/Xr16OsrIx3/P1n57i8vDwcOnQI7dq1o/sWAkD7pMZBUVERcnJy9RorKyvLt/wcaRiTJ0+u91hJSUkoKCigTZs2MDMzg6WlJaSk6FEWER2xsbFo3bo1zMzMvjpWSUmJlkEWgMOHD/MmN5iYmKBDhw5QVlaus8MuIcLEsHT3jBBC6oVlWXh6esLd3b3GSnwNDQ1Mnz4djo6OdKAXkLS0NFy+fBkHDhyAhYUFbG1toaenV215iC9V3TAkDev69etYs2YNCgsLa/x7Z1kW8vLy2Lp1KwYPHszbfvv2bSQmJqJv3771XgeY1CwyMhJLly5Fenp6rZ+BhoYGdu3aBVNTU9726OhovH//HiYmJlBXVxdmZJHk4eGBHTt2oLKyssb3WZaFpKQkVqxYgSlTpvC2X7hwAU+ePMHIkSO/OguMfF1ubi42bdqEgIAA3mfx5dI2DMNg+PDhWLt2LS1PIAArVqzAxYsXoaenh8WLF6Nz585QUVGh8yEilkpKSrB//36cP38excXF1d6XlZXF+PHjsXDhQrGeTSRodK7auGVmZuLt27eQkZGBvr4+PZQTkCdPnmDChAkAACcnJwwcOBBbt25FTEwMXrx4wRuXlpaG/v37w9raGrt37+YqrkijfRL3li1bhuvXryM4OLjOLohZWVmwsrLCsGHDsGPHDiEmFH1VnYmrvgM1PZaq6T2GYXj3Nr7sUEPIf5mJiQn09PT4ujo4OjoiOjqa7xgNADY2NkhJSUFUVJSwY4q0AQMGIC0tDVu3boWNjQ3XccReYWEh/v77b6SmpqKwsLDOpWzmzp0r5HTcoAIOQgj5F16/fo3ExEQUFRVBTk4Ourq61O5YCIyNjb9pPMMwVKEsQO/fv8e5c+dw7949JCUl8b4Pbdu2hYWFBcaPH4/WrVtzHVOklZSU4MqVKwgJCanxM7C2tqaHQ0IQGxsLd3d3hIaGIjs7m7ddWVkZffr0wdSpU2kZLQEqKCjAhAkTEB8fD5ZlYWBgAD09PaioqCArKwuvX79GXFwcGIaBvr4+zp07BwUFBa5jixRzc3Pk5+cjMDCQlmdqJDIyMhAREYH09HQUFxdj3rx5XEcSO4WFhYiIiKh2fDYzM6PuJ0JC56pE3C1YsACBgYFYv349xo0bB6D2h0N9+vSBrKwsAgMDuYgqFmifxK03b95g7NixMDQ0xN69e6GiolJtTFZWFhYvXoxXr17B19eXOvg1sLCwMDx+/Bj79++HmpoaRo8eDWNjY143mtjYWFy8eBHv3r3DggULYGhoiPj4ePj7+yMuLg4KCgrw9/eHpqYm1/8phHy3AQMGIDc3FxEREbxtNR2jS0pK0KNHD+jo6ODSpUtcRBVZJiYmUFFRwa1bt7iOIvZOnjyJAwcOoKSkhLftn6ULDMPwJmr98zxWVFEBByGEkP+Mf/MANDY2VgBJCCGkZvn5+bybsc2aNeM6jljYsWMH3N3doaWlhc2bN6NHjx7VxoSFhWHt2rVITU3FtGnTsHz5cg6Sii4TExO0bdsWFy9e5DqK2MvPz8fGjRtx5coVvs5AX97gWLhwIQIDA+Hr60vFZYQQIsL69OmDsrIyPHz4kLettgKOsWPHIj4+Ho8fPxZ2TEKEwt/fH0lJSThx4gQkJCQwaNAg6OvrQ1lZGdnZ2YiPO0lzqgABAABJREFUj0dgYCBYlsWMGTOgo6NT4++hWdr/XlxcHBwcHGBlZYVt27ahSZMm1caUlZVh1apVuHXrFs6fP4/27dujoqICy5cvR0BAABwdHbFu3ToO0hPSsFavXg1/f39s2bIFtra2AGo+Rru5uWHnzp2YMmUKVq9ezVVckTRw4EAoKirC19eX6yhizdfXF2vWrAHw+dlPVWGNhIRErT8jLhNUqEchIYSQ/wwqxiCENHbNmjWjwg0hu379OiQkJHDkyJFau2H16NEDhw8fxogRI3D16lUq4GhgmpqaKCsr4zqG2CspKcGUKVPw4sULyMrKolOnToiLi8PHjx/5xtnb2+P69esICgqiAg5CSIMIDw8HAMjIyKBTp058275F9+7dGzSXuMvJyYGhoWG9xtKyZ0TUrVq1ijd7FwACAgKqjal678iRI7X+Hirg+PcOHjwIhmGwadOmGos3AEBaWhobN27ErVu3cOjQIRw4cACSkpJYu3Ytrl+/jtDQUCGnJkQwpk2bhkuXLmHjxo1gGAYjRozge7+0tBSenp7Yu3cvZGRk4OTkxFFS0TVo0CCcOXMG2dnZdS6tRQTr9OnTYBgGK1aswNSpU7mO06hQAQchhHyj2NhY3lpcdaGLOkIIIUTw3r9/Dz09va8uZaanpwd9fX0kJycLKZn4GD16NPbt24f4+Hhan51Dp06dQkxMDExNTbFv3z60bt0ajo6O1Qo4evbsCWlpady7d09sZq5wIT8/H6mpqSgqKqp1/V6AHlgT0eDk5ASGYaCrq8t7KFq1rb5o+cuG16JFC2RkZNRrbGpqKj28ICKNjrfci4iIgJ6eHuTk5OocJycnBz09Pb6lJZSUlNCuXTukpqYKOiYhQmFgYIB169Zh/fr1WL16NX7//XfeeyNHjkRqaio+ffoECQkJbNiwgZYOEoCff/4ZwcHBWLRoEXbv3k3LmHEkISEBysrKVLxRAyrgIISQerp+/Tq2b9+Od+/e1Ws8FXAQUffgwQMEBwcjJSWlzocTDMPAw8NDyOlEX3l5Ofz8/HDnzp16fQZBQUFCTig+kpOT6/05bNmyRcjpRN/XWit+SUJCgh5OCMD06dPx4MEDzJs3Dzt27ICJiQnXkcRSQEAApKSksGvXrjpvPklLS0NbWxuJiYlCTCc+oqOjsXPnTkRHR391LD2wFiw6VxWeqgejGhoa1bYR7nTq1AnBwcGIjIxEt27dah0XFBSE3Nxc9O3bV4jpxA/tk7h1+vRpriOIvcLCwmqFxbXJycmpNnFOVlaWugURkeLg4ABtbW3s3r0bT58+5W2Pi4sDAHTo0AErVqzAjz/+yFVEkdasWTOcPXsWy5Ytw5AhQ2BhYQEtLS3IysrW+jM0AaLhycrKQk1NjesYjRIVcBBCSD3cvn0bixYtAsuyUFZWhpGR0Tc9MCJElJSWlmLhwoUIDg4GgDpnlQLUjlcQ8vLy4OzsjBcvXnz13x+gz0BQWJbF5s2bcfbsWbAsW6/vAhVwNLy+ffvC29sbqamp0NLSqnVcSkoK4uLiMG7cOCGmEw+//vorWrVqhYiICIwbNw5GRkbQ0dGp9cYHfRcEIzk5GZqamnwPUGvTrFkz6kYjAFFRUXB2dkZpaSmkpaXRpk0bqKio0HFYyOhcVfhqejBKD0u5N27cONy+fRtr166Fq6srdHV1q4159uwZfvvtNzAMg/Hjx3OQUvTRPomQz7S1tREfH4+QkBBYWFjUOi4kJARv3ryptgTUu3fv0LJlS0HHJESofvzxR3h5eSEjIwOxsbHIy8uDnJwcDA0N67y/QRqGn58foqKiUFxcjMDAwFrHsSwLhmGogEMAunbtisjISJSXl0NKikoWvkT/GoQQUg9Hjx4FAEyePBnLli2rda1GIhzp6em4fPkyXrx4gZycHJSVldU4jmauCMahQ4dw+/ZtyMrKws7ODl26dIGysjIVNAnRvn37EBMTg1atWmH69On0GXDE3d0dZ86cAcMwsLKyos+BI4sWLcK9e/cwZ84c7NixAx06dKg2JiYmBitXroSGhgYWLFjAQUrR5ufnx7em+IsXL/DixYtax1MBh+BISkrWa1xubi7k5eUFnEb8HDx4EKWlpRgyZAjWrVtHHX84QueqhHxmaWkJW1tb+Pn5wcbGBmZmZrzlBzZu3IhXr14hMjISlZWVmDRpUp1dOsi/R/skQj6zt7fHli1bsHDhQixduhQ2NjZ856NFRUXw8/PDnj17wDAM7O3tee/FxcUhMzMT/fv35yI6IQKnqqoKVVVVrmOIFS8vL+zYsQPA53//9u3bQ0lJiQophWzu3LmYMGECjhw5QgUy/0AFHKReqM0fEXcvX76EoqIiVq9eTQdxjnl6emLbtm0oKyvjfRZf7pO+3EaflWBcuXIFEhISOH78OMzMzLiOI5Zu3rwJKSkpuLu7w8DAgOs4YsvHxwcMw2Dv3r0YOnQo13HElqenJ6ysrHD+/HmMHTsWnTt3hr6+PpSVlZGdnY34+Hg8fvwYUlJSGDduHDw9PWv8PXSh+O/Rv13joKmpieTkZBQVFdW5tnhmZiaSk5NpqRsBePLkCRQUFLBz504q+OYQnasS8j9btmxBmzZt4ObmhtDQUN72qvOhpk2bYubMmXQsFyDaJxHy2aRJkxAeHo7AwEBs2rQJW7duRZs2bSAvL4/CwkK8ffsWFRUVYFkWgwcPxqRJk3g/e/v2bRgYGGD48OEc/hcQQkTJqVOnwDAMFixYgJ9++okKKzmipKSENWvWYMuWLXj69CkcHBzQtm3bOpeyqU/XUVHAsPXpu03E1r9p81fXbDtC/qu6d+8OHR0deHt7cx1FrP3999+YOnUqlJSUsGjRIpw6dQrx8fE4efIkcnJy8PjxY/j6+uLTp09Yvnw5DAwM0KNHD65ji5xOnTpBXV0dN27c4DqK2OrUqRO0tbVx5coVrqOINRMTEygrK+P27dtcRxFrRkZGfN0fqtS27Z+qCv7oHJb81+3ZswfHjx/H9OnTsWzZMgCAo6MjoqOj+f6+V61ahQsXLmDJkiWYOXMmV3FFkqmpKXR1deHj48N1FLFG56qEVJeTk4M7d+7g5cuXyM/Ph5ycHAwMDGBlZUXdggSM9kmE/A/LsvD09IS7uzvS0tKqva+hoYHp06fD0dGRJmURkREeHt4gv6d79+4N8nvIZ507d4aioiJCQkK4jiLWjI2Nv2k8wzCIiYkRUJrGhTpwkDpRmz9CPvvhhx/w6tUrrmOIvVOnTgH4/ICiZ8+e8PPzA/B5vUAAGDp0KGbOnImffvoJ+/btg6+vL2dZRZmSkhIUFBS4jiHWVFVV6VjcCCgqKtIN70bAxsaGbu4RAmDq1Knw8vKCm5sbsrOz4eDggIqKCgCfH9y9evUKJ0+exO3bt6Guro4JEyZwnFj0GBoa4t27d1zHEHt0rip833rjtSbidDOWCy1atMDo0aO5jiGWaJ9EyP8wDINJkyZh0qRJeP36NRITE3nd43R1daGnp8d1REIanJOT03ffs6DzpIbXvHlztG7dmusYYu9be0yIU08KKuAgdaI2f4R8NmvWLEyfPh1//vknxo0bx3UcsfXkyRMoKyujZ8+etY5RUlLCnj17MGTIELi6umLr1q1CTCgeLC0t4evri5ycHLRo0YLrOGJpyJAh+OOPP/Du3Tuoq6tzHUds9ezZE7du3UJxcXGdrf2IYG3bto3rCOQfWJbFx48fUVJSIjatLRuDli1b4ujRo5gzZw78/Pzg7+/Pe69Xr14APn82KioqOHz4MD1MEoDJkydjyZIlCAoKwsCBA7mOI7boXFX4GuJGqjjdjCXihfZJhNRMT0+PCjaIWKirc0Z0dDTKy8shJSUFVVVVqKioICsrCxkZGSgvL4e0tDS6dOkivLBipHfv3rh27RoKCgro2phDsbGxXEdotGgJFVInavNHyP94eXlhy5YtsLW15a3FJSMjw3UssdKxY0e0b9+e15Z6ypQpCAsLQ2RkZLW13keNGoW8vDzeElCk4WRnZ8PW1hYdO3bErl27qv3bE8ErKirCuHHjIC8vj3379kFNTY3rSGIpJSUFY8aMwfDhw/H7779TFwgi9h48eIATJ04gKioKJSUl1WYJHTt2DImJiVi5ciU9wBCgDx8+wM3NDTdu3EBqaipvu5qaGq9bGXUPEpz9+/fDw8MDc+fOxbhx4+hmIAfoXLXx+OOPP7Br1y706tULTk5O0NfX5z2YiI+Px+nTp/HgwQMsX74cU6ZM4TouIQJB+yRCCCH/VFlZiQULFiA0NBQ///wzJkyYwHfdUFBQgHPnzuHw4cPo3bs3Dhw4QPecGlhGRgbGjBmDnj17YsuWLfSchzQ6VMBB6tSvXz8oKyvTMgRE7NFaXI1Dnz590LJlS1y6dAkAsGjRIly/fh0XLlyAoaEh39gRI0YgOTkZT58+5SKqSPP390daWhpcXV3RokULWFtbQ0dHp84bUTY2NsILKAZcXFyQn58PT09PSEpKwsLCAjo6OnV2gZg3b54QE4qH8PBwPH/+HLt27UK7du1gZ2f31e8CrVkqfJWVlcjJyYGSkhLXUUSai4sLDh06xDeDmmEYvHjxgvf63Llz2LBhAzZv3owxY8ZwEVPsFBcXIy8vD/Ly8lRIIAQDBgwA8PlmYNXyNS1btqz1+MwwDIKCgoSWT1zQuWrjEBgYiAULFmDhwoWYPXt2reOOHj2Kffv24eDBg9S5hogk2icRUh3LskhKSkJOTg7Ky8trHUfXz0RUnThxArt378bRo0fRt2/fWsfdvXsXs2bNwvLlyzF9+nQhJhR9/v7+ePPmDY4cOYKWLVtixIgR0NLSouMzaTSogIPU6bfffoOvry9CQkJolhwRa0ZGRt/8M9T+qeHZ29sjJSUFDx8+BPC/m33z5s3D3LlzeeOSkpIwYsQItGzZEiEhIVzFFVlGRkZgGIb3kK4+FeBfPsAj3++fnwFQ++fAsmy1h6ikYVR9DvVFxX2CkZCQgHv37qFDhw58S/6VlpZi+/bt8Pb2RmlpKdTV1bFhwwb06dOHw7SiKSQkBDNnzoS8vDwWLVqEgQMHYsmSJXj06BHfvic7Oxu9e/dG//794erqymFiQgTjW68Z6PgsGHSu2jg4OjoiKSkJoaGhdX4GlZWV6NOnD9q1a4czZ84IMSEhwkH7JEL+Jy8vD7t378alS5dQXFxc51i6fiaibMSIEfj06RMCAwO/OnbQoEGQkZHhTWgkDYOOz41PWloaQkNDkZCQgMLCQsjLy6Ndu3bo3bu3WC7PK8V1ANK4LViwALdv38aaNWuozR8Razdv3uQ6AgHQs2dPPHv2DMnJydDR0YG1tTUOHjwIV1dXFBcXw8zMDJmZmThy5AgqKirQv39/riOLJJoBwT0bGxtqndgIiOPFQ2N09uxZeHp64vDhw3zbDx06BE9PT97rtLQ0zJ07F76+vrTWcgM7ffo0GIbBtm3bMGjQIAA13/xQVlaGuro6FbkKyNixYzF69GhYW1vTEikcOXXqFNcRCOhctbF49eoVdHV1v3rOKiEhgTZt2tCxgYgs2icR8llBQQHGjRuHpKQkqKqqQkJCAoWFhejWrRtycnKQlJSE8vJyyMjIoFOnTlzHJUSgUlNTYWBgUK+xzZs3R3x8vIATiR86PjcehYWF2LRpEy5evIjKykoA/5uQCHy+Xhg9ejR++eUXyMvLcxlVqKgDB6kTtfkjhDQmT548wbJly/Dzzz/z9jV//PEHtm3bxndjkGVZ6Orq4uzZs2jZsiVHaQkhhAiDjY0NkpKSEBkZCUlJSQCfu2/07t0bxcXF2LVrF7p27YpDhw7hr7/+goODAzZs2MBxatHy448/QlJSEqGhobxtjo6OiI6OrjZDxd7eHi9fvsSTJ0+EHVPkVc0gkpSUhLm5OUaNGoWBAwfSWr6EEE5069YNTZo0QWhoKCQkJGodV1lZid69e6O0tBSRkZFCTEgIEVWTJ0/+7t/BMAw8PDwaIA2pcuDAAbi6umL8+PFYv359teuFvLw8nDx5EseOHYONjQ02b97McWJCBKdXr14oLi5GSEgImjVrVuu4/Px8WFhYQFZWFg8ePBBiQkKEo6ysDJMnT8ajR494z3QMDAzQqlUrZGZmIi4uDomJiWAYBl27doWHhwekpaW5ji0U1IGD1GnVqlW8NkJZWVn1mlFEBRyEEEExMTHBjRs3+LY5Ozujc+fOvHXrZGVlYWZmBgcHB+oaRAghYiAzMxOqqqq84g0AiI6ORn5+PgYPHoyhQ4cCANasWYNLly7h77//5iqqyCosLKz37KGKioo6H+SRf69qxkpERATu3r2LkJAQyMrKYvDgwRg5ciTMzc3/j737jorqWtsA/hx6EURAEaQIiGJHwQ6C2FCpChYUeyFqxJqoMSZ2jUaNATUWrEhioUasIIqKAoIVUBSQJiggSC/DfH/4MdcJRYzMHDLz/ta6a1327GE9946cOeXd76buTYQQoenWrRtiYmKwf/9+LF68uMF5+/fvx/v379G/f38hpiOEiLKoqKgGX6s9F6pvTeunr9E5U/MLDQ2FjIwMli1bVu/rysrK8PDwgLq6OjZv3gwTExO4uLgIOSUhwjFo0CBcunQJ3333HXbu3IlWrVrVmVNSUoLvvvsOFRUV1GWaiCxfX1/ExcWhXbt22LhxI6ysrOrMuXnzJn766SfExcXhzz//hJubm/CDsoAKOEijqI0QIeS/oE+fPujTpw/bMQghhLCgsLCwznY2Dx48AMMwsLCw4I3JyclBT08PycnJwo4o8lRVVZGZmfnZedXV1byWyaT5OTs7w9nZGTk5OQgODkZQUBBevHiBgIAABAYGQl1dHba2trC3t0fXrl3ZjksIEXELFixAdHQ0vLy8EB0djWnTpsHQ0BBqamrIy8vDq1ev4OPjg/v374NhGMyfP5/tyIQQEdHQAsSHDx9i3759aNWqFZydndGpUyfeMenly5e4cOECiouL8e2338LExES4ocVAWloatLS00Lp1awDgFXVXV1dDSup/j6lcXV3h5eWFc+fOUQEHEVkeHh64efMmwsPDMWzYMDg4ONQ5JgUFBaGoqAgKCgpYsmQJ25EJEYi///4bDMPgwIED6N69e71zLC0t4eXlhQkTJiA4OJgKOAgBPu5nTYi4WbNmDQCgXbt2vKrw2rGmYhgGW7dubfZshBDxk5WVBQCQkpJCu3bt+Ma+xD8fcBMiKuTl5ZGbm8s3FhMTA+Bj+/ZPSUlJ8XXqIM2jb9++uHz5MsLCwhpdGRQYGIjS0lIMGDBAiOnEj4aGBubOnYu5c+ciKSkJgYGBuHjxIt68eYNjx47h+PHjMDQ0hL29PT0w/Qqenp4AgDZt2mDq1Kl8Y03FMAwWLVrU7NkIaQnMzc2xfv16bN26Fffv3693RTyXy4WUlBTWrFkDc3NzFlISQkRRfR19EhMTsX//fgwbNgy//PIL5OXl68xZvHgxVq1ahf379+Ovv/4SRlSx8+lWEbWfwfv379G2bVveOMMw0NLSosJ7ItL09PRw7NgxLF++HBkZGfDx8akzh8vlokOHDti9ezc6duwo/JCECMGrV6+gr6/fYPFGre7du8PAwACvXr0SUjL2Mdz6+oURQogYMzY2BgAYGBggJCSEb6ypGIaps+c7If9FtSt0DQwMcPHiRb6xpmIYBvHx8c2eTVwYGxuDYRi+z6B2rKnoM/h6w4cPB/DxItvb25tvrKkYhsH169ebPZu4mzx5Mh49eoTTp0/D1NQUmZmZGD16NNq0aYOIiAi+uebm5pCTk6PPoZnFxsbC1dUV6urq2LNnD/r161dnT+urV69i9erVqKiogL+/Pzp37sxyavFz//59/P3337hy5Qo+fPhA56tfqfa7WF9fn++aoXYL0sbUzqHP4OvRuWrLl5SUBG9vb0RERPAVXKqrq8PCwgKzZs2i7wQBqq6uhr+/P27evIm0tDSUlpY2eIyic9WvR8ekluvbb7/F7du3ERERUe9WBbWKi4thYWEBCwsL7Nu3T4gJRd/o0aNRVVWFsLAwAMCGDRvw559/4uDBg7C0tOTNq6mpgbm5OUpLS/Hw4UOW0hIiHJWVlQgJCcGtW7eQnJyM0tJSKCgowMDAABYWFhg7dixkZWXZjkmIwPTu3RuGhobw8/P77Nzx48fj1atXePTokRCSsY86cBBCyD9s27YNAH9VeO0YEZ7p06d/9e9gGAYnTpxohjTiq/bmXk1NTZ2xL/0d5N+p7Zzx6YoU6qYhfLXbQ3x64dyULSM+RfsoC4aDgwMePnwId3d3DBw4EI8ePQKHw4GjoyPfvLS0NOTm5mLo0KHsBBVhffv2xcKFC7F//35Mnz4durq6KCgoAAC4u7sjKSkJWVlZ4HK5WLlyJT2oY0n37t2RkZGBV69eITY2lu04/3mLFy8G8LEDxz/HiPDQuWrLZ2RkxLueLioq4j2Y+PR6mwjGhw8fMHPmTCQkJDTp3zmdq349Oia1XA8ePIChoWGjxRsA0KpVKxgaGvI6+pHm07lzZ4SHh6OyshIyMjIYPHgwfH19sW/fPpiYmPC2Vvn999+Rn5+P3r17s5yYEMGTkZGBo6NjnfsXhIgLLS0tJCUlIT8/H6qqqg3Oy8/PR1JSEjp06CDEdOyiAg7SJFVVVQgJCUFERASSk5NRUlICRUVFGBgYYOjQoRgzZgykpaXZjklIs3BycmrSGBGs+lrsfim6AfX1EhMTmzRGBKd2dcrnxohghYaGAgDf3ry1Y4RdkyZNQnR0NEJCQnDt2jUAH7dOcXd355sXFBQEABg0aJDQM4qDJUuWQEdHB3v37sXr16954+Hh4QA+rrReuXIl3ZgSsurqaty6dQtBQUEIDw9HRUUFuFwupKWlMWzYMLbj/afVV6xBBRzCR+eq/y1KSkpUuCFEe/fuRXx8PNq2bYs5c+bAxMQEampqkJCQYDuayKJjUstVUlKC9+/fN2nu+/fvUVJSIuBE4sfS0hLXrl3D3bt3YWVlBWtra3Tp0gXPnj2DlZUVDAwMkJeXh5ycHDAMg7lz57IdmRBCiIBZWlri+PHjWLlyJfbu3QtlZeU6cz58+ICVK1eiurparO5j0BYq5LOSkpKwZMkSpKam1lsFXts2dt++fejUqRMLCQkhoqg5CjiA+vc+JYQQInoSEhKQkpICTU1NmJiY1CniCwoKwvv372FjYwMNDQ2WUoq+6upqPHz4EM+fP0dRUREUFBRgZGQEU1NTyMjIsB1PbMTGxiIoKAiXL19GYWEhb7uOvn37wt7eHmPGjKn3xgghhBDRYWlpiby8PPj7+8PIyIjtOISwyt7eHklJSdi/f3+jD39u3LiBb775Bl26dEFgYKAQE4q+oqIi3LhxA8bGxryOfG/fvsXq1atx9+5d3jwVFRWsWLECLi4ubEUlhBAiJHl5ebCzs8P79++hoKAABwcHGBkZQV1dHbm5uUhKSkJgYCBKS0uhpqaGoKCgRjt1iBIq4CCNys/Ph729PXJzcyEvLw87Ozt06dKF98fz4sULBAUFoaysDG3btkVgYKDY/PEQQgghhBBCSEuRnJyMoKAg/P3338jMzOQV3+vr68Pe3h52dnbQ1tZmOSUhRBw9fvwYCQkJKCgoQFVVVb1zGIbBokWLhJxMtPXs2RO6urq4ePEi21EIYZ2Pjw82bdoEOTk5zJs3D1OnToWKigrv9YKCApw5cwaHDx9GeXk51q1bh6lTp7IXWMy8e/cOmZmZkJOTQ6dOnfg6XxJCCBFtiYmJ+Pbbb5Genl5vR3UulwtdXV3s27cPxsbGLCRkBxVwkEb98ssv8Pb2Rr9+/fDbb7/VW5zx/v17eHh4IDo6GrNnz8aqVatYSEpI8wkICGiW30MtwgkhhBB2lJSU8Lb8U1RUZDsOIUJhbGwMhmHA5XKhpqaGsWPHwt7eHj179mQ7mkiKjo5ult/Tr1+/Zvk9hLREjx8/xurVq5GSksIbq+0I9KnasYSEBGFHFGkjRoyAvLw8goOD2Y5CCOu4XC5WrFiBkJAQ3jFITU0NqqqqyM/PR35+PrhcLrhcLsaMGYPdu3fTtryEEEKIkFRWViIkJAS3bt1CSkoK756evr4+hg4dirFjx4pdV1cq4CCNGjNmDDIyMhAeHg41NbUG5+Xm5sLKygo6Ojq4dOmSEBMS0vxqb35/Lbr5RETB9OnTv/p3MAyDEydONEMa8TR8+PCv/h0Mw+D69evNkEZ8rVmz5qt/B8Mw2Lp1azOkIfVJTk7G0aNHERERgXfv3vHG27ZtC0tLS8yePRv6+vosJiREsExMTDB8+HDY29vD3NwckpKSbEcSac1xzcAwDOLj45spkXiic9WWKy0tDU5OTigvL8fYsWMRExOD7OxsfPPNNygoKMDDhw8RHx8POTk5TJkyBYqKili8eDHbsUXKzp07cfz4cVy/fh2amppsxxELdExq+f766y8cPnwYGRkZdV7T1tbGnDlzMGXKFBaSEUIIYcvDhw8RFRWF7OxslJeX8927e/v2Laqrq6GlpcViQiKOqICDNKp3794wNDSEn5/fZ+eOHz8er169wqNHj4SQjBDBWb16dbMUcGzbtq0Z0pBPeXp6ftF8asP79ZqjLRmtpvs69Bm0DPQ5tGwhISFYu3YtKioqUN/lDcMwkJWVxdatWzF27FgWEhIieKWlpVBQUGA7hthwc3Nrlt9z6tSpZvk94oq+n1uuH3/8EefPn8dPP/2EyZMnw9XVFXFxcXz/X0dGRmLFihVo164dfH19IS8vz2Ji0VNaWopJkyZBUVERe/fuRfv27dmOJPLomPTfkZycjOTkZN75k4GBAQwMDNiOJTYSExORnp6OkpKSRudRd2NCiCBlZWVh1apViI2NBVB/V7h169bhwoULOHPmDPr06cNWVCKGqICDNMrU1BQaGhoICQn57Nxx48YhOzsbDx48EEIyQog4+rQ1+D9RG17BiIqKapbf079//2b5PeIoMzOzWX5Phw4dmuX3iCt/f/9m+T1OTk7N8nvI/zx//hwTJkxAdXU1evTogVmzZqFz585QV1dHbm4ukpKS4O3tjadPn0JKSgp+fn7o3Lkz27EJIYQ0AzpXbblGjBiBgoIC3Lt3D1JSUvUWcADA/fv3MWPGDCxYsADLli1jKa1o8vT0RFFREXx8fCApKQkLCwvo6ek1WihDXVC+Dh2TCGnclStXsGPHDrx586ZJ8+meHiFEUAoLCzF+/HhkZmaiffv2GDx4MO7evYucnBy+Y09MTAymTZuGOXPmYNWqVSwmFk1paWkICgpCjx49YGVl1eC8Gzdu4NmzZ3BwcICOjo7wArJIiu0ApGUzMDDA06dPkZiY2GgVeWJiIl69ekX7KxNCBKqxm0mlpaVITU3F7du3weVyMXXqVLRq1UqI6UQT3ThiHxVetAxUeNFyHTlyBNXV1XBzc8MPP/zA91qbNm1gZGSEsWPHYuvWrTh58iSOHj2KHTt2sJSWEEJIc6Jz1Zbr7du36NixI6SkPt56rN3WqbKykm//6gEDBkBbWxtXr16lAo5m5unpyVsAUV1djevXrzfYbbR2AQQVcHwdOib9d3C5XLx//x7l5eXUFl9Ibty4gaVLl4LL5UJNTQ3GxsZQV1eHhIQE29EIafEiIyMxaNAgtmOIlCNHjiAzMxPDhw/Hrl27IC8vD1dXV+Tk5PDN69u3L+Tk5BAZGclSUtH2119/wdvbGwcOHGh0HsMw8PLyQlVVldhcM1ABB2nUuHHj8OTJEyxcuBBbtmyp90vi7t27WLduHRiGga2tLQspCSHioik3k9LT07F06VJERkbir7/+EkIqQgghbIqOjoaysjK+++67RuetXLkS/v7+uH//vpCSEUIIIeJLXl6eV7wBAEpKSgCAnJycOqvmlJWVkZycLNR84sDR0bFZtoclRJRERkbiyJEjiI2NRXl5ORiGQXx8PO/1Q4cOISUlBd9//z1UVFTYCyqC/vjjDwDA9OnTsXLlSr5iPkJIXWlpafD390dgYCCys7P5jlXk612/fh3S0tLYsmVLo93JJCQkoKOjg/T0dCGmEx+3b9+GnJwcLC0tG503dOhQyMrKIiIiggo4CAEAV1dXXLx4EU+ePMHs2bPRqVMndOrUCWpqasjLy0NSUhJevXoFLpeL3r17w9XVle3IhAhFZWUlCgoKUF1d3eAcquBnh46ODvbs2YPRo0dj//79WL58OduRCCGECFBeXh6MjY0hLS3d6DwZGRl07NgRiYmJQkpGCCGEiK927drh7du3vJ8NDAxw48YNREdH8xVwFBUVISUlhR7kCcD27dvZjkBIi+Lp6QkvL696t+WtpaSkhICAAPTr1w/jx48XYjrR9/z5cygrK2PNmjVUXEZIA4qLi3Hp0iX4+/sjLi4OwMeOQZ8WxZLmkZWVhY4dOzapWE9RURFlZWWCDyWG3rx5A21t7c9+L0hISEBbWxtZWVlCSsY++qsnjZKRkYG3tzc2bNiAkJAQJCUlISkpideCEfj4h2Nra4v169d/9sY5If9l1dXV8Pb2RmBgIFJSUhq94PtnBT8RLl1dXRgaGuLSpUtUwCFgjx8/RkJCAgoKClBVVVXvHIZhsGjRIiEnEw/l5eW4ceNGkz6DrVu3CjmdeMnLy+N9Do0V9zk6OgovlJhQVFTke0DUmHfv3kFRUVHAiQgh4i4kJAQBAQGIj49HQUEBOBxOvfPomkHw6FyVPb169YK/vz/ev3+PNm3aYMSIEThy5Ah27doFdXV1mJmZ4d27d9i6dSvKy8sxcOBAtiMTInB0TGJPREQEPD09oaioiKVLl2LEiBFYvnw5Hj58yDdv1KhR2LBhA65fv04FHM1MSkoKOjo6VLxByD9wuVzcvXsXfn5+CA0NRUVFBe+5Q5cuXeDk5AQ7OzuWU4oeaWlpVFZWNmluXl4ebdUuIBUVFU1+riwjI4PS0lIBJ2o5qICDfJaSkhJ27dqFpUuX4vbt20hJSUFJSQkUFRWhr68Pc3NzaGtrsx2TEIGqrKzErFmzEBsbC0lJSUhJSaGyshKampooLCzkfXHIyMhAXV2d5bQE+HjTIzs7m+0YIuvx48dYvXo1UlJSeGO1+yZ/qnaMbkA1v9DQUKxduxYfPnzgjdVe4H36OdR+BlTAIRjp6enYsGED7ty50+i82s+BCjiaX/fu3XH37l2EhIRg7NixDc4LCQlBdnY2hgwZIsR0hBBxwuVysXz5cly+fLnRYu9P5xPBoHNV9llbW+PChQsIDw+Hk5MTTExMMG7cOFy8eBELFizgzeNyuZCXl4eHhweLaQkRLDomse/UqVNgGAbbt2/HyJEjAaDeQgI1NTVoampS1z4B6N69O168eMF2DEJajOTkZAQEBCAwMJC3KKX2+kBJSQmnTp2CsbExmxFFWm2H1vz8fKiqqjY4Ly0tDenp6RgwYIAQ04kPDQ0NJCcno6KiArKysg3OKy8vR3JyMtq2bSvEdOyiAg7SZNra2pg8eTLbMQhhhY+PDx48eICRI0di586dmD17NuLi4nDjxg0AwIsXL3DkyBEEBwfD2dkZCxcuZDmxeMvMzERKSgrtVyogaWlpmDVrFsrLy2Fra4uYmBhkZ2dj4cKFKCgowMOHDxEfHw85OTlMmTKFVrsLQHx8PDw8PCAtLY0FCxbg0qVLSEtLw5YtW1BQUIBHjx4hLCwMUlJSWLhwoVid3ArTu3fvMGXKFOTl5aFPnz54/fo18vPzYW9vj4KCAjx9+hR5eXmQk5PDqFGjICkpyXZkkTRlyhTcuXMHq1evxosXLzB9+nS+i+/8/HycOHECx44dA8MwmDJlCotpRdeHDx/g7e2NmzdvIi0trdFVEdR1gIiq8+fP49KlS+jbty+2b9+O1atXIy4uDvHx8Xj//j0ePXqEo0eP4unTp/j555+pqE9A6Fy1ZRg2bBhu3rzJ9//vjh07YGRkhMDAQGRkZEBeXh5mZmZYsmQJPaD4SrXtpKWkpNCuXTu+sS9BW8E2PzomtQyPHz+Gqqoqr3ijMerq6nj+/LkQUomX+fPnY86cOfjrr78wadIktuMQwoqioiL8/fffCAgIwOPHjwF8LNqQlZWFtbU1HB0dsWDBAsjKytK5kYCNHDkST58+xc6dO7Ft27Z653A4HGzatAkMw8DGxkbICcVDv3794Ofnh0OHDuHbb79tcN6RI0dQXl6O/v37CzEdu6iAgxBCmiAkJARSUlJYt24d5OTk6rzeuXNn/PLLL9DS0sLvv/8OIyOjJl0UkuaVl5eHuLg47N27FxwOB5aWlmxHEkmHDx9GaWkpfvrpJ0yePBmurq7Izs7GkiVLeHMiIyOxYsUKREZGwtfXl8W0ouno0aPgcDjYvXs3Ro0ahaioKKSlpWHChAm8Oa9evcI333wDX19fXLhwgcW0ouvIkSPIzc3F4sWLsXjxYri6uiI/Px87duwA8PFCz9/fH1u2bEFeXh4OHTrEcmLRNGLECEycOBFnz57FH3/8gT/++AOqqqpQU1NDXl4e8vPzAXy8KTJp0iSMGDGC5cSiJzs7G66urnjz5g11HSBiLSAgAAzDYNu2bdDV1eWNMwwDVVVVDBs2DMOGDcPatWuxdu1aaGlpidUNKGGhc9WWQUJCAhoaGnxjUlJScHd3h7u7O0upRJe1tTUYhoGBgQEuXrzIN9ZUVGApGHRMahlKSkpgZGTUpLkcDgcSEhICTiR+Bg8ejI0bN2Lr1q14/vw5Jk6ciI4dO9Z7n5UQUXPz5k34+/vjxo0bqKys5HVcMjMzg729PcaMGUNbdAiZm5sbzp8/j4CAALx58wbOzs68hSjPnj3DixcvcOrUKcTHx8PIyIjvnitpPjNnzkRAQAD279+PkpISzJ07l6/DfW5uLo4ePYrjx49DSkoKM2fOZC+skFEBByGENEFycjK0tLR4N6Bqb4JwOBy+FdWLFi2Cj48PTp06RQUcAtC1a9cmzeNyudDS0qI2vAISGRkJRUVFODs7Nzhn0KBB2LNnD2bMmIGDBw9i2bJlQkwo+h48eABlZWWMGjWqwTmGhobYt28fHB0dsX//fqxbt06ICcXDrVu3IC8vjzlz5tT7uqSkJJydndGqVSssXboUx44dw9y5c4WcUjxs3LgRPXv2xB9//IGMjAzk5eUhLy+P97q2tjYWLFgAFxcXFlOKrt27dyMrKwuGhoZYtmwZevfuDXV1ddrfWsjS0tIQFBSEHj16wMrKqsF5N27cwLNnz+Dg4AAdHR3hBRQDSUlJ6NChA/T09AD875qhpqaG70HQDz/8gEuXLuHo0aNUwCEAdK5KxFFt54xPO+9RN42WgY5JLYOqqioyMzM/O6+6uhqpqal1CtDIl/nc/TtfX9/PFitRURkRJQsWLADDMOByuejYsSMcHBxgb2+PDh06sB1NbCkoKODIkSP45ptvcO/ePdy/f5/3Wu13NpfLhaGhIQ4ePAgZGRm2ooo0IyMjrF27Fps3b8aJEydw8uRJaGlpQUlJCUVFRcjKyuItAlqzZg26dOnCcmLhoQIOwlN7YvVptX5TH5bWohMrIqqqq6v5tuOQl5cHABQWFvK1aZeWloaenh61WhSQz63YlZeXR8eOHWFlZYXZs2dDSUlJSMnEy9u3b9GxY0dISX08jagtYqqsrOQ7mR0wYAC0tbVx9epVugHVzPLy8vhWD9V+FuXl5XyrV4yNjaGvr4/w8HAq4BCAN2/eQFtbm/edUPtwrqqqCtLS0rx5NjY20NDQQHBwMBVwCJCLiwtcXFyQnJyMlJQUlJSUQFFREfr6+jAwMGA7nki7ffs2pKWlcfToUbRv357tOGLrr7/+gre3Nw4cONDoPIZh4OXlhaqqKvp+bmZlZWXo2LEj7+fa7+SioiK0bt2aN66oqAgDAwNe22TSvOhctWUoLi5GQkIC1NTUGv0eTk5ORl5eHrp160ZbR3yFsLCwJo0R4aNjUsvQt29fXL58GWFhYbC2tm5wXmBgIEpLSzFgwAAhphM9zdFxj7r2EVHUunVrjB8/HnZ2dtDU1GQ7jtjT09NDQEAAzp8/j6tXr+L58+coKiqCgoICjIyMYGNjg4kTJ0JWVpbtqCJt6tSp0NbWxq+//ooXL14gIyOD7/WuXbti+fLlsLCwYCkhO6iAg/DUnhTV1NTUGfvS30GIqGnXrh3ev3/P+7n2BOvFixcYOHAg39y3b9+irKxMqPnERWJiItsRCD4WytTefALAK5TJycmps5JXWVkZycnJQs0nDlq1agUOh8P7ufahUFZWVp0b5DIyMk1aaUS+nKSkJN9FXG27y7y8vDoPsdXV1elvQUgMDAyoYEPIiouLoa+vT8UbLLt9+zbk5OQ+u4Xc0KFDISsri4iICHpA1Mzatm2LwsJCvp+Bjw+o+/Tpwze3sLAQxcXFQs0nLuhctWU4e/Ysdu7ciQ0bNjT6vRwTE4OffvoJq1evxowZM4SYkBDhoGNSy+Dm5oZLly5h/fr1UFJSQr9+/erMuXr1KrZs2QJJSUlMmzaNhZSiIzQ0lO0IhLQotra2CA0NRWFhIfbs2YO9e/fCzMwMDg4OGD16NG2fwiIZGRm4urrC1dWV7ShizdLSEpaWlkhPT8fLly9RXFyMVq1awcjICNra2mzHYwUVcBCe+h6M0sNSQj7S19dHVFQUb8sUMzMznDt3DocPH0bfvn15qybOnj2Lt2/filUrJyJ+2rVrh7dv3/J+NjAwwI0bNxAdHc13A6qoqAgpKSnUYk4A2rdvj5ycHN7PRkZGuH79Ou7cucN3g/zdu3dISUmh1YwC0r59e+Tm5vJ+1tXVBQDExcVhzJgxvPHKykqkpaXRdhJEZGlra6OqqortGGKvtivQ5441EhIS0NbWRlZWlpCSiQ8dHR2+rhp9+vRBQEAATp8+zVfAcfPmTWRkZPB16yDNh85VW4br169DUlIStra2jc6ztbXFxo0bcf36dSrgICKJjkktQ9++fbFw4ULs378f06dPh66uLgoKCgAA7u7uSEpK4rVpX7lyJTp37sxu4P842haCEH67du1CcXExLl26BD8/P8TFxSEqKgrR0dHYtGkThg0bBgcHB7HrMMCm6OhoKCkpwdjY+LNzExMTUVRUVG/xH2leOjo6tNXr/5P4/BRCCCGWlpYoLy9HVFQUgI/t8LW0tHD37l3Y2NhgyZIlmDJlCn766ScwDIOpU6eynJgQwenVqxfy8/N5XWlGjBgBLpeLXbt24datWygtLcXr16+xcuVKlJeXw9TUlOXEosfU1BTv37/nFXHY2NgAAH799Vf4+voiKSkJd+/exTfffIOqqioMHjyYzbgiy9jYGHl5eSgpKQHw8buCy+Vi7969ePXqFQCgoqICGzZsQFFREXr16sVmXLGQmJiIa9euISAgoNH/kObl4OCA1NRUvHz5ku0oYq2iooJv+6bGyMjIoLS0VMCJxI+FhQXKy8vx8OFDAMDYsWOhoqKCkJAQTJ48GTt27MDKlSuxaNEiMAyD8ePHsxtYRNG5asuQlpYGTU1NKCgoNDpPQUEBmpqaeP36tZCSESJcdExqOZYsWYJt27ahXbt2eP36NQoLC8HlchEeHo7MzEyoqalh+/bttO0lIUQgWrVqBRcXF/j6+uLq1atwd3eHpqYmysvLcenSJXzzzTcwNzdnO6bYcHNzw+bNm5s0d8uWLVRoTISOOnAQQkgTjB49Gh8+fOC1ypeVlcUff/yBJUuWICUlhbeCUUpKCnPmzMHEiRPZjCuyEhIScPLkSQwePBh2dnYNzgsKCkJkZCRmzpxJ3VAEwNraGhcuXEB4eDicnJxgYmKCcePG4eLFi1iwYAFvHpfLhby8PDw8PFhMK5qsra3h6+uL8PBwTJo0CZ07d8bMmTNx7NgxbNy4kTePy+VCTU0Ny5cvZzGt6LK2tsbFixdx69YtjBkzBkOGDMGAAQNw//592NraonXr1iguLgaHw4GUlBQWLlzIdmSRdeXKFezYsQNv3rxp0nxHR0fBBhIzc+bMQWRkJBYvXoxffvmFipVYoqGhgeTkZFRUVDS6R295eTmSk5N523uQ5mNjY4PU1FQUFRUB+Ngm/7fffoOHhwcePnzIK+wAADs7O3pAJCB0rtoyFBYWNnlvdxUVFeoAS0QWHZNaFicnJ9jZ2eHhw4d4/vw5ioqKoKCgACMjI5iamlIHFCEpKytDbGwsUlJSUFJSAkVFRejr66Nv376Ql5dnOx4hAqerq4ulS5di6dKliIyMhL+/P65du8Yr9svLy4ONjQ2cnJxgb2/f5HMq8mW4XK5A5pKmi46O/uL3iEsnFIZL/+pIIzgcDsrKyiAtLV3nJuDjx49520X06NEDs2fPpr26iNipqanBkydPkJGRATk5OZiYmEBNTY3tWCJr48aN8PX1xcmTJxv9oo6Ojoabmxvc3Nzwww8/CDGheKipqcG7d++gqKjIO+5XV1fjyJEjCAwMREZGBuTl5WFmZoYlS5Y0qRUdaR7BwcF1PoO5c+dCQ0OD7WgiqaKiAk+fPoWmpia0tLQAACUlJdi+fTsuXrzIW93epUsXrFy5klphCsiNGzewcOFCXsGSsbEx1NXVISHRcLPBbdu2CTGh6FuzZg2qq6tx6dIlcDgcGBsbQ09Pr8GbrwzDYOvWrUJOKfp++OEH+Pn5YeHChfj2228bnOfp6QlPT084OTnR34KQFBcX49atW7xrhn79+qFr165sxxJZdK7aMlhYWKC6uhqRkZGfnTto0CBISEjgzp07QkhGiHDRMYmQ/6msrMT+/ftx+vRpXifLTykoKGDatGlYtGgRFdMQsVNaWoqQkBAEBATgwYMH4HK5YBgGDMOgf//+OH78ONsRRYqxsTFMTU3h4+Pz2bl2dnZIT0/nK8gnzcPY2PiLtpxmGAbx8fECTNRyUAEHadThw4exe/durFmzBtOnT+eN37x5E4sWLQKHw+F9kXTp0gV//fVXo6u9CCHka9ja2iI7OxsxMTGfnWtqagotLS0EBwcLIRkhhPDjcDjIz8+HvLw8FbgK2OTJk/Ho0SO4ublh5cqVdKOPBbUX3E29tGQYBgkJCQJOJX6SkpLg6OiImpoazJgxA3PnzoW6ujrv9dzcXBw9ehTHjx+HpKQkLly4QJ3KCCECs2jRIoSFhWH37t0YM2ZMg/MuXbqEZcuWwcrKCgcPHhRiQkKIOJk+fTq6dOnSpEU+W7duxfPnz3HixAkhJBMflZWVmDdvHqKiosDlctG6dWvo6upCTU0NeXl5SEtLQ2FhIRiGgZmZGby9vZu8PSAhoiY9PR3+/v4IDAxEZmYmXUM3g+LiYnz48IH3s7W1NXr27InffvutwfeUl5cjOjoaP/30Ezp16oS///5bGFHFipubW4OvlZWV4fXr1ygqKoK0tDRMTEwAAKdOnRJSOnbRFiqkUXfu3AHDMLC1teUb37VrF6qrq2FlZYXevXvD398fz58/x+nTpzFnzhyW0hJCRN2bN2+go6PTpLna2tpNbqVPCCHNTVJSkrYnEJLnz59DWVkZa9as+aKqfdJ8Fi9ezHYEAsDIyAhr167F5s2bceLECZw8eRJaWlpQUlJCUVERsrKyeEU2a9asoeINQohATZw4EaGhofjxxx8hKysLa2vrOnPCw8Px448/gmEY2oaUECJQUVFR4HA4TZqbkJDQpIVD5Mt4e3vj/v37UFFRwffffw9bW1u+Ao3q6moEBwdj586diImJwdGjR+Hu7s5iYkLYo6OjgyVLlmDJkiW4d+8eAgMD2Y70n3f8+HF4eXnxjT19+hTDhw9v0vv/+YyUNI+mFGMEBQVh27Zt0NbWFqsuolTAQRr1+vVrqKurQ1VVlTeWnJyMpKQkdOnShbc6wsbGBmPHjsXVq1epgIOIvKKiIqSnp6O0tLTRlabisheXMNXU1HzRfnOVlZUCTEMIIaQlkJKSgo6ODhVvsIgKOFqOqVOnQltbG7/++itevHiBjIwMvte7du2K5cuX05ZOQlJYWPjZa4baLbgIETWWlpaws7NDcHAwFi1aBH19fZiYmPCKyh49eoTk5GRwuVzY29vXW+BBCCFsqKqqanQ7RvLvBAYGgmEYHDx4kLeK+lNSUlJwcnJCx44dMWXKFAQEBFABByEABg4ciIEDB7Id4z+Py+XyXZc1pYuonJwcdHR0YGtri7lz5wo6ImmAvb092rZti9mzZ6NPnz5iU/hNBRykUe/fv4eRkRHfWHR0NICPRRu19PX1oauri1evXgk1HyHCFBcXh507dyIuLu6zc8VpLy5h0tTURHJyMj58+ABlZeUG53348AGvXr1Chw4dhJhO/Lx+/Ro3b95EWlpaow8nGIbB1q1bhZxOPERGRiI8PLxJnwG1fxWckpIS3Lt3D+np6SgpKWn0c1i0aJGQ04m+7t2748WLF2zHIKTFsLS0hKWlJdLT0/Hy5UsUFxejVatWMDIygra2NtvxRF56ejr27duHW7du8bXorQ9dMwgWnauyb/v27dDQ0MDJkyeRnJyM5ORkvpvlMjIymDVrFpYsWcJyUkIEj45J/w3l5eVITU1F69at2Y4icjIzM6Gnp1dv8can+vTpA319fWRmZgonGCFELHz77bf49ttveT8bGxvD1NQUPj4+LKYiTTVo0CBoamrizz//pAIOQoCPq93Lysr4xmJjY3l70X1KRUWFTqyIyIqNjcXMmTNRWVkJaWlpdOjQAerq6rTaV8gGDx6M06dPY9euXdi4cWOD83bv3g0OhwNzc3MhphMfXC4XW7ZswZkzZ+pUL9eHbkA1v8rKSnh4eCA8PBwAmvQZEME4duwY9u3bh/Lyct7YPz+P2gcVVMAhGPPnz8ecOXPw119/YdKkSWzHIaTF0NHRafLWc6R5vHz5Eq6urigqKgKXy4WMjAzU1NToe1jI6Fy15ZCUlMTKlSsxc+ZMhIeH49WrV3xFZZaWllBTU2M7pljKzMxEUFAQ3r59ix49esDJyYk6DggIHZPYcf36dYSGhvKNvX79GmvWrGnwPRUVFXjy5AkKCwthZWUl4ITiR1lZGQoKCk2aKy8vT0U0RKRMnz79q95PC7Oa3+LFi6Gpqcl2DPIFVFRUkJKSwnYMoaECDtIoTU1NvH79GoWFhWjdujWqq6sREREBGRkZ9O7dm29uYWEh2rRpw1JSQgTr999/R2VlJUaPHo3169fTTSaWzJo1CxcuXMC5c+dQUFCAuXPnokePHpCQkEBNTQ2ePn2KI0eO4Nq1a5CTk8OsWbPYjiySvL29cfr0aTAMg2HDhsHExARqamp0w0+IvLy8cOPGDcjLy8PZ2Zk+A5b4+flhx44dAD5W7vfq1Qvq6ur0OQjZ4MGDsXHjRmzduhXPnz/HxIkT0bFjR8jJybEdTSxxuVykpqaioKAA1dXVDc6jreaIKNqzZw8+fPgAMzMz/PDDD+jatSvbkcQSnau2DNu2bYOEhASWLVsGdXV1ODs7sx1J7Pj6+mLPnj1YtGgRZsyYwRt/9OgRZs+ezesCwTAMLl68iCNHjtDfiQDQMYkdiYmJ8Pf35xvLzc2tM1YfFRUV6gwkAAMHDsSVK1eQl5fX6H3V3NxcJCUlYcyYMUJMR4hgRUVF/av3fbogiDQv2gr2v6W2Q5aUlPiUNTDcz5X9ErG2adMm+Pj4YMiQIZg6dSquXbsGf39/DBs2DAcOHODNKykpQf/+/dGtWzecO3eOxcSECIapqSkYhsHdu3chIyPDdhyxdu3aNaxYsQJVVVUAPq7qUlBQQGlpKTgcDm+146+//oqRI0eynFY0jR07FikpKdizZw/fdlpEeEaMGIGsrCycPHmyTkcsIjxOTk5ITEzEd999RwVjQtIcD0NpywLB+PDhA3799VcEBwfX6eD3T/QZfL3abS3l5OTQs2dPvrEvQYU0zWvAgAGoqKjArVu3Gt3ujwgWnau2DN27d4eBgQGCg4PZjiK2FixYgFu3biE0NBRaWlq88SlTpiAuLg5du3ZF9+7dERoaioKCAmzYsEFsWlILEx2T2BEVFcX3wNTT0xNaWloYP358g++Rk5ODrq4uBg8ejFatWgkjpljJyMjAhAkT0LlzZ+zZswfq6up15uTm5mLZsmV48eIF/Pz8aGtkIjL+zTYdT58+RVBQEDgcDhiGQUJCggCSEdLy5efnY8OGDbh69SrMzc1x+PBhtiMJhfiUqpB/Zf78+bh06RLu3LmDu3fv8h6MfrpXFADcuHEDHA6HHiIRkcXlctGxY0cq3mgBRo4cibNnz+K3337D7du3UVVVxdtfXEZGBkOHDsW3336LLl26sJxUdGVkZKB9+/Z084lFOTk50NbWpu9dliUnJ0NNTY2KN4SoOWrPqX69+RUXF2PSpElITU2FhoYGJCQkUFJSAlNTUxQUFCA1NRXV1dV8xQbk67i5uYFhGOjr6yMkJIRvrKmokKb5VVRUwMDAgIo3WEbnqi2Duro6pKWl2Y4h1l6+fAlVVVW+4o2srCzExcVBV1cX586dg5SUFFxcXDBp0iQEBwdTAYcA0DGJHf3790f//v15P3t6ekJTU5NWXLMoJiYGU6ZMwZEjR2BtbY2RI0eiU6dOUFNTQ15eHl6+fIlr166By+Vi7ty5iI6OrrdA2dHRUfjhCflKU6dObfLc5ORk7N27l/f3IC8vDzc3NwGmE2/Z2dn4+++/kZCQgIKCAt6i0X+ibWwEo7HthbhcLvLy8pCRkYHKykrIysrWeTYtyqiAgzRKQ0MDFy5cwNGjR5GamgotLS24ubnByMiIb15UVBSMjY0xbNgwlpISIlidO3fGmzdv2I5B/p+xsTEOHDiAiooKvH79mrePMhXZCIeysjJtI8QyVVVVWhHUAsjLy6N9+/ZsxxAr/9zHmrQM3t7eSElJweTJk/Hzzz/D1dUVcXFxOH36NICP3TmOHTuGQ4cOQVdXF1u2bGE58X9fbeeMTx/KUTcN9unr6/MKiwl76Fy1ZRg8eDAuXrzI25KXCF9+fj709fX5xu7fvw8AGDNmDK8Fde/evdGhQwe8ePFC6BnFAR2TWobExES2I4i91atX87aDAMArQv5U7WsHDx5s8PdQAQcRVZmZmfD09OR13ZCWlsakSZPwzTff0PeIgPj4+GD79u2oqqriLYb4dNHPp2O0jY1gNHV7IVNTU3z33Xfo1auXgBO1HFTAQT5LU1MT69ata3TOxo0bhZSGEHZMnz4dy5cvx/Xr1zFixAi245D/Jysri86dO7MdQ+wMGDAAYWFhKCsrg7y8PNtxxJKVlRX8/PxQUFAAFRUVtuOIrT59+uDBgweorq4Wqz0Y2UQtdFum0NBQyMjIYNmyZfW+rqysDA8PD6irq2Pz5s0wMTGBi4uLkFOKllOnTjVpjAjXxIkTsWHDBjx48ACmpqZsxxFbdK7aMixevBihoaFYvXo1du/eTZ8FC6qrq+usIo2LiwPDMHydCYCPHVNycnKEGU9s0DGJkI+o2JiQ+uXm5mL//v04d+4cqqqqICkpCScnJ3z77bd8Bfuked27dw+bN2+Gqqoqli5dipMnT+Lly5c4fvw4CgoK8OjRI/j5+aGiogKrVq2qs6idNI+TJ082+BrDMJCXl4eenh6UlJSEmKplYLjUQ5gQQprkt99+w4kTJ7Bo0SJMmjSJVr8TsZWWlobx48dj7Nix2LBhA1UgsyAvLw9OTk7o0aMHdu3aBQUFBbYjiaWnT59iypQpWLBgAbXiJWKtT58+0NDQwOXLlwEA06ZNw4MHD/DkyRO+4iYul4shQ4ZAW1sbZ8+eZSsuIQL1/fff486dO1i3bh21y2cJnau2DAEBAUhOTsbRo0fRpk0b2NjYwMDAoNHzVlpV3bysra3x/v173Llzh/f/+7Bhw/Du3TtERUXxfRbjxo1DQUEB7ty5w1ZckUXHpJaHy+UiNTUVBQUFqK6ubnAeFRwQQgTpw4cPOHToEHx8fFBeXg4AGDVqFDw8PGBgYMByOtG3cOFC3LhxA8ePH8eAAQN4nUQTEhJ4c/Lz87FgwQKkpqbCz88POjo6LCYm4oYKOEiTVFdX48qVK7h//z5ycnJQXl7Ot9/T06dPUVZWBlNTU0hISLCYlBDBGD58OAAgJycHHA4HANCmTZsGV08wDIPr168LLZ8oCggIAAC0atWK1/WkduxL0E3A5hcdHY1nz55h165dMDAwgLOzM/T09Bq9GUs3PppXQEAAsrKysH//fqioqGDcuHGf/Qzob6H5ZWVl4ebNm9i6dSsGDx6MiRMnomPHjo2urKPVE4JVVlaG2NhYpKSkoKSkBIqKitDX10ffvn1pxaMA9enTB506dcK5c+cAAPPmzcPt27dx69YttG3blm+us7MzUlNTERMTw0ZUkbZmzRro6+tj/vz5n5176NAhpKSkYNu2bUJIJj5q9++NjY0Fh8OBsrIydHV1G71moH2Umx+dq7YMxsbGfK3ym/LQ+tMb5uTrrV69GoGBgXB0dMTMmTNx+fJlHDhwAAMGDOA79lRWVqJv374wMjKCv78/i4lFEx2TWo4PHz7g119/RXBwMMrKyhqdyzAM4uPjhZSMECJOysrKcOzYMRw7dgzFxcXgcrmwsLDAsmXL0K1bN7bjiQ1zc3MAwO3btwGg3gIOAEhPT8fo0aPh4OBA189EqKiAg3xWfHw8PDw8kJGRwXfh/emBbNu2bTh58iS8vb0xaNAgtqISIjDGxsZfNP+ffyPky9Xe8NPX1+fti1k79iXoc2h+X/o50I2P5kc3xFuGrl27ftF8+lsQnMrKSuzfvx+nT59GSUlJndcVFBQwbdo0LFq0CDIyMiwkFG2jR49GVVUVwsLCAAAbNmzAn3/+iYMHD8LS0pI3r6amBubm5igtLcXDhw9ZSiu6jI2NYWpqCh8fn8/OdXNzQ0xMDH03NDO6ZmgZ6Fy1ZXBzc/vi99BWUM0rJSUF48eP563qBT7+e/f29sbAgQN5Y6GhoVi0aBFcXV2xfv16NqKKNDomtQzFxcVwcXFBamoqNDQ0UFxcjJKSEpiamqKgoACpqamorq6GnJwcevbsCYCOSYSQ5lVZWYkzZ87g8OHDyM/PB5fLhZmZGZYuXQozMzO244mdHj16oEuXLrhw4QIAYMaMGYiKisKDBw/qFFna29vjw4cPCA8PZyEpEVe0WTdpVE5ODmbPno2CggL06NEDw4YNQ1BQENLS0vjm2dvb48SJE7h+/ToVcBCR1NheXEQwHB0dwTAM38rd2jHCLuogwD5akdUyfGkdNNVNC0ZlZSXmzZuHqKgocLlctG7dGrq6ulBTU0NeXh7S0tJQWFiIQ4cOITY2Ft7e3pCWlmY7tkjp3LkzwsPDUVlZCRkZGQwePBi+vr7Yt28fTExM0Lp1awDA77//jvz8fPTu3ZvlxITD4dA5lQDQiqyWgc5VWwZ68Mk+fX19nDp1Cl5eXkhNTYWWlhZmz57NV7wBAH///TeUlJRgYWHBUlLRRseklsHb2xspKSmYPHkyfv75Z95K69OnTwP42J3j2LFjOHToEHR1dbFlyxaWE4uuqqoqhISEICIiAsnJybzOiQYGBhg6dCjGjBlD12tE5Jw7dw779+9HdnY2uFwuunXrhmXLltF3L4tUVFRQWVnJ+7lNmzYAgIyMDHTu3Jlvbk1NDfLy8oSaTxStWbPmq38HwzDYunVrM6Rp+agDB2nUxo0bcebMGUycOJG3T2NDrYRMTU2hpaWF4OBgltISQgghhBBxc/DgQezduxcqKir4/vvvYWtry3fDr7q6GsHBwdi5cyfev38PDw8PuLu7s5hY9Jw/fx7r1q3DwYMHYWVlBQ6Hg/Hjx+P58+eQl5eHgYEB8vLykJOTAwDYt28fRo4cyXJq0dPUDhwcDgdWVlaorKzE/fv3hZSOEEIIIYQ9Dg4OSElJQUREBFq3bt3g/W0fHx9s3rwZGzduhIuLC0tpRVdSUhKWLFmC1NTUehc41Hbi3bdvHzp16sRCQkIEo7Ybk6SkJOzs7DB69OgvLqj/tLsl+XouLi5IS0vjXRP/8ccf2Lt3LxYvXoxFixbx5qWmpsLW1hZt2rRBREQEW3FFwpd2rKyPOHWxpA4cpFG3bt2CnJwc1q5d+9kvFB0dHaSnpwspGSGEEEIIIUBgYCAYhsHBgwdhYmJS53UpKSk4OTmhY8eOmDJlCgICAqiAo5mNHj0aMjIyvBWmkpKSOHz4MFavXo27d+/i2bNnAD6ucFmxYgUVbzST6OjoOgUYb968gaenZ4PvqaioQFxcHHJzc+uswCaEEEIIEVVpaWnQ0tLidYaTkJAA8LHYW0rqf49IXF1d4eXlhXPnzlEBRzPLz8/HrFmzkJubC3l5edjZ2aFLly5QV1dHbm4uXrx4gaCgICQnJ2PWrFkIDAyEqqoq27EJaVYcDgcBAQEICAj4ovfR9lrNb8CAAXj69Clev34NPT09jBs3Dr///jv279+PsrIymJmZ4d27dzh48CA4HA6sra3ZjvyfRx0rvwwVcJBG5eTkwNDQEHJycp+dKysri4qKCiGkIoSQ+tXU1KCgoIAu8AghRIxkZmZCT0+v3uKNT/Xp0wf6+vrIzMwUTjAxoqSkBHt7e76xdu3awdvbG+/evUNmZibk5OTQqVMnvhvk5Ovcv38fnp6efIX2b968gZeXV6Pv43K5kJGRwYIFCwQdkRBCCCGkxVBSUuL9d3l5eQDA+/fv+bbuZRgGWlpaSE5OFno+UXfkyBHk5uaiX79++O233+q9d7d06VJ4eHggOjoaR48exapVq1hISkjzo+20Wp5Ro0bh6tWriIuLg56eHrS1tbFy5Ups374dR48exdGjRwF8vH7W19fH0qVL2Q0sApycnNiO8J9Cd89IoxQUFFBUVNSkuW/fvuVVMRMiahpbyfhPkpKSaNWqFTp06AATExMqJmhGycnJuH37Nrp16wYzMzPeeGVlJXbs2IHz58+jsrISmpqa2LhxI8zNzVlMSwghRBiUlZWhoKDQpLny8vJ0vipkbdu25bspTpqPsbEx3w0Qf39/qKmpNbqPspycHHR1dTFixAjo6OgII6ZY+ZKVdBISErxrBiMjI95KYEL+i7p27QoAMDAwwMWLF/nGmopWln6d6dOnAwA6dOjAW91YO9ZUDMPgxIkTzZ6NkJagXbt2yMvL4/2sra0NAIiPj+fblqCmpgZZWVmorq4WekZRd+PGDUhLS2Pv3r0N3itt06YNdu/eDSsrK4SFhVEBBxEZYWFhbEcg/9CrVy9cvXqVb2zmzJno3bs3AgICkJGRAXl5eZiZmWHixIlNvu9ESHOhAg7SKENDQzx69AhZWVmNVgkmJibizZs3GDp0qBDTESI8/1zd2Bgul8ubKykpCRsbG6xdu5YKOZrBmTNn4OPjgwMHDvCNe3l58e33npWVhUWLFsHPzw+GhobCjinyhg8f3uS5nxY0mZmZwc7Ojv4WmsGX3BD/9AFR7UUH7SXbPL7kpvg//xasrKyoE0EzGThwIK5cuYK8vDyoqak1OC83NxdJSUkYM2aMENMRIjgjRozAiBEjeD/7+/tDT0+P2pKyaPXq1V+8lzUAtG7dGpMmTcKiRYsgIyMjgGTihc5VhY/L5QL4+ODzn2Nf+jvIvxMVFQXgYxHNP8ea6t8cv8jn0TGpZejcuTPCw8NRWVkJGRkZDB48GL6+vti3bx9MTEx4Rd6///478vPz0bt3b5YTi56srCwYGRk1es0GAOrq6ujcuTNevXolpGSEEPI/ffr0QZ8+fdiOQQgYLl0hkUb4+Phg06ZNsLS0xO+//w4ZGRm4uroiLi4OCQkJAIDi4mLMmjULT58+xS+//AI7OzuWUxPS/Dw9PVFYWAhfX1/U1NTA1NQUxsbGUFRURElJCRITExEbGwsJCQlMmTIFUlJSSE5Oxp07d1BdXQ0DAwOcPXsWioqKbP9P+U9zdHREamoqHjx4AElJSQAfu28MGTIEZWVl2LVrF/r06QMvLy+cPXsWEydOxMaNG1lOLXqMjY3/1fsYhkGrVq2wdetWjBw5splTiZd/+xkAgJSUFL7//nu4ubk1YyLxVPs51N7sru+0ur7Xatvy1h6zyNfJyMjAhAkT0LlzZ+zZswfq6up15uTm5mLZsmV48eIF/Pz80KFDBxaSiobo6GgAHzs59OzZk2/sS/Tr169Zc5GP2wnJysrW+zdAhGP16tWoqqrClStXUF1dDS0tLb5rhufPnyMzMxPS0tIYNWoUqqurkZycjKSkJDAMg759++L48eOQlpZm+3/KfxqdqxJxVFusIScnh169evGNfYn+/fs3ay5Cx6SW4vz581i3bh0OHjwIKysrcDgcjB8/Hs+fP4e8vDwMDAyQl5eHnJwcAMC+ffvo//dmZmpqCg0NDYSEhHx27rhx45CdnY0HDx4IIRkhLV9ZWRlv6yfSPAoLC6lDK0vS09ORnZ0NJSWlOudJHh4eDb5v8uTJGDRokKDjtRhUwEEaVVVVhcmTJyM+Ph6Ghoaws7NDYGAgUlJS8Ntvv+HFixc4f/48srOz0a9fP5w8eZIq9olIKiwshIuLC+Tk5LB3716+VS21UlJS4OHhgfLycpw7dw6tW7fGmzdv4O7ujhcvXmDJkiX45ptvWEgvOoYMGYJWrVrhypUrvLH79+9jxowZGDVqFPbt2wcAKC8vx6BBg9C2bds6rdDI18vMzMS1a9ewa9cu9OzZEy4uLujatStfQdP58+fx+PFjrFixAubm5nj16hXOnz+PiIgISEtL4/z58+jSpQvb/1P+006fPo0dO3Zg9OjR9X4G586dw+XLl/Hdd9/B2dmZ9xn89ddfAIBTp07xbUVEvlxUVBQePXqE3377De3bt4eDg0OdzyEoKAhv3rzBkiVL0LlzZ7x8+RIBAQFISkpCq1atEBAQwGvfS/6dgIAApKam4siRI5CQkMDIkSPRqVMnqKmpIS8vDy9fvsS1a9fA5XIxd+5c6Onp1ft7HB0dhRv8P8rY2BgMw0BfX59387V2rKmoTT4RVZWVlZg+fTqysrKwfft2DB48uM6cyMhIrFmzBu3bt8eJEycgKyuLR48eYenSpcjOzsYPP/yAadOmsZBedNC5KiGkJaFjUstQVFSEGzduwNjYGJ07dwbwcTvw1atX4+7du7x5KioqWLFiBVxcXNiKKrJcXFzw9OlT+Pv7N1rYlJiYCEdHR/Ts2RPnzp0TYkJCWp6SkhKcOnUKJ0+e5DtWka/Xs2dPWFpawt7eHlZWVtQJUUhqamowduxYvH79Gr///jtfV1Hgf/eX6itdqL0PJS7PoKmAg3xWfn4+li5diqioqHr/MLhcLgYMGIDffvsNKioqwg9IiBBs3rwZvr6+uHz5cqP7haenp8PGxgaTJ0/Gjz/+CAB48eIF7O3t0a1bN/j5+Qkrskjq0aMHunbtyncBt3//fvz+++/YuHEj3wW2o6MjkpOT8fjxYzaiirQHDx5gxowZmDp1KtasWdPgvO3bt+P06dM4fvw4r1Bg69atOHnyJBwdHbF9+3ZhRRY5YWFhWLRoEVatWoXZs2c3OO/48ePYsWMHPD09ea17Dx06hN27d2P06NH47bffhBVZJCUlJWHixIkYNmwYtm/fXu/FXlVVFVavXo2wsDD8+eef6NKlCzgcDlatWoWQkBC4urpi/fr1LKQXHf+8uGvofLWh12rVdpcjjavt3qOlpYUdO3bwjX2JU6dONWsu8j/5+fn4888/cevWLaSkpKCkpASKiorQ19fH0KFDMXnyZGrHLiC//fYbDh48+NkHEwkJCXBycsKCBQuwbNkyAB/Pr6ZOnQoTExP8+eefwooskuhclRDSktAxqeV79+4dMjMzIScnh06dOtFWlwJy/PhxbN++HVpaWtiyZUu9q6jv3r2LdevW4c2bN1i9ejVmzJjBQlJC2FdcXIwTJ07g5MmT+PDhAwC6Z9HcPu2qq6SkBBsbG9jZ2VG3UAG7desW5s+fD3Nzcxw5cqTO68bGxtDS0sL48eP5xu/cuYOHDx/i8OHDMDc3F1ZcVlEBB2myW7du4cqVK3j+/DmKioqgoKAAIyMj2NjYwNramu14hAiUtbU1lJWVERAQ8Nm5Dg4OKCoqQlhYGG9s5MiRyM/Pp9Z/X6lfv35o1aoVbty4wRubPXs2IiMjcfHiRb7OKLUdB+Li4tiIKtLmzp2LJ0+e4M6dO43e2KiqqoK5uTl69uzJOyErKSnBoEGDoK6uzvc3Qr7MtGnTkJqaitu3bzc6j8vlwtzcHPr6+jh9+jSAj6uDBwwYACUlJdy6dUsYcUXWkiVLcPv2bdy+fRsKCgoNzistLcWQIUNgYWHB6xSUn58PCwsLaGtr83UVIl+uubYDooICIgrCw8Px/fff48OHDw1u66SsrIxt27bRNZwAjB49GlJSUrh48eJn59ra2qKyspKvW5yVlRVKSkr+1bZE5H/oXJU9R48eRVxcHCwsLDBp0qTPzv/zzz9x+/ZtmJmZYebMmYIPSHhquxG8ffsW3bt3F6t21MJGxyRCPqqsrMTUqVPx5MkTMAyDTp068XVOTEpKwqtXr8DlctG7d2+cPn2atpUjIqWiogKHDx/G5cuXkZGRATk5OXTv3h3z58/HgAEDAAAcDgfHjh3DoUOHUFRUBC6Xi7Zt22LOnDl0rtTM0tPTERgYiL///hupqakAPl4va2pqws7ODvb29jA0NGQ3pAhav349zp07h0OHDsHCwqLO68bGxjA1NYWPjw/feHR0NNzc3DBx4kRs3LhRWHFZReWkpMmGDh2KoUOHsh2DEFbk5uaiVatWTZrL5XKRm5vLN6aqqsrbR5P8e4aGhnj06BEePHgAU1NTZGZmIioqCurq6nW2tcnOzoaamhpLSUXbkydPoKur+9lVKdLS0tDV1cWTJ094Y4qKijAwMEBycrKgY4q0xMTEerdy+ieGYdChQwckJibyxmRkZKCvr4+kpCRBRhQLMTExMDQ0bLR4AwAUFBRgaGiImJgY3piqqioMDAyQnp4u6JgijwovCPno2bNnWLx4Maqrq6Gjo4MpU6bA0NAQ6urqyM3NRXJyMnx9fZGWloYlS5bgzz//RI8ePdiOLVLevHmDTp06NWmujIxMne8ADQ0N2l6oGdC5KjvS09OxZ88etGnTBtu2bWvSe8aOHQsvLy/cvHkTo0ePhqampoBTipeQkBAcPnwYrq6ufN0qk5OTMXv2bL57FI6Ojk3+3MiXoWMSIR/JyMjA29sbGzZsQEhICJKSkpCUlMTXTVFCQgK2trZYv349FW8QkVJdXY2ZM2fi4cOHvH/v5eXluHPnDu7fv4/ff/8dvXr1woIFC/Ds2TNwuVxoaWlh7ty5cHZ2pu09BEBHRweLFy/G4sWL8eTJEwQGBuLSpUvIysrCoUOHcOjQIXTr1g329vYYN24c1NXV2Y4sEp48eQJZWVle0VJT9evXDyoqKnznSaKOCjgIIaQJ1NXV8erVK7x+/Rp6enoNznv9+jVevnyJ9u3b840XFBSgdevWgo4p8hwcHPDw4UO4u7tj4MCBePToETgcDhwdHfnmpaWlITc3l4rOBKSyshJv375t0ty3b9+isrKSb0xaWpouxL8Sl8tFRkYGuFxuo1tC1NTU8OZ9imEYyMnJCTqmyCspKcH79++bNLegoAAlJSV8Y/Ly8mKzbyMhRPB+//13VFdXw9nZGRs3boSEhATf65aWlpg5cyZvxYuXlxcOHDjAUlrR1Lp1ayQlJSE3N7fRG3zv3r3Dixcv0KZNG77x4uJiumZoBnSuyg5/f39wOBwsWLAASkpKTXqPsrIy3N3dsWnTJvj5+WHRokUCTileLl++jMTERPTt25dvfNu2bcjOzka7du1gaGiI2NhYBAQEwMLCAmPHjmUpreiiY5Lw1XaykpOTQ8+ePfnGvgS10W9+SkpK2LVrF5YuXYrbt2/X2e7P3Nwc2trabMckpNmdPXsWcXFxYBgG48aNQ+/evVFeXo7w8HDExsZi+/btUFdXx9OnT6GhoYFvv/0Wjo6OtKWTkPTs2RM9e/bE2rVrcefOHQQGBiI0NBTPnj1DfHw8du7ciYEDB9a75Qf5MpmZmdDS0vpXRUlaWlrIzMwUQKqWif76CSGkCUaMGIGTJ09i4cKF2LNnDzp37lxnTlJSEpYtWwYul4tRo0bxxnNzc5GWlgZTU1NhRhZJkyZNQnR0NEJCQnDt2jUAgKmpKdzd3fnmBQUFAQC1gRUQQ0NDPHv2DP7+/nBycmpwXkBAAHJycng3TGqlp6dDVVVV0DFFWrdu3RATE4NDhw5hwYIFDc47cuQI8vPz+W48cblcvH79mirHm4Guri5evnyJiIiIetv+1YqIiEBGRkad7443b97UeXhHCCH/VlxcHFq1aoX169fXKd6oxTAM1q1bh0uXLiE2NlbICUWfhYUF/Pz84OHhgX379tXbDS4/Px9Lly4Fh8OBpaUlb7yoqAipqano3r27MCOLJDpXZce9e/cgISGBcePGfdH7bG1tsXXrVty9e5cKOJpZYmIilJWV+dp/5+bm4s6dO2jbti1CQkLQqlUrhIeHw93dHX5+flTAIQB0TBI+Nzc3MAwDfX19hISE8I01FcMw1BVLgLS1tTF58mS2YxAiNJcuXQLDMNi4cSNfV6z58+dj1apVCA4ORlpaGszNzbF3794mdwInzUtCQgIWFhawsLBAWVkZrl+/jrNnzyI6Ohp37txhO55IKC0tbbTY+/z581BUVKz3NWlp6TqL40QZFXCQz6quroa/vz9u3ryJtLQ0lJaW1rufMvDx5Pb69etCTkiI4H377be4efMmXr16BQcHB/To0QPGxsZQVFRESUkJnj9/jqdPn6Kmpgb6+vp8N57Onj0LLpfb6MM90jQSEhLYvXs35s2bh5SUFGhqasLExKTORbiuri7WrFkDGxsblpKKtmnTpmH16tX48ccf8eLFCzg7O/PdFHz16hUuXLiAkydPgmEYTJs2jffao0ePUFBQgMGDB7MRXWTMnTsX0dHR2Lt3L549e4YJEybUOSZduHABV69eBcMwmDdvHu+9kZGRKC4uxsiRI1n8XyAaXFxcsHXrVnh4eGDFihVwdHTku8goLS2Fv78/du/eDYZh+C7Sk5KS8O7dO1hbW7MRnZB/rWvXrl/9O+iGuGBUVlaiU6dOn13JIisrC319fbx8+VJIycSHh4cHbxXdiBEjYGlpWef7+ebNmygtLYW6ujq+/fZb3nsDAgLA4XAwZMgQFv8XiAY6V2VHcnIytLW1v7g4tXXr1tDW1qYtIgQgPz+/zkr2qKgo1NTUYNy4cbyHQ1ZWVmjXrh19NwsIHZOEr3YBg5aWVp0xwp41a9ZAX18f8+fP/+zcQ4cOISUlhbZ2IiIjKSkJysrKfPeFas2fPx/BwcGQlpbGjh07qHijBeByuYiNjcWdO3fo/KiZKSoqorCwsMHXG9vmtaCgoMHiDlFEBRykUR8+fMDMmTORkJDQYNHGp6gNOBFVSkpK8PX1xc8//4xr167hyZMnePLkCd8+jQzDYNSoUfj555/5qginT5+OKVOm0MlXM+ratWujD5Ds7e2FmEb8ODo6Ij4+HidPnsTx48dx/PhxSElJQUFBAWVlZaiqqgLw8WR3xowZcHBw4L336dOnsLKyqrPtDfkylpaWWLNmDX755Rdcu3aN15HmU1wuF5KSkvjuu+/4thN69+4dpk6dCjs7O2FGFknTpk1DdHQ0rl27hs2bN2Pbtm3o0KED70FdZmYmOBwOrzPTpzdjb9y4ASMjI1rlSP5zmnJNIIzfQerS19f/ohbt+vr6Ak4kfjQ0NODj44OVK1fi2bNnuHz5Mq5cucJ7vfbffs+ePbFz505oaGjwXhs6dCj69OkDXV1doecWNXSuyo7i4uJGtxttTOvWrcWqHbKwlJeX1xmLjY0FwzDo378/37iGhgYSEhKEFU2s0DFJ+E6dOtWkMSJc/v7+MDU1bVIBR0REBGJiYqiAg4iMoqKiBu9l154/6enp1dvBjwjPs2fPEBwcjIsXLyI3N5d3/dajRw++72fy77Vv3x6vXr1CcXHxFz0vKyoqQnp6Ojp16iTAdC0LFXCQRu3duxfx8fFo27Yt5syZAxMTE6ipqTXYkpcQUaaqqop9+/YhPT0dERERSE1NRWlpKRQUFNCxY0dYWFhAR0enzvuocENwuFwu3r9/j/Lycr6VFUTw1q5di0GDBuHIkSOIi4tDVVUVr3pWQkICffv2xdy5c2FlZcX3vqlTp2Lq1KksJBY9M2bMwIABA+Dt7Y07d+4gLy+P95qamhrMzc0xa9YsGBsb873PwcGBLjqaiYSEBPbt2wcfHx94e3sjKysLr1+/5pujpaWFOXPmwNXVla/Qdf78+U26cUVIS5OYmFjv+PHjx7Fr1y4MGjQIbm5u6NSpE9TV1ZGbm4uXL1/i1KlTiIyMxKpVqzBjxgwhpxYPEydOxM8//4zAwMBGj/OBgYHIycmhrQoERF9fHxcuXMC9e/cavGYYOHBgnff92wffpH50rip8ioqK+PDhw796b1FRkVitphMWVVVVZGRkoKqqCtLS0gCA27dvg2GYOlu8VlRU0L0LAaJjEiFfhsPh0EJRIlI4HA5kZWXrfa22g6KysrIwI5H/l5mZieDgYAQHB/M6wnG5XGhra8POzg729va0+KEZmZmZ4cWLF7h48SImTZrU5PcFBQWhpqZGrDpqUQEHaVRoaCikpKTg7e0NIyMjtuMQ0iLo6OjA1dWV7RhiLTIyEkeOHEFsbCzKy8vrtGKvbbX4/fffQ0VFhb2gIm7YsGEYNmwYSktLkZaWhpKSEigqKkJXVxcKCgpsxxMLxsbG+OWXXwB8vPFd+4Cosb0ESfOqbXM8bdo0vHr1CikpKbzPQV9fn689MiGi6tq1a9ixYwc8PDzg7u7O95qWlha0tLQwdOhQ/PHHH9i+fTs6dOiAESNGsJRWdE2ePBkvX77EDz/8gCdPnmDatGno2LEj7/XU1FT4+Pjgzz//hJubGyZOnMheWDEwcODAegs1iPDQuapwaWpqIikpCR8+fPiiBxAfPnxAWloa3XMSgD59+uDq1avw9PTEvHnzEBISgtTUVPTq1QutW7fmzeNwOEhLS6t3QQppPnRMIqRpOBwO0tPT6b4GIUSg/vzzTwQFBSEuLg7Ax6KN1q1bw8bGBvb29nWKXUnzcHBwgI+PD3777TeYm5ujQ4cOn31Peno6fv/9dzAMI1aLEqmAgzQqPz8fenp6dCFNCGkxPD094eXl1Wj7dSUlJQQEBKBfv34YP368ENOJJwUFhTpdHojwKSkp0Q0OlhkaGlLBBhFLx44dQ5s2bbBgwYJG582bNw8nTpzA8ePHqYBDAIYPH8777z4+PvDx8YGUlBRUVFRQUFCA6upqAICkpCTCwsIQFhZW53cwDIPr168LLTMhwkDnqsLRv39/PH/+HOfPn8fs2bOb/L5z586Bw+HU2dKDfL3Zs2cjNDQUhw4dwqFDhwB8PM7/8/OJiopCeXk5evbsyUZMsUPHJCIuoqOjcf/+fb6xN2/ewNPTs8H3VFRUIC4uDrm5uVQIS0TO5/79f+71xYsXCyKW2Pr5558BfOyAYmVlBXt7e1haWvK6lhHB6NWrF0aNGoWrV69i0qRJWLduHUaPHl1v16WamhpcvnwZW7ZsQWFhIUaMGIFevXqxkJodDJc2ICaNGDFiBOTl5REcHMx2FEIIQUREBObNmwdFRUUsXboUI0aMwPLly/Hw4UO+/Xrz8vIwZMgQWFtbY//+/SwmJoQQQkSfmZkZ9PX1ce7cuc/OdXFxQUpKCmJiYoSQTLw0x8MghmH4zqkIIaSpkpKSYG9vDzk5OZw4caJJN1cfP36MGTNmoKKiAgEBAejcubMQkoqX8PBw7N69G6mpqdDU1MTcuXPh4uLCN2fp0qW4fPkydu3aBVtbW5aSEtJ8unbt+tW/45+dXsmX8/T0hKenJ++hHJfLbdK2KFwuFzIyMjh06BAVcRCRYWxs3Oi//9rHtI3Noeu05jVt2jTY29vDxsaGtq8RspKSEkydOhWJiYlgGAZqamro06cPtLW1IS8vj7KyMmRmZiI2NhZ5eXngcrno0qULfHx8xGrLP+rAQRo1evRoHD9+HG/evIGmpibbcQgRitoLPQMDA1y8eJFvrKnoQk8wTp06BYZhsH37dowcORJA/Se2ampq0NTURGJiorAjipyAgAAAQKtWrXirpWvHvoSjo2PzhRIztdX3bdq04e1/3FhFfn0YhsGiRYuaPZs4iY6OBgDIycnxVifWjn0JcdqrkYgPLpeLjIwM1NTUQEJCosF5NTU1yMjIaLSLFvn3Tp48yXYEsVLb8URPTw/e3t58Y01FHU++Hp2rthxGRkZwcXHB2bNn4ebmhm+++QaTJ0+ud0vLgoIC+Pr64uDBg6isrISLiwsVbwiIlZUVrKysGp2zefNmbNq0CYqKisIJJcLomNQyNMe5Jp2vfj1jY2M4OTnxfvb394eamhosLCwafI+cnBx0dXUxYsQI2taJiBS6F9TynD59mu0IYktRURG+vr7YvHkzAgICkJubi2vXrtV5zsPlciEpKQkHBwesW7dO7Lacow4cpFGlpaWYNGkSFBUVsXfvXrRv357tSIQIXO3qRX19fVy6dIlv7EtQ8UDzGzhwICQlJXHnzh3emKurK+Li4upUIbu4uOD58+d4/PixsGOKlNoKcX19fYSEhPCNfQmqEv/3GvsMPncaVzuHVlR/veb4W6DiPsF7+PAhoqKikJ2djfLycmzdupX32tu3b1FdXQ0tLS0WE4omNzc3xMTEYNGiRY22da1dhde/f38qNiD/ebXXBwYGBnzfC1+Cvp+/Hp2rtixVVVVwd3fHnTt3wDAMJCUl0alTJ+jo6EBBQQGlpaXIyMhAUlISOBwOuFwuhgwZgoMHD1K7aiIS6JjUsh0/fhy7du3CoEGD4Obmhk6dOkFdXR25ubl4+fIlTp06hcjISKxatQozZsxgO67IMTY2hqmpKXx8fNiOQgghpIVIT0/H5cuX8eDBA7x9+xYlJSVQVFREu3btYGpqChsbG7Et6KMOHISnodW8gwcPho+PD0aPHg0LCwvo6elBXl6+wd9De3GR/7r6Ci+oGKNlKCkpgZGRUZPmcjicRlcBk6ZxdHQEwzBo27ZtnTEiHLXfq23atKkzRoSndrXEpw//aQVFy5GVlYVVq1YhNjYWwP/a835awLFv3z5cuHABZ86cQZ8+fdiKKpIWLFiA6OhoeHl5ITo6GtOmTYOhoSHU1NSQl5eHV69ewcfHB/fv3wfDMJg/fz7bkQn5aqGhoQAAKSmpOmNEeOhctWWRlpbGkSNHcPDgQRw7dgwfPnxAYmIirz3yp8XHSkpKmDVrFtzd3em6TQhycnIQExOD7OxslJWV0fWEgNAxqeW6du0aduzYAQ8PD7i7u/O9pqWlBS0tLQwdOhR//PEHtm/fjg4dOvC6qJDmERoaCllZWbZjEEIIAOqq21Lo6Ohg3rx5mDdvHttRWhzqwEF4GqsI//SfSWNzaAURIUSQLC0tUV5ejvv37/PG6uvAUV1djf79+6Nt27a4cuUKG1EJIYQISWFhIcaPH4/MzEy0b98egwcPxt27d5GTk8P33RATE4Np06Zhzpw5WLVqFYuJRdOZM2ewdetWVFdX13u9wOVyISUlhTVr1vC2gyKEECK6SkpKcPPmTcTGxiInJ4e3mk5DQwN9+/aFpaUlbdkhBEVFRdi0aRMuXryImpoa3vin50geHh64du0a/Pz8/lX3UUL+C1xdXZGamsrrENSQmpoamJubw8DAgNrrE0KICKOuuqSlow4chIcqwgkhLV3fvn1x+fJlhIWFwdrausF5gYGBKC0txYABA4SYjhBCCBuOHDmCzMxMDB8+HLt27YK8vDxcXV2Rk5PDN69v376Qk5NDZGQkS0lFm6urK/r16wdvb29EREQgNzeX95q6ujosLCwwa9YsdO7cmcWU4uPx48dISEhAQUEBqqqq6p3DMAwWLVok5GSEEHGhqKiIsWPHYuzYsWxHEVvl5eWYMWMGEhISIC8vj549eyIpKQnv37/nm+fi4oIrV67g+vXrVMBBRNaLFy+gr6//2XvfEhIS6NChA3XiFYCsrKwvfg9tf0kIEZTGummUlZXh9evXKCoqgrS0NExMTIQXjJD/RwUchGf79u1sRyCkxeJwOCgrK4O0tHSddn+PHz/G2bNn8fbtW/To0QOzZ89Gq1atWEoq2tzc3HDp0iWsX78eSkpK9Z5oXb16FVu2bIGkpCSmTZvGQkpSU1ODgoICqKqqsh1FbOXk5CAnJweGhoa0spGIvOvXr0NaWhpbtmxpdJs/CQkJ6OjoID09XYjpxIuRkRG2bdsG4OOK39LSUigoKEBJSYnlZOLj8ePHWL16NVJSUnhjtZ0SP1U7RgUcwpOZmYmgoCC8ffsW3bt3x/jx42nbCJbQuSoRFydPnkR8fDz69u2LvXv3ol27dnB1da1TwDFgwABIS0vj9u3btLUKC+iYJBxcLhcZGRmoqalp9Pu3pqYGGRkZoKblzc/a2ppWuxNCWoxTp059dk5QUBC2bdsGbW1t3r0OQoSF7hYQQkgTeHt7o1+/fvjrr7/4xm/evAlXV1dcuHABt27dwoEDBzBt2jRUVFSwlFS09e3bFwsXLkRubi6mT5+O0aNH49WrVwAAd3d3DB8+HB4eHigtLcWyZctola+AJCcn4+TJk4iJieEbr6ysxKZNm9CnTx8MGTIE1tbWuH37NkspRdvjx4+xbds2hIeH840XFxfD3d0dVlZWmDRpEszNzXHhwgV2QoqBnJwchIaG4sWLF3zjXC4X3t7eGDVqFExMTDBt2jRawSVAWVlZ6NixI1RUVD47V1FREWVlZYIPRaCkpAQNDQ0q3hCitLQ0zJo1C69fv4atrS3at28PAFi4cCGmTJmCrl27gsvlQlZWFrNmzaLiDQHw9fVF//79ceLECb7xR48ewd7eHvv27YOvry9+/PFHzJ07l287A9J86FyVkI9CQkIgJSWFXbt2oV27dg3Ok5aWhq6uLl/xH2k+dExqGbp164aCggLs37+/0Xn79+/H+/fv0b17dyElEx9aWlrQ1NSs9z8qKirgcrm8bRc1NTV557KEEMIWe3t77N69GwEBATh79izbcYiYoQIOQghpgto9Mm1tbfnGd+3aherqalhaWsLDwwM6Ojp4/vw57ZMpQEuWLMG2bdvQrl07vH79GoWFheByuQgPD0dmZibU1NSwfft2zJ07l+2oIuvMmTPYtm0biouL+ca9vLzg4+ODiooKcLlcZGVlYdGiRbwiG9J8zp8/j5MnT9bpNrBz506Eh4eDy+VCWloaZWVl+PHHH/H48WOWkoq2U6dOYfHixXj58iXf+PHjx7Fz506kpaWhvLwcMTExmDFjRp0tPUjzkJaWRmVlZZPm5uXlUZcsIrIOHz6M0tJS/Pjjj9i5cyc0NTUBfDx3Wr9+Pfz8/HDs2DEoKCggMjISc+bMYTmx6AkPD0dRURFGjhzJN759+3aUlJTA2NgYzs7OUFFRQWRkJM6fP89SUtFG56qEfPT69Wtoa2s3aQsCJSUllJSUCCGV+KFjUsuwYMECcLlceHl5YcaMGbh27RqSk5NRWFiI5ORkXLt2DTNnzoSXlxcYhsH8+fPZjixywsLCGvxPZGQkoqKi4OHhASkpKYwfPx5hYWFsRyaEEAwaNAiampr4888/2Y5CxAxtoUJ4/s0+dPWhvemIKHr9+jXU1dX5WlomJycjKSkJXbp0wcGDBwEANjY2GDt2LK5evUo3xQXIyckJdnZ2ePjwIZ4/f46ioiIoKCjAyMgIpqamkJGRYTuiSIuJiYGsrCwsLCx4Y5WVlThz5gxvhVefPn3g5eWFs2fP4sSJE9i4cSOLiUVPbGws5OTkMGDAAN5YWVkZAgMDIS8vj5MnT6J79+44cOAAfv/9d5w8eRK7du1iMbFounfvHqSkpDBixAjeWE1NDY4ePQoAWLlyJfr06YMjR47gxo0b8Pb2xpo1a9iKK7I6duyIxMRE5OfnN9p6Oi0tDenp6Xx/N6T5PX78GAkJCSgoKEBVVVW9c2jrDsGIjIyEoqIinJ2dG5wzaNAg7NmzBzNmzMDBgwexbNkyISYUfS9fvoSqqirfNXFWVhbi4uKgq6uLc+fOQUpKCi4uLpg0aRKCg4MxceJEFhOLJjpXJeR/JCUlmzSvsLCQtl4UEDomtQzm5uZYv349tm7divv37yMqKqrOnNruD2vWrIG5uTkLKcWbsrIyvvnmG+jp6WHFihXo3LkzRo0axXYsQgiBiooKdSojQkcFHITnS/ehqw/tTUdE1fv372FkZMQ3Fh0dDeBj0UYtfX196Orq0ooJIZCSkoKZmRnMzMzYjiJ23r17Bw0NDb6bgXFxcSgqKsKoUaN4fxNr165FcHAw7t27x1ZUkZWbm8tbWV0rOjoa5eXlcHBwQM+ePQF8XGV04sQJxMbGshFT5GVnZ0NDQ4OvaOzx48fIzc2Fubk5rxOQkZERzM3NqSWygIwcORJPnz7Fzp07G9yTlMPhYNOmTWAYhu97mzSfx48fY/Xq1Xw3Nbhcbp3ri9oxKuBofm/fvkXHjh0hJfXxMr/2e7qyspLvODVgwABoa2vj6tWrVMDRzPLz86Gvr883dv/+fQDAmDFjeJ9N79690aFDhzpbcJHmQeeqhHykra2N169fo7S0FAoKCg3Oe/fuHV6/fo1evXoJMZ34oGNSy+Hq6op+/frB29sbERERyM3N5b2mrq4OCwsLzJo1i7bjZdnYsWOxdetWHDt2jAo4CCGsKy8vR2pqKu9ajhBhoX9xhKexzhlv375FdXU1gI8PTVVUVFBQUMA31th+moT819XU1KCsrIxvLDY2FgzD1CkgUFFRQWZmpjDjiQ0fHx+MHTsWbdq0YTuKWCssLKzznfHgwQMwDMO3qkhOTg56enpITk4WdkSRV1xcDG1tbb6x2mPSkCFDeGNSUlLQ1tZGUlKSsCOKhYKCAnTr1o1vLCYmBgzDYNiwYbwxZWVl6OnpISMjQ9gRxYKbmxvOnz+PgIAAvHnzBs7OzigtLQUAPHv2DC9evMCpU6cQHx8PIyMjTJgwgeXEoictLQ2zZs1CeXk5bG1tERMTg+zsbCxcuBAFBQV4+PAh4uPjIScnhylTptAKXwGRl5fnu6mkpKQEAMjJyYGOjg7fXGVlZfp+FoDq6uo6nWfi4uLAMAz69+/PN66urk5bawkInasS8tGwYcNw+PBh7N+/HytXrmxw3q+//goul4vhw4cLMZ34oGNSy2JkZMQr+i4qKuIVONWeN5GWoX379lToSghhXX5+PjZs2ICysjLqzNRMfHx8MHz4cLRv357tKC0eFXAQnob2ldu6dSt8fHwwefJkuLm5QV9fHxISEqipqUFKSgpOnTqF8+fPY+TIkdQWnIgsTU1NvH79GoWFhWjdujWqq6sREREBGRkZ9O7dm29uYWEhFRgIyKZNm7Bt2zaYm5vDwcEBw4cPp+1SWCAvL8+3UgX4+NAaAExNTfnGpaSkmty2lzSdoqIisrOz+cZqV/j+8zMAQH8nAiIjI4OCggK+sYb+FuTl5VFTUyOsaGJFQUEBR44cwTfffIN79+7x/hYA8LaS4HK5MDQ0xMGDB+nvQQAOHz6M0tJS/PTTT5g8eTJcXV2RnZ2NJUuW8OZERkZixYoViIyMhK+vL4tpRVe7du3w9u1b3s8GBga4ceMGoqOj+Qo4ioqKkJKSQn8LAtC2bVtkZGTwrXaPiIiApKQk+vTpwze3uLgYrVu3ZiOmyKNzVUI+mjVrFs6dO4ejR48iLy8PEydOBIfDAfCxEPnFixc4duwYbty4AU1NTUyZMoXlxKKJjkktl5KSEhVutEA1NTVIS0sDl8tlOwohRIRNnz69wde4XC7y8vKQkZGByspKyMrK4ttvvxViOtG1adMmbN68GV27doW1tTWGDRuG7t27sx2rRZJgOwBp2c6dO4dTp05h8+bN+Pnnn2FoaAgJiY//bCQkJGBoaIiff/4ZmzZtwsmTJ3H+/HmWExMiGObm5qiqqsLy5csRFhaGH3/8Efn5+Rg8eDDfze+SkhKkp6dTBaGAmJqagsPhIDw8HMuXL8fgwYOxdu1aajEqZIaGhsjOzsaDBw8AAJmZmYiKioK6ujoMDAz45mZnZ0NNTY2NmCLN2NgYeXl5uH79OgAgISEBDx8+hLa2Njp06MA3NzMzE+rq6mzEFHkdO3ZEeno6b9usgoIC3Lt3D8rKyjA2Nuab+/btW/pbECA9PT0EBARg/fr1GDBgAFRUVCApKQklJSX07dsXP/zwA/z8/Or8fZDmERkZCUVFRV7BTH0GDRqEPXv2IDExEQcPHhRiOvHRq1cv5Ofn4/379wCAESNGgMvlYteuXbh16xZKS0vx+vVrrFy5EuXl5fUW/JGv079/f5SXl2PTpk14/vw5fvvtN7x58wampqZ82xdUVlbi9evX1MVSQOhclZCP2rRpgz/++AOqqqrw9/eHq6srHj9+DODj9/KMGTNw48YNqKur48CBA2jVqhXLiUUTHZNansrKSjx8+BCXL19GQEAA23HIJ6qqqrBt2zZ8+PChTrdLQghpTlFRUQ3+Jzo6GsnJyaisrISpqSlOnjxJW801k+XLl6N3795ITEyEp6cnnJ2dYWVlhQ0bNuDWrVt1OlqKM+rAQRrl6+uLdu3awcnJqdF5Tk5O2Lt3L3x9fRu9cUvIf9X8+fNx6dIl3LlzB3fv3gWXy4WMjEydyssbN26Aw+HU2VaFNA8fHx9kZWUhKCgIf//9N16+fAk/Pz/4+/tDQ0MDtra2sLe3p/1KBczBwQEPHz6Eu7s7Bg4ciEePHoHD4cDR0ZFvXlpaGnJzczF06FB2goowFxcX3L9/H0uXLkXnzp2RkpLCG//U8+fPUVhYWKdtO2keNjY2iI+Px9y5czF69GjcvXsXFRUVdf4WcnJykJ2djYEDB7ITVEzIyMjA1dUVrq6ubEcRO2/fvkXHjh1523fUrhytrKzkK3QdMGAAtLW1cfXqVSxbtoyVrKLM2toaFy5cQHh4OJycnGBiYoJx48bh4sWLWLBgAW8el8uFvLw8PDw8WEwrmhYsWIArV64gICCA90BIQkIC33zzDd+8iIgIVFdX1+nKQZoHnasS8j+9evVCcHAwjh49iqtXryI9PZ33Wvv27WFjY4N58+ZR0YAA0TGp5aiuroaXlxdOnz6N4uJi3vinn8WPP/6Iu3fvwtvbG3p6eiykFF2f69ydm5uLhIQE5OXlQUJCgu/8lRBCmtvJkycbfI1hGMjLy0NPT486NTWz+fPnY/78+cjPz0dYWBhCQ0Nx7949+Pr64s8//4S8vDwsLCxgbW0NS0tLqKiosB2ZNVTAQRqVkpKCTp06NWluu3bt8PLlSwEnIoQdGhoauHDhAo4ePYrU1FRoaWnBzc0NRkZGfPOioqJgbGyMYcOGsZRU9GlpacHd3R3u7u5ISEhAYGAgQkJCkJ2djaNHj+Lo0aPo3LkzHBwcMG7cOGhoaLAdWeRMmjQJ0dHRCAkJwbVr1wB87I7i7u7ONy8oKAjAx9VdpHnZ2tri+fPn8Pb2Rnx8PG9s9uzZfPNqHx5R4YBgzJgxA+Hh4YiNjcXx48cBfOwE8c/ivpCQEAAfH16T5ldTU8PrEEfYIS8vzyveAMC7wZGTk8O3dQcAKCsr097uAjJs2DDcvHkTioqKvLEdO3bAyMgIgYGByMjIgLy8PMzMzLBkyZI6nYLI19PX18epU6fg5eXFu2aYPXt2ne/hv//+G0pKSrCwsGApqWijc1VC+KmqqmLVqlVYtWoVysrK8OHDBygqKlLHDSGhY1LLwOFw4O7ujjt37gAAOnTogPfv36O0tJRv3tChQ3Hu3Dlcu3YNc+fOZSOqyPL392/SPC0tLaxevZrOkwghAkWL3dilqqoKZ2dnODs7o6KiAnfv3sX169dx8+ZNXLlyBVevXuVtRVq71UrHjh3Zji1UDJc2EyON6N+/P2pqanD37t1G90iuqKjAkCFDICEhgaioKCEmJISQjytJ7927h8DAQFy7dg0lJSVgGAaSkpJ4+vQp2/FEVkJCAlJSUqCpqQkTExMwDMP3elBQEN6/fw8bGxsqpBGQ9+/fIy0tDZqamvW2YY+MjERJSQnMzMzEumJZkGpqahAWFobk5GRoaWlhxIgRkJOT45tz/PhxZGVlYcqUKdDX12cpqegyNzfHuHHjYGdnhx49erAdRyzZ2dnh/fv3uH37NgBg165dOHr0KLZs2YLx48fz5hUVFWHo0KGQlpamawZCiMDRuSohpCWhYxK7zpw5g40bN8LAwAC7d++GsbExXF1dERcXh4SEBN682m3m+vbti1OnTrGYWPQ0VsDx6Wr3Ll261Pn7IISQ5rZ48WJISUnhl19+afTZJxG+R48e4fr16wgLC+NtW80wDDp27Ijhw4dj2LBh6Nu3r8h/V1ABB2mUu7s7bt68CRcXF2zYsKHBP4j169fj7NmzsLKyoj2tCSGsys/Px9q1axEeHg6GYfguxAkhhIgeY2Nj3jmqvr4+HBwcYGtriw4dOrCcTHz88MMP8Pf3x507d9CmTRs8fPgQkydPhqqqKrZv3w4zMzO8e/cOW7duxa1bt2BlZYUDBw6wHZsQQgghhIiJiRMn4unTp/D390eXLl0AoN4CDuDjVpkfPnzA3bt32YhKCCFECHr06AEjI6Mmdwci7EhPT+cVc8TFxaG6uhoMw0BVVZXXVUtUUQEHadSTJ08wZcoUcDgc6OrqYsqUKTA0NISamhry8vLw6tUr+Pr6Ii0tDZKSkvD19UXPnj3Zjk0IETM1NTWIiIhAUFAQwsLCUF5eDi6XC2lpaTx58oTteIQQQgTo0w5MxcXFYBgGDMPA1NQU9vb2sLGxoT1LBSw0NBSLFi3Ctm3b4OTkBABYsWIFLl68yFcAzuVyIS8vD19fX9q+gxBCCBGS169f4+bNm0hLS0NpaSkauhXMMAy2bt0q5HSECIepqSnatGmD69ev88YaKuCYNGkSnj17Rh1dWRYZGUlbChFCBGb48OFQVFTkbWFGWr7CwkKEh4cjNDQUd+7cwYMHD9iOJFBUwEE+KywsDKtXr8aHDx/q7cDB5XKhpKSEbdu2YcSIESwkJISIq8ePHyMoKAiXLl1Cfn4+70aUiYkJ7OzsMHbsWLRp04bllIQQQoShsrISYWFhCAwMREREBK8qX1paGlZWVrC3t4elpSWkpaXZjipyampq8O7dOygqKqJVq1YAgOrqahw5cgSBgYHIyMiAvLw8zMzMsGTJEireEDB6UEcIIQT4eL9uy5YtOHPmDLhcboPfB7WogyURZSYmJtDT00NgYCBvrKECDjs7O7x58wYxMTHCjin20tLS4O/vj8DAQGRnZyM+Pp7tSIQQEbV582b4+voiNDQU7du3ZzsO+UJVVVUif3+PCjhIk+Tl5cHX1xcRERFITk5GaWkpFBQUYGBgAAsLC0yePBnq6upsxySEiIG0tDQEBQUhODgYaWlpvJtQenp6sLOzg729PXR1dVlOSQghhE2FhYUICQlBUFAQHj58CC6XC4ZhoKysDBsbG2zYsIHtiIQ0O3pQRwgh5FNHjx7Fzp07wTAMhg0bBhMTE6ipqUFCQqLB99R20iJE1NjY2CAnJwfR0dGQkpICUH8Bx4cPHzB48GB07twZfn5+bMUVK8XFxbh06RL8/f0RFxcH4ON5rZSUFHVBIYQITEFBAcaPH4927dph3759aNeuHduRCOFDBRyEEEL+MyZNmoTHjx8D+Hgx16ZNG4wZMwYODg7o3bs3y+kIIYS0RBkZGfj7778RHByMV69e0UNrIrLoQR0hhJBPjR07FikpKdizZw9sbGzYjkMIqzZt2oQzZ87gu+++w6xZswDUX8Cxc+dOeHt7w93dHR4eHmzFFXlcLhd3796Fn58fQkNDUVFRwSs+7tKlC5ycnGBnZwc1NTWWkxJCRJWnpyfy8/Px119/QUpKCoMGDYKhoSHk5eUbfM/ixYuFmJCIOym2AxBCCCFN9ejRI8jKymLYsGFwcHCAhYUFb+UEIYQQUp/q6mpUVVWhqqqK7Sgiq7i4GAkJCVBTU4OBgUGD85KTk5GXl4du3bpBUVFRiAnFw4ULF8AwDD2oI4QQAuBjEWv79u3pO4EQAHPmzIGfnx9+/fVXlJSUYOLEiXyvZ2Zm4tixYzh9+jRat24NNzc3lpKKtuTkZAQEBCAwMBBv374FAF7hhpKSEk6dOkXbLRJChMLT0xMMw4DL5YLD4SA8PBw3b96sd25tV1cq4CDCRE+9CCGE/Gds2bIFo0ePRqtWrdiOQgghpAXLz8/HxYsXERQUxGu7y+Vyoa6uDltbW5bTiZ6zZ89i586d2LBhQ6MFHDExMfjpp5+wevVqzJgxQ4gJxQM9qCOEEPIpZWVlWr1OyP/T0tLC7t27sXz5cnh5ecHLy4vXpaxPnz4oLy8Hl8uFvLw89u7dC1VVVZYTi46ioiL8/fffCAgI4OuqKysrC2trazg6OmLBggWQlZWl4g1CiNA4OjqCYRi2YxDSINpChTRJeXk5bty4gYSEBBQUFDS4gpFhGGzdulXI6QghhLRkiYmJdBFOCBG48vJyXLt2DUFBQYiMjASHw+HdhB05ciTs7e0xePDgRreTIP+Oq6srHj9+jKioKCgoKDQ4r7S0FP3790efPn1w6tQpISYUD+bm5mjfvj3Onz/PdhRCCCEtwIoVKxAWFoa7d+822g6ctCx0/SxYr169wu+//47w8HCUl5fzxqWlpWFlZYWlS5fC0NCQxYSi4+bNm/D398eNGzdQWVnJW8FuZmYGe3t7jBkzhrdAy9jYGOrq6rh9+zbLqQkhhJCWgQo4yGeFhoZi7dq1+PDhA2+s9p/NpxVqtSdhtKc4IYSIlv3792PhwoX/6r3Pnj3D7Nmzcf/+/WZORRpTU1ODgoICWjVExEJERASCgoIQGhqKsrIycLlcSEpKYtCgQbC3t8fIkSPpoYWAmZubQ15eHteuXfvs3JEjR6KiogK3bt0SQjLxQg/q2HP16lUEBQUhNTUVANCxY0fY2dlh9OjR7AYTU2vWrIG+vj7mz5//2bmHDh1CSkoKtm3bJoRkhAhXWloaxo8fj7Fjx2LDhg20ylRI6Pr5v6GqqgqvX7/Ghw8foKCggI4dO0JOTo7tWCLF2NiYtz1Bx44d4eDgAHt7e3To0KHeuVTAQQghhPwPbaFCGhUfHw8PDw9IS0tjwYIFuHTpEtLS0rBlyxYUFBTg0aNHCAsLg5SUFBYuXIi2bduyHZkQgaJuNEQc7du3D4qKil/c7v7x48eYM2cOiouLBZRMfCUnJ+P27dvo1q0bzMzMeOOVlZXYsWMHzp8/j8rKSmhqamLjxo0wNzdnMS0hgjVv3jzef+/WrRvs7e1ha2sLdXV1FlOJl8LCQmhqajZproqKChITEwWcSDx5eHjg5s2b2LZtGz2oE6L169fj3LlzAP630OHVq1cIDQ3F+PHjsWXLFjbjiSV/f3+Ympo2qYAjIiICMTExVMBBRFJOTg4WL16MXbt24eHDh3B2doaenl6j3bL69esnxISiia6f/xukpaXRqVMntmOIhdatW2P8+PGws7Nr8jUDIYQI2sWLFzFy5EjIyMiwHYWQelEBB2nU0aNHweFwsHv3bowaNQpRUVFIS0vDhAkTeHNevXqFb775Br6+vrhw4QKLaQkRrC/tRkMFHESUbN++HfLy8pg4cWKT5sfGxmL+/PkoLi5Gr169BJxO/Jw5cwY+Pj44cOAA37iXlxd8fHx4P2dlZWHRokXw8/OjNrACUlhYCG9vb9y6dQtpaWkoLS1tcC7DMIiPjxdiOvGgpaUFOzs72Nvb079zlqioqCAjI6NJczMyMqCsrCzgROKJHtQJ3/Xr13H27FkAQJcuXdCvXz/U1NQgJiYGL168gJ+fH4YNG4YRI0awnJQ0hMPhULETEVlubm68f99JSUmfLVSic9XmQ9fPhAC2trYIDQ1FYWEh9uzZg71798LMzAwODg4YPXo0b/sUQghhw4oVK9C6dWvY2dnB2dmZti9rgYqKinDjxg28ffsW3bt3x6BBg9iOJFRUwEEa9eDBAygrK2PUqFENzjE0NMS+ffvg6OiI/fv3Y926dUJMSIhwUDcaIs5+/PFHbNq0CT///DPk5ORgb2/f6PyYmBgsWLAAJSUlMDExwZEjR4SUVHzExMRAVlYWFhYWvLHKykqcOXMGUlJS2LVrF/r06QMvLy+cPXsWJ06cwMaNG1lMLJrS09Mxbdo0vH37Fk3ZlZB2LhSMsLAwtiOIvV69eiEsLAyXLl3CmDFjGpx36dIlvH//HlZWVsILJ0boQZ3wnT9/HgzDYOrUqfjhhx94//9zuVxs3rwZPj4+OH/+PBVwtFAcDgfp6elQUlJiOwohAqGlpcV2BLFE188tW3FxMe7fv4/09HSUlJQ0eo22ePFiISYTPbt27UJxcTEuXboEPz8/xMXFISoqCtHR0di0aROGDRsGBwcHvvsahBAiLF26dMHz589x+vRp+Pj4oFu3bpgwYQLs7Ozo+kCIQkJCcPjwYbi6usLFxYU3npycjNmzZyMnJ4c35ujoKFadE6mAgzQqLy8PRkZGvJ+lpD7+kykvL+fbF9DY2Bj6+voIDw+nAg4ikqgbDRFnU6dORXl5OXbu3Im1a9dCXl4eI0eOrHfuvXv38M0336CsrAxmZmb4448/oKioKOTEou/du3fQ0NCApKQkbywuLg5FRUUYNWoUbGxsAABr165FcHAw7t27x1ZUkbZz507k5OSgQ4cOmDt3Lrp37w5VVVVayUvEzsSJExEaGooff/wRsrKysLa2rjMnPDwcP/74IxiGafJqVPJl6EGd8D19+hRycnJYtWoV37GfYRisWrUKfn5+ePr0KYsJxUN0dDTu37/PN/bmzRt4eno2+J6KigrExcUhNzcXAwcOFHREQlhBRa7soOvnluvQoUM4cOAAysvLG51X21mXCji+XqtWreDi4gIXFxekpaXBz88PQUFByMrKwqVLl3D58mWoqKiwHZMQIoYCAwPx7NkznD9/HiEhIXj27Bni4+Pxyy+/YOTIkZgwYQJdJwjB5cuXkZiYiL59+/KNb9u2DdnZ2WjXrh0MDQ0RGxuLgIAAWFhYYOzYsSylFS4q4CCNatWqFTgcDu/n1q1bA/jYkt3AwIBvroyMDDIzM4WajxBhoW40RNzNmTMHZWVl8PT0xPLly+Hl5YWhQ4fyzbl9+zYWL16M8vJyDBgwAAcPHoS8vDxLiUVbYWFhnQd1Dx48AMMwfKtX5OTkoKenh+TkZGFHFAuRkZGQkZHBqVOn6MGpkGRlZQH4WFTcrl07vrEvQZ9X87K0tISdnR2Cg4OxaNEi6Ovrw8TEBEpKSigqKsKjR4+QnJwMLpcLe3v7egs8yNejB3XCV1BQACMjI8jKytZ5TU5ODh07dkRSUhILycTL/fv34enpyVdE8+bNG3h5eTX6Pi6XCxkZGSxYsEDQEQkhYoaun1seHx8f7N69GwDQrl07dOnSBWpqalR8L0S6urpYunQpli5disjISPj7++PatWt4//49gI8LSW1sbODk5AR7e3toamqynJgQIuq6d++O7t27Y82aNbh69SouXLiA+/fvIzg4GH///Te0tbUxfvx4ODk5oX379mzHFUmJiYlQVlbm2xI5NzcXd+7cQdu2bRESEoJWrVohPDwc7u7u8PPzowIOQgCgffv2fC1qjIyMcP36ddy5c4evgOPdu3dISUmhKnEisqgbDSHg3Vw6cuQIlixZgj/++AMDBgwAANy8eRNLlixBRUUFBg8ejP379/P9bZDmJS8vj9zcXL6xmJgYAICpqSnfuJSUFF+nDtJ8ampqYGBgQMUAQmRtbQ2GYWBgYICLFy/yjTUVbRshGNu3b4eGhgZOnjyJ5ORkJCcng2EYXltqGRkZzJo1C0uWLGE5KSHNp7q6GgoKCg2+Li8vz7cgggiGsbExnJyceD/7+/tDTU2t0ZbscnJy0NXVxYgRI6CjoyOMmIQQMUPXzy3L6dOneV013N3d6RqZZYMGDcKgQYNQWlqKkJAQBAQE4MGDB0hNTcXevXvx22+/oX///jh+/DjbUQkhYkBGRga2trawtbXFmzdvcOHCBfj7+yM9PR379u2Dp6cnhgwZAmdnZ1hbW/OeDZGvl5+fD21tbb6xqKgo1NTUYNy4cWjVqhUAwMrKCu3atROr+3n0r4w0ytTUFD4+PsjJyYGGhgZsbGxw4MAB/Prrr5CSkoKZmRnevXuH3bt3o6qqCoMHD2Y7MiECQd1oCPlo5cqVKC8vx+nTp7Fw4UIcPXoUeXl5WLZsGSorK2FhYQEvLy/IyMiwHVWkGRoa4tGjR3jw4AFMTU2RmZmJqKgoqKur1zkmZWdnQ01NjaWkos3IyKhOIQ0RrNpimbZt29YZI+ySlJTEypUrMXPmTISHh+PVq1coLi5Gq1atYGRkBEtLSzoWCVjtNRsh4mbEiBEYMWIE72d/f3/o6emJ1f7IhDSmuroaV65cwf3795GTk4Py8nKcOHGC9/rTp09RVlYGU1NTSEhIsJhU9ND1c8uRmZkJdXV1LFq0iO0o5BMKCgpwdnaGs7Mz0tPT4e/vj8DAQGRmZtbZHo0QQoRBU1MTixcvxuLFi3Hs2DH8+uuv4HA4uHXrFiIiItCmTRtMmjQJs2bNgrKyMttx//Pq29YsNjYWDMOgf//+fOMaGhpISEgQVjTWUQEHaZS1tTV8fX0RHh6OSZMmoXPnzpg5cyaOHTuGjRs38uZxuVyoqalh+fLlLKYlRHCoG03LUlJSgnv37iE9PR0lJSW81b3/xDAMXZwLwLp161BeXo7z589j7ty5KC8vR3V1NaysrLBv3z66+SQEDg4OePjwIdzd3TFw4EA8evQIHA4Hjo6OfPPS0tKQm5tbp10vaR5Tp07FqlWrcOfOHQwZMoTtOGKhvi0iaNsI9m3btg0SEhJYtmwZ1NXV4ezszHYksWRtbQ1zc3M4Oztj2LBhtCpISPLy8hAQENDgawAafB1Ane9u8vVCQ0Pr3daGEHEUHx8PDw8PZGRk8K6b/9m5LDg4GCdPnoS3tzcGDRrERkyRRtfPLYOqqirU1dXZjkEaoaOjgyVLlmDJkiW4d+8eAgMD2Y5ECBFDRUVFCAoKwoULF5CQkAAulwtJSUkMGTIEubm5iI+Px8GDB3Hu3Dl4e3ujc+fObEf+T1NVVUVGRgaqqqogLS0N4OM2cwzD1OkyXVFRwevIIQ4YbkNPvQhpRHBwMAIDA5GRkQF5eXmYmZlh7ty5tOKLiKzNmzfDx8cH4eHh0NDQwIsXL+Dg4ABZWVl8//33fN1onj17hrFjx+LXX39lO7ZIOnbsGPbt28dXnfnPr7Lalu0Mw4hVVaYwcblcrFq1Cn///TcYhsGIESOwZ88eelgkJDU1NVi5ciVCQkJ4Y6ampjh06BBfAZmnpyc8PT3x/fffY9asWWxEFXk7duzAuXPn8O2332LChAlidSFBSK3u3bvDwMAAwcHBbEcRaz169EB1dTUYhkGbNm3g4OCACRMmoFOnTmxHE1nGxsZftIXTP9GWToQQQcrJyYGDgwMKCgrQo0cPDBs2DEFBQUhLS+O7Tn727BkmTJiAqVOn4scff2Qxseii62f2rV+/HsHBwbh9+zYtuiKEEFLH3bt3ceHCBVy/fh2VlZXgcrnQ0tLChAkT4OzszHv2+fTpU+zZs4e3mOvo0aMsJ/9v8/DwwNWrVzF//nzMmzcPISEhWL9+PXr16oWzZ8/y5nE4HJiamkJHR0ds7j1RAQchhDTB3bt3MW/ePKxfvx6TJk0C8PGh3bFjx/hu2tZ2ozl79iw6dOjAVlyR5efnh7Vr1wL4eMO8V69eUFdXb7TN6+LFi4UVT2StWbOm3nEOh8MrIBg7dmyDe8gyDIOtW7cKLJ84S0hIQEpKCjQ1NWFiYlLnIVJQUBDev38PGxsbKrIUkKqqKqxYsQLXrl0DALRp0wby8vL1zmUYBtevXxdmPLHg6ekJLS0tjB8//rNzAwICkJGRQd8Nzax2exQ/Pz+2o4i1/Px8BAQEwM/PDy9fvuR9J/Tu3RsTJkzA2LFj6YFFM7O2tv7q30FdhASrqKgI6enpKC0tbbBrHwD069dPiKkIEY6NGzfizJkzmDhxIjZs2ACGYeDq6oq4uLg6Cx1MTU2hpaUlNjfEBYmun1umnJwcODs7o1+/fti6dSvk5OTYjkQIIYRlWVlZ8PPzg5+fH968eQMulwspKSlYW1tj4sSJGDJkSL0F+9XV1Rg2bBhKS0vx4MEDFpKLjkePHmHq1KngcDh843v27IGNjQ3v58jISMyaNQvjx48Xm/MkKuAghJCvQN1ohMvJyQmJiYn47rvvqJuAENWuLm2o08k///s/X6dOKERUFRQUYPbs2byWip9DfwuCYWxsDFNTU/j4+Hx2rpubG2JiYuhzaGZr1qzBxYsXERERgdatW7MdhwB4/Pgxzp8/j5CQEBQXF4NhGMjJyWHMmDEYP348zMzM2I5IiEDFxcVh586diIuL++xc6oRCRNWIESOQm5uLe/fu8R5WN1TA4ejoiPT0dHoI0Qzo+rnlSk1NxapVq5CdnQ1bW1vo6OhAQUGhwfm0zRkhhIiuWbNm4f79++ByueByudDT04OLiwvGjx8PVVXVz75/2rRpePDgAX1nN4Pw8HDs3r0bqamp0NTUxNy5c+Hi4sI3Z+nSpbh8+TJ27doFW1tblpIKF/VpI4SQr2BnZwc7Ozu2Y4iN5ORkqKmpUfGGkDk6On5Ve3BCRNXu3bsRHx+P1q1bY+LEiejWrRtUVVXp76UFq70pTprX4sWLERoaitWrV2P37t0NdqEhwtOrVy/06tULP/zwAy5dugQ/Pz9ER0fDz88P/v7+0NPTg7OzMxwdHWk/eCJyYmNjMXPmTFRWVkJaWhodOnSAuro6Hf+J2MnJyYGhoWGTOg3IysqioqJCCKlEH10/t1wJCQnIzc1Fbm4ujh8//tn5VMBBCCGiKzIyErKyshg1ahQmTpz4xR35JkyYgIEDBwoonXixsrKClZVVo3M2b96MTZs2iVVXUSrgIISQJoiOjoaSkhKMjY0/OzcxMRFFRUXUhlcA5OXl0b59e7ZjiJ3t27ezHYE0gsvlIjU1FQUFBaiurm5wHh2Tmt+NGzcgJSWFkydPokuXLmzHIU2QlZXV6Co78u9ER0dj8uTJOHr0KEaOHAkbGxsYGBjQisYWQFZWFo6OjnB0dERGRgbOnj37f+zdd3QV5dr+8WvSKy2hBQgdgyDSETECikd6QgkqTbCBgmI99oYNFQWliChdFAWSAEe60nsIiBiCoUkggISWTkgyvz/8sV8jCQTJ3pPy/azFek+euWevS1/J3nvmnvvR9OnT9ccff+iTTz7R+PHj1aFDBw0YMEBt27a1Oi5QKCZMmKDMzEzde++9euONN+Tn52d1JMASXl5eSk5OLlDtn3/+yRStQsL356Jp9erVevbZZ2Waptzd3VW9evUCPWENACiZXn31VYWEhKhMmTL/6vxevXoVciJcjY+Pj9URHI4GDgAogEGDBqlly5b65ptvrln73nvvaefOnYzhtYNmzZpp586dysrKkosLb2Eo3ZKSkvTJJ59oyZIlSk9Pv2oto8HtIzk5WbVq1aJ5w8FiY2MVGxuba+3MmTOKjIzM95yMjAzt2LFDCQkJatasmZ0Tlj4vvfSSbex3YmJigbazoYHDsQ4cOKAFCxZo8eLFtma/smXLKjU1VatXr9ZPP/2k22+/XZ9++ik38FDs7dmzRz4+Pvr444/l5uZmdRzAMnXr1tUvv/yihIQEBQQE5FsXGxurEydO6M4773RgOsCxpkyZIknq16+fXnjhBfn6+lqcCABgpUGDBlkdAbgq7n4BQAH9c3/SwqpFwY0YMUIPPPCApkyZopEjR1odp1SKjY3VkSNHJEm1atUq0FQaFL6UlBTdd999OnLkiCpXriwnJyelpqaqRYsWOn/+vI4cOaKsrCx5eHjolltusTpuiVWjRg1lZ2dbHaPUWb16tSZNmpRr7Y8//tDLL7981fMub5/y4IMP2jNeqcSEn6IpJSVFP/74oxYuXKhff/3V9negTZs26tevn+655x6lpqYqIiJCM2bM0ObNm/Xhhx/q/ffftzp6sXGt3zvXYhgG/77twDRN1apVi+YNlHrdunVTdHS03n77bU2YMCHPvxMpKSl6/fXXZRgG28PaAd+fi46DBw+qXLlyevvtt9niBgBKqezsbM2ePVuLFy/O9f7co0cPDR48mAdG7ezy9+dKlSrpmWeeybVWUKXpOzT/NQJAITt//rzc3d2tjlEiVahQQa+88oref/99/frrr+rXr59q1aolT0/PfM+52pNGKLj9+/frhRdeUFxcXK71+vXr66OPPuJClINNnz5dhw8f1v3336+33npL/fv3165du2xTgpKSkjRjxgxNnTpVgYGBeu+99yxOXDL16dNHH374oWJiYnTzzTdbHafUqFatmlq2bGn7eceOHfLx8cn395BhGPLw8FBgYKC6du2q5s2bOypqqTFnzhyrI+Bvtm/froULF2rlypXKyMiQaZry9/dX79691bdvXwUGBtpq3dzc9NBDD6lnz5669957tXbtWuuCF0MRERG2m0D/poG7NF18cqQGDRroxIkTVscALNevXz+Fh4dr/fr16t27t3r06KHz589LklauXKnff/9dCxYs0MmTJ9WqVSt1797d2sAlCN+fix5PT08FBATQvAEApZRpmnriiSe0fv36XN/d9u3bp9jYWG3ZskVfffWVhQlLvoiICElSnTp1bA0cl9cKqjR9h6aBAwDykJKSoqSkpFxrmZmZSkhIyPecy+PZ4+LiVK9ePXtHLJXuvvtu2/9ev3691q9ff9V6to0oHImJiXrwwQd14cKFK25O/P777xoyZIiWLFmiihUrWpSw9Pnpp5/k5uZm+7D7T2XKlNGoUaPk7++vd999V02bNlVYWJiDU5Z8Dz74oPbs2aPHH39cr7/+ujp16mR1pFKhV69eufYaDQoKUoMGDWgiQKn3xRdfKCIiQvHx8TJNU05OTgoODla/fv3UsWNHOTs753uuv7+/GjRooN27dzsucAlSp04d3XHHHXJycrI6CiQNHjxYzz77rFavXs17M0o1V1dXffXVV3r66ae1fft2jR8/3nZs1KhRkv66mdGmTRt99tln3NguJHx/LppatWqlzZs3KzMzkwlNAFAKRUZGat26dZKkDh06qE2bNsrJydH27du1bt06bdy4UeHh4erdu7fFSUuuDz74QJJybWN2eQ1XooEDAPIwc+bMK8az7927N1cDwdXw5Ip9XO+TjWxlUzhmzpyp8+fPq3LlynrllVdsH3C3bdumMWPG6M8//9SsWbP0/PPPWx211Dh69KgCAgJUtmxZSbLdMMrKyso17q9///6aNGmS5s+fTwOHHVzeiuPMmTN68sknVaZMGQUGBuY7FcgwDM2aNcuREUuF2bNns4c1IOmzzz6T9Nf0scvTNqpUqVLg8xs3bszI2OtUtWpVnThxQocOHdKFCxfUo0cPhYaG6qabbrI6WqnWtWtXxcXF6b///a9GjBih++67Tz4+PlbHAixRoUIFzZ49W+vXr9eKFSu0f/9+JScny8vLS/Xr11fnzp111113WR2zROH7c9H01FNPaf369Ro7dqxeeeUVq+MAABxs8eLFMgxDzzzzjB577DHb+sMPP6wvv/xS48aN05IlS2jgsKO/P4x1tTX8xTC5uwUAV5gwYUKuBg7DMK7ZDODh4aEaNWqoe/fueuSRR676lCNQnISEhOj333/Xd999p6ZNm+Y6tmvXLj3wwAO66aabtGjRImsClkLNmjVTvXr1NH/+fEnSo48+qo0bN2r9+vVXPMnVt29fHTlyRFFRUVZELdGud/SxYRjat2+fndIAKO2efPJJhYWFKTg4mKeoHcQ0TW3dulURERFatWqV0tPTZRiGGjZsqJCQEPXo0UMVKlSwOmapc7np/tSpU8rOzpYklS9f/qoNlqtXr3ZYPgAlG9+fi6YdO3Zoz549GjdunBo0aKDevXurRo0a8vLyyvecVq1aOTAhAMCe2rZtq6ysLG3btu2KyYnZ2dlq06aNXF1dtWXLFosSArnxeA1sIiMjC+V1QkNDC+V1ACs9+eSTevLJJ20/BwUFqUWLFpo7d66FqQBrxMfHq0qVKldcfJL+aiSoWrWq4uPjHR+sFKtUqZLOnDlj+7l69eqSpJiYGLVv3962npOTo4SEBGVlZTk8Y2nAmD+URg0bNpT015YRP/74Y661gmKLM/uYMGGC1RFKHcMw1LZtW7Vt21ZpaWlavny5IiIiFBUVpX379unjjz9WcHCwevXqpY4dO8rV1dXqyKXC8ePHr1g7e/ZsvvU0PAEoTHx/LpoGDRpkezhr3759eu+9965az+dVAChZkpKS1LBhwzy3vXR2dlbNmjW1f/9+C5IhP9nZ2dq9e7f+/PNPNWrUSIGBgVZHcigaOGDz0ksvFcqFCxo4UBKNHDlSVatWtToGYIm0tLSrThqoUqWKTp486cBEaNCggdauXWvbv/f222/Xd999p88//1xNmza1ba0yYcIEnT17VrfeeqvFiUsmxvwVLUuXLlVkZKRiYmJ0/vx521PX/8TF2BtzeSJZTk7OFWvX+xpwjKysLB04cECZmZkKDAxUuXLlrI5UInl5eal3797q3bu3EhISFBERoUWLFmnNmjVau3atypQpo65duyosLEw333yz1XFLtNmzZ1sdAUApxvfnoikgIMDqCAAAC2VnZ8vd3T3f4+7u7vleR4L9bNy4UfPmzVPnzp3VvXt32/qff/6p4cOH2yYZG4ahJ554QiNHjrQqqsPRwAGb0NBQnjwB8lGa3hiAvFzt/YH3Dsdr3769Vq1apc2bN6tDhw666667dNNNN+m3335Thw4dVKdOHZ05c0anTp2SYRh65JFHrI4M2I1pmnr22We1fPnyAjUG0DxwY2JjYwu0BvtLT0/Xvn375OLioiZNmuRZM23aNH3xxRdKTU2VJDk5OalTp0566623VL58eUfGLVUCAgI0YsQIjRgxQtHR0YqMjNSyZcs0b948HT16VNOmTbM6YonWunVrqyMARcLgwYMLXOvs7CwfHx9Vq1ZNLVu2VIcOHeTiwmXjf4vvz0XPzz//bHUEAADwD4sXL9ZPP/2kYcOG5VofM2aMYmJi5OHhoUqVKik+Pl6TJk1Ss2bN1K5dO4vSOhafxGEzZswYqyMAxcLu3bu1fft2nTx5UhkZGXr//fdtx/78809lZWXR2V8ILl9sqlatmm2bguu5ACX9dWFk1qxZhZ4NsNq9994rNzc32+8aZ2dnffXVV3rppZe0efNm/fbbb5KkcuXK6bnnntM999xjZVzArhYsWKBly5apefPmGjNmjF566SXt2rVLMTExOnfunH755RdNmzZNe/fu1VtvvcW0OJQYK1eu1EsvvaSuXbvqk08+ueL41KlTNW7cuFxNS9nZ2Vq5cqUSEhL0/fff5zk+FoXLycnJdrPONE2ayAA4zPbt2yUp1++gf8rr2KxZsxQQEKCxY8eqWbNmDkgKAABgfydOnNDEiRPzPSYp3+MSD/naw549e+Tj46NbbrnFtpaUlKSVK1eqTJkyWrRokapWraqFCxfq1Vdf1XfffVdqGjgMk6sHAFAgCQkJeuGFFxQdHS3prwschmHYxjhJ0muvvaaFCxfq22+/5ULHDbo8crROnTpaunRprrWC+uf/f/DvBAUFyd3dXf7+/nkeT0xMVGZmZr6NS4ZhaPXq1faMiL85ffq0jh8/Lg8PD9WrV48n5+woMjLyus+heaDwDRgwQNHR0Vq+fLlq1qyp/v37a9euXVf8/n/llVcUGRmpmTNn8nQ2SoTLnzu/+uor3XHHHbmOnT17VnfddZcuXryoevXq6fnnn1eNGjW0c+dOffTRR0pJSdEHH3zA7yQ7OXHihCIjIxUZGamjR4/KNE2VKVNGnTt3Vr9+/dS4cWOrI5YogwcPVps2bdSqVSs1bdpUbm5uVkcCioTt27frl19+0WeffaYqVaooJCREDRs2lLe3t1JTUxUbG6vFixfrxIkTeuqpp9SgQQMdOHBAkZGRiouLk4+PjyIjI1W9enWr/1GKFb4/AwBQ9AQFBV11CtblW+VXq+E+Q+Fr3bq1KleurCVLltjWVq9erZEjR+q+++7T22+/Lemv//+0a9dOzs7O2rBhg1VxHYor+gBQABcuXNCgQYN0/PhxValSRbfffrs2b96sU6dO5aoLDQ3VggULtHr1aho4btDlvas9PDyuWIPjXbx4UcePH79qTX7HGRHrWBUrVlTFihVzraWnp8vT09OiRCXXSy+9VOD/vi83/XGztPDFxcWpWrVqqlmzpqT/+52Tk5OTa7rAq6++qmXLlmnatGk0cBSCadOmadeuXQoODtZ99913zfp58+Zp48aNatmypYYMGWL/gKXA3r175erqqjZt2lxxbNmyZcrIyJCHh4e+/PJL202iOnXqyNnZWa+88opWrFjB76RClJaWphUrVigiIkJRUVHKycmRs7Oz7rzzToWGhuruu++mscBOtm/frh07dkiSXF1d1aRJE7Vq1UqtW7dWs2bNcn2fAEqT8uXLa/LkyfrPf/6jMWPGXPE7qFOnTho2bJheeuklTZ48WfPmzVP79u01ZMgQvfDCC1q6dKmmT5+uN954w6J/guKL788AABQtrVq1sjoC8pCWlnbFZ9To6GgZhqG2bdva1gzDUEBAQKnawpcGDtgkJCQUyuuwdQRKoq+//lrHjx/X3XffrbFjx8rT01P9+/e/ooGjefPm8vDw0JYtWyxKWnLkdXONG27WuLyFDYqf1NRUzZkzR7Nnz9bmzZutjlPihIaG5nuBNS0tTUeOHNH+/fvl6uqqe++9V66urg5OWDqkp6erVq1atp8v36hLTk5W2bJlbeve3t6qU6eO9uzZ4+iIJU58fLzGjRun8uXLF/g9omvXrpo0aZLWrVune++9V1WrVrVzypIvMTFRNWvWzPN3y+Wx+cHBwVd8P+vRo4feeeedUnXhw562bNmiiIgIrVq1ShkZGTJNUzfddJNCQ0PVo0ePfJ/ARuEZOnSooqKitG/fPmVmZioqKko7d+7UlClT5OzsrMaNG6t169Zq1aqVmjdvLm9vb6sjAw4xYcIEGYahd999N98GMldXV73zzjv6+eefNWnSJH3++edydnbWa6+9phUrVmjTpk0OTl388f0ZAICiZ86cOVZHQB7Kli2r48eP2x58k6StW7dKklq0aJGrNjs7W15eXg7PaBUaOGBz11133XCXt2EYiomJKaREQNGxevVqubq66r333rvqU+xOTk6qUaOG4uPjHZgOsK9evXpZHQHXKSUlRbNmzdLs2bOVlJRkdZwSa8yYMdes2blzp1566SWdO3dOX331lQNSlT4VK1bUhQsXcv0sSYcOHbpiGtaFCxeUkpLi0HwlUUREhLKzszVs2DD5+voW6JwyZcpo+PDheueddxQeHq4RI0bYOWXJd/78+Xyb5/fu3SvDMPLcG9bV1VUBAQE6duyYvSOWeB06dLA1dFeoUEH9+vVTr169rnvbP9yYF198UdJfzZO7du3S9u3bFRUVpV9//VWZmZnavXu3fvnlF3311VdydnZWw4YNbRM6WrZsKR8fH4v/CQD7iIqKUt26da95odvLy0t169ZVVFSUba1ChQqqU6cO1zb+Bb4/AwAAFMwtt9yidevWad68eXrggQe0ceNGxcTEqH79+ldMmD569KgqV65sUVLHo4EDNkzOAPKXkJCgWrVqqVy5ctes9fb2Vnp6uv1DAcVEamoqTzoWgosXL+qrr77S8uXLdezYMXl4eKhRo0Z67LHHbOPzs7OzNWPGDE2dOlXJyckyTVMVK1bUww8/bHH60qtFixb67LPP1Lt3b02bNk2PPvqo1ZFKnBo1auSaqtGsWTNFRkbqm2++ydXAsW7dOh07dizXtA78O1u3bpWTk5O6det2Xed1795d77//vjZv3kwDRyFwd3fX6dOnr1g/d+6cjh8/LsMw1KhRozzP9fLyUnZ2tr0jlngnT56UYRiqU6eO7rjjDjk7O2vx4sVavHhxgc43DEMvvPCCnVOWHl5eXmrXrp2tcely88aOHTsUFRWl3bt3Kz09Xb/++qv27t2rGTNmyMnJSTfddJPCw8MtTg8UvtTUVJ07d65AtefPn1dqamquNU9PT7bzAAAAgN0MGDBAa9eu1ejRozV+/HglJyfLMAwNGDAgV93u3buVmpqqhg0bWpTU8WjggM3PP/9sdQSgyHJ1dVVmZmaBas+cOcNTXID+b6T46tWrFR0dbXWcYi0rK0tDhgzR7t27ZZqmJCkjI0ObNm3Stm3bNGHCBDVp0kTDhg3Tb7/9JtM0FRAQoEceeUR9+/bNd2QyHOPmm29WzZo1FRkZSQOHHQQHB2v79u3avXu3mjZtqq5du2rcuHFaunSpjh8/rmbNmun06dNavny5DMNQ7969rY5c7B06dEjVq1dX+fLlr+u8smXLqnr16jp06JCdkpUuNWvWVGxsrI4eParAwEDb+uUtszw8PHTzzTfneW5iYmKBGpNxbaZp6uDBg9f93/XlEbE0cNiPm5ubWrdubduGMSsrS3v37tWOHTu0Y8cObdu2TRcvXtS+ffssTgrYR2BgoA4cOKANGzYoODg437oNGzbo2LFjatCgQa71EydOXPd7PQAAAFBQwcHBevPNNzV+/HhduHBB7u7uGjJkiO6///5cdQsXLpQk3X777VbEtAQNHABQALVq1VJsbKzOnj2rChUq5Ft39OhRxcfH256GB0qbP/74QxEREVq8eLFOnDiRa/86/Hs//PCDdu3aJcMw1K1bN916663KyMjQ2rVrFR0drTFjxsjf31979+5V5cqV9eSTTyo0NFQuLnzUKyo8PT11+PBhq2OUSJ07d9aRI0eUnJwsSfL19dVnn32mUaNGaffu3dq9e7ettkePHnrkkUcsSlpypKSkqGbNmv/q3Mv7m+LGBQcHKyYmRqNHj9bEiRPl4eGhpKQkff311zIMQ8HBwXJ2dr7ivDNnzujEiRNX7CeL68eY/OLl5MmTOnLkiO3PxYsXrY4E2FVYWJjef/99jRo1Ss8995xCQ0NzTUZMS0tTRESEPv30UxmGobCwMNuxuLg4nT59WnfddZcV0Yu1hISEG34NpiQDAIDS4oEHHtB9991nu/fm5OR0Rc2QIUM0YMCAUjVVl6v6AFAA99xzj/bu3auPP/5YH3zwQZ412dnZeuedd2QYhjp37uzghIB1UlJStHTpUkVERNhulJqmKRcXFwUHB3NzoxAsW7ZMhmFo9OjRuS6sPvbYY3rhhRe0ZMkSHT16VHfccYfGjx/PFKAi5vz58zp8+LA8PT2tjlIiVa9eXe+++26utTZt2mj16tVav369bcuhVq1alapRi/bk7e2tpKSkf3VucnIy22oVkkGDBum7777Tpk2b1K5dO9WuXVt//PGHUlJSZBiGHnrooTzPW7lypSSpVatWjoxbIuX3vQBFw+HDh23TNqKionTy5ElJf31OdXd3V6tWrdSiRQu1bNnS4qSAfQwcOFA7duzQqlWr9O677+qDDz5QtWrV5O3trdTUVB0/flzZ2dkyTVP/+c9/NHDgQNu5a9asUf369dW1a1cL/wmKp7vuuuuGHmIwDEMxMTGFmAgAAKBoc3Jykr+/f77H69at68A0RYNhXp7DDVxFVlaWVqxYoW3btunUqVPKyMjQrFmzbMf37t2r9PR0tWjRIs/uKKC4S0tLU0hIiI4dO6Y2bdqob9+++vrrr7V//34tWLBAv//+u+bMmaOYmBjVr19fCxcuZMsClGimaWrTpk2KiIjQTz/9pIsXL9q29vDw8NAzzzyjHj16XHViDQrutttuk2ma2rZt2xXH4uLi1KNHD7m5uWnNmjXy8/OzICHyExsbq/fee09RUVG69957NX78eKsjATcsNDRUcXFx2rJli8qUKVPg85KSktS2bVvVr19fkZGR9gtYikRFRWnUqFE6c+aMbc3JyUnPP/98vg0cPXr00IEDBzRnzhxuXKNE2b9/v6KiorR9+3bt3LnT9vfCNE35+vqqefPmtoaNW265Ra6urhYnBuzPNE3NnTtX06dPz3MyREBAgB5++GH179+fyYmFJCgoSIZh6EYuucfGxhZiIgAAgKLp4YcfVlhYmO6++26+n/0DEzhwTTExMRo1apSOHTtm+/Lxzy91S5Ys0ezZszV9+nS1bdvWipiAXXl5eenrr7/W448/rq1bt+a6idq3b19Jf10YqVu3rqZMmULzBkqsgwcPKjIyUosXL9aff/5pe1+oWLGievTooenTp8vHx0cPPvigxUlLluTk5HwnB1zexqBmzZo0bzjY3Xffne8x0zR19uxZW3NT2bJlNWrUKAemA+yndevWtibW/JoE8jJ//nxlZ2erdevWdkxXurRs2VKrVq3S2rVrdezYMXl7eys4OFiBgYF51p89e1Z9+/aVYRhq1qyZg9PistTUVC1btkwRERGaO3eu1XGKvccff1zR0dFKSkrK9dm0c+fOatmypVq0aKGbbrqJm9MolQzD0MCBAzVw4EAdPHhQhw8fVlpamry8vFS7du1S+TSjo9StW1ehoaHq3r27ypYta3Uc/H9btmzR2rVrdfToUaWlpeXbaGMYRq6HFwEAQOHbtGmTNm/erLJlyyokJER9+vRRgwYNrI5VJDCBA1d16tQphYSE6Pz582rcuLE6duyoxYsX6+jRo9q3b5+t7rffflOfPn00YMAAvf766xYmBuwrMzNTCxYs0MqVK7V//34lJyfLy8tL9evXV+fOndWvXz+5u7tbHRMoVElJSfrf//6nyMhI/frrr5L+ujnt6empTp06KTQ0VG3btpWTk5OCgoLk7++vjRs3Wpy6ZAkKClKLFi3yvclzreOwj6CgoGvWlC1bVu3bt9eTTz6pGjVqOCAVYH9xcXHq2bOnPDw8NGvWLDVp0uSa5+zZs0cPPvigLl68qMjISL6Qo1TasmWLwsPDtXr1amVkZEhSru/V+HcuP+1eo0YNDRkyRMHBwbznArDM/PnzFRkZqZ07d8owDLm4uKhjx44KDQ1V+/bt5ezsbHXEUikzM1OjRo3S2rVrJemaE1IMw+A9GgAAO5s5c6YWLlyouLg4W8N948aN1bdvX3Xr1q1UbxNOAweuavTo0fr222/Vr18/vf322zIMQ/3799euXbuu+BDbokULBQQEaMmSJRalBVDanTp1SqdOnVLdunXl7e1tdZwS45ZbblFWVpZM05STk5PatGmjkJAQ/ec//5GXl1euWho47IMGjqLp+PHj+R4zDEOenp4qX768AxOVfPlNorke7CteON544w398MMPcnd31+OPP677779f5cqVu6Lu/Pnz+u677zRlyhRlZmYqLCxMo0ePdnxgwCJHjhxRRESEFi9erJMnT0r666ZRuXLl1L17d7322msWJyz+/t5Q6eLiosaNG6tly5Zq2bKlmjdvfl1bPQElXWpqqlJTU+Xt7c13ZjuLj49XRESEIiMjlZCQIMMwbL/7Q0ND1ahRI6sjlirjxo3Tl19+KU9PT/Xt21dNmzaVn5/fVbcCZ2ocAACO8euvv2rBggVatmyZkpKSZBiGPDw8dO+996pPnz5q1aqV1REdjgYOXFWnTp2UmJiorVu3ysPDQ5LybeAIDQ1VfHy8du7caUVUAKXAnj179OOPP6pt27bq0KGDbT0lJUXPP/+81q1bJ0ny8PDQa6+9pj59+liUtGS5/FRjmTJl9Pbbb6tz585XraWBo/AFBQUpICBAvXv3zvP4xIkTr3pckkaOHGmveIDDFGTqSUGwr/iNu3TpkoYPH65NmzbJMAw5OzurXr16qlGjhry8vJSWlqZjx44pLi5O2dnZMk1T7dq105QpU9jXFCVeSkqKfvzxR0VEROiXX36RJFsjbMeOHdWrVy+1b9+evwuF5Ny5c4qKitKOHTsUFRWl2NhY5eTkyDAMGYahevXqqVWrVramjooVK1odGXCoQ4cOadq0adqwYYNOnz5tW69YsaLat2+vhx56SLVr17YwYcm3Y8cOhYeHa+XKlUpNTbX9bgoNDVWPHj1UqVIlqyOWeJ06dVJCQoJmz56tli1bWh0HAADkITMzU8uXL9fChQu1fft2maYpwzAUGBioPn36KDQ0tNR8bqKBA1d1yy23qG7duoqMjLSt5dfAcd999+m3337T3r17HZwScKz09HRFR0fr8OHDtidXateurebNm8vT09PqeCXaG2+8ofnz52vmzJlq06aNbf3NN9/U999/L0lyc3NTZmamnJycNG/evAKNdcfVdezYUSdOnJAk2wemHj16qGfPngoMDMxVSwOHfVxuosnP5Y9zV6th/CtKspkzZ2rs2LFq27atBg0apHr16snf31+JiYk6cOCA5syZoy1btuiFF17Qgw8+aHXcEsM0TU2ZMkUzZsxQUlKSbd0wjFxjqX19fTV06FANHz78qk85AsWZaZrauHGjIiIi9PPPP+vixYu2vweNGzfW3r17+YzkICkpKdq5c6d27Nih7du3KyYmRllZWbbPSYGBgWrRooWtqYPtVlCSLV26VK+88kqu30l/ZxiG3N3d9f7776tr164WJCxdMjIytGLFCkVGRmrbtm225r6BAwfq5ZdftjpeiXbLLbeoatWqWrlypdVRAABAARw/flzh4eGKiIiwTTNzdnbWHXfcobCwMHXs2LFEX2OigQNX1aZNG/n4+Oinn36yreXXwNGxY0dlZmZq06ZNjo4JOERmZqYmT56sb775RqmpqVcc9/Ly0sCBAzVixAi5ublZkLDk6969u44fP65du3bZ1tLT09W2bVsZhqHZs2erUaNG+uKLLzRhwgR1795dY8eOtTBxyWCaprZu3aqFCxfqp59+Unp6uu0C+K233qqePXuqa9euKleuHA0cdjJo0KAbfo05c+YUQhKg6Fm1apWeeuopjRo1SsOHD8+37ssvv9T48eM1YcIEderUyYEJS77U1FStW7dO0dHROnXqlK3BtXLlymrevLnat2/PmHaUWAcPHrRtkXL69GnbDdKqVauqZ8+e6tmzp+rWrctnJAtlZGRo165d2rZtm6KiovTrr7/q4sWLts+zlSpVsk3yA0qS/fv3q0+fPsrKylLjxo01dOhQNWjQwNbkGhcXp+nTp2vv3r1ycXFReHi4GjRoYHXsUuOXX37RM888oxMnTqht27aaPn261ZFKtPbt28vPz0/h4eFWRwEAAAX0+++/64cfftC8efOUlZVlWzcMQwEBAXrmmWfUvXt3CxPaj4vVAVC01a1bV7/88osSEhIUEBCQb11sbKxOnDihO++804HpAMfJzMzUo48+ahvbVLZsWQUGBsrPz09nzpzR0aNHdeHCBU2dOlXR0dGaPn06I5HtIDExUVWrVs21tmPHDmVkZCgkJES33HKLJGnYsGGaNWuWoqOjrYhZ4hiGobZt26pt27ZKSUnRsmXLFBERoejoaO3evVu//PKLPvjgAwUHB1sdtcSi+cJ6gwcPliRVq1ZNH3zwQa61gjIMQ7NmzSr0bKXdjBkzVL58eQ0bNuyqdY8++qhmzZqlmTNn0sBRyLy9vdW1a1ee3EWpExYWZptAaZqmvL299Z///EehoaG5psXBWh4eHrbPspJ04sQJTZs2TfPnz9fFixf1559/WpwQsI+vv/5aWVlZGjRokF599dVcx8qXL6/69eura9euev/99zV79mxNmzZNH374oUVpS4eLFy9q5cqVioyM1NatW5WdnS0nJyfVrVvX6mglXocOHRQeHq7z58+rXLlyVscBAAD5SElJ0ZIlS7Rw4UL99ttvtvV27dqpb9++SkxM1A8//KC4uDi98MILSk9PV1hYmIWJ7YMGDlxVt27dFB0drbffflsTJkzIc6pASkqKXn/9dRmGoR49eliQErC/6dOna9u2bSpXrpxefPFFde/ePVeDRlZWlpYsWaKPP/5YUVFRmjZt2lWfAsa/k5KSourVq+dai46OlmEYateunW3NxcVF1atXV1xcnKMjlng+Pj4KCwtTWFiY4uPjtXDhQi1evFgJCQn6+eefZRiGzp8/rzfeeEOhoaFq3ry51ZGBQrF9+3ZJUp06da5YK6irbXGDf+/3339X7dq1r/nv18nJSdWqVVNsbKyDkgEo6X799VcZhqEyZcroxRdfVLdu3eTu7m51LPzDqVOnbNupREVF6fDhw5KU53YSQEmyY8cOlSlTRv/973+vWvf8888rIiJC27Ztc1Cy0mfHjh2KjIzUihUrlJqaKtM0VbduXYWEhCgkJESVK1e2OmKJ99RTT2nNmjV65ZVXNHbsWHl5eVkdCQAA/M3WrVu1YMECrV692rb9X+XKldW7d2/17dtX1apVs9UOGjRIkZGReumllzRjxgwaOFD69OvXT+Hh4Vq/fr169+6tHj166Pz585KklStX6vfff9eCBQt08uRJtWrVqsSOqgEWLVokwzA0ZcoUNW3a9IrjLi4u6tWrl2rVqqUHHnhAkZGRNHDYgbe3t06ePJlr7fJFphYtWlxRz1Y29lWjRg09/fTTevrpp7V161aFh4dr1apVSk9P1/z58zV//nwFBgYqJCRETzzxhNVxgRsye/ZsSX89xfvPNVjLNE0dO3ZMOTk5V937MicnR8eOHeOGHYBCZZqmkpKS9MEHH2jXrl3q2bOnWrVqZXWsUi0+Pl47duyw/Tl+/Ljt2OX3gPLly6tVq1a2P0BJdObMGQUFBV1zOqibm5tq1apFk2shi4+PV2RkpBYtWqTjx4/LNE2VK1dO/fv3V2hoqG2CKBxjw4YNuv/++zV58mT95z//Ubdu3VSzZs2rNnKEhoY6LiAAAKXQyZMntXDhQkVERNg+Lzk7O6tjx47q16+f7rzzznyv9YWGhmrmzJk6cOCAg1M7hmFyBRPXcPbsWT399NPavn17nk82mqapNm3a6LPPPmMEHUqsJk2aKCAgQMuXL79mbZcuXXT8+HHt2bPHAclKlwcffFDbt2/XhAkT1KlTJ+3bt0+9e/dW9erVtWrVqly1bdq0Ubly5bRixQqL0pZOaWlpti1Wdu7cKdM0ZRiG9u3bZ3U0ACXUoEGDFBUVpREjRmjkyJH51k2cOFETJ05U69atab4BUCiOHj2qhQsXasmSJUpISJD017SlqlWrqkePHurZs6dtLH5QUJD8/f21ceNGKyOXSAcPHrQ1a0RFRdm2RPn75S5/f3+1bt1aLVu2VOvWrVWvXj2r4gIOc9ttt8nd3V3r1q27Zm2HDh2UkZGhrVu3OiBZyfbDDz8oMjJSu3btkmmacnFxUfv27dWrVy+1b9+e7XYtEhQUJMMwbO8NBZmOyHUMAADsq1GjRsrJyZFpmqpRo4b69Omj3r17q1KlSgU6//I1wZL4ns0EDlxThQoVNHv2bK1fv14rVqzQ/v37lZycLC8vL9WvX1+dO3fWXXfdZXVMwK7KlClT4PGKnp6eKlu2rJ0TlU5hYWHatm2bnn76aTVo0MA2/vifI7L279+vCxcuqHXr1lbELNW8vLzUp08f9enTR/Hx8YqIiNCiRYusjgWgBBs2bJh27NihSZMmaceOHRo4cKDq1q0rPz8/nTlzRgcPHtTcuXO1bds2GYahxx57zOrIAEqIwMBAPfPMM7mmka1evVoJCQmaOnWqpk6dqptvvpmtRu3s8iTQvzdsVKlSRa1atVLr1q3VqlUr1apVy6J0gHUaNWqkzZs3a+nSperatWu+dUuXLtXJkydzbUuKf++NN96QYRiqU6eOQkJC1L17d5UvX17SX9vvZmVlXfM1PD097R2z1GHaEgAARY9hGOrcubP69euntm3bXvf548aN08WLF+2QzHpM4ACAAnj++ee1YsUKrV27Vn5+fvnWJSYmqmPHjurSpYs++ugjByYsPT755BNNnz5d2dnZkv66YDtmzBi5uPxfT+KHH36oGTNm6PXXX9eAAQOsigqghEtJSdG+ffvk5+enOnXq5Ft36NAhnTlzRjfffLO8vb0dmLD0+Pbbb/X+++8rKysr34lxLi4uevnll3lfAGBXqampWrp0qSIiIhQdHS3p/57y9fDw0LvvvqtOnTrJ3d3dypglSlBQkKpXr56rYaN69epWxwIst3r1ao0cOVJubm566KGHNHjwYFWoUMF2/OzZs5o1a5ZmzJihS5cu2SZd4sZcnvTwbxmGoZiYmEJMBAAAUDSdO3fO1uiK3GjgAIACOHbsmPr06aMGDRpo3Lhx8vf3v6ImMTFRzzzzjH7//XeFh4erWrVqFiQtHc6dO6ejR4+qatWqeY7T2rJli1JTU9WyZUu2dgJgN9OnT9fHH3+st99+W/369cu37ocfftCbb76pl156SQ8++KADE5YucXFxmj59ujZs2KDExETbur+/v4KDgzV06FA1aNDAwoQASpv4+HiFh4dr0aJFubZY8fb2VpcuXRQSEqKWLVtanLL4O3nypKpUqWJ1DKBIeuONN/TDDz/YGgoqVKhgm1J29uxZSX81ut533316++23rYxaYgQFBd3wa8TGxhZCEgAAABRXNHDgCtOmTdOuXbsUHBys++6775r18+bN08aNG9WyZUsNGTLE/gEBO4uMjMxz/ciRI/r666/l5OSke+65R/Xq1bNd+Dhw4IBWrVol0zT1yCOPqGbNmgoNDXVobsBeJk6ceMOvMXLkyEJIAhQt/fv31549e7R9+/arbrOVlpam1q1bq1mzZpozZ44DE5ZeycnJSktLk5eXl3x9fa2OAwDaunWrIiIitHLlSqWnp0uSnJyceMoagN3Nnz9fX375pY4dO3bFserVq2vYsGFXbEuKf+/48eM3/Bo8EAQAAFC60cCBXOLj49WlSxeVL19eS5cuLdAF76SkJHXr1k3nz5/XypUrVbVqVQckBeznauMuL//KzG88+9+P7du3z04JAce6kRGwpmnKMAz+PqBEuuOOO+Tp6alVq1Zds/aee+7RxYsXtX79egckAwAUVWlpaVq+fLnCw8O1c+dOPiMBcJhDhw7p8OHDSk1Nlbe3t2rXrn3VbQABAACAwtKwYcMbfo3StNWci9UBULREREQoOztbw4YNK/DTimXKlNHw4cP1zjvvKDw8XCNGjLBzSsC+WrVqZXUEXENqaqq2bt2q+Ph4paamKr9eRMMw+J1UiOrUqaO6detaHQMoMi5cuFDgxtVy5coxChkAIC8vL/Xu3Vu9e/fO82l4ALCXOnXq0LBRDGzZskVt27a1OkaxdvkGUZ06dfTjjz/mWiuo0nSDCAAARyiMeRKlaSYFDRzIZevWrXJyclK3bt2u67zu3bvr/fff1+bNm7lZimKP8fZF24wZM/T5558rIyPDtvbPN27DMGyTH/iddOM8PDyUkZGhQ4cOydPTU6GhoerevbvKlStndTTAUuXKlSvwzbdjx46pTJkydk5U8l3e0ql8+fIaMGBArrWC4r0BgL1cunRJp0+flqenp8qXL59v3blz55Senq4qVao4MB0AoKg6evSoIiIitGjRIp08eZLGgRt0+RpRTk7OFWvX+xoAAKBw8GDb9aGBA7kcOnRI1atXv+rFpryULVtW1atX16FDh+yUDACk8PBwffjhh5L+2tajSZMm8vf3l5OTk8XJSraNGzdq2bJlWrRokXbu3KnffvtNH374oTp06KCQkBB16NBBLi58pEDp06RJE/38889atmyZunTpkm/dsmXLdO7cOXXo0MFx4UqoiRMnyjAM1a5dO1cDx+XGvauhuQ+AvS1YsECjR4/Wf//7Xw0dOjTfusjISH300UcaPXq0wsLCHJgQQEm1Y8cOSX81399yyy251q4HE0kdJyUlRcuWLVNERIR27dol6a+mAb5b37i8bhBx0wgAABQnfCJELikpKapZs+a/Ords2bI6fvx4IScCgP8zZ84cGYZxzYviKFw+Pj4KCwtTWFiY4uPjFRkZqcjISK1atUqrV69W2bJl1b17d4WGhqpx48ZWxwUcpl+/fvrpp5/0+uuvy93dXXfdddcVNWvXrtXrr78uwzDUr18/C1KWLCNHjpSkXM3Gl9cAwGorV66Uk5OTevXqddW60NBQffzxx1q+fDkNHAAKxaBBg2xNrkuXLs21VlBsGWF/pmlq8+bNCg8P108//aSLFy/ampBvuukm9erVSz169LA4JQAAAKxmmMwDw9+0adNGFSpU0LJly6773C5duujs2bPatm2bHZIBRVP//v21e/duLnI4yK233ipfX19t3LjR6iiQFBUVpfDwcK1YsUKpqakyDEN169ZVaGioevToocqVK1sdEbC7F154QUuWLLFdMG/atKl8fX2VnJysX375RYcOHZJpmurZs6c++ugjq+MCAOyoffv2cnZ21s8//3zN2rvuukumaWrNmjUOSAagpBs0aJAkKSAgwDa18vLa9WBLWfs4dOiQIiMjtWjRIv3555+S/m+LDl9fX82ZM0dBQUFWRizxIiMj5efnp+Dg4GvWbty4UYmJiQoNDbV/MAAAIElKSEjQpk2bdOjQIaWmpsrb21t16tRRu3btFBAQYHU8h2MCB3KpWrWq4uLilJSUdF37tCclJeno0aOqX7++HdMBRRN9cI7j6enJXuFFSMuWLdWyZUu98cYbWrlypRYtWqQtW7bok08+0ZYtWzRt2jSrIwJ2N2bMGFWuXFmzZ8/WoUOHdOjQoVzbebi5uWno0KF66qmnLE4KALC3s2fPqmHDhgWq9fPz0/79++2cCEBpkVfjBc0Y1kpOTtb//vc/RUZGas+ePZL+un50eXJfaGiohg0bJnd3d5o3HOCll15Sy5YtC9TA8eWXXyoqKooGDgAAHCA1NVXvvvuuFi9erJycHEmybYEsSU5OTgoJCdGrr74qb29vK6M6FA0cyKV169bav3+/FixYoIceeqjA582fP1/Z2dlq3bq1HdMBKO2aNWumnTt3Kisri31hixAPDw+1adNGJ0+e1MGDB3Xy5Ekam1BqODs76/nnn9eQIUO0du1aHTx4UCkpKfLx8VH9+vXVvn17+fn5WR2zRDt27JiqV69e4PqffvpJd999tx0TASitfH19deLEiQLVnjx5Ul5eXnZOBABwtHXr1ikiIkJr1qxRZmam7QZEy5Yt1bNnT3Xp0kU+Pj5WxyyVuE4BAEDRcunSJT3yyCPavXu3TNNU7dq1Vb9+fVWsWFGnT59WXFycDh8+rIiICB05ckSzZs2Sq6ur1bEdgrtfyCUsLExz5szRhAkT1LJlSzVp0uSa5+zZs0cTJ06Uk5OT+vbt64CUAEqrESNG6IEHHtCUKVM0cuRIq+OUehkZGVq5cqUiIyO1bds25eTkyDAMtWvXToMHD7Y6HuBQ/v7+fA6ySK9evfTmm2+qe/fuV627ePGi3nvvPc2fP1/79u1zUDoApUnDhg21efNmbdmyRW3bts23bsuWLTp9+vRVawAAxdOwYcNsE/lq1aqlkJAQ9ezZU9WqVbM6Ggro9OnT8vDwsDoGAAAl3nfffaddu3apUqVKGj16tDp06HBFzbp16/Tmm29q165dmjdv3r/aJrA4ooEDudSvX19hYWH64YcfNGjQID3++OO6//77Va5cuStqz58/r++++05TpkxRZmamwsLC1KBBA8eHBlBqVKhQQa+88oref/99/frrr+rXr59q1aolT0/PfM8pjfuj2dv27dsVERGhlStXKi0tTaZpql69egoNDVXPnj1VqVIlqyMCKEWSk5P1wgsvaP369XrzzTfzHKcYGxur5557TgcPHrzqewYA3IjQ0FBt2rRJL730kr788ss8R+LHxsbqxRdflGEYCgkJsSAlgNImMzNTy5Yt0/r163X48GHbnuK1a9dWcHCwunbtKjc3N6tjljhly5ZV79691aNHD1WtWtXqOKVOQkKCjh8/nmstOTlZO3bsyPecjIwM7dixQ0eOHNHNN99s74gAAJR6//vf/2QYhr744gs1atQoz5r27dtr0qRJ6tOnj5YsWVJqGjgMk9lh+IdLly5p+PDh2rRpkwzDkLOzs+rVq6caNWrIy8tLaWlpOnbsmOLi4pSdnS3TNNWuXTtNmTKl1IyuAS574IEHtGvXLsXGxlodpVQo6J7ilxmGoZiYGDulKV3++OMPRUZGavHixUpISJBpmipfvry6deum0NBQNW7c2OqIAEqpRYsW6Z133lFqaqqqV6+usWPH6tZbb7UdnzFjhsaNG6fMzEw1bNhQY8eOVd26dS1MDKCkMk1TDz/8sDZv3ixnZ2fdfvvtatq0qXx9fZWcnKzdu3dr8+bNys7O1u23365p06bZ9vUFAHvYu3evnn32WcXHx+e5fYRhGKpRo4bGjh1boCm8uLbnn39eP/30k9LT02UYhm37lJCQEN177725tk8JCgqSv7+/Nm7caGHikmnixImaNGmS7efLW9kUhGmaevPNN/XAAw/YKx4AAJDUokULVa5cWUuXLr1mbdeuXXXq1Cnt3LnTAcmsRwMH8mSapqZMmaIZM2YoKSnJtn55BOBlvr6+Gjp0qIYPHy4nJycrogIoRfJ6ivFaaK65cffff79++eUXSZKLi4vat2+v0NBQdejQQS4uDPNC6XC9DWR5oanMfuLj4/XCCy9o9+7dcnFx0RNPPKHevXvr1Vdf1ebNmyVJQ4YM0bPPPkvDMQC7SktL06uvvqply5ZJUq6bRZe/S3fr1k2jR4/Oc2IQABSWo0ePqlevXkpNTZWPj49CQkJUt25d+fv7KzExUYcOHVJkZKRSUlLk7e2t8PBw1axZ0+rYJUJKSoqWLVum8PBw7dq1S9Jf7wfu7u7q2LGjQkJCFBwcrEaNGtHAYSezZs3SrFmzbD+fOHFCrq6u8vf3z7PeMAx5eHioRo0a6t69+zW3ZwQAADfu1ltvVd26dRUeHn7N2t69e+vgwYO2+xQlHQ0cuKrU1FStW7dO0dHROnXqlG3MYuXKldW8eXO1b9+ei04AUMIFBQXJMAzVqVNHXbt2zXNbrWsZMGBA4QcDHOjfNJDlhaYy+8nOztbEiRM1depU5eTkyNnZWVlZWapUqZI+/PBDtW3b1uqIAEqRffv2aeXKlTp48KBSUlLk4+Oj+vXr65577im09xQAuJrnnntOP/74ozp06KCxY8fmmvxwWUpKip5//nmtXbtW3bp10yeffGJB0pLt6NGjCg8Pt02zlP5qFihXrpzOnTtHA4eDBAUFqUWLFpo7d67VUQAAwP/XpUsXHTt2TOvWrVOFChXyrTt79qzat2+vatWqafny5Q5MaB0aOAAgDx988IFq1KihgQMHWh0FsNzlBo4bsW/fvkJKAwD5y8jI0BNPPGGbuuHs7Kxp06bptttuszgZAACAY91+++1KS0vThg0b5Ovrm29dUlKS7rzzTnl6emrLli0OTFj6bNmyRREREVq1apXS09Ml/dXMUbNmTfXq1Us9e/ZU1apVLU5ZMkVERMjPz0933nmn1VEAAMD/N2bMGM2cOVO33367xo8frzJlylxRk5SUpKefflpbtmzRkCFD9OKLL1qQ1PFo4ACAPFytM//uu+9WkyZNNG7cOAuSAY43aNCgG36NOXPmFEISAMhfbGysnnvuOR06dEgeHh6qXr264uLi5O7urmeffVYPPvig1REBAAAc5tZbb1X9+vW1YMGCa9b26dNHBw8e1O7du+0fDEpLS9PSpUsVGRmpnTt3yjRNGYYhwzDUunVrzZw50+qIAAAAdnfmzBn16NFD586dk5eXl0JCQlS/fn3bln9xcXFatGiR0tLS5Ofnp8WLF191UkdJQgMHAOThag0cjF0sGhISErRp0yYdOnTItr1TnTp11K5dOwUEBFgdDwDgQDNnztSnn36qzMxM3XzzzRo7dqwCAwM1btw4zZgxQ6Zpql27dhozZky++14DAACUJN26dVNmZqZWrVp1zdp77rlH7u7u+t///ueAZPi7+Ph4RUREaNGiRTp+/LgMw2CCJQAAKDViY2P15JNPKj4+Ps8p4KZpKjAwUJ9//nmp2o7UxeoAAABcj9TUVL377rtavHixcnJyJMn2tIokOTk5KSQkRK+++qq8vb2tjAoAcICHH37YtmXKQw89pGeeeUaurq6SpBdeeEF33HGHXnzxRW3cuFEhISF699131bFjRysjAwAA2F1ISIjGjRunTZs2qV27dvnWbdq0SfHx8XruueccmA6X1ahRQ0899ZSeeuopbd26VYsWLbI6UrF39913S5Jq1qyp6dOn51orKMMwtHr16kLPBgAAcgsKCtKPP/6opUuXav369Tp8+LDtgd3atWvrzjvvVNeuXeXm5mZ1VIdiAgcA5IEJHEXTpUuXNHjwYO3evVumaap27dqqX7++KlasqNOnTysuLk6HDx+WYRhq1qyZZs2aZbuJBwD2cvLkSf3vf//Tvn37dP78eV26dCnPOsMwNGvWLAenK/mCgoJUqVIlffjhh2rbtm2eNRcuXNBrr72mVatWycnJSTExMQ5OCQAA4FjZ2dl68skntW3bNj355JMKCwvL9ZBDWlqafvjhB02cOFFt2rTRhAkT5OTkZGFioHBcfjq3Tp06Wrp0aa61gmISCgAAsBITOAAAxcZ3332nXbt2qVKlSho9erQ6dOhwRc26dev05ptvateuXZo3b54GDRrk+KAASo25c+dqzJgxunTpkm0S0N/7o/++ltcYQNy4u+++W++9957KlSuXb03ZsmU1YcIE/fDDD/rggw8cFw4AAMABBg8enOe6aZq6ePGiPvzwQ33yySeqWrWqKlSooLNnz+rkyZO6dOmSXFxclJSUpKFDh9JsjBLhp59+kiS5uLhcsQYAAFAcMIEDAPLABI6iqV+/fvr111+1YMECNWrUKN+63377TX369FGTJk30ww8/ODAhgNJk69atGjp0qCpUqKCnn35as2fP1oEDBzRjxgydP39ev/zyi8LDw3Xx4kW98MILql+/vlq3bm117FLv8OHDql27ttUxAAAACk1h7AfOxAEAAAA40tGjR7V48WI1btw4z4d1L1uzZo1+++03hYSEqEaNGo4LaCEmcABAPpKTk7Vjx47rPnZZq1at7BGrVDt48KBq16591eYNSWrUqJHq1KmjgwcPOigZgNJo9uzZkqRPP/1Ubdq0UUREhCTptttukyR17txZjz76qIYNG6bx48crPDzcsqz4PzRvAACAkoYJYwAAAChuvv/+e02fPl1ffPHFVesMw9CkSZN06dIlPfPMMw5KZy0aOAAgH3FxcXmOITUMI99jf6+JiYmxZ7xSKSsrSx4eHgWq9fDwUFZWlp0TASjN9uzZIz8/P7Vp0ybfmgoVKujTTz/Vvffeq8mTJ3Nx3U727NmjXbt26eTJk0pNTZW3t7eqVKmiZs2aqUmTJlbHAwAAsKtevXpZHQEAAAC4Lhs3bpSHh4fat29/1bo777xT7u7u2rBhAw0cAFDa3cgOU+xOZR8BAQGKi4vT2bNnVaFChXzrzp49q7i4OFWrVs2B6QCUNufPn9dNN91k+9nV1VWSlJaWJi8vL9t6jRo1VK9ePW3ZssXhGUu6yMhIffHFFzp69Gi+NYGBgXr88ccVGhrquGAAAAAAioyXX375uuoNw9D7779vpzQAAECSTpw4oerVq8swjKvWOTk5qXr16kpISHBQMuvRwAEAeYiNjbU6AvLQvn17zZw5U88//7zGjx+vMmXKXFGTlJSk559/XllZWerYsaMFKQGUFuXKlVNmZqbt5/Lly0uSjh07pgYNGuSqzcnJ0ZkzZxyaryTLycnRiy++qP/973+2pkkfHx/VqFFDnp6eSk9PV3x8vFJSUvTHH3/o5Zdf1oYNG/Txxx/LycnJ4vQAAAAAHOnydpdXc/nmkWmaNHAAAOAAFy9etD0Qdy1ubm5KS0uzc6KigwYOAECx8eijj2rx4sXasmWLOnbsqJCQENWvX1/+/v5KTExUXFycFi1apLS0NPn5+enRRx+1OjKAEqxq1aq5Jj80bNhQK1as0KpVq3I1cBw5ckRHjhyxNXjgxr3//vtasmSJDMNQr169NHDgQDVq1OiKut9++03ffPONIiMjtXTpUpUtW1ZvvPGGBYkBAAAAWOVqW1mmpaXpyJEj+vHHH5WcnKwRI0aocuXKDkwHAEDpVLlyZR06dEgXL16Uu7t7vnUZGRk6dOiQKlas6MB01jJM5vwDAIqR2NhYPfnkk4qPj89ztJZpmgoMDNTnn3+uoKAgCxICKC3Gjh2radOmafny5apZs6aOHTumzp07yzRNDR06VC1bttTp06c1ZcoUJSQkqF+/fnr77betjl3sxcTEqE+fPnJzc9P48eMLNG1pzZo1GjVqlLKysjR//vw8mz0AAAAAlF7Jycl69tlndeDAAYWHh9OADwCAnb366qsKDw/XE088oSeffDLfuokTJ2rixInq1avXVZsySxIaOAAAxU5mZqaWLl2q9evX6/Dhw0pNTZW3t7dq166tO++8U127dpWbm5vVMQGUcHv27NHzzz+vJ554QqGhoZKkmTNnasyYMbkazEzTVO3atfXtt99yEbAQvPXWW/r+++/14osvasiQIQU+b/r06froo490//3366233rJbPgAAAADF0+nTp9WxY0eFhYXpzTfftDoOAAAlWlxcnEJDQ5WTk6MHH3xQjzzyiPz9/W3HExMTNW3aNM2cOVPOzs5auHChbrrpJgsTOw4NHACQjzVr1ui3335T/fr1de+99+Y61rZt23zPe+KJJzRo0CB7xwMAFFG7du1SZGSkjh07Jk9PT7Vs2VL9+vWTl5eX1dFKhM6dO+vUqVPasmWLPDw8Cnxeenq62rZtqypVqmj58uV2TAgAAACguAoNDdX58+e1du1aq6MAAFDizZ07V++++64kyTAMBQQEyNfXV8nJyUpISNDlNobXXntNAwYMsDKqQ7lYHQAAiqLU1FS99NJLSk9PV3h4+BXHz507l++5EydOVN++feXp6WnPiACAIqpZs2Zq1qyZ1TFKrD///FOBgYHX1bwhSZ6enqpZs6bi4+PtlAwAAABAcXfx4kWdOXPG6hgAAJQKAwYMUPXq1fXJJ5/o999/17Fjx3Idb9iwoZ599lkFBwdblNAaNHAAQB5WrVqlCxcuqF+/fqpXr16eNQ0aNNBrr72Way0iIkKRkZFauXKlQkJCHBEVAIBSxTRNOTs7/6tznZ2dxQBCAAAAAHmJjY3VH3/8oSpVqlgdBQCAUqN9+/Zq37694uPjdeDAAaWkpMjHx0f169dX9erVrY5nCRo4ACAP69atk2EYuu+++/Kt8fX1VevWrXOtVapUSREREVq3bh0NHDdo8ODBkqRq1arpgw8+yLVWUIZhaNasWYWeDUDpNW3aNO3atUvBwcFXfY+4bN68edq4caNatmypIUOG2D9gKeDv768//vhDly5dkqura4HPy8zM1JEjR3LtpQkAAACg5EtISMj3mGmaOnPmjHbt2qVp06bJNE116tTJgekAAIAk1ahRQzVq1LA6RpFAAwcA5CEmJkZlypRRo0aNruu8WrVqqWrVqvrtt9/slKz02L59uySpTp06V6wVlGEYhZoJQOkWHx+vcePGqXz58rbGsmvp2rWrJk2apHXr1unee+9V1apV7Zyy5GvZsqUiIyO1ZMkS9e7du8DnLVmyRGlpabr33nvtmA4AAKBoOXjwoGbNmqXt27fr1KlTunjxomJiYmzHFyxYoJMnT2ro0KHy9va2MClgP3fddVeBrhGZpqnGjRvrqaeeckAqAABKt6lTp6pXr16qWLGi1VGKHBo4ACAPp0+fvmqnn6enpzw8PPI85u/vr8OHD9srWqkxe/ZsScr17/nyGgBYISIiQtnZ2Ro2bJh8fX0LdE6ZMmU0fPhwvfPOOwoPD9eIESPsnLLk69WrlyIiIvThhx+qYcOGatiw4TXPiYmJ0YcffijDMBQaGmr/kAAAAEVAeHi43nrrLV26dMm2jdw/b2InJSVp0qRJqlOnjrp27WpFTMDuAgIC8j1mGIa8vLxUs2ZNdejQQaGhoXJx4bYJAAD29umnn+rzzz9XcHCw+vbtqw4dOvzrbZNLGsNkE2gAuELjxo3VsGFDzZ8//7rP7du3r/bv369ff/3VDskAAFbp37+/fvnlF23cuFHly5cv8HkXLlzQ7bffrqZNm2ru3Ll2TFh6jBo1SitWrJCHh4cee+wx3X///apQocIVdWfPntV3332nr776ShcvXtQ999yjzz//3ILEAAAAjrVnzx498MADkqRBgwapU6dO+uCDDxQTE6N9+/bZ6hISEnTXXXepW7du+uSTT6yKCwAAgFLm8ccf14YNG5SVlSXDMOTn56eQkBD16dMn12T20ohWUgDIQ7ly5XTmzJl/dW5iYqLKlClTyIkg/XVhyd3dXX5+ftesPXPmjC5evHjVpywA4HocOnRI1atXv67mDUkqW7asqlevrkOHDtkpWenz4Ycf6syZM4qKitKECRM0efJk1a1bV4GBgfLy8lJaWpqOHj2qgwcPKjs7W6ZpqkWLFvroo4+sjg4AAOAQX3/9tXJycvTWW2/pvvvukyS5u7tfURcQECB/f3/t2bPH0REBAABQin3xxRc6c+aMIiIiFB4erkOHDmn69OmaPn26mjVrpr59+6pLly7y9PS0OqrDOVkdAACKosDAQJ04cUKnTp26rvMSEhJ08uRJBQYG2ilZ6XbXXXdp1KhRBap9+umn1alTJzsnAlCapKSkqFy5cv/q3LJlyyo5OblwA5ViHh4emjVrlkaMGCFvb29lZWVp//79WrVqlRYvXqxVq1Zp//79ysrKkre3t5544gnNmjUr3+3PAAAASpro6GiVKVPG1rxxNZUrV9aff/7pgFQAAADA//Hz89MjjzyipUuXat68eerTp4+8vLwUHR2tV199VXfccYdee+017dq1y+qoDsUEDgDIw2233abo6GjNnTtXzz77bIHPmzNnjgzDUNu2be2YrnS7np2/2CUMQGHy9vZWUlLSvzo3OTlZ3t7ehZyodHN2dtaTTz6poUOHav369YqOjtbJkyeVmpoqb29vValSRc2bN9edd94pHx8fq+MCAAA41Pnz59WgQYMC1RqGYec0AAAAwNU1bdpUTZs21Wuvvably5dr4cKFioqK0sKFC7Vw4ULVrl1bYWFhCgkJyXMr5ZKEBg4AyMN9992nr776StOnT1eLFi3Uvn37a57z008/afbs2XJxcVG/fv0ckBJXk5qaKldXV6tjAChBqlatqri4OCUlJV3XVllJSUk6evSo6tevb8d0pZePj4+6du2qrl27Wh0FAACgyChXrlyBp4rGx8cXaKtSoLhq2LDhDb+GYRiKiYkphDQAAOBqPDw8FBoaqu7du2v27Nn69NNPlZ2drUOHDumjjz7Sp59+qi5dumjkyJEldho+W6gAQB4qV66sYcOGKSsrS0888YRGjx6tAwcO5FkbFxenN998U08++aRycnL02GOPqUqVKg5OjMsyMzO1ceNG7d+/X1WrVrU6DoASpHXr1srJydGCBQuu67z58+crOztbrVu3tlMyAAAAILdbbrlFZ8+e1c6dO69at3r1al24cEEtWrRwUDLA8UzTvOE/OTk5Vv9jAABQKhw8eFAfffSROnTooI8//lhZWVny9fXVAw88oE6dOsk0TS1evFg9e/ZUVFSU1XHtwjCZLw8A+Xr99dc1f/582zjRMmXKqFq1avLy8lJaWpqOHz9uG6dvmqb69Omj9957z8rIJcrEiRM1adIk28+maV7XaNchQ4boxRdftEc0AKVQXFycevbsKQ8PD82aNUtNmjS55jl79uzRgw8+qIsXLyoyMrLAY6wBAACAG7F27VoNHz5ctWvX1uTJk1W7dm31799fu3bt0r59+yRJe/fu1bBhw3T27Fl98803NHGgRJs5c6bGjh2rtm3batCgQapXr578/f2VmJioAwcOaM6cOdqyZYteeOEFPfjgg1bHBQCgVElNTdXSpUu1cOFC/fLLL7rcvtCiRQuFhYWpS5cucnd3lyQlJiZq3LhxWrhwoZo1a6bvvvvOyuh2QQMHAFzDokWLNHHiRMXHx+dbExgYqCeeeEKhoaGOC1YKTJw4URMnTrT9bBiGCvK25e3trW7duumVV16Rh4eHPSMCKGXeeOMN/fDDD3J3d9fjjz+u+++/X+XKlbui7vz58/ruu+80ZcoUZWZmKiwsTKNHj3Z8YAAAAJRaL7/8siIiIuTu7q6WLVvq999/V2Jiovr376/ff/9dO3fuVE5OjgYOHKjXXnvN6riA3axatUpPPfWURo0apeHDh+db9+WXX2r8+PGaMGGCOnXq5MCEAACUTlFRUVq4cKGWL1+ujIwMmaap8uXLKzQ0VGFhYapTp06+59599906e/asdu3a5cDEjkEDBwAUgGma+vXXX7Vz506dOnVKqamp8vb2VuXKldWiRQs1btxYTk7sSlXYkpOTc0046dSpk2655RaNHz8+z3rDMOTh4aEKFSo4MCWA0uTSpUsaPny4Nm3aJMMw5OzsrHr16qlGjRq26UzHjh1TXFycsrOzZZqm2rVrpylTpsjV1dXq+AAAAChFTNPUpEmTNG3aNKWnp19x3N3dXY8++qhGjhxpQTrAcfr3768jR47YvsflJycnR3fccYfq1Kmjb775xoEJAQAofe69914dPXrUNnm9bdu2CgsLU6dOnQp0HXXQoEGKioqyTZcrSWjgAIA8JCQkyN3dXX5+flZHwd+8/PLLqlWrloYNG2Z1FAClmGmamjJlimbMmGFrMpOunBLk6+uroUOHavjw4TT5AQAAwDLnz5/XunXrtH//fiUnJ8vLy0v169dXx44due6BUqFly5aqXbu25s+ff83asLAwHT58WFFRUQ5IBgBA6RUUFKRKlSqpd+/e6tu3r6pXr35d52/YsEGJiYnq1auXnRJahwYOAMhDUFCQWrZsSbc9ACBfqampWrdunaKjo6+YztS8eXO1b99e3t7eVscEAAAAgFKtRYsWcnNz06ZNm67aXJ+Tk6N27dopMzNTO3fudGBCAABKn59//lkdOnTgwbc8uFgdAACKKvrbip7ExERt3LhRgYGBat68eb51O3fuVHx8vIKDg3maCIDdeHt7q2vXruratavVUQAAAAAA+bj55psVFRWlyZMnX3XLoMmTJ+vcuXNq3bq1A9MBAFA63XXXXVZHKLJo4AAAFBvz5s3TpEmTNHbs2KvWnTx5Ui+//LJGjRql4cOHOygdAAAAAABF04ULF5SWlnbVh1UCAgIcmAhwnGHDhmnHjh2aNGmSduzYoYEDB6pu3bry8/PTmTNndPDgQc2dO1fbtm2TYRh67LHHrI4MAABKMbZQAYA8BAUFqUWLFpo7d67VUfA3ffv21e+//66dO3fK1dU137rMzEy1aNFCN998s77//nsHJgQAAAAAoGg4cuSIJk6cqPXr1ys5OfmqtYZhKCYmxkHJAMf79ttv9f777ysrK0uGYVxx3DRNubi46OWXX9aAAQMsSAgAQMk1ePDgG34NwzA0a9asQkhT9DGBAwBQbBw/flwBAQFXbd6QJDc3N1WrVk3Hjx93UDIAAAAAAIqOffv2adCgQUpNTS3QFrE844eSrn///mrVqpWmT5+uDRs2KDEx0XbM399fwcHBGjp0qBo0aGBhSgAASqbt27ff8Gvk1YBZUtHAAQD5yMzMVEJCwr8+n9GjhS81NVXVqlUrUK2Pjw8NHAAAAACAUmns2LFKSUlRo0aNNGrUKDVq1Eh+fn5WxwIsVb9+fX3wwQeSpOTkZKWlpcnLy0u+vr4WJwMAoGSbPXu21RGKFbZQAYA8BAUF3VA3H6NH7aNjx45KTk7Wtm3b5OzsnG9ddna22rRpIy8vL61fv96BCQEAAAAAsF6zZs0kSWvXrlXZsmUtTgMAAACgoJysDgAARZVpmv/6T05OjtXxS6TmzZsrNTVV8+bNu2rd999/r5SUFDVv3txByQAAAAAAKDpcXV1Vu3ZtmjcAAACAYoYtVAAgH7fccovGjx9vdQz8zcCBA/Xjjz9qzJgxysnJ0f333y9XV1fb8UuXLumHH37QmDFjZBiGBg4caGFaAAAAAACs0bBhQx0+fNjqGIDDTZw4UZJUvnx5DRgwINdaQRmGoREjRhR6NgAAgIJgCxUAyENQUJBatGihuXPnWh0F//DJJ5/oq6++kmEY8vX1VcOGDeXr66vk5GTt27dPycnJMk1Tjz76qJ577jmr4wIAAAAA4HDr16/XsGHD9OGHH6pnz55WxwEc5vK2yLVr19bSpUtzrV3rVsjlGsMwtG/fPkfEBQCgVNmzZ48OHTqkypUrq23btrmO9e3bN9/zHnroIXXt2tXe8YoMJnA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_24.png\n" + ] + }, + { + "data": { + "image/png": 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g1qxZCAwMZPIGERERERERkRHSawLH0KFDAQDZ2dnYvn17rV/fsGFDACi33qyrqys6duwIURRx8eLFR46TiIiIiIiIiIiIiIiIiIiISEp6TeAICAiAlZUVAGDp0qW4cOFCrV6fmJgIAMjKyir3XJs2bQAAmZmZjxglERERERERERERERERERERkbT0msDh5uaGWbNmQRRFFBcX44UXXsCGDRtqtJzK7du3ERUVBeB/lTgeZmtrCwCQy+V1GzQRERERERERERERERERERGRgek1gQMAZs+eDT8/P4iiCJVKhRUrVmDkyJHYsmULUlJSKnzNuXPnMGvWLKhUKgiCgB49epRrk5+fDwBo0KCBPsMnIiIiIiIiIiIiIiIiIiIi0jtzQ3zIV199hTZt2mD16tVQqVS4f/8+vvzyS3z55Zdo0aIFmjdvDicnJygUCty6dQsJCQk6r3/xxRfLvefdu3cBAI6Ojob4KxARERERERERERERERERERHpjUESOABg5syZ6NOnD5YuXYqYmBjt9qSkJCQlJem0FUVR++c5c+agT58+Os9nZmbixo0bEAQBTZs21WvcRERERERERERERERERERERPomiA9nSxjIqVOnsGPHDkRERCAvL6/CNu3atcObb76JUaNGlXtu7dq1+O677yAIAl5//XXMnj37X8WRnp6OU6dO4cqVK7h8+TKuX7+OkpIS+Pj4YOvWrf/qPYmIiIiIiIiIiIiIiIiIiIhqy2AVOB42YMAADBgwAGq1GlevXsX9+/eRkZEBlUoFR0dHdO7cGW3btq309U5OThg/fjxu3LiBMWPG/Os49uzZgy+//PJfv56IiIiIiIiIiIiIiIiIiIioLkhSgaO+CA4Oxp49e9C1a1d07doV165dw/fff88KHERERERERERERERERERERGRQklTgqC8CAgIQEBCgfZyWliZhNERERERERERERERERERERGSqZFIHQERERERERERERERERERERGTqHrkCR1paGlxdXesilhp78OABmjRpYtDPNGaiKEJZooaiSAW1UgONRoQoihA1+N//IQJVLLYjyAQIQun/y9qZmctgbmUGuwaWpdupVkRRhDxXAWWRStsnEAGNWHVfAACE0u9fEEr7xcrGHLYNrQwSt6kQNSJUKg0URSqolBpolBoIZgJkZkLp9y77+88ANBoRaqUGZhYyWFiZwdLapIsf1ZqoEaFUqFEiV0Gl0kBUa6DRABBFQChr8/fvFABAQLlfHAGQyQRYWJvB3tEaMv4m1UjZ+FBSWDo+qJTqqscEAaX//v/+c9kibYIAWNlawL6RFceDf6mkSAVFkQoalQYqlQYatQhB+Oe//b/9b1eAIAiwsDSDzLz0t0nUiNBoRFham8PG3oL9UQNKRekxkkYtwtxCBrVahEqhhkb99/GSurQHxJqMzw8TAHNzGWTmMshkAswsZDC3MIOljRkEgf1SnbLfp7JjV41ahEb993GspnTseHilSEEoHZfNLMxQ9vXKzATtd29hxe+9Jir63iECGo0GimL1v9oPBEDn2Ako6xsZLG3MYGVroZe/izEoOx4t7QcRapUIRbFKO0aUjQNlbUv3i9LXlv1zL/3eZTrfvblF6bhhbi7jOFEBUSOiWK5EXPxtFJcU6fWz7O3s0b5De/4+VUNRrEJxgRKKEvX/fv//7WLBf59L29hbwsbBgt99LYgaEYX5CiiKS88dRBF/H6/+fbRag3kMQSgdmx/+TRIEgefS/1B2blD2XamUGiQkx6FYoeffJHt7tG/P3ySgfB+IAO4aYFwAADtbO3To0IFjdCU0GhG5GXIoi0vnL6r30Al0Vf6eV7K0MYONvRV/j2rg4TmlsnNoTQ3HaUEQYGZeeo4gyARYWJnxfLkWyo6NVEqNdh6oSmLFc31C2QTfQ+fQZuay0nkmM55L14ZGrUFhngIlf88vAQDE/52f1UTpOZ7udy37ex8R/v6NMrOQwdLGnP1SDVEUUVKoQkmhEsoSTbk5pAr9vSuIKBsPzGFlY8HfpQqUnTMrFWqY/T3veTfxDoqKCvX+2TbWtmjr0Q6WNuaPfb888pHG6NGj8cILL2D69Olo2LBhXcRUqdzcXGzatAlbt27FhQsX9PpZpkKj1uDu5UzEXXiA7PtyKEvUsLAyg1qpQUFuCRSF6lq/p5ll6Qm2Ri3CvqEl2vRsAt+n28Dc0kwPfwPjIooiCgtLf8Tu387GtVMpSLmV+/eBlgZKhQYalajTHkCFP0SCDLCwNIOVjTmatHZAlyHucGlhjwaNHB77Hy4padQapMblIiOpAOmJ+Ui5mYMiuRJmZqWT32WTS1a25rCwksHe0RpNWjpApdIgJ7UQTds0RAffptwfqqBSqJGdWoj87GI8iM/D/du5yMsoRJFcBY1CU7PJvwrIzAQ4t7RHp77N4ORmj6ZtGzKRowoatQbxV7Nw71I60uLzUJyvhAigWK6ARlnz9xFkpZPgDRrboH0fVzg3t4ddQys4NrXlflALyTeykXA1ExlJ+chJK4IIDVQKDTRqVLs/mFkKsHe0hl1DS6hKNFCrRTg3t4Nbu0ZwbGrHfaEayTeykXg9C1a25rB1sETCtSykxedpE19rNilYMZkF4NjEDq6tG0CtFuHY1BbN2jaCm2ejOovfGGnUGtw6l4Z7lzKQnVYEiBooStRQFKtQIldXuk8IAmBuJYNzc3s0a9cAiiIN1EoNGrnawsnNHq06O0FmxgKFlSkbFzKTC5D7oBBm5gJKilXISilETlqhzjFqbQmyssQBGRyb2qORqy0sbczg3tEJrbu61OHfwjiUHY9mpxaiMF+B4gIlRI2I9IR8FGSXQKPRoGFjW9g3skJhvgJFeYrSfaSwLBlW1CYeW1jIYG1vAXNLM6gUGljamMPawQKuLR1g29CK40QFMpIKcPdyOma8E4QrN8/r9bP69PLF2XOn9foZjzNRFJGdmYcz4bcRfzmzNHGgmt+iqs6htWRAi/aO6DmyJdzaO8LW1pbn0FUQRREF+QW4eCQRidezkJFUAI1KA1EQoVGh3LhcbR8IgKW1GSytzeDYzB4NnK1hBks0a9uI59J/S76RjaTYLNg2tIKltRlSbuYgK00OpaYExXklKMxXVnmMWqIsxsKtpUtKLwkKhpWFddUfKAD9/Nqi/Tgmb5R5uA9s7C2QnSrHy/Ofw434S3r/7O6dvPHLunA4NeO8xj+pVWp4d/fFxWvRev0c7x4+OBsVATNz/h5VRqPWIO5iOq4eT0F6Uj5UxWqo1bVPsLSwksHeyRrWduZo2ckZbp6OPF+uhlKhRtSeu0i6no3CAgWUxSqolKXJ3ABK+6DsKnRNCYCZOSCTyf4+R7CFtZ0Fz6VrQK1S4+7V+0iMzUZibBZyUguhUmmASsbpGh2r/k2QATb2FnB1d4KiWAPV39f2nJrZo01PF7Tu6sJ+qYBGrcHlo0m4GZmKrPuFUKs1EGt5GVRmDtg3skLTNg3RvL0T2vu48hj1IRlJBbh3JQPynBK4NLdHzoNCZCQX4UFSFgrzFKXz2VWo1bGqrPQGdgtLGSxtLODURIYiz2SjmF995ASOoqIirF+/Hlu3bsUzzzyD5557Di1btqyL2LQSEhKwbds27NixA0VF+s9kNiWpcbmIv5yBrBQ5AMCxmR0Uf0/EKktqn7wBAGqFiLIjgLzMElw5ngwLGzO0aO/EE4sqiKKIgQMHIiIiQq+f06eXL05HRsCMg3etadQa3L6QjtiIZKTF50NVrNFmzCqB/2Ujy4DCXBnMLGQozFWgIKsEjVs6wMxchsJ8BbJTC9G4pYNkf4/66uGLEQXZxYi/mons+4VQKTX//g46nfcXkR6fj5hCJZp7OiLxehacW9jD3ELGhIIKpMblIj0hHxnJBVAWlZ4AyHNLapW8AZRWiFApNMhKliMq/S4auFjDsakd7BpYwaWFA9r1bsI7V6ohiiKKSwqRl5uH3NwCyAuLoK5gMvzh9sBDJ3tKoKioEIVyq9ILc3YWKCgowN3rRcjOtEV2Zi68erfg5FMlNOrSKkqFeUqkxuXhQXweSgpVdfO7pARy0gthYVV6oTonrRDWdhZo0sqBv0eVUClUOB0Wh+Sb2SjMV0CtEqEqUVV78geUVgVSFmuQnpCPkkIV3L0cYe9kg+zU0uRZKxvzx/7kTp9S43KRmVyArPtyWFnL8CA+HzkPCqEoeoQspr+JGkCtEaHRqFGYXwJLW3M0cG4AO1aQK+efiTTmFjJY2pqjWK5C0d/JHBCAzJQCpCfkQ9SUJvv9MwFWVInQqESoSjQoKVHBoZE1LGzMSytuKdR4kFgA68xiFMuVKClScVL2IfLcEpTIVdjw9e/IysjD/Vs5yM0oqnJcqM0ElMwSsHewhm0jK3Qb2BpqpYZjQgUMdf4MAAMGDMCJEyd44boChuqHLu17YdOKHTyX/ltZhZLcB4XQqEXk5xRDUaiGsgTQKMxhaWYO1PBnw8rCGlYWNtW2u3QoFT2HtYW1HStjAX9Xh5EJ0Kg1UKs0KMpX4t1n10KeXwARKJ1TreZ8obaJNGbmApya28GtdWOk3S09J+EY/T9lx0glhSq9f1ZxgRIpt3Pg7uWs9896XKXczsHVE8l4EJ8PpUJdWmXgX5xDK0s0yL5fCAsrAfmZJVCUqOHUzBbW9pZ1HrOxyEjMR/Z9OXIzi1CiKIJKUbNqNNUmDihLEwZKlEUQrNVo0bgRUuIzIIoiz6UroVap4dunH6JjovT6Oe1bdsPqD3+BWi0iP7MYmSkFMLOQwdrOkv3yt7IbpzVqDe5eSseV00lITyzQVhWtqD1Q9f6gUBejoECO3Jx85Oflo0W7JmjWrhGvf6L0nLm4QAkbewtkJBcgI7EA8twSiAoLWJqZQazFYUtNjlXNNIBMYw5BYwZBFIxmfvWRr5gEBQXht99+Q2FhIX766Sf8/PPP6N27N8aOHYuRI0fCxeXf3TGVkZGBw4cPY+/evYiKKv2BE0UR5ubmmDp16qOGrePMmTP48ccfcfbsWQDApUuXsGrVKsycORO2trZ1+ln1iVKhRkZSAbLuF0AQAHsna5iZyyDPKoGiSFnrrLPKqBQanN8fj7yMIhTLlWjd1ZknFpUwxISQoliN+CuZaN2F/VCZsgoQ8twSaNQiZGYCbBwsce9KOq4eS0Zxgari8mZ/b9Oo8fdkeWnma2GeAoUFJWjgaANBECDPLUFjcNLpYQ9fjMi6X4D7t3NRmFtSowtytfsgIDetGAXZqXBwtEJmsj0cnK1hbW+BB/GWvMv0b8q/94H0hDxoVCKKC5VQKzTV3tFYHbVCRHZKEbLTimBta46slNJKNm17NYabp6PJf+8VMWRy39lzp3lh4h80ag1y0uTITpMjLycfBVnFKCmufjK2NndMQAmkJStg4SDCoYE9E/2qoFKoELn7HhKuZ6EguwQQSyuU1XasUCtF5KUXIVkAmnk6opGrjdGc3OmLUqFGZooc92/nQGYGJNzKQkFmUQ0TZ2q3P6izSiDIBDRwtkbDxtXcEWyCSivB5SM1MROOTWxQXFiC3Pv5KMguRlZmAdQKTbnj1JpMPqnUCtg7WcHCyhzK/BI0sLKGg7MdE5wqULpkk4iMJDnyMxVQygVYmVd/8bNMdRNQMhlgLrOCOSxRVKDkmFCF2pScJv0xRD9o1CKK8xU8l/6bjYMFigtVyH1QCJlMgKgWITMHlHk1XTKi9lQlGhzcdBXj53TjXBIAu4ZWsLa3gDyndN5IUawqXTrU0gaiCMg01Z8zPKxGiTQCAKUFbOwtYGVngez7HKMflhqXi6wUOd595jtkpefV6DW1rkYDwNLWDC6ujZBwNQvN2jTiuUMFlAo14i6kIyUuHSVF/1tmsarxorrj1RIlIC8sxOWThSgukqNlZxd4dGnGGxQrkB6fj8yUAiz97xzEpV7V++d19uyJ/27cxXPpCqTG5UKl0P+BkkYlIiu1EM5udnBwtkZBVjGyUgqQmZTPfoHh5la7d+qN0F/3msx1t4dXE/ingoICFBbKkZenQe6DIhRkFpcuvassrQhUkyTXiv5cKRVgrjGDmVKAhZ0Ima0NMtNFJMWZobF71ecO9bna4iMncHzwwQcIDAzEsmXLcOLECYiiiKioKERFReGzzz5DmzZt4O3tjQ4dOqBNmzZo2rQpGjVqBBsbm9I7SouLkZ2djbS0NMTFxeHGjRuIjo5GXFyc9jPKBvDBgwdj/vz58PT0fNSwtbZu3YrFixdDFEU4OJR2ZElJCdatW4eDBw/i119/RaNGjers8+qTnNRCZKXKoVED5pYyqMQSFGQrkZdbgOLiqsstArWfjL0RnQSNSoSljRladHCqg7+BcREEAX/9eRQXjt7F9dMpyM8oAgRAUVTxSV+t7uIyE9CgsQ2atHaAlbkNsu/LYW1rwRO8f6isHDUAFOQUI/FaVo0PukQRpUlQMhEahRr56cVQKdSQ5ylg18gKLTs782L1Q8ru6s1OLUBmihzFBdWX0noUaoWIvMximFuZwbGpDeQ5JchLL+Jdpn/L+XsfKClWIT01q8Zrx9Z4XFACShUgl8uRn10CtUoDZYnG5L/3ygjVrcVbB1R/33FtYcVqKGXKEsvyMovx3qppuJl4We+f2c3LG999uo0XJypx9WQK7lxNQW5GIURRhEYNqJWV/zhV+ZukBEruF0Gw0cBZYwelWo3MdBFZ9x3QpFUDff0VHltZKXLcv5OLogIFPlz3Mm7peX9o59YVPyz5DRlJch6vPkSpUCPrvhzT3pyEq7dj9P553Tp6Y9OKHSaT4FTVBNTDiorleJCchdyMIshzi6Gu6wkoJSBaKGHpYIfsjFzY3ZfB1rlmx0f1efKpJmraB2VWfrwZJ0OuQ1HD6qEKZTE+/e/zAIBPn/0FltXd7W4hoF2vJhj+XNdaxWVq/fDtpz8i+vAd5GUUQZAJVZYFr20fAICljTmcmjRARmoOnFpZQy6vWcKUMfdDdkYu8vPykZtdiAYu1rBsYIbMtAIUl9Ss8kCtJ8X/Fn/jPi5HNEC7Xq41am/MfWDhIALmKhQUFCC/AJDnlUAlKlCiVECtFqFWVH8C/W/6ISujGJa3RLTq6gJrR+B+QgY0MiXsXGSVjtHG3A9llAo1ku9mIC0xFyqNQq/xqErUyM3Kh3m8iIQbtnBt06hGr3vc+6E20hPyMPO9yXpfUsjUlrKp6ficlpyJvNx8aAyU6SqKGmSm5yApzrzaC6XA478v1LQflAo1Uu5l4vM3vodVAyDuQgZy0gqrTGT6N8dJZuYCLM2tkJedB8FCBduGllBBgeIiDZLuZsChmUWF/fI490Ntj1VFUdTOD+mTokSJ5LgH0ECBZu0aVdv+ce8DQ1VDLLsGqi/1udpinczSt2/fHhs2bEB0dDQ2bNiAY8eOaXeIuLg4nWSMmnp4wnXo0KGYNWsWevbsWRfhal25cgVLliwBACxatAgKhQJffPEFunfvDqVSiatXr+Kjjz7Cd999V6efW1/Ic0sgzy4BBBEfrnsF1+Mu6v0zPVt0w+aVO9DUo6FRT/7VVlniwIOEfNy7kIOSbEBQWwICUJNraTXJ1FfJZSjKFGHjJiDbRCZha+PhChA5aXKoNCWwaWAJmYUMuemFuHc1E4pKyjBWeYFIAAQBkJkLUGQVwcLKHCnxgNNlO5PJxqxOWbWH3AeFKJIrUZBVAmWJBgpVzSeSgFomlQGAEshNlyG/qQLNPRuhuFDJO1j+Js8tQVG+Au+tnIZbSfq+SNcFn8z4P9g3tDKJ7722JxkAsGn1r7h0Mh6J1zNRUlj1BYqanuyZmQuwa2gF985OUCs0cHJtiOS7GTU62QYe75MMoGb9cP92Nu7H5SE3uwAw0E+1UqlCYaEcBQVWNbo4YQr9UKYwvwTPvvIUYuP0OwnYs1tvHD/1V42/V1Ppg1sXk5CekoWSIhXUKj1mWP5NI6qRfDcdorkKNk5CtUttmUo/pCfmIzM9B2JdrOFUAyqVGhpBCYW6uEZ3rzzO/WAsE1D1efKpOobsAwDa46VqbQYwt3bvzX6omRr3wb/EfqiZWv8mfV/zpuyDmuPYULF6vS/U0uPeD7WZx4iPTYNKqf9zBkWxEnFX7sPN07FG7Xmsqh/Xbl/E0ICuNW7/uO8LxnKc1K9fP5w6deqx64f6vC9cv3MRgyd1qXH7x7UPAKCwsLBe9sG/cerUKRQWFsLOzk7qUMoRRD2kHsXHxyMkJAS7d+9GSkrKv3oPNzc3PPXUU5g0aRJatWpVxxGWmj17Nv7880/4+flh6dKl+OWXX/D555/Dx8cHn3/+OcaMGQONRoOdO3fCy8tLLzHoS00OrO5eSsf1U/dRVKjElz+9jti7+p0QBwCPpp3w1X82oPeTrWqUofw4H1gBNesHjVqDhGtZyLovR8LVTORnla41jb/vLK1Mbcv8ycwF2NhZoGFjW5hZyODa2gEtu7gYfWZsTU8yyi7UZafJ8cG3M3ElNkavcXX16omQ33aZxEkGUHU/pCfmI+FaJnLTi5CekI+cB3J8t2sB7j24rve4WjfphI9eWYNO/d1gZi5DSZESOWmFcG3dEO37uJZLcDLmfnjY3UvpuHc5A299+iLu3L+m15g8mnbCe0Hfwq6hFdr3aVrh9/5Pj2s/1OeTjNriCXf98Dif7BlLP7AP6gf2Q/3wOPeDXC6Hvb291GHUiYKCgno5+VQdY+oDgP1QX7AfpMc+qB/YD/XD49gPPFatH7gv1A/sB+mxD+oH9oNh6CWB42E3btxAREQELl68iBs3biAlJQUlJSU6baysrNC8eXN06NAB3bp1Q//+/dGhQwd9hgW5XI6+fftCoVDg119/hbe3t04Cx9atWzFt2jRERETgtddew1tvvaXXeOoSD6zqB/aD9NgH9QP7oX5gP0iPB7f1A/uhfjCmfmAf1A/sh/qB/SA99kH9wH6oH9gP0mMf1A/sh/rhcewH9kH9wH6oH9gP0isoKICDg/EsOfw49gHAfcFQ9L7QeYcOHcolY+Tn52vvALa1tZVkh7t+/ToUCgUsLS3RrVu3Ctt4e3trk08eJ8ZUvub06dP1tnxNddgP0mMf1A9yuZz9UA+wH6RniPUWqXrsh/qB/SA99kH9wH6oH9gP0mMf1A/sh/qB/UBUivuC9NgH9QP7oX5gP0jP1tZW6hDqjLu7+2P79+G+YBh6T+CoiIODg+RZUnfv3gVQulSLhYVFhW1atmyp0/ZxwZ2HqBT3hfrhcT0QqcjjfGD1uMZdkce1Hx7HmCvzuPYBwH6oLx7XuP+JfVA/sB/qB/aD9NgH9QP7oX5gP0jvce4DzifVD4/rv5+KPK77w+MYc2Ue1z4A2A/1xeMad0X69ev3WP59ZDIZ1Go1srKyyq30UJWMpHxcPnUP92/norhACaVCU2lbhbIYS0NnAQDenfR/sLSwrrStmZkA20ZWsGtgiSYtHGFtbwFFoRpung3h3tG5ypicnJweu+rSZR7HfzuVqc/7giQJHPVBbm4uACAxMRG+vr4AAIVCAQA4f/48fH19oVQqAQAZGRnSBPkv2dnZ4dy5c1KHUScsLS3r7c5THWtra6PpB6D07/O44b5QP8hkMmg0GqSkpCA1NVXqcB5Js2bNHtsDK/aD9NgH9UNZP6jVamg0FZ+wKYpVUJaoIcgEyGQCEpLuoURRrPfYLM2t4dbEHQ7ONRtzzc3NjbofHlZSqEJ84l2994OtrS3atm1b4/am0AeKYhVK5CqoVRpo1CLSc5OhUNV8ouTfsDS3RuNGbrCwNINgJkAQAAsrM1haV3z6agr9AACiCJQUKg32m2RjbYPmTVtCrRJhbimDla0FqvqaTaEfysYHlUKD1IxEve8LNtY2aOfZrsbtTaEPyhQXKBGfdA8Kpf73BStLa7Ru5VHpb9A/mVI/qJQa3L0bh+KSIr3GZWVhDVfnFrCyMYe1fcU3Yf2TsfaDSqmBslgFjVqEmYUMaqUGapUGqZlJeh8bLM2t4dKgGcwsZLBtYFntPvE494ExzSfZ2dk99vNJ1f0mlR0jqRRqpDxINMy+0LAZLK3MYWVnXqPx4XHdH2o7LgClCVC3b95BsUK/Y4O1lQ3cmrjDwrry84SHPa59ANTynEEjIi+zCKkZSXo/VgUAS4vSeQwrW+PeF4D/9UNN9gVRBErkStxNiEOJnvcFS3NruDq2gEwmAAIgCALMLc1gZWsOCyuzCl8jk8ke635wcXGpcXtRFJGXG4uOQxqj45DGNXpN4MLaHwPYWNvCvWlryMxlsLQxh4Vlxd+9MajJvlBSqEJxgQJqVWlSbFp2IkoMcO5mbWWDNq3bwtKmZudu9XlfEEQTTSleu3YtVq9eXaO2giAgNjZWzxERERERERERERERERERERGRqao3FTjOnTuHS5cuYeDAgWjfvr3eP8/KygoA0L17d2zfvr3CNseOHcPMmTNhY2Oj93iIiIiIiIiIiIiIiIiIiIjIdNWbBI7Q0FCEhYXB2dm5XALH3r17ERERAWdnZ/j5+cHDw+ORP69hw4YA/reUSkXKnitrS0RERERERERERERERERERKQP9SaB49KlSzA3N8fo0aN1toeFhWHhwoUAStcq+vHHH7F06VKMGTPmkT6vdevWAICUlBQolUpYWJRfQzMhIUGnLREREREREREREREREREREZE+yKQOoEx6ejpcXV21S5uU2bBhAwBg0KBBmDBhAlQqFd5//30kJiY+0ud17NgRFhYWUCgUuHTpUoVtoqOjAQA9evR4pM8iIiIiIiIiIiIiIiIiIiIiqkq9SeCQy+VwcnLS2Xbnzh3ExcXB09MT69evx9KlS7FgwQIUFxfjl19+eaTPs7e3x8CBAwEA27dvL/f8vXv3cObMGQAoVxWEiIiIiIiIiIiIiIiIiIiIqC7VmwSOBg0aIDs7W2fbn3/+CUEQ4O/vr902ZcoU2NnZ4dSpU4/8mbNnz4YgCNi5cyd+//13iKIIAHjw4AHeeecdaDQajBw5El5eXo/8WURERERERERERERERERERESVqTcJHO3bt0dycjJSUlK02w4cOAAAGDJkiHabpaUl3N3dkZyc/Mif2a1bN7z33nsAgI8//hjDhg3DxIkTMWLECFy9ehUeHh74/PPPH/lziIiIiIiIiIiIiIiIiIiIiKpSbxI4xo0bB41Gg48++giJiYn47bffcPXqVbRs2RKtW7fWaSuTybTVMh7VSy+9hB9//BGDBw9GUVERbt++DTc3N7z66qsICQkpt6wLERERERERERERERERERERUV0TxLrKhHhEGo0GU6ZMQUxMDARB0G5/99138dJLL+m09fX1hZ2dHY4cOWLgKImIiIiIiIiIiIiIiIiIiIjqXp1U4Pjhhx+gUCgeLRCZDBs3bsSUKVPg7OwMW1tbBAYG4oUXXtBpFxsbi9zcXLRo0eKRPo+IiIiIiIiIiIiIiIiIiIiovqiTChxeXl5wc3PDO++8g/Hjx9dFXJVaunQpfvzxR8yePRtvvPGGXj+LiIiIiIiIiIiIiIiIiIiIyBDqpAIHANy/fx/z58/HM888gwsXLtTV25bTsWNHTJ48GcOGDdPbZxAREREREREREREREREREREZUp1U4OjWrRsUCgUEQYAoihAEAU8++STmzZvHpU6IiIiIiIiIiIiIiIiIiIiIqlEnFTgOHDiAp556SvtYFEUcOHAAY8eOxbJly1BQUFAXH0NERERERERERERERERERERklOqkAkeZa9eu4euvv8aZM2f+9wGCAEdHR8ydOxfPPvssZLI6W7WFiIiIiIiIiIiIiIiIiIiIyCjUaQJHmWPHjmH58uW4deuWzrIqbdu2xfz58zFkyJC6/kgiIiIiIiIiIiIiIiIiIiKix5ZeEjiA0mVUQkJCsHr1ajx48EAnkaN///5477334OnpqY+PJiIiIiIiIiIiIiIiIiIiInqs6C2Bo0xxcTE2b96MTZs2QS6XQxAEAIBMJoO/vz/efPNNODs76zMEIiIiIiIiIiIiIiIiIiIionpN7wkcZbKysvDdd99hx44dUKlUpR8uCLC1tcXMmTMxbdo0WFpaGiIUIiIiIiIiIiIiIiIiIiIionrFYAkcZe7du4fly5fj8OHD/wtCENCsWTO88847GD9+vCHDISIiIiIiIiIiIiIiIiIiIpKczNAf2Lp1a6xZswbbtm1D9+7dAQCiKCIlJQXz58/HM888gwsXLhg6LKLHmkajQVZWltRhGLWOHTvi+eefr1HboKAgdOrUSc8REZEp+/jjj3H58mWpw6AqqNVqREdHY9++fUhISJA6HCIiMoC0tDSpQyCqNxQKhdQhmDz2AdH/rF+/Hunp6VKHQUQEgGM0UZkRI0bg7bffrlHbd955ByNHjtRzRPWHuVQf7O3tjd9//x379+/HypUrER8fD1EUcenSJUyZMgVPPvkk5s2bhxYtWkgVIlG9ERcXh5MnT6JTp07o3bu3drtCocDSpUsRHBwMhUKBZs2aYdGiRRg4cKCE0RonURRRm4JFBi5uZHLkcjnOnDmDxMREyOXySr9vQRAwZ84cA0dnWmJiYhAZGYnU1FQUFxdjyZIl2ucePHgAlUoFNzc3CSM0Ttu3b8eOHTvg6emJgIAAPP3002jUqJHUYZmckydP4r///S9Gjx6tU0XuwYMHePXVV3H9+nUApb9Fs2fPxty5c6UK1WQlJydj165dePDgAbp06YKJEydCJjN4DjuRQRUUFODs2bPVHicB4O9SHRs+fDgGDhyIgIAADBs2DObmkk25EElu4MCBGD9+PPz9/dG5c2epwzFJ7IP6YcyYMQgICICfnx+cnZ2lDsdkrVixAqtXr8agQYMQEBCAoUOHwszMTOqwiAyuoKAASUlJcHR0hKurq85zBw4cwH//+1/t+fNbb72FZs2aSRSpceMYXf/k5uaisLCwyvNnznHXveTkZDRt2rRGbdPT05GcnKzniOoPgy+hUhGVSoVff/0V69atQ3Z2NoDSiW4LCwsEBQXhtddeg729vcRREknniy++wLZt27Bu3ToMHTpUu33lypX4v//7P522VlZWCA0NRdu2bQ0cpXHz8vKCt7c3tm3bVm3bgIAAxMbG4sqVKwaIzPT8+OOPWL16NYqLi7Xb/jmUCYIAURQhCIL2AirVrbLKWefPnweACr/vDz/8ECEhIfj111/Rs2dPqUI1Sl988QV2796NnJwc7THT8OHDERAQwCQ+A1qwYAH++OMPbN++HV27dtVuf+edd7B3715YW1ujSZMmSExMBABs3LgRAwYMkCpco/Xbb79h5cqVmDNnDl588UXt9osXL2L69OnaE3BBENCvXz9s3LiRSRxktNavX49169bpHCdVhMdJ+tGlSxeoVCoIggBHR0dMmDAB/v7+aNeundShGa3333//kd9DEASdJGSqG15eXhAEQfvngIAAjB8/Hg0bNpQ4MtPBPqgfyvrBzMwMQ4cORUBAAAYPHszjUQN77bXXcOLECe047ezsrB2n27RpI3V4JuX06dM4evQoEhISqrxYKggCfv75ZwNHZ/zWrFmDtWvX4vPPP0dAQIB2e3h4ON5//31tfwiCgGbNmiE8PBwNGjSQKlyjxTG6frh37x7WrFmD48ePIz8/v8q2giDg2rVrBorMdNTmutszzzyDq1evmsx1t3qRwFGmoKAA//d//4ctW7agpKQEQOlO4eTkhFOnTkkcHZF0/Pz8cO/ePURHR2uzwxUKBQYMGICioiIsX74cPXv2xNq1a7F9+3ZMnjwZixYtkjhq41LTgSQuLg4TJ06Es7Mzjhw5YqDoTEdoaCgWLlwIoLRPunXrBhcXlyonPnhnad3Lzc3FpEmTtBmy/fv3R0REBNLS0nQuBJ07dw7PP/88ZsyYgfnz50sYsXFSKpU4fPgwQkJCEBERAY1GA0EQ0LRpU0yaNAkTJ05kJTM9Gz16NDIzMxEVFaXdlpeXh/79+8PW1hY7d+5Es2bNEBISgg8++AAjR47EmjVrJIzYOM2aNQvHjx/Hn3/+qXM3xHPPPYcLFy6gY8eO6Ny5M/7880/k5OTgs88+w+TJkyWM2HSkp6drKzT16dNH6nCM3rZt2/D5558DAJo0aYIOHTrA2dlZOzFYkS+//NJQ4ZmErKwshIeHIzQ0FLdv39Z+9927d4e/vz/Gjh0LOzs7iaM0Ll5eXo/8Hkxm0o/r168jODgYu3fvRm5urjbpeNSoUfD390f//v2lDtHosQ/qh0OHDiEkJAQnT57UJg+4uLhg4sSJmDRpElq3bi11iCYjMzMTYWFhCA0NRVxcnHac7tmzJwICAjBmzBjY2NhIHKXxUigUePPNN3H06FEA1Vcv5visH1OmTMGlS5dw5swZnZumR4wYgZSUFDz77LPo2bMnfvnlF1y+fBmvvfYa3njjDQkjNk4co6V3/fp1BAUFVVu18mGxsbF6jsr01PS6W1ZWFkaNGgVbW1ucOHHCQNFJq14lcABAUVERTp06hU8++QRZWVm8M6ieuXPnDn7++WdERkYiLS0NJSUlOllnwcHBSE1NxbRp0zgxVYcGDBgAe3t7HDhwQLvt7NmzePHFF/HEE09g9erVAIDi4mL069cPjRs3xsGDB6UK1yj8/PPP2LJli/ZxcnIyrKys4OLiUulrSkpKkJmZCQAIDAxkEo0eTJw4EbGxsViwYAGmTZsmdTgm65tvvsGGDRswYsQILF++HDY2NpgyZQouXLigM15rNBr06tULbdq0QWhoqIQRG7+0tDSEhoYiPDwc8fHxEAQBgiDAx8cHgYGBGDVqFCwtLaUO0+j4+PjA1dUVf/zxh3bb4cOHMXfuXDzzzDP47LPPAJROTA0YMABmZmYmc5JhSCNGjEBxcbFOwndKSgqGDx+Oli1bYu/evTA3N8fFixfxzDPPoE+fPti6dauEERu/4OBgbNy4EfHx8QDK36ny9ddf48qVK1i2bFm5sr30740ZMwb37t3D3Llz8eqrr7IsuMQuXbqE4OBg7N27FwUFBRAEAdbW1hgzZgwmTZqkszQm/XthYWF18j4TJ06sk/eh8hQKhTbp+PTp09qk42bNmsHf3x+TJk1ieXY9Yx/UDxkZGQgNDUVoaCju3bunTR7o3bs3/P39MXr0aFhbW0scpemIiYlBcHAw9u3bB7lcDkEQYGtrizFjxsDf359VRPWgrJK0jY0NAgIC0KNHDzg7O1d5U5aPj48BIzQNQ4YMgUwmw19//aXddv36dUycOBE9e/bEb7/9BqB0nmn48OFo3759nR1vUXkco6UzY8YMnDp1Cp07d8abb76Jzp07c6kzAwgLC9P5TYmMjISDgwM6duxY6WtKSkpw+/ZtFBYWYuzYsfjmm28MEarkJFuQVS6X486dO7h9+zZu376NO3fu4NatW0hNTa1xthMZVmhoKD799FMolUqdUloPy8vLw9q1a9GmTRuMHTtWijCNUm5ubrn1taKjoyEIAgYNGqTdZm1tjVatWiEuLs7QIRqd/Px8nfW0BEFASUlJjdbYGjBgAN5++219hmey4uLi4OzszOQNiR0+fBgWFhZYvHhxlXenyGQyuLu7a5ePIP1xdXXFa6+9htdeew1RUVEICQnBgQMHcPbsWZw9exYNGjTAU089hYCAgDq5U5VKFRYWlkuMOX/+vHapjjKCIMDNzY2Z+nqSlZUFDw8PnW1nz54FUHpB29y89JSne/fuaN68OW7evGnwGE3JwoULERYWBlEUYW5uDkEQoFKpdNp06NABmzdvxuHDhzF16lSJIjU+ycnJcHFxwZw5c6QOhQB069YN3bp1wwcffIB9+/YhNDQUUVFRCA0NRVhYGFq1aoWAgAD4+flVmSROVWPiRf1naWmJsWPHYuzYsUhNTUVISAjCw8ORmJiI7777DmvXrkW/fv0QEBCAkSNHwsLCQuqQjQ77oH5wcXHBzJkzMXPmTJw/fx7BwcHYv38/oqKicO7cOXzxxRcYO3YsAgIC0K1bN6nDNXo9evRAjx498OGHH2L//v0ICQnBuXPnEBISgpCQEHh4eCAwMBATJkyAk5OT1OEahT179kAmk2HDhg1MZJVQdnZ2uXmhc+fOAQBGjRql3ebq6orWrVtrk/JJPzhGS+f8+fOwsbHB5s2buXSNASUnJyMyMlL7WBAE5Ofn62yrjIeHh0ldd9N7AkdBQQFu376NW7duaRM27ty5g9TU1ArbM3mjfrp06RI++ugjAMCLL76IkSNH4ssvvyy35tPo0aPx9ddf488//2QCRx2ysbFBRkaGzrayAytvb2+d7ebm5rzbrg5MnDhRm+UtiiJefPFFtG/fHh9++GGF7QVBgJWVFdzd3eHo6GjIUE2KjY0NmjZtKnUYJi8lJQWtW7dGo0aNqm1rZ2eHoqIi/QdFWn369IGnpyfc3d2xbt06qFQq5Obm4pdffsG2bdvQq1cvvPPOO+XGD6q9hg0bIjk5WVsxDgDOnDkDoPz4rFarYWtra/AYTYFKpYJSqdTZduHCBW0Vmoe5uLggLS3NkOGZlN27dyM0NBSNGzfGokWLMHjwYAQFBeHChQs67YYPHw5BEHDkyBEmcNQhJycnJgLUQ1ZWVvDz84Ofnx+SkpKwfft2bN68GfHx8fjmm2+watUqDB06FFOnTtVJ/iMyRk2bNsWcOXMwZ84cREZGIiQkBHv27EFERAQiIiLQsGFDTJgwAc8++2y55EyqG+yD+qFXr17o1asXPvroI53kgR07dmDHjh1o164dAgMD4efnhwYNGkgdrlGztraGn58fxo8fjy1btmDFihVQq9WIi4vD119/jRUrVmDMmDGYO3cuWrZsKXW4j7W0tDS0aNGCyRsSk8lkyM/P19lWdqPoP/vG3t6+XDI+6Q/HaMOysLBAixYtmLxhYCNHjkTz5s0BlF53W7hwIVq3bo1Zs2ZV2L7sulvLli3RqVOnKpeINTZ1lsCRl5enk6RR9l96enqF7atK1GjSpAk8PT11/iNpbdy4ERqNBp9++imeeeYZAKUTUf/k5uYGFxcXXLp0ydAhGrW2bdvi4sWLiI6Ohre3tzZLzcXFBW3atNFpm5qaylJPdaB58+bagQQovSDaoUMHlu6TWM+ePREdHQ2VSqW9o5oMz8LCAgqFokZtMzMzddbUJP0RRREnTpxASEgIjhw5ApVKBVEU4eHhAX9/f2RkZGDnzp2Ijo5GUFAQVq9ejZEjR0od9mOta9euOHbsGP773//iueeew8mTJ3Ht2jV4enqicePGOm0TEhK4VISeNG7cGElJSSgsLNQmyZw4cQJmZmblSh8XFBTw5FyPfv/9dwiCgJUrV1Y5Mevg4MBqKHowePBg/PHHH5DL5VzOsh66ffs2goODsWvXLu1EeMOGDSGXy3H48GH8+eef6N+/P1asWMHfKTJ6ubm5uHHjBm7evKndH2QyGXJycvDzzz/jl19+gb+/Pz788EMuA6gn7IP6wcrKCg0bNkSDBg1gZmYGtVoNALh16xa+/PJLfPvtt5g5c2alFzXo0d25cwchISHYtWsXMjMzIYoiGjRogHHjxiEjIwN//fUXdu3ahYMHD2Ljxo1MPngETk5OnB+qB9zd3REXF4e0tDS4urqiqKgIJ0+ehI2NDTp37qzTNjMzkxVoJMAx2jA6duyIu3fvSh2GyfHy8tKpArRmzRp4eXmxsmIF6uTq18CBA5GZmVlue3XVNBwdHcslanh6ejKzuB46f/48GjRooE3eqIqrqytu375tgKhMx4QJExATE4NXX30Vffv2xcWLF6FWq+Hn56fTLiEhARkZGRg8eLA0gRqxjh07wszMDAqFggdGEpozZw6ee+45/PDDD5g7d67U4Zis1q1bIzY2FllZWVWeyCUkJCAxMRG+vr4GjM70JCYmIjg4GDt37kRaWhpEUYS1tTXGjBmDyZMn60wwvfPOO1i/fj3WrFmDtWvXMoHjEU2dOhVHjx7FokWLsGrVKuTn50MQhHIVBWJiYiCXy6tcz5H+PR8fH+zcuROff/45XnrpJezfvx/379+Hr6+vTtUThUKB+Ph4JofrUWxsLJo0aVKjiW0nJ6dy1fzo0cyZMwd//fUXPvroIyxZsgTW1tZSh2TyCgoKsGfPHoSEhODy5cvaik2+vr6YPHkyRo0aBblcjrCwMPz444+IiIjA0qVLsWTJEqlDNxqZmZm4fv06cnJyqryD9J/n1qQfJ0+eREhICP7880/t8rzu7u4IDAzEpEmTkJmZie3btyM0NBQ7duyAnZ0d3n33XanDNirsg/rh7t27CAkJwc6dO5GRkQFRFGFnZ4dx48YhICAAGRkZ2L59O44dO4ZVq1YBAJM46pBcLsfevXsREhKCixcvaq9heHt7IzAwEGPGjNHeuJiRkYGVK1ciJCQE33zzDX777TcpQ3+sDR06FKGhocjJyalRRVfSj2HDhuHWrVt49dVXMWnSJBw9ehRyuRzjxo3Tqeqdk5OD5OTkcjdFkP5wjDasGTNmYNasWdi1axeefvppqcMxWUeOHJE6hHqrThI4MjIyIAhCpQkbDg4OaNeuXblEDVYJeHzk5OSgffv2NWprSiVsDOWZZ55BVFQU9u7di0OHDgEoPal49dVXddrt2rULAFh6Vw+2bduGNm3a8KBIYk5OTli4cCGWLFmCy5cvY/LkyWjdujVsbGwqfY2bm5sBIzQNo0aNwpUrV7Bs2TJ8+eWXFbZRq9X4/PPPIQgCRo8ebeAIjV9JSQn27duHkJAQREdHQxRFiKKIDh06YPLkyXj66afh4OBQ7nWWlpaYO3cu9u7dizt37kgQuXEZNGgQPvnkE6xatQq5ubmwsrLCSy+9hGeffVanXUhICACgf//+UoRp9GbNmoUDBw4gPDwc4eHhAErvTnnttdd02p04cQIqlYoTUHpUUlKCFi1a1Kgtk2LrnqurK7Zu3Yr58+dj1KhRGD9+PNzd3atcvokXrfWjrOTxwYMHUVxcDFEU4eLigkmTJiEgIECnBLulpSWmT5+Op59+Gk8++SSOHj0qXeBGJDExEZ999hlOnTpVZbuypBruC/qTlJSE0NBQhIWFITU1FaIoatd7DwgI0Jm/cHFxwUcffYQpU6Zg0qRJ2LNnD8/B6wD7oH4oKirC3r17ERwcjJiYGAClv0E9evRAQEAAxo0bpzO3MXz4cERGRmLatGnYvn07EzjqwLlz5xASEoL9+/drx2dHR0f4+fkhMDCwXJVjoHSfWLx4Mc6cOYPY2FgJojYeb7zxBv766y8sXLgQy5cv5xKjEnn55Zdx4MABXL9+HUuWLIEoimjYsCHefPNNnXYHDx6EKIqsRq1nHKOlM3jwYCxcuBCffPIJrly5oj1P440QVF/UWf15URRhY2ODtm3blkvUaNq0aV19DEmkUaNGNV4vPDExkck5dUwmk2HFihV45ZVXcPfuXTRr1gw9evQolyzTsmVLvP/++7xYqgcuLi6wsLCQOgyTN2LECO2fjx8/juPHj1fZXhAE3t2rB0FBQQgODkZ4eDju37+PgIAAFBYWAgCuXr2KmzdvYuvWrdqlJPz9/SWO2PgMGDAAcrkcoijC1tYW48aNQ2BgILp161aj1zs7O7NMYB157rnn8Mwzz2gr0shksnJtXnrpJUydOhWtW7c2fIAmwMPDA1u3bsXatWtx7949uLm5Yfr06ejbt69Ou927d8PBwQGDBg2SKFLj17hxYyQmJlbbrri4GHFxcWjVqpUBojIt169fR0ZGBjIyMvDTTz9V254XrevWunXrEBYWhsTERIiiCJlMhkGDBmHy5MkYNmyYzl2N/+Ti4oL27dtrL+rRv5eeno7nnnsOmZmZ6NmzJ+Lj45GVlYWnn34aOTk5uHLlCjIzM2FtbY0nnniiyn6hf2/Xrl0ICQlBVFSUNtm4bdu2CAwMhJ+fX5V3X7dt2xYdO3bExYsXDRewEWIf1A/R0dHapIGioiLtxdKnn34akydPrrI6nI+PDzp27Ijr168bMGLj9OSTTyIhIUGbuNevXz8EBgZi5MiRNZrvc3NzQ0pKigEiNV4nTpzAs88+i++//x5PPPEExo0bh1atWjHZ2MAaNGiAkJAQBAcH4+7du3Bzc4O/vz9cXFx02iUnJ2PEiBF44oknJIrUuHGMrh/Gjx+PiIgIbN26FVu3bq2yLa8z6JdcLseZM2eQmJionfOuiCAImDNnjoGjk0adJHB8//338PT0hLu7e128HdVDXbt2xdGjRxEdHQ1vb+9K2x0+fBi5ublcwkNPOnbsWGX5dZZ60p/+/ftjz549yM3N5ZrUEqpuaa5HbU81Y2tri40bN+K1117DmTNncPbsWe1zAQEBAKA98fjhhx94h7UeFBQUoFu3bggMDMS4ceNqfefKBx98gLy8PD1FZ3pkMlm5yY6HtW3b1oDRmKYuXbpg3bp1VbZZuXKlgaIxXT4+PggPD0dYWFiV65du27YNCoWCVWnq2OHDh/HOO+9AFEVYWVmhRYsWXLPawL799lsApRd5yqpt1OaGli5dusDcvM7uszFZGzduREZGBubOnYu5c+diypQpyMrKwtKlSwGUVooLCwvD4sWLkZmZifXr10scsXFasGABAMDGxgajR49GYGAgevXqVePXN2vWDA8ePNBXeCaBfVA/TJ06VVu52sfHB4GBgXjyySdrfJ5sY2MDtVqt5yiNX3x8PJo0aaIdn2taNa7MzJkzMWnSJD1FZxree+897b6QkZGBLVu2VPsaJnDoh729PV566aUq27z99tuGCcZEcYyWXnJyMoKCgnD//v0aXUPgdQb9+fHHH7F69WoUFxdrt/3z+y4bP0wpgUMQ+a+OauDo0aN49dVX4eHhge+//x4eHh6YMmUKLly4oM0Cv3LlCmbNmoWsrCz88ssvVSZ60KMRRRHZ2dkoLi7m8hAGkpycjIkTJ8Lb2xsrVqyocskOIlOhUCgQHByMgwcP4saNG8jPz4etrS08PT0xevRoTJ48WbtuLNWtGzduoEOHDlKHQUSk49atW5g4cSIsLS3x8ccfY/z48XjhhRe05wwKhQLbtm3DN998A3Nzc+zevbvWk+dUuYCAAFy9ehWBgYGYP39+hUtpkX69/vrrCAwMxKBBg7i0qITGjBmD1NRUREREwMbGptzcRZn9+/fjrbfewrx58/Dyyy9LFK3xmjhxIiZPnoynnnoK9vb2UodjktgH9cOAAQMwceJEBAYGsvqYhI4cOYKhQ4dWWDGRDCMoKKjWr6nujniixxXHaOn95z//wZ49e+Du7o4ZM2agU6dOcHJyqvI8rnnz5gaM0DSEhoZi4cKFAAAvLy9069YNLi4uVY7Xc+fONVR4kmICB9XY+++/j7CwMFhZWaF37964efMmMjIyMGXKFNy8eRPR0dHQaDR4/vnn8eGHH0odrlE6ffo0Nm7ciPPnz6O4uLhc2ab169fj7t27ePfdd6sss0W1Fx4ejri4OGzatAmOjo4YPXo02rRpwzJ/REQmYs2aNQAAR0dHTJ06VWdbTZlSlrhUYmJiEBkZidTUVBQXF2PJkiXa5x48eACVSsXkVz3bvn07Pv30U4iiqF07tri4GO3atUNiYiJKSkogk8nw5ZdfsnpcHevZsyesra0RERHB5AEyaT169ECLFi2we/duAMDzzz+P6OhoXLp0qVyZ/CFDhqBRo0bYuXOnFKESkQlQqVSsrkRERFTP9O/fHwUFBThw4ACaNWsmdTgma+LEiYiNjcWCBQswbdo0qcOpV3j0SDW2ZMkSNG/eHJs2bcKpU6e027dt2wYAsLKywiuvvGIy2U+GtmbNGqxdu7bKUk0ODg4IDw9Hnz59WNavjv2zzF/Zv/uqMIGDiMh4rFmzBoIgwMPDQyeBo2xsqIoplvkztJSUFMyfPx/nz58HAO33/XACx+rVqxESEoJff/0VPXv2lCpUozd58mS0bNkS33zzDS5fvqzdfuvWLQBAp06dsGDBAvTt21eqEI2WjY0N3NzcmLxBJs/MzEynClzZXY2ZmZnllrRxcXFBXFycQeMjItPC5A0ikkrZUuxt2rTBnj17dLbV1D9vICUyFkVFRWjTpg2TNyQWFxcHZ2dnJm9UgEeQVGOCIGDu3Ll4/vnncezYsXLl8ocNGwZnZ2epwzRKJ06cwJo1a2BnZ4e33noLI0eOxDvvvIOYmBiddk888QQ+++wzHD58mAkcdaxPnz5Sh0D/kJKSglOnTiEuLg5yuRx2dnZo06YNBgwYwLuryWTExMRg586duH79OrKzs6FSqSpsJwgCDh8+bODojEtZgqqjo2O5bSSt3NxcBAUFITk5GU2bNkX//v0RERGBtLQ0nXZ+fn4IDg7G4cOHmcChZ3379sWOHTuQlpaG2NhY5OXlwdbWFu3bt4e7u7vU4RmtPn36ICIiAgqFApaWllKHY/IyMzNx/fp15OTkVDo+A0z61oemTZsiIyND+7hly5YAgAsXLmDMmDHa7QqFAgkJCUx6MgBRFHHv3r1q9weed+sP+6D+UCgU1fYD5zT0JzU1Fbt379aO0UqlssJ2giDg559/NnB0RHWr7GYTjUZTbltt34P0h2O0NNq1a4ecnBypwzB5NjY25ZLsqRQTOKjWGjVqhAkTJkgdhknZunUrBEHAV199hVGjRgFAhZNMzs7OaNasGWJjYw0dotHjmov1h1wuxxdffIFdu3ZpT0DK7rQGAJlMhgkTJuCDDz6AnZ2dlKEahffff/+R3+Ofd8FT3Vi2bBk2b95co5NpXph4dBUlazCBo37YuHEjkpOTMWLECCxfvhw2NjaYMmVKuQSOXr16wdraGqdPn5YoUtPj6uoKV1dXqcMwGW+88QaOHz+O5cuXa9eQJcNLTEzEZ599plO1siJlx69M4Kh7Xl5eOHDggDbJe8iQIdiyZQtWrVqF9u3bo23btigpKcGiRYuQn5/PikB6lJeXh2+++QZ//PEHioqKqmzLO3z1g31QP6hUKmzevBk7d+7E3bt3qzyHYz/oz7Zt2/DVV19BqVRqz5Ef7ouHt/EcWn+USiX27t2LEydOlLspa/DgwRgzZky5Jc/o36noGgGvG9QfHKOlNXXqVLz33ns4deoUBgwYIHU4Jqtnz56Ijo7mknMV4LdB9Bi4dOkSnJyctMkbVXFxccGNGzcMEBWR4SmVSrz88suIiYmBKIrw8PCAp6cnGjdujPT0dNy6dQt3795FWFgY7t27h59//pknfY8oLCzskd+DCRx17+DBg9i0aRM8PDzw8ccf45tvvsHVq1dx8OBB5OTk4OLFi9i6dSvS0tLwwQcfoH///lKHTKQ3hw8fhoWFBRYvXgwbG5tK28lkMri7uyMxMdGA0REZTlZWFubOnYuVK1fi3LlzmDRpEtzd3WFra1vpa3gXV91KT0/Hc889h8zMTPTs2RPx8fHIysrC008/jZycHFy5cgWZmZmwtrbGE088ATMzM6lDNkrDhw/Hnj17cPz4cYwZMwYDBgyAr68vzp49i/Hjx6Nhw4YoKCiAWq2Gubk5Zs+eLXXIRqmgoADPPPMM7t27B1dXV8hkMsjlcnh7eyMnJwf37t2DSqWCtbU1unbtKnW4Rol9UD8oFApMmzYN58+fh5mZGczNzaFQKNCsWTPk5uaisLAQAGBpaQkXFxeJozVeZ86cwRdffAEnJye89dZb2LJlC27fvo2ffvpJew4dGhqKkpISzJ8/H56enlKHbJRu3bqFN954A/fu3SuXyHTt2jXs2bMHP/zwA1avXo127dpJFCWR/nGMlp6fnx9u376Nt956C3PnzoW/v7926UUynDlz5uC5557DDz/8wBvl/oEJHPSvlJ1gVJUxznJ/dUcul9f4xEGtVkMmk+k5IiJp/Pbbb7hw4QKaNGmCRYsWYejQoeXaHDt2DJ988gkuXLiA//73vwgKCjJ8oEbkyy+/lDoEqsDvv/8OQRCwYsUKdOzYUVsq393dHe7u7ujatSsmT56M119/HZ9//jm2b98uccSmKS0tDWlpaWjbti0rAulRSkoKWrdujUaNGlXb1s7Orto7W6huxMbGIjExEXK5vMp2rD5Qd4KCgiAIAkRRxPXr17F48eIq2/Murrq3ceNGZGRkYO7cuZg7dy6mTJmCrKwsLF26FEDpuVpYWBgWL16MzMxMrF+/XuKIjdPIkSOxbds2nfWsv//+e3z11VfYs2ePtlRyhw4dMG/ePPj4+EgUqXHbvHkz7t69i2effRaffvoppkyZggsXLuCXX34BUHrX6Y8//oj169ejZcuW1f5mUe2xD+qHbdu2ITo6GqNGjcKyZcswffp0XLhwAX/99RcA4ObNm9i4cSP++OMPBAQEMKlMT7Zs2QIAWLFiBXx9fbU3q5RVYRo9ejReeeUVzJo1C6tWrUJoaKhksRqrrKwsTJs2DRkZGbCxscFTTz2FDh06wMXFBRkZGbh58yZ27dqFuLg4TJs2DTt37oSTk5PUYRPpBcdo6Y0YMQIAUFRUhK+++gpfffUVHB0dK70xiMtT64eTkxMWLlyIJUuW4PLly5g8eTJat25d5Q1apnLtmQkcVGP37t3DmjVrcPz4ceTn51fZlpOBdcvJyQnJycnVtlOpVNqsTdIPlvmT1u7duyEIAtatW4fOnTtX2GbIkCFYu3Yt/P398ccffzCB4xFNnDhR6hCoAlevXoWrqys6duyos/3hUq+WlpZYsmQJhgwZgnXr1uHbb7+VIlSjdunSJezZswf9+vXTSSgrKCjAvHnzcOzYMQCAtbU1PvzwQ/j7+0sUqXGzsLCAQqGoUdvMzEzeUaFne/bswbJly8otYVMZJnDUHVOZxKjPjh8/DhsbG8yYMaPC583MzBAQEAB7e3u89dZb+PHHH/Hyyy8bOErjZ2VlBW9vb51tdnZ2+Pzzz/Hpp58iKysLNjY2HA/07M8//4SlpSXefvvtCp9v0KAB3nzzTbi4uOCLL75Ajx49EBgYaOAojRv7oH7Yu3cvzM3N8eGHH8La2rrc8+3bt8fXX38NNzc3fPfdd/D09KxRBV6qnUuXLsHZ2Rm+vr6VtnFycsKKFSvw5JNP4vvvv+cNLXWsLNG1T58++PbbbytMznjrrbfw5ptvIioqCps2bcL8+fMliNS4qdVqFBUVwcLCAlZWVjrPXbp0Cdu3b8eDBw/QpUsXTJ8+ncdLesIxWnoVXW/LysqqtD2X1tKPskQaoPR8+vjx41W2N6Vrz0zgoBq5fv06goKCIJfLq6y6UaYmbajmevXqhf379+PIkSMYPnx4pe127tyJwsLCKk9G6N9jmT/p3blzBx4eHpUmb5Tp3Lkz2rRpgzt37hgoMiLDKigogLu7u/Zx2Um3XC7XObl2dnZG+/btER0dbfAYTUFwcDB27NhRbmxetmwZjh49CqA0kaaoqAgfffQRPD090a1bNwkiNW6tW7dGbGwssrKyqrxDKyEhAYmJiTxO0qO9e/di3rx5EEURVlZWaN68OZydnaUOy2QcOXJE6hBM3v3799GiRQvt3UJllRGVSqVOkvfo0aPh6uqKP/74gwkcBmZmZobGjRtLHYZJSEhIgJubGxo2bAjgf/vDP9e3njJlCtauXYsdO3bwwkQdYx/UD3FxcXBzc9PebFV2AUitVusspTVnzhxs27YNW7duZQKHHuTk5KBDhw7ax2XjcmFhoc5yc+7u7mjXrh1Onz5t8BiN3V9//QULCwusWrWq0vM2R0dHrFixAkOHDsWRI0eYwKEHmzdvxooVK/D+++/jhRde0G4/duwY5syZA7VaDVEUceLECRw5cgS///57uUQPenQco6VXVpmJpFXba8mmdO2ZCRxUI8uXL0dBQQE6d+6MN998E507d+ZkrAEFBQVh3759+Pjjj+Hg4FDhWtUHDx7E4sWLYWZmhueff16CKI0by/zVD2Vr/9WEtbU1VCqVniMyTUqlEunp6bCxsYGjo2Ol7bKzs1FUVIQmTZronHzQo3N2dtZZlqDs9+bevXvo0qWLTtvCwkLk5uYaND5Tcf78eVhbW+skBBQVFWHnzp2wsbHBli1b0LlzZ6xbtw7fffcdtmzZguXLl0sYsXEaNWoUrly5gmXLllV6l5xarcbnn38OQRAwevRoA0doOjZs2AAACAgIwIIFC9CgQQOJIyIyLDMzM50J7rKkyszMTDRt2lSnrYuLC+Li4gwaH5GhOTg4aP9cltiUnZ2tk0QjCALc3Ny4P+gJ+0B6KpVKZ6m/sn7Izc3VmTeysLBAq1atcOPGDUOHaBIaNWqkU7WvbC4jKSkJ7du312mr0WiQmZlp0PhMQUpKCjw9Pau9puDi4oL27dvzpiw9OXXqFARBwPjx43W2L1++HCqVCkOHDkX37t0RFhaGGzdu4Jdffqm0uhw9Go7R0uIyivVDbGys1CHUW7yaQjVy/vx52NjYYPPmzdqsQDKcXr16Yfbs2fj+++/xwgsvoGXLltp1e1999VXcunULKSkpEEUR8+bNK3fiQY+OZf7qBzc3N9y6davau6yzsrJw69YtNG/e3IDRmY7g4GAsWrQICxYswLRp0yptFx4ejq+//hqLFi1ilngda968OW7duqV93LVrV+zZswfh4eE6CRyXL19GfHy8zhrwVHcyMjLKfbdRUVEoLi7GhAkT0LVrVwDArFmz8PPPP+P8+fNShGn0goKCEBwcjPDwcNy/fx8BAQEoLCwEULrc0M2bN7F161Zcu3YNnp6eXMpGj+7cuYNGjRppk2WITE3Tpk2RkZGhfdyyZUsAwIULFzBmzBjtdoVCgYSEBO4nelZQUICzZ88iMTGx2mqic+fONWBkpqFJkyY6F0BbtGgBoLR65ZAhQ7TbNRoNUlJSmHyvB+yD+qFJkybIzs7WPi47f7h58yb69u2r0/bBgwcoKioyaHymolmzZkhISNA+7tixIw4cOIBDhw7pzKPeu3cP9+7dq/JmFfp3zM3NUVxcXKO2JSUlvBFIT+Lj4+Hi4qIzrxoXF4dbt26hQ4cO+OGHHwCUVowbO3YsDh48yAQOPeAYTUTVkUkdAD0eLCws4OHhweQNCb3xxhv48ssv0aRJE8THxyM3NxeiKOLo0aNITk6Gs7MzvvrqK5bg1ZPalPkzMzNjCWs9GTJkCJRKJebNm4e8vLwK2+Tl5WHevHlQqVQYNmyYgSM0DQcPHoRMJsPEiROrbOfn5wdBELB//34DRWY6+vfvj4KCAm2W8vjx42FjY4Nt27bh7bffxrZt27By5UrtSfbDF42o7hQUFOiU2wVKk14FQcCAAQO028zNzdGiRQukp6cbOkSTYGtri40bN8LDwwNnzpzB/PnztXcuBgQEYOHChbh27Rratm2LH374AZaWlhJHbLwaNGiA5s2b86I0mSwvLy9kZmZqq2QNGTIEoihi1apV2rtIS0pK8NlnnyE/P5/LaunR+vXrMWjQIMydOxdLly7FmjVrsHbt2nL/lW2nute+fXukp6dr73jv378/RFHE6tWrdarDfffdd8jKytJZ2oDqBvugfvDw8EB6ejrUajUAoHfv3hBFERs2bNCpCLF9+3Y8ePAAHh4eUoVq1Hx9fZGXl4f4+HgAwLhx42BmZobvv/8ey5cvx9GjR7Fjxw7MmDEDarW6yiWs6d9p06YN7t69W+3d1rGxsbhz5w7atGljoMhMS3Z2tnZJpzJRUVEAoFOt0sPDAy1btmQlFD3hGF2/5efnY9euXdi4cSOX1CLJMI2RaqRjx464e/eu1GGYvIkTJ+Kpp55CTEwMbty4gfz8fNja2sLT0xPe3t68IKFHLPNXP7zyyivYtWsXTp8+jWHDhmHChAnw9PTULmVz69Yt7Ny5E4WFhXB2dsYrr7widchGKS4uDq6urjplYCvi6OiIpk2bssyfHjz55JOIjo5GQkICvLy84OLigsWLF+Pdd9/Fvn37sH//fu1dpr1798brr78uccTGyc7ODqmpqTrbzp49CwDw9vYu157jtP60atUK4eHhCA4OxsGDB8sdJ40ePRqTJ0/m2r165uvri+PHj0OhUPDfu0Ty8vKwefNmHDt2DAkJCdpqNBURBAHXrl0zYHTGb/jw4dizZw+OHz+OMWPGYMCAAfD19cXZs2cxfvx4NGzYEAUFBVCr1TA3N8fs2bOlDtkobdu2DStWrABQendjhw4d4OzszOQyAxsyZAgOHTqEiIgIDB06FMOHD0eHDh1w9epVDB06FG3atEFmZibS0tIgCAJvRtED9kH9MGTIEBw/fhyRkZHo168fRo8ejW+//RYREREYPXo0unTpgvT0dMTExEAQBEydOlXqkI3SE088gYMHD+LChQto1aoVWrRogXnz5uGrr77Cpk2bsGnTJgCAKIrw8PDAW2+9JW3ARmjcuHG4fPkyZs+ejcWLF6Nfv37l2kRERODDDz+scIkPqhsajaZcpZ+yG1F69+6ts71Ro0ZITk42ZHgmg2O09Pbu3YsNGzZgypQpOpWj4+LiMH36dKSlpWm3+fn5VbpkL9WdmJgYREZGIjU1FcXFxViyZIn2uQcPHkClUsHNzU3CCA1LEKuqIUn0t+PHj2PWrFlYunQpnn76aanDITI4b29vuLq6Yu/evdW2HTduHFJTUxEdHW2AyExPbGwsXn/9dSQmJlY4CSuKIlq2bInVq1fDy8tLggiNX9euXdGxY0ds37692raBgYG4ceMGLl26ZIDIKCkpCXv37kVSUhJsbGzQp08fDB8+HDIZi67pw4svvojIyEh89913GDlyJK5fv45JkyahRYsWOHTokE5bX19fNGrUCAcOHJAoWiL9u3PnDp555hlMmjQJCxculDock5OamoopU6bg/v37VS4V8TCuN1u3SkpKcOXKFTRr1kw7sSSXy/HVV19hz5492oSaDh06YN68eRg0aJCU4RqtMWPG4N69e5g7dy5effVVmJmZSR2SScrPz8dff/0FLy8v7fIEDx48wHvvvYeIiAhtu0aNGuE///kPl1zUA/ZB/ZCRkYEdO3bA19cXvXr1AgDcunULb7zxhs7Ncubm5pgxYwbefvttqUI1SRcuXEB4eLj2HLp3796YPHlyuUqL9OgUCgWmTp2Ky5cvQxAEtGvXDu3atYOzszMyMzNx69Yt3LlzB6Ioonv37vjll19gYWEhddhG58knn0RKSgpOnjyJhg0bQqVSYfDgwZDL5YiKitJJxB89ejTkcjlOnDghYcTGiWO09N544w0cOnQIu3fvRtu2bbXbX3nlFZw4cQJNmjRB27Ztcf78eSgUCnzzzTcYO3ashBEbr5SUFMyfP1+79LQoihAEAdevX9e2+fDDDxESEoJff/0VPXv2lCpUg2ICB9XY1q1bsWLFCgQGBiIgIAAtW7aEtbW11GERGURgYCCuXLmCsLCwKpMCYmNj4efnh65du2LHjh0GjNC0KBQK7N27F8ePH8fdu3chl8thZ2cHDw8PDB48GGPHjuWdv3rUv39/mJmZ1egEbtCgQVAqlThz5owBIiMyrN27d2PevHkwNzdH+/btcffuXRQXF+Ptt9/GzJkzte1u3LiBCRMmYNSoUfjuu+8kjNg4RUVFwcHBoUZJe7GxscjPz0efPn0MEJlpOn/+PObNm4eGDRvC398f7u7uVU6Asy/qzoIFC7Br1y60bdsWb7/9Nrp37w4XFxdWHagn1Go1srKyYGNjA3t7e6nDMWrdunVDw4YNebGhHktPT0dycjKsra3Rrl07mJuzQLChsQ+kp9FocPnyZSQlJcHa2ho9evSotuor0eMuPz8fn332Gfbu3QuNRgOgtCpc2SUqmUyGcePG4eOPP4aDg4OUoRqtzz//HNu2bcOAAQMwdepUHDp0CGFhYRg2bBjWrVunbSeXy+Hj44NOnTpxjtvAOEYbxhNPPIHc3FxtJV2gNOly8ODBcHZ2xr59+2Bvb4+jR4/i1VdfxcCBA7Fx40YJIzZOubm5mDRpEpKTk9G0aVP0798fERERSEtL00ngOHfuHJ5//nnMmDED8+fPlzBiw+GeTzU2fvx4REREYOvWrdi6dWuVbVmO999LSUmpk/cxpVJChsAyf/WLpaUl/Pz84OfnJ3UoJqljx46IiIjA6dOnK9wXypw+fRrp6elVtiF6nI0fPx43btzA5s2btcc948ePx/Tp03XahYeHAwD69u1r6BBNQlBQEHr37o1ffvml2raLFy9GdHQ0j1P1rFGjRrh+/ToWL15cZTueM9StkydPwsLCAps2bULTpk2lDof+wczMDI0bN5Y6DJPg5OQEFxcXqcOgKjRu3Jj7g8TYB9KTyWTo3r07unfvLnUoRAbj4OCA5cuX46233sLJkyfL3ZQ1cOBAtGjRQuowjdrMmTOxb98+nDp1ChERERBFEZaWluWW3v3rr7+gVqvLLatC+scx2jCysrLK/d5ERkZCo9Fg3Lhx2qT7oUOHokmTJpy70JONGzciOTkZI0aMwPLly2FjY4MpU6boLGEDAL169YK1tTVOnz4tUaSGxwQOqpHk5GQEBQXVuBwvC7v8e8OHD3/ku+Q4GV73pkyZgj179uDy5cuYPn16tWX+pkyZInXIRHrj5+eHU6dO4b333sP//d//VXjXe2xsLN59910IgoAJEyZIEKVxGzlyJHx9fdGnTx/07duXF+ok9J///AfTp09HQkICmjVrhiZNmpRrM3jwYHh7e3PiQ49qc+zJ41T9OXfuHGbMmAGFQgFRFGFtbQ0nJyepwzIZBQUF8PDw4JggofDwcPj6+qJZs2ZSh2LSBg8ejD/++EN7QYikce7cOXTv3p3l7yXEPqgf7t+/z3GhHnjxxRfh6+sLHx8f7hf1QIsWLfDss89KHYZJcnV1RUhICDZt2oR79+7Bzc0NQUFB8PT01GkXGRkJLy8vDBs2TKJIjRvHaOkVFxeX23b+/HkIggAfHx+d7a6urjrVIKjuHD58GBYWFli8eDFsbGwqbSeTyeDu7o7ExEQDRictJnBQjaxYsQIpKSlwd3fHjBkz0KlTJzg5ObEcrx6wckb9ZGlpic2bN2vL/N26dQu3bt0qV+Zv/Pjx+Pjjj3nwRUZt/PjxCAsLQ0REBPz9/dG/f3/06NEDDg4OyM/PR0xMDCIiIqBWq9G/f38mcOhBUlISkpKSEBoaCqB08sPHxwe+vr7w9fWFq6urxBGaFkdHRzg6Olb6PKvQ1B85OTmwsrKSOgyj9e2336KkpARDhw7FggUL0KZNG6lDMiktWrSAUqmUOgyT9t5770EQBDRv3lx7kcjX15dJNQY2Z84c/PXXX/joo4+wZMkSLv0qkeeffx7W1tbo3r07L5pKhH1QPwwfPhwtWrRAnz59tOdrHBcM7+zZs4iMjAQAWFlZoUePHtpxunv37lyigExKs2bN8OGHH1bZZtGiRQaKxjRxjJaek5MTkpKSoFQqtd/7yZMnIQgCvL29ddqWlJRwGUw9SUlJQevWrdGoUaNq29rZ2aGoqEj/QdUTgshb0KgG+vfvj4KCAhw4cIBZ42TykpKSWObPAF544QUAQPPmzfHll1/qbKspQRDw888/13lsBBQWFuKDDz7Avn37AEAnoa/s0GLcuHFYtGgR73zUg9jYWJw5cwaRkZGIjo5Gbm4ugP/1g7u7u/YE0MfHhwkdevLxxx8jMDAQXbt2lToUk1JQUIC8vDzt4+HDh6Nr16749ttvK31NcXExoqKi8Mknn6Bdu3bYvXu3IUI1Od7e3pDJZDh16hQsLS2lDsfk/N///R9WrVqFP/74A+3atZM6HJM0e/bsSsflsjGZiZaGce/ePcyfPx+pqakYP3483N3dYWtrW2l7Ls1Y9yZMmICbN29CFEXtvmBtba29aFp2oYIXTfWHfVA/+Pj4aI9dy/qhRYsWOol+HBf07/Dhw9okjsr2i7I+6datG/cLItIrjtHSe/PNN3Hw4EHMnDkTr7zyCvbu3YuPP/4Y3bp1w/bt27Xt1Go1vL294e7ujj/++EPCiI1T79694ezsjAMHDmi3TZkyBRcuXChX9WTUqFHIz8/HmTNnDB2mJJjAQTXSs2dPtGrVSruGOxGRvpUty9GmTRvs3btXZ1tNCYLA8mZ6dv36dRw8eBB37txBQUEB7O3t4enpiVGjRtW6v+jfEUUR169fR2RkJM6cOYPo6Gjk5+cD+N8EYcuWLXUOhKlueHl5QRAEeHp6IiAgAE8//XSNMsbp0axZswZr167VPn54wqM6oijirbfewquvvqqv8Eyar68v3N3dERwcLHUoJkmlUuHll19Gamoqvv76a3Tr1k3qkEySKIqIjY3F2bNncfbsWURHR5e7cNeyZUvtxOxTTz0lZbhGa9++ffj666+Rmppao/Y8Z9CP3NxcREVFafeHW7dulbtQ0bNnT+3+0KtXL4kjNj7sA+k9PC5ERkbi3LlzVY4LTMDXv5ycHJ394vbt2+X2i169emHTpk0SR/r46tixI4DSOb09e/bobKspLhGuXyqVCgcOHMDZs2eRlpaG4uJinZvgrly5gqKiIm2SPtU9jtHSunjxIqZOnQq1Wq2zfeXKlRg9erT28enTpzFt2jRMmjQJS5YsMXSYRi8gIACxsbE4fvy4dgneihI4EhIS8MQTT8DX19dkbthlAgfVSGBgIHJycnDo0CGpQyEiE1FW2tLa2lp7AaJsW238c806ImMniiIuX76MjRs34tChQ9qTP16YqHtffPEFdu/ejZycHAiCAAsLCwwfPhwBAQEYOHCg1OEZre+++04ngePh5cwqY21tDXd3d4wfPx4vv/wyzMzM9B2mSXrllVdw9epVnDp1ikst6tn7779f4XaVSoV9+/ZBrVbDy8sLrVq1qnQdWUEQOAFlAGWJlg8ndOTn50MQBF6Y0JPDhw/j9ddfhyiKsLKyQosWLbSTgZXZunWrgaIzbTk5OYiMjNReyL59+7b2Oe4PhsE+kF5V4wLAfpBCdnY2oqKisHv3bp5D15GyG3o8PDy0lVv/zU0+sbGxdRoXlbp27RrefPNNJCUlac+l//lv/ssvv8SWLVuwefNmLgtrIByjDe/o0aNYsWIF7t27h2bNmuHll19GYGCgTpu33noL+/fvx/LlyzF+/HiJIjVe//d//4eVK1di4sSJ2irs/0zgUKvVePXVV3Hy5El8/PHHeO6556QM2WCYwEE1Eh4ejvfeew+bNm3CgAEDpA6HSDIKhQL79u3D8ePHyy2hMmjQIIwdO5Ylw8nopaWl8Y6gekYURVy9elVnErCwsFB7Iu7p6ckyf3qiVCpx+PBhhISEICIiAhqNBoIgoGnTppg0aRImTpzI5bX0zMvLC97e3ti2bZvUoZi88+fP44UXXsDbb7+NGTNmSB2OUSurAPQop/O8MGE4Go0GV65cwdmzZxEREYEzZ87w4pAeBQQE4OrVqwgMDMT8+fPh4OAgdUj0D5mZmTh79iz279/Pi6USYR/UDxqNBjExMfjpp5/YDxLIysrSXiw9e/Ys7t69qz22cnBwQFRUlMQREtW9tLQ0TJgwATk5OejSpQuGDRuGXbt2ISEhQee35+rVq/D398fUqVPx0UcfSRix6eEYXb8UFBRAFEXY2dmxGo0eFBYWYsKECUhKSoKvry8CAgKwceNG3LhxA8HBwbh58ya2bt2Ka9euwdPTEyEhISZz/Y0JHFRjy5cvx++//465c+fC398f9vb2UodkMkaMGFGr9oIg4PDhw3qKxnRduXIF77zzDhITEyucLBcEAe7u7li+fDlLVutJSkoKrKys4OzsXG3bzMxMlJSUwM3NzQCRmZbOnTtj4MCBCAgIwLBhw7geo0RiY2Nx5swZnD17FufOndOeUAClZUofXlO5urtOqW6kpaUhNDQU4eHhiI+P195d7ePjg8DAQIwaNcpkTjIMac2aNWjWrBn8/f2lDsXkpaSk4M8//8TXX3+NwYMHIyAgAC1btqy0AgQAjtP/0po1a+rkfebOnVsn70PlXbt2TXtB6Ny5c5DL5dpxum3btvD19YWvry+efPJJiSM1Pj179oS1tTUiIiJYDaieePiO0rNnz+LOnTsAoJ0M7927N3x9fTF9+nSJIzVe7IP6QRTFcuPDw4n37dq1g6+vLy+W6knZcgVl59Fld7eX7Qfe3t7ac+jOnTvzQh0ZpUWLFuHXX3/F5MmT8dlnn0EQhAqXKwAAb29vuLm58YYgPeMYTaYuPj4er732GuLi4io8fxNFEW3btsX69evRvHlzCSKUBhM4qEbKEgjS0tK0a0I5OjpWWY6XCQR1p6Yl5sruwmNGZt1LSEjAxIkTIZfLYW9vjwkTJqBt27ZwcXFBRkYG4uLiEB4ejoKCAtjZ2SE0NBStWrWSOmyj4+Xlhd69e+OXX36ptm1QUBCio6NZXk4PunTpApVKBUEQ4OjoiAkTJsDf3x/t2rWTOjST4evrq103WRRFtG7dWnshyMfHBy4uLhJHSFFRUQgJCcGBAwdQXFwMAGjQoAGeeuopBAQE/KvysUT1Hde1JlO3ZcsW7QW5vLw87QU5Dw8PncTKmiQj07/Xv39/uLm5ITg4WOpQTNqff/5Zbk13URRha2urc5G0S5cuvEiqJ+yD+iE2NlYnYSM/P18n8b6sH5h4r18TJ07EjRs3tPuBjY2Ndj/o27cvOnfuzGUW9Sw8PBzOzs4YNGhQtW1PnjyJjIwM+Pn56T8wEzNy5EhkZGTgzJkzsLa2BlB+uYIyfn5+SExMRHR0tBShGjWO0US6FAoFgoODcfDgQdy4cQP5+fmwtbWFp6cnRo8ejcmTJ8PKykrqMA2KCRxUI7W9yMAEgrqVnJxc6XNFRUW4d+8efv31V0RFReGjjz7CgAEDTCoTzRD+85//YM+ePRg6dCiWL19eYQWagoICzJs3D0ePHsW4cePwzTffSBCpcatNmfygoCCcO3eOv0V6kJWVhfDwcISGhuL27dvazNju3bvD398fY8eOhZ2dncRRGrey0vmurq6YNWsWxo8fz/Lg9VBOTg62bduGdevWQaVSabcLgoBevXrhnXfegbe3t4QRGp+UlBScOnUKcXFx2mXO2rRpgwEDBrDSgwFwXWsydWXjc5MmTTBkyBBt0kbjxo2lDs2kvPnmm4iIiMCpU6dY+UpCZfuDjY0Nevbsqb1A3aVLF14kNRD2Qf1Q1g8A0LJlS/j6+qJv375MvDewf55DBwQEwMLCQuqwTEptb8rinJ5+dO3aFW3btkV4eLh2W2UJHM888wyuXr2KK1euGDhK48cx2rDef/99AECTJk3w9ttv62yrKUEQsGTJkjqPjagyrHlONbJlyxapQzBp1SVjtGvXDiNHjsSKFSuwePFi/P777waKzHScPn0a1tbWWLZsWaXLB9nb22tLhkdERBg4QvonuVzOk3E9cXJywvTp0zF9+nRcunQJwcHB2Lt3L2JiYnDx4kUsWbIEY8aMwaRJk9C7d2+pwzVKffv2RUxMDFJTU7Fo0SJ88cUX8PLy0p7w9e7dm0k0EhFFESdOnEBISAiOHDkClUoFURTh4eEBf39/ZGRkYOfOnYiOjkZQUBBWr16NkSNHSh32Y08ul+OLL77Arl27oNFoAEBblQwAZDIZJkyYgA8++ID7hh4xGUNaI0aMQLdu3bBy5cpq277zzju4dOkSqybqgSiKSE9Px+XLl2Fra6ste8wlSA3njTfewPHjx7F8+XIsXLhQ6nBMmiiKsLCwgI2NjfY/XpQwLPZB/SCKIpo2bYqhQ4fC19cXffr0YQK+gbm7uyMxMVF7Dv3tt9/qVD9hRVHD4L3E0rO1tUV+fn6N2j548AANGzbUc0Smi2O04YSFhQEorXxVlsBRtq2mmMBBhsYKHERGRKFQoF+/fujXr1+drYtNpbp37w5PT88aleH19/fHnTt3EBMTo//ATExNKnAoFApERkZi1qxZaNGiBQ4cOGDACE1XSUkJ9u3bh9DQUERFRWkvnLZq1QoBAQHw8/Pj3UV1TKlU4uLFi9r1ey9evAiFQgFBEGBmZoZOnTppJ6O8vb0rXfaM6kZiYiKCg4Oxc+dOpKWlQRRFWFtb44knnsDkyZN1kpkUCgXWr1+PNWvWoGPHjrU+aSRdSqUSL7zwAmJiYrTJMp6enmjcuDHS09Nx69Yt3L17F4IgoGfPnvj555+Z4EdGiZXKpBcdHa0thRwTE4OSkhIIggCZTIaOHTvqJFra2tpKHa7RioqKwqVLl7By5Uq0b98ekyZNgru7e5XfeZ8+fQwYoWnYtWuXdn9ISkrSJlU2atRI56Jp27ZtJY7UeLEP6ofvv/8ekZGRuHDhgs64wAR8w0tNTdWeP0dGRmorHguCACcnJ539wsPDQ+JojU9tjlVHjx6NtLQ0XLhwwQCRmZYpU6bg4sWLOHTokLZKZUUVOGJjY+Hn54fBgwdj/fr1UoVrtDhGG1bZvJuDg4P2Jqp/Mxc3ceLEOo2LqCpM4CAyMgEBAUhKSsKZM2ekDsWojBs3DgqFAocOHaq27ahRo2BlZYXdu3cbIDLjtmbNGqxdu1b7+OG7qWvipZdewrvvvquP0KgKSUlJ2L59OzZv3gy1Wg0AMDMzw9ChQzF16lT069dP4giNk0KhwPnz5xEVFaVN6ChbtsPc3ByXL1+WOELjU5a4FBISgujoaO2apR06dMDkyZPx9NNPV3ln3dixY5GUlIRLly4ZMGrjs2XLFixZsgRNmjTBokWLMHTo0HJtjh07hk8++QRpaWlYuHAhgoKCDB8okZ7VZlKc5ZD1T6FQ6CRaXrp0SSfRskuXLvD19dXeAUZ1p6wkdU3PHQRBwLVr1wwQmelKSUlBZGQkzpw5g8jISKSkpAAo/e6dnZ21FykmT54scaTGi30gvbJxoeyC3T8T8Dt37qxdemvgwIFSh2sSkpOTtftEZGQk7t+/rx03GjdujOPHj0sc4eMtJSVFZ1nwoKAgtG/fHh999FGlrykuLkZUVBTWr1+PTp06ITQ01BChmpRt27bh888/x5AhQ/Ddd9/B0tKyXAJHQUEBpk2bhitXruDrr7/GU089JXHUxo1jNBEQERGBY8eOISEhAYWFhZVWbBIEAT///LOBo5MGEzjoX4mPjy+3tnirVq2kDosADBkyBNnZ2bwYVMfWr1+PlStXYuPGjRgwYECl7U6dOoUZM2bgP//5D1555RUDRmic1qxZo1NNpmwitjp2dnYYN24cFi5cCGtra32GSP9w+/ZtBAcHY9euXcjKygIANGzYEHK5HCqVCoIgoH///lixYgXLMOrJ3bt3cebMGRw7dgzHjh3TXrzgXdZ1r3fv3pDL5RBFEba2thg3bhwCAwPRrVu3Gr2ed8DXjcmTJ+Py5csIDg5G586dK2139epV+Pv7o1u3bti+fbsBIzRNKSkpOHXqVLlzhgEDBmjv9KK6VdMEjqysLIwaNQq2trY4ceKEgaIjhUKB6Oho/Pbbbzh06BDHZz0aPnx4rV9z5MgRPURClUlKSsLZs2dx5MgR7XfPRBrDYh9IT6FQ4MKFC9qEjvPnzwNgP0ihqKgIUVFRCA4O5hhdhx7lpixRFPHJJ5/gueee01d4JkupVOLZZ5/FtWvX0LZtWzz11FPYuXMn7t69i2+//RY3b95EcHAwUlNT0adPH2zZsqVWN9PRo+MYTaakqKgIb7zxBk6ePAmg+qW2TGl8Npc6AHq8hIWFYd26dUhMTCz3XMuWLfHaa6/Bz8/P8IERAGDv3r1IS0uDp6en1KEYnRkzZiAmJgZvvPEGXn/9dQQGBuqUtywsLMT27duxZs0ajBgxAjNmzJAwWuPx4osvakuTiaKIkSNHomvXrli1alWF7QVBgLW1NZycnAwYJRUUFGDPnj0ICQnB5cuXtSflZdnho0aNglwuR1hYGH788UdERERg6dKlXDewjsTHx+tk6mdkZAD43wGvq6srfHx8pAzRaBUUFKBbt24IDAzEuHHjal0O/4MPPkBeXp6eojMdd+7cgYeHR5XJGwDQuXNntGnTBnfu3DFQZKZJLpfjiy++wK5du6DRaADoTtbKZDJMmDABH3zwAUuFP6KwsLByZV9v3ryJF154odLXlJSU4Pbt2ygsLKywWg3VvXv37mlLtZ89exaZmZlc/13PmIxRfxUVFSE6Olq7P1y9ehVA9RO1VHfYB/VHcnIy7t27h3v37iE+Ph4A+8FQiouLcf78eW3yzJUrV7TVQ0VRhLm5Obp37y5xlI8/BwcHNGvWTPv4/v37sLCwqHR53bI5PXd3d4wfPx7jx483VKgmxcLCAhs2bMBbb72FyMhInTnWN998E0DpfuDr64tvv/2WyRsGxDGaTNHKlStx4sQJmJubY9SoUejUqROcnZ352wNW4KBa+OKLL7Bt2zbtgNGoUSPt2uI5OTkASg+0pk6dig8//FDCSI1PeHh4pc+JooiMjAzExMTgr7/+giiKeP/996ucuKXae+GFFyCKIi5cuAC1Wg1zc3M0a9YMTk5OyMrKQmpqKpRKJczNzdGjR48KBxhTKu+kL++//z48PDwwc+ZMqUMhAJGRkQgJCcHBgwdRXFwMURTh4uKCSZMmISAgAC1btiz3moyMDDz55JOwsrJCRESEBFEbh+DgYO0J3YMHDwD874SuSZMm6NOnD/r27QsfHx9WyNKjGzduoEOHDlKHYfK6d++Otm3b1qi87qRJk3Dnzh1cvHjRAJGZHqVSiRdeeAExMTEQRREeHh7w9PTUnjPcunULd+/ehSAI6NmzJ37++WdYWFhIHfZj699WKgMADw8PbNiwAS1atNBXeCYrMTFRu2xKZGQk0tPTAeiO0w+va13R8RKRsSgpKcH58+e1icaXL1+GWq3W7g/m5ubo2rUrfHx80LdvXy61qAfsg/ojISFBmzBQ0fjg6uqKPn36wNfXF4GBgVKGapROnz6tPYe+fPkyVCqVzn5QtrSZr68vevXqxWquelCb5f7IMI4fP44DBw7gxo0byM/Ph62tLTw9PTF69Oh/VdGMaodjtOFUdX2tNnjzet0bPHgwMjIysGnTJv4b/wcmcFCNHDlyBLNnz4a5uTmCgoIwffp0NG7cWPt8eno6fvzxR2zZsgVqtRrff/89hg0bJmHExqVsDd+qlN3ZGBQUhIULFxooMtPh5eX1yO9hSuWdyLitW7cOYWFhSExMhCiKkMlkGDhwICZPnoxhw4bBzMysytc/99xziImJ4f7wCB7+TXJxcdGulezr64vWrVtLFxiRBMaMGYOkpCQcO3asygpMWVlZGDJkCJo3b479+/cbMELTsWXLFixZsgRNmjTBokWLKqzwcOzYMXzyySdIS0vDwoULERQUZPhAjURsbKx2LBVFEQsXLkTr1q0xa9asCtsLggArKyu0bNkSnTp14h0tejBs2DCkpqYC+N8FucaNG2vHaB8fH47TZDKmTp2Ky5cvQ6lUVniR1MfHB7169YKNjY3EkRov9kH9sGDBAkRGRiItLQ1AxeODr68vE+/1rGxutazCRpcuXbTfP/cDwwgLC4OzszMGDx4sdShEkuMYbVg1ub5WE5zLrnvdu3dHs2bNOE9XAS6hQjXy22+/QRAELF68GBMmTCj3fOPGjbFgwQJ4eXlhwYIF+PXXX5nAUYf8/PyqHGBsbW3RqlUrDB06lHdw6cmXX34pdQhE9ca3334LAHBzc9NW22jatGmNX9+lSxeYm/MQ5FGMHTsWPj4+8PHxQZs2baQOh0hSQ4YMwU8//YR58+Zh1apVaNCgQbk2eXl5mDdvHlQqFY9R9Wj37t0QBAHr1q2rdEmbIUOGYO3atfD398cff/zBBI5H4OXlpZPQt2bNGnh5eWmXnyPDu3//PlxcXHQSNjw8PKQOi0gS0dHRMDc3R7du3bT7g7e3Ny9EGBD7oH7YtWsXAN3Ee44PhvfP/aC2y1/So+MxKtH/cIw2rOqur5F0mjdvXu3NoKaKFTioRvr27QsrKyscO3as2rZDhgxBSUkJzpw5Y4DIiMgUFRcX46+//sL169eRk5MDpVJZYTtBELBkyRIDR2f8Xn/9dQQGBmLQoEE8+CWTMWLEiEd+D0EQcPjw4TqIhh6WmZmJp556CtnZ2bC1tcWECRPg6ekJFxcXZGRk4NatW9i5cycKCwvh7OyMXbt2VVmpg/49b29vuLq6Yu/evdW2HTt2LNLS0hAdHW2AyIgMIy4ujomVBlY2Prdq1QqbN2/W2VZTHJ/148SJE7xIKjH2Qf3w3//+l4n3RESkg2M0Uanvv/8ea9euxf79++Hu7i51OPUKEzioRrp27QovLy/s2LGj2raBgYGIjY3F5cuXDRAZEZmaP//8EwsXLkReXp52W9lQ9nAyQdmyQixtRkR1gUtp1W+xsbF4/fXXkZiYWGFimSiKaNmyJVavXl0nfUkV6969O9q2bYvQ0NBq206aNAl37tzBxYsXDRCZafj4448RGBiIrl27Sh0KkcGU/aa3adNGmzxW2995js9ERESGExMTg507d+L69evIzs6GSqWqsB0TLPVHpVIhLCwMx44dQ0JCAgoLC1HZZUL2AxHpk0KhwIsvvoiCggIsXboUnTp1kjqkeoP1y6lGnJycEB8fD6VSCQsLi0rbKZVKxMfH865GMilqtRoxMTF48OABOnfuzGVs9OjatWt48803YWFhgVmzZmHfvn1ISEjA4sWLkZOTg4sXL+LIkSMwNzfH7Nmz0bhxY6lDNkpKpRLp6emwsbGBo6Njpe2ys7NRVFSEJk2acMkUPcrPz0diYmKVJ9wA0KdPHwNGZXz+/PNPqUOgKnh5eWHPnj3Yu3cvjh8/jrt370Iul8POzg4eHh4YPHgwxo4dC0tLS6lDNWpubm64desWsrKyqjwfyMrKwq1bt9C8eXMDRmf8tm/fjh07dsDT0xMBAQF4+umn0ahRI6nDItKrsvH54WNNjtlERFSd3Nzcas+h3dzcDBiRaVi2bBk2b95c5fdehhVf9SMvLw8vvfQSrl+/zn4gIslZWlri559/xjvvvIOAgAB4eXmhZcuWlS4nZEoV13k1hWqkd+/e2Lt3L1auXIkFCxZU2m7lypXIy8vDkCFDDBid8QsPD8f777+POXPmYO7cuZW2W7NmDdauXYtly5Zh/PjxBozQ+J08eRL//e9/MXr0aJ3v9sGDB3j11Ve1d2wJgoDZs2dX2U/0723atAlqtRorVqzAE088gcjISCQkJMDf31/b5s6dO3jttdfw22+/ISQkRMJojVdwcDAWLVqEBQsWYNq0aZW2Cw8Px9dff41FixYhMDDQgBGahgsXLmDZsmW4cOFCtW0FQcC1a9cMEJXx4oXm+s/S0hJ+fn7w8/OTOhSTNWTIEPz000+YN28eVq1ahQYNGpRrk5eXh3nz5kGlUmHYsGESRGm8nn/+eezevRs3b97El19+ieXLl2P48OEICAjAwIEDpQ7PpJw+fRpHjx6t0R2NP//8s4GjMy4Vjc8cs+uX+Pj4Gt/hayqTsYbGPqgf5HI5zpw5g8TERMjl8ir7Yc6cOQaOzjQkJiZi9erVOH78uE5V14rwHLruHTx4EJs2bYKHhwc+/vhjfPPNN7h69SoOHjyovSlr69atSEtLwwcffID+/ftLHbJRWrVqFa5du4bGjRtjxowZ6NGjB5ydnSGT/T97dx5XU/7/Afx1WmgTKlq0SIsyhIQhUXayFBUisg5j35cxxtj3NVnLhDCjzZalEInRak2UVlJatJe28/vDr/t1p0VG957m3vfz8ZjHY+65n9vjVce9555z3p/3R4LraGKJjtHc+/jxI86dO4fg4GDEx8fzJgO1a9cO/fr1w7hx4+qcwEi+D8uy2L59O+7cuYPKykpER0fXefwVp/cCLaFC6iUmJgZ2dnaoqKhA165dMXny5Gpri3t4eODx48eQlJSEl5cXtaduQLNnz8bdu3cRFBQEVVXVWselpaXBysoKVlZWcHV1FWJC0bdixQpcvnwZf/31F19b6iVLlsDf3x8yMjJo3bo1UlJSAAAnTpyAubk5V3FFlqWlJYqLi/Ho0SMAgKOjI6Kioqq1PI6JiYGNjQ0mTZqEtWvXchFVpE2dOhWhoaEICQmpc2bvx48fYW5ujl69esHNzU14AcVAZGQknJ2dUVpaCmlpabRp0wYqKip1zow4ffq0EBMSIjylpaXUWaORyMrKwsiRI/Hx40fIyclh9OjR1c4ZLl68iKKiIigrK+PSpUvUua+BlZWVITAwEN7e3njw4AEqKyvBMAzU1NQwZswY2NraQlNTk+uYIqu0tBQLFy5EUFAQAHx1ViMt3UFEGcuy2Lx5M86ePQuWZen9wAHaB43HyZMnceDAAZSUlPC2/XN/MAxDS8EKUFxcHBwdHZGfnw+WZdGkSRMoKyvXeQ59+/ZtISYUfdOnT8eDBw/g4+MDY2PjGq/plZaWYv78+Xj48CH++usvur8gAP369UNWVhZ8fX1hYGDAdRyxRcfoxiE0NBSLFi3Cx48fa9wHDMNASUkJe/fuRY8ePThIKPqOHTuGPXv2APi8LG+HDh2gpKRU5/FZXCZPUwcOUi9GRkbYtGkTfv31V0RGRtY425dlWUhLS2Pjxo305aqBvX79GioqKnUWbwCAmpoaVFRU8Pr1ayElEx9Pnz6FgoICX/FGXl4ebt68CUVFRVy8eBHq6urw9vbGL7/8gnPnzlEBhwBkZWXxnVxUtUouKSmBjIwMb7uRkRF0dXURFBREBRwCEB8fD1VV1a+2ZW/ZsiXU1NQQHx8vnGBi5ODBgygtLcWQIUOwbt06KCsrcx1J7BUXFyMqKqpatb6pqSnf5xNpeH369MGIESMwduxY/PDDD1zHEWvKyspwd3fH/PnzkZKSgnPnzlUbw7IstLW1ceDAASreEABpaWkMGzYMw4YNQ3p6Onx8fODn54ekpCS4urri8OHD6NGjB+zt7TFo0CAqfmpghw4dwp07dyArKws7Ozua0UjEmru7O86cOQOGYWBlZUXvBw7QPmgcfHx8sH37dgCfr1WYmJhARUWF9oOQVXWNNjMzwy+//AJjY2OuI4mdFy9eQFVVtdrfvqpwCfjcVXHLli3o168fDh8+jP3793MRVaRlZ2dDR0eHijc4Rsdo7r179w5z5sxBYWEhVFVVMXHiRBgaGvImoLx+/Rpnz55FWloa5syZg0uXLlGnPwHw9vYGwzDYvn07Ro0axXWcRoUKOEi92djYoGPHjnBzc0NwcDAyMzN5z6moqMDCwgLTp0+Hvr4+hylFU0ZGRr2LYtTU1KiAQwCys7OhpqbGty00NBTl5eUYNmwY1NXVAQBjxozB7t278eTJEy5iijwFBQVUVFTwHjdv3hwAkJqainbt2vGNbdKkCd69eyfUfOIiOzu73hc7lJWV8erVKwEnEj9VRWU7d+6km28cq6yshKurKzw8PFBQUFDteQUFBTg7O2P27NmQlJTkIKHoy8vLw7lz53Du3DkYGRnBzs4OI0aM4B0jiHAZGRnh6tWr8Pf3x71795CQkMAratLV1UXfvn0xfPhw+uwSAlVVVcyZMwdz5sxBWFgYvL29cePGDTx69AiPHj2CoqIiRo4cyVtnlny/q1evQkJCAsePH4eZmRnXccQeLWXDraqLsXv37sXQoUO5jiOWaB80DqdPnwbDMF9dgpQIVnh4OGRkZODq6lrjMn9E8AoKCqClpcV73LRpUwCflxdSUFDgbVdWVoahoSEiIiKEnlEcqKqqUpFAI0DHaO4dO3YMhYWFGDp0KHbs2FHtGoWlpSWmTp2KFStW4Nq1azhx4gR+++03jtKKrvfv30NdXZ2KN2pABRzkm+jr62Pr1q0APn/pqroY++WXLNLw5OTkkJWVVa+x2dnZvC/ApOEUFRVVO4hHRkaCYRj06tWLt41hGGhoaCAmJkbYEcWCmpoa0tPTeY8NDAwQGBiIkJAQvgKOjIwMJCQkQF5enouYIq9Zs2Z4//59vcampaVBTk5OwInED8uyaNu2Ld0A5RjLsli4cCECAwPBsiyaNWsGbW1ttGrVChkZGUhOTkZ+fj5cXFwQExODgwcPch1ZJPn6+sLLywtXrlzBy5cvsWnTJmzfvh2DBg3C2LFjae1kDjRp0gQ2NjawsbHhOgr5f927d4eBgQG0tLRw+PBhlJeXIzc3F2fOnIGnpydMTU2xZMkSdOvWjeuo/2np6enQ1NSk4g2O/ZulbEjDe/v2LdTU1OimBIdoHzQO8fHxUFZWpuINjn369Ant2rWj4g0OKSsro7CwkPe4qhtfYmIiOnbsyDe2qKgIubm5Qs0nLoYMGYI//viDd9OUcIOO0dwLCQmBrKwsNm/eXOv11aoVB4KCghAcHCzkhOJBRUXlq12+xRUVcJB/TUFBgQo3hMTQ0BDh4eF48eJFna3BX7x4gXfv3sHU1FSI6cRD8+bN8e7dO762fn///TcAVLvQXVFRQTesBaRbt27w9PREeno6VFVVMXToUBw+fBi7d++GlJQUzMzMkJGRgT179qCsrIxu2gmIsbExHjx4gIcPH/IVMP3Tw4cPkZGRUecY8u8YGhrWu4iGCM7FixcREBAAeXl5LFmyBPb29nwnfaWlpbhw4QL27t2LwMBA+Pn50Q1tATA2Nsavv/6KlStXIjAwEN7e3nj48CGvC4S6ujrGjh2LMWPG0AUqInZYlkVwcDC8vb1x+/ZtlJeXg2VZ6OrqYuzYscjMzMTFixcREREBJycnHDhwAAMHDuQ69n+WkpISnSM3ArSUTeOgqKhIy/xxjPZB4yArK1utoysRPl1dXeTl5XEdQ6y1adMGsbGxvMedOnXC1atX4efnx1fA8ezZMyQlJdG5m4DMnTsX9+7dw+LFi7Fv3z76fOIIHaO59+HDBxgaGn51AqiCggL09PSo672ADBgwAOfPn0dWVha9J/6BCjjId8vPz8edO3fw4cMH/PDDD3SjTgCGDRuGsLAwrFy5Em5ublBVVa025sOHD1i5ciUYhsHw4cM5SCnaOnXqhLt37+L8+fOYMGEC7t+/j+joaBgYGKBVq1Z8Y5OTk2vcR+T79e/fH+fOnUNQUBDGjRsHQ0NDODs74+TJk9iwYQNvHMuyUFZWxpIlSzhMK7psbGwQEhKCVatW4ejRozW2XY+JieF9Jo0ePZqDlKJt8uTJWLJkCQIDA+lGG4eqWl4eOHAA5ubm1Z5v0qQJJk6ciLZt22L69Onw8fGhAg4BatKkCYYPH47hw4cjLS0N3t7e8PPzQ0pKCg4ePIhDhw6hV69esLOzw8CBAyEtLc11ZJFTUVGB4uJiSEtLV+sI9/TpU/z111/48OEDOnbsiGnTptGNbgFKSUmBl5cXLl68iPT0dLAsCxkZGQwbNgwODg58HSKWLFmCY8eOwcXFBYcOHaLjynewtLSEj48PcnJyaBYRh2gpm8ahZ8+euH37NoqLiyErK8t1HLFE+6Bx6Nq1KyIiIlBeXg4pKboUzxUHBwf8/vvviIiIoI5jHOnduzeioqIQExMDIyMjjBgxAvv374enpyeysrJgZmaGDx8+4Ny5cwA+Xw8nDc/d3R29e/eGp6cnhgwZAgsLC+jo6NR5nJg3b54QE4oHOkZzT0ZGpt6dfnJzcyEjIyPgROJpwYIFuH//PhYvXoxdu3ahdevWXEdqNBj2a70kCQHg7++P48ePw9HREfb29rzt8fHxmDZtGt+SBjY2NrxlVkjDKCsrg4ODA16+fAkFBQWMHDkSnTt3RrNmzZCfn4/Hjx/jypUrKCgoQIcOHfDnn3/STYkGFhwcjJkzZ4JhGCgqKiI/Px8sy+K3337D+PHjeeMeP36M8ePHw9raGrt37+YwsXi5fPkyLl68iLdv30JWVhZmZmaYMWMGFdIICMuymD59Oh48eABJSUn07t0bXbp04ftMevDgASoqKtC7d2+4ublRa2oB2L9/Pzw8PDB37lyMGzeOboRyoEePHmjevDkCAgK+OnbQoEHIzc1FaGioEJKRL4WGhsLb2xtXr15FRUUFgM+dtUaPHo3x48dDV1eX44Si4/jx49izZw9Wr16NyZMn87bfvXsXc+fORUVFBa+bWfv27fHnn3/S0n8N6NOnT7h27Rq8vb0REREBlmXBsizat28PBwcHjBo1Cs2aNav19cOHD8fbt2/x9OlTIaYWLVlZWbC1tUXHjh2xa9cu6srHkU6dOkFdXR03b97kOopYS05OxpgxYzB8+HD8/vvvdD7AAdoHjcPz588xYcIE/PTTT3QTlGMrV65ESEgI1q5dS8sWcCA2NhZbtmzBhAkTMHjwYACf7zmsXLkSZWVlYBiGt+yZmZkZTpw4QTdMBcDIyIjvbw3Uvpxc1bnby5cvhRVPbNAxmnuTJk1CREQETp48iR9//LHWcX///TecnZ3RvXt3nD59WogJxYOLiwvy8vJw9uxZSEtLw8LCAlpaWlRUBirgIPW0YMECBAQE4MqVK9DT0+NtnzlzJoKDg9G6dWvo6ekhMjISpaWl2L17N3WBaGAZGRmYP38+Hj9+XO2AXvU2NjU1xf79+6t1hCAN49y5c9i3bx9yc3PRtGlTODs7Y/HixXxjfv31V1y4cAGbN2/G2LFjOUpKiOAVFRXhl19+wbVr1wDwn+xVfSZZW1tjw4YNX21FR+o2YMCAWp9LT0/n3ZBu2bJlrV9uGYZBYGCgQPKJMxMTExgaGsLLy+urY+3s7PD69Wu6MSpkubm5uHTpEnx8fHgXnSQkJFBZWQkAkJSUxNixY7F27dpa1zwl9efs7IzQ0FDcv3+ft6Y1AIwcORKxsbGwtLRE586d4evri5SUFCxbtgzTp0/nMLFoMTMzQ2FhIViWhZycHKytrWFvbw8TE5N6vd7JyQnh4eF0gbae/Pz8atyempoKV1dXtGjRAtbW1tDR0amzkIM6MzW8fv36QVlZGT4+PlxHERthYWE1bn/x4gV27dqFdu3awc7O7qvvh+7duwsqosijfdA4pKam1rj97t272LJlC3r37g0HBwe0bdu2zhsTGhoagoooFr4sJP6nyMhIVFRUQFFREdra2nWeQ3t4eAgqIvnC27dv4e/vz5uU1b17d/Tv35+WPhOQVatWfXOxAE3W/T50jG6cvL298csvv0BRURHLly+Hra0tX6es8vJy+Pr6YteuXcjLy8PmzZsxZswYDhOLJioqqx0VcJB6GTx4MHJzc/Ho0SPetszMTPTt2xfKysq4du0aFBQUEBQUhNmzZ6NPnz44ceIEh4lFE8uyuHXrFm7evIm4uDgUFhZCXl4ehoaGGDRoUJ03+UjDqKysRHZ2NpSUlGo8kXjz5g3KysrQtm1bqhInYuHly5e4efMm3rx5g4KCAigoKMDAwACDBg2qcWkV8u0a4u8oTl9uhWngwIHIysrC/fv36yxUKiwsRJ8+faCkpIRbt24JMaH4un//Pry9vXHr1i2UlZWBZVloaWnB3t4eY8aMQVZWFv766y/4+PigpKQEzs7OWLlyJdex//OsrKxQUVGBe/fu8bbFx8dj+PDhaN++PS5evAgASEhIwPDhw2FiYoI///yTq7gix8jICCYmJrC3t4e1tfU3d3+IiYlBXl4eevToIaCEoqXqQlNNqi6z1OfiOB2fG95vv/0GHx8fBAcH01I2QlLX+6G+GIZBdHR0AyUSP7QPGgdjY+Pv/hm0H74fnUMTQhoTOkY3TizLYs6cOQgKCuJ1XdfR0YGysjKysrKQlJSEvLw8sCwLKysruLq6UqcUAaCistrRwnukXrKzs6Gpqcm3LTQ0FJWVlbC2tua1bbe0tETr1q3pYCIgDMNg4MCBtC41hyQkJKCiolLr8192qCFEHBgbGzfIRSpSu1OnTnEdgdSid+/euHDhAtavX49t27ZBUlKy2piKigqsX78eJSUlMDc35yCl+Hj79i18fHzg6+uLtLQ0sCyLJk2aYPjw4bCzs0OvXr14Y1VUVPDrr7/C0dERY8aMwdWrV6mAowF8/PgRBgYGfNuqZht92aZaV1cX2traePPmjVDzibqLFy+iffv2//r1VHj5bWgGXOO1YMEC3LlzB2vWrKGlbISEugVwj/ZB49AQ8yRpruX3E5cbO4SQ/wY6RjdODMPg4MGDcHFxwenTp5Gbm1uta668vDycnJwwd+5cKt4QkG3btnEdodGiAg5SLyUlJdW2RUZGgmGYajO0VFVVqUKZEPLdVq9e/d0/g2EYbNmypQHSEMIdmgndeM2cOROXL1/GlStXEBERgYkTJ8LAwAAqKirIzMxEbGwsPD098f79e8jKymLmzJlcRxZJly5dgre3N8LCwsCyLFiWhZ6eHuzt7WFjY1Pn7Gs9PT0YGxvjyZMnwgsswiorK1FcXMy3reqcwczMjG97ixYt8O7dO2HGE3nfU7xBvh2tf9x4BQcHY/z48XB1dcXgwYNpKRshuH37NtcRxB7tg8YhJiaG6wgEgK2tLdcRCCGEh47RjZe0tDQWL16Mn376CREREUhISOB1vdfV1UW3bt2oGJxwhpZQIfXSt29fFBUV4eHDh5CWlgbweRZdcnIyHj58iObNm/PGjho1Ch8+fMDff//NVVyR9vjxY4SGhiItLQ3FxcV8VeUfPnxAeXk5VXUKUFJSEu7evYvk5GQUFRXVOjOCCge+H7W8JKR2qampaNq0KZSVlb86NisrC58+faJjg4A8ePAAixcvRm5ubo3V+CzLonnz5ti7dy969+7NQULRV3W8kJWVxdChQ2Fvbw9TU9N6v37x4sV48uQJXVRpAEOGDEFqairu37+P5s2bo7y8HH379kVhYSHCwsLQpEkT3tihQ4eisLAQwcHBHCYmhIiif66jTEvZEEIIIcLVEMt8MwyDwMDABkhDavLmzRt4eHggNDQU6enp+PTpE19XdS8vL6SlpWHq1Kl1LhlLCCFEMKgDB6mXrl274ubNm3BxccHMmTPh7++PxMREmJiY8BVvVFRUIDk5GVpaWhymFU2pqalYvnw5IiMjAXy+IcQwDF8Bx4EDB+Dt7Y2zZ8+ia9euXEUVSSzLYvPmzTh79ixvdm9dqIDj+1HLS0Jq179/f5iZmeHMmTNfHbto0SJERETQ8mYC0rt3b1y7dg1nz55FcHBwtWp9CwsLODo6QklJieuoIqtDhw5wcHDAiBEjeMv6fYu9e/cKIJV46tOnDzw9PbFkyRJMnDgRAQEByM7OhpWVFV/xRmFhIVJSUtChQwcO04qmvLw8uLu78xUc14bWUW54q1evhq6uLmbNmvXVsceOHUNCQgJ95xUAWt6mcXBxcYGGhgbGjBnz1bF+fn54+/Yt5s2bJ4Rk4oP2QePg5+cHZWVlWFhYfHXs/fv3kZmZSV2BBMDY2BjdunWr1zm0k5MTnUN/p4botEfLFQiOj48P1q9fj7KysloLXvPy8nDo0CG0a9cOw4cP5yKmSKNjNCHV5efnIyUlpc6J04D4nO9RAQepl2nTpuHWrVs4duwYjh07BuDzQX3atGl840JDQ1FSUoJOnTpxEVNk5ebmwsnJCe/evYOamhp69+6NBw8eID09nW+cjY0NvLy8EBgYSAUcDczd3R1nzpwBwzCwsrJCly5doKysDAkJCa6jiSxqeUlI3b6liRo1XBMsJSUlzJs3j06mOfLzzz+DYRi+AgHCjVmzZuHatWsICQnBgwcPwLIsmjRpgvnz5/ONu3PnDioqKqotq0K+T1paGhwdHfH+/ft6fe7TsaHh+fr6olu3bvUq4AgODkZ4eDgVcAgALW/TOLi4uKBbt271ujHh7e2N8PBw+i7VwGgfNA6rVq2CmZlZvQo4jh49ivDwcCrgEID6TMb653jy7926davG7Tdv3sTu3buho6ODSZMmQV9fH8rKysjKykJcXBw8PT2RlJSEpUuXYtCgQUJOLR6ePn2KX3/9FQAwZcoUDBw4EFu3bq1WsDR06FDs2LEDt27dogIOAaBjdOORnZ2N8+fP4969e9UmZfXt2xfjx4+nSVkCFhUVhZ07dyIqKuqrY8VpIgoVcJB66dy5M1xcXLBnzx4kJiZCXV0dM2bMwNChQ/nG/fnnnwBALcIb2IkTJ/Du3TsMGDAAu3btgqysLBwdHasVcJiamkJGRgYPHz7kKKno8vb2BsMw2Lt3b7V/94QQ0pgVFhbylj8jRBTNnz8fGhoatV4kJMKjqqoKb29vuLm5ITExERoaGnBycoKBgQHfuNDQUBgZGcHKyoqjpKJpz549SE1NhZ6eHhYvXozOnTtDRUWFZi82UhUVFbRvCPl/VR1GCXdoHwgWFQP8txQXF0NSUpLrGP9pbdq0qbYtLCwMu3fvhr29PX777Te+59q1a4fu3btjwoQJ2LBhA3bu3ImOHTvW+HPI9zlx4gQqKyuxfv16jBs3DgDQtGnTauM0NDSgoqKCp0+fCjsi+Qc6RgtOUFAQVq5ciby8PL5jdW5uLh4/fownT57Aw8MDW7duRf/+/TlMKroiIyPh7OyM0tJSSEtLo02bNnQd4/9RAQepN0tLS1haWtY5ZtOmTdi4cSOti9bAAgMDIS0tjc2bN0NWVrbWcRISEtDS0kJKSooQ04mHt2/fQk1NjYo3OLZu3TrY29tTlx9C6qG0tBShoaF49eoVNDU1uY4j8tLT0xEeHo60tDQUFxfTzAghatGiBc2GaETU1dWxdu3aOsds2LBBSGnEy/379yEtLQ03NzeoqalxHYfUoaKiAikpKWjWrBnXUQhpFFJTUyEnJ8d1DLFG+6BxyMjIgIyMDNcxxFp8fDxiY2PRunVrrqOInCNHjkBOTg6rV6+uc9yqVatw+fJlHDlyBCdOnBBSOvERGRkJRUVFXvFGXVRVVREXFyeEVKQudIwWjBcvXmDevHkoLy+HlpYWJkyYAD09PaioqCAzMxPx8fE4d+4ckpOTsWDBApw/fx4dO3bkOrbIOXjwIEpLSzFkyBCsW7cOysrKXEdqNKiAgzSof7PuOPm61NRUtG3bFi1atPjqWHl5eRQXFws+lJhRVFSkg0cj8Ndff+HChQswMDCAnZ0dRo0aVa/3BSH/dS4uLjh06BDftsjISBgbG9fr9VQlLjj5+fnYuHEjrl69isrKSt72Lws4Fi5ciICAAPj4+MDIyIiLmCKtU6dOePLkCSoqKmimHBFrBQUF0NXVpeINIQoLC8OjR4/4tr1//x4uLi61vubTp0+IiopCZmYmfvzxR0FHJERoYmJiEBMTw7ctKysLfn5+tb6mpKQEYWFhSE1NpWVgGwDtg8YhNTUV796949uWn5+PsLCwWl9TtR8SExPRoUMHQUcUCx4eHjh16hTftufPn2PAgAG1vubTp0/IysoCAPTp00eg+cTR8+fPoaOj89WlL5s0aYK2bdvi2bNnQkomXnJycmBoaFivsTQDvuHQMbrxOXjwIMrLy2FnZ4cNGzZAQkKC7/l+/frB2dkZ69atw4ULF3Do0CEcPnyYo7Si6+nTp1BQUMDOnTtpaeR/oAIO8s2Ki4sRFRWF+Ph43npQ7dq14y3fQRqetLQ0SktL6zU2KyuLCmkEoGfPnrh9+zaKi4vr7IJCBGvSpEm4cuUKXr9+ja1bt2LXrl3o378/7Ozs6OSaiLwvW/kxDFOvNrzy8vKwtrbGwoULBRlNbJWUlGDKlCl4+fIlZGVl0alTJ8TGxuLjx4984+zt7XHjxg0EBgZSAYcAzJgxA87OznB1dcX8+fO5jkP+X3h4OIKDg2tcQ7Zbt25cxxNJmpqaKCsr4zqGWHn06BFcXFz4Lm6/f/++WtHlP7EsiyZNmuCnn34SdESx5u/vDz8/P0RHRyMnJwcVFRU1jhOndZQFKTAwsNq//aSkpK/OtK5qCz5lyhRBxhMLtA8aBx8fn2r7ITY2FpMnT67X6+3t7QURS+zk5+fzFdIwDINPnz5VK66pibm5ORYvXizIeGKptLS02nLgtUlPT6/3tXDybVq0aFHv/ZCSkkITGhsIHaMbn6ioKCgoKGDdunXVijeqMAyDtWvX4tq1a4iMjBRyQvHAsizatm1LxRs1oAIOUm+VlZVwdXWFh4cHCgoKqj2voKAAZ2dnzJ49m2Y/NrC2bdsiJiYG2dnZdbYIT05ORkpKCnr27CnEdOJh4cKFuHv3LrZu3Yrff/+dKpA5snbtWqxcuRKBgYHw9vbGgwcPcP36ddy4cQNqamoYM2YMbG1tabkIIaJlI4RjypQpsLW1BfD5i+3AgQPRqVMn7Nu3r8bxDMNARkaGlpUQsFOnTiE6OhqmpqbYt28fWrduDUdHx2oFHD179oS0tDTu379P7xEB0NTUxKJFi3DgwAFER0fD1tYWenp6dRZcamhoCDGheElPT8fy5ct5s0z/WXx27Ngx9OjRAzt27ICqqipXMUXS6NGjsW/fPsTFxUFfX5/rOGLByMiId3wGAF9fXygrK8PCwqLW18jIyEBbWxsDBw6ElpaWMGKKHZZlsWTJEly/fr1eBa/1GUO+rk2bNjAzM+M9DgsLg4KCQq3Fq1XfV7W1tTF8+HCYmpoKK6rIon3QODRr1gzq6uq8x+/fv4e0tDRUVFRqHF+1H7S0tDBixAiMGDFCWFFFmq2tLXr06AHg8+f8lClTYGhoWOtSfwzDoGnTptDS0kLLli2FGVVsGBgY4NmzZ7hw4UKdhUoXLlzAhw8fYGJiIsR04qNTp04ICgpCREREnYX1gYGByM3NRd++fYWYTnTRMbrxKS0thb6+/lcLB5o2bQpdXV1aTkhADA0N8f79e65jNEoMS2eqpB5YlsWCBQsQGBgIlmXRrFkzaGtro1WrVsjIyEBycjLy8/PBMAwGDhyIgwcPch1ZpBw9ehR79+6Fra0ttm7dCgBwdHREVFQUXr58CeDzOsqzZ8/G/fv3sW7dOkyYMIHLyCInLCwML168wK5du9CuXTvY2dlBR0enzvXnunfvLsSE4ik9PR0+Pj7w8/NDUlISGIYBwzDo0aMH7O3tMWjQIKreFJDalo2o+kwCaNkIQVq9ejV0dXUxa9YsrqOINRsbG8TFxeHmzZu8goB/Hp+rWFtbIzMzs1qrffL96ruUUBWaaS04hYWFsLOzQ0JCAhiGQe/evWFoaMg7Z4iNjUVISAhYlkW7du3g5eVFa/k2oPLycsyYMQNpaWnYsWMHXfTmgJGREbp16wZPT0+uo4i1Cxcu4Ndff4WpqSm2bduGVatWISoqCtHR0fj48SOePHkCNzc3PH/+HOvXr4eNjQ3XkUUSvR+4R/ugcaD90Dg4OTmhffv2tRZwEMG7cuUKli1bBklJSYwePRqTJk3i3TwtLS1FXFwcPD094efnh8rKSuzcuZMKmgQgKCgIs2fPhq6uLlxdXaGrq1vtOsbz58/x008/ITs7G2fOnKEOigJAxwbujRkzBllZWbh79+5Xx/br1w/Kysrw8fERQjLx4u/vjyVLlsDFxQUDBw7kOk6jQh04SL1cvHgRAQEBkJeXx5IlS2Bvb893U7S0tBQXLlzA3r17ERgYCD8/P7oI0oCcnJzg5eUFPz8/vH//HnZ2digqKgIAvHjxAq9fv8bp06cRHR0NAwMDjB07luPEosfJyYnXdSM2NpZXSFMbujkkHKqqqpgzZw7mzJmDsLAweHt748aNG3j06BEePXoERUVFjBw5EnZ2dlRA0IBo2Qjufe0ziAhHUlISNDU169XNoVmzZkhKShJCKvHzrfXoVL8uOH/88QcSEhJgYGCA3bt317i2cmxsLJYsWYK4uDh4eHhgzpw5HCT976ut1W6rVq0QHh6OcePGwcjICDo6OrV2o2EYBlu2bBFkTLFz69YtNG3alOsYYs/Pzw8Mw2Dr1q3Q1tbmbWcYBkpKSrCysoKVlRXWrFmDNWvWQENDgzdLmzScU6dOoVmzZlzHEGu0DxqHrVu30hIEjcDp06e5jiD2RowYgZiYGJw4cQK+vr7w9fUFAMjJyfGucwOfz9emT59OxRsCYmlpCVtbW/j6+sLGxgZmZmZISUkBAGzcuBGvX79GREQEKisrMWnSJCreEBA6RnPPwcEB69evx8WLFzF69Ohax128eBHp6emYO3euENOJj+HDhyM2NhYrVqzA3LlzMW7cOCgoKHAdq1GgDhykXpycnBAeHo4TJ07A3Ny81nEhISGYPn06evTogVOnTgkxoehLSkrCnDlzEB8fX+PyHSzLQk9PD8eOHUObNm04SCja+vfv/82vuX37tgCSkLrk5OTA09MThw8fRnl5OW87wzAwNTXFkiVL6MSjARw7dgx79uyptmzEP7sOlJWVwdTUFD/88APOnz/PYWJCBKNr167Q0NDA1atXedtq68AxbNgwZGdnUwcOItJsbGwQGxuLa9eu8d00/afk5GQMHToUBgYGuHjxohATig4jIyMwDPNdBUkMw1T7rCJEFPTo0QOKiooIDAwEAEycOBGRkZF48eIF3/rWhYWF6NOnD3r06IGjR49yFZcQQggRK8HBwTh27BgiIyNRUVHB2y4pKQlTU1PMnDmTlu0QMJZlcejQIbi5uaG4uLja802bNsXMmTNpCVgi8jZt2oTz589j/PjxmDRpEtq2bct7LjExEZ6enrznf/nlF+6CirABAwYA+NxpveqY0LJlyzonolSd54k66sBB6uXVq1fQ1NSss3gDAMzNzaGlpYWYmBghJRMfOjo68PPzg5eXF27evIlXr14hPz8fcnJyMDAwwNChQ+Hg4EAzvgSEijEaL5ZlERwcDG9vb9y+fRvl5eVgWRa6uroYO3YsMjMzcfHiRURERMDJyQkHDhygdlzfyd/fH1JSUti1axdat25d6zhpaWloa2sjISFBiOnES0lJCe7cuYOXL18iJycHZWVlNY6jWdaCoampiaSkJBQVFdW5DERGRgaSkpJoOQMi8pKSkqCvr19n8QYAaGtrQ19fH8nJyUJKJnroYup/Q2lpKXJycvgKi/+pPl2cyLcpLi7mu/gqIyMD4PMSgM2bN+dtl5eXR7t27fD06VNhRySEEMKhp0+f1uscmmZbC4aFhQUsLCxQXFyMpKQkFBYWQl5evs7OcaRhMQyDefPmYdKkSbh79261+wxWVlbUOYiIlKoigdp4enrC09MTUlJSaNGiBd85nKSkJG7fvo07d+6ITeGAML17967atuzs7FrH1zS5XVRRAQepl5KSkq9eiK3SvHlzpKenCziReGrSpAkcHR3h6OjIdRRCOJeSkgIvLy9eGzOWZSEjI4Nhw4bBwcEBZmZmvLFLlizBsWPH4OLigkOHDlEBx3eiZSMah1u3bmHNmjXIy8vjbauaif3ll1mWZamAQ0CsrKxw/PhxuLq6YtmyZbWO2717N1iW/eoJIyHiRJxOugWBCjgar/Lycri7u+PixYtISEios0sKLbsoGK1atUJubi7fYwCIj49H165d+cbm5uaioKBAqPnEjb+/P/z8/BAdHY2cnBy+2dZfoveD4NA+aBweP36Mixcv4uXLl/j48WOtxX3iNLNU2J4+fYpVq1bxTTKpOl/+UtU2KuAQLFlZWVpul2MtWrSoc+kIInh0jBaOmooEalJWVoaMjAy+beXl5Xj37h1dwxAQWsmhdlTAQeqldevWePPmDa8itjaFhYV48+YN7wIJIYQ0pE+fPuHatWvw9vZGREQEWJYFy7Jo3749HBwcMGrUqBrXD2zSpAnmzZsHf39/vHnzhoPkokdSUrJe43Jzc+s8bpB/Jzo6GgsXLoS0tDR++uknXLt2DcnJydi8eTNycnLw5MkT3L59G1JSUvj555/puCwgU6dOxYULF+Dm5oasrCw4ODjwTrZzcnLw+vVrnDx5Enfu3IG6ujomTJjAcWJCBEtbWxtxcXF4+/YtNDU1ax2XkpKC2NhY6OvrCzGd6Js3bx6kpKSwY8cONGnShOs4Yqm0tBRTp05FZGQkJCUlISUlhdLSUqirqyM3N5e3vnuTJk2goqLCcVrRpaWlxddVo2vXrvDz88OZM2f4Cjju3r2Lt2/f8nXrIA2HZVksWbIE169fr9dyT7TCc8OjfdB47Ny5E+7u7vX6G9MNIsFITk7G1KlTUVJSghEjRiA8PBxpaWn4+eefkZOTg8ePHyM6OhoyMjKYMGECXccghAgUHaOFi4oEGq8ePXpwHaHRogIOUi+9e/fGhQsXsH79emzbtq3GG3cVFRVYv349SkpKvrrUCiGE/Bvm5uYoLCwEy7KQk5ODtbU17O3t670sgbKyMi3n0QBo2Qjuubm5oaKiAnv27MHgwYMRGhqK5ORkjB07ljfmzZs3mDNnDs6dOwdvb28O04quli1b4ujRo5gzZw58fX3h5+fHe65Xr14APp9kq6io4PDhw1BQUOAoqXhIS0vDlStX6tUO2cPDQ8jpxMOgQYPw6tUrLFiwAHv27KnxpmhCQgKWLFkClmUxePBg4YcUYUFBQTAwMKDiDQ55enoiIiICgwYNws6dOzFt2jRERUXhzp07AIDXr1/jxIkTuHz5Muzs7PDzzz9znFg0WVhYIDQ0FI8fP0aXLl0wfPhw7N27F/7+/nj37h26du2KjIwMXL9+HQzDYMyYMVxHFkleXl64du0aTE1NsW3bNqxatQpRUVGIjo7Gx48f8eTJE7i5ueH58+dYv349bGxsuI4scmgfNA43b96Em5sbdHV1sW7dOuzevRsvXrzAzZs3ecX3p0+fRnp6On755Rf07t2b68gi6fjx4ygqKsJvv/2G8ePHw9HREWlpaViwYAFvzMOHD7F06VI8fPgQ586d4zCt6MvKyuKdt9W11Bx9LhFRRcdo4aIiAfJfRAUcpF5mzpyJy5cv48qVK4iIiMDEiRNhYGAAFRUVZGZmIjY2Fp6ennj//j1kZWUxc+ZMriP/Z61evfq7fwa1yhccannJrYKCApiYmMDe3h7W1tZ1Fg/U5JdffuFbboL8O7RsBPciIiKgqKhY581PPT09HDhwADY2NnB1dcXatWuFmFB8mJiY4PLly3Bzc8PNmzeRkpLCe05NTQ1Dhw7FzJkzaf1YAfP09MS2bdtQVlbGm7X45QyVL7fRrEbBcXZ2xuXLlxEdHQ1ra2v06dOn2jnD/fv3UVFRAR0dHUydOpXryCJFVVW11pa7RDj8/f0hJSWFtWvXQkZGptrzhoaG2LFjBzQ0NHDw4EEYGBhg0KBBHCQVbUOHDkViYiLy8/MBfF7Sb//+/Vi4cCEeP36Mx48f88aOHDkSM2bM4CipaPPz8wPDMNi6dSvfkrwMw0BJSQlWVlawsrLCmjVrsGbNGmhoaNDF9QZG+6Bx+PPPP8EwDPbs2QNjY2NeoaWWlha0tLTQqVMnODg4YP78+di4cSP++usvjhOLpocPH0JeXh52dna1junVqxf27t2LKVOm4MiRI1i8eLEQE4qHlJQU/P777wgJCalzXNV5G920bnjGxsb1HishIQEFBQW0adMGZmZmcHBwoC6KDYSO0YSQr2FY6r1D6unBgwdYvHgxcnNza7zwzbIsmjdvjr1791K1+HdoiLX/GIbBy5cvGyAN+dK3trykfdDwXr16hfbt23MdQ+x9/PgRw4cPR05ODmxsbODg4IBt27bh6dOnePjwYbVlIy5fvkydBxpYp06dYGBgAB8fHwDA5MmTERYWhqioqGo3jIYNG4aysjIqKhOS4uJi5OXlQV5env7dC8nff/+NqVOnQklJCYsWLcKpU6cQFxeHkydP8mY1+vj44NOnT1i+fDkMDAzowocAvX//HkuWLEFUVBQA/jbgVd+hunXrhl27dkFdXZ2TjKJq06ZNOHfuHG7dugU1NTWu44ilbt26QVlZGTdv3gQATJw4EZGRkXj+/DlfF8uysjL07t0bxsbG1M5XiAoKCnDv3j28ffsWMjIy6N69+zfdxCDfpkePHlBUVOR9B616P7x48QISEhK8cYWFhejTpw969OiBo0ePchVXJNE+aBx+/PFHyMjIICgoCADg6OjIm2X95fekrKws9OvXDwMGDMD+/fs5Siu6TExM0LZtW1y6dAkA4OTkhPDwcDx58qRa97KBAwdCWloa165d4yKqyMrIyICtrS2ysrLQtWtXJCUlITs7G6NGjUJOTg6eP3+OrKwsyMjIYPDgwZCUlMTWrVu5ji1yvufeg5SUFFauXAknJ6cGTCSe6BhNxJGLiwuAzx2NJ06cyLetvhiGwdy5cxs8W2NEHThIvfXu3RvXrl3D2bNnERwcjISEBBQWFkJeXh66urqwsLCAo6MjlJSUuI76n0ZfTBsnannZOEyaNAnNmjXD9evXqT04h2jZCO4pKCjwzbJu3rw5ACA1NRXt2rXjG9ukSRO8e/dOqPnERWpqKpo2bcrXXUNWVhaysrLVxmZlZeHTp0/Q0NAQZkSxUHXzc8+ePejZsyd8fX0BfL5YDoDXBeWnn37Cvn37eIVPRDDU1dVx7tw5hIWF4d69e9XOGfr27Yvu3btzHVMkzZs3D7dv38aiRYtw4MABtG7dmutIYqe8vBwtWrTgPa46HuTm5vKdJ0tLS0NHRwevXr0SdkSxpqCggOHDh3MdQ2wUFxfzLaVVVWScn5/P++4KAPLy8mjXrh2ePn0q7Igij/ZB41BQUAAtLS3e46ZNmwL4fFPuy3NlZWVlGBoaIiIiQugZxYGsrCykpP53K6RZs2YAgPT0dL79AwCKioqIj48Xaj5xcOLECWRmZmLevHmYN28eHB0dkZ2dje3btwP4vDy7r68vNm/ejKysLBw7dozjxKIpJiYGZ86cwfbt2zFkyBDY29vD2NgY8vLyKCwsRExMDC5cuIDr169jxYoVsLOzw5s3b+Dl5YU///wTW7ZsgbGxMczMzLj+Vf7T6BjduDx9+rRey/GKS+GAoLi4uIBhGOjq6vIVcDAM89VJ01VjxGk/UAEH+SZKSkq8L1lEMGxtbbmOQGpALS8bh/LycigrK1PxRiNAy0ZwS01NDenp6bzHBgYGCAwMREhICF8BR0ZGBhISEiAvL89FTJHXv39/mJmZ4cyZM18du2jRIkRERCA6OloIycTL06dPoaysjJ49e9Y6RklJCXv27MGQIUPg6upKBbNC0L17dyrUELIzZ87A0tISf/75JwYNGoRevXpBT0+vxqKyKnRe17Bat26Njx8/8h5XdZl5/fo1r6isyocPH1BcXCzUfIQIU6tWrZCbm8v3GADi4+PRtWtXvrG5ubkoKCgQaj5xQPugcVBWVkZhYSHvcVVBX2JiIjp27Mg3tqioiG+fkYbTunVrfPjwgfe4Xbt2uHPnDsLCwvgKOPLz85GQkEDXnQTg3r17kJWVxfTp02t8XlJSEnZ2dlBQUMCiRYtw8uRJWuZMAG7fvo3Nmzdj+fLlmDZtGt9zioqK6NGjB3r06IEffvgBW7ZsgYaGBgYMGICOHTtCQ0MDe/bswenTp6mA4zvRMbpxePr0KVatWoWEhATetpqW3hW3wgFBqbr+0LJly2rbSHVUwEEIIfXw4sULqKqqVmux++UBvUmTJtiyZQv69euHw4cPU8tLAdDW1kZOTg7XMcj/U1JSwvLly7F8+XJaNkLIunXrBk9PT6Snp0NVVRVDhw7F4cOHsXv3bkhJScHMzAwZGRnYs2cPr007EYxvWY2QVi4UjJycHL7ltaSlpQF8vgAuJyfH266lpQV9fX08fPhQ6BkJEYYvZ65UVFQgKCgId+/erXFs1XdYuljSsHR1dREaGoqKigpISkrCzMwMFy5cwPHjx2Fqasq7GfTXX3/hw4cPtDQgEWlaWlp8s0W7du0KPz8/nDlzhu/GxN27d/H27Vu+WaikYdA+aBzatGmD2NhY3uNOnTrh6tWr8PPz4yvgePbsGZKSkmiJOQExMTGBr68vPn78iJYtW2LgwIE4ceIEdu3aBRUVFd459JYtW1BSUlKt8JJ8v/fv30NTU5NXXFy1TERZWRnvHA743EFRVVUVly9fpgIOAXB3d4eysnK14o1/mjJlCo4fP46TJ09iwIABAABnZ2ccPnyYt1wm+ffoGM295ORkTJ06FSUlJRgxYgTCw8ORlpaGn3/+GTk5OXj8+DGio6MhIyODCRMm0OS4BlDT9Qe6JlE7KuAg36y4uBiRkZHV2iGbmprWObuLNJxPnz4hKSmJ9/fX0dHhtWAkgkEtLxuHUaNGYdeuXYiMjISpqSnXccgXals2gghG//79ce7cOQQFBWHcuHEwNDSEs7MzTp48iQ0bNvDGsSwLZWVlLFmyhMO0BPh8vPjyohRpOC1atEBpaSnvcVUl/9u3b2FoaMg3trKyEllZWULNJ04KCgrw9u1btGzZEqqqqnzP3bhxA+fPn8eHDx/QsWNHLFq0iG5QNDAbG5tqM4WIcPXr1w/37t1DaGgoevXqhaFDh2L//v148OABhg4dio4dOyIjIwOPHz8GwzC8trGEiCILCwuEhobi8ePH6NKlC4YPH469e/fC398f7969Q9euXZGRkYHr16+DYRiMGTOG68gih/ZB49C7d29ERUUhJiYGRkZGGDFiBPbv3w9PT09kZWXBzMwMHz58wLlz5wAAw4YN4zixaOrfvz+8vb0RFBQEW1tbdOnSBdbW1rh69Sp++ukn3jiWZSErK4uFCxdymFY0SUpK8l2/rrqempWVBTU1Nb6xKioqtIyNgMTExFRbercmDMOgTZs2iImJ4W1r0qQJdHV1+YrSyL9Dx2juHT9+HEVFRfjtt98wfvx4ODo6Ii0tDQsWLOCNefjwIZYuXYqHDx/yjtOECAsVcJB6Ky0thaurK86cOcPX+q+KnJwcJk2ahLlz51KbOQF59OgRDh8+jPDwcFRUVPC2S0pKokePHpgzZw61qhYQannZODg7OyMsLAzz5s3D77//joEDB9KNCiKWevfujRcvXvBtW7lyJTp06ICLFy/i7du3kJWVhZmZGWbMmFHtRioRntLSUoSGhuLVq1fQ1NTkOo5IUldXR3JyMu+xsbExbty4gYCAAL4CjsTERCQmJvK1aiQN648//sChQ4ewceNG2NnZ8bb7+flh9erVvC408fHxCAsLg5+fHxQVFbmKK3K2bdvGdQSxN2TIEOTl5fFuTjRt2hRHjx7FggULkJCQgNTUVACAlJQUpk+fDgcHBy7jEiJQQ4cORWJiIvLz8wEAzZo1w/79+7Fw4UI8fvwYjx8/5o0dOXIkzbIWANoHjcOQIUMQERGB5ORkGBkZQUVFBZs3b8bKlStx7do1XL9+nfcdyczMDPPnz+c4sWiysrLC3bt3+WZQb9++HQYGBtXOoRcsWAAjIyMO04omNTU1ZGZm8h5ra2sDAKKiovgKl0pLS5GcnEzX+wSEZVm8ffu2xmUivlRZWckb9yWGYSAjIyPomCKPjtHce/jwIeTl5fmuXfxTr169sHfvXkyZMgVHjhzB4sWLhZiQiDuGpV7OpB5KS0sxc+ZMhIaGgmVZNG/eHNra2lBWVkZWVhaSk5ORm5sLhmFgZmYGd3d3mmXawNzd3bFr1y5UVlbytsnKyvKtmywhIYFly5Z9tQUa+XaOjo6IjY1FWFgYgM83KLZt24ZJkyZh7dq1vHHPnj2Dg4MD1NXVcfv2ba7iiqzJkyeDZVlERkaisrISCgoKaNu2ba2dHxiGgYeHh5BTihYXF5fv/hm0RiARFS4uLjh06BDv8dcuePyTs7MzVq5cKYhoYm3Xrl1wc3PD9evXoaOjg7dv32Lo0KFgWRZTp07ltUM+cuQIUlNT4eDggN9//53r2CLJ0dERT58+xd9//83XoWzAgAFITU3F+PHj0bVrV5w5cwbPnj3DnDlz+Ga3ECKqKisr8ezZM7x9+xYyMjLo0qULlJWVuY5FCCcKCgpw79493vuhe/fu1ZYqJYJF+6BxePv2Lfz9/XmFA927d0f//v15y0oQImqWLl2KGzdu4NGjR5CXl0dISAimT58OHR0duLq6Qk9PD58+fcKGDRvg7e2NH3/8EX/88QfXsUWOk5MTwsPDsWjRIr7uM/907Ngx7NmzB927d8fp06cBfL4G0r17d6ioqOD69evCiixW6BgtPCYmJmjbti0uXboE4H/vjSdPnlSboD5w4EBIS0vj2rVrXEQVGyzLIjExETk5OSgvL691nLhMYqcOHKRe3N3d8ejRI7Ro0QIrV67EiBEj+Ao0ysvLcfnyZezcuRPh4eFwc3PD7NmzOUwsWsLDw7Fjxw4AwODBgzFt2jQYGhpCTk4ORUVFiI2Nhbu7O27cuIGdO3eic+fO6NatG8epRQu1vGwcQkND+R7n5+fj2bNntY6nav3v5+LiAoZhqlXc10fV66iAg4iSL98L9X1vyMvLw9ramtrwCsjgwYNx8+ZNREVFQUdHB5qamli2bBm2bdsGNzc3uLm5Afi873R1dbFo0SJuA4uwd+/eoVWrVnzFGy9fvuS1gP3tt98AAD179kT//v1x584dKuAgYkFCQgKdO3dG586duY5CCOcUFBQwfPhwrmOINdoHjYOmpiZmzZrFdQxChKZ///64evUq7t27h2HDhsHc3Bw9e/bEo0ePMGLECDRv3hwFBQWoqKiAlJQUfv75Z64ji6QZM2YgLCwM+/btw4sXLzB27FgYGRlBXl4ehYWFePXqFby9vXHz5k0wDIOZM2fyXvvw4UMUFBRg0KBBHP4Goo2O0cIjKysLKan/3SJv1qwZACA9PR1aWlp8YxUVFWlZJwHKy8vD7t27cfnyZb4J6zVhGAbR0dFCSsYtKuAg9XLx4kUwDIMjR46gS5cu1Z6XkpKCra0t2rZtiwkTJsDPz48KOBqQh4cHGIbB0qVLq7XLkpOTQ+fOnbF//364ublh586d8PDwoAKOBkYtLxuHrVu3ch1B7MybN4/rCOQriouLERkZiYSEBBQWFkJeXh66urowNTWttTsN+XemTJkCW1tbAJ+LAQYOHIhOnTph3759NY6vai1atewWEQwTExPcvHmTb5uzszM6d+4MPz8/vnbIDg4OkJOT4yip6Pv48WO1dtPh4eEAwHeRT1VVFW3btkVSUpJQ8xFCCCGEEAJ8Xl7xn+fQbdu25TqWSBs4cCA8PT2hrq7O2+bq6opt27bh6tWryMnJAQC0b98ey5YtQ48ePThKKtr69euH1atXY8eOHQgICEBAQEC1MSzLQlJSEitWrEDfvn152zMyMjBx4kSMHDlSmJEJEYjWrVvjw4cPvMft2rXDnTt3EBYWxlfAkZ+fj4SEhGpdOUjDKCgowLhx45CYmAhVVVVISEigsLAQ3bp1Q05ODhITE1FeXg4ZGRl06tSJ67hCRUuokHoxMTGBhoZGvVpjDRs2DO/evcPTp0+FkEw89OnTB5WVlQgJCamzowDLsjA3N4eEhATu378vxITii1peEkK4UlpaCldXV5w5cwaFhYXVnpeTk8OkSZMwd+5cOskQkNWrV0NXV5dmzv3HfOvSN6T+unTpAnV1db62oosWLcKNGzfw559/wsTEhLd93LhxePnyJZ0zEJGUnZ2N8+fP4969e9VuDvXt2xfjx4+n4j4iNkpLS3Ht2rUa3w8WFhYYPnw4fVcVMNoHjUd4eDiCg4Nr3A9mZmZcxxMLFy5cwPHjx5GSklLtOS0tLcycORP29vYcJBNvFRUVyM7OhqysLF83PyI4MTExcHd3R0hICLKysnjblZWV0adPH0ydOrVacT5peHSM5s4vv/wCX19fhISEoGXLlnj8+DHvPG3btm285Xi3bNmCe/fuwdLSEocPH+Y6tsg5cOAAXF1dMX78eKxfvx6Ojo6IiorCy5cvAXzuznHy5EkcO3YMNjY22Lx5M8eJhYcKOEi99OnTB61bt4aPj89Xx44ZMwYZGRkIDg4WQjLx0LFjRxgbG+PChQtfHWtvb4+XL1/i+fPnQkhGCCGEC6WlpZg5cyZCQ0PBsiyaN28ObW1tKCsrIysrC8nJycjNzQXDMDAzM4O7uzvf0meEiBJ/f/96txitrKzEqlWreEvTkYY1cuRIxMfH4/bt21BVVUVxcTEsLCxQWVmJsLAwSEpK8sYOHDgQ5eXlCAoK4i4wIQIQFBSElStXIi8vr8ZlthiGgaKiIrZu3Yr+/ftzkFB8bdq0CbGxsfDw8OA6ith4/vw5lixZgpSUlFrfD1paWti1axdfkR9pOLQPGoe3b99ixYoViIqKAlB9WUYA6Nq1K7Zv316tbTtpGCzLYvny5bh69Srv76+kpMQ7h87OzgbweX8MHz4cu3fv5jKuSDp16hQYhsG4cePohnQjk5+fj6KiIsjJyfGWkSCCR8dobt26dQtz587F1q1bed12ly5diqtXr/JN+mFZFrKysjh37hwVNQnA6NGjkZCQgODgYDRv3rxaAUcVT09PbNq0CRs2bBCbQktaQoXUy48//ogbN24gKysLysrKtY7LzMxEbGwshg0bJsR0oq9FixZITU396jiWZZGamooWLVoIPpSY6d69O5o1a4br16/TSUYj8unTJyQlJfGqk3V0dNC0aVOuYxEicO7u7nj06BFatGiBlStXYsSIEXwFGuXl5bh8+TJ27tyJ8PBwuLm50dJmAlZcXIyoqCjEx8fzPpPatWsHU1NTyMjIcB1PpK1cuRItWrRA79696xzHsixWrFiBq1evUgGHgFhZWSE2NhazZ8/GmDFjEBQUhMLCQlhbW/MVb+Tk5ODdu3fo2rUrh2kJaXgvXrzAvHnzUF5eDi0tLUyYMAF6enpQUVFBZmYm4uPjce7cOSQnJ2PBggU4f/48OnbsyHVssREdHc27eUoELzk5GVOmTEFhYSEUFBQwevToau8HPz8/JCcnY+rUqfDx8YGOjg7XsUUK7YPGITMzE46Ojvjw4QMkJCTQt29fvv3w5s0bBAcHIzIyEhMnToSPjw9UVFS4ji1yzp07hytXrqBp06aYOXMmHB0d+bphffz4EZ6enjhx4gT8/f3RrVs3ODo6cphY9Gzbtg1aWlpwcnLiOgr5h2bNmlHhhpDRMZp7VlZWuHv3LuTl5Xnbtm/fDgMDA1y8eJFvOd4FCxZQ8YaAJCcnQ0NDA82bNwcAXmf78vJySEn9r4TB0dERhw4dwoULF6iAg5AvLVq0CMHBwVi0aBH27t1b44lEZmYmFi9eDDk5OSxcuJCDlKKrc+fOuH37Nv744w84OzvXOu7UqVPIysriW2OcNIzy8nIoKytT8UYj8ejRIxw+fBjh4eGoqKjgbZeUlESPHj0wZ84cdO/encOEos3Pzw+rV6/G3LlzMW/evFrHubi44NChQ9i5cydGjBghxISi7+LFi2AYBkeOHEGXLl2qPS8lJQVbW1u0bdsWEyZMgJ+fHxVwCEhlZSVcXV3h4eGBgoKCas8rKCjA2dkZs2fP5ruBTRrW/PnzcfLkyVpnpVQVb1y5cgUaGhpCTic+ZsyYgRs3buDly5fYsmULr0PQP88Nbt68CZZlaV1rInIOHjyI8vJy2NnZYcOGDdWWVezXrx+cnZ2xbt06XLhwAYcOHaI2vERk7d+/H4WFhbC0tMSuXbtqbIm/aNEiLFu2DEFBQThw4ADNeG9gtA8ahwMHDuDDhw8wMTHB7t27a+ywkZKSgqVLl+LZs2c4cOAANmzYwEFS0fbXX3+BYRjs2bMHAwYMqPZ8y5YtMW/ePHTo0AE///wz/vrrLyrgaGBKSkpUJNAIJSUlVZuIQoUCgkfHaO5JSEhAVVWVb5uUlBRmz55N11CF7Mtjg6ysLIDPhZWtWrXibWcYBhoaGoiPjxd6Pq7QEiqkGj8/vxq3JyYm4sSJE5CQkMCgQYOgr6/PazMXFxeHgIAAsCyLGTNmQEdHBzY2NkLNLcpCQ0MxefJkSEhIwNraGpMnT4ahoSGaNm2KT58+4fXr1/Dw8IC/vz9YlsWpU6fo5nUDGz16NIqKihAQEMB1FLHn7u6OXbt2obKykrdNVlYWxcXFvMcSEhJYtmwZpk2bxkVEkTd79mzcvXsXQUFB1b7ofiktLQ1WVlawsrKCq6urEBOKPhMTE2hoaOD69etfHTts2DC8e/cOT58+FUIy8cKyLBYsWIDAwECwLItmzZpBW1sbrVq1QkZGBpKTk5Gfnw+GYTBw4EAcPHiQ68gi6caNG1i8eDGaN28OT09PtGvXju/5qnbJVcUbHh4e1JpagAoKCuDl5YWEhARoaGhg7Nix1Yq/9+7di7i4OMydOxcdOnTgKCkhDa9nz56oqKjAgwcP6iz8/vTpE8zNzSEpKYlHjx4JMaF4q60dLxGM3r17o6ioCMHBwXXesMvLy0Pfvn0hKyuLhw8fCjGh6KN90Dj07dsX2dnZuHXrVp3nz+np6RgwYABatmxJy1ILQOfOnaGiooJbt259deyAAQOQmZmJJ0+eCCGZ+Fi4cCHu3buHhw8fUpfKRsDX1xeHDx9GSkpKtee0tbUxZ84cur8jQHSM5l5qaiqaNm1a54oDVbKysvDp0yeaECQAQ4YMQVlZGW7fvg0A+P3333H+/HkcOXIE/fr1442rrKxEnz59UFRUhMePH3OUVrioAwepZtWqVXxrPH2pqt7H39+/1ueOHDkCAHSAb0A9evTA4sWLsXfvXly5cgVXrlwBAMjJyaGoqAjA//7+S5YsoeINARg1ahR27dqFyMhImJqach1HbIWHh/Pa3g8ePBjTpk2DoaEh770QGxsLd3d33LhxAzt37kTnzp3RrVs3jlOLntevX0NFRaXOi08AoKamBhUVFbx+/VpIycSHoqIi5OTk6jVWVlaW14aONKyLFy8iICAA8vLyWLJkCezt7flu2JWWluLChQvYu3cvAgMD4efnR9+PBGDIkCH47bff8Ntvv2H69Ok4f/487/OJijeEr6rrTF0WL14snDCECFlpaSn09fW/2rWvadOm0NXVRVxcnJCSESJ8hYWFMDAw+Opsa0VFRejp6eHNmzdCSiY+aB80Djk5OTA0NPzq+bOqqioMDQ3p2CAgcnJyaNmyZb3GtmzZkne9lTScOXPm4M6dO9i8eTM2bNhQ6/0HInibNm2Cp6cn735CixYteBNRcnJykJSUhNWrV+P58+dYu3Ytx2lFEx2jude/f3+YmZnhzJkzXx27aNEiREREIDo6WgjJxIuhoSGCgoJQWlqKJk2aoHfv3jh37hwOHDiALl268K5pHzx4ENnZ2ejcuTPHiYWHCjhINXTzv3H66aef0LFjRxw5cgSRkZGoqKhAYWEhgM/LRnTr1g2zZ8/+6vrv5N9xdnZGWFgY5s2bh99//x0DBw6kEw0OeHh4gGEYLF26FDNmzOB7Tk5ODp07d8b+/fvh5uaGnTt3wsPDgwo4BCAjI6Pe6/6pqalRAYcA/Pjjj7hx4waysrLqrBTPzMxEbGwshg0bJsR04sPb2xsMw+DAgQMwNzev9nyTJk0wceJEtG3bFtOnT4ePjw8VcAjIuHHjkJ2djf3792PatGnw9PSEoqIib9kUdXV1Kt4QgsePH9e4rBMh4kJXVxcfPnyo19gPHz5AV1dXwInIl6gBrXBpamoiNze3XmPz8vKgqakp4ETih/ZB46CmpoaSkpJ6jf306RPU1NQEnEg8mZqa4sGDBygoKKhxqYIqBQUFePPmDfr06SPEdOIhPz8fP/30E1xdXfH8+XOMGjUK7dq1q3NyCt2naHi3b9/GmTNnICUlBScnJ0ybNo1vmYKMjAycPHkSp06dgqenJ8zNzWFlZcVhYtFEx+jG4VvOD+hcQjD69euHgIAAPHjwAJaWlujfvz/at2+PFy9ewNLSEu3atUNWVhbS09PBMEy1e0KijAo4SDWnT5/mOgKphbm5OczNzVFcXIykpCTe2nTa2tr1nolN/p2pU6eCZVnk5uZiwYIFUFBQQNu2bXlrcv0TwzDw8PAQckrRFxUVhZYtW2L69Ol1jps2bRrc3NwQGRkppGTiRU5ODllZWfUam52djaZNmwo4kfhZtGgRgoODsWjRIuzdu7fa8gTA5+KNxYsXQ05ODgsXLuQgpeh79eoVNDU1ayze+JK5uTm0tLQQExMjpGTiac6cOcjKysKZM2cwc+ZMaGlpwd/fH+rq6jh16hQVbwjB+PHjoa+vDzs7O4waNQpKSkpcRxI7eXl5cHd3x927d5GcnFzn7FGGYWgGUQNzcHDA+vXrcfHiRYwePbrWcRcvXkR6ejrmzp0rxHRk3bp1yM/P5zqG2Bg9ejT27t2LkJCQOr8rhYSEICUlBUuXLhViOvFA+6BxGDx4MNzc3PDixQv88MMPtY578eIF3rx5g1mzZgkxnfiYN28e7t+/j19++QU7d+6ssVtWaWkp1q5di8rKSjpGC4CTkxMYhgHLsoiJifnq+TF9VxWMc+fOgWEYbN68ucbvq61atcKKFStgZGSEFStW4OzZs1TAIQB0jP5vKSwshLS0NNcxRNKQIUPQpEkT3vI0kpKSOH78OFatWoUHDx7gxYsXAD53Clq6dCkGDRrEZVyhYlgqGyKEkK+qb7eBKgzD0NrKAtCxY0cYGxvjwoULXx1rb2+Ply9f4vnz50JIJl6cnJwQHh4OLy+vr16AGjt2LExNTXH27FkhJhR9fn5+SExMxIkTJyAhIYFBgwZBX18fysrKyMrKQlxcHAICAsCyLGbMmAEdHZ0afw51g/g+JiYmMDQ0hJeX11fH2tnZ4fXr13j69KkQkom3pUuX4urVq2AYBqqqqjh16hS0tbW5jiUWfvzxR+Tk5IBhGEhJScHKygpjx45F3759qXOZEKSlpcHR0RHv37+v9+wgKixreJs2bcL58+cxfvx4TJo0CW3btuU9l5iYCE9PT97zv/zyC3dBCRGwiooKzJ8/H48ePcL8+fNhb28PeXl53vNFRUX466+/4OLigp49e+LgwYOQkJDgMLHooX3QOBQXF2Py5MlIS0vDunXrarzxEBgYiI0bN6J169Y4ffo0ZGRkOEgq2sLCwhAZGYmDBw+iefPmsLOzg56eHu8c+s2bN/D29kZubi7mz5+Prl271vhzqCPEv9e/f/9vfs3t27cFkES8/fjjj2jatCnu3r371bH9+vXDp0+f8PfffwshmXihYzT3jIyM0K1bN3h6etY6prS0FKGhofjpp5+gqamJGzduCDEhycjIwLt37yAjIwN9fX1ISYlXTwoq4CCEkHrw9fX95tfY2toKIIl469OnD1iWRUhISJ3jWJZFnz59wDAM7t+/L6R04uPs2bPYsGED9PX14ebmVuNavh8+fMC0adPw5s0b/PLLL5g0aRIHSUWXkZERb+YKgBpvjNb1XBUqNPs+AwcORFZWFu7fv893ov1PhYWF6NOnD5SUlHDr1i0hJhQ9qampXx1TVlaGJUuWICUlBfv376+xgKmqsp80rPLycty6dQve3t4ICQlBRUUFGIZB69atYWtri7Fjx1InFAFasWIFLl26BD09PSxevBidO3eGiooKFc8I0YABAwAA6enpqKioAABISUmhRYsWyMnJQXl5OYDPs4pq+v4EfD5uBwYGCicwIQI0efJksCyLqKgoVFRUQEpKCurq6lBSUkJ2djbS0tJQVlYGKSkpdOnSpcbPKups+X1oHzQOq1evRmlpKW7cuIGKigq0bt0a7dq14+2HhIQEpKenQ0pKCoMHD66xMwTDMNiyZQsH6UVH1Tk08PlcubZz6Lq+N1FHCCIKOnXqBCMjo3pPjouJicGzZ8+EkEy80DFa+FxcXHDo0CHe46995v+Ts7MzVq5cKYhoYq3qOqmFhUWN34HEGRVwEPIfUlBQgEePHiElJQWFhYV1zqybN2+eEJMRIhxz587F7du3sXLlSjg7O9c6zsPDA1u3bsWgQYNw8OBB4QUUE2VlZXBwcMDLly+hoKCAkSNHonPnzmjWrBny8/Px+PFjXLlyBQUFBejQoQP+/PNPajPXwJycnBrk59Cyad9n3bp1uHDhAkaMGIFt27ZBUlKy2piKigqsWrUKV65cgb29PTZs2MBBUtFhbGz83T+DLrwKx4cPH+Dr6wsfHx8kJSXxLoz06NEDY8eOxZAhQ2iJrQbWu3dv5OfnIyAgAGpqalzHEUvf2rWvJtTJj4gKej9wj/ZB4/DP4vt/g/bD9/s33R9qQh0hyH9dv379UFxcjJCQkDqv1ZWVlcHc3ByysrL16tZBvg0do4XPxcUFLi4uvMf1PTbLy8vD2toaa9asoQ5ZAmBsbAx1dXU6vtaACjhINVUXxtu1a4erV6/ybasvujDe8I4dO4bDhw+jpKSkznFVlYN08CaiKDQ0FJMnT4aEhASsra0xefJkGBoaomnTpvj06RNev34NDw8P+Pv7g2VZnDp1itpbCkhGRgbmz5+Px48fV6tWrvpqYWpqiv3796NVq1ZcRCRE4FJSUjBq1CiUlJRAXV0dEydOhIGBAVRUVJCZmYnY2Fh4enri/fv3kJWVxcWLF6n7wHdqiIscAC0bIWzh4eHw9vbGjRs3UFRUBIZhoKCggBEjRmDs2LHo2LEj1xFFgomJCdq2bYtLly5xHUVshYaGNsjP6dGjR4P8HEK49G+6WNaEOlv+e7QPGocvbxZ9D5qoRQhpCEuXLoW/vz+mTp2KFStW1Dpux44dcHd3x8iRI7Fz504hJhQPdIwWvvz8fOTl5QH4fO164MCB6NSpE/bt21fjeIZhICMjAyUlJSGmFD+9evWCpqZmvboCiRsq4CDVVF0Y19XVxbVr1/i2fQu6MN5wPD09sXHjRgBA69at0b59eygrK9fZ4mnr1q3CiicWjI2N0a1bN5w5c+arY52cnBAREUFFTAJy9OhR7N27l+/fv5ycHIqKigD8r3hgyZIlmDVrFicZxQXLsrh16xZu3ryJuLg4FBYWQl5eHoaGhhg0aBCvjTghouzBgwdYvHgxcnNza23D27x5c+zduxe9e/fmICEhjUdRURHOnDmDAwcO8JaXAD6fa0ycOBE2NjZit6ZpQxo+fDhYluWdwxFCCCGEEELIl2JiYmBnZ4eKigp07doVkydPrjYRxcPDA48fP4akpCS8vLwabCIFIY3J6tWroaurS/cPODZr1iw8efIEDx48qLGzsTijAg5C/gOGDRuGxMREzJs3D7Nnz6YPMg4YGRmhW7du8PT0/OpYJycnhIeHUxcUAQoJCcGRI0cQGRnJdwNIUlIS3bp1w+zZs+lGKSFEaLKzs3H27FkEBwcjISGBV8ykq6sLCwsLODo6UsU+EWssy+LevXvw8vJCUFAQysrKAAB6enrIyclBVlYWGIaBvr4+jh07BnV1dY4T/zcdPXoU+/btw+XLl6Gvr891HEIIIYQQQni+ZZKPpKQkFBQU0KZNG5iZmWHkyJF0Tt2A/Pz88Ouvv6KsrKzWiSjS0tLYuHEjbGxshB+QECI2QkND4ezsjDlz5mD+/Plcx2lUqICDCERxcTFkZWW5jiEyTExM0Lx5cwQHB3MdRWx9SwGHnZ0dYmJi8Pz5cyEkE2/FxcVISkri3SzV1taGnJwc17EIIWKiqtuYvr4+dQ0gpAZJSUnw9vaGn58fMjIywLIsZGVlMWzYMDg4OKBLly4oLy/HrVu34OLigtjYWAwePBgHDhzgOvp/Unl5OWbMmIG0tDTs2LEDJiYmXEcihBBCCCEEwL9fDrNq+cUtW7Zg0KBBDZxKfMXFxcHNzQ3BwcHIzMzkbVdRUYGFhQWmT59OReFEbKSnpyM8PBxpaWkoLi6mZcuEKDU1FVeuXMGBAwdgYWEBW1tb6Onp1Xl/WUNDQ4gJuUMFHKRBFRYW4vTp0zh16hQePHjAdRyRYWlpCRUVFXh5eXEdRWzVt4AjPj4etra2UFZWxu3bt4WUjhBCCBeMjIzQunVr3Lt3j+sohDQaJSUluHbtGry9vREREQHg8wyuH374AQ4ODrC2toaCgkK11xUWFmLAgAFgWRaPHj0SdmyRsHr1apSXl+PatWuoqKiAkZERdHR0ar3wwTAMtmzZIuSUhBBCCCFEHL179w4BAQHYtWsXOnXqBHt7exgbG0NeXh6FhYWIiYmBl5cXnj59iqVLl6JPnz548+YNvLy8EBwcDGlpaXh5eaF9+/Zc/yoip6CggDc5rqZzNUJEVX5+PjZu3IirV6+isrKSt/3LzuoLFy5EQEAAfHx8aDkhATA2Nv6m8QzDIDo6WkBpGheaKkgaREFBATw8PHDq1Cnk5eVxHUfk9O3bF5cvX+Z9kSKCV/Xv+UvPnz+vs93fp0+fkJWVBQDo06ePQPMRIiyTJ08GALRp0wZbt27l21ZfDMPAw8OjwbMRwrXmzZtDTU2N6xhijz6TGo+1a9fi2rVrKCoqAsuyaNasGUaMGAEHB4dqJ+WbNm1CUVERr4BAXl4e+vr6vKIP8u18fX3BMAyq5mi8fPmyziX9qICDiKpjx47B1tYWrVq14joKIYQQQv5fWloadu3ahYkTJ2L16tXVnjc2NoatrS22bduGXbt2oWPHjhgyZAiGDBmCLVu24NSpUzh58iS2bdvGQXrRpqCgQIUbROyUlJRgypQpePnyJWRlZdGpUyfExsbi48ePfOPs7e1x48YNBAYGUgGHAHxrjwlx6klBBRykVp8+fcLx48dx/fp1vH37FjIyMvjhhx8wa9Ys9OzZEwBQUVGBkydP4tixY8jPzwfLsmjVqhWmT5/OcXrRMnfuXNy5cwe//vortmzZAhkZGa4jibz8/Hy8e/eO95hhGHz69IlvW23Mzc2xePFiQcYTe4WFhfj777+RkpKCwsLCWg/cDMNg7ty5Qk4nWkJDQwEA7dq1q7atvmpaT5MQUWBoaIj4+HiuY4i9+nwmVX0OsSxLn0kCVNUtztTUFA4ODhg6dGit31uvXLmC3NxcvgKCvn37QktLSyhZRRG1eSXksz179vBa8NrZ2cHS0hKSkpJcxyKEEELE2uHDhyEvL4/ly5fXOW7p0qXw9fXFkSNHcOLECQCfZ8CfP3/+m69HkbqVlpYiOjoaaWlpKCkpgY2NDdeRCBGaU6dOITo6Gqampti3bx9at24NR0fHagUcPXv2hLS0NO7fv0/n3AJQtTw1qY6WUCE1Ki8vh5OTEx4/flztxqiUlBQOHjwIExMT/PTTT3jx4gVYloWGhgZmzJgBOzs7NGnShKPkoisxMRHLly9HWloaRowYAS0tLcjJydU6nr5wfZ93797xijVYlsWUKVNgaGiItWvX1jieYRg0bdoUWlpaaNmypTCjip2TJ0/iwIEDKCkp4W375+dU1exThmHqnHlKvq7q5FhGRgYmJiZ8275Fjx49GjQXIY1BQEAA5s+fj/Xr12P8+PFcxxFbdX0mFRcXIyEhAX/99RdSUlKwYsUKtG/fnj6TBGTbtm2wt7eHnp7eV8f++OOPyM3NpeM0IaTBzZkzB8HBwSgvLwfDMFBWVsbo0aMxduxYvqJkQgghhAhPz549oa2tjQsXLnx1rL29PZKTk/mWVrSxsUF8fDyePn0qyJhioby8HIcOHcKZM2dQUFDA2/7ludmvv/6KBw8ewN3dHTo6OlzEJESgbGxsEBcXh5s3b0JDQwMA4OjoiKioqGrXKaytrZGZmUnLvRKhog4cpEZ//fUXoqKiwDAMrK2t0blzZ5SUlCAoKAiRkZHYtm0bVFRU8Pz5c6iqqmL+/PmwsbGBlBT9kxKUly9fIjMzE5mZmfjjjz++Op4KOL5PmzZt0KZNG97j7t270w2fRsDHxwfbt28HABgZGcHExAQqKiqQkJDgOJnoqunfPL0PCPls0KBBWLZsGbZs2YK4uDjY2tpCT0+POmUJ2dc+k/r16wcnJyesXbsWBw8ehI+Pj5CSiZ9Vq1ZxHYEQQnD48GFkZWXB19cXPj4+iI+Ph7u7O9zd3dG1a1fY2dlh2LBhkJWV5ToqIYQQIjZKS0vx4cOHeo398OEDSktL+bZJS0tDWlpaENHESkVFBWbPno2QkBAAn6+Bf/z4EUVFRXzj+vbtiwsXLiAgIAAzZszgIiohApWUlARNTU1e8UZdmjVrhqSkJCGkEj9+fn5QVlaGhYXFV8fev38fmZmZYnPvk+62kxpdu3YNDMNgw4YNsLe3522fNWsWli9fjsuXLyM5ORl9+vTBvn37aI00AQsMDMSSJUvAsiyaNm0KTU1NKCkpcR1LrJw+fZrrCASf9wPDMFixYgWmTp3KdRxCiJgzNjbm/b+npyc8PT3rHM8wDKKjowUdi9RAUlISv/zyC/z9/XHgwAHs2LGD60iEEEIESFlZGTNmzMCMGTPw+PFjeHl54dq1a4iMjERUVBQ2b96MYcOGYezYsejatSvXcQkhhBCRp6enhxcvXsDX1xe2tra1jvPz80N6ejo6derEtz0lJYWuhzeAP//8E/fv30e7du2wZ88eGBkZ8boOfMnCwgKSkpK4e/cuFXAQkVXfZRZzc3MhLy8v4DTiadWqVTAzM6tXAcfRo0cRHh5OBRxEvMXGxkJRUZGveKPKrFmzcPnyZUhLS2P79u1UvCEER44cAQA4ODhg+fLlaNasGceJCOFGfHw8lJWVqXijEcvPz8edO3fw4cMH/PDDD+jVqxfXkQgRmG9diZBWLuSWgoIC9PT08ODBA66jECJwLMsiMTEROTk5KC8vr3Vc9+7dhZiKEG506dIFXbp0wdq1a3H9+nV4e3sjPDwc3t7e8Pb2hq6uLuzt7TF69Gi6MUQIIYQIyKRJk7Bq1Sr8+uuveP36Nezs7PiWXnzz5g28vb1x6tQpMAyDSZMm8Z578uQJcnJy0Lt3by6iixQ/Pz9ISEhg7969aN++fa3jZGRkoKWlhTdv3ggxHSHCo6mpiaSkJBQVFUFOTq7WcRkZGUhKSuItLU4aHl0vrRkVcJAa5efn880q/VLVmmc6OjpQVlYWZiyx9ebNG7Ro0QK///47GIbhOg4hnJGVlYWamhrXMcSev78/jh8/DkdHR75Cv/j4eEybNg3p6em8bTY2Nti6dSsXMUXa5MmT6z1WUlISCgoKaNOmDczMzGBpaUlLnjWQW7ducR2BfKO8vDzk5eVxHYMQgcnLy8Pu3btx+fJlFBcX1zmWugI1vLCwsHqPlZCQ4B2faVKEcMjIyMDGxgYjRozAqVOnsGfPHlRUVCA+Ph47duzAnj17MGzYMMybNw/a2tpcx/3PS01NrfdYSUlJyMvL03uhgdE+IOR/Vq9eXe+x/zyHru0aOfk2NjY2iI6OxqlTp/DHH3/gjz/+gJSUFOTk5FBcXIyysjIAn2/kTZkyBaNHj+a99vnz57C0tBSbWdeC9ObNG2hoaNRZvFGlefPmePv2rRBSiR86RnPPysoKx48fh6urK5YtW1bruN27d4NlWQwYMECI6UhNMjIyxGrZarp6T2pUUVGBpk2b1vhckyZNAACKiorCjCTWZGVloaGhQcUbROx17doVERERKC8vpxvQHLp+/TpiYmJgamrKt33r1q1IS0tD69atoaenh8jISPj5+cHCwgLDhw/nKK1oCg0NBQDecaGmSuWanvPw8ICGhgZ27dpF7cIbQJs2bbiOQL7Bo0eP8O7dO7opR0RWQUEBxo0bh8TERKiqqkJCQgKFhYXo1q0bcnJykJiYiPLycsjIyFRrSU0ahpOT0786Z2vfvj0mTpxYYwdM0nCqZvZeunQJWVlZYFkWioqKsLa2RmZmJu7cuYNLly7h5s2bOHHiBMzMzLiO/J/Wv3//b34/KCoqwszMDI6OjjA3NxdQMvFB+6Bx+Jab/18W95mZmcHBwQH6+voCTCc+fH19AXzbOXTV4y5dumDLli3Q1dUVRlSRtmbNGvTq1QsnTpxAVFQUysrKkJubC+Dzv39TU1PMmDEDlpaWfK+bOHEiJk6cyEFi0VNRUVHvpSCKiorE6mapMNExmntTp07FhQsX4ObmhqysLDg4OKCiogIAkJOTg9evX+PkyZO4c+cO1NXVMWHCBI4Ti4bU1FS8e/eOb1t+fn6dkyFKSkoQFhaGxMREdOjQQdARGw26+0XIf0D37t3x4MEDlJaW8gpoCBFHc+fOxYQJE3DkyBHMmzeP6zhiKyYmBoqKinytLjMzMxESEoJWrVrB398fCgoKCAoKwuzZs+Hj40MFHA3s1KlTePLkCfbv3w81NTWMHj0axsbGkJeXR2FhIWJiYnDp0iW8f/8eCxYsgKGhIeLi4uDn54fY2FjMnDkTfn5+0NTU5PpXIeS71XWSx7IsMjMz8fjxY3h5eQEARo4cKaxoIs/FxeVfv7akpKQBkxAAcHd3R0JCAsaPH4/169fz1rI+c+YMgM/dOU6ePIljx45BW1sbmzdv5jix6KlakiYqKgrl5eVo2rQp2rZtyzs+JyYm4tOnT5CWlkaXLl1QXFyMpKQkxMTEYN26dbh//z7279/P8W8hWgoLC+Hv7w9vb288efKEd1OuW7dusLe3x7Bhw3iTVzIzM7F37154e3tj9+7dOHfuHJfR//M0NDQAAB8+fOAt5aSgoMB7PxQUFAAApKSk0Lp1axQXF+Pjx4+4desWbt++DWdnZ6xcuZKz/KKA9kHj8C1twSsqKpCbm4vc3FxER0fj7NmzWLlyJZycnASYUDxs3boVb9++xdGjRyEjI4OBAwfCyMiI93549eoVAgMDUVJSglmzZkFJSQlv3rzBzZs3ERUVhSlTpsDPz4+W2moAVlZWsLKyQlFREZKTk1FYWAh5eXloa2vXuYwBaRhqampITk7+6uS4vLw8JCQkwNDQUIjpxAcdo7nXsmVLHD16FHPmzIGvry/8/Px4z1UtCc6yLFRUVHD48GHqgNJAfHx8cOjQIb5tsbGx9e42LU6THhiWFpchNTAyMoKGhgbGjBlT4/MuLi51Pg+Abq42oDdv3sDOzg729vZYs2YN13EIEYraWsndvXsXW7ZsQe/eveHg4IC2bdtCVla21p9T9YWYNBwzMzNoamryfbH19/fHkiVL4OzsjFWrVvG29+3bF+Xl5Xjw4AEHSUVXbGwsHBwcYGVlhW3bttVY3FdWVoZVq1bh9u3bOH/+PNq3b4+KigosX74c/v7+cHR0xLp16zhIL5qKi4sRGRmJhIQE3gUoXV1dmJqa1vkZRb6fkZHRV2euVJ3yDBgwAPv27YO0tLQwoom8+vzta8OyLBiGwcuXLxs4lfgaPXo0EhISEBwcjObNm/MKOP75N/b09MSmTZuwYcMGsbr4IQyVlZVYtGgRQkJCsGrVKowaNYqvs2VpaSkuXryIHTt2oFevXti/fz9YlsX169fx+++/Iy8vD1u3bqX24A0gPDwc3t7euH79OkpKSsCyLFq2bAkbGxvY29ujXbt2tb52wIAByM7ORlRUlBATi6bt27fj9OnTmD59Ouzt7fmKh9+9e8eb9Thp0iSsXLkSHz9+hJeXF1xcXFBaWopDhw6hf//+HP4G/320DxqHM2fOYPv27RgyZAjs7e2rFd9fuHAB169fx4oVK2BnZ4c3b97Ay8sLf/75JwDg9OnT1BXoO6WlpcHW1haGhobYt28fWrZsWW1MTk4OFi5ciNevX8Pb2xsaGhooLCzEzz//jNDQUEyfPr3ONvuE/Bds3LgRZ8+exYoVKzB16lQAqPG8YefOnXB3d8fs2bOxcOFCruKKNDpGNw7Z2dlwc3PDzZs3kZKSwtuupqaGoUOHYubMmVBWVuYwoWjx8PCAh4cH7/H79+8hLS0NFRWVGsczDAMZGRloaWlhxIgRGDFihLCico4KOEiNvnYx9p+t5GpCF2MbTlhYGJ4+fYq9e/fC0NAQY8aMgZaWVp1VyVWzvwj5r2qINUZpbXfB6NixI/T19fkKODZt2gRPT89qJw/29vZ4+fIlnj9/zkFS0bVgwQLcv38f9+/fr/NYUFRUBHNzc1hYWODAgQMAPp+YWFhYQFNTEzdu3BBWZJFVWloKV1dXnDlzBoWFhdWel5OTw6RJkzB37lzqoiUgdc1IZBgGcnJy0NHRgaWlJW8WBWkYDTEb9PTp0w2QhACfl5pTVVXF9evXAQCTJk1CREQEnj17xje7jmVZmJubQ1NTE3/99RdXcUXSyZMnsWPHDri7u9f5efPw4UNMnToVK1aswLRp0wAAt27dwty5c9GzZ0++C1rk2w0ZMgTJycm8QrFevXrB3t4eAwcOrFcBn5OTE8LDw+maxnfy8fHBL7/8gp07d9Z5ofXq1atYtmwZNm7cCDs7OwCAl5cX1q5dC0tLSxw5ckRYkUUO7YPG4fbt25g7dy6WL1/O+8yvyR9//IHt27fDxcUFAwYMAAAcO3YMe/bswZAhQ6hD03davXo1rl69iqCgoDq7aGRlZcHS0hLW1tbYtm0bgM83UgcOHAg9PT1cuXJFWJFFXkZGBtLS0lBSUkLXsYUoNTUV1tbWKCsrw+zZs+Hg4IBFixbxCjjevXuHkydP4syZM2jevDmuXbtGnWcEgI7RjVNxcTHy8vIgLy9PHTeExMjICN26dYOnpyfXURodKuAgNaKLsY1LVUFN1QWor6Gb1g3v1q1bAAALCwu6ASckRkZGDfJzYmJiGuTnkP/p27cvioqK8PDhQ95F8KFDhyI5ORkPHz5E8+bNeWNHjRqFDx8+4O+//+Yqrkjq3bs32rRpgwsXLnx1rJ2dHVJTU/m6oIwcORIpKSl4/PixAFOKvtLSUsycOROhoaFgWRbNmzeHtrY2lJWVkZWVheTkZOTm5oJhGJiZmcHd3Z06PxBCBKZr167Q19fnHRtmzpyJ+/fv4969e2jVqhXfWDs7OyQmJiI8PJyLqCJr5MiR+PTpE27evPnVsYMHD0bTpk1x+fJl3jZzc3OUl5fj0aNHgowp8oyMjNC6dWuMGTMGdnZ237xkXHBwMDIzM2FrayughOJh7NixyM7Oxp07d7461srKCkpKSvD29gbwuZvNjz/+CCkpKerk9x1oHzQOkyZNQmJiIu7fv1/nOJZl0adPH+jq6vKWPystLUXPnj3RrFkz3Lt3TxhxRZaFhQVat27N+zdelzFjxiAjIwPBwcG8bcOGDUNaWhp1Z2oAXl5eOHHiBJKSkgBUv469Y8cOPH/+HDt37oSqqipXMUXanTt3sGTJEt6ylhISEqisrISMjAyvc5msrCxcXV1pEoSA0DGakM98fX2hrKyMvn37ch2l0al9kSsi1qj4onGhJSC4N2/ePKirq+P27dtcRxEbVHjReHXt2hU3b96Ei4sLZs6cCX9/fyQmJsLExISveKOiogLJycnQ0tLiMK1oKiwsxMePH+s1Nicnp1pnCFlZ2X+97AH5H3d3dzx69AgtWrTAypUrMWLECL4CjfLycly+fBk7d+5EeHg43NzcMHv2bA4TE0JEWevWrZGVlcV7XHXTOjo6Gv369eNtr6ysRGpqKm+tZdJwUlJSoK+vX6+xioqKiIuL49vWpk0bKsRvAK6urrC0tISEhMS/er2FhUUDJxJP8fHx9X4/qKio8L0fJCQkoK2tTeeE34n2QeMQExNT57JNVRiGQZs2bfj+5k2aNIGuri5iY2MFGVEs5ObmolmzZvUaW1JSgtzcXL5tioqKeP/+vSCiiZU1a9bA19cXLMtCSkoKDMNU+07avn17uLu7IzAwEBMnTuQoqWizsrKCl5cXDh48iKCgIF4hR3FxMaSlpWFpaYlFixZBT0+P46Sii47RhHxGRfO1owIOQv4DqGiAey1atKC1zgj5f9OmTcOtW7dw7NgxHDt2DMDni03/bAcbGhqKkpISdOrUiYuYIk1bWxtxcXEIDg6u8yZDcHAw3r59C0NDQ77t79+/r3HNX/JtLl68CIZhcOTIEXTp0qXa81JSUrC1tUXbtm0xYcIE+Pn5UQEHIURgDA0NERQUhNLSUjRp0gS9e/fGuXPncODAAXTp0oVXZHnw4EFkZ2ejc+fOHCcWPbKysnjz5g0KCgrqbLlbUFCAN2/eQFZWlm97SUlJvW8ukdrRWuCNg7S0NBITE3mfSbUpLS1FYmJitS5lFRUVkJeXF3RMkUb7oHFgWRZv3779alfdyspK3rgvVa39Tr6Puro6EhIS8Pz5c3Ts2LHWcc+ePUN8fDx0dHT4tmdkZKBFixYCTinarly5Ah8fH7Rq1QobNmxA37594eTkVK2rSf/+/cEwDG7fvk0FHAKkp6eHffv2oaysDElJScjLy4OcnBzatm1LnzlCQMfoxiMxMRH37t1DcnIyioqKqh2HqzAMgy1btgg5HRFn/246AiGEiJlOnTohOTkZFRUVXEchhHOdO3eGi4sLDAwMIC0tDW1tbWzYsAFDhw7lG/fnn38C+LzcB2lY9vb2YFkWCxcuhKenZ7UOG0VFRfD09MSiRYvAMAzs7e15z8XGxiIjI6PBlikSZ+/evYOOjk6NxRtf6tq1K3R1dZGamiqcYGLqzZs3WLduHYYOHYquXbuiQ4cOfM97eXnBxcWl2vuFEFHRr18/lJWV8dro9u/fH+3bt8eLFy9gaWmJsWPH8tZJZhgGM2bM4Dix6OnRowdKSkqwZs0alJaW1jimtLQUa9asQUlJCX788Ufe9pKSEiQkJEBNTU1YcQkRqC5duqCgoADbtm2rc9z27duRn5+Prl278raVl5cjMTGx2vJP5NvQPmgcOnTogI8fP/ImP9TmxIkTyM7O5vsOy7IskpKSaEJRAxg5ciRYlsXs2bMRFBRU45i7d+/i559/BsMwGDlyJG/727dvkZqaSt0IvtOff/4JhmGwd+9eWFlZQVJSssZxzZo1Q5s2bfD69WshJxRP0tLS0NfXh6mpKYyMjKh4Q0joGM29iooK/Pbbbxg2bBi2bt2KM2fOwMfHB76+vrX+RwQjLy8P+/btg62tLbp16wZjY+Na//vntT5RRh04CCGkHmbMmAFnZ2e4urpi/vz5XMchAFJTU3H//n0kJCSgsLAQ8vLy0NXVhbm5Odq0acN1PJFnaWkJS0vLOsds2rQJGzdupIpwAZg0aRLCwsIQEBCATZs2YevWrWjTpg3k5eVRWFiId+/eoaKiAizLYvDgwZg0aRLvtXfu3IGBgQGGDx/O4W8gGhQVFSEnJ1evsbKysnxLDJGG5ePjg/Xr16OsrIw3W+KfMxzz8vJw6NAhtGvXjv79E5E0ZMgQNGnShLf8oqSkJI4fP45Vq1bhwYMHePHiBYDPneWWLl2KQYMGcRlXJC1cuBD37t1DQEAABg8eDGtraxgZGfGOz69evYK/vz/ev38PGRkZvvOKa9euoaysjK+og/w7kydPrvdYSUlJKCgooE2bNjAzM4OlpSWkpOhSWUP4+eefERISgnPnzuHZs2ewtbWt9n7w9fXFs2fPICUlhZ9//pn32jt37qC4uBjdu3fn8Df476N90DjMmDEDYWFh2LdvH168eIGxY8dW2w/e3t64efMmGIbBzJkzea99+PAhCgoK6JjdAGbNmoX79+/j8ePHmDNnDpSVlWFoaMjbD69fv0ZWVhZYloWpqSlmzZrFe623tzdkZWVhZWXF4W/w3xcTE4PWrVvDzMzsq2OVlJRoWTki0ugYzb3Dhw/zJh+amJigQ4cOUFZWpiWnhSwtLQ2Ojo54//59rd1PvlSfMaKCYcXptyWEkH8pNTUVV65cwYEDB2BhYQFbW1vo6elVa3v8paqL56Rh5ebmYtOmTfD390dlZSUA8LUiZRgGw4cPx9q1a6m9JRFpLMvC09MT7u7uNXZ20NDQwPTp0+Ho6EgnHwKybNky3LhxA0FBQXXOisvMzISVlRWGDRuGHTt2CDGheHj69CkmTJgAAHBycsLAgQOxdetWREdH4+XLl7xxqamp6N+/P6ytrbF7926u4hLCiYyMDLx79w4yMjLQ19enG9QCFBERgaVLlyItLa3G4y/LstDQ0MCuXbtgamrK2x4VFYUPHz7AxMQE6urqwowscqq6jFX9/Wu67FXTcwzD8PbNl7Mcyb9348YNrFmzBoWFhbW+H+Tl5bF161YMHjyYt/3OnTtISEhA3759670+PKkZ7YPGwcPDAzt27OBdw/gnlmUhKSmJFStWYMqUKbztFy9exNOnTzFy5Mivdv0jX1dSUoL9+/fj/PnzKC4urva8rKwsxo8fj4ULF1IXAgEwMTGBnp4e3yx2R0dHREVF8Z23AYCNjQ2Sk5MRGRkp7Jhio7CwEH///TdSUlJQWFhY57IRc+fOFXI68UDHaG4NGDAAqamp2Lp1K2xsbLiOI7ZWrFiBS5cuQU9PD4sXL0bnzp2hoqJC17JBBRyEEFIvxsbG3zSeYRiqFBeAgoICTJgwAXFxcWBZFgYGBtDT04OKigoyMzPx5s0bxMbGgmEY6Ovr49y5c3WuP06+X3p6OsLDw5GWlobi4mLMmzeP60hi6c2bN0hISEBRURHk5OSgq6tL7V2F4O3btxg7diwMDQ2xd+9eqKioVBuTmZmJxYsX4/Xr1/Dx8aEOQQKwYMECBAQEYP369Rg3bhyA2i8E9unTB7KysggICOAiKiFETJSUlODq1asIDg5GYmIi7/jctm1bWFhYwNramm4MCVBoaCiePHmC/fv3Q01NDaNHj4axsTFvRmNMTAwuXbqE9+/fY8GCBTA0NERcXBz8/PwQGxsLBQUF+Pn5QVNTk+tfRSR8+PAB586dw/3792t8P4wfPx6tW7fmOqZIo33QOMTExMDd3R0hISHIysribVdWVkafPn0wdepUWuZSSAoLCxEeHl7t/WBmZkYdRAVowIAByM3NRXh4OG9bTedtJSUl6NGjB3R0dHD58mUuooq8kydP4sCBAygpKeFt++dtQoZheBPm/nleTRoOHaO5Y2JiAhUVFdy+fZvrKGKtd+/eyM/PR0BAAC0n+g9UwEEIIfXwb06iY2JiBJBEvO3YsQPu7u7Q0tLC5s2b0aNHj2pjQkNDsXbtWqSkpGDatGlYvnw5B0lFX35+PjZu3IirV6/yzSL68qRu4cKFCAgIgI+PD12IIiLJz88PiYmJOHHiBCQkJDBo0CDo6+tDWVkZWVlZiIuLQ0BAAFiWxYwZM6Cjo1Pjz6FK/+/TsdmBhQABAABJREFUp08flJWV4dGjR7xttRVwjB07FnFxcXjy5ImwYxJCCBGS2NhYODg4wMrKCtu2bUOTJk2qjSkrK8OqVatw+/ZtnD9/Hu3bt0dFRQWWL18Of39/ODo6Yt26dRykJ4SIg/z8fN5NumbNmnEdhxChWL16Nfz8/LBlyxbY2toCqPm8zc3NDTt37sSUKVOwevVqruKKLB8fH6xZswbA5+vdVTexJSQkan0NTdYiomjgwIFQVFSEj48P11HEmomJCdq2bYtLly5xHaXRob6phBBSD1SM0TjcuHEDEhISOHLkSK3dBXr06IHDhw9jxIgRuHbtGhVwCEBJSQmmTJmCly9fQlZWFp06dUJsbCw+fvzIN87e3h43btxAYGAgFXAQkbRq1SrerBQA8Pf3rzam6rkjR47U+nOogOP75OTkwNDQsF5jqQUjERVhYWEAABkZGXTq1Ilv27egdZOJKDp48CAYhsGmTZtqLN4AAGlpaWzcuBG3b9/GoUOHcODAAUhKSmLt2rW4ceMGQkJChJyaECJOmjVrRoUbROxMmzYNly9fxsaNG8EwDEaMGMH3fGlpKTw9PbF3717IyMjAycmJo6Si7fTp02AYBitWrMDUqVO5jkMIZwYNGoQzZ84gKyurzmWRiWBpamqirKyM6xiNEhVwEEII+c/48OED9PT0vro0hJ6eHvT19ZGUlCSkZOLl1KlTiI6OhqmpKfbt24fWrVvD0dGxWgFHz549IS0tjfv371O1vgDFxMTw1iytCxUJNDy68dk4tGjRAunp6fUam5KSQifmRCQ4OTmBYRjo6uryiseqttUXLflHRFV4eDj09PQgJydX5zg5OTno6enxtXJXUlJCu3btkJKSIuiYhBBCOJKfn4+UlBQUFRVVWzbiS3S+17AMDAywbt06rF+/HqtXr8bvv//Oe27kyJFISUnBp0+fICEhgQ0bNtBSZgISHx8PZWVlKt4gYu/nn39GUFAQFi1ahN27d9NSNRwZPXo09u3bh7i4OOjr63Mdp1GhAg5CCCH/GV9r6fclCQkJukknIP7+/pCSksKuXbvq/HIrLS0NbW1tJCQkCDGd+Lhx4wa2b9+O9+/f12s8FXA0vNOnT3MdgQDo1KkTgoKCEBERgW7dutU6LjAwELm5uejbt68Q0xEiGFU3FDQ0NKptI9wqLy+Hr68v7t69i+Tk5DpvDjEMg8DAQCEnFH2FhYXVCotrk5OTU60IVlZWljo2NaCHDx8iKCioXu8HDw8PIacTD7QPGoekpKR6Hxu2bNki5HTiISoqCjt37kRUVNRXx1Khq2A4ODhAW1sbu3fvxrNnz3jbY2NjAQAdOnTAihUr8OOPP3IVUeTJyspCTU2N6xjk/9ExmjvNmjXD2bNnsWzZMgwZMgQWFhbQ0tKCrKxsra+hCYoNb/r06Xj48CHmzZuHHTt2wMTEhOtIjQYVcBBCCPnP6Nu3L7y8vJCSkgItLa1axyUnJyM2Nhbjxo0TYjrxkZSUBE1NTb6bRrVp1qwZdUIRgDt37mDRokVgWRbKysowMjL6pgInQkTJuHHjcOfOHaxduxaurq7Q1dWtNub58+f47bffwDAMxo8fz0FKQhpWTQVkVFTGvby8PDg7O+Ply5d1zuitQkUCgqGtrY24uDgEBwfDwsKi1nHBwcF4+/ZttWW43r9/j5YtWwo6psgrLS3FwoULERQUBABffU/Q+6Hh0T5oHFiWxebNm3H27FmwLFuv/UAFHA0vMjISzs7OKC0thbS0NNq0aQMVFRX6d8+BH3/8ERcuXEB6ejpiYmKQl5cHOTk5GBoa1nmtjzSMrl27IiIiAuXl5ZCSotuDXKFjdOPg6+uLyMhIFBcXIyAgoNZxLMuCYRgq4BCAX3/9Fa1atUJ4eDjGjRsHIyMj6Ojo1FpII07fk+gTmhBCvkFaWhquXLmCly9fIicnp9b1uagqVjAWLVqE+/fvY86cOdixYwc6dOhQbUx0dDRWrlwJDQ0NLFiwgIOU4kFSUrJe43JzcyEvLy/gNOLn6NGjAIDJkydj2bJlta7vTog4sLS0hK2tLXx9fWFjYwMzMzNe6/uNGzfi9evXiIiIQGVlJSZNmlRnlw5CCPke+/btQ3R0NFq1aoXp06ejS5cuUFZWpgJLIbO3t8eWLVuwcOFCLF26FDY2NnzfR4uKiuDr64s9e/aAYRjY29vznouNjUVGRgb69+/PRXSRcujQIdy5cweysrKws7Oj9wMHaB80Du7u7jhz5gwYhoGVlRXtB44cPHgQpaWlGDJkCNatW0cdWxsBVVVVqKqqch1D7MydOxcTJkzAkSNH6GY0h+gYzb0LFy5gx44dAD5/HrVv3x5KSkpULCNkvr6+YBiGV8T08uVLvHz5stbxVMBBCGmUqKUWtzw9PbFt2zaUlZXxDuRf7oMvt9GBXjA8PT1hZWWF8+fPY+zYsejcuTP09fWhrKyMrKwsxMXF4cmTJ5CSksK4cePg6elZ48+hE5Tvo6mpiaSkJBQVFdW5tnhGRgaSkpKo9ZkAvHr1CoqKili9ejV93hACYMuWLWjTpg3c3NwQEhLC2151HGjatClmzpxJn/+EEIG6desWpKSk4O7uDgMDA67jiK1JkyYhLCwMAQEB2LRpE7Zu3Yo2bdpAXl4ehYWFePfuHSoqKsCyLAYPHoxJkybxXnvnzh0YGBhg+PDhHP4GouHq1auQkJDA8ePHYWZmxnUcsUT7oHHw9vYGwzDYu3cvhg4dynUcsfX06VMoKChg586dNAGCiDUlJSWsWbMGW7ZswbNnz+Dg4IC2bdvWuWxEfTrwkm9Dx2junTp1CgzDYMGCBfjpp5+oeIYjdJ2udgxbn76ehBBO/ZuWWnVVqZFv9/fff2Pq1KlQUlLCokWLcOrUKcTFxeHkyZPIycnBkydP4OPjg0+fPmH58uUwMDBAjx49uI4tcoyMjPgqMqvUtu2fqopr6P3xffbs2YPjx49j+vTpWLZsGQDA0dERUVFRfH/bVatW4eLFi1iyZAlmzpzJVVyR1L17d+jo6MDLy4vrKIQ0Kjk5Obh79y5evXqF/Px8yMnJwcDAAFZWVjTLjhAicJ06dYK2tjauXr3KdRSxx7IsPD094e7ujtTU1GrPa2hoYPr06XB0dKRiWAHp1KkT1NXVcfPmTa6jiC3aB42DiYkJlJWVcefOHa6jiDVTU1Po6urC29ub6yhiISwsrEF+Tvfu3Rvk55D/MTY2/qbxDMMgOjpaQGnEFx2jude5c2coKioiODiY6yiE1Ig6cBDyH0Attbh36tQpAJ9vXPfs2RO+vr4APq/bCABDhw7FzJkz8dNPP2Hfvn3w8fHhLKsos7GxoQusjcDUqVNx4cIFuLm5ISsrCw4ODqioqADw+ebp69evcfLkSdy5cwfq6uqYMGECx4lFzw8//IDXr19zHYOQRqdFixYYPXo01zEIEbhvvfBaE7oY2/BUVVXpHK2RYBgGkyZNwqRJk/DmzRskJCTwusfp6upCT0+P64giT0lJCQoKClzHEGu0DxoHRUVFKiRuBAwNDfH+/XuuY4gNJyen775+R99VBeNb53PT/G/BoGM095o3b47WrVtzHYOQWlEBByH/AdRSi3tPnz6FsrIyevbsWesYJSUl7NmzB0OGDIGrqyu2bt0qxITiYdu2bVxHIABatmyJo0ePYs6cOfD19YWfnx/vuV69egH4fIKnoqKCw4cP0wmJAMyaNQvTp0/Hn3/+iXHjxnEdhxBCiJA1xIVUuhjb8IYMGYI//vgD79+/h7q6OtdxyP/T09Ojgg0OWFpawsfHBzk5OWjRogXXccQS7YPGoWfPnrh9+zaKi4vrXKKACNbkyZOxZMkSBAYGYuDAgVzHEXl1dc6IiopCeXk5pKSkoKqqChUVFWRmZiI9PR3l5eWQlpZGly5dhBdWzMTExHAdgYCO0Y2Bubk5rl+/joKCArp23YiwLIuPHz+ipKRE7JdvoiVUCPkPoJZa3OvYsSPat2/Pa7U4ZcoUhIaGIiIiAnJycnxjR40ahby8PN6SN4SIquzsbLi5ueHmzZtISUnhbVdTU+N1paGZRoJz4cIFbNmyBba2trw1S2VkZLiORQghhEN//PEHdu3ahV69esHJyQn6+vq8i+JxcXE4ffo0Hj58iOXLl2PKlClcxxU5RUVFGDduHOTl5bFv3z6oqalxHYkQzmRlZcHW1hYdO3bErl27qp03E8GjfdA4JCcnY8yYMRg+fDh+//136irKof3798PDwwNz587FuHHj6IadkFVWVmLBggUICQnBzz//jAkTJvDtg4KCApw7dw6HDx+Gubk5Dhw4QO8XIrLoGM299PR0jBkzBj179sSWLVvomirHHj58iBMnTiAyMhIlJSXVujAdO3YMCQkJWLlypdgUPVEBByH/Af369YOysjIty8GhPn36oGXLlrh8+TIAYNGiRbhx4wYuXrwIQ0NDvrEjRoxAUlISnj17xkVUsVZZWYmcnBwoKSlxHUXsFBcXIy8vD/Ly8nQRRAhozVJCCCH/FBAQgAULFmDhwoWYPXt2reOOHj2Kffv24eDBgzQDtYG5uLggPz8fnp6ekJSUhIWFBXR0dOqccT1v3jwhJhQ/LMsiMTEROTk5KC8vr3VcXTOFyb/j5+eH1NRUuLq6okWLFrC2toaOjk6dNyhsbGyEF1AM0D5oHMLCwvDixQvs2rUL7dq1g52d3Vf3A30mNbwBAwYA+HzDrmoJ2JYtW9Z6jGYYBoGBgULLJw5OnDiB3bt34+jRo+jbt2+t4+7du4dZs2Zh+fLlmD59uhATEiI8dIzmnp+fH96+fYsjR46gZcuWGDFiBLS0tGgfcMDFxQWHDh3i6xLKMAxevnzJe3zu3Dls2LABmzdvxpgxY7iIKXRUwEHIf8Bvv/0GHx8fBAcHi011WWNjb2+P5ORkPHr0CMD/LnzPmzcPc+fO5Y1LTEzEiBEj0LJlSwQHB3MVV2TFx8fj/v376NChA99yQqWlpdi+fTu8vLxQWloKdXV1bNiwAX369OEwLSGCY2Rk9M2voTaZhBAi2hwdHZGYmIiQkJA6ZytWVlaiT58+aNeuHc6cOSPEhKLPyMgIDMNUu/BUE5Zlq12UIg0nLy8Pu3fvxuXLl1FcXFznWCp0FYx/vh/qM4ua3g8Ni/ZB41C1H+qLPpME41vPoekY3fBGjBiBT58+ISAg4KtjBw0aBBkZGd5EOiIYqampCAkJQXx8PAoLCyEvL4927drB3Nxc7JcuEDQ6RnOP9kHjEBwcjJkzZ0JeXh6LFi3CwIEDsWTJEjx+/Jjv752VlQVzc3P0798frq6uHCYWHimuAxBCvm7BggW4c+cO1qxZQy21ONKzZ088f/4cSUlJ0NHRgbW1NQ4ePAhXV1cUFxfDzMwMGRkZOHLkCCoqKtC/f3+uI4uks2fPwtPTE4cPH+bbfujQIXh6evIep6amYu7cufDx8aH1rgVg7NixGD16NKytrWmJFI7cunWL6wiEEEIamdevX0NXV/erF54kJCTQpk0bKuwTABsbG2r13QgUFBRg3LhxSExMhKqqKiQkJFBYWIhu3bohJycHiYmJKC8vh4yMDDp16sR1XJFFHQS4R/ugcaCboI3DqVOnuI4g9lJSUmBgYFCvsc2bN0dcXJyAE4mvwsJCbNq0CZcuXUJlZSWA/xUXA5/PF0aPHo1ffvkF8vLyXEYVWXSM5h7tg8bh9OnTYBgG27Ztw6BBgwDUXEyjrKwMdXV1sbqOQR04CPkPoJZa3Hv69CmWLVuGn3/+mfe3/eOPP7Bt2za+AwrLstDV1cXZs2fRsmVLjtKKLhsbGyQmJiIiIgKSkpIAPnffMDc3R3FxMXbt2oWuXbvi0KFD+Ouvv+Dg4IANGzZwnFr0VFUoS0pKonfv3hg1ahQGDhxIawUSkTZ58uTv/hkMw8DDw6MB0hBCSHXdunVDkyZNEBISAgkJiVrHVVZWwtzcHKWlpYiIiBBiQkKE48CBA3B1dcX48eOxfv16ODo6IioqijeDKy8vDydPnsSxY8dgY2ODzZs3c5yYEEIIEX29evVCcXExgoOD0axZs1rH5efnw8LCArKysnj48KEQE4qHsrIyTJ48GY8fP+ZdxzYwMECrVq2QkZGB2NhYJCQkgGEYdO3aFR4eHpCWluY6NiFERP3444+QlJRESEgIb9s/z9+q2Nvb49WrV3j69KmwY3KCOnAQ8h+watUqXjunzMzMelWNUwFHwzIxMcHNmzf5tjk7O6Nz58689dJkZWVhZmYGBwcH6pIiIBkZGVBVVeUVbwBAVFQU8vPzMXjwYAwdOhQAsGbNGly+fBl///03V1FFWlWVfnh4OO7du4fg4GDIyspi8ODBGDlyJHr37k2zT4nICQ0NrfW5qn/vNdVFf/kcvS8IIYLUoUMHhIeHw9XVFfPmzat1nKur6/+xd+dhNeb//8Cfd/uOipQWlcgeZS8lW2ilLJF9aTCyzmBmzNgZBkNhLFnTjKV1ZI0IUSl7Ee2lqJT25XR+f/h1vs60yEfn3HXO63Fdc13T+7xP15Nb3ee+79f79caHDx/Qv39/IaYjRHhCQ0MhIyODZcuW1fm6iooKPDw8oK6ujk2bNsHExAQuLi5CTkkIIYSIl0GDBuHSpUv44YcfsGPHDigpKdWaU1xcjB9++AHl5eXU3VhAfH19ERsbi3bt2mHDhg2wsrKqNefWrVv49ddfERsbi7///htubm7CD0oIEQvFxcWN7s7E4XAaXKwiaqiAg5AWgNo5NV99+vRBnz592I4hNgoKCmq1H3348CEYhoGFhQVvTE5ODnp6ekhMTBR2RLHg7OwMZ2dnZGdnIzg4GEFBQXj16hUCAgIQGBgIdXV12Nrawt7eHl27dmU7LiFNor7iyUePHmHv3r1QUlKCs7MzOnXqBDU1NeTm5uL169e4cOECioqK8P3338PExES4oQkhYmXBggWIioqCl5cXoqKiMG3aNBgaGvJ+J7158wY+Pj548OABGIbB/Pnz2Y5MiECkpqZCS0sLrVq1AgDeTb6qqipISf3fbTBXV1d4eXnh3LlzVMBBCCGECJiHhwdu3bqFsLAwDBs2DA4ODrWun4OCglBYWAgFBQUsWbKE7cgi6d9//wXDMDhw4AC6d+9e5xxLS0t4eXlhwoQJCA4OpgIOQojAqKqqIiMj44vzqqqqeFtkigsq4CCkBTh16hTbEQhpFuTl5ZGTk8M3Fh0dDeBT2/DPSUlJ8XXqIE1PQ0MDc+fOxdy5c5GQkIDAwEBcvHgRb9++xbFjx3D8+HEYGhrC3t6eHhJ9gzVr1gAA2rVrx1tJWjPWWAzDYMuWLU2eTZzUtVI9Pj4e+/fvx7Bhw/D7779DXl6+1pzFixdj1apV2L9/P/755x9hRCWEiClzc3OsW7cOW7ZswYMHD+rsHMTlciElJYU1a9bA3NychZSiIzMzE8Cnz5zt2rXjG/sa/y1OJk3j89bsNefnDx8+oG3btrxxhmGgpaVFRd+EECJiPD09AQBt2rTB1KlT+cYai2EYLFq0qMmziTM9PT0cO3YMy5cvR3p6Onx8fGrN4XK56NChA3bt2oWOHTsKP6QYePPmDfT19est3qjRvXt3GBgY4M2bN0JKRggRR3379sXly5dx48aNBjsvBQYGoqSkBAMGDBBiOnYx3Lp6PRNCCCHN0OTJk/H48WOcPn0apqamyMjIwOjRo9GmTRuEh4fzzTU3N4ecnByuX7/OUlrx9eDBA/z777+4cuUKPn78CIZhau1ZRxrP2NgYAGBgYICQkBC+scaiYyAY33//Pe7cuYPw8PA627/WKCoqgoWFBSwsLLB3714hJhQfVVVV8Pf3x61bt5CamoqSkpI6t7QBPv080LmBiLKEhAR4e3sjPDycr/BVXV0dFhYWmDVrFjp37sxiQtFgbGwMhmFgYGCAixcv8o01FsMwePHihaAiiq3Ro0ejsrISN27cAACsX78ef//9Nw4ePAhLS0vevOrqapibm6OkpASPHj1iKa1oqOm69/nPw9d24qOfh29Dx6B5GD58OIBPD6q9vb35xhqLPqt+u5rzsb6+Pt81dM321A2pmUPX0IJTUVGBkJAQ3L59G4mJiSgpKYGCggIMDAxgYWGBsWPHQlZWlu2YIqt3794wNDSEn5/fF+eOHz8eb968wePHj4WQTHTROZqQ+sXExMDV1RXq6urYvXs3+vXrB1dXV8TGxvLOw1evXsXq1atRXl4Of39/sbmfQR04CCHkP6ZPn/7N34NhGJw4caIJ0pDPOTg44NGjR3B3d8fAgQPx+PFjcDgcODo68s1LTU1FTk4Ohg4dyk5QMde9e3ekp6fjzZs3iImJYTtOi7d161YA/CtJa8YIux4+fAhDQ8MGizcAQElJCYaGhryOQaRpffz4ETNnzkRcXNwXb8gC+KqHq4S0REZGRrzzRGFhIe+m+OfnEfLtajpnfN7RgbppNA+dO3dGWFgYKioqICMjg8GDB8PX1xd79+6FiYkJb2uVffv2IS8vD71792Y5cctXc/6trq6uNfa134P8b+gYNA81LcA/f/jcmLbgn6PPqt9u8eLFAD514PjvGGGfjIwMHB0da93LI8KhpaWFhIQE5OXlQVVVtd55eXl5SEhIQIcOHYSYTjTROZqQ+vXt2xcLFy7E/v37MX36dOjq6iI/Px8A4O7ujoSEBGRmZoLL5WLlypViU7wBUAEHIS1KZWUlQkJCEB4ejsTERBQXF0NRUREGBgYYOnQoxowZA2lpabZjtnh1tZv+WnTBLRiTJk1CVFQUQkJCcO3aNQCftk5xd3fnmxcUFAQAGDRokNAziquqqircvn0bQUFBCAsLQ3l5ObhcLqSlpTFs2DC247VoTk5OjRojwldcXIwPHz40au6HDx9QXFws4ETiac+ePXjx4gXatm2LOXPmwMTEBGpqapCQkGA7GiGsU1ZWpsINAanp7vClMSJ8lpaWuHbtGu7duwcrKytYW1ujS5cueP78OaysrGBgYIDc3FxkZ2eDYRjMnTuX7cgtXnx8fKPGiODQMWgeQkNDAXzaXuu/Y0R46irWoAIOQj6xtLTE8ePHsXLlSuzZswcqKiq15nz8+BErV65EVVUV3dNrAnSOJqRhS5YsgY6ODvbs2YOUlBTeeFhYGIBP3URXrlwpdoV/tIUKIS1EQkIClixZguTk5DorLmtaA+7duxedOnViIaHoaIoCDgDo379/k3wfUltcXBySkpKgqakJExOTWgUzQUFB+PDhA2xsbKChocFSSvEQExODoKAgXL58GQUFBbxWo3379oW9vT3GjBlT58UgIaLA3t4eCQkJ2L9/f4M3NW7evInvvvsOXbp0QWBgoBATigdLS0vk5ubC398fRkZGbMchhBDCssLCQty8eRPGxsa8FVrv3r3D6tWrce/ePd681q1bY8WKFXBxcWErKiGEEEKIUOXm5sLOzg4fPnyAgoICHBwcYGRkBHV1deTk5CAhIQGBgYEoKSmBmpoagoKCGuzUQQghTaWqqgqPHj3Cy5cvUVhYCAUFBRgZGcHU1BQyMjJsxxM6KuAgpAXIy8uDvb09cnJyIC8vDzs7O3Tp0oX3werVq1cICgpCaWkp2rZti8DAQPpgRQgRmMTERAQFBeHff/9FRkYGr6hMX18f9vb2sLOzg7a2NsspCRE8Hx8fbNy4EXJycpg3bx6mTp2K1q1b817Pz8/HmTNncPjwYZSVleHnn3/G1KlT2Qssonr27AldXV3eXrKEEODJkyeIi4tDfn4+Kisr65zDMAwWLVok5GSEsOv9+/fIyMiAnJwcOnXqxLdKnhBCCCFEHMTHx+P7779HWlpanV2kuVwudHV1sXfvXhgbG7OQkBBCCBVwENIC/P777/D29ka/fv3w559/1lmc8eHDB3h4eCAqKgqzZ8/GqlWrWEhKiPAVFxfzthNSVFRkO45YMDY2BsMw4HK5UFNTw9ixY2Fvb4+ePXuyHU0kBQQENMn3Ebc2c8LA5XKxYsUKhISE8G56qKmpQVVVFXl5ecjLywOXywWXy8WYMWOwa9cu2mJLAEaMGAF5eXkEBwezHYUQ1j158gSrV69GUlISb6ymO9bnasbi4uKEHZEQQgghRKCioqKa5Pv069evSb4PIc1RRUUFQkJCcPv2bSQlJfHurerr62Po0KEYO3asWK54J4SQ5oIKOAhpAcaMGYP09HSEhYVBTU2t3nk5OTmwsrKCjo4OLl26JMSEhAhXYmIijh49ivDwcLx//5433rZtW1haWmL27NnQ19dnMaFoMzExwfDhw2Fvbw9zc3NISkqyHUmk1RTMfCt6SCc4//zzDw4fPoz09PRar2lra2POnDmYMmUKC8nEw44dO3D8+HFcv34dmpqabMchhDWpqalwcnJCWVkZxo4di+joaGRlZeG7775Dfn4+Hj16hBcvXkBOTg5TpkyBoqIi7Qf/DYYPH/7N34NhGFy/fr0J0hDCrunTp3/z92AYBidOnGiCNOKJjkHzsGbNmm/+HgzDYMuWLU2QRnw1xTU0wzB48eJFEyUihIgzOkc3f48ePUJkZCSysrJQVlbGdx5+9+4dqqqqoKWlxWJCIo6ogIOQFqB3794wNDSEn5/fF+eOHz8eb968wePHj4WQTHx4enp+1XxqSS04ISEhWLt2LcrLy1HXKYxhGMjKymLLli0YO3YsCwlFX0lJCRQUFNiOITZWr17dJAUcW7dubYI0pCGJiYlITEzk/YwYGBjAwMCA7Vgir6SkBJMmTYKioiL27NmD9u3bsx2JEFb88ssvOH/+PH799VdMnjwZrq6uiI2N5Svgi4iIwIoVK9CuXTv4+vpCXl6excQtW1O0k6YuKIIXHx+PtLQ0FBcXNziPOpV9G/p5YB8dg+aBjkPz4Obm1iTf59SpU03yfQgh4o3ODc1XZmYmVq1ahZiYGAB1d6r8+eefceHCBZw5cwZ9+vRhKyoRQ1TAQUgLYGpqCg0NDYSEhHxx7rhx45CVlYWHDx8KIZn4+HzLiP+iltTC8/LlS0yYMAFVVVXo0aMHZs2ahc6dO0NdXR05OTlISEiAt7c3nj17BikpKfj5+aFz585sxyaEECJAnp6eKCwshI+PDyQlJWFhYQE9Pb0GH0xT1wEiikaMGIH8/Hzcv38fUlJSdRZwAMCDBw8wY8YMLFiwAMuWLWMpbcuXkZHRJN+nQ4cOTfJ9CL8rV65g+/btePv2baPm07Xbt4mMjGyS79O/f/8m+T7iiI5B8+Dv798k38fJyalJvg8hhPxXamoqgoKC0KNHD1hZWdU77+bNm3j+/DkcHBygo6MjvIAiiM7RzVNBQQHGjx+PjIwMtG/fHoMHD8a9e/eQnZ3Nd20QHR2NadOmYc6cOVi1ahWLiYm4kWI7ACHkywwMDPDs2TPEx8c3WLEZHx+PN2/eoGfPnkJMJx4aetBTUlKC5ORk3LlzB1wuF1OnToWSkpIQ04mPI0eOoKqqCm5ubvjpp5/4XmvTpg2MjIwwduxYbNmyBSdPnsTRo0exfft2ltISQsQNl8vFhw8fUFZWRq0VhcjT05NXZFlVVYXr16/X27WmpsiSCjiIKHr37h06duwIKalPl/k1W5xVVFTw7V89YMAAaGtr4+rVq1TA8Q2o8KL5unnzJpYuXQoulws1NTUYGxtDXV0dEhISbEcTWfRAgX10DJoHKrwgpOlERERg0KBBbMcQOf/88w+8vb1x4MCBBucxDAMvLy9UVlbSNcM3onN083TkyBFkZGRg+PDh2LlzJ+Tl5eHq6ors7Gy+eX379oWcnBwiIiJYSkrEFRVwENICjBs3Dk+fPsXChQuxefPmOj+83rt3Dz///DMYhoGtrS0LKUVbYx70pKWlYenSpYiIiMA///wjhFTiJyoqCioqKvjhhx8anLdy5Ur4+/vjwYMHQkpGCBFnEREROHLkCGJiYlBWVlZrv+RDhw4hKSkJP/74I1q3bs1eUBHl6OjYJNsMEdLSycvL84o3AEBZWRkAkJ2dXWvVnIqKChITE4WajxBh+euvvwB82m985cqVfAVMhBBCCGm+UlNT4e/vj8DAQGRlZfFdV5OmcefOHcjJycHS0rLBeUOHDoWsrCzCw8OpgIOIpOvXr0NaWhqbN29usIOrhIQEdHR0kJaWJsR0hFABByEtgqurKy5evIinT59i9uzZ6NSpEzp16gQ1NTXk5uYiISEBb968AZfLRe/eveHq6sp2ZLGko6OD3bt3Y/To0di/fz+WL1/OdiSRk5ubC2NjY0hLSzc4T0ZGBh07dkR8fLyQkhHCroqKCuTn56OqqqreOdQRQjA8PT3h5eVV5xZbNZSVlREQEIB+/fph/PjxQkwnHrZt28Z2BEKahXbt2uHdu3e8rw0MDHDz5k1ERUXxFXAUFhYiKSmJHmoTkfXy5UuoqKhgzZo1VOBHCCGENHNFRUW4dOkS/P39ERsbC+BT58TPC5NJ03n79i20tbW/+BlJQkIC2trayMzMFFIyQoQrMzMTHTt2bNRCK0VFRZSWlgo+FCGfobMgIS2AjIwMvL29sX79eoSEhCAhIQEJCQm8duHApw9Vtra2WLdu3RcfbhPB0dXVhaGhIS5dukQFHAKgqKjI92CiIe/fv4eioqKAExHCnqqqKnh7eyMwMBBJSUkNFhD8tyMEaRrh4eHw9PSEoqIili5dihEjRmD58uV49OgR37xRo0Zh/fr1uH79OhVwEEIEplevXvD398eHDx/Qpk0bjBgxAkeOHMHOnTuhrq4OMzMzvH//Hlu2bEFZWRkGDhzIdmSRVVZWhps3byIuLg75+fmorKyscx7DMNiyZYuQ04k+KSkp6OjoUPFGM/LkyZNG/TwsWrRIyMnEBx2D5iE3N5d3HBoqvnd0dBReKDETEhKCgIAAvHjxAvn5+eBwOHXOo2toweJyubh37x78/PwQGhqK8vJy3j2NLl26wMnJCXZ2diynFE3l5eWNfnYgIyODkpISAScidI5mh7S0NCoqKho1Nzc3F0pKSgJORAg/KuAgpIVQVlbGzp07sXTpUty5cwdJSUkoLi6GoqIi9PX1YW5uDm1tbbZjEnz6QJWVlcV2DJHUvXt33Lt3DyEhIRg7dmy980JCQpCVlYUhQ4YIMR0hwlNRUYFZs2YhJiYGkpKSkJKSQkVFBTQ1NVFQUMC7wJaRkYG6ujrLaUXXqVOnwDAMtm3bhpEjRwJAnQ+L1NTUoKmpSV2BCCECZW1tjQsXLiAsLAxOTk4wMTHBuHHjcPHiRSxYsIA3j8vlQl5eHh4eHiymFV2hoaFYu3YtPn78yBureSDx+TmCy+VSAYeAdO/eHa9evWI7BsGnBxKrV69GUlISb6zm3/7nasbowUTTo2PQPKSlpWH9+vW4e/dug/NqjgMVcDQ9LpeL5cuX4/Llyw0ufvh8Pml6iYmJCAgIQGBgIG+BVs3ftbKyMk6dOgVjY2M2I4o8DQ0NJCYmory8HLKysvXOKysrQ2JiItq2bSvEdOKFztHsqunenZeXB1VV1XrnpaamIi0tDQMGDBBiOkKogIOQFkdbWxuTJ09mOwapR0ZGBpKSkhrVeot8vSlTpuDu3btYvXo1Xr16henTp/N9wMrLy8OJEydw7NgxMAyDKVOmsJiWEMHx8fHBw4cPMXLkSOzYsQOzZ89GbGwsbt68CQB49eoVjhw5guDgYDg7O2PhwoUsJxZNT548gaqqKq94oyHq6up4+fKlEFKJtpr2rVJSUmjXrh3f2NegLYWIKBo2bBhu3brF14Fs+/btMDIyQmBgINLT0yEvLw8zMzMsWbKEbo4LwIsXL+Dh4QFpaWksWLAAly5dQmpqKjZv3oz8/Hw8fvwYN27cgJSUFBYuXEg3xAVk/vz5mDNnDv755x9MmjSJ7ThiKzU1FbNmzUJZWRlsbW0RHR2NrKwsLFy4EPn5+Xj06BFevHgBOTk5TJkyhbonCgAdg+bh/fv3mDJlCnJzc9GnTx+kpKQgLy8P9vb2yM/Px7Nnz5Cbmws5OTmMGjUKkpKSbEcWSefPn8elS5fQt29fbNu2DatXr0ZsbCxevHiBDx8+4PHjxzh69CiePXuG3377jYpomlBhYSH+/fdfBAQE4MmTJwA+PZCWlZWFtbU1HB0dsWDBAsjKytLnUyHo168f/Pz8cOjQIXz//ff1zjty5AjKysrQv39/IaYTH3SOZt/IkSPx7Nkz7NixA1u3bq1zDofDwcaNG8EwDGxsbIScUHx8/PgR3t7euHXrFlJTUxvs/CNOHbKogIMQQppAbm4uYmNjsWfPHnA4HFhaWrIdSSSNGDECEydOxNmzZ/HXX3/hr7/+gqqqKtTU1JCbm4u8vDwAny4EJ02ahBEjRrCcmBDBCAkJgZSUFH7++WfIycnVer1z5874/fffoaWlhX379sHIyKhRRQbk6xQXF8PIyKhRczkcDiQkJAScSPRZW1uDYRgYGBjg4sWLfGONJU4Xe0S8SEhIQENDg29MSkoK7u7ucHd3ZymVeDl69Cg4HA527dqFUaNGITIyEqmpqZgwYQJvzps3b/Ddd9/B19cXFy5cYDGt6Bo8eDA2bNiALVu24OXLl5g4cSI6duxY52cmIjiHDx9GSUkJfv31V0yePBmurq7IysrCkiVLeHMiIiKwYsUKREREwNfXl8W0oomOQfNw5MgR5OTkYPHixVi8eDFcXV2Rl5eH7du3A/h0neDv74/NmzcjNzcXhw4dYjmxaAoICADDMNi6dSt0dXV54wzDQFVVFcOGDcOwYcOwdu1arF27FlpaWvTg+hvdunUL/v7+uHnzJioqKnhdBMzMzGBvb48xY8bQlgQsmDlzJgICArB//34UFxdj7ty5fJ1bc3JycPToURw/fhxSUlKYOXMme2FFGJ2j2efm5obz588jICAAb9++hbOzM69w4Pnz53j16hVOnTqFFy9ewMjIiO+ajjSdrKwsuLq64u3bt9Qh6z+ogIMQQhqha9eujZrH5XKhpaVFLakFaMOGDejZsyf++usvpKenIzc3F7m5ubzXtbW1sWDBAri4uLCYUrSlpqYiKCgIPXr0gJWVVb3zbt68iefPn8PBwQE6OjrCCygGEhMToaWlxXtQV/PwmsPh8K3YWrRoEXx8fHDq1Ckq4BAAVVVVZGRkfHFeVVUVkpOTaz1YJV+vpnPG56vWqZsGIaS5ePjwIVRUVDBq1Kh65xgaGmLv3r1wdHTE/v378fPPPwsxoej50nWar6/vF294U2GfYEREREBRURHOzs71zhk0aBB2796NGTNm4ODBg1i2bJkQE4o+OgbNw+3btyEvL485c+bU+bqkpCScnZ2hpKSEpUuX4tixY5g7d66QU4q+hIQEdOjQAXp6egD+7xq6urqar9D+p59+wqVLl3D06FEq4PhGCxYsAMMw4HK56NixIxwcHGBvb48OHTqwHU2sGRkZYe3atdi0aRNOnDiBkydPQktLC8rKyigsLERmZibvAemaNWvQpUsXlhOLJjpHs09BQQFHjhzBd999h/v37+PBgwe812qOC5fLhaGhIQ4ePAgZGRm2ooq0Xbt2ITMzE4aGhli2bBl69+4NdXX1r1qoJaqogIOQZqbmBtTnK0sbWzxQg25ANb0vVfbJy8ujY8eOsLKywuzZs6GsrCykZOLJxcUFLi4uSExMRFJSEoqLi6GoqAh9fX0YGBiwHU/k/fPPP/D29saBAwcanMcwDLy8vFBZWUkXGU2sqqqKb6smeXl5AEBBQQHftkLS0tLQ09OjrTsEpG/fvrh8+TJu3LgBa2vreucFBgaipKSE9stsAjdu3GjUGCHiqKioCHFxcVBTU2vw81BiYiJyc3PRrVs3asXbxHJzc/k6M0lJfbrlUlZWxtf9wdjYGPr6+ggLC6MCjm/UFCuwxGkVlzC9e/cOHTt25P0c1BQZV1RU8N0AHzBgALS1tXH16lW6ZmhidAyah7dv30JbW5t3zVZTLFBZWQlpaWnePBsbG2hoaCA4OJgKOASgtLQUHTt25H1dc14uLCxEq1ateOOKioowMDDgbfVBvl2rVq0wfvx42NnZQVNTk+04BMDUqVOhra2NP/74A69evUJ6ejrf6127dsXy5cthYWHBUkLRR+fo5kFPTw8BAQE4f/48rl69ipcvX6KwsBAKCgowMjKCjY0NJk6cCFlZWbajiqw7d+5AWloaR48eRfv27dmO06xQAQchzUzNzaPq6upaY1/7PUjTiY+PZzsCqYOBgQEVbLDgzp07kJOT++JWQUOHDoWsrCzCw8PpIqOJtWvXDh8+fOB9XXMT5NWrVxg4cCDf3Hfv3qG0tFSo+cSFm5sbLl26hHXr1kFZWRn9+vWrNefq1avYvHkzJCUlMW3aNBZSEkLExdmzZ7Fjxw6sX7++wc9H0dHR+PXXX7F69WrMmDFDiAlFn5KSEjgcDu/rmgdCmZmZtY6JjIxMo7o4kYaFhoayHYHUQ15envdQAgBvkUN2dnat7nwqKipITEwUaj5xQMegeZCUlOR78FOzZURubm6tBxXq6up0HASkbdu2KCgo4Psa+FTY2qdPH765BQUFKCoqEmo+UWRra4vQ0FAUFBRg9+7d2LNnD8zMzODg4IDRo0fT9ikss7S0hKWlJdLS0vD69WsUFRVBSUkJRkZG0NbWZjueyKNzdPMhIyMDV1dXuLq6sh1FLBUVFUFfX5+KN+pABRyENDN1FQpQ8QAhpDmpWUH0pVZmEhIS0NbWRmZmppCSiQ99fX1ERkbytkwxMzPDuXPncPjwYfTt25dXrX/27Fm8e/eOWl4KSN++fbFw4ULs378f06dPh66uLvLz8wEA7u7uSEhI4LUfXblyJTp37sxuYEKISLt+/TokJSVha2vb4DxbW1ts2LAB169fpwKOJta+fXtkZ2fzvjYyMsL169dx9+5dvgKO9+/fIykpiTqgNAFqw958tWvXDu/eveN9bWBggJs3byIqKorvwURhYSGSkpKoLbUA0DFoHtq3b4+cnBze17q6ugCA2NhYjBkzhjdeUVGB1NRUahkuIDo6OnxdNfr06YOAgACcPn2ar4Dj1q1bSE9P5+vWQf43O3fuRFFRES5dugQ/Pz/ExsYiMjISUVFR2LhxI4YNGwYHBwfq8sAyHR0d2vaYBXSOZl9UVBSUlZVhbGz8xbnx8fEoLCysc+EW+Tba2tqorKxkO0azJPHlKYQQQkjzEx8fj2vXriEgIKDB/0jTKy8v52v12hAZGRmUlJQIOJH4sbS0RFlZGSIjIwF8arerpaWFe/fuwcbGBkuWLMGUKVPw66+/gmEYTJ06leXEomvJkiXYunUr2rVrh5SUFBQUFIDL5SIsLAwZGRlQU1PDtm3bqA0yIUTgUlNToampCQUFhQbnKSgoQFNTEykpKUJKJj5MTU3x4cMHXhGHjY0NAOCPP/6Ar68vEhIScO/ePXz33XeorKzE4MGD2YxLiED16tULeXl5vK5xI0aMAJfLxc6dO3H79m2UlJQgJSUFK1euRFlZGUxNTVlOLHroGDQPxsbGyM3NRXFxMYBP13JcLhd79uzBmzdvAHy6xl6/fj0KCwvRq1cvNuOKLAsLC5SVleHRo0cAgLFjx6J169YICQnB5MmTsX37dqxcuRKLFi0CwzAYP348u4FFhJKSElxcXODr64urV6/C3d0dmpqaKCsrw6VLl/Ddd9/B3Nyc7ZiECB2do9nn5uaGTZs2NWru5s2bafGDgDg4OCA5ORmvX79mO0qzQx04CCGkEeLi4nDy5EkMHjwYdnZ29c4LCgpCREQEZs6cSSveBeTKlSvYvn073r5926j5jo6Ogg0khjQ0NJCYmIjy8vIG9wAsKytDYmIirzUpaTqjR4/Gx48feX//srKy+Ouvv7BkyRIkJSXxup5ISUlhzpw5mDhxIptxRZ6TkxPs7Ozw6NGjWvtlmpqa0koJQohQFBQUNHpf8datW1OXPwGwtraGr68vwsLCMGnSJHTu3BkzZ87EsWPHsGHDBt48LpcLNTU1LF++nMW04qG0tBQxMTFISkpCcXExFBUVoa+vj759+0JeXp7teCLN2toaFy5cQFhYGJycnGBiYoJx48bh4sWLWLBgAW8el8uFvLw8PDw8WEwrmugYNA/W1ta4ePEibt++jTFjxmDIkCEYMGAAHjx4AFtbW7Rq1QpFRUXgcDiQkpLCwoUL2Y4skmxsbJCcnIzCwkIAn7Yr+PPPP+Hh4YFHjx7xCjsAwM7OjgrwBUBXVxdLly7F0qVLERERAX9/f1y7do33ADs3Nxc2NjZwcnKCvb19oz/Xkq8TFRX11e+hrgNNj87RzQOXyxXIXNJ4c+bMQUREBBYvXozff/+dClk/w3DpXx0hzR6Hw0FpaSmkpaVrPSx98uQJr0V+jx49MHv2bNpDUAA2bNgAX19fnDx5ssEPrVFRUXBzc4Obmxt++uknISYUDzdv3sTChQt5N72NjY2hrq4OCYn6G0pt3bpViAnFw08//QQ/Pz8sXLgQ33//fb3zPD094enpCScnJzoOQlJdXY2nT58iPT0dcnJyMDExgZqaGtuxCCGECIGFhQWqqqoQERHxxbmDBg2ChIQE7t69K4RkJDg4GIGBgUhPT4e8vDzMzMwwd+5caGhosB1NZFVUVGD//v04ffo0b9X75xQUFDBt2jQsWrSICi0FpLq6Gu/fv4eioiLvHkVVVRWOHDlS6+dhyZIljWpfTb4OHYPmoby8HM+ePYOmpia0tLQAAMXFxdi2bRsuXrzI61jZpUsXrFy5kraTELKioiLcvn2bdw3dr18/dO3ale1YYqOkpAQhISEICAjAw4cPweVywTAMGIZB//79cfz4cbYjihxjY+Ov2qqJYRi8ePFCgInEE52j2WdsbAxTU1P4+Ph8ca6dnR3S0tL4iv1I01izZg2qqqpw6dIlcDgcGBsbQ09Pr95ie4ZhsGXLFiGnZAcVcBDSAhw+fBi7du3CmjVrMH36dN74rVu3sGjRInA4HN4H3C5duuCff/5pcFU8+Xq2trbIyspCdHT0F+eamppCS0sLwcHBQkgmXiZPnozHjx/Dzc0NK1eupJutLElISICjoyOqq6sxY8YMzJ07F+rq6rzXc3JycPToURw/fhySkpK4cOECdaQhImn69Ono0qVLowr2tmzZgpcvX+LEiRNCSEYIEUeLFi3CjRs3sGvXLowZM6beeZcuXcKyZctgZWWFgwcPCjEhIcJRUVGBefPmITIyElwuF61atYKuri7U1NSQm5uL1NRUFBQUgGEYmJmZwdvbu9HbAxJCSFPicDjIy8uDvLw8LcYiYi8tLQ3+/v4IDAxERkYGGIZBXFwc27FEjpubW72vlZaWIiUlBYWFhZCWloaJiQkA4NSpU0JKR4jgFBUV4ePHj7yvra2t0bNnT/z555/1vqesrAxRUVH49ddf0alTJ/z777/CiCpWaorKGluqIE7nBtpChZAW4O7du2AYBra2tnzjO3fuRFVVFaysrNC7d2/4+/vj5cuXOH36NObMmcNSWtH09u1b6OjoNGqutrZ2o7f3IF/n5cuXUFFRwZo1a76qWpw0LSMjI6xduxabNm3CiRMncPLkSWhpaUFZWRmFhYXIzMzkfehas2YNFW8QkRUZGQkOh9OouXFxcY0qAiSEkP/VxIkTERoail9++QWysrKwtrauNScsLAy//PILGIah7bWIyPL29saDBw/QunVr/Pjjj7C1teUr0KiqqkJwcDB27NiB6OhoHD16FO7u7iwmJoSIK0lJSdpylJD/T0dHB0uWLMGSJUtw//59BAYGsh1JJDWmGCMoKAhbt26FtrY2ddQlIuP48ePw8vLiG3v27BmGDx/eqPf/99kcaRqLFy9mO0KzRQUchLQAKSkpUFdXh6qqKm8sMTERCQkJ6NKlC2/lnI2NDcaOHYurV69SAUcTq66u/qp9zioqKgSYRnxJSUlBR0eHijeagalTp0JbWxt//PEHXr16hfT0dL7Xu3btiuXLl1P7VyEoLCxEWloaSkpKGvw9RXuWsquysrLB7Z4IIeRbWVpaws7ODsHBwVi0aBH09fVhYmLCK7B8/PgxEhMTweVyYW9vX2eBByGiIDAwEAzD4ODBg7yVo5+TkpKCk5MTOnbsiClTpiAgIIAKOAghRMwUFBR88Rq6ZssbInwDBw7EwIED2Y4htuzt7dG2bVvMnj0bffr0ocJvIhK4XC7f7/zGdH2Qk5ODjo4ObG1tMXfuXEFHFEtUwFE/KuAgpAX48OEDjIyM+MaioqIAfCraqKGvrw9dXV28efNGqPnEgaamJhITE/Hx40eoqKjUO+/jx4948+YNOnToIMR04qN79+549eoV2zHI/2dpaQlLS0ukpaXh9evXKCoqgpKSEoyMjKCtrc12PJEXGxuLHTt2IDY29otzac9SdpWVlSE5ORmtWrViOwohRMRt27YNGhoaOHnyJBITE5GYmMh3Y0pGRgazZs3CkiVLWE4q2iIiIhAWFobU1NQGHw4xDENbawlARkYG9PT06ize+FyfPn2gr6+PjIwM4QQTUykpKbh161ajfh7EZT9rYaNj0DwUFxfj/v37SEtLQ3FxcYPHYdGiRUJOJx7S0tKwd+9e3L59m6+Nfl3oGpqIu0GDBkFTUxN///03FXAIEJ2jhef777/H999/z/va2NgYpqam8PHxYTEVIfWjAg5CWoDq6mqUlpbyjcXExPD27P1c69at6QaUAAwePBinT5/Gzp07sWHDhnrn7dq1CxwOB+bm5kJMJz7mz5+POXPm4J9//sGkSZPYjkP+Px0dnUZvMUSaRkxMDGbOnImKigpIS0ujQ4cOUFdXp+40Anb9+nWEhobyjaWkpGDNmjX1vqe8vBxPnz5FQUEBrKysBJyQ/FdGRgaCgoLw7t079OjRA05OTtQJhYg0SUlJrFy5EjNnzkRYWBjevHnDV2BpaWkJNTU1tmOKrIqKCnh4eCAsLAwAvriii87bgqGiogIFBYVGzZWXl6cCSwHhcrnYvHkzzpw5U2vFY13owUTTo2PQfBw7dgx79+5FWVkZb+y/x6Om4JIKOATj9evXcHV1RWFhIbhcLmRkZKCmpkbnYgGaPn36N72fCl3Z17p1ayQlJbEdQyTROZp9ixcvhqamJtsxCKkXFXAQ0gJoamoiJSUFBQUFaNWqFaqqqhAeHg4ZGRn07t2bb25BQQHatGnDUlLRNWvWLFy4cAHnzp1Dfn4+5s6dix49ekBCQgLV1dV49uwZjhw5gmvXrkFOTg6zZs1iO7JIGjx4MDZs2IAtW7bg5cuXmDhxIjp27Ag5OTm2oxEiVPv27UNFRQVGjx6NdevW0cM4IYmPj4e/vz/fWE5OTq2xurRu3ZpWvAuIr68vdu/ejUWLFmHGjBm88cePH2P27Nm8VSwMw+DixYs4cuQIFXEQkbR161ZISEhg2bJlUFdXh7OzM9uRxI6Xlxdu3rwJeXl5ODs7w8TEBGpqavQ7R8gGDhyIK1euIDc3t8HPSDk5OUhISMCYMWOEmE58eHt74/Tp02AYBsOGDaOfBxbQMWge/Pz8sH37dgCfVvv26tUL6urqdByEbPfu3fj48SPMzMzw008/oWvXrmxHEnmRkZH/0/s+L2Yi7KnpJColRY8QBYHO0eyjrTuaHy6Xi+TkZOTn56OqqqreeeKyTTjD/VJpFyGEdRs3boSPjw+GDBmCqVOn4tq1a/D398ewYcNw4MAB3rzi4mL0798f3bp1w7lz51hMLJquXbuGFStWoLKyEsCnFY4KCgooKSkBh8PhVfD/8ccfGDlyJMtpW76muJimlpffrma7Jjk5OfTs2ZNv7GuIywcrYTE1NQXDMLh37x5kZGTYjiM2IiMj+W5CeXp6QktLC+PHj6/3PXJyctDV1cXgwYOhpKQkjJhiZ8GCBbh9+zZCQ0P59qmeMmUKYmNj0bVrV3Tv3h2hoaHIz8/H+vXrqQUsEUndu3eHgYEBgoOD2Y4itkaMGIHMzEycPHmyVqdEIjzp6emYMGECOnfujN27d0NdXb3WnJycHCxbtgyvXr2Cn58fbYEpAGPHjkVSUhJ2797Nt/UrER46Bs2Dk5MT4uPj8cMPP9BiHxYNGDAA5eXluH37doNbI5Om879sS/Ds2TMEBQWBw+GAYRjExcUJIBn5kry8PKxfvx5Xr16Fubk5Dh8+zHYkkUPnaEL+z8ePH/HHH38gODi41k4E/yVOz3uofI6QFmD+/Pm4dOkS7t69i3v37vEKBT7fswsAbt68CQ6HQzcLBWTkyJE4e/Ys/vzzT9y5cweVlZW8PTNlZGQwdOhQfP/99+jSpQvLSUVDU9QXUo3it3NzcwPDMNDX10dISAjfWGOJ0wcrYeFyuejYsSMVbwhZ//790b9/f97Xnp6e0NTUpKp9lr1+/Rqqqqp8xRuZmZmIjY2Frq4uzp07BykpKbi4uGDSpEkIDg6mAg4iktTV1SEtLc12DLGWnZ0NbW1tuh5jWXR0NKZMmYIjR47A2toaI0eORKdOnaCmpobc3Fy8fv0a165dA5fLxdy5cxEVFVVngbKjo6Pww4uQ9PR0tG/fnh5KsIiOQfOQmJgINTU1Kt5gWXl5OQwMDKh4Q4imTp3a6LmJiYnYs2cP7/wsLy8PNzc3AaYTXw1tbcPlcpGbm4v09HRUVFRAVla21vMH0jToHN18ZGVl4d9//0VcXBzy8/N5i3f/i7Z1EoyioiJMmjQJycnJ0NDQgISEBIqLi2Fqaor8/HwkJyejqqqKb3GpuKACDkJaAA0NDVy4cAFHjx5FcnIytLS04ObmBiMjI755kZGRMDY2xrBhw1hKKvqMjY1x4MABlJeXIyUlhbenOD1IbXqhoaFsRyD4v84Znz8YpW4a7OvcuTPevn3LdgyxFx8fz3YEgk+rg/T19fnGHjx4AAAYM2YMr+Vr79690aFDB7x69UroGQkRhsGDB+PixYu8bReJ8KmqqlK3pWZg9erVvPbrAHhFyJ+ree3gwYP1fh8q4Pg2KioqtM0fy+gYNA/y8vJo37492zHEnr6+Pm8RFmk+MjIy4Onpyeu6IS0tjUmTJuG7776j318C0titbUxNTfHDDz+gV69eAk4knugc3Tz4+Phg27ZtqKys5C1W/HxB6OdjtK2TYHh7eyMpKQmTJ0/Gb7/9BldXV8TGxuL06dMAPnXnOHbsGA4dOgRdXV1s3ryZ5cTCQwUchLQQmpqa+Pnnnxucs2HDBiGlIbKysujcuTPbMUQatTFuHk6dOtWoMSJc06dPx/Lly3H9+nWMGDGC7TiEsKqqqqrWConY2FgwDMPXMQX41KEgOztbmPEIEZrFixcjNDQUq1evxq5duyAvL892JLFjZWUFPz8/5Ofno3Xr1mzHEVtUbNw8DBgwADdu3EBpaSn9PmIJHYPmoU+fPnj48CGqqqp4hcVE+CZOnIj169fj4cOHMDU1ZTuO2MvJycH+/ftx7tw5VFZWQlJSEk5OTvj+++/5FhCRpnfy5Ml6X2MYBvLy8tDT04OysrIQU4kfOkez7/79+9i0aRNUVVWxdOlSnDx5Eq9fv8bx48eRn5+Px48fw8/PD+Xl5Vi1alWtxdSkaYSGhkJGRgbLli2r83UVFRV4eHhAXV0dmzZtgomJCVxcXISckh0Ml/rLE0IIIYS0OH/++SdOnDiBRYsWYdKkSbTil2VcLhfJycnIz89HVVVVvfPooVLTs7a2xocPH3D37l0oKCgAAIYNG4b3798jMjKSNwYA48aNQ35+Pu7evctWXEIEJiAgAImJiTh69CjatGkDGxsbGBgY8P0M/Bd1GGhaubm5cHJyQo8ePbBz584G/+4JEXWpqakYP348xo4di/Xr19OqRRbQMWgenj17hilTpmDBggW09SLLfvzxR9y9exc///wzbVvAko8fP+LQoUPw8fFBWVkZAGDUqFHw8PCAgYEBy+kIER46R7Nv4cKFuHnzJo4fP44BAwbwOj/ExcXx5uTl5WHBggVITk6Gn58fdHR0WEwsmvr06QMNDQ1cvnwZADBt2jQ8fPgQT58+5St85XK5GDJkCLS1tXH27Fm24goVFXAQ0oJUVVXhypUrePDgAbKzs1FWVsa379azZ89QWloKU1NTSEhIsJi0ZQsICAAAKCkp8Va214x9DbohLlilpaWIiYlBUlISiouLoaioCH19ffTt25cqlwVszZo10NfXx/z5878499ChQ0hKSsLWrVuFkEx8DB8+HACQnZ0NDocDAGjTpk29//YZhsH169eFlk+cfPz4EX/88QeCg4NRWlra4FyGYfDixQshJRMfq1evRmBgIBwdHTFz5kxcvnwZBw4cwIABA/g+J1VUVKBv374wMjKCv78/i4kJEQxjY2O+bSMacxPw85tT5NsFBAQgMzMT+/fvR+vWrTFu3Djo6elREQ0RS1FRUXj+/Dl27twJAwMDODs7f/HngQpdmxYdg+YhMzMTt27dwpYtWzB48GBMnDgRHTt2bPC+BXUgaHrTp08HAMTExIDD4UBFRQW6uroNXkN/fi1Bvk1paSmOHTuGY8eOoaioCFwuFxYWFli2bBm6devGdjxChI7O0ewzNzcHANy5cwcA6izgAIC0tDSMHj0aDg4OdH9bAPr06YNOnTrh3LlzAIB58+bhzp07uH37Ntq2bcs319nZGcnJyYiOjmYjqtBRAQchLcSLFy/g4eGB9PR0vpuyn59Qtm7dipMnT8Lb2xuDBg1iK2qLV3PzW19fn7dfcs3Y16Ab4oJRUVGB/fv34/Tp0yguLq71uoKCAqZNm4ZFixZBRkaGhYSiz9jYGKampvDx8fniXDc3N0RHR9PPQxMzNjb+qvn/PV+QplFUVAQXFxckJydDQ0MDRUVFKC4uhqmpKfLz85GcnIyqqirIycmhZ8+eAGgLIkFISkrC+PHjeSu4gE//5r29vTFw4EDeWGhoKBYtWgRXV1esW7eOjaiECJSbm9tXv4d+JzUtKqIh5P987TU0Fbo2PToGzUPXrl2/aj4dB8Gga2h2VFRU4MyZMzh8+DDy8vLA5XJhZmaGpUuXwszMjO14hLCGztHs69GjB7p06YILFy4AAGbMmIHIyEg8fPiwViGNvb09Pn78iLCwMBaSirbRo0ejsrISN27cAACsX78ef//9Nw4ePAhLS0vevOrqapibm6OkpASPHj1iKa1w0cZ7hLQA2dnZmD17NvLz89GjRw8MGzYMQUFBSE1N5Ztnb2+PEydO4Pr161TA8Q0cHR3BMAxfhV/NGGFXRUUF5s2bh8jISHC5XLRq1Qq6urpQU1NDbm4uUlNTUVBQgEOHDiEmJgbe3t6QlpZmO7ZY43A49LMjAA3tWUqEx9vbG0lJSZg8eTJ+++03XrX+6dOnAXzqznHs2DEcOnQIurq62Lx5M8uJRZO+vj5OnToFLy8vJCcnQ0tLC7Nnz+Yr3gCAf//9F8rKyrCwsGApKSGCRcUY7KNVcc1LZWUlQkJCEB4ejsTERF7XPgMDAwwdOhRjxoyhawUBog4C7KNj0Dx87dpJWmspGLRqWvjOnTuH/fv3IysrC1wuF926dcOyZcvoekyI1qxZ883fg2EYbNmypQnSkM/ROZp9rVu3RkVFBe/rNm3aAADS09PRuXNnvrnV1dXIzc0Vaj5x0blzZ4SFhaGiogIyMjIYPHgwfH19sXfvXpiYmKBVq1YAgH379iEvLw+9e/dmObHwUAcOQlqADRs24MyZM5g4cSJvT7T6WjqZmppCS0sLwcHBLKUlRHAOHjyIPXv2oHXr1vjxxx9ha2vLd9O1qqoKwcHB2LFjBz58+AAPDw+4u7uzmFg0NbYDB4fDgZWVFSoqKvDgwQMhpSNEeBwcHJCUlITw8HC0atWq3nOzj48PNm3ahA0bNsDFxYWltIQQQoj4SEhIwJIlS5CcnFznw9Cajot79+5Fp06dWEhICCGEiLaaDgOSkpKws7PD6NGjv3qBz+err8nX+9rOM3WhbjREVLm4uCA1NZV3z/qvv/7Cnj17sHjxYixatIg3Lzk5Gba2tmjTpg3Cw8PZiiuyzp8/j59//hkHDx6ElZUVOBwOxo8fj5cvX0JeXh4GBgbIzc1FdnY2AGDv3r0YOXIky6mFgzpwENIC3L59G3Jycli7du0XP+jq6OggLS1NSMkIEa7AwEAwDIODBw/CxMSk1utSUlJwcnJCx44dMWXKFAQEBFABRxOIioqqVYDx9u1beHp61vue8vJyxMbGIicnp9YqeEJERWpqKrS0tHjV4BISEgA+FZNJSf3fx2xXV1d4eXnh3LlzVMBBCCGECFheXh5mzZqFnJwcyMvLw87ODl26dIG6ujpycnLw6tUrBAUFITExEbNmzUJgYCBUVVXZjk0IIYSIJA6Hg4CAAAQEBHzV+2jLiG9HnWcIqd+AAQPw7NkzpKSkQE9PD+PGjcO+ffuwf/9+lJaWwszMDO/fv8fBgwfB4XBgbW3NdmSRNHr0aMjIyPC60khKSuLw4cNYvXo17t27h+fPnwP41DFlxYoVYlO8AVABByEtQnZ2NgwNDSEnJ/fFubKysigvLxdCKvK56upq5Ofn040/AcvIyICenl6dxRuf69OnD/T19ZGRkSGcYCLuwYMH8PT05Csge/v2Lby8vBp8H5fLhYyMDBYsWCDoiISwRllZmff/8vLyAIAPHz7wbcPFMAy0tLSQmJgo9HyEEEKIuDly5AhycnLQr18//Pnnn3Veoy1duhQeHh6IiorC0aNHsWrVKhaSEkIIIaKLtohgn5OTE9sRCGm2Ro0ahatXryI2NhZ6enrQ1tbGypUrsW3bNhw9ehRHjx4F8On+tr6+PpYuXcpuYBGlrKwMe3t7vrF27drB29sb79+/R0ZGBuTk5NCpUye+xXLiQLz+tIS0UAoKCigsLGzU3Hfv3vFWApOmk5iYiDt37qBbt24wMzPjjVdUVGD79u04f/48KioqoKmpiQ0bNsDc3JzFtKJLRUUFCgoKjZorLy9PPwtNxNjYmO+iz9/fH2pqag3uWyonJwddXV2MGDECOjo6wogpVhrqfvJfkpKSUFJSQocOHWBiYkKFZk2oXbt2fHtgamtrAwBevHjB1+q1uroamZmZqKqqEnpGUTN9+nQAQIcOHXiriWrGGothGJw4caLJsxEiTF27dgUAGBgY4OLFi3xjjUWrGomounnzJqSlpbFnz556P/e0adMGu3btgpWVFW7cuEEFHIQQIuK+pvuDhIQE7xrayMiI12mRfJ0bN26wHYEQQurVq1cvXL16lW9s5syZ6N27NwICApCeng55eXmYmZlh4sSJjX4mQZpO27Zt+RbIiRsq4CCkBTA0NMTjx4+RmZnZYPVyfHw83r59i6FDhwoxnXg4c+YMfHx8cODAAb5xLy8v+Pj48L7OzMzEokWL4OfnB0NDQ2HHFHkDBw7ElStXkJubCzU1tXrn5eTkICEhAWPGjBFiOtE1YsQIjBgxgve1v78/9PT0qBUji/7bEaUhXC6XN1dSUhI2NjZYu3YtFXI0gc6dOyMsLAwVFRWQkZHB4MGD4evri71798LExIRXRLZv3z7k5eWhd+/eLCdu+SIjIwF8emj937HG+tp9lwlpjrhcLoBPBWL/Hfva70GaztcU0Xz+cKjmpmCnTp0EmE58ZGZmwsjIqMHrBQBQV1dH586d8ebNGyElEy/Dhw9v9NzPC47NzMxgZ2dHn1WbAB2D5uFrio3/exysrKzEbrWpoKxevfp/ug5o1aoVJk2ahEWLFkFGRkYAyQgh4ojO0c1Xnz590KdPH7ZjEAKGS3dtCGn2fHx8sHHjRlhaWmLfvn2QkZGBq6srYmNjERcXBwAoKirCrFmz8OzZM/z++++ws7NjObVocXR0RHJyMh4+fAhJSUkAn7pvDBkyBKWlpdi5cyf69OkDLy8vnD17FhMnTsSGDRtYTi160tPTMWHCBHTu3Bm7d++Gurp6rTk5OTlYtmwZXr16BT8/P3To0IGFpKItIyMDsrKydf79E+Hw9PREQUEBfH19UV1dDVNTUxgbG0NRURHFxcWIj49HTEwMJCQkMGXKFEhJSSExMRF3795FVVUVDAwMcPbsWSgqKrL9R2nRzp8/j59//hkHDx6ElZUVOBwOxo8fj5cvX0JeXh4GBgbIzc1FdnY2AGDv3r1itVejINQUa8jJyaFXr158Y1+jf//+TZqLEEKAT13L/ldSUlL48ccf4ebm1oSJxJOpqSk0NDQQEhLyxbnjxo1DVlYWHj58KIRk4uV//XlgGAZKSkrYsmULfW76RnQMmoea41BTPFDXrfi6XqvZhrHmfhP5NqtXr0ZlZSWuXLmCqqoqaGlp8V1Dv3z5EhkZGZCWlsaoUaNQVVWFxMREJCQkgGEY9O3bF8ePH4e0tDTbfxSxUVpaytumlHybtLQ0ZGVlQVlZuda5wcPDo973TZ48GYMGDRJ0PLFE52j2FRQUUPduIYuKigLw6Z5ez549+ca+Rr9+/Zo0V3NFBRyEtACVlZWYPHkyXrx4AUNDQ9jZ2SEwMBBJSUn4888/8erVK5w/fx5ZWVno168fTp48SatLm9iQIUOgpKSEK1eu8MYePHiAGTNmYNSoUdi7dy8AoKysDIMGDULbtm1rteAi3y4gIADJyck4cuQIJCQkMHLkSHTq1AlqamrIzc3F69evce3aNXC5XMydOxd6enp1fh9HR0fhBiekiRUUFMDFxQVycnLYs2cPXzeCGklJSfDw8EBZWRnOnTuHVq1a4e3bt3B3d8erV6+wZMkSfPfddyykFx2FhYW4efMmjI2N0blzZwCftjJbvXo17t27x5vXunVrrFixAi4uLmxFJYQQIiSnT5/G9u3bMXr0aLi4uKBr1658BZbnzp3D5cuX8cMPP8DZ2Rlv3rzB+fPn8c8//wAATp06xbdlI/l6Li4uePbsGfz9/Ru8OR4fHw9HR0f07NkT586dE2JC8ZCRkYFr165h586d6NmzZ50/D+fPn8eTJ0+wYsUKmJub834ewsPDIS0tjfPnz6NLly5s/1FaLDoGzUNkZCQeP36MP//8E+3bt4eDg0Ot4xAUFIS3b99iyZIl6Ny5M16/fo2AgAAkJCRASUkJAQEBvO0ayf+moqIC06dPR2ZmJrZt24bBgwfXmhMREYE1a9agffv2OHHiBGRlZfH48WMsXboUWVlZ+OmnnzBt2jQW0ouX4uJinDp1CidPnuS7rib/m+rqaowdOxYpKSnYt28fX4dd4FMhAcMwdRaX6evrIyQkhJ4zCACdo9nXs2dPWFpawt7eHlZWVtRlSQhqft/U/G75fKyxxGkrWCrgIKSFyMvLw9KlSxEZGVnnLzQul4sBAwbgzz//ROvWrYUfUMT16NEDXbt25buxt3//fuzbtw8bNmzgeyjn6OiIxMREPHnyhI2oIu2/FxX1/SzU91qNms415Nvk5eXh77//xu3bt5GUlITi4mIoKipCX18fQ4cOxeTJk6mln4Bs2rQJvr6+uHz5MnR0dOqdl5aWBhsbG0yePBm//PILAODVq1ewt7dHt27d4OfnJ6zIYuf9+/fIyMiAnJwcOnXqRK2PCSFEDNy4cQOLFi3CqlWrMHv27HrnHT9+HNu3b4enpyevffKhQ4ewa9cujB49Gn/++aewIouk48ePY9u2bdDS0sLmzZvrXDl67949/Pzzz3j79i1Wr16NGTNmsJBUtD18+BAzZszA1KlTsWbNmnrnbdu2DadPn8bx48d5xUtbtmzByZMn4ejoiG3btgkrssihY9A8JCQkYOLEiRg2bBi2bdtW5wOiyspKrF69Gjdu3MDff/+NLl26gMPhYNWqVQgJCYGrqyvWrVvHQnrR8eeff+LgwYNfLO6Li4uDk5MTFixYgGXLlgH49LM0depUmJiY4O+//xZWZLFTVFSEEydO4OTJk/j48SMAun/XFG7fvo358+fD3NwcR44cqfW6sbExtLS0MH78eL7xu3fv4tGjRzh8+DDMzc2FFVds0DmafZ93yFJWVoaNjQ3s7OzEprsDG2q6TWppaWH79u18Y1/j1KlTTZqruaICDkJamNu3b+PKlSt4+fIlCgsLoaCgACMjI9jY2MDa2prteCKrX79+UFJSws2bN3ljs2fPRkREBC5evMi3+r1mJV1sbCwbUUVaU7WUFpeTvCCFhYXhxx9/xMePH+ttAauiooKtW7fS7yYBsLa2hoqKCgICAr4418HBAYWFhbhx4wZvbOTIkcjLy6N24UTk1XRJeffuHbp3707tX4nIOXr0KGJjY2FhYYFJkyZ9cf7ff/+NO3fuwMzMDDNnzhR8QDEzbdo0JCcn486dOw3O43K5MDc3h76+Pk6fPg3g08rgAQMGQFlZGbdv3xZGXJFVUVGBqVOn4unTp2AYBp06deLr2peQkIA3b96Ay+Wid+/eOH36NLXEF4C5c+fi6dOnuHv3boOFrJWVlTA3N0fPnj15D5aKi4sxaNAgqKur832GJV+HjkHzsGTJEty5cwd37tyBgoJCvfNKSkowZMgQWFhY8Lq85uXlwcLCAtra2nwdYcnXGz16NKSkpHDx4sUvzrW1tUVFRQVfZ10rKysUFxf/T63exVl5eTkOHz6My5cvIz09HXJycujevTvmz5+PAQMGAAA4HA6OHTuGQ4cOobCwEFwuF23btsWcOXPo82oTWLduHc6dO4dDhw7BwsKi1uvGxsYwNTWFj48P33hUVBTc3Nxom3ABoXM0+9LS0hAYGIh///0XycnJAD7dz9bU1ISdnR3s7e1haGjIbkgi1mgpICEtzNChQzF06FC2Y4gdQ0NDPH78GA8fPoSpqSkyMjIQGRkJdXX1WlsXZGVlQU1NjaWkoo0KL5qH58+fY/HixaiqqoKOjg6mTJkCQ0NDqKurIycnB4mJifD19UVqaiqWLFmCv//+Gz169GA7tkjJycmBkpJSo+ZyuVzk5OTwjamqqiI7O1sQ0QgRupCQEBw+fBiurq58HbESExMxe/Zsvn/rjo6O2Lp1KxsxCWlyaWlp2L17N9q0adPof9djx46Fl5cXbt26hdGjR0NTU1PAKcVLfHx8ndua/RfDMOjQoQPi4+N5YzIyMtDX10dCQoIgI4oFGRkZeHt7Y/369QgJCUFCQgISEhL4OvlJSEjA1tYW69ato+INAXn69Cl0dXW/2IVMWloaurq6ePr0KW9MUVERBgYGSExMFHRMkUbHoHmIjo6GoaFhg8UbAKCgoABDQ0NER0fzxlRVVWFgYIC0tDRBxxR5b9++RadOnRo1V0ZGptbfuYaGhti0bG8qVVVVmDlzJh49esQ7/5aVleHu3bt48OAB9u3bh169emHBggV4/vw5uFwutLS0MHfuXDg7O9N2Bk3k6dOnkJWV5RXMNFa/fv3QunVrvnMDaTp0jmafjo4OFi9ejMWLF+Pp06cIDAzEpUuXkJmZiUOHDuHQoUPo1q0b7O3tMW7cOKirq7MdmYgZKuAghJBGcHBwwKNHj+Du7o6BAwfi8ePH4HA4cHR05JuXmpqKnJwcKrIhIm3fvn2oqqqCs7MzNmzYAAkJCb7XLS0tMXPmTF6Vv5eXFw4cOMBSWtGkrq6ON2/eICUlBXp6evXOS0lJwevXr9G+fXu+8fz8fLRq1UrQMUVKzUorOTk59OzZk2/sa1ArxqZ3+fJlxMfHo2/fvnzjW7duRVZWFtq1awdDQ0PExMQgICAAFhYWGDt2LEtpCWk6/v7+4HA4WLBgAZSVlRv1HhUVFbi7u2Pjxo3w8/PDokWLBJxSvHC5XKSnp4PL5Ta4nV91dTVv3ucYhoGcnJygY4oFZWVl7Ny5E0uXLsWdO3dqbfdnbm4ObW1ttmOKtIqKCrx7965Rc9+9e4eKigq+MWlpaSqu+UZ0DJqH4uJifPjwoVFz8/PzUVxczDcmLy//VXvDk7q1atUKCQkJyMnJafAh3Pv37/Hq1Su0adOGb7yoqIiuob/S2bNnERsbC4ZhMG7cOPTu3RtlZWUICwtDTEwMtm3bBnV1dTx79gwaGhr4/vvv4ejoSNuPNrGMjAxoaWn9TwUxWlpayMjIEEAqQufo5qVnz57o2bMn1q5di7t37yIwMBChoaF4/vw5Xrx4gR07dmDgwIF1bkNEiKDQ2ZAQQhph0qRJiIqKQkhICK5duwYAMDU1hbu7O9+8oKAgAKAW7USkxcbGQklJCevWratVvFGDYRj8/PPPuHTpEmJiYoScUPSNGDECJ0+exMKFC7F792507ty51pyEhAQsW7YMXC4Xo0aN4o3n5OQgNTUVpqamwozc4rm5uYFhGOjr6yMkJIRvrLEYhqFVWwIQHx8PFRUVvtaWOTk5uHv3Ltq2bYuQkBAoKSkhLCwM7u7u8PPzowIOIhLu378PCQkJjBs37qveZ2triy1btuDevXtUwNHEunXrhujoaBw6dAgLFiyod96RI0eQl5fHV9TH5XKRkpJCK7uamLa2NiZPnsx2DLFkaGiI58+fw9/fH05OTvXOCwgIQHZ2Nq9AtkZaWhpUVVUFHVOk0TFoHnR1dfH69WuEh4fXuX1BjfDwcKSnp9e6tnv79m2tYgLy9SwsLODn5wcPDw/s3bu3zs65eXl5WLp0KTgcDiwtLXnjhYWFSE5ORvfu3YUZucW7dOkSGIbBhg0b+Dolzp8/H6tWrUJwcDBSU1Nhbm6OPXv2NLrLKPk6JSUlDRZ7nz9/HoqKinW+Ji0tXauojDQNOkc3TxISErCwsICFhQVKS0tx/fp1nD17FlFRUbh79y7b8YiYoQIOQlqIqqoq+Pv749atW0hNTUVJSUmtFVs1GIbB9evXhZxQtElISGDXrl2YN28ekpKSoKmpCRMTk1oP7nR1dbFmzRrY2NiwlJQQwauoqECnTp2+WL0vKysLfX19vH79WkjJxMf333+PW7du4c2bN3BwcECPHj1gbGwMRUVFFBcX4+XLl3j27Bmqq6uhr6/P94Du7Nmz4HK5Dd48JLXVPGTT0tKqNUbYlZeXV2sVdWRkJKqrqzFu3DjejUArKyu0a9eOimiIyEhMTIS2tvZXP9Rp1aoVtLW1qeWuAMydOxdRUVHYs2cPnj9/jgkTJtQ6P1+4cAFXr14FwzCYN28e770REREoKirCyJEjWfwTiIY1a9ZAX18f8+fP/+LcQ4cOISkpibbXEoBp06Zh9erV+OWXX/Dq1Ss4OzvzFVu+efMGFy5cwMmTJ8EwDKZNm8Z77fHjx8jPz8fgwYPZiC4y6Bg0Dy4uLtiyZQs8PDywYsUKODo68j0sLSkpgb+/P3bt2gWGYfgedCckJOD9+/ewtrZmI7pI8fDw4HV+GDFiBCwtLWudo2/duoWSkhKoq6vj+++/5703ICAAHA4HQ4YMYfFP0PIkJCRARUWF7990jfnz5yM4OBjS0tLYvn07FW8IkKKiIgoKCup9vaEtj/Pz8+st7iDfhs7RzRuXy0VMTAzu3r1L95CaUNeuXb/5e4jT4jgq4CCkBfj48SNmzpyJuLi4eos2PketFQWna9euDZ5o7O3thZiGEHbo6+t/VZs/fX19AScSP8rKyvD19cVvv/2Ga9eu4enTp3j69Cnf3u4Mw2DUqFH47bff+FZbTJ8+HVOmTKEbJF/p1KlTjRojwldWVlZrLCYmBgzDoH///nzjGhoaiIuLE1Y0QgSqqKiowW20GtKqVStqhywAlpaWWLNmDX7//Xdcu3aN17nvc1wuF5KSkvjhhx/4tl18//49pk6dCjs7O2FGFkn+/v4wNTVtVAFHeHg4oqOjqYBDABwdHfHixQucPHkSx48fx/HjxyElJQUFBQWUlpaisrISwKefiRkzZsDBwYH33mfPnsHKyqrWlqXk69AxaB6mTZuGqKgoXLt2DZs2bcLWrVvRoUMHXuFARkYGOBwOr3Pi5w/pbt68CSMjI+oe1wQ0NDTg4+ODlStX4vnz57h8+TKuXLnCe73mOrpnz57YsWMHNDQ0eK8NHToUffr0ga6urtBzt2SFhYX13kOt+Qyrp6dXZzcU0nTat2+PN2/eoKio6KvuAxUWFiItLQ2dOnUSYDrxRefo5un58+cIDg7GxYsXkZOTwzs39OjRg+8YkP9NY55tCuN7tBRUwEFIC7Bnzx68ePECbdu2xZw5c2BiYgI1NbV6ty4ggsflcvHhwweUlZXxrcYmRBxMnDgRv/32GwIDAxv88BoYGIjs7Gxqzy4gqqqq2Lt3L9LS0hAeHo7k5GSUlJRAQUEBHTt2hIWFBXR0dGq9jwo3iKhRVVVFeno6KisreXvA3rlzBwzD1NoqqLy8nH4GiMhQVFTEx48f/6f3FhYW0mo6AZkxYwYGDBgAb29v3L17F7m5ubzX1NTUYG5ujlmzZsHY2JjvfQ4ODnRTkAUcDocWQAjQ2rVrMWjQIBw5cgSxsbGorKzkrQKWkJBA3759MXfuXFhZWfG9b+rUqZg6dSoLiUUPHQP2SUhIYO/evfDx8YG3tzcyMzORkpLCN0dLSwtz5syBq6sr3++k+fPnN6oYjTSOvr4+Lly4gPv379d7DT1w4MBa7/tfC2bFHYfDgaysbJ2v1XR0VVFREWYksWRmZoZXr17h4sWLmDRpUqPfFxQUhOrqauo8KkB0jm4eMjIyEBwcjODgYF6XSi6XC21tbdjZ2cHe3p4WJzaR+Pj4OsePHz+OnTt3YtCgQXBzc0OnTp2grq6OnJwcvH79GqdOnUJERARWrVqFGTNmCDk1e6iAg5AWIDQ0FFJSUvD29oaRkRHbccRaREQEjhw5gpiYGJSVldVq2VTTgvfHH39E69at2QtKiABNnjwZr1+/xk8//YSnT59i2rRp6NixI+/15ORk+Pj44O+//4abmxsmTpzIXlgxoKOjA1dXV7ZjEMKaPn364OrVq/D09MS8efMQEhKC5ORk9OrVC61ateLN43A4SE1NrbOwiZCWSFNTEwkJCfj48eNX3fz++PEjUlNT6bpCgIyNjfH7778D+FQsU/NwqKH9x4nwcTgcpKWl0XERsGHDhmHYsGEoKSlBamoqiouLoaioCF1dXSgoKLAdTyzQMWBfTfv7adOm4c2bN0hKSuKdG/T19fna5hPBGzhwYJ2FGoSIIgcHB/j4+ODPP/+Eubk5OnTo8MX3pKWlYd++fWAYhgqMBYzO0ez5+++/ERQUhNjYWACfijZatWoFGxsb2Nvb11oQRATj2rVr2L59Ozw8PODu7s73mpaWFrS0tDB06FD89ddf2LZtGzp06IARI0awlFa4qICDkBYgLy8Penp6dJOVZZ6envDy8mqwTZOysjICAgLQr18/jB8/XojpCBGe4cOH8/7fx8cHPj4+kJKSQuvWrZGfn4+qqioAgKSkJG7cuIEbN27U+h4Mw+D69etCy0wIEV2zZ89GaGgoDh06hEOHDgH49Dtm9uzZfPMiIyNRVlaGnj17shGTkCbXv39/vHz5EufPn6/1770h586dA4fDqbXFEBEMZWVlKhAQsKioKDx48IBv7O3bt/D09Kz3PeXl5YiNjUVOTg49xBMSBQWFWp1niHDRMWgeDA0NqWCDiI0vnY+/9PrixYsFEUus9OrVC6NGjcLVq1cxadIk/Pzzzxg9enSdHciqq6tx+fJlbN68GQUFBRgxYgR69erFQmrxQ+do4fvtt98AfOoIZGVlBXt7e1haWvI6uxLhOHbsGNq0aYMFCxY0OG/evHk4ceIEjh8/LjYFHAxXnDaMIaSFGjFiBOTl5REcHMx2FLEVHh6OefPmQVFREUuXLsWIESOwfPlyPHr0CHFxcbx5ubm5GDJkCKytrbF//34WExMiOE1xQcEwDN/PDiHNXX17936N/3ZtIk0nLCwMu3btQnJyMjQ1NTF37ly4uLjwzVm6dCkuX76MnTt3wtbWlqWkhDSdhIQE2NvbQ05ODidOnGjUzdUnT55gxowZKC8vR0BAADp37iyEpIQIlqenJzw9PXkPIrhcbqO2ReFyuZCRkcGhQ4eoiIMQQghpYsbGxg2ej2seSzU0h+4bNY3i4mJMnToV8fHxYBgGampq6NOnD7S1tSEvL4/S0lJkZGQgJiYGubm54HK56NKlC3x8fGgLUiKypk2bBnt7e9jY2NB2TiwyMzODvr4+zp0798W5Li4uSEpKQnR0tBCSsY86cBDSAowePRrHjx/H27dvoampyXYcsXTq1CkwDINt27Zh5MiRAOq+wFBTU4Ompma9+3kRIgpOnjzJdgSxUlM4YGBggIsXL/KNNRYVDny7pqh5prppwbGysqq1L+x/bdq0CRs3boSioqJwQhEiYEZGRnBxccHZs2fh5uaG7777DpMnT65zG7/8/Hz4+vri4MGDqKiogIuLCxVvfKOa1aJt2rTh7UHd0ArSujAMg0WLFjV5NnFjbGwMJycn3tf+/v5QU1ODhYVFve+Rk5ODrq4uRowYQVtrNYGAgAAAgJKSEm9FXM3Y13B0dGy6UGKGjkHzEBUVBeDT75iarm81Y1+jX79+TZpL3NR0DdXT04O3tzffWGNR19BvR/+Omw9FRUX4+vpi06ZNCAgIQE5ODq5du1br3jaXy4WkpCQcHBzw888/0xYeTYTO0c3T6dOn2Y5A8On3Tnp6OqqrqyEhIVHvvOrqaqSnp4vVvVXqwEFIC1BSUoJJkyZBUVERe/bsQfv27dmOJHYGDhwISUlJ3L17lzfm6uqK2NjYWtXgLi4uePnyJZ48eSLsmGLl0aNHiIyMRFZWFsrKyrBlyxbea+/evUNVVRW0tLRYTEhI06jpeKKvr49Lly7xjX0NKiwTjOPHj2Pnzp0YNGgQ3Nzc0KlTJ6irqyMnJwevX7/GqVOnEBERgVWrVmHGjBlsxyWEiJjKykq4u7vj7t27YBgGkpKS6NSpE3R0dKCgoICSkhKkp6cjISEBHA4HXC4XQ4YMwcGDB6k17DeqWVWqr6+PkJAQvrEv3WapmUMdyQTD2NgYpqam8PHxYTuK2Gjo5+Fr0M/D/46OQfPQFMeBiu+/Xc31soGBAd9x+Bp0jiaiKi0tDZcvX8bDhw/x7t07FBcXQ1FREe3atYOpqSlsbGyouLWJ0TmakPq5ubkhOjoaixYtanDbrJqui/379xebxaXUgYOQZqa+VVuDBw+Gj48PRo8eDQsLC+jp6UFeXr7e70N7BDat4uJiGBkZNWouh8NpsFqQfJvMzEysWrUKMTExAP6vRfLnBRx79+7FhQsXcObMGfTp04etqIQ0iboKL6gYo3m4du0atm/fDg8PD7i7u/O9pqWlBS0tLQwdOhR//fUXtm3bhg4dOojNPo1syc7ORnR0NLKyslBaWkqfh4jIk5aWxpEjR3Dw4EEcO3YMHz9+RHx8PK898ueFBMrKypg1axbc3d3ps2oTqPn90qZNm1pjhF2hoaGQlZVlO4ZYcXR0BMMwaNu2ba0xIhx0DJqHmo4Dny8moS4EwhcaGgoAkJKSqjVGiLjT0dHBvHnzMG/ePLajiA06RzdP1CGreViwYAGioqLg5eWFqKgoTJs2DYaGhlBTU0Nubi7evHkDHx8fPHjwAAzDYP78+WxHFhrqwEFIM9NQ9eXnP64NzaEq8aZnaWmJsrIyPHjwgDdWVweOqqoq9O/fH23btsWVK1fYiCrSCgoKMH78eGRkZKB9+/YYPHgw7t27h+zsbL7jEB0djWnTpmHOnDlYtWoVi4kJIaLM1dUVycnJvJXv9amuroa5uTkMDAyoRaOAFBYWYuPGjbh48SKqq6t545+fGzw8PHDt2jX4+fn9T11sCGnuiouLcevWLcTExCA7O5u3mk5DQwN9+/aFpaUlbSFECCGEEEIIIUTsUYes5uPMmTPYsmULqqqq6jwmXC4XUlJSWLNmDW/7UnFAHTgIaWao+rJ56tu3Ly5fvowbN27A2tq63nmBgYEoKSnBgAEDhJhOfBw5cgQZGRkYPnw4du7cCXl5ebi6uiI7O5tvXt++fSEnJ4eIiAiWkoqPJ0+eIC4uDvn5+aisrKxzDu3vTkTVq1evoK+v/8XztoSEBDp06ECdUwSkrKwMM2bMQFxcHOTl5dGzZ08kJCTgw4cPfPNcXFxw5coVXL9+nQo4iEhSVFTE2LFjMXbsWLajEMKqzMzMr34Pbb1ICCGEEEKI+Giom0ZpaSlSUlJQWFgIaWlpmJiYCC+YGHJ1dUW/fv3g7e2N8PBw5OTk8F5TV1eHhYUFZs2ahc6dO7OYUviogIOQZmbbtm1sRyB1cHNzw6VLl7Bu3TooKyvXeYK/evUqNm/eDElJSUybNo2FlKLv+vXrkJaWxubNmxvcQkhCQgI6OjpIS0sTYjrx8uTJE6xevRpJSUm8sZoOQJ+rGaMCjqbF4XBQWloKaWnpWi3Cnzx5grNnz+Ldu3fo0aMHZs+eDSUlJZaSijYul4v09HRUV1c3uB1BdXU10tPTQY3vBOPkyZN48eIF+vbtiz179qBdu3ZwdXWtVcAxYMAASEtL486dO7TNASGEFdnZ2cjOzoahoSF1QxEga2trWk3XAlRXVyM/Px+qqqpsRxFbdAwI+SQjIwNBQUF49+4dunfvjvHjx9N2c4QQVtE5WvBOnTr1xTlBQUHYunUrtLW1sXXrViGkEl9GRka8v+PCwkKUlJRAQUEBysrKLCdjD30SIYSQRujbty8WLlyInJwcTJ8+HaNHj8abN28AAO7u7hg+fDg8PDxQUlKCZcuWiV01oLBkZmaiY8eOaN269RfnKioqorS0VPChxFBqaipmzZqFlJQU2Nraon379gCAhQsXYsqUKejatSu4XC5kZWUxa9YsKt4QAG9vb/Tr1w///PMP3/itW7fg6uqKCxcu4Pbt2zhw4ACmTZuG8vJylpKKtm7duiE/Px/79+9vcN7+/fvx4cMHdO/eXUjJxEtISAikpKSwc+dOtGvXrt550tLS0NXV5Ss8I4SQpvTkyRNs3boVYWFhfONFRUVwd3eHlZUVJk2aBHNzc1y4cIGdkGJAS0sLmpqadf7XunVrcLlcXhteTU1N3mdZ0rQSExNx8uRJREdH841XVFRg48aN6NOnD4YMGQJra2vcuXOHpZSijY5B85CdnY3Q0FC8evWKb5zL5cLb2xujRo2CiYkJpk2bRh37BMjX1xf9+/fHiRMn+MYfP34Me3t77N27F76+vvjll18wd+5cvm0ZCSGkqdE5umWwt7fHrl27EBAQgLNnz7IdR2woKytDQ0NDrIs3ACrgIISQRluyZAm2bt2Kdu3aISUlBQUFBeByuQgLC0NGRgbU1NSwbds2zJ07l+2oIktaWhoVFRWNmpubm0tdBwTk8OHDKCkpwS+//IIdO3ZAU1MTwKefkXXr1sHPzw/Hjh2DgoICIiIiMGfOHJYTi567d++CYRjY2tryje/cuRNVVVWwtLSEh4cHdHR08PLlS5w+fZqlpKJtwYIF4HK58PLywowZM3Dt2jUkJiaioKAAiYmJuHbtGmbOnAkvLy8wDIP58+ezHVkkpaSkQFtbu1Ht75WVlVFcXCyEVIQQcXT+/HmcPHmyVqe4HTt2ICwsDFwuF9LS0igtLcUvv/yCJ0+esJRUtN24caPe/yIiIhAZGQkPDw9ISUlh/PjxuHHjBtuRRdKZM2ewdetWFBUV8Y17eXnBx8cH5eXl4HK5yMzMxKJFi3gLJEjToWPQPJw6dQqLFy/G69ev+caPHz+OHTt2IDU1FWVlZYiOjsaMGTNqbRFLmkZYWBgKCwsxcuRIvvFt27ahuLgYxsbGcHZ2RuvWrREREYHz58+zlJQQIg7oHN1yDBo0CJqamvj777/ZjkLEDG2hQkgz87/s11sX2sNXMJycnGBnZ4dHjx7h5cuXKCwshIKCAoyMjGBqagoZGRm2I4q0jh07Ij4+Hnl5eQ22kEtNTUVaWhoGDBggxHTiIyIiAoqKinB2dq53zqBBg7B7927MmDEDBw8exLJly4SYUPSlpKRAXV2d7+cgMTERCQkJ6NKlCw4ePAgAsLGxwdixY3H16lUqpBEAc3NzrFu3Dlu2bMGDBw8QGRlZa07NCt81a9bA3NychZTiQVJSslHzCgoKaNsCQojAxMTEQE5Oju8zaGlpKQIDAyEvL4+TJ0+ie/fuOHDgAPbt24eTJ09i586dLCYWTyoqKvjuu++gp6eHFStWoHPnzhg1ahTbsUROdHQ0ZGVlYWFhwRurqKjAmTNneJ2z+vTpAy8vL5w9exYnTpzAhg0bWEwseugYNA/379+HlJQURowYwRurrq7G0aNHAQArV65Enz59cOTIEdy8eRPe3t5Ys2YNW3FF1uvXr6Gqqsp3vzQzMxOxsbHQ1dXFuXPnICUlBRcXF0yaNAnBwcGYOHEii4kJIaKMztEtS+vWrambqxA8efIEcXFxyM/PR2VlZZ1zxGmrdirgIKSZ+dr9eutCe/gKlpSUFMzMzGBmZsZ2FLEzcuRIPHv2DDt27Kh33zkOh4ONGzeCYRjY2NgIOaF4ePfuHTp27AgpqU8fI2oenFZUVPAVMQ0YMADa2tq4evUqFXA0sQ8fPsDIyIhvLCoqCgD4/t3r6+tDV1eXKvUFyNXVFf369YO3tzfCw8ORk5PDe01dXR0WFhaYNWsWba0lQNra2khJSeHtj1mf9+/fIyUlBb169RJiOkKIOMnJyeF1JqsRFRWFsrIyODg4oGfPngA+dXA6ceIEYmJi2IhJ/r+xY8diy5YtOHbsGBVwCMD79++hoaHBV2QZGxuLwsJCjBo1iveZde3atQgODsb9+/fZiiqy6Bg0D1lZWdDQ0OC7Vn7y5AlycnJgbm7O6+JqZGQEc3NzapUvIHl5edDX1+cbe/DgAQBgzJgxvPsbvXv3RocOHWpteUMIIU2JztEtR1lZGZKTk3nnCdL0njx5gtWrV/MVyXC53FrPSWvGqICDEMKKhjpnvHv3DlVVVQA+FRG0bt0a+fn5fGMN7f1O/nc+Pj4YO3Ys2rRpw3YUsebm5obz588jICAAb9++hbOzM0pKSgAAz58/x6tXr3Dq1Cm8ePECRkZGmDBhAsuJRZO8vDzfh9aa/eiys7Oho6PDN1dFRQWJiYlCzScOqqurUVpayjcWExMDhmFqFZe1bt0aGRkZwowndoyMjHhFZYWFhbxCAnHfq1FYhg0bhsOHD2P//v1YuXJlvfP++OMPcLlcDB8+XIjpCCHipKioCNra2nxjNefnIUOG8MakpKSgra2NhIQEYUck/9G+fXt6SCcgBQUFte5vPHz4EAzD8K02lZOTg56eHl0zCAAdg+YhPz8f3bp14xuLjo4GwzAYNmwYb0xFRQV6enpIT08XdkSxUFVVVWs1b2xsLBiGQf/+/fnG1dXVaSsbIjJ8fHwwfPhwtG/fnu0o5DN0jm4Z8vLysH79epSWllJXXQFJTU3FrFmzUFZWBltbW0RHRyMrKwsLFy5Efn4+Hj16hBcvXkBOTg5TpkwRq666VMBBSDNT3/67W7ZsgY+PDyZPngw3Nzfo6+tDQkIC1dXVSEpKwqlTp3D+/HmMHDmSWi0KwMaNG7F161aYm5vDwcEBw4cPp+1SWKCgoIAjR47gu+++w/3793mrJQDwtvPgcrkwNDTEwYMH6RgJSLt27fDu3Tve1wYGBrh58yaioqL4CjgKCwuRlJREx0EANDU1kZKSgoKCArRq1QpVVVUIDw+HjIwMevfuzTe3oKCAis+ESFlZmQo3hGzWrFk4d+4cjh49itzcXEycOBEcDgfApxvmr169wrFjx3Dz5k1oampiypQpLCcmhIgqRUVFZGVl8Y3VfF41NTWtNZ8+I7Gruroaqamp4HK5bEcRSfLy8nydyYBPD62B2j8PUlJSjd4OjTQeHYPmQUZGBvn5+Xxj9R0HeXl5VFdXCyuaWGnbti3S09P5uvaFh4dDUlISffr04ZtbVFSEVq1asRGTkCa3ceNGbNq0CV27doW1tTWGDRuG7t27sx1L7NE5mn3Tp0+v9zUul4vc3Fykp6ejoqICsrKy+P7774WYTnwcPnwYJSUl+PXXXzF58mS4uroiKysLS5Ys4c2JiIjAihUrEBERAV9fXxbTCpcE2wEIIV927tw5nDp1Cps2bcJvv/0GQ0NDSEh8+vGVkJCAoaEhfvvtN2zcuBEnT57E+fPnWU4sekxNTcHhcBAWFobly5dj8ODBWLt2LbUvY4Genh4CAgKwbt06DBgwAK1bt4akpCSUlZXRt29f/PTTT/Dz80OHDh3YjiqyevXqhby8PHz48AEAMGLECHC5XOzcuRO3b99GSUkJUlJSsHLlSpSVldX5wIJ8G3Nzc1RWVmL58uW4ceMGfvnlF+Tl5WHw4MF8D4OKi4uRlpZGKy2EoKKiAo8ePcLly5cREBDAdhyx0qZNG/z1119QVVWFv78/XF1d8eTJEwDAoEGDMGPGDNy8eRPq6uo4cOAAlJSUWE5MCBFVxsbGyM3NxfXr1wEAcXFxePToEbS1tWt9Ns3IyIC6ujobMQmAyspKbN26FR8/fqy1Mp40DUNDQ2RlZeHhw4cAPv2bj4yMhLq6OgwMDPjmZmVlQU1NjY2YIo2OQfPQsWNHpKWl8ba1zM/Px/3796GiogJjY2O+ue/evaPjICD9+/dHWVkZNm7ciJcvX+LPP//E27dvYWpqyrcNY0VFBVJSUqjDMREZy5cvR+/evREfHw9PT084OzvDysoK69evx+3bt2t1piHCQedo9kVGRtb7X1RUFBITE1FRUQFTU1OcPHmStuMVkIiICCgqKvIW59Zl0KBB2L17N+Lj43Hw4EEhpmMXdeAgpAXw9fVFu3bt4OTk1OA8Jycn7NmzB76+vg3+wiNfz8fHB5mZmQgKCsK///6L169fw8/PD/7+/tDQ0ICtrS3s7e3RuXNntqOKBRkZGbi6usLV1ZXtKGLJ2toaFy5cQFhYGJycnGBiYoJx48bh4sWLWLBgAW8el8uFvLw8PDw8WEwrmubPn49Lly7h7t27uHfvHrhcLmRkZGpVg9+8eRMcDqfWtiqk6VRVVcHLywunT59GUVERb9zR0ZH3/7/88gvu3bsHb29v6OnpsZBS9PXq1QvBwcE4evQorl69irS0NN5r7du3h42NDebNm0c3PQghAuXi4oIHDx5g6dKl6Ny5M28PXxcXF755L1++REFBQa2W7aRpfKkjZU5ODuLi4pCbmwsJCQm+z6+k6Tg4OODRo0dwd3fHwIED8fjxY3A4HL7PSMCntsk5OTkYOnQoO0FFGB2D5sHGxgYvXrzA3LlzMXr0aNy7dw/l5eW1jkN2djaysrIwcOBAdoKKuAULFuDKlSsICAjgFdxLSEjgu+++45sXHh6OqqqqWl05CGmp5s+fj/nz5yMvLw83btxAaGgo7t+/D19fX/z999+Ql5eHhYUFrK2tYWlpidatW7MdWSzQOZp9J0+erPc1hmEgLy8PPT096rIrYO/evUPHjh1527XXdJupqKjgW6Q4YMAAaGtr4+rVq1i2bBkrWYWNCjgIaQGSkpLQqVOnRs1t164dXr9+LeBE4klLSwvu7u5wd3dHXFwcAgMDERISgqysLBw9ehRHjx5F586d4eDggHHjxkFDQ4PtyCKnurqa132GsGfYsGG4desW355z27dvh5GREQIDA5Geng55eXmYmZlhyZIltVYVkW+noaGBCxcu4OjRo0hOToaWlhbc3NxgZGTENy8yMhLGxsZ8eyuTpsPhcODu7o67d+8CADp06IAPHz6gpKSEb97QoUNx7tw5XLt2DXPnzmUjqlhQVVXFqlWrsGrVKpSWluLjx49QVFSkjhuEEKGxtbXFy5cv4e3tjRcvXvDGZs+ezTev5sERPaQTDH9//0bN09LSwurVq/n2GSdNZ9KkSYiKikJISAiuXbsG4FNnS3d3d755QUFBAD6trCNNi45B8zBjxgyEhYUhJiYGx48fB/Cps+h/i+9DQkIAfHpAQZqevr4+Tp06BS8vL9419OzZs2udi//9918oKyvTuYGIHFVVVTg7O8PZ2Rnl5eW4d+8erl+/jlu3buHKlSu4evUqb0uhmq1WOnbsyHZskUXnaPZRMX3zIC8vzyveAMArmMnOzubbqh0AVFRUkJiYKNR8bGK4tNknIc1e//79UV1djXv37jW4T3J5eTmGDBkCCQkJREZGCjGh+OJyubh//z4CAwNx7do1FBcXg2EYSEpK4tmzZ2zHEznm5uYYN24c7Ozs0KNHD7bjEELE3JkzZ7BhwwYYGBhg165dMDY2hqurK2JjYxEXF8ebV7OVUN++fXHq1CkWExNCCBGGDx8+IDU1FZqamnW2YI+IiEBxcTHMzMxolaMANFTA8flqui5duoBhGCEmE09xcXFISkqCpqYmTExMav2dBwUF4cOHD7CxsaFFEAJCx4B91dXVuHHjBhITE6GlpYURI0ZATk6Ob87x48eRmZmJKVOmQF9fn6WkhBBx9PjxY1y/fh03btzgbffEMAw6duyI4cOHY9iwYejbty99bhIAOkezZ/HixZCSksLvv//e4DM3Ilh2dnb48OED7ty5AwDYuXMnjh49is2bN2P8+PG8eYWFhRg6dCikpaXF5tknFXAQ0gK4u7vj1q1bcHFxwfr16+v9sLRu3TqcPXsWVlZWYrUXVHORl5eHtWvXIiwsDAzD8D28I03D2NiY9+9fX18fDg4OsLW1rbWnOCGECMPEiRPx7Nkz+Pv7o0uXLgBQZwEH8Kl18sePH3Hv3j02ohJCCCGEEEIIIYQ0KC0tjVfMERsbi6qqKjAMA1VVVV73UUJEQY8ePWBkZNTo7n1EMH766Sf4+/vj7t27aNOmDR49eoTJkydDVVUV27Ztg5mZGd6/f48tW7bg9u3bsLKywoEDB9iOLRRUwEFIC/D06VNMmTIFHA4Hurq6mDJlCgwNDaGmpobc3Fy8efMGvr6+SE1NhaSkJHx9fdGzZ0+2Y4uF6upqhIeHIygoCDdu3EBZWRm4XC6kpaXx9OlTtuOJnM+7nRQVFYFhGDAMA1NTU9jb28PGxob2pSOECI2pqSnatGmD69ev88bqK+CYNGkSnj9/Tt2ZBCglJQW3bt1CamoqSkpKUN9lDsMw2LJli5DTEUIIaa4iIiKoLTUhhBBCyH8UFBQgLCwMoaGhuHv3Lh4+fMh2JEKazPDhw6GoqMjbpoawIzQ0FIsWLcLWrVvh5OQEAFixYgUuXrzIt5Cdy+VCXl4evr6+YrNdOxVwENJC3LhxA6tXr8bHjx/r7MDB5XKhrKyMrVu3YsSIESwkFC9PnjxBUFAQLl26hLy8PN5DIhMTE9jZ2WHs2LFo06YNyylFV0VFBW7cuIHAwECEh4fzqsGlpaVhZWUFe3t7WFpaQlpamu2oIo0elhJxZ2JiAj09PQQGBvLG6ivgsLOzw9u3bxEdHS3smCKPy+Vi8+bNOHPmDLhcbr2/i2pQlyxCCCGpqanw9/dHYGAgsrKy8OLFC7YjEUIIIYQ0W5WVlXSflYiUTZs2wdfXF6GhoWjfvj3bccRWdXU13r9/D0VFRSgpKQEAqqqqcOTIEQQGBiI9PR3y8vIwMzPDkiVLxKZ4A6ACDkJalNzcXPj6+iI8PByJiYkoKSmBgoICDAwMYGFhgcmTJ0NdXZ3tmCIrNTUVQUFBCA4ORmpqKu8BkZ6eHuzs7GBvbw9dXV2WU4qfgoIChISEICgoCI8ePQKXywXDMFBRUYGNjQ3Wr1/PdkSRQw9LCfnExsYG2dnZiIqKgpSUFIC6Czg+fvyIwYMHo3PnzvDz82Mrrsg6evQoduzYAYZhMGzYMJiYmEBNTQ0SEhL1vqemqp8QQoj4KCoqwqVLl+Dv74/Y2FgAnz7XSklJUYcsQgghhBBCxEh+fj7Gjx+Pdu3aYe/evWjXrh3bkQjhQwUchBDSCJMmTcKTJ08AfLrJ16ZNG4wZMwYODg7o3bs3y+lIjfT0dPz7778IDg7GmzdvqHBAQOhhKSGfbNy4EWfOnMEPP/yAWbNmAai7gGPHjh3w9vaGu7s7PDw82IorssaOHYukpCTs3r0bNjY2bMchhBDSjHC5XNy7dw9+fn4IDQ1FeXk5r/i4S5cucHJygp2dHdTU1FhOSgghhBBCCBEWT09P5OXl4Z9//oGUlBQGDRoEQ0NDyMvL1/uexYsXCzEhEXdSbAcghJCW4PHjx5CVlcWwYcPg4OAACwsL3mpr0nxUVVWhsrISlZWVbEcRaRcuXADDMPSwlIi9OXPmwM/PD3/88QeKi4sxceJEvtczMjJw7NgxnD59Gq1atYKbmxtLSUVbeno62rdvT7+PCCGE8CQmJiIgIACBgYF49+4dAPAKN5SVlXHq1Cmxar9LCCGEEEII+T+enp5gGAZcLhccDgdhYWG4detWnXNrOn5TAUfTKyoqQlxcHNTU1GBgYFDvvMTEROTm5qJbt25QVFQUYkL20NNHQghphM2bN2P06NG8fbhI85GXl4eLFy8iKCiI1/qYy+VCXV0dtra2LKcTTfSwlJBPtLS0sGvXLixfvhxeXl7w8vLidaLp06cPysrKwOVyIS8vjz179kBVVZXlxKJJRUWFVk4TQghBYWEh/v33XwQEBPB1T5SVlYW1tTUcHR2xYMECyMrKUvEGIYQQQgghYszR0REMw7AdQ+ydPXsWO3bswPr16xss4IiOjsavv/6K1atXY8aMGUJMyB7aQoWQFqSsrAw3b95EXFwc8vPz6+0ywDAMtmzZIuR0hAhPWVkZrl27hqCgIERERIDD4fAeko4cORL29vYYPHhwg1t6kP+dubk52rdvj/Pnz7MdhZBm4c2bN9i3bx/CwsJQVlbGG5eWloaVlRWWLl0KQ0NDFhOKthUrVuDGjRu4d+9eg60uCSGEiKZbt27B398fN2/eREVFBW+FnJmZGezt7TFmzBheIb6xsTHU1dVx584dllOT/4qPj6fCGpbRMSCEEEIIIcLk6uqKJ0+eIDIyEgoKCvXOKykpQf/+/dGnTx+cOnVKiAnZQwUchLQQoaGhWLt2LT5+/Mgbq/nx/bxSsOZmVVxcnNAzEiJo4eHhCAoKQmhoKEpLS8HlciEpKYlBgwbB3t4eI0eOpId3QkAPSwmpW2VlJVJSUvDx40coKCigY8eOkJOTYzuWyEtNTcX48eMxduxYrF+/nlZQEEKarerqauTn51NHpiZmbGzMa3/csWNHODg4wN7eHh06dKhzLhVwCMb+/fuxcOHC/+m9z58/x+zZs/HgwYMmTiVe6BgQUtvVq1cRFBSE5ORkAEDHjh1hZ2eH0aNHsxuMECLW1qxZA319fcyfP/+Lcw8dOoSkpCRs3bpVCMkIES5zc3PIy8vj2rVrX5w7cuRIlJeX4/bt20JIxj7aQoWQFuDFixfw8PCAtLQ0FixYgEuXLiE1NRWbN29Gfn4+Hj9+jBs3bkBKSgoLFy5E27Zt2Y5MiEDMmzeP9//dunWDvb09bG1toa6uzmIq8ePh4YFbt25h69at9LCUZdSZqXmRlpZGp06d2I4hdrKzs7F48WLs3LkTjx49grOzM/T09Bqs3O/Xr58QExJCxEViYiLu3LmDbt26wczMjDdeUVGB7du34/z586ioqICmpiY2bNgAc3NzFtOKnlatWmH8+PGws7ODpqYm23HEzt69e6GoqPjVLY2fPHmCOXPmoKioSEDJxAcdA0L4rVu3DufOnQPwf4vg3rx5g9DQUIwfPx6bN29mMx4hRIz5+/vD1NS0UQUc4eHhiI6OpgKOJnbx4kWMHDkSMjIybEcRawUFBY2+dmvdujXi4+MFnKj5oAIOQlqAo0ePgsPhYNeuXRg1ahQiIyORmpqKCRMm8Oa8efMG3333HXx9fXHhwgUW0xIiOFpaWrCzs4O9vT1tR8AieljaPHxtZyYq4CCiys3NjfdvPiEh4Ys3NRiGwYsXL4QRjRAiZs6cOQMfHx8cOHCAb9zLyws+Pj68rzMzM7Fo0SL4+fnRZ9omYGtri9DQUBQUFGD37t3Ys2cPzMzM4ODggNGjR/O2TyGCt23bNsjLy2PixImNmh8TE4P58+ejqKgIvXr1EnA68UDHoHkqKCiAt7c3bt++jdTUVJSUlNQ7lz6rNo3r16/j7NmzAIAuXbqgX79+qK6uRnR0NF69egU/Pz8MGzYMI0aMYDkpIc1HYWEhbt68iXfv3qF79+4YNGgQ25EIAA6HQ4vnBGDFihVo1aoV7Ozs4OzsTNvIsaR169ZIT09v1Nz09HSoqKgIOFHzQQUchLQADx8+hIqKCkaNGlXvHENDQ+zduxeOjo7Yv38/fv75ZyEmJEQ4bty4wXYEAnpY2hxQZ6bmpaioCA8ePEBaWhqKi4vR0A6FixcvFmIy8aClpcV2BEIIAQBER0dDVlYWFhYWvLGKigqcOXMGUlJS2LlzJ/r06QMvLy+cPXsWJ06cwIYNG1hMLBp27tyJoqIiXLp0CX5+foiNjUVkZCSioqKwceNGDBs2DA4ODnzHhTS9X375BRs3bsRvv/0GOTk52NvbNzg/OjoaCxYsQHFxMUxMTHDkyBEhJRVddAyap7S0NEybNg3v3r1r8DqhBu123jTOnz8PhmEwdepU/PTTT7x7GFwuF5s2bYKPjw/Onz9PBRxE7ISEhODw4cNwdXWFi4sLbzwxMRGzZ89GdnY2b8zR0ZG6PrCMw+EgLS0NysrKbEcROV26dMHLly9x+vRp+Pj4oFu3bpgwYQLs7Ozo71uIevXqhRs3buDSpUsYM2ZMvfMuXbqEDx8+wMrKSnjhWEYFHIS0ALm5uTAyMuJ9LSX16Ue3rKwMcnJyvHFjY2Po6+sjLCyMCjgIIQJDD0vZR52Zmo9Dhw7hwIEDKCsra3BeTScUKuBoelTcRwhpLt6/fw8NDQ1ISkryxmJjY1FYWIhRo0bBxsYGALB27VoEBwfj/v37bEUVOUpKSnBxcYGLiwtSU1Ph5+eHoKAgZGZm4tKlS7h8+TJat27NdkyRNnXqVJSVlWHHjh1Yu3Yt5OXlMXLkyDrn3r9/H9999x1KS0thZmaGv/76C4qKikJOLHroGDRPO3bsQHZ2Njp06IC5c+eie/fuUFVVpdXUAvbs2TPIyclh1apVfH/XDMNg1apV8PPzw7Nnz1hMSAg7Ll++jPj4ePTt25dvfOvWrcjKykK7du1gaGiImJgYBAQEwMLCAmPHjmUpreiIiorCgwcP+Mbevn0LT0/Pet9TXl6O2NhY5OTkYODAgYKOKHYCAwPx/PlznD9/HiEhIXj+/DlevHiB33//HSNHjsSECRPo710IJk6ciNDQUPzyyy+QlZWFtbV1rTlhYWH45ZdfwDBMo7vMiQIq4CCkBVBSUgKHw+F93apVKwCfWu8aGBjwzZWRkUFGRoZQ8xEiCJmZmQA+FSy1a9eOb+xrULFB06OHpeyjzkzNg4+PD3bt2gUAaNeuHbp06QI1NTW6GUsIIWKqoKCg1mfPhw8fgmEYvu4PcnJy0NPTQ2JiorAjigVdXV0sXboUS5cuRUREBPz9/XHt2jV8+PABwKcFEjY2NnBycoK9vX2j91wmXzZnzhyUlpbC09MTy5cvh5eXF4YOHco3586dO1i8eDHKysowYMAAHDx4EPLy8iwlFj10DJqfiIgIyMjI4NSpU3R/Qojy8/NhZGQEWVnZWq/JycmhY8eOSEhIYCEZIeyKj4+HiooK3zZ+OTk5uHv3Ltq2bYuQkBAoKSkhLCwM7u7u8PPzowKOJvDgwQN4enry3S96+/YtvLy8Gnwfl8uFjIwMFixYIOiIYql79+7o3r071qxZg6tXr+LChQt48OABgoOD8e+//0JbWxvjx4+Hk5MT2rdvz3ZckWRpaQk7OzsEBwdj0aJF0NfXh4mJCZSVlVFYWIjHjx8jMTERXC4X9vb2dRZ4iCoq4CCkBWjfvj1f+zIjIyNcv34dd+/e5SvgeP/+PZKSkmjVBBEJ1tbWYBgGBgYGuHjxIt9YY9HWHURUUWem5uH06dO8rhru7u58K64JIYSIH3l5eeTk5PCNRUdHAwBMTU35xqWkpOi8IQSDBg3CoEGDUFJSgpCQEAQEBODhw4dITk7Gnj178Oeff6J///44fvw421FFRk1hwJEjR7BkyRL89ddfGDBgAADg1q1bWLJkCcrLyzF48GDs37+f77MraRp0DJqX6upqGBgYUPGGkFVVVUFBQaHe1+Xl5fkWyxEiLvLy8qCtrc03FhkZierqaowbNw5KSkoAACsrK7Rr147uqzYRY2NjODk58b729/eHmppag1v8ycnJQVdXFyNGjICOjo4wYootGRkZ2NrawtbWFm/fvsWFCxfg7++PtLQ07N27F56enhgyZAicnZ1hbW3Nuw9Lmsa2bdugoaGBkydPIjExEYmJiWAYhretnIyMDGbNmoUlS5awnFS46F8ZIS2AqakpfHx8kJ2dDQ0NDdjY2ODAgQP4448/ICUlBTMzM7x//x67du1CZWUlBg8ezHZkQr5Zzc2Ntm3b1hoj7Kr5XUTYQ52ZmoeMjAyoq6tj0aJFbEch+HST9sqVK3jw4AGys7NRVlaGEydO8F5/9uwZSktLYWpqCgkJCRaTEkJElaGhIR4/foyHDx/C1NQUGRkZiIyMhLq6eq3zc1ZWFtTU1FhKKn4UFBTg7OwMZ2dnpKWlwd/fH4GBgcjIyKjVzpp8u5UrV6KsrAynT5/GwoULcfToUeTm5mLZsmWoqKiAhYUFvLy8ICMjw3ZUkUXHoPkwMjKqVdxHCCFsqWv715iYGDAMg/79+/ONa2hoIC4uTljRRNqIESMwYsQI3tf+/v7Q09PD1q1bWUxF6qKpqYnFixdj8eLFOHbsGP744w9wOBzcvn0b4eHhaNOmDSZNmoRZs2ZBRUWF7bgiQVJSEitXrsTMmTMRFhaGN2/eoKioCEpKSjAyMoKlpaVYXjtTAQchLYC1tTV8fX0RFhaGSZMmoXPnzpg5cyaOHTuGDRs28OZxuVyoqalh+fLlLKYVbcXFxbh//z7S0tJQXFzMqwL8L4Zh6IHeN6prmw7auqN5sLa2hrm5OZydnTFs2DCqOmYBdWZqHlRVVaGurs52DALgxYsX8PDwQHp6Ou/c/N+OTcHBwTh58iS8vb0xaNAgNmISQkScg4MDHj16BHd3dwwcOBCPHz8Gh8OBo6Mj37zU1FTk5OTU2taACIeOjg6WLFmCJUuW4P79+wgMDGQ7kkj6+eefUVZWhvPnz2Pu3LkoKytDVVUVrKyssHfvXiocEAI6Bs3D1KlTsWrVKty9exdDhgxhO45Yyc3NRUBAQL2vAaj3dQC1zt+EiAJVVVWkp6ejsrIS0tLSAD5trcUwTK2OceXl5byOHKRphYaG1rnFE2FfYWEhgoKCcOHCBcTFxYHL5UJSUhJDhgxBTk4OXrx4gYMHD+LcuXPw9vZG586d2Y7com3duhUSEhJYtmwZ1NXV4ezszHakZoPh1vf0kRDS7AUHByMwMBDp6emQl5eHmZkZ5s6dSyvjBeTYsWPYu3cvX6Xyf3+F1rR2YhiGKpSJyOrRoweqqqrAMAzatGkDBwcHTJgwAZ06dWI7mtjYtGkTfHx8EBYWBg0NDbx69QoODg6QlZXFjz/+yNeZ6fnz5xg7diz++OMPtmOLnHXr1iE4OBh37tyhIhkWZWdnw8HBAfn5+ejRoweGDRuGoKAgpKam8p2Lnz9/jgkTJmDq1Kn45ZdfWExMCBFV1dXVWLlyJUJCQnhjpqamOHToEN95wtPTE56envjxxx8xa9YsNqISIhRcLherVq3Cv//+C4ZhMGLECOzevZsKwIWIjkHzsH37dpw7dw7ff/89JkyYQA9EhcDY2PirtuD9L9qSl4gqDw8PXL16FfPnz8e8efMQEhKCdevWoVevXjh79ixvHofDgampKXR0dBAcHMxiYkKE4969e7hw4QKuX7+OiooKcLlcaGlpYcKECXB2duY9c3v27Bl2797NK8w8evQoy8lbtu7du8PAwIB+z9SBCjgIIaQR/Pz8sHbtWgCfLgJ79eoFdXX1BluwL168WFjxxIanpye0tLQwfvz4L84NCAhAeno6HQcByMvLQ0BAAPz8/PD69WveTZHevXtjwoQJGDt2LD3MFrB79+5h3rx5WLduHSZNmgTg003BY8eO8d2kqunMdPbsWXTo0IGtuCIrOzsbzs7O6NevH7Zs2UL7h7Nkw4YNOHPmDCZOnIj169eDYRi4uroiNja2VjGlqakptLS06MKQECJQcXFxSEpKgqamJkxMTGo9QAoKCsKHDx9gY2NDxfdEZKxZs6bOcQ6HwytqGjt2LCQlJeucxzAMtmzZIrB84oCOQfNVWVmJFStW4Nq1awCANm3aQF5evs65DMPg+vXrwownkqytrb/5e1AXWCKKHj9+jKlTp/JtywsAu3fvho2NDe/riIgIzJo1C+PHj6dzg4AVFhYiLS0NJSUl9Xb7BoB+/foJMZV4yMzMhJ+fH/z8/PD27VtwuVxISUnB2toaEydOxJAhQ+osBqyqqsKwYcNQUlKChw8fspBcdNRsj+Ln58d2lGaHCjgIIaQRnJycEB8fjx9++IFWybHI2NgYpqam8PHx+eJcNzc3REdHUycUAXvy5AnOnz+PkJAQFBUVgWEYyMnJYcyYMRg/fjzMzMzYjihWqDOT8CUnJ2PVqlXIysqCra0tdHR0oKCgUO98asPb9EaMGIGcnBzcv3+fV0RTXwGHo6Mj0tLS6AKbEEIIaWI1q93r61L53///7+vUxfLb0TFonvLz8zF79mxeG/YvoeNACBG0sLAw7Nq1C8nJydDU1MTcuXPh4uLCN2fp0qW4fPkydu7cCVtbW5aSirbY2Fjs2LEDsbGxX5xLXYGa3qxZs/DgwQNwuVxwuVzo6enBxcUF48ePh6qq6hffP23aNDx8+JDO2d9ozZo1uHjxIsLDw9GqVSu24zQr1C+PEEIaITExEWpqalS80YLU3IAigtWrVy/06tULP/30Ey5dugQ/Pz9ERUXBz88P/v7+0NPTg7OzMxwdHaGurs52XJFnZ2cHOzs7tmOIlbi4OOTk5CAnJwfHjx//4nwq4Gh62dnZMDQ0bFQHFFlZWZSXlwshFSGEECJeHB0d6fqLZXQMmqddu3bhxYsXaNWqFSZOnIhu3bpBVVWVjhUhhDVWVlawsrJqcM6mTZuwceNG6rArIDExMZg5cyYqKiogLS2NDh06QF1dnc4NQhQREQFZWVmMGjUKEydO/OoOJxMmTMDAgQMFlE58LF68GKGhoVi9ejV27dpVb4cycUQFHIQQ0gjy8vJo37492zHIV8jMzGxwFTxpWrKysnB0dISjoyPS09Nx9uxZeHt7IyUlBX/88Qf27NkDKysrTJ06FYMGDWI7bosXFRUFZWVlGBsbf3FufHw8CgsLqdWiAFy/fh3Lly8Hl8uFrKwstLW1G1WlT5qWgoICCgsLGzX33bt3VNFPCBEKLpeL5ORk5Ofno6qqqt55dH4momLbtm1sRxB7dAyap5s3b0JKSgonT55Ely5d2I5DCCGNoqSkxHYEkbZv3z5UVFRg9OjRWLduHdTU1NiOJHZ++uknODg4QEVF5X96v5OTUxMnEk9RUVGYPHkyjh49iv/H3n1HR1Wubx+/dhqptIQWIHQMghw6IkZA8UhPKEGlCTZQQKxH7IoNFQWliChdFAWSAEe60nsICBgCoUkggISWTkiy3z98md+JJBA0mZ1kvp+1WMc8+96zLp1DZmbPve/n/vvvV6dOnVS7dm2mG4sGDgDIl6ZNm2rXrl3KzMyUiwu/Ou0lJiZGMTExOdbOnz+viIiIPM9JT0/Xzp07FR8fr6ZNmxZyQvzV4cOHtXDhQi1ZssT2ZUWZMmWUkpKiNWvW6Oeff9Zdd92lzz77jC9R/4GBAweqRYsW+vbbb29a+/7772vXrl2MWiwEU6dOlST17dtXL730knx8fCxO5Jjq1KmjX3/9VfHx8fL398+zLiYmRqdPn9Y999xjx3QAHE1iYqI+/fRTLV26VGlpaTesZRQyAJR8SUlJqlmzJs0bAACbvXv3ytvbW5988onc3NysjuOQBg4caHUESBo9erRtO7+EhATNmzfvpufQwAEAsBk+fLgefvhhTZ06VSNGjLA6jsNYs2aNJk+enGPt999/1yuvvHLD865tn/LII48UZjz8f8nJyfrpp5+0aNEi7du3z/bfv3Xr1urbt6/uv/9+paSkKDw8XDNnztSWLVv00Ucf6YMPPrA6erGWn/2T/04t8u/IkSMqW7as3nnnHcZcWqhr166KiorSO++8o4kTJ+Z68SM5OVlvvPGGDMNgmyEAhSY5OVkPPvigjh8/rkqVKsnJyUkpKSlq3ry5Ll26pOPHjyszM1Pu7u664447rI4LFJqYmBgdP35cklSzZs18TY1DweI5KDqqV6+urKwsq2M4nJtdN7oZwzC4ZoFi79rfg4oVK+q5557LsZZf/F0oHKZpqmbNmjRv2FlWVpbmzJmjJUuW5Hif1L17dw0aNIgbdy3ARMq88f9GAMiH8uXL69VXX9UHH3ygffv2qW/fvqpZs+YN9+S60V3AyJ+qVauqRYsWtp937twpb2/vPC8+GYYhd3d3BQQEqEuXLmrWrJm9ojqkHTt2aNGiRVq1apXS09Nlmqb8/PzUq1cv9enTRwEBAbZaNzc3Pfroo+rRo4ceeOABrVu3zrrgDubSpUsqVaqU1TFKJA8PD/n7+9O8YbG+ffsqLCxMGzZsUK9evdS9e3ddunRJkrRq1SodOnRICxcu1JkzZ9SyZUt169bN2sAASqwZM2bo2LFjeuihh/T222+rX79+2r17t21iVmJiombOnKlp06YpICBA77//vsWJgYJ18OBBvfTSS4qNjc2xXq9ePX388cc0EdgBz0HR07t3b3300UeKjo7W7bffbnUchxEeHm77nPZ3bmjgS2uUBOHh4ZKk2rVr2xo4rq3lF38XCkf9+vV1+vRpq2M4FNM09fTTT2vDhg05XhcOHDigmJgYbd26VV9//bWFCR3T3LlzrY5QZNHAAQD5cN9999n+ecOGDdqwYcMN6xmHXDB69uyZYz+5wMBA1a9fnxd2i3355ZcKDw9XXFycTNOUk5OTgoKC1LdvX3Xo0EHOzs55nuvn56f69etrz5499gtcAiQnJysxMTHHWkZGhuLj4/M859p2QrGxsapbt25hR3RILVu21JYtW5SRkcFdExZydXXV119/rWeffVY7duzQhAkTbMdGjRol6c8P6q1bt9bnn39Oww2AQvPzzz/Lzc3NdoH8r0qXLq1Ro0bJz89P7733npo0aaLQ0FA7pwQKR0JCgh555BFdvnz5ui9LDx06pMGDB2vp0qWqUKGCRQlLPp6DoumRRx7R3r179dRTT+mNN95Qx44drY7kUGrXrq27775bTk5OVkcB7O7DDz+UpBzbvV5bg7UGDRqk559/XmvWrOF1wU4iIiK0fv16SVL79u3VunVrZWdna8eOHVq/fr02bdqksLAw9erVy+KkwJ9o4ACAfLjVbn22Kygcc+bMyfGhA9b4/PPPJf05ZebatI3KlSvn+/xGjRoxku4WzZo167rthPbv35+juexGmDhQOJ555hlt2LBB48aN06uvvmp1HIdWvnx5zZkzRxs2bNDKlSt18OBBJSUlydPTU/Xq1VOnTp107733Wh0TQAl34sQJ+fv7q0yZMpJk+7IoMzMzx3uffv36afLkyVqwYAENHCgxZs2apUuXLqlSpUp69dVXbRfFt2/frrFjx+qPP/7Q7Nmz9eKLL1odtcTiOSiarm3tev78eY0cOVKlS5dWQEBAnhNdDcPQ7Nmz7RmxRKpSpYpOnz6to0eP6vLly+revbtCQkJ02223WR0NsJv/vSnuRmuwvy5duig2Nlb/+c9/NHz4cD344IPy9va2OlaJtmTJEhmGoeeee05PPvmkbf2xxx7TV199pfHjx2vp0qU0cKDIMEy+ZQQAALdg5MiRCg0NVVBQEHey28nEiRNzNHAYhnHTRjF3d3dVr15d3bp10+OPP37DySj4e3bu3Km9e/dq/Pjxql+/vnr16qXq1avL09Mzz3PY2xEASq6mTZuqbt26WrBggSTpiSee0KZNm7Rhw4br7njv06ePjh8/rsjISCuiAgUuODhYhw4d0vfff68mTZrkOLZ79249/PDDuu2227R48WJrAjoAnoOi6Va3rTEMQwcOHCikNI7DNE1t27ZN4eHhWr16tdLS0mQYhho0aKDg4GB1795d5cuXtzomAAd17Yass2fPKisrS5JUrly5Gzb3rVmzxm75SqI2bdooMzNT27dvv24qU1ZWllq3bi1XV1dt3brVooRATtz+ChQxERERBfI4ISEhBfI4APBXEydOtDqCwxk5cqRGjhxp+zkwMFDNmzfXvHnzLEyFgQMH2pppDhw4oPfff/+G9WyvBQAlW8WKFXX+/Hnbz9WqVZMkRUdHq127drb17OxsxcfHKzMz0+4ZgcISFxenypUrX9c4IP3Z3FSlShXFxcXZP5gD4TkomtiuwBqGYahNmzZq06aNUlNTtWLFCoWHhysyMlIHDhzQJ598oqCgIPXs2VMdOnSQq6ur1ZEBy2VlZWnPnj36448/1LBhQwUEBFgdqcQ6derUdWsXLlzIs54b6P65xMRENWjQINcttZydnVWjRg0dPHjQgmSOo0GDBpL+3N7sp59+yrGWX450bZUGDqCIGT16dIG8INPAgZJs2bJlioiIUHR0tC5dumTrVP4rR3pBLwoyMzN1+PBhZWRkKCAgQGXLlrU6Uok1YsQIValSxeoYDs/f39/qCACAIqR+/fpat26dMjIy5Obmprvuukvff/+9vvjiCzVp0sS2tcrEiRN14cIF/etf/7I4MVBwUlNTbzhpoHLlyjpz5owdEzkenoOiie0KrOfp6alevXqpV69eio+PV3h4uBYvXqy1a9dq3bp1Kl26tLp06aLQ0FDdfvvtVscFCtWmTZs0f/58derUKcd2u3/88YeGDRtmmwBkGIaefvppjRgxwqqoJdqcOXOsjuBwsrKyVKpUqTyPlypVKs/vGFAwrk2Tzs7Ovm7tVh/DEdDAARQxISEhdFQCeTBNU88//7xWrFiRrxdrR3pBL0xpaWk6cOCAXFxc1Lhx41xrpk+fri+//FIpKSmS/tzzvWPHjnr77bdVrlw5e8Z1CHyALhp++eUXqyNA0qBBg/Jd6+zsLG9vb1WtWlUtWrRQ+/bt5eLCRyIABaNdu3ZavXq1tmzZovbt2+vee+/Vbbfdpt9++03t27dX7dq1df78eZ09e1aGYejxxx+3OjJQoG50LYPrHPbBcwDcmL+/v4YPH67hw4crKipKERERWr58uebPn68TJ05o+vTpVkcECtWSJUv0888/a+jQoTnWx44dq+joaLm7u6tixYqKi4vT5MmT1bRpU7Vt29aitCVXq1atrI4A2F1MTEy+1vAnrlYCRczYsWOtjuDwrn0RVLVqVduoy1v5ckj688LI7NmzCzybo1u4cKGWL1+uZs2aaezYsRo9erR2796t6OhoXbx4Ub/++qumT5+u/fv36+2332YSTQFZtWqVRo8erS5duujTTz+97vi0adM0fvz4HA0zWVlZWrVqleLj4/XDDz/kOp4OBWPPnj3asWOHzpw5o/T0dH3wwQe2Y3/88YcyMzOZFIESbceOHZL+70uJ3Jr3cjs2e/Zs+fv7a9y4cWratKkdkgIo6R544AG5ubnZXnednZ319ddfa/To0dqyZYt+++03SVLZsmX1wgsv6P7777cyLgAADs3JySnH5wRuAoIj2Lt3r7y9vXXHHXfY1hITE7Vq1SqVLl1aixcvVpUqVbRo0SK99tpr+v7772ngQIlx+vRpTZo0Kc9jkvI8LnFDHezLMHlnAgA5XBs5Wrt2bS1btizHWn4ZhmEbOYeC079/f0VFRWnFihWqUaOG+vXrp927d1/33/rVV19VRESEZs2aRUdzAXj99de1aNEiff3117r77rtzHLtw4YLuvfdeXblyRXXr1tWLL76o6tWra9euXfr444+VnJysDz/8kGaaQhAfH6+XXnpJUVFRkv684PTX3z3XnrvvvvuOL6hRYu3YsUO//vqrPv/8c1WuXFnBwcFq0KCBvLy8lJKSopiYGC1ZskSnT5/WM888o/r16+vw4cOKiIhQbGysvL29FRERoWrVqln9rwKgBDt37pxOnTold3d31a1bl+k/KHECAwNVqlQp+fn55Xo8ISFBGRkZeTYWG4ahNWvWFGbEEo/noGiKiIi45XP4/Fx4Tp8+rYiICEVEROjEiRMyTVOlS5dWp06d1LdvXzVq1MjqiEChatWqlSpVqqSlS5fa1tasWaMRI0bowQcf1DvvvCPpz2tMbdu2lbOzszZu3GhV3BJj0KBBat26tVq2bKkmTZrIzc3N6kgOJzAw8IbTyK59VX6jGr7vgT1xxQAA/uLaHnTu7u7XrcFasbGxqlq1qmrUqCHp/95QZWdn55jw8Nprr2n58uWaPn06DRwFYP/+/XJ1dVXr1q2vO7Z8+XKlp6fL3d1dX331le1iYO3ateXs7KxXX31VK1eu5AJUAbt8+bIGDhyoU6dOqXLlyrrrrru0ZcsWnT17NkddSEiIFi5cqDVr1tDAgRKrXLlymjJliv79739r7Nix110I6dixo4YOHarRo0drypQpmj9/vtq1a6fBgwfrpZde0rJlyzRjxgy9+eabFv0bAHAEFSpUUIUKFXKspaWlycPDw6JEQMG7cuWKTp06dcOavI6zvUfB4DkoekaPHp3v/7bXmvL5/FywUlNTtXLlSoWHhysyMlLZ2dlydnbWPffco5CQEN133318mQqHkZqaet3/36OiomQYhtq0aWNbMwxD/v7+bG9QQHbs2KGdO3dKklxdXdW4cWO1bNlSrVq1UtOmTXN8D4HC0bJlS6sj4P+bPn26du/eraCgID344IM3rZ8/f742bdqkFi1aaPDgwYUfsIiggQMoYuLj4wvkcRiX//fl9oU/TQBFQ1pammrWrGn7+dqb26SkJJUpU8a27uXlpdq1a2vv3r32jlgiJSQkqEaNGnJ1db3u2LWtC4KCgq77vdO9e3e9++67fNgrBN98841OnTql++67T+PGjZOHh4f69et3XQNHs2bN5O7urq1bt1qUFCh8EydOlGEYeu+99/K88Orq6qp3331Xv/zyiyZPnqwvvvhCzs7Oev3117Vy5Upt3rzZzqkBOLKUlBTNnTtXc+bM0ZYtW6yOAxSIa9uPwjo8B0VTSEhIng0cqampOn78uA4ePChXV1c98MADuX7uxt+zdetWhYeHa/Xq1UpPT5dpmrrtttsUEhKi7t275zmtBijJypQpo1OnTtkaxiRp27ZtkqTmzZvnqM3KypKnp6fdM5ZEQ4YMUWRkpA4cOKCMjAxFRkZq165dmjp1qpydndWoUSO1atVKLVu2VLNmzeTl5WV15BJn7ty5VkeApLi4OI0fP17lypXL93vXLl26aPLkyVq/fr0eeOABValSpZBTFg00cABFzL333vuP73owDEPR0dEFlAgoOipUqKDLly/n+FmSjh49et10gcuXLys5Odmu+UqqS5cu5dkUtn//fhmGket+mK6urvL399fJkycLO6LDWbNmjVxdXfX+++/f8M5dJycnVa9eXXFxcXZMB9hXZGSk6tSpc9MLS56enqpTp44iIyNta+XLl1ft2rX5OwLALpKTkzV79mzNmTNHiYmJVscBClTPnj2tjuDweA6KprFjx960ZteuXRo9erQuXryor7/+2g6pSr727dvbbnAoX768+vbtq549e97yFslASXPHHXdo/fr1mj9/vh5++GFt2rRJ0dHRqlev3nXT4k6cOKFKlSpZlLRkefnllyX92bi3e/du7dixQ5GRkdq3b58yMjK0Z88e/frrr/r666/l7OysBg0a2CZ0tGjRQt7e3hb/GwAFIzw8XFlZWRo6dKh8fHzydU7p0qU1bNgwvfvuuwoLC9Pw4cMLOWXRQAMHUMQwOQPIW/Xq1XNM1WjatKkiIiL07bff5mjgWL9+vU6ePJljWgf+vlKlSuncuXPXrV+8eFGnTp2SYRhq2LBhrud6enoqKyursCM6nPj4eNWsWVNly5a9aa2Xl5fS0tIKPxRgkZSUFF28eDFftZcuXVJKSkqONQ8PD0aGA/jbrly5oq+//lorVqzQyZMn5e7uroYNG+rJJ5+0bT+XlZWlmTNnatq0aUpKSpJpmqpQoYIee+wxi9MDwP9JSUnhjl+LNG/eXJ9//rl69eql6dOn64knnrA6UrF35swZGYah2rVr6+6775azs7OWLFmiJUuW5Ot8wzD00ksvFXJKwP769++vdevWacyYMZowYYKSkpJkGIb69++fo27Pnj1KSUlRgwYNLEpaMnl6eqpt27a2G+GuNW/s3LlTkZGR2rNnj9LS0rRv3z7t379fM2fOlJOTk2677TaFhYVZnB7457Zt2yYnJyd17dr1ls7r1q2bPvjgA23ZsoUGDgDW+OWXX6yOABRZQUFB2rFjh/bs2aMmTZqoS5cuGj9+vJYtW6ZTp06padOmOnfunFasWCHDMNSrVy+rI5cINWrUUExMjE6cOKGAgADb+rWR3+7u7rr99ttzPTchISFfTQa4Na6ursrIyMhX7fnz5+nUR4kWEBCgw4cPa+PGjQoKCsqzbuPGjTp58qTq16+fY/306dMqV65cYccEUAJlZmZq8ODB2rNnj0zTlCSlp6dr8+bN2r59uyZOnKjGjRtr6NCh+u2332Sapvz9/fX444+rT58+eW77BAD2dG2biTVr1igqKsrqOA7r9ttvV40aNRQREUEDRwExTVNHjhzR0aNHb/k8GjhQUgUFBemtt97ShAkTdPnyZZUqVUqDBw/WQw89lKNu0aJFkqS77rrLipgOw83NTa1atbJt356Zman9+/dr586d2rlzp7Zv364rV67owIEDFicFCsbRo0dVrVq1W74OV6ZMGVWrVu2WX9OLMxo4AADFRqdOnXT8+HElJSVJknx8fPT5559r1KhR2rNnj/bs2WOr7d69ux5//HGLkpYsQUFBio6O1pgxYzRp0iS5u7srMTFR33zzjQzDUFBQkJydna877/z58zp9+vR1e2jin6tZs6ZiYmJ04cIFlS9fPs+6EydOKC4uznYHMFAShYaG6oMPPtCoUaP0wgsvKCQkJMfdo6mpqQoPD9dnn30mwzAUGhpqOxYbG6tz587p3nvvtSI6gGLuxx9/1O7du2UYhrp27ap//etfSk9P17p16xQVFaWxY8fKz89P+/fvV6VKlTRy5EiFhITIxYVLMSh54uPj//FjMJHUfn7//XeFh4dryZIlOn36tO0La1jLw8NDx44dszpGicCWQkDeHn74YT344IO2a0pOTk7X1QwePFj9+/dnurGdnTlzRsePH7f9uXLlitWRgAKVnJysGjVq/K1zy5Qpo1OnThVwoqKLqwYAgGKjWrVqeu+993KstW7dWmvWrNGGDRtsY6tbtmzJiL8CNHDgQH3//ffavHmz2rZtq1q1aun3339XcnKyDMPQo48+mut5q1atkiS1bNnSnnEdwv3336/9+/frk08+0YcffphrTVZWlt59910ZhqFOnTrZOSFgPwMGDNDOnTu1evVqvffee/rwww9VtWpVeXl5KSUlRadOnVJWVpZM09S///1vDRgwwHbu2rVrVa9ePXXp0sXCfwMAxdXy5ctlGIbGjBmToznsySef1EsvvaSlS5fqxIkTuvvuuzVhwgQmYqFEu/fee/9RA4BhGIqOji7ARPir5ORkLVu2TOHh4babH0zTlIuLi4KCgvjC22KXLl3SsWPH5OHhYXWUEiGvz8kA/uTk5CQ/P788j9epU8eOaRzXsWPHbNM2IiMjdebMGUl/vj6XKlVKLVu2VPPmzdWiRQuLkwIFw8vLS4mJiX/r3KSkJIfa7s8wr835BFDkZWZmauXKldq+fbvOnj2r9PR0zZ4923Z8//79SktLU/PmzXPtnAWAvysyMlKjRo3S+fPnbWtOTk568cUX82zg6N69uw4fPqy5c+fyQaOApaamKjg4WCdPnlTr1q3Vp08fffPNNzp48KAWLlyoQ4cOae7cuYqOjla9evW0aNEixrSjRDNNU/PmzdOMGTNyvQPY399fjz32mPr168fdpQAKzJ133inTNLV9+/brjsXGxqp79+5yc3PT2rVr5evra0FCwH4CAwNlGIb+yWXGmJiYAkwE6c/3SJs3b1Z4eLh+/vlnXblyxfYcubu767nnnlP37t1vONUPhS8mJkbvv/++IiMj9cADD2jChAlWRwJQQj322GMKDQ3VfffdJ1dXV6vjOJyDBw8qMjJSO3bs0K5du2zXWU3TlI+Pj5o1a2Zr2Ljjjjt4jlDihISEKDY2Vlu3blXp0qXzfV5iYqLatGmjevXqKSIiovACFiFM4ACKiejoaI0aNUonT560fdj+6xcQS5cu1Zw5czRjxgy1adPGipgASqgWLVpo9erVWrdunU6ePCkvLy8FBQUpICAg1/oLFy6oT58+MgxDTZs2tXPaks/T01PffPONnnrqKW3bti3HF0d9+vSR9OeHvzp16mjq1Kk0bxSyrVu3at26dTpx4oRSU1Pz/OLCMIwcjZcoOIZhaMCAARowYICOHDmiY8eOKTU1VZ6enqpVqxZ3DwEoFElJSXlOfbs2FrZGjRo0b8Ch1KlTRyEhIerWrZvKlCljdRyHdeTIEUVERGjJkiX6448/bO9PK1SooO7du2vGjBny9vbWI488YnHSku++++7L85hpmrpw4YKtsaZMmTIaNWqUHdPhr1JSUrR8+XKFh4dr3rx5VscBCtzmzZu1ZcsWlSlTRsHBwerdu7fq169vdawS76mnnlJUVJQSExNzvCZ36tRJLVq0UPPmzXXbbbdxwwlKvFatWtluQMzrptDcLFiwQFlZWWrVqlUhpitamMABFANnz55VcHCwLl26pEaNGqlDhw5asmSJTpw4oQMHDtjqfvvtN/Xu3Vv9+/fXG2+8YWFiAIA9ZGRkaOHChVq1apUOHjyopKQkeXp6ql69eurUqZP69u2rUqVKWR2zxMrIyNCoUaO0bt06SbrpHaeGYeR43QYAFG+BgYFq3rx5nl/w3Ow4UJIsWLBAERER2rVrlwzDkIuLizp06KCQkBC1a9dOzs7OVkcs8RITE/Xf//5XERER2rdvn6Q/3596eHioY8eOCgkJUZs2beTk5KTAwED5+flp06ZNFqcu+QIDA29aU6ZMGbVr104jR45U9erV7ZAKf7V161aFhYVpzZo1Sk9PlyQ+u6FEmjVrlhYtWqTY2Fhbs0CjRo3Up08fde3alS3/Csm1SWXVq1fX4MGDFRQUxO97OKTY2Fj16NFD7u7umj17tho3bnzTc/bu3atHHnlEV65cUUREhMM0ndHAARQDY8aM0Xfffae+ffvqnXfekWEY6tevn3bv3n3dh4nmzZvL399fS5cutSitYzp79qzOnj2rOnXqONQ+XIUpr7sZbwX7KAMoTOPHj9dXX30lDw8P9enTR02aNJGvr+8NtzFzpE5xK6WkpCglJUVeXl68LgMoNDRwANeLi4tTeHi4IiIiFB8fL8MwVLZsWXXr1k0hISFq2LCh1RFLrDvuuEOZmZkyTVNOTk5q3bq1goOD9e9//1uenp45amngsJ9Tp07lecwwDHl4eKhcuXJ2TIRrjh8/rvDwcC1ZskRnzpyR9GfT07XfWa+//rrFCYHCs2/fPi1cuFDLly9XYmKiDMOQu7u7HnjgAfXu3VstW7a0OmKJ8r/NfC4uLmrUqJFatGihFi1aqFmzZre0lQRQ3L355pv68ccfVapUKT311FN66KGHVLZs2evqLl26pO+//15Tp05VRkaGQkNDNWbMGPsHtggNHEAx0LFjRyUkJGjbtm1yd3eXpDwbOEJCQhQXF6ddu3ZZEbXE2rt3r3766Se1adNG7du3t60nJyfrxRdf1Pr16yX9uYfs66+/rt69e1uUtOTIz10q+cE+ygAKS8eOHRUfH685c+aoRYsWVsdxeEePHtX06dO1ceNGnTt3zrZeoUIFtWvXTo8++qhq1aplYUIAJU1gYKD8/f3Vq1evXI9PmjTphsclacSIEYUVD7Dczp07FRYWplWrViklJUWGYahu3boKCQlR9+7dVbFiRasjlijX7u4tXbq03nnnHXXq1OmGtTRwwBElJyfrp59+Unh4uH799VdJsjU9dejQQT179lS7du3k6upqcVLAPjIyMrRixQotWrRIO3bskGmaMgxDAQEB6t27t0JCQni9LgAXL15UZGSkdu7cqcjISMXExCg7O1uGYdjeH7Vs2dLW1FGhQgWrIwOF5urVqxo2bJg2b94swzDk7OysunXrqnr16vL09FRqaqpOnjyp2NhYZWVlyTRNtW3bVlOnTnWo12caOIBi4I477lCdOnUUERFhW8urgePBBx/Ub7/9pv3799s5Zcn25ptvasGCBZo1a5Zat25tW3/rrbf0ww8/SJLc3NyUkZEhJycnzZ8/P1/jn3DrZs2apXHjxqlNmzYaOHCg6tatKz8/PyUkJOjw4cOaO3eutm7dqpdeeon9fOEQ0tLSFBUVpWPHjtkmDtSqVUvNmjWTh4eH1fFKtDvuuENVqlTRqlWrrI7i8JYtW6ZXX33Vtn/4XxmGoVKlSumDDz5Qly5dLEgIoCS69mVpXq79PrpRDePZ4QjS09O1cuVKRUREaPv27bYvSwcMGKBXXnnF6nglRocOHXT69GlJsn351r17d/Xo0UMBAQE5amnggCMxTVObNm1SeHi4fvnllxyfGRo1aqT9+/fz9wHQnxODwsLCFB4ebpui5ezsrLvvvluhoaHq0KHDDSeOIv+Sk5O1a9cu7dy5Uzt27FB0dLQyMzNtnxsCAgLUvHlzW1MH262gpDFNU1OnTtXMmTOVmJhoWzcMI8d1PR8fHw0ZMkTDhg1zuN8/NHAAxUDr1q3l7e2tn3/+2baWVwNHhw4dlJGRoc2bN9s7ZonWrVs3nTp1Srt377atpaWlqU2bNjIMQ3PmzFHDhg315ZdfauLEierWrZvGjRtnYeKSafXq1XrmmWc0atQoDRs2LM+6r776ShMmTNDEiRPVsWNHOyYE7CcjI0NTpkzRt99+q5SUlOuOe3p6asCAARo+fLjc3NwsSFjytWvXTr6+vgoLC7M6ikM7ePCgevfurczMTDVq1EhDhgxR/fr1bc19sbGxmjFjhvbv3y8XFxeFhYU5zH6ZAArXwIED//FjzJ07twCSAMXHr7/+queee06nT59WmzZtNGPGDKsjlRimaWrbtm1atGiRfv75Z6Wlpdm+CPrXv/6lHj16qEuXLipbtiwNHHAIR44csW2Rcu7cOdsXQlWqVFGPHj3Uo0cP1alTh78PwP84dOiQfvzxR82fP1+ZmZm2dcMw5O/vr+eee07dunWzMGHJlJ6ert27d2v79u2KjIzUvn37dOXKFdvreMWKFW0TwIGSJCUlRevXr1dUVJTOnj1ruzmxUqVKatasmdq1a+ewWyO7WB0AwM3VqVNHv/76q+Lj4+Xv759nXUxMjE6fPq177rnHjukcQ0JCgqpUqZJjbefOnUpPT1dwcLDuuOMOSdLQoUM1e/ZsRUVFWRGzxJs5c6bKlSunoUOH3rDuiSee0OzZszVr1iwaOFAiZWRk6IknnrCNtyxTpowCAgLk6+ur8+fP68SJE7p8+bKmTZumqKgozZgxw6FGzNlL+/btFRYWpkuXLuW6VyPs45tvvlFmZqYGDhyo1157LcexcuXKqV69eurSpYs++OADzZkzR9OnT9dHH31kUVoAJQnNF0D+XLlyRatWrVJERIS2bdumrKwsOTk5qU6dOlZHK1EMw1CbNm3Upk0bJScna/ny5QoPD1dUVJT27NmjX3/9VR9++KGCgoKsjlpiDRo0SJJUtWpVffjhhznW8sswDM2ePbvAszma0NBQ23Ri0zTl5eWlf//73woJCckxWRfAn9Mgli5dqkWLFum3336zrbdt21Z9+vRRQkKCfvzxR8XGxuqll15SWlqaQkNDLUxc8ri7u9tewyXp9OnTmj59uhYsWKArV67ojz/+sDghUDi8vLzUpUsXpuXmggYOoBjo2rWroqKi9M4772jixIm53kmdnJysN954Q4ZhqHv37hakLNmSk5NVrVq1HGtRUVEyDENt27a1rbm4uKhatWqKjY21d0SHcOjQIdWqVeuGY6glycnJSVWrVlVMTIydkgH2NWPGDG3fvl1ly5bVyy+/rG7duuVo0MjMzNTSpUv1ySefKDIyUtOnT7/h1Br8Pc8884zWrl2rV199VePGjZOnp6fVkRzSzp07Vbp0af3nP/+5Yd2LL76o8PBwbd++3U7JAABwbDt37lRERIRWrlyplJQUmaapOnXqKDg4WMHBwapUqZLVEUssb29vhYaGKjQ0VHFxcVq0aJGWLFmi+Ph4/fLLLzIMQ5cuXdKbb76pkJAQNWvWzOrIJcKOHTskSbVr175uLb9udr0D+bNv3z4ZhqHSpUvr5ZdfVteuXVWqVCmrYwFFyrZt27Rw4UKtWbPGtrVQpUqV1KtXL/Xp00dVq1a11Q4cOFAREREaPXq0Zs6cSQNHATt79qxtO5XIyEgdO3ZMknLdIhaAY6CBAygG+vbtq7CwMG3YsEG9evVS9+7ddenSJUnSqlWrdOjQIS1cuFBnzpxRy5YtGWNWCLy8vHTmzJkca9e+AGrevPl19WxXUDhM09TJkyeVnZ19wz3PsrOzdfLkSd7kosRavHixDMPQ1KlT1aRJk+uOu7i4qGfPnqpZs6YefvhhRURE0MBRCDZu3KiHHnpIU6ZM0b///W917dpVNWrUuGEjR0hIiP0COojz588rMDDwplNm3NzcVLNmTZr7AAAoRHFxcYqIiNDixYt16tQpmaapsmXLql+/fgoJCbFNr4T9VK9eXc8++6yeffZZbdu2TWFhYVq9erXS0tK0YMECLViwQAEBAQoODtbTTz9tddxibc6cOZL+vJP6r2uwP9M0lZiYqA8//FC7d+9Wjx491LJlS6tjAZY6c+aMFi1apPDwcNvrtLOzszp06KC+ffvqnnvuyfOaa0hIiGbNmqXDhw/bOXXJExcXp507d9r+nDp1ynbs2vXscuXKqWXLlrY/AByLYfLtFlAsXLhwQc8++6x27NiRaze+aZpq3bq1Pv/8c8a4F4JHHnlEO3bs0MSJE9WxY0cdOHBAvXr1UrVq1bR69eocta1bt1bZsmW1cuVKi9KWXAMHDlRkZKSGDx+uESNG5Fk3adIkTZo0Sa1ateJiCUqkxo0by9/fXytWrLhpbefOnXXq1Cnt3bvXDskcS2BgoAzDsH24zs/dcgcOHCjsWA7nzjvvVKlSpfK1H2z79u2Vnp6ubdu22SEZAACO48cff1RERIR2794t0zTl4uKidu3aqWfPnmrXrh3b+RUxqampti1Wdu3aJdM0ZRgG71VRYpw4cUKLFi3S0qVLFR8fL+nPz2tVqlRR9+7d1aNHD9s2ToGBgfLz89OmTZusjAzYRcOGDZWdnS3TNFW9enX17t1bvXr1UsWKFfN1/rVrs7xe3JojR47YmjUiIyNtW6L879ezfn5+atWqlVq0aKFWrVqpbt26VsUFUAQwgQMoJsqXL685c+Zow4YNWrlypQ4ePKikpCR5enqqXr166tSpk+69916rY5ZYoaGh2r59u5599lnVr1/fNsbsr+PiDh48qMuXL6tVq1ZWxCzxhg4dqp07d2ry5MnauXOnBgwYoDp16sjX11fnz5/XkSNHNG/ePG3fvl2GYejJJ5+0OjJQKEqXLp3v7To8PDxUpkyZQk7kmLgDomho2LChtmzZomXLlt1wz8xly5bpzJkzObY+AwAABePNN9+UYRiqXbu2goOD1a1bN5UrV07Sn9v7ZWZm3vQxPDw8Cjsm/j9PT0/17t1bvXv3VlxcnMLDw7V48WKrYwEFJiAgQM8991yOyTNr1qxRfHy8pk2bpmnTpun2229nG2o4HMMw1KlTJ/Xt21dt2rS55fPHjx+vK1euFEKyku3axPT/bdioXLmyWrZsqVatWqlly5aqWbOmRekAFEVM4ACAfPr00081Y8YMZWVlSfrzjdfYsWPl4vJ/vXAfffSRZs6cqTfeeEP9+/e3KmqJ9t133+mDDz5QZmZmntNoXFxc9Morr/AcoMR68cUXtXLlSq1bt06+vr551iUkJKhDhw7q3LmzPv74YzsmBOxnzZo1GjFihNzc3PToo49q0KBBKl++vO34hQsXNHv2bM2cOVNXr161TdMCAAAF59pksr/LMAxFR0cXYCKgaEhOTtaBAwfk6+ur2rVr51l39OhRnT9/Xrfffru8vLzsmNBxpKSkaNmyZQoPD1dUVJSk/5ui6O7urvfee08dO3ZUqVKlrIwJFKqLFy/aGixhP4GBgapWrVqOho1q1apZHQtAEUYDBwDcgosXL+rEiROqUqVKrqPltm7dqpSUFLVo0YKtbApRbGysZsyYoY0bNyohIcG27ufnp6CgIA0ZMkT169e3MCFQuE6ePKnevXurfv36Gj9+vPz8/K6rSUhI0HPPPadDhw4pLCxMVatWtSApYB9vvvmmfvzxR9sF2PLly9umM124cEHSnw1+Dz74oN555x0rowIAUCIFBgb+48eIiYkpgCRA0TJjxgx98skneuedd9S3b98863788Ue99dZbGj16tB555BE7JnRMcXFxCgsL0+LFi3NsseLl5aXOnTsrODhYLVq0sDglgJLizJkzqly5stUxABQjNHAARdT06dO1e/duBQUF6cEHH7xp/fz587Vp0ya1aNFCgwcPLvyAQBGRlJSk1NRUeXp6ysfHx+o4QIGLiIjIdf348eP65ptv5OTkpPvvv19169a1fWF9+PBhrV69WqZp6vHHH1eNGjUUEhJi19yAvS1YsEBfffWVTp48ed2xatWqaejQoddtfQYAAArGqVOn/vFj0HD8z0yaNOkfP8aIESMKIAn+V79+/bR3717t2LHjhttgpqamqlWrVmratKnmzp1rx4TYtm2bwsPDtWrVKqWlpUmSnJycmAoEAAAsQwMHUATFxcWpc+fOKleunJYtW5avL6UTExPVtWtXXbp0SatWrVKVKlXskBQAUNhuNI762tu4vLYT+t9jBw4cKKSEQNFy9OhRHTt2TCkpKfLy8lKtWrVuOK4aAACgJPgn29iYpinDMPjMUAjuvvtueXh4aPXq1Tetvf/++3XlyhVt2LDBDsnwV6mpqVqxYoXCwsK0a9cu/j6g2GvQoME/fgy2OAMAa7hYHQDA9cLDw5WVlaWhQ4fme6JA6dKlNWzYML377rsKCwvT8OHDCzmlY0pJSdG2bdsUFxenlJQU5dUDZxgGzwGAAtGyZUurI0D/d+Gjdu3a+umnn3Ks5RcXPuyjdu3aNGwAAFDMbN26VW3atLE6RolQu3Zt1alTx+oY+P8uX76c75usypYty1ZCFvL09FSvXr3Uq1evXKf6AcVNQdy7zf3fAGANGjiAImjbtm1ycnJS165db+m8bt266YMPPtCWLVtoHigEM2fO1BdffKH09HTb2l/fxBqGYbtzhefgn7k2/rVcuXLq379/jrX84nlAScD43KLh2u/77Ozs69Zu9TEAAAAgnThxQuHh4Vq8eLHOnDlDo+s/5O7urvT0dB09elQeHh4KCQlRt27dVLZsWaujObSyZcvmuxng5MmTKl26dCEnckxXr17VuXPn5OHhoXLlyuVZd/HiRaWlpaly5cp2TAcUDhrCAKD4ooEDKIKOHj2qatWq3fADRW7KlCmjatWq6ejRo4WUzHGFhYXpo48+kvTnWNLGjRvLz89PTk5OFicruSZNmiTDMFSrVq0cDRzXmmRuhEYaAAUttwsfXAyxv507d0r68wuKO+64I8farWCyDQAA1khOTtby5csVHh6u3bt3S/qzydXFhUuU/9SmTZu0fPlyLV68WLt27dJvv/2mjz76SO3bt1dwcLDat2/Pf2cLNG7cWL/88ouWL1+uzp0751m3fPlyXbx4Ue3bt7dfOAeycOFCjRkzRv/5z380ZMiQPOsiIiL08ccfa8yYMQoNDbVjQgAAgP/Du3agCEpOTlaNGjX+1rllypTRqVOnCjgR5s6dK8MwbvpBDwVnxIgRkpSjkenaGgDAMQ0cONDW3Lds2bIca/nFVjYAANiXaZrasmWLwsLC9PPPP+vKlSu2pvzbbrtNPXv2VPfu3S1OWfx5e3srNDRUoaGhiouLU0REhCIiIrR69WqtWbNGZcqUUbdu3RQSEqJGjRpZHddh9O3bVz///LPeeOMNlSpVSvfee+91NevWrdMbb7whwzDUt29fC1KWfKtWrZKTk5N69ux5w7qQkBB98sknWrFiBQ0cAADAMobJLGegyGndurXKly+v5cuX3/K5nTt31oULF7R9+/ZCSOa4/vWvf8nHx0ebNm2yOgoA5Klfv37as2cPX07bSUREhHx9fRUUFHTT2k2bNikhIUEhISGFH6wEGzhwoCTJ39/fNhnr2tqtYGsiAAAK39GjRxUREaHFixfrjz/+kPR/W8r5+Pho7ty5CgwMtDKiQ4iMjFRYWJhWrlyplJQUGYahOnXqKCQkRN27d1elSpWsjljivfTSS1q6dKmtEblJkyby8fFRUlKSfv31Vx09elSmaapHjx76+OOPrY5bIrVr107Ozs765Zdfblp77733yjRNrV271g7JAOvEx8dr8+bNOnr0qFJSUuTl5aXatWurbdu28vf3tzoeADg0JnAARVCVKlUUGxurxMTEW9r7MjExUSdOnFC9evUKMZ1j8vDwYP9LAMUCvbn2M3r0aLVo0SJfDRxfffWVIiMjaeD4h3JrvKAZAwCAoiMpKUn//e9/FRERob1790r68/3ptckDISEhGjp0qEqVKkXzhp20aNFCLVq00JtvvqlVq1Zp8eLF2rp1qz799FNt3bpV06dPtzpiiTd27FhVqlRJc+bM0dGjR3X06NEc28O6ublpyJAheuaZZyxOWnJduHBBDRo0yFetr6+vDh48WMiJAOukpKTovffe05IlS5SdnS1Jtq2oJcnJyUnBwcF67bXX5OXlZWVUAHBYNHAARVCrVq108OBBLVy4UI8++mi+z1uwYIGysrLUqlWrQkznmJo2bapdu3YpMzOTPWMtdPLkSVWrVi3f9T///LPuu+++QkwEwNHRMAMAACCtX79e4eHhWrt2rTIyMmxfBLVo0UI9evRQ586d5e3tbXVMh+bu7q7WrVvrzJkzOnLkiM6cOcN7WTtxdnbWiy++qMGDB2vdunU6cuSIkpOT5e3trXr16qldu3by9fW1OmaJ5uPjo9OnT+er9syZM/L09CzkRIA1rl69qscff1x79uyRaZqqVauW6tWrpwoVKujcuXOKjY3VsWPHFB4eruPHj2v27NlydXW1OjYAOBy+hQSKoNDQUM2dO1cTJ05UixYt1Lhx45ues3fvXk2aNElOTk7q06ePHVI6luHDh+vhhx/W1KlTNWLECKvjOKyePXvqrbfeUrdu3W5Yd+XKFb3//vtasGCBDhw4YKd0AJC3c+fOyd3d3eoYAAAAhWLo0KG2iQI1a9ZUcHCwevTooapVq1odzeGlp6dr1apVioiI0Pbt25WdnS3DMNS2bVsNGjTI6ngOxc/Pj2t2FmnQoIG2bNmirVu3qk2bNnnWbd26VefOnbthDVCcff/999q9e7cqVqyoMWPGqH379tfVrF+/Xm+99ZZ2796t+fPn/61tSwEA/wwNHEARVK9ePYWGhurHH3/UwIED9dRTT+mhhx5S2bJlr6u9dOmSvv/+e02dOlUZGRkKDQ1V/fr17R+6hCtfvrxeffVVffDBB9q3b5/69u2rmjVrysPDI89z2Cuw4CUlJemll17Shg0b9NZbb+U6xi8mJkYvvPCCjhw5csPnBwBuVXx8vE6dOpVjLSkpSTt37szznPT0dO3cuVPHjx/X7bffXtgRHVpGRoaWL1+uDRs26NixY7Y9fGvVqqWgoCB16dJFbm5uVscEAKBEK1OmjHr16qXu3burSpUqVsdxaDt27FB4eLhWrVql1NRUmaapunXrKiQkRD169FDFihWtjgjYTUhIiDZv3qzRo0frq6++ynULp5iYGL388ssyDEPBwcEWpAQK33//+18ZhqEvv/xSDRs2zLWmXbt2mjx5snr37q2lS5fSwAEAFjBMZuUBRdLVq1c1bNgwbd68WYZhyNnZWXXr1lX16tXl6emp1NRUnTx5UrGxscrKypJpmmrbtq2mTp3KWLNCkN99Mq8xDEPR0dGFlMZxLV68WO+++65SUlJUrVo1jRs3Tv/6179sx2fOnKnx48crIyNDDRo00Lhx41SnTh0LEwP29fDDD2v37t2KiYmxOkqJNGnSJE2ePNn28//uEXszpmnqrbfe0sMPP1xY8Rza/v379fzzzysuLi7XUeCGYah69eoaN25cviabAQCAW/Piiy/q559/VlpamgzDsG2fEhwcrAceeCDH9imBgYHy8/PTpk2bLExcMv3++++KiIjQkiVLFB8fL9M0Va5cOXXt2lUhISFq1KiR1REBS5imqccee0xbtmyRs7Oz7rrrLjVp0kQ+Pj5KSkrSnj17tGXLFmVlZemuu+7S9OnT8/1ZDyhOmjdvrkqVKmnZsmU3re3SpYvOnj2rXbt22SEZAOB/0cABFGGmaWrq1KmaOXOmEhMTbevXxpJe4+PjoyFDhmjYsGFycnKyImqJl1tn/s3wBWrhiIuL00svvaQ9e/bIxcVFTz/9tHr16qXXXntNW7ZskSQNHjxYzz//PM1MAArU7NmzNXv2bNvPp0+flqurq/z8/HKtNwxD7u7uql69urp163bT7Z/w95w4cUI9e/ZUSkqKvL29FRwcrDp16sjPz08JCQk6evSoIiIilJycLC8vL4WFhalGjRpWxwYAoMRJTk7W8uXLFRYWpt27d0v68/1QqVKl1KFDBwUHBysoKEgNGzakgaMQPPTQQ/r1118lSS4uLmrXrp1CQkLUvn17ubgwhNkebvXmn9xwQ1DhSU1N1Wuvvably5dLUo4GjWvXWbt27aoxY8bkOvEVKAn+9a9/qU6dOgoLC7tpba9evXTkyBHbawsAwH5o4ACKgZSUFK1fv15RUVE6e/asbSR4pUqV1KxZM7Vr144PFnAoWVlZmjRpkqZNm6bs7Gw5OzsrMzNTFStW1EcffcRepQDsIjAwUM2bN9e8efOsjuLQXnjhBf30009q3769xo0bl+MO32uSk5P14osvat26deratas+/fRTC5ICAOA4Tpw4obCwMNskCOnPL0vLli2rixcv0sBRCAIDA2UYhmrXrq0uXbrkug3vzfTv37/ggzmQv3PzT264IahwHThwQKtWrdKRI0eUnJwsb29v1atXT/fff3+BPYdAUdW5c2edPHlS69evV/ny5fOsu3Dhgtq1a6eqVatqxYoVdkwIAJBo4AAAFFPp6el6+umnbVM3nJ2dNX36dN15550WJwMK3ocffqjq1atrwIABVkfB/wgPD5evr6/uueceq6M4tLvuukupqanauHGjfHx88qxLTEzUPffcIw8PD23dutWOCQEAcGxbt25VeHi4Vq9erbS0NEl/NnPUqFFDPXv2VI8ePVSlShWLUxZ/1xo4/okDBw4UUBoAQFE0duxYzZo1S3fddZcmTJig0qVLX1eTmJioZ599Vlu3btXgwYP18ssvW5AUABwbDRwAgGInJiZGL7zwgo4ePSp3d3dVq1ZNsbGxKlWqlJ5//nk98sgjVkcECtSNJj3cd999aty4scaPH29BMsB6//rXv1SvXj0tXLjwprW9e/fWkSNHtGfPnsIPBgAAckhNTdWyZcsUERGhXbt2yTRNGYYhwzDUqlUrzZo1y+qIxdrAgQP/8WPMnTu3AJIAAIqq8+fPq3v37rp48aI8PT0VHBysevXq2bYgjY2N1eLFi5WamipfX18tWbLkhpM6AACFgwYOALhF8fHx2rx5s44ePWrbzqZ27dpq27at/P39rY5X4s2aNUufffaZMjIydPvtt2vcuHEKCAjQ+PHjNXPmTJmmqbZt22rs2LHy8/OzOi5QIG7UwME2HnB0Xbt2VUZGhlavXn3T2vvvv1+lSpXSf//7XzskAwAAeYmLi1N4eLgWL16sU6dOyTAMpj8AAGAHMTExGjlypOLi4nKd3GSapgICAvTFF1+wrRAAWMTF6gAAUFykpKTovffe05IlS5SdnS1JtjuGJMnJyUnBwcF67bXX5OXlZWXUEuuxxx6zbZny6KOP6rnnnpOrq6sk6aWXXtLdd9+tl19+WZs2bVJwcLDee+89dejQwcrIAEqI++67T5JUo0YNzZgxI8dafhmGoTVr1hR4NkcXHBys8ePHa/PmzWrbtm2edZs3b1ZcXJxeeOEFO6YDAAC5qV69up555hk988wz2rZtmxYvXmx1JAAAHEJgYKB++uknLVu2TBs2bNCxY8dsNynWqlVL99xzj7p06SI3NzerowKAw2ICBwDkw9WrVzVo0CDt2bNHpmmqVq1aqlevnipUqKBz584pNjZWx44dk2EYatq0qWbPnm1rLEDBCQwMVMWKFfXRRx+pTZs2udZcvnxZr7/+ulavXi0nJydFR0fbOSVQ8JjAYb1rd53Url1by5Yty7GWX9xZWjiysrI0cuRIbd++XSNHjlRoaGiORsrU1FT9+OOPmjRpklq3bq2JEyfKycnJwsQAAABwNGfOnNF///tfHThwQJcuXdLVq1dzrTMMQ7Nnz7ZzOgAAABQlTOAAgHz4/vvvtXv3blWsWFFjxoxR+/btr6tZv3693nrrLe3evVvz588vkP1nkdN9992n999/X2XLls2zpkyZMpo4caJ+/PFHffjhh/YLB6BE+/nnnyVJLi4u163BfgYNGpTrummaunLlij766CN9+umnqlKlisqXL68LFy7ozJkzunr1qlxcXJSYmKghQ4ZwURwAAAB2M2/ePI0dO1ZXr161TXH933sq/3ctt+0MAAAA4FiYwAEA+dC3b1/t27dPCxcuVMOGDfOs++2339S7d281btxYP/74ox0TIjfHjh1TrVq1rI4B/GNM4AD+VBD77zIJBQAAAPaybds2DRkyROXLl9ezzz6rOXPm6PDhw5o5c6YuXbqkX3/9VWFhYbpy5Ypeeukl1atXT61atbI6NoAS6sSJE1qyZIkaNWqU6w2K16xdu1a//fabgoODVb16dfsFBABIYgIHAOTLkSNHVKtWrRs2b0hSw4YNVbt2bR05csROyXAjNG+gJElKStLOnTtv+dg1LVu2LIxYgF0xWQkAAADFyZw5cyRJn332mVq3bq3w8HBJ0p133ilJ6tSpk5544gkNHTpUEyZMUFhYmGVZAZR8P/zwg2bMmKEvv/zyhnWGYWjy5Mm6evWqnnvuOTulAwBcQwMHAORDZmam3N3d81Xr7u6uzMzMQk7kuPbu3avdu3frzJkzSklJkZeXlypXrqymTZuqcePGVscDCk1sbGyu20cYhpHnsf+tiY6OLsx4gF307NnT6ggAAABAvu3du1e+vr5q3bp1njXly5fXZ599pgceeEBTpkyhaRlAodm0aZPc3d3Vrl27G9bdc889KlWqlDZu3EgDBwBYgAYOAMgHf39/xcbG6sKFCypfvnyedRcuXFBsbKyqVq1qx3SOISIiQl9++aVOnDiRZ01AQICeeuophYSE2C8YYCf/ZNc7dswrHK+88sot1RuGoQ8++KCQ0gAAAAAoai5duqTbbrvN9rOrq6skKTU1VZ6enrb16tWrq27dutq6davdMwJwHKdPn1a1atVkGMYN65ycnFStWjXFx8fbKRkA4H/RwAEA+dCuXTvNmjVLL774oiZMmKDSpUtfV5OYmKgXX3xRmZmZ6tChgwUpS6bs7Gy9/PLL+u9//2v7Etrb21vVq1eXh4eH0tLSFBcXp+TkZP3+++965ZVXtHHjRn3yySdycnKyOD1QMGJiYqyOgFxcG398I9cuipimSQMHAAAA4GDKli2rjIwM28/lypWTJJ08eVL169fPUZudna3z58/bNR8Ax3LlyhVbI9nNuLm5KTU1tZATAQByQwMHAOTDE088oSVLlmjr1q3q0KGDgoODVa9ePfn5+SkhIUGxsbFavHixUlNT5evrqyeeeMLqyCXGBx98oKVLl8owDPXs2VMDBgxQw4YNr6v77bff9O233yoiIkLLli1TmTJl9Oabb1qQGICjuNFo49TUVB0/flw//fSTkpKSNHz4cFWqVMmO6QAAAABYrUqVKjkmiTZo0EArV67U6tWrczRwHD9+XMePH7c1eABAYahUqZKOHj2qK1euqFSpUnnWpaen6+jRo6pQoYId0wEArjFMZmoDQL7ExMRo5MiRiouLy3XMnGmaCggI0BdffKHAwEALEpY80dHR6t27t9zc3DRhwoR8TTZZu3atRo0apczMTC1YsCDXZg8AsJekpCQ9//zzOnz4sMLCwrggCwAAADiQcePGafr06VqxYoVq1KihkydPqlOnTjJNU0OGDFGLFi107tw5TZ06VfHx8erbt6/eeecdq2MDKKFee+01hYWF6emnn9bIkSPzrJs0aZImTZqknj173vDmFQBA4aCBAwBuQUZGhpYtW6YNGzbo2LFjSklJkZeXl2rVqqV77rlHXbp0kZubm9UxS4y3335bP/zwg15++WUNHjw43+fNmDFDH3/8sR566CG9/fbbhZYPAPLj3Llz6tChg0JDQ/XWW29ZHQcAAACAnezdu1cvvviinn76aYWEhEiSZs2apbFjx+a4Ocg0TdWqVUvfffcdTd8ACk1sbKxCQkKUnZ2tRx55RI8//rj8/PxsxxMSEjR9+nTNmjVLzs7OWrRokW677TYLEwOAY6KBAwBQZHXq1Elnz57V1q1b5e7unu/z0tLS1KZNG1WuXFkrVqwoxISA/axdu1a//fab6tWrpwceeCDHsTZt2uR53tNPP62BAwcWdjzcREhIiC5duqR169ZZHQUAAACAxXbv3q2IiAidPHlSHh4eatGihfr27StPT0+rowEo4ebNm6f33ntPkmQYhvz9/eXj46OkpCTFx8fr2leGr7/+uvr3729lVABwWC5WBwAAIC9//PGHAgICbql5Q5I8PDxUo0YNxcXFFVIywL5SUlI0evRopaWlKSws7LrjFy9ezPPcSZMmqU+fPvLw8CjMiLiJK1eu6Pz581bHAAAAAFAENG3aVE2bNrU6BgAH1L9/f1WrVk2ffvqpDh06pJMnT+Y43qBBAz3//PMKCgqyKCEAgAYOAECRZZqmnJ2d/9a5zs7OYsgUSorVq1fr8uXL6tu3r+rWrZtrTf369fX666/nWAsPD1dERIRWrVql4OBge0RFLmJiYvT777+rcuXKVkcBAAAAAAAOrl27dmrXrp3i4uJ0+PBhJScny9vbW/Xq1VO1atWsjgcADo8GDgD4i0GDBkmSqlatqg8//DDHWn4ZhqHZs2cXeDZH4+fnp99//11Xr16Vq6trvs/LyMjQ8ePHc+zhCBRn69evl2EYevDBB/Os8fHxUatWrXKsVaxYUeHh4Vq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_44.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_52.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_86.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_93.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[REVIEW] state traversal figure -- the highlighted category should be a visible cluster of coloured points, and the black traversal path should trace a curve through it rather than collapse to a single dot\n", + " --- pathway_summary_c_98.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... and 29 more not shown (raise N_TO_SHOW to see more)\n" + ] + } + ], + "source": [ + "state_figure_paths = resolve_paths(CONFIG[\"analyze\"][\"state_traversal_figures\"], pattern=\"*.png\")\n", + "print(f\"Found {len(state_figure_paths)} state traversal figures\")\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Per-category figures must actually differ from one another.\n", + "#\n", + "# A previous run emitted ten figures for ten categories with no cells assigned.\n", + "# Every figure was the same background scatter with a degenerate traversal path\n", + "# collapsed to a single point, identical below the title band. File bytes differ\n", + "# (the titles differ), so only a pixel comparison catches it.\n", + "# ---------------------------------------------------------------------------\n", + "hashes = {}\n", + "for p in state_figure_paths:\n", + " hashes.setdefault(image_content_hash(p), []).append(os.path.basename(p))\n", + "\n", + "duplicate_groups = {h: names for h, names in hashes.items() if len(names) > 1}\n", + "print(f\"{len(hashes)} distinct figure contents across \"\n", + " f\"{len(state_figure_paths)} figures\")\n", + "for names in duplicate_groups.values():\n", + " print(f\" identical below the title: {', '.join(names)}\")\n", + "\n", + "check(\n", + " \"state traversal figures are not duplicates of each other\",\n", + " not duplicate_groups,\n", + " f\"{len(state_figure_paths) - len(hashes)} duplicate figure(s); \"\n", + " f\"groups: {list(duplicate_groups.values())}\"\n", + " if duplicate_groups else \"\",\n", + ")\n", + "\n", + "# These can be numerous -- show a handful rather than all of them.\n", + "N_TO_SHOW = 8\n", + "for p in state_figure_paths[:N_TO_SHOW]:\n", + " review(\n", + " \"state traversal figure\",\n", + " \"the highlighted category should be a visible cluster of coloured \"\n", + " \"points, and the black traversal path should trace a curve through it \"\n", + " \"rather than collapse to a single dot\",\n", + " )\n", + " show_image(p, title=os.path.basename(p))\n", + "\n", + "if len(state_figure_paths) > N_TO_SHOW:\n", + " print(f\"... and {len(state_figure_paths) - N_TO_SHOW} more not shown (raise N_TO_SHOW to see more)\")" + ] + }, + { + "cell_type": "markdown", + "id": "08c90de8", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "56e7813e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Automated checks: 26/32 passed\n", + "\n", + "MODEL QUALITY FAILURES (6) -- these invalidate the run:\n", + " - K_selection found a model meeting k_select_thr (not a fallback): k_selection_met_threshold=False, suggested model_order=None\n", + " - evaluation reported no collapse warning: Only 11 of 120 retained categories are populated (arms: [8, 11]). The discrete latent has likely collapsed -- model_order is not a usable estimate of the number of cell types and downstream Analyze results will be dominated by empty categories.\n", + " - populated categories are at least 50% of model_order: n_populated_categories=11 of model_order=120 (9.2%); per arm=[8, 11]\n", + " - avg_consensus meets k_select_thr: avg_consensus=0.0259, k_select_thr=0.95\n", + " - MMIDAS lowD recovers t-types within 0.15 of the PCA baseline: PCA=0.909, MMIDAS=0.431, gap=0.479 (limit 0.15)\n", + " - ConsType confusion matrices span at least 50% of model_order: 11 populated of model_order=120 (9.2%)\n", + "\n", + "21 figures/items flagged for manual review (see inline images above).\n", + "\n", + "======================================================================\n", + "VERDICT: the pipeline ran, but the model it produced is not usable.\n", + "Do not treat the passing checks as validation of the outputs.\n" + ] + } + ], + "source": [ + "n_pass = sum(c[\"passed\"] for c in CHECKS)\n", + "n_fail = len(CHECKS) - n_pass\n", + "\n", + "# Checks whose failure means the model is unusable, as opposed to a plumbing or\n", + "# configuration problem. Reported separately so a run cannot be waved through on\n", + "# a favourable pass count -- any one of these failing invalidates the results.\n", + "QUALITY_KEYWORDS = (\n", + " \"populated categories\",\n", + " \"avg_consensus\",\n", + " \"K_selection found a model\",\n", + " \"collapse warning\",\n", + " \"recovers t-types\",\n", + " \"ConsType confusion matrices span\",\n", + " \"has cells assigned\",\n", + " \"not duplicates\",\n", + " \"KEGG pathways were mapped\",\n", + ")\n", + "\n", + "\n", + "def _is_quality(name):\n", + " return any(k in name for k in QUALITY_KEYWORDS)\n", + "\n", + "\n", + "quality_fails = [c for c in CHECKS if not c[\"passed\"] and _is_quality(c[\"name\"])]\n", + "plumbing_fails = [c for c in CHECKS if not c[\"passed\"] and not _is_quality(c[\"name\"])]\n", + "\n", + "print(\"=\" * 70)\n", + "print(f\"Automated checks: {n_pass}/{len(CHECKS)} passed\")\n", + "\n", + "if quality_fails:\n", + " print(f\"\\nMODEL QUALITY FAILURES ({len(quality_fails)}) \"\n", + " f\"-- these invalidate the run:\")\n", + " for c in quality_fails:\n", + " print(f\" - {c['name']}: {c['detail']}\")\n", + "\n", + "if plumbing_fails:\n", + " print(f\"\\nPLUMBING / CONFIG FAILURES ({len(plumbing_fails)}):\")\n", + " for c in plumbing_fails:\n", + " print(f\" - {c['name']}: {c['detail']}\")\n", + "\n", + "print(f\"\\n{len(REVIEW_ITEMS)} figures/items flagged for manual review \"\n", + " f\"(see inline images above).\")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "if quality_fails:\n", + " print(\"VERDICT: the pipeline ran, but the model it produced is not usable.\")\n", + " print(\"Do not treat the passing checks as validation of the outputs.\")\n", + "elif plumbing_fails:\n", + " print(\"VERDICT: model-quality checks passed; fix the plumbing/config \"\n", + " \"failures above.\")\n", + "else:\n", + " print(\"VERDICT: all automated checks passed. The [REVIEW] figures above \"\n", + " \"still need a human eyeball.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3800231", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c25c880f", + "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": "3.10.16" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tasks/wdl/Glimpse2SVImputationTasks.wdl b/tasks/wdl/Glimpse2SVImputationTasks.wdl index 1175cb4e93..fd9ec9b11a 100644 --- a/tasks/wdl/Glimpse2SVImputationTasks.wdl +++ b/tasks/wdl/Glimpse2SVImputationTasks.wdl @@ -2,10 +2,10 @@ version 1.0 task MergeSampleChunksVcfsWithPaste { input { - Array[File] input_vcfs + Array[File] input_vcfs_or_bcfs String output_vcf_basename - Int disk_size_gb = ceil(2.2 * size(input_vcfs, "GiB") + 50) + Int disk_size_gb = ceil(2.2 * size(input_vcfs_or_bcfs, "GiB") + 50) Int mem_gb = 8 Int cpu = 4 Int preemptible = 0 @@ -14,7 +14,7 @@ task MergeSampleChunksVcfsWithPaste { command <<< set -euo pipefail - vcfs=(~{sep=" " input_vcfs}) + vcfs=(~{sep=" " input_vcfs_or_bcfs}) mkfifo fifo_0 mkfifo fifo_to_paste_0 @@ -89,12 +89,12 @@ task MergeSampleChunksVcfsWithPaste { task ExtractAnnotations { input { - File imputed_vcf - File imputed_vcf_index + File imputed_vcf_or_bcf + File imputed_vcf_or_bcf_index Int batch_index String docker_extract_annotations - Int disk_size_gb = ceil(2 * size(imputed_vcf, "GiB") + 50) + Int disk_size_gb = ceil(2 * size(imputed_vcf_or_bcf, "GiB") + 50) Int mem_gb = 2 Int cpu = 1 Int preemptible = 3 @@ -104,12 +104,12 @@ task ExtractAnnotations { set -euo pipefail # Ensure index is localized so bcftools can use it for random access if needed - ls ~{imputed_vcf_index} > /dev/null + ls ~{imputed_vcf_or_bcf_index} > /dev/null printf 'CHROM\tPOS\tREF\tALT\tAF\tINFO\n' > annotations_batch_~{batch_index}.tsv bcftools query \ -f '%CHROM\t%POS\t%REF\t%ALT\t%INFO/AF\t%INFO/INFO\n' \ - ~{imputed_vcf} >> annotations_batch_~{batch_index}.tsv + ~{imputed_vcf_or_bcf} >> annotations_batch_~{batch_index}.tsv bgzip annotations_batch_~{batch_index}.tsv >>> @@ -130,7 +130,7 @@ task ExtractAnnotations { task RecomputeAndAnnotate { input { - File merged_vcf + File merged_vcf_or_bcf Array[File] annotations Array[Int] num_samples @@ -138,7 +138,7 @@ task RecomputeAndAnnotate { String output_basename String docker_merge - Int disk_size_gb = ceil(2.2 * size(merged_vcf, "GiB") + size(annotations, "GiB") + 50) + Int disk_size_gb = ceil(2.2 * size(merged_vcf_or_bcf, "GiB") + size(annotations, "GiB") + 50) Int mem_gb = 6 Int cpu = 1 Int preemptible = 0 @@ -204,7 +204,7 @@ EOF bgzip aggregated_annotations.tsv tabix -s1 -b2 -e2 aggregated_annotations.tsv.gz - bcftools annotate -a aggregated_annotations.tsv.gz -c CHROM,POS,REF,ALT,AF,INFO -O z -o ~{output_basename}.vcf.gz ~{merged_vcf} + bcftools annotate -a aggregated_annotations.tsv.gz -c CHROM,POS,REF,ALT,AF,INFO -O z -o ~{output_basename}.vcf.gz ~{merged_vcf_or_bcf} >>> runtime { @@ -224,10 +224,10 @@ EOF task CreateVcfIndexAndMd5 { input { - File vcf_input + File vcf_input_or_bcf String output_basename - Int disk_size_gb = ceil(2.1*size(vcf_input, "GiB")) + 10 + Int disk_size_gb = ceil(2.1*size(vcf_input_or_bcf, "GiB")) + 10 Int cpu = 1 Int memory_mb = 6000 String gatk_docker = "us.gcr.io/broad-gatk/gatk:4.5.0.0" @@ -237,11 +237,11 @@ task CreateVcfIndexAndMd5 { command <<< set -euo pipefail - if [[ "~{vcf_input}" == *.bcf ]]; then + if [[ "~{vcf_input_or_bcf}" == *.bcf ]]; then # Normalize BCF input to a bgzipped VCF for downstream compatibility. - bcftools view -O z -o ~{output_basename}.vcf.gz ~{vcf_input} + bcftools view -O z -o ~{output_basename}.vcf.gz ~{vcf_input_or_bcf} else - ln -sf ~{vcf_input} ~{output_basename}.vcf.gz + ln -sf ~{vcf_input_or_bcf} ~{output_basename}.vcf.gz fi bcftools index -t ~{output_basename}.vcf.gz @@ -266,12 +266,12 @@ task CreateVcfIndexAndMd5 { task FilterVcfByInfo { input { - File vcf + File vcf_or_bcf Float info_threshold String output_basename String docker = "us.gcr.io/broad-gotc-prod/bcftools-vcftools:2.0.0-1.24-0.1.17-1784569943" - Int disk_size_gb = ceil(2.2 * size(vcf, "GiB") + 20) + Int disk_size_gb = ceil(2.2 * size(vcf_or_bcf, "GiB") + 20) Int mem_gb = 4 Int cpu = 1 Int preemptible = 3 @@ -280,7 +280,7 @@ task FilterVcfByInfo { command <<< set -euo pipefail - bcftools filter -i 'INFO/INFO >= ~{info_threshold}' -O z -o ~{output_basename}.vcf.gz ~{vcf} + bcftools filter -i 'INFO/INFO >= ~{info_threshold}' -O z -o ~{output_basename}.vcf.gz ~{vcf_or_bcf} >>> runtime { @@ -390,7 +390,7 @@ task ConvertInputArraysToManifest { } output { - File output_manifest = "~{output_filename}" + File output_gvcf_manifest = "~{output_filename}" } } diff --git a/verification/VerifyGlimpse2SVImputation.wdl b/verification/VerifyGlimpse2SVImputation.wdl index 3273a0ff9e..60a8dba897 100644 --- a/verification/VerifyGlimpse2SVImputation.wdl +++ b/verification/VerifyGlimpse2SVImputation.wdl @@ -21,18 +21,18 @@ import "../verification/VerifyTasks.wdl" as Tasks workflow VerifyGlimpse2SVImputation { input { # popped posteriors vcf, one per chromosome - Array[File] truth_imputed_vcf - Array[File] test_imputed_vcf + Array[File] truth_imputed_vcfs + Array[File] test_imputed_vcfs Boolean? done } - scatter (idx in range(length(truth_imputed_vcf))) { + scatter (idx in range(length(truth_imputed_vcfs))) { call Tasks.CompareVcfs as CompareImputedVcfs { input: - file1 = truth_imputed_vcf[idx], - file2 = test_imputed_vcf[idx], + file1 = truth_imputed_vcfs[idx], + file2 = test_imputed_vcfs[idx], patternForLinesToExcludeFromComparison = "##" } } diff --git a/verification/test-wdls/TestGlimpse2SVImputation.wdl b/verification/test-wdls/TestGlimpse2SVImputation.wdl index 38ff69e122..322b584245 100644 --- a/verification/test-wdls/TestGlimpse2SVImputation.wdl +++ b/verification/test-wdls/TestGlimpse2SVImputation.wdl @@ -75,8 +75,8 @@ workflow TestGlimpse2SVImputation { # Collect all pipeline outputs into a single Array[String] Array[String] pipeline_outputs = flatten([ - Glimpse2SVImputation.imputed_vcf, - Glimpse2SVImputation.imputed_vcf_index + Glimpse2SVImputation.imputed_vcfs, + Glimpse2SVImputation.imputed_vcf_indexes ]) # Copy results of pipeline to test results bucket @@ -99,15 +99,15 @@ workflow TestGlimpse2SVImputation { if (!update_truth){ call Utilities.GetValidationInputs as GetImputedVcfs { input: - input_files = Glimpse2SVImputation.imputed_vcf, + input_files = Glimpse2SVImputation.imputed_vcfs, results_path = results_path, truth_path = truth_path } call VerifyGlimpse2SVImputation.VerifyGlimpse2SVImputation as Verify { input: - truth_imputed_vcf = GetImputedVcfs.truth_files, - test_imputed_vcf = GetImputedVcfs.results_files, + truth_imputed_vcfs = GetImputedVcfs.truth_files, + test_imputed_vcfs = GetImputedVcfs.results_files, done = CopyToTestResults.done } } diff --git a/website/docs/All_of_Us/RNA_Seq_QTL/overview.md b/website/docs/All_of_Us/RNA_Seq_QTL/overview.md index feba7644e5..8d0819976e 100644 --- a/website/docs/All_of_Us/RNA_Seq_QTL/overview.md +++ b/website/docs/All_of_Us/RNA_Seq_QTL/overview.md @@ -41,8 +41,8 @@ The table below reflects the original end-to-end run order, including steps that | 9 | sQTL metadata | Calculate phenotype groups | [Calculate Phenotype Groups](./calculate_phenotype_groups) | [CalculatePhenotypeGroups.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/CalculatePhenotypeGroups.wdl) | Merge covariates for sQTL TensorQTL run. | | 10 | Covariates | Merge covariates (genotype PCs + phenotype PCs ± groups) | No dedicated page yet | [MergeCovariates.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/prepare_QTL/MergeCovariates.wdl) | Run TensorQTL cis permutations for eQTL/sQTL. | | 11 | Association | TensorQTL cis permutations | No dedicated page yet | [tensorqtl_cis_permutations.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/tensorQTL_cis_permutations/tensorqtl_cis_permutations.wdl) | Recalculate FDR and prepare significant loci for fine-mapping. | -| 12 | Fine-mapping prep | FDR recalculation + SuSiE input preparation, including required AF calculation for downstream aggregation/annotation | No dedicated page yet | [calculateAF.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/prepare_QTL/calculateAF.wdl) | Run SuSiE per phenotype window. | -| 13 | Fine-mapping | SuSiE fine-mapping | [SuSiE Fine-Mapping Workflow](./susieR_workflow) | [susieR_workflow.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/susieR_workflow.wdl) | Aggregate SuSiE outputs across phenotypes. | +| 12 | Fine-mapping prep | FDR recalculation + SuSiE input preparation, including required AF calculation and genotype dosage checks for downstream aggregation/annotation | No dedicated page yet | [calculateAF.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/prepare_QTL/calculateAF.wdl) | Run SuSiE per phenotype window. | +| 13 | Fine-mapping | SuSiE fine-mapping (requires the [`susie_rscript`](https://github.com/broadinstitute/warp-tools/blob/main/3rd-party-tools/aou_qtl_finemapping/20251210_rfast_removed_susie.R) SuSiE runner script) | [SuSiE Fine-Mapping Workflow](./susieR_workflow) | [susieR_workflow.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/susieR_workflow.wdl) | Aggregate SuSiE outputs across phenotypes. | | 14 | Aggregation | Aggregate SuSiE outputs and annotate | [Aggregate SuSiE Workflow](./aggregate_susie_workflow) | [AggregateSusieWorkflow.wdl](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/AggregateSusieWorkflow.wdl) | Consume required AF outputs from step 12 for interpretation/reporting. | ## Practical Run Notes @@ -67,6 +67,7 @@ The genotype preparation stage (Step 1) uses tools available in the [warp-tools] ## Additional Processing Notes * **FDR, AF, and SuSiE prep:** after TensorQTL, recalculate FDR, filter significant loci (commonly 0.05), calculate AFs, and format SuSiE-ready inputs. +* **SuSiE runner script:** `susieR_workflow`'s `susie_rscript` input is provided by [`20251210_rfast_removed_susie.R`](https://github.com/broadinstitute/warp-tools/blob/main/3rd-party-tools/aou_qtl_finemapping/20251210_rfast_removed_susie.R) in the [warp-tools](https://github.com/broadinstitute/warp-tools) repository. * **SuSiE runtime guidance:** preemptible VMs can reduce cost; pinned Docker SHAs improve reproducibility. * **Aggregation inputs:** for `AggregateSusieWorkflow`, use fine-mapped `SusieParquet` outputs and required AF resources from step 12; do not use full/all-tested parquet outputs. * **RNA-level aggregation status:** `aggregate_rsem_results.wdl` is available in WARP for cohort-level RSEM aggregation; an equivalent aggregate `rnaseqc2` workflow is not yet available in WARP and currently requires external processing. diff --git a/website/docs/All_of_Us/RNA_Seq_QTL/susieR_workflow.md b/website/docs/All_of_Us/RNA_Seq_QTL/susieR_workflow.md index 28a175137e..df798e9ad9 100644 --- a/website/docs/All_of_Us/RNA_Seq_QTL/susieR_workflow.md +++ b/website/docs/All_of_Us/RNA_Seq_QTL/susieR_workflow.md @@ -9,13 +9,13 @@ className: aou-doc-page | Pipeline Version | Date Updated | Documentation Author | Questions or Feedback | | :----: | :---: | :----: | :--------------: | -| [aou_9.0.1](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/susieR_workflow.changelog.md) | January, 2026 | WARP Pipelines | [File an issue](https://github.com/broadinstitute/warp/issues) | +| [aou_9.1.0](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/susieR_workflow.changelog.md) | August, 2026 | WARP Pipelines | [File an issue](https://github.com/broadinstitute/warp/issues) | ## Introduction to the SuSiE Fine-Mapping workflow [`susieR_workflow.wdl`](https://github.com/broadinstitute/warp/blob/develop/all_of_us/rna_seq/susieR_workflow.wdl) performs cis-window fine-mapping using SuSiE from TensorQTL outputs and dosage matrices. -The workflow subsets phenotype/genotype inputs for a target phenotype ID, then runs an R-based SuSiE script to produce parquet outputs including credible set summaries and full fine-mapping tables. +Given an `InputMappingTsv` (one row per ancestry) and a target `Ancestry`, the workflow first looks up the ancestry-specific genotype dosage, covariate, sample list, phenotype BED, and TensorQTL permutation file paths, subsets those phenotype/genotype inputs for a target phenotype ID, then runs an R-based SuSiE script to produce parquet outputs including credible set summaries and full fine-mapping tables. ## Inputs @@ -23,14 +23,10 @@ The workflow subsets phenotype/genotype inputs for a target phenotype ID, then r | Input variable name | Description | Type | | --- | --- | --- | -| `GenotypeDosages` | BGZ-compressed dosage matrix. | File | -| `GenotypeDosageIndex` | Tabix index for dosage matrix. | File | -| `QTLCovariates` | Covariate table for QTL model fitting. | File | -| `TensorQTLPermutations` | TensorQTL permutation output table used to select phenotype-specific loci. | File | -| `SampleList` | Sample metadata file. | File | -| `PhenotypeBed` | Phenotype BED matrix used for expression/splicing values. | File | +| `InputMappingTsv` | Tab-separated file, one row per ancestry, providing `gs://` paths for `GenotypeDosage`, `GenotypeDosagei`, `PhenotypePCsOut`, `PlinkAF`, `QtlCovariates`, `Sample_list`, `VCF`, `cis_qtl`, `genotype_pcs`, `pgen`, `phenotype_bed`, and `psam`, keyed by a `mapping_inputs_id` ancestry code (AFR, AMR, COMB, EUR, SAS, EAS, MID). | File | +| `Ancestry` | Ancestry code used to select the row of `InputMappingTsv` to use for this run. | String | | `CisDistance` | Cis-window distance parameter. | Int | -| `susie_rscript` | SuSiE runner script path. | File | +| `susie_rscript` | SuSiE runner script path; see [`20251210_rfast_removed_susie.R`](https://github.com/broadinstitute/warp-tools/blob/main/3rd-party-tools/aou_qtl_finemapping/20251210_rfast_removed_susie.R) in [warp-tools](https://github.com/broadinstitute/warp-tools). | File | | `memory` | Runtime memory (GB). | Int | | `NumPrempt` | Runtime preemptible count. | Int | | `OutputPrefix` | General output prefix. | String | diff --git a/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/README.md b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/README.md new file mode 100644 index 0000000000..e5b3c10379 --- /dev/null +++ b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/README.md @@ -0,0 +1,202 @@ +--- +sidebar_position: 1 +slug: /Pipelines/Glimpse2SVImputation_Pipeline/README +--- + +# GLIMPSE2 SV Imputation Overview + +| Pipeline Version | Date Updated | Documentation Author | Questions or Feedback | +|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:----------------------------------:|:------------------------------------------------------------------------------:| +| See [changelog](https://github.com/broadinstitute/warp/blob/develop/pipelines/wdl/glimpse/sv_imputation/Glimpse2SVImputation.changelog.md) for version information. | See changelog | Terra Scientific Pipeline Services | Please [file an issue in WARP](https://github.com/broadinstitute/warp/issues). | + +## Introduction to the GLIMPSE2 SV Imputation pipeline + +The GLIMPSE2 SV Imputation pipeline imputes structural variants, SNPs, and INDELs from a manifest of input GVCF paths (or from GVCF/index arrays that are converted to a manifest). It uses GLIMPSE2-based phasing, panel-informed bubble processing, and cohort-aware merge/re-annotation to produce final per-contig imputed VCF outputs. + +## GLIMPSE2 SV Imputation Summary + +The `Glimpse2SVImputation` workflow is a WDL-based pipeline for structural variant imputation using [GLIMPSE2](https://odelaneau.github.io/GLIMPSE/). +This top-level workflow acts as a gateway that scales to large cohorts by splitting samples into batches, running preprocess + batch imputation subworkflows per batch, then merging per-batch results back into cohort-level per-contig VCFs. + +The workflow processes requested chromosomes independently, extracts/reformats bubble likelihoods, phases and ligates imputed chunks, pops/marginalizes collisions, merges batch sample columns, recomputes AF/INFO across all samples, optionally filters by INFO threshold, and indexes final outputs. + +![](pipeline.png) + + +### Pipeline Features + +| Pipeline features | Description | Source | +|-------------------------|----------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------| +| Assay type | Structural variant imputation using GLIMPSE2 | [GLIMPSE2](https://odelaneau.github.io/GLIMPSE/) | +| Overall workflow | Manifest normalization, preprocessing, chunked phase/ligate, pop/marginalize, merge, and AF/INFO re-annotation | Defined in `Glimpse2SVImputation.wdl` + imported subworkflows/tasks | +| Workflow language | WDL 1.0 | [openWDL](https://github.com/openwdl/wdl) | +| Sub-workflows | Gateway workflow + `PreprocessPLsGVCF` + `Glimpse2SVImputationBatch` | Imported from sibling WDLs in `sv_imputation/` | +| Genomic processing | Contig-by-contig processing with nested shard/region scatters | Workflow scatter logic | +| Cohort scalability | Input manifest splitting via `sample_batch_size`, then batch-level contig merge | Gateway orchestration | +| Algorithms | GLIMPSE2 phase/ligate + custom paste/concat + cohort AF/INFO recomputation | Task commands in batch/task WDLs | +| Data input file format | GVCF/GVCF index manifest (or input arrays converted to manifest) | Workflow input block | +| Data output file format | Per-contig imputed VCFs with index files | Workflow outputs | +| Containers | GLIMPSE2, GATK, bcftools/samtools, Python, custom SV tooling containers | Runtime blocks | +| Resource optimization | Parallelization by sample batch, chromosome, chunk, and pop region | Workflow architecture | + +### Inputs + +This gateway workflow expects manifest- or array-based GVCF inputs plus SV panel/chunk resources. + +| Input | Description | +|---------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------| +| `input_gvcfs` / `input_gvcf_idxs` | Optional arrays of GVCF and matching index paths. If both arrays and `gvcf_manifest` are provided, arrays take precedence. | +| `gvcf_manifest` | Optional two-column manifest (`gvcf_path`, `gvcf_index_path`) used when arrays are not provided. | +| `sample_batch_size` | Number of samples per batch for gateway-level scaling (default: `1000`). | +| `output_basename` | Basename used for intermediate and final outputs. | +| `preprocess_panel_bubble_split_sites_only_vcf` / `_idx` | Panel site resource used during preprocessing. | +| `extract_bubble_likelihoods_extra_args` | Optional overrides for preprocessing extraction behavior. | +| `paste_regions` | Regions passed to hierarchical merge in preprocessing. | +| `chromosomes` | Chromosomes/contigs to process. | +| `genetic_maps_tsv` | TSV map from contig name to GLIMPSE2 genetic map file. | +| `ref_dict` | Reference dictionary used in header updates. | +| `chunked_panel_json` | JSON describing chunk bins and regions by chromosome. | +| `extra_phase_args` | Arguments passed to `GLIMPSE2_phase`. | +| `glimpse_phase_cpu_override` | Optional CPU override for chunked phase tasks (defaults internally to `4`). | +| `pop_glimpse2_panel_resources_json` | JSON with panel resources needed for pop/marginalize processing. | +| `info_filter_for_inclusion` | Optional INFO threshold for variant inclusion (default: `0.0`, meaning no filter). | +| `pipeline_header_line` | Optional additional header line to add to output VCF headers. | + +### Workflow Tasks + +The top-level workflow orchestrates batching, per-batch preprocessing/imputation, and contig-level merge/finalization. + +| Task / Call | Purpose | Input Dependencies | Key Function | +|------------------------------------------------------------|------------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------| +| `ConvertInputArraysToManifest` | Convert input GVCF/index arrays into a manifest | `input_gvcfs`, `input_gvcf_idxs` | Supports large submissions via manifest abstraction | +| `SplitIntoSampleBatches` | Split manifest into sample-level batches | Derived manifest + `sample_batch_size` | Enables gateway-level cohort scaling | +| `PreProcessGVCFsBatch` (`PreprocessPLsGVCF`) | Preprocess one batch of GVCFs into merged preprocessed BCF | Batch manifest + preprocessing resources | Produces one preprocessed BCF/index per batch | +| `RunBatch` (`Glimpse2SVImputationBatch`) | Run per-batch chunked SV phase/ligate/pop processing | Preprocessed BCF + chromosome/map/chunk/panel resources | Produces per-batch per-contig popped posterior BCFs | +| `ExtractPoppedAnnotations` | Extract AF/INFO annotations from each batch contig VCF | Batch popped VCFs + indexes | Captures per-batch annotations for cohort recomputation | +| `MergePoppedContigVcfs` (`MergeSampleChunksVcfsWithPaste`) | Merge sample columns across batches for each contig | Arrays of per-batch contig VCFs | Creates full-cohort contig VCF | +| `RecomputePoppedAfInfo` | Recompute AF/INFO across merged cohort and re-annotate | Merged contig VCF + annotations + sample counts | Restores cohort-correct AF/INFO values | +| `FilterPoppedContigByInfo` | Optionally filter low-confidence variants by INFO | Re-annotated contig VCF + threshold | Removes variants below threshold | +| `IndexFinalPoppedContig` | Finalize VCF/index outputs | Filtered or unfiltered contig VCF | Emits `imputed_vcf` and `imputed_vcf_index` per contig | + +### Outputs + +Upon successful completion, the workflow emits final per-contig imputed outputs and matching indexes. + +| Output | Description | +|-----------------------|----------------------------------------------------| +| `imputed_vcfs` | Final per-contig imputed VCF files | +| `imputed_vcf_indexes` | Index files for final per-contig imputed VCF files | + +## PreprocessPLsGVCF summary + +The `PreprocessPLsGVCF` workflow preprocesses each input GVCF in a batch and hierarchically merges all preprocessed outputs into a single batch-level BCF for downstream GLIMPSE2 phasing. + +### PreprocessPLsGVCF internal processing + +| Step | Purpose | +|-----------------------------------------------------------------|-------------------------------------------------------------------| +| `ParseInputManifest` (`ParseVcfManifestIntoArrays`) | Parse manifest into parallel arrays of GVCF paths and index paths | +| `PreprocessPLsGVCF` scatter (`PreprocessPLs` task) | Run per-sample `extract-bubble-PLs` to generate preprocessed BCFs | +| `PastePreprocessPLsGVCFs` (`MultilevelHierarchicallyMergeVcfs`) | Hierarchically merge preprocessed BCFs across configured regions | + +### PreprocessPLsGVCF outputs + +| Output | Description | +|----------------------------|--------------------------------------------| +| `preprocessed_pls_bcf` | Batch-level merged preprocessed BCF | +| `preprocessed_pls_bcf_idx` | Index for `preprocessed_pls_bcf` | +| `num_samples` | Number of samples represented in the batch | + +## Glimpse2SVImputationBatch summary + +The `Glimpse2SVImputationBatch` workflow is the per-batch subworkflow used by `Glimpse2SVImputation`. It takes one preprocessed batch BCF and runs chromosome-level chunked phasing, ligation, header normalization, and pop/marginalize post-processing. + +### Batch internal processing + +| Step | Purpose | +|----------------------------------------------------|--------------------------------------------------------------------------| +| `ChunkedGLIMPSE2Phase` | Run `GLIMPSE2_phase` over each chunk for each chromosome | +| `GLIMPSE2Ligate` | Ligate chunk-level phased outputs into one chromosome-level BCF | +| `UpdateHeader` | Rebuild/update headers with dictionary and optional pipeline line | +| `PopAndMarginalizeCollisions` | Apply pop/marginalize processing over one or more regions per chromosome | +| `ConcatPopAndMarginalizeCollisions` (`ConcatBcfs`) | Concatenate regional popped BCFs into one popped BCF per chromosome | + +### Batch outputs + +| Output | Description | +|--------------------------------------|------------------------------------------------------------| +| `glimpse2_bubble_posteriors_vcf` | Per-chromosome post-ligation BCFs with updated headers | +| `glimpse2_bubble_posteriors_vcf_idx` | Indexes for bubble posterior outputs | +| `glimpse2_popped_posteriors_vcf` | Final per-chromosome popped BCFs for upstream cohort merge | +| `glimpse2_popped_posteriors_vcf_idx` | Indexes for popped posterior outputs | + +## MultilevelHierarchicallyPasteVcfsStreaming summary + +The `MultilevelHierarchicallyMergeVcfs` workflow provides scalable multi-level merging for squared-off single-sample VCF/BCF inputs. It performs region-first hierarchical batching, optional streaming/localization, and final region concatenation. + +### MultilevelHierarchicallyMergeVcfs internal processing + +| Step | Purpose | +|----------------------------|----------------------------------------------------------------------| +| `CreateBatches` (L0/L1/L2) | Split input files into hierarchical batch fofns | +| `MergeVcfs` (L0/L1/L2) | Merge each batch within each region with optional streaming timeouts | +| `FinalRegionMerge` | Collapse remaining per-region intermediate files when needed | +| `ConcatBcfs` | Concatenate finalized region outputs into one merged BCF | + +### MultilevelHierarchicallyMergeVcfs outputs + +| Output | Description | +|------------------|-------------------------------------------------------| +| `merged_bcf` | Final merged BCF across all input samples and regions | +| `merged_bcf_idx` | Index for `merged_bcf` | + +## Glimpse2SVImputationQuotaConsumed summary + +The `QuotaConsumed` workflow computes submitted sample count for service quota accounting from `gvcf_manifest`. + +### QuotaConsumed internal processing + +| Step | Purpose | +|--------------------------|----------------------------------------------------------------| +| `CountGvcfsFromManifest` | Count GVCF entries in the manifest and report `quota_consumed` | + +### QuotaConsumed outputs + +| Output | Description | +|------------------|------------------------------------------------------------| +| `quota_consumed` | Number of submitted GVCF entries counted from the manifest | + +## Glimpse2SVImputationQC summary + +The `InputQC` workflow validates manifest-level and GVCF-level readiness before SV imputation. + +### InputQC checks + +- required manifest columns are present: `gvcf_path`, `gvcf_index_path` +- manifest has at least one row and no empty required values +- GVCF paths are unique +- GVCF and index extensions are valid (`.vcf.gz`/`.gvcf.gz` and `.tbi`) +- GVCF and index basenames are matched +- paths use valid `gs://` format and are accessible +- GVCF file size is below the configured threshold (default `10 GB`) +- GVCF headers are compatible with the expected reference dictionary +- each GVCF contains exactly one sample +- sample IDs are unique across all provided GVCFs +- PL and GT FORMAT IDs are present in each GVCF header +- each GVCF is vcf version 4.x + +### InputQC outputs + +| Output | Description | +|---------------|-------------------------------------------------------| +| `passes_qc` | Overall QC pass/fail status | +| `qc_messages` | Aggregated QC failure messages (empty when QC passes) | + +## Important notes + +- Runtime parameters are optimized for Broad's Google Cloud Platform implementation. + +## Contact us + +Help us make our tools better by [filing an issue in WARP](https://github.com/broadinstitute/warp/issues); we welcome pipeline-related suggestions or questions. + diff --git a/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/_category_.json b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/_category_.json new file mode 100644 index 0000000000..75ee33fb96 --- /dev/null +++ b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/_category_.json @@ -0,0 +1,5 @@ +{ + "label": "Glimpse2SVImputation", + "position": 5 +} + diff --git a/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/pipeline.png b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/pipeline.png new file mode 100644 index 0000000000..e9ac96b8f7 Binary files /dev/null and b/website/docs/Pipelines/Glimpse2SVImputation_Pipeline/pipeline.png differ diff --git a/website/docs/Pipelines/Multiome_Pipeline/README.md b/website/docs/Pipelines/Multiome_Pipeline/README.md index 19136596a6..b25b34a84c 100644 --- a/website/docs/Pipelines/Multiome_Pipeline/README.md +++ b/website/docs/Pipelines/Multiome_Pipeline/README.md @@ -85,6 +85,15 @@ Multiome can be deployed using [Cromwell](https://cromwell.readthedocs.io/en/sta | run_peak_calling | Optional boolean used to determine if the ATAC pipeline should run Peak Calling; default is `false`. When set to true, the pipeline takes the ATAC h5ad produced by the JoinBarcodes task and performs peak calling to produce a cell by bin matrix and a cell by peak matrix. | Boolean | | vm_size | String defining the Azure virtual machine family for the workflow (default: "Standard_M128s"). | String | +:::warning BWA machine size not available in your region/project +The ATAC component sizes the BWA-mem2 alignment machine from three inputs — `num_threads_bwa` (default 128), `mem_size_bwa` (default 512 GiB), and `cpu_platform_bwa` (default "Intel Ice Lake") — that are internal to the ATAC subworkflow and are **not** exposed as top-level Multiome inputs. If a Multiome run fails immediately at the `Atac.GetNumSplits` or `Atac.BWAPairedEndAlignment` task with no execution logs, the requested VM shape is likely unavailable (quota or capacity) in your project/region. Override the three inputs directly on the ATAC subworkflow using Cromwell/Terra's nested-input syntax, e.g.: + +```json +"Multiome.Atac.num_threads_bwa": "16", +"Multiome.Atac.mem_size_bwa": "64", +"Multiome.Atac.cpu_platform_bwa": "Intel Cascade Lake" +``` +::: #### Sample inputs for analyses in a Terra Workspace diff --git a/website/yarn.lock b/website/yarn.lock index c7908ccc74..6d88a7166f 100644 --- a/website/yarn.lock +++ b/website/yarn.lock @@ -2791,10 +2791,10 @@ "@jridgewell/resolve-uri" "3.1.0" "@jridgewell/sourcemap-codec" "1.4.14" -"@jridgewell/trace-mapping@^0.3.20": - version "0.3.25" - resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.25.tgz#15f190e98895f3fc23276ee14bc76b675c2e50f0" - integrity sha512-vNk6aEwybGtawWmy/PzwnGDOjCkLWSD2wqvjGGAgOAwCGWySYXfYoxt00IJkTF+8Lb57DwOb3Aa0o9CApepiYQ== +"@jridgewell/trace-mapping@^0.3.25": + version "0.3.31" + resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.31.tgz#db15d6781c931f3a251a3dac39501c98a6082fd0" + integrity sha512-zzNR+SdQSDJzc8joaeP8QQoCQr8NuYx2dIIytl1QeBEZHJ9uW6hebsrYgbz8hJwUQao3TWCMtmfV8Nu1twOLAw== dependencies: "@jridgewell/resolve-uri" "^3.1.0" "@jridgewell/sourcemap-codec" "^1.4.14" @@ -3173,10 +3173,10 @@ resolved "https://registry.yarnpkg.com/@types/estree/-/estree-0.0.47.tgz#d7a51db20f0650efec24cd04994f523d93172ed4" integrity sha512-c5ciR06jK8u9BstrmJyO97m+klJrrhCf9u3rLu3DEAJBirxRqSCvDQoYKmxuYwQI5SZChAWu+tq9oVlGRuzPAg== -"@types/estree@^1.0.6": - 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picocolors "^1.1.0" + picocolors "^1.1.1" update-notifier@^5.1.0: version "5.1.0" @@ -9403,10 +9434,10 @@ wait-on@^6.0.1: minimist "^1.2.5" rxjs "^7.5.4" -watchpack@^2.4.1: - version "2.4.2" - resolved "https://registry.yarnpkg.com/watchpack/-/watchpack-2.4.2.tgz#2feeaed67412e7c33184e5a79ca738fbd38564da" - integrity sha512-TnbFSbcOCcDgjZ4piURLCbJ3nJhznVh9kw6F6iokjiFPl8ONxe9A6nMDVXDiNbrSfLILs6vB07F7wLBrwPYzJw== +watchpack@^2.5.1: + version "2.5.1" + resolved "https://registry.yarnpkg.com/watchpack/-/watchpack-2.5.1.tgz#dd38b601f669e0cbf567cb802e75cead82cde102" + integrity sha512-Zn5uXdcFNIA1+1Ei5McRd+iRzfhENPCe7LeABkJtNulSxjma+l7ltNx55BWZkRlwRnpOgHqxnjyaDgJnNXnqzg== dependencies: glob-to-regexp "^0.4.1" graceful-fs "^4.1.2" @@ -9499,39 +9530,46 @@ webpack-merge@^5.8.0: clone-deep "^4.0.1" wildcard "^2.0.0" -webpack-sources@^3.2.2, webpack-sources@^3.2.3: +webpack-sources@^3.2.2: version "3.2.3" resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-3.2.3.tgz#2d4daab8451fd4b240cc27055ff6a0c2ccea0cde" integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w== +webpack-sources@^3.3.3: + version "3.3.3" + resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-3.3.3.tgz#d4bf7f9909675d7a070ff14d0ef2a4f3c982c723" + integrity sha512-yd1RBzSGanHkitROoPFd6qsrxt+oFhg/129YzheDGqeustzX0vTZJZsSsQjVQC4yzBQ56K55XU8gaNCtIzOnTg== + webpack@^5.73.0: - version "5.96.1" - resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.96.1.tgz#3676d1626d8312b6b10d0c18cc049fba7ac01f0c" - integrity sha512-l2LlBSvVZGhL4ZrPwyr8+37AunkcYj5qh8o6u2/2rzoPc8gxFJkLj1WxNgooi9pnoc06jh0BjuXnamM4qlujZA== + version "5.105.0" + resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.105.0.tgz#38b5e6c5db8cbe81debbd16e089335ada05ea23a" + integrity sha512-gX/dMkRQc7QOMzgTe6KsYFM7DxeIONQSui1s0n/0xht36HvrgbxtM1xBlgx596NbpHuQU8P7QpKwrZYwUX48nw== dependencies: "@types/eslint-scope" "^3.7.7" - "@types/estree" "^1.0.6" - "@webassemblyjs/ast" "^1.12.1" - "@webassemblyjs/wasm-edit" "^1.12.1" - "@webassemblyjs/wasm-parser" "^1.12.1" - acorn "^8.14.0" - browserslist "^4.24.0" + "@types/estree" "^1.0.8" + "@types/json-schema" "^7.0.15" + "@webassemblyjs/ast" "^1.14.1" + "@webassemblyjs/wasm-edit" "^1.14.1" + "@webassemblyjs/wasm-parser" "^1.14.1" + acorn "^8.15.0" + acorn-import-phases "^1.0.3" + browserslist "^4.28.1" chrome-trace-event "^1.0.2" - enhanced-resolve "^5.17.1" - es-module-lexer "^1.2.1" + enhanced-resolve "^5.19.0" + es-module-lexer "^2.0.0" eslint-scope "5.1.1" events "^3.2.0" glob-to-regexp "^0.4.1" graceful-fs "^4.2.11" json-parse-even-better-errors "^2.3.1" - loader-runner "^4.2.0" + loader-runner "^4.3.1" mime-types "^2.1.27" neo-async "^2.6.2" - schema-utils "^3.2.0" - tapable "^2.1.1" - terser-webpack-plugin "^5.3.10" - watchpack "^2.4.1" - webpack-sources "^3.2.3" + schema-utils "^4.3.3" + tapable "^2.3.0" + terser-webpack-plugin "^5.3.16" + watchpack "^2.5.1" + webpack-sources "^3.3.3" webpackbar@^5.0.2: version "5.0.2"