diff --git a/.github/workflows/regression-test.yaml b/.github/workflows/regression-test.yaml
index d686a6f..1eeb528 100644
--- a/.github/workflows/regression-test.yaml
+++ b/.github/workflows/regression-test.yaml
@@ -69,3 +69,5 @@ jobs:
         run: >-
           nox -s regression_tests-${{ matrix.python-version }} --
           ${{ inputs.starting_revision }} ${{ inputs.end_revision }}
+        env:
+          FORCE_COLOR: 1
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 2e98831..7d47c39 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -28,6 +28,7 @@ jobs:
         uses: actions/checkout@v5
         with:
           fetch-depth: 0
+          submodules: recursive
 
       - name: Set up Python
         uses: actions/setup-python@v6
@@ -42,7 +43,29 @@ jobs:
           path: .nox
 
       - name: Install nox
-        run: python -m pip install nox
+        run: python -m pip install coverage[toml] nox
 
-      - name: Run regression test
-        run: nox -s benchmark-${{ matrix.python-version }} -- main
+      - name: Run tests with every array backend and generate coverage report
+        run: nox -s coverage-${{ matrix.python-version }}
+        env:
+          FORCE_COLOR: 1
+
+      - name: Generate coverage XML
+        run: coverage xml
+
+      - name: Run Coveralls in parallel
+        uses: coverallsapp/github-action@v2
+        with:
+          flag-name: run-${{ matrix.python-version }}
+          parallel: true
+
+  aggregate-tests:
+    needs: test
+    if: always()
+    runs-on: ubuntu-latest
+    steps:
+      - name: Coveralls finished
+        uses: coverallsapp/github-action@v2
+        with:
+          carryforward: run-3.10,run-3.11,run-3.12,run-3.13
+          parallel-finished: true
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5d63a46..f0f0c69 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -8,6 +8,7 @@ ci:
 default_language_version:
   python: python3.13
 
+exclude: ^glass
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v6.0.0
diff --git a/glass/.all-contributorsrc b/glass/.all-contributorsrc
new file mode 100644
index 0000000..b5146b9
--- /dev/null
+++ b/glass/.all-contributorsrc
@@ -0,0 +1,107 @@
+{
+  "commitConvention": "angular",
+  "commitType": "contributors",
+  "contributorsPerLine": 7,
+  "files": [
+    "docs/CONTRIBUTORS.md"
+  ],
+  "linkToUsage": false,
+  "projectName": "glass",
+  "projectOwner": "glass-dev",
+  "contributors": [
+    {
+      "login": "ntessore",
+      "name": "Nicolas Tessore",
+      "avatar_url": "https://avatars.githubusercontent.com/u/3993688?v=4",
+      "profile": "http://ntessore.page",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "paddyroddy",
+      "name": "Patrick J. Roddy",
+      "avatar_url": "https://avatars.githubusercontent.com/u/15052188?v=4",
+      "profile": "https://paddyroddy.github.io",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "Saransh-cpp",
+      "name": "Saransh Chopra",
+      "avatar_url": "https://avatars.githubusercontent.com/u/74055102?v=4",
+      "profile": "https://saransh-cpp.github.io/",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "ucapbba",
+      "name": "baugstein",
+      "avatar_url": "https://avatars.githubusercontent.com/u/87702063?v=4",
+      "profile": "https://github.com/ucapbba",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "arthurmloureiro",
+      "name": "Arthur Loureiro",
+      "avatar_url": "https://avatars.githubusercontent.com/u/6471279?v=4",
+      "profile": "http://arthurmloureiro.github.io",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "mwiet",
+      "name": "Maximilian von Wietersheim-Kramsta",
+      "avatar_url": "https://avatars.githubusercontent.com/u/49800039?v=4",
+      "profile": "https://mwiet.github.io",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "joachimi",
+      "name": "joachimi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/4989590?v=4",
+      "profile": "https://github.com/joachimi",
+      "contributions": [
+        "ideas",
+        "research"
+      ]
+    },
+    {
+      "login": "NiallJeffrey",
+      "name": "Niall Jeffrey",
+      "avatar_url": "https://avatars.githubusercontent.com/u/15345794?v=4",
+      "profile": "https://nialljeffrey.github.io/",
+      "contributions": [
+        "code",
+        "ideas"
+      ]
+    },
+    {
+      "login": "ARCHER2-HPC",
+      "name": "ARCHER2, UK National Supercomputing Service",
+      "avatar_url": "https://avatars.githubusercontent.com/u/60643641?v=4",
+      "profile": "https://github.com/ARCHER2-HPC",
+      "contributions": [
+        "financial"
+      ]
+    },
+    {
+      "login": "connoraird",
+      "name": "Connor Aird",
+      "avatar_url": "https://avatars.githubusercontent.com/u/61978554?v=4",
+      "profile": "https://github.com/connoraird",
+      "contributions": [
+        "code",
+        "review",
+        "maintenance"
+      ]
+    }
+  ]
+}
diff --git a/glass/.git-blame-ignore-revs b/glass/.git-blame-ignore-revs
new file mode 100644
index 0000000..e98ceab
--- /dev/null
+++ b/glass/.git-blame-ignore-revs
@@ -0,0 +1,6 @@
+# applied ruff-format - https://github.com/glass-dev/glass/pull/199
+b42067f6776dcd763827000d585a88e071b78af3
+# applied prettier - https://github.com/glass-dev/glass/pull/227
+f9bac62f497a7288aa71fd4dbd948945c37f854e
+# applied ruff-linting - https://github.com/glass-dev/glass/pull/232
+e58d8616c03b8447aba90996905a98b42f18ba0a
diff --git a/glass/.github/ISSUE_TEMPLATE/blank.yaml b/glass/.github/ISSUE_TEMPLATE/blank.yaml
new file mode 100644
index 0000000..9bf3502
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/blank.yaml
@@ -0,0 +1,11 @@
+---
+name: Anything Else
+description:
+  Create a blank issue for anything that does not fit into the other categories
+body:
+  - id: anything_else
+    type: textarea
+    attributes:
+      label: Add your issue here
+    validations:
+      required: true
diff --git a/glass/.github/ISSUE_TEMPLATE/bug_report.yaml b/glass/.github/ISSUE_TEMPLATE/bug_report.yaml
new file mode 100644
index 0000000..eea75cd
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/bug_report.yaml
@@ -0,0 +1,61 @@
+---
+name: Bug Report
+description: Create a Report to Help us Improve
+labels:
+  - bug
+body:
+  - id: describe
+    type: textarea
+    attributes:
+      label: Describe the Bug
+      description: A clear and concise description of what the bug is.
+    validations:
+      required: true
+  - id: reproduce
+    type: textarea
+    attributes:
+      label: To Reproduce
+      description: >-
+        A minimal working example of code to reproduce the unexpected behaviour,
+        this will render as `Python` code so no need for backticks.
+      value: >-
+        import glass
+
+        ...
+      render: python
+  - id: expected
+    type: textarea
+    attributes:
+      label: Expected Behaviour
+      description: >-
+        A clear and concise description of what you expected to happen.
+  - id: actual
+    type: textarea
+    attributes:
+      label: Actual Behaviour
+      description: >-
+        Be a specific and detailed as you can. Paste any output or stack traces
+        of errors you receive.
+  - id: version
+    type: input
+    attributes:
+      label: Version In Use
+      description: |-
+        Can be found by
+        ```sh
+        python -c "import glass; print(glass.__version__)"
+        ```
+        or
+        ```sh
+        nox -s version
+        ```
+    validations:
+      required: true
+  - id: additional
+    type: textarea
+    attributes:
+      label: Additional Context
+      value: |-
+        - Python version:
+        - Operating system:
+      render: markdown
diff --git a/glass/.github/ISSUE_TEMPLATE/config.yml b/glass/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000..6d50a38
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,12 @@
+---
+blank_issues_enabled: false
+contact_links:
+  - name: Raise a GLASS Discussion
+    url: https://github.com/orgs/glass-dev/discussions
+    about:
+      If you would like to start a discussion with the wider GLASS community
+      about e.g. a design decision or API change, you can use our Discussions
+      page.
+  - name: Join the GLASS Slack
+    url: https://glass-dev.github.io/slack
+    about: We have a public Slack workspace for discussions about the project.
diff --git a/glass/.github/ISSUE_TEMPLATE/documentation.yaml b/glass/.github/ISSUE_TEMPLATE/documentation.yaml
new file mode 100644
index 0000000..582c191
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/documentation.yaml
@@ -0,0 +1,27 @@
+---
+name: Documentation
+description: How Can We Improve the Documentation
+labels:
+  - documentation
+body:
+  - id: section
+    type: textarea
+    attributes:
+      label: Which Section of the Documentation Needs Improving?
+      description: Please provide a link (if it is a specific page).
+    validations:
+      required: true
+  - id: problem
+    type: textarea
+    attributes:
+      label: What Can be Improved About This Section
+      description: Is it incomplete, incorrect or difficult to understand?
+    validations:
+      required: true
+  - id: suggestions
+    type: textarea
+    attributes:
+      label: How to Improve This Section
+      description: >-
+        Do you have any specific suggestions we could take to improve the
+        documentation?
diff --git a/glass/.github/ISSUE_TEMPLATE/feature_request.yaml b/glass/.github/ISSUE_TEMPLATE/feature_request.yaml
new file mode 100644
index 0000000..de0b519
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/feature_request.yaml
@@ -0,0 +1,32 @@
+---
+name: Feature Request
+description: Suggest a Way to Improve This Project
+labels:
+  - enhancement
+body:
+  - id: problem
+    type: textarea
+    attributes:
+      label: Is Your Feature Request Related to a Problem? Please Describe
+      description: A clear and concise description of what the problem is.
+      placeholder: I'm always frustrated when [...]
+    validations:
+      required: true
+  - id: solution
+    type: textarea
+    attributes:
+      label: Describe the Solution You'd Like
+      description: A clear and concise description of what you want to happen.
+  - id: alternatives
+    type: textarea
+    attributes:
+      label: Describe Alternatives You've Considered
+      description: >-
+        A clear and concise description of any alternative solutions or features
+        you've considered.
+  - id: additional
+    type: textarea
+    attributes:
+      label: Additional Context
+      description: >-
+        Add any other context or screenshots about the feature request here.
diff --git a/glass/.github/ISSUE_TEMPLATE/question.yaml b/glass/.github/ISSUE_TEMPLATE/question.yaml
new file mode 100644
index 0000000..d553883
--- /dev/null
+++ b/glass/.github/ISSUE_TEMPLATE/question.yaml
@@ -0,0 +1,24 @@
+---
+name: Question
+description: General Questions About Using GLASS
+labels:
+  - question
+body:
+  - id: topic
+    type: dropdown
+    attributes:
+      label: What is the Topic of Your Question
+      description: Please indicate the topic in the title of your question.
+      options:
+        - Installation
+        - Usage
+        - Documentation
+        - Other
+    validations:
+      required: true
+  - id: question
+    type: textarea
+    attributes:
+      label: Add Your Question Below
+    validations:
+      required: true
diff --git a/glass/.github/PULL_REQUEST_TEMPLATE.md b/glass/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..1b44d1b
--- /dev/null
+++ b/glass/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,33 @@
+# Description
+
+<!-- describe you changes here
+make sure your PR title starts with "gh-XXX: " where XXX is the issue you are
+solving -->
+
+<!-- for user facing bugs -->
+<!-- Fixes: # (issue) -->
+
+<!-- for other issues -->
+<!-- Closes: # (issue) -->
+
+<!-- referring some issue -->
+<!-- Refs: # (issue) -->
+
+<!-- add a one liner changelog entry here if this PR makes any user-facing change
+## Changelog entry
+
+Added: Some new feature
+Changed: Some change in existing functionality
+Deprecated: Some soon-to-be removed feature
+Removed: Some now removed feature
+Fixed: Some bug fix
+Security: Some vulnerability was fixed
+-->
+
+## Checks
+
+- [ ] Is your code passing linting?
+- [ ] Is your code passing tests?
+- [ ] Have you added additional tests (if required)?
+- [ ] Have you modified/extended the documentation (if required)?
+- [ ] Have you added a one-liner changelog entry above (if required)?
diff --git a/glass/.github/dependabot.yml b/glass/.github/dependabot.yml
new file mode 100644
index 0000000..caff9f8
--- /dev/null
+++ b/glass/.github/dependabot.yml
@@ -0,0 +1,9 @@
+---
+version: 2
+updates:
+  - package-ecosystem: github-actions
+    directory: /
+    schedule:
+      interval: weekly
+    commit-message:
+      prefix: dependabot
diff --git a/glass/.github/pr-title-checker-config.json b/glass/.github/pr-title-checker-config.json
new file mode 100644
index 0000000..72cf139
--- /dev/null
+++ b/glass/.github/pr-title-checker-config.json
@@ -0,0 +1,13 @@
+{
+  "LABEL": {
+    "name": ""
+  },
+  "CHECKS": {
+    "regexp": "gh-(\\d+):\\s"
+  },
+  "MESSAGES": {
+    "success": "All OK",
+    "failure": "Failing CI test",
+    "notice": ""
+  }
+}
diff --git a/glass/.github/release_notes.sh b/glass/.github/release_notes.sh
new file mode 100755
index 0000000..486db80
--- /dev/null
+++ b/glass/.github/release_notes.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+# This script generates release notes by extracting changelog entries
+# from merged PRs since the latest release tag. These should be placed
+# in docs/manual/releases.rst and tidied up before publishing a new release.
+
+# Get the latest release tag and date
+LATEST_TAG=$(gh release view --json tagName -q .tagName)
+RELEASE_DATE=$(git log "$LATEST_TAG" -1 --format=%cI)
+
+echo ""
+echo "=========================================================="
+echo "Checking PRs merged after $RELEASE_DATE (tag: $LATEST_TAG)"
+echo ""
+
+# Get all merged PRs since the release date
+gh pr list --state merged --limit 100 --json number,mergedAt,body,title | \
+jq -r --arg date "$RELEASE_DATE" '
+  .[] |
+  select(.mergedAt > $date) |
+  "\(.number)"
+' | \
+while read -r pr_number; do
+  # Get PR details
+  pr_data=$(gh pr view "$pr_number" --json body,title)
+  title=$(echo "$pr_data" | jq -r .title)
+  body=$(echo "$pr_data" | jq -r .body)
+
+  # Extract the changelog section and remove HTML comments (including multiline)
+  changelog_entries=$(echo "$body" | \
+    sed -n '/## Changelog entry/,/^## /p' | \
+    sed '/<!--/,/-->/d' | \
+    sed '/^## Changelog entry/d' | \
+    sed '/^## /d' | \
+    sed '/^$/d' | \
+    grep -v '^[[:space:]]*$')
+
+  # Skip if it contains the template text (unchanged template)
+  if echo "$changelog_entries" | grep -q "Added: Some new feature"; then
+    continue
+  fi
+
+  # Only output if we found actual changelog entries
+  if [ -n "$changelog_entries" ]; then
+    echo "=== PR #$pr_number: $title ==="
+    echo "$changelog_entries" | awk '{print "  " $0}'
+    echo ""
+  fi
+done
diff --git a/glass/.github/test-constraints.txt b/glass/.github/test-constraints.txt
new file mode 100644
index 0000000..d3c772e
--- /dev/null
+++ b/glass/.github/test-constraints.txt
@@ -0,0 +1,10 @@
+numpy
+--only-binary numpy
+
+healpy
+--only-binary healpy
+
+healpix
+--only-binary healpix
+
+--prefer-binary
diff --git a/glass/.github/workflows/pull_request.yml b/glass/.github/workflows/pull_request.yml
new file mode 100644
index 0000000..faf30b3
--- /dev/null
+++ b/glass/.github/workflows/pull_request.yml
@@ -0,0 +1,25 @@
+---
+name: PR Title Checker
+
+on:
+  pull_request_target:
+    types:
+      - edited
+      - labeled
+      - opened
+      - synchronize
+      - unlabeled
+
+jobs:
+  check-pr-title:
+    if: >-
+      github.event.pull_request.user.login != 'allcontributors[bot]' &&
+      github.event.pull_request.user.login != 'dependabot[bot]' &&
+      github.event.pull_request.user.login != 'pre-commit-ci[bot]'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Enforce PR title format
+        uses: thehanimo/pr-title-checker@v1.4.3
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          pass_on_octokit_error: false
diff --git a/glass/.github/workflows/regression-test.yml b/glass/.github/workflows/regression-test.yml
new file mode 100644
index 0000000..f9b3d3f
--- /dev/null
+++ b/glass/.github/workflows/regression-test.yml
@@ -0,0 +1,19 @@
+---
+name: Regression tests
+
+on:
+  pull_request:
+    branches:
+      - main
+    types:
+      - opened
+      - ready_for_review
+      - reopened
+      - synchronize
+
+jobs:
+  regression-tests:
+    uses: glass-dev/glass-benchmarks/.github/workflows/regression-test.yaml@v0
+    with:
+      starting_revision: ${{ github.event.pull_request.base.ref }}
+      end_revision: ${{ github.event.pull_request.head.ref }}
diff --git a/glass/.github/workflows/release.yml b/glass/.github/workflows/release.yml
new file mode 100644
index 0000000..0624f80
--- /dev/null
+++ b/glass/.github/workflows/release.yml
@@ -0,0 +1,86 @@
+---
+name: Release
+
+on:
+  release:
+    types:
+      - published
+  workflow_dispatch:
+
+jobs:
+  dist:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      id-token: write
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v5
+
+      - name: Replace stable version with the release version
+        if: github.event_name == 'release' && github.event.action == 'published'
+        run: |-
+          # store the release tag
+          tag="${{ github.ref_name }}"
+
+          # regex patterns
+          badge_pattern="s#(readthedocs\.org/projects/glass/badge/\?version=)stable#\1${tag}#g"
+          url_pattern="s#(glass\.readthedocs\.io.*?)/stable#\1/${tag}#g"
+
+          # perform the replacements
+          sed --in-place --regexp-extended \
+            --expression "$badge_pattern" \
+            --expression "$url_pattern" \
+            ${{ github.workspace }}/README.md
+
+          # store the tag since the git checkout is now dirty
+          echo "SETUPTOOLS_SCM_PRETEND_VERSION=${tag}" >> "$GITHUB_ENV"
+
+      - uses: hynek/build-and-inspect-python-package@v2
+
+  publish-pypi:
+    if: >-
+      github.event_name == 'release' && github.event.action == 'published'
+    needs: dist
+    runs-on: ubuntu-latest
+    environment:
+      name: publish
+      url: https://pypi.org/project/glass
+    permissions:
+      id-token: write
+    steps:
+      - name: Download distributions
+        uses: actions/download-artifact@v6
+        with:
+          name: Packages
+          path: dist
+
+      - name: List distributions to be deployed
+        run: ls -l dist/
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+
+  publish-test-pypi:
+    if: github.event_name == 'workflow_dispatch'
+    needs: dist
+    runs-on: ubuntu-latest
+    environment:
+      name: test-publish
+      url: https://test.pypi.org/project/glass
+    permissions:
+      id-token: write
+    steps:
+      - name: Download distributions
+        uses: actions/download-artifact@v6
+        with:
+          name: Packages
+          path: dist
+
+      - name: List distributions to be deployed
+        run: ls -l dist/
+
+      - name: Publish to TestPyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/
diff --git a/glass/.github/workflows/run-examples.yml b/glass/.github/workflows/run-examples.yml
new file mode 100644
index 0000000..07819d3
--- /dev/null
+++ b/glass/.github/workflows/run-examples.yml
@@ -0,0 +1,54 @@
+---
+name: Run examples (reusable workflow)
+
+on:
+  workflow_call:
+
+concurrency:
+  cancel-in-progress: true
+  group: >-
+    ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+
+jobs:
+  run-examples:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          cache-dependency-path: pyproject.toml
+          cache: pip
+          python-version: 3.13
+
+      - name: Cache nox
+        uses: actions/cache@v4
+        with:
+          key:
+            examples-${{ hashFiles('pyproject.toml') }}-${{
+            hashFiles('noxfile.py') }}-${{ env.pythonLocation }}
+          path: .nox
+
+      - name: Install nox
+        run: python -m pip install nox
+
+      - name: Run examples
+        run: nox -s examples
+        timeout-minutes: 20
+        env:
+          FORCE_COLOR: 1
+
+      - name: Generate HTML
+        run: nox -s examples -- html
+        env:
+          FORCE_COLOR: 1
+
+      - name: Upload artefact
+        uses: actions/upload-artifact@v5
+        with:
+          name: Examples
+          path: examples/**/*.html
diff --git a/glass/.github/workflows/test-examples-on-label.yml b/glass/.github/workflows/test-examples-on-label.yml
new file mode 100644
index 0000000..91cdd39
--- /dev/null
+++ b/glass/.github/workflows/test-examples-on-label.yml
@@ -0,0 +1,17 @@
+---
+name: Test examples
+
+on:
+  pull_request:
+    types:
+      - labeled
+      - opened
+      - reopened
+      - synchronize
+      - unlabeled
+
+jobs:
+  test-examples-on-label:
+    if: contains(github.event.pull_request.labels.*.name, 'run-examples')
+    name: On label
+    uses: ./.github/workflows/run-examples.yml
diff --git a/glass/.github/workflows/test-examples-on-push.yml b/glass/.github/workflows/test-examples-on-push.yml
new file mode 100644
index 0000000..5fd3f2e
--- /dev/null
+++ b/glass/.github/workflows/test-examples-on-push.yml
@@ -0,0 +1,26 @@
+---
+name: Test examples
+
+on:
+  push:
+    paths:
+      - glass/**
+      - examples/**
+    branches:
+      - main
+  pull_request:
+    paths:
+      - .github/workflows/run-examples.yml
+      - examples/**
+      - glass/**
+    types:
+      - opened
+      - ready_for_review
+      - reopened
+      - synchronize
+
+jobs:
+  test-examples-on-push:
+    if: github.event.pull_request.draft == false
+    name: On push
+    uses: ./.github/workflows/run-examples.yml
diff --git a/glass/.github/workflows/test.yml b/glass/.github/workflows/test.yml
new file mode 100644
index 0000000..68bccd9
--- /dev/null
+++ b/glass/.github/workflows/test.yml
@@ -0,0 +1,146 @@
+---
+name: Test
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    types:
+      - opened
+      - ready_for_review
+      - reopened
+      - synchronize
+
+concurrency:
+  cancel-in-progress: true
+  group: >-
+    ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+
+jobs:
+  tests:
+    if: github.event.pull_request.draft == false
+    name: Tests
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.10"
+          - "3.11"
+          - "3.12"
+          - "3.13"
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          cache-dependency-path: pyproject.toml
+          cache: pip
+          python-version: ${{ matrix.python-version }}
+
+      - name: Cache nox
+        uses: actions/cache@v4
+        with:
+          key:
+            tests-${{ hashFiles('pyproject.toml') }}-${{ hashFiles('noxfile.py')
+            }}-${{ env.pythonLocation }}
+          path: .nox
+
+      - name: Install nox and coverage.py
+        run: python -m pip install coverage[toml] nox
+
+      - name: Install ubuntu dependencies for fitsio
+        run: sudo apt-get install -y libbz2-dev
+
+      - name: Check version
+        run: nox -s version
+        env:
+          FORCE_COLOR: 1
+
+      - name: Run doctests
+        run: nox -s doctests-${{ matrix.python-version }} --verbose
+        env:
+          FORCE_COLOR: 1
+
+      - name: Run tests with every array backend and generate coverage report
+        run: nox -s coverage-${{ matrix.python-version }} --verbose
+        env:
+          FORCE_COLOR: 1
+          ARRAY_BACKEND: all
+
+      - name: Coveralls requires XML report
+        run: coverage xml
+
+      - name: Run Coveralls in parallel
+        uses: coverallsapp/github-action@v2
+        with:
+          flag-name: run-${{ matrix.python-version }}
+          parallel: true
+          base-path: glass/
+
+  aggregate-tests:
+    needs: tests
+    if: always()
+    runs-on: ubuntu-latest
+    steps:
+      - name: Coveralls finished
+        uses: coverallsapp/github-action@v2
+        with:
+          carryforward: run-3.10,run-3.11,run-3.12,run-3.13
+          parallel-finished: true
+
+  build:
+    if: github.event.pull_request.draft == false
+    name: Build
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          cache-dependency-path: pyproject.toml
+          cache: pip
+          python-version: 3.13
+
+      - name: Install nox
+        run: python -m pip install nox
+
+      - name: Build SDist and wheel
+        run: nox -s build --verbose
+        env:
+          FORCE_COLOR: 1
+
+  docs:
+    if: github.event.pull_request.draft == false
+    name: Docs
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+
+      - uses: actions/setup-python@v6
+        with:
+          cache-dependency-path: pyproject.toml
+          cache: pip
+          python-version: 3.13
+
+      - name: Install dependencies
+        run: |-
+          sudo apt-get update
+          sudo apt-get install -y pandoc
+          python -m pip install nox
+
+      - name: Build docs
+        run: nox -s docs --verbose
+        env:
+          FORCE_COLOR: 1
diff --git a/glass/.gitignore b/glass/.gitignore
new file mode 100644
index 0000000..931ccb8
--- /dev/null
+++ b/glass/.gitignore
@@ -0,0 +1,15 @@
+glass/_version.py
+
+.*.swp
+.DS_Store
+__pycache__
+_build
+build
+dist
+.env
+.coverage*
+coverage*
+*.html
+.ipynb_checkpoints
+.jupyter
+.*_cache
diff --git a/glass/.matplotlibrc b/glass/.matplotlibrc
new file mode 100644
index 0000000..f8ab379
--- /dev/null
+++ b/glass/.matplotlibrc
@@ -0,0 +1,6 @@
+backend: agg
+figure.dpi: 100
+font.family: DejaVu Sans
+legend.frameon: False
+legend.fontsize: small
+savefig.dpi: 100
diff --git a/glass/.pre-commit-config.yaml b/glass/.pre-commit-config.yaml
new file mode 100644
index 0000000..fe575ef
--- /dev/null
+++ b/glass/.pre-commit-config.yaml
@@ -0,0 +1,98 @@
+---
+ci:
+  autofix_commit_msg: "pre-commit.ci: style fixes"
+  autofix_prs: false
+  autoupdate_commit_msg: "pre-commit.ci: update pre-commit hooks"
+  autoupdate_schedule: monthly
+
+default_language_version:
+  python: python3.13
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      - id: check-added-large-files
+        args:
+          - --enforce-all
+        exclude: |-
+          (?x)^(
+            docs/_static/.*\.png|
+            examples/.*\.ipynb
+          )$
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: check-symlinks
+      - id: check-yaml
+      - id: debug-statements
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args:
+          - --fix=lf
+      - id: trailing-whitespace
+  - repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.10.0
+    hooks:
+      - id: python-check-blanket-type-ignore
+      - id: rst-backticks
+      - id: rst-directive-colons
+      - id: rst-inline-touching-normal
+  - repo: https://github.com/adrienverge/yamllint
+    rev: v1.37.1
+    hooks:
+      - id: yamllint
+        args:
+          - --strict
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.3
+    hooks:
+      - id: ruff-check
+      - id: ruff-format
+  - repo: https://github.com/adamchainz/blacken-docs
+    rev: 1.20.0
+    hooks:
+      - id: blacken-docs
+        additional_dependencies:
+          - black
+  - repo: https://github.com/pappasam/toml-sort
+    rev: v0.24.3
+    hooks:
+      - id: toml-sort-fix
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.6.2
+    hooks:
+      - id: prettier
+  - repo: https://github.com/igorshubovych/markdownlint-cli
+    rev: v0.45.0
+    hooks:
+      - id: markdownlint-fix
+        args:
+          - --dot
+  - repo: https://github.com/Lucas-C/pre-commit-hooks
+    rev: v1.5.5
+    hooks:
+      - id: forbid-tabs
+  - repo: https://github.com/rhysd/actionlint
+    rev: v1.7.8
+    hooks:
+      - id: actionlint
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.18.2
+    hooks:
+      - id: mypy
+        additional_dependencies:
+          - numpy~=2.3.4
+          - jaxtyping~=0.3.3
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.8.1
+    hooks:
+      - id: nbstripout
+        args:
+          - --keep-output
+  - repo: https://github.com/shellcheck-py/shellcheck-py
+    rev: v0.11.0.1
+    hooks:
+      - id: shellcheck
+        args:
+          - --external-sources
+          - --shell=bash
diff --git a/glass/.prettierignore b/glass/.prettierignore
new file mode 100644
index 0000000..7cfa7f6
--- /dev/null
+++ b/glass/.prettierignore
@@ -0,0 +1,2 @@
+.all-contributorsrc
+docs/CONTRIBUTORS.md
diff --git a/glass/.prettierrc.yaml b/glass/.prettierrc.yaml
new file mode 100644
index 0000000..3204891
--- /dev/null
+++ b/glass/.prettierrc.yaml
@@ -0,0 +1,3 @@
+---
+proseWrap: always
+quoteProps: as-needed
diff --git a/glass/.readthedocs.yml b/glass/.readthedocs.yml
new file mode 100644
index 0000000..3c7f439
--- /dev/null
+++ b/glass/.readthedocs.yml
@@ -0,0 +1,15 @@
+---
+version: 2
+
+build:
+  os: ubuntu-24.04
+  tools:
+    python: "3.13"
+  jobs:
+    pre_install:
+      - python -m pip install --upgrade pip
+    install:
+      - python -m pip install -e . --group docs
+
+sphinx:
+  configuration: docs/conf.py
diff --git a/glass/.yamllint.yaml b/glass/.yamllint.yaml
new file mode 100644
index 0000000..3a0c657
--- /dev/null
+++ b/glass/.yamllint.yaml
@@ -0,0 +1,33 @@
+---
+extends: default
+
+rules:
+  anchors: enable
+  braces:
+    forbid: non-empty
+  brackets:
+    forbid: non-empty
+  colons: enable
+  commas: enable
+  comments:
+    min-spaces-from-content: 1
+  comments-indentation: enable
+  document-end: disable
+  document-start: enable
+  empty-lines: enable
+  empty-values: disable
+  float-values: enable
+  hyphens: enable
+  indentation: enable
+  key-duplicates: enable
+  key-ordering: disable
+  line-length: enable
+  new-line-at-end-of-file: enable
+  new-lines: enable
+  octal-values: enable
+  quoted-strings:
+    quote-type: double
+    required: only-when-needed
+  trailing-spaces: enable
+  truthy:
+    check-keys: false
diff --git a/glass/CHANGELOG.md b/glass/CHANGELOG.md
new file mode 100644
index 0000000..bce4d5b
--- /dev/null
+++ b/glass/CHANGELOG.md
@@ -0,0 +1,214 @@
+<!-- markdownlint-disable MD024 -->
+
+# Changelog
+
+All functional changes to the project are documented in this file.
+
+## [2025.2] (21 Oct 2025)
+
+- gh-578: mutable argument should be empty list rather than `None` (#579)
+- gh-552: add docs on all-contributors (#565)
+- gh-573: add keyword `rng` to functions in notebooks (#574)
+- gh-583: fix flakiness of `test_generate` (#584)
+- gh-575: bring back inv_triangle_number (#577)
+- gh-572: make `multalm` private (#576)
+- gh-487: reorder alms in \_generate_grf to glass ordering (#533)
+- gh-568: convert `_weight` funcs to classes for `_windows` funcs (#589)
+- gh-495: allow `zeff` to be computed automatically (#590)
+- gh-566: add example on galaxy redshift distributions (#567)
+- gh-616: use `position_weights()` in galaxy redshift example (#617)
+- gh-124: use `cosmology.api` over the `cosmology` package (#537)
+- gh-621: bump `array-api-strict` version to `2024.12` (#625)
+- gh-639: fix compute_gaussian_spectra() for empty spectra (#640)
+- gh-652: make `array-api-strict` an official dependency (#653)
+- gh-407: Port functions in fields.py (#642)
+- gh-633: switch to `dependency-groups` (#634)
+- gh-408: porting straightforward functions in `shells` (#643)
+- gh-667: Make JAX an optional dependency as intended (#672)
+- gh-409: Port straightforward functions in `points` (#663)
+- gh-691: rename envvar to `ARRAY_BACKEND` (#692)
+- gh-693: Add script to generate release notes (#694)
+
+## [2025.1] (21 Feb 2025)
+
+- gh-163: drop `Python 3.9` & `NumPy < 2.1.1` support (#391)
+- gh-368: add type overloading for `lensing.from_convergence` (#395)
+- gh-384: rename all internal functions to `trapezoid` over `trapz` (#392)
+- gh-440: remove scipy as a test dependency (#462)
+- gh-443: `if` should be `elif` in `fixed_zbins` (#444)
+- gh-445: place all public function in `__all__` (#446)
+- gh-465: enable installing glass through git archives (#483)
+- gh-470: instructions to install using conda (#481)
+- gh-478: `glass.core.array` -> `glass.arraytools` (#524)
+- gh-493: add module for Gaussian random fields (#494)
+- gh-496: functions for legacy mode (#497)
+- gh-501: change imports to be directly from `glass` (#512)
+- gh-504: deprecate old generator functions (#523)
+- gh-508: create standalone bibliography page (#518)
+- gh-509: `inv_triangle_number` -> `nfields_from_nspectra` (plus make it public)
+  (#527)
+- gh-513: make algorithm a public module (#514)
+
+## [2024.2] (15 Nov 2024)
+
+- gh-188: add docstrings to all functions and tidy docs (#381)
+- gh-336: support Python 3.13 (#337)
+- gh-358: add static types support (#368)
+- gh-131: rename `gaussian_gls` to `discretized_cls` (#345)
+- gh-328: efficient resampling in `ellipticity_ryden04` (#341)
+- gh-137: deprecate `redshifts_from_nz` in favor of `redshifts` (#333)
+- gh-328: fix shape mismatch bug in ellipticity_ryden04 (#332)
+- gh-315: add broadcasting rule in ellipticity_ryden04 + tests (#317)
+- gh-198: enforce `python>3.8` & `numpy>1.21` (#326)
+- gh-260: remove glass.core.constants (#261)
+- gh-107: add all public functions/classes under glass namespace (#221)
+- gh-168: move examples into repository (#169)
+- gh-156: add FITS catalogue writer tool (#158)
+
+## [2024.1] (16 Jul 2024)
+
+### Added
+
+- A new function `combine()` that evaluates the linear combination of radial
+  window functions with given weights.
+- A new function `effective_cls()` which combines power spectra using a list of
+  weights, which models what happens in the simulation.
+- A new function `position_weights()` that returns weights for `effective_cls()`
+  to model the result of `positions_from_delta()`.
+- A new function `multi_plane_weights()` that returns weights for
+  `effective_cls()` to model the result of `MultiPlaneConvergence`.
+- The `glass.core.algorithm` module.
+- The new `partition(method="nnls")` function computes a partition with
+  non-negative contributions for each shell.
+- Function `redshifts()` to sample redshifts following a radial window function.
+
+### Changed
+
+- The default method for `partition()` is now `"nnls"`.
+- Both `partition(method="nnls")` and `partition(method="lstsq")` now have an
+  additional integral constraint so that the sum of the partition recovers the
+  integral of the input function.
+- The output of `partition()` now has the shells axis as its first.
+
+### Fixed
+
+- Now uses the updated intersphinx URL for the GLASS examples.
+- A bug in `effective_cls()` that caused arrays to be one entry too long if
+  `lmax` was not given explicitly.
+- A bug in `partition()` with the default method.
+- `partition()` now works correctly with functions having extra axes.
+
+## [2023.7] (1 Aug 2023)
+
+### Added
+
+- Function `getcl()` to return angular power spectra by index from a list using
+  GLASS ordering.
+- New `linear_windows()` and `cubic_windows()` window functions for shells.
+
+### Changed
+
+- The `gaussian_phz()` function now accepts bounds using `lower=` and `upper=`
+  keyword parameters.
+- The `partition()` function now returns an array of weights to approximate the
+  given function by the windows.
+
+## [2023.6] (30 Jun 2023)
+
+### Added
+
+- `deflect()` applies deflections to positions
+- `from_convergence()` returns other lensing fields given the convergence
+- A new `glass.ext` namespace, reserved for extensions
+
+### Changed
+
+- The `glass` module is no longer a namespace package
+- The point sampling functions `positions_from_delta()` and
+  `uniform_positions()` now return an iterator
+- `ellipticity_gaussian()` and `ellipticity_intnorm()` accept array inputs
+- Use pyproject.toml for packaging
+
+### Deprecated
+
+- `shear_from_convergence()` is deprecated in favour of `from_convergence()`
+
+### Removed
+
+- The `glass.all` meta-module is no longer necessary
+
+### Fixed
+
+- Incorrect extrapolation in `glass.core.array.trapz_product()`, causing a bug
+  in `glass.points.effective_bias()`
+
+## [2023.5] (31 May 2023)
+
+### Added
+
+- Allow dimensional input to the sampling functions in `glass.points` (#80)
+- The `redshifts_from_nz()` function supports `count` arrays (#83)
+
+### Changed
+
+- Position sampling returns counts alongside points (#80)
+- `redshifts_from_nz()` no longer returns `gal_pop` (#83)
+- Move core functionality that is used by other, user-facing modules into the
+  `glass.core` module (#88)
+
+### Removed
+
+- Remove profiling functions (#89)
+
+## [2023.2] - 1 Mar 2023
+
+### Added
+
+- The `glass.lensing.MultiPlaneConvergence.add_window` method to add a
+  convergence plane given by a window function.
+- The `glass.shells` module for shell definitions.
+- User functions to save and load Cls
+- This changelog added to keep track of changes between versions
+
+### Changed
+
+- Instead of an array of shell boundaries and `MatterWeights`, the shells are
+  entirely defined by a `RadialWindow` window function.
+- The `glass.lensing.multi_plane_matrix` function now takes a sequence of window
+  functions.
+- The arguments of `glass.lensing.MultiPlaneConvergence.add_plane` are called
+  `zsrc` and `wlens` instead of the more ambiguous `z` and `w`. The properties
+  `MultiPlaneConvergence.z` and `MultiPlaneConvergence.w` that return these
+  values are similarly changed.
+- The `glass.points.effective_bias` now takes a single window function as input
+  and computes its effective bias parameter.
+- Some type hints added to library functions
+
+### Removed
+
+- The `glass.lensing.multi_plane_weights` function, replaced by the
+  `glass.lensing.MultiPlaneConvergence.add_window` method.
+- The `glass.galaxies.constant_densities` and `density_from_dndz` functions,
+  since densities can now easily be partitioned by window functions for shells.
+- The `zmin, zmax` parameters of `glass.galaxies.redshifts_from_nz`, for the
+  same reason.
+- The `glass.math.restrict_interval` function, as shells are now defined by
+  window functions instead of sharp intervals.
+- The `glass.matter` module, in favour of the more appropriately-named
+  `glass.shells` module.
+
+## [2023.1] - 31 Jan 2023
+
+### Added
+
+- Initial wide release for GLASS paper
+
+[2025.2]: https://github.com/glass-dev/glass/compare/v2025.1...v2025.2
+[2025.1]: https://github.com/glass-dev/glass/compare/v2024.2...v2025.1
+[2024.2]: https://github.com/glass-dev/glass/compare/v2024.1...v2024.2
+[2024.1]: https://github.com/glass-dev/glass/compare/v2023.7...v2024.1
+[2023.7]: https://github.com/glass-dev/glass/compare/v2023.6...v2023.7
+[2023.6]: https://github.com/glass-dev/glass/compare/v2023.5...v2023.6
+[2023.5]: https://github.com/glass-dev/glass/compare/v2023.2...v2023.5
+[2023.2]: https://github.com/glass-dev/glass/compare/v2023.1...v2023.2
+[2023.1]: https://github.com/glass-dev/glass/releases/tag/v2023.1
diff --git a/glass/CITATION.cff b/glass/CITATION.cff
new file mode 100644
index 0000000..64abe68
--- /dev/null
+++ b/glass/CITATION.cff
@@ -0,0 +1,40 @@
+cff-version: 1.2.0
+title: "GLASS: Generator for Large Scale Structure"
+message: >-
+  If you use GLASS in your research, please cite the associated paper.
+repository-code: "https://github.com/glass-dev/glass"
+url: "https://glass.readthedocs.io"
+license: MIT
+preferred-citation:
+  authors:
+    - given-names: Nicolas
+      family-names: Tessore
+      orcid: https://orcid.org/0000-0002-9696-7931
+    - given-names: Arthur
+      family-names: Loureiro
+      orcid: https://orcid.org/0000-0002-4371-0876
+    - given-names: Benjamin
+      family-names: Joachimi
+      orcid: https://orcid.org/0000-0001-7494-1303
+    - given-names: Maximilian
+      family-names: von Wietersheim-Kramsta
+      orcid: https://orcid.org/0000-0003-4986-5091
+    - given-names: Niall
+      family-names: Jeffrey
+      orcid: https://orcid.org/0000-0003-2927-1800
+  date-published: 2023-03
+  issn: 2475-9066
+  issue: 84
+  journal: The Open Journal of Astrophysics
+  title: "GLASS: Generator for Large Scale Structure"
+  type: article
+  doi: 10.21105/astro.2302.01942
+  volume: 6
+  eid: 11
+  pages: 11
+  eprint: 2302.01942
+  primaryClass: astro-ph.CO
+  keywords:
+    - Astrophysics
+    - Cosmology
+    - Nongalactic Astrophysics
diff --git a/glass/CODE_OF_CONDUCT.md b/glass/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..ae4ad82
--- /dev/null
+++ b/glass/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity and
+orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behaviour that contributes to a positive environment for our
+community include:
+
+- Demonstrating empathy and kindness toward other people
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Accepting responsibility and apologising to those affected by our mistakes,
+  and learning from the experience
+
+- Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behaviour include:
+
+- The use of sexualised language or imagery, and sexual attention or advances of
+  any kind
+- Trolling, insulting or derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behaviour and will take appropriate and fair corrective action in
+response to any behaviour that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behaviour may be
+reported to the project maintainers. All complaints will be reviewed and
+investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behaviour deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behaviour was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behaviour. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behaviour.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behaviour, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+<https://www.contributor-covenant.org/version/2/0/code_of_conduct.html>.
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+<https://www.contributor-covenant.org/faq>. Translations are available at
+<https://www.contributor-covenant.org/translations>.
diff --git a/glass/CONTRIBUTING.md b/glass/CONTRIBUTING.md
new file mode 100644
index 0000000..d49de2f
--- /dev/null
+++ b/glass/CONTRIBUTING.md
@@ -0,0 +1,346 @@
+# Contributing to GLASS
+
+If you are planning to develop _GLASS_, or want to use the latest commit of
+_GLASS_ on your local machine, you might want to install it from the source.
+This installation is not recommended for users who want to use the stable
+version of _GLASS_. The page below describes how to build, test, and develop
+_GLASS_.
+
+## Installation
+
+The developer installation of _GLASS_ comes with several optional dependencies -
+
+- `test`: installs extra packages used in tests and the relevant testing
+  framework/plugins
+- `docs`: installs documentation related dependencies
+- `examples`: installs libraries used in the examples and a few notebook related
+  dependencies
+
+These options can be used with `pip` with the editable (`-e`) mode of
+installation in the following way -
+
+```bash
+pip install -e ".[examples]" --group docs --group test
+```
+
+## Tooling
+
+### Pre-commit
+
+_GLASS_ uses a set of `pre-commit` hooks and the `pre-commit.ci` bot to format,
+lint, and prettify the codebase. The hooks can be installed locally using -
+
+```bash
+pre-commit install
+```
+
+This would run the checks every time a commit is created locally. The checks
+will only run on the files modified by that commit, but the checks can be
+triggered for all the files using -
+
+```bash
+pre-commit run --all-files
+```
+
+If you would like to skip the failing checks and push the code for further
+discussion, use the `--no-verify` option with `git commit`.
+
+## Testing
+
+_GLASS_ is tested using `pytest` and `pytest-doctestplus`. `pytest` is
+responsible for testing the code, whose configuration is available in
+[pyproject.toml](https://github.com/glass-dev/glass/blob/main/pyproject.toml).
+`pytest-doctestplus` is responsible for testing the examples available in every
+docstring, which prevents them from going stale. Additionally, _GLASS_ also uses
+`pytest-cov` (and [Coveralls](https://coveralls.io)) to calculate/display the
+coverage of these unit tests.
+
+### Running tests locally
+
+The tests can be executed using the `test` dependencies of _GLASS_ in the
+following way -
+
+```bash
+python -m pytest --cov --doctest-plus
+```
+
+### Array API tests
+
+One can specify a particular array backend for testing by setting the
+`ARRAY_BACKEND` environment variable. The default array backend is NumPy.
+_GLASS_ can be tested with every supported array library available in the
+environment by setting `ARRAY_BACKEND` to `all`. The testing framework only
+installs NumPy automatically; hence, remaining array libraries should either be
+installed manually or developers should use `Nox`.
+
+```bash
+# run tests using numpy
+python -m pytest
+ARRAY_BACKEND=numpy python -m pytest
+# run tests using array_api_strict (should be installed manually)
+ARRAY_BACKEND=array_api_strict python -m pytest
+# run tests using jax (should be installed manually)
+ARRAY_BACKEND=jax python -m pytest
+# run tests using every supported array library available in the environment
+ARRAY_BACKEND=all python -m pytest
+```
+
+Moreover, one can test if a function is compatible with the array API standard
+by using the `xp` fixture in tests. Further, the corresponding RNG `Generator`
+can be accessed using the `urng` (a unified RNG interface) fixture. This will
+`parametrize` the test to run on every array library specified through
+`ARRAY_BACKEND` -
+
+```python
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import types
+
+    from glass._types import UnifiedGenerator
+
+
+def test_something(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    # use `xp.` to access the array library functionality
+    # use `urng.` to access the corresponding rng functionality
+    ...
+```
+
+## Documenting
+
+_GLASS_'s documentation is mainly written in the form of
+[docstrings](https://peps.python.org/pep-0257) and
+[reStructurredText](https://docutils.sourceforge.io/docs/user/rst/quickref.html).
+The docstrings include the description, arguments, examples, return values, and
+attributes of a class or a function, and the `.rst` files enable us to render
+this documentation on _GLASS_'s documentation website.
+
+_GLASS_ primarily uses [Sphinx](https://www.sphinx-doc.org/en/master/) for
+rendering documentation on its website. The configuration file (`conf.py`) for
+`sphinx` can be found
+[under the `docs` folder](https://github.com/glass-dev/glass/blob/main/docs/conf.py).
+The [documentation is deployed](https://glass.readthedocs.io/latest/) on
+<https://readthedocs.io>.
+
+Ideally, with the addition of every new feature to _GLASS_, documentation should
+be added using comments, docstrings, and `.rst` files.
+
+### Building documentation locally
+
+The documentation is located in the `docs` folder of the main repository. This
+documentation can be generated using the `docs` dependencies of _GLASS_ in the
+following way -
+
+```bash
+cd docs/
+make clean
+make html
+```
+
+The commands executed above will clean any existing documentation build and
+create a new build under the `docs/_build` folder. You can view this build in
+any browser by opening the `index.html` file.
+
+## Releases
+
+To release a new version of _GLASS_, there should be a commit that includes the
+following changes:
+
+- The changes since the last release are added to the [changelog](CHANGELOG.md)
+  under a section titled `[yyyy.mm] (DD Mon YYYY)`. A new link to the changeset
+  is added at the bottom of the file.
+- The [release notes](docs/manual/releases.rst) are updated with the new
+  version. The release notes should translate the changelog entries into prose
+  that can be understood by non-developer users of the code. If there are
+  breaking changes, a release note should explain what the changes mean for
+  existing code. There exists a script that helps generate an initial
+  [draft of the release notes](.github/release_notes.sh) from the changelog
+  entries. We recommend running something like
+  `bash .github/release_notes.sh | tee -a docs/manual/releases.rst` to generate
+  the draft and then editing it to make it more user-friendly.
+
+Once these changes are merged into the `main` branch, a new release with title
+`glass yyyy.mm` should be created in the GitHub repository. The description of
+the release should be a copy of its release note.
+
+Creating the release will automatically start the build process that uploads
+Python packages for the new version to PyPI.
+
+If any _GLASS_ extension packages depend on the new release, new versions of
+these packages should be produced as soon as the new release is published to
+PyPI.
+
+### Versioning
+
+_GLASS_ follows [CalVer](https://calver.org). There is no difference between
+releases that increment the year and releases that increment the month; in
+particular, releases that increment the month may introduce breaking changes.
+
+The current version number is automatically inferred from the last release
+(i.e., git tag), subsequent unreleased commits, and local changes, if any.
+
+The target is to have a new _GLASS_ release once there are significant changes
+to the code's functionality.
+
+## Nox
+
+`GLASS` supports running various critical commands using
+[nox](https://github.com/wntrblm/nox) to make them less intimidating for new
+developers. All of these commands (or sessions in the language of `nox`) -
+`lint`, `tests`, `coverage`, `doctests`, `docs`, and `build` - are defined in
+[noxfile.py](https://github.com/glass-dev/glass/main/noxfile.py).
+
+`nox` can be installed via `pip` using -
+
+```bash
+pip install nox
+```
+
+The default sessions (`lint` and `tests`) can be executed using -
+
+```bash
+nox
+```
+
+A particular session (for example `tests`) can be run with `nox` on all
+supported Python versions using -
+
+```bash
+nox -s tests
+```
+
+Only `tests`, `coverage`, and the `doctests` session run on all supported Python
+versions by default.
+
+To specify a particular Python version (for example `3.13`), use the following
+syntax -
+
+```bash
+nox -s tests-3.13
+```
+
+One can specify a particular array backend for testing by setting the
+`ARRAY_BACKEND` environment variable. The default array backend is NumPy.
+_GLASS_ can be tested with every supported array library by setting
+`ARRAY_BACKEND` to `all`.
+
+```bash
+# run tests using numpy
+nox -s tests-3.13
+ARRAY_BACKEND=numpy nox -s tests-3.13
+# run tests using array_api_strict
+ARRAY_BACKEND=array_api_strict nox -s tests-3.13
+# run tests using jax
+ARRAY_BACKEND=jax nox -s tests-3.13
+# run tests using every supported array library
+ARRAY_BACKEND=all nox -s tests-3.13
+```
+
+The following command can be used to deploy the docs on `localhost` -
+
+```bash
+nox -s docs -- serve
+```
+
+The `nox` environments created for each type of session on the first run is
+saved under `.nox/` and reused by default.
+
+## Contributing workflow
+
+Every change to the repository should come out of an issue where the change is
+discussed.
+
+[Pull requests](#pull-requests) should always follow from the discussion in an
+existing issue. The only exception are minor, obvious changes such as fixing
+typos in the documentation.
+
+The discussion in a pull request should only be about the low-level details of
+its implementation. All high-level, conceptual discussion belongs to the issue
+to which the pull request refers.
+
+### Pull requests
+
+Pull requests to the `main` branch should have titles of the following form:
+
+```markdown
+gh-<issue-number>: Subject line
+```
+
+The body of the pull request should contain a description of the changes, and
+any relevant details or caveats of the implementation.
+
+The pull request should not repeat or summarise the discussion of its associated
+issue. Instead, it should link to the issue using git's so-called "trailers".
+These are lines of the form `key: value` which are at the end of the pull
+request description, separated from the message body by a blank line.
+
+To generically refer to an issue without any further action, use `Refs` and one
+or more GitHub issue numbers:
+
+```markdown
+Refs: #12
+
+Refs: #25, #65
+```
+
+To indicate that the pull request shall close an open issue, use `Closes` and a
+single GitHub issue number:
+
+```markdown
+Closes: #17
+```
+
+Changelog entries are collected using the following trailers, and later parsed
+into the [changelog](CHANGELOG.md) for the next release:
+
+```Text
+Added: Some new feature
+Changed: Some change in existing functionality
+Deprecated: Some soon-to-be removed feature
+Removed: Some now removed feature
+Fixed: Some bug fix
+Security: Some vulnerability was fixed
+```
+
+You can use any of the other common git trailers. In particular, you can use
+`Cc` to notify others of your pull request via their GitHub user names:
+
+```markdown
+Cc: @octocat
+```
+
+### Acknowledging contributors
+
+_GLASS_ uses [All Contributors](https://allcontributors.org) to acknowledge
+different types of contributions to the project. The contributor data is stored
+in `.all-contributorsrc` as JSON and is rendered as Markdown in
+`docs/CONTRIBUTORS.md`. The markdown file is further rendered in
+[_GLASS_' documentation](https://glass.readthedocs.io/latest/#contributors).
+
+We encourage people to minimize contribution types, keeping them restricted to
+`code` (usually includes `docs`, `infra`, and other related activities),
+`ideas`, `research`, and `funding`.
+
+To add a new contributor to the JSON and Markdown file, comment
+
+```markdown
+@all-contributors please add @<username> for <contribution1>,<contribution2>,...
+```
+
+on a PR/issue (a relevant PR/issue if it is a code contribution). The comment
+will trigger an automated PR from the all-contributors bot. Contributors should
+be added one at a time to avoid conflicts; one should wait for any existing PR
+by the all-contributors bot to be merged/closed before triggering a new one.
+
+To acknowledge a contributor without a GitHub account, add an entry to
+`.all-contributorsrc` manually and regenerate `docs/CONTRIBUTORS.md` using
+[all-contributors CLI](https://allcontributors.org/docs/en/cli/usage)
+
+```bash
+all-contributors generate
+```
+
+It is also often useful to run `all-contributors` CLI for debugging or fixing
+generated files locally.
diff --git a/glass/LICENSE b/glass/LICENSE
new file mode 100644
index 0000000..b2dd4a2
--- /dev/null
+++ b/glass/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022-2024 The GLASS Developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/glass/README.md b/glass/README.md
new file mode 100644
index 0000000..b35fd00
--- /dev/null
+++ b/glass/README.md
@@ -0,0 +1,104 @@
+# **GLASS**: Generator for Large Scale Structure
+
+<!-- Essentials -->
+
+[![PyPI](https://img.shields.io/pypi/v/glass)](https://pypi.org/project/glass)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/glass.svg)](https://anaconda.org/conda-forge/glass)
+[![Documentation](https://readthedocs.org/projects/glass/badge/?version=stable)](https://glass.readthedocs.io/stable)
+[![LICENSE](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+
+<!-- Code -->
+
+[![Python Versions](https://img.shields.io/pypi/pyversions/glass)](https://pypi.org/project/glass)
+[![Test](https://github.com/glass-dev/glass/actions/workflows/test.yml/badge.svg)](https://github.com/glass-dev/glass/actions/workflows/test.yml)
+[![Coverage Status](https://coveralls.io/repos/github/glass-dev/glass/badge.svg?branch=main)](https://coveralls.io/github/glass-dev/glass?branch=main)
+[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/glass-dev/glass/main.svg)](https://results.pre-commit.ci/latest/github/glass-dev/glass/main)
+
+<!-- Science -->
+
+[![arXiv](https://img.shields.io/badge/arXiv-2302.01942-red)](https://arxiv.org/abs/2302.01942)
+[![adsabs](https://img.shields.io/badge/ads-2023OJAp....6E..11T-blueviolet)](https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..11T)
+[![doi](https://img.shields.io/badge/doi-10.21105/astro.2302.01942-blue)](https://dx.doi.org/10.21105/astro.2302.01942)
+
+<!-- Community -->
+
+[![GitHub Discussions](https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github)](https://github.com/orgs/glass-dev/discussions)
+[![Slack](https://img.shields.io/badge/join-Slack-4A154B)](https://glass-dev.github.io/slack)
+[![All Contributors](https://img.shields.io/github/all-contributors/glass-dev/glass?color=ee8449&style=flat-square)](#contributors)
+
+This is the core library for GLASS, the Generator for Large Scale Structure. For
+more information, see the full [documentation]. There are a number of [examples]
+to get you started.
+
+## Installation
+
+Releases of the code can be installed with pip:
+
+```sh
+pip install glass
+```
+
+or conda:
+
+```sh
+conda install -c conda-forge glass
+```
+
+If you are interested in the latest version of the code, you can pip-install
+this repository:
+
+```sh
+pip install git+https://github.com/glass-dev/glass.git
+```
+
+## Citation
+
+If you use GLASS simulations or the GLASS library in your research, please
+[cite the original GLASS paper](https://glass.readthedocs.io/stable/user/publications.html)
+in your publications.
+
+<!-- markdownlint-disable MD013 -->
+
+```bibtex
+@ARTICLE{2023OJAp....6E..11T,
+       author = {{Tessore}, Nicolas and {Loureiro}, Arthur and {Joachimi}, Benjamin and {von Wietersheim-Kramsta}, Maximilian and {Jeffrey}, Niall},
+        title = "{GLASS: Generator for Large Scale Structure}",
+      journal = {The Open Journal of Astrophysics},
+     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
+         year = 2023,
+        month = mar,
+       volume = {6},
+          eid = {11},
+        pages = {11},
+          doi = {10.21105/astro.2302.01942},
+archivePrefix = {arXiv},
+       eprint = {2302.01942},
+ primaryClass = {astro-ph.CO},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..11T},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+```
+
+## Getting in touch
+
+The best way to get help about the code is currently to get in touch.
+
+If you would like to start a discussion with the wider GLASS community about
+e.g. a design decision or API change, you can use our [Discussions] page.
+
+We also have a public [Slack workspace] for discussions about the project.
+
+[documentation]: https://glass.readthedocs.io/stable
+[examples]: https://glass.readthedocs.io/stable/examples.html
+[Discussions]: https://github.com/orgs/glass-dev/discussions
+[Slack workspace]: https://glass-dev.github.io/slack
+
+## Contributors
+
+This project follows the
+[all-contributors](https://github.com/all-contributors/all-contributors)
+specification. Contributions of any kind are welcome!
+
+Click here to see
+[a full list](https://github.com/glass-dev/glass/blob/main/docs/CONTRIBUTORS.md)
+of our contributors' profiles.
diff --git a/glass/docs/CONTRIBUTORS.md b/glass/docs/CONTRIBUTORS.md
new file mode 100644
index 0000000..35716a1
--- /dev/null
+++ b/glass/docs/CONTRIBUTORS.md
@@ -0,0 +1,27 @@
+
+<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
+<!-- prettier-ignore-start -->
+<!-- markdownlint-disable -->
+<table>
+  <tbody>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="http://ntessore.page"><img src="https://avatars.githubusercontent.com/u/3993688?v=4?s=100" width="100px;" alt="Nicolas Tessore"/><br /><sub><b>Nicolas Tessore</b></sub></a><br /><a href="#code-ntessore" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://paddyroddy.github.io"><img src="https://avatars.githubusercontent.com/u/15052188?v=4?s=100" width="100px;" alt="Patrick J. Roddy"/><br /><sub><b>Patrick J. Roddy</b></sub></a><br /><a href="#code-paddyroddy" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://saransh-cpp.github.io/"><img src="https://avatars.githubusercontent.com/u/74055102?v=4?s=100" width="100px;" alt="Saransh Chopra"/><br /><sub><b>Saransh Chopra</b></sub></a><br /><a href="#code-Saransh-cpp" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/ucapbba"><img src="https://avatars.githubusercontent.com/u/87702063?v=4?s=100" width="100px;" alt="baugstein"/><br /><sub><b>baugstein</b></sub></a><br /><a href="#code-ucapbba" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="http://arthurmloureiro.github.io"><img src="https://avatars.githubusercontent.com/u/6471279?v=4?s=100" width="100px;" alt="Arthur Loureiro"/><br /><sub><b>Arthur Loureiro</b></sub></a><br /><a href="#code-arthurmloureiro" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://mwiet.github.io"><img src="https://avatars.githubusercontent.com/u/49800039?v=4?s=100" width="100px;" alt="Maximilian von Wietersheim-Kramsta"/><br /><sub><b>Maximilian von Wietersheim-Kramsta</b></sub></a><br /><a href="#code-mwiet" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/joachimi"><img src="https://avatars.githubusercontent.com/u/4989590?v=4?s=100" width="100px;" alt="joachimi"/><br /><sub><b>joachimi</b></sub></a><br /><a href="#ideas-joachimi" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-joachimi" title="Research">🔬</a></td>
+    </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://nialljeffrey.github.io/"><img src="https://avatars.githubusercontent.com/u/15345794?v=4?s=100" width="100px;" alt="Niall Jeffrey"/><br /><sub><b>Niall Jeffrey</b></sub></a><br /><a href="#code-NiallJeffrey" title="Code">💻</a> <a href="#ideas-NiallJeffrey" title="Ideas, Planning, & Feedback">🤔</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/ARCHER2-HPC"><img src="https://avatars.githubusercontent.com/u/60643641?v=4?s=100" width="100px;" alt="ARCHER2, UK National Supercomputing Service"/><br /><sub><b>ARCHER2, UK National Supercomputing Service</b></sub></a><br /><a href="#financial-ARCHER2-HPC" title="Financial">💵</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/connoraird"><img src="https://avatars.githubusercontent.com/u/61978554?v=4?s=100" width="100px;" alt="Connor Aird"/><br /><sub><b>Connor Aird</b></sub></a><br /><a href="#code-connoraird" title="Code">💻</a> <a href="#review-connoraird" title="Reviewed Pull Requests">👀</a> <a href="#maintenance-connoraird" title="Maintenance">🚧</a></td>
+    </tr>
+  </tbody>
+</table>
+
+<!-- markdownlint-restore -->
+<!-- prettier-ignore-end -->
+
+<!-- ALL-CONTRIBUTORS-LIST:END -->
diff --git a/glass/docs/Makefile b/glass/docs/Makefile
new file mode 100644
index 0000000..d4bb2cb
--- /dev/null
+++ b/glass/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/glass/docs/_static/android-chrome-192x192.png b/glass/docs/_static/android-chrome-192x192.png
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diff --git a/glass/docs/_static/spheres.png b/glass/docs/_static/spheres.png
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diff --git a/glass/docs/conf.py b/glass/docs/conf.py
new file mode 100644
index 0000000..a72cb4e
--- /dev/null
+++ b/glass/docs/conf.py
@@ -0,0 +1,116 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+
+# -- Project information -----------------------------------------------------
+
+import datetime
+from importlib import metadata
+
+project = "GLASS"
+author = "GLASS developers"
+copyright = f"2022-{datetime.date.today().year} {author}"  # noqa: A001, DTZ011
+version = metadata.version("glass")
+release = version
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "matplotlib.sphinxext.plot_directive",
+    "myst_parser",
+    "nbsphinx",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx_autodoc_typehints",
+    "sphinx_toolbox.more_autodoc.autoprotocol",
+    "sphinxcontrib.katex",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# List of warning types to suppress.
+suppress_warnings = ["ref.citation"]
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+#
+html_theme = "furo"
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+html_logo = "_static/logo.png"
+html_favicon = "_static/favicon.ico"
+html_css_files: list[str] = []
+
+
+# -- Intersphinx -------------------------------------------------------------
+
+# This config value contains the locations and names of other projects that
+# should be linked to in this documentation.
+intersphinx_mapping = {
+    "array_api": ("https://data-apis.org/array-api/latest", None),
+    "jax": ("https://docs.jax.dev/en/latest", None),
+    "jaxtyping": ("https://docs.kidger.site/jaxtyping", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "python": ("https://docs.python.org/3", None),
+}
+
+
+# -- autodoc -----------------------------------------------------------------
+
+autodoc_typehints = "description"
+
+
+# -- napoleon ----------------------------------------------------------------
+
+napoleon_custom_sections = [
+    ("Returns", "params_style"),
+    ("Yields", "params_style"),
+]
+napoleon_google_docstring = False
+
+
+# -- sphinx-autodoc-typehints ------------------------------------------------
+
+always_use_bars_union = True
+
+
+# -- plot_directive ----------------------------------------------------------
+
+# Whether to show a link to the source in HTML (default: True).
+plot_html_show_source_link = False
+
+# Whether to show links to the files in HTML (default: True).
+plot_html_show_formats = False
+
+# File formats to generate (default: ['png', 'hires.png', 'pdf']).
+plot_formats = [("svg", 150), ("png", 150)]
+
+# A dictionary containing any non-standard rcParams that should be applied
+# before each plot (default: {}).
+plot_rcparams = {
+    "axes.facecolor": (1.0, 1.0, 1.0, 1.0),
+    "savefig.facecolor": (1.0, 1.0, 1.0, 0.5),
+    "savefig.transparent": False,
+}
diff --git a/glass/docs/examples b/glass/docs/examples
new file mode 120000
index 0000000..a6573af
--- /dev/null
+++ b/glass/docs/examples
@@ -0,0 +1 @@
+../examples
\ No newline at end of file
diff --git a/glass/docs/examples.rst b/glass/docs/examples.rst
new file mode 100644
index 0000000..4d4bfac
--- /dev/null
+++ b/glass/docs/examples.rst
@@ -0,0 +1,49 @@
+
+Examples
+========
+
+These examples show how GLASS can be used in practice.  They are often a good
+starting point for more complicated and realistic simulations.
+
+The examples currently require `CAMB`__ to produce angular matter power spectra
+and for the cosmological background.  Make sure you have CAMB installed:
+
+.. code-block:: console
+
+    $ python -c 'import camb'  # should not give an error
+
+If you want to compute the angular matter power spectra in the examples, you
+need the ``glass.ext.camb`` package:
+
+.. code-block:: console
+
+    $ pip install glass.ext.camb
+
+__ https://camb.readthedocs.io/
+
+
+Basic examples
+--------------
+
+To get started, these examples focus on simulating one thing at a time.
+
+.. nbgallery::
+
+   examples/1-basic/shells.ipynb
+   examples/1-basic/galaxy-redshift-distributions.ipynb
+   examples/1-basic/matter.ipynb
+   examples/1-basic/density.ipynb
+   examples/1-basic/lensing.ipynb
+   examples/1-basic/photoz.ipynb
+
+
+Advanced examples
+-----------------
+
+More advanced examples doing multiple things at the same time.
+
+.. nbgallery::
+
+   examples/2-advanced/cosmic_shear.ipynb
+   examples/2-advanced/stage_4_galaxies.ipynb
+   examples/2-advanced/legacy-mode.ipynb
diff --git a/glass/docs/index.rst b/glass/docs/index.rst
new file mode 100644
index 0000000..6a29e84
--- /dev/null
+++ b/glass/docs/index.rst
@@ -0,0 +1,43 @@
+
+***********************************************
+*GLASS* --- Generator for Large Scale Structure
+***********************************************
+
+.. image:: _static/spheres.png
+
+GLASS, the generator for large scale structure, is a library for performing the
+kind of simulation that is useful in the field of cosmology.  As the name
+suggests, the code was originally designed to simulate cosmic matter, weak
+gravitational lensing, and galaxy positions.  But GLASS is really a platform
+for a wide range of cosmological and astrophysical simulations on the sphere,
+thanks to its ultra-flexible design and open architecture.
+
+
+=================
+Table of Contents
+=================
+
+.. toctree::
+   :maxdepth: 2
+
+   manual/index
+   examples
+   user/index
+   reference/index
+   modules/index
+
+
+==================
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+============
+Contributors
+============
+
+.. include:: CONTRIBUTORS.md
+   :parser: myst_parser.sphinx_
diff --git a/glass/docs/make.bat b/glass/docs/make.bat
new file mode 100644
index 0000000..9a1acd2
--- /dev/null
+++ b/glass/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+    set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+    echo.
+    echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+    echo.installed, then set the SPHINXBUILD environment variable to point
+    echo.to the full path of the 'sphinx-build' executable. Alternatively you
+    echo.may add the Sphinx directory to PATH.
+    echo.
+    echo.If you don't have Sphinx installed, grab it from
+    echo.https://www.sphinx-doc.org/
+    exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/glass/docs/manual/extensions.rst b/glass/docs/manual/extensions.rst
new file mode 100644
index 0000000..e494bd8
--- /dev/null
+++ b/glass/docs/manual/extensions.rst
@@ -0,0 +1,4 @@
+Extensions
+==========
+
+TBA
diff --git a/glass/docs/manual/first-steps.rst b/glass/docs/manual/first-steps.rst
new file mode 100644
index 0000000..37e899e
--- /dev/null
+++ b/glass/docs/manual/first-steps.rst
@@ -0,0 +1,4 @@
+First steps
+===========
+
+The best way to get started with *GLASS* is to follow the :doc:`/examples`.
diff --git a/glass/docs/manual/get-in-touch.rst b/glass/docs/manual/get-in-touch.rst
new file mode 100644
index 0000000..32ea77d
--- /dev/null
+++ b/glass/docs/manual/get-in-touch.rst
@@ -0,0 +1,11 @@
+Get in touch
+============
+
+If you would like to start a discussion with the wider GLASS community about
+e.g. a design decision or API change, you can use our Discussions__ page.
+
+__ https://github.com/orgs/glass-dev/discussions
+
+We also have a public `Slack workspace`__ for discussions about the project.
+
+__ https://glass-dev.github.io/slack
diff --git a/glass/docs/manual/index.rst b/glass/docs/manual/index.rst
new file mode 100644
index 0000000..2f7e799
--- /dev/null
+++ b/glass/docs/manual/index.rst
@@ -0,0 +1,12 @@
+===============
+Software manual
+===============
+
+.. toctree::
+   :maxdepth: 2
+
+   installation
+   first-steps
+   releases
+   extensions
+   get-in-touch
diff --git a/glass/docs/manual/installation.rst b/glass/docs/manual/installation.rst
new file mode 100644
index 0000000..a3deb30
--- /dev/null
+++ b/glass/docs/manual/installation.rst
@@ -0,0 +1,87 @@
+Installation
+============
+
+Requirements
+------------
+
+*GLASS* works best on a fairly recent version of Python.
+
+
+The way to install *GLASS* is via pip, which will normally install missing
+dependencies as well.  If you are using a package manager such as e.g. conda,
+you might want to use that instead to install as many dependencies as possible.
+Use ``pip install --dry-run glass`` to list everything that pip would install,
+and then see what is available in your package manager.
+
+
+Install the current release
+---------------------------
+
+To install the current *GLASS* release, use pip::
+
+    $ pip install glass
+
+or conda::
+
+    $ conda install -c conda-forge glass
+
+
+Install a specific release
+--------------------------
+
+To install the a specific *GLASS* release, pass the version number to pip::
+
+    $ pip install glass==2023.1
+
+or conda::
+
+    $ conda install -c conda-forge glass==2023.1
+
+For a list of released versions and their respective changes, see the
+:doc:`releases`.
+
+
+Install the latest development version
+--------------------------------------
+
+To install the latest *GLASS* code in development, use pip with the GitHub
+repository::
+
+    $ pip install git+https://github.com/glass-dev/glass.git
+
+
+Developer installation
+----------------------
+
+If you want to install *GLASS* for local development, clone the repository and
+install the package in editable mode::
+
+    $ pip install -e .
+
+All of these words should ideally make sense if you attempt this kind of
+installation.
+
+
+Versioning
+----------
+
+There currently are a fair amount of breaking changes between *GLASS* releases.
+For production use, it is therefore **strongly** recommended that you install
+*GLASS* in a clean environment (venv, conda, etc.) and **pin the GLASS version
+for the duration of your project**.
+
+
+Announcements
+-------------
+
+To keep up with new GLASS releases, you can subscribe to our announcement
+mailing list, which only receives 1 email per release.  To subscribe, use the
+`mailing list page`__, or send an email to ``listserv@jiscmail.ac.uk`` with any
+subject and the following message body::
+
+    subscribe glass <Your name>
+
+where ``<Your name>`` is your full name, or ``ANONYMOUS`` if you prefer. You
+will be sent a confirmation email in return.
+
+__ https://jiscmail.ac.uk/lists/GLASS.html
diff --git a/glass/docs/manual/releases.rst b/glass/docs/manual/releases.rst
new file mode 100644
index 0000000..3495f77
--- /dev/null
+++ b/glass/docs/manual/releases.rst
@@ -0,0 +1,317 @@
+
+Release notes
+=============
+
+These notes document the changes between individual *GLASS* releases.
+
+2025.2 (21 Oct 2025)
+--------------------
+
+* Changed the testing flag to be named ``ARRAY_BACKEND``.
+
+* Introduced ``array-api-strict`` as a dependency to ensure that the array API
+  is correctly implemented.
+
+* Changed the way NumPy and JAX are imported internally so that GLASS no longer
+  explicitly depend on either of them. If NumPy is incorrectly imported, a
+  helpful error message will be provided to the user. This aims to reduce
+  confusion if a user suddenly requires NumPy just because they called a
+  specific function.
+
+* Add Array API support for functions in the following modules.
+
+    * :mod:`glass.points`
+    * :mod:`glass.shells`
+    * :mod:`glass.fields`
+
+* Removed support for ``Cls`` (angular power spectrum) as ``Sequence[float]``.
+  Cls must now be arrays (NumPy, etc.).
+
+* Changed the ``docs`` and ``test`` optional dependencies to
+  ``dependency-groups`` as these are not needed for the majority of users.
+
+* Changed ``zeff`` will now be computed if not passed into
+  :class:`glass.RadialWindow`.
+
+* The weight functions have been changed to callable classes.
+
+* Fixed a mutable argument bug in the legacy mode notebook.
+
+* Added a notebook example on effective galaxy redshift distributions.
+
+* Removed the ``cosmology`` dependency in favour of the new Array API-compatible
+  ``cosmology.api`` package.
+
+* Made the type hints more permissive for :func:`glass.glass_to_healpix_spectra`
+  and :func:`glass.healpix_to_glass_spectra`.
+
+* Fixed a bug where :func:`glass.compute_gaussian_spectra` did not skip over
+  empty spectra.
+
+* Added a script to speed up release notes.
+
+2025.1 (21 Feb 2025)
+--------------------
+
+* ``Python 3.9`` and ``NumPy < 2.1.1`` are no longer supported.
+
+* Improved type hints for :func:`glass.lensing.from_convergence`.
+
+* ``SciPy`` has been removed as a dependency for testing.
+
+* Fixed a bug in :func:`glass.fixed_zbins` that prevented the user supplying
+  ``nbins`` as an argument. The functions intended use is to accept eiher ``dz``
+  or ``nbins`` as an argument, but not both.
+
+* It is now possible to install ``glass`` both through git archives and from
+  ``conda`` (in addition to PyPI as before).
+
+* A new module :mod:`glass.grf` has been added with the machinery required to
+  generate Gaussian random fields.
+
+* New functions have been added for a FLASK-like (the predecessor to GLASS)
+  legacy mode.
+
+  * :func:`glass.check_posdef_spectra`
+  * :func:`glass.cov_from_spectra`
+  * :func:`glass.glass_to_healpix_spectra`
+  * :func:`glass.healpix_to_glass_spectra`
+  * :func:`glass.lognormal_shift_hilbert2011`
+  * :func:`glass.regularized_spectra`
+
+* All imports in the examples have been changed to be directly from ``glass``
+  rather than from a particular module. This reflects the authors' intended use
+  of the library.
+
+* Following the addition of the :mod:`glass.grf` module, the
+  ``generate_gaussian()`` and ``generate_lognormal()`` functions have been
+  deprecated.
+
+* A standalone bibliography has been added to the documentation.
+
+* A new function :func:`glass.nfields_from_nspectra` has been added to compute
+  the number of fields for a number of spectra.
+
+* The :mod:`glass.algorithm` module has been made public. This module contains
+  general implementations of the algorithms which are used by ``glass``, but are
+  otherwise unrelated to the functionality of ``glass``.
+
+
+2024.2 (15 Nov 2024)
+--------------------
+
+* All GLASS user functionality is now available directly from the main
+  ``glass`` namespace.  No more imports!
+
+* Changes to the functions dealing with Gaussian spectra:
+
+  * ``gaussian_gls()`` was renamed to :func:`glass.discretized_cls`, which
+    better reflects what the function does.
+
+  * The implicit call to :func:`glass.discretized_cls` was removed from
+    :func:`glass.lognormal_gls`.  Call :func:`glass.discretized_cls` explicitly
+    with the desired ``ncorr=``, ``lmax=``, and ``nside=`` parameters.
+
+* ``redshifts_from_nz()`` is deprecated in favour of :func:`glass.redshifts`,
+  as the former is almost never the right choice: the two-point statistics in a
+  linear bias model are coming from the shells, so the redshift distribution is
+  implicitly the same as the radial profile of the shells.
+
+* Several fixes to :func:`glass.ellipticity_ryden04`.
+
+* Added a FITS catalogue writer tool :func:`glass.write_catalog`.
+
+* Much improved documentation with docstring for all functions.
+
+* Examples now live in the main GLASS repository and documentation.
+
+* GLASS is now fully typed and passes mypy.
+
+* Python 3.13 is now supported.  Python 3.8 and NumPy 1.21 are no longer
+  supported.
+
+* The ``glass.core.constants`` module was removed.
+
+
+2024.1 (16 Jul 2024)
+--------------------
+
+* Further changes to the :func:`~glass.shells.partition()` function.
+
+  * The output of ``partition()`` now has the shells axis as its first.  **This
+    means that the new output is the transpose of the previous output.**
+
+  * A new ``partition(..., method="nnls")`` method that computes a partition
+    with non-negative contributions for each shell.  **This is now the
+    default.** The ``"nnls"`` method works much better than ``"lstsq"`` since
+    it does not introduce small negative densities, and should almost always be
+    preferred.
+
+  * Both ``partition(method="nnls")`` and ``partition(method="lstsq")`` now
+    have an additional integral constraint so that the sum of the partition
+    recovers the integral of the input function.
+
+  * The ``partition()`` function now works correctly with functions having
+    extra axes.
+
+* A new function :func:`~glass.shells.combine()` that evaluates the linear
+  combination of radial window functions with given weights.  This function is
+  the inverse of :func:`~glass.shells.partition()` and can be used to obtain
+  the effect of the discretisation on, e.g., a redshift distribution.
+
+* There is now a way to compute the effective angular power spectra that can
+  be expected from a *GLASS* simulation, including all discretisations and
+  approximations.
+
+  * A new function :func:`~glass.fields.effective_cls()` which combines power
+    spectra using a list of weights.  This function essentially models the
+    linear combinations that happen in the simulation.
+
+  * A new function :func:`~glass.points.position_weights()` that returns weights
+    for ``effective_cls()`` to model the result of
+    :func:`~glass.points.positions_from_delta()`.
+
+  * A new function :func:`~glass.lensing.multi_plane_weights()` that returns
+    weights for ``effective_cls()`` to model the result of
+    :class:`~glass.lensing.MultiPlaneConvergence`.
+
+* A new function :func:`~glass.galaxies.redshifts()` to sample redshifts
+  following a radial window function.  This should always be preferred to the
+  existing :func:`~glass.galaxies.redshifts_from_nz()` function, since the
+  redshift distribution entering the two-point statistics is in fact fixed by
+  the window functions.
+
+
+2023.7 (1 Aug 2023)
+-------------------
+
+* New radial window functions :func:`~glass.shells.linear_windows()` and
+  :func:`~glass.shells.cubic_windows()`, which correspond to linear and cubic
+  spline interpolation of radial functions, respectively.  These are
+  overlapping window functions, and it has been difficult to obtain accurate
+  matter power spectra so far.
+
+* The :func:`~glass.shells.partition()` function now returns an array of
+  weights to approximate a given function by the window functions.  This is
+  necessary to obtain an accurate fit of redshift distributions by overlapping
+  window functions.  For example, to get the array of galaxy densities in each
+  shells from ``dndz``, one would now do::
+
+      ngal = partition(z, dndz, shells)
+
+* A new function :func:`~glass.fields.getcl()` was added to return angular
+  power spectra by index from a list using GLASS ordering.
+
+* The :func:`~glass.galaxies.gaussian_phz()` function now accepts bounds using
+  ``lower=`` and ``upper=`` keyword parameters.
+
+
+2023.6 (30 Jun 2023)
+--------------------
+
+- There is some support for simulating the deflections due to weak
+  gravitational lensing:
+
+  - The :func:`~glass.lensing.deflect` function applies deflections to
+    positions.
+
+  - The :func:`~glass.lensing.from_convergence` function returns one or more
+    other lensing fields given the convergence.
+
+  - The ``shear_from_convergence()`` function is deprecated in favour of
+    ``from_convergence()``.
+
+- The ``glass`` module is no longer a namespace package.  The new ``glass.ext``
+  namespace is reserved for extensions instead.  This is done to follow best
+  practices, so that a bad extension can no longer break all of *GLASS* by
+  mistake.  The ``glass.all`` meta-module is no longer necessary.
+
+- The point sampling functions :func:`~glass.points.positions_from_delta` and
+  :func:`~glass.points.uniform_positions` now return an iterator over points.
+  This has lead to orders-of-magnitude improvements in memory use and
+  performance when simulating galaxies at Euclid/LSST densities.
+
+- The ellipticity sampling functions :func:`~glass.shapes.ellipticity_gaussian`
+  and :func:`~glass.shapes.ellipticity_intnorm` accept array inputs.
+
+- A bug causing incorrect results from :func:`~glass.points.effective_bias` has
+  been fixed.
+
+
+2023.5 (31 May 2023)
+--------------------
+
+- The point sampling functions in :mod:`glass.points` now accept extra
+  dimensions, and will broadcast leading axes across their inputs.  They also
+  return an additional scalar or array with the counts of sampled galaxies.
+
+- The redshift sampling function :func:`glass.galaxies.redshifts_from_nz` now
+  supports array input for the ``counts`` argument.  It accepts e.g. the number
+  of galaxies returned by the position sampling.
+
+- The profiling functionality in :mod:`glass.user` was removed in favour of
+  external packages.
+
+
+2023.2 (1 Mar 2023)
+-------------------
+
+- New user functions :func:`glass.user.save_cls` and
+  :func:`glass.user.load_cls` to save and load angular power spectra in the
+  *GLASS* format.
+
+- Some type hints were added to library functions.  These are mostly
+  perfunctory at this time, but there is interest in adding proper typing
+  support in the future, including use of the Array API.
+
+- The ``glass.matter`` module was removed in favour of the more
+  appropriately-named :mod:`glass.shells` module for shell definitions.
+
+- Instead of using an array of shell boundaries and separate ``MatterWeights``,
+  shells are now entirely defined by a :class:`glass.shells.RadialWindow`
+  window function.
+
+- Many functions have an improved interface thanks to the previous point:
+
+  - The ``glass.math.restrict_interval`` function has been replaced by
+    :func:`glass.shells.restrict`, as shells are now defined by
+    window functions instead of sharp intervals.
+
+  - The :func:`glass.points.effective_bias` function now takes a window
+    function as input and computes its effective bias parameter.
+
+  - The ``glass.galaxies.constant_densities`` and ``density_from_dndz``
+    functions have been removed, since densities can now easily be partitioned
+    by window functions using :func:`glass.shells.restrict` and
+    :func:`glass.shells.partition`.
+
+  - The ``zmin`` and ``zmax`` parameters of ``glass.galaxies.redshifts_from_nz``
+    have been removed for the same reason.
+
+  - The ``glass.lensing.multi_plane_weights`` function, which computed all
+    lensing weights at once, is replaced by the ``add_window`` method of
+    :class:`glass.lensing.MultiPlaneConvergence`, which adds a convergence
+    plane given by a :class:`~glass.shells.RadialWindow` at its effective
+    redshift.
+
+  - The :func:`glass.lensing.multi_plane_matrix` function now takes a sequence
+    of :class:`~glass.shells.RadialWindow`.  It no longer returns the list of
+    source redshifts, since these are now independently available as the
+    effective redshifts of the windows.
+
+- The arguments of the :class:`~glass.lensing.MultiPlaneConvergence` method
+  ``add_plane`` have been renamed to ``zsrc`` and ``wlens`` from the more
+  ambiguous ``z`` and ``w`` (which could be confused with "window"). The
+  properties ``z`` and ``w`` that returned these values have been similarly
+  changed.
+
+
+2023.1 (31 Jan 2023)
+--------------------
+
+- **Initial wide release for GLASS paper**
+
+  This was the initial full release of *GLASS*, coinciding with the release of
+  preprint `arXiv:2302.01942`__.
+
+  __ https://arxiv.org/abs/2302.01942
diff --git a/glass/docs/modules/algorithm.rst b/glass/docs/modules/algorithm.rst
new file mode 100644
index 0000000..5e70882
--- /dev/null
+++ b/glass/docs/modules/algorithm.rst
@@ -0,0 +1,32 @@
+.. module:: glass.algorithm
+
+:mod:`glass.algorithm` --- General purpose algorithms
+=====================================================
+
+.. currentmodule:: glass.algorithm
+
+This module contains general implementations of algorithms which are used by
+GLASS, but are otherwise unrelated to GLASS functionality.
+
+This module should be imported manually if used outside of GLASS::
+
+    import glass.algorithm
+
+
+Non-negative least squares
+--------------------------
+
+.. autofunction:: nnls
+
+
+Nearest correlation matrix
+--------------------------
+
+.. autofunction:: nearcorr
+
+
+Covariance matrix regularisation
+--------------------------------
+
+.. autofunction:: cov_clip
+.. autofunction:: cov_nearest
diff --git a/glass/docs/modules/grf.rst b/glass/docs/modules/grf.rst
new file mode 100644
index 0000000..bb3524d
--- /dev/null
+++ b/glass/docs/modules/grf.rst
@@ -0,0 +1,35 @@
+.. module:: glass.grf
+
+:mod:`glass.grf` --- Gaussian random fields
+===========================================
+
+.. currentmodule:: glass.grf
+
+
+Gaussian angular power spectra
+------------------------------
+
+.. autofunction:: compute
+.. autofunction:: solve
+
+
+Transforming correlations
+-------------------------
+
+These functions can convert between Gaussian and transformed angular
+correlation functions, and form the basis of :func:`glass.grf.compute` and
+:func:`glass.grf.solve`.
+
+.. autofunction:: corr
+.. autofunction:: icorr
+.. autofunction:: dcorr
+
+
+Transformations
+---------------
+
+.. autoprotocol:: Transformation
+
+.. autoclass:: Normal
+.. autoclass:: Lognormal
+.. autoclass:: SquaredNormal
diff --git a/glass/docs/modules/index.rst b/glass/docs/modules/index.rst
new file mode 100644
index 0000000..64c320a
--- /dev/null
+++ b/glass/docs/modules/index.rst
@@ -0,0 +1,9 @@
+=======
+Modules
+=======
+
+.. toctree::
+   :maxdepth: 2
+
+   algorithm
+   grf
diff --git a/glass/docs/reference/fields.rst b/glass/docs/reference/fields.rst
new file mode 100644
index 0000000..1cca978
--- /dev/null
+++ b/glass/docs/reference/fields.rst
@@ -0,0 +1,132 @@
+.. automodule:: glass.fields
+
+Random fields
+=============
+
+.. currentmodule:: glass
+
+The following functions provide functionality for simulating random fields on
+the sphere. This is done in the form of HEALPix maps.
+
+
+Angular power spectra
+---------------------
+
+.. _twopoint_order:
+
+All functions that process sets of two-point functions expect them as a
+sequence using the following "Christmas tree" ordering:
+
+.. raw:: html
+   :file: figures/spectra_order.svg
+
+In other words, the sequence begins as such:
+
+* Index 0 describes the auto-correlation of field 0,
+* index 1 describes the auto-correlation of field 1,
+* index 2 describes the cross-correlation of field 1 and field 0,
+* index 3 describes the auto-correlation of field 2,
+* index 4 describes the cross-correlation of field 2 and field 1,
+* index 5 describes the cross-correlation of field 2 and field 0,
+* etc.
+
+In particular, two-point functions for the first :math:`n` fields are contained
+in the first :math:`T_n = n \, (n + 1) / 2` entries of the sequence.
+
+To easily generate or iterate over sequences of two-point functions in standard
+order, see the :func:`glass.enumerate_spectra` and
+:func:`glass.spectra_indices` functions.
+
+
+Preparing inputs
+----------------
+
+.. autofunction:: discretized_cls
+.. autofunction:: compute_gaussian_spectra
+.. autofunction:: solve_gaussian_spectra
+
+
+Expectations
+------------
+
+.. autofunction:: effective_cls
+
+
+Generating fields
+-----------------
+
+.. autofunction:: generate
+
+
+Lognormal fields
+----------------
+
+.. autofunction:: lognormal_fields
+
+GLASS comes with the following functions for setting accurate lognormal shift
+values:
+
+.. autofunction:: lognormal_shift_hilbert2011
+
+
+Regularisation
+--------------
+
+When sets of angular power spectra are used to sample random fields, their
+matrix :math:`C_\ell^{ij}` for fixed :math:`\ell` must form a valid
+positive-definite covariance matrix.  This is not always the case, for example
+due to numerical inaccuracies, or transformations of the underlying fields
+[Xavier16]_.
+
+Regularisation takes sets of spectra which are ill-posed for sampling, and
+returns sets which are well-defined and, in some sense, "close" to the input.
+
+.. autofunction:: regularized_spectra
+
+.. function:: regularized_spectra(..., method="nearest", tol=None, niter=100)
+   :no-index:
+
+   Compute the (possibly defective) correlation matrices of the given spectra,
+   then find the nearest valid correlation matrices, using the alternating
+   projections algorithm of [Higham02]_ with tolerance *tol* for *niter*
+   iterations.  This keeps the diagonals (i.e.  auto-correlations) fixed, but
+   requires all of them to be nonnegative.
+
+   .. seealso::
+
+      :func:`glass.algorithm.cov_nearest`
+         Equivalent function for covariance matrices.
+
+      :func:`glass.algorithm.nearcorr`
+         Nearest correlation matrix.
+
+.. function:: regularized_spectra(..., method="clip", rtol=None)
+   :no-index:
+
+   Clip negative eigenvalues of the spectra's covariance matrix to zero.  This
+   is a simple fix that guarantees positive semi-definite spectra, but can
+   affect the spectra significantly.
+
+   .. seealso::
+
+      :func:`glass.algorithm.cov_clip`
+         Equivalent function for covariance matrices.
+
+
+Indexing
+--------
+
+.. autofunction:: getcl
+.. autofunction:: enumerate_spectra
+.. autofunction:: spectra_indices
+.. autofunction:: glass_to_healpix_spectra
+.. autofunction:: healpix_to_glass_spectra
+.. autofunction:: cov_from_spectra
+
+
+Deprecated
+----------
+
+.. autofunction:: lognormal_gls
+.. autofunction:: generate_gaussian
+.. autofunction:: generate_lognormal
diff --git a/glass/docs/reference/figures/spectra_order.svg b/glass/docs/reference/figures/spectra_order.svg
new file mode 100644
index 0000000..744f411
--- /dev/null
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@@ -0,0 +1,123 @@
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diff --git a/glass/docs/reference/galaxies.rst b/glass/docs/reference/galaxies.rst
new file mode 100644
index 0000000..ab8a7aa
--- /dev/null
+++ b/glass/docs/reference/galaxies.rst
@@ -0,0 +1 @@
+.. automodule:: glass.galaxies
diff --git a/glass/docs/reference/index.rst b/glass/docs/reference/index.rst
new file mode 100644
index 0000000..df26f96
--- /dev/null
+++ b/glass/docs/reference/index.rst
@@ -0,0 +1,15 @@
+==================
+Function reference
+==================
+
+.. toctree::
+   :maxdepth: 1
+
+   shells
+   fields
+   points
+   shapes
+   lensing
+   galaxies
+   observations
+   user
diff --git a/glass/docs/reference/lensing.rst b/glass/docs/reference/lensing.rst
new file mode 100644
index 0000000..0a8a028
--- /dev/null
+++ b/glass/docs/reference/lensing.rst
@@ -0,0 +1 @@
+.. automodule:: glass.lensing
diff --git a/glass/docs/reference/observations.rst b/glass/docs/reference/observations.rst
new file mode 100644
index 0000000..082d8bd
--- /dev/null
+++ b/glass/docs/reference/observations.rst
@@ -0,0 +1 @@
+.. automodule:: glass.observations
diff --git a/glass/docs/reference/points.rst b/glass/docs/reference/points.rst
new file mode 100644
index 0000000..e1e6681
--- /dev/null
+++ b/glass/docs/reference/points.rst
@@ -0,0 +1 @@
+.. automodule:: glass.points
diff --git a/glass/docs/reference/shapes.rst b/glass/docs/reference/shapes.rst
new file mode 100644
index 0000000..e252e4b
--- /dev/null
+++ b/glass/docs/reference/shapes.rst
@@ -0,0 +1 @@
+.. automodule:: glass.shapes
diff --git a/glass/docs/reference/shells.rst b/glass/docs/reference/shells.rst
new file mode 100644
index 0000000..4bfb0e1
--- /dev/null
+++ b/glass/docs/reference/shells.rst
@@ -0,0 +1 @@
+.. automodule:: glass.shells
diff --git a/glass/docs/reference/user.rst b/glass/docs/reference/user.rst
new file mode 100644
index 0000000..fbfa603
--- /dev/null
+++ b/glass/docs/reference/user.rst
@@ -0,0 +1 @@
+.. automodule:: glass.user
diff --git a/glass/docs/user/bibliography.rst b/glass/docs/user/bibliography.rst
new file mode 100644
index 0000000..d167edc
--- /dev/null
+++ b/glass/docs/user/bibliography.rst
@@ -0,0 +1,45 @@
+Bibliography
+============
+
+.. [Amara07] Amara A., Réfrégier A., 2007, MNRAS, 381, 1018.
+    doi:10.1111/j.1365-2966.2007.12271.x
+
+.. [Binney85] Binney J., 1985, MNRAS, 212, 767. doi:10.1093/mnras/212.4.767
+
+.. [Bridle07] Bridle S., King L., 2007, NJPh, 9, 444.
+    doi:10.1088/1367-2630/9/12/444
+
+.. [Bro97] Bro, R. and De Jong, S. (1997), A fast non-negativity-constrained
+    least squares algorithm. J.  Chemometrics, 11, 393-401.
+
+.. [Catelan01] Catelan P., Kamionkowski M., Blandford R. D., 2001, MNRAS,
+    320, L7. doi:10.1046/j.1365-8711.2001.04105.x
+
+.. [HEALPix05] Gorski et al., 2005, ApJ, 622, 759,
+    https://healpix.sourceforge.io
+
+.. [Higham02] Higham N. J., 2002, IMA J. Numer. Anal., 22, 329.
+
+.. [Hilbert11] Hilbert S., Hartlap J., Schneider P., 2011, A&A, 536, A85.
+    doi:10.1051/0004-6361/201117294
+
+.. [Hirata04] Hirata C. M., Seljak U., 2004, PhRvD, 70, 063526.
+    doi:10.1103/PhysRevD.70.063526
+
+.. [Johnston19] Johnston, H., Georgiou, C., Joachimi, B., et al., 2019,
+    A&A, 624, A30. doi:10.1051/0004-6361/201834714
+
+.. [Lawson95] Lawson, C. L. and Hanson, R. J. (1995), Solving Least Squares
+    Problems. doi: 10.1137/1.9781611971217
+
+.. [Padilla08] Padilla N. D., Strauss M. A., 2008, MNRAS, 388, 1321.
+
+.. [Ryden04] Ryden B. S., 2004, ApJ, 601, 214.
+
+.. [Smail94] Smail I., Ellis R. S., Fitchett M. J., 1994, MNRAS, 270, 245
+
+.. [Tessore25] Tessore N., Joachimi B., Loureiro A., et al., 2025, A&A,
+    694, A141. doi:10.1051/0004-6361/202452018. arXiv:2408.16903.
+
+.. [Xavier16] Xavier H. S., et al., 2016, MNRAS, 459, 3693.
+    doi:10.1093/mnras/stw874
diff --git a/glass/docs/user/definitions.rst b/glass/docs/user/definitions.rst
new file mode 100644
index 0000000..f4f9aaa
--- /dev/null
+++ b/glass/docs/user/definitions.rst
@@ -0,0 +1,74 @@
+===========
+Definitions
+===========
+
+The *GLASS* code uses the following mathematical definitions.
+
+.. glossary::
+
+   deflection
+   displacement
+      The deflection :math:`\alpha` is a complex value with spin weight
+      :math:`1`.  It describes the displacement of a position along a geodesic
+      (i.e. great circle).  The angular distance of the displacement is the
+      absolute value :math:`|\alpha|`.  The direction of the displacement is
+      the angle given by the complex argument :math:`\arg\alpha`, such that
+      :math:`\arg\alpha = 0^\circ` is north, :math:`\arg\alpha = 90^\circ` is
+      east, :math:`\arg\alpha = 180^\circ` is south, and :math:`\arg\alpha =
+      -90^\circ` is west.
+
+   ellipticity
+      If :math:`q = b/a` is the axis ratio of an elliptical isophote with
+      semi-major axis :math:`a` and semi-minor axis :math:`b`, and :math:`\phi`
+      is the orientation of the elliptical isophote, the complex-valued
+      ellipticity is
+
+      .. math::
+
+         \epsilon = \frac{1 - q}{1 + q} \, \mathrm{e}^{\mathrm{i} \, 2\phi} \;.
+
+   pixel window function
+      The convolution kernel that describes the shape of pixels in a spherical
+      map.  No discretisation of the sphere has pixels of exactly the same
+      shape, and the pixel window function is therefore an approximation: It is
+      an effective kernel :math:`w_l` such that the discretised map :math:`F`
+      of a spherical function :math:`f` has spherical harmonic expansion
+
+      .. math::
+
+         F_{lm} \approx w_l \, f_{lm} \;.
+
+   radial window
+      A radial window consist of a window function that assigns a weight
+      :math:`W(z)` to each redshift :math:`z` along the line of sight.  Each
+      radial window has an associated effective redshift :math:`z_{\rm eff}`
+      which could be e.g. the central or mean redshift of the weight function.
+
+      A set of window functions :math:`W_1, W_2, \ldots`, defines the shells of
+      the simulation.
+
+   spherical function
+      A spherical function :math:`f` is a function that is defined on the
+      sphere.  Function values are usually parametrised in terms of spherical
+      coordinates, :math:`f(\theta, \phi)`, or using a unit vector,
+      :math:`f(\hat{n})`.
+
+   spherical harmonic expansion
+      A scalar :term:`spherical function` :math:`f` can be expanded into the
+      spherical harmonics :math:`Y_{lm}`,
+
+      .. math::
+
+         f(\hat{n}) = \sum_{lm} f_{lm} \, Y_{lm}(\hat{n}) \;.
+
+      If :math:`f` is not scalar but has non-zero spin weight :math:`s`, it can
+      be expanded into the spin-weighted spherical harmonics :math:`{}_sY_{lm}`
+      instead,
+
+      .. math::
+
+         f(\hat{n}) = \sum_{lm} f_{lm} \, {}_sY_{lm}(\hat{n}) \;.
+
+   visibility map
+      A visibility map describes the *a priori* probability of observing an
+      object inside a given pixel, with pixel values between 0 and 1.
diff --git a/glass/docs/user/how-glass-works.rst b/glass/docs/user/how-glass-works.rst
new file mode 100644
index 0000000..1be43e9
--- /dev/null
+++ b/glass/docs/user/how-glass-works.rst
@@ -0,0 +1,250 @@
+
+How *GLASS* works
+=================
+
+.. note::
+   This page gives a fairly high-level overview of how *GLASS* works.  See the
+   :doc:`list of GLASS publications <publications>` for additional in-depth
+   references.
+
+
+The main purpose of *GLASS* is to produce cosmological simulations on the
+sphere.  The full, three-dimensional past light cone of the observer is
+discretised into a sequence of nested shells, which are further discretised in
+the angular dimensions into maps of the sphere.
+
+
+Radial discretisation
+---------------------
+
+The discretisation in the radial (line of sight) direction is done in *GLASS*
+using the concept of a :term:`radial window`, which consists of a window
+function :math:`W` that assigns a weight :math:`W(z)` to each redshift
+:math:`z`.  In the *GLASS* code, the :class:`~glass.RadialWindow` named
+tuple is used to define radial windows.
+
+A sequence :math:`W_1, W_2, \ldots` of such window functions defines the shells
+of the simulation.  For example, the :func:`~glass.tophat_windows`
+function takes redshift boundaries and returns a sequence of top hat windows,
+which are flat and non-overlapping.
+
+.. plot::
+
+    import numpy as np
+
+    import glass
+
+    # create a redshift grid for shell edges
+    zs = glass.redshift_grid(0.0, 0.5, dz=0.1, xp=np)
+
+    # create the top hat windows
+    ws = glass.tophat_windows(zs)
+
+    # plot each window
+    for i, (za, wa, zeff) in enumerate(ws):
+        plt.plot(za, wa, c="k", lw=2)
+        plt.fill_between(za, np.zeros_like(wa), wa, alpha=0.5)
+        plt.annotate(f"shell {i + 1}", (zeff, 0.5), ha="center", va="center")
+
+    plt.xlabel("redshift $z$")
+    plt.ylabel("window function $W(z)$")
+    plt.tight_layout()
+
+Given such a sequence of window functions :math:`W_i`, *GLASS* discretises a
+continuous field :math:`F` (e.g. the matter density in the universe) by using
+each :math:`W_i` in turn to project :math:`F` onto the sphere,
+
+.. math::
+
+    F_i = \frac{\int W_i(z) \, F(z) \, dz}{\int W_i(z) \, dz} \;.
+
+This results in the sequence :math:`F_1, F_2, \ldots` of integrated (projected)
+fields, which are :term:`spherical functions<spherical function>`.  *GLASS*
+then simulates the (radially) continuous field :math:`F(z)` as the (radially)
+discretised fields :math:`F_i`.
+
+
+.. _user-window-functions:
+
+Window functions
+^^^^^^^^^^^^^^^^
+
+*GLASS* supports arbitrary window functions (although the computation of
+:ref:`line-of-sight integrals <user-los-integrals>` makes some assumptions).
+The following :ref:`window functions <reference-window-functions>` are
+included:
+
+.. plot::
+
+    import numpy as np
+
+    import glass
+
+    plot_windows = [
+        glass.tophat_windows,
+        glass.linear_windows,
+        glass.cubic_windows,
+    ]
+    nr = (len(plot_windows) + 1) // 2
+
+    fig, axes = plt.subplots(
+        nr,
+        2,
+        figsize=(8, nr * 3),
+        layout="constrained",
+        squeeze=False,
+        sharex=False,
+        sharey=True,
+    )
+
+    zs = glass.redshift_grid(0.0, 0.5, dz=0.1, xp=np)
+    zt = np.linspace(0.0, 0.5, 200)
+
+    for ax in axes.flat:
+        ax.axis("off")
+    for windows, ax in zip(plot_windows, axes.flat, strict=False):
+        ws = windows(zs)
+        wt = np.zeros_like(zt)
+        ax.axis("on")
+        ax.set_title(windows.__name__)
+        for za, wa, _ in ws:
+            wt += np.interp(zt, za, wa, left=0.0, right=0.0)
+            ax.fill_between(za, np.zeros_like(wa), wa, alpha=0.5)
+        ax.plot(zt, wt, c="k", lw=2)
+    for ax in axes.flat:
+        ax.set_xlabel("redshift $z$")
+    for ax in axes[:, 0]:
+        ax.set_ylabel("window function $W(z)$")
+
+
+Angular discretisation
+----------------------
+
+The projected fields :math:`F_i` are still continuous functions on the sphere.
+They therefore require further discretisation, which turns :math:`F_i` into a
+spherical map of finite resolution.  In *GLASS*, this is done using the
+*HEALPix* [HEALPix05]_ discretisation of the sphere.
+
+Any spherical map is a discrete collection of spherical pixels :math:`F_{i,k}`,
+:math:`k = 1, 2, \ldots`.  There are two ways that values can be assigned to
+pixels:
+
+1. Each pixel is set to the average of the field over its area, or
+2. Each pixel is set to the function value at its centre.
+
+In the first case, the discretised map :math:`F_{i,k}` is approximately a
+convolution of the continuous projected field :math:`F_i` with a pixel kernel,
+usually called the :term:`pixel window function`.  This convolution is then
+sampled at the pixel centres.  In the second case, the continuous projected
+field :math:`F_i` itself is sampled at the pixel centres.
+
+*GLASS* can simulate either kind of angular discretisation.  The only
+difference between the two is whether or not the pixel window function is
+applied to the spherical harmonic expansion of the fields.
+
+
+.. _user-los-integrals:
+
+Line-of-sight integrals
+-----------------------
+
+The `radial discretisation`_ determines how well the simulation can approximate
+line-of-sight integrals of the form
+
+.. math::
+
+    I(z) = \int_{0}^{z} \! a(z') \, F(z') \, dz' \;,
+
+with :math:`a` some redshift-dependent factor, and :math:`F` a continuous field
+simulated by *GLASS*.  Integrals of this kind appear e.g. when simulating
+gravitational lensing or the distribution of galaxies.
+
+To approximate such integrals using the discretised fields :math:`F_i`, three
+additional requirements are imposed on the radial windows of the simulated
+shells:
+
+1. Every window has an associated effective redshift :math:`z_{\rm eff}` which
+   is, in some sense, representative of the window. For example, this could be
+   the mean or central redshift of the window function.
+2. The window functions of shells :math:`j < i` vanish above the effective
+   redshift :math:`z_{{\rm eff}, i}` of shell :math:`i`,
+
+   .. math::
+
+      W_j(z) = 0 \quad \text{if $j < i$ and $z \ge z_{{\rm eff}, i}$.}
+
+3. The window functions of shells :math:`j > i` vanish below the effective
+   redshift :math:`z_{{\rm eff}, i}` of shell :math:`i`,
+
+   .. math::
+
+      W_j(z) = 0 \quad \text{if $j > i$ and $z \le z_{{\rm eff}, i}$.}
+
+In short, the requirements say that each shell has an effective redshift which
+partitions the window functions of all other shells. In *GLASS*, it is stored
+as the ``zeff`` attribute of :class:`~glass.RadialWindow`.  Functions
+that construct a list of windows for shells should ensure these requirements
+are met.
+
+To approximate the integral :math:`I(z)` using the projected fields
+:math:`F_i`, it is evaluated in the effective redshifts of the windows as
+:math:`I_i = I(z_{{\rm eff}, i})`.  Inserting the partition of unity
+
+.. math::
+
+   1 = \frac{\sum_{j} W_j(z)}{\sum_{j'} W_{j'}(z)}
+
+into the integrand, and exchanging summation and integration,
+
+.. math::
+
+   I_i
+   = \sum_{j \le i} \int_{0}^{z_{{\rm eff}, i}} \!
+            a(z') \, \frac{W_j(z')}{\sum_{j'} W_{j'}(z')} \, F(z') \, dz' \;,
+
+where the outer sum was truncated at :math:`j = i` using requirement 3.
+Conversely, the remaining integrals can for :math:`j < i` be extended to
+infinity using requirement 2.
+
+Now the crucial part:  If the radial discretisation is sufficiently fine,
+everything in the integrands except for :math:`W_j(z) \, F(z)` can be
+approximated by its value in the effective redshift :math:`z_{{\rm eff}, j}`,
+
+.. math::
+
+   I_i
+   \approx \sum_{j < i} a(z_{{\rm eff}, j}) \,
+                    \frac{1}{W_j(z_{{\rm eff}, j})} \,
+                    \int W_j(z') \, F(z') \, dz'
+   + R_i \;,
+
+where :math:`\sum_{j'} W_{j'}(z_{{\rm eff}, j}) = W_j(z_{{\rm eff}, j})` by
+requirements 2 and 3 above, and :math:`R_i` is the remaining contribution of
+shell :math:`i` to the integral,
+
+.. math::
+
+    R_i
+    = \int_{0}^{z_{{\rm eff}, i}} \!
+            a(z') \, \frac{W_i(z')}{\sum_{j'} W_{j'}(z')} \, F(z') \, dz' \;.
+
+Overall, the approximation of the integral by the projected fields :math:`F_i`
+is
+
+.. math::
+
+   I_i
+   \approx \sum_{j < i} a(z_{{\rm eff}, j}) \,
+        \frac{\int W_j(z) \, dz}{W_j(z_{{\rm eff}, j})} \, F_j
+   + R_i \;.
+
+It depends on the application whether :math:`R_i` is best approximated as zero,
+or
+
+.. math::
+
+    R_i
+    \approx a(z_{{\rm eff}, i}) \,
+        \frac{\int W_i(z) \, dz}{W_i(z_{{\rm eff}, i})} \, F_i \;,
+
+or set to some other value.
diff --git a/glass/docs/user/index.rst b/glass/docs/user/index.rst
new file mode 100644
index 0000000..c5682d7
--- /dev/null
+++ b/glass/docs/user/index.rst
@@ -0,0 +1,11 @@
+==========
+User guide
+==========
+
+.. toctree::
+   :maxdepth: 2
+
+   how-glass-works
+   publications
+   bibliography
+   definitions
diff --git a/glass/docs/user/publications.rst b/glass/docs/user/publications.rst
new file mode 100644
index 0000000..c6a7473
--- /dev/null
+++ b/glass/docs/user/publications.rst
@@ -0,0 +1,10 @@
+Publications
+============
+
+The following publications about *GLASS* exist.
+
+.. [Tessore23] *GLASS: Generator for Large Scale Structure*.  Tessore N.,
+   Loureiro A., Joachimi B., von Wietersheim-Kramsta M., Jeffrey N.  The Open
+   Journal of Astrophysics 6, 11 (2023)
+   [`arXiv <https://arxiv.org/abs/2302.01942>`_,
+   `ADS <https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..11T>`_]
diff --git a/glass/examples/1-basic/density.ipynb b/glass/examples/1-basic/density.ipynb
new file mode 100644
index 0000000..9d5b70d
--- /dev/null
+++ b/glass/examples/1-basic/density.ipynb
@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Galaxy distribution\n",
+    "\n",
+    "This example simulates a matter-only light cone up to redshift 1 and samples galaxies from a uniform distribution in redshift.\n",
+    "The results are shown in a pseudo-3D plot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Set up a galaxy-positions-only GLASS simulation. The setup for angular matter power spectra matches the definition from the [Matter shell definition](shells.ipynb) example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.colors import LogNorm\n",
+    "\n",
+    "# use the CAMB cosmology that generated the matter power spectra\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = lmax = 128\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# linear radial window functions\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute the angular matter power spectra of the shells with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up lognormal matter fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - HEALPix pixel window function (`nside=nside`)\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# generator for lognormal matter fields\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Galaxies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constant galaxy density distribution\n",
+    "z = np.linspace(0.0, 1.0, 100)\n",
+    "dndz = np.full_like(z, 0.01)\n",
+    "\n",
+    "# distribute the dN/dz over the linear window functions\n",
+    "ngal = glass.partition(z, dndz, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The goal of this example is to make a 3D cube of the sampled galaxy numbers.\n",
+    "A redshift cube is initialised with zero counts, and the simulation is run.\n",
+    "For every shell in the light cone, the galaxies are counted in the cube."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a cube for galaxy number in redshift\n",
+    "zcub = np.linspace(-zb[-1], zb[-1], 21)\n",
+    "cube = np.zeros((zcub.size - 1,) * 3)\n",
+    "\n",
+    "# simulate and add galaxies in each matter shell to cube\n",
+    "for i, delta_i in enumerate(matter):\n",
+    "    # simulate positions from matter density\n",
+    "    for gal_lon, gal_lat, gal_count in glass.positions_from_delta(\n",
+    "        ngal[i],\n",
+    "        delta_i,\n",
+    "        rng=rng,\n",
+    "    ):\n",
+    "        # sample redshifts uniformly in shell\n",
+    "        gal_z = glass.redshifts(gal_count, shells[i], rng=rng)\n",
+    "\n",
+    "        # add counts to cube\n",
+    "        z1 = gal_z * np.cos(np.deg2rad(gal_lon)) * np.cos(np.deg2rad(gal_lat))\n",
+    "        z2 = gal_z * np.sin(np.deg2rad(gal_lon)) * np.cos(np.deg2rad(gal_lat))\n",
+    "        z3 = gal_z * np.sin(np.deg2rad(gal_lat))\n",
+    "        indices, count = np.unique(\n",
+    "            np.searchsorted(zcub[1:], [z1, z2, z3]),\n",
+    "            axis=1,\n",
+    "            return_counts=True,\n",
+    "        )\n",
+    "        cube[tuple(indices)] += count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualisation\n",
+    "\n",
+    "Lastly, make a pseudo-3D plot by stacking a number of density slices on top of each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# positions of grid cells of the cube\n",
+    "z = (zcub[:-1] + zcub[1:]) / 2\n",
+    "z1, z2, z3 = np.meshgrid(z, z, z)\n",
+    "\n",
+    "# plot the galaxy distribution in pseudo-3D\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection=\"3d\", proj_type=\"ortho\")\n",
+    "norm = LogNorm(vmin=np.min(cube[cube > 0]), vmax=np.max(cube), clip=True)\n",
+    "for i in range(len(zcub) - 1):\n",
+    "    v = norm(cube[..., i])\n",
+    "    c = plt.cm.inferno(v)\n",
+    "    c[..., -1] = 0.2 * v\n",
+    "    ax.plot_surface(\n",
+    "        z1[..., i],\n",
+    "        z2[..., i],\n",
+    "        z3[..., i],\n",
+    "        rstride=1,\n",
+    "        cstride=1,\n",
+    "        facecolors=c,\n",
+    "        linewidth=0,\n",
+    "        shade=False,\n",
+    "        antialiased=False,\n",
+    "    )\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/1-basic/galaxy-redshift-distributions.ipynb b/glass/examples/1-basic/galaxy-redshift-distributions.ipynb
new file mode 100644
index 0000000..c0bdb4c
--- /dev/null
+++ b/glass/examples/1-basic/galaxy-redshift-distributions.ipynb
@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Galaxy redshift distributions\n",
+    "\n",
+    "Simulating continuous redshift distributions from a pre-constructed set of density or lensing shells (be it GLASS or other simulations) comes with an important caveat: the two-point (and higher-order) statistics are **fixed by the shells**, no matter what redshifts are ultimately assigned to the galaxies.\n",
+    "\n",
+    "Within a particular shell, the individual galaxy redshifts are effectively dummy variables. The clustering and lensing signals are fully determined by the definition of the shell (and these internal redshifts have no impact).\n",
+    "\n",
+    "This example notebook shows how the choice of matter shells fully determines the final redshift distribution of galaxies, and how this affects galaxy clustering and lensing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, a number of required imports."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import glass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will require a cosmology object below. Here, we construct an arbitrary cosmology from CAMB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arbitrary example cosmology from CAMB\n",
+    "import camb\n",
+    "import camb.sources\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "lmax = 200\n",
+    "\n",
+    "# basic cosmology\n",
+    "pars = camb.set_params(\n",
+    "    H0=70.0,\n",
+    "    omch2=0.25 * 0.7**2,\n",
+    "    ombh2=0.05 * 0.7**2,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get cosmology object for GLASS\n",
+    "cosmo = Cosmology(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example, we want to simulate a normal-looking redshift distribution such as the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = np.arange(0.0, 5.0, 0.01)\n",
+    "nz = glass.smail_nz(z, 1.0, 1.5, 2.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 567
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(z, nz)\n",
+    "plt.xlabel(\"$z$\")\n",
+    "plt.ylabel(\"$n(z)$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation setup\n",
+    "\n",
+    "For the simulation (GLASS or otherwise), we have a given set of redshift shells. Here, we use a comoving distance grid as an arbitrary example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zgrid = glass.distance_grid(cosmo, 0.0, 3.5, num=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then have to set the radial profile of the shells. Here, we use a tophat profile in redshift as our first example. Later, we will see how this impacts the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shells = glass.tophat_windows(zgrid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are all the ingredients that define the radial discretisation of the simulation. For a GLASS simulation, these are usually user choices; for *N*-body simulations, they might instead be given."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching the redshift distribution\n",
+    "\n",
+    "With the (matter or lensing) shells defined, the clustering or lensing of each sampled galaxy is fully determined by its associated shell. The only choice is *how many* galaxies are assigned to each shell: having relatively more galaxies in a specific shell will make the clustering or lensing signal from that shell become more prominent in the final galaxy sample.\n",
+    "\n",
+    "**The clustering and lensing signals are fully determined by how many galaxies end up in each shell.**\n",
+    "\n",
+    "We expect that if we assign galaxies to shells according to their desired $n(z)$, we will recover the clustering and lensing signals to the best of our abilities.\n",
+    "\n",
+    "Fortunately, we can predict exactly how this happens, at least for linear galaxy bias."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In GLASS, we can use the `glass.partition()` function to match a given $n(z)$ to the radial profile of shells. For tophat shells, this is trivial; for other shells, it requires techniques such a non-negative least squares solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ngal = glass.partition(z, nz, shells)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00125538, 0.00564149, 0.01252906, 0.02165866, 0.03282337,\n",
+       "       0.04573353, 0.05985262, 0.07430384, 0.087862  , 0.09896062,\n",
+       "       0.10572553, 0.10658204, 0.1004076 , 0.08627026, 0.06721855,\n",
+       "       0.04606434, 0.02695859, 0.0130401 , 0.0051696 , 0.00194282])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the result of splitting the n(z) up over shells\n",
+    "ngal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It bears repeating: after the shells are fixed, this relative distribution of galaxies over shells is the only remaining degree of freedom. Within each shell, the clustering and lensing signal is given by radial profile of the shell itself. The relative galaxy numbers `ngal` say how we combine this signal; the true $n(z)$ is hence the sum of the radial profiles of the shell, weighted by `ngal`.\n",
+    "\n",
+    "Using the function `glass.combine()` we can produce the effective $n(z)$ of our simulation, given a set of shell and their relative weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nz_eff = glass.combine(z, ngal, shells)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 567
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show input n(z) and effective output of the simulation\n",
+    "plt.plot(z, nz, label=\"input\")\n",
+    "plt.plot(z, nz_eff, label=\"simulated\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"$z$\")\n",
+    "plt.ylabel(\"$n(z)$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given a fixed set of shells, this is really the \"best\" (or at least close) effective $n(z)$ that we can sample."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Effective clustering and lensing\n",
+    "\n",
+    "Similar to the above, we can also compute the expected galaxy clustering and lensing signals: if all we do is put more of fewer galaxies into each shell, their combined signals are a linear combination of the signals of the shells.\n",
+    "\n",
+    "To demonstrate this, we must first compute the spectra of the shells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute angular matter power spectra with CAMB\n",
+    "spectra = glass.ext.camb.matter_cls(pars, lmax, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compute the effective clustering spectra for `ngal` galaxies in each shell, we can use the `glass.effective_cls()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clustering_weights = glass.position_weights(ngal, bias=1.0)\n",
+    "clustering = glass.effective_cls(spectra, clustering_weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lensing is a non-local effect that happens along the line of sight between a galaxy and the observer. Therefore, we must first convert the weights `ngal` into a set of \"lensing weights\" that take this line-of-sight effect into account.\n",
+    "\n",
+    "How the lensing is computed is fully determined by the shell definition in GLASS. However, there *is* an additional degree of freedom here; namely, the effective redshift of each shell (see the GLASS paper for more details)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lensing_weights = glass.multi_plane_weights(ngal, shells, cosmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.40172249e-03, 3.96236548e-03, 6.14957033e-03, 7.88046658e-03,\n",
+       "       9.09531219e-03, 9.76151091e-03, 9.87782573e-03, 9.47783633e-03,\n",
+       "       8.63145117e-03, 7.44327199e-03, 6.04631015e-03, 4.58919458e-03,\n",
+       "       3.21865127e-03, 2.05760202e-03, 1.17603538e-03, 5.87348465e-04,\n",
+       "       2.49054056e-04, 8.57266500e-05, 2.05607514e-05, 0.00000000e+00])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lensing weights are an array of numbers much like ngal\n",
+    "lensing_weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute the lensing signal from the lensing weights\n",
+    "lensing = glass.effective_cls(spectra, lensing_weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having both the clustering and lensing weights, we can also compute the cross-correlation between clustering and lensing (galaxy-galaxy lensing) by providing both sets of weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cross = glass.effective_cls(spectra, ngal, lensing_weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point, we can **predict exactly what our simulated signals will look like**, without running a simulation at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 433,
+       "width": 578
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the expected galaxy spectrum from the simulation\n",
+    "ell = np.arange(lmax + 1)\n",
+    "plt.plot(ell[1:], clustering[1:], label=\"clustering\")\n",
+    "plt.plot(ell[1:], lensing[1:], label=\"lensing\")\n",
+    "plt.plot(ell[1:], cross[1:], label=\"cross\")\n",
+    "plt.legend(loc=\"lower left\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"$\\\\ell$\")\n",
+    "plt.ylabel(\"$C_\\\\ell$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can compare this with the expected signal from theory using the **original** $n(z)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# boost accuracy for power spectrum computation\n",
+    "pars.Accuracy.TimeStepBoost = 5\n",
+    "\n",
+    "# no effects on top of clustering for demonstration\n",
+    "pars.SourceTerms.counts_density = True\n",
+    "pars.SourceTerms.counts_redshift = False\n",
+    "pars.SourceTerms.counts_lensing = False\n",
+    "pars.SourceTerms.counts_velocity = False\n",
+    "pars.SourceTerms.counts_radial = False\n",
+    "pars.SourceTerms.counts_timedelay = False\n",
+    "pars.SourceTerms.counts_ISW = False\n",
+    "pars.SourceTerms.counts_potential = False\n",
+    "pars.SourceTerms.counts_evolve = False\n",
+    "\n",
+    "# angular scales\n",
+    "pars.min_l = 1\n",
+    "pars.set_for_lmax(lmax)\n",
+    "\n",
+    "# use the _original_ n(z) for theory\n",
+    "pars.SourceWindows = [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=nz, source_type=\"counts\"),\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=nz, source_type=\"lensing\"),\n",
+    "]\n",
+    "\n",
+    "# compute theory spectra\n",
+    "theory = camb.get_results(pars).get_source_cls_dict(lmax=lmax, raw_cl=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 433,
+       "width": 578
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the expected galaxy spectrum from the simulation\n",
+    "# overplot the theory using the original n(z)\n",
+    "ell = np.arange(lmax + 1)\n",
+    "plt.plot(ell[1:], clustering[1:], label=\"clustering\")\n",
+    "plt.plot(ell[1:], lensing[1:], label=\"lensing\")\n",
+    "plt.plot(ell[1:], cross[1:], label=\"cross\")\n",
+    "plt.plot(ell[1:], theory[\"W1xW1\"][1:], label=\"clustering (theory)\", c=\"C0\", ls=\"--\")\n",
+    "plt.plot(ell[1:], theory[\"W2xW2\"][1:], label=\"lensing (theory)\", c=\"C1\", ls=\"--\")\n",
+    "plt.plot(ell[1:], theory[\"W1xW2\"][1:], label=\"cross (theory)\", c=\"C2\", ls=\"--\")\n",
+    "plt.legend(loc=\"lower left\", ncols=2)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"$\\\\ell$\")\n",
+    "plt.ylabel(\"$C_\\\\ell$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For an in-depth explanation of these differences at large scales, please refer to the GLASS paper."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear windows\n",
+    "\n",
+    "We have seen that the quality of our simulated galaxy redshift distribution is closely tied to how well we can match the individual radial profiles of the shells to our desired $n(z)$. For tophat windows, as used above, it is clear that this match will never be close unless the shells are very thin compared to the width of the $n(z)$.\n",
+    "\n",
+    "An alternative is the linear window function available as `glass.linear_windows()`, which is an overlapping triangular profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shells_lin = glass.linear_windows(zgrid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 567
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the linear windows\n",
+    "for shell in shells_lin:\n",
+    "    plt.fill(shell.za, shell.wa, alpha=0.5)\n",
+    "plt.xlabel(\"$z$\")\n",
+    "plt.ylabel(\"shell profile\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The reason for this choice is simple: if tophat windows correspond to nearest-neighbour interpolation of the desired galaxy $n(z)$, linear windows correspond to linear interpolation, which greatly improves the quality of the effective $n(z)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 567
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ngal_lin = glass.partition(z, nz, shells_lin)\n",
+    "nz_eff_lin = glass.combine(z, ngal_lin, shells_lin)\n",
+    "\n",
+    "# show input n(z) and effective output of the simulation\n",
+    "plt.plot(z, nz, label=\"input\")\n",
+    "plt.plot(z, nz_eff_lin, label=\"simulated (linear windows)\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"$z$\")\n",
+    "plt.ylabel(\"$n(z)$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recomputing all weights, we similarly find a much better agreement with theory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lensing weights for linear windows\n",
+    "lensing_weights_lin = glass.multi_plane_weights(ngal_lin, shells_lin, cosmo)\n",
+    "\n",
+    "# recompute the matter power spectra of the shells for linear windows\n",
+    "spectra_lin = glass.ext.camb.matter_cls(pars, lmax, shells_lin)\n",
+    "\n",
+    "# compute the observed spectra from the weights\n",
+    "clustering_lin = glass.effective_cls(spectra_lin, ngal_lin)\n",
+    "lensing_lin = glass.effective_cls(spectra_lin, lensing_weights_lin)\n",
+    "cross_lin = glass.effective_cls(spectra_lin, ngal_lin, lensing_weights_lin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 433,
+       "width": 578
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the expected galaxy spectrum from the simulation using linear windows\n",
+    "# overplot the theory using the original n(z)\n",
+    "ell = np.arange(lmax + 1)\n",
+    "plt.plot(ell[1:], clustering_lin[1:], label=\"clustering\")\n",
+    "plt.plot(ell[1:], lensing_lin[1:], label=\"lensing\")\n",
+    "plt.plot(ell[1:], cross_lin[1:], label=\"cross\")\n",
+    "plt.plot(ell[1:], theory[\"W1xW1\"][1:], label=\"clustering (theory)\", c=\"C0\", ls=\"--\")\n",
+    "plt.plot(ell[1:], theory[\"W2xW2\"][1:], label=\"lensing (theory)\", c=\"C1\", ls=\"--\")\n",
+    "plt.plot(ell[1:], theory[\"W1xW2\"][1:], label=\"cross (theory)\", c=\"C2\", ls=\"--\")\n",
+    "plt.legend(loc=\"lower left\", ncols=2)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"$\\\\ell$\")\n",
+    "plt.ylabel(\"$C_\\\\ell$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All in all, using linear window functions is therefore a simple way to greatly improve the quality of a simulation, and is our recommended default for GLASS."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/1-basic/lensing.ipynb b/glass/examples/1-basic/lensing.ipynb
new file mode 100644
index 0000000..b3eaf9d
--- /dev/null
+++ b/glass/examples/1-basic/lensing.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weak lensing\n",
+    "\n",
+    "This example computes weak lensing maps (convergence and shear) for a redshift distribution of sources.\n",
+    "The lensing is simulated by a line of sight integration of the matter fields."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Simulate the matter fields, and use them to iteratively compute the lensing fields.\n",
+    "\n",
+    "To obtain the effective integrated lensing maps of a distribution of sources, the fields from each plane are collected and added with their respective weights.\n",
+    "\n",
+    "The setup for angular matter power spectra matches the definition from the [Matter shell definition](shells.ipynb) example.\n",
+    "CAMB is also used further below to compute the theory lensing spectra."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# use the CAMB cosmology that generated the matter power spectra\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = lmax = 256\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# linear radial window functions\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute the angular matter power spectra of the shells with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - HEALPix pixel window function (`nside=nside`)\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up lognormal fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# generator for lognormal matter fields\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lensing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will compute the convergence field iteratively\n",
+    "convergence = glass.MultiPlaneConvergence(cosmo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Galaxy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# localised redshift distribution\n",
+    "# the actual density per arcmin2 does not matter here, it is never used\n",
+    "z = np.linspace(0.0, 1.0, 101)\n",
+    "dndz = np.exp(-((z - 0.5) ** 2) / (0.1) ** 2)\n",
+    "\n",
+    "# distribute dN/dz over the radial window functions\n",
+    "ngal = glass.partition(z, dndz, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The simulation is then straightforward: compute the lensing fields for each shell and add them up using the galaxy densities as weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the integrated convergence and shear field over the redshift distribution\n",
+    "kappa_bar = np.zeros(12 * nside**2)\n",
+    "gamm1_bar = np.zeros(12 * nside**2)\n",
+    "gamm2_bar = np.zeros(12 * nside**2)\n",
+    "\n",
+    "# main loop to simulate the matter fields iterative\n",
+    "for i, delta_i in enumerate(matter):\n",
+    "    # add lensing plane from the window function of this shell\n",
+    "    convergence.add_window(delta_i, shells[i])\n",
+    "\n",
+    "    # get convergence field\n",
+    "    kappa_i = convergence.kappa\n",
+    "\n",
+    "    # compute shear field\n",
+    "    gamm1_i, gamm2_i = glass.shear_from_convergence(kappa_i)\n",
+    "\n",
+    "    # add to mean fields using the galaxy number density as weight\n",
+    "    kappa_bar += ngal[i] * kappa_i\n",
+    "    gamm1_bar += ngal[i] * gamm1_i\n",
+    "    gamm2_bar += ngal[i] * gamm2_i\n",
+    "\n",
+    "# normalise mean fields by the total galaxy number density\n",
+    "kappa_bar /= ngal.sum()\n",
+    "gamm1_bar /= ngal.sum()\n",
+    "gamm2_bar /= ngal.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "To make sure the simulation works, compute the angular power spectrum of the simulated convergence field, and compare with the expectation (from CAMB) for the given redshift distribution of sources.\n",
+    "\n",
+    "We are not doing the modelling very carefully here, so a bit of discrepancy is to be expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jINe9e3eTNevWrRsCKsXVTRKyRdolS5Y0x8yCQGuXgb/88ccfrkLo4sWL+/3nJcfvv//uOqYOHTo48ubNa86zmPHs2bOBPjwRiVDcUcxdaqVKlTIFy3ny5HE0a9bM7NjKly+fY/jw4QkKmKtWrWp2ndkLmL///ntHuXLlzA5i7nLbsGFDgp/BHWVNmzZ1ZM2a1RR6V6xY0fHqq6+6bmexc//+/R0FChQwj1GiRAnH5MmTXbcPGzbMkT9/fkdUVJQpsiYWdXP3HHfc8fF4vDyWb775xvV9EydONLvjWBBev359j7vY+LN79+7tyJ07t3ksbiLi8SZVJM7Cb17HQnB/vX9H8T+BDdFC06lTp0yzK0bEN954I4Id+0ywdwTTqCz84ycUf+OnnYULF5rz/GTAXh6BxKJxflIhFpL/+eefruwWs0ih0PpeRMSOmR72KWJGKRSaL4bS+7eW2CIEU6pWMZs/l9fsnnzySdd57rwIpgJt7pCwB0RaZhMRETsFSBEiteqP3Hc4WLsyvv/+e1OEGAwF2mnTpsVtt92Gxo0bI2vWrK7ju3LlSkCPT0REgocCpAgRiACJgchjjz1mzjN7FchibRYVsvjP2rHBwkHuHOGOEGJ6eunSpQE7PhERb3B8BytltLzmewqQIkQgAiR69NFHTaBEDJD81R/jWlhzZS0x3n777a7r27Rp4zrP3RsiIiKkACnCAiQ2GkusF4Y/sHmYVevDbZ6BqvVxrz+yMINkNR9jgKQ9CyIiQgqQIgCbdFlTjxkc2XsARUqxtr1BpD2DxB0MrEWiPXv2pMruPhERCX4KkCLAv//+a2ajpfbymn2NvHTp0uY8O7Vye32gAiS2OmDnWjv7Mpt2s4mICClAigCBqj+ysAOr1X/I6keUmtj59Z9//jHnq1at6qqJsrRs2dI110cBkoiIkAKkCBDoAIk464zZG2L7fGtAY2rXH9mX1yz58+d3TcLmTjfOMBIRkcimACkCBEOAxC2oDzzwgDl/+vRpfP755wEv0LazN43UbjYREVGAFGEBklULlCLc2cUhg9u3Azt2AFyu2reP29KA48cZ8QDnzwNstJjELjD7Mts777yTajvGEivQjuQAiUN7N27cGOjDEBEJWgmLMSTsMAixAqQiRYq4Oke7sC/RgQPA/v3xQY91sl/m+bNnk/9DWePDrfP8mikTkC8fUKAAqhQogI8LFMDvBw7gwMaN+OO991DhzjvNbciSBf56/lYGiZOnb775Zo/3K1myJMqVK2cKyFesWIEDBw6gAI8rDLEhptVcjoESu4qLiEhCCpDCHHsPnTx58r/ltcOHgU8/BWbMAHbujL/sa8wkWWM7mF3iz9i0yVzs6jwZPXv+9z033BAfKPFUpAhw661AqVLxp5Iluf3M6x18LNK2skdWMbYn3M1m7bD77rvv8PjjjyMcffDBB67s3YIFCxQgiYh4oAApzDF7xP/JHKjxCpfHOBstJTPHGLjwewoWZDUzt6QBly/HP4b9a2LXMfN08GD8+aQwkOIpsQJpBk1WwGSdGEQVLQpERydreS2x+iN7gPTKK6+4drOFY4B0/vx5E/xZrP5YIiKSkAKkcPbHH8jxyivYCyAfL//9d8LbCxeOPzH4YRBkBUL28wyQrhezFaxV4lLewYP4YtQorPvxR3AB666KFVEmR47423hikOTJnj3xp4ULE16fMWN8hqlcOYCZEOvEZb1kFmhbKleujKJFi5qhuj/99BNiYmKQLVs2hJN58+Yl2EGoAElExDMFSOHmxAngiy+Ajz4C1qzBVYsnDHq6dAEeeig+C5MamHXKlSv+VL48bi9WDA8wqAHw9unT2L52rRmBYvDNm0XgfON2PzmXChNgA0wu3/H05Zf/Xc9sV6VKuG3zZrQHsJ5LbFWqXOMwo0yx9rhx43D58mXMmTMHHTt2RDBmBVeuXGl2BXLgbkp89dVXCS4rQBIR8SzKoeFTXjl16pTJLjDLwHEVARUbG59ZYVDEHVgXLya4mZdmAWj2xRfIdt998cXTAXbXXXeZbAbNnj3bzERLEn9Njx79L1jiUpx1nrVU11rCIxaMV6jwX5apWjWgYkXAFmQsXrwYjRo1MufbtWuH6dOnI5icPXvWjIs5ePAgOnXqhKlTp6ZoeS1v3rxX9aA6fvw4cjCLJyISAU4l8/1bAVKoB0gsgG7QgGmFq2+rUgXP7tyJ92NiEJUrF44ywAgSs2bNcm2tv/fee/H99997/2AMjhgorV+f8HTs2LW/l3PpGCxx+a16dVypUgX569bFsRMnzI4/Fnhn5DJekPjhhx/QokULcz46Oto0tSxevHiyvpftC+xjVSzctXfHHXf4/FhFREL5/Vt9kELd3LkJg6PcuYF+/YANGxDz008YHhOD4wFsEJmYe+65x7QdsDJI1igQr7ClQPnyQKdOwJtvxmfTjhzBtDffxD0AnuVSEjNHJUpc/b1sc8BC7vHjzdJj2vLlsfvMGcwHMOTMGfzBom22OQgSc/n/2ykuLg6jRo3yanmtadOmrvNaZhMRuZoCpBRig0MOO02s4WCqs9flPPdcfN+i0aPN0tGWLVtcNwVbgMR5aI899pg5zyTmhAkTfPsDoqKwZMcOzAEwHMCBcePiG12eOgUsXw68/XZ8LZaHxpmZL18Gw4dnAFR79dX4gnUWs7dtGx+A/fprfO1TgAMk+uijj5KVGbTvXuNy2pNPPum6TQGSiMjVFCClUM+ePc28LvvuqICyN3BkTQ2XjIJoxEhSHn30Udfg2EmTJuGiW+3U9bK2+LP4mkNqDe7Kq1UL6NWLQ+His28sbGfWafhwttRGHAu83THw5CDbp54CatcGuLuN89sGDgRmzozfgednO3bswE7WW7kFPu+++26Kdq9xma08M25OCpBERK6mACnU2Qtu3bpRB3uAxCGxbZmVAVfEjuDrr7/22WNfuHDBNUqDGb8bkmpXkD070LgxMGSICYKiDxzAo3fdBVbrjODGQAZX7uvUXJpbuRLgEhcL37k7kF26H3yQaUbO8khZv6lk+PHHH13nn3jiCdfOv/Hjx5tAKSn2YnMWnxcrVgzpuDSpAElExCMFSKHOnkFyGyMS7AES2Zd6kpMJSa7169fjijNA8WY5tP4DD+Bb5zLbK/Xrx2eZ/vgDmDgRePhhzy0SWEfFIbzMTrGlgBV4DR0an6Fy2z12PctrnGvHQMcKLj/55JNEv4/Bk1UEz+W1xo0bm8wdd8NZmalY7oQUEREXBUjhFCAlkkHKnDmzqyA62NSrV89keOiXX37BJudIkuuVkgaRiRWRWxkadtV2sJcTm1E++igweTLA+i6OMGHgwcwTgyi2EXD/f/PTT8CwYayKjg+YeCwDBsQv1zlHoCQ3I8YWBMQZcRUqVMCgQYNct7/11luJBjkMrOzLa1bmqJQzyOPSJkeyiIjIfxQghekSG99Q/3Z2zi5durTZEh6MWB/EbIjlvffe88nj2keMeJNBypkzpxnoSrt27fIcuHHH4L33xtcuLVkCxMQwMgPGjgXat48v7LZjAMPbWUTPpcW8eeOLxLt3B5gB4v+vRLpuLFu2zLWMxh5SVl1Vw4YNzXXbt29PMEIksd1rVtbJHiAR2wWIiMh/gvNdU647g8Q3TG4DD+blNUvnzp2RxXnsbHx4OrFxI15kkNKnT4+KbAbpBatPk5VFuiZmZlgo36dPfFdvjkZhZobDgTnXjRkod6z/mTQJ6NoVYD8jBlU8z2DHtlPOvrzGAMnyFIvGnd7kDrtr7F7j8pqnAEl1SCIiCSlACtMapFCoP7KwYdeDLG42CbEz+JQBxXU4efKk6w2fk+oZJHmjVatWCZoseoXDdPnc3n8/voaJW/JnzQK4PMbmjO5dzdlzidkk/uw8eQCOOpk5E0tmzzY3MxPYpEmTBMFSOWfg9euvv5qTHQMrdt92X16jWzns10kBkohIQgqQwilAstXAhFKARPZlNhZrX0+D9zVr1lxX/ZGFdVvVmBFyFn1zqe26cR5dy5bAG2+whXX8shxri6w6JXsdGZdPmYm67z4s27oVXCh7rnhx5LQFVVxqs9civcHHTWT32v3335/gNmWQREQSpwApXGqQMmdmeiFkAyRmemqyrxCYaPkDS5cuDViBtp19NIfXWaSk8P8ba52efx6YPz9+PMoPP8QPE7bNR8sM4D4AL7HZJWuXGGRNm8Y1NDO0tiDbDDhHuFj1RPbda6ypsmbMWXLnzm2uJwVIIiIJKUAKlwxSIlv87du5g519y/8LL7zgdRbpegu0UzVAcsfBuffcEz94+NAhdnjE/GLFcNh+HzbUZODToQObSSH9k09i5L33IsrZldwaP8K+SYktr7lnkfbt23fVEFsRkUimAClcAiTb0gy3e1sZAQZHnt4Yg1H79u1RsmRJc54ZJPexGinNIHEIob3OxhvcAWg9BtsQsOeQJyyIZzBy+PBhsxTHLNhvv/2G3bt3e//D06XDpQYNcN+xY2B+qNWNN8LBILJAgf/uw9EpH36IBydMwK6oKAzjcU6ebI4jsd1rdtrJJiLimQKkUGd96rcFSBz8ao3tCIXlNQsDuVc5+8xpyJAhrp14ybV//36TDSHWD11vewPW+FhZJB4LC6Rr1KhhRnXcfPPNyJs3r9mBx55JWbNmRb58+XDLLbeYPkV33HEHihcvbobxemvFihVmVx87HGVllohduvfuje+vxIaVtszhTQ4HnucS5eXLuFCpEvJ//TWyJbK8ZlEdkoiIZwqQQhmDB2vERIjuYHP3f//3f665aRs2bMAXX3wRsPojT9v9Ob6ES3h//vmnCUSZUTp37lyi38tu3o888kiimadrsWfRmjdvHn+GQR/7H7FhJZfhPvuM29ngsAWDRQ8exOhLl7AfwHe5ciEdC9c9LFkqQBIR8UwBUiizvzHbMkihHCAx4/Paa6+5Lj///PO4xLlnAag/sgda97IhpA2zRsweMYvEbBKzSszStGjRAh06dDCDeK2fz+Wuxx57zKuaKnuAdOedd3ou8n7gARYcIWrvXnxWpQrW2W8GUJuF3RzQW6ECMG5c/NgUJwVIIiKJcIhXYmJi+G5nvgbMgQN8y40/tWzpuvrhhx82x8bTmjVrHKGoSZMmrufw9ttve/V9e/bs8ekxnThxwnH27FlHXFxcsu5/8OBBR548eVzH89FHH6Xo5+3fv9/1vVWrVk3W96xfv97cvwLgGAs4TkZF/fc7Yp0yZnQ4OnVyOJYtc1w4f94RHR1tvqdy5copOj4RkXB+/1YGKQy7aNszSCwyDkUjRoxwnX/55ZeTtcOKNUJWDyTOKytUqJBPjyl79uxmrh3rkpKD9UgTJkxwXe7Tp49ZlkuuefPmeeyenZRKlSqhadOm4GCUvqzj6tIFmDIFqF37vzuxQzebcdatiwy1amFgrlzI4CzSvlaWi0uMbORpjT0REQlXCpDCrIs23+CsAKlo0aKuER6hhgXW1s4rLlGN5vyya+BUenbRJi5vJTeQ8SfWLz3EnkaAKbbu2rVrokNlk1V/lAxDhw5FhgwZzKlb794Ag6RffgH+/BPo1y9BfyWsW4eRR45gD4DBZ8/igK3JprujR4+ibt26ZjQMg7AYNrkUEQlTCpDCLIN08OBB1xtXqNUfuXvllVfM7jCrQ/S1Cp39UaDtC2PHjsVNN93kal+QnGCPQdR8No50jmJhjVNy1a5d27QZYCG5VfBulC0bPyjXGmdiq9HKwy7dAPKzWSfHm7DLt1s2ac6cOTjFtgIAli9fbmquGDSJiIQjBUihzL7s5AyQQrlA2x37D7HY2cq+DB8+PNULtH2B/ZimTJniymg9++yz2LSJi2BJB3snnMXUzNaw4WdKsP8VWwx4lDEjJwQDv/1mAqHt1arhsvOmaGa3ON6ERd1cluOQXmerBQZIdmvXrkX9+vVx4MCBFB2biEgoUIAUZkts4RQgWR21MzlnzHFG27///pusDJI1Qy1YMJAYOHCgOc9deVymsnpVXWt5Lbn1RynGgO2OO7B35Egwv/UyA1HbPD+TRWrblr9IiH3vPSxxHhMDPmu0yebNm82yW1L/X0REQpECpDBbYgu3AIlvxH379nUFFqyv8eTy5ctYt26dK3tizRgLJiw2Z0sAq8fTiy++mOh9OSbE0qxZM78eF7f6Mwf0AoAH69SJH3PiPE5j2zakefJJrI+JwRDOhGvcGMuWLUOxYsXMzTt37kSdOnXUiVtEwooCpFAW5ktslqeffho5nIXFn3zyiamvccfrLnB3VpDVH9llzJjR7ACzRr+MHDnSjC9xx7oeKxvGjtyFCxf263Fxxx+7gNMfO3bED8rduNH0VjINKZ3yA+Ai5/tz5uCW997D8pkzXWNY9u7di3r16l1z6VBEJOwCJM6VktBZYuOkdp7CAbfXDx482LVL75lnnkmy/ihYAyRrG/6wYcNcbQm6dOli6qvsFixY4Npu77flNRvWRlkNI9mGwASaXH7jz+ZIk9WrMS9bNjPuhNJxafDNN1Gwdm2sq1cPTZytJA4dOmSaWVqF3CIiEREgsbDUnkJftGiRmUslwbPExt1rVsFsuGSPLL1793b1Nfr+++/NLqpQKND25KmnnjI7zYiDbQcMGJD69UdurACJgRnbJdjtzZ8fd8XEgLmiGXnzAhkyuPopZZ40CfP//hsz8+RBMecuyuTs0hMRCZsAiUsDjz/+uPnKIZysBWHjOwmeJbZwXF6zsFDbXrPDjJK9qaG1JMW2AJUrV0Yw4zFyqdBa1po0aZIJ+qyskhUgsYeVFUj5W1IjR6x6qL8B/Pnkk4zqAAZ1HHPCDNSlS2h75Ai287kAmD5ypNez50REQi5A4qfK119/3dSDcD4Waydatmzp36OTFGWQwjlAIjZctN7I+fs3e/Zsc/7s2bOm549Vs2Ptegtmt9xyS4JMC9sZMKhYv369aYxJ7DPEZo+BDpCsf2e6++67WbQEvPUW1+OAIUOAG24wt7ERQTf2njx3DtuYXd67N1WOXUQkoAHS22+/baaScxmHNSD/+9//TG8XCZ4apHAPkNgL6NVXX3VdHjJkiGmoyH48zLwEe/2Ru27dupnhtsSgiBla++61lHTP9leAxFYECxcuNOc5nDdB48k8eQD2pmKg9OKLiLvxRnN1ejar3LgRjhIlAO5APHgw1Z6HiEiqB0jTpk0zO4XYvI7LbFzG0LbeAIuwDBK1bdvWFQTx9/Hzzz8Pqfoj9+LoiRMnuorpv/nmG9MxPLW299uVLFnSY4DE7fzM0FkBW3S0h5cMtlQYOhTR//6LhTVrwio5j2Ix97hxTJdxKyLgHAMjIhKWu9i4RZnLGB07dkzwaV6CpwaJtStFihRBOGJQ8dprr7kuW8u9llDKIHkaaGuNieH2eS7DpRb77wwDJKu+y9492yyvJSV7dlT54QdUuuEGvA7gnHU9B9uOHBkfKI0axbSU356HiEjAd7H99NNP2sUWRBmkC2nTmh1RVLp06aAY1OovDRs2NNvJiR2cv/32W3M+c+bMKMt5YyGmTZs2roG2qb17zdMyGwf+WkXWVv0RC8utf/OksEFnt6efBpsy3MzvZ2bKqqPi6BR2E2dbgM8+c40wEREJq11sHAGhXWxBEiClSYNt//zjqsMJ1+U1O3sWyVKlSpUUzywLxoG2gQqQrKaPxA9D3JhhfSjibjr2o0oOdj5nvRJLze/dvh2bZsyIn/1mBe2sWerUifNg2C/EP09GROQ6aRdbOCyxcXltyxbX1ZEQILEGrkOHDgmuC7XlNTvON5sxY4bJgLVv3z5Z2Rp/F2rbC8avubxmw/YFfI2wDHr7bbZA53RbwP68OBqGWWgGgxs2mHqnmjVrmuL1K1eu+OIpiYgEdhdbv3798NFHH+H3339PcgCn+CmDFAE72BKbbWbPGIVSgbYnHLDLdgVffvmlWdIKdICUovojN4899phrVtv8+fOxZMkS4LbbgHnzeEX8ecu8eXBUrox/6tfHvpUrMXnyZEyfPt0XT0lEJLC72NivZd++fSbDxGUODuTkp2AWcVsN8MSPAVKE7GBzx6G0PXr0cBUZ169fP9CHFNLsARIH/y5evNicZ/G2NWQ3udKnT4+XXnrJdblnz56m+74p/m7aFPj9d2DqVDiKFjW3Rzkc6OxwgK8oXDx97/XXEzQCFRFJbVEOP7wKcZYTg6mNGzea4ZXhOHqA86ayZctmMmpcHgmI9Ok5xt58Gq8YG2v+rZlROXfunGsgarhjHyR2pWZQyNo48R5r2BhoWkN/Law9fP/99736f8PZc1YTTypXrpwZG9OpUydcunQJne67D6V/+gnPAYgfRxyPA3NOPP00yrLPkqfWAiIifn7/TlGAtHnzZnzxxRcYOHBgsgs2w1XAAyQGRgyQOD+rdm1kWrPGLG8yUOD/JxFvVKxY0QTadrNmzfK63pCZqFatWmHPnj0JrufrB/9+uAuR8qZNiwWNG6PcokVIY68/YtDLXkp+Xj7ly+Dw4cOxf/9+kwm3xsCISOS+f6foo9mIESNMZshTcMRPnVtshcKSelv8z0VHu2q/ImV5Tfy/zGYtlTVu3Njrx+NS/M6dO80SvX2uHFsJWMERG2XO+OknVJw7F44//sB8+6iYlSuBGjU4i4XtxuEvrJF67rnn8O677yboSyUikStFAdLKlSsT3dqfMWNGdO/e3QRRkroB0inbJ24FSOLLAKlBgwZm2e16cLn3/vvvNztf16xZg65du5rAy8pYcdBw3bp1zeW0pUrhrxEjwL1urqo6Jrk//JDtvgEu1zN76mO//vqr6zw3m4iIpChA2rt3rymMTcwTTzyB7777zhfHJSnoon3MtnNQAZL4MkBK6e61a+Est48//tgsuXHGG8fEWLvdLNwtuzp7dlRki4DoaMQ5h+Hi1ClgwACgUiVgwQKfHhfn+Vm0RC0iKQ6Q2CX3wAGWT3rGPjTslySpm0E6bDuvAEmCOUCysJEkl+4yWF22bW644QbzYYt50bfi4vDaww9zsu9/jSa5Y5P9lNq0AXbv9nmAxFIBFpiLSGRLUYBUr1498+kv0QeLjr5qB0ww4+467qphcz4uHYbUtmJbULSfn6wTeYMTSQn77w8H2NqH2KYm7nSzdmK+8cknODNmDAdCxhdtWzhihqNl3nrrupbdjh8/jn/Y3duJr2H2yyISmVIUIA0aNMhMH0+siHHFihWpOmDzenDW1Pjx4029AXft8CtrrEJxiW3PsWPmK0dVXG+9iEQ27uzgqJAcOXIk6GOU2goWLIgHH3zQVdDN5pFmJ9vy5QAb1ObL998HhUGD4seWrFjh1c9av379VdfZWxOISGSKTmn9AHd5PPnkk2jatKkZErp7927zCYxbgTmG5IEHHkCo4DgDflq8fPmyOTHtH4oZpGOXLpmvWl4TXxgzZgyOHTuGjh07BvQ42E7Enu0140fYE6lLF66Dsfvkf8tuGzcCtWqxhTdTQl4vr1lUhyQiKe7Axp1q3BLL/gFt27bFzTffjDx58piJ5BUqVED//v19cmBLly5FixYtzCdJTqa3JrbbvfPOO6bAkzvoatSoYQo+k4vHzIxY0aJFzc9o0qQJihcvjlAMkKxzCpDEV/g3F2js3m0N7eWSFztxu7DVyPjx8ctulSv/d/3EiUDp0jg1fjya3Xmnmet2rWV/BUgi4olXLWrr1KljghG+iEydOhUffPCB2SY7d+5c1/bd63X27FnThZdBkCfsqzJgwAAMHTrUvMDxvs2aNcNhW6+U2267zbzIup/YDI4jU3744QfzwssxKTx+BmWhuMRmBUiBqhcR8Rd23LY3nbwKl934wWjsWFZ3x1935Ahu7N0bgxcswK+TJ5uRR8kJkPjaxTpKUoAkIv9N+vRC6dKlzckfmjdvbk6JGTVqlMlmPcwdLoAZhTB79mxTqzB48OBEawssX331lWlZwJ15dM8995gaJBaie8JGjPZBvOzEGSwZJCtUsp6LSLiwz4BLtC6IA4vZn+3//g9gBvurr8zVDfkawAa3w4dj8//9H8raB+Q6nT592jVTkh+y+MGJO3E525CjV6yASUQiT0j+9XOGE4uquSxm4QsZL7NQPDk4gJNZI6bfuaWXy4ZJ7QBjA0wWsFonfn+wLbFxe7RIOOHfpBWksIt/kgoVAqZPRxwzw84lQjYReDEuDhlr1UKcrRmkZcOGDa7dqxy0zR2txHmGrK8UkcgVkgHS0aNHTVCTz9rJ4sTLBw8eTNZjcLApe7xwFAK7+bL+KKl5U0OGDDF1V9bJfbZUMARIARuaK+InrC+0mtMmtz/RzltvRRnOVuNGDOd1t5w/j6g6dYC+fRMsT9vrj+wBEmmZTSSyhWSA5CusTWAqnan7cePGJVmYyoZ2DEDsp2CrQVIGScJ5mY3Z3r///vua92d2mWXZzwLoWaMG1jivj2KmiINvy5UD5s411ylAEpGwCpA43DJNmjQ4dOhQgut5OX/+/IgIHmqQAh60ifgBm7mmpD8R571ZWj7/PN7r0gWDuGxmXcmlM9Y3duqEv507X/l6wkBMAZKIXHeRNj/Nbdy40ewaYzGjXVJLVb7A3SbsycRtv61btzbX8Rh4uVevXogIqkGSCA2QrL/5xNiHzfJ1gsvppefMwTdHj4ItbhtbN372GWYC4CvGlrJlzXKefdOJAiSRyOZVgMTt/F26dDG1QO64TOWLOUZnzpxJMNdt165dZlcad2qxdxG3+HMqeLVq1cwMODa3Y2sAa1db2NMSm0RggHStQm1+ULKWzQoVKuTKKLPRZOfOncFtHb0yZ8aYtGmR5tQp5GHLEACruSv18GFkyZvX9Hbj6w0DJBZwW69pLNpm37Vg6BElIqnA4YUSJUo4nnzyScfBgwcd/rJ48WJuLbnq1LVrV9d93n77bUfRokUd6dOnd1SvXt2xcuVKR2qJiYkxx8OvAVGrFisqzCkN4MiQIUNgjkPEzy5evOhImzat+XurUKFCkvfdtm2b67WiZcuWruvj4uIc//d//+e67e4qVRzbK1Vy/Q2ZU+7cDse0aY577rnHdb89e/aY723fvr25/MQTTyT58/fu3et49tlnHb/88ovPnr+IBOb926sA6YYbbnDs2LHDEYnGjx/vKFOmjOPWW28NbIDkfHG/EBVljiM3X9xFwlTZsmXN7zk/DF2+fDnR+33++eeu4Oall15KcNuJEyccxYoVc91+4403OtoBjiP2IAlwbLj1Vkdu533mzZvnmD17tut7cuTIkeRxNm/e3NwvZ86cjgsXLvjs+YtI6gdIXhVp33fffaZvUCTq2bOnSb2vXr06KJbYtMVfImmZjT3Q7EvvSdUfcfndLnv27KYDf7p06VzNXtlSko98pVUr1/0qbtsGVh/d5+yTxOV8CxtJss2HJwcOHMC8efPMec6n5A5ZEYmwGqTx48ejXbt2WLZsmZm/Zr3gWPqwq62kSpH2aWeTO9UfSbgHSOx+bxVqJ9bB371A2x3rFV9//fUEQU/O0qWR9ptvOL8I4CaPY8dMbRJ/2jfPPecaBm1hfRLHGLmbziaVtg0rDK483U9EwjhA+uKLLzB//nyz64OZJHvRIs8rQEq9AEkZJInEQu3/41iRaxRouzeStfTr1w+LFy/G999/by6zWSz4GtahA9CwIS537450ztvaXLqE2gB6APj6GgESXxftGCCJSOjyaont2WefxUsvvWRSzRz2yhcM65ScRm5ynZg1cguQlEGSSO+FxKU3a0aip+yR/UPcxx9/bLJJWbNmRe/evf+7MV8+pJs1C71y5sQx51V5AdMOgOFPLsDjaxyv++233xJcl9QsSPf+bVeuWD2/RSSkAyTWAbRv316DHAPlwgV+XDZnlUGSSMBxI+x/llSAlFT9kTu2C+HcxpMnT6JmzZoJb4yKwo7bbze1Sd/Yru7A7BWATIsXX/V4X375ZZJz3hLD7ytQoIA5XgVJIsHFqwiH/YdY7CjB00VbGSQJZ6xztIZJb9u2zXxIS2n9kTt+wGMHbU/YVZt9+tsySLrvPsRlz26uZ1elJ2fPBp54IkEvMvvymtWNm4Xa+/btS/IYWFfFIIrB1DWH8YpI8NcgsWnayJEjzY4NDnp1L9IeNWqUr45PPNGgWonQZbZNmzaZTMv27dsTLLt5EyAl5YknnjB1SmwMedcnnyDqxAn8WKQImltF2B98ACxaBEydij+yZnUFN8xG1atXz9WFm8tshQsXTvTnMIjCNWqbRCSEAiS+SJnCxmR0tg0377zzjjn5olu419RFWyKQex2S/TILtK0AKakC7ZQs6dkDLmTKhAElS+LbrVvBj39ZeB3bDdSujSM1a5oXUi6QdezYEXnycA9cPGaG7r333kR/DtsGWFS/KRIGARI/WUUq9kHiicWg2bJlC8xBaFCtRCD3nWz3339/ggLt06dPJ6v+yFs333ILJmzdip8YoFWtivQMoOLi0HD5cqwE0CUqyhyTPSt0rUJt9wySiIR4gDRs2LAkd4g8//zz13NMci0aVCsRKKmdbL5cXksMZ7QR21T+Pno0av78M9t1I+rKFfAnro2KQoZp05DriSdMCxQO9La2+rPOiG0FOBuOu+c8BUjKIImEQYD0DZuq2Vy+fNl8+kmbNi2KFy+uACkAS2zKIEm442tLhgwZcPHixYAGSLRrzx7UfO45/JwpE/INGoQyADKwPqlvX6T97js0LlUKszdsMJktDt6eNGkS+vfvbwrDd+7caWqbWGjOAdsWBUgiYRAgrVu37qrruOT00EMPoU2bNr44LkmKltgkAnHHWZkyZcyyFQMPBkoMmGjNmjV+D5BuueWWq5bDfoqJwRsAXgPQ17px0SJMy5ABXdk/yeHA8uXL8fLLL7tqpXj8DJDs9UfWY/J2tU8RCQ4++0vkGzSbRyp7lAq0xCYRvszGTRJbt2415xmA8OSrAu1kZZCcARIzVxfYnZuz2D75hAdgrs9y8SJmAJgI4KkePRIspR05csR8tV9HzChxnpuIBAefflRhZ+3EBjmKD2mJTSKUe6E2u1CzMNpqsti9e3e//WxPAZI12iRHjhzI36kTt/gCtuLxRwHM2LULVWyPc/jwYY8BEmmZTSTEl9jGjRuX4DILEPnJZ+rUqWjevLmvjk0SowySRCh7gMQC6IkTJ2L//v3mcsOGDc0YJH/Jnj27ObH7NgMk/tyDBw+a26pUqRI/kzJHDrbHxvn69RHbsyeyArgVwAoAzwF4M4kMkhUg1a1b12/PQUT8HCCNHj06wWWumbP3BztsDxkyxJuHlJRQDZJEKHa4towdO9bUIVHBggVNN2tuFPEnZpFYg7l7926sWrXKc91TVBQyPfkkGr/2Gl7bswe3A+CQlJEA7gTw7b//mru51yDZM0hcQmQAZe+plBQGa/fccw9y5cqF2bNnu2qzRMR7Xr2aRHK/jqBoFKkMkkQoFjdnzpwZ586dcwVHDIo4ssNftUeeAiT+/c+aNSvJwvBs1aqh1p49eAnAYGc9QxMA1Tmq5NtvPWaQ+NrK5UJ25GZ905QpU9C5c+drHhez91bPpblz56JVq1bX/VxFIl2Ka5C4pb9x48am1X8kYpNIjhFYvXp10NQgseeK+7gXkXDEbDV3srmPNqpVq1aq/Hx7HZI9QOISm7t27dqZ7trjCxTAyZkzsdd5/Y2XLwNt2qD21KnI7CGD9Msvv5hdeSxd+PTTT5N1XNYyI11r/puI+ClA4hvxxo0bU/pt4sclNmWPJJJUqFDBdZ6jPXr16pVqP9seIFlLZOyozx5N7nhsW7ZsMcXkOdu2RbP8+c3ONsvta9eCzQlucwuQfvjhhxRn663Cb7LqokQkALvYOnXqhA8//PA6f7T4aolN9UcSSdhwkYFKixYtTJG2KY4OQIBk4VzKxI6hVKlSyJkzpzmfLl8+tAPwWJo0cGSOzx0xF/YbgOezZAEfgZtdZs6c6fr+f/75J1nL+QqQRIKkBolr5JMnT8bChQvN2nuWLGZ0Y4KUt6TeElshZZAkglSsWDFg2+E9BUjJbUyZN29e83VibCzeXLIEh5o0QclTp0wB97CzZ8EBJA85gyJ7SQOXz4oUKZLkY7PdgUUBkkgAM0hMGXPNnUs727ZtM0WL1ulawxnFdxmkOADnlUESSdUicW8DJPuOtEPZs+OhW2/F67bb7+VwWwDum/ytYHDRokWmjcp3332XZAbJHiyJSCpnkBYvXnwdP1J8FSCdYw8q1SCJpJpMmTKZgbP2LI2nAu1rBUjshXT45Emzu21V1qz4NDoamU6dQmG+vgJ4EcBw54cgBkj169c3tVasaWINaMuWLV2PxSW4o0ePui4rgyQSwAwSe4Bwh0Vit0nqLLGpi7ZIYJfZ+OGkZMmSKQ6QmPGxirzX582LDR9/jEXO29IA4OS2+QDyOwu1OfCWwRFxyc3eQ+nYsWMJXo8ZICX2+iwifg6Q+AJhdYO14x+qpzV68U8GSU0iRVKffWgtC7STO1zWqkFyD5BYxF349ttNE0lOsrRKshuzWzjnuv3yi2ktYsdhvfbHcp/pppFPIgEKkPjpxNOuDX7KYU8eSZ0ASU0iRVKf/UNgcpfX3DNIDHDi4uJcARI7gadNnx6vcGQKawudO98YUj29eDEyvPhignqIpAIk0jKbSCrXIA0YMMB8ZXD0/PPPm4629nXw3377DbfdZu/qEX4C3kmbP/c8S7O1xCYSCPbXuHr16nkVIG3dutV1ngESs1C33nqr2QDzO19X16/HgpIl0dTZLbzSjz/iZ/ZWYhlDMgOk0qVLe/X8RMSLAIm71KwM0qZNm5A+PTeoxuP5SpUqYdCgQQj3Tto8nTp1yjSIS3XnWJodTxkkkdTXunVrvPLKK2bESUpGetiX2NwDJHr99dfN4/bo0QOZihTBS1Wq4McVK8xON/bJr+Xc5fawMkgiwRcgWbvXHn74YYwbN05vzIGgQbUiAZUmTRo8++yzKf4+ewZp586drvM5cuQwX++++25zstxcvDhGr1iBXwB8ydon3pfDbgF8NX8+wOxShgweA6TkbPVnrdKIESPMh90333zTYwsDkUjmVQ0Sd21wOKQ7No/kpyDxIw2qFQlJzDhbMxPZbNc9g5RYrROnPrLSyf6K244ZIs6f27nTYzB0rQzS3r17zfLgiy++aDp3Dxs2zMtnJRK+vAqQJkyY4HF9u1y5cnj//fd9cVySzC7apAySSPBj7Wbu3Lmvuj6xAMm+W4570u4H0APABevKtWvZpRLFNnCvW/IDpLNnz6J27dqmZtSybNmylD0ZkQjgVYDEP74CBQp4TCFzlpCk7hKbMkgiocFeh5TcDJLdxxkzogZrmKwrYmLw7OrVpk6J/ZOSEyDNnz//qn51rGmyN5sUES8DJM4FWr58+VXX8zpuV5XUXWJTBkkkNNjrkFKSQbLceeed2AjgdgB77rjDdf3/APycJg0Kp0lzzRokduK23HTTTa7zK1euTMEzEQl/XgVI3bt3R79+/fDRRx/h33//NSfWH3HKNm+T1F1iUwZJJHQDJKtI2x0/bNp3CpO1a+40gKn33guMGYPLzttqx8bid4cD9a+RQWJRtuWxxx5znVeAJOKDAOmpp55Ct27d8OSTT5pPOTz17t0bffr0wZAhQ7x5SEku7WITiYgMEnfL2TM83GXGzt2WHTt34lz37mAnpj3O6/LGxZmRJQ8dPIg4WyG4pwwS58p16tTJdf2KFSu8fl4i4Sja22JD7lbjuBF+6tiwYQOOHz+OF154wfdHKAlpF5tIWNUgJZZBcl9mK1++PEqUKJGgbsi8Bjt3uW1wPjYX2YY7HLjMDJNtZhudO3fO1UOJm2qKFi2KwoU5IhdYtWpV4BrgioRLgGTJmjUrbr/9dvOHmyFDBt8dlSQ7QOKnQDasE5HQyyBxGkFS45nshdoVKlQwH4by5ctnLjPQsXogsbx6Ytu2mHXbbYgfYAJkmDePs1CA3393Pcaff/7pGmTLx6M7nLVMHBXF20XkOgMkbgtlerZWrVrYt2+fuW7q1Kn45Re2NZPUqkHS8ppI6AZIiS2vWUqVKnXViBMri8Qdw3///bfr9rwFCmDV3XejuTNgMv75J75f0oQJHIGQoP6oYsWKCQIkT3VI33zzjVkZsAbrikQSrwIkNhZr1qyZyV6sXbsWF53zgjhBevjw4b4+RkmiBknLayKhGyAltbxGXbt2RfPmzdGhQwe0adPGXGdfZrPXDXH5jtml+c4ltyPFi8ffcOkS8PjjwEMP4S9bNsnKINWsWdNjgMRxKG3btsXLL79sOm2LRBqvAiTOC2JDyIkTJ7o6wxKbjzFgCmccVFu2bFmztBgQbktsyiCJhG4N0rUySAyg5syZgy+++ML1WmsPkOztVvjY+fPnN+dZtD310UeBPn3+e7BPPsFjn3yCm9wySKxFstj7I3344Yeu8/rgK5HIqwCJnyw8TbFmK/2TJ08inHFQ7ebNm7F6NQcABH6JTRkkkfBdYrvWstuaNWs8Bki0n40fx44FvvwSyJLFXFfyzBnwO+7Lnt11LPyQZbUTsM91sz+2akwlEnkVIPGP0D5N2sL6I0/NzcR/S2zKIImEjuzZsyfYVOFNgMQlNzbrdeceIHG4+OXLl4H27XFs9mycd+5W47CTL/lBlstmDofZlWwFS9wVR6dPn05QT8oNOSKRxutGkX379jWzfPjHtX//fnz22WcYNGgQevTgtCBJrSU2ZZBEQoc9GElODZInDFY++OADjwESl9+sOZksd+Aw2oEDByJPw4YosHcvZjvva/ptP/UU0LGjeU2xlv44biQuLg4LFy6MD66cjh07hgsXLrgG7X777bcJOnKLhCOv9ocPHjzY/BE1btzY9NXgchtTsAyQ2DBS/Ei72ERCmn1mpTcZJCuLxALuKVOmmMtcImOJAwMwXsfdxexpZK8d4sDbFgBeBODqWDdtGrB5Mypmy4Z1zuCHZRKse3LH3crFixc3wVmvXr1Mi4Lt27drvJSELa8bRT777LOmOeQff/xhdj4wNcvdDpI6GSR+trukDJJIyLFnkLwNkGj06NGuJTXWJfF1mapXr24yR3Zc1uvYsSPatG2LuKFDETdzJl884m/ctAnvrFqFZs77sg7JU4C0Z88e17Bb4ofjH3/80evjFwl219VhkJ9aypQpY85bf5ySOgGSBtWKRHaAxOW5RYsWmd1mXbp0SXAbRz799NNPpg6JW/9nzJiBOnXqJHyAsmWB1q256wZZLl0CQ6LnAPy+Zo0pm3C3d+9e89XeTJKPz7FTIuHI60aR/KNkB212geWJ5ydNmuTbo5NrBkjKIImE7lb/6wmQiC1H3nrrLVSqVOmqOW7M9CxYsMDsOr4qOCLWKq1axQm4rjcDLshVeOklWCXZhQoVShAgnT17NkFzSgZhVmdukXDjVYDEzqos0m7RogW++uorc+L5/v37ax5bKtUgKYMkEpratWtnajY5RoS1Qv7CZbUmTZqY2qRE8fXj66+xplUr14iSijt24FcOx3VrIskA6a+//koQELGWatu2bX57DiIht8T23nvvmSaRXNO2tGzZ0jQeY5H2sGHDfHmM4iGDZJVqK4MkElqYzTl48CCyZMmSoNFuwERHY/8jj2DorFn4jK0ImEUCwE5vP+XNixm2AIk1p+6YRbL3ZhKJ6AwSt39Wq1btquurVq1qdkGIn3BkgHPrrTJIIqHdDykogiNbXRRrkKqzEbDzOvZLuu+DD/CEs740sQCJdUgi4cirAKlz584mi+RuwoQJePDBB31xXJKMHkikAElEfFUXtR1ADQBznddHx8biPYcDb3M5bffuBAGS1V2bAZLqkCQcpb2eIm0WAVqToNk0knN8uJtiwIABrvuNGjXKN0cqV3XRJi2xiYgvd9axX9K9AN4A0N95XS8AZY4cQQ/n+BHuoGNJxc8//2yaS7J4W922Jdx4FSDxU0SVKpwXDezcudN8zZ07tznZP2Fo67+PKYMkIn7AD1ps23KJy/gAYgG8VagQ+g8bhiuPPoq0DgcaA5hz7Bha8vW+fPkEu/EYJClAknDjVYCkNecAceuiTcogicj14odZBjxWryMqzNltjzyCd+fNQ/vp05EPQAkAKwFMveEG/JmbVUr/BUjFinHfG8e7xc93E4nIGqTz58+bLqqWf//9F2PGjHF1WA1n77zzjuk9cvvtt6f+D9cSm4ikwjIbWQNxr9SoAb7acRQJMWf95I8/opVte7815HbdunW46aabTNsXjqMSibgAqVWrVvjkk0/Mec7tYWt7Nivj9Z6Kt8NJz549sXnzZqxezU2wgV1i4ywkNoQTEfF1gGQySM6vHDLCVpPWlv8ohwPNFi3CROcyBDNI1kYdjiT54YcfsMZZryQSUQESp0TXrVvXnGcLe84DYhaJQdO4ceN8fYySSICk+iMR8RV7TZE9g8RmkWxJwDWDj+66C2cHDXLd51EAnMZ2avduc5kbdSyexpWIhH0NEpfXrKUdLqu1bdsW0dHRZkcbAyXxfw0Sz2l5TUT8nUFioMSls0OHDqFBgwbmtR5VqiC2a1ekuXwZTdh9e/Ro4IEHEtQw8f4iEZdBKlGiBL799luTSp03bx7uvPNO1xRoZTX8SBkkEUnlGiQqV64cGjVqFB8cUceO2DlhAuIrj4C8x44BNWog/65dru9ht3CRiJzFNmjQILNroUaNGq55PcwmVa5c2dfHKIkESMogiYi/l9gSk6VpU7AL3l/WFUeOYNbp07jfeVEZJInIJbb77rvPzBPioEL7FOnGjRujTZs2vjw+SWKbvzJIIuKPDBIzRawtTUquXLnwNwCO212YLRuqxsQgI4BpAIpzZIkzg8TdbK7Mk0gI8fq3ln88zBbZf/G5m6106dK+Oja5xjZ/ZZBExB8BUsGCBZE2bdKfnzNmzGiaQ54E8FC+fDjQvLnrtuG8bulS1LvjDhQqVMjjDDeRYKewPpSoBklEUmGJzSrQTm5QdeDYMSzq2BGDbbe1OHYML/32Gy4ePIgpU6b4/HhF/E0BUggvsSmDJCK+wqAoX758rq39ycHxUnT8+HH8u3s3XgfQjs2Enbc3BLACwKW/XJVKIuE/rFYCv8SmDJKI+EqGDBmwaNEirFy5Evffb5VaJy+DxPEiGzduNOfZTJLdkL4DzHiSUgCGcsoCm+sGYgKBSGplkC5fvmyKsbdv3+7tzxRvaYlNRPyI2/m7deuW7Oy0lUGi9evXu86vAlADwJ/OyzkvXwYaNADmzPH4OGfPnkWHDh3QpUsX18BckZALkNhR1fqkIKlMS2wiEqSF3dtss9noX+d4kp+tKzi/s2VL4MMPr3qcl19+GdOmTcPUqVMxefJkfx+2iP9qkDp16oQPPfySS+pkkLi+zzGQyiCJSCDZM0iecIdbMwDTrStiY4FHHwVeeonrcq772d9P2HxYJGRrkK5cuWKi/IULF6Jq1arIkiVLgttHjRrlq+MTDwGStdCmDJKIBHOARBcBdADQqFMn5P700/grX3wR4FgSDjdPmxanTp1y3T9btmz+PGQR/wZI7GlRpUoVj2nVqKgobx5SvAiQlEESkWAaT0KZM2c28zrtmCta27kz7uT7xoAB8VdOmsSJtsC0aQnqjmKZZRIJ1QBp8eLFvj8SSXYNkjJIIhKsGaTbbrsNv/7661XX79u3D+jfHyhUCOjcGWBQNGcO4urXB8Msa66bRpRIyPdBWrZsmalFqlWrVvwvPmAK7H755RdfHp9YuF7vzCBZpdrKIIlIsGWQ7r33Xo/33c9sEbGFALf9O5fSoteuNb2SSjjvpyG3EtIB0syZM9GsWTNkypQJa9euxcWLXGUGYmJiMHw4m8yLz1244CpqVAZJRIIxg9SiRQv069fP432tD9JG/frA8uXsTmkucnYbc07VlUGSUA+QXnnlFbz//vuYOHGi2fZvqV27tgmYwtk777yDsmXL4vbUbnjmtsWfOAdJRCRQsmfPjltuucXVQ+mzzz4zH5yTzCA5eyYN+ugjbJ0yBYed3buZi2Lxxu2HD5uNQM8884yZ7/n777+n0rMR8UGAtHXrVtSrV++q67n74ORJbuwMXz179sTmzZuxml1hA9hFmzsH06RJk7rHICJiw2Hls2fPNh8cWV5hZbWtLFL//v1dA82tDNLOnTtRt25dvPXWW+j6zDMY2qgRFjkfLzOAb9lgcvBgjBgxwrzODrCKukVCIUDKnz8/duzYcdX1/AOxPk2Ij6mLtogEodKlS+PJJ5802STL6NGjcezYMdPyhe8XVoDE3WodO3bEGWdG/LfffsPqbdvQHMCXtp1Dld56CwOdl5cuXWpGmYiERIDUvXt39O3b1/xyc1s/U6dMrQ4aNAg9evTw/VGKumiLSEjJmTOn+VqwYEHz9cCBA2jfvv1V2XcuoV0G8ACAt23XvwmY4bee2smIBO02/8GDByMuLs7MZGO/Cy63cdAhA6TevXv7/ihFg2pFJCQVKlQIa9asMee//ZYLaJ4xR9SHRdqsc3Ve9z8AeQGsWLoUpUqVcg3GZf3r6dOnzVKeSg0kqDJIzBo9++yzOH78uGkayenPR44cMfN0xE+0xCYiIYibd+z4YZqrEO64+YVeBfAYG0Y6r38IQBW+tzibT7KU4/HHHzcfyFn7JBJ0fZAoffr05peaOw20oyp1AyQtsYlIKBg4cCCmT59u+ubVr18fc+bM8dgKoHlzViLFmwignXNMCVXcswdo2hQ4fhzz2UPJiaUeqk+SoAqQunTpYmaxcTeCBKYGSRkkEQkF3MXWrl0700h4yZIlaNSoEUqUKIG0aRNWeNx9990JLp9q3Bj9SpWCa0obu3PXq4coez8lAKtWrfL3U5AIFe1t5ui1115DyZIlUaRIEfPJYNKkSdi+fbvvj1A81iApgyQioYrvIQySLDly5EC1atUS3KdXr16IbtwY9dld27ryzz/R68svEV+NFO+jjz5KnYOWiONVgMRgiLsK9uzZg5EjR5rlNfa04HbPws7OqOJjqkESkTDCvnn2AIkf+nLlypVgZAnnuq1nHRMnNThvy3v+PDjQymrVy93UIkFXg8Rfav5C8yt7YDBl6mk2j/iAtvmLSJhlkSx8H+HmH05o4FIbZ33y/YQBEv0NYGiTJnA4L3PAyU8AmgL4999/A/YcJLx5FSCxBTyH1PKXmlv+L1y4YL5yyOC6det8f5Sibf4iElZeeOEF13mWbNB9991nOnPXqVPHXC5fvryrE/fSbdtw6MsvscT5PdwW9AOAO0+cwKlTrkol08SYEw/mzZuXqs9Hwo9XfZD4y8xM0dChQ9G2bVvceuutvj8ySUi72EQkjLCP3tdff20CIBZue8K5bizd4HgnfvguW7MmuNn/cwBtmYVynj/42mu40Tko/amnnjL9lt59913TfsZ9oK6IXzNI/EVlHyTuHmCPCzYCe+CBBzBhwgR1PPUX7WITkTDCJbU2bdqgVatWSd7PWmajEydOmK3/bAHwsXNQOt/ECo4YAQwdyi6SCZpRcuecSKoGSJUqVUKfPn1M9M8InX0tuJ7MtGaZMmW8PhhJgnaxiUgEsgdIljh25W7e3DSVdBk2DHFPPpngTe3DDz9UnyRJ3QCJv3Br1641gwhbtmyJhg0b4tNPP0WFChVM4CR+oF1sIhKB7rnnHo/jRBo1boznANhbTka//75ZcrPKv//8808z7UEk1QIkDiGsUaMGPv/8c9MLacqUKTh69KgJmjjFWfwXIPGT03llkEQkQnBaw8aNG81QW77XcLWCRdxWY8mxAMbdfjsczsaT7Z3F21mc3//PP/8E8Ogl4oq0mS2qW7eushgBqEGy8kj6txeRSGHNaatSpYqpW2LvvcuXL5sCbw5Of+/0aazNmRPvHj6MzM7t/2wDwBCKZSB09uxZZMlihU0ifsogMeXJX0o2h3z00UfNicttMTEx3jycpCCDpABJRCIZs+cs8GYmqWDBgua6LVu2YMrhwyYwOuG8X3UAyzjPbft2PPTQQ+Y103247cKFC9UOQBIV5fCigm3NmjVo1qyZ2YLJQbW0evVqnD9/3gwSZJQf7th3g51gGRSmSrDCBpxHj4LT79igPzY21tUfREQkEnEl45df2Ff7P+UBMOSJD52AE1mzotaZM9jivGy95bED9x133GHOf/PNN2jdunWqHrsE//u3V++w/fv3N8XZXNvlTjaedu3aZVrDe5rSLL5dYuMnKAVHIhLp2GbGHUuyawGwJoPmOHPGZJKs0SRc/aDvvvvO9T1cthNx59W7LDNITz/9dIJpzDz/v//9z9wmPhYbC1y4YM5qi7+IyH/lHp5w+AhDp7XOy9ZokiYA9u7da65zXzzZunWr349XIiBAYkpq9+7dV13P4bV68/aDc+wdG09b/EVE4tWsWTPR649GRaGBMzCyRpPM5tDbCRPM5UOHDiX4npkzZ/r9eCUCAqT27dujW7dumDZtmgmKePryyy9NsXbHjh19f5SRToNqRUSuwpWLW265xXW5XLlyaNeuHcaOHWva0Zx27mSzQh/2RyrPkSTvvntVgOTpQ79ENq+2+b/55ptmF0GXLl1w5coVc126dOnQo0cP19BB8SENqhUR8YjdstmsOGPGjPjhhx9QrFgxcz3nhR47dsyMJrkfwPsAusfvTAJ69kTLAgVMRsly4MCBgD0HCaMAidsrGaGPGDECO3dyXxVQvHhxZM7MDhTicxpUKyLiUYMGDbB9+3bT46hAgQKu6xkgcfs/sSz7MfZEAvCM8/bHDhzAJQCc/cBqJAVI4pMAycKAqHz58q7Bg+InGjMiIpKoEiXY/CQhBkjunnUGSda8h17OAu4uCpDEg+jrSWsyOGJakyeenzRpEsIdG42xq+vtt1ubRlO3Bkm72ERErs1TgERjnAFRfHEI0AHA9wBOHzjgagEg4nWA9MILL6Bv375o0aIFvvrqK3PiefZH4m3hrGfPnti8ebNpjJlqlEESEUmR3LmZG/JsKoDWzrmW1AzA3NhYHNu2DcuXL8dzzz2Hffv2pdqxShgtsb333nuYOHFigh1rbBxZsWJF9O7dG8OGDfPlMYpbgFRIGSQRkRRnkF599VU8+ywX2uK3/Dd1Zo9yAGBP7fN3340nT57ExhMnsHLlSjOKRCKXVxkkDgmsVq3aVddXrVrVtatN/LfNXxkkEZGk5c+fP8FlliYUKlQowXXLAdyVKRP2Oy9n2rUL3584gVIAFi1aZIIkvadFLq8CpM6dO5sskrsJEybgwQcf9MVxiZ22+YuIpIg1Z82SK1cuj1ml9FWrmq7b1miSogA43e12Z8NJToiQyHTdRdpsDslThQoVzLIbZ4QNGDDAdRIf0DZ/EZEUuemmm0z7GQs3E+XNm/eq+9WoUQP/AKjDcSNZslw1mmTcuHFmOPjFixfx119/XTWiRMKXVwHSH3/8gSpVqphonH2QeGJBHK/jbevWrTOn9evX+/6II5GW2EREUsy+25g9kTxlkOrUYWgEHGZPpbg4LHEbTdI2NhabNm1C8+bNzQ7mIUOGpNrxSwgWaS9evNj3RyLJXmJTBklE5Npq1aplxmARR4+4B0hsLsnxJJaD58/jLgBfAGjjHE3C7576+ONYvGqVuc/rr7/ucWLEpUuXzI5ujj5JbEZcYpiV4qD3IkWKXFU7JSG4xCapSNv8RURS7KGHHjJBB40ePdo0N86QIYPrdi7BsTbJjqNJ2gH4q1Yt15tk11Wr8PI1ftYHH3yATp06maBs/36r7Dt5GMRVr14dpUuXxqlTp1L0veI/CpBCgWqQRERSjK+VGzduNL3rODuUWEtkD5CyZct21ffFMns0ZQpGZ8zouu45AJOcyy4XLly46nv69OHQknjTp09P0XE+8MAD5mtMTAw+//zzFH2v+I8CpFDg1klbGSQRkeTJnj07ypQp4/G2m2++GWnSpDH3cVegYEH83KyZGUdi9dfuBuBbAJ3atDH1Taucy27u0qb1fooX2+hIcFCAFGIZpHPOdXMREbk+OXLkcNUnuWeeuBxXv359vAPgfufSG90D4Km5c/HPmjUYPny4x8dNly6d349dgjBAYnTbuHFjMz1ZUjdA4ueKjDfeqMHAIiI+YG37dw+QrELpBg0amK8zneNITjpvr+FsMnk0kZFTzEpZzpw5Y2qh+vXrp1lv4R4gMTLmmq6k/hKb6o9ERK7Pp59+ar6yOLtDhw5JBkgcn2Wd/xlAXQDWhLZbAczYvx+XVq686mecP29NeQOGDh2KKVOmYOzYsZg2bdo1j099lkJ8iY2V+mwUKambQVL9kYjI9WFB9Nq1a03htvV66h4gFSxY0JUJmjFjhinAHjNmDP5gd20Am533Y+iUpnFjYMGCBN9/1lYW8e6777rOJ2e2mwKk4OFVJRln00yePNn8z+b8NfeamFGjRvnq+IR/MGfPgotqyiCJiFwflihUrlzZYy2Sxd6Bu3bt2uZkrZzscXbd/s75Nc25c4hr3hzch/a5bVnNYt/xlpz6UfsuOwnBAMnqpE3btm1LcJvqY/y7xKYMkoiIb7lnkNjs0V2BAgVc508AaOoMiNhQMjo2Fp/xPgDecssg2bHw+1rOneNWHAkG6qQd7C5dQpRzmrSW2EREAhMgsWaJTSf37Nljum//+eefuA/A2wCedN7nTQCFONbElkFKrDYpMQqQgoe2+Qc7NYkUEQl4gMRB7HPmzDElJD/99BMefvhh0x+pJ4Bnbffrzw7e8+dzreyqXWsnT1r74BKnACl4eN/NioVqmzdj9+7dZgaNXcuWLa/3uMSiMSMiIqkaIBUuXNjj/cqXL29OxDrcokWL4qWXXgK7IR3kuBHnm2rN3btxsEoVPOzWpVsBUgQESH///TfatGljJhyz5siqurfqj2Jj2ahd/NFFWxkkERHfci/StvcxSgr7G3F4LQuxJzuDpK9Ya8Qdbps3gyNtNwA4YKvfZWDFJELu3Lk97lpTgBTiS2x9+/Y1LdoPHz5sis64Frt06VJUq1YNS5Ys8f1RRjJlkERE/Mrb0SDFihXDX3/95bo8B0BDNpB0Xq4E4FcApWzJhW7dupnAyuI+100BUogHSCtWrMCwYcNMBMx1WZ7q1KmDESNGJBjYJz6gGiQREb/irmyrIeRbb3EfWsqCJDtOZ6sFYJd1u7Pr9h22+8yePRtHjx71GBApQArxAIlLaNYbNYOk/fv3m/M33XQTtm7d6tsjjHS2JTZlkEREfC9DhgxYt26d2aHNFZKUGjhwYILL250NJdc6L+cCsAjA3bb7sOD7vffeS9A+ILk73SR1eJVXZJHahg0bzDJbjRo1MHLkSKRPnx4TJkzwWP0vvskgqQZJRMQ/mEGyskgpxRWVU6dOYeLEia7rDnGWm3OOW1NnXdIsAF2d/ZO4E44jSNwpgxTiGaTnnnvOtX2Rvxi7du1C3bp1TUQ8btw4Xx9jZFMNkohIUGMtLhME7k4DKLdrF352Bl7MSLChZC8WdB9kSffVFCCFeAapWTPONY5XokQJbNmyBcePHzc7AdRJ279LbMogiYiEjgI33YRB9eph8/Tp6OG8js0lX5w3D/M83F8BUhg2imQfCQVH/l9iUwZJRCQ0tG/f3rwvFipa1HTbftl224sAuN7i/q6ZnF5JEmQZpAEDBiT7QTWs1oe0i01EJOQ8//zz5kT16tXDm2++iRcAHAMwxnmf3s4CbtYlXbENuv3222/RunXrgB27pDBAYoW/BIB2sYmIhIQmTZpg4cKF5nz37t2RLl06c541upaxAI6zE7fzDfgBANkBM9fN2r/G2t67777bbH6SEAiQNKA28Bmkc1FRyZoGLSIiqY+72IYOHYoGDRqYwbaW7Nmzm2DHGss1lUtpAKYDyOjc/j8fwL0AYpwJCdb3sgmzVg1CrEib0W1iuN5qpRXFtwESsmRRnZeISJBi00hPW/dp2bJl5r1z3rx5uHLlCr7nhicA3wHgxLY67LqdNi0aX7liRpbs2bPH9En63//+l+rPQ+JFOdwHwSRD5cqVE1y+fPmy2erPdu3FixfH2rVWe6zwxZ4X2bJlQ0xMjH+Xvdq0Ab791pytVrAg1uzb57+fJSIiftWrVy+88847rsu3AWY3W17n5Z3OvknsxF29enUcOnQI5cqVw/fff2+mVkjqvX97lUHyVI/EH8j5MhxiK/6pQYpSqlVEJKQxe2S3HsCOjz9G3hdeAHbvRnHnaJI7ObZkFQeXAP/++y+WL1+eoJZJ/M9n4SijsJdeeknLaz7msAVI6bOzlE9ERELVxYsXr7ouY4UKwPLl2OPMZnD4yFLnuBILd8L17t3bDIWfP58VS+JvPs3XMV3Fk/hO3Gn2Yo3f3ZBFO9hEREKap3FcZpmncGGMadMGvzmvywGA++H+a8sMjB8/Hr///ruZZkFHjhwxNU8HDhxIpaOPLF4tsbmPE2EZE/8HTZ06Fc2bN/fVsQn/bZ0Bkrb4i4iEvn79+uGzzz5LMNg9X7585mu6/PnRGMA3tvltLObuDGCa7TFWr15tvj744INYsGCBySpxOU6beIIgQBo9enSCyywcy5MnD7p27YohQ4b46tjEtotNg2pFREIfX8c3b96MHTt2mEHvTCpYr+0McM46t/t/CqAdgybncFtmlN63PQ6LjFn7S2vWrMEff/yBClyqk8AGSNyxJqkj6nx86zBlkEREwgOTCrfeeismTZrksYCb3ZI6AMhapAia79ljamHeA5AbwCvO+1rBkWX69OkKkHxMewaDmcOBaFuApAySiEj44kqMpdgtt6D5v/9ihO12znLj+o2nhbSvvvrKlLtIgDNIic1lY3owY8aMpgNoq1atzABbuQ7nz7NRlTmrQbUiIuHtiSeeMCUsx48fx+eff843VWR46y0MGjgQbzrv04/D4QF0s81vI9Y0MTPFrt0sdeGOt0yZMgXomURwo8iGDRuaZpCxsbEoVaqUuW7btm1IkyYNSpcubf5HMVj65ZdfULZsWQQrDg/86KOPzLEOHjwYnTp1Cq5GkUeOAHnj24exUG/vu++iR48e/vlZIiIScBcuXMD58+eRIwerjuJVrVoVFdeuBRfk0jivYwfu9rx/Io/DHW89e/Z0ZZVUwJ3y92+vltiYHeJQvv3795sthzzt3bsXTZs2RceOHbFv3z7Ts6F///4IVps2bTIROo+dOwL4y3TyJKfjBOeYEdUgiYiEP67C2IMjy8fOgbZWF6WWzg7cfFfwdP8vvvgCO3fuNDPhKlWqhLP2sVWSLF4FSG+88QZefvnlBG/YjMZefPFFU5XPgaovvPCCCT6C1V9//YWaNWuaX0amIfkLNHfuXAQVW5NI7WITEYlM6dJxLxvAoVN3AYhv/gLUA/AzgCqFCl31PXnz5kWHDh1MwoIJAQZMkgoBEtNShw8fvup6Nq2yKuu5DmpNLvbG0qVL0aJFCxQsWNCkBr91ziOz4zwbDgdkkFOjRg1XW/bkKF++PJYsWWKyRidOnDDn+YsUVJRBEhGJeCwHsTwzfz4+6twZR2yz3D7asQM3eXif5vZ/i6f3bPFDkTaX2B555BG89dZbuP322811XKYaNGgQWrdubS4zWOE2Rm8xHcisDn9O27Ztr7p92rRpplj8/fffN8HRmDFj0KxZM1P/xMiZbrvttqvm3hDbtLM2qk+fPmjUqJHJft1xxx2mhiqoKEASEYl4derUwc8//2wySVz5YM+julOnggNHigIocuGCySQ1dA65JS6vuQ+VlxRyeOH06dOORx991JE+fXpHdHS0OfF89+7dHWfOnDH3WbdunTn5Ag/zm2++SXBd9erVHT179nRdjo2NdRQsWNAxYsQIr35Gt27dHD/88EOit1+4cMERExPjOu3Zs8ccF8/7zaxZfPLm9Azg2LZtm/9+loiIhIRly5aZ95/CgGNv1qyu94ndgONmwNzmfurRo0egDzto8H07Oe/fXi2xZc2aFRMnTsSxY8ewbt06c+L5CRMmIEuWLK7sDU/+wKU71jexUNzC7Y28vGLFimQ/jpVyZNaJGS9moBIzYsQIk2myTix8S80Mkrb5i4iIlVFine8d992HtL/8gn3O9wa+K/3obAPgTktsqbTEZg+UKlasiNR29OhR02LAml9j4eUtW7akaKmQ67QM6rjdP23axP852FfC3v+JtVZ+D5LclthUpC0iIvTSSy+5zg9o0QKPfvYZ2FSnlLMFQBO3FgAzZ87E7t27UbQoF+XErwHSokWLzIlRaVxcXILbJk+ejFCQkmxThgwZzClQu9jOR0Wp6ZeIiFwlNlcucEz8SgAFANQGMNXZJ8n+7vzhhx8mCKwkaV4tsfEf+M477zQBErM53AVmP/lb7ty5TUH1oUOHElzPy/nz50fYsGWQ4jJnVqMvERG5Clvr7AZwj7McA86eSf/tfYu3YcOGABxdhGWQuHPs448/RufOnREI6dOnN51FGaBZu+aYxeLlXr16IWzYG3s5a7tERETcAyRa5wyMZkdFIY3DAbZq/hfAWNt7p/g5g8Qi6Vq1asGfzpw5g/Xr15sT7dq1y5znGiqxHoiF4lOmTDFNHzmCg60BHn74YYQNBUgiIpLMAAnO7trLbWOzxgB40HnemhbBzeHsWyh+CJAeffTR+EF6fsQGV5UrVzYnKyDieVbuU/v27U3zLF7mbjkGT+yE7V64HcriTlv9UoFo7WATEREP3DNDuZ5+GsPcxpS0tAVI99xzj3mvZGKB5ydN4pS3/zARccVDD8FIk9bbYXrc0r9w4UKzi81qg24ZNWrUdR9YgwYNXEP2EsPltLBaUnNz5eRJWL/2aRQgiYhIMpQrVw6VoqORKy4OPZ1v9NMBPLJnj2mU/OOPP7rKZWjOnDl44IEHTCbqgw8+wBNPPGEaMHMjUyTXvnoVIG3cuNHV44gdPe0i+R/T12KdY1soXfbsAT0WEREJHWt+/x1PP/UUmsTEoNTq1eAe7A8OHkTjRPr9sXUNAyQGR/Tbb79hx44dKFmyJCKVVwHS4sWLfX8kcpU4BUgiIuIFJjHmLVjAGSNYkicPGsTEIKuzkSSH3P7poe7X3cWLFxHJrqtR5ObNm81apX0oLTNIHDIr18/h/IVlH4uMOXIE+nBERCTUpEuHEZUqIW7pUjRydtleCJjzf9nupgDJRwHS33//jTZt2mDTpk0mILJqhazlNXa5Ft/tYjODarNlC/TRiIhIEOJgd0vDhhxZm1CWXLnQyhkY1QDAboE/uQVJ27dvNyO7cI2gKZJ4tYutb9++uPnmm00Xba5Z/vnnn1i6dCmqVauGJUuW+P4oI1TUuXPmq8aMiIhIYurVq2fGYd17772mR6G7PHnymAaSd7E2yXmdFSSVcV6+//77EwRadNq2kzoSeZVBYmX7Tz/9ZDpaM+LkicPzONC1T58+ZnitXL80F+In6WhQrYiIJGX48OGJ3la+fHnzlZv8mwJYAKBaIpkku7179+KRRx5B3rx5za42ttbp168f2rVrh0jgVYDEJTQro8Egaf/+/ShVqhRuuukmbN261dfHGLHSOtd/lUESERFvWbvOEwuSFjuDpM0eVosu2WqM6ddff71mC56IXmJjNGrNdGFUOXLkSCxfvhzDhg3DLbfc4utjjEyxsUh7+fJ/NUjKIImIiBfYr9DOCpKs5Ta2V17qDJjsLrkFR5Zjx44hEngVID333HNm9hkxKOIYkLp165pmU+PGjfP1MUYm25gRLrEpgyQiIt7Ili2bSWS4B0lfP/EEVjkv53IutzVIxuP99ZenBbnw41WA1KxZM7Rt29acL1GiBLZs2YKjR4+aou1GjZioE18GSMogiYjI9XjqqadMax677DffjCbOJTa6wdkniWNJksL3/EjgVYDkSc6cOdVF248BkjJIIiJyPcqUsfasxStUqBC4T+1uAN85r8sIYCaA/8bdXs0aGh/ufBYgiY/Z+k9oF5uIiPgCB7tXrVrV7DgvXry4uY77pf8PwKe23VtTAfRP5DFefvnlsJ6DaolyREo5uo9xbg3XdWNiYvwTvCxfDtSpY86+BeDJc+eQKVMm3/8cERGJSHz7Z2PJn3/+2VzmGhCriO2hzzhnoBRfdZwQd7AXKFAA4fr+rQxSCCyxnY+KQsaMTHyKiIj4BstiOFv14MGD6NChA3Llzo3eAIba7tPHueTm6eP57NmzEc4UIIXAEtuVjBlV3yUiIj7H95Z8+fLhiy++wKFDh9CxY0cMA/AQgPhGM0BrAJyRkdfte1etsvbAhScFSCGQQYrV0pqIiPgZp2J8/vnnZnf6FADNAcQ4b6vOKRoAytruP3HiRDOTNVwpQAqBAMmROXNAD0VERCLH119/bZpLLgLAStg9zuvZBnqlWxsAFmyHKwVIQSrWPiRQAZKIiKSSChUqmGkZHD7/7w034A4Aa523seHMLADP2Oa1HT9+3PW9O3bsQOvWrc1s1lDfA6YAKUhdsrVyj1IPJBERSWX169c3O772ORyowlWN9u1dt70K4EsAG1asQK5cuVw74UaPHo1Zs2bhmWeeMV9DmQKkIHXpJBvBx4tWgCQiIoGUOTPwxRfA8OFwODcNMVz6BUAxjihp0AAvvvgi3n33Xde3TJ48GaFMAVKQumILkNJmyxbQYxEREQEDoyFD4PjmG5xyXlXZufzWAsBLL72U4O7ff/89XnnlFaxYsQITJkzAaXvpSAhQgBSkYmOsvQNAGgVIIiISJKJbtTJ1Sdudl3M4R5W8xvcrt/s+//zzqFWrFh5//HEMHWrvsBT8FCAFqThbH6QMOXMG9FhERETsbr77blQDMMN23dMAfgKQWG9t1ieFEgVIQcphC5DS52B8LiIiEhwmT55sltnaAehraypZD8A6AE0R+hQgBakoWx+kTLlzB/RYRERE7Nh9+8yZM/jxxx/RccUKExhZ/ZLyAZjvnCOaPsF3ATNm2HNO8TgTjf2UWLMUTDi0V4JQ9Llz5uslAFmUQRIRkSCTJUsW3HXXXWZEyUpnwfZUZwduGgCgMYAHAGx2XteuXTuz1HbgwAHcdttt2LJlC7Zu3Ypp06aZ2//9918ULVoUwUABUpBKc+GC+co8UlLThkVERAIpp7NOlt377gHMwNuRrJ8FUAnAGgCDAFgNAPr375/oY7E5ZZcuXRAMtMQWpNJcvOgKkG5QHyQREQlS6dKlQ8OGDc35AgULYttdd6FfrVr403k7p4m+A+AHAAWv8ViXLnHdJDgoQApS6Z2/JCzVVgZJRESC2Zw5c7By5Urs3r3b1CW9t3w5Zg4ejHG2+zC7xKDpoSQeRwGSXFP6y/F7ApRBEhGRYJcxY0bUqFEDadL81wkpa548Zofb3QAOOK/LDuAjBlQACnt4HM5169evH5o1a5ZgxlsgqAYpGF26hLTOIX8MkIoqgyQiIiGmfPny5uuPAMqxDxKArs7bWMj9B4CBAD50ayxp4Yy3CxcuIEMGVjOlPmWQgpGtB5KW2EREJBTVqlXLdT5tnjxmaY3LbPuc13FGxCRnc8lSiTwGl+sCRQFSMLL1QDofFRWw6FlERMRbWbNmNTPYGCh9+eWXppB7jjObZB9jy/LujQCGcanO7THOnz+PQFGAFOQB0qX07m22REREQkP37t2xfPlyNGrUCDNnzsT06dOxets2dANwF4C/nffjO93zzmW3O23fHxsbG6AjV4AU9EtsVxQgiYhIGMiRI4dpFGk1gpzHOiUAnxUrZpoiU3Hn9WwbWQjARWfLm0BQgBTkGaQrGd0TjiIiIqErg61shAtoD+7ahUPz5mGJ7T73A9jK2qSZM4G4uIAcpwKkIHQlJsZ1PlYBkoiIhLnCTZuib4UKYA/tw87rsnAn286dQHRgQhUFSEHowtGjrvOOLPwVERERCR/Dhw83Rdxjx441l6OiorBg4UI0mTLF7Ghjg0kWm0yrUSNgx6gAKQhdOMaJNk4KkEREJMwMGTIEMTEx6NOnj+u6vHnz4sEHH8RJwDSYvAlAmXvYGCAwFCAFoUsnTrjOR2XNGtBjERER8YdoD0tn7MTNrFKVKlUwbcECtG/fHoGiTtpBHiClUZNIERGJIH369EmQWQoUZZCCvEg7TTb2GhUREZHUpAApCMWeOuU6ny47R/uJiIhIalKAFIQcp0+7zqfPkSOgxyIiIhKJFCAFIYetk3aGXLkCeiwiIiKRSAFSEIqyddLOqABJREQk1SlACkLRtunFWfLmDeixiIiIRCIFSEEozYULrvOZc+cO6LGIiIhEIgVIQSidc3rxOQA3qkhbREQk1SlACkLpLl82X1mJdMMNNwT6cERERCKOAqQglF4BkoiISEApQApCmWJjzdezUVHIkCFDoA9HREQk4ihACjYOBzLGxZmzF9KkCfTRiIiIRCQFSMHm/HnX/5SLaTVLWEREJBAUIAUbWxftS+nTB/RQREREIpUCpCBz6cQJ1/krCpBEREQCQgFSkDl35IjrfGzGjAE9FhERkUilACnInDt82HU+LnPmgB6LiIhIpFKAFGQuHDvmOu9QgCQiIhIQCpCCOEBC1qyBPBQREZGIpQApyFw8ftx1PloBkoiISEAoQAoyV06edJ2PvvHGgB6LiIhIpFKAFGSuxMS4zqfNli2gxyIiIhKpFCAFmdjTp13n0+XIEdBjERERiVQKkIKM49Qp1/n0CpBEREQCQgFSsDl71nU2Y65cAT0UERGRSKUAKchEnTvnOq8ASUREJDAUIAWZNOfPu85nyZs3oMciIiISqRQgBZk0Fy64zmdWgCQiIhIQCpCCTNpLl1zns+bLF9BjERERiVQKkIJMemeAFMfzahQpIiISEAqQgkyGK1fM17NRUQBPIiIikuoUIAWZjLGx5uv5aP2vERERCRS9CweZTHFcXAMupEkT6EMRERGJWBEbIL3zzjsoVqwYMmbMiBo1amDVqlWBPiRcvHgRWa3z6dIF+GhEREQiV0QGSNOmTcOAAQMwdOhQrF27FpUqVUKzZs1w+PDhgB7XqRMnkNF5/nL69AE9FhERkUgWkQHSqFGj0L17dzz88MMoW7Ys3n//fWTOnBmTJ08O6HGdOXTIdf5KhgwBPRYREZFIFnEB0qVLl/D777+jSZMmruuio6PN5RUrViS5/HXq1KkEJ1+7cvKk63xcRiuXJCIiIqkt4gKko0ePIjY2FvncmjDy8sGDBxP9vhEjRiBbtmyuU5EiRXx+bCULFnSdr1Snjs8fX0RERJIn4gIkbw0ZMgQxMTGu0549e3z/Q7is1qYNcOediKpUyfePLyIiIsmSFhEmd+7cSJMmDQ7Z6n2Il/Pnz5/o92XIkMGc/KpoUeDrr/37M0REROSaIi6DlD59elStWhWLFi1yXRcXF2cu16xZM6DHJiIiIsEh4jJIxC3+Xbt2RbVq1VC9enWMGTMGZ8+eNbvaRERERCIyQGrfvj2OHDmCF154wRRm33bbbZg7d+5VhdsiIiISmaIcDocj0AcRirjNn7vZWLB94403BvpwRERExIfv3xFXgyQiIiJyLQqQRERERNwoQBIRERFxowBJRERExI0CJBERERE3CpBERERE3ChAEhEREXGjAElERETEjQIkERERETcROWrEF6wG5OzIKSIiIqHBet++1iARBUheOn36tPlapEiRQB+KiIiIePE+zpEjidEsNi/FxcVh//79uOGGGxAVFeXTyJZB1549ezTjLQLp/79nt99+O1avXo1gFYjj89fP9NXjXu/jePP9Kf2e5Nxff5Ph93fJsIfBUcGCBREdnXilkTJIXuI/auHChf32+PxD1B9j5NL//4TSpEkT1P8egTg+f/1MXz3u9T6ON9+f0u9Jyf31Nxlef5dJZY4sKtIWkaDXs2dPBLNAHJ+/fqavHvd6H8eb70/p9wT771Ww6xnk/37Xe3xaYgsyTOcyso2JiQnqyFz8Q///RYKL/iYjlzJIQSZDhgwYOnSo+SqRR///RYKL/iYjlzJIIiIiIm6UQRIRERFxowBJRERExI0CJBERERE3CpBERERE3ChACiLvvPMOihUrhowZM6JGjRpYtWpVoA9JREQkIilAChLTpk3DgAEDzHbStWvXolKlSmjWrBkOHz4c6EOTINGmTRvkyJED9913X6APRUQAM36kQYMGKFu2LCpWrIivvvoq0IckPqRt/kGCGSPOjRk/frxr1hvn//Tu3RuDBw8O9OFJEFiyZImZHzRlyhTMmDEj0IcjEvEOHDiAQ4cO4bbbbsPBgwdRtWpVbNu2DVmyZAn0oYkPKIMUBC5duoTff/8dTZo0STDrjZdXrFgR0GOT4MFPqhyOLCLBoUCBAiY4ovz58yN37tw4fvx4oA9LfEQBUhA4evQoYmNjkS9fvgTX8zI/lUjoW7p0KVq0aGGmR0dFReHbb7+96j6qQRMJ3b9Lfsjl6zgz/xIeFCCJpIKzZ8+aujK+2HqiGjSR0P27ZNaoS5cumDBhQioduaQGBUhBgGnZNGnSmLVsO15m2lZCX/PmzfHKK6+YQmtPRo0ahe7du+Phhx82BZ/vv/8+MmfOjMmTJ6f6sYpECl/8XV68eBGtW7c2taK1atVKxaMXf1OAFATSp09vivsWLVrkuo5F2rxcs2bNgB6b+J9q0ERC8++Se5weeughNGrUCJ07dw7g0Yo/KEAKEkzjTpw40exQ+uuvv9CjRw+T/uUnFwlvya1B4wtzu3btMGfOHBQuXFjBk0iA/y6XL19uluFYu8RibZ42bdoUoCMWX0vr80cUr7Rv3x5HjhzBCy+8YP74+Ic2d+7cq/44JXItXLgw0IcgIjZ16tQx2X4JTwqQgkivXr3MSSKLatBEgo/+LkVLbCIBpho0keCjv0tRBkkkFZw5cwY7duxwXd61axfWr1+PnDlzomjRoqYGrWvXrqhWrRqqV6+OMWPGqAZNxM/0dylJ0agRkVQaE9KwYcOrrueL78cff2zOc8zMG2+84apBGzdunGlMJyL+ob9LSYoCJBERERE3qkESERERcaMASURERMSNAiQRERERNwqQRERERNwoQBIRERFxowBJRERExI0CJBERERE3CpBERERE3ChAEhEREXGjAElERETEjQIkEfGbBg0aoF+/fgh1of48Uvv4Bw0ahNatW6fazxPxBwVIIiLiU+vXrzeDXUVCmQIkEQlqly5dCvQhSAr/v2zYsEEBkoQ8BUgiYWju3LmoU6cOsmfPjly5cuHee+/Fzp07Eyy59OnTB//73/+QM2dO5M+fHy+++GKCxzh9+jQefPBBZMmSBQUKFMDo0aOvWqopVqwYxowZk+D7+Mbo/ljJPS7r2Hr16mV+Tu7cudGsWTOPj8X79e7d29wvR44cyJcvHyZOnIizZ8/i4Ycfxg033IASJUrgxx9/dH3PxYsXzfPOmzcvMmbMaI5l9erVCR6X39+lSxdkzZrVPO+33nrrqp8dFxeHESNG4Oabb0amTJlQqVIlzJgxI5H/G8n7N0/Ov6U3z9ly5coV8++aLVs28+/6/PPPw+FwJPs5Jff/y969e3H06FHz/SKhTAGSSBjiG+aAAQOwZs0aLFq0CNHR0WjTpo15E7RMmTLFBD+//fYbRo4ciWHDhmHBggWu2/n9y5cvx3fffWeuX7ZsGdauXev347KOLX369Obnv//++4k+Hu/HN+tVq1aZwKFHjx5o164datWqZY71zjvvROfOnXHu3DlzfwYnM2fONN/H2xlM8I3++PHjrsd86qmn8PPPP2PWrFmYP38+lixZctXzZiDxySefmGP7888/0b9/f3Tq1Ml8X1Ku9W+eHCl9zvbvS5s2rfm+sWPHYtSoUZg0aVKKnlNy/r9weY1BGAMtkZDmEJGwd+TIEaYKHJs2bTKX69ev76hTp06C+9x+++2Op59+2pw/deqUI126dI6vvvrKdfvJkycdmTNndvTt29d13U033eQYPXp0gsepVKmSY+jQoa6fY7//tY7L+p7KlStf8zm5P4crV644smTJ4ujcubPrugMHDpjHX7FihePMmTPmOX322Weu2y9duuQoWLCgY+TIkeby6dOnHenTp3dMnz7ddZ9jx445MmXK5HoeFy5cMP8Ov/76a4Lj6datm6Njx47JPl73f/Nr/Vt685zt31emTBlHXFyc6zr+XF6X3OeU3P8vL7/8sqNevXrXvJ9IsFMGSSQMbd++HR07dsQtt9yCG2+80Szf0O7du133qVixYoLv4XLS4cOHzfm///4bly9fRvXq1V23MytQqlQpvx8XVa1aNVmPZ38OadKkMct2FSpUcF3HJSji8+JSHp9T7dq1XbenS5fOPMe//vrLXOZ9WFtTo0YN1324HGZ/3jt27DDZmaZNm5plOOvE7Iv7cmFSx+v+b55cKXnOdnfccQeioqJcl2vWrGn+f8TGxib7OSXn/wszSFpek3CQNtAHICK+16JFC9x0002mPqVgwYJmCat8+fIJCmsZHNjxzdN9qetauERmr2MhBiHXc1zEZajk8PQc7NdZAUFKn1dSzpw5Y77Onj0bhQoVSnBbhgwZUny81rEl99/SH885uc8pOf9fGCDdfffdyf7ZIsFKGSSRMHPs2DFs3boVzz33HBo3bowyZcrgxIkTKXoMZnj4pmsvYI6JicG2bdsS3C9Pnjw4cOCA6/KpU6ewa9cuvx3X9ShevLirfsYegPA5li1b1nUfPm/WCFl4jPbnzfsyaGDWizVM9lORIkW8Pr6U/Ft6w/6caOXKlShZsqTJQvnqObGwn9lH7WCTcKAMkkiY4e4mLrtMmDDBLOHwTW/w4MEpegzuhurataspWOYSE3d9DR061GQ57Ms0jRo1wscff2wyQ9yZ9sILL5g3XH8d1/Vg9oMFzdZzKlq0qCmU5tJSt27dzH24rMTzvA+Plc/72WefNc/b/m/DRogsYmaWhjvhGDwy8OKyIf/dvJGSf0tv8N+bBfKPP/64KeZ+++23XTv0fPWcuL2fx1yuXDmfHbdIoChAEgkzfDP/8ssvzZZyLl+xfmbcuHFmm3ZKcJfTE088Ybbi802SO8D27NljtsdbhgwZYrIcvA9rlF5++eVEsx6+Oq7r8dprr5kAgLu8mO2oVq0a5s2bZ4I3yxtvvGGWnBioMHAYOHCgCRbs+DyZ8eHOL2ZMGNBUqVIFzzzzjNfHlpJ/S2+wdcH58+dNzRWDmL59++Kxxx7z6XPi8lrp0qWvudQoEgqiWKkd6IMQkeDHLfqsT2HWwcq4iIiEK2WQRMSjdevWYcuWLSbjwAwKe/ZQq1atAn1oIiJ+pwBJRBL15ptvmsJqFjdzizebRbJJoYhIuNMSm4iIiIgbbfMXERERcaMASURERMSNAiQRERERNwqQRERERNwoQBIRERFxowBJRERExI0CJBERERE3CpBERERE3ChAEhEREXGjAElERETEjQIkEREREST0/0CjROjJCwmjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the angular power spectra of the lensing maps\n",
+    "sim_cls = hp.anafast(\n",
+    "    [kappa_bar, gamm1_bar, gamm2_bar],\n",
+    "    pol=True,\n",
+    "    lmax=lmax,\n",
+    "    use_pixel_weights=True,\n",
+    ")\n",
+    "\n",
+    "# get the expected cls from CAMB\n",
+    "pars.min_l = 1\n",
+    "pars.set_for_lmax(lmax)\n",
+    "pars.SourceWindows = [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=dndz, source_type=\"lensing\"),\n",
+    "]\n",
+    "theory_cls = camb.get_results(pars).get_source_cls_dict(lmax=lmax, raw_cl=True)\n",
+    "\n",
+    "# get the HEALPix pixel window function, since the lensing fields have it\n",
+    "pw = hp.pixwin(nside, lmax=lmax)\n",
+    "\n",
+    "# plot the realised and expected cls\n",
+    "ell = np.arange(lmax + 1)\n",
+    "plt.plot(ell, sim_cls[0], \"-k\", lw=2, label=\"simulation\")\n",
+    "plt.plot(ell, theory_cls[\"W1xW1\"] * pw**2, \"-r\", lw=2, label=\"expectation\")\n",
+    "plt.xscale(\"symlog\", linthresh=10, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.yscale(\"symlog\", linthresh=1e-9, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.xlabel(r\"angular mode number $l$\")\n",
+    "plt.ylabel(r\"angular power spectrum $C_l^{\\kappa\\kappa}$\")\n",
+    "plt.legend(frameon=False)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/1-basic/matplotlibrc b/glass/examples/1-basic/matplotlibrc
new file mode 120000
index 0000000..0f9ea77
--- /dev/null
+++ b/glass/examples/1-basic/matplotlibrc
@@ -0,0 +1 @@
+../../.matplotlibrc
\ No newline at end of file
diff --git a/glass/examples/1-basic/matter.ipynb b/glass/examples/1-basic/matter.ipynb
new file mode 100644
index 0000000..3ed5c98
--- /dev/null
+++ b/glass/examples/1-basic/matter.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matter distribution\n",
+    "\n",
+    "This example simulates only the matter field in nested shells up to redshift 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Set up a matter-only GLASS simulation, which requires angular matter power spectra and the sampling itself (here: lognormal).\n",
+    "\n",
+    "The setup for angular matter power spectra matches the definition from the [Matter shell definition](shells.ipynb) example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# uses the CAMB cosmology which produced the cls\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = 1024\n",
+    "lmax = 1000\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# triangular radial windows, equivalent to linear interpolation of n(z)\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute angular matter power spectra with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - HEALPix pixel window function (`nside=nside`)\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3)\n",
+    "\n",
+    "# set up lognormal fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# this generator will yield the matter fields in each shell\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Run the simulation.\n",
+    "For each shell, plot an orthographic annulus of the matter distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a 2d grid in redshift\n",
+    "n = 2000\n",
+    "zend = 1.05 * zb[-1]\n",
+    "x, y = np.mgrid[-zend : zend : 1j * n, -zend : zend : 1j * n]\n",
+    "z = np.hypot(x, y)\n",
+    "grid = np.full(z.shape, np.nan)\n",
+    "\n",
+    "# set up the plot\n",
+    "ax = plt.subplot(111)\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "# simulate and project an annulus of each matter shell onto the grid\n",
+    "for i, delta_i in enumerate(matter):\n",
+    "    zmin, zmax = zb[i], zb[i + 1]\n",
+    "    g = (zmin <= z) & (z < zmax)\n",
+    "    zg = np.sqrt(1 - (z[g] / zmax) ** 2)\n",
+    "    theta, phi = hp.vec2ang(np.transpose([x[g] / zmax, y[g] / zmax, zg]))\n",
+    "    grid[g] = hp.get_interp_val(delta_i, theta, phi)\n",
+    "    ax.add_patch(\n",
+    "        plt.Circle((0, 0), zmax / zend, fc=\"none\", ec=\"k\", lw=0.5, alpha=0.5, zorder=1),\n",
+    "    )\n",
+    "\n",
+    "# show the grid of shells\n",
+    "ax.imshow(grid, extent=[-1, 1, -1, 1], zorder=0, cmap=\"bwr\", vmin=-2, vmax=2)\n",
+    "\n",
+    "# show the resulting plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/1-basic/photoz.ipynb b/glass/examples/1-basic/photoz.ipynb
new file mode 100644
index 0000000..e4d1be4
--- /dev/null
+++ b/glass/examples/1-basic/photoz.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Photometric redshifts\n",
+    "\n",
+    "This example simulates galaxies with a simple photometric redshift model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "The simplest galaxies-only GLASS simulation, sampling galaxies using some\n",
+    "redshift distribution.  Then add a model for photometric redshifts with\n",
+    "Gaussian errors.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "\n",
+    "# how many arcmin2 over the entire sphere\n",
+    "ARCMIN2_SPHERE = 60**6 // 100 / np.pi\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# galaxy density\n",
+    "n_arcmin2 = 1e-4\n",
+    "\n",
+    "# photometric redshift error at redshift 0\n",
+    "phz_sigma_0 = 0.05\n",
+    "\n",
+    "# parametric galaxy redshift distribution\n",
+    "z = np.linspace(0, 3, 301)\n",
+    "dndz = n_arcmin2 * glass.smail_nz(z, 1.0, 2.2, 1.5)\n",
+    "\n",
+    "# compute the over galaxy number density on the sphere\n",
+    "ngal = np.trapezoid(dndz, z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "Simulate true and photometric redshifts.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sample the number (not density) of galaxies from the Poisson distribution\n",
+    "n = rng.poisson(ngal * ARCMIN2_SPHERE)\n",
+    "\n",
+    "# sample n true redshifts\n",
+    "ztrue = glass.redshifts_from_nz(n, z, dndz, rng=rng, warn=False)\n",
+    "\n",
+    "# sample n photometric redshifts\n",
+    "zphot = glass.gaussian_phz(ztrue, phz_sigma_0, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plots\n",
+    "Make a couple of typical photometric redshift plots.\n",
+    "\n",
+    "First the $z$-vs-$z$ plot across the entire sample.  The simple\n",
+    "Gaussian error model only has the diagonal but no catastrophic outliers.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(ztrue, zphot, \"+k\", ms=3, alpha=0.1)\n",
+    "plt.xlabel(r\"$z_{\\rm true}$\", size=12)\n",
+    "plt.ylabel(r\"$z_{\\rm phot}$\", size=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now define a number of photometric redshift bins.  They are chosen by the\n",
+    ":func:`~glass.equal_dens_zbins` function to produce the same\n",
+    "number of galaxies in each bin.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nbins = 5\n",
+    "zbins = glass.equal_dens_zbins(z, dndz, nbins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the photometric bins are defined, make histograms of the *true* redshift\n",
+    "distribution $n(z)$ using the *photometric* redshifts for binning.  Use\n",
+    "the :func:`~glass.tomo_nz_gausserr()` function to also plot the\n",
+    "expected tomographic redshift distributions with the same model.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tomo_nz = glass.tomo_nz_gausserr(z, dndz, phz_sigma_0, zbins)\n",
+    "tomo_nz *= ARCMIN2_SPHERE * (z[-1] - z[0]) / 40\n",
+    "\n",
+    "for (z1, z2), nz in zip(zbins, tomo_nz, strict=False):\n",
+    "    plt.hist(\n",
+    "        ztrue[(z1 <= zphot) & (zphot < z2)],\n",
+    "        bins=40,\n",
+    "        range=(z[0], z[-1]),\n",
+    "        histtype=\"stepfilled\",\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    plt.plot(z, nz, \"-k\", lw=1, alpha=0.5)\n",
+    "plt.xlabel(\"true redshift $z$\")\n",
+    "plt.ylabel(\"number of galaxies\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/glass/examples/1-basic/shells.ipynb b/glass/examples/1-basic/shells.ipynb
new file mode 100644
index 0000000..90c52c9
--- /dev/null
+++ b/glass/examples/1-basic/shells.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Matter shell definition\n",
+    "\n",
+    "This example demonstrates how matter shells are defined, and their angular power\n",
+    "spectra computed.\n",
+    "\n",
+    "The the angular power spectra are saved here, so that they can be reused in\n",
+    "other examples without recomputing.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute\n",
+    "Here we define the shells for these examples, and use CAMB to compute the\n",
+    "angular matter power spectra for the shell definitions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "lmax = 1000\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zgrid = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# triangular radial windows, equivalent to linear interpolation of n(z)\n",
+    "shells = glass.linear_windows(zgrid)\n",
+    "\n",
+    "# compute angular matter power spectra with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot\n",
+    "We can do a quick preview of the shell definition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the individual shells\n",
+    "for shell in shells:\n",
+    "    plt.fill_between(shell.za, np.zeros_like(shell.wa), shell.wa, alpha=0.5)\n",
+    "\n",
+    "# plot the total of all shells\n",
+    "all_za = np.linspace(0.0, zgrid[-1], 1000)\n",
+    "all_wa = sum(np.interp(all_za, shell.za, shell.wa) for shell in shells)\n",
+    "plt.plot(all_za, all_wa, \"-k\")\n",
+    "\n",
+    "plt.xlabel(\"redshift $z$\")\n",
+    "plt.ylabel(\"radial window $W(z)$\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/2-advanced/cosmic_shear.ipynb b/glass/examples/2-advanced/cosmic_shear.ipynb
new file mode 100644
index 0000000..0767934
--- /dev/null
+++ b/glass/examples/2-advanced/cosmic_shear.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cosmic shear\n",
+    "\n",
+    "This example simulates a galaxy catalogue with shears affected by weak lensing, combining the [galaxies](../1-basic/density.ipynb) and [lensing](../1-basic/lensing.ipynb) examples with models for the intrinsic galaxy ellipticity and the resulting shear."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The setup of galaxies and weak lensing fields is the same as in the basic examples.\n",
+    "The setup for angular matter power spectra matches the definition from the [Matter shell definition](../1-basic/shells.ipynb) example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# use the CAMB cosmology that generated the matter power spectra\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# how many arcmin2 over the entire sphere\n",
+    "ARCMIN2_SPHERE = 60**6 // 100 / np.pi\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = lmax = 256\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# linear window function for shells\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute the angular matter power spectra of the shells with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up lognormal matter fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - HEALPix pixel window function (`nside=nside`)\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# generator for lognormal matter fields\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lensing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will compute the convergence field iteratively\n",
+    "convergence = glass.MultiPlaneConvergence(cosmo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Galaxies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# standard deviation in each component of galaxy ellipticity\n",
+    "# this is very small so that the galaxy density can be small, too\n",
+    "sigma_e = 0.01\n",
+    "\n",
+    "# galaxy number density per arcmin2, over all shells\n",
+    "n_arcmin2 = 0.01\n",
+    "\n",
+    "# localised redshift distribution with the given density\n",
+    "z = np.arange(0.0, 2.0, 0.01)\n",
+    "dndz = np.exp(-((z - 0.5) ** 2) / (0.1) ** 2)\n",
+    "dndz *= n_arcmin2 / np.trapezoid(dndz, z)\n",
+    "\n",
+    "# distribute dN/dz over the radial window functions\n",
+    "ngal = glass.partition(z, dndz, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Simulate the galaxies with shears.\n",
+    "In each iteration, get the shears and map them to a HEALPix map for later analysis.\n",
+    "\n",
+    "In addition, generate the galaxy ellipticities, drawn from the intrinsic normal distribution.\n",
+    "The standard deviation is much too small to be realistic, but enables the example to get away with fewer total galaxies.\n",
+    "\n",
+    "Finally, apply the reduced shear from the lensing maps to the galaxy ellipticities, producing the galaxy shears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number of HEALPix pixels in the maps\n",
+    "npix = 12 * nside**2\n",
+    "\n",
+    "# map for galaxy numbers\n",
+    "num = np.zeros(npix)\n",
+    "\n",
+    "# map for sum of shears\n",
+    "she = np.zeros(npix, dtype=complex)\n",
+    "\n",
+    "# simulate the matter fields in the main loop\n",
+    "for i, delta_i in enumerate(matter):\n",
+    "    # compute the lensing maps for this shell\n",
+    "    convergence.add_window(delta_i, shells[i])\n",
+    "    kappa_i = convergence.kappa\n",
+    "    gamm1_i, gamm2_i = glass.shear_from_convergence(kappa_i)\n",
+    "\n",
+    "    # generate galaxy positions uniformly over the sphere\n",
+    "    for gal_lon, gal_lat, gal_count in glass.uniform_positions(ngal[i], rng=rng):\n",
+    "        # generate galaxy ellipticities from the chosen distribution\n",
+    "        gal_eps = glass.ellipticity_intnorm(gal_count, sigma_e, rng=rng, xp=np)\n",
+    "\n",
+    "        # apply the shear fields to the ellipticities\n",
+    "        gal_she = glass.galaxy_shear(\n",
+    "            gal_lon,\n",
+    "            gal_lat,\n",
+    "            gal_eps,\n",
+    "            kappa_i,\n",
+    "            gamm1_i,\n",
+    "            gamm2_i,\n",
+    "        )\n",
+    "\n",
+    "        # map the galaxy shears to a HEALPix map; this is opaque but works\n",
+    "        gal_pix = hp.ang2pix(nside, gal_lon, gal_lat, lonlat=True)\n",
+    "        s = np.argsort(gal_pix)\n",
+    "        pix, start, count = np.unique(gal_pix[s], return_index=True, return_counts=True)\n",
+    "        num[pix] += count\n",
+    "        she[pix] += list(map(np.sum, np.split(gal_she[s], start[1:])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Compute the angular power spectrum of the observed galaxy shears.\n",
+    "To compare with the expectation, take into account the expected noise level due to shape noise, and the expected mixing matrix for a uniform distribution of points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compute the expected number of galaxies in each pixel\n",
+    "nbar = ARCMIN2_SPHERE / npix * n_arcmin2\n",
+    "\n",
+    "# normalise the maps by the expected number of galaxies in each pixel\n",
+    "she /= nbar\n",
+    "num /= nbar\n",
+    "\n",
+    "# get the angular power spectra from the galaxy shears\n",
+    "cls = hp.anafast([num, she.real, she.imag], pol=True, lmax=lmax, use_pixel_weights=True)\n",
+    "\n",
+    "# get the theory cls from CAMB\n",
+    "pars.NonLinear = \"NonLinear_both\"\n",
+    "pars.Want_CMB = False\n",
+    "pars.min_l = 1\n",
+    "pars.set_for_lmax(lmax)\n",
+    "pars.SourceWindows = [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=dndz, source_type=\"lensing\"),\n",
+    "]\n",
+    "theory_cls = camb.get_results(pars).get_source_cls_dict(lmax=lmax, raw_cl=True)\n",
+    "\n",
+    "# factor transforming convergence to shear\n",
+    "ell = np.arange(lmax + 1)\n",
+    "fl = (ell + 2) * (ell + 1) * ell * (ell - 1) / np.clip(ell**2 * (ell + 1) ** 2, 1, None)\n",
+    "\n",
+    "# the noise level from discrete observations with shape noise\n",
+    "ntot = nbar * npix\n",
+    "nl = 4 * np.pi / ntot * sigma_e**2 * (ell >= 2)\n",
+    "\n",
+    "# mixing matrix for uniform distribution of points\n",
+    "mm = (1 - 1 / ntot) * np.eye(lmax + 1, lmax + 1) + (ell + 1 / 2) / ntot\n",
+    "mm[:2, :] = mm[:, :2] = 0\n",
+    "\n",
+    "# the shear pixel window function for HEALPix\n",
+    "_, pw = hp.pixwin(nside, lmax=lmax, pol=True)\n",
+    "\n",
+    "# plot the realised and expected cls\n",
+    "plt.plot(ell, cls[1] - nl, \"-k\", lw=2, label=\"simulation\")\n",
+    "plt.plot(ell, pw**2 * mm @ (fl * theory_cls[\"W1xW1\"]), \"-r\", lw=2, label=\"expectation\")\n",
+    "plt.xscale(\"symlog\", linthresh=10, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.yscale(\"symlog\", linthresh=1e-9, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.xlabel(\"angular mode number $l$\")\n",
+    "plt.ylabel(\"angular power spectrum $C_l^{EE}$\")\n",
+    "plt.legend(frameon=False)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/2-advanced/legacy-mode.ipynb b/glass/examples/2-advanced/legacy-mode.ipynb
new file mode 100644
index 0000000..f88a765
--- /dev/null
+++ b/glass/examples/2-advanced/legacy-mode.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Legacy mode\n",
+    "\n",
+    "This example demonstrates how to use GLASS in a FLASK-like \"legacy mode\", where both matter and convergence fields are simulated for a given set of n(z).\n",
+    "\n",
+    "It is generally **not recommended** to run simulations in this manner: as one can see below, the very small lognormal shifts of the convergence fields mean the lognormal transformation is overall ill-defined, and the simulated angular power spectra require regularisation. In other words, the simulation has to run with slightly different spectra than predicted by theory. This doesn't generally happen with \"GLASS-like\" simulations."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We begin by setting up the simulation. First, import some required libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also import GLASS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function for plotting spectra."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_spectra(*spectra, labels=None, log=True):\n",
+    "    if labels is None:\n",
+    "        labels = []\n",
+    "\n",
+    "    nspec = max(len(cls) for cls in spectra)\n",
+    "    nfields = glass.nfields_from_nspectra(nspec)\n",
+    "    fig, ax = plt.subplots(\n",
+    "        nfields,\n",
+    "        nfields,\n",
+    "        figsize=(1.1 * nfields, 1.0 * nfields),\n",
+    "        sharex=True,\n",
+    "        sharey=True,\n",
+    "        layout=\"constrained\",\n",
+    "    )\n",
+    "\n",
+    "    for (i, j), *spec in zip(glass.spectra_indices(nfields), *spectra, strict=False):\n",
+    "        ax[i, j].tick_params(axis=\"both\", which=\"both\", direction=\"in\")\n",
+    "        for c, cl in enumerate(spec):\n",
+    "            if log:\n",
+    "                ax[i, j].plot(np.arange(1, cl.size), +cl[1:], alpha=0.5, c=f\"C{c}\")\n",
+    "                ax[i, j].plot(\n",
+    "                    np.arange(1, cl.size),\n",
+    "                    -cl[1:],\n",
+    "                    alpha=0.5,\n",
+    "                    c=f\"C{c}\",\n",
+    "                    ls=\"--\",\n",
+    "                )\n",
+    "            else:\n",
+    "                ax[i, j].plot(np.arange(cl.size), cl, alpha=0.5, c=f\"C{c}\")\n",
+    "\n",
+    "    for i, j in zip(*np.triu_indices(nfields, 1), strict=False):\n",
+    "        ax[i, j].axis(\"off\")\n",
+    "\n",
+    "    ax[0, 1].legend(\n",
+    "        handles=[\n",
+    "            plt.Line2D([0], [0], c=f\"C{c}\", label=label)\n",
+    "            for c, label in enumerate(labels)\n",
+    "        ],\n",
+    "        loc=\"lower left\",\n",
+    "        frameon=False,\n",
+    "        fontsize=6,\n",
+    "        borderpad=0.0,\n",
+    "        borderaxespad=0.0,\n",
+    "    )\n",
+    "\n",
+    "    if log:\n",
+    "        ax[0, 0].set_xscale(\"log\")\n",
+    "        ax[0, 0].set_yscale(\"log\")\n",
+    "\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inputs\n",
+    "\n",
+    "At first, we will use GLASS to define some mock redshift distributions. These could come, e.g., from a galaxy survey."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = np.arange(0.0, 5.01, 0.01)\n",
+    "\n",
+    "dndz = glass.smail_nz(z, 1.0, 1.5, 2.0)\n",
+    "\n",
+    "nbins = 6\n",
+    "zbins = glass.equal_dens_zbins(z, dndz, nbins)\n",
+    "\n",
+    "nz = glass.tomo_nz_gausserr(z, dndz, 0.05, zbins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the redshift distributions — these are pretty much arbitrary and only for illustration purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(layout=\"constrained\")\n",
+    "plt.plot(z, nz.T, alpha=0.5)\n",
+    "plt.xlabel(\"redshift $z$\")\n",
+    "plt.ylabel(\"sources $n(z)$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the first set of bins for galaxy density, and the second set of bins for convergence. This is completely arbitrary, and only used to make bookkeeping easier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# density\n",
+    "nz_1 = nz[:3]\n",
+    "\n",
+    "# convergence\n",
+    "nz_2 = nz[3:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute the input theory spectra of the fields to be simulated. Here, we use CAMB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import camb\n",
+    "import camb.sources\n",
+    "\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "nside = lmax = 256\n",
+    "\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "\n",
+    "pars.set_accuracy(AccuracyBoost=2.0, lAccuracyBoost=2.0, lSampleBoost=2.0)\n",
+    "pars.Want_CMB = False\n",
+    "pars.Want_CMB_lensing = False\n",
+    "pars.min_l = 1\n",
+    "pars.set_for_lmax(2 * lmax)\n",
+    "\n",
+    "pars.SourceWindows = [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=nz_i, source_type=\"counts\") for nz_i in nz_1\n",
+    "] + [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=nz_i, source_type=\"lensing\")\n",
+    "    for nz_i in nz_2\n",
+    "]\n",
+    "\n",
+    "cls_dict = camb.get_results(pars).get_source_cls_dict(lmax=lmax, raw_cl=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the spectra are computed, we collect them into a list in the order that GLASS expects, using `glass.spectra_indices()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls = [cls_dict[f\"W{i + 1}xW{j + 1}\"] for i, j in glass.spectra_indices(nbins)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the spectra using the function defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_spectra(cls)\n",
+    "\n",
+    "fig.supxlabel(\"$\\\\ell$\")\n",
+    "fig.supylabel(\"$C_\\\\ell$\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare simulation\n",
+    "\n",
+    "Now we can prepare the GLASS simulation. For \"legacy mode\", we turn the redshift distributions into (improper) GLASS \"shells\", by manually creating `glass.RadialWindow()` instances. Here, the \"shells\" do not have to be strictly increasing in redshift, since we do not compute the weak lensing iteratively, but simulate it all in one go."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shells_1 = [\n",
+    "    glass.RadialWindow(z, nz_i, np.trapezoid(z * nz_i, z) / np.trapezoid(nz_i, z))\n",
+    "    for nz_i in nz_1\n",
+    "]\n",
+    "shells_2 = [\n",
+    "    glass.RadialWindow(z, nz_i, np.trapezoid(z * nz_i, z) / np.trapezoid(nz_i, z))\n",
+    "    for nz_i in nz_2\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then set up the lognormal fields for the \"shells\". The density fields get assigned a fixed lognormal shift of 1 (i.e. the default). The convergence fields get assigned a redshift-dependent lognormal shift from the fit by Hilbert et al. (2011)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# density\n",
+    "fields_1 = glass.lognormal_fields(shells_1)\n",
+    "\n",
+    "# convergence\n",
+    "fields_2 = glass.lognormal_fields(shells_2, glass.lognormal_shift_hilbert2011)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then combine the density and convergence fields into one single set, and compute their Gaussian angular power spectra for simulation using GLASS's iterative solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fields = fields_1 + fields_2\n",
+    "\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting both lognormal and Gaussian spectra shows the impact of the small lognormal shift on the convergence fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_spectra(cls, gls, labels=[\"lognormal\", \"Gaussian\"])\n",
+    "\n",
+    "fig.supxlabel(\"$\\\\ell$\")\n",
+    "fig.supylabel(\"$C_\\\\ell$\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regularise spectra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The very small lognormal shifts of the convergence fields, coupled with the fact that the line-of-sight kernel for weak lensing overlaps that of the density fields, means the fields have a complicated correlation structure. To visualise this, we can compute the per-$\\ell$ correlation coefficient $R_\\ell$ (see GLASS paper for details) for all fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rls = []\n",
+    "for (i, j), gl in zip(glass.spectra_indices(nbins), gls, strict=False):\n",
+    "    gl_ii, gl_jj = glass.getcl(gls, i, i), glass.getcl(gls, j, j)\n",
+    "    with np.errstate(invalid=\"ignore\"):\n",
+    "        rl = gl / np.sqrt(gl_ii * gl_jj)\n",
+    "    rls.append(rl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the resulting $R_\\ell$ shows the very strong correlations between the fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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599xzZd68eSSfAAAAGSyuyedTTz0lb731lrz44ovy+uuvS21trTgcDlm/fr1MmDBBvvzlL8ezOAAAAGRy8qlmz55tNqW33w8ePCgtLS2yefNm+e1vfxvv4gAAAJDJyWc0bfWsrq42x+PHj5crrrgikcUBAADA5iydakn7fwIAACBzOYYyldLdd98tp59+ulRUVMjf/d3fydatW4973o4dO2TlypVm1DsAAAAwpNvu999/v6xYsUKqqqrklFNOkddee03mzJkjb775phQXF8sjjzwiv/71r2XXrl0SDAalqKiIdxoAAABDa/n8xS9+YVozd+/eLZs2bZKdO3fKaaedJv/4j/8oU6dOlQceeEDKysrkrrvukv/7f/+v1NfXi5Xuu+8+OfvssyUvL09KSkosLRsAAABxbvnUpPN//a//Fem/OWLECPnf//t/y/z58+XMM8+Uf//3f5dJkyZJsvh8PrnqqqvMiPsnnngiafUAAABAHJLPrq4u06oYTVs81Z133pnUxFMtX77c7NeuXTuon2tqaupx7vF4zIbkIi4AAKSXIY1211vp2p8zLNwKWllZKalq9OjRps9qeNN+rUg+4gIAQHoZ0jyf2r9TWznPOOMMOeuss2Ts2LFmTXcdCZ+qampqegyOonXNHogLAAAZ3vK5YcMG08fz8ssvl46ODlm3bp3cc889piX0K1/5iowbN86s367TMT377LPywQcfxFzJxYsXm+R2oG379u0xlaEJjm6a3OgUUcni9Xpl2bJlZp+J5fdGXOxRPgAA8ZIVjL5/PgTa2rlt2zZ577335M9//rPZ63bo0KFQAVlZ4vf7Y6pkXV2dHD58eMDnaNLrdrsj59rnc+HChdLQ0HDCPoV6O7exsdEkOb3PrZYO5SfiNdLhfUl2+cn+bwAAIC4rHGl/Tx1w9K1vfUv++Z//Wf7rv/5LDhw4IPv37zdruf/0pz+N+Z3Wyex1INNAW3TiOVSrVq2K+bm9r0ef93UcvU+l8vurw2Bf40RWr16dkHoN5n1Ra9asSVj5A5Ubr/LD7yUAAEkXTDO7d+8Obt26Nbh8+fJgQUGBOdatubm5z+c3NjZqy6/ZT548ucf5QPS5J3M9+ryv4+h9KpXfXx0G+xr9Cb/GhAkT4lavob4vauLEiQkrf6By41W+0vcy1rgAABCrIQ04srMlS5aYfqhh06ZNM3tdiWnu3LnHPT/c60BvSWr3gPDUPr2n+Okt+rkDXY8+7+s4eh9drt3L768O0ddi6dER/tlE1Guw70tf7088yz9RufEoP3xdxdjTBgCA5Pb5THV79+410/kgMSPVR40aNaSfJS72jAsAALHK+OQzEAhIbW2tFBYWmsFRiJ3+SjU3N5vVrxyOoXUrJi72jAsAAElNPvfs2WMGA+Xm5vb5eHt7uxmpPmbMmFjqCAAAgDQRU/PHqaeeKs8//3y/j7/00kvmOQAAAEDMyeeJGk11DlBu7wEAACBs0KPddRRt9MTtOvm73n7vTZ+jKxxVV1cPtggAAACkqUH3+Vy+fLn8+Mc/Pqnn6kvfe++9ctdddw21fgAAAMjkls8vf/nLUlBQYBLLO+64Q6655hr53Oc+1+M5Ojo5Pz9fpk+fLjNmzIhnfQEAAJBJyefs2bPNplpbW+WKK66QM888MxF1AwAAQJphnk/mk4w75vm0J+JiX8zBCiCTxG15zZaWFjl69GifI+DtPM+n/kPKSjr2W0mHuCQOcbEvVp8CkAliSj47OjrMAKQnnnjCjHrvT3hNaTvSFpzwh35RUVGyq5MWdEYETVDC7+1QEJf4Iy7pHRsAyIjk89Zbb5V169bJpZdeKl/4whektLRUUk341qH+Q8o/pvEVy21Z4pI4xMW+6MoAIBPElHz+n//zf+TGG2+Uxx57TOzkjTfekAceeEA2b94s+/fvN6swaYIMAACA5HLE+i299zRLdqCj8KdOnSqrVq1KdlUAAAAQr5bPSy65RP7whz/ILbfcInYyf/58swFIHB1c2OnP6MkyYpbtzOJWO4CME1Py+aMf/Ui+9rWvyc0332wSUB3V7nQ6j3teWVmZpEKH/2gej8dsSC7iYt+4dPoDsuZPNebc5XKK0xm3yTMyxvfmnS5uF8kngMwS078W48ePN/utW7eaEe+pONo9rPf0MUuXLpVly5YlrT4IIS42jovDJcWzv2bO58yZI3Pnzk12tQAA6Z58LlmyJG1uGfWeOobWNXsgLvaNi04L1BUI3XZ3u2mRHuptdwDINDEln+nUAsXUMfZEXOyJuAAAhiounbS8Xq9s2bJFDh06JOecc46Ul5fH42UBAACQZmJeRPihhx6S6upq+fznPy+XX365vPfee+Z6fX29SUKffPJJsZou9fnuu++aTe3atcsc79mzx/K6AAAAIE7J51NPPSULFy6UCy+80Aw4il7XXRPPc889V5599lmx2qZNm2TatGlmU4sWLTLH2kcVAAAAKXrb/Wc/+5mZ6/OZZ57pc2336dOnm5ZRq+mo2+hEGAAAAGnQ8rlz584BJ3PX+T37SkoBAACQmWJKPktKSkzfzv5s27ZNqqqqYikCAAAAaSSm5PMrX/mKrF69WhoaGo577MMPP5Q1a9bIxRdfHEsRAAAASCMxJZ/33nuvWb3ozDPPlHvuucdMOL9u3Tr55je/KTNmzJDhw4czyAcAAADxST5HjBghmzdvNqPd169fbwb5PP300/Lyyy/LNddcIxs3bmTOTwAAAMRvknlt3Xz88cfNVldXJ4FAQCoqKsThiHkKUQAAAKSZuKxwFKZJJwAAABCX5DO8QtCYMWN6nJ9I+PkAAADIbINKPseOHWsGFbW3t4vb7Y6cn4gOSgIAAAAGlXzqOu2abGZnZ/c4BwAAAOKefF5//fUDngMAAAADiWlIeldXlzQ1NfX7uD6mzwEAAABiTj6///3vy9lnn93v4+ecc47cfvvtvNMAAACIfaqlDRs2yHXXXdfv41deeaX84he/kJ///OexFANkDF2owR8ISqc/KJ2BgAQCoXN/MCiBgHTvg9IVCEq7zy/tnV3S7guYn811O6XA45J8j1NcDoccafXJwaYOOWNEkQwvykn2fxoAALEnn7W1tTJy5MgBV0Dat29fLEUAaaej0y+HW31S3+yVI20+ae7okuaOTrP3dgYkEAzGtbyiXBfJJwAgPZLPYcOGyY4dO/p9/KOPPpKioqJYigBSXlNHp9QcaZN9R9tlX0O7NLR1ntTPOR1Zx7asLHGYfei6HudmO82W43aa53f4/NLi7TJblz8oxbnZUlmcI1XFuQn+LwQAwKLkU9d0f+yxx+Qb3/iGTJs2rcdjW7ZskdWrV8tVV10VSxFASjra6pOddS3y8cEWc+u7t8Icl5QXeGRYgVuKcrLNeUGOS/LcLnE5ssTtdJgEEwCAdBNT8vmTn/zE9PucOXOmXHzxxXLGGWeY6x988IG8/PLLZt13fQ6QCbRv5id1LfLnmgbZe7Q9cl2nwq0qypFRpXkysjRXqotzJCc71FoJAECmiSn51D6dmzZtksWLF8uLL74ozz//vLmut9q1NfT+++83zwHSmbfLL+/uaZA/722QVq8/knCOKcuT8cMLZVxFvuR7YvpTAwAgbcT8L2J1dbWsW7fOjNKtq6sz1yoqKlj5CBmRdP65plE27z5qBhEpHW1+xsgi+czIYinMCa0EBgAAjolbc4wmm3qbHcjEpLMs3y2zxpWZlk4dEAQAAOKQfO7Zs8fsx4wZ0+P8RMLPB1I96Xxvbyjp1Dk2o5POCcMLGSAEAEC8k8+xY8eaFs729nZxu92R8xPx+0P/UAOpSFs3361pkK17GiItnaV52TJr3DCZWEnSCQBAwpLPf/mXfzF9O7OzQ33ZnnrqKbGrVatWyQMPPCAHDhyQqVOnysMPP2xG5QMnS1s3t+w5ahJPX1doFSGSTgAALEw+Fy1aJE8//XSktVMHGt19993ypS99Sexk/fr1pq6PPvqozJo1Sx588EG54IILzIT49EvFQHTpyj1H2uTD2iYzbZJOn6TKC9wy89RhMn54AUknAAAxcAzmybm5udLW1hY5f/311+XgwYNiNytXrpSbbrpJbrjhBpkyZYpJQvPy8uTJJ59MdtXShraA97WlIk0wdQWi//64Tp780y55fus++cvBZnO9sihHLpo6Qr75t6fIxCpaOwEAsLTl87Of/axJ7JxOpxQXF5tr77zzjuTkDLxu9OWXXy5W8fl8snnzZrnzzjsj1xwOh5x33nny1ltv9ftzTU1NPc49Ho/ZkkUTOW9XQNp8fmn1dpl9e6ff3P7VrdMfMI/rXjdNlHRNcH9AxB8Mmha8Y9d0LxKUUHIYnSOGE8bwtfBDx841qTy5Ot8yZ5xZoSee4hkX/W/t6AyYddSPtnWalYcONXvNPnxbXekE8JOqC+WM6iLWRE+RvxcAQOoYVKbw85//3CyX+Z3vfMec6+13vaZbf/Q5Vg44qq+vN+VVVlb2uK7n27dv7/fnRo8e3eN86dKlsmzZMrGiX2F9i1eOtPrkaJtPGts7zdKMzR1d0tV9yzeTmbg4s8VTPcGcX3/DDbJgwfWhRDr0/yY5DifJet7VnZD7/EHp7E7QdaDQQO9pntsppwzLk3EVBTKuPF9czkHdFMg4yfp7AQBkWPL5N3/zN7Jz50755JNPzO32uXPnyl133SXnn3++pLqamhqzMlNYIlpxtAXyQFOH7DvaHml1a2rvHPBnPNkOyct2Sp5H1/12mjW/3S5HZJ/tDG0uZ5Y4srLMHJPOrCyzwo45doSu691i/SIQvmkcnqQgcsU8Hjnsfk7oSP+3r0kNjr1aSE62IyFxEXee/OJ/9plzl8spG/96OKbXzPc4zXrqw4s8Mrwwx+wrCjwsjGCzvxcAQHoaVPL53nvvySmnnCITJ04024IFC+Siiy4yg3rsory83HQL6N0XVc+rqqr6/Tn9hzT6H9N40FvfdS1e05+w5mibSTo7/ce3vJXkZZv5Ikvy3GY0dWme2yRHeR6nSSwzmcYkOydfpo/rjCTBPRLn0P9HEmt9LJyQu7uTcj32uBzmPdXEk1bN2CXi7wUAkBkGlXxOmzbNjHa/9tprzfnu3bulpaVF7ETnH50+fbq8+uqrcumll5prgUDAnN92220JLVv7FNa3+EyiqQnnvoZ28XYe60uoct1OGV2aJ1XFOTK80CMVhR7TxxD90/fs/Ck9u1EAAIAMSD77Gu1+4403it3oNEvaKjtjxgwzt6dOtdTa2mpGv8c72Wxo6+xONttl79E2MzCo923zkSW5MroszySdOmUPt3cBAECmGlTyqZO12320u7r66qulrq5OlixZYiaZP+uss2TDhg3HDUIaLB3IcrjVZ/pr1jZ0mGRTB7FEy3ZmycjSXJNoasKpfQmZngcAACAkKziIyRk3bdokV155ZWRNd23BO9GPWz3afShTxmgi3djY2KMPmw4O0lHoZmBQk1cONnfI4RZfZNLxMB3QU12cE2rZLMuTqqIccy2T9feeWv0a6Im42BfvK4BMMqiWT72N3Xu0u65wpHNopoPdh1tl56EWOdjkNYln70Qz3P+wssgjlYU5Mqo0T6pLcjJ+UBAAAMDJGvSM4C6Xq8do969+9au2Gu0ei/2NHfLe3sbIuQ4EMolmUWhwkE44XpTjos8mAADAEMW0HM1TTz1l9l6vV7Zs2SKHDh2Sc845x0x3lIrGDsuXLn/QzPuoLZtFuSSaAAAA8RTz/eKHHnpIqqur5fOf/7wZWKRzgYZXGtIkNJXWU9fpjz4/vlwmVBZKcV42iScAAICdkk9t+Vy4cKFceOGF8sQTT/QYfKSJ57nnnivPPvtsPOoJAACATE8+f/azn8kll1wizzzzjFnpqDed7P3DDz+MpQgAAACkkZiSTx35Pn/+/H4fLysrk8OHY1uHGwAAAOkjpuSzpKTE9O3sz7Zt2wZcTx0AAACZJabk8ytf+YqsXr1aGhoajntMb7evWbNGLr744liKAAAAQBqJKfm89957zepFZ555ptxzzz1mdPi6devkm9/8ppmQfvjw4WaJSwAAACDm5HPEiBGyefNmM9p9/fr1ZrT7008/LS+//LJcc801snHjxpSd8xMAAAA2m2Reaevm448/bra6ujoJBAJSUVEhDgdLTgIAACDOyWc0TToBAACAhCefLS0tUlNTY45Hjx4tBQUF8XppAAAApImY742/8847Mm/ePCktLTUDj3TTY13daNOmTfGpJQAAANJCTC2fb7/9tsydO1fcbrfceOONMnnyZHP9o48+kl/96lfyxS9+UV5//XWZOXNmvOoLAACATE0+7777bhk5cqS8+eabx00mv2zZMjnnnHPMc37/+9/HWk8AAABk+m13bfm85ZZb+lzFqLKyUm6++WYz3RIAAAAQc/Kp0yl1dXX1+7hOQM+USwAAAAiLKTM8++yzZdWqVbJ79+7jHtuzZ4888sgj5tY7AAAAEHOfz/vvv98MKpo0aZJcdtllMmHCBHN9x44d8uKLL4rL5ZIVK1bwTgMAACD25HPatGmm36cOKnrppZekra3NXM/LyzNLbura71OmTImlCAAAAKSRmCeZ1+Ty+eefN8tq6vKaiuU1AQAAEPfkUwcbaWtnUVGRSTZ1hHu0pqYm0wqqt98BAACAmJonv//975tBR/3RwUa33367WOm+++4zddKkt6SkxNKyAQAAkMDkc8OGDXLllVf2+7g+9pvf/Eas5PP55KqrrpLvfve7lpYLAACAE4vpfnhtba1Z4ag/I0aMkH379omVli9fbvZr164d1M9pF4FoHo/HbEgu4mJPxAUAkJSWz2HDhplplfqja7xrf9BUMHr0aCkuLo5sTBFlD8TFnogLACApLZ86ndJjjz0m3/jGN8y0S9G2bNkiq1evNrfAU0FNTU2PRJlWHHsgLvZEXAAASWn5/MlPfmL+AZo5c6ZcccUVsmTJErNdfvnlMmvWLNMios+J1eLFiyUrK2vAbfv27TGVof8duuk/oitXrpRk8Xq9smzZMrPPxPJ7Iy72KL834mKvOgBAKskKBoPBWF5g//79JjnUFY3C/cD0H6VLL73UrICk/T5jpfOHHj58eMDnjBs3Ttxud+Rc+3wuXLhQGhoaBvw5rbMmyY2Njabevc+tlg7lJ+I10uF9SXb5xMW+dbDDfwcAWCXmmeCrq6tl3bp1cvToUTlw4IDZ9FiTv3gknuFJ63UJz4G26MRzqHSd+lif2/t69Hlfx9H7VCq/vzoM9jVORLtuJKJeg3lf1Jo1axJW/kDlxqv88HsZL+HXike9TiY2iXxfBlN++Liv6/EqHwDSXjDN7N69O7h169bg8uXLgwUFBeZYt+bm5j6f39jYqC2/Zj958uQe5wPR557M9ejzvo6j96lUfn91GOxr9Cf8GhMmTIhbvYb6vqiJEycmrPyByo1X+Urfy3jGJV71OpnYJPJ9GUz54ePo6/H4m43H3wwApIq0W3pI+5xqS2xYeCDUa6+9JnPnzj3u+eFeB3rby+/3R7oO9J5Kprfo5w50Pfq8r+PofXS5di+/vzpEX4ulR0f4ZxNRr8G+L329P/Es/0TlxqP88PV4xiWe9TpRbBL9vpxs+eFrifrMiLEXFABkRp/PVLd3714zbQwSMyJ61KhRQ/pZ4pI4xCU9YwMAqSLjk89AIGAmyy8sLDSj5hE7/ZVqbm42fX4djqF1KyYu8Udc0js2AJAqMj75BAAAgHX4ig0AAADLxG3AUUtLi5liqa+G1DFjxsSrGAAAAGRq8tnR0SHLly+XJ554YsBJ4MOjQwEAAJDZYko+b731VjOtka5m9IUvfEFKS0vjVzMAAACknZgGHJWUlMjVV18tjz32WHxrBQAAgLQU04AjnWrlc5/7XPxqAwAAgLQWU8vn9ddfL62trfIf//EfkqqYtzD+mE/SnoiLfREbe2L+VcCGyecnn3wiX/va12T69Olyyy23mFHtTqfzuOeVlZWJXbFiS+Kwko49ERf7Ijb2xMpTgI2Sz+hvggN907bzaPfGxkbTd1U/XIqKipJdnbSg61TrP4INDQ1SXFw8pNcgLvFHXOyL2KRvXADEebT7kiVLUv72Trj++mHNB3Z8xfK7QVwSh7jYF7Gxp1T/dw5Iq+Rz2bJlYkdvvPGGPPDAA7J582bZv3+/PP/882Y6KAAAACSXI54rHH300Udm0+Nk0kFQU6dOlVWrViW1HgAAAIjz8prvvPOO3HHHHfLmm2+a0ZbhvqA66fw//dM/yYwZM8Rq8+fPNxsAZCLtyt/pH3J3/oyX7cziVjtg1+Tz7bfflrlz54rb7ZYbb7xRJk+ebK5r6+evfvUr+eIXvyivv/66zJw5U1KhY3k0j8djNiQXcbEn4mLv2HT6A7LmTzXm3OVyitMZcztDRvnevNPF7SL5BBIlpk+ku+++W0aOHGlaPauqqo7rD3rOOeeY5/z+978Xu+s9RcnSpUtt26c1kxAXeyIuNo+NwyXFs79mzufMmWMaCQAgbVo+dcR778RTVVZWys033yw/+clPJBX0np6EVhx7IC72RFzsHRudaL4rELrt7nbTKj2U2+4AbJp8at/Orq6uAef3TJVVIZI9PUlHp1+a2julxdslbT6/tHq7pNUXOtZbaNp/y+y7AuYfFZ2dNbpLkvZPcmSJuBxZ4nQ4zN7l1OMscTkc3fvQtch59+PZvc5Dzwu9htvpEE+2Qzx6604LyLC4oG/Exb6IDYC0Tj7PPvtsM6L82muvlVNOOaXHY3v27JFHHnnE3HrHMW2+LjnY5JUjrbp1ytE2nxxt9Zkk0+40+dT0U5Pe6M744cPrzx4reW76lgEAgP7FlCncf//9ZlDRpEmT5LLLLpMJEyaY6zt27JAXX3xRXC6XrFixQqymUz3t3Lkzcr5r1y559913zTKfugSoVbr8Aalr8cr+xg452Nhh9o3tnf0+P9/jlHyPS/Ldru69U3LdTnG7HJLtDG9ZZh9O/fTGmraCBiUoepfN7w9KVyAg/kBotKvuw+faYhq63vNc91rX6HN/97m3KyC+rtAsBvrYMYykBQAAFief06ZNk40bN8o999wjL730krS1tZnreXl5cuGFF8q9994rU6ZMEatt2rRJ5s2bFzlftGiR2S9YsEDWrl2b0OlNDrf6ZPfhNtlzpFX2HW0/broTbSUsy3fLsHyPlOZnS2me25yX5GWbW9t2FAgExecPmM0sxtr9n6QJb3hxVt3l2LT+AADAPmK+R3rGGWeYFYR0js+6ujpzraKiIql9PXVkZwxL1g86MattbJePD7XIJ4dapLmjZx9YbbmsLs6RqqIcqS7OleFFHsnJTq0kzeHIkhyHM+XqDQAAUjz51H6cKnzrOnze2969e3ucW3mr26qEc+9RTTib5ZO6Fmn1HuuvqYN0RpXlypiyfDllWJ4My3czWTEAAMBQks+xY8eaRKq9vd1MLB8+PxEd9Z4OGts65cPaRvmwtsmMSg/T0eDjygtkfGWBjCnLM30yAQAAEGPy+eSTT5pkMzs7u8d5OtNBNtq6+cG+RtlzpC3Sx1Fvp59WUSDjhxfI6LK8pExDBAAAkNbJ5/XXXz/geTrR6Y8+qG2UbbVNPaZB0lvpZ44sNoknCScAAICFA460z6cOLsrNze3zcb09r4OQUqXPp043pAOHtJVT+3SGFXhccsaIIjljRLEU54VafQEAADB4MXVOPPXUU81I9/7o9Ev6nFTxx+2HZMMHB0ziqb0JxlXky0VTR8h3Pn+qnH16OYknAABAMls+TzSdUWdnZ8osr6kmVxeZfp16W33KiCIpyiHZBAAASGry2dTUJA0NDZHzw4cP9znlkj7n2WeflerqakkVo0pz5dvnnGrmtQTsSL/wNbV3SX2r16w85cjKEo/LYWZc0GMz/i8oZrUrXQRA+yXrlyjmaAUApGzy+S//8i/y4x//2BzrSPeFCxearb9/KHWVo1Sh/z1pPngfKcbb5ZdDTaElWvc3tsuBxo4eA+BOxufHl8vfjC1LWB0BAEho8vnlL39ZCgoKTGJ5xx13yDXXXCOf+9znjkvi8vPzZfr06TJjxozBFgFkBJ3Gq6HNJ0fbfNLQ1mmSyvZOv7T7/GYeWV0tq6Pz+ERTWzOHFbjNcqaBYFC8XQGz6d+k9oTRL1Dmi5QOogsEJM9NqycAIIWTz9mzZ5tNtba2yhVXXCFnnnlmIuoGpBVNDg82eWX34VapOdou+xvapUvvj59AUW62WZ61qliXaM2R4YUecbGQAQAgEwccLV26NH41AdJUY3unbN/fJB/tb5KjbZ09HnO7HFKa55ay/GzJc7vM4gW52U7J97jMFF+FOS76awIA0kpMyafq6OiQX//617JlyxZpbGyUQCDQ43G9/ffEE0/EWgyQcn01Pz7YYhLO6Dljs51ZMrY8X0aX5pkBbmX57rRfJQwAgLgln7t375Z58+bJp59+KiUlJSb5LCsrMyPddT338vJy0z8UyASBQNBM1aUJpy7J2ukP3VLX3HJUaZ5Mri6U04cXiMdFSyYAIHPFlHz+8Ic/NAnnxo0bZdy4cTJ8+HBZv369nHPOOfLQQw/Jv/7rv8rvfve7+NUWsKG6Zq9JOHccaDYDhcK0VVPnjp1UXcicsQAAxCP5/OMf/yi33nqrzJw5U44cORIZVOHxeExi+tFHH5lpmF555ZVYigFsR5NMTTY16dTkM0z7bE6sLDRJZ2WRh1vqAADEM/lsa2uTsWPHmuOioiLzD622hIbpqPgf/OAHsRQB2IZOe7TzUItJOmuOtplpjcJTH+lSrJpwjh2Wb86ReTr9ATna6pMjbT450uIzA810NgP9/uFyZInT4TB7lzP0+6GDyj43pjTZ1QaA1Eo+x4wZI3v37g29kMslI0eONLfgL7/8cnNt27ZtkpOTE5+aAkmgqwjtOdIqH+1vlk/rW3tMjTSyJFcmVhWajRHp6ZtQahLZ0tFl5lLVLyDRex1Y5u0MPaepozPyheRk6NRZJJ8AMlFMyee5554rL774YmTKpeuvv15WrFghR48eNaPen376abnuuuviVVfAEq3eLtlV32oGDe053NYj4dTJ3SdVFZlb68V59ONMq2VLO7rkUFOHmYv1YFOHHGn19ejDezK020WZmTrLLaX52eJyOHS1U7OgQJc/ENoHdOFTkZJcfn8AZKaYks/FixfLO++8I16v1/TzvOuuu6S2tlaee+45cTqdcu2118rKlSvjV1sgATQpONDUYaZE0tZNPY5uwdJJ3scPLzADhyoK6MeZDrTlUhNMXbZUlyzV4/6WLdW5WPV3IMflEE+287i9x+Uwc7LqFxOdo5XfDwBI8G133cL0Fvvjjz9uNsDOt9IPdiebe4+2meSj90pDlUU5clpFvoyrKJDyAubiTPVWTW3F1EQzlGy2y+FW33G3yB1ZWVJe6JbKwhwTfz0uyXVLTraD+AOAnSaZB+w876auj65rpx9u9cqhJq8cavaa896Jh65/PrI0V8aU5cmp5flSyNRIKZtoNrR1mjjrLASHmjtMS7b2y+ytODfbLFda1b1pqzbLlgKAzZLPPXv2DKmQ6NZRIN6tmM0dnSbJ1E0HfWhyqaOONQnpb+10XbZyREmuWWVIBw6x0lAKfrHwdpk4m3i3+aS+2Sd1LV7zO9Gbriw1vCjHJJvVxblmr6PNAQDWG9Snr06rNJR/oHW1I6utWrVKHnjgATlw4IBMnTpVHn74YTMfKVKzf96nh1tNctnSnWCGk019bCA67VFJXrZZP314occkILon8bA3TSB14FdL9xY+1pg3tA38xUKnMyrXWBd6pKLQE7qFXuBhCiwAsIlB/Qv85JNPpkTrkK6ytGjRInn00Udl1qxZ8uCDD8oFF1wgO3bsMKswIbXoQJDfvn+g38c92Q5zm7wox2VaNEvy3CbZLM3Ta9niIOlIyu1vnYrI5w+YW946JZEmlKHpiQLdx6Fpisxzuo/1sVZfV5+3yQf6YqGbDvjRZFNHmxNzAEiT5FOnUkoFOsL+pptukhtuuMGcaxKqqyxp8qwj9JFaNKHU/pih5DLbnB/bu1grPYne/Lje9Kc1iaNJNEPzX+r8mIOZ87K/UeY6ilxbqQs8zu596MuFJpgae5JMAEg9cbn3qFMtbdmyRQ4dOmTWdS8vL5dk8fl8snnzZrnzzjsj1xwOh5x33nny1ltv9ftzTU1NPc516ijdkFzhuFw4odjsiYu94rJz/xE50OQVl8spTqerz9ZJnYpIN7crNC2RtlTrFwZNLo89Frqmx5pk5nv0mC8VQ8FnGYC0Tz4feughWbZsWWRZzd///vdm8vn6+nqZNGmS/NM//ZN8+9vfFqtoudrHtLKyssd1Pd++fXu/Pzd69Oge5zpxvv53IbmIi73jkj1stGS53PL3N98oi/7h/zsuqWT0uPX4mwGQ1snnU089JQsXLpSvf/3r8uUvf7lHkqmtn5qEPvvss5Ymn0NVU1Nj1qcPo6XAHohL6sSF2NgDfzMA0jr5/NnPfiaXXHKJPPPMM3L48OHjHp8+fbppGbWSJr26utLBgwd7XNfzqqqqfn9OP6yjP7BhD8TFnoiLfREbAHYX0z2xnTt3yvz58/t9vKysrM+kNJHcbrdJel999dXINV1nXs9nz55taV0AAAAQx5bPkpIS08eyP9u2bRuwtTFRdJqlBQsWyIwZM8zcnjrVUmtra2T0OwAAAFIw+fzKV74iq1evlltvvfW4xz788ENZs2ZNUvp7Xn311VJXVydLliwxk8yfddZZsmHDhuMGIQEAACCFbrvfe++9ZmT5mWeeKffcc4+ZgH7dunXyzW9+07Q66oTumgAmw2233Sa7d+8200C9/fbbZrJ5AAAApHDyOWLECDOn5oUXXmhWFdJVTZ5++ml5+eWX5ZprrpGNGzcmdc5PAAAApNk8n9q6+fjjj5tNb3Xr4J6KigozsTsAAAAQbcgZYltbmxlVrktXhmnSqf0qSTwBAADQlyFniXl5ebJr1y7TzxMAAAA4GTE1UWpfz9/97nexvAQAAAAySEzJ549+9CP5y1/+It/61rfkzTfflH379smRI0eO2wAAAICYBxydccYZkcnkdYnN/uh0TAAAAEBMyafO4UmfTwAAAFiSfC5btiyWHwcAAECGietUSwAAAMBAmGoJAAAAlmGqJQAAAFiGqZYAAABgGaZaAgAAgGWYagkAAACWYaolAAAApEbyGa2lpUVqamrM8ejRo6WgoCBeLw0AAIA0EdOAI/XOO+/IvHnzpLS0VM4880yz6fG5554rmzZtik8tAQAAkBZiavl8++23Ze7cueJ2u+XGG2+UyZMnm+sfffSR/OpXv5IvfvGL8vrrr8vMmTPjVV8AAABkavJ59913y8iRI800S1VVVcf1Bz3nnHPMc37/+9/HWk8AAABk+m13bfm85ZZbjks8VWVlpdx8882ycePGWIoAAABAGokp+XQ4HNLV1TXg/J76HAAAAEDFlBmeffbZsmrVKtm9e/dxj+3Zs0ceeeQRc+sdAAAAiLnP5/33328GFU2aNEkuu+wymTBhgrm+Y8cOefHFF8XlcsmKFSt4pwEAABB78jlt2jTT71MHFb300kvS1tZmrufl5cmFF14o9957r0yZMkWsdN9998krr7wi7777rhmF39DQYGn5AAAASOAk85pcPv/88xIIBKSurs5cq6ioSFpfT5/PJ1dddZXMnj1bnnjiiaTUAQAAAAlIPnWwkbZ2FhUVmWRTR7hHa2pqMq2gevvdKsuXLzf7tWvXDurntK7RPB6P2ZBcxMWeiIt9ERsAdhdT8+T3v/99M+ioPzrY6Pbbb5dUoEuCFhcXRzb6qtoDcbEn4mJfxAaA3cXUJLlhwwa57rrr+n38yiuvlF/84hfy85//XOxO16XXFtwwWgrsgbjYE3GxL2IDIK1bPmtra80KR/0ZMWKE7Nu3T2K1ePFiycrKGnDbvn17TGXoh7Vu+kG9cuVKSRav12tWh9J9JpbfG3GxR/m9ERd71SEasbFH+QD6lxUMBoMyRJp4XnPNNfLP//zPfT6+aNEieeaZZ+TAgQMSCx3IdPjw4QGfM27cODO6PUz7fC5cuPCEo921f5TemmpsbDQf2L3PrZYO5SfiNdLhfUl2+cTFvnUgNvYsP9n/DUC6iqnlU6dTeuyxx2Tr1q3HPbZlyxZZvXq1zJ8/X2Klo+d1LtGBtujEc6h0wvxYn9v7evR5X8fR+1Qqv786DPY1TkR/hxJRr8G8L2rNmjUJK3+gcuNVfvi9jJfwa8WjXicTm0S+L4MpP3zc1/V4lW/130yyP0uSXX5/dYh3XABECcZg3759wREjRgRdLlfw8ssvD/7oRz8y22WXXWau6WM1NTVBK+3evTu4devW4PLly4MFBQXmWLfm5uY+n9/Y2Kgtv2Y/efLkHucD0eeezPXo876Oo/epVH5/dRjsa/Qn/BoTJkyIW72G+r6oiRMnJqz8gcqNV/lK38t4xiVe9TqZ2CTyfRlM+eHj6Ovx+JtN5t9Msj9Lkl1+f3WIV1wAHC+mAUfap3PTpk2mT6auaKTzfSq9PfGNb3zDrICkz7HSkiVLZN26dT0mwlevvfaazJ0797jnh3sd6O0VXYs+PE1J7+lKeot+7kDXo8/7Oo7eR5dr9/L7q0P0tRh6dER+NhH1Guz70tf7E8/yT1RuPMoPX49nXOJZrxPFJtHvy8mWH76WqM+MZPzNJPuzJNnl91eHeMUFQJz7fEbTl4meZF4HAaWCvXv3mqlJkJhRt6NGjRrSzxKXxCEu9kVs0i8uABKYfKYqXZlJR+0XFhamTMJsd/or1dzcbFq9h7rSFXGJP+JiX8QmfeMC4HgZn3wCAADAOnyVAwAAgGVIPgEAAGAZkk8AAABYhuQTAAAAlolpns9oLS0tcvTo0T7nQxszZky8igEAAECmJp8dHR2yfPlyeeKJJwZcez086S8AAAAyW0zJ56233mpWE7r00kvlC1/4gpSWlkqqYW68+GPOQnsiLvZFbOyJuNgT869m+DyfJSUlcvXVV8tjjz0mqYpVQRKH1VrsibjYF7GxJ+JiT6w8laEtn/ot7nOf+5ykMv02Gv4l1jXpETtdD1k/bMPv7VAQl/gjLvZFbOyJuKRvXJDCyecll1wif/jDH+SWW26RVBW+DaIfCnwwxFcst5iIS+IQF/siNvZEXOyJbgypK6bOEj/60Y/kr3/9q9x8882yefNmqaurkyNHjhy3We2NN96Qiy66yPQH0V/OF154wfI6AAAAIM4tn+PHjzf7rVu3mhHvdhnt3traKlOnTpVvf/vbcvnll1taNgAAABKUfC5ZssSWzd7z5883GwAAdqHjezv9Qx7jm/GynVm2zDlgcfK5bNkySacOzNE8Ho/ZkFzExZ6Ii71j89GBFnlnd4P5h9rldIorO1vC/2SH/+3WXfgfct1F/knPyurx3PBZ9HOir0uP693PjS6n12scKyZ0Nfy47h3m8SyzDz+uM+mYI/P4sZ8JH/e8Hv7ZPl5H993lh4+jXyd0HjoeVZonbpcj7nHp9AdkzZ9qzLnL5RSnM27rvGSE7807Xdwuks90wG9+t95TYSxdujStkutURVzsibjYOzbu6gmSO26GOZ8zZ47MnTs32dVKKTecM1bcLnf8/2YcLime/TVzTlyQyWJOPnWVo1//+teyZcsWaWxsNBPqRtNvkgP1B7WL3tNg0IpjD8TFnoiLvWPjysmXFm+XOXe73ZLtDiVSOqtzMOoWcO9ZnkOPB/t+btRzuo8ix7o7dhx1vdfrRZcT/dxA9+ub50cdm+vdrxF6LHQc6PG8Y48N+DrRz42U28fr6D+MTkdC4qJTA3VpoSYu3C0Yym13pIeYks/du3fLvHnz5NNPPzUTzmvyWVZWJg0NDWaQUXl5uRQUFEgqsMM0GPrh5+0KSJvPLx2dfunyB6UzEDD7rkDA3B4KbaGkXv8Qc7KdkuNyiifbIR6XI+36w9ghLjgecbEvYmNPxAWIU/L5wx/+0CScGzdulHHjxsnw4cNl/fr1cs4558hDDz0k//qv/yq/+93vYiki7fgDQTnc4pWjbZ1ytM0nDWbrNK0UmnTq40Oleacmo7m6uZ2S5z52rPs8t8tc0+foXpPVRHzDBwAASEjy+cc//tGs7z5z5szIfJ7aeqe3EjQx/eijj2ThwoXyyiuviJVaWlpk586dkfNdu3bJu+++a1plx4wZY1k99L3QJPNAY4ccbApth5q9J0wwtRVTWzO1ZVOTQ5dD91mR20ShW1FB8fmD4u30m9ZSX1fAPN7u85tNWk+ujk6HtqA6TFmajIaOHSZBzfc4pcCjCavL7PVcj8ONq9G31FyO0GukW8srAACwUfLZ1tYmY8eONcd6O0ETD20JDZs9e7b84Ac/EKtt2rTJdAcIW7RokdkvWLBA1q5dm9Cymzo6Zc/hNtlzJLSZRLAXTeyG5bulJC9bSvLcUpqXLQU5oSRPWyQ1+RusLn+gxy173bebfVfkPPox3WvyqImwP6DnIs0x/rff+IVTpTAnO8ZXAQAA6Sym5FNbEffu3Rt6IZdLRo4caW7Bhyd237Ztm+Tk5IjVdAShtgxaQcupb/HJx4eaZeehFjnc4uvxuLZaDi/ySGVRjlQV50hVUY4U52bHvYXQtJA6HZLvObmQBgLachowU39oq6nOPdfZncDqXhPXVm9X9+aXVl9or0lrf4aSNAMAgMwSU/J57rnnyosvvmimWVHXX3+9rFixQo4ePWpGvT/99NNy3XXXSbrRhLOu2SsfH2qRjw82m1vrYZpTVhfnyOiyPDllWL5JNvXWtt04HFmS4wj1/xwMbSnV//7oOfqUJq+MRAQAAAlNPhcvXizvvPOOeL1e08/zrrvuktraWnnuuefE6XTKtddeKytXrpR0oa1+H+1vkvf3NfZo4dTWzVPK82X88AI5tTx/0AldKgkl0scnmUz8CwAALLntHj2AR2+xP/7442ZLF9rKd6CpQ97f2yh/OdgcWRpNE86xmnBWhhJOjyt9E04AAIB4YYWjfni7/LLjQLO8t7fR3GIPKy9wy2dGlcikqsK0buEEAABIevK5Z88esw+3dobPT8TK6Y1idUhbOfc1yvYDzWYgTriVc3xloXx2VLHpz8l0QgAAABYknzqtkiZe7e3tZsm28PmJ6GpHqeD1HYdk656GyLlOgaStnFOqi8xE7YAd6PRZ2udYZyXQFa9Cq1t1zwvrcJh5YP06F2xAzD68sAAAACmXfD755JOhZR2zs3ucp4tRpbnmNvvpwwvkMyOLzXk6/fchNbt/6FRe4YUKdN/Yfmx2hZMxd2KFTBtTmrA6AgCQsORTp1Ia6DzVjSsvMBOl62TvgBWzJ9S3eM3yqrrp3Kod3Zse65Kr3s5Q14/edIECbe3UFaY6OnV+Vr90dgVNq6d+X9IWUZ2ZIHwMAIBdxJRl6ZKVuoTmNddcE7mma7nfd999ZvolnWrpH/7hHyRV6NyXJJ5IFJ3Yf+/RdvlrfYvZa+J5Mmsh6NKmlcU5UlnokeriXLNoQX+30cNzsAIAYFcxZVp33HGH5OXlRZJPXUP9sssuk2HDhsmIESPMspa5ubly8803x6u+QErRZFATTZ2mSxcl6L3calFutpTlZ0tJrjvSN1O33Gyn5HucZtnVwUzjReIJAEjr5PPPf/6z/PCHP4yc/9u//ZuZXH7r1q1SXl4uV199tTz66KMkn8g4mmRu0wUJ9jb0WAFLB66dVlEgY8ryZGRprmnVBAAgk8T0L19jY6Np5Qz7zW9+I+eff75JPJUe//a3v429lkCKtHLWNoYWJNBlV7sCoXvqbpdDJlQWysTKQjOITbt3IL1pH9xDTV5p8/nNkrT6pSPf7RRPttNM3ebtnsatLN+d7KoCQGoln9XV1abPp9q/f79s3rxZbrjhhsjjLS0t4nA4Yq8lYGM6QEjnhdVWTh2ZHqZ9Mz87skQmVBWwAlYa0/mAG9p9Jtnc39hhVkQ7fBL9ebXl+2szRltVTQBIj+TzkksukYcfflg6Ojrk7bffNuu7a5/P6Nvy48aNi0c9Adu1ch5s8poFCXYcaIosu6pzbWor52dHlUhlkYc+mGnQfUIHhh1t85kZCXSOVZ1dIDwrQavPH1mMorfCHJeU5LlFG7q1BVR/Vmcv0LlXtTVcf1cAIBPFlHzee++9UldXJ08//bSUlJTI2rVrpbKy0jzW1NQkzz33nHzve9+LV10B2y67OkyXXR1ZLJOri5jQPYVpQvnp4VapOdIutQ3tcqT1WEv2QHSi/+GFOVJVlCNVxaGtv/68zEgAINPFlHwWFBTIL3/5y34f27t3rxkND6Q6neBdE04dtd5z2dUCOXNksYwsYUGCVNXU0Sl/rWuVTw6FpsDSuVJ7z6mqfTOLc7NNa6Z2oQjNSuAwU7PprATMSAAAJy9hQ221r2dxcXGiXh5IOJ3kXZPNj/Y3mf58YZqIaMLJsqupSVsetW/uJ3UtJunULxbRtBX71PJ8GVGSKyOKc4kxAMQZk8wDvW677jzUYm6t1xxtiwwaoZUz9Sf4r21sl0+6WzijlyjVUGqSedrwfLPKWSkj0AEgoZhkHhmvuaNTdh9uk7/Wt8ru+tbIFElqREmOTKwqMtMk0QKWWjr9AdlzpM0kmxrb6An+9cvEmGF5Zs7VcRX5rGwGABZiknlkZCvY/qYO+bS+VXbVt/YYOBS+7TqpO+EszstOWj0x+NvpOqG/Jpy7zaChtsgsBEr7aert9NOH58uYsnwz4hwAYD0mmUdG3ErXPpv7GkIjmHUexujpcfS2q45SHlueb1rBKgqYIimV+uVqTLXlWhPO5o6u45YvPa0i37RwancJJvgHgORjknmkVctXs7fLtGRGb9H9+6JbwcYOyzMJ59hh+dxSTwG6UpBO3q6rSO1vaDcTuveOrdORZQYKnTIsz2x8kQAA+2GSeaRcgtne6Te3V4+2+kzyoROA63ljm6/HbdbeLWAjS3JCI5hLcmVYvpukxMZJZlN7p4mtTvCuW12LT460+I6bBklDOKzAI2PK8uSUsjyzalC2ky+8AGBnaTvJ/KpVq+SBBx6QAwcOyNSpU02SPHPmzKTUBbFpbOuU//+T+lDC2ebrd0UZ5cjKkrL8bKkozJGKQo8ML/SYPRO/2+fLg64QpKv96Ko/rb4uaWrvMommbg1tPnMrvb+lKXUy9+riHKkuDk2DVFnsYelSAEgxaTnJ/Pr1681Iex3sNGvWLHnwwQflggsukB07dsjw4cMtrw9ik+UQs3Z65DxLly7MlpLcbCnNzzZLGJrjPLdp4dRbr7B2AFdHl19avX4zolwTyvBykj32Xj32H9d62RddelIndS/L90h5gVvKC3XvkaIcFy3WAJDi0nKS+ZUrV8pNN90U6X+qSegrr7wiTz75pCxevDgpdcLQFXpc8oXx5aEkMy+UdLq4tWoL/76pxgz4OYl8sgdtic5zhzb9wlDca9PrJJkAkJ5csSaYJ/MPhN9/bH69RPP5fGbg05133tmjnuedd5689dZb/f6cdhOIpv1XdUNyheMyoUx/VQPiyRYSTxvFxdvRIR0dXnG5nFKQ6+lOKF2RxDL6ON/jMgO78rKdxDCB+CyzJ+ICxCn5XLJkyXHJpyaan376qbzwwgsyceJE+epXvypWqq+vN3UI9z0N0/Pt27f3+3OjR4/ucb506VJZtmxZwuqJk0Nc7B2XLE++SDAg9yy+Q36wnLjYAX8z9kRcgDglnwP94ejUS3/7t38rEyZMkFRQU1MjRUVFkXO+kdoDcbEn4mJfxMaeiAtgQZ9PnQP07//+7+UnP/lJj7XfE00nuNdVlg4ePNjjup5XVVX1+3P6oRD9wQB7IC72RFzsi9jYE3EBjklox6v8/Hyz3ruV3G63TJ8+XV599dXItUAgYM5nz55taV0AAABgUcvnBx98IA899FBSbrvrNEsLFiyQGTNmmLk9daql1tbWHqsvAQAAIMWSz1NPPbXP0e4NDQ1m3Xed41MHHlnt6quvNpPf64AonWT+rLPOkg0bNhw3CAkAAAAplHzOmTPnuORTz0tLS+W0006Tr3/961JWVibJcNttt5kNAAAAaZJ86nKaAAAAwMlipmcAAABYhuQTAAAAqTPavaOjQ37961/Lli1bzCAjndaodx/QJ554ItZiAAAAkOnJ5+7du2XevHlmOc2SkhKTfOoAIx3trktc6oTvBQUF8astAAAAMve2+w9/+EOTcG7cuFH+8pe/SDAYlPXr10tLS4v89Kc/ldzcXPnd734Xv9oCAAAgc5PPP/7xj3LrrbeaidwdjtBLaQKqa9ZqYvqlL31JFi5cGK+6AgAAIJOTz7a2Nhk7dqw51jVrtX+ntoSG6XKWb775Zuy1BAAAQFqIKfkcM2aM7N271xy7XC4ZOXKkuQUftm3bNsnJyYm9lgAAAEgLMQ04Ovfcc+XFF1+UpUuXmvPrr79eVqxYIUePHjWj3p9++mm57rrr4lVXAAAAZHLyuXjxYnnnnXfE6/Wafp533XWX1NbWynPPPSdOp1OuvfZaWblyZfxqCwAAgMxNPvW2u25heov98ccfNxsAAAAQl+SzpqbGjG7XPp7hieYfeeSR4543evRoueqqq4ZSBAAAANLQoJPP999/X6ZNmyYPPvig3HbbbeZaa2ur/OAHPzCj3XWqpTC99T5p0iT5zGc+E99aAwAAIDNGuz/22GNyyimnmPk9e/vFL34hu3btMtsnn3wiI0aMMM8HAAAAhtTy+dprr8nll18emVQ+WmVlpUlMw3TA0UsvvcQ7DQAAgKG1fOo67norPZrO8Tl16lQpLCzscf3UU081678DAAAAQx5wpHN4RisuLpatW7ce97zefUABAACQ2Qbd8jlq1Cj585//fFLP1efp8wEAAIAhJZ/nn3++/PKXv5RDhw4N+Dx9XJ+nzwcAAACGlHzqlEqdnZ3ypS99STZt2tTnc/T6eeedZ553++23804DAABgaH0+x44dK88++6xcc801MmvWLDn99NPlzDPPlIKCAmlpaZEPPvhAdu7cKbm5ufLMM8+YQUcAAADAkAccffWrXzX9OX/605/KK6+8Is8//3zkserqavnOd74jd9xxh0lMAQAAgJjXdh83blxkAvnm5mZpamoyUy0VFRUN9SUBAACQ5gbd57MvmnTqOu92SDzvu+8+OfvssyUvL09KSkqSXR0AAADEO/m0E5/PJ1dddZV897vfTXZVAAAAEK/b7na1fPlys1+7du2gfk67DUTzeDxmQ3IRF3siLvZFbOyJuABp3PI5VKNHjzYrNYW3FStWJLtKIC62RVzsi9jYE3EB0rjlc6hqamp69FnlG6k9EBd7Ii72RWzsibgAKdbyuXjxYrNO/EDb9u3bYypDPxR00w+ElStXSrJ4vV5ZtmyZ2Wdi+b0RF3uU3xtxsVcdohEbe5TfG3GxR/mwh6xgMBgUm6urq5PDhw+fcOont9sdOdc+nwsXLpSGhoYT9sPRWyCNjY3mg6H3udXSofxEvEY6vC/JLp+42LcOxMae5RMXe5af7P8GZEjLZ0VFhUyaNGnALTrxHKpVq1bF/Nze16PP+zqO3qdS+f3VYbCvcSKrV69OSL0G876oNWvWJKz8gcqNV/nh9zJewq8Vj3qdTGwS+b4MpvzwcV/X41W+1X8zyf4sSXb5/dUh2XFJlc+yky3Xrp9lSJJgmtm9e3dw69atweXLlwcLCgrMsW7Nzc19Pr+xsVFbfs1+8uTJPc4Hos89mevR530dR+9Tqfz+6jDY1+hP+DUmTJgQt3oN9X1REydOTFj5A5Ubr/KVvpfxjEu86nUysUnk+zKY8sPH0dfj8TebzL+ZZH+WJLv8/uqQ7LgMVC87fZadbLl2+yxDcqXdgKMlS5bIunXrIufTpk0z+9dee03mzp173PPDvQ60Gd/v90emw+g9LUZv0c8d6Hr0eV/H0fvocu1efn91iL4WS4+O8M8mol6DfV/6en/iWf6Jyo1H+eHr8YxLPOt1otgk+n052fLD1xL1mZGMv5lkf5Yku/z+6pDsuJxMvezwWTaYcu30WYbkSok+n4m0d+9eMwUGEjO6c9SoUUP6WeKSOMTFvoiNPRGX9IsLkivjk89AICC1tbVmiVAdNY/Y6a9Uc3OzjBgxQhyOoXUrJi7xR1zsi9jYE3FJ37gguTI++QQAAIB1+MoAAAAAy5B8AgAAwDIknwAAALAMyScAAAAsQ/IJAAAAy5B8AgAAwDJpt8LRYDEHW/wxN549ERf7Ijb2RFzsibikfmwyPvnUXz5Wn7Df6hPEJXGIi30RG3siLvZEXFI3NhmffOq3nvAbVVRUlOzqpAVdi1f/qMPv7VAQl/gjLvZFbOyJuNgTcUn92GR88hlubtdfPn4B4yuWWxnEJXGIi30RG3siLvZEXFI3Nmk54OiNN96Qiy66yPQ50DfghRdeSHaVAAAAkK7JZ2trq0ydOlVWrVqV7KoAAAAg3W+7z58/32wAAAAnGqHd6Q8muxopLduZNahuEGmZfA61k2w0j8djNiQXcbEn4mLv2Oysa5WtNU2i/xa4s7PF484WR1aW6Mwnutd/JBxZoWPdh86PXcvq3jsdx457PvfYNX2Oy9m9d4T3DnMt+jz8uEN/OEPj4u0KyL9v2W/Os10uyc4O/ROs74i+r/pem3dHj0O77sePPRbah58Y9bPhx7ofj84Dejwe9VrdRZlX0bBojEK/J93n3b8ret3pCL2OXov+XYr8TK/fmfB1cxz5mT6eFznOSlpcOv0BWfOnGnPucjnF6SQ1GqzvzTtd3C6Sz0HrPeXC0qVLZdmyZUmrD0KIiz0RF3vHxl09QXLHzTDnc+bMkblz54pdaOKhianH5RC3bs7ufdSxx+Xs3oc3p3iyQ4/rXs81YUm1uGS5PFI06wpbxiXZIklqVDIb/uITejxLpp9SKmeNLon/Z5nDJcWzv2bOiYs1SD679Z5ugVYceyAu9kRc7B0bcedJQ1un+INByc52iys7WwLBoAQCYvbBYGgf2kK3HQPR17qf1/O54Z89duwPhJ7T5Q8ddwaC4vcHpEv3gWBkr1uYPt/XpVsg5tt8fSWlmqy6nA7Tymq28LEzS7K7j3snuPrzmuOYugaD3edZcY9LfkGhHG71ib4b7my3ZLvd5lgFu9/r0LFIUP/P7KMe735Mjnss6vlRx2G9X6/nax07N/E0MRXzPoR+F7rPu2Mdjrv+bPj90p/3R363wr8XPX/HQj9z7Lg3fb2u0C9bv++ht9Mv8aZx0WmB9HdVud3cxRnq3+NgkHx2Y7oFeyIu9kRc7B+boU29nRiB7iRFk9SuQMDsff6ASUD1VrTuw+eha/7INW9n6Nwb9VylffQ6/V3S4o1/fb/9+VOlODc7IXEpjW/DXUoKJ9r+6EQ2cnwseTUpcXc+qscFnvinLHyWJQfJZ5I/kJu9+uHZJR2doQ/b8IerfkCb/jLdtxv0FkROtlNyur/d57qdUpjjMscAYGfmVqpoy6OeOWP+3OwrKY0cdwa6W2FDSa4/EDCJajjp1WNf93NDn7XBPstA4oT7tx77nUCmScvks6WlRXbu3Bk537Vrl7z77rtSVlYmY8aMsbw++kGmt1oONXfIoWav1Dd7pbG9U1q9fnNLIhaahOo3dN1K89xSUeiW8gKPOWe9WgDpmMjmOPSLuGYtsbdOdmmLqz9gBuLoIJrwABgAiZOWyeemTZtk3rx5kfNFixaZ/YIFC2Tt2rUJL19bMfcebZeaI21yoKnDJJt9fbtW+iGntxI0iYzugK/9k4713wn1kTHf7DsD0t7plzaf35TT7gttBxo7eryuvk5ZfigR1X14K8pxkZQCQDfTJ1SHcgOwTFomnzpSTfuNWEXLqmvxys5DLbL7cJscbOro0dk7nAxWFHpkuNlypCQvW4pysyXf7RxyMqjJqLagNrV3SWO7T+pbdPPKkRafuZ2kCWnvpFQ7BWuraK7bZcrWpDfP7ZLcbD12mNYEPS7MyTZ1BgAAiKe0TD6tSjj1FvrHB1vk40PNZmRpNG1lHFOWJyNKcqWyKDG3wbW/5/BC3Y6/zd/Q3mkSUZOMtvrkqG5tnaa/kyapIrr1T6takpstFYU5MqxAW1DdUpLnNgmpjhY1z4nMFBd6vrbc0qoKAAAGQvI5SE0dnfLB3kb56ECzNLUfSzg1IRtbni/jKvJN0qkth8nsExW+zT6hsrBHUqotpc0dXdLq6zK37tt8Otgp0L3vvo3fGTDHmqzqJgdPrtxb5owzragAAAD9IVM4CZq07T7SJu/tbZBd9a2RW+p6C/vU8gIZX1kgY4fl2/42tSalpflus52IJqP1zT6pa+kwLaWHW3wmcdXO+f31XwUAADgRks8TJGAf1jbJe3sbe7Ryji7Lk8+MLDatnDppcTrSFswxw3TL6/Px6D614UPuuAMAgBMh+ewjqdrX0C7v722Ujw+1RFZi0NUzplQXyWdHlZjb2Zkuum8nSScAADhZJJ9Rth9oknd2HekekBNSVZxjWjknVhWmbSsn7E/74OrAMR1Apl0gdN5Yf/dCBJ7s0DRduulsBbrXKbx0cJl+eTplWJ5UFuUk+z8BAACD5DOKDsTRxFP7ck6s0lbOYv7RRsJa2HV1q8a2TtOXNrScoK6+EohsPn/QrGV8tM1nFiQYKpezgt9jAIBtkHxGOWNEkRm1Prm6qHv1DCB+tOVyV32LWYBA51/V2QYGQ5dT1WmvhuV7zF6n2oosPtC9rKC2kOqxrpOc7Qit1DKMbiIAABsh+ew1yGbamNJkVwNpRFs2P6htlI8PNoemrYqit8yLc11msYE8t9N06whvbpeueRxa8aokV2coyDbJJlJnhgztItHU0WWW0A0t3OAynzF6Z0WXc9QvDkxNBiAT8ckHJCDx+Gt9q7y/r8GseBWeDUBbIUeV5sopw/JlREmOVBR4WNYvTeLd4uuSQ01es7pZbUO7WYBCVxnriw7Q09+JkaW58rUZoy2vLwAkG8knECct3i75YF+j2bT/cNjY8jyZUl1s9rRephbtexse6KWrmB1bmOHYggza7aGv1Xy11Vq7POj8um3eLvP7oYPAws/V1waATETyCcQ4cGiPWYCgUf5a12pusapct9P0IdaZEnRZUqROPPc3dpgW65ojbeY4HNOBaBeKsgK3VBXlSHVxjpklQxPP3svNamuo3nLXGQmYPQNApiL5BIZAW7227W80Sae2iIXprVSdJeH0igJuqadQwnmwySs7DjabvrnRrdZK++MOK/CYZFKPtZ9mrjs0rZX25dQvGjkup2nhPBFtDbX7SmgAkGgkn8AgkpTaxg55f2+DfHywJbLMqCYTugDBZ0YVS3mBJ9nVxElqaPPJttom2X6g2Ux3Fabx1OVydX7U0aV5UpyXndR6AkC6IfkETkD79G3f3yzv7WuU+mZv5PrwIo9MHVUiEyoLac1KoVjqFwdNOnUlszAdgT6uosDEcuywPFqtASCBSD6BAVo5P9wXWmY1PHJZkxRNUHSZVe3Xh9QYja5zq2o3iZ2HWsygH6XdMbV1UweDnVqezxcIALAIyScQpdXbJR/tb5IPa5vMKOcwndRdBw+xAEHqONrqM7Hctr+pRz9OjaV2k5hUXSQFHj4CAcBqfPIi4+ngoU/qWsz2aX1bZHSztoSNH14gZ4wslhHFOceNXIb9NHd0mpbqvxxoNiPVwzzZDplUVWhaOSuLPMQSAJKI5BMZ28Kpt2B101uy0dPp6ATwZ4wolvGVBczLmSKxDCec0f04Nb/UgUNTRhTJuPJ8+nECgE2QfCIjdPkDpiVM5+TUTVeiiZ6+sbzQY1o5tT9nGWuh274/bl2zV3bVt8qnh1tNXKNjObIk13xxGF9ZyG11ALAhPpmRtq1hmpQcaOyQ/Y3tJtkMDzQJ0wFDmnCePryAieBtnmw2tXeZVs2ao22y+3CrtHr9PZ6jE7trsjmhskAKc5gaCQDsjOQTKZ+Y6ByN9S0+OdziNfsDTR3SFDVvY1i+xyljyvJkdFme2ZOk2JMuXaktm3UtXtnfEForXZemjKb9cTWOpw7LN8uWEksASB0kn0iZJFMTkMOaZLaGkkw9PtLqPa5FM9zfT1elCS93qJveTmegiX3o2uahLw5eqW8OrZ+uW+8VhpTTkWUGCo0oyZVTyvJNv1z6cAJAakrb5HPVqlXywAMPyIEDB2Tq1Kny8MMPy8yZM5NdLQzBoeYOeW7zXvF2huba7M3lCK2rPSzfI+UFbqksyjETwDNYKLn8gdAXBh2BrvvGtk5paO80Cace927NjFaUm21iqV8eNOHULhKshQ4A6SEtk8/169fLokWL5NFHH5VZs2bJgw8+KBdccIHs2LFDhg8fnuzqYZCKcrJN4unIypLS/GyTZOpcjZqc6HFxbvZJrauNxHwx0C4OTR1d0tKhiWYo2dR9q6+rx0Cgvujtc42jLktqtsLQGurMpQoA6Sstk8+VK1fKTTfdJDfccIM51yT0lVdekSeffFIWL16c7OphkDQR+dbsU6QkN5tbrTbzXx8eNP0z+6O3ywtzXGbUubZmagx1rXT9wlCSq0mmg64QAJBh0i759Pl8snnzZrnzzjsj1xwOh5x33nny1ltv9ftzTU1NPc49Ho/ZkFzhuOhY9LZWL3GxWVwKXQHp9IiU5OdIWWGuSTSLTLKZbY7z3E6SS4vxWWZPxMWeiEtypF0zUn19vfj9fqmsrOxxXc+1/2d/Ro8eLcXFxZFtxYoVFtQWJ0Jc7B2XS/9mnHx7zkT588tPypwJFfK5MaVy+vBC00cz3+Mi8UwC/mbsibjYE3FJjrRr+RyqmpoaKSoqipzzzcceiIs9ERf7Ijb2RFzsibgkR9oln+Xl5eJ0OuXgwYM9rut5VVVVvz+nv3zRv4CwB+JiT8TFvoiNPREXeyIuyZF2t93dbrdMnz5dXn311ci1QCBgzmfPnp3UugEAAGS6tGv5VDrN0oIFC2TGjBlmbk+daqm1tTUy+h0AAADJkZbJ59VXXy11dXWyZMkSM8jorLPOkg0bNhw3CAkAAADWSsvkU912221mAwAAgH2kXZ9PAAAA2BfJJwAAACxD8gkAAADLkHwCAADAMiSfAAAAsAzJJwAAACxD8gkAAADLkHwCAADAMiSfAAAAsAzJJwAAACxD8gkAAADLkHwCAADAMiSfAAAAsAzJJwAAACxD8gkAAADLkHwCAADAMiSfAAAAsAzJJwAAACxD8gkAAADLkHwCAADAMiSfAAAAsAzJJwAAACxD8gkAAADLkHwCAADAMmmXfN53331y9tlnS15enpSUlCS7OgAAAEjn5NPn88lVV10l3/3ud5NdFQAAAPTikjSzfPlys1+7du2gfq6pqanHucfjMRuSi7jYE3GxL2JjT8TFnohLcqRdy+dQjR49WoqLiyPbihUrkl0lEBfbIi72RWzsibjYE3FJjrRr+RyqmpoaKSoqipzzzcceiIs9ERf7Ijb2RFzsibgkR0q0fC5evFiysrIG3LZv3x5TGfrLp5v+4q1cuVKSxev1yrJly8w+E8vvjbjYo/zeiIu96hCN2Nij/N6Iiz3K7424JKcOWcFgMCg2V1dXJ4cPHx7wOePGjRO32x051z6fCxculIaGhhP299Cm9sbGRvML2PvcaulQfiJeIx3el2SXT1zsWwdiY8/yiYs9yycuqR+blGj5rKiokEmTJg24RSeeQ7Vq1aqYn9v7evR5X8fR+1Qqv786DPY1TmT16tUJqddg3he1Zs2ahJU/ULnxKj/8XsZL+LXiUa+TiU0i35fBlB8+7ut6vMq3+m8m2Z8lyS6/vzokOy6p8ll2suXyWTbwZ8kai8q38m92QME0s3v37uDWrVuDy5cvDxYUFJhj3Zqbm/t8fmNjo7b8mv3kyZN7nA9En3sy16PP+zqO3qdS+f3VYbCv0Z/wa0yYMCFu9Rrq+6ImTpyYsPIHKjde5St9L+MZl3jV62Rik8j3ZTDlh4+jr8fjbzaZfzPJ/ixJdvn91SHZcRmoXnb6LDvZcvksG/izZKJF5Sf6b/ZkXyPtBhwtWbJE1q1bFzmfNm2a2b/22msyd+7c454f7nWgTcV+vz8y7ULv6Rd6i37uQNejz/s6jt5Hl2v38vurQ/S1WHp0hH82EfUa7PvS1/sTz/JPVG48yg9fj2dc4lmvE8Um0e/LyZYfvpaoz4xk/M0k+7Mk2eX3V4dkx+Vk6mWHz7LBlMtn2cCfJVaUb9Xf7IlikxJ9PhNp7969ZqoFJGYU4ahRo4b0s8QlcYiLfREbeyIu9kRcUjc2GZ98BgIBqa2tlcLCQjNqHrHTX6nm5mYZMWKEOBxD61ZMXOKPuNgXsbEn4mJPxCX1Y5PxyScAAACskxKj3QEAAJAeSD4BAABgGZJPAAAAWIbkEwAAAJYh+RyE//zP/5SJEyfK+PHj5fHHH7e8/Msuu0xKS0vlyiuvTMq0CTpP6pQpU+Szn/2s/Md//IfYRbLjooiNPWNDXPpGXIhLf4iNPWNzWbrFZcAp6BHR2dkZHD9+fHDv3r1mtSRdFaG+vt7SOrz22mvBl156KXjFFVcErVZbW2tWilL79+8PjhgxItjS0hJMNjvERREbe8aGuByPuBCXgRAbe8bmtTSLCy2fJ+l//ud/5IwzzpCRI0dKQUGBzJ8/X/7rv/7L0jroNw+dkywZqqur5ayzzjLHVVVVUl5eLkeOHJFks0NcFLGxZ2yIy/GIC3EZCLGxZ2zmpllcMib5fOONN+Siiy4yE5/qhLIvvPDCcc9ZtWqVjB07VnJycmTWrFnmFy5MJ6PVX7wwPd63b59l5ccqnuVv3rzZLKMVj9Uhkh2XeNQhVsQmMeXHirgkpvxYEZfE1SFWxCYx5adjXDIm+WxtbZWpU6eaN7gv69evl0WLFsnSpUtly5Yt5rkXXHCBHDp0iPKj6Led6667TlavXm2reqVyHYhNepdPXOxZfrrFxQ51IDbpXf6ReMYlmIH0P/v555/vcW3mzJnB733ve5Fzv99v+jWsWLHCnP/pT38KXnrppZHH/+Ef/iH4y1/+0rLyo/t9xNrnY6jld3R0BL/whS8E/+3f/i2m8u0al6HWIYzY8DdDXIjLUOvFZ1lmxIa4hGRMy+dAfD6faUo+77zzItd0TVI9f+utt8z5zJkz5YMPPjBN7S0tLfLb3/7WfDOwqvxEOpny9Xf2+uuvl3PPPVe+9a1vJbxOdojLydYhkYjN0MtPJOIy9PITibjEVodEIjZDLz8d40LyKSL19fWmD0NlZWWP63p+4MABc+xyueRnP/uZzJs3z3S8vf3222XYsGGWla/0l+Gqq66S3/zmNzJq1Ki4/WKeTPl/+tOfTNO89hXR/37d3n///biUb9e4nGwdFLE5vl78zRCXvspXxOX4evFZlpmxqc/QuLhi+ukMc/HFF5stWf7whz8krezPf/7zEggExI6SHRdFbOwZG+LSN+JCXPpDbOwZmz+kWVxo+RQx0wY4nU45ePBgj+t6rtMKUH5y2KFeya5Dssu3a70yvXy71ivTy7dzvZJdh2SXb9d6lWdo+SSfIuJ2u2X69Ony6quvRq5plq/ns2fPpvwksUO9kl2HZJdv13plevl2rVeml2/neiW7Dsku3671cmdo+Rlz2107Ce/cuTNyvmvXLnn33XelrKxMxowZY6YZWLBggcyYMcN0Ln7wwQfN9AQ33HAD5SeQHeqV7Doku3y71ivTy7drvTK9fDvXK9l1SHb5dq1Xppffp2CG0CkK9D+397ZgwYLIcx5++OHgmDFjgm6320w9sHHjRspPMDvUK9l1SHb5dq1Xppdv13plevl2rley65Ds8u1ar0wvvy9Z+j99p6UAAABAfNHnEwAAAJYh+QQAAIBlSD4BAABgGZJPAAAAWIbkEwAAAJYh+QQAAIBlSD4BAABgGZJPAAAAWIbkEwAAAJYh+QQAAIBlSD4BAABgGZJPAAAAWIbkEwAAAJYh+QSAIVq7dq1MmjRJ/vEf/zHZVQGAlEHyCQBDsHPnTvn7v/97ufrqq+Wll15KdnUAIGWQfALAEFs9zz//fKmoqDAbAODkkHwCwBC8/PLLcvHFF8s777wj06ZNS3Z1ACBlkHwCwCDV19fL+++/L3PmzJE//OEP8qUvfSnZVQKAlEHyCQCD9NZbb0lJSYnU1tZKa2ur/N3f/V2yqwQAKYPkEwAGSW+1f+Yzn5FHH31UvvGNb0hubm6yqwQAKcOV7AoAQKrZsWOHOJ1OefHFF2Xbtm3Jrg4ApBRaPgFgCH0+33jjDfn6178up556arKrAwApheQTAAbJ4XCIx+ORe++9N9lVAYCUQ/IJAIMQDAbl6NGjct1118nIkSOTXR0ASDkknwAwCGvWrJF3333XDDoKBAKyePFiee6555JdLQBIGSSfAHCSOjo65De/+Y3853/+p5SWlsppp50mzc3NctlllyW7agCQMrKCeg8JAAAAsAAtnwAAALAMyScAAAAsQ/IJAAAAy5B8AgAAwDIknwAAALAMyScAAAAsQ/IJAAAAy5B8AgAAwDIknwAAALAMyScAAAAsQ/IJAAAAy5B8AgAAQKzy/wC7uB6OaKto1wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 660x600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_spectra(rls, log=False)\n",
+    "\n",
+    "ax[0, 0].set_xscale(\"log\")\n",
+    "ax[0, 0].set_ylim(-1.2, 1.2)\n",
+    "\n",
+    "fig.supxlabel(\"$\\\\ell$\")\n",
+    "fig.supylabel(\"Gaussian correlation coefficient $R_\\\\ell$\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The problem with such strong correlations is that they cannot be realised by actual Gaussian random fields. In other words, when trying to sample fields from these Gaussian angular power spectra, we would find that the associated covariance matrix is not positive (semi-)definite. GLASS comes with a function to check this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.False_"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glass.check_posdef_spectra(gls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, GLASS also comes with a function to \"regularise\" the spectra. There are multiple ways an indefinite covariance matrix could be \"regularised\". The default method in GLASS is to find the nearest valid correlation matrix, which keeps the auto-spectra of the fields unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg_gls = glass.regularized_spectra(gls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After regularisation, the spectra form a valid covariance matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.True_"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glass.check_posdef_spectra(reg_gls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And, thanks to the \"nearest correlation matrix\" approach, the Gaussian angular spectra themselves have changed very little due to regularisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_spectra(\n",
+    "    cls,\n",
+    "    gls,\n",
+    "    reg_gls,\n",
+    "    labels=[\"lognormal\", \"Gaussian\", \"regularized\"],\n",
+    ")\n",
+    "\n",
+    "fig.supxlabel(\"$\\\\ell$\")\n",
+    "fig.supylabel(\"$C_\\\\ell$\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the Gaussian angular power spectra fixed, we are finally ready to simulate the fields."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulate\n",
+    "\n",
+    "The simulation in \"legacy mode\" requires all simulated random fields to be correlated. We can therefore carry it out in one go, converting the iterator normally returned by `glass.generate()` into a list of all maps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maps = list(glass.generate(fields, reg_gls, nside, rng=rng))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QaUnZERYVE2iD/dNWWJpgF8+2SXsdERVgYZGBsiPUSWALkDcGhvha5c0+G6odtB0P51l396Rt2p5sLfWOVBFwjs1obxtVkEDmAdA/NDCtZg7IGsmKcl1dHaNiNpsahi0pKUQMIswmDnuwacSCEoKV6YyPI3tikmzxeWRgWptHFeRTlgUxQX0X1zg3u2gDfdOSg2LTIqKDbHFxWUWokB3cZ1pWpE2MzukzCIpcNWZ8Nt9NdqexflAML/ePzSTDkpUVYW0QMStrIlXWrV8nwJFfGK2sB2TM7PSCgEtsXIhNTMxbZFSAdXWM2eNPFN3tLeJeH4D1zI/L7ebpNkvIi7KJ0Xm7vbambN16z/V24OF8BciJSaGSFjU1jehsk1lsuNxtuTuT9Bkp6RE2O7MoVr+zdVRZC/wwNRP8HkE5Pre2ot9WFlctNS/SuprHLDox2AnSk0OsqWFEssbmxhGdXfxxRkaYzjjnPzohxMaGZgTuwRBkUOZnF83Lx1PncICgIcJPmRSuOS4uUPLKG1e6LLcgypqbRi0o0FvEoafnOtkTginsxUDPhLItkAMEMJCVBAOAJK6fBSbhnNaU9dt425iVHM205PQICw31s/LrXbJ/KB4gYaor+2QnCLR6uiYsNSNcWZ3e3ikrLI7XMxwanLUtO1Ls8oU2G2wdt0OPFwh7QK60tYxaYmKIsj2f/PTWu7xD3r/LHWz8J6+nf1Rmvv7ectwUS23emmTn32i07Ycz5EwxHByokdE5GxuctaWZJbH86J2J2MkiIEfggMNW0iVKbN74grSWTbcGrXBLgoIADv7IwJRkRMioADKwGbAO+w9n2tmTLRYa6mPttcNWtD3R0jKj9B1V5T2K+HHQfQ0jlrM53tJzoq2vc0zX1VMzZHnbk2SEkC+RLmV5eXta5fUe27ApXhKOiEh/SSEiowLt6qUOFXrzO7Mjc/axz221hrpB6+uhmNTDUjMjraluUMCKAnMMVF/XuLU1jtimXSnK6lB4Vnam3Y59tEQGorqiX12xyH6srNxWkFOyMcEmxmZtCNlXXJAADQAElre9dVwAA/037HB8Yoj+OXm8yfILYqyhfsjCw3ytt3PSUjLCbXR0TowQBri9atD8wv3sL//u/ru9hdzrfbC+/y9XLTkj0p7/6iWLKYq1jVsSrbtjTJkEpESw86Oj8zZYN2zZ2xMlj2AfEmTz39RKuCRSjuN1Mh/UZLEGOiaseHuiAP7V0222dX+aWL0NpfGSXvF7MH8D3RNy5BRiS0oxNicZA4H5j75xVXYBNhJyYnRoWh2kCC747szMcIEDHDdZEGwDBABnaHBozoo3JdjZV+stJiVUwQPXxvfwM3w3/9/dNqKCVIKCqpt9FhLppyAhMy9GZAS1Y9ii9sZRS0gLlX3ielPTw6Urr7zZbZ0V/bb1WJZ1dExY6aYEu3m9x7bvTlFwhVQLW8K1wQjz/aovm15U8XpHy5jFJ4fYYP+MssFcW3CUv4IXSJLAIF8VrWJDCHxwh8g83Rpw9/rPWH/4xZetYFO8AgCyeDD1nKXmmiHbd3+OJMaQc5x9QPKN852Wmh0hpQABN8HyyMicsohIhCDjktLCVRuVXRKrv4dEgCxorxmyqLQwi4n2V8YfmTGBfkx0oM3OLqlLZHx8oPY5GcmQMEeqXHGxS52kcvKj5E+5FpQWOkdTCxYc4me9XeOSVHp60IHqtsg8GkwkJlHrMatrqK8bts1bE62+dtAJ9teZJaaEihilXiMpPdxG+qdU8B0TFyg7h3KCAKenY0yfOdg3KWwDhtLvzS9bVFywgqnRkVmLiAwQAcJz7OufseLSOMm8luZXLD0zTMTkhedrrWB/mp5BUJC3njffhU3kz7i/qeklNdGJTQq1pYUVPQ/kZ/w+OA081N8zaR9+yp0J/c9a7mDjP7i+9fVLVlAcf6coO8Qqy3ttsm9ahgBWHgais2NcP4skAec9PTBtG7Ylil2gqDN3Q5y98PUrFhATaAmZ4RYa6mvZ+bHqFMXBe/l7NyyhINp8vD11IDAwsQnBakUHW8DCYeNgSbcGBftYREyQHX/mlrSWFIzlFccIgGOAkEEEhvuJ4YBtxGgkJIbIqFSW9do99+fYT791zTYfTJfDJ4NBDUlX64gMHilQ9JPpaaH6NyshIdh6eqas59aAbb8/2/wDvS0lNdzqqvt1zxWXuqxoW6LkFWQhaKWXnhlhne3jMratjaMWFee03yzdkmzjE2RMbgsQDPdPidVobRjUdwUG+9no8IztPZSpFO/Q4LQMDoZlYX7FAgK8VWyKhh1Z2kjbmO15MNdqqgZs8/ZkBVUsRx8eafW1Q+qMw++TwUGGwrMnC/PoYxvu2t5yr/fOeut4kw32TMh5Bfh724UzrVZYHGdnXqyzwh2JyiK6TC1nqr97QpKfzNwYBf+BQT5i91//5+u2/dE8Bdc0fEAehP0o2BCnguyylxtt55MFAusQGgT9rK27UiVJ4P89vDysp33Ubp1ut50P58o5YysIvFk3rnTa6PCcjTQO27YHcgRCIAQI1PM2xNmtmz2SUCCvSM8Isz5sxtKqHTiSpdbWAPaOzklbIgjxXK/MCyQJgQBZXAARdqqhvN+yimMtMS3cKm/2Su5YXdan388piBLAh42lC1Z6ephqxCBoABCADc4pgCcg2FdZ4KpzHbb5SKa61lVV9Ci4WFpywFhsbIDlFcVLkkUGmaYXSFI41wAuQEZT5YAVbk2wtrZx2Q7s2a1zHbZhT4qAi68vAGmdRUcF6D3y3a7W3nt2p97F3eVe7yU8gIyPDOJ9D+XbW6/VK6s/A8kY5S8SgWYNEAhk+JAoIoeamVywwaEZSZII7tm7+CN+HkkUjD3Zh6gof/l6sv/4bAB6W+OQ/CcZksNPFEoyCMC+dqlTfhsMkF0QLRsEccnZBPxz5skuDg3NKdCAiIAYQEoFdkGaNE1R+bxTD0Kh9sjQrKWRAVlYkcwRm0V2hCAmpyDOpmcWFRg01g3r3BGgkOHgHJHxhAiNjPSzoCAfNY1A2QEJ0tczIVl2eKS/EwxMOPIoVBZ0tfTwXCdiBSxA4XliQpCCJoIDyAGym5C4kA+O3MqRpUO+Un9KEDUxsWBFmxKlkvD187ahgSlhBT4DQpP3Vnml21aXVi2zKEadvDKyInQt2NDhoVn7zd91d8n8f7rcwcb/zfXMT8otryBWRdXogf3C/JSB4FCVbE603o4xZRk6W0as5mafHXmsQIcXpv7660126GPFipi7GkasaEfSO7rJmJgApfRoE4sBINpH6oDz4xWN9k6rZgLdM06/rmZQ2kl+5/hPKi19Y7zaRz7zG8ftM997SBkHiraQXmGsOFQAIPTfgAoc9fDInD3wcIEYfiQdHHTW+vXrpbXcvCNF/3/8tQbz8fVQxqXtZp/+LCQpxPwwhr6e0llzwEeGZ8WIjg/PWn9Fv+14okBOHCDR1TVpk4Mz0k9TNF57q0/XRFaksCjW+vqn9Z2kNMf6Z6xgY5yCFJgNDElmbrSyF7AOMB4YU8DKtTebLZxWmQFe6syDkZulS0ZGuHW3TygFu/ORPKuvGrTI2EBJWDAepIJJs6KRT00L059huGFMyPjExwfZmR9XWcl9WUrBpqaFysD/2hd33dX9517vHlCRkBRqbQSzAzMWEOprJZsSFEyzR1lV13ps3wO5KoYknU/NFXu8+kavbTuQpn2MtAoniSMMC/OTPvvmlU4rKIpVzRaF3C31Q5aeEyUZI7+PFLK7Z0qgHFtBoweCE4IDD6/1OnsUhKdtS7StO1Pt5tVOy8mLkYRpYmzeOlvHdBZG+qctCk328Kxt3ZUi6RYSDQJtrocCT8iE1NRQOVrATnpmuOQL/Dl2D4YQ8IBsg8wL9oqMznD/pAIcVkxiiC3MLVtleZ9N903ZtiOZshuQJpffbFLQElcQY6VbEq2jZUTXQJYFu4WdA+jw3aMd4xaTFWFp6eECc9EUoVN3MresLnfRMQGyAwRwZDPj44IsuyDGhgem7caZNotKDbX8wlinoxVAa/W2bHN1Zb/FxgdaX9ekhUT46+w3N48JjB29L9f6+6ZsqH9SQRmZKT7gv//pobu8A93r3bCe/1mVAHFr06h5+XlZAZn4kVkRWfgVZSRnFkVEdraPivHn/CKB+uFvH7f7/3CX9jAEF8Ta5efrLGFrgrKKkJOAs/TsKNkQCrepcyCoRg6IPcAnhUcEyE9yJvHrEHYQBFMjc+YbQrtbT51liDT2O6oA6rQIHvjZnMJYq63sE7lBJpSAA3CNvcAvcg472sftwJFM2SbAOzUdZC9RU1y92GFJySF27a1m23YsS8+FQIDz11A3JJsITiGDSNcspE+3KvqlvujrHlcAI6JkcNaiIv1s0/YUBQcEZdXlPfo8CAkywlU3emy9l4ctk33JDNe98ew4z1wz9rChdsgmsG0pIZKSg11czxB764K82O/aqgGdd+wWwR5yzzY6aEUFWO6GWBsbmlYzG+wheINgLzjY1+mUt3bbnQH9v7Hcwcb/H+vK1S51SIDpLrvWpYIlIvNbF7tsdnDGCg5nWEFJvCUnhdiLz1erEwIGgQhf3SJu35b2seJmj7pIYVTIKPR2TYq94P/JGNy62mMRiU7GgkMECOEQEq0TecfGh0hCRCBBhxacOsYHmRP1FsMDU0r/kTLlACJBQJdNb3wOE9pk9NkcMK7j2muNSjfCsgAUcP4YHv5NIDA+OifHTvFXxfVuHWQYTNgWJBJkRshmbCiJ18+dfKvROm4NWPHeFOvrmxEDQktcUrAAebTi3Z3jYlAwYEiy9h3LfkdqQW99dmPbnWK4hdkli4wJFPtDodtU75Slb0+ybbtSrfJGt7Ijtec6LGt7kjVd6rKZwVnb+0vFkoTAklRX9EqvzvP/yT9eVl9wngMGg2zS+Ois5FXf/cfLlpLtFKAC2mBDYYEpand10bp5tct6uiYtrzBahpAMSX5+zN3emu71X7hefqnWbl7psnsfzrdzp1qcwuSuce2PsAh/nRdABjVD3VWDduCJQumAcUwAYpwVoKD6ao/tujdLrCHBBYFvU8OQZedG6yyHhgfYtQvtlpYZru/FVsAgOjVPwXbmRPMdgLJeZxI5IcAkKjZQn1m0MUEZRWob8vOj7e23GuVsG2sGNP+GLODc8KxlFsWqpoqsLME8JMTy4ooygFt2JJsf9Vuzi7ItsJx0snn6uzfU0nakYcRyj6SrU11766j1N4/ZL//GTmtuGpYUBMlS7sY42Qvui8YPddd6lQkp3pkkm4f9OvVqveVuiNG5JyihYxZML4QHXXParnTbPZ90mjvwXF75/k3bcV+27ArBEfaRgaMXz7QqOMAuk3VJSwsV2bP3WLYCBWRfNN6AXSX7gX0gayTbnB6hwAMphyPPDLSay92aERAaH6ysiTMPYFXfR/0bP0+2BDnIUx9zd7z5IK03Xm+wRpQMsYEC9bR/BZRzJjmrLoKMABlfoqzfliSrqegVKdffOWHpuZEC0BuK4yQdohiaALvidLskTQTG1DngB5ESo4LYujNFTSPwYWQErp5pVx0GDWAg2SA7YOfBChRjIw8CwO/Zny5fiGxzz8EMu3y+3Yo2osYYUtYCP871oRIgM9HWOKxzxJnnHgDo4AX2PxlYPj8k1GnUMj44Y0VbElWLytlBxrhpW7K1NA7pXlmVV3ssKiFIs3t4NvrM6UXLyYuWBBw/S1E513D1YrvqQDjLEAPYCeyVx/p1ugbOKzYPqSW+mXPZUDVocyOztuvBXNlaCEuuH4xCINHRPqHW3Mi6IGvIkJx+uc5Kd6U4GZRAb9W/8bs0xcEeQL6CgULotHenZpUMDkEb0na6aA0PzghP8Zl89mc+727D/f9ruYONf2d99W/P2yNPFFpD44hSnAQDZApoQYcmktToYO+0eRAMeK63rTuTzd/fW5F4QUmCGHIkDRnZUZI/IAkieMjKi3lnCjcsQ031gK0tr9neI5nW2jBkEVGBtry8YsWlCVZ+o1sZDuocMGSw/0g1WLy2TdtSlGFh4/MW0S7T/g0njkEikCFSd/prL9uP/uK0xZXGWevJdtv36VIFAPw8GRKkHQQZXAMLUIIcgYJTQAupWiRYBBpkMjBAFFfSFQOmA4APsMJAIPsgsKGGg8CJtp0YhE9/aYddudCudO6Ft5ostyTWmin2jg1U/+/5iQULiXYGCFJ81ljebwcfyRcAoyNVeLifABaGBJkDMggACLIPT6YVr65ZRIS/WmuSpeEZU3ReWdZnh45lSz7SdLbDkrclyoAj92ARCPI8CYJYFMfVVfXbvQ/kqY0nHX7Qi9Zf6jLvIB9Lzo3UO+G91lYOqMPW7/6Be+7H+3F9+xuX1UYSGcPF1xotIS/SpiYX1fUMeWFj/bDIBZh9nCbnSYF957i6nxCMd5X12pO/sUvM3vUrXQIY2BOcOUMzaVig1rHr1+lsURtFBxeYOuRDkTFB+l3OHNfy6vM12r8UkPuG+Fh+UazdutptG3enSBd+/JU62743TRJO9i306M696SqqxtnTKjYtPdTa2yaUDcCh0smGlZYdpbPOGWusHba0rHDJHpFJtDaPWXxSsHmsX6/mDmRccLI4Z2b5cO4IqJA6kDHBltAh6/t/fMKyD6eLafUL9bVHHi+0H/7TZXv4lzdJ5vH2C7UiVIIi/S0zK0KsLYAANhg7iqwEOwRDShDCOSf4wu5RY4LNvHap453OVyuLABh/YxIJmVaXHAUCCLkGjSM6mkZtw+Z42RoAI0CO7wJIIBWjEL54Y4J1d47Z/sNZNjG+oPNOtrW3e1JBRmp6pDIoE10Tmisy3TtlX/3xh+/2lnWvX8D6529cVmDeVDuoOgTAPcCafxh4B6jNyI7WOeasIgOkuxqZA7LndJejloFGCy7yEFIQKfHNi50KtAkaaqoGJREiA0DWgAyI5E3+3vJRrv0XGxdkdVUD5h/ko5+jNhPZkTq/tY4qw8HeX7sjY0LFsHNvmnw8ZGnZ9W6LiPCzgf4ZS0kJ0ZyL8HB/DdSluJuf7WwZdmZxLazIt4MrmOfFmQVTIDfkv+l0V13Zq++mpgOJlss+4Id7e6ZUR0kTGmwPcsWU1DCBdeqqCkvilfWpuzVg4VEB5iui1Ve2hJpRmtlgG7EJSMT4nprKPkmcsA3IoLBrCSnhwhdgBDKc3Nva8qpqYRor+m3j3lS1Bie71Nc7JZzF8+b5EDjyrLAnXENVWa+trq0pAGLGV1/PpFQiBDAR4f526VyrCCaa1fD8wAnI33lfNLb4/f/u7nb1/7ncwcb/2/rT339DqVDazJL2A0CvLDmDaHBEsJCACgqi+LtLZ9vEFFw43SojAjtYcaNbB6yne1K97vt7py0xOUS6YoooidR55Dh1DkjFzV7NmIhPCrPFxRVlQ6JjnWgZg0XHhFqkBL6eFh3lr4NGHQdBCNkNWD2yBQQGyLeYeUG2A2dM8WNRUazV1AyqOLWnc8LWe3pIYgHrwJRf2FMYlt370gV0fvLDCgEIpBNoKvksUoqwr7fOd6p2ZLRv2vbckyW2EnaPycEEYnSi2r0/XSwF2ReMIgCAzlGkbfM2xNuFs636rue/c8P8wnxt58EMpYFBBrS+I2C6+lazZW2Ot6SUcDl7ggauEUlU/s5kpWEJABgSyHugaxY/S2anpX7YJjonLG1roopNYR5IY8P8draOq7i9p3tC7wamBm160+k22//xYttQkmDDI7NigNHAv/KzKgUxakMY6ScgwvuhHgYGFn35aOuorS6t2Wf+aL+9+Vq9FRbFuTtcvcfXv333ms4H8x8ICNhfZAY6W0ZtcmpBgSZsJGcCJwOgyMpDnnNbYAOQDZjdsjdVdRVYWH6fhgicBwAKTBkkBoE+GmSKrnHcMJPIJciUsg9xsrB8OH0yahAETPQNC/OVDYEtnZhYtN0H0rWnkSfS9hUgPjO1qO8DjHBtZAiT0iOVjcFpc2YACQQTrgwr9gU2liYKq8urlpkfZbVXum3rPZly0DRU4Iyy93HaXTWDtvfhPNkbgAzfRcaQbAifhySLLCFacciHnPxoq68ZtA0lcZaZFWnP/KDcvJgDEBuoYu6dTC9PDJHkjAYTh+7Jttqqfuvrm7LJkXkr3pIg2/zqN69Jo568K1mF5Tw3pB5FpfH6TtmdMN933g/2g2dAYXxH86il50TqeoKDvRU0AsAIaCiW571iM3jeSLI620YUkATjD1ZWNbSUgASbzf4ge43NrDzZakUH07WHAKb3Hsu521vZvf6DRENf95Q99KFCnTOC89ItCZJBu2Q1gFGWq4UrgUZObpQ1NgzLHwHKkf+4BudSY0lhNrIlAoDCDbECvbDnsPVIEwkskP0GhvlKioRdYBYGkkgkS5B3zMg48Uq9ZedHSQbN9YARXJIgJFFkF6i9IEBgCG9v3bBtOpgu0I9cmz0MgEcOhCyKzKaX53pHCj2+IDtAFo9zQ00mtU5kB6nXoFAbO4PUiuJ0shWcWzIcnCW6Sw70T9uV8+2WlROp76HuAtyCXUJ23o2cMy7ICQbqh9957ki9OWPUYhBc8fnUYIAP6s53qg4tKzdaBG75tS7Ztc62cc36AoPQHY9nhSyK2s74hFCrLOvWNaOeoPW36s4q+y2HBhm0GvbxdOpT/b1V00HQAc4D25D9QNrZ0jBkM7TgTw7Te+bMMwcNu0fWBeIT2zc1tSByArtDbd09Rxxp2Qd9edoHfP3V/zwtVgD9XtGmBHv969fEquM0kRMw9IYhUXQ7wXHB3jEbg41274N5OjSkIekMRRqODc9GDgn2FTuBFrquZsD2HcrUfAuAOECmp33Mdu/PEPPP773xbJWmbUfmRVlw2Kh+BscOo5CTEynHTScrulbVl/fbhq0J0m37BwzqAHd1T0kCRXCAgWFwH5pvMjEYQRmgDTEq7sYQyTiuOuAofHxOfas3bkuy5KRgGQbY1L6+TqVE6RjTXD9os8OzNjk6pwnDdKlobRix+JRQTSymmIoIHwNG5gKAAevQ0ziiZwSb8fJPKiwtL8reeKHWVhaWbePObEkZkKdhlDQZNMzfdj2Q4wwL8naK0hjkw2EfSgwS4K+p6reCDbH6XjTjGCcMOL25eU90t6q80WtD7eO6drSXsMcpGWFijNFxYpQwUrzHvCMZCkY62sfEtGzbnSomOyjMT1pUWvMSmBBw8j0MH+N5YtT2HEiXJryzY8xy86PlWH7200pLvsNYuxmO98b6wfeuS37DHuSdU/9Ai0ayDLSBZiGzY+8QNLS3j9vGzYnWUNlo6fdkSY7QWNOvtpHbd6epzWpDTb/YL84j54tGCMWbE9X5hMYI1CIMNM9YQXGs9mVyaoSALcz7ueNNFh0XJBBBOj8+JdzKr3ULiHCNnDFALnVVTNrtah8Vy0bGg7Pv0o2PdE6o2BEgAvNJdrVlbkmZRlZ/37Tdpvd+qK9t3Jps1y93WGJquAgGOslRc3XkI0X6GSRWtLx++lvX7IGPl0h+deCxAqso65W8gLOIgwWksygcLypNEIhhKWOz6tRxkFU88XSVBScEWU5JnDKXuYXRIk7IFCPTYLgggQYZBhew4746Wkdt70c2yE5Rb8EzBywMN4/ayoYY1cdQnwbjaZOLVnmuw/Y8nKfuPswuCAz2saBgP4tcXbPTP6tWQwsAHe+c6yKrg71Ny45WJoT7ySuIkd3nWo4/V23phdFWfrPHhhqGbf8ThbKlGw6mOzNBcqKs7la/yCTkogQo7iGD751aLIJo1/7lvJARJ+jkn8baATUfoT4Txn1kcMqWF1clA0bJ0Ns6ZlUVfWLnWWkZEQLfdJ1iAC3zb+iCtH1Pmo2MzNqVCx3CG+w5vhcJ0dHtKSLrCFaoRSA7yueBHVBVtLc7GfmEVKfhA7UNGdmRalEPMAb4X73QLj+PeoLzN9I7rXOldrO+2LdFBTdkY7s7JuQHwQ4FmxNVkA0pyFmGlGA2Bv6O2krOBaRlWFSg/ewr563wvixJnCA6zj9XYwklcSJVUVzws/GJwVZzvcfyNsWLWMEGkNWFJCTIIXC5fq5DcrCU9HA9R4g8zg4Nb6jpTMuMFHlSsilJpAJ/h2+FTAB3gFsgPfk+Ms5vfb9MNSPYVFpekxmCfBisHRLZQFMeaj1L9qeKqAH7EUBSw4kyg++lgJx75h3wWbQmhyAKQlZ1p5smGIkBgrwTMsM8Z6Ro4BneAT7g5Et1Iih9CV6GZ+wTn9piH9T1gc1s0O+aDcNmd7WPI9VJtgK2m0wFf0/qEBYTZ8bPEEComKhnwg4czhILQHRL5MzGzS+MkXMhlYYmEcaPonEcH4Pu0CNT9AhTjoY5ODFYgQ1dnA48WWhXT7TY9iOZAttsdECFdI5M7/b0UBs4iiu5Jn6Gv2cTo0UmgAFUcDioobhyoU2AA8nX0ftzxBCQRUCXqXXbrOZWn+Xmx0gLWnG6zQp2O8P2yB7QUQrjys91No1a6c5k3cP69SZWoaVq0PI3x8uoAYroHsNzSsmItKqyHrGyXA/yDzIFZFdoj4fMAUPO3zmDigI0eyQrM0JMCj/PUEH6fMckhFhNRZ8MIawQTCKgIC4pVN25musHnMzM/nQx0OxmAgc0oaRu+XxSqlwXBoE/xzAi/corjFXKFQkGq6VmyDIKogUIYZXpy/3s05WWlBgsCdnq8prd+3Ce2BSkcj/+y7O29yOF1t4xIcN3+FiOQCggFUYDUAmTQhbm81/YeXc3vHv9X66//5tzyuix55El3fdUsdUyeVuzZ7zlqGC1OcvXTrVa3qY47euYhFCruNFjW3amaPouoARHSUYT54vj5FyyN5EN9fQ6ciBkUwQtZ080qykE+5WfAewfPpptx99oUDDB+eKcM6iSNq20o4W1dM25ILDl8/lOMpxovjnDmHOcIA6cDGZhaZwCeMA0A8JgLEu2JFpv57h1dk6ofS12hHuBtUQGAODesjNV+5yW1ciHyOhyRs++1mCBkf7KfDIcCylDU+OI+vl/4tObrX9gWh1gmOUDe0sWhwAeDTt1Ujw75v50NIxYUla4Phf2kn8gVpxrXRHAh110OtBMKpuIVIuuWAz4gyVldo5ztj3UAQzggS4cMAMZAkHBTB/qsCBVYGp5Ph01Q1aE7DUQ6eeyZC0EUmSfCRRfe65KdSTUhh1/vUEyKuwkRfIrq2uWf4esIehEQnLrZrekZHTcgnXm2gFxBHe9HaMCWNjpy2fb7C/++t67veXd698JMshs46NoZgKR1NM7LSDpyipefaPJtt+XrT2EveB8AubZs5xD/DX1ASHBZM3XBNYhBJn/0tMz6dQy1A8JFBPMu3w2wBRyjowcwBcZD0ED+x92nwwCdYwEHAy1JXhBzoPygOy9l/d6W1xgps1tze7wC/S2mbE5TfxmD+I/6V4JS0/9JJiAegfaTZfuSFJggJQJ+SK+FfITzNFwa9AOPkRzlX7ZKaaGD/ZOiIygvglsg+0hK4LN4mdo6csgUQIbyEeyhk6nKF+RIATf2ATwAQoNMhqcSyRlu/an6/qwk2SVy652iWghiwvxyLURFEFALq/cITQi/fWckHNL0jaxIJmb671AhFC0z/DD4aFpa6seUj0Mv0dzj9zCGIuOD7GLp1stJMzXtu1MVWYCe4h9ZsWnhKlOjSw174ahpdhg3gPZEt4b9aMEiOAlbBqkLnYewgQcFUuDgLZRXRfr05/bbh+09YEKNk6faRXwh6XGOWMc8ovjdXCRPoTeiZphKaX/jwqUIyM9NjYyo64vEyNzyhBQ2EQ0DZvOpmODwm51N49aJL2n+2dssnvCQlNCLa84Vg4HA8LThrEEGDAzI7c01urL+q3nWo/99csft5s3e2Rw2JgUQfL5dIGC+YC150BxeAFB144320e+sF1sICwnfwfzTxcMtIvXX26w/R8rEhva0TomzaHTT9rLKk+327r1Zsc+XqqDzH3DyJDOpeMMhWVJScEC6J2NI5ZZGC1QBDjHUGEg6UbRUO2wt0gPMAIJySF6DszICAr1UU99DAQStIz0cGvvGBfrp9Rn/YgOPoEdRujyqVZ75GPF0kcCUJCSEWxgnJF+8Plo1tmwpDiRYdBph0COAv7UjEgV0AOeqKXIK4oRC8G7Zpvz7GFUMNC8c6aMx8UE2tUbPfo7OoZhSJyC/jAxTcffaJSMRRNapxetmVkByaGStPE8KEQdHJyRrpPfA0xt3JYsA61sU26kdXVOqGh3oHtcdTYbNsTe7aPwgV6/+5mfq14Ax8Y7IxCFMQcwXjzVKvaKok+csIswIKCAYTv/ZpMdfaJQDpFuMK6WiwAH3j0SIabgAkIvn2vT93EeG/jzqABlvvhOukLhoLFF/AMbzoIBZd8h72GI38k3m5SiB6zSVpvGBPw+ZAegYahzQvbmsQ9tsMqKPrWRpLsUTGDAHeafzCI1ZQTjFJjj4AnECUBU3B7mq8AKHTlZHEAUf8dZZ3YHGmxYTTK91JswoXjsTqaXJg479qULXNBuF7vB+YNM4fcIDpiZgU1QXdT4nM3MLIsE4HtxyEwCJ2DZezBDjSjI4NJyF+kHEgcAB+CDwYTYON5XTUW/hccAyhYtKydCZ7+pbkC2jgnkSFLpbEWWg/qrwCCn/e/23anW1j4uCRuyDDr7VVX0ysZfPNFiBx7I1XURKDmTjX00KLDjarcVH81U0S7MNi17YWp5RnwGASoDzKjJo+EF98fAssvHWyx9Q7RY8bNvNqq+i8Cyu2vK/vzLR+/ySfhgrz/7w7essCRWZ5n9z9A63hu+AEY8LjFYvo0ZLUh9utrHVVfB38FiJyaHiVgENyCfUQOVsTn5KdQS7H8CWHwH/6aRCoE0nZgefLTAOpQZmZGPx9ezHwG7+HmyGABbPz9vgVXAPQtgjJSKYCcp0Wn9Sh3E7MisZRfFqGV++fVuBeFkPDin+GmIMq6Vs0ntCGcRsg5SpLNtTEXsdI0kcOCMESTQhQmSD2yB/Do+IUiZVDIy4AUwDMw+PpDAHfsIFqFhBHVtkDh8Frak5mKXHf14yTsF2LTlpXsmRCiZHoKyDHBH37SCmvMnWlTEzuK6sWkQQOARaimwYyg3IEHJkCzPLpmHj6cdfXKDirvxx5DIfD+ZIdQn3DMNdBg8SGctAjgCOUgMZKvNdQMilJF/8/90FKUJCKurY1w+gucUHOQ0s8FeQjooyKoatHueKJRPIJORlR+rPYFPoCHPeO+Ubd6fpj0CsQGWmJ5ZtuH2cfvb7zxmH4T1vg82/vHvzjsBQ6ifAD/AlcWf9XVPvjPsBUYL53bpVItl5UapiBu9P0HF9ROttudBR9rDP6TT0SFSJ7HvUJbSb2ilAaVlb7eaV4C3bTmcLonCaP+0BUcGCHDDAmI8kAHhpPi7pdllO/x4gQ4Ah46DiWMlS8BETjpUHDySZQNDszY+Pi8G7+K5Vsm0yJJw4AaHZp12ekHeDoORHGb1VX1iQYnOq8532tajmTIIRNZkSTjk6BqpicB5k1Zl4igzONYx7XNDtJgT2Jnh4TkFHvlF8WqDS0cmjBIBGawhw/3Qc8LMwh6gzyYz0ljnFJvzGRRh8yxhHan1OHgk21raxpRB2LEnzeprBsRs8n7o+33lWzcteUeCJW+GeV0nCQWBBH26t2xPVi1La+uY6mvQTxLM0NqOIIK5J7AQ10622uYDaTIMSKCYQI4RBRgQKABuYEswOk10GfrkRjGi7W3MNYix3LxoO32iWXU3GFqYIU199/WSNrWrc1JMJveOYR9sHLH00jiBSALRXfvSJQPBIWCA9x7MFODBSHEv7Jvf+0O3zOq/Yh1/u0lAwNXFjWAQpvH86RYLRRudGGpnfl5jRz9aImdBdxKcFu8RhwFApEHDhuJYTZ9GXkNXI4JiNS0I8VXASwBAdrPscpeyCtgcgnqYUXq4+wT5qI0y2Tt0xzRSIBPCvoW57O2YVF0CQQMZVros0T0NgoBsWXf7qJhFirgJZlnouqnfIBPLYMzTbzfLPiEFgEBgNZ5oVTOLx35zp8675mF0jKkwFCkHBdQENYAuWHnsEfIO7h29+PTEomVmR4g5pJtObhEdpG6rt76G8wV6q/0sGb6+rinVXlDHRfaSfQ7gYlG74qqBaagZUBaRM4hcg+fIewG04Kgpwk1OdXTYahhRPaTv5fooFK+tHrBN25Ik58JuvPKnZ+2eP9mjDjmh0QGaoQOYo1aLQndIB2wQwRTd97Bfrg5cVVedNsWcb54lgWjtrQERL6qb8QO8eVtr84gaR5w73aJnkZEZoUwOXpTiU4Ac3cFoA869YZ+xrXwn3wWoBMACHgGMCSnovxftvvtz7+r5+KCsk6daBOSZ2eIf4a/ujwy9410CvHnPnC0y38iS8FdkqzmjdDQiGAYD4M+RHeLvmOfAuy+70aNzRNtq/gzQDJClfotg3DfIW7VJ7HNXswWCEPwF/o3aClZb67gVbYxTEESQTqaS/Ujw4RqwWV89aJk5kcIsFGqTXS8ojFEAjKyXn0UaRAaF66VTHotrgfykgQMBAyQkADm/OFZZWQIQ7A5nG8kgvpUmE7mFsTqnN651S2qtWR8LK7pHSFQCMcmufDylOuBnUV+QKaWLE+eX34EUQJ1A5pPMIHaRzBHnBJvU0zZhxdsSrblpxA4dzbZL59oUtED4gF0gdqnFYJhgbEKorp0gETvG8+OasMFkpsg+EIjwd7zTylNtllgSZ3v2Z6hTJ7YbWTS+m2wugUB/95QduC/Hqst79V7IxkI0gRe4B/wBkjWI0I1bkqyqsk/+AlVEQGSAgo8Af4cggagAS1Cj2tk2onul+QyBHxiIv6cObqhz0rYeSFP25Ykni+z9ut63wQbggoPCgYLlZlJ3bEaYdZT1W3xhtBwRxgXwV1/drw3J5oO1o83cPffm2MnjjU4b1KRQZQQoWiq/3qVaC5gzImckATB8MxMLYtIxDhgu0u1dtwas9ECaNhhdLCSZGJjRz8HY02Lu1qlWu+cTpRqsBxtB0SIGC7YUp0enJbocAEYoPCdNm5UdqZqCwEAvOd6WplGLCPdVVgPm9OLZNjGgrY1DFpsUJpkQrSxTU8Ikc8BgujpVwBzGJQTb1eMtlrc1QUNvSGk+/eWztuFYlsCLt4/TCpaOEbn5sfbcD8otPjVEgRbsLw63tmbAbpX32aatiWJ+KKYl6KJDS1J+tAw1h42gTMFF7aCcNN1qaip7Jdc48Wy1roEhYBzIs6/Um6ePh/mG+alwDWPWQw/sxGDbtTfd2ltGLDw6UCwlA8VghJyC1FAZKd4xQQmyMhhViurozkX7YuRwFLGRRdHwxEh/pV0J7gCI9OpGS195uct2HsnUn5Ft4jrf/EG5ff5PDwqosgfQqhOcULfz3F9fsMd+e5fFJwRbS9Ow5HTIX9Dc806RsqHphE1CFkY6mSP4K5/94KVV/yvW1/7hgoD99PCsJWVFyDnCOCLNYV+TZUQaqCLM4RllE3A0sGFzI3NWsCVesxXIhpWf7bCiXUliqWEPyeIhbWSPibWfXBSJwV7VhNq0MNmDm+c7LDAqQNmFsqvdGm4HOIaBhz3DQZWdarPNRzKk+UVKyDUC3JFTAVph58lw0kufbjUuGQUZAAIEAAWd7egiQ4YB8qS9bUyBNGl9gZChWSdLp8zqjDOZOC9KATy27+HHCqyxYUj3RKCQmBwqlg97AFFCH/0Nu5OtpW7Edh3OUEcZyBpmXvDzsMJIMAbqhy0wNkizPOhxTxYU6SJTkdGHo5lubx1TcANYog6E4J0sJMxmW4WTPYTgADwghwIE/uxvLihTi2SCgGbjjiTpx7GbzCOhgwxAANtMUIBtcwEK/AByOGRvPHvsE4FQbVmfsrYMM0Sbj93GRgAmefYUwlMTRvBHNyykEtiSkWEny8w9Aab4GWpHCGho1TnZO2XbDmcoIMXu97aNW3h8kHXe6FVL3Z0P5ioI4ZmxyFwTrLmzHb+Y9eMf3JQU7+bVbkuIZ1966c8hoMhA0rAAkI6tAHxy1jw912mvB0X4qQ6BIAE5LRIczixnjqYC1Ae6MiT4IIJNfCtBAXIazhx2BeKg9WavReVEaX/xefsO0nhhQkAT23HxzSaLSQ+z8aE5S0CG5OupYJ9gRDOgRudki/A7nD0Y8oGmUfML95fUp2R7onwM8h5khtt2p+hakPKOjy3ofLK6y/o0f4qsPIt7qr7ea4HRAWoHTwaG+4PAoIEFEksUH9g2irFj4oP095rL0zWue+EfroszSzYFHEIAxGdwZvl78A01lFXlvbJvEIGQn9gI7hHfzDvA3mAjqG0BmFde7hYgj4kNlqwTu4oP5/dob087X9QGkIkEGZxJV9BHIMi5hajBbna3jYvQqWX2VpTzLmmxX3G1Rw1kCCQgOLg+mlakZ0doT/Au+bnZqQUno9Q0KlvOM6Dr5YVXGiytOFb2ln1BAINtgpBpYxjq+nV6DhBHSLjWrV9vqemhapyDDJ+GNxAQdD38+399wt5v630XbHz321e0qYlEC6glGJ1V6hE2A2YeJoIDUXG9R2k6onJYOdLiMNhIIEiV1tUM2ezonB14MFc6S5wWqXjS7RQpNdb26/vqKwasYFO85BQc/oGWMcvbHC8ncuqr1+yXv3pMHaf4XbqpMDkXyQUabJhVrgujxGtwTQqF8STowaE5NRgB+l6Kr4na0Szj8NqaRpWWRyqAI2wH4Hutl2EjYwCYZro2IBpHjt6S3weQDPYzNMtXjvD8qRa1iEN3/cCTRTo4ZAbofHPjfIcVbUnQ9QFqYCBwsuWn2uzJL+wQU1N2s0dZicmJebF2sBe0/aPQmg4OHCAYCVeHDNoBA7ZhggFVtJ1LTouQDvO1H1dafHaEWCV+H4NFhqClYdAq32yxyNxIK9wYp3Z6GHUOb9ONXnXLSVNNx6oAPEYK1gaHfv6ZKhmnzY/kStqAIcLYkx1iJgLPAODnGjQGkIQZrqroFyDC4ETHB6mmB0YUMEZ7wQcezrdbFb0K8ipv9sg4ca+wOdXMGymNU9EgLfgGKvotJCXUfvOPD1gzIJBAMDFUhhaQAtDlOn7pVz64BWT/2R2leJ41V7pt88F0ORu6G6FNppUjex9niWOkxSPsIeeU1smAaxwC55KFpMnVTpqf7R+YFXOGHABCg3fHvnGkmdNytOxBPh+igrPHZ+F0AQBM68Xpa9DenCPbhNHn89mXpOoXppfsvscK7OqlDoEFgmtsFxps7Bsp/7YGp30rxAVd8DjvZCvYV2QPADkttcOWnBWua8FREogzI6TsR1X2xz99Ui16JadacMA+zCSZCGq+kEbiIPk86pCoTUN+xTlBngEYxxZw7+zxQ/dm27lTrQImOPAY9vfsouwGkiZIFD6f4B/AMNDn/A62qqd7SrYX+0VA8PN/K7fSXcnqqEeWxQ/AHxv4zjweyB+eP5KS9LQwZTkV1CeF6n3wXKnhoBMe9uja5U4brBqwpE0JApa0BabFJyQF1wKbOzVOy1IvZYOxmchZsV90qOMdAPKu/KTaInMiLL0kVrbkxrkOK96eqOJ+3h2NNLDvEC8Uq5af79DcBLoculp6s7gmQBqdbShYZ98QcJEx5lp+5/fdrbT/s4bu0bwD7TyL/XP9TLsVbUvUu2awJtKjsdFZu6kGDF6y/T4+XtoXAED2LGeDYZY0i4FBZw9S2wFrjd9jhhZZbrpOsTex6exF/PTNU21qshAbE2TXLndICuyaNwWoJRBHpUBNFWCYfQVuAZi7ZLlcC8PvqEcAxFJIDunANYAt8GV1l7tEbhJUj3ZN2qHHC3R/zMbg+/DByI+oL0AChd8im8B1sDexX5CeBOlqIb2yZrWn2ywiK0L3zcJearbX5KLsGKQFUkhUEgTrFKJT7A2Bif3g57FXwk8hvrKBYA+INz6Dn4MUoREPEkSCIghACBfsGZ+BfAm/3nizz3Y/kKP74LmSdUKtgUyJtvhgD0gF4Q3InKRQ2R2eL8RKfnGcXb/cJWJ2cmpJtXBcD3siOsYpAufdQRThOwgkXGQh+If3gbwK+0IWSHPJ8qL1/Lg3J8Ptp4wNeIx3Oz4yJ1IqISlUdnBkYEqkD417kJqqTW+Qr2UyBwmZaMCdQHJh2SJjgu3I4Ux7v6z3TbDxlb88rZcEMOXfgExqMjJyY+TkMOJXL3RYbkGUAGNKZpQcIQtQqg4Cs4tOj/zuKTFVaHdpFckGpp3dC89V6VCLabzVJ/0fG4vMye4jmRqeBxhnY1PshMZ42640SbI4QLSDJLUIU0HqkwNeX9Gv4TJcL4ed1JpqFeKCJdVgsRkxcBgiDi96cowfUTxOiwPKtddc7TH/yABLSQvV52AkSTMSYOAImQdAmjAqLkiHnPQlRgBgAABBz0yLO5geAjDa/sLkoHeMSwqTs4U14Vnw5wBm13wNJA9bdiYriCD1yIHkmsis0EITyQPvgbahQ4NOhwieLTUdQ8Mz0qpzqAkeuCfkTHR/eOEfr9jWh3IEDgGEr/2g3IISgu3mdyvss//8oIwKTAPGiIzIYA+Tfp3COboAUVAHOIJ5pSCN64LJ5V4pvmO/vPTzGisojrOR/imxKqRvKSR/5dlqi4gJsO7aIZsZnLEN92Qqs0JhKwXiAC0kYwQvSGPYOwS2qSmh2oNvvFJn/a1j9qHPbFFWB005c0VGm0et6AjZsVW1HMVxoOvHsBKAvvpirXtC8f/D9bd/fVYZLcA1jRjGeqfs8KMFVlXeY8WbEjWckUyc+sanR8hBwmRjBm+81SxG3SfQW+8wINhHUgRIBGoD6PTEHuKsEthT7+XSQZ99vVGgHqfy9o8rNdSu4GC6wApABraLPYDDAdAsLa7q7OAMYeaQNdK9iv3KuSZbB5hm0bENCQ9niz1GVzuKl6kzQ1pF5hJ5EM4T0oOaBzGDbeN28J4s2SpsATVeGw+ky4bQeY+uTtIde3kos4udAwAjeUSHTuYRsMP9trSMKbjmWZx4s1FSCoINsq/YQAZ6CaSsX2fbd6Xa5fNtygTz+8zDARQQ2NAdji5XdKkhw8J9cmYAg2dONkt+AFtLRplsBZ1+kClgs3kePEcyF5fOtduufWn25vM1sr20s8SBHz6WrWw1tpM+/pxnuothL3HksLS0F+XskbUlSCGI4b0CWlwD1MgE86yRaAH+AKIUvGJLeX7MA7l1rcdWl1YtOSdCdgEZVu0timlv6/3QrYje/wREyDnQ8QOIYNUBoxA62K2omCDZYoglMuVIRsgWkcn91K9uu9tH6j253nyr8Z1GLo58eEG+aHx4Rs0IsNmqsUMyTAOGO9kpNTsh8DjdZhnFsSIg8zZEC+gDsgGi+Etkgfhj9rOr9gnSIIimA0sr2qcNNUMWQXbP20MNUlAU0DobkgwyDQLENQAQ/0w2ggAIgopMGr4NeRIBOGw9tSBIvvHdyPHwaYDsxppBzapC6gnY5WfJqPF57DW6NhLIY3/ANrDtXAP1DwREEJvM0qI4HtkVciXONZkUsr7MEelH7fFUsUgRGqcQDLz98xpLz4/WeaTtP/6UDlecXadFv5PJhEyksQ0BDjUSQ72TyrQS8KuGpLzf/ML9bPcBunOuKpBoaR6VgoPnw7sKIZgI9ZU8jIwHdV7Yj4tn2iy3IFq2lIYyepZm8s0QDkiqkUyBKXge+u/MSDVr4PewizxvbDn1p2AXiCKuuaasXwX0BGdkUHmnSLi4Pz6P+yHIYIjgUMeE+YX5SSFBtqSxfkTvEpzhknetI1uKrCspVM8WjNXeOq7nt3VHsrAh10FQw37l89lvqCQ+8lFnsOl7eb3ng40//u3X7PBD+XJo6CAB2uF3DgyHjowFIIP0I1M1Q0L9VezNdFkvP0/bfTjTRgamBTwA/2iF09IjJKEKCfZRiouIF0dE8TQABMaqtWlEKbyHaP14o1tsFkbt0pk2tYh94jObJYGi3/rEyKzaUhZvipd2n6FYdI7IyomSw3FA8oQYv5pb/XbkvhxNyO7qmBR7APsNk8LEztYr3bbniQIZyraWMRlLMhU4KXp6AxwAG3EJQbYwv6Lvab/ZZ7fX1ixtS4KuuWRjgorCkXYBznccyNDnwdJOTMxLUoBhxPFTAwLrhvE5/Xqj+noD5jF4MCNkZjC6yKR4Jmot6+UUniFz4j1QNErbP9LNm3ckycjynAEAmqXRMaZgkOIsDnZd9aCNdU7Y4vSiJRRRXO8hABkeGWAv/2uZZZbGyTEz9Kuvm3oJij9XFTi88Uq99sWJv71sT/7lQT0fulORwYApcIE3goGejgn7/G/ssps3uwVQACMEI6RzCb5mZpdl3GG5m8r7LDw5VOl0nAvXSlAIawTLi7FFA85nk4Wi2E/DxHy9LDQyQNNaaYt4m4FkdKjw99IwIYoDuXcMOJOhR3umbNtB2unOyNn82pd23eUT9t5YT/+oTP+mnuGtn9daZn6kmDO6EzlaZA+BYxwt9oH2jgBMGfSJeQF8FnuSIPXMK/UWTmF2uJ9AL46op3XcgqMCLCU1VCw0qXTOdUZGuPYEGQkXA+6qDZAmOcTXMnKiFWRT79HeMiwQ7GrjiN6a/UzNFe2rlWZvGlZBKcXL1F7wfU5gvyq7QpaCDOH5Nxp1nQASMiXVlX2q8cBJ5W2IVSHmwsSC+Yf7SZpIhyqGiHr7earoG7vG5OGpyXm1uATgAnyRhAAGCBY2bExUDQJsG+cEcALRwuyBks1JkqFCBsA4DgxMv8PUOt1q5sWuuiaGY6dZsH5kSXheOPyZ6UXVngFSyDbA8iElycyNkISRzyfgCAr2VVtc2GRq6ggWUzIiVK/lsvcAQIIn2t9SOEovfzJWAwwR81wviRkBILJIbBwa+c/9+nax4BSEArBoFoGN5BkjqyOoQ6qBPUBqNTE8p2wnkhDAE/aE/QSIgBRhb0Aucf/YM34HqSVMaXZulEAhoJQ2n9SCka1+49U6Zd4un2uXnaF9NpIsmNhf/2+77+r5eq/JpThX+DSeN4E8QBUgyAIEso8hzjh3qVlRamfO+2C/A8Cpx+SdotFHcufaQwQUvZ0TGqbHYv+QkSdYxvZwFvCHdHhsoulMiK8AOAXLmjfl4ymyi/dLTQDtlZlwj++H3CODoXq//ema30WtxMI8EurAO/K/EUm1+FwyYvgX2qpCpIZG+KmBAWCXmo6tu1PV1IFzDvFCLUFv+4QlZYTZ3PyKgm7VI/l6qXaIjD5DBivL+1QHxvwOADmES0t5v20/lqXMDRIoMjoM1gMoM9yXTCBEBRnKlNQQkZhkJk681XinOcqEMo19zNI56tQuQj6AR+j26Kq1oJMXXT3BE4D+6JhA4R4AOGCdulbwAGD/7MkWfX/JpnjZK0hZyJ/I6CDV5uHLsWVgqsLiOP0dNgnMQ5DANZM1mZmaVyAK5kERQfaa90FQqgzF6Kzs3MWzrerShzydv+N9EIRCVLOXIJZuUgvElPFIf+vrotA8Vrhwbe221BXcM/U/SHOP3pdjrS0jCiiHe6clgeN7yU5jy0SQBCI/DbIblzuVDfrSb+2x9+p6zwYb3/+Xq3JMpLVIEdbXDr2jG+TAkA6DMXK1giRVj7RqeW5ZMhxSohx2tPQYgvmFZU2+5dDBFFI0Rt0DGukTLzIXYkUFlkM1Q3b0c5sVvBDEoPfHWbAZYNaIemHMqm70Wn4J/fNvW0vjiKb8cmgAlXRSunqmzbIKosQUOtO+fSw7P8b+7ctn7cgnSnQ/pNla60c0HOzU07ds2/05MogcZDEE/VMyZEy/RcZDcBEXF+h0rfLzEiPHRuY7YF6m+qctPCnEum702KZ7s9QVBgafGSAcTowjhzoVBm9s3tGdJwfr8BNcwLYxdZR76Oqe1LPGSAPeMB4UpiMPgVEgcHEK02Z1qAlCkI4BwgBMADFa9nEoaSMK2MO44+Tp/gJwue+hfHvpuWpbW1mVDAGwVLI53tGSrqzZ4UOZVn6r3y6cQWqVpmAInTgF/hRxwygCvDRboMUpxKX+BYeBAXz+7y7Zvo9ucIYaTSyoqwyfTYcrDju9xMPC/GxsZE6GlKK28y/WWlh6uM2Pztmnf2u31dUOqLsPxhnmFQNEP3/YGYI/upIlpoeqFWJ6lvP3pJH5ThYBz+riirJb7FXYUphagJWLnWKgmVtW8e8XfAroK/DzdbTJ4ws2Mz6vokdAKZkjOqilZkUK+CIVGGwasdjcKIdZjvSXsyNICAj0Vt0EoJ8zRbaN4Jv9BnhlDxHAd1cP2o77cuTE9x/JUp0ErD6BJoCBTmuJ2Q6BgUOluJrAGmCioVarTs3UW/9aZilbE2VrcI6w8cgmyq52KlgiWMahIpkANAF+0VPDxtWcbpfumuxEelaUOlDBAgKA2MvIxjiTtLrGUWNnKHofH5i1GDITa7ctIytS98AzBAzjEAm+qOmixz22ipoHyA7mBRH0Y1uZipyVF60CdAri1Zmma1LPEnbOVexKHQKgna5s2CwyBXSVoVieiedkLylQh9ghMIQ00FyO26YziK5614F0PZua8n4NOkOGonM2MityBHvEmTrzcr3lbIxTYAL4IIMMCCw/064hoQQavGfAJACN80aQRZEmdXg3r3Teka0GihBBvgWbDYGDhJLnwRySYw/kKXBRTSC1e8tOm172IOCGzz31TJUaTlAYfO6NRnvww0Wyw72dY05LcH9vBSOu7kQEhHklse/U+/GdkdEBshM0oCDTQqc0d+vc/+t16nSrAjtInvbaYdv/QK58D0Tdyuptp4Vzi1PHQ2c3gCd1eWLA51cU6PPe2HPS3Af7yk/RYpkaRLBDXn60gn5aRs9NL2mKfGfDiB14KE9nHjUCSgLOklrXj8yJiIAk4P+R8ASH+mj2BjI7aizIpBGMQmJyBtgbLgKKJgqAYQZc0t2J4b9IeK+82WTegd4WGhck3/3cP1+39OIYAWBIDacRxrz8MNgEwIw/Jcgik8h1YUtYdISCiCGw5dx3d03KdqqIPC7Yjj99yw49USh/RlYwOSXEqm/02T2P5is4GeqatKKtCepcheIDyfnw8KzNTy9Z6fZEXcvwyLw1n21XvRXPl5bZZPIh56jFyCiMlq9kr5ORJZgIDPaTdJqggvs681qDBRGg+XqKxNMAxZkldabUPI94Ooc5TSgglcF3ZKBYE3f+mxloZCiQoqnQnq6V1NVuRQYZJvKEzAYYiICDQBCSCUzDnzFbBRwJYsZOQAwQcJE9oQCczlzIJLEJBIwESvgfyJHVFWeYMyRE9J19QvYGySlyL2wZ8lXUJ+Ct2FhqZT2d898+YdnZERr2SmBDXdtv/u5ee6+t91yw8ewzlZaUEiaDTIZgapr5DuNyWuVXuu1Xfn27pncCynGKtK+FqeLgob1mcWApLqKFIRsWIBIa7mcVFzqtdHeK6gfQRx//t3J78LNblYbMLohRJF37XJ199MuHBK4B38gU6FIDs0b+DkNGxgAnxWTe7XtS9f+wnqTUkFaxUZEQ0QUlNi7Irl/pFIhB90g/eKQ59XVDiuIJHI4cyxHQgbkHEPHzpAs5eDg3WLITL9TaZ35zlx1/vV7Ag374HGJSjQAOipk7bvap+9XM0KwlbUuw4h1J6vzCZ5c9XWMlHyZjYjK8tOKsKu9TahmGH1AWGeknA4jjBzgBYmBjGuuHlKXBoOI8eb5OHQoAx1Q4XVIUK+aSyJ6iVXTt9NFGH4uxgJGEqcRgc2+AEhyBJphL4z2qIssPfWqTZCHowcnm0CmLugqGj+WTcWoZdYIWpogXxSjgXF1ZtaamUfU757pgbMvebLaNx7KUVRrum5J86dTxJhky2paivYZxhn3lfbUhrdibKsYMcMeSLjc5zF7//k3bcixL4DAqJkDPBtBLzQvzDACvrtob3tWr371pW49lCvjAhjktkW87+u4QXzmh2MRg1d4gUYHdIGjkMz7nntehVVMzoHNH5gHjSxZNRZc0eZhy5JG8S4J4MltkxZADQS6wf9mr7DvkjeOjyI7CdZY465d/WGX3/+Z2AcibFzps6/40AXKGQu19vECOHLYKp4icgSm6vFccK61iyQ6QncAhqs88g7YWIAKC7MLztXJy6Pj3PJQrmQEOH2cGSUGwQmcmMp2cH+oFAAMHjmTrvqjDgBTg+p0ua5Oaek1WjuYGfCcsPNkPMnQMv8LBe3s52dSWS132xG/skqSPDAS/S7csCi+Pv1pvB+/N0bkaHZ23TduT7OTPawVsjj1aoMCDoIJaMRg75KPUjBG8FGyMs/Kz7aqPIQvSWt5nWZsT1LGH1dY8rC41kBrqxtM0cicYCJBkBB0614kNACC67o8OP5ArSJgI/plLQKaJz3jp+2V2+IlCSS6xxbx/hq7i7AFYZLRGe6ftnscKnDkq17skc0lKD5ftpaMV2Q3sG5lXAACLdwGzzOcxbLSrYUTzVTiPP/3mNYvKCBfwI0vFs2PvAW7I4D750WJ789V6PXuXrQdgkBVvqhuWLJO191CmOpERdBAc8p7S0sIEmOh2ONAwbGklcWoiATAj0OMeKNhHAnfwQMZdPH3vnvXyS7UK+gD0PB/2J+Qi5xXbDQD+4VfO2a5H88QUszramGcRLP/CmYKUg8wrPpQhf8P54d252py6ahlg6i+cbLHo2ED9HKQGbdsZvKcAZmlV2W58DISZayHTQpp48VybFZXEC3wCTgGXEBFkKSgOp9AYFp3zj1QIX0+hekJyqHwW7x55Hp+D3Bn5IUET8l/ODrUHyKE422Q4qAeCJEMSSUepyrea7cBHix12nQxIhL8wCW3yeXYE0GTdeIZkITljapLQN6kgCxtGN0zUB2RspQ7onRYRUktx9c4k2UFIRXwyIJ36EbJK1MtSC8qZolMVdQsU4GPjOiFW08OtdEuigitXswiykywa6ZCRxrYTAE1PLIiIgyzm+45/r8we+dIOW1xcVodRSF+GcALWK8t6tQ9QaKBYqa7stbpbgxabGCSZJ74A4oZghGxrUUmcyBHIEpQbkKxILpFEuiSXYJe3X6yz/FJnpAHvjkw5ewRyCr/O57G32Cvsi4joIA0BJaAkYOD5cT/YK/AQ6gzNNasZ0O+DTakLhPjAdkA+speb6galXKFZUHCIn93/QJ69V9Z6ew+tnz9fpcnRbFBqJ0iFI0+hpRlgrGhzggAIzgcwAENYdbNXxgRHSx/09pYxGSOYew5u4YZYC5I0KlZ9kK+/3aLDyooUQ+5pZ79bbj//5jWBl0Nf2qq/q6sdUuEPXY+YAcFhd2RFI0orAjxxSAQaANm01FBnKranh64DNoyAgIi+ucwZWkenKA4RU27F+CeGKqCggAoDRsoNAOzh6aFpmqQ0WWffbLLf+pMDFhTgLcPEpv3s7+zWfePEf/i/zstoJJXG2Zf++qh9/C8OWV5pnGQTZGS6yvsVaPBsMQAYCVJ5FNBzyMmwDFQ5BgwWEidNbQWpZhggHCsGDcNGEMZhZWifywEQVLz+Sp21t43KudadabfTL9WpUBNHgbQNQMV/tzcPK5qHbcKYqS3v7JKCMIaJvfZSnd261CWNPKzN/Ni8DiFD/QCTCfFBkmEU7kiS0yAIooiOrhgsABLytB0P54nhRPdOAHXuVLOyZBQHP/v1K7puCrzRpsJqkVlRHYyvp4wPhpeMDS174zfEClDQix2jQqDBdVM8BxsBA8x7wRHwuYnFsTLsABGMGMEGA+VyN8bZGFrVvEi78qMqseUA2g1FccrOuJzrB33xDHr7puQMyeYBztDMEzjjnHFG7En+YX+yaIMMIICp0rC8bqaDe0h+FEh75xG60A1r3+37dKnm5hCMsOeQESA5GKwZFhtFvRdgFWCITPD8C3VOLcHCsvYbxYfXX21UAKmizPhg27k33S6+UGdHf3mjPfxr2yxvW6IyYEgeKDiHQGmqHxH4pVgSEAJ4AKxLRz23JGddXdErAAp4INDgPOHAAejN9SPOwMHoQJ0JyACAAcCVvQnbVno0y06+XCdQzwKs1FQNOoNMU0Ktv3tcklIcORKfBz5WYpm5kbJzl95qEoFCNz0IgYc/UqRgLjjKX0E3Haiwazja9NJ4gXMyxlcvtitTcO1ihwIHbBLZCbrHcO5x0sgXyDxAECFNYzYNYBxGf8eeVIEPNNk4YQAdtoasAYW5NGTIKXSmpIv5653UvdGQA9kbZ4cCdzoRwhoTCFKnh3yTGhlksTcudspOq0XtOlOWg2wJtopaDEequWbxuZGSUhFYIodD7gYY4LtVc9EwLHKI4AF5DtkeV31AcnqYDXZNivGUTfz2DbHtZIcBbNhUWFnkFU/+2nZp5TVkrnNCQStFpMxKQtZKjeIHfX3vn68KGEP+4IuoacLPUatJlg5fhrwF2TF2HzCJ7yWIltwxxFfnnv3y8Oe3qdCa/UnXKALqgdYx+QoVLC+tST4DEdRyo1ckJsEAXeuYw6G5Vxc7FQBAJiDZoq6DQLG7e1LXyIwagkaCBQgoQDjvHXktqgZ8CXUUNGvgXBCEs6cInsWM00Wqc1zNG+oq+yVhfuNfyx0lwfSSiANmvJC1oC4MoIvyAb/KGUjcnCD7QMfKuPgg1V1Sq6Kp3d2TUmXgx5kBA9kBMUBBM5k1sA2D+Thr3CN+jKAfXAOpRpc+yINROvoVxwoPAaYBzhAhkLcsAnoyLEfvz9W7AFNtvydLpCb4CDIPFQJ2G98MngKDgHXI2pCZBtxDSvIOwUsHP1Fsl062OHY61FfPB3+MsgHFA5lZyA18bmvruK4DuyrJ953On9hGCsIvHG82f3+n3pOz9/3/cVr7A6k0crSUzEjd09zorF36uTMdHIkbpAOd/cAHtE3mvNPBk8wNOGYN9UT3pPwUzxx7xnthEZhCWPR3TygLg1/LzAxXZ1AyPOwdvpMAjQwSQQw2B//09a9esPfKek9kNqjL2L4/XS+ITcgBSM+OVkqbKDY4yEdyIOYiACKY+IoDe+u7N233kwUqyk3NirDuzkkbJmOwLVFMFqk0Nh+Fj89+74amRwP00THDbFEMBftBwaL01QvLAiy8ZF42hgYgvmVHqjYyWmYKxBlTTx1HH33iN8QIOEwNzdjWA+maPUFad/O2JBkvZDJsZJw9nQ1cc0B4LVfedqaJUwCNAUPXDHuGU6aWw9XyFgYQ2RZsKfIJjOU0Q4S2JOizkOHAYjqtXifsxpl223ow3QH3reMaioV0gcOOLIHBXgQcAAc+m3vnGhkqRuQPywLIovgRkMWzP/FslYpidzyQIwlEY+OI5eY5aV/1Km8ZcVjmO8P/mMxauiVZBVcwskgsMPQcTO79J392yjY8kucAEl9PRfNKMavwbtA270yx8hs9uj9YFA4oDBQgCq0poEx/l+R0xEGTyewSCuQAAxQMA4r4bMAV4IWgh5oLgIhPgLeeGe0/MbBvPX1LQAODTPCBk6AtJsXz9COnAB5Awu8jKzv1k1u27YGcdxgiMlcARJfkB+OhfulXu23Dbtpx3pbMhwCjrWlMrfkIQgA+rmnEMExk4m6+3GCbH879wE0l/6u/OKVCR7qOqSd617jAL1pqptqTro+LDVAwQNofUItDJAhFtoCkDge4dUeKCsVpg0tgS3Coqc8RAZrbUlszKEfE7yOv4WwSgO55JE8DriAn+D3atsbGBllz86icJwCV/QtwphsbGQ6cNHKmoo2JdvLFWtkYsix1tYNy1GRaAP+AmNTcSAUmpPqRWzHDBtv02st1slc40/rLXZZ2Z9gVQASWjSGZSAMIhsm2vfDta5ZUHKvZGLsOpivzSyYXRpUF0MGBE4Rj5wjckShge7ABZE8SEkPU1hrZFNJHAD5ABKaNgVth6vJEvdWEwO+2PWmSR7HHccA4RRxncIiPeXk6vf9p7zkzMG33fXKjsidIFng3yFQonAW8aa5AdKDVVPZbWkbYOxPWcfQ4fYIaTVCeXpBsTkX2zcPv1IVRfAlTiSTr5WerbPveNAEcABXZD7TgZC0JliBPAGXIW8k6URvT0T6mrlT+Qd4CLWeevmWHP1GqrA4SO1ptY6cAtMxi4R1wlgFnsLj4HiQ11GnFxwVL542W25VVkz48LkR1gJAozrDR2/osCLRn/umSHXmqWJItJpknpodJpsF+p3aEBUDjfiGkAJrvZR33f6TDFHIkSEYauXR0TkqSooGLo3PmG+BtpZsTNc+Isw2RRFbrx9+/KckSGQvOCraVPU83ImQ7NAeARISAKrvWbfsPZaotKfsKfwnQw+cBiJm5w5nkvFA/iHSXzAS+E6KJsznYO+kEEEsrktGlpIZbxbVutW1mr7PUrvWO5JjfRbYL+EWmRcaMpgIdZLZ9HAkR/gS8MTe3oszgreMttuORPAUlSHmpIcLX3yrrlu2iRTb7nCYoN690KXtBLQf+mWzj8Rdq1dWu7Ey7le5LVTMYCql5vqhFSrYmqm4EHIS0DDIVW8B0bph++WUavah18JIacFCzSQ0E5B3ZARbPGhITFQLBIAEbklLkgswjwTfyHMlQjA7RgYoMgaeCQPwfOIH6MeYIkS3iTKBooaYGYgV/TS0bnUTBKdgLyIWhgRm9f/ynZK1zy7L9nCf2S3/LmKTMZJrHhmdFAONnIRt5v7ybrrZxK92W5NSaokhIYTir6fls2pmkfegapQD5tedghrImyF4JVmtu9QtjgR8ggCA5wJDMVEOyBZbi/ghcsC0Qmtw/GIlxAbxbuvRBXjfWDtkRpHvNwzY4NKegmuzoux0PrH+3G5VnflyuAnAOOrpCHBrAdGJ8Tj3VKbJF+z83vqCNAPODzADWovjeLOvsmrLZoVm9LIoiD3y0yFJSQgUskCwAGjmUOcWxcvTS3g7M2I/+xxkx3+iPAclIIdDkaXI4veX7p61ks1Nwfau8Ry+ejcZQGkAQhxJNOAVR6LWPPFZosXHBNjHgpOoBPfc9mC95j8edOhM2GUwNBwujt3FvqrrmUJykCeW3bytSx7iSbizWbIl5BWH9Ff22bVeKAg262yD9wBHjyAAtAOMrF9vl9JjaLW1quL+FhPvatTeaxMTzXaQ7AR0YX+RXHl4eam1HpuLQPdkK4ii0Jn1KK13YR8A8szT4WSQtZec6lGFAk0l0D1tHhycCteUlh0HAuZKpAfDzO2SoAIgupu+TX77Hdu1Nk4HC+MFSh4T6KpVJ4Rv9yAcqB6znKoPXVhU0AQ6/++en9Pt0D4I54tpgMV795+sy9hMd42pbDODj2aEJpfCTlCQsL6AM6RmdMDjEgAKyObvuz7bUtHAFn7DhpHjJOvHsAZgYEAIDjDDGiXaBFNHSjABgBQBx2uauV8AD+MSA8cww1mS2XvzaFRlrJiN3dow77FuwrwruSetiJDH4/hQaeq23P/rN1+z115yC+Pfz+vY3LuufRz60Qd1hANecMUCfpkunhKkug+fIHiMYR7sNs56WGia2nDk7ZBvRbuNgcMYswCJ1Ejxn9h/tXXGiOAJAxYvPVQvc7rg/R6waTCjggZoO3iFZRFqvIrHkswC5OFjABtlVCAJNvfb2sPs/UqSzBdgB6HIGmFS942C67TicIZICTTWBAI4eQERWM1da8VVlau/75U2SXuI8AeecZwIt9hyNLpDtPfb5bTpnew5nCtDgHGEf6RgHWIf9hwBAgnb9cqcdeSRfe3qkmfkcqcoOIWXA4WJ32Ys4YFhHikMhJ/hungfSL2Q9GqCF7GtgyiJjg1XzgY3j3GKvACzM5tj/OCxznwA455aMhz9dgApiNdCQAIX3iL1RZ8CuScvOpa5kQEH8xRPNqusiwwR7eeKlOgFuCkvZDzTW4N8/++5Nu++xQr1j3gNttsnWEGgQCBWWJKh5B+efRZYSe8l9QeQACrCPJceyFHgiw4BNpmMcIIJro56Ldw5BwP2jn4fhJOACJKG1hzVVIenonL35k0q78GqjnT/TquwvRAefh04e+QTDESFr8DGQZnQY4nvY48jiyLphZ2CueafsVfbo//qrM/aTHzoNEt7v7axferFGYJ0MomtoLANdkZzwnJFE+wX6qLCZZggE1sidIKBOn2gSsIX9pkCXvYd/1IBOAbwVNVbhrGWmO/a97FqnzqoG0gb6yBZALiB9hEhjT6GUoJMQHYQIygnA8dUsMo6QEuwzshX4Q4gkiEKCjIb6YZ0x5IIw3tPM6lEBNMXmQ7IxdG8jE0hLfvT9kJzgBfwb/qDgYJr2Kov2uQTSBGOcD2pKqIPAlkByFZXG6/s450iQIMcS0kI1v+vwkxtsbnZZMmWyaGCM3A3RIl/ABixsBoE79wtBBthHdqj2tIurNju1qHPE83Zqo5w9jqTRGYDqIUIH2RF+F5+IyoTgjn1NwwtsIJiDz1cb8gBqNNZkv1VXkR+tbCG+nSwQ9m9swDmTgH8ISrIZEMc8TwA/jSlQFoDbKETHBnV1TGgfUVu3+8FckRLYR+YHUbdK5lntdOkcSBYMbBborUCFhY3n+UBaky1FQguGoLMUvht72do0LGnuNp53oLfI8Y565z1z/s+faJat4J1wnqkhY5o6sn4CEgJgglk6nFJDyFwgrgV5J1iB2kPqeSAnvvlPF+3VV+rs3brelZmNEydb5EQBtnR1uHm9WwUybEw0+rBtaPPnZ5ctIZGuSIHvVPyTdmOEfX5pnKZk8v9seBhmIseyC532wFPF1nmnHSMbhv7U9/5SqZwnTvatb96w8Iww23JPppwd3Z4wAKTsnAmjw3IirmmQgOnR4VnJdob76C4VYVkZ4XbzZo9TRLiwopT66dcbJEkg3QhbJWZ2aNaGW8fUzhX5D4Pg1PM6KkDD76gjqDrZao99aaeMBQVrHGqvO9eD8eMQA5ArrvdKi8oBIQXKoScDgTEAaJPS5UDwu7x1ghPYc4IbZnhk5cfqGXe3j+lQ1VcN2tY9FC6b2EqAC8wLYIBDS4E5h7KhatBCYwLF5GLcF2bphBOjieSepP8zw2XIYeNGW8fswOMFCgzaaoZs075UXU9ObrTdKu/V5wP0rr5Yb5sfzFEamAGL1JXsOZBhp95uUibLmfzprewGBZsYQaaO8n4ogsXpKLhhoND4vMAAHaMAUMwV4ZpgqUjHXny13u7/5CaloDFwsCMMLgIkNlzv0RCuvE3xllsYp/1ZX+20EoVhKCiJE2M6AjuaHGJXn662I5/bLNaWIMMlBau+0Wsf++xWTX9n1kEd04+3J8vZvfnDCtv5QI5VXOm2+z60QQEjmSzqWQheeHZki3iXgMRbFX3St1KchmN88NECPad9e9Ps/da+kiJdzt5Ix4Tmr9DyEHYYWYOkPl2T2m84ZwI52OWN25JUQ0U2DjAGGAVMK1WeHSWZUm11v1hDAg9aSU+MzatjGSBkQ0mCXTjbqiLQA8eyxTDRXhJWkV7wAEG6kDU3DOsauD7AH3bm1HfKrPixPO1RAEdyargc+p4DmdbWNqZsIXaNCbhosMnK4LDpUAPbBzsKI5i/IVagmmtHDunaw4BiNTG43q37YK9ylih+5GdcDCPBBqCKBhd0oSElz/1yrz//aZUVFMXYxZfrLbkkztIywsXMUTDOfkLesIhePcqR/SGnJDuChh3Hi/Mb7pkS647zzC+MFYNMEEbgRyYHfTTnmmslC4FjBYTR7enkG42WlBysz1YmuXpQAQXnHmeP1E1sf4S/5umobbe/l85AKwXj92ZZRmakNTfx/L0ldyC7CnCALXW17MWOvvz3l+3+L217Z6YPgQ8kA58PmCSLQpHoxm2JcvpkKF11W5w75jHNTS1q2BcSEd4xmTPONu8esom9hX3nuqmXYwgZ5A6F69h/ZCM0H4FhbrzYafn7UnVPAEmCYoITsm3YNrLbFIzvPZihKeNkXd96ocY2706R3G2CbF1JrLJ4NO+4db7TAuOCbENpnDoiIqHbeAcMvV/WlatddvZEs4WG+cr/05Y6PiVMvgufyD7rZb5DlL8jkWZeQt+0Mt68H3wjMitqB/EZ1BhC1Dk1VqO2c1+a2ri3No5aULifGq6gaiCLip+LinSKeCF88KWra2vK/JHtIogg08/+xj4TXJDNhFWXfOdO1lMtV7Oi5JepySRoJDvKfoqOC9GZobYAn8WegdxUg5aUUCkTyLxhz1xtuCE5ggK9ZAddgzo5u1xHZVmf7p3GFpBcdOfi/rEfMPeultqAYc7YqTebLCSMwGVJ2Vau4ezLdbZpf5pqEgiK+BxkVJwbsiVgEDJAkKK0dFbWJcRP+5cADTnx8R9V2KO/tl1AX2QIjWv8vRRY8DnUZbqaSYClyO4Ctgl8yFKCVbAt9bUDqmvB9+EPXS2jOc+QOxAyBF7MwwKI0y2Q9wZ29PBaL5zFPsAe8H1n3mjUwER8AbJtFoEg4L27bsg2H0pX0wiKzpFFsUcIorg+OtD1NI1YyS58twkvUc/JLDKCPNnruCBhr4a6IRWMN1UP2b77cywrM0LBZXfnmDXf7DMPH09Lzo20zTtSJDGFQEKeiz2D3KG+g2tGRkuQhkQTfMSf8zyDQnz05zW3BmysdcxWFpZt34c3yNZR//PgQ/n2blrvuszGH3zhZcmRKAiWNnZ8ToXRsLqAeRYblW5LFGSTjaiqdNrSMh0UFighPUyFRKRNeehsKjpKYWiSs51OVWQ1YDZwfluPZsrwsEh17f1kseVsT1QbR67h0tk2/R2HiTSphvcNziiqJoCB6Vc//aFpgV0G0X3tK+cUBLDpcfgAlkJatt4pHOVzCDoAo8W7UwSO6S7Fodh7MFODhk7LwPqpC9ZL37wqkMXmIqXKPQCUAVOwdP/4+HO6Rg6gCormljXxF4fZ0zBsk0Oz2oR0iUBGBJDFmRF1A7LpLkOnlLLr3QIw6EyXZhaturJf4KjqSrckFMe/eUODyQbbJ5S5wLA+9enNOuywzqQ3N+1IVhEW4NspXPMR83zfg3n24S9stzIAPMWgGQDHBTEIgOUzP6pU1oosxVN/sNfK32pWJghDDXOAAUCSAaCATeDZ0+2DNDfBJ3IYfhYghzEKj2C6+ogOaFJauJ18s1HFcMUb48WGwfpQR7IFqdr0gtghGg8QhAI4YKQYFoiUDeNRcaNL39XbN2MdHRM20TEhPSU/++Ff3iSwsvnJQnUfgy3jHVRd6LQ9+9KtcHO83bjSoQ5CGCGAESltABesOfeTlhOpGg7YVdgbWgUjPyF1Wlfdr1Q4jk2DCNduq9MVYPWF56r1Lv/p7987+s3/3frZTyvtu196Xazi9Ni8bdybInDIPJaEVFLPozqDsRANQT5ivXGS6KQVxA/M2I696foZAgF1olteVXCO0WZ+A4CUrmQEixhunA092ulGxv5MSglRxokUv4r6ltfkdGnegLSQDkWwqXwv34Fd+Pzf3yfAB0ikXeSJn1bJbvzbN6++w4gT8AKUYPE1cTghxA4cydRZQGcMqMI+ELACJnBqfD6MJqCEND3yhfbGESsucSQ2MH2awHuxSywXWTnul0BD3zm1aDVV/ZrQm1cYrefwyGe3St6HvAkQzhnmu7Kywm3jzmS7/6F8azzZapU3e512wUg8bzsdrFKyI5RpPHZ/rp4J+5GWsti5Y/fnaeglIJt/YDWxAcqUzCxK082zRGaiDHBMgJy5iIqFZdl0ArjaigHZX2w6tTm8nL0P5AgwESzCgFK3QYYCYECwxdwNSBgIHpjvY7++RUEd4JwGHIAnAkHsJBljACKdoJBLkFWmboMzyvuhRgRbgTyUs0dBKXaZ542zp0aGZwIIQpaHryEIRAZJhy4050hvabWJ/4INv/dTmwQuAKkXzraJ9YW9pekHWnquw5HnTMgGz80t6V0AWiF2nvz8Nj2PvPwonYNNh9L1DLDt7BlaAf/NV87Y+2X99OkKyY3R6SOToU0qfggSAjadpfavPh7aX9hHzbsI8pZcB8DP/sev0iqeNdI5IaBL4xhkSASRsOc8x8ysSL039iZngf0JiES6zed31QyK8EBiiE8Ci6AAgLDD5sCas/cg6wj6wRfMnMFvc83YgIaLnQo8IZTwNYBPzrd8VmSAanTAIZIQ+3kpM8+AX/aN2vO3j0mG3HClW/NqCDb4fHzRtTPtqkHiOghkeTbYOQILiAoUHar/bBgS/mHvEfhTk0Z2iGd3hcnf2xKVwQGL4Wc7aoeV9WcuGJiDRig0x6AuNTM3Rpl+gjhqH5mYzXd85Dd2idgD83BOwDoEQGQ0RDTEBDkEQ0q45p9wRpA87dmfLokbQJ1Ag7NGcE4bYMgYPpsaSDpjgi8I0pBQ032QbpwEfNgygn5a7nLWIYf5bEjLAleX0PpBERgQv+wP1bPkRyvrQGaZ8+zK6ETHBihAQU3z6Cc3WXh0kLLhkBwoLJDVEbBgG5FavvrN67Kx7KttB9IUqNyq7BNGgbR96FObbPe92SIsKq53KfNB1oYicgr7yWRBhJFJ5/kNDUxJIePIq1Z1zUjc6YoaHROgOlAa3oAjkJqxL//nn5+0d9N6V2U2aGOJZIl6h+pTbfbYF3dIqkPHHqWpGofU1xhgTzSLgwNs1V3psoKdyc5GvtAuLSuTg+/5SJGVX+u20c4JW5hcsF/57/udoV650QoI0tNC5QRh1wDaRNQ4XeRayFswMBgOHA0Au6A4VqlO9MMAQWRZGD8AMhtc7VuX16ziapdS4Di15lsDtvf+XN0fB4bDQoq+s91pDdnfN61on1qDN79zU2w+jpbDSJcLjAUbqOpaj6VQW7FundqlhkUHKMKFkQTcY3g5LAAMMg6wkBjcHbvTrK93wl7/5nXb/kSB2rXBTPBzfC/O7fxzNbbAAdubYlMDMxYUE2ijLaNWtDdFMqb0jEgNEqPv9bUXGyy6IMoCw/1thuGDiSGSUQAejv/0lkWkhNrs5KK6PWzaniJDTT1HbHaE2IkbrzTYU3+wT/INDCazUdIzw2Vsq0+3WerWRN1Xa82QZRXFyMBTh/LKi7XKxLBgWFwDDzm4gDUMrQYJUkg1PGclG+P1rMjmEDTQxQEwdvp4kzIwpNbZ+BjZ2Tm0kYliH9Wrn9Z/RXH2z7/1hu34eJEND83ZE08VCxjipNg/FJwODjoDfgCkgFn2HYzmUNOIBcO4nGm3e7+wVUAP4wkQg62qeL7OAmMCzDfU1/K2JwkYEvyQJeNnnD7sfjYyMm/7D2fa8ZfqLCDU15F2oIH39tBUZ/YwTBRFwgBoZpM88iubbc/uVHsvr7/7X2cFqDh79WfbpZlnwdqRYQMwvvntG7b/E8XaXyrGjA9RGhzJD+w9RAApbSR4nE3MHICWwJNWy9gVMl3oo5nVQvEo55VAGwDId5Bd5b1IHjE6p/eO/aAZwYk3GtV5hMwkTKja2P60yvbcnyNmCodMhvSez2/WniRQApAQLOUXxVhiirPnIRCwGVwr+wRbw5knQwFAOPGTW5a9I0m2A0vNvcJUAqx7uuiqE6KfY98C1rFdLlYVlowiUM4TEi2cWX/7uCVlRSirV7IlUc72xM+qbe9DuapHAehDvAAeIHS++ycn7PCnSm3L9mQ1p4C9d7UFpqYBdlc1bIurkvaRhcGmEIwRaCBdwKkjqXjtW9fV/pL5IQQjMPgACrIhAH3Yajnl8j7dT1JSsBhqzhWafAIsgBXPzSVXBCjy/65Bq353wBmBPWeNMyP7mhSmvUNmiaDONftgsHdabDath8nuQrqwmJ+BvQPUkDnifUJGUF+CDpyBidQHwUoTGJCRuXiyxRLTQhVIqAX4zKJderPZskpiZZsBD9hfbDVyGwJapCMEshA3kEicb9huiDGeH2eAlr98bnfbuOUURMsOIXVDC08RO6TVcO2QHfxokXW0jYkISi+MftdruP9366+/fEY1iS21wxbJEMioALVSTkwKttbaIdn+gs3xkuEgnwb0I0djH8GW449vXe22TXtS9c4BaZAA/DcKBmwHzx95C1lIPg/why/DdwDEAbgUDadkOZlTV/2Qq+UxEkz8tTrP3VFQ1Fb2inhC3gM45KxJurWwYiVbkqyxdkBZCLor7r4nS/uHrCLEotOydb32J9kTgKcDzk17CBDN9yHDqynrM/87TD5+EkANhlFbVxrWRPk7jWoKopVVpPU6XbogNjlXGi66tCrikfuAHEtJC1MWhPtBfs151vC6YF91seKaeDbImiBZyQBRBwUhGxjopX3L/m+uH1AGEJuMTcFOYJfocke7X2wNNYpIYjurBuz+T26UP1ML+bRw2Tla63LuCLywZUi3yXi4FrVt2B6eHbbbxfT735F5YdPJMhCAQjbwu7xDAjzkniwk3Mw8gRAmG4S9orsgz1Ctan29JN1GFk5Aw+dCHpGNpKkHzwncAeHkGrLYVDtkD32kWM0juD6CvMG+SdXq4nP4bL6fQOTW1R7bfjBdOA6ZnCvDTH0N2XQktRSl85zAGXQJpSMf9pzsKnuBTBXScTJT/AwSVFonq74o2Odd08HyXZHZ+MY/XrRvfe2SDAQGgAOSyfA3htb4eYndxPDT9UE9llfXxHY2n25XO9GDTxTqIJ891aIOUxiZ7UezJDPhM4p2JtvHf3ePAN/snRedlBgkwEmg0X2nRz4vmc1DmpPIn6m4pGXRHtOphciYtrE3znfIEaA9xClq2uPQjH4Xpnz3kSwZfBiY1PwoMRowBNevdOmg9/dNSlpTWBRvI73TilIBi4/+xk6l3hVoeHuoRRzsHWnHoOgAp34k2MfikoKt4WSrtdQMWWgk0yqHrQ0daaCPDmRfy6hAKLpOngnp94/9yQFpo5lSTLQOk4Bho02wX4S/HfvsZrXaLd2dbAnxgeosw2GgWJxnru49Xh4WFB9kYy3O4UOmRibAqZGYU9tLmCJ0y7DzgC7SlDuOZWsCKQHOJ/74gL3+4wqxKBQ3MYTx1rkOSQrC0sKs9YIzr4S2vWRsaEtJbUd4mK+Ky44/U6WUIwYxKztKh55ibxwvKU+CtnvuyxHgISv2b1+95BSQDszIEQF0yJBRNyKN+fCc/hv2GinDjp2pYkphDL749QcV2JBOZ8o4tQA4GcALjoahaV2tY8p6ACaRqsCyhAB2Z5YsbX+adXU680hIExO4Edzs/5VSmxudt4DoQDk5pFvn32yS0UK2AYMJmOB9UByGxpf+2w0XOiRZoxsZYA8QBjtGUSuFvpHp4TYyNC1m82v/8N7Lcpw526ZOU0OdkyIUeJ6f/KMD2lu8V/TOBA84vY0P50jGhpYVw8reAMDxTHCcOAraS4sE8GXS+7j2HxlNHD/OGWeAI62/3itQB2AFwBJoODUIjiPBWQD+X/lhhT7/m7/9phwTzpn3jt1ij6UWOAM42bNojlP3JOt7OAvYEAoFt+5izs6co+MfmBJ4xU4AdNiXgAACBrIJEBO0VMZpsqiDgFED1JKOBwhhdwAMtIXFybzxLzf088g1YOsIVgExr3z3hhwTTSr4ObKnLDJixbuZ69Et0oAgXjr2zjF77flq2//xImUUOuiIRKeVYB99Dqwd+w7mlowGtoZ9zP0MDcyqxoz2nYAEAg2e47ZHcwWYKPCsuEGziVjVQsDA4ig5gwTOOH8AFMETMgpkqQQlZHL4LiQjZB7Q7mO/NUC0aVhBHcHV2ZPNNjTE4DMfAXxX0wmAUNXNPl0DDGRmXrTlFERZQkKQQBx99QF5tMYlMCOTQWBAIMU5IyNJ8ME5RvpKZgHWl451DFPMvZMx4nkDkgjadt+XbX290/pcpGdIKa683qhsO/fc104Hv1W1C2bSOYw0YEadZ/qnFWCT2UaOt+9Ytu6Pa8fu8nuAbNX4bEuUXeczS3claybArxz8rnzre22dv9Buf/K7b6jOgHd1+JF8ZSA57/c/mq9aOGZbMDyNd0HdEAXF7F0ymQTEkEU8j5Rcp5kEgAzgD6nIc4O8w8YQaFC/hX3Brnf3OAE3uILmIGQo9t2XLRBHJoBsA1K77o5xBYfsIfaYulOF+Kp+h/PP71HADVjHl+O36SJH5g4GH9kj2UGKobk+9gvkqVrFe3tKAgT2eOVHFWqvj92hcQHZmsbaYWtqHLV4MEmjMyST/U1NKXt5y85k6y/vk80hkIDkoOsdZ5AsYn//jM4YzxepNk1tOhpGZAM5s2rfX03mg4nkvro+CBiK2/k7rhHZOM+R76X5C4w/YJogmwWGIfi//Eaj3gd2kawNDL7LFiNxj6V75P40e/v5mneG+10+02rPfuuayA5J49sdGRTZQjAJRANYhL3POaCWks8DLykLoOncPnqfamYzuyySgt8Fx2A73vzGdQUmBAfYeggjTT2PDFAtHTgRHAr+zMmNEqlEJ7DYhFCpKMjqYqvAOHwumBI7Tz1mCFPOacW+fp3+TvNPcmPk0wkQ8DkErQQ6Ow5laC+5nllP06h+rvZWv+peIBLwLbw/5FS06cdfMNwRu8oiKCOw4J1g2wmmwSoMCqWhzne/fUX1TvZBDzaYm4GT4ICjiUcuRBs0jMwIPeZ9vMSCk9bMKYiR46ip6BfDc+/nt4gFJnomEiZ9CMAEfKilarCPWqYCHtmsdHUhGs3KjdbBxHnClmmOQ9u4ajNwljhTvp90KZuWTVF7sVOg+/O/u0cDb5gGCqi5+lqjomHYuepbA2LGmI2QkBkuZxMdG2wPPl6gCLaoNE7dSWBYYTN/9qNyZVwAlDOTi6r34HvprU80zaHGgAHYuTdYvJPfKxdIPvCJEhlcui3RESkDeRiMYqivFe1Ikg4cCQ6yJgwkh7H+1oAMDYcLAKG2j/nRqpuAEeZgZeZEOy0CKXry95Y0hPQf7SiREFAonbyVwlgfqz/TLuaAWgyGEJL9gQFh8CH6YYws2QQAgT7Pz8saa/qV4nG17AQoAc5nZhdt3fr1ds+vbJQMCmaWzlgw/BxCngtGMyYrQm0uMULP/aRSbOrgwKymb8NmA2JwOhRxwUo88IlSBTW8OzphjQxOCRzy+3sOpNvy7LIK8EjTo5n+t+/ftM7WUX0/xi6/OE5ymcyscLHZ9ARHqsX1MIiHDBbGCfD26vM11lY/bCMNw/bAk0WaJI8x5hnSHYu0My1Q62/0SkfuRWapd9raG0dtbnjWzj9TZT7UidQMKbNDMAe4o81yakaEffl7j9vmPSliaHnPAC9AHRkjsjFk5ejEg/OEsf1vTz1j75X1j393XlkjWKWDD+cpgHaKYpedjN3Kmow9rBDs2vTMsm3elWxpaaEC6YB3ZEcYe2wEbBCAGfsAMcAeQudM/QDyBjT5tMOEweY8MUWb4EMDuDzXOy1cfZw5NrBSfA4MNcFm8q5ksakADRwee0n95lPC1GiAQLzpeq9IBwJVJBAELNgoVxtK6jJoDsHCKTMLRJr/jAg5JJwuGQFmM5ClI6sCSCGzwgJIKxPnsV4Olja1kCEf/r090nIDStCWnznRIof34S/uUG0BpAayJppF8Ew4J1z/vQ/nyybxZzhZQBuygZqbfbon7AdnFSnX+jvEDN2TXn+xzq5d7pAck4C/uwXn62XdLeOS+QGmYHF5lnkb4jUQDea2eFO8bC3fCUBnHhGLziwARYAAZxbJ2FvPVgnEHHwwz376k0rtceQLfsE+2g8AGd4/jDTMNNpnwBQaeYIV7AwLiRrnEXuF1ArHLRkGPfCvd+lMQWRBNLG/yLADCgqK4tTCmt79yNhgGTcdSNP7ItglG8+14vABHBBj7CXswtKdomM6AAUwy6m81459olTSIIAOrGZRaYJVv91iNTd6RUbhwzjLXCsF5TshLTrGVNQK6QKpooGEZGiGZlTDyOffPNWm90eWl2f0+G/v0vn5zY8/a++VBVECkKS9Mg0R8NsU9bPnuLf2llHVABCY0i2Ic1lXMaBuRPgkQHleUby98kyVPg8QT8CGTSCD1N48pqxUal6U3g17gfcM0EYaC2YAFLP/ANhgD+q/NNTyzuBg3hEZKMiGy2fbRTAhl2Mvk4Ejm8Y7IAgFSLJ/NHujeURSNzLqYAMCbeRx4BBIVlqgEoQiyQGQQo4x4wb7oFaqScGyd2T/2AddTaMWnhkh28G1UPPlarP6od/Zo2AKJQakBmeQ9rHsdWoserunlL2sLu9RBiMiIci6y/sEusERDJgEvNIchUANqerS4rKIVc4cEijaZHOP1LvV1w7qLCB9RIaK3cT2FexIcuZGQH7MLikYgxwA8xHQ0USBbFVaQZTsGO16U9LDRFBy5nmOklnOLqmjEx3CkD4S5PNvZG5gJO6bc46thnglUOcs8n7pnqVAs2NMwQSS07QDafKjZGmdxhbTOlfYRT67s3FU75rzBdFEFpdFsEgWlIwTdT7YZUYB8BkQS54e6+SPUbvQLY/AChVEd9uo/AM/j0ycz2Cp/Xaor/Ymi4wkwSvy1vT0MNu8PUW+BfwI4cCzxDdhn7ET+CHeLbaE7n3sMyTrSN4gfbknfAH78sc/uPnBDTaYm4EEgMPKRoSF3/VovvV1TsjQ0E4Q465iwVA/tS7DgaRlhWsgDpuGxeFiEB8Ggqi2u3VcDhaAgpYXzTtdg3CYQXc6OCCBgFWmnWtLw5CKJAHC65ncGhMkIM7PAlhwXrF5UVZ7rcd+8L2b0lrDiGPIM7clCcxg3ImeH3m8UJtuuH9GYBtJFbMs/CP8HdYgMkDyqPPP11hcYrBllsY60XSEn+Q+AFNqVNCUI8mh7S2RcW/XpFq6HfpUqYAkzhvnWXGp02IS6ICzLHYGBqbqao8MhgKXwWk5b+nTtyTKMRIVcxAIxEg996AplmZxvY2Pz0sbe/gxBvxRcD8s4EfQcuKtRid7Ut6nOg7kVHSeobiToA39JkYlYUuCGOTcvBi1A1VXpdggPScAAYvrmhqdFxtKWhpdM9OB2Qcs3gH3xzRNDDOyGrT1HDSMLbrE1cVVBRJFG+PkcHH2SK2Ys/Dac9XvFO2lpobazj3pypRs25XmFBXPLyt4i4gPFChrvNYjg00quK97Sof6zdfqZURgTjCEm7Yl2nTftH6upXlMcxMISDBiBKjHHsozTx9Pi8hy0sjHPlLkdBbJhF0zGX1WamG0AiFkfPuOZFp2YZQlbIixTQ/n2qZtSQqkoxhiFuYUKBKoYQDrKe738tAkV2RUtVV9tn1XmpwBMhxALAz5hbebBRSpBYEhfC/osgGqgPeikgS7fK5dDhEgj1MHPHCucQiwRZxzjDWsM4wxdgKDzL5mngpMZ311v2wFjPjVCx1iwHjmTK8lw0jtQkxCsFXe6LEHfqlUzNSNk60OSzQ+J0DC3gaQEiTSghZGjX2dnRdpMbGBduHtFl0LewMmiX0KuL7ySoNlb0nQXm1sGNFEe7S6tJmsvtmnjAUOhgyiHNrYghgsHCKsFfcC6KHYOCUjzK6ea38nSwvpwMwXnhVFpi1VgwI60qVLlkX9wZQ+m7MDW0oATpDC9/C5nG+CFSRd7c0jcn44deoGOPPYLyZY93ZO2u6jWXJq2FCcJnZAUtSMcO056i/I7jhFoyuaJoxu2othoIPTAoO0zeQa6awC0YIEgCwzUk+KswEFdFchgzAwMGsXTrfKwUNu0FocBpuajbNvNCgjSvE7mRRsLRkAuvYQCCGZRE7BgFMyEJA+nBuXhIo2vASotOZEQgVY9/bxko0oKEnQ/ZPlpJ8/nwWgg6hBZgb4R4JCzRiE1a0r3bLT+AK6fHU1O+QQ34UfwD/pXfROWX6R01iCOQsEewCDRx4rVIMBpFbUn8RtjDe/O80gsDmcaWx6Lvtoyak1QjJH5oM9SbafrjYUkmK32XsphTG2aUuiMuDI7DgzR+/LtcisCPv7vzln7/Z19VqXZL8EahCGBGT4Yf4B9GXn065+UfuCLCc2nP1INyXeJ2QW4JKsVFJmmOZUkR0DC+BL8IkEqEhmnQ6TTuc39h9kDk1DeMYEMjRnQC9PAKqWpTQl4ewwT2dxVQw/Q2SZCN7WOi7bgjRrGRAd6S/WmTMEcGy4M3W6t2lUfhsSk30OSBzrm1bAS5YSJp5GDOwjiD98ByoGfDc+h3sm2KfFPefHN8TXNm52yIJUstqDjm8ig0GAQnZf2ZquSSs/2aYMBuCWOjBqLlicRXAA+3rLfdkiPmHt+U4yNPhm7Cq1HjwHSEJwFNcIPuEcEiChGEBeyvmCzMXm8A7Zn+AgZsbA1HOt3Dc1ixCxKEAyMsPlc7GXZGyxgQ31jrIEbMSzHh2cVX0H2Q1XrQ72hpo9AjipYHzpFOmhFrUs3i+ycIrt+V0yhiyAOFiFn8Xu0GUOsoLAgMYM2HQWihIIGuoDsUcpGXQMpQvnpFrlQ4AjacJPNVcPKhMPyYEt5/3QNAO7gT0fG5sXYcDP0xENKR02mzbpYC/V25xtl010anCcobPq+kUwkRCsrqfgR0iMgaZR3X/z5S7ZE54xz5qAkvol7B/NM7hv3qEy/kOzd1XxcFdqNmhjSceo/ffnKJXGQwKssnAaDFRB8wsTTXSPU8U4wA5SBM4wOPTKTF/sah/VZsfQkEpno7Kp0HeyTr/dpN+nTzaHBEkFHQRunGnTEDUKnEkVYhhoX4lOWEPpYoNlhNQGLi9GzDHOlS4oFELx2AAZHGCiVQayEMTAmMKU0Lu5aHuinBGs1c7H82UIduxJV4aFORsYNIbxNFX0WwFOOjlM15yRHm43rnba0OCsmDpSsikZkfbt33zdYgqjbcvhDBWjtZf3KQii5eK5Uy3adEgMiPZ5Djgy7hWpBcCFqPz6mTbLLorRcMPdhzN1sLgvOmwRCCBhgpkha0PrzJTkUHv26UqxOYrmm0fEKiIhe+Ob1+2eX90kkM/zJYom/dfXNKrOQQAadhfsKc+Brh4vPV+tIl6+a2Rgxu5/vFA1JRR98h5490g7OEg8X+QLpJTRKXN4mRAKi0H3IZgkUpqNb7falsfzxPLCaKMjhS1qpV7mQaQbzsFFHjfQPamAEiaXfXH89UYNDoNx4YAT2GGkXB0gMKbMAoGOAsS++PQtabwBWEhIYI7Zu90KkBMV6AA0xtGap4TIkGRlk870U/odeQMBrBiXCD/1FefaGCp26IFc7WENYEwLV+EtMjYNBEsLt4S4ILt1q1/vEW027QnJ4vAzsP44LDqJUQOCU2M2CWCc1s85pbH2mc/vsHfToqCdttUAKYJJmEWeFYsMGQ4OWRhSQow1hpt9Q0vbgb5JFUOW7ky2pYVlSQsmOsfNJ8jHMulEF+PMiiFwQO4CqQFgwMg33eyzReSJ2xP1DGHPyUbQcYb93tk1ITaTNpDsIxUBBxL8hCv7AilA1xXAJ+CXPco9aLhcXrTm9Lz29av2xO/tuVPMDTPnTC/mfFFTQrYUBo9sHY4S0A+zSZaSn6HIm1okzvWxB3LVpU5DK+9Id2AEARjsAw2KTArR70M2AJAILEa7J23nPZnqqQ+5j6HHVmCzyi932c5DGWopC2tHcA84xu5yjth7OKfuxhGLzwwXcQM5oEB/S7xqFWD2sNecf1hMQDgAGhKnq2rAwtLCJQnkvNJ6lz1KphM7yFm+ealLg1hhX5G1alhruJORJUtVfq1LYAngg2OHHeR8s8evvNpgOTuTVZ8xNDhl5dd6LDU9TCwedofvoW112ZUu2QpYXN4p74nzOdI+LqktbCPBlbIUw3Mqnkdfzv5BrkRDCQApAA32nEwyQPLapQ5136LNJbIMCAxXkTKOnwnPAMPgYG9rLuu3/G0J8kurK07bdN4RtXUwudhnADWZDVqww2a+8Wq9ivv5LmS7vCt8H7aKwIkBgAS/ADTqRWCTsUm8OwCJ6hJW1lRoSrBN5yD2CqD8C7+x295N6+kflTnDYL0cCSOae3wsgBR/I9ne6pqAJxn22LhAgWQIA/YMz50Ai4wH9o7htJdOttr9dPY712bJKSEKKJH5gCN4XgSi7Dtkv4BeAmrOTl/fjFQL+HGybAxVEwHQPipdP+cbP8XvQ/pgw9Hy40PpWkRrWYILghaAHraCn7n4s1rbcDRTZ539AZBmIXtjLzjZvPXvSMnJSjizNTx0Xsi8M/EbghN7T70FnZuQZIJTKs91WOameNk8bAffAQHK/eI/wCbsTT4DbMPzZAgigREZXyRSBFEUq1OgDvmKUgBSi0wd2X2ylwQcEFoEG+w1zjUkItctlUKon3wPthCCwnWmUEng0/k7aje4V9eEcGo6Cao1AFBzzZzumWQ0Dh/Nth/+a5nuPycvRkXfnBskbsg4eT4QeU43Qg9hPDKd2EpsmiNTDVDjjNCYAO2N65e7LCY2QNJtiBMKzaklyc6LVkCgrl7qruWQd+BOngskDe8C+5aSFiEiHCka3496Bj9EQIvaAnUIgRCBAfeXtTleZ4+sMZldAjn2Epk6/AD1J9kFjo2reLvVogqiLZmatf4ZYVnOdE31gNQ3BONkQjgzfB/EPAQN2JX7IEAiYExKc7LkEOPI6xjOCElUV9FvMckh9lu/t+/9ndn4wuM/1gZZml5ULQOpNzIbLkaNtDUBxNT0gg44GjTYOZw9G4lWt7BDFNNosBLtETucGgsOBgwPAQIHFmMEWKdLAOw0XVfYIBjomPQwZx5CZIAMFsEKRgbHCJOJJp4UKI6cgAgQiiawBsnDtNN7/vKJFutsHbdX/7VMnRvIEjAohumc9z1VLIDEZwOA+DepOWo6tu5MVoHqjROtOvSPf3qzgA7Xg3RidGxOQJFNSBEVnaBogRscH2S5WxN1b4Pdk/bo57aqhzb3SbtPImJa0V14odZpVbvmHAoMsaZwzy3ZI58odbIJpfF6VsgHuDYcOpt0vGfS+trGbP/RbGWSTr7VaJu3JsnZLy87rA21BAII2xOlMccwwzZwiJG4AOQIWDBe535Qac31QwJIdFTCUZA+xhjvOpRhrU1DTm/uQG8BDVgHSQ+Sw8Qss9Bg866ryvsFQjBEMKYEnGjnvfw9lc3qbAZsrRfTwP3sQi/dM2nxCcFit0jfBjO3IyFIxhhHhiOAyUKLCSAkJc1BBZxSK/D9vzqrvycdz15Nz44QW8hz4M/nRufEXHqRql9ec1iTgmgr3pGk7jKkQkmpEmjQBcwlB4CZJ5iCrdLwuZwIBakYfjFH69cpa4Icjv36+gu19s9fv6JWnbCWBEFIvXBKaN55njhA+qfTurSwOF7dhDBY7HWY0u//y1V7Nw3oQ6/OPbZVDsgIl25OkKEH5LK4H4JLV2YOG4CTgxRAflC8jdahU9LTEgDGFMTY1iOZIgcoQGZPsqdg+lytFjnvAVH+ahfa1zCiYADGGS08QQ2NJ6ornAJlbMvlF+vktNjvMKYUSRNAqptKpJ86saD/xoDz/gk0OOPh6WHKcpD1A8BwD7CcTWfa5RTJlpLq57qe/84NBUIEXEgoXANEabGMPOfFn96So8U2IQPU79ONJMghSdgLgMukpBDVfLGvxvumlJLnmthrBUWxduTeHE0CJ1DLL42VUx7umBAQgEHDRiD3Q0pAppig58Of3WINZztsoHFE9Q0f+uRG27w1WQCEfdrbPiGSg+ydJp5vS5IkZfu92TY7NCMbiE1n/xOMUG9FhgTwt//eHDnofYcyZKsA4xTWElBg07h33ztBvXTLscH29sv1AjelhzNkt5hQ7jCEqWIQj9ybLSBDJkma7KgAS0wLV/aLs4VGnYJWsggQFbRURZ7EvgKUIqMKRCJaN6RMB/aGtqq8J/wUgJjPJ5OFLWP/cN/8PsW/yMsAFTDqOHiP9evto1/cLnYW0oRgB/BAfQi+icwOXdDwLzEpoQ4p0jkuxp49yB7esjfVqir6bHF6UUEgQSWsO5lbbCVSH9ogUx/gDJxd0n7iXgCbNAKIjA6SXIYOXLSWf7es73zrioqaXTMseM4UN+cVxtqNy12S6QBA1ZGIrkpkqiH8avr1zKlJ4GfIePC7ED+VZb0WHheo+Vn4fvAD+wqfRKcgAdZpZ54Fe5x3CMAFfwDYIG3w4+xbghDYb9rm4iuo24CdZo933ejRXCoCDQY19pClC/KRH8Vm42N6avDn87bpwRxHgZHh1HniGwkEqcfQlPD5ZXUSQ1nAdV073qwGKpxnAnl8GIQd9tLV0ZJgBoBP8EBXMph69hutUPEJyPNg9WnXnZTizA3hjLBHCJgIQMk+UDz+859UWkfbiLAP+Az7iowZGxx2J7PENTNAEDvEPuL/r19AmkhX0BCnVoWM0ficQDokBJkfVBQEGPhBcACkJXsXrAN223ZPpuw7zwGZG2QZ75Zzdelih+wi9o7W+AB7Ag0yH5xHFrgNGTcjCAhmuHauiaCMe22qH7aMvEjVPeLzCX4IViFf8JuQywRtZBipXSG7gKyf6+X5UfcBmcoZJfjXkOORGRE8BLDgLgYpqrPd/Mo7WIKFaiZrU7yGz7IgLsBfZE0hm6lZYe9zvahZ+HdiaZzwKM+UOXA0GsBmM6gQzIFtoQ7XNeuH+XJgDrJhDIJkT7Ca6waU0cdmQXwwaJAGNMxg475RFLxvgw1SuaEpoXqJgALkLQzQ4+FcfblBrVzpcMSmQ05TdrzFXv2HK86LWFpVK0PSzHQnopsBC5kCmRFeDPUDrmJACocBBhh5p7PCmgbdwPpjaDgspMEBo7wsuh0B3AAnpBrZDBwaNj3RJEbb0XN7yNAjz1rRsBcf86Jt3vp1MlpE4PFJYQqU+G/uZc9j+QqE9sNct47KWGHccMgYUTTkUVEBMiQwtzDzOCJYFdqj5m+Ot63bky3/QJrun3uAwUAGVLIxQR0O3v5pldXe6LP++iHL3pGs+hFSnmOjs7p3mIbKy91y9s4QufXKXuD4SJPSVo8DGIeT8/awt39eo/Qr3wPLj7yEd4LDhsmF0WXKNjpmntPtOwMHYX9xFjwjirKz7slUf3AOBq3kaC1JcTdsUNGGGCsoitc74sDh5GkfDOjgZ5CfUcAO+wfYO3RvtrpgIEHB4dPKkoMXlh4u5m/b/jSxPBhhDD4OZt/BTNVi8P208d29L13sC6xp+fkOOTBYET4XpwBrIRla37QAMH3CSeFSWFZxo0dZmisX2lT/AhMG0/v6c9WWlRWhORn8nrrX3JmHAIOF8yfN3NM9If1x+ak2gUNkAG+8Uq/giXa6sKbUDj38WKEyegSJMBM877jEINt5IF3SAj5XjHiQ030DJtQpcktVSpz9QGtGhlh1tE0IVHMGYGUuXuywd0OgwblIy43U2d95X44MPPeEw8fB8gxJYwM0kOLxrAHFyJjIQowPzYpNxtBTOAkLzERcyezWHP0uARZniPbHkBYEtLDQNFWITw+zzI0YdQewoVWGQCAwAbAgt2IeyoGPFr8TNNIVD5kegJbvgL2iEByJJHVK1061SjZZ92aLJvHi/NkHdF/hjHEPpfdnq40hewJdblxskJXsSVE9hrTKe9NUQAzwdyYLr7PNO1Ps9ZfrZOO2bnc6spGxgX1rrxrUeQG8AqSoBYFNDI0LkvQSnTXMGnsNRoxzAyjIyI5WYEVAcv71Bu1Hh6kMlhwCyROBUkfrmOUdSNOsGZzy+dMtam/pMKo+FpscokJSABL2gT125L4csfU5pXEKMGYm5mWzYUYBBpx1bBDZAaSNOF8cNLa3uWFIgQGkBQ6bM0inG9WF0WkomY4wU44Of3XNzr7ZJN9Aq3Q+k3bnGo7IENQ1AgdvR3PPeQn1tzdfrVcjgbBofwF6mO2exhGdHwBY+XlHb855wbYQGEFoQBTAumNTAEm8l8WFVRtqJiPqoQADsEjmCAKBhf3jeUqWxu81Dzu1bD6ekjgBugAb8zOLyqZgBygkVVHr9ILkXDDY7F869pXsSJKP4lrZo/U1g9rjvCd8amZWlO4X+RDPh+CDoA0ChWASVhdmHDkn6oJ3Q50WWYPY2ACBKTpDqvZmkYn1s/boh4v0XPElgEBAq1+Yn4g0ZW+ml6ytZdTCw/z0finIfunLF3TeAIKcP+o8kcohXYbdj6S4f3BWTDKBChko5Li+fp6S5kKqnT/dKh/Ds2xrGpVUEFKD80fgwf4g8/rR39urYYA3L3SK+U7fHK8MIlkx1ukf3bJdD+RKNsWQNjpnnn2r0YKC/eQ/GcBJRo2MGbVkkAKATP459rESBRGvPHNLn0ldAzgHTIOvZQ/Vl/XJX/H/7AlAOt3OcvIiBY6pLcRGvPFync5C5RvNkorR6ABgDPmK/eP3ILtoDczeYx9CXEBkgJV4JtRBEJhtui9bdQ5kBFA4eCNXX1wRccE5wRZh8wD9BNtkp7g3/C9ECX+v7lBDs8I6ZEYIniEFOFPM9QLMa3AyRFL/tK6RelYwEgRwXe2gisnpOgYZQJBKkEhnT7KeSCBRSIB98DOQb/gDcAwBILadc40v4LtpdZuaHmoLC6sWlxqqIIZnBAbB/0D4cV4hgnjv2G4COPYje4Q6GAhg/IgzRHlVdXYoU5AzEbQWlzokKdeD36BTXtnbrRYU7CvFB3uTrmScB7Jf3ENn26gyIwS0vF9IdmwLUs/N25PUHIG9WFneJ1KC/YLkkjpTVmc7HTNnJTGHHKUlPP6Td4AvJUDjDL7vZFR/8X+etLlxekuvs2OPFdqL37tpn/7dPdoEGEQYMBaHnI1KmhhHTqTupNFJK4VKt4YzRf/HBsM402EJwPXyT6ssKS1UGxtNM84TYHLjWpc22EOPFojN4vCjq2OzwSCy4fheWBBA+elnqmzT0UxnmN30krIAvEwcKKw3AQcAmewEDqjsXLs6OtU8W2uP/dk+pempCWETYthvnmi1ox8rERMo5j0lVEYAxp4DxgJskabjOXAgSK2ySDXyGUg6MAo4TIbXEaWz8bjXUz+utPs+s1lyCWQYsC9kApA6UHDLYaQdHXMiKHxEBw8I4HrIbnR2TYq1kR7eY72CE0cvOG0JKaHW2zkhKQ7F7Hl7U9+ZisvzqbnUZb50X4l2ZGsMWmRjV77aKGkTgRF6bqQaGzcnql0sgaBreisOIS0lVAwGxgEWCmPN37EPSPW6Ovow1A8Dhx6fRatjUrWwEIANUr2AI4wi4JH5IXxGVFyIvfV0pR16vFCyEeQP2mc853C/O/pYh/WIjgmUMcSJw4Rj+NijHNAX/u6SHfzlEjE2ACQYtcG2cUvKiVQPffYEwaS0t91TNtM/bUkbYvRMyaoAZpBJYbxpc3rtcqeMD8z05VcbbBUWLzLA8jfFCZAB3gBOb7xQo/tdZVBbTIAYMT9fL2tvGVaHFoIlpAf8DiBULR4zHPbP1cqTeyA7g9SN9ZGPOu1k/6sXrCr77+03Gh1DHuIjxpXgkK5Dbz9XbYtTC1a8P03O5MSz1RbExOtAbzFzgDMCb1LrBACkhmF/aG1IYTTvC0aMNDoOgX2h4nDYZ2QSneMy0ux7JrVCKgDKyJrNI5VcWLHszfGSMBF48K55d8y1wTHGxgXL8L/wg3IFGaT6W6sHrXBromq8qG9SVm1vus439Qs4GZ49LKLkfY2jFkWLx9RQgZ6W670WXxjtZAXml5UdYX/yHh98pMDq64feaavIHotPCFETCQIYpDTYVBUGj8wqGC672qWggjNG4LLxYLpsCoEYdSxIQtEcS5c9s6R/d1YPijTZvNdpE+rM+Fi165c7rOlMh6XuTNJnEDTXVvUrwxwS6i9JH1lG3gFnBiYPdg/JIWcd0I4Ek+999h8v2cGPFFlD7ZDtPpAusoRsMCwspA8FkMgYlMn297LaG71WvDNZQVRdVb/FxgYpCMNWokHGVsPKYiuQ1OKsOadkJWABmZnA52Dfjj9XbbuPZclmcs/UBQHgayv7zdvfW7MtAGDco4iAhRXJLHCOAEICzY6WEf05TCv7laLVbWrSsGjXL3UIIGBLsLfsMZ6F5rMsraoLFpkhgg32FWeVycaADNV8UccT4a/ZHthWpJzUEZx5qc5SCqJ1dqjX6DjXYaUP54rIgu1lOC32jD2sQJkavPYxPXf2AJIhQBByDWQiBIwqNA/303V/6bf23BU78PWvXlDgRHMOWFZmofj4eggk844BmdwjtTA0BujtGFWrVVetHS2qqQlkdgwBGfUJSOLYP/gc/Gp4hL/kxsh1H/5IkSSzZHqxqfVVfbI7fA62goCMRVDrkv/iT5BzFRbF6XPTsiKl3w8M8hahg0yJmVFr1IKG+Wk2EoEnkjhmZLFHOUPYXt4P5xMmnL+n4By2H98FkHWx0XQPUnaA+U2hZBRmFYwg6WNf1pb3W3ZhtK4TWSkBCnuZ/+b8ofDANyEth6wjICewwb85w3xvS8aXvyFO+IcgBLBNtyya0oA7lHlZcLK3AH2w1cnjjeooed8nSvV7+CZ+DtIQco/heQRJkMCA7eqbvbb/vlxlnPDJ1OTxbnmOdHokOwWuYa+TMdAsIIYizq+oXo9aTPwE2TmeHY2CwiP9FezFxgXpWiFraO9LrQTNMVCtECBx5sBqrsGZ4Ar2BCTP3LzThnzrrlS7eqFd9oHnTXOb+IQgEQDYETJU1FFQR0aWjWCKbA1+ZHpqQUECM0AIcCnaZ/+Am3iP7GenU5dZ5aUuK9gSr4AZMpxmBtQTgfXAW9g6cBj1JmrPTgvjUIf02Ht/jqS6SPOO/6RSdpyfJeh0STghTrDzkKGoYRgUTOMQsAaBEeQ0dougHd8B3iI7SvYfAlllC7GB9qu/tuP9kdn4wy++bN3XujWcKCIuSIxW1qY4+7d/uqx0WFR8iA42ThkDTscStGeMoIdxoHiYVpBIAGCbVIgXG6jWhBoCFResgW1MeWWTIGV59m8vSsdadatfxp+iMLTYO7YnSwrFJueQM1EcIIzTczoarLOCfamORIGim7F5O/1qgw4Kxgxnoqng88s6tCoMD/G1nNxIO/ilrY5caWFFWYFTL9Tq/vc+li/nTgEXDAjFUl//s5NKyeEcT/9LuYrPePEz4/NidsveapZTJFKFKWAjsnno0IMjJSi5cbZdxZcP/do2O/VctRw+hwLQrOE/IzMyCgQ/58+0qrYEdotDit6TIi4kKBhgNh+SENoK4gBgQWBUGT6UWxij4XaHP16ig8x30yIQ9mxmcFYpSoIM6hGQKGHoE7YmWF/XpAzGBAZoedXa7kxOpXbi59+4IqNIqFVTNyQmHs8O24EBo01gdEyQCqhgNDj0J483ScrC+yZ44l0B3lxpSIwgGnOKOhkmRCCKVO3pvzhjxz7qBHtkVgjIMDLqgpYQKgehbNjwnJ4fhvns641yCgBX9kJ2TpQd/vRGFWDhrMgKjfZNqyMYQYcz1GhY2m6kK2LXCqMV2LJ/cR6AAMAg6WeyabBq06PzjmY7NcxKDqVbeGKwXX+dOiOnSA1J4Pa9aTY7PGeJ6aHSogOYqdUg6KYYjn3KlNlzL9U5DEgV4C9Y5wkjjyPiuZBCVVvT3in70z940/6r11/9z9PK+EEUaGhecqiCedg5gnAK/SiGe/hXtzotF1fWpOUdpYvL4IyeHQ0IfvyDcmWSYGlgoWDFkY+hvb5xqs0ZVNk95cjkYgA0k3IQDHji+zC4/DkBPzYEphx97vbDGbblULrkFgQpnE2YerWKHl/Q7w0NTuudbDuYbhkZYcqObt6fps5h0gfT7GIeaUuHrd02O//jKrFLzc1j2u9qgeixTnsGu8Q7uudjxQqkYZ6oUyHLCHvIWfvRD8qt7EaPHDnvFsdIdgFpJ88I54ctAsjwd2QusSvYEsDahj0pGqSHc6e4ledHW0YkePwMjpszRNvkyKRg2TYKcAmMnv/6FRvtnbZDnywRa7mhNF6AH2KCRRaCPU9GCWcKwYFTZ78xVBVGDWIFhwdgP/RUkZVd6rTUtFBpxwGUEE3YYto9YjvVsjg1XGc7YwMsPx1sxuzDT5WoADeQIt1AbwXNzP/JyouW78Dp4kyry/qUReG6aEdL9z8kMZyr8pu9sp9IQ9hzMJRkZ8c7xpUl4PNhU7EHu/Zl2KXz7cqq8S74LOwFZAQAj6wj74P7YmAs4AUgzB4+/0KdAD3ZKsghfo69zHMgS0G2EWYRGwI4xO8QIOHjaNahJgQFsQK6Rz9SrDapgCRAwWO/s0vZPHThZEMIgjTkNtAJ2v39vdWljp9hkQWgiw8BGhnol799TbUc7P+k1LC7Mgj0n79xWYQSNQlcF+eLZ0ubce4Z4Eg2GzuaRIOEix36e5he6nDobsizaySwpbW1vxP88bzx05wp/uF9UEALLkDKW7opQeQT2QLkRAQaAH58E+Ccc7CyuKL9xLA87BMMP36DRQ0DQC87P0bvl2eODcc+41foJId0kvOFauPk16/rGZPRv/BSvTNXZ2ZJIJa9jp/jswkS8Lt8v7pw0gkuNUL3T2aM4NaxUX6qc8VXExBBjnIdBAQ8A0g47BXvHqKJjBG4AuLJFWRwzWTf2OvgKOaW0RKfP2dBpmo+Q4iPAngW2UDuk85YBM68r5JNiboP6oHamof1bCguh5mHbY9mBgxkLGRfTMA7naWoXcTpk4EA98H6A5bBNrxX/BU1liyAOPuexjoE8thx2pMjWafzVvHGBAVtNIxApoktVs2pJPcQpreV3YEM8fBkzg1NgtaU4cC+E+zeeK1R+CqdAcOzS/by16/qPXCfvDfmjtB1S9JJbw8FGFfeaJSkC/k0gRzEANJPvo/rJ3glm4Z9T98QbTfebLbRnilhlizao887TSB4nzwDSGnIdQKRyrdbhIOC45yRC0ja+P5DT24QaeIEovPqbIrigT0u/IOzMVNWGCUJZwF701w3pC6v2BiCQDKG4Fi6ehF0OSUMZj/8/o33frDx1b89bzODM2rRBqjiYEyOzqn7zwMfK5H+Dr0bjqB4U4JS8xwQDOK2fWkqdkrJjLDJwRkV0HRUD+rvOIAAeB4mjuLQsWxFgU5Rno/d86lS6ZzVNWFoVvMZAAuV1QNy6I5sYJ0lZoaLIeOzcN6uLADgt+J8p7oW7DiYrk179WybdVK05edlFWW91lXeLxCMvIXMidPbP0L3S+ru/o+VyPATEWNkcFCk3gEKh58q0gHBue3+5WIVPWGYkjLCdW0bDqY7XQZ8PDQJm84PL//dZdu4Pcle+84N6zjbIblTQorTKYnOFIAUwAe9rAFSpOAoMBphEu3skhx4k9o6YuhC1H4SA6hZJp7rBXYUAUf6254PFwrsI3uouzUgAEOBIVkgl5aawC/7QKoKkGYGpjW7AwaX1GF/Rb8Y98qzHbbv/lylTWmjh/PGwdDmFidCfQ4HhbQ3QQ59ptFDUkDL4f/hV87JwMNaky0i1Y2x4z6RGAGk0CZihDAQHG4MLilr6lySN8TYkc9slOabewRwsycAONRe8G8MDwwxemACAaQGo00jYpHYUziomup+27knTQ6FAu+nPrnR4lLItPVKigaQBWR1XOlWATOgISYm0OmAEuJrXd2T2r8arEYtQt+UUut0UuF6ljGSTMIO87MDHynSs0ejiyF8+RtX7d6PlQgYw/zDhgIi6JxFnQFgBlnfrgdy9GyiU0I0tVZF7Bq4yHOJUTr26utNOoO0MvyvBBr/+p1rFhLsbY1VgxpWdO17FUoLc1bp8uEMtVtQFuO5f7ioe0KOh1wCff6HP7VJRhdnhqPF4TjANEwg98073cfytiYIyCCRqLzarawnzpf3iEzOaXV7W8CL7CDvElkRTD3Pn7/DhvBzw0PTNtg4IufB2bnwZpPqZVw1JGQsL5xttatvNWs+ihzc2m05IPYj2a4P/9E+yTDpkkWwpIYTOZEiOAD+/Jv7pnCdoBbbx57k/1nMy2GxVwRmx5yOLhAHsGs4c4b8ce/UZxG00G1JLXcHZ/TOYUvZDzhx9Oqcg7JzjpySlq4uVnX3/gw5TQAdzz95Y5xkBcgrcHxIlPr7ppSBIjMHoONcanBVsCPpI6vJ5/IdODGAHHNMOPMERAl8Xtu4amSQPRBk/PwrFwToyTKXbEoSIQOhwe8wxKr+Wo9966uXRAxQN8dCJoE8EntNgFN+tl3dCfcezRKZModMRO1hmXuy5NSFBTh6d5w0dTqQK/TQ3/2gM3SVQBTQSFB2+UKbJvYOdExoLggBKLV4BDSw1jh0gjIcempupLToYrCTw+zIR4u0L8miUER/7VSble5I0u92lvfb4ftz5D9gg1n8OV2FIMSG2icUJGMjINR4fleOt+i+K95qFkDCF5A5oaYIFhZApjkLrWPa15fOtyl4J1OCz4VYgQWFOd77RKGAG+8MP0hASoH2f2Wgwf5gMVek+yqyv1kVvrOPVQsVHWB1Vc5AM0dW5GkT4wvyOfgXbAISM7IZnBkKwwGrnAFsCmSdqxA6d0+qOsvBHCNzrrrRK7tNhx4N7UwMUZYA8HXrYpfah1ZV9qvNLUPmeK6oA2hpis2FyISpx8ewnzmXSD6x61/7/bdUG0N3I4aMxpXEKGC4dr7DUopjFQxikyHLeC+0iyUrQNMa5MPqOjW7pICae2VflF3rUQDFeScQg7jSvKAwP2VIqfXUVHCPdRbg760z57TJDdUz4TMJBDRFfHFVzwefxD2QOSETIRnQ2JzISkhS/CJTvQHW+Nmx7kmRDzsOZ9rZ1xoUMJBR4ryQuQVrHP9RhXAOZBLyI+wgRGV325jkRkjFT/281qmr8PJQdoh6LbXRXnVkTgxKBtBTk8X74R7BBbx/MAs2FjaeJiI0lEGBAGDv7WYq95qyi2SaAdRkwLHB2C3IDeS0yDn5e+RDkJ4EXHufLFRBPz5fmfOSWEkSlQleWdM7Bjfy3BTU+3vZfb+0UWfo4tvN2gcoFbhnSGSeE3JAMjrYETLt2x/IUQ3nyz+q0PsiSHz72ze0/66ca5ffIQMTHOWv6wGPUccGkYwfwmZhX8JCfZy97O/ldNbSoNfbkrQRbLNUjxPkbTdOtcqGcE2aGF837JAbrePyN3QAg9DF7tIqH/9bWzv43pVRMUyET8cgc3ikQSZC9EK/OiL9PCkuGEEOLc6Ql4qRB4STRgYkfOwTpRqq1lU3JJlDRGaEOrywurumlP1g8wC4ALW0pSTNxMIAb9ubqjQTrQJDYwOVemtvGrWjjxZIG082hE2pVoX9M9Zytt2yDqRpQzEs6+1nbllcbuQ7tSNzkwvWV9Zvez9epEnlzH4A3FBERXRLR6i09AiBZMDtyMC0ovCe3ikxWTjQqnKndzYORgaHQXG0whuesfTsaG1+JnI/9evbtaF//u1rlrEpXqwELBsHBWPKtF2CL9jtymdrLPu+LG1GukVhYMtv9Njhe3Pspe/flIzo9Et1FpMR5rTKnF/R/AiYUQybZFG3+m2gbth2PpQjMOBMDHUGzlw70WKf+NJO1b6cf7XB0jbEiAlh0BqdOwhsaN2qFHCwr9h7x+kFqH3k0aeK7PTL9fbgU8UCchRyITdhOjmLLjRolQFtMK/ll7pUXE/AR3YGhw/DNTm+oJaxyEh4X1PDsxYW50gsyDChlUSS0tU2qggfY0xAyTPiet54rto+9qtbrPoWhaHr1NmHFCeLe7l8rs3yCmP0fii453s5wNRDUBeBsQYsEFA98sXtSlsic2ORKUKfybPEmMAm4VpbrnZbVH60gombz9fa1icLFDDTdpSJ0rA3FPvzrHB81L8QCAKmeHYF2xOdWQ4JQQpcuR6et6tLGMCj7KpT0Epgxe/CzBAgu6bMotN/9dkqSd5aL3RY1r5UnZmP/dIm+0Wu3//8i/boJzfZs9+8ItZm90N5es+AZoz6nn0Z9sxPKiRXoCgfLTYsVf/AtAIJ6gMw6AB99g0Alf3B84U5Zp/xLNinc2PzOuOAX7Vq9PGQU0UmxeRh2ESM+cnnqu0jv7ZNTQbYAzBSOE8YQZwLjR8Idmhzq6J1DeK7bYONw/bhX98hQMz1w6QvM+thfMHy9yTrffUwayLcX5kP5AiwlmQYsQXKii6s6Dscyc2qJA1km9DVcw4BN+xTAnuYJ0gQmHBaIy7PL9uDHy/Rd6P5xm5xfpnH8cTHSyQPpbkF55ZnC2sIAAXQkN3hmXG+cE48M/YTEifqENTZx9dTTrKyrE8ggixCb+uYsh6uFtBIcgDSgNlzx5ukt4YYAQBS7wbIIXgH3MJW01oaFv7EjystsSROskGAm7qDNYzoXfP9MI5cn9protuHMUwPl04bwMEzI1AAWGC7rj5faw9+cZt1to2rYcY8zOmd7jhkFn/0+2/bvl/b4mQeE4LVIhzJEmcMHTl+iOCVPZaVE22TUwt6x4AB9hmAhudH8EVL1n/7uwtWtCdFzK1Ldsnz4T54v001Q5ZbFKMsJxJM1ywj7DSSKt474J46FCQ2ZLy4Nj6D7xTQYkL6oiO7g7BgVskmDYJEkx1o0THBNjoyIw09g9QIQAGuEFlMmqYOhXvSoMkOZL4eagzAPIWOlnERVJwDAKtaLUcweyHQPLw87NjR7F94zSY1Bchn0Lh3Noyo+xYE3cSoU/fH3mN+i5Pxui1CRRnF4VlLpI6H/buyprMKkIdYo8kJ+4HGJa6pz2SkCRrY3/w/YBzyjZlVI5Jceqsl9Zs/KLeHP7dNzVAIdvlZWiJDOAJsAc2cQTWMCPeXBJEABaKQbB+2g/MJeKfgGixDMIGPnJpYlHSTOi98omoL0hxJJu+T80Vwr5lgYf7aQ0iBAIwQWLwj9hmdGPkz5jeoY1b/jCSk2D7kpTQW4bwDKiHpaKiAvI5p6cgPCVBh96nX6O6eVBckCrtDIv2FW/BxSLTTMiK0l9m7SNDIsCF7wt8gr8KnoFbYdTBD2WPOC39OdgK1AOQjbV1pfcvZwP+Q9cimYUczxefe8puQP7xjMJ9mb/Q4A/EIMpG+tjQ62S0avdRVUzsWJTw40DWpTBdEBv4AvEBd3Xf+8Lj90fceteOvN8gHYMc4V3TYws7cfKvZHvrcVhGC2FfOPvaWdviQyMy6KiiOlaSMLDmEDASopFGrayo0xy8xER3CC8CuoAb5WaCXTU7RfGdZ54pmFGRikcfxzqidxWYHhvq9kxFm/9JggmAG382exQaB86glJkPl2O154Qk69aGYoOZQjU4aR4WJqAPm9yjqRx6OT4C8InuKnURRgk0sPZiu7+WzyfTK3mDng30VGCG3o8OrGh3Eh0j9857KbDCxECfLA+WgYgxw2hxOihYB8kQ5MHNkLkh9cdhJ33H42CwYUooVf/p0pfV3Ttj2e7IsEz11LAU1S1Z3rddi4wMt5w6Ig/FlwxJwwH6gfZtoH5eR7++ZFluN1IdDvu/eHGUzOECk92Cv1VVk/TorvNc1jMvRRW87mqWol4i87rUmsXMf+7MDCib2PJirTiDcFwsDwX1epTXi5LwOLgeY4kEAVsPlbmVvaIlIcTWBCM6blqnaIPRtr+2Xtj8xN8pe/lm1wGMwfcUXVyQlKzvjDORiXgDsD5vxvgfz7PNfe8DCKASqGrCzz1YLtMACkN4kIKJGIHdTnA22jttAy5iAuas/P8YShr/ldLttPZYpQwOoIxBj0wN8AGcYUQz+YPWQAg1qVpASMEyNaBn2gSwPmx9W7tLL9dIbYgGor0jICLfqyl5nUjBZk6o+fT7ROVpODBfPEHAVmRhsew5lSHeOwYexVsrYz1NsFkaYwPGpz2xRJ5mDR7MFXhrOtevdEnByAKnTuVXWIyMEmDzySL4OMd8Hu0ygQVDJz5MtgekFwOEgeK7sRdgvNqxT07JqPdd77PAvlWhWAfsNsI+BQb+pgUz1aGeRTqwqtZu9M1lFbe2V/RaWGioHQDo8PRPN+pQznTnM6XCF4UdOSEHv83970R7/zBZnzseWRKs83S7AQuoUFhspAAbnVRoEnGlXwSzPrrN+RHK1+bF5nSnkFs9+44oFRfrbSOeE7f9okYIoWLhvfe3SL8oM2J//2QlLK4yWUaeY8N5fKhXzdOtCpybHwwA2No/oGhvrB7WfyAAd10yFILHqtDflWWAfmm/2SV9McAZrzbwXAoNFWPD0MNuyL1WNFug6hDSyeBMtpmedmg9/L03K5mx96LNbpROH2UNOifyILAfyGd510eZ48/ZzWokCFp3APMJ23p+jWhneGXsnNMrfgmKDLKEoRjYJRjF/Y7wyGb5+3iJa2puQyqyqJsApzvPS/xP4wJAj6yRDpqFiYX5WcbFLgTg95QE7BQQj3RNWsj1Rw69e+0mlwCTgBaeBA6UbF4OekFUgsaHY/erJVjGNgAn2Ps+S2hYkRDga5JhMWaebEQEI9VBILk49U6X21NgWsjHbD6XLQdEmGFtKoAEoBNwwK4hADUBy5mSLnCT2m1aMmtBOz/+qfrWmJdBAs859lx9vkZMEiPHcOfeQSwR12A8CMb4fFhL2raVuRKAEZ0tXKvaAf4RT/E+BN4Xq+x7Ntyco6E8KVtHk9k+XCqDwOex/Cn/xEdgXPYuhaf031/Ev/+dJ2XfVUzA3qXfqnSARCRp2d/9jBSpaZ08QGDIsjOdDBh0/EhwVYBVn6Ta2XsM7yXygh+czCCKQxbFHeK88p7BwX4FhsmrsQzJ8LQ2jTivktnHZPMAVXQCxVzDodFiC7ea8sADR2I7KG92S7rCwjWQ/UlJC1PefbDLvinpGOmg5LcZD1PiCOiMAJe7rD77w8i/MDlCQLtDusV7yT4D77qPZIoEunWuXX0GSjFLhZ9+6KtaVYAoQDuvbcqFLtTtkuwb6Z2x2eknPgHMEM0vtFcXTfD5+h7onfAMZILK9nGGyTLDvJFY6qwZEUmy7L1vtjcnwwebzPNkHnCuawmzZnqyzQR0Sew/SC39NTRX+QjVEY3P6f4F/f0d7zzXTdbJwQ6yePTbZyajOC2tgJ3jmKlQeR9LcLQnWxCgt9m9r/7fWj2i/8t0+1Fx40cAhVH4WrFB+s0dKBOoGODP4G4IRfBiYg4wcNoLvEZGpOtcQ+YUI2rVX9CszhO8kCMS+Om1twWiObA3VAtfNftVsH9VRjirQcI0jIGCB9CCjn5waoQY32F7OP4u96QLUdFwiaIR4cdUsbd6RrCwIn0V2YmrCKShnYYd4x2SJkeHjTzmzqlOJDLCq8h67/4vb7If/ckO4jkAdzPnAwwVOBqd9wv7g7+/XnCKuEUxGgwCwDbJsCEj/IOfdYEep5akp75d/5Z7TsqIkwWIuCnYEtQNEFnbKlREm0KJJA8EU+w/MIWLB21NZ7f3HctS5UAqGOw2MwFv4f+onwFOQnAB9GshgC7FrThtzH5EjtMsl84d/3Lg1UXsPZcjNcx3yNQQv2BVwGrJzfvehj5Zo1hb7l4wr/hUyEnKpoXZY0jwazoBFsXt0fiQDhRrpPRNsfOvrl+ScMXrSxs87/ZMB8xw8HhoPEAMdHR0oMIDxoV0nkic6qwDgednIDmClYBlvXugQ47BpW4o2CS0FAX+3yvqkcaW+wun/vM4uvFSnQx2eFWFvf7/cklNDLPdwhlJmMJ2wK8yeoEsNRX4ARH4eBzykoW8rKsijuxApMaRBFDUWPZoncM6BJnDBUNBfG4fCQcDw07WCdHxRUZw21LkX67Q5YW92PpgjnSiF74B72ABSfqzG2kEZ2bobfUr97z+cqZS7pqfnR2mQC10MaBWHc6cAmYAHx8IEbdiy7btSJUXZfG+Wom1XG1GeCz8PS9R2ut1yNsaJSSay5kAB8GDLf/Wv7rFbl7utt3NMjCTPneieDAydbqhBQFZ1z+c3qwBUOks6JKSGiqVnbgIyFw5de/uEvnvLPZlWvCdFz5pgAVkYaUcMHUwAwBu2Ey0hfbVLtyYr0KTI+/qlTovLd4r96VhC4e7s5KIKyOh0ATirq+mXMSWVTfRPsTxGFoDUcK1HxjAG9o7OWtFOkdRQ/6QCBIAP8gwCCdhBAAZ6SAJG9mtbq1MUSgCAwycLA6PG9G+kGuxTpIC0q4S9IcPRUDEgyRf7t/F4iyPvyopUwBCbG+VMMe6bVoCMYQScYIQoEOf5IAFAkgbzGVscownNnBtYIyQayARITROEwr4euifbdh3OsCO/VGof+kSpHAW1JASnfuF+Vn++U0EvgTrp6dLdyVZ+rkNsSWJamOaRvPySU1/0n7n+x/9xwunCsYCDWLMwinOnFsQI0W2FfYGNAGzCBrIHAMGAeJ7LiTcbNdlbsh7kAIsr9tCnNsnRAsjQ5pPGj44PsuSscJ0JghA5x2AKMCf0/njfyDRoabl5R5ICeoCNU9PhZPRwpOo93zEmMoOUM0CQd3fxeJOMOYaYIAON7s596ZJlwUBSs8DcBuwB3wVbSSaVejRID5huAAlAhn/jVHEoZEIp4GevElQQLCEbSSWbsrKmFpa8a2YusDe5f+wfbbVxdDhbpCTc52DzqIIoBnuSYYDhgp0DtHD2KTCf6JtS8DM+tiAbyOfnZkcoW8p10g2N95O+NVG927EtyLl4Bryf+pp+BXeAXwiMky/Xi7mnL39P27iIDWwa5BJAD2aTa6ZlNFlAmGAycc//02Xb80SBsiBkeHnX7ec6FGhjw+hiA2MJQKTLGPscrTqFktxTU50zpDV3V7IVbUywDdsT1QJyeGBGewDmj8Ceotr41BDbUJIgez3Z6dgjsic8P84xIJHn9/h/2ynHj31pqB1QMEMQipYfOQK2CQCBfb9+rsMeerRQ9V0wsMi5uAcKW7M2J9iFM20K3ABFgDgadzTXDd8Bzglqmc7wMrTUvP9c5FhTi86AsPQwe/s7ZZINY3/wm02NoyKGuAaIKuSvgGHmbhCksA+xIQSyVTd6BEjJ/iCVZa+rhoG2pSdbxSbj59iHO/anSYLGPSORC44OsD/+7dd+IcXgEFPUiaDjpxaA/yboYW/BjvMP10HThPwdydbaNOK0Be+etB/902XL2psisgogzjnZfTBD3eroEMifDw3MCgQCXi+/WG+L8ytO+9XxeQV4BOH4TlpOI/Ur3JWsvUJwzV4liBExGOijxhS8e2SxyhwyfTuY9sbOVGf8NQE9mTjkfCzkLnwHNoRsEuQKASlZRfYP4B+mXMXgiyvyf1wX/lkdJpdW1SwiKSVEwXJaZqSlZEfIhuOXyZq4gCwZGnwJ5w3ATEaH66m40Cm7BznGez10NFv2CB/B4FsWdTLYFp73xiMZdu6tJnW6IpPG8+IM8N0AUvYM2RBIKb4X+XdgsLc6TuK7eHc8L7pxgYEAv4BXfhYp3+VXGoQJwGr8rDLyIY5MFDtGYKGhgnPLkmtzXipea3SI6ZVVnTf2SOsdwolnSXBPTReBJt8HLqsq61UbfeagQE5DOKOc4ZlRQ1pVNaDrJeOJ3UAhQ0aPug+exWiX05yCvUI2jeGRDPgjMETij+IG28MsJIhKsrizY/O2MLVgpVuSFXAysBU7ToBFIAkeAMCDK7nugCBf6+yc1P4E627bk2bHf1YluT1BDuoI9k3VhU41NYJQ5N3Nzq2o4xpZD+oxVA8TEyRikwGve+/LsZ//rEoqCVRA2A3sBJk7OqUh60JJQ/0wMltHdtUpIg08QyYJjOpqUY4dweZcvtz57pdRATBIG61n0mpysIAaDw0Q98LXLtvuxwtsYGDams522MNf2K5CZQzjxi1JmulA+o7imsrrPbbvWLaKyTEipPJhDHFkPECiTAIWdMKulmE4VTYT+lYOC46N6B6HgtYyIipQOjiiQuZXSIuHYwmCIVn7fzH3H1B2n9l14PuRyDkUcioUcs6RAEkQzDl1s7vVanXLipYlWbLkefYb56ixpWdZkqVWVqszm02ymSMIEkTOORdCoQqhkHMk3/rtfxXfvHnLbyy75Zm7FhdJoOre//3CCfvss08u8OZVDUFcHIizZy7HOODZeblsvk+Uq1rKUDLN1pkWLt7Hb+9NsAANY2R2rj+aOQeaJT0bBOLC6SuZCn2h6XyZ98S4atr59Vtx2gIWr3WrG0of03M7tfuMT2inoMHj5g9NINq0vqk8/avzk7VD5zVlogn4rkr1lGOU1TWH+/1KkeaTDI+adffwBByG+XEEkioNoCs+qI8xbdh3utz75IQkWq3qKUqxLhongLbmRQ1GuRPy6rAaGgNJhb7YDyoraEoCaFl+09ELmdr84KPjgt57+fsqyKyqGoMG94wCB8nK6IlbtwNnS1cyw/YfvaK2RxW0PjouJW1l6chVnr4SBC3/rumU4IsR5zA4fk6VgX7uS1Mj/ckJNDWcyb5Ye8GeswBhMJNhxeu7S4/anuFPK7dqVn7wK9NTimWEWlWgBM14tQJlLwnVO2/uTnAC+aCKMnZS/zjGoNVTB5YPXtlZ5t07MkGLShJeJqTf+dJE/f4L28qUO4aV9e/tL0OmVKpWptCiwUlmVYMYB2tb9R2djfGFqChzQ4SdX/1HTbtPlpHTBpQTJ0imtklwfOdj4xJ4Ka2jcVi/H9egn299Y12SLQ7aWVZhGTCiV5l7x/AkT0d2NEfy9aPvbsn0ZNRJd1ZPAi6zNePoz6JF9e4U6WMorWBT0yTHGz5t+zaldkSfcOzHjKMGc3u+s/Xg1DlglRW9X4JPg6CcNfdEAiTZEARC3QRBAgZBvMoUu6Ahn42wNnu2nwilRdJBjUYVadS4mlT1yGBfv3itjJs2IGAEMEEQIYkXRFsHAY2gkURyPw2oZGHPXyk71h8t42cMDCWQod/65t7y85L+dUfK0BG901wsWXDPlr2yq9z3+ckJCqhj+Y6nmi/lPqHRuPvO73Hv1a19QAJnznNsWHMk/SQcHp4/WobkHOWJHdaHkaCZrv7JSxV/vn/XACn+nM2z7qgNjFFnFKmW5m72GOUJYkdVCH1Kgvzun2/I4E+VQve2gaLNT0xNkv7maztz7uwDp//05yaXl3+4NYAGGqb7gFJquKs98DxoM3/2L94vd39xctm+8VgZXNsjPTT2R2IksVj2wf7cMcnizo3H0k/22M/NLhs0RXZul7sMYHj5j9eU2hkD8wxQVo29RxvOl3seGRt7yfcISnZtOZ5BhrjtqkD33Dc6Ag1sjiBx0X2jU8mFxB8+eCa0L8naWy9tL9PmDEkCZy2mzhyS9zy451R67ARhEFESxQJbzl+wI7D1TM4p4Oqv/+WSCESMvH9EZpeosKxddjA9jSrEbPK7b0Jpb4+/UEkFVEHgnVX+Q9JqD3dsRhO8PVKyi58cn+DIXgkD8PXN5JCI/LjU6v7dv3o/wbLvNWJ0TdQFgT6CJWdJssxGoog6m+4MOyqwE5x6sfl+xnrx0frdGnc0B8Bie9mMVgrg8he2l/5TBiSR0qfjfG5ccbgMqusVwGcdwGxo9/hHqkHOuUQazXDt6oYE4ZKR1qGye/acCh3S2RMgAz5Qk6HIpMzZHFU0IJF4g78TPBJnAUz4TMIlAlLfW9Dp/qjobd7YFBlTs1bMSWCHL5y6XMZN7p+zIGbgr4m68FUqH+6Xc8YX87F8OKDsgx/tiCKmSgzKD9ll96j1FUnh6zdTQWD/CfCQ9GXn+A93G4gS8YEObQPEov4JbsUn9kozPoAGMIOS5D6zt1gg+rCObzte5j46Nn9e0dnaxl5LTlRD3FEvYAgZa0mMewHw8z5ob/w/8OO913dFBhytDAglvgglmyw1atPA7mFUAJ6JdkT+t02lomVw8SNfnBJWin3rP6RHztO+jUfLPU9NyHvpYZh7T10AH0koMQDVBPfdGuzdeTIyvPy5OMp+vP/y9ihvsh9iIPsqgXTfrB3bjgIMqHB+UP88+45tFM36566yQcBP4hh8tX3EWCCkAdzZv+t4OXioUgeVVGWY36bG2LXv/qePUx22jlXFu/9n5xQdSmLE74kn0AFVqiRAKuz8DFBaBRCYz566L3yO+ymek3ARIwHUZO8+LT9WivWPvbIhILo7wXqnsm97c5AIQT7HMcy02F0nQ//4rW9+Pg5egL5j8/FMyuXcKT2semdvWXj/qDgoCyqoHDSid1C6kWP7JnA8e/ZaUB0NxtB5/D3TvTWWxSAdOp3nyeRR07SD8l8I6iXzxAnWOCWpUD616C6V7N7F0FgKpXPxxozuUz69VUmy3nXvqEpDurZ3jDdKiMzcs5IVNDRPBmnjbe6MO2uDckMy/MyJfadKx27ty03Jz+g+eQ9rgE/8yBMTQuPyD7SUfKoDy+h6r63LD0ctg3M/d/hs+do/uScls9DEjl4oz//HZflZiMTCe0aWTRsac6BUadAeXHIlSVQMjrNVWpWTEXho3FVWd5g9gwYu/Q4QMg5TsGftq+E8FxNsU+4Q5B49cj7NyKQW60b0TIBgPTavPpILZ20lfs6H3xFMu6Sa8BxwCYUA3WTjN3+0I04QCuP3cHrpYEs6odPQlmryeaU4IeAUNC5+YEyy/QPbT5Q9b+8rK7+zNZJ2kB6GTTM1xMTlEpDs3nE8jgqNoppofj7ORtJjjZc+v7UMnlhR9DjxOxbVpdIjkBfgSTQEvAwlY/TyX6yPcYRm+P5UsVQSOCmyk5JVr91LD4ZSc/8zk6qBcy3KYc6VBMu54/xmLR6R6eV3PD6utNN8W9O5PPnVGWma8xlQYMG7YAhSLzDGq29VG2KMle+d/XEtcq4CYrNHRk8fmKCcE2zafiJ3zFCyH9cLn9T7r/rwQNZg4Ihe4RWjE+q50YOiuX/Kw2PKHYtGBGE0Ldz3Gju+b4wrYMELvYwjb51fkDkq7dvG0a56Y09AAQ7TGUItTMB945MgdJPnDmlRmrtVXvr+ltgJ+xW99Ktm1txMH5WgTnAPWIiqzeDu6bVBQbF+7tT4OOeKGgBImDRjYM4NZJFIxMw7h5exEysE0ecJviU1/r3uB9uTfG/d0FQGDe9ZjtafyUwO93XynMG5V/c8MDrOt/eo3tHGh6oPJcu7salSO9KMOBNSfrSq9DVWPR6cv3k0vg/nJGF56rnJCTDR/FRG3XU0sNaAltAB0RdnzppDaKGGbKMADNolkAaCeLGh6EsGfV2g2NSxXfjKzqBEQTLjhcIoseOEUU4e/sXZcfgSHE3TPQZ3D4r6/W9vKs2Hz6UqGgWXyzfyHVQgPcPyJfvjQPHn0U+9B9QWRWPh5yaWfXtPl6e+NCWJpwQAJcb3NYRLRXPY0B5BjkdO7FdGzK0SLs/pZyDibMAzf3dugIk471uf5PnM34BKA2godUFLyU0DuPgUNodvQM30e5te25P5GWw7+zZwUI98fsOBU6GqcNpAFd/fXRZu6ZmSCEmod+9oTnDrvQSX6Bb1W0+kquMl2Jnw4MhSe+ew+ERJSipNGpsbz8deQIJHj+mT/qtDe07l9yTN+/aeik3QdI4Hzo5JhFV0VT5V2klwAgUEb9BUQaRA6Mf1khwCEPv07ZxgjrKWM0poRVLDr7kD1y8TMCnxx2x/6xBFIBr6GWYAv64PCBd/zOzBqY4E6a/pkn4WFcXhdwwrtXU9o9inH2vd0gNl5IS+qbgTWzFED1AH/W7lsRMW4INMg/Zz6NPemz2p6d0xgbQKhHPBNvPNerUkpH4PG4IP1ociueNrMgtlcPcEfnyiRMPzS1BIyGco3OBuoXBD3DN8b2yfMmnmoATc1oSfYUd2GX53+EzusCq6IFDCyCdbQ+vFzqm6pLekD1D1ZhTOnD90ImANqs5di0dm/VGnBPfsiHtCHj53YESfME+WLa2Pwh5ZavcriPiaI9mj5qbzLZW8jtkbk6qvnrtWFj4zoXRpaW7f8OLOyl7fVuIzKxnddgGQ0NwE53pb7R0QhK+QeEs4MRQkX+4fNSg+0j5ZQ+ubWOLVXXl2lHN0cXdC1VXfxcNfmBKK0MYPDiR5pfImQQG2iVvYwPn3jozv9gL8SaRQtvT4ipXYc/fj3T9dnz449n789IFJDACYqGiCeN/JM6In6hXFBmE/xUhsGfaDeOVY4/myffPRcvXS9Wq+2QDTzc9lz1RviNnw12JRlQ2AD9/kZwgEOCfDVPkPnElvp58RI+za0Zz3Jnmvh86ZdU58/51bT8SXiXutVdcu7QL6o/VKqNCF+Qr302egc1l/L+wBw2p/nK8fa7Lx53+8qsycN7Rs29iU7LtjT4N2bgQpPNJwrnzui1PLw0+OL6++sK289ebuBLoUXqbPG1K2vF8fdP3VF7eXYRP6JRmQya96vz7lQkYCQuuyy9iVF/VkqE5AK5StGXuNPhmkNXVQ5Cc5Fdk4FFEige9PnlCgDLWziQyW0j1VK/KXUAoa/BB/G75pY2OkFg2FQ+ty2AVGZiowIC7i409OTIKhD+C1P1ubANDnDa7tlSCf8yZpKoC0oYKG2Qtrq+ZCB+LY+ejya5TMhT5+IbQZzhzHUSCx7Ye7Stf+Xcvyb28pfTNT4mS4plcuXE8A8cgvzE4glcmol64nQ80Mjb6d49RcVDx3Tc4fvLE7z+fzUFGU2MkEnkYTOX8tjo0D12TdKv2HU426AlH3eQJkBsrLPA4Brsss4PI5DrhJxS6A7+N7Ke25QGgYX//1N4Kk4RxaZ9STybMGp5Ts+V2q9761KYkqzqELkgm/PaqS/NwFw9P/YQBPp07toxiF6z79ztoy/0uTy6KvTQt6wwnPvqM2Ouia/qD8aFGcAWMa6suwHqEhfON3l1doYZ8u5Yu/vjAOj3HwPFDBWfePCmqEYicI9L6aGR9+bHy+H+17gTMOrmBRA78pxxSBGEyByHO/sbB88P2tCQiUh6EhHFXUw5rOx4n4XKofkhyOzaCeYSNrEgDSmx8/cUD2VpWgW42ptmfjaEx/Rl/hXNwvzg7NSrDonKqu/Pk/fy/qHl4SjMd+emZ5+PFxQYbd4f/R13e/tSHfXxI9a8Gw8EhxaSWPq7+1OaIEeih2rzqSUrfmzO2bGlPRsq8SpyON51KRghKFUoZucPVmyr0SYT0OOLpzHx6TuyN5YB8E7BChzI6oP51mSn8noTNdlT2y7u4tJzJhEpWYW+EQa3SWHJibwzkqR7tLqIKeI70EHdokyID4Qd/t986NR7O2HNaeHcfSUKi8zkl6DonlF//JotACVQPYrtpxfcJB9qyQLEHWxjWHQ1kkMYnKp78IlYxqz2vPb0lF1ncPJeHS9bLz48OpqA3or4HxSno2JM/WUOmd/DP7JxBG/XE+PLsgQbAsWf7oh9tzv6H0GsspWSndK8tXjYTtYvs2bzyawVp+/+5nJrTQ+romkakd1j3BnbkWNaNrPrvr6JTsgwAAeiyo01j5g79YF1BAE/3aDw+mWfPM/tNRv0E5M0eh/+DuqUJ4JjYFMCHAWb+kPoEKm016t3efrqGM6e1wPwWj7LoAjkOH9psToMqHP49eIMB2NqB7Ai1JjL3yrBIgFJS9Sw7EN/i+Kp6AH2j43Dtqy7JvbUnTqjMx6p66oPMSGNK0BE8Mm0SPBY54Trz3ajryyYBp1oBt8KzN+06VnVuOly3LDydJGz95UFnw0Ojsp0SS7SQgICHdufVYadOhqqRTmbHGntE0d4g7lH/8tAEJnoBUqhvf/4OV0etvPHgu9wNFh28ljLJxuV6dW6F5CIyAAYIRr2/8+Zr/YTvAlpAdZrNJpZ46ymeezlnb8IPtsWWz5w5NsDNhyoCcNb1zmvHvumdU7qfA1MveO/+ej/23F4Jl35/qIXDBd9Ib477yl2ydgbiAN/uHAeCz7SebQP0Q6KSS9NBj43Lm5y2oq6qxqxpaUN+qb2P8FHbik1Ss9FahbrLpn9z4JFWu6bOGJJEbUtuzbNnYGF8i6Nu5ozlAKtBHzxHgbdKsQflOqiASH8mm36UeJ0iN+mK39qX5xOVQeSQUeieh1CqzAktJZAa1XrwWe+L3JKgBT3afSD8QkRj2HzVNUOtn0yDco2MqHO6g52G7BLpH9p9Jb9LKpQdSESVUYv1RWNlFimaoY37+8MGzide8PIfKn7k77iObOOnxMUku3bV9O6vgVgUjNMUdx6PsJlgPQ0Ivw41Pqgb59dVkeKg8MNB5AYwCgAFCqW51bh8JWYl1BGPOXg3dbvzkAUlm2FsJ9B2Pjc09A/oBMCQJAGvxV6rfPTvn+QERu7adqCbIj+gV8FFsxW7N/tyE+IvQ/np2Km99Y0MCefa64dCZAGZUOiX85rGpEkoU2OgNS+o/k6ftVdO5NO1sjo+xhmxy/Zbj8fevfmtjesH4JkOcJc3iQv4EGARY1VhvT8bOGpz4AKBm7ANQbO/6pvhYohFonc6C7ztp2oCyeUl9esf8WYYZr2lo6RspqXJEqe/i9fLGn62LfcCwYEfE0LVjayLq8H87GtVv/29LY8iVRzk6h4dhEyBAJXwh1BdfhGNgtC1Who316pzgRBLh9xkEVBCL4yKna572PT3n65X2tNfaFYeTgQpGGHfIlMNMeePosUvlkSfGB1GSKZO31BwFKat4xucyHG3iuL5lyft7y7r39pepdw1PUKNxWxAqa2+d1hxVloazZc3SA1HEmr14RBBTCJiGHXxPP0NHH8oqo/adGACIClRy7ceHyuJHxyaIf/NP1pVedb3KpDmDc4ghYpquDGCBqMri0UpccgEW6T+l1y2bmsJXNNDOgSMnp7ROMs2ltJ2azndtO1569e1SmvacKnc9NjalXlUFdAdoE8SWcYXk/vmvvlFqFw7N1GPzCTgBFK1pD41KwLXkhW1l6l3V5GLrcbil7G9/BB2aGU8dPFNm3TsiFDa/H+5fl/ZBdVw2vTJmZnDwpDOhJRoa7f2m9Y05B5w++pYhSYZ5cT449Awr/iuVC9SATCNtvhj0RtVCNUIDqM/AyX3x91aU3iNrMv/Dubn3/jHl9Ve2hyMN5cGnVB3Q90AOEZKuSrZrbWMGugnwlSMZLUELA776zb1l7sOjg3AOraWQc7oc2NFcumgSnTogvOxZc4aGjsJBUNhhCD+GkEIqarrkuamhoNhIZjh59AHTRiHNjU3nP1Py0szfvUeHGGt3YubMIeWt13dGlrD51OUkmYyO8jonpOR85ODpIP4csPsgMdSAr3FPSVywKUkR3BFmcJ8Et08+Namcu3CtbFp/JBUJDvif/cv7/7vswOuv7Yyh37yuIVK9kOi9qxrKiNmDP5taPzxKKhVyQ19cAA0FW/He/jTZXtQLYULskO5JUCS2UOBMZyXAcOOTJO/OlTXwT2vvC4e2d1dzmTJzcBB006ehgJXO+CfVEMWxfcvgQd1jcHGLlY+9t+BFcEnJ7OMP6vNc7q3EyBwPa3lge3MCP3/Htjgrqb7V9i6b1jdE6ebSqctl6rxq0BQahRK9hm1cWzbkQP2pVOEkmJqWnftwbE9QqGlTxk3on4DZPQJeWLeP3tidYY+SeDMI0JOe+fU7QjlzliWmJKtf/+G28pM/M7vU11cyqK3Dw1T+Pn5tdxkzc1CSODbUP6PH9I3z5xzXvrsv9NeHfmJq+MH09iXiaGzVENNzn1HOvATnnKIkefLEfmXVqsPhh3OW7jk7i46B5nCsxUFLJFoDcIoqBB3QD/Cyp04fFBqqhnTPqAJucrrkSGNjkqITF8q4iQOy1/bOnRI4kSwV0PE3zozz4iVoUQV3FyQzzj46E7sluGGL7S8039A2AgImeg8aZjDXJ5lPM2rKgCQt7jD6EsTXoLj2qGP48h3bBkUEDrnzU2YMSpUYQAYsWr264TPFIZXECEJ0aptkCBiBksPRUwkChFlPd9WdFYgDDqCj/ASKlmBBwPzaNzaU8XOHBEiIDGyXdvndJHj49wdO58/4RN/L+eAHBVsCcud/357mnE/N/8eOXgjA515R6mK/H3m0kgX+72kIB9ZZ48xAahmkKklPMNOjYzj7EkGVSYlXlILOX/lserQ92r3icBk9b2jucJTSInFbKVQ5D86BMyCWsDaQWvTCpS/vKN0Gd08SwQ/zfdtXHA7laOrdw8MS+No/WJh76Ty7wxqTsSTQcgV2ZhV95y/Wp48G0KOyBXlOgH/ZULs2iVXS32RQpuGM7dsEPOjYsU1mK0yZOyTvvW754dDzBJmQfvuMWSH4JTDAJlS9hOeTbNQO75WkwEs/InutiqVaKI7R7A2oWvLOntgPldFWtSM2wkuySbkScu5esAXovxs+PhQ5+laqtoZ4fhIyL3HjC9lDdDZJSM6gwP70lTJ9wbAAx4QPCBvYFz5MxcT8GlX8g/Uny5Y1jRkZIOlCDQIMCWzdeyAo/6qysOL57WXxV6eVnZuP5V4BOseMrYkIDrVLZxazAq0ZYAQ0YA+tk8qN74iminb9SYuYBnldU9rRydw3n23fgDcZPNh8ORVEoKA9UNm1f+hbBpaKEVuHsJ46fTU9PaGFtW+TCje7Jua5c/HIz2j+7op7Ix7x917YD/qB+cLxkwcFaE3v3u235ftjXejzcl4J6yx+eGyAbndF/4jnF9O12tsIU7T04HoWTAe9MkRmJBTxJ2/tSdwjiWVPAqAcr0DmBYtGpM9F75xKHZsmNhCv2AvnTbVEhUN1hhopVg4/9OMY/vljSzZo9qesNqRHSk4ChIP1Z9KXMG7e0GqD9p8OSgcZuH7lZpmzoLYseXF7mXzH0FxIFxcaq9FRpshwnz91OdrpFj9Dl45eCE8SqixZgPKQ/nPQKBB8uGRfggCIRCbkytxaskZczU0fHixzHxqdS8EwbFh9OJfqree3lp4DuyWQ97veIwoFNZ3jECGNkLkTe0+lhCvIYOSziLdV1Cc0HJ+n4VqQCHXmMKFnEp7jDecTRHEAGuGtFyPxzOcml4bG85Gr01TNGehRGD+5aoxmsBkwWuwOXobhGYxz+HwMF04rJGPz2/vyPFMfGJWZG0cOn4nhOLDrZPnc12aUN1/dlVIx1ETAZk3wVgWrLiWlo4l31QYlnzC1f5Isxhw1RbBlzSU4w2t75KJCHefMry2vfndzZjtksq7ZCOZONJ3PBVBBuXn1RhlY16saJnTzkwwl87MMD/RFQKY6wGh6DvsWRQw6/gypgUGrj4RvqbJlTwQUEpvJ0wdW3PIB3ZIc4KAKGCH3vh8n7WLRy+/Wr0tQGg5JMocWh+LifHAULqs5LnPuH5XmcyiP5laOXwO8v2c8NMpDwiFBix4bFwOiehe0dcfx7PfEKQNSBrd+km3Iku9hzkTvQaSReyfxUV1hlHavOVJ6DO0Z1F/A4P05Nc9ufayJsx6ZwKgEDYm6ih4PlR9J4oWTlyO9qQrj3Kn0Qc1Q9FrXhqER5EMIAQCzZw4ux05cjCKLZ926oqFMXTisfPVn5vx3y107n5Bo++BeQJSW/nB7pJsjx6oHq/F86d6tQ/YNAkZdZtqsQeEzCxTdIdQCXHi0BM/7zve2lMe/OiPOi0NW8dNoS9oTYuNeSfJVkMxCaKXI4OS6L6hY7izeMzTQ92WQBS2qY60ykBxRNZPlk/T5bN3YmJL/pnVN6bW684HRcWgC1xOHzpXeg7uXe+4fVamw1NVEBtrd2bvnZBIciLeEcdmru8pdT4wPV1vPEDQKnQJiy4mpvFBPce7ddfstGFGV1KBoLsPSV3eVMVP650z5ft6fnWI3VTnIM7JN6GrAEsE9aUYyiKMm9as4z598GqRUYqzCw75A4DhzSKi1Xr38UKgWkgC9T5r8Vas5JWger+EzqbwJcnxvz0tVz/MAWVBMKPI4e6HO7DsZSqQkN5VRk3A1YW47UR77xTlx+s65oFqCBJXUQI5eUfXmnYt9hcYufXF7uefZSWXVB/Vl7OR+6dGCzEum9YK0zitxF/v37xI7ixopEYKACzoEr86D3yOfyo54n8X3jy5/+VsflpFzhpSxE/tHMYj9GT97cIIyd5AN5fAh6FB2k3uXvLKzLLh/VCq8EH2fKXG0b8AzVXHUPhSxEWYFmF2wsC7DAw0KZG/QI9jY+x4aG7l364FeJUAXNHkPvXF8F5vMRgBi7rxnVFmnMnb9Vtm373TsjX0UYLXSbvQWqXIKejSTnm04V6bfNfyzyfbQWwFv25Y7yj498+zk/+6qRqrsHdoGuZYI2sPn/92H5b6fm5l10yxsqJz+IT7P3qruoP6gxEiC/L4zi+qYGSktcxncGTYmNMbaqicu/TgC1pbp23ynRBA1DKiGL08NTYDqTLqHbJTET0LGPjq/0+cMyX6FIn2l+gw0U7QnPUSt/TqqwvZKpV/gTeUIFS00wgtXExgLHCUmbL+AFMuifuPRMmbO4JxN8v+q/NTzXmwZzMsfOV+S5PR5dGmXvVHh05+EJtvKyYfqN9SfKeOn9C+NjRcicc6fiGmsA2qi4J69YEf3bzoWOpGz6cy5C2ym+8Bf8s++LxsM2PH8egt8tjPnbABgAKHvvbozohcPPTkxFfXWvlFVQsl1Ja9/IypLgA97F9Dn1idV4H79VuIwsybMuJo4c1BsS9Ur1i37qaLhu/gza2qOiTipdYQC4DASsvr42t6e746qhxYkpuBHJIkarx/6yelJTNwF1FPfw1kS/7DPwDqsBXZCNUjC/vafry91C4alkomWp29Owui8YVb43sBldkXvMLsnMQKseOnHmTF3SMQrDE9UDVJR2LHqSGnfrUPpP8x09DZlWF1N1og/b6g/mbPOpwPs7bF4RpKJtUAMSC8uwNH9eeSpiakQSWpUhglNoMWq2NsL77Vz6/Eye0FtAPqhtT1TfZKgbdnQmLVjM9jMfv27tFQ32qfSZg8k3//LP76n/N+CRqUpHHKkjL9i6YFkujvXNoa/J7BvPHS2rH1vf+g6S7+1Ob/TqWv7HNzZ940Id+ztP14XJ/jW97ZEylKiIfBHw+DgHDaNoNF7plvctX3mX+Bzc5ToCO++tTvvzaCjQRlcdqvl8syYNaTs2ny8zL5/VNALyBtk3Ge++/KONEw3rG/M/0NYZPlTZg4pu3dWPHt8f2hSl/5dE4C6ZBBU6NTSV3dGSUqgQkXFxX3r5R1ly492R6LN4WEEaW4LelweRq/xwNkcbDKfkhFNp2RsGenzjecSBKA+9RvcI8GMQFtW7vnReDgxjpQCAXRi1qNjyoJnJiRA16S19IXtcfI9+ndNgymDDvkXkDCEqDneX9AjQBg2fUASjUefnRTnJogdP6Ffefrzk2Oc/b6+DBcKyonjKKHq0qdzJgtDAzhQh1SDvIDHjJPb2tyeJkYOhKGEZpF5vESFrE+Xsn9Pc/YTGq/PhzMViPhH8BUpyYfHZPBjiyJe3m/Y8J5BBbyHgYEoJIJHxgeiaG22rDoS43Jqb8WxdlacJc5k9dt7MwGVI1v71t5IMpKZFcByIPYj/SFKoS2DkchEmhwq0FJmRC1hWGlVZzr3wYrKY15JGrFb6BSk5gQy+NrQgk2rGzKNWGmYM5738JgyemxNysASDQZNEyu1CuVc6JTqDYfRdPh8OUFG8EYlpwrtE/BJ7Dd9dDDlfTNdUMXIRXbq2K5qGmvfJsbMgCM9IJLHt9/aXRokPUN6JvB64ItTEqS8+16VuP5NXv/5dz5KxcK+ZTopWtKhs2XFO/tK39E1cVYrvr0lyCUD7Z5wYoJmdDdJx7ljF0MPQz1Y9taect+DY8tdi0elevkzv3lndM4FqjTSOUPD2SQDVMKgSqopkMzj208kGLAHDLNmUXvI6VsX1UulfKo3/szfvf6Ha8rF05dzvgVmFGZiT8b0TWIr+B41gUrLjaq83eb2nAE0DEGg8r6AnyPWlCpwu3nlZtm9trFMmNi/3P3k+NCUKK4JLK9cuR77hM7ROuiscfPR0JJUzla+tif3E/rF+Aseho2p1lGy6i44U8CRZe/vz7kW/BNAsB6EF/TL5d4uGFb2bDqWZk33vXXGxa2bn0bFT4AHBKqS2qsZgKU5/xo6Zq0eqRYpysEqTCV3VIDqvvneXuRtBXf7955OAPzYkxPKqo8rVRO6+gAllYSGIxdCWRDQcoDmzwjC7AH6j3Ogb0GV19pKMog1CIo0Z0oMO/bsFHQYeGO93TEo4LIP9sVe2DNVFUk3CqZg0rqpqB7SsBk72Cc25a//84oEeMARNJxXXtpevvwbCxMMCUAErai91oyNECBy1uiiptaT3LbXmpAF8aR+rU/AhR0Vndb58FyqSEPqepaxE+n6l/KNf/1BEkmSmyo7B/ZWyjtRLzx2sZw8XO2hnj8+Am1z8KCqkdo5VxFo/d6qW/pqVIxUYQQfkGXPAtkNPfjQ6QT+KjL6tvDzVZP9jGBOlcx3cT537zhR/sO//+BvbAfefHN3JQ7SoW0ql4Kl1v6DB//u7CoIOnMl/ZXAnv79Kh9uKJ2A0RpmzsT1W/GBr39nczXf5cCZ2Eq+SaLhM/SDCcoqf9A7sYIz6Duiuxzafyo/jyqjGZvYAkEQaoT+3x1HgXO3nEfqRR+9tCP20sudOnLwbKrwaE8fvLc3QT4bK5AGMAg6GzNMuH01ufnqjVCn04e07VgCWpVvPH/AEQq0Krvf07wfueu1DWX06JpIrLpX0Hzv59zqe2Xn+S+AF/uUfoNPS+xs95rO6bt0z9GIVUVXfnQgfqyVykcFDcC5+JmJFQAysFvOE5qpZA/oI6ZAffS5fIrECv3R/VNx8DLzSGzhd7v26hT6NJow6h6gEJURqOw++ce9ve32ir3ixbeIDXasaUy88NY3N6bHhiQ++yBxRzkSePvHdwc0C5oz2HJNQ9UE/va+xBP+u5KavVHN9hKYa7Te05z3J5/u/vYf0yf+BsuAT3ifQijgtqVnVqwi/hCP6SlVWeXXH/rZWbGvzpM+O30cvft1K2dOXamoi53a5ZyTIpe022/vQezHfdR7Yu9On6nsARvnvR7/2swydFTvzITzXCih1D3ZNIkkkG7BXXW5z86DynfPPl1ScYig0Q0zMbqXdh3blrde2xlguUvLOffzYkOfZ3+xS8ZN6p+qOlaBeGjfruMBJgBw1l2sBEyy72y3mBmoY23984e/93H5vzzZ+Pf/+v1UDJTzTADXGH6FgaHQItC/div0Jtn0G/+vVWXmE+OiSiQAwD1EE2jefzqSqw49yo8A9fiek1H/UWryIntav6dq5BToUw9RYYCWCZBtuFK7JANCdvrg2WrQSm2vZOlRcxndO4eLswzHsW/nNAxOnTskpUWzLDgk1BpKWg72vLuGl+mzBoeXLbC8de1WGsKgw3icDAnu8c4l9WX2HcNiSPVz1I3uXWrvrA3KLCGC6E6ZNThItkDLhMghI3qmn8IzyqzTyDt5YAJO78kpm5fAEApsvBwCQZw5JVB6fNhHvjAljYytMypUfxiDsfMMDKzoa7SjOVcX7N0Xt6VErG9FBg5J3bP5eJyfplffiTGTlGzaCEkTgHWLw0Jjsn6cGMcg8IZQ41JL+Hq2BMVoBDjcnIwp2Yxkqh3j+iVhHD+2b5mxsDaoE3ROo5jkbvodw6LMoDLEUEg4BTOG4+RitDSZ7UR3uKKv51YuOl4mp+GiQPPf++N1cRQaIVVQnvuHd8bB4s1DfBm0RU9PCFLh+9bNHpwmLwErxSmO7MXfXZGEFWrAIQgsJAGMDERAQGRNIOWttA0Nfhli2TKjQYLtXI+fRKnqWjl95HyMSvc+XaJ8xhAd3qvZrF0a7SS0AmEVDTKRR3eeiOGD3EN7nY9xk/uV4SNqEky//+1NWWPB1cwHRpaJ84cmMFV1IpOJp8wZJDCfMbi8++L2nNFDB07lDmaY5bmrn/Xf+F4qIu4BKsR/6+v9JfszsdVwNAMsgQXuqrt/69rNBEnQdH00AuR9O5uzTvZQsqiZVe8Kxapx4/ulyuD1wrc2lr17misq3s7jqca4KyhNwAp2Qd8ApSkomfPSo0XWMf1IQ3qmTOz9IKe4vc6vpMjfQRChTyR6pz8+tvTsVylRRVN9U1NQ4KpBuWTqNWMsAMLrdc4YdueLY4U+usOS3i6GYvXoULqgjgzrmQne0Lr6/afSfAlBBEwANlDgBPOcFTTdeXdmZj0wMgGYwMYsgsjx1vbKmukNQNmUeAnQu3SrhBTw4m9cvVnqV1bUEU2D4WTvbC7P/PTMBDUStKjsDegaJFZ/G8qQsjpJaWotlIOguZIBvXEQNUHMwAHdkpB4z3df2xWHzLYBH5xx5+zRZybm7wXtmmmtmcnjnKxmRv7C3Xj40fGxQc9+eVr2WhCjidn9v/eZiQFyUA6thReakqoCu9CuU9sEauicwI0Mejt3pUJENe6uOpx7ocKlIqBHg1P12QPH1sSH8B1nDp5J07WZGZpNM++Gjv7xizkzegHXvbuvHN7ZHGBDYzGHr2GXCESqj8cvpunSmddQPHXGkDJmTE2qy0A4771tSX3mm3DgvpNkMkPs+neJlC8ASqPrvLvryodL9qfBHLffs6Fiet7jjRfKN/79h7HTggMJGyCCH3X+Pni5Etbg44BMgkf9k/s/OhgwBIIJkUd3IZTiu0ri73poTM4fcGPFsgNJfFQV3CX+8c0WEO+/5fXyS9tCi0WXquYzdIy8pqARlbd16J4Kq8CHzbFukib2IFW6cX3z53v3ngqNdcq8SqFRMob6ygazke788ncll1U1DDjmDvr+pNtVBQT/qtQqApJrNmPDR4dyx9Cm+Mdq0CXq1oVUvbsNEkxeTqVPkHj1zJUoRfG1JLTd07ET+qf6p28J1UgFS9KHDipRcc+AWMeOnC/rPj4U1bcdyw4FMEQ5Yl9IkL7zg62xJXwpv23aNTR55Yf1SXYrSrlp17cSsH/8/v68N/qLF1pNBjRevxU2Bpvk+4hT2J/96xoT4LoDn//S1MhWo3BKyFSaxk+oxBIADECO3EOS/w1ny7Gj6FJXwkoBcrFVUQFlX+cMqxgI6EInLpZhQ/XbdU2DtHNv7SUtGAhiJd+JpLC5P0CEMTMGpfr78FdntKhR3RZbMmHygNyzVK6H907fDX/LVut/tXYSn5EzBqb5WrxpnyTI4raZc4bmDEnEVBEkzdZWY7WKONsNaHzmazNiL1HhgUfANz5PHLdnl9kT5lt0ju93T1DhJQACb3HhwntHRhgHFdX3sweoS178k/gMHYm/ktDPnDssfs75ctb1R0oagJCe12dnP4+er2a+DO312Ww28Zf7TGBG/MgX1R84G0YP9U/0NlK9hw6djX0Sa0k62GXrhSnkzEqCfI7YTcUK8OuZ/G4ElDBzzl9NLN148FSqKRJNCml+7390/sb/MI3qd/7D0nJ07+lSU9sz6GprM/HBHc3lq79yRxqSNr2zr4y8Y1gujMUWVGb4nSmxpy+nhIO3LgBwgF//7uZMOjWEz8yICXOHJFDWsPTwY+NS8kYvgLyFcjB3WAIxPE8XwUVK9333jmXtqsPpdTh+5FyeS6Cl5M/IWPSl7+xNQrF1Y1MuiGBZ4C6wbTh0Lj0h+MsurKYbGaQmIAfIBmTKaZf24Q979jMnL3+GuJve3KVnpzQ6M1SQsalTB5VTpy8nqP32/2t5mXrP8HLx4o387h131yVJYPRdyN2rj5S7nq6k2pQiBQYCW4oZkBHVGci5MruAafqcwWnQsiaqKQ7/qUNny8xFdTmAkB+DgCh8Lfn+tsxg0HPAyOD0aXCC1KA7qbYsXDQyJcHps4aWv/idZalQ3PvsxBgz5UxIBuf28V9tKs/8ozvTP8ChCTg4C8E+h2iNHFzZuTLnk89NDkca0uMF8dv07v4y+d4RqSZAMQTBqRQ0nCuHd50ss++py1wQFAmDjBhXlRWoYQZTtVwYAx6V3aEgLtPmN/aUiQ+MKgc3Hi19xvRJpYaD5rSUaO0pQ/Xxu/vKiHE1qbKpikhqvJw7zp7xQOPz95JlRsT/Q8Y5ZbcI/3ThFyfFiJEh9T4qNpyoAIFBlRxxrugTVCIkxn5XiV1ptnZErzTOJbhAG7lIjvTTOH1otfsjuPGejCZeJycqUdKYTNqZegoahTVUBfvOf/yo3POlKQnsOATlXqiLJlSo38r394f+kV6Y4+dD2RPQvf9Ha8v3t/7qf5MdeOP1XeEDc/polAI7hl0CrHLh+7kzzoXkRnLFkZn38cAXpiRAWLG0vjz+uUnpY6joQSWUJkFT6FDNl8sjn5uUu+PMuBecmaSWQ+Jsrav/dk8Y5QyjG98/fSgUiqLxPrpv/k6Qar1QF2fNG5rys5cKD3viheN+YFVDmf3omCRPuNRQK7MZtq86UgaMqQlqr5qYxvXVR8qwUb2DHtnzKETVdCqbaOCnR6Imhn/StMGhrgmaWqUx3/vDteWRX5uXoFsiCYkWnIQ2cu5a9m7AwO75fmuXHyr3Pz4+Qe/mVUcCmOi3UflZ93595HHZSAgu5SbfV2Oyihwbo98KUmyNUxVruadmjOxY15TGWk39esPYGaioRlsBOlWlBLz9Oud3nHeO87239kSthfO2X59+UpK4obviju9derAs+PyEoLYoXIIXPHRNkmiV6360qwycPjC0KQnkkrf3hnIB8bUGejPQ39AQDCekrtVKz1C9AMrgYFfKSm3jPAWUXbu1T3DpjFlrdsU9FISgW+iXQinQI8EXPfDw2NwFcsrOrjsvCZJMr1p2ID0zmfex51R6s1Szdq5rKl/4hTlJagQIkULv1iHcehX0zGboUE1Bf/ubGwNu+BmBsM9CzZHwQb4BY0cPnCm/+Bt3lp27m0PxFJDZz/jWvScDOvju/Nzy9/aX+54cnwAJMuwMQPuBYtYNoqmqaf6G31n2w+1l7mNjE2hAP/0Mmy0w50/GjO+bNUVT4bt++P0t5Z/88/v+m+zAhg2NCYbdFUqR7r/vzCc5f0ArvUsSNv4e3UxQit7cbVD3ACVsnPvt/KvsSjrZE/2JA0f3Tm8nOVj9i5IC7028RJAP2QfwOFsqQvyZZnqVQL5/69qmgI7Wgw0C6jgPqJbWAG0FeHB898kydEr/auBrn67pB3RGJfZAK3/mfPldd0gwzSfYJ8ANWysxNh+LvbOe7LEKDj/p3n/0ys4yasbAUI8N5uPX+TOAojVhO+wvIEmySO5UzOG78xtsdybLt9hV3HyVhO2bjpWuvapp46imgFUJr8nczhoEHMURwAIA9ErfCznsnh3K7q0nAhBFya+uJnbcufG+hp36viTxgUYSZ2yJZpWi0X1T7eF7rU0GjvbrFlVLEq8SKZ/t2dkA+5FEL+qiV5NEiNUAcHybF/vFr7urwBNzuZx/fWTuM4ope+HvUaDMSHvgmYlVpeHs1QTn5Osn3jEsNpm4inskxujQknBhEogf/Gx6Av1/w/n4UHcWWGxPIzIyuGdizG/+p4/DRrA27rFEEJBgijnlQEIhKsIG6QFju/XomJiDf4388fYTpWldY5n52Niy8a29Zc7jY2Pb2CMVG2ccRQzFU9LAtgF/ANVAVcI44lDPa48Ak1H0bBGVcA61BkyaPTi9GICepa/tCgvFGgODTzRdKPc+Ni4JIPuoB0b8SymUvwa28IN8hfcFivz937ir/F9S2fgHX/tBddjmVFk2FAiaCn1zUKkrUZkSREJKXBbNyTjNSmMOuAtIx9rETwEUp/vol6aWurF9yvRHx5SBY/uUNa/szqZyWg44DWSX/cyZq5Vm+N7mUAOg1ByNDYCsCGYEhriaEA+X3yEXIEPSHXr0jbOhaZUYE0EO/WoJB66dP4egueQuh4Dug29tTrYrO2UUjzadS8APsVfd0LNg8ybqJ7j9tgTWkfLr07UcPHw2KOcH71Y0lQxgMhW3b+dQCRgQcEFK3xP7JWHieAVF1kiJb8r0IeXPfvPNGBjOwHdn1CRPGmKV4hgshnrgmJqyaWVDjKtAifNk/IZOG1BmqtrMHJRnlchYf/xMMmwOOYUgF/j572wqvWt7lvGzBoWOoqS/d8exlC4hOw/80uw4LgaVg43MXef2cRDKwHiAd94zIpJ5g4biNJ5ICdCaogvYwyd+cU6oBYyP7wE1SCNV9w5l4uxB5dX/tLIcbjiXRIPz2fTSzqpBuMVYBR2DjMbYNpXZs4fks+/92vTSsKO5TF9cl7Iho8mA2BfPDuGF2GqqkgTjt0ImVKQydLJvlxgP1YgBQ3uVcycuRSJV3wjnLNFFv7BfP/fv7v+ME46Dax+dU453x4rDUTfx8+Rq8eA1c9fvOV2+8/ffTpAjGfGdrCs5QPzL8VMGZX00Hwv0nBHPbJBSFJk6tUs/EWQTCsOBc9aCjfUfHiw//PrqMuexsdXguFufhFqA1oBCBWGxxugfApL3nt+S76KfQE/Fr379yfKPfvlH/039Wugh7hyZwZS+z17NXUUZ82zu5TvPbw3iVFUXKkrbk1+bGUQyQzZH9S4v/MX6UDkkEIIl90NwyflL2DXY64sRAAMXVLTWfXQgJXNBv4myqnIkIgXLEhNIrjsNWWM0VTw4LmfB2XEOqSkJWhhpjs4auuMCCParlUInmGs1vrWT+oe+iP+LGqBpsHsaGrvlPKikQFQNybxx+UbkIdkfFQ3Vo1aKgz0XVH/1tx7ILA9BAqqHz+SUBH2SGfdW8s8ZzZxfTTbm2M3DsV8AnI+e31badmybgNUa+TwvDk9w5W4ICiT1kv+LzZdi89wzCCpbMH1hbXqynHFiG2wl223dUGMFMBz8+nfrEzziREPDfR+OWwAjaNJsikOu6VLF7plfuyPny1mxl+46sQmUJs2In/uNhamOZ5bS2aulqWXIH7ld55eNxFsW/OtbilJOm9uDKJOV9N+ZN3PodAJ0n0GSt6JOlgz/sse7Nx5LpQH44nlXfXygvPhXG7LemtVTuWg4GxAJZUU1sqZfl3xHIhcSdairyhDQyrl67CenxR/g+Uu+2EM9fmyLuSNEG6DLwAlV6/79u6ZRXeAtEIkefse2qczaCw28fIU5SM6j/olD+07lO3rOVl9JEWfO3cNDmxFY2yPJLKoIIO+j13bnbgC0fA7bOnZhbWyzRND7CorYbXbPZ/O1QJ7IZ+84HtUig/n+z14qoXjyAkKy6/wpe2of9O0drj+ToOyVb29K9UJTt0qAuzT3wdGpdKiKsneCf/MAVO/Te2Fy8l21eU/B4Ia1DQGuqr6QNknW0HdUa9kdCC0fxaYCqdYvOximwwNPT8h3rALhCqmXRE6Y1C/VIjZfsvLFX5kfZTL3Hyh1GOd9fm0SXJVAZ1TPhWSbzagqD50qSe1rt+I/vJ/k1345g6ojFdOgZ4LXOx4ZkyqcAFzALnhkd/h4d/v4McDWiNhGgNbw2p6JgVqnXasUq9BTlrOP/NmKV3eX4aN6lYto1hqKDbo8fzV+AxtELMA2uFfiEr4Iu0Eir8LBXiy4b1R56AtTWgYv38je+m+N8h98Z0s+H52bapsXFNw6AXv4JuvV2gsh9pJ4sTEayz0TENN5S9JvUGbn9rGD+iKdVXsNsLhw/ko1f+vC9aDyYW8crSinoea1JHMBuI5eSCVnxPi+AY3Elekxadcmwj/2FWhljyRu/CSwSoxYN6ImYiSSNcpwnhcoqWLhLqgc6s9DoeSr/vq3l5UFj41rES/5JM9VzUI5lTjQHZAQSJTcccpdt912WwQqPKvza63v++r0xJRP/8r8fP9jRy9W8UifqudV5U6iIWkGplhD8YWqqIo3IKP/kJ4BHN2r9HkcPJP9FqNMnT8059R3/fi9faXP0O6JUau5P6UMHt4jPgIgLGlPEt2rmr0kzvO9+XL2HxCDEfI/Qqf6H0o2Rk6vFJFcLMa/adfJNBQygsm2jl4sj/ydmUFX0WdUC95+fXdK9ZQylDQ5jCQco2vy5V7/i3VpamyVmLTAdOcdDP+vFIbz6uKZ+4A6AYmHsglQHVbvueRHO/KMFpa6DCTNQiptyZTfeXVnWbOkPgGSyeUH8I2hkZ9+GupXJBK7dyxLX69KyALFDh2gyhfKE39vbp5r6R+vD2K6f281pZgRouPvgMr8IVUmWmpgdZg2r28IZxklAZo4cGK/aOZzxD/450sTPLmQFAEkSQZfLXt3bz4fn5qh5bygcHd8ZWq4+1UjVsmhFGxMmEzZ4nRQ0M6d2yY4J6VpnwzUa9h/JqVXZVFoDOcTRZ+L1+PYGM3mo1UWz0FlmFeHtqHB4P+ZiqwKksa84b1D51CScxghNGhW1lQAxZhYB0ZBAN6hW6VCYlaKEiEjjctOJrQyMDfLW3+1IckgQyFohCTpAaoZ0zuUIFQgyi0THxtbtq9oSBMdecFL565GSIDTlTAdgZa2OM47Hx+bgETPTCbNn7xUGg6cDnWHwXaxIckch3OLE86Jcf4UmkizQi0gAKa6Qh89I1TS2q2iSNGjQ3nhT9fmPfxTTQm/mPKr801bncb26y9uL++8viuqEFBWEoqTPz+unNjZnB4ed0aTtPUP/WVbRe0wuwUaK0AT8D75E1PjPJ2xUycuxQlJPKBO5FxVBCRUpoY7u4yxZkH7IUni/KDZHA1ESVPsT/7agpytP/oX75epMwZlb82O+D974T2rKFC8cRaH1vWOvPKFs9eCBI0e36+s/vhgufPRsdVaXKvQVME5ZD6zccgN9+1auvU3nPO2BNTQp/d/sDW9BgYrzlxYG5TGOji31jyDom67LWuWplOSgS2DjiCp6bfR0Ie7OrBb0M/dG8zQORnKpfONViSh59hTeWkZ2EgaESUryVtLn8HGtYeDQN5oabD0dwE79jSHolc3snecVKha3dqnwvjkV6aX3qNqytCRvYI6AwPIPYeDTTns9JWAGALNEWP7pO+EjKQAgjQoxxznbTDXjVsJgp0DNA3BMxqH88m5f+E37iyLn56YwJxjb0WIBWTuYagPt99W3nptV6qD0+4YmjupqdXAywwdPX8tie26Dw8mgBGUQrwkFs6bMr9g9LGfmZkzLBDBNVcZdbfw1VWHDJqz32ypO+TOAaQ8j2fgGM3qwPNGaZXYb9t6LGjzkpd3lDueGBcKR4ZNvbc/E3hRazhMtpYdTW/DqUupqHLivh9U7sDBs+m7k2hKGMzsGDmmJmvdsUeHMvPh0aESAk6cA0IP5hC4UwKSVOL6VkINc+8dUXZJ+tq3TbWUaAH75QwIFsjGvvn81rLhgwPZD8ING9c1pv/IwL5R4/pnjSkKNe07ldlLehlQFlVQUdBMwEbTy9R0SGg3g0JPpcLpzwEnKEmZxzG+f3rUVDhVIqwjW87GDRtRU2bOqy0nDp1NgkM1UeVTf56+EX4UMCOJJQkOLYWSSoDdqdCgL99I3yO6iP1E8wF6/J+9+CZJs9+RoEhYUai8IgLw+cllydMsk/gAAQAASURBVOu7yhNfnpa+HN+vdThsReu5PfdyNKnOnh1SRaoSl6byzg+35Vz4vgAcL4mCCg86K0TfGc/cig4VSEAByJrg7xNHQEfxmQIzd2GnPorD57KXFDQBBFUidysBKcGYgIwoqAuHpQdK5VKCB/QQfLEl4hiKkXqvItM+uEe+C5BFsPe/p4cJyL2fc9gqSCBoBXyq/Np/1UyA7fTZgwNMikNULyQkrbL9KHcXJFR7TpUundumZ2XNu/vLA1+emu+Hxi3mEkQ7o/ycBBpNmZ30PGw/NTJ3nn9nCyUi2CCSdUmD4N53Mqnd+9zx9PgkUEQvgJnAnNnzh+des0kSS4E2cBebQmVZoiN49zySbe+v+oVS2ToHBDDBvoqn3LGqObl9fD377tn0i27fUA2ldD8AGZlF0XQ+ey6u27f1eO6Mf8QoYrMMvSMCMLBbwEkqpYYK2lu2C20KtY9fnThdD9C53DP2U4+JQJxttEa+4+d/aV6ql6pl6Oee63DD2cSAkheArhjS+Tf4VV9F45FzZd7CurynREms6exjwKBIA6jFs/aOMpeEOIpl126GiUCtSoIi/q2qGVUPVuhS7drG99tHNh1A99Bj4wNAOX/WeurswaGKAXkB/ZIId/PI4Wqopn5roDrauOS834AuqQLxdeiCPtvQW0DV//Rk49//myXlwNbj+XIOFDROA+SVDElrnwzNYCmGizzj66/sSGaJD4+v6fC8/IerojvOQAhSPnx+W1n8xSlBhDSyOgA2jvqLzWcUBMATpgwKF0/gid5CHpHz0TSmWZQqw+xFddkUG+aiQBSg3mYWDB9B8adzGTSqd3n7u5uD3nTpRw6w0oEWuENcDI7R0KsJSMLy3gvbYuhdfA7+/r83O0mDS8RAGMQVdJ6RcVDHVBORBQmaFR3m9JM0X2xp0uqa2Q8C8Qd/Y34M9aNPTAgy5/1tPoTwo7f3pvnLhdTMqQGRk6VERZrNFG2Ob+L4fjl00EGHVBOtYMhh128gwegzuFtZ/sfrQ4vggNEDBFmSPQZu2+Zjmf+B482ZuZBKusqSvmtrEK60q1lOUBnusGDok08zvE5AB310SVWfBN/W3u+bDv/qD7ZWhnpnpTDy0gtbo8oTFZvx/WLwSQ2mMRNH/fy1Msv8kxak2Z9Bu9G6OJw9b+4tk2dUgQ5kzMuaQ6shAtWQm3NBLSHKnp0yxDkDmLpVjfBmsmiulRCTPTRhVGCn0oRugpe6bfnhrBuDzEn96IVtaRrsjO6y62SaSO0tZ85Z4bArCUM7p9w/sty4crN079UxDZyMnuRy+pyh5Su/vrAMmTogSlJeLjj5V2flxvVPsh4QJk7LelPKiPTh3CEJAAUheJmQb98H8n744Lly79MT4tBUDKwHkQSqNXiduMdeUNwFd9cl+WH0OZn7vzw1awY0kFD9+k89/1+1A5CONMW1uS28WGePes/BA6fKLU3bql99u5TL567G8KOcuNOb1jclMJNo3P/YuHBn/T2Kn6ARRc6U25n3jgh/f8LEfgEolOOdE2vHGRFaIJUMFdXgxvFDz+0RO8CZ497qr1Jp9Ro7Y2AUN0gWQ6itrWdx/9gMDkgi4fdUqCS7EFaolb3l3JS7nZfQTiYMKOMnDQhK5AUw6NG9UnyDIKtwsIsAFM8kqaJUJZCWmGvehE7aZ/fa+5B8FNyiGqrAQAChWhvf2Z9n3bSioWxdeiD9HO6AwJjdEqChsQFhJH32xt3etbIhd9maOiuED86fupLKiHPO6VMHihoflaBTl8tDz00OzQBa7NmdbUEtVT3VHIEbW/fhSzvSPG9vW5tXnR/Jln6ZnM8DnDFlvWtl7/qjpX7f6VS5VcIzRXhAt/LS9zYHXSNe0W94NbxMYoOSYbClM4uDzK6ioAlmnDcvfOVQNq7dCr0J8MFuCUz1U/jOG96vD5XuPo64yeycG0HPJfbuKETSnrFbAwfhYt9IEmsNNBObRi+4UyWieMafSUIkbkNG9S53PjGuHN5zKvt749KN0qlXp8pmn7wY+yxJnHvfyCRzvjfUE6VWQEBVjwKZ5MadJbzB/rOt7AudfWvmfqo4jx5NrrnENrLHTz0zKf0NlH3YHkICqcZpzDajaWNT9pmNxx/XS0NeVGUbYu+cSMwkaKELHzoTEMGzoPSSqf2TP1z5/7eqIXhE7xVYsZupHN78JE2yQ+YMyfwHPktAKaGvKicnYmf4OZ8lAMZ8ENQI7vSUqBAQYlBhVu1UlZXE+ocyz7QFw3L2Q1cb3COVisaG82XzhiO5f3wtQIX/U4Vq065N1tT50GunUidZZw/EEdbTP2gkkGODJ3dtPJagr51mXo3J124lbln0xPgEbwJFfU7srAZ3Z13yzZ6gHEoy2A2zM9xfZy+zamoqIRNnRuKJbtiLcue+k7nT4g0AqX00mLGSsj6byrF9IrMdGleX9mXW4rr8m1207tZfDMUHqWIHod5zMj0VDXtPR/aXnxd7AQP0gqEIZtBcz06J6zw3IIh9BzxY84jcjO0XyiRbvnFdQxnCHm48GhAL5cyaWENnGD2JfeFn/W5son6AxgsBEPS/qLKollgr+yV5Q4XVGwLIFmdmmvj4vgni+www8K8mVQ9nRiwIpEEL9z3Tc/Ppp0mo+TiVRvRBfU1L39uX6rk1Qg2yLwRd9BPwC0BdIArbnMF7Y/vF937wzt7EMoY7im+IG/RqERlBSxLrsh9AVEmzWGnUuOpc1fTulPNDAhmA5TP4a8mLv/PMXtT3Js+qkmR0K+A9CXVgDJsmhgSkGIYMoHXG0MIBF5gdlNaAa6/9aHuS21TBenXKucy4gt6dy7aVDWX0uGqsArvjOcVT1pN0ubtpr9lEa0shkzIjyiNbYI7W/9Rk45LR9qaZXqqGt7nkmklm3FUhoQ51RtIbmHTkfByF0s30xRVKZGNqZw8J7cfF27TqSKkZ06dcOn81gesHr+wMDUVJp5UbKdsTnJEHFGThCHM6jJCKiYQHsiNQsZHUBd78z6uTATsES7+3pbTr3C4BGclW6gJ9R/YOraP/iF7JwKE7uv5l1IalQcwFGCg4l09eShe/UrXkAT1m/p0jYjQ5HgipQ6R5eey4vilnC6p79+yUng6yv5o6KR9B5Q7sP1P27GrO+nE8KGkb1x8JZSfNg13bl9Hj+5ThoYD0jgOACD7y+PiswTTZarvbY9wHuuybjwapaz56Ppm1GSCapGTcSnySAWorv/BHj0ddw2esX30otClIgot1132jMmNEYE6ZQ2CfSkHjhaCZhl3hwL77/S0pW1aDw6pyLgMiKA86evJyzoF5BwIVyc/G1/fmYlw4eqEcqj8TBB5K6DK2Ko+Mm9g3DtH+McBeBnzZS+/DMauuWPNxsweH23zn16ZViWm/qvFbgilRgq4KMiBisn0InVItRNjrzofGlCU/2BaHIklENUNNUqIkFeyMMQb3PjgmDgJH89rF6ymTS3IlDc4c+sFjX5gS48PpqUqhDwlMBMdK0+7HFD01vTom2OBIJGb6QQTOkk69FlB5lMNJMwemEdLQO2pMSqH+TvCMCkX9zH4IEjSKOdMoJFCnvTuPlQ4ZXnk5NJ6t6xpDoWOE6g9UgSgFtuYj59NUFzS3d+co8zCmnCkEybRb6j0C/v/aC6qoPE/OmJKXKhhDrCfo9nZt8lwQtT4Du8VBAyJ2rmksxzYdS6Bz5cyVyBGiB0mMN69vynNSLOOYOGRgwZZ1TS0TljvnzEu4KHWoqo6fOSgVKw5BI6FqAiPcr1/nVDcM9OQMFtw9MjMLrKVzCDFHqSM+4HNQdzavOZIgWvA6oq5XpLGvGUzau2MCKPcBkCLRQJe696ExoW65E5ve2pdKF6SbHThy4EyZPnNIqgTTF9QmaEU/evMv1wf59RnvfHNjReHs3DbynFOmDYqDJmnIaXPYgsz6PacSNIxZMCwN01DXL/3agvQncUoCEZ/tXDqDgBn3UgPjxjWNCag5Nbxpd4T9IjSAAsDpekk8JEDm2fieKFfQN/fnu7/8VoIBvSJzF9XFxqiMqZaS8cz97NguakeAHU6dzRX0sYMSIvbOq+ewHkVBSpAOAXVv3Ouf/+U7sgbur4qU3hjPJgi1r5yjIEBAZv2gu0AQPUboVdBHNk01SKDKMas4SSicmTkPjU5T9+vf2RSkDg1KFWLazAq1R5uJkt+pS2XX9mP5Tn7GupqwvPCRMVGC8r0FcprNvY9gMZSam5+WO+4flfs5akKfVAnMlMLVjrzytqPxL/YUuiz4/vCdveVY04V8L4klWw1s81Jtatx2ohzefqKc3neqPPDo2LJzAxDqZvZccqyai7YF+PnGP3kvTfTxvxuOxk65R1/7xblBQQWlgg6fy7be/4XJZf2aIwn0SNartkjKBaaZdr+xMRVxdleAqvr3X3vt31I1ueuBBFBIErwkv+/99ab0KlHFMXSXH+dP2UfPxPZE2vTS9fiJg/vOxP63NkULStlAfskdpEDlvu7ff+Yzmpazy35kuObVmwFi+CN3j7w7gNF5cF5u3biVYBnQxRZIaFUFV39wIPvgdXDf6dhfZxnNRvILve/Ru2OqaAAbgfjalYdjd4FO7ALwhE+w9hI8inJ+D0AKeLv/kbE5060D91TVPBd027OycREyqOsd3yBxd/8lRALSnsRm2rWpKvKN55KEGSSIUib5XrZkf2g1khzqmCpGEa64eC3BomRAwu/3KMkB11QFrKeqWzU/5PacW2Akey5w9zMq0ct/tDOKdG+/titUOd+llbauF0AsApAAehKY0d+wcWVDefe7mythnpbZKyrpPWs6xSapMIpL2GuJBZ8leVOt0USvyX7fCvL7dWX46D6xPQJtYgb6Dvh5fWiqxGbbCOo9t+QNui9B4m+HzRpcZt5dl/OwfcvxUMfEPdbP52m25p9USCLYg0Vw5nLivcw9q+uZmM/ems1FmYu9dc7StL3/dGyr5MwdV310XtnfHRuOxg6Ys2afvJwZ8Zkk73u/vSySvXq7JGBJRPt3y/4QWfEdxSDOHXlkUsHuMmDPuZOgobkdOnA6FCpn1kwgvX78os8G4rChCx4dGyAzFN7LN1I55a+8n0ph9efXA3aJLaoZYSW2WRLiXv5PTTZGjq0p0+cOjfqOTFMXPeStFRUR4EOgpIs/82sLwkOD4EHu6PzimeNbnzhyPuojdz44Os3YLoPhI1fPXslmUqOCltGKV05k+Ek7CkhyUTq1K6s/PlDq6nqWZ5+bEgeEm2rBBPoP/PKcsn0j7nL3cufnJubQaDAymVcTlCqLABmVSZnIAfvw9d2hQ0hsIBkcMNRo0ZemlIZ9Z/LeJA1d5BtpcroeNAASxuhPvGNoEHTJCyOy78Dpsui+0THmDq6kyoUgdYqewIhriuUQBOoutj+LTGe7NmX/9hNxig6ycvx3/3pjSr4mnkNGIrd37WZQMaoSZPAgAw4nw37vg6NTfUkT2ckrMWqQAtz3rR8fzmEWtEBRGAK0MrrPqhH+jiMI/3l839Kpd8ckTXo4KEZU6Fc1cAZnO02f9KhH9866Kd37Hit+tLPc9cVJZdumY2XQhH5RrUChEeCjAUFAJ08ZGFTYy35x+tNmDE6VCeK67ePDVaNd29sTlEFgh4/vm8RNqd3fOYcoHVBYBsblgazjfzLiGz6oj8KIQEISWzd9YGhrppxqCrtrkbko15KAShQobDFkAkfozPxFI8qKP1qbsvvK13eHJy2IfPeN3XFc3guC7HMlYygknkU1jIzyvh0nk3QzJBIb5x0/1fk6U3+63L14VPZgy+oj+T4f/nB7ef/t3dlHgTYanjvmrJAlpEymidk9EZRnSvjEgUnAGQyqVM/+5PQYDcpNnpc2+IBhPUIJyzCmBlzqTmXs2D4pz+LxdqDG0aPjZ4P0/muvOfeOSCAlsI184ImLcRhj5g2JyIP97z+gexIrSbI7MGXB0ExR59jHzxxYKcaNrAnio1G7XQti6ftTjol09fyhQV3cX/fPmXn/5e2h8qlUWZdhw3uVt17YFsqHRAvKKgBmfNGKNPwKVKyLnhCIlsY6c2MYYfs8clyfPI/qCnqcdYfGHtp9qvTJLJUKJZX0s0e+39Ej58vbL2wrv/bbD1foLEpHx7bliS9OTT+AOTcckT337IOmDYxj5Hjv/YmppV9f091L/gzlA5rHdvhsql3TZg4ufQZ0LR+oINT1CsqO5maN2BHJqvJ4Jstevh7np2IMGIE24jU/9/fmV6pro/omgPNc0GSVpHkLhmefQz+qP5P3c17IOC95fmuZNntI+QcvfyGSkosfGReqpFI/2wP0MMiQ7RSkHqk/k2Bl4lyzCm5P0mtGh88eMqxHefX3V1cqWVTmIJ+Xrueeo/H81V+sK6MMT2s8n+Rt8rRB5Y47q4nO6KmCHNU3fsR3AKygdkL5jx27UE4crwZ9ceL2Bc1DZYpDjnLXzU/SY9O5b5f0pO3aeiKBhfeSBBxZ01jZ6N0nE6So3mSvbyuhjWgi9VIZkBBw0vq8VDkFK5I86P6h3ScTfKG92CvS6BICCQKqmDMU9aeB3eLz2KN9Oyr578wT2XY8SQJf1E+1rlO7Mv6OYeWFv9qQaiV6rYRGpVdvlyCbzfnav70/qD67PO+h0aHw8hvvvb07gZdKUuucItUv502Fw1kNbZA4yuEzuRd8aDXrqBJU+OC1XfEX/7UXqiC/orIIiRdQAR/5ivu/Oj3oLLtmXdgtdEggk6RR4sF3Og/OSf/B3VI5VOFfs6ISeHHWBdet91FSItn78K83h9GgyoAqBlACuJCaVa3wQhlLLyRa9cXrVb/a+auhSTrzwBK/N/vu4WnwBy5C8k1u9x1ahxRr4rbW6HYZZtmzYxkwqGvp2aNTGrXJFgugBaD8iyR69oOjPpOZjaJdw9n4UsnBxfPXQsGB7OsFlLgnsO/ULrbRmXLfUWT1Lfnegm+gGqqP2EC/jRdAEPItidFn6Q7qU8O9FxySkLfv7h3biOlgrdNPcntF0553/6gkGGIrNDbfib11VvUdOctDppEabyqPPTMx79/aUyM5BGhI+ClU+Y7uIECnc6+OZcTMwTnn9upk86WyZuWhzHyRyPau0e95eyp/6YXddiy0ZlUoZ/wLX51RJi8ekYoElblUT7YeLZOnDyoTpg5IVYavR79vFfbYsfpIKnZeGZqqZ61D28QnqkSdulRiC1/86sxQHVH9+e93X9mZ2NV/W1+VDr5bvOUM8dOSAxVU8c2GZYeyT4AFiZOXCm+oW+LD67fKF74yvcxdPKKizZ5h9zslFtm26Wh+h/DLqIW1OWfex5qjnaFk+e8IwZy5nNhNQzxgox2aZ7+uia0SyLfERc7xoofGljZtbytDR/YufQd0jU0R6ziDAEvP5OclTsQ+xG18pZ9BnWTHUOX5AOAjZtKRhvOhbK1deiDP/j9Njcq0cA5Dkysj6ksIepT4fDHTj01XZJwYQUFvBvu0aLD7WVw/aBRDfWxXc6TM8EczD0MDoWmR4/sFbSXP5r1RkjgVhpj0JeoFg22WBYm0SFKaD3H8QkvZsJKBg6hCPmWAqg9vvb4r1RRBhqCcw+BM3nx1Z5qzHNwMjmlzewvFi1rI+XLu8NnSd2zfKLZkTsbhc+Wu+0e3aDnfVpa+vy9TaFUVOH28Pqi5xmh8R5sEUXzj5R3l2vmr5f5nJiXY8cwcERR+27KDZeDE/kFAfS50ZePHh0rt+L7hnbfv1DYSgP5OYsU4bVlSX66cuVp+4bcerCbMXq+kBMN1VwWStWeGR4+ybvXhrCH0HBXHpWEEoLh7djQn4J3/5Lg4Zv0LSp84fDh95jVQqWrecaIseGp8EqP6jw+XMfcMT5XitT9ZW77wGwuTWL7zxq4gxZcgB+P6xgniqzNkHDxO4Z/97vIyR4Jpmu/lSk7U59lfBxwFp++gHjFA4fHvOJbGQY1TfsewGbxJgc4rP9wWhHL27KHl5R9uzVnCoXf+OFrGCYrEOEIjGeNqeFClYQ+FUTZk9FxyBhuC5dzaAwaGoTzVdCHNvnc8NDrBJuTJGWHQxk8aGA6sGRo0yFXYnDtGHUqNVyyQi5zi2auR74wC2I4TcWLOPIRCsG1glwBLYOJZt205Xh54ZGx56RsbStd+XRJwQAJJILrCEmj/z8FBHyA7ZlWgrnGqJA9VNKCanALOstKseyW5RCNrneodp3zB9OhK0U2gzCD93V9Z8P8zLZz18L2goBIXd6b+wOkELftNr39gTBIc92rlkvrS3WdKrM5ejUQr2VeUAc79zOmrKf0KACQPPlt5G+oicBUERFVnUI/QUyDv+oWgroy2qgWHjssc7qs+oRbFOMnqzk3HSt24Pklsdm49Vqmjnb6SoHvw4G4JuhhgQZPgwpllmNE+gAWmxWo8pyKmIXzvjhMBG8yY8azQIk5G5XbCFFOhz+UzJCYc04pXd5V5j4zJHXD2DHicsWBYqisahTkCSBNKoecXEBELqN93Jlr0gpkxE/rGGejBcZa9zGsR3LlXzlGr+hGFOhQWz7bmowNl4DDa/R0jPGDYJsAIBYxyimDjQtOFzD3ybNDUS2evlCEjqum07uXkqYMyNFVQA6VWaREUmhi8Y/OxVFGsv6BVr47GcbQx3x+w5C6inXGK7iDVG9U+688uQv45UgGhswnJVWVDN137xt4ycv7Q7Pctw/Im9su9gwCj4thjgWuVcN0I8CV48d1abTDVusxiOHMld1xlsOnQ2fSSoedAJNkcwIugBYUBLWHpu3vzeehiBtRKvidOGxx7prqGijv7vpGxE57PHZR0t1KMBaaZDXHhWs6Gs4YK0aqM5/egh5VKT/dUj45vPR4alvPiTq55Y08ZNGVAmT5rSKnfq1p7uUyc0j/gkvVkI2bNHVZeemFbRYvr0Db0tyCUHdrGnjtTePgApKhmXb6Ryh8lqGFTBkS+VcDBX6MXAkkIV6jmtFaZBfH/7F/e//9lB/7+T3wvVSMiEBIYVVy2jI3SyH3kyLkg89aKnzTDiF1yV8hdU78R4En43A30HtUR+yqIXfTgmARpXvz5+vfry/RFddnPgBvdO6bvwXsAN/fvP52p7L6zqgPRCkmteShRNWu+lAqCz1n22q4y576RuYPWnxKV3g/3SFK0aW1jufuB0dlH1Vf2zXtRejzdcK488NzkVGYjX9qhTX4HjUmiZGaUHgksD2sKXIjoxOUb8clR/zt1OfdYHON8VHLBnQLKTZo2IDEOBUyS4pSNnD3iH1B26ycY9tzOj7VXVVixZH+ZccewPHvflt5NNlW1QeC6/J29GaDHp/HrSVLG9800ar2vEkzApTMENFMtodKFkvzB67tK15aBdfYQ8GUPACx88V/89rLy3N+dm6TAd3YXWkPMQwfO5LMyy+fi9fgh9HT7IzaUePg7/Qt+9pDhh3OHJFFolUnnO4FgBqD6zprB+SmKUc6BypXfj8iH5KBD26yf+MtaAeRUItlKe6FiCti53AJeq8oDxVTqJWetqphiHbaNf1IJN6yUDDFBIC9r23r2xcPOCWaEP9uz7UQGj7oTYkD2DjVbzCkpMhYC9Tk9rgYRj++feIW9ZAvFL/U7mwPIb97QFHsqjvW9nB0VC/1wlNyIJKjibdl0NBUQjA6VLnRSCayz6DtZM8m4Myde5FuPHrlQajKKoWdAk6MN59PDQobXeQb8TZhdUbzWLDtY/u3vPPq3X9kwjAYPmYFTmrOwqCJrlh3KRRRAQFQ5H7znDa/tiZH3MwK1PcsPfVZ2coh7Du+VicgyRI3cUHiXkbGDfjireyDPmlkMCjp7pSqR1p+qSkajaj4rP+PTQtEpX0gQlP6VxjOo5PrN0EZW/eWm6KEve35bdKkZIEPQBLKQQAbcoV+3pD6HB3JuI+98akLk4hhFTvahJyYE3WNAOVGlQBQdA1r0PrgMgi7Zow0KgnP9Vpl/d12pG19NtnQhGAubT86Nc5k2Cz2qTS47lKj7wG5RQcGv0wxvXRgeqK2DaD7II784J++NtibwcJmhxji99Tuao+ftu7cO10EXkRjhaD78+PiUyMzYUHXBw8bZ5LgZu317T6bJKAMPe3UqEyFAB8+mIuWCnGw4HyfVZ3zfoAt//Yerwg+ce0dt6Teyd/5bOV2jov2A3Op9eO7vzMpl9OIYJIcal3DzDUXbtq4puv5K+a0BoCRJOZ0x5CgjU3fwdBKN6TMGl2Uf1YdmEISnAbf1SlAkRtPlpc2NHy4QgaRbjwP1ZxM4DBjYI2fZCwLK8fn/oSP6hMuL52lmyMxFw6uBaiculI9e2BZeo+a+H357U4Lm4aNrUl6GzgpQX/iDlSmVq7pkLsCu5vKFvzOrtOlQKZEwehBA/Sah/Qytppky4ByKYEPPwo+8f9vb4/hUNvx72Xv7kiiadxLdfPzUnp2CDkF+JNXuoDIqNR2GhmEUwO9afzRqJSh+fka/EkRXQKRMr0Ki0kSmUVn5//gCCpDyO3H8Qv5f34FeBGdeED92Yr8oqaT5z6wKVYlbn+Yso7MJFggkWEt7ksSj/lRZt6YhtED38tKZq5Wyy6DumdYNqHDGUHA4CIYRbQLqpayPiiLAcq/tlz0+dvhcEPBRE/smadi8vjHBhv2RpFw+fy2JKGeEw+vz2AwosOQMJYe9s+aSIJQIiOvb//7jnFd3wLP6c4mAF3Teq6JTnQ29gOOERKkm+vnFT4yvpt6euJgAEgJvMrlgg+JIZXc6lCkzBgWcGTGmdzl//mpoeKh6h7YeT6Ldq2/XSvWo5bOhocQPHnx8fKWC1Kld6T3Az3SL4MKLf7AydFLfmcOaPntoaJMSDecR+izAHzW+bwICSB1b9r1vbSxt29yWRPzD13flvGzf3JizdenEpQoxP3I+5Xd3QyWCYMVoE7C7tE/gatK2xFxgf/cjY+PsJ04xiPFw7rnkbdb8yj4YXGkGk8TluV9bkDsLCdbECOFjH1Uf+QrrkWb7k5cjgOGuQ8M50SUv7YhdDPp56nLoDRIN+zmotkKEBdHQQ/sj0di8tjEVJTRdiRHU8Zb+qdtuS0LE761bciAB48NfnpbKrCDf/kOOnX9gy+FD58qGVQ2x/UCNhx4bl3VH/zGEzPpuWnowlQprD0kfP7l/efQX5kQK1mwB76kCJknRi3Tp8s2oKAo8+Qm+SrIISNIrpNoErf7oh9tjs1Vk+S/3bMTYvmXdSgpBt8WWE9iYtGBY1dR6owLlrB2aJsCha9d24cKzQc4i//N/fA2Z0j/Jp0Z/AZ8EWwV/ybt7QqFjs50JiYIzbr2xFQRAaDEYBAIofg19WSVAEuDZ71g0ImttfSQe1nvGPXW5c4I+sYH+BX2V6H8Sm+bdJ8vSF7bFj5APHzWmJhQZvoAcKP8BiBCACvTYRM9mLVGNgV16m177g9WZp8CmJkmrqwn40DrV+6mvzoj9Pd54Ps8qKbd/gDPrq7/AHjnzKkh8suCc3c5chhMXs1YCXwpn3hd6TdJfNdK/+TYACHEZ5yVzfi5eD+sDMKHPxmezuZJISRn/s/bDg3l2wb7AW69kK8I9fvrA2EwATuVL2iW4nznXRPSbiUP8DuTcuuv5EnsJgO95dFzOl6RE9Utio4IgTolc/uPj4m8Bo86/Sj+7qrpdScq3DXov0BbvOTMo9pItKQnavNegwd3TpyUxQ/1FGSS0w2/ozXNXNq09kkSbfTOAkoCLs+5MCMozT2bn8bLuo4OprOk30L/oHpBKtu4qyell6N4hAIsEfT/q3bCesc38u2ov4JIdYLd9JvvvXLnrEnfrBYzxnnwCRoRkT0xGCv3KFQNLq9kt7jMmTMYpjDP/6Uao8+y6cyOwjzDGSGIhA5JgPfjspLLig/qWOScdonRlYK51Ea/y22wUfx9g48Sl9G36fmJy9xetT48S5oE+X0NYVXIVAMxWmTprUO5ip07t44+9xD/2TDVp+p21qRa7A1hNf+uVDcO7OBLGeJSFung9QQ5Hq3HGxfqzf/pe6TO2T5k8e3B598/W578XPDw6lYd3/nR9GX738JQgfTHVCkGtxt/WORvee9Cw7sn2aLDLeqFEEg2lbQvw+jc2ZKifjfcMrdN+NSb5e2VZKJNNs8EQQgFfK0ousF37QX0ZPXVANkhWrRFMhQHq5QBBQ/EEX/j+liBfJ45eyGVgQCVSHJZD6fByUoyBjeWsGVuf7QCprGQk/ciaSMGZGPve97aUJ3529mdZ/9vPby09B3WLgslP/b155bt/uT40L30FVePRzTKktnfKXEHKPzhQ7vnJqeG9mjoOeYIcG0DUdWC3DNbSe7B6+YGgyNZExutSkGDb+sruaNrjGLcqOjjIrXMR0LiUUfGGHUioL+PGaVEG6dKnSxqHrG2Gdh05l72HEkydPrisXXUoPQiCABx2yQpE0del+OVZIEAfv7s3KMyaDw6UBQ+Myns5E/n3qUu5mBICspUu1pGtx9NDIPixdoycgYXUN6zBmg/qy71PTQx9QGMXqg0lEhebAbCv9kJfzf1fmBKH6n3yGRslN9V0XnM/amcPDs0OkiQ5Xf7dbWXucxNjUPReSJ5RwSCwFEpaeY/+vXX1kUz0nvHwmEwbbmw4kyR62+amcEVHT+hXGupPJ2lyhiTKkEAceefzGOSa5GOHNlGuQA3TuM4QVQjP1Rje3SgCKFZ9qwnLzjcDJxAfXtc7xpdKm/ey7q1NyagGzi1HitqI29mjV8eyd9mhMvrO2tK4s7l07tM5CQNn4XvZk3sXj8x5/Wf/6K00gAvWNMsePniq7Nx6ogwbXk10h5IJfgWQDBTQwDRsAaqKnLL1e6/vLhePXyx9R/TKXXNPAAectWCkGkzYPsHtg4+MCw3KC50B2ir5lBQ5I5BAtMT99WeCfHO0aD/swODa3mmAP9N0vkyeM6QqE5+tZBXp8puDYVbMuMfG5n09L9vB7vhZ9QPJDtuCT3z6NLnmNgnC3Rc/x245V9tWNJQv/fL8qDZR9lr+QX0QaHSHN17eXqbPGRKnwIHbc2sEaBAIs4NQcOdZYjZz3tAg+1S9+vbvUvavb4ok54G9J8uuHc1JqDiikSic12/FDrKTEmXrGKda1zvqdOtWHs7dhWAKep0fd3D1kvoy887aVDnJ4Xpx2s64ICTn5pNP4igFiQJRzZiNh85EOrRz5/ZBMTXHOrcfvrqrDBhFjaw2s2b0Iq14e295+ItT8/3S1zd3aNC/1pk23lMF8+1vbypdB3Qt0+YYRlpJbko62Q7BgCDfsEyVG8AQ+pSXuxv++qiqGmxoac2Q7klOBLECvoPbmyMM4GfefWFbWfzUhHK4/lTuhO/l99yVde/tL+27tC8DRvbOHRfIARs0eQPHgAFEGlAGPb8K9KjRfXOv2SKVifq9p1JZ0gvBL6k6oim20qzQmSh1qfbYbwE2m8s/oW251+ytYFPCKWDILB6qQ5rPW4a9sdECCOu5Zf2R3H1/z9e53/X7z5RLzZfKw1+iXHc9geG4yf3LocPnWlSWPknP0WsvbC39B3VL5ZM9U5lC27Em7l8q7FdvVLNLThD+uFL+1W89lLUnja0itvCZCaFNp/dh+cFU2xJgtMh/C/yqidKd4z/ZHA3GaFfoGV7uEPR/t72a2DcVOMk9uyESlcCZCK/iiyaiR0blApqNYUFYIXNj+nZJ3EBeWNJKvIGfyjDT+tPxc4J6tCCVbLZNMiyhoArWrWfVmwXoUal29gRuY8b3yfs4U+5a6NUCdT6lY9vIN0sq0IQEkB4GUBHFtt6dE6xH8bC2Z/op3I0M4Lv1SVn9xt5SN2tQbI294UsBJxq4VcgF9AfrTyXhwsYQO/FxkeLu0SE2l31iQ9Gf0Q3Tw1rXOywBqD2bpnLeq+Vcs5+Ced9l1pyhAXQ1c2NkWHu9a+wKWyFAdaaBRc6YGMnaUeecPH1wZv+02m1/BzF3FpwdoJKgHAjw5DOTytFjFwISm9sDhOTzgNGYCu4EmyPZm3fn8PgAAIc4D3AgHnGP/IzP8ZLI+m/nk4COxMK8Jklqa+VAbNdw8GxEAQYPr4kIAds9MnayUq8CBtpTlcajxy6lMmCtyWdjy7irlJ+cE+COs5Yq85EKBHMWJD72AcgDmJAE3Lxyo0ydMyQsFHb06vmrpVf/rpnRI07UyyMeoLbpvqk+tzbSq1RISJ3PVa/vKfd9aUrZsfVYqPuPPTspdpt9A1h7b71/GfwMBJeIdVTprvqqDRHuXytevS02AztJI3sGunZsG2aAsyZxFy9V4x1OpGfWGUX30yLROi8MXfS3/uDJv73KhuZL5WWbs3N9U5pa6nedLKdOXqxUUtYdKT/5vy4qNYMr/XBD+ei+GwLngPeb0DfO8NV/8kHoFTYHugplTMPysoPl8qnLCUbw/jij/RuOlj1LD8bYSFgM92vfrUOCRwFWpiaevlyWvrorl54h8p4Oexw3XWYa94YxtSgVQQBcHO/pgjlkFhLPcMVbe0vTvtPhsbkAuKEuD06lzRO8O7gMB7SwMqq08c/GMH741p4kM+MmDgj/2wHnbMLJXnowQQ6nykH83k+9mLLy8Al9y5mGc2Xc9AHl1Ze2l0mGW03pHyf61p+uTSUByoDfJxgaMG1gsvtDyw/l8kBHOclRMweFcqEx8g/+H2+Vd/9gTYIhBrNryyFGeZr/E5NLt35dyrLXd6c/gXEUQEHYoagZpONCX6gmTaYh9Hw1oEmz64XjF4Nocj5Kdpy2hn0X9S9/f0VoHpAIyijK1c4LahOkDHKlERh6Bj11Wbv27VI+fnNPzsaB/acibey9fC+Bh2zes2/69vay9p19QcE4d0GcJnoIaXjnNdV8FZUMtKuxLcmks8J4+24Cjml3D8974G8yEgbKOS+CdOjWYz8/O87A92DMXeAFX5qUM+m/owRz5XrOD0PbnxTg9ZtJNKCn9pMMKVQAGi2gMD1aQxgjCqXY+PbeNJtBeTlRF94d8b0g8M4Nug5dcupU9NkliybAHtt0tFLs+PBgudB8qezZWA1jZJRR5jRUclR6WTRvQtIO7mwOCkLO0JmXWHt+/UkCdUmtNRTUnTtyvrTtWFVVrJN9996tr71v7U2VBVqnNwCSxUlLIPGEUVo4NSiWs7fynX1B0BnFbt07pXKjBN2nrlfpCVnq2Sl3KMFuw7k0zjKyGvedQ/SdUKXcny7tIuerQXvr2sZUzHrVdCqr39hTlcYHm/1QyYEy2Jyykv9jX5wah3S2hTrpTk2cMiBnaPrnJ+aeQ5NUKP28oGUPKsKAbjmjZIwhjZzwqmUHcw6AJigXggO2AVdecOjnVDn7D6ioc2bxmOrLBvheaHH6xARlzsxf/OJrZeUPdwR91OsC2WSnfGfr6jPGzx+aQXfuMRoFo++1fX1THKeKHr645nn2RmCLSkf17UIjTfp+qQRAYJ1LzZGjJ/dLgzJnrxqmx4zk4X0PjY0jVeXxvVQjVFGcD7QKzlQyJ1mXAGxc31TWrWqIDdKX4LtfvnA9iVWf2p5Retq8sSnzZujUO3vsEtSW+IXvp9HcxHJJmIBNhU1wJLh3v08evZCKskpZ7uH1W6lksJGcoyRJ703t2GrmERSfw1/57a3lNCWnpguh+32a370ZlSyOVjK9f+fJvN/IGYPKmj/emNkf6FfOpD1g63DnBV6vvrwjaDiFQHYAFSaNt9TmenZKwIPv7IViQ4ZcEmLPqVXp7/K7AmT+auLkgQEArK8zaNYK+gOFO0irpm8UO/61NfljzyJqcO5quP/8TOaybDueago7NmJkryi5WXt3dcG9o1KxRr0413ypDBhQzUZ65OlJOYNopARWnC+a//ZcX0sGmFLpOn0lc4L+9/jkie3NZcS8ITmr5M+3bGxM8mxfBJ/smYb+NN0OrKR5fSYKWeukZ8G3dfY92ULS9gRJyGXzeahJ6KICZnRPiQ+fIIjyHnfcPSL0mNbZF4Ca8exG29vLwEHdkiBIavh3gCH/bV0//MG23A0+m30UkHo/e18BPW1D2ZHYUhEMC+Hk5VTVBGSZVH74bOyECmpmO3Rsm/4pVFHBtO9vT8Qt6ED6ePTXSVqp77FPwKyHvjItfUQHNjTFzu5XwbpQUYH5YDRcZ99Zc1dU5/ZsZV8qOtGmdU2xXXwPgEKflTPBJ/kOKrueJeqSpH8bJcID44tVC1HtJLpVlfbTABrOLZt9rlFDdpskGWymarX/1n/iWQlkBAFHHzt4Osm/mRTWz7MDdPy3PjnKVZIu4jziCvEjAIrPE5cACFWR0fApLrHF/LOXpn7nRGVKrAXY9TnYJ86H+wswcY7Ecf6MQqpnt/5HD51NFTH34fbbMiRWr5gKmIC98eC5JATmuAERDENtFc8Q5yx/Y08FgAzpEYUrd9d9dz48A9CR3SOs4o5ltlpN5/LIs5MrVbJPP43iXL/B3UNhEhuhpolLl721N38GtLdW7NLunc1JlO0be3vHE2Pjj1FMqS+K8dw5yaxBrBInFOO77h2VxJoNxQKwRsRIxG2Sef6BXSQqgpGhcmTt0bTFADs2NGXvnaVxk/ol/mHbyJGrzOnnQNUWs/1NXn/jZAO1QoDfo6ZzufORseXtP19fZt9VNVYp33MgDgk0e+2Kw5mQ3XNA15TEof93PzYu0p5P/NvFoWN5MWqoNRxP935dy4ip/VNGZQBcYE1yi78yrfzsz80JAk+2rkf/rmnWkblbzOYWx4kGZZMoGaBrkHGNslS39hX37PC5cmjPqbL21T1JQsjbady0MQkgr94s185fK3c+Mqa8/M2NybqjYtFwLmVniQx5xoObj6bJ+TLd/p3NQaBb6SD3PDI2/QdKtSuXHiiL7x8TZyXjXvilScnqJ80YVB56fHz5/L+5JwnVgW0nMkmZtC/uKcOW5talB8r0x8bGeLlkqCFKmGPH90kg+cQvzwsdy8HO8KSbn+SCuIh3fmFS+fw/u6dFvaRDnLELLkBoOnwuF+KKxI4KQstAI+V+TcoMCsPP4NkfBk25mjqP+QuDR/VOUK90DQGAIrYqM0CPJQ0CFb+LEsIY3bx2M0EM1Ay9QiLhZxgRBmHUlAFBMectqItj1ehpAFemUVMYmTOs3PvP7wo/2PPgiau6SCYE7wfrTwcZ4ASgzoyyBlt/xjg5A0e2HU8S43eixHP9k6zr4ofHlv0fHkxjrYuov4Uj8r0gqS5XpsV37ZAKA9oRBEPfkd9BZVGhUHomegD9VbKWENQJAIf1rBDJxvM5qwsWj8zshRMHz5bTJy4G4YBezJ5XG97w0abz6ZnxrPpf+g3pnjXYteV4ef7bm8rExXUxXqMfHp0poZ//udlBsCDdkGuIkeZf3O+RU/uXhr2nysPPTSkHdp/KPkt0rcepYxeryszhc0E1SPRC6Gc/OS4BW5R8OrQJDUozcuvry//6vjJx2sBy9tjFnJOV39hUnv38lLLxo4O5K4JznxPH26VdGTt9QKShcZ2dQUoyHCGVp889NzV0EQG75mOIiwqAAMWgNBKqP/0zs5MY9ZvcP99bsCCQJURw7wOjUwae+8iYIICqEBB+1SYNjxpcNRyqNESi9+TlBNXukbVSNbz/oWqiMmrOzLm1QT4NwJp174gEv1FbO38ts3bw2ZXjnVm0Ey9IPTTVi4SgaoI7KUiDjHFKKniZfN50IWV0vU2cgGRy3t+bXe796rSy4o3dZeiw7tFit/eCcvsAoeXQVERWLa1PUHV47+n0i/Ue3D2VN0IQEhsJC1oPZ6GZWfVk8ecnJblQuTt2VO/S7dX69+mSsypYbNh3Oii/6iQAAW3EHWIn3WNBk0RWcmGNOVuAAbujciUBIhXOgVJNcvYkmnwCu+BeAZ6m3VOXz+NkBYGqa4InQWXrjJsVf7UpQiLs0It/siY0p849OsYZxge0VBG9PL8AQOLOBodO2rVD5CAP7D5ZJj0xtvQZ1ydrlaFpk6up7RB2n8Uh66HzMkPn1158LhRMd12ComdHNV8gBdGT5DnnaGIqBhB1CDVHzv7eff+oUJ2smUAaLcEsFs25JGn1+5kbIhE5cuhsxEb4JQDYni3H03+I1jl5TjVnwVmwj4IalYc0F1+4Wpa+vTdqWWtXN6RqhOqpkuy1/pXdCQgBBfpOiJ4A/wSCz35hSoRd/P1f/4ePAsC4L63AgrWZMXtI+im8INKQeSi976rRv/U186lxpU4P1OZjSVJUbc3oENg5WwL9zKSZX5sgjB0G0KEV6hdct7oh6yPQ5f8r2uitKP35blVF8XJiCvaaaqMAid1P/wI1soNnMlcIIszWH9eH06ld+oAk0ir5qKbuj2GYZFoFrSPnVUPPnE0gACAHbYs9ZidURlRm969sqNT8DHAc0qNcbr6Uior1YytRYPg3CaSKUjWRvArcl7yyM3f9tT9dm/4Iz+v+CCj1pHoONMNK8riUzn265ExSYuOz+QXUcLOpBPf6w9giVCbDPFGmxR9UHFW99bqonEl2JR2fflJiByTMrdXX9SuqnhnVJkmrBEm/KHXA1kZjz852qlI9+dUZsWPQ/fjSoxczG0ZSqT8CjdhEchUne+692Hx0czZb1dyeXTx/JWdBYmueEtDHukuiJMH8jIqhBmqqSqePXkxgy1eonmsEv+128WLHnIVMQzffreFsxBusVxLgxorKSSKa2qcXSjRfMXr24PgTCWfNoK6ZMeO8sZuECTa/ta8MGNI9PnHm3CG5zxIVINaIKf2TkHvmxA/oeFuOJh7gT4Baqo0oYBgwzoDP2bm16i08ceJy+obdkdaJ5WheKqATpg8IjfutH27L2ZMsiDcz0BGlqq53gHNnTk+H5vm3/2RdNSPj2IWwYlQ7rUFrHzImCHBE7yH6FPCIbRw/oX+e3X1jNyZNHlB++L0tGWwqeR0wvFc1dPf220K7lTCq/vca0j30/dbZLc7t32qygVs7enRNUDmvuU+NL+uXHwrysnldU/iosvG+g7qXSYKRc9eS0Y5kgI5dTID5yOMTKs118whWHs6BzfCShjMJ2hmAe5+dlEMNdaJG4SKuWHEo6C0em4Y9AWWrPvGxhnMxxoZleQlQoSAMFdqFngmIIs3xOYvqyhwOqLZngo2ojQzuUZV8e3Ysz/7S3DjW4eP7JEFqNeIQNc5fCfhrv3FnNTE33Mc2KYu6pDY8yPr4fkFgBtf2KH/6X1Ymc1fW1KzLsDKUz39nc2ZcOLg2UsWG1GloNI3nYrwnLKwNatx8/FKCXQGI7wJlEuS7DLlgzZejhPHAI+MS4DnkDgsaCXQCIgE9U8HhGDnBw+say9zHxsZwMriyVkG87zd5Gkd3oKLhNF0IiqQPZs/yw5FV9J2heUqvONXe26XxUtqTjVubja/viYNmFKEF7721O0GF96WaQF0Lysj4oahwqofqT5Y//ldLYsB/6pfnR1dcAOcVjquJ1P26JuF1+aHrBkZRD4EOuFQuiPK9yom1UHY2AVwiSbNe4iLYZvit1wdv7Skznxyb7+x7/vV/WZXyMFRb+VgFZMqMIbnkAhHGQpI6YEjP8sGbu4OkS1YF/A89Oyk0Ez1DvhODLfhRptSro+Sqb+CTm5+UBQ+OTuVD2Rg6XF9/Os+VpPD81dBflPYF4mSiDYajAsKho3mMGVOTvfPzEE/ynQz+0YNnYxQXPD0+CRWeLpSbJKfKDqPjnDmf1SDKG+XVf7OsTJ8zLIj0/m0nchcZUPQA3+VGS/N49qFl6vGYyf2SzM/68uSycWNjGT6pX/aWY4diQhLddcEhBTo8Vwb5vgfH5C4ImJe8vy/8W5UoPN7wXlWgRvQOrUBQ+u1vbkiADfXXwOg8EQrwoq4FxQVMZLL4kXM555yY4VvuI4d7PJKI7RIUC8Ahgeg7uN99a6oeMYmO2QeS77seHhOnxZmcaTnTqhYfvrE74IH9BxR4H4gcgMWASQ3k6AD48O7ZgP7dyprlh3LPIIjOJyDhW7+/IjaEMgvJ6S1rGsucB0YF6eTU8eBTmtfb06V9yucCYyV4tsqQQ05NUud8Sa5UeaHB/l4PkKqpdSBxChzZufpI+MzW+Ojhcwn8NKSjBXz55+fEhjpPy1/bFelNgQhHbK8lNBoyBTSkMUPNHNk7ynW42dBS1JR1Kw+VEZP6leHzhiR4Zu8Of3w4z7ToyQlJogEX5H0hf+lzqutVqUJ1bZ9esC/9q8UZOOVlTVTKJFoCNHRa/4/qYU+cHYFg1KY6tsvaCTbJP46a0DfV2ll3D08VAJKegV1tb0/lWuUQHWrYxH5VFWzvyYhs2FuBhPPIflBPsmbu3tOfm1xm3lPXQpe4Ug3GM4On+VLsmaThnvtHxZl7SdJVowFg/KTqxdO/MDfiENYaanjH3XVlPAW7gdUwNJQmfRhAJ0mLWSESRbQX9kMyqZcHEII2iK4rCeOvBN4P/szMsvqjg3kfKK+zEjWaKzfKdv0K128lsa+dOaii5mqm7qln8mo5dxbd9dOya82Rsv+9/alCGAj6O197MT5Fst76ArDwoV16dgrtVUUOmFU3tk/OIZuoqXbJ23sS8AJpVBxvb6HXoB21IWU9RJ/Jbal8op5JmK23++pcARfEHYA1suvOvDvER/Iz9lQswGZR/pEY6rnyWRJMNt8ZEZhLjtnKM82XypmmC6GUoP+ozEH6gQb49s6t9Rvd0gRsfQTx09BMD59NdTY9R1MHxgayefYL3e7aJXKot8U+Smr1paLJCjqphHmh46As8cdAPWh5nyHdy9Zlh0K1Fox7jwy4PV3Rw72AH5IT9ww7A/iafowTl1oUpG4vH6MzDugaGpp7Izhki1So73t8fH5Gsi5BFlMMNaBuV1X5Bs5E6nZPc6r8vqcgVIWLTRaPAWetjQTNvdXzRclQNZVMtyBYf5HnX7h4ZNlOlfLarbL548PxTwAi+wy83rrhSKSS9ddIrHwPghnmTokRpxkwe+ZKgnRAnXiBHL29T5M84O/s5SQyeoKsSWSzb3ySfkNAkz6K9Gp2bR/fwJcDRCUa1rqp/kzs8vA7VO5KKhRAELErpkUqIapmxy4GfPbequ7A6OVL9qcqJbECIPh8YgiAYf5ZHNuxQ5v0K4sRH392YqoL/JLYw5nlK5uOnM30djbZWqWpvaZLKvdmeWhb4N/4A/HD4786L35Yj1H6nS9ci8Klc3744JnEvaSADQJkF/TNqObuadkXwIW99+ITa4b2iHCM++K7S4YkPMBqVC/nyeul31oWGfMMXPzbTDZMJ33tP60MUuOBBS5z7qpLiXDRg6OzURbRl8Cn1vwnoxWs+TsOR6lVkC27gthbCIkGx34GLeed/fmsS8mKr8SAQ1scBjK3jAt+HYlLvFsvkpXUNPxMw/qmLAS6APRJAgKllulzbIKwSvVHYHElgaayUWuzVNStNEd+WpJN2nQX7v3vbw0HW3JgqCBn5z1NT6YOEL5eNMpvKwcOnskMDkEC+dx3/3JDJYdK476uZ0qZkK6/+pM1yRoZZMZY0gUFQIPBtxZsaW4UmOJFy2xDAfvk0wTmAomgk1MHJqmAxCx/c29oOQyb5Ag/EbrIoYwcA00p5YmnJ5Zhs4cETcCZdaA8vwCXU9J/Y9ibdVO+puChN+ALv74gaJULCwWkCiFIk0RKBFuTHw5S8++Cz1dyw5K5HZuORVFHEsYx2VuouNf2VUfiQM0GcVGn3FMXQ+LcSLTM46Bupgqjb8c6n22+nKqWfdq0tiFVhve/uyUXRUDq/fXcMEKSCAOv7npuUhriXSglVAG7QTtde2isv5bgb97ddaXHgK7l/Re3p8T+1tfXJomxJsqWEgiICuekEoPOw/C9/53NSVYlFxIuawJdFajoQ+nYsU2UwzhKazLvnhFZX8kAWgsE8oMf7YhYgoTPflhfjVvhxs4YHGqIpkoIL0PEAQoMWhM91DsVQTrqmv7RCSGSmsA4a/QMCYzX2hUHQ79hvEeO7l0e+1/vjDE7e+hMEm3GGZJNPpT+/tY1jZ/ZAU4pzaA9OgVcaJWZ1CTsrLkz+KAcit6W6K7X9U4QZ++XLd2f5KVSI/rks0S8/sDZ0K84bvsoAXbeGrYeD5p3vuFcvl+MN2nnHh1j6N37d/98QzjuKIGoWCpqkLHl7+wLzYJ9EUy20sWU0723fqyTpy9HHpXhF6BL6DnL1rNKXnfStMGp5A0f1yfvDZUXWKQyuac5web1i9dS0UUp8F4csSAXDZB61oaPDqY52NmkYAS53bHlWNm97Wi594nxsR2UdKBTgmjrxgFIeFHSSFZ6dvd1w4rDCR45f5UhdAVBpz3jRDWbS9RWvbO3LLp/dJK1fqNrStdu7WMnRk/oG+qUwAO/WX+XnhVqLY99bWb6gDS6c2RQWecWUmYdBd0qd4beQX3tt+AlKoIT+iVZdMfGTRtQDq1oKPM+PzGBDQTUd8cr97PQR/ZIRVDl1f6xA9u3HIuQBpBEJdT/qwyTPW/XQomR9FtjQamAzucJ3gRnH7+2O+c60rjj+yfRZ1/ZfIlVhtXtbS6b1jcm+JP4+FwoP3BpzarDoSYCLVRt1n18KA2vknxziSR3EE/Bg8btcXOGxNZvXtcQupT7uGHdkUg0C5SshzO15Ue7PwPd9KFx4Ow/SoXfve+hMUGm//yfvpfKZGz22H5JSNhy9CB32HkEAKnknd5/KkmI59mx9WgCZ3Zz8sxBkWLfsrzhs/0AzjiTwBaBI3sHAeUrqOJ5H8g5hoCzMv+npmWNSUb/0h89kX1yHlpf6QejTjaoa84HepPgWVCDxqPisWZJfQJevovvdk6wGiQ8Rw5T+bktayMpcxadE8HVtiX1n01kFtTxjc7wgDFVc6++JcCG92PHBODoTW/92fqqAfvohUrQ4OjF+B+JAJDF0Mc889Ae6YnkV9wTcqie5cSBs0nkXv3GhvwcW+/MqYBCmPksZ0yi7zNVQvQ/sUM+g/3r1bdLmAOCXC+ouvfye5JY+yFgJUkreZBMqKJLPqffU5fn9x0lBwAu8ZFKrmTQaAAgrGfiJwT2/LZ/S/4aDpyKbfFevi/QzZ6a9+AuiAfYHYHzD5/fmvjre/96aRItCZbEVlyUXsbuHQLioKbl/jdfTP+bxMV8DAwKAIqqL3ou39SjT+eAr1gL/Ku5HipSXp//+TlJjMwpct92bz8am+L7W0uIvOqLRAwIB4RQFVaFydyl2ypKvOTl1s1KwpVsPT8kAZKg9+zfNbGIPjgVUesgvnO2yNuzFWy8c+YfgiSSfKqKKLqe29oDRZy/k40XcncmTiZW9Gl6T/2cxMb9hPSr1PGBzpGqlYrUru3NZe/eU6E9nT5ztUycNiBAAn+Piues+Ud8++6rO7M+Gr/5EnEU3wYQQD2X7PkzdD0AGkaOsyb2S9LYu3PlD7u1D0CkYT69xNduJnHxnTBSgDyqx4cOno79VbUmHIQlpN9DDOVlPb2sgz+T0AIMUQyf+cd3xea1CpL8rTSI/8HvLksJi9EQSMhqo5Pc0jAksPH/1CsgCwJZDTU+QrClImETRk8YELTFBx/Y0Vxm3TU8jsH0UptKDUEwI9h00KEJLpU5AKLvGbOGRl5zRF3voDQWuRpC0iYouwbBhQ+OiXHQSAOpuffBseXr//aDNJW7eAxztRm3YripHckSqbigSXnZREaegXH5XXYLrOS8+hubytyvTktDbU8l2mE9KhnIFuRPkCLhMcGyOmC3gmqRUzW5Vc+JzB2qZuM0evs7z6a5kzY92cbWph9GRyJkTVG0JCLLPtiX5A9irUzGaEAlBK5QLpxZfHQOk2OSjKG4QBNT1jtq4E2VbzIAPkNj6onGcxlC8+zXZgRN37/mSJm6eERQolb5RGVNzwfRlZxRUOrSvWNZ9e6+THRFK9q++Wg5uK6x9KrrVR54YkI5cuh00Boo+a4f7S5//8+eyjR0Lw7YczvUQR9qupS+/brFWTLCdL6rJKZj2bHpaCpnAnFZf5Q+zl2NAVUif+Wl7akOPPfFaWXPvorHzXC58FABl0wmbyLwpDlDyt5dzaV2RK/sBTlne052j0a64XamP89VgTh3NXQbAQ9HKiAR/PYZ0btMnjawrHh/f3nwmYkJ3PBGBbzOl6qY4Xj+/dYPt5fz9OIH94iUnXvTOljn4pkrZdHDYytKS/2psuyvN5cB0waUMVP75/JrBqVUwzBLuL7+86+U5/7t4pzlA/tOlxnzhpYRI/skeBAQQYgZo7y/Bk2SoMOroHTdNzaXB359XgyYO6cyRdbV87710vbSuz8j9mmMKooKCg4H5P9NeUUlsQZpOKzpEqUrlS5BLjqlNRaIQbqonnB47oAAefZdtQm2GPPmpnMJqJe/sqvUjK5JUKOqqDII9eGUfL7vWJ1jEr69y/pVDZkeDY3LhOFRfXKHTZ21l14oW5OmDoxDsqbOE3UpgfORLcfKXU9PTFAmmdLPw0YpUbNNEDVSju4zJyzQkUCMHt8vyK4z6ewy4uia/g2dVH0w0FGFF31OT8mjX5hStm1qDCCDcgEtgqqzB4ag6etA21I5CKqFPtW9CmY3fXy4DBzdO2pj7lO7luBHuVx1xVqpwtg39DLBOkWtVn121QzOjF2UHKjQCBrstYAZHUfSzQGvArJM7Bt75XfsJ3BGQMT+na4/XWbcMyJnJVrtbW7LM0bZqF/XBP/kbFH+Us3q3K7s332qdMV3pztP/WlozyDf7DVHKxm13rs2Hy+Pf3FqeePFbZnwzPlFyrSlAZxD9J5sdhSWCF0sPVDu+6lpoSI1H72YRM9w1tbARjKh0fPhx8cl8DQQzVA/swD4Lwg2PrtAFbi0Ql/RgmFl67qmUju6d+6L88aWOA8+Gx1Sf4sG3I2rDqdyKgAWEAgyJAZvv76rSuyH9EwSZqCkBNr5FUAIeiSH6B8UcCQbRCRaUWlzd5wB33vM2L7lT357WZQF9+86GSqeqiraIIEOwYT9lHwCm6yRc2ZQHV9J8tfeEQeAuNorQS+FHYEie0h1i+9zX/HA+VH0GoAeerTkZ+VHB+I3BDLQZVHDkT0nww5gE/RYOhOQZCATn5/+r3PXkoRLGOy9aodEhL2Gmmv4DR1v54kEplUAZuL29Zxz1QvrBvjQHK1v0s+gQDqHkkDzbNavOhSQBpIPAHTezEZq3tlcnv7VO6qAueFMqiQYAWw8X2sGh4DausyaNywUQ/8N6PD8KnO14/qEdsi/sBON9WfK01+ZnucKtYe4QKd2ofHqEZFQ6feJEt3B00mKAAWmNkuS+Uy9Q+bFSLAJEEgCt2w8GtUs4JRYwzkmjc/e7/jgQBk8c1CZOWdoEo8da5uiDiTO0PO09LVdZUBtz8wzcqclnJIGVbf0gtSfTmBN5hxQIg6QAGrcTi/twO55NrGOf3vl+dmxkb1iH9CAJEsUz2bfMTwVCeeAD3AH+SZrYSgyW2VdgUYSIWcuvQenLidg5g8AxQ0Su1mD8uf2A7iHsiwOS/I0GBrfKT1wzoqYEHtAEiIudC89pwRPZVI8YKp6L/TSXtWZdi/ZKgpeQFD3wSt9OX5Pr8nhqgdXXxG6X+POk6lesmtio1RrR1X9RmHhHL0QYBHw7O4DS1VH+azMiGk4m58FDPNDnTtXYhuEPtLv0qIydvXqjc8GGOc+ml7fmYLhqdAtxZvACiCDRESCKuj3fSSc28nKj+yd2A1Dwd2x9/aBAquzpkcGuBOQ6iLlxQtlzvxhiQnFAuJGtl41XhVX/4Y7bMYROXL2hQ9OMnSskn2n8DdqSv+yf+vx8h++/vSPv7JBd1f50YM4EOgQHgRaxZlZUKU1h4qsWwzH7SWBr4BEojF7/vA43n1bjpddqxrK7LvrYmQdPoO3oLcMrSFHgkGXLrrfg7sla5WhcOyQfopHftZFMSOid79uuVB9hlaKN6hbkDEVEAH5Uz87OwvVWnY1B2DJ19fFYISfduxiPoeSkM90AAS+EBdGV4Ogi6WicPfPzyy1db3KnZ+fWGbdWZtDhibRtLM5/N7WEjqkkZqQ31NG3YtnPa5PGil9D3JzkHxGBm3Hxfz4g/oEjBnE06tjns1lTuk3iPmFsn1LUxC4t17ZGboEaTvPJXh1MRg/qCSkznvKggVmqCTQJJUAKJV9cdjxAnGHBQwCpce+NLV88O6+fIdRc4cmwOI8XRgcTYgcI8nZCdwrh9ahTJw9uNSO6JPDr6p1oeliGTepf7iYUGSUJ3M6HvmN+ZUTGditNDdfyn5acw5J1Wfle/uDehzf0RwjsemNvXHILiROM1oUPi60OzNBhvfOfo8Y3iscRAjIunUNLbz5WwmeHnx0XBlW2yPSgQfrz5R7npwQRyh4NbWWIVWxkUBSClM2RrubetfwOCuVKfxl2T3jTk1lxLRqcvVSzVPDe5bV6DLdOwTpuePOEZEelny4yNAYSanegnseGxdEnzFWfsZR7juwW4wF1MX5nPvFSWX2orpU0CC2uJf4q35m+ZL6UnfP8LJphX3sVu55eGwCv7/87Y/KmuUHs5YCmaiANJ3PXACJhgRYc9nn/sWiGBZNdM6BdUI1MoxSIoh3errpQgw+uoh7zqBve78+6JdEY/3qI0mCBLFxQj06ptrl7DlXmpGhf4wnByFAmzZvSKbHVyjo9aynKlDt9IE5awz5F35iWlBXsqz2QxCjiiLI02DJYOrp8H4XTl3+jPLCsZAnZZD1PsWxKK03nqtk/i5cLQcPVaXfSXcOT7DCWbzzxu6gPoIJCbTAxmyZqJscu5T34JAkLBBo5/iFr69OE+bJYxfD6WdDoO+cr3vtnkj2DU781r9bGgnp1gqNv68mfLdPoGmNa4d1jz2laLdtzZHPHFpNbc8ETvW7m1OWB3QAFFT7BKsSDcCBfhTOWCNp+lMuVGgeqsCat/fFuXFSkEcBvu/jBV30QvO8jMYxsOoNsi/umibJUaNrgtKighxuOBeFHkEO+pkgVlAcoAJtZFLf0q9/NWhSQkROG11TdcdZ8DkADhxtza6QM/+96LFxGVg2fkr/NPiPn1SJM1BzEYip9qCrnD15OQFIqrN/12Ts9uFoz19UF8WroLNnLieIdjZHjeqdBNQZRKuFzAoEJDN6ZpxbinMC+Knzh0aed2Btj1Tl7bfn2LvmSNZIJdV3rCZJdy53PTQmdp3MrPWEFAuABMm7th0P8ikBVZWq6E9dAyo5F+3atsleP/W5SUkQvY819dqzsznoOxGC739nU5kyb0iqGV78ofMiQUULdYcFSUAa4i01vTuH6kfmU+VQ4rhrw9HSeOR86CmSCMnEmaMXSvceHZLQmUsjeRZQCgoBVMQ++G0Vds3Kvjc7+sYfrI5PcYZ7EVZBg73xSYArFD/VQs+IZsen1/Tr8pmql+/n2Sns+Pfe9U2hHEK07ZsgyPcBCqmQBmAzvKxPRZVmJ9FtMxOng+bczmXu3XUJegVLKkPmCAjSremshbVl1F3Dq0be7h1il1zQDLvt2TFxByl71SH+9p03dyfBUi1UTQDUQLwldu4DOyIYVdlMBfjU5dwtVQqfm0HCePAXr4dqaVZXGs6nDggQ8J1/sSQ2hi23JuyminNmkV29mf2wxuy4u64PQC8d3zv2ruFZM0ky2ybR8LnsDZDl4c9PDvotVln9zr7sg2eR5HmGVond99/Zm9+zBmg17DAQGN1ctUuVAmVGddTvmeqtMs22+29U6ATfqw4lYRObAOl6krXtUs2UOXvyUs4zX67KK1kAHvpeVDwBkdTdNIID8iS+aVZvEWQQ4xjmyzazFyhwwBnP4267+60S4/qFVGsqwPtqqoFAPGcEiKAKwhfpc0LfVjF2xtxf78dPowH2NrG8plPEbAA7KjDugmS9tQpCcEWwbf2cw6j7TeyfNTCVmz33bPpA+Co23xlG06od0TuSvSoLbHEU3tIjdSlCEark6KoC/1st+wroANZQsSSu0dSkraBz1at0+FyAAXGY+E212Tm35+yWc2Ld0z89qKqke3/V0bQa3Fb1sYhvgRNsPP9JLp+9VTkBrrrbYp9q9hLlxi6xK2YTSUAGtTAafuzJxpRZ1QG4eOZqPlDygGvOuGZ0+vFKjxqihFtr83EfTaJVxrQQS97Zk0Bw4uxBUWdZ99GB/K4MvnefrmlA5hiObTpWZi8eEfQD31U5HGpjUc0zgIyahrttQ1MyOKU7k1pf/NbGILqM0euv7IwjefflHeG8u4DoFLiigryOoTi1TRBw6EDVQKwh9md+fWEuGwOC+pNJiw3nQz3o1atTefMHW8v2pQeT6PQd2COGguES1D/x0zOTyeKfazBjZJTuHFaSg3i3B9cfjRPzGUte3RVal0AdlxdCe+zgmaCspnT6/pKlB5+YEAQjQ9P2nE5wrdIheGaUBL4aw3Oxe3VKafHpZyfnIrhUnpWxZWQEMp7nh7+3Iv0GkJQ0rbcMiHPRX/vu5lxYPGMqBdZPE6cZIpJAAY4ZCxX/uU0oa5KpNDVfv5kGa0nRgi9PyaU9c/JygneUk2kPjY6RUxr1wsft0q19yvMCY4lVJ6Xztm1K75G9owRC5ld1gOFwSVd8WJ9GbEEB2hnjs2n9kbLkg/3ZC1WzV/9gddTRVE18x3/x8DfL2vfqy/Q5g+OcBIjoOIL56fOGJBjnaFGtGEbG9OEvTsmauGx6hJSOm09cLBNnVc109oDSD9ULAR6E1Dr7foxXzYCu2RfGMAlp9yrIQx8cG+pc5wrlH9YrSRQHhLbmvSWTElh8VneAyILgEkqhAnX17JXQnRju91/dmQnls+4bWRoPncvgR9UmASaH7ztAR6BNzjN+8AV889OXY4CgntZb0Pvwb87P/wsADx06Vw07a0GDFn95arn73pHhVnM8UBMBlvWhsuJMMNBBbM5cCQLju6AJUrVgE4AK9ZuOZa8l56ohhzcfK1eovB0+G344umSlOHQzSBPkE2rprHNqgoLt79eXmswUqGYfuPPmoOzbeLSs/f62nFHlYkH3PQ+NyV7RdG/Ve4cuojJyeBJWdClO1R1TCZJkTJ41KPxU983d8tKkSRmKIacaFwGF3l2iGHY2CDqd+ttTrVDFe+pX56cRVhAyXlNqm9vjeDRBomUx6Ove3pdJ4lDtRY+PD6Kbisr1W3GaDLvEgHQkBzZngSb2y0nYo8BS0zkgAiUSQML5k5eTjHCsCx4fF6BC4Dt2TDV/hkIQu2nt2Gx3hhQ0zjHEVbDjO6vGuQt47bPnDi3XLt8oDVuOJ1ift1Av3W3lo7f2VE6rZ8cEKno4BLGCTWdKU/i4lnvBUZp1wx4BYSTi1u5ESyAHHDLjhKwppPns8Utl2fPbg1bjKY8cU5MExNRndALJff8hPXJG0TsE184VWtrQ2l6hYH3wvS2pnmjuFQQcazoXZ66iLZla/tKOKiDr0TFNvuRZOVgVKGcA4BAVv6kDyoaPD2XNzDX48I09CZD0vqx+a29UuPJ3u0+VwdT0rt6IDxEsOFepQg3oWvZuby7vvrU74iHUDqHegnWVUP0Y1trPoWQ4n84NtNqsFNWLVnltdBiBApCvYS/51XaR4F36/a0J9iRVfl7/jO+nmvXX//aDVPhGTuibM7NzSzU8VYVPsGKvrZmgbeqMIZ8NkoOYowA/+ivzSu3w3rHVkF62CJee7+e7Ll68Fp/h+Ul5UqZTdQttaYh5G20CjkHMR0wdkOnzKgLWSoUyoFwoTVV1G0CmAge1/WjJ/lTrCCygQnr5ju6PmEJSopkeKsxWUEtLlTGSthcqv0f5rlfnBLTuBiCFmp73yzruO5Og0mBeyYT3Nv/HAGBJZiZdhz6FntSlPPiT03LOBfIQYQmmv0ebEs/s3Ho83+HY/tPlwV+ak/OgauJ7bvz4cBk2vGcSl1ahE/0a/BlABXpvvdwHsQkknUqRRM/6BGW/cSvVWBUUgb446ZGfnB60G8CzdWVDpch5W4mvQwn0rKq5bGmlOHYsFFn/LbCWHKH/ArH4sdDTzN/p0TEJXiu1NTOCTl8JSj52fP9Q7zSVu+f8h+SMFHpEAfp0yXNbe43mrSyW8XMGp//K3kYi2J27UVGxVXHZBr5MTLnsxR3ZX+euNWG1Dvwy2Xrv7TlVKbwAeM6iu6PyZw6U/0d7Z4Mkt5EHHtkn1acdG4+VRvu1/XiSKTYKFdnzAblQlMQ77lJUpAb2CHjLVrg31osEu+G0msIP7GtukRsmKnF7gI5qTkebVLWdewCM7+6eYGqIIdll5wP1tkefTqHz8ifU48wMcS/MiXIGQ2Wm5Hj7bVGAY0fQpICBqkPECdxRFGGsI+8vYTOCgQ0Vk2x5c098LhUuf183um96m7ycPXbZ87MXKsTsmGqU/ULt/1tJNiAXGSrSt3M15Gx476DaGiPJvVYl/vZBiSAbMmaBmFImKpMAr3591Sjkn+5De0RJ50g91P7TLLJNdSGf/LX5WQC9BwL0AS26xJymRhv/FjDgHQtgRoysiQrSvY+Pj1PWYCSg27TsUCoPDjoHSrvehcXdRj264yenZEjU0NoeqSRwWCuX1edwz1swPJSAj9/eG5UVGWlkGXc1l1/61/fn5wVbkKqgOxptSca2vT0BAidRD8k8cSlSnRyHwG/UnMEVgrmpqUyaWTWXCYi+/nsr0phFkcvr+T9dm8SlmkCNRtAmzcvW38TIIKonLoUm8+6buxN46LfgdCCfHBoE1f/jwIbbScbw+s1cgrs+PylIQyTjxlZDBiHge3adyMAtl5fx+sH3tuRSCTxUJuY/OCpUJRcfX1XWvPwjw686hI/OYG1cfyQqTSom0G4N/b6jS2Rf8GgZSnSwatZFyWWh3Qwx5ugFLxeOVgoYguZd79dX6hc1XXKWpswenAxd4sRgZ/LqlmO5mKoPj/7SnJwrydPajw6ViU+PLT2HVdNtGXKGyUvQib7SytlGQSAjp3pCDUmgL2i2dtB4Rtx7MHCZ9v3Q6DimKxevJaCxbgJAilCqTVAlgazGZYYNegVFwyPfvvVoEgrrwvEJtlCqBD7uQ4Vw3EhplkFh/AXGnP2zv3JHBsbt2dacO0HFyGyTgUOrAXzep6Iz0fc/VxqbzgchfflP1qS8fucDo1uaE6/mznHSnOmBPacqwyUAbBmcaD+qAYFd0vzufexbDFI3amWdo0OvKkVKD7KqemdYlYBUQiXZ9nPO4yA9UQJ33NPObcuMxSMSlEJ8Gw5VyI/9gUxteH57goaxE/tnGBH7sv3Dg1EYkqhYH+pQkFuIaM3wXmXxz84IPQTyygm6p56bUbcuAgRJBwc6sK5XnJGAlXPT4yDRFMjYxxlzh6RZ2gt6/dqPdgQhg04KEgApf/gzL2dQniY7FQGOVOJ5aNmhzKK4celG0NbVP9yRgFJ1UNIxcWL/7FE1KPN0qgVQ0Z3bjodyRcGNc7M/HONbr+0sTQ1n0jPGnjz02PiUw1UjIJvsGrQt8wBuuy0Nl0uf31pqR/VNBdW508dEeSyzizq1C1XQHaNmwyE5y5JjgQ61lA1L6pPwvvOdzeU6VanIU58OoEAA4MkvTwtq5uwc2lL1gOAwCwKgZI/9nVn5e/MorLs5CoKExgNnUyXjyKDWhmI5E/qwCDOoTtz50OjyG//pkfTy2TvnAYXT3fDf/A/aHuBg3sLh8UFsJsqHe62J88GvTq8C+mMX0oeSBssNTUneVVGGzRocDnrmN7S5LQ6c0wUSCCL6TeqfChHkXyUenUh1pmOPalhs207tyti5QxKAEGp45CenpS/E+jp/qA4ZpGmaeJdqmKHka9zkfrEnL39rU/wZmUsUFzZV0OUM4fzrFQFGoHChUuobEeSpSPp+gLPrF64lmOaLzLxwHiD2Akrvo/phr2c/MS5+A0LKPw8YUgWUGbK7+8Rnij783Mcf7s/3dw8hpf5bAKYHUzO7Z2eX60b0LJfPXEkCJlBRoQsd8OrN0H71uLFfnslZl8gbGhn/PaYmVBGBM3oNwIc/gtYKZuxHAC00mH2nAvgBYyKvevxC1Yzbr2uCIkGfRmX9etlDtu1iRb/MLAXgUMe2ZePqhjT7Tom8cY8ElEBJcQEQRzXAPqi8ST7fe3VXKn7EIwwd7aiaduJSmojtlSDy6tmr2Xt3v7HxQsQhVJjMKSKvfWh5Q/yZCiHfy74AioCJ6csYSrWsbYb6CsYBFQns3t6XO2xehN8zjI4/Yh+80LvYNtPbBZoDSW03/H8afweN7VMuRkWqQ2ycV3oi9jWXCVMHJmAFeqIPfuP3V8RXmRPDrvLd1k1vDaCCbxIjACqcOQmePXb3BLDun7WwzpcMTO1Hzetm3qfh8JlUFlA83W+23F1lZ9g2krAa+tFU+UwAGoAKJd178pEPf3VGKhDAWTFEVESPno9fPnXgTAAJMZo4Ryygoqm/Agg1edqg2GOfp6dSNQENUZwmZnKOxXrTF9flnKpEkI9mQ9yr99/cUwYM7JY7DZxj4zKn6szV2Fqf59y27dAm/SLumqTx2BFiHbdVKovt2pRTp4nBXK4qH0MNjO6Tgb0S/VbGDVsN3DXSgD8U3zjTQK8599Tljkvg9LdYC3chfZsNKJOfRCLf+4gbNyw9EJsjDgSuiDMA6/wpoMf7PPe/3JX+aP5LLImSyCaTy1737v7YevtvXwHJnqe6k1TLyt9OsgGxOnT4fA6kkiXU2IcyAKoeMjFldrsAxcTJu/Op8eXUvjMph61f01AmLqzNwhiqpmTu1bN/l2SZnJuAQNkJ+jJ73rDynd/6MMHZZ8160wal3G3xNcMpJbcOTNIgBMHas+lYDorSKB4ddZFw2I5eKIf2nUqpTFAi+HEJTc90MabOHBSHtnVFQzZFmY+h7z+8aiSGsJzaczITuw3dg0Claad356BQreXe5oNn0zTLgRsa2GNQtwSHHAe0I0hor86h2XBgjD2nMP/uEQnUISkuyIKHRsfYqX6gRlAVePKLU2J8/L19cEghb9Qk8Gp9T5cDkuvZVJigwi6PYNqaCMrmLBhe3v/rTTEAEh/7yIExPpAlClAVp7dNGTO2puzadiIXWTMlo5yZDm1uK9Na+NFQ8iQ3qw6XuxaPDJ8cDYA+tMCVVCRlERfU4LU7FtVFU1qGz5FAya9duZkBfC//xfqoTRg49smNWzEsAqARC2sTEJhrEaneK9CkgQm8lGEFWmhw0BXJaaa8Pzou/RN3PzIm08z9vQZO2blAm6O6eP56+cEfrIqxZ7hl8n/0r95PsGSPBNMoQZBkMwhIaKKUOeOCZwpEgicD00jKCs4F25y0EvdL/+sH4Qoz0C64oKTVOXlfFxzaKiDDeYZgeTlveMZKqYyVvfKMknwBk8riGPSrwd3K9IW11QTcG5/kOfWPqEBCoBkO+6BRL0HNHcPyPoaxOQsMlkTGGkJhps0dGnlqr0x1v3ErVS+VLkknY7fyw/pUoARpQWF6d8nwPN9ZibkKlqqBgpl/YoL0O/tDJYHA+57W9RjlEnz+Lu0jqYnS5nz5OwHf409PjLyvM8SQkgLVP7LgqfFR4dHL471Rxrxwkh94dGySAkppBAkEW+5yPxXKo+fLMA1vLYpF7JVqpTuZ8ni/LmXqgmE5p4zqC3+5PgH1qUNny8dv7U2QeGInRBkN8kbui/W945dml3seHJ2zzChzxPboS//snjzXzIXDMnF99D11KekTGuCcPv6oPvTJA9ubk4xKAqbOG5KgG5WNRLTPxIvf+O7+JPsaD1EKUZUoWUm+3ZGUxi9fT1WzQ/cOoYOh+Cz+0pSAJ/5hQ19+YWsoeQAYydTEGdWcB4EBlJQ9SBP/+/uz7uQ42RW2u25C3/K1X1+QZP7N13aGhggJjwLeyYvlnmcnBSlGZeEEQ2k7r0G+Y/aOY544fUBZvfRAejOGD+8ZFNrPfPPrq4O46hFAedI8jl4JSJkxf2gmi5sGz8az0QIR788muI/uFiliqDwfIogCREl0BZ/e9+Ol+8vhg2fTMJ/ZMW1uyzPZY4gvGwgo27v1eGhp7gQ1JvNQ0F0kT6/8cFvVCNypbX5HIum8v/mtjenZIAuaZtS9p9Mzs3//6QRMq/98YxqmsQKgkdvWNmY/VNfZc5U8lMJQvAZ0L7s3Hg1fHzDivPBbqrloy86JWU/Om4Bv8sJhqe6xM9TM7KUKl4ns7hZFNaCQJANXX2XTnpLoBWxUZ+W2VOLYcD5XwBmK3rlKFSnKVhsr+U0a/QKt+AngX+hkt4W+DDAL7XPG4PQnttJG2Bm2S2WaCpFnVA11TwRiXpI4geXxlj1zziC7kFl+zl1T4d+752RZ/+GB7LdA1/dhZ9BMO3dpFxUhPk01Qh8C30CtEPpcO7J3kg5VeoCfoYs+UzVu0LAeCeKgwV7uw5w7a4P+Uy48f+Zq2bf3ZPwu+i/7Kagz4Rlwx06yWWgpGB2AR9/jK//u/swa86rmTbVPj4mgU/+E91AZwaFHH3QWnI0JD47KXphHhIrHb6HkYgNYP2IPfJhEz0scszeVtd6RjnU/7ntuSgYKRxYfJb2uJvddxUcAKmmyt2aytMogExVByZIAJnY6fjH76dyKZfgt6Dn7lHU+fzV0pIst/0YJFpQ7V61iKex83eg+6XEwFsBZrejhn+Z+aG5HU33/nT1hzPguKEn6Kkj4ohrxM/aHepPkgt9Ko/8T4xJAu6N68gBH/DOKJQCQRDd/rz+TYl/6TBowG9qWfeubysWzV2IPJS78L4quhFHPbeT/e3Yo/Qf3SMLvucQG77+2KzEvgM2+SCYlOgJ7Psz6PvrspM/UP0+fupzqSFStGkk/3wx919lNQn/oXJJOoB9aMIUtz+oOigOxYQCSkhKx5Hs/2pHvaf/MftJnhh7ID1AH1Hs8/e7h+S4SN4I2AI/b292eu1r1flSqZHpNiYMAEJ35wUO6l2Xv7C0DJ/RL8s03uqPAE3HV1nWN2UvJyt9KspEpwl3bZaM1q0F4DP1R4q/4/53TezBx6oCUyJVhBQaf/5eLoqbjoaHO29/bn2F/Dg4tdwZEYMKwcpwQDcERw/6Ff3hnkMhOvTqWd76/JcE/Go8KgeZJXMNWPrUS2eFdJ9NM51JxeGk6VuFY21AObj2eoEMJyvsvundU2bj0QAx3uP0u4rAepXZS/wQRLrBg99yZq3HyjMDsR8ZEDaOmvyaoptLceCGlXailZ0ANeuRLU4OIHzhwNqVlwY0gjuFXoYhazA1KHGdi1CQytLzJluE2Q5iUw11yyIeGWvKE1ouBsd5Quk1v7s1nkujUrLrnvfogWIwUA+XiafZaePeIyLehNWmO9NmCW3M4os5Fx/ztvSm9uvg4kLLobZuOBmWoGmP7xGChMumRUS42jwBXfPuKhtK2U9skWxIAwZSGzt3LD6XS46IvfHRsefF3l8ex4AySubNWLgLngR88YFDXTGqdMHdw+f1/9FbkWilz4KibTtuwrjEJE67v2rf2xol+8/dXJPmi9HOi6UICQHv50ZJ9VdB3+23lpb/emO+VwXkd2kYa0nwRa7Rx+eHwn/uO65MKF+lXlJKZ943M2lKg8juqOucbzwd1Zxigz847h3xi+/E4ZE2BcZCTSDei/LXJ/j/3H+5NdUVpUtCFZtRvGG7lp6ESQqQkwrjdglwJIQPnzHAKc+4cXuo3oCRcTdPYm89viZPZvbaawowuMnd+bWZwnNh2PJry3dzFsTVZZyiuYIjBb1ULQ/lo2qw/oWuMXFThrlXBpvIr5SVnQ2DB6EKkrANOrzXu1LVD6dKjY1n57v4gzM//zsf5zv5hCFU+CBL0hbbvPx0pwn54+BxYjw6hWXFg3QZ1S78XlCoKLBdx3LsnCGbcfv9fvJ+kYutbe0OxFFwICNYvPVguHruQO85AooxJXDesOZwkKtPcP/k06DHETdACgRMQSO4F3gIrtCroJocUvf1zVbVG/xYjT7zCWi9+emIZMrqmrF96oPQepXn2Ynnlny797I5lAm3T+VRD176+p5J+NjiuG1SqomTMmV8bZRKD0ewzO8fhCKZxwzMoSSPvJ5/Gjoa+eexinvNY08Uy/4lxZcasIaVvy3wQThFY8ebzW/PsoSy1b1O2vrMv/O+tG6uJxSphzjI0j01ROXzxm5tyBqyF98gU3D6dE3hpREQzmXPHsDQ2jxhXE5oB2olEnDIcewSRvnDiUiUosbkaLOhzBNyAKM4MX9jMDkGe/qysSY9OQc2GjeiVZIbN2LrpaO7E+6/sDKrcfOhczsRHb+4JMIT24C7/1C/OLaeaL5f+o2tSCWIvfa8lb+xK1d33cW7IqQrQUQrcQ9UtyTz/wT+1qilKOKBzkES+QhWN7yKfCtT44N29uV/Lf7SzrHt1d6odAwZ2zVR0jtezQUQFPWOJj/TuFLsk2OiIknf77eXs4aqi+Xf/+Iky5+7hn1HCHvnClASD0+cOLfv2nMrdcA6JqrA79zw1oQwZ2j0BLRooWl078wu6tg+SjVYsCFaB27fjZBJ1ialAmg2MLOw1/QSnyo/+aHX6USDjqF98nz5GoA3OPJDFnXUuVQ7ufWBM7gElKvvKfrDR9z01ITQg554KTwVSXS0jobOd2iUBIkXNblHjmnLHsDSpqpisW3YwVTnrvmNLU747qosEpFKW65Q+gqaGStlHBcRryszBoUJPvW9kEH7Jmlhi3IyBZembuxO8szkCe8GcaoaKscDPGeeDoPhANy/JEYorP4gXv3udQXptAka4g5Bv/HTsC5Urdsn0ZGuAvsifovhVTb+3xVei9X78yq7sxR4iFE3nI7uMo8+muVOUJSUss+ZUEuqR7+3ULoGgINLLWVVxVb0QyEsMMwxwyoByRVM9+60Ktf90KkZo5SrkzrxeDnvjfFKk6tyrY6qfKvBoVgDQLWuPJDhld9wvATE/w24K+FU9nR+xAj9ur9gUfiPJ2ZZjpeHQuVJX1zM23L2KqhEp1YZziQ0OHz4X+jhbIEj1T2vvDX/k/OkRUs2QQAGn0sDfdL4c3NGcigfZ1bAHVh1J9Upyad/FHWRd3TP9GsBbMSOlLWcOjZJqlO/l+5Fk5rv0kemf5WP5bxVCiYT/nzB/aGnfuX1A1lYxIGemW9d28S2SM032vXp2yrqzHeyqF1YBu8T/UDWTvABoUeysCdvaOjOpa9f2CdTbtzR9V2t2NgknFsSkKf2r3todFYVJIsZHsk+og+mL7loNNwayAYkkd2YJeU/xCEATeNI6gwgQwb6q8lhniSj2iTtgjpQeZupXPsM9jcxyczU35o57RiRWAih5AfOdWc9AGEMfc2tS/mNPNrxxJUPXpnzp78xKI40H4mw/fHtPmrddBNxwD075Q/CC9+1LQMOhIY/+wpxwWtF1XApcQiUlAcbGdUcqx3aSnFyfHBSGTzLTd0TvqMnUDOsRRYwqUDafoLGFJnIhdJLxkwflcukD4eBkcy7x4mcmlgNrG+MoJAiy5h61PdNsw+l4LxUXGsga6VBSUMBmzBlSydn17JgSo5IrY2CmQZsObcrKJfVB7TgzdACIHBQaAiFD5XzRJs6dvJJkQTlWlQIl5Vv/4O0gHD/5v9xV7n1oTFl8/+hQhWy2hlOv1iFWaGwSETQezfpj76lL4rT8vf1BOnuYdH6pGjyUATzdO6ScfeL4hag0MchUhEivmc4LPWOc1ryzL/SAseP6JfCVrMiiTSIXEHEEKkhv/WBrGoiU4Sm27Nx2NIfW1HIVA4i1AA2KdLL+dLkQycHuMTyCGbdE/8SHL2wLokwNhtF774VtZdrMoZH1pMLDudXNH5ZeF9/Hd+bAasZUE1JxfX/6Hy+qgisT6E9cylRtfTsutAvYimYxCuRgG+pPpwLCMGl6HDauT0V1O3ahvP97a5LwWaul7+0LRVDgLkBKwnfrkyRZo6cPzPc1z6Brn85lSG3v8ECHzx6cagUHrxqxffvxJGZx+ChgXTuUTS1N4oKdIFU1nbM25ClRSRhiPw8xEsQwtOHJQoev3yp9RtdUFKbD50qH7h3TJzP1ztoEy2g0R5rOlwuC3SfHBR3U4KXiKIFoWN8YiotSLwOh8sEoO7tLvrkpiT5FJeff94OicLioAibJKnm3PgcHlEFxHdtUg6WenpAzOuuxMeWj9zUmNiQ4cL5VMyUj+kTcmdoJfaNGVtFgzofLrsfgo3f2BCHVm6R5kHNCb0Iv7Ny3S5k8fWAZzEEPpYp1oaz45pYyckr/cv+XpyWo5YicO0HSvt0nE4Qd2X0yVT3UIbYHMCL4wpMVALvv+KYcLH7ymmWH8v3e/PamGF4zazwD4w0tVcmhJNJraI8ybeagrO+D//TOsv3jQ5kHIXAixZim2s9PqlTkunXMXKLn/+OyOGv7AnEkOQyRou4G7aut7ZE7IljinJxRVWTVFhUUzqydwGlQjzTzsr/rPz6UuxQ+rkDPcLGj5zO4dNL9o5KEGfS1g6xzt44pzTtfRBvchUHDeyTJ/N7vLIvD9/mqQakADeyRAA4KKGnzInWr3wiVZvumo6mM6rtRiXSeTfkWJKkynDNgbtKAVJY+emlHqpTL36vEJgRX7DV0uX7HiQBEkLr0+F24XurG1gTdnXFnbdZoKFUoCejQXqmoLX1vb1mweESQXa+nzGM6dLpFFrxLgJ1Qfc5dDdVH8Ln+/frIA0PhyIZ+9x+9l+oBqqS7IMn47u+vDOKoRywDSG98kjupOV+1edHnJ5f7vjo99E9/Pv+BUdV8mwNncj8AMlDJV5/fGlCBrVYRXvPSjnL/c5PzPvqxgG9R6enfJXaNIAoUklMX1KimqDQYbKpaILk2e0mQKfCYa4ZVvy4BwgTTm97dF3pM74EUIk8HZXVmzCYISHL3iJy7535tQZkwqZpRotdIwymalf0AAO7EVz98JmdN0IJvLiDXwxZVtoNnQ20bNbJ37LKzyHbzS9ZYZV3S4ozg56sUSfIFRoC317+9qbTtUMmvW2f2hC9y3k0mBnCp2ABzUGWcM68e3TtmeCF6kzs0a/GI2IpI2x6/lEQts5Xa3V5+9DsrctfRdT0b288u8AXW3jr4PXRDQfru9+tTTX/yp2cmhmk8eCpnTxUxkvXt2lSqmB3bpfq2dW1TkmWS/ZI81GPU7sa1jUHVO/fpXMnTnrmS6oDnJYTDp6JFffDevsyoSjP98QuxPZI8dknfoABXguPcJ9GvqZQE06g9oFsmwEtqu3RuH7Ea9B9g0Jixfaqesu7tY7usr8RSQiSOkczoydDf5D3YeCph/Jr9P3f+ejlSfyYiAs6kfjJgF78UOdl9JyN/6ryhvVGnYqP5KlU/Aahn1IsHuJQYRW71wtWg+ZI0dxzbRNIs8eQfwy6g6NV8OeCvGKtuYt/MfNKLiQ42867hCZbJk1svMYt9wZoQU9jPo3qT5gxOImSdJDZAPt/hvofH5B6IQcUJVNOcQYkXpUDVDf7A8/OXf/4rbyS5CwWyV6fsAcoZMRq9wukD6ds174X226b97eX9H1TywQZXOhPukJgwoNXRc5npcQIoOKKm6htTwa/rnT4ltsR3Aeroh9FXXDe6kojHcBErtu4rcEdFTQzNTmxZWTFw9BUGQDtwJkCp92EzzRyyh2hqzjz7rh+Hz5Eoo16iyKGNUZPs1LFd2fBefYQ1UJ29R87xDaIr1VwuYKnvp5K84tVdsXN/K9K3f/Vnq+OIbILyI26wDV329t5kWQcOno1Rl8lDVGSY+M3jJvRrCeYv5/IfbYRIDgoKiuIEqRd84JAqueJDK0dyItBmQYBDceLAmdJ9YDVI6+CeU9nsQUO7Z8MsDgcCQVRFaTp4rly/dL0MHlOT2QzKfxA4AaSGyKHDewVx81ycDhlDaLD3tikWJc2jV28m+NuxsqHMeXBU1AtsoMuK79tKnRJ06i3Z+MGB0smsgZ4dg+If33MyU4htFAPtgvuOHNX7v7u6PPib80OR+vN/+m65+yemVM3Fl2+kLJlDf40ywaepJilR46tDaQYN7F5ef2VHeKcMoUstsNVHgkMrWPXdfG9UFslPNKJPXU5WzUkL+BnN40qNwzTAXY8T3LGuqTz43OQYCIcsXOFNTWXTRwfLhHlDEqQLOGXEkGKGW5LJMXy4ZH8mSzIojBTjKJmUfDLcL/3nFeXZ31hY1qwwHLL3Z9k/CWQvBltCq+dk4JBu+RzPmP6Ns1eTkFLgevxzk6K69M7vrCxTvjQpw/qm3TE0lxp1wZ65VBIazkjgpx/H+fTcLpuzAGU8ZqBP53b5p1WmVoDkUuGsM06CklaUGH3u9IkLOZeQcBQ1iTNHnvkdtQY4dUugyfCRKoYeKQUrjfpclALGd9Nbe5MgOL+ctWBIoC8gEWhwrNNnDgkd4I1Xdgad8JkcCIPv0nMQaB8delRDsjyXQBQtACUGDQGF5Hu/vzJneAWZ2TF9EoCrMuAmEzPwXu7uOmd9QW2MDeMpQXr7B1vLA5+bVF75s7Vl/qNjE/jtWNNYbjPYjQEmp2xw48QBSbadxV2bjpURE6q5LgIo39szK6FzlpSTBGGqVhnMN6omQQK56jTW3fokQbPAioHnpJ0rCY+gQuVi/5bjZcy0Aal+aQquaHFXg/J5dsEB/rlnsGYclHV//KmJOQ8CfKBFmjzJAh48U9a/sKMs/MrUUJCAEuzalGkDYtM4UDaQ48MJtxdK0Ry7z6XUxCmgBHCS5jcIBre/trvMeW5i7rRkng21FuxL6xn3OeRvAQlmvUC9Du46WebeU5ckRqD7+DMTI+bAyUgwJKSqnBIQ7z1r7rBKVezdvXGo5gKNHltVNNx/e4wCwOqzAymLD+sVTr5gQqDtPNiXLe/tLz/1jxdV/Tcd2ya4qBte9Q4ZrLlvy7Hy1E/NyB3hHJ0X9s37aXqktCdgfv+FbelrWvXW3rLo6Ql59qUvbCsz7h2RO+X/OeQX/2p9efIr0zPcyuCoJ35zQWwDv6A6BHFrLfOrVFPVQveCyAtM9C9UDZkqg1cT+LHhmZzcqV0QPwGd90IfGTigW6ht6IVAIN/9zsUjy+s/2pHkEQABxBEAS5LZuvUrGtLnYN/QuDhy6LO9hmLX72hOD4uqNfDJ3kskJFLuNqqr6pC7ILBgv0lou+8cuzPKNmQexPqmMnVhbWg2Kio3r9yM6hYAiL13bvVssVv2EXrr/qg8CrqqZu+rCWQCcOCQN1ZT7vlcPuK972wuX/tHdwfUUalHXyHZzG5aE+fmyNrG8ov/5v4INag62Ss9etYWui5I27nlWO4rxS7PCA2v/FylQCWZFSRp9lV1clc8C+BBYrN7+4kAihIf1WU+nC9HddaHofKL0uZcoqlYK3dF8i4AI3SxeX1TKjECIsg3JBhirF9QAuqe+tktm461nHWU41tl6oxBqXpYD0myM6xSLQ5A6wPMOGcqL2KFDesasrbukESBbdIz6MyQrReH+Hu+Wg+qdSDZCwwRFGJJAF8oUxLkYLtUClXQgKL7954sZ09dyQBU984erP/oYCTQI7OLOn2jokNlxpVhksQZTl/Jd9BD5hzt33cyyS2QiY1gM/3jnEc5s+FsmbuwLuyCUDgHVlQ6NpBtUaVgg5L09ajoVPx8hnGiUm06Vnr165JKGyr7mnf3lykLhmafiRE4/2yR9UXfU/20b+wjyX6URmApBN8rkv2C8iEVaGqP4uvG98veiR8BJxfOX4lCZaXihsHRtko6Gs+F2iMZc5ajYlrbs6rEdWgbWy8+otyGOotSDIRRkTC/SrXDc2/fdjxDJr2qhvBqPpz9lYiLodhAKmHWiY/yGRImNjgiFxr6e3cK2MU/9+jZKfd1x+Zj5b7HxqcHED0S5VpLgD4n1bHDq4+UkXfWprqjUmmOke9hLdgZgCBQnSCQc7rq/f0BfNgXwM16QzgXDMt+YcKYtaTy1qt3x/R9ub+SIqqTYl2/J/kkNBRQq33bnGffhz/kk1R6x0/qnz/zM9hAI4EZ7dskhv+ZX5j3469sWHQPv3nDkRgYXft0p2kaR0P5wJmyYc2RXDSHmxpUUMZDZ1LKYxh84a7dK4kwBxyiqipRle9Mg6yCd18KWszBQdJSvunbpZw5dDYbPnZyvzThKcdVkmklJToX0eFb9V/WlfGzBgW1c8gof3i/nduP5ZB6bgfFxFvBeuceHcuhNUdyMT8x9TSNq+0SaNvo4VMHJLtFJaBmoHFTNmvSrsOnKScj3tu1iSKOoG/arMGlbuqAsn3j0UwDtlmMHl4wI3Tvr83Nd3FJfuL/uSjlKrJ1KkKQFYGEA5XmuOaLQUz8OUUtTlLgy8ELVMMnPns1gUUacGt759BYCwipF9UgwRdEGgqg+Slc0akDcpEg2g7mjcuVIeUUHDhKJdb37qcmlH3bTsS4qDChg9lDBnLpq7vK9/9gZTiMnMaKpfVl/Su7Mn0a2iZJk2CMWTwinHKJhsTA3uxefaR0H9C1TJ4zuNxz/+igYoJdRoOevsDZmUrCcfFambuwNo1fHNXgmQOScLr8npnBohaEtuAMQQY4YFx2RmzvjhNl/6ZjZcvHh2OokhyM7F2mzh6c5FBgS8Fh0IBulYRp20qmk8LZWTMSajqlj8KlzJCuN/ckAOaYffbOdU1l7Og+cYiMM8QznM1hvcqmNUdyvoOc3ryVRPXpX5lfjp+4nERUgB4krUPbsur57SnXDhteE2lW5/+xpyZUk8NrOgVlpAEO6fffDz83JXQfzoDj1nvE+J03PG5+bYK/I2uayo7Nx8v0+0ZGQcS5dk78vWGPnKIE3D1+4T8ui7Nwr7/7Wx8moHFWpi8eEUSG7PFVksmDqqmsnIE1cL6amy+nb+bBz01OQKvsDgFt1xJkSCZVXNxfTthzBum5cqPs2HI8DlwSZH+Vwxk8zt4LDcSepCn22q2y+KkJ5eC+02la5cjcCWAAyiIVEvfmzkUjUpX02SgaZthkFsKE/kHuACaU7JyXaLvPGRyHSxwBXcXL3YO2Haw/m0BKAOL5vCC0GYQlAb3xSaqe7oUgmGGHZFFlE6CSlxwwqEcUfCB6Ei10FkgceiEetOFP6BvW1eTn5W/sSaKhVI+qUKHx7SLm0DroTIBB2OC1H20vb768Pc+19PXd6YUSCHsJvgAIng/9CM0t/TxUuHp2im2DqvpzAdGU+0dWUpsXriVhU70USAh09L5NmT80DpVd1X/BDm9edSSUTvQViA0KH4DC7xt8l2rXO/vKuPlDY6cy7f7Yxchcm5EhgTRz5cv/+r6oAglmJf96hewVOydo4YjZhOjTX7xWNi0/HGeNKkhdCTpHKtc5NWsjTa6osMcuZt9VxN5+Y1dstpeqpvuDM368/kzOujOGSgXdj0Z+v25JAlMRPX+9UkpyL25+GrvpHt771ITy+g+3hdoi+Pb3zrNAEohGNANNkZ1xVjh/Ut1ENDzKsOG9gi5Cfg1m276+KTRfwYtGX0CZ6l8CoVtAqF6ho1Sym7eFyukMV9z9k+Heu998TlUprQYbCtzWrThUeo+qKf/lN98MAENREGBmnUk+H/zoUGz9k780N6Ip7hJZWrLP5ly475KCj97eGwqj5IvUMVuArivAA8QI5PQgOL+tcYR7yl5KNPRZ2U8+URIokXR/ADXAH+eET1i57EBZtdwsmP4BXczrAigJEtlZtCP9BIA1iQLapP+HTmvWVXWWyLln5vmo0lfDCG/El0tSJYM+271CvQTU4a47Cyp17765K+dBv4F+PUiyvQSySJQAItaJXZb0sPMZaFrbM0GjyqGqqM8LJX1knwzvhDxLlOwRyg7BGn0rbKOKDMRf/5lqqPsn8ZIQSTx9Ntl/STkQU9KARiapwupA/anfSJGqEhjx3FUvXSkHW4Yis9vASWeaTWqVxpZ8mm0liWdHtjmPA6q5Q/0Hd0+S7I4CBShL2XOCDdZFQoIOqspILTET4hsvJBCmJEd0wX3lg1WfqgGzlephBHeaLwcQU80lXuFOqYqKt4hsqFbZW1Rga+Eu611AjwPOsrf22/1rlb/N0LsOlaSyaj8qN2Codd5UpojX9khiHp+eKtPwsn/nyfjW9NJcNL+sV6bEX716K/st3kXJAwJIJK3Jhg8OpGezfuuJxH+HD50rd94/OhRClbDNKxs+m+T+0h+vjs9b9BNT4v/FQZvWN6WC6/7zee5zepCGdk/M7Ky01yPZv1vOBX/FP2PS8E+nT14MhYsYA8na1obuVtVMZ9ganW44F3U4lFZJu7hjy4am0NLq368vQ4f1iH9pnXE07+ExqXw3NV7Ie/23vv5GycZP/9zcZLW7VjTE6atsMCQM1Pa1jWXkjIExQowhpyDYRoFy+aGVptWu/GB/vjDUwiIrBRoQo9lbkF1dVI1Nt0rToTMJFpXHTRx1CKYsGJbgFeIva9UM9+KfrEnz5sof7UxQ4kIv/icLg5pNnzm4bFjZkMZPFI901F+7Uba+Wx8qSwzfJ5+WRfeNLpMWj2iZkNqpbFzZEM40B+og6gc43JJl0gaftaCawDpu3tAECHi1DP0zPzMrQ89QQATXBzQEXqkCfN+XKtTeDw+Wl76+JoY7U3+junF7OX/uWnnsp2aE5hR1Ag1Ep68E1dqy+kgMqGZS6I7gWeIBwXPRDuxsTsMo5FLJG4q3ZmU1r4Eil2Fq0FSULciUMruDcvzQ2QQTqj+Chme+OKUsemZiqkoqQetMcT17JYkMLn0nMzYGd08273NdUM3tPck01vXKxGYGFw3l4V+YXRbeMzKJjSTVM507eTnPqZ8HjUhAK3HTdyHwFbQ99cWpGcgIYf3G7y4PAkBC8MOXdpRhQ3uG2ywoEAg99UvzErSfPXg2XFFZuoFZ0MrMe+jVMY7t4zf2BBGQfN73+cll4vyhQdYEA037T5cNyw/n0vlMGvcv/mBLkl+/ywCrTpBZFSBz3NFu79mxPPsPFsbokW2lgEOytzWI2bPtRCaaHzlyLsiXCezrVxz+TMUKqkE+U9leoM9Irl15ODSJ9i1y0ofqT5bRE/rlvOOqosMJHrzQfyBUrYOVoHKcLxWoaZS6Wl5oLe5ozaheZe/b+xKMVxPNK26u/z7adC4o2rFtx8ugSf1K3aK6BAD6JKY/OiYKaCpLbMDXfnZumthobR/dcixJGdQzyJSS9qzBQY007GoE14sjmZBIZRAcWeDenbOmHKCASwIDjIBCcrCokM42UMOz21sVI4kXnqk1V7YnQjGkrmcZMLBCahnlkaP7RNWGo/CeAiGlcgb/2Z+YFkMuyH7t+S1BgVEYUJHQX6xjn8HdQssZXtszSfWIup7ZczQPL6iZNTSTgwXGpXevIJScOWqFHgsJnmqLio+zInnQ+MxuccTK0+Sk3WdOr01bU9Q7tSjoXA83HDXx2Z+dHRUogbnq5uUWA++z0E8ENXrXgApkSW9v2yb3EuhBJjYT6ynkHTufgClzQ1qqGAI8z8eJQsdRgVShnDF2CC1LYHO++XISrVAhuncqw0b2TgCnj02PjKqD7zZqYt8g9wIv30kfBbqL5lIoJ9QPBe5qixyqZ+E8JfT27JW/Wh/1KvcMhxqtEUoLTUNvdNbYIkID+tAAXvrD6iZV4FMUE7t1aGmyPBYaqztudkX9rpMBEvR3eR6oqp4tfkKApLLYjyR6jw45S5IYd/DwAfz9m2XFsgNJAmfNGVoeeWpC7FJs/3HiJrcHKQZ+AW8ktxr82QFJk+ncAixKPlWS36uMHNkriaNg8uM396SPwn0YOm1AejoEEWieRxrOB/2EJro7gjUVPNVEqLC+FD4ytKbOtP8HJACnvOi779xxvCy8u+pBS1WE3Ou+0/nOpGAXf3Va1WOx5nDWDn1j6twhZdbT45JIC5xRVgWybE23wd1jA37tN+/KGSLvzYahPgs20ZdTOTsFaf8kNs25Un1TGRB8kghl0wTOrZSjj5dUim/ut/slmUwyfe5KqJkoO+6rKpZ1Q8HmLwRBgi10SvcMS4F8cwaVDeyWcyjo9P31UQY8OHslCei2VUcCJNjLOxeNLCNHV/Q8gajP1wyuydc5Y9/4XLQ6SRgGAlaC9924tiHVInentq4mgaq9F8Ow8yZJAyuAZ868uwzh12RsCKk7T5BFwOncpa+kV6dy9MiFzJJBw9Y3qUIvyFTpbEWagZHiARVLgaU7oMqmoiYuQYEElnp+NkjfndjjwLYTCZI9J+AQ6KDi3Kdvl3wPgAuAjm1gi5wb8svQ9Wr2QtVAL6BWUci6khs/UCXeEhAMDWdr0oxBWZ+5i6pKkQrjgU1HU1lgg1JZuqWy1D+JgORZzMSHA4l8TwCutQYWk2313+LMZUv2R8KZjDLA0H12zyST4hHJofPozkjISNNaP0AX++jljNa2zIwAqHiJU/j/LRuOlL5Dugc8URkx50fMZEo8poTnkYChO/kuei7s4fi5ldDHhNmDUsUksOJ7qH7s23UyIGfrOIMpdw+PfVPJUPkGsPjlQ4fOZm9QsJ0NsZv1FLPqYbnv0bFJ2MSGKo1sPRaKXjGqX/ZDTCBeWf7+vtgMyYTeKtURYFSXvl2SDLOf1g9Lgu2wvz/7vz0YkAr117rymwAea6/XlQ34W0k2vBxsC+P1/d/5OCUog7KgUmfOXksQ5RJ7GMjaSz/YmoOnlwLPbQwJVHzo3p0T/Pq5IYO6ZfOgYRy2SZQ4glVDumFDVRmTQ4oCRsOZGBWZHpWNEdMHllGT+5d+E/vncFFDEqDILk2enL2QMlS7HN59yw+Xpc9vK1/5x3enDCnQ0rgpwLG4jInDMLC2Z4yVhjYObv6iEWXKnbUJJDg6l94mV07/Rjl66GyME5SSo5bl3rh6swye0LfMuWdEefWl7bnUSl1d+nctX/4HC8r0mYPKZQPS6mqSqT7z3JQYIMgTZycwU/lZ/6Nd5Tf+2eL87ms/2BJFFoiI16hx/VO2XfzkhBweiB0qVLPeC0Nirt0on/u5OZlWzukJHh0YCQyHiOaguoIO8NZ3NpdVyw9WtLUj59Octev1fSkXcnJKb2R/0RMqrfpPoyzFQHGYDIWpzq/97soEMy7qa3+xrpxrOBfj5zk7de+QHhhqXhAWwcjMB0YFbeBE9u5oToC1bvnhBExPfG1GDEO7Tu3Ks78wt3zjv6zMGan0s6+FfiJh+eXfejDr5rzgjTs7qSqgTVy7WRY8PDrPQ/0mqFoLUo+m180l696hdOrVKWcQWiOAa2o8W6nO+C63NNlWIgKhkO07lc9e8cH+OJeF94/KGkQbv3c1j0Gw13N4z6iTeTGgejRU01RFBNDnGs7G2TIwqicaQl/6d8vCD/dShoUSfvOfvRdjTP9bEBiJO0PW9p8qw2p7xcF7QcyuUB27cSu69ig/HC2FDudu3BNjExCtfWdfgjbBAcRKkKHsetezE+N4Vf0ouggSnEWfwWm2vqCg6IHT7hsZqgg0XP9CNZzsZgylO4dWJNCv33+qUjh6mXTr+RhBTgxVwNnyPdEMrEkVbFz9TK4S6i/w5LjT3HbxWmh5/t7Z9X4qGQIqjqmaw3GhzJgx+DP0TwKcAXcrDpXtmxuraeqjaoL8otNAq9wH5wedi4GPkkzXiv+LUkPhTOLkjgo2BCTOHMWdr//Dt2IL0YzQyMgAm08DleVwUDA4PeiWxmVqWPqifC+InqDAOnPc3pPs8ratJoHfHgeCpkolyWfe+8jYIPSc/g/+ZE2mUrfOdhHUjh3fp8y9Y3juliAQZcE+Hzt6odSvbywNeyo0U9ULeMRZ4ve6KxTONIlL5iRE5gWp0o0cT766UsVS6YSQo02a04MeBD3E1ZfczZo1NGpD9s59gORCTk8fOR8lN4FGKqlDeuZu+451I2pSRTCvxtml6hbFFNziazfLA1+cml4DdxCdQICvQRQi6r460xMnD0ygLTHgJwgxCOzJNeL2s4HAGFUh5wR/GvUS/UofFmDCGRWg6oWiEiOgy/ya+bVJeuyfHgZ2VJURfRL9FCVBsOB8Sd4kZUQGOHm9C55RoCxoo/QikbVHzpx7xn8CG5xbgQWb8+6L28vQGYPK/LvrgsSj/h5c1xSAThDvcybPGJQBlBplUY+hpoJNM3tMRBd0uSfOvMSWL97x9v5y49L1qqdgVUPZsxPN8LZKNKLxfFn98cHQL+99cGx8tOqe/kwI8H0PjUkPVesr1JWRveN32QQJu0CUQIdkho90ntFVUaF37RTgni/DR/SMTXUOUHU8l+BFAAVwdM5aJXfZJ/sdn4gS17Fd1lPfh3MnONaPwLbj9Atsq96rT8umdU35eeBfUG5B7LCeCYT19M1cVJd1oKh25eqN3BPoMXTfGRHws0UqBILlDE4bWM3VEDNsXHM4oBKkWkUE6q5HUsLNDrWqsuk50N+DZpXp64fO5GyLK3DsI05x5krOuDVxlthIey/BJbARGdqbt/Lf1Nz4EOg0Cro1E3t5fvQtDcX6vvgx+yFQBSYCL020T+wwbUDOmTUELIanr4q583gSnwxqpVrVcD6AYuOO5gC0aMr2JvKoJIt7d04y5ue9n+/Vmgj5bMmqn/dnerPcd5XVOQ+Myvep1Afbx95acxWIgUN7xSapdgOAnS/vBTyW1J886Z52Dygt8CYxbIaImEky6F4R5+DHli3dH0DL8wC53eNqCC27UfXjoOS1ggsALqCeqhogiu/0nVRa2GzAmEr9NIp+F66mSsSX6YnlYwDR+lEAVM4J/z6ESBBV1e3H4h979+8SsQAxDyCd3Dzbpk+WjLsYAktFJUZV1+iEEaP7RumvmhfTKQkOfyExBx6nl2Jw91RC0BbHT6n2l0+VUE+YNrAMJEF95Ub6pylWZkbcvlNhauj5iFT19IFlxbv78pyeN703Z65mZk9fU88vXw/wDwD5m7z+xsmGYBziz8j8w999NF+a1ODlyzfL9hd3lp2bj2dqZNPh8wnYH31qQgy+aaumuTrwsjlGCMfQhrz8w21BiBioBIBdO5S3X9uVzzm0rRog5RIxqAJ/xoFiB061oLpx/+mUPGfOHRKD/aPvb8mCcgicECNALg2f7blfX5jS8bKl9SmzQiCUKxllVC5Bn+d67EmqGxcjrbr9gwOfDbBzOHDwdmyuBuVwWFHTwllceqAqZzVWg140lFYTMS8FAdmypjGXa/SMgUHXXJSuZP4az5b5d9RGSjLlyCmVoxagMJijFtWVVS1TpaHJKGSSE5xlTWcqBg6k4KFVkYV2+CNfmZ4GLIYUYv+lr0zPEDHqNxot/ewTP1kFtYKbkdMGJJBC1Xjo8fHpielV2z360icPkSvtV6YurkuQAdVBHxAgUogQAKMvCFY6dG8fVICzPrXvdJkw1wTcHul1MMWb0Vlw78gg8xStUB5UTSglHN10rCx7bVc+20W1DpBgxkPTf5sObSMFLBhsOHA2TlUCLJiFordOJPaer3x9dS4EY84gfOHL00Lj8Jo5Z1joFCo+HIHGNy9Dvlxe51JjIHWUykF8mt4TwbcKnbXP87VtU7ZsbEy1hRFzKbt175TAG10FajVkTJ8k2kqREBn9Duh8UL6ZD46KSsmo8f2SrPq+i//e7GpA2+JRMQYCMgjDijd2x+CdFSQeP1/e+EPzMq6WY8cvlIs4mzVdEiTMWVQXKorPETh4dkgf5SeBz+IHRqevKOj8ruZU1/SYkLy09zu2HMuZZqSdY0ZNUNc64TiB6oXqexIhsDfWtdcIPTgVauj/nUP3b8l3tiQBVBV89hfnlrsfGB0ah1I4OWSIOvrCC9/fkrMMrICUo3nYP0G85Li+pfeCE+f8IGTsBfvA0R6sP5lEVAWGc/jTP1wVB97KgX/v7T2RxfTdSCJzmoJpe4Gbnem2fbrENkFdA4AM7JHqCXRn//YTCWaP7DsdKVRBrT2njHT/z85IUiJJFwCQktR74D6TV2UDBPCQJH+mesuYt6qTQJDnzRsW+7h764mAIIIsaik+R/CAS+/58LutAypl3eT+Qan1FLChAjsgCTQOJYpj57ydZdXLqYvqSocWuUx/rvG9PnS93gnC2Am9WZr32UJnJJN2W1BtFLmDG5rKu3++Ied+3KRK+nLThqb0UwnW//j3VpQPl+wLMg9VExwJGogy7N93OspPOMLpazh4Nk2jAjxVGu/lPSR/kgIlfHbQeVEJgM5aP7QaCHkmxB+zXzcj+GGNu3bpkN+fNHPQZ1PU/axgWIUdeIIuQdffe0kM2DPN9qm0kgj9f3P3H2B+ned54P2yofeOQR2UGfQOopAgQYK9NxXKsmQp7vLaThwndnqy66yzsR3HduK4SZYsq0uk2BvYQABE72VQBh0z6L0D5Hf97jN/br7dL/tlbUlWcq6LF0lg5v8/5z3v+5T7uZ/7uVQ5dPtOBU1/mIQbkvkXv7Moe8X9nzh8Pggye6GRWVVy/64TCaDo3gM2nAF+QkDa2nK6TJ85JJSQlcv3JahFN2ZbAAmof6hfgi9qVvtWHwy4JlgSiHXu1znvgv+DlL/wB++X917fUTXnHzkXoIwdEXyObBvm54zYN0A+AdXI24eXyTdXFe7ZDzWW1nUtCY4IBgiKBMpsvkqV9QVg1Wbl6N/5r7s8+U8JBoqUKqA5DxrZUUlUQ1z26tF9lf8kUCGoEqA6s0ke2lfD2VBhoelk0s3e0t8mU/d9ZIQlud4NZNVe9XmowX6Xb1K1UI3md+Y9PCbf3b1nh7JOA/egbgFXVHpUyzKnpE+n0FolzoIyYIZkGdggwZbMUdMTqAmEJUXONXABvWX3piNJNIc39E4VB8qP6eHdC9zct/2Dsu0dGOJqHwNtJC6pAh08leonGpWzBnwAoHh28Yg18byZDbbzRCpZgA57G4CC+kyYgr0SnBMOYfvMHdHvp+cFRdDa1EYV6IHwb/sb0CMZcNb5HNVt79y5RT3yjtgptOUuA7qmhwK1yr5iWxa/3RzwwFqyscQDxkwYUFa+uC33QTnSfrUGqWLvq8SCho/olaqotSX/itrKHwriATzWyD3YJ+hgbJcgl2jO3NtGhAngeuGv1wbstMcAlJUwwAexB5gyzrreYe8JnQyoyd5kjlPHmz7qU9Mkb36KvUzCXjP4Gy81BfTgn9hwFVhSzeh7wB0JLhtNoEVVlA3nlyRsQHA9c86lPbxz+7HsNUmkqqYeSU3e9i7lUSA8lkHUHC9fi33ie+2FJHQG4p5Wgf4w1WEsAe8l6pFL9pYv/crLsaH8ifMXaefNhwKqAZ9d9pt3BBzkH9pJZFrPlrs+NTkAhTjKmRWrTJo1OO8gkvXbjiaB0QeoaiK5tX+AMz/UZMMiKOVTxfnLP11RJk0dVJo2HylHtx0tnft2Kl17d4yaBG3qg7tOJDCD5NkAsif0nLee21LJXF64kqEljDzHiG6lnJVZERp+9p8s7bq2jyFBE/AiqKDo6l/+VnPZ8e6eigs8ZUB5753mGEwB5zkOcdORsm/LkSQ2EJ8l7+wqw6YOjJzuXQ80BgVhQCwYfrvPJTcoEIZYLV+2N82WB1tOl5/9V3flwGcI3cg+McqoNzjTAiuHEoo5ZFSvZH8MA/R6zfNN4bF6iUqc5iEoG3KODz06vqx6b285dehsKiuMaCTeyNJdvpY+D5vL5oXiMNI2zujRvRMQMLzWlnqVTedA6wvAIxcQU7uw6aEjNYenWf/7f/R+6DFQxRXPNZWFr20P4mR9ty7em4Pn+u5X1pRJNw8qj/yDuQkyp84bFj4/9FvCJEJmOKN7PrRnjKIgXxLwk//8zqB/0IiH//7cPO/GdS05RBAJRmO72R/De0bZigFQLSHpSeJ4sMnavTulQqREGom4nccrJY7uHcro8X1L87YjpVvvjmXl0koOlZFxf9657/F7g6fVJVh795lNeW/QYM6EkV301o5MiZWsODy4jWSPb71zZLl85nJKkpI3XHLUFA4cTeX157fEkFfD8q4LimymAsne1/9qbfoq7Nu5D49JY5W9t38bB/1hef/1HRn0pOKir4SiEOREgIIDL3mB8EUCumeHHG7G32XiZ/+GPtm3AgbvadbHx5fT5gJsaA1iJ4Ek1rBy0Z7SY3jP0rT1aOYNSFgE4FSUGDSqXNRr7rq3IciJsjDql6SBUb98zoDAHukvqIxUFRRAZmuX4BXVbVvTsSQ8qI0SyUxO71sh3AwXB2qomiQcKoj64P1bT0pX4Y6/uC2KTCguUB1/ZjYBRMsaS+CISijhZzCYALB3pY5FqAHSrZogcPTeDd5yVjM1eXivsnphcwIaKCWaEjDj219fF2OPzpMA9PDZoHnOMuqkwImN2ri+JXZLEnTPkxNy3m+5Z3QZM35AggkBSJp6T16McddPYx3RKlxQ8g3v7sn+g3BpvEWDccY3rDqY33/601OCihmU6Z679a0SHoiShMC7h3bTeWdHOB5DTJ0fzhJnH3VUQMphqyIOH90nDo4aDtvbtJk888XwjyXvbAN7a79xPihh1KyOWef6ngl804N23XWxacAAlAhn8PCmo6FIak5ubT2TROqOu0blmQEb/Qd1TTXDdGyobtObuzLjgJrY/Y+OTaKgUVIS0aFHh/LWNzekWZ8yj6qx98peCQrYLvaGjZHI6SthLzXEUmNBPasFDDjHkmU0N/duHdjNBMrHzpdv/qelScB79+6Y79m+7ViqTehlfAH0GxcbIs+m+qcWkOAo1xKvX/pndySBlShPnzsk9yQggXqObOxbehMx6dspkuWQVnLnaCJ8HB/1u7/6YlRqBFkCDWslSeAL2RXgjbkS217eXuZ/fGJ575sb8wwCWr1TqrqQfsHAbZ+ZnBkuBq/689Vv704Ao4dE8GHdJFrEKVDz7FNnw2ygk+ZTHLtQhs8ZmkTEvpUY6m1gi6rp0ZTh2n80Yd0eNEW9dknA+K5tW4/m3lUWnE0XKXf2TbI3YUZdpMfRke3/NNSevBj/ge675K3mvGvvleLbHQ+NyZmA8gtO73h4TBk8uHsqh3yP8wHwjJLOpsMBk/RZYhagHAk8+bUTR8+XIfV6Og0ku9YmnHJ9QDL7TOXE2Zd82GfEF9JwLKnp0zmBlnMo0SMBLlkcd/OgSuJ5cv9U82sVPAEwWVnvXEK1q/l46CfWx1nUeMweAQ78Dl/KdvLr3tmjT0zIGqiOQOBfeX5LemnsNUPyVENnzB4W4FRfTW2CNDDNOqous9XemyBXr5JKsPOpMuMdE5eR0JLfFdM41+xCVBvH9sucplrCrwLlQsOS+J8+cKpMmFpXTh08k+RcUOosZaCnnqYBXWMHBOF10+qyFxpnDEpFDmDlc4Gsgm0xkvs1OyZKid3aJaZDG4rAQ9SUVJG6lslTB8Yn8CnOmCQYoLNmFSGbSWXxO7uSLI1o6BWqp94ye50Np4zFH0uGiTGEEn/gVBJzPQ4SAEmIPSHO2b10Xzl+5Fzk5u97ZGyqD+y8tXamMFEAqirmGBrxhR3bpT+LX+evBPXeGbnmJLid28Wfsz32B7DWuxfgR8yl5Uzuq9Y/5iw5s+kfGdkntDdVqlq/r3vxjyoHv3fPZ6aWMQ83JElGMxavaLQH5u1bVTXKu+zHiAbp4yEkNLxHVCUBNLfOH5kZMmyKeBPQ1q79Talq+nl20L8Jobj4UBPHf6jJBrqDgyOh6NazQziNH//U5DJ0yoAy47ExlaLG2H5ZbA3GmilVJDhVkzsFxWRWd285Wlqajn1UjpRBLnp2czIyGTsqh8rG0FGUfo6UzWtbP0KcZMOaie/9e9NzD2mw7dsp5ShIgVL8XU9NKP1H9coi4ahBxKC7nCxZTsEC1RTczxXv7Ar3c+ac4cnul77dJlt2+Fy+S0CcffBhCa9VaW7M1AFxrsryEHfOkVNyuKH4tM1v/eSElEttamX9Bx4Zl4DHzz3/7MYkHyPH9Q36sGffqSgG4fw5JB26dUiwePpYhVjGIKk09O2SBiKVJH0zb72xPQOulN2UzjQQkjAkTQu5k9FLYBhOHMa7fmpqAgA83/pbhqb0qCqkJPm537g9Q6dUOXrXdUnQDlnA0RZUMmCMmew8jaa431CUru2DlgvUvB9BGgMuo+ZQyZvKjKFt+j8YFz+vL8bh5Dg0xXFCn/sn89PLocxH437t1zdGrlRpTzD54KPjsl7WU5CH44+64D5NE37i8zOqhtgPSzjZpl/e2KEaJrhu1f44F8G19Vu+GO3nWvYGh0bhzIEmCwoJhBZzllQ9oP0Sqd3v7EnyJOBV1qwf1bfs23OyjBjVswyYNCBldcobDC0DLQAd2DaobsYd9WmcY1Ct1bRbhiYJTiBx4kKcMZRJ0JBhYaRmD5t23TnrpOokWFWtIlZwcO+pBI5QIj8rsR0wrEcZNa5PufuBxnLP/Y3pM4CIQVah6Wl0HdAt5yK9EZPqggJBUq2DpKFnXVXJsP6S85Xv7SnHj59PJah2oQ5aZwmVsyFgQyOTyC19ZXsqjkFVr35QXvvq2mpK/Zh+qTh97V+9GSfJkHISNz/cGEOtdP7eM5tzX3p4VMKoylGCmrdgZKoc1oXz4ChV5nZsOJSAHLInuYXECJrMCbn7oTEBRh756Zlxxmhd6G8czEOPj4/zH1hXNddxHtbZ2Rk7tl+SGyiefROOcrsqEDS5F6ItuHcBCASR7Ej6DOq6BU1zSWIFpE/94uwEwJIbgZfAFqJtqKl1+fpX11Z00mE9y5rl+0PhccYEDWyFYG/rukPZv2xJZBl7dyrLXq9myVAbswckVJAtA/uyP0f2KhNnDY6DkSi7x+98fV2+C/cYcqhyLHHJ56xuKfVj+uQ79cw411BKwaNKi8QDVXPuT00J5emOBxqTNNu/UGBos3cKXKkb1CNrngRsVK/cO4DDmSNvLSH1HjXr3v7UhNh7NNyXvr0hzdGAAXNlOG4lfQim59FMjJrn3LHx9jQnKzG2D3O//buEVrBr69FycMfxsnfz4fikxtmDw1EHFrz/3q7y8GPjEuR5B3mGE4LKC7HdNd48G+fzCFo898WVoYNt2QIQgp5eSROngNhlDYA7qgNofiqZKJYCC3vMkFKO/LFfnpM9mEF59b2SfAOwBCRQS/ZDQHXPL84M9fWxX55bJaHd2pVZ8+pD4WXX+VLniOPHZycT33dEz9Kvf9d8lrNOFlTVjt2xTvwLdSdqO3wTMIZqnaocKqL7A1iEZvrBh9kL/Jnqqvvlm+e3VQ1c+hdVZi1+1vLKtfSRJdHu1zlUtk1vNud9UoaqG9qtGtzXo2MCfskCeyxY9118rv7JV79ZzRLiawGGmRrdtX3Wdc/OY9nL7GGoj51vin+UFGj4Rlfp0rNDObjVIL/+SfhV2fgvQTXbUqv+q6Y5H5JFlWiJmPPEHnpua5h5JZevJbEAkqg+etcUgyRN/s5Q0ZpssEAxjb19OwW4DEXohqrnwB51H3uaK9Ec+xYrwNrqrfEc7LF/DL61V62dPiA9sLj/UYdrV/lh9rU2m+mNV5vi262pe4TEQ775Vz/Htgt204ewtiXrtWFda+waW7pi2b40lvNF7CnZXPNw+tZVNLqf/c355a1XtkX9T5M+SpO4QhVRRV0wrU9N9dZE+Ze/vDp9vU2bq94Qz3TsUCWSo7IjoaBUygdHPXBi1X/m3cY+dbgpMRY/I3lQkXp/YXP2wc2zh5aGht7l3Td2hDavUsue6VX0/GeOXQiQgNWCorR2yb7IPdcu58O6ESASJ4iN2OvP/Ys7AmqsXrE/ggS1SeoSX8nH1YtXyvpFezKg8WjrmfhhMaN9aO1Vc9gOazp4RAUGsff9Itl7Yxg3/KrkSQIIxBK3sodEdDAX0BTZ6IF13bNmUTlsQk+8PnRK3yWR0CMMeHc2u6Eg7zqRfbFq8Z68AzHaqDlDKrnfax8EXNQ0jqYs0WBH+KS1C5szToHQDjCC/XEfwCc0LfYvZ4Wwxor9VT/1gdNl0PDuP9xk4567R5fTR8+HjoOzBpHG83dYty7dl8VT1pX1UmaCkuh7oOCEV2eTybRvf7CxdBvcLUGR0qbm6zs+NiHGJVkmnmYrZ13Ja82YNywNp9CS539/acq3HM39D40tRyIxNyhlQ4ZJ4EBB4tDOE+FPMgYOHUoJ1Z8v/9xLZd2qfZmCrmR3xyNjy9ZVLWX7tiNxMPoKUJ4y9Of85SDTgkBoIRqBadmGGkKglV5lxqbfQkTJydocDr8AGGVA1QOiIDFo3nk0f+bARDmjrlsMESOuqdHhonpycPXBlOcFJJ6H8Wpe1xoKl+uW2+uj/MVwcrrvfmtjUF1OJYo20wZG3k0mqpHdQcSrhC7j/DLQrVuPlEXf35ID8a3fX5wmfYOIJE8Ug9y/BMFhOrgST/jDMnlyXbi9AjPGiNN44+WmKMFI6hi2oBdDegSFEYz5PqiHDc/o2KzK1CdxS/dWtAXfJZCSGGVC5doDOVgzPjM5zytTh1j99me/E/UWVtvh4mg5LZQ4B18ymMNR16UsfampHNxwKHJ83t+eNS1J1DZuaI2TxYfl9JVEXf2HVNSILe9WxsS+sQ8lSD36dErT1UO/OieGF9pgAjh04dGPTyzLX9xWdr7ZnP4bRti73Lb+UPiuvks/BqWlu+5rjEFl1CVk0DiBhIocKhhnaF2VdR3ulHhPnC+TZwwJBxNPnUyrALS2prXhhd5JAIH+XZNUoQ1RnXriE5PKt//Lskp++PSlGByBpITBf1Nz8mwkJ51f1Q7oDaNWm2XSr1+XnP3adf99jZlkzQCaVEsKVdO+6sCgxj5JCA9obL3yQZk4vz7AgXPMqE18fGyCfklFVJSOnE8V0TPUTRwQNQ1leY5C4qTyQJXKnhRASIzfeWFr9jQ5SAYerUWfFmRfsIJqJEAW6L31UlMMPnoKaprqAMTb93HykkfVC84Dz17FFc3nzFF9XNfFljkHEmmNxUr+zojkTKCi8iCgC0q241iZOXtoKloqBuar+FnBnCDbvUm4JHGQOj+DesLOXWvrYbF3VCm8f0GRAYINEyt5Q3b2nT9blaB91NSBSRzhIFSfrK/KA5ok5+S/BQrsL9tEzhk6ilbhfEAOATP2F8Uv6kd7dx4PB985cFnj8O9H9E7g42ehjWgFzrHgWCLNpqMgSO66dGlfnvvGulCDBGxmZVAZqvocekccAFVUQMtWQjfRiiBnep+oTaXfpK2yMXxs3zL33lGhKqiAOJMbUaYOnspn+MzXXt6aIMnPU3RSlRg+pk+U9eY/Oi78/B49VDQ6B4gRPPBbqE3WWJVJYAU5ZOecFYmDgERguPLNXeXWR8bE50g8vS9AlAps1dP1YfYJhT/PZLbEqPpeoUpZMwkbEQIJtwCc3RIsQxIhiAIB79/Z++7vLU5/EPuD3kl613ld9XqlCAXwsT/ve2hs7Ln1kHwv+ubG+BR9G5JhqD6UHT3Sc6BdqYgEPT9ZIeoVHeNKKuEoYZroO/fuFGEWPyfZAxJKKlT1nGNnv3b9wi/dkmGr4yYNSFWEP1jx7p4ESb6TPPT0BxqS0AFRVKEFOVQXCTdYOwEY9NvvSojY8Om3V/MV2EJAhiA9NOb91cRyPlOyJSi2pqlynrlc+vTvViHiBhM+OT59Ss6bM29GFtvrvqiovfvKtvL2y9tiB/Tt5HlvuiGS2ap27ssa1mR32S6AFxEMgwyh696jQNf7MJdLNQVyr6dGkkKlDTjAZwJUAJ3emQRERVZMQ1yAPap6Ec6niVfQGJ80qEcqXwJZVXznYtkr21PdkfgSaXjxL1bGjvJtL3xlTeyA/ql9+08nQHcBovgx53HK9MFB/yXK+inZJlVkFdK9a1pTrRIIq0x+iG2x+XAC+O9/b1Mqz6izqhmovOIjoKJZERkYOn1Qki3xx4KnJ2XPANEkXc7cE5+anKqJvjBJPbvLfqgmat4GiqFO3vtAY6qrErr0Wpy8mOTpsZ+cGnEBlTBN96ou+grsS+wGe1lsOG3u0KyP3504Y1DOtr2mAkYoRrWcr2PHJErObEDgb23IsxtauHdda35f8oASzyZ2MNjx3lFhlYQON6hbhqRafzYd4GX/ST6BL8QdBO8fRijicOIiAKn4D1DNpgEWVKfY+4lT6jKnTkwiKVH9YFvEVdbXPjcWAKiWAawHTiXGs5fci35SsbKzsvylbVG527PrWNoPJE/OHnvk86ljikHI4Fs3YgRsEL85Z97w7CN+jb+w5yUeZrAA4thqZ/+Hmmy4bCTBTIxPWzkSxeJn/sn8vCSXh73t7tEZliWzFMgK1qmCCAY1bn/qJ6eWZe/tSpPxvEfHliOtZ+PQIPV4yD5D5i+4I1mpCVvwOOcnJgbpNUzLhpxy69BoRa95b2867jU63XLnyLJ38b4kLA6q0jS0/M3ntpSP/Ye7kz0L4CQbEMGHf3JqUEUImwDCZtX8yOFMvXVYFl6mePf9jaV9l3aZOszRbF7TktKaywGQNHg2XEpqRxqylM0EYkrEyqPvvLIt3GU9CoJJhoiz8F3KWdR0+k3on8TM5nMYbTQbnSNXknTfUBuJRCat/4Nbq1L8TTdkIy1+ZnM4izjpKgNQpwz8wT+94boYyPopA8snf2lOArObH2xIo6CDSM0JvQTPUrA5ekzfaD9zsFtMv11zIChAuNwGeE0ckIBP8MH4CiTcg8OL1gXt8f3eJwcpiWMIIdrX0OVaz6aqkaF2a0woPR5D7QAIqr1/nw1tn/D42KC6jWa3mEa/rpoUyzFJgP33K9/ZGOcx7Y4ReS979lbDAVXfXv4P7yeQ44DwJjlvScqmJXtzMOnqT7ijvix5d1fuVxldAnHq+MWybEkV6Kik+Ltl39yYfUTxpv/EAeWz/+auMmxoj7J86Z4gDp/9wuyUmzlAlQw0LQYHAi5wtsZUxjgUwVAC9baZBwlgrlxLdQXF7fx5PG0lTw2Bpyr9e70Wg7oHnejRNnxILwmkWqmeQtYLf76iHNh/qiz42MTQBPUsbFxJT78SRIDMUXOy18zxUPmDVkloKJxBz80e4Fz+r5dkzRRWPUR+R8DAOHsPnBn5RmieoIbBIzCgQRCfdfa84WX92oNl6Rs7omC3aWNr+L+csHcSCWrqMTvQYErWU4BUP6l/HPOQxj6RO3V2Ny0/kGorRBUy/f3vbUzFxJ5BD5o0oy7AgISUdDLH6j3Ys6g2kC1GFg1n9FhD6ioxABQfCl8zZg1JlVHCiv6iQRRo4az07tc5FTV7gDEW8KCXWUdBLEdgH0c28OoHZfE7O5MQsVGMPrqe/UGOmRP2PZIv1T8D6kIj6XhjknENiyp705+eGPqCi1CGoJVtkWRCqzPfAiptonPz8QTOqA2qy+gEkF8JPXumEbKWxKgCjqRO0rUaqOjnKPkIfvSqsaPOjXL8nl0nI5AwYEDnNKWjWHFQzj/ABQ2mps5D6te5PLjnZJTjvvulVQkg2WsBukBINYd2fvpkBlTTg1VUcd8FlyNG9olNRbcj6oB+4meo+Dz/7Q2xM/PvGhV7L+gSCEiKNKFG7ntoj5wRCXkFxFR845VL9+TMNY7vm8nLkoKzJy6k0i44Rqdh92beNaJCwHt1Cv2PzWKvNJ9LGJ2rYUO7hxoiCKW29/z3N4UaKLhkD4FAKMQkzvWrEf+wj9zr+jX7y+TpQ9Ks/9Sv3RpkOnOo9p6M+iM7M+G2YZH/BnRAQVUtDc1kKyGVE+8ZmaTSfQIKBKkLv7y2sqWXrobTP6y+V4ABPQxED9Jz1adz1v32+xrKxRMXIi+/YunuJN1mKaDO6HcZN6nuo2F7//U1eMagTCMmNQwYQHNlByoa5A1BbSVBnkEiza987OduTszAPzqHgikzFOwx9iLSqnXdEyQCJSTxmcquInvkXCpq3q81FVv4DP0/bKd9nWowVkWvjqHZ2LdERg7opxrdN1XU7n07pwLi/uJzNIEP6lae+f0lGUBIwnnGnKE5u55r4mSI84cJfEeN7hXQC4gB4ANq2Jv6LAAB7L7v5C8kbi7zM/ycP5dM8Pn2oXeFMpwG5PH9wtX3Hkn5C+hnzBycwP9Y21mY/+T4ADL8ueR25gMNWS/XDWb8DO4eX8UOaHy2jsCASjgDar8vtolq4Khp3hnFonaxq5/6+7cEXAQSxnZdqqat8y233zkyvwdA1i8G6JAQAwnFeoJ3NlKl1762DyOwsKk1iZS4AyJuDe0tVSj2hc12/0BJe8M7juJjO1W1ijYMFPB9gAjPeNu9o8MiwTyQwKJg7th6LIOggeA1ijf7yHYueHRc7LZ3CQQZM7Zf+orQrNh074n/GNHQO8nRzu3H0x8FUHJ+awpMzgffq+pG3n3xs1sSD6Du8h+q5z5LrCk2siaGLp87c7GcO34h737j+kMB0ghASFLZLsmw3hcAkfkj/ANgy3nITKnLH0RW9/UvrU4FAkigPwh9TT8Z21arcmlrQBN96Gdmxpeyh2JGQiSSJL4cRWvUmApAMkwaqNC7b9UfIlnjVz1nBoCu3B97Zx/5vPn3kr6tFLt+6MnGpz87I9k6ehPDJzNWBlWaFkDhqtYyUgYZumYROScUEI3FaFSbNx0K7cJDKesxwrK9PvU9g2rbRIw91EXGphGr25Duca6Z0NutfRk0vHeCdr/fe0j3qHPItjmYaT8xMapNkJlM3V22t4ydUgXGNdUhZdTBg7oFlUe74DRl6ji2gifBz6p3drf1RPQIHcDvkeLr2r19pB4XPDQmxglnEPo5ZsLAyN+immnuO4E6dMP1MbyrX9tRhmgaHtQ1QwHp23PeqFOe0SE3DG7ilIHJwnFQ8eagf81v7opx9t+oXYycIC89DXtOtCkhdSrHD58tj39hTrLs2vAmG47R0Axv7fBuGWfovHfGGd66YGQ2dhqxr7+u7N16NEZbwHD21KUYGegIg5Eekw/weJUYr+V7NXuvWbI379k6fOvr67JfHBhVFcbmS//5/aDoZAwZvymzh5Tzx86X576+Lus+5ebBOTTuRxBgTaI+NKJn1h6yYsNnEvTAbrlPB4Ehcp+41f2HVjMr7Dv7RcDLKaE+3PL3ppZ1r+5M4Fk7MJsWNpfuhhDtP5XDpbKG5lWbT6LPyNRUCQIkX7YvuJzx1PhyrPVsAvN+/aqhReY3SBocavv5gcfHxfgwaGhYgjHSgGvf2lX2bz4SNRzJJ2Sndl4koPYoStwDD4+N4pi94x1aZ2dCsjt5zpA0VELblLAZ1Y5tiIg9ev7YufLQT89M34HPljCpMN7+QGMU3t7+zqYEIugt48b3T/AhMPZdStx3PzGhbHl+e5znQw+P/b/ZgfvubfiIOwwlsSc4CVSkIO4fUqs5Hc6/ZNIwJUZP/whVItWcYWP6xNAJYqwhXrx5ERAuCaT9IHEUyAtmBas7VhxIUqDhGOfcPB2gRzVo73KZO29EnAiKAFrT+pUHy6EDZ9pkNC8m4VYRrA3Zwv21RyHkHISkbEh9r9AdVKAgw4ZvQdoF9JAl+8d+RRmDaGoihog1NvbJuYCCSRacf31a3inZWWtSS9Y5d++ZtOr8x8YlwSBWIPmBSH/3WxuyDnqL0LEE6K+8sCVTwL1nJf6gW4cq/XjvkmOUWKBCVpKlVXM6pR8S1KahawrcuK41/UOol9BSyTgbrfLD0QiaOR3N9/Z3Gu9VIZqPJ6iXOAvkBMWomKqOkGBnn2CIYJqtkHTpq7BPUEPNaJo0e3DZt/dUGTiqVyq26A0b1hwsM+YMSXIPPBEoVLSYrqG6aeJ3oXfpFyQfLlFRGRbUevdUaKDKKKIct94QdFkcfOicNY0K1dCeoRL4Ge9U5Zi9Wfq1DQmIzxw8HZoSW8KPZYDfmKqp1j8SAvtX4CpJu+2OkUluBT7Qb3bCWhqOyu/Yd2z2hQuXc94F5cAEQJt1Eujrx+A/BXdAK4lyJX9+LTNJSJ6z286ms8I2kpu+49GxWSvVJ8ixSe0Cj/qGfuWIRulPTCivfHFVbCXbDjn32YI4e1HS8s73NufvVDNn3DWy9BvVK89O0lmijq7LBqqYPPDg/0mhql3/7F/eFXaAZBrzwJ6A1PLzLgEnH2Xt3Bfb/PL3t6QvDL1N0g5IUGkV1FobFEYKYfZQhqpdD1w82dbg3LeabXTucmykd3CkrfonnmBDnWd2hm0iLJDZPFQHO9zwETcehcd3+Vn0EbbAe3rwF28udYO6BoxSgbUW7KH7QTWrKDjXJUZ4/42dUYOrzdiRpEronRvDS9k2doH/ByLqg/CzAFbVD3uGNLTnYN8yr+H85Xzf1vWGZXYrX/nSqqiYkRzes/lI4gI2LBLKdd1SIRKIEmWZcPOg2BrAI9UpVCsABBYA4ErwS5FIP4/Lz0Vs4XRFAZSsEb8QF6kmqxBK6CHphCSSYLcJf4hdJEViGXuwpnQE5Xd/27ceiQ/0d+yMpF3SIV5k14AckuCMH7j2QdXMXGeew3VRE2OXgAeZmN2/S/wB+9up041VZUEj/vYjbX2zncqYif0Cakn0fLbPVGGwnu5bjxaaZwZRnruU2BD4y75RbGKrvI+3Xttehg7vUeobeieRYjPFLxJOjdNUwAiykKlVvTm2/3Q5efhcfJx9L9lqbTmVWPb+x8aVMy16dK7PoOf0C/XvnIrc2KkDsrZiDvvZ/DQiOyocBIBMJnd/4skRY3oHIP7l37k/NkmyIC7WA5QZV+cu5c8AkacPny0v/NWaALGAUvbR97eubYl/Fe85O6pKVXJxJdUM/s9a69skZ853SNbYjEwoB4J2pvZ5uvz6b9zxo0k2XBD4oLPnL+flQiW8fOVHKISL0xgzoV82DW1yN02beNnCneUL/+i2KAtwkNA9KAHnjncKzV/xVnOCJpk8hDPo9cWrUSkSsJ05dr507nRjjAXKhYqKYElg6tCkXHT4XCWB2obecBo0kDkW//7KHy4N2qfh2kt5763mDM9CKYDAojtxFvMebMyh1FSUQzWoyrBtPvSrZ/90ebmh/Q1l+6YjeVm4qRDUFW80x/C6NBMxeNPuGVUmThkU/qSsUtIG1fYc1F0EuJBIhsl6Qs0YCCoVP/FvFuTQuhhYz+PwCjTRIzasPFCVvRfvzeaTwXsXaBHh/PbomDKxteyrGfqe0UH1IxVp8npbr4wDsHf3yUyGrpQdrpVzh89GScLBfuzJiTGwggEGQPOh9VIOHNbQuyrhoTtcuhon6P69I+/ElHaoOgcPsUJfOHf4XNTKZPOQF6gIji6DAnmUrb/2Z6vCGWeIvGOSgpne29Cn3PvAmJQulRyhGvYM7ihu72mzMw6fjVqOz+FUP/tP5wedUsIWRI24ZWgZM65v7otBDPJ00w1tXN5qsCIOKcelZ0ZiqOkLN16ZWSLhYgxcXhFuNL66+/f3DvvYxsq4SVBGzhhU6sb0KQeoFy3fH8QxlZSmI6FH1QYOHmw9E+fBYcf5nrpQho3snaBDEKPyA+EUEDiHL35tbTXI8obryrFtx8u+XceDcL79wtbSb0Dn0Do2rW+JPO7k+cPzDrxHw5LsJ3xV/69S8PwXV5a7f31uBiz+ty7JOGphz36dMkPEvmTACD2YteDsS/JVKj1HTfVEUsYhk61FxwrVYfepSEv7OYGgvWy9nGcG0fulNGPtoO4c6a7VB6u/UwE6rarwQZr67cuRI3om4HBvmpa9P4534Z+vrhpL1x1KZRCVgKFn8DdvqsQsBDeCU4H/ka2Q7HNZb+/BXnae2KGGaXVB5TM1tnvH0BpCUzH87uq1BK+Cfoiu51KdS7PqpWtp9MS7FahL6AT8wAOBgaBDIO6ZySNv3tga3XSBxcpl+xKoQN0MfaO44x1YZ9VUexJXf/zkKvCwL+wJ6PKDj4zN+5E8H9hxPNUmyQXePhra+nUtSU4i6HHT9XkmdgHytnrRngRalMM0nkMCBYOeT4WIc2OdnMvZtwzPc7JPtSFpF9GLllWStmyhPhPvEUKpLO/dAETMLNELZI87y2hkkkTJxbLFu3NGb/vkxHyOSt+Gl7fnXKSfZ++JJL64/9ZYAK8hlr327ohyoHl63xQC3Zt9CLkEHjiDH//CnNBMNdQKSIBbEhmJK6dsYCkfIhHiyNl6l2DD76syvfTMpjLttmEJ4qyDta0a4w+lFw4tBzIqqRNsX732Yeh87vHU3pOxXfbX2eMXyq5Nh6Meo3Jij7JL+PUqQih8gkL7fsU3N6bKqNIpGaba4+x86h/dFqDI/s9U73NXEmyoLquMAlogzc6T/avXwNqiXIbm1nwsSLrv/m9dmfd0+mL8lxkhFHrsfwpuvntXU9VADjzz2ZEQvXAlnx21sLOXE2DxEZFLHodK2Tm9QSprqqGCIUmlJBi46MxLopzF3v07p1JikJp9gG4MjPTMEltVQ0E/kEGCV4E5x/NO7QeVPn+PpSExlZC5+HqVB6CdhFBFBVVb3MPnzVowIoG+4Nx+Q9PD3vCd4iPxDFlrTcBYD/Wj+2Y/UOYUwKrooGsCedauPBBbQDXTRcRD7wpQxPBDSQyhFmsMSVcJCDh1fWVXDWoTDOqj9Z1sMxCS6h7VOH5fHGMPOHtrVh6Ib7ZeeoPcB1vh/UgsfJa4iS1geyhaufedO46FJnqo9XR556Wm+Hn2QbIqUCadK/bTYySOkdBLHvVJ6g/194QSxIPolWixVCBV5Nk1/n3MzEFt1c0OYZBQ4bIXvEcJuITVc2FJSAj9DlvivaiYs42Lnm8KvUjFll2gMCoYF9Czz9avNi1e7KnqZBYOoFesKbEBpKBvoXj5syioHkbNa5+KNrvWMKl/mT1/RCiDqrxAAnbYOq96f09pnFM1kgMpAbkqcu5bpUE/yNlDZ5PkNo7rmzOfGOH4xXIaTa9zu9g8Z8h7oZrq39kf/bqGlcL2R7K46Wh+btDIXmXGnSMC1HpGZ9g7e+CnZwQIEmu6Nqw4kETlla+uzXuwP/k2tqHWBuB3VXxnzhrSJkYy+KPv/5ElGxpGLdSu5pN5Ycp821e3lOuuL9lUehSoIwlecMKVhmR0NsE1nf17TpYJkwfGcK75/tYE/Zo6IZ8CovkPj03ggm/sz5VKob+f/PTU8KplnQI+QYMF4hQ5I8kBA4Tbe2DFgXC7HXplNgHYsJEMxZXMOzCAbeq0Qfk7B/Nky5k4Co1HS17elsabqTOG5CXI0qE7EEMvVDAiez669WheGORdQ5DvwDvVgKsEjJ/MmDFwjKmDjTeqSiKg4gRVULZtOpwDJOGAmtvYGaTXv1J/mjuvPkGrjQJBhigo+TMGePEoaLNur6ax/8I/uq2iuFFF6dM5yJ7NzZlT3bIeNozmTGuqkc/mJpmGtsDQCkZQthhEhrvPiF4pL/reAweryZ0uQYPPwA2ViKG77V7XEuWZiwK9Xh1DW2KQcVZp7ev5ib767lMpY4+aNbg0bTqUd8gYRpaOGk239gksIAj3/fzMMnHaoNBMrLsqx5kTF0ID2bT+YKo5aeo8e6lMnTE4xoOc52f/8W0xjErZElQOSmWA4xJoSgg415qqUZqke3RME2rntmRQwIiy5t37fMYeP9xU04c/OSnJoX2rWuR+M2vi0rWU511QREZ28Xu702Q4fe7Q7AGlVhWIJ//hrUn60J3sZdWKDW80R/1EsMjockbO05x5I8qiV7bHiEAyGicMTLPn976yJspAt97fkM+01rf91JQYRY1pdz0xPs47vTZkIyMffUP2qQADQiy4QtXRHzN5Wl2kbLcv3Ztn/m9dKCP2bWiT947OM9sbaFIX2wLqXcv2p7qkUum9mTJvH+l30qPAyEJQ3BOhBqXo117YWlVjTl8qz395ddm5uiWJg7MtwVJRFfRCfa3Bod0n4wh8ZyZaZxhol2qonTkgEwcGodzbfKLUzahL4kltTY9Bp96dIjawf9fxKJ8IvjkKa7P6/X2ld0Pv9FWo0qx8dUcSC9Ure1pCFVUtwa1JxBeqxn4UBMGy+RHL32wORQZVQtLLidlLEhB7Cdqmoa9Dm1oXyURnF71G4tm+W0WJyUT766sETnC2bW1rmb1gRBJittD7g1hae/fnzNuL77+4LTaJbdM3Zo9bH9KoZCOXPrulTJs5pEo0T1+Ms0lFekC3qkegw41lzZu7yk0db4yKCVSyR537qxAyyLDPlDCwLYJHHH/OSvLhu6jRzZw3vPRBIet4U4AhexRCKOj1M/aHabu1ZmQ0mJ3rD5XWPSezjzw3JT17RUIXFTBKgk+Mjc8BaKEkQjg1N7NL9rVEJZLF+06VY9vxxG8It5zfQjfYvfN4eemv1iSRIpOJ9hF5ztF9I/XIJukrXL/qQAAGtkBw7SwJcFSvVB/RZzWrGjDG9jnTbKIKR0CvjjeWz/3CrDJx4oDqzzccqhKsntbs+nL+6Pmy9LtbSsOcIfl/AZbherPvHpU+IRVCyRVfefuCkTl/5sQ8+1uLkhgOnze0HDx4NgkpVDWqc707BU0XxKLkCAztFxVt7+rtZzeXCZMH5P43rN6f5EXQ7u+caxUHyK9Az+Tz/9alioj+4h2h7qj6CGRVPKwRWvJJfnt4jwqMeGNnquzsJCoJ/6tPjU1GpdUnQN1Q5VJl3z4EiNgzfCMbZxp1SxstR+VJIj+0jX7MHmVA4nD9Ex9W9EaUtwzLqyZYC3bJxqLPeg/2oASS/+UzVeP4UQlEEpJBJGRPhKa19u3d2VuYGt6rhDPNzYfOZo8C7ATYEieJjaQSiGAt7IXQXC9eLSvf3Z2BnebSRN701MWoZgoevaf7Pjkpe5kwjz0jqXCmVRbYQo3qbLwqlM/XX0IJkuR65PN7dEwV70xbMEounH/XL8Em+yw2jN2rM/n7xIWy+Ptb8uw16WKsAGeD6MLegEYVvcbZ7z+8R/phJXWCdf/m26098DD9RH26xJ6oCunH0RB/dPfJ2Kln/uOS7Ek+DPXJu0JHYsf0Y3ln9olELz1gQ3rkHUXu/Mbrk+QJjNOD8WGJWJCz6t0OBiAeOVfGThwQG4YNwvej3LO1NWARwu+ZDAd1b+yOPSHQBnqqXIsTMp+M0uioPnkulySB/eNTo5Q6uKrcizkka6GSnbwUJkJ6Zbt1SBIkgWTn7QcVevQ7DfFiU9O7O3S4oUx+sCH7UvUvAx97dUqC4GyzYfo17DXnzXOcOXIuiRfQA+slFNheHZPo8g1AobBGOtyQ77XvsCJueWhMaOOa9IE0+i1rYwcADCbIL1uyJzLwqvzs2t/kuu7DWk3xb3B96c+WJXjTtNl1YNfSuWu7svml7eWBL9xcli9sLjcvGBGE1EAkL/OVb20og0f3DgcUjYn8JD60lyqztvlkoHt2nyq33VU1cqKeUINZ9FJTmXbb8GSuDrFSmODfYbEgNraeCzuOQ/DCZs4dGtSf0SIPyoAq4b38rQ1laEMVWKZB9NC5GKCdGw6VulG9w+3jJZe+2JTnmnvnyATxdP5HNPaJsRNEQ41Slhzcvbz6l6tLt7YhUf5ME6KJ04zi7u3HUkXIBModR6PDLjGJItCJi9HY1uT27NfWloc+PikbzGbhYCH9BiIpAbsEvsdazkQnXaCvkdnfQYwcMIYtamAnzsdIKzNWY+ortRCbC7ry5X/8arnrF2ZW0oTtbkjSc88DY2LQUaFQbRhah53UpIPKKGhwcrggMkl0cEWvK3EOg4Z2Lyve3lW69++S4GvevaNTcuMMBE3QfcGdMr9kMJPkd58ocx9szOb3PDUqkGoEGUpUA39uw3dp+2fCuH6ZO+JgqfTcOn9EJIKtJxQFOigBqEq0H5Y+qDdNh4MoRi+9zUGlWfy1HWXKA6PL3qZjGb4IkXFwJTsMn36JAUN6hleqAVbyxzFogIU8kR/dsKYlFLRaWVPFxO/iiQtcobuMp+SCpDCaxn2fmRa1NvNRBDWQaEjijvWt2ecOur4e6InBa4wkgybxZJA4K0GQgErABFUSGKj+CNZRd9CBoG+9+3RJ78KW1S1l2rxh+WzGUpKsagi1XGqdr78+imh03/NuTl4sT3xsUp7x/+n697/9VoI96G1Veu2UYBNv3TlRETSrwv6DGkLTBR2M+PSbB1dI/8WrUdFQGfPf6ILACQGI2SAVenstvFGIfxDuDjfm3jlQzsM71VtAa17QAkRQOXDuIGIclABPoGkfORsqmoQjju46UW5sf0Pmj/gs1UBUQPsJkkZqun+/LqVp0+HSd2DXsvWdXTnvqGwMv32FUmVwor3z+FMTyqoV+xLg4h4LBtmyBLi9OwVIIUWtHwnnF31KQOU8ejf49c71wte2RRRAY7K9vuHN5nL3p6ckIJd8QA5dEGGooMRH0iUIEmihk3J2hjDZk9YVWutc7G4+UU4cOF3GTBuYAJN4AVTUOgvqVMPqVTevfpAANgn72WrYF1rl0OHdI4AgiYGUWuOdTcdCE3VvpFwlTey7IGb3igNl2IxBUV8JiDSprryzcHvZuLA5TcSD63ulx8TvqsJYU47y7vsay7e/uqY89NTESD2iUinpe4cqE865PihACQAHF53dF2hADx97ckLeH7sAjPKPRmnJt0Qb9eu+h8aU73xjfZJQZw0CXje4WwJo6DcUnI1i61TcTHW2PhNmDkp1acZtw/OZfJPAWSAoQLdmfJRAGKrKYaMZOr9AHgFMrWqv0i2B89x413pTXHj8kHcUndA9Dp8rM++sfGtmvTT2SdDJNlsTVRgBvHfo/wFdBgjuaa5mZNSq3NYKwGEP8TnsAh44xSJSm2dOayjuUV786ppyzycm/X8JRPz/ul56cWs1zX1Ez0jr6rGx5wVqkgr7TmVozWs7yuS7Rpaty/ZnsvUxldHeHfPOrYN71Avl+VJhaqsYY08A3Nhje40fMqPK+5CIJRC/8kEAImulusxWSrIAD94hewRUE6z7Log1eVnUOEmwmIJdVNVEO3Kh6Aiw2Nqtmw6XO+4ZHbGD9DK1nImN9Y5XvLcnPHiJDbqt/QUgIpcr6LTGEgyVMoEu0EEfB7veqXP70F31EfADAldJL5vpEvj7fffp/UkWcf2dAz04CZ47SsZ7xx6Ko9B5Iinft1LhAg6TCVZV8TOUCLesPxjfan34PkG96iKEXYVy//rWMk7PqinlF6+m6vjeWzuzd8UbbLC15Jf4OpUydGYJm2qqZ7OWkjdVrH3bj0e4wZUhgG00Tc36DRP65fOAHkAYlT6MDpU6VPjZt9eHMVE3tHvsvt9n0+xXQbL+GkmavdG87XgZNKx7VDpRBVXDJBBpNj9+LucMYOM9RIL5husTuLOjfLI9wD/VqLb8mmeXDIppnF/gpM+hEInOBnzyHeh1FOlSjWAzT11IoidZlawQauAHxE5iKN/JDugrdtn/ACiJmc/zXu1RfYsBqHoAi69PBZf8P79r/4nz2HeUXj042C3Wx/nASkEbVPGPPVf5H9MvSbQ18IxRLezRMVV1YPGa9/eX+x4bl4q59wFYJCLw4EP/d0r1D7Wy4RLwW9R2nW9Kw5SM7slfvzXKIvUTKsfKaVMLCII8vEeyWwbyme9sTLMwhBKv9sq5K0ET8eGObDmcgEdZl+PBX5x+e/1HvRMct0REknD48PkMF7OA9z48tppTQO3qntFpYkrj5yHc2vZxohbu4acnB62A3Ck1nT1+vpJ6a+wTFY8lzzVlI5iV0LF7h3BJlcuvXLiaw5RhT5evBRXkaL2kT/7avPKTvzCrvPvNjQmW5txZIY5KXBILCBkOp80wfurAMm/+yGTbLSgThyrNcNmvTQMxsRHefHVbefu7m8rJI+dzsCU3Dvk9j42PI+VoM4Nj78mUwebeXp9EIwayb5cgDwcOqlZQz7hQuvfoFONnYNq8z09N4qLk/cwfLwuKdbDlTJwrSpPAB4UDX/G1l7YmEKjpgaMoQf0NHpKMMGwak/WbdOvXJQjPT//K3DSqoSEIsvFMGVMOZNWyfZnJ4JJVSwYpS1hTUrALX94W9BRysfr7TQmgBQ8SFe99+fJ9MWIxAFMHlZ1N1VCy2gHWvGrPee8MBycDjbb2jOwtt43INHlZ/+f/1YI498YpA7Jv9u2tJFytLaMFtQmPuHM1SXzbxiO5b6pdAmxqKdNmDY7SDVSVUaCMhKqQYY9d2scRSISIBDz8iUmZmL565f4yfHjPihKw6kBZkVkQN5TJczXKnkugoCHR+2BU7SmJnH4Bhx6CtPSv1qXq8PJ3NiaYoyICPXcJ5jkjex7qhrs6bvrAIIooc2h8AivnDy0M8j1idO8oGXFSkjXczaPHKxnX/6eLZKh9KFhiuCFHpF2pYTmDZF/HT6prGzB3NpK8S767OcGFQE4wAa3tP6R7/t9eVDl0Jg1RiyqdAW9tjha9smlda3oPJC5mruhdcGU6q1kNV/S19EgVSOka/coeRBfyb/xzaK6gJE2D+LYDu0aGk/hAnuv0pYrKc/ZSFEKUyY9vr2Z4zHyosXz2l+YEZeLg0Ikm3zI0CnCCCU3SAh1nl5BBZD0Pnirf/d33ksSZqSEAVJ2BGPqebRsOl359u5RVy/dH6ELzp8oduV1gDRCj8ZZhWQOVE5QOAYh/ODSVIk54+IiepV2ndpEFPkNScnSvUHXYG3uMHKtGSjN+JFdsrgDtzde2lYbGvmX69ME5R+xZONsaF6+rGv8FkJBT/WhNa1rzvJlgf/B07r9HX0pG16WaBKml9GL/SuwnLxhZTrSeDXAxdHjv8vorTbGlQ2cOToBinbetPlgO7z8dO895C2RUwwBIfo+Ovuq5gMzetd/SfH+tmlMkMVVNssfttyvnr5Ql71YqTmyFhEEyKFGXdAhU9G6x08CugQLkkb0qoYJj52OPJRAc8MY1LaF0oUhAGsmvo14OJNt7rOqncI6spf1mpglqjmBVMgQ5BDAAU9hIn+XvNV+z92gRLoET+6rBF/oJrHA+XJTOqCwBY5yfwxsOlVMnLyQJlMRIsJte3xmUGSoblboeHcozX1pZ1r2/v6xbui+zsfTsSM5Qsagcob+wZdbJ2gik3Z+gZPyswRFW+f931eYZSC4vnb4UX/zO6ztSUfQ+9SayW/OeGp99+BO/Mjdn/fK5yxkiO6KhX/yYADGzLi5fC9jw3ve3ZMikypGqpfe99IWmUOFUMr0nawbZt1cE6S7PoXcK/QcwxYfyA/aU/zZgFQBluKHqO+RZ8gxZR5FWDeNTABwUqpzxqve0JbZh/YoD6RnkE1FrHnxqQkBPZ13QLkERb5A8rvV0CGolHKns7z2ZRE81Iu92zrD0jgBBxQR6sQSNAX6OncvvQKcBn/YuwEEyyb4LFv0/0NAaqAA0bT0Se4bqLTh2aWyGWlPvdMYlL+xRql5oQicvJnlxlgSxtz85Pj8jgQpQ9Mq2gED8osTI+qAzseH6pTR4W0e9hewbX6p/I5O+r30YWXV7ddVzW+PHrDXbNWQk6euL+X9+VqIBgFHhQ00aNaZP2bC2pdz5wJj0G2aoq4GtJyqGjR695qaK+qXKMWd+fZJNVbKqz/FahjRTX7Rm3p++LBQuiYJYQELg3fk7CalqtnuSZAKUMTwyyO/i1dAevTe2WGXSWZK4YsGIewg7+G++NTO5bqqmuwMjVY08pz9/8xsb8m+Ucap0gE62DDAAqHH2nSe/h5oMFPLc4gI9wqq7GD2AxMQaHSuKlD45wISqMan9KDnuPxVg1Hmxl+wjPlvPEJAaMFMNE6zeGVuuEmEOmPcM1NB3+De9/laVDdcrr26Lk5Dx4+gp1zJQaBJQjgX3ji5f/N/eKnc+PTnD1e779NRUImzaSdMG5gDbHJyTYFngRft9/OzBaYyxQLJmwWScT7f2KeGNbuiTRdeAFFrRgC4JJiGzNoUgG9cOUs5wfeffLyoP/dLsBMcCV8hvEpbWatqpF4bqkubmq9dCw2HIaiW5bYv2lNs+MSGDXabePDiop4Nfa5C+c8HosnXb0agGqb6gX1C/4JwlKuTgOPU+EJumIyndLfzq2tJ/8oDQmXwGZyXwrqa0fpgAlsoHTqA10+DEuFknB9cBFsiNauibhEcAzrHiBpoNwdmS+xNE+my8b88DnYNiC2AkHNCjqBUcPpsg+fDO4+Xuj09MSd0mJLWI8gRZYDQ5MH0tEh/rN6K+Z1n33p68s2XfbyozHmpIGREqYLeaRM3ACb5rzWBQOEizYXaeXSZvPf03qhDFDEGjgZCZXXDkbPabZIlhw4VU3hUYQ/WVDiUEjI/g4YGHx0WNq6IFXC4tO4+XQaN7J1BkXByqhV9ZWz7x6/OyD7wP+0KAay05+d69OpUli3cnQEYpU2lAd6ExbniY59Evcf/DY8vrLzXl4ENqHGIIgecU2K1dtS97GYqkyqdx0gA+XhMCTV0Dz/72u0dlDSBeknOBiPdu7/St656kjhM5efRcgm9yp7ve3FUGzxpceg7onFK6ahbuL4WnaLoP6Bb5VRQXQQ5H5rykGXLbsfLYp6eUv/7d98rTv3pL5aQGdUuSiqNL0u+2efX/XXbgT//z0iS0JAsFydAaSG+NjgJIcFZVECVYmXR8Qa9XFfQx1kAIqBvDyvi6/KxgV+JOL1+1S/JoP2hsF1DZexqs7XfnC6XsgY9PjB0QKHICoUV1bleamo6GbvO931tc7v570/LdbA5pY+u1dWFzuePTk+MMGFlVOCVkCYGAmmODbpGwpYvvMgdiwWPjYwedj0xqvXS1Qge3a/ysb0tYq8riieMXw1mXpKhovmnydN9OSejf+NaGMs701qsfBECh4NQNb/rqB3kv0HZghGQ4zu3gqQRQrVuOlPt+cmocKoAArZOaCadMQGHnot2ROHzgYxNjM5wjlRCJir4w0swSXOeP1CvwwB5kD7p2uSnrJOjQpB6JX31mkOCWM6EOGWDVuVfHMnlqXQIjAQzpaxU11SH0kt1bjpRhjX1if44evZC96MxYhyXPbS2P/OzNqcbVKmmoJ4JU+9KeragSH6YXp9+gqnFekMgeuHcBPcRXQmGdiXTo6WITBPICfJUf1Qzvyjty1qzRicPnSq/+nZMoSoBw7qF5ADX7UlAqGGPLJagCFRVVlTQN57jTqMPvvro9g0dROwAazq/94qoh9mg4AgyX4YaovZuX7o16nu8CaFlD9GFqi55dkO732aWv//7i8rH/ZU6S6H5tdsHnCWIFZ57JbJvxtw+P3W5AUzQt+tzlJGn6gpa/ubOMmNg/QbqeGXuVzfKZAi6+xWBBVFG/83NfmPvfZQf+0398LwCcS+Cc+UXbj4VSnOFuA7tm7+r5s6crYOBqgnvnhuiMvTZ0aFWJE3jZb97zprd3l8/+5u2p4gvM2dn3X9pWHvyp6VUwTur0huszX4EkueZeQ1DZSnGGzxdDSHS2rTsU2WxBNEotP85GsNmuHVuOlEkzCRlUTel6t9y3REdjNMpka+u5VDn4H7GHc8TfizlU0YChLtXp1e/uKY/85NTIj/KLG5buixCOd2uf+Fy0WQm6AFgVzOeLqVRInI/hM+rij13WTgwQBa3BPdKX5BwJjlVaBKlsvB44e5HSoEZuYJqql8TAmjgDZte0HjqbaoTzImHgW/UYpc+jR4ck2Pyd8ygRdw7QZdlpcQQUH5ghYPe99pT7Eq/5HP5W8Cx2A04Nq+8RQAsL4PrrS6jghCAAKhI61Xw+aNjw7knegR18vPfjZ9A50YckAXocAgZfpt54PqCUGEyQXg3M+zDVcCCe4LpGTVYJExsAktJE31zJfc+/vyE2AdiJdYLC5pIUqWjy0fZUjY3BlogL59xWn+qN/Q+MEMTbj/qNPrj6Qd6psykZ0UvmPKu0Ze/tOxnbZj+izGKOEFOgNDV2Qv9qdpjBfDuPZy+y31s2HIyNBepZl/ETB0aEgu9Ff5ckAbElvhRTVXrsMesLqJWAWBvx7YWzl/Lz9qH1sebsnx6zWrVOEvZP/+Vd5W96/a0qGy7lZkbMw3DsuIo25PL395a1395UbfKpdWXxC1vL/Z+ZFrUA2eXNc4dmUiJjI7s/d/JCeeXbG8qhA6fL4LF9y549svfr02jmwDMwFEmo0nBuFRewczjwFGA4Xi/WBUHyM1RsXJzN5/63u2MgZLU+GyrPGJiwLMDHNTUfRKJhY3spNrXrwLZj5a7PTEm2jz8JmbdB3Duak59f9O6uZLKyT866ZU1LEP3n/+17MQIcLiP13b9cnSm77ulM69nMZLDxOSBGldHk7BgYsoM06jX6qQgwZJ7LAYWuoCJJGhgHyjItbQOrIlm44VASCAdACV5QLCtG4fJcnJoEQPIhoBBIc+aatKbeNjyokcCYgbBZa1emlnbvUIYP65FG5QceGRsUl4SeKsZDPzczxkZGv//A6fL2wu1x2K4zbVUppXrqRyphnC/H46ChN1nTt17fVk0vvuG68CNl8Xu3Hw9vWW9QDZlh/KFUgh4VKU5LGRZFT7O6ZmtOU+LjkjTZrxIbBv3j/3Be3oNDr99h3Zu7QunYs/N42bb1cBINwS2jfhOKTeebkpB8478sD4r4zotNUU3TBIiaY39BhiFhUb+i4NCMNneyaqI9czlIKEUewaW1F3ReOXc5RjXNa5eu5v1OmzU0wWm//t3SXG1fqVCRY2TUfRcKUv0d9ZkqLPiVjK56f18Cc0nGjq3VhG5OAg0HnUkA4z2RoTbA7Wu/v7h84pfnxiFBO0xPFWgPGd7jo8mj/z0Xig8kqEmT4Nj+qZg1bzwc5yc489xK4pBJnyvRdA8QbYinhBdg8PIfLU9Q734go7jCqhvQYYbWUCEGu7NBetHf/7Dc/YlJZdHL27LGvmvM5P5xLNTH0AqsHcfn3NUqJU/8g1sypVxCNGPusOwtqPS4u0fGkaf8rD/qyrX0cHHekM5MnD52IfLGgk9Uw+nzhgcZdP4l9THWl67FIQj6BR72UN2QnqEsKPfrbeJMncnps/WBtUv1c8aCEUmIKS7Zt8d3HE8ArEoy944RCWpQI9yLZGDLhkOhZU1bMCLvmviEhM7Z4TBXv9mcBPn2pyelGoeXv3vr0dhSweW2FQdCQWGj7Lf1a1uD8kVCm/pZDzKzl1JdXPLWzjgq1Ky9Guk/+CD2y55pWd1SLpy+lEqWilKa19cdSDPtudOXyoE9J8voif1DG7XWd9/fEO41e9yrX5cyZcGIJIeqjs67C2gg+dCPIpDTpNiy6mASbxKyzqjkGAU3oh+bWmM/0IOmz66ou+yXaibQAeoLVBAcUObCW/dn3gGp9K5dqt4XiQZfAu1X+Xj9G+vj7CW4aAzsz4zZQz6aeaQiqgpYiRF0Lj17dcg0aP1LGdi3/VjOweqXtyXAgzTa9xTVUPIyiXlk7yghSiTff2dXPpfaFJvpfUHh9adEoe5z0/M+BM5slz2BHixwgjhLwG95bGyb8k7/+AMBiCRPv4vK0G0PjomPARgJVDauOZi9TN4WZ12grn/Iukp+/nsvtkby6x8+VTBtojqVSGADKipQgqCJ52VH9ZQJclDd8M75iMwTOHwudEIJmzM166HGsvCVbZlyz0Y429MXjKiG+LU15wPMiJJIrv7rRMMlMASUed8SDZ8btcmle2M3BJ/2I/tFyMR6oFCiM/LzKhUqhX7GRT3PnhPcOiMZpCZI69Ex3w8QpRLXrXvHMnnukCj8qAAbsnq8+UQautkFgB9ajN4JlVLf6zskHpILtr5990rimr/jvw+3VuvEFlYNywLHimq7c9vR7AdUUueHzdRHJXglhGIvAreIYQikzZthI6HiETigPnn6YuScT5yo1MAkjT4fc0IQvW83eeQ9Zd+ukxHPCC1JhXDn8fRAun/rS6AEbckzUHnim9HbnU3vwvepqIkBxVH+EU+ibd5yx4j4sQzcPF0BM7WeFetq/wLr2Al2V/XRmZU0Wo80+/ep5uBYi307Vf4Oh0rrPHsO7IS3n9+Std+/bH8ZaNbWrmOpCEUhki2/ULFufJ64Vlzqu/iJCA9duFqGDFMxPxFgzGdHDGRo91SPUYAP7juRgF2FDXjCtgJHiKPwI8D2Xr07V71ha1sDnnzrdxalehkZdcwOqpyjeqc6cbCNPujZ/ZleE3YIhVkCCPiQaAD10L3YJeuost+9W7tQe4EJkn/0Lq0CwC57IzTRDjeGOdC1VyWxDsgAyv9trr91sjF37rDIRI6fOiDBNWPm4Jk0O+PpCcmMVQL6tck5rnquKZmohdn57u4EyuExD+xaRozrl8F/MiqOCK/ZC5ax1aTjXCoaDKtSM3RXyREdQQmdw3cglFIZYw2PSmKaZfycTcBhalpy2G3cHCx9Ak1H8p0QNfzSOEZ63yN7pgFOQMpYKLObpNl/QLc83/LXTRj/sGoa7EFKtFuZ+lBjBt39vT98IMGiAIFKjkmPEgQ/f/8vzSp9+3WJYZL5m4nBkLhH07UNwDly+Hy0+aMWM7hHAos+fTvHcHL0vovUHGONbzpsRJ+shWBBVQjFxPBCaMpVQ8luHpxkhpGTvdpE1Jxk/tBvAbvLpue00CA08o+fMiCVEcG74ELVQNIlsVDudFCsIWTUwZZ02vQQfca64uCTYKuMCVSYnJ2mKAEApaVje08FYRAkemeCLwbLWi14ZGxQBoMIq+Dtg3ymBMK6CKI5LUhGhj4Z0HUEze5cDG67Lu1THYOEoErYDxI28q8MmVL62FuHlrnmeOC4ppHqxqg1bV++P/fsHQgi7396ctm77VjkjiEQ9tWAgV1zn4QQBJ4H959MkqWadetdo8r7r5ox0TeVMzx9A90E0hBY/TzeuStKJXjTksf9p8rWjS1pNuVYNHJKKl78z8uD/Phuzz9lRl1K6A8+Ni6fiYrg7+jmJ4lb1xrkHoqMo814pqH+/JUy5yEyuBfKGy82xbju3VMFuRMnDSx3LRj1320Hfv4Lc5OsomsZpEnBigwjB+k8ez7PjMLHgEZOdVjPsm5NSxB4ibFA6b4v3FwmTBoYOhDkDN1r4Qtb8y5QNgEMuK0Clahg9an6fwaN6pWqjr0gwF36xs4gZhKGnj3ax7DXwIPIBuopmVr3keLJvlUHYswhw5A8cyxMwa6pTumLMXCTHj+kyj6GYHJeEsRa75eACoUR+CEwXPp8UwWY7DmVHhHykM7pqLH98q58J/vjeQXa3o19igoFVR49b1joABKSvTuPpeLhPXI+qooHVhyMcIL9PnFyXfmDf/hy7s06QBnHzqlmbkjaNSFKKO97akJoKX7uyZ+bVV7/3sbsGTaD6pz18fnWb/3rOwPoVHQSfVfX5fzNuqfaG9YGonzPz04vs26rxCdQCdF4OGG9TipYpML1agjQ+vXvHISzpna06LUdVf8XBPP660IvQVEwd4XEq8SUkAAa5i1PjU8f1ck9J4M8csTOBvun2uHzgDPOPMe/d/eJqJRZa8GFd0Fj/8zhCnVGFdLPIgGF8KlCqY4LZgyyklgOaOwT/jrEUiX11jtGhmbgWkX9CPe7w02xYcAM1UzJlnWX2F8+dyV20jwK1BGKeYIjFU42SjWPv4Tu6kMYPLxHfIaKTaSc7x4Vu4wvjVJsjZ79y1W59/SqdbqpDB3cPbQtlQkBGfslaBX021vWyH1ZH7LhSyHMJy9kr+3ecaJcOnM5fojCkOc5uuNYNftg/aHy6GPj/7vtgAqIoASIp9LM3kpm7fPQinedCOptf6DlCDS9O/x4lK5Zd46ID3Xh0GsAT7N0r05JmtjgEWP7lrXv70vw2rlbh6yBCroksXlta94PcE6wJElwJu0t1VMJF7vsfEjgvJebbx2etZfY2fcocNZPEqGZ1v7j2yT7ABGAoITFZ9j/5mh5f+Iale133tgRBBzthx1QgVHlwaWXWKLS3frJCQlKPWumTdf3CmimMrlt27HYR7GF4E+PCkDJ37sn/hGNUUykymsNFn5vU4BQlVRKXnwnmqHPVn0UcLItEHdKcYAJzA9/hi2ROWKXrqUK4fPYtS3rWvOZ7t/ZYYcWfqcaHEl+Fn2I0p9ERKWEPwEUAzBUOl57dlPWEfgKzITiS/5S4T15KfMbnBPTugXG3jUZaGdEtQV7JDTgM5diG197cWtiF9/D5gEZCAhIwJwLfhxtLUpPkf2uekCJ8fg7Ygt9BnSJrC3acUNj70JYqfdQDdc3lo/943kfKZmq4rp3PXcusRKwi2Q/kIi/cq7QDgEeZr7ZE/5xZSxAj45l1h318WPde3aKWAA7xJdJ5v2MmRn6G51P4IL9oufSde/fm571BxqI19je48fOlTvvb0iybY00dlN1VQ3ZtLqSuI8s88bWSra7rdGbXUYjVdWdOXd4zoCKnN+1h/2OPaPKU5Pgz+DJUb3Lxvf3lz0rD5TP/r2by9/m+lvTqGrXt76xNnQeiB/j8tXffa9MubM+wfr+5pOl76Cu0W+WcMjiZYecvKAHtUHmbbLi4hebsmEFJ16MjV8rd7/7+o7yuZ+7uXzxvyxLT4bJwErW+HqCdvxMRk0THl4x42QitT/zsiA0HKXNS52m0iffWU7tP1Vue3hMDhvHCQGl1U9ejjKQZpm3v7UxZWll3e0rD5ap8+vzDG9+ZW25+fFxZd+ek6UHNZiTl9JgRVXI4CClNxvf5hLcvEP9Y+6QZOxUSTZtOlR27zyWe4VKkJDcunx/GT9nSGla01L6DOtRjh84XRonD0gza7jb0Ue/WN5/q7kMHd0r/PgdWw4HDZCQWS9BrhKrZG73Ls2NH5QeFGA0dHXrmCBVIxN96Kc/PyP68GgmJp5SV/H7tenV3hO0ZsuGlhzA5d/aVCY+0phSNmRQcIqa4RJckhLGm2boHVLNowIfpTsOUeDC0LgcruN0x3eeKC3rWsotj4/L4Qxn/2I1GMlzrF2xL8+OUwt1EBzVjJEgCX1CIGXPRBKxf5fSf1A1WMjB06AN8WW8lTFVHRwyZXvqMi76/PTz7Q/vXGBhDdAUoFHK4Xp8Fr/bHGNDRvM61Rf9SmcuxzlA6wdBJ9/bnQMdVbFj5z7SdodYqGDs3HI0lBa9SYJ0z8bZvvmdjaVjr05l6pwhOSeCNXQ+vw8ph4iqAHLKAiAXZENiiK4kEGcM9bxwfJIOSLT7szaGFZkngxpWaYxX8xesE+M3Zcag0rdv5zJj+uC/sR3wzgTaUGUqaYJWAY3Bad4rI8lhMG7e44Hdx3LvDChkujZsEFp5tOV0/u3yWZ4Fqnfb3aMS/B/ZfbL0HNw9BtIcD6jcbshxl3ZB6jTqQj6bdx5PsqdShOMuuPTdVTNtzwTYzorkm/wzewOw4MhQgvL51vP4hXLH3aPL1o2t6U2bNHVg0DXB4ZTpddW0831VLxUbw/5ZW1Q9gQLZRxVXSKIA2rlU+odaW38CBIu/vblMuGdkEgvInGCJ8hwnlYbInSfiCIAiQBsIOQqE75PkSlKPHL2QwJ7Dad56rNx2f0PsoHdj3VAt2Am8br0BBB58zntv7CgPPjkhSS57853fe688/itzcwZUXlXIdm87Vrr26ZQqkbMVmtjuE0Ea0V0ABOfapL+HT+yf4Ac1VFVHUoRCKShe/MyWzMGZNb8+VFd7XADs99kIXHf73NFhg1Ry1r+2s0y8e0QZgZrnXG49Wj77C7NSRbXG9o8mWM5bECVA8lnAAjbImttnHC6giYqeORkXMuPhaL5bYsaekOUePbZPEGH9CPzHM3+yPNLL7PuK13eU2x4bF9Uo9vuRx8eHJiNBAAYQGtmx6mBpmDmoreKlUbaSeRXYAmSg7/YhKuobrzRFHAXliDqQNUGPMWVYEozWt/KNnZmF4WKnNM+q/OuNFCTpU3Opio9s7B2E2/6CxEpy3lm4sxzecaycO3Ku3PLEuOwtfpv9sFcld2bLQEqBMp/7mVl/IzvwxT99vxpYW9c91DtnuCaF7DwIuvUdCVxfeHZz9rO9LSBSvXWvL31tXenO1tf3+EjkRLUD4g+EULGQ4GugZ+ciC9zuhiSr/H2tqVUyKShFJ165dF+EDfT1SR7YZokAMc+oDaF2kWc/WiUUzgkar34hZ0nAzBfyHSShBaUvfW9T6dC1XZ6Ff8QU0Lvic+3DqurRN4H2++/taVONuxzJbHs2PP0PPqykWy9cSaUDcKo/gH8D5gka0R41OwMugA/uXeAOkJVESOZVdTSXA6vci3v1d6oykh2+lb0TbAs2VY8BtN6J86DiRJkR9fKyWIr0b09iOBezpvY9kIAt9DzsojPHjqpoAIzYH+AqO2qCO89ntgygVIWRcIo4w15l67dsRJVWgbyUZDKJZ0MlWetdH9x7OuqhAm3KWkCX9KB8WMqtd46MPHDjtIHpe4rS1paqYRx74NDBM0HtJVX2SHoeJB5TBybe4/+8A0lGBjy3ycK6ho3sk+qltRB0A1L5ZHtxz+7j6ZlD+c0stDZxAxdaXuZRkJLt17VsRFsd0j2CHCqnEiyJKgBBVYMfAQaz32KI7315dYQ53v6TVeV//e7T+cwXv785VWYDivkIQCtf6r6iumg2GvDl7OX41sgCX7oW6qj1xiRhmyTdfBvApqrCXM3aAyHN4Ir6Xvf2+UwsCftYv99v/NM7y9/2+oEmGxQoZIuQUojJS19alaFilZb29WXH5qOZinzLPaMSyAkWNX1BKGWMMlWOAYcxsnLnqmEyDiJkw2YXbPTo3rFCnLdWcrFe4p33N4YTL/BNORVt5PCZ8vL/vrg89b/e8X+qOkB4hvfK4fBCagNolN8kGUcOno7Bs/hkvhw0B4uDNRUXh87kVxKd0BmbDoJnszg4kW9sOlz1Hazcl8THEgtuBThe9BJ82cY+Qfw8E9pC7cULzlFKzJHAd9Z7cmL3ibLgqQkfrQNJQ4fcd3IwDGLzzmNZO8ZEMybOqmBB0MtYrV55IAYMqgrtnTp9cPkaBa2eVfXIxj195Hx54ienJLhiqPGsGasMdNl+JM/H8dq0l86buNmhGp7Xr2soBt6xg2hDL392a5nz5NgYewfRnzHaDKqN7CByDKgsjC70StBrHyine34BFAcA4cPJNCQNP3HX5iNl/kNjkghB0m+9Y0Q405x4pQxyLYGR4MS78+cSI+oOFCWsY+uBk5lxgZbmnVL+Qqmh7uWz8HoFBt7RtmX7y2M/e3NUi0gvo43hYQusomgyrn9UUqCbnAWD++wXV5aG6VWgA4nS4CoR8LOk/KCm/o7aSxqgO9yY/a7xjFPDxXz1pa2pCtm7+hOgtd4LBFdwYP0YKwFCpRR0qTSM6l12NB9PM3pNGlqCKdCBhN5y54gYTPvn5a+tLd0Hdw89j9HdtfNYguRd24+X3/rdB/9GduAbX1uTCgJUkXFHUfIOXZxZhg/27JSzhX/+ylfXJGDz3Sp+aIwM8+b1h0IxU6mShNuLpJcFysq8BAVQVVBpBLoqoSvf3FUaZ9SFcqVhV6DFxvgZSD4lk/q2gWWaF50Re6GWgCqva1ZFoTA8EspJStSk2+yZ1nMBNqCPpD1xaVU3aeBzblBKSBq6pcCAo9FTETR1/IAk4ShyZG85eIBBRa/qkeBWv5vzIRDbu/pgaZw7NA7IfQrUnFNJowoXECQACp7zh5WDIwkO+XvvneZUKvUVUTMRXKss2BOcj0BNoALJrtScrpUjB85k8jMKk3VAv9TH8ZX/9H4ZPal/yv4qPpJAgYm1feW7m0qH7u3LgnsasgbeC2rL9i2Hg1C6nI+G8f3Ls3+wtEy4e2SqsvlzE4SH9cwZdV5wjCXnGcw3sJLKzLO22XOJEZshyPOswIe1C5vL0GkDP6Jj4IJDLQV4bHLLgTPl0plLZdy0gVnTnJ0BXT8Cr9gciCpJzWk3D83+REvwjvzsype3lyd+aU4cOWTWvp40rS73P3hYj/L+ot3huAMPQhvp0j4Sv4I9KknOJvtkL6EPRtp5x9Gyb8fxDJeFfDob9oIAiBgARB6aDokUNJimzo/5bmCKz5ZQSnA9s2AeAGa92U0oOzugwuIz/bt58+Ey47b6smnjoXL1osFoXRJ04e4HTGk5k+RcIu1CeyO2onp97z0NfyM78OILWzILhO9JbwD73wb2sc8qCei9UHMBGyqqvcMm2pvWE80NnZKd8BmSZfsCWGZugMBUzMC/qFyqhhneCb3Wc4S+4jslvvy5ijJFucimn7kcdoT1pAbHN7LDLmeAHDIaGGCAb1Mpbd+1ffbTlLZqoQbcGlUrPRwHz2Rvkidmb/kDZ0Kil9jjxuurOObqtQBCfIKk2/71vPxf3z6GEpbQfNmZTCw/VTEm2C2BqjgBbRUlyT6PIMnpi6FUqtKgD4snVCSt25Y1LWXUhH5JzPQZ+T4gge85dbpSYhKDSF4BBpvXt5bb720ohw6YD3Vd9paA2x7BGgGOipesW+OYvuXNr60r/Sb0L0//5NRUVvjW0eMGxJ+99Jery4BxfcOO8N5RrbzjmjwuSrX3FJn/uq5R5XLG2SrPisa86NnNZcod9bEXntMz1QZsAqlqvw9cQ/mmsDduRl0SCNUgFCl7RqwGPEoj/ulLoXdZA2cAo8L5V+3KDJ2BXcsXf3dRuevjE9Ov5XsBSmICIIukfdz4fvEz/A2Q2jtN9W1Yj6zhPY+MS2/G8Ppe2R9UtiQHbM/L31xf+gztXg2OXba/9B7eM0nE3n2nU+kcpKfmVNWjJsaL3D4Z3qYjuYdaXIzyDNyWsLKpYgJnCiA++/6G+BMApWfDAqLUt/yd3eUCquVDY6rhmOcv51yyN+ztzq2HYh8keqqTvuvhR8aVv+31t6ZR1a6Pf3JKeeJjE8vgYb2CLr75vU3l079+W1CNfTuPJ6gaMLR7GT62Tw4jlSAqTxyzwSke6JIy5smLyfoYYEFpLUCBGOqzkA2/+9bOGBCGKA1bDLNAa/yAbGAolCYa3EuJxubVB8NPtCFqjXnb17WWSVMHx9lIQKA60DtVAgdMIsPoQJNspnFTTTE+ErRUtWLje3vLwr9em0oG2cTKUV2KKo4XpSSvxC7AdLAFMzJ3o+uvu/76sm39oZSRITqCR9w6h+1I8/Hw9b/5h0urabEnL5axswZXDZmd2yWLJmFm86mWCBj0Kwhuog1+qNJhd3GmmsKgIk9+fGIcoGBo/fIDUbDaGarJdWX+3aNDm6Kg9czX1pUxDX1C9xHMC8DRwxxOnPMMeenfOSVBaKteCvegmoW+4vO9l8Y763P/ehoO7j9dPv705Eq5oV+XzExR2t26+UjoKkve3pXAjtHjPASdOMSQcZUCB8b/c5acioZ5qI19YkYDo4d6JGBf+e6ulGPJvvlZUqL2xJuvbw8aCqESBEcTXhKZJE0SayBTRfMSDAoAOV9l/kunLwdBphO/y8Tu4+ezx+wLBxQapIdkzXt7Yrigd3d9bGKMQhREWs+mvOuS8NgjtKxRLHDcVaRwZymreS4law3+kHprZn/6N3U2pXPzIFAJJ0+uCwrLYalUocxROVr43JYybdaQBJZRs2k+HvW1qTdXgZv3yVF16tO5NI7pk2BblUQAfsv8kX/jRMP1yU9NDfI1786R5VOfn5FqkaqZvcQYes+LF+7MvnSWH/jMtBjiFV/dkH186uiFSE4OHNQ19ypYEkRAFDXLRie/X5fMA3nj1Uo1rnX7sbJpTUsUpdYvbA4SDPkRoEleORe0yA6d2+X9SkhPnjifQACKyJGjHGrQHzOpfxAiQR9DK9HYtOJgKgUqQwJ+jksi1w9f+ija0Y1JEN0vVFUwh9KE08yxOm/Q56CvbZKb9uSuHceTCJ8+VQ0k1MOm4un+rhkQ1qdTee/NasCX4YcZStn+hiqweG9Pnsleh/gRKODkM30+08WvtnH8j5fxk0lctiRA9ryCVRebajgr9ScUA4m1/gb8Y7YZ+lk/vm+oRGyP82od0Do42XufGJ8qz97dx+JoPSs7RkBh6MjeQU2BJzjjQ2YNTgJj/dhua2/CsQSrphf/zivbyshRvXKPoRvNHBK6quof0ARIkonfZ1Ad2ge4sj9c9z04toyb1D+ggQZjKOfwET3K4FG9EpAYFIsGFUrLiQttspkfJCFIwN16Ju8LtVIQzOn2FUwcPReKy9lWyWWFcqsKqoJFYn3NwfQIqpxI3KC5v/eJb+U5NyzeWx54bHx8AwDKdG1nfXxbgoTiKyBT8QoV5MzF8Kc1bbrYFwFSrU+oZffJAGpkcT2j5My5ElzaA6SFgXT2s+eXfKE4j5tWF/sh0bj+xhsybVsgRyEOyARg0MiLOnbi4JmcPXfwN000XOQxM0n9uhI/XpMAR3W01wWCgnuBtHtAF2HvDQb1/M48FbdUo49WlJNMNx7eO3Q1ogWqxOxIzuuO47F3Ln0G0G+/4/0AHPgWSSOgTDCr8qsi7L2R6q0NTpMg8J/OnovYhf1hnoQ1bJzcPzLDkH4JPVvkMwSpkglnnF9i2wF/kgZJVNWvdyF7R1IgkRHUoeCGgn32UiaIS+S9a1W7UI46VcCjZ4E8A3EFgeyHz8tAXnt0eDVbY+mbzbEx7KPp5gLVxz8zLXtUsrDwpabIkKumA9QAOdaCIpr3wnY7i86xtQDARhJ5RM/ci8Q6sv8nLoRSxnZ/5jfmh7om5pGs6PsBTrJB0+4aEfUq8VkENqhU3VIfoY0hGA9bDmUvSD5tOsmYQbhYLuwtau8tj4wpG5fuK4f2n8oAYWCJ3gRAqPVimyRL9od9NO+Bhvhdv6sXzv5DaQM8sklssZhh2Vu74m/Fc/yodxjVsEtXy7Pf2ZDBgioi69YcTEUNVQ47Q3VJ1RhjRFLJHvt79KfMG+lNHr9f+et/906SGd+3b9/p9HCgO0uOegzsGvaNn6f2ptolfhlR3yP2zfo526q07s8QXPGp/i32ROKD1i+ptj8l6O9+e2NZu+JA2bJ4Xxk1vS52zt7yGZ7TXBWxNHCif2Of2Ba/52x6x9gcoQ13bhcxB3HRDyrR+IEmGy60ILJnqhb3Pj05ikFZzKkDy5qV+1PGe/0/vF+atxlcNby0b39T2bm2tTzzO4tjdB//1OTy5GenJWOU6VtECyHb9fcZSnPyYjlzvFJRUCZiOObfNSpcwvDnGbDjF4LoKotDbI2Y95l9B1aNbgKW6bcPD51CuRiVwUGQJEg+NE96iRq+qmbZnnEesscTh8+HmiFQ69yvS6b34hZD0fSscBz42IIQUxw1fHmJAiVIigmvjD4jbDOEArT9aJBam9ZnvvS9jZET7tG7UxwTg4VuZD0W3NsQ3rryOQ6sQ1/jfZqR4VCbMfLKf14RJF4vAxm0Z769IQ3EslozNCQiI+8aWU213HMijl1jGVoaRB2KaYNyoJ7dWpM+g9xtXWnozI1l2XNbQ+NS7o3E4PYj5e3vbcoUdhxLWTZ+8Ly7RpW/+vLqIL2VDN4Hqb6gLzFyhjw6lAyw0qeEDsLNSEt6HCxrx6h4V4JuaDVjDeFzUBe9si3vb8DwnjGiemwWfnltnI65LhdOXcxkUUjbkpeawkX2OY1tQ4EEM4z00cNnygt/vTZrefxgJTP44M/OSGXBgKq3Xm4KUsGhKH8e23UilBrP//Cnp5a17+4uG9eiIVxNEqby88gnJmb9du8+mSnZB/acKid2Ho8jc++1KbbuQ+BvHcg4KtFzWi5ByojhPVNi1ldhn33xj5dFHtnn/vm/Xlg2LGwuu9e3lqc+Oy2BPIqEpP2hT07OOdB4hqLAOS1bvCvKTfYNZ6YyINgfO6aSff3bXD/7i3PKG69sSxUCRUcgXul7XyvbNx8JvQOyiOZWP7xneLGf/T/uSQI05ebBCQRrE9L9N+dmD6pKeWcQ7itt9LytGw+V3sN6lJs6tyu33DWyNNwytOxf2xLUc+2qA+X5ZzalaVSybgDaxmUHMqBTksnGKP3bpxwTFAxgwRgz/Mte21G2rjuUBn6BeYaXbjxUBSO9O4Zm5c9UAgQ6TWtbK338ja1xDgJxifLYyQPSGKhpve+ALuWFP1+RhBVPngNU9UTBgDSzcySe7/v89GoOytVrOSsCawGkqg/wYOi0uny2ygUUy89KeFW/JAlmbaCPXDJnQ+9V946phgkAJTicC9uGSklyGPIrwIYkZspv09E047LPkMFUG4lpCPIyIbdCPSGA7C3lMrYZvUFlw7k1N0Wy60zj6XNeEhqBEtvr3iMdfP5ybNCQ+h7VtO2BXVPdUL24/zGqctUcAcHgkreac1YEEA98fkYQVwkHe8q+mHzMJ0Cv4+ibjsZxC/T1L1hv962x189J2CT6lK3Yjab398e5ZwDY+StBY/V33f8TU1Kxd84hxPamnjyJod9L9fry1fQF/PxfPJqAY+bdqHDX2uSnr2RvHN5z6iPhBZOmvRsBjQRL9UMCsWfF/tAlrCtarCRLoNB/aPcEgAvua0hwyr6j0qkWoSEPnT6ovPZXa+JT7Nc5t9cH2CG8ocqlYV3C1H9QJbzwtf+yLDY+dN3+3TJ/qWu/ztlL824d/re2A//L35+XRmIN79ZHBc26Zeo1ShkfMGFAwEmN6vp4nH9IveCPvwOgoRa7VHwkHnoCCXBI+GqCACgmkg62RXCLNsuWSgiyD7Ycyc+ilLBDzqLAV1APQZYoSH7YBQGzJM2feR8AoT1rWsqJ5uMBDIGKkkOJvsqcNaSaeGDn8ay93sbGVBRPBZQC9NifaLjWmCw1IICMs14/tlFFoCYk41kkjcePnk8spTrKZxJcEOOg2vn+DF0jWnKxEhVBAUNv1D8Q9aYBXZLAqtzvXtsSgFD/iTgAPcZZzeyS1jOhTfpeqlRVbwB51KtlT9OxVHA8o5ijliii96o0OFMSEjYBzXrYkO7p2ZMMs7koOJgregXEcuz23n0nA+YI6g3xZOMlRxIdATbbKxGXVFjX91/dkcAfSEPRlG9ng0N1C+W5R84I3z5uQr+ICkhk9EQ5s6jSfIgqlaqI51NBGjSiZ5696k1qCVNBwgK0sR8F9eIVtlnsChQy8wjoXc3P+TB+1dBOwEOAojF9Ew8Auu76yampDtv3w4Z1z/ewY+KYs21VRLYjz2tWSqeqWmGQKQBLUmu8At8x/eYhYUiIQSRqnhOADaDxriSVfcb0DUVw8h3Dc4aIspBOPrbvVJIG76nl4JnSMLFf3pfvxEAguuOs2bv6ccXJzp04+QeVaPxAaVS165nvbchLJnvLaFlcXGlB7k2db0rAagqnxYvaw8UrOYDoMRrg3vn+ltJvVO+U2yDG02cPjTN++/uby8wFIxPIkU0TbNEHZ7if/MSkBPOmR+q0x4FlwAUCo0b3rSZzrzuYg4paxOlCWJW1GF1a3hAUqkgCIBw+QZ6NrAFo+5aj0W0OCn3hahxWbWqyACVKGNc+LMvfak5pDJLKCKFeoRkY8e5zHDzfA0389jfWfSTBJiMWONkw7puU5pgpA2LYGGVGk0NtnNA/9BuNoRyXDYNjhw4mAYIKDR9VNdxB7lE9/D6EPA7bTBO0jC2HqqDqgw8TZFMRQlOy0QRgmXJ69YP015igLvlALVu/en9K0fpsIBqQJQNtKDqcP3Gh3Hz78LL0tR1l1l0j42QYryEj+oQOVuNr+2xGxWGwJ9p1aReVoj1NR9MImQb+666L4WdEafZ7ZwZTnaLaIXAlqzm/Pmg0GgNngct851MTsqdQC1ApSJoKrATxDAzngQsM5fUONS9y2oIkB7xTn04VV3bJnqA2AgBBJWSRHCeFK4irgW2GjJkeP+rWoQmiNj7XVCY+2pj7ZzhVGaC9Ki6CDUnYsEkDst/cn+ZflSIJENqCfh/JDsdLv717v86pIEHXISN6YThg8zmcKQkmIy2hEUiqZnTr2znOVaKjnIwrj+qB/8r428847C89szlGSfDmz71n796++emfn/MDsQN/8p+WlKaVB8uMO+tTaRCUbTYlvku7rD3jrN8H8o36I4HSeA2VR6XhRD0fAy4Al3hXlbazQeohlJ/69NTyta+uyeCpPg16ZbqGi204HyROEibIVdrWzGfdBMEcEWMqoHQv6763pdz7CzPjsCQaqpsqAs5Q0Nb398YpaHAndQoVhBoznfPvbSjf+Y+LQyNQJRI4eB+cmCZOCe+jvzQ7+0wCxVFBGVUxIaLbNh0OpZJ8qsqjJMylfHpTAAEAAElEQVQ7WruyUo/DV+fQK4nYi0E9gRoADhWJ2XfUBw1MZfX4+VRbXn1uc2SV9Qw4Qwc2HS4P/dT0snldS9m7+XCZNNeerZJ26lSqFVA3dlnztF41591aEaBwZs2Q8U44eYnwsUPnMjHe5V6HDO2e5xHAC7i9S4p2UHPvt8a19x5wmDkyfQb6HQTLqibmXEDaoL3QVgGhCgnAgMMO1WhQJScLVNmyprWMntgv9oeKIdunYn3Ho+OSsKHrSQIF5bs2Hi6f+9VbkoCr2phJQrxi/Zr9sXUCTI39Ku6bV7ekV6RSIDpb5s6vTzWS+AlfpIKrMXPkyJ4JmO1vwQvb6b1XVQVUD2o1XeLjVizZG0ETNAoT5Rc8MT6N9NQD3/3GhtJjeM8yYkK/VONRXdOTcKrqX7E2qrBsuP4E7xuIAPAAjlhDtk0QfHzfqQBVnbp3iF21Bu+/tr3MuW909nzoOmsOpLJh3UirstHWyFkAagiUfvOfL/iB2IF/9c9fC7Ay/6nxqdxpZIW+DqjrkqqWZPP5v1xVxs0enGGNJMFVGswc2LrlcAJI5wYVW6WHfVVtZ3OxAuwLQBzVsC0rD5bp84cHvODrXH5GcmjdM8ywZ6esO1aB6/Illen28c+Hj5yP71GdkpAIVFUkJOh8ZYZ9NpivdCVBK5ACQCixBzC+99r2cmOHm8roxmrYG1VC9+0zUIzZd8mLyoIEEPtCUi+GAQyowBGJ8Y5Q0u0vMy/WvbGzzH64MdVawKd+iO3v7C4P/OzMJMPHDp8LVdH5l4z6TkGqc9p68FQZUNc9wzrtc/14nkMFEzsCiBfxi8V7kpQNa+xddm06UqbeOjR+2vNKzL0rfkX/oNiHLXQG33lha+aTqQRZR2CgixCD+IovfOFr61J1HjuxX2yE51O58jnskFhO1Qm9iFQ/26hymhkxvTuFTkfBrzaATpwGdNOTlarJ+as55xQjBdMqVuwwMAM9zxlSwZTcoFsB2VDgKWWJrdDWVXLYf1fmWLQNSVRJUkkz4Pnjvzg7DAXVU2CL509j/eVr+Q6ff7ottiEV7F0fa4uVbrzphiS7bKV4zJBPFYfMIkqP2+VU+ewbf4ZaKjmTRIoPnW1XgLtLVwPMSYyslRjDc0WFq4dJ4WczUqB73855x+LEqBiuOBB5cRc7K1mjaleBIdQ8P0gMLsbyWZLEH5Qd+KEkG66fvueLpcewHmXUlAGhUE2bO7SagItjdvxC2fXunvKP//iRTNjFh1vzzu5M3iWJJrhCHYDoCeAvQM3mDUugSpPccBcGWTnKRt2+7WiyfJmnxkkvBspjkRkDmRsn5TBDjgTrt905KsG+F+xnoPvQXksx8+YhZdWq/WXCxIFpOhTUPf/s5jJ6NLWb7pF0Jflqo2kQ7je4W77b5p45Z3h57eWt+R2cRdQbB1Bprgo0KolC9yZjF5QIqlGqODfKPCd3nSgT5w1LyVKQQdVjYF23GFmopeBZACH4sJFrjXOSIwieTU8ms3lXNRFW0IxPvvzPV5cvfPHxIMlmcUABVXv8nEDVUJ15d4wq69ccSEa9f/fJrLVhMbJpxlgGLVGiuCRIPNR8osy9d1QM9OplkMQPy00dbgySSe6VgpXBRO7D4YVCqsYwMiSBH/4ntybIYnw3L95bfurXb0tpkAO3vgIbjXucoGm6gnIVJ4P/JBmelZNRKuWc0I3si1rvSCZzUhfq3rFKujpVwdLCr68v8z82McEk3qZ9I7CH7Guec+Hptuw/U/oN6JxEzucGPe3bOUE7AyNhU9qk+kFVRnBMOxyqqtmbgTaZGx/cRb3jzde3BTk2QwWdhRHE9VW6zpwI6NGZiykZuwQoqjfoYK99d2McAsS136hqoA9KYtXL0zFa3zXjhaZWIX6kVqtBcH/92++WOY+PKS0tZ/Pni760tvQa2SMymYYF/vI/mPcDtQNf/fLKsnHFgdKlb+fSsr41dIlZDzSkUkWxZuuKA+X2R8emUR3ixiAL1si+osC5PI9zjstKPcW+VQkywZfBVqWgNhK09EYy0CfSCPruC00ZfkYxTW+TSiqwQABonQXQkhjnVMINCYb62nd6yybdPChBM0dUqTtVPR349wbSqYD6LMigQUcCe0ikxOaxJyeWnSZhn7lY1q08mMqe5AglkvysJAKliX0TLJDGNAmYQsst80fEobFpQAQKQmdOX0yyBX2H7Lqg8YaE+jkIlqoNZAqtQpDKkQmSJN209a2vwE3ChnIhYGMn0FoE0NYYguZSpRDoZ9rusXMVna1Hp0imojHgy9/34JgkwRqYvRNBEmlv1RJ7sn3ndjlTqJ7eG8ft+wQafv/COfZyaPn2b79bbv30pKpBVjDUn0BA1XRaTXjvlQBlzcoDsdWSF2eZPSHA4eyzH9bSOrALqiSSMslnde8dYwfQo/RMoGvYL2dV1+q6BSl0z9B/VJTaxHj3UXHlL6bn5/23mzOgVQCyom0waUUzvRT7LzDz7qCeqJhQ81GN/RKAJDnadKicPnYhwJU9Qy6Xw6c2A73m19iog6a6j+0boYMXvrKmjJ4+MHLBEoMM+Wx/U3r0rIP7CwvgxsrmUROSjOvb8O5c1s9k92lzhpTlb+8qI8f1DY/bjIqpd9SXTSsPZuBuJaF5rXzi6Sk/UDvw8stNCXRf//q6JFVNL2wr9//KrOy5KKClN6VqWpXUEgD5/vc2VX5tSLeqH+f05VS8J08bHLpbpLkvVVx6l6CP/VCRQCs1VwWoaCAcP7firV1l1KT+OQ9sqb34xtfXl7mPNAbg04toOr09XJvsTBJ35i3DMmRTb4b7Eczzj/Yq6rCExKR19oN/cx/7Vh0sNz/YkP3Dfvt8suXen4vCZOeu7QK8QOXFAIA7Z9R9iXO8Kwmrnp9aZURQSZre85stNKChd7n7vsYAcYJJ/stcDT4tA+ra3xjapXVSuX31S6tLl4Fdy20PNqaiqaqkkZwv56/EKXzmpnWtpaP5HQMqsGsz+nLfTgEMBbWqRtB+cZAAGwXX2qBV6w+h/Dh+8oDs+VpvDFrvlvWtqWz6rAxdretW3n12cxk/t+qXwtgAIPKFWC/ip9q8I3tc5RDTQgM+CpIzjVLoGSdOrUtsBwgQJ5mlZYDljNuHV+IVbTMl0KC8Q9deM4y6Gfb8QfyMe0NhJvpBJZPdZeP0TZKDdva8UwpOkiuBukQX2JmhqgdPl+OtZ8uwUb0zPFEvlP5gVaDDR86nP1TcJzlt3XeqTJhWl2RbAmFP8Y+qwoAWvsW5tQ9VaoDX7B/QAQXbuVels+7OvH3EFquoWnd7n82WIPJzYhWsH4kbNoXfE0tYd0Dro5+dVl5/YWviHQC6733iyYk/UDvwQ0k2XH/2x0ujFNJzQJdk8PiaU2cOjWpRTRmIk1K2rFDmHinzUQdxSKj+GFoiUPTCURIE2gzJ5rUt5bFPTYlzee3r60qfEb3KgAGd87OhWmkmva8hSIOmLhUSKjuQPTQBhnr8xAFBG5XzZMeCaHx8GaFy3eGDZ8rQET2Dgtp4Gnk4CxvAYDXJyfqVBxL4afyb/8jYoJaCRwH6kIbeoSbhH2ooF9yhdHguvOCWfacjscp5vvknK8vMn5gUBy4oqWXLEjNSr/OfGF+mTRtUNm89EnoM+cxaDwsnvn51S9mzaE/53G/dncZ1B4J6EorX2IkD0pw59s4RMVh40898dW155JOTM0XZAdKkrfn2jec2l+Gje1cSu/SjL15JKXjlwuYypG2qsgY4KJFS8etfWlMe+cKs6IkbHKcciLOuxM2JhHayvjVIEW35SVPqUqo8eEDTb7cgTgIClZqm13aWx3/tlhxawSh6ht8X5GmcY+wlF8qCeIcCh/cX7QpqdHjz4XLfZ6YG0WGwIEpQc0owlJ3mP9iYoIysI779K19bW0ZPr8u7krwI7DkF7zbTlC9eSbB2bPeJMrCh4lPTo7bm+JScucRRoAtlYMi8MwGxUvvAcf3KHXePqhDVda1lzM2DExiQ5JMI28fuH6pmaM64yf1TWiXdi3LQWWNWx5vKXfc1lLWr9pfNa1rLsNG90uzovPgcPVIS2y3LD6Qp2nA86PGhfafK+SPnythbhlbygVc14137aPhS1VhXKdlISjQO+/f0adUskh/0pWEciiVBVp3hSFRRGDlrmlk7bSph1GmyxqcvJHCPA+vXJQE3bq/9IHCL9vwluuSVhK6AQ3DI0N/RNpn+hb9cVcbMHpLkTeXGFG5UsQgBHDmXhmQXCUcI93/43PfKA/9wboL60FsOnonxpwoCja/NVHDfEgpqa6iG+mcyuGzPqTKorkuCFgE9pwjkOH/Gu+4W9FAAz6GxuAInZf0qoO4d+6KSZm8a6qfpGvVwwqT+Zc3yA3EC3htKICqfIAj1U9DtEmwcPnA6lMSJNw8OPUePkuDa+wW0LHx2U5m9YGQcERQRYrfmxW2lz9i+6a958bnNCWbcJ+qUycvsBeqdpF+AwTYK3tkSl164KAf2aB+am6qwgAQC63ypvO3ZeazsWNsaG4n+KBjfa2janCGpNBzdfzoSkfamypXLexZ0urw/iZbvloDdcdfo2Jv08vTsmJ439uZrv/V2GTV/eCrqmmNVE25dMDLSqKrrAhj2R4VClZBiFGEHayGB/dL/9lb51K/fFuQfLdaaH9h1MmCZe+PEBWbpU+vbKT4ElScCA22ywPyIuQWku1UP0C9XvLcnSmwblh/IfkJblGRIXnr0q9SB1q/YXzp27xA5c0EVhJj8uWoxvyaJGTseZapLKszOACTcfuRPvAP+0B6HbqbZONPcr4/dcEbYmFQGWk/Ht/BFi95qDoK++u3doaqgQP4wrj/+w8VVc7O5FXXdyvamI0lunDXsBfsmVA5DFXt1jt9xea+Lv7OpPPKLs3JuVKz4N9RiNt7+d2aBmf6eEECHDjeEbiip5Hdq87Fw7qG2fCiKKgDJVWveRZtWRRKMoZ0I0oB+qY6Hsmi9zcChqHV9KsMACe+Cvd7wzu7SMHtwEqcaO8A5YX/5FTGQoHDn9qPl8N5TZdT4vmX7Bs37FWUNyFapQl1L0I1FgR0gcUYtVHWVmJkAj7JUa1AHaPKbKqYEbgYO7V72bDlaGqYMKOvf3l0e/Pz0svjNnam0Cq79ngnv46YOjE1lfwBkBluOkyScu5zeBtVdFx+J6p1BrOf9Xbd8n2q0PSbJUnFU8aH+JGFXUbPHvWsA0H5zwjJY73ySbcwSIBMq19zbRyTRc899SfwP6JK4RpKA5m59axVWZ5uNQLEWXLMLaJgq4jUZcxWe1a9sL8NuHpxYku9RuY3k+dFz1ZiDY+fL7FvqE+PooTBp2xme3MZ8qCkQ+vdU4i5t/a57dp2IH7Pv7AuJEFCPoEDXnh0DoknYrKtYRBz3zstNZejIXol7sV4kJxK7zDUb1Tu/i0Hh3WJysPMAEPaG3UZPFzc469s1+08dmHX1e4A1IBHf7myp+BnoSzig8lVXUykE4tnzqG3WafSYfrF9wFFJnX0PHGpaf6i079a+/OvfuvcHbgN+aMmG6y//fFmMGoPnpSgdUtnxcjVxQRtRbJSbvv+N9ZE3k+G/9/K2Mm3esASdsn8Nh+Fvt7+xrFy2r1ymDW0iZf9K2cTnLH23uU267KYECSvfbC7jbh4U5wAVchBUCQwdExwaRHP2xIUyIRJolTqJbFmj2JZVB8ujPzmtvPjtDaVrb5NXr5RP/OTUOGMZpcYvGxeNSyOT77Rptm5qTXlRIKQEZnghTi1ur5eKhsDALXppW/TsG24ZVvZvPVIap9fl5zmDVYt2J1GCPChBvvX6jjjKDDEa1SfDrJQ4qVwJaqAm0Cj3Q3Jz3vyRSSJkppAi90bZgHKM73z8525OCVClBcoPLYEEMtKuTd/bUqZ/amIcGycoiBPkK+fJ/m1+z202Qm0ytfXH1dzcdCQoSybrCnyufZDfVZLzXjXT7Wk7GKRfz8jIJ5qe/kGa4yqDd77c/cjYUB1Qkbw775BijGQUCqvcCQ22RjJ9wQME6Mj2Y6Xv6N5RDII2Mk4MqfuoOLlncuigp57f+uGpWwcUA+jFkMEUWq4rf/rv3k1FLYo5Ww7F+RsqpMJjX0IjICrHW85GphYvNBWYThX6kZ4cDbqrW0rP+p5l1q3DPhogKNDxjuxBgaok84v/7p3Se3TvckW59/yVHHhN1hzVtSuVig2UCK8fYgJF2rbxcBkKaa/vVdavbQk/nlKZpvyzbU2MEj9BnwAShU6fAYez4vWdZc4DDUFEn3xqUvlhXl/+i+VJbhhXVUTXwu9vLj0Hdo1h9I6Ul9Fq8HENbxMI29cQJPQWKB8UeeykulAjCCVETnBXpeolUEGBM5NjwrSB2SfVJN+zqeTsW30wssqQnl3NJzOUDf++EhroHl184IOgDDiAqmA/+F5qK+Pm1wdRg8Y7c5SD8OH3t1WgIGlUWwR2dOPvvLch75sjApaYZUPaVPLnevjx8eVb6AUdbkzy/t3//d3yqX99Z5w5iWOVjiaJWK+OQckFKqgFmVR86mKpb+idMz5mbP9UGjWLsnvH9pwshzcdLnf99PRUtyT4nkvwglbA0Xzz37xdnviNeaG1CRZMmZ46e3CoIWgpnN6JI+fKFROR63umr8Ees1YqdfYeJ1qT4U4iTSp1cPcMDxWsRWGnS/sEIuzhnXePznn2Tthx5xMnXGUIcmoN2DBBhh4w1ck0/F5Xqob3y9fKnp0nyvS5Q3IWvXPrKqHWBKwCtXvzkdKFgERj77JpTWsmgrdowjfl/OMTYjdQTlCXIIDsPhsJoQZAAUbQU4l6QJntAVWoWqOzQLA2SE0l47Uvri4Dpwwop9HZHmxIhQL1ih0X0EbW+OTF0rLnZOk7uFt8gaG2gjaIeOOESmmMIIYKjWAA3/2d724qA8b2La//m0Xl1575xEfKXtZOUPvmK9tC1WHfqqCMOlVVmZMI+nkgkufRcO5C9R1YV/HpIad72ipvepgapg5ML9HjT/xgkcz/6/WH/2FR1KJ69+8cpN1gS1Q+oAxbat+gQrtnPhaybJ3QRQyjk7hJ7shYCx5roUuafNvWyBnwc299d2Pp39AnwgASFO/Mj6MQOyskkN/59sby8V+5JQP9/B1Kij4Z+8Pe0E9oKKt+Fv1ngEFxBFquOEDQLVHxXlTY2SD7UnUEa8J/S7D4N9VtykRQdCMAEvR2qgJkScvRI1Vgvuq9PWVYQ+/QWkjZJgm7EXuhUnBzOfdrlu0rx3ceL7c9NeGjaoDkG32WqIXzCMUXdNbmJqAf2TOCYWdNhf+2J8dHkdP8oFRaAWIdbiw7th8PC8HfVcPh2sWvWUf3LGh1T5J/yTPwVS+NpBg4JJB1djyvSikKFNBAgF6jjwETvGfBrmdk31Toa1QrwjKANdQ7ICPbnGqRIY9tk7udMftY4q3ajapGZVLlyLkFUKDFep8rl+wpd9zfWM36On8l5xzVdtrMwbFB8xeMiq8HCAn2VW0wRsj0o7ZqKpeQoMN7t+IzwT1KpQqcWJedAE6qchJHskb6CuvQVDceLt3bhujZR96RJMrlmc1B0usnVrav+EsU7yObD5d7PjM1SRuAwJqjuBIi2brpcPoT+Y1ho4xAOBSqWdSpzqMf+6yr1Qy4Pp1TaQFWAOlUtZw7ayqRZY+8t6d/YtoP5fz/UJMN13e+tT5d+wbJCOogwtCf117aGufn67346CAfOx8jxNlKRt749oYyqLFPufv+xgScECeojWBZkCZYc91x16hIwS18eVsCtd51FQXj1PGLGS6Wpr7Wsx8lJ2lyOna+nNp7sjTMGZoy+ejRvcurf7Ky3PXT06rG0f6aFLuWRW/vzIGFNuE+OnxQQ8FfNKahfb7TnA/Sgz06xtmkIe7ClaA0ypo2jYPkQENDoP3pe+jcLvQXmS0Dcxj96aYbMuiNExUMpHmo440JEpUVlQ8ZWAfLWq5ctjdohUDI23QgBLrPfXN9mTprSBrGR4zqmeEs06EK+IyN/VIlevWPV5Reo3qWez45OcOGbLw3vrepzFowIvQdJTwBmjJfzYBxqq99bW258+MTE6Ao0aFYKREKpJe+tTOlRIhI+Iyd2uUQ41RCIqpys16SqpzJIGc4koa93ZrBq4ZQRh1/WBAB8RDkkInTu6PMyBGhKlkLaylQqiFdekYgGioMDhV5ZHvI/jAXw3txADVlKSFDIn0O5BPn0jwGCi8SLn0Wf/3l1ancCPgh2Kgp54+dL/1H9Aw3+v0XmsrshxqzdyI3uO5Qmuf3NZ8oDeP6phq2fMneKK6d2HU8M1sEYzVeN0No71OFIU2rguR68dnNaVbb33yiPPTxSTH8jMa+5qNB0yVpo0f1Lq+93BTEi+Pxvnr06ZwZFdbfXoSWkDkU6CgVU55h3ObMqSheP+zrj35/UZCr0ZMHBOFR9qZGJAGX0L30pdVlzkMNcWQqOVNmD86esufxc02nlkSMm0DG9GwqNRwgdRoo0aa1LTk3aEgayBlfzl9AIFB3cQY92viwkhDIGSlMASGntm9daxk+dWCUeCBQOMEpe5Pyru+ZsyVZrVA0AU5FaWQHUNqgtbi0EvdJ0wa1ce47ZE+Hlz2qT3nhz1aUh352ZpyFz4TsjiPTudtAzt6pKKr+oj6Gb9vxxrJtbWupG9U7NAzBmEbTZS9vL3MebMy71QAPddX/w2ZBaTM8b6Cm7A+SwOlhsb85n0UvbC1jZtR9FBwBOkL1uXAlCCobAN1kFw05tSdVnFSI0Ztqw8EkCnt3VVx6wa2f4bRUKwBMgqlNi/eWz/7avKiwCYTZSc5PUKMxkbJeTXocrZCghP419wZgEYT72Zran7VGX2APVamXvr0rwytrs1m8H5UGDeYqLSgkf/Xbb5fZj475iM8MXa4NvANQoeS8+Y0NZdaDDZUoRpvMJ/qhQKPWaBwZ05YKDbc26JA0/de+sK3c/MTYOH8Xqhk/oTIP4bWnJdA+59tfWhUgAc0CUMJ+AS/uurch9A9rxiYZpupShfBeKCcC5ryv2MxO7eJHyF7yKSqA7NOS13aUoY29Kyn3Y+fzM3ff11DNG9py+CMOf0Wj+LCiZXW4qdx3799ceer/zfUHv7codr2aKVU169ZmG0CtBcHsp7Ve8tzWMv2eUR+h1QAjSaB9Z02JlUjG/XltbsHq9/eV7r07JdDcurY1FGAzXvDWodnhr5+4GLqRyggqCroZcYWXn98Sf+vdqf6xC2IU1aS1q/aVJhLoPz0zflPQvH3VwTJtfn3EDtgUiYFg/P2FOzPxG+hjjb0z1Dj3JPgGuHz9S6sy50M13rMIWj2HSrk9QGTF8GAxkSZycYJzw2ZJdJxte37Vsv1JyP0sOyWRAeqRSPUZN88dmvECqH8ujAS0IGvtEqzqh6BiaS+kInPtg4+mpBPnUWmxfyqJ9mooH+BOooAiJNDXV8Uv+Rxn1VlQ3QVcuF97HxVRksLuAfvWrj5YnvrEpLJ1y6HYQWqPdz0+PkNxzfqQ9GmUJv0M2V/zxs7Ea/aPtbRHJB5iB2qVkgQxkWq1PrRI2Y7sGfq7xGHl8n2VPHn3DqFnqjCIJdkYwJZ3hZ2Acg+QkrCcO3M59tZ3sH0o/ipg7NxG9MNh3dPHqsINiJAYY7RQLmVrrSNgZf+eE4kV/dmGNQdTQa4xG4CrEiXxH8Bs6aLdpaGhd5K+9BfzMWaenTJ8uK6cPFrNV/FOCNkYCUCOX3+i6rg9Vu21a6kwZ2ht87FUgN2nqpV3Z6q5JKeW8Lqe/vQPJ9H4kSQbrhUr95c3Xm6KYRdcaAQjXwuJNRp+yowhQam8EI2JNNsF1ZzgDprPD43JwuHOv/TMpkx41eSjMc5iUlug+ASJEhwKNqHBFy5cLps2HApigsbgcDLSNrmyH+6bHonxtwyNbJshRjijkgMvwSHyb2VRh94Lk+TsaDpabmh/YxwI52PTMzgaAzVsq95w0oK7L/2zN8qCz09NMMsBc5gHQ+/pUM4qP84eUq5eIH3aJVUOwTAKDLRNaVGiA5HWPCo7F4gKdMhxXj5zqcx7bFyMtbVzOCU1DJdDDlE0vIbDU5bGy9+1tiXa0YIFyCvjCBVXurzn/jFl8aLmNDArP0IiGR4b2GdavyFDuudZvCf9CoJoKN7apfvK0z8zM4GkIGH/3pNJAG+68Yaqgel4peAi+HNQWtoCQD0aEiaGgoqDHh8Ns9UBq6ZhMg6qYhVien1Z8/au0qFnx3Lr3aPisCRPa9/aFYk8F2qcvhxJoee2lowduoGAEx8a4vs7/8vzZdT8+iAD25qOZe0HDesedIxTQgFhFGvIq0Devmxd21JmPTYmn+mg1ugeAhWIhcNs3zF6AtOtS/eVhlmDy8G9p8LFFfyRAhbUSKBrSBdq3YDB3eIMaW0L7sjzUaHhcLzH8HG3H08w6R2pzAiwauVRe0HgCA2yDoMGd4tzhk5bX3xR9y0p+0e/eUf5UV7r17eUV17cGqqHJJLx69WrUxJRzYhQW0bdvsQpH93Yr2xafzB7IOpzbfLEegdqlBiB4wt/vLxMe7ghPTYLHmgs7721MwlaZrT0R9U6nH1ouGBkLOsqRw0ksKYRCThR7XFrKHkWOPg+79PkentMciaRYBOWv7+3zCIrvPdEkjezXiQ10LeqX+j/HDzGSb39SlN54PEJZdWyPQE7AB8BWi5UTYWqKdR4BIbAEc9LLpGjY18EsALFlr2nUtWAuLGduMCAC+fK56GKooCOndQ/a6jhUh+beT0kxmmto16sebO59BzRqwys6xo5RVOZJTZpZOzXJYE7KqHqAucueXrju5uS9Nt3PlNyjwonWUAJc9/2HBlLzfIqM1A2aJyAZV+zmURXsqYSKe/AwNf+Q1ROeoZ7rDLEpgIc9F8IxgTSWYMPSQvrlfkwYIqk316W4NlLkiCDqMjN8jXb2wabOVuCAM8dRZhO7SKxDanGJae65cw7E5IKKnNTpg0KdUJy5PnwzlGrzPEQaJpL4r4hyaqNGxbtKQ/+1PTYjQ1rD3xULRo7rn+C3BqNkf1HG7TO7oV9EdSq4HlfkiMynbfcMSLvt3H8wCQb7L+Kpn2s0sUfqiJXSd2NCRSHjuidCvbAYd1L85qWnLkRUwdmnVv3nyl1Q6kaDowwguTGAF4VWqpRP8rr3UW7gmIDe+xxtBTiJ6qxqhaSaucCBVgjP4qLoYfQ7DWL9oR+pyH61RebEpSpLqos1nop9mw6XGbfMzo0YwHwinf3RNYdK+KOe0aXpe/uyoA59pff4+P0RahqoL7YZxkOXNc9iYgEB8WVshTE28wcgjSox6jR/FskRU2MH97ro8Fo7IvkqVefak4PieLV391cRtw1Iv1T6Us8cCYgoYoA+8MX6WMd0dAvyae9b7I0oI+yn+Z2vhFlxl5T7efnJOB6NqwD28MHC/hRbdivACWHzuYso4CqdItP+CdnpRa4SyIiC9/2GdVslLOp7kgCqibwa5lpNLCxd/oQrVdkiIf3SpUCS0B/nIoH2WqADEBP03Qt+UrfXPo0KolhfYuRFr/yQYYW3nFfQxrTSc9TB1VJDhUwszTOp3m5W/f2GaxX69cjuMJmZz7J1Q+SaLOrlLZczi2xjMm3DU/s98AjY9P/qaIlGZQE6o2wvtYyjJz+ncuGFQcjie7MbVq6rwyfNCB2rUZ1srbbNxyKxPSGZfvLyEn903+lEuJZJQNTZgwORV5Cwh9IxIgUSKTYU/ResQA7A+SwF/L8I6q5LWjSYjagqjMjFpKc+Fy0ZN9DPAkdHCtlx7YjERV6/zubSz+S3UO7591L2lwEDVCJiXOIXdnHf/ov7/qhnvsfSbJRq3AoMcvGlGxdyn5Kuhw5XXqSaA4qdG3YCLKzZ0OpGjmxfza9izqMPggBmSzwG//2nbLgc1PT6Kh5++SJi+XY3lNlzr2jctg5zg+vaaI5VVa8tL3MeXRMuO04nJACwTOEdQ6d6ENK4O3LrubjQX4dCMGzYIdBlNjg3yqJCeahchxjKDpHz6fsKSv1sxy3y98xeA6jq1a6c0mmRo7unawST33ZK9vLpHnDgl7e/9CYsslQO5KGmt72nCjvfGdj+bl/sSC0CU6ec9707u7M7bip001l7v0NoT2hHAnCDBxrbTlV3l/YXO772MQoIG1ZfygBtV4HGx86p/HL5yfQGtMvzlF534GxhlHk2nqkTJxWlwBi2NDu4asPr+9dFr62rRzccqR07te5PPTUxPKV336n3P+56Tn8yoj+zbmnl+bwudKxR4dy/XWlTJwysPTu0yV0IqgUY+1n9q7YHyrE8Z0nyph7RobaJjN//k+Xlx7Depa5C0ZmrTaubil9B3YNwilRPHP4XHnqc9MTyEAS3n25qdx6b0MaUGc/PSGlVM9hsJ0ypEDD7BeH1D9UtExr79yp4hWjPKCyQEq8L0gkA4CnrXdHgLfy7V1l+u31mTQMdYc2eb+m9169eDWSy8rjmshwy+1PQZl7RNfzeahpAhbqPaoOC1/bXk7uPlEaZw2OUWds9WnYR9tXHCg3drypDG4kC3ld3j+pSGcKqr7jvb3lwZ+ZmbUUEDsTBmuiqkU6tHO7DPiSKP9tZmn8bS7Dvt57fUdKw0/86i0f9RRoeBw/u9KTN2/kyI5jURfz/+Mn9I809Pd+f0n5/L9akD0jgJJIQPZQLARiqhH29PPfXJ/v6tyjY4AJiYS1rKE4AnXBm3Ot6fuBx8en/wcVQSVCZWTazCGRQ0RrIBWYBv1vrC+Xz11JU6nzrDESMntk/+moh0HNoJIoHSRUe47slTkfoVYt2xueLHSwNjPle3+5ugwc0TM8Yb/34vc2lcOS1D6dyu2Pj8u5OXLwVJyf4JqMoiCNyt3wht6hg6jcprl7+9HYRsm4JIwjqxRfqsFikCyDPQW6QJHv/t7i0ruhd7ntPgP5qgZPKC+kE+1BUCPQ4djZM0nW8f2nc28zbq4oaCo/9lXoIp0rQQ6VAzaF0IT7MR0Y6IBG5XvYM7MG2D7OX9Cowlc1rFfy1hob0Tbxu/VNABuccSizZxNoXD57Oc3a7AsFGlxpQIL3rxEU9ZTgBkBI0qDpf+TYPqlIOPuSMzbPuXKWok6z50SGli17b08QRDYussSQ4m1HKhW53ceqnoG67uFSS0wXLdwZkRCgFNCM7HdkrftX8yV6AAlOnA+oI0iQ7OL9SyIin9mjej4zg4hgeBd67kYRJakjQ3lDgjjfax8DWMZPHlTefG1b/BcasoQ9+6X1TBKaydMH5bzbd5BW/9Zzg7IoaBNE++wfVUXj/3r95z9476Ohoyp6S95uznwPCV3dkG7xSc70wa1HQ2cFGB1RuWo5ncnSKNFAgjRzt1FxMjG7J8rbdQEe2VmUFsmLBNi6EuYQsAma9fbpxQoL4fK1JMWZ8TBxYPY/qq9eOci3KrqqPT+o6gzguOfz0/Jd9o0gqmPbWXCWxScS9Je+sjoT5O2DarBh1cMAYLJ3Id16ipyxGv9fgEnJzefZVwARNt1nq8bpR0Iny7Ru59BQvr5d4hcroZIbQ6GUCAwa2v0jtTIxie9zxiT+YoQlC3em/0h1bMr0QUG7/X6N7kh8wX4FmoQStP5QGTWuT8AA/QCoRM6zijsGgzMTJH//6XL2xMUkuACiVHm7tE9iJPYANKNWqU6rXrj/zEQh8T2yqsLUEh4JxqbVLWEK+N6TR87n70llHzB/6IbrAi6hKQ0ZoxLV+SMFKspSGCX2iQo5ZgFAT4KFGqX/w5oDQ9Yu2RsVUL2k4g7VtEivh5J+IXtLZcdn8T/uTeLPd+irkbizgZQX9eXowxPEA1LatcPeaJe1UnEEEql81YCVWrUPwKqfU5XKHnTpdUZ/zKyjDVVF3/6lmEYACXATgHFMv/SxYAZpAWiY0C+/I7EE5Ema9+09VSZMGpjEhRS2ajT7+INSofyRzdn4f7qe+njFC584bVBUlDh5pSslS8aGNJsNT1ecLJtgV3AISXP4BXgWi9HkbMh52gC3Pj0hDpLBwNU8uOFQ6VvfMygJGVtlKKi0xk3Su14SB2kQWKRYr1Y6+LLncDOX7M1GSxn/8LnwwtFk0CzcNzR9/l2jYxxk5puXVUN/zF4gkVkFiNU926iuRd/fkonJZNhqagxzb68vU28enMNggKAm7Jl3jwqio+qCex+1pe3H8t+oU10GdE2isfGtXUE6KUbR2x9369DywKenxEBAEwRvgk9SrQzbQ09PzmGHyg8ziKhP56z10QNnsvEZ0jf/el2cmLVhBO69f0yCWH0Z3ofv82ymZ3JuZ05eDLrDIN79iUnlpo435T4f+8XZyfgdeOiMC1dY6XHwiB7l/ofHBH1nOL//V6vjZCM7SDJydO9Sj9Zz3XVl5pNjIzdLvtZ7+o3ff6jc9ejYJISuefeMjtHfsriSJTWVXjDjMJ/SON+9Q3nrmU2l54ge4d5CavV5CNpUUhg8ARNqh6Tsw5RmqxIr9SJ7jSNhPCSr3rlA5tv/+7sxIlQdpt46LMgnbXf7D+UCh7q3CbZTBsTg4kQq4Zr+qarw3Hc2lvPnL1fygYO6xkBBuSVmENhPfGpyZkagWNhXAgNG3jNOu3NEmTx3SJrCOA7JnbV9+9ktoQx+/l/cmcAabRFaqqfgjk9MTM8RY3ahrdfp7yrRqA370vTauGBEejTe/fPVZdfuk+XOJ8a3Tcu9VM6hqNwyNPx/Du97396QZ7nrp6aWr//B4iRskgLvdMDALtmXEDH7PSong7uXuXeOzOdQ8Fq5aE9Z8kJTGr2dy6iVDO2Zd1w/suqPQPkTrKE8CUYMR1Sd+6kvzC6b3t8fBKx+Uv8yZMqA8sYX16QpD1pnivmU2UOS6HCqAgWXSpazYv9kdkaaJ+nflwTT7765M4OdJJvmAS1fvKeMHN2rPPGrc8uYWYNzLxqzzcmhYCf5FUi47np0XJycvWUNjraeycA69lTAq6+FeIXAU1CNkuW7Z8wakn1DWnXg1IFl/kNjAsKwF+hbAm5VJzKvJldLbPSBoxZo3r//E5NSqnce9VQIQpxj54R94mD9GboPm67pEOovIEENEzBVA1SvZZ2cX3SPdSv3hc5AAv3t72wK0kepSXO3IM2fV5PSP0yixB6z2Z4VMmtmgEQDACDA2rvyQIZJqq6ghqp6zppfn36ZyOQeOhf0E6qKbhSFtytX8y7YeIp6Ahx7wfO99lJTggwB26gx/aOwZYYL+phqtyqdxAq14omnJpbvf21thpFKSNEfPLPkwoySS23VjL07jpe5t40IMGRv6P2D/KLrok/Mvm14NRBy9YE2GsbJqnK1yVyCG0PxFHCzPwAN8rtmEwDBIOD2FHWZY0fOlzXL98Xv6tvwmWmk7d7h7yzRcP3iL98aX2xfacbGXUdl4hNT+erRMSjs5FuHZr+pFkkooO7ek6AfeBBFyfY3Jvnj//h7/kRyq+LFzwKVAkiduRxfhkLsnROjwW13H8AoAV6GD7bRrs3EktCjuaEOqwpWdMoPy6T7RqXSTbVQcsEWZGr35LoEeCrKzpW5WXyq2Tfs+St/sTqxBJqr4FszMsoekDRzYrYejVQy0RUKd3pAVUwDIuytJH2Bs3pA/K7vWrtoT8UcIMLR/sbIRUuyIy++8XCq2pIc/ZaSBpd9KLg1x0v/S89eHTKLZvKMIYmJVJ0E1XyePtM0JzcdzZoYRurc6Hv51h8sSf/PxCmDAm7pw/F7kt3B9cC8DwPKsMtAQ2uvWqoHJop8H1R0PsCqRNyYgFA/Vx7MmYnkcMd2pb6xTyrCbBxBHtVtSeDTPzUt1SoUI7LHfn7dkr2JXVDOXABTfgS1aM78EbGBAETxGCqneETiNeO2+iQagGZADV/EfhMXSUy67WhU45xPFWMgkEnnwAh9qqqTgAujC8QIaFYEWICCp09dSF/Py3+yomJEoJu2nA5ILV50iYHYUsIVkkE9Nvad/WKwIAYJ8Ns6SjSA5miE7Np9D4+NLecL9b72rqsEduwxia//xt44uPlwYhS+Q2xlL/0oEo0faWWjdv3u//F2EgqJggO+bMnuUF70c9ikpjjubjby/nTZZzx853bpN2jacDhonlKxF6U3QkICGfE5m9a1ZNNSpBlE0WAwhZKq6XfwoG7J9AX/nAMHoMRmIzFoSletmw6X+pmDgtZ5gXog8L+hLyuW7I6hqM28ECgKPr/8R0sjWbt25cHSpVu7GIDn/nxFue2JcdlgHM66VQeCuDCQuLJkHx1EpdxLpy6Wu5+YEETPZoC0MRZQ1BNHzn50nwaPcXRbNxwqo8f1jUoMypTDueLl7WXw1LoEp5A/utd1o3oFaWndebwMHNkryBYEx+FguPGEcYKhrBIHfQGqRLJqJVzlYkjDvt0nYjyhwByqAMif419HqeHo+Ri0ZQt3ll5DzBcZWjp2uClZtYCS42ZsVBJMp6bfr6GLMXUxDIJiKjoLHh6b4MjnSow4Dhm+dX/zte0JzO2P6bOGlWPHz5cVS/eWo3tOlhV/uqZM+fT4Mu/RsaEnacRds2JvjBrDKoB375kIfvjMR8ozgkX/7bvee2lbuXz2Urn9yfG5P5m/pI7yAzUqAYx7hVBDqqnNZIAZBYuh3XOfSvQPPzE+Dby1ac0+i3P0vYwfbrRgk0Ew5EhFTRJwrPVsEhYVLb0sGujMIOnWp1MSEcYCSqMkLBihOCJYUI3pFL78jSm9Ql0Z3VB/KEusO1QGjeyVQEiJ1VTqf/v7D5Ufh4ssrvcpSGSBJK3uc9CQnqFY4tzj2AMSUMYEVZxGa8uZ7DnnGIVP2RuV7PXvby5XDfns3SnOQkl+28YjmUMBuYKcewfH2hoo0SXtd9PrGd7Th8+VOXeNrAZrdasmTXNm0DBUi1SSjpwvw4b1SGCnMsoRR/Vo8Z4yZkL/yDCyVfpIqExJNg1zEuRALU0SN0hKI6UkUuO7PSB5hJI54wIfdCDv15/ZY6f3nSrdhnQP2k6e1bNB2ZwNNAl7UCAgaCF+sPb9/UEBnS2OXbLxyOPjM7AL0os+o/qggmMvOtecEicqERHYsI2qGsALSbmmT7QLcyjSUPjBB9mX7I65D86S90lG2DNDA1EJJSdshs8IV3p4r/ReqezZp4IWwRjN+4V/uaaMvqM+3z1+8sBIWTp31gVyZzCXZzaHRlXD90soqDbpg3D5PkhhrakeLSeIbRDg60r/wT3C4Sc8wck7o9BWZzh9KsfP5768W7ZSgANY0lOX/paON6X/i4OPzSRG8v7eDJq9sf2NZerMQR9xpP2dIEfDtoQRUgm5pXCF1gCJ9J0CVspDwAgVXzz4Grdf8yllsFB6L1yJwiK1IQklCgeamSqAoHPpK9vLhDmDYzs1wi/68toy99OTkhS5rCuQSSD5Uz89q/w4XH/6n5cmQdWvgyYtWfC+VWAE0Cgi3r1q8+13joxtUF3mK1Gf2Ff7FTAx744RqSxbR2IObK2qEXCSDfE+XMRVfDY/ilLrLAnUrSV/perA/0sOJYaq2uxvpN1vuqGsf39/mXPPqJxTiYg9CKjTFyeQVeVb+sq2Mtcka2pE39xQ+o7tVybPqEvc4z2aPZPBmyOpCR2NHK7ZI6igE6YMiK1b+fqOMm3BiLxPvk1VlapZVNuOnk+l0+ejR4lZ7E2+DlgicYFyS5IFsVSOdm0/Xu54oDEJ+luvb4+PQD0SCOv7UnXUnwKs8tm1qpGeD4mToF/8wjazT1BxojTsrdgqdhxIpgq/raKc853oq5J8lWPUTDYHXbBxbJ/YZuvKRrKLLtVRQTVqLVsS2dtLV6um8X0ns589l2d3eQ9nUWFPX847i5pT9/bpaRw7rm++T/Ko+nG4DUB2dpxz1aH3Fu4s9z0ytq1KdbwS5ujZIYDkBYpT/TvHjqLdqYpWQzxL4kYVM0mMPWQ9+Itbbq8PLU6c6x4lpV3aZI4leBItIJAYEA2umrXWLjYFaCRuRaVkSyRcGDViKT9n/0UFcc/JsnP1wfLkz82KfRHHSK6wgih8AY1GNvRLXE38QsypKpIeJ/03h8/FHn7hV279kZ31H1llo3b92j+aX959bmt579sbk1FOmjY4DqsqPR0LmojnSjf+9gcby8WTOvyvi+69sjQq0OvfXB/DwJDjwePpHms5EwrQvHsbosADeWdcBPIMN5RKQxC+Gz3ksRP6h++mxE/d5YHPT88mVFoz08Hft1eO3H8qiKUg5Zl/+U5QuVtvr+QUGyb1L6uW7CuTp9cF4Xz9mU1l9gMNaXJ87yvr8nJVSQT+So44pAwlgwplnHrLsI/G0jNWsli8XdQJcrQafR0UQQP0gkyi4Owc49KzQwxO17puMTCZSLrzaIygspoD8MTnpicwkxwwnow5xJFDlQVHEeHStXCZGV/N21G4IqF29YOKanbhSmg+O5tPxMBQDtFgZk2hPN7hhJsHBV2EpkVetlfHvBObGTXD+0NfYLj9vASws8FFa1vjRBrG98vhdCn5Cep8PyqFA3n25IUEcn4fh1/iNHHywDJ+Zl35xB/cW+54ckLW3PdArRkQQZEAUxMf48DB6g0SWKElCY7efnVb+at/9FomUN/66NgYM5OUa6IAj3x2Wjl+6Fw16OrDD0NDwY0140CAC4UjvyoBoBy16M2dUepKI7qk7cDplJcFW+7JPbg4rle/ti4lXInGgofGlH37z5Qj5PO6tk+iDXHSQ2SIoHdpGBNOv2R0/ZK9GSDZdUCX8tTTk6MMxkFzoibMQ1Kht6o9JAc5Eontj0ui4fr0Z2cEMd+zSo/T0ASiprlz1qT6UA7Gje+fwNosDBU4MpYqCBJajkSjHZrZWcPe6nuWiycupEdp26I9oejMu290Wbl0XwxxVcX8ICVovRQqSYte25GKm33TkXLJhYpjXc3PaJ/PSDOlHp5O7fLdytbuiWZ6UNku7Uv9qF7ljb9YHcrPm19cEwqN84g6xylBqhh4fQGSd0n88ld3xCYJJO0H5x5vN+h9Y784lW0rDmSt6qfVBUXkbCC1EEoBzfJFe0JDJc0t0eCUJGhspQBZsEqmWa+Gpm0c3UVvN8cJmrfznX/xVu5/1bu706uw6PXtZdlzTXGikFeOT3WlNgcEEOHnIZiCA5WpQebk4NU3H83Aw0/8xJT8jKTg3PELAThUpfv26xohhHcW7kylyrvXvyfpkpwv+f6W0njXyCQUuPBspCCLeABeu3UcUl81TY4e3zf0JTQjwZqqBZqAShKHLahJlevy1QT3kotML794tWzffDh2tibPqQKkuqCypNfPuX/sqYl55xKRyC+bo3TRQDDTintGOMOaoiLpkcGR1iPTt1+nBGf2KoQ0gMyJC7HZ9iA/ghKL/20fCgZrNsqARsiotWQvfM62piPxT5pVSZ2iWJlhIMBx3iHQ7Irz7j5JYLPLbCVFngd+edZHzftmJ+CJo3n9uCQaLlK75pFovgYORVq5Z6ckoNbLebHHJXq1Hj7Pwa+wr2yl6jsFMv7HIFVJBooKMM6ZgQyzJ2aN2Ecq72IHUrj8zQt/vjLJmj0E/BGsq5JZO/ej+uH33/vGxgCAgDXBXxSrGiuFt6k3D40fEF8IuAeP7ZtejQx5fWxsqqiSwQwCbJsWbS8A/ABFU2bUVXKlF69WnH6B7ZDuSaQWPbc1AjYDBncNEGlvCSYxORrGVbNDDAe131S5yLs7J1HyOkqs4brs9fseH5/eoZaDpxJ0qvjw+6ve2Z19JBXLLJ0PPyxLXmzK86EAAmRRQ2+48brEHWwMW2mPZhJ8uxtCfaUWJ8lQIdUTdPudo0KZR1fTV+QsqogCDgXS5sWwkyqFeqAAPCSbgTwSjcnT6jI3Qw+V72MrVBEiEHDtw5xdQb61lbyxy/5eDBf0XlX57KVSP6pvqRvULZ8p5lOxdU4A0oDAiVMHlldf2BqbUckPX19N9m5/Q3noyfFtDfwUxYZk/6FGOdNkfpuX72/rEzkXANH62BvAM5XezAbq3r5sXrovCbF+KcmzSrPKqntVVdfnieXAl6n0AcDsSUA6QCKDYo9UogWSMVXdBz4z7aMBioBQQFLGA7Qpm6JWAniB1Wi2/JHqGx9GWOJHmWj8nVQ2/uvrj/9ocWhUXiQD7aUu+97m0ndcv3L3Y+OyuQT7rvVv7CwLfmJy2bj+ULl5ztAkGw4ahFszrOEweM5LX99Z7vv4xDJwQNc0+XiBHK1sXpbHuKtAQEgcTtJ4vsfLpqjAURoUaMNlIBRd6u3Hy1Vytt3ap9y78A+Xl/m/OCMl/cMHTn0k7wvNdkBTujpyNlUDEp0ayi5dulKWvLQtvHQ/371bhyAc9zwwJoijJqaNb+4qI+cMyWEXJLzyV2tKt8Hdy1mD7rq0K9NuHVbef3VHmTh3SAKO44fPpNxoUBjkF8IIUcsB3dQalQr0CA5GYyGNfkiw6hAH5PArC7oEY4wPp7hz29EMSpPojJ80MM1eyvdKs+QI3395W3pLbGgBLoPpEqgrXfsstLBZ945OX8MHV66VTn07l8c/MSkGlXym9yIolxSu+JPVZeicujJuwcg49gwuPH8lpX9o4MZFe8qI6XWp3CiZX7tCc/9KEJu3/8vKUjdtYLn53lHhswq8qYWgaaBuNL21q/zCv703Q64gxQ6y4GfuvPrIwAqszp2/GvTaQZZAWce3Xt0eJY+a5OyR5hPlsc9Prxzj1qNRM7lw7lIZMrRngktrid7z3jvN5cD6Q6XXyF5JQmvUwN27jpVrVz8MvU0izQhKXihfCIKsSxpu+3YK+sLx2WuQWU5z66qWMnZGXbTNrZFzA7EQDFPWsIeTzHW8KUOfBMaCP4HaP/4nP9pG8P+31+/8u7eTUNkPkkxGfuSYqiEPUk5WUNXn9gUjQ32QaGq2ZJBRbLwHZ7U2H2PkhH5l+9rWcusDDVXQtqm17N93OpOg33tjRygFzn6GhZ2pqorhxbeeLfc+MT4UAw2kzoFzQvGjfnhV8uc8JJL2DYclaLx67cPSvPFwvreGwnPmePe33jky348yQ0XL+fO+g6qN6pUKmWqpuRoqckPN3OjfJSpYpIs5kCXPbgmFUk+Re3viYxNDExJwaRpXWSXla70kRfoL7GUVMrTCg3tPl+59KloKJA2oIGiBsAns0SQE96gCRCSmzx6ShDyVgIHdAk7wElGiG903dAC/Z59xXs5rrUKsR0ZSJzEBXKDDCNBQqHyHn6GcBZlml8/jdQ/vnqqvwE8Ph3cugF++aHcEPD7+q7fkOaB5zWsOllHT62Jj7BVVApd+DA4ZNQu1gSMXbI2dODC/q/LjTFgj8pCCBiACIQ/BoXMjeHNffITzp3KG4y848SwGcKHuUNyREKKRsvkmYt/90Jjy3lvNmYsAMBCACA4kcYJfyabfFTxA8VHpasPHrI09JGBi+1WR2BRAEZvxyvc3lR59OkVe/a1vb8ysiEc+PyN7ycwigar3gzpMlYZtIudqDwaQ6kK55vofqyTjv9XXmVlVl68FYedL+R/7hJ9SKfTOUc5cqL3O/taNh8s9j44rG1bvz9onUbyq8nahLVC+nMBVT4AkwjnGGIjCIxni9PF0SFP3ijeay1BT2nu0z2R1n+X8Rsq5azVgVDDNn0vyX//G+jJkUv/4PdUE0vTjZlRUKrRHNkJV034VdLPTYgzzsCTG9iHQ4nDr2XD6gY+rFzaXOQ81BuGWUOqz8Jwo0c6aoPoAX3fz4OwrZ8ye3Lz+UJl/7+hqQvqArvEH+s4k2udPXywdOrdro0x2yvND6+1Nfy+p08wtkHaGVdT4aH0+eirJy0tUdu84np7PDOltOZteKYkRmyyBAjqICQiatBCKodCZ6sZ1Add8lwTD/CDVRP0uu9p8HGDNWpBB14sEfddzph/C3/PpoTy2DS4lGuNMoIpJ/gX5AAPr5EwTvpg4c1DZtetEuX3BqMQFNdnkMEkOnMlcGwM2nRXPzud6b/aIpFBckF4NA4CPnksliz9SmSASQqCgNhQYYARM9f0AJraWL7Bf+w3oVvbtqpTHJKBsVdWQX1WXa0kysA2guHlta6rTkjjVmQiG7D1Vzhy7UDr37FDOHr+QqexiFEyHjcv3R1YX6HmgTTbeWuhr2rrpSFgnYlIzSX7hl275Oznff6fJRi3h2LpkX4IyNJaNG1qjWw6JhQi8/ueryqf/5Z05lLiR+GhoMA6z5MDhU37TfO4gUaRAoYGWcfwcMk6hQ6MhjkGWze/ZdbIMHtq93Pfg2PIf/9Ub5eZ7RmWjQEpkvBxDmss/LKVrl/YVCne24hxDmEmzqSh069khBk1z2bf/+P0y/8nxQREYH8Egp40mtuitHTE4+gckMALdbVuOloGDu8ZxOVwOHSPm36o9y76zuUx/bExZ/+qO0nhHfQyojPnAzuNl/PS6JDOvv7ytdCKPuWRv+eTfvzWGkXE1jEtma4Lz5NlDQifS4KYMSe3hp39hVnnmOxvSWxFaWq9OSexM/p59u4nqZzNwzZC6kYbjdNSs1iXN8wc2HCoTbxuWw/rzvzg7yZLKBPqCLB3nWwAlONEAqUFPRUACRX1h5eK9Zeatw3KAqFBAPQRJ3/7nb5V//szTQWUlaXVtyA7DsPjFpvL0F2an2qEqIemA/mZWxsQBQbd2MoQ453tOBmFQdfAulBk9m4bTA8sPlFlPjUswgcJBoUrDMDTga7+7qIy9bVgMeDTvD55K0MA4Wnta/hpvqYNIDiiKMPT2mqALlxyqOHxEzzh55WXIL8oWIwZ9gBSjsQng9u89VXr07hg+fhzgzmMfNXt5dgiGhNbfLXx2c7n3qQmhbNQGqlFe0/NDGWzntsNBbhhMUoZR/bjyQXmgbcjdj/v1e//+nWqI48CuSagMVLzj8fEpa6OYcAaoKJwxbreKFz6s9/PWC1szeE1Aqe9Hcm2NnEFrKbngkPYs2Vs69+lUJs8fnp4Pg9Q6de8Q6gqKG267cjY1OTQffQOpaC3eWzr16phqBqQzfOaLVxM0CBztexWan/i1WxPkL164o3zqczPKnr0n0+hJnlhgOG3W0ChlsQeH9SkdOJVqh0ZYw6ug2KNG9w7FEU3U+WVzUDIbx/dLid5eIG9r7/pMzkmlTYOt9+65ld4FA5yz59kJITcboA+aRrV3JTwCb8lM+w43JLDgADmod1/dXo7vOFYGTasrpw6eKfd9ouq3EzCYY8I+JKldsidBmdkU6INs5qtfXl2uXf6gDJs1uHoH/TrHrtYkKt0zVM69kIfESRcYSAhUEiQLqqfuO6j05WqYH5nbJS81lZN7TpXP/693RWyBPQWcCCLcj3eqb45zR7+LstM3N5bH/ultsW/eJeTUmoUu07dTee9bm8rtT09MwKShV0Al8HL20VNQagRmAjS/L7lFcbXmKhvR+r/6QWnediwV54nzhsW3XGmbiQCNN1hWYrmtbXovO4wr7vsFLc493yWRrSkrkbFGF1XxBsQJeMIdP3S2TJ83PPZcD55gz7MJLAEjNSBNQKoyJVD0GY88Or78j3B977sbwo2HQFOEQ4ECzhw6cKpsWHGgPPbpKfG//CP/LoniU53XQ3tPRVXNfqmFNX5GwM9/oUinF+fAmfydANG+pXpmr6kw8i1sLABTbwy51Pl3jco7RpFWAUdx0tyvYuDz7SUAkX2H1uh81WYf1M6UwbESB9+jMiFZN4cLYo+6qSLiXfI1qDYoMXyY8+Fz3It5IWwbaWiJt6RMo7VeQecZgu6KUMHuk7lvAi6oPz5HMgvkgMKLTYAR4iaN3YDPWnM7nxs7cvBMmBg1iWE9BFB2wMzS57aWW58Yl2ojpoFn8rOajT2LgB5Qo6emJh0N6KvRpuxZsRw2CXtvvcVmfdua0WugDpsi9nPWUafYEsqLqlVEA4BtVJ74Vvvc99WGW0p+MDP8LjYLP6t6wi4CNCQaYkPJIBvMVm1vOpaGc7Fd7RlQP9HO+Gb7ThwjUQP4qIIvX9hcHvvstND6N69qKf3re2Rg4PYtrdXQzVUt5bw5V/OGp0pXDWC8HDtgndHYMrupvneSRkmrOVTd+3eJ+Il1YGe8F3EvcAgToAaOPveH75e5Hx+fWKQ2td2ZIXaAakhAQ2yiT3H+7SP+zs7233my4fr2N9eVTRsPlYceG5eX6o52vre3jJo3NAPj0J8qZYJTKZ1CE9FEHBZOiGN1SPE1V729q0y7fXgMh5KYTaR526bxkg244/Bs/I2rD5YuPTvm87w8lRCBru7/7U2H0yQkiBylx+RkNf7dy4RQCvp9rwnOrr1L9penf2tB6Dw2JOfl4DrcAttbbx8RSV880dMtZ4JOXTh+vvzkr82LcZQEoJGN0G+RKc8fJkCQIMnkcfgYMNn+ge3HSuPUAUEZoQAoaDaaQCJKI6cupeRnHaHbtcPMsLhnh9/a+Q5GzFpZGxt5w8oDpd+gbkFdJDErn9laZj4xNs4fwqQRuX3HG8uEyQPL5g2tqSTI6AWBSq3KmJTFICqQQlUM68/pul7/yprSs75XmTizrix9oSkTvm+9d3SMmMoIh8BwcgTeH66lZ/nOHy1NszMje7D5RJk+b1gMuc8lj8gZcwLWjLEXUDi8khMNctBJW73WtM9QG9JX6fmfivSd9+U5cVPRGSQLEhrvZ+eGQ2X4uL5pMA4db/fxMnPu8OyX/ftORQkEmqvZWYN5NUjwhiDRpohPunVo+OJ+BtLz/S+tKn3qewaJh1BQohE4caSeozZhnUFE7+IMDCCTlEHcR4/pm4CU8bVmDC4kRpnac0Clf1SNXz/I4X/UTswT0QCrojZ4cPcYW8GkPS6BfP5bG3J+6NSjKkpwTchtj445pm+SATQr07ShS86BPU/VCrUmGv3kp3t0CGd70/pDqUC8+9q29E8oy7ugqJzj1hUHSt+RvaI+x/GhWeiLQN9ZvXx/Bqy5IJ5BwI6dqyZn7ziWipqhbYI/7x+qzulz0s4q5ydAlHxA/jlGdBn0Ic5TU6r+LAEDmwWl05cg8YLGko5Og+PArqnI2svEMCCM6BjQPkmCBlrldmdGQF2rythb1gTVQF+F8zNwCLR+fwAWqC+Hbg0zI+PS1aqREbJsWFyXdmXgkJ5xxDuaDiehh0Te80Bjzo1gwT05y+yLYWQU1PQUbF/XWoaN7VP2Urgb2zfAEPTNRR6dTDBKrOqKhno21z3pmZC87zJ/Z1j3VI59diWmoMfjhszW8e6XPNdUZtwzspK8TjP4h0n0V721q0y/o76sW7ov52/m3GrWQE2ViAwl2y1gr1XBBDLQbZRUSC7qlR4dQY3gCfilT4+WPblWIBS6QioWH3yYgEeVM/0GpgtfuhpwQLDDrh9pORNpbPK+LgGJQX14/rVBptYHzaiinx0qt907OjZMH4xA9M3vbSoLnhyfapx+BcHGJz81tfyPdNVmcbh/lTDAj+dBk1O5Z5NReiWUqL5spHfhz9g9zdpsJXlTg9okpvwxQMnfC/JQniSeGAnoqmhEgmZn8eUXtiTxdRa952pGQudU3nr36xxEXlLrcwygzDs6eTHJe008QcAnQUWlIlhi+CgZa5XDTQuby/QHG0J180zsuHk1BlTql+DrJQ692pDpTIcPG8Jcpg+rCsvVDyL3jA4q8XVhOrBXEswN61oSc5xmC3ccrcCMDz4MfdClv0BAj7HgHoBgkhjPzQag2gAhti0/UB787LQkeyZpU6pkGyS6fBSb5L8FzYA4wj+t61pL/dwhSaZrNgigB1yQXIvliNCI7SDtL3xpVcRPxFXsPQBGVZLdJ9FaCTV8GFod+0HkRh9N7QIomWHE71XqVe0Skzgj3jFlOs+VnpH1LaFtiYesK5oz1U0JpsvPSuo9kPinRuGz3myMREgCwY6YtaWqKG5DKQUKocZJHswncS9+TpXp3OmL6fW55b7RWQ+Ahs8BDlsb8zskFmhw3rtLL5k1lYigrmrg13jOLuzberSMmT4wg/xc1h9AAtCp9bmx2fyL72eHf+Iz08vf5fVjkWzUrt/8ledL/yHdUz42dZsT5SDo5AvgoRAQD5UJAVlQy7pucZYJ1N5qLgMGdA63smHBiLLsL9aU8U+MDYouODyBczuwS5BI8yao/ky/bXh54y/XlLs/NzVlTk7GwZ45e2jZvuVwNLTffn5L6VffsxwxJ+HWYaFvaM71Z7XlU47nnCAADtg7LzaVIQ29y133NZZ9uLLNx0JdsAkE5ts3HC6f/OmZpXknLfqbsqFRbASvu9YfCvXoE1+YU5o2aXwvmWp+91MTk3jgib703Jay+tktZf5nJsew1VB8m47EIlREP8zbC3dEnQQSywFznH5Gg6GABA8ZX3js5P5lwJCepXXfiaB3OMK7mo6VISN75nOgZQ7i83/wfuncr1NoHTfPr48akntU6nX/xw6cKZNnq4QIus6Hmz16dK8gOFVDNjk8KlP7oxOtbEru2MGulCmqUuLm9/eX2x8b91FCo0lNsCGhUS1pXnWgzH9qQtZegkd95OUXtmYdx47vV9avackUZ8GQ3xEkERGA4DJg/tt3hyPZpV0COxUIyRiurrkhJjFPe6QxHG9OXnLiPpSDJWNQYUaPgUdlEvhCPCDjb72+LdxqhsGFH/z281vL7nf2lI//6zviuNCmGMDXv7a+1M+oy/4hK4wWYObHPU9NyHtzoYWQO/R8aSBb1xIFCgpg9kx4yZeulRMHTpf/4788Xv5Hvv7qSyvynKSqp94zsuzecaKMHtsnhnTL5sMJ+NHPoN7WV7Dm7wQFBB3sM8krSWLSiyqdkCxUqPWL95YHPj21vPVSU+k/qGuoNNA11CJopTWs+MUf5P81ODovzsa7L28rl05fLGNmDgriesfdo8vX/sN7pXHukCTZnBs1F8ibXjF0CPQ/QIihehpCnVPVV43XhAbsx0wZ5kz7dC5rl+8v4yYPiLPUYM1ekU5m6+i2czYb3t5VJsyvT5IpSDKPwf0JPKDx9qDK2uHdJ8uICf2SGHBs02cPTfKt2gYMEAxYG+ALxa0eQ7vHbtjvKCeSEfZ12es7yryHxuT+2BOUNyAAlJg9GTdhwEfNtyqzHJtgnmQpapueFo49+310nzhYIIdLMqbq6Ty5H0kJ5w+9g/4L9uwFPH3vG5ppAq4gPZXsMygZA4J6CuihxWhnnfp0LiMm9gtNDnAhCURZg/oKWAw7NW8AXcl7dg/s/gnNnP27VAn79ddlDdy7s6knptbXI6hi69l//VsSkVf/em2582MTYuNcgjZI6bhJ/QPi3LJgVHrlDKNl7/gC55qOvz4ba7ia1PKk/tnDkiaDbQEVGQ6571SC0JWvbC9DptUleBYQzp03omxcdzBBnOBq3/ID5cKJC+UrS36+/I98AR+AO5T8VMu9y0Pbj6XCDbVlq/k+NlSgv27lwXLn/Q1JAPr36xRJ1Qx7zPyMTuH6L3p5W3n4J6aUZ/58RZn7QGN5//UdkcnnQ1SU9ICh3/psiYNAXrAXCuD5ywlqDUv1Xkn0qrrWZoTYH3xD+Ph9u8RnONdEOygdktIlclPNrrqQP5NIiW8E+irZZvmonEt8Nfo2jO1b1ry/vwwc0i2J9uTpQwIw6GswHwbdkw0zVBR9R4IjSOX32ASCD+7F3xGSEACzGYJ9e5yvU0XJs9f3SnXXc9hHk6fWJcm19hIAiZ/fkTyjGhFoIFNbGwRo/0qEJYEUNyUi1p9/lOxhHQAgQlk9eTF2l60QxHs3KjjL3tipaaSMJRfc4caAIxJHFM9av8bqlfvzWZnW3a3DR4IWKjAq284k8FAs4Zyxce5N0glUti/Yl9BVtx+LeMbid5srEQxKlh1uavvOSwEfNFerTLEhvlMlY96D1SRyfued17aX7r06hGGgV1R/L2WzW9t6z9JTMbhH3ilb4hzz1ySyJZmos6oi7Bt7Zso3eiw7XAO+AES+jy9Ak9Ps7+rco0PUS2sSuz7ffbJrbJr1/s1/vqD8OFw/VsmG6+VXmkIveO2PV5Tpn5gQlMnhlSlCqJe+WzXJytZsGBWOKdPqomQDcVOC5SQ0DqbhV2P4mUtByzkqATaVmp/4GYG+hvQT0cS/5+nJ2aReFDlGfE/IioMhcGZ4/IPuBRmrzd9w+DRCvfLClqg+qRC4bDLlsEzfNHn2wKnIJjJku5qOlsc/PSXSa0p0C1/cWmbeMizBikMdRSgD/wZ3C0fQpOrBo3oF5Vuz4kAZN7F/kjDN0ls2HAqdRKCuEtOy52QZBEE5fL7Uj+gRgyNZipxqt/YJokxRdtCo6mjsVgLGfbSZzb746995twydMSjKOza7wxyFqJ6dss6QDD0zfk8TL8QT/1sS5zBS6LH2Kbcu2h36EQOmfIsWRLubLFzPgaQrbwxf1TMZtCW4WLV4T5k4oy4Vl+Ydx0MJY+C8Q5/5/F+vLQNG9CybXtmRpJJjad5xosy5vT4GnjEeNLh7UBQNYBTOBFsMpDIvI4mr26mNGkcWkdFEn7LGz/7VmnKjxvepA7MfcLI5MQkCni6DBNY1+0EQef7clXyGtZQooJlwfmk0HNknVC17iJKSwFMJnYHiAAWAGstVNt57bmtkTxktyj8QUj0HOOQcrXWFdN06rz7I6QvfXl96tc2sQRlicH75H/xoB3T9sK7vfmd9HI0qhQCPEaWOBDWEiDm7777QlEDBWRMMdq45N7SrTOJuTRVOw3nTlqPp2aA+9fCT4yNEgb4Qvuyx8xUK3K9repVIUXtfvl/FCLVp+es7S7fB3UpjY58kv/bimuUVdRA9SsVCsOocqsRx5oJR+8ufsRMcl5L2fQ+NLc3bj4QWddd9DdkrgtWl75C0/qB06dGxLLh3dNBTCiYqJdB0ZwTIcau9eORsnhNFTND8xX/3Tjjsj//8zfk8fw9FpfB2+70NcbxoC/j7/l4VzLlE6cN9pj7DZjojUF9VGmIcAlh2TnKAXsD2+kwBDAoB+0padvL8+gRTDRMqtRyIGwlcKDDQAQorGccvphZoDV59cWveE3W6N/58debhoNRqiDfvQsIlwNHYaB1f+eb6Mnb6wLK7+WToh5HMfGVHuf/nZsY2N47pF2QYckh90ERiM1IkMOysNXBvgI4TeOD9qgFu0Eu0JH19QAMV8sOHzoUGWqts1uiNABJ7Zd/2Y6X34G7573Vv7S4NswdHHcbAsIqXfV0qTIClV//TivLAr8xKBYgm/6ULV8usW4dVwwU3tMTuCjpNatcH473W1/dMz86J4xcrJUPVWVTYBij6+bLszeZy56Pj4ivYOKImq5fsLR2D7l9ffvOf3Vn+Z7iwHgSSbIALCDR2yoAEZhIC9hkCDjCyz/grfHl/p/dOwKs6iO4nKZSwT5xal/kU9ugbz21OsiC5gxwLEE0WV000I4afpFrm+3ftPFEObztapi8YUdFqu3WI+AaJfECG6pZ35Of4cQpHgmTvA3JOiEXFQAK7+tUd5Z7PTA1dWRX2zntGJ6ivhridTuVUj6jeBsCqfkT+BSrP36keopzWJknzz9B3Z1tiSkJ2x85jkUtFG5PIWxv0RQqZ7Ki+Ckg6WzBlxqCy8v19sYfTbx6cXsStm1tTqbT3gYniMGwBQhNm8ai2i2v4vshGH78QACAqc42SH1O1b/io4uN81Tf0zZ7XTC6BAth891sbAqCajUIgQqKEAQJQ8rziIOcUoOwMA4TFMapf5PFRpA/tO10eeGpCQFI2SpULqi/RcAFUnHfPLzG0p1QWMWWAVZQB9aKhJukxk8xQuJJ8Nm05nJgSi8Z/o90CtjBWvFuX59NTRGyC3Wcz7DdUU9TQrZsPlUM7T5Tptw+vkqGDZ6oZUhMHlBtuuiGAkT1t/Yh/uH90Te8HUwX9UqIrdrIOYgt737NaE0kl+lpom+a8dbop6/2Zz88sPy7Xj12y4fr6V1fncHPOQSV6dczhv3nOsPKdb64vh7cfK4PH90vpSnKBa4tKIziQEaYZa2C3ZPHQNFnm7HnDgxJTC+K0ZdGblx8o939K0N8ayo8sHwIKPfRZlsZLdwhRalp2nwx3m/wpQ4OiBH1y1aZiShL69umYYIW8Ju7gM9/aEOMDpdy8uiXIv01v+mXroTM5/LsW7y11MwZVw9c6KfeSO1QabJfDp5EqdJION4ZnqJFy7crKaAjABfMqGipAjBkHxoAyuIvf2ZX5I/M/Nj7oT6eu7crRXSfKLQ80lEmT60rzrhNxYFCZYSN7Z66EKogDKTs+cuRc0FK9IzMfaQzFAQf2a3+0tEy9fXjUqjQtv/lX67LZNbUPGNUrjhgN49jBs+VMy5kMQ4Mu7t5ypFx/4w2l3+BupV//zjl4qgwOu/KiIE5yUQ1p6pRSJ0RCTwLDDuFpbT5Rpt4C8Wsf7qTn5hgEpJKghx8bX95+Y3sM370PjQlCraFLk7n+kkceG1/WrGmJIowkSkOuQFU14uSxC6HGePcCWUmLxnhBi2RT0kFlQxImmVTqhDwwFhII7/7dZ7eEIta1T6cy7ebB2Z+rX9+Zyee3PzQmzydhw0OeMmdIWfHGziDB+nfciyqYgFWDXPO246Ge2AOSFYbH/pBcuGfvxl65/77G8j/T9cU/fT8B2pvPbg5qPXx83zy3qsBzX1tb6kb0ynlBv4HE64XQU/XS9zcH+UMV1MdRQ8yjsHbtg3Ln3Q3lL7+4skyaMqCsfn9fubHDTfkczYA3tb+hNIztX15+fksmvwp2IdCoC5vXt5ZBw3rE6eiX2bH9SNBITZMGTAbp6nRjAnOBIqU1lQLN4dAuwwgFCapszQuby80fH599IBBWcbDHnR9OEGIm8JeccyTEG3Cm0YkMPBw6sX8oJPa9RFRfFO76q99aX0ZNGpDBf+SnJexEGu79+MQga5Om1IXfLjBS2qfQpiq7YsW+JDXAG0H6qAn9AvTs2X086CNQwzBT1Q19VqoxFKH0WkBB8cMFyrVp3qg/mT1w7kq4y7W5RUv/bHX5xT99NM2oLftPlxmmsO8/GZS+1uD9wrOb04RZs6nQa+egNuGdvaOTzz4JZlQ2nI3aUEO/J+Hw3eyLtVz0/NYyTLKeKctdM4AVZRdaahinBErA5jxZE+ssCHFPbARFHAiiRMA7dH8SfhQza+Hs13qp/A7Kqn2AmssHiQjMxYhUbfcOqW55t+SHJbkqE/ZPlGQ63hhBBJVkwavnNwUaIsv+23vvvrkjQYazr+LPLrjsYQpP/zNdX/qzZTm/bLvKVNPWI6GqAvf090Ds0fIkrlgPEgy0we/8/uIybNaQBOeCVmsjgWUHatOu7RUUG0mqvUZyWWDqXIojVL7RN1HUBJCCeDNW/B1VJYHf6mX7AoRKPPRSScBV/jSjqwwIbiOiUEP0B3dLQ7ALzU6Az0aYpaDCriqJMu2ZUSoF6wawVUMdP8jzZWL0+tZIw4qVAia8vSs9JS73p09NDGPf+nuJtGfUC0aoQPzAbujFRMXevu1IAAuf75xRT9KcbN1VGwEYKq9DR/TMvYxo7Jtk2mWPNmKhtDVbo5GKVzwb6jqamfMlkMdE2brhcJk6e3AqGY98emqSMQADChJbf8tt9WXN6oNJGKwpCWTghqqD2M09em7nfeuWw2XkqF6la7eO+btaIuEea70vLnLc4jWUdYmCYN28CjayNhhR4u739NmojgNC0TG9/7vubYyIBxtJMKbrwK6ZI5T5LdSzpg7MPYkzDaCkrkiqGuBx4KDKTpf8LNXBv/6ttzLUFnDp50z8tlbolDUatGetQJIPy0vfWF+mzCXQcyk0bIP7JFtArsYJA8vOrYdiZ/buPF4mTK8L5dJMqx+n68cy2fivp4wyIBB7h1S26aVyFLJbjkKVgdMNH/+mG5LJptl2+5GqzN82GE126EClnLbtaNCMt19syhyPMePxhasDhDNP2lAW7NBsWnWwjBzfL5QiVCOlrx3Nx+OYIQ42FR4uPiGHQ2FEGYwxWPbajtJ/dO9kmoJun6/xcsbdI+O00MUEqQ7BW89sDo1Bk5KyI8MmWHJJGqqS8aUcEtWRTe/uKZ//jduTrEB0BdWuajjc+VC9GAjPq+wWytnArkGGUMEYA4kMhP/Vl7YmIFPaZUAdPIYiw6XmDivf/+7G8OIhGhBdJVvPv2fv6XKZTGbfTgkEGPMVr2wvM+4dFbnGBx8fl+rTmlUHk6xp5JeJa4hUQZDc9e3TOQPUUKSUgB1wgZ1/O5iaZkkaU4Gi7nJi94ly+sDp8vAXZoe2JeEUUNK817Py3L9fXOb/zPQ0DlITeeOZTRlexlCOHlsF8hIehpIjpywFPZo4aWAay7/52++W+352Rsqr9gFuZxppu3WIkoUJ5/Wzh4T+QrYu5fbrrw/6Y/CUBPP5L63MvpJUCIAYECiU6g80SELlXgVtyub6E6grKa2iiClfa/YUjOTdWYdrlQqLfSZYPIsekn6EDzMg63/m649+f1HOr8oOx51p9HtPxrFyECqRwAgUoic+Nqm8vXB7kMcxEwaWJYuao8R2U7vrI7+aabQbWss5znFsnwQeLmIIzrz3ubf5RBk1Bq/7Ws6PvWTegoBCMlA3qEcCQgGigMD9UAeRPDvjbIszWZMahcIJRAUZkEjIozI9wMG7hGL7fjr7bNiir64vCz4/NfLQgmlBSCpxbfQu9yXQEtwf3X+6TLtlaOyNJJdNUqlg6zh3DtXede6guhn816Y3zzZ271YFFvakfU4i0ZpI4FCFWjYdLk/8wqyPVFMEO5np0aNDZL3XLttX6ob1qPqdADAHNC+2D6jy9M/MzHeqlAh+0BIMWHVfHKNnUI22Jva5BImqlF4l6jGe6ZVvbyh3PT4+z2XwnmDBnk8wtHRveeDxCRERkDwZgKfqZfDroT0n83tAhqYthwISnDp5IQGfSuwbf72ufOyX56aqPH7KwDTlL317V7nzwcbSs0fHUFv0kElYhgzumnU81HK2NC3aXRrnDU/yA3RBf6BaBUzAvQZcHdh9qjz45IRIabZQPepZiQugV3l+waR3hxIskURldV25eq1seXVnueWTExLg8m1LvrGx/PS/vacc2Hci+6U2bC36/QdPB2gjM279f/Uf3lb+Z74Wvbc7wWc1QPJYzoRzCFEePwmVj8hBhwxLS0P9+tbsRWAW6XDzHqqJ8ZUMNvYCanTfod1jI/hojf0AhUiOnr1U1qw8kHkU7AJ7QtgFwKUKCLRCu/QdPhNA9P6i3fGPkvRRDX0/YkuohC95a2eG3xoAp2fC/hYcoz316t051EtJEsEDFbRDhp0+OjbJPhnVUP2oxc0dEtvC1jinejzch4BbJcWaSKBVFsQLLsGzIBjoidI3c97w0IaTPO86Ue7++MQqprlwJXRyz2j4rOq6e+mPWtilXcDdClS5qRzcdzrDNtlEwjJmRVGi2ryxNRVU1TrDksVDPk8gjwXAnrofvkySwYerSrFBhGEohLKtRg+ooACNN6xpKQ1jq3hItV/sB0TWayJe8n7Na+FvNcyrNj/z1bWxyey28y2Rce/W0zthz1SMJHzAIEkZ1gPfDtAE3tREOgCZEqbaFHYxJACAz5EYzLltRFn46rbYeXEbEIod0xOoL+YmPYa31+feJC/eRSXLfSLS9/aV70FRRYsTz4mphgztngq1/SdmQeljT70HvbBsaehiXapmeNWUGdMHlx/H68c62fivKx0yOguOXyswlVQobWVASv8uMeRekAAQMsGo6A0QZJokboO5GCJBHQRJH8ToyQOSSJAZQ39QRvM9C1/elgoIh6MxC5rVsvlwJjBrLMTl9HMMnumXFI8M+sOlh64IEtAkBIQ71rSUSbcMjVMSXDNSMvPp/x/2zgPczqu42ku6vffee7/qvdqW5F4pxhhD6BBIIKSHBBJCAgFCqKETqjG2AfcuW733e3V77733pv955+gqhhBC/lgIW3uex5Z0yznf+b69Z8+sWbNmbYoFsNATTh5ttX9XlVNlCTNnCfcUBI1Ay/diwMPGIPMnGUFF48Aztbr73WuMO0oQQUnz/Nl2e3+cbFKSpwQJegYy4Anm/e2eDQ1NGroBgnbqaJsCgn1tsbOhWPgnH6/WrnesNHoRPMyV61JseifNuP4hftYnwQGOBj/BDEEQCcQi9cnk5Rr77cA++WiVVtyaZ5uRhlAf1EQCfGwQHk7PIxk3YE6MFUmChfMjuSGQ57NQokSJCJlgnFt+caw5S2T5qMqQrOBsKR/iiD3Slf42E2XD5gz9x2f3aaxnXJvfWGRVgao9TVp7W54F/DwTrpeNTlBEVQbHhMoFn/+RrxzRH/ztNRa8QjkDaeEZMo8ABBMpQwIEgh74nSR63HfuzVNfPqrCW/JMUpADjDUIRx4Hv/fJKqNjEBRVnujQG969xri4POdFrn1n64gKl8VZ0xwSejVVvcZx//gndupqMigVlMyRG0UCGy4+1YyTx1pUebZLJVQGQatI9F+o19ab8+0ZoFh27nCr1lyTac/Mc+gsNdTL5uZsz9Tj3ztpVMOEjAir0qGZHhvLXIg62w8otpDs1xxt0w1vXWHBMYEvByfrBToAaHNb3YBufGOJNQuz31g7+C4ORw4GT+KxxPjRJJCgq6xtDmeS6dVrk412CdABqgb1gcokg6IYTMfByl47vadRSUWeado0odIrgRIOjeimvsPUYnoQLg4hhR9NQyt+gwMPH3jzbYUqO9uhupp+DXWMKCUv2no0oJgRQAE+8PNj3WO67b4VJofJviAZI9nb/sZiDyd5as4SF/wynxcfg3gFlTkqFXCp2cNctw0W9Vpi/oeEgWbWF56oNL4xNAzuTdORVpXuylJzdb/8w/1tfgp9DMxbAcHr6BgzIQ2bHD8zb5VHnjmAhM0IWrig/o5R7bqzyO451EOCPZosoWTVVHZbkzn+msoDVJYyKFCrk00N8Q/esVqPP3Le/NQahkKOz9gzAsBBPhf/RCKBXyOI6ENOsyTODnoG8vEcSGoIFkylzmQxA2wdUkXl/uLnWFdQqUDqaagn4K0o8wyn5VpRkztf1mnqRABu+COe53DrsFZfl6ni0kS9+FyNPvSnr+0k49cNBib4ZY/SrMyhgtrR4rRnqFBUqqDwAf6xNgjKCfBB6TknOJtRE2RtDA14kl/EITiLeL4g/vxJFZJn2dI0bBL0a3Zk2Qws+vXwE5zFAHkkJ/TXFG9I0ZFHqrT6llwDMKEewdDg+wdfarAmYKNKz86bZCqJLN9n3+FPSDo4Y3jmBP3EN7wXlF8CdUA/GAD0QjJbhCQHFUbYFCQ7rBHkvZeiqJhG0uBvlXh8AWp1dZU9l2h+MzMLJkpAsMtrkqiR8EBhou+R64dGijAGfgdggJ4z1NnwM631g7Y3izelmj/hfkMvwhdgrGPAieaKXmtmrjnbrdXb0q3nIWtZnGci+Oy8UaigydO/wHuSPK9cl3ypx4GYjIQB0Lixpl83va7EeiwAN4l38JUGtiIx7bXU4hD+a6vsVcFaJplHWZM8oC/nOzEDsR6ANOcrlQaATXwFiQ1qpATz0M3xOY9+76TyViUaSMH5TaWU+ArQivtC3yYVTvw2nxkxGsDUFx44p6icaFMtiyfBmJ03/2Gqh6GeJn9Tl2ofsfsKKIkfJOEjHkIMh0oecR5Ua2uA7xlXTm6UzTMpKomzJG3jtkw1NQ3+3jMbXhXJxsuDDaatUuKkvMlC7R+Y0jU7svXsYxXKyo2yg5+NxEECalhYmmjIBFQhAu8zJ9uMw48qAdktyCM0AjJhULlDu+tVvMpTDoPCAcrA7I81dxQY4t7dPaae5iEVrky04IMS33MPlmn7bQWW2OBU0LlHWWGwa1xzU7M2aC86NsQOa8qjBNIoB8ATRKd7w42eQWxoze95vtZQQDYPBzuzRMyWwLn2KIwwhwAEYXJyxoJsNhH0AAIaMmaCbA5IUJNnvnZcJbfmGr0BRA0nDRrL++EIQAPS08KN7/f9T76otPUpFmigX79lV45RCJBXZZEjH0zVhO/TuEcmjXOpPt6uvDVJxsdkA3F4g/CT8EEdmbkYAIDCmeLL5KyhBr/4wSmt2Z5xKUDkegkCSJqgaBCYJF98ZmxEPjeOApTwFw+c1cZrs/TEt47bTIu4xBB1NA1brwV8aBKXigMtyt+YcpFWF6IXfnRW2RtSzIFBkQAZpISJeIBNW45AtzrdEBNUvrobBrVsY4pWrkkx1AJHDqeURBP6A4EViIk5Re+lhjhCwags77QyeU/HqNKzIwxlwinweUAneEbIo8KvZvDZHfcuN9oFzhSu/O6flds8FiozTAUniCFwI+DZvjNHG9an6mq2n/z4lMLCAw1Zr67yHJ5UqeA7gxQzZwb+M5x76EcdiB7kxnpkAYcnLTgwVaKhaevHOPx0jYqgsj1Rrds/sF7nTnWYLjmgBT0TRq9BIWVg0tA6kDsSXw4cKDHIlZLgkhjzuuzRTduyLAnCj2zYkmnB69ZrsgzFZEZMbl6sDbe78dZ8C1r5DAzfZJ8SPINeEUiXnwVJ9TXaCNxuDkDWMkE5wRXrBl/BvmB/crDhX5C/JcBnz0ML9PbzMmCEhmIopicPtWjZmiR7LxKezKwoU+4Dpdz98/MKTwzVSM+YbnhDyUWkmCR6yO4dwRFAD/0tVDNovGSKtVE3mHy7PtWaIuGKg+QzIwCwgz4qqGcgk/hflJJs6FVEoCUi9KgwqZuEnYAd1JTApqu6T9vvLFRFebcld4uf1e755KyuvznfKFEc0AQCPGd6YJCopE8FZBa0lGSNe9XVOmwBS2VZh0mCEvg9/5OzuuEtK1R9EaiCpvro145q5fXZFnCgVDR70V9CYyWJ4NkSlBmtMi7E5itxJpFcjfaOm+R4dbUHCWYOANUZ+rbwA0w4JyFtrh9QYKifSVVCoyO4xOdzVkDnpbpJ5Y5gE+oVykWmPNM9/pqvZPw2fiA9M9pEO0jcGfo51DlqFWWSEOjEBGucSexLehStShHgY76afUcs8Pg3junuP91sVEYacAm88bkvPFVt/HzADc4TKnoAkOfOdOmandn2HoBCnKfsR6rhObnRqqvts2dHXyk+ncoLPoGgllkgrC+STOINqENH9jVaXxXnIog5FUzAVMDGm24t0MP3n7H35TPgh04zd6okzuSRAd7wHYChXAMBOAAF4CvJLsH44vR0fCEVdBgGUJMRueB3SVRYV6DjKG5yzWdOdRjIGpcQbLEE9OLO9lEl06ge7GtJG2ues5QAGcYAa5IhpRh+kUSM6JL1SkM96oEwL6CeMQuCPjRoYgws5fzHFwEccu1U9fBHndV9uuHNyz0COhelvnluALKAbsjT4g84V6FOLs5MYx9CCWNIIWcDE8x5jvhLkjf8EZWW6IRQ+wzILAMacvZboz+JVXSQ3R/8P/MuqFhRGed50WPLNHRAYBIvrgkgHIovFFJ8EKwXGu+Zsj7YNaZNO7ItyWD4JFUPhhXvuKXAelg8dNlB8/uoaZGEsE6YxcWaBfwlAeV18UMA0FC4WNsMyH012Ksq2cBefKneFhSHKajU2MiMDU9j4ZOV2qTH1cmmcU8DKOVHKCw01+DkbRBTSphS0iJt5sNiEAhVBW4fiwG1Hw6uYweatOu2QvV0jRjqYDrUwX5WrsfGhqdNhQCaDwEAByiIORsUvh2caJARDlKuFe1l/mQRkXmDmhIAgKIcfaRK9/zlVnN4fB+khYydw5S/ExyD2oMekNTw2FiAJDsEvvnF8bbgoeeQ9SJnx2YCOWTjcO2GDNQOmFwiiGT5mS7d987VlkAxjA4HWQcKURpniAgBAwcqm5m5D1Z+9Pcx6gqNjiBEJDrW27F0iTkgnB6a8vzc4uyTRR302kMt1hTHMyHwYOOAFoAuscG4xrKzXVqYXdBI+7Du/sMNRkvAWWPQiHDIhx+vVkyBJ7HpPNWh5TfmqKWm3w537hHviSNjOjRoDAngAAPVLjZ0Gzc60NcoOadebNAtb19lyV9RaaJRGUDPCaI48AmqaOLm3oFO9nYy3X1Q0dEBNsQPzjDT5qnC4EComMET52AbZgrsxQZuDoLExOCLg76iTAUFzidG+ZZAGXTk3Ol2SypQXWEKLY6cZ0UJH2Rj+fLEK7j7fn/sxMk2HdzToJz8WJtIT1AJteyaXTl2n6DqEVQwQZy9Mjs+Y+uDsjdVNwLbRSQMPjQ0JvYkSCO0RpCt4uWJhgTu++ZJfe7pt6miqsdoBSi73PqGEptfQWWrsqzLeL8k4SBaGGuZZ07TIw3/SBuSwFD1BKRgKCN7hCAdeeidN+QZ6sbQQhq3CXZQ5Cssib80cI8AODYm2GS5EYggIeaAayjrtqZxApXCZQl2uLJXF7nLJ56utX23qNdeU9GrkMgAq2KC7PFeHHgEEQwJRYEu+OKk8NbGAbsP0KEI9EHgPDx3jxzxgZ9V6IY/WGlUjs3XZOqxH5y2gKivqk83/eFa4xbzuaBh0YRNTxL+k/eFVmaUT18vU8hBWY0GfJIWj2LLgt1Tm9zeOWpBJX4OqhlgyeMPlWmsa0w33rfC9h/AC2AT7784HNOEPbyWaGR42g5tEj/EPGiIZ47Sc/efVXRWpFUnNt6cZ5Xmxd4KQC2eIQpRpeuSTWoWBTOovOxZnl9GTrS9L2uQYIGePRJCgAjQau4/fqqqole9lT02O8qmyS9ZYkAF6wHZ4Z5uKifepoB4x+uL9dxTVao+3WUS5yga0chfftpTfcEnrF2TcqW34O+Fffvrh+1PfC/reVE1iZ49FBtbzvdox+uLjRJMlR2AYVGFiYCWvU+1uLaiy852El32c8vZLm27q9AAOZLIp753UrkbUrV2U5onuR+fsd8nOef58uxsHktkgGfuRu+4ye5SMedZQ+Fi7zBgDdo2VEfkaNkD/B3lxPNn2i3gps/yxedqtWJVog0i5udoUuc9DQBt7L9UMV3s6eLfJqDhC81QFvR7JFpl+9rL28vk3JHLhoaOKAYUJoJlEnf8CzEUCRAgDeg+foTPQoBI4oB/JGEiqMePAGqiJEl8ZSMGMiPVWN9vAB4iG3zu+QUmss97aJELyBkHWLO0nfUn2pVbFGssDirNVCZpyKYXhWZ6kmtoYJyN+Ce+B1OAr9F/gjjCYlJCLETfGIk/1wbL4OTRNosNURqjckEiBmgKIIOKV0pmpCU7VKCJKwARiftgLdAHQi8Ye3jv7nrzCVRH6I1sru+3Z3n4qRqtuyHnkgoX8QTJCOAN+554AWYEs5V8fL2smZ0qJkY/IF9bjCmtRzcjUhXlnRYLEctu2JJh8WrVqU7tekOJ+Zc45nQMTVmvKJ/11UShftUlG4v2+GMVevaHp3XH+9aZxjbZO0E5gSHJw6G9jeZ4aM6tONvpUa7wQxlhwRwT5W9+h7LcHW9ebtw8hi5x6I+NTxv6TEmVjWfZf1yIOSLQzEe/dUxrdmWb8hBKAtAMkEukqdiC2JEpQ/nPnWw3fl5RSYIe/8V5Syq62kZ1/ueVuuaDa60kSiDBpnz6G8eVsjFFTXuatOb1habXDJJHrwMZNnJ4zLjYsD3DDlaoUByIVBhIqtgwLG6yZj4/ZWMCJhpI+ftxKFkZEZb1gxoiFxgbB7Xqgh778Rml5kXZZyOxwJl4+XnbwU+ADioDok+wsjgtvZtJtTuyjQ998oHzWnNPsSlXzE3NmbwfTo+AnU0HxYxFRrM/wQOoIAo6VKk4rLkHOJt6K2F7hunQYAeNCKWM1etS7LNSsiTg8TTSoiN9waMItjgAbGrOZOQox5JsELThNG2aa/eooZ1sfuYt0PDJ79OQh1NjQCKNYygY+fh7W3UFFLf1TJdufscqa8Ti/nD/4Jry+ZDwxOHi9IzT3T5sAQgHBNQVbBFtp5RLcMgBgaNrKuu2NZOWEW5cVZJSyvzHj7aqr7rPqjXZOZEWnO3f0/B7PwX8SsrkFkOtO99lCBJVShwxdDsSbwJB1gb0GQJPKhNQK/ADBB0c1gAOAAokqSBdxw61mFwzdCAS/333l6lgV5YF3VQ9oBWcONSs2IQQoyZxSPJarDmUqgAsOAjg7UbHBXsoE3UeqULWb1IyAUWQjh9psbkg0HOqK3vtdz0oqfTYd07YAZ+zNkmttQPadlOegQfwhfmz8nSXclBa8vU25SVoJAQ9BPT4OgZuguwmJoXaa9J/hGIRB6JHLSra/AavRQWCZlaU75DjJHABXQcdpEqLAASVScAIQBsSEJB2ro++Eg72R795XMuvzTBwA552RhYVjRktX0VDbZtRNFBy4bThcIbmdGF+wSrEPBcCJatyhPsbNxpQBlSQZ8C+g+oFAkoiyHVxb5lTwOR09jCgCAc9VEN8IoEUfGckjRenveOLOdS5H9ZoXN5pVBLkd6EwkZRwbeH4uul5a/KnsgVYsziV2oQEUiMsoCEo5L35/Phd/AX3DV8FwsnPP/d4pc1/AVSC0okAAUkwSkcgpS/99Jy2vaHYPh8IKTLj9GegRhQRH6Sh3gnricMvTwxM6tNfvu1Kb7nfS/vUP+42NgPSzPR2wiyIiw225NsS1+k5awKHAssaoBqNBDFnwOIAWBILwK8Tx9qUlRVh5w3nxdF9TcrOj7IexetvzjP/zkwFgEWeGa+BH0HhihiEvYVKE0EkySR7kcoWZxXVEc5T1poJHTR7zin6KVEYQhgARUbWOOfiYvLKGUEyzs+yBmkKxu/QcwiwaInU8LT5KBIARBAA6aA2Lsp7L1uB4lSLqVIuiq9QSeXnQdTZW/hCzlMCXuIEpP9R7CTBYpYLyRXVQCSm6YPiM81MIxnuGRTaUNajlVvTLGkCiMMvkegBOtBLxfqmyZrgn/MuNT3cfAbsAnwjwCyJGsqhi/O/ABrwrzZxu3vMYgJEUrh/iDSY8liYn30fn0ZyyPUszlnDCOqH+yYVERtor031BAYGMR33mWdllPYlSyxGaYUaybX7enmSS1gWKWF2fqBmR7/ZmeOtFmux5znbSagO7W+0mIv47aVHK7XpxhyjSWGsDWLQRZlgm9N2slPrrsuyHh+EHzgrYHhQPYa6uyhQws+feKrGVELpCXnD3cv0arNXbbKxaB++76fKWplowR3OnQWL3CtDrjq7RpWTFaV9L9XZYmFgD4jBooIDKBglRZN1XCI7DDlQCYYp0aOrDa2GW0SmTumNxIUFYw3ceTG2cDyObNBQCqNoLFwwpI5F8uDnDyhjQ4od/KCYIKi3vb7YynX8bl/XqAXjlB055AmUaERkkdnBd3FCOYE7hxoRODMiOKzjU8NMMcbTWMwQuaXqbB2yoVw0HIEo2Ptaw6OH588mnZicUy8TRm/NNzS0ubxbuasSDYHlkH/up+ds4Bz3kg2AgyE42vNsjVULuO6SlcmGNILcgmxAw0JFq6m8R1tvzTdlrI1bM4z+w4blP9CUpmPthrKS6Dz8rWNKzI+28iAViFOPViumMEbRKaEaQOIvJcycMnSQzduzjApHoMh9wWkxcwSUBCd46qUGU7iin4YNi8OCxpaSHWHPgwSBRmzQSpIDHC0KEI3H2xVXFOepeHgt0blj7dp6Y641w1OqpSGTYWw0/q1ck2z/Bo3luqh2wV9HKQqHS0WCRIy5JVDxCOoYEEhw4ZENjFR4eIA9exKmxPgQHTrQaOvHpt5K1ntD0paRFWHPtrQ04UpvsVeFfeLjz9uBzmyKxQnRlK6p0hFMs4cJ5uHZGtKNLnl6mO0JDt7y3Q3aeGehHToccFCRQOuhL5B8Q2uAc0+QAbr84NeP2oRz9isJJsp0BMUg3Bxyi03ZNDmCWkOtIRjmkB3vHdeNd5daEyL0A1BHkPrmxkF7/stXJdk8BtZPczMTpT0qSDQl8x4kzxy67CloPXsfrdS22wt0dG+jCpbFW1JP4MGwMw58Jlsv35pu/Ww/+Nx+7XrzMquOwuNeVMyCIko1Dl9CD8uep2u0ZVe2HXj0ZaH2RfBOAIA/Ienp6x6xP0HcAFeQ8sYPlx9v1zW3FejIgWZT4CMQIxinwkkzN/cbWiT3EdrQ8CCKcP52b0BzTx5rs6ouAZZnJofXJbUnKi4MPOT18Hnco0X5XAAI9r4Nvartt2oEAAk/A52M4J7GWqrY+EGTSx30qN+QVNAMjAQ2gzlXruYZjFhggjIVymAka6CdfJ0gcKRrTNGpYZaoEbwwRRzqaWNtr4kRkMAyz+jJH59R8bokOz9AiwlwTh1vs0oLwSxrhaCPis9ioMV6RTwEqhnB0623FV7pLfaqsPt/eNKeeW1ln0mrksjSWI1U/aZtmVaRx88C4tGcGxYdYOcryTQJKZV69hn9EgTAVD0qznfbGkSmFN9ArwhBOeuRXp+Ckjg7u6Ebs089oOa8xRX8HZYAwBrAEevdMyTzgvVYMH/LBG2gD8d7QCyqLoiHoDZEgApVD2oOlc2Xnq+zXpNFVTP2I/vouSerFRHpb+cbzAySHD4fTdu8F34N1J5gHBojv08VlmQZAMzeNzroklgDikuIrrDvV6xJNvZEZk6M9u+pt5iCChGfHwGVU4dbte2GXDtXAVoAC/ANnJuAfDwDhhoiP8zeh8aJv0PVD/qjyb1epEDiT+tqB0zZCQEYwBJAC3pUsjIjDNChGZznSQJPxYgJ3bGZERa7sZeodqAQyDWxp3im9F9lFHn6ZfBp7N+QYB/VHG/XtjsLLbEiMV2kzgI8Mj6AyghVBtZGW22/AqMDrf8CsLryeLuikAzP8FSzAbswKg+hzCtC/YvP3oBYQLSdIyREJHIkrFwXPo9zgLNhEdyqr+k3MJhnwroC8KWSiv+4/Y4ivVrtVZ9sLNpPf3LG+i/Q0GbBkIFC1WFzt3eMGppAsoF6CJUNyocoxjDa/ubbC63Je1FyktfBGS3Kq3E4xCF/OjptBwkHHhvzuR+fVcryeOMDg4xAi+JABs1DLYMmyOefrlZ3Ra+y1yZp/ZYMfffvd6tkV5YtwtIVyeYIQGNwIKD70A1osEaql03D5oJ+8e0/eVor3lSs3sZBUzqKQTmhd9z0163UeeGCBQ6GJPh56+AzNUrNj1b1oValr060e7FIdSIYwcFNDE/JL8RPZQ+eV+FdBRbUcKgfub9M6+4pMfrC2VNtNtEWSde+zlEre9pk6zUeJRWSuM62UWXleAYr0VRKE9zP/+WAMq7JUFRMoCF6BABtDYMmx9naNKScQo/+NWoSDKUiIINeBXrAvQP1mBmbsYCBRG7BSsNeFrxQ7sSZ8jnhZbJRSTpIetic8HB7esZMspjACQoIHNv/+ORLWnNz7qUhPwSOOD0SMRIaON6Ua0FhCUB4Lc9EZ8qWC6Y2g6whicHirAJKszhIkkuCEih8h/c26PrbCi1gwimCmLF2rIHxXKeh4Vw/dDfUKqB58b4153tsoj3KKrfd/up1KlfSfvHzMgsE2LOg+JSdabRFuhgaASACvGxEA6A8sI8XUTAQbAJjjIOBBJhnzKFEcojR+ExjITNzGut6zT+Mjc1q0/ZMoz1xcDQ3DVvwjM+hFN7WPmLr58DzdYqOZ0hfnI4fbrHGyIOPVqlwS6pJyhLQAnQUFMVp/7O1NgF95+uLbc0de7xaW95QZGAG1D4TY2COx+iM0rKj9Ng3jmnDbflWvSOIYMI6yXB3+5DO7G+2BOemd66+pM2OoAG+jiosvoAAnCRtkRrIugehw69AXzi8u17RKLswz6h7zNA/Aisqh4tgDYgwM4+o+pD4M2GYZI6fu/HWArs/0FYIuNkvD33liO5471pDZUGJUZ7jPoEkbtiSbj0Oi0pcJIj8h78hIWO/Ls5WAkXk9wiU8CdUVah80ExNIGBiEwPjOvJ8vU3wPbK7/qIiX6B9PipZ8OQBWw691GC+l/eFygHFE647940AioCK/Q81hb4sAkJ6YlasTbLPRWB59NlaxWRHGaiFdCWVIPpSuKdw1nl2UF5LViZZ8EqCTFAK6n7kxQblL4tTZ+eYBTVvf/e6K7yjXp2GqAz9MQBmbfWDuv3e5Za4skYWJcuh+0BToaeH835RTY2ACEANEIznhmgLAXb5wRYVbkg1KXj6EOmlYn21NA/ZHiI4Z28hFAFCToCK2hsAAXKlgBIT49MWPPNegFAE/0WlCSZWAZMCCfiuzhGrGlCBgMZZtb/ZADpeBxCC895mYsUG29nFumEPspboEUKsgko8e4LPxp6lSld7slMJKEZd7N8kYGZPMQsKo5/DKnjzzOsAfZfdAxJpqnhUXmABeEQPgk3BCV9WXe4BeAA41m5MM6CT14YFkZISZvcGifdrbs03GihzsDBiLq4dhTGoUVRDqIpas35mhPkyk/mlYts/YewD+sbYY9CcSC6gkRPj8R5UQgnmkfKFfg0givw9wOXmndlWzcJ/8iwxGtShpxEH8P6LM5rwnww9hfbE7C8a8UmgUi+OBSBGwwcsChbxWRf7PQAoochyD7i30MpR2+psGzYfD1gMmLh2U7oBTYceq9K1bypRcCjqaZ0mOgA7hmSH6+O+Eje+Fvo0XzPJxqI9cP9pC+pANyi/cSgSGIBq40CYfxCbE2Va8gSyoFv0CbBwPI1kMlSZg59gk0ZQU23oHTd6Dxry0GB2P1urG2/JNydBwEEJjgA5MSnEMmNKjwTYIKgpNGA3DSosKlAH/v2EbvvoZttQbBAOGRoQCXQsgF6crDs6bYuMBIbGapzS0T2NJjWLItQb3rNWnR3DRu9is1KhAe0koAHdJNg6fqBZ67amazfykW8sNaoVG5H3ZTMQpBMIU8HBuXEY8nk9mtxR9vMEblBCCDgIwqGpgH7SJLnYH7N3d60pTYAcLEoGcu/pL8GBxSIbR79Jqmc4Ekgd18Dv48z5GogMSSJoK6pXOEoCeqhRVAVAA5AFZEgO8rOF12RakyUN/LwWE99BSUBoQZ7gYOJc4KOjf44OP1KTHPIYy/7+T+5R+tY07bg+91JzL58dRRn+5JnQY2EIg/cSO5xwvsjysTaYMcB94ffoIWqu7tOWG3ItSUS9C9oHje5pKWF67ukqc5J7flqmbW8svihhN39peCTBHsHdez+w8QrvoNdO8yjrDNllpI1JzEHfCQIWZVw9TXheFnBbE1/HqCFfW2/INQoW9Af8AoMnCexZw6DozER5+0c2W08PgTGSmdAvUSiip4A1yTpEBQ/kcM/Pz6t0S5qHwuPvbdNtTz9coU1vXWZcfJrGadZkL1E94cDkd7JXJdq6HIG6SH9AeriqznWbktvajamqrWJvBRn9E6S+ubLPwIPIOE9jI+udSgN7kcOrrWlI3n7MaQizKhuNx9Ci6FcCQMB/IVfNULT9z9VaVYjKBckYByjrPjU90u7bnudqtevmfLU09dv9pmk1f2WC+Q36YUjiEeTg4CRQoRKBUALoHRVEBmUymZdggqbszoZBFaxMMGSPe8qhRHWX6iw/T5LIQQwKyzPkID/1XJ2SlyfYdZCAmEpd+6jdCwIdfBX7FhSWfimGuSKtS4J5+lib9YARAO5/pMIkT69/20rP2pi/YFxtEE2eP1RIkk+G5mGoXFHdYNhm3ckOxeVGW/AAOku/l/H+z3UayMLvwtuHJ4/iGM+Tz0UTMqAHgA2BCwEcwZP19Hkt1bvf/9qalXGl7InHK03k5NT+ZsWkhWmoZ1w7biu04a4M36PahaRyanqUerpHLImAfjzQPKT19DYOTthZTUJLNQAQk6CeRH2xRxRkHgCDfkviibbWYau206iNzHp785ApXTW3DHvWU4qHWYH8K0kH1Q2b5XRRmnXF6hQdPdhodL2DLzaoZEWCJdXVR1pNxIA4ITLW0yOalBJmfg4wBf9CQkDsQyKy/6ka6/nBn7ChmHdBMk7TONUSZmbkF8dZ5ZI5H0cONhndib4IgnjO8SO7G5SeH23Xzb5nD1NNoZ8Tv9JQ3qO0/GhL7KmQdnWMmUTt4hBWwBqEMaiIkIxB/eJ6j+EvSmLt2qgEUOUB6YfOzrXDuFiUHYcaDd2ThJ94gOQG5UH8AOYBh/0UFRtk8Qz9bMjQEy90tQ6ZTDdfZ98DbPDsQsIDrF+jsaHfQGgEIEiySBwASAAbu7vGzedw/tPUDn39/JkuG9qLSAhgLBVtfFLlkVZFZUUZaAElk4QGcBJ/wudYwjTvCY/kLhVkWDAAEYA+xBr05pL0keSQwCJGAp3zjW9arteKveaSjUX7mw8/YYclKkOURclYCWDJPEEvQMVO7m009B80E8TKMyzGz0r9jeU99j2yXA5aUJGayl5LGDhUOFw4JMj6MZShmC4NInnve9ba1xiSA13oax9+Urf96SaTgSToJRAl0ARBozzKI4DDy0HfUNtn/RhG8xpkCnmw1q9PVW39gPbtrjP5OjYQlCQOYJwdT3D1Bk/jGpuVqg3NlQQa5460KSolzDiNBEoEARycvC6TKtkABCYEXwTQNL/SW9LaNmqoO/QE0ACuF8UuNiVSsf/xpUPW5/Hil4/p9r/Zoh996Fnd/PfbzDmAvFC5YFowHFWoV9x7NjnBF44ZNQqM98Zn8BloPGdDE5BUn+5U4epECxTI7rl2rhHlH5QlUPlCNYJngCIITe4E7aBFxptuHDA0Af1r0xMfn7YhfSRPoIYEMHByb76jUBmp4RoYmtKRA43moEAfjzwBpSvWqA6UXVlHlDa5NhIkBhOhxLVibaoNIQQJQob1kYfKjJPv6ZvxoL7QcZhfgkoSSQbVm/u/ftSkRqlyePks/b2XrXu1GrN6CORA8+D3cighAuBRYQm0ZBQpQg5LglQaiKsqu00ZDglDgu7E9HAbBgndkcTV0PfJOavuUR39j4+9oPydWbbuCW4JFpFdRCaT9UgiCueYIBQVqB23FtjeBK2DN0wgQLXOqq6dYzZPBIOiyHV2V/VqrHtcH/jcjXrm8UoLYm1mRU6U7V8ONfaIVdbWphitCbAC33VsX6NykNtcskRHHq1SWFq41l+baSg8e5r+BpJjggCU1AiMSaygSuEnGmr7jaZxvrxbaamh5mOgK+FjSNyhdrA/QBLZAwAtvDZ7+tjTtXrThzaZ6hN7tvZomxKKYjXaP6HSNUn2OnsfLNOam3ItACegWb0+zYI1QApQU4IjqrIv/eisCWgASJA0QvUALCGoIQjCb7CvQUr5uk3VpWfn4vA0aAgkQvCz2bsECFAgqTQNDU5ZJQhxAAI8OOz4OJuQ3DFsIBTAEYEmZwQBGRRS/BEJJddCQsW5Ql8cCQy0E4CT+mNtSiiOs+AjOibIkk2CHCihcO2pvpFwsaY4KzZsePUjmL+P9qPvn7CkmZ6IttMd2nxnoU3zxggoAdqgH50+2qqUzAhTeqLaDh2uv2/MzhKag6m2seaYg8TaJ1EgOE26uO45y/ED9BzSiwWAxOwG69PwXmroNn4AehHBOGch64eK6ZPfO6m73rvW9iNgHgkvQF9r45A27cgyam9Xy7DRpwmMqVhA6QXEY43im6BVsqbo12D4Z+aKBPN/BLw2/6NhwHwPAIkl8LHB9h5QskDdkdtmDR9+rs6Gg9JoTtXR0ycSr/0vNRjF6aWHym3OEc3dBP/4PSoJNEdDg2SmBBLwNL931/SpYH2KAQmLtFVA1ubGIV17Y571QPB5oLvRqzAxOmOxEUkiiQlKWdDQUNDiZ6gqQsNkzsWi+hwJ1qJxf9mDyEhDkSOZeOjrR7Tq2kxjitAQz7PEl/BM+BniIdYHvZxUNIkNEIKg6oSPph8W6hPPFx9I9RQAFL9z9JlaZaxIMGD7/PEO8yVIqOM/brqtUOdOtRlti2oQcQEVXPw3vh3j/aBz4i88AM6AXvf6Ur3W7DWbbLycVkGm+41PvqSiTam2qeHdw89lQ1A+A7noah20hIQNhSLMXW8o0YE99UZTIig3BZXZBc9wMCQmfbx0ak+T1u3KtsVOUxhIJ9QMApjm+j6jJLBwOODYAIsNnKCDSJoR3LDg2PCoFFCCHB2iIWrEJH1pUiOwZUEfebxay3dmGS2HmQwE5mTNoGMEMjg/GibrDjTrdR/aZBk8vFNmCnCYgdJDXeBQozFy3eYMvfBMjR2YIHw2qCvY15KshJxIa3aisY5rh+sNdeLY4RaTySPAgo/J5FQcF4kb6GzV4VZtvq1Az3zruMLTIxSdHm6SnThe6GX0dZD0UA2g8sOGhkoAfxx02RpHByYNMSCpoRJw/KUGJWRFmpNCYYYkiNekOZepwPSWjDMXIzbQ09wVCsIRbIoW3Nst12SZM6NykJHhUbgAicLghgYE+ei663ONy0uZFgdMxYhqV2ZGhPY9UKbYwli9/q0r9LMfnVZwZKAS4lGbGjKnQokVo8kNp02FCATF5INbhy0QggaDoz29t0mzk3PKWJ1o1SoOp1djo9er0X7+szJ7NjTkXvfmZbbWcH3IWIJcsS7bm4e1dkuanv/xWd3+3rVGl+JZ0uBH5a14WaKefqLS1lhH24hVQAlgy0632x4EFWWvgFCydgEokJCOTvIIMRCk0Ifz2PdOasut+cZJ5muU2dFUJyjJzY2y+TcY1C28MwcRfumlp6qVnB5uiiqIN3AAMnwONI4hYzR5cvgSoJPwoBxD8kA17syRVtsfqzanGiJLD5P1QIT423yAW9620pTU6IkC5Xzu6WpD3whauGaoHzat3uZEDNrfqebii04+WaOVN+VYwkbFeNm6ZAuqoSfgcxb7LkxGcmzGehI42KEXhYTBJWeS74IhlF0Ng1q1Nd38Af6LigxN9iTpBBj4Cyqei3OT8B287vnDrdp0S97FadCDVqElwId6QqJAEmSBwti0fP18rCpBFavuTJcmByYUXxJvPT/4O6N/ILBR228zMqDh5hfE2gyOwqJYu3YQaYArKGcAOCChDOSE902SxjBTaCWIkIBYggQbIjzBMLcLdl3c25rn63XTB9a+phDM32c7fqLNwELOvoJSz6BaQKvFOVNI6EIntmF8/h65YYLt8eEpG/TJOQ4IAGgBsMb+HRubNvoUz5RKFmg31XX8O4pjnM3w/wuKY22yMxV3gl3m50DfIhngTCMAbansVc7yeNufI0MTl4ZYMgH92l05FlS/9EKdIsL9rNoCeAaIBhWPCdWLKDoAIfsxJyfKqh3GpGAGxMCUMRNIvjkTqQjix6jy8H2qq1R17byND9Ej/3rQ6NAoTGbmxhpDhD1JrxGJi839qO6xM5X5HON941bpMFn9BI8cdOX5bgN7aNrnT2IXfAiJA3GTMQoYujk4YQp4JF/4CRI1wFT27qFna5VB78rojDFMiFv43Ow14hvGCPB7xx6uMBU8qHJUGdmPNMXjw6BBMmUcKhrUSJ4DAT4S3rBhTp9sN79KrEMFcrGCapTziVmLFZlGD6jCs2dd4O9JHA0wLus2MNpTCffMakI0gKpV7UWJdio9VD9IVjiPqKoBrvS3jZha4j1vWanXqr3mk41fTTyaGzyb6txj1Vp5Z4GhGpQkTWUkPkQNDQN20LGZCB45fMjeOSg4QBaHZIFUkAiQuCwqUHE40tD0zBNVxrun0ev5xyuNvxwTG3hJiQXkEK4ek7aZCnv+XJdHI9/bM6KeRAZ0gwDEj8CeqeLIzyaHmWpVX+2A7vzYVpvDsO+FOpsciioSighD3eNGFUA1CUSd18KBkmFzmJ891a6qp2p13btXWtUA3f2t12RbbwblPYxeA4InSo3W5BrgY47g9JE2pWZG2GHPgUnFiKCK18dA+UB+UKrgQKaqAPLCxHMCBsq2SFVGZUWa4gY8VpAh7gEqLciKWkATFWTqPKb0scD7+Nq1kHQxXwT0hPIrzh01MBITpG837Mj2NPfGh15swpOpFNFTA02GwARu/tnj7UpKCzPKQn/7qJIyIywwOXegxQYN5pTG2Xp44vOHteYtpZcaSwmcSFxAuXG4oDMEJjSCEbxwQBCA0TfDMMjm051af3Ou3RukLxk21VzXq3e9z1EkrnS1g4OC58o6ZUhXTEygHSiDPRPG733s60etskWCyh7CmLfS0jpsyQWBOQcRBxBNfhj9GiBYKExROSMwIDhBCQtapkcBZ8wOSypk5w+1an52XmtvyLFKJug/aBogA+X6gFA/a1h+8Ydn5e3vpTf+yWZ7DVA+9i4HcfnPKnXfP++wQ5fG5ut25ZiUrqF10YGe/qb+8Uv9Wqg9tTQPm+oUgAYHYH/zkFZtSzf5SxIcA1IOtei2e5ZZdaemsscOVxIa9j6vBwCDf2RyO42L/M7eZ2qUVxRjFZKffv+UMnIirXrCPiPIQHKXr0F1AjiAnoocJoAAiRr+lSZWQBT6XqCMMgEYGibHFO9B8kcFmXkFBPEEECj6ECRAt2CuD76Pa6RKwWwi60WbZpigRw4UdBGaCNQukjhmspAc4ccQwiAYwSfufq7Gw8uemDVfVXGu24aNkWiggNhwoFkrb8k15JVJxKvWpxiaiy8bHUYVx9fWAYIf0N3SMqP1iZt+qJs+ttWqQn/zseuu8E64uu3pp6t1eG+jipfHG8K992fntfyaDOPZQwlaVA9iX1I9r6ns0+oNKRbEUsnwJPoMmk3UC49W2FqF1rTYB0WvEMkCVUPWEcGx+YSIQLVAyaJ3r39cZ4+2GfWZcxZqLj2hoOLQnSZGpg3UgpKL0AXXQW9ha2O/vRaxB0Bf+Z5GlV6Taecnv0fVk34DQIeBliFtuQWq4awF1cQVsCxA6uk1BaGnksi5D018sHfMKn/sE6ozSPZTDSa5WOxnpWJL9ZSkgj1FZQIRF6oPnI0AigAfNiE8M8J+np5M4piMjAijZEPzmh6ZssF3gJgwTLh3gCEAHpXlXVb9JLmbHJ81tU/8GRRVfA/mad729K/hvxZp4twT/o5Igw3gO9iiAD5ruL8lYFDcoMsxtoDKBb6C5BD/w2ci3gD4XbMuRT/50RmLzfChzCmjV2ux14PZI/SzMngP1gYCQCjkAfwQOzLfyYYbzi3oyIEmO2eg9VPFRiyGWOfGG68OVsNVlWy83B579LwtSnopoBLYgksO93ytsd/4dOj4g/ojG8mBSqCO3jJUKLL2xnpP3wKqR0UrE6w8n5QeZSpJINqPf/+UTRy/7q4iq4zQ1Ghc8QAfQ7pJYjgAQRBIFHhNmqdZyH5+PqaGwSLl+gii2cRk22T9ZM+UHGlqBK0jYRnqGFVWaZzWb0o3/iVoIRstuyDWkiU2KUExm4jXhSpB4AIq0dQ0ZBMpbZZE14ih/tAKcLyUa0FXofzglEgyas/3KjgywFQWTh9q0fZb8k0lAh4nijzQD5DMJIiH3tB8tE2BUQEq3JBiCRt8biw7P1aH9zXaZuY62HzWjHZxcit0q93/cVrhNGt2j8knyEc7711uDZxwskEgcMxUYtjgSA7S/JaWEeGZ0BnoazSq9voB47PTyE5QxrOgAZMhRjx7+LQ7b8zTsYNN9j1oanxukimCC5Iv6DAkOvRhVJztuCRJnJ0bY3QVmpFRKGtu7FdaRpQFLQcePK+7/2Krdu7IvtJL3tmvsS99fr8dFBjTe0kkkEIF/YQ2w2He1TZkE7+pXG67NtsoQ1T20F1HUQXFFni2HK6spb0/PKf7/v5avfRMjTZfl2UBKAc3gTk0nIpj7br1rSuttwPlOMQBqGrccnuhiRnEMBm3e8zTiBzkY6+LryEYoJJJFRZ6ZFhiiA0RQwWHSh/BCpQsGlrxDUj0EiCwR0hw0PiPTww2EARQgCn3/Nwil3jvc7U2eIvPBVWIg5+kCOSQw5vKz+vuXW7XBTpKjwf9VFwfzfe9zcPKXRZn+wPUksCGiiFgAfN9ilbEq7KsR4WlcZeuGcqHr78nyML38rNUIthvi0bfFNVOAhcEIkgUoZIRyJM4MXgNMOT8qU6jvOGn8KUEQ/DoCZg42I88VaNr3lBsCRvSyDTNIu3LZ6URnOoJErTpmR51GfqpSJJAq0maoGIynHWROgu/2tZNhKfnCn4+1SAqqyjQ8Dmsl6a6X4MNA4rIjNS/fPX2K7LOnf1m+8F3j1vfAGuA4B4qLecXz5AeJ5qV4e1zptDvQe8CiQQ/y56nl4BgFLotCTvN3ewx1hhVUSqIiwNFoSBa4JocbucVsUJOXqzRfhiKuRlFs5peozYB1pHsIsUOuAGqjtISFU5YF5zr9IABfgKknTvapp13FRmduvylRhVu9ahpcrZypuHXiPh4HYZm4uPolWBSOPuPKg9BN+wM9i7AGnuDNc95TDXwzIEWrdiSZteN7wAEoNLLZ4qJC7KqAv2ei/L8JB7Qw9mzi1Lxex+vUsn6ZPMlNrdnzkOn5uwFPNhyTabRnPge/oqeMAaNknQR7xCXIJTBe3JfAHzo9aSSwPf4LIi34Fvod6Gnldei9xUrQS69otujLhYbbPEWsQ0Uch9fEo55qzTRsP+TLx5S+qoEo2MZkDo1Z8Aqyp99veMGxAD+ZGZHmuiLsUUKYdBMW/+X9XJ1oh641Cpi5/Y06cs/vUdXm121ycbLG8jYdJTocRLQnDioGBhFI3ZyJuPj54xiA20ItJ3eBRuCEx1kPH9Qi0WlopjEUHMgSLVRgsdRkPUjseob7Ke4nCjTymbhwuUD/Tr6ZI1ueddqHdrTqNBwP73+jctsgBiLmk07P3fBEhkWMcYsgFUrErV/f6MhcaDtbDrL6PsnlZIWdmkiOp+N/oXCYlRmGi145nA9sLfe5n8w1yE9Lezi8KulOvZcnRIKYkwa99o7Ck2u9+C+Bg33TVgZ+dgT1Vp5Q44lQKAmI6PoVY9bkmDJQdOAoYY4VXTOKZ3SyNYLupcYYjxsnBFZPveH36FPAuQRpQ4cFM1hBFkgOPCncRLwtEnQqIQQBNh01OYh5a1IsNfg+XAfCHxAKJETbeSAD/e35GZ0YFIFpXE6+lStstYkXapKLA7XATGhHAz1jQAFp0lwtmFbhqGjHhoMsnmeIIhG+Gd+eFopy+KNgrPjhjxV1/QpKTHUDhz0thne9/o3vva4l69F+/Q/vagt27MskeCgZSggYMCLj1ZqrGvUqhwoJHHQsR8rK3qUkxt1if6HVGQuPU7Tc0YTIMEALYcDPNI+ogLWnL+3Z8gbVcFQP6vagTZy0NI/kJgaaopyNiE3Idiqd+xVKhXQ+wguAAOysyL17S8d1rrtGXZYVp7tsr4M5gZAj6CSwL6FDsBBOD4xZwcexnBQjxTloAXCXV0eZTmoHyC5JOpUUvFpJ1+oV8bKRPkHeKu9aVhpWRFG/UlIDFHZiw1adl2WzdsABaUiQ0M6iQoBEqAIhz97n71JBTggwDMPhzk49JNQEXz6iSoL1qmgohAE5QA6A/e2sDjBZiPwdcQvuF9UTeipIygk6TCqlK+XTr3UaFPg8T0055JgPftElW65gwSuyarOqNOhkFVYmqifffu4dryxxFBcKj6dLcPyCfI10MLUuYYmdPCpGm2+Oc/8Ip+HYAhJbyQ8qWwRpCDjCVhy651FpulPosoaiYwOtj6aB792VCGJofqHf77+Si9xZ78l+EBIRGBMskoy3tc+qtTsSAuMYSBQkSA55hwDyCKIJDlmX7MPALo4E1FmBLH3NE8H2BqEUgX9CWSfRIG9CDDHewE6Hn6m1uZ2FV+XaWsLsAw/QoM0KkacqyS07HG+X13RYwl6c0WvIpJDDZAAgHzk+6f0pvettWQJYIwqIjPIOCuhaTK9nvOMRJ59RYLNbBkM8I6fw/+QXJ053KrgmCCjaRHUc0+oUKQiUT84adUdm581joyrr448VqUb37HKUzWF5hTsa4E3FU18JXNQAEFhG5Bc3HBLgVURSXpIRki0SGo4YxkNsJahnszVaR2ys53kZ2FB1vOGz+VekzzxuTnD8SEACtxb/CvAJfEQvgj5bQYYc734+ycfq7DYCDoT/bP4QPa5h3YpvfDgORPqoAeWXksSOnw4PqP2ZIfueMdq9fWMGihFnLDjhlxV01sDbT03xibPEyvte65GcYkh+uCHt+hqtas+2fjVJrLFMjocxojoQOUVxFojcFJqhGXpHc1DJk/KIUfzIQ3EbAyMzQINiowfzj8cYA4nVGQIQPY/XaOMwhhzSDgfmrSKSxNMbhFNe5QnQBYxk9gN8jW0LiQ60NA5kgOuLTou1JA8NiD9KKCPlHaxkz+r0PY/8KCPBOcEG6awlRujl56r1X1vX6WHHjin62/Ks6nLDPxhQimlYAInKCA4MozNuYjqsUyYwg3CCccUyc2t12Wbs2DyqSUQj1cpf0u6JTj8G5WXtBUJFvBXn+1S5EWZXhwA1Ab4kpRJSQQKl6EYNnHJaXt0yr2UlR9nyC/Nax0tw0pKCzfKBdUfSpb9rcNKL4g2J8d1nnq6VqlrkqxciSQpza1UPEA2aRjl+WamR+hrn9tn3FIQH5xtV/eYDeKiKrXv6RqVrE2yRnO07rlGkj2TwvVaampE8MOrK3ptGjXKXlwTwQXJ5Yf+dOsVXMXO/q/2b5/da2uYPctBRsDPfoQ6BC1q/wt1VjpH6pCJ5eWn220dMWeFk2rCaJihaq0b0J33rdAP/mWvstenWOPludOdWrHa069D7wUVB9YkwQQ9TejWk0BwqBMoFC9DEnuJBSYkKwWFsaZQh+9gX4O8m2rT9LwFwew7rotgm/6JBCbxjs6YzC77m0n0BMR8nrbGIeUXx15qJqfaefZkh+2H5FQAixSrUICcgvpBEVmcHM6BDI2D9zt9pMVoIMeeqVVCYawFIynJoUaLAJ08f6hFK6/JvDg4q9/AFqokUKqoeASF+SvNmjJnjCZ1Yn+zgiIDzH9RMQI9heYALQGqwqEHz2vFrXm23xJTIkwus2ZPk5bflGOVHT4vlV98NJKSJI2gqfgBghz8Jc90CRr9ZZ2GMJN0cH3QN0l+uOfMPoi7OCcETjmKP9Aj6bHh57hmKt4kPiePt1kQVXuuW6Xrk1Vb3e8SjNcA+wH6LInmMDTp2GALYJlTAVjA+cgZBQCAiAxJMIkmIBb9XQBqkwOTyiqOtblQAHy8Bj0+9fWDxhwgADfKVVSAVTIWhRmIIVhX1WU9yimKMV8E8GgqWG3Ddh5DeUIshrUKIMfr8PuJDMlrHrBmb3ovABoA1wjCoTlR0VxUQYPeTE8av89cG3o7YUDQU9p9vltFW9PVWj9o1Gybhv18nc2hGurzqHXynkbR7BtXR/uo0tLD7H5RReHcB4QhdgEAWBSzoToL9ZkznteOSgwxgAN2AT2v+CL7XtOgTYDvLu/R2jeVWGIDG6GjdcRmJBF/JSQE2x4n+bNZWjb1fMEkufk3NCvkgQGJObsZekjcYxPkAS0RDUoB7Jy3GAowhiQL/4xZ9YqJ47EhFoOYZH47VElvTyLo55ldQnzA76F+uDgYFRWydVsydMutBVd6Kf9emEs2/hs7drxVVWVdKjvapq0351lDE1w+mhuRij15pNUCTQKNRaldKiJM9CTJYNOfPdFmwfjkRfSyo35A00jAlsRZsF1V1qN737nahg+C2LPZ0Miur+o2hDErJ/qSRjcBBaghyi80kp851W4HH42ML91/Tku8lhhNqHiTR82EjZmcHGbSljhEmrFBawhU4GmSIL1cOg5NaTbpE184rI1vKbXNuGFjuuoaBiw4Ly/vssOeAIkNS0UAR8O8AqTlCMQILHhJHPJiEN/QOKRRJIiTQq1aAeeRSgZBPZKXzO6ITwkzJwQ1g9KoTRjPiDAqG2VLkhOSPdALUBzuBfbMT8+ZohPzTAiCElMjDNHASUPlogGLTU9ggZOl8R7lDgb4MHivYFmcJRKLSC+9FiRvTGq9/uZ8vfhcjakTlZ3tsEY/gpUXHyhTUFywIhJCdNudRTpzqk0HH6nUVx6694quV2eXx77x1UMmygClbpFKQRCxfWeOrc30zCjV1/TaAY9kJPuMfYFTpccKSiD7f3GC/b7vnTGRgLX3ldr+pppHz5I1UHaPGZcbmiWBBAcy+58qA9XMvs4RG/pGIgICz17A77BvCGIYWMhgUr5Oczh0D8APqEpNzUM2r+fwnkZrhCW44bCmb4UghonVJasTdXJvk5LzoCEF2xAvkFibFM503rFpo5pyXbwu9wJqJ/sNCiGHMAhf2elOXVhYMPU2KIh8Hz16DvTpmTnzSwAL0EtAKrlfVIYBa0ySm/lHveMWtFmD9+CkilcmmNY9lAySHa4BHwt9C+WoJx84a1Lm/C6HP0BAy4EWrb+n2ORzCSK4XpTHMHwh+57XoJcLPjyBi81b6Rk3ah3+nGumyZ6A6eyzdVp9c679zom9TcosjjW/g/w4vHKu858+f8uVXrLOLoO9tKdBLz5dbTL2NrujfVSr16UY3YpKB3sc2hRrhjWGbCnrcFH0hECXfk5UJ2kUP00PAT0SMYFaszFdxw81XRJtWExgARYXJZzZ8wBxnllWEyZS46l8+FgVlrORa9h5U56tXwZsAiASf0ClJEZBOp/ExeTdoUC2jmjlxlSTf0WameTjwO56bduVY5RF5gshXgPAwt6mYk/sYBOumTMSxPUsWI8IAjCV57ptkB5KbJzFJF7I8rOH6WNqbRlWRmakMS4ACtj3xBmAIOWHWpW/NtkEI3q7PdQk6JiISwAi8tmhIsEigCURFO5voAXXAwDKn3FJ4UZDBSwguUMFj89aBgUrwzPoGeom1SiqtThp9i6fn88z1utpbifGohrC/aAXBKomQA6UMwR3SFKokNB/STJC/wfvA6UamhSKnv/4mZuu9JL9vTOXbPwW9ld/9Jg2XJel3T89Z7QCeNrWrP1ohUm0UWUgaGVD0zREBsxBTnNnYUmCjhxstNcBUYAaBRK3OBnT6+LvwRPEyKJJAo7taVR8WrgdkhyANDONMLnax8uaMDEOXdAGkhqUT1CM4DCmAoO8LolNXGaECoriTWGCzU3igfPgvaGCsYFIYqAf0KvBRqcJDSWFXEt2lloShJNjVgGNXlAM+Lw2i8R7qW1gms6YCwI/koZSGtD4HEzphgJSfbRNG27OM6nLtDXJxoXEMaDuAGrD9RDQ0Bdhw5DCmCbqaTwnYKHC9MB3Tmhuas50cv3C0LCPMP4raCgJBYEKKLFnXgYypJ5prPBmrUkX3e/hKXsvPg/IJfK0BSUJl5r6mJiO8zEebVaUmlqGLIGhFwVjt5iMZVyIm+p7ldn3v3PMklf2I1UG1gJI408/s0/b7imxPYGaCsErKCID9aDncegSlNPAzBpHKY6koad5WKu3phsdBwonPWLL1yRb79PzT1XbeoyKCvBMvQ3xM99C8zNGkkEvxYsPl9vcICogNKySwLD2kVyGjlT9VK2S1iYpLTfKEu0DT9eoeG2yHaYJFxMk9ilByaFHKrXprkIL1OEfo7R14MV65RVEXwweZiyBoc8JH0AgBB0UbndPdZ/RkCb6xrXxjgJDewFkSlcmGg0KvwFSSVWXz0W1l4ZZ6Eb4L2bmkEAw74Mp3db/0j9ufwJy8JnwldxLU/q6iFZaf1Zq2KW5RKi+4JdsYFdKqN1/kiWAAsAUo7Fd7Ffz0C/mzX9QiYbjjf9kjgE0TNDRw/ubLMAhGCF447OWP1KlwtvzjepFL81f/51r9L6a7FOffNGoh0cPNtuZiyQ2/XvlZ7tUWBJna4s1Rz8HVGxAiUW0nDXMmsaHPPqlwyq9KdfADBD9zLwYJTKLBtn74Sn1dgxb9Z4+BcAHqms0mxOEA3JCT6QSgeITlbWZsWlt3pmjk8dabaYFACbULeINgnUSFhu6V96l449UKu+6zEuVUSoQDNLjugje6Vcg3giNDrT9yufCT1DRwzi3+RmSKyqfUIwAEDJyoq0PCpoVQCi0dNTy2PckSPEpEeYfMZStoCMSuLe3AuZ6Gt5Jjoh7aKQ3cZqBCaNEA9aEGl1r1pgL0Nsxrgv6GJ/DJM3jgk0Exi/Y92KCtmCJCT4WNgViDRhJ2WKjPMkX/jyVZBKGip+PyRT39ozaMyPuomeLOABfSX/W+X1NKticZjQ5EpVPf8X1Y/0mc8nG/9I+/Jaf6ob7Vlimz0HFcKzTDJljkmZlj+56Q6kqznfbJqF/IT453BpB21pHFBcfZBUOqg6gl/w+BysUCdSZ2FAom6BUwGFJ6RPdbAILDtfhzlGFxIcYJaC7rEuZG1Ot/Ei/BMEymxM6BrxqNh9JT/3Zbu14fbHxCftbhuz3eyp6dNPbV+nMyXY7SJevSTHZW0qfZPOotIAmWB9CVY8lOnwdxwQ9hISAg3vLNdmqr+sz5wpS+OiDZdp+fa6hIjg1Mv+qM12aGZ+VX6ifUS2Ywgn6C1UEyTcSis7KXnPMGaVxxjUnKeCajh5sMuSHFUpAVlnebZ+LxAr0AmUfJjSj6EHAwDVxz0FJCRL8/b0MaWAGye5na6yHAnUK9MCPH2425+vttcRDy0oNtQDNM3jJS101/UoqiDEEhmcBh37v/ef07effeaWXoLPfA/uL9/1CN9xdamgm9BroPQTf7EcONwzeL0AEFYKG2l7bJ/gFa3qMCLCAAeQTPjAHJ3+nGoCMZXpGuNIzo62iyoENqkiTNHsKLjiCCu0VvcpakXBJMQl0r7a235SZAC+o5nHYNtT0KwI/cXHqN7zvzvJuhaGc0zVmimmAIPSZUZUlgAGUwDi8Qe5QfqIqSkDeUD9os2HoDwHhJVDCX9CEyR4iWD/xfL0KNqSYD7MJwgdatOGmXOvJio4NMgoK1CiUoPAtgAsNzL0wWfGlBlAQ+FSc6zJFvpnJOWu6BSyYn55XRHyQBTNUO6j8UlkYaBtRWgGVXG+FhvtbsyZgCNfpczFBhDLC8E0GIxavSDRkeNmyREOUy8u7rdoKmESvCJQNrqGrY9R8NpSY7OJYHXukSt969u1XeAU6+32wj3/0WQvSAQ880vheGu6fsLXFejt9vN32D3uPJMJr6VKT3OWcQoafsw0wq/x0hxKSPTRjgmL8BOpsBMfMYWCvhIX6G+0Z5gMN6odfbNCqTWmW+JOAc04DEiw2JsMuoF8CH0WMQF8Ws55QhwRUI9knYcJf1NUNeKhZsUHmHwjO6edAuckzeG/Smq2p8HO9gHHI5VNFBXiFgkQPCJUQgDhkwyNjgnTmZIftMeTC8RHMDgFcBFggsScRKixNMBABlTlem3tJHwh0MoYhkgjUHGlTSEKIqfdFJIaYj11UEARwbCrv0Yqt6VZ1ITngmhGhMNpT74SWrUhURyvU+CAd3d1gipMH7y9T4U05VuUlASTZgf5Goz4AJ2qeO27Ms8SPPhIAVZ4R8rXEZINtI/rct+660kvwVWMu2XgFqh4oMdFwBbeXzffiszXmREDWCKZB6+BNmqJR24jyizxKBXXne206J5k/jcmnj7cax5JGy5b6PuvNQAGL1+Tw5/f3/uicktYkyoeqRhwqCp6mKmgaCSkRtkGhNS0OC0T+junGXZ3D5gypADDNE3SeawJpGBmcMFSCeRg0i6PUgvOoqvAMH9y8PVNTU7Om9ACq0N0+qsBQgvhUa8hCGq+zY8QOaqt+BPmqr97jvEbaRpVzXaa9F4EUJUwSBnjSTNHl+ssPtCh3bZINSmT6MAERn2PJ0qW66c5CnTvZbqhoQUmcJVSoBoGK0EuDwg/lU1Aj5AihlHBfKiu6DU0mecOJEODhoGjWeujLh5SzPsVQmfNnu6w0Dorpmb0gPf+Ts8pek2Tl4X/4J8e7dvY/25f/bb9n6v3cglEFCLxRNoPqQBBAbxfUI9YiNAmCCSbVc4ARXHOgUr2jIgAIAaAAIsqhRiDMftn7ZLWCLnKfkbbNK2To5pwpWnG4s5dRvaNfgSoGa5l9QbKRV+SRYORQJSDi79AJaUhnbkxtRa+JYYD60++RkRll9CrohT31AyrZkGIBD1XBE4dbbM9gVC0ARPA3+DmaZUEr8RsE+twTEFgMtBc5S6qaqOJdWJA1joNMIiQx3DepklWJVh2l0oq/IWCj8ZpmS6oSXDv7FAQUCsvgwKRqT7QrLDVcpSsSLJnopPfF5L9DDVCBMsFgwIrybkOiqXa89FilgmODVLo8wSM52j1qinzwzGnUJZAi8Vm61IOcvvO966/wCnP2arBP/sMLHkrwxIwFyvRKklgv9giwBzpah7XpumwLuKEHE9iyj6gIADqw7qg88jqmIMkAu4tnFdLLgG78DIg6FTekVqEEQZ2CYoSQAz1di1ULKqOdbaMa7xlTCoNq44ONuQBIwSBA9lsn/WZULOKCNDwyo7BQz7R7khLmRTEugDOcBAHqZ0vz0CXFPv7NteLH2luGreIYkxhm4i4ACeMDk4pJDlVOXoxJRtMPx7nf0zNhPoxKKVUDFK9QdzJwMSrArodkgveGpgpAiT+sPNWpwlUJBqiQTOGTADSgQtIDt+jn8BNnT3eaHDh9GvgiaFEIWpDEASaQWHE9DHO2GTwXAQkonvga7g9SxB75/0m95w+dbP3/r7lk4xW2ysoea4LkrkI5aKzpV+uRNhXcmKN5GrhC/EzpCAnIwcZBo00lFsdZgLFxa4YlFAf2NOjkT8q08k3FRos4dbjVqFP0IVANINDoaR/RQN2AcYjZcHz91juKrCR7/kyHIQ8gciY9i7Z/Spiho2jvUyaFDsLgKxwKqlIMvcH5UJWgMoIToJl1245sq7zgyGiYhhpiFQHk+5YssSB9kX5EYyiJFhse4z0IEHC4dZXdOvF0rYLjQ7R6W7pHMz8qSC88Xqldtxd6Khytgx5n3DmmvIIY2/xNjUP2Gbgv8NCpeuBEqyu7NYbTGJwylJf7RDMfHMyTB5utikJ15Yt/8Ywpe/R0j5tuf2hMkJWAQTvgiFtz3cVp0qA+112bdaWXkLPXiNgEe5BDD/QQ5RZQcmh+9FaQQCAOQcKMsfegAjFUM29lgvHACT6MLhXoY9N2S1Yk2H4ChSRw2HpzviF5NFAj5wwoQJJuKGGgr/Y9VK6k5QkaaB3WzjcU29dNpra+3yqMJAgE0vRtnDvdrv0PnlfBNRmGnrIP2RcNe5uUuinV1KLY11AZkAEGZCChKT/XbZOwo6ICrXcFuhSVQhIV0FcS/NaGPrV3jNkgMiqX0BpBQGmwPH6g2aYjM/WcewIqSsIFBQTfyOfF37RRacyJMsCCe4Z/pUJSsizRKrIMZ+voHDWUErGIuspeFSKJOzRpjbcEevgnKiYEe4Wl8Vb5HRqY9EjzNg0ZB51q0fobctyQPWeviP3rZ/bYfkDqHaVKEHLWZ1aeR+iBZJoKHX0MeUVxRoFibSPoUFvTZ/sfKlZ4VIANgqOCx3mMJD90R87hlJQwAxWhWYVG+JvKG0k8PUlJaZF29hrgGeBte/3xfz+ma95SatUQj+z7iCntAQIC9nGeEytAo2IgKVUGzljkXQkXOTOhjAJqsGc5cwEPqKxyrhs93NtDKWVf4/f4PXwHDemADDRUs/9O7G7Q6msz1dw8ZEqPBPhUZPCFVBm6u0Zs9AACLtwnGsC5fyQJ7FcqQfTF0aBP7IK4DT0i9K8AHAD+UG0m6cHHcl3IA8ekhlnTOn6Svi98AEAs/37x8UpLjugfdWIvr5y5ZOMy2wP3n74ohTlkTdSULjnICfqRaoUSAQ2BzcGCh5Ntsx2Cfa0SwYY6hPTsyIw1KcXEhhiSCAWDQ5MmaDS3oWxAJ0JR6aXnay1QgHaxWJFgE5GQsAEp38Kb9EwA9reeBZSaSE7Y5KO949q4M1vJFysFqNvAm6RpjWtD3QWaEo3fKfnRRgkhMGBmBvJ80Ergk3M9lCDjmWQ+NGFTO00P/0yX0RjY/ElJIYY00HhOIxyBFY4NJambbivQ4784b7MrQHOhhaCcxYAhqxwR7JzusHsQnxxqNBb42wwJI8FCko57BFJ5vpwJqyEWZIH2Queg4fXue1xQ4ezy27e+dtj031mDp15q0PJt6VYpiI4OsgOeQ5h1DMJpU4xTI+xQZc0TfFMtYAYI38cXIKnJPmZvYxy+GBUFozakhFtzJqgpqCH7n6rggRfqVLgs3t4jNCLQs6+n50yCm0QIChRDq2jk5j2pqMJHZ8gVog7Ix266JtNQRo/G/VJ7jcUAhQF8oKbsV5rWCQDY2301fVq1I0vHn61TaHKoVm1MNVoZ/SskJKC0BC8ELuFRgZZo1Z7sVHpJnAVJUDEmoC/MepTxoFKuWptilV8CKKOQZEXZlG+SLiYr81nwIyjX9feMm8rNYjMndA2Q4i07slVd3nlVS1I6+93Zu3Z9VxEZESpcnWjVTM6suck5XXtzns3aAv1n33qq7J5J8wTgpnjJmZceabRK/AAVCar5xBVUTwAMETOgP4IK6kTfhDbfmGv0bgRpyo+3KyUnyva0KeGlhOnwI5XK35aufgbVpYWpvWNUt99VbHLR7OGzz9SpZFeW9aDSEM++B7hj1hZUZ3qYPPM1Quxsh+7M+cpZbxVYhnR2jtj1kKwsDi31D/C1zwDDg5gHmviiahZ9JtAeiTcALfAxUK/5N2DEsSMt5uMAC6me8nf8n2eCe6TRsZj/Q5xFFRPBl+07sg0w4XqZp4Wf7O0bN3YF9wNVPJK6T33x1iu9RF6z5pKNK2T/8a2jFiyAROJMcDAmRRcTZOpLJAAMD0ThYPX6NKNUnDvTZbJq2MQkMzgu2KwK+JgggDSKMTyHg5jG7uycaCWnhV+acHycgWExgUb3Aj1gg9I4CWWDZYAmNOhffHyIoY+1JzoUnhauwqI4HXis0qhOi41jY52j2nnvMvsdDnGCInjdDEDDidLDAVrINRL845BQq4JGRlM9HHcSkvKznbr3rSvV0TVqAQavwTWAbNIUByUKDicOy6Tq5i8YRWV6atb6VPjsTCYFoUUxiCCto2XQOJtZ2dH63r8dUN7qJFf+dPZ7q3bFZHqUZzhku1FN6/JMnIYmwN4CNaSJkUFUJBxQMupr+zU1PmPUwkWxBhJ1gA0qKuwX6BdQBBjM1dYyZEkBwQBykPgeEE/2JVTEyFjP1G18CL8PGEEgwM+bqo7XUqtiQnWgsoBMJpVN5pLgRwA3qC7ye1RP5ufmjZYJVx2AonR5os6d8Uz3PrK/yQIEkgh8EcOx8Dc0ZELZgPbEayN+UVvtqe6Aui72plAFxa/xuhVnOy9KbS8x6hPVFwIzlKcqznVaUsXclIN7Gy34cFO7nf2+zvcoWZlkVGr2McE5vRv4BnokkWmmWRyUn4ZxAD+oRSgsEtxDC0pODbcAHgNMoz+ipWlAZ463G8iJWAN73CMDPe/psYgLVdmZDut3YkgoFX6+R1KQXxBrsQkDgxl2R1UAOX7OZeZfUFE4drjFgAoku6GEIqLA2U0MYLL6swsGWswveGjM9LZi9IXx2fgT/xYW7q/m5mEDP6GOmdhO16jFG0xDpzEbhgN7HhVOqiCemSHIecfbrBKqNwCV9MWgpodADFZf16/xkWmrDCOKgVrYH3/EgQu/a3PJxu+h08EJkLGDPpCNQ/uheXPVmmT7GRB5UEk4y4sKDihMEUwQwDMUj00I/5KnC2/cy3uJYuM8g+ugG9DoCV2KCsuylcmGQjz41cMWtDOECild5oxQdTn2ZI1W7MoyKgSviX40ClTwSAn4aXAFZcC50H8CPSKnINaCCiozNi01Mcy0yZETJlgi6GDTxyeEaMPmdOOeHthTbz/HgB3QXEqwoK4EGaA3TDclqYGzahPAk0LteouK4y0IqTrfpXvesvJKP0Jnzl6RWR8crqxvEoPkpFAL1tnnNJdCsyKphqeMD6AxlP4NKIsECyD7BBj0YzHdt3pfk25612qr8gEQUInkAObn9+2u08IFae3GVBssOjc9p6y8KBNaoJJwZF+TURDXbkv3NJ/Oztv3ejpHVHm+x4aMUZkhASIxAFwgWPjFD04pw0QWfIzuAcqIL+AzwaHu7xpTcka46eZ7+3lbtQYwgWSCz04lhqCKBGZ0eNr8FvM7SEIqyruUmR2l+IQwvfBsjVavTTZVICiiLbUD+ucvOAlaZ6+NiigDAAmcqVCiEAf9Kjc3ypJ6qJHM7KBHhHkRnKO1h1usmXrN9gzba1QEqOgRHxCQ0/xdUJpoSUB/35jFC4giEAMwr4JKJipriDYQP3D+JqZF6PzZTqMkIdU9PT2r2HgPLQx62BPfPK7psRmbdQOVCgBi09ZMAw6gV0KtwlrbRuw1SKwAF0/tbZKXn7eyimLtfarP99jwXfpPAWCotuJvSKKgbI50jpri3arrs60HrrlpSF2VPSqA6smA4ASPb+A+rFydpJtvcTMufl/MJRuvIqeD6kojk4YTgq2/gME8djCHBVgJkzIqQfdjPzlrcyCslEpGvyrRkAESmOyCWDu8oTuBDJDpL8rLTU3O2GAy6AwkKPBJacaGN7koBQcnkuZ1qiXwNRtOdih/Y4qhKCcONCsrP9r6IfrgQMcGWfLD/ApUICiHVpV3GmoJFYz3pDJBJQe0AhSUf9MMTmIBLQLElMSJIIVBiP0DEzaI6w//ePOVfiTOnP3O7Z8/sVuJicE6tbtBwXEhpptPRRQ0n6ZGKhsmp83gsKoeC/obG4dsr6KlT1M6vgAFFxoz2eNQHQhmULIb7J9Qf/+kxoendN0t+cZrBvlkEnr7sTbFlcardHWSBTmdnVRll2qod0IhkQHmi1DmgUZlg+6OtqmnvNvU6IpvyLYBm1RyoYEAWIRFBZoKXnPtgGKTQ60fA2nNsw9XyC/EV6W35JryC4pxDNLDBzQ3eSadUxXBB/39P+660o/EmbPfuX33m0ds/1IVYK+zJ6gWAkYQhFP555w32VaEGmbnLQmAVonR94HaFfNjqPhR/UNQBcCC5AHfAUOCxMBUpY63W/WCGWMAFtC3qKYiwFJ+tsMS/JCoAAMVoDoDSgIcEl7CniB5QfgFZgZ9lkUlnooN9Cv6S7q6xq2xnBlbxB/4I/zaHHTMuGCVHWuzyeoZa5JsnACfDx/E5yDpcnTo339zycZrxO5Z/mWFJAQrIjNSySiuoFMdHmhVhMWSI46HSgEHtklB+nnb/A0UaAhSKIciJUnwT+IBFYmyZUJ8iB3uNKEZV9trqVU0cEJp6ZHa/1K96WKDotTU9FtQQGID/xL0cn5mXlko58ygCnVBPR2jVhbOL45TR8eIJibmrIGWwIjBP9XP1+t7+95zpW+pM2evOvu7v3hKgexb5sQUxtoef/HJaiWmhmr56hR1XaRdsEehOuAXmOhL4zSUKA74xb4JkgcADVSdoD0SqMCL5sig4kHigOoNv0ugQ58UilqgqKCWIKdwwHkdmsjhnCMCQTCEz6mt6jWedGR8sIb6JzXD9QT52OfAtxB4/O3Hd1zpW+rM2avOnnu+1hKGhvIeRaWGKb8w1pILqFmc6/Q4wVqg+omCJGIO0CehWdHb2dMyrOSsCFNvRDq3pbHfqJO8Jn4DkIKRwChF0QBPrxb+Ad9Sjax2TJBKbKigv4GViLdgNJEz1BdwlGQDSjTMDHpAh0k+siKtqjk6NquJkSklpoYpOz9OO3dkX+lb6uz/aC7ZuIrt8JEWoyXhYFCRyciJsSCEJnGoGqAlcCCZDUIDJwFCRKS/VTgoi7J0KKna0C5/H6N38XWqE7FxIUZ1IrlZuTLpSn9UZ86c/QbqJvK0oJ/0ekCxgsKELxgamLCqCEACNAsUZQgwQEMBLwAUZmYWDKjAUH+iERREEjUXAhGmAlP9+OTn3FRdZ85+X+35F+oMmETBid6s0RGU3DyzgejFhOI0OTil8ORQ691AOIaKKgM6iRWQ2aXKAtBBhYU+UuIBZtTQu4mgC3M6HLXp6jSXbDhz5syZM2fOnDlz5uyy2NLL87LOnDlz5syZM2fOnDm72s0lG86cOXPmzJkzZ86cObss5pINZ86cOXPmzJkzZ86cXRZzyYYzZ86cOXPmzJkzZ84ui7lkw5kzZ86cOXPmzJkzZ5fFXLLhzJkzZ86cOXPmzJmzy2Iu2XDmzJkzZ86cOXPmzNllMZdsOHPmzJkzZ86cOXPm7LKYSzacOXPmzJkzZ86cOXN2Wcz78rzsa9OmpqY0MzNzpS/D2RU0X19f+fv7X+nLcHYFzfkBZ84POHN+wJnzA7+9uWTjf+FYMjIy1NXVdaUvxdkVtPj4eDU2NjoHc5Wa8wPOMOcHrm5zfsAZ5vzAb28u2fgtDQQDx9LY0KSQkFBduHBBFy5Iuvin/dv++Wu+/ivf5wsLi19bkBbsZzw/Z39feNnfPT9+8fsXtLDAf/L8efHf/Lzn6xd/5tLPLX5Nnp/h6/Mv+zn7/sJ/fm3x3/zJ1+xaLv7bvn/xfV/+3+JnufQzL/v6L/2c52cuzC94fmaev3v+be/D13lPvnbxmvhz8Wc8v6P//Lu91sX7dvF1Pb/jeQ1u0svf59LPv+w6PV+/+B6LX/ul7y2+t+x15i5M6UjXF20tOOdyddpv6wf+y/7nq7/y78V96dmz//m9xf35nz5g0U94fAG//Wv34a/6Avu5X96zr4gfeNl+/yVfdHEv/vJ1eD7Dr14nH2bB9rxnT//nnvzPve/Zuxf9Am/6Sz5icW8veD7D/CvoB+zzeW74pde99Hdpdh4/8AXnB65i+1U/oF/d1/9dHPB/8AN6BWOCS1+3tb/4Xv+5xy/twV/6nf/cp7/kT14BP2Cf7ZWKCS797KKPuXgf/8vvLV7jr/7+y3yF8wOvmLlk439poaGh///JxssdxG+dbPyy4/mfHMuvC/T/a7LxcifyG4KMl//7173PyxKJ//Iz/8W5XbyGS5v9P53GpaD+Vx0LX1t6QQtLL/7O0gu6sPh3vr/kZcnGkgUt8G++t+Ti7y65oAWPO9fCkot/e9nX7H7yL3t2F3+Ovy+5+HP258XXufhezpz9Nn7gfwwyXpak//bJxst+97dINjxf+03Jxv+nH3hZMPFLr/+y6/pvg4yXf/+/+IDfkGwsXNz7/50/WPIK+gG7xy/b97/kDzw/48zZy/3A/3ey8b/wA3oFY4JfTjb+6x7/tcnGxX368p9/pfyAJ9l4hWKChZf7AM8+tr97PMCv+IOLX1v8/yUfsPhv5wdeKXMN4s6cOXPmzJkzZ86cObss5iob/0sbGRn575ELR6N6ddGoXobOXPrv0rO4+PlBNi4+i3lNX+nl5+xV4gccjepVRKP6VT9wsbLh8dO/6g+kOecHnP2KH3A0qtcIjeqXfILnnjg/8MqYSzZ+S2ORBQcHKyMz/UpfirMraKwBR6e6es35AWeY8wNXtzk/4AxzfuC3N5ds/Ja2ZMkSjY2NqbW11Xiazq5OFCslJcXWgrOr05wfcOb8gDPnB5w5P/C/M5ds/C8Nx+KcizNnV7c5P+DMmTPnB5w5++3MNYg7c+bMmTNnzpw5c+bssphLNpw5c+bMmTNnzpw5c3ZZzCUbv6X5+fnp4x//uP3p7Oo0twacuTXgzK0BZ24NOHNr4H9nSy64Vnpnzpw5c+bMmTNnzpxdBnOVDWfOnDlz5syZM2fOnF0Wc8mGM2fOnDlz5syZM2fOLou5ZMOZM2fOnDlz5syZM2eXxVyy4cyZM2fOnDlz5syZs8tiLtl4mdEr/7GPfUwJCQkKCAjQjh07VFtb+1v//qc//WmbJvnhD3/4sl6ns8tnX/3qV5Weni5/f3+tW7dOx44d+61+74EHHrBnf8cdd1z2a3R2ec35AWfODzhzfsCZ8wOvnLlk42X2mc98Rl/60pf09a9/XUePHlVQUJCuv/56TU1N/Y+/e/z4cX3jG99QaWnp7+Ranb3y9tOf/lQf+chHTM7u1KlTWrZsmT3/np6e3/h7TU1N+rM/+zNt2bLld3atzi6fOT9wdZvzA84w5weubnN+4BU2pG+dXbiwsLBwIT4+/sJnP/vZS18bGhq64Ofnd+EnP/nJb/zd0dHRCzk5OReef/75C9u2bbvwoQ996Hdwxc5eaVu7du2FD3zgA5f+PT8/fyExMfHCpz71qf/2d+bm5i5s3Ljxwre//e0Lb3vb2y7cfvvtv6OrdXY5zPkBZ84POHN+wJnzA6+sucrGRWtsbFRXV5eVShctLCzMSmeHDx/+jb/7gQ98QDfffPMv/a6zV5fNzMzo5MmTv/QMly5dav/+Tc//E5/4hGJjY/XOd77zd3Slzi6nOT9wdZvzA84w5weubnN+4JU378vwmq9Kw7FgcXFxv/R1/r34vf+Om0eJjbKps1ev9fX1aX5+/tc+/6qqql/7OwcOHNB3vvMdnTlz5nd0lc4utzk/cHWb8wPOMOcHrm5zfuCVt6u2svHjH/9YwcHBl/6bnZ39X79Ga2urPvShD9lr0UDk7Oqx0dFR3XffffrWt76l6OjoK305zv4/zfkBZ/8Xc37gtWHODzj7v5jzA/+zXbWVjdtuu81Koos2PT1tf3Z3d5v6xKLx7+XLl//a16DMRrPQypUrL32NbHjfvn36yle+Yq/p5eV1WT+Hs1fGcBA8K573y41/x8fH/5efr6+vt0awW2+99dLXFhYW7E9vb29VV1crKyvrd3Dlzv4v5vyAs5eb8wNXpzk/4Ozl5vzAK29XbbIREhJi/71c5o5FtHv37kvOZGRkxFQo3v/+9//a17juuutUVlb2S197+9vfrvz8fP3lX/6lcyyvIvP19dWqVavs+S/K1eEs+PcHP/jB//LzPONfffZ/+7d/awjHF7/4RaWkpPzOrt3Z/785P+Ds5eb8wNVpzg84e7k5P/DK21WbbPyqLephf/KTn1ROTo4yMjL0d3/3d0pMTPwlrWQcyp133mkLDudUXFz8S6+DPF5UVNR/+bqz339D5u5tb3ubVq9erbVr1+oLX/iCxsfH7cDA3vrWtyopKUmf+tSnrEz+q884PDzc/nTP/tVrzg84c37AmfMDzpwfeGXNJRsvs7/4i7+wxfSe97xHQ0ND2rx5s5555plf4l9SLqN5yNlrz+6++2719vbaICeaAEG0eP6LTWItLS2mSOHstW3OD1zd5vyAM8z5gavbnB94ZW0J+rev8Gs6c+bMmTNnzpw5c+bM2dWrRuXMmTNnzpw5c+bMmbPLay7ZcObMmTNnzpw5c+bM2WUxl2w4c+bMmTNnzpw5c+bssphLNpw5c+bMmTNnzpw5c3ZZzCUbzpw5c+bMmTNnzpw5uyzmkg1nzpw5c+bMmTNnzpxdFnPJhjNnzpw5c+bMmTNnzi6LuWTDmTNnzpw5c+bMmTNnl8VcsuHMmTNnzpw5c+bMmbPLYi7ZcObMmTNnzpw5c+bM2WUxl2w4c+bMmTNnzpw5c+bssphLNpw5c+bMmTNnzpw5c3ZZzCUbzpw5c+bMmTNnzpw5uyzmkg1nzpw5c+bMmTNnzpxdFnPJhjNnzpw5c+bMmTNnzi6LuWTDmTNnzpw5c+bMmTNnl8VcsuHMmTNnzpw5c+bMmbPLYi7ZcObMmTNnzpw5c+bM2WUxl2w4c+bMmTNnzpw5c+bssphLNpw5c+bMmTNnzpw5c3ZZzCUbzpw5c+bMmTNnzpw5uyzmkg1nzpw5c+bMmTNnzpxdFnPJhjNnzpw5c+bMmTNnzi6LuWTDmTNnzpw5c+bMmTNnl8VcsuHMmTNnzpw5c+bMmbPLYi7ZcObMmTNnzpw5c+bM2WUx78vzss5eDbbrq0cUGuCj7LhgRQV5q3dsTo294+ofnVJuYpgCfL00OjWnuBBfrU8OU+/klHrH59QzOquUcF8F+XopMsBX/l5LFejtre7JKZ3tHFNz/6QSw/0VHuCtU83D+vk7V13pj+rMmbP/xm7496Py9lqi/MRQxQT7qH98Ti39Exocm1ZWfKh9r39sWglh/roxJ1rtY5PqGJlRXd+k0iL8FRHopZhAX4X5+ioqwFeDUzM62zOi8o5xZUYHmG851DCs++9bfqU/qjNnzv4bu/WbxzU+Navi1AglhvpqdHpebYNTGhqfVk58iJYuWaLKjhFFh/jp3atTVD88poaBKVV1jSkpIkBxIT6KCvRWcnCgksMDNTE9p4r+ER1sHFFebIB9b3/jiL79xuIr/VGdXQFbcuHChQtX4o2dvbJ289ePKTTQR6creuTr66U1RXGqahlUTESgChJD9MMnKxUVHaKdq5PU3Deh7LggFcUFqW98Rg0D0/L3Xqr0SD8tXSI1DUyrY2hK+4636roNadqeFa7+iVmVd03Ye3ktWaLZ+QVFBPqoZ2RaQf7e6huZ0tDEjAJ8vRUe6KsVKSFakDQzt6CYIB/V90+ppmvMfj861E9jU3MaHp+Rj/dSzS9ckI/XUm3NiZDX0iXaXdWvR961+grfUWfOXn227d8OKiTQR2UV3fLy8tLaZQlqaB9WUmywipJD9bWfnFRCSrSuX5uiivZhlaSEa0VikIan52zfz85f0PLEIPl7e+l897haBqf0s8fO6I5blun2ohg1DE2qrH1M/j5eloRMzswr0M9bHQMTSowMVHPvmIZGp+XltUQhgb66uSTG/MXA5JwiA7x1vntClW3DWrp0iRIiAjU0PqPhiRkF+3ubD/D2Wqpr8iI0vyA9crpLz31g/ZW+pc6cveos/6+eVXCwr9qa+6UlUnFpkgYGJpWXHqG8uGD963cPKSMvSTetS9H+im4Vp0dpc0aoJubm1T48o4UL0tbUCPMDxzqHVNc7qe9/d7fe+vbrdN+yRB3rHNThhiGLOTjzRydnzR+09Y0rLylMNR0jGh6d0szMgvz8vPTmTSkK8fVSy9CMQvyWWixxsrJHgYHeykuJMP/Bz0eGBSg82Fe+Xkt1bU6ECE6/trtBh/9y25W+pc7+j+aSjVeJvfF7p9TeO6b0hFA1dowoIzFU+XHBhir6eS01B5EU5qumwWm1DkwqPz5IU3MLVn2o75u0Q315Sqh9rbl/StkxgYoJ9lb36KwF+IfrBnTiRLMKihN1XVGccqID1Do8Za8bHeSjYN+lmphdUHqEv461jupEbZ/8/Lz1BxuS1T0+q7n5C2obntHIxIyyYgI1u3BBz5xo19DQlP7u7iK9WDuklEh/lbeNKCrET5syQnW0eUwZUf463zFmCUjn4KQWLlxQaKCvTlf3anh4Srs2pKkwPkAzcxdU1zel5w43a93yBP34LQ4ldXb12Zu+f1rNXSPKTApTVeOgijIjbb8F+i61vU5VgsO8tm/KAIHc2EANTswp1N/LKpJ9YzMqTAjS5MyCJRLFCUEK8luqzuFZ+fss1dNnu3T2RKOWr8nQDaXxSg33U8vQtMZm5hUd6G3vMTQ1p/yoYB1tH9ZLZV3y9vbSn1yXqdaRKUNDq7rG1TU0qeVpEXbNTx5u1sjwpP75D1bqYNOIksL8dLJ5SNGh/tqZE6HddYPKjArQ2bYRhQb4qmNwwhKPID9vnavtU1/PiHZtzdKypGDzVQAXT+xv0pZVSfqPe0qv9CNx5ux3bvf+6IyqmwaVmxahivp+rS+Ot33l671EYf5exlIYn5k3gC81OlAZkf6q7plURKC3EkIB/6aVEuZr53Rj/5Q2pIVoaGpeveOzig/x0Y/2t6j6fJvWrM/SxuwoJYf7qmfMEysAGvh6LVH9wJSuSYvS2Z5hPXGuWwsLF/R3u/JU3jei7rFZHa8fUGf3mDaUJFgi8uLxVg30jugz711nccpi3BEXHqA3lsTrkcoeFcQF6kjjkGbnFtTZP66gQF/zAzWNA+ps7dMNOwtVmhSiJUuk1qEZPbmvQbdvz9QXbi+40o/E2f9gLtn4PbN/eane/qztnbQy5OT0nEpSQi2ICPBZaqXMstYhozesSQ7Sc9UDhizcXgDqOCEep6/3UqM1jM7MasmSJZYIHGke0czsgm4qjNLk3LxeqhlSXfuQ1uTFqrp9WP2Dk8pMDrOEhGAhPMjXqhLdQ5PKSwgxZ0PiAfJZEh+omr4pu04qIhWdoypJCtHAxJxKE4I0MDGrvok5e1/sbMuQ8hJDLWnBERIIQcvYkBGmkugwfeNYi1IjA7Q9PVLlfaP2vhgJBk6lrmdCsaF+Gp2esyCEz+TvvURlLUO6vjjWg8RIWp8SortXJF3Bp+fM2Stjn9vbYA11ZR1jGp+eM1+wJiPCDvz4EF8NTc7pdNOAMuNDtS41RE+V9yoyxE9vXZ6kmsFRLeiCVQeSQ/wtoPBaIo3OzFmwTxLylmWJGpye0SPlPTpT3avtK5N0ur5fra1DWrs8Ueszwu3QDwnwEScEFYsV6RFanhhs9Knh6XmtSwnRoeYRCzygTJ5tHdGylFCNTc9rRWKwusZmLHgZGJ+Tn89SNfaMKSs2WPGhPgrx81JD/7S6hqe0MTNMy2PC9cVDjYoJ9dddBbFqHBm3JGlm/oIlMEG+S3WmdUSRwX6aW7igYD8vq5gQsByt7dM965NU3Ttln/OajAjdXBx3pR+hM2f/Z/v20WZNzi7oSNOwBeADY9PaURirrtEZlSYEqqJ7Usfr+pUUHaTs2EAdqu1XVlyo3rMmReW9Q3YujkzNKyciSAHeXhY/DE3P6HDrsJ3TH9mcoYnpeX3xUJP2HmvRTVsydbSiW5VnG3XLLct1c1G0HjjRaQE/1tA2rGtXJGpXVpTO9Y6qsmtctxfG6KGz3VblyIn217HmEa1MDTVAY2VCiDrHpwwgaBuYUlyYnwbGZ5UY7md+JSvKTz1jczrVMqyVqWHamRmjf93fYDTs1xXEa2BqWq2jk5qeu2DnPH7jUF2/VVGJA/ABiWEeytcLJ9v1JzfnqLJn0pKu69KjtS0v8ko/QmcXzfVsXCH7xpEm2yi7qweUEhmo3Bh/y/a7RmYUH+prG6hrRCqID9be6l5zIB3jU6pqHFBP94iaW4dV3xWmlrZh3Xddll5sHDSkAtSR1yVg7xiZ1cjUnIoTAhUe4KOAsKWWaIBqtPaN6QM7sjQyPWf8TBIZHFhmZIAhG+3Ds2odmtK9qxPVNDylz/60XLMzsyouSbRgAkfB647PLMh76VIF+ixVbGyATrSNmUPwWiodbxjQyaP1evsbVyvc30vPnOlUdHiAhsamrXrSOTKr/vF+o1OlZoWrY3xC59pGLRgiyMG5Qt1oHZrW/vIuo4JEhfprbHLWOKI5iWE60TKi5IgABXgv0UOnuyzAiQ3yUaivj2oHxi3Qmpq9oL/bmX2lH7kzZ//Fvn64SaF+3vrF2R6lRgVaANE5Oms9EVQq0yIDVN87YQfxS5W9SokJVkPfhM7V9auusk0tidFq6ApTd/eY3nNDjn5R1W0oJnuGwAJ0c3hqXn1js1qdEmxABBWMptFxlXdOqLZtSH//uiJ1jU8rMTRBQeuS7FDPjAgwcKBteFptQzP602uyVDs4rg9/5YDGe/u1YmuJBfbQLQAfQEVBL/EdIVEBOtI6aj7BZ+kSVbQO6vjBGr3z3vXWE/KTfc2KjQ4yIOWWFfGWJAxN9qt3ZErbcyPUNjapF6oH7P5A15qande716RY0PTi2U6lJ4bK18tfY1MzFpQUp0Xo6fJeRYX4KzbEV9852qbG4QkDXKj69k7MaHp+QYMT884POPu9tC8faFRqaIB+crrTzrY1KSHqHpsxUCGSakSYv6o7R7WzKFYvVvQqPTZEJ9vGdbyqR2XP7lVU8TI1pkaoq2tMd61M0P3nOpQT46+yzgkD/aBBL9USNfZPalt2mL3uEi3R8fYBAyBOVnTrO+9br7KeEcWtS9Z929IsfkgOCdS9a5PsXG3om9IfbcrQ+f4Rve4fntJc7QmlX3+LcmICLYnYlh6hjvFJTc3MKz7YVxF+Pnqssk8BvksNINnfNaKHf16jO+5YqTD/QP37o1VKTAyxyugb1ibqZMuoVWBrW4a0MTNNbWMTerqq/5IPgDXxtzty7Wd+crBFuSnhSo0KsAot+351YZy+s7/FYoS0mCB9YX+DyvqG7b4OTc8aNWtmfkF1/ZP6xPW5V/qRX3XmKhu/g0pFRee4kiP8DaUHrWfBg8wR5FOxKI0L1r6mIQvSmwcmVdM2ZHQpnAOo46ZlCarpGNbKjEhtTgtT98S0BQT0QiQE++lM15jSI/z0+Uer5e/vrbs2plrvBd8HgWwbmlbH4KQ2ZoZb6XFZYpCOtY6pJCFAL1QOWJBDEECwkBzqp4aBSWVFBujR8l4VJgSbsyJAwco6RtXeN67VWVHW40EAQFABmkHjWFv/hPx9vazUScCUGemnyEAfS2rGpheUExWowy3D6hyeUkliiKZmFwyVwLkOT8/qsTM9Gh6bVm5yuFUx1qUF2/v2T3gqOzW9UzrT0G/ff3pPrRbmF7Rza7bq2ocVGOijxAgP4sH9xp471aFb1iTpTMuwccS3ZITqwVNdyk8MsWDug5syruj6cHb1+IGTzcPKjQ+2BH16dsH8ABWBvJgAozeujAvXoXZPkF3VPaFTVb2Kjw2yxPvEuS7ddU2mjtX2aUtBrDalhGtgesaSAVD/9LBAneocUVZUgP7me6fsNd55a4FVBvJjAtQ+MmNgQkXbsF63Kl6n28a1KT1U+xuH7c8nz/dZVaEgLsCSlLTQAAvYE4L99bNz3YZUjk/PW/LOfj/dMqK6liFtX5GowtgA2+v4kOcr+pQUFaS6zhGrjq5KC7VekBVJQQrx9db47JyJTKyIC9VTtX2q7x7T6nT2+hLzJRtSwjQ+N6ev72kyCuaK/FiFBfpoW0a4RmZm1Tc+a1Sumt5JHTrfrYL0SL2wv17Tk9O646ZinanpNY54UXqkJqfnlRzpbz9//4sNunNLutFK+Jy35EXrm0datCI1zCot712ffoVXiLOrpWJ5oHZAxcmhBiwCnCHEwpm1JSvMztpt6TEq6x5W59iUyjomtP90u7y9lyotOUwnTrboI/cs1wP7W/SGTalakxjmCaAHx+31V8SG62D7gBJDfPWhL+3X7PSsPvi29eoZndFdRbE60TFiwMCRym59eFe2nqzo09bscD1+rkevXxGvF2sH5efjpc0ZYZpfWFB6aLDaxycU7OOt+091akd+pPrG54wmRcP3ubYRnSzr0m3bMrU8IVBVvZPmbx461aW0mGAdKutSSXaUduVH6UDjsHZkR8hn6VLNX7ig9tEpbUiK1JcONam+Y1jr82LN/wC63pgVo9mFBX3wh6c0Ojqla9enKSbET9vTI0yoBn+G8ERt75SeP9Gu9ORQHTvRovm5ed1xQ6H2n2q3e7p9VbJROlenh1mS8tkHy3Xjlkz1DE+ZX3nPmlR9fn+DVqSGmi/+w43OD1wuc8nGK2xv/fFZpUcHaG9Fr4IDfLSrKEY/P9GhuIgApUcHqXNoypKOU5U9Sk8JU33ToK5fn2olR77e2D2qiGA/+3tcmL9ig300NDmv50+2qyAzUnWtQ7p1TbLxsuv6aexeYijF1swwC+hJXkA116QG69nz/VYlGBmf0YrMSG3PCJe/l7e+e7xVwf4+VpJtahtWYVaUJQctg9Nq6RvT8NiMVmVHqZ9SbXKwGvunjRMe5u+th/Y36Ya1KaZaRWMoajME7YcaRzQ5M6ewQF+jXLxrY6pOdYxa/wjvszwtXGeah/S2dckampq9hDS+UN6tdTnR5jBBUk82Demu5XE62zmuE/X9WpsdbVQLVmn38KTesCpB1b2TqmwfMfUcgh+cCdUh6GUkKDi5hu5RrUgPN0TlqXPdRjGBilKSEqaW/kkLhFr7xpUZG6y67lEL/oZGpnTkr1wjmrP/u93x7RPanBOphw+3KiLMXzcUx+rH+5sVExmotZkRKmsbUZC/j46VdSo7I1Jnyzp16zXZhuDBfa7vHFFokK8igvwMNSSQJxj/xf4mFeVEq7K+X6/fkq7wAC+dbhtTdLCviTVcXxBlKCY/SwVkfUaofnayy7PfR6dUnBapW3KjjVLxj89XKzrE36qgLZ0jxv+G2kiloa1/3CqQqzKjDBhYS9IwOC3vpXDCvfXj3fW6YUOqUaO8li7VhqxwCz5eqB40ZBNkkSbRP9ycZr0djb2Tau4Z1ab8GJ1qHNSfX5ttKOgwQhFT83ryZLuuX55ofoFD/3jjkN6yOlGnOkf1xNE23bIu2So88/MXrOH9wzdkGyJb3jyoNdlR5h/ONA+qJDVcpxsGDPCAalrXNaI1mZF2n35xrF0xEQFGS9uSG22IKKgpgUdWbJCqOketmjo4MqVDf7H1Si8hZ68B2/y5/fqTndn62INlSkgI0eacaH3v6WpFRQVZsnCgtl+FSWF6ZF+jSvNjtGd/rW67vshQfM7uhpZhhYT4qjgjUiF+3sqI8rNK45NHWpUYH6zzFV16/a48JYT6WhKTEBGg3uEp3b0qQU2DU9ZrdbZlWNcXRevHB6gGRKipe1RpscF6/7o0o12/74cnlZQQYqAGNKmizChdnx+pF+uG7DpgHkChJFG/vijKmALEGKhPUZ3YsjrZztKZmTn9wZY0o1bff7zD9hYUbPbVH21O188rekwYhsRiW1Gc9lf26PN3lqhxeFwNg5O2hx8+1KJ3XJth/os9+8z5Pv3Jlkyd7h7Slx+t0h/ckKtTzUPmJ8+cadM3PrhJPyvrsQptaWaUooN99MOnq3XD5kydre8z35uTEGq+iHgAIOXxwy3KSg1XZ9+4dq1IUFPfpAlTEL8QR5xtHtTIGCIXS3Xgz7Zc6SX0mjGXbPwf7W33n1NBfJBqeia0OjVEPz/VaTQmAomN2RF2kCP/ShNU+4Cnp4LAdlNOlPZU9aooJVwbUkP0Z98+rvyCeGXHhyoz2t96G0AE6ZX4+tN1hmyQFECrAqEka2fD3ZQXpbhAfys59k/O6mDDkL3/+owwPX622zZNSmyIKbyc75rUkYpuQwxxbGFBnuarwbEZ43vynvzOvesS9a2XmlSaGW0NZcXxQZqZn9eR5lGjTJFMtHaMaH1JvEYm6AuRfX1kckbd/RPWvL4sOVQF0UF6srpPUUE+OlzTp9ykMFOtwDjg85PCjNaBnW0a0NqsKKv4ZEcFGN0LlIfKzKmWEW3LCbekh54RGlo7hmdMJQv0gyb3g3UDpmjDfeEet/WOqygtwu55V++47t6cqidOd+rOVYmq6Jqwe9czPKn48ABrTAft9PVZqvhQP6ugtA5OK8TfS5+7Nf8KrzBnr5aGzbVpYTrZMqJbi2L0pd0Nnv4pXy9dWxhrimsnmoaUFRdsaD6VR2xLbpR+frRN24rjtC45VO/6/F4VLUtRXnKYNU1HB3nrdPu4ViUH6Z8frLDX3LgiSatSQ1TWOW59WKNTs/rApjRFB/qpdWRCbaNTevZ8ryUBNxZF66GTnerrn7Bk4qaiaJ3tGNfzJ9qs8Xrj2jQTZEBZrrlvXJuzI2xP/uxkp96yPklffbZey3KiFeLvra3p4cb3RsoSyezTDf3q6RnX1pVJGp+as8ro9NyCKdN1do0pJz3CfGBpTIh+eKrDfMxzZzutKsn+M1/SM6rlmVGGOkJ5er6sS7euTDDKQ1qYv6nj0KNB0PRiVb+hrwhXAKrAy0bs4pbCaI3Nzml4ck5Hm4bN94IY8x4ESoUp4UbRIpB685Y0/Xhfs969I8OqO4Pj0+aTQHPbukaVmhBqPrE4KdiCH5pnA/289G+3OT/g7LcDG3fkRerxsl796ZZMffCB05qamrPv7VybIj+vJTpU3auNeTE63z5iyTuVQPovP/3zSt23I0slsSF662de0Mq1mSpIDrege0NymO4/02m+5U++fsQqebdcX6RNWWF6prxP/SNTGhmZ1rffttr2Zm3fqOoGJ/QwCXZ4gO5YFqtv7mkyRarC7Ci9fW2yDrcN68fP1mp8ZFwb1mcYeEjSvb+yV+/ckmpn8ANHO/SOTSn62ouNykoMU1SIr+7Ij1XD8JieqehXSVKonj7dYWIua4vjDTRMCg8wgLCqedBijcLcaN1WGqOiqDD924FGrU0P00NHWpWRGGbgYESQj45W9WhlToxVNqbmLugHuxv0V3fkyxeKtre3JubmrCoaRIXlZKf+eHO6BqZmVNU3bsyI3rFZ3VYQbf0hxAiV3ZPGFOEew5TADyNYgaT3+do+vf+mHH3+Z5X6u3uKdb5rQg0945qZmzegobG+Tzl5cUqPC7lIOZvXmbZRe4Y/uHfZFV5hr15zycb/0v7s8SqjMj1R1a/nDzQqLy/GUHJQgTWpIeoamTVKBAfvC2c69aFdWWof9XAvv/i9w/rAfetNVpaNuCInRo/vrlZBUYIlGcjALksItBIjaN8hAugAHzv8aNRGZjI1Jlg35kWpvHvMqEU4ogdfalRqSpgpOR38zo9139+8T6WJQTpYP2RBP1+H9/m9vc1Gz0KWkoMeBPWe5QnqmpjUsRbkLJea8+Nnm1CayApXz/isHjnVadWW2DB/narpVVfniAICfRUbG2wVmzUZ4dbMiaFYVd81qtqGAd20KU3NvePmTCvbhqzqQfDD53uuol9jU7OWcLyxMF6NI2Mmc7e7ZtDmfMSGBVgTGVWIyqZBrcyNMc45yQcVGBQxCEBONQ5YsMRrk+w9ea7Hmt23lcartmtU1fUD2rAsQYPjyPJ6qSgxxAI/qBwEGiCl/Bu7qThaBxqGLYFamxFmqM3GVUlq7RlTdmKYKXq5mSHOsL9/tlZrk8L0UFmXntlTq9LSJFuHoHnQEqAZgM6h+EYy8Tc35VujIyIK//TX/64//cf3GVLY1DGiLcXx+vfv7tPyDXlalRtr+3JZUqD1O7A0HzzRaZUJmiLptSKATosN0d2l8TrWPqyOkWnryXrwhVpFRAQajejsTx/SG//8PdqQEWozLpYnh9ihTD/Hvz1Za1VSDvrp2Xn7PO9el2LzMyp7JqyxHB/GLJ3TraN6x+pklfeOmB8AoMiMC9GBsi61NvcpKCRQQUG+yk2P0Mq0MNv/wb5epkwHlaqhaVA716eatCV+4Mj5Luu5YK/xPj871aWpmTkVpkTofWtTVDvgkceGuoXmP+IX+fHBOtM6rPKqXq0uiTfghHuL4g7+j78fqOwxJDIixE/X5EVZNZng63WbUnW+Y1SHT7Vr/Yok28NJUYHKiIIuJkOJSUQw/BH3/l2bUo0rDhixOSdKX37onLZvyjAVwIK0CPMPD7195RVdf85+P+wL+xpUEhtqqP3Pn61UUUmi7VXk4K/Pj1Ln6IydjVCOf7C/RZ+5o8T6paju//kH/1Uf/eyHdbxxUL2Dk7prXbL+7sOfV86td+iuTWnWM1mSEKSIAG/FBPjrMy/UamZmXteWxquifVQ9gxOKjwzUO9am6KnafguWOdcffqrckpGUzHjVP/mo7vrTd2lXXqSOtowZU4Lq4TXpkfqrh8u0sSTBKv4k3ISCH96cqZ7JKbUMT1rQjxjEymRYEr365I0FerSqy9gIvj5eWp8dqQd2N2hkaEyBwYHW07l5bapy4oKMKgWAAN2xumNYra3DumZNirEQkqOC9MzBJpUWxFoSxPt+a2+zAapUXj96bZYqOkcs2f/i/kaLSUYmZ7U2K1L7znfr3IlGrdmUozeuSTQ/A0gBlRR57ccOtWhkZEqZmZG6a1WCvvVcg/37L99YpDPt43r0+SotW5asnr5x++xxwUj3LjVpX66Nqiw+cXJyVn99U54eOd9jgOX2/Gj9y3cOmwT4odPt2rk+zRgbD7xtxZVegq8ac8nGb2GPnOvUc7WDxvf/6akuk27lEDxd16uQID9DxWlUWpEQrG8farWgYF16iGXEqEpxgNHA9Mj+Jr1uW4ahf4VxATrROq7hyRmF+Pso1N9bh6p6rOeCORW8flFisPU0gOARKMC7xDHwfpQtt+dFyc/bA5Gi1kCAEB/sY/8O9vXRY+d7rRzK4B2ax/c1DNu18P7LkkN0/54mLc+LUWQwjZSghBPalhNh7xkd5GszOJbHhRlXmsY1DmoqJRGh/qYCQ9BxpnFAb9+UojBfH9uYx893a+OyBFW3Deum5fF2LUcbhpQcFaidWZGq6h+zIMpzb8ZNtrN9aNoCn+yYAPv5/TX9GhiZVoC/tzJiQ+xre060KjDQ1/ifa9JC9UJlnynTwGvPTAlT3/CUaio7dd/tJdbADjf0RPuoOdf08AB9/vk6+fh4KT4iwN4LDil898KYIH3m2VrlJIVZMkdSl5UQqvRIT89HTlSAoTVw2ylPr8uMsMrS+tRQvckpX11V9lxFrx4416Wb8qP09QPNykv0VOYOV3Sbpj2Up4yYIG1KDdOX9zUqITzQ1N+ah6at8smBzvcf2l2ne6/Psf6tNckhOtIyopquUQMsEDvYc67T5lBkJYdpYmrO9jmN3vQ1kHzQL0HJPyM22GZWvGltoh3uNGz3Tczaz/l5LzWedZC3t753ok33rUrSodYhLU8INp42CT+vfUNxtL7+bIPWFcUZlSAy0EvlbaMGCuBPoum3mpkztSjUrL56sEkDo9PWkJ6eGm4KNFCx8IV/d3OB0RQePt+t/SfbtLYkwSgbr1+bZHvombIepUYH6d7liarsH7UAoWVwxoIEApPWgSlLAOCuE+g8dqZbHVQbkkItQKH34sHnqpWUHK7C9EiT8KVamZcUriPlXdbf0tUzrub6Tn3wLWsNqHjbqiSd6By2PpOCqBD98Q9PKS0lzAJCwIfbi2PVMTpl3/vMi3VKigzU8MSseocmTeGnNDnEemxWJoZYdXd33ZBaeseMFsd8op1ZEbq91OPnnF0d9lJVv750oFFvWpWgf326VtctT7DEmfObgNnf19voTG9enqB/2V1nfuE961N0vnfUKm/9o9MqpFLxWJnecdcyYwZcmxFpdMN91X22h7bmRekne5q0sLCgDcsSVds+rD/YnGI9S7wXEvTfPdSqOeStE8NU2TqkP9yebslGgJeX6ofGFR/sp4nZOUUH+NnAzU+9WKuPXpej+8u7jEp5/5kOO/M6+sb0xzuy9M+PVOn2jSkmkc1ZzDl8S0ms+R76Lqk2FkZ7erf+5OGzmpiYVUdrn/KLkpSXEm6sgrO1ffrKm5abr9jd2K9HDzSrODdard2jetPGVAMGHj7QbGDHH29MV9XgiML8fFXRM67YEG9jLxDnEGtQfekan9L3D7SouWlAW9alWayUHO6nL/3kpAqKk6xKytDhp4+3aX1RvHYfa1FcXLAqzrVpbm5OH33XBh1vGtbfXpejYx0DFhtsSY3Wzf/8nLZsyLTqJyDqPaUJqhsaU3FUmL5woMHEJqCSQkH38VmqG0vjVNc7pfXpIaYSiJQ/foDY53THuO7Mj9XWXKd89ZvMJRu/oRwKp5HhNZ0jM7bJGZIXHOir5Oggy8YJ/EHF/by9rOxO9aKqY1TbciPVNTqrI7V9etO6JJsPAXUCKhXVgqahKQu4qUzAW2STospCVSQ2xMeC/dzoAB1rG7OS4Ob0UIX6+Gh3w4Dxp+FRgvzfXBCtQ83Dmp6/oLwYfysdcmgXxAaod2LOONR54SHqnfTIx33qR2eUmR2jxoY+veWmAmVF+ZskHQ5gXWqwWodnrJkcSgSNlZQm4WfDF0d2sqN/XIlRQaaWAT8cOVr+pK+DzwOquTYxXEfahwyVeLGix+4lAT5UrXdtTNELdYN2D56q7Lf7h5Y28zrY8F2Dk1qTFWUVCXos6jpGrBGdRrDdFb1WBh2anDWqU2qErzkm3vNQeZehq/P0fYQH2BBDHHZGpGdGAPeQz/rImW7t21OlrdvztTI9XEfrBpSdEKqUcD9DnD2o7FJTBqPCggIOpdqa/gmT+KNZn4MBTXOSkdreCRtiRJD25PvWXukl6+wy2D0/OK2/viZbLzX1mR9oHZxURf2A8agz4kNN0hU0DKoPa4OqBjMrDtf168aSWJOAfvZkhz50Q7aqeiesbwmw4PUlcdasSRWRKuZjx9oUEOBjfqB5cEbZUX4am1lQcWyQ9jYNmzzl9qwwxQcG6CdlneobmTbaIqo0ryuK1UuNg7Z+ATGON49YMyUy1J2j00oN81duZIj6J2ZUNTCqj33jsFIy4tTTNaR33lmiothAU5EbGJ/RTQXRqh+YtIR6YMyj9gTQAQ8bBBD+98DwpFVIc2KCVN3tabjuG51WcWKw+R/8yfbUKL3Y3G9+gM/m67vUBnbRE/HhbZkW6Lx5eaJ+fLrDKgiLHGmQWlBMtP2ZTEwyU9U6pO1FsVbNfPBImzbkx5oMdkywr3Kj/W1QWNPApA6e7lB0dKAmSV5C/bQxN9r2/sokfJunepES5q8HTnZq9wvntW5zrrblxehgbb8KoK1F+pnwBkg0HPgVCSF68FyX/mxLpvkdVHiONo7Y7I9x7tXmdBPhaB6cUkPXiHy9vfTMH6670kvW2WUamvvV15fq/rNtVhUnyW9qGTLwbtOKRJshRbJMPMD6vWdVgu09KmxvWJtk6/A/XmjQJ99QrJqBcT1/vtdoSx/Zmqln6/tMxQ2Q4JvPNygmKtAk3Su6J7Q+LcQastcnhevx6l6bk3NTQZTSQoL0hYONVo3jvEY85q6COD1R26PR6QXzHzAbOFevy4qwMwxgrTAuzPb16Z5BffhfnldYdIQN/XvP60q1PD5ET1b1WQD+lhWJ5p8wYgLARejF9FhRaShvGtDg4KRWFsQZKMqMHKq6JOqcv/RxNvRP6d7SRPOd+JDvP0PlNUBBQT7y9/PWJ67P1z88X62/35VncrddAxOam1uw6k1cdJDdy1uWxep486hiQnz1zNFW61MjRvra03XasSbZ+q9IhKB6D07O6rmKPh0/0azkVE8fFzHB6zal2fvfnBOjc73DighArdLT8L57b40KS5LN7+6r8TTuM+y4ZWjKqh3MKdqQFKHPvFSnL95VYlXNs71Devp8nyrq+jXYP6Z7by409ghiHGcbB2yo6hPvdfHAr5pLNl5md377hE2zffO6JNOzB0lDshX1hnMdYyqKD1JiqJ8dXOH+3iZbe2dprPY1DlvAmhzmp8fKerQpK8I4fhyeLHb4wCDm3GjUknAQW7LCDfGs7xm3RnC4iiCdoH0EDX1jc7ZhDRVFbSo6yA48UHm0rEHVKP+/aXmC+qdQp5pVdpS/NVZD0yLYgBa1jaE/faMW9JxuGrLpnNCyoDgMDk7pupWJquse09qMcOOKfu6FOrsX1xTG6mjDgFKig+xQJwFCfhb1F5qx4FdSgsT5IH2JOkRl57hNDie54vMwTwP6RHiwnzV/Qy1r6J3Q60vj9EdfP6K0jChdUxJvdI1AH6aLTtt7P4fyzupEuw9l7aOXphTPzM6bc0mNDrbnxNegWjX3TWpzdpiqeqZs2CCoMHQz0N/lqWGq7hqXn6+XTSzl85KwcX9wjAQI5U392lmaoNL4YEsuzndPaktamKHXVE+45yRfPA/UsAhKkOmDhtXcNmJqGR+/t1TPVvQbxcMp27y67ZZvHFP/0KT+9PoctY9Om849w+16xmbsubPGi2NCVD0wZn4ABZc7imJ1unPUaD0JIX66/0SHdhZGq6JrUkMTM8ajxp9UdYx4ZmZkRal/3NN3dKpj3FTcqP6xt+nzig321cAk0tBzKowNUm3/hCX8q9LCdLZtVBFQl2ICrG+ja3BCf7wlw/wAfVspIQE63ztme8ezfpfozvw4QxFp7nyput9oh9A2QefaO0Z03ZoU68UqTg4zpau/ebjMQJDb16fomdMdykkOV1KEv1VWDlR0W7KBRGVpdpRSI/ztoEVVC573kcYRGypKcpYd7W+9Z3xuAJK4UF+rWvB5QXvf8E/PKjk9TrdsSLUAnz6NpqFJzV2Q0c/evjXNkFVUrjjQUZTj/vGZM+JCLtGzeN3G3gndURqrqp4J6w2JDPO3ahG9X+syw00NjIrxnmOtpuzV1Duu9JggkwfHD5yq7dWda5OUF8VE9Vl7LiCWn3iu2oYNUsWC006Cc6hp1KS/ac7nGuobBzQ8MKrPvm+9nq7o120lMXrzyuQrvZSd/R/9QFPrsL5y30qd6Bo26VeG31Hxe7FmQFuyI0xN6WTXoJ1f57sn7Lwt7x21szcqwEc/ONquW5fFGph1rHFIufEhVmUDjICus6skziRmr80M1wOnOw0kS44MMIAPAC8tPEBtI1Mm+sDcCPbwS9WDpgr1ZFmv7Qf22LGmYTV1juhfX1dqfgCqVLS/n1rHJvTgyU7bB5yX71qVoprBEQX6eNuMHWIBekg7BydUU9tnczb6RqeMGr4zJ1J/8f3TFsfcfV2Wvv9EhTasTjX1StgWTxxuNZEbeiLTEkJM4ZHKZAqCFoG++kV57yVVSM54wNTd5d1WBSFewj/Qu/lnWzO166OPKiI2QvfckG+Us6KoUJOtpar42Il2a7LHt3znQItmZ+e1JjfGfEhz56iyU8JU2TCgVQWx1nvWPjipN69ItLlj33ux0dgQVHJhP9y9JkGPnu2x/fzC/jrde0uR9WuUJofaTCDAkmdPtOsd12WYZO7g1IzOdE7o7SuS9JdPVNj9IgZZkxlhM0R+dq7H+kmZiM7cr3OnWzQ2MKx//+gN9nzetiZZt5a4mT/YVZ9skGDYYRXsY8jkA3sadevGVEMiekdnlBDmZ1l9bTcom68h2gyvImF454YUO5CRcuWwXZMcbE4BpBK1BjJusnMQw1sLYzQ9P6+DzaNGWcJhXZ8TqaNtIyYV97Mz3Tp1tkM5uTGWoGDQeeBid41Mmc42SQQUBq7n5tJYa2wigGkbnLRy6r0rE3WkDcqAlx3QVFoY9neuh9kVTAr1ULqYxMthziGcFOprTZiPnO7Wm9ckqrZ/0sq0zMlAmg9ZzKn5BUMJPvmjM9q2IV1lNX16584s/cfuBpXmRJsTQ/WlJDXCSsRQygpjA42veaJpUPERgfZ5kKVE6eLZPTUmWftHb1ppqCAB10t1gxbUoyaBGg33iDLwdw+3altetCU7qMekRfprcHLOeKygFQwIau8f133rkxXi462vH2w2fuWTpzvV1DhgCj9bM8IMzcyJD7HAq398VrWdnknmBB8kJQRlWdH+nkFoYX6m7MX1E9Cd6/BUL+DEchjwGXmucNShpyH3R0AKH57X57oKo4P1eFWvvvmG4iu8wp39Nvb67570BMMhPkZB+v7zddq5LtXQdA4hkP1FaWqefUlSsE42j1i5/z2bUg1IONA8bBVPKHZjM3M2R6aud0JpUf62l6iAvH1Vsg3Te4qA35+qxpxVJp6t69e1mRH6zpFWnTjdpqzsGI2Pz5qcc2ZCqKlN0dxIsAs4AHpKleAPNiartn/KMxG8n36LC/rDjWna1zJg1Cp8ANXHt65KVFkPw/6k8x1jhl4SLLFWCRCyAEFm5/TdAy16z9Y0W/v4KD43PSG7ciI1OjtnKlbv+KdndcftK3WyvEvvvzlXn3+oXOtXJRtdhOnfBYmh5pOofG5JD7dECUoGlV0C9gNnOlSSG61HHzttXO+P3Lfa+rBQ1EMBB+QUqgT9LiRHuZFB+vqhZm3MjjQ/0D48bQAIPgpqB1VnPv/xym597I4CBfv46B+eqtLtqxP1yPF2VZxr1T13LNeu7EjzD8tSwy1pwkeeaxxQVJi/7X9obgRl+EdU7jLDAy6BD8h67m8cufTM8Bk0oRN4MGSNRn5AFqpRNPNTtYZusTYhQg9XdrkJx68ioQdozPhxwKUf72nSirwY5cUFWR8WRqzA36Ey7yiI0v76IU3PzOtNF4UN8APYupRQa2qmb4NEF6BgT1WfrbG/2p6t9tFJ/eBEu7LjglTdOaY/2pSu755o0zvXJOvTu+tUVtahxOQITU/PGU0zOTbYfBTnHYH9ycZB8wFjYzP661vyjMFQ0zNlYBpxwsd35emFRk9VgbVOUvz+jWk62zNiVQqYEJyDb1qTYAAAyXx+ZIiGZ2b1T49W6i9vzdO5rnHbc/g+/MAdBbEanplRQlCAbrzvn3Xfn79dLxxu0Z/cVaC//tJeXbez6JLiY2Z04KXK546sCHWMTZm6FOc8NLPH9zZozbIEPf7tnys0f5k+ePcKq6iyfw40eColqRF+yo8ONNGLNfER+srhZuXFwyyR/SyU8Py4QNtrVCVheTBV/Hvv22DqXXf86x599N5S/eRIu44+fURve/cNuqckQf/0Qo0xJUiaekdnDXCEZk3vGOAQ9/WW4mjbx9lUW2fmdL57XJtTw/VwWbfiw/zUMTRtYA09sL29E3rz9nRLtMq6xm2Q6Y15kTrTOWbMjO3pMdbv8rc7rt45P1dtsvHPu+sMYaBCwCbcmBVhKkdHm0AlvAwd50AhED7eNmYzMRJCfCzYZFOyCFcnhBsXGfSAIVQE8mlRnsCa/oc7SmLt+yAeBNBsDjLnJ4+1a01BrA6XeXjGm3Oj9dSpdu1cnqAXy7p1/bIEcxAg+9GBnt8D7URtqSg+wNSmqAIQGIOkghyyKUFBmQrK9W9JDzVHgfQswfFty2M9Sk6+XpakMAnY32upfl7WY9SPE61j2neiTW+8NtMcRUpkgE33pK+EDUnFYF1ujAUrNFLxOY/V9yuaib/L4mw2R/PAtKF9KEoxcAiHzXXj2KAynOsct76VjLRwu0aazkMDvBUV6GN8TUrDMwvzhmCQAFX3TJrU356aQUuMaO4GsanrHte23Aj7GogCFY+bC6ONngZaQrBCQAanHaUpkgpUtagiWWUoNshUrEAmvJYs1fP1AxZ87D3Vri3LEy34e6Gh33i03zvZrqhgP2t6pzLzljVJ1jx6Z1GsPvFYpa5ZlmAywKA9m9LC1DMxbWVvOLUMQmKNcb+/elfhlV7yzn6NffKFOqMwEFBWtSMRGWMB8dGmEUP+UWFCRpnnfrB52CocSaF+5gdMyGF6VitiIzQ5O6/64VH9/HS3rTEqghzQlR2jeue6ZDvAOYQIVOhRwB452KLNyxKtWTIvJ0o3lcTqJ4dadeOKRD16tNUU1JiLA8LJ+oWa6VFOWmpzadgLVGBZuyRF+DJ6Ltjn7YNT1sR5Q26kzeGhokh1FaU5aBbsuUNNI9bbhNrLD06165qcCFO++vkzFfqTN6+w3pDc+CAVxHgC7ehgbz19okM3UnWcWTBEMC8hWAerek2m9+3rU9QwOGHcZsQc2C8oX5GwEATEBHnbnJ2y7jH9x+OVys2LNZ/GPp1bkMl5L0sINkQWv4kvgPtdZv4qSA+f7rb7hsIVvPHy9lFdlx9pn5cKA4ALVdOagQmbbQTvHAldnkV1+7BVQgAzqPBCzypM8IhF0I/BEFBoKnC6dx9t0XXrUvWuVcl6tKZbN2TG6JvHW6waimIXABNVGfp37lueqH94plrrc6IsYMyOCzYBkb7JaQ1Mzis3MlDd49N2L/Cxzg/8/g7VA9yif+poTa92lsbbmX2wftBDyY0J1IHKXv319bl6sXFA+bEBSg4OtJkwnKOD07NaHhduVfiT3YP61guNio0ONBl21lx524g+tDlD7eOTah+ZtnMBcBPK4Y92N2hdSbwefvS0tl9bqDeujNe39jVra2Gsybj+zT0lOts+YecoqD+JDuAFFb7bi2N0umPMzmj8AL6MeADwE5/AHAxkoG8qjLZmb4J/6MqAFAwPzY0K1P0nO/Se9akK8/PRZ/fW6/bSWJ3pGNc3fnREH//DLSapuxoBiIQQPVXTb0kY/SRv35Fp/qepb8LuD36AKsifX5etltEJHWse1TXZ9Hp5wFfuJ5+XPkoG79b0TerffnBUy1dnGPsBdgUxBEAINFP25NyFBUsoqNYcbh7V3cXx+ucXakxxDol8wN0TzSOmygWVlSZwQID3rkvV4fZBHW0Y1B3L4/RcZb8BiwCeG4rjzTfvLe9SWnyond3EUUUxwUoIDtBPyzsNoHnxYIPu2JmnP9qQpu+fbtMdeXH6/pkO87/QWvH/71mdom+fbNUH16fp75+rUXFyiMl8FyQEaVlciPqnZgwcLY0JNZAJ210/eFX6gasq2fjo0zV24FG2TAz1UfPQjCFWk3MLhuyvzoywwwfOLhsDZ8DdKYwLtCagMKTUOka1LCXUpFGh8lDyxBkR3FJ6pJ8C5SMk5ri18A93rE2xZGVidl51PUwDDrVA+UjDoA20OlI/oNNnrEnEXAABAABJREFU2/X9P9pijZPZEUGmMsM1gOCzWBND/EyLmutnw55tHzOO8r+/fbWOdQxZckLFAyWYkthgPVs3oOeOtOiPb8szOhNBN5sKNJDf/9nTFUpOi/olTetnz/cZUgsFibIqQToBPrMvoCOZzv2FC5ZU0bexOjlEzUNThiiA8kAXw+lwHS+VdWlTQZwlHKAqUCQI0k62j5lCFWgq6CyI0Js3JKm+b1rH6vr0R9dkWqUIhBm1GfonSOxALmkop0GM16LKcUNelN0THBSJIdOWUasi+EC1J4/BiEtkzo1y9PD0jCE4fC3Mz9tk9DIjgvVYdZfdaxrjCKhIViL8fexQeKa8xygnSZEB6kbOr3HQ0xsSHaiEyEBrcC9rHbGqCckhAQ8Dkb65t0mB/j66piDa0KydhTGWIFL9+NAWN0jwStqfPlZlVSwOZVAnUHXQNNDsHx5o1fbiOFsjcLDpWWA9ERxAE9pTP6yUCD/Vso9TQqznyRSOIvxtbTX1jFkSjVIJ1c/GxkHFxwdrbGxWG0riLQjngIbitCYtzN5nT82AbiyO1u6qAR040qSH/vQalfcPa1l0uJ5v7DNqJX0XTcNTl2hGULXghB+qG1RVfb9++J71erqu13wMUtPoRqyIC9eTtb36xd4GfezuYqMF4QfYuwQn7Pv7f3FambmJJgWLhDR+6tGzKEHNWSWivm1YcdGBun1ZnH0dqd7FBnWU8lIiArQlLdx6wqp6JlXe6ulJSY8ONJWYnx1o1us2p2nuwgXzpXyWjAh/HW4ZNQoFVFD8LOjsB3dm6XT7mA0u/JsbcrW3adDAEaozgBg8J6ymZ9wQ5LhQP5uq/LoV8VZJOt4yalLASOziM0en5tTeP6GiZM9AL3wRQ9BoOgUwoq+NZMB7yVLlxATrh2falBsdqJq+CROGwI/5eXmpcWjS5qVAHQORxQ+cqewxQIrKU0mWh0p2uG5Am3Oj7PygKkSv3aceqVJKYqhVopETvm15vH0OBgn++fasK70Vrmr740cqDaSDHsV6IFhlf1LZ/PTDFXrHDTkWD0B/gsvfNzZrfoO+n5+f7TbZVywpzEdlXZMXe5z8rNcRJTXWB+cb59ypE01atjLN+hwIdmE1NA9P6qXqAUue6a+kD+BNKxL08Llu7T3UqEf/4lrrsVoRH64narrtHN2YHKEjbUPKjw1Ude+E8mIC7Qx+/GSHRkdn9PMPbNT3T7caGLA+JVQzCwvKDg/RvtZ+fe2xKv3TW5aZ2AlJEQIJAAGc6d/60RHlFqfpD6/PsjlW+LqfnuiwhnMAgcPHWxSXEKaP31Fo4hfXFsRYIA/4wTmYHhWoHRlR6p6csmbvxw63WhM4cyvY8997ukYffVOxzeqhzwkmAIDcS3VDGpueNQpYeIi/mtuG9am7S00y/0xdnz5zV4l+UdVjgAt0x+VJQTZAFMq5gYfeS60qeaa+T2/ZlKJgX2+jsrN3ARNIzABZ6A1dnRNt5y8Jyi05cWodndDcwoIla9dlRhroAsgKsLAmMdRYIgwZpZoL6HG+Z1wP7mvSmqI4U7ODlXHwdLslWMREDBBkVMETJzv05k0pJn7BmQLj5Y+/fEDr12fanLGvvNSoe9YnmUgOVaO/vz5HV4O95pONf3iu1hq6CKZZoGS0HDwECXD+okP9rNnyrWsS1Tsxo2cr+iyTxlmszoyyAPL5I816/fZM40ay4Z8422OHCkEwqg5IRu6tH1J6ZIANu6JZGYdCwEtSwXAenBmDpDjQGZwHsk8G39oxqls3pFjjJYkHjo5Grier+i2owXZlxqhjbMLoTLwWGT7JAAEulQykMEHq2PjrM8JNfenFhiGbOg7H8Mz5bn3kdYV22IK6EyiBtCGrhxEksehBPeBh4yTz0aTuHTNlje7eceNDIoHHHI5FGgIbib4HDtrTLUPGUefz7q0ZsHkCNHpT+YgK8jVkknuO48Hh3FuSYJxMpCdRyDnSOmLoLao9VJpOnmhWbEKESvNibJDhu6/N0NDUnClL4ERAVx443mEDu8pah7U+K9LoLhMz87olP0rV/RP6ys/K9dabC6xR/HjLmPJiA+zZkxwhN3iL8bsnLXB59nib3nt9llWHkPbbUhCjZfHBerZmwBAktgk8UJr0qSwRIB2v67dGdhw33+fZkJhsyYm0xGJdSog1+YMw0cBKcgTKCQ+V9UjJ+1+dfv/vxD67p95ABvjJBNqsA9bQuY4Jq/wRgFe0D+sj2zPVMTapR872eKhK0I0KYo169/T+Br3j5nwVxQZpaHpWPznWoRtKYq368LqCOHWMT+jRsl7Tkf/qY1V2CO1Yx+C7UTv82O80FjO8DllLBniCfCKtyFyKXWuSLVjfURRjQUBJTKh+dLZTmVEe3vPNOXGqGxwz1SkOvwONI6rvHjE6AIBHQ8+Y0Sxa+8d114o4xQf6Gz3rbNOg/X5VRaf+5d1r7YAFdUcljtkyDPQk6VmkFjHD5kRFtyUH7Hv8FX9npsbG0gStSA4y6WpQvPHpBWvWxuhnQ6L2uvwou/7HzvWoKClUlR0jig8PNIR3bVKoqvrHbf0z/fcty5JU2TdsvWY3ZEfr4YoerUwKtOGA9GWcP9eu8KgQk7ylMf9912ca8EDvDAleUVygocDrc2O0/3yXbl2dZP6ePQkKerJ7WB//yl69+y3rtTYlRHvqh0wWPNTP2/Yo6j93rIjXiZZRG1D6g+fq9FevLzC//4O9zdpUHG/9W/gBEixe9yNbMvWLqm5LKFhP5xr6FBrsZ36T5zA6MaOwYD/dvszjX0jGjneMWJWaSuydhTHGJ6dSxfVT8frUTXlXdH9cTX6A/UqSCzoOLY7+QihFrGNUIY/W9OkztxercmBYD57oMvqsSc6WxKmma1wHjjXrr968TFnhQXb2IzxybX60nWuvL4xT08i4vn+4zaqIn3ukWj0dA3rjraWq7xox6Wh6NVo6RnXzumSrOFY3DWrXqkQdqx9QV9eotqxIUlXbkO5YmaD4ED/lR4XqK0eajS6E3ZIbp2PtA7a/+6amdbJtTC+cbNdN65Ktcsr5y1BNegj+9JosA9VeaurXSwzVG5tRzfkW3f/RG+zaOcuYwzM1PWezP6ieBPkh8jBne/vZY63Wk3jdhnQL2gHTevsntLk0QauSgvRMZb+Bp/hWKsMM0OOMRyhnU06kTTL/yYlOrUoL19nWYaOs07+xLDbU1J/oa2lDorooUZXdI3qxYVBvWZaoLxxs0p3FMfrxyU51009S2SVvX29tXJNq83IASQFYz7WPmkDPztwIfe7JWu1cmajHDjbrzdci6jBnw4jvKkjQ8w09+thHv6s3fuBu3V4UY0I1DD7m80PDfORkh966KcUqIFRLP/6dE/rse9dZlfoTD53XxuWJFlcQVxHzEBN88sZ8/ex8h8o7PHLkvQMTRqlKiqGqu9TWGIDNjQVRquiZ0PVZ0drfMmjJKTSxt65I1N7mAWNj3JwXrYMtQ/r0za9dP+CZqPYatK8cbLSZGCDgh8u7dKCq1xqsaT7eU91vaDeKEVQt4FByeCMRS38Bg13esSnVDkkW0fbVKYZqkITsruzXPWtwAj7G1//O8Tb94ky3IWgc1AQXUKTYlHCvOYBuLo2zEh5cY/iAUDVILAg0ujsGzUFwDSQi8KmRmSWYhQdMn8UvqrpsrgdUpV+c7bFggeSlunNE2dEBNpeCpIIm8pXxYRa4b0wL0aqMCF2bH6Wi/BgLcDPDguw12SgkAE+c6zYJWaoI+2v6TM+eoVfvY+DVxZIo17g8N9oQn321g3rHBqTxZixpW5scrLy4QENBsuNC9O9P1trhSoXnZMOATQstq++3Q5keCBS2yjrGrKH8wfNdlqzRE0GTFdSrLzxwxhCCawui9Qd3LjNFGe4/gc7PT3fpWOOwORqeKYkO04JJADdkR6o0LljbMsIVH+qrsdl5q3b82d2lVslpHJhWWIC3BXxBPl4WjGD01kCLQXZ0XXGcVYAqGUaWFKY95d1Gm2nqGVVubJDNK7n/NEjPEu0912l8XFQnKFFzGG3MiVJKdLAOHm6wqeXLEoPUMjxtk0trGRg0u6CjrSOq7/fMCCHQ4mf+6slqUz5zdvn8ABXNc21j2n+uU+XNA4Z476vstWom6+/NKxMMsUTCFW4u64U1THXyj67NVHnbsMkgXr/Zg/7Dw378XI8NvUP4AErF144068FTXabYRtK7c0OazZfgsDt3ziMxeVNJnPz9fUxymXV+99pEG86XHhus+soWO4QJgKq6J7S/blBfOdikQF8v66sCIPhZZaeeru63RPpHR9tNoACrbB+xSm1L16i9Hz0TJdFhOto2bAkvzehvWJNoMwBQuMmLCDXKEpUXesEeOdamp891Kz3c3xTaaN58545M/dFN2Tpa1mmBFvNwstPC7YB+obJf79uQqrbBaaOeclDmxASoIDbQkovPPVRuNIzoED+jWg6OTps0LdY0PGnDPg81jKgoPlDfPtlms3yWJQTpG0dbTHzhA//yglUw0cm/dVeBSVliJH48swN1gzpxvsv6IwAgStIjVRQXoDvWJpv/uzbDM5iQ+UGdwzP6hw9uM3/vocP6GSDCPsa3khzQvLshPVT7q/u0rjTB/MCBuiGrWByp7DbaAwlXYUKwtuZE6F/2eEQ0Ttf32yAx/CT+m2eKZCkzUI4fb1ZMgEcRrKxnTIdq+ixBQr3u5+d7DJ3lfkEtXZUYon98vk63f+v4Fdwpr2379tFmSzQ4Q3huBPjw8s+3DeuHh9pUmhCoty5LMnpxdJi/0SMRXYmgV6plWH++K8dmW1Q1Dui2a3NNKARaDIDDm1YnmqDD6uRge4+fne5SR8eI0RrXlybovXevNCCtqqbX5uowB4a4gJgC4YgP35htPY5UBBtOnjf6H9XRY80j+uHRdn36pTqrvGeEB9iQyQfOddg+RqDiay81qb5nzORZjzcMmGJcdcOAre/t+THKjAqy6dsoLC3LiNT7d2WqeEW6VQ7WJkRaUj2GxH1mlJ472W7J96q4MJO/puLxkdty9ddvKjEEv7FlyM7c8HB/G0T86LlefXhThsraPXL2f7sj1ypFVAPWZ0Xosz88aRRyAm7YG61do3pqf6P1Y5b3jZrc9BMVfQb8fHpPvSr7x40e+tl9DdqcGaZ73v8VqyS+f3u6rtmSpcysaOXGB6u3d0wPHe/QkfpB7T3cpEgU4YambfgoqlTvuj5bO9KjDbw41TSkU10DNhrgzz/2NqtGn+wYNcYElEmu+3DDkFEjSRYRmPj27iatWJli4OjDp7ss0YB+xfgDGBxIbr9ueZze/+AZi6s6Byb0N9flmJ+MvtifuiEz3EQ8mO+TGhJkwh+ojjG09PmqAR0r79IX9jdaTII65/GOYW1Ni7Dz6tovHdJr0V5zlY0/eazKw1nuH9d1BZS0560fAORiVVKwJRQgmi9UMQzO04PBHSALbe4YUWZymE3RJSEhoIcXWNM3Zb0PHCj8Lk1AVoqcnjflBoJ6FKNAFfef6VBxTpQF/3uOt+l9N+da4yCNhFArUKNYlRysY62jxvE/3jZqi54EAkUUqiFvXp2owy0jxhvckBFmaAOL0gbkdI7ZBrQGqSEOS5nSCmg9aCK0grtXJeix8l7tzI+0qbpxwb7WwMrG59B7z5ZUC4Qp3bJZnjrXbUEzA/AWaVcetNdfe8q6lJ0UbhUgf+8l1idC0sYcDSb9FkUH62QnAfWEfT506telhdiUYaabV/SOG4IHl9xUdfomjbpCJYWvU5lBeQP6RmyQrz73VK0K0iMsUWEmAc1uIK8MGaxoGTQnvSkvRrvPdmp1Xqyhn0jm3bIy0YIyeOygE9tzI+3e0Lz3xFNlioqP0rY1yYoK9lV567ByExl2KPt9yufrMyPk67XEgrWKixLCJKhFqRE2kHB2dkHJMUFKDA+wROtQXb/etzlNDUOTig/21QMnOkyGFEWfZalh2lfRo035MdZEVtE4oNamPuUWxOtTtxbpVPeQVVDSIv3s+UAVITijvHy1lFQvt5HEgWxRAby1NNbK9lAiCABISusGJiy4ZuYEpXgCUOgC0IzqW4ZUlB1lKiNM56Xv6H2b0kwHnwABYw+juPJ4pUcqkqR4eVq4cbMJuJ850a78jAjj+R+p7NGd65KNXtM9Mm2VjJXpEVqRSEVv1Dj+exuH7PpISjBQxA9tzdBj1X3my25bFmvc8HF6Q/qnLLDITQiz/VDbMWyfYVtelPU5UbHEf719XbJ+cLzdONhQB7IigjQ2O2tc6RfKuvXnu7JV2Tdu/gzKIAkHVMiqmj594I4Cq+a0DCD/GGSKVMmxIRY4gNAT5PeOTtvsmesKY1QSE6Ky3lEDS6CRAlIwFXlP3ZDNG8BHQB8i6Ef1jcm/VFrZZ6jUAMTQ0Am9iIrOXz54zmYAUU3A75KsL0sJUW3PpPkBBhdS1Xz8UItJYNZ2jpoqzhs3pyovKtAau39xpke7CqMNyHj4ZJdefOK4wpPitWtbjtEgqG6uzYwwtUEaZXmO67OjbB9i5lN9lmj3uS6tyonRqbo+o6pkpIRZbwZ+AJDmI9uzjD4S6O2l7x1pU1Z8qCpaB7U5L1r7q/q0KS/aOPbMIKitbFPRslR94XXLdLxr0NYWM414PiSAZzom7P3/bufV20j6SvdmVndPGFOBs5zzHvoRlGDkXeuGxpUeGmhD+QjQOffSaGzuHFVb56gKsyJtON/Pz3Rb0vz5O6H1eBJnfAFnHDSbH57q8FT/hie1LjtSp5vp/4rWj/a3qDgzypIFBBzyE4Lt+Vb3TulEtccv5EQGGmJOnyX+AMNHkAxxNiG9/aNznarpHNYfbc0wah/BOutzL2IL2dHmuxA7YF/cUhpn1Mz3bUjTl/c3mh/52qFmvW1tkpqHp7QiLkxtYxNqHZ42oOFTdxTrmbo+rUoKMXluANlAf09y9GdvKLYYgQb31elheuJUp8JD/AzQYP/iYznz8F34gWWxIaodHNeRxmGjmaOWBaCzv3bAJG73NPeZUif043VJYRZkQyeip7NrdNqSOHwae+HO/Hi95euHbRggrAFiNOILKqv0g1S3DtnML87bbzx4WnffWmoqmyjD3bsjW5tTI9QxPqkvP1uvt25LM8nbzz1erer9xxSSnqU1a9K1PT9Kuyv6dNvyONX3TamsdUiDQ1M2RBgmBFRTfBT34IE9Ddq1LlVPH2q2xKKkIMao2lTKGeD8T3cUWZO+95Il+ubBFpWmhluVbFdpvCl78rOAM/gTBhPml6bp2/et0pGOfvPtVGi6xzx9vcSMJEKvpXjgNZNsbPrsPpNDAy2mAtE86FFtompAM+Sp9jHb7IkRASZXyWHJIURASlM2w2HY3BxsbGpQTriAcPgJ4pfHheonZzqNdrAzN1IHmka0PBFkasKah+9enWBD83gNpGE/uCPTUKuNqZ7J3qgpwGEmUADpJ5OGBw7vkN4InBEHMSVIaD0kHwRG9GDEBntbAkAgk5fgGXKHY8QRUQamnEjQerq2VzGRgZZEXZcXad+n0ZWkJD48wAIJEFqG8XGwIuXLYcr70dh65/JYpYUG6elaFCM8lIAXz3XqHdvSjXbw/9j7y2g/y6vrG14Q2+7u7nsn2XF34gkJwR1aoFCo0VKnLTXqUCjF3QKBhAhx951ku7u7e4Q+4zf/oe+X58t7P9d9X22v+xyDASRbzv95Hsc61pprrjmb+x2DjkjCge4yx4J8J4UVXhd3L4xSp2Rpsp/ukc8a5e0YaCNpQSEDMyw463SBQJMYsgOhOVDSqUFRki342Vz8fvS0182OEO0CZOdPeyssLtTTPtmWbb94ZL6SKy4f17H/9A2h84KKV0KAkxIAZP7wIwn2HK8DgoMEozTmXm7JCLJnjlZbRoSnhnkpBhkMr+odVCeHgDkwdEkmZtMyglRgclHwRfs6SYubr2HdEHCXxjsGblHuARWpquux761PkiEYXQ2eJ4g5aAmfFW47AY1BeIIdFA+Q4//rVP6/di348wmbEe9nk0McMxjQVuhCEsC9ncbbgYpu7W8SdYo9DmyKbqgD7x+ttfBgFMuuFc0RIEExxMtZCCQdqbnh3vZuXrOS4OvTAuxIda+SaqiNKLI9PC9KnGuSm+b2QXtyXbIdqenRmuJqHhqWShTDmsQgim5mkGo6h9XVYCYLdJCwzNcRnOFQExf4Ojp2HFwUNhRQxDDWHXMaHPzMmRRUdlpkiINPvmFigMQW6CRgsJcY5ilVLNYeSCoXhyoDm9A0dxV0SFUn2MXJPilxDGTjBbLlZL19Z3W8zDvR36cbGuLmpATrbGOfRCUCPSfYvnON9u018fbBuWa7cYrD7O5s3YDWNWg+4g6NHYN288xQ7QdoSKhQhbm62PnWHgEfk6O8tYe/VIViVu7F/VW2dHKInk2cl6t9b3OeTUzwt2078+znD81RAgN4QBEBb5r3yjP90m9Ec1oMgZd0qBOFFPjpik7bmBmsxOih6RH2o13FKgygs1DIrIrz1+wMAhUN7YNSBcKVfEpaoOK8ElN/NyHRUCm+pFfyTG6cGKj3h9jFc0eqhY7/8vYMUXXofL17sMp6e4Zs+fxYR6Ea4SnQCaoZs0RJAU6aodv+wLT/tr3073wt/MsJWwj1N8Rd5y9eNcQEzgOvCePtpTN1VlLVJUEQaFQkuXQWmZH81QcF5uvrYr7ezjqnOU8pqnEFx7AXUPCuiSH2SXHLP8E96HlIxdJ5rGzus6fXptpT+8usqXVAcz1/uD7Ndla02XXRiKz8Q8m++7hxMrmlk8l6wkCSYpruBB13FOK6Rh0O2SOXr+geiWPEAag3eE9BzUZwAhCUOADghl8Wg+HltT0qljr7Ru3+2eHq1iAAkVXSZgszgtXVIfcgxtHJDbla6Jd3D4hWeN+UMPN0Hmdv5zTqmQZfVef62YYU83WaYO8XNGvWDVM+fCsAFPDbSA911/r+2Y1p9sLRGntgXqTeAQXbilQH3Qwlraqabvv62kQBwfw9hVuIm7OdaOiyj882KklnRpYiB+okzIgXj9Xa5CgfvbNUX0+768VTNntyqB0+U2vf2JiqOABzY1mKr74HYQpmckI8x9v0UE8p7hEHAHHwJgP0+/RknX1lWYxc0p9akWRfeeu8rZwWKsBo46xwW5cYZCcaOlTgnC1oVTE0NHTRpqQFae4FY2FUOYO9nCy3tls+bPhxUADenBGsXIrrG+9kW3tLjz3zyGwVVqyt37x93gjyKxcnqJiaFu3tMGSdwBkxIlU+BvS3fmWq/btf//Y0quXPn1br6ZZZ4ULaUD+Bk0mBwEGODj2HEHSb+EA3BRM3pzFSV2LBg+qBinMYoh7FQuIA6h5wJNb8Paj67w9UCLlnIZ9r6FcH4mR1n82L8lRRQlsTRBFK08jIZSHsBAZ0mqE1naxCM9oxvMxFAcAFx4/gAUqGAsvp2n6pMhW2DAsNo5CgA0AxQlK/N6tBhz8UL2RfSTw4mBq7Bi0u3MtCpYE9Rvf2eUG7fg/mQ4fO1IkjzuAnB3YoEm3Ng7avqNOOVvTY5EhPBQEKDToNJN4UbRQaBCjujc2oCj+nRRU4rUgOVg5yCg3+jjkSglekp7NDFapzVMn0pDB3cVULWwZVgFBYUaihzsF9gFgQ7Lg3NPV3F7Tbi3sqbWpKgLpKFIhtQ7h5jlFg/MrtM0RhoSgjOIMac1HUkAT87c0TdryiV92ct842KGnBsI9njYQx91FU02WBbk5K3ChKkTEk6PVcvKggBOrI302O9bXgYHcN+5EE8h5Ap6Bo8PWsmRhfR0KKag+O6/tKHa1sUNDqrhEVc8gn02ZNiPWVCRqHF0UGhR+FFOjqrvw2mULe/V7e/6VV/P8ZB946V293zAqzlt4RvQf2EoGew/dIeY/e1dRwV6HXrF0KWNDqIyXtQtJunx+pNUCyjZzz+kmBWgO8Vw6mstZB++G2Aqto6pXiybGaXkccqOmX6hOdTRD7GVGeOmz6cdoeGtF+paMAXxcvFoaZWY8U+ewJLroBYZ7jlTQQJ0jQE308FFeIAcQNEgxQNArb/dnN2kd0KkngSTw4jCsaeywt1ldfQzdye0G7bc5qFgDS2Nhn2/YUW23bgDW09dvEUBcVXLga0wk9XN4jvfmshj7bW9Wh+6ELCNXiu2sSbH9ZtwoSnhcUREyxUF/jPtiHxIKvrYiVEg5zXSTf4e4ueu7QwogPzLMgsEA8TA9ySA2frO2zqr4B3UdauJeKQ8y0tue2qNvyp20lksxuphvrPE5AAHsOOeC7bpwi8QwObgoNEnlUdaBagrj+9cWDtrekU3HgmSPVoldBeeGz0YksaB6SSR/gASaA0LiQAuUzNw8Oa86Pwmz5xCB1UkNDPRRDKRAoQrlP3s+XmB2ILdSb3WVdSmy2FbZbqI+rhYd7ClF/60S9kPaEaB9LSQmSoAAJbKyPk7q8Kb5uovrhns7Mx8ZXz9vKF878N++uf59rxd/O2PsXGhxxoP+ifVrQZm+ebRRKDOD4xpkGnWXQZQCx5kV7WrCbk+XVdNm2c00C6H54c5p9ZUm03Ts7XOfp6lQ/AXUg0+QJ5AEPvXPedp2pVz6wr6xL5yyFxsaUACXwZd39to6O6vAla2sbUCFNTtI1Oiqe/ta8NsUG9jWUZopi8hXmu+Q34+UsGhP5TLKvh8BG8pEvFZ2kQOdGItohWjIdCmIANOsYHyfLym226SmBOrPSwj2V9L95vN72na5Twfzie2etuW3A8is6bUNagJQnMbX7MK/ZTlUzF+kqcYnDNe0qzi9UdwnM+M2mdHszq1Fr+0xRq6jQn+S3atgaBgbmvYCJj61N0P5nT1F0x3s5lN+gnbGnUN4bP36s7cxrk6Ewn+uz4g4r7+4XTXJGgp/iwOQID/vweJ3tyW22n39UqL0EWAtImd3WYxcvXpY9wYbFcfI+Il6jLEphMC0YE+DLdr60zf7w2kn7ILdFUrQAFZwL5CiYAy+eEuqQAa7qspgAV1s3I0xGp0+sSZQxYWF7rz4LecVdS6JtYoKfznBmZOlARYV5qAsOZZsODKEAIAcWxRvnGlQo/v2UowPrQzHXMWzPby+1opYhmzEt0tImhlpZQ4/mXWaFeQmUSfPz0KzdZ+ebRNla9MxJm/uHY/bvfP3bFht/OVplX/+0WCYtIHxna/rUamfom8Q9p673qlb2BOsYvCiOPKgyVSzqErEhnuIhcziBRkFlgVM/PdzBSUYm8aaUIHMeN8ayyzq0QUkK9x8uk2EPSiyrknzF62ehQY/YV9SuRJvhcIbGSloGLathUDKUUIEu1PfL/ZKE4uT5BmsfuCgXXgYycahO9XO3zw+X24cFLVJkIVDk1vWo2Nhb0CY9bpRd9hW0Scuay81pnBIXDlv44gRDDj6qajoMzIpERXhZRISXZZ2p0mGLKgXPDPS1rWdYhYA8BDpH7HRpu4oHLlzPi9qGJf3ZOuigj4AS4KwNX5oAzu8CkaMLg8oMgYkNvqe8SxU6SRxtVFqlHO5ljb16NtCuVPA1DNqkUDcNkPMOvkSSOdCf2pSqJAxEFiUclDs2TQsRwkzxgdTgknhvu1DZqS7DO6ca1HmI8HO1H31tgVravBc6OefK2m17bpudre1V0J4e4W5DQ5fsbBOyt/9QRybOy80WRHnb9sJ2IUOTIzwlxYncMBSqW6c4uhEkoviHQKWhyKBjw2cmYPOzoOrF+bso8PBecGBlMBBlDoo66C7hvq7W0Tdi75xoUPLKwFpquJfmhXifeDNwoCBwAFr/f6//9+vl07V6RnfMCFU380xdv/Ypfg8g3CCPBDkUikD2QAd3nW2wF3aW2ecFHfKwSIvwlus38cLPZbx08pfFewtZZu0gZgCKf76o1Xp7R23s2Gtt7+48dUQo7K9P8RcNYl2av1V2DNtnOS2iJa6YFSkDqQt1vUo+G7pHpO5GfAFFBYzYdaBUCPykcE/JQx4q77ZJ/l722f5Se/50rXjUqDYRzzDN/Cyr0WL8Xe32eREO9C8tUOsRo0v2IjMhFBYL4710uDPHxQB0gI+Lxcf6WGSMv53dd15f9+v38pXsDI9elsoa3iF4W7CWiZOsXehZgDK5zQNWWt8tSdcrV/4huhRmXMQmlNvoBkH7zG4Y1LNKDnCWtOZb55s0R4VUdqQXhdQXFh3iYUWVnSrAobOcyG9Rl3RNqp+GuNmfdHIoPOgC/vH2yQJxqlv67OUTdXp+9y6OFsjAO8gM8bZ1KX62J6dJsZXEimIvPtjTfvjYMlHSqtoHNUiPwy/DuwdLu/S86Kr09o7YmfpOJQvcE93d5bG+9mF2izquDLyjTjU51NWCvJ1FTyEpDfN2lqJgepCr0NzmHub2vlDHlmePahhD5ySn7H/NzQV7mLfrOJsY7inqRnKYl+XXdtszeyvtXHGb7SrvtCkxvuLRI+aBHCjXbw9Wqnv/f6//9+uD7EaBjiD4Byp6NDuIwAPPfkqUl+TJkVfmTGwfHhFd6JOjNfadl8/ai8drNW+HWlFD70WdQ8w37i/vVud7e367Olice3g9wMOniPD0dLKj245buJcTJtx2a0aQ7SjtsG/Ni5HR55vH66RauH5etIA/TDkp0Mk5mD3YfK5Z0rMwHF5466QVNQ9ojbKnPs5tsVA3Z3t/e4H9Yl+ZfbCvQjMjrP2DJR0SosFz56tzInTufW1JjEARVLM4dxCpIcn+kjq2KDnAlqYHquhNT/S3kMgAK96+wwL9Xe3bb2ZLJAZhg4Ona2VaGunl8Iz6+65yPV/545S0W357n1XUdguQGx11iEw8uTRBRQfeUuzd6rYBgTsMsAOAoOb264PlNiPGWzMf5EBcsZFeVlbRaTsKOuQhdux8gxW2DdnXZ0dp3gLQhQJtw8wwa27ut9/fNlHMjPyaLntqV7GAuW9vSlP3gjnJzAhvSd6+daha7wQzztrOYQEVj983W4AsIOq6WeFWWN9jv99bIYAFdbm1SX7W3txl52q71eV+41SDeU0YZ3dODFE3BX8QusSAgsjssp9/tCTeoT4Z72MHy7o1g8VzZr6LjjMzaQCK5V2DUjAtbepTLgYQPTczVPELSeH1k4Jscpy/HbrQaD/YUmCfHa60/dWdtjDZX47qzKOsnRRkTk5j7elDlTbxyQP273j929GoSC6g9KDBTGLHUBHJHC/3RGmHfWNRtJ2s69OCZnCJqhcd6EnhHpKkAy3oG76sw4s5Cg5Oqm9Q/Nc/zrZv3jFNSB1KL/nVnTY7OUDtxpumBis45Bc020Mb0lRw0KpfGuNtua39GjIk6aZCr+8eliTkGYy6UgIkkUZCOT3Oz06Xtdu0OD8h2sw/ME8Bsse9QM/qGLqoQuBwicMEC4QBc6kLzf02ORg9+LG2u7xTXwPKSSJPwvDByTr75fpU+6igVQfoX/dVmqe7k+ZJSGwwtenpcfAhWcAeHuNtemKALYr2stfONFhT24DFR3gpWfd0GSdKB8PRSSEe1jlwUfKedBRSQ9yE6LNRSPoJSJ9eaFbSw33wLtJDXMzLyfF+eK68L4akSFRO1vdYXl2vlKugMYHo8NxARXhnBDmCJQgN98H9ZoS4ifPJAY9W+R8OV9qt00IkowuyADWORCctxE10BmgiBETUfUBOoSedL2231VNDhQ67TBhje8/UWWq8ny1Duap9WAPfFBkJga6WFuCqbhTzKuGB7kJ739pbLh4oLWsKMwIhF0UP8p6gZDm1PRr2pfBBzQNlE+R0UeUCacbkjLmh1081iKYBIspFlwe07VBBm1BflG0i/F1teYKvUGbWVHn7kFS/nrzuP4fD+f/l2prXLLqa27ixknc809Sj/U03ifcGf5a9ACXvRGWPxQe6WnHTgIy1UB/h63j+KKiQKA8MXbTrp4Xqfb3wzmn70QNzlQh8lNsiXfrEUE/Lq+q0BxdF2StH66yhode+uTFFxTQShwsifex4fbfQVOaNiDt47fB7v/TOYe2cKmq16UkBdjSnyeZkBGutMAsV5DrBzjX269C9IT3QyrsBTa61HXntWjtLE70t0MVJsWZGqJc+9/byNnX02Dugm+jZv3m41l66I9NeOFtna5L87Nc7S9U9XZYRLEro6wdrlGSTKDHEGhbmqYT3ltQg+9WBMhsauSyBBIAIuQ6PHyOEbVK0j6Q9KZD585nRnpIOZ8+j/U/X8JUD1fIGYRiTxIl5MyiXvB8KvQCXCaJJ0O0lqT5f0WGrJwcrjrKn+CzENCgkMd7O8i4g5jIsy7BlerCL7chr0/t6YmGcfXdbgeZOTtb1qqgkXoJsEjNBeUGjC0ra7cE1CUpaQGwpcjbNjVRCB6iy81i1TU4N1FwL5pyoZW3Oa7E5MV6W4edpn5W1STp4YnKAVOb+9HGBPbguWYUDM14AA9w3z+DV3RV27/I4O1TcYZOjvDSHBgpLnGdtVrT2qzCE0roq3td+vbfclqQGKP6xFkmY8P/Zn9uignb8+DGWEuYlF3NMGjlzAHvgrD80K+q/ewv+S1xnKnuUCFMYs66Zp8AIknf78aEqe//h2fan41UCAt7NapIEOR00EmpmhRCGoMjHQwNzx/rmfnt8dbxVdI7Yr/92yJ594joLc3e2Px6qFLWS93fibK09ff80e/K9PPP2drbHVyeo2EDNkqFr6EWAUgBmdLoArpJD3CUHPyspQCDDR0drZGx5JqdZs4TItgOOUGBgwsuMxCOzouxwbadmHd8/3ywA45ZJQVKXOtPYY8ui/fVn20tbpAbFLBVUYua68PnY8dgce2p/hd2WHmw/2lkkJTxoQfx+xFxgCcSEe9r53CabNyNCndZvzY22Rz/O07NNDvdSocIZzHXoXL0tmRauXKCMeQlPJ3UoYJJAUScmYGT820+K7e5lsSqituW0KJmO93azocuXrXP4okA9aM48o5fP1VtJXbfNSw0SswHKNZ0b/m5ymJsUv8q6BgSshPi5igGREuBs755uUFz4ydIEu+e1s/b3O6coHuKhQ1eBJJ/YTzwMCXC1rPP19sv7HNTk7Xntkgu/bXGsYjID629sy9csF9RYutNpAW4yWGXuLcnfQz4bf3vnrK1fnWFLE33sZ+/l29fXJyluQYelQL3QMGDzoj3s1x8X2Y9uTJVQAHkOOdIYgY4X9bWAuIhJAGh/dUqYfe2DbLt3fqSKUObaEBPg5+7KbpaqHfnQ9Fgf25QSbAer2+V1hlAAJqXrMxxU1X+H69+qswGCGeTqpLYciQYJ+LmaXlWMuEBmRvso+JNcgyxNi3SXDBoKIiRr8GWP5bXIhZqDaFlqgMyYWvsvCn3DZfbTU3UykUK9yNnZIY13+M0t9puPinQPy+bGKDE8Ud6lg5Z74EBhtgIKkGQePZyEqC9PDxRKCF9wRUaQxfhOsDB/NxlD4fOwLbtZnN5Tha0KlvxcNhoFSGyQuwxxmDHAeZPuCE68h2u61YmhWGEWhZbb33eVqdAIcHOSwznKT0+uSVHiW9M5ZL/5IN8CvJwtMMDVntyQbD/ckCR5Nlr4DKZ29Y1YRpyfzP1Y2AzLQitCzo8EnwBbXNGpwmBnTot+N38PXYKhbj4nBysJuKMIGWfBrk7imtLdgCd/vKFTuvmoYjS29NuWrCZLCnIYnxFkGZyiEKDwgmNOECUAY6rGYU7LksD3t1O1usdn91QoSLBpGbC9CWPBzhFRWJDknD85VAo3BAEcix++LkZdk+L6biU5yAuikw6tAY3tT/JabUqEh4ZGf7e7XJSNG2aGqSXOfYFQsbaIuwzOUtyQwOAo7evrrBb4ijR/rRHeObKBcC2zKjstHeUq53EOpayLVyQzeKHGYVQICg/lA7O0m2aE6vsDPJ1s89YcK24fVCI5I8RLeue0pxFA+J9+EQfY/yBEJJ7Qk05WdEmViY7b3NRAta6nR7ipZb8g3tve2VOhhDK/if36D/v8bIP2KwkqHgizkwIETkCtumlDpr2xt0LdJl93J+03Cr7s9zfbj9/M1oAkzrcUFp/ltYtXXNDRp5jDeqOrVdjYp0OvpmPYNk4L1d5g9gDFJChc0WGeVlTfIyDh1f3VSjK5J+IAF4kTyBoS0uxLDnQMovIa+uXF81Zuk5JUEvBI7wmiiv35k2J7/tbJ5uM2XnGAr2f4MyLQXQpYv3knRzMpoJ4/ui7Bfnf7JCXxDJjvrW635tYBiw32sNlR7tpvFE0kwOsnB6mLSbcUqgGqdR+dabDMEFdROOFG+7tMUKGBBwEoJ2pS4R5OFuDiZOOvvdacxo6RnDdy18jxzonwEPgB/YMEiQSIYghqJEIYdIIAKjDkA0TguQLo8IyJ3b86WK5k/MlPirSvQcxwN75tYrDOhBsyg6T4t3B6uLjvfA3d5J9tTFGhyQA33c5FMyJEu/j9Z6W2Jj7AtuS3Wmqwm71zvE4JGkDBdzYmiyJHIuDmNkFxhuKAf5hVQfCCtePt7aRi9a6ZoSouoGKS0O4taJWCXUooXY0JUjGk+MJzYdeFJp0fdHBxm8fw9RvLYpXghXi72MtvHrfsVpzpzeaF+ajTBGpPV/9/+oUIBL4y7YOjSuiO1nXa4cI2DfnSFb5jWZzO1I0ZgdY5fMnWTgyQ2z0JO7GZ4u25HWU6k+LwQknzt4UTg213SZdc4m+/dZb98v08e/FUnQV7uah4INntOnXAvvHsMZuSHqR5jL2lXQKQ3j1eZ8Wdvdrn/OyIqyIEYb4uVtTYZzfNDFMi6ek0xu5bFqsuZ0qin2UVtmqY+QcvnhZA99KOUp0X/7B/iNLsOnas/j/Q00neHMxLnqrstoqefvs56H3XqDxdgj3H6ez8xZsX7K37punsJPco7+23361LNXf3CVqzv3gtS94/zs5jlay/9NAssSgYuN5T2SYgBUl3ilpoXQAMxMUHVsSL2sReQkIXVU0+86Job6kvwV7AkHPDvCgNvec0DmgNZ/h7WpC7k+iN5Ek7ytqtvg91vVa7a3Ko3LePFrQoBhBvOFdRuTxd02vv5jSpWzIxxtcyIzw100YsRVIfOvQ3tuRZcKCbfXdrvmI0ecPXZ0XabRnB6kw/ujxWnYdVixMsxNVFIMOx4xX2zoOzJBwE2IQZ4ZL5cYqzj754WtLCW/LbJNf9jVfP2SMf59np8k574fFFDmPP7lHtz72F7dqLnPXMuBBzN59vUde4uG3IHpsXbflNQ4oBec1Dtg9mzYEKyQCH+boKoEAZsZMcbXOh1jBUd8DxT07VS34Zui45yB//fthONnQqB5kb6qN3cLSmxx7e4shL/x2uf4vOxpoXz1pmlJeSa9B0ECi4dphgwZmEu8hwDcPHDAixaLhePVIrlO58cZvNSg/WIG9BeYdNTwuy6xJ9xOHMqe1WK3JXcYf5uI4XDWhHfpsUoVBtQRsaOVwO33AfCh2HYhGcZfwdMiO9ZKwDzQBuNii6OI71/XY2v8VuXxJru3OaVLgsTwuwExXdanfSimNWgkOZKp4EGiSQ6pYEiU399pFau3V+pJA/0ASKAziKKCOtmhIqTXuoPqBsDGpnl7bbzfMi7aMTdebj7Wxz4n1F0UCakefEcyEgvIupzMIoBSwGXVekB2rQWQf64EUL8pyg4oHDEjMu+IvMaGw+3WAzkgKEuLLBCKhpgS5KkOgKFFZ02sLJIfp/5g9oWbMpCHBQQPjMOHyiQQ3qAXWqbeCiEFKKF6gTIBsEjVd3l9ucyaEW6e0w4eFnkmiAJrb3X5Q03q6C9n92XEjoWRtp/h52uLZLz7C+a0ieHHga4ARPt4ok47r0QM3hQH+CZgYNjrkUDoKuwct2Iq/ZPD0naO14u4yzeH8n23K+RQcNGtsEPxJJkiGSI4qljoHLKrQIhDy7pzcX2o1L4rQmcJ6nMOPvaPmSkPBceGcME86M9xNdDjUP3jXtVJ4jwZfPwfOjJcvgKANoDNbhO/D+XZPtf9K15K+nbE6Cr/jtKHeUtA0p0aNzAVWGQc62riG7Z0Gkna7ukyM2CfvzR2vMecJYy0MhLNNR0OVXdOgZL4/ztb2VXZZV1m7fWx5v++UYbDYrwkPt8x8si7cLLX3aKygQ0XmAkgVSRSHNu4GiA42GTkhe64A6o2dqB9ThO1LWbTlFrfbI2kR7ZW+lBk83TAnWTBJcffThP8lt071D/aHWQOe9utMhcAFN6OXd5Xb/inh1FNmf8HspsknWb8EEs6JLkquAH5jZ7cV9fGG0bT5SLdnYpakBonCw1okDy1N8FTP+8GGeff2GNNuV06LD84apwZbXhAfGOO0dvEdY+1DMiAP4zoBMPvNZqa2dE6UEBHCEbgTeI05jxmgA+2ROkwQQ6DYXN/SK6srvY2/jK4Cb8bGqXok6ACAQQ5gFQeyBGMTP5WuJEW/vr5R86IwoD8tvHtLfk0DSDaSLiYY9w+hQQNxdxus58Nw4kBlw52cRe1CyoiC4IT3AthW1K7HCN6GxZ1SxXXEgzlPzNyR4Lb2jkqdEdWb+1DAL9Zqgjguc87ggD9E5KFRB1fl97FXmuCgcnK7+Gf//rb+dslvXpunsSvKn+3RJsYALyg6nL2BKYV23LUjxlyTz6klBMmWjY7pkqsM7AdAHUGJOlIfc7k8Ut1pDfa9NnxxqH92baf+Trtm/O2prpoRYop+rEvGTDd0CrQqaBiWOgGR6eVmb/eLuyfJr4dyDmvibQ+USbaiuaLWp06Lk+ZBX0WHrpodJKpVuYHZJm/3p1kl2or5bKmXXxfrYjz8ptDfvnWb7q9okGgK1DUO7Lz0jiOvEfy5moJB2RoUx3tfFPitsF0LPHCKGdT/amGxPflAg+dgbZ4fbp2cbLTnS225IDdRcKIa6+FuRjK5K8VUhkxjoKjDrtR3F9tV1KQKzAE7zKjttePiy9fQM2cPXp4hqjcw+SStxiSHtZdPDbcu+MouK9hWNilhBygeACMUKpP17fz5oP3l4gb13qFr3df+CSCXHJPZ8TqikUAMBu9jv00LdrWP4ooqjB26crFysf/QLzZgiiOPOLEZZh207XGWT04I0D5Vb1WnfWBqr+FHQMizZ7pugqJV0yH6A+BDhRaExRiZ7XCTjJOoY9e48XiOBlo3pgXawsls5AjkYIj2fZrdKLfBvx2vU2WRPbpwcpDmXW9OD7c2cRt0/MRkJf7zUHp0TbX86WqnzYtmkYOU60FWZt0KK9khVr4a/MSA+l9NoQ/1DtmpZsk2PcqhwfXK+xeKCPbRvZyX46fNTRBK/+Td5FmsS40JECW7+yWd21+1zNI8zJ8JLc2f8Od0khHf4fgrc3Wfq7ZaF0bbtTIMtnRysvf/a/ipbNjVMXTiu45U9tiE9wPJbB+yjw9U20D9ic6ZF2Ad3/2vnA//SnY2f7ikTenHTlGC71hzqTVwcNByAIAMUGnAzSTgJ2qDhUIy8J4yz1EgfK6jqEr8SRIg5i2+sSdBcA5sVJSaGcg5V9tjMaA9RrKgcWbCPv3pOiczadH8pRTDIiVENHESGzEG+Z8X6SLKRgMDvhD5Q2tirliaHTWKsj4qV1Zkh6qLgAlxc7pDSBA1Eeo8FDl1oYpi7kACoN6jDgGTeMi9CXwf1BidjnDpvmB5miRHeQsjhecN5Zpjx8Nl6e2BZjALOptkRartxqMWgdjR0UQUORlygsfFR3kLp1mT4W1Kol72xr1KbEKSUwEw3B1QSpIQDs7H/otqjtPqQBuXPcusY+LwiCtLQpcsWCuUr1lemevvO1otDDsqM3Byb/76pYUqWOVwJ0NA+kN6jgwRn8lRllzY8zxhFDpIoNP0xw0NhDLSIYiKvrsdmx3jYX7aXWmFZhxXmNQiFOV/dIz7+Pc8d19cxJEwCiMfF4jhv+8OOsn8O5YNu4MAKmkgBtCO/XVxvvg+OL9LFmbG+Sv4YwoeCNjvOV88S5IQB8RkR7pLNVZfJ00WFF50Wum4cDGnJAfqZFDVwUk9W92pYnoMC/jCUMQbUoauRoCCXuflEne4xMtTB7yR5hPJD8pJV22eNPcNax/gNUOzA4f7TkSr7T79+tqdcsYCBTZL8vqsDuRR5oN2ouLA32FfQ3iiEkW2ma4jyTLi/m5VUdNroyKhNGD9Gz/A7axKkaMIwP2srM85PA4rI4koCc9gxo3TPX48r4b85M0gyzHB00aAnuWZGqaVvVN00ZpBA4zlk+AcKJmoiHF4xUd5K4r+6PM6WZwRavI+rFRW32ujlK9bcf9Humx72T8lNCo39xV0StOAQ54B/dG2iYt/kUDfFt2BfV7t5VrhNTQ1yOFUn+Or3U2jsPlVnj1+faDvPNdrXVsWLIsrXkCCRVEf4umguBNGCtNQgzZXdMzfcEkI87Y8fF+mgpQimIACFDHNzVhwA/GgZQB1vrH13Q5KKEQ78c9Vd1jV0RQkCNAkS9WlpQSqG9pyu05wWBynJNkX4N2ZHif5IwQ6KicdEZdeInjXy4cfLOrQ/MTnFD4c4gDY+5ouLY731HCg4mFuDWvbdF89YYUmbZZ+pVBeErgZUzut/t99i/SbI7XxWjKe45SuSfOz77+fJ68LNhbVzxaZFuEtpaFmij5S4ME7jc0FrnZQcIKUb3g2JlLqgKf5KGEAbiWsoEAECsVdnhnjrzOAc4ZmTyM2aGS2qK12Lvx+rtUNlPVbVOSrp08KGPsXCk/nNkhrlOaVF+kgVraRlQIgt3HLiCbQ0CjOS54buYQFOg30IazjbzN8esb+drLH/9AsPC/55eEmMqGmlHYN28YsrKuL4f9TLEqO8VThj/kY3ONLXWYWau/NYzWU0NnTbyJDDlwrE/idrky3Sa4IGnpODXJXUvXauwRZG+pqX0xir7RsyJ6cxtvaPR8Q4wJvrIJQrDycraewRgo+EK0AYtB2KehBn4nZD34hADZJe4hOFP+aVP7gxRYXGtGAvK8pzzEZBFYICDr17WVqAksm/H6m1G9OZG71Wxf6v78mUnxSzgojR+Ho521evi7E5U8MFSq3MCFTM4Zz94EiN/WBjihL+P9w/1eYm+2tNbpoSpEIjNczDTtf1ax3OnJ+souLJTamWEultP30zWzS/wuYBu2NaqM61CHcXC3JzxAG8iFzGjbGnH5qpOTSo3Tuym621d1Sza7wLzqy1C2JEGTx0rsHCA9wE1AIWzgh3s+8vjLPDNT2K54C3M8M8rbxzREAC3jwAuRQl+HNAr7pzWZzmXrOb++3G1EDFZGLBa0dq9ZluevQlO3u6yk7uz7XoAHc7XdunNbPu94ckq/vumSbN4X18vlmKWF955Yy1dg5ZoK9jZpbOEOthSZy3/JJ4l+QDXAtnRtrU6dGaA4Qy+9axOluQ5KdiIiXcW53ym1IDReWkGFsRF6i4xgVIC318zfqp6hhjnPyjTwslzNPYd9H2lHapEKU7veNErYUEu+sZzEoNtF1nGkT5g/IKlZ88j9kf6HwfXmi2kuYBG2K2q6FVcZ0BckRS/lWvf8nOxjPHqoVy4YQLjSCnusumxvoKSUTRB7T6QEGrBnxRXkkLcVXbNNh9vORn69sHlGCQtNMuZbiLJILE4oV9VXbPwqh/JvbMI5Q39trS9CChYLTFth8ss3kzoiS5drKi2xYn+UhTv75vVI6YKLxQ5EBhgMYEb/9IWactSPC1guZBdU2iPVztWIMDRWgfuKyD76NzzUruccjGG4NDpKpj2Grb+sU/RP4QCgID2UigIdWGfjcdA+Qk63oc6kcsaJLV8naHAyhUHH/38XK0nhzjI5SNYTI6Mg6+oOlzMszaP3LJapr7xNdcNSlIyS+ymdAzoBHx/2w+iiWkP6Ea8TtJZOBsE6iQZ4QbCS+SYdWK1gG1eenQQLE4X9ujQTVax2OuvVZ+GXQNDhS2aV5kURqOzJctp7JDKmIkF2zOnSUdFuDuKBoPn2uwWRODtfFWJPnau2eb7LbpIfLvoL3M/AL3eq6y026ZEaqhVAbRJoy9Ru1T7gWzP54VhSWfEafywrZBO13RpVkWChucVvNqu21+or+oJoHu47UGUfmhVdvYPmAtLQO2YWGMOjEkENkNAyoQD55vtGVTQ/VsUZeC0oOpH0gm1K6clgFJeNJ9OZ/fYt/amCwUHpf3E5XdagsjVfjsnkpbPyPM9uW32kMLIqUMxOHFLAqITEnbiN4jiQlr6UJtj1A10CIP1/F278ww0XBuzQyz/6QLVRlMnyj6QMdK6nskh8iVW9+n+Yv3DleLTjMlMUDzD9Aqkv1dNCRKHAB9gi6FARRFKTQb3L9/vqXQvrsu0U7U9CmJ6x2+KHnTxakBmvPJquu37XuLbfH8eFEEkGTdNCVYcwfVPUMW7eViH+Q06zBFqQqUGoWjTy60CF2ks3lTRqCFebjYsfoOFTAc7CgwvXG8XnNb8Jz5DFKh6URFbUSJBz4dFOplTb0qcmeHe9lLp+sst7jNfnJzmpyvSWa4on2cFCvpLrT0jKgbARAA1x9lJQ4rhrO5SAA4sJgnYk8fPd8o6g+FC/cPksjeQcBgcPSKkN0h1PB6hgSsSFK8yzH3xWwRhQ+uxCRSFHwIb9D9gx7GLAImeZhdYhQGBQtpa7oDm0/Vi8o1P5Eh0C9ENcR0kK7evAhvezu7Ud4eADn7T9XZ3CmhAikoLuhi3DMzzI7V9AlEwWgPzv3pwlb7xso4C3B2kpM7iebm83Q8LtnStEBR7pAZR2p6boSXnazvlfQtymRQGtlPdDvwxiH+xPk7aa/tyWoQhRKBkIa6Trt9Tarmu/DxIGGg27X1WI2tmRNp3UOXVJhwnTxVZZtWpdn1yf5KWAFliJP8+ffummq59f22MsXh34BMJ7TTH20ucAiA5DXbd5fHy+wUmVS3CeMEBqGoRaEVfVWyGY8PJNrPF7RYWqK/uvF0l9amB9p/0nW8rNueOVH9z/9H1OTG6SEC+E5V9sgX6y8fF1homKfNTA4UeECcnhflpc4CoBDeFwCWgH+sfSROOSPufz1LSfa+cocJLWIJHb0jtiYzWPsFZaSdO3PsuhUZ8myBXkexg3wqg7/Tg701dxDm7WTBHshPX7FEPxd7/nC1xEzOVPfZ12ZEmL/HBDtcjYCM6QxjrvK5vVW2ZlqoaJgApxQDnBfQa8kJksK8xALIq+60ecwMRXjbCydr7PSZGvvzw7OsqZ810KkiCglalNU0QzRwUTLUJU19Av2YI9hV1Kmim/0/9hoTOIaYA+fyq1tyLDou0B5YEi0VywWxjntjMJsiWh4h3aNSpLp9ZqjWHh0SOh6AYww/78hqtOhQD5sX5y0hF/Kb13aX21dWxkvl68YZYersBge5yXuDTuUbh2ss0M9VoinkMYhg4E2CcSozcJjezYnztgt1fZaV12KzJoWokF+c4K0B9E34idT0qNhHwZEZUFQ3/3BPpgW7Olv9wJA8L545UCXTQ854iXLkt2rfXxfjJ9W9c1XdtijZz3ZmN6soKqvtsYw4XwG0nL+navps+/5Si4pxSPqXlzTbgzdlqkDqGb2oYXLA5L9+lGdfvT5N7BT8Pnh35y/U2ZqlSfbQNIf08KmGHjtc0m4nDxfb9746z6o6huxrMyLt90cq7bYpwbrve18+Y6vnRtnR3Gb7880T7a2cJgHeXOS6Fe0jFuPnJNAdFa39FxotwM/VThwpsevXTrLFid6W6OVus+O97V/p+pcrNq57/rRaiCTQ8IlJFKisOTRB2149XidTt+qOIauo77EbZoULnUsPcZNKQ1yQu9r3HOAgcyBobF4kYuEYZtX1SQGKRUdCHeXpbCfqerXRKTZoh/L9HN7orfvjJjp40W7jMOwbVXWO8hKbDBQSFIHNSNUP6sTg2btHazWUCXqBYYzMATODJMdGMk3VjqoJFSq0BhJ1OgK01Ggv3pkZKjMakHoM9EgKJjBkHephK9P8ZdjzZXAiof/KzAi76a6nbPGDdwlxoyCAX4r6AoglPEAQdIICSdraDH8tUi7kgilC2MQk2sx4MAD13O5KcTpRQODg7u0fsdvmRNjfdpZZWoK/EFuSP4z95oX52u7KdgU6ghlumVTrcKtJEuElci2J8xLyvyWvVck0CDPPgAQLmhO86bsXR9tn2c0qILbltForFJU4P4sPcBG9hZ8PosFnp2AB9ekfuaL3TFfiSwUe3i9Dq7ik46RKkvNZXptclnFIJ4F//ZMctdPTw700T4NiGTQV6HL8bFQvVk0Klus6awUZQtqquUVtFhGBhOc/LNTPVUVJnO8EKVrB7aaYXJkeoFb2h0dq7Jc3pgmR4R4/OVlnfr4uSrTO1fRYUrBDfYeLwog1WNE2ZFMjPOxkZbeQeCgeMb5O+tzQS6B4sN5ZZ6kxPqIQTYv1UQG486Hp9p8iYcnaoftEQo+XAUkESmaIAzyzu0JtZvitHA73LIqyI2Vdcq5lrgjXbApyBulIGtCIlwxidbetnRgo/xkSBLpPrKkMf3fbVd6hQ4/9zCEAPxdqH1Kv7CmKyVumBAt08Ed+GkCke9gWxnkLvQT1Y2bh0/w2GWo+vaVYSfWNcyJsf1GbJLFvmRVmWbX9UimCz42aHOuHtU1nBjole4JE6MFp4ZLMZF2U0Tk5XG4ubs4WFeVlt0wLkdIVbsQk7PsLWu1Xq1Ns2c0/URzgfgFEiKPsC+IiRQB7bUmKnw5wpL4xD6UQooNHp4W9Tjzie0BXv/1Klvn5u5uLy1gbC1jRP2rfXBWvIdDJyHy6TZCaFLLBy2IDbFd5qwoDOnpQfnaXdCp+orpS1jaomM5gI9eu0k6BHzxXihE6BfMiPe2H7+fZ/Svi7INjdfbVpQ4ln/buYUuJ8tE+AFhin4MsUoQTjzkjHBLBXyhpRCQCIAEElK+h47siPcCC3SfYu2cbraNr2H6wJlFCEAgCTJ+TYFOiHU7kUEjo2pKIgRifLG61G2eG692yU3EgJoYfO1JqCanhSmRQ2EIUhGKAd4EZKHx3ZkdQlSLxeubuTKvoHhSV8u2D1ebqOk6F3uf5bZYU6iFKJp8LwAkEuKFrWIUuCS/xB1rHnBhPxerKjlHrGLioffEhcSDOV5+ZJO9gUbvteWSm/Sdcq/9+VvuDgiEywE1SwygC/XlfhT20KNp++VGhrcDNvnPIKut77f6l0bb5TKNtnBpibx2uEa2YpOxQWbfWBkIxPEuG+IkjqEZBlYX6zHOfH+6j4oEuNnRhnn16mIM6s+N8k0A6iusbJwVZVfeQJfm62emGXtErZ0d7yudJkq2BXvba+Qa7OSPIvvbyGfPzc7PV00PteKnDGJL5pmzULyM9lbcAqEyJ8VHOAvuAd625LaexdmNasB2r61CHBhrX3n1FNmbsGEubFGGPLY6xZw5U2u2zw0TLIS6+fs80m772+7bwgTuts3tYkvheLo45VsRRmFMYHL6kGUFmClDTQxQBAA2aMGwL5gaJQeQxd2eG2vrvfmCeocHm6+8hydqRkUv29G0T7ZfbSyw91lfPcNvRavveDSm2LiXENuc1aD/SHbl9UogdrO4SGHrf3HB1+lHjwnsI4Ce3pV9KmJyDyFeTrwG0PvzKWZmLvrijzL67KVl+N3RnYaPQdQBAoJiAuoZgEGqiX4q3UNiTX/D1yOUyu8I7vFDba6vS/OS99fsdZTZ+/LX2l00T7dOSVnvmL9ssbeF0uy4zRApz0Ow1p2mmjuXOC01259wIdXmImdmtfbY7r9VOf37GYqemKVdKifax+ACHPO+O0/W2dla4hIseXRhteS0D9vK2Atv8rYXK76CUPb+rQtT4725MlhgGNDpoZTw71iPnAjkHRRhjA3TW6YLeguDIyKj8ncghOau2HKyw6ZNClC/MivYQuH3g0Vn2r3L9S9GokPdD45xD7wJDU1EOBSkOocRAN7l2M39Awo8PxrSkAKFQVLa0D6/HBbJ1QOgC+vD4adBS33W+0Rqa+i2I5GHCWCV02fX9qoRJAhnYJOlouOpwC70KZQcMcwI8nGxuvK/9/XCNFsA7R2u1ADiMmbkgMWGAWHMkvo6AhZRiUqS3KlDoXUgcUpSgCz8pxM1umhLkmAXwcxI9CmMrNsPpwhbLjPCwvZWdllXnWOj+buMVTNFy9sONuLpXiwt0m6Szq2/U9lR02Y2Pf1UdHAIpQYJCg002KdTVJkZ625lzdUpiGZaHLkUyzsbk92cVt4q7WtDQK/lgkBeQS2YW1k4M0kzLA4ujFUhp25GMdPSN6l2cr+2TQRGUFhBC6Y0XtKsjtTDBW8nahZI2DaAVtA7Zjz/IFwWIhIp3UdbQq7mMwtZBbToCL0gkCDGoDFrha0mOanuFQJNY4X0CrYGkhpmIudEeOixAiQlUDOkTWJmh+Nq8KHH8kdZcnuInJOVc/aAQy7jEIJsR42NlLcilIlNrKmoY6p0V6aHBLRBkHMlJQqGz0YH68S2pGvj38ZighI5kHz8E7pcZGCh3Wy802wtb8u2m+VG2t6JLa4d7S4j0tnNnquzvnxZYR8+wkCxUrFhvX/K+ec90e3iPqOKczmsWHYSDj3cOXYD37OY2Xq3dzGhvy6rssuKyDqm1/ScMgN87K0xzWGeyatXaRoWHw2hmrI/tKe5UIczFYTInPUhoUs/QRVHw7pkdplks1gizQ+xt/hvpRuJAoOsE0XzoFJ2o6lFXDyUp1pCUv9oGpSLjMMsbK0U56EBLkn3t2f2Veo9/3F6mwx+lqn0lndrLJL25LQNC5Qtbhqyns98SIr0kEwu//8654YoDJNbMP9w5PdSSgzlcnPXuWd+oWIHSz4rysLfzmpVcQBUL8ZpgN69KkXISQ8b7S7rs4qUrWhNQdKCKvnahwdZ+4z4BGVzEidI6Cpqx6nIgO3nyWKll1fQqzjBMSrxjH1H80EHNKW2XCh3PB+lPD09nOaE/sDDKnlgWbz9Yl2QVncPW1d6n+yhr6lN35Cz0pV4Hzxs5WLp5dIAjfJylWCPlr+ouIf8obT3xXu4/CyvW8vmydtElKruHZJ6HGh9u4G8frzN/7iHC29Yl+4mmwkWnNcRjgtBc9gnvaUmst82MdNdnI0bn1nSpM0oM+u7iOCWG2Y0DdueMUCUZByq7bfuJGktIjRAyyTA4FD2KAfjRdEKmhbtbd/eI0NyPclo0W0IXlvf53LcXKmkkySCZw9Wd+TC68Mhuc32Q1WR/eT/bvnl9kmRRWSMUlfjv1FZ32q/fPCclNGIynSDANRIpaBVQI6C7MWu4Js3fTuU2C3nG1b65Z8TmxXpq3VC0YJSYEuoudDY3t+E/Ig48trXYvrcoTl42oLbXxfuIJicDuMnB6gqFXKWr0G3jrIDmwnwWz/nB62KE/DNAXcVsXLSXilLYDhXVXeY2boy6iWlX0Wvk54/WO2Yk4M/ThUaBjE4/HehF6UEqIm+YGGi/3FosoOsHH+apW8HsHWDW9DAP/W5UpNhTn5W0W1t9m+hduIpzJn9rZZyEX9hjgJ2Pzo22++aE650DctJ1oGDZn9Vg6UFu9ucTNUocJZ3s72rfuWem3bg2w7zcxts7WQ7DPaiX7DfiAEPjK79+r2bCKIT5PQez6pUTIM9Osp6175wAwZQgF1HIANigMwNi1dT32NFzDTYwcknqlFuL2y0iLV6eEL+6Od2evjHdXr9/htX1DVt5caOS24PZTVJs+7yww7Lre/Rs6HTyd5ghE9uYVaPQaO4cFEha0N5vv/ikyMpbB1VcAYQg6RvkwaD7ZfvHF/+Qz863NyYrKfdwHq/cY3G0txQ+uSg4gl2cNccBdZrcbnmsn905NVQ5I0DG2aJWdcEoAL45N8Yy/L3klfaLDSnW1Nhnb+Y02faTtRaYmmK3zo3QDB5diJqOQYk5QHtO8nOVDDcUyFfO1Fvb8KglMeQd4GZv//Zmu3tpjJ6znkVRmwRCNs6JUPeUePxxXqs9/+EF++uDM+2Vc/UqjABN6JoSfx//6zFrax8Ue2dPQZulBbopNqcGuojyVtQ6rPfx1ZkRdja32U7U9ajLS76FeiFnWXi4l5gYCJSgRpZ1slxy0P8q179EsQEf+853crU4x48ZY1G+Lo629MhlS/R3tjB3Jy2U8qY+eWM09Y1ae+eQ7T9TJ9RyXpKfAhCDQqvT/bXgGBTkkKcoWDY5xL69LkFmeSx6DpNof2fpxl8Vf9H3kCjSfiW40AEBRWax8XOSw73tZHmnhQW5KwiQfOJPAQJOd4DBYu4B3vGmRTFW3dIvNQcODBQbSJTOlHeo/VpG4nTtNTYp0NPO4WIa4ykeOMZZfB74zThHQlvC0I6BUzYNiTAHMxsup6DFCqu75P9AwnXwZLWSI7oYDC9OjPKxSD9ne/NoreYckH3j/3HO5eCnw8FGPXDV/AdHUdqiaaGO4B0U4GaBJNOYCY51KEJxTz6+rtLDhlpGEkayDTpIckwQo4PS0T2k58xm4hDdMCtCSQ/ozz3XxclIjauufdASwjzto2M1onAwPNvSPijzNLoNqHHx/ko6hkRp4wCgtbirqEOfNSXIWVx1Br/pRg2MXNHX3TU9VJ+PxO6Vk3WiMBzLa5b/BXMqsyLdFQiSI330vlem+albNC/SS7KiSPidrOmzx69PUjFDMEBrHf4/awQUBeoGXicMnIPAcz/L431EWQnxdVEykp4eopYpSCv+AcjsYVJ049qJNjI4IqlFfh5J2wcn64WifnS2Uc9cbrC+LkLMb1sU7dDtb+qzuo5BIfF0+qIC3W1qtI/Q/VUZgVLe4fMwy3HXu7n273b94UiV3fJmtlr0dMAoBB/cmGH1vSNKImM93fR1rKX0KB9r6Bqx6roeO5BVL54wrXGGjt8+02g3TAnS/ABdjPymAYEQCAM8cX2SRXu6igLIHk4PcbeKxl59PzxkimaKCLogrN3zdf36b6hSJB8xwZ62B8WiOF8heezLObHeKsQpOkkiGGis7RiyTatSrblrSAIEJN0cACCXDBUGujhbXsug4trUIG+hX0vjvBU/fnODoxWfFuQsJJZDj04uKP3Fy1fU1aSg9fN0stNZtXausMWSoxzdgs8/z9d6be0eUiG6MD1Ia/L1g9V2iuHXby6QuSkHO4U0yZgGHk/WSX9+7sRgmZexB1ibQUHuSnB8nCZo2L6qe1hdQjdPV/vsYLndPj3EFif7ax7uUHW3tQ1cFhL95XrlIvEnbq2fGW6lNd2iEN21LFb0VQ5QzPdiQj3t+a3FQnL9/FxlOEZiOTMxQM7CxLjSzkGBJbyPA+cabVt+mzo3MyLdRSndXtSudYMAA0pUOBfn1fepW/27A+VW2D5gnxyqlKnWg4uibVGMlwbvpyT5awaOrhXa/euT/GxGiLfM3HYWdtjjG5K0lijetua2iuogqoqLk9yfZ8T52vFcKLIO76MV8b76/Z4u46VilJwWYh9lNalLNzvCQxKYUHLWLknQHBHqZu29w3qff/28QnM6r+6v0nvhnADtzGkasK+vTVA8bO9xUG8pgPCXCmRuIN5Pw/fXTwmxebNiFCOm/eqw3f5Ojv27XS+errG1L2VZdqUD5EHg4MmH54kCuyrVz5L8PARA5ha1Sj6UvX3iTK3lVXVoPy1K9lcB/8aRWilQkbxS6J+r7VP3mcHov9yVafG+7lIcch83VkPc+NMAbga4jRVVjjXLvtbZXTegBDozzFViKMzVfXqhxRZkhtqr2wolUYoJ47HaXq1n6NHMU9K5u+XGaRIj2Tg5UPLnv91aop/NGor2xjyvQ7McS2MCJHe6JsFfxo/vPjDTDlX1yANiQ2aQ4g5y2qx/QBQYIKzJIB8X23+w2M5kN9qM9GB1wz9/f7+KcBJgCt8bFkTLaPOlPZV2JLvJnn1yg8575lIa+0Ykt4wP1Gv7Ku2JDcmWmRakr8dbgu/nbCYuhLq5WLCHs+2v6ZBcN92XDz7LtV/dkKbCjNxkd0WHYicXsrY8V7ryFMbIyC/DGbwMR+5eyVBXNvSKMgrIERnuZU+9lW39ly6bOx2tlgGBl2tnRUjKG2C2pndIJqrMzX56tMZeOVOnvbU+LUCF3stZ9TZy5YqeP53uh5bH6IyENv/wO+dtR1mbvbr5vFX3Dtkf75ui5/vI6gRbtzBW+wavjk8vtNp35sXY5EBP0ZsQGXl4dYI+Q0fvsOJArI+bo/Pu4ixhokkJ/rZtb7GkxPH6Wh7jbzdODRYFysN5nCWlhtpvdpQ6aPSwTBoH7cH5kfLcuDhyUc8YCj05FMUsc1o/e+uCwC0pdXk524GqLvvlbRl2utyRQ7V2DkqUBLDTGRArxk/504ZZ4ZaYFqEYMuvpI3bj6xf+m3f1v0Cx8c1txRrUSw7GNC3Ynt5aIrM7LuYnOLjfz262qaHutiDJX5uL5G1uepD4gNCjSNI4YPGEgOvMIqR9R1eBVhrDlnzQZ09WCzmiYgS9RhKSdh3XicpeIR8ELuhEDDyzURmyBoE7mdfsUH+ZEmxrZ0doJoRB8F2n69Q+B0UvaGCo9wuhbCkR3vqZdA9Qx/k8q1GIPTJ6DFWBaP9ga4FQmM/y26xv+KLQdw5bFsvevBZJWMJJhk8dE+AmwzA4iaCBK+bGiEJGIgBy+eD1aTp4ncaPVQDmsId+cfvcCPP1mGDn6gfsVHmnZhEIdMjxgeZvmhUuNJe2I3r/KClwNbX0azaDWQCMfE5V9Vian7vNSA3U0CQ0JGSCNeTaO6KNgWEW7wcpOygsIHgM4YZ6jlOXALUvAiUKL+E+zvKSYBOGBrtLG52guXRyiLw8CFb5LYP6GTxD3icFHYjktEhPPQP45SRNBTWdGu5kiO5ISYf41LfDXx57rd01I1RrxN/XRe7iHNCHKx3UlKUJ3nrfcLMJmqAPfzlaLbRbsqjOTlI8gRM8PdrTXjpVZ9VNfba9qEMHDz8DRI1uxIpEX3vjbIPQUAzSNmQEaJicLg5JG3QViogoLycrb+61h2/NFMrFgVPV3CtVEbiXgd4u6sR9erjKViT46r725LWqrY2sKcZ//F4MxTBuIzFmP9A6VrE8fEWeCgT4uMc/t3+X64GPCrQ2KMYemx9t33njgtArupO8MwrNF8/WaTD7+hlhSrrYMwunhNl31ieKHoXUMgUIHiYUD1CokCQkePOcQAuvfPGF/fF4lcw+oaqUtg6at4eTLYzyFo2RbiUqVnRGGQSm4EbmGICCQ4PEHn+K9ekBduvSWCFbzEy8t7tU1AAONBB17o9uAkgoA9nsMWQYt52utzWTg4U+QlmkU/Xg2+dF13ztdL3oFPtZr67jFD92XGiSedeUcJJUh0FlUXO/nTrjGAhesyTBHl2TIGTSw3msPf2NBQJQ6DpS/MO55t4fXRmnQok4QCcNHjV7JzPKW+sJFBhwAAoF8YFDV4ppnUNCzUAaTzd1i34i88tpYTZnWrgK/fcP16gTCgJI56GwulPrtqW5T5+DmMzMGu+uqb5TCRfrtKtrSM8MmW/Wc2Qkw5ZfiPs9NyNY+w5aIxLE0EuJjQdLujSQCYJLvCa5gVpHEgLyT6FJvPg8t0VO7fdMd5hnfWtRrFB/kHDoXOpYliNKcUmIOQgkRm99A6P2wsk6+9nuEjtc0SMVqygPV0v1d1OyuDDRx57aV2Zt3UO2uaBVPiT8DBBkOmgURsT44+WdVtfaL6oa7xZ0k248iCbnT4gbXaFe++adM/SeOntH1FlCUpQOd1yEl20/VWfv7SyyG1IDdL5tOeeYEfrbzZNUbNNRp5t+Y2aQkgq6G+Lkj7lG4BA0w5ziNgt+cIv9u1yADVxz4nzsl2tS7Ibf7ReglOjrKvoY6+qFM3WSjP7q6kQV+Jw5KxfF2w9XJYkiBOjH2bB+Wpg5jx0jVDi3cUjPhTMS4QgSzSf3lGqP0+HLbx4UReqG5EDzd3ayz0s61U0qqOu2cK8J6pABgkj5sX/UCkvbLfd8rS2L87ZHN2Wok88Z9PYnOVKopEMJ4Fhe16PzbFKklx0s7dYa+cnGFBmLXj850B7bkievKIar1/zxiCWEedmTO4p0n+/mNWnvIcf8wakGWzcpSGdfY/ew9kxpU6+dPuEw3bth7UT7w71TREOCjfHG7+8QZZE4APDIWU9e9dONKRJAyblKCcYziOdy25xw0fK+uz5R3TRRvBEmaOO5XSsKKBTD9qFRyeN+nt1k4e4utmJZqs2fG2/bitvtzUPVypkQN2GusbaxzwK8Xay2ukMdG7pOzLUwfN9Y3SyQjf1Ox4B5isQQTz0nHz93nWdJMT42Iz1I1HHM+F45XWedfSPm5zLOnj9S4/AyKqxSnICh8dqpelG2t+/Itd9D9w51s/cO12iAHdqbj/MYKY2h0hcWFSjfHdbCR/mtAmDXJvrZzvONtr203Wrre+yp/WX2890lcobHGDXd3+Hw/cCCKFuc5GtP7CjS2vugsEWdTQGHfu7a30tivO2vp2vsgzNNAkqWxfvI0DMqxMN6+0ftdF2fBXqMM39nBzPisXvnyNCxpq7Hqup7rLmxS2MCaanB9smRanvlkzzR0XiOqCQyz/PKLZNELS1uGbBYP2d7ZF6UbAmyCloUD+LCPZXbMMfEvJ/3ne/+zy02ntpXoaSN1iaH4t+O1Ih2gjrItDB3HXZwlWlNgVjx/6glkVxjNEU1SYeDTTQj0k0c26LmQb08T+fxcrJkKIz5BIaroTyAuIMcUYiQqJ9r6hNliaRehlzDl7QhoFJRpDAUSQIUGeahhPXFo7Vql6PYBJUoPNRDxQUIFBuUJBf529LGHnOeMEaytyipoCDEomCmYsK4a624tkume0jfQidiodHG5zMuSnBI93L/+IMweIgCCSoq965KtKqrXwcqSeJAQi8vCLfx+lw5Ze2WU9WpRJbDGfSW4VmQ35ggDxkGQVmQLvXYa8Q7J+BwoHGQc/3g+iQFdhw2UXJJDnGzPx2uVMsZJ1+4g1OTAyw1yNWmRXjos6dFeut7fHyclRhBWztU2i1VGZxLZYAzfFmJNZQt6G8M/0+L8RbqNH7cGK0HaHLwMKFlBXo62/m6PiHDA6OXLDLIQ0IADOfzrtjI66aGikKFuguzEufrB+yvh6qEaHFIEMCYuVme5KuEB+oWwSyveVAIJKZLUiu6fEVtTAYEaY3/4WCF7iXMw0lSlLdNDbaKkmYldodymuWPQmcHxSwQOBAzkNqJ8X5W2TVsmyYHqT2MEhCcYpKeMDcXUXJYl6yDhUl+Fh7gbiFBDiWV65J9LTXax9KT/VXodfZftEBvZyEeZ5sdnFVM66CGQB1kn+DMStAG8aYTBqKDIylSgShU/KtftHrj/Z2FgBMHfrOnzG67Ll7qIMm+rnpuJAPsaR+n8Vr7jiTOWXM1yJUqSNPhSfHVgU7B2NztSDBAGvkeFN5qukeVlNHVExXHy0kdKuRt6ZhBn2IQlw4C8wEUKHQwueDDJ8f5WUiIhz13oMr25TSrqCYRDgv3UmLAumEG6qF5kUKb6GKwNrafb9IsQGfHgOgaAADsffYAUp0c6By+rFXkjeH+81koCEqbB2RqycGGGlx9a7/94K4pVgcvecy1QlihiOFXcb6+33xdx4vud/RCo+05W6/vISHHsBKOP1S0jGgfdZLZkwzEc48Uyjwn9gyxgdj3842pSlSYvThQ3KGO8S/2lNqUcHcl06xhzDMZ+FyQ4K1ZEdaeZKH93fQM4NgTB9qGLlpSaoi6mCir4UBMkQjSd6ywRWAM3aMvAQbWA3stu6JdcrNQXaAKgbQyLAoFZWKwixJ1TAgfW5eoWaiTFZ1S/Tpd22+/3VWmebru0Ys2Pd7PvrY4xtak+Ml742RuswotZjBIJihCaqo6FbfpznBf7H+SPwpZOhkDo1/YQ3MjrCC71tKCXGzbwQq7UNqud4h8uPv4cYoDvKvEcC+Hes6kIM3CLEj0k6oMNJk4H3fNdwGQ8HkRKKHAQLmMGTVUEOMivS0tPUTCJgA6qG0V1HTZqcZOJajQ7eiUnW8YsDnhXrZyYpAQ5DmxXlK9aeoZtQWZYTZ3ZvS/RRzABfy6ZMxOHYaJ3/ko125ekyY6JV1l1MvY6yRcsd6c92PsK7PCtTbokh2u7ra8nAaBcl+ZGS4JWozVdBYOjup9suYodDnroV3ik0ROgDrhxGgfO9nUbe9lN4kChTwz6znOx9nWJ/vbnHgfCZDMS/C1aRnBFhkbKE+mD4/VqtvHevUN9JIJH0AXRf+3VsQph0Fh6fIXX9g2VITah6zkQpn8GvC/QgiktXdY8vhccaFeonKdKm2XyS1Gc8xsMpP2+v4qAVyAEvX1vfbK95ZYa/ewPDX+uLvc9uW3aNZkW0G7VNBI9rceqrTXPjqvOECSHu3noj24PNVXKlVQNWFoMLOB9CwUbe6Vs5yvo4v66xvSRev+MK/Ftpyut5tmhtsPdhbZkkQfq21CVnuM3bEgSt3AW6YGK+7CmKA4CAnzkXT4zDAvO1bZa23DIzZlVrzNjnR3UJa6+rRnAWjwGvrayjgViwBEgKbsD+aviss7LSbIXWp2cxL9lPtsuGmW5O7x1GrvGrKegVF76Ykltn5GuDpBizJDpBb61KdFyiEbB4ZszexI+8aaeFuX6q/zeOf+EgFT20s77ImVCWKqNNe1y2MpxNfV6jsGlbw/+k62/J1Q6OOc+sb8aDt1tNTmRLrba68fUdcWlThyMfzg1qb6K+9DafJYTY9tTAtwGKImB1hWRYfArbQQTxXH8i6h4JkTZQlR3paWEWrTkwPtlukhFhrqYUkpwYphsluI9rELZe12oLJN7JayWodc+4e5LTY/3Ne+c32Sxfm4CBBj6J4OF92TpYuTbfGzJ/9nDYhjSHQsp8k2zI4QJSq/qtMmxvopmJAEgIZh8obsK6ouHLiYvKBZ/HF2q1RlqCKpJkHiQMegL3DwcTgglQqiwX/vKOmUKhVIJ4ZeHPAkCEgrxtFGdB6r4iLE1VncwjAfKAD2TylU5kTgjNf1jkjzGdk5Wqske1CHSFrxQGBDIG3q4zJGXZNzha0yjoMTeiqrzhbPiVHgig9wVvLOYUKyAx3nSF6L3TQnXBue35vo42Y7yjocLbuBSyoCZka521PvF9iECWNs0dRwK27o0UKmBU/ycL6mR4NDDGyijDQ31svhz0FyjzJTlIfuGwMb9LZRkbl3TrjUorqHLynwccAz18FhW9bYKxnG1Qm+9qvPSy0j2ldoP/xxNiOUrmc/yLFHbpqkwoLigA25NatRCBHmYBRRJMckIjiKO4251vZVdCsB83KbII3rTTPChBZBG+F5E5A+LWoTLYp3wzMIcB1nE8aMURs9u65XP3dqpId4mASU9vZBm5ISqIKH53S8qleFFkFy3aQAySSj6kISjhKXv7eLrcvw15DYKyfqbEasr4IahwNdMhA0UF4QiiQ/F6vpHVFBDLKKcpSPl5PFg8KEuGioiyF8OLG3LoK+cEWD2iCk/t7O+jkM58KVRZkEtAX0F3QsI8JLn5HilwPxkUXRtrPI0a3BkBJO7ap0fxWrG5IDlBAeqOmUhCgHBFQX6FSos/H8poa52Zm6frV2SaJRI+Hd05FjruHZ65PtX+miq4lM8p1LYq2iY1j+EdFBHnKAFi3Pz0HHIRDvLepwqIOEuVuYxwS5AVPosRZYKyToKB+xftnDvEeoazhtc8jAO54c6lATQ2GOoM8QKF41qCRxQDPbE+3p5uigBLspDlBskPgjE7kiztdKOgfUqoeCg8kmxTH7jCKD98L/F7QM6fNRKB44XmnrlibqID19vkEyinwelJyy63rszmmhogww47HzZK09uDJenUzoo3QQ3s1rllkUHgskrLOjPOypzQWaa9i0JM72ZjVIjYtZpIVJvpqhIhnh2VCszY3zktAErXiKaYQYmC/bl92kDtHhkg57eF6kEonWwVGtReIAtCQuqKvQAlBVenJ7kehOdFl4P6hpUeD+4fVT9u17ZipxJzmh8H33cI1miqbG+wmIATCZFOomSW0ckLeVdNjh8w2WHOtrpdVd9vDKOCn7kQgSB1bFB9gzJ6sVz4iJ7L9wT2dRXpivOVPRJflRwCeSks9yWq2hsc8yEv1Fa5wZ7m77yrutoqlXz+PuWWEa6CcJpKhgfiQmzNNumBSos+L3e8ptTrJDxQmgifXzZYeAuIfEabnkfscIKX36s1IVGKgE4RPy7L5KnWGfH6uyr61PUTKy7Uy99rKvt7PeeXKoh9YItD6KWuIRhTA0LMCrC3W9VlbTbT/dkGJvZzUqns+L9VIxeefMMFE778gI1j1uLW2zjGBXe/t0g0wX2R+AGqgGoXSEKiOgG/5DhwrazNV5nJVXdtrSmZH24o2p9q8mbf3WnnL73o0p6rxBPeSdMRQLQr4k3lsAACpcyA8TI4iPmMfSyUJkAfovRdzHJ+vV8QdQoxvNecWMDdQ3GNM7K9rlE4EPBhK25BCRfq7qXiJ6wrnOcDCUzV8eKFc3i+QxzNNBI8StnpmA4q5+nd0MOWMqytnAvCZrlZiF5DtzASjE0aU4eKDI7r91upB3aMjLZ0WqiIJqV1TXbU9cF6+OPIn0h7uK7Cd3T1FHBpUplC2fO1EjxSUk0DGzmx7uZn9Czr2p2x64IcNe3Vpovv7uFhToJnlmChwKEk9XB+I+WeIwqF6OV64C9ZB8YH9Os4xG6cT9YHG8NQ8Oq7jlsxDLiB0UfnR6oQbfNyXMfrqrWF171h33SIcOdce/PLPDnvj2OoFDdPDJmV7fW6mZrltnO+bVEKJIDnKxVD93Jdvv5zXbjn0ltmRBvJ3JabLf3zXZzooh8g8VlbdlhNrj2wqkKghgypXi6ybQ6aOiFjua32LxEV5SqNLPy2qyuoZei47wUl6ELPEHeS3yWsPU8GuLoqRAyNmOQAuzO0iI3zM1VCDLo6+ft8UzYHs4fL+IixT0dNahVMd4u1jF1TgQ5OJkP/ggT3EOMSA+07ffybaFU8Ns274S++Ytk1QEvLSzzLy8nDVnCN0uOcRDMRb/MAncjH5hBbVdNiXGV4AVfmbQx56+a5JUzciVyFeQ731sQbRmQO+d7FDoeze/SZ5QT28vVXd6c1azOmCAcwjyYOtQ2zZgcxP87IPDVZYa72f7D5bYprUZ9tKNaf/ZnY3UH+1VgCWxQ+WFw2xxRrAShoLGPqtuH1LHggMckJ0pewZeQGwIOGwWgih/DnoWF+gqnjPIwJRQV/0OEGuKFsfhMFaLHC3tYE8nIUN4MjBsREFDV4JqHr1uNiWJLxXmJ+earLwTSckxcsAm0WvpG9EG5WfTYtx5qtYKarvtg9MNmvGgW3CkqF0HyQ82JiuYII1IW5DgyaaEdw0tAodJTLVImB9bHqtDFr44B3XHiGOIkKDFwYFm/76SLlEAKDRw2kXpCR4qiSybhuFV0H902RvaB1QEMeuBIszKJF95WvDM75gTpkMO5I5uz9EqFGpMiQPBk4QElan756LwMWjPHauxm2eGiSOJOZ0c0j0n2NHSTotNCNBcy5xoB13jnUM1NinWzyorOoS4orCFgg7IKJzjnOYBbTIkAZND3O3u+RFW2DKoYM77pTN1oaVX7xAzMX83RyJ+uLLXtuHW2TqgmQwuvo+DdWGyv3V3DVjP4KieNwuabgtJKIIBKEyRuKFPPjPK03w8nRXged7QqZCOBF2Rtv7FL6yitV/vmiSTZ/i3ozXiZdO65TNNSfQXf5YDBO41Q7oLoz3lJEsrFhoPvgeLM4JsbUaA3EJTApztZBVqar4aoEV+k0KDw5F1yZAtFDKSIxJgVJJIdmbH4eNyUYXJz3YW2wunay3Rz1nrmXVPF4ruHAWOBvEbB6T0Q1FLx+PHSxKkwBMS6GapQc726wMV9q9yJT6xW5LDIHbbshrE7V0xMUifHTUdOMnHKrq1vlhb0EeQZibpZC2RUBIHABha+0YtLdxLUqCsgZQAFxWMZ+v7NfyIagiHC/MvHBYcmiW13XawsFXcbwoN5qRApZEypLMFRRLp1ZcOVGvPA1zgZsuF43Z977B4/iCd7+wotDMVnfb20VrRsFAVQhaZGak/PThDc1Qkgm7uTqK8AQZMDnbXocM80nefP6mY89MbU3WPDKpTHIMAclh1XKX6MB/EcDxzFPCL6UgwYPjYijiLRw68qMOWJvmoS4ssMs8HAABPG0QUNqUFSi4TJO3+xVGip4Dqcg8cShxeF2p6ZI7GYGhpQ689MC9C4ACFBmp0X58Zpc7i+eouFX4nyjstOiFEyQnS0MRFEoy5E0OstqZL6jYyqNueq3s5UtNrJ+t79B6/uTZBQBGFBs+N2QrWMXHwUG2HjBspoCkQmHc6Xttrr5ytt+r2YR2exK0LDQNKiJjNaahuUQxnaBKXZToPyH/fMztchQaGYxhqoWqIVCQd8+ruEaliobDDZyKGs/9LmvtVoFHwAtj89XC1EjhfJwqfMTY9NdDumhMuym5d76hcgzemBMhhnMSEbun1M8NtQUaQ3T0z1KbHeAuUgo66NsXfYnycdE5MR9J9zLVCxunox4R72Z6yLpsd563ZRT7j2kmBEtBA3eqxD3PsD0errqpmDdq8RD/FVzyXSKwpjIkD0LkoViYGudsLN08SZSc5wc8Wxnr+S8UBqJ54wtAN/9vnFXYmv8WWJPvZrFhve+9EveWUddiH55t11nM2AoBh5AgQABUIURTHrOUVoeeZCf62+XitOsxRXs5K5KCwtg6N2InGLhUDBW2DNjHQXRTX2uZ+df/ochAjoEwBUNT0DVpDc7+dqe1XnP/5+/lW0ObokP7xSKXO5p2n6628e1DxiU7Zix9esD2F7fbCrnKdLcSU09mNygHe/eFyGYfi9TAyfEl0L7pf90wKtSlxflbQPmAP//6g5jT//sgcqeUtiPaUXxjDyqxVEkipEA2M2oGybvmwfOe2yRJMSUoJEkUKhsTJii5bEOslIAAvJ+JAHCa/sZ4CYO6eFKpzkv1+x7wIAT1IRB9v6LKDVynGu3Jb1WVkD+TX9dhdM0PtdG6TPfRGlhQon79xosV4uqrriAAEXYaAxAQHqu82Tt2bN/dX2XUzwq2utlsmpoAO73942oLcxqtDcrqxR2fqs4/Mthh/F3tobaK9fLJOtE8KFqRlUbcDVADcQbKbvCirqdf+cLRS+djKqSGiblGknmsYsAfnRFjV6fNaW0gKF7YzO+tm8ZHe9p1lsYrl7NN1iYH26NwoCw52F/U9t7VPAg5g76fzW6ROyZxWReug9j9dY0wEf7O9xGGn4O6qzwPI+b1VCWLUILWbluCnQmDsOIeSHV979/J4m54cYI8vi7Pp0d6Kk+dqum1+lJdlhrhrHnBhMmbJY8SiGDv2WouP9bFjNb12x6ww5UqeE8bLZ2pfZbedLW6z+984Z788UKYB8qPVvXbT3EjtCc57zhDOjcK2IVud6KezEC+avY8vsNszQ2zS1Ci7OT3IfrCr9D+3s7HpNcciyIz0VKJIgrfnXKNNSwmwEzlN9r3rk+z1E/Vqp3P4soDzC5rs+qWJagnSRmRhkMCxqPFbIDFmeBtddNr1HEgEXJDkLRdabcPkQHGmlyR4297iLqHdoJrIk6FbXds3qEqXQJ8S6mGtVw3sqGjhXDM3cnNakNpv/FnX0CUFlGUp/vbKngqLCPNUwtPdPyot9hmpQaJ0VXY69NYZigbdYlOFe0O5GZEUJGZMFFhQF1BPYRCpsKPfTlf32MQwD3HJURt5YV+1zUkLtJkRHvZ5caeSaJKMzy802dzUQA2TVfUO2AdZzdY/MKo2cXCAm4oRgilqE1/K+mJOh9slCTHoL88XDjjdA9Cfi198oc4RvHa6DnQomCn5+vxoe+1MvbTooZfQcmbT0rWAj8qcB9xAZjNAX3/xQYE2y8DQJXGLScwDPcZbcoCLDtYdhR12y6Qg854w3mr6CdZfWHXXqMzDytpGFGjoahXU9xiLE11uOkSgTBQEp2t6rbNvVCpfW7NbbHjksj28KEpGbAQAzIDoMDEHMifCU7MYUyLcFaxxAkaZCLWc8toemzcxWMkYVATug8Dc3TciHj/oMFrefz5YpS7QrsIOuS/vKeq0rLxmu2lJrJ0s7ZDXxbqMAHv7ZL04siT7tNzFgR+4pMKFZwwFg0TkQnW3lIFA03g2JFUUo/BtoT98c060/e1MnWYyoI6gukNiC2rMuuKgIwkDYQWhywzytJzWPimmsM74HHQ6SNAau0dU3BOokHlkHuGuqeH233mtfOGMulDTor2kFIOx4+dn6m3ptDC5p/9kQ4r9ZX+lOnBorle09FnW6Uq778YpegbbclqUENLJoMADLTuR3WjLZkZqz2KaB6WCZx7r62wvHq6xbyyJsY9zWzVgjAMrMqEg1wxgXxfjbw39Q/ZJfptjTYd56JDBM4bnCLrI4f7tuTFW3zdoGEDDXX7nRL1tmh5qz3xabNHR3trnQ0MXra62SzFrYQxdhKF/ejzwmUkaoGeCbtOVhcJFkZ3b0K84MDvE27JaemxvYbvWUH5jvy1O8LFnd5bbapLXKG/NBDDDE+PrbFvPNtjSScF2fWKgFXf2Celtau7XDAVFCcPFxDw6HOwdEgFi7KKUACtuHrAwH2c7W95hk2J8ldQzu8J8xgcXmkVrLK7qkvwlEr4/XpFgf8GV3MuhdpUZ7qZEnwIP6o4HRmmDl7ReQZF/9G6e6JODDEanOroGxGaQY6/x4zXYiZOv+4SxVt83pKFOhuYBSYi7JNoUQwhcDI1c0jwTSSQ+G/0XL6tA4XcjLwviDa/8yXXJ1j16yYYvXbGs+n4hlAAZ00I97NUzDUriAQ9Q/yMOsN+hZyydEWFTwx3vmXeEi/PQ8CV1BPi8D06PsCe2FdijS2JEUSHhhXZ3+ESVfXVjuh3IaxFl5fqJAfbykVolDFzM53DRIfmy+wWdj+KVZIhzgASTPcv6IXYEeYzXvn18foz96mCFUNuzdf3yFiL5w+wxv2VYZrZ0qqBsQAOEQrG/usPiriLAxGKGfwFWYBBA2aOL9ebZRt3n3dMi7L/zmv+n43q+0JJI6OX9cqHR7lsep5mi7y+Pt59tK7blU0KstmNYggenj5bY9746VyDhq/urVaTMSfBVNwzK3qefF9rdGyYqt4Bff7yuW0Uus3c/+TDf/nj7JHvmSLWtSPO3s7V9Si5J+vHIWpWAmtWQPG3kc3TVMwaTPfIVDC9fP9Vgf7o+zZr7hvU1LUMj9tc9lfKkePGzYouI8NYsEmdnWUGtPXjXbFuX4G/nW6GzfGFnq7t1hnO2IFSyLa9NuQcUOAbTP8trl3jArFAf21bSajuyGmzNNAdAiHv1n7YU2Z0r4m1lbIB9b1uBRVwF0d46UislpTWJQXa2sUvyzjk5DZLIxT18QWqgzvuV6Zj7/cPCPSbY3w7X2LL0QIGVDJsfz22yKckBDgXMOD+BWB/lt+hd5Vd2KrfgnHrxzin2xPZC83afIBAIIRxAMby9kJX1c55gtb3DFunprDj97bezVYTjsbVqRpi6eJxnmcHuFu3laj/fV2Y/WZpgrhOYzxu2kctXLKvRoSII7ZVkn0KwoLFfhRZnHJ2Vh2ZFWu/FiwKDobTflBlkv/60WEXU83dm6uegGsUwN++SDuXkQA/7zf5yW54WoFk75nMxZq1o6rOami5bPDNSEsbQTtn30POIpXSPM6J87KHpEXbL30/ZLzal2kd5rcort+e12/79hfb4fbNtf36rvDoANP7yuWOehmLotgVRAljIxShacAbH6w1gFG829j1FMxdxgC4NZoecNd9fGGuPfpIvIRJymDg/Z51d8d4uKj7Ijdam+Vuoq4uo1qsTguyNCw02PcxdXSpfpwkSx+B3UwAxBvDjZQn2kx1FdvfcCPvqzMj/rGJj+fOn9UBR0sDg5bOcFvvKnHAl8LTPd+e1aJGgsAONAiUSEjFkQPk33MA0X097LbtBQTSrpE1D4qFX1aJwAGegkpkFAjoJOWhIiq+HnWvukTQjknU1PcOi5bRcpQvwwilYQJbpOHDtu9BkM1ICFaQoenB+5GLACWlD5GEZHESOk8RhR26LNlJZSYt985bJ+iwk9WwqPtvBcw1SFAJ54nfiQk7rH6fKxGAHlYsiBHlYnhGzCVAVHPQMxwL9UoYTtAR5WDozIGO0Gt/aV2k/2pRie0u6zMNlnNqNtEzjAhx6zQy5k5zCYf5SS9/ffZyqX1BCOhjwAKE6MHQFugg6kp3baLevSrGTJW2ia+UXtdr6RXGq8rk3BsrOFbVaeoKf1bcNmLuLYyCfpOrkuXq7c1WSkE6chB9eHS9jRJIH6BoojOFESkDL8PO0d/ObheCT2AR5OKmzQeGwu7Dj6ka8YkuTHYo7IPtcbFiHv8F4yZWC6FZ1Dit5ge4GwseF2RqblCGq9w5VWxQJ0IhDNYdZFFrgWObwPgmmFGag46BTBF6eIc9k+8k6mz85VHxWTAIZDD1wus5WzyNhGuNozdb26X5J8NEy57lxuCBxyjwIxUZGkKuU1racb9az4gDgsAgNcNPg6fzUQPGtvV3HaR4HVZXy4mabNTtWX78+I0DI7RlmFdr6RXMBkeOwhorHZ5kR7m5FbYOWX9+noMkzgqfMADMdm+c3pth/x7Xkr6c0uFhR12OPrIizN47V2qNLY208iVH/iL1/sl6818hgd0kV7ypoV/BlXxOMV6b42fRQH/vrqVolXqfym23VjHAV7iD1FOgYPqE2xWdODXQVPWGSv5eVdPVJXejOKaFW2jWoOMD+hPt9vKLXCqs6LTLEQwOerEsKyeRwLw2RcthT/IEwQZ9i7gJJUhBYOOI4+u7IbbX+wYtWlFtnP31gtr19uMZWzwhTIlTfe9F2n6q1u5fFKXFhrUwJ8bDRK1fshWO1eoeg6vD+GUTnMKEgXpbiJ4Uc5p64mE1gjoGEHDCFpCUjzE3dAwYfn7op3d4516TnBfWU2MisBhQBVJrokOBwzKFDLKD4BslsZNanvkeD2Z0Do5aE9KL7WA04XjhTaV+/c4Ydym9REZ5X0Gy3rkgS6k9RQHw6hbFcrK+V1fdYiL+bQAnidl52vT12a6aS7c+OVdvjNyRboMsEcZ8xu/N1Hq+1PHjpilS5Xj7XoIRi/Di8UZzUDYA69crJeoEnzGusmRio/cgzIYGjqCxpG1IyzhrACZqOmKPIcJerMYU/CkXEC86Ml3eVWWSElwoKVOEwLWR+7UuzM4As6GtIXjKgzhqg8KQz/fquUiUlvCN8k9r7L9nuY1W2cWm8vGCg6UKB1IxFHYqH10gUI9gX80OHZwMxhWSrfXhUz5i4d/uMUPvTZ2UWF+ll1Q0OpR68WOiAcc48s6PcKvIqbPnaqUqS75oeZt0jFyWDLD8BXxfLDENpzK5SaK/YslgfK+oYEHXXz2OCnhFr2dfNIdbxyk3/Z6kUX14L/nxCnZfi0nZ76o6J9udd5fbt1Qniw1f3DdqLB6o12+fv72IPz4uyl07U6f1zvrCuNqYH2NKEQHv6cIVEFhAHQOEM8IK1Ny3SQ+8AYDHU28lmhXuqy5no42GN/UP2QU6LPTwjUsUZxRsFIAaZOwraLSu32WZlhkpFjrOmvGVAgip0yhgCX5PuJ64/HVO4/rAlVkwKlgJVRoirbctuse7uYSs9cd5+/7Mb7bfv5dpX16cqmYYWjQnkhvlRKigRgbklNViF9pO7SiQmABiLdxiflUKIs/WeeRHKj0DwQaoxtMRAc3KIq717plHoPw7b5BjHi1rtJ6uT7Nmj1TpvAS2qOkc0g8b/08m4NSPIjtQiyf6FDY5e1lovbh5U8Z6TU2+rlyTqOfMz4f/ja3L+SIF9/9GltuV4rTy/LmQ32MblyXIEP1TRI3rUp2cbJVOdW96hGUTus7ahzxpqWu0bd0zXHtp9okaUuXA3Fwt0cxJI0jNyyTzxB7t0xaYH+9jfs+o0K8EsG+ufTj3g5Pc+ypPqEzOv0L+gN/JeMeRlX52t7lW3FNA4KdBF3RBAlPQAd9td3ql9AlBQqSH4a+3ZjwssPMIxI0t38o6Zocr5ZOhY1SugBq+bDH8PO1TbqVyBAojY97OXT9t1S5J0JiNHT45x6Ey9QEjyNihRuI3DcjiU32KhzLBVdCpfAshSMfGFWbyvi/WOXrSXD9XoHf9gVaJ9950ci0eWv7LTvrIqQTN/108LlXfYbz8ssNYzx23jI7eoCw+1qnPkor1zlVJIbMD5HUCjuGPQChoH7KEZEZbd1qMOE4Ul7zuvsV9sGMSBPr5vyn8GjWrhX07IXIZB1jB/N1FgOCTfPN0oNIKDHW1yZFwJFDwcFnprx+A/jdp25rfby+frlShz4fCISzPmWlBP3jxaJ2oVFAUOWF4cLsCHajpFy6KQqO0dEfoDZznN303B6Hh2ky1LD9IhwEak6kcRieSRAx3OL0NNUCw4RFjAKIDQReGwEbdx/FjLjPW1RXNjlRiQsL61vcje2FuhxY1iDhdVKgM7FBo//6DA5if4iHLx9r4KIW2otLDZGbh+bluJlFg4SPEB4X6gGWAANTnCS4nC4eI2BdevrU6w5/dXWWKQqxIwvAYIsjWdw0rYaT3C4cV1koSbZ8xCxGCIwxupVlQMFsZ56SDefq5RLeSvbUzXQlyYFqiuwtc3pAilB3Utbe6T9j8u7szB8JlJmuk6IYM5ZWKIhsA5fKF65DUNWsvARXMfN06DzX879v/TgS/v6dfwGvQmUAAKDWY8uEeQTwIOgfdkVZ8SSTok3Gcofh1jr9V74jOCllA40l597WS9EMLjha32qzeylAzyDK+fGymUA2MmNKkHCbYXv1DSQisd5BylsPfPNWvOhaSAa2Kwmz24Is4mhrrIdAeU6HxJu/3o5lQNHpL8UEAjdQjSkBzmqQKWZJRBWjocHEx000j8SPxDvF0k3YoqBnQiklwU0rYcrlbrFz4nBe6clEBbuihRQZfAB6WCP2cAetPUEKGiJL8FTQMqGCta+pW8k1zyM0m6X/o0X8+RNcznpLvwf/qa98fjmq9hKD88xMPyW4ZEa0N0AS40B9H0RH9LRhlq3Bg7UOoYii6rdlABpMiT3WJ/OObQDkfNY1Kiv82P9LZkf4afXey5zyu0P4vre8TN//PeCplpHqjpsE9z2yTviKHSkig/0VCmhXjaZzltdvx8g906P1LIN4cWhwByoiDxCA3gP4E/CyZSUF5wcmWtTwzwUMJJUc2VGe9vk6ZFa6bBy8vJnnn9lL2wo1QDvVAgSQTphNJGJ8H40ft5chxn3b2+u1xAC/Qr4h8c7d+8lyvXbjo208JcFeNa+y7ZJ9mtoubwdZ+db9bXfG1VvP16e4koW/we7oHCF3U75lbooCAPy3pgv/M8QR5PFrYqzkK3wzGXwUm6lC/srrSWln578LbpohounRhkD8yOsG/dmK6C/KPzzfKoYV+hoiT1v3FjNHhK5xchhBkzokUpkoeMv6veResQJnxjLLd5yJ49VOVA8caNscqeARXp8MyJAyRH8NVJrn6+PFFJEfKwyL3SgWAAmDhAIgHdNTPIQ3Mq0FqQuoZe+8yhaoefSl6z/fjvJ/UciUl0Mpi1Yr9V1/dKiANZcBJ0nilJO0XkW1mNQrTppEMLBSj69V2T5LKMqAFx4ODZOnv63ky9YwChD880ChwpZe4twltUnyVpgeqk1nePKrFdGu2rTjL/DUCUn9Ng+0u75JmxMNHXkqJ97J39lbYmPUDrCuQfZZ5bb50jyhTdAMzQ+POmzkHbNCVIzwda2enqPs0uEVMBILh3zrdwLyf7yzvn1H0lNrK3Fj1z8r+l0NCcXbS3pacEWm3PqAX6u9qzeyrs/bwWdc3ZR9NSAwWqUTzjRXIqp0nnMckUMy3f2lao8xqgcVJygM0K9ZZyF4nfczvKJHu672iFBBb+sK9CymmY4/1+b4XO2uq+ARnmXWgcUneTZA352O9sSlEHmIKctcRMB7HYbfxYO3S4RDNbdCunBnuqCCUJJJ6wd0lgOaugEcZMn6Q9GBfvb799+mPNMbFeFkwJcxgK+zlL6Ab6zdfeOCcfCkQoXtlZplmdKB9nKenhKfLd546ry81M4mKJ5CDFfEXddgxdASGgo+K/ct+CSHviwzzRm2TsCaJ96YoKGLprrFtUF/l87IdrzKE+RwykgxiXECh1xTsmh2j24mcfF1pleZvdf99CdUMBd76/KN4ev3WS9hZGy+xbqKco9cFiYI6Bjtw9M8JszsRgmzglSkUuz4vimzgweNkBoADqvbKvyjwnjJNhXtfwRTlwQ3EGbKYYTAtw0KdfvGOKaKnRIZ4CRD/IbrZEb3d1rhZF+qnjuyklyAqqOtVJ+PhIjebSfra1SPRUwJBHnz1+lbI0webNiJC8dlSwh5WVtinPBIDmLAKMXZfub0UtQ5LTBRygG0JH2t91vH38g+tsY0agilnYLtt2F9qf78lU3Enwc7J3TzZo6J9CY3Kcv4pDcsGC8k5RdGGDLIzwlRkjcYDnknvonGYy+L4bMgNt3pQwe/dQtd03P0LS/ij7TUoLsq8+cbeEf6BRXmjuV7HV2NQnRVd+zqm6XttR0mFZ1Q6aPF5oxLa0MEcn/bevnlLsB6yW2eXPDvz7FxvofLe2DpiPt7PkUasae8UFJiHDuAuUnITvdGm7JQe5ic/OhbrBiikhkj3DEZqLhPHIhQZVhAROHDxJIKo7hyXdxuAylxxzQz2VBLA5SO6RfIUriCMvC+1YbY+Sr5moSng7TNQoakDLKSJoNeG9MCnaR4sJgysM9UA6aLshs/be1lyp/ySEeMgQh+AtM6jKTnksUASsSvYTsk6nJinASSoxaGovnx2phcuw8JLpEUIX2big4PDI1y+M0aFAJevnMl6yrdv2lzooA3vKrbqxT4dyeoiLEt2EcC+bHuJp7k5jxMtlroMhYYIbiRdDdyTvm4/V6jDlUNw4JVh0EgIrSdXZ+gHzdhqrhT5/UogSChIINg/JCpsD/W9kHDlMQSsZpqIj0ds7KtUvghkF2bmcRhUA+/NbxMXn91CkMPzN4GxP74j9ZVe5ENnhy1+I3sJMC5rbzFlw4Uey4Ve7Hd4WzuNU3IBo4ZvBcODuUtS4unWIwFVn/gGEB6QBgz1UM4L9XG3jyjQN78MFRXwA+snw6GX73op4rQVJx/k6SaYQZ2W6GSAKFKwEUWhWXcOXLMhtgtAYDji4oZMS/GxHfodQLy6cyrv6Ruy2KSEaBOS+QZpiQzyF/OKn8vaFRqEqjT2joqDNmhgsBJdWLQGIw+GB1Ql6H4fyWvSMX/8421ZeVVmCZocEMF07ilyUz+DXo0SGyghrl87B+xealXihvsX+2HBdknivIJygGRTWCDX8n7pufStb9AIScBK7guI2O1vSJkWx7y6JE3IL7e1ITrM6VHTlQHspYm+cHyUjTAADBA2IA0ezmxQ36O7V9A3ZC8dq9PzuXxoj9BDgAtUp4gAGmswY0TEoauwV776gvVcdxX2VndoPi2ZEWKgHLvLDDn8dr/FKkHGPRnOd5AEPD2QoD1b06GCFBvPbfWX2xz99pu9DDhuKJIkyvNycc9U2bU68fWtDst2WGSyhCnjlHPAoEUGxWjMnys7V9dnpym5bPz9az4rimf3F79+wNEGxkiQGRR66iR9uy7ZbpobYu/srZZAFMj81wl1xAgUTDjDUaXDjprCgQGGeBeUnqDTQNV7e5Wjxs6buWRglZ3CUffycx4vjTuudQ2vFrEiheCTzuPxCsaIbRBzg8MQPA+rU4unhOoSbm/ul7vdlvM46V6uiHslfVOMocD4822Rv5TTqWTHoDt2KApwEjtkCDNJAc1nbdD2O1fTZ8id3CEBxIyHxdZarMUqCULdey2qwnMoOzdTg+xHv5yzlQOb7UsK9NEsWFOBqa5anOoxJL30hNTs6Sbyr39yUrjWCT0UC3jYNPaJoccZkXai3PxFv2/pFWeKCIoZPCkXSY/OibWp6kH2S26r5KZIUlLD6B0ftmwtjVKCQIHPgR4V62I5DZdqHvz9Uaa0DF7W/KRw2rEhRR9fTfYKQU1QLcVHn+/ZnN0kg4L3NZ21dip9iO0pi9V2OzwKdBaUzdcTUtZ6gLio/42/HagQ+QCNDre226yfazemBlt84oPOWDuD/Se42/hkImzBcD8Bz4ni5PF56+kbsD5sybGKoq9bIoXP1NiPCXbKmUGa4vr4u0R5dHCPfGM6WxVAi81t0XqAYSdL+gy0FovZ9ZXmcujurFicIrECwhOv1A9Vacyg9ktzW9QzpfN5R3mZ9/aM2Y3KoOvCHK7p1nqFyxJm+7XitRADu3pQpQO/tqxKpzIrsym2xB948Zz95/DnLaRwUaAY4cu2119qiKB87sz/H4udMs8dWxckglxlHBGqYteC+2ocu2vXzouxQaafmrh7D38l5vOhwiGbE+jjbnTdkSmWSuO8zAfO/a+2td0/YrZOD7YP9lTpfA+h0hLvLR+j6ORG2MMpfYi8MSVPYkO8cLO5QDMLPanKIm/1hc4Ek7pkFpMPEOf7rdakyysM3Bw+J/v4Re+iGDAmiUBiT8EJ/JmdhViEx1Es5D1SmDYvjRNMuzK0X3bh7BGEIVzt/qsLOF7WqgAaphzr8620l9syJahVCxPkHnzthfZcuifqzITNYjAvAY6jDnQMX7aOCVlv51Od6ZhScAGnfWxBrxxq6zHnMGA3043fz/R1F9viKeIvxchHAAVUc34nntpeal4eTGAKsjfbBi6JBkQOQd738tdkaxmYf4bcFhWpPSZfEb3bvK7JfbS8Vk+DOKSHKA+r6BzWozlkBDWzxggQ5sU8M97CD5T22fGKQxFmeXp+uMwYABEou1L+3X9tvq+P97fs7Cq334iWJBd00M8zu+soK3Q+FNsP3fE7os7BT9mHuW99ne7Zl2c2pQeY24VoBObn1vQJFQ0NQvYKud1mgI5+JYpU48NzBatkp0OEDEL/5+smiaQJgQyFkpuXpQ5X/vjQqjPpQ36FTQVsc/qyCLi+voN3umI5fw5AGtUlkoVTR+iYpJrhyAHCtTwqw3xwot6aWAYsJ9xQqwGENasOC7Bu8aE7MMng6uMQcMgQEihKcLVdOCxNCAUpf09grkzmGjZcm+iigoDLA35NknCttU3FE8oEUHCjYnquDoaAt/FyKmVPn6mzVwjjRaCamBEqajaDOBZKFrFvbwEXzdh5rOc1Dcp9GfeBLDj/Ot3DVeRYkpAxD09KERwwlhsWDE+WD8yKtb/Sy/fK9PLttRYLNCPOwz0u7zGWCQ20Lb4b5E4M1iHnwVK3FxftJCpBOCQkl9CIGa984WG03zo2wj0/UiaaSGO2tjQXVq3/0shJw0BvQ+cbGPls5O1JtTBJaOiYkA58er7MfbkiSE/jnWQ1CQhlQXxzjbWcb+0T9Ienh8/Ae6fp86ZxNsn33oigVcEuSfFXccVBmhrha7+hlockkj3Q9KPqggjHo5+npZIlKGB3merz7d/eU2W/ummSfFbSra9DXd1HUqN6BUVucGmgfHq6yWxbFCNEjaSMRB9EPAYG+fMW2nG9R0GdjgnaQ1ARfVRGB8rIjv01dOJ4fQ6EML09N8Ndw77IEbyvrGJbBI7LL9y2JlmIKhS0Xzqh0ILhPKBcUByRJqGHA48ZBfO+BEps7N04FNYfS5CgfKUdRxCUEuNiunBa7eUao1jcBfeuZBhlT8rPjg9yvuiKPsbz6Xt0nbV/WFBQRkCPc1ik64HjuK2pXERwf7KlDbvNV6lZpdbetnBGurtlXZvzv5WyufznLlqf6q1gngYUexgVFDDfke2eESeGD5Jr9CM2LJJlnyD7FkZtD/aaUIHtyT4nVN/RaUqyvArhccsdcq7kO0HUQNUfBPsai/Rmi/0Lv9KOj1bZ6VqSeKWIIZ3Oa7JHrk3VY0tEjwSOhZw3gKM6sQmtTt123MF6eMCiy7MlpFh1mdlqQkh0483v2F9vN6ybatgOlNmNKhIZBz1Z0aGaKRHdhlJfV9w/LtfxCU78GAjuHLyreUfCjQsVnhcol/xTPCdrv7A8KY5JXFGO+vyrBmgZG7ekP8u1r16cIVd1c0CJaHEXT+4eqbMO8KMWPg6frLDLSS9QsCiT2Iig9YhO/31ZqG+dF2o4zDZqlSE/0F82OZw8dRTNYE8bY60drrbGh1zYsibOihl6t1cVJftpTnx6ttqdvn6j5uH3nGjWoimTvqnh/O1rXpQQaiWwSIzqrUq7qdhTFeeUd9uDyWBl1fRkHOBN4LlApKDjoVpLsk2RC+3nvYJUMr/AuYu6N9Q5t7lcvHLFPfrHWXs9qsMr6Xmtv67fYOD8V7tdlBNkz7523x++cqj3MWtIsyTWm4XxoB68crrEnVyfL14T4nN00oHgMp/rLOEBCw3siiWQuaPG0cKm9rUj2VVcecy1Q+m+uTdRsIIU8vwuzVrqbfBYoc+fr+yR3jQY+idXklAA7cLTCpk2NlJQ7HXB8ohjGjQxwkw8M/O9bZ4UpLjO79M7BKtFx8RxieJz4TzcXwGVNRqAobUgbs6ahwxIfuBe8oej0srbxMkBd8a2zDUpQ6xv7bP3cSD3/GyeF/G+NA3RRMDFj9hB6CiIPnLsksbAH7skMteahYXMaM0adTigwvH/iHe7bGLIWtgxrwPnHnxdbdW2PRYR7in4M/Zp9X9bca62tg6Jfcc7RCZsY7imJYZK457YU2KbrEoV6063DBPOpR+YJjOI+2IPkIygYUiBTjJadybNb71mq2Mz62Z3XqvnMFVNDdYbRUXr7neN21x3zbNu+YkkNM4+2+Xid+fo428w4H3U+ctp6pcaW1zKgNYgKXWXnsAaRMXhjz3Becp98HfkQbAwQYYDJfadq7bn7plrL0LD97O1ce+quSZbo7WFv5zUpVgAO/nFzvt27NllgFnEgJsZH3kyg9NDEiHOLor3s51uLbXKiv53MaVJMQyY9xMtZn72sZVDgHcI5P3s/34YGhu2utal2IKfZnJzG2lcXRNqRyh79/JcemG7v5TRbdmm74gDyzkuj/excS7dAXhgKxADAyuL2QXU36AAyV/ajjcm2JadVHVxAAPZJWoCb6NVQvTKC3Oz1Mw16rwy7v7arVDNg8aGeNl1GniOiWT789b/aiQ9/Yn8+Xq2Cpra80dKnxIgtcOP0EPvuk5vt+9/bqE434BR7knNhcZSvZm5+8VGhvffQLNte2qo/217WpsFrTIDJDelaQD3CdPXTgjbbf7jc1ixLUlFCbsW5S37TUNthv3twhpy9Mfjk/D9Q2KZzYNyYawTsVLSPyB/q5PkGGx0atUXzYuWFw+wv4DEdK/buwbxm5Zs8kwMFrRKjoLAp6xyyv36cZz+9a7JAHQAs1guFyemiVgl/HC5DqdJLjB6AcgxjKU5/tjzJfrCjUJLxCMysjguwPx2r0mgDINGNi2OkrrYs2f/fq7NBR4OBJgIfjpYgbVz9I1ekcoAsGSoLBAEOXjYKLwOuHf9NEoaCDkM9v9xXZnl5TbZyepheBtK3DIvtPFzhkDtN8LfoAHdRi1hMyJ4iEQlqDmJHsP5wX5kKDVy6WWhIwUFBou0I7Ypkny4C0o5pyQH6uajUIE85IylAbTAKG4IJhRMmeiSfGxbGWISfixYWEpog01S6Ryp7paaFLjiHPepTUGdAF1E2aoQz7j5ObW9ka0F1OaRLm/uVYNAFmhrja++fdzhfZ2YEi0Ne2jkkUzhe3LmqTrXb+Xwrknzt1uUJOlyOXmiwj47W6OCGr03CC38SehScbCTY2nqGdWBrcHnksig+ICu1tT2WkeSvLgw60w4DrssKEhQaKK9wNTV02dQoR0eEDQhvOcx7gm0+2yiFGlRXcD3esrfE9p2u1QFLYgGHk5YhxQMcaboSFGA78tott5Eu1Bf6h6DLveIOfyK3WfMRdB8wRExK8Ldj1X06PPGo+N76RNG8vjI/UgpD3t7OopVQLLBxD+U0SeGIgPZ5QYfdOCXYhi5d1mGNZCatRJJHklwObKgntBxBbGnJh/i5Sj2Ez0CyzAW141ur4h3J8chloQXcH1K1fLby1gHJrJLo8w8a+hxOBeUdFpcUbDNjcCj2t3A/Nw2K98PB9XKofIFUgryA6ECteWBJtNB6jKNQQKOg4JC+eUqwCjk5Kpd0yl38eFmnkBXuCW8GOgfQRXB/f+lwjdYn8xI3zI0USs4h/7/TXRSTLgoNgiGHDxxlnmHf8GXbmt0qqkPz4IgO+Zwmh4EcSBgoLB3HstoeS/R3kYrX45/m2YWsGls/N0odQop64sT7n1xQUSy3bH83dT9QLePzIVcr/5x+h7TzS++dteLKLnv2/qkqiqE+tA1eEt8amkGEl5OSIIyvJmeGK97QrTtc3K44kBHnJ/Ww7QfLlHh8/97pShTvXJ0iTjkxjHktEkZURkAH6YTtK+/SgcMcDdK2oEwUIOo0uI+VLDKGlFBKMczEZI4EA7SdAfD3LjRrTSQnBdh7h6slJoFkONfR0g5LiMbI8B+2LtnP1syP1uFy4Gydvb2rVJ+TOYaPLrTIKOuDA1V23ZRQIWxQ6tjDHMAAFswtQY+qrmxXzIFWhekc65R1hhPvL2/NsIb+Ec3FtTZ3S30NdPSVrHoBDCQs759tEiUB2iPo2+atOVKzwYyPuSI08ek0kshLNKKoQx2TnXltStp5nxj1odaHLv58kvMzdaKEgcjy7+lzk+yd7CYBCmFBbvbbezPt24tj7f55Efbx8VpRuLLr+hTLEf14/2ClDm46IlsutNgts8KlXc/7xSgLxJZ9xT+o9wmhlrz5FzrU8VjJq+4S2oh8L/uc6yebUkUJo1NDzGAm64G5kYoHCAMQB3CQhq4QGe6pAegzOc0WFeOvYWCoGKyzqrZB6+oZUWeVThQzHgAhZe3D9vqecnvi+iStAeSAAVwYFCXGPcReHsKY1Gx3fqti7JnKTpsb7yM/JJzWWRu8R5IXFHWIA5XV3Xb3slh1zekSr3nxrP3vzAeuB63uGxXyyh6jWNf+Pdtks6I9LLu1V+7uzMiQREJfy63vE32lsKRdaDLXV986Z4f3Fdi6uVHqruHvxM98/elX9QzWzY6QYSrzcpivUSCvjQ9QwX1p9JI6h8889arll7bb3l+tVwJOd4P3zLNmfotZr9npQZYa52szV86QYSZ78819lTY7wU/Gk8eK2+zzk7WiIT37g5W6N2Y2KbLpkqJGRuzFdwMaJ+vm0/w2fd355j5r7BvVOR3l4aJ8gHlMBCE2n2mUAAJ74tOzDTqXoefetyrBPilqU+cGX5/v/v20lXb3yUaAsx4Z9MSkAMVS1IZgRxAHdp+stQ92FOr8Sw9ytqd3l+n+of3cvDDaAgNdtdcALSgKmEWjU/b6yXprb+qwyZPC1OFBiTE10lvPgff2l3uniBK5JMHHGus65JdFF4Z5OvYyVF9or8zJbStos8Ml7fbBWwettq5HCpKAdrwDD+ZY/R1J8wvHa1VoIElOFxmqMQAR+cD1i2JFR9x7rMr2FHZKHIe5ivjly+2Hu4qtvmNAip1v/XiFPXfzJLttVqj98eMiu+vBNVI+BQjgXP/71iJ5JOF0jqAGXbDm/mH5Wn0Hk8XukX96X5AboGjH8+fvmaVdMDdWKmk1rf2W7Otmkd7QR6+1vz0617IaBq2lbVBre164j/1+XaoUQRu7hhX3YEqQbwUGuqswOXKyWpK8t00PsTumhFhj56DWFYUyMfmzMw0qpJnVyW8dsJd2lNqzD878J/vjj+9mSyKdZ/eL9amyZ2APvH6gSkXw2couu3NWqHKaF87UCmCm0wGw/ci7F5RLV1W0q2uIKMo72c3qPv7bFBu3vZ0jB24SN5ILEh4SWy5aUvNojY5DNcchyUWiT9KLPBttQw5jpBzbBi7b/uIOBcmFc6I1AxDt72xrk/y1iX5w20QN/u3JalAy1dw55JCFHT9G/Eu40RsWxNi+/FbbtCReL5UklYUE/w0uIAURRjQg6hQhUDhQIwJNyarqtnbxXk0/94bJQbZyUYKGckhMp0Z4yM0TWhAvmMSPoEcSC69yUoCHZYa72tGiNhURFBM4DkOjoTNzpLRT9Cb+DCStsLFPCeUWHC69nYW2YRI4NdRDSAkUIRx8CXbMczQ19VtL95Ccy/FnWBjlY7dOd6BTcANps7PBmQNh4YHGMPyZf/CM3TIzzNYm+uuwWpbkIwUtBqb/dPdkDTERcOngkPzhbgrV50BFjwayPjlabRuXp9jx8g4dYhR4PGvQfqhfuBJTpUNjIbjftDjWbpkXIZlI7p33TVIDasCGjvN1Vns2PdRNUm3MSlBAQd/AXR7OJ+jGZ3lt8veg5U5BAqcTFRwkkQnw8FeZCdo4I0wJBsg5yGN4sMNAC+496hl0sEgwSf6uS/WXoADraUqwhxIIVIGgpnGxDkgS2KzICOLHQuCjDZzVMKBELSHIVWZCJ6ociiMU2Hhf4Ldy/FiZBn1Rq6J1jg466BD0Oeg8/Gwu0Bo6OzxPZmD494XKTouN9tHhAqLLesCTApSKTgZc1/L2QbsvM8wywj1tUaKvFLocDvGu4g6DsH+8M9/yWVshnhqcvGV+lH18rEbKPnQZ0Pb/3yGHifpctL+LCg0uEh+oKxSZFHHIlbJv+Cct0PWqKd41lu7naUXVXfIMmZYUYPW9IzKwnBjjZ7esn6i9RLcKCUOoAX/+5nx93/t7y1RcAipQ/HM9f7xG7/bOVYlyvb3vlmnm6TlBqDxJLkBHIPzieC/bkBYgUIA3Pyva05pbB7SfDxe2qQsgcykfJ8lAbliWJJPQ0cv/UOePvUsBSkJOcQGliLjBgO70YC+bGekuvwOoVxTmcSEeVo35qOt4aaMzO4VCGZxlOMmxgR6252SNDshJQW5299RQSUGuzgiw6HBP25LVaKunhKpQzcttEDqFv8tnxR12W1qwfCVALDctjZci3ofH66yrd0R7mqSHZ5jz6U51+NYnBmgvYEYHesfaefubCyy/pF2mnpITDnSWNDbvk4Qp0sPF/vxBrq1fliz0jiQYYIM4Q9eEdz050lOxuKapz7744gu7eUmsKIIUCismh+idFbcMac9RsAEsQducHulpm9ICNOyPBxIFHAjyqrlRSspB6YnTHR0g+A7VOGSmSVShJOwv7bbkGB8pDkETInE9WthqgYFuomAQb/iMHP7QPLigbQCwcCGTzUGPVCizLcw+MRjKXkU9Cc51jJerw0iVmFzRY4hPYXjY3jtsW/PbrHlgxHKbBuX/gAjHsUPFWksUIZ+db7KYaG/zcJ8ghcVQV2c9YzocSZHeAhauXPmHUHWe0ZGCVkuI9dVsAwXM4hgv+95NaZqBQ6LzQ4zranvs67MipdCGy/my1ACdQxhFEkv42W99cEbrLznKR8/2G9cn2YvbS61n4KJm+UrKO+2Zq/NQ/5XXir+dUSeGRB7hEj4XRSwzCWvS/DSzVNU5qvlFFKQoYplFmRzsbYXlHfLKmjEp2Cp7BjUHAyL/5NcXCiikKMWDiBjyzsuPq4uMBC0gWUl5hxJZzgrU/aCY4Qv11uEau+9796jYhj7FDBZrm24C6x/PhapuZO+vtRsyAq0ov1H77P39leqwQaNmiPsbS2I16J1X1aF8gDPwoxN1loaaHYXNF19YXnWnnOwXRPpIAIFcY3d2kxU3QRMcls9MVe+gcoent5VqH+BNk+LnqZiAwd+pk5Va55OD3CWVuyzW1x5aHK1i+pnPK+yeJTGaM9y69YLOeCR29zJYPCXcvjY30gYHhu3+jRmijv3k9Qui9kBtdPVwFVBx4b3N9rXZEbYi1oFoY4B7e3qwRfi52sFfr7eC4lZ1oyhC5kZ7yvcJ6t8zh6vMc/w4++ovdoiqfKykQwUSZzTg5Ad7StV9Tg52yEyXl3XYtW6etmlhjFzXAQT570a6sSWdOvPxpwJs5vwmsb8vM9SWJflqQJyzg+L/9pVJ6tz/fHepCnM62tAvmfP7ypwIq+0Ztsb+YZn7zpwcIrUsYgT5xPG8ZnNxnWAJ4d6SwkYNkAvQgRmWR5bGSPyH/IDnLZGFeG/FJOjvSByjHsYZ0tDQZ7F+bophGbG+9llhu7odMDiI/384UmlFnX12srZfcYA4fOp4mT7brzek2R+3l9nEiaEW4Ouioifcw0WFCsUFTuP1XUM2fvy1AtbpdFJ4TEkPksAOcv6cf3/+2kwBT3Qmf7a9yLafbbCn1yTbulnhtiTJR8UuRTRdYmIMhcabL32utQW7hW73U/dm2i9fO6u1xzl45ky1vZ5V969Po/pSZcLh+uimSo5FOyfG0/62p1IIe1PnkOVn19q37pwu1JGkEJ7kO1lN+h4/l7GqRFdNDJQBF8UBQ3zwEBlqoSXPS6XlT4ucrgloBeiOaE5Dl/RyJga52fbiDkmKgpBxYBF4oURAnyHZwEQH5R4kF0m8Dxe02O1zIiwAw8Chi2pvgmDy9QyUQxeg+0JgZHPwc0miSXLYAKfKOx2DQPFeUn+qax8U4sm9okLDYQyKgUxjdVWHrV+SKMRcyi0VHfbodbHqbDBEDzWHxIXkDPQJOgmt3aXwlas6raqqy3555yRJmvK7CQRoTVM5c3HAcDBtyWtTEOY5kvgyF7F6aqgQSLoMPBN+/7QId7XgSChqO4eVdJM8qbU8/loZAiI/CXeewgx9dxkGgko1DOpzwY9E5x7jIahcbGAc3jk0b00PlocIAYkhVLoLXHq/XQ4fEIf3hJOkGknq+T2tfReVvCBHifwcQTM11ENI+bxIL8m/QX/AyKkBLmqsr2VXdpqbyzgZHDEgxionQFJYPLe30n6+PsVO1PdoziO7cdDqOwftxkycfq8RjYWASqGBAROcbPjZ0CdaOwdt2aRgh4GZ9wR9Lg405ktIUBisRZaSw/TVI7VC8TdMDRE6RSFZ0uhQq7oOCd3Cjn9KcWaEuFlx65BQEA4pkNNHnj1mGZMj9J7Dw70sIczTzhW1ic9JkMDki/cZ5OUkVIzkJzPMMd8EDQXna5Bm3hOzBxkxfna6oEVBBtfZNZODhJyC2mBuxed7785J/yVxYOovD6m4h59NIQX/lWHc9Wn+9uutJRYb4SVvlFMHC+w7Dy1UUId6CIXimYNVlhTmUIHCfBJfFhBvDvPsvGb76roUHUgM1hMP4CnTvSOxomhu7HWgUMwYRPo62ewwb/swj1kQV6H87HvWFwcVks+omZxg7ife23I1n3SNfXi42h6/PtFCXF2sdWhYHUqEFTCoxGSNoUzkOKFsci90Jumk4DwMzeccng3eLuIEA3BQQLAfGfiliCLh+lJWsbSw0VZfl6LCiT1QXNtt31keL+7y6Zr+fyohgUozg0bXZvOJOs20IbkK7fGv90+znaUdiruL4rzsQtOgBkgpOEmEl8T42Ktn6nXYULhXtvRbU+uArZoe9k8PEmIqX58Z6mKHynoE7PAMSUxBf1nzrHHAHSgTs+J8rKn3ogoYDlAM9vhsoJ7EcWaGKHZ5B1wMtjN/8p35sdY2NGIf5DTbzZOCbODSFcltI8Jxur5PHV8KPlRw6KAQtwFq6jodz/tEfostmhQsozEHVekaJUovZdVpsJYYxmzgwoxg23uuQUPXxHS6mMxSMNCLwMcvtxbbs7dNtm0lbTKCJV4z+/HYslghvBevXFE85dA/frrGFs6JEWWPpKuuqU/0XChXqPH0X7xivs7jZDzKBYiGpDrzGS+fqLPe/lG7Z0Gkbc9t0xkCTSIcLnlGgKgkJK8IPBBzmQucF+Wh+IOM6Mbvb7HJ81KtorTFYuIDbVKcn53Kb7G5E4NtSay3fDmgZrD+6cAQR5bF+2je5XBFjwr7L+msW0/V2cy0IDt4tt5SEvyspWPQbpoVLilm9j9nFzN4H9w9+b8kDqT9eJ9kQ7lwlD5b1Coq6V1TQu0bb563tER/nT2HPz5oD31jgwDI65P8beDSJfvu2zmKvYg64KaNIdvzB6o0zPvJliz74cOL1DVgWBya34woxB5atPaWJ/mKk8/Zdr6uX2v5umh/ey+/WeaeeHQwt3OkvFtS88SI3LZemaXePz3czjT2iIb9p/dz5JWDrwK+N1CYSdo2TQ5U0ZsZ5aOiB6ABrwgSOz4DzvZFbcOSzgUZX5rgrS5+cX23ADk6cAzwonqGkz1zkgUXKm3+4lSdGcyBkjs9Ojda3j/4X6iT2H9J1FeUjwDl3jxer9mAHecaBS48f1umbSluEUCIrxPzjZyhXxrWLoz2tj8eqJAzNsPXhy806vfNSg+SFC9zmwLXxo+xzFA3nTHkPpgrIwOf3ewwM+Q8/HRvsQWHetvdi6LFSuB+oWQuT/LRz4EJQlHfejUfIEcCOEAsJreyw567ebK1DozYX49Vy3Uck13kalnzWwrbbF60h55rgpeHlXT3KW4DLNZ2Dtm0SE979tMiGZueKmmzmGBP5VjLY/3tiR1FdtfMMMkK5xS1yufi1Z2lkt/FOfzd040SvkAZcFKghz3+fo69cf90ez270eZHe8q3Yu+pWvv9nZjyXWNjr7nWDtd0K+c6c6zUbr5hqtYV8Q2BF7xELn9htiklwOr6h2T2d64ZGuAVmxnuoL3fmhEkY0ZYBQ8tj1XXF/EYxAAWz4ywTamB9ub5BoGhdM7JVQBFl8X4SN43ysPNFt/1tKWtXm4Fp4stdUaS1FiP5jaLRr86wU9dCfIRmBRr0vxtX2mXPTAtXIIoR6odM8PcE0yb57eXavh818FSmzEtUvLfj65LtEOlXVLETAjzEhi5+Z5M+5ftbFCVrUgLcPCt6xxOvfDG0AmHTw3F6daZofbpj1YIDQDN+jy/3faWdam1ui7Rz/bmNtuCJH8pwqB5z4ulnQnnFd18Aj7VHtQVfh6HJfxZkNGTpe3SLuYQZKEwJ0JBAA3Hy2mMkH2G5zoGL2tWAFQDfwYCxJdoNvMUn5d2So+fTbogxlN0GZQOGGSluAFh4nfw8kgwKDw4iJHzJcHoGr6iw5vEMtjLSQuHz/HJZ9lCrdxcxlvmpDD9/DjfCfqZX10cLbSK5IXhStAq7ojfgRsoCRC8/87Bi1KBevruyTL94QDn7594+awQXdrocF25UH1hSIzAySGOUlRsmKcUnfjMMyPc9b0kCxQMXCT3JEMc8HhokDjTWkRphuqXxJ4kjyRoWaKPVL+gbi1N9hP1jQOLZ8zzwOyM5IZW86nGbuu/6EDTkXojyNFiBYmky8QwM74B/BkX6AgzJbOvBh08A+gALUh00DYqWx2D66CudBTomOHNcCy3Wa1BqHd0gqAU4d4LXYLaehIumpVdonnR7oUryzo9Xt2r9jWINAUgfNaMpABJHZM0UeA01PfovWNIx9exLhju4jDfX9CqpJWCkmeHBCD0D1qcKDB1DzrUgZraB+xUdZ9UZzg8ghhM7hhRdw6lNGg0tMVvWTdRVA5QTB+PCRq8/+p1MQr0uFTz/Bkch1OPszqHkfv4sbYtt01dIvS7QY15dsmRPvp6ksTrkn1tcoyPhpxJcEHoKVCROfyvuphruGVGiJ4j8yoMMTOQikfBBJTVeoft5ukhtud3mxwUDz8ne/NUg+0s6RD6fM/kUFFJUISSr0iMt+hRc6dH6D2GujtZoKeTDjTJO1/+Qp/vTO2Auj47zzbKZZjOGA7RiYGu6mKQDHCBclJIw5+muGQN0EHhd7HmMWk8XtVnb2c32tbcNhXziD2gokLyQKwBPWVv0EHsHXZQskgQeeaPzo9WQc3agFNOEYtfDAkI//3hlnN2+fIVJWLM79ABZnaEjhlqMohfONElpDNa2i66EfcOIvXijlI914buUQ2hQgnjvXOoEwu+9fwJFcJ8LcCNlKCcGUp30hwccQ5EPpKOaai74vDKeB8lGajanG8YdJh7Ng9ovwAEsIb5rMy7MORPUcLvIH6hdLQuzV8FLtSM1en+KljgBZMEwicH9URum07H+dZuDZqzF9hvPI/t+e32UW6L5EWnBHralrMNkieli8dQJPH1uiQfJTQAVsR20EyG7YmTFJckmFChpkd52rrpYbbtWLW1NHZbdWWHilVQ04VpDuAAJahFmaH2WWmbPhMdrRkRbur+7S3tkmkj6oEkagVlHTZ3ZtRV3w+TGlRH+4BAFMwT8bOhi9/cD5j0hR0tbrfPizq1luicMDcwPy1I308SgeBGsJ+b1bf2C3iZF++jggowqbn3ot4Z4BemssyY3XnXPMX9P3xtlnlflQO+e3G0ABTWLwXG9ekBVtXaLyaA5p2uMfskp1WFBpRWP1dm865Yepyf/j401EOdLKiHdKUYJKXwoHCkU/tfdTFj8o2VcTrzdhwqF8UVlPePhyrl3dLQOqD3uO+Fh/45v/HU7lLbW95t182KtB8ujpfiWXKEl0Q6kAXnrLr15plax65jx1pGuIdAJqghxHGS+ZO1fTp//r6rXEUwiTZAFuaWvCv2KN0UBGmQAN1V3q6zBqESTOOImax5T283++B8s4wUkahmVhI/BxK5YC8XdeehVZNglzQ5VBVZ+zIQ9ZlgP1mWaDdlBGodtfQMqSDgHEOGlf322b4SvVu63XMWpdjUGB/7/pI4FTSY0ua1kWSP0dpkZgxgls+ZV9FpP339vGji0JP5Gb+7MUP7tbylX9//9WeOSdENKh+JOp39SC8XATmIifD8AH3c3MbZ2iQ/ofm3pjlYBMUNvdoTFNZI+YKMI6Oq4eUIN4FzCUlBFhXqqc4wBSPdNAoS6JqvPbdV+QBeQ8xksBfw19me22rTI9wsyNfVyrv7tfc547ydxuuZf4bq6Ol6W5noa6l+nvbWyQb7y/EqAUAfZ7coDgBQbLvQbDMnhyqGP7Qo2hYleNnO7GatIUQiKFqQtaeT+tzmHOts6bTO9j77IKvJRkYuaaYTkIKidtO8KNtR1qa1D/g8N8pDRe6755uttBNfJWYnL2t4fv06RxEuV/YoL+VJrC1mfcu7B6QKVdTRr67phZpue+VUvdZM8yCCAdfYksxQUZ2Wp/oJBMVoM6uwVWAYRS85KiCOAxS7xnaVd6qjCSC7+iubBE69/dR6AXkAJMwfk4vhs0UudkNagOaheFYoJiL7vjm7RfsKsJxzBGBvSlqQ5rsyJoaJsXP/ynitI8AcZtA4Oyh8/iuv/9Ji4/4P8yU/CbICnYeFDwKEXOTR84327aWxdseMUHv7eJ1tLW1T4n6gqN1unhos+dALpe0y1EmO9FbCTVJxpJBhXR9bmOBtvaNX5Ao8NdxNbTkQRFqdOy80qehgYAbePi+MTQlKyQX6RHBhNoDFgYJUtLeTqAMcFKper71G93LTzHAlbbTBoDKRoDMQjcwmKikgjQRyeLiljT1qs9MOXBkXoPtBYQRTO5JcUP+bM4NkroIe9rmSNluzKkPJFFw5Oj4USkjpYez06uEaqYRQXMgXIojOAgPl45W8V58+p8+zdVeBUDEOotq+YWtud5jC4CZK8srvZbiMYJvX1i+UAhSQRB1eMX4jP/+oUAVhfsugBXpOUJBEwpFNBG+Q583GzarpdTgoj73WpsX42M3TgsUf5ODk+6u7RtQOn5gUoC4MPE0KHu6BQMzMBQUeqDCDt3zWpu5h0TN++m6e/CegX0CZq+wetmmJASpOUejh328fqFTChgoNhQ5BmwOZZH7dxADbV9Vlp6p69S5JONHeh2u7enG83b46RW3SGXG+oiDRAgVlRrKWoXha0zxrDnreP4fY05uL9P8knD+8IUX3TcDlszJvgzpQVQtmUKYBrDWJfkJ1ZkW4W21ttzoUFHoMhDLo/yW9huQ0M9xdylXQX3btL5Y6F19D0Gc+iJkhEhECG5MhIKZQ5wgSicEeKm4owHFYJjk/VdGlJC3lqrEkxdqW3FYpuB0t61Lyys9iL9Alo5OFFv+PX81SoYZi1q1pQdIW50Ck9f/wlqL/z3HgwY8Kta4pXh9bHKM/K6vq0oGza3eB/fL6VPvO0jh75UCNkj1khXfmtdoj86N0aJ3KbbLfH6m02CBH61xzSEVtNivWS/QmNONxfaYjCQ0JpI33x7wOBwao5ffXJsrLBT4tQ8v4m5BUE1c4EJAN5kCG3oCymTqeY6+R0teHp+rtjvmRlhbsUMZhKJ/3dLLOIetY2dQr9BzzSdYjoAoqOnSobk0Pdcz+dA7a158/abvy24QQfWthrK1P9tc9n8xrtqXXpWkAEIQRJBbQA2SdGYo/fFoiF+/WqzGNQW86DBz0FDoNZ88KbHj/o7MCdyhMOegq6/AXGWc/+8oMxWCSGZ4da+ZgbYeGybMqOvVZQL/umhZqj7xwWp+B2RLiNcXS1v2lAkqaOgZtdoynDjrU9L402puX7G+3TwtRt+fLuQUGWWdEe9h1ixOV8FPIcr/cA3FoSWqA1jZF3uHSLs1+NXQMyhjviTcuyJMCNScKv7PNPaLMoUhGUR7v72ovbMmXBj/IafeAY26stH3EartH7c4ZobazvENyuCQQACvEB+Rtb1mTat+4eaK6Cejd08lK9XOXotjeM3Wak6AIo5AbvOSgTFKQ/eHDPBUPHM5P3zpRM27QQhENofhbOT9Wqop07vKaBmxtop+AhwXRXpZ9tkomoohcHIQm5eVsWZWdKnKivJ3UmULYYEFakG3fmWfbspuFNro5jdHzhLJLt5xu2cDFSxropjuIGePEq54AXHwtSDFx5OOcVsuI8JIS3YwwT3v5ZL2SYqTSifdwvCmoUfJiDyBG8fgLpwTOQcm9PzPUHl4aY4/OitJw8Vc2F/x/jgOPbS1WoYtAytM3ZZiHp4sV5DboHD75/mf20l1T7feb0u2lfdX2aUmrLY/zsc3nmu1361JV3G/ZXWRP7S/TOYncOPsUShlAHPuQmbQ3zjWIehXq5ypVQWJkaX2PzgoK+z/dMdmSfRyzKrsrHXN7xAKKRtQrmUFiPd2WESJaKx1izWrW9trL+6vtjqWxtn5igMUEuKqbyczDnpJOKX6V1nXbwWJUNF2lqNjQNmDr0/0lkf2d+TE6R3ZWtNntv90nPyHmIl64dbIG4Tk3SDLjE4NEeaGYSgv30mDyW9lN6i794r18y67vd/ik1A0IvEKWlTiVFuNj3YU51tkxYK++uNtmJwUocS7s7LP84nad108/PFvzT+x13gMJ7KfFLQIAYHAwJwGt5YkVCXbzLz7Xnv28st1ya7pk4PuXV4474kBTn02PcBe4e7yiW/5OnJV0xh+eHSk6L3sVQLVlcFTCPw99c6M6/tDRiEHcDzO60EA/Ot+iTgfUITxVapr7NJP2o3dyVbhxRtJVPFTbYSnh3pYe5qE1hPDKb3/3iY255lrFPWI9Es6navv1zB9ZHG3v5TfpjGe4Hbok+dCmlan22N0z1aEih4GZgOT57DBf21XSaa/tKBZjhFhPDofoAv8NWPLH97JliMdefvGOTCuq7JS3WHygm3JTHMPza7oUj5nbWhDhqy4wFPVjWw5KApwzbPOFFoGwe7MaBFTHerlK+pkzLjPJ37Z8lmPbzzcqH0ERle4V/5AToDqV39FrN2cG26y0IEkRT4lhHosC+hrNvhFr8Yh5I6tRohbEtOnB3vbc0Wq7c2qIfZrdKiEWwDIoy0josyaI7d/520nbltWovPhHi+Psxzem2tNrUrSXmLX6l6NR3fTGBR0WXPAHoXdgIgSijaQpSQFcdPh78K8ZGiUxZPCTi/YnvHpeMMWJHCZD3TSoR5LMgQoC86WSQIyPk715vM42TgtV4g+vlsJjZbyvFbT3S0JuzcQAVZhQDzjwQZVIoGXm19Avaleqr6cGZ750osU5EoUlBljhZ0O7IVnkkAUZ3ZPbIpQ2IchdCCpqWqicnK/tEdWHrg6VPskihxWcfhQZcHKlfXjbxGA729RjL3xaqCGvKQkBDg5riKPTQEBgc7LoQPlog7PhuT8S/ed2V9q8jCCpXHFv75xq0EKfGO/nQCeGHMkWNAkSUFB2qE0UcyD88SEearGfqe9TAUOH43svn7W4hAApOdFN4FmdOVdn82dFy3zm3f0VtnpulFAFVgtJA5QQigG6HLvy262orEPzMSQrXMzocIDyeSgqj1X2iIOJOtL9s8JFLZgR56OqnN+J3CQDuCh6sQ64BzY0SWBakKvVdI/ooIb/ygwAVC0SAIIRwZeEmgQQ1P75PZXm6+0sRJF5krnh3vZZaYfNiXQXSoD0KgnF1DB3217YLpQHLfZjJ6tsypQIJT6O9eskdIH1guzc6swQDfCR0LFtGjuHxAeeG++ngTYQT4o8iofFkX5W0TtgR5Ga9XYEDQ4flGSQuGzuHdHvPVXeYasmBonWMXz5itYMSQY+DPBMQUPo1FEo7Snvsg0p/jp8HF4Q/1AyC48dZ/PC1mHtJ2Qxub/bpobIXXVKOJ4Bo/r9dA5xG+ZivZF4TAn0sp7RS3KLhTKz+VyL7X54xv+y8hQKWbx3Ch2eEYGPZ1bS2CNKIUpRUO9oJYPwgLQwxwMqDKIN3YqvIxgSUCeFuOn945DO3ndooF8jGh1J+gsHqmTwxOfB1I0Lp9czTV2KKyszAiUWcaG2xzp7R4SEdQxe1BpFnnFtur9NCvSyn+0tU/BlTuov+ypsemKACjIUotgndEC52Jto2sOnJnkj4Ttf32++konut6a2AbtvUZSFuDmMK0Hd3RgonjDeyroHlDCgqAOf92dvXZDDb1oUsekLPX8uOiUkXCRmE4Pc7ZP8ViWrON1+fXG0/fi9PFs+O0oHE9S914/Va/g6PsJLCSNrD0oTnQC6V0g9c8CzZlq7Bi01ykfzUajIcTHU/NXfHbCYxFC7d0mMkgquvfsKbfa8BFuY5Gd/2wptIf6f7xoZYSSmSXqhCzx7uNqKCprsoRsm/tN3hL3AWoWDj0cNXjl4yHxwsl7mVfgfTInzVRyAhnbiQqOtmRdlpU19lhLqaZVtA5Yc4i4fjklB7jr4GUJl353As6NnWB2CKQn+Ohsmh7rZZ/ltctR9ZneFhQe5O6hhEZ7yYfiwoEVxsnFg2E5W92n2Z1G0t72f0yzQA7GOAweKbNrseFFAABugJxFjoM5uPV1vt8yLtBPlXQICSFrLGnsVw1dNCrYTFV02PRo5Tc6pf9iahEAr7aSD0S4/KM4MzjHkqP3dx0uFblq0t8z78EVgWLj/0iXFAUAyOpbEIUAiipQUPzfR5W5JD7ILLb3aI1zQafD1+TIOhHuOtz0Fbdojjy+Otfeym4XwUzBSDN87OdRONyFe4HhPxI3Jgd7WOjBq7+U02XVJvvbaiTo7+Njs/2XzTkAMRA6gNdN1ATGnsEbIgqKI2LYu2d9eOV1vGyYFCFlFDhYO/5ZT9ZYQ6eVgM/i72Bf2D0sPdJPHEMPbUHChrl09btQ9e3ZvpW2YEaYz6lSlw+jwT9enW3Zrt4bQOQfpHiOi0dI2YM/fnmkFnb2ivfzlUJWUsuZF+9udb51Th+SBWRH2rbey7Z7lcRKToTM3P8L7n/GTJPCNQzW2LDNE6DZr70BZl+aOkMI/daHRfn3nRAtwwbiuX1TxCHdHty+vrUems3dkhGi+567f7beMzCgVTZz390wLte7RS6LCQSs619hny2P95PcA8AZg8aub0uz+Px+xDStTdcawtt4+2WCdnUNSZyRhLO/sF42aHIaiA9SbWBAd6G4FVV2i4qyO97MD1Q6xiUnBbnbPN18x37SJ9qt7Mm3z+RYBmTve228p86fZPYui7OevnLG7N07SWUeuwmwhnXLW4pr4QLv9hZPWUNVkTz6yUD+TOMZ5RHxHEWpurJdyIYrf339YYH++f6r9cW+5RQZ56Hexl7buyrdbr58kvw1MPDnnAAw4i+eG+9jx+i756xA3dld0CtDMLWm3BZmhikezw7zsRUxUp4fZb3aUahYKuvCKVD+ZBv7tbJ19JTNM5x7rnb0JFfOt3CaBzfih7d6WZdMXZ1jAVU817g1WDVLImBfeOjtctCPeN7kn5o50roj92+gqRnjZtDAPmcDemRluF+q77aO8Fp0ZU0McZqNc5LfM8TDHVtTcr9jl7+yk7i+AMrEASX7WJKIgN84MtzR/d9ta3G53TQyx/TV0Mx2+ZH/eWS4WEHYGxDfUQd84XKO99vSGdPv7mVqbHeOlzhuCID9ZGm/7K1t1XpOrhkH5D4DKOWRvXWiU18pTO4vt1BML7F+m2Nj46vmrw33uUlgCTUfGr7ys3VbMjxE/GtoK3HBQRecJY9U5QLqVGYTGnmFRCOAhUiGTgJLw8YB5ORyq8AYJVAxS0ua7cWqw6FClrYP63esS/O2d3CZ5MoBya+Bo5LIOWDYwtJ1zhS02JSXQ1qb6q9JGjYf20xunHA61mAjhVQFSCnWI4ojOyZKMIMlKEjRRuuCiiwPKDFeeIAkFhksSgwHOQu/Xp/prUZHUHchttuWTQlS45Df0KQlggBHliFMNPRre4xlQpLX1jZin83i1LOkeMKSK+SAqPXwOELZz5R06YPh9IC817YOWA6cfBC/SUzMizDdAjXhkQbQdqe4RusJmbO0bdRgIRjsoCfxcWqPpEd763QR5WmoEEAabaYkeucoNXpUeKIoOlI07p4Xau+eahJDw3HkPLHwKSVAgni8J0vR4P9Ew4GVvzmnRYPvg4CW7eW6EEu2dxR3aGOfq+qX8QeLI59Lw6OgXVoBjaGawbb3QbCsnBqkFyQbnAAHlR9mHz8cBQxud7gNBKyHUU/rfBAsCLVQc1hRzPHy2cE8nDVzBk+adP//WaduwbpKkLfeXdTsMhDikvCcI8aNzwQFKEQey9dh1sTo0UE4i8ELX8HIbLwReogOXvpDiECIAcDxBC7gXAgqDpZnhbra/uNPKq7ssIMBNCDAJDJufQo5OEYk4yTLrg5+xPD1QaifM6qxK8rXtRR1Kvhj0RFgBShnF18kqxxwDByzr9ExtvxLaL+edKKSmBXuLEwyyjVIcaBEzOb9dnfi/FAdW//2s5gIYrqWzQQCraO23A/sK7aaNU3QolrYOaJ8BQmCuSIcJieolmSHW0AW/d5zNi/a0pv6L8q9ARAEkFs6vDNoqe9VdwIwpq6JD3Uy6AAzm+7iOt1vSgsW/pdgmDnBAwfNlfxNgOXTP57do6PS2ySHWNTKqDhH+KM8frdHXQZsjEeVAZZ9A0Xv/VIMtSA3Q8+Uz8TV0WIkDeDtQENEx5XDQ9w2wB+im9Sqx/zivVQX6Zydq7eHV8TYw6jBjhPbImrx1UrBUalDgYY1AE4N6x7panewn6UiG3KECocrGeiYRPZnfLHoMayMjwtOq24fs6KlqzRggYgGai4oSceeJ6+LtYJUjySQ5B8lD3Q7hBkAeZuiIcchlEn8p9ogDIMOnK7ts/cRAKTlhsobR1L4Sfu41du/UUHvueI3ii+Koyzjty9qeizYp2EXUJBBM3hnt/wBnJ/v7qVqrqOnWQXjn4miL8nLW/AESzRjcMVRMHCA5hiJH4VVY32M3TAm2t4/V2tqpobY3v1UKK8zHQDWJ9HC1jwpbtc6Rdr1Q3GZBQW76e947NC5okfw9xQTrSX5CE8bpvTH3B0jy1NNbbf3tS+zGSUH2cW6L1bZgJOahe4KSC4+d7jPv/8iFRnv65gzbWtxmk0LdNEz+0tFaJY5Qu4gPrDsAH4wO6YizdqCmkAzRpZsd5W6f5rRZaUWn7ndtZrCSMzrvU0JdrX3IITyCfDrxmKL5jjnhFuzqZG+fa7SvzIiwzfkt2tsd/SOWHu6ld4uRK946FFzQtEA1D0MXHb6kmIExGDOMi6Io+i/JJRnqYffgqAV4ONmf1yX9LxcbgCXQknhOINDlrYO29Y3Pbf3dK0WlosDiHXx+vMaWzorQ13x+rslmpARe9ZYZI5VFVJt493hfZUR6i44xO9JTBXhRZZctnxpqe8412nfXJmj9Mu9IvnHX5DB7O6dBawghly9nOpAabxLlcNTychqkOvfY/GjNbr11ttG+syDGfvZ5ibqIdPpRYqPjyj4FzALgW5jsMFsklsGqYI+kh7oLMWcPEwe4SBYRCpgV4WGn6/ttQZSnivK5iX726rZCKQtBmYRaA1BBEUZugmodiTnvM8Tb+Z/zkXRF6NSCxDMoz1omieZZ7z9RbbevTJQC0bwEP33/rj0FtnZFuk2NdEi0IjoRQodgbapmO6AKsjZ9XMdJxnrplFDFCeR4UXDMjPZWt1e0MqexKmLpQlNIvnSwWvS4H69NUrFGB/YrU8Ls5/vKrL17yPy9XUTFYQ6R4WrYBW+daRSdkTiQEOBkk/y97NcHy23/3gILDA+0r69PEjhBUbw41iHRjIkxDBPmZ8mrKErz6noEvv5xW6ndsTjG3txXYQkxPnpXzKnGervZX0/XihrHu953qNTmzI4V0EXRQHxlfSHDy0A/wK3b+DEqipBZ13zauGvspz98xZbcvUFmphh8InYREexhicFu1jt8RcwWutCcAa9sL7b3Hp1rP99bKsXLIFcn+/HHBZYS66Nz5kBVl8BUQF8Gt0/kNkmkwMN1vPx6GOrPCHa19842WUlZu94TYjfkcayxDekBon9CcUUtjLOP2PnYohj5Iv1kZ5H9fn2azj5iNzkKcyEAPRRQ7DfW0OQob0nc7izr0LomLk4Kd/jQrE8K0plzorFLCqV02olxr9yUZv8SNCok8+6YGqLNiH46aNOJglZtgIeuTxG6+f6+CiWjcNsItFzw9JFgxI365HnHA8IB+N0DlVoUtMxBNXBtJRkkQULBRspVwR62q6BDbXNQ00iv8UKtOHD5bxI8pNwkOYjWvtt4JYdo7TNECCUCnjOJ3ROvZglx4aFzsJM4osENR/jznBYlraCKSPHB0+TrSPYxEQOx3FfQpsSRAUYZ7Pk72WtH6+zQqRoNDHJ40TpDVpfBVA44Da4VtOj+kG8kMBDIOAw57BlwBGUlsY31cbIbp4cKDWOxJAe52IFzDZJbIxiCvoGU83ckUGxwqEyHzjdqI5EMELxYfClBLnp+DLoiP0yhtPN8oz4PGuynilqFgBE8eRYcTsjKcvFzZ8T6qgCgMKCtSZHQ2Tci2V/UIDDh4fNScECz4LBAwYWEAcRtL+h8RoAKBm8vJx3cuF2CGIAO55Z3OCg+w5fUvUCOja4A5opbsprUIZMHgYeTEHSCHyjqXw9VyUDLwXu/1iZG+djkGF9Rpvgc+w4US1ksLcxdahklLQP2951lGpwiofr4QIUdK2q15x5fpEQWmsX/Q9xfQOlVZt++8IJYpdzdvSolqbi7EyGQ4N5YA01DN3RDG7TQSDdNQ9O4ayBBkhB3l0qlknJ3d684d/zmm/R3xnfvGeece8//3j0GA4jUu9+9n2c9a80151wMEWS6OhuStjqFxoihVympQRMBmoojFUk6yAuIZUf3oE2N9rQ3NpUqQWP+A0kvgjgu5rHgGQ7qyjOHSwtNgOnSt0wMUeIDSsYhgOD0iu0fHulzErwVXL46UC1knqJsf1W3giZ2mKzLbUdr9Hmg9iTaJDGgstwbNBpa/gx4CnIbLorFByfrRMUDmWZAJEUq1JH/O4O+ABx+Nj1Ktn84LIGqbzlao+D+1P3TVLi9ufaU9jYOLrxL3juHzrWTwyyrosP2Ha7QfJfnvy20V77MFmhBocaANSww6dDw7O+YHi4ONvQSCgXWNJ9DkfXaYQaZ9SjZZS8RB9CkcKjKraqhx+5eHK/9tq+qUwUwa/eel3aprc3FYUTHAsQMa9JP9lergwgKhciSGAZYwLOv6mA2wNW25XST7SnvknsYxSGD/V7ZXGqbdpdaaeeA6IrjQtw0yJFClE6N6B6lbSryPj5RL8vrJSm+KhIBD/Iv29tSaDAlffXEULnhACBwQOzNqhMPHhtG1kppU58KeQoNBKAU05sPVEpECt/3NNPseyianfWemUgMzZCEYsOxWj2rCTHeGpZG5xcwRHqNAFe7YWyQ9nGor4tNS/I39+HDbHGyg+6E1355dZeeH1QICjSBAd4jFAdAyJgWT/cCvdEHmbXqIN05L9p8fZ1FdzjVCM3qKhUauSVtdrqmy5q6z8h2GqSRuMIMoTc2l8pDn4KcYnVeko+SEBKiZzYXab8uTfTVepgzLtTGRXlbRWOPEu5vP9kp9zVNsfccrgm7L3yVbztkt3u1ffB9nubavP/CTTLSQLtH3EPLAaKMJfHYUFd1luGVY288d1yI7a500AQpSCnucPwhDvz5qzwVSlB7oM4SB7BtJonDOpyJ7yREaM8wkFgyNULUQ7rjFKwUbyDbuCeSSCM8h8vPu/zn+mJ1splwTaEDYk4xO3Dmgn2zp1wJhnSKvWdscZKPaJScR5yZ8Lk5WzHIoAOCXWl5d5/NCPeWUcOkSA+t9Sc2Fv0vx4E7v8ixvyxO0vP4eG+VzuKvdleIm//UU6utuKrTXvk0U+cSFBT0IwBe8b7OdvO0cM0sYH4NzpR/WJNrv/3HdhUMrEsSluuSAuxwdbfoaU8tT3BM4w5xl06G9U48nxDqbo99lysXMjR9JGusR/YttDEAlbb2AfvD3ePEaCAO4KzEWb/46e81qA7QhDWEhobYevvYEPv31nLN4YryHmGzI3306+QOxOWqDoej5bdHa9VRhvLNuka8+5dvC+3rrYUyPMDSHuoXjnZMgeddsyfLKjtUADEwGGDpmmRfC/dxVpcE+3fyq8KOXpsa5iVjB57flYQSSlZUtI/tzW3S0EKAAXj3ixeMcgy39HW1r3/IlQNZR8eg7atp19yxKeHuOsthkYxLdmhtd51sULJ93dhA257VIMCTC4Ex1Bwo8iSwODphwuAxfLg9MSdWVKN3T9TZqbwmCbZ5fsRy6I+8g73l3fbnRYmyqwesAUjDshbA8sXHZqnIru0+p/OO70ahUV3vGFrHrIsjuY3S5hLryQ1/816mtCoy8InxsdvGh1hq0EjNo0AkzvO6McUxUfuGpWkqWmBMUGR/8cLb9sPectlDs84PVXTZ7z89JZ0T7+P1r7LtnU0l9tarP7W2zkH7Pr9VeSZObsxS4jlDb4z3drE7xgQLzLv7mkRbV9iseW//2lxqRe190tTQSXjo7aMCKCqqu2xMlLcdyK63lDhfmzXKX13j53eUKvZB5QNAvXVhvL10bapFe45UDOA8ogPX1HvWshv6bVyoq909Pdx6e8/ZI28eserefkuL8rF3s+oE8NEFZe7Y62tzlK9Aaa9t6LW7JofK5hn6uUYHeDpZAi59XiNtR36LPb+n3Mq6ejUYdmGcty1I8FZ8un9t3v/3xQaTgUH6sxp6RUdgcwZ6OquVx3RbuLAcQIunRdr6HUUSJzPtE0tHCojazkGrqOm2B5Ylqqr86aIYmz8x3LbsL5dugqQQZIBNfIWTz0FOIgE9AwoGSQzBH39+0Cza17wc/k5KiLsKDXQCKdE+ume0IKBEHM50HqaMj7D8Skfblarxh9PNQuixe7ttaqilRvvIhx/tBogPwiMOPrnEDIBqjLDtB8plmUbiiy4hNdLbIqO8JRqmoodeBM8TylWAs5P0BnMnhiv4xfm7XJ7qbDo05cjl5bDABAVlcTG9msQG/UCo+wibnBokmztmYdw5MdSOZjeIxoUtW3ZFuxyWGNpG0ACFoD03P95b/w8aCMUDCgoJG5uV5AI3kqnJAUrouBd0JmXtg1okkR4jlTyTAIAykXTQOUoNdrUxMT4STC+K99b716Ajhp9FuinognJSCNWBLkW4aSopQ8muzQhUkoPIi0A1N87Lbp0RocJF3Qd/V5sQ5aFkObuiQ25MVNkkjYVVnaLIERzrus5K9KZuUlmXgtFb//zWqlv7bdKoANGtnn9oqmhaFG58HgczBzvPDBRl0bQoUUtIfgcHL6g9+/bX2XrmXCQnfG8QKNr3zGYhCYJ+wFRR+JcUWFOSAsQtZlAWm56+IT+TNcR+2JvdIDpPaKCbPNrRIeVWd2qyKMnS2AAvmxTqqUMHNAik/2RVl2ZkwLteNTZIAllfl+Hi9+48XiuzBIrL1Agve2xFgtbx2zsr9R3X57TqveFKRXL16e5KIaYkfNMTfAUAsA/wthflsKlfLWKoCgzl/J+97v4yR8kULhxwxelEQIPp7uoX+gjKyv2tWjJKRTiFN0PdoJLwvehOMljosZtGC3V++rok2Suv312mZ4HO4mSjY2gm3aGSFkfXCCSZOMC6FsAwYpiKZZ458QLqBhxtClNiAiACLXUh+0NwarrKzly8aLePCbYxE6PteH6zEHiSjjWHarQOEPHCZ0+L9dHePlzbJXQK9xliAF0y4kCA10j7fnuh1iHPO6uuz0bH+5m3r5v88udF+6gDyJrlivV007OaNS5MxTNor+gl/J6vk1BfX6Z0dw7ITQUTjUQfN303ECdohXRpu7vPmJ+Hk903JdyOZ9cr8Smt6dK6gnZyz5IErfknF8Zp7gSOYC29F+yHrAYdYAwKVRfo3EUhxJrtkRGkApUkEJ1DXkufhPap/u4W4AZdrM/qes4oZkNxIg4snRxu+TWddh22q0MdFEp+BjbguMtw37wDaDU3ZgRZrJerkheMAjDx4B7pwt2UHmT3LYiRTomEhU429AcSTorUiamBKjIAWCgImEcDCMDAN4rPOD9n+ya3RSYS7/7pDd3rxHg/gWD//stqmz8uxFL9XR12pcOHyL+f5B19zLK5CTYm3k/vHT0Gxddbr/+gZyoaRipWzGclpkX8jSMaVqNQrFh/12QECayalRFswa4j7I4FcdJ/0W0F9EqP8bWm9n77bn+VqLcMLV23q1RmJTjLoOlh4GCyj7umT/edPW/byjpUaGC28cyXuUI1758TpSR9JMYVgSNtX1adFdZ1KSZDaXn25lQraxmQAyT0OTjjgYg/+y8Ixf14Z4WoxwjR5yQ7zm3iz/dFrVbTdVbfa+2BKp1dd3z+Px8HHltfqPWd3dIlvSJdCnjyrY0dcsACoOPZYGGNnjMxzNMemBWpcwhxLq5yHR0DcqocPnyoPXdzmt19xwxbv79SHTtQ72MNnSqAAWaYLk/StCzNT4wAqKgU26zV68cGWrCvq4o2tGp8X9wJATVZ4yGBbrLjpTsE3azj7Dl7YlaMhceF2N4TtbZpf4U6sF/vq1RMXV/Yar9aFm8TUoOstP2M7ahsE4gBEwJdEfbBzQNnbFSUt63dcFqo98mabttW1m4ZiQ5b2Q93V9m1Cf7mNmyYgFHMRiaEeCv+8HMxMoF+Q9yCOi5DmpHDNKSQOM1Qz0+zG2xpLHa/P1pdc6/iO9QZEls31xH2q4VxVlDYrFkNmTlNYjfU9g7YL24dq3zpzdvH2sHSdrtrQogKfGg2FFoPjg/Xs2pq6LTtJxtES75+SphiNyYXab6eGtaH/fv8KACRYVbZ2q9EFzCWnIrYd9uieKts6rGfjAnVGYQJC4DDrelBsiCmCCK2kYf9YUGCTQz3sRRfd7tvdqTyBChr0BCfmh1nd8yOstHhHpZC3pjorzlCWGcDwly3MMnGJPoLMKhv6bOs+l6dtzsr25W/YT8PRT7CZ6R9+Jc3VLSg/yVH+vfbT8oOfXqot9Y9TI6bF8TrfZP3rViYZEunR4lpcvbsRRktfP23dxS/YOFAeQIAah08KyoWwPd3h2sENjPv48nlCSrwlkyPsnD3kfbAtaMEgmKKwDObMSbEGtr7NfMIWQHW6u+tzba6vkHbfKDCpod7WWFHjyX4uokyCRj1/sk6USuPFbXY/S/uUl782+WJ5uXjqjg9PtTFDp6s11wxzkYKmTcemqwc4INdlRYW7CbhOLkyToWR3k6iyDMHCRACgb8ob2a2Jq9R1uzo4L7bW2Ezoj3/H+s3/h8XGxE+zhIWg7aRFINegLRfvHDRfjY3WgcE4mOCBDMqqA4JviQHtK14yQRyviT8bVq6x4taLCMtyHKLWuRpzqGyKA4ruwvyE+bCspXDhIA9P9lXCwbBYFywhwIzVSxuKXDhQDvh/9MaZMNSjHDeYwv32YkGVYG4fFAts7hAXEFHEANBhaAlSzeBTgzCPtrBfDYHkfzfU3wtZVSQPMQR9vEPyCHD2XB/Kmp1tEmhx44PcZdDAB2RrOJW0SDoLBBI4Z/C16cNCN2Miem3zInW32vqO6vAw6LigKA9HuTvGGp2tKbHYqK9ZQOJAP/+GSS6V9ux0jYdgARU3gtVMUXHhFhf+9l7mfb8NwXiRmMlyLAy3IIQG4F28JkkBaA1niOG29+2l8rDGd4kF1QBkDO+PwODZiX6qT1PsNFQrI4zSrh4jgRVNgsH2ufH6q2+f0AcVqaJgwxxODCsBlSBrgnFBOgkwRSB+vbsRpud7G9LEn31/ujmwE0niaTzw3uGUgLlhqQHyt2Tv16lgJ1Z1KIE5OVvC9UyxaKW+74ySRRnF7ijII4+LsOkd5mTAQf3oriwokU4Dxetj0ONgqNz4KJNifIQje/LvVU2O8lPh9frG4tlk0n3KqemW7S/yXE+FhfoKmEsCAuFAgF1Vry3/ZSZAy0DCrzsB6xwN5Q0S+hHwsyv4QzD4fPQnCglFT4jh4vSQ2eDIum2udFKVLkv1jQJF58zOz1IyR3WuoguQUdwqvnlsngVbxRkDCBjWvR7OyqUINBB5N+guPz8dWsO/k/HAbpF+084xJ9YfvLseRZnB8/aX1al6PPoEpAkLJ0Vq+eBgJC9TWwAJCAOUMTzXEHD6IJOHhtqO/cWa1otz5iZEBRFWLlyrySz7MOd+a2aq0CygS6H5AIUDHEhLigfbyuVtXOkr4uQeA46dGGsVWguOKDMSfY3d/cRMl9grQKMcDGVlqKb74irR7K/s7otdN1Ai7lnOOV0SnBooWNEEgKKD2oIfxr3OQoxhNT83rWJ/jqk3YYNFf+abs/X+6tURCBUphhameSnQ7OwuNVunBcrVxOSBnzjoS9wT3R50CeRDFEwRsf4WGKIpz29NMEemRWl+4N6lV/XrcSC5L1t8Jy0ABMS/Oze1w/ZLz/OFi01JsLTEqO8FAeIkRNCPMxj5BAr7RiQKJ+Y9dQ3ebbpWK3ioGYlXfrRgtwcIv53vsqyaYl+ouQ5Cr6rhJ7isEdRh1YEhJkC/t/7Kq2ko1fCRzRfJAnws9/dWCj9hJ/LcAl4oUByIZ4/kNdk00cF2rIkXxWgPIPwQDfFC4SP7A+KRIAAnMg4X37x3M+UrB3CcjXYw/6xocSq2wYsp7lPYmC+K4U6gm7AKtBTCkiceBgi29p7zm69d5EcBtFYQUniwsiDDit0SzRAxK+VYwJVNL/0fZGVYhHac0baK2jF05L8LCHITXGNrsfYlAAl26z1X9+YKgdEBn+yJr/JarKv85uk9ZmX4K2/g0nI0vRA+9PNqaKj4kIISsnUday2f748QSCVLG4vmUSzgDyrpoYrjlJsf5XVqLiGk8+zq0ap00msJLkjcX99U5lia5Kvi9Y/yR06pbVvf/s/HQcQHZPwELuO1PQIOMAtbLBv0D65f5KExiT7aCympAZp3ToPHWI1bY75NszKunD+ggq21eODZRpCHJgzMczWf5dpn2zMl03v8oQAxZrrkxwJEg5+GLcgoodew7wVYgPfHZc0AAGmdv/z6xy5f+EkNyPeW2cqBQLrE+H3awcr7Za5MebsPNzGgdqPHKqCnt/HuOJITa9YAcy+mRjioXOP+QnsBdYwbAl+PzjcT4UFs6BYb4mBLpaUHCgb5py2biWSnHv3ZIRYcWuvHKf2Hq1Wl3zj4WoBKnw/3udDEyPME5rw3mKbNSlClJ2q3n7Zn3Np5lekh/KB/oFz9tHxegGdAHDv3TfBHpoaoc7Z1werNXAXqhyzy4iVgIfMMrr7lX127d92Kw7ExgdYaICrEG9ibbq/h/bHlvIWO17dY67Dh9o9Hxy33Vn1AhWIC0ziZr+Td7z64REbE+trm8sd1FSKIcx1QMzp5qMtwwGT7/i7HwrscHWbFXX02leZjcqroBl+sa3Evi5osiRfV1HOGZJLccNk+bzydhuT4Cdb6byKdgGQuAeiFcZQAjYGeRe5IRo4wJD7nnlYGiu6t7iBYUvPujzV2qXkG/t0uhP8m5i962iNwMO2gXO2bFKoNGw3PHm/rU4N1EDo4xUYapg0MhQdK9L87Xcrk+zDDfl2w4QQ2Rz/+YciudXV9Ayq04SxzgyAEq+RAo2L8hts1vhQO53fbL9YHGcvPTRZcWzC6GDrv3DBXttUap+ewprY1M3kzKRgeWhutP3j8VnqRlFUP39TmuJAYcugvXRruhzCHMN+L9rWkg5p3h5bEmvNbQ6RPXkW+/LLfVX26p1jJciniw9di/z3hW8LFffGBXrq2c6fHKF84Ns319r/Z8VG6u93WmFDj4Y1kdRSARNsQPCvmRlja7ObLKuyQ+0sAiSBZVaMpzYQrTmSHpI+5gZ8sqvCXIYNUUHx+MJYBccpGSEWGuxmB4/XWGlHv1rCeM9zgFE4HC5pU6IFL3hrQbsCC21tEggOf1p998+J1MwEkhwsXWnnkXyTFHMf+JtLc+HrGDxDQsnPzC1vt5tnRgox4xJfFkG763A7kduk/6ddHuflLFtfkvBQbyeL93fWP2xIdXi6GGTUo0OZz85u6pVtHnQLJnnTmqSbQFKBhzTTObkauwZsQkqALAv5M+hYmNUwIdpL7j0vfXRcVqygySQ1FBN87/cP1NjRGmYC/Khih0MDtJIiJcHHVf7UJOzR0T62bGqkqFRQNkgACBS02NCYeDsPsbL2s5Zf2W7vHK4xL2hLyQFK0HGm4aB1DLe7ym64ZpSGb9Gu59dcQJ1chltZ6xnZAl83NkjTrmmLE3x5/gQmWtmghThGPXptog4BNgkBjH9TnUPFS470tte/zJLHN5uBQX7HTjUITWG2B2uGhANRPO+Ze1sc46+1dd2kMFnYInCHPpNT262CjkOW58UwIY8RQ6WlIYmkA0XbkLVAZ41p79gzoxsCNQRlBiGBj0kh+sTyeB3UIDjP35KuAUSbc5ttcaqfCiwKAJ49vt0kQguSvO2BaREW7eX4LFza1n26U/aUBEMS5/res3IRkesUqHnfOQ0GAh1q6D0r9yV40MdL26y4eUBOU6WNPUJjSXwJTrug5tV2aV9hjECyD1JMEoVTD7QUqFbsQbi/fNfnvy+ShuSecaFqW3/wpxU24a97/4dxIPk3223r6SYd0pnFjkQ7zs9hgbxi0Sh772iNHS7rsJrmPs1FgR6HLWzP2Yu2NNlXRR6H5bSMYPvHNwWijTAsDy4wNqbLFiRbdLSXbd9dZJmNnVqneIefrOpQ92hPbpOoPflNAwqkHALjw9y0puHfst7+emu6jAUYgsacHp4rriG0o+mUZoR7qDgeG+ur5IX1h2YCwfLPl8YrMQUMOHd5ujTvKbugWesN5C7By03vbGqinywREwKclRTQ5aJYbWkbsGMVndZ/9qKGaCLuBUHbUd5hXl5OikfLJkMNGyLxJMk7+xS9yuyJ4XLjGRXguGem585P8bcUfxf701sHRcmRCPzcBWnhiGHQczLreoSEYTPJGmSCNt2eaA8XW5HmQO99/d3t1gWxGkhHsspMA+hVFPAbi9q0FyiISSAZkEhhMy45QB09KLPE2DPogC79KEFnHJbgXWdEkYCeQHzCrSkpwNkenRdjd44PEQIPsFPV5UhQcPtCZzBvlJ89c2uaBKQkQtwrBSgaGe4VCsN7X2fZvqpuURXmJXrL1IG9x76k64SzEKiq+4ihKqZXJgYoBv1kVqRtO1SlYafQqOgucl7Q6ST+M7UZAe91UyNUdJP0Qz0gFvEOoDdQoCaHuClJ4iw7VtFhid7uWg8v3jFae4+i8fkb0+yOiSH21dE6zVWhiGK98Pfp6mEdjJbjxdWpehfEoWP5zbbxq/22raBNVDHeAQkciTIzo0iUcSYi2eXXcP1BI8haQaC7q6jDAj1G2MnLE85xtGLQ6Tsbi6yuqc/WoBOIIw4MtXExPnaivld6RnR4zEOSuJ7EuWPAfr8uT+fzzyZH2Na8Vvv8Xw9a+jO7/odxIO7JLfbS13l2+/xY+2h7meg/0DRGhXrY0mUZ9vyeUtsFRa683VYmBaojfNPoIFHBfjIxVJ2A68cH2+zJkfb4vw5aiKuT1hkzkTC5WL16ooag4dxzoLZd3aS8th7bm9csfv3e043S1+0r67aPj9YJELs2PUCGHNATMUP46JFp6twsiPGRSQxaP86PcZffKyg61Cd0IBT+7EHo24eOVNhfb0wTFYbkvgpqU02XAC8of1AiWTPRHq72Q06LLZ8aYYvGheg7Rfg6W6Kvi85ZGB/rshq1j++ZFm6nWrttfUGr3LgiIr3UJbl+RqSoPgy+Jb4BTjG0c+XSNFuS5KNCbH9Ft86DG6aGW7yXq/3xzQM6y88A4joNtQsXLgnIe/iTLDvR0COtD8NtAXX+/fUpdf1dhw2zh6ZGKoadO3vOHrsxTXsQfeWkGC9H8h3iam8cccyLyqvrlQaE/48K97T4SC8xMt45VGPZ9X2aTYaG9t5bJ9r4UAcYc7yuxwJdHMM6QcmJYS9cm2r3TQiz3NpudYOvjB4g/tCFvHd6hL1530RRMKt7BvT9MUHhDKP7GRPmaV9+kyWtIsPvGJR4bFeO4gB7HxAGpzv0T9w3+xstCTH2jytH2cbthTL/qKnv0V6iq4XgOrO6197dWmqRHs62ek6MRXs59CEwQHimKozReXSfs8RgdwFCjkHLrTYj0tdONvTaRz+bZl5OQwWa/mFpkv1hSYJ9frDGbhoXbMcKmpUHk0fs2ldqcYmBMuv5+tHpFjDSSTGO+9746TZR6eaND9WfpRhGb4lBAZog5vLkNg1IL1fXO6gBtYAVDL1+ZXeFigsc8ziHsbcF/H74b7u1JihkyQN4VgvHh0pHwvvfmNMiUJ58YHxKoPKqBz4+Iev1p2bH2KHyLtv43uMW9fim/2+KDWg6eMfj5MMmZ4oyARHxMcEK1B5kF+4fAhYC9+fH64UOQfMRonnhkoRhMzJC7Fhtn32xt0rIPa0xiYgHz9uEjBAFCJJYuM5wL+GacsCyCJbE+cjrnZfFw2KTMeyKzY+dGxUwU8kJwiQcp6s6JW46WtyiQ52x96j8STK4eMERQW4KdF8fqpUYc1dRu15KVfuAPb06WY44azbm2seZ9UKQQcPoWhAgKEoQizItHZccWrxUmQi0SM4leHcbJmFQbsOABJMchAR8qnMcbBhKMy3aQ4GJS97sXiMlRC1uHbDli0bZluN1lh7qSKomRnvKEpgBaKCHVKqgmDyfIZcTEQZp4QaVXdUpSgvfh0SaCwF2ZnW3Nh5Ds+g48fzxg6fTwzRgugSIjKB7UFTgkHSkvEuHLHoMEvSSpn4VCTw77IJJ4LC63F7k4J7zHXhHTPfku7IxoGnQUSFhoZqH0kCwZUMU1XVZTVufRcYGKHjhp80BGxvtrc1LQgLqwbvDdeiTQ7X6DqAoWElyGCDG5SBB4+JwP7qgBCGrol2JDkg4HFw46Gw4LBT5/AlRnuKZcvFuoNLRYoSadxKxa6iHNfWd16AuiqAvshrkjEZix/umYIayQJAkacJLHd/wV3aUWVmHo5MVGupuHhERor2wfwiMu4vaJPbm9+kS8H7xbsey9ER1j4wLWBsgRKxfnt39U8N1EIrb6T7Clk0IFTd31pgQDQvj76CNYjgkWgpsTfPremRsQEB+eEqEPbE8Qc5pj315ysZHe+m7PrIg5n8YB1bNjLSkMC91zUZF+Qg9peNEUYHzEMENJO2h2ZF6NlwM4mKA1vG6PiXHCupeTrZgSqQdqOqy97aX26aidokRATJoM8+fnaA/x7plXYH04FDEs2UP3pgSqGfOu+KQRsOzM7NO7wRHkEWxPoofUKDQ2lCkvLC+2A6calABc9OoQKF1rOMhl5Pc8WlB+llvbymVNgMxP+5U7Mff35AiKugHa0/a3/eWS1BLEUsc4EAiRtT1nJUl7PVTw2UXS2KDRSr3BIWMtYHbCVxlpsJu2VPqmLfS1K8ZFD+fE23Lkv3sqxON6t5ipUzXiHgDrez65aPt613lNjfJV/EVmghD/R6cHy2KKkUDNDO6ZwwO41mD+mI7yTAsKEUU7awDEPRDhc1yWIHWhPAc8IMuQlKsj+g4E2K8NDcFpI6J9XwPaBX7GFrp46Q4AApMnKzoOCPgBVoTBTJD0D4/2aBDL8TLQRslDnT0n5cuItHHVR1rErxnPz2tf7OWvF2G2rH8Jh2AETGB6nYQB041DFhyaogE2ugUCup7dSYsGxtsr20tc3RLuvpEHwSs+dOtaaJNEgegsfIuoCoeL2xRkUnxA30Q96CJoJeVjqGLs5N8bR3UU/fhWnt0bSlq4Frvq2mzGWE+Vtw2oL1Nskhn5spwPz53RUaQNFIqWC9eFNUFBP7pb/KsuKNPiaAPYIafv9U09cqog84AQnzMNYiLOFCBxmOxTYcNigtsAsA7BpQR57l+uyDeJoY7EmfO1Z9ck2jtDCKdECZKDzogHL5AtOn01jb12omqLkv0ddbPf3punP1p1SjzGDHMbnz9kBJYBm0+c13y/zAOPLA0weZOjlD8nDYmRJQU9kBFS5/Opymx3gKPXrg+1Vr7GeJ6lX2T26zknG5YY895DeAbH+Fu1y9NVffp39sq7O+7y2WTzf12YZCxIEXPhPyBvc78Awb2QQPk/d4zNlSWoyR83iOGG4ZVCISJ+6daemRLCp8ehy9YAUz9fvrz07b7cKUKzIcnhdu3+yrltIZ2jmRy6fwkret/fl+knILu2cxIL5ng/GpFgjriL7+11x7/NkemKxSXGUGuOgeY44IJx/WjA+zaaZH26MwoMQJIiul68e74HrHBHiruSW6/XXtUA0oPlHXa2YsX7YXlo+yOjBD74kSDQKrihm7tB/YzwuL7b5koncHdc5gHc5WtnBBit2YE22+vT7ay1gGL8nSWFmBqgq+FR/mqw8D+e35bie3MbrAn7prkSPg9nKQfQX9GIo5pQnFlh84lcoj5c5PVNeA7Vjf22vHydrthXKCeMYNodxa02LQID52vvAtyQTqusE/QoLBnn/o+117aU+ZwyvR31RkOUMTFOQqtjSSYZ/Lshyf+w2ZgTe88WqP8Ln5UuAw86CafYhjmjFG242iNhiVvPdWo3OHBa+LtX1vKHLTSjj5L8HHRUOEPH5spl0HO4+tSAwTIfJXbZNlFLaKyVXUPqHOO2cTcSB9bl9UkMBNt2FtHa2xetJeKSPJCaGx0J7eXNdu1CQECj17aWKIOzTcACMVtind13Wft98uT9P1wV+zv6dfcsn/trrCb/33ISrp6ZdmL8YZ7VKz19J8THR7QAVH9nsxaUc4/zqqXxnNWlKelhboKnADkZyBj1sla5eGAGn9fniK9BYAC5+rvfzpd9LjJ6cGiceVVd8pNEGMWzpT65j5Z7C+K9VU36C+Lkuz9u8eLZjv5Nz9I+kAX5bOH/u+50/0/cqOCz81ih3pQ3NQvVJ9DmWSURQ2qRSLwfUGLtAccVBQJJ/Oabeb4UB1gVIoPzYi0HaUdOjw3nai3ZeND1Xq+4m2fVdMjS8K8+l4tTjb83CQfJZx0GaBVgXhzEHBIcrDivJIc7qVD6sq0YISmILm0zkB6odxwkJAQ4f2MixC/zqFLgOQQxRkjJMBVlqbkSEw1v396hHQdJIFuI4YqcSbowb0EAVs1NlBIKocSrWJaZ7irgDKxMHgOWOqSOFAQVLY7JmXz+ySVJDw7K9skGA/2dBJnn5YbVACExbRW0QrAiWcYG84CLBAcKuZF+SrJHj5kiBysOPy3FXUIYadbMy3aXR2gjV8ftEkLx9mKjEBxwAlmJR392szb81psQYq/HSztkAgVTQgX6AxJARudidW0n7/NapSoeXthu34+qNjUOG+JvvHkJpiRGIBu0onamd1oyVHe+hkh3iM1jI7kaMOJepuaFKDngdsUrhYcFAQ/gj+0OAIwqDIoMu+aJOh4eac6N6VVnbZqeoSKAQ5ihPQcwgwHZBp6YUOvkhl+5vqD1XbdjEh9J2Y3LEoPtMJGxxAzKDa4NJxu6bGvD9bI0hB05/2dlRYS6CqhmK/XSOvErevqqyV8ZwAPSBiF2IIxwdbWd17iWDjwCHhJWEEZafNDB0iL8TFvZ+h8DE88q/ulcKYbxf5A0MxhwYCtSUn+1t57TkkkawP095YxQVbcPnCZT282J9pLg4dIellvPEN0IIfLOi3c11lB1eHHPUR0Mfy9sbiFBgY6wu8ROIXW+I9UGxoaAMkhzx/O9lv7qm3f41P/L+MAPM5KupuTw+xYVbclB7lK0M9/k1Dy/pjS+sHxWmlWcCjhe+4/XGFL5yWKBkgIwo3jy9ONSjS+OFxrS8cE29GKTu09ivbdxZ3qVOXV9wix4bplQrDoNYADC2N9ZEmJHeW+kk6tr32nGm1skr/2Pgknz4iLBB9UDdpVQdOgDm8KvYXJvvbl8Xp14sLcRlpt76CQ+12nGm3IkKts+fgQvbcduc32q/mx6rLSnQjDUcRpiDpGUA9Z8wxMgz55xSyBidxoJUCkKVgQXicGuCp2ULxwYLK+NaTUfYTdmRFiW8pa5LoGjY55ALx/uiR0AOG2N7QPWHSgm1U09dr81ACbE+FjWyta7ZpYf4l9r75sDIEY9dOTDYq3FCpzE7xtS36bbfl0s01fPV9J89RQb9H3yjodnS9mltw0KVRxkcOejiTdYrjp7C9+VpSfi8CKz/ZX26MLY8QHRmeFwcOCZF8VhqBqXHQeSN4ohvdDbY3xVmGARgWR8LVjg4UqotcT/TWvRecIZ8qVWEknkc4UXa+CpgEBAySdxCpoGdCTbpweIRSbtcL8I1y8sNmG9sYASGzNoU18tKXEVs+NdXQXTzfavfOi1c1j7zEkblG0n6YWv7Kx1GZkBGtvvPJtoYWFechFikOzo3NQQxndAGMiHXOJMESA+lrbeVbcdOgKDpt3B0KKVupATpONS/RXR0HvqLFXTkbJfjhFdan4O13dKdOC2oYeDR+sax/Qs6PA5nncNz7MDtV16hlTkN2aFixK2vGqHoFZdGfo7Owv7fjPcC72Gh18qIAg8J+eapB7ZGP3eSWTmIeAuOMiyHBSzi3iFoAgRc6r28vt6FMz/7txoLCiQwP8GNKK+xFrH90SZzvxny4CQAvgFwkrhcf6TaftjhvGO3QWw4faI5Mj5E6EaPj1vZW2KC1Qif3kaA/NzcERKDHYTec4rAEnp2H2xNwYFd7oQe4fF2a7qhzvkXVBYvb9rlKbOTFCSSeW29CtMbHAqhpmxuoxgbKJZi9C3aPr9N7+antodpSFuTpbcWePBl2iR8o7VWvPPzhZ3Q1MQX4zL06FxMcMEY3yVLxDg8YcJ7oIS5J9ZY/PGUDyTGfn3glhdpAZHfktcizDMY7ZMBHuzvZDMfNwhmgQH+Lwx6dF2bbSJnt1U5mtmBKm7gKaH6jPDBJkECraDLoRcPXvmRtlE4O8bV9tu4rgxr5BxSloPfeNC5URALRmgAU0kxQzG99ZazNvu9YSQ9xtdXKgtZ85K90QGqaC2k5ZApPwoi9FHE1yK9OD5n7pDqYmOAq7tzYW20u3j1anIyXMwwrrezRgD9DhirlNTXOvpUR6q8CgW8oUe3I2aL4Y+DwwN8p+9eYRzeuB4kg3GCc5zBv4XIBZGCWMBeA6VNGtswx9y+6iDiup7NCe/NniWDlhYqKieVJpQfbMDwXm7+XQvqABgZHw0icnRJdGT3WyqNX+dP0oe+6HItGylmcE2ZwoP2vpO2P3v3NMQwRxV/vNvw+aX7CPCmrsg8kjYal4ujkJYGe465vrC+3RlckCtwAP6pt67fElcVbcNmij/J0FcqDHpZvP3BjWBmMXYHmQz20ubVNsg9bG/VZWddr8SRGKKejMyDUpLH4+JdK+zHUI+unEPTE92grbe+yTo/XSSuPoJe1FQatyNM6WE+XtotIz/42C6t2sWksLxub7R8fMsCh3y20ckIsY64ZBmOzjJgZNpvjbM1/nWcFfF/y/19moaOwWN5gqMgXesIsDxYZbyA9FtIvzCagWinlU/whzcEviUktqxFD7PrdFFA5EWExFpCrj4GChkrRzaFC9czjzsCg0ojycRSugIstr7pNnMd71uC6QYMcGuytBQZz+xtfZepAsuO8PViugscgQL+PgQBKPe0xv/zkrax3Uiw33cNKmZuJ5R49jmjOJQHv7oCwc+Z5zYr1kx4YvdVvfBTkyUe1T3Hx/oMoWRPupuifBowV/tNohZMPa9/2NhbJzxRqUw5hyD+ETh8H+2nbNbOAQhjfNhFOEtkrmu89JSP3MwgRZMF6TFiBLv8rufrkV7alqV/dEGpHecwpEHLBQFqj06fzgN//rny+WwFJc1/Ze0SVy6vqUGPMzoU0wR4Rn1do9qENhX06TuLZ5pdilXbLvTjapKGBaNX+OQ9XZaai6VkfhhR6rUQBhc5LI0X6sLGvSuy+txoLtknzlmd76k5kROpg0Ud7PVUUh1CFarnQp2AS8S/4sxgDcH78Gb5zAOyUtSJxZHFegx3BhGZkWSgfmkg50ElyQgeBgN1kY8983TAyxI+WOJJYuBsHsWH2XCuVHF8XoPkB4fzIvSkUiQ9i6+86p0ECsjiMa746k4aEFjiFOcf4jNXyOZ8IBfqSMScImG9vJyQFKVknmqtr6NeQPX3/ocyTMIHdcdHsSwr1EQ4B3zQFK0UPCR9eLe6ZI5hlgY4rom79PgcyhyfejbU2CRGeBGSYyM+g/b00Dg0Iu3txebsX13XLnoNgFTf8+u1mffxTRa0WH9BR0fVZPCP7vxgGMCcbF+9mXh2pk9wvtDzoLBy1IIsl6QXuf2vI9PedsaqyXFcPpnxKtIqN3wGGv+kFmnZzMXttQLH2DhHaNvbKW5KDnHbFvKX7ZC0wdDnEdqaIXhIbZOsxRIDliT/PrWA4ydZ1996c3D6rzR0KBOFfUwaFXCxHasrdUAZwhYG1tA0JX8ThHtHiissOGDkUsjIAZ5PRHq6npsk+zGtQ5vT7V4Y5Hy53ukSYIRzBv5ZJ9/EOhXZccJEoRznagxXQ10BfhiPevNSeton1QQ7F4Vlygkbm1Xba5tEWFPxam7AEoh+nhXkoWSbRxDfv7ihQBEjdODNE6qujp1/rfWNpipW2DQk5Z/6CXOABBa2NdoPuZk+htv/ntzfo1dSbbexQH4CYDoDAzgpiKYJXEIDOvSc/si10YdwxaNs5/fedsc06zzUwLtLf3VkmPx74mXgD8bMuqtx92FDoEvOkBmqBM/KoqqRfChyaOC6c9iqe/3JSmghmUFOMC9jR2oFMjPERbBaWD8sY9UmigWQJlJr5RkFNY4rQEgprs73J5gvc5AQLsZagY6BlIknC+4eex5x9ZFKOkKzbAza5J8VOc3V/bob36+LI47R06XT9fmaRhYugC+/rPCUlPCPPUADSKIvY4g0ApNLi/sZGeKvJl1Vnbo6SdPTk3I1jCb4qhPYUtsmRFJHqgykFtBHnkGhvhKQEslBfOtqdmxVqEL7rCq61l8IztL0aTxxTmq21jSYv9fW2+wwGo44ztK+9SosP/z4v10tRmujHoU1iP1T390jC9trHEDpc5uvrEJQq9b042yyUR63Z0f2it6NBBRfvvXd+uO66BY2/uqlTXCUcfZisRYzB5ARys7hoUiICbE7TjzTsK1JkjFwAlx3zlmW3FoqIxuAzjFfRAe45Uai1iEQwSDt0VJy/m3Nw0MVhucspJmnvteGOHLYoNEOhDkYUzZFKiv7qLvMPHf/+VNInkJWt3lWntAPRhtf3+F8cVz6GaVld1SlRf1zdg6f5ediinUcWsf7C3WBl0rDKPltvrh6pkof/w1EgVGnSG6ZjjwsT509p/zl77PNNuTg/TGXbH2BAbcfUQrQVotSnBrvandw+rU4UbHffIuf/w5EjbfKTGPj5Za2/uqLTV0yNk7IHl6rTkAJlTQKNakBJgb9w4WnvlV8sSpPuDwRDiNty+K2pSd4i4DwXn3o9PaB/T5eTsHhPgKWfIXz9zj/SexEwKVmg5dNV4drhAMs+GHKS5vd/+/f5+3eMbX59SvpJzslpdMvSqq2ZH270v71GuATDJs9U08c0l9sOOInVwsMsurO3SwNTa0/mavXaisEUx+6YpYfZtdrOt+dVcFRrEewBPCg2MVVaNcrhicu4BtDEjCbCORJgcLj7Q1ZJifCw+wkvvFGR/Uri77IEpoGanBlpyiLsKAgBRitGQcF/lBxSi6BmZZTUhKcAenBkppgczWpj+/fj1ozSA2GXY1fbb+yYr8Sfe0bWor2nXwEVyDFzewj1G2N/uGqM4BXULuruvj7O0a9Vtg4rnzN/ARIacCjOGv39XKIo8JhAbi1t1/tHRxyZ4/ig/mzMhXEYwPKe/LEqUoxkFUkVnnx0ubrOC2i4BUesKmuyJd46LkgZ9E7dQinXODWZPMVvMzdnRUS9pH7Cqnn7RsN/cXCpXOqi/UOGI03S/T5W3ifa/H5czxPvdZ+zJlYn/73Y2XthdLjE3VSY3RnFBwkIwQyVPYosXNnQU3IcQBTMdNrPGMfyK2QG0r6AjUMHH+TmcpeCgQwP445o8S0vyV2IB4sK0UBYvQjmEP7xAhEcgU3Dw2fx0BbZXtqpdzeFNEkWyCJcfLiL2gSSAHEwKcLgIRftIPIi7AFQaXCZOVHSqMmcDg36D8MHDy28ZsEPFbUI+4chR0JCwUNWTVPm7DbWdhY77ZDPwuSzsKdGOFtTm/DYVWKAKbEgq2syaHvlqf3miQQuEZIbDnoOOTU0352B2gyZVspmwPtxW2C6UcfAsVB2HBgbRPcgazxC0hs+njQ0FalK4m4YD0eZ8d3+1hfq5aIYHBxv6FwIYSQmWhyQp/Cz+n8ILRBjRcefAeQ3Cg3rBJF7EumhxQEUpMKBHlTT02NxkX/s+s172diQdXHRWduwusrtXZYjCRSElbYyHkw5KaD1oHECu3jpYI/44KB+HL8g9nFOKPSZ43zw+SFQNChGCLR0EDkjoNj+dHiGrSorXVRkBco0ikTpW3WfNlx0wEGxdunRJiBgdHBB9igs6RnTqfjolUp0NnEuq2/ptdUagfZfTojbu5BhvTdn+/J+f26qHbrLjuY0WEe4pT/Pc+h4VchyOwa4jVRR8meWw6qXwwCniy8xGrT3QX5BaBFr+fi42JtJLzlK/eOOwvfzQFDtR12d5NZ0q3GnTThkVYDMiPaWlgZ7Gs4Cfyf1Q+DEjhYSQe6Q7h10eLhn7Srs0wOnadH/bXdqlNUdHkfdyZd4GiCFrmYSKvYHxACHhm6N1Dmeylj7pGZ5Z8P8b5vbfXv86WKkii32LsA6AgO/m7zJMnRMOzmvifbRPQ9xGioNLAnikqkd0L/YOawHaJPsGNIUi/VRtj61OD7SH3zhsYzLClPSiedqQ0yq+PnQVAAmQIDozHECsfz4ffu6m0hZRGfhcOYidvWCFNV22clywECOojqxv4gS2pEzwxSaZRHBmpKcdQDx42coVlPR4YbM9tijWXIYOtcyGHs3MIflCp0GHAveUILcRltvcbwm+I6UdITHic0mCmK+AoJNnS7eRd0EcIOHAGhFO7J3MqjhQZfHB7gJsiAMcinRwme5KopCa4KcOZ3qgq63Pb1WcoFOI5oU4QEeVvX8ltvK+KSgolmdFecj3nzgAakx3FK0XMRV0nT/Hu4BvzTu4QiXjsCfWgnbjtU/iv+d0o62cFCqKHnQU1h3DJOl2UCwBJHy8p8piwjw0B4N7AkTasDHbfvPgDLn08euQ6rg39nE1wxCnhIly+8KuUjmvUVBuKWnXIL6cll7tS2g5uHjhXoAiaJIAAQAASURBVAQlgXkzJMvE07e2lNqrt42RYw/7GcpKY/8ZCXd3lXYqCUQTUdnSazV13RYU5GbXjQkSnQUKC10DxN2/nhNrJ5u6pFujY3v/pHDNBSGhwNyBYuqH976x6366WrbtkZFempGDFopikzkLMZ5u1jF4zt45WqMuIwUJ4NSb+6sUEzQ53muEfcG7CHDVBHDoG3f+Y6+98bNpElijweDMqKzpsimjg21hvGMgK+J3LnQjc5J8lYgCMIHYE5ehEcV6OYvvTTeb8+7WCSFKHKDQcDaBKINWo5ubFeulLjxnOf76nLmgpdjQL5ocLroQNNUnZkb/X8aBD45XK+Erauy1m8cGSZPIz2IeUE3XGRWFN4wKUBc90A0u/KA19A3a1sJ2dXVYxwAVW/PbRIm8ISPQyjsGNZzvFzNibNHT39mE6Ym2ZHSgxXk7q8PBOUI85Z0mBLvLPRH9EGctpg/3jg2zTaXNOgMAGeg4MEvkVEmb/XRBtL4fsYA1BRWVzrOnu5PVN/baNZPC7Nb0EHvreI32L4UL+7Wkusv+ct0oUcu2V7SJbvXkdUkC3a4Mcxzl5yYrZOiUr+wst9kp/lbU2GdNHQNyqVwS76MOIro74gEMBeLx8tEB6iQ+uyDB/rSj5D9mLORUzLVCe0SHAJA0GSOIQFebFu4lBzHeOWsKsIuYAcBFjsWFdvJEUYuNZpbO1VfbymR/vQcAwH/srXBYp7s7qcOKno3Em04UxhrQ1+mQ8fyIE2hjfshplZMguQaCa7opJMx0WsfH+Gh/om3aV9CqKeP//r7QJo8J0c8FZKDzv/Ob/fb2czfYu/uqzdNthJ4DcVbGOJ0D9pv5ceY8dKhmVtDB9hs5wj7MrFNXCOohtCuS9iUJPtKFMJ8CAwTWM8DhH984YPtfWqlODnb6r9yYLp3fyCFDpJFBP0qBQDGDfiIyxt9+tsDRnaXrRkyHKvqH+fESkZMDsc6enhNnv9qQZzFB7mJPvHegxo5vO27X3jJLzl/EExzp6MpjSRzp6WTJfh6iYb16qFJOpuim0vzcZHfL/XLuofHbeKJedsIUFHeNDrYVf9tt7z40VTa1uH0SjwsLm23c6BBbOTpAYBJnHtRdtFXQPQFsm3vOKxeAEQPVFQfBut4BuVI1tvXZ7dMjBCgCPOFYSNeX/wbUWJ0RYPsqsPl3UV5AsQTL4dP1ubZyUbIKxZsnhdiDk//7wMP/1mLjwXX5OizZBBwi4u/7OCmgk9R+fKjOHpkdJZ40VSMPngSHIHeqskOIBcEXShTuC8cLmm1qWpBQmjGRnuoUwNPnACOR5e8hfmMDERzGBLto4NrMWA/ZyH54vE6OUCx+NsS+8m4dPgRyDks20NIkX6vvPSM6FMglFSX3AbLL4RHr6WIfnqhTgk6xA5KDOp/vp8FX9d3W0NCjKdkgT3jJ9w2etzmJvhpKR+KyNqfZxodDQTmn+6RwAC3YVdimpMVBcxlm//yh1HzoTIwO1CYDBYIWREV5oJg/664EqrztjAo4Av6MMaFW3tyjzTgt1lFF0wWCGgJn29vb2e6YGaHiT8OcfJxsf3G7+biP0H0ylA/h08aiVqGv0ByOVnVbW/cZ6+49azNSAjUop61jQM5UFBb4yaM9gQYDRQU6yM6iDrVOQSNpR7//5iZbdMMsCevW7q+yJ1ckyL3qy+xGFRmdXWc0CRYhGY5bFF8IRJ+cE2uv7q8Q4sn6OV1HG/tqPW+GLHJYwDmmiGVtsdHo2tChIuGh2GACNe9webKvfZ5Fh83NZkZ42/riVrUfQb/ZjCFuTnLU4TMoglkD2PyyJtnkJGgkdiSlFJi4s5B0gT6ODnUIM7kcQ/l+lFaCwWpwppUIdg7K852AB8cVxIQEAbEt9wlSi9MG6BFiTig5BBkSTQ2orO/RcyFpLK/tkiXj1Fhv6U0YHAQKwWHIcLS/riuwjOQAHTTQpdiD0d5O9uaeKvPzdHRVSPQJ6rircBhH+4zUzxoX4i7KIkk4SQEIO4foyhR/BWAoNnSOsCzFNpPDlSSW5IPvvu6esf8nP31mkRDsKDh4RyD82M/imvTG9gr79dIEido5rNZju+c6QvQqBhKBsFBEQCEATcZxasWsaL07nhXvjpjA2kUzpQI9hLkxpu4iSfEP+W02J97L4jzd7IXdpZpOj9uSv/MIIaHsL2aL8D7piNFOxwlN/996Rgkp64CuytI0P0v0crd3Mmv0bm7OCBKdACQTUEEzH6o7raK0yR65aawsA0mSoT5xIMZ4uIqy8PaRGlm10rkkieB+QZG/zmzQxHsKeJL3v6zJs/hYH+0BLsTkDCcE9aR7xX1NiXK3k3X96p69u6HQFk6PUqIIYj030VuJlcSkHWft/e9yLSjESw5EvEPQRVz0cOqiC8hAVDqXTN59/3idOhS3Tw61DadbhHrDfZ85OkgHGzNFcONChzI7ytOO1fXYrEgvcamJafC5a1r77NZJoSrYX/zdm7bs4dv0fj7aWmIv3D5adpp0rGj5gwqnxfkKmUdbAR2DguWl61NVWEDp5J0wiJJ9QAeLgZWgrMR0NHd0DkgcOfzQHnFvCC5xW+OwvHNsqKYBs2dnRviKXkBMQ//GvsSKMrelT0gf+4LP4RwAcaVAINkiDnOOcH4QWynIMb6Yw/DM083qLtIpDPBx1tyeFeNDRMnlUEZwzIDKyeEeOvu25TZrXwPEAPgQe17fXSnKy44TdRKKw9tmXZNYoqGCosV6qq3vtoAAV5t9eXhcdecZCaJZS5iw/OmLXJsyNkRJEZoPxNBpga72l/WFFh3qYVNivJToa8JySYedO39RQmeK7Pkx3raWYm/kMCtt6lUXg3lCd48LtTU5TUo+mfaO615ug2NKO4NcSTxxL9zy04n/pzgAoou1LjQRaK6IlknISdTe3lquc4G9SFx6a1+V5iNMY3J1XrM0NDMT/cRPhwb23cZT9vDtk+xoaZs63bxbDDAoYNefatLPWD7aX/kA4AJUz/U5LeqeMh/gp19k2yur0+XYxBpcc7pRGkQclbjYK3eODrEDNW2KWXSemJ8CGMU8lofnRFm4q7MKDbrNv5gdLcMCZhqBODPLBrOR/GNF9uwvFsj8gqK9sqnXfrMo3iK8XLSunt9TZivS/SW+JRdgP9PhpAuYHOGlhI49/+R7mTZzcpQtT/ETOOI+fKhoYaz7Y1U9KiRuGRcsNzni0GtfZolmRJHCz7wu3f9yHPhRnYz3vsszvwAPmzM2RJ0+tI7EirUnGi0xxENaR4bpTgnxVjLf2jZgv12RaC9vK1Vnh+7ukmmRdhQDDLcRKopBv6H3fJHTqGfHfCZodgB+OEJh6oP995OPvGyrn7hPnej3NhTY2w9O1nnwXladjAGw5505NvQ/wyYBsj7cUmJfPjzF/rC1SPpevj8AX21zr6VF+4r1sbOiU86JdO8BSclFeP/rsxs1t4KO4Yf7q2Vk86vZsfZ1frN0jtMj/OyDrFoNOnxqXqw0FYBJx+q7VWTyzDjbeMYycEkNENDJPBQALC6o2wB8dOmI8//eUSE3TIqLcamBVlLdabfNiFDeB80L+3320LVJfvqc9cdqLdDPRUXFgnhvaUN/+fkpdTY+2VhgcQn+stlmgjrmGAAZ7PPK+h4ZChAnVk4OV/F4uKJbsgLOUzo/f/+mULrCMeHuypU4M9EkPf7ZSQm9YU8k+7pJq0JeTAEyNsJdYNTKxEB790StQBAYMvVt/TZixBB7dkGizkByGNgAnLV59X12uqTV7pkXrb+LpGD3o1P+62lUKNw7+s4pqQXRBMmEZ/rR0TolhAxs+z4PX/WrRO84eLDMYT0X7CJaSEVtlyYp5tV2W5jXCJtw2d2EyhyLNrljDBuipG92grcqPSgKHBxs/tL2QR2U9d0O72vQY7imcCODnEdaYW2n0CgOFYIZD40iiEQcmsXX32bp8N22r0yBiw2Jd35ZbbcQcBJsggM8WXi2cOwJjgzE4TtRaEBjWpbmr/HujtHyTJwcokAG77MA5yXXofpMTUFnAnU3+oxLdsusKAVXB23LtDhZ9JpNEecjage/zgYCVQDRmhnjYZNifWxVRqCoGCBxBFAuDqVpqYEKhPwdDiUKJYYVcZDws3Bbwa8duhtJJgcn4qiEEA97bGGsDnISPHyguUhuofdEe4+0zIZuLWSSirLaLk235e8zgdovIV5UJqzg2BQIpt87WqsFzRTNlGhvJeK8P4oPksf7poVbx9mz9otZ0VovBDgOXLysNeSsZUCDhXLqesXF5/AnMDDZHH4kaw8HMCpyJp/mtfRbbICLjQlytwM1nZZV1iZ0OK+JLo3j3Qe7Dbfqph65VUA9YYAWCQebE1QGPjiifRJr/g0Puv2y7SYIGlxHrDPhj2INSwGA+PWHIzVyO2EPfHasXvdKR4Ik5WhJqywo39xRIYciaFHjY32U1N0yPtjaOgaVaBNYQr2cbGGyj80cHaxhbOwbDhvoGCSfOLG9trVcyAmHr2adnL1g3x+tlWc7Li4UoEzRliCypEPPkwLOy3mIEpTPMhE/D7G+c5dU6Le2D4iywOT1sWFumnbNs2CgFHtq8Sgf7UEOxWtSHT7x/+1FC1v6HETPjf1KoEGG/7S5WLMAbpoeYV9nN6koJpE8tK9IqDy0GETRLe0DVtzYa4dL21VYUmiwF2W/6zNCw7RAu+CRMliSLmdRc5/tLmiRbol1Qhyge/nRyTrFD5JTDmgQ8bKGbiVKtPjX7K/WOsPhhzWHg93Ha47pIN2yp0TPI8B5hLzzQap5f4Vt/Q77ycHzokxygDJM7oUHJ+vQA6igcLp3SpgOVPY/yQKUT4woNGyT1rcTmp1Btd1xe0P8zT3/dHmiNEsgSGic0H+wrgBIoNCsYCbAmYuy0ebPY5TBIClMN5hTcbp+QF0winueL4UGtJsY75ESm2uAYcdZu3ViiFryrI+1R2ttXV6LhXqP1PNYc7xB1phpkd727MpkJe8zEvwknqT4pGMGVW9auKfQWjQpJGcHs+rtjimh9tUJB53Md+JMJQebT9aLooRV93vHa21WvJfdOz1chQZzEKD4dfVjDd0llyGKs9/Oj9eBzjqHqoWYlPdKcreDOTI952x1csB/HAHR2xC32U8MAETbQdFMRxKqFELzHRWttjOr3pJDPexARbdmEzEhOs7HWcXCiYJm0SKIE5wPXMRhDlKSZbo0we7Qri7p/QMyEMeh20Gn5F3Cm+fAnxntISojLowUYq/trtD7pHNEQbP/VIMO+ue+K7SJ8b4SDE9LD1YcuGNSiNU19gpk4TOwGyWhwc2IQoWuLZ0lOOpXpr6/dFk7QrGFaQG6LuhtDDO7bkq4QIIs0bYuqtMb6wuNdqgFug5TovXyngrFBWhknDuAJxR3vF+GokKf2VPcqZk7fKfbJ4bIRhqrTYr5//+LdQZ6zllHx9vrcsy98+0jSo4euSZWBgfsZUC8Y/sKVGgwTyiM7l/vOWk9vznRqGf863unKi6j7YAae2UuDejtQ9MjtWcPlXdrgCLDg+lw1bf2CWR5aV+56M+fnK63TfmtFufrKkANvRN6mdc3FCs3yGzscIAN3efstY+OSHOz82C5gKA4LzdbW9hsGzbnqhAtbO8X7x0mB92AmfHe+i7vPrtMhjCVtd2672eWOOglCKNJU0leAQr8PBzUXvIGijFyCUAqtGU8/1/dPFrajgOV3QLZvs1tkd6VfIBk8bHp0VbfgwZzhOLDhAlRAoimxvvYXeNDhERzBoa7O3j3nt6ucopaEOMtIArzHOLRz2ZGWlZJq2a7fLm/2t4+XqMzDb3B16ea7NXVo7W23/zJOJsR6WE/nR+tZ0/coAP8wYk6uycj1L7IbVR+RBK6bv0pe3JxvL19oFqx0mfKPAEyH2wqsmhmygwM2senGuzaUX72i8WxymU47xovxwFohBQavG8GQXKxRhKDXNU5xiDms5MNtvlQle7lllSHa2Tf2UvSikHl5wyhoEuP9tXayG7uVvwAiN5Y1CRtBJPnvzzdZOND3fTM0bFhOQ7de0act54D1HL2F4wKQHKAsx9ONyk3AozCrEizggAqzlyw2ePDHGL1CC/zdx1uyxP87dUtZYon7CEMIAD5mKnVf+aCbT9QoVlJj3xwwsaNCrCC+h6bOSXKGhp67dap4VbXimkOXakfpfdEB/fAsgSLCHa3ccHuAqOxUOa5c738bZG5uzup0Miu7ZF9NIMKWaMrpkWqQ4R2EgoZHUSAa549+2R1cpA99k2OuprEFQBkrpQwT1kjM+sI4BVAt7xtUHbxDy2OlfXxKH8Xe3r+/zXT4X9rZ+PPO8pEGSFxRGCk4F/XI1SBxANk9IV1hRo4hHjxWG6TNubCCaG6eWzq+rBq9XBS0ioOdOdZBUjQdGgV+MKHBzjoC6FYo/U7eN0c5lRatPj35LVYoDd++VcpgYrwHqmfT8JNQQBaDlKN1z+BAMoGnFRENPAEaTMxKbofJxR/Vx1YV6xA+SwWMFxcEKkNuS2yNOVzSagQIrGBoa9MjfHQgKzarjPSY1CAcU8c8hwe3EtTtwMpZ6GAgkD9+Gh9nj15S4buE6R54+kmdVVI3H8+P0aILfcHcsbhThLAfW3Oa1UCUI4oelaUqBIc0NBgaGkmRnkLxcLfGv0IG5UNAlUh1meE6GaITfcfrbJRo4LECSdRhp9I4sl7ZcNit4s7CAgCKCloLEklFCtQTTlo0H51Hqpf+/ponfyfcV1g2Fhmba/oac8uS7Zzly4qCaQFC3eZowPuJB0FxJzcG5xYtBE8LygBbN6SNkchSwCFU03yk1PWbisvH3jSB7Sfte0n6mwV/MfBCxJo3r8ozvKb+jVkCXez6aODtb545xQGvKP86g4Hf7gPdyNHzX2opF0dKFrNoD9YwkZ4OYnTDNUJGhcFGUXo5GhP3SP8XpJpOgEkqCTpBKDKhm5rqO+xGRPChajxOXOjfGxNbpN+H30DBRtJEgLjyrYBDR9i7SAOJIk9WtyqqaBcJY3dtiw9UMJ/XJZAzhhaRDEdHuCqNQA6Q7LFGi+q7LAFY5lIPlSJE++L30MID6IJisjhQHuXn4W1YqifqyyFx6YGWgXdhWj86J1EM+PZ8rN+Pz9W98OEcdAiwscD0yOstuesbc1tthWjA3WAQwv7y9p87XOGXB3Pa5LWhThAgcX+bW3tV3F7w7RwJZJQBbgwUODPfL63yiIvz7qgNU5xzjPm0GZAJZS/7TlN5u0OTecqfT+CIgUPVEpiATxpkF80WHRImbDc2sv3PiejA9ZCdnGrdXUNWni4p42N9tZnQ80kCFPUgAbOjPJUsaZEuPe8kvk1WY3ay0UNveqsCgFq6Jcmhu9DQgwAguaLi/iIuB0vdwT86WEe9uI7++33D80UIMA+2l7Q5qBgVndpFsL20g47ltdsd86J0vOZE+elZ067nyS4qqrLrpsdLTqkhgA6D9VwqTGJfjogKSRAt6FLQJsJ9HAUxnQHcCY6mlVrMbF+skOlm0rhg5CTOEDRS5cAuglJNXGIZJqCCqDjcGW3wzefIav+IwU+QGP64+oUzUdI9HGxo3XdmkL85q1j1OVZW9AkQATbXE16jna44tE9HDliiO08UmN/vT3dBs4z7POiXJw0V8jbSd1i4gBx6lRxq3jsxAtiMgDMhoNVml0D8LRue7E9vjpVcYDO1ntbSkUH4n2zT9g7JDy5le1y7uEcI/YTa46UtIkmgbh0z8kGm5YeJLAIkSVTrEHBk0MdFs7MvKFzmtvcp3WHGx+xntiIBgFTidrqDhuTESqaDWuRhOmznAbFkaWpvmIC0FlAl4OzI2YcXKCN0X4u9u3+Sps+JkTPiwL6vhkRcj/i85roGIW62dqDNRYX4amCnK4tgEpFS7+APQob4vgVsS3dONYhaDVaMEwSKJyJS7jUBfu62N4DpbZgTqLlFLfahJRAR6eWM6DPQal5fkmC7vFXPxSrQ8X13MpRVtM7YO/vrbKH58VogvbUCHd75qs8rdlJ6cFy/KGbA12R9w7qSz7A9eCSOJ35mMIQrydFeQpRZoYJ1tcMV2PI7tHKHsUsNIJ3jw21I3VdyhkokOkw0B1C68h+Q9sJIAgYyCBGACyoZmhIi5oHBEagT7zivtba0mMhoV62fEKoA5zoPae9gLA5NcpHdL7X9lXKepqfz+DFV/dXygoV+uBN44MEmEJX470keLuo60A3lTOAvd0x4Bg8SPcJy3O6x8/+4hV76sWfK/aQ8EKrpEAqKmy25+4aY1n1/fbNtiL7/R0ZSgABX3Da+iyrQUV+Y0OPup6lDT02Jd5Xa3D9wSqtG7j7zy5KVBx4+POTAiRCvUYorrNvSEoLS9rMy2ukTU0JEMBF7kRex56lUIU1QH7FJAAAVUwGADmwLQbUIz7QsVw4ykfP7Zn3M+2LJ2bbvuoOJcoMBWWI6Md3jtPa+8eBSrs21V+uhOj7xkV7qYgg7yHG79xdaN/9bpH0Ep1nLqiALWwdkCaNPcQMK8BCbO1/sihOHU/srllz3+0ssb/cmWGnGgfs/S+P2+8fmCoQblKUu/3501O2akGCdaJf7BywZaMDZHlbUttpt04LVzfvSuf8SEGzBfm6yJBg3Z4KWzgl0lICR9r6U83qRB4oaBEVi+eaHuIiq+ZyHFTPXZLJEc8NYJeucW/vOSsvqrfYpBCbmRakKfZPzIi2jcXNtim7Ud20v35XaCFBbnKAA/Cji0qshZkCmPrWmpM2e2a8vndFfbf9anG8DCU4a67MdPrbV7k2Nj1YtDjordC2yGs4a+eOC9VzIn6NjvByGCL0nZM9NYDE4aoe5Ui8H4r3CAye1h61e+6eqQGXK+fE6mwgfrOWOdZeWuqIA/8lnY0DJW3afAgmcSPikG/uxGngkjVwgHWetadWJQmVQZtBFRgR6i5UhGAJMkgSc3z9bm123GO6Bx22alSBX+6pEJ8e0RwtwsauM5rufeRUg6hVCFbru3h5cJFHCq0GAbnw44/ieUPnAKXGehOdAZxlUASSOV4cNCTa4lzcC8kQXDcOLCZDgszsz2+2wrJ2FRpYtvp7ONDlY2Ud6oxcvOSgZhGcEX2yOLEXo2gC3SGJJJmjvZt0eXYDhzyFxcnsOiFnc6bHqO3+wq9fla0mU9gr67pt4egg2dvhKDI6xlei6G1Ha+zjQ7U6YKChgDTQRYC3i7sXhQabnUKDNj4uJoequ+3+fx+279ef1IRyvjPJMS0zKuEbFiWpGDpc2KwhSbUt8HVHiovJIuXQ5XCBKkVSerymT9OHQXdwHCLYU2gg/lt3vN5C/Fy1WBFmPftVnrjGN08Ntw8yazWIh3b95sx66RXYvARqEh5aghz+DBN7a0uZOmSgcgw7k6i8/awWNu+AorHg229s88kGFY7cG4kTvv8g0/zDACYODuYs7C5oFbqEyIkOB65JJPOpwc42LckhTkdnQvDn3UKTuzJc7qbp4aJGkGRD52vuPaciTNahA+f0LjsHz+vXmfCNowhrmIvgw4Ts39+aJlcWkjQS850V7fquU6I95GzDACYSAxK4lan+6npdSVTgbmPhSbuZyewUognerlqnq1MC5ObUBlUGC92LP+qgpJil28H7fXJpvMRm7ImWzgEVdPuz6x2FUGu/TQn1stlRXtqTdEoWjQ66bAvqaMeXFdY5ntdVpo4cxS6i5yvXpqO1DoviC5eEavE+sKflXWFpTGH+1KpkC/Z3VeLDkKy4SC+bFIZLzRAb6TTUMpL9rWDrTu0beaj3n1Ngxnnu3c2l0rOsTPcX5QZtwMmiFjuYVSeNT0XHoEwAsP3EfhtHMpIv3j38aJ4jXQGKs8rWQXFVuY+a9kHFLzqsTNbl8PfH9tPfxa4dEySuMoUj6/VgTqMVl7TKZnBzUZue05UYuLO0U0U8hQsHMt1NEiUEdiS0DFh650C1vhfWztgki67i6aCpHTlepd+7adV4IUfPPflP+yeHjbezkNL7Fsbae4dr1fGBDkFSwtDEf24pFQIKDY/4SIKF7/6Ro5UqNEgeEcb7uA6XkHhLabvd9vIe27ctWwMA6ShEezqr+zkrwdtuW5KkRPBYSavopSdLWkWJWTnaXwgwFz+TIgR9ELx+UG/iQJi3k2iNIGZQB9cer7e4KG/bXd6l9fDQm0ckXrxvXpS9crDCvi1sFo3wza3lKtIw1MDJjf1BQc77XzQ10n7z6WlzGjrEDpd3KqEhXrKeAHD0nYcNsZIN3ynBZB9iIECsAjVVYeQ0xOZMiVKiCgD07bE66+nq1+RdvndZDTSHq1UgLUgPsr1F7aLsUHCg02HYFkU5Sd/tc6K05/i5i1L8tX6IA/weiTmJRe85h9ibDh0gBHucMwGdTlyopz3/k3F6nuwNtA+IOPmuzDr5y5p8O3v2ghI0XGpuHhNk7X3ntA5ZTzMjvOyBxXE2KtDFbkgNNA/XEXKQ4XzBOWj99ydUlPEzABx2Z9ZaQoCLzj7otk8sSxBtleSF+IRD43YsQs9ftJNVnTYlxEeW0SQRFBSAW+x/dy937cXyvEo7ltvoSCYb+2UJ/N9eH3+fI2Qc955X9lXI2vTChR81d4E1An3tmRtGWVy0t9ZfUICrdDzMuaBrwzmWluhn1VmntZ9BXR3mF5ckWP1ga6mlJfvbT6dH2tIxQXasqlcuRnnFraJ9YG0M2FNd2a7nfeJUvYxJMAChkGAoH7TK6s6zmg/FQDjcxmA94JJJQU2RQLwAXKLwxu6bQgiQCeEtOo36+h5bnujnoPO4DFfSRkznO4OIUwiTCyV5O3RRkb4jJdKGRvPc9hIxBTCywSlOn+tOIvmj5sSwhn/yh4etc/CC/eHxVzQclmIJOtML94yzz487rLkfXpWqRJfC4Lmv8m1dTrO1dA3qrLlw4YKmPB/dk6e4AqU3MpzBmler0/45GqsXdlhxZoFmKa0/XGsTgrws1s9J+qfbF8QqnkNhZS7Zt4dqBKDcNDbIThe1iMLDeU9XGDr4yfoBW3u6yUYOvVogydCrrrKUUDf76niDzGMmTIi0b/JbZFZw47ObbMupJntwTpS9uK/C3j1RJ7Dgd2tybFykpxJ31jCuVdCIOF9/ftsEu+nve1SokU8RB+j0I7Km2CHHYc1Wbd2oLg1doF0lHQK7Z02KlK4zzGO43bZqnJLi2XFe9vb2CmuvaVQMItdpanHQY4ljuCl+dqDG1h2sdtDmG3vs9ME8Fa5cj69M+o9kYMVotIz9oi1DvdyXVfef/QC4RLcG+hz7h7Nh5dhguXC+9vhMe3pZooxXoOV/kdMgs4pH50bbixuKtd8pllMDXKXfw8VqTKjDGnhamJf945GpYvowB4v9Fu3lKhDh5tQg+/rv76or6+I6Qm6FOw5VSuMLMLZ8dKD9akWigLOK1n51XMmbmL/GHtmOC2SIt92VESIAi2Jm6dhg/bdfZIjcXZsLimz93gqL8XWS8H1KmKdy4v/Szsaa7HrbmN9qRZWdduuMCPv96/vs1SfmyIsY1JfrRGmbLUwPlFsGImDamNCDCNjcIMgr1esvp0dbaVefEGyQMqgKB8u7FKjzy9otLsJL3tQgiKByC+N9bM3JRgVNguVN44NV3KDwJ2kFoWYqNT/7pnFBFurqLP/iwxU9SgLYhN9lNchVioSSwIdVLZUhbW4QVar5I5k19tPrU+W0tWJcsJyauKBHXBHSEhAJLqASIMcgr0JR+89L64A4lkPyUBkCt2gNH0IjAVLM5yFUuy092N49Xqufu/t4rT20NF6uHRQTdHAQnTO1G5cPkmZag9C/8mu7RKXg4NPMDqZu0hJu7xe6ywKHrgICwP3STaGTwP1SjClotAwI4Sqq7bLpSf5y73DMthiqCeYHqrst2X+kUGq6NlgkgiixELESBZWDgwgKgusJbVDajVnVPY6ZEsOGSNBGUUalTNBDjHXvxHChHaIqVPUoOQYNYYowm42DeiG8SV9XW5vTZDmlbepagMox8XVXQZu6UFDtEGOCDnOQggJx2HJggrZy0HHgrt+Sb9ddkyIeNMk0iff8tCAhDxQwiMcQzs2N87LXd1bYT+dEqdXNMwU9Ialjh/Bs+Ddrc3aslw5RiloOOgoYUCKSJgLVkbwmu3VWpGOeR8cZWRTmNvWrgKPzsnBSuFBgXDyaW/ttyYRQhxOM9wgNF8KVZFos7liXROlCDAf/nncAIkyQJcEmUOCIBTUn1d9VlMKC6k67a3q4LGGZiPzq90X2yPJEcVvvmRpu7x2o1pRnkqqlaf723q5Ku3MWzhs9DhrjUFww8O0fpmDJVGSKJw4CkJA/LYzXXlh3qsHW57UKBPj1qmR79OlP7aN/3C1O+thYHwXxfXlNdueMCPvyaL0sHuHu427ioMMw98a0pl+4JtlK2nvtVFOvimEoldhn8r6y85stOtLLHpsVbR8cr9NBvzLZTzotLglfJ4bIlAIx+6RouOrDL09LvmRPzI4xL6fhVtDeLbtqQAGsedGP4CoF4BAe6mG3TQxR0g3tjTXNsMedO/LtmYem23fH62z1xFDpCXgudFIJ1kzVxmmNC9tL9llGuKfiDIfOtCgPO1jZrQ4bAvifTY2S61NWba+6uCS8xIzb0oLtLztLRAv8fH2OPfuTCULwOXhwEoKHvfFonc3KCFbxR0eCZCW/ulPi09Lmfols2evQ1zhUWB8ADJgIUMyBzOOdPybGR4mL6BYjh1h+Y58AGOxnZ40iDlwQOEKhQhK6objFRgU6C0SB+80gPUdnc5jDza6+W1Ql0C6SFPYJBySzZyQ0dXfSwQ7dB4E4F/SyJ2fG2qH6DnUuGPbFvYLWfnG8QX+mp+esLRkbYvOjfe2VAxXiyC8cG6I9MzPBx7bkNIsagY0txSGOiDx/3jkd0CufDR0RutDaDadt9fJ0mUdgmoD7IEYXDIalI8SMhUUzY21Fkp+9vKvMfjItXGsehyGohXTDKWbpGvMZrIFrR/mLasc+JwbwXCkCiJlokE6VtNq9c6NUJHHIM3wsu6FfjlgghdfMiFbSSvGfU9Ridy+Ms6qOM3qWKX7u8tSfl+Al0xIMAeieIP5EeI0Osq6pVxQO6Bejo300QT3J290+zKqzEwUt9puViaLYQMv9zQdZKnq+zGywn06LsH/uqRACjgEKtsOf7auS/S9xQPTSCw5beGIC5y2UXPZtZcdZzS+6IhQnDnyV3WT7DpbZOz+fbjff+Zx99cnv7Pfr8m1mRrD2yfrMevvDNUn2/PYS0ScRStONZSI7xfqP9qPmXK25d6LlNXbbwZou2d4zJI7BbbVtfVZc1Gxh4d72yqp0+8cBR5cOTvxrB6pUgELJWT0myPZXdAnJR8xOLGUQJbnCP1ak6B2dbOwUaEcxd8Um+3BmtTm7OKmzef+0cMcci7ZBmbF8crTODmw4ZC8/c719dazeVo4Nkn4MS12AGwAeqOR8Bv8P/ZEiBXSav8/zXxDnJWoUZzV76OdToyynrUuuYJyb7EPubUVioD209rRojC++ttVe+/1yuUKF+DhLa4Zd7reHqjV4EmodxRMda7QQ7A0s3jlL0TxgrYqjIK5TWPbSiWGvjwpytXWHalRYQX/joljIre/Tu84ubbXVU8Ik6NcsimFD7P5xofZ1fqO+D0UkDnHYBUNh9HYdrj+Hycac1AAVBKfrmbV10WbFedqnR+oEhjHNnPgEGwSwR/nA2Qv2/DXJcnwCtP02p1k6lPunh9u/d1cq5+nsHJTG46GJ4fbEhnzr7TurIb/8uQWj/GQMA+uAmMs9M9QZut0VAw7+iQ/yUBzgev+rE3bDtaMl+qejBuVufLS3wLPRCX629tsTdt/tU+yWlCANobxtfIhm6HBm8N6JKZxLnMHNnQPqgN45JsS2lrXpMygYOT+YT0GX40RNjyhKD86MlA05wCTnLqAN32/XkSqbNyVSz56zbH9mrT114yjLbRwUEyUjwNPWFjTb4jhv+66gVfoV9CRolYjh5D2nClpUsBeWt2ueDnNOYrxcpRk6fqrBXvvJOLls4bh234s7bc3vFtkXpxrt/vHh9tedJdII7zpeY6tmRdv6Q9X2yxUJMnChaMfNlJyK98Vaw+mPYhqQCLru6tHB/3XFxh+3lwo91DAdWtKD5xXAuRG6CDjFzEnwUsDlzzHcCREbvHGSSzZneoCbvXOkxm4eF2xv7Km068eHCFkA0WZhyp7RZZg2D+I6vUQmYHo6WaK/s+ZmgCQQ5AlmDMbDVpGJ3bjU0D4mGLHgVP229Nm0eB/pL97aXWl3TA8Xrxv9B+JMEK0r1n8kVEn+I+Xeg/gVhGRSpJt+j++EAIjEBt45LgigXnQocFKZnuSn5GHg7EWJp4pbB9VR4aAigHOgpoe629acJpsc72t5dT328+lR9rfdZUqiQCkmxflqU5IgUEWCKGaXtZq76wi15EjaMsLcrbH7nFwf4NIj3CtvHVDydvPoIDn+EERnxnupEJgQ5iYbM+ggFBq0CEnoDpV22KxEHwncv8hqFKWHZIHnjCibIVzzkv0tq7pbbVRmfzA9PTjY3eKC3XWoXyk6SHhP1/Wo+0B3gDWBo9e/39mjNpwGJXqNEKpL0QllA8QBpIRNRTsXO2LcRDicoV4w54LJrky75c/TPaO9DN0AtJXviJMZyNR1YwIdPN+rr1I7lTY2mgVs/xCTzoj1VGt43YFqefwvSvaRtSNrltkgX+yptORYH3n/VzX2WGNjr92yIE7rEooLdLzbxgRLMEwHB64jjl4kcbxPdEpc8PEdcyrOyZoZq9crRUlOTbfFBbmpk4HrSGf3GbtjRoQDna3u1vtneiqJMs+UgorgRFEEGlXeflZFCgODWPt04aAZkrBAP6QDBQ+UAuzKnBYSWqgZCIrpCEANDEBw23lGftr7cFMKdLUdOU2Wd6raXntspkTodCwQCeIcUl7XLfSMffnWqlH6nn/fVyF3N9jJ2JHyD2JrgpLigOdIW5zso+SEP0PyRaeIhIWCGh3ImEBPe3ZrkT0+O8ae31IsdInvcrDCYfDgmAEyUsUkQZ530TN4ToV0kp+LXK00Z+AitLth2isER7qExAGoRcQjDmYCc1mTQ2RKgvryxhJ7cnmCiouTpa32y0VxcvkhCeHPQn3CVYpkASoddAo6nWg1WJ9MHi9rPyOKQFZjjyh9u4s7ZOsLvYc9BkUNSpW0Yg29FunnophIxwDBMZNlZ6cFip73u/nx9sS6HIsN85QuipktFLysX6gDzGFhPoWvr7OdOXNeYkIGLfH9c2s6VXCh74KexRRx5jCA+n6b3SQkDt/0BbHe9ureCiUg7G9xrHEAym1RHMS15O97yoXca9K470iJJV/bU6GuDw4mzK3B6IE4EBnpaWE+Llpn0DnpFkFZIq7QSeOqau61pDBP+/jF9+2ep+8VFQ8KGvdK3ELHxyR54gD3RLcThyl5y3ecsa3ZDXJF4wBFNAvqV4XlZl2fKHOAFswuQIPCBOm7ZkcqDkKJ9R85QsP4oE+Q2BGLl11O+NftLrdJGcFCPHcUtWvNzkjwtc93llt8jLcO2PzSNuvrPWvXzYtTIUoMg4L66JQoq+rpsx0lnf9xFoz0HimHrVsmh4iCh9PQpAg3vR9+nfjA+hUltKRN+ju6MaDAaKceWxIncTqTwJlH89i8GM0roGhkDzARODrQXVbOTKEHtON5sb9wpmPNEy9YM+iDoGmgMYKmik0qyDMxgc53fl33fwxL+DU6rQxeRSS/9XSTZR8rt3d+Pdf+vK7AensG7caFCaJ07T9SJToYGrZPbk3X+339UKVQaZBlaLvEMoaH8g4Al2A2MPQMJygKN85a9piD5nxWWrHJId5294eZ9urNGRr4tnpSmEV4jpSZBc+Wogcty5U4AFhJl3Bxqr9Feoy0/gsXtD842/jZ2OQCLlGEsA44H6L83XQG8x4prO+dESGtERPP/37XGNtT0aXhkO/dNd4+zK5X95tcgDjOc0a7Rozl7Ir1cViQM58kNRBa96BNxBK3hRkpV8mS9eDRSvvN7WOEwqM1nBtPTnTeMQwzyVd0LpJLOgd/XJtvt82Ntg2Z9fbSdan2yGcn5TYFhY2ZLQwCZk0Ra7Cn3n6kWh0hOslJsb4CPMU+KG4VBbvzzHmtK86NX86IVjL/7p4qu3lKmAC02zKC7dfrclWo0kngXYDa40B4/aRQmx/tb49/l6sznoQTExXm8Px+U4FyNQYD00Uqaei20rJ2i4jwtDBfV+1H3hH0Uejl355s0plBTgCS7+sx0ra8/qHd8/uHFAeYkYa2h/OU7j0mLuRYdLzI74jTDAxlAOPm3GZLDfeyOVFeGqkwKcRTxRfxGMpsuOdw+/50i+azfbm1yH55Q6qh7w5zH6GZMh+dqlf8ZnA09MbbM0JsbV6TponPmh5rU2O8bGdhm6zYmRvy4fo8S04J0nfJyqqxocOHin5FTIeyD4j66JRIK+voVbHN2uLs08DNolb7yZQw5TIk7ejsWvrPyV72zvkx/wF7vs9qUKHPOuMsgK3DlPrBixc1JZwRAc+tSrEXt5dahL+bDB7W7K8SDQ69qIbLtg1IQ3G0tldaVYp5HErZCydq+xWzyJ0YoIsOhLhBpz8h0EXNAOhU0NvJH6F2IxGge0I36cDmTPv8r6vsrxuLrLK0yR6+ZaxoouvX7LOJC8drb+98ZPJ/HY0KhIUqEAoH4+QnRbgLcQaFu3lCsATWUJgINiQAaSFu/2m30J6luq7rG7Q7J4RqEnZciIcSMRYwYjRao3tP1OlAqm7o0YaEgoVjABsqr7nfwWluGxQ1gcVJNQ4Hk4nVUIsKSttUZBB4JewMdLNDpe3SE4B44RKDu5UCWe95y67pks4AZBRk9Ye8Nm0wCgeqcJIePgfKEQuargTUIJBohiYxiCwlwkszM7BHo2OAqJEJogS80+Vt9vUPubY42VfPgUmhVy4GzYA64OIS6OWYfQFKzlRzHEY4dEksCByIOEnwSSgYsIK3N0JYCr8JER4S0zFgaFdhu900JsjRzgxzsxza39HeetlU6LTwJ4Z4KvGl9UYyT0AAAaNdX9rSr2EuC1P85f/OcyLIYm0H+kaQY2NNj/DQwgTRAZG7Pi1AQZ6Nx6RhvJkf++kcvQtaxmxm2obcO/Qo1gUcSNrnrJvyjjO2Lb9dRQlDr1TINTmm3X5yoEaiXriLFBoUKswAAWUhkaaoivAYac+8d0wCf7o7XPBAEXPR3SIQLp8SbstT/WXJivjO03W4fXO4Vu5goAuss4xYX4uL9bHdOY06yD/dmGfjIzzsq9NNKiDpOvEeQEeYkxAfgm7nvP1rXa4+E9chppozKwAqAAkdbVfeJdPr4YXSvUqP8bV1JxpEdaLQ+8XcGHvhu0K5L/HdsMhjTVA0EZxmRnpYepCLxK60T0mIKLbpVrF/2Ee4grlc1qBQaEBtpOgsqOmUPge7PgAA7p/kmEKCQIM7yg0rRktINgjV6zIqec0oP3tySbzNTvCSG8eVK87L2b7aCwXgkixpWTvQzSgyGAJFF4Okjnvg54B2M4wtxN1JCRpCs8ruPntybqzltvRaSpS3DhcGG5EQMDkZylRKgIv0IwitMUmg+MH+kfk6xBje6fRwL+mLth6slDscxTrc55yCFqHRzKAB+WKKOYPbmNYNfXJ7UYfsq8MD3SRoZthRip+r2uDEjjUnGiXM5TDmc0FloGOQ1G7Mb1PHb391p6X4u8gnn+7m+FEB2is4vsH57x68YDPCvbR/Dxe22Fcbc2RbSGyBYkOBzLp44MNMu3V6hKWHuskFig5ZoOsI0S6gFvLM0hJ8lQDg0ML+gyZIIvPnRUkW5e6ihIQWOgl1ff+g5hw8PjPa3t9dJbtN7HwxpqCDyH4k4U/3d5cjGqjgtrJ2JQd0ZFhzDMnEXpdEGbSQZJlnDnX1wWUJQgbZ6/OjfWRuwD3iMMNANmIChT9dtAPZ9fbLPz+oDiegzXenmqURg8ZGB5gYRCJAQQdQxJwg4iyzMpIivNXdO9HQa1mVXfbK9jLFjig/x8BI3i/F2fHSNgkxOSOgGz769z1W2T2g78oV7+uk5Pfb0y0CadC5zE/0FmDGLCC0DAARzOFhDUMlTU/0t9g4X3G3oT+98dlxaTQ+zK6zvOYB6WfoANXjV98xKPQW0OSZtw+p4/XOrko7Wd2tBGDzqSbdL0UD6/tgUauMGrhmpAfZR4drJabms3+3ONGe+uy0DDaglHCGkdyND3eVIw/iVmgMCGCxa5ernv9IvR8cedhzUKko3OlQ8OvsmZlxnqLLMbxtfqyX7I75uxQazLlAE8Uw3FtWjVOy09d7xmZMihD1bFV6oL310BTRQwqrOv8TB8LdR9on2x3T2gECsegmfjFw7Ffz4qyz84zOW2I6HcfJ4e7KA1gzrJFDFV1W3NFrr98yxkq7ei0j1k/C3I+O1QkkJIE6frrRpuLaVw6tqdMWJflYb/9ZaTvyW/sceUVtl82N9FUx8+U3WTY1xlMUIZ5dRSXDX4fInYgkd0qCn5436w1t6ecnGmSgMXNMiFX39ltmYbOND/QS3Q80/+1dlRbk4Sj8iTckwhSvaCR3l3fKHhlq3NgAT1lhA8wtmROv840ilgKP42hBtK/O3E2nmmzzzkJ1BzC7wdaeeACFddWLO+2OudHqIAJ+wUDA6ndFYoAcsegGzJsUblNSAkUbpOvE+YWO751bxliIi7Oc2zi7AXvyWrvlpvfa6nTRNB8YH25bStts6fhQh0kIlu/d5+Q4OTc9SP+9taxF9q/YykKp5Nf+faTKFqQF2t7idr03wMWGxl7NnkH3CqBEZ2bxKF/d08f7q+3BaRECHYgLrCtm8zz8p0cUS9EK8Ny/yW7S2ZRZ36t3B2WV54mGi0L605P1KnRYpyTpFB4U0M9uKhQ7gdjOWUg8zC1qtS3Hai0+wV+FxtgAD7vrd99q0DFxgO87IczVuvrO2XvHaqyurd+uWzzK5iR4ay1gE4v72aZjtZaQFKiOCFd0nL+NHR0i/RZr8m/vHRSIBA0qu7FPVF+6FHTXuDestZkG/tCz6y2vssOe/TpPuU9MpJc0pMGuTvbJnko5UK07Vme+dLmdhtpNs6LtzYPVMndBIvD3G9Pt4fcy7dE50XqGACsUGvPivdRVCHdz1vBR4gDOjLLjTXAwB/78ZZ7yJ8BQGC3EMwDsus6zintQ0bnP65MCFFcBJogD0I0p1keFedp9982zdw/XKN+7dkmKGA73jA+1Lf+83W6bHCpA9n/l+l8qNm76ONs2FrbZA4tiFLhZhGgoOAzZxLTVQM9JwkE6eIEsZhABpubyJaERcdOMsOcQh3+JDoJEAMvFURFedsf8GNtf0W2LxoUooWK4DouFhIFOw5RQHxsb5qrFA+oACshC5ZBn0UeEeQhdxtkGsTH39tC0SG1Kj8toR3X7oCUEueseCI4k5CA2oE68aMTQINosclBKknbad/ASSZ7f++qEZoYg5PzhdLPQSugSJLkk/8xjYCGyEfh7M6fFyoqUz2MAIP7cFE0jRw6T09Pxyk6hhpqXcemSHazt1LAvFWkXL+lQ1XBD7MgiPYTU0y7HYYVklZ91sqFflf7ydH85DCFmHDEEJyInJaDMrOA7LU32s/q+QbX7CBryqEdT8yPJBIMER6o9R3JIIrRtT4nug8UMGgT6Sit/F5xqp6EqXghACLhokV9pz/r7OGuAGoniolgfUbk4+6H/YP3IZmBiNRS63MIWO1DUJj2MCgfvEULeSNI1OM/XVe4z6AHe2FSqgpbuUFkl6NdQFQ9lHQP22iNTZR/MZ4wN9NT907ngudEShnrGnBKoSV8crJGrGEgxQeZKy1DC2+ouJTod3WfMP9BTKCwtY4TPJMR0fzjwSuq7daCDqEVG+djceG85ZNBx4aAB+WU9EIyuywhUMgcvVEHM18luGB+szgJi3tf3V9ljy+K1jmfG+9iz/94vT3Vapuw1ptSCtCO+59q0q1gHIMJW6HVoIUhE6FSRBFF4Y+PMQZcS4S2kn3XOcJ7P1p2wuABX8/N2FjKtydKxXioYJ44JEbKN08qaEw3WPnhOzkfQ/YgBS98+LmHiQ9fE692TqHafvaD9xh7FVQk/efYm+x8EkG4Aa/uDI7VyXqEjA2oE9Ytu197MOlG/EFrSJRwV7mUPL0+UQHrx+BBpleAuEzhBeqHOTAzxlj5l6FVX672smhsr8ABQAbpJbIy3zYvz0t4gqafoe2R6pK09UC3QwcdtuKgIzLng+d45Ncz2VHaKgw3ShFMRBx9riEMTpB2dy6RRARo6xaTw1z8+KotdpifvyGvRPgj3Gi7BLZ9JUcVeBnWmiB0/Ieo/7nN0Edfvr9T6Dgv10M+nuI9TQT1CWoA1+U0STfJ+eG8UMYAZDHlD3Aga9frRKrk6nazuUqzFoYpnQFL2bUGLXTMuRHEAtzES0WsT/YTALk7wse5zDjCHGMFzovPKHiQOICKF1kjMGjhz3rZtzxNSxr6ALkq3i5j3LUO5EEOec4grofsQB3hPrH1oap9uLlYhf0NKgKWGuWuPscb4/nRumEvC+j12okYzTLiH0019cj5kPdOVAHVEvAwAhBj1hc9PqWMzZ3SwhrBx0UEr6eyzD56eL7ClvLHHMvy9pAGhEAUZZ4/TGWQqPXbI7++sFNI3MHBOsz8AjOg0s4eKCprk0AOtKzYpVHEDEGB8iJt9erROe6e9c1DI7ZWZP0mpYXZteoBlxPnJSISDntjJMweUu3NyqHm7jbCPd1aoC8W7xoIY5JL189KuUvvDTSnSqZDI/PJvO+zI6QbR1AA7sGinY4MNMe9s4/YCUWYpsDlrWANYwCb4uMhMAZYAduKAHGNifSUqxw6X+PT+GxstNdTdvBGJ+jEf6iq7JtHHHpgcbmPSgxW3SF7f2F9lHWfOKpnmzxIDpv39gP11U7HdtyROHR9myvD+KTKhBn1f2GJTUwPViWX/13Sds6yGXhVBz+8slUA4zGuk9Hk/lLYo7m87XGXbSztFR2LfQQ975uZU21jcZrPGhgp8wfUPzSgCbCjM0yJ8bWwUDlFmY4Lc7NZV4wRMYdaA/XtoqLtdn4jFOvbcJnDm0VlR9t1Bh8U1Bd6+kg51/kraBu3p5Yn2/sk6GxXrY8+tyxcoxLPn/KV7B7MBG2gMOFKDnFUAvfH2DnVEoJfTvSBfYTgwa7yh64xtyW+zLWWtDofBUE9LHR0uMTPPgj/zyaZCFe8zpsYoH/jiWL06TYh+oa38bV+FvXWw2gLcR+g8vQL8hPu62mOLY9UpfvlAhZ29dFGd0kjPkfr5dHwwt0Angfsd74b9zpkB958CeEqEuzr2rGmKLvZGeriHNfWf1f7lnGa/Ak4A5B7ZlqX3m5bgp84N1vaAyh9l16mDxncnrylo7Rcll+4h+QIdkA/WntQav29smIr86voeGZO8szZbOsWXd5QL6Nm2s9D25jWps4I7EkX+xBAPvVNira/nSMXkNYdr7NHnNuuMXDA5wjraeh0atIouWR9/9Jfr7J0DNTJ8SQvwVKdkUYqfaKgAQDAZAGVwy/wYCnC4u0wCWBdohPlzGAPgqIrRDAn2+KnxdrTK4SC3JNbPvshs0Lle39wnVgS0U+Jp+vQ0u3dWhMYWLEvzFwWTn0E3JzTITSYK/t7O0mvgWMb6wokOujQXluAv3z1We+a2scH2wG++skPZ9Xa4utey6ntlQ/yvI1X2fVGLTHe+35yrLiOg3bSxoXpHTEBP8/VQPIMpA0DCOcNZhFb6b/vK5Yb3t6des5QQd+mlR4e4CsRaEudrf1qYaBEMlr34o4rsP28ptrbBs5JNhAa7Kwb8lxQbJHC0i7fktv7HzpWKnGqXw54KHnoB6B9JIUk+czTErw1zV3BHNA36k1nUYmt3lNqK6VHibROkb54QYgvivMWBxCmATUNABHVCWEw1DAWlpqdfRQSDcbCzi/AcITEsIlZaSijx2cCgqKeqO9Wu3nRZqEjiqXbowDk7UtxqH399QjQHLB1B6UB/D+c3i2KF2OhKl4IkD6/8vfkt6iDMnOlQ4a+YGm5hfq46PHgWxeUd1ox1G0NajtRomix0LCgyaAoOFbdKIA2yysEjf34fJ21uEGCoPgxhEp+6qkOfwbMGKeagx/aVli6Bk27Gv3ZXCFElmC+O9ZEIUZPXfZzE2WR4DwuQWR7F7Q4rz+Z+PJe7ZJXoOnyo0EaEeSTRvBsco3ACAcWBmz1pYqRa42g1EKGStI/2d5clJ0kTByqCLYYa4iEf5+NkYR4jpK2geqersja3SUkvvO3q6i5bPT1SYnKoGuK4ZgTrz4IusGZI3EA9SQoJkKcLW2xChGMa6MrpkUL54L+if2CWAFzNGC9ni/d2t3HRCKqH2ZF6RwJysoZ7v1qtXVq9oEPQvO6fGyXtwjWTI0SL4nshRj6e22S3z4/VeudQk+DWeZhQbhVraf62KateszZoHxdWd+rQozuAcxm0HpBj9ECgODkNffp7TKiO8HOxm2dGigvKQcPzD3R3uChx0KPTOVjSpiGWy5eNtqWTwvT9OcgTgtz0Z0gYdxS0WXxioG3LatDPmR3jqXf/m2/zbG9ltwrizaca9Qz5fCbZIxg7XtYmOs9Dt020nNpu2VqSDEDPIgiSVBzOrBFFJ6+KrtZQJRvoUAjOpwpbZFlLwYtBBIcfFCJsJPl/DBFIMkGqr9A52G9oNL46Wmfjo72ULJD4UjRlFrbYZxvybP6kcAV3AuJd08Kky8BJiHdAYslhxxRU+LkcxhMi3TU9FfoWOgg0V8QW7IbnxtLB8VC3qO/cBQtzd7J9OY1akyRpDyyMsRVjglRsUxRgofjGZ8dEw7ooCs4wG5fgbzszHWJ0Yg7UM5Ju0XKYmbG/SoMjp81K1HBN3mmor4sSHvZiQbGju0pR8uZX2Sog4aFDnaNTiGUxjkg4wED5ZBYE64vJyHRX+RlMco7wHG6FVR1as9itdvWelXiVIu9Ufb8AFwqOP35fKN0IidANowJ1r3DTOfh25zdr4nT/+UuyP2VqrGiuWKMWtas7SiygMxvrPdK+zmoS1Q0HL+hePH8mZ4+dFKsOMZRZvguH+vRQb5sc7nBw4/NVRHs7CTQCFYZyQiHMaUjs/fQUou5L1t13zgoKW+zhZQk6xKD+YWm8aFasXIcAgAAGSBaP1/WKXgvym1PUKhE2icbPVqfZrGisL3vsxgV033xEc432cLFkP3cN7cSx6HBdu/bknmKHtTDT2yn+EIGzrh6/Jk6Ugpvnx6rjCEqKC9S+zDr75U3pSiaJTYg80VMgvmWdoPNhSvrCscHqVmNxi65nfIK/EFvQdMCZu6eESU+3r6xbxUVmXb+st3+6OFbDAnH48XEarrkYnBXEPM41XGTQsCxbMdZWz4oSkIV4HPEwMQHQbGNOq8UlBtku5mMMvVoFJF3Wu98/JjEzXd2vDtZYXlO/us+AZoAbDHkj6f3Zz1fY6doee3yWQxTNXuV9PPtDoR09XmVz4r3t4OkGJeR0BPcdqdI6zz5dr0ng2JMi0OWdcFF4v721TBTESG8n7XPmh7T04FI4QnnBlwdrpOHEpQq3qH1VXbbleJ29ty7bJqQHWU4lNp3DNeDxjvRggRDMiCFJBXgDGAJgxGCGeVFFzY5uQ3l3n8ALHKMwA7ghLcDumBgiCgpDhaFcfbypWE5Kn2Q22J3zokXdXBzvY23dg/bLT7Lt5Y+O2r7KbnVpNJh4dLBt3lemOEBso0NL/H3zcI1NDvOwP3+abQ/NiLS5yyeK7oVTIjREwFhyhgPHqizAw0lzxV58aZ3QeeIJ7kB0Lz/cWeEY/hblI+E8LpK8o2kJjm4HKPPb+6otKcjFWlr7L3fynRRHyMVYE4CSvPspUR724PuZGvKIQQw0H8DObSXtoih/c7TWijt7lZjSZSJpzK7rM2dE2PmtFuXpaqN83DU3isR+e0G7qO/YEkM/5ZkunhJhocmx0uExO4uci/i3JC7Aron3tTA3Z5uX5C0AJS3AVWAbCHyqn5s6fG5ebho4+frRatkpQ9fNLmi2P983UZPY0fruym+21cvS1BWbk+its5XZEwz3ZL9iTww4CQUMiuUbf7hGAxSL6rvs+oWJtmpcsMwRGIMwKdzH7p7qcK/MauxUHMTIAsAAnZe38xDFLnK/J5bF2/cnG+3BlamWEArjxmH4sft4jb3w0yk6A4l5zEpanOCtOMCZef+UcDua1yTdDMX+vzYUy3IWiuxHB2t1zlR1nrWHZ0RKP7Mxz5E/Qx/jLP7V8gS7f1KYYlOCl5v5jmTorsNwAGrv0dJ2e2lbieaZoavAYIB8FXCAbjFx4KuTTRabGKIY6jZ8mN0+Jljn9tK/7bFNZS3Ku4gDgOBojufEeOncOHayTgXQg398RAyHvyxJkpkOIIDrsKF2z4eZdvp0nS1J8rFPNhVZbLC7VXWfsX3bT1tcsIedzqz4ryk2bpwQLOpAZ88ZvXQOZ5JyFjwoDA4ux2p6tUmgIXVdpsdwUdHzcgnC8D8jgtwsPMJLtnkk4VTItLRxOCBZOZXToGSVooCEjfYVF12Gf2wvc/CNnYfJPQGkAptdXixe5hzwJF4cpEx3/iGnxebHeYufTiHDwZ8e7qlFGxjmJ8Fe/+Vkk8LkmeVJqqSxo6XVT1GVV91hLsOG2NREP/v4aJ0qcv486DELkISTynTWuFDZqNEqvX5ahHjs+MITIHEDSgjxtB0nG2yRnIacNeCPS+3dEB+L93cUSrRgb58WroWEzuWJuTEK3H5uDjetSA8ni8DG0tfFjpZ32uI4X8tr7VXQYZHCGX56XpymtpP40n0CzRDXv/2MUGUEWQwYoqjakNNi12cECm2dl+Qj/QxBcmyEhz06PcqSQzxs2xFHgODdD1y4YCdr+zVfgAMVXnuEm4sOY8gBcV4O3vddixztZCpjdA9ocBhCA1WLopTATTcM9C8l0lsbhI3PEDDoDgjBqMqvmxkltxzoE9CAPj9WL9EoOhzahvAeEdKuK8DPHST7kv3x+W90WJM80Hl47IXttjO3WXaToKw/5GK/eUmbnIn0tLhJzlbOiNSz4mcw/Aj0nOQNd6NTDX3S+6RE+6hYor1JwGQ4ETxikFMSUzoV0e6uOhj4DiTnJPQ9gxfsRFWXCr4YTxehdwjvaZEyfZ21jGBLtJkQ7J8R1F4ytxFDxYun4CQp4Dvhj88cE7pwBJt9pxps2YRQoUmTojxsfCx0G0cngOA1J8pb64Vfg5rW2jEgVycKTX4fy0YSkAeuTdFaRkjP/uNKiUHPcpXdMDvKHpwXpZYsn8/BBLrMQUsyDGrCVOMj1Q7bRDlkIabj97GgdhtuS9P97YNDtYoDoYGuFh7lKx4yQl0Ke/48dpJxfs6WdapeyS3viwOfYYSgUXQyn/0WRPeCBbiMkJVx++B5IcjPf19kz6zJk80tSTVUIJ4LttH40yP+hsZT3nZW4AIHo4ePg7oEuMGeozv71h1jbeEoX1tzrF76MX6NybNM4F41PUKaptr6Hs1guTIfBEtC1vSsCWGWVdiiovSRm8bIBYppyFCkNu4ps4nJAbL9nJMepOTxyXeOCSmjA7k0PkDUNK2tH80eWhCt4pmD77llyZpCDaKOJWKY20gNUMOLHb3QzSlBdqDOMRRK3c7aXvvr0lF6pux9pttfmelDl4uu3Fe5TfbCpmJ1gzHguHtSqPYFbjQnanotKdhNlMMXliZrr+Jm5DCyYNjkJdtc0C4bTn4+ehqMOSaEuyqWJPq4a+//7LpR2kP4zdOhjA/1sFmTIkQvwGEGcIWZP1CSRsf76b1Bj2xp67fDRS2yHqUoIw5wkMNZZuDcqzvLhSgSw/i+7FsGkK7NozvyoxDKpx97RV0xRL1Q7DAz2J3bbNenBwiJRATc0TmoWE63D6E+WrG7F8frnZ45c0EmFVgW0zmjy0wSgC6M+UacJ3RF+SwK+isCfShP1W0DFuvpqm4KiHS8v4ueLRrEr4/XW1XPgI0OcDgY8fPifUfKPprkmftF/0YMBqghrsMXR/+DCQYxg+vcuUvqNnEef3i01jYdqbZrp0dqIjCUvcmjAnQu8N3peC2O9bPUGCxKfxT4gAsiU5mD3RyOiXTWx8b52VO3jdE6uX5qhGIbeqMZkyKFXD/MMMbbRguoo2A+WtllWXV9on1FhXsqudpX1KaCF5MNnh1dWN5vZLC7AJY/35RqL35XKKqhn5+zBYb42OateZYe7bD7xv74ka9OiSJWUOLoVkOpZIgcsdD3ctx64otT0klSsGUVtYo+RwfsJ68dsKc/Pa3ivrLzjAbNPnJdsm3Jb7c7x4dIW4Szzv6qLpuR7Bie6OrpKhoQXRYE3XSwtz49VxRR9J504jVZu7FHhf5Tt4y2v28ttYrqLp0h0LGJe9DBcf6aNSXK9p9uFAXnoZ+vFAsC6nV8lLd9uTbT5o4Lsa8OVOs7A3Td+of1Aqa47hkbZmnBrjoLidlPrUgU8BrgPtzevGWMjQ1yl9GEKGxe7pbi62ExUd5W3XnO7swI0YwJzl66otB6/n5dmt4t4vFxwd5y6WJd7qnsUiz5rrBJmhmmyWPO8pOJoSp8MQfgPVNYcr5veHyGpUd528bD1XIf48wDWHlxV5l5ODm6WIvSAkTvAUlnvyQHuivnePKGFJ0d6E9JlLFVnjM+zN7dWSmNA10NciXML+icAL4BVkDv2bSv3EoqO8SsuGlerAoQ8iZA7Oe3lsi9CzoR3St0CBjRfHm6XvmJh8cIe+C+F21TQZstHxssa3/shinSbx0dZIeyG+zDw7VWmNeg2BnsDs1+qIYEY7BCnOvuPmNLpkZaRoiLZnEw1ZzZNLiCXTs5XN1XOpzEAXJD5AbQxzifT1V3WYy3qxy/xHjwdpZ2BPrbn77Ot4rufrmDYfX7fW6LzY72tBvGBSmG0dWn8JoYDRB1tdykKIbp3rAeUgNclD/hSIdpxprsRntpd5ntOFEvu1pA2QcZ4pgSKDABcPFkQ6/NjvSz6+fHK/fFoQszHgyV/Ec6CYBuHBjU+nzuJ+NUVN2zNEm5OHF+0dIxOpP/8fis/5piA8cjWrLwq+GsQQPhcAGNY+ItSag83Nv6taCwfOQhgGSDtmCdWFXfo5dJYkKVNDs9SJoLAjCOSHMTvFV5XTc/wTbsLhHyT7sNLvKMaA9x4Wlpk5QhAJyZ6CtKDa0f9B8PL4mV0A5kH/Tvh8w6Swn1kJgUHicCHmgOvHwEd/ExjsAW7DlSNqgcZJ9nNyowgvoSeNBI8Jmb8tts16lGq6nptvsXxMge7ct9VULbaYchRKZijg3xlNMNyWnvwHlVlbzIxGhv8UjHJPiJa4hWZNaYEKEXPJunNxUoeBIACpsG7F8/lFhRQ48OM4RhzsOGWmZZu6piXv7O8i5tWJKHvdUdSvILm/r0M1jU/9xfqaCJkOhoTc9/klsSbxJH6Vq8RqrQo5UO+sf8Ag4t/PUpwhAuP/VNngZRzZ0QrsOCYW/f5LbIOpJhR/w8Wse4ioS6jbRj1b2ijkyNdFdwmxHhJZoCIj8GJPL9+GwO0DGB7koU+H9Zmrb0Czkem+QvKgJrhT/Pdyqo7JCehaob1ImDnsM/2stZBQDvnUKOVu61owPst7++Ts8KWhfD+mbMT9OETGh7JDIkcFjb7SlsFfcZ721axRSHfA6FKt8f/igIEUk/tnU4bxDkeBfQpRClYtvMZ40OdlFAgDNN25w2OHQcviuHyIIEbxUgiNuhQWzOb5OAFQtBUHqsnosbuhVg0bcQfEFbSaKSQ9ysuq1fzwQkGNeQUyVtSio6es5YepyvbTnZIEtROlS8M5IMkgTWMwkFaBSCcbXT4/x0GIGikvxAiSRQolkABdUMG5KkbgdHHnQdHj0HDEXD2Hg/mxrtEBvTCcMlCNvarp4zSmyIB+zljEB36+k/Z7m5DVrLAAXsJ/YWlr5MY711XqwdL2jW0Df40HSPEAuvmBtv677LkkMJE+GZzD0lws2yqrt0r9xjiMtIu3Z8iL4zCTPiyl+sTFRyBprKHuG58Ewb+s6I14oGB8SpsKFHNAAGUFFksC6Ybo6A8+X9FUqUcSYC+eSdNTf32xdZDbKGrKjotHvmx8jm+Z2tZdrntKFJBplhkB7va6ePFOoZ9/SdFVc4K6/Z0lODRCuYkBSgpPZkZaddvyhZCC5dyAe+zBa1DBtcDpRnPjklfRDr+/XDVYpXBwqatS7YXz+UtMnhiee9oaRFSSRdAQk/SYg25KmIi/dxts2l7Zbo7aaECGAFahw/28drpAaFsq7oeFyb4q/OFKARZgLExTvfPabDZu6EMPGsFyV627uZtdIC4LbHmth0ol7PPdjF4cSzp6rNliXDpXfMmaEIpovHnuSe6ZwS9zloGY5HEgIllQMNUThDLp9cEKeuBt8FC1E0GlCSoORyEQcQmDPMDwCEv/vh5iLFjLsmh9qfX3lcNAdNge89ZxNXzJa4kYnMgT4uSirCg9xtJ64ugS56jnU9ZxTTEOeunBhqWVVdigPECfYTeyu/okNrJqu0VV2336xM0npRBy7URQVUlJ+LrclrtCmxXgLPMCtgb1w3yt/GRvvIdQwaGmcLXGychFgv0BiwvgQcIAaBhnO2QBNmqBx7nuKKBOT6KWGiV8AQoJgbNyrQvtpZLlQYajNAABRZQCfOmazmLhUtdAooHIi17EsKKDR3HXz/9gE5tgEukejS4UfQzbtj347yc7UDVV2iJjIQD6t63O8WJDF4NkCgJIU44AhFInseITiUsx82ZElfRMLm7e1spY3dSlixxn7xZ9OFICOyzm0a1LMnl6DzufvDtRo8yRBadHd0FkCihw8fosQ+0svFfjI3SnGAPZ6SFmK/viFZOQtFAPH7+8x6xc/SzgEBCfOTfcTGIFGMi/E2D09nC/F0DFVdu6dCZjTPbCu2SE8ne3xBrBXXdNrmgjYrK6q3d4/W2svr8jXjAiH35uN19swXOVo/xLcjpe0CUokvWDUDahEHcBHLymkQHQcK3aRRgbYg1kv2y7ffNk1FJ92/6984rBg4NS3IDpV12oMv7xG9mZj3xPo8a+gflC5Supwff7QNJc36PWiGULrYBxQi5EFQAm9/84j2y/QIT+kNcC4j0Rwd5CyNBhoUOog/5LaI0gfFcmWKn5gUdFtxxmKY4rJ/7NeaYvAk87Gg3UHHuW9quPY7RfDr3zBbxVRwfF/QageqWu2m0YHKR65LClAcSAl2FVUTIBkwC9R9XJCX3TEmREAwwA3n7tJ4X5s/Osg+f2SaCltAGHReCKgp+K6Ak1PSglRIpft66oyHWv3HV7YrDmKd/MJrv1QXhudC/jj6ptU6Qwraei0qwlMupZMnRqrLib6G+IqeCNAZZ8hHlifIaRUq4fRID+VggF84MsqZr7JDndS37x0vO10sy8mB2LuBXs7276M1KmrRShB76RrenRFqdy2IlaUzlFlkCkyS93d2Uv7I/Axo92Eh7ravuM2ivJw0M47vhFMr8ZjOzKwkP4HbjEkg/vIPg1Tf/vCA/tznWY0StwMGEqNY39lNXTq7AeU4MxgESXHHecIeB8Q9mNtkHx6o1egDQDTiBcY00f6uiseRHs7/NcUGCVh2VZeSpgPl3QqCIBS0PGkFkrxj8UhCS2BHs4CLSf3h/UJqQJXHJ/vry2YVNEukTRUK9zevqkMJFYhWcqCz7FevnZcg1xg+79O9VaKE3DshzCal4UV+1vZVd+r3aIn7uzBrYphtzWuzvblNQjwJciRgLC4CLYp8WkUbTzWLi8cmhJ7FgiNJxK6OSpKKDf0AHFNQLdAaNjwbgOoWJPuD3ZVycGFmyJJkH4vxd5VnN98Hx5wXbx+tCbRw1/l7JB4EVO6X4Edwf2ByhDjYtMWgJ1w3NlA2i6vSAuQ+BF0ItBcO80RsT4va7ekF8TocQY6gz9CmpKBjUzJ0ChE3ojgSWNq5oLHwNkGBclp6Lc4Pd58+8ThrG3tFzYGTyeI/VNGtgoMLESY/n0R/Zlqg1bb0OuYCjAvSNFJcZwjeWNSSUHb3nbXt5R12vL5bRSEHLc4PBM2j9V224XidNvjh4lbZzVJA0qlpHTwrhIJJyiSDJP+v7yi3ObGetvZko507d1H0Eg54d/cRQmpJIqBKqZMU6SX9wNRoDxV7IMasR+xOSdRvGxes4Dol0kH1mpXoZ4cKmyWIZKPiYEaX4I1t5dpgJBRoeRhMxaGEQJoE/XhVtzQ/UNeSIhzCUAo1nsm72ytEqyDoIXZHzC2609Cr5Wby1h/fVHtTTktVPfJ+h1Z3tLhFyRXJNeJ4TYtGSH32gg4QCmM44idKWm1fXrP2DeuWaegUD1DuSBQIimNjfXXIcfBCf8Odjc/g/nCmoNtIosZ90WJGi4Q7E3REkgmeAXQozemA/2xMjr1KbV5QZN5jarCbZjwQByhImBGCVTXPjM9AuI4TBiJPAjaJC0UhVpZ3Tw+3zmO7ZTe55USDDhfW8Yn8JrnVcUjijkI3gM4NwZRi4NbUIFu+LEPOOE5DrrZXNxTLvODp2bGWkeivzur2ijYlUVB76Kix7+lm7shusMbe8/quDBa84ruHfgyKFocnnTbiAG1z3vfEMDdRAWVzeuGidD0g79AeGtr6xAumMO7qHNC+xlGENj3zXYhNPDuKTpJQ/tx3f71etCJm5gwMnLepGcGi0NAJIViDLv5+foKja1PbpYFL2OzyDnGWm5vobTcvStR32JpZb0tT/GxzYZu9dG2qnh+8aNaVtC4NPZboN9LWn2hQYS7Ra4ynJYR5KgFgsCYxCSAgI9RVlJ9b0oIkAKTDQ0cOZJ13zAWIwWR4uqkUhVAo4EsDKD21IN6O1fZK0M66Tfd3uG6RxPM+OMCx7YW+yNwN1t2h2k4N/2SuDYnZ6aJWW5DkoCNU9vTbh0fqZJaBoQcuhy/8UGyzIj3t85MOtJHClvUJtZEBhxT12G1yLYz1EbVlQby3KDsLp0WJUsvhSOfu9rHBmu0AAMavzUryt42Ha6y2yTF/QsXIsCGi9vDZmTU9NirUwz7aV62OJLRH9ub2wnZ77tNs3TeW5Ox1Py9H8fTndfm6F94LXTKSHeIg35048PFzb8oGlrXOntBQvs5BzXPhvyn20amwr6E4tLYO6GxCU4Srzd6cRttwpFYFQKiPszrtgAIUprPHhamDgLU0MSUgwMUOlnU5WAENPSq6iUeOKeRX6TwmZoIi17b2qYNB8YImgb1H8oQZAXGBC7ooa4Z3x3uAlsz19aFaAWI8r+PVveY1YphAMcC/xZPC7WB+s52u7JAYHhH1b5clmFWdtnhvF9uX3aBZBWikckvabG6Sj9YccQDLa840nhVuOg9OCLf0lUtlqTv86qvttx9n24bCVvvnylRLjXU4O5JAc8Zw5k0L87UgT2f7JqtJcQAaKgg/HSDOYZ4BbkmI17cWdahw7e49K30RZzTJJBOiicPE9d99kaOJ7FXlrXa6pM3uvXGctXUN2kDvgDmNHKZ49/trk2zupHD73dw4mzsmRDRpDFA4p/au+4uAmrHx/lZX0263LIy3CbG+msVBAVHTM2ivXpemfKCisceyK9rthukRArduSQ2y60YH2E9uGKdnzt4BfDle02tv3zpGHUSopMTxuUm+lpnTKNcmAJHPthTrPWN7mp7sL8rU5pJ27eFdVW2iXmHG8JvZsZrdMDUlUEVxgo+zOlUew4dr72GUQsed2IZAPSu3SXnMh3ePt+/yWtS57z13wWZG+IqxEBLqaZlNnULqmQtFTggFlUL7ZHOX/ZDTLEOCz7eXaW7ICs5Zr+FW1zNof9hYYLOT/W10nK80b39Yny9aF8k48QXmBWddZ+eANIHJQS52+9xo3c81cX6aIr86KUDP/e67ZtqMMB9r7DsjLcODk8NlukG3kTiyKCPYXvjspLV1DGoUAnuI83TVy3sUB2Dq4Ni0PrNOa/FXC+JsRYqffZfXajc/8pbdNSHE7lwYp04AA2qJGQ+8lylgADYJ5zS5XpzfSD0DzvBP/vqmvbOtXPt9S3mLWDh0PfecqFX3p+PMOTvW0Cm6MFqr6upOrUHyzQ15rRKwf7i52L480SBaNno9zdE5f8munxmlvAw7X86XqORIaZ7Jacm5MWggBgxeBuuJS3RGYAQQB+kIEQdg3RyuAYjzMg/X4TrPYHLEBrpJc8nPoCj68ET9f02xQUBiYdPWrGhyOBh9uLfaESB84Jw7eLkgqyAeoEDYwi69b7X0FvB+eQlFFR3yt5fr0GXRsJe7k4ZxERR5QQjYSIAnpwaJa5sa6yNHEKaFMpU8zs9Fjh5UtkKC5JB0lbj3LCAOYBxyUoNctEhA6/NK2sTVHxPlpUTu2nR/yyThu3DRodtQonhJXG+CNigYwZDiguqbDkZaor+dO3vOwgLd7LuTjUpUarrParYGB2FpU5/EQIh60AqQ8M5M8NPz45BjUcL95Mps6FZwpyhgwAzBGs/7/JZ+TbW8UvjA/Su+jDLCtyb4IpBds61YNK3V44PtWGWP7d18UshsacugRLnY23LgTIt0F0pHQsY9OWhvg475HZennMMPZ4HRcsSxCEEVLfYIHyfNeCBZAs1HDBXn5yS6U7T3SAUYhr0g8orxcYjpmeys+8mqU8sQtBYUfuOhKvFn4TiDPuFljhFAQpCrOmNsNAYIIch74p3jEpQjbI7ydJZXeTle1sOH2u7sBvtsa7EOo9+sy1VLkoQHobla2N3nbFNhm6hHnWfP26v7Kq2sfdBuGRfkGNKXHKBBVCQYtDI5fHg3/BwQuoauQaH+VPIfHalVtwMkhs27u6hNyBd6B+6XogM+N9x6knICS0aQmxBn0EQS5t+88DNbNjrQTtd2a48wIAkEJDLIXZO7EcFziJPUUEiFB9BVwtr5ohLGJWOCVeSyztn07+xzCMzX7iixmtY+GxvhrsOlrqXPWlr6xTfvxAXF21naD7zIGXSGdTB7CoocgxnbOwYVdOhcgFySvIBWs88o4PPqMTjAG/2M1vH5y4gIiQpJED8DGgqBELvcVpJ2bxdZU16TFmDXpPnr4A9yHyZwYuHDd8mmGqQP5AckiIGcxAHcdehuwluma4G4ksD87olaccxxbFmX3WypCX5W3tRjWytabe+xarm47cF16tKPWru5LT1CZNjHy8aHmr/rULlYkSiRQDHttqisXQU+dqG8M+YOoI9q7j5rpxsdLldw3VkXc0cHyVMci0kX5+FK3rjPlOQAa6lvM3eX4fbqljL9+qmWHpsiysuP4jnfkBFop5p75KmOo9IdMyMuz2MYIrHu1Ag3mTDsr21XHGBaPIMlKYRIVLDUdXTsLqkYWTjOMU+EriP8dFBRhh2+veaExP8PLIi2nSWdlrX5gJD649XdokwSA6GQYRABQkrMpEsDRed0S7dNTPT/j30yCT+DBAEB+PNMfGaSvayVe87YjNHBEpW+uKNUa5uOIXROui5rtxRouCfOT8RTDCOIaSDVoOLsDaZ1owHw93WxG2dFaS4IM2jgjDOvA/cmrr2HKmxMvJ/95JW9esfEgQj3kXZrWrC1NENVG2K7c5rsjS9PCjl85OMsi/Z3sQqKwKuvUpJIHAWFliXr+Quy9UX/8fPZUTpzrp0WoeSFpJSDFromFwW2rLK7zygWQdf9585yiVPp4Hl6u9inR+rVka1qc8xFgoIFMEVsxqWRBAKHHy6eLSLcJ194VBoB1gadHnSO/N0pqUGalUH3hPhJkQ9NEKQRTREoLloB1jOIOd+dzs6L3xeJUvjVtmLtCWbzrEjws9KaLqur7bL4QBd1GRms+eXxBtt6qkExnE42Tol0D+gEkmgNXI4DXOEeI+zGjCDLLmnT/j9Zg/3zcNFj+Dd7C2CPhHXYsKulyaNYYN3f+8o+q+4+I1FqXm2X3T0rwh6YGSGdBLEA5HbmfbfbF9mNEmADTiCqbm3qMh8cEUN8HG6Jo4Nsa06zjfJ1Ne+Rw+yfhypt2qgAC/R3kVA6dVSAaFVQf3bsL5WgHPdALOHpEH9f2KR4TYFxy7Rw83IapsGpaBfYf0xNZ2BnYW2XOgFoD/68PFmFGoAqk5nRnJDUArj9ZHG8FbQM2PjxERL3Al4i6E5KCbGakznSQfzq01MqFnZWtMqVC7SYeTMPT45UNym/uV+ax389NEX3AChY13PWbspgoOqPtrW8RcXd4tGBVlfXI5SZMw2BNGg63TSstccnBcgNjYKutmfAQrxGar7Y317eoHgP7Qd9Q8nmTZac5K+J47XdZzS8D+onVEsAFwwZ1p9uVlJ9sLbNpiT6K3ZCMXrjQLXD6a2rz6aGeVlJRYdllbQohpJjXTM9SmfKQ2uyNZCTOIBJBOfC+19nCUDGZQlr8ezmbjtc1asuPMkt6+/ajEDR4VxdR9ij1yZaThPmJQP2/rFaS4v2UVefou+7z3ap6LjrH3u1r1IjvMTQ+OX0KBV6XJhqPPvPHYoDd7152KG7bOxUPkYXZHtFq4Bmin7OVWYafVfQYi+uSlVcfuLWMTrDATmg3EEPHTZ8mChKaKDQSaHDYhjqc5uKNF+K/DFiyhR7bmOxJfo6wNAgH2cBXgN9DmoyZy2gMF1bT7qcPk42NsRNjlz3L4zRjBvmGuEyyfXwNfF2vLBZawYqHOY7WNumJPnre5B/L0vxs9ljQuzC+QuiajJ36Q8fZwuM+mxzoWWWdyivvTYhwE6ir2zsUFeVzk9AgKu9uLHYjha2qLCDAga1miHSixJ9rLquR7GGDgjxAJAX4Bt9FgAgOSQ5Xn3PWdHNeZYwOP5Lig2KDLyO+eDfLowXn3HeaKZ69tjreyvliMCiY3Ij6AgvALoVCR3tq1szgsVhxX50VnqQuNCZdT16UAi9EBqzKLeeYAroRb0gugT8Hg+7tLxDCeA1k8JUCIAAkwiSlMHDBtnmoeJHDW2AzoSCJwVMzxmJvfIKmuVEhXAbCsuZMxfFRyVRYXHC/yXpJnlExJ5T2GLzUwO0ySJ8cXMZYuMyQjXwDGcCgjlBcVuRY5ggyXBmTZ91DzKm/pzs2ZL8nOXwhC81zhQUbBy8BOsv3t5os2I97ZdL421Joq8Ej8wkIfmjcBkxfIiSPIITehOSJagVJHi4hYyJ8FS7e3a8pyWMjZeIl/dEYsuAGahj8PLHRHpqyi0tNw6GLbkt2gwg3tgykphS4ZKoN/eds9mjg1TtEpAQcXPoQBkjgfz8cJ2m0mJdDFjMcCrQZETCHBR4tzMhFC9w+OwUeASZ7s5+vc8vd5RZZUuv6EpsbhBGLBU9Rg4Rh5M/M2tqtNAqDt7sxl5770itTcgIUZJAe/Bf947XvYJGYKU7JdhLDj0cECTDJJG4f1G947EOGg+Pn0RqSZyf6AkklRu/O24bNueYl5eTiiiKYzjSiBQ5ACbG+Ij6teFUk41N8FMnhNkXJJwM6QJZTE/0E42DQoZg3TLgOLRx02K4D98BbjCUJNYYA5TQwCBURafCYcaskSvzVeikQPGaEOqmvUbnKDbSS9Q7aGZh/q46BBdMj5YItrR10JoHzlh7+4BdMyVC6+WWyaE6uDAj6OgYVAKG1SWuKly056GUffj5ESUJdBJB8Dn8IvxdNTWcwo37mRjhakFuw6yslaFtPwo9H6fnMtT+cV2aBuUhXIMO+KcNhRpExt/zGumYfUELGlcskEBiwQMTwsUjXTQ+1G6cGWWvbS2zA9Wd6mxBEcHwgEQRahLfk8DIvlua5qe4gGCN93nPNYmy9Kyq6lCxCcoMMMGQNOwuZ0f46GDDoY39QxwgQaFgKSluUYJJYf7ukVo9Iwp33O6gxJVVd0lvoqFk+yst63SD5jBAPUHcynpPGxsloXNbW5+0BRhhbMhv1SHNs2fKONbIuZXttju/RS1n1iUgCc+SbhidxHBPJ/v2tU9tXoynvXBzut2QEiiNGQUegA5mGcRInn2Qx3A7VN5px2v7FPz5nNmzEjWJF9HmzFgPS5o1Sc+ShHlfUav2JQcQmiaSMmI0OpjCsnb7ZF+1Opvo5TaebJReARSRvYW724qpERYV6K4klc4Wa/r5Nbl2+5RQWerCrS5td6ypW5eO0n0yvZcEDkrn0ZxG8/EZaX6uw0UJAIWvqWi2M+cu2JtrT4sOxFoI8RxpG/dX2oo0fxW0i2bFqaN83ZIUfU+Q6OymXs1lmTg2XCBJTLCHrXt6vkSuWJjS0ZgX5afp6STRrHliOe8kzNVZdEYO0J/++7Con8vi/C3Q11Ecf//PD+zbr46Ym9sIFVG4r0ElySpukT0pwtVQLydZVU4bHWLBPs729dFadZXpaHHeoEXDYhvKkLpz58/rv3HWw5oYoI1kkEKSOEwBmBLmKTtOBOaIsEnKoTZQ4KL/AIgaHeimZJkCZmxqoECWwvoeS4nzFXWKick4k6GjaVIc6Bf9EAADnj2JMB3dnp5zKlKIu0dqHCABAyZdXIbZu/9cJyBkaYKvZkhAI0uM8BIdiguwa3yom2xYGW6G/TudTCaw02H68oFJ0pHcc32GwImHPsuy2yeFqkMu98miDhWg0HQbmnoVBx6dHGk59b1ym/vTvePtD1/m2p7qNg0qHRvmbtMTfeWa+Oe1+QIHon2cJE4HyCBm7dmZb76uQ+3RG0dbaWu/nTpSLDoorne4SjIM8NCRCjnysPbRpPmNdNK7gQYUGelAjXGh5MyGLgzlu6K6U2uJ540xCUAFoAXWx4cOllpqmIcV1HXZHbOjZHcclpHm0EH1DsjZB0CKIWoMNOa8ev9ErbRPn+8sE80QahE5EsJ6usLnLl3SmcWzZQ4FSPzHD0yye8aE2NJRWHkPE+2NKy7CU5x7fm1zbovtquiUFTdF53V3zFNiCbVsXJiLxS1eIrcqLHbfWF+kePTL2TF2sK5DRR5nAEg109H/+mWungvINjbzaLMWxfoKgD3e0GUrpkWKckiHlM4Wa/Dpt4/ao/NjhNIfyW2y2p6z+r4/u2287jWnqV+IPecdIuuIEHcBoAwBpJufm1WuP/+HNw9KSwQYSuzffrha3QNizh33LlJ+dvd1o2XvTqcehsZDX52yaVNi1EEccvXVdvi1W1Q8R0R4STu8JD5Q3calSb4CBJnNtb0IW31nAeLXj/K3Zb/boJlc00K8VUDmVXfampfesS1vfGJDhg7RMyxuc4xJ2JVZq2nwFM9oKXE0RJ8b6Odiz6zNk7nJr2bGKA8YPzZM9t7IDiiQWKvHqnotwtPJMoK9HMOey7u056H/kdvwvF2wiQ9w1fqaHumuuEHhTO5GtwmbcvRwxMLpExnsOERdxUljw8QMSk0JspsmBMvtEGOU9tYeu2FpmvYj+SWF4IpJYTrrOCsPFbXaicZuUejQfDLD6eVn39PPnR/jrc4MuczkceHKHSVqr+iW2B8w4Y1dFVbY6NBS/28f6nfv13ly+6Adi3MRvHgqN/QPUJC2ZDfYvPRAq2oblE3ouFA3uWCAaBBwmDwL5/N4RacCv5xWmgctPcRF/GCQIipQDgl8puHjw5mlyiOo1rf2qWUNlSQHlBskJB5u3VWqgqEA4LJE8gPaiWCI5JWDVUlgj8PFhs+EarO7sFVeyvz398ccky6x6iMhpFAgeHJYkexRcULRYUPiBAASGuQD9cpLSSnoDw4pUIRAzemG8HIZ1DI9I0QHzZWWOl0BOhZMlkTEiyj8uc9zJJikKKEQofocHeuYy8HBivc9GzK3vldtdapeXH2mJ/jKixtffDnQ1HTb2EhP+T2TsMKjZaASSRMdI5IpkqzrUv0lSqSzALrD4C2421gQI9Z9dEWiChGKOapYko+v9lXZvPGh8j6/fUqY7S7p0ORiggKVMIcPkz+ZakwyiDc/64LCB1QF28b3NuTbz1alOsRN0KagznUOqgPCsykqabVF06L0/0nBrhLBMR+AgAG6Gug63A5UdNvCeG91j7DMW7l4lIqcRJD2tkFx9OlOUaCCDCL6hA2wv7BVQ/OglbEmuaCUQd2BkkOCRDLG/TIr4MzFi5bf7LA8PF7eLkoXWpi7ZkaI6kCnAXoRk+slAAt0V4JIVw+ULruhT88F0VpmXZ/eP0jGlWmmZc29VlbVaVNHB4vjj/0e75HCBl99qHQ4DjEgEnrC+0frVOTAcycx4b+vTLQn6aN4mBbnOCDKm3ptcqy3EggSPFCfTw/WyMYP5wxa5rNTA3VP7C8J8+h61PeL9sC+4t0vTfSxss5B+/pQjd01K9L+8W2hzZkYJutphNLvH6yxWyeFiIuL6BynkW+O19k1Y4I19R36BRqc8vZBFUvsQ1rBDgOGZgVZAjKUHlAbWve8G4YeUoQtS/PTgYJDBtO4W7vPqBvE3oNKciCnUUUj4AVxgOKY4h7LXMWBwlZrauoV9VL2secvKZbwc+GEg85RCIzXkLUR9uWhGqHH8JEpHklKSA5JOCiYt2c63DsojAEUiAt+Ps62emyg9gvuLRygdJyIJ6DdoGZQlaBgkaxfmTbNnsIEgbXL/AvWzR/fP2bLFiRbpI8jDmw9VqP1wRrkgibE9Ho0FKDsdBX+vr3M5qUGKj7gxEWndU9hm81K9JWTC3sBDQFFB5aMIKYUYDjb3ZQeZOcuXZTdKOBKRryv4gC84i37K+zPd4wW+ACgQwwhIXh9fZEtmhqp7/7zRbEaQoleh5jEesf964/fFtgtsyJVyHx4oEbUtOLGXrt3UphlNvTYh+vz5SaFaJ4OE3uCIhGggdiZd6rWbsCRpv+sOlvMBdpb1aFnR/fb02mY7Srr1MTvfVWd9ummArt5cZKKcSgdzGEihhNjoMOxbii8id/bc5vtl3NjbVtZh1zsQPMADfafqLNpY0K0PtAh0oF4cGqEksFctHCXfhQaeeECphA/2i8Wx8oqleKSpIB1yKwWprTzrIhtvs7DNRsKDc31qQGi/kRdfrc8K8dw1XbLz2u0BTNi1PGTbbOfkyhKdAGxDoXiCjU2xsPVXj1QKUEvySwJABfxg645cYCuKQUxCQLFNUPk6GqyP6A7vrenSmcL80/QGKH5kM04ReiIIaL97S7rsiImT6cFinp859gQdeK/PlhtP1sca89+elrnFZ1QbJX/saPM7pwWLse9gtoumzfKzzafbrZbJobYJ4dqpacEcMAi22PEUKHga7KbbFV6gL1/pNamxProHGbi9NPz4+xATac6Sp/uqxJoddN42ApDZELy+pEq0c+ERgd7CGX+5lCNDCsWTQ5XfkIMgVL5XWGrgByMCKpqu3V+kUSx7zhj7oOOVNur/IRZLTNH+SsewVMHTFw9J1rvinhKPFqR5qecZ82eCps+JkTPnff+9e5y6Qn+tGKUtQ6esaz6Pqvv4Cw6L50KZ0dX16AYHcumRcpynrODooHcJ6/W0dFDl8Y5/oeXt2sYLu+FmWZMnJ84OljUTe4VTVVtzxlZ9oqS7O9uj31+yhZOCBX1iunQRxs6NYsHaiIUawpX8ghysklh7vr7sgrvPGN3jQnRc/n4VL1tP1Rl186O0SgCzqGte0vtvUemWWlnvyyWyWX8nIfaE28esSXzkuzo6QZ79sYUdUWh+hCrATqxrP7Ze5n2m5tTJd5/fmOxHN14JndPDBWI+Pqak/arO8bZwdIOUcYpJgAZ0fixz04eLbV7bp2sM519Mz3Cy3aUtyteMOCTZ0EX587RIfZVfqNtP1Zr9y+K1dkCNbvq8h6ALo8GlQI0I9xTsRZmChPM3zlRq+GSAGRcH36XY4vmMMCPnMLNdp9utN8vS1KHNKe1V8Nst51q0Hsnv/vnbRl2uLZLQDhJPM8IR9PpKYFygeQcDXEfoflfmBL8fEaUvXO0Ru59nAMnax0W3zjvZR6rsIXzkmQkwTnO8/juQJUlRPvIBnt9DpQ1T1sc62/Pbi+2xrZ+C/BxFv0LfRAgNc8K9zv0QddPCrOd+a3KgdE7MrsEi2OoWm/trJTr4r6TdTZ8+FC7c3akLJM577k4W77LbVHX9ZqMINnE/2ZOrH1X1CQq9E/nR9lv3s20pndX/e/vbCh5PntRhQaoCMGJgxXqDgmX6EZtg5rZgJf3B4dq9OcJ5ngZT4xyIGSggiQdqPSnRblLA0BCwiKl4uZw4aJtxIOCQgA1xM1lhFr/WLBeNylMnHk6Dhw+LATQPhA0TWQ+Wmarpkde9r0fYVkV7Rpcsv5Uk6WHOIt+AFrKwcIio9VMsAL5YdgLCAGoKi+cRA0HDag2TIWG/8h/w2F7f2upFTYxtdhZFTi6AugQtMsRt6XG+8kTG30Ch4um0Lb02pJRvpYU5qVWKokrEy+5180SsF2ljhHtTdrsSkzbB4WS8Xs8dwoH2sR0jxzc/n757CPcoWsT5YsFqZOoMbwjAibCVQoNDqhjdT0a4IagcGFagD4bVPNofrNsebn4ewQuNgs/84YZkZZZ0qrDlHtADEiwCnVzsjUHqu25bwvlvoDF6OcHaqyM1mt1lzZVfa9DrJ+RHqJEgOdb1sgk7otW3dQrUWW4n4v97a4xQrGwUkRPwDRN7oFDmQOTC1tTKm5oei7uiN5NBwfBErETQmpQCZ4PiA/viSQDQeb3p5v187fntWgSL0kcwjKeYW1DjwI7xRpBDLSKdQqStDQ90DoZ1OY6Qu5R0B0ojn7YV67BgWx2CkioCrT9QQ65Lw6uio4zsgf9aneFTBVABaEYTI7xtl9dm6jnMMrfWU4WvF+6TDsOVigphAKDtiC3uV+JGpxQ9g90Bmg0IPoEtPtnRpivm5PWxYHTjaLlgJqzx2jpU+gX5dVLp8EAQ4oObJj5fjOjPIRUHarqsSP5zVZV2WHbTtSbm9NQ0eT+9U2+Emc6FzPGhV4eznbR3txdofVJoUESSGJH8UK3kHcMGIFz2nsHqrVOcUbbXtxhM+O8VPxRaHARH27OCJIWA7Em+qsbxgUK5eLvgRC9t79acy3QGPl4OAYDCgCZG2UJkUxBZdaGw2mLQgOaB9fR/UV213zmAl0QmMGATQCCNZmNEkbm1vXoAAcpxsGN9j9xjHePPejSVD85cjFvA1vekCA36RCw5cZuGBoOHaAXvynU32dvsZe4cK7DXvmuOVGWkuCnbiAUOzonOlRru+zGsUGibJJoUaw8/8BkUTEYMEdyiC0zgmk6GBxudJ/Yc6wTDrWGvrPm6uyIedhtM+MEXUBKmIcKaQTKUHtI1KDzaAhqTZe1Y03uMtx2VHSIf0znF+c1urMaSpVdb264zFy4qE4ZnG1Nh24ZlEsThQlWj1BLbhnvmHNE5+atzSX2l/WFKsiC3IbbX7/Ot8qKdpkpTI31tsquAft2f5VNGBum+2P2BV1E9jj6CQoPLDI/+OUsUYPozILAtp05q2KF5AyuM2g9FqdwwqEjXTzvEH4ieAUEQEQ5OcZTCCF7nP2JhoLnThx461C13gdFLYAWZ9uSqRHqPJVWdCjW0sWFcoZ9OG40JOo3Xx5mhbsNE8T5c+xz5gdAoaPjAd/+871VKjSgiBF7oWEeIdmN87I3v811DOM8e0ET0ueP8rO/3zdeTmdTQr207ohXdBY2b8tXkUiXDf0B+jeGueIax5nD/aKfA0SCNvzArEhZv7Pvj+c1iRqGtS5rJSPAXXScvJOVMjfhrEI7te5QjZ4Z2ja6lRQaOw5VWmlRk2071ShXppaBM/bvb/N0RiI6nzo+TAU1ANJzG4q03hkeRx6QFOphQW4jZI0LePDUwjitX+ifdDkpm/dVdWsQJIUaTk90UDiHX1w6yjaVtMk2Nc7Lxf68cpTNS3YI2L/PabFH15wSzSU90kuAA+8bsO931yZZRrK/tIkMAKQo/tfBqssOZVfbrh+y7N4FMcpL0GdW1veIfvzliUaBohQffDc0U9sLOywjxkfJLsnj1kNV9rMZkfpZxEh0qv7+rmJ2UHwyXBjgEw3AY5+eVKEBes+MEwoNhnU+sSzBfnVDirqq2VWdEssTqz1GDNNEd8TVTET3cx6hvf3hn5Zr0vlHW4qls1owJVLgJFRunhlAGPEdAIW8oqFvUDod9gC6wg+y6uyj7WVyeCPBpcMR4jFCujfyIcwRcMbC1YuY9dnpBvvnwUoV5dwnLkl0mJjzdPWQq61pYFDi4qNlHdZz5oKdahywh28cY5l5TXYnAufaXntsZrQDlHAdaf/8tsD+satc2hRAqXv/ttuqK5qttK5LRT+2u+98l2fzZsXbvuJ2WV3vPFYju/6G5j4xD0iA1/95uYqFpcm+ilt1vQMacwCwjIsYZ/DdGSFW1NljUyM8rL6qWXGYwpB4CisBvcu1if5aK4BN5HScMfHBHvbk+jwrqOqwb7IaVaCyzm5fkSpwCetnnudji2LNa8Rwaxk8KwMTHAPvnBEh/Q4an3V5LYpRFPt//ft6W5HgrySejjjnAgY2Ja2OPPOGsYH2+alGuybVz55//4jyVvKsr+mUJPravx+bIXt5HOMO5zU5BmenBdmWbw6pwwpFDNrY2oJGu39yuNwNoZtDNWUcAGfFylH+duvEEA2NhfpYcXkGkZz1zl+SNS5rPD+7Uh0ZnjPa49e+L9JsFezM2ccUGhs2nrLTJyr1mYA4COY/2FBgLc299t2pFktKCf6frh/+l4qND25KFQc4N7/J3JyGia5D8UAwApmEYxvo4SQeMmjg8tGBEjdRvXGYYV2K5z9OVHijY2HKwcGBgA0jSS1II2IXDnuoD1xwpxG3zkjwsalxDtSIg48HTJClQgMd2neyXn8Pjt01i9P0cGfFe6mqR4wF9Yo2P0j/kZP14prCzyVJJLEZNypAHD4m6m7FTz/GS0kuYkSml8I15mcSuGeOCtDk9CWTI2xOnKfsDlnU0FqoKrEC3JjVoGm7iJ5HDh0ixIU2OofZbz86KVoVLS4SqmC3ESrgGGTI8xKFJ9JDm57DElQeezK8pfk1Ah9BAicXhEQldd1yu4FmARpIEsmwv/7zF2W7SQsXC7X4ABf5YeO2wCaGf8lhyWEIEvzCqhRbNSVchw/JzdHsBjtS3qUqn6LOzWW4ECcKMLjoIJdrTjXJShE3EQqp040DFuDrbDfOjVEQ4flD3ZBmpeeMxMey7vVzlViaJJ53wFC51oFztiTNMYWZd/7y5lJx1Flf2043WqjrSNtT3mVfHKmTDuCuxQkSX1VVdaoAyKnpViCq7z4v3QEdkdbec/bZnioFdWhut00IVqJV1T6o4uRwTqPsaDlssLDMqeuxdw7XiHNLIMeZiDJnYmqQrchwdL5Yg9B7froiScYFFAAaaMUk06Z+0UN2ZNXbh7sqLbum2/aWd1tSnK9siknoJsf6qLD9w2enhR4xbZvE9ZvMelk3nxk4I1ElQQqbZrpb5Z0DSgiY/guSypqHX0tRItevOC8lMMEBrppWX93u6GjRwaI16h/sbXk59QrE3CvdCwby7a3o0vvGTg8O9pRxoWp5k5wi8r9uXpzQe+6bGPDF7aNVYBfm1ulnpQS6KBED5dtZ3GkZCX6yZKQIAW1ZOTbYbh8dovUPcMDkctrKIL9lTb3qxiF0xu+eQwtOKAERigV/Bi901iKzbFgTJCcg6Xw3xIh6Xucv2V+/KdB7PJjdoLjBvli2dLScwJiAzvTwpeNC1AmSh3lNn2WebrAl6YGWFOKuqc9YdIJuk7TMHxOs4Vo44YGOMtuksrrTth6rVXepofeseOTwj7FkXRTvI6/31RmBds3oQNFVsJRec7hWLnysPagctNEZilRd222PvHZADieOWT8OG1+e6dLJDrQZWgNDqHivACvQH/fnN9uUaA8l+BRtcmtj+FrXWU13JnEmLkPXJGnNrOpWXMSS+J3v85R4QxFamehvt6QEiT7GWicpBUWnuH7jjnESXYJAcx05Wqm1SxxnfgbgC8AJNrwkYPz3vw9Vi6oaFuSmRP4kdMF4X3vg2lF259RQ23CiXokzIADdHCiWoe5OQqcBR6DFAv4wwIohYyRfFEeACH/6Kk8HNJ0jaLYxXq4SnL66qUwDAR+/JUNd3Lz8RhVHJC3M3anpPqOkkPXPnnx7a7ncxoidzEcQkNN6RsgiNK5DZR12ZvC8ngk8+Jd2lgl4oIuF1olYPHNCuGZA8D3QVMku/b7J6rRgF06xRDGCTTYdz53Ha+3DPVUq2CgGk5ID7UR1jzqTM1MCVQA98V6mnsPRui793Le3V4gLf+niJYETCGGZMUOCiSXltUl+NjvKUwkOwEkGnas2B+UJdzCK/JgIT9FRENliWYq7EDqz8NhgO51VrSSD57B8ooMGxvPk3JgY7maJ8X52zbxEgSvsPfblHdckWXCwQ4dCDNjz8ylW3TFoBQeyFLsWxXuro837ovs8b4JjvsGm4nY5Fd44KcTunRCh36f78cnhOttWjDHM1SqwocshWl6ZGGBbituld3Ie6pjzwzqGOcA65eyjCASYXJ7mr443dOzRYe76vUfePa6EE50B5zWJ4KIV4xU7KHA+PVZvd8yKFMjZ1jkges3+A6X2wLxoSw/3kiaFJG/2xHD97PuuSbB/7Kmw0eEeNinCTTQjDCPWHqiyYB8Xq+4ZkPPQL66Jk206iS2A1uq0ALt+cpitz260t/ZU2r83lUg3yr0zUwIL1bcOVFtL24Dd9qsvbO2xOtHXyAl8nUZoAPGtC+PlJslZdtOkUMUBci9mNXx3vF4ALug9BRyFBxb0nJ1orXi/nO3kFThHcq7x7L892Wh/f/+QQBLMQm5LDbLHp0bpDCY2cBHL+Lvv3DnOfn1zusV7uqtzeWDDIWlrc2s6bQJU4IwQxVcMGraWteucemlXmc2fEqE8D6c9OtIzZyXYyw9NsccXxtrO7EY7WNFjHl4umh9GHoYeIjrSy+aP8tc5DHX0r9ckC2TgHreVdCh3+8Onp+3hmZECrehijAp0t9ePVNvvPsyyv20usafvnaLub+aJau0rDGmiPFzllke3mFiZU9Vhf/2+yE4RB5yH2y8WxomxgFkAs8O+2FRg6447hM/QwLDLffjzk6Jkw8KA+kY8nTszVkNiRWkPdVU+/NofV9kn2fXKIzNG/R/c/QWUXte1rYsOW1apSsXMzFxiZpZlyczMGCd2EsdJHCcOOYmdmJlRtiywmJmhpCoVMzOTSmS/9vVfcjvvvrPv2Ts72fe2t9LcnCil+v+11pxjjtFHH70HymCatbn1ZJNt2ldl7+ysEoMCk8f45DBJQ0OphBqO0tTTn5xQ0Yx8OSMBf15W4Jg39PC0+i5H5wVWEPuU/fzwuEh5jFC4frq/TjRk6F50kvAVA8iMivSSUA6ePOq0nDqt/CcmKVy0QLruxK075sdbW/85e25LiWaFoPBdd9VIe+iWseo0yTah45SYKfEJfpovJgb8W4oNLhRLbluYZGE+zvbZwXolHUi80SWQe/bQS5Qo80CXH2mwP28vVfVW19Ar5JbABVhPEop7M8EhysPVKlr75XjKQ8wM95BaAIGVAEMCSKUFykDBQOIKGkryicrQmKQAoaqgjiTW16UHqdtCcsyMA8Y+UDE4cI7kNNqGgzX2tztG6NCkSr92RJASIdSKaP0R2EACGPIhWHHIo6yRFO1js5JQ0XG4apKEYxpY33NGA6U1XYNW0Nhvd48L16KmZZoazRCcj1rFJHSg7klxvvaHO0YoISms7RS1geExpvs5uOhAcIjwbJCD3Lq/yp6Zn2gzUwPtva0V6qAQCECyqWZJLGalBepAB62HNgTiRheIxbM4K9BGpQWJelbS3C/ahFQ+At3t1OlzOkBpOzKcubKgVYf1seoe+2RrudAd3isHIKoG/afOqtgi0ecAwxAKDiP3i0rEosxAW7m5SMkYixNaChxBEGcKEFBpDHHQJieA8ufobEO3Ab1ffaJZz2nlfiTcTMhwbl2fOhSPzIy2v2wvk2sxg+bST28/pUTz/fvHaX1mRHgqcQeJpN3rcuFAvW1mtFyQg72cVYAhWYtXAe8azjLrhWCXFuahuQD+N/NDoB0EEwYPI32cdXAhu0jixfDr8ZoecZnR5WbdJ4V7KZHnM8emBEo/vrVzQAkcA2dILU6Ph05wXoeCu7sjEUJrnoKY+/rbBwfs3muz1G1guD8tcLjazRQ+W8o7NYAZ6+siJATkAmlhOLAkq/7uw6y8qtNa+s9quJ99Q9JHQGtt7LSIKD8917U7SqVwRTuZAxUUiHcBBxuaHgfiglh/GxXmKg37G8YEi+Jx8UKh6PFbx1g8/jYHai2nvk8cUAoSfj/oPxfoNFSIZzYWa80hG13b3KtnRCxgVoduHHsswnO4JDdxfgdRJwFFP55kz8djmDqOeBeQDIAS8Q9gA/RNhpkXTohQQRsW6iFzPwzBHHHAWZKoD0+K0sHM+kaGd/uRWvvH3aMtxddNAAVFAgcqyQ/SniT7KOxFejn40gRoFJlI4mYkOtTQ4JFDF9xT0GI1FzyFSHChXP14WrTUrtg7E9ODbGyMt724o1zPGmpLSryfPf/gBLWoUdCjcMR486ZRwZJgPVDRZZWtjt95Raq/rd5cZL+cHW/Xjguz19aUqJtH9w8who4Q8RGzs0dmxyh28udQq0hCapARzgq0pOQgK6nqlEcFCCnJQWKIpwpoUMmvjzXKhG1ZQbPWJ+7lb39bKA4ziQ6/8+vjTer2AJLw/+OijGDF5HgfdcMQyLhmVLAtXZevuAAIgN9Nb+8ZvXd8GHgvVyT6S3GKWQiKptEpgZYV6WV5LX321RH8ci6xtQdqRQ343Y1pUoIiFv16cZIoBBRMafG+ohSSoFF8rfrpTK1jFGTWFbWpKIfXT+LDAPkD82Pt1Klz8jbpO3NWKDwdYP4OCXNCkLuNyQoRjQqUl04FA6EkmewhukppIa7q6EAxhMZCpxOVPOYdKLpY1/gpkdwiLTx1ZKiK+KbWfoEbgAsUBFBZoeS29p0zZ5dheibEBd5hUrS3Pf23TXbvjWNkfDcu3EMyyS9/fEixYkdVh1V3n7KxYR6ii3Aezo52yIfy3ri/47mN8p6hGIe6BKggGfBjJy0syiFa8s36fM0RMJOHQSd+WbyLyQl+Elbg2V+bEiQVG4ba750UrnP7hwTi0kvsz7++WupBf9tcJnrf3oJmydmyT+m+8kyyi1vt5VVF9rM1BdrP0EUuXpzBxAEU0ZhX5HtAk5oT7W9VPX0qysaF+iiRppCl4OV9EW8LWwZ01mEWuGxfjc2N8bf7FidL0IVE72Bhs92aGarzCPnafZU99rt5SRJPoDtakFNjh3Ib7Z0nplu0p6uofDePCjY/1C1dnWx0sENZkW7f5HBvh/qln5vMZCNCPdR5g5ZNd4TzcfOxBs3N0MkBJWZP/mJ2vARW6H4QPwCanltfpDP+1vGhkv7+6zNXS1nt4x3Mvg63+r4Bu3NcmE2L9La1ea0qxLgwkPtyRbY9ND7SHp8Ta3/+/ISeg7qlLk6ikDKbN3VEqP1iSaLOEM4z8jTmFpjDumdiuMWnRqgTQ4yBJkgcSI3wVhzgHEHemz3+WW6Dzpg3D1fbS58cMhvuoaKEWUak2x3yyWYfHK4TewMgB9oWZ+8Nox0zBMvW5Op5FLWesqXHGgWGSEExJVA5yJKkQPvtt4WWGOLws0qK9RWt9GhTpyiagM7Q0znbXrtvrL23v1bd9VduG2mPLT+pvTR3SqwAT+6D+LvjtwuUS05PDbB/7K5QVxOQh3i8IDNIBn79/WdtRBT79DsbEeNr46PcFS96OnrsilEhtnBmomZii6s6ZeyLd8Xto0J1NrLmxkd5iuJ9bUqAbS5oUwFBJ++BMRHqLNLZSgn3kjiSq8tQFWAIuFTWdCn/oCvBfBadGdYi3SYu5tcAkW+dE6eC5We/+NDuvXmc6NRjwjxsUoSHvfD+fsXCI42dEvOYH+drs9MCBQQvjPVXwQH7ICPCyw7vL7O+s+flSO8+9DL75kSzJQcOt7K1qywgxFc0xQ+/Oqo4gG8ZPh4AqCiXkSNBufPxcLYHxkbqXCT/RkrYz8NBRf+3FRu8TCpGNvfj06PVroYOQWDZfrDGNh9vFNe1kurK01ltcgIoQZ7NRMJGYAPJI0mE1/38jjIFg69zm4U4coi/d7hWGzs7r8mh2+7hJJrERXM/NpGX+zBRM06Ut9nkGA91Pah+f/xJtqgzq0+2amGDJtFixgHzpbtGmb+/q316pF4yg2wCzIP2l3dKhUk0pdYBuy0rRAcBQzkc8rSXuRgaY+gN2hIP+/oxwUqiUKZYebBOwTy/tU+yl7S54dZSdSIZyyFO8oA1PcoLIJEMYYN20jaD88jBA1qD+ywXwX9URog8Emj/xYR5yqeB3wk94tucFiVPPOOOU+c1WMhhwSGBlCAJARf0qScXxotKBvf/kxMNCkK424rznuSn+4CLXt05aEvS/S0Yx2qM3kI8fjDVYtPw3nC6JTnhXYFqw/PeV9Ell2gPr+H63byrxsYeyR1TnDGoRmFHUYFHQ1ETShdeSugpsvB/qG/staW7qyXpCrJO051BV4b40NBuaOoTd5DZG2YPJsZ46HA92tQtpQQ6G9BjKMBe+jpXmzg92EWJ6cMzovUs5HoejMEXBWOnXcfQFKZVp89J4QZ0h0TO332oEmeGGkEEoKnxXeG8QvtirdC5QoqUixkTcXsZ7q9zdINAylCdQnGKhAU3VRA19gtqVVdMjNDfYW5JdA+nITZ3TqrEAdID3GRGld8yYE/dMcZ2FrXq+xDolh5tsJeWntChW9Tep6KLuQtoeRh67Sxq0zM/VtsnOs7ocDfLGBkhVTAkl+Pi/aVwRfcA9BXn6CPVDhoZhSfF0gu7y9VtdHceIu+S//VyyOVeIqrjL2bFq7Ch0NLg5uFaW7m3xg6UtqmF6+x8mZJzftczN6RKLQ73W9YvfGViCt2vJ1fnKXn5PNfRmWAdg7Kg/ELiRHClm5EZ6K71JunloOEq6uGg7itothtGBImW4ufpYtf9faf2IQccyCo8WSSVoQp+9KPJUud4Y0+V7a7uVIJDd4ChXXjN7EkKqHtGhjmU20J9bPXGfD1TfidxgD14sLJbGup3TolQwdTQ0GvLD9apUAGZhYpJO5v1xAF03+QIdbow4INHC9JP8E4Ix2NhqM2LDbCPhXBS1Pqbr7tDIQkwAiUc1OjgjKMLT5HKd2CQEHQfvr3mTPrOiM7KdwXJxJCPPQ6VCmT4V1cmCyB460C1vX6wWgPSKHKx/+kY8T7glLNmr8kItIBAd1Ep5Bhb7ugK8C5IcCP9XBQHGjoHLNxjmGbNUL8Byff0dhNtARS7trbbxqYFai4GVbgbRwbbydYeJQjMMzDMnxaMN8VlAj0YuIeSNTolQJ1ehpZR7QHUIn6cyGuS9DoGZNsP1wp0IdHZVwfFYrgdrWgXZYl7+uMn2YoDI0Pd9LyfXpSoswUTMVznET9gvuCBhQnqflKMrTlSpyKmvcdBSWVu5YaMIHVyMGzFIRsqEKIUrPm88nb7KrfpByowPiQU+gVl7VovC0aEiK9e1TZgCxN9LcHbXT4TFIIUhounxeg5jQhxxAHOpcuvGisGQJKvu5V3nNKM2nMPTdZcI+8T8YVfLc21597YLbDsWHO34gDxGBnQm+cniiYmWfSyLgErKCFmzRxtt0yPst9fnmwJyUHW1Ngt0YG9hS02PtRL3UnW1Uf768zLdZj9cn2h5oT4ncQ8PuvihTIf75+Y9OcrUi0+0NXCAtzEbDiU32zvrinUviwrrLdTA6c1uM+9kNxPTPATD58BeOIAcROq2P1fHtf6wp+E9Uu+8NDS40oKs3MarK3zlO5jXIiXxF7kyRLjoWIRiVPAh5/NjLN5qf6SIx3/5HIpTr6NwtL57+zJlSft6vRA0cxW/GaBujUvbiq1DaVtAjrpEnxzvEnzPNCcmJu7b3SYHWrosunRAbZrb5meMTMg7AHm5vh59uOj82P13Zk9RLCBQgVZZxJ0zOhQdOPZ/vbyZCHkeyscRQ9FLBSd1BhfGXVOjQ6wz5GwbutVxxqRE+Ihye34SfHyeNhd2W2xcf4SqwHcwAvps6MNegcUAbBGUDibGOcj0I3vKePQoZdprvQns2NtQYqvvX+0zn6/tVSMjIcnReo7Q33W3GQgJpI9dkNmkAyQXb3wjfK0NQdq1HGrbsEb6JwUJYmfuRXtlhngbqt2VcosE9NinwAvxfX9xQ4DSQppupDMAd07OtxymrssLNBNZp8wLhakOmZVEf3p6DltL6wsVCdV1gh9p+x38xIdxotdfbZ980kBnwgR7TnmUE1iBnF3TavF+7jajrwWzYcAFv7u0+PaWwzi01F4/oZ0AT9IGpPAs6rXnGiyp+6bLCoqVGb8mTLiEGIYFCiztbzD7hkdrsLzox1V8npZerJJtE7EIljzzIAgeUzegk8FAExFVaf2+tPXpWg+FOXDRydE2aQwH/s0t8FSgh2zr9Dlsqs6BCwALpJHXf/QtTKvjvRwtequU7KU+Mvj0zR7ys9syGuz6363zv70zDuKa9uqEEk5pZyZvf/obWPt04N1yg8Qy6DQIa+NWbjYbpoZa6/ekGWxSaGWl11hH+yt0XNckuRvHx2uV/GH4ha57P1fnbCO02cEMmvvezr9e4sNgikGffmVHfbXTaU2MdxTZlY4ZnY0d0gqkIU1MytEB0Nt9xklBQQjghSJ4dLdVXJSpiuAfBcceVAUElg03kFFCVZI8ZGojI7x1lwDF4gg3Omtec1SfvgKmkNKoDjY0EpAdAhq0HnyS9uEnnPYZjf0W1KEt4YTH5oWpSAKIsAG5DuByoMKfZvXIooCcnNIrlb39tsnP5mmg5vvlBnkJu4wyc7ceB8VHgT+26ZFyhgLPiQykAz4LPtsh3huFD4UF9CYoGlgCjcn1ldFArSSi/b0LFgoJRQ5HDwkfzw32ogb8lqFOmC+R+JEWx9Uk04LBQWHulSFYr2EKoAQN/acFSeTChekbkVOi606UCtVH54LaAHtSLiQJDw8LzYrm+PvG8qEDj5+VbKCyuTkANt2oNrGxHiLfgViA3WHahkFBhIQCpl5mcF244wYzX9QaD20JEXfm/to6jsruVs6I9w3B9efNharQEJRicPdw8NJMscctmw4ugfMPBwradXGGZPqmC/hoM1M8LethR1a/KAvIMzNbf2iE8ADBe3wcR1qr6wp0d9FmhX0mIKO1i73wCaCJkfxgPb+7VMirLS6S9+FgW0KA5chQ/QdcVhHd59im/dMwMuI8ZNSSA0OtVvL7f7JEQoASOcyN0FCzbwASM6flxcIDSI4kwRRXJF8UcTxDNGCJxGpbuzRe/4qp0k/x3tlgA6lMZ4l+whfhStmJyqYkhTTfWEug9kZBrUp3pceqFN7l6SRQfexsb6S70Tyjg7KXXPjNItB8UrxjagDz4fuBOIIizP8NfRP0OIAhsp38eJ7gyTDs31mdb7NivIT2lTX3GvNda02DTR3yKV20+xYDYdyf7xvBtQoUngGL64okjob0qo8f0wUcXWGjoVwgWZwfIfLyBNkHTUoDmWSDkAMUNPPdldroPv1rRU2MyNYScPGky3SH//uu++0VnPym0URnBrvo2FQZjw2lrbbE7NiHYP23zkkfTsZME3zUzfggyO1NiHaQ7KJDM2VdPba6l/Pt1APJ3kToNhUUN8j3jryiCSx0AnuXRCvmAVCSyJCPPr6zW90qJNwYK5JF5aYhBfGogTHXqQ4JbFRrGwfkEQt9LfDxa1SLqHoxiyNzgMeQwArvPPmnjNaI8QB9ulF8QEGv5lFgJPLeypt6RflDMrYqrwWh7Jcur8EMJBKXVvUZvvL2qUck1vQIvoakpp/21CiofE/3DVKSOfcEcG2ZnOBTU30t/yaTh3WPD86GSQ/DCuSqCwZF2Z3zYtXJ5du6N3z4xUnKTgpHPD84F3yfkCqf7L0hP63pHT9XKUKNSGTgdhLdbDTmTpc1yPZc7rnM8dHCv1mFnB0epDobhTUFGIcrHiaoIaDQMDlsxLVkf/lJydUBFahSBY03Fp6z8lAjWKWdXD0grfG3bNj7MHZ0VZQ3i7qHl13ngudIKhzj06PlhILAicUzdw/4iGa++o6ba98k2ePz4pRfJmcFSLgJ8Ad6uIl+uenHx+3tSWtUk6ia3iytseR2Lo6qQsJ4o2xGUUPCfOb+6tUAEo9Lwcw5FKdERTa+Gvcev1Y+3BbhZTViEk7SrrsnXUlopB4uTnZm5vLBaRhQLajrNMmJgdq9mZ9aasAgsevS1fnODLI3V7dU2nBns4StaAwJSbcMjZUZyEJNh4XFGIXr8wgfFbO2SdfHbGfrcy1RfGO+FzT2GtVZY12xfQ4Ifq/uX+iFLKgMKPsVNQ2IOSZs/LeF3dosB9klXyAWaefzYwVcHK4rk+/38fTxfYUtlpvV68tHhOqzm374GkbHeGhWIz8J7NXzyzPs0nJgfajj49JaCM20ENzfQCWpSWt6s5eMybENpW0W0iQu318rN6enZ+oAolzVCZ4g2fVaeHCrI7EeG9tu4Z885q6bPOzC6QmBW14RJC7EnpMVa/LCtI5D4h0x8JEFTYkf9xzmI+LffvS+3bXqDAxNb7Jb1ZnbkyEu+UVtdqSxECJbkjFcZiTksyjJ+pVWDL7g3keHmKAAChKvXOgRsUAbAfOhdqOAeUoKHeSnzHLw3P+xYw4JYwMRvNvDFyZdbh5UrgtP9lsf1xWYItT/VVkQanmfIJdgMBEWWmbugqcNa/trtK8yTs/nqbvds+cWPt6RbZM6rYeqxcST0fkiblx9u6hWhkkLsoItFtnREk2eFKst+hRD82PVV4IxRYaEGAHOQHru7alz+5595BDEbP3rLoKYFz4nXCW4o+BGtLnJxstN69JP3PllSOtqrlX0v3xMT72+toSqbuxt5/fWCxmxq7KLil2zZkcoy7mw6/vU+FypL5HACW5CXLKbk5DbXDwrOLgzIwg+93tWfazBQl2tKBZa4E893hVh3wt6F798eo0+/P6Yq0Pzhvew7jUQBkVcn9/+uKkAF6uiSNCRR3FgwZWEPH8tjf32/LCZhWnANw8c2IYHdSdld1i4/xyZrz8Me6fFGG/31jsoJef+87e2Vyu85bvDoC7+Iosu+3J2+3pD4858quWXnXEXl6Rb5uzG8zP09n+/m2RLSDPbe61l/ZUSlUPkBoj2DHJAfanH00XED4+M1gy4QzHz4vzEfODz3xkSpQFujiL3g1NCzW1/8r1X1Kj0ubbWS4lITYRCRTJNLKjbEoNpnWf0pCf2kIlrXKARcMaF8oV+6qVLGro9UiDuThfZmfOnldbi8MPhRw8BjBMAqXHdwDpShL79u5BuQSjgoABGqo+8OJoL0In4vMYKiYwUXF5DRsqRBNVIRYVhzIFTc/p85JKxK2VeyD4gjzREVkY72uv7K7U4UjAywx2FaIFikkbjkSP6hSncZJpkiZoFyyckagWVHRJQrR54LSk9JBXJIChgsXswE2ZwTZw7pzcHEN9XFSthvsTcAjspvYym5sDkEKIgwNkDApXSZOjs4C2NhU/iQbSiNBI8MTAORW+N5xPui6g5ag7gK7zikE2eFeggbi0b8x3IJS09fksknHmJ6Aq4GzJwR/qOdRa+hzywtw/74MOBMk2yRXBiwCHegmtRRB4giEoVStUL+ZunIfqoDtQ3aONjpID74ihZTY4iiokDRz2GFldVMAiGYBbu29jtl1/8xRRLQhoULDwYIFLuSDN32EaV9GtuRkZObo5iYPL4BrzNJLb7DttMxJ9hSTR2rxvVLjQB4ap6OiQILAGGBRbMDLEKtpO6UAnQQVt42Bu7j4lNYenXt1jC+alSRGNgWNmjKCIXFRfYdiXxIHikefABX+d+8Olnu/Dc6HopBCHrz852V/dBfifW8o7lGRTiINm0V2BB813INk5fea8uh+gKFBUWAc8A7oPdGsYAEX6k/8O7/eDw7W6t8VZOEIP6oAnUQE0YNCRBOJobb8QeAL77EQfzRlAKWJQFFWcLfmtNhZ3Xech9rPpsbqntw5UCcGG4gC1hk7TxFBf+9vu8h9ca2lZs2ey85rtxtmxlhXkZuWdpzS4ODY9WO7q72+tkIoLBxEqX/9YVaTkJDrMU8PTIPw8Y4L30coOFRbo5tMuZmCOuRSkmQEaoD1wEOLzwxXn62KeTkPtjX3Vds/4MJkUsS7RFAcZxLF7XoKvnTl/XsZePAMKATjMT67MVVcvM8ZXe7pCBfZ3mnFh0JCWPggUyQw0NBJ+kMcxIZ72bWGr3ZgeJC8MpFBBb5HWJg5wsNw1OkwKPn/bWSb1N+a9SGpZ/xQszG/xTkEpKfo4VOjwXjczVs8AhA2+N0IdHNzxzKyc+962Ha2zp69MMl/nYVbTO2DZdf3q+gKykNQTr0kqMQpEZ/+KdH/tMdZmuDff8TvNpvBsmcOAokpSl+AzXNKhdA4uotokeLyX0nZi8Vmh3iNDHP4yFIoIf/A7atr69R1Zp6CjSHWmBLooUWCvM4P0jx3l+g50T/28XETD4hAFMWeY9+/rSy1/5Sq7/sl7NAxd0uIYgtxe2amZHzrBfC862Mguf3ehi7Msu0mSxYumxzmSmbZ+gVX7yxyKh8/OSbD3jtXKVwY5VYp25lzyStttzuhQFQMc/HQd6eABYBAzMfq87/frbdGS0SoQmHHAZ+Pjg/W2KDPAlh1ukIhGSoCL4j7GYLz3o4UtFhfhJWSRREJFipTaBqV4s3hsmLqXv5mbaN8UNCv24CqNvCroPuuMIV3+LuemTER7T0t9ivjOecGMFIIPK3Kb5RtENwfZ3Rd2O2hLvHOGurkooiJ9h4tKGejmpO9JTKQgQj0QtP6usWG2pqhN73v98UbNKnIGPDjBMe+yIqfRXt9dpZjG0DZn/cRwP3vtYI1mEokPvFviQfaxGvvJzSNtcpiPnWjtsldXF4vey7qHYgXltLNz0P50U4b9+L0jAgvowP5kZqwVtvVrdoEOH5RD1MCg2PLcj9b16XxEpQepZ84cCh7ACdDiNH93c73sMvv91hJ7YFKEbS3v1J5akuKv9XqiqU8UNBJI1hDFQGq4lz0wJtyueHG3hYS4W0qkt92E51d1u95FTn2/iuTkEHfbld8sCXOeEeuWgn9yhJcKgl/NSrCcli5bcbxJs2asaeJvRVOP6FWhni72+MqTUsVkTogYgzIX32tmlK+KsV8tz7OsBIf8/5Ydxfb4zaPVvZmQFCB6G+coe4zZPkAc6KHv3T1WMaS2b8D2VXfb2j1VEvfg/RJrWJcLk/3kscC7/svqYs3a0dVnrbLPMI+FWQH9rOv0WRsZ4G17ah2GqL6uTqI7pwa5qFMCUFvc3Ceq1vRIH7mbs/+Iu3TwEOhhlon7e3RSlH2Z22gz4rwF/pBD0FH77bpC+a3RMWLm6eGZ0QLJYFIw/P2zT45bw/b1du2T99kE6OWF7fbQxAiZOx+t6LRbx4VqTZJkk+MEDB9m/efO2du7qq0ov8GmTY6VYuXh8k7FAbrprM/Pbh9tHx2rkXLfdOY9ano0iM8838RRYVL1JHZSgLNXv9pZYSNTA9WVWfjY+7bgxlmKC+QgmAkz7wmL473dNRIaIFYHDXe2pTlNysV2Hqy28aPCJHpE0cMV7+siUBUjxvvmxtjXh+rt71elqxjDy2JjbpPiADNidJ4A7wCcMYRk1IC8m3dHwcPcIFRN5H0/PFKnz5kY4WlJ/h721NoCFfQwfaROeuqcZgszIzyVy0IH31/dI0VKZo0RLvjiWKPMECmQ8S7bll1v00eEKIe7OjP431dscI36/Q5bPC5MyQ9DKyx0Dur8ig5xU0EYMMDq6z+jCrq8/bQCL4PDfBzJD66Pq4812K/nJ4pftj673q4eF6YWmJuTY0IfaTgC7sVEmwvuJwMt8Pyptq5K8bdfLsuze+fEmMewIeLrkwyThOBOfOP0GG1c1C0+PlyvWZFfPP2+PfGr238wMUoOGi6N+cdnxdor2yv02fD5QOcoFirbBjTMyIEGSsy9EujFJY3xka8EhzmUKjYeAZF2OQPeEZ4O50g2KwPV/PcJUR5q/XE4UOQQpECzOHz4PBbUxrwWodO0J+lOwKP/+7I8mz0pWtJxV2YE2McH6tRFgucMfYjha0y1piUw5EayOyC+L0UEZmAFdd0yziIY8mxJglEM+dXVyfbcsnwhiXSXSEhR90H9iWcJ0kArPCXKRxJ5nYNnlDSAeHM/UJEowuICXa1VSjmmgUWGjJBChE4F2kkiRYDi8ylmth2usdgob20QZBGhvUEtoSV6omHAIQ/aOagWZt/AGRuf6K/h3KunRIomghgB9Da8DPDPIMmAkkGHBZTMzc1J9DaUIXjWOw/X2pTRYeIhMuDOd4DDD/2E4oACjSJy68Ea++k1yXpfKJ+BhuIZAKUEzmuQ21AVNiQn8iDxGGpvbCgXkoVvxLxYf3vzULWVNfRIVSMjzF3BikLvhqwgrUOKrBhfFz0L0Ar5idT0OGYMfIZJhe2qkUF6licb0bh3Fe2Dd0JBBWecdenrNkyFoqM4dNJnRPmh0PW91XWc0tAkdKw/ri+2RaNC5NoqE7JhQ9RB5L5pb+N0T6KJy+qeyh6peGAUh7xsXk2n/WRGrF1xwa354hX30w1229w4vVMGWDngiQUomEBp4B9QfgZ/H5sbq2AKQAAtQcZr5x1D6az15+Yl2fryFltzsM5umx6lQgx0nYJ+8KxjtoCkkOSOxAraCtKXSFvTibl/TLg99uVxu2tmtKRXt5R1qvvC2th6oEYKSlwTwrzsD5uKNbT+i199ZH/8/R0/JJnox3+wvdL+cFWa/W5tgX4eaVD4w6CVBfW9KqDo/rEnkWkleqJeNS3FXzSIkvpuu2NSuLkNvUwcXH5uQaqvtPW5GHwmRoFKUthRHBIPZeonn6LT+v10xxAggAbDs6Izube0zRamB9qLX+XYvKlx6g4+MjXKXtpWrjmEyelBoiJglhgZ4qHOBfGELiCIGWDE5rJOmXPRkaOjAqpPoZtf1GKv3zvGHnjzgDTcEwMdtAjWFPufhICifPfJJrmxw0vuPH3GvjzRKKUkQJLN+Q4kPTUYnxbHUC/7jIvZsV3FbZovAHCAcnZR1pzkKDzUQ4Xe9RlB9vreKh2gDCjuKu/WvBNJFN1e5MxRj9txpNaunxkjsIWZDLq5GJZyMPNOKtoGpIr43oZS6cfjMTEpM1jPmsMeEQToJriMQ+tBTAOJYLqIx8vaLC7U03YfqbXf3Jxu2fUDKu7TQt20R+8cHSYKGF2/zsFz8pmCRsiz+svXBaLuMOB/S0ao/X1vpR062WgpcX42IsJDZwid1GvSApSMcvDTmWUOkJjNXtpZ1G7xwe565quP1Nu9M6J01u4o7rSrMgI0hwEdJtJjuOU0915QHXNRvAWppBykqObM4lmUt/SpqzcyyNOe/DpXvhbESYoK4j2fS7zHaXtmZrDuA2UxJEWJEXTyUYHjDHhucYrNSXbMe1y8gh9Ybo9cl6mCrKn7tAx/40IcSkZcFCIACOo8LEqy2t5TAqp498Rl9jMxD9Wk5+Yn2ec59bZ0S6n95JpUFVesrYtFLsAf6mVDlVg5W11rv4AKupV0aZ+ZHW8PLj1hi0YGi2YFxQXpduLjR2vy7aGr0wWMEaPv/OCI8oafP7/ePvnDVRpCJoYxVP+3dSX2yg1Z9sgXjo4J9GUSVYAggA3mCxCR2FvSLoADEARq0vysYJ0NBwqa7eHZMeK9YyTJLAjUQZ9hDuoJ6k4Y8xHDEWWA6w8oRgcGCjOMg/QQNxXkJJd4m0DzZd6DeUY6Bi9+cdxmTY2zwooOe/7adPvFNyeVGyHSQPK76UC1kuKrMwLVkQOwAkCAyvvekVoBcigd0d2kW0j3HHri8h9PtXm/XW8ZIyNtXJyv1uS4cHfJPHPe0iHFWBfn9ofHRYjCyiwaXi50fL4+1qSOdGqQq8PLw89ZZzPxnH9/frDebpsQqoSZtSbFwZZT6loyB0NO9KOJUfaLdQW6j9vHhGr+CqEHvguzgdVVHZaZHmzZx+vs+gXJ+t0UlgCi+I8AIrLGyMMoPJ79KNtCwrytob7TFs1IUOzed6TWRmeFSrVuTLCX7a3ttG35LaKJkd/RTfKn81vWbn+8LVNmxRSzV43EaLrDnpoeaztr2uWfISp+Va9iKmv1laUnLCzS16ZkBNtPJkfbc1tLbfmaHJs1I+kHCW7O/SkR3qL3UgACOrOHSeibek9LMICCkoJ62e5K+8VVyZo3xeeM+VWMGum68z5PtnULKKaDxDPnoltIx4UL0A+wBWl6KMH3fXREwD8XMQ8RHuISYw7L91bbjdOiBEIkeLnb8sIWzVmjnjg+ztfWHaqzP1+bbvNT/7/jwL+cRsV145QImX7Ba473d/DjuX56ebxNSgmUFCOJMsnNzuIObTwOT5BgEAWUaggsj8yItt01ndq88LjhHcJ1YzGhWEAyP/3C0C5ydySDBK2YSG8laQw8f3i4zsanBWnh/nVVke3IbtDLpjLv6uwXh5VgxbAWhy/t5keeulWOtiS4+BrQ/rt+XKgtz23WoBtB+KbJEeIOU1zA2SVAEfxxBabzgZISG4FhrLzSNgUbWv0yRusYFAePJKb91BlzGzrU3txcoaKCQ5HhR4qThvYBfVeSVy6COkgejpN9A2eFgDHETpAjCUxK4ruZzA8JSnhsgCrz3/FUmIwzttMQc3caYh9urZD8pwb2Krq14Sk00JqmIiaA8f2fXJJoa/LbbNooh9KF7tfHWQdYQw8887NKptNiHNSmvbUd9nl2gzTseW5InF49IlCUC4o3uhfjIt1VdGGYg2IKFBnkcKswFwxxlypGTkW7PlPqXr2Dai9SaFCYQIUgKBFo8R2BMoIyB4cyIgCxPs5SQIPPTBeEQgNKGbKiHFz7S9tFg5iXHqjDFgNBCgycOHccqBKyBDoI95/uExcux9D1oOYwlEXnh8QZehodHpKNLaUdGkR9f0+NErFNO0q0D/KbTmnGBPUeaHaf5DSIm/zwjCgZ5rBuKLIpUo/U99pVWY7uHkO6n+ysUrHATAnKaUhAQwnDvIrOCNQoAimfRxGIygoHE2gyRpmgJgSuseHujqARzhCZu3i2dLUY8scM7bHZMVZ4IZiQpMIZBcmgSJiS7K+uBQocGLLxPElm+Psg049MixZS9X+97l4QL6dXuiS0khlEZc/+8fo0IR8hAa5y2AaxXX6i2b49WKt9xOFPHGB9Hq7qtp9Mj7GtVW2iad04NVLoIFx6EEzkguG0X5scZCOivNW9oKuFpwgSvlD+GPBEtnFCWpCe3S+XnpR7PYgYxXZXW5eKRdY8Mw83jgvVPMCDP71Jal8Lk31VaECZu2N6lH1xosEuHxWqWRA8RNjjUN6gBFF8Q728b3y4dXefFtUQqWKKX1SwkEU9VN2rA5hEYmy0p20saJeTPfKJuI3Tuqcz8tH+Wrm1MvfDM8ebheKKmbfUQBebHRNgnd2D6lAyr4Z3AodLelqI1hNovPOQIRqCxAyKTgP0TYybNH81bKhoBYANO8s61RHENG58cqCKTWZImFsCsHj57tH25r5qWzwzQeseAGVUiLtl1/WJDkZXA9PL5CgfUZu2V7fZy7sqdSiTyDEgjcobsZJCA/SX4Uc/Fyf9g6QrXRxU6JA5Ff2kukeJ49xxDmUnUFfQWGh9qNFQnJKEs65JShg8DQtx16BkaoKf9hrFK+uGAgZwBylotPg5f+hE/OGmDLtmbJgSRiTIoTphDooaYUP3GanfjQ3xckirDxtiO47W2cTkAFFcMlMDJe/IXqAYhUbDoQ3Ch8LXK1vKVQSt3ZSvQpM4sGBylN0xO8Ymhnup0ABY+vU1KUqgWDskS2UN3ba3pstuHh2iApM9/e7WSsc76Ttn14wMtMmRnooJ0SEeAl7E/3elOPxO64T3srm8Qz/DPAJzD/x+/h70I5IH0EzFgf4z9gbOwfVd9svFSVbddkoACLQ4Zi9J9Jt7z8gXgO7JxzurbGdlp74PiSAFQVVrvz27KMUquvv/f+PAlel2oKxD7xye+rVjQgQ+vXBjpgx4GQSGijEjI8he3V1lb2woU+yBsknxR3yDAvarWfG2qbzFDpS02Z9uz9IaXxjvZ6v2Vtmby3P1rB+fGGnzM4PULaEjjpIhOQH7ac+hKnt+R7k8XYhrt76yRwZxrE+MSDsa27S36ObmtXaLLvfammJ74L6ZGp7Ho4bvj+jB3TOj7f3sOnkPBPq52i0jQwQ25NX32qhobz1rOPB/XJhsDQ09dvnYUCk10hXfkV1vpYUN8vwC6ITixCD5G7urVKB7D3ey17ZVSPiDc+nD3dUCHwrLHZ4ZKHBhJMf8QJyvs902MsJaOgZ0z6NCPGz2qFAbEehpaRkOb4zHF8SZl7OTzR4Zaj4+Llq7KB0+viRJcRbg48WvT6pwhD68q7pDQMc106IFFDBcnuDjqo7QN49Ptce/ybGbrsyymCB35SRXX3As5wyCkskwfGy4l/xr6PT8ZlWBJYV66d7X5rXJRwILAnIHili6OZn+nqKHjQn20axOjKeb3lGCt5tEdcobe+zaKZFay1C+QMAj/Fy1RlEqpNDg4ixdPCrYEhL97abRwfKmuTzeT0UQcxaIMtw2LlRnyPWpwdp3dOA/eXyK3T4t0lzdXOx4aas8imZMiLLqhh4xHpj/YG5sfLyvlLvwNRmZ5C+lwfg4X/viSIPyEa5Qd4x6L7V3j9XZhpwme3lzmRg4q9bnicLGM7rn6gytodkx3vajFSc1r/nGT6aZl5sDzAU0xNwY76A7xoSK/QHd7oNdVSpciLk/YqYm3k8U7ZHJgfLq+sNnJySWQj+Ejh3r9su8RjFpyANHB3mJXTIj2ltFFGfN9CgvUcDw3SAO4K7+5xsyxGgZE+kwPwWYxteL+Ak9nZwEAHVTeZvAAiSjKebJ8V64IUMzxP/V658qNqBSkHhyaMCxReccbif60iRvaDtT7YMI030I8XcYzlFxebsOFTed9i+DfiR9JMy7i9tkoIXOdEa0j83IDNagJgMpGtKN5QC5TEgxtvLZlR3SxGeeAYpLZUufZST624KxYQrAbsMuNR9fdw1soxxE0gn/f2xqoDYAFwkukmuo+UDToJ1LUTQp0U9ulQT3tFB3aerj9gv6/NmRenHQCdDw/fmZRxbGaV4D6g2DSBlhbgqMIOy0IZn7IDkiQK0/UKNNCnIFHYbqERS3uee0eNdc7xypkZ47QYqWIIk61ISioha1Uqm+QT6Y75iT6HPBCfy81XedEp2lsfes3TMnRn+ftjozA8xtEODZyCgEgWYSwHaUdKqlSiLPQCpzKaBJ8QGuKijSg1ylmV7b2qduBUUASDL0K2gjUI14xxxSOKCjCLKlqEO8dwbe62jzOQ+xXRXd+lmc5ilqOBD4XgyvUdy9vK1cBSWKGCDZPF/oFlBhSDZQ8kHHHaoefips/GkJjnmVY5VdSoRQlwGxYGA3aDgt2+9tbHKAihUSTYI1brtbLhxASBZCP/lyTZ68RUgoOTQp2K7JCnSYcl3oYkDNoagGuZiSHCDX+LuuSpdizaRIiuFL5fBOYKSNSaFAJwI+P+gxiPUtY0J00H24r9beWFciPe25o0JEW2KvnGzk+3ynNcEz4VkjSUhyzqFCcU/yAjefdzEiwlNBJr9pwI7XMwh+qaUHuEsfG8fRnJx6u312rEwfMdMiYZoR5Sv/APbM+5vLdE8kWcxeQWHhQAFJocv22LQoO5Jda58crrMHxjtoE//r9ctZcTos2dd0K+gegqbur+mWNjxJMomKv9tlKkIiQjyUiMqbxdvhN1JS3WmH6rr1jAnEX++tscKGHnFCkyN97Kqp0Q799H4HaHBtauAP7tF4DWAwibobA+iAEax5pAPnjsP19JTWXlhUgMxE95R1KtCTqI1ODVKywBqEKhPn5WrXpQTK04OEg0QUPwYEGJALTQ1xl+qeI25cKklUZhmId3SjSG4fX5xg4eGeCvoUPchUg95ysLGn0IKn+8XfBxmk6waaHygzq8tEGYPKBN0C/vBPV+dLWQoZzTd3VykObC3rtPLKDq0PkpEVBS2iBdwwMkgJOWAECdGVmYGaTXhwYbw6VG7DhqrY5flTlJCEMnOxq6RT6igr8lrUNaK9fnmyn+Q1sxt7VawBBmSFuImaWtnUY1NjPHVA0WZnQBgQhSSetcNamhXrbR9vKpXpJ9z8t/fViFJGJxQuMjEZ9RmoNXPSg5TIMpvATNsTX5xQHOXd0u2ly8v5kuQHlcnxbOgGkYwcq+/Vny0aESxqJsZtUBgb+wd1ltAxDR7uop+ZlBEsXwW65pF+w232xCjNoNF1pwAlSXnpg30SDylv7rN1xxvlTExCQ+zAvBQu9LgYb3U7eafjkgLkUfHYzaN1ns2OddD36B4yyAqyDwhG9+PatACdQcSIx2bG6F29vKXcXlldrO8xe0SIrc5p0drYVdat2MFsIuAJcejdm0YIlebMYr1OivKQKSNJDLQ4ePhI836T06S4Rxz4JrdZMyiH9xbbvQsTZPSKZwgxEpdqpNpBO99cXyrAgPfI4GhwgJvOOt4l3xkw4MDRGs1z/O/iwB8XJKi7zvMBoU72dVdXGqSYdy4zzdPnlBBK/SzaR/QY1iIFoiiAh2psZVGzwBzlGO8esW+PN5nzZUPkVXXH4lTRNjjj6JAj+Qm/fuH4CFEdEX8JDfO2OUk+dqCoRXLnY0aE2fSx4ZphA/waMyVZdFrmfLjIE5j3YY0hvUuSn+braY9NjJLEPwkgssEoJn2NcIjTEHU295c4BEE4/x76IlsKU8RmOmOs7btmxVjW6Cg9u8LWAdHucHenU82ezG3pstbWAf2OvfnNigMAfxmJfnrXdH7YX1ByozzcbPpfd9pP5sZZSW2XPfFJtqhLO6s7rLnZ4esDXemD7DrbV9hsP54bZ4GuQ9V1QejjoYmRVtjWZ7+6JUNeN1wAsI29Z6QSCpVof2GLfXm8URStf+yrFDtiQbyPhFGIa1sr25S0NnSdFuiBWEheSatMWIk3+MGQnEKLRSQFJUH+N7TZt78+LhGSLRVt9smReivr7NPa2FrZLoDl0Y+OWX3HgN0+MUyeLkuygkQXmvvMGse5H+urswFPLQotgFgS8XlpAbpvSSbXdym/XDImVGtq6dFG5TMUlICX5Aw+Lk76e3PGR6jLDnibGDhcANWu/ZV2oKrH6voGpLj2zAubLDTUQ6Dcu1sqlO/cPT5cneZrR4foHULZh8pIF3tEnL99tKbAHr5ppLr1qOzBGOBc7D17zvbuLROASmfo4bEROnOPVnXaX2/P0p57emmu/fGbfPt6c4lmPTfkO97TZ0ca5AROEQAYQ46Q+9dFohcHuDio66hFdvY76OV3jwmzb4tbFAefWZGvsyfRx01rNznYzbI37rMHr0gUMISkObMudDluyAgW0PPxzmp1hjj/cCqny0T+g1oqn//TWbG241CtPbu20B6f4hDb+bcXG1xwTUHAUE1iKh60j4ScZBPaBbxm0DSqIlSbkJDjgMNM6kRpm907JUIGOsjTciNQkCg6jtb3CN1HTWRbCeZKZ/TiMHeDk8Y/JAzV1V0qeOCcuVwIAmz2mo5TKnTYSFT5UjKROd4lQpSg5NB653v99pt8DUQRAKmYGcYi6d+Z1ywnzHFRHDAgTUF6UCS2oG5QZ0DSLtK6Pt5dI/58fm2XWtagXhRRtFjhDhPo6PLw99GlBnEnCQLpA8nk59BUJ7D5D3cSlxL0imDFvdAahyN596Jktdk4lGm5giqi0IS8LwEYpIffBzXo3PeO9hieGwyngTJCBaGYQPGpq/e05Pj4jM7+M1bQPCB5TFSX2Lgo5hDwkBpGXhCuN0PqFIX3jY9QZ4n7AdknQSNx21HRZeNTA7UJlx+oldQsSdfF+RN+nu9M0k+bn2eGshTSsAy6oapDMUXxBC/7QGWP7Szr0vf+dHOZihpoEnSDoOV9faBWaBoSe3RZKEI4vMobuu2TY/VCY/fmNKrwY+AW0QCu52/P0jo5VNkjBH3KxBi9E5JGmUT6uUgZCI496DiJ3ZbcRnV9QFJZjyOjfRR4m9od3SzM8qAGIjGIAhWqMiSYqD9w0Eja1d3B0WQNTx0ZJldQvsfxnHoL9hyq90CCxjsmwPHcaI+y9khgKRig3zGUyndAzQz0EQf4EaFuChR/316uRJG/GxbhI9QKne5vjjXqdx5v7rZVxx0KbyCOHf2ol10iyVX2D2uYZ1/dNmB/31ou2sGNo/5jXiayeBRHFKyr8luUUPB+3lpTLNoL8oo7Szqtu++Mfj/FTm5tt/jih/Ob7WeLEmxzTqOGbVmboHSon+2r7RKdDr8EOkoNvYNCCZsHBhV3kBTl3vNzalVkUdxx8JHYIB0IUghqyr4gDvCdkNwleTle0iaEGp4xMzy//CLHVhW2ClXHVwcTRdYrSQvc28xwdw2loyRF65v9ER/ioUF2Yocc5gub7aVvi/XsC2o77XhVl9r5xMG1OU0aGMdr5/YZ0eooMEiIBj+HMqAFMe3bow06PKDCcaAjcwkayPqDVgQQQiJ+zfQYIYB0zShEADygZh2u6VWxyp5HpQ/RBf4ua4/hTwYT0wIvUDPcnKQQVVHXbSOTA3RIgn4BCiAdOTHKXTEMahTrhpY9A+Ac3CtPNCvOPzbZkXgCONFh5DvQpaUgunZGjP4es3htHacE5rBHOcDZU6jPYBp2sr5HdJi9ZZ2WX9YudTZ8c/g+JFUo3MGj3whd5dx39vryk0L3s8I91aGkA4SBnuLAqGDRtZjDQ2+ewc5X9lf+0Lnk80lgSbp4vygTUpTQPYQmsmTxCNHhQn1ddZ/w8f1dhllJU7/+riSjj9ZrQJ8CRHSc0aFSRkJcA4oc1NQoH2fbXNBus0aHKg6AdH51sllrlLMK2gNFBTRDurujM4JFFz14pFpdZAq6TYXtkuGkSOffR+o7tH5A+6GxrkNyONRNTs0ADhTilQ09oswQ659bXywdfN7huCmJeuagmG9srxRvPru5017eWKZYFB/trfkUfndeW6/FBrrZ3sJWiQqwlv6yudSeuW2E5h3+o4sZBTriFMsv7a7QOURX/nfvHbLjZa1ah3Sy6up71LG/JT1EXPHtRW2i0/79jhG2fFelvApQaHzx/rGShifRhVYE4If7emmnQ10vt61bdDXHezA7sf2opETVTbjQjUkPx4jXcQ5iAsvzJl/ILmyxIFdncc/Z89DgiBs/ev+IfXi8TmAkYMs/1pfq7zP3hGcGcQWa0JMzY6X2AxV5fIqDinJRihcq2l+/OKE9XVzbqS77u7urRX3+Yl+tEkaoVQ9enqDZGVQZ75sbq44z34Hr3c0VmgFlbeLH8ZurUpT80cGhOKI4k4ljRpDWInHuWHGr5l3Zo3hw8P15t7i+Q48hDrKm+VkKoghPPIu+0+xhU1OfHc5ptMkTMT89qziA5wqO19dmBMrR/r2d1Y7OfF2vGCCZSQFSwHtteZ49NC5Cz5hzDqAQ80riLWcf7uCAi9/sqbKKmi47UNOt/bArv0VgwpRRoQIDlx5usORIb0nIrtlWYrdeO0rrlXwMdckvjzTap8fqbVVxs7r6f3jHIf06NtZbXWvW2mfbK+WPctOYEPnGIDY0N9XfNuwotVcPVKvA511y0WFGPRTRjg8fm6zviJ/G1Bgvu+mWyTYxKcBGRvlYbLinOjE+zk7yWeL3Aqy+u6lM+QvrhzVy9+IUGx3ioc/CCHfzUYeIwrt7a+yKeSliXbCuPsnBa+iUgLgYLzfleVCNcaOHFkYOsHNPqcN4MbdBa573DUhPrnWgsl2U3HPffyfJ288PNUiRswIj2PxmrUMktp+9MsVuSguyX63MF9WaM+eqOxdc8G/zsEc+Oaa8+0RLlz3x+XHlAOx/4hDrAqd0fOQ+3V6p2Eys+9vWMvvTbZn2lyVp/1TN8E8XGxwuJOjoWIPWHqnsss2Haiwh0kscSlqpAR6OdhPV5Zc5jZYe7qlKnE3Dpod/zEG463CNbSvqEIWEw3x/Zbccm6l4SYIlN9rcbyMjPG1Jqr+jLTkyVGglm4giBe8FKAkgqyhOkFyVl7VKApRWGgcKAY8ChYSRA9rX10VmNAfqui3ed7g1dQw4BkwT/O0fKwtF6yIZhbayZm+V2m0MmfNCadOh+FRaUKfFBucOvn/qBTdHEiEctCeEesukDMdyKk26GjwvBqLYkPxO0CeGnUiUsYpnkBU5tpYuh9M2LTfoZFygj139p3UQ0vYkqHCoc6jcMi5ErXwG20hEQ7xdpIbDIsc0kc2N0QtcPQbacFIfHelw2K1vH1CSc6CyWxQbZkUYKN+U16LEkCLh1jGhoo2UdPRpcYIOgMiCeoAMsHHzqh3Dd0haQn2q6zylgIGEJAVpS6uDr86VV9UurjpFDwoQ4lxe5mi7ck8EX9SkOJTumBdvc+K9NVjKxXAsMxnQQOii8E44cHlnN04IU4Hl43qZ/WxxotBngj1tQDYYCOzICA9LDHK117eWi0NJMkFBRcJLB2hnFVrYLtbEwH1Dn81ID1KLm8MUfisNVT738blxOkBw7NyQ61CWaEBhZ+CcDiM40XIgT/ATugvPmgBIYbUkw1+I843z0X3vFSeXhJwCD6UiElkSK4bOKPgIFHeNChUyRWBHy57CjE7bh/trFQinJ/urg4EKFrMqJP+YYsG1ZI2CxuHICzpOd0p68D1ntB8Z8r1pbIjWAwlBmL+bpKlBwf6ji8FhGccVtQjdZsgOfuns8ZGKA/dNi9Qz9fdyUWL82v4qFRP8Ge+PAWR8I5gRWbMxz1bltKiAqe0Y1JwRaGB+VYcdrekVso+qHPMLi5P8Ncu1eH6qDDlJBPHGoRily8c8D2v/y8P1VpjXIAfUAB+HZ8KZMw6fGZJahv88PJzthrQg21zZqmS8qaVfh/K0tECZLoJ2UVgeqe21r9bk2smGPh1KABqsG5KC/F1H1B1jWBckFKrF5Hhf0TsK8xttQqivxCZk6tl6ShQ14uecaIc3A3r3ILSY/TFzVd83qPmKvrNnrbf/tE2KIyE8JxEL1rmni5PU3nivFB0UIlz8DDQCULUnJker4KVQifBx0R45VNujWAF6xUGPihLdUAarUXIDkNld2qEiH8QMauH0RB9buqdGhQIym4hujE8NssrufiG63AfxjoOev4eULlRYCrqsRH8JfxQ29Fpj16C1djBH5mJHjtcJTQO5zy1vV1xjpuJweYeeB11O9jJ7lgKOwUU6ND++PsMuT3HwsaHhMbcGRYekk6KfQ5F3Cmr98yVJWsccnj+/IlH3f+bMOc0VUtDzs8zCAS78eVWRpYU4ONP8nMswR8ePIpShV+gk0J1QQuLZYRpKB5CinXm852/KlBgBRoFQK4jzdGwQBiBJYLiZsxDvF6i1cV7uNjcjSAIMdFFdhg2xhTMTbFN+m+Ime5NYxxwkBSZdAwofzkGKxSenxMhslMSFoooimW7f6zsr9WfMLPD8Y/ycRXGBWsW8w5SUAJ3BJEk4xhcUt8orh3OYOMH6YX1BAeXijEkI87JvTzRrr/9H12fHGwTMIVaCUlxOTbc993W+PXrTKAFaT86Ld6jzRXhp3yIkERfkLlXIoCB3a+gbtIcuT7AgNyf78pMdGk4fHeRpubU9Vt97xm4ZG6JO0bbiDr0XgMGZyf6iwB0ra7frbp8tqhJnRnSoh94rRRrADNL8K7Ob7MTRKits6NN80DsHa621pUdAJIg8iaOb2zB7cEyErS9tsbmxvpp/YP0hTPPER8e1bzi7T7b02TffHFIXh5yFtc+a5XmfXPaN+fi5CzXmuUF9JBeCpoenx7Rof+scPKtz9URtt0zXoOXcnBmq7772ZJvoO6/urFQ+0Hn6tGZJ8BuhkzPqAr0OAQq6jnS56JaOSvTXu2Lt8n7x07o8zV/Uqd/NTVCHDbEJPzcnh49WfZ9iLGuZ7s+1M2OVa7C38SBCMXN7YattKu3QeTI7M8jmJvva5+sLVNQCDNwwMlgKT6VdvYqnPAcuknpyOBJ47pN/403k5DRE7umcqW1t/TY12tOWfnNUwCZCOahjAhrPnx4n9oWEHoY5BIHYA8Qx6ODQaJ99YJLWGWshPcTd3tpZ9cNaRFWQfcx/yNMoJmDF0PW4d3qUugzkbNB8yQvoeixM8VMcwBgWcI79QkwlX+A8/CynUU7dKKIdq+uXGz3dxTKkpl0dRQeiI0ufmK6z6IUbMjUL6TAqHVQiT9eOWEmO8NDYCOVucd7udvXECNEp8VUBIBg3LlqGl6kpQWKDQDHcfqxe+dPRhh7zdx1m+2u7FBNeWJyq3JbB9U17KvU8okM97eWdFaK4IaYCuMAIA88AzynOhqumRKkzTQFw97w4GWAyX8LFcwKcIMb+/tpU3T/n4eQkf/tgf619U+iYxfqvXv/UgPjF66drioT+XRwAY5EjMzlvUrSQXg5CBiShILAA/7q8wKaMYkB3mCr3d/bVyB2Xts65CwkgG4kk/ooMf/13oaZDLrWlRxrs9vGhVthySnKZmFzhk+E3/DKh+JjZYTDHosQNkcID7iIoLYO4OMMy+wF3ExoLClhU2FA22AxjYnzUbiXZI9CzYLacaLTLLhtiV40OkQ9Hc+cpLT6MnkhkGD7CSKfv9Fk7XtBiqfFoRPvKYA3OM8kjRQ1J4uQ4T1FhMAA6VtkhmgAoAocqikOY93x4tF5GT2q9VXTbrHhvJZ7w6Ui+kcs8Ud8n1SS8IUgvkn1dbcgljpY1nFIONJLii+7C6nKc/16UBYIJRQhFzpufHxZ6QIeFNjLFAq6hKHetP1RnD8yL1e9gwaUFDReNC8WDRHjXPsNsb3mXjY30UCFD8ALFRT3pZFWH+NGSP04JENWLJJ4gAsWBBIKA8e6OKhtkfsfbRUpHtP6r2/otLdRDawXK1Nx4b3vvQK1UNhQcPJzMB6UiD1fJtbEOuDeUKZYerJMMH4gh3xsndSgTrnTChlwqydGLg/HwwTU75OYk+gOID9LNmdE+P8wU8R14NkgZoozFc6DwZV2wnkjC2cTMyiDDzHriZyK9htnB6h4VbzjeUwzTYeEwHxXqKvpbTXOfeKd8HxSi4C6jrgbHlQSMoS4O/Dq4muHuovSx2TEFpECi2CCAhfu66ndwrxhjccDTNQL5Z5aDIES3haKQYIRZJIP6oN8gUNCbUFeiq8AeJBJclxloh+p6tEZ2FbfaH+Yn2/g4r//bOPD0+mLbkt0g5JD3hOtweWWnLZoSJVECVGFYB5j3gUQ//f5RmzctTvFiQbyvvbSzQmaQB6u6pQKCTwFUFFRhoJ6hF+/nepm6hO/vq7UHp0RaQUu/guZtmSF2pKlLSQAXbuogXSSodF1JAAjkFGfIQW482azO05QoT3t+fYndODFcSRZmoCh94aqOAhB0zXh/VyWqHBokqffPjBbVhW7W6dPnLSuBotFZcYe4gTLIsZNNNio9SJ4QJe0DmmnhnbLGQKk5sEHJUIjB0wH6CcU8gIloYilBEhfAmBTluvVF7TYngSL3vIyWUMDhYERMoby2yx6aG6viCnoBRfS60lYrbXJ4kFyRGaDCXaZqtY6EgI4o8phQ7/jz9746ardeM0ozOyOC3eTlMD7M03ZUdspR+ydXJ4uCyYEj4YrCdnlqADok+rtoKBEKGwU6h+jnhxs09Fxc4/BXAV2dkexvWwtaFCuJA3SuoZdiQPbXNSU6kPH0IQ6wP0k0UOQJ83JSHLw1M0Rmnuw17oED04OhYi9X0UcAY0iIxka6a/ZheiambI6BdklLM0DuNFTFBWsbpHDIJabnz0WMZu1Lsaq5V/Q91jGJHEh7uKez1hvIPQADa5M9BphBzAX8QVIYsGFxVtAP9K+d5V1WWNOlooKLooN4mRniKnpeZ+9pWzIyWAkC8Y49K3nSRofSEck435OkGWCIAXGSI74rtNLqjkH9HHFAdJYAF/19Zg0BFkheUdBB3ZDOObETfwGoSPiskFDQGcHIC6CCuM4+IbY8MTVG0rgkkOuzG+z1G7JsRKTDPfw/uv60rcxWHKhVLoDDdlCQm1Dz62dEKzZBQdtf1qEYBUXnob/vtIXzUnUu3jEy1P68tdSuHRVs3+Y0K64z5MvehJYKlau0wyH+Qof4uQ1FEnTBDflQVa89PjHKTjR3Cpgh8WNwHgVMZgjpbPNcZsR4yZn84johuYLv/6NPs+2hhfHq0DNDhGzyknHhtvpwnYUHuYuuyjmMKmZr+4D9ZkmyvbGn2mrqupXoQ6PivGBGCLYC4CrF9KxJMXbP6DAr7ujV3j2Q32x+PtCth0hWnIKTQW32KjSXwxWdMqXlPEXp6G1EEFIcceD1fdWSvmZGq7RjQM/x0clR9rt1RaJh/frKZM2lMufV1ntGHQCKXXKW2yeFK3GmkAO9Z/00dg0IIIPyTBL65TfH7KHbxitnmBXta4XtvRbr5WqrClts1cYCe+KWUTrD+K6xPi627HiTfvfoeD8bG+ZmXxxtlCpcqJuLCvDX91UpwW5s6beIYHflH1dkBdmXe2sswHe45twQreCZT4n0tOdWFui5o0iGsSxSsIVVnXb56FDRl5TgjwqTHwhdS1gSWYHMG5glBLjbn3eUW6jnMHXyyS9eXVsiWWjWNMIRANPEG+IAM7QH6jvVkWV9rDhYaxHMPA1H8W2oXMIr6rvVKQOoBmQhB8ETDHqky9AhVt5+SkamsHYoWOVr0tRr148P0zA/9EtmzhL9htuuym7NEF45OVIABZ9L7KAD+d6OKhVh14wJ1TkNO4PuNt0v1FR53jwrQFi6t8QgKNMAmhR9szKDlQfxfGG8cMaMl6rh91Jd5d+A73z3ZSebBZa7Ow3VwD3xDfls8hNAVKjPABlQ1sh7YCi9ek2GvX6wRvTxlQdq7cM7R1tGuPv/fLHxHoo7bYMqKKiaaQGNivRS8sVgKEgGLougkhcdxjnsQP+5xoZ6WGXXgGYCaP/xYGhBM9QK1xxNe9y5Qa0Y3kSOi/Y/m5VEhaDYO3BGnF2SaDidOF8SaPk8hltJOvjciwN6DMTBSUZViCBEkIBWwmEP+lDb4Uhy6KzAAyZIRuNwfOkl0pO+Z3aMg77RNKBhvIO1jsSM7xjjPdye+7bQRib66z4udj/gekJxAvFs7xlUhcwGIilGTnC4y1ANItHdAKXlcORanduqASnUbkCZ2TAk3Cw8igMKlUmR7kJ+kAQGWZLKR8egtJ6RTEViGFSZgILGMlQctKp56+cvaFtj7nbLjGiL9Xax+l6H1CiLHpSktLHXklGIQCK1qltDR9A95mQEXdAkPycFHAbiOay357do7gLZNdr9/D5kFT8/0mDdfaeFwpfUddtdU8Lt/V3VNireX+8AI78pmcEKyCxJhqtQz/nZx8dt7sRIbWicQqFladN5udi3Ba1SvGANclATUAuaBjSPADUD5JPiAKrKjuJ2rQGQZoqHvZU9arWDkkIdgZbCmkGJZmyiv96pFJuGOszdQO0RMmCNXj86WAgbbeWF6QEqiglqp899r8/HCInB/KQgNwWHbSWdDiO0c7gZDxV3lL97dVqATJpAzDBDGp3gb229py0zzF1tbjieIFNo/NOVY12SVFA80TUbPHVGQ250QRjc5TPg3HM/N6QH2smWXs0D4bq96kCN3Tg1ShJ6JNIg3SD2JOAU6rxrCiMKHpIfOibcD/d/3/jI/9s48MHhaiFnFDN8TxJCAIY1h+ssyN9VexbzNdAukGwKUWQxceUlmYMnntfWo64P/hcUViTxby7LsT/cO0aDzqip0EHivUHLoKABfaYAa+k+pYMPJ27exZXJ/vbq3iqhRNCt6KhwkPDuKDBBixi6u5n1Vtel5BD6CskiNEl4xwx8k1TDISZh590nhHgo+G/aV2U/uy7VQtyGaeAfI6oNZa2Obt/wyyzRx9V+/sUJy0oOENefOFBU36O4VNnQLZM/fg+oOxLFbf1n7KPtler0IPeIHC1u37g+w93/8nCDDZxy0Fwigzw0iE8iTtdrX3m30L9Zcd5SVcKcsazjlBJJ/HPiAt2VhOK1ABcfsYbZdEvPnbf1BY4Dh/dxsqzNentP2+NXJinJYJ4J6cj1xe2KN4AIyLkyP7GpoE1F66rDdbZwZKg6fYAIHMq4iFPEfLKr2kbE+9sdWSFq91+M8y+sL1VniY4HdMcHpkfZ39eV2rSsYIkzvLm6yOZNjFKnjCLk/nmxFuQ6zH7xWY6kO4mHIKrcq6io3q72aXaDTYn1knQr8yTIhdIp5Kzh/SPry76B7sR8FPGYPxsV6q5EgPVz95hwJVbM8NBlgW4xPc1BkWEPAOJcVLvaU9Imqs59UyNt9ckWrdersgI0JEoCgcM3oBIHOTQh/AJQvWJAn3ikdTjcQZMlaYBrjXwnHca8inaJrAC8TIn3sUhPZ60x7pUOKxx63gWFPd3m48eqbZjLMJs8NlKqRDxXrlkJ3ra1uMPuHxOhgdATdb0aBv5wY6k9dW2KhpLh5iM+wlrHW4p1TkJJPACQ4AKcO3UOKqn9b+c1/tdr6fF6+YTAPOBcgdYT6etiK3ZVWkyUt+hp8xJ8ZILKGU4n+xAdbVcnya8m+HjYnpo2GxHoJf+BgTPn1RH6yysb7cs/X6suGvMMrM81xW22+0SDo6vl7ixaCv5TdMmQL89v6LV7x4bb7zcUa6CWfU3XDrPP899BRxwipgRgwINjI2xdWYuGe0GPmdtjL8PEIDYgMIIYA+/u8701NjYpQKDU+m3F9qs7Rlmct6uKm0UJgfaWiofz6jok+rrZrX/dZmlZESoqia07cps09F9Q3mEhQW56JtCOr0kP1L395ctc8/Qabv+4Jcv+vKXUHr+gpsk5uvJYg0QpnJ2HWHigu7o3OJ1juIgdAfH/qrQAy2vpE6MCZBuEn9iPiM6ceB/7xWcnNI8wNdnf0gPdVBgsO9GkfcK5ghJjX0+/vfzQBM0FVPX2S5HonSO1Kl7pXOHJgMfYx4frtN+RfJ+fEah8AxZIaWOPXTc6WPO3v/oy16aOCpXcfFP/oArGYHcne/Kdw/JOGj8qXKDJr5cky6ASWiHg0DNvH7BpUxME7CACcM/8eOU4z68olN8H+/TZuYlW2N6tgnlkkJecwqFS0oEm5pGbFDYNaH9zLpF3EpugO+FhxZ9xTwsSHG7oqC2hivju0VqBVHQ0sys7dS94llHYsC541jA/AKEo9J6+IlHUMuhn144KEuBFHODzy9sGxazYmNtkY+P8bE6sj3yuoE9zRfkyj3mJ1iuu8BjyEQcQP0JYZU9xm12RFWipvh76/yTF3zwgKv8X28plTEssOLDpmPlGhduorFArKGm7ECvP2tVIcR9vlvjC0aYOW5ndrJnNP3923N57dLJkleMjvSw+0KF6Spe1ouOU9ib5KbEUthEdPOjsXHeN+Y/plP8WGhXXveMi1Xmg8ifwsWB5IcnhXjYi0lMUEV4uEoKg9wRSzPOY0wDNQzsbB14qOChTDGTR6vXwGCaaFpUhhyYzCQzfXTEyWNK1BCd4h7z4lHBvO17bo6rwdysKbF6Knw3imVDXZRnhHupSIH22bne5Y87gzDnNhcT5DFd1vL2oQ61OJPRQh5CKy3AnhxtmbY8OaxACBjpRgeCgAElkAS3LbdZLAXUcE+wtlOW6SRE2JtzdNuU2C60nwQAhJRgSOHH1lirJ2e9t6a4qISMUGhRkF9VuUB9hSIgqlUKDdixBm0qa5ALNZgIHUqUk8SxAaDtQDfje16cHCSXlvzOYDlI+I8HbdpZ3C0ngmXB4kLwkhHlKSYkFBdeaFiOdH9p99R2nlKCDxkFDYaCQzcP32Sku4Xe2L69ZzwsUiKoY6gxSlAxpgjKl+LmpJY5616g4Px3M4QFuQtRRiWFAkUr/mkkR2nRpMT6SHkWpAsrHuKxgBUS+K27YFBoENQ7uURcqbL4r64bki/cLFe6qlAAhqAx+0pHgvWG+B60LylCgx1Bbv6XQdpRDr3MEEZ59YrgDxZ8V42NTwn3UPeJ3UkxSjIDoujldJtlMZnngmI4IcbM1OS2iL1Cg8AxpgVMowJ2ndU3yzkGe4OdsU6I8rLCqwz46XGeHS9ukkgMKjKIUXTmKPnwfeJ6YWZIoo+GNvwKBD/Tshlmxku3be6xelDUu0AcGL+k0Pr+1TB0xCkf0zxOivLWnSKS4mCngYKYjQCLN2kR1iUIDbjwD31z/p0KD6+6xkXovmeGeKtq5T7mWJwWoUIXyw7vdtr/KoR50vF4FG1J/OBefaIa73WGv7q5UHFiY5BgKxN0Y+WEUN3j/+DZAd7plcoTNTPEXCEByTyHKe2Md8Qwe+fCoUEDea1Fdl42O8VFiT7f0m29PaD3T0aIVjSIZawTZxxnJAVZY3q7DCICC983vwDk+LdxTw4j7chrtimkx5jr0Umu4wN1//UCV3jvF9rRwX82VYGxJF+Or3dUCKtTZC3SVmzwUL7qPrDda/m9vKNX7v396lG0q6tD7hUKAfwDtdJRHSNDxy8Bwjc7NztIuS/XxlHdFcuBwDZADRDBvQcFB8fTrWQl6PiQ5rAmENUgStpR2StmENUriDSrHoCRdZ7oYIGAU8EjqAprwTkjSeMfMs9w6OkSocRxu03nNUoLbcQy1pGH21d4ah4ErdDJfZw32h7q7WJq/h727r0a0rUXjw9XVDfFz1V55YG6MYiyJ3Z3zExyFQZS3vXnbKMVo6DsZyQEqqogf0ErodKF2R9I5P9lXXkckeHQeLtIwmQV7ZFyk/JLoZuyr7NF7RbiDg7W+Z9B8hg+xNauP21e5jZqn4GKtQUnibMVIDFd3CnaSMZ4rCcq4eD91V+ho3TomRKpVGD2iNHisplcddgpfBt/ZU1CtMBZEhQ7BARTjpkV7WW5xq720q9LWH65TMoykK39Ockwh9XWOQ8kR9JFzj8QIwOa+KRHygrj32iybOTHajp5sUkJJ7GOWg5hHQfuTZTkqNIj5UJ3TEv1l7EaxzoUpKDLSdICRJgb95pwh9nB+Haju1Vr8PxUaXDeOCBU6CmjHWpXPktMQu2FmjIpc9ggzCBt2lAiF/nJTiT0yLcoWpvkrB9hf1y4Dy1+vLVBMxAGetTth3ij78FCdfXqswXxdnDT/cmdWqN0/N1YoOJ4J5BfMgCHPzpqhkLjzlT3249mxGvQvre/WuYUgwx14h3y8XrkKFMjjLV3a88QF9vqC9AA7cqTKrkhwCGlIDGHoJTLZpRDU7EJ+sy2Zm2wRHi4qptnLv9pQqIIEeiYdhpaBQbvv+pH20PQoe2+TQ4wjMcJbfkux+K34uWn2Fep3Tc8pe21NiQUEetifbkiXMR5rgHOU9f/5nuoLppAmU0bWHbQ+5pNmRfnbk1Njpaz5t81lElM43NglBbixUMgenSSQK7uhV4bH4+P9bH6Mv20sbrf1RW1280hEEIZKIfO6mTG2ZE6y4sgbB6q1z3/8dY465LBV/H2HS6jg7b3V9qMp0VbWOigaJ3R11umBk03KGVBPK2ztt7hob+VD+P4kBbhbRoCHvbCu1K6ck2SPXpOmZ0mHOL+l3355TYpmy7geummU1jsF6xcPTtCfYQocFeElXzVA5tcOVAmlxxdoZVGL3T8hQgUpnUNyEzpEdALLqzrtD/OTLC3IVWuDQgMgAv8jumbFHf2K0fs2HLV/7K2UTxVrF+YKZnjEsyVJgXZ9eqglQi/2HS5aEmAXMtKI0QDaPDbF0fnGhJR5lhXZjcrBmA1cPCJYf4e5LsQYFiT7qaNHFxnlun2Hq+2FneX21bZyMVdgh2CUy3rN8veyV/ZWivYGU4X4c3l8gGSNn5gda6NjfO2B+2drbuRkYavNnRApBbp5Kb4SKEAQ4vpX96rQAIRlgHzkiDBbW9xm3t7OikfEn+P5zeoKAioHDHfSzBZFEeuf84I5wn+20PhvFxtcoKFwx1CCITgTYEEScUX9evUJ++xAnSUl+NmOk022cFSokhiSPZBwEli8GbIiPFWNvb69UlVxVpyfqAAM1bC56tv77f2DtWpfo/FMwCbBZkgQVA6UikBBQsVFckY7ltY+AQ5ayy9vStdCw+gIWd3SdhDwQSH1zB2QAKOeRHcG2g/JbqCni5Imkt3HFsSpEwIdgcOEgU7k4xgIZvEcaOiUys7u4nZRBPAAGB3ual+tPK6FTysL2gEW93C2Se757khs0iUBYabFxueCqnPgwcnmey1O9pMcIEHp+swg21TWLnRow0mHBj/UKr4bnHD+LjzPL3ZWCf2/fXyYpQc56A751R0yJER1qbKpVwGLAMZQ9qHyds1cQHVKDPWyo1VdUuJKCHSVAyWHGP4htJJJJpkNAAWalRUsJZGRCXRzhmnoFY8T0BYCDJxn6f2fOafF5uc+TCjazEhfrQP027kH5Gzb+87YpBhPW1/aJmoKHQLUtBiWOpLbJBUYkEIoQSD4BH88Jyj2LtKnoB2RrKHAgXAB7UXpc589r6BBwsD7oJty/RUZOgxJrgm0Gw7X2bHCFnHVQRq3VbUpyWBzZ9c6+MocJgx91XWeFhUMIyk6EKBg/K7ZsT763XRzJKnXNqhClbUBPxdpOeYvokI8hESlRfpYVVWnHJm3l3bKURjFnskxniqeCNQH85skKShOcOMpJe5sfJ4ds0v5lR2iP0FVQpqOpGlBRqDlVXbo80jQQDmgY3DQ0m28amSgjU8OsL+uKNScBjQUZH4Z3kbJBQWsi+jmfyqQXGKiGtCx4BkTFJnJgev+8dJDMjiKifW1HQUttnhihChedHRQ9WFol6A6Ps7Hjh6vsxc2lmqdgqRDSViU6OcwH6rqsOe3lwplYr0zOMghD92CIIlMKnQghijZs6DA90yOUGcLcIAE/aWfTBOCSWH06ZYyO9HUK7WlCbE+AkHgFuOWy1yRuqOdjiSPvcmefOrKJB2QDOgeq3UkYovTA3S/zLxgesX3gxJBLGQ+ACWnb749rgKKYmxShKe9trbkgjqQwxn6+gnhKi6Q7KYw5VkSezj0kCwkSaETs7+wWTMJt40IsS9ONtrKglYhngx6ImvJe8DhlkFpZoTeW1skXvtTixNF40Op7Fhxi+Ls/oourXcKITpbdPe2F7RoLoNYBTiAAzdgEvNbH24qVbJON/docYve++y0QCXgo1MCbO+xOsuM99OwJl0myWf3nrZjzZ22LK/ZHpzsOASJ6SSfc5J8bREJXe9ZUVw1eO/niEl0YVcUNtvukg4hsLwD3uOxYzWKA18fbdQ+mhjpoQIt3G24CpeL4A/UyvRoXymAbSnpVMEEwMA+wBuKd9k2cE5zQPMWZur3s7+YN0FUgnthL60tbbGVRU2KeVx7yxzUK77L71cXaS3QrQFco5PIsGuwl7MkW/kuUJd2lbTb/soeW3uwVudXSWO34mNJx4CotyQAPLey0lZRrjjriIkby9pVSFE00EXffLDGPr7g3l3Uimy6Y74k3MfFpo0Js8N4O0yIlKw8hRtza0i40/lkjREDmRXijMBAdH6Kr2aqkEz99Scn7I7xYXoudPROVndqbV6kZv1nrzAPZ7EDSO7pBkH5XL67SvH17Y/22qf76yw41EdmvNfOjpdhGN8HRPqbIw3ac7PSAu3wwXL729oS5RUkU7yb+8aF6wzam11vP16Rq8+TVGeAu6jWN02LEqhE0osgw6zJsbof9txP58TZyDA3i/R2si+zG+2Tf9ytRJnO9LPvH5GaF6IlqCMCkCWnhdoftpaoYCWWlLc55gwTAhwU7WevSdE7ghtP5xqe+02jQ3QOkRtsLm9RVxlqNnM2UIbT/N1sxbfHrUOUFycBYs8vyxedj73e3tJl982Otr3V3WI4MLtV0X5aORazIy0tfZYU7SNzQRgAmuEbESaJXv5BuhzQZU1Bq7wfus6csfy2XttT1WpvfHHUrkwMtJfvHyea0VuHa+xgbqO6kRRyGw7WKHnmDCPhh71Aog5Cv2B0iK3NbrS5aQEq7H7y6l797JbydttyoFrnIypxFGSBAa52KLtOPlLJ/q42PtZHsb514LR9dbJB6nl/vS5dRR6xipkwLAiuTQ0Wi4WuLYBmKiwOr2F651+ebJS7Nu+FmA4FqTCvXuADsy3kA5MiPGx5fouFujqonjxP1t/lqX7yZCvt6LXPD9erk8Z+gd6FFw+WBbw75siSx6VKqQ6xBPY1Bfy23WWS919R0GSfHK9Vp5mO25rcVs088D1v+MdOgTY/W5qjc4UuPaA6IPb8OF91hqO8XOzzg3X2dXaTaFvM8ZbUddmxhh51fCaNjRQgGx3lbRWlLXbN+HB78NNjFugxzD7NabC7kQe/xGxhoq+t31Mhg0poqcz4IoIE2E4Xa/aECMl34zy+qaBdwAuzTT+/LsUqqjoFtGCQjWALwN/PZ8Xb4jR/e/aqZBWaj762z+4fH6FuGkIKULXIvwCyAP//O9d/u9j4xcxYKXkwhDUzwVsJKB4B89P8bMb0JA0t0baBB0sXpK6tX8FLL7rfMXhKIsCw4rzMIHHjSHSwTR8452jnoioBek5RQFHB4iKxhAqBygibHg4jEoDQixy64WeV6BF8HEXQGQUhggEDZKCQII9QRWgbgxjyZyTgs5N9LNJ7mDjfbHRQKipWKlEWOPzX1dCfLhsiyd/31hYqsQAJgnNJkUOrdHNhh9130xir7Tylg+twXZ/08AmoXHRKkHZNDx6udhW8WVqCJFANbf12/9RI8ch3VHaJggWFgQqcxIo2Ljz3IK/hNirczfYWteqAgSNI0UQ7HXSC70xCBNoQHeRhk+J91EGJDnKX/wnXdZlBCshcIOm0o0E7Sb5I8jB6wugGNJzfA2rIO4SaxLNeMjVahQEdH4oongnDcygnwctM9/NU0kSVz8YoaDqlliBBmb/PIcjAGknz1qIOyRiTAMFrTglyUTGAyR6bHNMh1Ll6z+CeidfLJQ7/DY9hlhzgoC8RhPke/F7QaHw9+PwZsV4q+ugcoODC2li7r0r3eu2IIJs7OsymjgjV9+d3gNjyWRTP0B7wx+AZM7S841CNUEgkUkGax4ShTjRUA1xQvRiqpqhiLdwyPcpWbi+zcXE+dqymRxQYZDmdhg5RwblgcvQPqmqstYmR7lrHvLeTTf0WFuSu1jwqEiSin63O0/5BjSfSz8WunxShz8UdmAIGvjit2Lum0nEYah09g6LRgXKDCl/0YKhpG7Alkx0HNIl4TICrtXUOSAWKBOCpGQ638P/M9djkaD3f9EhvJYoINoBuMh8wY3aqDn0Sfyh2HGB0I0Cejtb0qHuHmhnrbd7UWLtpUoQ6CawpqEto04NYIZMql/Bz3ykBONGIVOE5y2/ss417KxyUNWYcknxEEWIIn3kPuKt0SAnW7FOKEhJekCH2/MwEH+2/5v6zmjdgj6MmAweZ5+1zwS2X7u2y7EYhX3yPabFeOoTdoHU6X2Z//uCQulvsAw5f4hrDeqtymu2uG8dqdoOkiXmIhBgfFd7MFV01NcpivF1E7+P7ELsoiAAx4L4/MS9ez3hlYas6IyjGEQOIEzgR3zQ+VAALQ+sYQbX0nZOPBfrvM8ZFqOjg+SGbSpKDARqIO0IWSdHeoqMw93DnqFB1vkhueW4UqVCZUgJcVUxdMTlKIhgohMFxpjgBHaarR0ycNZ5BRzclwsRCpwsURLqxJBRpgZ7qQHAA8hnMY2yuaBUIxLsnptGNgze/MrfFTlR3ytmWjgg8e97Bn+8fq2Tv6KGKC1z8c1JvImaNDnOzUDdnJQDIHF+M++wxgBQoscQBnKPp9kLjpXOGnv+KraUCJh6eFGlXT4qwyaPCRA3mM1D+gRqVX9GuQoKDn+95z8woofQUlBQwGANClyKmoUPfceH78azj/F3stpnR9tGKE5LtZE2GukM1dnQakZCeOyVWnHOSDtbBrBhvJbIkTftqekTLY/3MTfTR/NJrHx/U58ISYL8xaAqdjw40a5zvQcL7s8sTHMZq332vuMPaX5HfIkluhtp5ziT+dB22lnRqfhFKXXqwiwqO/0ocuDYrRAUSs083Zgbb+EgPqd6NiPCyKbPTraPjlP3lunSBM7wvOO8Ul8wS8BxHhLsrft2wJMvunxOtXIL74Ds19EHvGGK3zo6VGzVJKQp8dCjZcwicfP3xFj23jXlttiDFVwaqDMlD49tZ0qG1xz4GbCRXQeABZ2n22qhwD+27ur5TFuaPxL6HzInvGh8mAIkkldjOMP+HB+u0/3BqXpzqLxdx9hgx++nnlqmbRDfqyhGBFu/rbBkpAfKxue36MUq0WZubytttZFqQxCRCXF3s0RtHinpE3MNYGoCKWTyZ4DZ02W+uTdW7w8eJ4WY6GeQD0EsBDe6cEiGmxPwkX+19wEdmuV5YXWy3Xj1C4J/TpZdalIer7gVDP0BZ0fzi/exIbqPOM+TWkUEfgAbUPqhngps7RR2mlvffMFJn1Z6CZktO8NPcELnd0kMNKt5QVSLHWFeIV5VjcP5ITa98OC5P9LXUYAeNHk8ZgE+kar8tahLQitqXuplN/ZLnf21vlR0obtX82QvIjUd4CKT6y4PjRaM/uGaPnjPzKhRPWoMZgZrrhM7K7+Z8YxYNyhp7IK+uW4j+TVnBioU3pgZZSeugzcgKsU9W5er3Pr8oxR5dnGSTx0fr7Icd0DFwXh4Yu4/WaR89NT1ODITf3jFShn1Q+iiu1+Y0iwILkIF1g0xaz52XvxNFyJ3To+y3f1ltD8yIFpsAdSloYcz8URiMHxfl8A1KC1TeiBjAt4Wt8l75Nr/Vpo4J1zO9Ls0hz//888v03g5VdKgTPXVkqH2d36j8lvwI0ON4Xb+9ftcomxzhLQAb003iKcJNeA15OjlJ1OO6y9Msp6VHVOzZY8JkFuvj4pgzeXau4xz6f6zY4LppZJgWG9q9JMsMf32yp0btJhbltsJ2HYo4fLP5eIAMbzN4zYA3VBQSWJKBTcfqhf6w+U809AmxAClHBo3B6hlRPpqDIJgW1XRaeLC7EkACOnMdXBycOAXTcq5qd8gNclG9wjtLCEJH/FItWgaRQFu5xsX62qgwNw2c+Q0fqhdJQsfgekvngLok/L0XvylQK5IN8fXGQntgcbICCEktm4cih+KFYMesA466IDvjw92tjvkW5yFKui+P9xc/DgM5giCHXUPnKQ3k0Q5+d0+NKmbkSHETJQFo7D+lZANVCgIfiSMX3Yjdxa1qlYLsw9llkaPTDPojtSZnxyyInMGHO1lmrJ+GpRk6Y/5jfKy3ElLawFB2qOJJVqlsWcRQwqTjL35jt4bcQMsnRHiKs4/aFofz3pNNorMwNwJveHdNu5JgEniCNrQneM8g8RR7tPFq2gcclLEgV9FjmMEAUYUqQVWNuhgmdtPHRVpLj6OIvHl0sBRoeI8YMYE2q3Nx9ry+J54UN40JFhobE+ShIXHW3tH6XnG/UYKZPjpcRmQkBiAzrEdQdQI8RQyH2fVjQ+WczJD83AQfKaFcNytO3Su43fiz7EHnvrr7h0OeQoyEi+/BJnv0qhQVAHSneN/Zpa2SF+VnQZdIRHk+IHkgtRSkvFvWdmyAm+6LYhTaU3Scv2gRBBzWGwU06wdlJzpIuQ19Ml6S90r3GbtlYpgOQZKSk5XtWtfVXYMatOPPD1b3ykUWf4Grx4VJrOE/Q5/6v143jAjV93Ma4nBHJQ58uL1Sg64YXkGnYyYGCU+KO1Sd2Fso51CALc9Guu9S3fOWo/WaA1DnrqHfYY7VNWgLUvzsYHGrLYzzV/eBA7+qscfGZoWqwMWrBifXi7MjUFvoCKD8QvJ4MbEl0Yr2H26uAAqgud1nbH1Okw5HuL0z471FuwlwdVAqW/vO2JQ4b6FC7CuAjyde2ye/GPwoPvzqqFr/fCfQzAPlHeo4Mrty86gQJUkjIz2l8gJvt66pV+ua539NcpCGPlnjrBkkwuk8gIBNHhFir26vUGKPvO6YSE916HAghhYwNy3QWnrP6fk7D7nU0qJ8pYZ09MLwIGoz/Dndjt4zjq4pLX9ENSiwABkmZ4ZoHshlyGUq4lBLo/gj6QMpZUiezmuMr0P1z9vVSQlbY/uA6KIoBbFP8UCh2JIMd+8ZO5zXrL+Hwdy3J1tsV3WrFAop7qE4+rs7yYiL4oXkS7S3pj5raOjVLIHkIX2G2ZQEP/NyHirxhVc3lAmdv3FJlmIq7+aX8xJlosj94BfAsCoXBQj7EbO2uyaGq6uCCAQ+MOxvnhHSrnzGgqkx4mjX9Z7S+0WDH+lmBsynRHvo9zw8N8bWHajWHAv3uja3xR68JlMHMko6dArolCHXS3ENpYgZEYAZgC4lBg9OEKIODfijA3V2oqRVM4Qo+mFshusvyQ7F0eYLswPEDzw1oAKK0lDbKxrZqAlxFuQxzDEMWtNjAe6Xad8Ry0DpYRFgKoYUJwOfd0wIEzW3snXAjhe3qmA51thjN44JdnDRq3otLcRVydiNUyKFlP5XCo2L180jw3RWgqxz73OTfO2jDcWioHp5OdvLuyuV2BAnKJCRuEdRCKnRCE9nxXLYBcy9ZBe16J6ICXSquTfi5B3MueQ22g1pwfr/OacA6K6/Y47kQgHVkIDlvpgRSPFztx9PiraNR+s1+0iRwXp2gGPD9OygvbKeoApCAed3Pjo1SiINdGxQVGPmEwpMVW2X/n9yi1ufXmbjoac199mrb221Hz9xpQr14oZuW3GsSQptqMk9OS1Wn4H4wc7DtXZtcqA1dw4oHrUNnpYiFVTs93ZWCanecKRetKQtJ5ttyexE++vaErEdvt1epg4Ma4q9mRQwXDRkqG8U7HT5ONuX7q+x3TUd9tjCeH0GV+upQc1rUcyyDwF8Ls6yIVdN141nBhX9tolh6hBwDgIW8/cYPgaIZR7R3XWYcgrWOq7Wj8+ItlR/d/vVzHjlBQAEzNLmFDRL4QnA4eOj9bazotUywz2kSoZPGlTbL3ZW2qbDtQJEKF54zrt2FFpqmId5ujkK8jnpgUrgETT4/Zd56sD+8qlrRHWjg/jRPWOt5dSgVKHe3F9tf9lUonXE/dB1hiHy8PQoq67vUa6XSycS0YCyVgHC5CbzZyaKMYAfCO+A73eypktxF0VJ1svPr0m2HUdq7fOTjVKVQtDgl/eMV4F++GSj3TkhTLkTHR1osjzDr4416vymoKIA+PwvN2pdcD78ZnWBVdd12/3jIjR8X1nbrQIKijkg4+qSVoH0xEgAVroT5Exf5jYqXk+7dpaFezvmlKFAXbQvgAFRgVhBdY8KFp7L+pI2u31UqFgTUKT4vnTtoaE9OD1KOXhePWqLngJ975wZLQrq29el2n/3+pcUG1wcYigUgBYwR4BcHLzL+al+ajfFB3vq4X6wo0oLD6SRRUBFSXVOe4wHhVsnlRjFCwgoF4guhwM697hf3jgyWA+I7gGLfd2ucm0YNloa6jvDh2gIZ19Nl9Bykk8Sr9yGAR0s6YGuSsJQv6GIiQvxtIzg4boHWvFXpvrb1tJO0a0wsyOJhuMqecWuU/bMTWlKypXQjo6QBCEFTFqYuy1M89PMAgl4TdcZS0C5yttZ7UT4+3fOiLL8+h4lDJ/nNuqQiPJ31Uvme3LIhAW42fwEHxsX56vORmubg0NLckJSuSG/TRKjVKYXLe5xCc6McnRkSNQZ1GOxUfxBBWLhYBKFWRGFB8ocSKdSBPx9Z7kCz+a8FovydJGSS0YIC/Y7C3F30sKFLsZBhRKOi8tlUuUANWQICg8E0BX0zmk/ouDxyYYSBTKUl0gC+S4UGRRiUBJwMyeQoOHMPbB54iK81MalhU9HhgSQRJj2HXKed48Ok950TlGrvhP0JSgkaEiTjPHsUoLdlFQS1Oms7Crvln8GsrltfWdUgGBSgywwfiiLkvxU+HGYgFxwADHQzHMDeUVl6/N9NaIjgIr4DHOyBal+Qp/Son0sMshd6wZPjswwD2mAs1bzCh0zHPwe7hkkl8AwMtTN0sM8pCmOeRdyeEgf82c8lxFRXrpHhpzbenBS/17vi6FYOJs4fN84MeIHJA6kh+fMXhE/Oj1Is09woDfltyo4wdXk/aK4hd8LAZMi6qsDtSrUWHusbVB9ZonYJ//sRXENEs0zIA7cPSvaiht67JaxoeoYIHO8o6zLXtpQqg4KyB0zOLhuk8BSzOPyC/LEnyPpiYIVSD5Sg+yhn86NszUlLXZdRpC+NwOi/N2Vmwv1XED3KAApfNaVtNnemk4ZWPYOMvD2nfwoxoS6y82ewhvBBAq/i91DDgNa+kuS/YWw789rkqgBBTVFE21xaJ4vPTZZnQ+e//y5qRqQh3oIlQZ3aEyrxoR6WFFbv6SuiQUIK5D4I4AAVZGk/M3DNXagvEvyj1CAKLjoKIQGuOk7TEyEhnhehxnJFodt28BZdW+ya7rFa2fwtmXgjLqxJNQkF8SLvEbHu2Qv4/eD+h5UQ4adKWwofKBE0dH41boCUSLYUwy9QlGkU1HTNahBZWIsHG7+fWV6gHV1DVpTU68+H48ehsqZ0yAukjBEhXvam6sKtFeRytxf0eMYcsfFvuu0Oj+TUwJtepK/BBcwBeXvMp+hOFDWpuQDAONQba98CZ5enGRPz4gzT5chllvUKtQVJb5XNpWLcsI+IyFLDHRTQcgBvSKnRWsOs7B5cT4qHLOC3aROA0IJ4n1rRohiZ4qvh7oxvCu68hQwrIEvDtTLa+C2WbE/dAaZ2UISeUSiv7qPrFHczBlWZyaNoiU3v8mKmhzmtcR8nhOAAMPp6XTxY3yFABPnmAMhCSNeQccgriBjjlQoSS2x4KFJkYpjzF3Q0UL6m/kfnOpZhzx/kqQHxkaIcvHh4Tr7/GC9DFpf21phNbyvnkG7eVqUkkf22Gsby3U+8l4verqArDP/9c9edOxfXFeq8wGQ4blbM0UXgs7EO06P8tG8AF4fFPYkYXiS4M3AGUJMKqzrVnGCYApgF2fWxVh3sK7bXrwuUxQ35OuRzqZrQowGZU7zc8jZIv/K9dXJJg3K0z3hXJEPVmW3XZ8aJMEEzlDQd1TqOBMRrgCYgGO/OMVP7t+NbX0qoPGfCA91GAAj9f7RH69R5wgA47qbJtvB0nbFZboGz8xJVHI4JcxXCkhQmaAZ3jHf0Um6a0qETCo5I/+2u0IWAMG+rnasvt9CLgz/ug8fardkBNvicWHKcc6eOSukOcbH2XJbu21TUbtAU/bhPWPDbXdth/YBgCWfxbuFwsn7drA7hlhpU586ut+eaNI7uiUjxFbvq1an5r5Pj6m7xrph0DzIfZgUKQEyo72G63myllmPV2UEWFlpm1VXd6rgz2/ttcqePhXp5HgIzERFeds/VhRaQ+8Z272vQmcT3weDRcmUezjZjdOi7I7ZMRpWf3RilArueXPTtH9LKzrMz8VJa6K47ZTlN/Xb2/eNlVISIEB2ToOk/T88Xm8//jDbii74CyG8g4AIuRpx/qUdFZpXw+BzUXyA6OwMXkPXh66N/DiS0kivjwr1tg0FULfcVXgBSL19oNo+3Fap0YDrZsQI5CPeXpkVaCODPNUdCmHe77vv7ZmPskVPAjhn1i2/qFXmqwgSsP5R/WQ8YEyIp6VG+1h6gp+KB+Zopo0IUR4NaAyoDIPzpavSrLp9QMAdne+nZ8brTABwvn50kNYCM4mMBRA3+Ex/F2d7ZnaClM3+tqPcXtpeYY3dg/bjT7Lt4PEGAfwPX56g98l58ff1pVpfEg7qdvijEUuIsf+K619WbOAmjGttvM9woYsoFCzMCFSSzIsnGWYeAq76RaM3kqiatj6LD3ITDw8EGpUIUBvQMVruVJu0S0mQaGGBBP51fYkOIHiMJKkzJ0SqeEi+IFGKjOj0KB8llffNjFZViJkgaDsBATQgu7xdMxe4CTMDwsXBQlLMJqKt5eR0qWVGeiuJpgUHGiJVre++17AdyAPfGdMhNhafSxcBIx8S8E82l2rwiRYx98QLpMVWWtkhvWk2MEkSL5TFATLW3Nxnd44OE2+dFjaHQ1qsr7jUfAZGdsy70GUAjWThUXTE+pFcDNWBS0LqMJu5RIceG3pReoD+O3xneIsc+jnHqqUOhAQehxkDh39eX2yxngxwn7GiZkcA4+/TAYFHShIGp/bK8eF6ZhyuoFgkbniSgJBD2Rk/ItS6T52XHC3DewRZkmnuUx2ghh4NaXK4vrK+TJKudHZwNB0V42OfH6rXmuCdcRF8S7p6ddBkJvlrHXDoz88IstLmPrUMw/0cvhAk0rwritqJ0Qz+DbHlOc1K4uFOO6QvL1GigYoI6g6gUpnBw+WSjCwkBRfdD2YJQC7ZgAwFos4Byra9uFPJDBf3dUWqn721ulCHIcPOT12fqu9Ht4DBupP1vVLZAKWH/seBOjvBW/xoKBok6Tg603khMO6u6VQhTYHC76TViSIWhSOHIJKQCii00GvQah+m2Z9vi1rt44N1olJQxDAEXFXTZUmhHuLgE4gI0BGeLqLksEdIRKCfccDSCXjjmpR/Og5Ap7prQpiSa+T/PttTY1ePCFKHjeBGEYK6C8pIeBXwHgEO6LhQdJOgEYydnRw+ByTEqOHE+rqINsW9c2hy/Wl9kZQ/SCo4EMaNiZSKmrqAnaeFDl2VFCjk74EZUaLDQcVjkLjnjMNRfeWBGtG+Dtf0SdkMWUOoc3R3AEHgLaM8lBHto/dAHKAgYHgTLxzoMJuO1mvt0sHgcJTx35lzdrCswyq6Buz99SUy1/rsaIOKE4b2lh1psBMFLYoVoEgUWQwQs+4WZwYI3f/J1Bh7ammu1lFdY6+NSAm03QUtegbvb61QR46imU4v1CW+H1KndHs4cKGYMXVD8UIy09J/RoPzACpQuEj+3IZeZjl7cvUzd08O11qGAvO7NYVKvImz7A+8Ifh99R39Qo3hHJMc4I3A94GD7+08VN4I0JgywhwzSVPHRkjy9UqocSGuMnOjWGMdEkMPlrQqHoCYYq4IgsgzZ55v0bgwe3Vruc4JUGRAKCSDoYXRdclMDtBa4dC/anyYuqN41zAPQsF6cQYOaV5QVbpWb+xlYP9S217WJWU+uszQqJjPwtxtUwXFgqfELQCPAD2IkZEBbuLd83mbD1RbRXef3vPXx5sEbvD8+NnHZsTYEy9uV1KHNwWzghQtOVUOg7cjFZ0S69hT1S1DTeLAFSn+1to9KElaCjASWGiSnDNf5jVKYAGwgLiFYhFdFwpO4gRABoUZdM2Nea0W4+Wijtzq4hb7w7eFdt2IIBmQIr1eU90h7x2ocBTUFJ8oKKbH+Wl4lbk8xFaIGeNDfOylJcn/dBxgWPxXi5PsqqQg++nUWPvjsny7dWyIaCV00bno6Pp4OVv/WZQAL1VyxLC6Omf4G3zvKH6I1QKuugdtXJiHZGQBGfCggUb2u3WFKuI4F1AeGjUu1lYUNKtQxlgYwAcEOsnX3X62IMFqWvt0ZmWFulllT79i7AsrC9WJxUCUNYjcMbkEeQXfi647jt0YYFKskIzz96IC3bXXEbHZmddk1S19clpmP0MTIvFGgONQY6d9urVcXj6AMcQBukooMu7c5+j0AHyRd7B+yXugcbY099hv5iTarW/sV4Le1jZgk8dHqfNJ/HlhWZ4ondCDK9sG7GBdl/IYRGI495BsJh5whtPhPFLdK1lbAFtMJhOCPa26tV9xr2LLJgk3/GRhvIDPKUl+9szaAhsX7isFxdLWfuumQ3r6vDq4dGio57zpAADu30lEQVSIhfdckWy/vilD3w/p5FC34TY1wldgBRLNdI6gWDL78Mj1WcoP7xoRqrxFdOzCNvtyZ5UAkKszA+3Otw7ITFX0q4oOu3V2nP3x2yIBrTAPyNc2lLVbdVe/FKoQSiGewBJ44IpEScGibsYcW2Ftp+IGn3XT2FCbFuMo4p9ckas99XlugwwfmeWBifPusTopS31wrFb7AFABQQbWH35cafG+UgTjmX6xodCK23s1H/fe4VrFYu6Vzv2bD06w2+9/UfHzs7019tNrUywm0N1Ka7rUOdt8rMEemBxpX+U2Se6d73LHiFDFg9yqDlGy+TdKlJxDSPsSR5gz5t2UdPXIF4gOMXFif3Wv4pG+16F60TlXF7XampJmu/nVvXbPhHDLiPKRn1JbU6e8VbgAGIgDzJTgnQTdE1AZuvHirADLCPCy316g8f6/ptjgunVUmFQKUAa6e1K4DKzg/HEobs2uFx1k195yoYKzo/2U6NPm+mZHhRILFghKJyFuzuIC89II2iDyXF8fbdLP+Xg5htZYjIEeDsdttPELGh0KOnn1PXJC7Th13tyHXaaiB247/Gj+Hi0kFLNQ5wEJ4TATB7y6S8XI5tJOtQkzY/xkJEUbkQuHdFr3HCrinJ89r5kJ2vb8DoaWeXkPTYnSfaMKwMEKWghnbmtOkwO97DttfQNnhKjR0oXLiGIBtBGcOd89WKPPo7U+NjlQiThuqlT2DJJSqRNIWJhoycd4Ddfg8Jps2mpmxyva7bPVJ9XCnxjhIeR/7ckW20cwbO3TcHc5Gtljo6UywSKFdkWSPoyhq31VSgL57jgI88zhPWf4u9ve0nbJtNEN+Hxrmc1KDZQR2eH6Hum5f7q51A6caLBbRoaoWGBYMNHPxV7YXa6kACSXAmtMjLcoHKKUuQ5VMKCSZyifBB3UD9nFbfktllvTLXWxxu6zCnpXpweq+IJHzu/LCvcQOsRBw+AaLpjI0r25vlSFF4g475kZBTYT3Yrixh7pWacHuKlgxMWaRGt7Rad9uKlMTsHFFR1S6Vg8LlwoOfLAzO7wjMdFe2iQGgSGDgpdlj/fniUKjKQVKSKHX6bhzx9Pi5EUJIpjtDUpfqsaeqTwQEBktoB1SaHNYcOAHh0SKH3MrJDg8P+jjrW3tM225LdoroPWLJxzvv9P3jsiShcFPYOedLagfpFYPn1NstYwBxQD9uj1o7PN84IXvCQjQEGSPTc3xYEE/ncu6FR/3Fxih2t7dbjzbjjYaE+vP1Bt6eFeduRotRU196kIhffq7z3cvt1Zoed8x7gwe3hKpDTbSUJQyjlz/ryKWdbiO3tqhPywVinOoNXEBbrZfePD7VhJiwZyOWDg0O+r67C2gTPm4zzMrh/j6DZQbJGcyNsnKUDcZp47hyNzWDlIi156iX2d1yykLznGR8IIqL/x7knyAElACGvb+qy1pVfc8nf316iriZwtoMOv5yTovq+ZEetwVh86RLMxK/ZUW2Kop505fVbdAYb/EcrAoR5fIAqoGePC7e3DNQIFQK9Ri0oIcLUrR4Wo+ECCm64PxVhr1ymrYp/5OOYlUBbhcFxzpM7eemebBiYnR3sovnxzvNl2nWhQp5Mu2rHmbpuycIwG04l9UHo4jFG/+sO2Uh1kDClC8VKCd+qsTQnzEU2QgxAQ6fUVeXb1iED7/GiDba9yzFu8/MUx232wSgg7Knl05RCseHLVSR2IcJuJAygMQl/iPWOqxjpnfYCu7ylqEy0MNamv9lTbppwmGbgVSC3pvN05JtR2FbUKRCF+gPBzryjNcEHJgmrz+roSqSohjU4cA9kHxIDOiDM1g/OoEUnO9Ei93ivDrx+vK5a2/MGcBimIXTUhQs/81jlxGvQkbi5O99dZwGeuL+C79duXv1modUHM8XV2MvdhlyqBZD3U1vfYjpJOxWHWUEVtlwApwCPkqGUuetmlAuMoInm/qP9AReL3ADjNjva3ZQdrJWs+I9rbPthdI1lpiu4HXt5tB4nzbQP2xOUJovruONFgOfUD9sc7R4piQZduXW6zfZvbIvlwreH+c3bbqBBJg/M5/ydvnf/MdXVmsD289Lh9lttgb9w1WmqIFELIhm7aXyUqC3480L1uzgixtw5UW2SIh23ZXaZk/DdzE+3H02OUvHIBrnCh5AVA96tv8mxStIc8b5CrpXM7Ispbz/lIQYtcnpEWRdQFqkzzwCkZI143KljxmWIUStrg+fM2Y0y4QAzorIm+ruY9zMn2nWySF9F7R2vlrRUe7qm5HQoV/iGpx2AOiWic4/NP1Kj4wHE9wWe4Jfi5qBP5tytSFYfvnBvnYCd0D9qK48321sqT6vReSuBlJiuvRTOfiGIgrkBsvmVBkr20r9LOnzsvIAFDXiiGeBfR9Xv2lgyBjAiWcP9HqzptSoS3ZTd128Z9VSq0/rK80N7+7eu2owI6kLsYCW/uqZbYBGAXxeehhg4bd/PVovUQ+24fGSImAj4pP19TIEABVc0vDjeouC2p7rTr0oIlPEOXA5n6F784btemBdpH2fW2tbLV3tldbX95Z49t2pwnVTiEWki+473d7MY39ivWo/gEKPHA/FiZMhKTMQEFLGKWBcbL0p0VFhnqIZGE19cU2/tbKmRytx9q7LCh9tDECNt2QRWPHGNekq+tym0R0wFpWpQGV+2tspfWFMv7ZkG8nzrU7E9/N4eXz8aTLfK7WhDv43A831kptgPywas3FUgy+qut5VpPzEZxDmHGS8wjj7xtZKjeIV5kiHCgcLXiw6eUL3JOZfl7Cmhj1obisavrlH2T26y9hygFAkmol3G/scEeAkkE0g8fKmCaf2MncMuIYHWGyc+QWv7t0jx7Z0ulXZsSYB9srbCthR06y2/59SrbnV0vw8Q/3jFCNF1m8zh//v7oJBXipdVdEhWgm7S2sE2zcsRRZlG4iF1ZEf+cp8a/3Gfjf3c9ubpIXDxeGPrIfr7DpadPIsRDv3JksG3Ma5GZFagsZnmgsMhaCqWt7rKEIHfRUphXmBnnpYTyiz3Voh4sP9Yo7ikt9c82FFtyUoDdOi5UvE8CBVJlzEjQUSEhpr3F5jlYD4/WLMLLSY6UdCG2H6m1iVkhWhAMoiITynwCaEaS33CZcvF7qtoHtIBpu6LwE+gzXENV2/KbhaYzHImyybacRtGtpiX5SQoO1RiSSQbeceYG2WVoksQeJaNgPzdHS97VSR0HtIzxEdh2uMaWTIkWUsfm31vSJkWaB2dGy9SQxQcHmeFDkHIUVmYmetuHe2o0BA5CRQImJ2h/NxVHoMmgsgxs0w0hsaCLsyW3ybw9nCUpXNg8IMSQwEjCIRlD7+FS6qAtCuUJtQbQVzYYSBuHLOaFJLxB3sPlI0LwEKUtDO8NUweDgVvUjvh5hr8JChR3fEf+N8gwQQ+DOQ4+Lp477U2+x66iNiUnqCjU1XXbwinRShoY6OdCqeX5mzOkSU9LEIlEvjf+BiSC0NHwfZGLeZ+jLUjyRPucThcu5SScd04Ms/f31Ai9yEzwU/DDvJDAc7iu17KrOuyxqdH26dF6y0PPehQ+A0Ns5dYSmz0pWugD3icUHyA9JFYgMiQX/Bw0G+YEoA+REFJIcV+seQpWkiiKIgoWkgcKMhJDjLDohKlADXQk3ZKH9XdI832+uUyHIepWXARanivDtAQnDlf2EfK6uICCaHFg8pkkUBzItGWX3TXyXxIHHl5eID8M9nx7+ymLi3QkkCCXnp7D7NbxofbG5gqbMzJEawtFqoenRMm5naIXug9O47SOeW6gv1Vdp+zTnVX244Vxkmf+2ew4zUu89NkRSx8RYQ9Oi9LcDh0/1lKUr8sFFP0SGxfio7kPOMzDLrtESM7J5j5r7jlrq7cW2ewpcaLixV94tsQOOovQMU4096jgA5mDY04cOFHWZr5eLjYr2c8+310tPxGEMOgI7spvkVET9KANxxvM39tFLs3cJ4OjIFPEIpJjKHohF+IAsYIYgeTntrJOW7W50O65KkOKXqieMYvR3j5gz1yTIkGF/UUt9rO58TL94n/TIkevH8lafHn4zm3dgwI2SNpZZyh6MaQPTZLCHVoUaxS1OygmizICJZsK3RARBYon4jIJAhRWvCpI6HkndJKJJSQQ7C+eH3soyNtFa4nDnP1OYkSyAvBAMqsZmc5TkkiFz3yssktSlsQuDPJ4VtAZ6WhjWAm9FvQWKg3PgGQcFZmWll67YnqcZjHYK8h7LttRYW/fPUbflS5okKuzqHQUKckRXopHxENmhEDveA/wnUnuG3pOSzEO+e6fzk+wl7eVix6yYEqM4tPsJB8N8u6p6bQ9BS329PwEe+9grRWWtdvkkaGK4Su2FNvimQlKLChukC2lgKJbzp7nrOHZQLWji8F3AFDLLmuTXDBrhj1JssH8BrGcrjPFSEl9t6hgxGgKahTUiOGsOZ4XdIe3V+RabHyApUb66H0TB0DZpZwT5Kp4y9A4INTLW8sl4sC7BDFGVWxXaafudcND4/4lceCer07a9oM15urqpD2h4r7rlNU29Ahk+vGcOHv4lT325K2jzW3Ypbb0QJ39flGKBESIm7wPcgVHx/eceO3Hm7vtsx1V9uzVKfbGnir744JkO9jQab96dZclZUTaU4sSbVVei4zZRsT5aw1D6wJknBDmK7VBkkjWPUAdXkeAQOvWnbAbrx2jtcQz5lmRC4Aex3i62Z7aDlG0MWIl8QdNJsY5OV1md00Ktz8vL5AJ8OOL4qVIJz+Wc9/J3PbzbeXm7+8qVoCb8xApa7L2UMJj3xfUdJo3RozOQ0UFLm7qt0WpfvIfW/ptjj15+xiJ4CA8sfpovbW2Dthfb82USSXeHb9fkiJOPvGwsvWU3Tc23J7dUCRDODpDOJoDjo6M91fBveNorUzv6Oaz/o5W95ivu5NMWLnvOakBompBxcNFnK43qWJmgKfezYQwT/v8eKP2Hua16oA29uns5+eIwdgScH90EvHUWTAiRAIIvcyQqti7VJRylOgoYPCUaG7tt4x4PwGp0KzpaDK7MznKQ34RsCaQdl62v1bysBRDDK9fPjVW8U5u5f7D7eNNpbb8R5PlBp/s52rBbi72VV6jbcuul6IXVEXuMyvUVR1NREcQdAHoYfCd2DU4eN5eujHLnttcbNmHK+yOa0daU9egXZMZKGPWtSVttie30Z67MtXe2FdlJeUdNnNsuIATgLPLp8aocF+cEGhbK9v0eTBFyI2YxcBrZcnEcEvwG277JQjzvWZBPDyc7YHZ0erukYtBveI5897IB/DgmJgWpMIV4BGz637ia9dprVti8l8+PGhZo6N1DhJzWd/cIzQsTBgB2DDyywh0sz+sKZKBIewA5nahVi471qi1u+Pxifb/2mKDK/2ZrVoACdE+duBQlaVmhOoh6WF3n7La2m77x50jbXNZpxJJqC9lVZ12zdQoh8HboQZpNxMEmzoGNIRJYCaocihwUNIpQMoQQyISQgayC+q7xVVEuYIElzkKDeddWIAMPfLycFNcmtOoViAXcm4Kwl1n7LqUQFtW0KxhoKL2Xgv1cNaA5qQkf7Vw1xa1i4ahlvz6UnHtCBAUNJcn+tk7B2ocA7HDHG11/pzFwiZEGQm6j/S9ex3DmiSKu/Ob7Y6pEbpvCrSJ6cFCFWhRwh/kd3SePiv+NRuKQyg5xF0tRimHSDLSWR4aUH+4aBXzZlHVgAawYm2uvfjYZAtzc5E2PUogwa7Okl17d3e1dPTvHBtqW8sdhw2IPbJ0qFyhEa0WaW233Ts+XGgYBwYtV+ZdSFpJ6uVQ3H1K9ByQ/MnJARq86uo7o2FPWppDhlyqtv4br662X/zsKiGn8GdJBp5ZkGgV3f1Csmmt875LmvpFUYj2d/D2WR/wmylCkVwGASGR4c8pDJt7HTrWJIxcdBdIljpOnZXYAEgTKClyj6BWUPpA/GkJQ12I8uOQQSnjjN4Xw+qsOS46HyQwoNvyRQl2VbIA4lbdOSjvBmZ2kJxDKhSFIQ495Py4RgZ5yDyIjgVrFmSMzgTflxkQDihoTCjZMNTFoUdXj6QLRHL1iWYFANBwvFEivR2tfgo3DYXiJuwz3LpOnVWAB41nvdBCRef/hZVFNiEz2A7nNdm0kWH24RcHLDkzWogtz/K/Q5v4310jfrddztt+fq6Wc7TSQiIDpKjDfkQhq6Soyb56cqatLGpWAYsU7OGcRls4OcqyQobbR3trbbjzUK0rEHVmEUDD2DMETlBNZj0uFpDsZzoEB8valTDOSA+yw+UdNjLKW4UdXHcOKt4nh+O8WD8NxtGupqtEB5R3i379PSPD5OB7S1awlXX1ad/8bnWhzR0ZIpGKz0802rgod3VCkA0dkxWiRBZ1nZszgmUwheIOXRDa+1CQoAuRNHIvUC9ZVvhCkMBUtp8WZ/rpq5Ptg321VlrSYotmxCuBvjhbwT3gYEu3hMTwcGW3Ytz+kjZbMiJIgEeYp5Nmbuj6ci9IkJKYR4d7aibn82+O2kdPzxVq9UVuo82McyjhNPSfsr+sLrawYHf70ZQo21LeIVpPY8eAZsegit0wJVLFMK7Zj06KsneP1GrNc0ixv6H9seY4NBsbey0xxldDnJMS/VTME5uhn+RVdQhxvHxcuP3916/bk398RO+EtUrxDR85v6NblEaAgLL2U5JSH+6MeIWjQMHwFaUmlGeqMXXsO2sNXadFVSTG90jwYYjiKD9PrEWxjnjD3A/38fHWCimuEDugjEKzY+C+tHVACjTEXdYJg5pQEvHoIfGnU82MHxfO36y/0pYB0ZuIBwALN40JsbV5reJcs+8/PFqvOECyNT/OT0VvVpDDCJW9ua+iWxRU4gszarOj/EQbXZXTorMzPsBFXS8oJMxiAbrwXvHWIHaQmHOfADXEbwQkUNEiQeG+UH5kXqLr9Bl7bmmejc0Mtp37KuyOxWn28strLHJkut0yK/bfEgcm/W239fc7nM9LcsrN09/H5k6P136FHliSXWL7X7tFCjrsdYaDN+2tlBgIwNyrm8pFt6Lw4qwlOScWE+NIZqEbkawRh+laHizvtIlx3rZ0T42o0EvGhClBZz6Is7e554yUnHhXrIcbUoPktfHBtkoL8HOVLDiUI4QmHhoXYX/bXW53jQoTbStwuIs98MlRu35qlM2P9ZcE6W1jQrVW7nlxp02YGCswAcrM/aPD7am1BTIERHUP9SMKmyMALq5Omie4IslPSkV0xJivYC7iw1Un7aWHJkhi/cj+Urv12tE6x+hqIXvbceqMFbT1CixAKppBeJgYqw/V2S8XJ1kldLzhDjdqYi3nP95G/f1nLDbWR0DTR18etHV/WCIFrZf2VkiwAwUn5kge+/iYRUd62zNzEkQrpHgBgYde+fmGYtGmeJfMifx4crQkgvkcQARAMTmUI3hy7jurKGu1tLRgMQ4wAFx5uE5KV8QBOkft7f324OJk+92T/7B7fvOI1gsdZAwy78gKtePNncpTiAPMORTUO1QaUanjjEfiF/EJDBxzmnsU83aVdIiKytlAsc05iqGl/Mj6z1lGkKu5Dx0qEIl9g+jDpKwQiVlAIyyv65YiIjN6UIq4H9Yb+5rCl3hLEUccwLeI90ennE4nINmCNH8Be9tym+zBGdG2sahd1Gx8M/66q9wmxXrb4epuGW4yc5Pk66aOAsP7+KstSPU1r2FOArsvTwi00vZe+9vGEkuP8ZUhbd2FogKgvry2W+DLhNRAqRlKiGLoJepeYdpLbAJY5s+hy9U199pfrk63xoFT9vRHxyVHf3DjYXv+l0vsF0+9YwGjJ9rdi5JEFfxXDIX/24sNrkdWFEhxJ8jXVYccqASSfctPNFtMAHxHU2XFYmCYigNVGyffkcjePTHcWvrOSDWF4WTkCjlUQBqYObhtQqgS4M93Vulhzx7haPvRHmZAFP13WvV0O5DNZQJ/ZxX8vSHSIR8T7S1e3Ce7q2XUMz/eV0Nnogg5XybVmIUjHIZyoEeYAOLMCRJI4ke7e9nuKker3He43T01QgPBKH0wDJkY7a3h+MqO07J/pxKGZoGcHlUtBxjPhQOK6pTfc/+USGtC0aC6R9QzVEFQv7l2bKhcVSmCMKFCkYEkE6oWdJFv91VbU327/fbeceKQQ/05WNwixBS+8m/ePWiTJ8XpOairsqtasxR0Fq4ZGSh6GooyOPmmx/lKhpEBRuYE2DTQGbzdhulgJPlG6hhlprU7y2zO5Gi7PMmhyMPmZ+iKYoz3y1DqzqJWIZsUXBjWseCRd5WLdHWXNgnviC7B3PERQjRI8glGrBlkaHH2pKhKCRguyVsKBDYW8mws311H6kRZI8GK83fICLOe0A6Pj/bW5ud9gWT5D3eSNDBIKRet25yGfkuCxz/omAkCUYCewXeUrrfUSpxUgDAjhMRrTlOflV0Y9uIAxJyJljzoI88KSuCrWyuE5kUGuKsNStEDarmvrMNGRXpJGQ3t/bGpgVL5AVGA7sYwc0yop12R6q9AykwFLWXQdgoqEiAGG6HZOJx63aSewiF3JK9JppgTUgKFvnE/JBDsG5JHOjkETpRvcBJnqJ0E/p9Rn/rPXPcvy7OC6k7zch9micEeVt95ym4aEWwfHKq10VHeSr4uOilDh6PoT/F3ta9ONFlZbbf96opEax5waM2jNndlkr84xwADOdWdmjeCKvDa6iK9r5tmxgq1/mhPjegVFAdNPadFXSPR/9HEaHkn0M1BJhdUDbSeBIwi5/qUIKGemHuR3IKOXjc+XEkQaw1X4ONNPZpjmBDtIRTpY+arTp22yGhfe3R2jAZroUTlFLXI7RZBAfZqzIU4AIWBGQAonqxtOm/wx5nn6iUpnBcvo7D1J1vVQYXz3dzSb48tjLOsAC/7ILteyQdxhUPqZINDKW/N3iqrq2iwP/1oujw0KERYX/gPoaT11Es7bMK0ZO0thnIBGdIivS23ssMemRltb+6sssdmRNtzKwosLd5PCCumhwASxJu1OTgND1ViAV0FIAQj0JUbC6Ticm16oGag6BwhqsH9ctARN77ZV2N3zIjWECYdNxTfSBpJIPNK2yQ5TRcE+U3kegFN+N+xgR5a1yhX0dGdEO8rydrm/tPq6k6J9hQ1gs7qxp1lNmlcpN5VlN9wrRmSy2XbKyw10c+WZAaoEAHlhk7zj50V6j5DV7gqM0AqcMyVQMcA6KEAxvmX/5/OADGDpC6vvlcIK27NeCIAcjGHgKIPcsgMqx4tb5e09f2TIuzFTaXm4jLU4oI9NMjM92P9rclp1nOk+7ZyT5WUx4id/Mz6/DYrq+2ylGgfdave2lete5ydEiCggmKJpPyBMRH2/I4yq2nskYwnCRBxcNeBKotPCLDkMC+JjZAQcbaQoBKLKcToeOw90WDJsb6iNhF/npgW82+JAzd+fFyzOOx1EjZmj55fnGq/31Jis1L8tI+IjRQCqBhCZWN4FunTvOJWe/XO0aJBMYME0n1NcqAokuQAnJP4dJCQ/vrDY3Zm8Iz95u6xFu7hbL//tkhnK3OPrCOKE6iRT06Jtq/yGjT7su5kqxDgGdFedsdft9nNV40Q2LCxvMW25LUoH2hpH7D7Z0ULQKRDEuE+3A7Vd4t1QK4CFe79VfnW0dxhCelR9sxVyXaiqU9o/5GcBps4KkziKkfrem18hIdAitd3V+msAJhibiHUd7iNj3S31bmt1tDSZ3+4MlWmmB/vq7UZKQG2IbvBGuq77aV7x1iSv4e9sr9KoCf3QAxHDAVgbv2uMmsrLrZnn75GRo1zUvzt9dVFNi4rROp/z7y0zdLHxFtajK9Ni/W0r481KXY2t/XrM3+5Is9+f1WqBolTE/xtZpKPjQzwsrLuPs14kcf0DZwVYDB/TKhM6TaUtNvny4/ZksszZDRa2I56kkPGG+EFgEPA3ldWFdkfbs1QgQQwhuwyzBeAMkzlcL7mDNi2r8JGjwizrr7TKlqYm6Abwpxkbk2XwEGKPb7T6pMtdn1WkPx/mINatf6kxSWFWLC/q2brMNUl73trVYFlpAWJpru/pke0LKSfX95TKRCBuPbwpCgB0YCOzEExEwV7YPWJJol2wFxh39BVxIOIMQDcuZcVNtvRUkd3GOVLBEcobBDd4Tn8eEaM/W5VocR1MmN8BQ5Xd50RCwOhFgRjUKj7fFOpzZsUZSPCXEURW53XaoUVHRpq5+x7dm2BANv7pkbqnCEWNHeftscnRdmPluVYL+yFtMAffHW27Ci2KZPiBIKxplE7Y46QvIBYTCFGbvPRtydt6sQYzZ1QOL9wRdK/PAb8S2c2/tfr9atTbFwSA3yXqHUPCryhEMTXy5q6B4V00RojWQMB3nKyyVqh0vgOt87OQfFyqSyp4mjpY4DEz9J242XjwbCtoE3Dbb+7MkWJMyZq+H2AxNOSA/lgCJgk+71DtUIxQDNQnWGuA0rK1LQgteopNOZE+1lGmIOjduukcFt5sE5qL99m46lxmQYakUtVwofcXpCbXTUlymanB2qDwD+cm+yjwUkGsOQkHeYh5ZDyzgGbleIv7fLs4larrnUkyi9/lSPuKodCadsp8XBR0iGxRdEGihOHEy7F0EtAFUkc4UFT3dZ1Dtr0UWF2xdwUDeOiaw7Sj7MqKC/PELlFvt+6LQUqnpCz5FmC5hKsCXJv76yS8sfq9SdVzFV3D4qHyYa7f0qEnjnzBwQ1+MqgaSMzQ1QcgGjQVqXYAHlHvhCknX8WZwXpeSH5SrCBAgQ1ioKS2RX+Pgv/qWuShYpidIODK6oqdIMY7AeZIXFgc3HvKJWRZNG94R1TaJCYoF5DcUMBxmFMEYhSBMUinHk6B19lN6ntS+IARY1ny2BugOtQ3evEMC/5NRBEwn1dbRGc7IEzmgOB50jgeWbpSXXOODzpAJEgkPiwaQk0HFybizuU7ILwihpyYe5InYbxYdLXZi4jNc5XbvDL9tXYaxvK1OE7sSdPyQCyyAwrQhMEpVhztF7D6aD6oNKgR4HeLjLp4/4ZiE+J87XZWcHSK2cmhDUG4sLan47qCoOhzkM1gMz9S+Xt31RocL1zXZqNSXDMgRwoatEg8PKTzTYzyc9yaruVQKIAw7rBnZXDva530CJ9h9vAwBkrvqB6RndnZOhwdRlIrEEHiS1fHW0U73RUWpC9f884W3+s3raXdNqUlAC7YXKEZGgpvgmq/PPclmJRMhiwJ+lFQYrZphtnxyrwH2josKtTgiQLTOKGW/mHWys0u4QSCXGAWRAKDWSbiQMhIe5226IUWzIm9AfaICot982Pt4fGR2o4G1dZ6BdwZ/HHGRvhbjtzGq2wqEX3/vN/bJfTORzu+t5BdTmhNtIaxwwvONBNB8ivNxQp2YFaxXoOc3e2lKDholyyD665cpR9vqfGrswI0Fq5ZlKkDhA6gG//Yo721JcfbpY3CKaJoME+HsPUVudAp9Bwc3Oy9RvzbPXhOitq71OBTnHzkwsyqFAi4bt/faJJXbp5MxI0/0NRMTrY4S1DUpcegBfHd+ri3js7RhQCYgizaKOivZQEAChwiDOsT5L8+1sypNJSUtVpV48MtvSQ4RJ2gCJGDAKdhJpE3ILySgKOAAPPcMGMeCWWuHJDc4KCwdqHnnTXuDBJrVJoQMGl0FicFagCFF8lhm0xEaP7wb0iS47pFR12+PrXjQpSMlLS0i8/GxQS73t5j+gJdKo6egdFUzpU4wBdiAPMaawpaBPlpby8XUPgfCeeExS9n82MU/EtOkdygAaE39tcbr/94qQdOtFg+cuX630hp8vn/2hatM4vqHkUGlwfZNcpvuDfRBygq4Ea1pRxkZrNw2MCqV/3oZdpbRKLZ8R7O8QanIZYZ0efkl06Yv+uQoNr6R0jBPCBFG853iAADTVGZJYRkzlW0WGzon2VdELtAJCp7O43Xw9nrfuqnn6JkwDooVpV0OGgXa87VKv7+vxwg2iZaanBtvnZBfb2xjIBFnfNjLIrJzh8CaCwIvZAQv7Al8ettLlftBkKSeYkMLd74vZxWleYc941OsJunxgmp2+8jP62olBA3S/ePKCkG3+lm8eGWCciEue/Nx8/d3v8nskCBTgTKTSYKwK5/+WMOBUaVyT6y6OF7gNxHSrlR1vKbfeOQoFsj/z0A7EWJqUFWdPAoOYhrxoVIpGFx+fGWmycn50+f97u+fSoZoWgI7LemN9ADAW69A3zEu3qO+ba+hON9sT0GKHu912eaGfOOiSEP332cs29fvmXty3C3WG8Ny/VT0qgMAvGpQTaQ+8eFj1s+/ZCKUntb+hUEkr8++mMWHN1GWpRoY5c4xvieayXPXTLWKto7NH7TvVzkxs9s0MTw5g3gn52yn59Y6qEcqA/M4OHrDU0NwAwZjXY49UtvfbGg+P1niorOmzJ6BC7MjVA83bsF/4c4LC4s0fUWEAqKGSsIbmgX5EpsA437cLqDrFn6CxzXvx8huPdUGgcqu2xn32da7eOCRGgeOe4MEmK0xWk60hucO+oMP0binhsiKdUFaGtI3P/+LQYiR7MuvcV7TsEJBAtgnHAbASgJcAO5+3yky1WV91mtbVdynk4j6FTcj6/cFW6hsQp9ieMDNVsxnOfHLdHX9lj6zflWdGqFaKcrS5utV/MT7S/LE6zQ7V9onsRF3l27x6tUxESHORmYyLclQ/gdzN3ZqIsBLAz2FzRZr7Ow9Tl5UyaeiEOcH/93f02JspTVO1/R6Hxb+1sXLx+s6lEkl+LMgOEwHFNifG0dfmtcg8GpeUbgKAfym+26yZHqPWDkgxzD7TnkZ5jwBPeG4gHtCvapIcquqwCVGNKpJJgpNrg5eOsyLwH3QkQfPwFSFhJBjl8CGhUyThERkV6aYGnhHra8h3l9tiSJCFLoKUEM4oeDgOSPf6cljt0JRY1L4mEjaEsKmh+76Q4B62JJK6287QD+UYBqqRVspcn6xybkcVLgKf9j7stGuoMoxeVtZuPj4vdNyNKdBwOJg5QEiWq7Z35LTY+wV9tMa5rkgNsU3mb7pHvwvej7ciwG8kA6MGxinZLCPW0zfuqbP7kaCXyQW5DNTyNokJIqJfdOImCAvTWbF1uiw4B0AjoawRSaAIkBCD7cxK87Y0dVfJJIZGI8HbS34P6E4+DaEWHDsnLU/2tsgM3UFe1BOkuEHRQroHfHew1XAPyF5NhfFAISFCymKFBOYgDHoQvLNBddIKiui67eUKoaHR6ju5O2mwEPdrM3ANI9fGGftt6pM4eWRin6v9QZY/mZZoIzi5D5a4LjYV7Y8Yip7H/B5RgWryX1GgKW/uFGkEzeftgjb5Le9cpG5Xgr4OhGJOvYCQsvSXTCB2PgEFRdKKyw0bH+uq5kFioo1ZM4kE35BIN+rHRcTpmfaEkEx/sUL/hHqBTsDUZqGSd0f25dWyo7ans1rrEZIj1RscnPcjVPjlYrz3GHA3PQQO3Tg5FFPYWn0FiRhHDdyfYc0BgwvU/cf18bbFmDB6eGS0lIPjtU6M87ZucZpsMJe7CnA7J0FdbSuRdE+HlLNWjdccbNXh564gQ83FxsuWFDqEI5J9Bq9fntdmJgma7aXas+K9/X1ci2Uj2PIdIdkmrzcgMVkJOkYnmOs+ouq1fB8GmHSUWnxSkLhTP84uNxfb727MUc8o7+1XcSGHqgi8Pv4POWg6mgr2DmkUC/VuZ3SRqBIF+epKvfp5OErEMiUSK7aW7q+zWGdGKbaI5+rpKIIAkksI12t9V3yunsMU8PZ1lyIaSGmtPh+xwJwE3+FOMTvDXeyWhv3dkmG2pbBNVizgQ5+diAS7Qhr4TtxqFHzoYzC2s31lmsybFaFiU2ARS/Mm3Jy0wxNvunh2j4VsKGhIdfJGId8QVnhlccvYbCd7VaQH2/IZiu3NapAqpRH/H4OLWQqQ/PYX6EmcfmRxlB+q65H1U3tWvn2Wvby9qV8JEbEMGk+dEtxvhBg7Dw+Xtco/mvXGfRRUdcnhn7RbWdtkD0yLVjaCLynwOa57nyM/yzqZEeglE+nRdoT1zW5b2FvuJpAnqJPGEDgjviYN/QZKv1K7Yn3RgF6Vz7jhZbQ+KT5dagpeHvbi7XNQxZmeumBgh0KysslN8/AVZQXagvNOmJvhoJgj0FLGBCfF+Sjy4PwbT4fJXV3ep20EHCToqyDz0KcAS1iAXwBCxTKpn9f1CKvnsO8eGicoa7eui9y2gIcpbstpv76pWfJTrtDog38mfhDlF1jDnCbRVYgHrjWRobpy3LU4P+h+LA5uO1NmLN2baJ9n12qNXpvjbp9kN4szzfelKQ4d67q299txDkzUvV9U9aF/sqlKH4pHxkQICPsyuUxzgeTIU/vahGttzqMqumpOke3r0ncMWn+Cvoop9hmfFjJEhmtlA9ICYrLmQ5j51nr9aftTiUiLMz2+41h1n44ePTlKCvaumXe8j2H2otQ6cUyeC2SRkxrMb+iWrjUz+o1OjpZKHzwX0tRnJ/pq/IabXdQxaWqibcpUXVxXZY1ck2qrjTdY/cEbUY2ZNmQujk80aZ/j8ZFGrntsnD4y3v+wsU6IKhYc4DrD4yb5aGxHrqzkAEt5n5yTY7po2iaAw34GEM5QcPGe+zGnSmQMoB0i7ZVepzZwaZ/OSfLTmmTN8+aMDFhUfKsA02ddV9O3lx5skq7q3okfeGRj/Egfp5HEG3TMm3J7bUGRPzI6VuA4ywOxF7g2AlfjtPtzJfjs30ZYVNNnCeD8VUgBy5BJrC9pkIMic6Z0Tw/U9KIoAo/nv647W28JRoaI5d18Ypo6L8bHoAHcBDY9Pi7alJ5qs59QZm5bgq3iwr7xLVDX2PDLydKFeXZptLz40wTychv4wGwiFnSICER6EetgzmDQiR82aIE4sygiQCABFStfgWc2vvbK3yqrqu627e9DuWZgg9bwyJHr9htvNkyJE5RoX42Vjgr3s0xMNYrLwLIgDgOL4zFGw0M1hngn6KlTHX8yLt+V5LSqS6KgD2HLB6oDpgxgGdE5yQlRL39xfJXraruJ2nRHMyTCH8/bWCrnR00HjorNOB2pXdae6vdDnyD0RNmB/kP9C1Z2T/N8XiPkf72xcvJ6bl2CPTIlSMkWA44UR8Ej4HNyyQcsIcrMwzJMygy3a21noNUpDBAjoHiz44o5ecRp5QaOivCzBx9W83IapNc/QICgfQz8poR6WgWpDhJvkKvcVt2ojEZhJ/An2GDmBtNOZ6O07LXlTAs5dCxKU2JGowtsmSSN5hKNNd4X/j0VMoQHaTReEe+EzSQw5ZCoYhvVztlgfZ3HKP35/izYe6BrUDHiqFEhQzN5Yma/Er7a51yFr2Nir7kJ8mJcWA7+fYgHJNe6BwIo+OUaDedUdtv9ko9RkKM6gI4B+0BJeXdimQgOKGh0AnCvhRE8ZHW47j9aJR458IIfOvYtTLSQALf3TUrOheJif5i/koHPAIXuL0gcuvQyBUrCtyW+z2ycxoH5elTDD9iuzG+V7wDuCr8r3hbPMZsFngCSAIMA7mJPip1Y26wG1BDjtKMJQQDAczQHIc+HnoXRNSQvS8+WwBeladrRRQQBUlUOJxGjDkTrR4uggoH7EEBS8WumWuzlbQqCrqEpNrf0W6u1i48I9NMRFsrinske0l735zVItw70bDi1JSWHLgP12XaEoKbTT4R5v2Fvl8AqJ89OmR46WpAwFkT3lXUrWxsb5iUKC0AEKSXSkuPfbZsVYUoyPXZceaBG+Do1/ErTYIHfJY7In+FkG8FFj48Ci4wO6RqcDsQEM6ni+oT6uul/e0cW1ufFks+0ubhPVjGFPVIooXNCSZ3iXZw7lAiT6f6rQ4PrrokT765I0+xJH1/PfqZCERkZ3AUO4spYBKQJxKN80N8FiMDfqGBQtD8S9pLZLB1RVd788Eig0MN8LdXXRvT9+ZZKt2lejgIwoBVQNEFtUmMYkQRns1MwAe8Wh7T5UiW6Yt7MlpgQ7/B/83dQ5feTqVNH1mK8hEWef8L0WxAUILYIetKWoXXGEWIDxExSQSQmOIWwO+byGfov3dZEkNPNFbz37mpL1AH9XyTt3dA9qvulIYbO9svSEvltDKxTIy6yyvsfc3YdJJpxEc06Sr/ZUeriHo5P43ffin4PWYpLIDM5RuM0eQ/WdiKmfHaq35fkt9tb+Gqtp7tX+v29apP7u1PFRtvNgtfY6hxD0wYeuy7SQIDclxEhf8+fQW9ycLtNeJGZA58opaXWoOXkNs69zm+yR2bFKYjEsRQISSd8npsTIdZy4SnH0xfFG/QwUK2ieHPIUlzOTfEWlothAfAIa7OxYL5lhSVOe2a+OAXVgodoumhD+g/gFXjgk1sSl6XFe6kpRvB3Ia5LiHPsbtRfWym2XJ9vXRxo1pxLmOUwUhfp6B41rUoSXbT3kABIYykeMYd3eKklyk1whOAGww5zYT5blSFEN92pmgj5bX6QiRU70332nBB+j0WPVPbYit1lUqQUZgYqTUHqPFbfoGTJ7dsvsOIuN8LLHJkSJKse+oOsB0rslu0GdWGbkoEDiMYDBYEVTr2g3pZ0DNjnGQyAXSSdFU1aIm94tQ7O7Srvs3S0V9vHOKqkc0s0tae7XjNrcWF8VGfy9qtpuK2/q+R8rNC7Gga8fmiDqCu+LeAQqDaedLh3nO3NrGKD+8r5JFujmJBNWkjmUqgDmkB0muWcAf39Zu5gKzOMhz/7MbSPsqzW51tA3aH7+btqTIPfEvylZIXq/zB9wZuQ39qmI1hke7GqjJiSog8jcAZ3TB65MU2fv05wG5SQITWDWtjA+0O6bGG4z4rzldYMIB8khXY7z339nc7KCBVylhXvpfjJDXNW1Yb7ot0/8Q2cyfgxfHaq3js5TcinHO+qFjw+riIJXPzLM1Rrb+vU+o6K8LL+9R7LiaRHeKkqYH8AfgwKX4oTPL6/psp3VbRbkNsxqoIQ39tk/tpTZN/nN9vsNxVZQ5QAB/3RtukDZWdPibOvOEittH7TG3jOa8XnglnEWGuwucDXQ1VlGigC7Ia7Dte8BDAELN+8ssZzKDinJ4Q/x1Nx4sSF0zpz5TkI+LyxOtSgvZ3VB+Dz8hJgnPNHcLXoQSqIUwTiec7bzHgED6F7ekBakIoTZAehe0NuJ1xTld8yLlygNjtt0a/64vljdEPIK5h84G5n1JTeh0HjrQI2NDHa3G69ItxfXluhcJelnz1SWt+pcZ0YKB3LWI6pdnI8ff5Mt80TAlk0lHZL/hub85Ne5YmjgC9bV1q331tTSbzPHRqi7QGy7d1yE7SvrtPeP1GkW5NZxoRq4P1jZZdmFzfbtkTrt10VToiwi1MP+sihFVHyUzNjbd0yNtPc2OVTZmDskDqT5e4hCxneTAWp7r2ZtKDSgewNGzYn1ljgOgiX4GjFP+Puv8xV//r6zQp1+4s+8WH/t/bGxPlZe1Wn7Cpv/rYXG/0hn4+L10m5kubCvH2LzUvzUygZFlpeGl4vkIgmMW3Ib7Ta6G67OKky+zG5U6/XWMaFqPYLW4iXBw7luXJioDffNjZE9/JvbK9WKA4GgiuTlguJTMIAawbEH9aOIoNKkzU2gAS2anuijxILEF0lHWqvwA9v7zigZLKzqsMtHh6pg4IAhSIxKDNB3pHpGf35hur+9vLrEEmJ8RN2i/Q9yDt2JBIeLjcjQNUO+WYn+4ghjpIY78u5j9Zpb4NBN9ne14429SnRoN9Jh+ejbPHv1oQmSWwNpwBBx87F6bS6KFNAtnulFZJWCgUP+i13Vds/saCUcz3+abTOmxAqNJUljpgOaEbMlbMBEf4dqB54GIJU8awJEflWHzckIFhVCw5aeTqJybSnp0CHIhr17fLg9v67YHpwdbS1954To0BnhgpvPpuH9Qzu4dkyI6BYEM2RZ4eBzqHPw6zn1DdqkMRFCkUM9h9mnW8qUgC0YE6qgiCcFFAcSRFrHBJ97p0RKIpXqn0M9M8LL3vnqmE2aHG9jo721QUFMGBSmmifxpBAkGeC7DZw6KxoLiSP3R/EAF5u/RxCiUITbDTeWAVI48ounRKlQ5b4WpsLJ7ZMLOJ0blGV4liB1tOZZa8g6cxDixXFRiYyihgSInYg2P0EIdFPJqftQoV0Y/FU39FhYkEPfHfoHAZpDmqRmY0G7ldV0afCdz2Hdjo/2sFfXlgoN9vd0cDEp0ijKv77zX6M69V+9/r6rwrYVtuk7YtSIogtFMXucbt/MGC871tBry3dV2pNXJlmCt7tQuQ+O1ilJQkYYSgVJcEnbgK05WCeXU+gnTyxOEJLz52+LzNvboVKG+h0qaySgF6WvUYRCMAI51pq2fhsV7WPt/Q7xCTwWKDZpRVM40pkI83GW2yrX/mP1dt+iRFEqef6NyDTH+SkO0EUhCbh9bKg99vp+GzEyXApwFBAUniTEMu88972QcpJBhnwnwKUOd79gntlnB7IbbPG0aHXtkGZmHooigIOcZ/Pht3n21iMT7ZMj9fJRgC/+za4qdWYANcYnB6g4KGnotnnpgaIDEX/e21JhD82P1WHzk3/stJmzUhzmV8Muk6Y8anwlbYOOeRAS6nPnRf3A6JACgM7gscIWWzwhXDGLiwQZ2uTGkg4lM3Ca75sUYc+vLbYfzYsVZYLhfeYfHF2+76TWNQzn9vPfi6YCsILC19gIN8UBqGkrtpaa07DLrKezz6ZOitWzRRDk7WU5FhDsbbNGh2oInCQLamBCmJfVtveLJvPQ9CitL8zXeE/MCL792UEbPzXRJsf7ORTiBs+JRkOiznsHIKKTm13ZId4zBap8fjwYuKebg9iHKbGky4hGPsUM32vlliK7bl6S1XegMPT9D+qKUHMAJaAM0sEn9uOHxPAxXTO40xtOtqhA5XsQUwBQGGD/6nDDD3x+1gVrCMCGGFRU2aH1zXtjL/MOGXiGAvbRwTqtyWsnR+ozATsokJ/++LjmBjiXONdO1nYptn55+4j/x+LAloJWzSg9NS/BVhW0WH37gOIa93VDeqBtLG23T1bl2AsPTbCsQEf8fu9YncyC/7Ag2Yo6eqQ2tr+2yz7fWm6PLU6U3PkfbkjXZ/xqaa5FhntJje2OyRFawyhH0mlm7hH/KxztAXMo9sfF+Cgpw7Mi5YLqGs8O2jDnRXqou+U19Oms2batwH5290TtVzpT0F/pcrsjXRroZnm13fbUzDhb+MTnNnbOKEsM85TMKu+Ss8DHZYg4/fiSPftNvvV0n7JZE6P0HgW2tvXb7v0Vdv3CVJ1FyE1vrGjVeY7AAH4a767Ks7cenmifHquXShfdww83lCgOUKSgNkU3dfm+alswNkz3RKfzpbWl9uNF8eY5zMlu/d16mzA9WXsIejNFNn+H70AcSPN3lyQrgNy4CDf7x7pSiRIAWl41NUqdUc44znLo8MQBJFfDQz00y/L6jkp7ZEa0lXec0vrlPGP99Q2eF+WVeEWC/tj0aOs7e07FxlUpAXaytUdJO+d3SIS/NTd02OSJsSoEeRdvvb/T4jLibMG4cNHyj+NIj9FzqIc1tvcrtvzq8iTdb0Frv7oW7K+3Pj1gWWNjbXpakOIxcYBkfWSUl+XUdKtTfrS23w7mN9nAwFkblxFsoyPczYNOem2f9hq0rMLGfhU4v1mQJI+NIC9n+/zbXFsyP1X5DWcDv4v7ATD3dhumTinvnHk/5pc9hg7VvBbvDVlvijJfj2GWHuIuBa3KrgHR6ulGjwl3FyUeEIJ5MopxYrGG/qO9Nf8LEDUhxsuS/dzsN6sKJF9+75wYh+dU96CYAbf9bbvduCRT5y1518HKbuVu71/YM/9/UWxw/WpDiZx7i9oGNHiXFe1j06O9NABKa46NCCLQ0XdG6gEkWVRryJNtL+7QoOWxun4hGGmhoH7fycAF1JOF9XVOk3SwoVAQsDC/Q87w9sxQLeRnVuXbw3NjZKK27FCdxQR7Su6NjQL1hwKFgfOsBD8pg6w7Vi/KDBcHBxcoCbSgiwPRdFW4D9BjKED83IQ4HyU2DACdrOmUsylVMAcThQCtYwIahxyJKpQvCh6GgHkbJMQkwrR5KYDYzMwFEJz5mU/XF9vDV6XIfZwDg4R59IVZE3T2ScahPJ2CxnHpJTY93ktcV5A3kEIKLpAuNLaXZAXp/t/aUWmTkgOl276yoMWuSwu0nJZeDefzfYvqu2xheqCSpjhQ5y6HaRgJsxSyWgf0rqBmkAx7XzC8a4cmMfRSJVhbCtpk1uNI1i+zFbsqLTXBT50hFISuGRWsxQ+CwuaJ8XPRJpyWGqChULj8JOJcvHsGOTceb7DwQHebluAtmh6qLaAyPIO120ssKsZXRRItWBJMhuzumh5pnQPnFfww+QE9hovL4BbypBSjvM/DVT3qgOHgS2v11W0VVlzQYDctzpCTPM+ewV7ua1ayr325v9bmZ4WI2gA/mCQLJI4hZC7ea3FDtxIVkE/mErgdKGSgZhRxUyN87O+7K1REgQDTXaJw4cLci8QRd3T2Cl0kEpOPdlWrFYsEHkkOM0EUn6BkaWGeQjNI5OnG0d1Y9+BY+3/y+v2WMqGXGGq9saHcZo4KtTkxPlbV46Da0dXhYl6H9jZ0RDpYKAAxq0UQP1TdJ4Sb7h/PAooC8orwX988VGONXQOiUyIhzGB6ZWu/BvkACu5896D9+rpUdd1WHG2QnCg0JzpUOwpa5P2AUlFyrI94rW+sK7WpI0O1rksauxXEZ6YGqmsBIMD72JDbLOoFMz2AAMcqOzSvxN6l+N2d06gZpbHpwSowmUsAjWJwD1oF9Bm6fQxEpoTi12Oa6YFzT5zBsBH0fUykh2SCJ8T52mtfHrNf3DVWXR5iC4c4kqYk76je0AGFrkaCzL65Oj3AdiEj6zxEQ84cTMdqe6TPf8v4MK0/AJuZGUE2N8ZPc0Eoa+2t7ZBUJxQM9iazDuydsWEeQvtITDgAiUMUcMQe5s4oskmA4bczmEhBRhzYkNdqd4wNlZsvdKeVOytsNF3RYDfbcLRBso90J5F7JLmgK/3N3mqbOzrMJkV4WmP/oLlcdqkNuRS/FEf3du2hOnW08FAgqUwOdhV4wZ5bsanQ4hICJTvK3oTi9fXuKvvp4gTtPboWCxJ9ZRr76uZyS47ykWoZBzpFCL8v0tdFw8sLk3ztz2uKrbSg1u6+cYySeYAG6CWrjjdLaOP5lYX24IJ421HsoFIgKoKG/bTUQMVKAKT9+c326yuSbFlOs9YAxScFouuwS/X+eP4v7qnQzCFFjjqB57/XOyA5uJi04fQN+o9J5XvbqrRvfrIgTjNkq/dWma+viw2lMxTjK2oY3SaQfUCUf5XM9X+n4BgX4i0Vngdf22dL5iSLUpXX2it6E+8PNJs9i5IX85uci1AEvzzSIKnpE/V9Ojt5zv+f9s4CPM/ybP8X1OLu7mmTNHV3o6VAcRm6wYANBnNn7i5sjOE6oAUKbWmpUvdU0qRxd3ep5n/8zrfhY/tv38c2GLA+13FAmuTNK89z39d9yXmdZ0PXKRVZKH796fqx9svtpbperDtiBlgQ9xc129O3ThTsaNH33rSf3z1F80pb81tsVKSPgj9s9Z5Ku2V+vOIBIC43TYqwLz16QHNBdPihkmVAGs0F4IE7K9vULX16e4XNHR1mEyN97Gh9lxiJ7l+YqA45gS1JNArgS+en6LwkHiDZxC8jdvfwuiK7bHqs9i4EEqwVCGXGRrkYLgmECSpnJ/nZGzlNNi3R3773x232vc/MkSgeJCwkAAvjA7WOXs6r1/7n7KPjyfX41OQYe2hfpfYV3TE0cmBBohtw28xoDSbDwIkfRNMFVj5E+IBAcX3ptgd4uykeY0aJvUNyXwq0uMEVB7j8d7+Nj3VB42CAivPxlMp4RadrsHndsSa7e0aMrUEmwG2oPbc6xy6elyKI29p9VfblZanSkuI90/UgSXtqfbFdNjPOpsX4WHFrn2BiwNxIvihkrN1XbV5ew+zWWTG28XiLOpEUU5nxgpY6MSnYFmeGqlO2OD3YfvNyrv304+PMa9gwkZYwJ0uC8vNVBdJXGoT6M8e5qaBVcSHxwYIkf81uVuSV2VfvWaBZLkLpBakB9sjmMvvRlen22WcP25euSBUBz5XjQ6WFtnFvhV05O17Xh2vw5r4q+81NY21FToPOa84Czj+KM/jsK0eF2W92lilehKadggtxF3MjxK6BHi6EEDOnrHPis0c3lVlzc7f98JaxOuN+8Pg+i08O00zJvAmRgttBvHOssl3aZg9f896yTn1gMKp32o8uTtEQFjc/ItBTNxAqWZ/hQ8+pPLqqfwQQMEZR6SCoA3fe2N4nZ5IR5m6XjA5WIE6Fig7EvMww+8vhWleFKNBDEAWGXTj8EJJp7Ou3fTXtdt2MaG1eDjhvzxFy8ggnMaybX+gSBYTRg8WOaNyScRE2OtxD1WAOPYYb0dbYDC43xt9e3F5hF48JkwjdhuPNwhrDWnGkskNMNwxCAnUh48Y4IKigzEzwtS3Z9VINpR03KcZHVXiq62xa2K5om6F9AG6ThQcrAtX1pq5TYlQQ3CHE09btKBX3M5AlRNQ2HKo9N6xpll/RJofCTSYpAC6BM+JvgRVNjA+wYM/htqWozS5BxMd7mNX19OlQK2rrER0k7CpUzuHd5lDHj6CwmVffq0FyAiEqAgwrc0B7nMNqVneekKDUoapu25iPuvBQm5UcIKYWqn0cutfNTxDjz/PrC+2yMaEKWpj5YAMEeA6zhAA3zYVUNPepOgEfNu+XbhGv//SbhXZ0+ctWVNGmvwWbj4Iu6wixsc9dn2kTkoOtAqcU6qkAC+YxOlE8B8wf0E4iVAS0RyJGXSc1PEZwhyYE+HmG0PdWddn80aH24H0ztfZI2LJLWlz3Pc7PVh9p0PAWnwunMzi3Q2AsCsoLTA6C1jqDaamhXhqO5lAB103FiuoLAlIcbOWNXUoMYb0BqgXrFIN0VFqBFhEYPry1XIGbruWkcGmwkGDCiEGVqrC0VRVt1hZBLHC9DzrRwL61KMke3FmuexAX5SMoGAwgEBIwI0P1iCBvXLS3bcpp0HpmjodrBFMXLGfoxTAEreG9c3M700aG2I+3FCnYJTEAjgCGl+o7FbvG7n4pRaNIC7UyA4z4D9jRiuu7dGCUl7Xq0E6O85fmBrz/V812sYMcyG8UbSR6EK/vrbI38ltsTmyAEpNLx4QpwXhye4V4+elcrsyqtS15zTY6zF1+hRkM3hvrh/eNqvPG/VX22t4qrQs+0+goH9twGApQ0/cEqtuL2pS0wpdOcIXRYYRmk6Cf97l2W4lmG57PqrNNpW2C4lQ29aj6v+9Y/dvEF3QvK1r7xT4EgQVCgUD+wCRvyG+1q6ZEqXVf3d2rAKi4vUuvAVSJPQTEko4lvvfFw3XqApGgkQQfLG2zgso2VeaAxsEMWN7ebwdrO21TQZutzm5SYYOgCs0a7hkHJPBVChp/eP6gXT0tWoQaPAfUwYEwQAV52E1z49SRpNq3uxRa3OHW3HNSs2SPrcyxvJWvWll5mzopwBRgMiLI4Tp/67YJwrWDMwfyRoB19cxYMRtxHhAsdZ48Zc/uq9E1AiIGDBE/ADV1Vk69FdV3qxq4q7JT2jB//vJ8fZbUUE87WtSseR3m3GD4+uOtExQoDJIz4Fe+sChJySNwH4Z6UfJGB4iZNSBSDHwfKG4WlTuFERimbp4QYUW1HUoSSBp5zuyiZp1fo0O91LmnSv+rtUViLiOY+PSieHXxqfbC0hgb5iOSCowuCYkGcN0POtHAGEhHw4BiI8kgDD5A7tKCvGxqlK/OEjqM6BxBVkC1GJZArLyqXT6VofxbJ0GgcVqwGB6Djsz9r2aLwIOiQ3ldp+BssCJdlBlmDe39trm00T52WYbW3+hgHxXlCNSgNYVAormxUwQ0FG0oMP5+Y4ldsSjVNfy7t9ympARZYrSvOgnPH6mVH/jTmyV29dRo6V58/fmjKkoCu/3u8hyRYRAgp40KlQ4KAbQSRo/hGuxnVvP3r+eLgjUjzFPQm1e2FMvvTI/3VXf2lUMN6sIASdpb0akOC597wUWjXexvwZ72ysZCKYn/dHORvZLfIP9SXNupwtdb+6v0XHQsCWyBJkFCcawewVVEjAMt3tdThbCbGIz3dbOy9h4Fp0Xt3er4L0oNsBmpwSLQwH8Qm7x0uE7K66/sqdL7PlbWYjuP1NhYBpMDPWxHXqNVdZ4Qe9gLR+rUvaeoynVF/oCkiiTm3mvH6t7+7rev26KJUWKb5DHMvZBApQZ52g0LEqQv1nXqtAqh3sOHWlFbt2i/oQwuXLXSqirbFTPCAnZNWpigWEBOf3r7RJs5MljzctdNDBdj3xeuzbCOE6dFKX7PjFj556f3uIatgbDzGYH3rjvebNv2VWpWEr+4qajNFk6JsSe/e5kC+ih/dzuU22DP7q4WsuPrr+TYM3dOEWTLFXudEWrm4U9MUjIhRsiGbpsyOsxey2sSeoNCNeMDO4/VC6oGtBhBRToSe4tbNZ/Se/Ks4jPmbTAEHFkD9d2n7Bcr8219brPmSH506zj5QtaNf7CvjU8JttOnmdmCgdNXiUZlZft/LNH4jycb2Ot3TrKnt5TZqtVHLNJ7hKbkOTjifN0VVFFpmh7hryoDODf4+KEyvX0m8CKXEjesE9xgbiA3COYRNi9tsunJgWKFYQiHgI9DGwfPYzkcGGLelN9qC9ODFYSg3sji+eyVo1RJh00Km5QYqK8sWgSY4It/8blt0pWYm+Bn+2s7bFJaiBIOsONXjg2xFVn1YnKgU1FY0mJ/2VUlLCgtTYI/DpKYILisz9g1U6JsYnKQOjMMqJPlXpQRouFVcPzI0hNckEBRnUNtlsQF51hc1qoDC6c4ZMgQJSgEnLT3p6eHquvDdaVCCA3o6mNNSm5omzFEuHZHmRw1jDZAKgjuSO6A7XBA43hZwOg/AH/i2uXUoJh8oVTKGV7jIKVCS+CBIB1wCCpPOGwcGa1JgklwmRF+7lbZcUIVYBIWjHYgcIqiyna7aEacPjPVPYIocLU8duXRRvGTA7U7WI4oI6I4J/WYeUl+dsW8RLv9gU9LTRn+bW/v4VLGJkggsUK7ALw5gQyfgcM7PnCE/s3B/cTOSvvir7cKGz4nI8xSgz10kG8oapWK971LXOw7rEswwlSpgtyoRA9TleWaadH67Gx+NBqe3F9tG7LrraGtT+ri0PCRJDIATBeCe0ngwWD6qn1V1tbRLxKDhekhEr0srmlXBW9neacqkCIxKGzSjAnJOeQAOOas/CZ1tXw8h9sdU6M1kEYQiVMjQIdFhL9FsXbr4VorauhWsr7nq3Psw2Kr75pkv34lzzY8s9oivdw0MMcgPtWvIReYDrbZMYGqyN49LdaaW3t1oCC2RqDLYfu95Tk6tDhIuc8oJQ8OhbNnl02O0iwAiSgQshFDhij5oqCxq6TDHt9dZVeOD9MMRnvXCXUOvv6x0bqGwBS5pjBpcX9ZM5dPiRJccvmzW6ToTRUW/PHUzHB7aHWBTU0KVEUN/P2CcREa5Mw+Vms/fzXfJsb4CDLF3qls6dO8ABCtOxcnSaCJbi4BOp+FdaVK355qQYMockDRSrANoxHrletz9Fid9grMdWfOUPl2CUESkPD+UAjH/wAroWL23MFa25hVI6jYuHBve2ZFlgoyQGq4NvgWePDxefgjYFO0/XkMHTT8MLTcJCPrsxs0x8Q+wl/QvaHrimI83Sn2KhAfKog8b7ifm+BMzNQw00SQz71jL61F6Ta/0a65LFMMOnQV8TtobpCYPbWvWpoidPgIECi6dJw8pfUA/eyNl6TZbd/8tH392jTLq+0WC93r+c12SXKQ9sSh6m5B8MCrM6fy6elx8ouQSvA9uPb7f/WWYLXMh6WHeupsWH5uMP/HN45RJZxAlir7G3urLMTDTax40LV/fF6cpUX7qRJ/47Qo+9W2UnWfEFRk7oJiFrScdKlGBXvoPQ0O0b6ys0Kq4ocLm23ZhAjBQYD2zT0nphYd7NLkoRMCLBdK9X2FzaLs3JvbYPn1PQqI+Uy3LnBVTDEKP6GhLmHH25ck25odZXaoosNqGrrt0Lfn24fF1t871b764E7Lev4lxQFjor0t0sfDYnw9VJSM9HK3ObFBNiY5yO6bFmv19V1W333S/nTbRP0+u7bb7n/8oPRo6B4QB6x+q+icNsKFgs2ht7PmcL20G7jmrFfijPnx/raloM1+sDZPBR46pwjfsaZ/csdEEW5cN9mloYHGAQUeRIEfuHmMmI/eeHmXXTUvwe6fHmcFbV0i/fjFs1l20bhw+9wVI0XsQJIM09iOncV23x922TUTwux4dpVFB7hbTfsJnb9AdG5emCjMP+8/1GOEZj2+dn2GztCfrsiVNg1zfTBHQgjBnMb4eH+d47t3FYvxE9HMrvYuwbpSI31FWjJldLgSbaBYz949Tdf8J5uKJGh8x+RoERkwT8ZnA8PvOXSoINbAwUkQMOIXBqPpHtOd4brvKmmzCRHe6ppDq83fSMSz97TE+x66daJ8LUUfKNaJLzj52YOpEa75V/YCnR0ex2wmCSWFkZvuXGqvby+z2Qm+uo97S9ukN/WDlcftUHm79uJbhW3yA0K3dJ6028ZESAD1ui/fZb+4fYISqchIX1tT2Gh3ToxS0XVdXrPYO9OiXMXE2ydFq8vL/YWdkAThnt9sUwI4LjXYEgJHqNDK3BudwKfumW61Lb3yQ3TCX1x1VGcW/oqO6zeuSVOiy1lx/5Ik+8baPHtsV5UY917YXqFr8MKxOs0NU6zGl8AgSRLywpZS23+oyrYfrLabZ8daQXGLEiTmgJj7wDd1dZ2w7cfqBSueMjnW9hc0meewoeoC4cdCQrzsc7MS7JaLkuSvSdCI+UYmB+k63744yVZtKbJVRxqtqLDJin5x8X90r/9HYVR/azc9d0QYR1EBQkk39EJhcP283dQ283UbYgdLW21mSpCt2FFudyxK1JQ/1HAsAoJ0qt10IvYVNStrRcX71tmxFuY5QiwiMFm09aMXcUoHOY5kHMPINT1arGwGAstL0sjwe7Qpdxa3q3pAAEPgAqacTJS2PbZ1W4Ho1aDB3VPSrjYnm4chTtq+Yefa4rT4Dtd1K7ipaD9hB0papXBOQAk0BBjITeMjbFt5m+AyVCCSInyUNKEI+7OXjllMjL9wedyl2+bF2Yu7q+TMqOYC3+G9w+oEqxHXkSooXZ3dxW06pEhuOGh5rbkjgxQUYPMS/ARtoIJHgsEGBgrAkCe4VSpK/A69DYalqbpT8SNJWLm/xjLiAxQQUS0i+KdLRRUWo52ZV9tlmdGuKi2sK8G+bnb31BhVIajG63GnzgqK9PpDf7HwGfMEpRgf4ytYCHSOKMUzvLZ6T5Va2TOSA3TPgUnhhJlPocoUGuYjmlMSIeAtVB/gNKcysnFnmT1xzzTpFhyqcQ0E5tb3CANJ1ZLDmGuIrglD4nQs0Hmgk8L6Q/mdtjAOHNpmAt0X9lRrQB5dDYQcn8uqlRNFURkax/7+UzYqNkBVHp4fSld47nHUiAEizEMCRfJAlZx1D40v4nOIIII3BQOKEwT7yzXasbfcUkaGilaXNjosStxTICGsL65TSpC7NfacEn3s/LRgKQqXNHbba5+caB9mu+Kxg2JxOnFuH7EnKSJwTWFnYl1zuC8dE2pPrC/WLMfyg3XqgLGXCRrpAgZ6Dbe3jtTZ7DHhtiWrxj6xIF6Kt9lNnRbr62Joy210wQy5LxwSR2u6lRxTdMCuzgy1guYei/N3sxWH6kVIwP4n2CfYZEAZP9DS2W8HdhXYLddPEfUs9Ltgbnn/sIqRPEChSMI+IdxHuHICU2av3jpWb+GBnjY90V+JEB2Je6bFigqUA5vCAvAZqt50eh54/IAFh/opEOJ9f/nKkfbsnhq7dlKEgg0IE3DjmnlJ8df1gOlpUryf1gLBVWqEr7rEMLYwS0RhgMNoaUqQRCTZ67Tjqf7PSPBT0ICPYsAedrVJUT6CjAGTQvMCuOFz2yts8qgQJdBASIDCQhnJADOFC4wANyPK21btQfH3QkuI8rUvz0m0gtZODbHTWcL3wOzz6q8es+CZF6lzy5wDsNGOrhP2hYuSRHfLc0SGeb+tkQQ0lD1CwWbb7jLz9fe02xYl2vKdlXbdzBglOndOi7FVeU22ZmO+rfzqQvmB1fnNGtoFJjkvxU/QXM2TnHYRYFC4QAOGzwIbGQHqm7nNSvqYnQKyE+493B7eUmb3LEiwt4rb7MYx4dKEoFM7LtrHXtlbJerryWmhmhukUJPk71rLEJ7QTaa4xft/bX+1pccHyM/AsoWmAiQQgkuhKn9mwA4WNAouisp12th4mzgyRAHE4JA1mk7+bsM0vwN1bHVnv/De102JVDCWVdZmG+6dah9mm/jDt2zyKKC6LtY8dYZ6Tynphb6aBDW3otUumxBhf15dYL+8bZzmHcaNDNbwdXFrr9aE7teeSps8Osx2HKy2mxYn21Ujw0RnSzczxN3NXslr1H6muEQH+nB5u2XG+CnxpiBw49hw7Yt4Xw976kCNCmt0hzH2Gd1WihBl1R2Wf6TEbr9lhujgf/96gYhnuC90CiCQwIdx/y9LDZWiNbArRB1X7qy0hBhfMbHRoWK9/mDJSPv+xkJBLCF7EPZ/bJgSki/+cbd5+nja6dOnbYTbCPvxLWPs1aMNdueUaDtU3yn6dPZ6ZIiXRO0geQCeDOkAjE7oL6Fpgbo3STTzXKA5WIfLUkMVmFPEBZEBkQNdIwwfNT3W13aUt9vipEB7o7BFZxD+ma708zsqRNXLvynoMiuFgB0zNZxH+BnO/HGxvvaXjSXW19NvkydE2w8vHmkFzV324NYyzTTQieLar/jlo+Y7aa6NGRMlsVJIJwi0f33dGHv5eINtOVgtFjcYPLnmO0s7FHNxfr+1o9guHHKhffb6sfbs5hL79NJke+VgnX1nSapgoW+8VWSrvrLAOvtP2c/fKralo0PkK68YEyJkAEgDzh/+Azo+b1SI7hc0zCSoL+6ttsz4QHUcoeiP8XW3320ptc8vSLBXjzUKogZ8j7WL/12xs9La2npVGCX2AO4U6+MpiFvXydNiZEP7hKTnN6sKVLhitmdnXqNdNSnSVsMoGuQp2BOw1Dd3ldv4jFB745GXLGLGXLt2fqLmCYlPSIigWsZg32Iovry9z57dVmGfvThJhCvAzj+oouMHmmxgtzx3VG3vgAAPS4vxt11HaxWcgm+ePT7Kdhyusa9eOVJ0c2mBXoJbMEzJ5syu6VKyAo4TJibgLjgB2ncIrqBdwSYH3gCz1agwz3N0X7D5mBh+wCR/7sUjNndchAaiafFdPjZUhy24UYJrZhTA8WnY98yAMLw4+nbYEE6eVdVlYbK/ffHR/faVj40VthIDd727rMMuTw+2LaXtqi5yKEOnuLW0XbRjYPOZT4G1oaytX90bMMQ4sh1Z1RoePQ5lZVyAAqIpCf62YkeFTRsdJn2G328pFeZ8//EG+9TiBKkHy5GMDLSCpl61naFeJPgHskXFH/rY++cl2J93VtiYWJdYHJuLakBVY5dmN5q66JpcKGw514lhU4I99A7eOlAl7DmHwCfGRVlNT6/95WCtMvXR0X5W3NClmQuGGSMD3BWgc2ACi+L6v76v2q6ZHu3iT291dXa4Nj99cJM9+f1lOjwR6kJoT7oFjb0S7Hro09OkV0KARXUUxiDu0bgwb3urrM3KEQwcPkQVxR5UmidGaHNXdZy0jccazN97hB0+VmeLZsTr4OCABgaCU6X6uim3SZVEb7ehavlxuFHBoKvBoDsVCzoa0FtCJQrdJoEdTk6CQ+7DbP2ucstMC7GGll4LC/K0izOCtF5gEGGdompKxQ2DUQzs6+L0QNtX3qVgg5kTAkEOQbQ9GGgkSYZp6a7FiWKc4roy6Dk53lcDtVSaucZrspvE7gTrFhVhWri3Toy2j4JReEA9PDraVxoFW/dXiUawra3PMkeGWElVu33hkhSr6zohOkGqPQSKrX1nbGdRqw4pKbm39snp1rf3aq/QMk9B5X74hbb1WL341+lcsI45dDnMx8cFSJn39qcO2oLxETYm3FOQmo9Pi1IifbzeNQsEFSSJNIkj/gAudeZEwMTrfSYHiZHtjt9us198aqqSEro0HFJACW8cF27bK9rUvUTh13v4MFt9vEndrdgwbyW6QIaYh4ryGWFbEekaeqGt2VZiU8dFWmFFm4Ltts5+u35qlBSPqcAhePrrzSViqtu8v9K+eU267Sjt0GdDaK+8o1dsgCTr2wta1PVhjQF3+PrFqfaLjUWaZxi0/aVt1tTaa1PTQlVBZZ/TNaWYsvNonQg56Bqu2VVuU8dEqOLOXFxpe7c9vLNCIlx0pHKr2nXtuc4kkyQlFG0YQKWa+dSWMtEAM3dQ3HJC3Rq6TF/5xXp76ruXKTDfWNimoVmRVlR32asr9tq6n18rvRWKKTCvMDeCv54c4WebSlvEZIVvwUfX1XeLxjPUc5igc49sLLXocG/bu6/cLrtolOhKEYak+wvjFX6QeUJ8qgva62JOI9jl3yT2BF9oM60B8hPt72I4RFyuql1wWwKDlZuLLTbOX6xXaanBds24UEHJSEDHRXjZWyUuykmKX7nn/o5h5FePNMiPAHUoauoXTBU4BZA7vn95c7Eq3nTmiqrbLSXaT+chQSKBHnsAGmyEIulukxRBmvCfZJ77d+y6pw5Z1rF6G5fuQggATfHwGK45KU/PYSKAuX9uvIKoqREB9tC+CrtoZKAosiEDIEkHbppT3aXkEAg0EF1U1xHUxQ+8eaBapA7Q0DJjwHkJ1ApNnaUpYXb9I3sFlYNS/LGdlfb9JaMsv63TxXIY62sTQv0Eu2s/6aIU31jQakcKmiw+2tfKqzvF6vadRam25Ltr7KEvzNXnomPAGfOXrFr71LRYO9LoYl1CrwYynId2VVh+SYslxPrbyAgfW5AQoAQCRjsUqIHr/fbFwzZrWoLVNHW7oLUVrfane6bZ77aW6Tz45JRo+9VbJTY21t9W7Sizn908Vl16oNu3jouwyq5ewa3npwbYQ+uK7c6LEjV0jJL3z64ZbV966ajduyRJZzQzDodLW3TdMxMCVZCjEwsMD9jli+sL7Ee3jVMR9YW3Sm1iepjFBrrZreOi7Uhtm/3sjUIbOvQCmz4qVAKX4xICrIj5thFD1REl8OYcp5v7i9WFmn2BTY8ZVa4Twod3/HiDPfXNxfoeMeilGYgk96sgQmFi58ofq2iBoQjOLEj3ibM2LzZQNODEFsx9MauYk1tn9149WnTqdAW/8pcjGug/dqTKFs1L1f2/f1GiPbarUucB7IOgQaAXh2qeuQ3KJ5ANCZoc7qnPjo96bnOpLZ4ao4SBTvDh4iZLivRTd/E3Lx61YSOGWUtdi81dkGZ3TouW5gZ26/gIe/V4o/wM8REitazRi5L97RfrizRTfHlGsAoIGPHZstEhio1WvHbYHvzCXNtW0m57jtbp9dHWwl9wRkFE8/T+aokwk+wxK41v+08yz33okg3su+uLbGteo31mbrzYDBjAYtCboerLMoJFkcmm3l3WafmVbRKs21PcokCZdjeLgQAA/nZgBVxgKos4c7DOCNYQvDO4SSYMlCrM183W7avSwMwRdCii/ITl5zBEeXkPLDld/apUMiTEAUMAV9rSr6yVwJDf4cyamnutrqbVPn75aEG3YKSBTiw9OUifj0NxdnKAqi4sLLjZa+q7JOgGDKCywzXkCDQGbDkOEepdDh0WkFSnCb7piLS73hOvT4ssp67XpsZ5KxAWNKrJFUQR5PIzWGP2l7UJ6kVQTJIF8wnXjCFtHB4BFR0PukVg0VNIQNyGqsOy4UC1zRwTIVx774kzgpHgvOiSgJcmECCYojKK0waiEe7pbgUt3dIlYTOQiME+huLxY9srrbGx2xZPi7W1O8tVBVo8JlywifzaTgU0sI3A4sPcBuww4G1X7K6y0YmBSiihkJwPg0+kl3WfPG2HqnsES8BBNnSdVFWMTP+SUUGqNqCEzCqnY4PjoSJD8DQlKUCVY2h/aWnTiWDT41hINOIC3AQd4RpSfaZqdMPYMGH1CQIRmQtyH2HbytoFqcDRgP0n4KcdDk83mPljJS02JzNMrGJQ/RFs/W5NoUVF+Fhn9wlhrZdOjtJa5j5TNYIdY19xqwaBw31dQ2Ekr9BXbi1skzgeEBReH5Gnx/dWq4vFADrXkPWBsOZHyR54s1AzR99dlmYrjtVrz+45UmujRwaL4Qcn6jZ0iA5MGHkIVIH9AHXi8IJBCWrK7aXtqkBz/UmA6VIW1XbaTVMjXRTBZZ0KxICgMHi3/K0yu2FegtY+CQNG9Z4DBf0V7j1/d9GoQOk/cJ8gMuA+k4gChShp6LSWlj6rLG2wT39sgtYPs1R7DlXbhMwIJdDsD2afwNoD+0D8D/E6KDkvSQm07MYurVNEA8HXE4QfqOy0MPar1zAl5FDu0v0rauqV5g1J6cgQNztS22sTo7xEYQnTDrNRwCSZb+O9kLTvKGpVIYbZFBhTksO8FexQAOHQr+06qUFw8MNPrC2QwBQ+lOT1pe0VYqHh4McYuO3qPWVLx4ZZaXO/jYn01LwTQ4x0ehekBEjno7CtW8+NH7hsVJAdrO2yiRHe9huRLdTZNUvTbfmaY5aUGqZ5EfwIA6hQF4Ofp5MX5jvCtuU02JWTI235rirLSAgQXA4oyBWz4qSnweApIpZAYWCzglGQ+9/SfUKY5z/uLFe3AqvuOKH3TxEF+Mm01GD5Epi36JaSjPI58eUUM1hbLq2l05rh4lreMz1WLDTodMDWhVjWljIExIYLrkNxhioy1VoCLC4bBSE0oaB3Z5YlxneEfeWJLEtPC7P2zn7r6zttl0+P1r4GpkJVH7IRSC2WTYpU4YviApAXYGAku3OSfAWliPT0sI6TJ+3B7eU2MtJHOj9AOnjdh67+aPmBe189bvuON9gvr820J7NqdN6u3ZRnYyfEKjGE4CXEa7iGh9FcgPb6mW0VlhTtZxkRXpYe7KWz/NG9VSpkca+ZX8EvHittsS8sTlaxgMSOGanylj6LDXC3R1/PsW/ePM5++1q+TR0TLpQCKAoXvfhZdaU5Y5lboEvOmQO1OkUfihcE5Ayrl1e2W3lOqX3n8xdpDbF2Hn7hgE2ZlqRCU21Dt10yJUqziIOD28z9XDE12hbGB9mG0ibdt/z6bpuR6KczaUt+s/YEkHDNuuY0yUccreoSRJFzHE0WZgvHhntYdl2vLUsNsa0VLXagrE0wMXwjFXV0M4iJ9pS5NFooymB0c4FwMa+ZVeXqwP/++YO2aP5IJW7EG398o1DQLGZJOaPp+rbB0iidmBPqNEsb6oIL5PNgXSK4r+5ysfmRsN0+Mcq2VrTaJUkh9s21x+3gnmK74arx9uwL+6S+fvfiRDHAEcdAI07HJj0hUCKgDPhfPSlC1fqUWD9LDvW0R17Olh+5IT1M87lo5YBcIBF4LbtRsxeIb94/M96+tCJbiSRxHvOvxDOctXRNZ4+NsCVJgfZaXqPuGUiPwRhqkJiGfce1Jc5knvJzM+Ltq6tzbUlmmGIJkh7IOTgjgFADTWMer66jX74Un43fuvfSFIv1dVfHlmLpDT9802bPHaVCEnM+18+OVXIAygMfgmL6E2vy7L6r07X/8ZPEhfgeyCAmR3uruBjt42FNPSfsJxuLbEpSoO0pbLZJSYFCzvwnGKc+VDMbf8++uzjZtn5uhj22u0rteSoOdAvYCEUtfXa0tkeLlJYkBxKMHWSAULKSgZLpga8Gq4y67dpd5Qoq39habEWlrbop0O6Cgdt8LlBExCo2ykcHSVVVh1goqFpjOLExkR4Kyhch1pVVawdLW2xNVq2cDQxEu7Pr5Mio5CfH+tkXbxynSrS4zlHa9BgubCU8yDBGsBGpaKaGuEuwDuE+4Ap7KjvVLWEtg+2TVPxL2UqqZsT4KJumpUmgS7AF9nBJSqBe+5fLc6RPQuWcoW9em0VOt+dAZbeCExKNi9KC5LDYHBgdEuh4USd+7UiDbclrEvMRVbuLJ0WJpYbqPkwJc8dH2rxEP1EFU+XPzW+y2rouW3u4TtSqtKwZWKaixuH+5I4qwcI4pOnQUF1FmIeEkSCACur18xOUkI1MDtTQNUw8BHRUdLh2zCugJK4Ay9tNDnv+mHDNltBapssDDzqK68C+qHqyJg6giwAV5gWme93Ye1Ltde4x3RuCeJz9/GSXkjZVCumzeAzTdX0zu0FsNbwua482LI6GQ35UuJeeC+dAIEKywn3bWdGhQW4cAIN0Y2J8Ba2iK0SwyaB3UACdhqH2yI5K+8NLR3QfZ4yJUBDMfBEsOjvymjRkPibKR5//pS2ltigjRIKJsNRwXyva+u1YXa/FB3vYa4fq5Fxez2sSMxrwHjpl2YerpNr9UUs0sB8uSbH935hrv9tWqlkD5jimjA3XAS4q0bYTtupYo+anPjYnTms2Iy5Q1xlGHoSYMBINruHGnaW655v3VlpudrV5Dhtij75VruBl/bEGPfbB5w5YTLSvYDkNde060OiA4uiB+SQHuwnGdlVmqD29q8rWH6yxl/ZUSfshPdRdGGMYQjhQ4qN87bt3TtUsFfeMNejj427NHX1Sp2Umiy4dlXD0H0hWfH1HKFmA/pA1xAEJ2QIJ/Q+f3G/5ZW02N97VfQQuQCLNvk0OdnHI49u+9dQhdbeg6uRwpPJGNQsoxq7SDl2btccaxARHl4yEHRgenUA0joA/PbqjUpV69jPzRbcvTVXQDcTs0TUFEkO7fFSwlda7AomcvEYNwSMuiJ6Q57ALrbrthOaTAjyH2+/eLLYNJS3qgEAZyX1As4Igqrb7hE1LCbJ7rxurYGXSpDibnBpsL+2sVGcamBS+EyhtdnGLCiMxod7yKQvHhct/UwiClhKSCiADg+JXPD8QJopVYMshTEBIEGHBkSEeKhg1dbgEWZeNDNbcE3S0VR39wk5z9jy3rVwc+XRi8HtPri+WD6J7CPSNRCOnqVP+FpIHfNuuqnYNfOKv8eMwG16ZhqDcUPkS6LlDgjxUVHl4e4X95oXD6rRcvoDh+CF25ZQoMUe9sb/GVh+tf1tb5cm1BXbD9GhVZBE8xB/go+iQAmtlPfsOH25/OVZnOU3d1tt/yvblN9qBXYXyAR+1RAPjfR98YJ79YEOhMO2QhsydlawqO/BJilskCu3dJ+2Oi1PMz22YClEMTI8M8rSXjtZb2wnOw7Oad8s5UqX9vO1gtR3ZkaNOz/eX5woaBbyIM+pXT+y2yCh/i/Z2t9amdiUBzONxbv5pY5mSevzG9Znh6hzRYXphr4tQgEFqEskJ0T4W5O1mo5KC7KdfWaLkm45XXm2n+Qf7S4vhnpmxdsPsWLE30pmdFxeoZBQjltle1aI4hGC4tKZDRc4HHtxqpRXtNifWX/5hfV6L5iiKGvu0R2/MjBAs9wuP7Fdn8OXDMBtdKMQBZxTdnnV5LUr+H9lWbp+ZGWf7K7rUHaHjeGhQ26T7pH11xTF7ZnuF1i1zXd/+5FRLCvUU1PFnLx6zS6bFCjJYe47yt6CkRZoz+MQFCf4SzKOQAXQQeNYDz2fb+uIWQZaBdfWfPKNEA5hldnO7GPi+/amZ+vzzLxpt40cG269eOa7CzJxUlzAqc7DMdDFvGRXsKYIQEgZiA7ocVyweZcer2mz58XqxjlLd5zp976UcO1bQpO9JfHZVtymBnRsXYEPPFYjws8tSQlX4pVhDojU5xlsw8h89ny20A/4QhMiTa/N1P4g1mdH94qwEy23psBMnTks3Azve3G17C5t03bHrJ4XbtWlh8tucYyQ38bF+Slx/uq7QHn8DgoR+u/+2aS72qcmRogV/7I1CW51VYx+fHCm48EPLD9u3bhpj0b4udAj+Ht/HfYWp8cev5VuUt4f9ekeZ1fT0yTcji3Bob7EUwT/oROND09l4pz1zsEqienuONwjHyk2nWk/VfXSIt7JGID4caDhubiDdAPDQMDiR8dHOpmoNGwTtagKJwxUuDmWq4AS6d0yLlqo1XYeVe6rsrkXxwsRxeMNLDG4X6BVBJdSsHFy8BtU/WJo4UOICPbRpqKaxwRGcAa/Ic4jZKtRD1Wiw0Kid3zIx0t4oaJZmxfcvS1OAgborBzPdBKA5QG0G2RMIfBAj4+dsdobaEQSDoQG2FdqsMCzR/udgJOkg4GUDkSF/bHq0sJ7QusK25AbOv6NfMAKqfNsKmiwx1EevSdeh88QZC/IYZn/cUiYhNeBdJCx0XWhP0hUZGeip4UwqOwwtLhoX4QrkEa5q6tWMCx2TkcHumlFgEIsKkR+t8DNndQA/vq/aGlp7LCLIy6obu0TbSmBEgjg9JchyqjttBkJpF16ggVScMSKNkAPQbYBuGA5s2uoM4U6IdVV4Jsb6yKERICCAxkwJGxguc4SjeD/MYAx2N2DmgrpSA4CRXmI7o5r9m7VFSgZpR5OEAkFbOMrl+Hg+Ohi8t+059TY7I0yJEu+R+73mUJ1dP9XFjkIFeUGKv+aFBhWi3UcMsYWpATo0CWKplm/Ia5HjRJAMWAwsRB39LmpeqkmsaSwlxE2B4dKkECtq77IHN5aoXcu8R2yIl4KwD4NTeS/spcM1dqyhx9bur7ap6WE6gAmyKUZMi/S3DaXNVtrUI/pggjDa++Ni/DTc29SL0upJJXBz0oJ1kC7PbrDkEA/B6e6bF2+bitvUlfzy/CTbXd2mxPPpt8rtvosT1SEDpkdgvru4VRAXuNJhYQHfDbSByjQ6LHhPZiuofDJQ+OK+GvvCgkQrVALq6kiSQA6qum863mhfXZAkwT0UjR+7baJtrWyWGNVXXzhqCWiiINIW5a21DG0lM2morwf7uSvRQqPmjexGBbSTwvxsb22b4GJzUoPEz446LgxGVMTa2vvtjvlxYpv63qvHtWeo8kLHTVfx0sxQVQkzkwK135kvokvgMWyo/XZ9sQS1YOBJCnQTvSr+jGQ+wRd14ZO2o7zTdmXX2ZJJkeYxbIg6gTA3EeDAUDVIzwxcg+oiw5fsoXumxNgPNhVqMN/fx03VXq9z7GCoCQ/S1DIfEYZvK+1QtxDIkNiqwj1t+d4qq6vrtthYP90jlOhhvxsf7aUuN2sC6md844ihF0j5F/a5qo4+zf7R9YLXPtBrhPw8hQgKLQSJDL9+9+Vc+YHJyUEu2uP6LhVu8K+nB0z7E98H3ebCCZHyAyQOMH+9uq9a8xy5DT2al0BzoB6NjMY++SdmVGDBenJvtf6Gzt2mwjZ1kzcerFa3i70v5XSCyihPK2zsV9GCOTk6WJelhtmxhnb76aoCGxnvb8VVHZYc4yc//fzNLmafj7qty20UFe5TG0ts2Qwoy11K0+jCzIoJ0gwECuLMW1AUYu6BDgCaEgQ2+IGXdlQowJ8bE2g/21oikbpH3ii0R++YLMG+opoO+/Glafbk4RrN8vxlV6V9an681XWdkmI45+Kaow3yr9V1XXbl9Bid8YkB7na4tlt+iGCSpIiznj0CM9NPrsqwyq4eGz5kiKh6EZ0bTIrfymm0X1yeYU8dqRGj5NqvzLMdlc02JtjPrvvtNs0hwmCH4B0CvOgAsTa2Zdebv5+b0BRfvixFc2XMG6QH+dq64iZbuafSPn1RoqUGeNvxlk7NSjAbSbfl61ePskgvD/vEH3dZZJSfZgOZhfD3d5OI3PeeyrI50xNE35rk52Ut/SdU4PveK7k2dXSYCg8XJwXq51xbig501HpPn5Y/Q6B0+uhwJe1KJqo7xZAlumJ/D82IVraftKyKdtesrg3YtxYk251/OSymJAq0wFFJgpjRRID3qmnR6vQiY0BhBDg6BTw0USi+zYr3scd3VNrh/SU2eny84KgUiHeXd4nWnrnNQSV3UAF0AdMCfaymq88KWnsUT5GkItSJ36VgzZ67YVK40B6TI3ztvqez5AfmZ4aLcOhAaZstyQgRbIriAok/MeaKrWV2+cw4K2/uPScU6a1kFhrcdcUttiO7zr59+SjNEKNjVljbKSIkdFPokuKv75oeI3QPBDkUe5dOjZGPofucVdWtwi/d9omR3upUwch3bWaU7Sxrsu+8lGPj0kIsu6BJgsM837pPT7EPi33okg3sl9tKra7j5NstayA8ZNo3jQ23776RL1aXlDh/0RhuLWiRo2YTU/nEkSeHeakiAXyAjPbkqbMKCHDU4ITBZdMuZxYCSXeybiA/cNQ/c6DWYoMZ4LlAlQaSgWd2VVlCuI94o6k2UWXGoUBRSABT3e7qKni7DRGlGAff8GFDbFGyv9pqzx2uVWWPn2/cVWZXzk9SKzTZ31NVt1cO1uozgcfmICfwRISMSiftYhiQDtV3CF/KYQt2mcovtJpUUgY/GwcoA6BjYvw100KwQ/D88uF61zxAmJcCeQJoDn3oLOHAB4IGFp5hdyjtqODxmZiDgW6NQUeG3Whtz8gIlTYK1fk/cC0z4NzvV9dk+5FadQwImBly4j1QZYaTvCi/3u66JtOOVXVaQWmrxUb7KthGCwHYxVUZoZrHmZXoJzG1kaFeur5gVVFlBzNNsnK4tFXsFsVVUBnHWM+JMwoKqR6C24fKuKGt1+6bn2B7KzuVeMJqBnf4ltI2mxXnJ6XgW8dEWF5bpwI6DLpAEiKehyAk1Nfdxkd72ps5zbomwBeoFkE9B9yLhA84FBA0rhfBDVuJ3YRz3XisXo6ayilJLqq+VDXgZv/ckiSx8lCpRcQSGNCmA9W2aHK0iyGtz8UkUVDfI52JzbmNdsnYMMFqIAVgzodqHEkz1b7UaD8FX5+d9T9sNP8Nhh+ALjSnukPXFQfOIOEXZsbbxx/frwQ6KT7gbSa4O6fHCL+/pbBVyXlmtK+LqensgKr5wBxmJvqZ9/AhUpK9YlqMmILySlolCEjnIC7AXcnJH3eVW1Kot6qoFBrYRz9/vUBsQPgUEhuqjySqg7An4FGsHeB/B4pbLDHcRwxR6FvAtMOcCdo2QBK27ym3Gy4eJRE3GE1gtWLInDXk5TFcVVdVF6O81MUM9hhuoR5utq2i1Y7VdAp3DZyOwHbVgRobFeuvGSLPEUNUjWts7rF5YyPU5QCyNS3K1365uUTDhrBEcdABlWTIErVtqr1UBfEDMCndMCVCtJKDxRUE+7ApI0PsQGGTTUkJloIzVf1fvllol0yIlHYPMwkbsmpFksB6JtAnKCdAhMll/458+9Fn52n4m+poWIinPit7lwLLXROjpQHA5wa/TSd4f3mHri+VXLpIrb1n3hb1JPi7c1GCPidQFq4/qvBHKtpFQ/7Ny1KlLQK99O0zYuRLd1a12cxofykr3zslxkraukXkQZBOdxq/BDkI3UI6ZDPifATHAE4LuQRdXwItZtNYB0ArSDYJNCmIiHSk75Q6c0BAGMwlgEJs8AV8fe8p+YHvXZ5mVd29KiCs2ldtc8eESyh03qRo+R66VBRAatr6BKHdmF0vlWHOxFcO1Vt6lK8oQ0mEOzpOSCQWWvU7p8baf5N9b0ORusnQf6IYzVkZE+FjP780zWY98Ia5ebhZQmKghOF+u7nEvnVxqgLbBzeV2ggEK5MCpWdFMsnegyjhhnHhWt9feznHPn1Rgr1xrEnn0revTRcbIH6Y5OR3u8osNshD8YK/13CbEetjX336sMXG+kvFngSCQhydOCjvpyb6KyHC7wCZQRwOkgB8z23jIy3QY4TmTEAuIIB58EC53XR5pi1NDrIA9+G2v67NnqRb5TtCPg9oLL6MeQXmA2Hui/BytzeKGgWPumFiuD25u0pFupe3lkkUmK4dXV+GjPOOVdsnrhknEgWIaZYkBtstj+y1OROidE0pVFEgZF5serSfXlPzKCdOivXza5emysdRwAHCWVnfJdjvJVOibV8JiZ2/TaWwN3yYff75w3blrDh1bTi3gaWSzFy/IFHXE/Ia9hTw2C3LN9kTv7zVnt5bo/OfTjEWH+KtQu63F6XYDzcXKcECaUAx6NXD9bYoLVhxHmgFusSsfWKOpqZe+/bVaVbbdUKkH/hmZA/WH62zxsYe+/1tE+zV4w2WU9aq2Qz8AHMzE8N87ZnDtfbAgiSrbeu3NUWNOn8pKPgwF9p/Wl0WoFgLEwLsxex6y4zwtNVHGyw92k8xGp0rRIOZOSHOgIiEbi4oDOZcIO95ZEOJRUf4yF/98Op0+8WmYn1e9u2jt06wwrYuERRtPlKnAu4LG4stKTFAsQv+ZEN+i2ITur5c1wcuHyUo4DP7amxygr8VNPRYXimCpCeEBmBm98M2r/mhTDYG7f7X8oRFJGjlcKQazaHFADfBHwkBBhaf2QGgSjPjfKzjxCllgQSog1V3gkRaoXXtLtEp6NpgOWhp6bUlk6OlCskmZZBMHNi+7lIOXrOnSiqVsFRQOZwa6SdGJYJUqA6hJl08NlwbNzHAzfKbXNhErurjrxyx8RNiRUPH4cNhysDSpMQABZqz4nxsQ2GrAhy49ltbe+3aBUkuIZ8zZ/U+wn0R6xqigIPhbvDYWdXd9tqGPPvDPdNVqaOiiDAdAS+B1WCiAaMVSRf0n1TPmFPZcLDa5snpDZUTgXlpZU6jDtj9BY1iUQLvCjMLA9rjQn3tL0frpKK5s6RdVYD5IwPF/gNjUHKEjyonXFOGoJ/dVCL8+7HqTjlIugBUXakawNPPRqbDyDA+tIbdp05pEJb7ynskWAFHToUKelCqSdIbaDshFiHgCPt3F9sv7ptlmwtaVc1czjDvzGjbUdJhy1/NsquvGK8qLQH7i7srpZcChGtWrK+9ntukYIR1gVG5Brt9CaJep8/Y917OtbuXJNne8g616eeNCpK4Fx0Q7mFJZbtNzwxX9wbomJJDj2EaAGRInbmMH67IFYzqriVJgrC09Z+2JD9P21TaqvXHsOehqh5r6OhTx+R4fZ+SOpwKiQRwNUgMEOmDK5vW7fHqdps/KljVu0E2Lx6L/f6KUfbfbMx0sddY2ySPVL+5F1dNCFeiyX3GF0B37apqdUptu63/lAgagPexhwjS2HccxAV1napGspZhGOFAunRGrPYMe+f5fbV6bYITqqiPbyy1y2fEWH4tVTU/mx0daMeaO5SUC9axvcxuWZgoX4OAFaxXrGMqfD97eKtNmjVKFXIokTlMCTgnJ7o40pemBNq6olaxn614q9Qaa5rt3psnK4Cms0FiCXU282ZK4LtPWWa4p6073mLr3yqwRz47S90IBs7xQ8weUHkFbgBZBZhzEt/n9taoog7D0aptpTZ7YpQCYg7WsaE+9mZxi4ICGKxIlEjmMIQExwb72qMHqwSdXHmoQYnfFePClPyydyHj4J7QxaQr+eAruXbvVeki8aA6yzAlcEnou6cn+KhKT/IOdDPFz8dKO7uF9ea5SNCAK/F7oCopkb4qlkyL9VHnGJ76QyUtdmBngf3xaws1uwCb4O82ltiXFifb8iP19vrynfaxW+aqy4E9t6NSLH6cA/PjA+xPuyuUjPI5CBh2FbUIgnvruEgFC19+4YjdtSTZ9pV3SPTw8vHhKmhx/1lH2XmNdtG0WOH2eZ90xcFgA2186ViDLUkJsM8/dkACbg/cNkEaMiTCmUG+trqoUUUfcPRAgKm8XjsxXHM3h4qaNAQNexiWXdZq31iSYnnN3fIDDNvS2cJ/rTzSKJ9JwYkCB/o1/8322L4KnbskYeD5uQ8ErwyA0/0F8ug1fKhmEqDLJpm8lLXX0a8ZGM50/DWBKsyJBKmILYYFeNiStEB7ameVVVd32nULEsVUB/nArzcWm5/3CMUGt02Osh++nmfXzIixYzVdYhFamhxqO6uaBZvk/fz4qf32pVsmiRJ+VpyvEl3OOvzO977ztI25ZIFdOjFS0CUqzwTZrEOgl5+cEKWOLT7k+dU51lFwzL757VuUZPB7yFootlyNJlVnv0gVLh8ZZL96q9Ryj9fbQ3dPlUBisLub6FGpkCMySqyE35ge621uQ4bY91ce14A83fhX3jhm6WOi7drJEdKnSg/yEuICHZen91VrvwJtJD4ggE0L8rE/7K2waXE+9vg2FxnEzbNibM3RRt0LBI6JOYA1MWv5q78ctu9+YqK6czNifSU6jFYZiIRbxkfa1vI2QQMD3IepO7O5rElxHIE1Q9t3z4tTN+J3m0vlP/kc82HL6z+pWcvVe6st/2CB/fabFwsa9skJ0Xb/S0fs51ePljjuplX77cZbZttFKQHW2HPSnt5aLggWru3KkaH2lddz9LzMNET5j1DHKTzQw74+L0mFzE8/m2V3LEpQIRfmsY/Pj9d7gIyA9bNla6EtXjjSLk1zzfqh2TE6yFfFiD/tr7SrMkLshu+9YdbZYr/5/nVaUyRuM6ICbVVBg7pyn50TL//PNbtlWpTmAlduL7OICB8JUSIqfLyk1Z78xCTbWt6kmApGw/sWJsjfPLW/RgUkYk4KyB+2JOMjkWy8M9gYrCAX1HUr2Lg0PUjVYVhWgBeRLXOw4xQ4pAkGCUC4cUBwCDapDLOh9pW26VCkGk9lHeEfguE5yWCFBxQ8Am1iboTkAIeOSnlUiIvzHFYnWtzg/YFqwSjD5vjq3CRbW9Ikx8Cw8Qvbym1MSrD0E8CWg2dmOHBfVZeYQyZE+IhBgaoYDmFGrLftqeySMyQZeH5vjX1iRrSw2AT5BAVAuwaHNGkTQqFIMABm2mv4MAX9a3KblZjBdgS1KtheKDiBofA+CcgYtCWQPVjeoaoGw2jgoGmVMmCPzseGzfn2zdunSOsC+AZzMlQhgBwQAN0xPVr6GWhw8HoMjQE5g3sbSFlBc5+cLE7yeFGzMNZSI69ut4Qwb2kIfHpWnASBuMawsrBpwHUyvErrmNdB9JBKAs6RYTaSSypDCzJdgj1UGgnOCQxVvfJ2dVUYrgfvDVvIDWPCFWCUVbbbN64aJfaSjblNes47p0YL54sqK0Ndh8rbxKbFtfB1H6LAbXtek901O1YaCqyVwdkcAllwsiRfPBeVXOZngHtFIdTT1m+PrsqzOVNi1JUCHgW8inWF2B5DwjB2sXa5fp+YEqUDCsYpHDz3nJ+zTqnWoGCKUZF/4dZxdj4ZA+RUDQmsCA5ZS7cigNjep2o8VLCwEpFEA7EkeWMfcZ+oduGIUQsGIsWeE7Vq90kL9XfXuqmo77S4cB+xAgGdCPEcbt964ZhNyAjVaxEc0AWJi3YFgomhXhrM21vZZZePDLbf7yhzzVpcPMpezauXH6BC/4fVBTZzfKSqcATOaKXAtoK4Ix3JWdGop3eLvpiEmsR3bWGL5qrwRQ9tLrWvLkmxFw7XKaAnGIdtiI4t3QkSq3UFLRrk5toEug3XYfeXI3V6n8yAEGgxzMz65DAvrusU2xYDoxXtfRpATYv0tfLmHnUHCBwYcKY6t2Zttv3kvtm2taBVMxZ0FiHxEKSwvc8+tzDRchp6tCYJfOm4TYj3E7UzHcbdlZ3q2JAglJY02+ypcXpfDO4mh/uKzvTLFyXba7muokd2RZvIMXgfVO9J9Jkzw6eAOUckFfpe9j5Ci8xx1LajMj9E5BLMQsHOQ8GBoJIuAYfy+vwW+8zUOPvhpkLpIP361vHyvwRK7K0vzU20P+wqV6ccYTzgtWnxARJ/5Syh8IWA4d1zYu2pPdXyxdwLicl6DRd8BMgvZws+iPfE/R9U+H7gkX22dOEomxTrrQDyQHmHrjPdcjryUYHn9nZ1h31hbqIdrOuQvhDVbe47ftcdn+QxXDNmnBNUm5d//IMX6PtP2tfeKFAHCtgss2vML3xzYYroZNccqLHPXJQgmAoUrkdqXNBbutAqDlZ0iuEHUpZEKGx7T6hiTpctLTFARQDIWyanh9rS1EArbusT8cSnfrfdxk2ItalJAZZf123Z+U3SMYHqmKIPitLP7K8Rde0tj+6T5tOjN423Jw9Va/3OTfazbz5+UMrhQL8DvEeoYr4sI1gzjaANliaHWFlbj4pWFBnumhRjr+TVq6sKrPE7K3LsqTsmS1m+qbPfTp06K/gsZySVdxisXsquV4GS+QaGvaH5/dO+Sqto7FLBgfWD7xgf5SWyG6A6P7putPZHaUePxCmToclu69X+BpZNNxS/9vqr++yhB5ZqzrO0skPwK56P2ap9uQ32/avSbV1hiwpnGsYva7FFmeHSkvnp9aM1X0i34fE38q2ns8fmzU622uYe7WdmN4prO+yzCxLtuQM16iiW1He5iDV6T0rnhOtGl+feWXH23ddckFCQFcxHvLazXMrrnKcgYSobusQSiK+am+QvtfUbJoS7lMvzmu2b85Pt0y8dserqDnv67mkqdqw80qDP+d2LUu2b6/KUBNT19NsPns+2BdPjpBmG+CbXF6aqT8+Plx8YHeOnAjE+N8BzqFg3MQpdzMaxb68bH6bfM+B//bdW2eVXTrKL0wKt7/RZe2l/rWJaPjP+lSIrYqZvHa2zh28ab3ktnYpHJ40MUcEahARzMpAaocU2N85fsQ3aVR9m+1AMiL+bAXKGxrhR6AoQDMBXD2MBrW9oDRlyJqiglU+gCa6bKsicRF/LCPQVvi7eD3z3GWE2wfDhtMVl7utul48OkWNSBtvabzNHh2nwEejTtv2VYgPh3wQyDAXCbMXwNVSsDIkSHK8qbLTJkQxND1HAOTEt1I4VN0voCxG6vLoe8U5TAaH1XtXVJ+E/qiJRvsP1WVnsCPqwoWFiaes7LZgUG55gmooF0AICWYbnqaCDS4UhAZ7xLcXtNjkOrY6hytg3bCtSAMNjmBlg8R9v6BM8ggMUju6Lk4JtXqq/Ze0rtdbOExp0JOD/3l1TNefAZwYGAIxskALzlimR+ixAu8rqO3UYohBOpQ+cNF0CPhOB+vhYP5s1IUoQKd4/eihXpIcoGGzoOSE8PgE7Yjsl1S4mKioyBPtXj+bgd81VMJBGRWhuop90CsCDM0h+9JzgEQE6gQ5/y2dbsbfKDtX2KDnDkdY1dktEDdYasPdcXzb2zsp2OX7uKboiMD/weQm0WFOwTMDsQhJHy5lEEhw9lQ0ONK4tokQ4QhwNueDG3Ea1t4VPvzJNyRgdJlrCOAggHmC5qeSiLMs1WDo6WGw9BGWIPhHIch+vGBsqqIlmPoZfqArm+ZZoDA6QDw6+g5m9cUK45TT2iEAB50v1eO2hWu1x5ocILhAGJNGAmSwjyNcifUZYsPsIVQ+ZW0Ipl3vD3gqhsj0h0raVdEg8DkjRFbPjNVdDO3/rjmL75EWJmqEisD1a3qbWN11XYI6Hs+sUsK8pbLCLEoK0PmCLgxAhK69RwqSXESS396kTCbSB+Y+Krh57YidDrKa9zUyUS0enSVjvxAhfaVcAeYJcgqSfBBsBNJKVI/Wo+16oBIrXK2jp0cHOfmOtTU4MtHXrc/SZ8YkwpBDUM3dV1w319AX2wIIUuykzwi5OD7ItG3OsvaNfhxrd1e98epbmjkimqWTyMw699Cgfu2d+vOU396oAgEI9xQyUtOlYTE8NVgUSdhx8HpoPl8xNtotHwSp3gc1ODZYwIrMoPadPC3rBsPzISF8VJxAJBGYG9S6UkXSsCEyADh6t6pToV2y4t02J8lFBh0QDcT18aEVjt0X6uHzqszurRDLCns5p7rDSijYJADL/x/vg3rMGgKiR1NA5Hh/mY0smRupzMv8C3psK7Jj4AFud0yS4B930mEAETk2qzwiDcc5Q+GLmA9+BFgnikRRdvnTLRCU16441aahYcK2mHjFH4buAp5Is4lvXokq+p0psSNNivMWaBqSC9wpRCn4BuNT5lmhgP70k1Z69eYwStlumRdpX5ifZ0cYOFdPa2/skuLj5YLUdq+1V8EYnZE5csPwviUZqkI9lBHsLAsieYJ1944qRKuhwjoSFeNkd46NsRXaD2Nmaek/YXdeOs1unRuk+rl592B64Nl0JNElDdmmLHa3v1nvbXd1iFcW1tnBMmK0rbrRbxrgY8Oi+/fSTk+xAboPdPClSz895AXPj7qIWdRZzmzrsiX1VOptgdWT4GJa0P28ttyf2VNnIhAB75mit4paJ8QE2a1SwEmrgfQS7B2o7NDdITFTZdlL78onDNXbRyAAVHaenBtm21XsszGe4YoA9+Y1iRETJvrEPGlezXyxLt89MjbHLM0PszXVHpT4vPbILL7Dvf36Ri1HTE1jgWQug6DYwIJKary1LtfXFrToz8R2cVTfNiFHXb8HESA1Bz4r3teKmXvvJrWMFHfv4xCjNa103KUJdHeZEh194oeIzoIsxwZ6Wf7xeorWQ4pDUk2jg25jvYm4T+ND4CG8blRioOKa+pceqm3psQnKwoOw5RS26HiBDnttfY0dqu5VQbilrFBve7z8xQXEbRSjuE7a5vFmfAdg16ItrFyWruwMBDT5ndKindMaYByGOZGYXFAhQOZTvKeIi3ExyR+eJOc0Hns8WrS4x2mfvnKPr+eLBOs2P6Gxv7tXsUHN7n12RFiKkDRC2X+8stR+9nKt4lIQ2KdjDPjM7zlKj/RWL4ZOWZoR86BONj0yy8c6kQ+JxbSe0uagqMUzITMLdM2MV2MEigXIsGDgYEcg8t1Q0C2rEgXa8xtUG52ZOiPERnhn78/YKDXCSXdKi5r9fryoUJeWXr8tQ2w0nsCgtUJUMDujWjj7hoz+5OEnwGdqHT+6rPscqY0oI6I4AGQAHOj/VXyKD6WHuwmlvON5sM9NC1bF5cEW2oFMEDyiakwBxwFAxL2roUVVNVf5ob7XTGdrms0yJ8dZhR4uS98whBASIgXogGz+7c7LgF/y9Kn9wunujRnzKthe1CjJQ0tGt2YTFC9O0Cfh8BOLAsuB4JxBHrTQxxNM1fBfqbk/uqtLQJxsZ6loc0rbCFvvN6kIle7Q+qeJTfQPLiGP+1hMHXMJVtV22t9pF0QpkhbYgTp+Kc4Cfu+0+Vmd/3lRmU2O8bWtZuxR1EUO8fnKETUv0sweeOWL7D9dooBJj/iSrsEkYWlScNxe2WVF9l3DkDAGnhLjYheIjfXXtuPckqvzs4PEGDVzyHGBPgVUAi6FzVt/Wp04a3Q7ufUKwh2Z0jtcB0RuizlJWZZfeQ32nKzGhiwKunIAUBwcWe29Jm35HskoFngoHzpLrzGA8jGSsr7HBfoLeUI2BphgKV2BTzBLQAr97Rox9aU6Cne/2wEIXPI0gG70I1p6GYg/W2i+uHK21ThX/+rQwy2/tsoxwT/v1hiJRSg4qZYOvxlH7jhiqjhaYYILpb63MVQWL7hWJHWvhgSez7Lr0MPv1PdP1+sARCcrZIyTTdQ3dUij+wrUZShCpUv56e4kSAp4Df8A8FgrHr+Y0qFjC/uB1Y7w97aWDdXbxmDBBJH/w+D69BnhcgnoUg4E6AROpRmTUfagOdF6HuTaC1535TTY5xktrd0lyoGBlkCNsK2oTHCS/rsse/tI8wRHpbADFgOkGRhPW9vrcZg19Frd128tZ9XbxxZkK6tmf+EP2DBU9qoKLMsMkGooAZXKgm/1mXbG6TXRUYQrER6071mg/eCFH/hBqZgYZJ0R66lqzD+755RZ1ihggJdEn0GcvMpvV1ntG3UggBAi0/mRlvi0bhU5RmzqhdFvumRdvC0YG2FefPGR7DlTaxuI2Gz58qAsKmkeX4kIJ472R2ywKWvwT8CNoMhlKHZkYqMIC9x9RWF6foUquB36XCjHrCRz+HVOiBJ1EJBV/yPwOayvaf4Sq5MDOFqcGCLrG+YP4Kb7uLfau5zCLDvPWIO7Dq/JFHsHsEB2RF7PqNCeIv5uUGKiiF2xdFJ7Ghfm77hMUuc29trkYjaZhCm4IOO6fHf9fN5/1r9jX5ie6OlbFLaKihviDIeMXjtTZ8k9Pk+huXLCn3ZARbscbOwR//N4rx+21vHrtL6CvJCCwPDG7SOLLWQxpwK2P7NW5wXoN9hihwsCnf75ZxAavfv8yJalU1YEI0g3lDM7OrRck8+f3z9bcF0KiX1mdq3Ocjn1N50kVBxHJ/fm2EsHoEJlcmhlqMf4eml+4LDNEJBf3/nST4MwErsydADu6bLRL84FkmqS0sqVPLGUUu1iPz28qUczBewYihG9gLW7Ib7UpcT62K7/JXvjZ9RqeZ91SGYdymfhgc2GrvXyw1lp7T1pZa4+Y+OYsyBCkL8LPXfOMDEzzOYkjKNoxi4gmRUaIp313eY78Lon2jTNjhIAApv7TJ/cr6I7wJAm6wD42Bsi5ySde/fmnlDxvKWi1V/MblERTUM6u7lYRh6QoIsrfVuyvsc8/cdDumBQtgh0Y8cYnBtpPrs6QiCnK4lu3F9nKfDRZhsl3HShw0ddeMzPWthS2iWKcOUuo/BEX5PUzx0bZnuoOwUxzanu0Vo5k14gUIDHKVyyAge4jBP3+yrwkKyxosqd2V9v+qi4VlLnWrIO1Oc2Cq143JtRym7oFX4U8hE44cQ6w1bFpIVZd22nff2S3vb6zXJ1b/DBCgxQUwkM87bLx4TZ06BANrlPcGRvuJxIib+8Rotp1IUAutB3lHSpef35uon1mxkfHD3wkYFT/CFJR3NBjk+N8bX1Oo5R+OZyvHxdmLf0nbX1ui4all44OFbYvyHOYsL5tPae0qAkUCOyBOsBVDoyJCgOqkFRE2dRrjjWq8s1MBcE9ASaVjZU7yu3G+Qm25XijOgUNtMBDqYj7K4iH3x4sOEE/hyo6E8sP1eswJBvdu7vIFi1IE4sUWhVUJ/6yFchVkLooMydFW2akt609Ui+KSJwGh31lU48qm4PigCQIMFHAnkNSzuM4RDnw00I99F7BGdM1IMil6gcPNrS4VAkmRAEDGLDHt5ZbRLCXnoNKDRUXcMpU8xnEnJgYqGsOgpsAgscxQMmA/qPri621ucuuu3iUhuM9zsGwxkbAH35S+hNUiel8YNwjghYGywjkCEJgHyPRgMaOSj/0sbSqCUCqm7ptbkaoqrYM9nE9CQT4ytIlCSDgI+ARtXCwu9rDQJVgvBhsa3IdgFTQ9SDhQ2GXZIlKJgEAOHLWAFhqoA3McbB+wI0uGhsuClycKckc7xUnw0HAteB3kb7D9HwktbDZYFQrMyJdonwkmOtymq24vE20t4hXUqmiqgGfNwcL8D/obwleGSbjgCJ5JjBe+SFXAf8gIRUcBolBbkr8CRwxONURsXrtaIM1tPXZNZMjNVjI3uF+NHedkB9gHVJkIFlkuJYZjE05DXb7zGjLbejTQfzkjkodbgSAwLToTnGvYB+577JUBRhuI4ZaS2ufxUf6aO+THBwqb1U1dGcZ+OzTdll6kD25p9pigr00mLljS65dcslYzScAVST5QHSOAsXazQU2cWKsLRsbYg+tKxH1LAcke4d9hvgke5y1z5zZbVOirLStT2uUnx2qdtHNzk8J0HsFMz020lMt+xmpQarOwSTF+iWph0Xq928UWaow12d18Na09lldS48Uc4+WNtu0kSGuLieq1WcHFPTgz+Ds/9HzR+3smbN262Vp0sQIC/QULGhOvJ/truhQ9W/l0Ua9J/Yt3Qm+zs0IU6FhdJiHHa3rla/iWhA4pob7CIJCIsM+nJMRpkIGGkoYnYpBiBOfgyALP0AxhaIUrFVlTT2CVTG/NzbCU5hq6HhhHfIfMdxquvuUcOAHMaA5QHYpMtClASJCsQV/SEfWRaM5oGJTgj8zNGfFtsUa5PVj/dzsreJ2ywj30BoAzofaPOx4aJAA6WKw/+Cxes18vLG91MZmhKljCsyP68u9eHpnlYbIocvFV1PkoRP6YWKX+TDZ19cW6Dxn/cMcRVWba/n9JSOtrK1buPbWrn67Yny4AjnOJealgGNBYHLlRMgUzooUBcYfmPC2H2+0ry9OsdeON9msBF/72Wv5mt+kAAT96ODcI3DpH1yTbt9/PU9kAOjtQGBz3bgwaT7szWu0b1ySavugLC5pkVYIPmVsQqBlFTXZob1FtuCi0bYkI1hQxRvGh0sUb0JqsD39wl6bMmukfW5+gt36k4127RXjVLCjkLohp1HB+rVjwiyrtktEGtDabixpfVt/DJg1vg7aawocrx1rlGDkioN1dt2kcEv09bKdlW3agwsTA6yup89+9kqexDrbe04IsYG/AemQEeuvmaJZ6WHa/wiWwpoEYQNzR5eNCbFvPrxHCt6fv2mCPbW+2Manhyo5X5wYZI8drLJl6cHygV09LqYrBqSZDSOeAg1CcRL/nBHmYauzXQkDBQAMSBcslnQt8EHseQQ38RUjwym2uGZl2YsU+ugyAnMHYkrcwevB8IlPYtYVtfC0IG8VoEo6umz5wXoVd4BvQinc1H1aj8FXA6vm9fB3o6J8VeyBep/Xo7MCIQFFRNi2QryHWrSXhz2xv0qQTOZUSohtek4KLkaxCEbKwzU9tml/lV06PdaeX3vcUkeGqiDJ/cQbnTxzxn7yWr4geX7eboozIZHIKm21tz7rKnx9lOwjm2wMWtKX1tmM8ZHKNDmUlEX7DpdUO3jLMC9EWHpEl4saMy1sIEkcDCycYF83iVjhfLYVtml+YXtBk2XG+ou//6bxEaqe7K1p10aAshQcMQc6NJwEtgQALV0nLdR3hKqaHH7grQmWv7c8x6KjfJW0RPm7i8qWLkxpW68WJ1WJ1p5T2gQ6yDhYm/t0wPFZgC6hAE7Xgt+TQABZIGvmgLx2XKgGkKlq8n1F+0mbGu1trxxt0KYh8Ge+hKoFysw4R4L2xtZem58Zpg7R+r2VNnZUiF0xOkSiQAghxgV5vj2XAMsKlbU3cxpFOYczmJ/oJwcHRIskB42HMH8PBRMkC7nVnXbjxAh7s6BFEBRwlQQnO4/U2g1z4/U+UOolu+d+4Cgef3qHpYxJtBmZ4aLTnJIRKjE2DvzrRodpMH9/eadmS+aPCtJ8DZVEXv/A8UYLDvJQixle8ENlbdqwVEIJEqloogOAo6abRdcFyBUzHDB/zBsdps/CdaXjRbuYzw42FqacY5VtYvkCAoPjIiCLDvKy/BqXCjwY6qq2E+pq7C9ps0syQwTdgJTA69x9ywhz1z1XF8djqKUF+NiB+jYraTkhnDe4bxwlmFeSnSvTQ+yy0f+j7OzYP7bYz662+VNjBXvhvqEiDhEAB0FtV7/WL36A4AItBTRVKDjwOKgUXYrLrq6mOo7JAdKQGJ8crEMCISoSyTdLmiw12MN+tbbI5o0J12sD/QGWgB+ADY2ZLt4DwQhwGQ75ux/ea/EJgeq+cYADxfzc3ATLqu2UaCMHG6xumjPxHKaqOkxD+CgOW2hcZ48K1nvgPZL0ALGk4kUic9+cOGvoPaHDj/VFoDs/0d9ePFSntYofoPvK/AeaFgw0VzZ3C863dEKkoGdoB4xMCbZ7ZsXa+qJWwQ1ig1xdURileF8UXCjuLEwLEYThprERGigl6GaNU5wQ/LPnpCp2sELRkQOSkiyOfJdexdNvFtmti5P171cP1GhIl0IH1f7f/X6NpU4bKzHRN3eV2+LpsXa0tEVQkNsmRFpeS5ftKG4TbOvqqdHye6gF4+/2ZddJNwUjkdyYVSNsNxANoI74tIZOF+Mcvl4sVo09llvULAruxRMitEa4/gQcFW2ugX7gOHQkmHUBJ0+xhaOT12bt5JS2qBpKNbyitV9zclAv3zI9SmK0dDmo1NKJm8Ls1onT8l906FL8ve1AfatII3gt3id+IKugSQHnDaPDbG6qq+Dj2P9uMfetsotnJ2ou0JvZHXRUgj0ktsj1Jh7IqukWOxX7lPUxKiFAlXvWblKEj4o/jV2nVXSg2gzt7vyJUQp2Pz8zXoHsY1nVNjfOz+7+w06799qxot0HXQCciqQFaC8MUUC4KLzBRhfr62kX/+BNi4oNUmd7UlKQbT1aZz+6KsPWl7TYzFhf6z11WjBfEoQQnxEKpgn0c0tbbcLIYJ2HdMLYh/gKZv2wvSWt1tDUYz+/brTmkDAKJ7WdJ+zKtBAplHPmMmcG1ep31hyXODJFVPYoMyufWBCvROz5dfkWE+tvX1uSaitzXWrXCPxBnsH8KHEM8QZBLyQdiCTfOzXW9tS0qhBHkYZCC9pDxFAk0oV1HXbPrDh74VCdipQUfiZHedl9v99pN185RsWIR7aUW1iQp2Ilko2vf/URS128SMxXT7x2zG68JN02H6pRIe6BBcm2q7rVdpe0WU5hs31qabIKEQiMUrDcuCXfMsfHSm9i7rgIe/bVIxYeHaR5O66B6xw+rQLusrGhuk/FzX22I6tancir5iZofg2UCQxQFKBr2vs0/4Yf4DW4/o1tveo84XdJFPYdqbUJo8PE/MnMFnHdjpx6+8xFiZoZiw/yELycuA+yodoel2I6SJcYXw/bX9sq4hsszM9NbFjbDlTbwqkxYuobH+djH1X7SMGo/p4V//Jie/rGTA1fM0AEdi6rrN3qe05YWqCXcL5QkxHMpgf66CAkKOAAJltt6z4pfDGD47RZwRgCJSJoYPCMluyawibh9lmQBIEYA9cv7KlWckHFkUQmu7JdQSxiOAxehnu42+zxUVZe0a55g0lR3nY4q0IMVBzYBCRU9AhoaelTjWDoEUrea8eFqTIIRhoY0p9X5WlTb8iu18A1yRVVPWBi4LS35Lfa8YZewafQlGCT4Fxe21ykKudt06JUCSWopeIPXere4hYrquuylsYOJTav5zTpcx/Na7Tm7pOqBALdQMkaRfO2jn7hXAlS6AxwPUjwUMSEXpDqH9cDKMD+Q1W2/HC9hfm4WBJINLYfqhHmff3RevMacaEtGxeuTQssi/c9bX6G3TI/XgkLwjYMiMGKQ+XiqYPV0hagczIjOVB0nMDOYgPchAWFZ57ggg1P5XF+WrDYZagwIshD8rn7SK2YnkaHeeoAgK5uzvhIG5MQJEjazHgfVcT4XApYh1wgDQ/eO5ASqqHQEnOADbnwQuE4SUAYzAWaBbsUwSDEA+iVMFsCDpv1RRcJppStxW1KVAlSvr+xUDAWxBvpRm06XGs55a02KyXInrhhtJNo/BNW8bvL7MmPZdrkeD8l6hkRnmLsoDpE9YoDHOICEtOLEoLV5eOa4+ipVJFQ0D5n/6KlQGLPgCFBPt2vpw7ViIefLhn43vEpwTqA4Fd/ele1ghm6U3Qp9hY1W0vPKRsV5KVCBVCipXMSraKiTfcdytisfSWqQDJwzHqFFY4AFjgU3TkSjbf2VSohoYhBlRsf9PvlR+1ITbe9tqdKg8TgpxF9+vWmEsut7xNTGaQYFEJQlidIbu8+YU+8eECD7Aw18jwk7ByamUlBtulYvaqX7U2uzuQT6OC099n2A5VS2UVPgC7djrxG7TuYuzBgTUAwqQaTvJAYEAgB1SQgY9/v3Jpvrx5rFMkDvoPr/ZfNJfaN69Nt1b4qVUZvmxmjQVFE90g+Jl40xT61OFEVQHf3YUo0mFvhfv10c5G9drheMLfLJkZKKZxOALhxmF9GJQXaovRgl3r3iTP2qcUJgovgn7fm1EttfPPucgljoh0Cq9eoCC+bPT5S4mGoljPfgwFtHHqhaaj14oxgl2iqv4cq30lh3noPdKu5VjPSQvUZgWLBlMRn9/ceIX0TVJQXJgRasMdQzWfRSX0zt1lJIzDWz688ZkVN/RJNJElbt79aicbNs2Pt4WvSnUTjn7DKB5fZn69Ntztnx2q9gBB4YVOJRXi52cQwP8uu75FAL/fu3mmxNmdsuPYI5xhKy5ybBKLc6+unRKo67ufnprmsnJIWe+5orf1gU5EKekAPl85PtfLWPp0NzOZkhHoKUnPpmDB782CNEpRUfxc1LPCjay/JsMqyRvkWOgnHD5famsJm2360TolGlJeHXovB8YlR3naBXWCr38yxP988XoWyiop2zXH86pkDStzZS7sRvvRxs+mjw+ybK3OV3L6wv9YqWvs0X8j8JJDvhtZe++MvXtAg/ecWJemMI9FmRmra6DB7Zmu5fFdrQ6vgXr/eUqJYaO2WAiUlKIHTTdyRWy9NmbKyVl2nsvouMfMBTWbgec3uChsZ6Wc7j9WLbYs9vuuFNZoNWTbatY9Y+99+4Zj96t7pKnbiS7+0JNnKqjpULCZeG3flUvv65SOFPvHwcrfdeQ2WFOOnDsPtT+y35XuqFCPddXGyHajsVIKeFuahOdQJk+PtnjlxEgkk5vn9/TNtCnO4SQH21qFaXfs3thTZuDiXQntmiLdgUdPHRiiw30JRdFy4i958yAXq5MC2SRzFMDefC8KKqakhKuJCeIMfuHxugi1OCxJ8le+5bv6+bhbn42nXZoba5Smh5jNiqI0N89b8Kh0m6HKhGr77L4fE2ocqOUkNdPgoy3/jujT5gY9yovFf0dn4e5zcLAw2EQcnG5TDgbYZfOZwlaNGSyDOcB9Kkhx6YKqpbrC5pFDbyTD1UFWqqCDSzgdnlx7moSop8wIMcRE8wFxS2dpnTajQhngp+IRzn8VW09htY5ODFEgw8Pz6sxvs7vuW6SAcVLq+fkyY5TX3KMgtaujWIGh1a5/NTfG3DcdbbF5qgNq2Gw7VWmqMvwY3PzE9WmxP6IDQjmMwm+4B8vazk/1VIdxU0CoGJIYUr5/p4menzd/ef0rDjGx67j7wMqAhDNwBnaDiR/LG7/ns8NfTqcFpsDFjfNwtq65TNJJcD7oAXG8+X5jvCA23wkcP48Wesk4FfgQFiB5VtfeLqYXgDIEcKkgE3zgfkiYC9iXpQQr6cB4YEDKcHNh1RHh2bS+wzAnxduecWAke8b7YvGvymvWeUYDGipp6LTPCS89D1RiYDIEYTB3oCnztiYMWHuFnn12SpMosHRtw82BrcTBg1ekC1Z5LPK+ZGGFhniMkCEcAyqFE9ROnjfYJDCLoAfB5WGusQ1RV0Ud4MbfOxkV42i9XFdrdSxIF5aDtv6/UJRpHsAsEiIDZsX/ffrqlREWFPcWtonslgb17aoygVayB3QhahvuITYz9zKGwfEuJhYV5S1OHYgT+Az8g0UeE+U6fFXSTgz0zwsP8z83bEBSwl1WR7DwhLQ0SAAJgoAHQl6INwIEOsxjQy5UPr7A7v3yzEllei/v/+VkJdrC+XUEElUqIFipaepXwDu7r5u7TYncZlRigv8EPgG8mQdla3C4OeGaggNxcOT7UfIYPFeafYOh3r+XbF64aJfgIyXdFZ58OeXwVnd3C+h7pdvD6DEOS9KYEuQtjTeUW4gz8AHNWQDfTgrxsd1WHngPcMwO6zBjgQ2MC3HXgvlXUJlrIHWWdgjC+VYA/C1RhhI4yQqncJ6CuCO8Bi2QOCn/JQDSqxlBQirGv1SWeOTbSS+x0OzZm29hpqfaFxcmqKAd5DLUEfw9RfnK/6WCxNwfFMeliFDX12eKUQDtQ3amhbu7b/b/faaGRAfbNq9M0uE+nMT3UQ/AHfBmfC1gVEA7pZSxI0HVdmdOkzjNnDOQYwPGYHeB+QouKAc3kPVyUGKCkE3ahcRFe9qOVefa5S5MFe6XLiaJ9Roy/Cizg8x+7LuOD3kL/NVo9IA1e2FWpLiWd6N9flWnHGtu1Jvk+KdxHOkWD9+v5N46bl4+7Ak8SFrrlnAN0GMD90yWnAAelPWua85DBX7qYHkOHSi+KdXq0pFm6NBjn5tZzVXNYGUluKE68+PNH7LZvflrJOR1KEtXfXJFh2Q3tVtDUZ3uKW9QhQGslKsBdFK2DZ9oz64ssLTVY6+tjE8Itt7HHMkO9BBuiE0ER4HhFm31+UZLiHMQ+6cJ+8ddb7aGvLBDMkFm0tyqate92l3ZYRoSXvXGoVsPdvOdH6EAuTLSkAA+hLhCmpIOA0CYFSt7jrBh/kSuwRznX2S8UfOgOQvoA+Qm6MF+ck2h7atoEQ3tuV5XObyDUzJ9MCPNVlR/YEsb5uzmvRQnSN5emWhF7b8DUEYEGHjRBepinvZHdYLvXHbBxc8faFxcnCzFC9yrAbbh8Uzn+M9pHZzJ+imtFMogezsdGh9uqgkbNZhDsf/nP+8zL19N+dFOmEjT01y5KCrC1BS02JdbLajpOSZCwtrFb2mPfvzpd1+SpfdXW3NanbhUiqczMofXkEiP2VhejoqVfnXbmg9Baefl4nXRNfrA6z76zbJTlNAEbN4mBQtW/JbdRsLVBQpT/BvuvSzYGbeYvd6jtjmOgkkQgxwYA0gMUhiHN17OblKVOhA3I183ymnrFEkX7kQ7CtNRg0RlGeI3Q8B7zGHQhUsK8bHasv9gfCCgHYQYEFy9tK5dAGFWADUVoaLjZM5tKbFJGmA5dhLNIAnzdh7uG0TyhpD1p+TUddt/seA0U0aJk0zLzAWTnS7MTbWdVi+0pbVf3BVwy74WhMJIWBkBxNrBT0Rkh+MY5od5JNZfDkM8+MSFAG45bTvBBcgEcg5YszB2IEtZ19Fukn7uqF7BAbMlvsYQQL8ut7rCbJkdYbkOvDuJXjzQI/rRme5nNnRxtr645apctGa3rTZIFLpXZENqnqJTD1MDrEqhx8ANVARdOG5hhVjo7VBJJXHBOsDnBNc+A96SEADkujPZ3fXOPVJDTmPs4Df3nMAWFYLMJYOgklDFQ23FC7VgcDJ/71Dm4BfhSgkHUQmmhTgz1t65Tp+zNohbNVhBs7CpoEk0pySQCi8DlpsZ6S2iM6iwBal59t42J8paoG58LXnUEAzk4cph1oZrpNVSJFo6H9cEhMSfO3363rVTwGHD6JDBXZLogOY69tzb5x1ulqswhD0SFQwqmFFr1rJkrM0Ps2f01OjiBTsFCdLSxy1ICPFQAQAl4RkaYpYe6q+rIocr+A4IHjh7Wqc3lLXotOpxgc9GKQWDrG9ekaS4A9hTWIVod40eFqAJPlf/lo/VSsWdd0kU83thnWcXN9sCSVNtW0abgmOB+U55r/uw3V4y2tUUNdriyU0rX/J45pIQgN/07p7rLbp4YYUfqu5RQcMAW13cpQEI5HV9HdxLYFIkTSQWsL8wcQLpBcrC9uF1FGKBEAdAvHqmVIvGKrDqJWFGthCWFYB1KzucP1IrV66l1BTZrYrRt2FpoS+anqhtZ19xjn70oUYPNdOzoptJ9JsHKjPHV66083GDJ4eCYT6lYMSbSQ8Eh7Hr4qGf3VEvDg+7GrLQQdVDoPiMOyiwElhbjElpkLoOiED6IIhF47V2VnUoASTwYrscPcP+AokIHOi4h0A6Xtkg0c2F8sKAVLx6rF0y0uLXP3sprEmwKP0AAByxlfpK/WG24fnSQ2P+I7hGI0E1FYwOlY/zOrpx6m54eqr2PH+BnCUHuKvBcmhJsP95YKD9wRWaI+QwbJmYZx957y/z2Jqlgk4TCVjQqxk/+nwo859Udk6PtZ5uKVGiE3vbKkWG2q7rFxof42Y7qVlu+s1IwJs6AJD9vW1/i6l7mVbWL5emSpBCxGLGviKgoEnAubdlbaT+5ZYwSzQd3ltvFGUH26KYyqYNDx05V+1fbSjSfhAYTxCS7Kjo14Pzjy4FXNavYhq7FiwdqxQ617ouz7dnD1YLtjol00dLCuMfsJ36ALv0np8TYjkqXDwFpkF3abDfNjLGH1xUrKK6v77L7r0xTgRJihtkJfkq0CbBhXOJ8R6AQNi5gwJsP1doXLkm2368vkbDv6u1l9synpmkoG1HQP24tk/7LL1fk2rTxkbb3cK0tnRWv5Bl41/euSrPXcptUqc9IDFTBFIjT4gmRlhHqoTkOznnmToBdoefDtYExD2rXn28qshmpwbZqd6XNGhshelvQBLuK25QEMug+ISVY157OSE5Nl+41ECgkBhgsp8tEIZduJj4GOB2ol86ekzYnLdg2Z9fbkrERdt3oCHW40BP52Bjg2z3ywcCpgVnD8Ec0MifO1/ZWdcoPENOsPdaouIKzHwTExydFitiCoe7n3iy0xTPibXSEh6DgJLFjo30UN94+Psq+ujpXZ9I902Kt/8wZuzj9v88PfORhVP/Idn5plujxpsb5igFh+ZpjqqinhLiJKu6RreWWFult85L95SA4lKgycwggPnXbzGhhe8k24UiHQYmA/QwsMWcGNFi4en+12BM48MdFeSr4vGZWrLkNudBWHmvUz3i+iemhEsV7NbvB1h9vsWh/dzk6uiFb812BCu340vZeVRjqO07YrpJ2VQcJhv6wp9z+tKZAmxAVXZIUqmGwIlGdI9GAq5vXolIDNItqDNAk4D8MJ1KFARbkO2KIEhTYN57bWalqPkalhZ/vO1KnFuk9VO9GDFELf2KUlwTmoHzk/R2t61EljwRpbLqL9WLGzBQ7kFOvtnVxcYs9sb1SnxHYGUFeONh1t2E6tHGADD+DR+RgJmGjBUvllICN7guHAJ8RMjq6JlR0EDUkYZmWFmo3To+2vMp223O0Tp+ZjkbfiTOq6tLtEIa13XWtoEblHpFcUZmcEB8gxprqmk5VT4rau+zRfVWCxNCtINmjjcxz7jtWZ2sP19nilADpnpCoMgPCGskva1XyEBvooUF3xKOkPOo2xG6kBe8+RLj9uCB3fZbtBc22v6jZ/ry7Qo6VQPNT0+KcRON9tP3fmGvP3zxWwdw35ifbMyuyFBgyP7M4LdB+8nq+jY72FUyGxHdzaatE7ahUrs6qtVvnxKpjpcHDkyfteE2nC6I45EKtp8quHlufVatAlu4ZkBkUdD99SYoOtMf3VYm1jE5iRnKgzU/2s8f2VtlLh116LYgUAlF6dneV3m9EoKfltXRrr6J1A3SQbtkX5yXaA+vy7I+v5spXAGsE6sW6gvkEES6Yk0g0qKLRTUHLg0AWsUAoW/EB88eEqwLPPsbo8P1xbbF8Gn7w1LnZi1Vv5koh+0uXpkjZ+KrxYTYpysseuGykePCZexLD3sggW3Og2uLi/G3E8CGWnhlpB3PrNSRfVdFqj+2q0gwShQH2HrTW6uT1ndHrMZDJ9cVHs+9Idqj2Y2EebhqoZL8BOwEDTTLR2HVK3ZAJSUF215w4O17ZZuveKrLC+i4lGvgBnntLSbuERykQMcy9t6JTxQwSPfwd5Bvs17LSVttT0m4FrV0SAqPzCYMW3eSevlN6vp2HamxtVo0oOB/dWSlfO1gUOZLXqICDWTW66Ih24eeAxd45P17kJPgBaCsRDiPx477/ZFOR/FxKmKfdMC7SSTTeR8v+/kJ75NoMu29anP3p+rH24quHVIUGP/+x8eH22WeybGpyoC0dHaLK9Ov5DbZif61lN3fYqoM1tmxKlJ6H86Gt/6SKeBCJMPMJIqGqu8c25zQogYa56oax4TqrvvOxDEGqf7GlxO6ZHiu4XGSol12WHmw/W19oD+4qVzFwb2GTAlD8EecGDFd5rV3apxTHnthWLhHNpz45xW7/y2F78MXDSlzppBHMEgCjqD0tys++PCfR3ixulubExv1Vdk1GiJ6PDu1l02MsOcrPPn91unyHRC2HDZE2D3oSzGWhQUWgTjL19GMbNF8GtS0d+k8vilcR4I+3T7LvbygQBHpdfotdOSHCfv1Kns5NyFtiYvxs++FadVdrKpvtkd2Vgl/PygxTQkchcVRcgJTWgUODCMG3fn1esq7D6qONSuyJFaJ83QVVAk4eHOxhQV5DRUd9vM5FzT0y0se+fekoFSZfffO44jAKfhDJ4OuePlCte1XV2GWLkwKlxr29rMM6+k7LJ+EH6Bjn59RYfkO3VbX12rfezFeXGJ0N5vGg1O3oO2nr3yq0bdl1tjDB3545UKvYh+7nywdq7Xh+o/b90jFhdvOECPkQ7g3d4S9dm655TtYW3WMG2pfvqNC9/dqaXMVDk+N9bd7IwP/KROO/urPx9+yHm4ptR0GzDrxNu8osMMjLFk+M1KbjsJ0Q7iPmCRY7YkxUO4HSUHWnRQgkAk50Fr5rIUMx6RJ9o+JGOxABIATiFqT4S8lzRXa9ggmgBYMiglRFd27Ns2uvHK9hQGYnIvw9dFiDr4aGlc3CMDVMBLTVylv6RKH34uZSa21st0lT4u3GSRFKACL83W1WnJ89+Fap9fScEvY41n+4NiPZPF2dyzNCrKilV8rcJeVtNn9StFrDDMkxOHq4tttKm3o0dLmAiovbEMGkqHxwUKK38WZWrYRlGK5kk1HpYzMfrurUEB4BD7RssGBwrRBTpH0rPZALLrAV28s1gxIU5GGTUoKl6E37ks6TKO/qe4TPxPkW1LSLDYvX8nEfKpgY0CMOeCAdJCUc8DhHKEDzazttUZpLsRynxD0h2EOjY3oUqu89asESFJKITE7wt4auU1ZQ22Hj4vwVuL15uFaMN1wHgrQ120ps4fS4t+kE6UJR4RADzpALdb9JOm6YFGF7K7oERYP2DgGp0uZeDXbdtiRZOO7frikSbOJgVY89dPV/T2v0o2i/3lZqL+6q1LzChh2l5uE5wpbNjBMRAjM3E8N9Vd0GprRwVIC6BHTd6HpyMB6r67ODBY02NilIiQf7loCSwIA1BcQq2d/LHt5TaUszgizO29NeOFYnaBB03SQZUB7D7HJ013G75dZZGh5GBI9BaX5HgEyln0No1ZYiCw3zsRtnxdqx2m4JOz67sdhqyuosc2KCYEQkAKj9XjEqWLjtjo5+mzEuQpSXKOuS8GPoiACBZA8Ul7VJbJAZMLofQBHBVdPdLChvs0/Mj1PXlqFROgLAlGB1W7mz0qZlhsm38F4ZqGX4dm9ZuyCYiBTyedkf7FGUnZldgfqX/fq7Fcfs9KnTFhsfJGjLtemh1tjXr8CHQVoGzunQMudWVttpU0eFaFAWAoX4AIQyTyiQ533hN5jZAMYF+x3kHiT6dCBJGjj8fd2HWlZ1j12bFirWGf5NRwJfiCYIsDoSIvQWmMVbnVVjH5seLcFYqH/XbSuxi2YlqCsOBITXfnZPjVjF8K0ErGsO1dp9CxJsY1Gb1gKQD4JG1s4La4/bPddkat7nOy8cs29cl67OzW+Wjfygt8J5bY/urbDvPXvYls5NsjWbC22E+wi7al6iAlzW7uyYAHvmcI0CQjRyDlZ3q4KNIC9JOt2rt7JqbMaYcM0FcMaF+7lpjRyq7JAqeXqgr31jbZ6w/qn+3vZsdp0IKYgx8AMUnF57q8QaDu61u798kytw3V1poed0tTj/eCwogMee32cRcWH2latH2baidpuT7Ge/ei3fyvMqLCY1xn59yzj75eYSwUPR8bjjif1WXw2l/SjNgGwsbFMcQozxmRlx6nzQjSsqa7Xxo0LV6bthapRmW6FkJSE/XNBkX70sVfsMwh0SEM53ipTPrc2z6ZNixNpGxxiVbmCP+ys6bHKsry1NCdV8Cz6MwqcLwjTk7Zjqqw/utJMnTtqYSYkWH+FrX56doIJi24kT0h95o7hRDG7QV+PP5o+LsK7+M2LiAvkBPAnmOeYeKc4Qo0Ffz7zuE1vK7MuXpKhLhG+ggEIiiFYWr1PR0WOv5zbZ2ChvaatRsIXMprq5R9oZ+C2gqJ+aGWM/W1doSZF+tmV3qc2dDrnNhTY60suifEaILWpSeqhmVUHBvLSz0n5xTaY9cbBaA/7EDMQkyUFu9p3H9tkXb55oKYEedv8j++xbt4wVkcdPlqba+WDnVbLxTvvmukIFrLCDzE30FYSKjUBQgcX5u9lr2Y02Nd5XhxqBxqGqbpsS562glySD9jmJQXffKYv0d1elc1qst4LhaF938T/D+Ux1lL9lUyaKAecCOSTww4MzG1DzQZ27r9TFOz8qDD2HHmlMLEoPUXKTX98jx8MMAVoMJDsu2tQLbGN+q3QhGFRakhoo1d4Tp1x83FCxcZf93IZJ0RrMNZzZvA8OY7JtKoFUdqH6pFKTU9ejIJ/qKtUDWnx8tgPVPerwwHXNe4AHen1Wjd0wK1bQE0T6eB3UkelGEJRThdx+sNoS4vzVaeE64IBI5sZHetgrh1zMQBhVTGh92YTcH7oef9lQbAEB7qpCwKM9NTVY1RYYw0jkGFCD2lRUeCjzlrXr/fK52OwoxtMVooK6OB2RNxeunKXP73tOnJXA0XVTo1Qx5vMyqEm1myQUi/Aeoc9G1ZUDZ+PxZpuRHGBb85v1mSAaIOgkaATfnRruYqN4dfVR++QNE+2XlzmBxYfRvrKmQAcfgeq8eH/pPjA/BNMI+yTOz11MSjMS/ERtDGvKvooudStyGnqViANvhDSAgJ6uJZVNKB4hURgZ6G1fXZ6t/fvpJYm2q6TDxRIXjCrwhZqNArpAIvLZmfH22x2l6ibC/8/fZEb5iHGNgGfOyGBV9YA8dp84ZZUN3ZovYR0zmxXj666O6qG8BhudEmzXjQ2zzSVtWssIg3JAsxejvN2kuh5yjqmPwJhEi+ICCTOFAqgdoXBmFolDs6K5VwPbkDBwoB8EpnDijB0paZbA5phoX1u1v9pumhVjGcE+wl8DIUNRmCSCvYh/fHX9cZs4IUYwA4yE6oJz9LuPk8SkBKnrQVEhxGuognKw7QTuf1qZa94+7qLK9Pd3F2xi97F6FYyq2vrsaH6T3b4oUQUQ9i1BROy564xfQSSRwW4YvKCk5Llx9/hJoBe8P7Q9bp0dq/kxKrH4R7q6PecKLFFe7hIIza7tla9cdaRBA+mrD9UpyKMbClyNLjo02vh7yDZWrj5iN1w1XtV1xz58dteKHGmYUNWfEeUSz6N4wH5Qd9rH017IrhPEaFd5py1KDrAD1V22MCHAtpS5qGbZsxAIcD7CRnawtM2+MDfBdle126KEIPvMXw5LfPRzl6eK/pizcVS4ixkzzs9N818Ihf76igz77oYCnT/MUcAOOTs1SDMYueWtUuXGZwH3FdV+TbtlxAYIFooUwLTIAHv2aK29si7Xxk+Isc+cE8Ljb6GvRleKPZ/s72mFrd02KdzfXsip1/uGcIXCJoQKnMHTYnytor1PRQ7mLxBP7uw6YQvHhtvC+EDBSvED6/dXW0iIp12SGWov7Ky0Ty6Is1EBPuoCsX//uLNcsFMhEE6dtadWZNnSxRmCQJHQ00kh7lmcFGDfWZlrt86N00wJsDWKhsyjwRyI6OmPn9xvHt4edub0GYuM9reRsf62ZXeZ3b4sTV3d19dm29dunyZ/R3EIqCbda7qx0T4j7LkDtTY7JcA2H2+2u2bEWH5zjxICEkYIhrjvL+yusjs0E9pnB4uaRVjz+bkJElwFFRLt42FHGtvFOEkh9LkdlXbF5Ch7fF2hTRwdpvfK52E+hI5NVKDrPv/ltaN2xdKM83Im67xNNgbti6vybXqsr3iSaSUOzm0g6Dds2IWWGO0nrQTmIBB2YsFcMy7MWvtOmeewC+3lww2WEuatrBZ+drD8x+t71b2AfQJqVxIEIDe+vm6ac6B1PyHWRwc5Lfqq+i4lJJvymgV/unV8pJV19Ni+yi4FvgxYQ8EI9GMzAj0JfpYR5G3PH66VinB2dZeCCAbYB+XugXBxZ8M93aRyHotYXkWHDleGxFYearCPTQq35Vn1NiqSmYozCnzWHak3d7ehVlrWZvddPlLO4UBFhxVWtIvj+dEX9tuNV0/Q8xOk85mpdkyO8ZYTwUHRZmXQC1rHmq6TghoBhcKAsHGNEA2icgO+nYoEJq0QxO+8of4csMQAN9ta0qHAam6Sv5KqrJoe0QJSdQbyQbCCQjlV2fyWbgUZVDEY3IPpYVJigDpHVGAvHR1sf1hXbAvGRyrAyavpVBJC4sE8zaHaLg1t7yxqFWsFVHgwSuXU9wiDi0MEk4lTWpDsL0GzSydFKnGkkk0QwvwFw2U/efW46ISB8Dj24bfPrMyTgCcaPfiBw/WdIgv43os5+v3okcFKAlij24vaJFoFtXMjLFZeI+yRXZU2OtpPiSiMIhx0sEYV1nQIPkhyzawPehEwLIX7eWj90lI/VtMlmtnCkha7//KRwv7Sfb1zQpQVtHVJpJIq47hoH+2vydFeUhaH/hnl4ScOVKujwBwC+39cjJ/+TXDMgKLnsCEW7e1hx1u6lGhsK2MI/bRgAui63DI1yl48WCvIEp8rOdjdVmXV2alTZ6ysuNG+dPN47auD5R2WW9hkl82Msz8+uctuvH6yzYhncH5AFfzc2i5bmh6kGRAKMsC4SGoY2KQA09x7WgP27D30P3hPP3qzQAkTECOSHPYiHRSJ7wW7rpG/23Bbk+/CXF+aEawAgs4F3Ux+T9DGc0LLjYJvQasrSaCgAp78zzsrlBAs31VloUEedu/MOPvMEwfthoUw8pgdruiQH4EQ5Or0UNsFm9aQC8RiN29UiLqUl6YF29H6Lj22veuEzcsIURdzXryfPbAix+5anKgENS3Q26q6ejWgCunE5x47YOPHRNhrjk7OR8YP3JIZbp0nT5nviOG2pbxZne0vP7LPhgwdYtMmxtjVY0NFskLhAFY3FN8p4k2J9LMfvFkgqvZFqQF2vLHXUoPclZwcKW5+W5yPuID4AL/AnCEdkZRQT8sqb9dsY15uvf3gE+NVhBsZ4WO3T4iynMYOe+lQvfwPiT36Lazth3dU2LKxYZYe5G2P7a+yUeFe6q4CLf7kzBiRVjQyzxDtYxE+Iyw1wNuONXVIBwKWzfrOflX3txe2SmQQgWQKJxQCpcuzt8YaG7uturTOvnLnTBHGVDb3WtbRWrtqYbI9/PhWu+LqKdrPCNL2nDpjrx1psJsnR1rHiVMuCu74AAkv0gGky0gnEfgUrwE8mUTu9icPqEOAAGtWVacYMyGfITaYFsnZf6Eg1SSBsEBeMSbU6rpOiIiFuTKKkIG+bireTk8OtGUpoXawvk1xBMVTulQ/2Fhgl48Ns18uz7HxmeH2vcWptuxX2+zeK0apyEzxmL+HGpd5qg0lLUJIwGqIIjzaX7dPirajjZ0iqqmp7bR7L0lRjDQ1IsA+9tBu++FNmfrcKX7e1nbipPwAdu3PNlnmuBjbfN80O1/tvE823mmfX5WvCgUdB/D+DPsx4M1BS4a6tbBVTAQs7MlJQVbf4cIWU6kcPuxCdTiChZEeqmoWQ0xghlHb5TBcf7BGrFdUEKjkn6QTkuArusprxoVqKGt6UoAgWbA2USVngZMho+NAxY6BLVqtBPlALWjVtvadUReEIeQXVx21+XNSlGhQPeUwBbqQ4Oduq4832eXpwfbo7ir7+NQodT947xiVRNqbi5MDhDGlGnm0oUufnYFrKO1y6l3XZXDYHkVxaGRxUCRRULUhVDcm2kdsNa9vLdXwOFUemLRiwrzFuIGDpSNEtZb33NbZb3M45AualCgRzBOc0S0iiTtQ0SUI2t7cBps4MkSD7PwcXZSiijbLTA5SNZGAAQpBhu/gAm8BBpPsL3pJDg70FsK8RliIu5v9YmuxWr+0VlvPJUOw/rAd3jhSb2PjA8SbTrDEAD/BB8EEHa21h2rt0gkRIhIgQeO6FaDIHOylFvXLt0/4gFeyY/+O3f9ansTlGLoEakiCyR4gJQ71HGZ/OVCroUeKB/PTQqy4qfdt3R7mEaDLpcIJux0+gOegA0dXgLmotw5WWWy0n4StEsJ9XTNPCb6iq7xrZow9sbvKpiW56E6BK7DGjla79gD+iG4l3RVmjJhfoFN4SXqQ3t/WItdg8kuvHLRFi0drzdJlIyFIC3FXgL8ip96uzQhToP/FRYkSG6NQwF6nA0Bl87rMMB3wHPFHGzu0ByZFw1R1Wgw05Xx+z+Ea+MaXoTnwHLMrXiNsW1a1ZaQEicGLqucLa/Ns4axEscSBfw/wdbPahm6Li/SVtsah/EZ1LoFYLswIlb6B+4ihVlndYUlx/tqnUFWj1Mt1XbujzC6aHqvBVVi43shpsryiZpuQEaZrDix27f5qi4/yFTUmxZ5LRwYpCAB3jg4BcygxJDtbilR15H7T3aVwQ+JG1xfF75mjgsUa09Dbr0orVU/8O92vl/ZW21WTIrX/uRewehFUohVA0cTxAx/9xIN1tyGvRXuM+RoKfnQFkvw87aebikQDHRrqpYAfRXm6gHTJR8X62/HyVgsP8tLZNDnWR91w4gA64fiEHXvK1OWrb+qxuAgfnYswJv55c5l957JR9qN1BTZzVMhfwTPpdEDPHuo9XIPTL+c0ai6Arj0JNgPm0Ow+vLtCkO2XH33NZlx7kf6dEeWtyj+FtUhPd1t+vN4uSw22L7541H51wxg7WNehwBwoc6jPcKto6bO7JkdL9I6gO6uhTZDMy9OCrbC1V10VZleAHMPKRrfy6tEhYm8iPkDzZlDUj5jmiRVZduUlmTY+2stezarTbEd2Tp1Nmxgtv7Fpb6XUshGyRFUdpin8167t+WKfHBnjryIf/pcO5VPLs+zj103Q9UEV/cndVVZV1WHj0kMVu0FbvP5InQX7uyuuICa6ITNMiWScr6flt3YJHpYa6G2/3F6qWTj8H51QkB3AJvGl+4ub7aLRoZZBzHbmrO2s6BB9eKK/hwpIv1xfZLfOipGPJH5kRnTDjhILCva2kfEB9uJt4z7opfyhMCfZ+Af2XFa1MMYHS1ttcUaIlbS4lLkvTg2UjgUVdtS4ydJhi+B3MKDAWT8lJVh4YVgHyN5Fv9jcbaWlbWrHjU4NFisCjFGfmhIrvDKwo4aOEzYlHkG5fleQE+olaBfBCNlzSXOfqhOTIn2srKNXQfi0eB9bznDSsVqLig20yyZHnRv0HNChCLUkcxPlHf0W4T3c9ld1aeMDG4Bqko4J2GienySCoa6b58frGkBHV9/bbyHuI+xQfaew0bDALBgVaKMCXaqZYDRJfJID3dTqpDIQ5O0mBwD0AKrP6npXggYDiMfwIYIf1HWcEg84jptqKq/v7wnU6aS1dZ+QUBb89syK8H5hqcERIhpEwAb9Xva5ivCUBH85w0E8KINxmecCHTY/yVlt10nNloAlBSpV1dRto2P8lMDRzWDIlpkYBuz213TYjGh/W5nXaDPjfNRdIcAjAPnN81mWNjpKHPpQ9EKh9+aOUqv4/WUf9JJ17H2wV47WqRMAwwjc+VC2njx1xq7JDBWzHNVLuPUZZgRqIHhCiIdtzm20CQkB6nTsKOnQGudwLahqs7KSZhs6bKgtmZ1g3iOGakYCbvWOEycFySiq77bLRgfLxwCjzIjy0QwVRqWOrgUChDBnlXX22O7STrtmdKg9tLPc9u0psVGjo+zqyVFv7x18AV0GtyFD1P0L9x4h3wYrExAA9g4aPgF0ILpOqgCQVdBody9M0B4eH+ZnTec6vwfqXNS8g35gbLCfVXb3qMNQ0tpvY8K87dVjDZplgb0Fpjlw2HRc8ktbVd2dmh4q/4BIGAPbg7TidC7wA7FBHupaULyhSszwK0YBB9go19jfbag19rj8FvcErPXC9GBBXuQrW/ptT2GzhBCBh/FaIR7DLb+51wrqe1Tk2VXaLigafomCDN0orgPdVtiFtla22Mxof3u9oMmmx/jYqpwm0W/TRf7dX7JsZHqkqqn4Aarcq988Zu3P3fwBr1jH3g97I6fBXs1ttJK6TluSGWbHaru0Pj8P/r+zx+q6TtnIIA91s9YXtGrmksIAc0EpET5CExCosy/ooGUXt1hpYZ2dOnnK7rl5ivYUZ9mXZicI+bC+tEmEIp+aGas5KroJFA2YKSO5gA4aUU0IWUYD1es7Yfuruu3uSdH2w81FtnZtts2YM9IuHxcmaDHoAuIUmNTYx8ebuy3W182KWnvVAUw8N98E9BAWKsTx8FlAuHgPzEFMCnMV7gi499e2q5sJKQNwr8wgXyvt7NZz76nusAVxgfanvRWuAh3FRbeh1nSOwn7HQRcZxscuSpZv0vxUkUvIjtkUIJu8toRVD9bqbxhwx0fANlnU2KOZiWhfNwtyG2E5TS4dNWKxoroOUdSn+Hsprtpd0SX9nxsXJap7wfwk2jeH6rqsrLlXhB6gU6rrulSkREgPHRMou4GNzYsNtLcqWtRBxjcz3/rMvhqpvXPPfv3sQYtPDhPjIaym6Kdtf/Ow9a3+9Ae8Yj985iQb78Juf/GYLU4NsD9sKbPM+ABtyulx3vb7N0vshlkxb2OdGVgi4GURVra4OgDTYn3EskTbk43H749WdYhOlpYolXV0MgjA2TBUR9icUMYxQAZzA/MbQLiAZNENGBPnr1kKnh9M44qselUIUaccG+Zlq3KbLLuoWZCEcanBglLQPUEAzN9zmKq0g7MJqGWvzW6wS8eEimq3uaNfvN4YrUTec25Lp94rjDq8f5idCFKAV9EKRtPjeE2H3TI50srb+8UZj8gf1tDpCl62H66xq2bEirEDSmGoPlcebVQ3AWYXBtEJzLh2VAhJUugSsTwRBsQZPvpWuXV3nxK8DbtoQqQOfqjsMIa06TxgfGXmg2FNWr8MhgNvIbkYpA6mM8TncnHkXyBYGBh9xNBQVI7ydre85m49H/oAVGPTEwLUdeH9fGaGKylz7Pywm547YjeODbfvvp5ns0eHqRuwMMnfvvBkln3xmnS1/Al26epRICDIppJIR2Jugp+VtgEfOCXBvrhAdw1pj4nxU+ANucL+6k5hkgmWYa1jndFJZN/zMwoLsLQQgBfVdihZBgqIKjnVSliSqL6SGE+J9LWnsmosK6fe3NyG2cwx4fIxz2yrUIeRij++hz3PwOOkWG975WCdXTY2TNjz9p4T0n1gD101MlS+5FBDmyBZED4AhURLB80LOr50Ktlr+4pb7PPzE6ysvc9W7KtWZZYkHWgj2Ov1+6vEiLM9t8EuHhchMcOn91dLe4PZOIoIzMWQ5OQ39qtbRHcIP0PxBIjmb9cWaWDUzW2oPtui8RH6OxICuhdHantVneSa4Qc01O01wmq7Tmj2qqSVeZWhwrxTxYWmF9+7cFSg7gWwlmD3EfItQCGS/bxtf12bnuvlrHpVY2dPjVUSRCHja/MTP+il6dh/0D65PMc+MS7S7nnukF0+Pdqq207YHROibMm3VtlP7pstv8A6Irjl/AJuR4DMOYhwY3Fbj2IFtGxmJgfZhqN1NjYxyNLC3G1CqL/tq221KCrxZhIKDPQZYSOGDlECQzHukvRgzTfRfScZgIp/S3a9hQZ42G2TI+33W8s03wX0e3Z0oP1ud7ntOVRjPj5udvHkKM0uQNKQmBRkU5OD1AmETpu4Y0K0lz27s8ouHR/x9nwGXZpQnxF2Y2aE9snu6haL8/EQ9b8S9Kou+QE6N3QHCNJX7Kq0B28Ya1XdvfardUV2yURmKgFCmVATy7eV2ZzxUXa4uMlunxMn0cVfbSmxxaNDVSCRXtY5QULiI+asKPIkhvnYidOuou/Xnz8qshzfQF8LDfUUuQuFXXQ/gDQzc8a1ZyYLH4LOFd3MQ/Ud6koAL4elDlkD/AAzdyA9gFsjwBswYrhQHnQ/Krt7bWyYn20qbZSuykM7KmzbhqN2xVWTLDnUUz7jOxclf9BL80NtTrLxT1rs/avt+7eMkRgNmwL40dojdXbJuHBVx6l4Vnf1K2MHg8xGBuoAjSQiXWw0aHTBGZMsBHi6GE5gwNlZ1SamBw5dKhFAjqg2wuRwqKJDKrsIElIBbGzusfAQL8E2oFOsoqoY6aUqBZoYzEOQqEA/uzAtxPaWtulQhdsaNeKlM2IFhfD3HG6TYn00a1DY0qsDFq0AOjXXjAnTUPTy/TUW4uca0KZlimgZw+lQh+bW90rUjM32QladFJx5rbkpAXaoqstyS1stKszF7EUFkMQMJ4izLW/uU2tzz5Fai431c0GnAjzkQIFoZVd2yEHjQJhlgSs8Jdr/bdFFriGQLZhruA8kKfyO6gWYSuAdQKvGhnvYmpxmPQ942blJfhp0JyDh2lOlgmaY+0mVmc9Z3eRKMoKh6ew9Kc2MR1484FQuHZMFffxF++19M+1gdZc1dZ2QuuwLW8vt0xcnqWIO6UNlZ9/bUCgGpAmgs+muxTMoaVL+pYrJXoWVBi2GmZEB9nphg9Zl/6kB0a9SFKATSaJPgg/VakZKsDW09Yp9LijAXQkIVT+oG6HzRiSP+QL2E3u6vq3XFo4KUiLBwHh1dYedOnXWFk2LtZKGTrHhUXhI8vewfTUdqqDCv89M2U3jIlQlZM4KOALvhwQKaAn7mmFrIEaoc58ZOCuyC+bYjla02cWjQ+QH8ivaVP3j+YCMNnafVMICsxQKzPiBLXsrRJnp6zXC4kO8NDSPT8itaBPRBq/p5T7M8stcz0XwhQga8A3m2KD+RIWc5yTACPYeJkYZBlCpiuIH0FYC4kEh5+K0QOHp8dH4DDpEQD4JFmG/4r0WVLbpHgb6u0ujBKXwh/70pvWt+9wHvAId+zCY+xWP2DPfvVQsjIjNzR8VLCKDn3xiguDFd0yJFk0+MFwSbhJvaFaP13XZ9AQ/nUkrD9VZc3OvIMggHijkTY8KtKcP16jrDokJ9KuEahQMCNqZB1u5LtcWzEmxosp2wbOg1B0V4SNfA8vUdePD1DlkloriAvuJ+OPasWHq0nGO5+XWqcs6d2qsHS9rtdGJgdLrGBPiI/pcYEUUF4kH7pwYLT+w4mi9Pjt7n73EkDf2iWlRdqyhxy5ODLbW/pP24PYywaNqW3oUH4E6OFLaYmMTAq26pdc+PjnSarv7JY5KAQMiDOKKDXsqBFF1dx9q6bEBKgpSvCmqaDc3tyEWEuCh14ZFKzk+QLEB+zQ8yNOuHhdmmwtbJbhMMaG+84RiAFTZw/3d1V2eGO1pq7ObrLGtV/HU5aNDbEtxm8SYQX/sKOsQ8x1GHEdHFVYuCi4+PiMUPy0YG26/++6fre/Arz/Q9fdRMifZ+Ddt1q922uUTIzQzwXATmfKfd1VqAxAMEyzDFoFAHIf0IKMCwXZWqUuFlEA5OdBD7BZU2tngULYOvdCstfukgu+R/P2Zs/bam8dtypR4JSpUEAhkJkR5CqsNcxYiMVQsEQgjS+c/aHi5zWC6YVKAsYbveQ42ldqsPSfljMCTwrQDDS14TCqWCCASvBypdKnjtnefFKaaAfNtJR3a2LyP3cWtVtfUow2J8wSHXVnZYempQcKMTo7zkXgYg2Qc8NnATs4OKMi45eJUwVH2VXVJZKu//7TEhz4+LVpOm1kImGqY1UC9t7LjhFrUJD8zE3zkjKmy8BnHhvjY3pp28W0DbSMZofU8CB97fV+1TUsP1eP3lLTqvYG1jfQdLugJ4j6jYgPURTqfB7oce/c27ntb7Ipp0TrYZyX5W1qQt/1sc7GF+rq7iAzchlpeRZuNSwqS+nyg1whx1Je2nLDd+Y2WEuVnaeGeos5+q7TdvIZfqCCDAUiCX8SkCNjHJgep6rfyjWM2f26q+XmNEH6bgUuEudbkNNncFH/5l+RAd9ta0i5iAyqj0F+CMb7gnB94eHelfBQq6lTp4Z3nvRbVdNjE5CANP4NPPljaoqLGVZmh6s7AmIchqufjOdw+NjHcVhyqt2VjQiS++drhBispa7XgYE9rbe2z+Fg/y86utczMCB32KKHD/oKfo1uAtgaY8117Su2TV4+xCRHetqm4TUOuJEQebkPtkzNi7E/by+Uv5qWFWGKgu0vjqKPfVmXVWnKkr/DcsGtRuQVmsiQx2DaUNiuwGx3pLRgqr4+PJXFbd6DaFo536eHAYEcAw5wbHdbS1n7beqzeRicEWlFtp7312ekf9BJz7CNgyV9eZ8tmxavTf/X4MJsQ5m/3rThqiRG+6hbSoSM5mDcm3ArqOkVuAiEKqAHmGkmiJ0lk2N3W5DVrbgARSzoKdOTZnxt3V9iUseylIbZ2c4EtXZBqYb4jFBjTNV08MtCe3Vdj100Is3aIX3zd7MXD9UoYSLo/NjZcHQPg1Z7DhtoP1xcoiScx4LwfZIYCjXDZtBglD68VNNjuvEaLCvGy68eFa/5xc36L4ht1Xz2H2yWjg+2hDaV28+xYi/Zxs9+/VWo5R6ssNCLAentPWcaoENv2Vp7mLzzch9mSjBAXoiHIVRB5+WiD1TT32PHsKrvm0kx1gZ49VKN5N1fsdKF9bm6C/eCNfPmBZZMiLT3Yy3pOn1Z88uzmEps5NtIWJfvb/moXygG20XumxNhzR2utqL7L5qQEWFZll7o4dG1J0rYcrrXrZsYKVVHU0K1YKC3KV0XU3IYeW7uv2iamhSge2HK/4wf+VXOSjffYVhyptYLmXmGsqT4U13aq5X/RxCipVXK4I9K1q6hVbbsWIAmB7gquLwLqEOJhD2+vsOxDFTZteqJYlKiyMwdCoHCopMVGRfurKlFe1fE2gwN4xflJVBYHbH1+q9qeqIBfPilK1K8wWLCZGGTk0CXJYJ4BVhcqlAQobHgG0EprOmxMYpD4rRdlhGhT7i/vsHEx0HD2a4NS7UehnIogRiWUIIbEYFaKC0KFc6xu69dgKDCyjYdqpeS8dHKU2pbT431ES3fF2FBBTwhUoPdD8Rd8KirGODIcGsKE8QFuwmKTlIAdhXauoLbT+k6cFjabdjYOmGonSdOihAD71spcYTFhr2rt6FPwQAWYIViwpzixxvY+VYah+3OgUY69F/bq0TrhoYFVHajstryqNmtp6bVlM2Jtb1GLDRlygWbBICOgYlZd3anknOD6Yg7REA/7zZvFlnOo1GbPT9ceqKzrtIXjIsT0driwWQkzsySlpa02dVykzUj0036+KNnfLrALpPejge1DNfbJixLt5f01Nic9RDNPFCSYKYN6MzHAXSrfqPqCv8ZPwE7D4GpmUqDllrXapRMjBUGkY7ggLVjzT77uw1V1ZD6KvdkGQYbncAUxzx6okeAfe6y197SESmG9oSjx+q4K+bsbFiZaRXOfXTQyQMOdaBF0nnBVSfkcPHdkgLvUxam6hgV72sQ4P+lV5Db02uQoH/m7bWUdllvZJvYs9HuYdyOpIGiiM3J1eoh9dcUxdYIho+jrO22T0kLkAwkugH7QRS6saLP4SF+bk+Lv+AHH3jM/AF0qe2JPRZeV1HdaeWW7GN0I3rFrpkTZk5tKtf/zc2stJNxVHFw6NUb6DL94Nc+qCspt2oJMJQH5xS22aGqskAqH85vs4inRtnpXudVVNtm8eSNtSUaQIIhLkwOVgDx/pFb7YeOuMnvgY5n2w79k2+JZ8fIRcxJ9NbMwLzXAJob523PZtYJwXzcxzGo6Ttm6w7UibqDjkZXXaMumRasrS6IOPS0JB4kG3VI6pykBnpp5TAtxQYueOVAjiCSxALOksDpSjKlqP2HPrD6umZW7rh0rgpZPTIyy76w+bp+7KEmzL8QT+CUKjSAW8JMb3yq0CRNjlagANYNhEyIXCgirjjdbTmmLDR16oaXFBWguM8jXTYUWOjA3jY+wn6wrUOJDd4LHTRsTrgSQwsaUxEDFJTuyqi0y0seunhhh9810/MB7ZU6y8T7bHS8d06AzVI5FDT0WHeAhJpMxYV62/GiDlLXDfYcriKfazzyFBGpOnTUfqPI83ez1/CbBoYALwEqFsBUJBUE2A9m0CWGDIiMHvvHcoVq9NsEHVdGm7tOCcHCnOfyZN6HFCP8zB+3SUUG2ubhNr9F/4rQ2I0HN2AiXKB8D8XDSU2Fp7T2j7gJK5gT1UPkN0vOmBnqKV7vlnGMpauxVNWDmyGAlDgj/Me+SXe2aAQHegFGphbUrMcxbvwfvikbHjWPC7ZlDNRrKBFZW3dqvzwvshJmOSfH+gpfxGWi/8ndcQyolvGfazXsru2xGnI+uFwPgdFWAh5BwMMvx28tHfcArxLHzwa547KCNDPfWGj1W3am9DA0kc1GoCIO7Bi4JOQSPGRfpZW4I1p0+I72KcC83W5nfIPIDAg748FGmXZndqIO4rrVXXVOS7LLGLvvq/CR7aE+FggwESqmMsi+rWqHEvVDsMYhgEpTAnsWBe8ekKFuR0yBmNRIjbMnkaLHesJd6T5+2rWXt6gDir4B0AFnKr+kUZJEuKq81LtTP6nv7RB6B38mt6xb06erp0fIJwDTYz9DgUrSgigjzFUb1kefCJ2AkAbeNi5RuAEPemodr7bMTJ11CZ8ysoHcDLS9im7xPoCf4AbrMBCzgrt8oaLar0kJtVX6T2L2YH6FwwWPRRXnyY5kf6Ppw7PywiT98y6ZnhKmrThGQvcz+/dKsBPvmunzBggWbqu8T3fXilEDzHjZUxTWY1KjIb69oUlGQRBqWTOa9Xs1u0B7OLSPYHmKj4wJsx5Fae/L2Sfa7XeWCIbHv6CYCfQZVASSyrKbDLp0UZfn1roo+xbpvL0qxh/dX2dGSZqupbhfl7zULkuy6dJcqOkXJp4/UWHqYp+a2gB+BGthyvNHSov2UeMxM9BMUrLKjR/NbzE+UNfVadkGT3X9ZirSFJkZ7SVG9sqXXqhu7bOn4SAv2Gmod/WfUaRwZ4a04AYg4XcePj4+05bkNEixmOB0/MMjQtym3UYxgu0vbFWNRhBjsnKL75T9imHkPH2Z/3l9pn5seb89m19qESNesZ25Dn1g7gVxuuHfqB71E/mvNSTY+IPvS6ny16Y7UduvwLmjqF380VUIShMmRPra/plNq1leMDhEWEpE4uiFsPLoLZOyoeJMEMJzJcQ2siew+KtRb6sUMOtb1uBQ23zzeIp0LBlmBDvCVQUmocxH0q2o7oUEqZhkY2i6oare4cxX/Vw/W2oSkIEG6YM1obe8TbrG1ByGgEaLkZXYEJ0CnBAx6cX2XaEOh3GQz7y7vkhjguLgAvWcqHzsKm+2BRSlW2dUrSk3EilBn3VnWKcgTyVdRS79eh0ol1Q1UkYF+0SIF34qqNwkIg+oZYZ5KMOgsEST9ZGORBl1/f4WTVDj24aTXpAK5tbxNXUKIGzj0ZyT6KyCHHW1VQbMV13XaHdOj7VhDtxJ+kgX2AsPd0vPxGCrlepim2FdbStpta1a1Jcf72/UTwsXaUtPTJ+jRYCWTeRCSb3DcaAnQMeGgJwnHx3A0MLuQXdJiCZG+tnhkgOAZ8NjDRgUsgzmGeZlhotbl9Qfnn9DecdGHe1pedbsGVpmn4DmBSh6v7rCpSS6abaqYuwqa7aeXpllJR7cdqe2RT4z0HmEbiloVzCQFeEjnAkVhfCAJ06IUf6vrOqmER7NzR+stOdxXsxoQZQBHAy61LCXEvvHGcUuK8D0vxbQc+/Dbx545bPdOjbPN5c2aaUTrCUIXINBAhNCJ+NmWYpGUfHVZqoaysarWXkGfKLaReDAQDTwTwT4KbnurOu2VraU2emSI3TopwgLwA929Fug2wv60u0IFB8RKh1yA4j3FTjfrO3VGe4qYAKY6YgEUsvfnNVpEiKfdOjnKntlfrb99M6dJe7G5vc+mj3SRQRA/UARMi/RWgbWKQmKEr+WVt9rH5yL2563CBQKqRyrabXF6sPwHbJdAmP9w3VjLbe60PRWdKmxODPOzF4/V2/hoT0v09RITFhTV+D86vtePDrVjjV2CniPEuvFgjY1NDda868IEf3ujoEWJx7LUULvj6YMWFe5tq++a9EHf8vPOnGTjQ+h0oIDlIA/3pXIxRPhrvkeEi2CBoJzND6MSSQcVxekJPqouMHB2tKZHQnhUC+ksXDI2zEI8wSgOERMLg1qwqNBh0KD3qEAraekTQ4QHrDIB7hIVq2npEfvD0dIWG5MQKIrcAM/hoqXkwCdpoAOB8T7o4PAV3DrDb+gUCCN94QWWGuxSDF6xv8YiAj0tKdRTSttUduC+5u82M0zu51LyjfIZoaCEmRWqFCNDvSTyRbBFoEPVYzDASQn0EA3pwepuJ6lw7L/Cbnz2iOAJtP/pyHFYwt9OcjE1zluFBahx2e/MO7B/ULEHk0zQMXzIENFcA0WiW4gezQ2z4ywpwN3chw6xotYeDXUzPwbTHdVVYAaH67rs+U3FYq4JDHC3pDAfy6lotUlJQXa4vNXGxwcIhoX/gEgC1ruM5EAVPuhGUI2dGuer97OzuF3wTGYyEOlibooCC8HMH98sttBgTxsf768OKx2J+2fG672tKW4UlTZ48wQ/D6kfo+eB/5mfFmwZwV62u6pDlcgJ8f7qdqCTkejvaU19/ZZb32e/Wjbyg76Fjjn2niAj2OMQHkD3ikAmUGVmwJalu6DVzGWBLqALCPEBgoGTE/wFbaTYuL2iTaQ0HeeIIW5amGRTo/wEj0boc2dJu02L97Ud50SL754SY1vKW+z5DcXmz1C1+zDNcKGjdem0aNtxvFFsVUChmG+iwPjYqlxLSQ1RcYH3dPrMWbtubJidHhjQ+12WGWKvHW0UayZJzYKEANFpf/u5oxYQ4GHTR4erKAFk67sXj7RgrxH2RlGDjQry1BA5LF2/3V1m9W19mgO7d0mipQX42Ct5DXaorNWmJgeqeEpxckwo0gA9StK+vzjlg76Fjp0zJ9n4CNFuJgd72sbsOosN8xHjDKxVVANgsAK2BBd0/5kzUsylpcpAmjDgY8OsvLVfrBG0Us8hFKRbQSeBNiODlQQRbT0nNEyJQ8CJ0IbMqgT2MUIJA2xSBys7VbU8efKMlZe1WNqoUBsV5WtFdV2WHuVrWWWtEteBmjIkxFOVUNrG3SfPWl59j3nz83MsF3QvwILDJEVgRZWEQTAYNdLD3M1vxHBVXtv6TwlW1n/6jBhkHrnWqVA6dv7ZdU8dUvdz1f5qDZMuTA1Q8QB4JHtKmjShdP7O2J93u2BU9a0uSNR1UyItp65XnUfmrwjwgWzRQSEw8R0+TJz+7X2nFJjAt0/iAgyTTijJThzMVwFudvLMGduY32rVTT3CP1dXNFlqWoRmJiCaQBl9Y06j5eXUmpunm8XHB9gkmG5C3MSYV9jQo4os0DGGxfmKH8iv71GCxKwa8yYUKvgbqDEptCCuR1UWHPzOsg7HDzh2Xtq1Tx4S0cITm8ts/MhgWzYqWAiG5p7T6ixwbo/091EHgQ4Gg+U19d0iXfnk7BjbV9ltVS09Ni7WV2ctSAA0fBjs9h4+VGKBnX0nlcTAIgmcSR1EvxGilp4c7aVuKc///MFaK63qsPb2XmttaLXktGibmRkuyPOEaG97aV+N7d90yNwDAy1zfKxlxAZIv6qgqVfwLXwUqAgEREE80FU9VNkpaChJFSrqDJLj36DKRrSwvLNHEHNYr7Jquh049EfAnGTjv8RSvvKmDmYoItMSA21UuJcO7+QADzHPdJ06LQjDqbNn1RkJ9x2mqgfVBYIVHA4HPhudSiiaFCQIGLS4OJuj4Dr7XQOcXf2nLdbfzWL9R9gTOystMy5ADi63ulP84MyTwGBDlQOldCj9CGKGneu21NZ327iRwVILb+not6hgT1VvoKNra+uz3B9d9AFfUccc++hZ0pfW2ZkzZ+3s2bM2bnS4aHaBHE6K8BHmm/3OXsYPHKnrVrEh3NPd6nr6NL+B3kxSoLtmGvrPnNWg9ugwD1UoQ91d3UmY3ihUYHRVmMViaPu3bxbb5FGhFuU73I7XdYvWl6JEsK+bigjs/dhAN3VEvEYMs+L6TisqbLIJYyKsq/+UaLmjw7z1dwimUcE89oOFH/AVdcyxj55F3fuanT512vp7+23ajGRblBasGaVLUoIsIdBTrFUUGyjebSprsfRQD4v09LDCti7bV9klxMP0WG9Bmgno0bBamhooyGOQ+wjNf24ua9HgN3EGCISYQHcRW/x8daHNGhMuKCQFSwohENtAn098QRGBjuqe0nZ1YhjOPrSv1KbOSFbcwL4PD/PS52DmqrK6w/J+sviDvqSO/ZvmJBvnsT20u1wiYzDEAIHICGd4nQrHWcup7xPbhdtwWKYuEEYcbQuoNpNCvdQFAb4V5e+meQwqqjBNAHegVYp6MMJ3MFd8dpbD6OCYYx9We/ZglTX1uPwAHVLEr7yGDxPmm+5BY0e/sNFDL7xA/25u7bUAf3cbHe2nAgJ+AIppgg58R3Fjr+bPgEiG+XvY+EgPdVM/OSX2g/6ojjnm2D+wl4/UWnkHJCxn5AemRPmYx9Ch1th3wl45XG/1aNr4ub/dLW1s7LbQUC+bnhosdXLE9oBQeo0YomIE1PbVzT02fNgQaVxAzY3dPCHqg/6ojn0A5iQbjjnmmGOOOeaYY4455tj7Yi6+Qcccc8wxxxxzzDHHHHPMsffYnGTDMcccc8wxxxxzzDHHHHtfzEk2HHPMMcccc8wxxxxzzLH3xZxkwzHHHHPMMcccc8wxxxx7X8xJNhxzzDHHHHPMMcccc8yx98WcZMMxxxxzzDHHHHPMMccce1/MSTYcc8wxxxxzzDHHHHPMsffFnGTDMcccc8wxxxxzzDHHHHtfbOj787T/ndbf328nT578oN+GYx+gDR8+3Nzc3D7ot+HYB2iOH3DM8QOOOX7AMccPvHtzko1/wrHExcdbQ339B/1WHPsALSwszMrKyhwHc56a4wccwxw/cH4bfiA+Pt7qHT9wXpvjB969OcnGuzQqGAQYhSWV5untYwMDA3Z2wOzswIANDJi+H3jH92fP/V6Ps3Nf3/G4d37/f30dfPwZc/3szMA7v/7Pf3/7c33lB+b62dv/neU/13s8fe7f+tnA4L8HH3/u54OPOfd8fK+v555L742fDX59+2/OvTde69zj+er67+zb/x54+2eDj/+f/86cGfw9z3HW9e+Bc1/5nusz+O9zX+3cY/7nud/5uzPv+O+s2cA7vz9jduZvvn/n70/1W/3xp7UWHOdyftq79QMD9vf381l+83/4jf/t6+DjXb7AtUf/Z6/b3/iCv/6qv3sP/ID2+9/s9bN/4wv+nq8YGHz+v7PP/z8/8PZjzr7tB1yPcfmSQT/wzr39nvkBvQi+4G8eN/AOP5D7pOMHzmPj3pNoFJZWmpe3z9/dp//buf6v+AHT1/cmJvif37t+p/1/zocM7tHTb5/5/+MLTr/tG/46Bvh3/YA+43sVE7wdX7j+PfD2Ywb+7u/+4Xn/dkzg+IH3wpxk4580bx8fOZe/DTL+3td3m0y8269/13H8f1//f8dCuvHXAcb/JA9/L8hw/eyvg4zT/0uywb8vPLeZL3zH94M/G3zMBX8VTPyPYxn83d/7jzehhEn/uRzEX30950D+9ncXnD2r//i7v/565n//7wK+nv7/f3bBGZdjcsyxd+EH/mGy8U8mF//o69/d+2ffnW/4d/3A/5ZsvL33z/mKt/3Bue8H9/XfJhr/2A+cfdsPDP4dH2Rwr/9dn/Dv+oHBZOOCv+8fHD/g2Dv9gPffSTb+r/P/X9n370w2/t2Y4H++d+3twWTjbR/wjjP/bT9xdsCG/M1j3is/8LfJxr8VE/yNbzj7d/b/4L9djxvc36f/l5jA8QP/rjkD4o455phjjjnmmGOOOebY+2JOZ+OftK7Ozr9boXBgVB8uGNXAuce8s4068O/CqM44w4COvTs/4MCoPhwwqn/JD/xfMCrHDzj2Dj/wj/apA6P6cMGoBmMF1/fvAYzK8QP/lDnJxrs0FqqXl5elJMZ80G/FsQ/QWAOsBcfOT3P8gGOY4wfOb3vbDyQ4fuB8NscPvHtzko13aRdccIF1d3dbVVWV+fj4fNBvx7EPwDo7Oy06OlprwbHz0xw/4JjjBxxz/IBjjh/458xJNv5Jw7E4zsUxx85vc/yAY4455vgBxxx7d+YMiDvmmGOOOeaYY4455phj74s5yYZjjjnmmGOOOeaYY4459r6Yk2y8SxsxYoR95zvf0VfHzk9z1oBjzhpwzFkDjjlrwDFnDfxzdsGAM0rvmGOOOeaYY4455phjjr0P5nQ2HHPMMcccc8wxxxxzzLH3xZxkwzHHHHPMMcccc8wxxxx7X8xJNhxzzDHHHHPMMcccc8yx98WcZMMxxxxzzDHHHHPMMccce1/MSTYcc8wxxxxzzDHHHHPMsffFzttkAxKub3/72xYeHm7u7u62cOFCKyoq+j//7o9//KPFxcWZm5ubTZkyxfbv3/9Xv7/77rstMTFRzxkcHGyXX3655efnv4+fxLF/1f6ve/m3tmLFChs5cqQeP3r0aFu7du1f/f673/2ufu/p6Wn+/v5aU/v27XufP4Vj/645vsAxxxc45vgBxxw/8D7awHlqP/3pTwd8fX0HXnvttYGjR48OLFu2bCA+Pn6gr6/vH/7Niy++ODB8+PCBJ554YiA3N3fgzjvvHPDz8xtoaGh4+zF//vOfB7Zt2zZQVlY2kJWVNXDZZZcNREdHD5w+ffo/9Mkcezf2bu7lO23Xrl0DQ4YMGfj5z38+cPz48YEHHnhgYNiwYQPHjh17+zHPP//8wMaNGwdKSkoGcnJyBu64444BHx+fgcbGxv/gJ3PsnzXHF5zf5vgCxzDHD5zf5viB99fOy2Tj7NmzA2FhYQO/+MUv3v5Ze3v7wIgRIwZeeOGFf/h3kydPHrj33nvf/v7MmTMDERERAz/5yU/+4d/gtMjpiouL38NP4Ni/a//svbzuuusGLrnkkr/62ZQpUwbuvvvuf/gaHR0duvebNm16D9+5Y++lOb7AMccXOOb4AcccP/D+2nkJoyorK7P6+nq1tAbN19dXbbM9e/b83b85efKkZWVl/dXfXHjhhfr+H/1NT0+PPfnkkxYfH2/R0dHvwydx7F+xf+Ve8vN3Ph5bvHjx/7peHnnkEa2rMWPGvMefwLH3yhxfcH6b4wscwxw/cH6b4wfefzsvkw2cChYaGvpXP+f7wd/9rTU3N9uZM2fe1d889NBD5uXlpf/WrVtnGzdutOHDh7/nn8Oxf83+mXs5aPz83Tx+zZo1uu9gOH/zm9/o3gcFBb0Pn8Kx98IcX3B+m+MLHMMcP3B+m+MH3n87L5KN559//u2Nzn+nTp16X1/vpptussOHD9u2bdssJSXFrrvuOuvv739fX9OxD4fNmzfPjhw5Yrt377YlS5bo3jc2Nn7Qb8uxc+b4Asf+U+b4gg+vOX7Asf+UOX7gPEo2li1bpps9+N9gVtnQ0PBXj+P7sLCwv/sc/M2QIUPe1d/QJktOTrbZs2fbyy+/LOaJlStXvuefy7F/zf6Zezlo/PzdPB7WiaSkJJs6dao9/vjjNnToUH117MNhji9w7J3m+ILz0xw/4Ng7zfED77+dF8mGt7e3bvbgf2lpaVoQmzdvfvsxnZ2doiSbNm3a330OWp4TJkz4q785e/asvv9Hf4OdG8K3EydOvMefyrF/1f6Ve8nP3/l4jHbo/3bvB5/XufcfHnN8gWPvNMcXnJ/m+AHH3mmOH/gP2MB5THMHrdnrr78+kJ2dPXD55Zf/fzR38+fPH3jwwQf/ihoNdoqnnnpKVGd33XWXnqO+vl6/h97sxz/+8cDBgwcHKioqRI0GzV1AQMA/pE9z7IOx/+te3nLLLQNf+9rX3n4893Lo0KEDv/zlLwfy8vIGvvOd7/wVzV13d/fA17/+9YE9e/YMlJeXaw184hOf0GtAeefYh9ccX3B+m+MLHMMcP3B+m+MH3l+z85nq7lvf+tZAaGiobv6CBQsGCgoK/uoxsbGxWkDvNBxNTEyM+JihStu7d+/bv6upqRm4+OKLB0JCQrTooqKiBm688caB/Pz8/9jncuzd2/92L+fMmTNw2223/dXjly9fPpCSkqLHp6enD7zxxhtv/44D6corrxRVHr8PDw8XT/v+/fv/o5/JsX/eHF/gmOMLHHP8gGOOH3j/7AL+95/ooDjmmGOOOeaYY4455phj55edFzMbjjnmmGOOOeaYY4455th/3pxkwzHHHHPMMcccc8wxxxx7X8xJNhxzzDHHHHPMMcccc8yx98WcZMMxxxxzzDHHHHPMMccce1/MSTYcc8wxxxxzzDHHHHPMsffFnGTDMcccc8wxxxxzzDHHHHtfzEk2HHPMMcccc8wxxxxzzLH3xZxkwzHHHHPMMcccc8wxxxx7X8xJNhxzzDHHHHPMMcccc8yx98WcZMMxxxxzzDHHHHPMMccce1/MSTYcc8wxxxxzzDHHHHPMMXs/7P8BLfxD18fR0REAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 3, figsize=(10, 5))\n",
+    "\n",
+    "fig.subplots_adjust(hspace=0.0, wspace=0.0)\n",
+    "\n",
+    "for i, m in enumerate(maps[:3]):\n",
+    "    plt.sca(ax[0, i])\n",
+    "    hp.mollview(m, title=None, hold=True, cmap=\"Purples\", min=-0.4, max=0.4)\n",
+    "\n",
+    "for i, m in enumerate(maps[3:]):\n",
+    "    plt.sca(ax[1, i])\n",
+    "    hp.mollview(m, title=None, hold=True, cmap=\"Blues\", min=-0.03, max=0.03)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then compute the realised lognormal angular power spectra from the maps. A helper function converts the output from HEALPix order to GLASS order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_sim = hp.anafast(maps, lmax=lmax, pol=False, use_pixel_weights=True)\n",
+    "\n",
+    "# reorder spectra from HEALPix to GLASS order\n",
+    "cls_sim = glass.healpix_to_glass_spectra(cls_sim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thanks to the power of GLASS, the realised lognormal fields correspond very well to the input spectra!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_spectra(cls, cls_sim, labels=[\"input\", \"simulated\"])\n",
+    "\n",
+    "fig.supxlabel(\"$\\\\ell$\")\n",
+    "fig.supylabel(\"$C_\\\\ell$\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/examples/2-advanced/matplotlibrc b/glass/examples/2-advanced/matplotlibrc
new file mode 120000
index 0000000..0f9ea77
--- /dev/null
+++ b/glass/examples/2-advanced/matplotlibrc
@@ -0,0 +1 @@
+../../.matplotlibrc
\ No newline at end of file
diff --git a/glass/examples/2-advanced/stage_4_galaxies.ipynb b/glass/examples/2-advanced/stage_4_galaxies.ipynb
new file mode 100644
index 0000000..79c21ba
--- /dev/null
+++ b/glass/examples/2-advanced/stage_4_galaxies.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stage IV Galaxy Survey\n",
+    "\n",
+    "This example simulates a galaxy catalogue from a Stage IV Space Satellite Galaxy Survey such as *Euclid* and *Roman* combining the [galaxies](../1-basic/density.ipynb) and [lensing](../1-basic/lensing.ipynb) examples with galaxy ellipticities and galaxy shears, as well as using some auxiliary functions.\n",
+    "\n",
+    "The focus in this example is mock catalogue generation using auxiliary functions built for simulating Stage IV galaxy surveys."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The setup is essentially the same as in the [cosmic shear](cosmic_shear.ipynb) example.\n",
+    "\n",
+    "In addition, we also show how to use auxiliary functions to generate tomographic redshift distributions and visibility masks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# use the CAMB cosmology that generated the matter power spectra\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = lmax = 256\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up the matter sector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 3.0, dx=200.0)\n",
+    "\n",
+    "# linear window functions for shells\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute the angular matter power spectra of the shells with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - HEALPix pixel window function (`nside=nside`)\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, nside=nside, lmax=lmax, ncorr=3)\n",
+    "\n",
+    "# set up lognormal fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# generator for lognormal matter fields\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up the lensing sector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will compute the convergence field iteratively\n",
+    "convergence = glass.MultiPlaneConvergence(cosmo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up the galaxies sector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# galaxy density (using 1/100 of the expected galaxy number density for Stage-IV)\n",
+    "n_arcmin2 = 0.3\n",
+    "\n",
+    "# true redshift distribution following a Smail distribution\n",
+    "z = np.arange(0.0, 3.0, 0.01)\n",
+    "dndz = glass.smail_nz(z, z_mode=0.9, alpha=2.0, beta=1.5)\n",
+    "dndz *= n_arcmin2\n",
+    "\n",
+    "# distribute dN/dz over the radial window functions\n",
+    "ngal = glass.partition(z, dndz, shells)\n",
+    "\n",
+    "# compute tomographic redshift bin edges with equal density\n",
+    "nbins = 10\n",
+    "zbins = glass.equal_dens_zbins(z, dndz, nbins=nbins)\n",
+    "\n",
+    "# photometric redshift error\n",
+    "sigma_z0 = 0.03\n",
+    "\n",
+    "# constant bias parameter for all shells\n",
+    "bias = 1.2\n",
+    "\n",
+    "# ellipticity standard deviation as expected for a Stage-IV survey\n",
+    "sigma_e = 0.27"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a visibility map typical of a space telescope survey, seeing both hemispheres, and low visibility in the galactic and ecliptic bands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vis = glass.vmap_galactic_ecliptic(nside)\n",
+    "\n",
+    "# checking the mask:\n",
+    "hp.mollview(vis, title=\"Stage IV Space Survey-like Mask\", unit=\"Visibility\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Simulate the galaxies with shears.\n",
+    "In each iteration, get the quantities of interest to build our mock catalogue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of galaxies sampled: 22,275,837\n"
+     ]
+    }
+   ],
+   "source": [
+    "# we will store the catalogue as a structured numpy array, initially empty\n",
+    "catalogue = np.empty(\n",
+    "    0,\n",
+    "    dtype=[\n",
+    "        (\"RA\", float),\n",
+    "        (\"DEC\", float),\n",
+    "        (\"Z_TRUE\", float),\n",
+    "        (\"PHZ\", float),\n",
+    "        (\"ZBIN\", int),\n",
+    "        (\"G1\", float),\n",
+    "        (\"G2\", float),\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "# simulate the matter fields in the main loop, and build up the catalogue\n",
+    "for i, delta_i in enumerate(matter):\n",
+    "    # compute the lensing maps for this shell\n",
+    "    convergence.add_window(delta_i, shells[i])\n",
+    "    kappa_i = convergence.kappa\n",
+    "    gamm1_i, gamm2_i = glass.shear_from_convergence(kappa_i)\n",
+    "\n",
+    "    # generate galaxy positions from the matter density contrast\n",
+    "    for gal_lon, gal_lat, gal_count in glass.positions_from_delta(\n",
+    "        ngal[i],\n",
+    "        delta_i,\n",
+    "        bias,\n",
+    "        vis,\n",
+    "        rng=rng,\n",
+    "    ):\n",
+    "        # generate random redshifts over the given shell\n",
+    "        gal_z = glass.redshifts(gal_count, shells[i], rng=rng)\n",
+    "\n",
+    "        # generator photometric redshifts using a Gaussian model\n",
+    "        gal_phz = glass.gaussian_phz(gal_z, sigma_z0, rng=rng)\n",
+    "\n",
+    "        # attach tomographic bin IDs to galaxies, based on photometric redshifts\n",
+    "        gal_zbin = np.digitize(gal_phz, np.unique(zbins)) - 1\n",
+    "\n",
+    "        # generate galaxy ellipticities from the chosen distribution\n",
+    "        gal_eps = glass.ellipticity_intnorm(gal_count, sigma_e, rng=rng, xp=np)\n",
+    "\n",
+    "        # apply the shear fields to the ellipticities\n",
+    "        gal_she = glass.galaxy_shear(\n",
+    "            gal_lon,\n",
+    "            gal_lat,\n",
+    "            gal_eps,\n",
+    "            kappa_i,\n",
+    "            gamm1_i,\n",
+    "            gamm2_i,\n",
+    "        )\n",
+    "\n",
+    "        # make a mini-catalogue for the new rows\n",
+    "        rows = np.empty(gal_count, dtype=catalogue.dtype)\n",
+    "        rows[\"RA\"] = gal_lon\n",
+    "        rows[\"DEC\"] = gal_lat\n",
+    "        rows[\"Z_TRUE\"] = gal_z\n",
+    "        rows[\"PHZ\"] = gal_phz\n",
+    "        rows[\"ZBIN\"] = gal_zbin\n",
+    "        rows[\"G1\"] = gal_she.real\n",
+    "        rows[\"G2\"] = gal_she.imag\n",
+    "\n",
+    "        # add the new rows to the catalogue\n",
+    "        catalogue = np.append(catalogue, rows)\n",
+    "\n",
+    "print(f\"Total number of galaxies sampled: {len(catalogue):,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Catalogue checks\n",
+    "\n",
+    "Here we can perform some simple checks at the catalogue level to see how our simulation performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# split dndz using the same Gaussian error model assumed in the sampling\n",
+    "tomo_nz = glass.tomo_nz_gausserr(z, dndz, sigma_z0, zbins)\n",
+    "\n",
+    "# redshift distribution of tomographic bins & input distributions\n",
+    "plt.figure()\n",
+    "plt.title(\"redshifts in catalogue\")\n",
+    "plt.ylabel(\"dN/dz - normalised\")\n",
+    "plt.xlabel(\"z\")\n",
+    "for i in range(nbins):\n",
+    "    in_bin = catalogue[\"ZBIN\"] == i\n",
+    "    plt.hist(\n",
+    "        catalogue[\"Z_TRUE\"][in_bin],\n",
+    "        histtype=\"stepfilled\",\n",
+    "        edgecolor=\"none\",\n",
+    "        alpha=0.5,\n",
+    "        bins=50,\n",
+    "        density=1,\n",
+    "        label=f\"cat. bin {i}\",\n",
+    "    )\n",
+    "for i in range(nbins):\n",
+    "    plt.plot(z, (tomo_nz[i] / n_arcmin2) * nbins, alpha=0.5, label=f\"inp. bin {i}\")\n",
+    "plt.plot(z, dndz / n_arcmin2 * nbins, ls=\"--\", c=\"k\")\n",
+    "plt.legend(ncol=2)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/git_archival.txt b/glass/git_archival.txt
new file mode 100644
index 0000000..8fb235d
--- /dev/null
+++ b/glass/git_archival.txt
@@ -0,0 +1,4 @@
+node: $Format:%H$
+node-date: $Format:%cI$
+describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$
+ref-names: $Format:%D$
diff --git a/glass/gitattributes.txt b/glass/gitattributes.txt
new file mode 100644
index 0000000..a94cb2f
--- /dev/null
+++ b/glass/gitattributes.txt
@@ -0,0 +1 @@
+.git_archival.txt export-subst
diff --git a/glass/glass/__init__.py b/glass/glass/__init__.py
new file mode 100644
index 0000000..eee2ac2
--- /dev/null
+++ b/glass/glass/__init__.py
@@ -0,0 +1,157 @@
+"""GLASS package."""
+
+import contextlib
+
+__all__ = [
+    "DensityWeight",
+    "DistanceWeight",
+    "MultiPlaneConvergence",
+    "RadialWindow",
+    "VolumeWeight",
+    "algorithm",
+    "check_posdef_spectra",
+    "cls2cov",
+    "combine",
+    "compute_gaussian_spectra",
+    "cov_from_spectra",
+    "cubic_windows",
+    "deflect",
+    "discretized_cls",
+    "displace",
+    "displacement",
+    "distance_grid",
+    "effective_bias",
+    "effective_cls",
+    "ellipticity_gaussian",
+    "ellipticity_intnorm",
+    "ellipticity_ryden04",
+    "enumerate_spectra",
+    "equal_dens_zbins",
+    "fixed_zbins",
+    "from_convergence",
+    "galaxy_shear",
+    "gaussian_fields",
+    "gaussian_nz",
+    "gaussian_phz",
+    "generate",
+    "generate_gaussian",
+    "generate_lognormal",
+    "getcl",
+    "glass_to_healpix_spectra",
+    "grf",
+    "healpix_to_glass_spectra",
+    "iternorm",
+    "linear_bias",
+    "linear_windows",
+    "load_cls",
+    "loglinear_bias",
+    "lognormal_fields",
+    "lognormal_gls",
+    "lognormal_shift_hilbert2011",
+    "multi_plane_matrix",
+    "multi_plane_weights",
+    "nfields_from_nspectra",
+    "partition",
+    "position_weights",
+    "positions_from_delta",
+    "redshift_grid",
+    "redshifts",
+    "redshifts_from_nz",
+    "regularized_spectra",
+    "restrict",
+    "save_cls",
+    "shear_from_convergence",
+    "smail_nz",
+    "solve_gaussian_spectra",
+    "spectra_indices",
+    "tomo_nz_gausserr",
+    "tophat_windows",
+    "triaxial_axis_ratio",
+    "uniform_positions",
+    "vmap_galactic_ecliptic",
+    "write_catalog",
+]
+
+
+with contextlib.suppress(ModuleNotFoundError):
+    from ._version import __version__
+
+# modules
+from glass import algorithm, grf
+from glass.fields import (
+    check_posdef_spectra,
+    cls2cov,
+    compute_gaussian_spectra,
+    cov_from_spectra,
+    discretized_cls,
+    effective_cls,
+    enumerate_spectra,
+    gaussian_fields,
+    generate,
+    generate_gaussian,
+    generate_lognormal,
+    getcl,
+    glass_to_healpix_spectra,
+    healpix_to_glass_spectra,
+    iternorm,
+    lognormal_fields,
+    lognormal_gls,
+    lognormal_shift_hilbert2011,
+    nfields_from_nspectra,
+    regularized_spectra,
+    solve_gaussian_spectra,
+    spectra_indices,
+)
+from glass.galaxies import (
+    galaxy_shear,
+    gaussian_phz,
+    redshifts,
+    redshifts_from_nz,
+)
+from glass.lensing import (
+    MultiPlaneConvergence,
+    deflect,
+    from_convergence,
+    multi_plane_matrix,
+    multi_plane_weights,
+    shear_from_convergence,
+)
+from glass.observations import (
+    equal_dens_zbins,
+    fixed_zbins,
+    gaussian_nz,
+    smail_nz,
+    tomo_nz_gausserr,
+    vmap_galactic_ecliptic,
+)
+from glass.points import (
+    displace,
+    displacement,
+    effective_bias,
+    linear_bias,
+    loglinear_bias,
+    position_weights,
+    positions_from_delta,
+    uniform_positions,
+)
+from glass.shapes import (
+    ellipticity_gaussian,
+    ellipticity_intnorm,
+    ellipticity_ryden04,
+    triaxial_axis_ratio,
+)
+from glass.shells import (
+    DensityWeight,
+    DistanceWeight,
+    RadialWindow,
+    VolumeWeight,
+    combine,
+    cubic_windows,
+    distance_grid,
+    linear_windows,
+    partition,
+    redshift_grid,
+    restrict,
+    tophat_windows,
+)
+from glass.user import load_cls, save_cls, write_catalog
diff --git a/glass/glass/_array_api_utils.py b/glass/glass/_array_api_utils.py
new file mode 100644
index 0000000..2bfb3f1
--- /dev/null
+++ b/glass/glass/_array_api_utils.py
@@ -0,0 +1,753 @@
+"""
+Array API Utilities for glass.
+============================
+
+This module provides utility functions and classes for working with multiple array
+backends in the glass project, including NumPy, JAX, and array-api-strict. It includes
+functions for importing backends, determining array namespaces, dispatching random
+number generators, and providing missing functionality for array-api-strict through the
+XPAdditions class.
+
+Classes and functions in this module help ensure consistent behavior and compatibility
+across different array libraries, and provide wrappers for common operations such as
+integration, interpolation, and linear algebra.
+
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+    from types import ModuleType
+
+    import numpy as np
+    from numpy.typing import DTypeLike
+
+    from array_api_strict._array_object import Array as AArray
+
+    from glass._types import AnyArray, FloatArray, Size, UnifiedGenerator
+
+
+class CompatibleBackendNotFoundError(Exception):
+    """
+    Exception raised when an array library backend that
+    implements a requested function, is not found.
+    """
+
+    def __init__(self, missing_backend: str, users_backend: str) -> None:
+        self.message = (
+            f"{missing_backend} is required here as some functions required by GLASS "
+            f"are not supported by {users_backend}"
+        )
+        super().__init__(self.message)
+
+
+def import_numpy(backend: str) -> ModuleType:
+    """
+    Import the NumPy module, raising a helpful error if NumPy is not installed.
+
+    Parameters
+    ----------
+    backend
+        The name of the backend requested by the user.
+
+    Returns
+    -------
+        The NumPy module.
+
+    Raises
+    ------
+    ModuleNotFoundError
+        If NumPy is not found in the user's environment.
+
+    Notes
+    -----
+    This is useful for explaining to the user why NumPy is required when their chosen
+    backend does not implement a needed function.
+    """
+    try:
+        import numpy  # noqa: ICN001, PLC0415
+
+    except ModuleNotFoundError as err:
+        raise CompatibleBackendNotFoundError("numpy", backend) from err
+    else:
+        return numpy
+
+
+def rng_dispatcher(*, xp: ModuleType) -> UnifiedGenerator:
+    """
+    Dispatch a random number generator based on the provided array's backend.
+
+    Parameters
+    ----------
+    xp
+        The array library backend to use for array operations.
+
+    Returns
+    -------
+        The appropriate random number generator for the array's backend.
+
+    Raises
+    ------
+    NotImplementedError
+        If the array backend is not supported.
+    """
+    if xp.__name__ == "jax.numpy":
+        import glass.jax  # noqa: PLC0415
+
+        return glass.jax.Generator(seed=42)
+
+    if xp.__name__ == "numpy":
+        return xp.random.default_rng()  # type: ignore[no-any-return]
+
+    if xp.__name__ == "array_api_strict":
+        return Generator(seed=42)
+
+    msg = "the array backend in not supported"
+    raise NotImplementedError(msg)
+
+
+class Generator:
+    """
+    NumPy random number generator returning array_api_strict Array.
+
+    This class wraps NumPy's random number generator and returns arrays compatible
+    with array_api_strict.
+    """
+
+    __slots__ = ("axp", "nxp", "rng")
+
+    def __init__(
+        self,
+        seed: int | bool | AArray | None = None,  # noqa: FBT001
+    ) -> None:
+        """
+        Initialize the Generator.
+
+        Parameters
+        ----------
+        seed
+            Seed for the random number generator.
+        """
+        import numpy  # noqa: ICN001, PLC0415
+
+        import array_api_strict  # noqa: PLC0415
+
+        self.axp = array_api_strict
+        self.nxp = numpy
+        self.rng = self.nxp.random.default_rng(seed=seed)
+
+    def random(
+        self,
+        size: Size = None,
+        dtype: DTypeLike | None = None,
+        out: AArray | None = None,
+    ) -> AArray:
+        """
+        Return random floats in the half-open interval [0.0, 1.0).
+
+        Parameters
+        ----------
+        size
+            Output shape.
+        dtype
+            Desired data type.
+        out
+            Optional output array.
+
+        Returns
+        -------
+            Array of random floats.
+        """
+        dtype = dtype if dtype is not None else self.nxp.float64
+        return self.axp.asarray(self.rng.random(size, dtype, out))  # type: ignore[arg-type]
+
+    def normal(
+        self,
+        loc: float | FloatArray = 0.0,
+        scale: float | FloatArray = 1.0,
+        size: Size = None,
+    ) -> AArray:
+        """
+        Draw samples from a Normal distribution (mean=loc, stdev=scale).
+
+        Parameters
+        ----------
+        loc
+            Mean of the distribution.
+        scale
+            Standard deviation of the distribution.
+        size
+            Output shape.
+
+        Returns
+        -------
+            Array of samples from the normal distribution.
+        """
+        return self.axp.asarray(self.rng.normal(loc, scale, size))
+
+    def poisson(self, lam: float | AArray, size: Size = None) -> AArray:
+        """
+        Draw samples from a Poisson distribution.
+
+        Parameters
+        ----------
+        lam
+            Expected number of events.
+        size
+            Output shape.
+
+        Returns
+        -------
+            Array of samples from the Poisson distribution.
+        """
+        return self.axp.asarray(self.rng.poisson(lam, size))
+
+    def standard_normal(
+        self,
+        size: Size = None,
+        dtype: DTypeLike | None = None,
+        out: AArray | None = None,
+    ) -> AArray:
+        """
+        Draw samples from a standard Normal distribution (mean=0, stdev=1).
+
+        Parameters
+        ----------
+        size
+            Output shape.
+        dtype
+            Desired data type.
+        out
+            Optional output array.
+
+        Returns
+        -------
+            Array of samples from the standard normal distribution.
+        """
+        dtype = dtype if dtype is not None else self.nxp.float64
+        return self.axp.asarray(self.rng.standard_normal(size, dtype, out))  # type: ignore[arg-type]
+
+    def uniform(
+        self,
+        low: float | AArray = 0.0,
+        high: float | AArray = 1.0,
+        size: Size = None,
+    ) -> AArray:
+        """
+        Draw samples from a Uniform distribution.
+
+        Parameters
+        ----------
+        low
+            Lower bound of the distribution.
+        high
+            Upper bound of the distribution.
+        size : Size, optional
+            Output shape.
+
+        Returns
+        -------
+            Array of samples from the uniform distribution.
+        """
+        return self.axp.asarray(self.rng.uniform(low, high, size))
+
+
+class XPAdditions:
+    """
+    Additional functions missing from both array-api-strict and array-api-extra.
+
+    This class provides wrappers for common array operations such as integration,
+    interpolation, and linear algebra, ensuring compatibility across NumPy, JAX,
+    and array-api-strict backends.
+
+    This is intended as a temporary solution. See https://github.com/glass-dev/glass/issues/645
+    for details.
+    """
+
+    xp: ModuleType
+    backend: str
+
+    def __init__(self, xp: ModuleType) -> None:
+        """
+        Initialize XPAdditions with the given array namespace.
+
+        Parameters
+        ----------
+        xp
+            The array library backend to use for array operations.
+        """
+        self.xp = xp
+
+    def trapezoid(
+        self,
+        y: AnyArray,
+        x: AnyArray = None,
+        dx: float = 1.0,
+        axis: int = -1,
+    ) -> AnyArray:
+        """
+        Integrate along the given axis using the composite trapezoidal rule.
+
+        Parameters
+        ----------
+        y
+            Input array to integrate.
+        x
+            Sample points corresponding to y.
+        dx
+            Spacing between sample points.
+        axis
+            Axis along which to integrate.
+
+        Returns
+        -------
+            Integrated result.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/646
+        """
+        if self.xp.__name__ == "jax.numpy":
+            import glass.jax  # noqa: PLC0415
+
+            return glass.jax.trapezoid(y, x=x, dx=dx, axis=axis)
+
+        if self.xp.__name__ == "numpy":
+            return self.xp.trapezoid(y, x=x, dx=dx, axis=axis)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            y_np = np.asarray(y, copy=True)
+            x_np = np.asarray(x, copy=True)
+            result_np = np.trapezoid(y_np, x_np, dx=dx, axis=axis)
+            return self.xp.asarray(result_np, copy=True)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def union1d(self, ar1: AnyArray, ar2: AnyArray) -> AnyArray:
+        """
+        Compute the set union of two 1D arrays.
+
+        Parameters
+        ----------
+        ar1
+            First input array.
+        ar2
+            Second input array.
+
+        Returns
+        -------
+            The union of the two arrays.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/647
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.union1d(ar1, ar2)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            ar1_np = np.asarray(ar1, copy=True)
+            ar2_np = np.asarray(ar2, copy=True)
+            result_np = np.union1d(ar1_np, ar2_np)
+            return self.xp.asarray(result_np, copy=True)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def interp(  # noqa: PLR0913
+        self,
+        x: AnyArray,
+        x_points: AnyArray,
+        y_points: AnyArray,
+        left: float | None = None,
+        right: float | None = None,
+        period: float | None = None,
+    ) -> AnyArray:
+        """
+        One-dimensional linear interpolation for monotonically increasing sample points.
+
+        Parameters
+        ----------
+        x
+            The x-coordinates at which to evaluate the interpolated values.
+        x_points
+            The x-coordinates of the data points.
+        y_points
+            The y-coordinates of the data points.
+        left
+            Value to return for x < x_points[0].
+        right
+            Value to return for x > x_points[-1].
+        period
+            Period for periodic interpolation.
+
+        Returns
+        -------
+            Interpolated values.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/650
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.interp(
+                x,
+                x_points,
+                y_points,
+                left=left,
+                right=right,
+                period=period,
+            )
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            x_np = np.asarray(x, copy=True)
+            x_points_np = np.asarray(x_points, copy=True)
+            y_points_np = np.asarray(y_points, copy=True)
+            result_np = np.interp(
+                x_np,
+                x_points_np,
+                y_points_np,
+                left=left,
+                right=right,
+                period=period,
+            )
+            return self.xp.asarray(result_np, copy=True)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def gradient(self, f: AnyArray) -> AnyArray:
+        """
+        Return the gradient of an N-dimensional array.
+
+        Parameters
+        ----------
+        f
+            Input array.
+
+        Returns
+        -------
+            Gradient of the input array.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/648
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.gradient(f)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            f_np = np.asarray(f, copy=True)
+            result_np = np.gradient(f_np)
+            return self.xp.asarray(result_np, copy=True)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def linalg_lstsq(
+        self,
+        a: AnyArray,
+        b: AnyArray,
+        rcond: float | None = None,
+    ) -> tuple[AnyArray, AnyArray, int, AnyArray]:
+        """
+        Solve a linear least squares problem.
+
+        Parameters
+        ----------
+        a
+            Coefficient matrix.
+        b
+            Ordinate or "dependent variable" values.
+        rcond
+            Cut-off ratio for small singular values.
+
+        Returns
+        -------
+        x
+            Least-squares solution. If b is two-dimensional, the solutions are in the K
+            columns of x.
+
+        residuals
+            Sums of squared residuals: Squared Euclidean 2-norm for each column in b - a
+            @ x. If the rank of a is < N or M <= N, this is an empty array. If b is
+            1-dimensional, this is a (1,) shape array. Otherwise the shape is (K,).
+
+        rank
+            Rank of matrix a.
+
+        s
+            Singular values of a.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/649
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.linalg.lstsq(a, b, rcond=rcond)  # type: ignore[no-any-return]
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            a_np = np.asarray(a, copy=True)
+            b_np = np.asarray(b, copy=True)
+            result_np = np.linalg.lstsq(a_np, b_np, rcond=rcond)
+            return tuple(self.xp.asarray(res, copy=True) for res in result_np)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def einsum(self, subscripts: str, *operands: AnyArray) -> AnyArray:
+        """
+        Evaluate the Einstein summation convention on the operands.
+
+        Parameters
+        ----------
+        subscripts
+            Specifies the subscripts for summation.
+        *operands
+            Arrays to be summed.
+
+        Returns
+        -------
+            Result of the Einstein summation.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/657
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.einsum(subscripts, *operands)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            # Using design principle of scipy (i.e. copy, use np, copy back)
+            operands_np = (np.asarray(op, copy=True) for op in operands)
+            result_np = np.einsum(subscripts, *operands_np)
+            return self.xp.asarray(result_np, copy=True)
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def apply_along_axis(
+        self,
+        func1d: Callable[..., Any],
+        axis: int,
+        arr: AnyArray,
+        *args: object,
+        **kwargs: object,
+    ) -> AnyArray:
+        """
+        Apply a function to 1-D slices along the given axis.
+
+        Parameters
+        ----------
+        func1d
+            Function to apply to 1-D slices.
+        axis
+            Axis along which to apply the function.
+        arr
+            Input array.
+        *args
+            Additional positional arguments to pass to func1d.
+        **kwargs
+            Additional keyword arguments to pass to func1d.
+
+        Returns
+        -------
+            Result of applying the function along the axis.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/651
+
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.apply_along_axis(func1d, axis, arr, *args, **kwargs)
+
+        if self.xp.__name__ == "array_api_strict":
+            # Import here to prevent users relying on numpy unless in this instance
+            np = import_numpy(self.xp.__name__)
+
+            return self.xp.asarray(
+                np.apply_along_axis(func1d, axis, arr, *args, **kwargs),
+                copy=True,
+            )
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def vectorize(
+        self,
+        pyfunc: Callable[..., Any],
+        otypes: tuple[type[float]],
+    ) -> Callable[..., Any]:
+        """
+        Returns an object that acts like pyfunc, but takes arrays as input.
+
+        Parameters
+        ----------
+        pyfunc
+            Python function to vectorize.
+        otypes
+            Output types.
+
+        Returns
+        -------
+            Vectorized function.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        Notes
+        -----
+        See https://github.com/glass-dev/glass/issues/671
+        """
+        if self.xp.__name__ == "numpy":
+            return self.xp.vectorize(pyfunc, otypes=otypes)  # type: ignore[no-any-return]
+
+        if self.xp.__name__ in {"array_api_strict", "jax.numpy"}:
+            # Import here to prevent users relying on numpy unless in this instance
+            np = import_numpy(self.xp.__name__)
+
+            return np.vectorize(pyfunc, otypes=otypes)  # type: ignore[no-any-return]
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def radians(self, deg_arr: AnyArray) -> AnyArray:
+        """
+        Convert angles from degrees to radians.
+
+        Parameters
+        ----------
+        deg_arr
+            Array of angles in degrees.
+
+        Returns
+        -------
+            Array of angles in radians.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.radians(deg_arr)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            return self.xp.asarray(np.radians(deg_arr))
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def degrees(self, deg_arr: AnyArray) -> AnyArray:
+        """
+        Convert angles from radians to degrees.
+
+        Parameters
+        ----------
+        deg_arr
+            Array of angles in radians.
+
+        Returns
+        -------
+            Array of angles in degrees.
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+        """
+        if self.xp.__name__ in {"numpy", "jax.numpy"}:
+            return self.xp.degrees(deg_arr)
+
+        if self.xp.__name__ == "array_api_strict":
+            np = import_numpy(self.xp.__name__)
+
+            return self.xp.asarray(np.degrees(deg_arr))
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
+
+    def ndindex(self, shape: tuple[int, ...]) -> np.ndindex:
+        """
+        Wrapper for numpy.ndindex.
+
+        See relevant docs for details:
+        - NumPy, https://numpy.org/doc/2.2/reference/generated/numpy.ndindex.html
+
+        Raises
+        ------
+        NotImplementedError
+            If the array backend is not supported.
+
+        """
+        if self.xp.__name__ == "numpy":
+            return self.xp.ndindex(shape)  # type: ignore[no-any-return]
+
+        if self.xp.__name__ in {"array_api_strict", "jax.numpy"}:
+            np = import_numpy(self.xp.__name__)
+
+            return np.ndindex(shape)  # type: ignore[no-any-return]
+
+        msg = "the array backend in not supported"
+        raise NotImplementedError(msg)
diff --git a/glass/glass/_types.py b/glass/glass/_types.py
new file mode 100644
index 0000000..a071f1b
--- /dev/null
+++ b/glass/glass/_types.py
@@ -0,0 +1,51 @@
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Sequence
+    from typing import ParamSpec, TypeAlias, TypeVar
+
+    import numpy as np
+    from jaxtyping import Array as JAXArray
+    from numpy.typing import NDArray
+
+    from array_api_strict._array_object import Array as AArray
+
+    import glass._array_api_utils as _utils
+    import glass.grf
+    import glass.jax
+
+    P = ParamSpec("P")
+    R = TypeVar("R")
+    T = TypeVar("T")
+
+    AnyArray: TypeAlias = NDArray[Any] | JAXArray | AArray
+    ComplexArray: TypeAlias = NDArray[np.complex128] | JAXArray | AArray
+    DoubleArray: TypeAlias = NDArray[np.double] | JAXArray | AArray
+    FloatArray: TypeAlias = NDArray[np.float64] | JAXArray | AArray
+    IntArray: TypeAlias = NDArray[np.int_] | JAXArray | AArray
+
+    ArrayLike1D: TypeAlias = Sequence[float] | FloatArray
+    Cls: TypeAlias = Sequence[AnyArray]
+    Fields: TypeAlias = Sequence[glass.grf.Transformation]
+    Size: TypeAlias = int | tuple[int, ...] | None
+    WeightFunc: TypeAlias = Callable[[ArrayLike1D], ArrayLike1D]
+
+    UnifiedGenerator: TypeAlias = (
+        np.random.Generator | glass.jax.Generator | _utils.Generator
+    )
+else:
+    # Runtime fallbacks (for Sphinx / autodoc)
+    # https://github.com/sphinx-doc/sphinx/issues/11991
+    AnyArray = Any
+    ComplexArray = Any
+    DoubleArray = Any
+    FloatArray = Any
+    IntArray = Any
+
+    ArrayLike1D = Any
+    Cls = Any
+    Fields = Any
+    Size = Any
+    WeightFunc = Any
+
+    UnifiedGenerator = Any
diff --git a/glass/glass/algorithm.py b/glass/glass/algorithm.py
new file mode 100644
index 0000000..34da86d
--- /dev/null
+++ b/glass/glass/algorithm.py
@@ -0,0 +1,270 @@
+"""Module for algorithms."""
+
+from __future__ import annotations
+
+import warnings
+from typing import TYPE_CHECKING
+
+import array_api_compat
+
+if TYPE_CHECKING:
+    from glass._types import FloatArray
+
+
+def nnls(
+    a: FloatArray,
+    b: FloatArray,
+    *,
+    tol: float = 0.0,
+    maxiter: int | None = None,
+) -> FloatArray:
+    """
+    Compute a non-negative least squares solution.
+
+    Implementation of the algorithm due to [Lawson95]_ as described by
+    [Bro97]_.
+
+    Parameters
+    ----------
+    a
+        The matrix.
+    b
+        The vector.
+    tol
+        The tolerance for convergence.
+    maxiter
+        The maximum number of iterations.
+
+    Returns
+    -------
+        The non-negative least squares solution.
+
+    Raises
+    ------
+    ValueError
+        If ``a`` is not a matrix.
+    ValueError
+        If ``b`` is not a vector.
+    ValueError
+        If the shapes of ``a`` and ``b`` do not match.
+
+    """
+    xp = array_api_compat.array_namespace(a, b, use_compat=False)
+
+    a = xp.asarray(a)
+    b = xp.asarray(b)
+
+    if a.ndim != 2:
+        msg = "input `a` is not a matrix"
+        raise ValueError(msg)
+    if b.ndim != 1:
+        msg = "input `b` is not a vector"
+        raise ValueError(msg)
+    if a.shape[0] != b.shape[0]:
+        msg = "the shapes of `a` and `b` do not match"
+        raise ValueError(msg)
+
+    _, n = a.shape
+
+    if maxiter is None:
+        maxiter = 3 * n
+
+    index = xp.arange(n)
+    q = xp.full(n, fill_value=False)
+    x = xp.zeros(n)
+    for _ in range(maxiter):
+        if xp.all(q):
+            break
+        # The sum product over the last axis of arg1 and the second-to-last axis of arg2
+        w = xp.sum((b - a @ x)[..., None] * a, axis=-2)
+
+        m = int(index[~q][xp.argmax(w[~q])])
+        if w[m] <= tol:
+            break
+        q[m] = True
+        while True:
+            # Use `xp.task`` here instead of `a[:,q]` to mask the inner arrays, because
+            # array-api requires a masking index to be the sole index, which would
+            # return a 1-D array. However, we want to maintain the shape of `a`,
+            # i.e. `[[a11],[a12],...]` rather than `[a11,a12,...]`
+            aq = xp.take(a, xp.nonzero(q)[0], axis=1)
+            xq = x[q]
+            sq = xp.linalg.solve(aq.T @ aq, b @ aq)
+            t = sq <= 0
+            if not xp.any(t):
+                break
+            alpha = -xp.min(xq[t] / (xq[t] - sq[t]))
+            x[q] += alpha * (sq - xq)
+            q[x <= 0] = False
+        x[q] = sq
+        x[~q] = 0
+    return x
+
+
+def cov_clip(
+    cov: FloatArray,
+    rtol: float | None = None,
+) -> FloatArray:
+    """
+    Covariance matrix from clipping non-positive eigenvalues.
+
+    The relative tolerance *rtol* is defined as for
+    :func:`~array_api.linalg.matrix_rank`.
+
+    Parameter
+    ---------
+    cov
+        A symmetric matrix (or a stack of matrices).
+    rtol
+        An optional relative tolerance for eigenvalues to be considered
+        positive.
+
+    Returns
+    -------
+        Covariance matrix with negative eigenvalues clipped.
+
+    """
+    xp = cov.__array_namespace__()
+
+    # Hermitian eigendecomposition
+    w, v = xp.linalg.eigh(cov)
+
+    # get tolerance if not given
+    if rtol is None:
+        rtol = max(v.shape[-2], v.shape[-1]) * xp.finfo(w.dtype).eps
+
+    # clip negative diagonal values
+    w = xp.clip(w, rtol * xp.max(w, axis=-1, keepdims=True), None)
+
+    # put matrix back together
+    # enforce symmetry
+    v = xp.sqrt(w[..., None, :]) * v
+    return xp.matmul(v, xp.matrix_transpose(v))
+
+
+def nearcorr(
+    a: FloatArray,
+    *,
+    tol: float | None = None,
+    niter: int = 100,
+) -> FloatArray:
+    """
+    Compute the nearest correlation matrix.
+
+    Returns the nearest correlation matrix using the alternating
+    projections algorithm of [Higham02]_.
+
+    Parameters
+    ----------
+    a
+        Square matrix (or a stack of square matrices).
+    tol
+        Tolerance for convergence. Default is dimension times machine
+        epsilon.
+    niter
+        Maximum number of iterations.
+
+    Returns
+    -------
+        Nearest correlation matrix.
+
+    """
+    xp = a.__array_namespace__()
+
+    # shorthand for Frobenius norm
+    frob = xp.linalg.matrix_norm
+
+    # get size of the covariance matrix and flatten leading dimensions
+    *dim, m, n = a.shape
+    if m != n:
+        msg = "non-square matrix"
+        raise ValueError(msg)
+
+    # default tolerance
+    if tol is None:
+        tol = n * xp.finfo(a.dtype).eps
+
+    # current result, flatten leading dimensions
+    y = xp.reshape(a, (-1, n, n))
+
+    # initial correction is zero
+    ds = xp.zeros_like(a)
+
+    # store identity matrix
+    diag = xp.eye(n)
+
+    # find the nearest correlation matrix
+    for _ in range(niter):
+        # apply Dykstra's correction to current result
+        r = y - ds
+
+        # project onto positive semi-definite matrices
+        x = cov_clip(r)
+
+        # compute Dykstra's correction
+        ds = x - r
+
+        # project onto matrices with unit diagonal
+        y = (1 - diag) * x + diag
+
+        # check for convergence
+        if xp.all(frob(y - x) <= tol * frob(y)):
+            break
+
+    else:
+        # nearest correlation matrix was not found
+        warnings.warn(
+            f"Nearest correlation matrix not found in {niter} iterations. "
+            "The result may be invalid. Please run with a larger `niter` value, "
+            "or run the function again on the returned result.",
+            stacklevel=2,
+        )
+
+    # return result in original shape
+    return xp.reshape(y, (*dim, n, n))
+
+
+def cov_nearest(
+    cov: FloatArray,
+    tol: float | None = None,
+    niter: int = 100,
+) -> FloatArray:
+    """
+    Covariance matrix from nearest correlation matrix.
+
+    Divides *cov* along rows and columns by the square root of the
+    diagonal, then computes the nearest valid correlation matrix using
+    :func:`nearcorr`, before scaling rows and columns back.  The
+    diagonal of the input is hence unchanged.
+
+    Parameters
+    ----------
+    cov
+        A square matrix (or a stack of matrices).
+    tol
+        Tolerance for convergence, see :func:`nearcorr`.
+    niter
+        Maximum number of iterations.
+
+    Returns
+    -------
+        Covariance matrix from nearest correlation matrix.
+
+    """
+    xp = cov.__array_namespace__()
+
+    # get the diagonal
+    diag = xp.linalg.diagonal(cov)
+
+    # cannot fix negative diagonal
+    if xp.any(diag < 0):
+        msg = "negative values on the diagonal"
+        raise ValueError(msg)
+
+    # store the normalisation of the matrix
+    norm = xp.sqrt(diag)
+    norm = norm[..., None, :] * norm[..., :, None]
+
+    # find nearest correlation matrix
+    corr = cov / xp.where(norm > 0, norm, 1.0)
+    return nearcorr(corr, niter=niter, tol=tol) * norm
diff --git a/glass/glass/arraytools.py b/glass/glass/arraytools.py
new file mode 100644
index 0000000..f9680ab
--- /dev/null
+++ b/glass/glass/arraytools.py
@@ -0,0 +1,227 @@
+"""Module for array utilities."""
+
+from __future__ import annotations
+
+import itertools
+from functools import partial
+from typing import TYPE_CHECKING
+
+import array_api_compat
+import array_api_extra as xpx
+
+import glass._array_api_utils as _utils
+
+if TYPE_CHECKING:
+    from types import ModuleType
+    from typing import Unpack
+
+    from glass._types import AnyArray, FloatArray, IntArray
+
+
+def broadcast_first(
+    *arrays: FloatArray,
+) -> tuple[FloatArray, ...]:
+    """
+    Broadcast arrays, treating the first axis as common.
+
+    Parameters
+    ----------
+    arrays
+        The arrays to broadcast.
+
+    Returns
+    -------
+        The broadcasted arrays.
+
+    """
+    xp = array_api_compat.array_namespace(*arrays, use_compat=False)
+
+    arrays = tuple(xp.moveaxis(a, 0, -1) if a.ndim else a for a in arrays)
+    arrays = xp.broadcast_arrays(*arrays)
+    return tuple(xp.moveaxis(a, -1, 0) if a.ndim else a for a in arrays)
+
+
+def broadcast_leading_axes(
+    *args: tuple[
+        float | FloatArray,
+        int,
+    ],
+    xp: ModuleType | None = None,
+) -> tuple[
+    tuple[int, ...],
+    Unpack[tuple[FloatArray, ...]],
+]:
+    """
+    Broadcast all but the last N axes.
+
+    Parameters
+    ----------
+    args
+        The arrays and the number of axes to keep.
+
+    Returns
+    -------
+        The shape of the broadcast dimensions, and all input arrays
+        with leading axes matching that shape.
+
+    Examples
+    --------
+    Broadcast all dimensions of ``a``, all except the last dimension of
+    ``b``, and all except the last two dimensions of ``c``.
+
+    >>> import numpy as np
+    >>> a = 0
+    >>> b = np.zeros((4, 10))
+    >>> c = np.zeros((3, 1, 5, 6))
+    >>> dims, a, b, c = broadcast_leading_axes((a, 0), (b, 1), (c, 2))
+    >>> dims
+    (3, 4)
+    >>> a.shape
+    (3, 4)
+    >>> b.shape
+    (3, 4, 10)
+    >>> c.shape
+    (3, 4, 5, 6)
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(
+            *[arg[0] for arg in args],
+            use_compat=False,
+        )
+
+    shapes, trails = [], []
+    for a, n in args:
+        a_arr = xp.asarray(a)
+        s = a_arr.shape
+        i = len(s) - n
+        shapes.append(s[:i])
+        trails.append(s[i:])
+    dims = xpx.broadcast_shapes(*shapes)
+    arrs = (
+        xp.broadcast_to(xp.asarray(a), dims + t)
+        for (a, _), t in zip(args, trails, strict=False)
+    )
+    return (dims, *arrs)
+
+
+def ndinterp(  # noqa: PLR0913
+    x: float | FloatArray,
+    xq: FloatArray,
+    fq: FloatArray,
+    axis: int = -1,
+    left: float | None = None,
+    right: float | None = None,
+    period: float | None = None,
+) -> FloatArray:
+    """
+    Interpolate multi-dimensional array over axis.
+
+    Parameters
+    ----------
+    x
+        The x-coordinates.
+    xq
+        The x-coordinates of the data points.
+    fq
+        The function values corresponding to the x-coordinates in *xq*.
+    axis
+        The axis to interpolate over.
+    left
+        The value to return for x < xq[0].
+    right
+        The value to return for x > xq[-1].
+    period
+        The period of the function, used for interpolating periodic data.
+
+    Returns
+    -------
+        The interpolated array.
+
+    """
+    xp = array_api_compat.array_namespace(x, xq, fq, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    return uxpx.apply_along_axis(
+        partial(uxpx.interp, x, xq),
+        axis,
+        fq,
+        left=left,
+        right=right,
+        period=period,
+    )
+
+
+def trapezoid_product(
+    f: tuple[FloatArray, FloatArray],
+    *ff: tuple[FloatArray, FloatArray],
+    axis: int = -1,
+) -> float | FloatArray:
+    """
+    Trapezoidal rule for a product of functions.
+
+    Parameters
+    ----------
+    f
+        The first function.
+    ff
+        The other functions.
+    axis
+        The axis along which to integrate.
+
+    Returns
+    -------
+        The integral of the product of the functions.
+
+    """
+    # Flatten ff into a 1D tuple of all ff inputs and then expand to get the namespace
+    xp = array_api_compat.array_namespace(
+        *f,
+        *tuple(itertools.chain(*ff)),
+        use_compat=False,
+    )
+    uxpx = _utils.XPAdditions(xp)
+
+    x: FloatArray
+    x, _ = f
+    for x_, _ in ff:
+        x = uxpx.union1d(
+            x[(x >= x_[0]) & (x <= x_[-1])],
+            x_[(x_ >= x[0]) & (x_ <= x[-1])],
+        )
+    y = uxpx.interp(x, *f)
+    for f_ in ff:
+        y *= uxpx.interp(x, *f_)
+    return uxpx.trapezoid(y, x, axis=axis)
+
+
+def cumulative_trapezoid(
+    f: IntArray | FloatArray,
+    x: IntArray | FloatArray,
+) -> AnyArray:
+    """
+    Cumulative trapezoidal rule along last axis.
+
+    Parameters
+    ----------
+    f
+        The function values.
+    x
+        The x-coordinates.
+
+    Returns
+    -------
+        The cumulative integral of the function.
+
+    """
+    xp = array_api_compat.array_namespace(f, x, use_compat=False)
+
+    f = xp.asarray(f, dtype=xp.float64)
+    x = xp.asarray(x, dtype=xp.float64)
+
+    # Compute the cumulative trapezoid without mutating any arrays
+    return xp.cumulative_sum(
+        (f[..., 1:] + f[..., :-1]) * 0.5 * xp.diff(x),
+        axis=-1,
+        include_initial=True,
+    )
diff --git a/glass/glass/cosmology.py b/glass/glass/cosmology.py
new file mode 100644
index 0000000..19134c9
--- /dev/null
+++ b/glass/glass/cosmology.py
@@ -0,0 +1,23 @@
+"""Module for cosmology.api utilities."""
+
+from typing import Protocol
+
+import cosmology.api
+
+from glass._types import AnyArray
+
+
+class Cosmology(
+    cosmology.api.HasComovingDistance[AnyArray, AnyArray],  # type: ignore[misc]
+    cosmology.api.HasCriticalDensity0[AnyArray],  # type: ignore[misc]
+    cosmology.api.HasGrowthFactor[AnyArray, AnyArray],  # type: ignore[misc]
+    cosmology.api.HasHoverH0[AnyArray, AnyArray],  # type: ignore[misc]
+    cosmology.api.HasHubbleDistance[AnyArray],  # type: ignore[misc]
+    cosmology.api.HasInverseComovingDistance[AnyArray, AnyArray],  # type: ignore[misc]
+    cosmology.api.HasLittleH[AnyArray],  # type: ignore[misc]
+    cosmology.api.HasOmegaM0[AnyArray],  # type: ignore[misc]
+    cosmology.api.HasOmegaM[AnyArray, AnyArray],  # type: ignore[misc]
+    cosmology.api.HasTransverseComovingDistance[AnyArray, AnyArray],  # type: ignore[misc]
+    Protocol,
+):
+    """Cosmology protocol for GLASS."""
diff --git a/glass/glass/ext/__init__.py b/glass/glass/ext/__init__.py
new file mode 100644
index 0000000..dd44fdb
--- /dev/null
+++ b/glass/glass/ext/__init__.py
@@ -0,0 +1,39 @@
+"""
+Namespace loader for GLASS extension packages.
+
+Uses the pkgutil namespace mechanism to find "ext" submodules of
+packages that provide a "glass" module.
+
+"""
+
+import os.path
+from pkgutil import extend_path
+
+
+def _extend_path(path: list[str], name: str) -> list[str]:
+    """
+    Extend the path to include the "ext" submodules of packages.
+
+    Parameters
+    ----------
+    path
+        The path to extend.
+    name
+        The name of the package.
+
+    Returns
+    -------
+        The extended path.
+
+    """
+    _pkg, _, _mod = name.partition(".")
+
+    return list(
+        filter(
+            os.path.isdir,
+            (os.path.join(p, _mod) for p in extend_path(path, _pkg)),  # noqa: PTH118
+        ),
+    )
+
+
+__path__ = _extend_path(__path__, __name__)
diff --git a/glass/glass/fields.py b/glass/glass/fields.py
new file mode 100644
index 0000000..1d674ae
--- /dev/null
+++ b/glass/glass/fields.py
@@ -0,0 +1,1117 @@
+"""Functions for random fields."""
+
+from __future__ import annotations
+
+import itertools
+import math
+import warnings
+from collections.abc import Sequence
+from itertools import combinations_with_replacement, product
+from typing import TYPE_CHECKING
+
+import healpy as hp
+import numpy as np
+from transformcl import cltovar
+
+import array_api_compat
+import array_api_extra as xpx
+
+import glass
+import glass.grf
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Generator, Iterable, Iterator, Sequence
+    from typing import Literal
+
+    from glass._types import (
+        AnyArray,
+        Cls,
+        ComplexArray,
+        Fields,
+        FloatArray,
+        IntArray,
+        T,
+    )
+
+
+try:
+    from warnings import deprecated
+except ImportError:
+    if TYPE_CHECKING:
+        from glass._types import P, R
+
+    def deprecated(msg: str, /) -> Callable[[Callable[P, R]], Callable[P, R]]:  # type: ignore[no-redef]
+        """Backport of Python's warnings.deprecated()."""
+        from functools import wraps  # noqa: PLC0415
+        from warnings import warn  # noqa: PLC0415
+
+        def decorator(func: Callable[P, R], /) -> Callable[P, R]:
+            @wraps(func)
+            def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+                warn(msg, category=DeprecationWarning, stacklevel=2)
+                return func(*args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+
+def _inv_triangle_number(triangle_number: int) -> int:
+    r"""
+    The :math:`n`-th triangle number is :math:`T_n = n \, (n+1)/2`. If
+    the argument is :math:`T_n`, then :math:`n` is returned. Otherwise,
+    a :class:`ValueError` is raised.
+    """
+    n = math.floor(math.sqrt(2 * triangle_number))
+    if n * (n + 1) // 2 != triangle_number:
+        msg = f"not a triangle number: {triangle_number}"
+        raise ValueError(msg)
+    return n
+
+
+def nfields_from_nspectra(nspectra: int) -> int:
+    r"""
+    Returns the number of fields for a number of spectra.
+
+    Given the number of spectra *nspectra*, returns the number of
+    fields *n* such that ``n * (n + 1) // 2 == nspectra`` or raises
+    a :class:`ValueError` if the number of spectra is invalid.
+    """
+    try:
+        n = _inv_triangle_number(nspectra)
+    except ValueError:
+        msg = f"invalid number of spectra: {nspectra}"
+        raise ValueError(msg) from None
+    return n
+
+
+def iternorm(
+    k: int,
+    cov: Iterable[FloatArray],
+    size: int | tuple[int, ...] = (),
+) -> Generator[tuple[int | None, FloatArray, FloatArray]]:
+    """
+    Return the vector a and variance sigma^2 for iterative normal sampling.
+
+    Parameters
+    ----------
+    k
+        The number of fields.
+    cov
+        The covariance matrix for the fields.
+    size
+        The size of the covariance matrix.
+
+    Yields
+    ------
+    index
+        The index for iterative sampling.
+    vector
+        The vector for iterative sampling.
+    standard_deviation
+        The standard deviation for iterative sampling.
+
+    Raises
+    ------
+    TypeError
+        If the covariance matrix is not broadcastable to the given size.
+    ValueError
+        If the covariance matrix is not positive definite.
+
+    """
+    # Convert to list here to allow determining the namespace
+    first = next(cov)  # type: ignore[call-overload]
+    xp = first.__array_namespace__()
+
+    n = (size,) if isinstance(size, int) else size
+
+    m = xp.zeros((*n, k, k))
+    a = xp.zeros((*n, k))
+    s = xp.zeros((*n,))
+    q = (*n, k + 1)
+    j = 0 if k > 0 else None
+
+    # We must use cov_expanded here as cov has been consumed to determine the namespace
+    for i, x in enumerate(itertools.chain([first], cov)):
+        # Ideally would be xp.asanyarray but this does not yet exist. The key difference
+        # between the two in numpy is that asanyarray maintains subclasses of NDArray
+        # whereas asarray will return the base class NDArray. Currently, we don't seem
+        # to pass a subclass of NDArray so this, so it might be okay
+        x = xp.asarray(x)  # noqa: PLW2901
+        if x.shape != q:
+            try:
+                x = xp.broadcast_to(x, q)  # noqa: PLW2901
+            except ValueError:
+                msg = f"covariance row {i}: shape {x.shape} cannot be broadcast to {q}"
+                raise TypeError(msg) from None
+
+        # only need to update matrix A if there are correlations
+        if j is not None:
+            # compute new entries of matrix A
+            m[..., :, j] = 0
+            m[..., j : j + 1, :] = xp.matmul(a[..., xp.newaxis, :], m)
+            m[..., j, j] = xp.where(s != 0, -1, s)
+            # To ensure we don't divide by zero or nan we use a mask to only divide the
+            # appropriate values of m and s
+            m_j = m[..., j, :]
+            s_broadcast = xp.broadcast_to(s[..., xp.newaxis], m_j.shape)
+            mask = (m_j != 0) & (s_broadcast != 0) & ~xp.isnan(s_broadcast)
+            m_j[mask] = xp.divide(m_j[mask], -s_broadcast[mask])
+            m[..., j, :] = m_j
+
+            # compute new vector a
+            c = x[..., 1:, xp.newaxis]
+            a = xp.matmul(m[..., :j], c[..., k - j :, :])
+            a += xp.matmul(m[..., j:], c[..., : k - j, :])
+            a = xp.reshape(a, (*n, k))
+
+            # next rolling index
+            j = (j - 1) % k
+
+        # compute new standard deviation
+        a_np = np.asarray(a, copy=True)
+        einsum_result_np = np.einsum("...i,...i", a_np, a_np)
+        s = x[..., 0] - xp.asarray(einsum_result_np, copy=True)
+        if xp.any(s < 0):
+            msg = "covariance matrix is not positive definite"
+            raise ValueError(msg)
+        s = xp.sqrt(s)
+
+        # yield the next index, vector a, and standard deviation s
+        yield j, a, s
+
+
+def cls2cov(
+    cls: Cls,
+    nl: int,
+    nf: int,
+    nc: int,
+) -> Generator[FloatArray]:
+    """
+    Return array of Cls as a covariance matrix for iterative sampling.
+
+    Parameters
+    ----------
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+    nl
+        The number of modes.
+    nf
+        The number of fields.
+    nc
+        The number of correlated fields.
+
+    Yields
+    ------
+    matrix
+        The covariance matrix for iterative sampling.
+
+    Raises
+    ------
+    ValueError
+        If negative values are found in the Cls.
+
+    """
+    xp = array_api_compat.array_namespace(*cls, use_compat=False)
+
+    cov = xp.zeros((nl, nc + 1))
+    end = 0
+    for j in range(nf):
+        begin, end = end, end + j + 1
+        for i, cl in enumerate(cls[begin:end][: nc + 1]):
+            if i == 0 and np.any(xp.less(cl, 0)):
+                msg = "negative values in cl"
+                raise ValueError(msg)
+            n = cl.size
+            cov[:n, i] = cl
+            cov[n:, i] = 0
+        cov /= 2
+        yield cov
+
+
+def _multalm(
+    alm: ComplexArray,
+    bl: FloatArray,
+    *,
+    inplace: bool = False,
+) -> ComplexArray:
+    """
+    Multiply alm by bl.
+
+    The alm should be in GLASS order:
+
+    [
+        00,
+        10, 11,
+        20, 21, 22,
+        30, 31, 32, 33,
+        ...
+    ]
+
+    Parameters
+    ----------
+    alm
+        The alm to multiply.
+    bl
+        The bl to multiply.
+    inplace
+        Whether to perform the operation in place.
+
+    Returns
+    -------
+        The product of alm and bl.
+
+    """
+    xp = array_api_compat.array_namespace(alm, bl, use_compat=False)
+
+    n = bl.size
+    # Ideally would be xp.asanyarray but this does not yet exist. The key difference
+    # between the two in numpy is that asanyarray maintains subclasses of NDArray
+    # whereas asarray will return the base class NDArray. Currently, we don't seem
+    # to pass a subclass of NDArray so this, so it might be okay
+    out = xp.asarray(alm) if inplace else xp.asarray(alm, copy=True)
+    for ell in range(n):
+        out[ell * (ell + 1) // 2 : (ell + 1) * (ell + 2) // 2] *= bl[ell]
+
+    return out
+
+
+def discretized_cls(
+    cls: Cls,
+    *,
+    lmax: int | None = None,
+    ncorr: int | None = None,
+    nside: int | None = None,
+) -> Cls:
+    """
+    Apply discretisation effects to angular power spectra.
+
+    Depending on the given arguments, this truncates the angular power spectra
+    to ``lmax``, removes all but ``ncorr`` correlations between fields, and
+    applies the HEALPix pixel window function of the given ``nside``. If no
+    arguments are given, no action is performed.
+
+    Parameters
+    ----------
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+    lmax
+        The maximum mode number to truncate the spectra.
+    ncorr
+        The number of correlated fields to keep.
+    nside
+        The resolution parameter for the HEALPix maps.
+
+    Returns
+    -------
+        The discretised angular power spectra.
+
+    Raises
+    ------
+    ValueError
+        If the length of the Cls array is not a triangle number.
+
+    """
+    if ncorr is not None:
+        n = nfields_from_nspectra(len(cls))
+        cls = [
+            cls[i * (i + 1) // 2 + j] if j <= ncorr else np.asarray([])
+            for i in range(n)
+            for j in range(i + 1)
+        ]
+
+    if nside is not None:
+        pw = hp.pixwin(nside, lmax=lmax)
+
+    gls = []
+    for cl in cls:
+        if len(cl) > 0:
+            if lmax is not None:
+                cl = cl[: lmax + 1]  # noqa: PLW2901
+            if nside is not None:
+                n = min(len(cl), len(pw))
+                cl = cl[:n] * pw[:n] ** 2  # noqa: PLW2901
+        gls.append(cl)
+    return gls
+
+
+@deprecated("use glass.solve_gaussian_spectra() instead")
+def lognormal_gls(
+    cls: Cls,
+    shift: float = 1.0,
+) -> Cls:
+    """
+    Compute Gaussian Cls for a lognormal random field.
+
+    .. deprecated:: 2025.1
+
+       Use :func:`glass.lognormal_fields` and
+       :func:`glass.compute_gaussian_spectra` or
+       :func:`glass.solve_gaussian_spectra` instead.
+
+    Parameters
+    ----------
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+    shift
+        The shift parameter for the lognormal transformation.
+
+    Returns
+    -------
+        The Gaussian angular power spectra for a lognormal random field.
+
+    """
+    n = nfields_from_nspectra(len(cls))
+    fields = [glass.grf.Lognormal(shift) for _ in range(n)]
+    return solve_gaussian_spectra(fields, cls)
+
+
+def _generate_grf(
+    gls: Cls,
+    nside: int,
+    *,
+    ncorr: int | None = None,
+    rng: np.random.Generator | None = None,
+) -> Generator[FloatArray]:
+    """
+    Iteratively sample Gaussian random fields (internal use).
+
+    A generator that iteratively samples HEALPix maps of Gaussian random fields
+    with the given angular power spectra ``gls`` and resolution parameter
+    ``nside``.
+
+    The optional argument ``ncorr`` can be used to artificially limit now many
+    realised fields are correlated. This saves memory, as only `ncorr` previous
+    fields need to be kept.
+
+    The ``gls`` array must contain the angular power power spectra of the
+    Gaussian random fields in :ref:`standard order <twopoint_order>`.
+
+    Parameters
+    ----------
+    gls
+        The Gaussian angular power spectra for a random field.
+    nside
+        The resolution parameter for the HEALPix maps.
+    ncorr
+        The number of correlated fields. If not given, all fields are correlated.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    fields
+        The Gaussian random fields.
+
+    Raises
+    ------
+    ValueError
+        If all gls are empty.
+    """
+    if rng is None:
+        rng = np.random.default_rng()
+
+    # number of gls and number of fields
+    ngls = len(gls)
+    ngrf = nfields_from_nspectra(ngls)
+
+    # number of correlated fields if not specified
+    if ncorr is None:
+        ncorr = ngrf - 1
+
+    # number of modes
+    n = max((len(gl) for gl in gls), default=0)
+    if n == 0:
+        msg = "all gls are empty"
+        raise ValueError(msg)
+
+    # generates the covariance matrix for the iterative sampler
+    cov = cls2cov(gls, n, ngrf, ncorr)
+
+    # working arrays for the iterative sampling
+    z = np.zeros(n * (n + 1) // 2, dtype=np.complex128)
+    y = np.zeros((n * (n + 1) // 2, ncorr), dtype=np.complex128)
+
+    # generate the conditional normal distribution for iterative sampling
+    conditional_dist = iternorm(ncorr, cov, size=n)
+
+    # sample the fields from the conditional distribution
+    for j, a, s in conditional_dist:
+        # standard normal random variates for alm
+        # sample real and imaginary parts, then view as complex number
+        rng.standard_normal(n * (n + 1), np.float64, z.view(np.float64))
+
+        # scale by standard deviation of the conditional distribution
+        # variance is distributed over real and imaginary part
+        alm = _multalm(z, s)
+
+        # add the mean of the conditional distribution
+        for i in range(ncorr):
+            alm += _multalm(y[:, i], a[:, i])
+
+        # store the standard normal in y array at the indicated index
+        if j is not None:
+            y[:, j] = z
+
+        alm = _glass_to_healpix_alm(alm)
+
+        # modes with m = 0 are real-valued and come first in array
+        alm[:n].real += alm[:n].imag
+        alm[:n].imag[:] = 0
+
+        # transform alm to maps
+        # can be performed in place on the temporary alm array
+        yield hp.alm2map(alm, nside, pixwin=False, pol=False, inplace=True)
+
+
+@deprecated("use glass.generate() instead")
+def generate_gaussian(
+    gls: Cls,
+    nside: int,
+    *,
+    ncorr: int | None = None,
+    rng: np.random.Generator | None = None,
+) -> Generator[FloatArray]:
+    """
+    Sample Gaussian random fields from Cls iteratively.
+
+    .. deprecated:: 2025.1
+
+       Use :func:`glass.generate()` instead.
+
+    A generator that iteratively samples HEALPix maps of Gaussian random fields
+    with the given angular power spectra ``gls`` and resolution parameter
+    ``nside``.
+
+    The optional argument ``ncorr`` can be used to artificially limit now many
+    realised fields are correlated. This saves memory, as only `ncorr` previous
+    fields need to be kept.
+
+    The ``gls`` array must contain the angular power power spectra of the
+    Gaussian random fields in :ref:`standard order <twopoint_order>`.
+
+    Parameters
+    ----------
+    gls
+        The Gaussian angular power spectra for a random field.
+    nside
+        The resolution parameter for the HEALPix maps.
+    ncorr
+        The number of correlated fields. If not given, all fields are correlated.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    fields
+        The Gaussian random fields.
+
+    Raises
+    ------
+    ValueError
+        If all gls are empty.
+
+    """
+    n = nfields_from_nspectra(len(gls))
+    fields = [glass.grf.Normal() for _ in range(n)]
+    yield from generate(fields, gls, nside, ncorr=ncorr, rng=rng)
+
+
+@deprecated("use glass.generate() instead")
+def generate_lognormal(
+    gls: Cls,
+    nside: int,
+    shift: float = 1.0,
+    *,
+    ncorr: int | None = None,
+    rng: np.random.Generator | None = None,
+) -> Generator[FloatArray]:
+    """
+    Sample lognormal random fields from Gaussian Cls iteratively.
+
+    .. deprecated:: 2025.1
+
+       Use :func:`glass.generate()` instead.
+
+    Parameters
+    ----------
+    gls
+        The Gaussian angular power spectra for a lognormal random field.
+    nside
+        The resolution parameter for the HEALPix maps.
+    shift
+        The shift parameter for the lognormal transformation.
+    ncorr
+        The number of correlated fields. If not given, all fields are correlated.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    fields
+        The lognormal random fields.
+
+    """
+    n = nfields_from_nspectra(len(gls))
+    fields = [glass.grf.Lognormal(shift) for _ in range(n)]
+    yield from generate(fields, gls, nside, ncorr=ncorr, rng=rng)
+
+
+def getcl(
+    cls: Cls,
+    i: int,
+    j: int,
+    lmax: int | None = None,
+) -> FloatArray:
+    """
+    Return a specific angular power spectrum from an array in
+    :ref:`standard order <twopoint_order>`.
+
+    Parameters
+    ----------
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+    i
+        Indices to return.
+    j
+        Indices to return.
+    lmax
+        Truncate the returned spectrum at this mode number.
+
+    Returns
+    -------
+        The angular power spectrum for indices *i* and *j* from an
+        array in *GLASS* ordering.
+
+    """
+    if j > i:
+        i, j = j, i
+    cl = cls[i * (i + 1) // 2 + i - j]
+    if lmax is not None:
+        if cl.size > lmax + 1:
+            cl = cl[: lmax + 1]
+        else:
+            cl = xpx.pad(cl, (0, lmax + 1 - cl.size))
+    return cl
+
+
+def enumerate_spectra(
+    entries: Iterable[AnyArray],
+) -> Iterator[tuple[int, int, AnyArray]]:
+    """
+    Iterate over a set of two-point functions in :ref:`standard order
+    <twopoint_order>`, yielding a tuple of indices and their associated
+    entry from the input.
+
+    Examples
+    --------
+    >>> spectra = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+    >>> list(enumerate_spectra(spectra))
+    [(0, 0, [1, 2, 3]), (1, 1, [4, 5, 6]), (1, 0, [7, 8, 9])]
+
+    """
+    for k, cl in enumerate(entries):
+        i = int((2 * k + 0.25) ** 0.5 - 0.5)
+        j = i * (i + 3) // 2 - k
+        yield i, j, cl
+
+
+def spectra_indices(n: int) -> IntArray:
+    """
+    Return an array of indices in :ref:`standard order <twopoint_order>`
+    for a set of two-point functions for *n* fields.  Each row is a pair
+    of indices *i*, *j*.
+
+    Examples
+    --------
+    >>> spectra_indices(3)
+    array([[0, 0],
+           [1, 1],
+           [1, 0],
+           [2, 2],
+           [2, 1],
+           [2, 0]])
+
+    """
+    i, j = np.tril_indices(n)
+    return np.transpose([i, i - j])
+
+
+def effective_cls(
+    cls: Cls,
+    weights1: FloatArray,
+    weights2: FloatArray | None = None,
+    *,
+    lmax: int | None = None,
+) -> FloatArray:
+    """
+    Compute effective angular power spectra from weights.
+
+    Computes a linear combination of the angular power spectra *cls*
+    using the factors provided by *weights1* and *weights2*. Additional
+    axes in *weights1* and *weights2* produce arrays of spectra.
+
+    Parameters
+    ----------
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+    weights1
+        Weight factors for spectra. The first axis must be equal to
+        the number of fields.
+    weights2
+        Second set of weights. If not given, *weights1* is used.
+    lmax
+        Truncate the angular power spectra at this mode number. If not
+        given, the longest input in *cls* will be used.
+
+    Returns
+    -------
+        A dictionary of effective angular power spectra, where keys
+        correspond to the leading axes of *weights1* and *weights2*.
+
+    Raises
+    ------
+    ValueError
+        If the length of *cls* is not a triangle number.
+    ValueError
+        If the shapes of *weights1* and *weights2* are incompatible.
+
+    """
+    xp = array_api_compat.array_namespace(*cls, weights1, weights2, use_compat=False)
+
+    # this is the number of fields
+    n = nfields_from_nspectra(len(cls))
+
+    # find lmax if not given
+    if lmax is None:
+        lmax = max((cl.shape[0] for cl in cls), default=0) - 1
+
+    # broadcast weights1 such that its shape ends in n
+    weights1 = xp.asarray(weights1)
+    weights2 = xp.asarray(weights2) if weights2 is not None else weights1
+
+    shape1, shape2 = weights1.shape, weights2.shape
+    for i, shape in enumerate((shape1, shape2)):
+        if not shape or shape[0] != n:
+            msg = f"shape mismatch between fields and weights{i + 1}"
+            raise ValueError(msg)
+
+    # get the iterator over leading weight axes
+    # auto-spectra do not repeat identical computations
+    pairs = (
+        combinations_with_replacement(np.ndindex(shape1[1:]), 2)
+        if weights2 is weights1
+        else product(np.ndindex(shape1[1:]), np.ndindex(shape2[1:]))
+    )
+
+    # create the output array: axes for all input axes plus lmax+1
+    out = xp.empty(shape1[1:] + shape2[1:] + (lmax + 1,))
+
+    # helper that will grab the entire first column (i.e. shells)
+    c = (slice(None),)
+
+    # compute all combined cls from pairs
+    # if weights2 is weights1, set the transpose elements in one pass
+    for j1, j2 in pairs:
+        w1, w2 = weights1[c + j1], weights2[c + j2]
+        cl = sum(
+            w1[i1] * w2[i2] * getcl(cls, i1, i2, lmax=lmax)
+            for i1 in range(n)
+            for i2 in range(n)
+        )
+        out[j1 + j2 + (...,)] = cl
+        if weights2 is weights1 and j1 != j2:
+            out[j2 + j1 + (...,)] = cl
+    return out
+
+
+def gaussian_fields(shells: Sequence[glass.RadialWindow]) -> Sequence[glass.grf.Normal]:
+    """
+    Create Gaussian random fields for radial windows *shells*.
+
+    Parameters
+    ----------
+    shells
+        Window functions for the simulated shells.
+
+    Returns
+    -------
+        A sequence describing the Gaussian random fields.
+
+    """
+    return [glass.grf.Normal() for _shell in shells]
+
+
+def lognormal_fields(
+    shells: Sequence[glass.RadialWindow],
+    shift: Callable[[float], float] | None = None,
+) -> Sequence[glass.grf.Lognormal]:
+    """
+    Create lognormal fields for radial windows *shells*.  If *shifts* is
+    given, it must be a callable that returns a lognormal shift (i.e.
+    the scale parameter) at the nominal redshift of each shell.
+
+    Parameters
+    ----------
+    shells
+        Window functions for the simulated shells.
+    shift
+        Callable that returns the lognormal shift for each field.
+
+    Returns
+    -------
+        A sequence describing the lognormal fields.
+
+    """
+    if shift is None:
+        shift = lambda _z: 1.0  # noqa: E731
+
+    return [glass.grf.Lognormal(shift(shell.zeff)) for shell in shells]
+
+
+def compute_gaussian_spectra(fields: Fields, spectra: Cls) -> Cls:
+    """
+    Compute a sequence of Gaussian angular power spectra.
+
+    After transformation by *fields*, the expected two-point statistics
+    should recover *spectra* when using a band-limited transform
+    [Tessore23]_.
+
+    Parameters
+    ----------
+    fields
+        The fields to be simulated.
+    spectra
+        The desired angular power spectra of the fields.
+
+    Returns
+    -------
+        Gaussian angular power spectra for simulation.
+
+    """
+    n = len(fields)
+    if len(spectra) != n * (n + 1) // 2:
+        msg = "mismatch between number of fields and spectra"
+        raise ValueError(msg)
+
+    gls = []
+    for i, j, cl in enumerate_spectra(spectra):
+        gl = glass.grf.compute(cl, fields[i], fields[j]) if cl.size > 0 else 0 * cl
+        gls.append(gl)
+    return gls
+
+
+def solve_gaussian_spectra(fields: Fields, spectra: Cls) -> Cls:
+    """
+    Solve a sequence of Gaussian angular power spectra.
+
+    After transformation by *fields*, the expected two-point statistics
+    should recover *spectra* when using a non-band-limited transform
+    [Tessore23]_.
+
+    Parameters
+    ----------
+    fields
+        The fields to be simulated.
+    spectra
+        The desired angular power spectra of the fields.
+
+    Returns
+    -------
+        Gaussian angular power spectra for simulation.
+
+    """
+    n = len(fields)
+    if len(spectra) != n * (n + 1) // 2:
+        msg = "mismatch between number of fields and spectra"
+        raise ValueError(msg)
+
+    gls = []
+    for i, j, cl in enumerate_spectra(spectra):
+        if cl.size > 0:
+            # transformation pair
+            t1, t2 = fields[i], fields[j]
+
+            # set zero-padding of solver to 2N
+            pad = 2 * cl.size
+
+            # if the desired monopole is zero, that is most likely
+            # and artefact of the theory spectra -- the variance of the
+            # matter density in a finite shell is not zero
+            # -> set output monopole to zero, which ignores cl[0]
+            monopole = 0.0 if cl[0] == 0 else None
+
+            # call solver
+            gl, _cl_out, info = glass.grf.solve(cl, t1, t2, pad=pad, monopole=monopole)
+
+            # warn if solver didn't converge
+            if info == 0:
+                warnings.warn(
+                    f"Gaussian spectrum for fields ({i}, {j}) did not converge",
+                    stacklevel=2,
+                )
+        else:
+            gl = 0 * cl  # makes a copy of the empty array
+        gls.append(gl)
+    return gls
+
+
+def generate(
+    fields: Fields,
+    gls: Cls,
+    nside: int,
+    *,
+    ncorr: int | None = None,
+    rng: np.random.Generator | None = None,
+) -> Iterator[AnyArray]:
+    """
+    Sample random fields from Gaussian angular power spectra.
+
+    Iteratively sample HEALPix maps of transformed Gaussian random
+    fields with the given angular power spectra *gls* and resolution
+    parameter *nside*.
+
+    The random fields are sampled from Gaussian random fields using the
+    transformations in *fields*.
+
+    The *gls* array must contain the angular power power spectra of the
+    Gaussian random fields in :ref:`standard order <twopoint_order>`.
+
+    The optional number *ncorr* limits how many realised fields are
+    correlated. This saves memory, as only *ncorr* previous fields are
+    kept.
+
+    Parameters
+    ----------
+    fields
+        Transformations for the random fields.
+    gls
+        Gaussian angular power spectra.
+    nside
+        Resolution parameter for the HEALPix maps.
+    ncorr
+        Number of correlated fields. If not given, all fields are
+        correlated.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    x
+        Sampled random fields.
+
+    """
+    n = len(fields)
+    if len(gls) != n * (n + 1) // 2:
+        msg = "mismatch between number of fields and gls"
+        raise ValueError(msg)
+
+    variances = (cltovar(getcl(gls, i, i)) for i in range(n))
+    grf = _generate_grf(gls, nside, ncorr=ncorr, rng=rng)
+
+    for t, x, var in zip(fields, grf, variances, strict=True):
+        yield t(x, var)
+
+
+def glass_to_healpix_spectra(spectra: Sequence[T]) -> list[T]:
+    """
+    Reorder spectra from GLASS to HEALPix order.
+
+    Reorder spectra in :ref:`GLASS order <twopoint_order>` to conform to
+    (new) HEALPix order.
+
+    Parameters
+    ----------
+    spectra
+        Sequence of spectra in GLASS order.
+
+    Returns
+    -------
+        Sequence of spectra in HEALPix order.
+
+    """
+    n = nfields_from_nspectra(len(spectra))
+
+    comb = [(i, j) for i, j in spectra_indices(n)]
+    return [spectra[comb.index((i + k, i))] for k in range(n) for i in range(n - k)]
+
+
+def healpix_to_glass_spectra(spectra: Sequence[T]) -> list[T]:
+    """
+    Reorder spectra from HEALPix to GLASS order.
+
+    Reorder HEALPix spectra (in new order) to conform to :ref:`GLASS
+    order <twopoint_order>`.
+
+    Parameters
+    ----------
+    spectra
+        Sequence of spectra in HEALPix order.
+
+    Returns
+    -------
+        Sequence of spectra in GLASS order.
+
+    """
+    n = nfields_from_nspectra(len(spectra))
+
+    comb = [(i + k, i) for k in range(n) for i in range(n - k)]
+    return [spectra[comb.index((i, j))] for i, j in spectra_indices(n)]
+
+
+def _glass_to_healpix_alm(alm: ComplexArray) -> ComplexArray:
+    """
+    Reorder alms in GLASS order to conform to (new) HEALPix order.
+
+    Parameters
+    ----------
+    alm
+        alm in GLASS order.
+
+    Returns
+    -------
+        alm in HEALPix order.
+
+    """
+    n = _inv_triangle_number(alm.size)
+    ell = np.arange(n)
+    out = [alm[ell[m:] * (ell[m:] + 1) // 2 + m] for m in ell]
+    return np.concatenate(out)
+
+
+def lognormal_shift_hilbert2011(z: float) -> float:
+    """
+    Lognormal shift of Hilbert et al. (2011) for convergence fields.
+
+    Lognormal shift parameter for the weak lensing convergence
+    from the fitting formula of [Hilbert11]_.
+
+    Parameters
+    ----------
+    z
+        Redshift.
+
+    Returns
+    -------
+        Lognormal shift.
+
+    """
+    return z * (0.008 + z * (0.029 + z * (-0.0079 + z * 0.00065)))
+
+
+def cov_from_spectra(spectra: Cls, *, lmax: int | None = None) -> AnyArray:
+    """
+    Construct covariance matrix from spectra.
+
+    Construct a covariance matrix from the angular power spectra in
+    *spectra*.
+
+    Parameters
+    ----------
+    spectra
+        Sequence of angular power spectra.
+    lmax
+        Maximum angular mode number. If not given, the maximum is taken
+        from the provided spectra.
+
+    Returns
+    -------
+        Covariance matrix from the given spectra.
+
+    """
+    # recover the number of fields from the number of spectra
+    n = nfields_from_nspectra(len(spectra))
+
+    if lmax is None:  # noqa: SIM108
+        # maximum length in input spectra
+        k = max((cl.size for cl in spectra), default=0)
+    else:
+        k = lmax + 1
+
+    # this is the covariance matrix of the spectra
+    # the leading dimension is k, then it is a n-by-n covariance matrix
+    # missing entries are zero, which is the default value
+    cov = np.zeros((k, n, n))
+
+    # fill the matrix up by going through the spectra in order
+    # skip over entries that are None
+    # if the spectra are ragged, some entries at high ell may remain zero
+    # only fill the lower triangular part, everything is symmetric
+    for i, j, cl in enumerate_spectra(spectra):
+        cov[: cl.size, i, j] = cov[: cl.size, j, i] = cl.reshape(-1)[:k]
+
+    return cov
+
+
+def check_posdef_spectra(spectra: Cls) -> bool:
+    """
+    Test whether angular power spectra are positive semi-definite.
+
+    Parameters
+    ----------
+    spectra
+        Spectra to be tested.
+
+    Returns
+    -------
+        Whether spectra are positive semi-definite or not.
+
+    """
+    cov = cov_from_spectra(spectra)
+    xp = cov.__array_namespace__()
+    is_positive_semi_definite: bool = xp.all(xp.linalg.eigvalsh(cov) >= 0)
+    return is_positive_semi_definite
+
+
+def regularized_spectra(
+    spectra: Cls,
+    *,
+    lmax: int | None = None,
+    method: Literal["nearest", "clip"] = "nearest",
+    **method_kwargs: float | None,
+) -> Cls:
+    r"""
+    Regularise a set of angular power spectra.
+
+    Regularises a complete set *spectra* of angular power spectra in
+    :ref:`standard order <twopoint_order>` such that at every angular
+    mode number :math:`\ell`, the matrix :math:`C_\ell^{ij}` is a
+    valid positive semi-definite covariance matrix.
+
+    The length of the returned spectra is set by *lmax*, or the maximum
+    length of the given spectra if *lmax* is not given.  Shorter input
+    spectra are padded with zeros as necessary.  Missing spectra can be
+    set to :data:`None` or, preferably, an empty array.
+
+    The *method* parameter determines how the regularisation is carried
+    out.
+
+    Parameters
+    ----------
+    spectra
+        Spectra to be regularised.
+    lmax
+        Maximum angular mode number. If not given, the maximum is taken
+        from the provided spectra.
+    method
+        Regularisation method.
+
+    """
+    # regularise the cov matrix using the chosen method
+    cov_method: Callable[..., AnyArray]
+    if method == "clip":
+        from glass.algorithm import cov_clip as cov_method  # noqa: PLC0415
+    elif method == "nearest":
+        from glass.algorithm import cov_nearest as cov_method  # noqa: PLC0415
+    else:
+        msg = f"unknown method '{method}'"  # type: ignore[unreachable]
+        raise ValueError(msg)
+
+    # get the cov matrix from spectra
+    cov = cov_from_spectra(spectra, lmax=lmax)
+
+    # regularise the cov matrix using the chosen method
+    cov = cov_method(cov, **method_kwargs)
+
+    # return regularised spectra from cov matrix array
+    return [cov[:, i, j] for i, j in spectra_indices(cov.shape[-1])]
diff --git a/glass/glass/galaxies.py b/glass/glass/galaxies.py
new file mode 100644
index 0000000..0eb9bbd
--- /dev/null
+++ b/glass/glass/galaxies.py
@@ -0,0 +1,423 @@
+"""
+Galaxies
+========
+
+.. currentmodule:: glass
+
+The following functions provide functionality for simulating galaxies
+as typically observed in a cosmological galaxy survey.
+
+Functions
+---------
+
+.. autofunction:: redshifts
+.. autofunction:: redshifts_from_nz
+.. autofunction:: galaxy_shear
+.. autofunction:: gaussian_phz
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+import warnings
+from typing import TYPE_CHECKING
+
+import healpix
+import numpy as np
+
+import array_api_compat
+
+import glass
+import glass._array_api_utils as _utils
+import glass.arraytools
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import FloatArray, UnifiedGenerator
+    from glass.cosmology import Cosmology
+
+
+def redshifts(
+    n: int | FloatArray,
+    w: glass.RadialWindow,
+    *,
+    rng: UnifiedGenerator | None = None,
+) -> FloatArray:
+    """
+    Sample redshifts from a radial window function.
+
+    This function samples *n* redshifts from a distribution that follows
+    the given radial window function *w*.
+
+    Parameters
+    ----------
+    n
+        Number of redshifts to sample. If an array is given, the
+        results are concatenated.
+    w
+        Radial window function.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Returns
+    -------
+        Random redshifts following the radial window function.
+
+    """
+    return redshifts_from_nz(n, w.za, w.wa, rng=rng, warn=False)
+
+
+def redshifts_from_nz(
+    count: int | FloatArray,
+    z: FloatArray,
+    nz: FloatArray,
+    *,
+    rng: UnifiedGenerator | None = None,
+    warn: bool = True,
+) -> FloatArray:
+    """
+    Generate galaxy redshifts from a source distribution.
+
+    The function supports sampling from multiple populations of
+    redshifts if *count* is an array or if there are additional axes in
+    the *z* or *nz* arrays. In this case, the shape of *count* and the
+    leading dimensions of *z* and *nz* are broadcast to a common shape,
+    and redshifts are sampled independently for each extra dimension.
+    The results are concatenated into a flat array.
+
+    Parameters
+    ----------
+    count
+        Number of redshifts to sample. If an array is given, its shape
+        is broadcast against the leading axes of *z* and *nz*.
+    z
+        Source distribution. Leading axes are broadcast against the
+        shape of *count*.
+    nz
+        Source distribution. Leading axes are broadcast against the
+        shape of *count*.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    warn
+        Throw relevant warnings.
+
+    Returns
+    -------
+        Redshifts sampled from the given source distribution. For
+        inputs with extra dimensions, returns a flattened 1-D array of
+        samples from all populations.
+
+    """
+    xp = array_api_compat.array_namespace(count, z, nz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    if warn:
+        warnings.warn(
+            "when sampling galaxies, redshifts_from_nz() is often not the function you"
+            " want. Try redshifts() instead. Use warn=False to suppress this warning.",
+            stacklevel=2,
+        )
+
+    # get default RNG if not given
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # bring inputs' leading axes into common shape
+    dims, *rest = glass.arraytools.broadcast_leading_axes((count, 0), (z, 1), (nz, 1))
+    count_out, z_out, nz_out = rest
+
+    # list of results for all dimensions
+    redshifts = xp.empty(xp.sum(count_out))
+
+    # keep track of the number of sampled redshifts
+    total = 0
+
+    # go through extra dimensions; also works if dims is empty
+    for k in uxpx.ndindex(dims):
+        nz_out_slice = nz_out[(*k, ...)] if k != () else nz_out
+        z_out_slice = z_out[(*k, ...)] if k != () else z_out
+
+        # compute the CDF of each galaxy population
+        cdf = glass.arraytools.cumulative_trapezoid(nz_out_slice, z_out_slice)
+        cdf /= cdf[-1]
+
+        # sample redshifts and store result
+        redshifts[total : total + count_out[k]] = uxpx.interp(
+            rng.uniform(0, 1, size=int(count_out[k])),
+            cdf,
+            z_out_slice,
+        )
+        total += count_out[k]  # type: ignore[assignment]
+
+    assert total == redshifts.size  # noqa: S101
+
+    return redshifts
+
+
+def galaxy_shear(  # noqa: PLR0913
+    lon: FloatArray,
+    lat: FloatArray,
+    eps: FloatArray,
+    kappa: FloatArray,
+    gamma1: FloatArray,
+    gamma2: FloatArray,
+    *,
+    reduced_shear: bool = True,
+) -> FloatArray:
+    """
+    Observed galaxy shears from weak lensing.
+
+    Takes lensing maps for convergence and shear and produces a lensed
+    ellipticity (shear) for each intrinsic galaxy ellipticity.
+
+    Parameters
+    ----------
+    lon
+        Array for galaxy longitudes.
+    lat
+        Array for galaxy latitudes.
+    eps
+        Array of galaxy :term:`ellipticity`.
+    kappa
+        HEALPix map for convergence.
+    gamma1
+        HEALPix maps for a component of shear.
+    gamma2
+        HEALPix maps for a component of shear.
+    reduced_shear
+        If ``False``, galaxy shears are not reduced
+        by the convergence. Default is ``True``.
+
+    Returns
+    -------
+        An array of complex-valued observed galaxy shears
+        (lensed ellipticities).
+
+    """
+    nside = healpix.npix2nside(np.broadcast(kappa, gamma1, gamma2).shape[-1])
+
+    size = np.broadcast(lon, lat, eps).size
+
+    # output arrays
+    k = np.empty(size)
+    g = np.empty(size, dtype=complex)
+
+    # get the lensing maps at galaxy position
+    for i in range(0, size, 10000):
+        s = slice(i, i + 10000)
+        ipix = healpix.ang2pix(nside, lon[s], lat[s], lonlat=True)
+        k[s] = kappa[ipix]
+        g.real[s] = gamma1[ipix]
+        g.imag[s] = gamma2[ipix]
+
+    if reduced_shear:
+        # compute reduced shear in place
+        g /= 1 - k
+
+        # compute lensed ellipticities
+        g = (eps + g) / (1 + g.conj() * eps)
+    else:
+        # simple sum of shears
+        g += eps
+
+    return g
+
+
+def gaussian_phz(  # noqa: PLR0913
+    z: float | FloatArray,
+    sigma_0: float | FloatArray,
+    *,
+    lower: float | FloatArray | None = None,
+    upper: float | FloatArray | None = None,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> FloatArray:
+    r"""
+    Photometric redshifts assuming a Gaussian error.
+
+    A simple toy model of photometric redshift errors that assumes a
+    Gaussian error with redshift-dependent standard deviation
+    :math:`\sigma(z) = (1 + z) \sigma_0` [Amara07]_.
+
+    Parameters
+    ----------
+    z
+        True redshifts.
+    sigma_0
+        Redshift error in the tomographic binning at zero redshift.
+    lower
+        Bounds for the returned photometric redshifts.
+    upper
+        Bounds for the returned photometric redshifts.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        Photometric redshifts assuming Gaussian errors, of the same
+        shape as *z*.
+
+    Raises
+    ------
+    ValueError
+        If the bounds are not consistent.
+
+    Warnings
+    --------
+    The *lower* and *upper* bounds are implemented using plain rejection
+    sampling from the non-truncated normal distribution. If bounds are
+    used, they should always contain significant probability mass.
+
+    See Also
+    --------
+    glass.tomo_nz_gausserr:
+        Create tomographic redshift distributions assuming the same model.
+
+    Examples
+    --------
+    See the :doc:`/examples/1-basic/photoz` example.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(
+            z,
+            sigma_0,
+            lower,
+            upper,
+            use_compat=False,
+        )
+
+    # Ensure inputs are arrays to allow lib utilisation
+    z_arr = xp.asarray(z)
+    sigma_0_arr = xp.asarray(sigma_0)
+
+    # get default RNG if not given
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # Ensure lower and upper are arrays that have the same shape and type
+    lower_arr = xp.asarray(0.0 if lower is None else lower, dtype=xp.float64)
+    upper_arr = xp.asarray(xp.inf if upper is None else upper, dtype=xp.float64)
+    if lower is None and upper is not None:
+        lower_arr = xp.zeros_like(upper_arr, dtype=xp.float64)
+    if upper is None and lower is not None:
+        upper_arr = xp.full_like(lower_arr, fill_value=np.inf, dtype=xp.float64)
+
+    sigma = xp.add(1, z_arr) * sigma_0_arr
+    dims = sigma.shape
+    zphot = xp.asarray(rng.normal(z_arr, sigma))
+
+    # Check for valid user input
+    if (lower_arr.ndim == upper_arr.ndim != 0) and not (
+        lower_arr.shape == upper_arr.shape == zphot.shape
+    ):
+        msg = "lower and upper must best scalars or have the same shape as z"
+        raise ValueError(msg)
+    if not xp.all(lower_arr < upper_arr):
+        msg = "requires lower < upper"
+        raise ValueError(msg)
+
+    if not dims:
+        while zphot < lower_arr or zphot > upper_arr:
+            zphot = xp.asarray(rng.normal(z_arr, sigma))
+    else:
+        z_arr = xp.broadcast_to(z_arr, dims)
+        trunc = (zphot < lower_arr) | (zphot > upper_arr)
+        while xp.count_nonzero(trunc) > 0:
+            zphot = xp.where(trunc, rng.normal(z_arr, sigma), zphot)
+            trunc = (zphot < lower_arr) | (zphot > upper_arr)
+
+    return zphot
+
+
+def _kappa_ia_nla(  # noqa: PLR0913
+    delta: FloatArray,
+    zeff: float,
+    a_ia: float,
+    cosmo: Cosmology,
+    *,
+    z0: float = 0.0,
+    eta: float = 0.0,
+    lbar: float = 0.0,
+    l0: float = 1e-9,
+    beta: float = 0.0,
+) -> FloatArray:
+    r"""
+    Effective convergence from intrinsic alignments using the NLA model.
+
+    Parameters
+    ----------
+    delta
+        Matter density contrast.
+    zeff
+        Effective redshift of the matter field.
+    a_ia
+        Intrinsic alignments amplitude.
+    cosmo
+        Cosmology instance.
+    z0
+        Reference redshift for the redshift dependence.
+    eta
+        Power of the redshift dependence.
+    lbar
+        Mean luminosity of the galaxy sample.
+    l0
+        Reference luminosity for the luminosity dependence.
+    beta
+        Power of the luminosity dependence.
+
+    Returns
+    -------
+        The effective convergence due to intrinsic alignments.
+
+    Notes
+    -----
+    The Non-linear Alignments Model (NLA) describes an effective
+    convergence :math:`\kappa_{\rm IA}` that models the effect of
+    intrinsic alignments. It is computed from the matter density
+    contrast :math:`\delta` as [Catelan01_] [Bridle07]_
+
+    .. math::
+
+        \kappa_{\rm IA} = f_{\rm NLA} \, \delta \;,
+
+    where the NLA factor :math:`f_{\rm NLA}` is defined as [Johnston19]_ [Tessore23]_
+
+    .. math::
+
+        f_{\rm{NLA}}
+        = -A_{\rm IA} \, \frac{C_1 \, \bar{\rho}(z)}{D(z)} \,
+            \biggl(\frac{1+z}{1+z_0}\biggr)^\eta \,
+            \biggl(\frac{\bar{L}}{L_0}\biggr)^\beta \;,
+
+    with
+
+    * :math:`A_{\rm IA}` the intrinsic alignments amplitude,
+    * :math:`C_1` a normalisation constant [Hirata04]_,
+    * :math:`z` the effective redshift of the model,
+    * :math:`\bar{\rho}` the mean matter density,
+    * :math:`D` the growth factor,
+    * :math:`\eta` the power that describes the redshift-dependence with
+      respect to :math:`z_0`,
+    * :math:`\bar{L}` the mean luminosity of the galaxy sample, and
+    * :math:`\beta` the power that describes the luminosity-dependence
+      :math:`\bar{L}` with respect to :math:`L_0`.
+
+    """
+    c1 = 5e-14 / cosmo.h**2  # Solar masses per cubic Mpc
+    rho_c1 = c1 * cosmo.critical_density0
+
+    prefactor = -a_ia * rho_c1 * cosmo.Omega_m0
+    inverse_linear_growth = 1.0 / cosmo.growth_factor(zeff)
+    redshift_dependence = ((1 + zeff) / (1 + z0)) ** eta
+    luminosity_dependence = (lbar / l0) ** beta
+
+    f_nla = (
+        prefactor * inverse_linear_growth * redshift_dependence * luminosity_dependence
+    )
+
+    return delta * f_nla
diff --git a/glass/glass/grf/__init__.py b/glass/glass/grf/__init__.py
new file mode 100644
index 0000000..9f3e017
--- /dev/null
+++ b/glass/glass/grf/__init__.py
@@ -0,0 +1,29 @@
+"""Transformations of Gaussian random fields."""
+
+__all__ = [
+    "Lognormal",
+    "Normal",
+    "SquaredNormal",
+    "Transformation",
+    "compute",
+    "corr",
+    "dcorr",
+    "icorr",
+    "solve",
+]
+
+from glass.grf._core import (
+    Transformation,
+    compute,
+    corr,
+    dcorr,
+    icorr,
+)
+from glass.grf._solver import (
+    solve,
+)
+from glass.grf._transformations import (
+    Lognormal,
+    Normal,
+    SquaredNormal,
+)
diff --git a/glass/glass/grf/_core.py b/glass/glass/grf/_core.py
new file mode 100644
index 0000000..c7574e9
--- /dev/null
+++ b/glass/glass/grf/_core.py
@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Protocol
+
+import transformcl
+
+if TYPE_CHECKING:
+    from types import NotImplementedType
+
+    from glass._types import AnyArray
+
+
+class Transformation(Protocol):
+    """Protocol for transformations of Gaussian random fields."""
+
+    def __call__(self, x: AnyArray, var: float, /) -> AnyArray:
+        """
+        Transform a Gaussian random field *x* with variance *var*.
+
+        Parameters
+        ----------
+        x
+            The Gaussian random field to be transformed.
+        var
+            Variance of the Gaussian random field.
+
+        Returns
+        -------
+            The transformed Gaussian random field.
+
+        """
+
+    def corr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        """Implementation of the corr function."""
+
+    def icorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        """Implementation of the icorr function."""
+
+    def dcorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        """Implementation of the dcorr function."""
+
+
+def corr(t1: Transformation, t2: Transformation, x: AnyArray, /) -> AnyArray:
+    """
+    Transform a Gaussian angular correlation function.
+
+    Parameters
+    ----------
+    t1, t2
+        Transformations of the Gaussian random field.
+    x
+        The Gaussian angular correlation function.
+
+    Returns
+    -------
+        The transformed angular correlation function.
+
+    """
+    result = t1.corr(t2, x)
+    if result is not NotImplemented:
+        return result
+    result = t2.corr(t1, x)
+    if result is not NotImplemented:
+        return result
+    msg = f"{t1.__class__.__name__} x {t2.__class__.__name__}"
+    raise NotImplementedError(msg)
+
+
+def icorr(t1: Transformation, t2: Transformation, x: AnyArray, /) -> AnyArray:
+    """
+    Inverse-transform an angular correlation function.
+
+    Parameters
+    ----------
+    t1, t2
+        Transformations of the Gaussian random field.
+    x
+        The transformed angular correlation function.
+
+    Returns
+    -------
+        The Gaussian angular correlation function.
+
+    """
+    result = t1.icorr(t2, x)
+    if result is not NotImplemented:
+        return result
+    result = t2.icorr(t1, x)
+    if result is not NotImplemented:
+        return result
+    msg = f"{t1.__class__.__name__} x {t2.__class__.__name__}"
+    raise NotImplementedError(msg)
+
+
+def dcorr(t1: Transformation, t2: Transformation, x: AnyArray, /) -> AnyArray:
+    """
+    Derivative of the angular correlation function transform.
+
+    Parameters
+    ----------
+    t1, t2
+        Transformations of the Gaussian random field.
+    x
+        The Gaussian angular correlation function.
+
+    Returns
+    -------
+        The derivative of the transformed angular correlation function.
+
+    """
+    result = t1.dcorr(t2, x)
+    if result is not NotImplemented:
+        return result
+    result = t2.dcorr(t1, x)
+    if result is not NotImplemented:
+        return result
+    msg = f"{t1.__class__.__name__} x {t2.__class__.__name__}"
+    raise NotImplementedError(msg)
+
+
+def compute(
+    cl: AnyArray,
+    t1: Transformation,
+    t2: Transformation | None = None,
+) -> AnyArray:
+    """
+    Compute a band-limited Gaussian angular power spectrum for the
+    target spectrum *cl* and the transformations *t1* and *t2*.  If *t2*
+    is not given, it is assumed to be the same as *t1*.
+
+    Parameters
+    ----------
+    cl
+        The angular power spectrum after the transformations.
+    t1, t2
+        Transformations applied to the Gaussian random field(s).
+
+    Returns
+    -------
+        Gaussian angular power spectrum.
+
+    Examples
+    --------
+    Compute a Gaussian angular power spectrum ``gl`` for a lognormal
+    transformation::
+
+        t = glass.grf.Lognormal()
+        gl = glass.grf.compute(cl, t)
+
+    See Also
+    --------
+    glass.grf.solve: Iterative solver for non-band-limited spectra.
+
+    """
+    if t2 is None:
+        t2 = t1
+
+    # transform C_l to C(\theta), apply transformation, and transform back
+    return transformcl.corrtocl(icorr(t1, t2, transformcl.cltocorr(cl)))
diff --git a/glass/glass/grf/_solver.py b/glass/glass/grf/_solver.py
new file mode 100644
index 0000000..6d59bda
--- /dev/null
+++ b/glass/glass/grf/_solver.py
@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+from transformcl import cltocorr, corrtocl
+
+import glass.grf
+
+if TYPE_CHECKING:
+    from glass._types import AnyArray
+
+
+def _relerr(dx: AnyArray, x: AnyArray) -> float:
+    """Compute the relative error max(|dx/x|)."""
+    q = np.divide(dx, x, where=(dx != 0), out=np.zeros_like(dx))
+    return np.fabs(q).max()  # type: ignore[no-any-return]
+
+
+def solve(  # noqa: PLR0912, PLR0913
+    cl: AnyArray,
+    t1: glass.grf.Transformation,
+    t2: glass.grf.Transformation | None = None,
+    *,
+    pad: int = 0,
+    initial: AnyArray | None = None,
+    cltol: float = 1e-5,
+    gltol: float = 1e-5,
+    maxiter: int = 20,
+    monopole: float | None = None,
+) -> tuple[AnyArray, AnyArray, int]:
+    """
+    Solve for a Gaussian angular power spectrum.
+
+    Given the input angular power spectrum *cl* and a pair of
+    transformations *t1* and *t2*, computes a Gaussian angular spectrum
+    that reproduces *cl* after the transformations are applied.  This is
+    done using the iterative solver proposed in [Tessore23]_.
+
+    The internal padding of the solver is set by *pad*, and the initial
+    solution can be passed as *initial*.  The convergence is controlled
+    by the relative errors *cltol* and *gltol*, and the maximum number
+    of iterations *maxiter*.
+
+    If *monopole* is provided, the monopole of the Gaussian angular
+    power spectrum is fixed to that value and ignored by the solver.
+
+    Returns a tuple *gl*, *cl*, *info* where *gl* is the Gaussian
+    angular power spectrum solution, *cl* is the realised angular power
+    spectrum after transformation, and *info* indicates success of
+    failure of the solution.  Possible *info* values are
+
+    * ``0``, solution did not converge in *maxiter* iterations;
+    * ``1``, solution converged in *cl* relative error;
+    * ``2``, solution converged in *gl* relative error;
+    * ``3``, solution converged in both *cl* and *gl* relative error.
+
+    Parameters
+    ----------
+    cl
+        The angular power spectrum after the transformations.
+    t1, t2
+        Transformations applied to the Gaussian random field(s).
+    pad
+        Internal padding applied to the transforms.
+    initial
+        Initial solution. If not provided, uses the result of
+        :func:`glass.grf.compute`.
+    cltol
+        Relative error for convergence in the desired output.
+    gltol
+        Relative error for convergence in the solution.
+    maxiter
+        Maximum number of iterations of the solver.
+    monopole
+        Fix the monopole of the solution to the given value.
+
+    See Also
+    --------
+    glass.grf.compute : Direct computation for band-limited spectra.
+
+    """
+    if t2 is None:
+        t2 = t1
+
+    n = len(cl)
+    if pad < 0:
+        msg = "pad must be a positive integer"
+        raise ValueError(msg)
+
+    if initial is None:
+        gl = corrtocl(glass.grf.icorr(t1, t2, cltocorr(cl)))
+    else:
+        gl = np.zeros(n)
+        gl[: len(initial)] = initial[:n]
+
+    if monopole is not None:
+        gl[0] = monopole
+
+    gt = cltocorr(np.pad(gl, (0, pad)))
+    rl = corrtocl(glass.grf.corr(t1, t2, gt))
+    fl = rl[:n] - cl
+    if monopole is not None:
+        fl[0] = 0
+    clerr = _relerr(fl, cl)
+
+    # this is a very basic Gauss-Newton solver
+    # see https://arxiv.org/pdf/2302.01942 for details
+    info = 0
+    for _ in range(maxiter):
+        if clerr <= cltol:
+            info |= 1
+        if info > 0:
+            break
+
+        ft = cltocorr(np.pad(fl, (0, pad)))
+        dt = glass.grf.dcorr(t1, t2, gt)
+        xl = -corrtocl(ft / dt)[:n]
+        if monopole is not None:
+            xl[0] = 0
+
+        # we know the "direction" of the step xl at this point
+        # make xl smaller (by half) until we get an improved solution
+        while True:
+            gl_ = gl + xl
+            gt_ = cltocorr(np.pad(gl_, (0, pad)))
+            rl_ = corrtocl(glass.grf.corr(t1, t2, gt_))
+            fl_ = rl_[:n] - cl
+            if monopole is not None:
+                fl_[0] = 0
+            clerr_ = _relerr(fl_, cl)
+            if clerr_ <= clerr:
+                break
+            xl /= 2
+
+        if _relerr(xl, gl) <= gltol:
+            info |= 2
+
+        gl, gt, rl, fl, clerr = gl_, gt_, rl_, fl_, clerr_
+
+    return gl, rl, info
diff --git a/glass/glass/grf/_transformations.py b/glass/glass/grf/_transformations.py
new file mode 100644
index 0000000..a481312
--- /dev/null
+++ b/glass/glass/grf/_transformations.py
@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from types import NotImplementedType
+
+    from glass._types import AnyArray
+    from glass.grf import Transformation
+
+
+@dataclass
+class Normal:
+    """
+    Transformation for normal fields.
+
+    .. math::
+
+       t(X) = X
+
+    This is the identity transformation.
+
+    """
+
+    def __call__(self, x: AnyArray, _var: float, /) -> AnyArray:
+        """Return *x* unchanged."""
+        return x
+
+    def corr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        if type(other) is Normal:
+            return x
+        return NotImplemented
+
+    def icorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        if type(other) is Normal:
+            return x
+        return NotImplemented
+
+    def dcorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        if type(other) is Normal:
+            return 1.0 + (0 * x)
+        return NotImplemented
+
+
+@dataclass
+class Lognormal:
+    r"""
+    Transformation for lognormal fields.
+
+    .. math::
+
+       t(X) = \lambda \bigl[\exp(X - \tfrac{\sigma^2}{2}) - 1\bigr]
+
+    The "lognormal shift" parameter :math:`\lambda` is the scale
+    parameter of the distribution, with determines the smallest
+    possible value :math:`-\lambda` of the lognormal field.
+
+    Parameters
+    ----------
+    lamda
+        The parameter :math:`\lambda`.
+
+    """
+
+    lamda: float = 1.0
+
+    def __call__(self, x: AnyArray, var: float, /) -> AnyArray:
+        """Transform *x* into a lognormal field."""
+        xp = x.__array_namespace__()
+        x = xp.expm1(x - var / 2)
+        if self.lamda != 1.0:
+            x = self.lamda * x
+        return x
+
+    def corr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        xp = x.__array_namespace__()
+
+        if type(other) is Lognormal:
+            return self.lamda * other.lamda * xp.expm1(x)
+
+        if type(other) is Normal:
+            return self.lamda * x
+
+        return NotImplemented
+
+    def icorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        xp = x.__array_namespace__()
+
+        if type(other) is Lognormal:
+            return xp.log1p(x / (self.lamda * other.lamda))
+
+        if type(other) is Normal:
+            return x / self.lamda
+
+        return NotImplemented
+
+    def dcorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        xp = x.__array_namespace__()
+
+        if type(other) is Lognormal:
+            return self.lamda * other.lamda * xp.exp(x)
+
+        if type(other) is Normal:
+            return self.lamda + (0.0 * x)
+
+        return NotImplemented
+
+
+@dataclass
+class SquaredNormal:
+    r"""
+    Transformation for squared normal fields as introduced by
+    [Tessore25]_.
+
+    .. math::
+
+       t(X) = \lambda \, \bigl[(X - a)^2 - 1\bigr]
+
+    The transformation is characterised by the shape parameter *a*,
+    which is related to the variance :math:`\sigma^2` of the Gaussian
+    random field, :math:`a = \sqrt{1 - \sigma^2}`.
+
+    The "shift" parameter :math:`\lambda` is the scale parameter of the
+    distribution, with determines the smallest possible value
+    :math:`-\lambda` of the squared normal field.
+
+    Parameters
+    ----------
+    a
+        The parameter :math:`a`.
+    lamda
+        The parameter :math:`\lambda`.
+
+    """
+
+    a: float
+    lamda: float = 1.0
+
+    def __call__(self, x: AnyArray, _var: float, /) -> AnyArray:
+        """Transform *x* into a squared normal field."""
+        x = (x - self.a) ** 2 - 1
+        if self.lamda != 1.0:
+            x = self.lamda * x
+        return x
+
+    def corr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        if type(other) is SquaredNormal:
+            aa = self.a * other.a
+            ll = self.lamda * other.lamda
+            return 2 * ll * x * (x + 2 * aa)
+
+        return NotImplemented
+
+    def icorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        xp = x.__array_namespace__()
+
+        if type(other) is SquaredNormal:
+            aa = self.a * other.a
+            ll = self.lamda * other.lamda
+            return xp.sqrt(x / (2 * ll) + aa**2) - aa
+
+        return NotImplemented
+
+    def dcorr(
+        self,
+        other: Transformation,
+        x: AnyArray,
+        /,
+    ) -> AnyArray | NotImplementedType:
+        if type(other) is SquaredNormal:
+            aa = self.a * other.a
+            ll = self.lamda * other.lamda
+            return 4 * ll * (x + aa)
+
+        return NotImplemented
diff --git a/glass/glass/jax.py b/glass/glass/jax.py
new file mode 100644
index 0000000..1ffbec7
--- /dev/null
+++ b/glass/glass/jax.py
@@ -0,0 +1,123 @@
+"""Wrapper for JAX RNG with a NumPy-like interface."""
+
+from __future__ import annotations
+
+import math
+from threading import Lock
+from typing import TYPE_CHECKING
+
+import jax.dtypes
+import jax.numpy as jnp
+import jax.random
+import jax.scipy
+from jax.typing import ArrayLike
+
+if TYPE_CHECKING:
+    from jaxtyping import Array, Integer, PRNGKeyArray, Shaped
+    from typing_extensions import Self
+
+    from glass._types import Size
+
+
+def _size(size: Size, *bcast: Array) -> tuple[int, ...]:
+    """
+    Return a size, which can be a single int or None, as a shape, which
+    is a tuple of int.
+    """
+    if size is None:
+        return jnp.broadcast_shapes(*map(jnp.shape, bcast)) if bcast else ()
+    return (size,) if isinstance(size, int) else size
+
+
+def trapezoid(y: Array, x: Array = None, dx: Array = 1.0, axis: int = -1) -> Array:
+    """Wrapper for jax.scipy.integrate.trapezoid."""
+    return jax.scipy.integrate.trapezoid(y, x=x, dx=dx, axis=axis)
+
+
+class Generator:
+    """JAX random number generation as a NumPy generator."""
+
+    __slots__ = ("key", "lock")
+    key: PRNGKeyArray
+    lock: Lock
+
+    @classmethod
+    def from_key(cls, key: PRNGKeyArray) -> Self:
+        """Wrap a JAX random key."""
+        if not isinstance(key, ArrayLike) or not jax.dtypes.issubdtype(
+            key.dtype,
+            jax.dtypes.prng_key,
+        ):
+            msg = "not a random key"
+            raise ValueError(msg)
+        rng = object.__new__(cls)
+        rng.key = key
+        rng.lock = Lock()
+        return rng
+
+    def __init__(self, seed: int | Array, *, impl: str | None = None) -> None:
+        """Create a wrapper instance with a new key."""
+        self.key = jax.random.key(seed, impl=impl)
+        self.lock = Lock()
+
+    @property
+    def __key(self) -> Array:
+        """Return next key for sampling while updating internal state."""
+        with self.lock:
+            self.key, key = jax.random.split(self.key)
+        return key
+
+    def split(self, size: Size = None) -> Array:
+        """Split random key."""
+        shape = _size(size)
+        with self.lock:
+            keys = jax.random.split(self.key, 1 + math.prod(shape))
+            self.key = keys[0]
+        return keys[1:].reshape(shape)
+
+    def spawn(self, n_children: int) -> list[Self]:
+        """Create new independent child generators."""
+        with self.lock:
+            self.key, *keys = jax.random.split(self.key, num=n_children + 1)
+        return list(map(self.from_key, keys))
+
+    def random(self, size: Size = None, dtype: Shaped[Array, ...] = float) -> Array:
+        """Return random floats in the half-open interval [0.0, 1.0)."""
+        return jax.random.uniform(self.__key, _size(size), dtype)
+
+    def normal(
+        self,
+        loc: Array | float = 0.0,
+        scale: Array | float = 1.0,
+        size: Size = None,
+        dtype: Shaped[Array, ...] = float,
+    ) -> Array:
+        """Draw samples from a Normal distribution (mean=loc, stdev=scale)."""
+        return loc + scale * jax.random.normal(self.__key, _size(size), dtype)
+
+    def poisson(
+        self,
+        lam: float,
+        size: Size = None,
+        dtype: Integer[Array, ...] = int,
+    ) -> Array:
+        """Draw samples from a Poisson distribution."""
+        return jax.random.poisson(self.__key, lam, size, dtype)
+
+    def standard_normal(
+        self,
+        size: Size = None,
+        dtype: Shaped[Array, ...] = float,
+    ) -> Array:
+        """Draw samples from a standard Normal distribution (mean=0, stdev=1)."""
+        return jax.random.normal(self.__key, _size(size), dtype)
+
+    def uniform(
+        self,
+        low: float = 0,
+        high: float = 1,
+        size: Size = None,
+        dtype: Shaped[Array, ...] = float,
+    ) -> Array:
+        """Draw samples from a Uniform distribution."""
+        return jax.random.uniform(self.__key, _size(size), dtype, low, high)
diff --git a/glass/glass/lensing.py b/glass/glass/lensing.py
new file mode 100644
index 0000000..8856034
--- /dev/null
+++ b/glass/glass/lensing.py
@@ -0,0 +1,687 @@
+"""
+Lensing
+=======
+
+.. currentmodule:: glass
+
+The following functions/classes provide functionality for simulating
+gravitational lensing by the matter distribution in the universe.
+
+Iterative lensing
+-----------------
+
+.. autoclass:: MultiPlaneConvergence
+.. autofunction:: multi_plane_matrix
+.. autofunction:: multi_plane_weights
+
+
+Lensing fields
+--------------
+
+.. autofunction:: from_convergence
+.. autofunction:: shear_from_convergence
+
+
+Applying lensing
+----------------
+
+.. autofunction:: deflect
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Literal, overload
+
+import healpy as hp
+import numpy as np
+
+import array_api_compat
+
+import glass._array_api_utils as _utils
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from types import ModuleType
+
+    import glass
+    from glass._types import ComplexArray, FloatArray
+    from glass.cosmology import Cosmology
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[True] = True,
+    deflection: Literal[False] = False,
+    shear: Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[FloatArray]:
+    # returns psi
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[False] = False,
+    deflection: Literal[True] = True,
+    shear: Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[ComplexArray]:
+    # returns alpha
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[False] = False,
+    deflection: Literal[False] = False,
+    shear: Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[ComplexArray]:
+    # returns gamma
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[True] = True,
+    deflection: Literal[True] = True,
+    shear: Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[
+    FloatArray,
+    ComplexArray,
+]:
+    # returns psi, alpha
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[True] = True,
+    deflection: Literal[False] = False,
+    shear: Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    FloatArray,
+    ComplexArray,
+]:
+    # returns psi, gamma
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[False] = False,
+    deflection: Literal[True] = True,
+    shear: Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    ComplexArray,
+    ComplexArray,
+]:
+    # returns alpha, gamma
+    ...
+
+
+@overload
+def from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: Literal[True] = True,
+    deflection: Literal[True] = True,
+    shear: Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    FloatArray,
+    ComplexArray,
+    ComplexArray,
+]:
+    # returns psi, alpha, gamma
+    ...
+
+
+def from_convergence(  # noqa: PLR0913
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    potential: bool = False,
+    deflection: bool = False,
+    shear: bool = False,
+    discretized: bool = True,
+) -> tuple[FloatArray | ComplexArray, ...]:
+    r"""
+    Compute other weak lensing maps from the convergence.
+
+    Takes a weak lensing convergence map and returns one or more of
+    deflection potential, deflection, and shear maps. The maps are
+    computed via spherical harmonic transforms.
+
+    Parameters
+    ----------
+    kappa
+        HEALPix map of the convergence field.
+    lmax
+        Maximum angular mode number to use in the transform.
+    potential
+        Which lensing maps to return.
+    deflection
+        Which lensing maps to return.
+    shear
+        Which lensing maps to return.
+    discretized
+        Correct the pixel window function in output maps.
+
+    Returns
+    -------
+    psi
+        Map of the lensing (or deflection) potential. Only returned if
+        ``potential`` is true.
+    alpha
+        Map of the deflection (complex). Only returned if ``deflection`` if true.
+    gamma
+        Map of the shear (complex). Only returned if ``shear`` is true.
+
+    Notes
+    -----
+    The weak lensing fields are computed from the convergence or
+    deflection potential in the following way. [Tessore23]_
+
+    Define the spin-raising and spin-lowering operators of the
+    spin-weighted spherical harmonics as
+
+    .. math::
+
+        \eth {}_sY_{lm}
+        = +\sqrt{(l-s)(l+s+1)} \, {}_{s+1}Y_{lm} \;, \\
+        \bar{\eth} {}_sY_{lm}
+        = -\sqrt{(l+s)(l-s+1)} \, {}_{s-1}Y_{lm} \;.
+
+    The convergence field :math:`\kappa` is related to the deflection
+    potential field :math:`\psi` by the Poisson equation,
+
+    .. math::
+
+        2 \kappa
+        = \eth\bar{\eth} \, \psi
+        = \bar{\eth}\eth \, \psi \;.
+
+    The convergence modes :math:`\kappa_{lm}` are hence related to the
+    deflection potential modes :math:`\psi_{lm}` as
+
+    .. math::
+
+        2 \kappa_{lm}
+        = -l \, (l+1) \, \psi_{lm} \;.
+
+    The :term:`deflection` :math:`\alpha` is the gradient of the
+    deflection potential :math:`\psi`. On the sphere, this is
+
+    .. math::
+
+        \alpha
+        = \eth \, \psi \;.
+
+    The deflection field has spin weight :math:`1` in the HEALPix
+    convention, in order for points to be deflected towards regions of
+    positive convergence. The modes :math:`\alpha_{lm}` of the
+    deflection field are hence
+
+    .. math::
+
+        \alpha_{lm}
+        = \sqrt{l \, (l+1)} \, \psi_{lm} \;.
+
+    The shear field :math:`\gamma` is related to the deflection
+    potential :math:`\psi` and deflection :math:`\alpha` as
+
+    .. math::
+
+        2 \gamma
+        = \eth\eth \, \psi
+        = \eth \, \alpha \;,
+
+    and thus has spin weight :math:`2`. The shear modes
+    :math:`\gamma_{lm}` are related to the deflection potential modes as
+
+    .. math::
+
+        2 \gamma_{lm}
+        = \sqrt{(l+2) \, (l+1) \, l \, (l-1)} \, \psi_{lm} \;.
+
+    """
+    # no output means no computation, return empty tuple
+    if not (potential or deflection or shear):
+        return ()
+
+    # get the NSIDE parameter
+    nside = hp.get_nside(kappa)
+    if lmax is None:
+        lmax = 3 * nside - 1
+
+    # compute alm
+    alm = hp.map2alm(kappa, lmax=lmax, pol=False, use_pixel_weights=True)
+
+    # mode number; all conversions are factors of this
+    ell = np.arange(lmax + 1)
+
+    # this tuple will be returned
+    results: tuple[FloatArray | ComplexArray, ...] = ()
+
+    # convert convergence to potential
+    fl = np.divide(-2, ell * (ell + 1), where=(ell > 0), out=np.zeros(lmax + 1))
+    hp.almxfl(alm, fl, inplace=True)
+
+    # if potential is requested, compute map and add to output
+    if potential:
+        psi = hp.alm2map(alm, nside, lmax=lmax)
+        results += (psi,)
+
+    # if no spin-weighted maps are requested, stop here
+    if not (deflection or shear):
+        return results
+
+    # zero B-modes for spin-weighted maps
+    blm = np.zeros_like(alm)
+
+    # compute deflection alms in place
+    fl = np.sqrt(ell * (ell + 1))
+    # missing spin-1 pixel window function here
+    hp.almxfl(alm, fl, inplace=True)
+
+    # if deflection is requested, compute spin-1 maps and add to output
+    if deflection:
+        alpha = hp.alm2map_spin([alm, blm], nside, 1, lmax)
+        alpha = alpha[0] + 1j * alpha[1]
+        results += (alpha,)
+
+    # if no shear is requested, stop here
+    if not shear:
+        return results
+
+    # compute shear alms in place
+    # if discretised, factor out spin-0 kernel and apply spin-2 kernel
+    fl = np.sqrt((ell - 1) * (ell + 2), where=(ell > 0), out=np.zeros(lmax + 1))
+    fl /= 2
+    if discretized:
+        pw0, pw2 = hp.pixwin(nside, lmax=lmax, pol=True)
+        fl *= pw2 / pw0
+    hp.almxfl(alm, fl, inplace=True)
+
+    # transform to shear maps
+    gamma = hp.alm2map_spin([alm, blm], nside, 2, lmax)
+    gamma = gamma[0] + 1j * gamma[1]
+    results += (gamma,)
+
+    # all done
+    return results
+
+
+def shear_from_convergence(
+    kappa: FloatArray,
+    lmax: int | None = None,
+    *,
+    discretized: bool = True,
+) -> FloatArray:
+    """
+    Weak lensing shear from convergence.
+
+    Computes the shear from the convergence using a spherical harmonic
+    transform.
+
+    .. deprecated:: 2023.6
+       Use the more general :func:`glass.from_convergence` function instead.
+
+    Parameters
+    ----------
+    kappa
+        The convergence map.
+    lmax
+        The maximum angular mode number to use in the transform.
+    discretized
+        Whether to correct the pixel window function in the output map.
+
+    Returns
+    -------
+        The shear map.
+
+    """
+    nside = hp.get_nside(kappa)
+    if lmax is None:
+        lmax = 3 * nside - 1
+
+    # compute alm
+    alm = hp.map2alm(kappa, lmax=lmax, pol=False, use_pixel_weights=True)
+
+    # zero B-modes
+    blm = np.zeros_like(alm)
+
+    # factor to convert convergence alm to shear alm
+    ell = np.arange(lmax + 1)
+    fl = np.sqrt((ell + 2) * (ell + 1) * ell * (ell - 1))
+    fl /= np.clip(ell * (ell + 1), 1, None)
+    fl *= -1
+
+    # if discretised, factor out spin-0 kernel and apply spin-2 kernel
+    if discretized:
+        pw0, pw2 = hp.pixwin(nside, lmax=lmax, pol=True)
+        fl *= pw2 / pw0
+
+    # apply correction to E-modes
+    hp.almxfl(alm, fl, inplace=True)
+
+    # transform to shear maps
+    return hp.alm2map_spin([alm, blm], nside, 2, lmax)
+
+
+class MultiPlaneConvergence:
+    """Compute convergence fields iteratively from multiple matter planes."""
+
+    def __init__(self, cosmo: Cosmology) -> None:
+        """
+        Create a new instance to iteratively compute the convergence.
+
+        Parameters
+        ----------
+        cosmo
+            Cosmology instance.
+
+        """
+        self.cosmo = cosmo
+
+        # set up initial values of variables
+        self.z2: float | FloatArray = 0.0
+        self.z3: float | FloatArray = 0.0
+        self.x3: float = 0.0
+        self.w3: float = 0.0
+        self.r23: float = 1.0
+        self.delta3: FloatArray = np.array(0.0)
+        self.kappa2: FloatArray | None = None
+        self.kappa3: FloatArray | None = None
+
+    def add_window(self, delta: FloatArray, w: glass.RadialWindow) -> None:
+        """
+        Add a mass plane from a window function to the convergence.
+
+        The lensing weight is computed from the window function, and the
+        source plane redshift is the effective redshift of the window.
+
+        Parameters
+        ----------
+        delta
+            The mass plane.
+        w
+            The window function.
+
+        """
+        zsrc = w.zeff
+        lens_weight = float(np.trapezoid(w.wa, w.za) / np.interp(zsrc, w.za, w.wa))
+
+        self.add_plane(delta, zsrc, lens_weight)
+
+    def add_plane(
+        self,
+        delta: FloatArray,
+        zsrc: float | FloatArray,
+        wlens: float = 1.0,
+    ) -> None:
+        """
+        Add a mass plane at redshift ``zsrc`` to the convergence.
+
+        Parameters
+        ----------
+        delta
+            The mass plane.
+        zsrc
+            The redshift of the source plane.
+        wlens
+            The weight of the mass plane.
+
+        Raises
+        ------
+        ValueError
+            If the source redshift is not increasing.
+
+        """
+        if zsrc <= self.z3:
+            msg = "source redshift must be increasing"
+            raise ValueError(msg)
+
+        # cycle mass plane, ...
+        delta2, self.delta3 = self.delta3, delta
+
+        # redshifts of source planes, ...
+        z1, self.z2, self.z3 = self.z2, self.z3, zsrc
+
+        # and weights of mass plane
+        w2, self.w3 = self.w3, wlens
+
+        # extrapolation law
+        x2, self.x3 = (
+            self.x3,
+            self.cosmo.transverse_comoving_distance(self.z3)
+            / self.cosmo.hubble_distance,
+        )
+        r12 = self.r23
+        r13, self.r23 = self.cosmo.transverse_comoving_distance(
+            [z1, self.z2],
+            self.z3,
+        ) / (self.cosmo.hubble_distance * self.x3)
+        t = r13 / r12
+
+        # lensing weight of mass plane to be added
+        f = 3 * self.cosmo.Omega_m0 / 2
+        f *= x2 * self.r23
+        f *= (1 + self.z2) / self.cosmo.H_over_H0(self.z2)
+        f *= w2
+
+        # create kappa planes on first iteration
+        if self.kappa2 is None:
+            self.kappa2 = np.zeros_like(delta)
+            self.kappa3 = np.zeros_like(delta)
+
+        # cycle convergence planes
+        # normally: kappa1, kappa2, kappa3 = kappa2, kappa3, <empty>
+        # but then we set: kappa3 = (1-t)*kappa1 + ...
+        # so we can set kappa3 to previous kappa2 and modify in place
+        self.kappa2, self.kappa3 = self.kappa3, self.kappa2
+
+        # compute next convergence plane in place of last
+        self.kappa3 *= 1 - t
+        self.kappa3 += t * self.kappa2
+        self.kappa3 += f * delta2
+
+    @property
+    def zsrc(self) -> float | FloatArray:
+        """The redshift of the current convergence plane."""
+        return self.z3
+
+    @property
+    def kappa(self) -> FloatArray | None:
+        """The current convergence plane."""
+        return self.kappa3
+
+    @property
+    def delta(self) -> FloatArray:
+        """The current matter plane."""
+        return self.delta3
+
+    @property
+    def wlens(self) -> float:
+        """The weight of the current matter plane."""
+        return self.w3
+
+
+def multi_plane_matrix(
+    shells: Sequence[glass.RadialWindow],
+    cosmo: Cosmology,
+) -> FloatArray:
+    """
+    Compute the matrix of lensing contributions from each shell.
+
+    Parameters
+    ----------
+    shells
+        The shells of the mass distribution.
+    cosmo
+        Cosmology instance.
+
+    Returns
+    -------
+        The matrix of lensing contributions.
+
+    """
+    mpc = MultiPlaneConvergence(cosmo)
+    wmat = np.eye(len(shells))
+    for i, w in enumerate(shells):
+        mpc.add_window(wmat[i].copy(), w)
+        wmat[i, :] = mpc.kappa
+    return wmat
+
+
+def multi_plane_weights(
+    weights: FloatArray,
+    shells: Sequence[glass.RadialWindow],
+    cosmo: Cosmology,
+) -> FloatArray:
+    """
+    Compute effective weights for multi-plane convergence.
+
+    Converts an array *weights* of relative weights for each shell
+    into the equivalent array of relative lensing weights.
+
+    This is the discretised version of the integral that turns a
+    redshift distribution :math:`n(z)` into the lensing efficiency
+    sometimes denoted :math:`g(z)` or :math:`q(z)`.
+
+    Parameters
+    ----------
+    weights
+        Relative weight of each shell. The first axis must broadcast
+        against the number of shells, and is normalised internally.
+    shells
+        Window functions of the shells.
+    cosmo
+        Cosmology instance.
+
+    Returns
+    -------
+        The relative lensing weight of each shell.
+
+    Raises
+    ------
+    ValueError
+        If the shape of *weights* does not match the number of shells.
+
+    """
+    # ensure shape of weights ends with the number of shells
+    shape = np.shape(weights)
+    if not shape or shape[0] != len(shells):
+        msg = "shape mismatch between weights and shells"
+        raise ValueError(msg)
+    # normalise weights
+    weights = weights / np.sum(weights, axis=0)
+    # combine weights and the matrix of lensing contributions
+    mat = multi_plane_matrix(shells, cosmo)
+    return np.matmul(mat.T, weights)
+
+
+def deflect(
+    lon: float | FloatArray,
+    lat: float | FloatArray,
+    alpha: complex | ComplexArray | FloatArray,
+    xp: ModuleType | None = None,
+) -> tuple[
+    FloatArray,
+    FloatArray,
+]:
+    r"""
+    Apply deflections to positions.
+
+    .. deprecated:: >2025.2
+       Use :func:`displace` instead.
+
+    Takes an array of :term:`deflection` values and applies them
+    to the given positions.
+
+    Parameters
+    ----------
+    lon
+        Longitudes to be deflected.
+    lat
+        Latitudes to be deflected.
+    alpha
+        Deflection values. Must be complex-valued or have a leading
+        axis of size 2 for the real and imaginary component.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        The longitudes and latitudes after deflection.
+
+    Raises
+    ------
+    ValueError
+        If neither an array nor the array backend ``xp`` are provided.
+
+    Notes
+    -----
+    Deflections on the sphere are :term:`defined <deflection>` as
+    follows:  The complex deflection :math:`\alpha` transports a point
+    on the sphere an angular distance :math:`|\alpha|` along the
+    geodesic with bearing :math:`\arg\alpha` in the original point.
+
+    In the language of differential geometry, this function is the
+    exponential map.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(lon, lat, alpha, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    alpha = xp.asarray(alpha)
+    if xp.isdtype(alpha.dtype, "complex floating"):  # type: ignore[union-attr]
+        alpha1, alpha2 = xp.real(alpha), xp.imag(alpha)
+    else:
+        alpha1, alpha2 = alpha  # type: ignore[misc]
+
+    # we know great-circle navigation:
+    # θ' = arctan2(√[(cosθ sin|α| - sinθ cos|α| cosγ)² + (sinθ sinγ)²],
+    #              cosθ cos|α| + sinθ sin|α| cosγ)
+    # δ = arctan2(sin|α| sinγ, sinθ cos|α| - cosθ sin|α| cosγ)
+
+    t = uxpx.radians(xp.asarray(lat))
+    ct, st = xp.sin(t), xp.cos(t)  # sin and cos flipped: lat not co-lat
+
+    a = xp.hypot(alpha1, alpha2)  # abs(alpha)
+    g = xp.atan2(alpha2, alpha1)  # arg(alpha)
+    ca, sa = xp.cos(a), xp.sin(a)
+    cg, sg = xp.cos(g), xp.sin(g)
+
+    # flipped atan2 arguments for lat instead of co-lat
+    tp = xp.atan2(ct * ca + st * sa * cg, xp.hypot(ct * sa - st * ca * cg, st * sg))
+
+    d = xp.atan2(sa * sg, st * ca - ct * sa * cg)
+
+    return lon - uxpx.degrees(d), uxpx.degrees(tp)
diff --git a/glass/glass/observations.py b/glass/glass/observations.py
new file mode 100644
index 0000000..34e6028
--- /dev/null
+++ b/glass/glass/observations.py
@@ -0,0 +1,366 @@
+"""
+Observations
+============
+
+.. currentmodule:: glass
+
+The following functions provide functionality for simulating
+observational effects of surveys.
+
+
+Redshift distribution
+---------------------
+
+.. autofunction:: gaussian_nz
+.. autofunction:: smail_nz
+.. autofunction:: fixed_zbins
+.. autofunction:: equal_dens_zbins
+.. autofunction:: tomo_nz_gausserr
+
+
+Visibility
+----------
+
+.. autofunction:: vmap_galactic_ecliptic
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+import itertools
+import math
+from typing import TYPE_CHECKING
+
+import healpy as hp
+import numpy as np
+
+import array_api_compat
+
+import glass._array_api_utils as _utils
+import glass.arraytools
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import FloatArray
+
+
+def vmap_galactic_ecliptic(
+    nside: int,
+    galactic: tuple[float, float] = (30, 90),
+    ecliptic: tuple[float, float] = (20, 80),
+) -> FloatArray:
+    """
+    Visibility map masking galactic and ecliptic plane.
+
+    This function returns a :term:`visibility map` that blocks out stripes for
+    the galactic and ecliptic planes. The location of the stripes is set with
+    optional parameters.
+
+    Parameters
+    ----------
+    nside
+        The NSIDE parameter of the resulting HEALPix map.
+    galactic
+        The location of the galactic plane in the respective coordinate system.
+    ecliptic
+        The location of the ecliptic plane in the respective coordinate system.
+
+    Returns
+    -------
+        A HEALPix :term:`visibility map`.
+
+    Raises
+    ------
+    TypeError
+        If the ``galactic`` argument is not a pair of numbers.
+    TypeError
+        If the ``ecliptic`` argument is not a pair of numbers.
+
+    """
+    if np.ndim(galactic) != 1 or len(galactic) != 2:
+        msg = "galactic stripe must be a pair of numbers"
+        raise TypeError(msg)
+    if np.ndim(ecliptic) != 1 or len(ecliptic) != 2:
+        msg = "ecliptic stripe must be a pair of numbers"
+        raise TypeError(msg)
+
+    m = np.ones(hp.nside2npix(nside))
+    m[hp.query_strip(nside, *galactic)] = 0
+    m = hp.Rotator(coord="GC").rotate_map_pixel(m)
+    m[hp.query_strip(nside, *ecliptic)] = 0
+    return hp.Rotator(coord="CE").rotate_map_pixel(m)
+
+
+def gaussian_nz(
+    z: FloatArray,
+    mean: float | FloatArray,
+    sigma: float | FloatArray,
+    *,
+    norm: float | FloatArray | None = None,
+) -> FloatArray:
+    """
+    Gaussian redshift distribution.
+
+    The redshift follows a Gaussian distribution with the given mean and
+    standard deviation.
+
+    If ``mean`` or ``sigma`` are array_like, their axes will be the leading
+    axes of the redshift distribution.
+
+    Parameters
+    ----------
+    z
+        Redshift values of the distribution.
+    mean
+        Mean(s) of the redshift distribution.
+    sigma
+        Standard deviation(s) of the redshift distribution.
+    norm
+        If given, the normalisation of the distribution.
+
+    Returns
+    -------
+        The redshift distribution at the given ``z`` values.
+
+    """
+    xp = array_api_compat.array_namespace(z, mean, sigma, norm, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    mean = xp.asarray(mean, dtype=xp.float64)
+    sigma = xp.asarray(sigma, dtype=xp.float64)
+
+    mean = xp.reshape(mean, mean.shape + (1,) * z.ndim)  # type: ignore[union-attr]
+    sigma = xp.reshape(sigma, sigma.shape + (1,) * z.ndim)  # type: ignore[union-attr]
+
+    nz = xp.exp(-(((z - mean) / sigma) ** 2) / 2)
+    nz /= uxpx.trapezoid(nz, z, axis=-1)[..., xp.newaxis]
+
+    if norm is not None:
+        nz *= norm
+
+    return nz
+
+
+def smail_nz(
+    z: FloatArray,
+    z_mode: float | FloatArray,
+    alpha: float | FloatArray,
+    beta: float | FloatArray,
+    *,
+    norm: float | FloatArray | None = None,
+) -> FloatArray:
+    r"""
+    Redshift distribution following Smail et al. (1994).
+
+    The redshift follows the Smail et al. [Smail94]_ redshift distribution.
+
+    Parameters
+    ----------
+    z
+        Redshift values of the distribution.
+    z_mode
+        Mode of the redshift distribution, must be positive.
+    alpha
+        Power law exponent (z/z0)^\alpha, must be positive.
+    beta
+        Log-power law exponent exp[-(z/z0)^\beta], must be positive.
+    norm
+        If given, the normalisation of the distribution.
+
+    Returns
+    -------
+        The redshift distribution at the given ``z`` values.
+
+    Notes
+    -----
+    The probability distribution function :math:`p(z)` for redshift :math:`z`
+    is given by Amara & Refregier [Amara07]_ as
+
+    .. math::
+
+        p(z) \sim \left(\frac{z}{z_0}\right)^\alpha
+                    \exp\left[-\left(\frac{z}{z_0}\right)^\beta\right] \;,
+
+    where :math:`z_0` is matched to the given mode of the distribution.
+
+    """
+    xp = array_api_compat.array_namespace(
+        z,
+        z_mode,
+        alpha,
+        beta,
+        norm,
+        use_compat=False,
+    )
+    uxpx = _utils.XPAdditions(xp)
+
+    z_mode = xp.asarray(z_mode, dtype=xp.float64)[..., xp.newaxis]
+    alpha = xp.asarray(alpha, dtype=xp.float64)[..., xp.newaxis]
+    beta = xp.asarray(beta, dtype=xp.float64)[..., xp.newaxis]
+
+    pz = z**alpha * xp.exp(-alpha / beta * (z / z_mode) ** beta)
+    pz /= uxpx.trapezoid(pz, z, axis=-1)[..., xp.newaxis]
+
+    if norm is not None:
+        pz *= norm
+
+    return pz
+
+
+def fixed_zbins(
+    zmin: float,
+    zmax: float,
+    *,
+    nbins: int | None = None,
+    dz: float | None = None,
+    xp: ModuleType,
+) -> list[tuple[float, float]]:
+    """
+    Tomographic redshift bins of fixed size.
+
+    This function creates contiguous tomographic redshift bins of fixed size.
+    It takes either the number or size of the bins.
+
+    Parameters
+    ----------
+    zmin
+        Extent of the redshift binning.
+    zmax
+        Extent of the redshift binning.
+    nbins
+        Number of redshift bins. Only one of ``nbins`` and ``dz`` can be given.
+    dz
+        Size of redshift bin. Only one of ``nbins`` and ``dz`` can be given.
+    xp
+        The array library backend to use for array operations.
+
+    Returns
+    -------
+        A list of redshift bin edges.
+
+    Raises
+    ------
+    ValueError
+        If both ``nbins`` and ``dz`` are given.
+
+    """
+    if nbins is not None and dz is None:
+        zbinedges = xp.linspace(zmin, zmax, nbins + 1)
+    elif nbins is None and dz is not None:
+        zbinedges = xp.arange(
+            zmin,
+            xp.nextafter(xp.asarray(zmax + dz), xp.asarray(zmax)),
+            dz,
+        )
+    else:
+        msg = "exactly one of nbins and dz must be given"
+        raise ValueError(msg)
+
+    return list(itertools.pairwise(zbinedges))
+
+
+def equal_dens_zbins(
+    z: FloatArray,
+    nz: FloatArray,
+    nbins: int,
+) -> list[tuple[float, float]]:
+    """
+    Equal density tomographic redshift bins.
+
+    This function subdivides a source redshift distribution into ``nbins``
+    tomographic redshift bins with equal density.
+
+    Parameters
+    ----------
+    z
+        The source redshift distribution. Must be one-dimensional.
+    nz
+        The source redshift distribution. Must be one-dimensional.
+    nbins
+        Number of redshift bins.
+
+    Returns
+    -------
+        A list of redshift bin edges.
+
+    """
+    xp = array_api_compat.array_namespace(z, nz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # compute the normalised cumulative distribution function
+    # first compute the cumulative integral (by trapezoidal rule)
+    # then normalise: the first z is at CDF = 0, the last z at CDF = 1
+    # interpolate to find the z values at CDF = i/nbins for i = 0, ..., nbins
+    cuml_nz = glass.arraytools.cumulative_trapezoid(nz, z)
+    cuml_nz /= cuml_nz[-1]
+    zbinedges = uxpx.interp(xp.linspace(0, 1, nbins + 1), cuml_nz, z)
+
+    return list(itertools.pairwise(zbinedges))
+
+
+def tomo_nz_gausserr(
+    z: FloatArray,
+    nz: FloatArray,
+    sigma_0: float,
+    zbins: list[tuple[float, float]],
+) -> FloatArray:
+    """
+    Tomographic redshift bins with a Gaussian redshift error.
+
+    This function takes a _true_ overall source redshift distribution ``z``,
+    ``nz`` and returns tomographic source redshift distributions for the
+    tomographic redshift bins given by ``zbins``. It is assumed that sources
+    are assigned a tomographic redshift bin with a Gaussian error [Amara07]_. The
+    standard deviation of the Gaussian depends on redshift and is given by
+    ``sigma(z) = sigma_0*(1 + z)``.
+
+    Parameters
+    ----------
+    z
+        The true source redshift distribution. Must be one-dimensional.
+    nz
+        The true source redshift distribution. Must be one-dimensional.
+    sigma_0
+        Redshift error in the tomographic binning at zero redshift.
+    zbins
+        List of redshift bin edges.
+
+    Returns
+    -------
+        The tomographic redshift bins convolved with a gaussian error.
+        Array has a shape (nbins, len(z))
+
+    See Also
+    --------
+    equal_dens_zbins:
+        produce equal density redshift bins
+    fixed_zbins:
+        produce redshift bins of fixed size
+
+    """
+    xp = array_api_compat.array_namespace(z, nz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # converting zbins into an array:
+    zbins_arr = xp.asarray(zbins)
+
+    # bin edges and adds a new axis
+    z_lower = zbins_arr[:, 0, xp.newaxis]
+    z_upper = zbins_arr[:, 1, xp.newaxis]
+
+    # we need a vectorised version of the error function:
+    erf = uxpx.vectorize(math.erf, otypes=(float,))
+
+    # compute the probabilities that redshifts z end up in each bin
+    # then apply probability as weights to given nz
+    # leading axis corresponds to the different bins
+    sz = 2**0.5 * sigma_0 * (1 + z)
+    # we need to call xp.asarray here because erf will return a numpy
+    # array for array libs which do not implement vectorize.
+    binned_nz = xp.asarray(erf((z - z_lower) / sz))
+    binned_nz -= xp.asarray(erf((z - z_upper) / sz))
+    binned_nz /= 1 + xp.asarray(erf(z / sz))
+    binned_nz *= nz
+
+    return binned_nz
diff --git a/glass/glass/points.py b/glass/glass/points.py
new file mode 100644
index 0000000..3691468
--- /dev/null
+++ b/glass/glass/points.py
@@ -0,0 +1,566 @@
+"""
+Random points
+=============
+
+.. currentmodule:: glass
+
+The following functions provide functionality for simulating point
+processes on the sphere and sampling random positions.
+
+Sampling
+--------
+
+.. autofunction:: positions_from_delta
+.. autofunction:: uniform_positions
+.. autofunction:: position_weights
+
+
+Bias
+----
+
+.. autofunction:: effective_bias
+
+
+Bias models
+-----------
+
+.. autofunction:: linear_bias
+.. autofunction:: loglinear_bias
+
+
+Displacing points
+-----------------
+
+.. autofunction:: displace
+.. autofunction:: displacement
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import healpix
+import numpy as np
+
+import array_api_compat
+
+import glass
+import glass._array_api_utils as _utils
+import glass.arraytools
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Generator
+    from types import ModuleType
+
+    from glass._types import (
+        ComplexArray,
+        DoubleArray,
+        FloatArray,
+        IntArray,
+        UnifiedGenerator,
+    )
+
+
+ARCMIN2_SPHERE = 60**6 // 100 / np.pi
+
+
+def effective_bias(
+    z: FloatArray,
+    bz: FloatArray,
+    w: glass.RadialWindow,
+) -> float | DoubleArray:
+    r"""
+    Effective bias parameter from a redshift-dependent bias function.
+
+    This function takes a redshift-dependent bias function :math:`b(z)`
+    and computes an effective bias parameter :math:`\bar{b}` for a
+    given window function :math:`w(z)`.
+
+    Parameters
+    ----------
+    z
+        Redshifts and values of the bias function :math:`b(z)`.
+    bz
+        Redshifts and values of the bias function :math:`b(z)`.
+    w
+        The radial window function :math:`w(z)`.
+
+    Returns
+    -------
+        The effective bias parameter for the window.
+
+    Notes
+    -----
+    The effective bias parameter :math:`\bar{b}` is computed using the
+    window function :math:`w(z)` as the weighted average
+
+    .. math::
+
+        \bar{b} = \frac{\int b(z) \, w(z) \, dz}{\int w(z) \, dz}
+        \;.
+
+    """
+    xp = array_api_compat.array_namespace(z, bz, w.za, w.wa, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    norm = uxpx.trapezoid(w.wa, w.za)
+    return glass.arraytools.trapezoid_product((z, bz), (w.za, w.wa)) / norm
+
+
+def linear_bias(
+    delta: FloatArray,
+    b: float | FloatArray,
+) -> FloatArray:
+    r"""
+    Linear bias model :math:`\delta_g = b \, \delta`.
+
+    Parameters
+    ----------
+    delta
+        The input density contrast.
+    b
+        The bias parameter.
+
+    Returns
+    -------
+        The density contrast after biasing.
+
+    """
+    return b * delta
+
+
+def loglinear_bias(
+    delta: FloatArray,
+    b: float | FloatArray,
+) -> FloatArray:
+    r"""
+    Log-linear bias model :math:`\ln(1 + \delta_g) = b \ln(1 + \delta)`.
+
+    Parameters
+    ----------
+    delta
+        The input density contrast.
+    b
+        The bias parameter.
+
+    Returns
+    -------
+        The density contrast after biasing.
+
+    """
+    xp = array_api_compat.array_namespace(delta, b, use_compat=False)
+
+    delta_g = xp.log1p(delta)
+    delta_g *= b
+    return xp.expm1(delta_g)
+
+
+def positions_from_delta(  # noqa: PLR0912, PLR0913, PLR0915
+    ngal: float | FloatArray,
+    delta: FloatArray,
+    bias: float | FloatArray | None = None,
+    vis: FloatArray | None = None,
+    *,
+    bias_model: str | Callable[..., Any] = "linear",
+    remove_monopole: bool = False,
+    batch: int = 1_000_000,
+    rng: np.random.Generator | None = None,
+) -> Generator[
+    tuple[
+        FloatArray,
+        FloatArray,
+        int | IntArray,
+    ]
+]:
+    """
+    Generate positions tracing a density contrast.
+
+    The map of expected number counts is constructed from the number
+    density, density contrast, an optional bias model, and an optional
+    visibility map.
+
+    If ``remove_monopole`` is set, the monopole of the computed density
+    contrast is removed. Over the full sky, the mean number density of
+    the map will then match the given number density exactly. This,
+    however, means that an effectively different bias model is being
+    used, unless the monopole is already zero in the first place.
+
+    The function supports multi-dimensional input for the ``ngal``,
+    ``delta``, ``bias``, and ``vis`` parameters. Extra dimensions are
+    broadcast to a common shape, and treated as separate populations of
+    points. These are then sampled independently, and the results
+    concatenated into a flat list of longitudes and latitudes. The
+    number of points per population is returned in ``count`` as an array
+    in the shape of the extra dimensions.
+
+    Parameters
+    ----------
+    ngal
+        Number density, expected number of points per arcmin2.
+    delta
+        Map of the input density contrast. This is fed into the bias
+        model to produce the density contrast for sampling.
+    bias
+        Bias parameter, is passed as an argument to the bias model.
+    vis
+        Visibility map for the observed points. This is multiplied with
+        the full sky number count map, and must hence be of compatible shape.
+    bias_model
+        The bias model to apply. If a string, refers to a function in
+        the :mod:`~glass.points` module, e.g. ``'linear'`` for
+        :func:`glass.linear_bias()` or ``'glass.loglinear'`` for
+        :func:`glass.loglinear_bias`.
+    remove_monopole
+        If true, the monopole of the density contrast
+        after biasing is fixed to zero.
+    batch
+        Maximum number of positions to yield in one batch.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    lon
+        Columns of longitudes for the sampled points.
+    lat
+        Columns of latitudes for the sampled points.
+    count
+        The number of sampled points  If multiple populations are sampled, an
+        array of counts in the shape of the extra dimensions is returned.
+
+    Raises
+    ------
+    TypeError
+        If the bias model is not a string or callable.
+
+    """
+    # get default RNG if not given
+    if rng is None:
+        rng = np.random.default_rng()
+
+    # get the bias model
+    if isinstance(bias_model, str):
+        bias_model_callable = globals()[f"{bias_model}_bias"]
+    elif not callable(bias_model):
+        raise TypeError("bias_model must be string or callable")
+    else:
+        bias_model_callable = bias_model
+
+    # broadcast inputs to common shape of extra dimensions
+    inputs: list[tuple[float | FloatArray, int]] = [(ngal, 0), (delta, 1)]
+    if bias is not None:
+        inputs.append((bias, 0))
+    if vis is not None:
+        inputs.append((vis, 1))
+    dims, *rest = glass.arraytools.broadcast_leading_axes(*inputs)
+    ngal, delta, *rest = rest
+    if bias is not None:
+        bias, *rest = rest
+    if vis is not None:
+        vis, *rest = rest
+
+    # iterate the leading dimensions
+    for k in np.ndindex(dims):
+        # compute density contrast from bias model, or copy
+        n = (
+            np.copy(delta[k])
+            if bias is None
+            else bias_model_callable(delta[k], bias[k])
+        )
+
+        # remove monopole if asked to
+        if remove_monopole:
+            n -= np.mean(n, keepdims=True)
+
+        # turn into number count, modifying the array in place
+        n += 1
+        n *= ARCMIN2_SPHERE / n.size * ngal[k]
+
+        # apply visibility if given
+        if vis is not None:
+            n *= vis[k]
+
+        # clip number density at zero
+        np.clip(n, 0, None, out=n)
+
+        # sample actual number in each pixel
+        n = rng.poisson(n)
+
+        # total number of points
+        count = n.sum()
+        # don't go through pixels if there are no points
+        if count == 0:
+            continue
+
+        # for converting randomly sampled positions to HEALPix indices
+        npix = n.shape[-1]
+        nside = healpix.npix2nside(npix)
+
+        # create a mask to report the count in the right axis
+        cmask: int | IntArray
+        if dims:
+            cmask = np.zeros(dims, dtype=int)
+            cmask[k] = 1
+        else:
+            cmask = 1
+
+        # sample the map in batches
+        step = 1000
+        start, stop, size = 0, 0, 0
+        while count:
+            # tally this group of pixels
+            q = np.cumsum(n[stop : stop + step])
+            # does this group of pixels fill the batch?
+            if size + q[-1] < min(batch, count):
+                # no, we need the next group of pixels to fill the batch
+                stop += step
+                size += q[-1]
+            else:
+                # how many pixels from this group do we need?
+                stop += int(np.searchsorted(q, batch - size, side="right"))
+                # if the first pixel alone is too much, use it anyway
+                if stop == start:
+                    stop += 1
+                # sample this batch of pixels
+                ipix = np.repeat(np.arange(start, stop), n[start:stop])
+                lon, lat = healpix.randang(nside, ipix, lonlat=True, rng=rng)
+                # next batch
+                start, size = stop, 0
+                # keep track of remaining number of points
+                count -= ipix.size
+                # yield the batch
+                yield lon, lat, ipix.size * cmask
+
+        # make sure that the correct number of pixels was sampled
+        assert np.sum(n[stop:]) == 0  # noqa: S101
+
+
+def uniform_positions(
+    ngal: float | IntArray | FloatArray,
+    *,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> Generator[
+    tuple[
+        FloatArray,
+        FloatArray,
+        int | IntArray,
+    ]
+]:
+    """
+    Generate positions uniformly over the sphere.
+
+    The function supports array input for the ``ngal`` parameter.
+
+    Parameters
+    ----------
+    ngal
+        Number density, expected number of positions per arcmin2.
+    rng
+        Random number generator. If not given, a default RNG is used.
+
+    Yields
+    ------
+    lon
+        Columns of longitudes for the sampled points.
+    lat
+        Columns of latitudes for the sampled points.
+    count
+        The number of sampled points. For array inputs, an array of
+        counts with the same shape is returned.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(ngal, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # get default RNG if not given
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    ngal = xp.asarray(ngal)
+
+    # sample number of galaxies
+    ngal_sphere = xp.asarray(rng.poisson(xp.multiply(ARCMIN2_SPHERE, ngal)))
+
+    # extra dimensions of the output
+    dims = ngal_sphere.shape
+
+    # sample each set of points
+    for k in uxpx.ndindex(dims):
+        size = (ngal_sphere[k],)
+        # sample uniformly over the sphere
+        lon = rng.uniform(-180, 180, size=size)
+        lat = uxpx.degrees(xp.asin(rng.uniform(-1, 1, size=size)))
+
+        # report count
+        count: int | IntArray
+        if dims:
+            count = xp.zeros(dims, dtype=xp.int64)
+            count[k] = ngal_sphere[k]  # type: ignore[index]
+        else:
+            count = int(ngal_sphere[k])
+
+        yield lon, lat, count
+
+
+def position_weights(
+    densities: FloatArray,
+    bias: FloatArray | float | None = None,
+) -> FloatArray:
+    r"""
+    Compute relative weights for angular clustering.
+
+    Takes an array *densities* of densities in arbitrary units and
+    returns the relative weight of each shell. If *bias* is given, a
+    linear bias is applied to each shell.
+
+    This is the equivalent of computing the product of normalised
+    redshift distribution and bias factor :math:`n(z) \, b(z)` for the
+    discretised shells.
+
+    Parameters
+    ----------
+    densities
+        Density of points in each shell. The first axis must broadcast
+        against the number of shells, and is normalised internally.
+    bias
+        Value or values of the linear bias parameter for each shell.
+
+    Returns
+    -------
+        The relative weight of each shell for angular clustering.
+
+    """
+    xp = array_api_compat.array_namespace(densities, bias, use_compat=False)
+
+    bias = bias if bias is None or not isinstance(bias, float) else xp.asarray(bias)
+
+    # bring densities and bias into the same shape
+    if bias is not None:
+        densities, bias = glass.arraytools.broadcast_first(densities, bias)
+    # normalise densities after shape has been fixed
+    densities = densities / xp.sum(densities, axis=0)
+    # apply bias after normalisation
+    if bias is not None:
+        densities = densities * bias
+    # densities now contains the relative contribution with bias applied
+    return densities
+
+
+def displace(
+    lon: FloatArray,
+    lat: FloatArray,
+    alpha: ComplexArray | FloatArray,
+) -> tuple[FloatArray, FloatArray]:
+    r"""
+    Displace positions on the sphere.
+
+    Takes an array of :term:`displacement` values and applies them to
+    the given positions.
+
+    Parameters
+    ----------
+    lon
+        Longitudes to be displaced.
+    lat
+        Latitudes to be displaced.
+    alpha
+        Displacement values. Must be complex-valued or have a leading
+        axis of size 2 for the real and imaginary component.
+
+    Returns
+    -------
+        The longitudes and latitudes after displacement.
+
+    Notes
+    -----
+    Displacements on the sphere are :term:`defined <displacement>` as
+    follows:  The complex displacement :math:`\alpha` transports a point
+    on the sphere an angular distance :math:`|\alpha|` along the
+    geodesic with bearing :math:`\arg\alpha` in the original point.
+
+    In the language of differential geometry, this function is the
+    exponential map.
+
+    """
+    xp = array_api_compat.get_namespace(lon, lat, alpha, use_compat=False)
+
+    alpha = xp.asarray(alpha)
+    if xp.isdtype(alpha.dtype, "complex floating"):
+        alpha1, alpha2 = xp.real(alpha), xp.imag(alpha)
+    else:
+        alpha1, alpha2 = alpha
+
+    # we know great-circle navigation:
+    # θ' = arctan2(√[(cosθ sin|α| - sinθ cos|α| cosγ)² + (sinθ sinγ)²],
+    #              cosθ cos|α| + sinθ sin|α| cosγ)
+    # δ = arctan2(sin|α| sinγ, sinθ cos|α| - cosθ sin|α| cosγ)
+
+    t = xp.asarray(lat) / 180 * xp.pi
+    ct, st = xp.sin(t), xp.cos(t)  # sin and cos flipped: lat not co-lat
+
+    a = xp.hypot(alpha1, alpha2)  # abs(alpha)
+    g = xp.atan2(alpha2, alpha1)  # arg(alpha)
+    ca, sa = xp.cos(a), xp.sin(a)
+    cg, sg = xp.cos(g), xp.sin(g)
+
+    # flipped atan2 arguments for lat instead of co-lat
+    tp = xp.atan2(ct * ca + st * sa * cg, xp.hypot(ct * sa - st * ca * cg, st * sg))
+
+    d = xp.atan2(sa * sg, st * ca - ct * sa * cg)
+
+    return lon - d / xp.pi * 180, tp / xp.pi * 180
+
+
+def displacement(
+    from_lon: FloatArray,
+    from_lat: FloatArray,
+    to_lon: FloatArray,
+    to_lat: FloatArray,
+) -> ComplexArray:
+    """
+    Compute the displacement between two sets of positions.
+
+    Compute the complex :term:`displacement` that transforms points with
+    longitude *from_lon* and latitude *from_lat* into points with
+    longitude *to_lon* and latitude *to_lat* (all in degrees).
+
+    Parameters
+    ----------
+    from_lon, from_lat
+        Points before displacement.
+    to_lon, to_lat
+        Points after displacement.
+
+    Returns
+    -------
+        Array of complex displacement.
+
+    See Also
+    --------
+    displace : Apply displacement to a set of points.
+
+    """
+    xp = array_api_compat.get_namespace(
+        from_lon,
+        from_lat,
+        to_lon,
+        to_lat,
+        use_compat=False,
+    )
+
+    a = (90.0 - to_lat) / 180 * xp.pi
+    b = (90.0 - from_lat) / 180 * xp.pi
+    g = (from_lon - to_lon) / 180 * xp.pi
+
+    sa, ca = xp.sin(a), xp.cos(a)
+    sb, cb = xp.sin(b), xp.cos(b)
+    sg, cg = xp.sin(g), xp.cos(g)
+
+    r = xp.atan2(xp.hypot(sa * cb - ca * sb * cg, sb * sg), ca * cb + sa * sb * cg)
+    x = sb * ca - cb * sa * cg
+    y = sa * sg
+    z = xp.hypot(x, y)
+    return r * (x / z + 1j * y / z)
diff --git a/glass/glass/py.typed b/glass/glass/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/glass/glass/shapes.py b/glass/glass/shapes.py
new file mode 100644
index 0000000..14019c1
--- /dev/null
+++ b/glass/glass/shapes.py
@@ -0,0 +1,362 @@
+"""
+Observed shapes
+===============
+
+.. currentmodule:: glass
+
+The following functions provide functionality for simulating the
+observed shapes of objects, such as e.g. galaxies.
+
+Ellipticity
+-----------
+
+.. autofunction:: ellipticity_gaussian
+.. autofunction:: ellipticity_intnorm
+.. autofunction:: ellipticity_ryden04
+
+
+Utilities
+---------
+
+.. autofunction:: triaxial_axis_ratio
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import array_api_compat
+
+import glass._array_api_utils as _utils
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import ComplexArray, FloatArray, IntArray, UnifiedGenerator
+
+
+def _populate_random_complex_array(
+    length: int,
+    rng: UnifiedGenerator,
+) -> ComplexArray:
+    return rng.standard_normal(length) + (1.0j * rng.standard_normal(length))
+
+
+def triaxial_axis_ratio(
+    zeta: float | FloatArray,
+    xi: float | FloatArray,
+    size: int | tuple[int, ...] | None = None,
+    *,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> FloatArray:
+    """
+    Axis ratio of a randomly projected triaxial ellipsoid.
+
+    Given the two axis ratios `1 >= zeta >= xi` of a randomly oriented triaxial
+    ellipsoid, computes the axis ratio `q` of the projection.
+
+    Parameters
+    ----------
+    zeta
+        Axis ratio of intermediate and major axis.
+    xi
+        Axis ratio of minor and major axis.
+    size
+        Size of the random draw.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        The axis ratio of the randomly projected ellipsoid.
+
+    Notes
+    -----
+    See equations (11) and (12) in [Binney85]_ for details.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(zeta, xi, use_compat=False)
+
+    zeta = xp.asarray(zeta)
+    xi = xp.asarray(xi)
+
+    # default RNG if not provided
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # get size from inputs if not explicitly provided
+    if size is None:
+        size = xp.broadcast_arrays(zeta, xi)[0].shape
+
+    # draw random viewing angle (theta, phi)
+    cos2_theta = rng.uniform(low=-1.0, high=1.0, size=size)
+    cos2_theta *= cos2_theta
+    sin2_theta = 1 - cos2_theta
+    cos2_phi = xp.cos(rng.uniform(low=0.0, high=2 * xp.pi, size=size))
+    cos2_phi *= cos2_phi
+    sin2_phi = 1 - cos2_phi
+
+    # transform arrays to quantities that are used in eq. (11)
+    z2m1 = xp.square(zeta)
+    z2m1 -= 1
+    x2 = xp.square(xi)
+
+    # eq. (11) multiplied by xi^2 zeta^2
+    a = (1 + z2m1 * sin2_phi) * cos2_theta + x2 * sin2_theta
+    b2 = 4 * z2m1**2 * cos2_theta * sin2_phi * cos2_phi
+    c = 1 + z2m1 * cos2_phi
+
+    # eq. (12)
+    return xp.sqrt(
+        (a + c - xp.sqrt((a - c) ** 2 + b2)) / (a + c + xp.sqrt((a - c) ** 2 + b2)),
+    )
+
+
+def ellipticity_ryden04(  # noqa: PLR0913
+    mu: float | FloatArray,
+    sigma: float | FloatArray,
+    gamma: float | FloatArray,
+    sigma_gamma: float | FloatArray,
+    size: int | tuple[int, ...] | None = None,
+    *,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> FloatArray:
+    r"""
+    Ellipticity distribution following Ryden (2004).
+
+    The ellipticities are sampled by randomly projecting a 3D ellipsoid with
+    principal axes :math:`A > B > C` [Ryden04]_. The distribution of :math:`\log(1 -
+    B/A)` is normal with mean :math:`\mu` and standard deviation :math:`\sigma`.
+    The distribution of :math:`1 - C/B` is normal with mean :math:`\gamma` and
+    standard deviation :math:`\sigma_\gamma` [Padilla08]_. Both distributions are
+    truncated to produce ratios in the range 0 to 1 using rejection sampling.
+
+    Parameters
+    ----------
+    mu
+        Mean of the truncated normal for :math:`\log(1 - B/A)`.
+    sigma
+        Standard deviation for :math:`\log(1 - B/A)`.
+    gamma
+        Mean of the truncated normal for :math:`1 - C/B`.
+    sigma_gamma
+        Standard deviation for :math:`1 - C/B`.
+    size
+        Sample size.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        An array of :term:`ellipticity` from projected axis ratios.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(
+            mu,
+            sigma,
+            gamma,
+            sigma_gamma,
+            use_compat=False,
+        )
+
+    mu = xp.asarray(mu)
+    sigma = xp.asarray(sigma)
+    gamma = xp.asarray(gamma)
+    sigma_gamma = xp.asarray(sigma_gamma)
+
+    # default RNG if not provided
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # default size if not given
+    if size is None:
+        size = xp.broadcast_arrays(mu, sigma, gamma, sigma_gamma)[0].shape
+
+    # broadcast all inputs to output shape
+    # this makes it possible to efficiently resample later
+    mu = xp.broadcast_to(mu, size)
+    sigma = xp.broadcast_to(sigma, size)
+    gamma = xp.broadcast_to(gamma, size)
+    sigma_gamma = xp.broadcast_to(sigma_gamma, size)
+
+    # draw gamma and epsilon from truncated normal -- eq.s (10)-(11)
+    # first sample unbounded normal, then rejection sample truncation
+    eps = rng.normal(mu, sigma, size=size)
+    while xp.any(bad := eps > 0):
+        eps[bad] = rng.normal(mu[bad], sigma[bad])  #  type: ignore[index]
+    gam = rng.normal(gamma, sigma_gamma, size=size)
+    while xp.any(bad := (gam < 0) | (gam > 1)):
+        gam[bad] = rng.normal(gamma[bad], sigma_gamma[bad])  #  type: ignore[index]
+
+    # compute triaxial axis ratios zeta = B/A, xi = C/A
+    zeta = -xp.expm1(eps)
+    xi = (1 - gam) * zeta
+
+    # random projection of random triaxial ellipsoid
+    q = triaxial_axis_ratio(zeta, xi, rng=rng)
+
+    # assemble ellipticity with random complex phase
+    e = xp.exp(1j * rng.uniform(0, 2 * xp.pi, size=q.shape))
+    e *= (1 - q) / (1 + q)
+
+    # return the ellipticity
+    return e
+
+
+def ellipticity_gaussian(
+    count: int | IntArray,
+    sigma: float | FloatArray,
+    *,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> ComplexArray:
+    """
+    Sample Gaussian galaxy ellipticities.
+
+    The ellipticities are sampled from a normal distribution with
+    standard deviation ``sigma`` for each component. Samples where the
+    ellipticity is larger than unity are discarded. Hence, do not use
+    this function with too large values of ``sigma``, or the sampling
+    will become inefficient.
+
+    Parameters
+    ----------
+    count
+        Number of ellipticities to be sampled.
+    sigma
+        Standard deviation in each component.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        An array of galaxy :term:`ellipticity`.
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(count, sigma, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # bring inputs into common shape
+    count_broadcasted, sigma_broadcasted = xp.broadcast_arrays(
+        xp.asarray(count),
+        xp.asarray(sigma),
+    )
+
+    # default RNG if not provided
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # allocate flattened output array
+    eps = xp.empty(xp.sum(count_broadcasted), dtype=xp.complex128)
+
+    # sample complex ellipticities
+    # reject those where abs(e) > 0
+    i = 0
+    for k in uxpx.ndindex(count_broadcasted.shape):
+        e = _populate_random_complex_array(count_broadcasted[k], rng)
+        e *= sigma_broadcasted[k]
+        r = xp.abs(e) > 1
+        while xp.count_nonzero(r) > 0:
+            e[r] = _populate_random_complex_array(xp.count_nonzero(r), rng)
+            e[r] *= sigma_broadcasted[k]
+            r = xp.abs(e) > 1
+        eps[i : i + count_broadcasted[k]] = e
+        i += count_broadcasted[k]
+
+    return eps
+
+
+def ellipticity_intnorm(
+    count: int | IntArray,
+    sigma: float | FloatArray,
+    *,
+    rng: UnifiedGenerator | None = None,
+    xp: ModuleType | None = None,
+) -> ComplexArray:
+    """
+    Sample galaxy ellipticities with intrinsic normal distribution.
+
+    The ellipticities are sampled from an intrinsic normal distribution
+    with standard deviation ``sigma`` for each component.
+
+    Parameters
+    ----------
+    count
+        Number of ellipticities to sample.
+    sigma
+        Standard deviation of the ellipticity in each component.
+    rng
+        Random number generator. If not given, a default RNG is used.
+    xp
+        The array library backend to use for array operations. If this is not
+        specified, the backend will be determined from the input arrays.
+
+    Returns
+    -------
+        An array of galaxy :term:`ellipticity`.
+
+    Raises
+    ------
+    ValueError
+        If the standard deviation is not in the range [0, sqrt(0.5)].
+
+    """
+    if xp is None:
+        xp = array_api_compat.array_namespace(count, sigma, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # default RNG if not provided
+    if rng is None:
+        rng = _utils.rng_dispatcher(xp=xp)
+
+    # bring inputs into common shape
+    count_broadcasted, sigma_broadcasted = xp.broadcast_arrays(
+        xp.asarray(count),
+        xp.asarray(sigma),
+    )
+
+    # make sure sigma is admissible
+    if not xp.all((sigma_broadcasted >= 0) & (sigma_broadcasted < 0.5**0.5)):
+        msg = "sigma must be between 0 and sqrt(0.5)"
+        raise ValueError(msg)
+
+    # convert to sigma_eta using fit
+    sigma_eta = (
+        sigma_broadcasted
+        * ((8 + 5 * sigma_broadcasted**2) / (2 - 4 * sigma_broadcasted**2)) ** 0.5
+    )
+
+    # allocate flattened output array
+    eps = xp.empty(xp.sum(count_broadcasted), dtype=xp.complex128)
+
+    # sample complex ellipticities
+    i = 0
+    for k in uxpx.ndindex(count_broadcasted.shape):
+        e = _populate_random_complex_array(count_broadcasted[k], rng)
+        e *= sigma_eta[k]
+        r = xp.hypot(xp.real(e), xp.imag(e))
+        e *= xp.where(
+            r > 0,
+            xp.divide(xp.tanh(r / 2), r),
+            xp.asarray(1.0, dtype=e.dtype),
+        )
+
+        eps[i : i + count_broadcasted[k]] = e
+        i += count_broadcasted[k]
+
+    return eps
diff --git a/glass/glass/shells.py b/glass/glass/shells.py
new file mode 100644
index 0000000..d460ab7
--- /dev/null
+++ b/glass/glass/shells.py
@@ -0,0 +1,990 @@
+"""
+Shells
+======
+
+.. currentmodule:: glass
+
+The following functions provide functionality for the definition of
+matter shells, i.e. the radial discretisation of the light cone.
+
+
+.. _reference-window-functions:
+
+Window functions
+----------------
+
+.. autoclass:: RadialWindow
+.. autofunction:: tophat_windows
+.. autofunction:: linear_windows
+.. autofunction:: cubic_windows
+
+
+Window function tools
+---------------------
+
+.. autofunction:: restrict
+.. autofunction:: partition
+.. autofunction:: combine
+
+
+Redshift grids
+--------------
+
+.. autofunction:: redshift_grid
+.. autofunction:: distance_grid
+
+
+Weight functions
+----------------
+
+.. autoclass:: DistanceWeight
+.. autoclass:: VolumeWeight
+.. autoclass:: DensityWeight
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+import dataclasses
+import itertools
+import math
+import warnings
+from typing import TYPE_CHECKING
+
+import array_api_compat
+
+import glass._array_api_utils as _utils
+import glass.algorithm
+import glass.arraytools
+
+if TYPE_CHECKING:
+    import types
+    from collections.abc import Iterator, Sequence
+
+    from glass._types import FloatArray, WeightFunc
+    from glass.cosmology import Cosmology
+
+
+@dataclasses.dataclass
+class DistanceWeight:
+    """Uniform weight in comoving distance.
+
+    Attributes
+    ----------
+    cosmo
+        Cosmology instance.
+
+    """
+
+    cosmo: Cosmology
+
+    def __call__(self, z: FloatArray) -> FloatArray:
+        """
+        Uniform weight in comoving distance.
+
+        Parameters
+        ----------
+        z
+            The redshifts at which to evaluate the weight.
+
+        Returns
+        -------
+            The weight function evaluated at redshifts *z*.
+
+        """
+        return 1 / self.cosmo.H_over_H0(z)
+
+
+@dataclasses.dataclass
+class VolumeWeight:
+    """Uniform weight in comoving volume.
+
+    Attributes
+    ----------
+    cosmo
+        Cosmology instance.
+
+    """
+
+    cosmo: Cosmology
+
+    def __call__(self, z: FloatArray) -> FloatArray:
+        """
+        Uniform weight in comoving distance.
+
+        Parameters
+        ----------
+        z
+            The redshifts at which to evaluate the weight.
+
+        Returns
+        -------
+            The weight function evaluated at redshifts *z*.
+
+        """
+        return (
+            self.cosmo.transverse_comoving_distance(z) / self.cosmo.hubble_distance
+        ) ** 2 / self.cosmo.H_over_H0(z)
+
+
+@dataclasses.dataclass
+class DensityWeight:
+    """
+    Uniform weight in matter density.
+
+    Attributes
+    ----------
+    cosmo
+        Cosmology instance.
+
+    """
+
+    cosmo: Cosmology
+
+    def __call__(self, z: FloatArray) -> FloatArray:
+        """
+        Uniform weight in comoving distance.
+
+        Parameters
+        ----------
+        z
+            The redshifts at which to evaluate the weight.
+
+        Returns
+        -------
+            The weight function evaluated at redshifts *z*.
+
+        """
+        return (
+            self.cosmo.critical_density0
+            * self.cosmo.Omega_m(z)
+            * (self.cosmo.transverse_comoving_distance(z) / self.cosmo.hubble_distance)
+            ** 2
+            / self.cosmo.H_over_H0(z)
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class RadialWindow:
+    """
+    A radial window, defined by a window function.
+
+    The radial window is defined by a window function in redshift, which
+    is given by a pair of arrays ``za``, ``wa``.
+
+    The radial window also has an effective redshift, stored in the
+    ``zeff`` attribute, which should be a representative redshift for
+    the window function.
+
+    To prevent accidental inconsistencies, instances of this type are
+    immutable (however, the array entries may **not** be immutable; do
+    not change them in place)::
+
+        >>> import dataclasses
+        >>> import glass
+        >>> import numpy as np
+        >>> za = np.asarray([0.0, 1.0])
+        >>> wa = np.asarray([1.0, 0.0])
+        >>> w1 = glass.RadialWindow(za, wa, zeff=0.1)
+        >>> w1.zeff = 0.15
+        Traceback (most recent call last):
+          File "<string>", line 4, in __setattr__
+        dataclasses.FrozenInstanceError: cannot assign to field 'zeff'
+
+    To create a new instance with a changed attribute value, use the
+    ``dataclasses.replace`` method::
+
+        >>> w1 = dataclasses.replace(w1, zeff=0.15)
+        >>> w1.zeff
+        0.15
+
+    Attributes
+    ----------
+    za
+        Redshift array; the abscissae of the window function.
+    wa
+        Weight array; the values (ordinates) of the window function.
+    zeff
+        Effective redshift of the window.
+
+    """
+
+    za: FloatArray
+    wa: FloatArray
+    zeff: float = math.nan
+    xp: types.ModuleType | None = None
+
+    def __post_init__(self) -> None:
+        """
+        Magic method to setup optional inputs
+        - Calculates the effective redshift if not given.
+        - Determines xp from za and wa.
+        """
+        if self.xp is None:
+            object.__setattr__(
+                self,
+                "xp",
+                array_api_compat.array_namespace(self.za, self.wa, use_compat=False),
+            )
+        if math.isnan(self.zeff):
+            object.__setattr__(self, "zeff", self._calculate_zeff())
+
+    def __iter__(self) -> Iterator[FloatArray]:
+        """
+        Iterate over the window function and effective redshift.
+
+        To be removed upon deprecation of ``glass.ext.camb``.
+        """
+        yield from (self.za, self.wa, self.zeff)
+
+    def _calculate_zeff(self) -> float:
+        """Calculate ``zeff`` if not given.
+
+        Returns
+        -------
+            The effective redshift depending on the size of ``za``.
+
+        """
+        uxpx = _utils.XPAdditions(self.xp)  # type: ignore[arg-type]
+        if self.za.size > 0:
+            return uxpx.trapezoid(  # type: ignore[return-value]
+                self.za * self.wa,
+                self.za,
+            ) / uxpx.trapezoid(self.wa, self.za)
+        return math.nan
+
+
+def tophat_windows(
+    zbins: FloatArray,
+    dz: float = 1e-3,
+    weight: WeightFunc | None = None,
+) -> list[RadialWindow]:
+    """
+    Tophat window functions from the given redshift bin edges.
+
+    Uses the *N+1* given redshifts as bin edges to construct *N* tophat
+    window functions. The redshifts of the windows have linear spacing
+    approximately equal to ``dz``.
+
+    An optional weight function :math:`w(z)` can be given using
+    ``weight``; it is applied to the tophat windows.
+
+    The resulting windows functions are :class:`RadialWindow` instances.
+    Their effective redshifts are the mean redshifts of the (weighted)
+    tophat bins.
+
+    Parameters
+    ----------
+    zbins
+        Redshift bin edges for the tophat window functions.
+    dz
+        Approximate spacing of the redshift grid.
+    weight
+        If given, a weight function to be applied to the window functions.
+
+    Returns
+    -------
+        A list of window functions.
+
+    Raises
+    ------
+    ValueError
+        If the number of redshift bins is less than 2.
+
+    See Also
+    --------
+    :ref:`user-window-functions`
+
+    """
+    if zbins.ndim != 1:
+        msg = "zbins must be a 1D array"
+        raise ValueError(msg)
+    if zbins.size < 2:
+        msg = "zbins must have at least two entries"
+        raise ValueError(msg)
+    if zbins[0] != 0:
+        warnings.warn(
+            "first tophat window does not start at redshift zero",
+            stacklevel=2,
+        )
+
+    xp = zbins.__array_namespace__()
+    uxpx = _utils.XPAdditions(xp)
+
+    wht: WeightFunc
+    wht = weight if weight is not None else xp.ones_like
+    ws = []
+    for zmin, zmax in itertools.pairwise(zbins):
+        n = int(max(xp.round((zmax - zmin) / dz), 2))
+        z = xp.linspace(zmin, zmax, n, dtype=xp.float64)
+        w = wht(z)
+        zeff = uxpx.trapezoid(w * z, z) / uxpx.trapezoid(w, z)
+        ws.append(RadialWindow(z, w, float(zeff)))
+    return ws
+
+
+def linear_windows(
+    zgrid: FloatArray,
+    dz: float = 1e-3,
+    weight: WeightFunc | None = None,
+) -> list[RadialWindow]:
+    """
+    Linear interpolation window functions.
+
+    Uses the *N+2* given redshifts as nodes to construct *N* triangular
+    window functions between the first and last node. These correspond
+    to linear interpolation of radial functions. The redshift spacing
+    of the windows is approximately equal to ``dz``.
+
+    An optional weight function :math:`w(z)` can be given using
+    ``weight``; it is applied to the triangular windows.
+
+    The resulting windows functions are :class:`RadialWindow` instances.
+    Their effective redshifts correspond to the given nodes.
+
+    Parameters
+    ----------
+    zgrid
+        Redshift grid for the triangular window functions.
+    dz
+        Approximate spacing of the redshift grid.
+    weight
+        If given, a weight function to be applied to the window functions.
+
+    Returns
+    -------
+        A list of window functions.
+
+    Raises
+    ------
+    ValueError
+        If the number of nodes is less than 3.
+
+    See Also
+    --------
+    :ref:`user-window-functions`
+
+    """
+    if zgrid.ndim != 1:
+        msg = "zgrid must be a 1D array"
+        raise ValueError(msg)
+    if zgrid.size < 3:
+        msg = "nodes must have at least 3 entries"
+        raise ValueError(msg)
+    if zgrid[0] != 0:
+        warnings.warn("first triangular window does not start at z=0", stacklevel=2)
+
+    xp = zgrid.__array_namespace__()
+
+    ws = []
+    for zmin, zmid, zmax in zip(zgrid, zgrid[1:], zgrid[2:], strict=False):
+        n = int(max(xp.round((zmid - zmin) / dz), 2)) - 1
+        m = int(max(xp.round((zmax - zmid) / dz), 2))
+        z = xp.concat(
+            (xp.linspace(zmin, zmid, n, endpoint=False), xp.linspace(zmid, zmax, m)),
+        )
+        w = xp.concat(
+            (xp.linspace(0.0, 1.0, n, endpoint=False), xp.linspace(1.0, 0.0, m)),
+        )
+        if weight is not None:
+            w *= weight(z)
+        ws.append(RadialWindow(z, w, zmid))
+    return ws
+
+
+def cubic_windows(
+    zgrid: FloatArray,
+    dz: float = 1e-3,
+    weight: WeightFunc | None = None,
+) -> list[RadialWindow]:
+    """
+    Cubic interpolation window functions.
+
+    Uses the *N+2* given redshifts as nodes to construct *N* cubic
+    Hermite spline window functions between the first and last node.
+    These correspond to cubic spline interpolation of radial functions.
+    The redshift spacing of the windows is approximately equal to
+    ``dz``.
+
+    An optional weight function :math:`w(z)` can be given using
+    ``weight``; it is applied to the cubic spline windows.
+
+    The resulting windows functions are :class:`RadialWindow` instances.
+    Their effective redshifts correspond to the given nodes.
+
+    Parameters
+    ----------
+    zgrid
+        Redshift grid for the cubic spline window functions.
+    dz
+        Approximate spacing of the redshift grid.
+    weight
+        If given, a weight function to be applied to the window functions.
+
+    Returns
+    -------
+        A list of window functions.
+
+    Raises
+    ------
+    ValueError
+        If the number of nodes is less than 3.
+
+    See Also
+    --------
+    :ref:`user-window-functions`
+
+    """
+    if zgrid.ndim != 1:
+        msg = "zgrid must be a 1D array"
+        raise ValueError(msg)
+    if zgrid.size < 3:
+        msg = "nodes must have at least 3 entries"
+        raise ValueError(msg)
+    if zgrid[0] != 0:
+        warnings.warn("first cubic spline window does not start at z=0", stacklevel=2)
+
+    xp = zgrid.__array_namespace__()
+
+    ws = []
+    for zmin, zmid, zmax in zip(zgrid, zgrid[1:], zgrid[2:], strict=False):
+        n = int(max(xp.round((zmid - zmin) / dz), 2)) - 1
+        m = int(max(xp.round((zmax - zmid) / dz), 2))
+        z = xp.concat(
+            (xp.linspace(zmin, zmid, n, endpoint=False), xp.linspace(zmid, zmax, m)),
+        )
+        u = xp.linspace(0.0, 1.0, n, endpoint=False)
+        v = xp.linspace(1.0, 0.0, m)
+        w = xp.concat([u**2 * (3 - 2 * u), v**2 * (3 - 2 * v)])
+        if weight is not None:
+            w *= weight(z)
+        ws.append(RadialWindow(z, w, zmid))
+    return ws
+
+
+def restrict(
+    z: FloatArray,
+    f: FloatArray,
+    w: RadialWindow,
+) -> tuple[FloatArray, FloatArray]:
+    """
+    Restrict a function to a redshift window.
+
+    Multiply the function :math:`f(z)` by a window function :math:`w(z)`
+    to produce :math:`w(z) f(z)` over the support of :math:`w`.
+
+    The function :math:`f(z)` is given by redshifts ``z`` of shape
+    *(N,)* and function values ``f`` of shape *(..., N)*, with any
+    number of leading axes allowed.
+
+    The window function :math:`w(z)` is given by ``w``, which must be a
+    :class:`RadialWindow` instance or compatible with it.
+
+    The restriction has redshifts that are the union of the redshifts of
+    the function and window over the support of the window.
+    Intermediate function values are found by linear interpolation
+
+    Parameters
+    ----------
+    z
+        The function to be restricted.
+    f
+        The function to be restricted.
+    w
+        The window function for the restriction.
+
+    Returns
+    -------
+        The restricted function
+
+    """
+    if z.ndim != 1:
+        msg = "z must be 1D arrays"
+        raise ValueError(msg)
+    xp = array_api_compat.array_namespace(z, f, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    z_ = z[xp.greater(z, w.za[0]) & xp.less(z, w.za[-1])]
+    zr = uxpx.union1d(w.za, z_)
+
+    fr = glass.arraytools.ndinterp(
+        zr,
+        z,
+        f,
+        left=0.0,
+        right=0.0,
+    ) * glass.arraytools.ndinterp(zr, w.za, w.wa)
+    return zr, fr
+
+
+def partition(
+    z: FloatArray,
+    fz: FloatArray,
+    shells: Sequence[RadialWindow],
+    *,
+    method: str = "nnls",
+) -> FloatArray:
+    r"""
+    Partition a function by a sequence of windows.
+
+    Returns a vector of weights :math:`x_1, x_2, \ldots` such that the
+    weighted sum of normalised radial window functions :math:`x_1 \,
+    w_1(z) + x_2 \, w_2(z) + \ldots` approximates the given function
+    :math:`f(z)`.
+
+    The function :math:`f(z)` is given by redshifts *z* of shape *(N,)*
+    and function values *fz* of shape *(..., N)*, with any number of
+    leading axes allowed.
+
+    The window functions are given by the sequence *shells* of
+    :class:`RadialWindow` or compatible entries.
+
+    Parameters
+    ----------
+    z
+        The function to be partitioned. If *f* is multi-dimensional,
+        its last axis must agree with *z*.
+    fz
+        The function to be partitioned. If *f* is multi-dimensional,
+        its last axis must agree with *z*.
+    shells
+        Ordered sequence of window functions for the partition.
+    method
+        Method for the partition. See notes for description. The
+        options are "lstsq", "nnls", "restrict".
+
+    Returns
+    -------
+        The weights of the partition, where the leading axis corresponds to
+        *shells*.
+
+    Raises
+    ------
+    ValueError
+        If the method is not recognised.
+
+    Notes
+    -----
+    Formally, if :math:`w_i` are the normalised window functions,
+    :math:`f` is the target function, and :math:`z_i` is a redshift grid
+    with intervals :math:`\Delta z_i`, the partition problem seeks an
+    approximate solution of
+
+    .. math::
+
+        \begin{pmatrix}
+        w_1(z_1) \Delta z_1 & w_2(z_1) \, \Delta z_1 & \cdots \\
+        w_1(z_2) \Delta z_2 & w_2(z_2) \, \Delta z_2 & \cdots \\
+        \vdots & \vdots & \ddots
+        \end{pmatrix} \, \begin{pmatrix}
+        x_1 \\ x_2 \\ \vdots
+        \end{pmatrix} = \begin{pmatrix}
+        f(z_1) \, \Delta z_1 \\ f(z_2) \, \Delta z_2 \\ \vdots
+        \end{pmatrix} \;.
+
+    The redshift grid is the union of the given array *z* and the
+    redshift arrays of all window functions. Intermediate function
+    values are found by linear interpolation.
+
+    When partitioning a density function, it is usually desirable to
+    keep the normalisation fixed. In that case, the problem can be
+    enhanced with the further constraint that the sum of the solution
+    equals the integral of the target function,
+
+    .. math::
+
+        \begin{pmatrix}
+        w_1(z_1) \Delta z_1 & w_2(z_1) \, \Delta z_1 & \cdots \\
+        w_1(z_2) \Delta z_2 & w_2(z_2) \, \Delta z_2 & \cdots \\
+        \vdots & \vdots & \ddots \\
+        \hline
+        \lambda & \lambda & \cdots
+        \end{pmatrix} \, \begin{pmatrix}
+        x_1 \\ x_2 \\ \vdots
+        \end{pmatrix} = \begin{pmatrix}
+        f(z_1) \, \Delta z_1 \\ f(z_2) \, \Delta z_2 \\ \vdots
+        \\ \hline \lambda \int \! f(z) \, dz
+        \end{pmatrix} \;,
+
+    where :math:`\lambda` is a multiplier to enforce the integral
+    constraints.
+
+    The :func:`glass.partition()` function implements a number of methods to
+    obtain a solution:
+
+    If ``method="nnls"`` (the default), obtain a partition from a
+    non-negative least-squares solution. This will usually match the
+    shape of the input function closely. The contribution from each
+    shell is a positive number, which is required to partition e.g.
+    density functions.
+
+    If ``method="lstsq"``, obtain a partition from an unconstrained
+    least-squares solution. This will more closely match the shape of
+    the input function, but might lead to shells with negative
+    contributions.
+
+    If ``method="restrict"``, obtain a partition by integrating the
+    restriction (using :func:`glass.restrict`) of the function :math:`f` to
+    each window. For overlapping shells, this method might produce
+    results which are far from the input function.
+
+    """
+    try:
+        partition_method = globals()[f"partition_{method}"]
+    except KeyError:
+        msg = f"invalid method: {method}"
+        raise ValueError(msg) from None
+    return partition_method(z, fz, shells)
+
+
+def partition_lstsq(
+    z: FloatArray,
+    fz: FloatArray,
+    shells: Sequence[RadialWindow],
+    *,
+    sumtol: float = 0.01,
+) -> FloatArray:
+    """
+    Least-squares partition.
+
+    Parameters
+    ----------
+    z
+        The function to be partitioned.
+    fz
+        The function to be partitioned.
+    shells
+        Ordered sequence of window functions.
+    sumtol
+        Tolerance for the sum of the partition.
+
+    Returns
+    -------
+        The partition.
+
+    """
+    xp = array_api_compat.array_namespace(z, fz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # make sure nothing breaks
+    sumtol = max(sumtol, 1e-4)
+
+    # compute the union of all given redshift grids
+    zp = z
+    for w in shells:
+        zp = uxpx.union1d(zp, w.za)
+
+    # get extra leading axes of fz
+    *dims, _ = fz.shape
+
+    # compute grid spacing
+    dz = uxpx.gradient(zp)
+
+    # create the window function matrix
+    a = xp.stack([uxpx.interp(zp, za, wa, left=0.0, right=0.0) for za, wa, _ in shells])
+    a /= uxpx.trapezoid(a, zp, axis=-1)[..., None]
+    a = a * dz
+
+    # create the target vector of distribution values
+    b = glass.arraytools.ndinterp(zp, z, fz, left=0.0, right=0.0)
+    b = b * dz
+
+    # append a constraint for the integral
+    mult = 1 / sumtol
+    a = xp.concat([a, mult * xp.ones((len(shells), 1))], axis=-1)
+    b = xp.concat([b, mult * xp.reshape(uxpx.trapezoid(fz, z), (*dims, 1))], axis=-1)
+
+    # now a is a matrix of shape (len(shells), len(zp) + 1)
+    # and b is a matrix of shape (*dims, len(zp) + 1)
+    # need to find weights x such that b == x @ a over all axes of b
+    # do the least-squares fit over partially flattened b, then reshape
+    x = uxpx.linalg_lstsq(
+        xp.matrix_transpose(a),
+        xp.matrix_transpose(xp.reshape(b, (-1, zp.size + 1))),
+        rcond=None,
+    )[0]
+    x = xp.reshape(xp.matrix_transpose(x), (*dims, len(shells)))
+    # roll the last axis of size len(shells) to the front
+    return xp.moveaxis(x, -1, 0)
+
+
+def partition_nnls(
+    z: FloatArray,
+    fz: FloatArray,
+    shells: Sequence[RadialWindow],
+    *,
+    sumtol: float = 0.01,
+) -> FloatArray:
+    """
+    Non-negative least-squares partition.
+
+    Parameters
+    ----------
+    z
+        The function to be partitioned.
+    fz
+        The function to be partitioned.
+    shells
+        Ordered sequence of window functions.
+    sumtol
+        Tolerance for the sum of the partition.
+
+    Returns
+    -------
+        The partition.
+
+    """
+    xp = array_api_compat.array_namespace(z, fz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    # make sure nothing breaks
+    sumtol = max(sumtol, 1e-4)
+
+    # compute the union of all given redshift grids
+    zp = z
+    for w in shells:
+        zp = uxpx.union1d(zp, w.za)
+
+    # get extra leading axes of fz
+    *dims, _ = fz.shape
+
+    # compute grid spacing
+    dz = uxpx.gradient(zp)
+
+    # create the window function matrix
+    a = xp.stack(
+        [uxpx.interp(zp, za, wa, left=0.0, right=0.0) for za, wa, _ in shells],
+    )
+    a /= uxpx.trapezoid(a, zp, axis=-1)[..., None]
+    a = a * dz
+
+    # create the target vector of distribution values
+    b = glass.arraytools.ndinterp(zp, z, fz, left=0.0, right=0.0)
+    b = b * dz
+
+    # append a constraint for the integral
+    mult = 1 / sumtol
+    a = xp.concat([a, mult * xp.ones((len(shells), 1))], axis=-1)
+    b = xp.concat([b, mult * xp.reshape(uxpx.trapezoid(fz, z), (*dims, 1))], axis=-1)
+
+    # now a is a matrix of shape (len(shells), len(zp) + 1)
+    # and b is a matrix of shape (*dims, len(zp) + 1)
+    # for each dim, find non-negative weights x such that b == a.T @ x
+
+    # reduce the dimensionality of the problem using a thin QR decomposition
+    q, r = xp.linalg.qr(a.T)
+    y = uxpx.einsum("ji,...j", q, b)
+
+    x = xp.stack(
+        [
+            glass.algorithm.nnls(r, y[(*i, ...)])  # type: ignore[arg-type]
+            for i in itertools.product(*(range(d) for d in dims))
+        ],
+        axis=0,
+    )
+
+    # all done
+    return xp.reshape(xp.moveaxis(x, 0, -1), (len(shells), *dims))
+
+
+def partition_restrict(
+    z: FloatArray,
+    fz: FloatArray,
+    shells: Sequence[RadialWindow],
+) -> FloatArray:
+    """
+    Partition by restriction and integration.
+
+    Parameters
+    ----------
+    z
+        The function to be partitioned.
+    fz
+        The function to be partitioned.
+    shells
+        Ordered sequence of window functions.
+
+    Returns
+    -------
+        The partition.
+
+    """
+    xp = array_api_compat.array_namespace(z, fz, use_compat=False)
+    uxpx = _utils.XPAdditions(xp)
+
+    parts = []
+    for _, w in enumerate(shells):
+        zr, fr = restrict(z, fz, w)
+        parts.append(uxpx.trapezoid(fr, zr, axis=-1))
+    return xp.stack(parts)
+
+
+def _uniform_grid(
+    start: float,
+    stop: float,
+    *,
+    step: float | None = None,
+    num: int | None = None,
+    xp: types.ModuleType,
+) -> FloatArray:
+    """
+    Create a uniform grid.
+
+    Parameters
+    ----------
+    start
+        The minimum value.
+    stop
+        The maximum value.
+    step
+        The spacing.
+    num
+        The number of samples.
+    xp
+        The array library backend to use for array operations.
+
+    Returns
+    -------
+        The uniform grid.
+
+    Raises
+    ------
+    ValueError
+        If both ``step`` and ``num`` are given.
+
+    """
+    if step is not None and num is None:
+        return xp.arange(start, stop + step, step)
+    if step is None and num is not None:
+        return xp.linspace(start, stop, num + 1, dtype=xp.float64)
+    msg = "exactly one of grid step size or number of steps must be given"
+    raise ValueError(msg)
+
+
+def redshift_grid(
+    zmin: float,
+    zmax: float,
+    *,
+    dz: float | None = None,
+    num: int | None = None,
+    xp: types.ModuleType,
+) -> FloatArray:
+    """
+    Redshift grid with uniform spacing in redshift.
+
+    Parameters
+    ----------
+    zmin
+        The minimum redshift.
+    zmax
+        The maximum redshift.
+    dz
+        The redshift spacing.
+    num
+        The number redshift samples.
+    xp
+        The array library backend to use for array operations.
+
+    Returns
+    -------
+        The redshift grid.
+
+    """
+    return _uniform_grid(zmin, zmax, step=dz, num=num, xp=xp)
+
+
+def distance_grid(
+    cosmo: Cosmology,
+    zmin: float,
+    zmax: float,
+    *,
+    dx: float | None = None,
+    num: int | None = None,
+) -> FloatArray:
+    """
+    Redshift grid with uniform spacing in comoving distance.
+
+    Parameters
+    ----------
+    cosmo
+        Cosmology instance.
+    zmin
+        The minimum redshift.
+    zmax
+        The maximum redshift.
+    dx
+        The comoving distance spacing.
+    num
+        The number of samples.
+
+    Returns
+    -------
+        The redshift grid.
+
+    """
+    xmin, xmax = cosmo.comoving_distance(zmin), cosmo.comoving_distance(zmax)
+    xp = array_api_compat.array_namespace(xmin, xmax, use_compat=False)
+    x = _uniform_grid(xmin, xmax, step=dx, num=num, xp=xp)
+    return cosmo.inv_comoving_distance(x)
+
+
+def combine(
+    z: FloatArray,
+    weights: FloatArray,
+    shells: Sequence[RadialWindow],
+) -> FloatArray:
+    r"""
+    Evaluate a linear combination of window functions.
+
+    Takes a vector of weights :math:`x_1, x_2, \ldots` and computes the
+    weighted sum of normalised radial window functions :math:`f(z) = x_1
+    \, w_1(z) + x_2 \, w_2(z) + \ldots` in the given redshifts
+    :math:`z`.
+
+    The window functions are given by the sequence *shells* of
+    :class:`RadialWindow` or compatible entries.
+
+    Parameters
+    ----------
+    z
+        Redshifts *z* in which to evaluate the combined function.
+    weights
+        Weights of the linear combination, where the leading axis
+        corresponds to *shells*.
+    shells
+        Ordered sequence of window functions to be combined.
+
+    Returns
+    -------
+        A linear combination of window functions, evaluated in *z*.
+
+    See Also
+    --------
+    partition:
+        Find weights for a given function.
+
+    """
+    xp = array_api_compat.array_namespace(
+        z,
+        weights,
+        *(arr for shell in shells for arr in (shell.za, shell.wa)),
+        use_compat=False,
+    )
+    uxpx = _utils.XPAdditions(xp)
+
+    return xp.sum(
+        xp.stack(
+            [
+                xp.expand_dims(weight, axis=-1)
+                * uxpx.interp(
+                    z,
+                    shell.za,
+                    shell.wa / uxpx.trapezoid(shell.wa, shell.za),
+                    left=0.0,
+                    right=0.0,
+                )
+                for shell, weight in zip(shells, weights, strict=False)
+            ],
+        ),
+        axis=0,
+    )
diff --git a/glass/glass/user.py b/glass/glass/user.py
new file mode 100644
index 0000000..b13b17f
--- /dev/null
+++ b/glass/glass/user.py
@@ -0,0 +1,200 @@
+"""
+User utilities
+==============
+
+.. currentmodule:: glass
+
+The following functions/classes provide convenience functionality for users
+of the library.
+
+
+Input and Output
+----------------
+
+.. autofunction:: save_cls
+.. autofunction:: load_cls
+.. autofunction:: write_catalog
+
+"""  # noqa: D400
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    import importlib.util
+    import os
+    from collections.abc import Generator, Sequence
+
+    from glass._types import FloatArray
+
+    if importlib.util.find_spec("fitsio") is not None:
+        import fitsio
+
+
+def save_cls(
+    filename: os.PathLike[str],
+    cls: Sequence[FloatArray | Sequence[float]],
+) -> None:
+    """
+    Save a list of Cls to file.
+
+    Uses :func:`numpy.savez` internally. The filename should therefore have a
+    ``.npz`` suffix, or it will be given one.
+
+    Parameters
+    ----------
+    filename
+        The name of the file to save to.
+    cls
+        Angular matter power spectra in *GLASS* ordering.
+
+    """
+    split = np.cumsum([len(cl) for cl in cls[:-1]])
+    values = np.concatenate(cls)
+    np.savez(filename, values=values, split=split)
+
+
+def load_cls(
+    filename: os.PathLike[str],
+) -> Sequence[FloatArray | Sequence[float]]:
+    """
+    Load a list of Cls from file.
+
+    Uses :func:`numpy.load` internally.
+
+    Parameters
+    ----------
+    filename
+        The name of the file to load from.
+
+    Returns
+    -------
+        The list of Cls.
+
+    """
+    with np.load(filename) as npz:
+        values = npz["values"]
+        split = npz["split"]
+    return np.split(values, split)
+
+
+class _FitsWriter:
+    """
+    Writer that creates a FITS file.
+
+    Initialised with the fits object and extension name.
+    """
+
+    def __init__(self, fits: fitsio.FITS, ext: str | None = None) -> None:
+        """
+        Create a new, uninitialised writer.
+
+        Parameters
+        ----------
+        fits
+            The fits object.
+        ext
+            The file extension.
+
+        """
+        self.fits = fits
+        self.ext = ext
+
+    def _append(
+        self,
+        data: FloatArray | list[FloatArray],
+        names: list[str] | None = None,
+    ) -> None:
+        """
+        Write the FITS file.
+
+        Parameters
+        ----------
+        data
+            The data to write.
+        names
+            The names of the columns.
+
+        """
+        if self.ext is None or self.ext not in self.fits:
+            self.fits.write_table(data, names=names, extname=self.ext)
+            if self.ext is None:
+                self.ext = self.fits[-1].get_extnum()
+        else:
+            hdu = self.fits[self.ext]
+            # not using hdu.append here because of incompatibilities
+            hdu.write(data, names=names, firstrow=hdu.get_nrows())
+
+    def write(
+        self,
+        data: FloatArray | None = None,
+        /,
+        **columns: FloatArray,
+    ) -> None:
+        """
+        Write to FITS by calling the internal _append method.
+
+        Pass either a positional variable (data)
+        or multiple named arguments (**columns)
+
+        Parameters
+        ----------
+        data
+            The data to write.
+        columns
+            The columns to write.
+
+        """
+        # if data is given, write it as it is
+        if data is not None:
+            self._append(data)
+
+        # if keyword arguments are given, treat them as names and columns
+        if columns:
+            names, values = list(columns.keys()), list(columns.values())
+            self._append(values, names)
+
+
+@contextmanager
+def write_catalog(
+    filename: os.PathLike[str],
+    *,
+    ext: str | None = None,
+) -> Generator[_FitsWriter]:
+    """
+    Write a catalogue into a FITS file.
+
+    *ext* is the optional name of the extension.
+    To be used as a context manager::
+
+        # create the catalogue writer
+        with write_catalog("catalog.fits") as out:
+            ...
+            # write catalogue columns RA, DEC, E1, E2, WHT with given arrays
+            out.write(RA=lon, DEC=lat, E1=eps1, E2=e2, WHT=w)
+
+    .. note::
+       Requires the ``fitsio`` package.
+
+    Parameters
+    ----------
+    filename
+        The name of the file to write to.
+    ext
+        The file extension.
+
+    Yields
+    ------
+    writer
+        The writer object.
+
+    """
+    import fitsio  # noqa: PLC0415
+
+    with fitsio.FITS(filename, "rw", clobber=True) as fits:
+        fits.write(None)
+        yield _FitsWriter(fits, ext)
diff --git a/glass/noxfile.py b/glass/noxfile.py
new file mode 100644
index 0000000..64704a3
--- /dev/null
+++ b/glass/noxfile.py
@@ -0,0 +1,146 @@
+"""Nox config."""
+
+import os
+from pathlib import Path
+
+import nox
+
+# Options to modify nox behaviour
+nox.options.default_venv_backend = "uv|virtualenv"
+nox.options.reuse_existing_virtualenvs = True
+nox.options.sessions = [
+    "lint",
+    "tests",
+]
+
+ALL_PYTHON = [
+    "3.10",
+    "3.11",
+    "3.12",
+    "3.13",
+]
+ARRAY_BACKENDS = {
+    "array_api_strict": "array_api_strict>=2",
+    "jax": "jax>=0.4.32",
+}
+
+
+@nox.session
+def lint(session: nox.Session) -> None:
+    """Run the linter."""
+    session.install("pre-commit")
+    session.run("pre-commit", "run", "--all-files", *session.posargs)
+
+
+@nox.session(python=ALL_PYTHON)
+def tests(session: nox.Session) -> None:
+    """Run the unit tests."""
+    session.install("-c", ".github/test-constraints.txt", "-e", ".", "--group", "test")
+
+    array_backend = os.environ.get("ARRAY_BACKEND")
+    if array_backend == "array_api_strict":
+        session.install(ARRAY_BACKENDS["array_api_strict"])
+    elif array_backend == "jax":
+        session.install(ARRAY_BACKENDS["jax"])
+    elif array_backend == "all":
+        session.install(*ARRAY_BACKENDS.values())
+
+    session.run("pytest", *session.posargs)
+
+
+@nox.session(python=ALL_PYTHON)
+def coverage(session: nox.Session) -> None:
+    """Run tests and compute coverage."""
+    session.posargs.append("--cov")
+    tests(session)
+
+
+@nox.session(python=ALL_PYTHON)
+def doctests(session: nox.Session) -> None:
+    """Run the doctests."""
+    session.install(
+        "-c",
+        ".github/test-constraints.txt",
+        "-e",
+        ".",
+        "--group",
+        "doctest",
+    )
+
+    session.posargs.append("--doctest-plus")
+    session.posargs.append("--doctest-plus-generate-diff=overwrite")
+    session.posargs.append("glass")
+
+    session.run("pytest", *session.posargs)
+
+
+@nox.session
+def examples(session: nox.Session) -> None:
+    """Run the example notebooks. Pass "html" to build html."""
+    session.install("-e", ".[examples]")
+
+    if session.posargs:
+        if "html" in session.posargs:
+            print("Generating HTML for the example notebooks")
+            session.run(
+                "jupyter",
+                "nbconvert",
+                "--to",
+                "html",
+                "--embed-images",
+                "examples/**/*.ipynb",
+            )
+        else:
+            print("Unsupported argument to examples")
+    else:
+        session.run(
+            "jupyter",
+            "execute",
+            "--inplace",
+            *Path().glob("examples/**/*.ipynb"),
+            *session.posargs,
+        )
+
+
+@nox.session
+def docs(session: nox.Session) -> None:
+    """Build the docs. Pass "serve" to serve."""
+    session.install("-e", ".", "--group", "docs")
+    session.chdir("docs")
+    session.run(
+        "sphinx-build",
+        "-M",
+        "html",
+        ".",
+        "_build",
+        "--fail-on-warning",
+    )
+
+    port = 8001
+
+    if session.posargs:
+        if "serve" in session.posargs:
+            print(f"Launching docs at http://localhost:{port}/ - use Ctrl-C to quit")
+            session.run("python", "-m", "http.server", f"{port}", "-d", "_build/html")
+        else:
+            print("Unsupported argument to docs")
+
+
+@nox.session
+def build(session: nox.Session) -> None:
+    """Build an SDist and wheel."""
+    session.install("build")
+    session.run("python", "-m", "build")
+
+
+@nox.session
+def version(session: nox.Session) -> None:
+    """
+    Check the current version of the package.
+
+    The intent of this check is to ensure that the package
+    is installed without any additional dependencies
+    through optional dependencies nor dependency groups.
+    """
+    session.install("-e", ".")
+    session.run("python", "-c", "import glass; print(glass.__version__)")
diff --git a/glass/pyproject.toml b/glass/pyproject.toml
new file mode 100644
index 0000000..e60c118
--- /dev/null
+++ b/glass/pyproject.toml
@@ -0,0 +1,237 @@
+[build-system]
+build-backend = "hatchling.build"
+requires = [
+    "hatch-vcs",
+    "hatchling",
+]
+
+[dependency-groups]
+docs = [
+    "array-api-strict",
+    "cosmology.api",
+    "furo",
+    "ipython",
+    "jax",
+    "jaxtyping",
+    "matplotlib",
+    "myst-parser",
+    "nbsphinx",
+    "sphinx-autodoc-typehints",
+    "sphinx-toolbox",
+    "sphinx<8.2.0",
+    "sphinxcontrib-katex",
+]
+doctest = [
+    "jax",
+    {include-group = "test"},
+]
+test = [
+    "cosmology.api",
+    "fitsio",
+    "pytest",
+    "pytest-cov",
+    "pytest-doctestplus",
+    "pytest-mock",
+    "pytest-rerunfailures",
+    "scipy",
+]
+
+[project]
+classifiers = [
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Astronomy",
+    "Topic :: Scientific/Engineering :: Physics",
+    "Typing :: Typed",
+]
+dependencies = [
+    "array-api-compat>=1.12.0",
+    "array-api-extra>=0.9.0",
+    "healpix>=2022.11.1",
+    "healpy>=1.15.0",
+    "transformcl>=2022.8.9",
+]
+description = "Generator for Large Scale Structure"
+dynamic = [
+    "version",
+]
+license = "MIT"
+license-files = [
+    "LICENSE",
+]
+maintainers = [
+    {email = "n.tessore@ucl.ac.uk", name = "Nicolas Tessore"},
+]
+name = "glass"
+optional-dependencies = {examples = [
+    "camb",
+    "cosmology.api",
+    "cosmology.compat.camb",
+    "glass.ext.camb",
+    "jupyter",
+    "matplotlib",
+]}
+readme = "README.md"
+requires-python = ">=3.10"
+
+[project.urls]
+Changelog = "https://glass.readthedocs.io/stable/manual/releases.html"
+Documentation = "https://glass.readthedocs.io/stable"
+Homepage = "https://github.com/glass-dev/glass"
+Issues = "https://github.com/glass-dev/glass/issues"
+
+[tool.coverage]
+report = {exclude_also = [
+    "class \\w+\\(Protocol(\\[(\\w+, )*\\w+\\])?\\):", # protocol classes
+    "if TYPE_CHECKING:", # type-checking imports
+], omit = [
+    "glass/_version.py",
+], skip_covered = true, sort = "cover"}
+run = {branch = true, parallel = true, source = [
+    "glass",
+]}
+paths.source = [
+    "src",
+    ".nox*/*/lib/python*/site-packages",
+]
+
+[tool.hatch]
+build.hooks.vcs.version-file = "glass/_version.py"
+build.targets.sdist.exclude = [
+    ".*",
+    "docs/*",
+    "examples/*",
+    "noxfile.py",
+    "tests/*",
+]
+version.source = "vcs"
+
+[tool.mypy]
+disallow_untyped_decorators = false
+enable_error_code = [
+    "ignore-without-code",
+    "redundant-expr",
+    "truthy-bool",
+]
+explicit_package_bases = true
+strict = true
+warn_unreachable = true
+
+[[tool.mypy.overrides]]
+disable_error_code = [
+    "arg-type",
+    "assignment",
+]
+module = "tests.*"
+
+[tool.pytest.ini_options]
+addopts = [
+    "--strict-config",
+    "--strict-markers",
+    "-ra",
+    "-v",
+]
+filterwarnings = [
+    "ignore::DeprecationWarning",
+]
+log_cli_level = "DEBUG"
+minversion = "6.0"
+testpaths = [
+    "tests",
+]
+xfail_strict = true
+
+[tool.ruff]
+fix = true
+force-exclude = true
+show-fixes = true
+src = [
+    "glass",
+]
+lint.allowed-confusables = [
+    "α",
+    "γ",
+]
+lint.future-annotations = true
+lint.ignore = [
+    "COM812", # missing-trailing-comma (ruff-format recommended)
+    "D203", # one-blank-line-before-class
+    "D212", # blank-line-before-class
+    "ISC001", # single-line-implicit-string-concatenation (ruff-format recommended)
+    "RUF003", # ambiguous-unicode-character-comment
+]
+lint.isort = {known-first-party = [
+    "glass",
+], known-third-party = [
+    "jax",
+], section-order = [
+    "future",
+    "standard-library",
+    "third-party",
+    "array-api",
+    "cosmo",
+    "first-party",
+    "local-folder",
+], sections = {"array-api" = [
+    "array_api_compat",
+    "array_api_extra",
+    "array_api_strict",
+], "cosmo" = [
+    "camb",
+    "cosmology",
+]}}
+lint.per-file-ignores = {"__init__.py" = [
+    "F401", # unused-import
+], "docs*" = [
+    "D100", # undocumented-public-module
+    "INP001", # implicit-namespace-package,
+], "examples*" = [
+    "ANN001", # missing-type-function-argument
+    "ANN002", # missing-type-args
+    "ANN201", # missing-return-type-undocumented-public-function
+    "D103", # undocumented-public-function
+    "PLR2004", # magic-value-comparison
+    "T201", # print
+], "glass*" = [
+    "D205", # missing-blank-line-after-summary
+    "D401", # non-imperative-mood
+    "EM101", # raw-string-in-exception
+    "EM102", # f-string-in-exception
+    "PLR2004", # TODO: magic-value-comparison
+    "TRY003", # raise-vanilla-args
+], "glass/grf/_*" = [
+    "SLF001", # private-member-access
+], "noxfile.py" = [
+    "T201", # print
+], "tests*" = [
+    "ANN001", # missing-type-function-argument
+    "ANN201", # missing-return-type-undocumented-public-function
+    "ANN202", # missing-return-type-private-function
+    "D100", # undocumented-public-module
+    "D103", # TODO: undocumented-public-function
+    "D104", # undocumented-public-package
+    "INP001", # implicit-namespace-package
+    "PLC0415", # import-outside-top-level
+    "PLR2004", # magic-value-comparison
+    "S101", # assert
+    "SLF001", # private-member-access
+]}
+lint.select = [
+    "ALL",
+]
+lint.mccabe.max-complexity = 18
+lint.pydocstyle.convention = "numpy"
+
+[tool.tomlsort]
+all = true
+in_place = true
+spaces_indent_inline_array = 4
+trailing_comma_inline_array = true
+overrides."project.classifiers".inline_arrays = false
+overrides."tool.coverage.paths.source".inline_arrays = false
+overrides."tool.ruff.lint.isort.section-order".inline_arrays = false
diff --git a/glass/tests/conftest.py b/glass/tests/conftest.py
new file mode 100644
index 0000000..8e5a2c6
--- /dev/null
+++ b/glass/tests/conftest.py
@@ -0,0 +1,242 @@
+from __future__ import annotations
+
+import contextlib
+import importlib.metadata
+import logging
+import os
+from typing import TYPE_CHECKING
+
+import numpy as np
+import packaging.version
+import pytest
+
+import glass
+import glass._array_api_utils as _utils
+
+with contextlib.suppress(ImportError):
+    # only import if jax is available
+    import glass.jax
+
+if TYPE_CHECKING:
+    import types
+
+    from cosmology import Cosmology
+
+    from glass._types import FloatArray, UnifiedGenerator
+
+
+# Handling of array backends, inspired by-
+# https://github.com/scipy/scipy/blob/36e349b6afbea057cb713fc314296f10d55194cc/scipy/conftest.py#L139
+
+# environment variable to specify array backends for testing
+# can be:
+#   a particular array library (numpy, jax, array_api_strict, ...)
+#   all (try finding every supported array library available in the environment)
+ARRAY_BACKEND: str = os.environ.get("ARRAY_BACKEND", "")
+
+# Change jax logger to only log ERROR or worse
+logging.getLogger("jax").setLevel(logging.ERROR)
+
+
+def _check_version(lib: str, array_api_compliant_version: str) -> None:
+    """
+    Check if installed library's version is compliant with the array API standard.
+
+    Parameters
+    ----------
+    lib
+        name of the library.
+    array_api_compliant_version
+        version of the library compliant with the array API standard.
+
+    Raises
+    ------
+    ImportError
+        If the installed version is not compliant with the array API standard.
+    """
+    lib_version = packaging.version.Version(importlib.metadata.version(lib))
+    if lib_version < packaging.version.Version(array_api_compliant_version):
+        msg = f"{lib} must be >= {array_api_compliant_version}; found {lib_version}"
+        raise ImportError(msg)
+
+
+def _import_and_add_numpy(xp_available_backends: dict[str, types.ModuleType]) -> None:
+    """Add numpy to the backends dictionary."""
+    _check_version("numpy", "2.1.0")
+    xp_available_backends["numpy"] = np
+
+
+def _import_and_add_array_api_strict(
+    xp_available_backends: dict[str, types.ModuleType],
+) -> None:
+    """Add array_api_strict to the backends dictionary."""
+    import array_api_strict
+
+    _check_version("array_api_strict", "2.0.0")
+    xp_available_backends["array_api_strict"] = array_api_strict
+    array_api_strict.set_array_api_strict_flags(api_version="2024.12")
+
+
+def _import_and_add_jax(xp_available_backends: dict[str, types.ModuleType]) -> None:
+    """Add jax to the backends dictionary."""
+    import jax
+
+    _check_version("jax", "0.4.32")
+    xp_available_backends["jax.numpy"] = jax.numpy
+    # enable 64 bit numbers
+    jax.config.update("jax_enable_x64", val=True)
+
+
+# a dictionary with all array backends to test
+xp_available_backends: dict[str, types.ModuleType] = {}
+
+# if no backend passed, use numpy by default
+if not ARRAY_BACKEND or ARRAY_BACKEND == "numpy":
+    _import_and_add_numpy(xp_available_backends)
+elif ARRAY_BACKEND == "array_api_strict":
+    _import_and_add_array_api_strict(xp_available_backends)
+elif ARRAY_BACKEND == "jax":
+    _import_and_add_jax(xp_available_backends)
+# if all, try importing every backend
+elif ARRAY_BACKEND == "all":
+    with contextlib.suppress(ImportError):
+        _import_and_add_numpy(xp_available_backends)
+
+    with contextlib.suppress(ImportError):
+        _import_and_add_array_api_strict(xp_available_backends)
+
+    with contextlib.suppress(ImportError):
+        _import_and_add_jax(xp_available_backends)
+else:
+    msg = f"unsupported array backend: {ARRAY_BACKEND}"
+    raise ValueError(msg)
+
+
+# Pytest fixtures
+@pytest.fixture(params=xp_available_backends.values(), scope="session")
+def xp(request: pytest.FixtureRequest) -> types.ModuleType:
+    """
+    Fixture for array backend.
+
+    Access array library functions using `xp.` in tests.
+    """
+    return request.param  # type: ignore[no-any-return]
+
+
+@pytest.fixture(scope="session")
+def uxpx(xp: types.ModuleType) -> _utils.XPAdditions:
+    """
+    Fixture for array backend.
+
+    Access array library functions using `xp.` in tests.
+    """
+    return _utils.XPAdditions(xp)
+
+
+@pytest.fixture(scope="session")
+def urng(xp: types.ModuleType) -> UnifiedGenerator:
+    """
+    Fixture for a unified RNG interface.
+
+    Access the relevant RNG using `urng.` in tests.
+
+    Must be used with the `xp` fixture. Use `rng` for non array API tests.
+    """
+    seed = 42
+    backend = xp.__name__
+    if backend == "jax.numpy":
+        return glass.jax.Generator(seed=seed)
+    if backend == "numpy":
+        return np.random.default_rng(seed=seed)
+    if backend == "array_api_strict":
+        return _utils.Generator(seed=seed)
+    msg = "the array backend in not supported"
+    raise NotImplementedError(msg)
+
+
+@pytest.fixture(scope="session")
+def rng() -> np.random.Generator:
+    """
+    RNG fixture for non array API tests.
+
+    Use `urng` for array API tests.
+    """
+    return np.random.default_rng(seed=42)
+
+
+@pytest.fixture(scope="session")
+def cosmo() -> Cosmology:
+    class MockCosmology:
+        @property
+        def Omega_m0(self) -> float:  # noqa: N802
+            """Matter density parameter at redshift 0."""
+            return 0.3
+
+        @property
+        def critical_density0(self) -> float:
+            """Critical density at redshift 0 in Msol Mpc-3."""
+            return 3e4
+
+        @property
+        def hubble_distance(self) -> float:
+            """Hubble distance in Mpc."""
+            return 4.4e3
+
+        def H_over_H0(self, z: FloatArray) -> FloatArray:  # noqa: N802
+            """Standardised Hubble function :math:`E(z) = H(z)/H_0`."""
+            return (self.Omega_m0 * (1 + z) ** 3 + 1 - self.Omega_m0) ** 0.5
+
+        def xm(
+            self,
+            z: FloatArray,
+            z2: FloatArray | None = None,
+        ) -> FloatArray:
+            """
+            Dimensionless transverse comoving distance.
+
+            :math:`x_M(z) = d_M(z)/d_H`
+            """
+            if z2 is None:
+                return np.array(z) * 1_000
+            return (np.array(z2) - np.array(z)) * 1_000
+
+        def rho_m_z(self, z: FloatArray) -> FloatArray:
+            """Redshift-dependent matter density in Msol Mpc-3."""
+            return self.critical_density0 * self.Omega_m0 * (1 + z) ** 3
+
+        def comoving_distance(
+            self,
+            z: FloatArray,
+            z2: FloatArray | None = None,
+        ) -> FloatArray:
+            """Comoving distance :math:`d_c(z)` in Mpc."""
+            return self.xm(z) / 1_000 if z2 is None else self.xm(z, z2) / 1_000
+
+        def inv_comoving_distance(self, dc: FloatArray) -> FloatArray:
+            """Inverse function for the comoving distance in Mpc."""
+            return 1_000 * (1 / (dc + np.finfo(float).eps))
+
+        def Omega_m(self, z: FloatArray) -> FloatArray:  # noqa: N802
+            """Matter density parameter at redshift z."""
+            return self.rho_m_z(z) / self.critical_density0
+
+        def transverse_comoving_distance(
+            self,
+            z: FloatArray,
+            z2: FloatArray | None = None,
+        ) -> FloatArray:
+            """Transverse comoving distance :math:`d_M(z)` in Mpc."""
+            return self.hubble_distance * self.xm(z, z2)
+
+    return MockCosmology()
+
+
+@pytest.fixture(scope="session")
+def shells() -> list[glass.RadialWindow]:
+    return [
+        glass.RadialWindow(np.array([0.0, 1.0, 2.0]), np.array([0.0, 1.0, 0.0]), 1.0),
+        glass.RadialWindow(np.array([1.0, 2.0, 3.0]), np.array([0.0, 1.0, 0.0]), 2.0),
+        glass.RadialWindow(np.array([2.0, 3.0, 4.0]), np.array([0.0, 1.0, 0.0]), 3.0),
+        glass.RadialWindow(np.array([3.0, 4.0, 5.0]), np.array([0.0, 1.0, 0.0]), 4.0),
+        glass.RadialWindow(np.array([4.0, 5.0, 6.0]), np.array([0.0, 1.0, 0.0]), 5.0),
+    ]
diff --git a/glass/tests/grf/test_core.py b/glass/tests/grf/test_core.py
new file mode 100644
index 0000000..0fa4a25
--- /dev/null
+++ b/glass/tests/grf/test_core.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass.grf
+
+if TYPE_CHECKING:
+    import pytest_mock
+
+
+def test_corr_unknown() -> None:
+    class Unknown:
+        def corr(self, _other, _x):  # type: ignore[no-untyped-def]
+            return NotImplemented
+
+        def icorr(self, _other, _x):  # type: ignore[no-untyped-def]
+            return NotImplemented
+
+        def dcorr(self, _other, _x):  # type: ignore[no-untyped-def]
+            return NotImplemented
+
+    t1 = glass.grf.Normal()
+    t2 = Unknown()
+    x = np.zeros(10)
+
+    with pytest.raises(NotImplementedError, match="Unknown"):
+        glass.grf.corr(t1, t2, x)
+
+    with pytest.raises(NotImplementedError, match="Unknown"):
+        glass.grf.icorr(t1, t2, x)
+
+    with pytest.raises(NotImplementedError, match="Unknown"):
+        glass.grf.dcorr(t1, t2, x)
+
+
+def test_compute(mocker: pytest_mock.MockerFixture) -> None:
+    cltocorr = mocker.patch("transformcl.cltocorr")
+    icorr = mocker.patch("glass.grf._core.icorr")
+    corrtocl = mocker.patch("transformcl.corrtocl")
+
+    t1 = glass.grf.Normal()
+    t2 = glass.grf.Normal()
+    x = np.zeros(10)
+
+    result = glass.grf.compute(x, t1, t2)
+
+    cltocorr.assert_called_once_with(x)
+    icorr.assert_called_once_with(t1, t2, cltocorr.return_value)
+    corrtocl.assert_called_once_with(icorr.return_value)
+    assert result is corrtocl.return_value
+
+    cltocorr.reset_mock()
+    icorr.reset_mock()
+    corrtocl.reset_mock()
+
+    # default t2
+    result = glass.grf.compute(x, t1)
+
+    cltocorr.assert_called_once_with(x)
+    icorr.assert_called_once_with(t1, t1, cltocorr.return_value)
+    corrtocl.assert_called_once_with(icorr.return_value)
+    assert result is corrtocl.return_value
diff --git a/glass/tests/grf/test_solver.py b/glass/tests/grf/test_solver.py
new file mode 100644
index 0000000..f680e09
--- /dev/null
+++ b/glass/tests/grf/test_solver.py
@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass.grf
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+
+@pytest.fixture(scope="session")
+def cl() -> NDArray[np.float64]:
+    lmax = 100
+    ell = np.arange(lmax + 1)
+    return 1e-2 / (2 * ell + 1) ** 2
+
+
+def test_one_transformation(cl: NDArray[np.float64], rng: np.random.Generator) -> None:
+    lam = rng.random()
+    t = glass.grf.Lognormal(lam)
+
+    gl1, _, _ = glass.grf.solve(cl, t)
+    gl2, _, _ = glass.grf.solve(cl, t, t)
+
+    np.testing.assert_array_equal(gl1, gl2)
+
+
+def test_pad(cl: NDArray[np.float64], rng: np.random.Generator) -> None:
+    lam = rng.random()
+    t = glass.grf.Lognormal(lam)
+
+    # check that output size matches pad
+    _, cl_out, _ = glass.grf.solve(cl, t, pad=2 * cl.size)
+
+    assert cl_out.size == 3 * cl.size
+
+    with pytest.raises(ValueError, match="pad must be a positive integer"):
+        glass.grf.solve(cl, t, pad=-1)
+
+
+def test_initial(cl: NDArray[np.float64], rng: np.random.Generator) -> None:
+    lam = rng.random()
+    t = glass.grf.Lognormal(lam)
+
+    gl = glass.grf.compute(cl, t)
+
+    gl1, _, _ = glass.grf.solve(cl, t)
+    gl2, _, _ = glass.grf.solve(cl, t, initial=gl)
+
+    np.testing.assert_array_equal(gl1, gl2)
+
+
+def test_no_iterations(cl: NDArray[np.float64]) -> None:
+    t = glass.grf.Lognormal()
+
+    gl1 = glass.grf.compute(cl, t)
+    gl2, _, _ = glass.grf.solve(cl, t, maxiter=0)
+
+    np.testing.assert_array_equal(gl1, gl2)
+
+
+def test_lognormal(cl: NDArray[np.float64], rng: np.random.Generator) -> None:
+    t1 = glass.grf.Lognormal()
+    t2 = glass.grf.Lognormal()
+
+    gl0 = rng.random()
+
+    cltol = 1e-7
+
+    gl, cl_, info = glass.grf.solve(cl, t1, t2, monopole=gl0, cltol=cltol)
+
+    assert info > 0
+
+    np.testing.assert_allclose(cl_[1 : cl.size], cl[1:], atol=0.0, rtol=cltol)
+
+    gl_ = glass.grf.compute(cl_, t1, t2)
+
+    assert gl[0] == gl0
+    np.testing.assert_allclose(gl_[1 : gl.size], gl[1:])
+
+
+def test_monopole(cl: NDArray[np.float64], rng: np.random.Generator) -> None:
+    t = glass.grf.Lognormal()
+
+    cl[0] = rng.random()
+    gl0 = rng.random()
+
+    gl, cl_out, _ = glass.grf.solve(cl, t, monopole=None, gltol=1e-8)
+
+    assert gl[0] != 0.0
+    np.testing.assert_allclose(cl_out[0], cl[0])
+
+    gl, cl_out, _ = glass.grf.solve(cl, t, monopole=gl0, gltol=1e-8)
+
+    assert gl[0] == gl0
+    np.testing.assert_raises(
+        AssertionError,
+        np.testing.assert_allclose,
+        cl_out[0],
+        cl[0],
+    )
diff --git a/glass/tests/grf/test_transformations.py b/glass/tests/grf/test_transformations.py
new file mode 100644
index 0000000..fd201a4
--- /dev/null
+++ b/glass/tests/grf/test_transformations.py
@@ -0,0 +1,96 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+import glass.grf
+
+if TYPE_CHECKING:
+    import types
+
+    from glass._types import UnifiedGenerator
+
+
+def test_normal(urng: UnifiedGenerator) -> None:
+    t = glass.grf.Normal()
+    x = urng.standard_normal(10)
+    np.testing.assert_array_equal(t(x, 1.0), x)
+
+
+def test_lognormal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    for lam in 1.0, urng.uniform():
+        var = urng.uniform()
+        t = glass.grf.Lognormal(lam)
+        x = urng.standard_normal(10)
+        y = lam * xp.expm1(x - var / 2)
+        np.testing.assert_array_equal(t(x, var), y)
+
+
+def test_sqnormal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    for lam in 1.0, urng.uniform():
+        var = urng.uniform()
+        a = xp.sqrt(1 - var)
+        t = glass.grf.SquaredNormal(a, lam)
+        x = urng.standard_normal(10)
+        y = lam * ((x - a) ** 2 - 1)
+        np.testing.assert_array_equal(t(x, var), y)
+
+
+def test_normal_normal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    t1 = glass.grf.Normal()
+    t2 = glass.grf.Normal()
+    x = urng.random(10)
+    np.testing.assert_array_equal(glass.grf.corr(t1, t2, x), x)
+    np.testing.assert_array_equal(glass.grf.icorr(t1, t2, x), x)
+    np.testing.assert_array_equal(glass.grf.dcorr(t1, t2, x), xp.ones_like(x))
+
+
+def test_lognormal_lognormal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    lam1 = urng.uniform()
+    t1 = glass.grf.Lognormal(lam1)
+
+    lam2 = urng.uniform()
+    t2 = glass.grf.Lognormal(lam2)
+
+    x = urng.random(10)
+    y = lam1 * lam2 * xp.expm1(x)
+    dy = lam1 * lam2 * xp.exp(x)
+
+    np.testing.assert_array_equal(glass.grf.corr(t1, t2, x), y)
+    np.testing.assert_array_almost_equal_nulp(glass.grf.icorr(t1, t2, y), x)
+    np.testing.assert_array_equal(glass.grf.dcorr(t1, t2, x), dy)
+
+
+def test_lognormal_normal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    lam1 = urng.uniform()
+    t1 = glass.grf.Lognormal(lam1)
+
+    t2 = glass.grf.Normal()
+
+    x = urng.random(10)
+    y = lam1 * x
+    dy = lam1 * xp.ones_like(x)
+
+    np.testing.assert_array_equal(glass.grf.corr(t1, t2, x), y)
+    np.testing.assert_array_almost_equal_nulp(glass.grf.icorr(t1, t2, y), x)
+    np.testing.assert_array_equal(glass.grf.dcorr(t1, t2, x), dy)
+
+
+def test_sqnormal_sqnormal(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    lam1, var1 = urng.uniform(size=2)
+    a1 = xp.sqrt(1 - var1)
+    t1 = glass.grf.SquaredNormal(a1, lam1)
+
+    lam2, var2 = urng.uniform(size=2)
+    a2 = xp.sqrt(1 - var2)
+    t2 = glass.grf.SquaredNormal(a2, lam2)
+
+    # https://arxiv.org/pdf/2408.16903, (E.7)
+    x = urng.random(10)
+    y = 2 * lam1 * lam2 * x * (x + 2 * a1 * a2)
+    dy = 4 * lam1 * lam2 * (x + a1 * a2)
+
+    np.testing.assert_array_equal(glass.grf.corr(t1, t2, x), y)
+    np.testing.assert_array_almost_equal_nulp(glass.grf.icorr(t1, t2, y), x, nulp=8)
+    np.testing.assert_array_equal(glass.grf.dcorr(t1, t2, x), dy)
diff --git a/glass/tests/test_algorithm.py b/glass/tests/test_algorithm.py
new file mode 100644
index 0000000..3e7dd43
--- /dev/null
+++ b/glass/tests/test_algorithm.py
@@ -0,0 +1,153 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass.algorithm
+
+if TYPE_CHECKING:
+    import types
+
+    import pytest_mock
+
+    from glass._types import UnifiedGenerator
+
+
+def test_nnls(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    """Unit tests for glass.algorithm.nnls."""
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in nnls are not immutable, so do not support jax")
+
+    # check output
+
+    a = xp.reshape(xp.arange(25.0), (-1, 5))
+    b = xp.arange(5.0)
+    y = a @ b
+    res = glass.algorithm.nnls(a, y)
+    assert xp.linalg.vector_norm((a @ res) - y) < 1e-7
+
+    a = urng.uniform(low=-10, high=10, size=[50, 10])
+    b = xp.abs(urng.uniform(low=-2, high=2, size=[10]))
+    b[::2] = 0
+    x = a @ b
+    res = glass.algorithm.nnls(
+        a,
+        x,
+        tol=500 * xp.linalg.matrix_norm(a, ord=1) * xp.finfo(xp.float64).eps,
+    )
+    np.testing.assert_allclose(res, b, rtol=0.0, atol=1e-10)
+
+    # check matrix and vector's shape
+
+    a = urng.standard_normal((100, 20))
+    b = urng.standard_normal((100,))
+
+    with pytest.raises(ValueError, match="input `a` is not a matrix"):
+        glass.algorithm.nnls(b, a)
+    with pytest.raises(ValueError, match="input `b` is not a vector"):
+        glass.algorithm.nnls(a, a)
+    with pytest.raises(ValueError, match="the shapes of `a` and `b` do not match"):
+        glass.algorithm.nnls(a.T, b)
+
+
+def test_cov_clip(xp: types.ModuleType, urng: UnifiedGenerator) -> None:
+    # prepare a random matrix
+    m = urng.random((4, 4))
+
+    # symmetric matrix
+    a = (m + m.T) / 2
+
+    # fix by clipping negative eigenvalues
+    cov = glass.algorithm.cov_clip(a)
+
+    # make sure all eigenvalues are positive
+    assert xp.all(xp.linalg.eigvalsh(cov) >= 0)
+
+    # fix by clipping negative eigenvalues
+    cov = glass.algorithm.cov_clip(a, rtol=1.0)
+
+    # make sure all eigenvalues are positive
+    h = xp.max(xp.linalg.eigvalsh(a))
+    np.testing.assert_allclose(xp.linalg.eigvalsh(cov), h)
+
+
+def test_nearcorr(xp: types.ModuleType) -> None:
+    # from Higham (2002)
+    a = xp.asarray(
+        [
+            [1.0, 1.0, 0.0],
+            [1.0, 1.0, 1.0],
+            [0.0, 1.0, 1.0],
+        ],
+    )
+    b = xp.asarray(
+        [
+            [1.0000, 0.7607, 0.1573],
+            [0.7607, 1.0000, 0.7607],
+            [0.1573, 0.7607, 1.0000],
+        ],
+    )
+
+    x = glass.algorithm.nearcorr(a)
+    np.testing.assert_allclose(x, b, atol=0.0001)
+
+    # explicit tolerance
+    x = glass.algorithm.nearcorr(a, tol=1e-10)
+    np.testing.assert_allclose(x, b, atol=0.0001)
+
+    # no iterations
+    with pytest.warns(
+        UserWarning,
+        match="Nearest correlation matrix not found in 0 iterations",
+    ):
+        x = glass.algorithm.nearcorr(a, niter=0)
+    np.testing.assert_allclose(x, a)
+
+    # non-square matrix should raise
+    with pytest.raises(ValueError, match="non-square matrix"):
+        glass.algorithm.nearcorr(xp.zeros((4, 3)))
+
+    # no convergence
+    with pytest.warns(
+        UserWarning,
+        match="Nearest correlation matrix not found in 1 iterations",
+    ):
+        x = glass.algorithm.nearcorr(a, niter=1)
+
+
+def test_cov_nearest(
+    xp: types.ModuleType,
+    urng: UnifiedGenerator,
+    mocker: pytest_mock.MockerFixture,
+) -> None:
+    # prepare a random matrix
+    m = urng.random((4, 4))
+
+    # symmetric matrix
+    a = xp.eye(4) + (m + m.T) / 2
+
+    # spy on the call to nearcorr
+    nearcorr = mocker.spy(glass.algorithm, "nearcorr")
+
+    # compute covariance
+    cov = glass.algorithm.cov_nearest(a)
+
+    # make sure all eigenvalues are positive
+    assert xp.all(xp.linalg.eigvalsh(cov) >= 0)
+
+    # get normalisation
+    sq_d = xp.sqrt(xp.linalg.diagonal(a))
+    norm = xp.linalg.outer(sq_d, sq_d)
+
+    # make sure nearcorr was called with correct input
+    nearcorr.assert_called_once()
+    np.testing.assert_array_almost_equal_nulp(
+        nearcorr.call_args_list[0].args[0],
+        xp.divide(a, norm),
+    )
+
+    # cannot deal with negative variances
+    with pytest.raises(ValueError, match="negative values"):
+        glass.algorithm.cov_nearest(xp.asarray([[1, 0, 0], [0, 1, 0], [0, 0, -1]]))
diff --git a/glass/tests/test_array_api_utils.py b/glass/tests/test_array_api_utils.py
new file mode 100644
index 0000000..d182083
--- /dev/null
+++ b/glass/tests/test_array_api_utils.py
@@ -0,0 +1,98 @@
+import contextlib
+import importlib
+
+import numpy as np
+import pytest
+
+import glass._array_api_utils as _utils
+
+with contextlib.suppress(ImportError):
+    # only import if jax is available
+    import glass.jax
+
+# check if available for testing
+HAVE_ARRAY_API_STRICT = importlib.util.find_spec("array_api_strict") is not None
+HAVE_JAX = importlib.util.find_spec("jax") is not None
+
+
+def test_rng_dispatcher_numpy() -> None:
+    rng = _utils.rng_dispatcher(xp=np)
+    assert isinstance(rng, np.random.Generator)
+
+
+@pytest.mark.skipif(not HAVE_JAX, reason="test requires jax")
+def test_rng_dispatcher_jax() -> None:
+    import jax.numpy as jnp
+
+    rng = _utils.rng_dispatcher(xp=jnp)
+    assert isinstance(rng, glass.jax.Generator)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_rng_dispatcher_array_api_strict() -> None:
+    import array_api_strict
+
+    rng = _utils.rng_dispatcher(xp=array_api_strict)
+    assert isinstance(rng, _utils.Generator)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_init() -> None:
+    rng = _utils.Generator(42)
+    assert isinstance(rng, _utils.Generator)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_random() -> None:
+    import array_api_strict
+    from array_api_strict._array_object import Array
+
+    rng = _utils.Generator(42)
+    rvs = rng.random(size=10_000)
+    assert rvs.shape == (10_000,)
+    assert array_api_strict.min(rvs) >= 0.0
+    assert array_api_strict.max(rvs) < 1.0
+    assert isinstance(rvs, Array)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_normal() -> None:
+    from array_api_strict._array_object import Array
+
+    rng = _utils.Generator(42)
+    rvs = rng.normal(1, 2, size=10_000)
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, Array)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_standard_normal() -> None:
+    from array_api_strict._array_object import Array
+
+    rng = _utils.Generator(42)
+    rvs = rng.standard_normal(size=10_000)
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, Array)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_poisson() -> None:
+    from array_api_strict._array_object import Array
+
+    rng = _utils.Generator(42)
+    rvs = rng.poisson(lam=1, size=10_000)
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, Array)
+
+
+@pytest.mark.skipif(not HAVE_ARRAY_API_STRICT, reason="test requires array_api_strict")
+def test_uniform() -> None:
+    import array_api_strict
+    from array_api_strict._array_object import Array
+
+    rng = _utils.Generator(42)
+    rvs = rng.uniform(size=10_000)
+    assert rvs.shape == (10_000,)
+    assert array_api_strict.min(rvs) >= 0.0
+    assert array_api_strict.max(rvs) < 1.0
+    assert isinstance(rvs, Array)
diff --git a/glass/tests/test_arraytools.py b/glass/tests/test_arraytools.py
new file mode 100644
index 0000000..88b2df2
--- /dev/null
+++ b/glass/tests/test_arraytools.py
@@ -0,0 +1,175 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass.arraytools
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+
+def test_broadcast_first(xp: ModuleType) -> None:
+    a = xp.ones((2, 3, 4))
+    b = xp.ones((2, 1))
+
+    # arrays with shape ((3, 4, 2)) and ((1, 2)) are passed
+    # to np.broadcast_arrays; hence it works
+    a_a, b_a = glass.arraytools.broadcast_first(a, b)
+    assert a_a.shape == (2, 3, 4)
+    assert b_a.shape == (2, 3, 4)
+
+    # plain np.broadcast_arrays will not work
+    with pytest.raises(ValueError, match=r"shape mismatch|Incompatible shapes"):
+        xp.broadcast_arrays(a, b)
+
+    # arrays with shape ((5, 6, 4)) and ((6, 5)) are passed
+    # to np.broadcast_arrays; hence it will not work
+    a = xp.ones((4, 5, 6))
+    b = xp.ones((5, 6))
+
+    with pytest.raises(ValueError, match=r"shape mismatch|Incompatible shapes"):
+        glass.arraytools.broadcast_first(a, b)
+
+    # plain np.broadcast_arrays will work
+    a_a, b_a = xp.broadcast_arrays(a, b)
+
+    assert a_a.shape == (4, 5, 6)
+    assert b_a.shape == (4, 5, 6)
+
+
+def test_broadcast_leading_axes() -> None:
+    a_in = 0
+    b_in = np.zeros((4, 10))
+    c_in = np.zeros((3, 1, 5, 6))
+
+    dims, *rest = glass.arraytools.broadcast_leading_axes(
+        (a_in, 0),
+        (b_in, 1),
+        (c_in, 2),
+    )
+    a_out, b_out, c_out = rest
+
+    assert dims == (3, 4)
+    assert a_out.shape == (3, 4)
+    assert b_out.shape == (3, 4, 10)
+    assert c_out.shape == (3, 4, 5, 6)
+
+
+def test_ndinterp(xp: ModuleType) -> None:
+    # test 1d interpolation
+
+    xq = xp.asarray([0, 1, 2, 3, 4])
+    yq = xp.asarray([1.1, 1.2, 1.3, 1.4, 1.5])
+
+    x = 0.5
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == ()
+    np.testing.assert_allclose(y, 1.15, atol=1e-15)
+
+    x = xp.asarray([0.5, 1.5, 2.5])
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == (3,)
+    np.testing.assert_allclose(y, [1.15, 1.25, 1.35], atol=1e-15)
+
+    x = xp.asarray([[0.5, 1.5], [2.5, 3.5]])
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == (2, 2)
+    np.testing.assert_allclose(y, [[1.15, 1.25], [1.35, 1.45]], atol=1e-15)
+
+    # test nd interpolation in final axis
+
+    yq = xp.asarray([[1.1, 1.2, 1.3, 1.4, 1.5], [2.1, 2.2, 2.3, 2.4, 2.5]])
+
+    x = 0.5
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == (2,)
+    np.testing.assert_allclose(y, [1.15, 2.15], atol=1e-15)
+
+    x = xp.asarray([0.5, 1.5, 2.5])
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == (2, 3)
+    np.testing.assert_allclose(y, [[1.15, 1.25, 1.35], [2.15, 2.25, 2.35]], atol=1e-15)
+
+    x = xp.asarray([[0.5, 1.5], [2.5, 3.5]])
+    y = glass.arraytools.ndinterp(x, xq, yq)
+    assert y.shape == (2, 2, 2)
+    np.testing.assert_allclose(
+        y,
+        [[[1.15, 1.25], [1.35, 1.45]], [[2.15, 2.25], [2.35, 2.45]]],
+        atol=1e-15,
+    )
+
+    # test nd interpolation in middle axis
+
+    yq = xp.asarray(
+        [[[1.1], [1.2], [1.3], [1.4], [1.5]], [[2.1], [2.2], [2.3], [2.4], [2.5]]],
+    )
+
+    x = 0.5
+    y = glass.arraytools.ndinterp(x, xq, yq, axis=1)
+    assert y.shape == (2, 1)
+    np.testing.assert_allclose(y, [[1.15], [2.15]], atol=1e-15)
+
+    x = xp.asarray([0.5, 1.5, 2.5])
+    y = glass.arraytools.ndinterp(x, xq, yq, axis=1)
+    assert y.shape == (2, 3, 1)
+    np.testing.assert_allclose(
+        y,
+        [[[1.15], [1.25], [1.35]], [[2.15], [2.25], [2.35]]],
+        atol=1e-15,
+    )
+
+    x = xp.asarray([[0.5, 1.5, 2.5, 3.5], [3.5, 2.5, 1.5, 0.5], [0.5, 3.5, 1.5, 2.5]])
+    y = glass.arraytools.ndinterp(x, xq, yq, axis=1)
+    assert y.shape == (2, 3, 4, 1)
+    np.testing.assert_allclose(
+        y,
+        [
+            [
+                [[1.15], [1.25], [1.35], [1.45]],
+                [[1.45], [1.35], [1.25], [1.15]],
+                [[1.15], [1.45], [1.25], [1.35]],
+            ],
+            [
+                [[2.15], [2.25], [2.35], [2.45]],
+                [[2.45], [2.35], [2.25], [2.15]],
+                [[2.15], [2.45], [2.25], [2.35]],
+            ],
+        ],
+        atol=1e-15,
+    )
+
+
+def test_trapezoid_product(xp: ModuleType) -> None:
+    x1 = xp.linspace(0, 2, 100)
+    f1 = xp.full_like(x1, 2.0)
+
+    x2 = xp.linspace(1, 2, 10)
+    f2 = xp.full_like(x2, 0.5)
+
+    s = glass.arraytools.trapezoid_product((x1, f1), (x2, f2))
+
+    np.testing.assert_allclose(s, 1.0)
+
+
+def test_cumulative_trapezoid(xp: ModuleType) -> None:
+    # 1D f and x
+
+    f = xp.asarray([1, 2, 3, 4])
+    x = xp.asarray([0, 1, 2, 3])
+
+    ct = glass.arraytools.cumulative_trapezoid(f, x)
+    np.testing.assert_allclose(ct, xp.asarray([0.0, 1.5, 4.0, 7.5]))
+
+    # 2D f and 1D x
+    f = xp.asarray([[1, 4, 9, 16], [2, 3, 5, 7]])
+    x = xp.asarray([0, 1, 2.5, 4])
+
+    ct = glass.arraytools.cumulative_trapezoid(f, x)
+    np.testing.assert_allclose(
+        ct,
+        xp.asarray([[0.0, 2.5, 12.25, 31.0], [0.0, 2.5, 8.5, 17.5]]),
+    )
diff --git a/glass/tests/test_fields.py b/glass/tests/test_fields.py
new file mode 100644
index 0000000..8c7dac8
--- /dev/null
+++ b/glass/tests/test_fields.py
@@ -0,0 +1,801 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import healpy as hp
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    import types
+
+    import pytest_mock
+
+
+@pytest.fixture(scope="session")
+def not_triangle_numbers() -> list[int]:
+    return [2, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20]
+
+
+def test_iternorm(xp: types.ModuleType) -> None:
+    # Call jax version of iternorm once jax version is written
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in iternorm are not immutable, so do not support jax")
+
+    # check output shapes and types
+
+    k = 2
+
+    generator = glass.iternorm(
+        k,
+        (xp.asarray(x) for x in [1.0, 0.5, 0.5, 0.5, 0.2, 0.1, 0.5, 0.1, 0.2]),
+    )
+    result = next(generator)
+
+    j, a, s = result
+
+    assert isinstance(j, int)
+    assert a.shape == (k,)
+    assert s.shape == ()
+    assert s.dtype == xp.float64
+    assert s.shape == ()
+
+    # specify size
+
+    size = 3
+
+    generator = glass.iternorm(
+        k,
+        (
+            xp.asarray(arr)
+            for arr in [
+                [1.0, 0.5, 0.5],
+                [0.5, 0.2, 0.1],
+                [0.5, 0.1, 0.2],
+            ]
+        ),
+        size,
+    )
+    result = next(generator)
+
+    j, a, s = result
+
+    assert isinstance(j, int)
+    assert a.shape == (size, k)
+    assert s.shape == (size,)
+
+    # test shape mismatch error
+
+    with pytest.raises(TypeError, match="covariance row 0: shape"):
+        list(
+            glass.iternorm(
+                k,
+                (
+                    xp.asarray(arr)
+                    for arr in [
+                        [1.0, 0.5],
+                        [0.5, 0.2],
+                    ]
+                ),
+            ),
+        )
+
+    # test positive definite error
+
+    with pytest.raises(ValueError, match="covariance matrix is not positive definite"):
+        list(
+            glass.iternorm(
+                k,
+                (xp.asarray(x) for x in [1.0, 0.5, 0.9, 0.5, 0.2, 0.4, 0.9, 0.4, -1.0]),
+            ),
+        )
+
+    # test multiple iterations
+
+    size = (3,)
+
+    generator = glass.iternorm(
+        k,
+        (
+            xp.asarray(arr)
+            for arr in [
+                [
+                    [1.0, 0.5, 0.5],
+                    [0.5, 0.2, 0.1],
+                    [0.5, 0.1, 0.2],
+                ],
+                [
+                    [2.0, 1.0, 0.8],
+                    [1.0, 0.5, 0.3],
+                    [0.8, 0.3, 0.6],
+                ],
+            ]
+        ),
+        size,
+    )
+
+    result1 = next(generator)
+    result2 = next(generator)
+
+    assert result1 != result2
+    assert isinstance(result1, tuple)
+    assert len(result1) == 3
+    assert isinstance(result2, tuple)
+    assert len(result2) == 3
+
+    # test k = 0
+
+    generator = glass.iternorm(0, xp.asarray([1.0]))
+
+    j, a, s = result
+
+    assert j == 1
+    assert a.shape == (3, 2)
+    assert s.shape == (3,)
+
+
+def test_cls2cov(xp: types.ModuleType) -> None:
+    # Call jax version of iternorm once jax version is written
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in cls2cov are not immutable, so do not support jax")
+
+    # check output values and shape
+
+    nl, nf, nc = 3, 2, 2
+
+    generator = glass.cls2cov(
+        [xp.asarray([1.0, 0.5, 0.3]), None, xp.asarray([0.7, 0.6, 0.1])],
+        nl,
+        nf,
+        nc,
+    )
+    cov = next(generator)
+
+    assert cov.shape == (nl, nc + 1)
+    assert cov.dtype == xp.float64
+
+    np.testing.assert_allclose(cov[:, 0], xp.asarray([0.5, 0.25, 0.15]))
+    np.testing.assert_allclose(cov[:, 1], 0)
+    np.testing.assert_allclose(cov[:, 2], 0)
+
+    # test negative value error
+
+    generator = glass.cls2cov(
+        [
+            xp.asarray(arr)
+            for arr in [
+                [-1.0, 0.5, 0.3],
+                [0.8, 0.4, 0.2],
+                [0.7, 0.6, 0.1],
+            ]
+        ],
+        nl,
+        nf,
+        nc,
+    )
+    with pytest.raises(ValueError, match="negative values in cl"):
+        next(generator)
+
+    # test multiple iterations
+
+    nl, nf, nc = 3, 3, 2
+
+    generator = glass.cls2cov(
+        [
+            xp.asarray(arr)
+            for arr in [
+                [1.0, 0.5, 0.3],
+                [0.8, 0.4, 0.2],
+                [0.7, 0.6, 0.1],
+                [0.9, 0.5, 0.3],
+                [0.6, 0.3, 0.2],
+                [0.8, 0.7, 0.4],
+            ]
+        ],
+        nl,
+        nf,
+        nc,
+    )
+
+    cov1 = xp.asarray(next(generator), copy=True)
+    cov2 = xp.asarray(next(generator), copy=True)
+    cov3 = next(generator)
+
+    assert cov1.shape == (nl, nc + 1)
+    assert cov2.shape == (nl, nc + 1)
+    assert cov3.shape == (nl, nc + 1)
+
+    assert cov1.dtype == xp.float64
+    assert cov2.dtype == xp.float64
+    assert cov3.dtype == xp.float64
+
+    np.testing.assert_raises(AssertionError, np.testing.assert_allclose, cov1, cov2)
+    np.testing.assert_raises(AssertionError, np.testing.assert_allclose, cov2, cov3)
+
+
+def test_multalm(xp: types.ModuleType) -> None:
+    # Call jax version of iternorm once jax version is written
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in multalm are not immutable, so do not support jax")
+
+    # check output values and shapes
+
+    alm = xp.asarray([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+    bl = xp.asarray([2.0, 0.5, 1.0])
+    alm_copy = xp.asarray(alm, copy=True)
+
+    result = glass.fields._multalm(alm, bl, inplace=True)
+
+    np.testing.assert_allclose(result, alm)
+    expected_result = xp.asarray([2.0, 1.0, 1.5, 4.0, 5.0, 6.0])
+    np.testing.assert_allclose(result, expected_result)
+    np.testing.assert_raises(
+        AssertionError,
+        np.testing.assert_allclose,
+        alm_copy,
+        result,
+    )
+
+    # multiple with 1s
+
+    alm = xp.asarray([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+    bl = xp.ones(3)
+
+    result = glass.fields._multalm(alm, bl, inplace=False)
+    np.testing.assert_allclose(result, alm)
+
+    # multiple with 0s
+
+    bl = xp.asarray([0.0, 1.0, 0.0])
+
+    result = glass.fields._multalm(alm, bl, inplace=False)
+
+    expected_result = xp.asarray([0.0, 2.0, 3.0, 0.0, 0.0, 0.0])
+    np.testing.assert_allclose(result, expected_result)
+
+    # empty arrays
+
+    alm = xp.asarray([])
+    bl = xp.asarray([])
+
+    result = glass.fields._multalm(alm, bl, inplace=False)
+    np.testing.assert_allclose(result, alm)
+
+
+def test_lognormal_gls() -> None:
+    shift = 2
+
+    # empty cls
+
+    assert glass.lognormal_gls([], shift) == []
+
+    # check output shape
+
+    assert len(glass.lognormal_gls([np.linspace(1, 5, 5)], shift)) == 1
+    assert len(glass.lognormal_gls([np.linspace(1, 5, 5)], shift)[0]) == 5
+
+    inp = [np.linspace(1, 6, 5), np.linspace(1, 5, 4), np.linspace(1, 4, 3)]
+    out = glass.lognormal_gls(inp, shift)
+
+    assert len(out) == 3
+    assert len(out[0]) == 5
+    assert len(out[1]) == 4
+    assert len(out[2]) == 3
+
+
+def test_discretized_cls() -> None:
+    # empty cls
+
+    result = glass.discretized_cls([])
+    assert result == []
+
+    # power spectra truncated at lmax + 1 if lmax provided
+
+    result = glass.discretized_cls(
+        [np.arange(10), np.arange(10), np.arange(10)],
+        lmax=5,
+    )
+
+    for cl in result:
+        assert len(cl) == 6
+
+    # check ValueError for triangle number
+
+    with pytest.raises(ValueError, match="invalid number of spectra:"):
+        glass.discretized_cls([np.arange(10), np.arange(10)], ncorr=1)
+
+    # ncorr not None
+
+    cls = [np.arange(10), np.arange(10), np.arange(10)]
+    ncorr = 0
+    result = glass.discretized_cls(cls, ncorr=ncorr)
+
+    assert len(result[0]) == 10
+    assert len(result[1]) == 10
+    assert len(result[2]) == 0  # third correlation should be removed
+
+    # check if pixel window function was applied correctly with nside not None
+
+    nside = 4
+
+    pw = hp.pixwin(nside, lmax=7)
+
+    result = glass.discretized_cls([[], np.ones(10), np.ones(10)], nside=nside)
+
+    for cl in result:
+        n = min(len(cl), len(pw))
+        expected = np.ones(n) * pw[:n] ** 2
+        np.testing.assert_allclose(cl[:n], expected)
+
+
+def test_effective_cls(xp: types.ModuleType) -> None:
+    # Call jax version of iternorm once jax version is written
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in effective_cls are not immutable, so do not support jax")
+
+    # empty cls
+
+    result = glass.effective_cls([], xp.asarray([]))
+    assert result.shape == (0,)
+
+    # check ValueError for triangle number
+
+    with pytest.raises(ValueError, match="invalid number of spectra:"):
+        glass.effective_cls([xp.arange(10), xp.arange(10)], xp.ones((2, 1)))
+
+    # check ValueError for triangle number
+
+    with pytest.raises(ValueError, match="shape mismatch between fields and weights1"):
+        glass.effective_cls([], xp.ones((3, 1)))
+
+    # check with only weights1
+
+    cls = [xp.arange(15.0) for _ in range(3)]
+    weights1 = xp.ones((2, 1))
+
+    result = glass.effective_cls(cls, weights1)
+    assert result.shape == (1, 1, 15)
+
+    # check truncation if lmax provided
+
+    result = glass.effective_cls(cls, weights1, lmax=5)
+
+    assert result.shape == (1, 1, 6)
+    np.testing.assert_allclose(result[..., 6:], 0)
+
+    # check with weights1 and weights2 and weights1 is weights2
+
+    result = glass.effective_cls(cls, weights1, weights2=weights1)
+    assert result.shape == (1, 1, 15)
+
+
+def test_generate_grf() -> None:
+    gls = [np.array([1.0, 0.5, 0.1])]
+    nside = 4
+    ncorr = 1
+
+    gaussian_fields = list(glass.fields._generate_grf(gls, nside))
+
+    assert gaussian_fields[0].shape == (hp.nside2npix(nside),)
+
+    # requires resetting the RNG for reproducibility
+    rng = np.random.default_rng(seed=42)
+    gaussian_fields = list(glass.fields._generate_grf(gls, nside, rng=rng))
+
+    assert gaussian_fields[0].shape == (hp.nside2npix(nside),)
+
+    # requires resetting the RNG for reproducibility
+    rng = np.random.default_rng(seed=42)
+    new_gaussian_fields = list(
+        glass.fields._generate_grf(gls, nside, ncorr=ncorr, rng=rng),
+    )
+
+    assert new_gaussian_fields[0].shape == (hp.nside2npix(nside),)
+
+    np.testing.assert_allclose(new_gaussian_fields[0], gaussian_fields[0])
+
+    with pytest.raises(ValueError, match="all gls are empty"):
+        list(glass.fields._generate_grf([], nside))
+
+
+def test_generate_gaussian() -> None:
+    with pytest.deprecated_call():
+        glass.generate_gaussian([np.array([1.0, 0.5, 0.1])], 4)
+
+
+def test_generate_lognormal() -> None:
+    with pytest.deprecated_call():
+        glass.generate_lognormal([np.array([1.0, 0.5, 0.1])], 4)
+
+
+def test_generate() -> None:
+    # shape mismatch error
+
+    fields = [lambda x, var: x, lambda x, var: x]  # noqa: ARG005
+
+    with pytest.raises(ValueError, match="mismatch between number of fields and gls"):
+        list(glass.generate(fields, [np.ones(10), np.ones(10)], nside=16))
+
+    # check output shape
+
+    nside = 16
+    npix = hp.nside2npix(nside)
+    gls = [np.ones(10), np.ones(10), np.ones(10)]
+
+    result = list(glass.generate(fields, gls, nside=nside))
+
+    assert len(result) == 2
+    for field in result:
+        assert field.shape == (npix,)
+
+    # check ncorr behavior
+
+    result_default = list(glass.generate(fields, gls, nside=nside, ncorr=None))
+    assert len(result_default) == 2
+
+    # ncorr = 1 (forcing only one previous correlation)
+    result_limited = list(glass.generate(fields, gls, nside=nside, ncorr=1))
+    assert len(result_limited) == 2
+
+    # non-identity field
+
+    fields = [lambda x, var: x, lambda x, var: x**2]  # noqa: ARG005
+
+    result = list(glass.generate(fields, gls, nside=nside))
+
+    np.testing.assert_allclose(result[1], result[0] ** 2, atol=1e-05)
+
+
+def test_getcl(xp: types.ModuleType) -> None:
+    # make a mock Cls array with the index pairs as entries
+    cls = [
+        xp.asarray([i, j], dtype=xp.float64)
+        for i in range(10)
+        for j in range(i, -1, -1)
+    ]
+    # make sure indices are retrieved correctly
+    for i in range(10):
+        for j in range(10):
+            result = glass.getcl(cls, i, j)
+            expected = xp.asarray([min(i, j), max(i, j)], dtype=xp.float64)
+            np.testing.assert_allclose(xp.sort(result), expected)
+
+            # check slicing
+            result = glass.getcl(cls, i, j, lmax=0)
+            expected = xp.asarray([max(i, j)], dtype=xp.float64)
+            assert result.size == 1
+            np.testing.assert_allclose(result, expected)
+
+            # check padding
+            result = glass.getcl(cls, i, j, lmax=50)
+            expected = xp.zeros((49,), dtype=xp.float64)
+            assert result.size == 51
+            np.testing.assert_allclose(result[2:], expected)
+
+
+def test_is_inv_triangle_number(not_triangle_numbers: list[int]) -> None:
+    for n in range(10_000):
+        assert glass.fields._inv_triangle_number(n * (n + 1) // 2) == n
+
+    for t in not_triangle_numbers:
+        with pytest.raises(ValueError, match=f"not a triangle number: {t}"):
+            glass.fields._inv_triangle_number(t)
+
+
+def test_nfields_from_nspectra(not_triangle_numbers: list[int]) -> None:
+    for n in range(10_000):
+        assert glass.nfields_from_nspectra(n * (n + 1) // 2) == n
+
+    for t in not_triangle_numbers:
+        with pytest.raises(ValueError, match=f"invalid number of spectra: {t}"):
+            glass.nfields_from_nspectra(t)
+
+
+def test_enumerate_spectra() -> None:
+    n = 100
+    tn = n * (n + 1) // 2
+
+    # create mock spectra with 1 element counting to tn
+    spectra = np.arange(tn).reshape(tn, 1)
+
+    # this is the expected order of indices
+    indices = [(i, j) for i in range(n) for j in range(i, -1, -1)]
+
+    # iterator that will enumerate the spectra for checking
+    it = glass.enumerate_spectra(spectra)
+
+    # go through expected indices and values and compare
+    for k, (i, j) in enumerate(indices):
+        assert next(it) == (i, j, k)
+
+    # make sure iterator is exhausted
+    with pytest.raises(StopIteration):
+        next(it)
+
+
+def test_spectra_indices() -> None:
+    np.testing.assert_array_equal(glass.spectra_indices(0), np.zeros((0, 2)))
+    np.testing.assert_array_equal(glass.spectra_indices(1), [[0, 0]])
+    np.testing.assert_array_equal(glass.spectra_indices(2), [[0, 0], [1, 1], [1, 0]])
+    np.testing.assert_array_equal(
+        glass.spectra_indices(3),
+        [[0, 0], [1, 1], [1, 0], [2, 2], [2, 1], [2, 0]],
+    )
+
+
+def test_gaussian_fields(xp: types.ModuleType) -> None:
+    shells = [
+        glass.RadialWindow(xp.asarray([]), xp.asarray([]), 1.0),
+        glass.RadialWindow(xp.asarray([]), xp.asarray([]), 2.0),
+    ]
+    fields = glass.gaussian_fields(shells)
+    assert len(fields) == len(shells)
+    assert all(isinstance(f, glass.grf.Normal) for f in fields)
+
+
+def test_lognormal_fields(xp: types.ModuleType) -> None:
+    shells = [
+        glass.RadialWindow(xp.asarray([]), xp.asarray([]), 1),
+        glass.RadialWindow(xp.asarray([]), xp.asarray([]), 2),
+        glass.RadialWindow(xp.asarray([]), xp.asarray([]), 3),
+    ]
+
+    fields = glass.lognormal_fields(shells)
+    assert len(fields) == len(shells)
+    assert all(isinstance(f, glass.grf.Lognormal) for f in fields)
+    assert [f.lamda for f in fields] == [1.0, 1.0, 1.0]
+
+    fields = glass.lognormal_fields(shells, lambda z: z**2)
+    assert [f.lamda for f in fields] == [1, 4, 9]
+
+
+def test_compute_gaussian_spectra(mocker: pytest_mock.MockerFixture) -> None:
+    mock = mocker.patch("glass.grf.compute")
+
+    fields = [glass.grf.Normal(), glass.grf.Normal()]
+    spectra = [np.zeros(10), np.zeros(10), np.zeros(10)]
+
+    gls = glass.compute_gaussian_spectra(fields, spectra)
+
+    assert mock.call_count == 3
+    assert mock.call_args_list[0] == mocker.call(spectra[0], fields[0], fields[0])
+    assert mock.call_args_list[1] == mocker.call(spectra[1], fields[1], fields[1])
+    assert mock.call_args_list[2] == mocker.call(spectra[2], fields[1], fields[0])
+    assert gls == [mock.return_value, mock.return_value, mock.return_value]
+
+    # spectra size mismatch
+    with pytest.raises(ValueError, match="fields and spectra"):
+        glass.compute_gaussian_spectra(fields, spectra[:2])
+
+
+def test_compute_gaussian_spectra_gh639(mocker: pytest_mock.MockerFixture) -> None:
+    """Test compute_gaussian_spectra() with an empty input."""
+    mock = mocker.patch("glass.grf.compute")
+
+    fields = [glass.grf.Normal(), glass.grf.Normal()]
+    spectra = [np.zeros(10), np.zeros(10), np.zeros(0)]
+
+    gls = glass.compute_gaussian_spectra(fields, spectra)
+
+    assert mock.call_count == 2
+    assert mock.call_args_list[0] == mocker.call(spectra[0], fields[0], fields[0])
+    assert mock.call_args_list[1] == mocker.call(spectra[1], fields[1], fields[1])
+    assert gls[:2] == [mock.return_value, mock.return_value]
+    assert gls[2].size == 0
+
+
+def test_solve_gaussian_spectra(mocker: pytest_mock.MockerFixture) -> None:
+    mock = mocker.patch("glass.grf.solve")
+
+    result = mock.return_value
+
+    mock.return_value = (result, None, 3)
+
+    fields = [glass.grf.Normal(), glass.grf.Normal()]
+    spectra = [np.zeros(5), np.zeros(10), np.zeros(15)]
+
+    gls = glass.solve_gaussian_spectra(fields, spectra)
+
+    assert mock.call_count == 3
+    assert mock.call_args_list[0] == mocker.call(
+        spectra[0],
+        fields[0],
+        fields[0],
+        pad=10,
+        monopole=0.0,
+    )
+    assert mock.call_args_list[1] == mocker.call(
+        spectra[1],
+        fields[1],
+        fields[1],
+        pad=20,
+        monopole=0.0,
+    )
+    assert mock.call_args_list[2] == mocker.call(
+        spectra[2],
+        fields[1],
+        fields[0],
+        pad=30,
+        monopole=0.0,
+    )
+    assert gls == [result, result, result]
+
+    # spectra size mismatch
+    with pytest.raises(ValueError, match="fields and spectra"):
+        glass.solve_gaussian_spectra(fields, spectra[:2])
+
+
+def test_glass_to_healpix_spectra() -> None:
+    inp = [11, 22, 21, 33, 32, 31, 44, 43, 42, 41]
+    out = glass.glass_to_healpix_spectra(inp)
+    np.testing.assert_array_equal(out, [11, 22, 33, 44, 21, 32, 43, 31, 42, 41])
+
+
+def test_healpix_to_glass_spectra() -> None:
+    inp = [11, 22, 33, 44, 21, 32, 43, 31, 42, 41]
+    out = glass.healpix_to_glass_spectra(inp)
+    np.testing.assert_array_equal(out, [11, 22, 21, 33, 32, 31, 44, 43, 42, 41])
+
+
+def test_glass_to_healpix_alm() -> None:
+    inp = np.array([00, 10, 11, 20, 21, 22, 30, 31, 32, 33])
+    out = glass.fields._glass_to_healpix_alm(inp)
+    np.testing.assert_array_equal(
+        out,
+        np.array([00, 10, 20, 30, 11, 21, 31, 22, 32, 33]),
+    )
+
+
+def test_lognormal_shift_hilbert2011() -> None:
+    zs = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]
+    shifts = [glass.lognormal_shift_hilbert2011(z) for z in zs]
+
+    # computed by hand
+    check = [0.0103031, 0.02975, 0.0538781, 0.0792, 0.103203, 0.12435, 0.142078, 0.1568]
+
+    np.testing.assert_allclose(shifts, check, atol=1e-4, rtol=1e-4)
+
+
+def test_cov_from_spectra() -> None:
+    spectra = np.array(
+        [
+            [110, 111, 112, 113],
+            [220, 221, 222, 223],
+            [210, 211, 212, 213],
+            [330, 331, 332, 333],
+            [320, 321, 322, 323],
+            [310, 311, 312, 313],
+        ],
+    )
+
+    np.testing.assert_array_equal(
+        glass.cov_from_spectra(spectra),
+        [
+            [
+                [110, 210, 310],
+                [210, 220, 320],
+                [310, 320, 330],
+            ],
+            [
+                [111, 211, 311],
+                [211, 221, 321],
+                [311, 321, 331],
+            ],
+            [
+                [112, 212, 312],
+                [212, 222, 322],
+                [312, 322, 332],
+            ],
+            [
+                [113, 213, 313],
+                [213, 223, 323],
+                [313, 323, 333],
+            ],
+        ],
+    )
+
+    np.testing.assert_array_equal(
+        glass.cov_from_spectra(spectra, lmax=1),
+        [
+            [
+                [110, 210, 310],
+                [210, 220, 320],
+                [310, 320, 330],
+            ],
+            [
+                [111, 211, 311],
+                [211, 221, 321],
+                [311, 321, 331],
+            ],
+        ],
+    )
+
+    np.testing.assert_array_equal(
+        glass.cov_from_spectra(spectra, lmax=4),
+        [
+            [
+                [110, 210, 310],
+                [210, 220, 320],
+                [310, 320, 330],
+            ],
+            [
+                [111, 211, 311],
+                [211, 221, 321],
+                [311, 321, 331],
+            ],
+            [
+                [112, 212, 312],
+                [212, 222, 322],
+                [312, 322, 332],
+            ],
+            [
+                [113, 213, 313],
+                [213, 223, 323],
+                [313, 323, 333],
+            ],
+            [
+                [0, 0, 0],
+                [0, 0, 0],
+                [0, 0, 0],
+            ],
+        ],
+    )
+
+
+def test_check_posdef_spectra() -> None:
+    # posdef spectra
+    assert glass.fields.check_posdef_spectra(
+        np.array(
+            [
+                [1.0, 1.0, 1.0],
+                [1.0, 1.0, 1.0],
+                [0.9, 0.9, 0.9],
+            ],
+        ),
+    )
+    # semidef spectra
+    assert glass.fields.check_posdef_spectra(
+        np.array(
+            [
+                [1.0, 1.0, 1.0],
+                [1.0, 1.0, 0.0],
+                [0.9, 1.0, 0.0],
+            ],
+        ),
+    )
+    # indef spectra
+    assert not glass.fields.check_posdef_spectra(
+        np.array(
+            [
+                [1.0, 1.0, 1.0],
+                [1.0, 1.0, 1.0],
+                [1.1, 1.1, 1.1],
+            ],
+        ),
+    )
+
+
+def test_regularized_spectra(
+    mocker: pytest_mock.MockerFixture,
+    rng: np.random.Generator,
+) -> None:
+    spectra = rng.random(size=(6, 101))
+
+    # test method "nearest"
+    cov_nearest = mocker.spy(glass.algorithm, "cov_nearest")
+    with pytest.warns(UserWarning, match="Nearest correlation matrix not found"):
+        # we don't care about convergence here, only that the correct
+        # method is called so this is to suppress the warning
+        glass.fields.regularized_spectra(spectra, method="nearest")
+    cov_nearest.assert_called_once()
+
+    # test method "clip"
+    cov_clip = mocker.spy(glass.algorithm, "cov_clip")
+    glass.fields.regularized_spectra(spectra, method="clip")
+    cov_clip.assert_called_once()
+
+    # invalid method
+    with pytest.raises(ValueError, match="unknown method"):
+        glass.fields.regularized_spectra(spectra, method="unknown")
diff --git a/glass/tests/test_galaxies.py b/glass/tests/test_galaxies.py
new file mode 100644
index 0000000..d22e941
--- /dev/null
+++ b/glass/tests/test_galaxies.py
@@ -0,0 +1,314 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    import pytest_mock
+    from numpy.typing import NDArray
+
+    from glass._types import FloatArray, UnifiedGenerator
+
+
+def test_redshifts(xp: ModuleType, mocker: pytest_mock.MockerFixture) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip("Arrays in redshifts are not immutable, so do not support jax")
+    # create a mock radial window function
+    w = mocker.Mock()
+    w.za = xp.linspace(0.0, 1.0, 20)
+    w.wa = xp.exp(-0.5 * (w.za - 0.5) ** 2 / 0.1**2)
+
+    # sample redshifts (scalar)
+    z = glass.redshifts(13, w)
+    assert z.shape == (13,)
+    assert xp.min(z) >= 0.0
+    assert xp.max(z) <= 1.0
+
+    # sample redshifts (array)
+    z = glass.redshifts(xp.asarray([[1, 2], [3, 4]]), w)
+    assert z.shape == (10,)
+
+
+def test_redshifts_from_nz(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip(
+            "Arrays in redshifts_from_nz are not immutable, so do not support jax",
+        )
+
+    # test sampling
+
+    redshifts = glass.redshifts_from_nz(
+        10,
+        xp.asarray([0, 1, 2, 3, 4]),
+        xp.asarray([1, 0, 0, 0, 0]),
+        warn=False,
+    )
+    assert xp.all((0 <= redshifts) & (redshifts <= 1))  # noqa: SIM300
+
+    redshifts = glass.redshifts_from_nz(
+        10,
+        xp.asarray([0, 1, 2, 3, 4]),
+        xp.asarray([0, 0, 1, 0, 0]),
+        warn=False,
+    )
+    assert xp.all((1 <= redshifts) & (redshifts <= 3))  # noqa: SIM300
+
+    redshifts = glass.redshifts_from_nz(
+        10,
+        xp.asarray([0, 1, 2, 3, 4]),
+        xp.asarray([0, 0, 0, 0, 1]),
+        warn=False,
+    )
+    assert xp.all((3 <= redshifts) & (redshifts <= 4))  # noqa: SIM300
+
+    redshifts = glass.redshifts_from_nz(
+        10,
+        xp.asarray([0, 1, 2, 3, 4]),
+        xp.asarray([0, 0, 1, 1, 1]),
+        warn=False,
+    )
+    assert not xp.any(redshifts <= 1)
+
+    # test with rng
+
+    redshifts = glass.redshifts_from_nz(
+        10,
+        xp.asarray([0, 1, 2, 3, 4]),
+        xp.asarray([0, 0, 1, 1, 1]),
+        warn=False,
+        rng=urng,
+    )
+    assert not xp.any(redshifts <= 1)
+
+    # test interface
+
+    # case: no extra dimensions
+
+    count: int | NDArray[np.float64] = 10
+    z = xp.linspace(0, 1, 100)
+    nz = z * (1 - z)
+
+    redshifts = glass.redshifts_from_nz(count, z, nz, warn=False)
+
+    assert redshifts.shape == (count,)
+    assert xp.all((0 <= redshifts) & (redshifts <= 1))  # noqa: SIM300
+
+    # case: extra dimensions from count
+
+    count = xp.asarray([10, 20, 30])
+    z = xp.linspace(0, 1, 100)
+    nz = z * (1 - z)
+
+    redshifts = glass.redshifts_from_nz(count, z, nz, warn=False)
+
+    assert redshifts.shape == (60,)
+
+    # case: extra dimensions from nz
+
+    count = 10
+    z = xp.linspace(0, 1, 100)
+    nz = xp.stack([z * (1 - z), (z - 0.5) ** 2])
+
+    redshifts = glass.redshifts_from_nz(count, z, nz, warn=False)
+
+    assert redshifts.shape == (20,)
+
+    # case: extra dimensions from count and nz
+
+    count = xp.asarray([[10], [20], [30]])
+    z = xp.linspace(0, 1, 100)
+    nz = xp.stack([z * (1 - z), (z - 0.5) ** 2])
+
+    redshifts = glass.redshifts_from_nz(count, z, nz, warn=False)
+
+    assert redshifts.shape == (120,)
+
+    # case: incompatible input shapes
+
+    count = xp.asarray([10, 20, 30])
+    z = xp.linspace(0, 1, 100)
+    nz = xp.stack([z * (1 - z), (z - 0.5) ** 2])
+
+    with pytest.raises(ValueError, match="shape mismatch"):
+        glass.redshifts_from_nz(count, z, nz, warn=False)
+
+    with pytest.warns(UserWarning, match="when sampling galaxies"):
+        redshifts = glass.redshifts_from_nz(
+            10,
+            xp.asarray([0, 1, 2, 3, 4]),
+            xp.asarray([1, 0, 0, 0, 0]),
+        )
+
+
+def test_galaxy_shear(rng: np.random.Generator) -> None:
+    # check shape of the output
+
+    kappa, gamma1, gamma2 = (
+        rng.normal(size=(12,)),
+        rng.normal(size=(12,)),
+        rng.normal(size=(12,)),
+    )
+
+    shear = glass.galaxy_shear(
+        np.array([]),
+        np.array([]),
+        np.array([]),
+        kappa,
+        gamma1,
+        gamma2,
+    )
+    np.testing.assert_equal(shear, [])
+
+    gal_lon, gal_lat, gal_eps = (
+        rng.normal(size=(512,)),
+        rng.normal(size=(512,)),
+        rng.normal(size=(512,)),
+    )
+    shear = glass.galaxy_shear(gal_lon, gal_lat, gal_eps, kappa, gamma1, gamma2)
+    assert np.shape(shear) == (512,)
+
+    # shape with no reduced shear
+
+    shear = glass.galaxy_shear(
+        np.array([]),
+        np.array([]),
+        np.array([]),
+        kappa,
+        gamma1,
+        gamma2,
+        reduced_shear=False,
+    )
+    np.testing.assert_equal(shear, [])
+
+    gal_lon, gal_lat, gal_eps = (
+        rng.normal(size=(512,)),
+        rng.normal(size=(512,)),
+        rng.normal(size=(512,)),
+    )
+    shear = glass.galaxy_shear(
+        gal_lon,
+        gal_lat,
+        gal_eps,
+        kappa,
+        gamma1,
+        gamma2,
+        reduced_shear=False,
+    )
+    assert np.shape(shear) == (512,)
+
+
+def test_gaussian_phz(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    # test sampling
+
+    # case: zero variance
+
+    z: float | FloatArray = xp.linspace(0, 1, 100)
+    sigma_0: float | FloatArray = 0.0
+
+    phz = glass.gaussian_phz(z, sigma_0)
+
+    np.testing.assert_array_equal(z, phz)
+
+    # test with rng
+
+    phz = glass.gaussian_phz(z, sigma_0, rng=urng)
+
+    np.testing.assert_array_equal(z, phz)
+
+    # case: truncated normal
+
+    phz = glass.gaussian_phz(0.0, xp.ones(100))
+
+    assert phz.__array_namespace__() == xp
+    assert phz.shape == (100,)
+    assert xp.all(phz >= 0)
+
+    # case: upper and lower bound
+
+    phz = glass.gaussian_phz(1.0, xp.ones(100), lower=0.5, upper=1.5)
+
+    assert phz.__array_namespace__() == xp
+    assert phz.shape == (100,)
+    assert xp.all(phz >= 0.5)
+    assert xp.all(phz <= 1.5)
+
+    # test interface
+
+    # case: scalar redshift, scalar sigma_0
+
+    z = 1.0
+
+    phz = glass.gaussian_phz(z, 0.0, xp=xp)
+
+    assert phz.ndim == 0
+    assert phz == xp.asarray(z)
+
+    # Pass floats without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.gaussian_phz(1.0, 0.0)
+
+    # case: array redshift, scalar sigma_0
+
+    z = xp.linspace(0, 1, 10)
+
+    phz = glass.gaussian_phz(z, 0.0)
+
+    assert phz.__array_namespace__() == xp
+    assert phz.shape == (10,)
+    np.testing.assert_array_equal(z, phz)
+
+    # case: scalar redshift, array sigma_0
+
+    z = 1.0
+    sigma_0 = xp.zeros(10)
+
+    phz = glass.gaussian_phz(z, sigma_0)
+
+    assert phz.__array_namespace__() == xp
+    assert phz.shape == (10,)
+    np.testing.assert_array_equal(z, phz)
+
+    # case: array redshift, array sigma_0
+
+    z = xp.linspace(0, 1, 10)
+    sigma_0 = xp.zeros((11, 1))
+
+    phz = glass.gaussian_phz(z, sigma_0)
+
+    assert phz.__array_namespace__() == xp
+    assert phz.shape == (11, 10)
+    np.testing.assert_array_equal(xp.broadcast_to(z, (11, 10)), phz)
+
+    # shape mismatch
+
+    with pytest.raises(
+        ValueError,
+        match="lower and upper must best scalars or have the same shape as z",
+    ):
+        glass.gaussian_phz(xp.asarray(0.0), xp.asarray(1.0), lower=xp.asarray([0]))
+
+    with pytest.raises(
+        ValueError,
+        match="lower and upper must best scalars or have the same shape as z",
+    ):
+        glass.gaussian_phz(xp.asarray(0.0), xp.asarray(1.0), upper=xp.asarray([1]))
+
+    # test resampling
+
+    phz = glass.gaussian_phz(xp.asarray(0.0), xp.asarray(1.0), lower=0)
+    assert phz.ndim == 0
+
+    phz = glass.gaussian_phz(xp.asarray(0.0), xp.asarray(1.0), upper=1)
+    assert phz.ndim == 0
+
+    # test error
+
+    with pytest.raises(ValueError, match="requires lower < upper"):
+        phz = glass.gaussian_phz(z, sigma_0, lower=xp.asarray(1), upper=xp.asarray(0))
diff --git a/glass/tests/test_jax.py b/glass/tests/test_jax.py
new file mode 100644
index 0000000..fed9887
--- /dev/null
+++ b/glass/tests/test_jax.py
@@ -0,0 +1,97 @@
+import pytest
+
+jax = pytest.importorskip("jax", reason="tests require jax")
+
+import jax.numpy as jnp  # noqa: E402
+from jax.typing import ArrayLike  # noqa: E402
+
+from glass.jax import Generator  # noqa: E402
+
+
+def test_init() -> None:
+    rng = Generator(42)
+    assert isinstance(rng, Generator)
+    assert isinstance(rng.key, jax.Array)
+    assert jax.dtypes.issubdtype(rng.key.dtype, jax.dtypes.prng_key)
+    assert jnp.all(rng.key == jax.random.key(42))
+
+
+def test_from_key() -> None:
+    key = jax.random.key(42)
+    rng = Generator.from_key(key)
+    assert rng.key is key
+
+    with pytest.raises(ValueError, match="not a random key"):
+        Generator.from_key(object())
+
+    with pytest.raises(ValueError, match="not a random key"):
+        Generator.from_key(jnp.zeros(()))
+
+
+def test_key() -> None:
+    rng = Generator(42)
+    rngkey, outkey = jax.random.split(rng.key, 2)
+    key = rng.split()
+    assert jnp.all(rng.key == rngkey)
+    assert jnp.all(key == outkey)
+
+
+def test_spawn() -> None:
+    rng = Generator(42)
+    key, *subkeys = jax.random.split(rng.key, 4)
+    subrngs = rng.spawn(3)
+    assert rng.key == key
+    assert isinstance(subrngs, list)
+    assert len(subrngs) == 3
+    for subrng, subkey in zip(subrngs, subkeys, strict=False):
+        assert isinstance(subrng, Generator)
+        assert subrng.key == subkey
+
+
+def test_random() -> None:
+    rng = Generator(42)
+    key = rng.key
+    rvs = rng.random(size=10_000)
+    assert rng.key != key
+    assert rvs.shape == (10_000,)
+    assert jnp.min(rvs) >= 0.0
+    assert jnp.max(rvs) < 1.0
+    assert isinstance(rvs, ArrayLike)
+
+
+def test_normal() -> None:
+    rng = Generator(42)
+    key = rng.key
+    rvs = rng.normal(1, 2, size=10_000)
+    assert rng.key != key
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, ArrayLike)
+
+
+def test_standard_normal() -> None:
+    rng = Generator(42)
+    key = rng.key
+    rvs = rng.standard_normal(size=10_000)
+    assert rng.key != key
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, ArrayLike)
+
+
+def test_poisson() -> None:
+    rng = Generator(42)
+    key = rng.key
+    rvs = rng.poisson(lam=1, size=10_000)
+    assert rng.key != key
+    assert rvs.shape == (10_000,)
+    assert isinstance(rvs, ArrayLike)
+
+
+def test_uniform() -> None:
+    rng = Generator(42)
+    key = rng.key
+    rvs = rng.uniform(size=10_000)
+    assert rng.key != key
+    assert rvs.shape == (10_000,)
+    assert jnp.min(rvs) >= 0.0
+    assert jnp.max(rvs) < 1.0
+    assert isinstance(rvs, ArrayLike)
diff --git a/glass/tests/test_lensing.py b/glass/tests/test_lensing.py
new file mode 100644
index 0000000..9ae78b1
--- /dev/null
+++ b/glass/tests/test_lensing.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+import math
+from typing import TYPE_CHECKING
+
+import healpix
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import FloatArray
+    from glass.cosmology import Cosmology
+
+
+def test_from_convergence(rng: np.random.Generator) -> None:
+    """Add unit tests for :func:`glass.from_convergence`."""
+    # l_max = 100  # noqa: ERA001
+    n_side = 32
+
+    # create a convergence map
+    kappa = rng.integers(10, size=healpix.nside2npix(n_side))
+
+    # check with all False
+
+    results = glass.from_convergence(kappa)
+    np.testing.assert_array_equal(results, ())
+
+    # check all combinations of potential, deflection, shear being True
+
+    results = glass.from_convergence(kappa, potential=True)
+    np.testing.assert_array_equal(len(results), 1)
+
+    results = glass.from_convergence(kappa, deflection=True)
+    np.testing.assert_array_equal(len(results), 1)
+
+    results = glass.from_convergence(kappa, shear=True)
+    np.testing.assert_array_equal(len(results), 1)
+
+    results = glass.from_convergence(kappa, potential=True, deflection=True)
+    np.testing.assert_array_equal(len(results), 2)
+
+    results = glass.from_convergence(kappa, potential=True, shear=True)
+    np.testing.assert_array_equal(len(results), 2)
+
+    results = glass.from_convergence(kappa, deflection=True, shear=True)
+    np.testing.assert_array_equal(len(results), 2)
+
+    results = glass.from_convergence(kappa, potential=True, deflection=True, shear=True)
+    np.testing.assert_array_equal(len(results), 3)
+
+
+def test_shear_from_convergence() -> None:
+    """Add unit tests for :func:`glass.shear_from_convergence`."""
+
+
+def test_multi_plane_matrix(
+    shells: list[glass.RadialWindow],
+    cosmo: Cosmology,
+    rng: np.random.Generator,
+) -> None:
+    mat = glass.multi_plane_matrix(shells, cosmo)
+
+    np.testing.assert_array_equal(mat, np.tril(mat))
+    np.testing.assert_array_equal(np.triu(mat, 1), 0)
+
+    convergence = glass.MultiPlaneConvergence(cosmo)
+
+    deltas = rng.random((len(shells), 10))
+    kappas = []
+    for shell, delta in zip(shells, deltas, strict=False):
+        convergence.add_window(delta, shell)
+        if convergence.kappa is not None:
+            kappas.append(convergence.kappa.copy())
+
+    np.testing.assert_allclose(mat @ deltas, kappas)
+
+
+def test_multi_plane_weights(
+    shells: list[glass.RadialWindow],
+    cosmo: Cosmology,
+    rng: np.random.Generator,
+) -> None:
+    w_in = np.eye(len(shells))
+    w_out = glass.multi_plane_weights(w_in, shells, cosmo)
+
+    np.testing.assert_array_equal(w_out, np.triu(w_out, 1))
+    np.testing.assert_array_equal(np.tril(w_out), 0)
+
+    convergence = glass.MultiPlaneConvergence(cosmo)
+
+    deltas = rng.random((len(shells), 10))
+    weights = rng.random((len(shells), 3))
+    kappa = 0
+    for shell, delta, weight in zip(shells, deltas, weights, strict=False):
+        convergence.add_window(delta, shell)
+        kappa = kappa + weight[..., None] * convergence.kappa
+    kappa /= weights.sum(axis=0)[..., None]
+
+    wmat = glass.multi_plane_weights(weights, shells, cosmo)
+
+    np.testing.assert_allclose(np.einsum("ij,ik", wmat, deltas), kappa)
+
+
+@pytest.mark.parametrize("usecomplex", [True, False])
+def test_deflect_nsew(xp: ModuleType, usecomplex: bool) -> None:  # noqa: FBT001
+    d = 5.0
+    r = math.radians(d)
+
+    def alpha(
+        re: float,
+        im: float,
+        *,
+        usecomplex: bool,
+    ) -> complex | FloatArray:
+        return re + 1j * im if usecomplex else xp.asarray([re, im])
+
+    # north
+    lon, lat = glass.deflect(0.0, 0.0, alpha(r, 0, usecomplex=usecomplex), xp=xp)
+    np.testing.assert_allclose([lon, lat], [0.0, d], atol=1e-15)
+
+    # south
+    lon, lat = glass.deflect(0.0, 0.0, alpha(-r, 0, usecomplex=usecomplex), xp=xp)
+    np.testing.assert_allclose([lon, lat], [0.0, -d], atol=1e-15)
+
+    # east
+    lon, lat = glass.deflect(0.0, 0.0, alpha(0, r, usecomplex=usecomplex), xp=xp)
+    np.testing.assert_allclose([lon, lat], [-d, 0.0], atol=1e-15)
+
+    # west
+    lon, lat = glass.deflect(0.0, 0.0, alpha(0, -r, usecomplex=usecomplex), xp=xp)
+    np.testing.assert_allclose([lon, lat], [d, 0.0], atol=1e-15)
+
+    # At least one input is an array
+    lon, lat = glass.deflect(
+        xp.asarray(0.0),
+        xp.asarray(0.0),
+        alpha(0, -r, usecomplex=usecomplex),
+    )
+    np.testing.assert_allclose([lon, lat], [d, 0.0], atol=1e-15)
+
+    lon, lat = glass.deflect(
+        xp.asarray([0.0, 0.0]),
+        xp.asarray([0.0, 0.0]),
+        alpha(0, -r, usecomplex=usecomplex),
+    )
+    np.testing.assert_allclose(lon, xp.asarray([d, d]), atol=1e-15)
+    np.testing.assert_allclose(lat, 0.0, atol=1e-15)
+
+    # No inputs are arrays and xp not provided
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.deflect(0.0, 0.0, alpha(0, -r, usecomplex=True))
+
+
+def test_deflect_many(rng: np.random.Generator) -> None:
+    n = 1000
+    abs_alpha = rng.uniform(0, 2 * np.pi, size=n)
+    arg_alpha = rng.uniform(-np.pi, np.pi, size=n)
+
+    lon_ = np.degrees(rng.uniform(-np.pi, np.pi, size=n))
+    lat_ = np.degrees(np.arcsin(rng.uniform(-1, 1, size=n)))
+
+    lon, lat = glass.deflect(lon_, lat_, abs_alpha * np.exp(1j * arg_alpha))
+
+    x_, y_, z_ = healpix.ang2vec(lon_, lat_, lonlat=True)
+    x, y, z = healpix.ang2vec(lon, lat, lonlat=True)
+
+    dotp = x * x_ + y * y_ + z * z_
+
+    np.testing.assert_allclose(dotp, np.cos(abs_alpha))
diff --git a/glass/tests/test_observations.py b/glass/tests/test_observations.py
new file mode 100644
index 0000000..a67af4d
--- /dev/null
+++ b/glass/tests/test_observations.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+import math
+from typing import TYPE_CHECKING
+
+import healpix
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import UnifiedGenerator
+
+
+def test_vmap_galactic_ecliptic() -> None:
+    """Add unit tests for :func:`glass.vmap_galactic_ecliptic`."""
+    n_side = 4
+
+    # check shape
+
+    vmap = glass.vmap_galactic_ecliptic(n_side)
+    np.testing.assert_array_equal(len(vmap), healpix.nside2npix(n_side))
+
+    # no rotation
+
+    vmap = glass.vmap_galactic_ecliptic(n_side, galactic=(0, 0), ecliptic=(0, 0))
+    np.testing.assert_array_equal(vmap, np.zeros_like(vmap))
+
+    # check errors raised
+
+    with pytest.raises(TypeError, match="galactic stripe must be a pair of numbers"):
+        glass.vmap_galactic_ecliptic(n_side, galactic=(1,))
+
+    with pytest.raises(TypeError, match="ecliptic stripe must be a pair of numbers"):
+        glass.vmap_galactic_ecliptic(n_side, ecliptic=(1,))
+
+    with pytest.raises(TypeError, match="galactic stripe must be a pair of numbers"):
+        glass.vmap_galactic_ecliptic(n_side, galactic=(1, 2, 3))
+
+    with pytest.raises(TypeError, match="ecliptic stripe must be a pair of numbers"):
+        glass.vmap_galactic_ecliptic(n_side, ecliptic=(1, 2, 3))
+
+
+def test_gaussian_nz(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    """Add unit tests for :func:`glass.gaussian_nz`."""
+    mean = 0
+    sigma = 1
+    z = xp.linspace(0, 1, 11)
+
+    # check passing in the norm
+
+    nz = glass.gaussian_nz(z, mean, sigma, norm=0)
+    np.testing.assert_array_equal(nz, np.zeros_like(nz))
+
+    # check the value of each entry is close to the norm
+
+    norm = 1
+    nz = glass.gaussian_nz(z, mean, sigma, norm=norm)
+    np.testing.assert_allclose(xp.sum(nz) / nz.shape[0], norm, rtol=1e-2)
+
+    # check multidimensionality size
+
+    nz = glass.gaussian_nz(
+        z,
+        xp.tile(xp.asarray(mean), z.shape),
+        xp.tile(xp.asarray(sigma), z.shape),
+        norm=urng.normal(size=z.shape),
+    )
+    assert nz.shape == (z.size, z.size)
+
+
+def test_smail_nz(xp: ModuleType) -> None:
+    """Add unit tests for :func:`glass.smail_nz`."""
+    alpha = 1
+    beta = 1
+    mode = 1
+    z = xp.linspace(0, 1, 11)
+
+    # check passing in the norm
+
+    pz = glass.smail_nz(z, mode, alpha, beta, norm=0)
+    np.testing.assert_array_equal(pz, np.zeros_like(pz))
+
+
+def test_fixed_zbins(xp: ModuleType) -> None:
+    """Add unit tests for :func:`glass.fixed_zbins`."""
+    zmin = 0.0
+    zmax = 1.0
+
+    # check nbins input
+
+    nbins = 5
+    expected_zbins = xp.asarray(
+        [
+            tuple(xp.asarray(i) for i in pair)
+            for pair in [(0.0, 0.2), (0.2, 0.4), (0.4, 0.6), (0.6, 0.8), (0.8, 1.0)]
+        ],
+    )
+    zbins = glass.fixed_zbins(zmin, zmax, nbins=nbins, xp=xp)
+    assert len(zbins) == nbins
+    np.testing.assert_allclose(zbins, expected_zbins, rtol=1e-15)
+
+    # check dz input
+
+    dz = 0.2
+    zbins = glass.fixed_zbins(zmin, zmax, dz=dz, xp=xp)
+    assert len(zbins) == math.ceil((zmax - zmin) / dz)
+    np.testing.assert_allclose(zbins, expected_zbins, rtol=1e-15)
+
+    # check dz for spacing which results in a max value above zmax
+
+    zbins = glass.fixed_zbins(zmin, zmax, dz=0.3, xp=xp)
+    assert zmax < zbins[-1][1]
+
+    # check error raised
+
+    with pytest.raises(ValueError, match="exactly one of nbins and dz must be given"):
+        glass.fixed_zbins(zmin, zmax, nbins=nbins, dz=dz, xp=xp)
+
+
+def test_equal_dens_zbins(xp: ModuleType) -> None:
+    """Add unit tests for :func:`glass.equal_dens_zbins`."""
+    z = xp.linspace(0, 1, 11)
+    nbins = 5
+
+    # check expected zbins returned
+
+    expected_zbins = xp.asarray(
+        [
+            tuple(xp.asarray(i) for i in pair)
+            for pair in [(0.0, 0.2), (0.2, 0.4), (0.4, 0.6), (0.6, 0.8), (0.8, 1.0)]
+        ],
+    )
+    zbins = glass.equal_dens_zbins(z, xp.ones_like(z), nbins)
+    np.testing.assert_allclose(zbins, expected_zbins, rtol=1e-15)
+
+    # check output shape
+
+    assert len(zbins) == nbins
+
+
+def test_tomo_nz_gausserr(xp: ModuleType) -> None:
+    """Add unit tests for :func:`glass.tomo_nz_gausserr`."""
+    sigma_0 = 0.1
+    z = xp.linspace(0, 1, 11)
+    zbins = [(0, 0.2), (0.2, 0.4), (0.4, 0.6), (0.6, 0.8), (0.8, 1.0)]
+
+    # check zeros returned
+
+    binned_nz = glass.tomo_nz_gausserr(z, xp.zeros_like(z), sigma_0, zbins)
+    np.testing.assert_array_equal(binned_nz, xp.zeros_like(binned_nz))
+
+    # check the shape of the output
+
+    assert binned_nz.shape == (len(zbins), z.size)
diff --git a/glass/tests/test_points.py b/glass/tests/test_points.py
new file mode 100644
index 0000000..6d57005
--- /dev/null
+++ b/glass/tests/test_points.py
@@ -0,0 +1,438 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import healpix
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+    from types import ModuleType
+
+    import pytest_mock
+    from numpy.typing import NDArray
+
+    from glass._types import FloatArray, IntArray, UnifiedGenerator
+
+
+def catpos(
+    pos: Generator[
+        tuple[
+            FloatArray,
+            FloatArray,
+            IntArray,
+        ]
+    ],
+    *,
+    xp: ModuleType,
+) -> tuple[
+    FloatArray,
+    FloatArray,
+    IntArray,
+]:
+    lon = xp.empty(0)
+    lat = xp.empty(0)
+    cnt: IntArray = 0
+    for lo, la, co in pos:
+        lon = xp.concat([lon, lo])
+        lat = xp.concat([lat, la])
+        cnt = cnt + co
+    return lon, lat, cnt
+
+
+def test_effective_bias(xp: ModuleType, mocker: pytest_mock.MockerFixture) -> None:
+    # create a mock radial window function
+    w = mocker.Mock()
+    w.za = xp.linspace(0, 2, 100)
+    w.wa = xp.full_like(w.za, 2.0)
+
+    z = xp.linspace(0, 1, 10)
+    bz = xp.zeros((10,))
+    np.testing.assert_allclose(glass.effective_bias(z, bz, w), 0.0)
+
+    z = xp.zeros((10,))
+    bz = xp.full_like(z, 0.5)
+
+    np.testing.assert_allclose(glass.effective_bias(z, bz, w), 0.0)
+
+    z = xp.linspace(0, 1, 10)
+    bz = xp.full_like(z, 0.5)
+
+    np.testing.assert_allclose(glass.effective_bias(z, bz, w), 0.25)
+
+
+def test_linear_bias(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    # test with 0 delta
+
+    delta = xp.zeros((2, 2))
+    b = 2.0
+
+    np.testing.assert_allclose(glass.linear_bias(delta, b), xp.zeros((2, 2)))
+
+    # test with 0 b
+
+    delta = urng.normal(5, 1, size=(2, 2))
+    b = 0.0
+
+    np.testing.assert_allclose(glass.linear_bias(delta, b), xp.zeros((2, 2)))
+
+    # compare with original implementation
+
+    delta = urng.normal(5, 1, size=(2, 2))
+    b = 2.0
+
+    np.testing.assert_allclose(glass.linear_bias(delta, b), b * delta)
+
+
+def test_loglinear_bias(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    # test with 0 delta
+
+    delta = xp.zeros((2, 2))
+    b = 2.0
+
+    np.testing.assert_allclose(glass.loglinear_bias(delta, b), xp.zeros((2, 2)))
+
+    # test with 0 b
+
+    delta = urng.normal(5, 1, size=(2, 2))
+    b = 0.0
+
+    np.testing.assert_allclose(glass.loglinear_bias(delta, b), xp.zeros((2, 2)))
+
+    # compare with numpy implementation
+
+    delta = urng.normal(5, 1, size=(2, 2))
+    b = 2.0
+
+    np.testing.assert_allclose(
+        glass.loglinear_bias(delta, b),
+        xp.expm1(b * xp.log1p(delta)),
+    )
+
+
+def test_positions_from_delta(rng: np.random.Generator) -> None:  # noqa: PLR0915
+    # create maps that saturate the batching in the function
+    nside = 128
+    npix = healpix.nside2npix(nside)
+
+    # case: single-dimensional input
+
+    ngal: float | NDArray[np.float64] = 1e-3
+    delta = np.zeros(npix)
+    bias = 0.8
+    vis = np.ones(npix)
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, vis), xp=np)
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # test with rng
+
+    lon, lat, cnt = catpos(
+        glass.positions_from_delta(ngal, delta, bias, vis, rng=rng),
+        xp=np,
+    )
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: Nons bias and callable bias model
+
+    lon, lat, cnt = catpos(
+        glass.positions_from_delta(ngal, delta, None, vis, bias_model=lambda x: x),
+        xp=np,
+    )
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: None vis
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, None), xp=np)
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: remove monopole
+
+    lon, lat, cnt = catpos(
+        glass.positions_from_delta(ngal, delta, bias, vis, remove_monopole=True),
+        xp=np,
+    )
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: negative delta
+
+    lon, lat, cnt = catpos(
+        glass.positions_from_delta(ngal, np.linspace(-1, -1, npix), None, vis),
+        xp=np,
+    )
+
+    assert isinstance(cnt, int)
+    np.testing.assert_allclose(lon, [])
+    np.testing.assert_allclose(lat, [])
+
+    # case: large delta
+
+    lon, lat, cnt = catpos(
+        glass.positions_from_delta(ngal, rng.normal(100, 1, size=(npix,)), bias, vis),
+        xp=np,
+    )
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: multi-dimensional ngal
+
+    ngal = np.array([1e-3, 2e-3])
+    delta = np.zeros(12)
+    bias = 0.8
+    vis = np.ones(12)
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, vis), xp=np)
+
+    assert isinstance(cnt, np.ndarray)
+    assert cnt.shape == (2,)
+    assert lon.shape == (cnt.sum(),)
+    assert lat.shape == (cnt.sum(),)
+
+    # case: multi-dimensional delta
+
+    ngal = 1e-3
+    delta = np.zeros((3, 2, 12))
+    bias = 0.8
+    vis = np.ones(12)
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, vis), xp=np)
+
+    assert isinstance(cnt, np.ndarray)
+    assert cnt.shape == (3, 2)
+    assert lon.shape == (cnt.sum(),)
+    assert lat.shape == (cnt.sum(),)
+
+    # case: multi-dimensional broadcasting
+
+    ngal = np.array([1e-3, 2e-3])
+    delta = np.zeros((3, 1, 12))
+    bias = 0.8
+    vis = np.ones(12)
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, vis), xp=np)
+
+    assert isinstance(cnt, np.ndarray)
+    assert cnt.shape == (3, 2)
+    assert lon.shape == (cnt.sum(),)
+    assert lat.shape == (cnt.sum(),)
+
+    # case: only the southern hemisphere is visible
+
+    vis[: vis.size // 2] = 0.0
+
+    lon, lat, cnt = catpos(glass.positions_from_delta(ngal, delta, bias, vis), xp=np)
+
+    assert isinstance(cnt, np.ndarray)
+    assert cnt.shape == (3, 2)
+    assert lon.shape == (cnt.sum(),)
+    assert lat.shape == (cnt.sum(),)
+
+    # test TypeError
+
+    with pytest.raises(TypeError, match="bias_model must be string or callable"):
+        next(glass.positions_from_delta(ngal, delta, bias, vis, bias_model=0))
+
+
+def test_uniform_positions(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip(
+            "Arrays in uniform_positions are not immutable, so do not support jax",
+        )
+
+    # case: scalar input
+
+    ngal: float | FloatArray = 1e-3
+
+    lon, lat, cnt = catpos(glass.uniform_positions(ngal, xp=xp), xp=xp)
+
+    # Pass non-arrays without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        next(glass.uniform_positions(ngal))
+
+    # test with rng
+
+    lon, lat, cnt = catpos(glass.uniform_positions(ngal, rng=urng, xp=xp), xp=xp)
+
+    assert isinstance(cnt, int)
+    assert lon.shape == lat.shape == (cnt,)
+
+    # case: 1-D array input
+
+    ngal = xp.asarray([1e-3, 2e-3, 3e-3])
+
+    lon, lat, cnt = catpos(glass.uniform_positions(ngal), xp=xp)
+
+    assert not isinstance(cnt, int)
+    assert cnt.__array_namespace__() == xp
+    assert cnt.shape == (3,)
+    assert lon.shape == lat.shape == (xp.sum(cnt),)
+
+    # case: 2-D array input
+
+    ngal = xp.asarray([[1e-3, 2e-3], [3e-3, 4e-3], [5e-3, 6e-3]])
+
+    lon, lat, cnt = catpos(glass.uniform_positions(ngal), xp=xp)
+
+    assert not isinstance(cnt, int)
+    assert cnt.__array_namespace__() == xp
+    assert cnt.shape == (3, 2)
+    assert lon.shape == lat.shape == (xp.sum(cnt),)
+
+
+def test_position_weights(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    """Unit tests for glass.points.position_weights."""
+    for bshape in None, (), (100,), (100, 1):
+        for cshape in (100,), (100, 50), (100, 3, 2):
+            counts = urng.random(cshape)
+            bias = None if bshape is None else urng.random(bshape)
+
+            weights = glass.position_weights(counts, bias)
+
+            expected = counts / xp.sum(counts, axis=0, keepdims=True)
+            if bias is not None:
+                if bias.ndim > expected.ndim:
+                    expected = xp.expand_dims(
+                        expected,
+                        axis=tuple(range(expected.ndim, bias.ndim)),
+                    )
+                else:
+                    bias = xp.expand_dims(
+                        bias,
+                        axis=tuple(range(bias.ndim, expected.ndim)),
+                    )
+                expected = bias * expected
+
+            np.testing.assert_allclose(weights, expected)
+
+
+def test_displace_arg_complex(xp: ModuleType) -> None:
+    """Test displace function with complex-valued displacement."""
+    d = 5.0  # deg
+    r = d / 180 * xp.pi
+
+    # displace the origin so everything is easy
+    lon0 = xp.asarray(0.0)
+    lat0 = xp.asarray(0.0)
+
+    # north
+    lon, lat = glass.displace(lon0, lat0, xp.asarray(r + 0j))
+    assert np.allclose([lon, lat], [0.0, d])
+
+    # south
+    lon, lat = glass.displace(lon0, lat0, xp.asarray(-r + 0j))
+    assert np.allclose([lon, lat], [0.0, -d])
+
+    # east
+    lon, lat = glass.displace(lon0, lat0, xp.asarray(1j * r))
+    assert np.allclose([lon, lat], [-d, 0.0])
+
+    # west
+    lon, lat = glass.displace(lon0, lat0, xp.asarray(-1j * r))
+    assert np.allclose([lon, lat], [d, 0.0])
+
+
+def test_displace_arg_real(xp: ModuleType) -> None:
+    """Test displace function with real-valued argument."""
+    d = 5.0  # deg
+    r = d / 180 * xp.pi
+
+    # displace the origin so everything is easy
+    lon0 = xp.asarray(0.0)
+    lat0 = xp.asarray(0.0)
+
+    # north
+    lon, lat = glass.displace(lon0, lat0, xp.asarray([r, 0]))
+    assert np.allclose([lon, lat], [0.0, d])
+
+    # south
+    lon, lat = glass.displace(lon0, lat0, xp.asarray([-r, 0]))
+    assert np.allclose([lon, lat], [0.0, -d])
+
+    # east
+    lon, lat = glass.displace(lon0, lat0, xp.asarray([0, r]))
+    assert np.allclose([lon, lat], [-d, 0.0])
+
+    # west
+    lon, lat = glass.displace(lon0, lat0, xp.asarray([0, -r]))
+    assert np.allclose([lon, lat], [d, 0.0])
+
+
+def test_displace_abs(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    """Check that points are displaced by the correct angular distance."""
+    n = 1000
+    abs_alpha = urng.uniform(0, 2 * xp.pi, size=n)
+    arg_alpha = urng.uniform(-xp.pi, xp.pi, size=n)
+
+    lon_ = urng.uniform(-np.pi, np.pi, size=n) / xp.pi * 180
+    lat_ = xp.asin(urng.uniform(-1, 1, size=n)) / xp.pi * 180
+
+    lon, lat = glass.displace(lon_, lat_, abs_alpha * xp.exp(1j * arg_alpha))
+
+    th = (90.0 - lat) / 180 * xp.pi
+    th_ = (90.0 - lat_) / 180 * xp.pi
+    delt = (lon - lon_) / 180 * xp.pi
+
+    cos_a = xp.cos(th) * xp.cos(th_) + xp.cos(delt) * xp.sin(th) * xp.sin(th_)
+
+    assert np.allclose(cos_a, xp.cos(abs_alpha))
+
+
+def test_displacement(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    """Check that displacement of points is computed correctly."""
+    # unit changes for displacements
+    deg5 = xp.asarray(5.0) / 180 * xp.pi
+    north = xp.exp(xp.asarray(1j * 0.0))
+    east = xp.exp(xp.asarray(1j * (xp.pi / 2)))
+    south = xp.exp(xp.asarray(1j * xp.pi))
+    west = xp.exp(xp.asarray(1j * (3 * xp.pi / 2)))
+
+    zero = xp.asarray(0.0)
+    five = xp.asarray(5.0)
+    ninety = xp.asarray(90.0)
+
+    # test data: coordinates and expected displacement
+    data = [
+        # equator
+        (zero, zero, zero, five, deg5 * north),
+        (zero, zero, -five, zero, deg5 * east),
+        (zero, zero, zero, -five, deg5 * south),
+        (zero, zero, five, zero, deg5 * west),
+        # pole
+        (zero, ninety, ninety * 2, ninety - five, deg5 * north),
+        (zero, ninety, -ninety, ninety - five, deg5 * east),
+        (zero, ninety, zero, ninety - five, deg5 * south),
+        (zero, ninety, ninety, ninety - five, deg5 * west),
+    ]
+
+    # test each displacement individually
+    for from_lon, from_lat, to_lon, to_lat, alpha in data:
+        alpha_ = glass.displacement(from_lon, from_lat, to_lon, to_lat)
+        assert np.allclose(alpha_, alpha), (
+            f"displacement from ({from_lon}, {from_lat}) to ({to_lon}, {to_lat})"
+            f"\ndistance: expected {xp.abs(alpha)}, got {xp.abs(alpha_)}"
+            f"\ndirection: expected {xp.angle(alpha)}, got {xp.angle(alpha_)}"
+        )
+
+    # test on an array
+    alpha = glass.displacement(
+        urng.uniform(-180.0, 180.0, size=(20, 1)),
+        urng.uniform(-90.0, 90.0, size=(20, 1)),
+        urng.uniform(-180.0, 180.0, size=5),
+        urng.uniform(-90.0, 90.0, size=5),
+    )
+    assert alpha.shape == (20, 5)
diff --git a/glass/tests/test_shapes.py b/glass/tests/test_shapes.py
new file mode 100644
index 0000000..77498f3
--- /dev/null
+++ b/glass/tests/test_shapes.py
@@ -0,0 +1,202 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from glass._types import UnifiedGenerator
+
+
+def test_triaxial_axis_ratio(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    # Pass floats without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.triaxial_axis_ratio(0.8, 0.4)
+
+    # single axis ratio
+
+    q = glass.triaxial_axis_ratio(0.8, 0.4, xp=xp)
+    assert q.ndim == 0
+
+    # many axis ratios
+
+    q = glass.triaxial_axis_ratio(0.8, 0.4, size=1000, xp=xp)
+    assert q.shape == (1000,)
+
+    # explicit shape
+
+    q = glass.triaxial_axis_ratio(0.8, 0.4, size=(10, 10), xp=xp)
+    assert q.shape == (10, 10)
+
+    # implicit size
+
+    q1 = glass.triaxial_axis_ratio(xp.asarray([0.8, 0.9]), 0.4)
+    q2 = glass.triaxial_axis_ratio(0.8, xp.asarray([0.4, 0.5]))
+    assert q1.shape == q2.shape == (2,)
+
+    # broadcasting rule
+
+    q = glass.triaxial_axis_ratio(
+        xp.asarray([[0.6, 0.7], [0.8, 0.9]]),
+        xp.asarray([0.4, 0.5]),
+    )
+    assert q.shape == (2, 2)
+
+    # random parameters and check that projection is
+    # between largest and smallest possible value
+
+    sorted_uniform_rnd = xp.sort(urng.uniform(0, 1, size=(2, 1000)), axis=0)
+    zeta = sorted_uniform_rnd[0, :]
+    xi = sorted_uniform_rnd[1, :]
+    qmin = xp.min(xp.stack([zeta, xi, xi / zeta]), axis=0)
+    qmax = xp.max(xp.stack([zeta, xi, xi / zeta]), axis=0)
+    q = glass.triaxial_axis_ratio(zeta, xi)
+    assert xp.all((qmax >= q) & (q >= qmin))
+
+
+def test_ellipticity_ryden04(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip(
+            "Arrays in ellipticity_ryden04 are not immutable, so do not support jax",
+        )
+
+    # Pass floats without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.ellipticity_ryden04(-1.85, 0.89, 0.222, 0.056)
+
+    # single ellipticity
+
+    e = glass.ellipticity_ryden04(-1.85, 0.89, 0.222, 0.056, xp=xp)
+    assert e.ndim == 0
+
+    # test with rng
+
+    e = glass.ellipticity_ryden04(-1.85, 0.89, 0.222, 0.056, rng=urng, xp=xp)
+    assert e.ndim == 0
+
+    # many ellipticities
+
+    e = glass.ellipticity_ryden04(-1.85, 0.89, 0.222, 0.056, size=1000, xp=xp)
+    assert e.shape == (1000,)
+
+    # explicit shape
+
+    e = glass.ellipticity_ryden04(-1.85, 0.89, 0.222, 0.056, size=(10, 10), xp=xp)
+    assert e.shape == (10, 10)
+
+    # implicit size
+
+    e1 = glass.ellipticity_ryden04(-1.85, 0.89, xp.asarray([0.222, 0.333]), 0.056)
+    e2 = glass.ellipticity_ryden04(-1.85, 0.89, 0.222, xp.asarray([0.056, 0.067]))
+    e3 = glass.ellipticity_ryden04(xp.asarray([-1.85, -2.85]), 0.89, 0.222, 0.056)
+    e4 = glass.ellipticity_ryden04(-1.85, xp.asarray([0.89, 1.001]), 0.222, 0.056)
+    assert e1.shape == e2.shape == e3.shape == e4.shape == (2,)
+
+    # broadcasting rule
+
+    e = glass.ellipticity_ryden04(
+        xp.asarray([-1.9, -2.9]),
+        0.9,
+        xp.asarray([[0.2, 0.3], [0.4, 0.5]]),
+        0.1,
+    )
+    assert e.shape == (2, 2)
+
+    # check that result is in the specified range
+
+    e = glass.ellipticity_ryden04(0.0, 1.0, 0.222, 0.056, size=10, xp=xp)
+    assert xp.all((xp.real(e) >= -1.0) & (xp.real(e) <= 1.0))
+
+    e = glass.ellipticity_ryden04(0.0, 1.0, 0.0, 1.0, size=10, xp=xp)
+    assert xp.all((xp.real(e) >= -1.0) & (xp.real(e) <= 1.0))
+
+
+@pytest.mark.flaky(rerun=5, only_rerun=["AssertionError"])
+def test_ellipticity_gaussian(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip(
+            "Arrays in ellipticity_gaussian are not immutable, so do not support jax",
+        )
+
+    n = 1_000_000
+
+    eps = glass.ellipticity_gaussian(n, 0.256, xp=xp)
+
+    assert eps.shape == (n,)
+
+    # Pass floats without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.ellipticity_gaussian(n, 0.256)
+
+    # test with rng
+
+    eps = glass.ellipticity_gaussian(n, 0.256, rng=urng, xp=xp)
+
+    assert eps.shape == (n,)
+
+    np.testing.assert_array_less(np.abs(eps), 1)
+
+    np.testing.assert_allclose(xp.std(xp.real(eps)), 0.256, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)), 0.256, atol=1e-3, rtol=0)
+
+    eps = glass.ellipticity_gaussian(xp.asarray([n, n]), xp.asarray([0.128, 0.256]))
+
+    assert eps.shape == (2 * n,)
+
+    np.testing.assert_array_less(xp.abs(eps), 1)
+
+    np.testing.assert_allclose(xp.std(xp.real(eps)[:n]), 0.128, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)[:n]), 0.128, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.real(eps)[n:]), 0.256, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)[n:]), 0.256, atol=1e-3, rtol=0)
+
+
+def test_ellipticity_intnorm(xp: ModuleType, urng: UnifiedGenerator) -> None:
+    if xp.__name__ == "jax.numpy":
+        pytest.skip(
+            "Arrays in ellipticity_intnorm are not immutable, so do not support jax",
+        )
+
+    n = 1_000_000
+
+    eps = glass.ellipticity_intnorm(n, 0.256, xp=xp)
+
+    assert eps.shape == (n,)
+
+    # Pass non-arrays without xp
+
+    with pytest.raises(TypeError, match="Unrecognized array input"):
+        glass.ellipticity_intnorm(n, 0.256)
+
+    # test with rng
+
+    eps = glass.ellipticity_intnorm(n, 0.256, rng=urng, xp=xp)
+
+    assert eps.shape == (n,)
+
+    np.testing.assert_array_less(xp.abs(eps), 1)
+
+    np.testing.assert_allclose(xp.std(xp.real(eps)), 0.256, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)), 0.256, atol=1e-3, rtol=0)
+
+    eps = glass.ellipticity_intnorm(xp.asarray([n, n]), xp.asarray([0.128, 0.256]))
+
+    assert eps.shape == (2 * n,)
+
+    np.testing.assert_array_less(xp.abs(eps), 1)
+
+    np.testing.assert_allclose(xp.std(xp.real(eps)[:n]), 0.128, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)[:n]), 0.128, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.real(eps)[n:]), 0.256, atol=1e-3, rtol=0)
+    np.testing.assert_allclose(xp.std(xp.imag(eps)[n:]), 0.256, atol=1e-3, rtol=0)
+
+    with pytest.raises(ValueError, match="sigma must be between"):
+        glass.ellipticity_intnorm(1, 0.71, xp=xp)
diff --git a/glass/tests/test_shells.py b/glass/tests/test_shells.py
new file mode 100644
index 0000000..37b2844
--- /dev/null
+++ b/glass/tests/test_shells.py
@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import dataclasses
+import math
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    import types
+
+    import glass._array_api_utils as _utils
+    from glass.cosmology import Cosmology
+
+
+def test_distance_weight(cosmo: Cosmology) -> None:
+    """Add unit tests for :class:`glass.DistanceWeight`."""
+    z = np.linspace(0, 1, 6)
+
+    # check shape
+
+    w = glass.DistanceWeight(cosmo)(z)
+    np.testing.assert_array_equal(w.shape, z.shape)
+
+    # check first value is 1
+
+    assert w[0] == 1
+
+    # check values are decreasing
+
+    np.testing.assert_array_less(w[1:], w[:-1])
+
+
+def test_volume_weight(cosmo: Cosmology) -> None:
+    """Add unit tests for :class:`glass.VolumeWeight`."""
+    z = np.linspace(0, 1, 6)
+
+    # check shape
+
+    w = glass.VolumeWeight(cosmo)(z)
+    np.testing.assert_array_equal(w.shape, z.shape)
+
+    # check first value is 0
+
+    assert w[0] == 0
+
+    # check values are increasing
+
+    np.testing.assert_array_less(w[:-1], w[1:])
+
+
+def test_density_weight(cosmo: Cosmology) -> None:
+    """Add unit tests for :class:`glass.DensityWeight`."""
+    z = np.linspace(0, 1, 6)
+
+    # check shape
+
+    w = glass.DensityWeight(cosmo)(z)
+    np.testing.assert_array_equal(w.shape, z.shape)
+
+    # check first value is 0
+
+    assert w[0] == 0
+
+    # check values are increasing
+
+    np.testing.assert_array_less(w[:-1], w[1:])
+
+
+def test_tophat_windows(xp: types.ModuleType) -> None:
+    """Add unit tests for :func:`glass.tophat_windows`."""
+    zb = xp.asarray([0.0, 0.1, 0.2, 0.5, 1.0, 2.0])
+    dz = 0.005
+
+    ws = glass.tophat_windows(zb, dz)
+
+    assert len(ws) == zb.size - 1
+
+    assert all(
+        z0 == w.za[0] and zn == w.za[-1]
+        for w, z0, zn in zip(ws, zb, zb[1:], strict=False)
+    )
+
+    assert all(
+        zn <= z0 + w.za.size * dz <= zn + dz
+        for w, z0, zn in zip(ws, zb, zb[1:], strict=False)
+    )
+
+    assert all(xp.all(w.wa == 1) for w in ws)
+
+
+def test_linear_windows(xp: types.ModuleType) -> None:
+    """Add unit tests for :func:`glass.linear_windows`."""
+    dz = 1e-2
+    zgrid = xp.asarray(
+        [
+            0.0,
+            0.20224358,
+            0.42896272,
+            0.69026819,
+            1.0,
+        ],
+    )
+
+    # check spacing of redshift grid
+
+    ws = glass.linear_windows(zgrid)
+    np.testing.assert_allclose(dz, xp.mean(xp.diff(ws[0].za)), atol=1e-2)
+
+    # check number of windows
+
+    assert len(ws) == zgrid.size - 2
+
+    # check values of zeff
+
+    np.testing.assert_allclose([w.zeff for w in ws], zgrid[1:-1])
+
+    # check weight function input
+
+    ws = glass.linear_windows(
+        zgrid,
+        weight=lambda _: 0,
+    )
+    for w in ws:
+        np.testing.assert_allclose(w.wa, xp.zeros_like(w.wa))
+
+    # check error raised
+
+    with pytest.raises(ValueError, match="nodes must have at least 3 entries"):
+        glass.linear_windows(xp.asarray([]))
+
+    # check warning issued
+
+    with pytest.warns(
+        UserWarning,
+        match="first triangular window does not start at z=0",
+    ):
+        glass.linear_windows(xp.asarray([0.1, 0.2, 0.3]))
+
+
+def test_cubic_windows(xp: types.ModuleType) -> None:
+    """Add unit tests for :func:`glass.cubic_windows`."""
+    dz = 1e-2
+    zgrid = xp.asarray(
+        [
+            0.0,
+            0.20224358,
+            0.42896272,
+            0.69026819,
+            1.0,
+        ],
+    )
+
+    # check spacing of redshift grid
+
+    ws = glass.cubic_windows(zgrid)
+    np.testing.assert_allclose(dz, xp.mean(xp.diff(ws[0].za)), atol=1e-2)
+
+    # check number of windows
+
+    assert len(ws) == zgrid.size - 2
+
+    # check values of zeff
+
+    np.testing.assert_allclose([w.zeff for w in ws], zgrid[1:-1])
+
+    # check weight function input
+
+    ws = glass.cubic_windows(
+        zgrid,
+        weight=lambda _: 0,
+    )
+    for w in ws:
+        np.testing.assert_allclose(w.wa, xp.zeros_like(w.wa))
+
+    # check error raised
+
+    with pytest.raises(ValueError, match="nodes must have at least 3 entries"):
+        glass.cubic_windows(xp.asarray([]))
+
+    # check warning issued
+
+    with pytest.warns(
+        UserWarning,
+        match="first cubic spline window does not start at z=0",
+    ):
+        glass.cubic_windows(xp.asarray([0.1, 0.2, 0.3]))
+
+
+def test_restrict(xp: types.ModuleType, uxpx: _utils.XPAdditions) -> None:
+    """Add unit tests for :func:`glass.restrict`."""
+    # Gaussian test function
+    z = xp.linspace(0.0, 5.0, 1000)
+    f = xp.exp(-(((z - 2.0) / 0.5) ** 2) / 2)
+
+    # window for restriction
+    w = glass.RadialWindow(
+        za=xp.asarray([1.0, 2.0, 3.0, 4.0]),
+        wa=xp.asarray([0.0, 0.5, 0.5, 0.0]),
+    )
+
+    zr, fr = glass.restrict(z, f, w)
+
+    assert zr[0] == w.za[0]
+    assert zr[-1] == w.za[-1]
+
+    assert fr[0] == fr[-1] == 0.0
+
+    for zi, wi in zip(w.za, w.wa, strict=False):
+        i = xp.searchsorted(zr, zi)
+        assert zr[i] == zi
+
+        # Using design principle of scipy (i.e. copy, use np, copy back)
+        assert fr[i] == wi * uxpx.interp(zi, z, f)
+
+    for zi, fi in zip(z, f, strict=False):
+        if w.za[0] <= zi <= w.za[-1]:
+            i = xp.searchsorted(zr, zi)
+            assert zr[i] == zi
+            assert fr[i] == fi * uxpx.interp(zi, w.za, w.wa)
+
+
+@pytest.mark.parametrize("method", ["lstsq", "nnls", "restrict"])
+def test_partition(xp: types.ModuleType, uxpx: _utils.XPAdditions, method: str) -> None:
+    """Add unit tests for :func:`glass.partition`."""
+    if (xp.__name__ == "jax.numpy") and (method in {"nnls"}):
+        pytest.skip(f"Arrays in {method} are not immutable, so do not support jax")
+
+    shells = [
+        glass.RadialWindow(xp.asarray([0.0, 1.0]), xp.asarray([1.0, 0.0]), 0.0),
+        glass.RadialWindow(
+            xp.asarray([0.0, 1.0, 2.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            0.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([1.0, 2.0, 3.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            1.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([2.0, 3.0, 4.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            2.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([3.0, 4.0, 5.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            3.5,
+        ),
+        glass.RadialWindow(xp.asarray([4.0, 5.0]), xp.asarray([0.0, 1.0]), 5.0),
+    ]
+
+    z = xp.linspace(0.0, 5.0, 1000)
+    k = 1.0 + xp.reshape(xp.arange(6.0), (3, 2, 1))
+    fz = xp.exp(-z / k)
+
+    assert fz.shape == (3, 2, 1000)
+
+    part = glass.partition(z, fz, shells, method=method)
+
+    assert part.shape == (len(shells), 3, 2)
+
+    np.testing.assert_allclose(xp.sum(part, axis=0), uxpx.trapezoid(fz, z))
+
+
+def test_redshift_grid(xp: types.ModuleType) -> None:
+    """Add unit tests for :func:`glass.redshift_grid`."""
+    zmin = 0
+    zmax = 1
+
+    # check num input
+
+    num = 5
+    z = glass.redshift_grid(zmin, zmax, num=5, xp=xp)
+    assert z.size == num + 1
+
+    # check dz input
+
+    dz = 0.2
+    z = glass.redshift_grid(zmin, zmax, dz=dz, xp=xp)
+    assert z.size == math.ceil((zmax - zmin) / dz) + 1
+
+    # check dz for spacing which results in a max value above zmax
+
+    z = glass.redshift_grid(zmin, zmax, dz=0.3, xp=xp)
+    assert zmax < z[-1]
+
+    # check error raised
+
+    with pytest.raises(
+        ValueError,
+        match="exactly one of grid step size or number of steps must be given",
+    ):
+        glass.redshift_grid(zmin, zmax, xp=xp)
+
+    with pytest.raises(
+        ValueError,
+        match="exactly one of grid step size or number of steps must be given",
+    ):
+        glass.redshift_grid(zmin, zmax, dz=dz, num=num, xp=xp)
+
+
+def test_distance_grid(cosmo: Cosmology) -> None:
+    """Add unit tests for :func:`glass.distance_grid`."""
+    zmin = 0
+    zmax = 1
+
+    # check num input
+
+    num = 5
+    x = glass.distance_grid(cosmo, zmin, zmax, num=5)
+    assert len(x) == num + 1
+
+    # check dz input
+
+    dx = 0.2
+    x = glass.distance_grid(cosmo, zmin, zmax, dx=dx)
+    assert len(x) == np.ceil((zmax - zmin) / dx) + 1
+
+    # check decrease in distance
+
+    x = glass.distance_grid(cosmo, zmin, zmax, dx=0.3)
+    np.testing.assert_array_less(x[1:], x[:-1])
+
+    # check error raised
+
+    with pytest.raises(
+        ValueError,
+        match="exactly one of grid step size or number of steps must be given",
+    ):
+        glass.distance_grid(cosmo, zmin, zmax)
+
+    with pytest.raises(
+        ValueError,
+        match="exactly one of grid step size or number of steps must be given",
+    ):
+        glass.distance_grid(cosmo, zmin, zmax, dx=dx, num=num)
+
+
+def test_combine(xp: types.ModuleType, uxpx: _utils.XPAdditions) -> None:
+    """Add unit tests for :func:`glass.combine`."""
+    z = xp.linspace(0.0, 5.0, 1000)
+    weights = xp.asarray(
+        [1.0, 0.90595172, 0.81025465, 0.72003963, 0.63892872, 0.56796183],
+    )
+    shells = [
+        glass.RadialWindow(xp.asarray([0.0, 1.0]), xp.asarray([1.0, 0.0]), 0.0),
+        glass.RadialWindow(
+            xp.asarray([0.0, 1.0, 2.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            0.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([1.0, 2.0, 3.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            1.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([2.0, 3.0, 4.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            2.5,
+        ),
+        glass.RadialWindow(
+            xp.asarray([3.0, 4.0, 5.0]),
+            xp.asarray([0.0, 1.0, 0.0]),
+            3.5,
+        ),
+        glass.RadialWindow(xp.asarray([4.0, 5.0]), xp.asarray([0.0, 1.0]), 5.0),
+    ]
+
+    result = glass.combine(z, weights, shells)
+
+    assert result.shape == z.shape
+
+    # Check sum of result
+    np.testing.assert_allclose(sum(result), 929.267284)
+
+    # Check integral w.r.t z has not changed
+    np.testing.assert_allclose(uxpx.trapezoid(result, z), 4.643139, rtol=1e-6)
+
+
+def test_radial_window_immutable(xp: types.ModuleType) -> None:
+    """Checks the :class:`RadialWindow` class is immutable."""
+    wa = xp.asarray([0.0, 1.0, 0.0])
+    za = xp.asarray([0.0, 1.0, 2.0])
+    zeff = 1.0
+
+    w = glass.RadialWindow(za, wa, zeff)
+
+    with pytest.raises(
+        dataclasses.FrozenInstanceError,
+        match="cannot assign to field 'za'",
+    ):
+        w.za = za  # type: ignore[misc]
+
+    with pytest.raises(
+        dataclasses.FrozenInstanceError,
+        match="cannot assign to field 'wa'",
+    ):
+        w.wa = wa  # type: ignore[misc]
+
+    with pytest.raises(
+        dataclasses.FrozenInstanceError,
+        match="cannot assign to field 'zeff'",
+    ):
+        w.zeff = zeff  # type: ignore[misc]
+
+
+def test_radial_window_zeff_none(xp: types.ModuleType) -> None:
+    """Checks ``zeff`` is computed when not provided to :class:`RadialWindow`."""
+    # check zeff is computed when not provided
+
+    wa = xp.asarray([0.0, 1.0, 0.0])
+    za = xp.asarray([0.0, 1.0, 2.0])
+
+    w = glass.RadialWindow(za, wa)
+
+    np.testing.assert_allclose(w.zeff, 1.0)
+
+    # check zeff is NaN when redshift array is empty
+
+    za = xp.asarray([])
+
+    w = glass.RadialWindow(za, wa)
+
+    assert math.isnan(w.zeff)
diff --git a/glass/tests/test_user.py b/glass/tests/test_user.py
new file mode 100644
index 0000000..cad998a
--- /dev/null
+++ b/glass/tests/test_user.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+import importlib.util
+import pathlib
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+import glass
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+# check if available for testing
+HAVE_FITSIO = importlib.util.find_spec("fitsio") is not None
+
+delta = 0.001  # Number of points in arrays
+my_max = 1000  # Typically number of galaxies in loop
+except_int = 750  # Where test exception occurs in loop
+filename = "MyFile.Fits"
+cls_file = "Cls.npz"
+
+
+def test_read_write_cls(rng: np.random.Generator, tmp_path: pathlib.Path) -> None:
+    cls = rng.normal(size=(10, 10))
+    glass.save_cls(tmp_path / cls_file, cls)
+
+    assert pathlib.Path.exists(tmp_path / cls_file)
+
+    with np.load(tmp_path / cls_file) as npz:
+        values = npz["values"]
+        split = npz["split"]
+
+    np.testing.assert_array_equal(values, np.concatenate(cls))
+    np.testing.assert_array_equal(split, np.cumsum([len(cl) for cl in cls[:-1]]))
+    np.testing.assert_array_equal(cls, np.split(values, split))
+
+    npz = glass.load_cls(tmp_path / cls_file)
+    np.testing.assert_array_equal(npz, cls)
+
+
+@pytest.mark.skipif(not HAVE_FITSIO, reason="test requires fitsio")
+def test_basic_write(tmp_path: pathlib.Path) -> None:
+    import fitsio
+
+    filename_gfits = "gfits.fits"  # what GLASS creates
+    filename_tfits = "tfits.fits"  # file created on the fly to test against
+
+    def _test_append(
+        fits: fitsio.FITS,
+        data: list[NDArray[np.float64]],
+        names: list[str],
+    ) -> None:
+        """Write routine for FITS test cases."""
+        cat_name = "CATALOG"
+        if cat_name not in fits:
+            fits.write_table(data, names=names, extname=cat_name)
+        else:
+            hdu = fits[cat_name]
+            hdu.write(data, names=names, firstrow=hdu.get_nrows())
+
+    with (
+        glass.write_catalog(tmp_path / filename_gfits, ext="CATALOG") as out,
+        fitsio.FITS(tmp_path / filename_tfits, "rw", clobber=True) as my_fits,
+    ):
+        for i in range(my_max):
+            array = np.arange(i, i + 1, delta)  # array of size 1/delta
+            array2 = np.arange(i + 1, i + 2, delta)  # array of size 1/delta
+            out.write(RA=array, RB=array2)
+            arrays = [array, array2]
+            names = ["RA", "RB"]
+            _test_append(my_fits, arrays, names)
+
+    with (
+        fitsio.FITS(tmp_path / filename_gfits) as g_fits,
+        fitsio.FITS(tmp_path / filename_tfits) as t_fits,
+    ):
+        glass_data = g_fits[1].read()
+        test_data = t_fits[1].read()
+        assert glass_data["RA"].size == test_data["RA"].size
+        assert glass_data["RB"].size == test_data["RA"].size
+
+
+@pytest.mark.skipif(not HAVE_FITSIO, reason="test requires fitsio")
+def test_write_exception(tmp_path: pathlib.Path) -> None:
+    class TestWriteError(Exception):
+        """Custom exception for controlled testing."""
+
+    def raise_error(msg: str) -> None:
+        """Raise a custom exception for controlled testing.
+
+        Parameters
+        ----------
+        msg
+            A message to be passed to the exception.
+
+        Raises
+        ------
+        TestWriteError
+            A custom exception for controlled testing.
+
+        """
+        raise TestWriteError(msg)
+
+    try:
+        with glass.write_catalog(tmp_path / filename, ext="CATALOG") as out:
+            for i in range(my_max):
+                if i == except_int:
+                    msg = "Unhandled exception"
+                    raise_error(msg)
+                array = np.arange(i, i + 1, delta)  # array of size 1/delta
+                array2 = np.arange(i + 1, i + 2, delta)  # array of size 1/delta
+                out.write(RA=array, RB=array2)
+
+    except TestWriteError:
+        import fitsio
+
+        with fitsio.FITS(tmp_path / filename) as hdul:
+            data = hdul[1].read()
+            assert data["RA"].size == except_int / delta
+            assert data["RB"].size == except_int / delta
+
+            fits_mat = data["RA"].reshape(except_int, int(1 / delta))
+            fits_mat2 = data["RB"].reshape(except_int, int(1 / delta))
+            for i in range(except_int):
+                array = np.arange(
+                    i,
+                    i + 1,
+                    delta,
+                )  # re-create array to compare to read data
+                array2 = np.arange(i + 1, i + 2, delta)
+                assert array.tolist() == fits_mat[i].tolist()
+                assert array2.tolist() == fits_mat2[i].tolist()
diff --git a/noxfile.py b/noxfile.py
index 3063fe9..20476f6 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,10 +16,16 @@
     "3.13",
 ]
 DEPENDENCIES = [
-    "array_api_strict>=2",
+    "array-api-compat",
+    "array-api-extra",
+    "array-api-strict>=2",
+    "healpix",
+    "healpy",
     "jax>=0.4.32",
     "pytest",
     "pytest-benchmark",
+    "pytest-cov",
+    "transformcl",
 ]
 GLASS_REPO_URL = "https://github.com/glass-dev/glass"
 
@@ -36,43 +42,50 @@ def benchmark(session: nox.Session) -> None:
     """Run the benchmarks."""
     session.install(*DEPENDENCIES)
 
-    revision = ""
-    if session.posargs:
-        args = session.posargs
-        if len(args) != 1:
-            msg = f"Incorrect number of revisions provided {len(args)}"
-            raise ValueError(msg)
-        revision = args[0]
-    else:
+    if not session.posargs:
         msg = "Revision not provided"
         raise ValueError(msg)
 
-    # Verify revisions have been provided
+    if len(session.posargs) == 1:
+        revision = session.posargs[0]
+    else:
+        msg = (
+            f"Incorrect number of revisions provided ({len(session.posargs)}), "
+            f"expected 2"
+        )
+        raise ValueError(msg)
 
-    print(f"Running benchmark for revision {revision}")
     session.install(f"git+{GLASS_REPO_URL}@{revision}")
     session.run("pytest", "--benchmark-autosave")
 
 
+@nox.session(python=ALL_PYTHON)
+def coverage(session: nox.Session) -> None:
+    """Run tests and compute coverage of glass."""
+    session.install(*DEPENDENCIES)
+    session.run(
+        "pytest",
+        "--cov",
+        env={"PYTHONPATH": "./glass"},
+    )
+
+
 @nox.session(python=ALL_PYTHON)
 def regression_tests(session: nox.Session) -> None:
     """Run the regression test."""
     session.install(*DEPENDENCIES)
 
-    before_revision = "main"
-    after_revision = ""
-    if session.posargs:
-        revisions = session.posargs
-        if len(revisions) == 1:
-            after_revision = revisions[0]
-        elif len(revisions) == 2:  # noqa: PLR2004
-            before_revision = revisions[0]
-            after_revision = revisions[1]
-        else:
-            msg = f"Incorrect number of revisions provided {len(revisions)}"
-            raise ValueError(msg)
+    if not session.posargs:
+        msg = "Revision not provided"
+        raise ValueError(msg)
+
+    if len(session.posargs) == 2:  # noqa: PLR2004
+        before_revision, after_revision = session.posargs
     else:
-        msg = "No revisions not provided"
+        msg = (
+            f"Incorrect number of revisions provided ({len(session.posargs)}), "
+            f"expected 2"
+        )
         raise ValueError(msg)
 
     print(f"Generating before benchmark for comparison from revision {before_revision}")
diff --git a/pyproject.toml b/pyproject.toml
index 88f198b..d04b62c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,15 @@
+[tool.coverage]
+report = {exclude_also = [
+    "class \\w+\\(Protocol(\\[(\\w+, )*\\w+\\])?\\):", # protocol classes
+    "if TYPE_CHECKING:", # type-checking imports
+], omit = [
+    "*/_version.py",
+    "*/noxfile.py",
+], skip_covered = false, sort = "cover"}
+run = {branch = true, parallel = false, relative_files = true, source_dirs = [
+    "glass/glass",
+]}
+
 [tool.mypy]
 disallow_untyped_decorators = false
 enable_error_code = [
@@ -76,6 +88,5 @@ all = true
 in_place = true
 spaces_indent_inline_array = 4
 trailing_comma_inline_array = true
-overrides."project.classifiers".inline_arrays = false
 overrides."tool.coverage.paths.source".inline_arrays = false
 overrides."tool.ruff.lint.isort.section-order".inline_arrays = false