"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
"Cell biology","latchbio/spatialbench","CHANGELOG.md",".md","1136","29","# Changelog

## [Unreleased]

### Fixed

#### Graders
- Fixed all graders to implement `evaluate_answer()` instead of `evaluate()` to work with the runner's direct answer passing:
  - `LabelSetJaccardGrader`
  - `DistributionComparisonGrader`
  - `MarkerGenePrecisionRecallGrader`
  - `MarkerGeneSeparationGrader`
  - `SpatialAdjacencyGrader`
- `NumericToleranceGrader` was already correct

#### Package
- Added `[tool.setuptools.packages.find]` to pyproject.toml to fix ""multiple top-level packages"" error during installation

### Changed

#### Evals
- Updated `label_set_jaccard` evals to use consistent config format:
  - `xenium_kidney_typing.json`: Added explicit `cell_types_predicted` field requirement to task
  - `vizgen_de_temporal.json`: Changed from `ground_truth_marker_sets` to `ground_truth_labels`, updated task to request `cell_types_predicted`
  - `vizgen_tissue_composition.json`: Changed from `ground_truth_marker_sets` to `ground_truth_labels`, updated task to request `cell_types_predicted`

#### Documentation
- Changed installation instructions from `pip install spatialbench` to `pip install -e .` in README.md
","Markdown"
"Cell biology","latchbio/spatialbench","CONTRIBUTING.md",".md","4496","173","# Contributing to SpatialBench

Thank you for your interest in contributing to SpatialBench! This document provides guidelines for creating custom graders and submitting benchmark results.

**Note**: This repository contains example evaluations only. The full 98-evaluation benchmark is maintained separately to prevent overfitting. Contact [kenny@latch.bio](mailto:kenny@latch.bio) for information about contributing to the full benchmark.

## Table of Contents

1. [Creating a Custom Grader](#creating-a-custom-grader)
2. [Submitting Benchmark Results](#submitting-benchmark-results)
3. [Code Style](#code-style)

## Full Benchmark Information

The complete SpatialBench comprises **98 evaluations** across four platforms:

| Technology       | Evaluations |
|------------------|-------------|
| Xenium           | 30          |
| Vizgen (MERFISH) | 31          |
| AtlasXomics      | 25          |
| Seeker/Curio     | 12          |
| **Total**        | **98**      |

This repository contains 7 representative examples. The full benchmark is withheld to ensure performance metrics reflect genuine spatial biology capabilities.

If none of the built-in graders fit your needs, create a custom one:

### 1. Subclass BinaryGrader

```python
from spatialbench.graders.base import BinaryGrader, GraderResult

class MyCustomGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        ground_truth = config.get(""ground_truth"")

        passed = True
        metrics = {}
        failures = []

        if agent_answer.get(""some_field"") != ground_truth:
            passed = False
            failures.append(""Field mismatch"")

        reasoning = self._format_reasoning(passed, failures)

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_reasoning(self, passed, failures):
        lines = []
        lines.append(f""Custom Grader: {'PASS' if passed else 'FAIL'}"")
        if failures:
            lines.append(""Failures:"")
            for f in failures:
                lines.append(f""  - {f}"")
        return ""\n"".join(lines)
```

### 2. Write Unit Tests

```python
def test_custom_grader():
    grader = MyCustomGrader()

    agent_answer = {""some_field"": ""value""}
    config = {""ground_truth"": ""value""}

    result = grader.evaluate_answer(agent_answer, config)
    assert result.passed

    agent_answer = {""some_field"": ""wrong""}
    result = grader.evaluate_answer(agent_answer, config)
    assert not result.passed
```

### 3. Register Grader

Add to `spatialbench/graders/__init__.py`:

```python
from spatialbench.graders.my_custom import MyCustomGrader

GRADER_REGISTRY = {
    ...
    ""my_custom"": MyCustomGrader,
}
```

### 4. Document

Add to `docs/graders.md` with:
- Description of what it evaluates
- Config schema
- Example usage
- When to use it

### 5. Submit PR

Include:
- [ ] Grader implementation
- [ ] Unit tests
- [ ] Documentation in docs/graders.md
- [ ] Registry update
- [ ] Example evaluation using the grader

## Submitting Benchmark Results

Help build the leaderboard by benchmarking your model:

### 1. Run Benchmark

```bash
spatialbench batch evals/ --model your-model-name --output results/
```

### 2. Generate Summary

```bash
spatialbench leaderboard results/ --output summary.json
```

### 3. Submit PR

Include:
- [ ] summary.json with results
- [ ] Model name and version
- [ ] Date of evaluation
- [ ] Any relevant notes (hardware, runtime, etc.)

## Code Style

### Python

- End files with newlines
- No docstrings or comments (per project style)
- Use `|` for Union types: `str | None`
- Imports at top of file
- Use available types (`list`, `dict`) instead of typing module

### JSON

- 2-space indentation
- No trailing commas
- Sorted keys (for ground truth labels)

### Commits

- Clear, descriptive commit messages
- One logical change per commit
- Reference issue numbers when applicable

## Review Process

1. **Automated checks**: Tests and linting must pass
2. **Ground truth review**: Verify analysis is correct and reproducible
3. **Grader review**: Ensure grader logic is sound
4. **Documentation review**: Check clarity and completeness
5. **Approval**: Two maintainer approvals required

## Questions?

- Open an issue for questions
- Tag maintainers for urgent reviews
- Join discussions for design decisions

Thank you for contributing to SpatialBench!
","Markdown"
"Cell biology","latchbio/spatialbench","spatialbench/__init__.py",".py","408","17","from spatialbench.types import TestCase, TestResult, EvalResult
from spatialbench.graders import BinaryGrader, GraderResult, GRADER_REGISTRY
from spatialbench.harness import EvalRunner, run_minisweagent_task

__version__ = ""0.1.0""

__all__ = [
    ""TestCase"",
    ""TestResult"",
    ""EvalResult"",
    ""BinaryGrader"",
    ""GraderResult"",
    ""GRADER_REGISTRY"",
    ""EvalRunner"",
    ""run_minisweagent_task"",
]
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/cli.py",".py","14946","402","import click
import json
import time
from datetime import datetime
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor, as_completed

from spatialbench import EvalRunner, TestCase
from spatialbench.harness import run_minisweagent_task, run_claudecode_task, batch_download_datasets

def _run_single_eval(eval_file_path, agent, model, keep_workspace, run_id=None):
    eval_file = Path(eval_file_path)
    start_time = time.time()

    if agent == ""minisweagent"":
        def agent_fn(task_prompt, work_dir):
            return run_minisweagent_task(task_prompt, work_dir, model_name=model)
    elif agent == ""claudecode"":
        def agent_fn(task_prompt, work_dir):
            return run_claudecode_task(task_prompt, work_dir, model_name=model)
    else:
        agent_fn = None

    try:
        runner = EvalRunner(eval_file, keep_workspace=keep_workspace, run_id=run_id)
        result = runner.run(agent_function=agent_fn)
        duration = time.time() - start_time

        output = {
            ""eval"": eval_file.name,
            ""passed"": result.get(""passed""),
            ""test_id"": result.get(""test_id""),
            ""model"": model,
            ""timestamp"": datetime.utcnow().isoformat() + ""Z"",
            ""duration_s"": round(duration, 2),
        }

        if ""metadata"" in result:
            output.update(result[""metadata""])

        return output
    except Exception as e:
        duration = time.time() - start_time
        return {
            ""eval"": eval_file.name,
            ""passed"": False,
            ""error"": str(e),
            ""model"": model,
            ""timestamp"": datetime.utcnow().isoformat() + ""Z"",
            ""duration_s"": round(duration, 2),
        }

@click.group()
@click.version_option(version=""0.1.0"")
def main():
    pass

@main.command()
@click.argument(""eval_path"", type=click.Path(exists=True))
@click.option(""--keep-workspace"", is_flag=True, help=""Keep the workspace directory after completion"")
@click.option(""--verbose"", ""-v"", is_flag=True, help=""Verbose output"")
@click.option(""--agent"", type=click.Choice([""minisweagent"", ""claudecode""]), default=None, help=""Agent to use for evaluation"")
@click.option(""--model"", default=None, help=""Model name for agent"")
def run(eval_path, keep_workspace, verbose, agent, model):
    click.echo(f""Running evaluation: {eval_path}"")

    runner = EvalRunner(eval_path, keep_workspace=keep_workspace)

    if agent == ""minisweagent"":
        click.echo(f""Using mini-swe-agent{f' with model: {model}' if model else ''}"")

        def agent_fn(task_prompt, work_dir):
            return run_minisweagent_task(task_prompt, work_dir, model_name=model)

        result = runner.run(agent_function=agent_fn)

        if result.get(""passed""):
            click.echo(""\n✓ Evaluation PASSED"")
        else:
            click.echo(""\n✗ Evaluation FAILED"")
    elif agent == ""claudecode"":
        click.echo(f""Using Claude Code{f' with model: {model}' if model else ''}"")

        def agent_fn(task_prompt, work_dir):
            return run_claudecode_task(task_prompt, work_dir, model_name=model)

        result = runner.run(agent_function=agent_fn)

        if result.get(""passed""):
            click.echo(""\n✓ Evaluation PASSED"")
        else:
            click.echo(""\n✗ Evaluation FAILED"")
    else:
        click.echo(""\nNote: No agent specified."")
        click.echo(""To integrate with your agent:"")
        click.echo(""  1. Use EvalRunner programmatically in Python"")
        click.echo(""  2. Pass agent_function that writes eval_answer.json"")
        click.echo(""\nExample:"")
        click.echo(""  from spatialbench import EvalRunner"")
        click.echo(""  runner = EvalRunner(eval_path)"")
        click.echo(""  runner.run(agent_function=my_agent)"")
        click.echo(""\nOr use mini-swe-agent:"")
        click.echo(""  spatialbench run evals/qc/seeker_qc_basic.json --agent minisweagent"")

        result = runner.run()

@main.command()
@click.argument(""eval_dir"", type=click.Path(exists=True))
@click.option(""--agent"", type=click.Choice([""minisweagent"", ""claudecode""]), default=None, help=""Agent to use for evaluation"")
@click.option(""--model"", default=None, help=""Model name for agent"")
@click.option(""--output"", ""-o"", type=click.Path(), help=""Output directory for results"")
@click.option(""--parallel"", ""-p"", type=int, default=1, help=""Number of parallel workers"")
@click.option(""--keep-workspace"", is_flag=True, help=""Keep workspace after each eval"")
def batch(eval_dir, agent, model, output, parallel, keep_workspace):
    click.echo(f""Running batch evaluations from: {eval_dir}"")

    run_id = datetime.utcnow().strftime(""%Y%m%d_%H%M%S"")
    click.echo(f""Run ID: {run_id}"")

    eval_dir = Path(eval_dir)
    eval_files = list(eval_dir.rglob(""*.json""))

    click.echo(f""\nFound {len(eval_files)} evaluation(s)"")

    if not eval_files:
        click.echo(""No evaluation files found!"")
        return

    if not agent:
        click.echo(""\nNote: No agent specified. Use --agent minisweagent to run evaluations."")
        return

    click.echo(""\n"" + ""="" * 80)
    click.echo(""STEP 1: Collecting datasets from all evaluations"")
    click.echo(""="" * 80)

    all_uris = set()
    for eval_file in eval_files:
        try:
            eval_data = json.loads(eval_file.read_text())
            test_case = TestCase(**eval_data)
            if test_case.data_node:
                if isinstance(test_case.data_node, list):
                    all_uris.update(test_case.data_node)
                else:
                    all_uris.add(test_case.data_node)
        except Exception as e:
            click.echo(f""Warning: Failed to parse {eval_file}: {e}"")

    click.echo(f""Found {len(all_uris)} unique dataset(s) to download"")

    if all_uris:
        click.echo(""\n"" + ""="" * 80)
        click.echo(""STEP 2: Batch downloading datasets"")
        click.echo(""="" * 80)
        batch_download_datasets(list(all_uris))

    click.echo(""\n"" + ""="" * 80)
    click.echo(""STEP 3: Running evaluations"")
    click.echo(""="" * 80)

    if parallel > 1:
        click.echo(f""Running {len(eval_files)} evaluations with {parallel} parallel workers\n"")

        results = []
        completed = 0

        with ProcessPoolExecutor(max_workers=parallel) as executor:
            future_to_eval = {
                executor.submit(_run_single_eval, str(eval_file), agent, model, keep_workspace, run_id): eval_file
                for eval_file in eval_files
            }

            for future in as_completed(future_to_eval):
                eval_file = future_to_eval[future]
                completed += 1

                try:
                    result = future.result()
                    results.append(result)

                    status = ""✓ PASSED"" if result.get(""passed"") else ""✗ FAILED""
                    click.echo(f""[{completed}/{len(eval_files)}] {eval_file.name}: {status}"")

                except Exception as e:
                    click.echo(f""[{completed}/{len(eval_files)}] {eval_file.name}: ✗ ERROR: {e}"")
                    results.append({
                        ""eval"": eval_file.name,
                        ""passed"": False,
                        ""error"": str(e),
                    })
    else:
        results = []
        for i, eval_file in enumerate(eval_files, 1):
            click.echo(f""\n[{i}/{len(eval_files)}] Running: {eval_file.name}"")
            click.echo(""-"" * 80)

            start_time = time.time()

            try:
                runner = EvalRunner(eval_file, keep_workspace=keep_workspace, run_id=run_id)

                if agent == ""minisweagent"":
                    def agent_fn(task_prompt, work_dir):
                        return run_minisweagent_task(task_prompt, work_dir, model_name=model)
                elif agent == ""claudecode"":
                    def agent_fn(task_prompt, work_dir):
                        return run_claudecode_task(task_prompt, work_dir, model_name=model)
                else:
                    agent_fn = None

                result = runner.run(agent_function=agent_fn)
                duration = time.time() - start_time

                eval_output = {
                    ""eval"": eval_file.name,
                    ""passed"": result.get(""passed""),
                    ""test_id"": result.get(""test_id""),
                    ""model"": model,
                    ""timestamp"": datetime.utcnow().isoformat() + ""Z"",
                    ""duration_s"": round(duration, 2),
                }

                if ""metadata"" in result:
                    eval_output.update(result[""metadata""])

                results.append(eval_output)

                status = ""✓ PASSED"" if result.get(""passed"") else ""✗ FAILED""
                click.echo(f""Result: {status}"")

            except Exception as e:
                duration = time.time() - start_time
                click.echo(f""✗ ERROR: {e}"")
                results.append({
                    ""eval"": eval_file.name,
                    ""passed"": False,
                    ""error"": str(e),
                    ""model"": model,
                    ""timestamp"": datetime.utcnow().isoformat() + ""Z"",
                    ""duration_s"": round(duration, 2),
                })

    click.echo(""\n"" + ""="" * 80)
    click.echo(""BATCH RESULTS"")
    click.echo(""="" * 80)

    passed = sum(1 for r in results if r.get(""passed"") is True)
    failed = sum(1 for r in results if r.get(""passed"") is False)
    errors = sum(1 for r in results if ""error"" in r)

    durations = [r.get(""duration_s"", 0) for r in results if ""duration_s"" in r]
    avg_duration = sum(durations) / len(durations) if durations else 0
    total_duration = sum(durations)

    costs = [r.get(""total_cost"", 0) for r in results if r.get(""total_cost"") is not None]
    total_cost = sum(costs) if costs else None
    avg_cost = sum(costs) / len(costs) if costs else None

    steps = [r.get(""n_steps"", 0) for r in results if r.get(""n_steps"") is not None]
    total_steps = sum(steps) if steps else None
    avg_steps = sum(steps) / len(steps) if steps else None

    click.echo(f""Total: {len(results)} evaluations"")
    click.echo(f""Passed: {passed} ({passed/len(results)*100:.1f}%)"")
    click.echo(f""Failed: {failed} ({failed/len(results)*100:.1f}%)"")
    if errors:
        click.echo(f""Errors: {errors}"")
    click.echo(f""Average duration: {avg_duration:.1f}s"")
    click.echo(f""Total duration: {total_duration:.1f}s ({total_duration/60:.1f}m)"")
    if total_cost is not None:
        click.echo(f""Total cost: ${total_cost:.4f}"")
        click.echo(f""Average cost per eval: ${avg_cost:.4f}"")
    if total_steps is not None:
        click.echo(f""Total steps: {total_steps}"")
        click.echo(f""Average steps per eval: {avg_steps:.1f}"")
    if model:
        click.echo(f""Model: {model}"")

    if output:
        output_path = Path(output)
        output_path.mkdir(parents=True, exist_ok=True)

        metadata = {
            ""model"": model,
            ""timestamp"": datetime.utcnow().isoformat() + ""Z"",
            ""eval_dir"": str(eval_dir),
            ""total_evals"": len(results),
            ""passed"": passed,
            ""failed"": failed,
            ""errors"": errors,
            ""pass_rate"": round(passed/len(results)*100, 1) if results else 0,
            ""avg_duration_s"": round(avg_duration, 2),
            ""total_duration_s"": round(total_duration, 2),
        }

        if total_cost is not None:
            metadata[""total_cost""] = round(total_cost, 4)
            metadata[""avg_cost_per_eval""] = round(avg_cost, 4)

        if total_steps is not None:
            metadata[""total_steps""] = total_steps
            metadata[""avg_steps_per_eval""] = round(avg_steps, 1)

        batch_summary = {
            ""metadata"": metadata,
            ""results"": results
        }

        results_file = output_path / ""batch_results.json""
        results_file.write_text(json.dumps(batch_summary, indent=2))
        click.echo(f""\nResults saved to: {results_file}"")

@main.command()
@click.argument(""results_dir"", type=click.Path(exists=True))
@click.option(""--output"", ""-o"", default=""leaderboard.json"", help=""Output file"")
def leaderboard(results_dir, output):
    click.echo(f""Generating leaderboard from: {results_dir}"")
    click.echo(f""Output: {output}"")

    click.echo(""\nLeaderboard generation not yet implemented."")
    click.echo(""Results will be aggregated from batch evaluation runs."")

@main.command()
@click.argument(""eval_path"", type=click.Path(exists=True))
def validate(eval_path):
    click.echo(f""Validating evaluation: {eval_path}"")

    try:
        eval_path = Path(eval_path)
        eval_data = json.loads(eval_path.read_text())

        required_fields = [""id"", ""task""]
        missing = [f for f in required_fields if f not in eval_data]

        if missing:
            click.echo(f""❌ Missing required fields: {missing}"", err=True)
            return

        if ""grader"" in eval_data:
            grader_type = eval_data[""grader""].get(""type"")
            from spatialbench.graders import GRADER_REGISTRY

            if grader_type not in GRADER_REGISTRY:
                click.echo(f""❌ Unknown grader type: {grader_type}"", err=True)
                click.echo(f""Available graders: {list(GRADER_REGISTRY.keys())}"")
                return

        click.echo(""✓ Validation passed!"")
        click.echo(f""  ID: {eval_data['id']}"")
        click.echo(f""  Task: {eval_data['task'][:80]}..."")
        if ""grader"" in eval_data:
            click.echo(f""  Grader: {eval_data['grader'].get('type')}"")

    except json.JSONDecodeError as e:
        click.echo(f""❌ Invalid JSON: {e}"", err=True)
    except Exception as e:
        click.echo(f""❌ Validation error: {e}"", err=True)

@main.command(name=""list"")
@click.option(""--category"", ""-c"", help=""Filter by category"")
def list_evals(category):
    click.echo(""SpatialBench Evaluations"")
    click.echo(""="" * 50)

    from pathlib import Path
    import os

    package_dir = Path(__file__).parent.parent
    evals_dir = package_dir / ""evals""

    if not evals_dir.exists():
        click.echo(""No evaluations found"")
        return

    categories = [""qc"", ""preprocessing"", ""clustering"", ""cell_typing"", ""differential_expression"", ""spatial_analysis""]

    for cat in categories:
        if category and cat != category:
            continue

        cat_dir = evals_dir / cat
        if not cat_dir.exists():
            continue

        eval_files = list(cat_dir.glob(""*.json""))
        if not eval_files:
            continue

        click.echo(f""\n{cat.replace('_', ' ').title()} ({len(eval_files)})"")
        click.echo(""-"" * 50)

        for eval_file in sorted(eval_files):
            try:
                eval_data = json.loads(eval_file.read_text())
                click.echo(f""  • {eval_data['id']}"")
            except:
                click.echo(f""  • {eval_file.stem}"")

if __name__ == ""__main__"":
    main()
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/types.py",".py","926","26","from pydantic import BaseModel, Field

class EvalResult(BaseModel):
    score: float = Field(ge=0.0, le=1.0, description=""Score from 0.0 to 1.0"")
    passed: bool = Field(description=""Whether the eval passed"")
    reasoning: str = Field(description=""Detailed reasoning for the score"")
    successes: list[str] = Field(description=""List of things the agent did correctly"")
    failures: list[str] = Field(description=""List of things the agent failed to do or did incorrectly"")

class TestCase(BaseModel):
    id: str
    task: str
    data_node: str | list[str] | None = None
    grader: dict | None = None
    timeout: int | None = None
    download_timeout: int | None = None
    agent_timeout: int | None = None

class TestResult(BaseModel):
    test_id: str
    conversation_history: list[dict]
    notebook_state: dict
    duration_ms: float
    eval_result: EvalResult | None = None
    grader_result: dict | None = None
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/marker_gene.py",".py","11774","288","from spatialbench.graders.base import BinaryGrader, GraderResult

class MarkerGenePrecisionRecallGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        canonical_markers = config.get(""canonical_markers"", [])
        scoring = config.get(""scoring"", {})
        thresholds = scoring.get(""pass_thresholds"", {})
        precision_threshold = thresholds.get(""precision_at_k"", 0.60)
        recall_threshold = thresholds.get(""recall_at_k"", 0.50)

        if ""top_marker_genes"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: top_marker_genes"",
                agent_answer=agent_answer
            )

        predicted_genes = agent_answer[""top_marker_genes""]
        if not isinstance(predicted_genes, list):
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=f""top_marker_genes must be a list, got {type(predicted_genes).__name__}"",
                agent_answer=agent_answer
            )
        predicted_genes = [str(g) for g in predicted_genes]
        k = len(predicted_genes)

        canonical_set = set(str(gene).lower() for gene in canonical_markers)
        predicted_set = set(gene.lower() for gene in predicted_genes)

        true_positives = canonical_set & predicted_set
        false_positives = predicted_set - canonical_set
        false_negatives = canonical_set - predicted_set

        precision_at_k = len(true_positives) / k if k > 0 else 0.0
        recall_at_k = len(true_positives) / len(canonical_set) if len(canonical_set) > 0 else 0.0

        precision_pass = precision_at_k >= precision_threshold
        recall_pass = recall_at_k >= recall_threshold
        passed = precision_pass and recall_pass

        original_case_map = {gene.lower(): gene for gene in predicted_genes}
        canonical_case_map = {str(gene).lower(): str(gene) for gene in canonical_markers}

        true_positive_genes = [original_case_map.get(g, canonical_case_map.get(g, g)) for g in true_positives]
        false_positive_genes = [original_case_map.get(g, g) for g in false_positives]
        false_negative_genes = [canonical_case_map.get(g, g) for g in false_negatives]

        metrics = {
            ""k"": k,
            ""precision_at_k"": precision_at_k,
            ""recall_at_k"": recall_at_k,
            ""precision_threshold"": precision_threshold,
            ""recall_threshold"": recall_threshold,
            ""true_positives"": sorted(true_positive_genes),
            ""false_positives"": sorted(false_positive_genes),
            ""false_negatives"": sorted(false_negative_genes),
            ""num_true_positives"": len(true_positives),
            ""num_false_positives"": len(false_positives),
            ""num_false_negatives"": len(false_negatives),
            ""num_canonical_markers"": len(canonical_set),
            ""precision_pass"": precision_pass,
            ""recall_pass"": recall_pass,
        }

        reasoning = self._format_precision_recall_reasoning(
            k,
            precision_at_k,
            recall_at_k,
            precision_threshold,
            recall_threshold,
            true_positive_genes,
            false_positive_genes,
            false_negative_genes,
            precision_pass,
            recall_pass,
            passed
        )

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_precision_recall_reasoning(self, k, precision, recall,
                                          precision_threshold, recall_threshold,
                                          true_positives, false_positives, false_negatives,
                                          precision_pass, recall_pass, passed):
        lines = []
        lines.append(f""Marker Gene Precision/Recall: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")

        precision_check = ""✓"" if precision_pass else ""✗""
        lines.append(f""Precision@{k}: {precision:.3f} {precision_check} (threshold: {precision_threshold:.3f})"")

        recall_check = ""✓"" if recall_pass else ""✗""
        lines.append(f""Recall@{k}: {recall:.3f} {recall_check} (threshold: {recall_threshold:.3f})"")

        lines.append("""")
        lines.append(f""True Positives ({len(true_positives)}):"")
        if true_positives:
            for gene in sorted(true_positives):
                lines.append(f""  ✓ {gene}"")
        else:
            lines.append(""  None"")

        lines.append("""")
        lines.append(f""False Positives ({len(false_positives)}):"")
        if false_positives:
            for gene in sorted(false_positives):
                lines.append(f""  + {gene}"")
        else:
            lines.append(""  None"")

        lines.append("""")
        lines.append(f""False Negatives ({len(false_negatives)}):"")
        if false_negatives:
            for gene in sorted(false_negatives):
                lines.append(f""  - {gene}"")
        else:
            lines.append(""  None"")

        lines.append("""")
        lines.append(f""Result: {'PASS' if passed else 'FAIL'}"")
        if not passed:
            failures = []
            if not precision_pass:
                failures.append(f""Precision {precision:.3f} < {precision_threshold:.3f}"")
            if not recall_pass:
                failures.append(f""Recall {recall:.3f} < {recall_threshold:.3f}"")
            lines.append(f""Reasons: {'; '.join(failures)}"")

        return ""\n"".join(lines)

class MarkerGeneSeparationGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        scoring = config.get(""scoring"", {})
        thresholds = scoring.get(""pass_thresholds"", {})
        mean_auroc_threshold = thresholds.get(""mean_auroc"", 0.85)
        fraction_high_threshold = thresholds.get(""fraction_high"", 0.70)
        per_gene_cutoff = thresholds.get(""per_gene_cutoff"", 0.80)

        if ""per_gene_stats"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: per_gene_stats"",
                agent_answer=agent_answer
            )

        if ""mean_auroc"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: mean_auroc"",
                agent_answer=agent_answer
            )

        per_gene_stats = agent_answer[""per_gene_stats""]
        agent_mean_auroc = agent_answer[""mean_auroc""]

        if not isinstance(per_gene_stats, list):
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""per_gene_stats must be a list"",
                agent_answer=agent_answer
            )

        num_genes = len(per_gene_stats)
        if num_genes == 0:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""per_gene_stats is empty"",
                agent_answer=agent_answer
            )

        gene_aurocs = {}
        for stat in per_gene_stats:
            if not isinstance(stat, dict):
                return GraderResult(
                    passed=False,
                    metrics={},
                    reasoning=""Each element in per_gene_stats must be a dict with 'gene' and 'auroc'"",
                    agent_answer=agent_answer
                )
            if ""gene"" not in stat or ""auroc"" not in stat:
                return GraderResult(
                    passed=False,
                    metrics={},
                    reasoning=""Each element in per_gene_stats must have 'gene' and 'auroc' fields"",
                    agent_answer=agent_answer
                )
            gene_aurocs[stat[""gene""]] = stat[""auroc""]

        computed_mean_auroc = sum(gene_aurocs.values()) / len(gene_aurocs)

        high_auroc_genes = [gene for gene, auroc in gene_aurocs.items() if auroc >= per_gene_cutoff]
        low_auroc_genes = [gene for gene, auroc in gene_aurocs.items() if auroc < per_gene_cutoff]
        fraction_high = len(high_auroc_genes) / num_genes

        mean_auroc_pass = agent_mean_auroc >= mean_auroc_threshold
        fraction_high_pass = fraction_high >= fraction_high_threshold

        passed = mean_auroc_pass and fraction_high_pass

        metrics = {
            ""num_genes"": num_genes,
            ""mean_auroc_agent"": agent_mean_auroc,
            ""mean_auroc_computed"": computed_mean_auroc,
            ""mean_auroc_threshold"": mean_auroc_threshold,
            ""fraction_high"": fraction_high,
            ""fraction_high_threshold"": fraction_high_threshold,
            ""per_gene_cutoff"": per_gene_cutoff,
            ""num_high_auroc_genes"": len(high_auroc_genes),
            ""num_low_auroc_genes"": len(low_auroc_genes),
            ""high_auroc_genes"": sorted(high_auroc_genes),
            ""low_auroc_genes"": sorted(low_auroc_genes),
            ""mean_auroc_pass"": mean_auroc_pass,
            ""fraction_high_pass"": fraction_high_pass,
            ""per_gene_aurocs"": gene_aurocs,
        }

        reasoning = self._format_separation_reasoning(
            num_genes,
            agent_mean_auroc,
            computed_mean_auroc,
            mean_auroc_threshold,
            fraction_high,
            fraction_high_threshold,
            per_gene_cutoff,
            gene_aurocs,
            high_auroc_genes,
            low_auroc_genes,
            mean_auroc_pass,
            fraction_high_pass,
            passed
        )

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_separation_reasoning(self, num_genes, agent_mean, computed_mean,
                                     mean_threshold, fraction_high, fraction_threshold,
                                     per_gene_cutoff, gene_aurocs, high_genes, low_genes,
                                     mean_pass, fraction_pass, passed):
        lines = []
        lines.append(f""Marker Gene Separation: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")

        mean_check = ""✓"" if mean_pass else ""✗""
        lines.append(f""Mean AUROC: {agent_mean:.3f} {mean_check} (threshold: {mean_threshold:.3f})"")

        fraction_check = ""✓"" if fraction_pass else ""✗""
        lines.append(f""Fraction High AUROC (≥{per_gene_cutoff:.2f}): {fraction_high:.3f} ({len(high_genes)}/{num_genes}) {fraction_check} (threshold: {fraction_threshold:.3f})"")

        lines.append("""")
        lines.append(""Per-gene AUROC scores:"")
        for gene in sorted(gene_aurocs.keys(), key=lambda g: gene_aurocs[g], reverse=True):
            auroc = gene_aurocs[gene]
            check = ""✓"" if auroc >= per_gene_cutoff else ""✗""
            lines.append(f""  {check} {gene}: {auroc:.3f}"")

        if abs(agent_mean - computed_mean) > 0.001:
            lines.append("""")
            lines.append(f""Note: Agent reported mean {agent_mean:.3f}, computed mean is {computed_mean:.3f}"")

        lines.append("""")
        lines.append(f""Result: {'PASS' if passed else 'FAIL'}"")
        if not passed:
            failures = []
            if not mean_pass:
                failures.append(f""Mean AUROC {agent_mean:.3f} < {mean_threshold:.3f}"")
            if not fraction_pass:
                failures.append(f""Fraction high {fraction_high:.3f} < {fraction_threshold:.3f}"")
            lines.append(f""Reasons: {'; '.join(failures)}"")

        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/distribution.py",".py","5539","131","from spatialbench.graders.base import BinaryGrader, GraderResult

class DistributionComparisonGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        ground_truth = config.get(""ground_truth"", {})
        tolerances = config.get(""tolerances"", {})

        gt_total_cells = ground_truth.get(""total_cells"")
        gt_distribution = ground_truth.get(""cell_type_distribution"", {})

        total_cells_tolerance = tolerances.get(""total_cells"", {})
        pct_tolerance_config = tolerances.get(""cell_type_percentages"", {})
        pct_tolerance = pct_tolerance_config.get(""value"", 3.0)

        if ""cell_type_distribution"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: cell_type_distribution"",
                agent_answer=agent_answer
            )

        agent_total_cells = agent_answer.get(""total_cells"")
        agent_distribution = agent_answer[""cell_type_distribution""]

        metrics = {}
        all_pass = True
        failures = []

        if gt_total_cells is not None and agent_total_cells is not None:
            total_cells_tol_value = total_cells_tolerance.get(""value"", 0)
            total_cells_diff = abs(agent_total_cells - gt_total_cells)
            total_cells_pass = total_cells_diff <= total_cells_tol_value

            metrics[""total_cells_actual""] = agent_total_cells
            metrics[""total_cells_expected""] = gt_total_cells
            metrics[""total_cells_diff""] = total_cells_diff
            metrics[""total_cells_pass""] = total_cells_pass

            if not total_cells_pass:
                all_pass = False
                failures.append(f""total_cells: {agent_total_cells} vs {gt_total_cells} (diff: {total_cells_diff}, tolerance: {total_cells_tol_value})"")

        distribution_failures = []
        for cell_type, expected_pct in gt_distribution.items():
            if cell_type not in agent_distribution:
                all_pass = False
                failures.append(f""Missing cell type: {cell_type}"")
                distribution_failures.append(cell_type)
                metrics[f""{cell_type}_actual""] = None
                metrics[f""{cell_type}_expected""] = expected_pct
                metrics[f""{cell_type}_diff""] = None
                metrics[f""{cell_type}_pass""] = False
                continue

            actual_pct = agent_distribution[cell_type]
            diff = abs(actual_pct - expected_pct)
            within_tolerance = diff <= pct_tolerance

            metrics[f""{cell_type}_actual""] = actual_pct
            metrics[f""{cell_type}_expected""] = expected_pct
            metrics[f""{cell_type}_diff""] = diff
            metrics[f""{cell_type}_pass""] = within_tolerance

            if not within_tolerance:
                all_pass = False
                failures.append(f""{cell_type}: {actual_pct:.4f}% vs {expected_pct:.4f}% (diff: {diff:.4f}%, tolerance: {pct_tolerance}%)"")
                distribution_failures.append(cell_type)

        extra_types = set(agent_distribution.keys()) - set(gt_distribution.keys())
        if extra_types:
            metrics[""extra_cell_types""] = sorted(list(extra_types))
            failures.append(f""Extra cell types not in ground truth: {sorted(list(extra_types))}"")

        reasoning = self._format_distribution_reasoning(
            agent_total_cells,
            gt_total_cells,
            metrics.get(""total_cells_pass"", True),
            gt_distribution,
            agent_distribution,
            pct_tolerance,
            distribution_failures,
            extra_types,
            failures,
            all_pass
        )

        return GraderResult(
            passed=all_pass,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_distribution_reasoning(self, agent_total, gt_total, total_pass,
                                       gt_distribution, agent_distribution, pct_tolerance,
                                       distribution_failures, extra_types, failures, passed):
        lines = []
        lines.append(f""Distribution Comparison: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")

        if gt_total is not None and agent_total is not None:
            total_check = ""✓"" if total_pass else ""✗""
            lines.append(f""Total cells: {agent_total} vs {gt_total} {total_check}"")
            lines.append("""")

        lines.append(f""Cell type percentages (tolerance: ±{pct_tolerance}%):"")

        for cell_type in sorted(gt_distribution.keys()):
            expected = gt_distribution[cell_type]
            if cell_type not in agent_distribution:
                lines.append(f""  ✗ {cell_type}: MISSING vs {expected:.4f}%"")
            else:
                actual = agent_distribution[cell_type]
                diff = abs(actual - expected)
                within_tol = diff <= pct_tolerance
                check = ""✓"" if within_tol else ""✗""
                lines.append(f""  {check} {cell_type}: {actual:.4f}% vs {expected:.4f}% (diff: {diff:.4f}%)"")

        if extra_types:
            lines.append("""")
            lines.append(f""Extra cell types (not in ground truth): {sorted(list(extra_types))}"")

        if not passed:
            lines.append("""")
            lines.append(""Failures:"")
            for failure in failures:
                lines.append(f""  - {failure}"")

        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/numeric.py",".py","5052","109","from spatialbench.graders.base import BinaryGrader, GraderResult

class NumericToleranceGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        ground_truth = config.get(""ground_truth"", {})
        tolerances = config.get(""tolerances"", {})

        metrics = {}
        all_pass = True
        failures = []

        for field, expected_value in ground_truth.items():
            if field not in agent_answer:
                all_pass = False
                failures.append(f""Missing field: {field}"")
                continue

            actual_value = agent_answer[field]
            if actual_value is None:
                all_pass = False
                failures.append(f""{field}: got null/None value"")
                metrics[f""{field}_actual""] = None
                metrics[f""{field}_expected""] = expected_value
                metrics[f""{field}_error""] = float('inf')
                metrics[f""{field}_pass""] = False
                continue

            tolerance_config = tolerances.get(field, {""type"": ""absolute"", ""value"": 0})
            tolerance_type = tolerance_config.get(""type"", ""absolute"")
            tolerance_value = tolerance_config.get(""value"", 0)

            try:
                if tolerance_type == ""absolute"":
                    within_tolerance = abs(actual_value - expected_value) <= tolerance_value
                    error = abs(actual_value - expected_value)
                elif tolerance_type == ""relative"":
                    relative_error = abs(actual_value - expected_value) / abs(expected_value) if expected_value != 0 else float('inf')
                    within_tolerance = relative_error <= tolerance_value
                    error = relative_error
                elif tolerance_type == ""min"":
                    within_tolerance = actual_value >= expected_value
                    error = expected_value - actual_value if actual_value < expected_value else 0
                elif tolerance_type == ""max"":
                    within_tolerance = actual_value <= expected_value
                    error = actual_value - expected_value if actual_value > expected_value else 0
                else:
                    within_tolerance = False
                    error = float('inf')
            except TypeError:
                all_pass = False
                failures.append(f""{field}: invalid type {type(actual_value).__name__}, expected numeric"")
                metrics[f""{field}_actual""] = actual_value
                metrics[f""{field}_expected""] = expected_value
                metrics[f""{field}_error""] = float('inf')
                metrics[f""{field}_pass""] = False
                continue

            metrics[f""{field}_actual""] = actual_value
            metrics[f""{field}_expected""] = expected_value
            metrics[f""{field}_error""] = error
            metrics[f""{field}_pass""] = within_tolerance

            if not within_tolerance:
                all_pass = False
                if tolerance_type == ""min"":
                    failures.append(f""{field}: {actual_value} (minimum required: {expected_value})"")
                elif tolerance_type == ""max"":
                    failures.append(f""{field}: {actual_value} (maximum allowed: {expected_value})"")
                else:
                    failures.append(f""{field}: {actual_value} vs {expected_value} (error: {error:.2f}, tolerance: {tolerance_value})"")

        reasoning = self._format_numeric_reasoning(ground_truth, tolerances, agent_answer, metrics, failures, all_pass)

        return GraderResult(
            passed=all_pass,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_numeric_reasoning(self, ground_truth, tolerances, agent_answer, metrics, failures, passed):
        lines = []
        lines.append(f""Numeric Tolerance Check: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")

        for field in ground_truth.keys():
            if f""{field}_actual"" in metrics:
                actual = metrics[f""{field}_actual""]
                expected = metrics[f""{field}_expected""]
                error = metrics[f""{field}_error""]
                field_pass = metrics[f""{field}_pass""]
                check = ""✓"" if field_pass else ""✗""
                tolerance_config = tolerances.get(field, {})
                tolerance_type = tolerance_config.get(""type"", ""absolute"")
                if tolerance_type == ""min"":
                    lines.append(f""- {field}: {actual} (minimum: {expected}) {check}"")
                elif tolerance_type == ""max"":
                    lines.append(f""- {field}: {actual} (maximum: {expected}) {check}"")
                else:
                    lines.append(f""- {field}: {actual} vs {expected} (error: {error:.2f}) {check}"")

        if not passed:
            lines.append("""")
            lines.append(""Failures:"")
            for failure in failures:
                lines.append(f""  - {failure}"")

        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/label_set.py",".py","3309","92","from spatialbench.graders.base import BinaryGrader, GraderResult

class LabelSetJaccardGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        ground_truth_labels = set(config.get(""ground_truth_labels"", []))
        scoring = config.get(""scoring"", {})
        pass_threshold = scoring.get(""pass_threshold"", 0.90)

        if ""cell_types_predicted"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: cell_types_predicted"",
                agent_answer=agent_answer
            )

        predicted_labels = set(agent_answer[""cell_types_predicted""])

        intersection = ground_truth_labels & predicted_labels
        union = ground_truth_labels | predicted_labels

        jaccard_index = len(intersection) / len(union) if len(union) > 0 else 0.0

        passed = jaccard_index >= pass_threshold

        true_positives = intersection
        false_positives = predicted_labels - ground_truth_labels
        false_negatives = ground_truth_labels - predicted_labels

        metrics = {
            ""jaccard_index"": jaccard_index,
            ""pass_threshold"": pass_threshold,
            ""true_positives"": sorted(list(true_positives)),
            ""false_positives"": sorted(list(false_positives)),
            ""false_negatives"": sorted(list(false_negatives)),
            ""predicted_count"": len(predicted_labels),
            ""ground_truth_count"": len(ground_truth_labels),
        }

        reasoning = self._format_jaccard_reasoning(
            jaccard_index,
            pass_threshold,
            true_positives,
            false_positives,
            false_negatives,
            passed
        )

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_jaccard_reasoning(self, jaccard_index, threshold, true_positives, false_positives, false_negatives, passed):
        lines = []
        lines.append(f""Label Set Comparison: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")
        lines.append(f""Jaccard Index: {jaccard_index:.3f} (threshold: {threshold:.3f}) {'✓' if jaccard_index >= threshold else '✗'}"")
        lines.append("""")

        if true_positives:
            lines.append(f""Correct Labels ({len(true_positives)}):"")
            for label in sorted(true_positives):
                lines.append(f""  ✓ {label}"")
        else:
            lines.append(""Correct Labels: None"")

        lines.append("""")

        if false_positives:
            lines.append(f""Extra Labels ({len(false_positives)}):"")
            for label in sorted(false_positives):
                lines.append(f""  + {label}"")
        else:
            lines.append(""Extra Labels: None"")

        lines.append("""")

        if false_negatives:
            lines.append(f""Missing Labels ({len(false_negatives)}):"")
            for label in sorted(false_negatives):
                lines.append(f""  - {label}"")
        else:
            lines.append(""Missing Labels: None"")

        lines.append("""")
        lines.append(f""Result: {'PASS' if passed else 'FAIL'}"")

        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/multiple_choice.py",".py","1432","44","from spatialbench.graders.base import BinaryGrader, GraderResult


class MultipleChoiceGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        correct_answer = config.get(""correct_answer"", """").strip().upper()

        if ""answer"" not in agent_answer:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Agent answer missing required field: answer"",
                agent_answer=agent_answer
            )

        agent_choice = str(agent_answer[""answer""]).strip().upper()

        passed = agent_choice == correct_answer

        metrics = {
            ""correct_answer"": correct_answer,
            ""agent_answer"": agent_choice,
        }

        reasoning = self._format_reasoning(correct_answer, agent_choice, passed)

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_reasoning(self, correct, agent, passed):
        lines = []
        lines.append(f""Multiple Choice: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")
        if passed:
            lines.append(f""✓ Agent answered: {agent} (correct)"")
        else:
            lines.append(f""✗ Agent answered: {agent}"")
            lines.append(f""  Correct answer: {correct}"")
        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/__init__.py",".py","1214","31","from spatialbench.graders.base import BinaryGrader, GraderResult
from spatialbench.graders.numeric import NumericToleranceGrader
from spatialbench.graders.label_set import LabelSetJaccardGrader
from spatialbench.graders.distribution import DistributionComparisonGrader
from spatialbench.graders.marker_gene import MarkerGenePrecisionRecallGrader, MarkerGeneSeparationGrader
from spatialbench.graders.spatial import SpatialAdjacencyGrader
from spatialbench.graders.multiple_choice import MultipleChoiceGrader

GRADER_REGISTRY = {
    ""numeric_tolerance"": NumericToleranceGrader,
    ""label_set_jaccard"": LabelSetJaccardGrader,
    ""distribution_comparison"": DistributionComparisonGrader,
    ""marker_gene_precision_recall"": MarkerGenePrecisionRecallGrader,
    ""marker_gene_separation"": MarkerGeneSeparationGrader,
    ""spatial_adjacency"": SpatialAdjacencyGrader,
    ""multiple_choice"": MultipleChoiceGrader,
}

__all__ = [
    ""BinaryGrader"",
    ""GraderResult"",
    ""NumericToleranceGrader"",
    ""LabelSetJaccardGrader"",
    ""DistributionComparisonGrader"",
    ""MarkerGenePrecisionRecallGrader"",
    ""MarkerGeneSeparationGrader"",
    ""SpatialAdjacencyGrader"",
    ""MultipleChoiceGrader"",
    ""GRADER_REGISTRY"",
]
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/base.py",".py","1928","50","import json
import re
from dataclasses import dataclass

from spatialbench.types import TestResult

@dataclass
class GraderResult:
    passed: bool
    metrics: dict
    reasoning: str
    agent_answer: dict | None

class BinaryGrader:
    def evaluate(self, test_result: TestResult, config: dict) -> GraderResult:
        agent_answer = self.extract_answer_from_tags(test_result.conversation_history)
        if agent_answer is None:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=""Failed to extract answer from conversation history"",
                agent_answer=None
            )
        return self.evaluate_answer(agent_answer, config)

    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        raise NotImplementedError

    def extract_answer_from_tags(self, conversation: list[dict]) -> dict | None:
        for msg in reversed(conversation):
            if msg.get(""type"") != ""anthropic_message"" or msg.get(""role"") != ""assistant"":
                continue

            content = msg.get(""content"", [])
            for block in content:
                if isinstance(block, dict) and block.get(""type"") == ""tool_use"":
                    if block.get(""name"") == ""submit_response"":
                        tool_input = block.get(""input"", {})
                        summary = tool_input.get(""summary"", """")

                        match = re.search(r'<EVAL_ANSWER>(.*?)</EVAL_ANSWER>', summary, re.DOTALL)
                        if match:
                            json_str = match.group(1).strip()
                            try:
                                return json.loads(json_str)
                            except json.JSONDecodeError as e:
                                print(f""[grader] Failed to parse JSON from EVAL_ANSWER tags: {e}"")
                                return None
        return None
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/graders/spatial.py",".py","5338","126","from spatialbench.graders.base import BinaryGrader, GraderResult

class SpatialAdjacencyGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        scoring = config.get(""scoring"", {})
        thresholds = scoring.get(""pass_thresholds"", {})

        max_median_ic_to_pc = thresholds.get(""max_median_ic_to_pc_um"", 25.0)
        max_p90_ic_to_pc = thresholds.get(""max_p90_ic_to_pc_um"", 80.0)
        min_pct_within_15um = thresholds.get(""min_pct_ic_within_15um"", 60.0)
        min_pct_mixed_within_55um = thresholds.get(""min_pct_ic_mixed_within_55um"", 60.0)

        required_fields = [
            ""median_ic_to_pc_um"",
            ""p90_ic_to_pc_um"",
            ""pct_ic_within_15um"",
            ""pct_ic_mixed_within_55um"",
            ""adjacency_pass""
        ]

        missing = [f for f in required_fields if f not in agent_answer]
        if missing:
            return GraderResult(
                passed=False,
                metrics={},
                reasoning=f""Agent answer missing required fields: {missing}"",
                agent_answer=agent_answer
            )

        median_ic_to_pc = agent_answer[""median_ic_to_pc_um""]
        p90_ic_to_pc = agent_answer[""p90_ic_to_pc_um""]
        pct_within_15um = agent_answer[""pct_ic_within_15um""]
        pct_mixed_within_55um = agent_answer[""pct_ic_mixed_within_55um""]
        adjacency_pass = agent_answer[""adjacency_pass""]

        median_pass = median_ic_to_pc <= max_median_ic_to_pc
        p90_pass = p90_ic_to_pc <= max_p90_ic_to_pc
        within_15um_pass = pct_within_15um >= min_pct_within_15um
        mixed_55um_pass = pct_mixed_within_55um >= min_pct_mixed_within_55um

        passed = median_pass and p90_pass and within_15um_pass and mixed_55um_pass and adjacency_pass

        metrics = {
            ""median_ic_to_pc_um"": median_ic_to_pc,
            ""p90_ic_to_pc_um"": p90_ic_to_pc,
            ""pct_ic_within_15um"": pct_within_15um,
            ""pct_ic_mixed_within_55um"": pct_mixed_within_55um,
            ""adjacency_pass"": adjacency_pass,
            ""max_median_threshold"": max_median_ic_to_pc,
            ""max_p90_threshold"": max_p90_ic_to_pc,
            ""min_pct_15um_threshold"": min_pct_within_15um,
            ""min_pct_55um_threshold"": min_pct_mixed_within_55um,
            ""median_pass"": median_pass,
            ""p90_pass"": p90_pass,
            ""within_15um_pass"": within_15um_pass,
            ""mixed_55um_pass"": mixed_55um_pass,
        }

        reasoning = self._format_spatial_adjacency_reasoning(
            median_ic_to_pc,
            p90_ic_to_pc,
            pct_within_15um,
            pct_mixed_within_55um,
            adjacency_pass,
            max_median_ic_to_pc,
            max_p90_ic_to_pc,
            min_pct_within_15um,
            min_pct_mixed_within_55um,
            median_pass,
            p90_pass,
            within_15um_pass,
            mixed_55um_pass,
            passed
        )

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )

    def _format_spatial_adjacency_reasoning(self, median_dist, p90_dist, pct_15um, pct_55um,
                                           adjacency_pass, max_median, max_p90, min_15um, min_55um,
                                           median_pass, p90_pass, within_15_pass, mixed_55_pass, passed):
        lines = []
        lines.append(f""Spatial Adjacency Analysis: {'PASS' if passed else 'FAIL'}"")
        lines.append("""")

        lines.append(""IC→PC Distance Metrics:"")
        median_check = ""✓"" if median_pass else ""✗""
        lines.append(f""  {median_check} Median distance: {median_dist:.2f} µm (threshold: ≤{max_median:.2f} µm)"")

        p90_check = ""✓"" if p90_pass else ""✗""
        lines.append(f""  {p90_check} 90th percentile: {p90_dist:.2f} µm (threshold: ≤{max_p90:.2f} µm)"")

        lines.append("""")
        lines.append(""IC Proximity to PC:"")
        within_15_check = ""✓"" if within_15_pass else ""✗""
        lines.append(f""  {within_15_check} IC within 15 µm: {pct_15um:.1f}% (threshold: ≥{min_15um:.1f}%)"")

        mixed_55_check = ""✓"" if mixed_55_pass else ""✗""
        lines.append(f""  {mixed_55_check} IC with PC within 55 µm: {pct_55um:.1f}% (threshold: ≥{min_55um:.1f}%)"")

        lines.append("""")
        adjacency_check = ""✓"" if adjacency_pass else ""✗""
        lines.append(f""Agent adjacency assessment: {adjacency_check} {adjacency_pass}"")

        lines.append("""")
        lines.append(f""Result: {'PASS' if passed else 'FAIL'}"")
        if not passed:
            failures = []
            if not median_pass:
                failures.append(f""Median {median_dist:.2f} > {max_median:.2f} µm"")
            if not p90_pass:
                failures.append(f""P90 {p90_dist:.2f} > {max_p90:.2f} µm"")
            if not within_15_pass:
                failures.append(f""Within 15 µm {pct_15um:.1f}% < {min_15um:.1f}%"")
            if not mixed_55_pass:
                failures.append(f""Within 55 µm {pct_55um:.1f}% < {min_55um:.1f}%"")
            if not adjacency_pass:
                failures.append(""Agent marked adjacency_pass as false"")
            lines.append(f""Reasons: {'; '.join(failures)}"")

        return ""\n"".join(lines)
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/harness/minisweagent.py",".py","8628","253","import io
import json
import os
import re
import signal
import subprocess
import sys
from pathlib import Path

OPERATION_TIMEOUT = 300
EVAL_TIMEOUT = 600


class AgentTimeoutError(Exception):
    pass


def _timeout_handler(signum, frame):
    raise AgentTimeoutError(""Agent exceeded time limit"")


class StreamingLogFile:
    def __init__(self, file_path):
        self.file_path = file_path
        self.buffer = io.StringIO()

    def write(self, data):
        self.buffer.write(data)
        with open(self.file_path, 'a') as f:
            f.write(data)
            f.flush()

    def flush(self):
        pass

    def getvalue(self):
        return self.buffer.getvalue()


def _patch_agent_for_progress(log_file, agent_class):
    original_add_message = agent_class.add_message

    def patched_add_message(self, role, content, **kwargs):
        original_add_message(self, role, content, **kwargs)

        with open(log_file, 'a') as f:
            if role == ""assistant"":
                step_num = len([m for m in self.messages if m.get(""role"") == ""assistant""])
                f.write(f""\n[Step {step_num}]\n"")
                f.write(f""Assistant: {content}\n"")
            elif role == ""user"" and len(self.messages) > 2:
                f.write(f""Observation: {content}\n"")
            f.flush()

    agent_class.add_message = patched_add_message


def run_minisweagent_task(
    task_prompt: str,
    work_dir: Path,
    model_name: str | None = None,
    agent_config: dict | None = None,
    operation_timeout: int = OPERATION_TIMEOUT,
    eval_timeout: int = EVAL_TIMEOUT,
) -> dict:
    from minisweagent.agents.default import DefaultAgent, AgentConfig, FormatError
    from minisweagent.environments.local import LocalEnvironment
    from minisweagent.models import get_model
    import re

    class FlexibleAgent(DefaultAgent):
        def parse_action(self, response: dict) -> dict:
            content = response[""content""]
            actions = re.findall(r""```(?:bash|sh|shell)?\s*\n(.*?)\n?```"", content, re.DOTALL)
            if len(actions) == 1:
                return {""action"": actions[0].strip(), **response}
            raise FormatError(self.render_template(self.config.format_error_template, actions=actions))

        def has_finished(self, output: dict):
            from minisweagent.agents.default import Submitted
            full_output = output.get(""output"", """")
            for marker in [""COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT"", ""MINI_SWE_AGENT_FINAL_OUTPUT""]:
                if marker in full_output:
                    idx = full_output.find(marker)
                    rest = full_output[idx + len(marker):].strip()
                    raise Submitted(rest)

    original_dir = os.getcwd()

    agent_log_file = work_dir / ""agent_output.log""
    _patch_agent_for_progress(agent_log_file, FlexibleAgent)
    if agent_log_file.exists():
        agent_log_file.unlink()

    captured_output = StreamingLogFile(agent_log_file)
    original_stdout = sys.stdout
    original_stderr = sys.stderr

    class TeeOutput:
        def __init__(self, *streams):
            self.streams = streams

        def write(self, data):
            for stream in self.streams:
                stream.write(data)
                if hasattr(stream, 'flush'):
                    stream.flush()

        def flush(self):
            for stream in self.streams:
                if hasattr(stream, 'flush'):
                    stream.flush()

    agent = None
    timed_out = False
    try:
        os.chdir(str(work_dir))

        sys.stdout = TeeOutput(original_stdout, captured_output)
        sys.stderr = TeeOutput(original_stderr, captured_output)

        enhanced_prompt = _enhance_prompt_with_local_files(task_prompt, work_dir)

        enhanced_prompt += """"""

CRITICAL INSTRUCTIONS:
1. Do NOT wrap your code in try/except blocks. Let errors propagate so you can see them and fix them in subsequent steps.
2. You must write eval_answer.json BEFORE printing the completion signal.
3. Correct order: Perform analysis -> Write eval_answer.json -> Print 'COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT' as your FINAL line of output.""""""

        if model_name:
            os.environ['MSWEA_MODEL_NAME'] = model_name

        model = get_model()
        env = LocalEnvironment(timeout=operation_timeout)

        if agent_config:
            agent = FlexibleAgent(model, env, step_limit=100, **agent_config)
        else:
            agent = FlexibleAgent(model, env, step_limit=100)

        old_handler = signal.signal(signal.SIGALRM, _timeout_handler)
        signal.alarm(eval_timeout)

        try:
            agent.run(enhanced_prompt)
        except AgentTimeoutError:
            timed_out = True
            print(f""\nAgent timed out after {eval_timeout} seconds"")
        except Exception as e:
            if ""Submitted"" in str(type(e).__name__):
                pass
            else:
                raise
        finally:
            signal.alarm(0)
            signal.signal(signal.SIGALRM, old_handler)

            sys.stdout = original_stdout
            sys.stderr = original_stderr

            print(f""Agent output saved to: {agent_log_file}"")

            if hasattr(agent, ""messages""):
                trajectory_file = work_dir / ""trajectory.json""
                trajectory_data = {
                    ""messages"": agent.messages,
                    ""actions"": getattr(agent, ""actions"", [])
                }
                trajectory_file.write_text(json.dumps(trajectory_data, indent=2))
                print(f""Agent trajectory saved to: {trajectory_file}"")
                print(f""  Total message exchanges: {len(agent.messages)}"")

        eval_answer_file = work_dir / ""eval_answer.json""
        agent_answer = None
        error_details = None

        if not eval_answer_file.exists():
            agent_log_file = work_dir / ""agent_output.log""
            log_tail = """"
            if agent_log_file.exists():
                log_content = agent_log_file.read_text()
                log_tail = log_content[-1000:]

            trajectory_info = """"
            if hasattr(agent, ""messages""):
                trajectory_info = f""Agent had {len(agent.messages)} message exchanges.""

            error_msg = ""Agent timed out"" if timed_out else ""Agent did not create eval_answer.json""
            error_details = {
                ""error"": error_msg,
                ""timed_out"": timed_out,
                ""trajectory_info"": trajectory_info,
                ""log_tail"": log_tail
            }
            print(f""\nWarning: {error_msg}. {trajectory_info}"")
        else:
            try:
                agent_answer = json.loads(eval_answer_file.read_text())
            except json.JSONDecodeError as e:
                error_details = {
                    ""error"": f""Failed to parse eval_answer.json: {e}"",
                    ""file_contents"": eval_answer_file.read_text()[:500]
                }
                print(f""\nWarning: Failed to parse eval_answer.json: {e}"")

        metadata = {}
        if hasattr(agent, ""model""):
            metadata[""total_cost""] = getattr(agent.model, ""cost"", None)
            metadata[""n_steps""] = getattr(agent.model, ""n_calls"", None)
        if hasattr(agent, ""messages""):
            metadata[""n_messages""] = len(agent.messages)
        if timed_out:
            metadata[""timed_out""] = True
            metadata[""eval_timeout_seconds""] = eval_timeout
        if error_details:
            metadata[""error_details""] = error_details

        return {""answer"": agent_answer, ""metadata"": metadata}

    finally:
        os.chdir(original_dir)


def _enhance_prompt_with_local_files(task_prompt: str, work_dir: Path) -> str:
    contextual_data_match = re.search(r'<ContextualNodeData>(.*?)</ContextualNodeData>', task_prompt, re.DOTALL)

    if not contextual_data_match:
        return task_prompt

    try:
        contextual_data = json.loads(contextual_data_match.group(1))
    except json.JSONDecodeError:
        return task_prompt

    local_files = []
    for item in contextual_data:
        if 'local_path' in item:
            local_files.append(item['local_path'])

    if not local_files:
        return task_prompt

    file_list = ""\n"".join([f""- {f}"" for f in local_files])

    enhancement = f""\n\nThe following data files are available in your current working directory:\n{file_list}\n\nUse these local filenames to access the data.\n""

    parts = task_prompt.split('<ContextualNodeData>')
    if len(parts) == 2:
        return parts[0] + enhancement + '<ContextualNodeData>' + parts[1]

    return task_prompt
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/harness/claudecode.py",".py","6573","201","import json
import os
import subprocess
import time
from pathlib import Path

EVAL_TIMEOUT = 600

MODEL_MAP = {
    ""anthropic/claude-opus-4-5"": ""opus"",
    ""anthropic/claude-sonnet-4-5"": ""sonnet"",
    ""anthropic/claude-sonnet-4"": ""claude-sonnet-4-20250514"",
    ""anthropic/claude-opus-4"": ""claude-opus-4-20250514"",
    ""anthropic/claude-haiku-3-5"": ""haiku"",
}


def run_claudecode_task(
    task_prompt: str,
    work_dir: Path,
    model_name: str | None = None,
    eval_timeout: int = EVAL_TIMEOUT,
) -> dict:
    agent_log_file = work_dir / ""agent_output.log""
    if agent_log_file.exists():
        agent_log_file.unlink()

    enhanced_prompt = _enhance_prompt_with_local_files(task_prompt, work_dir)
    enhanced_prompt += """"""

CRITICAL: You must write eval_answer.json BEFORE signaling completion.
Correct order: 1) Perform analysis 2) Write eval_answer.json with your answer 3) Exit""""""

    cmd = [""claude"", ""--print"", ""--dangerously-skip-permissions"", ""--verbose"", ""--output-format"", ""stream-json""]

    if model_name:
        claude_model = MODEL_MAP.get(model_name, model_name)
        cmd.extend([""--model"", claude_model])

    run_as_claude_user = os.geteuid() == 0
    if run_as_claude_user:
        import pwd
        import shutil
        import stat
        try:
            pwd.getpwnam(""claude"")
            home_dir = Path.home()
            current_mode = home_dir.stat().st_mode
            home_dir.chmod(current_mode | stat.S_IXOTH)
            spatialbench_dir = home_dir / "".spatialbench""
            if spatialbench_dir.exists():
                shutil.chown(spatialbench_dir, user=""claude"", group=""claude"")
                for item in spatialbench_dir.rglob(""*""):
                    try:
                        shutil.chown(item, user=""claude"", group=""claude"")
                    except PermissionError:
                        pass
        except KeyError:
            run_as_claude_user = False

    env = os.environ.copy()

    start_time = time.time()
    timed_out = False
    claude_result = None
    trajectory = []

    try:
        if run_as_claude_user:
            env_vars = [f""{k}={v}"" for k, v in env.items() if k.endswith(""_API_KEY"")]
            cmd = [""runuser"", ""-u"", ""claude"", ""--"", ""env""] + env_vars + cmd

        process = subprocess.Popen(
            cmd,
            stdin=subprocess.PIPE,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            cwd=str(work_dir),
            env=env,
            text=True,
        )

        try:
            stdout, stderr = process.communicate(
                input=enhanced_prompt,
                timeout=eval_timeout
            )

            with open(agent_log_file, 'w') as log_file:
                log_file.write(stdout)
                if stderr:
                    log_file.write(f""\n\nSTDERR:\n{stderr}"")

            for line in stdout.strip().split('\n'):
                if line:
                    try:
                        event = json.loads(line)
                        trajectory.append(event)
                        if event.get(""type"") == ""result"":
                            claude_result = event
                    except json.JSONDecodeError:
                        pass

        except subprocess.TimeoutExpired:
            timed_out = True
            process.kill()
            stdout, stderr = process.communicate()
            with open(agent_log_file, 'w') as log_file:
                log_file.write(stdout)
                log_file.write(f""\n\nAgent timed out after {eval_timeout} seconds"")

    except Exception as e:
        with open(agent_log_file, 'a') as f:
            f.write(f""\nError running claude: {e}"")

    duration = time.time() - start_time
    print(f""Agent output saved to: {agent_log_file}"")

    if trajectory:
        trajectory_file = work_dir / ""trajectory.json""
        trajectory_file.write_text(json.dumps(trajectory, indent=2))
        print(f""Trajectory saved to: {trajectory_file}"")

    eval_answer_file = work_dir / ""eval_answer.json""
    agent_answer = None
    error_details = None

    if not eval_answer_file.exists():
        log_tail = """"
        if agent_log_file.exists():
            log_content = agent_log_file.read_text()
            log_tail = log_content[-1000:]

        error_msg = ""Agent timed out"" if timed_out else ""Agent did not create eval_answer.json""
        error_details = {
            ""error"": error_msg,
            ""timed_out"": timed_out,
            ""log_tail"": log_tail
        }
        print(f""\nWarning: {error_msg}"")
    else:
        try:
            agent_answer = json.loads(eval_answer_file.read_text())
        except json.JSONDecodeError as e:
            error_details = {
                ""error"": f""Failed to parse eval_answer.json: {e}"",
                ""file_contents"": eval_answer_file.read_text()[:500]
            }
            print(f""\nWarning: Failed to parse eval_answer.json: {e}"")

    metadata = {
        ""duration_s"": round(duration, 2),
        ""model"": model_name,
    }
    if claude_result:
        metadata[""total_cost""] = claude_result.get(""total_cost_usd"")
        metadata[""n_turns""] = claude_result.get(""num_turns"")
        metadata[""session_id""] = claude_result.get(""session_id"")
        metadata[""usage""] = claude_result.get(""usage"")
    if timed_out:
        metadata[""timed_out""] = True
        metadata[""eval_timeout_seconds""] = eval_timeout
    if error_details:
        metadata[""error_details""] = error_details

    return {""answer"": agent_answer, ""metadata"": metadata}


def _enhance_prompt_with_local_files(task_prompt: str, work_dir: Path) -> str:
    import re
    contextual_data_match = re.search(
        r'<ContextualNodeData>(.*?)</ContextualNodeData>',
        task_prompt,
        re.DOTALL
    )

    if not contextual_data_match:
        return task_prompt

    try:
        contextual_data = json.loads(contextual_data_match.group(1))
    except json.JSONDecodeError:
        return task_prompt

    local_files = []
    for item in contextual_data:
        if 'local_path' in item:
            local_files.append(item['local_path'])

    if not local_files:
        return task_prompt

    file_list = ""\n"".join([f""- {f}"" for f in local_files])
    enhancement = f""\n\nThe following data files are available in your current working directory:\n{file_list}\n\nUse these local filenames to access the data.\n""

    parts = task_prompt.split('<ContextualNodeData>')
    if len(parts) == 2:
        return parts[0] + enhancement + '<ContextualNodeData>' + parts[1]

    return task_prompt
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/harness/__init__.py",".py","415","7","from spatialbench.harness.runner import EvalRunner
from spatialbench.harness.utils import download_data, setup_workspace, batch_download_datasets
from spatialbench.harness.minisweagent import run_minisweagent_task
from spatialbench.harness.claudecode import run_claudecode_task

__all__ = [""EvalRunner"", ""download_data"", ""setup_workspace"", ""batch_download_datasets"", ""run_minisweagent_task"", ""run_claudecode_task""]
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/harness/runner.py",".py","6042","160","import json
from pathlib import Path

from spatialbench.types import TestCase, TestResult, EvalResult
from spatialbench.graders import GRADER_REGISTRY
from spatialbench.harness.utils import download_data, setup_workspace, cleanup_workspace

class EvalRunner:
    def __init__(self, eval_path: str | Path, keep_workspace: bool = False, run_id: str | None = None):
        self.eval_path = Path(eval_path)
        self.keep_workspace = keep_workspace
        self.run_id = run_id

        if not self.eval_path.exists():
            raise FileNotFoundError(f""Eval file not found: {self.eval_path}"")

        eval_data = json.loads(self.eval_path.read_text())
        self.test_case = TestCase(**eval_data)

    def run(self, agent_function=None):
        print(""="" * 80)
        print(f""Running SpatialBench evaluation: {self.test_case.id}"")
        print(""="" * 80)

        print(""\nTask:"")
        print(""-"" * 80)
        print(self.test_case.task)
        print(""-"" * 80)

        work_dir = setup_workspace(self.test_case.id, self.run_id)
        print(f""\nWorking directory: {work_dir}"")

        print(""\n"" + ""="" * 80)
        print(""Staging data files..."")
        print(""="" * 80)

        contextual_data = download_data(self.test_case.data_node, work_dir)

        data_context = """"
        if contextual_data:
            data_context = f""\n\nHere is the context of the selected nodes the user would like to use: <ContextualNodeData>{json.dumps(contextual_data)}</ContextualNodeData>""

        task_prompt = f""""""{self.test_case.task}

IMPORTANT: When you have completed this task:
1. Write your final answer as a JSON object to a file named `eval_answer.json`
2. The file should contain ONLY the JSON object with the required fields
3. After writing the file, you have completed the task

Example eval_answer.json:
{{
  ""field1"": value1,
  ""field2"": value2
}}
{data_context}""""""

        print(""\n"" + ""="" * 80)
        print(""Running agent on task..."")
        print(""="" * 80)

        agent_answer = None
        agent_metadata = {}

        if agent_function is None:
            print(""\nNo agent function provided. To run this eval, pass an agent_function that:"")
            print(""  1. Takes (task_prompt: str, work_dir: Path) as arguments"")
            print(""  2. Returns the parsed agent answer dict"")
            print(f""\nExample:"")
            print(f""  def my_agent(task, work_dir):"")
            print(f""      # Run your agent"")
            print(f""      # Agent should write eval_answer.json to work_dir"")
            print(f""      answer_file = work_dir / 'eval_answer.json'"")
            print(f""      return json.loads(answer_file.read_text())"")
            print(f""\n  runner = EvalRunner(eval_path)"")
            print(f""  runner.run(agent_function=my_agent)"")
        else:
            try:
                result = agent_function(task_prompt, work_dir)

                if isinstance(result, dict) and ""answer"" in result:
                    agent_answer = result[""answer""]
                    agent_metadata = result.get(""metadata"", {})
                else:
                    agent_answer = result

                print(""\nAgent completed successfully"")
            except Exception as e:
                print(f""\nAgent error: {e}"")
                import traceback
                traceback.print_exc()

        eval_answer_path = work_dir / ""eval_answer.json""
        if agent_answer is None and eval_answer_path.exists():
            try:
                agent_answer = json.loads(eval_answer_path.read_text())
                print(f""Loaded agent answer from eval_answer.json"")
            except json.JSONDecodeError as e:
                print(f""Warning: Failed to parse eval_answer.json: {e}"")

        grader_result = None
        if self.test_case.grader and agent_answer is not None:
            print(""\n"" + ""="" * 80)
            print(""Running grader..."")
            print(""="" * 80)

            grader_type = self.test_case.grader.get(""type"")
            grader_config = self.test_case.grader.get(""config"", {})

            if grader_type in GRADER_REGISTRY:
                grader_cls = GRADER_REGISTRY[grader_type]
                grader = grader_cls()
                try:
                    grader_result = grader.evaluate_answer(agent_answer, grader_config)
                except Exception as e:
                    from spatialbench.graders.base import GraderResult
                    import traceback
                    grader_result = GraderResult(
                        passed=False,
                        metrics={""grader_error"": str(e)},
                        reasoning=f""Grader failed due to malformed agent output: {e}\n\n{traceback.format_exc()}"",
                        agent_answer=agent_answer
                    )

                print(f""\n{'✓ EVAL PASSED' if grader_result.passed else '✗ EVAL FAILED'}"")
                print(""\nGrader reasoning:"")
                print(""-"" * 80)
                print(grader_result.reasoning)
                print(""-"" * 80)

                if grader_result.metrics:
                    print(""\nMetrics:"")
                    for key, value in grader_result.metrics.items():
                        if isinstance(value, (list, dict)):
                            continue
                        print(f""   {key}: {value}"")
            else:
                print(f""\nWarning: Unknown grader type '{grader_type}'"")

        print(""\n"" + ""="" * 80)
        print(""Cleanup..."")
        print(""="" * 80)

        cleanup_workspace(work_dir, keep=self.keep_workspace)

        if self.keep_workspace:
            print(f""\nTo inspect results:"")
            print(f""  cd {work_dir}"")

        result_dict = {
            ""test_id"": self.test_case.id,
            ""agent_answer"": agent_answer,
            ""grader_result"": grader_result,
            ""passed"": grader_result.passed if grader_result else None,
        }

        if agent_metadata:
            result_dict[""metadata""] = agent_metadata

        return result_dict
","Python"
"Cell biology","latchbio/spatialbench","spatialbench/harness/utils.py",".py","3807","128","import hashlib
import json
import os
import subprocess
from pathlib import Path

def get_project_root():
    current = Path(__file__).resolve()
    while current != current.parent:
        if (current / ""pyproject.toml"").exists():
            return current
        current = current.parent
    return Path.cwd()

def get_cache_dir():
    project_root = get_project_root()
    cache_dir = project_root / "".spatialbench"" / ""cache""
    cache_dir.mkdir(parents=True, exist_ok=True)
    return cache_dir

def get_cache_manifest():
    cache_dir = get_cache_dir()
    manifest_file = cache_dir / ""manifest.json""

    if manifest_file.exists():
        return json.loads(manifest_file.read_text())
    return {}

def save_cache_manifest(manifest: dict):
    cache_dir = get_cache_dir()
    manifest_file = cache_dir / ""manifest.json""
    manifest_file.write_text(json.dumps(manifest, indent=2))

def get_cache_key(uri: str) -> str:
    uri_hash = hashlib.sha256(uri.encode()).hexdigest()[:16]
    filename = Path(uri).name
    return f""{uri_hash}__{filename}""

def download_single_dataset(uri: str, show_progress: bool = True) -> Path:
    cache_dir = get_cache_dir()
    manifest = get_cache_manifest()

    if uri in manifest:
        cached_file = cache_dir / manifest[uri]
        if cached_file.exists():
            if show_progress:
                print(f""Using cached: {Path(uri).name}"")
            return cached_file

    cache_key = get_cache_key(uri)
    cached_file = cache_dir / cache_key

    if show_progress:
        print(f""Downloading: {uri}"")
    subprocess.run(
        [""latch"", ""cp"", uri, str(cached_file)],
        check=True,
        capture_output=True
    )
    if show_progress:
        print(f""Cached as: {cache_key}"")

    manifest[uri] = cache_key
    save_cache_manifest(manifest)

    return cached_file

def download_data(data_node: str | list[str], work_dir: Path) -> list[dict]:
    data_nodes = data_node if isinstance(data_node, list) else ([data_node] if data_node else [])

    contextual_data = []

    for node in data_nodes:
        cached_file = download_single_dataset(node)
        data_filename = Path(node).name

        target_file = work_dir / data_filename
        if target_file.exists():
            target_file.unlink()
        os.symlink(cached_file, target_file)
        print(f""Linked: {data_filename} -> workspace"")

        contextual_data.append({
            ""type"": ""File"",
            ""path"": node,
            ""local_path"": data_filename,
            ""id"": node.replace(""latch:///"", """").replace("".csv"", """").replace("".h5ad"", """"),
        })

    return contextual_data

def batch_download_datasets(uris: list[str], show_progress: bool = True):
    if show_progress and uris:
        print(f""Preparing to download {len(uris)} unique dataset(s)..."")
        print(""="" * 80)

    for i, uri in enumerate(uris, 1):
        if show_progress:
            print(f""[{i}/{len(uris)}] "", end="""")
        download_single_dataset(uri, show_progress=show_progress)

    if show_progress and uris:
        print(""="" * 80)
        print(f""Downloaded/verified {len(uris)} dataset(s)"")
        print()

def setup_workspace(eval_id: str, run_id: str | None = None) -> Path:
    project_root = get_project_root()
    if run_id:
        work_dir = project_root / "".spatialbench"" / ""workspace"" / run_id / eval_id
    else:
        work_dir = project_root / "".spatialbench"" / ""workspace"" / eval_id

    if work_dir.exists():
        import shutil
        shutil.rmtree(work_dir)
    work_dir.mkdir(parents=True)

    return work_dir

def cleanup_workspace(work_dir: Path, keep: bool = False):
    if keep:
        print(f""Workspace preserved at: {work_dir}"")
    else:
        import shutil
        shutil.rmtree(work_dir)
        print(f""Workspace deleted: {work_dir}"")
","Python"
"Cell biology","latchbio/spatialbench","examples/run_with_minisweagent.py",".py","1487","50","#!/usr/bin/env python3

import sys
from pathlib import Path

from spatialbench import EvalRunner, run_minisweagent_task

def main():
    if len(sys.argv) < 2:
        print(""Usage: python run_with_minisweagent.py <eval_file> [--keep-workspace]"")
        print(""\nExample:"")
        print(""  python run_with_minisweagent.py evals/qc/seeker_qc_basic.json"")
        print(""  python run_with_minisweagent.py evals/clustering/xenium_leiden.json --keep-workspace"")
        sys.exit(1)

    eval_file = sys.argv[1]
    keep_workspace = ""--keep-workspace"" in sys.argv

    print(f""Running evaluation: {eval_file}"")
    print(f""Using mini-swe-agent"")
    print()

    runner = EvalRunner(eval_file, keep_workspace=keep_workspace)
    result = runner.run(agent_function=run_minisweagent_task)

    print(""\n"" + ""="" * 80)
    print(""EVALUATION RESULT"")
    print(""="" * 80)

    if result.get(""passed""):
        print(""✓ PASSED"")
    elif result.get(""passed"") is False:
        print(""✗ FAILED"")
    else:
        print(""⚠ NO GRADER (evaluation ran but was not graded)"")

    if result.get(""agent_answer""):
        print(""\nAgent Answer:"")
        import json
        print(json.dumps(result[""agent_answer""], indent=2))

    if result.get(""grader_result""):
        print(""\nGrader Metrics:"")
        for key, value in result[""grader_result""].metrics.items():
            if not isinstance(value, (list, dict)):
                print(f""  {key}: {value}"")

if __name__ == ""__main__"":
    main()
","Python"
"Cell biology","latchbio/spatialbench","docs/specification.md",".md","6941","298","# Evaluation Specification

This document defines the standard format for SpatialBench evaluations.

## JSON Schema

Every evaluation is defined as a JSON file with the following structure:

```json
{
  ""id"": ""string"",
  ""task"": ""string"",
  ""data_node"": ""string | array<string> | null"",
  ""grader"": {
    ""type"": ""string"",
    ""config"": {}
  },
  ""timeout"": ""number (optional)"",
  ""download_timeout"": ""number (optional)"",
  ""agent_timeout"": ""number (optional)""
}
```

## Field Descriptions

### `id` (required)

Unique identifier for the evaluation.

**Format**: `platform_task_description_version`

**Examples**:
- `xenium_qc_basic`
- `atlasxomics_clustering_coarse_v1`
- `vizgen_cell_typing_astrocyte_v2`

**Best Practices**:
- Use lowercase with underscores
- Include platform name
- Add version suffix for iterations (`_v1`, `_v2`)
- Keep concise but descriptive

### `task` (required)

Natural language description of the analysis task.

**Guidelines**:
- Be specific and unambiguous
- Specify expected output format
- Include exact field names for JSON output
- Provide necessary biological context
- Mention any specific parameters or methods

**Example**:

```json
{
  ""task"": ""Perform Leiden clustering on this AtlasXomics ATAC-seq dataset. Use resolution=1.0 and ensure clusters are independent of sample batch (AMI with sample < 0.3). Return JSON with fields: num_clusters (int), cluster_labels (list of unique cluster names), ami_with_sample (float).""
}
```

### `data_node` (optional)

Path(s) to dataset file(s) in cloud storage.

**Single file**:
```json
{
  ""data_node"": ""latch://38438.account/path/to/dataset.h5ad""
}
```

**Multiple files**:
```json
{
  ""data_node"": [
    ""latch://38438.account/path/to/file1.h5ad"",
    ""latch://38438.account/path/to/file2.csv""
  ]
}
```

**No data** (for synthetic or embedded data):
```json
{
  ""data_node"": null
}
```

**Supported formats**:
- `.h5ad` (AnnData - most common)
- `.csv` (tabular data)
- `.tsv` (tabular data)
- `.bed` (genomic regions)
- `.gz` (compressed files)

### `grader` (required)

Grading configuration specifying how to evaluate the agent's answer.

**Structure**:
```json
{
  ""grader"": {
    ""type"": ""grader_name"",
    ""config"": {
      ""ground_truth"": {},
      ""tolerances"": {},
      ""scoring"": {}
    }
  }
}
```

See [graders.md](graders.md) for detailed grader documentation.

### Timeout Fields (optional)

Control execution time limits:

- **`timeout`**: Overall evaluation timeout in seconds (default: 1200)
- **`download_timeout`**: Data download timeout in seconds (default: 600)
- **`agent_timeout`**: Agent execution timeout in seconds (default: 1200)

**Example**:
```json
{
  ""timeout"": 1800,
  ""download_timeout"": 300,
  ""agent_timeout"": 1500
}
```

## Output Format

Agents must write their answer as JSON to `eval_answer.json`:

```json
{
  ""field1"": value1,
  ""field2"": value2,
  ...
}
```

The fields must match those specified in the task description and expected by the grader.

## Complete Examples

### Numeric Tolerance (QC)

```json
{
  ""id"": ""xenium_qc_basic"",
  ""task"": ""Calculate basic QC metrics for this Xenium dataset. Return JSON with fields: mean_genes_per_cell (float), median_genes_per_cell (float), std_genes_per_cell (float)."",
  ""data_node"": ""latch://38438.account/xenium_kidney.h5ad"",
  ""grader"": {
    ""type"": ""numeric_tolerance"",
    ""config"": {
      ""ground_truth"": {
        ""mean_genes_per_cell"": 44.6,
        ""median_genes_per_cell"": 44.0,
        ""std_genes_per_cell"": 15.0
      },
      ""tolerances"": {
        ""mean_genes_per_cell"": {""type"": ""absolute"", ""value"": 5.0},
        ""median_genes_per_cell"": {""type"": ""absolute"", ""value"": 5.0},
        ""std_genes_per_cell"": {""type"": ""absolute"", ""value"": 5.0}
      }
    }
  }
}
```

### Label Set (Cell Typing)

```json
{
  ""id"": ""xenium_kidney_typing"",
  ""task"": ""Assign each cell to one of these 20 kidney cell types: Pod, Glom-EC, EC, PTS1, PTS2, PTS3, Inj_PT, FR_PT, DTL, TAL, DCT, CNT, PC, ICA, ICB, Uro, PEC, Fib, Per-SMC, Immune. Return JSON with field: cell_types_predicted (list of unique cell types found)."",
  ""data_node"": ""latch://38438.account/xenium_kidney.h5ad"",
  ""grader"": {
    ""type"": ""label_set_jaccard"",
    ""config"": {
      ""ground_truth_labels"": [
        ""Pod"", ""Glom-EC"", ""EC"", ""PTS1"", ""PTS2"", ""PTS3"",
        ""Inj_PT"", ""FR_PT"", ""DTL"", ""TAL"", ""DCT"", ""CNT"",
        ""PC"", ""ICA"", ""ICB"", ""Uro"", ""PEC"", ""Fib"",
        ""Per-SMC"", ""Immune""
      ],
      ""scoring"": {
        ""method"": ""jaccard_index"",
        ""pass_threshold"": 1.0
      }
    }
  }
}
```

### Distribution (Tissue Composition)

```json
{
  ""id"": ""vizgen_tissue_composition"",
  ""task"": ""Calculate the percentage of each cell type in the tissue. Return JSON with fields: total_cells (int), cell_type_distribution (dict mapping cell_type to percentage)."",
  ""data_node"": ""latch://38438.account/vizgen_brain.h5ad"",
  ""grader"": {
    ""type"": ""distribution_comparison"",
    ""config"": {
      ""ground_truth"": {
        ""total_cells"": 50000,
        ""cell_type_distribution"": {
          ""Neuron"": 45.2,
          ""Astrocyte"": 20.1,
          ""Oligodendrocyte"": 15.3,
          ""Microglia"": 10.2,
          ""Endothelial"": 9.2
        }
      },
      ""tolerances"": {
        ""total_cells"": {""type"": ""absolute"", ""value"": 1000},
        ""cell_type_percentages"": {""value"": 3.0}
      }
    }
  }
}
```

## Versioning

When updating an existing evaluation:

1. **Minor changes** (typo fixes, clarifications): Update in place
2. **Ground truth changes**: Create new version with `_v2` suffix
3. **Grader changes**: Create new version
4. **Task changes**: Create new version

Keep old versions for reproducibility.

## Validation

Before submitting, validate your evaluation:

```bash
spatialbench validate path/to/eval.json
```

This checks:
- JSON syntax
- Required fields present
- Grader type exists
- Config matches grader schema
- Data node paths are valid URIs

## Spatial Biology Conventions

### AnnData Structure

Standard fields in `.h5ad` files:

- `adata.X`: Expression matrix (cells × genes)
- `adata.obs`: Cell metadata (cell type, sample, QC metrics)
- `adata.var`: Gene metadata (gene names, chromosome)
- `adata.obsm['spatial']`: Spatial coordinates (x, y)
- `adata.uns`: Unstructured metadata

### Common Output Formats

**Cell types**: Use `adata.obs['cell_type']` or similar column

**Clusters**: Use `adata.obs['leiden']`, `adata.obs['louvain']`

**Embeddings**: Use `adata.obsm['X_pca']`, `adata.obsm['X_umap']`

**Markers**: Store as DataFrame or dict

### Platform-Specific Notes

**Xenium (10x)**:
- 300-gene panels
- Subcellular resolution
- Quality control on transcript counts

**AtlasXomics (ATAC-seq)**:
- Peaks × cells matrix
- Gene activity scores
- Motif analysis

**Vizgen (MERFISH)**:
- 500+ gene panels
- Cell segmentation from DAPI
- z-stack imaging

**Curio Seeker**:
- Beads, not cells
- Background removal important
- Spatial registration to H&E
","Markdown"
"Cell biology","latchbio/spatialbench","docs/graders.md",".md","12580","588","# Grader API Documentation

SpatialBench includes 6 built-in graders for evaluating different types of analysis outputs. This document provides comprehensive documentation for each grader.

## Table of Contents

1. [Base Grader API](#base-grader-api)
2. [NumericToleranceGrader](#numerictolerancegrader)
3. [LabelSetJaccardGrader](#labelsetjaccardgrader)
4. [DistributionComparisonGrader](#distributioncomparisongrader)
5. [MarkerGenePrecisionRecallGrader](#markergeneprecisionrecallgrader)
6. [MarkerGeneSeparationGrader](#markergeneseparationgrader)
7. [SpatialAdjacencyGrader](#spatialadjacencygrader)
8. [Creating Custom Graders](#creating-custom-graders)

## Base Grader API

All graders inherit from `BinaryGrader`:

```python
from spatialbench.graders.base import BinaryGrader, GraderResult

class BinaryGrader:
    def evaluate(self, test_result: TestResult, config: dict) -> GraderResult:
        """"""Extract answer and evaluate.""""""

    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        """"""Core grading logic - override this.""""""

    def extract_answer_from_tags(self, conversation: list[dict]) -> dict | None:
        """"""Extract answer from agent conversation.""""""
```

**GraderResult**:
```python
@dataclass
class GraderResult:
    passed: bool              # Did the evaluation pass?
    metrics: dict             # Detailed metrics
    reasoning: str            # Human-readable explanation
    agent_answer: dict | None  # The agent's answer
```

---

## NumericToleranceGrader

Validates numeric fields against ground truth with configurable tolerances.

### Use Cases

- QC metrics (gene counts, UMI counts, mito fraction)
- Statistical summaries (mean, median, std)
- Dimensionality parameters (number of PCs)
- Any numeric comparison

### Config Schema

```python
{
    ""ground_truth"": {
        ""field_name"": numeric_value,
        ...
    },
    ""tolerances"": {
        ""field_name"": {
            ""type"": ""absolute"" | ""relative"" | ""min"" | ""max"",
            ""value"": numeric_tolerance
        },
        ...
    }
}
```

### Tolerance Types

**Absolute**: `|actual - expected| <= tolerance`
```json
{
    ""type"": ""absolute"",
    ""value"": 5.0
}
```

**Relative**: `|actual - expected| / |expected| <= tolerance`
```json
{
    ""type"": ""relative"",
    ""value"": 0.1
}
```

**Min**: `actual >= expected` (minimum threshold)
```json
{
    ""type"": ""min"",
    ""value"": 100
}
```

**Max**: `actual <= expected` (maximum threshold)
```json
{
    ""type"": ""max"",
    ""value"": 0.35
}
```

### Example

```json
{
    ""type"": ""numeric_tolerance"",
    ""config"": {
        ""ground_truth"": {
            ""mean_genes"": 44.6,
            ""median_genes"": 44.0,
            ""p95_mito_frac"": 0.30
        },
        ""tolerances"": {
            ""mean_genes"": {""type"": ""absolute"", ""value"": 5.0},
            ""median_genes"": {""type"": ""absolute"", ""value"": 5.0},
            ""p95_mito_frac"": {""type"": ""max"", ""value"": 0.35}
        }
    }
}
```

**Expected agent output**:
```json
{
    ""mean_genes"": 46.2,
    ""median_genes"": 43.5,
    ""p95_mito_frac"": 0.28
}
```

### Metrics

- `{field}_actual`: Agent's value
- `{field}_expected`: Ground truth value
- `{field}_error`: Absolute or relative error
- `{field}_pass`: Boolean pass/fail for this field

---

## LabelSetJaccardGrader

Compares sets of labels using Jaccard index.

### Use Cases

- Cell type vocabularies
- Cluster label sets
- Gene marker lists (when order doesn't matter)
- Any set comparison

### Config Schema

```python
{
    ""ground_truth_labels"": [""label1"", ""label2"", ...],
    ""scoring"": {
        ""method"": ""jaccard_index"",
        ""pass_threshold"": 0.0 to 1.0
    }
}
```

### Jaccard Index

```
J(A, B) = |A ∩ B| / |A ∪ B|
```

- 1.0 = perfect match
- 0.0 = no overlap

### Example

```json
{
    ""type"": ""label_set_jaccard"",
    ""config"": {
        ""ground_truth_labels"": [
            ""Pod"", ""Glom-EC"", ""EC"", ""PTS1"", ""PTS2"",
            ""PTS3"", ""DTL"", ""TAL"", ""DCT"", ""CNT""
        ],
        ""scoring"": {
            ""method"": ""jaccard_index"",
            ""pass_threshold"": 1.0
        }
    }
}
```

**Expected agent output**:
```json
{
    ""cell_types_predicted"": [
        ""Pod"", ""Glom-EC"", ""EC"", ""PTS1"", ""PTS2"",
        ""PTS3"", ""DTL"", ""TAL"", ""DCT"", ""CNT""
    ]
}
```

### Metrics

- `jaccard_index`: Jaccard similarity score
- `true_positives`: Correct labels
- `false_positives`: Extra labels (not in ground truth)
- `false_negatives`: Missing labels (in ground truth, not predicted)
- `predicted_count`: Number of predicted labels
- `ground_truth_count`: Number of ground truth labels

---

## DistributionComparisonGrader

Validates cell type distributions (percentages).

### Use Cases

- Cell type composition
- Tissue proportions
- Cluster size validation
- Any percentage distribution

### Config Schema

```python
{
    ""ground_truth"": {
        ""total_cells"": int (optional),
        ""cell_type_distribution"": {
            ""type1"": percentage1,
            ""type2"": percentage2,
            ...
        }
    },
    ""tolerances"": {
        ""total_cells"": {""type"": ""absolute"", ""value"": int},
        ""cell_type_percentages"": {""value"": float}
    }
}
```

### Example

```json
{
    ""type"": ""distribution_comparison"",
    ""config"": {
        ""ground_truth"": {
            ""total_cells"": 50000,
            ""cell_type_distribution"": {
                ""Neuron"": 45.2,
                ""Astrocyte"": 20.1,
                ""Oligodendrocyte"": 15.3,
                ""Microglia"": 10.2,
                ""Endothelial"": 9.2
            }
        },
        ""tolerances"": {
            ""total_cells"": {""type"": ""absolute"", ""value"": 1000},
            ""cell_type_percentages"": {""value"": 3.0}
        }
    }
}
```

**Expected agent output**:
```json
{
    ""total_cells"": 49800,
    ""cell_type_distribution"": {
        ""Neuron"": 44.8,
        ""Astrocyte"": 21.0,
        ""Oligodendrocyte"": 14.9,
        ""Microglia"": 10.5,
        ""Endothelial"": 8.8
    }
}
```

### Metrics

- `total_cells_actual`, `total_cells_expected`, `total_cells_pass`
- For each cell type:
  - `{type}_actual`: Agent's percentage
  - `{type}_expected`: Ground truth percentage
  - `{type}_diff`: Absolute difference
  - `{type}_pass`: Within tolerance?
- `extra_cell_types`: Cell types not in ground truth

---

## MarkerGenePrecisionRecallGrader

Evaluates top-K marker gene lists using Precision@K and Recall@K.

### Use Cases

- Marker gene discovery
- Differential expression top genes
- Ranked gene lists
- Feature selection validation

### Config Schema

```python
{
    ""canonical_markers"": [""gene1"", ""gene2"", ...],
    ""scoring"": {
        ""pass_thresholds"": {
            ""precision_at_k"": 0.0 to 1.0,
            ""recall_at_k"": 0.0 to 1.0
        }
    }
}
```

### Metrics

**Precision@K**: Fraction of predicted genes that are canonical
```
P@K = |predicted ∩ canonical| / K
```

**Recall@K**: Fraction of canonical genes found in top-K
```
R@K = |predicted ∩ canonical| / |canonical|
```

### Example

```json
{
    ""type"": ""marker_gene_precision_recall"",
    ""config"": {
        ""canonical_markers"": [
            ""NPHS1"", ""NPHS2"", ""PODXL"", ""WT1"",
            ""SYNPO"", ""MAGI2"", ""CD2AP"", ""ACTN4""
        ],
        ""scoring"": {
            ""pass_thresholds"": {
                ""precision_at_k"": 0.60,
                ""recall_at_k"": 0.50
            }
        }
    }
}
```

**Expected agent output**:
```json
{
    ""top_marker_genes"": [
        ""NPHS1"", ""NPHS2"", ""PODXL"", ""WT1"", ""SYNPO"",
        ""CDH5"", ""PECAM1"", ""VWF""
    ]
}
```

K = 8 (length of predicted list)
- Precision@8 = 5/8 = 0.625 ✓ (≥0.60)
- Recall@8 = 5/8 = 0.625 ✓ (≥0.50)

### Metrics

- `k`: Length of predicted list
- `precision_at_k`: Precision score
- `recall_at_k`: Recall score
- `true_positives`: Correct genes
- `false_positives`: Non-canonical genes
- `false_negatives`: Canonical genes not found
- `precision_pass`, `recall_pass`: Individual pass/fail

**Note**: Gene name matching is case-insensitive.

---

## MarkerGeneSeparationGrader

Validates marker expression separation using AUROC (Area Under ROC Curve).

### Use Cases

- Validate markers actually separate cell types
- Expression-based cell type validation
- Marker quality assessment

### Config Schema

```python
{
    ""scoring"": {
        ""pass_thresholds"": {
            ""mean_auroc"": 0.0 to 1.0,
            ""fraction_high"": 0.0 to 1.0,
            ""per_gene_cutoff"": 0.0 to 1.0
        }
    }
}
```

### Metrics

**Mean AUROC**: Average AUROC across all markers
**Fraction High**: Fraction of markers with AUROC ≥ cutoff

AUROC interpretation:
- 1.0 = perfect separation
- 0.5 = random (no separation)
- Close to 1.0 = good marker

### Example

```json
{
    ""type"": ""marker_gene_separation"",
    ""config"": {
        ""scoring"": {
            ""pass_thresholds"": {
                ""mean_auroc"": 0.85,
                ""fraction_high"": 0.70,
                ""per_gene_cutoff"": 0.80
            }
        }
    }
}
```

**Expected agent output**:
```json
{
    ""mean_auroc"": 0.87,
    ""per_gene_stats"": [
        {""gene"": ""NPHS1"", ""auroc"": 0.92},
        {""gene"": ""NPHS2"", ""auroc"": 0.89},
        {""gene"": ""PODXL"", ""auroc"": 0.85},
        {""gene"": ""WT1"", ""auroc"": 0.88},
        {""gene"": ""SYNPO"", ""auroc"": 0.75}
    ]
}
```

- Mean AUROC: 0.87 ✓ (≥0.85)
- Fraction high (≥0.80): 4/5 = 0.80 ✓ (≥0.70)

### Metrics

- `mean_auroc_agent`: Agent's reported mean
- `mean_auroc_computed`: Computed from per_gene_stats
- `fraction_high`: Fraction above cutoff
- `high_auroc_genes`: Genes ≥ cutoff
- `low_auroc_genes`: Genes < cutoff
- `per_gene_aurocs`: Full dict of gene → AUROC

---

## SpatialAdjacencyGrader

Evaluates spatial proximity metrics (cell-cell distances).

### Use Cases

- Spatial adjacency analysis
- Cell-cell interaction validation
- Spatial organization metrics

### Config Schema

```python
{
    ""scoring"": {
        ""pass_thresholds"": {
            ""max_median_ic_to_pc_um"": float,
            ""max_p90_ic_to_pc_um"": float,
            ""min_pct_ic_within_15um"": float,
            ""min_pct_ic_mixed_within_55um"": float
        }
    }
}
```

### Example

```json
{
    ""type"": ""spatial_adjacency"",
    ""config"": {
        ""scoring"": {
            ""pass_thresholds"": {
                ""max_median_ic_to_pc_um"": 25.0,
                ""max_p90_ic_to_pc_um"": 80.0,
                ""min_pct_ic_within_15um"": 60.0,
                ""min_pct_ic_mixed_within_55um"": 60.0
            }
        }
    }
}
```

**Expected agent output**:
```json
{
    ""median_ic_to_pc_um"": 18.5,
    ""p90_ic_to_pc_um"": 65.2,
    ""pct_ic_within_15um"": 72.3,
    ""pct_ic_mixed_within_55um"": 85.1,
    ""adjacency_pass"": true
}
```

### Metrics

- `median_ic_to_pc_um`: Median distance IC→PC
- `p90_ic_to_pc_um`: 90th percentile distance
- `pct_ic_within_15um`: % IC cells within 15μm of PC
- `pct_ic_mixed_within_55um`: % IC cells with PC neighbor within 55μm
- `adjacency_pass`: Agent's assessment
- Individual pass/fail for each metric

---

## Creating Custom Graders

See [CONTRIBUTING.md](../CONTRIBUTING.md#creating-a-custom-grader) for a complete guide.

### Basic Template

```python
from spatialbench.graders.base import BinaryGrader, GraderResult

class MyGrader(BinaryGrader):
    def evaluate_answer(self, agent_answer: dict, config: dict) -> GraderResult:
        passed = True
        metrics = {}
        failures = []

        reasoning = f""My Grader: {'PASS' if passed else 'FAIL'}""

        return GraderResult(
            passed=passed,
            metrics=metrics,
            reasoning=reasoning,
            agent_answer=agent_answer
        )
```

### Register Your Grader

Add to `spatialbench/graders/__init__.py`:

```python
from spatialbench.graders.my_grader import MyGrader

GRADER_REGISTRY = {
    ...
    ""my_grader"": MyGrader,
}
```

### Use in Evaluation

```json
{
    ""grader"": {
        ""type"": ""my_grader"",
        ""config"": {...}
    }
}
```

## Grader Selection Guide

| Task Type | Recommended Grader | Notes |
|-----------|-------------------|-------|
| QC metrics | NumericTolerance | Use absolute tolerances |
| Cell type vocab | LabelSetJaccard | Set threshold=1.0 for exact match |
| Tissue composition | DistributionComparison | ±3% typical tolerance |
| Top-K markers | MarkerGenePrecisionRecall | Balance P@K and R@K |
| Marker validation | MarkerGeneSeparation | Require AUROC >0.8 |
| Spatial metrics | SpatialAdjacency | Platform-specific thresholds |
| Clustering | NumericTolerance + LabelSetJaccard | Multiple metrics |
| Custom logic | Create custom grader | Full control |
","Markdown"
"Cell biology","latchbio/spatialbench","docs/adding_evals.md",".md","9280","346","# Understanding SpatialBench Evaluations

This guide explains how SpatialBench evaluations are structured and how they work. The 7 example evaluations in this repository are fixed to demonstrate the format. To contribute to the full 98-evaluation benchmark, contact [kenny@latch.bio](mailto:kenny@latch.bio).

## Example Evaluation Structure

The 7 evaluations in this repository demonstrate:

- [ ] Tasks from real-world analysis workflows
- [ ] Reproducible ground truth
- [ ] Appropriate grader selection
- [ ] Clear task descriptions with specified output format
- [ ] Local testing procedures
- [ ] Comprehensive documentation

## Full Benchmark Information

The complete SpatialBench comprises **98 evaluations** across four platforms:

| Technology       | Evaluations |
|------------------|-------------|
| Xenium           | 30          |
| Vizgen (MERFISH) | 31          |
| AtlasXomics      | 25          |
| Seeker/Curio     | 12          |
| **Total**        | **98**      |

This repository contains **7 representative examples** to demonstrate the evaluation format and grading system. To contribute to the full benchmark, contact [kenny@latch.bio](mailto:kenny@latch.bio).

## Detailed Walkthrough

### Step 1: Task Selection

Good evaluation tasks in SpatialBench:
- Represent real analysis workflows
- Have clear, objective ground truth
- Test specific agent capabilities
- Span diverse difficulty levels

Examples from the benchmark:
- Calculate mean gene count per cell (QC metrics)
- Perform Leiden clustering with specific parameters
- Identify cell populations using marker genes

### Step 2: Dataset Requirements

SpatialBench datasets:
- Are in standard format (.h5ad for most cases)
- Have necessary metadata (cell types, clusters, spatial coords)
- Are properly preprocessed (or evaluations test preprocessing)
- Are hosted in cloud storage with latch:// URIs

Example data node:
```json
{
  ""data_node"": ""latch://38438.account/xenium_kidney.h5ad""
}
```

### Step 3: Ground Truth Methodology

Ground truth is established by running analyses with standard tools:

```python
import scanpy as sc
import numpy as np

adata = sc.read_h5ad(""dataset.h5ad"")

sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)

sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)

sc.pp.pca(adata, n_comps=50)

ground_truth = {
    ""n_cells"": int(adata.n_obs),
    ""n_genes"": int(adata.n_vars),
    ""mean_counts"": float(adata.X.sum(axis=1).mean()),
    ""n_pcs"": int(adata.obsm['X_pca'].shape[1])
}

print(ground_truth)
```

**Document everything**:
- Package versions (`scanpy==1.9.1`)
- Random seeds (if applicable)
- Parameter choices and rationale
- References to papers/protocols

### Step 4: Select Grader

Match your task to the appropriate grader:

| Output Type | Grader | Example |
|-------------|--------|---------|
| Numbers | NumericTolerance | `{""mean"": 45.2}` |
| Label set | LabelSetJaccard | `{""labels"": [""A"", ""B""]}` |
| Percentages | DistributionComparison | `{""dist"": {""A"": 30, ""B"": 70}}` |
| Ranked list | MarkerGenePrecisionRecall | `{""genes"": [""G1"", ""G2""]}` |
| Expression scores | MarkerGeneSeparation | `{""auroc"": 0.85, ""per_gene"": [...]}` |
| Spatial metrics | SpatialAdjacency | `{""median_dist"": 18.5}` |

See [graders.md](graders.md) for full documentation.

### Step 5: Write Task Description

Make it crystal clear:

**Bad**:
```
""Cluster the cells and tell me how many clusters there are.""
```

**Good**:
```
""Perform Leiden clustering on the preprocessed data using resolution=1.0.
Ensure resulting clusters are largely independent of sample batch
(adjusted mutual information with 'sample' column < 0.3).
Return JSON with fields: num_clusters (int), cluster_labels (list of unique
cluster IDs), ami_with_sample (float between 0 and 1).""
```

Key elements:
- Specific method (Leiden, not just ""clustering"")
- Parameters (resolution=1.0)
- Constraints (AMI < 0.3)
- Exact output format with field names and types

### Step 6: Evaluation JSON Format

Example evaluation file structure:

```json
{
  ""id"": ""platform_task_description_v1"",
  ""task"": ""Your detailed task description here..."",
  ""data_node"": ""latch://path/to/dataset.h5ad"",
  ""grader"": {
    ""type"": ""grader_name"",
    ""config"": {
      ""ground_truth"": {
        ...
      },
      ""tolerances"": {
        ...
      }
    }
  }
}
```

**Naming convention**:
- `id`: `xenium_clustering_leiden_v1`
- File: `evals/clustering/xenium_clustering_leiden.json`

### Step 7: Test Locally

Create a test agent:

```python
from spatialbench import EvalRunner
from pathlib import Path
import json

def test_agent(task_prompt, work_dir):
    import scanpy as sc

    adata_files = list(work_dir.glob(""*.h5ad""))
    adata = sc.read_h5ad(adata_files[0])

    sc.pp.pca(adata, n_comps=50)

    answer = {
        ""n_pcs"": adata.obsm['X_pca'].shape[1]
    }

    answer_file = work_dir / ""eval_answer.json""
    answer_file.write_text(json.dumps(answer))

    return answer

runner = EvalRunner(
    ""evals/preprocessing/my_new_eval.json"",
    keep_workspace=True
)
result = runner.run(agent_function=test_agent)

print(f""Passed: {result['passed']}"")
```

**Test both cases**:
1. Correct answer → should pass
2. Incorrect answer → should fail

```python
def wrong_agent(task_prompt, work_dir):
    answer = {""n_pcs"": 999}
    (work_dir / ""eval_answer.json"").write_text(json.dumps(answer))
    return answer

result = runner.run(agent_function=wrong_agent)
assert not result['passed'], ""Grader should fail with wrong answer!""
```

### Step 8: Documentation Format

Each evaluation in `evals/README.md` is documented with:

```markdown
#### platform_task_description

**Platform**: Xenium/Vizgen/Seeker
**Grader**: grader_name

Description of what this eval tests and why it's important.

**Key Metrics**: field1, field2, field3

**Platform Notes**: Specific considerations for this platform

**Challenges**: What makes this task difficult
```

### Step 9: Contributing to the Full Benchmark

To contribute new evaluations to the full 98-evaluation benchmark:

1. **Review the 7 examples** in this repository to understand format and quality standards
2. **Contact [kenny@latch.bio](mailto:kenny@latch.bio)** with your proposal
3. **Provide**:
   - Task description and rationale
   - Ground truth establishment method
   - Dataset information
   - Grader configuration
   - Validation of reproducibility

The full benchmark is maintained separately to prevent overfitting and ensure performance metrics reflect genuine spatial biology reasoning capabilities.

## Common Pitfalls

### ❌ Ambiguous Task Descriptions

**Problem**: Agent doesn't know exact output format
```json
{
  ""task"": ""Find the major cell types""
}
```

**Solution**: Specify exact fields
```json
{
  ""task"": ""Identify major cell types and return JSON with field: cell_types (list of strings)""
}
```

### ❌ Too Tight Tolerances

**Problem**: Ground truth not reproducible enough
```json
{
  ""mean_expression"": 45.234567,
  ""tolerance"": 0.00001
}
```

**Solution**: Use reasonable tolerances
```json
{
  ""mean_expression"": 45.2,
  ""tolerance"": 2.0
}
```

### ❌ Missing Validation

**Problem**: Don't test with wrong answers

**Solution**: Always test failure cases

### ❌ Underdocumented Ground Truth

**Problem**: Can't reproduce analysis

**Solution**: Document all parameters, seeds, versions

## Advanced: Multiple Graders

Some evals need multiple criteria:

```json
{
  ""grader"": {
    ""type"": ""multi_criteria"",
    ""graders"": [
      {
        ""type"": ""numeric_tolerance"",
        ""weight"": 0.5,
        ""config"": {...}
      },
      {
        ""type"": ""label_set_jaccard"",
        ""weight"": 0.5,
        ""config"": {...}
      }
    ]
  }
}
```

**Note**: This requires implementing a `MultiCriteriaGrader` - see custom grader docs.

## Tips for Good Evaluations

1. **Start simple**: Don't combine too many requirements in one eval
2. **Be specific**: Exact field names, data types, constraints
3. **Document well**: Future you will thank you
4. **Test thoroughly**: Both pass and fail cases
5. **Consider difficulty**: Mix easy, medium, and hard evals
6. **Real-world relevance**: Does this test actual analysis skills?
7. **Reproducibility**: Can someone else verify your ground truth?

## Example Evaluations to Study

The 7 representative evaluations in this repository:

- **QC metrics**: `evals/qc/seeker_qc_basic.json`
- **Preprocessing**: `evals/preprocessing/xenium_normalization.json`
- **Clustering**: `evals/clustering/xenium_leiden.json`
- **Cell typing**: `evals/cell_typing/xenium_kidney_typing.json`
- **Differential expression**: `evals/differential_expression/vizgen_de_temporal.json`
- **Spatial analysis**: `evals/spatial_analysis/seeker_spatial_contiguity.json`, `evals/spatial_analysis/vizgen_tissue_composition.json`

## Additional Resources

- Study the 7 example evaluations for format and structure
- Read [specification.md](specification.md) for technical format details
- Read [graders.md](graders.md) for available grader types
- See [BENCHMARK_SCOPE.md](../BENCHMARK_SCOPE.md) for full benchmark information
- Contact [kenny@latch.bio](mailto:kenny@latch.bio) to contribute to the full benchmark
","Markdown"
"Cell biology","latchbio/spatialbench","docs/CLI_USAGE.md",".md","5333","235","# CLI Usage Guide

SpatialBench provides a command-line interface for running evaluations and managing benchmarks.

## Commands

### run - Run a single evaluation

```bash
spatialbench run <eval_path> [options]
```

**Options:**
- `--agent minisweagent` - Use mini-swe-agent to run the evaluation
- `--model <model>` - Specify model name (e.g., `anthropic/claude-sonnet-4-5`, `openai/gpt-4o`)
- `--keep-workspace` - Keep workspace directory after completion
- `--verbose, -v` - Verbose output

**Examples:**

```bash
spatialbench run evals/qc/seeker_qc_basic.json --agent minisweagent

spatialbench run evals/qc/seeker_qc_basic.json \
  --agent minisweagent \
  --model anthropic/claude-sonnet-4-5 \
  --keep-workspace
```

### batch - Run multiple evaluations

```bash
spatialbench batch <eval_dir> [options]
```

**Options:**
- `--agent minisweagent` - Use mini-swe-agent for all evaluations
- `--model <model>` - Model name for agent
- `--output, -o <dir>` - Output directory for results
- `--parallel, -p <n>` - Number of parallel workers (default: 1)
- `--keep-workspace` - Keep workspace after each eval

**Examples:**

```bash
spatialbench batch evals_full/seeker --agent minisweagent

spatialbench batch evals_full/seeker \
  --agent minisweagent \
  --model anthropic/claude-sonnet-4-5 \
  --output results/run1 \
  --parallel 6 \
  --keep-workspace
```

**Batch Process:**

1. **Dataset collection** - Scans all eval files and collects unique dataset URIs
2. **Batch download** - Downloads all datasets upfront (if using Latch data nodes)
3. **Parallel execution** - Runs evaluations with specified number of workers
4. **Result aggregation** - Collects results and computes summary statistics

**Batch Results:**

Results are saved to `<output_dir>/batch_results.json`:

```json
{
  ""metadata"": {
    ""model"": ""anthropic/claude-sonnet-4-5"",
    ""timestamp"": ""2025-01-12T21:30:45Z"",
    ""total_evals"": 12,
    ""passed"": 10,
    ""failed"": 2,
    ""pass_rate"": 83.3,
    ""avg_duration_s"": 45.2,
    ""total_cost"": 0.34,
    ""avg_cost_per_eval"": 0.028,
    ""total_steps"": 38,
    ""avg_steps_per_eval"": 3.2
  },
  ""results"": [
    {
      ""eval"": ""curio_mt_percentage.json"",
      ""passed"": true,
      ""test_id"": ""curio_mt_percentage"",
      ""model"": ""anthropic/claude-sonnet-4-5"",
      ""timestamp"": ""2025-01-12T21:31:15Z"",
      ""duration_s"": 42.3,
      ""total_cost"": 0.028,
      ""n_steps"": 3,
      ""n_messages"": 6
    }
  ]
}
```

### validate - Validate evaluation format

```bash
spatialbench validate <eval_path>
```

Checks that an evaluation file:
- Contains required fields (`id`, `task`)
- Uses a valid grader type
- Has valid JSON syntax

**Example:**

```bash
spatialbench validate evals/qc/seeker_qc_basic.json
```

### list - List available evaluations

```bash
spatialbench list [--category <category>]
```

Lists all evaluations in the package, optionally filtered by category.

**Categories:**
- `qc` - Quality control
- `preprocessing` - Normalization, dimensionality reduction
- `clustering` - Clustering algorithms
- `cell_typing` - Cell type annotation
- `differential_expression` - Marker gene discovery
- `spatial_analysis` - Spatial-specific analyses

**Example:**

```bash
spatialbench list
spatialbench list --category qc
```

## Environment Variables

### Model Configuration

**For Anthropic models:**
```bash
export MSWEA_MODEL_NAME=anthropic/claude-sonnet-4-5
export ANTHROPIC_API_KEY=your_api_key
```

**For OpenAI models:**
```bash
export MSWEA_MODEL_NAME=openai/gpt-4o
export OPENAI_API_KEY=your_api_key
```

**Supported Models:**
- `anthropic/claude-sonnet-4-5` - Claude Sonnet 4.5 (recommended)
- `anthropic/claude-opus-4` - Claude Opus 4
- `openai/gpt-4o` - GPT-4o
- `openai/gpt-4o-mini` - GPT-4o Mini
- `openai/o1` - OpenAI o1
- `openai/o1-mini` - OpenAI o1 Mini

### Latch Configuration

If using Latch data nodes:
```bash
export LATCH_TOKEN=your_latch_token
```

## Programmatic Usage

For more control, use the Python API:

```python
from spatialbench import EvalRunner
from spatialbench.harness import run_minisweagent_task

runner = EvalRunner(
    ""evals/qc/seeker_qc_basic.json"",
    keep_workspace=True
)

result = runner.run(
    agent_function=lambda task, work_dir: run_minisweagent_task(
        task,
        work_dir,
        model_name=""anthropic/claude-sonnet-4-5""
    )
)

print(f""Passed: {result['passed']}"")
print(f""Cost: ${result['metadata']['total_cost']:.4f}"")
print(f""Steps: {result['metadata']['n_steps']}"")
```

## Monitoring Batch Runs

See [BATCH_MONITORING.md](../BATCH_MONITORING.md) for detailed instructions on:
- Real-time progress monitoring
- Identifying stuck evaluations
- Viewing agent logs
- Performance metrics
- Troubleshooting common issues

## Benchmark Script

For running multi-model benchmarks:

```bash
./scripts/benchmark_models.sh <eval_dir> [workers] [keep_workspace]
```

This script:
- Runs multiple models on the same eval set
- Organizes results by timestamp: `results/run_TIMESTAMP/`
- Produces separate results for each model
- Includes timing and cost comparisons

**Example:**

```bash
./scripts/benchmark_models.sh evals_full/seeker 6 true
```

Results will be saved to:
```
results/
  run_20250112_213045/
    sonnet45/
      batch_results.json
      batch_log.txt
    gpt5codex/
      batch_results.json
      batch_log.txt
```
","Markdown"
"Cell biology","latchbio/spatialbench","scripts/cleanup_orphaned_processes.sh",".sh","877","32","#!/bin/bash
set -e

echo ""==========================================""
echo ""Cleaning up orphaned multiprocessing workers""
echo ""==========================================""
echo """"

ORPHANED_COUNT=$(ps aux | grep 'from multiprocessing' | grep -v grep | wc -l | tr -d ' ')

if [ ""$ORPHANED_COUNT"" -eq 0 ]; then
    echo ""No orphaned processes found.""
    exit 0
fi

echo ""Found $ORPHANED_COUNT orphaned multiprocessing worker processes""
echo """"

read -p ""Kill all orphaned processes? (y/n) "" -n 1 -r
echo
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
    echo ""Cleanup cancelled.""
    exit 0
fi

echo ""Killing orphaned processes...""
ps aux | grep 'from multiprocessing' | grep -v grep | awk '{print $2}' | xargs kill -9 2>/dev/null || true

REMAINING=$(ps aux | grep 'from multiprocessing' | grep -v grep | wc -l | tr -d ' ')
echo """"
echo ""Cleanup complete. Remaining processes: $REMAINING""
","Shell"
"Cell biology","latchbio/spatialbench","scripts/compare_models.py",".py","5925","179","#!/usr/bin/env python3
import json
import sys
from pathlib import Path
from collections import defaultdict

def load_results(results_dir):
    results_dir = Path(results_dir)
    model_results = {}

    for model_dir in results_dir.iterdir():
        if not model_dir.is_dir():
            continue

        results_file = model_dir / ""batch_results.json""
        if not results_file.exists():
            continue

        try:
            data = json.loads(results_file.read_text())

            if ""metadata"" in data and ""results"" in data:
                model_results[model_dir.name] = data
            else:
                model_results[model_dir.name] = {
                    ""metadata"": {""model"": model_dir.name},
                    ""results"": data
                }
        except Exception as e:
            print(f""Warning: Failed to load {results_file}: {e}"", file=sys.stderr)

    return model_results

def print_summary_table(model_results):
    print(""\n"" + ""="" * 100)
    print(""MODEL COMPARISON SUMMARY"")
    print(""="" * 100)
    print()

    headers = [""Model"", ""Total"", ""Passed"", ""Failed"", ""Pass Rate"", ""Avg Time"", ""Total Time""]
    col_widths = [20, 8, 8, 8, 12, 12, 12]

    header_row = ""  "".join(h.ljust(w) for h, w in zip(headers, col_widths))
    print(header_row)
    print(""-"" * 100)

    for model_name, data in sorted(model_results.items()):
        metadata = data.get(""metadata"", {})
        results = data.get(""results"", [])

        total = metadata.get(""total_evals"", len(results))
        passed = metadata.get(""passed"", sum(1 for r in results if r.get(""passed"") is True))
        failed = metadata.get(""failed"", sum(1 for r in results if r.get(""passed"") is False))
        pass_rate = metadata.get(""pass_rate"", round(passed/total*100, 1) if total else 0)
        avg_time = metadata.get(""avg_duration_s"", 0)
        total_time = metadata.get(""total_duration_s"", 0)

        row = [
            model_name,
            str(total),
            str(passed),
            str(failed),
            f""{pass_rate:.1f}%"",
            f""{avg_time:.1f}s"",
            f""{total_time/60:.1f}m""
        ]

        row_str = ""  "".join(v.ljust(w) for v, w in zip(row, col_widths))
        print(row_str)

    print()

def analyze_eval_disagreements(model_results):
    eval_outcomes = defaultdict(dict)

    for model_name, data in model_results.items():
        results = data.get(""results"", [])
        for result in results:
            eval_name = result.get(""eval"") or result.get(""test_id"")
            if eval_name:
                eval_outcomes[eval_name][model_name] = result.get(""passed"")

    disagreements = []
    for eval_name, outcomes in eval_outcomes.items():
        if len(set(outcomes.values())) > 1:
            disagreements.append((eval_name, outcomes))

    if disagreements:
        print(""="" * 100)
        print(""EVALUATIONS WITH DISAGREEMENTS"")
        print(""="" * 100)
        print()

        for eval_name, outcomes in sorted(disagreements):
            print(f""  {eval_name}"")
            for model_name, passed in sorted(outcomes.items()):
                status = ""✓ PASSED"" if passed else (""✗ FAILED"" if passed is False else ""? NULL"")
                print(f""    {model_name}: {status}"")
            print()
    else:
        print(""="" * 100)
        print(""No disagreements found - all models agree on all evaluations"")
        print(""="" * 100)
        print()

def generate_comparison_report(results_dir, model_results):
    output_file = Path(results_dir) / ""comparison_summary.json""

    summary = {
        ""models"": {},
        ""disagreements"": []
    }

    for model_name, data in model_results.items():
        metadata = data.get(""metadata"", {})
        results = data.get(""results"", [])

        total = metadata.get(""total_evals"", len(results))
        passed = metadata.get(""passed"", sum(1 for r in results if r.get(""passed"") is True))

        summary[""models""][model_name] = {
            ""total_evals"": total,
            ""passed"": passed,
            ""failed"": metadata.get(""failed"", total - passed),
            ""pass_rate"": metadata.get(""pass_rate"", round(passed/total*100, 1) if total else 0),
            ""avg_duration_s"": metadata.get(""avg_duration_s"", 0),
            ""total_duration_s"": metadata.get(""total_duration_s"", 0),
        }

    eval_outcomes = defaultdict(dict)
    for model_name, data in model_results.items():
        results = data.get(""results"", [])
        for result in results:
            eval_name = result.get(""eval"") or result.get(""test_id"")
            if eval_name:
                eval_outcomes[eval_name][model_name] = result.get(""passed"")

    for eval_name, outcomes in eval_outcomes.items():
        if len(set(outcomes.values())) > 1:
            summary[""disagreements""].append({
                ""eval"": eval_name,
                ""outcomes"": outcomes
            })

    output_file.write_text(json.dumps(summary, indent=2))
    print(f""Comparison report saved to: {output_file}"")
    print()

def main():
    if len(sys.argv) < 2:
        print(""Usage: python compare_models.py <results_directory>"")
        print()
        print(""Example:"")
        print(""  python compare_models.py results/"")
        sys.exit(1)

    results_dir = sys.argv[1]

    if not Path(results_dir).exists():
        print(f""Error: Directory not found: {results_dir}"", file=sys.stderr)
        sys.exit(1)

    model_results = load_results(results_dir)

    if not model_results:
        print(f""Error: No batch_results.json files found in {results_dir}"", file=sys.stderr)
        sys.exit(1)

    print(f""\nFound results for {len(model_results)} model(s):"")
    for model_name in sorted(model_results.keys()):
        print(f""  - {model_name}"")

    print_summary_table(model_results)
    analyze_eval_disagreements(model_results)
    generate_comparison_report(results_dir, model_results)

if __name__ == ""__main__"":
    main()
","Python"
"Cell biology","latchbio/spatialbench","scripts/benchmark_models.sh",".sh","2350","98","#!/bin/bash
set -e

EVAL_DIR=""${1:-evals_full/seeker}""
RUN_TIMESTAMP=$(date +%Y%m%d_%H%M%S)
OUTPUT_BASE=""results/run_${RUN_TIMESTAMP}""
PARALLEL_WORKERS=""${2:-6}""
KEEP_WORKSPACE=""${3:-true}""

MODELS=(
    ""anthropic/claude-sonnet-4-5""
    ""openai/gpt-5-codex""
)

MODEL_NAMES=(
    ""sonnet45""
    ""gpt5codex""
)

echo ""==========================================""
echo ""SpatialBench Multi-Model Benchmark""
echo ""==========================================""
echo """"
echo ""Evaluation directory: $EVAL_DIR""
echo ""Output directory: $OUTPUT_BASE""
echo ""Parallel workers: $PARALLEL_WORKERS""
echo """"
echo ""Models to benchmark:""
for i in ""${!MODELS[@]}""; do
    echo ""  ${MODEL_NAMES[$i]}: ${MODELS[$i]}""
done
echo """"

read -p ""Continue with benchmark? (y/n) "" -n 1 -r
echo
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
    echo ""Benchmark cancelled.""
    exit 0
fi

BENCHMARK_START=$(date +%s)

for i in ""${!MODELS[@]}""; do
    MODEL=""${MODELS[$i]}""
    MODEL_NAME=""${MODEL_NAMES[$i]}""
    OUTPUT_DIR=""$OUTPUT_BASE/$MODEL_NAME""

    echo """"
    echo ""==========================================""
    echo ""Running benchmark for: $MODEL_NAME""
    echo ""Model: $MODEL""
    echo ""Output: $OUTPUT_DIR""
    echo ""==========================================""
    echo """"

    START=$(date +%s)

    mkdir -p ""$OUTPUT_DIR""

    KEEP_WS_FLAG=""""
    if [[ ""$KEEP_WORKSPACE"" == ""true"" ]]; then
        KEEP_WS_FLAG=""--keep-workspace""
    fi

    .venv/bin/python -m spatialbench.cli batch ""$EVAL_DIR"" \
        --agent minisweagent \
        --model ""$MODEL"" \
        --output ""$OUTPUT_DIR"" \
        --parallel ""$PARALLEL_WORKERS"" \
        $KEEP_WS_FLAG \
        2>&1 | tee ""$OUTPUT_DIR/batch_log.txt""

    END=$(date +%s)
    DURATION=$((END - START))

    echo """"
    echo ""Completed $MODEL_NAME in ${DURATION}s ($(($DURATION / 60))m)""
    echo """"
done

BENCHMARK_END=$(date +%s)
TOTAL_DURATION=$((BENCHMARK_END - BENCHMARK_START))

echo """"
echo ""==========================================""
echo ""Benchmark Complete!""
echo ""==========================================""
echo ""Total duration: ${TOTAL_DURATION}s ($(($TOTAL_DURATION / 60))m)""
echo """"
echo ""Results saved to:""
for MODEL_NAME in ""${MODEL_NAMES[@]}""; do
    echo ""  $OUTPUT_BASE/$MODEL_NAME/batch_results.json""
done
echo """"
echo ""To compare results, run:""
echo ""  python scripts/compare_models.py $OUTPUT_BASE/""
echo """"
","Shell"
"Cell biology","finitewave/Finitewave","finitewave/__init__.py",".py","2508","110","
""""""
finitewave
==========

A Python package for simulating cardiac electrophysiology in 2D and 3D using
the finite difference method.

This package provides a set of tools for simulating cardiac electrophysiology
in 2D and 3D using the finite difference method. The package includes classes
for creating cardiac tissue models, tracking electrical activity, and
visualizing simulation results. The package is designed to be flexible and
extensible, allowing users to create custom models and trackers for their
specific research needs.

""""""

from finitewave.core import (
    Command,
    CommandSequence,
    FibrosisPattern,
    CardiacModel,
    StateLoader,
    StateSaver,
    StateSaverCollection,
    Stencil,
    StimCurrent,
    StimSequence,
    StimVoltage,
    Stim,
    CardiacTissue,
    Tracker,
    TrackerSequence
)

from finitewave.cpuwave2D import (
    IncorrectWeightsModeError2D,
    Diffuse2DPattern,
    Structural2DPattern,
    AlievPanfilov2D,
    Barkley2D,
    MitchellSchaeffer2D,
    FentonKarma2D,
    BuenoOrovio2D,
    LuoRudy912D,
    TP062D,
    Courtemanche2D,
    AsymmetricStencil2D,
    SymmetricStencil2D,
    IsotropicStencil2D,
    StimCurrentArea2D,
    StimCurrentCoord2D,
    StimVoltageCoord2D,
    StimCurrentMatrix2D,
    StimVoltageMatrix2D,
    CardiacTissue2D,
    ActionPotential2DTracker,
    ActivationTime2DTracker,
    Animation2DTracker,
    ECG2DTracker,
    LocalActivationTime2DTracker,
    MultiVariable2DTracker,
    Period2DTracker,
    PeriodAnimation2DTracker,
    SpiralWaveCore2DTracker,
    Variable2DTracker,
)

from finitewave.cpuwave3D import (
    Diffuse3DPattern,
    Structural3DPattern,
    AlievPanfilov3D,
    Barkley3D,
    MitchellSchaeffer3D,
    FentonKarma3D,
    BuenoOrovio3D,
    LuoRudy913D,
    TP063D,
    Courtemanche3D,
    AsymmetricStencil3D,
    IsotropicStencil3D,
    StimCurrentCoord3D,
    StimVoltageCoord3D,
    StimCurrentMatrix3D,
    StimVoltageMatrix3D,
    StimVoltageListMatrix3D,
    StimCurrentArea3D,
    CardiacTissue3D,
    ActionPotential3DTracker,
    ActivationTime3DTracker,
    LocalActivationTime3DTracker,
    AnimationSlice3DTracker,
    ECG3DTracker,
    Period3DTracker,
    SpiralWaveCore3DTracker,
    Variable3DTracker,
    MultiVariable3DTracker,
    VTKFrame3DTracker,
    Animation3DTracker,
    PeriodAnimation3DTracker
)

from finitewave.tools import (
    Animation2DBuilder,
    Animation3DBuilder,
    VisMeshBuilder3D,
    Velocity2DCalculation,
    Velocity3DCalculation,
)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/animation_2d_builder.py",".py","2748","87","from pathlib import Path
import shutil
from natsort import natsorted
import ffmpeg
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm


class Animation2DBuilder:
    def __init__(self):
        pass

    def write(self, path, animation_name='animation', mask=None, shape_scale=1,
              fps=12, clim=[0, 1], shape=(100, 100), cmap=""coolwarm"",
              clear=False, prog_bar=False):
        """"""
        Write an animation from a folder with snapshots.

        Parameters
        ----------
        path : str or Path
            Path to the folder with snapshots.
        animation_name : str
            Name of the animation file.
        mask : ndarray
            Mask to apply to the frames.
        shape_scale : int
            Scale factor for the frames.
        fps : int
            Frames per second.
        clim : list
            Color limits for the colormap.
        shape : tuple
            Shape of the frames.
        cmap : str
            Matplotlib colormap to use.
        clear : bool
            Clear the snapshot folder after writing the animation.
        prog_bar : bool
            Show progress bar.
        """"""
        path = Path(path)
        path_save = path.parent.joinpath(animation_name).with_suffix("".mp4"")

        files = natsorted(path.glob(""*.npy""))

        height, width = np.array(shape) * shape_scale
        cmap = plt.get_cmap(cmap)

        with (
            ffmpeg
            .input('pipe:', format='rawvideo', pix_fmt='rgb24',
                   s=f'{width}x{height}', framerate=fps)
            .output(path_save.as_posix(), pix_fmt='yuv420p')
            .overwrite_output()
            .run_async(pipe_stdin=True, quiet=True)
        ) as process:
            # Write frames to FFmpeg process
            for file in tqdm(files, desc='Building animation',
                             disable=not prog_bar):
                frame = np.load(file.with_suffix("".npy""))
                # Normalize the frame data to the colormap
                mask_ = (frame < clim[0]) | (frame > clim[1])

                if mask is not None:
                    mask_ |= mask

                frame = (frame - clim[0]) / (clim[1] - clim[0])
                frame[mask_] = np.nan

                frame = (cmap(frame, bytes=True)[:, :, :3]).astype(""uint8"")

                # Upscale the frame if necessary
                if shape_scale > 1:
                    frame = np.repeat(np.repeat(frame, shape_scale, axis=0),
                                      shape_scale, axis=1)

                process.stdin.write(frame.tobytes())

        # Close the FFmpeg process
        process.stdin.close()
        process.wait()

        if clear:
            shutil.rmtree(path)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/velocity_3d_calculation.py",".py","2621","87","import numpy as np
from scipy import spatial
from skimage import measure

from finitewave.tools.velocity_2d_calculation import Velocity2DCalculation


class Velocity3DCalculation(Velocity2DCalculation):
    """"""
    Class for calculating the velocity of the wavefront.

    """"""
    def __init__(self):
        super().__init__()

    @staticmethod
    def velocity_vector(act_t, dr, orientation=False, t_max=None, t_min=None):
        """"""
        Computes the velocity of the wavefront from a single source based on
        the elliptical shape of the wavefront.

        Parameters
        ----------
        act_t : numpy.ndarray
            2D array of activation times.
        dr : float
            Spatial resolution.
        orientation : bool
            If True, the angle of the major axis of the ellipse is returned.

        Returns
        -------
        tuple
            Tuple of the major and minor components of the velocity.
        """"""
        if t_min is None:
            t_min = np.min(act_t[act_t >= 0])

        if t_max is None:
            t_max = np.max(act_t)

        mask = (act_t >= t_min) & (act_t <= t_max)

        res = Velocity3DCalculation.calc_ellipsoid_axes(mask)
        major, medium, minor, theta, phi = res
        major_velocity = major * dr / (t_max - t_min)
        medium_velocity = medium * dr / (t_max - t_min)
        minor_velocity = minor * dr / (t_max - t_min)

        if orientation:
            return major_velocity, medium_velocity, minor_velocity, theta, phi

        return major_velocity, medium_velocity, minor_velocity

    @staticmethod
    def calc_ellipsoid_axes(mask):
        """"""
        Calculate the major, medium and minor axes of the ellipsoid
        that best fits the wavefront.

        Parameters
        ----------
        mask : numpy.ndarray
            3D array of the wavefront.

        Returns
        -------
        tuple
            Major, medium, minor axes and the angles theta and phi.
        """"""

        cov_matrix = measure.inertia_tensor(mask.astype(int))
        eigvals, eigvecs = np.linalg.eig(cov_matrix)

        sorted_ids = np.argsort(eigvals)[::-1]
        eigvals = eigvals[sorted_ids]
        eigvecs = eigvecs[:, sorted_ids]

        major = np.sqrt(2.5 * (eigvals[0] + eigvals[1] - eigvals[2]))
        medium = np.sqrt(2.5 * (eigvals[0] - eigvals[1] + eigvals[2]))
        minor = np.sqrt(2.5 * (-eigvals[0] + eigvals[1] + eigvals[2]))

        major_vec = eigvecs[:, 2]
        theta = np.arccos(major_vec[2])
        phi = np.arctan2(major_vec[1], major_vec[0])
        return major, medium, minor, theta, phi
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/__init__.py",".py","274","6","from .animation_2d_builder import Animation2DBuilder
from .animation_3d_builder import Animation3DBuilder
from .velocity_2d_calculation import Velocity2DCalculation
from .velocity_3d_calculation import Velocity3DCalculation
from .vis_mesh_builder_3d import VisMeshBuilder3D
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/velocity_2d_calculation.py",".py","2780","104","import numpy as np
from scipy import spatial
from skimage import measure


class Velocity2DCalculation:
    """"""
    Class for calculating the velocity of the wavefront.

    """"""
    def __init__(self):
        pass

    @staticmethod
    def front_velocity(act_t, dr):
        """"""
        Computes the front velocity of activation based on the activation
        times.

        Parameters
        ----------
        act_t : numpy.ndarray
            Activation times.
        dr : float
            Spatial resolution.

        Returns
        -------
        numpy.ndarray
            Velocity of the wavefront.
        """"""
        min_time = np.min(act_t[act_t >= 0])
        max_time = np.max(act_t)

        start_coords = np.argwhere(act_t == min_time)
        current_coords = np.argwhere(act_t == max_time)

        tree = spatial.KDTree(start_coords)
        dist, _ = tree.query(current_coords)

        front_vel = np.zeros(act_t.shape)
        front_vel = dist * dr / (max_time - min_time)
        return front_vel

    @staticmethod
    def velocity_vector(act_t, dr, orientation=False, t_min=None, t_max=None):
        """"""
        Computes the velocity of the wavefront from a single source based on
        the elliptical shape of the wavefront.

        Parameters
        ----------
        act_t : numpy.ndarray
            2D array of activation times.
        dr : float
            Spatial resolution.
        orientation : bool
            If True, the angle of the major axis of the ellipse is returned.

        Returns
        -------
        tuple
            Tuple of the major and minor components of the velocity.
        """"""

        if t_min is None:
            t_min = np.min(act_t[act_t >= 0])

        if t_max is None:
            t_max = np.max(act_t)

        mask = (act_t >= t_min) & (act_t <= t_max)

        major, minor, angle = Velocity2DCalculation.calc_ellipse_axes(mask)
        major_velocity = major * dr / (t_max - t_min)
        minor_velocity = minor * dr / (t_max - t_min)

        if orientation:
            return major_velocity, minor_velocity, angle

        return major_velocity, minor_velocity

    @staticmethod
    def calc_ellipse_axes(mask):
        """"""
        Calculate the major and minor axes of the ellipse that best fits the
        wavefront.

        Parameters
        ----------
        mask : numpy.ndarray
            2D array of the wavefront.

        Returns
        -------
        tuple
            Major and minor axes and the angle of the ellipse.
        """"""
        props = measure.regionprops(mask.astype(int))
        major = 0.5 * props[0].major_axis_length
        minor = 0.5 * props[0].minor_axis_length
        orientation = props[0].orientation
        return major, minor, orientation
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/vis_mesh_builder_3d.py",".py","3289","112","import pyvista as pv
import numpy as np


class VisMeshBuilder3D:
    """"""Class to build a 3D mesh for visualization with pyvista.

    Attributes:
    ------------
    grid : pv.UnstructuredGrid)
        Masked grid with cells where mesh > 0.

    full_grid : pv.ImageData
        Full grid with all cells.
    """"""
    def __init__(self):
        self.grid = None
        self.full_grid = None

    def build_mesh(self, mesh):
        """"""Build a Unstructured Grid from 3D mesh where mesh > 0.

        Parameters:
        ------------
        mesh : np.array
            3D mesh with cardiomyocytes (elems = 1), empty space (elems = 0),
            and fibrosis (elems = 2).

        Returns:
        ------------
        grid : pv.UnstructuredGrid
            Masked grid with cells where mesh > 0.
        """"""
        grid = pv.ImageData()
        grid.dimensions = np.array(mesh.shape) + 1
        grid.spacing = (1, 1, 1)
        grid.cell_data['mesh'] = mesh.astype(float).flatten(order='F')

        self.full_grid = grid
        # Threshold the mesh to remove empty space
        self.grid = grid.threshold(0.5)
        self._mesh = mesh
        return self.grid

    def add_scalar(self, scalars, name='Scalars'):
        """"""
        Add a scalar field to the mesh. The scalar field is flattened
        and only the values of the non-empty space are added to the mesh.

        Parameters
        ----------
        scalars : np.array
            3D scalar field.
        name : str, optional
            Name of the scalar. Default is 'Scalars'.

        Returns
        -------
        grid : pv.UnstructuredGrid
            Grid with the scalar field added.
        """"""

        if scalars.shape != self._mesh.shape:
            raise ValueError(""Scalars must have the same shape asthe mesh."")

        scalars_flat = scalars.T[self._mesh.T > 0].flatten(order='F')
        self.grid.cell_data[name] = scalars_flat
        self.grid.set_active_scalars(name)
        return self.grid

    def flatten_scalars(self, scalars):
        """"""
        """"""
        if scalars.shape != self._mesh.shape:
            raise ValueError(""Scalars must have the same shape asthe mesh."")

        scalars_flat = scalars.T[self._mesh.T > 0].flatten(order='F')
        return scalars_flat

    def add_vector(self, vectors, name='Vectors'):
        """"""
        Add a vector field to the mesh. The vector field is flattened
        and only the values of the non-empty space are added to the mesh.

        Parameters
        ----------
        vectors : np.array
            3D vector field.
        name : str, optional
            Name of the vector. Default is 'Vectors'.

        Returns
        -------
        grid : pv.UnstructuredGrid
            Grid with the vector field added.
        """"""

        if vectors.shape != self._mesh.shape + (3,):
            raise ValueError(""Vectors must have the same shape as the mesh."")

        vectors_list = []
        for i in range(3):
            x = vectors[:, :, :, i].T
            x_flat = x[self._mesh.T > 0].flatten(order='F')
            vectors_list.append(x_flat)

        vectors_flat = np.column_stack(vectors_list)

        self.grid.cell_data[name] = vectors_flat
        self.grid.set_active_vectors(name)
        return self.grid
","Python"
"Cell biology","finitewave/Finitewave","finitewave/tools/animation_3d_builder.py",".py","3596","107","from pathlib import Path
import numpy as np
import pyvista as pv
from natsort import natsorted
from tqdm import tqdm

from finitewave.tools.vis_mesh_builder_3d import VisMeshBuilder3D


class Animation3DBuilder:
    def __init__(self) -> None:
        pass

    def load_scalar(self, path, mask=None):
        """"""Load the scalar field from a file.

        Args:
            path (str): Path to the snapshot folder.
            mask (np.array, optional): Mask to apply to the scalar field.

        Returns:
            np.array: Scalar field.
        """"""

        scalar = np.load(path).astype(float)

        if mask is None:
            return scalar

        if mask.shape == scalar.shape:
            return scalar

        if mask[mask > 0].shape == scalar.shape:
            scalar_mesh = np.zeros_like(mask, dtype=float)
            scalar_mesh[mask > 0] = scalar
            return scalar_mesh

        raise ValueError(""Mask and scalar must have the same shape, or scalar""
                         + "" must have the same shape as mask[mask > 0]"")

    def write(self, path, mask=None, path_save=None, window_size=(800, 800),
              clim=[0, 1], scalar_name=""Scalar"", animation_name=""animation"",
              cmap=""viridis"", scalar_bar=False, format=""mp4"", prog_bar=True,
              **kwargs):
        """"""Write the animation to a file.

        Args:
            path (str): Path to the snapshot folder.
            mask (np.array, optional): Mask to apply to the scalar field.
                Defaults to None.
            path_save (str, optional): Path to save the animation.
                Defaults is parent directory of path.
            window_size (tuple, optional): Size of the window.
                Defaults to (800, 800).
            clim (list, optional): Color limits. Defaults to [0, 1].
            scalar_name (str, optional): Name of the scalar field.
                Defaults to ""Scalar"".
            cmap (str, optional): Color map. Defaults to ""viridis"".
            scalar_bar (bool, optional): Show scalar bar. Defaults to False.
            format (str, optional): Format of the animation. Defaults to ""mp4"".
                Other options are ""gif"".
        """"""

        files = natsorted(Path(path).glob(""*.npy""))

        if len(files) == 0:
            raise ValueError(""No files found"")

        if path_save is None:
            path_save = path.parent

        scalar = self.load_scalar(files[0], mask)

        if mask is None:
            mask = np.ones_like(scalar)

        mesh_builder = VisMeshBuilder3D()
        mesh_builder.build_mesh(mask)

        pl = pv.Plotter(notebook=False, off_screen=True,
                        window_size=window_size)

        if format == ""mp4"":
            pl.open_movie(Path(path_save).joinpath(f'{animation_name}.mp4'),
                          **kwargs)
        elif format == ""gif"":
            pl.open_gif(str(Path(path_save).joinpath(f'{animation_name}.gif')),
                        **kwargs)
        else:
            raise ValueError(""Format must be 'mp4' or 'gif'"")

        mesh_builder.add_scalar(scalar, scalar_name)
        pl.add_mesh(mesh_builder.grid, scalars=scalar_name,
                    clim=clim, cmap=cmap, show_scalar_bar=scalar_bar)

        pl.show(auto_close=False)

        pl.write_frame()

        for filename in tqdm(files[1:], disable=not prog_bar,
                             desc=""Building animation""):
            scalar = self.load_scalar(filename, mask)
            mesh_builder.add_scalar(scalar, scalar_name)
            pl.write_frame()

        pl.close()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/__init__.py",".py","481","9","from finitewave.core.command import Command, CommandSequence
from finitewave.core.fibrosis import FibrosisPattern 
from finitewave.core.model import CardiacModel
from finitewave.core.state import StateLoader, StateSaver, StateSaverCollection
from finitewave.core.stencil import Stencil
from finitewave.core.stimulation import StimCurrent, StimSequence, StimVoltage, Stim
from finitewave.core.tissue import CardiacTissue
from finitewave.core.tracker import Tracker, TrackerSequence
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/model/cardiac_model.py",".py","7531","240","from abc import ABC, abstractmethod
import copy
import warnings
from tqdm import tqdm
import numpy as np
import numba


class CardiacModel(ABC):
    """"""
    Base class for electrophysiological models.

    This class serves as the base for implementing various cardiac models.
    It provides methods for initializing the model, running simulations,
    and managing the state of the simulation.

    Attributes
    ----------
    cardiac_tissue : CardiacTissue
        The tissue object that represents the cardiac tissue in the simulation.
    stim_sequence : StimSequence
        The sequence of stimuli applied to the cardiac tissue.
    tracker_sequence : TrackerSequence
        The sequence of trackers used to monitor the simulation.
    command_sequence : CommandSequence
        The sequence of commands to execute during the simulation.
    state_loader : StateLoader
        The object responsible for loading the state of the simulation.
    state_saver : StateSaver
        The object responsible for saving the state of the simulation.
    stencil : Stencil
        The stencil used for numerical computations.
    u : ndarray
        Array representing the action potential (mV) across the tissue.
    u_new : ndarray
        Array for storing the updated action potential values.
    dt : float
        Time step for the simulation.
    dr : float
        Spatial step for the simulation.
    t_max : float
        Maximum time for the simulation (model units).
    t : float
        Current time in the simulation (model units).
    step : int
        Current step or iteration in the simulation.
    D_model : float
        Model-specific diffusion coefficient.
    prog_bar : bool
        Whether to display a progress bar during simulation.
    npfloat : type
        The floating-point type used for numerical computations.
    state_vars : list
        List of state variables to save and load during simulation.
    """"""
    def __init__(self):
        self.meta = {}
        self.cardiac_tissue = None
        self.stim_sequence = None
        self.tracker_sequence = None
        self.command_sequence = None
        self.state_loader = None
        self.state_saver = None
        self.stencil = None

        self.diffusion_kernel = None
        self.ionic_kernel = None

        self.u = np.ndarray
        self.u_new = np.ndarray
        self.weights = np.ndarray
        self.dt = 0.
        self.dr = 0.
        self.t_max = 0.
        self.t = 0
        self.step = 0
        self.D_model = 1.

        self.prog_bar = True
        self.npfloat = np.float64
        self.state_vars = []

    @abstractmethod
    def run_ionic_kernel(self):
        """"""
        Abstract method for running the ionic kernel. Must be implemented by
        subclasses.
        """"""
        pass

    def initialize(self):
        """"""
        Initializes the model for simulation. Sets up arrays, computes weights,
        and initializes stimuli, trackers, and commands.
        """"""
        self.u = np.zeros_like(self.cardiac_tissue.mesh, dtype=self.npfloat)
        self.u_new = self.u.copy()
        self.step = 0
        self.t = 0

        self.compute_weights()
        self.diffusion_kernel = self.stencil.select_diffusion_kernel()

        if self.stim_sequence:
            self.stim_sequence.initialize(self)

        if self.tracker_sequence:
            self.tracker_sequence.initialize(self)

        if self.command_sequence:
            self.command_sequence.initialize(self)

        if self.state_loader:
            self.state_loader.initialize(self)

        if self.state_saver:
            self.state_saver.initialize(self)

    def compute_weights(self):
        """"""
        Computes the weights for the stencil.
        """"""
        self.cardiac_tissue.compute_myo_indexes()

        if self.stencil is None:
            self.stencil = self.select_stencil(self.cardiac_tissue)

        self.weights = self.stencil.compute_weights(self, self.cardiac_tissue)

    def run(self, initialize=True, num_of_theads=None):
        """"""
        Runs the simulation loop. Handles stimuli, diffusion, ionic kernel
        updates, and tracking.

        Parameters
        ----------
        initialize : bool, optional
            Whether to (re)initialize the model before running the simulation.
            Default is True.
        """"""
        if initialize:
            self.initialize()

        numba.set_num_threads(numba.config.NUMBA_NUM_THREADS)

        if num_of_theads is not None:
            if num_of_theads > numba.config.NUMBA_NUM_THREADS:
                warnings.warn(
                    f""Selected number of threads ({num_of_theads}) exceeds the available threads ({numba.config.NUMBA_NUM_THREADS}). ""
                    f""Using the maximum available threads instead.""
                )
            num_of_theads = min(num_of_theads, numba.config.NUMBA_NUM_THREADS)
            numba.set_num_threads(num_of_theads)

        if self.t_max < self.t:
            raise ValueError(""t_max must be greater than current t."")

        if self.state_loader:
            self.state_loader.load()

        iters = int(np.ceil((self.t_max - self.t) / self.dt))
        bar_desc = f""Running {self.__class__.__name__}""

        for _ in tqdm(range(iters), total=iters, desc=bar_desc,
                      disable=not self.prog_bar):

            if self.stim_sequence:
                self.stim_sequence.stimulate_next()

            self.run_diffusion_kernel()
            self.run_ionic_kernel()

            if self.tracker_sequence:
                self.tracker_sequence.tracker_next()

            self.t += self.dt
            self.step += 1
            self.u_new, self.u = self.u, self.u_new

            if self.command_sequence:
                self.command_sequence.execute_next()

            if self.state_saver:
                self.state_saver.save()

            if self.check_termination():
                if self.state_saver:
                    self.state_saver.save()
                break

    def check_termination(self):
        """"""
        Checks whether the simulation should terminate based on the current
        time. The ``CommandSequence`` may change the ``t_max`` value during
        execution to control the simulation duration.

        Returns
        -------
        bool
            True if the simulation should terminate, False otherwise.
        """"""
        max_iters = int(np.ceil(self.t_max / self.dt))
        return (self.t > self.t_max) or (self.step > max_iters)

    def run_diffusion_kernel(self):
        """"""
        Executes the diffusion kernel computation using the current parameters
        and tissue weights.
        """"""
        self.diffusion_kernel(self.u_new, self.u, self.weights,
                              self.cardiac_tissue.myo_indexes)

    @abstractmethod
    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil based on the cardiac tissue properties.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue
            The tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        pass

    def clone(self):
        """"""
        Creates a deep copy of the current model instance.

        Returns
        -------
        CardiacModel
            A deep copy of the current CardiacModel instance.
        """"""
        return copy.deepcopy(self)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/model/__init__.py",".py","60","1","from finitewave.core.model.cardiac_model import CardiacModel","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stimulation/stim_voltage.py",".py","1676","50","from finitewave.core.stimulation.stim import Stim


class StimVoltage(Stim):
    """"""A stimulation class that sets a voltage value in the cardiac model.

    This class represents a specific type of stimulation where a voltage value
    is applied to the model at a specified time. It extends the base ``Stim``
    class and provides functionality for managing the stimulation process,
    including preparing and finalizing the stimulation.

    Attributes
    ----------
    volt_value : float
        The voltage value to be applied during the stimulation.
    """"""
    def __init__(self, time, volt_value, duration=0.0):
        """"""
        Initializes the StimVoltage object with the specified time and
        voltage value.

        Parameters
        ----------
        time : float
            The time at which the voltage stimulation is to occur.
        volt_value : float
            The voltage value to be applied during the stimulation.
        duration : float, optional
            The duration for which the voltage will be applied. The default
            value is 0.0, indicating that the voltage will be applied
            instantaneously.
        """"""
        super().__init__(time, duration)
        self.volt_value = volt_value

    def initialize(self, model):
        """"""
        Prepares the stimulation for application.

        This method sets the ``passed`` flag to ``False``, indicating that the
        stimulation has not yet been applied.

        Parameters
        ----------
        model : CardiacModel
            The simulation model to which the voltage stimulation will be
            applied.
        """"""
        self.passed = False
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stimulation/__init__.py",".py","246","4","from finitewave.core.stimulation.stim_current import StimCurrent
from finitewave.core.stimulation.stim_sequence import StimSequence
from finitewave.core.stimulation.stim_voltage import StimVoltage
from finitewave.core.stimulation.stim import Stim","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stimulation/stim_sequence.py",".py","2467","78","class StimSequence:
    """"""A sequence of stimuli to be applied to the cardiac model.

    This class manages a list of stimulation objects and applies them to the
    model based on the simulation time. It handles the initialization of
    stimuli, adding and removing stimuli, and applying the next set of stimuli
    in the sequence.

    Attributes
    ----------
    sequence : list
        A list of ``Stim`` objects representing the sequence of stimuli to be
        applied to the model.

    model : CardiacModel, optional
        The cardiac model to which the stimuli will be applied. This is set
        during initialization.
    """"""

    def __init__(self):
        """"""
        Initializes the StimSequence object with an empty sequence and no
        associated model.
        """"""
        self.sequence = []
        self.model = None

    def initialize(self, model):
        """"""
        Prepares each stimulus in the sequence for application.

        This method sets up each stimulus based on the provided model
        ensuring that each stimulus is ready to be applied according to its
        specified start time.

        Parameters
        ----------
        model : CardiacModel
            The simulation model that will be used to prepare the stimuli.
        """"""
        self.model = model
        for stim in self.sequence:
            stim.initialize(model)

    def add_stim(self, stim):
        """"""
        Adds a stimulus to the sequence.

        Parameters
        ----------
        stim : Stim
            The ``Stim`` object to be added to the sequence.
        """"""
        self.sequence.append(stim)

    def remove_stim(self):
        """"""
        Removes all stimuli from the sequence.

        This method clears the sequence, effectively removing all stimuli that
        were previously added.
        """"""
        self.sequence = []

    def stimulate_next(self):
        """"""
        Applies the next set of stimuli based on the current time in the model.

        This method checks each stimulus in the sequence to determine if it
        should be applied based on the current simulation time. If a stimulus
        is due to be applied and has not yet been marked as passed, it is
        stimulated and then marked as done.
        """"""
        for stim in self.sequence:
            if self.model.t >= stim.t and not stim.passed:
                stim.stimulate(self.model)
                stim.update_status(self.model)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stimulation/stim.py",".py","1624","57","from abc import ABC, abstractmethod


class Stim(ABC):
    """"""Base class for stimulation in cardiac models.

    The ``Stim`` class represents a general stimulation object used in cardiac
    simulations. It provides methods to manage the timing and state of
    stimulation. Subclasses should implement specific stimulation behaviors.

    Attributes
    ----------
    t : float
        The time at which the stimulation is to occur.
    duration : float
        The duration for which the stimulation will be applied.
    passed : bool
        A flag indicating whether the stimulation has been applied.
    """"""

    def __init__(self, time, duration=0.0):
        """"""
        Initializes the Stim object with the specified time.

        Parameters
        ----------
        time : float
            The time at which the stimulation is scheduled to occur.
        duration : float, optional
            The duration for which the stimulation will be applied. The default
            value is 0.0, indicating that the stimulation will be applied
            instantaneously.
        """"""
        self.t = time
        self.duration = duration
        self.passed = False

    @abstractmethod
    def stimulate(self, model):
        """"""
        Applies the stimulation to the provided model.
        """"""
        pass

    @abstractmethod
    def initialize(self, model):
        """"""
        Prepares the stimulation for application.
        """"""
        pass

    def update_status(self, model):
        """"""
        Marks the stimulation as completed.
        """"""
        self.passed = model.t >= (self.t + self.duration)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stimulation/stim_current.py",".py","1380","45","from finitewave.core.stimulation.stim import Stim


class StimCurrent(Stim):
    """"""A stimulation class that applies a current value to the cardiac model.

    This class represents a type of stimulation where a current is applied to
    the model for a specified duration. It extends the base ``Stim`` class and
    includes methods for preparing the stimulation and updating its status
    based on elapsed time.

    Attributes
    ----------
    curr_value : float
        The current value to be applied during the stimulation.
    """"""

    def __init__(self, time, curr_value, duration):
        """"""
        Initializes the StimCurrent object with the specified parameters.

        Parameters
        ----------
        time : float
            The time at which the current stimulation is to start.
        curr_value : float
            The current value to be applied during the stimulation.
        duration : float
            The duration for which the current will be applied.
        """"""
        super().__init__(time, duration)
        self.curr_value = curr_value

    def initialize(self, model):
        """"""
        Prepares the stimulation for application.

        Parameters
        ----------
        model : CardiacModel
            The simulation model to which the current stimulation will be
            applied.
        """"""
        self.passed = False
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/state/__init__.py",".py","137","2","from finitewave.core.state.state_loader import StateLoader
from finitewave.core.state.state_saver import StateSaver, StateSaverCollection","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/state/state_saver.py",".py","3427","123","from pathlib import Path
import numpy as np


class StateSaver:
    """""" This class provides functionality to save the state of a
    simulation model, including all relevant variables specified in the model's
    ``state_vars`` attribute.

    Attributes
    ----------
    path : str
        Directory path where the simulation state will be saved.
    passed : bool
        Whether the state has been saved.
    model : CardiacModel
        The model instance for which the state will be saved or saved.
    time : float
        The time at which to save the state of the simulation.
    """"""

    def __init__(self, path=""."", time=-1):
        """"""
        Initializes the state keeper with the given path.

        Parameters
        ----------
        path : str, optional
            The directory path where the simulation state will be saved.
            The default is ""."".
        time : float, optional
            The time at which to save the state of the simulation.
            The default is -1 (save at the end of the simulation).
        """"""
        self.path = path
        self.passed = False
        self.model = None
        self.time = time

    def initialize(self, model):
        """"""
        Initializes the state keeper with the given model.

        Parameters
        ----------
        model : CardiacModel
            The model instance for which the state will be saved or saved.
        """"""
        self.model = model
        self.passed = self.path == """"

    def save(self):
        """"""
        Saves the state of the given model to the specified ``path``
        directory.

        This method creates the necessary directories if they do not exist and
        saves each variable listed in the model's ``state_vars`` attribute as
        a numpy file.
        """"""
        if self.passed:
            return

        if self.time < 0 and self.model.t < self.model.t_max:
            return

        if self.time >= 0 and self.model.t < self.time:
            return

        if not Path(self.path).exists():
            Path(self.path).mkdir(parents=True, exist_ok=True)

        for var in self.model.state_vars:
            self._save_variable(Path(self.path).joinpath(var + "".npy""),
                                self.model.__dict__[var])

        self.passed = True

    def _save_variable(self, var_path, var):
        """"""
        Saves a variable to a numpy file.

        Parameters
        ----------
        var_path : str
            The file path where the variable will be saved.

        var : numpy.ndarray
            The variable to be saved.
        """"""
        np.save(var_path, var)


class StateSaverCollection(StateSaver):
    """""" This class saves multiple states of a simulation model.

    Attributes
    ----------
    savers : list
        List of StateSaver objects.
    """"""
    def __init__(self):
        super().__init__()
        self.savers = []

    def initialize(self, model):
        """""" Initializes the state saver collection with the given model.

        Parameters
        ----------
        model : CardiacModel
            The model instance for which the state will be saved or saved.
        """"""
        for saver in self.savers:
            saver.initialize(model)

    def save(self):
        """""" Applies the save method to each StateSaver object in the
        collection.
        """"""
        for saver in self.savers:
            saver.save()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/state/state_loader.py",".py","2307","81","from pathlib import Path
import numpy as np


class StateLoader:
    """""" This class provides functionality to load the state of a simulation
    model, including all relevant variables specified in the model's
    ``state_vars`` attribute.
    Attributes
    ----------
    path : str
        Directory path from where the simulation state will be loaded.
    passed : bool
        Whether the state has been loaded.
    model : CardiacModel
        The model instance for which the state will be saved or loaded.
    """"""

    def __init__(self, path=""""):
        """"""
        Initializes the state keeper with the given path.

        Parameters
        ----------
        path : str, optional
            The directory path from where the simulation state will be loaded.
        """"""
        self.path = path
        self.passed = True
        self.model = None

    def initialize(self, model):
        """"""
        Initializes the state keeper with the given model.

        Parameters
        ----------
        model : CardiacModel
            The model instance for which the state will be saved or loaded.
        """"""
        self.model = model
        self.passed = self.path == """"

        if not Path(self.path).exists():
            message = (f""Unable to load state from {self.path}. "" +
                       ""Directory does not exist."")
            raise FileNotFoundError(message)

    def load(self):
        """"""
        Loads the state from the specified ``path`` directory and sets
        it in the given model.

        This method loads each variable listed in the model's ``state_vars``
        attribute from numpy files and sets these variables in the model.
        """"""
        if self.passed:
            return

        for var in self.model.state_vars:
            val = self._load_variable(Path(self.path).joinpath(var + "".npy""))
            setattr(self.model, var, val)

        self.passed = True

    def _load_variable(self, var_path):
        """"""
        Loads a state variable from a numpy file.

        Parameters
        ----------
        var_path : str
            The file path from which the variable will be loaded.

        Returns
        -------
        numpy.ndarray
            The variable loaded from the file.
        """"""
        return np.load(var_path)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/exception/__init__.py",".py","73","1","from finitewave.core.exception.exceptions import IncorrectNumberOfWeights","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/exception/exceptions.py",".py","1398","47","

class IncorrectWeightsShapeError(Exception):
    def __init__(self, *args: object) -> None:
        super().__init__(*args)


class IncorrectNumberOfWeights(Exception):
    """"""Exception raised for errors in the shape of weights in the
    ``CardiacTissue`` class.

    This exception is used to indicate that the shape of weights provided does
    not match the expected dimensions. It includes details about the incorrect
    shape and the expected shapes.

    Parameters
    ----------
    number_of_weights : int
        The number of weights in the incorrect shape.

    n1 : int
        The target number of weights in one dimension.

    n2 : int
        The target number of weights in another dimension.
    """"""

    def __init__(self, number_of_weights, n1, n2):
        """"""
        Initializes the ``IncorrectNumberOfWeights`` with details about the
        incorrect shape and expected dimensions.

        Parameters
        ----------
        number_of_weights : int
            The number of weights in the incorrect shape.

        n1 : int
            The target number of weights in one dimension.

        n2 : int
            The target number of weights in another dimension.
        """"""
        self.message = (f""Number of weights provided ({number_of_weights})"" +
                        f""does not match the expected {n1} or {n2}."")
        super().__init__(self.message)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/command/command.py",".py","1330","53","from abc import ABC, abstractmethod


class Command(ABC):
    """"""Base class for a command to be executed during a simulation.

    Attributes
    ----------
    t : float
        The time at which the command should be executed.

    passed : bool
        Flag indicating whether the command has been executed.
    """"""

    def __init__(self, time=None):
        """"""
        Initializes a Command instance with the specified execution time.

        Parameters
        ----------
        time : float
            The time at which the command should be executed.
        """"""
        self.t = time
        self.passed = False

    @abstractmethod
    def execute(self, model):
        """"""
        Abstract method for executing the command. This method should be
        implemented by subclasses to define the specific behavior of the
        command.

        Parameters
        ----------
        model : CardiacModel
            The cardiac model instance on which the command will be executed.
        """"""
        pass

    def update_status(self, model):
        """"""
        Marks the command as executed.

        Parameters
        ----------
        model : CardiacModel
            The cardiac model instance on which the command was executed
        """"""
        self.passed = model.t >= self.t
        return self.passed
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/command/__init__.py",".py","120","2","from finitewave.core.command.command import Command
from finitewave.core.command.command_sequence import CommandSequence","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/command/command_sequence.py",".py","1567","60","

class CommandSequence:
    """"""Manages a sequence of commands to be executed during a simulation.

    Attributes
    ----------
    sequence : list
        A list of ``Command`` instances representing the sequence of commands
        to be executed.

    model : CardiacModel
        The cardiac model instance on which commands will be executed.
    """"""

    def __init__(self):
        self.sequence = []
        self.model = None

    def initialize(self, model):
        """"""
        Initializes the CommandSequence with the specified model and resets
        the execution status of all commands.

        Parameters
        ----------
        model : CardiacModel
            The cardiac model instance to be used for command execution.
        """"""
        self.model = model
        for command in self.sequence:
            command.passed = False

    def add_command(self, command):
        """"""
        Adds a ``Command`` instance to the sequence.

        Parameters
        ----------
        command : Command
            The command instance to be added to the sequence.
        """"""
        self.sequence.append(command)

    def remove_commands(self):
        """"""
        Clears the sequence of all commands.
        """"""
        self.sequence = []

    def execute_next(self):
        """"""
        Executes commands whose time has arrived and which have not been
        executed yet.
        """"""
        for command in self.sequence:
            if not command.passed and command.update_status(self.model):
                command.execute(self.model)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/tracker/tracker.py",".py","2817","102","from pathlib import Path
from abc import ABC, abstractmethod
import copy

import numpy as np


class Tracker(ABC):
    """"""Base class for trackers used in simulations.

    This class provides a base implementation for trackers that monitor and
    record various aspects of the simulation. Trackers can be used to gather
    data such as activation times, wave dynamics, or ECG readings.

    Attributes
    ----------
    model : CardiacModel
        The simulation model to which the tracker is attached. This allows
        the tracker to access the model's state and data during the simulation.

    file_name : str
        The name of the file where the tracked data will be saved.
        Default is an empty string.

    path : str
        The directory path where the tracked data will be saved.
        Default is the current directory.

    start_time : float
        The time step at which tracking will begin. Default is 0.

    end_time : float
        The time step at which tracking will end. Default is infinity.

    step : int
        The frequency at which tracking will occur. Default is 1.
    """"""

    # __metaclass__ = ABCMeta

    def __init__(self):
        self.model = None
        self.file_name = ""tracked_data""
        self.path = "".""
        self.start_time = 0
        self.end_time = np.inf
        self.step = 1

    @abstractmethod
    def initialize(self, model):
        """"""
        Abstract method to be implemented by subclasses for initializing
        the tracker with the simulation model.

        Parameters
        ----------
        model : CardiacModel
            The simulation model to which the tracker will be attached.
        """"""
        pass

    @abstractmethod
    def _track(self):
        """"""
        Abstract method to be implemented by subclasses for tracking and
        recording data during the simulation.
        """"""
        pass

    def track(self):
        """"""
        Tracks and records data during the simulation.

        This method calls the ``_track`` method at the specified tracking
        frequency and within the specified time range.
        """"""
        if self.start_time > self.model.t or self.model.t > self.end_time:
            return
        # Check if the current time step is within the tracking frequency
        if self.model.step % self.step != 0:
            return

        self._track()

    def clone(self):
        """"""
        Creates a deep copy of the current tracker instance.

        Returns
        -------
        Tracker
            A deep copy of the current Tracker instance.
        """"""
        return copy.deepcopy(self)

    def write(self):
        """"""
        Writes the tracked data to a file.
        """"""
        np.save(Path(self.path, self.file_name).with_suffix('.npy'),
                self.output)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/tracker/tracker_sequence.py",".py","1842","65","class TrackerSequence:
    """"""Manages a sequence of trackers for a simulation.

    The ``TrackerSequence`` class allows for the management of multiple
    ``Tracker`` instances. It provides methods to initialize trackers, add or
    remove trackers from the sequence, and iterate over the trackers to perform
    their tracking functions.

    Attributes
    ----------
    sequence : list of Tracker
        List containing the trackers in the sequence. The trackers are executed
        in the order they are added.

    model : CardiacModel or None
        The simulation model to which the trackers are attached. It is set
        during initialization.

    """"""

    def __init__(self):
        """"""
        Initializes the TrackerSequence with an empty sequence and no model.
        """"""
        self.sequence = []
        self.model = None

    def initialize(self, model):
        """"""
        Initializes all trackers in the sequence with the provided simulation
        model.

        Parameters
        ----------
        model : CardiacModel
            The simulation model to which the trackers will be attached.
        """"""
        self.model = model
        for tracker in self.sequence:
            tracker.initialize(model)

    def add_tracker(self, tracker):
        """"""
        Adds a new tracker to the end of the sequence.

        Parameters
        ----------
        tracker : Tracker
            The tracker instance to be added to the sequence.
        """"""
        self.sequence.append(tracker)

    def remove_trackers(self):
        """"""
        Removes all trackers from the sequence.
        """"""
        self.sequence = []

    def tracker_next(self):
        """"""
        Executes the `track` method of each tracker in the sequence.
        """"""
        for tracker in self.sequence:
            tracker.track()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/tracker/__init__.py",".py","120","2","from finitewave.core.tracker.tracker import Tracker
from finitewave.core.tracker.tracker_sequence import TrackerSequence","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stencil/__init__.py",".py","51","1","from finitewave.core.stencil.stencil import Stencil","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/stencil/stencil.py",".py","1622","47","from abc import ABC, abstractmethod


class Stencil(ABC):
    """"""Base class for calculating stencil weights used in numerical
    simulations.

    This abstract base class defines the interface for calculating stencil
    weights for numerical simulations. It includes a caching mechanism to
    optimize performance by reducing the number of symbolic calculations. Also,
    it handles the boundary conditions for the numerical scheme.
    """"""
    @abstractmethod
    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the stencil weights based on the provided parameters.

        This method must be implemented by subclasses to compute the stencil
        weights used for numerical simulations. The weights are calculated
        based on the tissue mesh and spatial step. Additional parameters can
        be passed as arguments or keyword arguments.

        Parameters
        ----------
        model : CardiacModel
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue
            A tissue object representing the cardiac tissue.

        Returns
        -------
        np.ndarray
            A numpy array containing the stencil weights.
        """"""
        pass

    @abstractmethod
    def select_diffusion_kernel():
        """"""
        Builds the diffusion kernel for the numerical scheme.

        This method must be implemented by subclasses to build the diffusion
        kernel used for the numerical scheme. The kernel is used to compute the
        diffusion of the potential in the tissue mesh.
        """"""
        pass
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/fibrosis/__init__.py",".py","69","1","from finitewave.core.fibrosis.fibrosis_pattern import FibrosisPattern","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/fibrosis/fibrosis_pattern.py",".py","1744","52","from abc import ABC, abstractmethod


class FibrosisPattern(ABC):
    """"""Abstract base class for generating and applying fibrosis patterns to
    cardiac tissue.

    This class defines an interface for creating fibrosis patterns and applying
    them to cardiac tissue models. Subclasses must implement the ``generate``
    method to define specific patterns. The ``apply`` method uses the generated
    pattern to modify the mesh of the cardiac tissue.
    """"""
    def __init__(self):
        pass

    @abstractmethod
    def generate(self, shape=None, mesh=None):
        """"""
        Generates a fibrosis pattern for the given shape and optionally based
        on the provided mesh.

        Parameters
        ----------
        shape : tuple
            The shape of the mesh (e.g., (ni, nj) or (ni, nj, nk)).
        mesh : numpy.ndarray, optional
            The existing mesh to base the pattern on. Default is None.

        Returns
        -------
        numpy.ndarray
            A new mesh array with the applied fibrosis pattern.
        """"""

    def apply(self, cardiac_tissue):
        """"""
        Applies the generated fibrosis pattern to the specified cardiac tissue
        object.

        This method calls the ``generate`` method to create the pattern and
        then updates the ``mesh`` attribute of the ``cardiac_tissue`` object
        with the generated pattern.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue
            The cardiac tissue object to which the fibrosis pattern will be
            applied. The ``mesh`` attribute of this object will be updated with
            the generated pattern.
        """"""
        cardiac_tissue.mesh = self.generate(mesh=cardiac_tissue.mesh)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/tissue/__init__.py",".py","63","1","from finitewave.core.tissue.cardiac_tissue import CardiacTissue","Python"
"Cell biology","finitewave/Finitewave","finitewave/core/tissue/cardiac_tissue.py",".py","2732","101","from abc import ABC, abstractmethod
import copy
import numpy as np


class CardiacTissue(ABC):
    """"""Base class for a model tissue.

    This class represents the tissue model used in cardiac simulations.
    It includes attributes and methods for defining the tissue structure,
    ts properties, and handling fibrosis patterns.

    Attributes
    ----------
    meta : dict
        A dictionary containing metadata about the tissue.
    special_boundaries : np.ndarray
        An array containing labels for special boundaries in the tissue mesh.
        This array is used to define Dirichlet boundary conditions as points
        with non-zero values are ignored in the solver.
    """"""
    def __init__(self):
        self.meta = {}
        self.special_boundaries = None

    @property
    def mesh(self):
        """"""
        Gets the tissue mesh array.

        Returns
        -------
        numpy.ndarray
            The tissue mesh array.
        """"""
        return self._mesh

    @mesh.setter
    def mesh(self, mesh):
        """"""
        Sets the tissue mesh array.

        Parameters
        ----------
        mesh : numpy.ndarray
            The tissue mesh array.
        """"""
        if mesh.ndim != self.meta['dim']:
            raise ValueError(""Mesh dimension must match the tissue dimension."")

        self._mesh = mesh
        self.add_boundaries()

    def compute_myo_indexes(self):
        """"""
        Computes flat indices of the myocytes in the tissue mesh.
        """"""
        if self.special_boundaries is not None:
            self.myo_indexes = np.flatnonzero((self.mesh == 1) &
                                              (self.special_boundaries == 0))
            return

        self.myo_indexes = np.flatnonzero(self.mesh == 1)

    @abstractmethod
    def add_boundaries(self):
        """"""
        Abstract method to be implemented by subclasses for adding boundary
        conditions to the tissue mesh.
        """"""
        pass

    def add_pattern(self, fibro_pattern):
        """"""
        Applies a fibrosis pattern to the tissue mesh.

        Parameters
        ----------
        fibro_pattern : FibrosisPattern
            A fibrosis pattern object to apply to the tissue mesh.
        """"""
        fibro_pattern.apply(self)

    def clean(self):
        """"""
        Removes all fibrosis points from the mesh, setting them to ``1``
        (healthy tissue).
        """"""
        self.mesh[self.mesh == 2] = 1

    def clone(self):
        """"""
        Creates a deep copy of the current ``CardiacTissue`` instance.

        Returns
        -------
        CardiacTissue
            A deep copy of the current ``CardiacTissue`` instance.
        """"""
        return copy.deepcopy(self)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/__init__.py",".py","598","30","from .exception import IncorrectWeightsModeError2D
from .fibrosis import (
    Diffuse2DPattern,
    Structural2DPattern
)
from .model import (
    AlievPanfilov2D,
    Barkley2D,
    MitchellSchaeffer2D,
    FentonKarma2D,
    BuenoOrovio2D,
    LuoRudy912D,
    TP062D,
    Courtemanche2D
)
from .stencil import (
    AsymmetricStencil2D,
    IsotropicStencil2D,
    SymmetricStencil2D
)
from .stimulation import (
    StimCurrentArea2D,
    StimCurrentCoord2D,
    StimVoltageCoord2D,
    StimCurrentMatrix2D,
    StimVoltageMatrix2D
)
from .tissue import CardiacTissue2D
from .tracker import *
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/fenton_karma_2d.py",".py","10064","330","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class FentonKarma2D(CardiacModel):

    def __init__(self):
        """"""
        Two-dimensional implementation of the Fenton-Karma model of cardiac electrophysiology.

        The Fenton-Karma model is a minimal three-variable model designed to reproduce
        essential features of human ventricular action potentials, including restitution, 
        conduction velocity dynamics, and spiral wave behavior. It captures the interaction 
        between fast depolarization, slow repolarization, and calcium-mediated effects 
        through simplified phenomenological equations.

        This implementation corresponds to the MLR-I parameter set described in the original paper
        and supports 2D isotropic and anisotropic tissue simulations with diffusion.

        Attributes
        ----------
        u : np.ndarray
            Transmembrane potential (normalized, dimensionless).
        v : np.ndarray
            Fast recovery variable, representing sodium channel inactivation.
        w : np.ndarray
            Slow recovery variable, representing calcium channel dynamics.
        D_model : float
            Baseline diffusion coefficient used in the diffusion stencil.
        state_vars : list of str
            Names of the state variables stored during the simulation.
        npfloat : str
            Floating point precision (default is 'float64').

        Model Parameters
        ----------------
        tau_r : float
            Time constant for repolarization (outward current).
        tau_o : float
            Time constant for the open-state decay of fast sodium channels.
        tau_d : float
            Time constant for depolarization (fast inward current).
        tau_si : float
            Time constant for the slow inward (calcium-like) current.
        tau_v_m : float
            Time constant for inactivation gate v (membrane below threshold).
        tau_v_p : float
            Time constant for recovery gate v (above threshold).
        tau_w_m : float
            Time constant for recovery gate w (below threshold).
        tau_w_p : float
            Time constant for decay of w (above threshold).
        k : float
            Steepness parameter for the slow inward current.
        u_c : float
            Activation threshold for recovery dynamics.
        uc_si : float
            Activation threshold for the slow inward current.
        
        Paper
        -----
        Fenton, F., & Karma, A. (1998).
        Vortex dynamics in three-dimensional continuous myocardium 
        with fiber rotation: Filament instability and fibrillation.
        Chaos, 8(1), 20-47.
        https://doi.org/10.1063/1.166311
                
        """"""
        super().__init__()
        self.v = np.ndarray
        self.w = np.ndarray

        self.D_model = 1.

        self.state_vars = [""u"", ""v"", ""w""]
        self.npfloat    = 'float64'

        # model parameters (MLR-I)
        self.tau_r   = 130
        self.tau_o   = 12.5
        self.tau_d   = 0.172
        self.tau_si  = 127
        self.tau_v_m = 18.2
        self.tau_v_p = 10
        self.tau_w_m = 80
        self.tau_w_p = 1020
        self.k       = 10
        self.u_c     = 0.13
        self.uc_si   = 0.85

        # initial conditions
        self.init_u = 0.0
        self.init_v = 1.0
        self.init_w = 1.0

    def initialize(self):
        """"""
        Initializes the model for simulation.
        """"""
        super().initialize()
        self.u = self.init_u * np.ones_like(self.u, dtype=self.npfloat)
        self.v = self.init_v * np.ones_like(self.u, dtype=self.npfloat)
        self.w = self.init_w * np.ones_like(self.u, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Fenton-Karma model.
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.v, self.w, self.cardiac_tissue.myo_indexes, 
                        self.dt, self.tau_d, self.tau_o, self.tau_r, self.tau_si, 
                        self.tau_v_m, self.tau_v_p, self.tau_w_m, self.tau_w_p,
                        self.k, self.u_c, self.uc_si)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()
    
@njit
def calc_Jfi(u, v, u_c, tau_d):
    """"""
    Computes the fast inward current (J_fi) for the Fenton-Karma model.

    This current is responsible for the rapid depolarization of the membrane
    potential. It is active only when the membrane potential exceeds a threshold `u_c`.

    Parameters
    ----------
    u : float
        Current membrane potential (dimensionless).
    v : float
        Fast recovery gate (sodium channel inactivation).
    u_c : float
        Activation threshold for the inward current.
    tau_d : float
        Time constant for depolarization.

    Returns
    -------
    float
        Value of the fast inward current at this point.
    """"""
    H = 1.0 if (u - u_c) >= 0 else 0.0
    return -(v*H*(1-u)*(u - u_c))/tau_d

@njit
def calc_Jso(u, u_c, tau_o, tau_r):
    """"""
    Computes the slow outward current (J_so) for repolarization.

    This current contains two parts:
    - a linear repolarizing component active below threshold `u_c`
    - a constant repolarizing component above threshold

    Parameters
    ----------
    u : float
        Membrane potential.
    u_c : float
        Activation threshold.
    tau_o : float
        Time constant for subthreshold repolarization.
    tau_r : float
        Time constant for suprathreshold repolarization.

    Returns
    -------
    float
        Value of the outward repolarizing current.
    """"""
    H1 = 1.0 if (u_c - u) >= 0 else 0.0
    H2 = 1.0 if (u - u_c) >= 0 else 0.0

    return u*H1/tau_o + H2/tau_r

@njit
def calc_Jsi(u, w, k, uc_si, tau_si):
    """"""
    Computes the slow inward (calcium-like) current (J_si).

    This current is responsible for the plateau phase of the action potential
    and depends on the gating variable `w` and a smoothed activation threshold.

    Parameters
    ----------
    u : float
        Membrane potential.
    w : float
        Slow recovery gate.
    k : float
        Steepness of the tanh activation curve.
    uc_si : float
        Activation threshold for the slow inward current.
    tau_si : float
        Time constant for the slow inward current.

    Returns
    -------
    float
        Value of the slow inward current.
    """"""
    return -w*(1 + np.tanh(k*(u - uc_si)))/(2*tau_si)

@njit
def calc_v(v, u, dt, u_c, tau_v_m, tau_v_p):
    """"""
    Updates the fast recovery gate `v` over time.

    This gate controls sodium channel availability and changes depending on
    whether the membrane potential is below or above a critical threshold.

    Parameters
    ----------
    v : float
        Current value of the recovery variable.
    u : float
        Membrane potential.
    dt : float
        Time step.
    u_c : float
        Activation threshold.
    tau_v_m : float
        Time constant below threshold.
    tau_v_p : float
        Time constant above threshold.

    Returns
    -------
    float
        Updated value of `v`.
    """"""
    H1 = 1.0 if (u_c - u) >= 0 else 0.0
    H2 = 1.0 if (u - u_c) >= 0 else 0.0
    v += dt*(H1*(1 - v)/tau_v_m - H2*v/tau_v_p)
    return v

@njit
def calc_w(w, u, dt, u_c, tau_w_m, tau_w_p):
    """"""
    Updates the slow recovery gate `w` over time.

    This gate represents the calcium channel recovery and decays similarly to `v`,
    depending on whether the membrane potential is above or below threshold `u_c`.

    Parameters
    ----------
    w : float
        Current value of the recovery variable.
    u : float
        Membrane potential.
    dt : float
        Time step.
    u_c : float
        Activation threshold.
    tau_w_m : float
        Time constant below threshold.
    tau_w_p : float
        Time constant above threshold.

    Returns
    -------
    float
        Updated value of `w`.
    """"""
    H1 = 1.0 if (u_c - u) >= 0 else 0.0
    H2 = 1.0 if (u - u_c) >= 0 else 0.0
    w += dt*(H1*(1 - w)/tau_w_m - H2*w/tau_w_p)
    return w

@njit(parallel=True)
def ionic_kernel_2d(u_new, u, v, w, indexes, dt, 
                    tau_d, tau_o, tau_r, tau_si, 
                    tau_v_m, tau_v_p, tau_w_m, tau_w_p,
                    k, u_c, uc_si):
    """"""
    Computes the ionic kernel for the Fenton-Karma 2D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""

    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        v[i, j] = calc_v(v[i, j], u[i, j], dt, u_c, tau_v_m, tau_v_p)
        w[i, j] = calc_w(w[i, j], u[i, j], dt, u_c, tau_w_m, tau_w_p)

        J_fi = calc_Jfi(u[i, j], v[i, j], u_c, tau_d)
        J_so = calc_Jso(u[i, j], u_c, tau_o, tau_r)
        J_si = calc_Jsi(u[i, j], v[i, j], k, uc_si, tau_si)

        u_new[i, j] += dt * (-J_fi - J_so - J_si)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/barkley_2d.py",".py","4734","167","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class Barkley2D(CardiacModel):
    """"""
    Two-dimensional implementation of the Barkley model for excitable media.

    The Barkley model is a simplified two-variable reaction–diffusion system
    originally developed to study wave propagation in excitable media. While it is 
    not biophysically detailed, it captures essential qualitative features of 
    cardiac-like excitation dynamics such as spiral waves, wave break, and reentry.

    This implementation is included for benchmarking, educational purposes, 
    and comparison against more detailed cardiac models.

    Attributes
    ----------
    u : np.ndarray
        Excitation variable (analogous to membrane potential).
    v : np.ndarray
        Recovery variable controlling excitability.
    D_model : float
        Diffusion coefficient for excitation variable.
    state_vars : list of str
        Names of variables saved during simulation.
    npfloat : str
        Floating-point precision (default: 'float64').

    Model Parameters
    ----------------
    a : float
        Threshold-like parameter controlling excitability.
    b : float
        Recovery time scale.
    eap : float
        Controls sharpness of the activation term (nonlinear gain).

    Paper
    -----
    Barkley, D. (1991).
    A model for fast computer simulation of waves in excitable media.
    Physica D: Nonlinear Phenomena, 61-70.
    https://doi.org/10.1016/0167-2789(86)90198-1.

    """"""

    def __init__(self):
        """"""
        Initializes the Barkley2D instance with default parameters.
        """"""
        super().__init__()
        self.v = np.ndarray

        self.D_model = 1.
    
        self.state_vars = [""u"", ""v""]
        self.npfloat    = 'float64'

        # model parameters
        self.a    = 0.75
        self.b    = 0.02
        self.eap  = 0.02

        # initial conditions
        self.init_u = 0.0
        self.init_v = 0

    def initialize(self):
        """"""
        Initializes the model for simulation.
        """"""
        super().initialize()
        self.u = self.init_u * np.ones_like(self.u, dtype=self.npfloat)
        self.v = self.init_v * np.ones_like(self.u, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Barkley model.
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.v, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.a, self.b, self.eap)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()

@njit
def calc_v(v, u, dt):
    """"""
    Updates the recovery variable v for the Barkley model.

    The recovery variable follows a simple linear relaxation toward the
    excitation variable `u`, simulating return to the resting state after excitation.

    Parameters
    ----------
    v : float
        Current value of the recovery variable.
    u : float
        Current value of the excitation variable.
    dt : float
        Time step for numerical integration.

    Returns
    -------
    float
        Updated value of the recovery variable.
    """"""

    v += dt*(u-v)
    return v


@njit(parallel=True)
def ionic_kernel_2d(u_new, u, v, indexes, dt, a, b, eap):
    """"""
    Computes the ionic kernel for the Barkley 2D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""

    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        v[i, j] = calc_v(v[i, j], u[i, j], dt)

        u_new[i, j] += dt * (u[i, j]*(1 - u[i, j])*(u[i, j] - (v[i, j] + b)/a))/eap
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/__init__.py",".py","333","9","from .aliev_panfilov_2d import AlievPanfilov2D
from .barkley_2d import Barkley2D
from .mitchell_schaeffer_2d import MitchellSchaeffer2D
from .fenton_karma_2d import FentonKarma2D
from .bueno_orovio_2d import BuenoOrovio2D
from .luo_rudy91_2d import LuoRudy912D
from .tp06_2d import TP062D
from .courtemanche_2d import Courtemanche2D
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/tp06_2d.py",".py","35424","1105","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class TP062D(CardiacModel):
    """"""
    Implements the ten Tusscher–Panfilov 2006 (TP06) human ventricular ionic model in 2D.

    The TP06 model is a detailed biophysical model of the human ventricular 
    action potential, designed to simulate realistic electrical behavior in 
    tissue including alternans, reentrant waves, and spiral wave breakup.

    This model includes:
    - 18 dynamic state variables (voltage, ion concentrations, channel gates, buffers)
    - Full calcium handling with subspace (cass) and sarcoplasmic reticulum (casr)
    - Sodium, potassium, and calcium currents including background, exchanger, and pumps
    - Buffering effects and intracellular transport

    Finitewave provides this model in 2D form for efficient simulation and 
    reproducible experimentation with custom spatial setups.

    Attributes
    ----------
    D_model : float
        Diffusion coefficient specific to this model (cm²/ms).
    state_vars : list of str
        List of all state variable names, used for checkpointing and logging.
    ko : float
        Extracellular potassium concentration (mM).
    cao : float
        Extracellular calcium concentration (mM).
    nao : float
        Extracellular sodium concentration (mM).
    Vc : float
        Cytoplasmic volume (μL).
    Vsr : float
        Sarcoplasmic reticulum volume (μL).
    Vss : float
        Subsarcolemmal space volume (μL).
    R, T, F : float
        Universal gas constant, absolute temperature, and Faraday constant.
    RTONF : float
        Precomputed RT/F value for Nernst equation.
    CAPACITANCE : float
        Membrane capacitance per unit area (μF/cm²).
    gna, gcal, gkr, gks, gk1, gto : float
        Conductances for major ionic channels.
    gbna, gbca : float
        Background sodium and calcium conductances.
    gpca, gpk : float
        Pump-related conductances.
    knak, knaca : float
        Maximal Na⁺/K⁺ pump and Na⁺/Ca²⁺ exchanger rates.
    Km*, Kbuf*, Vmaxup, Vrel, etc.
        Numerous kinetic constants for buffering, pump activity, and calcium handling.

    Paper
    -----
    ten Tusscher KH, Panfilov AV. 
    Alternans and spiral breakup in a human ventricular tissue model.
    Am J Physiol Heart Circ Physiol. 2006 Sep;291(3):H1088–H1100.
    https://doi.org/10.1152/ajpheart.00109.2006

    """"""

    def __init__(self):
        super().__init__()
        self.D_model    = 0.154

        self.state_vars = [""u"", ""cai"", ""casr"", ""cass"", ""nai"", ""Ki"",
                           ""m"", ""h"", ""j"", ""xr1"", ""xr2"", ""xs"", ""r"",
                           ""s"", ""d"", ""f"", ""f2"", ""fcass"", ""rr"", ""oo""]
        self.npfloat    = 'float64'

        # Extracellular Ion Concentrations (mM)
        self.ko  = 5.4     # Potassium extracellular concentration
        self.cao = 2.0     # Calcium extracellular concentration
        self.nao = 140.0   # Sodium extracellular concentration

        # Cell Volume (in uL)
        self.Vc  = 0.016404   # Cytoplasmic volume
        self.Vsr = 0.001094   # Sarcoplasmic reticulum volume
        self.Vss = 0.00005468 # Subsarcolemmal space volume

        # Buffering Parameters
        self.Bufc   = 0.2     # Cytoplasmic buffer concentration
        self.Kbufc  = 0.001   # Cytoplasmic buffer affinity
        self.Bufsr  = 10.0    # SR buffer concentration
        self.Kbufsr = 0.3     # SR buffer affinity
        self.Bufss  = 0.4     # Subsarcolemmal buffer concentration
        self.Kbufss = 0.00025 # Subsarcolemmal buffer affinity

        # Calcium Handling Parameters
        self.Vmaxup = 0.006375  # Maximal calcium uptake rate
        self.Kup    = 0.00025   # Calcium uptake affinity
        self.Vrel   = 0.102     # Calcium release rate from SR
        self.k1_    = 0.15      # Transition rate for SR calcium release
        self.k2_    = 0.045
        self.k3     = 0.060
        self.k4     = 0.005      # Alternative transition rate
        self.EC     = 1.5        # Calcium-induced calcium release sensitivity
        self.maxsr  = 2.5        # Maximum SR calcium release permeability
        self.minsr  = 1.0        # Minimum SR calcium release permeability
        self.Vleak  = 0.00036    # SR calcium leak rate
        self.Vxfer  = 0.0038     # Calcium transfer rate from subspace to cytosol

        # Physical Constants
        self.R     = 8314.472   # Universal gas constant (J/(kmol·K))
        self.F     = 96485.3415 # Faraday constant (C/mol)
        self.T     = 310.0      # Temperature (Kelvin, 37°C)
        self.RTONF = 26.71376   # RT/F constant for Nernst equation

        # Membrane Capacitance
        self.CAPACITANCE = 0.185 # Membrane capacitance (μF/cm²)

        # Ion Channel Conductances
        self.gkr  = 0.153       # Rapid delayed rectifier K+ conductance
        self.gks  = 0.392       # Slow delayed rectifier K+ conductance
        self.gk1  = 5.405       # Inward rectifier K+ conductance
        self.gto  = 0.294       # Transient outward K+ conductance
        self.gna  = 14.838      # Fast Na+ conductance
        self.gbna = 0.00029     # Background Na+ conductance
        self.gcal = 0.00003980  # L-type Ca2+ channel conductance
        self.gbca = 0.000592    # Background Ca2+ conductance
        self.gpca = 0.1238      # Sarcolemmal Ca2+ pump current conductance
        self.KpCa = 0.0005      # Sarcolemmal Ca2+ pump affinity
        self.gpk  = 0.0146      # Na+/K+ pump current conductance

        # Na+/K+ Pump Parameters
        self.pKNa = 0.03        # Na+/K+ permeability ratio
        self.KmK  = 1.0         # Half-saturation for K+ activation
        self.KmNa = 40.0        # Half-saturation for Na+ activation
        self.knak = 2.724       # Maximal Na+/K+ pump rate

        # Na+/Ca2+ Exchanger Parameters
        self.knaca = 1000       # Maximal Na+/Ca2+ exchanger current
        self.KmNai = 87.5       # Half-saturation for Na+ binding
        self.KmCa  = 1.38       # Half-saturation for Ca2+ binding
        self.ksat  = 0.1        # Saturation factor
        self.n_   = 0.35        # Exponent for Na+ dependence

        # initial conditions
        self.init_u     = -84.5
        self.init_cai   = 0.00007
        self.init_casr  = 1.3
        self.init_cass  = 0.00007
        self.init_nai   = 7.67
        self.init_Ki    = 138.3
        self.init_m     = 0.0
        self.init_h     = 0.75
        self.init_j     = 0.75
        self.init_xr1   = 0.0
        self.init_xr2   = 1.0
        self.init_xs    = 0.0
        self.init_r     = 0.0
        self.init_s     = 1.0
        self.init_d     = 0.0
        self.init_f     = 1.0
        self.init_f2    = 1.0
        self.init_fcass = 1.0
        self.init_rr    = 1.0
        self.init_oo    = 0.0

    def initialize(self):
        """"""
        Initializes the model's state variables and diffusion/ionic kernels.

        Sets up the initial values for membrane potential, ion concentrations,
        gating variables, and assigns the appropriate kernel functions.
        """"""
        super().initialize()
        shape = self.cardiac_tissue.mesh.shape

        self.u = self.init_u * np.ones(shape, dtype=self.npfloat)
        self.u_new = self.u.copy()
        self.cai = self.init_cai * np.ones(shape, dtype=self.npfloat)
        self.casr = self.init_casr * np.ones(shape, dtype=self.npfloat)
        self.cass = self.init_cass * np.ones(shape, dtype=self.npfloat)
        self.nai = self.init_nai * np.ones(shape, dtype=self.npfloat)
        self.Ki = self.init_Ki * np.ones(shape, dtype=self.npfloat)
        self.m = self.init_m * np.ones(shape, dtype=self.npfloat)
        self.h = self.init_h * np.ones(shape, dtype=self.npfloat)
        self.j = self.init_j * np.ones(shape, dtype=self.npfloat)
        self.xr1 = self.init_xr1 * np.ones(shape, dtype=self.npfloat)
        self.xr2 = self.init_xr2 * np.ones(shape, dtype=self.npfloat)
        self.xs = self.init_xs * np.ones(shape, dtype=self.npfloat)
        self.r = self.init_r * np.ones(shape, dtype=self.npfloat)
        self.s = self.init_s * np.ones(shape, dtype=self.npfloat)
        self.d = self.init_d * np.ones(shape, dtype=self.npfloat)
        self.f = self.init_f * np.ones(shape, dtype=self.npfloat)
        self.f2 = self.init_f2 * np.ones(shape, dtype=self.npfloat)
        self.fcass = self.init_fcass * np.ones(shape, dtype=self.npfloat)
        self.rr = self.init_rr * np.ones(shape, dtype=self.npfloat)
        self.oo = self.init_oo * np.ones(shape, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel function to update ionic currents and state
        variables
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.cai, self.casr, self.cass,
                        self.nai, self.Ki, self.m, self.h, self.j, self.xr1,
                        self.xr2, self.xs, self.r, self.s, self.d, self.f,
                        self.f2, self.fcass, self.rr, self.oo,
                        self.cardiac_tissue.myo_indexes, self.dt,
                        self.ko, self.cao, self.nao, self.Vc, self.Vsr, self.Vss, self.Bufc, self.Kbufc, self.Bufsr, self.Kbufsr,
                        self.Bufss, self.Kbufss, self.Vmaxup, self.Kup, self.Vrel, self.k1_, self.k2_, self.k3, self.k4, self.EC,
                        self.maxsr, self.minsr, self.Vleak, self.Vxfer, self.R, self.F, self.T, self.RTONF, self.CAPACITANCE,
                        self.gkr, self.pKNa, self.gk1, self.gna, self.gbna, self.KmK, self.KmNa, self.knak, self.gcal, self.gbca,
                        self.knaca, self.KmNai, self.KmCa, self.ksat, self.n_, self.gpca, self.KpCa, self.gpk, self.gto, self.gks)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()


@njit
def calc_ina(u, dt, m, h, j, gna, Ena):
    """"""
    Calculates the fast sodium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    dt : float
        Time step for the simulation.
    m : np.ndarray
        Gating variable for sodium channels (activation).
    h : np.ndarray
        Gating variable for sodium channels (inactivation).
    j : np.ndarray
        Gating variable for sodium channels (inactivation).
    gna : float
        Sodium conductance.
    Ena : float
        Sodium reversal potential.

    Returns
    -------
    np.ndarray
        Updated fast sodium current array.
    """"""

    alpha_m = 1./(1.+np.exp((-60.-u)/5.))
    beta_m = 0.1/(1.+np.exp((u+35.)/5.)) + \
        0.10/(1.+np.exp((u-50.)/200.))
    tau_m = alpha_m*beta_m
    m_inf = 1./((1.+np.exp((-56.86-u)/9.03))
                * (1.+np.exp((-56.86-u)/9.03)))

    alpha_h = 0.
    beta_h = 0.
    if u >= -40.:
        alpha_h = 0.
        beta_h = 0.77/(0.13*(1.+np.exp(-(u+10.66)/11.1)))
    else:
        alpha_h = 0.057*np.exp(-(u+80.)/6.8)
        beta_h = 2.7*np.exp(0.079*u)+(3.1e5)*np.exp(0.3485*u)

    tau_h = 1.0/(alpha_h + beta_h)

    h_inf = 1./((1.+np.exp((u+71.55)/7.43))
                * (1.+np.exp((u+71.55)/7.43)))

    alpha_j = 0.
    beta_j = 0.
    if u >= -40.:
        alpha_j = 0.
        beta_j = 0.6*np.exp((0.057)*u)/(1.+np.exp(-0.1*(u+32.)))
    else:
        alpha_j = ((-2.5428e4)*np.exp(0.2444*u)-(6.948e-6) *
                np.exp(-0.04391*u))*(u+37.78) /\
            (1.+np.exp(0.311*(u+79.23)))
        beta_j = 0.02424*np.exp(-0.01052*u) / \
            (1.+np.exp(-0.1378*(u+40.14)))

    tau_j = 1.0/(alpha_j + beta_j)

    j_inf = h_inf

    m = m_inf-(m_inf-m)*np.exp(-dt/tau_m)
    h = h_inf-(h_inf-h)*np.exp(-dt/tau_h)
    j = j_inf-(j_inf-j)*np.exp(-dt/tau_j)

    return gna*m*m*m*h*j*(u-Ena), m, h, j

@njit
def calc_ical(u, dt, d, f, f2, fcass, cao, cass, gcal, F, R, T):
    """"""
    Calculates the L-type calcium current.
    
    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    dt : float
        Time step for the simulation.
    d : np.ndarray
        Gating variable for L-type calcium channels.
    f : np.ndarray
        Gating variable for calcium-dependent calcium channels.
    f2 : np.ndarray
        Secondary gating variable for calcium-dependent calcium channels.
    fcass : np.ndarray
        Gating variable for calcium-sensitive current.
    cao : float
        Extracellular calcium concentration.
    cass : np.ndarray
        Calcium concentration in the submembrane space.
    gcal : float
        Calcium conductance.
    F : float
        Faraday's constant.
    R : float
        Ideal gas constant.
    T : float

    Returns
    -------
    np.ndarray
        Updated L-type calcium current array.
    """"""

    d_inf = 1./(1.+np.exp((-8-u)/7.5))
    Ad = 1.4/(1.+np.exp((-35-u)/13))+0.25
    Bd = 1.4/(1.+np.exp((u+5)/5))
    Cd = 1./(1.+np.exp((50-u)/20))
    tau_d = Ad*Bd+Cd
    f_inf = 1./(1.+np.exp((u+20)/7))
    Af = 1102.5*np.exp(-(u+27)*(u+27)/225)
    Bf = 200./(1+np.exp((13-u)/10.))
    Cf = (180./(1+np.exp((u+30)/10)))+20
    tau_f = Af+Bf+Cf
    f2_inf = 0.67/(1.+np.exp((u+35)/7))+0.33
    Af2 = 600*np.exp(-(u+25)*(u+25)/170)
    Bf2 = 31/(1.+np.exp((25-u)/10))
    Cf2 = 16/(1.+np.exp((u+30)/10))
    tau_f2 = Af2+Bf2+Cf2
    fcass_inf = 0.6/(1+(cass/0.05)*(cass/0.05))+0.4
    tau_fcass = 80./(1+(cass/0.05)*(cass/0.05))+2.

    d = d_inf-(d_inf-d)*np.exp(-dt/tau_d)
    f = f_inf-(f_inf-f)*np.exp(-dt/tau_f)
    f2 = f2_inf-(f2_inf-f2)*np.exp(-dt/tau_f2)
    fcass = fcass_inf-(fcass_inf-fcass)*np.exp(-dt/tau_fcass)

    return gcal*d*f*f2*fcass*4*(u-15)*(F*F/(R*T)) *\
        (0.25*np.exp(2*(u-15)*F/(R*T))*cass-cao) / \
        (np.exp(2*(u-15)*F/(R*T))-1.), d, f, f2, fcass

@njit
def calc_ito(u, dt, r, s, Ek, gto):
    """"""
    Calculates the transient outward current.
    
    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    dt : float
        Time step for the simulation.
    r : np.ndarray
        Gating variable for ryanodine receptors.
    s : np.ndarray
        Gating variable for calcium-sensitive current.
    ek : float
        Potassium reversal potential.

    Returns
    -------
    np.ndarray
        Updated transient outward current array.
    """"""

    r_inf = 1./(1.+np.exp((20-u)/6.))
    s_inf = 1./(1.+np.exp((u+20)/5.))
    tau_r = 9.5*np.exp(-(u+40.)*(u+40.)/1800.)+0.8
    tau_s = 85.*np.exp(-(u+45.)*(u+45.)/320.) + \
        5./(1.+np.exp((u-20.)/5.))+3.

    s = s_inf-(s_inf-s)*np.exp(-dt/tau_s)
    r = r_inf-(r_inf-r)*np.exp(-dt/tau_r)

    return gto*r*s*(u-Ek), r, s

@njit
def calc_ikr(u, dt, xr1, xr2, Ek, gkr, ko):
    """"""
    Calculates the rapid delayed rectifier potassium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    dt : float
        Time step for the simulation.
    xr1 : np.ndarray
        Gating variable for rapid delayed rectifier potassium channels.
    xr2 : np.ndarray
        Gating variable for rapid delayed rectifier potassium channels.
    Ek : float
        Potassium reversal potential.
    gkr : float
        Potassium conductance.

    Returns
    -------
    np.ndarray
        Updated rapid delayed rectifier potassium current array.
    """"""

    xr1_inf = 1./(1.+np.exp((-26.-u)/7.))
    axr1 = 450./(1.+np.exp((-45.-u)/10.))
    bxr1 = 6./(1.+np.exp((u-(-30.))/11.5))
    tau_xr1 = axr1*bxr1
    xr2_inf = 1./(1.+np.exp((u-(-88.))/24.))
    axr2 = 3./(1.+np.exp((-60.-u)/20.))
    bxr2 = 1.12/(1.+np.exp((u-60.)/20.))
    tau_xr2 = axr2*bxr2

    xr1 = xr1_inf-(xr1_inf-xr1)*np.exp(-dt/tau_xr1)
    xr2 = xr2_inf-(xr2_inf-xr2)*np.exp(-dt/tau_xr2)

    return gkr*np.sqrt(ko/5.4)*xr1*xr2*(u-Ek), xr1, xr2

@njit
def calc_iks(u, dt, xs, Eks, gks):
    """"""
    Calculates the slow delayed rectifier potassium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    dt : float
        Time step for the simulation.
    xs : np.ndarray
        Gating variable for slow delayed rectifier potassium channels.
    Eks : float
        Potassium reversal potential.
    gks : float
        Potassium conductance.
    
    Returns
    -------
    np.ndarray
        Updated slow delayed rectifier potassium current array.
    """"""
    xs_inf = 1./(1.+np.exp((-5.-u)/14.))
    Axs = (1400./(np.sqrt(1.+np.exp((5.-u)/6))))
    Bxs = (1./(1.+np.exp((u-35.)/15.)))
    tau_xs = Axs*Bxs+80
    xs_inf = 1./(1.+np.exp((-5.-u)/14.))

    xs = xs_inf-(xs_inf-xs)*np.exp(-dt/tau_xs)

    return gks*xs*xs*(u-Eks), xs

@njit
def calc_ik1(u, Ek, gk1):
    """"""
    Calculates the inward rectifier potassium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    Ek : float
        Potassium reversal potential.
    gk1 : float
        Inward rectifier potassium conductance.

    Returns
    -------
    np.ndarray
        Updated inward rectifier potassium current array.
    """"""

    ak1 = 0.1/(1.+np.exp(0.06*(u-Ek-200)))
    bk1 = (3.*np.exp(0.0002*(u-Ek+100)) +
           np.exp(0.1*(u-Ek-10)))/(1.+np.exp(-0.5*(u-Ek)))
    rec_iK1 = ak1/(ak1+bk1)

    return gk1*rec_iK1*(u-Ek)

@njit
def calc_inaca(u, nao, nai, cao, cai, KmNai, KmCa, knaca, ksat, n_, F, R, T):
    """"""
    Calculates the sodium-calcium exchanger current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    nao : float
        Sodium ion concentration in the extracellular space.
    nai : np.ndarray
        Sodium ion concentration in the intracellular space.
    cao : float
        Calcium ion concentration in the extracellular space.
    cai : np.ndarray
        Calcium ion concentration in the submembrane space.
    KmNai : float
        Michaelis constant for sodium.
    KmCa : float
        Michaelis constant for calcium.
    knaca : float
        Sodium-calcium exchanger conductance.
    ksat : float
        Saturation factor.
    n_ : float
        Exponent for sodium dependence.
    F : float
        Faraday's constant.
    R : float
        Ideal gas constant.
    T : float
        Temperature.
    
    Returns
    -------
    np.ndarray
        Updated sodium-calcium exchanger current array.
    """"""

    return knaca*(1./(KmNai*KmNai*KmNai+nao*nao*nao))*(1./(KmCa+cao)) *\
            (1./(1+ksat*np.exp((n_-1)*u*F/(R*T)))) *\
            (np.exp(n_*u*F/(R*T))*nai*nai*nai*cao -
                np.exp((n_-1)*u*F/(R*T))*nao*nao*nao*cai*2.5)

@njit
def calc_inak(u, nai, ko, KmK, KmNa, knak, F, R, T):
    """"""
    Calculates the sodium-potassium pump current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    nai : np.ndarray
        Sodium ion concentration in the intracellular space.
    ko : float
        Potassium ion concentration in the extracellular space.
    KmK : float
        Michaelis constant for potassium.
    KmNa : float
        Michaelis constant for sodium.
    knak : float
        Sodium-potassium pump conductance.
    F : float
        Faraday's constant.
    R : float
        Ideal gas constant.
    T : float
        Temperature.

    Returns
    -------
    np.ndarray
        Updated sodium-potassium pump current array.
    """"""

    rec_iNaK = (
        1./(1.+0.1245*np.exp(-0.1*u*F/(R*T))+0.0353*np.exp(-u*F/(R*T))))

    return knak*(ko/(ko+KmK))*(nai/(nai+KmNa))*rec_iNaK

@njit
def calc_ipca(cai, KpCa, gpca):
    """"""
    Calculates the calcium pump current.

    Parameters
    ----------
    cai : np.ndarray
        Calcium concentration in the submembrane space.
    KpCa : float
        Michaelis constant for calcium pump.
    gpca : float
        Calcium pump conductance.

    Returns
    -------
    np.ndarray
        Updated calcium pump current array.
    """"""

    return gpca*cai/(KpCa+cai)

@njit
def calc_ipk(u, Ek, gpk):
    """"""
    Calculates the potassium pump current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    Ek : float
        Potassium reversal potential.
    gpk : float
        Potassium pump conductance.
    
    Returns
    -------
    np.ndarray
        Updated potassium pump current array.
    """"""
    rec_ipK = 1./(1.+np.exp((25-u)/5.98))

    return gpk*rec_ipK*(u-Ek)

@njit
def calc_ibna(u, Ena, gbna):
    """"""
    Calculates the background sodium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    Ena : float
        Sodium reversal potential.
    gbna : float
        Background sodium conductance.

    Returns
    -------
    np.ndarray
        Updated background sodium current array.
    """"""

    return gbna*(u-Ena)

@njit
def calc_ibca(u, Eca, gbca):
    """"""
    Calculates the background calcium current.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential array.
    Eca : float
        Calcium reversal potential.
    gbca : float
        Background calcium conductance.

    Returns
    -------
    np.ndarray
        Updated background calcium current array.
    """"""

    return gbca*(u-Eca)

@njit
def calc_irel(dt, rr, oo, casr, cass, vrel, k1, k2, k3, k4, maxsr, minsr, EC):
    """"""
    Calculates the ryanodine receptor current.

    Parameters
    ----------
    dt : float
        Time step for the simulation.
    rr : np.ndarray
        Ryanodine receptor gating variable for calcium release.
    oo : np.ndarray
        Ryanodine receptor gating variable for calcium release.
    casr : np.ndarray
        Calcium concentration in the sarcoplasmic reticulum.
    cass : np.ndarray
        Calcium concentration in the submembrane space.
    vrel : float
        Release rate of calcium from the sarcoplasmic reticulum.
    k1 : float
        Transition rate for SR calcium release.
    k2 : float
        Transition rate for SR calcium release.
    k3 : float
        Transition rate for SR calcium release.
    k4 : float
        Alternative transition rate.
    maxsr : float
        Maximum SR calcium release permeability.
    minsr : float
        Minimum SR calcium release permeability.
    EC : float
        Calcium-induced calcium release sensitivity.
    
    Returns
    -------
    np.ndarray
        Updated ryanodine receptor current array.
    """"""

    kCaSR = maxsr-((maxsr-minsr)/(1+(EC/casr)*(EC/casr)))
    k1_ = k1/kCaSR
    k2_ = k2*kCaSR
    drr = k4*(1-rr)-k2_*cass*rr
    rr += dt*drr
    oo = k1_*cass*cass * rr/(k3+k1_*cass*cass)

    return vrel*oo*(casr-cass), rr, oo

@njit
def calc_ileak(casr, cai, vleak):
    """"""
    Calculates the calcium leak current.

    Parameters
    ----------
    casr : np.ndarray
        Calcium concentration in the sarcoplasmic reticulum.
    cai : np.ndarray
        Calcium concentration in the submembrane space.
    vleak : float
        Leak rate of calcium from the sarcoplasmic reticulum.

    Returns
    -------
    np.ndarray
        Updated calcium leak current array.
    """"""

    return vleak*(casr-cai)

@njit
def calc_iup(cai, vmaxup, Kup):
    """"""
    Calculates the calcium uptake current.

    Parameters
    ----------
    cai : np.ndarray
        Calcium concentration in the submembrane space.
    vmaxup : float
        Uptake rate of calcium into the sarcoplasmic reticulum.
    Kup : float
        Michaelis constant for calcium uptake.

    Returns
    -------
    np.ndarray
        Updated calcium uptake current array.
    """"""

    return vmaxup/(1.+((Kup*Kup)/(cai*cai)))

@njit
def calc_ixfer(cass, cai, vxfer):
    """"""
    Calculates the calcium transfer current.

    Parameters
    ----------
    cass : np.ndarray
        Calcium concentration in the submembrane space.
    cai : np.ndarray
        Calcium concentration in the submembrane space.
    vxfer : float
        Transfer rate of calcium between the submembrane space and cytosol.

    Returns
    -------
    np.ndarray
        Updated calcium transfer current array.
    """"""

    return vxfer*(cass-cai)

@njit
def calc_casr(dt, caSR, bufsr, Kbufsr, iup, irel, ileak):
    """"""
    Calculates the calcium concentration in the sarcoplasmic reticulum.

    Parameters
    ----------
    casr : np.ndarray
        Calcium concentration in the sarcoplasmic reticulum.
    bufsr : float
        Buffering capacity of the sarcoplasmic reticulum.
    Kbufsr : float
        Buffering constant of the sarcoplasmic reticulum.
    iup : float
        Calcium uptake current.
    irel : float
        Calcium release current.
    ileak : float
        Leak rate of calcium from the sarcoplasmic reticulum.

    Returns
    -------
    np.ndarray
        Updated calcium concentration in the sarcoplasmic reticulum.
    """"""

    CaCSQN = bufsr*caSR/(caSR+Kbufsr)
    dCaSR = dt*(iup-irel-ileak)
    bjsr = bufsr-CaCSQN-dCaSR-caSR+Kbufsr
    cjsr = Kbufsr*(CaCSQN+dCaSR+caSR)
    return (np.sqrt(bjsr*bjsr+4*cjsr)-bjsr)/2

@njit
def calc_cass(dt, caSS, bufss, Kbufss, ixfer, irel, ical, capacitance, Vc, Vss, Vsr, inversevssF2):
    """"""
    Calculates the calcium concentration in the submembrane space.

    Parameters
    ----------
    cass : np.ndarray
        Calcium concentration in the submembrane space.
    bufss : float
        Buffering capacity of the submembrane space.
    Kbufss : float
        Buffering constant of the submembrane space.
    ixfer : float
        Calcium transfer current.
    irel : float
        Calcium release current.
    ical : float
        L-type calcium current.
    capacitance : float
        Membrane capacitance.
    Vc : float
        Volume of the cytosol.
    Vss : float
        Volume of the submembrane space.
    Vsr : float
        Volume of the sarcoplasmic reticulum.
    inversevssF2 : float
        Inverse of the product of 2
        times the volume of the submembrane space and Faraday's constant.

    Returns
    -------
    np.ndarray
        Updated calcium concentration in the submembrane space.
    """"""

    CaSSBuf = bufss*caSS/(caSS+Kbufss)
    dCaSS = dt*(-ixfer*(Vc/Vss)+irel*(Vsr/Vss) +
                (-ical*inversevssF2*capacitance))
    bcss = bufss-CaSSBuf-dCaSS-caSS+Kbufss
    ccss = Kbufss*(CaSSBuf+dCaSS+caSS)
    return (np.sqrt(bcss*bcss+4*ccss)-bcss)/2

@njit
def calc_cai(dt, cai, bufc, Kbufc, ibca, ipca, inaca, iup, ileak, ixfer, capacitance, vsr, vc, inverseVcF2):
    """"""
    Calculates the calcium concentration in the cytosol.

    Parameters
    ----------
    cai : np.ndarray
        Calcium concentration in the cytosol.
    bufc : float
        Buffering capacity of the cytosol.
    Kbufc : float
        Buffering constant of the cytosol.
    ibca : float
        Background calcium current.
    ipca : float
        Calcium pump current.
    inaca : float
        Sodium-calcium exchanger current.
    iup : float
        Calcium uptake current.
    ileak : float
        Calcium leak current.
    ixfer : float
        Calcium transfer current.
    capacitance : float
        Membrane capacitance.
    vsr : float
        Volume of the sarcoplasmic reticulum.
    vc : float
        Volume of the cytosol.
    inverseVcF2 : float
        Inverse of the product of 2
        times the volume of the cytosol and Faraday's constant.

    Returns
    -------
    np.ndarray
        Updated calcium concentration in the cytosol.
    """"""

    CaCBuf = bufc*cai/(cai+Kbufc)
    dCai = dt*((-(ibca+ipca-2*inaca)*inverseVcF2*capacitance) -
                   (iup-ileak)*(vsr/vc)+ixfer)
    bc = bufc-CaCBuf-dCai-cai+Kbufc
    cc = Kbufc*(CaCBuf+dCai+cai)
    return (np.sqrt(bc*bc+4*cc)-bc)/2, cai

@njit
def calc_nai(dt, ina, ibna, inak, inaca, capacitance, inverseVcF):
    """"""
    Calculates the sodium concentration in the cytosol.

    Parameters
    ----------
    dt : float
        Time step for the simulation.
    ina : float
        Fast sodium current.
    ibna : float
        Background sodium current.
    inak : float
        Sodium-potassium pump current.
    inaca : float
        Sodium-calcium exchanger current.
    capacitance : float
        Membrane capacitance.
    inverseVcF : float
        Inverse of the product of the volume of the cytosol and Faraday's constant.

    Returns
    -------
    np.ndarray
        Updated sodium concentration in the cytosol.
    """"""

    dNai = -(ina+ibna+3*inak+3*inaca)*inverseVcF*capacitance
    return dt*dNai

@njit
def calc_ki(dt, ik1, ito, ikr, iks, inak, ipk, inverseVcF, capacitance):
    """"""
    Calculates the potassium concentration in the cytosol.

    Parameters
    ----------
    ik1 : float
        Inward rectifier potassium current.
    ito : float
        Transient outward current.
    ikr : float
        Rapid delayed rectifier potassium current.
    iks : float
        Slow delayed rectifier potassium current.
    inak : float
        Sodium-potassium pump current.
    ipk : float
        Potassium pump current.
    capacitance : float
        Membrane capacitance.
    inverseVcF : float
        Inverse of the product of the volume of the cytosol and Faraday's constant.

    Returns
    -------
    np.ndarray
        Updated potassium concentration in the cytosol.
    """"""

    dKi = -(ik1+ito+ikr+iks-2*inak+ipk)*inverseVcF*capacitance
    return dt*dKi

# tp06 epi kernel
@njit(parallel=True)
def ionic_kernel_2d(u_new, u, cai, casr, cass, nai, Ki, m, h, j_, xr1, xr2,
                    xs, r, s, d, f, f2, fcass, rr, oo, indexes, dt, 
                    ko, cao, nao, Vc, Vsr, Vss, Bufc, Kbufc, Bufsr, Kbufsr,
                    Bufss, Kbufss, Vmaxup, Kup, Vrel, k1_, k2_, k3, k4, EC,
                    maxsr, minsr, Vleak, Vxfer, R, F, T, RTONF, CAPACITANCE,
                    gkr, pKNa, gk1, gna, gbna, KmK, KmNa, knak, gcal, gbca,
                    knaca, KmNai, KmCa, ksat, n_, gpca, KpCa, gpk, gto, gks):
    """"""
    Compute the ionic currents and update the state variables for the 2D TP06
    cardiac model.

    This function calculates the ionic currents based on the TP06 cardiac
    model, updates ion concentrations, and modifies gating variables in the
    2D grid. The calculations are performed in parallel to enhance performance.

    Parameters
    ----------
    u_new : numpy.ndarray
        Array to store the updated membrane potential values.
    u : numpy.ndarray
        Array of current membrane potential values.
    cai : numpy.ndarray
        Array of calcium concentration in the cytosol.
    casr : numpy.ndarray
        Array of calcium concentration in the sarcoplasmic reticulum.
    cass : numpy.ndarray
        Array of calcium concentration in the submembrane space.
    nai : numpy.ndarray
        Array of sodium ion concentration in the intracellular space.
    Ki : numpy.ndarray
        Array of potassium ion concentration in the intracellular space.
    m : numpy.ndarray
        Array of gating variable for sodium channels (activation).
    h : numpy.ndarray
        Array of gating variable for sodium channels (inactivation).
    j_ : numpy.ndarray
        Array of gating variable for sodium channels (inactivation).
    xr1 : numpy.ndarray
        Array of gating variable for rapid delayed rectifier potassium
        channels.
    xr2 : numpy.ndarray
        Array of gating variable for rapid delayed rectifier potassium
        channels.
    xs : numpy.ndarray
        Array of gating variable for slow delayed rectifier potassium channels.
    r : numpy.ndarray
        Array of gating variable for ryanodine receptors.
    s : numpy.ndarray
        Array of gating variable for calcium-sensitive current.
    d : numpy.ndarray
        Array of gating variable for L-type calcium channels.
    f : numpy.ndarray
        Array of gating variable for calcium-dependent calcium channels.
    f2 : numpy.ndarray
        Array of secondary gating variable for calcium-dependent calcium
        channels.
    fcass : numpy.ndarray
        Array of gating variable for calcium-sensitive current.
    rr : numpy.ndarray
        Array of ryanodine receptor gating variable for calcium release.
    oo : numpy.ndarray
        Array of ryanodine receptor gating variable for calcium release.
    indexes: numpy.ndarray
        Array of indexes where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.

    Returns
    -------
    None
        The function updates the state variables in place. No return value is
        produced.
    """"""
    n_j = u.shape[1]

    inverseVcF2 = 1./(2*Vc*F)
    inverseVcF = 1./(Vc*F)
    inversevssF2 = 1./(2*Vss*F)

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii/n_j)
        j = ii % n_j

        Ek = RTONF*(np.log((ko/Ki[i, j])))
        Ena = RTONF*(np.log((nao/nai[i, j])))
        Eks = RTONF*(np.log((ko+pKNa*nao)/(Ki[i, j]+pKNa*nai[i, j])))
        Eca = 0.5*RTONF*(np.log((cao/cai[i, j])))

        # Compute currents
        ina, m[i, j], h[i, j], j_[i, j] = calc_ina(u[i, j], dt, m[i, j], h[i, j], j_[i, j], gna, Ena)
        ical, d[i, j], f[i, j], f2[i, j], fcass[i, j] = calc_ical(u[i, j], dt, d[i, j], f[i, j], f2[i, j], fcass[i, j], cao, cass[i, j], gcal, F, R, T)
        ito, r[i, j], s[i, j] = calc_ito(u[i, j], dt, r[i, j], s[i, j], Ek, gto)
        ikr, xr1[i, j], xr2[i, j] = calc_ikr(u[i, j], dt, xr1[i, j], xr2[i, j], Ek, gkr, ko)
        iks, xs[i, j] = calc_iks(u[i, j], dt, xs[i, j], Eks, gks)
        ik1 = calc_ik1(u[i, j], Ek, gk1)
        inaca = calc_inaca(u[i, j], nao, nai[i, j], cao, cai[i, j], KmNai, KmCa, knaca, ksat, n_, F, R, T) 
        inak = calc_inak(u[i, j], nai[i, j], ko, KmK, KmNa, knak, F, R, T)
        ipca = calc_ipca(cai[i, j], KpCa, gpca)
        ipk = calc_ipk(u[i, j], Ek, gpk)
        ibna = calc_ibna(u[i, j], Ena, gbna)
        ibca = calc_ibca(u[i, j], Eca, gbca)
        irel, rr[i, j], oo[i, j] = calc_irel(dt, rr[i, j], oo[i, j], casr[i, j], cass[i, j], Vrel, k1_, k2_, k3, k4, maxsr, minsr, EC)
        ileak = calc_ileak(casr[i, j], cai[i, j], Vleak)
        iup = calc_iup(cai[i, j], Vmaxup, Kup)
        ixfer = calc_ixfer(cass[i, j], cai[i, j], Vxfer)

        # Compute concentrations
        casr[i, j] = calc_casr(dt, casr[i, j], Bufsr, Kbufsr, iup, irel, ileak)
        cass[i, j] = calc_cass(dt, cass[i, j], Bufss, Kbufss, ixfer, irel, ical, CAPACITANCE, Vc, Vss, Vsr, inversevssF2)
        cai[i, j], cai[i, j] = calc_cai(dt, cai[i, j], Bufc, Kbufc, ibca, ipca, inaca, iup, ileak, ixfer, CAPACITANCE, Vsr, Vc, inverseVcF2)
        nai[i, j] += calc_nai(dt, ina, ibna, inak, inaca, CAPACITANCE, inverseVcF)
        Ki[i, j] += calc_ki(dt, ik1, ito, ikr, iks, inak, ipk, inverseVcF, CAPACITANCE)

        # Update membrane potential
        u_new[i, j] -= dt * (ikr + iks + ik1 + ito + ina + ibna + ical + ibca + inak + inaca + ipca + ipk)
        
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/aliev_panfilov_2d.py",".py","6061","204","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class AlievPanfilov2D(CardiacModel):
    """"""
    Two-dimensional implementation of the Aliev–Panfilov model of cardiac excitation.

    The Aliev–Panfilov model is a phenomenological two-variable model designed to
    reproduce basic features of cardiac excitation, including wave propagation and
    reentry, while remaining computationally efficient. It uses a single recovery
    variable coupled with a cubic nonlinearity to simulate action potential dynamics
    in excitable media.

    Attributes
    ----------
    u : np.ndarray
        Transmembrane potential (dimensionless, normalized to [0,1]).
    v : np.ndarray
        Recovery variable describing refractoriness.
    D_model : float
        Diffusion coefficient used for simulating spatial propagation.
    state_vars : list of str
        Names of the state variables to be saved and restored.
    npfloat : str
        Floating-point precision used in the simulation (default: 'float64').

    Model Parameters
    ----------------
    a : float
        Excitability threshold parameter.
    k : float
        Strength of the nonlinear source term (governs spike shape).
    eap : float
        Baseline recovery rate.
    mu_1 : float
        Recovery rate coefficient (scales v feedback).
    mu_2 : float
        Recovery rate offset (modulates u-dependence of recovery).

    Paper
    -----
    Rubin R. Aliev, Alexander V. Panfilov,
    A simple two-variable model of cardiac excitation,
    Chaos, Solitons & Fractals,
    Volume 7, Issue 3,
    1996,
    Pages 293-301,
    ISSN 0960-0779,
    https://doi.org/10.1016/0960-0779(95)00089-5.

    Attributes
    ----------
    v : np.ndarray
        Array for the recovery variable.
    w : np.ndarray
        Array for diffusion weights.
    D_model : float
        Model specific diffusion coefficient
    state_vars : list
        List of state variables to be saved and restored.
    npfloat : str
        Data type used for floating-point operations, default is 'float64'.
    """"""

    def __init__(self):
        """"""
        Initializes the AlievPanfilov2D instance with default parameters.
        """"""
        super().__init__()
        self.v = np.ndarray
        
        self.D_model = 1.
    
        self.state_vars = [""u"", ""v""]
        self.npfloat    = 'float64'

        # model parameters
        self.a    = 0.1
        self.k    = 8.0
        self.eap  = 0.01
        self.mu_1 = 0.2
        self.mu_2 = 0.3

        # initial conditions
        self.init_u = 0.0
        self.init_v = 0.0

    def initialize(self):
        """"""
        Initializes the model for simulation.
        """"""
        super().initialize()
        self.u = self.init_u * np.ones_like(self.u, dtype=self.npfloat)
        self.v = self.init_v * np.ones_like(self.u, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Aliev-Panfilov model.
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.v, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.a, self.k, self.eap, self.mu_1, self.mu_2)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()

@njit
def calc_v(v, u, dt, a, k, eap, mu_1, mu_2):
    """"""
    Computes the update of the recovery variable v for the Aliev–Panfilov model.

    This function implements the ordinary differential equation governing the
    evolution of the recovery variable `v`, which models the refractoriness of
    the cardiac tissue. The rate of recovery depends on both `v` and `u`, with a
    nonlinear interaction term involving a cubic expression in `u`.

    Parameters
    ----------
    v : float
        Current value of the recovery variable.
    u : float
        Current value of the transmembrane potential.
    dt : float
        Time step for integration.
    a : float
        Excitability threshold.
    k : float
        Strength of the nonlinear source term.
    eap : float
        Baseline recovery rate.
    mu_1 : float
        Recovery scaling parameter.
    mu_2 : float
        Offset parameter for recovery rate.

    Returns
    -------
    float
        Updated value of the recovery variable `v`.
    """"""

    v += (- dt * (eap + (mu_1 * v) / (mu_2 + u)) *
            (v + k * u * (u - a - 1.)))
    return v


@njit(parallel=True)
def ionic_kernel_2d(u_new, u, v, indexes, dt, a, k, eap, mu_1, mu_2):
    """"""
    Computes the ionic kernel for the Aliev-Panfilov 2D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    v : np.ndarray
        Recovery variable array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""

    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        v[i, j] = calc_v(v[i, j], u[i, j], dt, a, k, eap, mu_1, mu_2)

        u_new[i, j] += dt * (- k * u[i, j] * (u[i, j] - a) * (u[i, j] - 1.) -
                            u[i, j] * v[i, j])

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/mitchell_schaeffer_2d.py",".py","6278","219","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class MitchellSchaeffer2D(CardiacModel):

    def __init__(self):
        """"""
        Implements the 2D Mitchell-Schaeffer model of cardiac excitation.

        This is a phenomenological two-variable model capturing the essence of cardiac 
        action potential dynamics using a simplified formulation. It separates inward and 
        outward currents and uses a single gating variable to regulate excitability.

        It reproduces key features like:
        - Excitability and recovery
        - Action potential duration (APD)
        - Restitution and wave propagation

        Attributes
        ----------
        h : np.ndarray
            Gating variable controlling the availability of inward current.
        D_model : float
            Diffusion coefficient for spatial propagation.
        state_vars : list
            Names of the dynamic variables for saving/restoring state.
        npfloat : str
            Floating-point type used (default: float64).

        Paper
        -----
        Mitchell, C. C., & Schaeffer, D. G. (2003).
        A two-current model for the dynamics of cardiac membrane
        potential. Bulletin of Mathematical Biology, 65, 767–793.
        https://doi.org/10.1016/S0092-8240(03)00041-7
            
        """"""
        super().__init__()
        self.h = np.ndarray

        self.D_model = 1.

        self.state_vars = [""u"", ""h""]
        self.npfloat    = 'float64'

        # model parameters
        self.tau_close = 150
        self.tau_open  = 120
        self.tau_out = 6
        self.tau_in = 0.3
        self.u_gate = 0.13

        # initial conditions
        self.init_u = 0.0
        self.init_h = 1.0 

    def initialize(self):
        """"""
        Initializes the model for simulation.
        """"""
        super().initialize()
        self.u = self.init_u * np.ones_like(self.u, dtype=self.npfloat)
        self.h = self.init_h * np.ones_like(self.u, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Mitchell-Schaeffer model.
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.h, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.tau_close, self.tau_open, self.tau_in, self.tau_out, self.u_gate)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()
    
@njit
def calc_h(h, u, dt, tau_close, tau_open, u_gate):
    """"""
    Updates the gating variable h for the inward current.

    The gating variable h plays the role of a generic recovery mechanism.
    - It increases toward 1 with time constant tau_open when the membrane is at rest.
    - It decreases toward 0 with time constant tau_close when the membrane is excited.

    This mimics Na⁺ channel inactivation in a simplified way.

    Parameters
    ----------
    h : float
        Current value of the gating variable.
    u : float
        Membrane potential (dimensionless, in [0,1]).
    dt : float
        Time step [ms].
    tau_close : float
        Inactivation time constant (closing).
    tau_open : float
        Recovery time constant (opening).
    u_gate : float
        Threshold potential for switching gate dynamics.

    Returns
    -------
    float
        Updated value of h.
    """"""
    h += dt * (1.0 - h) / tau_open if u < u_gate else dt * (-h) / tau_close
    return h

@njit
def calc_J_in(h, u, tau_in):
    """"""
    Computes the inward current responsible for depolarization.

    This is a regenerative current:
    J_in = h * u² * (1 - u) / tau_in

    It activates when h is high (available) and u is sufficiently depolarized.
    The form ensures that the current grows with u but shuts off when u ~ 1.

    Parameters
    ----------
    h : float
        Gating variable controlling channel availability.
    u : float
        Membrane potential (dimensionless).
    tau_in : float
        Time constant for inward flow.

    Returns
    -------
    float
        Value of the inward current.
    """"""
    C = (u**2)*(1-u)
    return h*C/tau_in

@njit
def calc_J_out(u, tau_out):
    """"""
    Computes the outward current responsible for repolarization.

    This linear term simulates the slow repolarizing current that restores 
    the membrane potential back to rest.

    J_out = -u / tau_out

    Parameters
    ----------
    u : float
        Membrane potential.
    tau_out : float
        Time constant for outward current (repolarization).

    Returns
    -------
    float
        Value of the outward current.
    """"""
    return -u/tau_out

@njit(parallel=True)
def ionic_kernel_2d(u_new, u, h, indexes, dt, tau_close, tau_open, tau_in, tau_out, u_gate):
    """"""
    Ionic kernel for the Mitchell-Schaeffer model in 2D.

    Parameters
    ----------
    u_new : ndarray
        The new state of the u variable.
    u : ndarray
        The current state of the u variable.
    h : ndarray
        The gating variable h.
    myo_indexes : list
        List of indexes representing myocardial cells.
    dt : float
        The time step for the simulation.
    """"""
    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        h[i, j] = calc_h(h[i, j], u[i, j], dt, tau_close, tau_open, u_gate)

        J_in = calc_J_in(h[i, j], u[i, j], tau_in)
        J_out = calc_J_out(u[i, j], tau_out)
        u_new[i, j] += dt * (J_in + J_out)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/courtemanche_2d.py",".py","19798","642","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class Courtemanche2D(CardiacModel):
    """"""
    A class to represent the Courtemanche cardiac model in 2D.

    Attributes
    ----------
    D_model : float
        Model specific diffusion coefficient.
    state_vars : list of str
        List of state variable names.
    """"""

    def __init__(self):
        super().__init__()
        self.D_model = 0.154
        self.state_vars = [""u"", ""nai"", ""ki"", ""cai"", ""caup"", ""carel"", ""m"", ""h"", ""j_"",
         ""d"", ""f"", ""oa"", ""oi"", ""ua"", ""ui"", ""xr"", ""xs"", ""fca"", ""irel"", ""vrel"", ""urel"", ""wrel""]
        
        self.npfloat = 'float64'

        # Model parameters
        self.gna  = 7.8
        self.gnab = 0.000674
        self.gk1  = 0.09
        self.gkr  = 0.0294
        self.gks  = 0.129
        self.gto  = 0.1652
        self.gcal = 0.1238
        self.gcab = 0.00113

        self.gkur_coeff = 1

        self.F = 96485.0
        self.T = 310.0
        self.R = 8314.0 

        self.Vc   = 20100
        self.Vj   = self.Vc*0.68      # (uL)
        self.Vup  = self.Vj*0.06*0.92
        self.Vrel = self.Vj*0.06*0.08

        self.ibk = 0.0
        self.cao = 1.8 # mM
        self.nao = 140 # mM
        self.ko  = 5.4 # mM

        self.caupmax =  15     # mM/ms
        self.kup     = 0.00092 # mM

        self.kmnai   = 10
        self.kmko    = 1.5
        self.kmnancx = 87.5
        self.kmcancx = 1.38
        self.ksatncx = 0.1

        self.kmcmdn = 0.00238
        self.kmtrpn = 0.0005
        self.kmcsqn = 0.8

        self.trpnmax  = 0.07 # mM
        self.cmdnmax  = 0.05 # mM
        self.csqnmax  = 10.0 # mM
        self.inacamax = 1600
        self.inakmax  = 0.6
        self.ipcamax  = 0.275
        self.krel     = 30

        self.iupmax = 0.005

        self.kq10 = 3

        # initial conditions
        self.init_u     = -84.5
        self.init_nai   = 11.2
        self.init_ki    = 139
        self.init_cai   = 0.000102
        self.init_caup  = 1.6
        self.init_carel = 1.1
        self.init_m     = 0.00291
        self.init_h     = 0.965
        self.init_j     = 0.978
        self.init_d     = 0.000137
        self.init_f     = 0.999837
        self.init_oa    = 0.000592
        self.init_oi    = 0.9992
        self.init_ua    = 0.003519
        self.init_ui    = 0.9987
        self.init_xs    = 0.0187
        self.init_xr    = 0.0000329
        self.init_fca   = 0.775
        self.init_irel  = 0
        self.init_vrel  = 1
        self.init_urel  = 0
        self.init_wrel  = 0.9

    def initialize(self):
        """"""
        Initializes the model's state variables and diffusion/ionic kernels.

        Sets up the initial values for membrane potential, ion concentrations,
        gating variables, and assigns the appropriate kernel functions.
        """"""
        super().initialize()
        shape = self.cardiac_tissue.mesh.shape

        self.u = self.init_u * np.ones(shape, dtype=self.npfloat)         # (mV)
        self.u_new = self.u.copy()                                        # (mV)
        self.nai = self.init_nai * np.ones(shape, dtype=self.npfloat)     # (mM)
        self.ki = self.init_ki * np.ones(shape, dtype=self.npfloat)       # (mM)
        self.cai = self.init_cai * np.ones(shape, dtype=self.npfloat)     # (mM)
        self.caup = self.init_caup * np.ones(shape, dtype=self.npfloat)   # (mM)
        self.carel = self.init_carel * np.ones(shape, dtype=self.npfloat) # (mM)
        self.m = self.init_m * np.ones(shape, dtype=self.npfloat)    
        self.h = self.init_h * np.ones(shape, dtype=self.npfloat)      
        self.j_ = self.init_j * np.ones(shape, dtype=self.npfloat)
        self.d = self.init_d * np.ones(shape, dtype=self.npfloat)
        self.f = self.init_f * np.ones(shape, dtype=self.npfloat)
        self.oa = self.init_oa * np.ones(shape, dtype=self.npfloat)
        self.oi = self.init_oi * np.ones(shape, dtype=self.npfloat)
        self.ua = self.init_ua * np.ones(shape, dtype=self.npfloat)
        self.ui = self.init_ui * np.ones(shape, dtype=self.npfloat)
        self.xs = self.init_xs * np.ones(shape, dtype=self.npfloat)
        self.xr =self.init_xr * np.ones(shape, dtype=self.npfloat)
        self.fca = self.init_fca * np.ones(shape, dtype=self.npfloat)
        self.irel = self.init_irel * np.ones(shape, dtype=self.npfloat)
        self.vrel = self.init_vrel * np.ones(shape, dtype=self.npfloat)
        self.urel = self.init_urel * np.ones(shape, dtype=self.npfloat)
        self.wrel = self.init_wrel * np.ones(shape, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel function to update ionic currents and state
        variables
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.nai, self.ki, self.cai, self.caup, self.carel, 
                        self.m, self.h, self.j_, self.d, self.f, self.oa, self.oi, self.ua, 
                        self.ui, self.xr, self.xs, self.fca, self.irel, self.vrel, self.urel, 
                        self.wrel, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.gna, self.gnab, self.gk1, self.gkr, self.gks, self.gto, self.gcal,
                        self.gcab, self.gkur_coeff, self.F, self.T, self.R, self.Vc, self.Vj, self.Vup,
                        self.Vrel, self.ibk, self.cao, self.nao, self.ko, self.caupmax,
                        self.kup, self.kmnai, self.kmko, self.kmnancx, self.kmcancx,
                        self.ksatncx, self.kmcmdn, self.kmtrpn, self.kmcsqn, self.trpnmax,
                        self.cmdnmax, self.csqnmax, self.inacamax, self.inakmax,
                        self.ipcamax, self.krel, self.iupmax, self.kq10)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()

@njit
def safe_exp(x):
    """"""
    Clamps the value of x between -500 and 500 and computes exp(x).
    """"""
    if x > 500:
        x = 500
    elif x < -500:
        x = -500
    return np.exp(x)

@njit
def calc_gating_variable(x, x_inf, tau_x, dt):
    """"""
    Calculates the gating variable using the steady-state value and time constant.
    """"""
    return x_inf - (x_inf - x)*np.exp(-dt/tau_x)

@njit
def calc_cmdn(cmdnmax, kmcmdn, cai):
    """"""
    Calculates the concentration of calmodulin.
    """"""
    cmdn = cmdnmax*cai/(cai + kmcmdn)
    return cmdn

@njit
def calc_trpn(trpnmax, kmtrpn, cai):
    """"""
    Calculates the concentration of troponin.
    """"""
    trpn = trpnmax*cai/(cai + kmtrpn)
    return trpn

@njit
def calc_csqn(csqnmax, kmcsqn, carel):
    """"""
    Calculates the concentration of calsequestrin.
    """"""
    csqn = csqnmax*carel/(carel + kmcsqn)
    return csqn

@njit
def calc_nai(nai, dt, inak, inaca, ibna, ina, F, Vj):
    """"""
    Calculates the intracellular sodium concentration.
    """"""
    dnai = (-3*inak-3*inaca - ibna - ina)/(F*Vj)
    nai += dt*dnai
    return nai

@njit
def calc_ki(ki, dt, inak, ik1, ito, ikur, ikr, iks, ibk, F, Vj):
    """"""
    Calculates the intracellular potassium concentration.
    """"""
    dki = (2*inak - ik1 - ito - ikur - ikr - iks - ibk)/(F*Vj)
    ki += dt*dki
    return ki

@njit
def calc_cai(cai, dt, inaca, ipca, ical, ibca, iup, iupleak, irel, Vrel, Vup, trpnmax, kmtrpn, cmdnmax, kmcmdn, F, Vj): 
    """"""
    Calculates the intracellular calcium concentration.
    """"""
    B1 = (2*inaca - ipca - ical - ibca)/(2*F*Vj) + (Vup*(iupleak - iup) + irel*Vrel)/Vj
    B2 = 1 + (trpnmax*kmtrpn)/((cai + kmtrpn)**2) + (cmdnmax*kmcmdn)/((cai + kmcmdn)**2)
    dcai = B1/B2
    cai += dt*dcai
    # print (""CAI:"", cai)
    return cai

@njit
def calc_caup(caup, dt, iup, iupleak, itr, Vrel, Vup):
    """"""
    Calculates the calcium concentration in the up compartment.
    """"""
    dcaup = iup - iupleak - itr*(Vrel/Vup)
    caup += dt*dcaup
    return caup

@njit
def calc_carel(carel, dt, itr, irel, csqnmax, kmcsqn):
    """"""
    Calculates the calcium concentration in the release compartment.
    """"""
    dcarel = (itr - irel)/(1 + (csqnmax*kmcsqn)/((carel + kmcsqn)**2))
    carel += dt*dcarel
    return carel

@njit
def calc_equilibrum_potentials(nai, nao, ki, ko, cai, cao, R, T, F):
    """"""
    Calculates the equilibrum potentials for the cell.
    """"""
    ena = (R*T/F)*np.log(nao/nai)

    ek = (R*T/F)*np.log(ko/ki)
    
    eca = (R*T/(2*F))*np.log(cao/cai)
    return ena, ek, eca

@njit
def calc_ina(u, m, h, j, gna, ena):
    """"""
    Calculates the fast sodium current.
    """"""
    ina = gna*(m**3)*h*j*(u - ena)
    return ina

@njit
def calc_gating_m(m, u, dt):
    """"""
    Calculates the gating variable m for the fast sodium current.
    """"""
    am = 0
    if u == -47.13:
        am = 3.2
    else:
        am = 0.32 * (u + 47.13) / (1 - np.exp(-0.1 * (u + 47.13)))

    bm = 0.08*np.exp(-u/11)
    m_inf = am/(am + bm)
    tau_m = 1/(am + bm)
    m = calc_gating_variable(m, m_inf, tau_m, dt)

    return m

@njit
def calc_gating_h(h, u, dt):
    """"""
    Calculates the gating variable h for the fast sodium current.
    """"""
    ah = 0.135*np.exp(-(80 + u)/6.8)
    bh = 3.56*np.exp(0.079*u) + 310000*np.exp(0.35*u)
    if u >= -40:
        ah = 0    
        bh = 1/(0.13*(1 + np.exp(-(u + 10.66)/11.1)))
    
    h_inf = ah/(ah + bh)
    tau_h = 1/(ah + bh)
    h = calc_gating_variable(h, h_inf, tau_h, dt)
    return h

@njit
def calc_gating_j(j, u, dt):
    """"""
    Calculates the gating variable j for the fast sodium current.
    """"""
    
    aj = (-127140*np.exp(0.2444*u) - 0.00003474*np.exp(-0.04391*u))*(u + 37.78)/(1 + np.exp(0.311*(u + 79.23)))
    bj = 0.1212*np.exp(-0.01052*u)/(1 + np.exp(-0.1378*(u + 40.14)))
    if u >= -40:
        aj = 0
        bj = 0.3*np.exp(-0.0000002535*u)/(1 + np.exp(-0.1*(u + 32)))
    j_inf = aj/(aj + bj)
    tau_j = 1/(aj + bj)
    j = j_inf - (j_inf - j)*np.exp(-dt/tau_j)
    return j

@njit
def calc_ik1(u, gk1, ek):
    """"""
    Calculates the time-independent potassium current.
    """"""
    ik1 = gk1*(u - ek)/(1 + np.exp(0.07*(u + 80)))
    return ik1

@njit
def calc_ito(u, dt, kq10, oa, oi, gto, ek):
    """"""
    Calculates the transient outward potassium current.
    """"""
    ao = 0.65/(np.exp(-(u + 10)/8.5) + np.exp(-(u - 30)/59.0))
    bo = 0.65/(2.5 + np.exp((u + 82)/17.0))

    tau_o = 1/(kq10*(ao + bo))
    o_inf = 1/(1 + np.exp(-(u + 20.47)/17.54))

    aoi = 1/(18.53 + np.exp((u + 113.7)/10.95))
    boi = 1/(35.56 + np.exp(-(u + 1.26)/7.44))

    tau_oi = 1/(kq10*(aoi + boi))
    oi_inf = 1/(1 + np.exp((u + 43.1)/5.3))

    oa = calc_gating_variable(oa, o_inf, tau_o, dt)
    oi = calc_gating_variable(oi, oi_inf, tau_oi, dt)

    ito = gto*(oa**3)*oi*(u - ek)  

    return ito, oa, oi

@njit
def calc_ikur(u, dt, kq10, ua, ui, ek, gkur_coeff):
    """"""
    Calculates the ultra-rapid delayed rectifier potassium current.
    """"""
    gkur = 0.005 + 0.05/(1 + np.exp(-(u - 15)/13.0))

    aua = 0.65/(np.exp(-(u + 10)/8.5) + np.exp(-(u - 30)/59.0))
    bua = 0.65/(2.5 + np.exp((u + 82)/17.0))
    tau_ua = 1/(kq10*(aua + bua))
    ua_inf = 1/(1 + np.exp(-(u + 30.3)/9.6))
    aui = 1/(21 + np.exp(-(u - 185)/28.0))
    bui = np.exp((u - 158)/16.0)

    tau_ui = 1/(kq10*(aui + bui))
    ui_inf = 1/(1 + np.exp((u - 99.45)/27.48))

    ua = calc_gating_variable(ua, ua_inf, tau_ua, dt)
    ui = calc_gating_variable(ui, ui_inf, tau_ui, dt)

    ikur = gkur_coeff*gkur*(ua**3)*ui*(u - ek)

    return ikur, ua, ui

@njit
def calc_ikr(u, dt, xr, gkr, ek):
    """"""
    Calculates the rapid delayed rectifier potassium current.
    """"""
    gkr = 0.0294 # * np.sqrt(ko / 5.4)
    axr = 0.0003*(u + 14.1)/(1 - np.exp(-(u + 14.1)/5))
    bxr = 0.000073898*(u - 3.3328)/(np.exp((u - 3.3328)/5.1237) - 1)

    tau_xr = 1/(axr + bxr)
    xr_inf = 1/(1 + np.exp(-(u + 14.1)/6.5))

    xr = calc_gating_variable(xr, xr_inf, tau_xr, dt)

    ikr = (gkr*xr*(u - ek))/(1 + np.exp((u + 15)/22.4))

    return ikr, xr

@njit
def calc_iks(u, dt, xs, gks, ek):
    """"""
    Calculates the slow delayed rectifier potassium current.
    """"""
    axs = 0.00004*(u - 19.9)/(1 - np.exp(-(u - 19.9)/17))
    bxs = 0.000035*(u - 19.9)/(np.exp((u - 19.9)/9) - 1)

    tau_xs = 1/(2*(axs + bxs))
    xs_inf = 1/np.sqrt(1 + np.exp(-(u - 19.9)/12.7))

    xs = calc_gating_variable(xs, xs_inf, tau_xs, dt)

    iks = gks*(xs**2)*(u - ek)

    return iks, xs

@njit
def calc_ical(u, dt, d, f, cai, gcal, fca, eca):
    """"""
    Calculates the L-type calcium current.
    """"""
    tau_d = (1 - np.exp(-(u + 10)/6.24))/(0.035*(u + 10)*(1 + np.exp(-(u + 10)/6.24)))
    d_inf = 1/(1 + np.exp(-(u + 10)/8.0))

    tau_f = 9/(0.0197*np.exp(-(0.0337**2)*((u + 10)**2)) + 0.02)
    f_inf = 1/(1 + np.exp((u + 28)/6.9))

    tau_fca = 2
    fca_inf = 1/(1 + cai/0.00035)

    d   = calc_gating_variable(d, d_inf, tau_d, dt)
    f   = calc_gating_variable(f, f_inf, tau_f, dt)
    fca = calc_gating_variable(fca, fca_inf, tau_fca, dt)

    ical = gcal*d*f*fca*(u - 65) 

    return ical, d, f, fca

@njit
def calc_inak(inakmax, nai, nao, ko, kmnai, kmko, F, u, R, T):
    """"""
    Calculates the sodium-potassium pump current.
    """"""
    s = (1/7.0)*(np.exp(nao/67.3) - 1)
    fnak = 1/(1 + 0.1245*np.exp(-0.1*(F*u)/(R*T)) + 0.0365*s*np.exp(-(F*u)/(R*T)))

    inak = inakmax*fnak*(1/(1 + (kmnai/nai)**1.5))*(ko/(ko + kmko))

    return inak

@njit
def calc_inaca(inacamax, nai, nao, cai, cao, kmnancx, kmcancx, ksatncx, F, u, R, T):
    """"""
    Calculates the sodium-calcium exchanger current.
    """"""
    gamma = 0.35

    # Exponential terms with clamping
    exp_term = np.exp(gamma * (F * u) / (R * T))
    exp_rev_term = np.exp((gamma - 1) * (F * u) / (R * T))

    # Numerator
    numerator = inacamax * (exp_term * nai**3 * cao - exp_rev_term * nao**3 * cai)

    # Denominator
    term1 = (kmnancx**3 + nao**3)  # (K_m,Na^3 + [Na+]_i^3)
    term2 = (kmcancx + cao)        # (K_m,Ca + [Ca2+]_o)
    term3 = (1 + ksatncx * exp_rev_term)  # (1 + k_sat * exp(...))

    denominator = term1 * term2 * term3

    # Calculate INaCa
    inaca = numerator / denominator

    return inaca


@njit
def calc_ibca(gcab, eca, u):
    """"""
    Calculates the background calcium current.
    """"""
    ibca = gcab*(u - eca)
    return ibca

@njit
def calc_ibna(gnab, ena, u):
    """"""
    Calculates the background sodium current.
    """"""
    ibna = gnab*(u - ena)
    return ibna

@njit
def calc_ipca(ipcamax, cai):
    """"""
    Calculates the sarcolemmal calcium pump current.
    """"""
    ipca = ipcamax*cai/(cai + 0.0005)
    return ipca

@njit
def calc_irel(dt, urel, vrel, irel, wrel, ical, inaca, krel, carel, cai, u, F, Vrel): 
    """"""
    Calculates the calcium release from the JSR.
    """"""
    tau_u = 8

    Fn = 1e-12*Vrel*irel - ((5*1e-13)/F)*(0.5*ical - 0.2*inaca) 

    u_inf = 1/(1 + np.exp(-(Fn - 3.4175e-13)/13.67e-16))

    tau_v = 1.91 + 2.09/(1 + np.exp(-(Fn - 3.4175e-13)/13.67e-16))
    v_inf = 1 - 1/(1 + np.exp(-(Fn - 6.835e-14)/13.67e-16))

    tau_w = 6 * (1 - np.exp(-(u - 7.9) / 5.0)) / ((1 + 0.3 * np.exp(-(u - 7.9) / 5.0)) * (u - 7.9))
    w_inf = 1 - 1/(1 + np.exp(-(u - 40)/17.0))

    urel = calc_gating_variable(urel, u_inf, tau_u, dt)
    vrel = calc_gating_variable(vrel, v_inf, tau_v, dt)
    wrel = calc_gating_variable(wrel, w_inf, tau_w, dt)

    irel = krel*(urel**2)*vrel*wrel*(carel - cai)

    return irel, urel, vrel, wrel

@njit
def calc_itr(caup, carel):
    """"""
    Calculates the transfer of calcium from the NSR to the JSR.
    """"""
    tautr = 180
    itr = (caup - carel)/tautr
    return itr

@njit
def calc_iup(iupmax, cai, kup):
    """"""
    Calculates the uptake of calcium into the NSR.
    """"""
    iup = iupmax/(1 + (kup/cai))
    return iup

@njit
def calc_iupleak(caup, caupmax, iupmax):
    """"""
    Calculates the leak of calcium from the NSR.
    """"""
    iupleak = (caup/caupmax)*iupmax
    return iupleak



@njit(parallel=True)
def ionic_kernel_2d(u_new, u, nai, ki, cai, caup, carel, m, h, j_, d, f, oa, oi, ua, ui, xs, xr, fca, irel, vrel, urel, wrel, indexes, dt, 
                    gna, gnab, gk1, gkr, gks, gto, gcal, gcab, gkur_coeff, F, T, R, Vc, Vj, Vup, Vrel, ibk, cao, nao, ko, caupmax, kup,
                    kmnai, kmko, kmnancx, kmcancx, ksatncx, kmcmdn, kmtrpn, kmcsqn, trpnmax, cmdnmax, csqnmax, inacamax,
                    inakmax, ipcamax, krel, iupmax, kq10):
    """"""
    Computes the ionic currents and updates the state variables in the 2D
    Courtemanche cardiac model.

    Parameters
    ----------
    u_new : np.ndarray
        Updated membrane potential values.
    u : np.ndarray
        Current membrane potential values.
    v : np.ndarray
        Recovery variable array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""
                    
    n_i = u.shape[0]
    n_j = u.shape[1]

    for ind in prange(indexes.shape[0]):
        ii = indexes[ind]
        i = int(ii/n_j)
        j = ii % n_j

        ena, ek, eca = calc_equilibrum_potentials(nai[i, j], nao, ki[i, j], ko, cai[i, j], cao, R, T, F)

        m[i, j] = calc_gating_m(m[i, j], u[i, j], dt)
        h[i, j] = calc_gating_h(h[i, j], u[i, j], dt)
        j_[i, j] = calc_gating_j(j_[i, j], u[i, j], dt)

        ina = calc_ina(u[i, j], m[i, j], h[i, j], j_[i, j], gna, ena)

        ik1 = calc_ik1(u[i, j], gk1, ek)

        ito, oa[i, j], oi[i, j] = calc_ito(u[i, j], dt, kq10, oa[i, j], oi[i, j], gto, ek)

        ikur, ua[i, j], ui[i, j] = calc_ikur(u[i, j], dt, kq10, ua[i, j], ui[i, j], ek, gkur_coeff)

        ikr, xr[i, j] = calc_ikr(u[i, j], dt, xr[i, j], gkr, ek)

        iks, xs[i, j] = calc_iks(u[i, j], dt, xs[i, j], gks, ek)

        ical, d[i, j], f[i, j], fca[i, j] = calc_ical(u[i, j], dt, d[i, j], f[i, j], cai[i, j], gcal, fca[i, j], eca)

        inak = calc_inak(inakmax, nai[i, j], nao, ko, kmnai, kmko, F, u[i, j], R, T)
        inaca = calc_inaca(inacamax, nai[i, j], nao, cai[i, j], cao, kmnancx, kmcancx, ksatncx, F, u[i, j], R, T)

        ibca = calc_ibca(gcab, eca, u[i, j])

        ibna = calc_ibna(gnab, ena, u[i, j])

        ipca = calc_ipca(ipcamax, cai[i, j])

        irel[i, j], urel[i, j], vrel[i, j], wrel[i, j] = calc_irel(dt, urel[i, j], vrel[i, j], irel[i, j], wrel[i, j], ical, inaca, krel, carel[i, j], cai[i, j], u[i, j], F, Vrel)
        itr = calc_itr(caup[i, j], carel[i, j])
        iup = calc_iup(iupmax, cai[i, j], kup)
        iupleak = calc_iupleak(caup[i, j], caupmax, iupmax) 

        caup[i, j] = calc_caup(caup[i, j], dt, iup, iupleak, itr, Vrel, Vup)
        nai[i, j] = calc_nai(nai[i, j], dt, inak, inaca, ibna, ina, F, Vj)

        ki[i, j] = calc_ki(ki[i, j], dt, inak, ik1, ito, ikur, ikr, iks, ibk, F, Vj)
        cai[i, j] = calc_cai(cai[i, j], dt, inaca, ipca, ical, ibca, iup, iupleak, irel[i, j], Vrel, Vup, trpnmax, kmtrpn, cmdnmax, kmcmdn, F, Vj)

        carel[i, j] = calc_carel(carel[i, j], dt, itr, irel[i, j], csqnmax, kmcsqn)

        u_new[i, j] -= dt * (ina + ik1 + ito + ikur + ikr + iks + ical + ipca + inak + inaca + ibna + ibca)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/bueno_orovio_2d.py",".py","14412","476","import numpy as np
from numba import njit, prange

from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class BuenoOrovio2D(CardiacModel):

    def __init__(self):
        """"""
        Two-dimensional implementation of the Bueno-Orovio–Cherry–Fenton (BOCF) model 
        for simulating human ventricular tissue electrophysiology.

        The BOCF model is a minimal phenomenological model developed to capture 
        key ionic mechanisms and reproduce realistic human ventricular action potential 
        dynamics, including restitution, conduction block, and spiral wave behavior. 
        It consists of four variables: transmembrane potential (u), two gating variables (v, w), 
        and one additional slow variable (s), representing calcium-related dynamics.

        This implementation corresponds to the EPI (epicardial) parameter set described in the paper.

        Attributes
        ----------
        u : np.ndarray
            Transmembrane potential (dimensionless, typically in [0, 1.55]).
        v : np.ndarray
            Fast gating variable representing sodium channel inactivation.
        w : np.ndarray
            Slow recovery variable representing calcium and potassium gating.
        s : np.ndarray
            Slow variable related to calcium inactivation.
        D_model : float
            Diffusion coefficient for spatial propagation.
        state_vars : list of str
            Names of state variables to be saved or restored.
        npfloat : str
            Floating point precision (default: 'float64').

        Model Parameters (EPI set)
        --------------------------
        u_o : float
            Resting membrane potential.
        u_u : float
            Peak potential (upper bound).
        theta_v, theta_w : float
            Activation thresholds for v and w.
        theta_v_m, theta_o : float
            Thresholds for switching time constants.
        tau_v1_m, tau_v2_m : float
            Time constants for v below/above threshold.
        tau_v_p : float
            Decay constant for v.
        tau_w1_m, tau_w2_m : float
            Base and transition time constants for w.
        k_w_m, u_w_m : float
            Parameters controlling the shape of τw curve.
        tau_w_p : float
            Time constant for decay of w above threshold.
        tau_fi : float
            Time constant for fast inward current (J_fi).
        tau_o1, tau_o2 : float
            Time constants for outward current below/above threshold.
        tau_so1, tau_so2 : float
            Time constants for repolarizing tail current.
        k_so, u_so : float
            Parameters controlling nonlinearity in tau_so.
        tau_s1, tau_s2 : float
            Time constants for the s-gate below/above threshold.
        k_s, u_s : float
            Parameters for tanh activation of the s variable.
        tau_si : float
            Time constant for slow inward current (J_si).
        tau_w_inf : float
            Slope of w∞ below threshold.
        w_inf_ : float
            Asymptotic value of w∞ above threshold.

        Paper
        -----
        Bueno-Orovio, A., Cherry, E. M., & Fenton, F. H. (2008).
        Minimal model for human ventricular action potentials in tissue.
        J Theor Biol., 253(3), 544-60.
        https://doi.org/10.1016/j.jtbi.2008.03.029

        """"""
        super().__init__()
        self.v = np.ndarray
        self.w = np.ndarray
        self.s = np.ndarray

        self.D_model = 1.

        self.state_vars = [""u"", ""v"", ""w"", ""s""]
        self.npfloat    = 'float64'

        # model parameters (EPI)
        
        self.u_o = 0.0
        self.u_u = 1.55
        self.theta_v = 0.3
        self.theta_w = 0.13
        self.theta_v_m = 0.006
        self.theta_o = 0.006
        self.tau_v1_m = 60
        self.tau_v2_m = 1150
        self.tau_v_p = 1.4506
        self.tau_w1_m = 60
        self.tau_w2_m = 15
        self.k_w_m = 65
        self.u_w_m = 0.03
        self.tau_w_p = 200
        self.tau_fi = 0.11
        self.tau_o1 = 400
        self.tau_o2 = 6
        self.tau_so1 = 30.0181
        self.tau_so2 = 0.9957
        self.k_so = 2.0458
        self.u_so = 0.65
        self.tau_s1 = 2.7342
        self.tau_s2 = 16
        self.k_s = 2.0994
        self.u_s = 0.9087
        self.tau_si = 1.8875
        self.tau_w_inf = 0.07
        self.w_inf_ = 0.94

        # initial conditions
        self.init_u = 0.0
        self.init_v = 1.0
        self.init_w = 1.0
        self.init_s = 0.0

    def initialize(self):
        """"""
        Initializes the model for simulation.
        """"""
        super().initialize()
        self.u = self.init_u * np.ones_like(self.u, dtype=self.npfloat)
        self.v = self.init_v * np.ones_like(self.u, dtype=self.npfloat) 
        self.w = self.init_w * np.ones_like(self.u, dtype=self.npfloat)
        self.s = self.init_s * np.ones_like(self.u, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Bueno-Orovio model.
        """"""
        ionic_kernel_2d(self.u_new, self.u, self.v, self.w, self.s, self.cardiac_tissue.myo_indexes, 
                        self.dt, self.u_o, self.u_u, self.theta_v, self.theta_w, self.theta_v_m,
                        self.theta_o, self.tau_v1_m, self.tau_v2_m, self.tau_v_p,
                        self.tau_w1_m, self.tau_w2_m, self.k_w_m, self.u_w_m,
                        self.tau_w_p, self.tau_fi, self.tau_o1, self.tau_o2,
                        self.tau_so1, self.tau_so2, self.k_so, self.u_so,
                        self.tau_s1, self.tau_s2, self.k_s, self.u_s,
                        self.tau_si, self.tau_w_inf, self.w_inf_)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()

@njit
def calc_v(v, u, dt, theta_v, v_inf, tau_v_m, tau_v_p):
    """"""
    Updates the fast inactivation gate variable `v`.

    The variable `v` models the fast sodium channel inactivation.
    It follows a piecewise ODE with different dynamics depending
    on whether the membrane potential `u` is above or below `theta_v`.

    Parameters
    ----------
    v : float
        Current value of the v gate.
    u : float
        Current membrane potential.
    dt : float
        Time step.
    theta_v : float
        Threshold for switching recovery behavior.
    v_inf : float
        Steady-state value of v.
    tau_v_m : float
        Time constant for activation (u < threshold).
    tau_v_p : float
        Time constant for decay (u >= threshold).

    Returns
    -------
    float
        Updated value of the v gate.
    """"""
    v_ = (v_inf - v)/tau_v_m if (u - theta_v) < 0 else -v/tau_v_p
    v += dt*v_
    return v

@njit
def calc_w(w, u, dt, theta_w, w_inf, tau_w_m, tau_w_p):
    """"""
    Updates the slow gating variable `w`.

    The variable `w` represents calcium/potassium channel gating.
    It has different recovery dynamics below and above the threshold `theta_w`.

    Parameters
    ----------
    w : float
        Current value of the w gate.
    u : float
        Membrane potential.
    dt : float
        Time step.
    theta_w : float
        Threshold for switching between time constants.
    w_inf : float
        Steady-state value of w.
    tau_w_m : float
        Time constant for approach to w_inf (u < threshold).
    tau_w_p : float
        Time constant for decay (u >= threshold).

    Returns
    -------
    float
        Updated value of the w gate.
    """"""
    w_ = (w_inf - w)/tau_w_m if (u - theta_w) < 0 else -w/tau_w_p
    w += dt*w_
    return w

@njit
def calc_s(s, u, dt, tau_s, k_s, u_s):
    """"""
    Updates the slow variable `s`, related to calcium dynamics.

    The variable `s` evolves toward a tanh-based steady-state function of `u`.

    Parameters
    ----------
    s : float
        Current value of the s variable.
    u : float
        Membrane potential.
    dt : float
        Time step.
    tau_s : float
        Time constant.
    k_s : float
        Slope of the tanh function.
    u_s : float
        Midpoint of the tanh function.

    Returns
    -------
    float
        Updated value of the s variable.
    """"""
    s += dt*((1 + np.tanh(k_s*(u - u_s)))/2 - s)/tau_s
    return s

@njit
def calc_Jfi(u, v, theta_v, u_u, tau_fi):
    """"""
    Computes the fast inward sodium current (J_fi).

    Active when membrane potential exceeds `theta_v`.
    Models rapid depolarization due to sodium influx.

    Parameters
    ----------
    u : float
        Membrane potential.
    v : float
        Fast gating variable.
    theta_v : float
        Activation threshold.
    u_u : float
        Upper limit for depolarization.
    tau_fi : float
        Time constant of the fast inward current.

    Returns
    -------
    float
        Current value of J_fi.
    """"""
    H = 1.0 if (u - theta_v) >= 0 else 0.0
    return -v*H*(u - theta_v)*(u_u - u)/tau_fi

@njit
def calc_Jso(u, u_o, theta_w, tau_o, tau_so):
    """"""
    Computes the slow outward current (J_so).

    Consists of a linear repolarization component below `theta_w`
    and a constant component above.

    Parameters
    ----------
    u : float
        Membrane potential.
    u_o : float
        Resting potential (offset).
    theta_w : float
        Threshold for switching between components.
    tau_o : float
        Time constant below threshold.
    tau_so : float
        Time constant above threshold.

    Returns
    -------
    float
        Current value of J_so.
    """"""
    H = 1.0 if (u - theta_w) >= 0 else 0.0
    return (u - u_o)*(1 - H)/tau_o + H/tau_so

@njit
def calc_Jsi(u, w, s, theta_w, tau_si):
    """"""
    Computes the slow inward current (J_si), active during plateau phase.

    Active only when `u > theta_w` and controlled by gating variables `w` and `s`.

    Parameters
    ----------
    u : float
        Membrane potential.
    w : float
        Slow gating variable.
    s : float
        Calcium-related variable.
    theta_w : float
        Threshold for activation.
    tau_si : float
        Time constant of slow inward current.

    Returns
    -------
    float
        Current value of J_si.
    """"""
    H = 1.0 if (u - theta_w) >= 0 else 0.0
    return -H*w*s/tau_si

@njit
def calc_tau_v_m(u, theta_v_m, tau_v1_m, tau_v2_m):
    """"""
    Selects time constant for v gate depending on membrane potential.

    Returns `tau_v1_m` below `theta_v_m`, and `tau_v2_m` above.

    Returns
    -------
    float
        Time constant for v gate.
    """"""
    return tau_v1_m if (u - theta_v_m) < 0 else tau_v2_m

@njit    
def calc_tau_w_m(u, tau_w1_m, tau_w2_m, k_w_m, u_w_m):
    """"""
    Computes smooth transition time constant for w gate using tanh.

    Returns
    -------
    float
        Blended time constant for w gate.
    """"""
    return tau_w1_m + (tau_w2_m - tau_w1_m)*(1 + np.tanh(k_w_m*(u - u_w_m)))/2

@njit
def calc_tau_so(u, tau_so1, tau_so2, k_so, u_so):
    """"""
    Computes tau_so using a sigmoidal transition between two values.
    """"""
    return tau_so1 + (tau_so2 - tau_so1)*(1 + np.tanh(k_so*(u - u_so)))/2

@njit
def calc_tau_s(u, tau_s1, tau_s2, theta_w):
    """"""
    Selects tau_s based on threshold.
    """"""
    return tau_s1 if (u - theta_w) < 0 else tau_s2

@njit
def calc_tau_o(u, tau_o1, tau_o2, theta_o):
    """"""
    Selects tau_o based on threshold.
    """"""
    return tau_o1 if (u - theta_o) < 0 else tau_o2
    
@njit
def calc_v_inf(u, theta_v_m):
    """"""
    Computes the value of v based on membrane potential.
    """"""
    return 1.0 if u < theta_v_m else 0.0

@njit
def calc_w_inf(u, theta_o, tau_w_inf, w_inf_):
    """"""
    Computes the value of w based on membrane potential.
    """"""
    return 1 - u/tau_w_inf if (u - theta_o) < 0 else w_inf_


@njit(parallel=True)
def ionic_kernel_2d(u_new, u, v, w, s, indexes, dt, 
                    u_o, u_u, theta_v, theta_w, theta_v_m,
                    theta_o, tau_v1_m, tau_v2_m, tau_v_p,
                    tau_w1_m, tau_w2_m, k_w_m, u_w_m,
                    tau_w_p, tau_fi, tau_o1, tau_o2,
                    tau_so1, tau_so2, k_so, u_so,
                    tau_s1, tau_s2, k_s, u_s,
                    tau_si, tau_w_inf, w_inf_):
    """"""
    Computes the ionic kernel for the Bueno-Orovio 2D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""

    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        v[i, j] = calc_v(v[i, j], u[i, j], dt, theta_v, calc_v_inf(u[i, j], theta_v_m), 
                         calc_tau_v_m(u[i, j], theta_v_m, tau_v1_m, tau_v2_m), tau_v_p)
        
        w[i, j] = calc_w(w[i, j], u[i, j], dt, theta_w, calc_w_inf(u[i, j], theta_o, tau_w_inf, w_inf_), 
                         calc_tau_w_m(u[i, j], tau_w1_m, tau_w2_m, k_w_m, u_w_m), tau_w_p)
        
        s[i, j] = calc_s(s[i, j], u[i, j], dt,
                        calc_tau_s(u[i, j], tau_s1, tau_s2, theta_w), k_s, u_s)
        
        J_fi = calc_Jfi(u[i, j], v[i, j], theta_v, u_u, tau_fi)
        J_so = calc_Jso(u[i, j], u_o, theta_w,
                        calc_tau_o(u[i, j], tau_o1, tau_o2, theta_o), 
                        calc_tau_so(u[i, j], tau_so1, tau_so2, k_so, u_so))
        J_si = calc_Jsi(u[i, j], w[i, j], s[i, j], theta_w, tau_si)

        u_new[i, j] += dt * (-J_fi - J_so - J_si)","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/model/luo_rudy91_2d.py",".py","16323","515","import numpy as np
from numba import njit, prange
from finitewave.core.model.cardiac_model import CardiacModel
from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D
)
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    IsotropicStencil2D
)


class LuoRudy912D(CardiacModel):
    """"""
    Implements the Luo-Rudy 1991 ventricular action potential model.

    This biophysically detailed model simulates the ionic currents and membrane potential 
    of a ventricular cardiac cell based on Hodgkin-Huxley-type formalism. It was one of 
    the first to incorporate realistic ionic channel kinetics, calcium dynamics, and 
    multiple potassium currents to reproduce key phases of the action potential.

    The model includes:
    - Fast Na⁺ current (I_Na)
    - Slow inward Ca²⁺ current (I_Si)
    - Time-dependent K⁺ current (I_K)
    - Time-independent K⁺ current (I_K1)
    - Plateau K⁺ current (I_Kp)
    - Background/leak current (I_b)

    Attributes
    ----------
    state_vars : list of str
        List of state variable names to save and restore (`u`, `m`, `h`, `j`, `d`, `f`, `x`, `cai`).
    D_model : float
        Diffusion coefficient representing electrical conductivity in the medium (typically set to 0.1).
    gna, gsi, gk, gk1, gkp, gb : float
        Maximum conductances for Na⁺, Ca²⁺, K⁺, and background channels [mS/μF].
    ko, ki, nao, nai, cao : float
        Ion concentrations in mM (extracellular and intracellular for Na⁺, K⁺, Ca²⁺).
    R, T, F : float
        Physical constants: gas constant, temperature in Kelvin, and Faraday constant.
    PR_NaK : float
        Sodium/potassium permeability ratio (used in reversal potential calculation for I_K).

    Paper
    -----
    Luo CH, Rudy Y. 
    A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction. 
    Circ Res. 1991 Jun;68(6):1501-26. 
    doi: 10.1161/01.res.68.6.1501. 
    PMID: 1709839.

    """"""

    def __init__(self):
        """"""
        Initializes the LuoRudy912D instance, setting up the state variables and parameters.
        """"""
        CardiacModel.__init__(self)
        self.D_model = 0.1

        self.m   = np.ndarray
        self.h   = np.ndarray
        self.j   = np.ndarray
        self.d   = np.ndarray
        self.f   = np.ndarray
        self.x   = np.ndarray
        self.cai = np.ndarray
        
        self.state_vars = [""u"", ""m"", ""h"", ""j"", ""d"", ""f"", ""x"", ""cai""]
        self.npfloat = 'float64'

        # Ion Channel Conductances (mS/µF)
        self.gna = 23.0     # Fast sodium (Na+) conductance
        self.gsi = 0.09     # Slow inward calcium (Ca2+) conductance
        self.gk  = 0.282    # Time-dependent potassium (K+) conductance
        self.gk1 = 0.6047   # Inward rectifier potassium (K1) conductance
        self.gkp = 0.0183   # Plateau potassium (Kp) conductance
        self.gb  = 0.03921  # Background conductance (leak current)

        # Extracellular and Intracellular Ion Concentrations (mM)
        self.ko  = 5.4      # Extracellular potassium concentration
        self.ki  = 145.0    # Intracellular potassium concentration
        self.nai = 18.0     # Intracellular sodium concentration
        self.nao = 140.0    # Extracellular sodium concentration
        self.cao = 1.8      # Extracellular calcium concentration

        # Physical Constants
        self.R = 8.314      # Universal gas constant (J/(mol·K))
        self.T = 310.0      # Temperature (Kelvin, 37°C)
        self.F = 96.5       # Faraday constant (C/mmol)

        # Ion Permeability Ratios
        self.PR_NaK = 0.01833  # Na+/K+ permeability ratio

        # initial conditions
        self.init_u   = -84.5
        self.init_m   = 0.0017
        self.init_h   = 0.9832
        self.init_j   = 0.995484
        self.init_d   = 0.000003
        self.init_f   = 1.0
        self.init_x   = 0.0057
        self.init_cai = 0.0002

    def initialize(self):
        """"""
        Initializes the state variables.

        This method sets the initial values for the membrane potential ``u``,
        gating variables ``m``, ``h``, ``j``, ``d``, ``f``, ``x``,
        and intracellular calcium concentration ``cai``.
        """"""
        super().initialize()
        shape = self.cardiac_tissue.mesh.shape

        self.u = self.init_u * np.ones(shape, dtype=self.npfloat)
        self.u_new = self.u.copy()
        self.m = self.init_m * np.ones(shape, dtype=self.npfloat)
        self.h = self.init_h * np.ones(shape, dtype=self.npfloat)
        self.j = self.init_j * np.ones(shape, dtype=self.npfloat)
        self.d = self.init_d * np.ones(shape, dtype=self.npfloat)
        self.f =  self.init_f * np.ones(shape, dtype=self.npfloat)
        self.x =  self.init_x * np.ones(shape, dtype=self.npfloat)
        self.cai = self.init_cai * np.ones(shape, dtype=self.npfloat)

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel to update the state variables and membrane
        potential.
        """"""
        ionic_kernel_2d(self.u_new, self.u, 
                        self.m, self.h, self.j, self.d,self.f, self.x, self.cai, 
                        self.cardiac_tissue.myo_indexes, self.dt, 
                        self.gna, self.gsi, self.gk, self.gk1, self.gkp, self.gb, 
                        self.ko, self.ki, self.nai, self.nao, self.cao, self.R, self.T, self.F, self.PR_NaK)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil2D()

        return AsymmetricStencil2D()

@njit
def calc_gating_var(var, dt, alpha, beta):
    """"""
    Computes the gating variable dynamics based on the Hodgkin-Huxley formalism.

    Parameters
    ----------
    var : float
        Current value of the gating variable.
    dt : float
        Time step [ms].
    alpha : float
        Rate constant for activation.
    beta : float
        Rate constant for inactivation.

    Returns
    -------
    var_new : float
        Updated gating variable.
    """"""
    tau = 1. / (alpha + beta)
    inf = alpha / (alpha + beta)
    var += dt * (inf - var) / tau
    return var

@njit
def calc_ina(u, dt, m, h, j, E_Na, gna):
    """"""
    Computes the fast inward sodium current (I_Na) and updates gating variables m, h, j.

    I_Na is responsible for the rapid depolarization (phase 0) of the action potential. 
    It depends on three gates:
    - m: activation gate (opens quickly),
    - h: fast inactivation gate,
    - j: slow inactivation gate.

    Gating dynamics follow Hodgkin-Huxley kinetics with voltage-dependent time constants 
    and steady-state values. I_Na = g_Na * m^3 * h * j * (u - E_Na).

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    dt : float
        Time step [ms].
    m, h, j : float
        Gating variables for the sodium channel.
    E_Na : float
        Reversal potential for Na⁺ [mV].
    gna : float
        Maximal sodium conductance [mS/μF].

    Returns
    -------
    ina : float
        Fast sodium current [μA/μF].
    m, h, j : float
        Updated gating variables.
    """"""
    alpha_h, beta_h, beta_J, alpha_J = 0, 0, 0, 0
    if u >= -40.:
        beta_h = 1. / (0.13 * (1 + np.exp((u + 10.66) / -11.1)))
        beta_J = 0.3 * np.exp(-2.535 * 1e-07 *
                                u) / (1 + np.exp(-0.1 * (u + 32)))
    else:
        alpha_h = 0.135 * np.exp((80 + u) / -6.8)
        beta_h = 3.56 * \
            np.exp(0.079 * u) + 3.1 * 1e5 * np.exp(0.35 * u)
        beta_J = 0.1212 * \
            np.exp(-0.01052 * u) / \
            (1 + np.exp(-0.1378 * (u + 40.14)))
        alpha_J = (-1.2714 * 1e5 * np.exp(0.2444 * u) - 3.474 * 1e-5 * np.exp(-0.04391 * u)) * \
                    (u + 37.78) / (1 + np.exp(0.311 * (u + 79.23)))

    alpha_m = 0.32 * (u + 47.13) / \
        (1 - np.exp(-0.1 * (u + 47.13)))
    beta_m = 0.08 * np.exp(-u / 11)

    m = calc_gating_var(m, dt, alpha_m, beta_m)
    h = calc_gating_var(h, dt, alpha_h, beta_h)
    j = calc_gating_var(j, dt, alpha_J, beta_J)

    return gna * m * m * m * h * j * (u - E_Na), m, h, j

@njit
def calc_isk(u, dt, d, f, cai, gsi):
    """"""
    Computes the slow inward calcium current (I_Si) and updates d, f, and intracellular calcium.

    I_Si is primarily carried by L-type Ca²⁺ channels and governs the plateau (phase 2).
    The reversal potential E_Si is dynamically calculated based on intracellular Ca²⁺ levels.

    Calcium handling is simplified: part of I_Si is subtracted from intracellular Ca²⁺, 
    while a constant leak term restores it toward a baseline.

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    dt : float
        Time step [ms].
    d, f : float
        Activation and inactivation gates of the calcium channel.
    cai : float
        Intracellular calcium concentration [mM].
    gsi : float
        Maximal calcium conductance [mS/μF].

    Returns
    -------
    I_Si : float
        Slow inward calcium current [μA/μF].
    d, f, cai : float
        Updated gating variables and intracellular Ca²⁺.
    """"""
    E_Si = 7.7 - 13.0287 * np.log(cai)
    I_Si = gsi * d * f * (u - E_Si)
    alpha_d = 0.095 * \
        np.exp(-0.01 * (u - 5)) / \
        (1 + np.exp(-0.072 * (u - 5)))
    beta_d = 0.07 * \
        np.exp(-0.017 * (u + 44)) / \
        (1 + np.exp(0.05 * (u + 44)))
    alpha_f = 0.012 * \
        np.exp(-0.008 * (u + 28)) / \
        (1 + np.exp(0.15 * (u + 28)))
    beta_f = 0.0065 * \
        np.exp(-0.02 * (u + 30)) / \
        (1 + np.exp(-0.2 * (u + 30)))
    
    d = calc_gating_var(d, dt, alpha_d, beta_d)
    f = calc_gating_var(f, dt, alpha_f, beta_f)

    cai += dt * (-0.0001 * I_Si + 0.07 * (0.0001 - cai))

    return I_Si, d, f, cai

@njit
def calc_ik(u, dt, x, ko, ki, nao, nai, PR_NaK, R, T, F, gk):
    """"""
    Computes the time-dependent outward potassium current (I_K) and updates gate x.

    This current drives late repolarization (phase 3) and is voltage- and time-dependent.
    Reversal potential is calculated via the Goldman-Hodgkin-Katz equation 
    (with sodium/potassium permeability ratio).

    An auxiliary factor Xi introduces voltage-sensitive activation near -100 mV.

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    dt : float
        Time step [ms].
    x : float
        Activation gate of the delayed rectifier K⁺ channel.
    ko, ki : float
        Extra-/intracellular potassium concentrations [mM].
    nao, nai : float
        Extra-/intracellular sodium concentrations [mM].
    PR_NaK : float
        Na⁺/K⁺ permeability ratio.
    R, T, F : float
        Gas constant, temperature [K], and Faraday constant.
    gk : float
        Maximum potassium conductance [mS/μF].

    Returns
    -------
    I_K : float
        Time-dependent potassium current [μA/μF].
    x : float
        Updated activation gate.
    """"""
    E_K = (R * T / F) * \
        np.log((ko + PR_NaK * nao) / (ki + PR_NaK * nai))

    G_K = gk * np.sqrt(ko / 5.4)

    Xi = 0
    if u > -100:
        Xi = 2.837 * (np.exp(0.04 * (u + 77)) - 1) / \
            ((u + 77) * np.exp(0.04 * (u + 35)))
    else:
        Xi = 1

    I_K = G_K * x * Xi * (u - E_K)

    alpha_x = 0.0005 * \
        np.exp(0.083 * (u + 50)) / \
        (1 + np.exp(0.057 * (u + 50)))
    beta_x = 0.0013 * \
        np.exp(-0.06 * (u + 20)) / \
        (1 + np.exp(-0.04 * (u + 20)))
    
    x = calc_gating_var(x, dt, alpha_x, beta_x)

    return I_K, x

@njit
def calc_ik1(u, ko, E_K1, gk1):
    """"""
    Computes the time-independent inward rectifier potassium current (I_K1).

    I_K1 stabilizes the resting membrane potential and contributes to 
    late repolarization. It is primarily active at negative voltages and 
    follows a voltage-dependent gating-like term (K1_x).

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    ko : float
        Extracellular potassium [mM].
    E_K1 : float
        Equilibrium potential for K1 current [mV].
    gk1 : float
        Maximum K1 conductance [mS/μF].

    Returns
    -------
    I_K1 : float
        Time-independent K⁺ current [μA/μF].
    """"""
    alpha_K1 = 1.02 / (1 + np.exp(0.2385 * (u - E_K1 - 59.215)))
    beta_K1 = (0.49124 * np.exp(0.08032 * (u - E_K1 + 5.476)) + np.exp(0.06175 * (u - E_K1 - 594.31))) / \
                (1 + np.exp(-0.5143 * (u - E_K1 + 4.753)))

    K_1x = alpha_K1 / (alpha_K1 + beta_K1)

    G_K1 = gk1 * np.sqrt(ko / 5.4)
    I_K1 = G_K1 * K_1x * (u - E_K1)

    return I_K1

@njit
def calc_ikp(u, E_K1, gkp):
    """"""
    Computes the plateau potassium current (I_Kp).

    I_Kp is a small, quasi-steady outward current that operates in the 
    plateau phase. Its activation is a sigmoid function of voltage.

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    ko : float
        Extracellular potassium [mM].
    E_K1 : float
        Equilibrium potential (same as I_K1).
    gkp : float
        Plateau potassium conductance [mS/μF].

    Returns
    -------
    I_Kp : float
        Plateau potassium current [μA/μF].
    """"""
    E_Kp = E_K1
    K_p = 1. / (1 + np.exp((7.488 - u) / 5.98))
    I_Kp = gkp * K_p * (u - E_Kp)

    return I_Kp

@njit
def calc_ib(u, gb):
    """"""
    Computes the non-specific background (leak) current.

    This is a linear leak current contributing to resting potential maintenance.

    Parameters
    ----------
    u : float
        Membrane potential [mV].
    gb : float
        Background conductance [mS/μF].

    Returns
    -------
    I_b : float
        Background current [μA/μF].
    """"""
    return gb * (u + 59.87)


@njit(parallel=True)
def ionic_kernel_2d(u_new, u, m, h, j_, d, f, x, cai, indexes, dt, gna, gsi, gk, gk1, gkp, gb, ko, ki, nai, nao, cao, R, T, F, PR_NaK):
    """"""
    Computes the ionic currents and updates the state variables in the 2D
    Luo-Rudy 1991 cardiac model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated membrane potential.
    u : np.ndarray
        Array of the current membrane potential values.
    m : np.ndarray
        Array for the gating variable `m`.
    h : np.ndarray
        Array for the gating variable `h`.
    j_ : np.ndarray
        Array for the gating variable `j_`.
    d : np.ndarray
        Array for the gating variable `d`.
    f : np.ndarray
        Array for the gating variable `f`.
    x : np.ndarray
        Array for the gating variable `x`.
    cai : np.ndarray
        Array for the intracellular calcium concentration.
    indexes : np.ndarray
        Array of indexes where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""

    E_Na = (R*T/F)*np.log(nao/nai)

    n_j = u.shape[1]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        # Fast sodium current:
        ina, m[i, j], h[i, j], j_[i, j] = calc_ina(u[i, j], dt, m[i, j], h[i, j], j_[i, j], E_Na, gna)

        # Slow inward current:
        isi, d[i, j], f[i, j], cai[i, j] = calc_isk(u[i, j], dt, d[i, j], f[i, j], cai[i, j], gsi)

        # Time-dependent potassium current:
        ik, x[i, j] = calc_ik(u[i, j], dt, x[i, j], ko, ki, nao, nai, PR_NaK, R, T, F, gk)

        E_K1 = (R * T / F) * np.log(ko / ki)

        # Time-independent potassium current:
        ik1 = calc_ik1(u[i, j], ko, E_K1, gk1)

        # Plateau potassium current:
        ikp = calc_ikp(u[i, j], E_K1, gkp)

        # Background current:
        ib = calc_ib(u[i, j], gb)

        # Total time-independent potassium current:
        ik1t = ik1 + ikp + ib

        u_new[i, j] -= dt * (ina + isi + ik1t + ik)




","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/stim_current_matrix_2d.py",".py","2127","64","import numpy as np
from finitewave.core.stimulation.stim_current import StimCurrent


class StimCurrentMatrix2D(StimCurrent):
    """"""
    A class that applies a stimulation current to a 2D cardiac tissue model
    based on a binary matrix.

    Attributes
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    matrix : numpy.ndarray
        A 2D binary matrix indicating the region of interest for stimulation. 
        Elements greater than 0 represent regions to be stimulated.
    
    """"""
    def __init__(self, time, curr_value, duration, matrix, u_max=None):
        """"""
        Initializes the StimCurrentMatrix2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        matrix : numpy.ndarray
            A 2D binary matrix indicating the region of interest for
            stimulation.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration)
        self.matrix = matrix
        self.u_max = u_max

    def stimulate(self, model):
        """"""
        Applies the stimulation current to the cardiac tissue model based on
        the specified binary matrix.

        The stimulation is applied only if the current time is within the
        stimulation period and the stimulation has not been previously applied.

        Parameters
        ----------
        model : CardiacModel
            The 2D cardiac tissue model.
        """"""
        mask = (self.matrix > 0) & (model.cardiac_tissue.mesh == 1)
        model.u[mask] += model.dt * self.curr_value

        if self.u_max is not None:
            model.u[mask] = np.where(model.u[mask] > self.u_max, self.u_max,
                                     model.u[mask])
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/stim_voltage_coord_2d.py",".py","2178","68","from finitewave.core.stimulation.stim_voltage import StimVoltage


class StimVoltageCoord2D(StimVoltage):
    """"""
    A class that applies a voltage stimulus to a 2D cardiac tissue model
    within a specified region of interest.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    volt_value : float
        The voltage value to apply to the region of interest.
    x1 : int
        The starting x-coordinate of the region of interest.
    x2 : int
        The ending x-coordinate of the region of interest.
    y1 : int
        The starting y-coordinate of the region of interest.
    y2 : int
        The ending y-coordinate of the region of interest.
    """"""
    def __init__(self, time, volt_value, x1, x2, y1, y2):
        """"""
        Initializes the StimVoltageCoord2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        volt_value : float
            The voltage value to apply.
        x1 : int
            The starting x-coordinate of the region of interest.
        x2 : int
            The ending x-coordinate of the region of interest.
        y1 : int
            The starting y-coordinate of the region of interest.
        y2 : int
            The ending y-coordinate of the region of interest.
        """"""
        super().__init__(time, volt_value)
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2

    def stimulate(self, model):
        """"""
        Applies the voltage stimulus to the cardiac tissue model within the
        specified region of interest.

        The voltage is applied only if the current time is within the
        stimulation period and the stimulation has not been previously applied.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the voltage stimulus is applied.
        """"""

        roi_mesh = model.cardiac_tissue.mesh[self.x1: self.x2,
                                             self.y1: self.y2]
        mask = (roi_mesh == 1)

        model.u[self.x1: self.x2, self.y1: self.y2][mask] = self.volt_value
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/__init__.py",".py","271","5","from .stim_current_area_2d import StimCurrentArea2D
from .stim_current_coord_2d import StimCurrentCoord2D
from .stim_current_matrix_2d import StimCurrentMatrix2D
from .stim_voltage_coord_2d import StimVoltageCoord2D
from .stim_voltage_matrix_2d import StimVoltageMatrix2D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/stim_current_area_2d.py",".py","3050","95","import numpy as np
from finitewave.core.stimulation.stim_current import StimCurrent


class StimCurrentArea2D(StimCurrent):
    """"""
    A class that applies a stimulation current to a 2D cardiac tissue model
    based on a area coords.

    Attributes
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    coords : numpy.ndarray
        The coordinates of the area to be stimulated.
    u_max : float
        The maximum value of the membrane potential.
    """"""
    def __init__(self, time, curr_value, duration, coords=None, u_max=None):
        """"""
        Initializes the StimCurrentArea2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        coords : numpy.ndarray
            The coordinates of the area to be stimulated.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration)
        self.coords = coords
        self.u_max = u_max

    def add_stim_point(self, coord, mesh, size=None):
        """"""
        Adds an stimulation point to the area to be stimulated.

        Parameters
        ----------
        coord : numpy.ndarray
            The coordinates of the stimulation point.
        mesh : numpy.ndarray
            The mesh of the cardiac tissue model.
        size : float, optional
            The size of the area to be stimulated. Default is None.
        """"""
        self._coord = coord

        if size is None:
            self.coords = np.atleast_2d(coord)
            return

        tissue_points = np.argwhere(mesh == 1)
        dist = np.linalg.norm(tissue_points - coord, axis=1)
        self.coords = tissue_points[dist < size]

    def initialize(self, model):
        mask = (model.cardiac_tissue.mesh[tuple(self.coords.T)] == 1)

        if mask.sum() == 0:
            raise ValueError(""The specified area does not have healthy cells."")

        self._coords = self.coords[mask]
        return super().initialize(model)

    def stimulate(self, model):
        """"""
        Applies the stimulation current to the cardiac tissue model based on
        the specified binary matrix.

        The stimulation is applied only if the current time is within the
        stimulation period and the stimulation has not been previously applied.

        Parameters
        ----------
        model : CardiacModel
            The 2D cardiac tissue model.
        """"""
        inds = tuple(self._coords.T)
        model.u[inds] += model.dt * self.curr_value

        if self.u_max is not None:
            model.u[inds] = np.where(model.u[inds] > self.u_max, self.u_max,
                                     model.u[inds])
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/stim_voltage_matrix_2d.py",".py","1522","47","from finitewave.core.stimulation.stim_voltage import StimVoltage


class StimVoltageMatrix2D(StimVoltage):
    """"""
    A class that applies a voltage stimulus to a 2D cardiac tissue model
    according to a specified matrix.
    """"""
    def __init__(self, time, volt_value, matrix):
        """"""
        Initializes the StimVoltageMatrix2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        volt_value : float
            The voltage value to apply.
        matrix : numpy.ndarray
            A 2D array where the voltage stimulus is applied to locations with
            values greater than 0.
        """"""
        super().__init__(time, volt_value)
        self.matrix = matrix

    def stimulate(self, model):
        """"""
        Applies the voltage stimulus to the cardiac tissue model based on the
        specified matrix.

        The voltage is applied only if the current time is within the
        stimulation period and the stimulation has not been previously applied.

        Parameters
        ----------
        model : CardiacModel
            The 2D cardiac tissue model.

        Notes
        -----
        The voltage value is applied to the positions in the cardiac tissue
        where the corresponding value in ``matrix`` is greater than 0,
        and the ``model.cardiac_tissue.mesh`` value is 1.
        """"""
        mask = (self.matrix > 0) & (model.cardiac_tissue.mesh == 1)
        model.u[mask] = self.volt_value
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stimulation/stim_current_coord_2d.py",".py","2944","86","import numpy as np
from finitewave.core.stimulation.stim_current import StimCurrent


class StimCurrentCoord2D(StimCurrent):
    """"""
    A class that applies a stimulation current to a rectangular region of a 2D
    cardiac tissue model.

    Attributes
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    x1 : int
        The x-coordinate of the lower-left corner of the rectangular region.
    x2 : int
        The x-coordinate of the upper-right corner of the rectangular region.
    y1 : int
        The y-coordinate of the lower-left corner of the rectangular region.
    y2 : int
        The y-coordinate of the upper-right corner of the rectangular region.
    u_max : float, optional
        The maximum value of the membrane potential. Default is None.
    """"""

    def __init__(self, time, curr_value, duration, x1, x2, y1, y2, u_max=None):
        """"""
        Initializes the StimCurrentCoord2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        x1 : int
            The x-coordinate of the lower-left corner of the rectangular.
        x2 : int
            The x-coordinate of the upper-right corner of the rectangular.
        y1 : int
            The y-coordinate of the lower-left corner of the rectangular.
        y2 : int
            The y-coordinate of the upper-right corner of the rectangular.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration)
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.u_max = u_max

    def stimulate(self, model):
        """"""
        Applies the stimulation current to the specified rectangular region of
        the cardiac tissue model.

        The stimulation is applied only if the current time is within the 
        stimulation period and the stimulation has not been previously applied.

        Parameters
        ----------
        model : CardiacModel
            The 2D cardiac tissue model.
        """"""

        roi_mesh = model.cardiac_tissue.mesh[self.x1:self.x2, self.y1:self.y2]
        mask = (roi_mesh == 1)
        model.u[self.x1:self.x2,
                self.y1:self.y2][mask] += model.dt * self.curr_value

        if self.u_max is not None:
            u = model.u[self.x1: self.x2,
                        self.y1: self.y2][mask]

            model.u[self.x1: self.x2,
                    self.y1: self.y2][mask] = np.where(u > self.u_max,
                                                       self.u_max, u)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/exception/__init__.py",".py","84","1","from finitewave.cpuwave2D.exception.exceptions_2d import IncorrectWeightsModeError2D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/exception/exceptions_2d.py",".py","1100","38","class IncorrectWeightsModeError2D(Exception):
    """"""
    Exception raised for invalid modes in the CardiacTissue2D class.

    Attributes
    ----------
    mode : str
        The invalid mode that caused the exception.
    message : str
        Explanation of the error.
    """"""

    def __init__(self, mode, message=""CardiacTissue2D mode attribute must be 'iso' or 'aniso'""):
        """"""
        Initializes the IncorrectWeightsModeError2D exception.

        Parameters
        ----------
        mode : str
            The invalid mode that caused the exception.
        message : str, optional
            Explanation of the error (default is ""CardiacTissue2D mode attribute must be 'iso' or 'aniso'"").
        """"""
        self.mode = mode
        self.message = message
        super().__init__(self.message)

    def __str__(self):
        """"""
        Returns a string representation of the exception.

        Returns
        -------
        str
            A string describing the error including the invalid mode.
        """"""
        return f""{self.message} (Invalid mode: '{self.mode}')""
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/local_activation_time_2d_tracker.py",".py","3734","102","import numpy as np

from finitewave.core.tracker.tracker import Tracker


class LocalActivationTime2DTracker(Tracker):
    """"""
    A class to compute and track multiple activation times in a 2D cardiac
    tissue model simulation.

    This tracker monitors the potential across the cardiac tissue and records
    the times when cells surpass a specific threshold, supporting multiple
    activations such as re-entrant waves or multiple excitations.

    The activation times are stored in a array where each element is an array
    storing the activation times for each cell. Arrays can be not fully filled
    if faster cells activate before slower ones. In oreder to get the full
    activation times, the user should select the next closest activation time
    to the desired time base.

    Attributes
    ----------
    act_t : list of np.ndarray
        A list where each element is an array storing activation times for
        each cell. Preferably accessed through the output property.
    threshold : float
        The potential threshold to determine cell activation.
    file_name : str
        The file name for saving the activation times.

    """"""

    def __init__(self):
        """"""
        Initializes the MultiActivationTime2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.act_t = []  # Initialize activation times as an empty array
        self.threshold = -40  # Activation threshold
        self.file_name = ""local_act_time_2d""  # Output file name
        self._activated = np.ndarray  # Array to store the activation state

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model and
        precomputes necessary values.

        Parameters
        ----------
        model : CardiacModel
            The cardiac tissue model object containing the data to be tracked.
        """"""
        self.model = model
        # Initialize with a single layer of -1 (no activation)
        self.act_t = [-np.ones_like(self.model.u)]
        self._activated = np.full(self.model.u.shape, 0, dtype=bool)

    def _track(self):
        """"""
        Tracks and stores activation times for each cell in
        the model at each time step.
        """"""
        cross_mask = self.cross_threshold()
        # Check if there are already activated cells in the current
        # activation layer
        if np.any(self.act_t[-1][cross_mask] > -1):
            self.act_t.append(-np.ones(self.model.u.shape))
        # Update activation times where the threshold is crossed
        self.act_t[-1] = np.where(cross_mask, self.model.t, self.act_t[-1])

    def cross_threshold(self):
        """"""
        Detects the cells that crossed the threshold and are not activated yet.

        Returns
        -------
        np.ndarray
            A binary array where 1 indicates cells that crossed the threshold
            and are not activated yet.
        """"""
        # Mask for cells that crossed the threshold and are not activated yet
        cross_mask = ((self.model.u >= self.threshold)
                      & (self._activated == 0))
        self._activated = np.where(cross_mask, 1, self._activated)
        # Set inactive cells that backcrossed the threshold after activation
        backcross_mask = ((self.model.u < self.threshold)
                          & (self._activated == 1))
        self._activated = np.where(backcross_mask, 0, self._activated)
        return cross_mask

    @property
    def output(self):
        """"""
        Returns the activation times.

        Returns
        -------
        np.ndarray
            The array containing the activation times for each cell.
        """"""
        return np.array(self.act_t)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/spiral_wave_core_2d_tracker.py",".py","7127","250","from pathlib import Path
from math import sqrt
import pandas as pd
from numba import njit
from numba.typed import List

from finitewave.core.tracker.tracker import Tracker


class SpiralWaveCore2DTracker(Tracker):
    """"""
    A class to track spiral wave tips in a 2D cardiac tissue model.

    This tracker identifies and records the positions of spiral wave tips by
    analyzing voltage isoline crossings in the simulated 2D grid over time.

    Attributes
    ----------
    threshold : float
        Voltage threshold value for detecting spiral tips.
    file_name : str
        Name of the file to save the tracked spiral tip data.
    spiral_wave_cores : list of pd.DataFrame
        List of tracked spiral core data.
    """"""

    def __init__(self):
        """"""
        Initializes the Spiral2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.threshold = 0.5
        self.file_name = ""spiral_wave_core""
        self.sprial_wave_cores = []

    def initialize(self, model):
        """"""
        Initialize the tracker with the given cardiac tissue model.

        Parameters
        ----------
        model : object
            The cardiac tissue simulation model containing the grid and
            voltage data.
        """"""
        self.model = model
        self.u_prev = self.model.u.copy()

    def track_tip_line(self, u, u_new, threshold):
        """"""
        Track spiral wave tips in a 2D grid by detecting crossings of voltage
        isolines.

        Parameters
        ----------
        u : np.ndarray
            2D array representing the old voltage values.
        u_new : np.ndarray
            2D array representing the new voltage values.
        threshold : float
            Voltage threshold value for detecting spiral tips.

        Returns
        -------
        List
            List of detected spiral tip positions.
        """"""
        return list(_track_tip_line(u, u_new, threshold))

    def _track(self):
        """"""
        Track spiral tips at each simulation step by analyzing voltage data.

        The tracker is updated at each simulation step, detecting any spiral
        tips based on the voltage data from the previous and current steps.
        """"""
        tips = self.track_tip_line(self.u_prev, self.model.u, self.threshold)
        tips = pd.DataFrame(tips, columns=[""x"", ""y""])
        tips[""time""] = self.model.t
        tips[""step""] = self.model.step
        self.sprial_wave_cores.append(tips)
        self.u_prev = self.model.u.copy()

    def write(self):
        """"""
        Save the tracked spiral tip data to a file.
        """"""
        self.output.to_csv(Path(self.path, self.file_name).with_suffix("".csv""))

    @property
    def output(self):
        """"""
        Get the tracked spiral core data.

        Returns
        -------
        pd.DataFrame
            A DataFrame containing the tracked spiral core data.
        """"""
        validated = [df for df in self.sprial_wave_cores if not df.empty]
        if not validated:
            return pd.DataFrame(columns=[""x"", ""y"", ""time"", ""step""])

        return pd.concat(validated, ignore_index=True)


@njit
def _correct_tip_pos(i, j, u, u_new, threshold):
    """"""
    Correct the position of a detected spiral wave tip.

    This function corrects the position of a detected spiral wave tip by
    solving a system of equations to find the intersection of voltage isolines.

    Parameters
    ----------
    i, j : int
        Grid indices.
    u, u_new : np.ndarray
        2D arrays representing the old and new voltage values.
    threshold : float
        Voltage threshold value for detecting spiral tips.
    """"""
    # Compute various differences for both old and new voltage values
    AC = u[i, j] - u[i, j+1] + u[i+1, j+1] - u[i+1, j]
    GC = u[i, j+1] - u[i, j]
    BC = u[i+1, j] - u[i, j]
    DC = u[i, j] - threshold

    AD = u_new[i, j] - u_new[i, j+1] + u_new[i+1, j+1] - u_new[i+1, j]
    GD = u_new[i, j+1] - u_new[i, j]
    BD = u_new[i+1, j] - u_new[i, j]
    DD = u_new[i, j] - threshold

    # Compute intermediate values for solving the system of equations
    Q = BC * AD - BD * AC
    R = GC * AD - GD * AC
    S = DC * AD - DD * AC

    QOnR = Q / R
    SOnR = S / R

    T = AC * QOnR
    U = AC * SOnR - BC + GC * QOnR
    V = GC * SOnR - DC

    # Calculate the discriminant for the quadratic formula
    discriminant = U * U - 4. * T * V

    if discriminant < 0:
        return

    # Two possible solutions for (x, y) coordinates
    T2 = 2. * T

    if T2 == 0.:
        return

    xn = (-U - sqrt(discriminant)) / T2
    xp = (-U + sqrt(discriminant)) / T2
    yn = -QOnR * xn - SOnR
    yp = -QOnR * xp - SOnR

    # Ensure the points lie within the valid grid range
    if 0 <= xn <= 1 and 0 <= yn <= 1:
        return [xn, yn]

    if 0 <= xp <= 1 and 0 <= yp <= 1:
        return [xp, yp]

    return


@njit
def _apply_threshold(i, j, u, threshold):
    """"""
    Apply a voltage threshold to a 2D grid to detect spiral wave tips.

    This function applies a voltage threshold to the 2D grid to detect spiral
    wave tips by identifying grid points where the voltage crosses the
    specified threshold.

    Parameters
    ----------
    i, j : int
        Grid indices.
    u : np.ndarray
        2D array representing the voltage values.
    threshold : float
        Voltage threshold value for detecting spiral tips.

    Returns
    -------
    int
        1 if the voltage crosses the threshold; otherwise, 0.
    """"""
    if (u[i][j] >= threshold and (u[i + 1][j] < threshold
                                  or u[i][j + 1] < threshold
                                  or u[i + 1][j + 1] < threshold)):
        return 1

    if (u[i][j] < threshold and (u[i + 1][j] >= threshold
                                 or u[i][j + 1] >= threshold
                                 or u[i + 1][j + 1] >= threshold)):
        return 1

    return 0


@njit
def _track_tip_line(u, u_new, threshold):
    """"""
    Track spiral wave tips in a 2D grid by detecting crossings of voltage
    isolines.

    This function searches for spiral tips in XY planes by detecting where
    the voltage crosses specified thresholds in both the old and new voltage
    values.

    Parameters
    ----------
    u, u_new : np.ndarray
        2D arrays representing the old and new voltage values.
    threshold : float
        Voltage threshold value for detecting spiral tips.

    Returns
    -------
    List
        List of detected spiral tip positions.
    """"""
    out = List()
    size_i, size_j = u.shape
    delta = 5  # Safety margin to avoid boundary

    for i in range(delta, size_i - delta):
        for j in range(delta, size_j - delta):
            counter = _apply_threshold(i, j, u, threshold)

            if counter == 1:
                counter += _apply_threshold(i, j, u_new, threshold)

            if counter == 2:
                correction = _correct_tip_pos(i, j, u, u_new, threshold)

                if correction is not None:
                    out.append([j + correction[1], i + correction[0]])

    return out
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/ecg_2d_tracker.py",".py","4328","153","from pathlib import Path
import numpy as np
from numba import njit, prange
from scipy.spatial import distance

from finitewave.core.tracker.tracker import Tracker


class ECG2DTracker(Tracker):
    """"""
    A class to compute and track electrocardiogram (ECG) signals from a 2D
    cardiac tissue model simulation.

    This tracker calculates ECG signals at specified measurement points by
    computing the potential differences across the cardiac tissue mesh and
    considering the inverse square of the distance from each measurement point.

    Attributes
    ----------
    measure_coords : np.ndarray
        An array of points (x, y, z) where ECG signals are measured.
    ecg : list
        The computed ECG signals.
    file_name : str
        The name of the file to save the computed ECG signals.
    u_tr : np.ndarray
        The updated potential values after diffusion.

    """"""

    def __init__(self, measure_coords=None):
        """"""
        Initializes the ECG2DTracker with default parameters.

        Parameters
        ----------
        distance_power : int, optional
            The power to which the distance is raised in the calculation of the
            ECG signal. The default is 1.
        """"""
        super().__init__()
        self.measure_coords = measure_coords
        self.ecg = []
        self.file_name = ""ecg.npy""
        self.u_tr = None

    def initialize(self, model):
        """"""
        Initialize the ECG tracker with the model object.

        Parameters
        ----------
        model : CardiacModel3D
            The model object containing the simulation parameters.
        """"""
        self.model = model
        self.measure_coords = np.atleast_2d(self.measure_coords)
        self.ecg = []
        self.u_tr = np.zeros_like(model.u)

    def calc_ecg(self):
        """"""
        Calculate the ECG signal at the measurement points.

        Returns
        -------
        np.ndarray
            The computed ECG signal.
        """"""
        self.model.diffusion_kernel(self.u_tr,
                                    self.model.u,
                                    self.model.weights,
                                    self.model.cardiac_tissue.myo_indexes)
        ecg = compute_ecg(self.u_tr,
                          self.model.u,
                          self.measure_coords,
                          self.model.dr,
                          self.model.cardiac_tissue.myo_indexes)
        return ecg

    def _track(self):
        """"""
        Tracks and stores ECG signals at the specified intervals.

        This method should be called at each time step of the simulation.
        """"""
        ecg = self.calc_ecg()
        self.ecg.append(ecg)

    @property
    def output(self):
        """"""
        Get the computed ECG signals as a numpy array.

        Returns
        -------
        np.ndarray
            The computed ECG signals.
        """"""
        return np.array(self.ecg)

    def write(self):
        """"""
        Save the computed ECG signals to a file.

        The ECG signals are saved as a numpy array in the specified path.
        """"""
        if not Path(self.path).exists():
            Path(self.path).mkdir(parents=True)

        np.save(Path(self.path).joinpath(self.file_name).with_suffix('.npy'),
                self.output)

@njit(parallel=True)
def compute_ecg(u_tr, u, coords, dr, indexes):
    """"""
    Performs isotropic diffusion on a 2D grid.

    Parameters
    ----------
    u_tr : numpy.ndarray
        A 2D array to store the updated potential values after diffusion.
    u : numpy.ndarray
        A 2D array representing the current potential values before diffusion.
    coord : tuple
        The coordinates of the measurement point.
    dr : float
        The spatial resolution of the grid.
    indexes : numpy.ndarray
        A 1D array of indices of the healthy tissue points.
    """"""
    n_j = u.shape[1]

    n_c = len(coords)
    ecg = np.zeros(n_c)

    for c in range(n_c):
        x, y, z = coords[c]
        ecg_ = 0

        for ind in prange(len(indexes)):
            ii = indexes[ind]
            i = ii // n_j
            j = ii % n_j

            d = (x - i)**2 + (y - j)**2 + (z)**2

            if d > 0:
                ecg_ += (u_tr[i, j] - u[i, j]) / (d * dr)

        ecg[c] = ecg_

    return ecg","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/period_2d_tracker.py",".py","2515","79","from pathlib import Path
import numpy as np
import pandas as pd
import json
from .local_activation_time_2d_tracker import LocalActivationTime2DTracker


class Period2DTracker(LocalActivationTime2DTracker):
    """"""
    A class to track activation periods of cells in a 2D cardiac tissue model
    using detectors.

    Attributes
    ----------
    cell_ind : list or list of lists with two indices
        The indices [i, j] of the cell where the variables are tracked.
        List of lists can be used to track multiple cells.
    file_name : str
        The name of the file to save the computed activation periods.
    """"""

    def __init__(self):
        """"""
        Initializes the Period2DTracker with default parameters.
        """"""
        super().__init__()

        self.cell_ind = []
        self.file_name = ""period""  # File name for saving tracked data

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model and preallocates
        memory for tracking.

        Parameters
        ----------
        model : object
            The cardiac tissue model object containing the data to be tracked.
        """"""
        super().initialize(model)
        self.act_t = [-np.ones(len(np.atleast_2d(self.cell_ind)))]

    def _track(self):
        """"""
        Tracks and stores activation times for each cell in
        the model at each time step.
        """"""
        cross_mask = self.cross_threshold()
        cross_mask = cross_mask[tuple(np.atleast_2d(self.cell_ind).T)]
        # Check if there are already activated cells in the current
        # activation layer
        if np.any(self.act_t[-1][cross_mask] > -1):
            self.act_t.append(-np.ones(len(np.atleast_2d(self.cell_ind))))
        # Update activation times where the threshold is crossed
        self.act_t[-1] = np.where(cross_mask, self.model.t, self.act_t[-1])

    @property
    def output(self):
        """"""
        Property to get the computed activation periods.

        Returns
        -------
        pd.DataFrame
            A DataFrame containing the computed activation periods.
        """"""
        lats = np.array(self.act_t)
        lats = pd.DataFrame(lats.T)
        periods = lats.apply(lambda row: np.diff(row[row != -1]), axis=1)
        return periods

    def write(self):
        """"""
        Saves the computed activation periods to a CSV file.
        """"""
        periods = self.output
        periods.to_csv(Path(self.path, self.file_name).with_suffix("".csv""))
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/animation_2d_tracker.py",".py","4092","116","from pathlib import Path
import numpy as np

from finitewave.core.tracker.tracker import Tracker
from finitewave.tools import Animation2DBuilder


class Animation2DTracker(Tracker):
    """"""
    A class to track and save frames of a 2D cardiac tissue model simulation
    for animation purposes.

    This tracker periodically saves the state of a specified target array from
    the model to disk as NumPy files, which can later be used to create
    animations.

    Attributes
    ----------
    dir_name : str
        Directory for saving frames.
    variable_name : str
        Name of the target array to capture.
    frame_type : str
        Default frame format settings.
    overwrite : bool
        Overwrite existing frames.
    file_name : str
        Name of the animation file.

    """"""

    def __init__(self):
        """"""
        Initializes the Animation2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.dir_name = ""animation""   # Directory for saving frames
        self.variable_name = ""u""      # Name of the target array to capture
        self.frame_type = ""float64""   # Default frame format settings
        self._frame_counter = 0       # Internal frame counter
        self.overwrite = True         # Overwrite existing frames
        self.file_name = ""animation""  # Name of the animation file

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model and sets up
        directories for saving frames.

        Parameters
        ----------
        model : object
            The cardiac tissue model object containing the data to be tracked.
        """"""
        self.model = model
        self._frame_counter = 0  # Reset frame counter

        if not Path(self.path, self.dir_name).is_dir():
            Path(self.path, self.dir_name).mkdir(parents=True)

        if self.overwrite:
            for file in Path(self.path, self.dir_name).glob(""*.npy""):
                file.unlink()

    def _track(self):
        """"""
        Saves frames based on the specified step interval and target array.

        The frames are saved in the specified directory as NumPy files.
        """"""
        frame = self.model.__dict__[self.variable_name]
        dir_path = Path(self.path, self.dir_name)

        np.save(dir_path.joinpath(str(self._frame_counter)
                                  ).with_suffix("".npy""),
                frame.astype(self.frame_type))

        self._frame_counter += 1

    def write(self, shape_scale=1, fps=12, cmap=""coolwarm"", clim=[0, 1],
              clear=False, prog_bar=True):
        """"""
        Creates an animation from the saved frames using the Animation2DBuilder
        class. Fibrosis and boundaries will be shown in black.

        Parameters
        ----------
        shape_scale : int, optional
            Scale factor for the frame size. The default is 5.
        fps : int, optional
            Frames per second for the animation. The default is 12.
        cmap : str, optional
            Color map for the animation. The default is 'coolwarm'.
        clim : list, optional
            Color limits for the animation. The default is [0, 1].
        clear : bool, optional
            Clear the snapshot folder after creating the animation.
            The default is False.
        prog_bar : bool, optional
            Show a progress bar during the animation creation.
            The default is True.
        """"""
        animation_builder = Animation2DBuilder()
        path = Path(self.path, self.dir_name)
        mask = self.model.cardiac_tissue.mesh != 1

        animation_builder.write(path,
                                animation_name=self.file_name,
                                mask=mask,
                                shape_scale=shape_scale,
                                fps=fps,
                                clim=clim,
                                shape=mask.shape,
                                cmap=cmap,
                                clear=clear,
                                prog_bar=prog_bar)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/__init__.py",".py","1343","34","""""""
2D Tracker
----------

This module contains classes for tracking the evolution of the wavefront in 2D.

The tracker classes can be grouped into the following categories:

* Full field trackers that track the entire field and output the results in
  a single array.
* Point trackers that track the evolution of a specific point(s) in the field.
* Animation trackers that track the evolution of the field over time and save
  the results as frames for creating animations.

Each tracker class has basic attributes such as ``start_time``, ``end_time``,
``step``, ``path``, and ``file_name``.

.. note::

    Note that the ``start_time`` and ``end_time`` is given in time units,
    and the ``step`` is the number of time steps between recordings.
""""""

from .action_potential_2d_tracker import ActionPotential2DTracker
from .activation_time_2d_tracker import ActivationTime2DTracker
from .animation_2d_tracker import Animation2DTracker
from .ecg_2d_tracker import ECG2DTracker
from .local_activation_time_2d_tracker import LocalActivationTime2DTracker
from .multi_variable_2d_tracker import MultiVariable2DTracker
from .period_2d_tracker import Period2DTracker
from .period_animation_2d_tracker import PeriodAnimation2DTracker
from .spiral_wave_core_2d_tracker import SpiralWaveCore2DTracker
from .variable_2d_tracker import Variable2DTracker
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/multi_variable_2d_tracker.py",".py","3328","99","from pathlib import Path
import numpy as np

from finitewave.core.tracker.tracker import Tracker


class MultiVariable2DTracker(Tracker):
    """"""
    A class to track multiple variables at a specific cell in a 2D cardiac
    tissue model simulation.

    This tracker monitors user-defined variables at a specified cell index and
    records their values over time.

    Attributes
    ----------
    var_list : list of str
        A list of variable names to be tracked.
    cell_ind : list or list of lists with two indices
        The indices [i, j] of the cell where the variables are tracked.
        List of lists can be used to track multiple cells.
    dir_name : str
        The directory name where tracked variables are saved.
    vars : dict
        A dictionary where each key is a variable name, and the value is
        an array of its tracked values over time.

    """"""

    def __init__(self):
        """"""
        Initializes the MultiVariable2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.var_list = []  # List of variables to be tracked
        self.cell_ind = [1, 1]  # Cell index to track variables
        self.dir_name = ""multi_vars""  # Directory to save tracked variables
        self.vars = {}  # Dictionary to store tracked variables

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model and precomputes
        necessary values for each variable.

        Parameters
        ----------
        model : object
            The cardiac tissue model object containing the data to be tracked.
        """"""
        self.vars = {}
        self.model = model
        # Initialize storage for each variable to be tracked
        for var_ in self.var_list:
            if var_ not in self.model.__dict__:
                raise ValueError(f""Variable '{var_}' not found in model."")
            self.vars[var_] = []

    def _track(self):
        """"""
        Tracks and stores the values of each specified variable at each time step.

        This method should be called at each time step of the simulation.
        """"""
        # Track the value of each variable at the specified cell index
        # Make possible to track multiple cells
        cell_ind = tuple(np.atleast_2d(self.cell_ind).T)
        for var_ in self.var_list:
            var_values = self.model.__dict__[var_]
            self.vars[var_].append(var_values[cell_ind])

    @property
    def output(self):
        """"""
        Returns the tracked variables data.

        Returns
        -------
        dict
            A dictionary where each key is a variable name, and the value is
            an array of its tracked values over time.
        """"""
        vars = {}
        for var_ in self.var_list:
            vars[var_] = np.squeeze(self.vars[var_])
        return vars

    def write(self):
        """"""
        Saves the tracked variables to disk as NumPy files.
        """"""
        # Create the output directory if it does not exist
        if not Path(self.path, self.dir_name).is_dir():
            Path(self.path, self.dir_name).mkdir(parents=True)

        # Save each tracked variable to a file
        for var_ in self.var_list:
            np.save(Path(self.path, self.dir_name, f""{var_}.npy""),
                    self.output[var_])
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/period_animation_2d_tracker.py",".py","4174","113","from pathlib import Path
import numpy as np

from finitewave.tools.animation_2d_builder import Animation2DBuilder
from .local_activation_time_2d_tracker import LocalActivationTime2DTracker


class PeriodAnimation2DTracker(LocalActivationTime2DTracker):
    """"""
    A class to track the periods of activation for each cell in a 2D cardiac
    tissue model.

    This class extends Animation2DTracker to create and save a period map that
    shows the time interval between successive activations of each cell that
    crosses a given threshold. The period map is saved at each time step.

    Attributes
    ----------
    dir_name : str
        The directory name to save the period maps.
    file_name : str
        The file name for saving the period maps.
    overwrite : bool
        Whether to overwrite existing period maps.
    period_map : np.ndarray
        The array to store activation periods.
    """"""

    def __init__(self):
        """"""
        Initializes the PeriodMap2DTracker with default parameters.
        """"""
        super().__init__()

        self.dir_name = ""period""   # Directory to save the period maps
        self.file_name = ""period""  # File name for saving the period maps
        self.overwrite = False     # Overwrite existing period maps
        self._frame_counter = 0    # Counter to track the current frame number

        self.period_map = np.ndarray  # Array to store activation periods

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model and preallocates
        memory for tracking.

        Parameters
        ----------
        model : object
            The cardiac tissue model object containing the data to be tracked.
        """"""
        # Initialize the period map and state arrays
        self.model = model
        self._frame_counter = 0
        self.period_map = - np.ones_like(self.model.u)
        self._activated = np.full(self.model.u.shape, 0, dtype=bool)

        if not Path(self.path, self.dir_name).is_dir():
            Path(self.path, self.dir_name).mkdir(parents=True)

        if self.overwrite:
            for file in Path(self.path, self.dir_name).glob(""*.npy""):
                file.unlink()

    def _track(self):
        """"""
        Tracks the activation periods at each time step of the simulation.

        This method calculates the time interval between successive activations
        for each cell, updates the period map, and saves it to a file.
        """"""
        cross_mask = self.cross_threshold()
        self.period_map = np.where(cross_mask, self.model.t, self.period_map)

        np.save(Path(self.path, self.dir_name, str(self._frame_counter)
                     ).with_suffix("".npy""), self.period_map)
        self._frame_counter += 1

    def write(self, shape_scale=3, fps=10, clim=None, cmap=""viridis"",
              clear=True, prog_bar=True):
        """"""
        Creates an animation from the saved period maps.

        Parameters
        ----------
        shape_scale : int, optional
            The scaling factor for the shape of the period map.
        fps : int, optional
            The frames per second for the animation.
        clim : list, optional
            The color limits for the animation.
        cmap : str, optional
            The color map for the animation.
        clear : bool, optional
            Whether to clear the directory before saving the animation.
        prog_bar : bool, optional
            Whether to show a progress bar during the animation creation.
        """"""
        animation_builder = Animation2DBuilder()
        path = Path(self.path, self.dir_name)
        mask = self.model.cardiac_tissue.mesh != 1

        animation_builder.write(path,
                                animation_name=self.file_name,
                                mask=mask,
                                shape_scale=shape_scale,
                                fps=fps,
                                clim=clim,
                                shape=mask.shape,
                                cmap=cmap,
                                clear=clear,
                                prog_bar=prog_bar)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/action_potential_2d_tracker.py",".py","2087","66","import numpy as np

from finitewave.core.tracker.tracker import Tracker


class ActionPotential2DTracker(Tracker):
    """"""
    A class to track and record the action potential of a specific cell in
    a 2D cardiac tissue model.

    This tracker monitors the membrane potential of a single cell at each time
    step and stores the data in an array for later analysis or visualization.

    Attributes
    ----------
    act_pot : np.ndarray
        Array to store the action potential values at each time step.
    cell_ind : list or list of lists with two indices
        Coordinates of the cell to be tracked in the 2D model grid.
    file_name : str
        Name of the file where the tracked action potential data will be saved.
    """"""

    def __init__(self):
        """"""
        Initializes the ActionPotential2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.act_pot = []  # Initialize the array to store action potential
        self.cell_ind = [1, 1]       # Default cell indices to track
        self.file_name = ""act_pot""   # Default file name for saving data

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model, setting up
        the action potential array.

        Parameters
        ----------
        model : object
            The cardiac tissue model object that contains simulation parameters
            like `t_max` (maximum time) and `dt` (time step).
        """"""
        self.model = model

    def _track(self):
        """"""
        Records the action potential (`u`) of the specified cell at the current
        time step.
        """"""
        # Make possible to track multiple cells
        cell_ind = tuple(np.atleast_2d(self.cell_ind).T)
        self.act_pot.append(self.model.u[cell_ind])

    @property
    def output(self):
        """"""
        Returns the tracked action potential data.

        Returns
        -------
        np.ndarray
            The array containing the tracked action potential values.
        """"""
        return np.squeeze(self.act_pot)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/activation_time_2d_tracker.py",".py","2459","76","from pathlib import Path
import numpy as np

from finitewave.core.tracker.tracker import Tracker


class ActivationTime2DTracker(Tracker):
    """"""
    A class to track and record the activation time of each cell in a 2D
    cardiac tissue model.

    This tracker monitors the membrane potential of each cell and records
    the time at which the potential crosses a certain threshold, indicating
    cell activation.

    Attributes
    ----------
    act_t : np.ndarray
        Array to store the activation time of each cell in the 2D model grid.
    threshold : float
        The membrane potential threshold value that determines cell activation.
    file_name : str
        Name of the file where the tracked activation time data will be saved.

    """"""

    def __init__(self):
        """"""
        Initializes the ActivationTime2DTracker with default parameters.
        """"""
        Tracker.__init__(self)
        self.act_t = np.ndarray         # Array to store activation times
        self.threshold = -40            # Threshold for activation (in mV)
        self.file_name = ""act_time_2d""  # Default file name for saving data

    def initialize(self, model):
        """"""
        Initializes the tracker with the simulation model, setting up
        the activation time array.

        Parameters
        ----------
        model : object
            The cardiac tissue model object that contains the grid (`u`) of
            membrane potentials.
        """"""
        self.model = model
        # Initialize activation time array with -1 to indicate unactivated cells
        self.act_t = - np.ones_like(self.model.u)

    def _track(self):
        """"""
        Records the activation time of each cell based on the threshold
        crossing.

        The activation time is recorded as the first instance where
        the membrane potential of a cell crosses the threshold value.
        """"""
        # Update activation times where they are still -1 and the membrane
        # potential exceeds the threshold
        self.act_t = np.where((self.act_t < 0)
                              & (self.model.u > self.threshold),
                              self.model.t, self.act_t)

    @property
    def output(self):
        """"""
        Returns the tracked activation time data.

        Returns
        -------
        np.ndarray
            The array containing the activation time of each cell in the grid.
        """"""
        return self.act_t
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tracker/variable_2d_tracker.py",".py","1345","53","from pathlib import Path
import numpy as np

from .multi_variable_2d_tracker import MultiVariable2DTracker


class Variable2DTracker(MultiVariable2DTracker):
    """"""
    A tracker that records the values of specified variables from a 2D model 
    over time at a given grid point.

    Attributes
    ----------
    cell_ind : list
        The indices [i, j] of the cell where the variable is tracked.
    """"""
    def __init__(self):
        super().__init__()
        self.cell_ind = [1, 1]

    @property
    def var_name(self):
        """"""
        The name of the variable to be tracked.
        """"""
        return self.var_list[0]

    @var_name.setter
    def var_name(self, value):
        self.var_list = [value]

    @property
    def output(self):
        """"""
        Property to get the tracked variable values.

        Returns
        -------
        np.ndarray
            The values of the tracked variable at the specified grid point.
        """"""
        return self.vars[self.var_name]

    def write(self):
        """"""
        Saves the tracked variables to disk as NumPy files.
        """"""
        if not Path(self.path, self.dir_name).exists():
            Path(self.path, self.dir_name).mkdir(parents=True)

        np.save(Path(self.path, self.dir_name,
                     self.var_name).with_suffix('.npy'), self.output)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stencil/asymmetric_stencil_2d.py",".py","12749","434","import numpy as np
from numba import njit, prange

from finitewave.core.stencil.stencil import Stencil


class AsymmetricStencil2D(Stencil):
    """"""
    This class computes the weights for diffusion on a 2D using an asymmetric
    stencil. The weights are calculated based on diffusion coefficients and
    fiber orientations. The stencil includes 9 points: the central point and
    8 surrounding points. The boundary conditions are Neumann with first-order
    approximation.

    Attributes
    ----------
    D_al : float
        Longitudinal diffusion coefficient.
    D_ac : float
        Cross-sectional diffusion coefficient.

    Notes
    -----
    The diffusion coefficients are general and should be adjusted according to
    the specific model. These parameters only set the ratios between
    longitudinal and cross-sectional diffusion.

    The method assumes weights being used in the following order:

    - ``w[i, j, 0] : (i-1, j-1)``,
    - ``w[i, j, 1] : (i-1, j)``,
    - ``w[i, j, 2] : (i-1, j+1)``,
    - ``w[i, j, 3] : (i, j-1)``,
    - ``w[i, j, 4] : (i, j)``,
    - ``w[i, j, 5] : (i, j+1)``,
    - ``w[i, j, 6] : (i+1, j-1)``,
    - ``w[i, j, 7] : (i+1, j)``,
    - ``w[i, j, 8] : (i+1, j+1)``.
    """"""

    def __init__(self):
        super().__init__()
        self.D_al = 1
        self.D_ac = 1/9

    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the weights for diffusion on a 2D mesh using an asymmetric
        stencil.

        Parameters
        ----------
        model : CardiacModel2D
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue2D
            A 2D cardiac tissue object.

        Returns
        -------
        np.ndarray
            Array of weights for diffusion with the shape of (*mesh.shape, 9).
        """"""
        # convert fibrotic areas to non-tissue
        mesh = cardiac_tissue.mesh.copy()
        mesh[mesh != 1] = 0
        conductivity = cardiac_tissue.conductivity
        conductivity = conductivity * np.ones_like(mesh, dtype=model.npfloat)

        fibers = cardiac_tissue.fibers

        if fibers is None:
            message = ""Fibers must be provided for anisotropic diffusion.""
            raise ValueError(message)

        weights = np.zeros((*mesh.shape, 9))
        d_xx, d_xy = self.compute_half_step_diffusion(mesh, conductivity,
                                                      fibers, 0)
        d_yx, d_yy = self.compute_half_step_diffusion(mesh, conductivity,
                                                      fibers, 1)
        weights = compute_weights(weights, mesh, d_xx, d_xy, d_yx, d_yy)
        weights = weights * model.D_model * model.dt / model.dr**2
        weights[:, :, 4] += 1

        return weights

    def select_diffusion_kernel(self):
        """"""
        Returns the diffusion kernel function for anisotropic diffusion in 2D.

        Returns
        -------
        function
            The diffusion kernel function for anisotropic diffusion in 2D.
        """"""
        return diffusion_kernel_2d_aniso

    def compute_half_step_diffusion(self, mesh, conductivity, fibers, axis,
                                    num_axes=2):
        """"""
        Computes the diffusion components for half-steps based on fiber
        orientations.

        Parameters
        ----------
        mesh : np.ndarray
            Array representing the mesh grid of the tissue.
        conductivity : np.ndarray
            Array representing the conductivity of the tissue.
        fibers : np.ndarray
            Array representing fiber orientations with shape
            ``(2, *mesh.shape)``.
        axis : int
            Axis index (0 for x, 1 for y).
        num_axes : int
            Number of axes.

        Returns
        -------
        np.ndarray
            Array of diffusion components for half-steps along the specified
            axis.

        Notes
        -----
        The index ``i`` in the returned array corresponds to ``i+1/2`` and
        ``i-1`` corresponds to ``i-1/2``.
        """"""
        D = np.zeros((num_axes, *mesh.shape))
        for i in range(num_axes):
            D[i] = self.compute_diffusion_components(fibers, axis, i,
                                                     self.D_al, self.D_ac)
            D[i] = 0.5 * (D[i] * conductivity +
                          np.roll(D[i], -1, axis=axis) *
                          np.roll(conductivity, -1, axis=axis))

        return D

    def compute_diffusion_components(self, fibers, ind0, ind1, D_al, D_ac):
        """"""
        Computes the diffusion components based on fiber orientations.

        Parameters
        ----------
        fibers : np.ndarray
            Array representing fiber orientations.
        ind0 : int
            First axis index (0 for x, 1 for y).
        ind1 : int
            Second axis index (0 for x, 1 for y).
        D_al : float
            Longitudinal diffusion coefficient.
        D_ac : float
            Cross-sectional diffusion coefficient.

        Returns
        -------
        np.ndarray
            Array of diffusion components based on fiber orientations
        """"""
        return (D_ac * (ind0 == ind1) +
                (D_al - D_ac) * fibers[..., ind0] * fibers[..., ind1])


@njit(parallel=True)
def diffusion_kernel_2d_aniso(u_new, u, w, indexes):
    """"""
    Performs anisotropic diffusion on a 2D grid.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated potential values after diffusion.
    u : np.ndarray
        Array representing the current potential values before diffusion.
    w : np.ndarray
        Array of weights used in the diffusion computation.
    mesh : np.ndarray
        Array representing the mesh of the tissue.

    Returns
    -------
    np.ndarray
        The updated potential values after diffusion.
    """"""
    n_i = u.shape[0]
    n_j = u.shape[1]
    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        u_new[i, j] = (u[i-1, j-1] * w[i, j, 0] +
                       u[i-1, j] * w[i, j, 1] +
                       u[i-1, j+1] * w[i, j, 2] +
                       u[i, j-1] * w[i, j, 3] +
                       u[i, j] * w[i, j, 4] +
                       u[i, j+1] * w[i, j, 5] +
                       u[i+1, j-1] * w[i, j, 6] +
                       u[i+1, j] * w[i, j, 7] +
                       u[i+1, j+1] * w[i, j, 8])
    return u_new


@njit
def minor_component(d, m0, m1, m2, m3, m4, m5):
    """"""
    Calculates the minor component for the diffusion current.

    .. code-block:: text
        m4 ----- m5
        |        |
        |        |
        |        |
        m2 - d - m3
        |        |
        |        |
        |        |
        m0 ----- m1

    Parameters
    ----------
    d : float
        Minor diffusion at half-steps.
    m0 : int
        Mesh point value at (i-1, j-1).
    m1 : int
        Mesh point value at (i-1, j).
    m2 : int
        Mesh point value at (i, j-1).
    m3 : int
        Mesh point value at (i, j).
    m4 : int
        Mesh point value at (i+1, j-1).
    m5 : int
        Mesh point value at (i+1, j).

    Returns
    -------
    tuple
        Tuple of weights for each of the 6 points.

    Notes
    -----
    The order of the points assumes m3 is the central point of the stencil.
    """"""
    m_higher = m2 + m3 + m4 + m5
    m_lower = m0 + m1 + m2 + m3

    if m2 == 0 or m3 == 0 or m_higher < 3 or m_lower < 3:
        return 0, 0, 0, 0, 0, 0

    w0 = - d * m0 / m_lower
    w1 = - d * m1 / m_lower
    w2 = d * (m2 / m_higher - m2 / m_lower)
    w3 = d * (m3 / m_higher - m3 / m_lower)
    w4 = d * m4 / m_higher
    w5 = d * m5 / m_higher

    return w0, w1, w2, w3, w4, w5


@njit
def major_component(d, m0):
    """"""
    Computes the major component for the difussion current.

    .. code-block:: text
        x ------ x
        |        |
        |        |
        m0 - d - m1
        |        |
        |        |
        x ------ x

    Parameters
    ----------
    d : np.ndarray
        Major diffusion at half-steps.
    m0 : np.ndarray
        Mesh point adjacent to the central point.

    Returns
    -------
    np.ndarray
        Major component for the diffusion.
    """"""
    return d * m0


@njit(parallel=True)
def compute_weights(w, m, d_xx, d_xy, d_yx, d_yy):
    """"""
    Computes the weights for diffusion on a 2D mesh based on the asymmetric
    stencil.

    .. code-block:: text
        w2 --------------- w5 ---------------- w8
        |                  |                   |
        |               d_yy_1                 |
        |               d_yx_1                 |
        |                  |                   |
        |                  |                   |
        w1 ---- d_xx_0 --- w4 ---- d_xx_1 ---- w7
        |       d_xy_0     |       d_xy_1      |
        |                  |                   |
        |               d_yy_0                 |
        |               d_yx_0                 |
        |                  |                   |
        w0 --------------- w3 ---------------- w6

    Parameters
    ----------
    w : np.ndarray
        3D array to store the weights for diffusion. Shape is (*mesh.shape, 9).
    m : np.ndarray
        2D array representing the mesh grid of the tissue. Non-tissue areas
        are set to 0.
    d_xx : np.ndarray
        Diffusion x component for x direction.
    d_xy : np.ndarray
        Diffusion y component for x direction.
    d_yx : np.ndarray
        Diffusion x component for y direction.
    d_yy : np.ndarray
        Diffusion y component for y direction.

    Returns
    -------
    np.ndarray
        3D array of weights for diffusion, with the shape of (*mesh.shape, 9).
    """"""
    n_i = m.shape[0]
    n_j = m.shape[1]
    for ii in prange(n_i * n_j):
        i = int(ii / n_j)
        j = ii % n_j
        if m[i, j] != 1:
            continue

        # q (i-1/2, j)
        qx0_major = major_component(d_xx[i-1, j], m[i-1, j])
        # (i-1, j)
        w[i, j, 1] += qx0_major
        # (i, j)
        w[i, j, 4] -= qx0_major

        qx0_minor = minor_component(d_xy[i-1, j],
                                    m[i-1, j-1], m[i, j-1],
                                    m[i-1, j], m[i, j],
                                    m[i-1, j+1], m[i, j+1])
        # (i-1, j-1)
        w[i, j, 0] -= qx0_minor[0]
        # (i, j-1)
        w[i, j, 3] -= qx0_minor[1]
        # (i-1, j)
        w[i, j, 1] -= qx0_minor[2]
        # (i, j)
        w[i, j, 4] -= qx0_minor[3]
        # (i-1, j+1)
        w[i, j, 2] -= qx0_minor[4]
        # (i, j+1)
        w[i, j, 5] -= qx0_minor[5]

        # q (i, j-1/2)
        qy0_major = major_component(d_yy[i, j-1], m[i, j-1])
        # (i, j-1)
        w[i, j, 3] += qy0_major
        # (i, j)
        w[i, j, 4] -= qy0_major

        qy0_minor = minor_component(d_yx[i, j-1], m[i-1, j-1], m[i-1, j],
                                    m[i, j-1], m[i, j], m[i+1, j-1], m[i+1, j])

        # (i-1, j-1)
        w[i, j, 0] -= qy0_minor[0]
        # (i-1, j)
        w[i, j, 1] -= qy0_minor[1]
        # (i, j-1)
        w[i, j, 3] -= qy0_minor[2]
        # (i, j)
        w[i, j, 4] -= qy0_minor[3]
        # (i+1, j-1)
        w[i, j, 6] -= qy0_minor[4]
        # (i+1, j)
        w[i, j, 7] -= qy0_minor[5]

        # q (i, j+1/2)
        qy1_major = major_component(d_yy[i, j], m[i, j+1])
        # (i, j+1)
        w[i, j, 5] += qy1_major
        # (i, j)
        w[i, j, 4] -= qy1_major

        qy1_minor = minor_component(d_yx[i, j], m[i-1, j+1], m[i-1, j],
                                    m[i, j+1], m[i, j], m[i+1, j+1], m[i+1, j])

        # (i-1, j+1)
        w[i, j, 2] += qy1_minor[0]
        # (i-1, j)
        w[i, j, 1] += qy1_minor[1]
        # (i, j+1)
        w[i, j, 5] += qy1_minor[2]
        # (i, j)
        w[i, j, 4] += qy1_minor[3]
        # (i+1, j+1)
        w[i, j, 8] += qy1_minor[4]
        # (i+1, j)
        w[i, j, 7] += qy1_minor[5]

        # q (i+1/2, j)
        qx1_major = major_component(d_xx[i, j], m[i+1, j])
        # (i+1, j)
        w[i, j, 7] += qx1_major
        # (i, j)
        w[i, j, 4] -= qx1_major

        qx1_minor = minor_component(d_xy[i, j], m[i+1, j-1], m[i, j-1],
                                    m[i+1, j], m[i, j], m[i+1, j+1], m[i, j+1])
        # (i+1, j-1)
        w[i, j, 6] += qx1_minor[0]
        # (i, j-1)
        w[i, j, 3] += qx1_minor[1]
        # (i+1, j)
        w[i, j, 7] += qx1_minor[2]
        # (i, j)
        w[i, j, 4] += qx1_minor[3]
        # (i+1, j+1)
        w[i, j, 8] += qx1_minor[4]
        # (i, j+1)
        w[i, j, 5] += qx1_minor[5]

    return w
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stencil/__init__.py",".py","244","3","from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import AsymmetricStencil2D
from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import IsotropicStencil2D
from finitewave.cpuwave2D.stencil.symmetric_stencil_2d import SymmetricStencil2D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stencil/isotropic_stencil_2d.py",".py","5792","205","import numpy as np
from numba import njit, prange

from finitewave.core.stencil.stencil import Stencil


class IsotropicStencil2D(Stencil):
    """"""
    This class computes the weights for diffusion on a 2D using an isotropic
    stencil. The stencil includes 5 points: the central point and the
    four neighbors.

    The method assumes weights being used in the following order:

    - ``w[i, j, 0] : (i-1, j)``,
    - ``w[i, j, 1] : (i, j-1)``,
    - ``w[i, j, 2] : (i, j)``,
    - ``w[i, j, 3] : (i, j+1)``,
    - ``w[i, j, 4] : (i-1, j)``.

    Notes
    -----
    The method can handle heterogeneity in the diffusion coefficients given
    by the ``conductivity`` parameter.
    """"""

    def __init__(self):
        super().__init__()

    def select_diffusion_kernel(self):
        """"""
        Returns the diffusion kernel function for isotropic diffusion in 2D.

        Returns
        -------
        function
            The diffusion kernel function for isotropic diffusion in 2D.
        """"""
        return diffusion_kernel_2d_iso

    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the weights for isotropic diffusion in 2D.

        Parameters
        ----------
        model : CardiacModel2D
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue2D
            A 2D cardiac tissue object.

        Returns
        -------
        numpy.ndarray
            The weights for isotropic diffusion in 2D.
        """"""
        mesh = cardiac_tissue.mesh.copy()
        mesh[mesh != 1] = 0
        # make sure the conductivity is a array
        conductivity = cardiac_tissue.conductivity
        conductivity = conductivity * np.ones_like(mesh, dtype=model.npfloat)
        d_xx, d_yy = self.compute_half_step_diffusion(mesh, conductivity)

        weights = np.zeros((*mesh.shape, 5), dtype=model.npfloat)
        weights = compute_weights(weights, mesh, d_xx, d_yy)
        weights = weights * model.D_model * model.dt / model.dr**2
        weights[:, :, 2] += 1

        return weights

    def compute_half_step_diffusion(self, mesh, conductivity, num_axes=2):
        """"""
        Computes the half-step diffusion values for isotropic diffusion.

        Parameters
        ----------
        mesh : numpy.ndarray
            A 2D array representing the mesh of the tissue.
        conductivity : numpy.ndarray
            A 2D array representing the conductivity of the tissue.
        num_axes : int
            The number of axes to compute the half-step diffusion values.

        Returns
        -------
        numpy.ndarray
            The half-step diffusion values for the specified axis.
        """"""
        D = np.zeros((num_axes, *mesh.shape))

        for i in range(num_axes):
            D[i] = 0.5 * (conductivity + np.roll(conductivity, -1, axis=i))

        return D


@njit(parallel=True)
def diffusion_kernel_2d_iso(u_new, u, w, indexes):
    """"""
    Performs isotropic diffusion on a 2D grid.

    Parameters
    ----------
    u_new : numpy.ndarray
        A 2D array to store the updated potential values after diffusion.
    u : numpy.ndarray
        A 2D array representing the current potential values before diffusion.
    w : numpy.ndarray
        A 3D array of weights used in the diffusion computation.
        The shape should match (*mesh.shape, 5).
    mesh : numpy.ndarray
        A 2D array representing the mesh of the tissue.

    Returns
    -------
    numpy.ndarray
        The updated potential values after diffusion.
    """"""
    n_i = u.shape[0]
    n_j = u.shape[1]
    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = int(ii / n_j)
        j = ii % n_j

        u_new[i, j] = (u[i-1, j] * w[i, j, 0] +
                       u[i, j-1] * w[i, j, 1] +
                       u[i, j] * w[i, j, 2] +
                       u[i, j+1] * w[i, j, 3] +
                       u[i+1, j] * w[i, j, 4])

    return u_new


@njit
def compute_component(d, m0, m1):
    """"""
    Computes the component for isotropic diffusion in 2D.

    .. code-block:: text

        m0 -- d -- x ------ m1

    Parameters
    ----------
    d : float
        The diffusion coefficient.
    m0 : int
        The value of the mesh point adjacent to the central point.
    m1 : int
        The value of the mesh point opposite to the ``m0`` point.

    Returns
    -------
    float
        The computed component for isotropic diffusion in 2D.
    """"""
    return d * m0 * (m0 + (m1 == 0))


@njit(parallel=True)
def compute_weights(w, m, d_xx, d_yy):
    """"""
    Computes the weights for isotropic diffusion in 2D.

    Parameters
    ----------
    w : numpy.ndarray
        A 3D array to store the computed weights.
    m : numpy.ndarray
        A 2D array representing the mesh of the tissue.
    d_xx : numpy.ndarray
        A 2D array representing the half-step diffusion values in the x-axis.
    d_yy : numpy.ndarray
        A 2D array representing the half-step diffusion values in the y-axis.

    Returns
    -------
    numpy.ndarray
        The computed weights for isotropic diffusion in 2D.
    """"""
    n_i = m.shape[0]
    n_j = m.shape[1]

    for ii in prange(n_i * n_j):

        i = int(ii / n_j)
        j = ii % n_j

        if m[i, j] != 1:
            continue

        # (i-1, j)
        w[i, j, 0] = compute_component(d_xx[i-1, j], m[i-1, j], m[i+1, j])
        # (i, j-1)
        w[i, j, 1] = compute_component(d_yy[i, j-1], m[i, j-1], m[i, j+1])
        # (i, j+1)
        w[i, j, 3] = compute_component(d_yy[i, j], m[i, j+1], m[i, j-1])
        # (i+1, j)
        w[i, j, 4] = compute_component(d_xx[i, j], m[i+1, j], m[i-1, j])
        # (i, j)
        w[i, j, 2] = - (w[i, j, 0] + w[i, j, 1] + w[i, j, 3] + w[i, j, 4])

    return w
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/stencil/symmetric_stencil_2d.py",".py","7500","251","import numpy as np
from numba import njit, prange

from .asymmetric_stencil_2d import AsymmetricStencil2D


class SymmetricStencil2D(AsymmetricStencil2D):
    """"""
    A class to represent a 2D symmetric stencil for diffusion processes.
    The asymmetric stencil is used to handle anisotropic diffusion in the
    tissue.

    Notes
    -----
    The method assumes weights being used in the following order:

    - ``w[0] : i-1, j-1``,
    - ``w[1] : i-1, j``,
    - ``w[2] : i-1, j+1``,
    - ``w[3] : i, j-1``,
    - ``w[4] : i, j``,
    - ``w[5] : i, j+1``,
    - ``w[6] : i+1, j-1``,
    - ``w[7] : i+1, j``,
    - ``w[8] : i+1, j+1``.
    """"""

    def __init__(self):
        super().__init__()

    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the weights for diffusion on a 2D mesh using the symmetric
        stencil.

        Parameters
        ----------
        model : CardiacModel2D
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue2D
            A 2D cardiac tissue object.

        Returns
        -------
        np.ndarray
            3D array of weights for diffusion, with the shape of
            ``(*mesh.shape, 9)``.
        """"""
        mesh = cardiac_tissue.mesh.copy()
        conductivity = cardiac_tissue.conductivity
        fibers = cardiac_tissue.fibers

        if fibers is None:
            message = ""Fibers must be provided for anisotropic diffusion.""
            raise ValueError(message)

        mesh[mesh != 1] = 0
        # fibers[np.where(mesh != 1)] = 0
        weights = np.zeros((*mesh.shape, 9))

        D = self.compute_half_step_diffusion(mesh, conductivity, fibers,
                                             self.D_al, self.D_ac)
        compute_weights(weights, mesh, D[0], D[1], D[2], D[3])
        weights *= model.D_model * model.dt / model.dr**2
        weights[:, :, 4] += 1

        return weights

    def compute_half_step_diffusion(self, mesh, conductivity, fibers, D_al,
                                    D_ac):
        """"""
        Computes the diffusion components for half-steps based on fiber
        orientations.

        Parameters
        ----------
        mesh : np.ndarray
            Array representing the mesh grid of the tissue.
        conductivity : np.ndarray
            Array representing the conductivity of the tissue.
        fibers : np.ndarray
            Array representing fiber orientations with shape
            ``(2, *mesh.shape)``.
        D_al : float
            Longitudinal diffusion coefficient.
        D_ac : float
            Cross-sectional diffusion coefficient.
        axis : int
            Axis index (0 for x, 1 for y).
        num_axes : int
            Number of axes.

        Returns
        -------
        np.ndarray
            Array of diffusion components for half-steps along the specified
            axis.

        Notes
        -----
        The index ``i`` in the returned array corresponds to ``i+1/2`` and
        ``i-1`` corresponds to ``i-1/2``.
        """"""

        D = np.zeros((4, *mesh.shape))
        for i in range(4):
            ind0 = i // 2
            ind1 = i % 2
            D[i] = self.compute_diffusion_components(fibers, ind0, ind1, D_al,
                                                     D_ac)
            D[i] *= conductivity
            D[i] = 0.25 * (D[i] +
                           np.roll(D[i], -1, axis=0) +
                           np.roll(D[i], -1, axis=1) +
                           np.roll(np.roll(D[i], -1, axis=0), -1, axis=1))

        return D


@njit
def compute_components(d_xx, d_xy, d_yx, d_yy, m0, m1, m2, m3, qx, qy):
    """"""
    .. code-block:: text
        m1 ---- m3
        |       |
        |   o   |
        |       |
        m0 ---- m2


    dx = 0.5 * (u2 + u3) - 0.5 * (u0 + u1)
    dy = 0.5 * (u1 + u3) - 0.5 * (u0 + u2)

    qx = d_xx * dx + d_xy * dy
    qy = d_yx * dx + d_yy * dy
    """"""
    m = m0 + m1 + m2 + m3

    if m < 3:
        return 0, 0, 0, 0

    qdx = qx * d_xx + qy * d_yx
    qdy = qx * d_xy + qy * d_yy

    w0 = - m0 / (m0 + m1) * qdx - m0 / (m0 + m2) * qdy
    w1 = - m1 / (m0 + m1) * qdx + m1 / (m1 + m3) * qdy
    w2 = m2 / (m2 + m3) * qdx - m2 / (m0 + m2) * qdy
    w3 = m3 / (m2 + m3) * qdx + m3 / (m1 + m3) * qdy
    return 0.5 * w0, 0.5 * w1, 0.5 * w2, 0.5 * w3


@njit
def compute_component_(m0, m1, m2, m3, d_xx, d_xy, d_yx, d_yy, qx, qy, ux, uy):
    m = m0 * m1 * m2 * m3
    w = (qx * (d_xx * ux + d_xy * uy) + qy * (d_yx * ux + d_yy * uy)) * m
    return 0.25 * w


@njit
def compute_weights(w, m, d_xx, d_xy, d_yx, d_yy):
    """"""
    Computes the weights for diffusion on a 2D mesh based on the asymmetric
    stencil.

    Parameters
    ----------
    w : np.ndarray
        3D array to store the weights for diffusion. Shape is (*mesh.shape, 9).
    m : np.ndarray
        2D array representing the mesh grid of the tissue. Non-tissue areas
        are set to 0.
    d_xx : np.ndarray
        Diffusion x component for x direction.
    d_xy : np.ndarray
        Diffusion y component for x direction.
    d_yx : np.ndarray
        Diffusion x component for y direction.
    d_yy : np.ndarray
        Diffusion y component for y direction.

    Returns
    -------
    np.ndarray
        3D array of weights for diffusion, with the shape of (*mesh.shape, 9).
    """"""
    n_i = m.shape[0]
    n_j = m.shape[1]
    for ii in prange(n_i * n_j):
        i = int(ii / n_j)
        j = ii % n_j
        if m[i, j] != 1:
            continue

        # (i-1/2, j-1/2)
        w0, w1, w2, w3 = compute_components(d_xx[i-1, j-1], d_xy[i-1, j-1],
                                            d_yx[i-1, j-1], d_yy[i-1, j-1],
                                            m[i-1, j-1], m[i-1, j], m[i, j-1],
                                            m[i, j], -1, -1)
        # (i-1, j-1)
        w[i, j, 0] += w0
        # (i-1, j)
        w[i, j, 1] += w1
        # (i, j-1)
        w[i, j, 3] += w2
        # (i, j)
        w[i, j, 4] += w3

        # (i-1/2, j+1/2)
        w0, w1, w2, w3 = compute_components(d_xx[i-1, j], d_xy[i-1, j],
                                            d_yx[i-1, j], d_yy[i-1, j],
                                            m[i-1, j], m[i-1, j+1], m[i, j],
                                            m[i, j+1], -1, 1)
        # (i-1, j)
        w[i, j, 1] += w0
        # (i-1, j+1)
        w[i, j, 2] += w1
        # (i, j)
        w[i, j, 4] += w2
        # (i, j+1)
        w[i, j, 5] += w3

        # (i+1/2, j-1/2)
        w0, w1, w2, w3 = compute_components(d_xx[i, j-1], d_xy[i, j-1],
                                            d_yx[i, j-1], d_yy[i, j-1],
                                            m[i, j-1], m[i, j], m[i+1, j-1],
                                            m[i+1, j], 1, -1)
        # (i, j-1)
        w[i, j, 3] += w0
        # (i, j)
        w[i, j, 4] += w1
        # (i+1, j-1)
        w[i, j, 6] += w2
        # (i+1, j)
        w[i, j, 7] += w3

        # (i+1/2, j+1/2)
        w0, w1, w2, w3 = compute_components(d_xx[i, j], d_xy[i, j],
                                            d_yx[i, j], d_yy[i, j],
                                            m[i, j], m[i, j+1], m[i+1, j],
                                            m[i+1, j+1], 1, 1)
        # (i, j)
        w[i, j, 4] += w0
        # (i, j+1)
        w[i, j, 5] += w1
        # (i+1, j)
        w[i, j, 7] += w2
        # (i+1, j+1)
        w[i, j, 8] += w3

    return w
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/fibrosis/structural_2d_pattern.py",".py","4138","117","import numpy as np
import random

from finitewave.core.fibrosis.fibrosis_pattern import FibrosisPattern


class Structural2DPattern(FibrosisPattern):
    """"""
    Class for generating a structural fibrosis pattern in a 2D mesh.

    The pattern consists of rectangular blocks distributed throughout a specified region of the mesh,
    with the density controlling the likelihood of each block being present.

    Attributes
    ----------
    x1 : int
        The starting x-coordinate of the area where blocks can be placed.
    x2 : int
        The ending x-coordinate of the area where blocks can be placed.
    y1 : int
        The starting y-coordinate of the area where blocks can be placed.
    y2 : int
        The ending y-coordinate of the area where blocks can be placed.
    density : float
        The density of the fibrosis blocks, represented as a probability.
    length_i : int
        The width of each block.
    length_j : int
        The height of each block.
    """"""

    def __init__(self, density, length_i, length_j, x1, x2, y1, y2):
        """"""
        Initializes the Structural2DPattern with the specified parameters.

        Parameters
        ----------
        x1 : int
            The starting x-coordinate of the area where blocks can be placed.
        x2 : int
            The ending x-coordinate of the area where blocks can be placed.
        y1 : int
            The starting y-coordinate of the area where blocks can be placed.
        y2 : int
            The ending y-coordinate of the area where blocks can be placed.
        density : float
            The density of the fibrosis blocks, represented as a probability.
        length_i : int
            The width of each block.
        length_j : int
            The height of each block.
        """"""
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.density = density
        self.length_i = length_i
        self.length_j = length_j

    def generate(self, shape=None, mesh=None):
        """"""
        Generates and applies a structural fibrosis pattern to the mesh.

        The mesh is divided into blocks of size `length_i` by `length_j`, with each block having 
        a probability `density` of being filled with fibrosis. The function ensures that blocks do not
        extend beyond the specified region.

        Parameters
        ----------
        shape : tuple
            The shape of the mesh.
        mesh : numpy.ndarray, optional
            The existing mesh to base the pattern on. Default is None..

        Returns
        -------
        numpy.ndarray
            A new mesh array with the applied fibrosis pattern.
        """"""
        if shape is None and mesh is None:
            message = ""Either shape or mesh must be provided.""
            raise ValueError(message)

        if shape is not None:
            mesh = np.ones(shape, dtype=np.int8)
            fibr = self._generate(mesh.shape)
            mesh[self.x1: self.x2, self.y1: self.y2] = fibr[self.x1: self.x2,
                                                self.y1: self.y2]
            return mesh

        fibr = self._generate(mesh.shape)
        mesh[self.x1: self.x2, self.y1: self.y2] = fibr[self.x1: self.x2,
                                                        self.y1: self.y2]
        return mesh

    def _generate(self, shape):
        mesh = np.ones(shape, dtype=np.int8)
        for i in range(self.x1, self.x2, self.length_i):
            for j in range(self.y1, self.y2, self.length_j):
                if random.random() <= self.density:
                    i_s = 0
                    j_s = 0
                    if i + self.length_i <= self.x2:
                        i_s = self.length_i
                    else:
                        i_s = self.length_i - (i + self.length_i - self.x2)

                    if j + self.length_j <= self.y2:
                        j_s = self.length_j
                    else:
                        j_s = self.length_j - (j + self.length_j - self.y2)

                    mesh[i:i + i_s, j:j + j_s] = 2 # fibrosis element

        return mesh
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/fibrosis/__init__.py",".py","104","3","from .diffuse_2d_pattern import Diffuse2DPattern
from .structural_2d_pattern import Structural2DPattern
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/fibrosis/diffuse_2d_pattern.py",".py","2757","88","import numpy as np

from finitewave.core.fibrosis.fibrosis_pattern import FibrosisPattern


class Diffuse2DPattern(FibrosisPattern):
    """"""
    Class for generating a diffuse 2D fibrosis pattern in a given mesh area.

    Attributes
    ----------
    density : float
        The density of the fibrosis in the specified area
    x1 : int
        The starting x-coordinate of the fibrosis area.
    x2 : int
        The ending x-coordinate of the fibrosis area.
    y1 : int
        The starting y-coordinate of the fibrosis area.
    y2 : int
        The ending y-coordinate of the fibrosis area.
    """"""

    def __init__(self, density, x1=None, x2=None, y1=None, y2=None):
        """"""
        Initializes the Diffuse2DPattern with the specified parameters.

        Parameters
        ----------
        density : float
            The density of the fibrosis in the specified area.
        x1 : int
            The starting x-coordinate of the fibrosis area.
        x2 : int
            The ending x-coordinate of the fibrosis area.
        y1 : int
            The starting y-coordinate of the fibrosis area.
        y2 : int
            The ending y-coordinate of the fibrosis area.
        """"""
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.density = density

    def generate(self, shape=None, mesh=None):
        """"""
        Generates a diffuse 2D fibrosis pattern for the given shape and mesh.
        The resulting pattern is applied to the mesh within the specified
        area.

        Parameters
        ----------
        shape : tuple
            The shape of the mesh.
        mesh : numpy.ndarray, optional
            The existing mesh to base the pattern on. Default is None.

        Returns
        -------
        numpy.ndarray
            A new mesh array with the applied fibrosis pattern.

        Notes
        -----
        If both parameters are provided, first non-None parameter is used.
        """"""

        if shape is None and mesh is None:
            message = ""Either shape or mesh must be provided.""
            raise ValueError(message)

        if shape is not None:
            mesh = np.ones(shape, dtype=np.int8)
            fibr = self._generate(mesh.shape)
            mesh[self.x1: self.x2, self.y1: self.y2] = fibr[self.x1: self.x2,
                                                            self.y1: self.y2]
            return mesh

        fibr = self._generate(mesh.shape)
        mesh[self.x1: self.x2, self.y1: self.y2] = fibr[self.x1: self.x2,
                                                        self.y1: self.y2]
        return mesh

    def _generate(self, shape):
        return 1 + (np.random.random(shape) <= self.density).astype(np.int8)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tissue/__init__.py",".py","73","1","from finitewave.cpuwave2D.tissue.cardiac_tissue_2d import CardiacTissue2D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave2D/tissue/cardiac_tissue_2d.py",".py","1435","46","import numpy as np

from finitewave.core.tissue.cardiac_tissue import CardiacTissue


class CardiacTissue2D(CardiacTissue):
    """"""
    This class represents a 2D cardiac tissue.

    Attributes
    ----------
    meta : dict
        A dictionary containing metadata about the tissue.
    mesh : np.ndarray
        A 2D numpy array representing the tissue mesh where each value
        indicates the type of tissue at that location. Possible values are:
        ``0`` for non-tissue, ``1`` for healthy tissue, and ``2`` for fibrotic
        tissue.
    conductivity : float or np.ndarray
        The conductivity of the tissue used for reducing the diffusion
        coefficients. The conductivity should be in the range [0, 1].
    fibers : np.ndarray
        Fibers orientation in the tissue. If None, the isotropic stencil is
        used.
    """"""

    def __init__(self, shape):
        super().__init__()
        self.meta[""dim""] = 2
        self.meta[""shape""] = shape
        self.mesh = np.ones(shape, dtype=np.int8)
        self.conductivity = 1.0
        self.fibers = None

    def add_boundaries(self):
        """"""
        Sets the boundary values of the mesh to zero.

        The boundaries are defined as the edges of the grid, and this method
        updates these edges in the mesh array.
        """"""
        self._mesh[0, :] = 0
        self._mesh[:, 0] = 0
        self._mesh[-1, :] = 0
        self._mesh[:, -1] = 0
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/__init__.py",".py","643","27","from finitewave.cpuwave3D.fibrosis import Diffuse3DPattern, Structural3DPattern
from finitewave.cpuwave3D.model import (
    AlievPanfilov3D,
    Barkley3D,
    MitchellSchaeffer3D,
    FentonKarma3D,
    BuenoOrovio3D,
    LuoRudy913D,
    TP063D,
    Courtemanche3D,
)
from finitewave.cpuwave3D.stencil import (
    AsymmetricStencil3D,
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stimulation import (
    StimCurrentCoord3D,
    StimVoltageCoord3D,
    StimCurrentMatrix3D,
    StimVoltageMatrix3D,
    StimVoltageListMatrix3D,
    StimCurrentArea3D,
)
from finitewave.cpuwave3D.tissue import CardiacTissue3D
from .tracker import *

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/luo_rudy91_3d.py",".py","4033","133","import numpy as np
from numba import njit, prange

from finitewave.cpuwave2D.model.luo_rudy91_2d import (
    LuoRudy912D, 
    calc_ina, 
    calc_isk, 
    calc_ik, 
    calc_ik1, 
    calc_ikp, 
    calc_ib
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class LuoRudy913D(LuoRudy912D):
    """"""
    Implements the 3D Luo-Rudy 1991 cardiac model.

    See LuoRudy912D for the 2D model description.
    """"""
    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel to update the state variables and membrane
        potential.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.m, self.h, self.j, self.d,
                        self.f, self.x, self.cai,
                        self.cardiac_tissue.myo_indexes, self.dt, 
                        self.gna, self.gsi, self.gk, self.gk1, self.gkp, self.gb, 
                        self.ko, self.ki, self.nai, self.nao, self.cao, self.R, self.T, self.F, self.PR_NaK)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, m, h, j_, d, f, x, cai, indexes, dt, gna, gsi, gk, gk1, gkp, gb, ko, ki, nai, nao, cao, R, T, F, PR_NaK):
    """"""
    Computes the ionic currents and updates the state variables in the 3D
    Luo-Rudy 1991 cardiac model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated membrane potential.
    u : np.ndarray
        Array of the current membrane potential values.
    m : np.ndarray
        Array for the gating variable `m`.
    h : np.ndarray
        Array for the gating variable `h`.
    j_ : np.ndarray
        Array for the gating variable `j`.
    d : np.ndarray
        Array for the gating variable `d`.
    f : np.ndarray
        Array for the gating variable `f`.
    x : np.ndarray
        Array for the gating variable `x`.
    Cai_c : np.ndarray
        Array for the intracellular calcium concentration.
    mesh : np.ndarray
        Mesh array indicating the tissue types.
    dt : float
        Time step for the simulation.
    """"""

    E_Na = (R*T/F)*np.log(nao/nai)

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        # Fast sodium current:
        ina, m[i, j, k], h[i, j, k], j_[i, j, k] = calc_ina(u[i, j, k], dt, m[i, j, k], h[i, j, k], j_[i, j, k], E_Na, gna)

        # Slow inward current:
        isi, d[i, j, k], f[i, j, k], cai[i, j, k] = calc_isk(u[i, j, k], dt, d[i, j, k], f[i, j, k], cai[i, j, k], gsi)

        # Time-dependent potassium current:
        ik, x[i, j, k] = calc_ik(u[i, j, k], dt, x[i, j, k], ko, ki, nao, nai, PR_NaK, R, T, F, gk)

        E_K1 = (R * T / F) * np.log(ko / ki)

        # Time-independent potassium current:
        ik1 = calc_ik1(u[i, j, k], ko, E_K1, gk1)

        # Plateau potassium current:
        ikp = calc_ikp(u[i, j, k], E_K1, gkp)

        # Background current:
        ib = calc_ib(u[i, j, k], gb)

        # Total time-independent potassium current:
        ik1t = ik1 + ikp + ib

        # if i == 4 and j == 4:
        #     print(cai[i, j], m[i, j]) 

        u_new[i, j, k] -= dt * (ina + isi + ik1t + ik)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/aliev_panfilov_3d.py",".py","2465","86","from numba import njit, prange

from finitewave.cpuwave2D.model.aliev_panfilov_2d import AlievPanfilov2D, calc_v
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class AlievPanfilov3D(AlievPanfilov2D):
    """"""
    Implementation of the Aliev-Panfilov 3D cardiac model.

    See AlievPanfilov2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Aliev-Panfilov model.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.v,
                        self.cardiac_tissue.myo_indexes, self.dt,
                        self.a, self.k, self.eap, self.mu_1, self.mu_2)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        Stencil
            The stencil object to be used for diffusion computations.
        """"""

        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, v, indexes, dt, a, k, eap, mu_1, mu_2):
    """"""
    Computes the ionic kernel for the Aliev-Panfilov 3D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    v : np.ndarray
        Recovery variable array.
    dt : float
        Time step for the simulation.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    """"""

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ni in prange(len(indexes)):
        ii = indexes[ni]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k_ = (ii % (n_j*n_k)) % n_k
        
        v[i, j, k_] = calc_v(v[i, j, k_], u[i, j, k_], dt, a, k, eap, mu_1, mu_2)

        u_new[i, j, k_] += dt * (- k * u[i, j, k_] * (u[i, j, k_] - a) * (u[i, j, k_] - 1.) -
                            u[i, j, k_] * v[i, j, k_])

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/bueno_orovio_3d.py",".py","4114","123","from numba import njit, prange

from finitewave.cpuwave2D.model.bueno_orovio_2d import (
    BuenoOrovio2D,
    calc_Jfi,
    calc_Jsi,
    calc_Jso,
    calc_tau_v_m,
    calc_tau_w_m,
    calc_tau_so,
    calc_tau_s,
    calc_tau_o,
    calc_v_inf,
    calc_w_inf,
    calc_v,
    calc_w,
    calc_s
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class BuenoOrovio3D(BuenoOrovio2D):
    """"""
    Implementation of the Bueno-Orovio 3D cardiac model.

    See BuenoOrovio2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Bueno-Orovio model.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.v, self.w, self.s, self.cardiac_tissue.myo_indexes, 
                        self.dt, self.u_o, self.u_u, self.theta_v, self.theta_w, self.theta_v_m,
                        self.theta_o, self.tau_v1_m, self.tau_v2_m, self.tau_v_p,
                        self.tau_w1_m, self.tau_w2_m, self.k_w_m, self.u_w_m,
                        self.tau_w_p, self.tau_fi, self.tau_o1, self.tau_o2,
                        self.tau_so1, self.tau_so2, self.k_so, self.u_so,
                        self.tau_s1, self.tau_s2, self.k_s, self.u_s,
                        self.tau_si, self.tau_w_inf, self.w_inf_)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        Stencil
            The stencil object to be used for diffusion computations.
        """"""

        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, v, w, s, indexes, dt, 
                    u_o, u_u, theta_v, theta_w, theta_v_m,
                    theta_o, tau_v1_m, tau_v2_m, tau_v_p,
                    tau_w1_m, tau_w2_m, k_w_m, u_w_m,
                    tau_w_p, tau_fi, tau_o1, tau_o2,
                    tau_so1, tau_so2, k_so, u_so,
                    tau_s1, tau_s2, k_s, u_s,
                    tau_si, tau_w_inf, w_inf_):
    """"""
    Computes the ionic kernel for the Bueno-Orovio 3D model.

    Parameters
    ----------
    u_new : ndarray
        The new state of the u variable.
    u : ndarray
        The current state of the u variable.
    myo_indexes : list
        List of indexes representing myocardial cells.
    dt : float
        The time step for the simulation.
    """"""

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ni in prange(len(indexes)):
        ii = indexes[ni]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        v[i, j, k]  = calc_v(v[i, j, k] , u[i, j, k] , dt, theta_v, calc_v_inf(u[i, j, k] , theta_v_m), 
                         calc_tau_v_m(u[i, j, k] , theta_v_m, tau_v1_m, tau_v2_m), tau_v_p)
        
        w[i, j, k]  = calc_w(w[i, j, k] , u[i, j, k] , dt, theta_w, calc_w_inf(u[i, j, k] , theta_o, tau_w_inf, w_inf_), 
                         calc_tau_w_m(u[i, j, k] , tau_w1_m, tau_w2_m, k_w_m, u_w_m), tau_w_p)
        
        s[i, j, k]  = calc_s(s[i, j, k] , u[i, j, k] , dt,
                        calc_tau_s(u[i, j, k] , tau_s1, tau_s2, theta_w), k_s, u_s)
        
        J_fi = calc_Jfi(u[i, j, k] , v[i, j, k] , theta_v, u_u, tau_fi)
        J_so = calc_Jso(u[i, j, k] , u_o, theta_w,
                        calc_tau_o(u[i, j, k] , tau_o1, tau_o2, theta_o), 
                        calc_tau_so(u[i, j, k] , tau_so1, tau_so2, k_so, u_so))
        J_si = calc_Jsi(u[i, j, k] , w[i, j, k] , s[i, j, k] , theta_w, tau_si)

        u_new[i, j, k]  += dt * (-J_fi - J_so - J_si)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/mitchell_schaeffer_3d.py",".py","2753","97","from numba import njit, prange

from finitewave.cpuwave2D.model.mitchell_schaeffer_2d import (
    MitchellSchaeffer2D, 
    calc_h, 
    calc_J_in, 
    calc_J_out
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class MitchellSchaeffer3D(MitchellSchaeffer2D):
    """"""
    Implementation of the Mitchell-Schaeffer 3D cardiac model.

    See MitchellSchaeffer2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Mitchell-Schaeffer model.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.h, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.tau_close, self.tau_open, self.tau_in, self.tau_out, self.u_gate)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        Stencil
            The stencil object to be used for diffusion computations.
        """"""

        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, h, indexes, dt, tau_close, tau_open, tau_in, tau_out, u_gate):
    """"""
    Computes the ionic kernel for the Mitchell-Schaeffer 3D model.

    Parameters
    ----------
    u_new : ndarray
        The new state of the u variable.
    u : ndarray
        The current state of the u variable.
    h : ndarray
        The gating variable h.
    myo_indexes : list
        List of indexes representing myocardial cells.
    dt : float
        The time step for the simulation.
    tau_close : float
        The time constant for the closing of the h gate.
    tau_open : float
        The time constant for the opening of the h gate.
    u_gate : float
        The threshold value for the gating variable.
    """"""

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ni in prange(len(indexes)):
        ii = indexes[ni]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k
        
        h[i, j, k] = calc_h(h[i, j, k], u[i, j, k], dt, tau_close, tau_open, u_gate)

        J_in = calc_J_in(h[i, j, k], u[i, j, k], tau_in)
        J_out = calc_J_out(u[i, j, k], tau_out)
        u_new[i, j, k] += dt * (J_in + J_out)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/__init__.py",".py","333","9","from .aliev_panfilov_3d import AlievPanfilov3D
from .barkley_3d import Barkley3D
from .mitchell_schaeffer_3d import MitchellSchaeffer3D
from .fenton_karma_3d import FentonKarma3D
from .bueno_orovio_3d import BuenoOrovio3D
from .luo_rudy91_3d import LuoRudy913D
from .tp06_3d import TP063D
from .courtemanche_3d import Courtemanche3D
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/tp06_3d.py",".py","8415","205","import numpy as np
from numba import njit, prange

from finitewave.cpuwave2D.model.tp06_2d import (
    TP062D,
    calc_ina,
    calc_ical,
    calc_ito,
    calc_ikr,
    calc_iks,
    calc_ik1,
    calc_inaca,
    calc_inak,
    calc_ipca,
    calc_ipk,
    calc_ibna,
    calc_ibca,
    calc_irel,
    calc_ileak,
    calc_iup,
    calc_ixfer,
    calc_casr,
    calc_cass,
    calc_cai,
    calc_nai,
    calc_ki
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class TP063D(TP062D):
    """"""
    A class to represent the TP06 cardiac model in 3D.

    See TP062D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel function to update ionic currents and state
        variables.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.cai, self.casr, self.cass,
                        self.nai, self.Ki, self.m, self.h, self.j, self.xr1,
                        self.xr2, self.xs, self.r, self.s, self.d, self.f,
                        self.f2, self.fcass, self.rr, self.oo,
                        self.cardiac_tissue.myo_indexes, self.dt,
                        self.ko, self.cao, self.nao, self.Vc, self.Vsr, self.Vss, self.Bufc, self.Kbufc, self.Bufsr, self.Kbufsr,
                        self.Bufss, self.Kbufss, self.Vmaxup, self.Kup, self.Vrel, self.k1_, self.k2_, self.k3, self.k4, self.EC,
                        self.maxsr, self.minsr, self.Vleak, self.Vxfer, self.R, self.F, self.T, self.RTONF, self.CAPACITANCE,
                        self.gkr, self.pKNa, self.gk1, self.gna, self.gbna, self.KmK, self.KmNa, self.knak, self.gcal, self.gbca,
                        self.knaca, self.KmNai, self.KmCa, self.ksat, self.n_, self.gpca, self.KpCa, self.gpk, self.gto, self.gks)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


# tp06 epi kernel
@njit(parallel=True)
def ionic_kernel_3d(u_new, u, cai, casr, cass, nai, Ki, m, h, j_, xr1, xr2,
                    xs, r, s, d, f, f2, fcass, rr, oo, indexes, dt, 
                    ko, cao, nao, Vc, Vsr, Vss, Bufc, Kbufc, Bufsr, Kbufsr,
                    Bufss, Kbufss, Vmaxup, Kup, Vrel, k1_, k2_, k3, k4, EC,
                    maxsr, minsr, Vleak, Vxfer, R, F, T, RTONF, CAPACITANCE,
                    gkr, pKNa, gk1, gna, gbna, KmK, KmNa, knak, gcal, gbca,
                    knaca, KmNai, KmCa, ksat, n_, gpca, KpCa, gpk, gto, gks):
    """"""
    Compute the ionic currents and update the state variables for the 3D TP06
    cardiac model.

    This function calculates the ionic currents based on the TP06 cardiac
    model, updates ion concentrations, and modifies gating variables in
    the 3D grid. The calculations are performed in parallel to enhance
    performance.

    Parameters
    ----------
    u_new : numpy.ndarray
        Array to store the updated membrane potential values.
    u : numpy.ndarray
        Array of current membrane potential values.
    cai : numpy.ndarray
        Array of calcium concentration in the cytosol.
    casr : numpy.ndarray
        Array of calcium concentration in the sarcoplasmic reticulum.
    cass : numpy.ndarray
        Array of calcium concentration in the submembrane space.
    nai : numpy.ndarray
        Array of sodium ion concentration in the intracellular space.
    ki : numpy.ndarray
        Array of potassium ion concentration in the intracellular space.
    m : numpy.ndarray
        Array of gating variable for sodium channels (activation).
    h : numpy.ndarray
        Array of gating variable for sodium channels (inactivation).
    j_ : numpy.ndarray
        Array of gating variable for sodium channels (inactivation).
    xr1 : numpy.ndarray
        Array of gating variable for rapid delayed rectifier potassium
        channels.
    xr2 : numpy.ndarray
        Array of gating variable for rapid delayed rectifier potassium
        channels.
    xs : numpy.ndarray
        Array of gating variable for slow delayed rectifier potassium channels.
    r : numpy.ndarray
        Array of gating variable for ryanodine receptors.
    s : numpy.ndarray
        Array of gating variable for calcium-sensitive current.
    d : numpy.ndarray
        Array of gating variable for L-type calcium channels.
    f : numpy.ndarray
        Array of gating variable for calcium-dependent calcium channels.
    f2 : numpy.ndarray
        Array of secondary gating variable for calcium-dependent calcium
        channels.
    fcass : numpy.ndarray
        Array of gating variable for calcium-sensitive current.
    rr : numpy.ndarray
        Array of ryanodine receptor gating variable for calcium release.
    oo : numpy.ndarray
        Array of ryanodine receptor gating variable for calcium release.
    indexes : numpy.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.

    Returns
    -------
    None
        The function updates the state variables in place. No return value is
        produced.
    """"""
    n_j = u.shape[1]
    n_k = u.shape[2]

    inverseVcF2 = 1./(2*Vc*F)
    inverseVcF = 1./(Vc*F)
    inversevssF2 = 1./(2*Vss*F)

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        Ek = RTONF*(np.log((ko/Ki[i, j, k])))
        Ena = RTONF*(np.log((nao/nai[i, j, k])))
        Eks = RTONF*(np.log((ko+pKNa*nao)/(Ki[i, j, k]+pKNa*nai[i, j, k])))
        Eca = 0.5*RTONF*(np.log((cao/cai[i, j, k])))

        # Compute currents
        ina, m[i, j, k], h[i, j, k], j_[i, j, k] = calc_ina(u[i, j, k], dt, m[i, j, k], h[i, j, k], j_[i, j, k], gna, Ena)
        ical, d[i, j, k], f[i, j, k], f2[i, j, k], fcass[i, j, k] = calc_ical(u[i, j, k], dt, d[i, j, k], f[i, j, k], f2[i, j, k], fcass[i, j, k], cao, cass[i, j, k], gcal, F, R, T)
        ito, r[i, j, k], s[i, j, k] = calc_ito(u[i, j, k], dt, r[i, j, k], s[i, j, k], Ek, gto)
        ikr, xr1[i, j, k], xr2[i, j, k] = calc_ikr(u[i, j, k], dt, xr1[i, j, k], xr2[i, j, k], Ek, gkr, ko)
        iks, xs[i, j, k] = calc_iks(u[i, j, k], dt, xs[i, j, k], Eks, gks)
        ik1 = calc_ik1(u[i, j, k], Ek, gk1)
        inaca = calc_inaca(u[i, j, k], nao, nai[i, j, k], cao, cai[i, j, k], KmNai, KmCa, knaca, ksat, n_, F, R, T) 
        inak = calc_inak(u[i, j, k], nai[i, j, k], ko, KmK, KmNa, knak, F, R, T)
        ipca = calc_ipca(cai[i, j, k], KpCa, gpca)
        ipk = calc_ipk(u[i, j, k], Ek, gpk)
        ibna = calc_ibna(u[i, j, k], Ena, gbna)
        ibca = calc_ibca(u[i, j, k], Eca, gbca)
        irel, rr[i, j, k], oo[i, j, k] = calc_irel(dt, rr[i, j, k], oo[i, j, k], casr[i, j, k], cass[i, j, k], Vrel, k1_, k2_, k3, k4, maxsr, minsr, EC)
        ileak = calc_ileak(casr[i, j, k], cai[i, j, k], Vleak)
        iup = calc_iup(cai[i, j, k], Vmaxup, Kup)
        ixfer = calc_ixfer(cass[i, j, k], cai[i, j, k], Vxfer)

        # Compute concentrations
        casr[i, j, k] = calc_casr(dt, casr[i, j, k], Bufsr, Kbufsr, iup, irel, ileak)
        cass[i, j, k] = calc_cass(dt, cass[i, j, k], Bufss, Kbufss, ixfer, irel, ical, CAPACITANCE, Vc, Vss, Vsr, inversevssF2)
        cai[i, j, k], cai[i, j, k] = calc_cai(dt, cai[i, j, k], Bufc, Kbufc, ibca, ipca, inaca, iup, ileak, ixfer, CAPACITANCE, Vsr, Vc, inverseVcF2)
        nai[i, j, k] += calc_nai(dt, ina, ibna, inak, inaca, CAPACITANCE, inverseVcF)
        Ki[i, j, k] += calc_ki(dt, ik1, ito, ikr, iks, inak, ipk, inverseVcF, CAPACITANCE)

        # Update membrane potential
        u_new[i, j, k] -= dt * (ikr + iks + ik1 + ito + ina + ibna + ical + ibca + inak + inaca + ipca + ipk)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/courtemanche_3d.py",".py","6228","164","import numpy as np
from numba import njit, prange

from finitewave.cpuwave2D.model.courtemanche_2d import (
    Courtemanche2D,
    calc_ina,
    calc_ik1,
    calc_ito,
    calc_ikr,
    calc_iks,
    calc_ikur,
    calc_ical,
    calc_inak,
    calc_inaca,
    calc_ibca,
    calc_ibna,
    calc_ipca,
    calc_irel,
    calc_iupleak,
    calc_iup,
    calc_itr,
    calc_caup,
    calc_nai,
    calc_ki,
    calc_cai,
    calc_carel,
    calc_gating_m,
    calc_gating_h,
    calc_gating_j,
    calc_equilibrum_potentials
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class Courtemanche3D(Courtemanche2D):
    """"""
    A class to represent the Courtemanche cardiac model in 3D.

    See Courtemanche2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel function to update ionic currents and state
        variables.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.nai, self.ki, self.cai, self.caup, self.carel, 
                        self.m, self.h, self.j_, self.d, self.f, self.oa, self.oi, self.ua, 
                        self.ui, self.xr, self.xs, self.fca, self.irel, self.vrel, self.urel, 
                        self.wrel, self.cardiac_tissue.myo_indexes, self.dt, 
                        self.gna, self.gnab, self.gk1, self.gkr, self.gks, self.gto, self.gcal,
                        self.gcab, self.gkur_coeff, self.F, self.T, self.R, self.Vc, self.Vj, self.Vup,
                        self.Vrel, self.ibk, self.cao, self.nao, self.ko, self.caupmax,
                        self.kup, self.kmnai, self.kmko, self.kmnancx, self.kmcancx,
                        self.ksatncx, self.kmcmdn, self.kmtrpn, self.kmcsqn, self.trpnmax,
                        self.cmdnmax, self.csqnmax, self.inacamax, self.inakmax,
                        self.ipcamax, self.krel, self.iupmax, self.kq10)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue2D
            A tissue object representing the cardiac tissue.

        Returns
        -------
        Stencil
            The stencil object to use for diffusion computations.
        """"""
        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, nai, ki, cai, caup, carel, m, h, j_, d, f, oa, oi, ua, ui, xs, xr, fca, irel, vrel, urel, wrel, indexes, dt, 
                    gna, gnab, gk1, gkr, gks, gto, gcal, gcab, gkur_coeff, F, T, R, Vc, Vj, Vup, Vrel, ibk, cao, nao, ko, caupmax, kup,
                    kmnai, kmko, kmnancx, kmcancx, ksatncx, kmcmdn, kmtrpn, kmcsqn, trpnmax, cmdnmax, csqnmax, inacamax,
                    inakmax, ipcamax, krel, iupmax, kq10):
    """"""
    Computes the ionic currents and updates the state variables in the 3D
    Courtemanche cardiac model.

    Parameters
    ----------
    u_new : np.ndarray
        Updated membrane potential values.
    u : np.ndarray
        Current membrane potential values.
    v : np.ndarray
        Recovery variable array.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    dt : float
        Time step for the simulation.
    """"""
                    
    n_j = u.shape[1]
    n_k = u.shape[2]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        ena, ek, eca = calc_equilibrum_potentials(nai[i, j, k], nao, ki[i, j, k], ko, cai[i, j, k], cao, R, T, F)

        m[i, j, k] = calc_gating_m(m[i, j, k], u[i, j, k], dt)
        h[i, j, k] = calc_gating_h(h[i, j, k], u[i, j, k], dt)
        j_[i, j, k] = calc_gating_j(j_[i, j, k], u[i, j, k], dt)

        ina = calc_ina(u[i, j, k], m[i, j, k], h[i, j, k], j_[i, j, k], gna, ena)

        ik1 = calc_ik1(u[i, j, k], gk1, ek)

        ito, oa[i, j, k], oi[i, j, k] = calc_ito(u[i, j, k], dt, kq10, oa[i, j, k], oi[i, j, k], gto, ek)

        ikur, ua[i, j, k], ui[i, j, k] = calc_ikur(u[i, j, k], dt, kq10, ua[i, j, k], ui[i, j, k], ek, gkur_coeff)

        ikr, xr[i, j, k] = calc_ikr(u[i, j, k], dt, xr[i, j, k], gkr, ek)

        iks, xs[i, j, k] = calc_iks(u[i, j, k], dt, xs[i, j, k], gks, ek)

        ical, d[i, j, k], f[i, j, k], fca[i, j, k] = calc_ical(u[i, j, k], dt, d[i, j, k], f[i, j, k], cai[i, j, k], gcal, fca[i, j, k], eca)

        inak = calc_inak(inakmax, nai[i, j, k], nao, ko, kmnai, kmko, F, u[i, j, k], R, T)
        inaca = calc_inaca(inacamax, nai[i, j, k], nao, cai[i, j, k], cao, kmnancx, kmcancx, ksatncx, F, u[i, j, k], R, T)

        ibca = calc_ibca(gcab, eca, u[i, j, k])

        ibna = calc_ibna(gnab, ena, u[i, j, k])

        ipca = calc_ipca(ipcamax, cai[i, j, k])

        irel[i, j, k], urel[i, j, k], vrel[i, j, k], wrel[i, j, k] = calc_irel(dt, urel[i, j, k], vrel[i, j, k], irel[i, j, k], wrel[i, j, k], ical, inaca, krel, carel[i, j, k], cai[i, j, k], u[i, j, k], F, Vrel)
        itr = calc_itr(caup[i, j, k], carel[i, j, k])
        iup = calc_iup(iupmax, cai[i, j, k], kup)
        iupleak = calc_iupleak(caup[i, j, k], caupmax, iupmax) 

        caup[i, j, k] = calc_caup(caup[i, j, k], dt, iup, iupleak, itr, Vrel, Vup)
        nai[i, j, k] = calc_nai(nai[i, j, k], dt, inak, inaca, ibna, ina, F, Vj)

        ki[i, j, k] = calc_ki(ki[i, j, k], dt, inak, ik1, ito, ikur, ikr, iks, ibk, F, Vj)
        cai[i, j, k] = calc_cai(cai[i, j, k], dt, inaca, ipca, ical, ibca, iup, iupleak, irel[i, j, k], Vrel, Vup, trpnmax, kmtrpn, cmdnmax, kmcmdn, F, Vj)

        carel[i, j, k] = calc_carel(carel[i, j, k], dt, itr, irel[i, j, k], csqnmax, kmcsqn)

        u_new[i, j, k] -= dt * (ina + ik1 + ito + ikur + ikr + iks + ical + ipca + inak + inaca + ibna + ibca)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/barkley_3d.py",".py","2294","85","from numba import njit, prange

from finitewave.cpuwave2D.model.barkley_2d import Barkley2D, calc_v
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class Barkley3D(Barkley2D):
    """"""
    Implementation of the Barkley 3D cardiac model.

    See Barkley2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Barkley model.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.v,
                        self.cardiac_tissue.myo_indexes, self.dt,
                        self.a, self.b, self.eap)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        Stencil
            The stencil object to be used for diffusion computations.
        """"""

        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, v, indexes, dt, a, b, eap):
    """"""
    Computes the ionic kernel for the Barkley 3D model.

    Parameters
    ----------
    u_new : np.ndarray
        Array to store the updated action potential values.
    u : np.ndarray
        Current action potential array.
    v : np.ndarray
        Recovery variable array.
    dt : float
        Time step for the simulation.
    indexes : np.ndarray
        Array of indices where the kernel should be computed (``mesh == 1``).
    """"""

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ni in prange(len(indexes)):
        ii = indexes[ni]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        v[i, j, k] = calc_v(v[i, j, k], u[i, j, k], dt)

        u_new[i, j, k] += dt * (u[i, j, k]*(1 - u[i, j, k])*(u[i, j, k] - (v[i, j, k] + b)/a))/eap

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/model/fenton_karma_3d.py",".py","2840","99","from numba import njit, prange

from finitewave.cpuwave2D.model.fenton_karma_2d import (
    FentonKarma2D,
    calc_Jfi,
    calc_Jsi,
    calc_Jso,
    calc_v,
    calc_w,
)
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import (
    IsotropicStencil3D
)
from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import (
    AsymmetricStencil3D
)


class FentonKarma3D(FentonKarma2D):
    """"""
    Implementation of the Fenton-Karma 3D cardiac model.

    See FentonKarma2D for the 2D model description.
    """"""

    def __init__(self):
        super().__init__()

    def run_ionic_kernel(self):
        """"""
        Executes the ionic kernel for the Fenton-Karma model.
        """"""
        ionic_kernel_3d(self.u_new, self.u, self.v, self.w, self.cardiac_tissue.myo_indexes, 
                        self.dt, self.tau_d, self.tau_o, self.tau_r, self.tau_si, 
                        self.tau_v_m, self.tau_v_p, self.tau_w_m, self.tau_w_p,
                        self.k, self.u_c, self.uc_si)

    def select_stencil(self, cardiac_tissue):
        """"""
        Selects the appropriate stencil for diffusion based on the tissue
        properties. If the tissue has fiber directions, an asymmetric stencil
        is used; otherwise, an isotropic stencil is used.

        Parameters
        ----------
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        Stencil
            The stencil object to be used for diffusion computations.
        """"""

        if cardiac_tissue.fibers is None:
            return IsotropicStencil3D()

        return AsymmetricStencil3D()


@njit(parallel=True)
def ionic_kernel_3d(u_new, u, v, w, indexes, dt, 
                    tau_d, tau_o, tau_r, tau_si, 
                    tau_v_m, tau_v_p, tau_w_m, tau_w_p,
                    k, u_c, uc_si):
    """"""
    Computes the ionic kernel for the Fenton-Karma 3D model.

    Parameters
    ----------
    u_new : ndarray
        The new state of the u variable.
    u : ndarray
        The current state of the u variable.
    myo_indexes : list
        List of indexes representing myocardial cells.
    dt : float
        The time step for the simulation.
    """"""

    n_j = u.shape[1]
    n_k = u.shape[2]

    for ni in prange(len(indexes)):
        ii = indexes[ni]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k_ = (ii % (n_j*n_k)) % n_k
        
        v[i, j, k_] = calc_v(v[i, j, k_], u[i, j, k_], dt, u_c, tau_v_m, tau_v_p)
        w[i, j, k_] = calc_w(w[i, j, k_], u[i, j, k_], dt, u_c, tau_w_m, tau_w_p)

        J_fi = calc_Jfi(u[i, j, k_], v[i, j, k_], u_c, tau_d)
        J_so = calc_Jso(u[i, j, k_], u_c, tau_o, tau_r)
        J_si = calc_Jsi(u[i, j, k_], v[i, j, k_], k, uc_si, tau_si)

        u_new[i, j, k_] += dt * (-J_fi - J_so - J_si)

","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_voltage_list_matrix_3d.py",".py","2267","73","from finitewave.core.stimulation.stim_voltage import StimVoltage


class StimVoltageListMatrix3D(StimVoltage):
    """"""
    A class that applies a voltage stimulus to a 3D cardiac tissue model
    according to a specified matrix.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    volt_values : array-like
        The voltage values to apply.
    matrix : numpy.ndarray
        A 3D array where the voltage stimulus is applied to locations with
        values greater than 0.
    step : int
        The step of the voltage values array.
    """"""
    def __init__(self, time, volt_values,  duration, matrix):
        """"""
        Initializes the StimVoltageMatrix3D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        volt_values : float
            The voltage value to apply.
        duration : float
            The duration of the stimulation.
        matrix : numpy.ndarray
            A 3D array where the voltage stimulus is applied to locations with
            values greater than 0.
        """"""
        super().__init__(time, volt_values, duration)
        self.matrix = matrix
        self.step = 0

    def initialize(self, model):
        """"""
        Prepares the stimulation for application.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the voltage stimulus will be
            applied.
        """"""
        super().initialize(model)

        total_steps = int(self.duration / model.dt) + 1

        if len(self.volt_value) < total_steps:
            message = (""The length of the voltage values array should be "" +
                       ""greater than the total number of steps."")
            raise ValueError(message)

    def stimulate(self, model):
        """"""
        Applies the voltage stimulus to the cardiac tissue model based on the
        specified matrix.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the voltage stimulus is applied.
        """"""
        mask = (self.matrix > 0) & (model.cardiac_tissue.mesh == 1)
        model.u[mask] = self.volt_value[self.step]
        self.step += 1
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_current_matrix_3d.py",".py","1499","46","import numpy as np
from finitewave.cpuwave2D.stimulation.stim_current_matrix_2d import (
    StimCurrentMatrix2D
)


class StimCurrentMatrix3D(StimCurrentMatrix2D):
    """"""
    A class that applies a stimulation current to a 3D cardiac tissue model
    based on a binary matrix.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    matrix : numpy.ndarray
        A 3D binary matrix indicating the region of interest for stimulation. 
        Elements greater than 0 represent regions to be stimulated.
    u_max : float, optional
        The maximum value of the membrane potential. Default is None.
    """"""

    def __init__(self, time, curr_value, duration, matrix, u_max=None):
        """"""
        Initializes the StimCurrentMatrix3D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        matrix : numpy.ndarray
            A 3D binary matrix indicating the region of interest for
            stimulation.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration, matrix, u_max)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/__init__.py",".py","337","7","from .stim_current_coord_3d import StimCurrentCoord3D
from .stim_voltage_coord_3d import StimVoltageCoord3D
from .stim_current_matrix_3d import StimCurrentMatrix3D
from .stim_voltage_matrix_3d import StimVoltageMatrix3D
from .stim_voltage_list_matrix_3d import StimVoltageListMatrix3D
from .stim_current_area_3d import StimCurrentArea3D
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_voltage_coord_3d.py",".py","2157","69","from finitewave.core.stimulation.stim_voltage import StimVoltage


class StimVoltageCoord3D(StimVoltage):
    """"""
    A class that applies a voltage stimulus to a 3D cardiac tissue model
    within a specified region of interest.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    volt_value : float
        The voltage value to apply to the region of interest.
    x1 : int
        The starting x-coordinate of the region of interest.
    x2 : int
        The ending x-coordinate of the region of interest.
    y1 : int
        The starting y-coordinate of the region of interest.
    y2 : int
        The ending y-coordinate of the region of interest.
    z1 : int
        The starting z-coordinate of the region of interest.
    z2 : int
        The ending z-coordinate of the region of interest.
    """"""

    def __init__(self, time, volt_value, x1, x2, y1, y2, z1, z2):
        """"""
        Initializes the StimVoltageCoord2D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        volt_value : float
            The voltage value to apply.
        x1, x2, y1, y2, z1, z2 : int
            The coordinates of the region of interest to which the voltage
            stimulus is applied.
        """"""
        super().__init__(time, volt_value)
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.z1 = z1
        self.z2 = z2

    def stimulate(self, model):
        """"""
        Applies the voltage stimulus to the cardiac tissue model within the
        specified region of interest.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the voltage stimulus is applied.
        """"""
        roi_mesh = model.cardiac_tissue.mesh[self.x1: self.x2,
                                             self.y1: self.y2,
                                             self.z1: self.z2]
        mask = (roi_mesh == 1)

        model.u[self.x1: self.x2,
                self.y1: self.y2,
                self.z1: self.z2][mask] = self.volt_value
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_voltage_matrix_3d.py",".py","1459","48","from finitewave.core.stimulation.stim_voltage import StimVoltage


class StimVoltageMatrix3D(StimVoltage):
    """"""
    A class that applies a voltage stimulus to a 3D cardiac tissue model
    according to a specified matrix.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    volt_value : float
        The voltage value to apply.
    matrix : numpy.ndarray
        A 3D array where the voltage stimulus is applied to locations with
        values greater than 0.
    """"""
    def __init__(self, time, volt_value, matrix):
        """"""
        Initializes the StimVoltageMatrix3D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        volt_value : float
            The voltage value to apply.
        matrix : numpy.ndarray
            A 3D array where the voltage stimulus is applied to locations with
            values greater than 0.
        """"""
        super().__init__(time, volt_value)
        self.matrix = matrix

    def stimulate(self, model):
        """"""
        Applies the voltage stimulus to the cardiac tissue model based on the
        specified matrix.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the voltage stimulus is applied.
        """"""
        mask = (self.matrix > 0) & (model.cardiac_tissue.mesh == 1)
        model.u[mask] = self.volt_value
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_current_coord_3d.py",".py","3092","91","import numpy as np
from finitewave.core.stimulation.stim_current import StimCurrent


class StimCurrentCoord3D(StimCurrent):
    """"""
    A class that applies a stimulation current to a rectangular region of a 3D
    cardiac tissue model.

    Parameters
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    x1 : int
        The x-coordinate of the lower-left corner of the rectangular region.
    x2 : int
        The x-coordinate of the upper-right corner of the rectangular region.
    y1 : int
        The y-coordinate of the lower-left corner of the rectangular region.
    y2 : int
        The y-coordinate of the upper-right corner of the rectangular region.
    z1 : int
        The z-coordinate of the lower-left corner of the rectangular region.
    z2 : int
        The z-coordinate of the upper-right corner of the rectangular region.
    u_max : float, optional
        The maximum value of the membrane potential. Default is None.
    """"""

    def __init__(self, time, curr_value, duration, x1, x2, y1, y2, z1, z2,
                 u_max=None):
        """"""
        Initializes the StimCurrentCoord3D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        x1, x2, y1, y2, z1, z2 : int
            The coordinates of the rectangular region to which the stimulation
            current is applied.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration)
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.z1 = z1
        self.z2 = z2
        self.u_max = u_max

    def stimulate(self, model):
        """"""
        Applies the stimulation current to the specified rectangular region of
        the cardiac tissue model.

        Parameters
        ----------
        model : object
            The cardiac tissue model to which the stimulation current is 
            applied.
        """"""
        roi_mesh = model.cardiac_tissue.mesh[self.x1: self.x2,
                                             self.y1: self.y2,
                                             self.z1: self.z2]
        mask = (roi_mesh == 1)

        model.u[self.x1: self.x2,
                self.y1: self.y2,
                self.z1: self.z2][mask] += model.dt * self.curr_value

        if self.u_max is not None:
            u = model.u[self.x1: self.x2,
                        self.y1: self.y2,
                        self.z1: self.z2][mask]

            model.u[self.x1: self.x2,
                    self.y1: self.y2,
                    self.z1: self.z2][mask] = np.where(u > self.u_max,
                                                       self.u_max, u)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stimulation/stim_current_area_3d.py",".py","1245","40","from finitewave.cpuwave2D.stimulation.stim_current_area_2d import (
    StimCurrentArea2D
)


class StimCurrentArea3D(StimCurrentArea2D):
    """"""
    A class that applies a stimulation current to a 3D cardiac tissue model
    based on a area coords.

    Attributes
    ----------
    time : float
        The time at which the stimulation starts.
    curr_value : float
        The value of the stimulation current.
    duration : float
        The duration of the stimulation.
    coords : numpy.ndarray
        The coordinates of the area to be stimulated.
    """"""
    def __init__(self, time, curr_value, duration, coords=None, u_max=None):
        """"""
        Initializes the StimCurrentArea3D instance.

        Parameters
        ----------
        time : float
            The time at which the stimulation starts.
        curr_value : float
            The value of the stimulation current.
        duration : float
            The duration of the stimulation.
        coords : numpy.ndarray
            The coordinates of the area to be stimulated.
        u_max : float, optional
            The maximum value of the membrane potential. Default is None.
        """"""
        super().__init__(time, curr_value, duration, coords, u_max)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/period_animation_3d_tracker.py",".py","1733","49","from pathlib import Path
import shutil as shatilib
from finitewave.cpuwave2D.tracker.period_animation_2d_tracker import (
    PeriodAnimation2DTracker
)
from finitewave.tools.animation_3d_builder import Animation3DBuilder


class PeriodAnimation3DTracker(PeriodAnimation2DTracker):
    """"""
    Class for tracking 3D period map. Initializes PeriodAnimation2DTracker.
    """"""
    def __init__(self):
        super().__init__()

    def write(self, clim=[0, 1], cmap=""viridis"", scalar_bar=False, clear=True,
              prog_bar=True, **kwargs):
        """"""
        Write the animation to a file.

        Parameters
        ----------
        clim : list, optional
            Color limits. Defaults to [0, 1].
        cmap : str, optional
            Color map. Defaults to ""viridis"".
        scalar_bar : bool, optional
            Show scalar bar. Defaults to False.
        clear : bool, optional
            Clear the snapshot folder after writing the animation.
            Defaults to True.
        prog_bar : bool, optional
            Show progress bar. Defaults to True.
        **kwargs : optional
            Additional arguments for the animation writer.
        """"""

        animation_builder = Animation3DBuilder()
        animation_builder.write(Path(self.path, self.dir_name),
                                path_save=self.path,
                                mask=self.model.cardiac_tissue.mesh,
                                scalar_name='Period',
                                clim=clim, cmap=cmap,
                                scalar_bar=scalar_bar, format='mp4',
                                prog_bar=prog_bar, **kwargs)

        if clear:
            shatilib.rmtree(Path(self.path, self.dir_name))
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/animation_3d_tracker.py",".py","2042","58","from pathlib import Path
import shutil as shatilib

from finitewave.cpuwave2D.tracker.animation_2d_tracker import (
    Animation2DTracker
)
from finitewave.tools.animation_3d_builder import Animation3DBuilder


class Animation3DTracker(Animation2DTracker):
    """"""A class to track and save frames of a 3D cardiac tissue model simulation
    for animation purposes.
    """"""
    def __init__(self):
        """"""
        Initializes the Animation3DTracker with default parameters.
        """"""
        super().__init__()

    def write(self, path=None, clim=[0, 1], cmap=""viridis"", scalar_bar=False,
              format=""mp4"", clear=False, prog_bar=True, **kwargs):
        """"""
        Write the animation to a file.

        Parameters
        ----------
        path : str, optional
            Path to save the animation. Defaults to path of the tracker.
        clim : list, optional
            Color limits. Defaults to [0, 1].
        cmap : str, optional
            Color map. Defaults to ""viridis"".
        scalar_bar : bool, optional
            Show scalar bar. Defaults to False.
        format : str, optional
            Format of the animation. Defaults to ""mp4"". Other option is ""gif"".
        clear : bool, optional
            Clear the snapshot folder after writing the animation.
            Defaults to False.
        **kwargs : optional
            Additional arguments for the animation writer.
        """"""

        if path is None:
            path = self.path

        animation_builder = Animation3DBuilder()
        animation_builder.write(Path(self.path, self.dir_name),
                                path_save=path,
                                mask=self.model.cardiac_tissue.mesh,
                                scalar_name=self.variable_name,
                                clim=clim, cmap=cmap,
                                scalar_bar=scalar_bar, format=format,
                                prog_bar=prog_bar, **kwargs)

        if clear:
            shatilib.rmtree(Path(self.path, self.dir_name))
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/spiral_wave_core_3d_tracker.py",".py","1176","36","import pandas as pd
from finitewave.cpuwave2D.tracker.spiral_wave_core_2d_tracker import (
    SpiralWaveCore2DTracker
)


class SpiralWaveCore3DTracker(SpiralWaveCore2DTracker):
    """"""
    A class to track spiral wave cores in 3D cardiac tissue simulations.

    The tip tracker detects spiral wave cores by analyzing the voltage data
    on each slice (z-axis) of the 3D tissue mesh.

    """"""
    def __init__(self):
        super().__init__()

    def _track(self):
        """"""
        Track spiral tips at each simulation step by analyzing voltage data.

        The tracker is updated at each simulation step, detecting any spiral
        tips based on the voltage data from the previous and current steps.
        """"""
        for k in range(self.model.u.shape[2]):
            u_prev = self.u_prev[:, :, k]
            u = self.model.u[:, :, k]
            tips = self.track_tip_line(u_prev, u, self.threshold)
            tips = pd.DataFrame(tips, columns=[""x"", ""y""])
            tips[""z""] = k
            tips[""time""] = self.model.t
            tips[""step""] = self.model.step
            self.sprial_wave_cores.append(tips)

        self.u_prev = self.model.u.copy()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/activation_time_3d_tracker.py",".py","369","16","
from finitewave.cpuwave2D.tracker.activation_time_2d_tracker import (
    ActivationTime2DTracker
)


class ActivationTime3DTracker(ActivationTime2DTracker):
    """"""
    Class that tracks activation times in 3D.
    """"""
    def __init__(self):
        """"""
        Initializes the ActivationTime3DTracker with default parameters.
        """"""
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/vtk_frame_3d_tracker.py",".py","2340","77","from pathlib import Path

from finitewave.tools.vis_mesh_builder_3d import VisMeshBuilder3D
from finitewave.core.tracker.tracker import Tracker


class VTKFrame3DTracker(Tracker):
    """"""
    A class for tracking and saving VTK frames in a 3D model.

    Attributes
    ----------
        file_name (str): The name of the saved frames.
        dir_name (str): The name of the folder where the frames will be saved.
        variable_name (str): The name of the target array to be tracked.
        file_type (str): The file type of the saved frames ("".vtk"" or "".vtu"")
    """"""
    def __init__(self):
        super().__init__()
        self.file_name = ""frame""
        self.dir_name = ""vtk_frames""
        self.variable_name = """"
        self.file_type = "".vtk""

        self._frame_counter = 0

    def initialize(self, model):
        """"""
        Initializes the tracker with the model.

        Parameters
        -----------
            model (CardiacModel): The model to track.
        """"""
        self.model = model
        self._frame_counter = 0

        self.path = Path(self.path)

        if not self.path.joinpath(self.dir_name).exists():
            self.path.joinpath(self.dir_name).mkdir(parents=True)

        if self.variable_name == """":
            raise ValueError(""Please specify the target array to be tracked."")

        if self.variable_name not in self.model.__dict__:
            raise ValueError(f""Array {self.variable_name} not found in model."")

    def _track(self):
        frame_name = self.path.joinpath(
            self.dir_name, f""{self.file_name}{self._frame_counter}""
            ).with_suffix(self.file_type)

        self.write_frame(frame_name)
        self._frame_counter += 1

    def write_frame(self, frame_name):
        """"""
        Writes a VTK frame to a file.

        Parameters
        -----------
            frame_name (str): The name of the frame file.
        """"""
        state_var = self.model.__dict__[self.variable_name]

        vtk_mesh_builder = VisMeshBuilder3D()
        vtk_mesh = vtk_mesh_builder.build_mesh(self.model.cardiac_tissue.mesh)
        vtk_mesh = vtk_mesh_builder.add_scalar(state_var, self.variable_name)
        vtk_mesh.save(frame_name)

    def write(self):
        """"""
        For compatibility with the Tracker class.
        """"""
        return super().write()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/action_potential_3d_tracker.py",".py","375","16","
from finitewave.cpuwave2D.tracker.action_potential_2d_tracker import (
    ActionPotential2DTracker
)


class ActionPotential3DTracker(ActionPotential2DTracker):
    """"""
    Class that tracks action potentials in 3D.
    """"""
    def __init__(self):
        """"""
        Initializes the ActionPotential3DTracker with default parameters.
        """"""
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/__init__.py",".py","1622","39","""""""
3D Tracker
==========

This module contains classes for tracking the evolution of the wavefront in 3D.
Most of the classes in this module are similar to the ones in the 2D tracker
module. More information can be found in the documentation for the 2D tracker
module.

The tracker classes can be grouped into the following categories:

* Full field trackers that track the entire field and output the results in
  a single array.
* Point trackers that track the evolution of a specific point(s) in the field.
* Animation trackers that track the evolution of the field over time and save
  the results as frames for creating animations.

Each tracker class has basic attributes such as ``start_time``, ``end_time``,
``step``, ``path``, and ``file_name``.

.. note::

    Note that the ``start_time`` and ``end_time`` is given in time units,
    and the ``step`` is the number of time steps between recordings.
""""""

from .action_potential_3d_tracker import ActionPotential3DTracker
from .activation_time_3d_tracker import ActivationTime3DTracker
from .local_activation_time_3d_tracker import LocalActivationTime3DTracker
from .animation_slice_3d_tracker import AnimationSlice3DTracker
from .ecg_3d_tracker import ECG3DTracker
from .period_3d_tracker import Period3DTracker
from .period_animation_3d_tracker import PeriodAnimation3DTracker
from .spiral_wave_core_3d_tracker import SpiralWaveCore3DTracker
from .variable_3d_tracker import Variable3DTracker
from .multi_variable_3d_tracker import MultiVariable3DTracker
from .vtk_frame_3d_tracker import VTKFrame3DTracker
from .animation_3d_tracker import Animation3DTracker
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/variable_3d_tracker.py",".py","227","10","from finitewave.cpuwave2D.tracker.variable_2d_tracker import Variable2DTracker


class Variable3DTracker(Variable2DTracker):
    """"""
    Class for tracking 3D variable
    """"""
    def __init__(self):
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/animation_slice_3d_tracker.py",".py","3909","121","from pathlib import Path
import numpy as np

from finitewave.cpuwave2D.tracker.animation_2d_tracker import (
    Animation2DTracker
)
from finitewave.tools.animation_2d_builder import Animation2DBuilder


class AnimationSlice3DTracker(Animation2DTracker):
    """"""
    A class to track and save 2D frames of a 3D cardiac tissue model simulation
    for animation purposes.

    This tracker periodically saves the state of a specified target array from
    the model to disk as NumPy files, which can later be used to create
    animations.

    Attributes
    ----------
    slice_x : int
        The x-coordinate of the slice to capture.
    slice_y : int
        The y-coordinate of the slice to capture.
    slice_z : int
        The z-coordinate of the slice to capture.
    """"""

    def __init__(self):
        super().__init__()
        self.slice_x = None
        self.slice_y = None
        self.slice_z = None

    def initialize(self, model):
        self.model = model
        self._frame_counter = 0

        if np.count_nonzero([self.slice_x, self.slice_y, self.slice_z]) != 1:
            message = ""Exactly one slice must be specified.""
            raise ValueError(message)

        super().initialize(model)

    def _track(self):
        """"""
        Saves frames based on the specified step interval and target array.

        The frames are saved in the specified directory as NumPy files.
        """"""
        values = self.model.__dict__[self.variable_name]

        frame = self.select_frame(values)

        np.save(Path(self.path, self.dir_name, str(self._frame_counter)
                     ).with_suffix("".npy""), frame.astype(self.frame_type))

        self._frame_counter += 1

    def select_frame(self, array):
        """"""
        Selects the slice from the 3D array based on the specified slice
        coordinates.

        Parameters
        ----------
        array : ndarray
            The 3D array to slice.

        Returns
        -------
        ndarray
            The 2D slice of the 3D array.
        """"""
        if self.slice_x is not None:
            return array[self.slice_x, :, :]

        if self.slice_y is not None:
            return array[:, self.slice_y, :]

        if self.slice_z is not None:
            return array[:, :, self.slice_z]

    def write(self, shape_scale=1, fps=12, cmap=""coolwarm"", clim=[0, 1],
              clear=False, prog_bar=True):
        """"""
        Creates an animation from the saved frames using the Animation2DBuilder
        class. Fibrosis and boundaries will be shown in black.

        Parameters
        ----------
        shape_scale : int, optional
            Scale factor for the frame size. The default is 5.
        fps : int, optional
            Frames per second for the animation. The default is 12.
        cmap : str, optional
            Color map for the animation. The default is 'coolwarm'.
        clim : list, optional
            Color limits for the animation. The default is [0, 1].
        clear : bool, optional
            Clear the snapshot folder after creating the animation.
            The default is False.
        prog_bar : bool, optional
            Show a progress bar during the animation creation.
            The default is True.
        """"""
        animation_builder = Animation2DBuilder()
        path = Path(self.path, self.dir_name)
        mask = self.select_frame(self.model.cardiac_tissue.mesh) != 1

        animation_builder.write(path,
                                animation_name=self.file_name,
                                mask=mask,
                                shape_scale=shape_scale,
                                fps=fps,
                                clim=clim,
                                shape=mask.shape,
                                cmap=cmap,
                                clear=clear,
                                prog_bar=prog_bar)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/local_activation_time_3d_tracker.py",".py","404","16","
from finitewave.cpuwave2D.tracker.local_activation_time_2d_tracker import (
    LocalActivationTime2DTracker
)


class LocalActivationTime3DTracker(LocalActivationTime2DTracker):
    """"""
    Class that tracks multiple activation times in 3D.
    """"""
    def __init__(self):
        """"""
        Initializes the LocalActivationTime3DTracker with default parameters.
        """"""
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/period_3d_tracker.py",".py","247","10","from finitewave.cpuwave2D.tracker.period_2d_tracker import Period2DTracker


class Period3DTracker(Period2DTracker):
    """"""
    Class for tracking 3D period. Initializes Period2DTracker.
    """"""
    def __init__(self):
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/multi_variable_3d_tracker.py",".py","257","12","from finitewave.cpuwave2D.tracker.multi_variable_2d_tracker import (
    MultiVariable2DTracker
)


class MultiVariable3DTracker(MultiVariable2DTracker):
    """"""
    Class for tracking 3D variables
    """"""
    def __init__(self):
        super().__init__()
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tracker/ecg_3d_tracker.py",".py","3914","141","from pathlib import Path
import numpy as np
from numba import njit, prange
from scipy import spatial

from finitewave.core.tracker.tracker import Tracker


class ECG3DTracker(Tracker):
    """"""
    A class to compute and track electrocardiogram (ECG) signals from a 3D
    cardiac tissue model simulation.

    This tracker calculates ECG signals at specified measurement points by
    computing the potential differences across the cardiac tissue mesh and
    considering the inverse of the distance from each measurement point.

    Attributes
    ----------
    measure_coords : np.ndarray
        An array of points (x, y, z) where ECG signals are measured.
    ecg : list
        The computed ECG signals.
    file_name : str
        The name of the file to save the computed ECG signals.
    u_tr : np.ndarray
        The updated potential values after diffusion.
    """"""

    def __init__(self, measure_coords=None):
        super().__init__()
        self.measure_coords = measure_coords
        self.ecg = []
        self.file_name = ""ecg.npy""
        self.u_tr = None

    def initialize(self, model):
        """"""
        Initialize the ECG tracker with the model object.

        Parameters
        ----------
        model : CardiacModel3D
            The model object containing the simulation parameters.
        """"""
        self.model = model
        self.measure_coords = np.atleast_2d(self.measure_coords)
        self.ecg = []
        self.u_tr = np.zeros_like(model.u)

    def calc_ecg(self):
        """"""
        Calculate the ECG signal at the measurement points.

        Returns
        -------
        np.ndarray
            The computed ECG signal.
        """"""
        self.model.diffusion_kernel(self.u_tr,
                                    self.model.u,
                                    self.model.weights,
                                    self.model.cardiac_tissue.myo_indexes)
        ecg = compute_ecg(self.u_tr,
                          self.model.u,
                          self.measure_coords,
                          self.model.dr,
                          self.model.cardiac_tissue.myo_indexes)
        return ecg

    def _track(self):
        ecg = self.calc_ecg()
        self.ecg.append(ecg)

    @property
    def output(self):
        """"""
        Get the computed ECG signals as a numpy array.

        Returns
        -------
        np.ndarray
            The computed ECG signals.
        """"""
        return np.array(self.ecg)

    def write(self):
        """"""
        Save the computed ECG signals to a file.

        The ECG signals are saved as a numpy array in the specified path.
        """"""
        if not Path(self.path).exists():
            Path(self.path).mkdir(parents=True)

        np.save(Path(self.path, self.file_name), self.output)


@njit(parallel=True)
def compute_ecg(u_tr, u, coords, dr, indexes):
    """"""
    Performs isotropic diffusion on a 3D grid.

    Parameters
    ----------
    u_tr : numpy.ndarray
        A 3D array to store the updated potential values after diffusion.
    u : numpy.ndarray
        A 3D array representing the current potential values before diffusion.
    coord : tuple
        The coordinates of the measurement point.
    dr : float
        The spatial resolution of the grid.
    indexes : numpy.ndarray
        A 1D array of indices of the healthy tissue points.
    """"""
    n_j = u.shape[1]
    n_k = u.shape[2]

    n_c = len(coords)
    ecg = np.zeros(n_c)

    for c in range(n_c):
        x, y, z = coords[c]
        ecg_ = 0

        for ind in prange(len(indexes)):
            ii = indexes[ind]
            i = ii // (n_j * n_k)
            j = (ii % (n_j * n_k)) // n_k
            k = (ii % (n_j * n_k)) % n_k

            d = (x - i)**2 + (y - j)**2 + (z - k)**2

            if d > 0:
                ecg_ += (u_tr[i, j, k] - u[i, j, k]) / (d * dr)

        ecg[c] = ecg_

    return ecg
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stencil/isotropic_stencil_3d.py",".py","4923","150","import numpy as np
from numba import njit, prange

from finitewave.cpuwave2D.stencil.isotropic_stencil_2d import (
    compute_component,
    IsotropicStencil2D
)


class IsotropicStencil3D(IsotropicStencil2D):
    """"""
    This class computes the weights for diffusion on a 3D using an isotropic
    stencil. The stencil includes 7 points: the central point and the six
    neighbors.

    The method assumes weights being used in the following order:
        ``w[i, j, k, 0] : (i-1, j, k)``,
        ``w[i, j, k, 1] : (i, j-1, k)``,
        ``w[i, j, k, 2] : (i, j, k-1)``,
        ``w[i, j, k, 3] : (i, j, k)``,
        ``w[i, j, k, 4] : (i, j, k+1)``,
        ``w[i, j, k, 5] : (i, j+1, k)``,
        ``w[i, j, k, 6] : (i+1, j, k)``.

    Notes
    -----
    The method can handle heterogeneity in the diffusion coefficients given
    by the ``conductivity`` parameter.
    """"""

    def __init__(self):
        super().__init__()

    def select_diffusion_kernel(self):
        """"""
        Returns the diffusion kernel function for isotropic diffusion in 3D.

        Returns
        -------
        function
            The diffusion kernel function for isotropic diffusion in 3D.
        """"""
        return diffusion_kernel_3d_iso

    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the weights for isotropic diffusion in 3D.

        Parameters
        ----------
        model : CardiacModel3D
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        numpy.ndarray
            The weights for isotropic diffusion in 3D.
        """"""
        mesh = cardiac_tissue.mesh.copy()
        mesh[mesh != 1] = 0

        conductivity = cardiac_tissue.conductivity
        conductivity = conductivity * np.ones_like(mesh, dtype=model.npfloat)

        d_xx, d_yy, d_zz = self.compute_half_step_diffusion(mesh, conductivity,
                                                            num_axes=3)

        weights = np.zeros((*mesh.shape, 7), dtype=model.npfloat)
        weights = compute_weights(weights, mesh, d_xx, d_yy, d_zz)
        weights = weights * model.D_model * model.dt / model.dr**2
        weights[:, :, :, 3] += 1
        return weights


@njit(parallel=True)
def diffusion_kernel_3d_iso(u_new, u, w, indexes):
    """"""
    Performs isotropic diffusion on a 3D grid.

    Parameters
    ----------
    u_new : numpy.ndarray
        A 3D array to store the updated potential values after diffusion.
    u : numpy.ndarray
        A 3D array representing the current potential values before diffusion.
    w : numpy.ndarray
        A 4D array of weights used in the diffusion computation.
        The shape should match (*mesh.shape, 7).
    indexes : numpy.ndarray
        A 1D array of indices where the diffusion should be computed.
    """"""
    n_j = u.shape[1]
    n_k = u.shape[2]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        u_new[i, j, k] = (u[i-1, j, k] * w[i, j, k, 0] +
                          u[i, j-1, k] * w[i, j, k, 1] +
                          u[i, j, k-1] * w[i, j, k, 2] +
                          u[i, j, k] * w[i, j, k, 3] +
                          u[i, j, k+1] * w[i, j, k, 4] +
                          u[i, j+1, k] * w[i, j, k, 5] +
                          u[i+1, j, k] * w[i, j, k, 6])


@njit(parallel=True)
def compute_weights(w, m, d_xx, d_yy, d_zz):
    n_i = m.shape[0]
    n_j = m.shape[1]
    n_k = m.shape[2]

    for ii in prange(n_i * n_j * n_k):

        i = ii // (n_j * n_k)
        j = (ii % (n_j * n_k)) // n_k
        k = (ii % (n_j * n_k)) % n_k

        if m[i, j, k] != 1:
            continue

        # (i-1, j, k)
        w[i, j, k, 0] = compute_component(d_xx[i-1, j, k],
                                          m[i-1, j, k], m[i+1, j, k])
        # (i, j-1, k)
        w[i, j, k, 1] = compute_component(d_yy[i, j-1, k],
                                          m[i, j-1, k], m[i, j+1, k])
        # (i, j, k-1)
        w[i, j, k, 2] = compute_component(d_zz[i, j, k-1],
                                          m[i, j, k-1], m[i, j, k+1])
        # (i, j, k+1)
        w[i, j, k, 4] = compute_component(d_zz[i, j, k],
                                          m[i, j, k+1], m[i, j, k-1])
        # (i, j+1, k)
        w[i, j, k, 5] = compute_component(d_yy[i, j, k],
                                          m[i, j+1, k], m[i, j-1, k])
        # (i+1, j, k)
        w[i, j, k, 6] = compute_component(d_xx[i, j, k],
                                          m[i+1, j, k], m[i-1, j, k])
        # (i, j, k)
        w[i, j, k, 3] = - (w[i, j, k, 0] + w[i, j, k, 1] + w[i, j, k, 2] +
                           w[i, j, k, 4] + w[i, j, k, 5] + w[i, j, k, 6])

    return w
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stencil/__init__.py",".py","163","2","from finitewave.cpuwave3D.stencil.asymmetric_stencil_3d import AsymmetricStencil3D
from finitewave.cpuwave3D.stencil.isotropic_stencil_3d import IsotropicStencil3D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/stencil/asymmetric_stencil_3d.py",".py","16501","459","import numpy as np
from numba import njit, prange

from finitewave.cpuwave2D.stencil.asymmetric_stencil_2d import (
    AsymmetricStencil2D,
    major_component,
    minor_component
)


class AsymmetricStencil3D(AsymmetricStencil2D):
    """"""
    This class computes the weights for diffusion on a 3D using an asymmetric
    stencil. The weights are calculated based on the diffusion coefficients
    and the fibers orientations. The stencil includes 19 points: the central
    point and the 18 neighbors. The boundary conditions are Neumann with first-
    order approximation.

    Notes
    -----
    The diffusion coefficients are general and should be adjusted according to
    the specific model. The parameters ``D_ac``, ``D_al`` only set the ratios
    between longitudinal and cross-sectional diffusion.

    The method assumes weights being used in the following order:
        ``w[i, j, k, 0] : (i-1, j-1, k)``,
        ``w[i, j, k, 1] : (i-1, j, k)``,
        ``w[i, j, k, 2] : (i-1, j+1, k)``,
        ``w[i, j, k, 3] : (i, j-1, k)``,
        ``w[i, j, k, 4] : (i, j, k)``,
        ``w[i, j, k, 5] : (i, j+1, k)``,
        ``w[i, j, k, 6] : (i+1, j-1, k)``,
        ``w[i, j, k, 7] : (i+1, j, k)``,
        ``w[i, j, k, 8] : (i+1, j+1, k)``,
        ``w[i, j, k, 9] : (i, j-1, k-1)``,
        ``w[i, j, k, 10] : (i, j-1, k+1)``,
        ``w[i, j, k, 11] : (i, j, k-1)``,
        ``w[i, j, k, 12] : (i, j, k+1)``,
        ``w[i, j, k, 13] : (i, j+1, k-1)``,
        ``w[i, j, k, 14] : (i, j+1, k+1)``,
        ``w[i, j, k, 15] : (i-1, j, k-1)``,
        ``w[i, j, k, 16] : (i+1, j, k-1)``,
        ``w[i, j, k, 17] : (i-1, j, k+1)``,
        ``w[i, j, k, 18] : (i+1, j, k+1)``.
    """"""

    def __init__(self):
        super().__init__()

    def select_diffusion_kernel(self):
        """"""
        Selects the diffusion kernel for 3D diffusion.

        Returns
        -------
        function
            The diffusion kernel for 3D diffusion.
        """"""
        return diffusion_kernel_3d_aniso

    def compute_weights(self, model, cardiac_tissue):
        """"""
        Computes the weights for diffusion on a 3D mesh using an asymmetric 
        stencil.

        Parameters
        ----------
        model : CardiacModel3D
            A model object containing the simulation parameters.
        cardiac_tissue : CardiacTissue3D
            A 3D cardiac tissue object.

        Returns
        -------
        np.ndarray
            Array of weights for diffusion, with the shape of (*mesh.shape, 19)
        """"""
        mesh = cardiac_tissue.mesh.copy()
        mesh[mesh != 1] = 0
        conductivity = cardiac_tissue.conductivity
        conductivity = conductivity * np.ones_like(mesh, dtype=model.npfloat)

        fibers = cardiac_tissue.fibers

        if fibers is None:
            message = ""Fibers must be provided for anisotropic diffusion.""
            raise ValueError(message)

        d_xx, d_xy, d_xz = self.compute_half_step_diffusion(mesh, conductivity,
                                                            fibers, 0,
                                                            num_axes=3)
        d_yx, d_yy, d_yz = self.compute_half_step_diffusion(mesh, conductivity,
                                                            fibers, 1,
                                                            num_axes=3)
        d_zx, d_zy, d_zz = self.compute_half_step_diffusion(mesh, conductivity,
                                                            fibers, 2,
                                                            num_axes=3)

        weights = np.zeros((*mesh.shape, 19), dtype=model.npfloat)
        weights = compute_weights(weights, mesh, d_xx, d_xy, d_xz, d_yx, d_yy,
                                  d_yz, d_zx, d_zy, d_zz)

        weights = weights * model.D_model * model.dt / model.dr**2
        weights[:, :, :, 4] += 1
        return weights


@njit(parallel=True)
def diffusion_kernel_3d_aniso(u_new, u, w, indexes):
    """"""
    Performs anisotropic diffusion on a 3D grid.

    Parameters
    ----------
    u_new : numpy.ndarray
        A 3D array to store the updated potential values after diffusion.

    u : numpy.ndarray
        A 3D array representing the current potential values before diffusion.

    w : numpy.ndarray
        Array of weights for diffusion, with the shape of (*mesh.shape, 19).

    mesh : numpy.ndarray
        Array representing the mesh of the tissue.

    Returns
    -------
    np.ndarray
        The updated potential values after diffusion.
    """"""
    n_j = u.shape[1]
    n_k = u.shape[2]

    for ind in prange(len(indexes)):
        ii = indexes[ind]
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        u_new[i, j, k] = (u[i-1, j-1, k] * w[i, j, k, 0] +
                          u[i-1, j, k] * w[i, j, k, 1] +
                          u[i-1, j+1, k] * w[i, j, k, 2] +
                          u[i, j-1, k] * w[i, j, k, 3] +
                          u[i, j, k] * w[i, j, k, 4] +
                          u[i, j+1, k] * w[i, j, k, 5] +
                          u[i+1, j-1, k] * w[i, j, k, 6] +
                          u[i+1, j, k] * w[i, j, k, 7] +
                          u[i+1, j+1, k] * w[i, j, k, 8] +
                          u[i, j-1, k-1] * w[i, j, k, 9] +
                          u[i, j-1, k+1] * w[i, j, k, 10] +
                          u[i, j, k-1] * w[i, j, k, 11] +
                          u[i, j, k+1] * w[i, j, k, 12] +
                          u[i, j+1, k-1] * w[i, j, k, 13] +
                          u[i, j+1, k+1] * w[i, j, k, 14] +
                          u[i-1, j, k-1] * w[i, j, k, 15] +
                          u[i+1, j, k-1] * w[i, j, k, 16] +
                          u[i-1, j, k+1] * w[i, j, k, 17] +
                          u[i+1, j, k+1] * w[i, j, k, 18])
    return u_new


@njit
def compute_weights(w, m, d_xx, d_xy, d_xz, d_yx, d_yy, d_yz, d_zx, d_zy,
                    d_zz):
    """"""
    Computes the weights for diffusion on a 3D mesh using an asymmetric
    stencil.

    Parameters
    ----------
    w : np.ndarray
        4D array of weights for diffusion, with the shape of (*mesh.shape, 19).
    m : np.ndarray
        3D array representing the mesh grid of the tissue.
        Non-tissue areas are set to 0.
    d_xx : np.ndarray
        3D array of half-step diffusion x-components in the x-direction.
    d_xy : np.ndarray
        3D array of half-step diffusion y-components in the x-direction.
    d_xz : np.ndarray
        3D array of half-step diffusion z-components in the x-direction.
    d_yx : np.ndarray
        3D array of half-step diffusion x-components in the y-direction.
    d_yy : np.ndarray
        3D array of half-step diffusion y-components in the y-direction.
    d_yz : np.ndarray
        3D array of half-step diffusion z-components in the y-direction.
    d_zx : np.ndarray
        3D array of half-step diffusion x-components in the z-direction.
    d_zy : np.ndarray
        3D array of half-step diffusion y-components in the z-direction.
    d_zz : np.ndarray
        3D array of half-step diffusion z-components in the z-direction.

    Returns
    -------
    np.ndarray
        4D array of weights for diffusion, with the shape of (*mesh.shape, 9).
    """"""
    n_i = m.shape[0]
    n_j = m.shape[1]
    n_k = m.shape[2]
    for ii in prange(n_i*n_j*n_k):
        i = ii//(n_j*n_k)
        j = (ii % (n_j*n_k))//n_k
        k = (ii % (n_j*n_k)) % n_k

        if m[i, j, k] != 1:
            continue

        # q (i-1/2, j, k)
        qx0_major = major_component(d_xx[i-1, j, k], m[i-1, j, k])
        # (i-1, j, k)
        w[i, j, k, 1] += qx0_major
        # (i, j, k)
        w[i, j, k, 4] -= qx0_major

        qx0_xy_minor = minor_component(d_xy[i-1, j, k],
                                       m[i-1, j-1, k], m[i, j-1, k],
                                       m[i-1, j, k], m[i, j, k],
                                       m[i-1, j+1, k], m[i, j+1, k])
        # (i-1, j-1, k)
        w[i, j, k, 0] -= qx0_xy_minor[0]
        # (i, j-1, k)
        w[i, j, k, 3] -= qx0_xy_minor[1]
        # (i-1, j, k)
        w[i, j, k, 1] -= qx0_xy_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qx0_xy_minor[3]
        # (i-1, j+1, k)
        w[i, j, k, 2] -= qx0_xy_minor[4]
        # (i, j+1, k)
        w[i, j, k, 5] -= qx0_xy_minor[5]

        qx0_xz_minor = minor_component(d_xz[i-1, j, k],
                                       m[i-1, j, k-1], m[i, j, k-1],
                                       m[i-1, j, k], m[i, j, k],
                                       m[i-1, j, k+1], m[i, j, k+1])
        # (i-1, j, k-1)
        w[i, j, k, 15] -= qx0_xz_minor[0]
        # (i, j, k-1)
        w[i, j, k, 11] -= qx0_xz_minor[1]
        # (i-1, j, k)
        w[i, j, k, 1] -= qx0_xz_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qx0_xz_minor[3]
        # (i-1, j, k+1)
        w[i, j, k, 17] -= qx0_xz_minor[4]
        # (i, j, k+1)
        w[i, j, k, 12] -= qx0_xz_minor[5]

        # q (i+1/2, j, k)
        qx1_major = major_component(d_xx[i, j, k], m[i+1, j, k])
        # (i+1, j, k)
        w[i, j, k, 7] += qx1_major
        # (i, j, k)
        w[i, j, k, 4] -= qx1_major

        qx1_xy_minor = minor_component(d_xy[i, j, k],
                                       m[i+1, j-1, k], m[i, j-1, k],
                                       m[i+1, j, k], m[i, j, k],
                                       m[i+1, j+1, k], m[i, j+1, k])
        # (i+1, j-1, k)
        w[i, j, k, 6] += qx1_xy_minor[0]
        # (i, j-1, k)
        w[i, j, k, 3] += qx1_xy_minor[1]
        # (i+1, j, k)
        w[i, j, k, 7] += qx1_xy_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qx1_xy_minor[3]
        # (i+1, j+1, k)
        w[i, j, k, 8] += qx1_xy_minor[4]
        # (i, j+1, k)
        w[i, j, k, 5] += qx1_xy_minor[5]

        qx1_xz_minor = minor_component(d_xz[i, j, k],
                                       m[i+1, j, k-1], m[i, j, k-1],
                                       m[i+1, j, k], m[i, j, k],
                                       m[i+1, j, k+1], m[i, j, k+1])
        # (i+1, j, k-1)
        w[i, j, k, 16] += qx1_xz_minor[0]
        # (i, j, k-1)
        w[i, j, k, 11] += qx1_xz_minor[1]
        # (i+1, j, k)
        w[i, j, k, 7] += qx1_xz_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qx1_xz_minor[3]
        # (i+1, j, k+1)
        w[i, j, k, 18] += qx1_xz_minor[4]
        # (i, j, k+1)
        w[i, j, k, 12] += qx1_xz_minor[5]

        # q (i, j-1/2, k)
        qy0_major = major_component(d_yy[i, j-1, k], m[i, j-1, k])
        # (i, j-1, k)
        w[i, j, k, 3] += qy0_major
        # (i, j, k)
        w[i, j, k, 4] -= qy0_major

        qy0_yx_minor = minor_component(d_yx[i, j-1, k],
                                       m[i-1, j-1, k], m[i-1, j, k],
                                       m[i, j-1, k], m[i, j, k],
                                       m[i+1, j-1, k], m[i+1, j, k])
        # (i-1, j-1, k)
        w[i, j, k, 0] -= qy0_yx_minor[0]
        # (i-1, j, k)
        w[i, j, k, 1] -= qy0_yx_minor[1]
        # (i, j-1, k)
        w[i, j, k, 3] -= qy0_yx_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qy0_yx_minor[3]
        # (i+1, j-1, k)
        w[i, j, k, 6] -= qy0_yx_minor[4]
        # (i+1, j, k)
        w[i, j, k, 7] -= qy0_yx_minor[5]

        qy0_yz_minor = minor_component(d_yz[i, j-1, k],
                                       m[i, j-1, k-1], m[i, j, k-1],
                                       m[i, j-1, k], m[i, j, k],
                                       m[i, j-1, k+1], m[i, j, k+1])
        # (i, j-1, k-1)
        w[i, j, k, 9] -= qy0_yz_minor[0]
        # (i, j, k-1)
        w[i, j, k, 11] -= qy0_yz_minor[1]
        # (i, j-1, k)
        w[i, j, k, 3] -= qy0_yz_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qy0_yz_minor[3]
        # (i, j-1, k+1)
        w[i, j, k, 10] -= qy0_yz_minor[4]
        # (i, j, k+1)
        w[i, j, k, 12] -= qy0_yz_minor[5]

        # q (i, j+1/2, k)
        qy1_major = major_component(d_yy[i, j, k], m[i, j+1, k])
        # (i, j+1, k)
        w[i, j, k, 5] += qy1_major
        # (i, j, k)
        w[i, j, k, 4] -= qy1_major

        qy1_yx_minor = minor_component(d_yx[i, j, k],
                                       m[i-1, j+1, k], m[i-1, j, k],
                                       m[i, j+1, k], m[i, j, k],
                                       m[i+1, j+1, k], m[i+1, j, k])
        # (i-1, j+1, k)
        w[i, j, k, 2] += qy1_yx_minor[0]
        # (i-1, j, k)
        w[i, j, k, 1] += qy1_yx_minor[1]
        # (i, j+1, k)
        w[i, j, k, 5] += qy1_yx_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qy1_yx_minor[3]
        # (i+1, j+1, k)
        w[i, j, k, 8] += qy1_yx_minor[4]
        # (i+1, j, k)
        w[i, j, k, 7] += qy1_yx_minor[5]

        qy1_yz_minor = minor_component(d_yz[i, j, k],
                                       m[i, j+1, k-1], m[i, j, k-1],
                                       m[i, j+1, k], m[i, j, k],
                                       m[i, j+1, k+1], m[i, j, k+1])
        # (i, j+1, k-1)
        w[i, j, k, 13] += qy1_yz_minor[0]
        # (i, j, k-1)
        w[i, j, k, 11] += qy1_yz_minor[1]
        # (i, j+1, k)
        w[i, j, k, 5] += qy1_yz_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qy1_yz_minor[3]
        # (i, j+1, k+1)
        w[i, j, k, 14] += qy1_yz_minor[4]
        # (i, j, k+1)
        w[i, j, k, 12] += qy1_yz_minor[5]

        # q (i, j, k-1/2)
        qz0_major = major_component(d_zz[i, j, k-1], m[i, j, k-1])
        # (i, j, k-1)
        w[i, j, k, 11] += qz0_major
        # (i, j, k)
        w[i, j, k, 4] -= qz0_major

        qz0_zx_minor = minor_component(d_zx[i, j, k-1],
                                       m[i-1, j, k-1], m[i-1, j, k],
                                       m[i, j, k-1], m[i, j, k],
                                       m[i+1, j, k-1], m[i+1, j, k])
        # (i-1, j, k-1)
        w[i, j, k, 15] -= qz0_zx_minor[0]
        # (i-1, j, k)
        w[i, j, k, 1] -= qz0_zx_minor[1]
        # (i, j, k-1)
        w[i, j, k, 11] -= qz0_zx_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qz0_zx_minor[3]
        # (i+1, j, k-1)
        w[i, j, k, 16] -= qz0_zx_minor[4]
        # (i+1, j, k)
        w[i, j, k, 7] -= qz0_zx_minor[5]

        qz0_zy_minor = minor_component(d_zy[i, j, k-1],
                                       m[i, j-1, k-1], m[i, j-1, k],
                                       m[i, j, k-1], m[i, j, k],
                                       m[i, j+1, k-1], m[i, j+1, k])
        # (i, j-1, k-1)
        w[i, j, k, 9] -= qz0_zy_minor[0]
        # (i, j-1, k)
        w[i, j, k, 3] -= qz0_zy_minor[1]
        # (i, j, k-1)
        w[i, j, k, 11] -= qz0_zy_minor[2]
        # (i, j, k)
        w[i, j, k, 4] -= qz0_zy_minor[3]
        # (i, j+1, k-1)
        w[i, j, k, 13] -= qz0_zy_minor[4]
        # (i, j+1, k)
        w[i, j, k, 5] -= qz0_zy_minor[5]

        # q (i, j, k+1/2)
        qz1_major = major_component(d_zz[i, j, k], m[i, j, k+1])
        # (i, j, k+1)
        w[i, j, k, 12] += qz1_major
        # (i, j, k)
        w[i, j, k, 4] -= qz1_major

        qz1_zx_minor = minor_component(d_zx[i, j, k],
                                       m[i-1, j, k+1], m[i-1, j, k],
                                       m[i, j, k+1], m[i, j, k],
                                       m[i+1, j, k+1], m[i+1, j, k])
        # (i-1, j, k+1)
        w[i, j, k, 17] += qz1_zx_minor[0]
        # (i-1, j, k)
        w[i, j, k, 1] += qz1_zx_minor[1]
        # (i, j, k+1)
        w[i, j, k, 12] += qz1_zx_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qz1_zx_minor[3]
        # (i+1, j, k+1)
        w[i, j, k, 18] += qz1_zx_minor[4]
        # (i+1, j, k)
        w[i, j, k, 7] += qz1_zx_minor[5]

        qz1_zy_minor = minor_component(d_zy[i, j, k],
                                       m[i, j-1, k+1], m[i, j-1, k],
                                       m[i, j, k+1], m[i, j, k],
                                       m[i, j+1, k+1], m[i, j+1, k])
        # (i, j-1, k+1)
        w[i, j, k, 10] += qz1_zy_minor[0]
        # (i, j-1, k)
        w[i, j, k, 3] += qz1_zy_minor[1]
        # (i, j, k+1)
        w[i, j, k, 12] += qz1_zy_minor[2]
        # (i, j, k)
        w[i, j, k, 4] += qz1_zy_minor[3]
        # (i, j+1, k+1)
        w[i, j, k, 14] += qz1_zy_minor[4]
        # (i, j+1, k)
        w[i, j, k, 5] += qz1_zy_minor[5]

    return w
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/fibrosis/diffuse_3d_pattern.py",".py","3386","90","import numpy as np

from finitewave.core.fibrosis.fibrosis_pattern import FibrosisPattern


class Diffuse3DPattern(FibrosisPattern):
    """"""
    A class to generate a diffuse fibrosis pattern in a 3D mesh grid.

    Attributes
    ----------
    x1, x2 : int
        The start and end indices for the region of interest along the x-axis.
    y1, y2 : int
        The start and end indices for the region of interest along the y-axis.
    z1, z2 : int
        The start and end indices for the region of interest along the z-axis.
    dens : float
        The density of fibrosis within the specified region, ranging from 0 (no fibrosis) to 1 (full fibrosis).

    Methods
    -------
    generate(size, mesh=None):
        Generates a 3D mesh with a diffuse fibrosis pattern within the specified region.
    """"""

    def __init__(self, x1, x2, y1, y2, z1, z2, density):
        """"""
        Initializes the Diffuse3DPattern object with the given region of interest and density.

        Parameters
        ----------
        x1, x2 : int
            The start and end indices for the region of interest along the x-axis.
        y1, y2 : int
            The start and end indices for the region of interest along the y-axis.
        z1, z2 : int
            The start and end indices for the region of interest along the z-axis.
        dendensitys : float
            The density of fibrosis within the specified region.
        """"""
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.z1 = z1
        self.z2 = z2
        self.density = density

    def generate(self, shape, mesh=None):
        """"""
        Generates a 3D mesh with a diffuse fibrosis pattern within the specified region.

        If a mesh is provided, the pattern is applied to the existing mesh; otherwise, a new mesh is created.

        Parameters
        ----------
        shape : tuple of int
            The size of the 3D mesh grid (x, y, z).
        mesh : numpy.ndarray, optional
            A 3D NumPy array representing the existing mesh grid to which the fibrosis pattern will be applied.
            If None, a new mesh grid of the given size is created.

        Returns
        -------
        numpy.ndarray
            A 3D NumPy array of the same shape as the input, with the diffuse fibrosis pattern applied.
        """"""

        if shape is None and mesh is None:
            message = ""Either shape or mesh must be provided.""
            raise ValueError(message)

        if shape is not None:
            mesh = np.ones(shape, dtype=np.int8)
            fibr = self._generate(mesh.shape)
            mesh[self.x1: self.x2, self.y1: self.y2, self.z1: self.z2] = fibr[self.x1: self.x2,
                                                                              self.y1: self.y2,
                                                                              self.z1: self.z2]
            return mesh

        fibr = self._generate(mesh.shape)
        mesh[self.x1: self.x2, self.y1: self.y2, self.z1, self.z2] = fibr[self.x1: self.x2,
                                                                          self.y1: self.y2,
                                                                          self.z1: self.z2]
        return mesh

    def _generate(self, shape):
        return 1 + (np.random.random(shape) <= self.density).astype(np.int8)
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/fibrosis/__init__.py",".py","161","2","from finitewave.cpuwave3D.fibrosis.diffuse_3d_pattern import Diffuse3DPattern
from finitewave.cpuwave3D.fibrosis.structural_3d_pattern import Structural3DPattern","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/fibrosis/structural_3d_pattern.py",".py","4947","127","import numpy as np
import random

from finitewave.core.fibrosis.fibrosis_pattern import FibrosisPattern


class Structural3DPattern(FibrosisPattern):
    """"""
    A class to generate a structural fibrosis pattern in a 3D mesh grid.

    Attributes
    ----------
    x1, x2 : int
        The start and end indices for the region of interest along the x-axis.
    y1, y2 : int
        The start and end indices for the region of interest along the y-axis.
    z1, z2 : int
        The start and end indices for the region of interest along the z-axis.
    dens : float
        The density of fibrosis within the specified region, ranging from 0 (no fibrosis) to 1 (full fibrosis).
    length_i, length_j, length_k : int
        The lengths of fibrosis blocks along each axis (x, y, z).

    Methods
    -------
    generate(size, mesh=None):
        Generates a 3D mesh with a structural fibrosis pattern within the specified region.
    """"""

    def __init__(self, x1, x2, y1, y2, z1, z2, density, length_i, length_j, length_k):
        """"""
        Initializes the Structural3DPattern object with the given region of interest, density, and block sizes.

        Parameters
        ----------
        x1, x2 : int
            The start and end indices for the region of interest along the x-axis.
        y1, y2 : int
            The start and end indices for the region of interest along the y-axis.
        z1, z2 : int
            The start and end indices for the region of interest along the z-axis.
        density : float
            The density of fibrosis within the specified region.
        length_i, length_j, length_k : int
            The lengths of fibrosis blocks along each axis (x, y, z).
        """"""
        self.x1 = x1
        self.x2 = x2
        self.y1 = y1
        self.y2 = y2
        self.z1 = z1
        self.z2 = z2
        self.density = density
        self.length_i = length_i
        self.length_j = length_j
        self.length_k = length_k

    def generate(self, shape=None, mesh=None):
        """"""
        Generates and applies a structural fibrosis pattern to the mesh.

        The mesh is divided into blocks of size `length_i` x `length_j` x `length_k`, with each block having 
        a probability `density` of being filled with fibrosis. The function ensures that blocks do not
        extend beyond the specified region.

        Parameters
        ----------
        shape : tuple
            The shape of the mesh.
        mesh : numpy.ndarray, optional
            The existing mesh to base the pattern on. Default is None..

        Returns
        -------
        numpy.ndarray
            A new mesh array with the applied fibrosis pattern.
        """"""
        if shape is None and mesh is None:
            message = ""Either shape or mesh must be provided.""
            raise ValueError(message)

        if shape is not None:
            mesh = np.ones(shape, dtype=np.int8)
            fibr = self._generate(mesh.shape)
            mesh[self.x1: self.x2, self.y1: self.y2, self.z1: self.z2] = fibr[self.x1: self.x2,
                                                                              self.y1: self.y2,
                                                                              self.z1: self.z2]
            return mesh

        fibr = self._generate(mesh.shape)
        mesh[self.x1: self.x2, self.y1: self.y2, self.z1: self.z2] = fibr[self.x1: self.x2,
                                                                          self.y1: self.y2,
                                                                          self.z1: self.z2]
        return mesh

    def _generate(self, shape, mesh=None):
        """"""
        Generates a 3D mesh with a structural fibrosis pattern within the specified region.

        If a mesh is provided, the pattern is applied to the existing mesh; otherwise, a new mesh is created.

        Parameters
        ----------
        shape : tuple of int
            The shape of the 3D mesh grid (x, y, z).
        mesh : numpy.ndarray, optional
            A 3D NumPy array representing the existing mesh grid to which the fibrosis pattern will be applied.
            If None, a new mesh grid of the given size is created.

        Returns
        -------
        numpy.ndarray
            A 3D NumPy array of the same shape as the input, with the structural fibrosis pattern applied.
        """"""
        mesh = np.ones(shape, dtype=np.int8)
        for i in range(self.x1, self.x2, self.length_i):
            for j in range(self.y1, self.y2, self.length_j):
                for k in range(self.z1, self.z2, self.length_k):
                    if random.random() <= self.density:
                        i_s = min(self.length_i, self.x2 - i)
                        j_s = min(self.length_j, self.y2 - j)
                        k_s = min(self.length_k, self.z2 - k)

                        mesh[i:i+i_s, j:j+j_s, k:k+k_s] = 2

        return mesh
","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tissue/__init__.py",".py","73","1","from finitewave.cpuwave3D.tissue.cardiac_tissue_3d import CardiacTissue3D","Python"
"Cell biology","finitewave/Finitewave","finitewave/cpuwave3D/tissue/cardiac_tissue_3d.py",".py","1504","47","import numpy as np

from finitewave.core.tissue.cardiac_tissue import CardiacTissue


class CardiacTissue3D(CardiacTissue):
    """"""
    This class represents a 3D cardiac tissue.

    Attributes
    ----------
    meta : dict
        A dictionary containing metadata about the tissue.
    mesh : np.ndarray
        A 3D numpy array representing the tissue mesh where each value
        indicates the type of tissue at that location. Possible values are:
        ``0`` for non-tissue, ``1`` for healthy tissue, and ``2`` for fibrotic
        tissue.
    conductivity : float or np.ndarray
        The conductivity of the tissue used for reducing the diffusion
        coefficients. The conductivity should be in the range [0, 1].
    fibers : np.ndarray
        Fibers orientation in the tissue. If None, the isotropic stencil is
        used.
    """"""
    def __init__(self, shape):
        super().__init__()
        self.meta[""dim""] = 3
        self.meta[""shape""] = shape
        self.mesh = np.ones(shape, dtype=np.int8)
        self.conductivity = 1.
        self.fibers = None

    def add_boundaries(self):
        """"""
        Sets the boundary values of the mesh to zero.

        The boundaries are defined as the edges of the grid, and this method
        updates these edges in the mesh array.
        """"""
        self.mesh[0, :, :] = 0
        self.mesh[:, 0, :] = 0
        self.mesh[:, :, 0] = 0
        self.mesh[-1, :, :] = 0
        self.mesh[:, -1, :] = 0
        self.mesh[:, :, -1] = 0
","Python"
"Cell biology","finitewave/Finitewave","examples/tp06_fibrosis_3D_ventricle.py",".py","1661","54","
#
# Left ventricle simlation with the Aliev-Panfilov model.
# Mesh and fibers were taken from Niderer's data storage (https://zenodo.org/records/3890034)
# Fibers were generated with Rule-based algorithm.
# Ventricle is stimulated from the apex.

from pathlib import Path
import numpy as np
import pyvista as pv
import matplotlib.pyplot as plt

import finitewave as fw


path = Path(__file__).parent

# Load mesh as cubic array
mesh = np.load(path.joinpath(""data"", ""mesh.npy""))

tissue = fw.CardiacTissue3D(mesh.shape)
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = mesh
# generate 20% of fibrosis in the ventrcile wall:
fibrosis_pattern = fw.Diffuse3DPattern(0, mesh.shape[0], 0, mesh.shape[1], 0, mesh.shape[2], 0.20)
fibrosis_pattern.generate(tissue.mesh.shape, tissue.mesh)

tissue.add_boundaries()

# create model object:
tp06 = fw.TP063D()
# set up numerical parameters:
tp06.dt = 0.01
tp06.dr = 0.25
tp06.t_max = 25
# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, -20, 0, mesh.shape[0],
                                             0, mesh.shape[0],
                                             0, 30))
# add the tissue and the stim parameters to the model object:
tp06.cardiac_tissue = tissue
tp06.stim_sequence = stim_sequence
# initialize model: compute weights, add stimuls, trackers etc.
tp06.run()

# show the potential map at the end of calculations

# visualize the ventricle in 3D
mesh_builder = fw.VisMeshBuilder3D()
mesh_grid = mesh_builder.build_mesh(tissue.mesh)
mesh_grid = mesh_builder.add_scalar(tp06.u, 'u')
mesh_grid.plot(clim=[-80, 30], cmap='viridis')
","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/current_stimulation.py",".py","2585","71","""""""
Using StimCurrentCoord2D for Current-Based Stimulation
=======================================================

Overview:
---------
This example demonstrates how to apply a current-based stimulus in a 2D cardiac tissue 
using the `StimCurrentCoord2D` class from Finitewave.

Stimulation Setup:
------------------
- The center of the tissue is stimulated with a small square pulse (2×2 nodes).
- A current of 18 units is applied for 0.4  at t = 0.
- Unlike voltage stimulation, current-based stimulation allows effective excitation 
  of very small regions, which is especially useful for avoiding sink-source mismatch 
  problems in tightly localized areas.

Simulation Parameters:
----------------------
- Model: Aliev-Panfilov 2D
- Grid size: 200 × 200
- Time step (dt): 0.01 
- Space step (dr): 0.25
- Total simulation time: 10 

Application:
------------
This example is ideal for understanding how to trigger depolarization waves using 
current injection. The `StimCurrentCoord2D` class allows flexible control of both 
current strength and duration, enabling fine-tuned stimulus delivery.
""""""

import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
# All stimulation object have two types of stimulation: by current and by voltage.
# In case of current stimulation, we use two parameters: current strength and duration.
# stimulate the center of the tissue with a square pulse (2 nodes on the side)
# сurrent stimulation is set by current strength (18) and stimulation duration (0.4 model time units). 
# Current stimulation allows to bypass the problem of sink-source mismatch
# and stimulate even small areas of tissue to start a depolarization wave:
stim_sequence.add_stim(fw.StimCurrentCoord2D(time=0,
                                             curr_value=18,
                                             duration=0.4, 
                                             x1=n//2 - 1, x2=n//2 + 1, 
                                             y1=n//2 - 1, y2=n//2 + 1))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 10
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/coordinates_stimulation.py",".py","1069","35","import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
# stimulate the corner of the tissue with a square pulse (10 nodes on the side)
# of 1.0 V at t=0.
# coordinates are always form a reactangular (slab in 3D) area of stimulation.
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, 
                                             volt_value=1.0, 
                                             x1=0, x2=10, 
                                             y1=0, y2=10))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 25
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/voltage_stimulation.py",".py","2329","69","""""""
Using StimVoltageCoord in 2D Tissue
=====================================

Overview:
---------
This example demonstrates how to use the `StimVoltageCoord2D` class in Finitewave 
to apply a voltage-based stimulus to a rectangular region in a 2D cardiac tissue.

Stimulation Setup:
------------------
- The `StimVoltageCoord2D` class is used to define the stimulated region by its coordinates.
- A square region (6×6 nodes) at the center of the tissue is stimulated at t = 0 .
- The voltage value is set to 1.0, which for the Aliev-Panfilov model corresponds 
  to the peak excitation potential (resting = 0, peak = 1).

Simulation Parameters:
----------------------
- Model: Aliev-Panfilov 2D
- Grid size: 200 × 200
- Time step (dt): 0.01
- Space step (dr): 0.25
- Total simulation time: 10 

Application:
------------
This example is useful for learning how to define spatially localized voltage 
stimuli in 2D using coordinate-based methods. The `StimVoltageCoord2D` class 
is particularly useful for applying custom rectangular stimulation zones.
""""""


import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
# stimulate the center of the tissue with a square pulse (6 nodes on the side)
# we use voltage value = 1.0 V at t=0.
# The voltage value should be set between the resting potential and the peak potential of
# the model to ensure the stimulation is effective.
# in case of Aliev-Panfilov model, the resting potential is 0 and the peak potential is 1:
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, 
                                             volt_value=1.0, 
                                             x1=n//2 - 3, x2=n//2 + 3, 
                                             y1=n//2 - 3, y2=n//2 + 3))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 10
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/sequential_stimulation.py",".py","3138","99","""""""
Sequential Multi-Type Stimulation in 2D Tissue
==============================================

Overview:
---------
This example demonstrates how to define a sequence of heterogeneous stimuli
using different stimulation classes in a single simulation using Finitewave.
Stimuli are applied from multiple locations at different times, combining
`StimVoltageCoord2D`, `StimCurrentMatrix2D`, and `StimVoltageCoord2D`.

Stimulation Setup:
------------------
1. t = 0: Voltage-based stimulation in the top-left corner (5×5 region).
2. t = 70: Matrix-based *current* stimulation (5×5 region in top-right).
3. t = 140: Voltage-based stimulation in the bottom-right corner (10×10).

Simulation Parameters:
----------------------
- Model: Aliev-Panfilov 2D
- Grid size: 200 × 200
- Time step (dt): 0.01 
- Space step (dr): 0.25
- Total simulation time: 170 

Tracking:
---------
- Animation tracker records membrane potential (`u`) every 10 steps.
- Results are saved to the ""anim_data"" folder and exported as a 2D animation.

Application:
------------
This setup is ideal for:
- Exploring sequential pacing protocols.
- Testing responses to multiple localized perturbations.
- Demonstrating how to combine different stimulation methods in a single run.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
# Here we create a sequence of stimuli from different corners of a square mesh. 
# The stimulation object in the sequence can be of any type (class).
stim_sequence.add_stim(
    fw.StimVoltageCoord2D(time=0, 
                          volt_value=1.0, 
                          x1=0, x2=5, 
                          y1=0, y2=5)
)

stim_matrix = np.full([n, n], False, dtype=bool)
stim_matrix[0:5, n-5:n] = True
stim_sequence.add_stim(
    fw.StimCurrentMatrix2D(time=70, 
                           curr_value=5, 
                           duration=0.6, 
                           matrix=stim_matrix)
)

stim_sequence.add_stim(
    fw.StimVoltageCoord2D(time=140,
                          volt_value=0.5,
                          x1=n-10, x2=n,
                          y1=n-10, y2=n)
)

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
animation_tracker = fw.Animation2DTracker()
animation_tracker.variable_name = ""u""  # Specify the variable to track
animation_tracker.dir_name = ""anim_data""
animation_tracker.step = 10
animation_tracker.overwrite = True  # Remove existing files in dir_name
tracker_sequence.add_tracker(animation_tracker)

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 170
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

# run the model:
aliev_panfilov.run()

# write animation and clear the snapshot folder
animation_tracker.write(shape_scale=5, clear=True, fps=60)","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/3d_stimulation.py",".py","2100","70","""""""
Stimulation in 3D
==================================

Overview:
---------
This example demonstrates how to apply two opposite planar waves in 3D tissue using:
- `StimVoltageCoord3D`: voltage stimulation with spatial bounds (`x1`, `x2`, `y1`, `y2`, `z1`, `z2`)
- `StimCurrentMatrix3D`: matrix-based current stimulation with a 3D boolean array.

The example highlights that 3D stimulation setup is identical to 2D, 
with the only difference being the inclusion of the Z-axis (`z1`, `z2` or 3D matrix).

Simulation Setup:
-----------------
- Tissue: 3D slab (200×200×10)
- Stimulus 1: Voltage-based planar wave on the left face at `t=0`
- Stimulus 2: Current-based planar wave on the right face at `t=0`
- Duration: 10 time units

Application:
------------
- Demonstrates stimulation syntax for 3D using both coordinate and matrix methods.
- Visualizes resulting voltage (`u`) distribution in 3D using PyVista.
""""""

import numpy as np
import matplotlib.pyplot as plt
import pyvista as pv
import finitewave as fw

# tissue setup
nx = 200
ny = 200
nz = 30
tissue = fw.CardiacTissue3D([nx, ny, nz])
tissue.mesh = np.ones((nx, ny, nz), dtype=np.uint8)

# stimulus 1: VoltageCoord3D on left face
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(
    fw.StimVoltageCoord3D(time=0, volt_value=1.0,
                          x1=0, x2=5,
                          y1=0, y2=ny,
                          z1=0, z2=nz)
)

# stimulus 2: CurrentMatrix3D on right face
stim_matrix = np.zeros((nx, ny, nz), dtype=bool)
stim_matrix[nx - 5:nx, :, :] = True  # Right face
stim_sequence.add_stim(
    fw.StimCurrentMatrix3D(time=0, curr_value=10, duration=0.5, matrix=stim_matrix)
)

# model setup:
model = fw.AlievPanfilov3D()
model.dt = 0.01
model.dr = 0.25
model.t_max = 10
model.cardiac_tissue = tissue
model.stim_sequence = stim_sequence

# run the model:
model.run()

# visualization with PyVista:
mesh_builder = fw.VisMeshBuilder3D()
mesh_grid = mesh_builder.build_mesh(tissue.mesh)
mesh_grid = mesh_builder.add_scalar(model.u, name='Membrane Potential (u)')
mesh_grid.plot(cmap='viridis', clim=[0, 1])","Python"
"Cell biology","finitewave/Finitewave","examples/stimulation/matrix_stimulation.py",".py","2277","75","""""""
Matrix-Based Stimulation with StimVoltageMatrix2D
=========================================================

Overview:
---------
This example demonstrates how to define complex spatial stimulation patterns 
in 2D cardiac tissue using the `StimVoltageMatrix2D` class in Finitewave.

Stimulation Setup:
------------------
- A 2D boolean matrix of the same size as the tissue is used to define the 
  stimulated regions.
- Two 10×10 square regions in opposite corners of the tissue are stimulated 
  at t = 0  with 1.0 V voltage.
- This flexible approach allows arbitrary spatial patterns and can be 
  generated from images, data arrays, or procedural logic.

Simulation Parameters:
----------------------
- Model: Aliev-Panfilov 2D
- Grid size: 200 × 200
- Time step (dt): 0.01 
- Space step (dr): 0.25
- Total simulation time: 15 

Application:
------------
This technique is ideal for:
- Designing realistic stimulation setups.
- Applying stimuli based on experimental data or anatomical maps.
- Studying the effect of spatial heterogeneity in excitation.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
# stimulate two opposite corners of the tissue with a square pulse (10 nodes on the side)
# of 1.0 V at t=0.
# we create a 2D boolean matrix of the same size as the tissue 
# and set the stimulated nodes to True:
stimulation_area = np.full([n, n], False, dtype=bool)
stimulation_area[0:10, 0:10] = True
stimulation_area[n-10:n, n-10:n] = True

stim_sequence.add_stim(fw.StimVoltageMatrix2D(time=0, 
                                             volt_value=1.0, 
                                             matrix=stimulation_area))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 15
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/aliev_panfilov_3d.py",".py","2002","73","""""""
Running the Aliev-Panfilov Model in 3D
======================================

Overview:
---------
This example demonstrates how to run a basic 3D simulation of the 
Aliev-Panfilov model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 50

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Aliev-Panfilov model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
k = 3
tissue = fw.CardiacTissue3D([n, m, k])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov3D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 50
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
aliev_panfilov.tracker_sequence = tracker_sequence

# run the model:
aliev_panfilov.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * aliev_panfilov.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Aliev-Panfilov')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/bueno_orovio_3d.py",".py","2000","74","""""""
Running the Bueno-Orovio Model in 3D
======================================

Overview:
---------
This example demonstrates how to run a basic 3D simulation of the 
Bueno-Orovio model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Bueno-Orovio model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
k = 5
m = 3
tissue = fw.CardiacTissue3D([n, m, k])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters:
bueno_orovio = fw.BuenoOrovio3D()
bueno_orovio.dt = 0.01
bueno_orovio.dr = 0.25
bueno_orovio.t_max = 500
# add the tissue and the stim parameters to the model object:
bueno_orovio.cardiac_tissue = tissue
bueno_orovio.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
bueno_orovio.tracker_sequence = tracker_sequence

# run the model:
bueno_orovio.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * bueno_orovio.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Bueno-Orovio')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/luo_rudy_3d.py",".py","2113","75","""""""
Running the Luo-Rudy 1991 Model in 3D Cardiac Tissue
====================================================

Overview:
---------
This example demonstrates how to run a 3D simulation of the 
Luo-Rudy 1991 ventricular action potential model using the Finitewave framework.

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge of the domain at t = 0 ms
    to initiate wavefront propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Luo-Rudy 1991 model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
k = 3
# create mesh
tissue = fw.CardiacTissue3D((n, m, k))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters
luo_rudy = fw.LuoRudy913D()
luo_rudy.dt = 0.01
luo_rudy.dr = 0.25
luo_rudy.t_max = 500

# add the tissue and the stim parameters to the model object
luo_rudy.cardiac_tissue = tissue
luo_rudy.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
luo_rudy.tracker_sequence = tracker_sequence

# run the model:
luo_rudy.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * luo_rudy.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Luo-Rudy 1991')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/mitchell_schaeffer_3d.py",".py","2084","74","""""""
Running the Mitchell-Schaeffer Model in 3D
======================================

Overview:
---------
This example demonstrates how to run a basic 3D simulation of the 
Mitchell-Schaeffer model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Mitchell-Schaeffer model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
k = 3
tissue = fw.CardiacTissue3D([n, m, k])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters:
mitchell_schaeffer = fw.MitchellSchaeffer3D()
mitchell_schaeffer.dt = 0.01
mitchell_schaeffer.dr = 0.25
mitchell_schaeffer.t_max = 500
# add the tissue and the stim parameters to the model object:
mitchell_schaeffer.cardiac_tissue = tissue
mitchell_schaeffer.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
mitchell_schaeffer.tracker_sequence = tracker_sequence

# run the model:
mitchell_schaeffer.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * mitchell_schaeffer.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Mitchell-Schaeffer')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/tp06_3d.py",".py","2049","76","""""""
Running the TP06 Model in 3D Cardiac Tissue
===========================================

Overview:
---------
This example demonstrates how to run a 3D simulation of the 
ten Tusscher–Panfilov 2006 (TP06) model for ventricular cardiomyocytes 
using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge (rows 0 to 5) at t = 0 ms
    to initiate wave propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus to initiate excitation.
3. Set up and run the TP06 model.
4. Visualize the membrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
k = 3
# create mesh
tissue = fw.CardiacTissue3D((n, m, k))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters
tp06 = fw.TP063D()
tp06.dt = 0.01
tp06.dr = 0.25
tp06.t_max = 500

# add the tissue and the stim parameters to the model object
tp06.cardiac_tissue = tissue
tp06.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
tp06.tracker_sequence = tracker_sequence

# run the model:
tp06.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * tp06.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Ten Tusscher-Panfilov 2006')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/courtemanche_3d.py",".py","2315","79","""""""
Running the Courtemanche Model in 3D Cardiac Tissue
====================================================

Overview:
---------
This example demonstrates how to run a 3D simulation of the 
Courtemanche ventricular action potential model using the Finitewave framework.

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge of the domain at t = 0 ms
    to initiate wavefront propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Courtemanche model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
k = 3
# create mesh
tissue = fw.CardiacTissue3D((n, m, k))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters
courtemanche = fw.Courtemanche3D()
courtemanche.dt = 0.01
courtemanche.dr = 0.25
courtemanche.t_max = 500

# Here, we increase g_Kur by a factor of 3 to better match physiological AP shape
# with a visible plateau and realistic repolarization.
courtemanche.gkur_coeff *= 3

# add the tissue and the stim parameters to the model object
courtemanche.cardiac_tissue = tissue
courtemanche.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
courtemanche.tracker_sequence = tracker_sequence

# run the model:
courtemanche.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * courtemanche.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Courtemanche')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/barkley_3d.py",".py","1905","73","""""""
Running the Barkley Model in 3D
======================================

Overview:
---------
This example demonstrates how to run a basic 3D simulation of the 
Barkley model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 10

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Barkley model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
k = 3
tissue = fw.CardiacTissue3D([n, m, k])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters:
barkley = fw.Barkley3D()
barkley.dt = 0.01
barkley.dr = 0.25
barkley.t_max = 10
# add the tissue and the stim parameters to the model object:
barkley.cardiac_tissue = tissue
barkley.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
barkley.tracker_sequence = tracker_sequence

# run the model:
barkley.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * barkley.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Barkley')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/3D/fenton_karma_3d.py",".py","1999","73","""""""
Running the Fentom-Karma Model in 3D
======================================

Overview:
---------
This example demonstrates how to run a basic 3D simulation of the 
Fentom-Karma model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5×3 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 3D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Fentom-Karma model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
k = 3
tissue = fw.CardiacTissue3D([n, m, k])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 5, 0, m, 0, k))

# create model object and set up parameters:
fentom_karma = fw.FentonKarma3D()
fentom_karma.dt = 0.01
fentom_karma.dr = 0.25
fentom_karma.t_max = 500
# add the tissue and the stim parameters to the model object:
fentom_karma.cardiac_tissue = tissue
fentom_karma.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3, 1]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
fentom_karma.tracker_sequence = tracker_sequence

# run the model:
fentom_karma.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * fentom_karma.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3_1"")
plt.legend(title='Fentom-Karma')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/fenton_karma_2d.py",".py","1977","73","""""""
Running the Fentom-Karma Model in 2D
======================================

Overview:
---------
This example demonstrates how to run a basic 2D simulation of the 
Fentom-Karma model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Fentom-Karma model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
tissue = fw.CardiacTissue2D([n, m])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters:
fentom_karma = fw.FentonKarma2D()
fentom_karma.dt = 0.01
fentom_karma.dr = 0.25
fentom_karma.t_max = 500
# add the tissue and the stim parameters to the model object:
fentom_karma.cardiac_tissue = tissue
fentom_karma.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
fentom_karma.tracker_sequence = tracker_sequence

# run the model:
fentom_karma.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * fentom_karma.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Fenton-Karma')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/barkley_2d.py",".py","2251","86","""""""
Running the Barkley Model in 2D
======================================

Overview:
---------
This example demonstrates how to run a basic 2D simulation of the 
Barkley model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 10

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Barkley model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
tissue = fw.CardiacTissue2D([n, m])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters:
barkley = fw.Barkley2D()
barkley.dt = 0.01
barkley.dr = 0.25
barkley.t_max = 10
# add the tissue and the stim parameters to the model object:
barkley.cardiac_tissue = tissue
barkley.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
# add the variable tracker:
multivariable_tracker = fw.MultiVariable2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
multivariable_tracker.cell_ind = [50, 3]
multivariable_tracker.var_list = [""u"", ""v""]
tracker_sequence.add_tracker(multivariable_tracker)
barkley.tracker_sequence = tracker_sequence

# run the model:
barkley.run()

# plot the action potential
plt.figure(figsize=(10, 5))

# Subplot 1: Phase plot (u vs v)
plt.subplot(1, 2, 1)
plt.plot(multivariable_tracker.output[""u""], multivariable_tracker.output[""v""], label=""cell_50_3"")
plt.legend(title='Barkley')
plt.title('Phase (u vs v)')
plt.xlabel('u')
plt.ylabel('v')
plt.grid()

# Subplot 2: Time vs u
plt.subplot(1, 2, 2)
time = np.arange(len(multivariable_tracker.output[""u""])) * barkley.dt
plt.plot(time, multivariable_tracker.output[""u""], label=""cell_50_3"")
plt.legend(title='Barkley')
plt.title('Action potential')
plt.xlabel('Time')
plt.ylabel('u')
plt.grid()

plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/tp06_2d.py",".py","2026","75","""""""
Running the TP06 Model in 2D Cardiac Tissue
===========================================

Overview:
---------
This example demonstrates how to run a 2D simulation of the 
ten Tusscher–Panfilov 2006 (TP06) model for ventricular cardiomyocytes 
using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge (rows 0 to 5) at t = 0 ms
    to initiate wave propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus to initiate excitation.
3. Set up and run the TP06 model.
4. Visualize the membrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
# create mesh
tissue = fw.CardiacTissue2D((n, m))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters
tp06 = fw.TP062D()
tp06.dt = 0.01
tp06.dr = 0.25
tp06.t_max = 500

# add the tissue and the stim parameters to the model object
tp06.cardiac_tissue = tissue
tp06.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
tp06.tracker_sequence = tracker_sequence

# run the model:
tp06.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * tp06.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Ten Tusscher-Panfilov 2006')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/aliev_panfilov_2d.py",".py","1979","72","""""""
Running the Aliev-Panfilov Model in 2D
======================================

Overview:
---------
This example demonstrates how to run a basic 2D simulation of the 
Aliev-Panfilov model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 50

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Aliev-Panfilov model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
tissue = fw.CardiacTissue2D([n, m])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 50
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
aliev_panfilov.tracker_sequence = tracker_sequence

# run the model:
aliev_panfilov.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * aliev_panfilov.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Aliev-Panfilov')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/mitchell_schaeffer_2d.py",".py","2061","73","""""""
Running the Mitchell-Schaeffer Model in 2D
======================================

Overview:
---------
This example demonstrates how to run a basic 2D simulation of the 
Mitchell-Schaeffer model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Mitchell-Schaeffer model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
tissue = fw.CardiacTissue2D([n, m])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters:
mitchell_schaeffer = fw.MitchellSchaeffer2D()
mitchell_schaeffer.dt = 0.01
mitchell_schaeffer.dr = 0.25
mitchell_schaeffer.t_max = 500
# add the tissue and the stim parameters to the model object:
mitchell_schaeffer.cardiac_tissue = tissue
mitchell_schaeffer.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
mitchell_schaeffer.tracker_sequence = tracker_sequence

# run the model:
mitchell_schaeffer.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * mitchell_schaeffer.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Mitchell-Schaeffer')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/courtemanche_2d.py",".py","2239","79","""""""
Running the Courtemanche Model in 2D Cardiac Tissue
===========================================

Overview:
---------
This example demonstrates how to run a 2D simulation of the 
Courtemanche model for atrial cardiomyocytes 
using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge (rows 0 to 5) at t = 0 ms
    to initiate wave propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus to initiate excitation.
3. Set up and run the TP06 model.
4. Visualize the membrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
# create mesh
tissue = fw.CardiacTissue2D((n, m))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters
courtemanche = fw.Courtemanche2D()
courtemanche.dt = 0.01
courtemanche.dr = 0.25
courtemanche.t_max = 500

# Here, we increase g_Kur by a factor of 3 to better match physiological AP shape
# with a visible plateau and realistic repolarization.
courtemanche.gkur_coeff *= 3

# add the tissue and the stim parameters to the model object
courtemanche.cardiac_tissue = tissue
courtemanche.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
courtemanche.tracker_sequence = tracker_sequence

# run the model:
courtemanche.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * courtemanche.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Courtemanche')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/bueno_orovio_2d.py",".py","1977","73","""""""
Running the Bueno-Orovio Model in 2D
======================================

Overview:
---------
This example demonstrates how to run a basic 2D simulation of the 
Bueno-Orovio model using the Finitewave framework. 

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A square side stimulus is applied at t = 0.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 500

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Bueno-Orovio model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue:
n = 100
m = 5
tissue = fw.CardiacTissue2D([n, m])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters:
bueno_orovio = fw.BuenoOrovio2D()
bueno_orovio.dt = 0.01
bueno_orovio.dr = 0.25
bueno_orovio.t_max = 500
# add the tissue and the stim parameters to the model object:
bueno_orovio.cardiac_tissue = tissue
bueno_orovio.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
bueno_orovio.tracker_sequence = tracker_sequence

# run the model:
bueno_orovio.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * bueno_orovio.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Bueno-Orovio')
plt.xlabel('Time (ms)')
plt.title('Action Potential')
plt.grid()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/models/2D/luo_rudy_2d.py",".py","2090","74","""""""
Running the Luo-Rudy 1991 Model in 2D Cardiac Tissue
====================================================

Overview:
---------
This example demonstrates how to run a 2D simulation of the 
Luo-Rudy 1991 ventricular action potential model using the Finitewave framework.

Simulation Setup:
-----------------
- Tissue Grid: A 100×5 cardiac tissue domain.
- Stimulation:
  - A planar stimulus is applied along the top edge of the domain at t = 0 ms
    to initiate wavefront propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 500 ms

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply a stimulus along the upper boundary to initiate excitation.
3. Set up and run the Luo-Rudy 1991 model.
4. Visualize the transmembrane potential.

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 100
m = 5
# create mesh
tissue = fw.CardiacTissue2D((n, m))

# set up stimulation parameters
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 5, 0, m))

# create model object and set up parameters
luo_rudy = fw.LuoRudy912D()
luo_rudy.dt = 0.01
luo_rudy.dr = 0.25
luo_rudy.t_max = 500

# add the tissue and the stim parameters to the model object
luo_rudy.cardiac_tissue = tissue
luo_rudy.stim_sequence = stim_sequence

tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[50, 3]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)
luo_rudy.tracker_sequence = tracker_sequence

# run the model:
luo_rudy.run()

# plot the action potential
plt.figure()
time = np.arange(len(action_pot_tracker.output)) * luo_rudy.dt
plt.plot(time, action_pot_tracker.output, label=""cell_50_3"")
plt.legend(title='Luo-Rudy 1991')
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.title('Action Potential')
plt.grid()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/basics/3D/spiral_wave_3d.py",".py","3417","116","""""""
Spiral Waves on a 3D Spherical Shell
====================================

This example demonstrates how to simulate spiral (scroll) waves inside
a 3D spherical shell using the Aliev-Panfilov model with Finitewave.

A hollow sphere is embedded inside a 3D Cartesian grid. The propagation
of electrical activity is initiated by sequential stimuli, creating a
scroll wave that circulates within the curved geometry.

The resulting potential distribution is visualized with Finitewave's
3D mesh tools.

Geometry Setup:
---------------
- Domain size: 200×200×200 grid
- Geometry: Spherical shell created using a binary mask
    - Outer radius: 95 voxels
    - Inner radius: 90 voxels
    - Mesh values: 1 inside the shell, 0 outside
- The sphere is centered in the domain

Stimulation Protocol:
---------------------
- Stimulus 1:
    - Time: t = 0
    - Location: One side of the sphere (thin planar region near the edge)
- Stimulus 2:
    - Time: t = 50
    - Location: One hemisphere only
- This breaks the initial wave symmetry and initiates a scroll wave

Model:
------
- Aliev-Panfilov 3D reaction-diffusion model
- Time step (dt): 0.01
- Space step (dr): 0.25
- Total simulation time: 100

Visualization:
--------------
The 3D scalar field (`u`) is rendered on the shell mesh using
Finitewave’s `VisMeshBuilder3D`.

Applications:
-------------
- Simulation of scroll wave dynamics in spherical domains
- Study of wave breakups, phase singularities, and 3D reentry
- Modeling electrical activity in simplified anatomical geometries
""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw


# Create a spherical mask within a 100x100x100 cube
def create_sphere_mask(shape, radius, center):
    z, y, x = np.indices(shape)
    distance = np.sqrt((x - center[0])**2 +
                       (y - center[1])**2 +
                       (z - center[2])**2)
    mask = distance <= radius
    return mask


# set up the cardiac tissue:
n = 200
shape = (n, n, n)
tissue = fw.CardiacTissue3D((n, n, n))
tissue.mesh = np.zeros((n, n, n))
tissue.mesh[create_sphere_mask(tissue.mesh.shape,
                               n//2-5,
                               (n//2, n//2, n//2))] = 1
tissue.mesh[create_sphere_mask(tissue.mesh.shape,
                               n//2-10,
                               (n//2, n//2, n//2))] = 0

# set up stimulation parameters:
min_x = np.where(tissue.mesh)[0].min()

stim1 = fw.StimVoltageCoord3D(0, 1,
                              min_x, min_x + 3,
                              0, n,
                              0, n)

stim2 = fw.StimVoltageCoord3D(50, 1,
                              0, n,
                              0, n//2,
                              0, n)

stim_sequence = fw.StimSequence()
stim_sequence.add_stim(stim1)
stim_sequence.add_stim(stim2)

aliev_panfilov = fw.AlievPanfilov3D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 100
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

aliev_panfilov.run()

# visualize the potential map in 3D
vis_mesh = tissue.mesh.copy()
# vis_mesh[n//2:, n//2:, n//2:] = 0

mesh_builder = fw.VisMeshBuilder3D()
grid = mesh_builder.build_mesh(vis_mesh)
grid = mesh_builder.add_scalar(aliev_panfilov.u, 'u')
grid.plot(clim=[0, 1], cmap='viridis')","Python"
"Cell biology","finitewave/Finitewave","examples/basics/3D/ventricle_geometry_3d.py",".py","2938","93","""""""
Left Ventricle Simulation with Anatomical Mesh and Fibers
----------------------------------------------------------

This example demonstrates how to simulate electrical activity in a
realistic left ventricular (LV) geometry using the Aliev-Panfilov
model in 3D.

The LV mesh and corresponding fiber orientations are loaded from
external data (available at https://zenodo.org/records/3890034).
The mesh is embedded in a regular grid, and fiber directions are
assigned to the myocardium using a vector field.

Stimulation is applied at the base of the ventricle to initiate 
activation, and wave propagation is visualized in 3D.

Data Requirements:
------------------
This example assumes the following files exist in the `data/` directory:
- `mesh.npy`: 3D binary array (1 = myocardium, 0 = empty)
- `fibers.npy`: Flattened array of fiber vectors (same shape as mesh[mesh > 0])

Simulation Setup:
-----------------
- Model: Aliev-Panfilov 3D
- Mesh: Realistic LV shape, embedded in a cubic grid
- Fibers: Anatomically derived vectors per voxel
- Stimulus:
    - Type: Voltage
    - Location: Basal region (first 20 z-slices)
    - Time: t = 0 
- Time step (dt): 0.01 
- Space step (dr): 0.25 
- Total time: 40 

Visualization:
--------------
- The scalar voltage field (`u`) is rendered in 3D using 
  Finitewave’s `VisMeshBuilder3D`.

Applications:
-------------
- Realistic whole-ventricle simulations
- Exploration of fiber-driven anisotropic conduction
- Foundation for further patient-specific modeling or ECG computation
""""""


from pathlib import Path
import numpy as np

import finitewave as fw


path = Path(__file__).parent

# Load mesh as cubic array
mesh = np.load(path.joinpath("".."", "".."", ""data"", ""mesh.npy""))

# Load fibers as cubic array
fibers_list = np.load(path.joinpath("".."", "".."", ""data"", ""fibers.npy""))
fibers = np.zeros(mesh.shape + (3,), dtype=float)
fibers[mesh > 0] = fibers_list

# set up the tissue with fibers orientation:
tissue = fw.CardiacTissue3D(mesh.shape)
tissue.mesh = mesh
tissue.add_boundaries()
tissue.fibers = fibers

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, mesh.shape[0],
                                             0, mesh.shape[0],
                                             0, 20))

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 40
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
# initialize model: compute weights, add stimuls, trackers etc.
aliev_panfilov.run()

# visualize the ventricle in 3D
mesh_builder = fw.VisMeshBuilder3D()
mesh_grid = mesh_builder.build_mesh(tissue.mesh)
mesh_grid = mesh_builder.add_scalar(aliev_panfilov.u, 'u')
mesh_grid.plot(clim=[0, 1], cmap='viridis')","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/change_conductivity_2d.py",".py","2647","79","
""""""
Aliev-Panfilov 2D Model (Conductivity)
======================================

Overview:
---------
This example demonstrates how to simulate the Aliev-Panfilov model in a 
two-dimensional isotropic cardiac tissue with spatially varying conductivity. 
Conductivity variations affect wave propagation, simulating regions of different 
electrophysiological properties.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 cardiac tissue domain.
- Isotropic Diffusion: Conductivity is uniform within regions but varies across the tissue.
- Conductivity Variation: 
  - The default conductivity is set to 1.0.
  - The bottom-right quadrant (n/2:, n/2:) has reduced conductivity (0.3).
- Stimulation: A localized stimulus is applied at the center.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 
  - Spatial resolution (dr): 0.25 
  - Total simulation time (t_max): 30 

Execution:
----------
1. Create a 2D cardiac tissue grid and define spatial conductivity variations.
2. Apply a stimulus at the center.
3. Set up and initialize the Aliev-Panfilov model.
4. Run the simulation to observe how conductivity affects wave propagation.
5. Visualize the final membrane potential distribution.

Effect of Conductivity:
-----------------------
The lower conductivity region slows down wave propagation, potentially leading 
to conduction block or reentrant wave formation. This feature is useful for modeling 
heterogeneous tissue properties such as fibrosis or ischemic regions.

Visualization:
--------------
The final membrane potential distribution is displayed using matplotlib, 
illustrating the impact of conductivity variations on wave propagation.
""""""

import numpy as np
import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue of size 400x400 with cardiomycytes:
n = 400
tissue = fw.CardiacTissue2D([n, n])
tissue.conductivity = np.ones([n, n], dtype=float)
tissue.conductivity[n//2:, n//2:] = 0.3

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1,
                                             n//2 - 3, n//2 + 3,
                                             n//2 - 3, n//2 + 3))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 30
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/reentry.py",".py","2626","81","""""""
Spiral Wave Formation in 2D Cardiac Tissue
==========================================

Overview:
---------
This example demonstrates how to initiate and observe a spiral wave 
in a two-dimensional cardiac tissue model using the Aliev-Panfilov equations. 
Spiral waves are a key phenomenon in cardiac electrophysiology, often linked to 
arrhythmias and reentrant activity.

Simulation Setup:
-----------------
- Tissue Grid: A 256×256 cardiac tissue domain.
- Spiral Wave Initiation:
  - First stimulus: Applied along the top boundary at time 0.
  - Second stimulus: Applied to the right half of the domain at time 50.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 
  - Spatial resolution (dr): 0.3 
  - Total simulation time (t_max): 150 

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply two sequential stimulations:
   - The first stimulus excites a wavefront across the tissue.
   - The second stimulus, applied after a delay, breaks the wavefront, 
     leading to spiral wave formation.
3. Initialize and configure the Aliev-Panfilov model.
4. Run the simulation to observe spiral wave dynamics.
5. Visualize the final membrane potential distribution.

Spiral Wave Mechanism:
----------------------
Spiral waves emerge due to the interaction of an initial wave and a secondary 
stimulus applied at a critical time and location. These waves are relevant 
to studying:
- Reentrant arrhythmias (such as ventricular tachycardia).
- Excitation wave turbulence in cardiac tissue.
- Wavefront stability and self-sustained oscillations.

Visualization:
--------------
The final membrane potential distribution is displayed using matplotlib, 
revealing the characteristic spiral pattern.
""""""


import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 256
tissue = fw.CardiacTissue2D([n, n])


# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, volt_value=1,
                                             x1=0, x2=n, y1=0, y2=5))
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=50, volt_value=1,
                                             x1=n//2, x2=n, y1=0, y2=n))

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.3
aliev_panfilov.t_max = 150
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.imshow(aliev_panfilov.u)
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/chaotic_pattern.py",".py","2589","74","""""""
Spiral Wave Breakup and Induced Chaos (Aliev-Panfilov 2D)
==========================================================

Overview:
---------
This example demonstrates how to initiate a spiral wave in a 2D excitable 
medium using the Aliev-Panfilov model and subsequently destabilize it with 
two additional stimuli. This approach leads to spiral wave breakup and the 
onset of chaotic, fibrillation-like activity in a homogeneous tissue.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 homogeneous cardiac tissue domain.
- Model: Aliev-Panfilov 2D model.
- Stimulation Protocol:
  - **S1 (t = 0 ms)**: Planar stimulus to the top half of the tissue.
  - **S2 (t = 31 ms)**: Vertical stimulus on the left half to induce wave rotation (spiral).
  - **S3–S4 (t = 75 ms, 125 ms)**: Localized current pulses in the bottom center 
    to destabilize the spiral and trigger wave breakup.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 195 ms

Execution:
----------
1. A planar wave is launched at the top to propagate downward.
2. A second stimulus creates a partial reentry and initiates a spiral.
3. Two well-timed localized stimuli are applied near the spiral core, 
   leading to fragmentation and chaotic wave propagation.
4. The model is integrated over time to observe the evolution of excitation.

Expected Outcome:
-----------------
- Formation of a spiral wave pattern.
- Spiral destabilization due to extra stimuli.
- Emergence of complex, self-sustaining chaotic patterns resembling electrical fibrillation.

Visualization:
--------------
The final membrane potential is visualized using matplotlib. 
Chaotic activity is indicated by irregular, fragmented wavefronts.

""""""

import matplotlib.pyplot as plt
import finitewave as fw

n = 200
tissue = fw.CardiacTissue2D((n, n))

stim_sequence = fw.StimSequence()

stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, n, 0, n//2))
stim_sequence.add_stim(fw.StimVoltageCoord2D(31, 1, 0, n//2, 0, n))
# extra stimuli to break the spiral waves: 
stim_sequence.add_stim(fw.StimCurrentCoord2D(75, 3, 3, 90, 100, n//2, n))
stim_sequence.add_stim(fw.StimCurrentCoord2D(125, 3, 3, 90, 100, n//2, n))

# Set up the Aliev-Panfilov model:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 195
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

aliev_panfilov.run()

plt.imshow(aliev_panfilov.u, cmap='plasma')
plt.title(""Chaotic pattern"")
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/commands.py",".py","3707","103","""""""
Fenton-Karma 2D Model (Interrupt via Custom Command)
====================================================

Overview:
---------
This example demonstrates how to use the `Command` and `CommandSequence` interfaces in Finitewave
to inject custom logic into a cardiac electrophysiology simulation. Specifically, we interrupt the
simulation when the wave of excitation reaches the far edge of the tissue. This demonstrates how
to trigger actions based on the state of the system.

Simulation Setup:
-----------------
- Tissue Grid: A 300×300 cardiac tissue domain.
- Mesh: Entire domain is active tissue (`1.0` values).
- Model: Fenton-Karma 2D model is used for wave propagation.
- Stimulation: A voltage stimulus is applied along the entire left edge.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 ms
  - Spatial resolution (dr): 0.25 mm
  - Total simulation time (t_max): 1000 ms

Command Usage:
--------------
- Custom command `InterruptCommand` inherits from `Command`.
- At every 10 ms of simulation time (from 0 to 190 ms), the command checks if the wave has 
  reached the far-right side (`x = 298`).
- If any value of the membrane potential exceeds 0.5 on this edge, the simulation is terminated
  early by setting `model.step = np.inf`.

Execution:
----------
1. Initialize a square 2D tissue with a full mesh of excitable tissue.
2. Apply a uniform voltage stimulus along the leftmost edge (columns `0–1`).
3. Set up a sequence of `InterruptCommand` checks at regular intervals.
4. Run the simulation. It will self-interrupt once the wave reaches the far side.

Effect of Custom Command:
-------------------------
This feature is useful for:
- Saving computational time by stopping early based on user-defined conditions.
- Triggering intermediate analysis, adaptive pacing, or feedback-based protocols.
- Debugging or validation of wave speed and tissue responsiveness.

Visualization:
--------------
No visualization is included in this example, but users can integrate `matplotlib` or export
model states using built-in Finitewave I/O utilities.

Notes:
------
- The `Command` and `CommandSequence` classes allow flexible integration of logic and control flow
  without modifying the core model.
- This technique is extendable to more complex use cases such as region-specific feedback, pacing adjustment, 
  or custom logging.

""""""

import numpy as np

import finitewave as fw


# number of nodes on the side
n = 300

tissue = fw.CardiacTissue2D([n, n])
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = np.ones([n, n], dtype=float)
# add empty nodes on the sides (elems = 0):

# create model object:
fenton_karma = fw.FentonKarma2D()
# set up numerical parameters:
fenton_karma.dt    = 0.01
fenton_karma.dr    = 0.25
fenton_karma.t_max = 1000

# Define the command:
class InterruptCommand(fw.Command):
    def execute(self, model):
        if np.any(model.u[:, 298] > 0.5):
             # increase the calculation step to stop the execution loop.
             model.step = np.inf
             print (""Propagation wave reached the opposite side. Stop calculation."")

# We want to check the opposite side every 10 time units.
# Thus we have a list of commands with the same method but different times.
command_sequence = fw.CommandSequence()
for i in range(0, 200, 10):
    command_sequence.add_command(InterruptCommand(i))

fenton_karma.command_sequence = command_sequence

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, n, 0, 5))

# add the tissue and the stim parameters to the model object:
fenton_karma.cardiac_tissue = tissue
fenton_karma.stim_sequence  = stim_sequence

fenton_karma.run()","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/matrix_stimulation.py",".py","2589","86","
""""""
Matrix Stimulation in 2D Cardiac Tissue
=======================================

Overview:
---------
This example demonstrates how to apply matrix-based stimulation
in a two-dimensional cardiac tissue model using the Fenton-Karma 
equations. Instead of a single stimulus source, this method applies 
stimulation at multiple predefined locations across the tissue.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 cardiac tissue domain.
- Multiple Stimulus Areas: Stimulation is applied at four distinct points.
- Stimulation Shape: Each stimulus is applied over a circular area (radius = 5).
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 10

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Define four circular stimulation areas using `skimage.draw.disk`.
3. Apply the stimuli as a matrix using `StimVoltageMatrix2D`.
4. Initialize and configure the Fenton-Karma model.
5. Run the simulation to observe how multiple stimulation sites influence 
   wave propagation.
6. Visualize the final membrane potential distribution.

Application:
------------
This method is useful for simulating paced activation patterns seen 
in electrophysiology studies, where multiple sites are excited 
simultaneously. It can help analyze conduction velocity, wavefront 
interactions, and reentry formation.

Visualization:
--------------
The final membrane potential distribution is displayed using matplotlib, 
showing how excitation spreads from the stimulated regions.
""""""


import matplotlib.pyplot as plt
from skimage import draw
import numpy as np

import finitewave as fw

# set up cardiac tissue:
n = 400
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_area = np.full([400, 400], False, dtype=bool)
ii, jj = draw.disk([100, 100], 5)
stim_area[ii, jj] = True
ii, jj = draw.disk([100, 300], 5)
stim_area[ii, jj] = True
ii, jj = draw.disk([300, 100], 5)
stim_area[ii, jj] = True
ii, jj = draw.disk([300, 300], 5)
stim_area[ii, jj] = True
stim_sequence.add_stim(fw.StimVoltageMatrix2D(0, 1, stim_area))

# create model object:
fenton_karma = fw.FentonKarma2D()
# set up numerical parameters:
fenton_karma.dt = 0.01
fenton_karma.dr = 0.25
fenton_karma.t_max = 10
# add the tissue and the stim parameters to the model object:
fenton_karma.cardiac_tissue = tissue
fenton_karma.stim_sequence = stim_sequence

fenton_karma.run()

# show the potential map at the end of calculations:
# plt.figure()
plt.imshow(fenton_karma.u)
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/anisotropic_medium_2d.py",".py","2688","85","
""""""
Aliev-Panfilov 2D Model (Anisotropic)
=====================================

Overview:
---------
This example demonstrates how to simulate the Aliev-Panfilov model in a 
two-dimensional anisotropic cardiac tissue. Unlike the isotropic case, 
anisotropy is introduced by specifying a fiber orientation array, which 
modifies the diffusion properties of the tissue.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 cardiac tissue domain is created.
- Anisotropic Diffusion: Fiber orientation is set using a direction field.
- Fiber Orientation: Defined by an angle alpha = 0.25 * pi.
- Stimulation: A localized stimulus is applied at the center of the domain.
- Time and Space Resolution:
  - Temporal step (dt): 0.01 
  - Spatial resolution (dr): 0.25 
  - Total simulation time (t_max): 30 

Execution:
----------
1. Create a 2D cardiac tissue grid with fiber orientation.
2. Define and apply a stimulus at the center.
3. Set up and initialize the Aliev-Panfilov model.
4. Run the simulation to compute wave propagation in an anisotropic medium.
5. Visualize the membrane potential distribution at the final timestep.

Anisotropic Diffusion:
----------------------
Anisotropy is implemented by defining a fiber orientation field for the 
CardiacTissue object. The model automatically selects the appropriate stencil 
to calculate the diffusion term based on fiber direction.

Visualization:
--------------
The final membrane potential distribution is displayed using matplotlib, 
showing how the excitation wave propagates in the anisotropic medium.
""""""


import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 400
# fiber orientation angle
alpha = 0.25 * np.pi
tissue = fw.CardiacTissue2D([n, n])
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = np.ones([n, n])
tissue.add_boundaries()
# add fibers orientation vectors
tissue.fibers = np.zeros([n, n, 2])
tissue.fibers[:, :, 0] = np.cos(alpha)
tissue.fibers[:, :, 1] = np.sin(alpha)

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 3, n//2 + 3,
                                                n//2 - 3, n//2 + 3))

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 30
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/using_states.py",".py","4838","148","""""""
StateKeeper Example: Saving and Loading Simulation States
=========================================================

Overview:
---------
This example demonstrates how to use the StateKeeper functionality in 
Finitewave to save and restore the state of a 2D cardiac simulation. 
This allows a simulation to be paused and resumed at a later time 
without restarting from the beginning.

Key Features:
-------------
- State Saving: The model saves intermediate states at specific times.
- State Loading: The simulation is resumed from a saved state.
- Multiple Runs: The model is executed in three phases:
  1. First run (0 - 20): The initial simulation run.
  2. Second run (10 - 20): Resumes from a saved state at t = 10.
  3. Third run (20 - 30): Resumes from a saved state at t = 20.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 cardiac tissue domain.
- Stimulation: A small localized stimulus applied at the center.
- State Saving:
  - The state is saved at t = 10 (""state_0"").
  - The final state is saved at t = 20 (""state_1"").
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - First run duration: 0 - 20
  - Second and third run durations: 10 - 20 each.

Execution Workflow:
-------------------
1. Run the first simulation and save the state at t = 10 and t = 20.
2. Delete the model instance and clear memory using `gc.collect()`.
3. Create a new model instance and load ""state_0"", then continue the 
   simulation from t = 10 to 20.
4. Create another new instance, load ""state_1"", and run from t = 20 to 30.
5. Visualize the results:
   - First run (t=0 to 20)
   - Second run (t=10 to 20)
   - Third run (t=20 to 30)
6. Delete saved states to clean up temporary files.

Application:
------------
State saving is useful for:
- Long simulations: Avoids restarting from scratch in case of interruptions.
- Parameter tuning: Allows resuming simulations from intermediate states.
- Multi-stage analysis: Investigates different scenarios from a common starting point.

Visualization:
--------------
The final results are displayed using matplotlib, showing the progression of 
the simulation across the three phases.
""""""

import matplotlib.pyplot as plt
import gc
import shutil

import finitewave as fw

# number of nodes on the side
# create a tissue of size 400x400 with cardiomycytes:
n = 400
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 5, n//2 + 5,
                                             n//2 - 5, n//2 + 5))

# set up state saver parameters:
# to save only one state you can use StateSaver directly
state_savers = fw.StateSaverCollection()
state_savers.savers.append(fw.StateSaver(""state_0"", time=10)) # will save at t=10
state_savers.savers.append(fw.StateSaver(""state_1"")) # will save at t=20 (at the end of the run)

# create model object and set up parameters:
mitchell_schaeffer = fw.MitchellSchaeffer2D()
mitchell_schaeffer.dt = 0.01
mitchell_schaeffer.dr = 0.25
mitchell_schaeffer.t_max = 20
# add the tissue and the stim parameters to the model object:
mitchell_schaeffer.cardiac_tissue = tissue
mitchell_schaeffer.stim_sequence = stim_sequence
mitchell_schaeffer.state_saver = state_savers

# run the model:
mitchell_schaeffer.run()

u_before = mitchell_schaeffer.u.copy()

# We delete model and use gc.collect() to ask the virtual machine remove
# objects from memory. Though it's not necessary to do this.
del mitchell_schaeffer
gc.collect()

# # # # # # # # #

# Here we create a new model and load states from the previous calculation to
# continue.


# recreate the model
mitchell_schaeffer = fw.MitchellSchaeffer2D()

# set up numerical parameters:
mitchell_schaeffer.dt = 0.01
mitchell_schaeffer.dr = 0.25
mitchell_schaeffer.t_max = 10
# add the tissue and the state_loader to the model object:
mitchell_schaeffer.cardiac_tissue = tissue
mitchell_schaeffer.state_loader = fw.StateLoader(""state_0"")

mitchell_schaeffer.run()
u_after = mitchell_schaeffer.u.copy()

# recreate the model
mitchell_schaeffer = fw.MitchellSchaeffer2D()

# set up numerical parameters:
mitchell_schaeffer.dt = 0.01
mitchell_schaeffer.dr = 0.25
mitchell_schaeffer.t_max = 10
# add the tissue and the state_loader to the model object:
mitchell_schaeffer.cardiac_tissue = tissue
mitchell_schaeffer.state_loader = fw.StateLoader(""state_1"")

mitchell_schaeffer.run()

# plot the results
fig, axs = plt.subplots(1, 3, figsize=(10, 5))
axs[0].imshow(u_before)
axs[0].set_title(""First run from t=0 to t=20"")
axs[1].imshow(u_after)
axs[1].set_title(""Second run from t=10 to t=20"")
axs[2].imshow(mitchell_schaeffer.u)
axs[2].set_title(""Third run from t=20 to t=30"")
plt.show()

# remove the state directory
shutil.rmtree(""state_0"")
shutil.rmtree(""state_1"")
","Python"
"Cell biology","finitewave/Finitewave","examples/basics/2D/isotropic_medium_2d.py",".py","2023","66","""""""
Aliev-Panfilov 2D Model (Isotropic)
====================================

Overview:
---------
This example demonstrates how to simulate the Aliev-Panfilov model in a 
two-dimensional isotropic medium using the Finitewave framework. The model 
describes the propagation of electrical waves in excitable media, such as 
cardiac tissue, and captures fundamental excitation and recovery dynamics.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 homogeneous cardiac tissue is created.
- Isotropic Stencil: Diffusion is uniform in all directions.
- Stimulation: A localized stimulus is applied at the center of the domain.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 30

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Define and apply a stimulus at the center.
3. Set up and initialize the Aliev-Panfilov model.
4. Run the simulation to compute wave propagation.
5. Visualize the membrane potential map at the final timestep.

Visualization:
--------------
The final membrane potential distribution is displayed using `matplotlib`, 
showing the resulting excitation wave pattern.
""""""

import matplotlib.pyplot as plt

import finitewave as fw

# create a tissue of size 400x400 with cardiomycytes:
n = 400
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 3, n//2 + 3,
                                             n//2 - 3, n//2 + 3))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 30
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.figure()
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/animation_3d_tracker.py",".py","2700","81","""""""
Animation3DTracker Example
==========================

This example demonstrates how to use the Animation3DTracker to generate a
visualization of transmembrane potential (u) over time in a 3D cardiac tissue
simulation using the Aliev-Panfilov model.

Overview:
---------
The tracker captures snapshots of the selected variable during the simulation
and later compiles them into an animation (e.g. .mp4 video).

Simulation Setup:
-----------------
- Tissue: A 3D slab of size 100×100×10.
- Stimulation:
  - First wave is initiated from the lower half of the tissue at t = 0.
  - Second wave is initiated from the left half at t = 31 to create 
    wavefront interactions.
- Tracking:
  - The transmembrane potential (`u`) is recorded every 10 steps.
  - Snapshots are stored in the folder `anim_data` and compiled into a .mp4.

Execution:
----------
1. Simulate wave propagation using the Aliev-Panfilov model.
2. Save snapshots of `u` at regular intervals.
3. Compile snapshots into an animation after the simulation.

Applications:
-------------
- Useful for visualizing wave dynamics in 3D, such as propagation, collision,
  or reentry.
- Supports model validation, presentation, and educational use.

Output:
-------
An `.mp4` animation file in the `anim_data` folder, showing how `u` evolves
over time in the 3D domain.
""""""

import numpy as np

import finitewave as fw

# set up the tissue:
n = 100
nk = 10
tissue = fw.CardiacTissue3D([n, n, nk])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, n, 0, n//2, 0, nk))
stim_sequence.add_stim(fw.StimVoltageCoord3D(31, 1, 0, n//2, 0, n, 0, nk))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
animation_tracker = fw.Animation3DTracker()
animation_tracker.variable_name = ""u""  # Specify the variable to track
animation_tracker.dir_name = ""anim_data""
animation_tracker.step = 10
animation_tracker.overwrite = True  # Remove existing files in dir_name
tracker_sequence.add_tracker(animation_tracker)

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 100
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence
# run the model:
aliev_panfilov.run()

# write animation and clear the snapshot folder
animation_tracker.write(format='mp4', framerate=10, quality=9,
                        clear=True, clim=[0, 1]) # !Note: for ionic models use clim=[-90, 40] or similar to show the activity correctly","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/spiral_wave_core_3d_tracker.py",".py","3051","101","
""""""
Spiral Wave Core Tracking in 3D
===============================

This example demonstrates how to use the SpiralWaveCore3DTracker in Finitewave
to locate and track the core of a scroll wave (3D spiral wave) over time in 
a simulated cardiac tissue using the Aliev–Panfilov model.

Overview:
---------
- A planar wave is first initiated from the bottom of the tissue.
- A second stimulus is delivered from the left half to induce a scroll wave.
- The SpiralWaveCore3DTracker identifies the locations in the tissue where 
  phase singularities form — these correspond to the spiral wave cores.

Simulation Setup:
-----------------
- Tissue: 200×200×10 3D slab
- Time and Space:
  - Time step (dt): 0.01
  - Space step (dr): 0.25
  - Simulation duration: 150
- Stimulation:
  - t = 0 : Stimulus along the bottom edge
  - t = 31: Stimulus from the left half — creates a broken wavefront

Core Tracking:
--------------
- Threshold: 0.5 (voltage level to define wavefront)
- Start Time: 40 (after wave has developed)
- Step: 100 steps between core detections
- Output: x, y, z coordinates of scroll wave core and corresponding time points

Visualization:
--------------
The scroll wave core trajectory is visualized as a 3D scatter plot using `matplotlib`,
with the color mapped to the corresponding time of core appearance.

Applications:
-------------
- Useful for studying scroll wave dynamics and anchoring
- Helps analyze stability and drift of reentrant waves
- Can assist in identifying vulnerable tissue regions in 3D models

Note:
-----
This tracker provides sparse detection (once every `step`), and is best used
to observe long-term scroll wave motion rather than high-frequency detail.
""""""


import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 200
nk = 10

tissue = fw.CardiacTissue3D([n, n, nk])
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = np.ones([n, n, nk], dtype=""uint8"")
# add empty nodes on the sides (elems = 0):
tissue.add_boundaries()

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 150

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, n, 0, n//2, 0, nk))
stim_sequence.add_stim(fw.StimVoltageCoord3D(31, 1, 0, n//2, 0, n, 0, nk))

tracker_sequence = fw.TrackerSequence()
spiral_3d_tracker = fw.SpiralWaveCore3DTracker()
spiral_3d_tracker.threshold = 0.5
spiral_3d_tracker.start_time = 40
spiral_3d_tracker.step = 100
tracker_sequence.add_tracker(spiral_3d_tracker)

aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

swcore = spiral_3d_tracker.output

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(swcore['x'], swcore['y'], swcore['z'], c=swcore['time'],
           cmap='plasma', s=30)
ax.set_xlim(0, n)
ax.set_ylim(0, n)
ax.set_zlim(0, nk)
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/spiral_wave_period_3d_tracker.py",".py","3579","115","""""""
Wave Period in 3D Tissue
========================

This example demonstrates how to use the Period3DTracker in Finitewave to measure 
the wave period at specific locations in a 3D cardiac tissue simulation using 
the Aliev–Panfilov model.

Overview:
---------
The Period3DTracker detects threshold crossings (e.g., wave upstrokes) at 
specified cells to estimate the local activation period. This is useful for 
analyzing rhythm stability in sustained wave activity such as spiral or scroll waves.

Simulation Setup:
-----------------
- Tissue Size: 100×100×10
- Initial Conditions: Fully excitable tissue with no fibrosis
- Boundary Handling: No-flux boundaries using `add_boundaries()`
- Stimulation:
  - First planar stimulus at t = 0, applied to lower half of Y domain
  - Second planar stimulus at t = 31, applied to left half of X domain
  - This induces spiral-like propagation dynamics

Period Measurement:
-------------------
- Tracker: Period3DTracker
- Target Cells: 7 manually selected positions within the mid-slice (z = 5)
- Threshold: 0.5 (voltage level for upstroke detection)
- Start Time: 100 (to allow initiation to settle)
- Step: 10 (check voltage every 10 steps)

Output:
-------
- Mean and standard deviation of measured periods per cell
- A matplotlib errorbar plot shows variability across spatial locations

Application:
------------
- Useful for scroll/spiral wave analysis
- Can help detect regions with rhythm instability or alternans
- Supports investigation of how geometry or fibrosis affects pacing regularity

Note:
-----
For full local activation time maps and wavefront tracking, consider using 
`LocalActivationTime3DTracker`.
""""""


import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 100
nk = 10

tissue = fw.CardiacTissue3D([n, n, nk])
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = np.ones([n, n, nk], dtype=""uint8"")
# add empty nodes on the sides (elems = 0):
tissue.add_boundaries()

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 300

# induce spiral wave:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, n, 0, n//2, 0, nk))
stim_sequence.add_stim(fw.StimVoltageCoord3D(31, 1, 0, n//2, 0, n, 0, nk))

tracker_sequence = fw.TrackerSequence()
period_tracker = fw.Period3DTracker()
positions = np.array([[1, 1, 5],
                      [5, 5, 5],
                      [7, 3, 5],
                      [9, 1, 5],
                      [50, 50, 5],
                      [75, 3, 5],
                      [50, 75, 5]])
period_tracker.cell_ind = positions
period_tracker.threshold = 0.5
period_tracker.start_time = 100
period_tracker.step = 10
tracker_sequence.add_tracker(period_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# get the wave period as a pandas Series with the cell index as the index:
periods = period_tracker.output

# plot the wave period:
plt.figure()
plt.errorbar(range(len(positions)),
             periods.apply(lambda x: x.mean()),
             yerr=periods.apply(lambda x: x.std()),
             fmt='o')
plt.xticks(range(len(positions)),
           [f'({x[0]}, {x[1]}, {x[2]})' for x in positions],
           rotation=45)
plt.xlabel('Cell Index')
plt.ylabel('Period')
plt.title('Wave period')
plt.tight_layout()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/action_potential_3d_tracker.py",".py","2616","85","""""""
ActionPotential3DTracker
=========================

This example demonstrates how to use the ActionPotential3DTracker in a 3D tissue
simulation with the Aliev-Panfilov model.

Overview:
---------
The ActionPotential3DTracker allows you to monitor and record the transmembrane
potential (u) over time at specific locations within the 3D cardiac tissue.

Simulation Setup:
-----------------
- Tissue: A 3D slab of size 100×100×10 with default isotropic mesh.
- Stimulation: Planar stimulation applied at the left boundary (x ∈ [0, 3]).
- Tracking:
  - Two measurement points are selected:
    - [30, 30, 5]
    - [70, 70, 8]
  - Tracker records the value of `u` at every time step.

Execution:
----------
1. A 3D tissue is created and stimulated from one side.
2. The ActionPotential3DTracker records action potentials at the given cell
   locations throughout the simulation.
3. The recorded time series is visualized using matplotlib.

Applications:
-------------
- Useful for analyzing wave propagation, latency, and signal morphology.
- Can be used for APD measurement, restitution curve analysis, or comparing
  regional tissue responses in 3D.

Output:
-------
A plot showing transmembrane potential over time for each measurement point.
""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 100
nj = 100
nk = 10

tissue = fw.CardiacTissue3D([n, nj, nk])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 3, 0, nj, 0, nk))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[30, 30, 5], [70, 70, 8]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov3D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 50
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# plot the action potential
time = np.arange(len(action_pot_tracker.output)) * aliev_panfilov.dt

plt.figure()
plt.plot(time, action_pot_tracker.output[:, 0], label=""cell_30_30_5"")
plt.plot(time, action_pot_tracker.output[:, 1], label=""cell_70_70_8"")
plt.legend(title='Aliev-Panfilov')
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/local_activation_times_3d_tracker.py",".py","4023","124","""""""
Local Activation Time in 3D
===========================

This example demonstrates how to use the LocalActivationTime3DTracker to track 
the local activation times in a 3D cardiac tissue slab using the Aliev–Panfilov model.

Overview:
---------
The LocalActivationTime3DTracker records activation times at each node when the
membrane potential crosses a defined threshold. Unlike standard activation time 
trackers, it can store multiple activations (e.g., from reentry or spiral waves)
and enables detailed temporal analysis of wavefront propagation.

Simulation Setup:
-----------------
- Tissue: A 3D slab of size 200×200×10.
- Stimulation:
  - First stimulus: a planar front at y=0–5, applied at t=0.
  - Second stimulus: half of the domain (x=n/2 to n), applied at t=50.
- Model:
  - Aliev–Panfilov in 3D with dt = 0.01 and dr = 0.3 units.
  - Total simulation time: 200.
- Tracker:
  - `LocalActivationTime3DTracker` activated from t=100 to t=200.
  - Records activation times every 10 steps (step=10).
  - Activation threshold set to 0.5.

Visualization:
--------------
- Two time points (150 and 170) are visualized.
- For each, a 3D scatter plot shows all nodes activated at or after the given time.
- Activation time is color-coded using a viridis colormap.

Applications:
-------------
- Visualization of reentrant waves in 3D.
- Analysis of wavefront timing and conduction delays.
- Studying effects of geometry, fibrosis, or heterogeneity on activation dynamics.

Output:
-------
- Two 3D plots showing activation times at specified time bases.
- Printed number of LATs (activation events) recorded by the tracker.
""""""


import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 200
nk = 10
tissue = fw.CardiacTissue3D([n, n, nk])

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.3
aliev_panfilov.t_max = 200

# induce spiral wave:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(time=0, volt_value=1, x1=0, x2=n,
                                             y1=0, y2=5, z1=0, z2=nk))
stim_sequence.add_stim(fw.StimVoltageCoord3D(time=50, volt_value=1, x1=n//2,
                                             x2=n, y1=0, y2=n, z1=0, z2=nk))

# set up the tracker:
tracker_sequence = fw.TrackerSequence()
act_time_tracker = fw.LocalActivationTime3DTracker()
act_time_tracker.threshold = 0.5
act_time_tracker.step = 10
act_time_tracker.start_time = 100
act_time_tracker.end_time = 200
tracker_sequence.add_tracker(act_time_tracker)

# connect model with tissue, stim and tracker:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

# run the simulation:
aliev_panfilov.run()

# plot the activation time map:
time_bases = [150, 170]  # time bases to plot the activation time map
lats = act_time_tracker.output
print(f'Number of LATs: {len(act_time_tracker.output)}')

fig = plt.figure(figsize=(15, 5))

for i, time_base in enumerate(time_bases):
    ax = fig.add_subplot(1, len(time_bases), i + 1, projection='3d')

    # Select the activation times next closest to the time base
    mask = np.any(lats >= time_base, axis=0)
    ids = np.argmax(lats >= time_base, axis=0)
    ids = tuple((ids[mask], *np.where(mask)))

    act_time = np.full([n, n, nk], np.nan)
    act_time[mask] = lats[ids]

    act_time_min = time_base
    act_time_max = time_base + 30

    # Create a 3D scatter plot
    x, y, z = np.where(~np.isnan(act_time))
    values = act_time[~np.isnan(act_time)]

    scatter = ax.scatter(x, y, z, c=values, cmap='viridis', vmin=act_time_min, vmax=act_time_max)
    ax.set_title(f'Activation time: {time_base} ms')
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')

    cbar = fig.colorbar(scatter, ax=ax, orientation='vertical', pad=0.1)
    cbar.set_label('Activation Time (ms)')

plt.tight_layout()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/variables_3d_tracker.py",".py","3260","100","""""""
Tracking State Variables in 3D Cardiac Tissue
=============================================

This example demonstrates how to use the `Variable3DTracker` and 
`MultiVariable3DTracker` classes in Finitewave to monitor the evolution of 
model state variables (e.g., transmembrane potential `u` and recovery variable `v`)
at specific cell locations within a 3D cardiac tissue model.

Overview:
---------
- The Aliev–Panfilov model is run on a 3D slab of tissue.
- Two trackers are used:
  1. `Variable3DTracker` — tracks a single variable `u` at cell (40, 40, 5).
  2. `MultiVariable3DTracker` — tracks both `u` and `v` at cell (30, 30, 5).
- A planar stimulus is applied from one side to generate an action potential.

Simulation Setup:
-----------------
- Tissue: 100×100×10 3D grid of cardiomyocytes
- Time step: 0.01
- Space step: 0.25
- Total duration: 100
- Stimulation: Small region at the front-left corner

Tracker Details:
----------------
- `Variable3DTracker` is ideal for lightweight tracking of a single variable.
- `MultiVariable3DTracker` allows simultaneous tracking of multiple state variables
  at the same spatial location.

Visualization:
--------------
The results are plotted using `matplotlib` to compare:
- The `u` values from both trackers.
- The evolution of `v` at the measurement location.

Applications:
-------------
- Useful for action potential shape analysis.
- Helps compare transmembrane dynamics across different cell locations.
- Can be used to validate ionic models or study parameter sensitivity.
""""""


import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 100
nk = 10

# create tissue object:
tissue = fw.CardiacTissue3D([n, n, nk])
tissue.mesh = np.ones([n, n, nk], dtype=""uint8"")
tissue.add_boundaries()

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()

# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 100

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 1, 3, 1, n, 1, nk))

tracker_sequence = fw.TrackerSequence()
# add one variable tracker:
variable_tracker = fw.Variable3DTracker()
variable_tracker.var_name = ""u""
variable_tracker.cell_ind = [40, 40, 5]
tracker_sequence.add_tracker(variable_tracker)

# add the multi variable tracker:
multivariable_tracker = fw.MultiVariable3DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
multivariable_tracker.cell_ind = [30, 30, 5]
multivariable_tracker.var_list = [""u"", ""v""]
tracker_sequence.add_tracker(multivariable_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# plot the action potential and state variable v at the measuring point
time = np.arange(len(multivariable_tracker.output[""u""])) * aliev_panfilov.dt

plt.plot(time, variable_tracker.output, label=""u"")
plt.plot(time, multivariable_tracker.output[""u""], label=""u"")
plt.plot(time, multivariable_tracker.output[""v""], label=""v"")
plt.legend(title=aliev_panfilov.__class__.__name__)
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/simple_activation_3d_tracker.py",".py","3008","94","""""""
Activation Time in 3D
=====================

This example demonstrates how to compute and visualize activation times in a 
3D cardiac tissue model using the Aliev–Panfilov model and the 
ActivationTime3DTracker in Finitewave.

Overview:
---------
The ActivationTime3DTracker records the time when the membrane potential at 
each node first crosses a specified threshold. This is a useful way to visualize
the propagation of the activation wave across the tissue volume.

Simulation Setup:
-----------------
- Domain: 3D slab of size 100×100×10 with uniform cardiomyocytes (value = 1).
- Boundaries: Added using `add_boundaries()` to define no-flux edges.
- Conductivity: Uniform (1.0) across the tissue.
- Fiber orientation: Longitudinal (along the x-axis).
- Stimulation: Applied to a thin slab at x = 0–3 across the entire yz-plane at t=0.
- Model: Aliev–Panfilov 3D with dt = 0.01, dr = 0.25 units, and t_max = 60.
- Tracker: ActivationTime3DTracker with threshold = 0.5.

Visualization:
--------------
- Activation times are rendered using `VisMeshBuilder3D`.
- The output is color-coded using the ""viridis"" colormap to show propagation fronts.

Applications:
-------------
- Analysis of conduction velocity and wavefront dynamics.
- Testing isotropic and anisotropic propagation scenarios.
- Foundation for conduction delay studies in healthy and fibrotic tissue.

Output:
-------
- A 3D scalar field plot of activation times using the internal visualization
  tools of Finitewave.
""""""


import numpy as np
import finitewave as fw

# number of nodes on the side
n = 100
nj = 100
nk = 10

tissue = fw.CardiacTissue3D([n, nj, nk])
# create a mesh of cardiomyocytes (elems = 1):
tissue.mesh = np.ones([n, nj, nk], dtype=""uint8"")
# add empty nodes on the sides (elems = 0):
tissue.add_boundaries()

# add a conductivity array, all elements = 1.0 -> normal conductvity:
tissue.cond = np.ones([n, nj, nk])

# add fibers (oriented along X):
tissue.fibers = np.zeros([n, nj, nk, 3])
tissue.fibers[:, :, 0] = 1.
tissue.fibers[:, :, 1] = 0.
tissue.fibers[:, :, 2] = 0.

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()

# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 60

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, 3, 0, nj, 0, nk))

tracker_sequence = fw.TrackerSequence()
act_time_tracker = fw.ActivationTime3DTracker()
act_time_tracker.target_model = aliev_panfilov
act_time_tracker.threshold = 0.5
tracker_sequence.add_tracker(act_time_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

mesh_builder = fw.VisMeshBuilder3D()
grid = mesh_builder.build_mesh(tissue.mesh)
grid = mesh_builder.add_scalar(act_time_tracker.act_t, name='Activation Time')
grid.plot(cmap='viridis')","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/3D/ecg_3d_tracker.py",".py","3065","98","
""""""
ECG in 3D Slab
==============

This example demonstrates how to use the ECG3DTracker to simulate extracellular
electrograms (pseudo-ECG signals) generated by a 3D cardiac tissue slab 
using the Aliev-Panfilov model.

Overview:
---------
The ECG3DTracker computes simplified ECG-like signals based on the transmembrane 
currents in the tissue and their distance to virtual electrode positions located 
outside the slab.

Simulation Setup:
-----------------
- Tissue: A 3D slab of size 200×200×5.
- Stimulation:
  - A planar voltage stimulus is applied at the bottom edge (y=0 to y=5) at t = 0 ms.
- Measurement:
  - Virtual electrodes are placed above the slab (z = nk + 3).
  - Three positions are used:
      - Center:        [100, 100, 8]
      - Left-center:   [ 50, 100, 8]
      - Bottom-right:  [150, 150, 8]
  - Transmembrane potentials are sampled every 100 time steps.

Tracker:
--------
- ECG3DTracker integrates the contribution of the transmembrane current from all 
  active tissue elements to each measurement point, using a distance-based 
  approximation of the extracellular potential.

Output:
-------
A matplotlib plot showing ECG signals at the three electrode positions over time.
This allows visualizing the propagation of electrical activity through the tissue 
as detected externally.

Applications:
-------------
- Educational visualizations of how wavefronts generate extracellular signals.
- Comparison of ECG morphology at different electrode locations.
- Study of pacing, reentry, or arrhythmic patterns via pseudo-ECG.
""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 200
nk = 5

tissue = fw.CardiacTissue3D([n, n, nk])
# create a mesh of cardiomyocytes (elems = 1):

# create model object:
aliev_panfilov = fw.AlievPanfilov3D()
aliev_panfilov.dt = 0.0015
aliev_panfilov.dr = 0.1
aliev_panfilov.t_max = 50

# induce the spiral wave:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1,
                                             0, n,
                                             0, 5,
                                             0, nk))

tracker_sequence = fw.TrackerSequence()
# create an ECG tracker:
ecg_tracker = fw.ECG3DTracker()
ecg_tracker.start_time = 0
ecg_tracker.step = 100
ecg_tracker.measure_coords = np.array([[n//2, n//2, nk+3],
                                       [n//4, n//2, nk+3],
                                       [3*n//4, 3*n//4, nk+3]])

tracker_sequence.add_tracker(ecg_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

colors = ['tab:blue', 'tab:orange', 'tab:green']
plt.figure()
for i, y in enumerate(ecg_tracker.output.T):
    x = np.arange(len(y)) * aliev_panfilov.dt * ecg_tracker.step
    plt.plot(x, y, '-o', color=colors[i], label='precomputed distances')

plt.legend(title='ECG computed with')
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/spiral_wave_core_2d_tracker.py",".py","3051","97","
""""""
Tracking Spiral Wave Core in 2D Cardiac Tissue
==============================================

Overview:
---------
This example demonstrates how to use the SpiralWaveCore2DTracker to track 
the core of a spiral wave in a 2D cardiac tissue simulation. Spiral 
waves are essential phenomena in cardiac electrophysiology and are closely 
related to reentrant arrhythmias.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 cardiac tissue domain.
- Spiral Wave Initiation:
  - A first stimulus excites the lower half of the tissue at t = 0.
  - A second stimulus is applied to the left half at t = 31, 
    breaking the wavefront and initiating spiral wave formation.
- Spiral Core Tracking:
  - Threshold: 0.5 (voltage level used to detect the wave core).
  - Tracking start time: 50 (after wave formation).
  - Recording interval: Every 100 steps.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 300

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply two sequential stimulations to induce a spiral wave.
3. Set up a spiral wave core tracker:
   - Tracks the movement of the wave’s center over time.
4. Run the Aliev-Panfilov model to simulate wave dynamics.
5. Extract and visualize the spiral wave trajectory.

Application:
------------
Tracking the spiral wave core is useful for:
- Analyzing reentrant arrhythmias and spiral wave stability.
- Studying spiral wave drift and anchoring in different tissue conditions.
- Testing anti-arrhythmic strategies by analyzing wave behavior.

Visualization:
--------------
The spiral wave trajectory is plotted over the final membrane potential 
distribution using matplotlib, showing how the wave core moves over time.

""""""

import matplotlib.pyplot as plt

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, n, 0, n//2))
stim_sequence.add_stim(fw.StimVoltageCoord2D(31, 1, 0, n//2, 0, n))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
sw_core_tracker = fw.SpiralWaveCore2DTracker()
sw_core_tracker.threshold = 0.5
sw_core_tracker.start_time = 50
sw_core_tracker.step = 100  # Record the spiral wave core every 1 time unit
tracker_sequence.add_tracker(sw_core_tracker)

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 300
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

sw_core = sw_core_tracker.output

# plot the spiral wave trajectory:
plt.imshow(aliev_panfilov.u, cmap='viridis', origin='lower')
plt.plot(sw_core['x'], sw_core['y'], 'r')
plt.title('Spiral Wave Trajectory')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(0, n)
plt.ylim(0, n)

plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/ecg_2d_tracker.py",".py","3328","106","""""""
Electrocardiogram (ECG) Tracking in 2D Cardiac Tissue
=====================================================

Overview:
---------
This example demonstrates how to use the ECG2DTracker to record an 
electrocardiogram (ECG) from a 2D cardiac tissue simulation. The ECG 
signal is obtained from multiple measurement points at a given distance 
from the tissue.

Simulation Setup:
-----------------
- Tissue Grid: A 400×400 cardiac tissue domain.
- Stimulation:
  - A left-side stimulus is applied at time t = 0.
  - The excitation wave propagates across the tissue.
- ECG Tracking:
  - Three measurement points are positioned at increasing vertical distances.
  - The signal strength is computed using an inverse distance power law.
  - Measurement points:  
    - (n/2, n/4, 10)
    - (n/2, n/2, 10)
    - (n/2, 3n/4, 10)
  - Sampling step: Every 10 time steps.
- Time and Space Resolution:
  - Temporal step (dt): 0.001
  - Spatial resolution (dr): 0.1
  - Total simulation time (t_max): 50

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply stimulation along the left boundary.
3. Set up an ECG tracker:
   - Records electrical activity from multiple measurement points.
   - Uses an inverse distance weighting (power = 2) to compute the 
     potential at each location.
4. Run the Aliev-Panfilov model to simulate cardiac wave propagation.
5. Extract and visualize the ECG waveform.

Application:
------------
ECG tracking in a simulated tissue is useful for:
- Studying ECG signal characteristics in controlled environments.
- Understanding the relationship between wave propagation and ECG morphology.
- Testing the effect of different tissue properties on the ECG signal.

Visualization:
--------------
The recorded ECG signal is plotted over time using matplotlib, 
illustrating how electrical wave activity in cardiac tissue translates 
into an observable ECG trace.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# set up the tissue:
n = 200

tissue = fw.CardiacTissue2D([n, n])
# create a mesh of cardiomyocytes (elems = 1):

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.0015
aliev_panfilov.dr = 0.1
aliev_panfilov.t_max = 50

# induce the spiral wave:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1,
                                             0, n,
                                             0, 5))

tracker_sequence = fw.TrackerSequence()
# create an ECG tracker:
ecg_tracker = fw.ECG2DTracker()
ecg_tracker.start_time = 0
ecg_tracker.step = 100
ecg_tracker.measure_coords = np.array([[n//2, n//2, 10],
                                       [n//4, n//2, 10],
                                       [3*n//4, 3*n//4, 10]])

tracker_sequence.add_tracker(ecg_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

colors = ['tab:blue', 'tab:orange', 'tab:green']
plt.figure()
for i, y in enumerate(ecg_tracker.output.T):
    x = np.arange(len(y)) * aliev_panfilov.dt * ecg_tracker.step
    plt.plot(x, y, '-o', color=colors[i], label='precomputed distances')

plt.legend(title='ECG computed with')
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/simple_activation_time_2d_tracker.py",".py","2869","87","""""""
Tracking Activation Time in 2D Cardiac Tissue
=============================================

Overview:
---------
This example demonstrates how to track activation times during a 
2D cardiac tissue simulation using the ActivationTime2DTracker 
class in Finitewave. Activation time tracking helps analyze the propagation 
of electrical waves and conduction delays in excitable media.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 cardiac tissue domain.
- Stimulation:
  - A left-side stimulus is applied at time t = 0.
  - The excitation propagates across the tissue.
- Activation Time Tracking:
  - Threshold: 0.5 (membrane potential value used to define activation).
  - Sampling interval: Every 100 steps.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 50

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply stimulation along the left boundary to initiate wave propagation.
3. Set up an activation time tracker:
   - The tracker records the time of first activation for each tissue element.
4. Run the Aliev-Panfilov model to simulate wave dynamics.
5. Extract and visualize the activation time map.

Application:
------------
Tracking activation times is useful for:
- Analyzing conduction velocity in cardiac tissue.
- Detecting conduction blocks or delays in pathological conditions.
- Comparing different tissue properties (e.g., isotropic vs. anisotropic).

Visualization:
--------------
The activation time map is displayed using matplotlib, with a color-coded 
representation of activation delays across the tissue.

""""""

import matplotlib.pyplot as plt

import finitewave as fw

# create a mesh of cardiomyocytes (elems = 1):
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, volt_value=1,
                                             x1=0, x2=3, y1=0, y2=n))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
act_time_tracker = fw.ActivationTime2DTracker()
act_time_tracker.threshold = 0.5
act_time_tracker.step = 100  # calculate activation time every 100 steps
tracker_sequence.add_tracker(act_time_tracker)

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 50
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# plot the activation time map
plt.imshow(act_time_tracker.output, cmap=""viridis"")
cbar = plt.colorbar()
cbar.ax.set_ylabel('Time (model units)', rotation=270, labelpad=15)
plt.title(""Activation time map"")
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/local_activation_times_2d_tracker.py",".py","4218","129","""""""
Tracking Local Activation Time in 2D Cardiac Tissue
===================================================

Overview:
---------
This example demonstrates how to use the `LocalActivationTime2DTracker` to 
track multiple local activation events over time in a 2D cardiac tissue 
simulation using the Aliev-Panfilov model. Unlike `ActivationTime2DTracker`, 
which stores only the first activation time per cell, this tracker captures 
all threshold crossings during a specified time window.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 cardiac tissue domain.
- Spiral Wave Initiation:
  - First stimulus at t = 0 along the top edge.
  - Second stimulus at t = 50 applied to the right half of the tissue.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.3
  - Total simulation time (t_max): 200

Local Activation Time Tracking:
-------------------------------
- Threshold: 0.5 (value of `u` used to detect activation).
- Records all threshold crossings per cell during:
  - `start_time = 100`
  - `end_time = 200`
- Data is recorded every `step = 10` simulation steps.
- The tracker outputs a 3D array (num_events, x, y) with activation times.

Execution:
----------
1. Set up a 2D tissue grid and stimulation pattern to induce spiral activity.
2. Configure the `LocalActivationTime2DTracker`.
3. Run the simulation using the Aliev-Panfilov model.
4. Extract and visualize activation maps for selected time points.

Application:
------------
- Ideal for analyzing wave reentry, rotation, or drift.
- Helps evaluate activation frequency and reactivation patterns.
- Useful in quantifying arrhythmogenic behavior over time.

Visualization:
--------------
Activation time maps are plotted for selected reference time bases (e.g. 150, 170), 
showing the most recent activation at each location relative to that time base.

Output:
-------
A set of color-mapped images visualizing activation wavefronts at different times, 
with all threshold-crossing events taken into account.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 200
tissue = fw.CardiacTissue2D([n, n])

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.3
aliev_panfilov.t_max = 200

# induce spiral wave:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, volt_value=1, x1=0, x2=n,
                                             y1=0, y2=5))
stim_sequence.add_stim(fw.StimVoltageCoord2D(time=50, volt_value=1, x1=n//2,
                                             x2=n, y1=0, y2=n))

# set up the tracker:
tracker_sequence = fw.TrackerSequence()
act_time_tracker = fw.LocalActivationTime2DTracker()
act_time_tracker.threshold = 0.5
act_time_tracker.step = 10
act_time_tracker.start_time = 100
act_time_tracker.end_time = 200
tracker_sequence.add_tracker(act_time_tracker)

# connect model with tissue, stim and tracker:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

# run the simulation:
aliev_panfilov.run()

# plot the activation time map:
time_bases = [150, 170]  # time bases to plot the activation time map
lats = act_time_tracker.output
print(f'Number of LATs: {len(act_time_tracker.output)}')

X, Y = np.mgrid[0:n:1, 0:n:1]

fig, axs = plt.subplots(ncols=len(time_bases), figsize=(15, 5))

if len(time_bases) == 1:
    axs = [axs]

for i, ax in enumerate(axs):
    # Select the activation times next closest to the time base
    mask = np.any(lats >= time_bases[i], axis=0)
    ids = np.argmax(lats >= time_bases[i], axis=0)
    ids = tuple((ids[mask], *np.where(mask)))

    act_time = np.full([n, n], np.nan)
    act_time[mask] = lats[ids]

    act_time_min = time_bases[i]
    act_time_max = time_bases[i] + 30

    ax.imshow(act_time,
              vmin=act_time_min,
              vmax=act_time_max,
              cmap='viridis')
    ax.set_title(f'Activation time: {time_bases[i]} ms')
    cbar = fig.colorbar(ax.images[0], ax=ax, orientation='vertical')
    cbar.set_label('Activation Time (ms)')
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/animation_2d_tracker.py",".py","3267","89","""""""
Creating an Animation of Action Potential in 2D
===============================================

Overview:
---------
This example demonstrates how to use the `Animation2DTracker` to generate an 
animation of the action potential (or any other variablse you choose) in a 2D cardiac tissue simulation. 
The animation is saved as a sequence of frames and can be exported as a video or GIF.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 cardiac tissue domain.
- Stimulation:
  - First stimulus is applied to the bottom half of the domain at t = 0.
  - Second stimulus is applied to the left half at t = 31 to initiate wave break and spiral formation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 100

Animation Tracker:
------------------
- Tracks the transmembrane potential `u` during the simulation.
- Records a frame every 10 steps (`step = 10`).
- Frames are saved into the `anim_data/` directory.
- Existing data in the directory will be overwritten.
- After the simulation, `write()` is called to render the animation:
  - `shape_scale`: Zoom factor for each frame.
  - `clear=True`: Deletes all raw frame data after animation is generated.
  - `fps=30`: Frames per second for the output video.

Execution:
----------
1. Set up cardiac tissue and stimulation sequence.
2. Attach `Animation2DTracker` to the tracker sequence.
3. Run the Aliev-Panfilov model with configured simulation and tracking.
4. Call `write()` to generate and optionally clean up the animation.

Application:
------------
- Useful for visualizing wave propagation and reentry.
- Can be used in presentations, publications, or model comparisons.
- Helps in debugging wave dynamics and understanding tissue behavior.

Output:
-------
The animation is written to disk in the specified folder (`anim_data`). 
It shows the evolution of the transmembrane potential over time in the tissue.

""""""

import numpy as np

import finitewave as fw

# set up the tissue:
n = 200
tissue = fw.CardiacTissue2D([n, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, n, 0, n//2))
stim_sequence.add_stim(fw.StimVoltageCoord2D(31, 1, 0, n//2, 0, n))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
animation_tracker = fw.Animation2DTracker()
animation_tracker.variable_name = ""u""  # Specify the variable to track
animation_tracker.dir_name = ""anim_data""
animation_tracker.step = 10
animation_tracker.overwrite = True  # Remove existing files in dir_name
tracker_sequence.add_tracker(animation_tracker)

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 100
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence
# run the model:
aliev_panfilov.run()

# write animation and clear the snapshot folder
animation_tracker.write(shape_scale=5, clear=True, fps=30, clim=[0, 1]) # !Note: for ionic models use clim=[-90, 40] or similar to show the activity correctly","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/spiral_wave_period_2d_tracker.py",".py","3759","108","""""""
Measuring Wave Period in 2D Cardiac Tissue
==========================================

Overview:
---------
This example demonstrates how to use the `Period2DTracker` to measure the 
wave period at specific locations in a 2D cardiac tissue simulation. 
This is particularly useful for analyzing repetitive wave activity, such as 
spiral waves or regular pacing, and for determining local cycle lengths.

Simulation Setup:
-----------------
- Tissue Grid: A 200×200 cardiac tissue domain.
- Stimulation:
  - First stimulus applied to the bottom half of the domain at t = 0.
  - Second stimulus applied to the left half at t = 31 to initiate reentry.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 300

Wave Period Tracking:
---------------------
- A list of detector positions is provided through the `cell_ind` parameter:
  - Positions: (1,1), (5,5), (7,3), (9,1), (100,100), (150,3), (100,150)
- The tracker monitors threshold crossings at each specified cell to calculate 
  the local activation period.
- Tracking starts at `start_time = 100` and is evaluated every `step = 10` steps.
- Threshold voltage for detection is set to `0.5`.

Execution:
----------
1. Create and configure a 2D cardiac tissue grid.
2. Apply sequential stimulation to induce spiral or repetitive wave activity.
3. Configure the `Period2DTracker` with desired cell indices.
4. Run the Aliev-Panfilov model and track the period at each specified location.
5. Plot the average and standard deviation of the measured periods.

Application:
------------
- Useful for analyzing cycle lengths during sustained wave activity.
- Applicable in reentry studies, tissue heterogeneity analysis, or pacing experiments.
- Helps evaluate the spatial variability of wave dynamics and rhythm regularity.

Output:
-------
The resulting plot shows the mean wave period at each detector location, 
along with error bars indicating standard deviation over time.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 200
tissue = fw.CardiacTissue2D([n, n])

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt    = 0.01
aliev_panfilov.dr    = 0.25
aliev_panfilov.t_max = 300

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, n, 0, n//2))
stim_sequence.add_stim(fw.StimVoltageCoord2D(31, 1, 0, n//2, 0, n))

tracker_sequence = fw.TrackerSequence()
# add action potential tracker
# # add period tracker:
period_tracker = fw.Period2DTracker()
# Here we create an int array of detectors as a list of positions in which we want to calculate the period.
positions = np.array([[1, 1], [5, 5], [7, 3], [9, 1],
                      [100, 100], [150, 3], [100, 150]])
period_tracker.cell_ind = positions
period_tracker.threshold = 0.5
period_tracker.start_time = 100
period_tracker.step = 10
tracker_sequence.add_tracker(period_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue   = tissue
aliev_panfilov.stim_sequence    = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# get the wave period as a pandas Series with the cell index as the index:
periods = period_tracker.output

# plot the wave period:
plt.figure()
plt.errorbar(range(len(positions)),
             periods.apply(lambda x: x.mean()),
             yerr=periods.apply(lambda x: x.std()),
             fmt='o')
plt.xticks(range(len(positions)), [f'({x[0]}, {x[1]})' for x in positions],
           rotation=45)
plt.xlabel('Cell Index')
plt.ylabel('Period')
plt.title('Wave period')
plt.tight_layout()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/variables_2d_tracker.py",".py","3039","92","""""""
Tracking State Variables in 2D Cardiac Tissue
=============================================

Overview:
---------
This example demonstrates how to use the `MultiVariable2DTracker` to record 
the values of multiple state variables (such as `u` and `v`) at a specific 
cell in a 2D cardiac tissue simulation using the Aliev-Panfilov model.

Simulation Setup:
-----------------
- Tissue Grid: A 100×100 cardiac tissue domain.
- Stimulation:
  - A stimulus is applied to the left edge of the domain at t = 0 to initiate wave propagation.
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 100

State Variable Tracking:
------------------------
- The `MultiVariable2DTracker` is used to track both:
  - `u`: Transmembrane potential
  - `v`: Recovery variable
- Tracking location is set via `cell_ind = [30, 30]`.
- Variable values are recorded at every time step.

Execution:
----------
1. Set up a 2D cardiac tissue grid and stimulation pattern.
2. Attach the `MultiVariable2DTracker` to record `u` and `v` at one node.
3. Run the simulation using the Aliev-Panfilov model.
4. Plot the recorded values over time to analyze the local action potential dynamics.

Application:
------------
- Useful for analyzing the temporal dynamics of variables at specific tissue points.
- Can help validate model behavior or compare different cell locations.
- Ideal for creating time series data for further signal analysis or machine learning tasks.

Output:
-------
The resulting plot shows the evolution of both `u` and `v` at the selected cell, 
providing insight into the local electrophysiological response to stimulation.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# number of nodes on the side
n = 100
tissue = fw.CardiacTissue2D([n, n])

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()

# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 100

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 3, 0, n))

tracker_sequence = fw.TrackerSequence()

# add the variable tracker:
multivariable_tracker = fw.MultiVariable2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
multivariable_tracker.cell_ind = [30, 30]
multivariable_tracker.var_list = [""u"", ""v""]
tracker_sequence.add_tracker(multivariable_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# plot the action potential and state variable v at the measuring point
time = np.arange(len(multivariable_tracker.output[""u""])) * aliev_panfilov.dt

plt.plot(time, multivariable_tracker.output[""u""], label=""u"")
plt.plot(time, multivariable_tracker.output[""v""], label=""v"")
plt.legend(title=aliev_panfilov.__class__.__name__)
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/trackers/2D/action_potential_2d_tracker.py",".py","3084","98","
""""""
Tracking Action Potentials in 2D Cardiac Tissue
===============================================

Overview:
---------
This example demonstrates how to track action potentials at specific 
cell locations in a 2D cardiac tissue simulation using the 
ActionPotential2DTracker class in Finitewave. Action potential tracking 
is crucial for analyzing electrophysiological responses at different 
tissue points.

Simulation Setup:
-----------------
- Tissue Grid: A 100×100 cardiac tissue domain.
- Stimulation:
  - A left-side stimulus is applied at time t = 0.
  - The excitation wave propagates across the tissue.
- Action Potential Tracking:
  - Action potentials are recorded at two specific cells:  
    - Cell at (30, 30)
    - Cell at (70, 70)
  - Sampling step: Every time step (1 ms).
- Time and Space Resolution:
  - Temporal step (dt): 0.01
  - Spatial resolution (dr): 0.25
  - Total simulation time (t_max): 50

Execution:
----------
1. Create a 2D cardiac tissue grid.
2. Apply stimulation at the left boundary.
3. Set up an action potential tracker:
   - The tracker records the membrane potential over time at specified 
     cell indices.
4. Run the Aliev-Panfilov model to simulate wave propagation.
5. Extract and visualize action potential waveforms.

Application:
------------
Tracking action potentials is useful for:
- Studying cardiac excitability at different spatial locations.
- Comparing action potential durations across various tissue points.
- Analyzing arrhythmias or conduction abnormalities in excitable media.

Visualization:
--------------
The action potentials recorded at the selected cells are plotted over time 
using matplotlib. The graph shows the voltage dynamics of the 
excited regions.

""""""

import matplotlib.pyplot as plt
import numpy as np

import finitewave as fw

# create a mesh of cardiomyocytes (elems = 1):
n = 100
m = 100
tissue = fw.CardiacTissue2D([m, n])

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, 3, 0, n))

# set up tracker parameters:
tracker_sequence = fw.TrackerSequence()
action_pot_tracker = fw.ActionPotential2DTracker()
# to specify the mesh node under the measuring - use the cell_ind field:
# eather list or list of lists can be used
action_pot_tracker.cell_ind = [[30, 30], [70, 70]]
action_pot_tracker.step = 1
tracker_sequence.add_tracker(action_pot_tracker)

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 50
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# plot the action potential
time = np.arange(len(action_pot_tracker.output)) * aliev_panfilov.dt

plt.figure()
plt.plot(time, action_pot_tracker.output[:, 0], label=""cell_30_30"")
plt.plot(time, action_pot_tracker.output[:, 1], label=""cell_70_70"")
plt.legend(title='Aliev-Panfilov')
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/fibrosis/2D/labyrinth_propagation.py",".py","2718","85","""""""
Simulation in Complex Labyrinth-Like Geometry
=============================================

This example demonstrates wave propagation through a 2D cardiac tissue
with a custom-designed labyrinth-like structure. The geometry is created
manually by setting up regions of obstacles (non-conductive) within a
conductive domain. The resulting structure mimics pathways similar to
fibrotic maze-like or post-surgical scarred tissue.

Wavefront propagation is visualized using Finitewave’s Animation2DTracker,
and the result shows how the wave navigates through the complex network
of narrow channels and dead-ends.

Setup:
------
- Tissue size: 300 × 300
- Geometry:
    • Obstacles are placed in alternating vertical bands
    • Bands are offset to form a labyrinth pattern
    • `tissue.mesh` uses 1 (myocytes) and 0 (obstacles)
- Stimulus:
    • A short planar stimulus applied to a small strip on the left side
    • Time: t = 0 ms
- Model:
    • Aliev-Panfilov 2D model
    • Total time: 200 ms
- Visualization:
    • Voltage (`u`) is tracked every 10 steps
    • Animation frames are saved and compiled to visualize dynamics

Output:
-------
To visualize the result, refer to the generated animation (e.g., 
`complex_geometry.mp4`) showing how wavefronts propagate within the complex structure.

""""""

import matplotlib.pyplot as plt
import numpy as np
import shutil

import finitewave as fw

# number of nodes on the side
n = 300

tissue = fw.CardiacTissue2D([n, n])
# create a mesh of cardiomyocytes (elems = 1):
for i in range(0, 40, 5):
    if i%10 == 0:
        tissue.mesh[10*i:10*(i+3), :250] = 0
    else:
        tissue.mesh[10*i:10*(i+3), 50:] = 0

# create model object:
aliev_panfilov = fw.AlievPanfilov2D()
# set up numerical parameters:
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 200

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, int(n*0.03),
                                                 0, n))

tracker_sequence = fw.TrackerSequence()
animation_tracker = fw.Animation2DTracker()
animation_tracker.variable_name = ""u""  # Specify the variable to track
animation_tracker.dir_name = ""anim_data""
animation_tracker.step = 10
animation_tracker.overwrite = True  # Remove existing files in dir_name
tracker_sequence.add_tracker(animation_tracker)

# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence
aliev_panfilov.tracker_sequence = tracker_sequence

aliev_panfilov.run()

# write animation and clear the snapshot folder
animation_tracker.write(shape_scale=5, clear=True, fps=30)
","Python"
"Cell biology","finitewave/Finitewave","examples/fibrosis/2D/structural_anisotropy_2d.py",".py","2462","80","""""""
Structural Anisotropy in 2D Due to Interstitial Fibrosis
=========================================================

This example demonstrates how interstitial fibrosis can create structural 
anisotropy in a 2D cardiac tissue. The fibrotic pattern causes directionally 
preferential conduction, leading to an elliptical spread of excitation from a 
point stimulus.

Unlike fiber-based anisotropy, which is driven by directional conductivity, 
this anisotropy arises purely from the geometry of the fibrotic microstructure.

Setup:
------
- Tissue: 2D grid of size 400×400
- Fibrosis:
    • Type: Interstitial (structured, linear obstacles)
    • Density: 15%
    • Strand size: 1 pixel wide × 4 pixels long (aligned in j-direction)
- Stimulus:
    • Type: Voltage stimulus
    • Location: Center of the tissue
    • Shape: Square (10×10 pixels)
    • Time: Applied at t = 0 ms

Model:
------
- Aliev-Panfilov 2D reaction-diffusion model
- Simulation time: 30 ms
- Time step: 0.01 ms
- Spatial resolution: 0.25 mm

Observation:
------------
Due to the aligned fibrotic obstacles, the excitation wavefront becomes 
elliptical, spreading more easily in the direction perpendicular
to the fibrosis strands. This mimics real-world structural anisotropy seen 
in interstitial fibrosis.

Applications:
-------------
This example is useful for exploring:
- Structural sources of conduction anisotropy
- Functional impact of interstitial fibrosis geometry
- Wavefront deformation and vulnerability to reentry

""""""

import numpy as np
import matplotlib.pyplot as plt
import finitewave as fw

n = 400
# create mesh
tissue = fw.CardiacTissue2D((n, n))
tissue.add_pattern(fw.Structural2DPattern(density=0.15, length_i=1, length_j=4, x1=0, x2=n, y1=0, y2=n))

# set up stimulation parameters:
stim_sequence = fw.StimSequence()
stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 5, n//2 + 5,
                                             n//2 - 5, n//2 + 5))

# create model object and set up parameters:
aliev_panfilov = fw.AlievPanfilov2D()
aliev_panfilov.dt = 0.01
aliev_panfilov.dr = 0.25
aliev_panfilov.t_max = 30
# add the tissue and the stim parameters to the model object:
aliev_panfilov.cardiac_tissue = tissue
aliev_panfilov.stim_sequence = stim_sequence

# run the model:
aliev_panfilov.run()

# show the potential map at the end of calculations:
plt.title(""Structural Anisotropy 2D"")
plt.imshow(aliev_panfilov.u)
plt.colorbar()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/fibrosis/2D/interstitial_fibrosis_2d.py",".py","1550","49","""""""
2D Interstitial Fibrosis Pattern (20% Density, 4-Pixel Length)
==============================================================

This example demonstrates how to generate a 2D cardiac tissue with 
an interstitial fibrosis pattern using the `Structural2DPattern` class
from Finitewave.

Interstitial fibrosis is modeled as thin, linear fibrotic structures
or strands, typically aligned along fibers or tissue direction. These
structures act as barriers to conduction, affecting wave propagation.

Setup:
------
- Grid size: 200 × 200
- Fibrosis type: Interstitial (structured linear insertions)
- Fibrosis density: 20%
- Strand dimensions:
    • i-direction thickness: 1 pixel
    • j-direction length: 4 pixels
- Fibrosis applied uniformly over the whole domain

Visualization:
--------------
The generated tissue is shown as a 2D image:
- Green regions: healthy, conductive tissue
- Yellow linear elements: fibrotic, non-conductive strands (interstitial fibrosis)

Application:
------------
This type of structured pattern is useful for simulating how thin fibrotic
barriers affect action potential propagation, slow conduction, and create
substrates for reentrant activity.

""""""


import matplotlib.pyplot as plt
import finitewave as fw

n = 200
# create mesh
tissue = fw.CardiacTissue2D((n, n))
tissue.add_pattern(fw.Structural2DPattern(density=0.2, length_i=1, length_j=4, x1=0, x2=n, y1=0, y2=n))

plt.title(""2D Interstitial Fibrosis Medium with 20% Fibrosis Density and 4 pixels length"")
plt.imshow(tissue.mesh)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","examples/fibrosis/2D/wave_propagation_delay_2d.py",".py","2454","82","""""""
Propagation Delay Due to Diffuse Fibrosis
=========================================

This example compares wave propagation in two 2D cardiac tissues:
one healthy (no fibrosis) and one with 20% diffuse fibrosis.

A planar wave stimulus is applied from the left side of the tissue,
and propagation is simulated using the Aliev-Panfilov model.

The resulting transmembrane potential maps clearly show how diffuse
fibrosis slows down the conduction, causing a visible delay in
activation front propagation compared to the healthy tissue.

Setup:
------
- Tissue size: 300 × 300
- Fibrosis type: Diffuse (random spatial blockage)
- Fibrosis density: 20% (in fibrotic case)
- Stimulus:
    • Type: Voltage
    • Applied on leftmost 5 columns of the tissue
    • Time: t = 0 ms
- Model: Aliev-Panfilov 2D
- Time window: 20 ms

Visualization:
--------------
The resulting `u` (voltage) maps are plotted side by side to highlight
the delayed wavefront in the fibrotic tissue.

""""""
import matplotlib.pyplot as plt
import finitewave as fw

n = 300
stim_x1, stim_x2 = 0, 5  # planar stimulus strip

def setup_tissue(with_fibrosis):
    tissue = fw.CardiacTissue2D((n, n))
    if with_fibrosis:
        tissue.add_pattern(fw.Diffuse2DPattern(density=0.2))
    return tissue

def run_simulation(tissue):
    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1,
                                                 x1=stim_x1, x2=stim_x2,
                                                 y1=0, y2=n))
    
    model = fw.AlievPanfilov2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 20
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    model.run()
    return model

# Run simulations
print(""Running healthy tissue..."")
healthy_tissue = setup_tissue(with_fibrosis=False)
healthy_model = run_simulation(healthy_tissue)

print(""Running fibrotic tissue (20% diffuse)..."")
fibrotic_tissue = setup_tissue(with_fibrosis=True)
fibrotic_model = run_simulation(fibrotic_tissue)

# Plot results
fig, axs = plt.subplots(1, 2, figsize=(12, 5))
axs[0].imshow(healthy_model.u, cmap=""viridis"", origin=""lower"")
axs[0].set_title(""Healthy Tissue (No Fibrosis)"")
axs[0].axis(""off"")

axs[1].imshow(fibrotic_model.u, cmap=""viridis"", origin=""lower"")
axs[1].set_title(""Diffuse Fibrosis (20%)"")
axs[1].axis(""off"")

fig.suptitle(""Propagation Delay Due to Fibrosis"", fontsize=16)
plt.tight_layout()
plt.show()
","Python"
"Cell biology","finitewave/Finitewave","examples/fibrosis/2D/diffuse_fibrosis_2d.py",".py","1131","38","""""""
2D Diffuse Fibrosis Pattern (20% Density)
=========================================

This example demonstrates how to generate a 2D cardiac tissue with 
a diffuse fibrosis pattern using the `Diffuse2DPattern` class in Finitewave.

Fibrotic tissue regions are marked as non-conductive areas in the mesh,
and this affects wave propagation in subsequent simulations.

Setup:
------
- Grid size: 200 × 200
- Fibrosis type: Diffuse (random spatial distribution)
- Fibrosis density: 20% (i.e., 20% of cells are fibrotic/non-conductive)

Visualization:
--------------
The generated tissue is shown as a 2D image:
- Green cells represent healthy (conductive) tissue
- Yellow cells represent fibrotic (non-conductive) areas

This mesh can be used in simulations to study how diffuse fibrosis alters
electrical propagation, reentry, and arrhythmogenesis.
""""""

import matplotlib.pyplot as plt
import finitewave as fw

n = 200
# create mesh
tissue = fw.CardiacTissue2D((n, n))
tissue.add_pattern(fw.Diffuse2DPattern(0.2))

plt.title(""2D Diffuse Fibrosis Medium with 20% Fibrosis Density"")
plt.imshow(tissue.mesh)
plt.colorbar()
plt.show()","Python"
"Cell biology","finitewave/Finitewave","Tutorials/SpiralWaves2D.ipynb",".ipynb","532084","417","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Spiral Wave Initiation""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Temporal block""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|██████████| 1000/1000 [00:00<00:00, 3580.19it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 3600/3601 [00:00<00:00, 4181.81it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 10398/10400 [00:02<00:00, 4158.74it/s]\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 640x480 with 3 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""\n"",
    ""class UpdateMesh(fw.Command):\n"",
    ""    \""\""\""\n"",
    ""    Update the mesh of the cardiac tissue during the simulation.\n"",
    ""    \""\""\""\n"",
    ""    def __init__(self, time, updated_mesh):\n"",
    ""        \""\""\""\n"",
    ""        Initialize the command with the time and the updated mesh.\n"",
    ""\n"",
    ""        Args:\n"",
    ""            time (int): The time at which the mesh is updated.\n"",
    ""            updated_mesh (numpy.ndarray): The updated mesh.\n"",
    ""        \""\""\""\n"",
    ""        super().__init__(time)\n"",
    ""        self.updated_mesh = updated_mesh\n"",
    ""\n"",
    ""    def execute(self, model):\n"",
    ""        model.cardiac_tissue.mesh = self.updated_mesh\n"",
    ""        model.compute_weights()\n"",
    ""\n"",
    ""\n"",
    ""# set up the tissue:\n"",
    ""n = 256\n"",
    ""tissue = fw.CardiacTissue2D([n, n])\n"",
    ""tissue.mesh[n//2, :n//2] = 2\n"",
    ""\n"",
    ""mesh_without_block = tissue.mesh.copy()\n"",
    ""mesh_without_block[n//2, :n//2] = 1\n"",
    ""\n"",
    ""# set up stimulation parameters:\n"",
    ""stim_sequence = fw.StimSequence()\n"",
    ""stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, volt_value=1,\n"",
    ""                                             x1=0, x2=n//2, y1=0, y2=5))\n"",
    ""# stim_sequence.add_stim(fw.StimVoltageCoord2D(time=50, volt_value=1,\n"",
    ""#                                              x1=n//2, x2=n, y1=0, y2=n))\n"",
    ""\n"",
    ""command_sequence = fw.CommandSequence()\n"",
    ""command_sequence.add_command(UpdateMesh(45, mesh_without_block))\n"",
    ""\n"",
    ""# create model object:\n"",
    ""model = fw.AlievPanfilov2D()\n"",
    ""# set up numerical parameters:\n"",
    ""model.dt = 0.01\n"",
    ""model.dr = 0.3\n"",
    ""model.t_max = 10\n"",
    ""\n"",
    ""# add the tissue and the stim parameters to the model object:\n"",
    ""model.cardiac_tissue = tissue\n"",
    ""model.stim_sequence = stim_sequence\n"",
    ""model.command_sequence = command_sequence\n"",
    ""\n"",
    ""model.run()\n"",
    ""u_10 = model.u.copy()\n"",
    ""\n"",
    ""model.t_max = 46\n"",
    ""model.run(initialize=False)\n"",
    ""u_46 = model.u.copy()\n"",
    ""\n"",
    ""model.t_max = 150\n"",
    ""model.run(initialize=False)\n"",
    ""u_150 = model.u.copy()\n"",
    ""\n"",
    ""\n"",
    ""fig, axs = plt.subplots(ncols=3)\n"",
    ""axs[0].imshow(u_10, cmap='hot')\n"",
    ""axs[1].imshow(u_46, cmap='hot')\n"",
    ""axs[2].imshow(u_150, cmap='hot')\n"",
    ""plt.show()\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Two cross stimuls (S1S2)\n"",
    ""\n"",
    ""This section demonstrates how to initiate a **spiral wave** in 2D cardiac tissue \n"",
    ""using a classical **cross-field S1–S2 stimulation** protocol.\n"",
    ""\n"",
    ""The protocol applies two stimuli:\n"",
    ""1. **S1**: a planar wave from one edge (top to bottom)\n"",
    ""2. **S2**: a transverse wave from one side (left to right)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|██████████| 5100/5100 [00:01<00:00, 4119.51it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 14900/14901 [00:03<00:00, 4153.31it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""# set up the tissue:\n"",
    ""n = 256\n"",
    ""tissue = fw.CardiacTissue2D([n, n])\n"",
    ""\n"",
    ""\n"",
    ""# set up stimulation parameters:\n"",
    ""stim_sequence = fw.StimSequence()\n"",
    ""stim_sequence.add_stim(fw.StimVoltageCoord2D(time=0, volt_value=1,\n"",
    ""                                             x1=0, x2=n, y1=0, y2=5))\n"",
    ""stim_sequence.add_stim(fw.StimVoltageCoord2D(time=50, volt_value=1,\n"",
    ""                                             x1=n//2, x2=n, y1=0, y2=n))\n"",
    ""\n"",
    ""# create model object:\n"",
    ""model = fw.AlievPanfilov2D()\n"",
    ""# set up numerical parameters:\n"",
    ""model.dt = 0.01\n"",
    ""model.dr = 0.3\n"",
    ""model.t_max = 51\n"",
    ""# add the tissue and the stim parameters to the model object:\n"",
    ""model.cardiac_tissue = tissue\n"",
    ""model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""model.run()\n"",
    ""\n"",
    ""u_s2 = model.u.copy() # save the model at the moment of the second stimulation\n"",
    ""\n"",
    ""model.t_max = 200\n"",
    ""model.run(initialize=False)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 640x480 with 2 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# show the spiral waves initiation (t = 51) and final stabilization (t = 251)\n"",
    ""fig, axs = plt.subplots(ncols=2)\n"",
    ""axs[0].imshow(u_s2, cmap='hot')\n"",
    ""axs[0].set_title(\""t = 51\"")\n"",
    ""axs[1].imshow(model.u, cmap='hot')\n"",
    ""axs[1].set_title(\""t = 251\"")\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Equvidistant Stimulation\n"",
    ""This section demonstrates **periodic stimulation** (paced activation) of 2D cardiac tissue with **diffuse fibrosis** that is finaly causing instability due to a high fibrosis density.\n"",
    ""Here we use 35% randomly distributed **diffuse fibrosis** using `Diffuse2DPattern` - this introduces structural heterogeneity and **wavefront breakup**.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|█████████▉| 9999/10000 [00:01<00:00, 5216.09it/s]\n"",
      ""Running AlievPanfilov2D: 100%|██████████| 20000/20000 [00:03<00:00, 5326.11it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""\n"",
    ""import numpy as np\n"",
    ""np.random.seed(123) # for reproducibility \n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""\n"",
    ""# set up the tissue:\n"",
    ""n = 256\n"",
    ""tissue = fw.CardiacTissue2D([n, n])\n"",
    ""tissue.add_pattern(fw.Diffuse2DPattern(0.35))\n"",
    ""\n"",
    ""# set up stimulation parameters:\n"",
    ""stim_sequence = fw.StimSequence()\n"",
    ""\n"",
    ""# stimulate the tissue at the top border with a time step of 28:\n"",
    ""for t in [0, 28, 56, 84]:\n"",
    ""    stim_sequence.add_stim(fw.StimVoltageCoord2D(time=t, volt_value=1,\n"",
    ""                                                x1=0, x2=n, y1=0, y2=5))\n"",
    ""\n"",
    ""# create model object:\n"",
    ""model = fw.AlievPanfilov2D()\n"",
    ""# set up numerical parameters:\n"",
    ""model.dt = 0.01\n"",
    ""model.dr = 0.3\n"",
    ""model.t_max = 100\n"",
    ""# add the tissue and the stim parameters to the model object:\n"",
    ""model.cardiac_tissue = tissue\n"",
    ""model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""model.run()\n"",
    ""u_s2 = model.u.copy() # save the model during the fragmentation process\n"",
    ""\n"",
    ""model.t_max = 300\n"",
    ""model.run(initialize=False)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 640x480 with 2 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# show the wavefront fragmentation process t = 100) and the instability (t = 300) \n"",
    ""fig, axs = plt.subplots(ncols=2)\n"",
    ""axs[0].imshow(u_s2, cmap='hot')\n"",
    ""axs[0].set_title(\""t = 100\"")\n"",
    ""axs[1].imshow(model.u, cmap='hot')\n"",
    ""axs[1].set_title(\""t = 300\"")\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### S1S2 protocol with shorter S2\n"",
    ""\n"",
    ""In this section we initiate a **spiral wave** with **26% diffuse fibrosis**. Here we also use \n"",
    ""**S1–S2 protocol**, where the final extrastimulus (S2) is delivered at a shorter coupling interval \n"",
    ""than the preceding regular pacing beats (S1).""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|██████████| 16000/16000 [00:03<00:00, 4936.90it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 24000/24001 [00:04<00:00, 4842.59it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""np.random.seed(27)\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""# set up the tissue:\n"",
    ""n = 256\n"",
    ""tissue = fw.CardiacTissue2D([n, n])\n"",
    ""tissue.add_pattern(fw.Diffuse2DPattern(0.26))\n"",
    ""\n"",
    ""# set up stimulation parameters:\n"",
    ""stim_sequence = fw.StimSequence()\n"",
    ""# stimulate the tissue with a 2x45 prebeats, 3x30 s1 and 1x23 s2:\n"",
    ""prebeats = np.array([0, 45])\n"",
    ""s1 = prebeats[-1] + np.array([30, 2 * 30, 3 * 30])\n"",
    ""s2 = s1[-1] + np.array([23])\n"",
    ""for t in np.concatenate([prebeats, s1, s2]):\n"",
    ""    stim_sequence.add_stim(fw.StimVoltageCoord2D(time=t, volt_value=1,\n"",
    ""                                                 x1=0, x2=n, y1=0, y2=5))\n"",
    ""\n"",
    ""# create model object:\n"",
    ""model = fw.AlievPanfilov2D()\n"",
    ""# set up numerical parameters:\n"",
    ""model.dt = 0.01\n"",
    ""model.dr = 0.3\n"",
    ""model.t_max = s2[-1] + 2\n"",
    ""# add the tissue and the stim parameters to the model object:\n"",
    ""model.cardiac_tissue = tissue\n"",
    ""model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""model.run()\n"",
    ""u_s2 = model.u.copy()\n"",
    ""\n"",
    ""model.t_max = 400\n"",
    ""model.run(initialize=False)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 640x480 with 2 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# show the wavefront sequence (t = 159) and the stable spiral wave (t = 400)\n"",
    ""fig, axs = plt.subplots(ncols=2)\n"",
    ""axs[0].imshow(u_s2, cmap='hot')\n"",
    ""axs[0].set_title(f\""t = {s2[-1] + 1}\"")\n"",
    ""axs[1].imshow(model.u, cmap='hot')\n"",
    ""axs[1].set_title(\""t = 400\"")\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""base"",
   ""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"": ""3.12.6""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 4
}
","Unknown"
"Cell biology","finitewave/Finitewave","Tutorials/RestitutionCurve.ipynb",".ipynb","137565","532","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""id"": ""e4bfc0ea-408e-45a9-a4fc-e026929f2cab"",
   ""metadata"": {},
   ""source"": [
    ""## Restitution curve\n"",
    ""\n"",
    ""This tutorial demonstrates an implementation of the classical S1–S2 extrastimulus protocol for measuring APD restitution using the\n"",
    ""Luo-Rudy 1991 cardiac electrophysiology model in a 2D tissue.\n"",
    ""\n"",
    ""Protocol Overview:\n"",
    ""------------------\n"",
    ""- Tissue: 2D grid of size 100×10\n"",
    ""- Model: Luo-Rudy 1991 (LuoRudy912D)\n"",
    ""- S1 stimulation:\n"",
    ""    - 10 beats at 400 ms cycle length\n"",
    ""    - Current stimulus applied to a small region near the top\n"",
    ""- State saving:\n"",
    ""    - State saved at the end of the 10th beat (after ~3600 ms)\n"",
    ""- S2 protocol:\n"",
    ""    - Single voltage stimulus applied at various coupling intervals (400 → 25 ms)\n"",
    ""    - Model resumes from saved S1 state\n"",
    ""    - Response recorded at the center of the domain\n"",
    ""    - APD90 and DI calculated from the S2 action potential\n"",
    ""- Plot: APD90 vs DI — the restitution curve\n"",
    ""\n"",
    ""Reference:\n"",
    ""----------\n"",
    ""Protocol adapted from:\n"",
    ""\n"",
    ""Goldhaber JI, Xie L-H, Duong T, Motter C, Khuu K, Weiss JN.\n"",
    ""\""Action Potential Duration Restitution and Alternans in Rabbit Ventricular Myocytes:\n"",
    ""The Key Role of Intracellular Calcium Cycling.\""\n"",
    ""Circulation Research. 2005 Jan 20;96(4):459–466.\n"",
    ""https://doi.org/10.1161/01.RES.0000156891.66893.83""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""id"": ""f9e47aa7-206a-4cd1-b410-4dd8b08d07be"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""import shutil\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""# Setup\n"",
    ""ni = 100\n"",
    ""nj = 10\n"",
    ""cell = [ni//2, nj//2]\n"",
    ""s1_cl = 400 # ms\n"",
    ""num_s1 = 10 # number of S1 beats\n"",
    ""s2_intervals = np.arange(400, 0, -25)\n"",
    ""stim_amp = 50\n"",
    ""stim_dur = 1 # ms\n"",
    ""threshold_up = -20 # mV\n"",
    ""save_time = (num_s1-1) * s1_cl""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""id"": ""4dd13fc6-ae95-44f1-ad0f-e1e54660ecdb"",
   ""metadata"": {},
   ""source"": [
    ""### Step 1: Prepacing beats\n"",
    ""\n"",
    ""Finitewave’s `StateSaver` and `StateLoader` features are used to avoid\n"",
    ""repeating the long S1 pacing train for every S2 interval. The model\n"",
    ""is pre-paced once with 10 regular stimuli at 400 ms intervals to reach\n"",
    ""steady state, and the state is saved after the final S1 beat.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""id"": ""dec939d9-834a-4b32-b925-d070953e9f95"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running pre-pacing...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"",
      ""Running LuoRudy912D: 100%|██████████▉| 360000/360001 [00:32<00:00, 10982.81it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""# Pre-pace the tissue with 10 S1 beats\n"",
    ""tissue = fw.CardiacTissue2D((ni, nj))\n"",
    ""stim_sequence = fw.StimSequence()\n"",
    ""for i in range(num_s1):\n"",
    ""    t = i * s1_cl\n"",
    ""    stim_sequence.add_stim(fw.StimCurrentCoord2D(t, stim_amp, stim_dur, \n"",
    ""                                                 0, 5,\n"",
    ""                                                 0, nj))\n"",
    ""\n"",
    ""# Save state after 10 S1 beats to reuse for S2 branches\n"",
    ""state_savers = fw.StateSaverCollection()\n"",
    ""state_savers.savers.append(fw.StateSaver(\""s1_state\"", time=save_time))\n"",
    ""\n"",
    ""# set up tracker parameters:\n"",
    ""tracker_sequence = fw.TrackerSequence()\n"",
    ""action_pot_tracker = fw.ActionPotential2DTracker()\n"",
    ""action_pot_tracker.cell_ind = [[5, 5]]\n"",
    ""action_pot_tracker.step = 1\n"",
    ""tracker_sequence.add_tracker(action_pot_tracker)\n"",
    ""\n"",
    ""model = fw.LuoRudy912D()\n"",
    ""model.dt = 0.01\n"",
    ""model.dr = 0.25\n"",
    ""model.t_max = save_time + model.dt\n"",
    ""model.cardiac_tissue = tissue\n"",
    ""model.stim_sequence = stim_sequence\n"",
    ""model.tracker_sequence = tracker_sequence\n"",
    ""model.state_saver = state_savers\n"",
    ""\n"",
    ""print(\""Running pre-pacing...\"")\n"",
    ""model.run()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""id"": ""bfa9d8be-dddd-4710-a2c3-1771ec833766"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 1000x500 with 1 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# plot the action potential to visualize prepacing beats stimuli\n"",
    ""time = np.arange(len(action_pot_tracker.output)) * model.dt\n"",
    ""\n"",
    ""fig, ax = plt.subplots(1, 1, figsize=(10, 5))  \n"",
    ""plt.plot(time, action_pot_tracker.output, label=\""Action potential\"")\n"",
    ""plt.legend(title='Prepacing beats')\n"",
    ""ax.set_xlabel('Time (ms)')\n"",
    ""ax.set_ylabel('Membrane potential (mV)')\n"",
    ""plt.grid()\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""id"": ""396b913c-279b-43a2-a02a-e214efd5c0a8"",
   ""metadata"": {},
   ""source"": [
    ""## Step 2: S2 stimulus\n"",
    ""\n"",
    ""Restitution is assessed by applying a single S2 stimulus at progressively\n"",
    ""shorter coupling intervals (i.e., varying delays after the last S1),\n"",
    ""and measuring:\n"",
    ""  - APD90: Action potential duration at 90% repolarization\n"",
    ""  - DI: Diastolic interval (delay between end of S1 AP and start of S2)\n"",
    ""\n"",
    ""Only the S2 response is simulated in each run, making the protocol fast\n"",
    ""and scalable.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""id"": ""df61139b-4f33-4fed-97b1-8a59377e31dc"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +400 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 11099.97it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +375 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:06<00:00, 11452.43it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +350 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10303.80it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +325 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10199.24it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +300 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10483.13it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +275 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|██████████████| 80000/80000 [00:08<00:00, 9968.72it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +250 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10221.69it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +225 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 11230.84it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +200 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10743.75it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +175 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10595.38it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +150 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 10554.29it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +125 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:06<00:00, 11687.73it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +100 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:06<00:00, 11733.73it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +75 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:06<00:00, 11506.06it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +50 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:06<00:00, 11694.09it/s]\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running S2 at +25 ms...\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running LuoRudy912D: 100%|█████████████| 80000/80000 [00:07<00:00, 11361.14it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""di_values = []\n"",
    ""apd90_values = []\n"",
    ""\n"",
    ""# Run S2 branches\n"",
    ""for s2_delay in s2_intervals:\n"",
    ""    stim_sequence = fw.StimSequence()\n"",
    ""    s2_time = s2_delay\n"",
    ""    stim_sequence.add_stim(fw.StimVoltageCoord2D(s2_time, stim_amp,\n"",
    ""                                                 0, 5,\n"",
    ""                                                 0, nj))\n"",
    ""\n"",
    ""    tracker_sequence = fw.TrackerSequence()\n"",
    ""    ap_tracker = fw.ActionPotential2DTracker()\n"",
    ""    ap_tracker.cell_ind = [cell]\n"",
    ""    ap_tracker.step = 1\n"",
    ""    tracker_sequence.add_tracker(ap_tracker)\n"",
    ""\n"",
    ""    model = fw.LuoRudy912D()\n"",
    ""    model.dt = 0.01\n"",
    ""    model.dr = 0.25\n"",
    ""    model.t_max = 800  # allow repolarization\n"",
    ""    model.cardiac_tissue = tissue\n"",
    ""    model.stim_sequence = stim_sequence\n"",
    ""    model.tracker_sequence = tracker_sequence\n"",
    ""    model.state_loader = fw.StateLoader(\""s1_state\"")\n"",
    ""\n"",
    ""    print(f\""Running S2 at +{s2_delay} ms...\"")\n"",
    ""    model.run()\n"",
    ""\n"",
    ""    u = ap_tracker.output\n"",
    ""    t = np.arange(len(u)) * model.dt\n"",
    ""\n"",
    ""    # Find upstroke\n"",
    ""    up = np.where((u[:-1] < threshold_up) & (u[1:] >= threshold_up))[0]\n"",
    ""    if len(up) == 0:\n"",
    ""        print(f\""Loss of capture at S2 interval = {s2_delay} ms.\"")\n"",
    ""        break\n"",
    ""    ap_start = up[-1]\n"",
    ""\n"",
    ""    peak = np.max(u[ap_start:])\n"",
    ""    repol_level = peak - 0.9 * (peak - np.min(u[ap_start:]))\n"",
    ""    repol_idx = np.where(u[ap_start:] < repol_level)[0]\n"",
    ""    if len(repol_idx) == 0:\n"",
    ""        continue\n"",
    ""    ap_end = ap_start + repol_idx[0]\n"",
    ""    apd90 = (ap_end - ap_start) * model.dt\n"",
    ""    di = s2_delay\n"",
    ""\n"",
    ""    if di <= 0:\n"",
    ""        continue\n"",
    ""\n"",
    ""    apd90_values.append(apd90)\n"",
    ""    di_values.append(di)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""id"": ""23763527-7ef7-43a2-b648-2b23234ae50f"",
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 640x480 with 1 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot restitution curve\n"",
    ""plt.figure()\n"",
    ""plt.plot(di_values, apd90_values, 'o-')\n"",
    ""plt.xlabel(\""Diastolic Interval (ms)\"")\n"",
    ""plt.ylabel(\""APD90 (ms)\"")\n"",
    ""plt.title(\""S1–S2 Restitution Curve (Luo-Rudy 2D)\"")\n"",
    ""plt.grid()\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()\n"",
    ""\n"",
    ""# Cleanup saved state\n"",
    ""shutil.rmtree(\""s1_state\"")""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""id"": ""2be432a7-63b4-49e4-9c30-0ef1ee06e0f9"",
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""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"": ""3.11.0""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 5
}
","Unknown"
"Cell biology","finitewave/Finitewave","Tutorials/Anisotropy2D.ipynb",".ipynb","558462","478","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Anisotropy in 2D\n"",
    ""\n"",
    ""This tutorial demonstrates how fiber rotation affects the speed of the wave in\n"",
    ""different directions.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2025.97it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2001.84it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2063.55it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2068.86it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2048.25it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2106.63it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2499/2500 [00:01<00:00, 2112.29it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import skimage as ski\n"",
    ""import pandas as pd\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""\n"",
    ""# number of nodes on the side\n"",
    ""n = 400\n"",
    ""\n"",
    ""alphas = np.radians(np.arange(0, 91, 15))\n"",
    ""out = []\n"",
    ""images = []\n"",
    ""for alpha in alphas:\n"",
    ""    tissue = fw.CardiacTissue2D([n, n])\n"",
    ""    # add fibers orientation vectors\n"",
    ""    tissue.fibers = np.zeros([n, n, 2])\n"",
    ""    tissue.fibers[..., 0] = np.cos(alpha)\n"",
    ""    tissue.fibers[..., 1] = np.sin(alpha)\n"",
    ""\n"",
    ""    # set up stimulation parameters:\n"",
    ""    stim_sequence = fw.StimSequence()\n"",
    ""    stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 5, n//2 + 5,\n"",
    ""                                                 n//2 - 5, n//2 + 5))\n"",
    ""    \n"",
    ""    # create model object and set up parameters\n"",
    ""    model = fw.AlievPanfilov2D()\n"",
    ""    model.dt = 0.01\n"",
    ""    model.dr = 0.25\n"",
    ""    model.t_max = 25\n"",
    ""    model.cardiac_tissue = tissue\n"",
    ""    model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""    model.run()\n"",
    ""    \n"",
    ""    # calculate properties of the wave\n"",
    ""    labeled = (model.u > 0.1).astype(int)\n"",
    ""    props = ski.measure.regionprops_table(labeled, properties=(\n"",
    ""        'orientation', 'major_axis_length', 'minor_axis_length'))\n"",
    ""    props['orientation'] = np.degrees(props['orientation'])\n"",
    ""    props['axis_ratio'] = props['major_axis_length'] / props['minor_axis_length']\n"",
    ""    props['alpha'] = np.degrees(alpha)\n"",
    ""    props['density_calc'] = (np.sum(tissue.mesh[-1:1, -1:1] == 2) \n"",
    ""                                / ((n - 2) * (n - 2)))\n"",
    ""    images.append(model.u.copy())\n"",
    ""\n"",
    ""    out.append(pd.DataFrame(props))\n"",
    ""\n"",
    ""out = pd.concat(out)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Show the Wave Anisotropy and Properties\n"",
    ""\n"",
    ""This section demonstrates the anisotropy of the wave and its properties, including orientation, major and minor axis lengths, axis ratio, and density calculation. The wave propagation is simulated for different fiber orientations (alpha values) in a 2D cardiac tissue model.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 1400x700 with 9 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""cv_along = out['major_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""cv_across = out['minor_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""\n"",
    ""fig, axs = plt.subplot_mosaic([[f'{i}' for i in range(7)],\n"",
    ""                               ['axis_ratio'] * 3 + ['orientation']*4],\n"",
    ""                               figsize=(14, 7))\n"",
    ""\n"",
    ""for i in range(len(alphas)):\n"",
    ""    ax = axs[f'{i}']\n"",
    ""    ax.imshow(images[i], cmap='jet', origin='lower')\n"",
    ""    ax.set_title(f'{np.degrees(alphas[i]):.0f}')\n"",
    ""\n"",
    ""ax = axs['axis_ratio']\n"",
    ""ax.plot(out['alpha'], cv_along, 'o-', label='Along')\n"",
    ""ax.plot(out['alpha'], cv_across, 'o-', label='Across')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('CV')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""ax.legend()\n"",
    ""\n"",
    ""ax = axs['orientation']\n"",
    ""ax.plot(out['alpha'], out['orientation'], 'o-')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('orientation')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.set_yticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Effect of Fibrosis on Anisotropy\n"",
    ""\n"",
    ""This section explores the impact of fibrosis on the anisotropy of wave propagation in cardiac tissue. By introducing fibrosis into the tissue model, we can observe changes in wave speed and directionality. The simulations are performed for different fiber orientations (alpha values), and the resulting wave properties, such as orientation, axis lengths, and axis ratios, are analyzed to understand the effects of fibrosis on anisotropy.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4383.59it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4656.39it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4415.98it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4608.57it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4650.29it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4651.00it/s]\n"",
      ""Running AlievPanfilov2D: 100%|█████████▉| 2999/3000 [00:00<00:00, 4654.99it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import skimage as ski\n"",
    ""import pandas as pd\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""# number of nodes on the side\n"",
    ""n = 256\n"",
    ""\n"",
    ""alphas = np.radians(np.arange(0, 91, 15))\n"",
    ""d = 0.2  # 20% of the tissue is fibrotic\n"",
    ""out_fib = []\n"",
    ""images_fib = []\n"",
    ""for alpha in alphas:\n"",
    ""    tissue = fw.CardiacTissue2D([n, n])\n"",
    ""    tissue.add_pattern(fw.Diffuse2DPattern(d))\n"",
    ""    # add fibers orientation vectors\n"",
    ""    tissue.fibers = np.zeros([n, n, 2])\n"",
    ""    tissue.fibers[..., 0] = np.cos(alpha)\n"",
    ""    tissue.fibers[..., 1] = np.sin(alpha)\n"",
    ""\n"",
    ""    # set up stimulation parameters:\n"",
    ""    stim_sequence = fw.StimSequence()\n"",
    ""    stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, n//2 - 5, n//2 + 5,\n"",
    ""                                                 n//2 - 5, n//2 + 5))\n"",
    ""    \n"",
    ""    # create model object:\n"",
    ""    model = fw.AlievPanfilov2D()\n"",
    ""    # set up numerical parameters:\n"",
    ""    model.dt = 0.01\n"",
    ""    model.dr = 0.25\n"",
    ""    model.t_max = 30\n"",
    ""    model.cardiac_tissue = tissue\n"",
    ""    model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""    model.run()\n"",
    ""\n"",
    ""    labeled = (model.u > 0.5).astype(int)\n"",
    ""    props = ski.measure.regionprops_table(\n"",
    ""        labeled, properties=('orientation', 'major_axis_length',\n"",
    ""                             'minor_axis_length'))\n"",
    ""    props['orientation'] = np.degrees(props['orientation'])\n"",
    ""    props['axis_ratio'] = props['major_axis_length'] / props['minor_axis_length']\n"",
    ""    props['alpha'] = np.degrees(alpha)\n"",
    ""    props['density_calc'] = (np.sum(tissue.mesh[-1:1, -1:1] == 2) \n"",
    ""                                / ((n - 2) * (n - 2)))\n"",
    ""    images_fib.append(model.u.copy())\n"",
    ""\n"",
    ""    out_fib.append(pd.DataFrame(props))\n"",
    ""\n"",
    ""out_fib = pd.concat(out_fib)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""iVBORw0KGgoAAAANSUhEUgAABW0AAAJmCAYAAAAn96L+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdfbzP9d048OdHTo7cNXIzN4l1Gu24iYqWxoiiqBjGuNJisRThcq4o3WtcNEpRtHQ5YzEsFcXIucZKVIpinSKRuRlzW0Q+vz/e56TWtmu/66ok7+fj8X3M+Z7vzefbvq/z/nxe79f79U7SNE1FURRFURRFURRFURRFURRFXwtFjvUBRFEURVEURVEURVEURVEURUfFpG0URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpG0URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpG0URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpG0URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpO0JZt++ffr3769y5coyMzPVr1/fb37zm2N9WFH0ldi7d6/Bgwdr1aqV8uXLS5LE7bff/rnH9ejRQ5Ikn7vVqlXrqz/oKPqSrVy50mWXXeb0009XvHhxZcuWdcEFF8jNzf3cY1955RUXX3yxkiVLOvXUU7Vv3966deuOwVFH0Vdr0qRJkiRRsmTJz9wfx4voRLNkyRJt2rTxrW99S/HixWVlZbnrrrs+85g4VkQnin80BhTeXnzxxX/6uDhWRN9UL730kksuuUSpUqWULFnSD3/4Q0uXLv27j41jxj9X9FgfQPTVat++veXLl/vFL37hrLPOMnXqVF26dHHkyBFdu3Y91ocXRV+qHTt2eOSRR9SrV8+VV15p0qRJ//CxxYsXt2jRos/dF0XfNLt27VKtWjVdunRRpUoV+/fv9+tf/1r37t29++67brnlFrB27VrNmjVTv35906dPd+DAAcOGDXPRRRdZuXKl8uXLH+NPEkVfjvfff9+gQYNUrlzZ7t27P/f7OF5EJ4qpU6fq3r27Tp06+a//+i8lS5b0zjvv2Lx58yePiWNFdCK59dZb9e7d+3P3t23bVrFixZx33nmf3BfHiuhEsXz5cj/4wQ+cf/75pkyZIk1TI0eO1KJFC88//7wLLrjgk8fGMeN/lqRpmh7rg4i+GnPnznXZZZd9kqgt1KpVK2+88Yb33nvPSSeddAyPMIq+XIV/7pIk8Ze//EX58uXddtttn6u27dGjh9/+9rf27dt3DI4yir4eGjdubPPmzd577z3QqVMnzz//vHfeeUfp0qXBhg0bZGVluemmm4wYMeJYHm4UfWnatm0rSRJly5b93NgQx4voRPH+++/77ne/69/+7d889NBD//BxcayITnR5eXmaNWvmlltu+aQKPY4V0Ynk0ksvtXLlSuvWrXPKKacgrHitWbOms8466zMVt3HM+J/F9ggnkNmzZytZsqSOHTt+5v5rrrnG5s2bLVu27BgdWRR9NQqXIkVR9D877bTTFC0aFuQcPnzY008/rUOHDp+cUEH16tX98Ic/NHv27GN1mFH0pcrNzZWXl/dPk1RRdCKYNGmS/fv3y8nJ+YePiWNFFPHoo49KksRPf/rTY30oUXRMLF26VLNmzT5J2EKpUqX84Ac/8Mc//tGf//xnxDHjXxWTtieQ1atXq1279icX4YXq1q37ye+jKAo+/PBDlSpVctJJJ6lataq+ffvauXPnsT6sKPrSHDlyxOHDh23fvt1DDz3kueee++Ti/J133vHhhx9+Ml58Wt26db399tsOHDjwVR9yFH2ptm3bpn///n7xi1+oWrXqP3xcHC+iE8F///d/K1u2rLVr16pfv76iRYuqUKGC3r1727NnD+JYEUW7d+/229/+VosWLdSoUeMzv4tjRXSi+OijjxQrVuxz9xfet2rVKsQx418Ve9qeQHbs2KFmzZqfu79s2bKf/D6KIurVq6devXqys7MRljn98pe/tHDhQsuXL//cRjRR9E3w85//3MMPPwxOPvlk999/v+uuuw5Hx4fC8eLTypYtK01Tf/3rX33729/+6g44ir5kP//5z333u9/Vp0+ff/iYOF5EJ4r333/fBx98oGPHjm6++WZjxoyxfPlyt912m9WrV/vDH/4Qx4rohDdt2jQffviha6+99jP3x7EiOpGcffbZXnzxRUeOHFGkSKgTPXz48CcruwvHijhm/Gti0vYE88+Whsdl41EU3HTTTZ/5uWXLls455xw/+tGPTJw48XO/j6JvgiFDhujZs6dt27Z56qmn9O3b1/79+w0aNOiTx8QxJDpRzJw501NPPeXVV1/9p9/tOF5EJ4ojR444cOCA2267zX/8x3+AZs2aOfnkk/Xv39/ChQs/WQobx4roRPXoo48qV66cq6666jP3x7EiOpHccMMNrr32Wn379jV06FBHjhxxxx132LBhA3ySyC0Ux4x/LrZHOIGUK1fu71bTFi7L+HszHFEUBVdddZUSJUp48cUXj/WhRNGX4vTTT3fuuedq06aN8ePH+9nPfubmm2+2fft25cqVw99fkbFz505Jkjj11FO/4iOOoi/Hvn37XH/99W644QaVK1e2a9cuu3bt8tFHH4Fdu3bZv3//P3x+HC+ib6LCceCSSy75zP2tW7cGr7zyShwrohPa66+/bsWKFbp16/Z3l4b/rThWRN9UP/3pT/3iF78wZcoUVatWdfrpp3vzzTc/KQSpUqUKxDHjXxSTtieQOnXqWLNmjcOHD3/m/sKeIoXLNaIo+vvSNP3czGAUfVOdf/75Dh8+bN26db7zne8oXrz4J+PFp61atcqZZ54pMzPzGBxlFH3x/vKXv9i6davRo0f71re+9clt2rRp9u/f71vf+paf/OQn//Q14ngRfdP8vZ6DhO86oXIqjhXRiezRRx8FPXv2/JefE8eK6JsqJyfHX/7yF6tWrfLuu+/64x//6K9//asSJUpo2LAhxDHjXxT/QpxArrrqKvv27TNz5szP3P/444+rXLmyRo0aHaMji6Kvv9/+9rc++OADjRs3PtaHEkVfieeff16RIkXUrFlT0aJFtW3b1qxZs+zdu/eTx7z33nuef/557du3P4ZHGkVfrEqVKnn++ec/d7vkkktkZmZ6/vnn3X333f/w+XG8iL6JOnToAObNm/eZ++fOnQsaN24cx4rohHXw4EG5ubnOP//8f7kQKo4V0TddsWLFZGdnq169uvfee88TTzyhV69eihcvDnHM+BclaeH0aHRCaNWqlRUrVhgxYoQzzzzTtGnTTJw4UW5u7v9YNRJF3wTz5s2zf/9+e/fu9dOf/lTHjh116tQJtGnTxvbt23Xt2tWPf/xjZ555piRJ5OXlGTNmjO985zuWLVumRIkSx/hTRNEX52c/+5nSpUs7//zzVaxY0V/+8hczZszwxBNP+Pd//3cjR44Ea9eudd5552nQoIH/+I//cODAAcOGDbNz504rV65Uvnz5Y/xJoujL1aNHD7/97W/t27cPbNiwIY4X0QmlXbt25s+f75ZbbtG4cWMrVqxwxx13uPjiiz311FOIY0V0YnriiSf8+Mc/9sgjj+jVq9dnfhfHiuhEs3r1ajNnznTuuecqVqyY1157zS9+8QtnnHGG559//jMb78Ux41+QRieUvXv3pjfeeGNaqVKl9OSTT07r1q2bTps27VgfVhR9ZapXr57i797Wr1+f7ty5M73qqqvSM844Iy1evHh68sknp1lZWengwYPTXbt2HevDj6Iv3K9+9av0oosuSk877bS0aNGi6amnnpo2bdo0nTJlyuceu2LFirRFixbpKaeckpYuXTq98sor07fffvsYHHUUffWuvvrqtESJEp/8HMeL6ETzwQcfpDk5OWm1atXSokWLpqeffnp68803pwcOHPjM4+JYEZ1oWrZsmZYoUSLds2fP534Xx4roRPOnP/0p/cEPfpCWLVs2Pfnkk9MzzzwzveWWW9J9+/b93cfHMeOfi5W2URRFURRFURRFURRFURRFXyOxp20URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpG0URVEURVEURVEURVEURdHXSEzaRlEURVEURVEURVEURVEUfY3EpG0URVEURVEURVEURVEURdHXSNFjfQBftSNHjti8ebNSpUpJkuRYH050jKVpau/evSpXrqxIkRN3DiPGRfRpMS6CGBfRp8W4CGJcRIViTBwV4yIqFOPiqBgXUaEYF0fFuIgK/atxccIlbTdv3qxatWrH+jCir5mNGzeqWrXqsT6MYybGRfT3xLiIcRF9XoyLGBfRZ53oMUGMi+jzYlzEuIg+L8ZFjIvo8/6nuDjhkralSpUq+NdNKHYsDyX6WjiIX37qe3FiinERfVaMC2JcRH8rxgWfjot/F+PiRHcQ/3nCxwQxLqJPi3FRKJ5HRUfFc6hCMS6io/61uDjhkrZHS9CLiUESFTrRlybEuIj+nhgXMS6iz4tx8em4yDyWhxJ9TZzoMUGMi+jzYlzE86jo82JcxLiIPu9/iosTu6FIFEVRFEVRFEVRFEVRFEXR10xM2kZRFEVRFEVRFEVRFEVRFH2NxKRtFEVRFEVRFEVRFEVRFEXR10hM2kZRFEVRFEVRFEVRFEVRFH2NxKTtCaUsLkbxY30gUXQcyvhfPud/87woiqIoiqIoiqIoik5kMWl7QslAI1Qs+Ll4wX2lj9kRRdHXX2HStbj//wRsWVT/XzwviqIoiqIoiqIoiqITWUzanjAycAY/zkDtgp/Loigq4MKCn6MoOqq0ECMZqIIzC+77Z0nYwuRuYaydUXDf/ybpG0Vfdxn+cUX53/vdP3t8FEVRFEVRFEVRVCgmbU8YpdDaA9N6apr+VZEt/fETRxNLzYTEbay6jaKgOBoIkxlnor0QI20LflfbZ5OxxYX4KZz8qEBms4LXKFXw+6Jf3eFH0VeiaMHt05MSfy8pm/E3j/97Mv7OLYqif00cX6IoiqIoir5pYtL2hFAcP2HTAWd62+I6rX38dkl+dAqTBtC4IRdnkNlQSE7FC+XoRPXp735pYTKjNNryiwwl9xWjfhZK0b+zo0ndUkLbkQGoWvD8qkxG1VOo1AtZYrVt9M1SuGKjohADhVXpn07kcnRyo6xQsZ6BWkIV+qdf628TuoWvF0Unun802VH0b373zyZFoiiKoiiKouNNTNqeKE4rp3+VXyppr6dXNXcoG0VJyyb0pkjufnoSqgej6ERUWkjCtufu22nSh0HQmb6nSK9JbFch3Le4l4G/vJsrWwpJ2gbc3oNuhM3+hngofcItnYdwGJNQqbWQsDr01X+0KPrCfLoKtnBzy6t9tpK2uJDIrS6032kgTGg0QF2cT9UO6FHwnMLk7hmOrvaIExzRieh/SsIW/TuP+XtxEhO3URRFURRF3wTxrO4bq/Ak/hA+pBY9PKbesnz5jXk3rSo9PWEa5ae/5xLPufuBoc4Yt1j4WsTEUnQiyHD0u34mz7a25pIzXOYZ6+p/T9XL8m16OkuZUVtoQmY2aZdEMijVc/kkv57d1ZakFs1qeOe2b7vUs/LPreedft+WJFvUGMGf/1zZlWZr23Mhd+89lh82iv6P/jYx255BFVmMFUWFGYoqqBN+/+OzORX7uHzKDE+f05GV4RUu2LjIH1e2kHRPWf3p95gpxGRFfIitn/pdHJeib5rC0/DDPn9K/q+copcV4vLDghtH4+TTzz/8vz3AKIqiKIqi6BiKlbbfWNWFqqZg+h/aqndNvicbtbIMHyabwmrtdmwbW92UZT9TPWc7p57tsz06o+ibKsPRdiAZaG3wJXeo1WKDaja64LJFNvY+y5FtiV2nf9viFTwxmfxLSHskatXe4BqTKVlD6+dnqdl1i3OsVKnfOjXzt8jAxByaJTc4L1kUElufSUARqwmj40OGUAFbQUgEnYkbmVDR8P+8SZPlC9BZSNa2R0Mmna3VtCc1GL9Enyn3eerBTlq/OoueZO7a6SJ/IJfhq25SNc3n9/gRoRI3S1j1UaXg/eP8cvRN9PdagXy6l3NhNXvh+VjhjRCPZ6KdULEeN5ONoiiKoij6JopXQt9IGWhA0bP5XWv60nFpQjaVbbYSdYcxq2dr7SfPs6w/jUaxbCT7DpykZObbmC5cML/vaPVGFH2TFC7tLo49NMtwo/sZyuKzWpvx1uU0IemN98i/gl5jsI1lfdmE05K7pG3vkud8evDE6h68h9VUHcje0WHB92IKCp0+HUtnhPd1+OgxRNHXTmHCqLqQTF1GpQ6u/fM4jSzTa36um4ePsQAD0pdcZJLxrw1wV71B3nWGZhb7rj/Z0zvDvBHtuZIZZTq6PH8R2XRPxrh54hjf6bnaf7f4gV1Olf34O+wS2oqszkA+doqVttHxr7AivbBa/bCjleWlhO956YKfy+Ldgv8tJ4wfm3yyT0HjU8JEx2GMqstf9mBVwevs9dl4+XRFbxRFURRFUXS8iEnbb6TioWJ2RWrdmUXsJFxrL+VPyWqlcf+dtL9znrz0fOk1L5k5iA6teKXYd1GR+jlswZbJwkVDFH2TFCztvr0co9qxbweNqfraDmm1RO5bHUhmmowfpuVVP7xdBhb0Zw1u3EpWhUwTkwPue4oBS19iPzOyL9fxvactyOGVgndpeQeKMnbIIi8kpYXkbHGhMmpxwb/3iknb6OuncHOw6pTswOX4fV1ePGTSohvMaH65Q53IuIM9ebxe7nxJTUot3+uWzaPtL1fEB8VOUX72Pgvakz6TuKjNfC871w9PX2zDWUecfQ8q8M7abDtrZarSfaf03MT3+y30wuLmrG4qjEEx2RQd7z7dp7YifiLE2AK0FCYnXqF+59Af/TD+owrKUQlbliCLWs2UWbnFlcUeV9cqA595qCBhW1tI8L5S8D5/m7gtfO8YS1EURVEURceL2B7hG6kovUlfKaJGNRpmk1+hquSx1HfTbJ1HceMz1PzLbi9r6HW8jfvn02DpGjPTHxn96s+V//N7QjVgXL4dfZNk4GJ+UVGf2+6jKun7p7ni3mnSb4c+z+0yZ+o2mB4DeSvZzm/oUZ+Wq7hxIKbwcnJAzjP0PJxhcROs5LvJ06Dl9PBOZ8AiPMgfc1tw8YCCYziTHxX0J9FNqIyKoq+bUjiTpzvou3dkKPL7C62qz2UFHc962q93Yxofph2s2JFtzgrOTG63uAolZhxx2s59lKHlQLa3KekPK1u5bfNImzOPeCO9PBQW7sRCtiQH6IgsTvJxqCIsmYirPaLjW+GmYYWtD86kag9Z6ZtWp9+hd0uPpl0tTrspsuVyF7y6iPoMz7nJu2lt2elymkFtujXTd81Iu2Z82+RtP1fHqjCn0b80Z5YTksEVhQ0yC1srRFEURVEURcermLQ97v29E/KK9EyZjaHI5CSHpTsT5+Wv5s/YxkfNyhhw23i1kdOI1nAnT+hswNjxOvuN2Nc2+mYojJOyuJBTGyrZd7uH5g60e/XJzOB3VboyITxq3kHeHMmS0bRsjuvD0xbXYfJocgeFilvD+eNJ3w//fi90rJ1zGXaGy+YKaT2HVjJzc/i9u+F2ZHlkRnfvpnXoSVgOG0VfJxmoS98Oplz2Iw90z5H+JSno9UF+TvjfHm+hNxf5byf52Iz0Eb2up9n1KEYykHktUJpimfvCuLSUd9KmOuY/7fXR2M2IvjwLtbm3TX9tzPXAT3qyb8HfOa6YiIqOF4XJ2tJCy6kLadxBl42/cpNf+t78ddLrEj/tNc02FRxZXMIfe7VwpFPi3GSM6ndul+snFk77PqPKeXdKBQ80zrGkO2bQcsYS6aTE9F+2ZRyuLMdpLYWxrkrBrbQYM1EURVEURcenmLQ9rhUXTsgLN1NS8L+1NfjO0vDjM1y0fL7v7X6Dqxh/Fm+Oxg7mrWbZnaHIadkysq7n0DKeGN7DnhzOsVKsAIy+GUoJ2dGfcEYz5f/6nr05FZjNX086ldok81Pjh11NZY6kV1iedtKkOd0XPmJrTd58kGVCl+eduLEtW5aUcemkvPAW6/nx4U3a3cOs61qrg8bXvCbjTTqsYuKQbp5q1MLuYomPdtXTq2auP/o+pxJ7dUZfH4VJ0aJc2cxdDwzSbf1MS3KZvJZ0d2J4cqXFWJDPE2cxq0dr812i4ctvmrLtZ/RFfc7u+LKZk8Oi7yW3seAgSpDfidblF1uUdYG64zh0Gz1PKoiCoYw4mGOzb7tu96Oaph97Kx2uatpJaCly5jH5rxJF/5qif3MrLiRQm3JaZ3o2csUL00y97Vp9qjxu4iWYxpKJDXQc9LR0b+LQDB7fSctzWTusunqL8r3jOxYPbKT6bdvlLguThm/2xVDufbW/Uva6+pLxLp89gwOEc8MzHN2crHCMia0RoiiKoiiKjiexp+1xpzA5e0i4GGgg1PQ9IZyM78FSD3rYnGnhxL5/0kqHEWx6mPNx9kDWD2KHcDF94z08PqSTBaqpP+5V300WWYa3nYmzsVTojfahmFyKjk8NGFNVvX4veu3BcuZoFypf6/CXZLvn8ZZE1oukObTq/aRy5TCEift/ZixymnL2GkZs48appH2p1Gu3dRMr6bN+CxXYsaKqQyNpX3Se+9HwdJ6t3NRHVfLslKt+mq30QqaVuUKXC59U1yoyYdsx/G8TRYUKx5cK+Im7Zg9y08Ffsoo/p5erkzzNw/weOVlYiBVoMY869Cqayw7SOby9kzfnNvT9dKE/Dm3BStRHPlldSCUm6ua0vi94Hx8UHkIrpmV+W+slHMpm8eTWRlzDxolnSeqnrJz4lf4XiaJ/7B9t7pUhnCtloBGZjfgFffrd5wPF/cEPaE7eHeernbxkxEiajXzFE+jckYwf0KM3ylFr0Qb3tKCTXFlPYl2YOMwZFVZ85OXzHzvH+Peyd5m87OeU5ZW9tQ1wn7yWl/L7NT7bLz32tI2iKIqiKDqexErb40rhpjCFfcqqULUuP66IG4UL7dLU6qPxotc+6QJYOm3iwPVh6XbDbOygxj18P60kC7lDOrj6vOluGTna5ZMW2SNUEla2GS8LSeGLHa26jctTo+NJabKbeLRfVxf7vTQr0bj/a4zEGj5MG+jRj6wRJL9P7d1PuSkYwdb+ZHbnhhK4LiRsc5agCf++4y6qUbP3Fl3vedT3+y20pHG44PYiNy5Bfc7yJzelq3VMK/mtHzGD95InWcpWFQvWhMfJkOhYyxDGkDro6a50mFvWjnagxBF20nH20zLTmjbNLajdm8WOGmxtj3LkjLmdPGY8drnkQrIG8qNZU+QYwUqS+4/IH07+JHZMRz69KuaqW4vq6NA2HMU9vWj9GIMuvEvRmtx3TUFtbU2szBcmOIoq6BgdRcdAYRXt3/5cWFVb3CfxNK6RCz5cZGa/Nh6aO9B/GmzdM98zpxlNV76kSbfwTV6Kzm+hMj5meZtsaRvuacHQMVQpwcR23dw7pb/iGDGIbs/QZwxJHqM35IRZ+DWU8xe/9hP6E1vvRFGhWKcURVEUHZ9i0va4U1q4qK6Cuiw5FFpknkvY0OhMKpHfvKrOjUI9RbNTl3ioJHXLsGjVBbY/VpI5VN6/xU/Td3S7bWZ42Qt5smcrtUuEmtq+qx+VvnouZ7YL7yWLkrcXvHcpsd9t9PWXger8lp/On2bU0FtDn+cymMTOWZkuLPeK18eSnJRqMnSB0pNY35UtPcp4BffP5pSJ2EZm+jPVLnyLOxnV91bpWFTm8VOvNVY/TZoLPRRGIA9DqFFui3faZau5aIsRjW+nOTmzMIzmQ1/QdPmzyBInQqJjqwp6oQG/S9zSfTR5/PpjTEENvjdjnaqN6HUdL2bXk7m/iF/jvhmMWH07U9iYPB0SrD/gXjc725vGPvMz6fNFrEHWhTz+sfCVX8XLazm7ls9eT6/kL8pJhjCgH5ftY08bmqbvFDygLq52NCEVYyf6Knw6WZvxqVth/9hLFTQpL9BMn+vv88I5zb3rDDKZkuyzu0Oi3QTkoB3L07sMWMz6s5g5FtU478HVXtnJ0ObYzBv7su1Nct08eYy2yLmOey7Dfha0J73hZNZhJNVbbFdl4067Lz3ZiHSm0FYkTrZH32SfbklS+HNxR69VCopaPvm5uM9PvkRRFEXR11NM2h5XyqIPRZtQtAc/zjK4+nA/++5YmjE1vZLL23Eap9pFDk2xZ1949vjdNG/8gvK19xm/jMzG/Ln2d+iB0zGBDxT3m31d5FRDe8bVv9bN+cM0SJdolX7k/L15/LiHo0mmeBEQfV0VVqZ30Pq7s8jj2Xuaum95H4/f0YkVlK15QDIkVDmlkxL/5r+83D3kW5dpZHI62Q07uL8rs/q19vPdj9h4zlmWTGwgZ9ztkjZYyq5dJZ2arA6TJ+eyIas8c9CTv+7I5E6Sa1MqsK5dJRuuKs9BrGLxOa1Z0drR3oNR9FUp/BteHLXDRnxjqtp36UnundLf2Ot+piJsIz0dw7Aes2m8+TUleh7RLK3tUtxXh/tqF7zs/vA9z7ptk6wmm5zkY8aGdznwXMFlcjXmVaThi8I8ZAlapgUvsILJvX9OQb/P3SV54iDFHNQl/baq6ZkUTRxdgh5FX7bC5E5xR5M/RVEbDSjZjRfP1j/9hcxdnZk0wM3pIx7KGSjtmbjh1PEWNG8Cxh9kRO+wh4C+XJDcSt/QdiRJW4eK2XY0HMeGheUtGUmxZDVYcA0VDhQx/mGG1mL8UFpmowwjh/W1bCnTFl7BMEr3PGTw5HE0afKp4y38LDFZFX0d/b3v5afv+3vFIqWE9RiFydjzhdZxddBWmOBrVvBzQbyqIKwi/NsEboyLKIqi6Osnjk7HjQycyY9QCbsoM2mLRpaBR3b102Xpk85+6ixP+LHyi/YZ0Z6cjqQTOLtsU5dWyTNnGe3uoM9sdr6aKS16wCs1qZZWV6vOBl3qPCl/tVAssoTs5FF91zB82l3sYPu4kir03stvLhT6pL1fcHxxeXf0dVNKiJvXzX2wAyu5tGaekuvzVEyrci4L8mhZOywxtZRem3OpRsZGKlhmQzLfL3H1SbSRo/1v5lGU4skrRgx8xcRcetWgqTxvvtpQ2/rT1fequ5bdG5K2dSi75wCHmby+MyOp2WsLL7J2VXXfydngwx0Z3IJPGppE0VehonAKsBd1+F0jm64op1TmTiU2cvOOMWFC70nGt7tan8aPy11Ltwns6UfpzaybUkmD4Wu8/qlXHVCLBZOoP3GfPcMyzLvzkL4PPmrealpXQ2NuzMZqWk9HT9avpkZPGuSuMQ97VlC6MWll1m+tpObaLXrtprgpurWbqeucR007fIajSecPHe0jGkVftKKOVtTWLrjlYxU/as1fcAZTG12py/wn/XLzEPf0ZOhi9MNYMt5iVbJEW2T15L5JPLGbbo3oMIHHr+qk1znTHdg/L0zyfXTE7t7F/CXZrkm2MJZMo2obtD+iz/WYHY4uzSMZyODh49ya3uyuFveGLiIFfdk9p+DYN3xl/8Wi6P9PUWEypLhwXVF4/kb4u174O0IDt+KfutWhcQ1eHIGe/KZcOKcaQ8lm2+27pRzvlqNqFk2Qi6f3CDt7HMa7whjyvs+OJbH3cxRFUfT18JVV2t57773OO+88pUqVUqFCBVdeeaU//elPn3lMmqZuv/12lStXVrx4cc2aNfPGG2985jEHDx50ww03OO2005QoUUK7du1s2rTpq/oYx1ApVFFk3H7VH1ir25SJ8or9wKUl56mSzOPHmEy93HzDB91lcguhWrYnz5Zjr1LMCqckC4Y18e1X3/GtcgeUm0PLZ6g1aYOJqzGQrCUkPdKClgvhPhcik/Jr91E1ZUJpenb22R5u0f+vGBdfltLogz72HTjHi9fX41wWrw9VtFuTTZSg2S7sF87NO2Ile9ZnqDuBw8luPQ5n+H5aT/FMlg1q5ufXjeZWGr7FnnHhnQ69ypv5DcnkIv/trq73Mpa0KEu2IYun72mu66nTbcoJ73fPamYmG4zfTen3DvELYtL2qBgXX7bSwlLuujhErZb6X3GvKkN3Gn8QzdGGN5rW5GX6nP44u+lWgft6M/kg6SXUXLvF+KHUvZAB2eGy+6Y1w0H5h/cpXu6Q2rAjXBY7nWRcauSqvu59pr/97YqEhG0tYVOzgtcYfxD1SepTs86WsKNmF4onM7mMaS//NBy3BsL4c2LMP8e4OFYKEkN60KMRg0q7Nn3BwHSTmTPaGPH8DRY+9n1dNj6pZ6sHJDNTQ9uwOBeTeWNKTZsqhsm/36dX+2AaHdLyul3Hoee4rz1XnzWd68m8htcuzJL+tYjSPQ8pjuWrsuVNPV/VxWjHjuewlEP7w6iRHGDtY9XJ4kOn0AJlSFfRNms6v1nm7ydsj/+4iTFxPCpsXVBY2VoWjajVR5gprEDJG4WK2TPRk0t70LszOhc8vi3ac3E7qtbwuxcuMT1doma6RZ/O90nvTKSvJZ4ucTmTuHz2DGmtRPp6Ir0vcX76KpNr0COLH7f02dZzZR3vvaBjXETR58W4iI5nX1nSNi8vz/XXX+/FF1+0YMEChw8f1qpVK/v37//kMSNHjnTfffcZN26c5cuXq1SpkpYtW9q7d+8nj+nfv7/Zs2f7zW9+Y8mSJfbt2+fyyy/38ccff1Uf5RgJidGxFft5d3htG1VTb32+U8Zx5km80KI+m4WL7R70aMuCjajBDw4UcYZ3mRJOj1pOXuLP53xH3k70Zc5lHOhCjzJc1GO+A/VJDyYcZF/aPPRJW8iC0RhI+kIR715XQfWJax3tFRWXdv9vxLj4slRwfvpHabfEuMwjivuAauGbOnRqQTuEcWTMZWIn3hzEKx1rs5FZRQ+Z2JunULr2IdVsVLwYXuVG93vzCkwOy7V7VSBjJfqSW5tSyTgvT6Pt1OmSJSE6XuxWz+U1Fxm/O9SHGMXQiQxtxA1lSV5Phar14/8C+osS4+LLVLB89Pc1dErX8vZgl6+Z4ToPk01OK6a1uULXjo/6Xs11IWHaROgFffrRb+mknfSs9YA+U9GK+1dzYw3G/OnmMBrsIGMJZ+1jwW30mYUhpL0TLZJxzvS2pNwRVcsgi/XrMTe8ds655HcXJg63oTdK0KE5913Xh0nIPkVoAFS46dM3X4yLr9KnNxfryaUtXZAu0uqxJ6UNEpNa3GDUpFutcK7BK8ZZmbzgg9pkJTdI/5rQnGYdGfEgTyfrrEK5fPrUftwpi3nI9ewk4+Ew9TA5H5O5fwanJPkWnMfrkzm7G+edt1rT3JdC5WxzynXijFfXyGgeEsGvVc7yBxfp3HGyUd1vDas8DpNU5qlLOnFLI2GS5u9NsB/fy8JjTHzdFSZnCcUn1YXJwtoFv2sQkrWbGpu5po0H0p7076Xe3hc5tSGjWhqR5nhgXk+3jB9i9+GT/SzdwuJy9M9Qc8EbNm0sp1wyX8cHn/bO3GwPXTaQBxk35FrVvOf8vXmeat/JgR7YTU7W7ZZtbmbg1Xf73WOXOH9aHlc2o1YTISnc2vEcE8S4iKK/J8ZFdDxL0jRNj8Ubb9++XYUKFeTl5fnBD34gTVOVK1fWv39/OTk5CDMZFStWNGLECNddd53du3crX768KVOm6Ny5M9i8ebNq1aqZO3euSy655H983z179ihTpgz+A8W+xE/4RSqLi2l8tvQHCX1ZW626Wks3uL9JKBIsihvHcdP1w/1y9hD1r3rByksu8MFS3t5P3cqM33z0EqRbwU7dz85p6tLueaxEU/aMzXDS4Y+NKjbIbe1GUpRDi8LSPgvZcw2TDoaLjGbPkQxMWb0eq/DKMfhv8391EL+we/dupUsf+5n1GBdflOJap1muSjp8std88zTbeTVXcw8jupLTlvFPhUdfjD+mnZyTTFe3FtetGWN40f72fEyNgZjC+1vLqlxup6QmbiX/ivCN79yF3GkhrpqdxM6P2ZHW07j7a9yJ3lzy3O88W+5KSRvW51Kjmk92CU/OTZkwx9crfmJc8E2MC2jAf7Tz53tPVWno7pAAXYXZoe3B+IPkPMb6a6gxgf09iigx9wgvojLj+4dL73aHM5Sedcibnai5j8wmjF9JnyWowOSzwrv1aMvMgjjrUFBRO7HK0QWoPynBKc3RSVjKXVMotioaErdZd7BkWAMvO1e/QY8oN2KTM05a75VlTWi8Q4ibd4UpkcO+3BYJMS74dFzcgswv8RMeK4VnSgXW3mjmd9v4jrd9pJjzZqxmDjumMeFjhg5EfeTz8p3h29hhFfPq8Dr6FGPTQc6+jiETbjV80F2s4s35vJJ2cEYyU5Nn2NGOct1RjDcf5uwLsYPxa8Nh9CzDpN1HOzm3R/HDGRYXPeQvaRc/zZlm3YhKnnOJPuc9zvWoTTIq5bf5Qqxs9Y9XdfxvloMfwN0nfExwIsTFP1P4rSxsJVJKqFZtz90ZoSXB2tc5rS7ZwrInh/ltlm4dJuppkqZzX6ImT9dq7vJJi5gvxNV7LJnQQJOxr5jZnw5bqVPhJa/vPF9Sh3s2hyNI04Fu6Tray9NoOIz1d1Kjn7C8qoswKVg2vK0cDGf5M9ku/fhZH+wr7sCuUuFYf493F+BtYUKd///YiHFR6Jt5HhX978RzqEIxLqKj/rW4OGYbke3evRuULRsqNNevX2/Lli1atWr1yWOKFSumadOm/vjHP4KXX37ZoUOHPvOYypUry87O/uQxf+vgwYP27Nnzmdvxpwp3ny0dlZg5knmnU6vZBk804cbHGLCGRjCFMUtvtvyqbCuvuYCcsONw3VaYGOosMtCtvnCBfAnnFc3zxpSaTGPxgzxR9JAS1xxRKhlp61OMn03GKqHaaj+lJzFgelhAdFOr4eataubydEU4xuj/LMbFF6EOg3LM7d/B5nSwGw5nOIzzeq2Wt+5893clZyv3FySS2uO/0ptd/fB0dZuyfE22h0f3l1GUmk+nuo561Iat5VWpstOsneE5yfJU5QPhz2fuNLo1pcNEyt0ZLtLfTl7zRC6mhAvz5x680u938sqU2mrU+JvDPZNwIV3WiVIx+P8rxsUXpSxV27nr3kFOdpCVDNl6K2vCxkilp3Nh2sCe3gxNH/Xkda1cWuxZS9qzYzRb+4dL8m79KD3rEPMZkk6VOYmJK4Wq2zXoHTrqwI65IUV0YVpG0jPlIL3ep1MJeq3ilN4seArT0YXxeeRfg3PJuo7kslST4a/oN/oRS0Yz7KQ7vbz6Ih9mJ9Qqx2ktnagtemJcfBmKCuczhZutfqhk1e3anzVP3Z35zmux2uud0Ib8w/UMrYVvc3937ruThjXocC7bs0uqI9QUls4LeaA5DzN87V3mjUY+Zw9DMtOFZXmlTW3vfsyh2WjK2YNpseQpOvFxeq3vp1ne3VVVp5PCeV8pzMIrRQ9pdw/fTqbRnZq1t+hz5+N2vCq0HMlRkD8881Of8dPjzKcr1Y/vqlu+upjgRIuLf6YwYVtW2PBrAGf0omdng9Ph0rMT6Z2JqekwrbY/Gdq5WUXfLH/o0NBQ92h6zUv0xW4uf3iR13sJg0gL9GRN8gpNw2oLc1g19HxJ76MJ2674vj9SlobrSCqk3k8bhGVV2Ry4Xgjp3bgMJVj3TCXf/fhPdk6u4sP15bxcvZ5rJ45TdX1+wWcpbJdQ2vEcE8S4iKK/J8ZFdDw5JknbNE0NGDBAkyZNZGdngy1btoCKFSt+5rEVK1b85Hdbtmxx8skn+9a3vvUPH/O37r33XmXKlPnkVq1atS/643zJMlBRzaFvsJ4Og8MS7/F5nEHYCyOPRt1QlLRM4rw7V4f7szmvxWoz57OuTSU1hhRcSF/Ijh5YxUsfU8E207Kv0GxwqDh0R1jA9Ou0TzjN74LVJAfTMHm8lKx+/HL4EJcOyvMrP+WMhmLC6f8mxsUXIQPNVPrPdZaPyXZbzkilxxwyeBfmcM7Hr2pNqLwQTsMrtuWubff6YKAQM7NXs4etB5l/2UWmPfNTZ/x6m0Nv0m4XY5f/TFo38USxzjo/yU/Kkr+4qgW9kH+0E1oVzBrW2tlTWXd9JS0vpEHfNSxkxEaeWIa2fNg7ETaFqu14vzD4MsS4+KKE2rzM1TvdMn+0sssOuG8uNZK7vNmMG0ow7wrqfrxK6R48UPRaV9Sc7+ceVBrl5vOb9Gfhr3wjPriG3En8rntXDobxaEOX8iG2epL1VhhP8g63VhGVbtstrZiwAisYtu+ucAHdk+Jpg3BxXi4cadZbQmFTGdL3EuOH4mBI/vY7/RH2s7Ekw9fcRH8FT/yyq2y/XmJcfFnqckY3Jre2OJ2sXtrU3t0V3JdPMg13siLtQhnqlnzN2jXVbRrE8vQRA5oL1eL1+V2yzyo8lY6WbErdOCWkgPQsaCaVxaY7Qx5p9o7WGpy1xl5s3lXeoTbIZ+HatibfSd/Vj6o3Ot8t7lZuLAc6Hp0UaTaVsUN+pgKM4P61bBlWxtaP0Q6DyZ6ynCsToQ9olqOJ2jMKfv57jr+x6KuMCU60uPhHCjcQu5jGfRTZkq1Sus5D63tI2yZGXHK79e15umNzk13j0eRK6aSEt9tKyyaa9HrF/GSDyZMxAdNwD7PTwd7sj83cd26f0FpqAlsWlgkVuHncMyMcQRVhO80SyQvue5AdWbx1fTU7k1fcu7w/q8lcj8rsyOP+3lgZerKXfuyQdFUiv35Vy5xv4s4bbHotiwkNObWJcG52fG9KFuMiij4vxkV0vDkmSdu+fft6/fXXTZs27XO/S5LkMz+nafq5+/7WP3vMzTffbPfu3Z/cNm7c+L8/8GOiOJlNtDOHCvQc8YDtadPQnSytGpa1noRh3LrkZjvqM/M2jOO1CllhZroyNUduoTJZV7H+QcqNYN7D4XRre7JPl41PGjfiWu3SlyT/neowigF9x2uZjd3k9yWtkTAZ7Xh9bDi6DyZw6u597OJ4PMn/Oolx8UUoxbnl9Paw8zauZif3D+LXp3LfNuYUPSRrKpYVpncxgSsrTHXKY5jBsvbMLKiYbTl0iTQjkRZJZGxkV5mS6iWPMImf3jaNHJIRZM3fpDZyJ9NyRKh8r4L2Q+c51IdXky32LyzCDCbW6OYwOmdjEn8qkcXv2gkX0HHi42/FuPgiFMeZTKrq7DJvUpT1jcM2Lztxdr+wKuOW9A9KTzrEXMr1RhanJU+GVOhc+j38SJj0WMtT+8O3ddCUuySHUi3rU73xdlZzoB2aUHUi7efO0/Jc5t0pJGn3YzWn2mVPi9ALuu7Hq6iBJSFu0nIsyr6AaRxqHqoKdQmXz/nvVeU9shpxnQkFw05hsvbEiZ8YF1+UosI37Mzwv5NbG7H+BumtifNKvmTligsYWfANuxN5nO1NT7dpbu2+2mqdtcEy/MFFIelUFO/RaxbF0gt83x+lRxJPdKfG++QtOd+7mDyfqgWbWbafNs+4t67VbCrVm2yX8aAwiTGHHs35VXYXGvNExR5MI3M+NXqENlX5XWmZPKLhdHJzuXEElTrtdnZT1ndiQ5vy2noqnKM1O4UzWlOpD66iUmeyWwtRV9zx3h/6q4wJvulx8Y8U/q0tjJna1BqgzIHqVr/wHR/nlrQ8+Y4+OY/zZ55+rrkMPOBGz+VeGb7zs0jvLuKiO+YbMSlMJfQYhju5fyxPv9fcGd4NTTzK0MLvtS9418eT3ZbMQP0wKXhTCUamL6uaZmo0kOLp1coNI6vZJu2mc/PwMX706hRGs/VOfv0xrdOqtvco6b7aeBjFKJ1s0uecxyULeaRe93A6tut1oUXC8b1JbIyLKPq8GBfR8eYrT9recMMN5syZ4/nnn1e1atVP7q9UqRJ8bpZi27Ztn8x4VKpUyUcffeSvf/3rP3zM3ypWrJjSpUt/5nb8CEvz1KKtp7zfqqw3ne2jJM907Ek2Scqlrus5xp46/Ftyr3I9GZ0uNKt+a/U251swrEkozR2H9eTOLjglL0Pr5TRMM+WlV3u8WicnOeznHjT52s5aDHzKfeP6UI4lq0Pl09ltXmYW1Zq/pd6mDynDKf14uMy17DomrZG/MWJcfBEKKodu57ZJI0PFX41wut1jTLjA3QtrWTI6PCPrDuZV4fqkK6sxhUaVwyvtxczhWMWgLnc5VI1dTg05ohJ4FXV4smcra1tVd3n6Qqh+L0PFYtQYxrzh5BdsQFai/RGy6TUtN7xGF3I3UuPjd9W+orCfbZz4+LQYF1+E4riYyZ21uvZJL19ykQUteBZNBoYL5tyxrEhWG51c5JXrarOC8Q+yZD4XlqDhdajFjuupOIzX76TzVDpfz6hLbpUuS0x8tZtxL15r3auVvFiS9DCP9+xEJsuXZ2s9BaNxmBZDnnLLjNHyD9JtKluLHvJGu5oOTaFdT24sO8LHyQuMJmNQSNbm1uig7iyWJZtCtW5NVjonXFN/ZrObb74YF1+Uwt61zVjSGY3YxeAZ4+jHL/dzz3mMGBuqY9/YWpODTHCdy3MXOZSs8cZbNdXFu7m1TTxLWI49hWevaqr55hd0mzFTfic6T8AKmp7zks53hITT+OuvVgGvdclyprdDBWF3XhuSRVF2Dg59UTsffIIdvL6NyUvJb88HM/hr2lTWkNCRxNwQJxNzwsdKuqReTVurnrPdDz3PKLKefy20W70SY2po8ucFMpcU9PxRVniF4zOOvuqY4JscF/9MWbTltBu5pTMT2rlizTS7dnzb95qt42V+Dbt5uTe3uEfVrXRJrgx7Z6zn9dPp/Nhkvwl9EryPiXcyYmlYb1TMRy5NpnsbbZrPBHvT2pxOz5NoMiW0c6uelnHKhbw5t6Gy5xywfFS2PvmPk0uyOzWu47WWDeVut1CfipVpmmbJardJ+Rn7DBiFbuy/s4iK/YRrpQtZphG/I/SzPb4nMmJcRNHnxbiIjkdfWdI2TVN9+/Y1a9YsixYtUuNvGjvWqFFDpUqVLFiw4JP7PvroI3l5eb7//e+Dhg0bysjI+Mxj/vznP1u9evUnj/nmKGzoX5yVjDJIlWU7/TGnhVXCQp01SFslBiX9lR7HU2kf907o79dJC+2XzjO5Cm8nS0xeKWw205Zug6lYhvW9kMldhqnhXVf3nu4JP9bOUybpaWHftg47yYFn6JauoS9v7GxIDiuSszxUpbeZfVGWGy6aJGy9ceL1FPy/inHxRSqOM1S/bK3xPa92X7c+coeScyHGUimtrk/9kHBqMoE+TYVJDaGo4tBYoa3IM7RbTI9+YWOx1wcxaumtVCfrmk2adERH1j/F2dNfdsXw+SpnbrAy/wKlhc2clh4M1bqHcPaIcHRPzGX7wpIULOnbP7CIbtk8eNL1StkrLDE8fi8OvkgxLr4oxVGbyxuafHVn0/yY7pRK62mLEaMpn5Z0blrdvx0oollTGuSvoX9IKp2WVnfKRBY/jBKUK+hC8Hragduwm/HPXQ169c91kf9Ws/EWzSZQ5KUjrp4x3bwWnJasNqtba6ZxSc/fWZRcThkaPoY7+UPaxfcmrZPxFJ7jgd45WlbGNNZPouUwuvWayXuhEUL+SNxB+4OzWEKYmjl+l6/+q2JcfNHOQHv61zX1wit5+3xV++V7o2NNyoUcztC25HQL+8ln3/OOiXd0c5m5nuzWSqPn+F7uOlnTWdCtiQsxcxL5Fbl0dp6JVdjUianpYJ5hSbsGTMHHVL0wVB/CX5N830ryKMN91/VRr32+xb1Jix7gICUWHuFh6o4J1fFb0wae3dfaB06hDC2LoXKol+01hMXTSJsn6lhFb37vYlkNX/OfBut07eM2jS8nrZ/I8QunldlB/c6U7KagccNxJcbEV6WUMPXdR/X0ZH5Dh7ty9b/uXr+b1NXMKjiX8dPCX+LJD4d589nJBQwPzxwxOtxuSp/yxHk9VOm0U05zmgwLq55yrg9nQS1HL1HxDjrfw2NJB/Xy8jWYu4ZzQ5ueA1fR7H3ud2NIBE9Gj9AOblZWa3Py+cuqxOnJoxoNZkuyQW5/NKde+3yasKATCwbxq35dTM084p6xvD4fizjsJBZDE+Es7vjr8xzj4pskXhd8UWJcRMezryxpe/3118vNzTV16lSlSpWyZcsWW7Zs8eGHYdlJkiT69+9v+PDhZs+ebfXq1Xr06OGUU07RtWtXUKZMGddee62BAwdauHChV199Vbdu3dSpU8fFF1/8VX2Ur0gplKXZACX3bTd3ZAcLGrN4ZDh1GNojzEgv6FpQaJTJgJHj3dxijBoTwisUnmZcmFbVo9VDdrRAP5rsWuww8uvwy7OGuDQ/j4oszmutSu5Of2jRyuIHGdxunMy5jNWP3uyswJzJFD2JPpMeD+0HKwsH0LuG4/GE/1iLcfFFKC5896rwdh1ne1OfKo9rkIz3PqFlXz+qHdxICf47vZZv89riLC1XL1EKWWt4alfrEFTvCRs5LgyvfjitHSr+pvL6ZHTDcGpM4e0dZzKH0j0ZlHWXuospfSetB9PhMdo9w+KwAakqeCHZRxtyhlFizhH3rObmRWMMMZxTj98qpy9ajIsvQgaq0KOd85/Kc3X/6d7yXWu7Vdd4/WuqrgmPKl9nn11OtblYZcqypw6yyZpAyWSDGV0ud2EZcrt0cPfWgd4cTuuiMy15q4F0LH0qPu6OOYNpSr05+dYv4/XefFipiKRCqvUITkH7FfM4zPzkFHoKY0cxZq6lXjKN0azrVskTG5HD2vermzObp/DEnaiGg6FtSVZPlKB+sZX8hVDL/s0X4+KLUhTV6dbZmPQu6UmJLpuf9Iczi9jY9Szfm7GOEfSqRpKTqjPlJbOQ5iZ6rcjVcdDTrsibT1GWdQ8v13LZEhuE6YNSaRlpzxCBp+0LK6V0oUmLVyzJbsCrrFtSyZvnNfSXNFttYSvKOWOpY5Xls7KVQrkm2CG0FNmBhZz9Khe99rL2a+dpf9s8+Tn88UBTmob3njOcZs0xgz/6vmRbaq423lpU3xVd5xuQ9DDKINZzee9F8vef5YpXpzGKo5thHj9JghgTX4WLOfNGRbb8wLXpw2Zp78PGiV9ldnf3wVtYRIepLB+V/cl1xxkYMIY0rWTPBOreEV4pA9OLtmU3V06f6v2FZe28I9Pr0KKgFc421g6r7tBIFsCycJ85NGq+2PMleaIKw+fc5f5tQj+qpby2MEv7QfNCD/ahBYdeLvR97jas4MCGhWr0ltm0rMBpyTTbFDQQKoFiTM77ueZrnuZywklhXUdbiBwf52gxLo5HGZ+6FW6E1xa9hWmNM4XIKv43j4/+VTEuouNZkqbpV7Ku/R/1+Xjsscf06NEDYQbkjjvu8PDDD/vrX/+qUaNGHnzwwU8aRMOBAwf8+7//u6lTp/rwww+1aNHCQw899C83dN6zZ48yZcrgP1Ds//ipviwZwlZguKWb9I3EnNm8ng50y4OjzelLu2G8dkeWemfle/qt5i6/c1HYMGwoijKvoEVL6wrC3/vH0Ljg373RhR33UO507lg32G3dRzowgcyVvN6EuhcKWxM3xXByr+pgo2ouSMZo9irWcGr7P9u18tv05MlVrVyZTBAusY+nC+iD+IXdu3cfk6UKMS7+rzJQC7Xpcbbpj7XV8ayn7XwrU9nTD8jdSOddZIxkwfBwqtOkKXmLz7cxeUm3CrhHSOzmIRdDeLpHc132/8a+yeWlsxKvL6LKSZTrhB0smF/wWtV8UjllM07iza7hd6egVAlOGY1pTFzcTc9yuZISoS/nvGSTw7j6JMqNJslNWTETax37DZViXHC8x0VRtPZUepPL2y2iCVtz+AA1OpL8JJX+POE6VAxLWRvWZ/mr2WoWXa1cbfQTrlkXIpfxb12tTvK4DGQfKKLE7iPer1BWlWt2mjeZ1lmc/dbLHkoaalaNdCVJC7Qid0QHl3hOhQf3Sp9L6M60jlfoMvLJ0DYhi1eW1LYsWeNiYSXJ247uS94UDbvxZm4YZYqjRVpTdvK2MFh9Fb0HY1zw6bi4BZn/x0/1VSvo7+wMD6T3u+bgY0qce0Ty29SKWomGazCLmUNDnqhwe8hXcOMaZtS63MHkaZ3LkHF9GFdajhC+sNlsHUTFwkmJLsIERQnSFfxpR3W1qmwICdLhzFh1uR+Ve1oynE29wzLxvLSvFsk4C9O+qifjPjniVvvInC30zR2IzTzRN/yu3QghcTVDWC3Sj/suY0At8tdUldVuE39mzgrajWN9X2rcg+6MrNbX4JHj3Dr4Zncn/Qo+6bvC0vB/pXr9AO4+4WOC4z0uChX+xS0uJCpLU7S1qofybWxyVvheH6bHrIf8wB9sT6bJuZ6XH6ThOnJr0m2I0JP5MC9fEjqGNMBLBe9w3YEiStQ/In9t+ItduwybdlMkLW99sl2zqcLXbwmy2DOc/IPh+X3W4UEMwEE+qMMpWUJMDMS5LJ7LkfQCzW97wcw72Zd2cnXN6VwvxEgb4VxPOEbtkE+jZxZbNqOZuzsO9HsXy7vnUsbgVLydCsEX4+J/HxfH23nUV6W0EGuF/24a/rn2FE2/+6yzvWn8owPouV4YlTY5ep1dXGjmVlT4Xn5YcN+HBbdjfS3xt+I5VKEYF9FR/1pcfGVJ26+L4yNIMnApSvO7LDuuKK6vB02tfa2n1zR3MFmkIpo8Jvz9niAkjpbiRSE5e5JwzliBkR37Gtx9nJdzw3VFtyHhsU8vbu7ylYvohanCJjF5OMj7Q8qqsn5n6LGWjU4kA1JpzcSCq5o4I1ki6zmsZ31vaqyic/Zk05Ns/N7x07j/2A4gXxfHR1z8PRXxE5T2SNrdXqWcn4xH6NdpMNUqvGXdqWfJyA7VTa8mW7QvS1IbIzCW66aP8fCK/t48j7MroAzXvTVGfa9qmzyu6mPk9Thf0xkv2d+uiBJTjljWi0bLOZRFxlz2XEPRoqHP844RlBvInJG0Gxh66L4vnGb16cKiqRdo/uALtval4jiS7DRcJ/fIVVA7f4zFuOB4jouyqKvkvu/JL5Gl0tDdJg6nV0+Sfak0SWyfWtKbztZ07ktcE3rQTtrRTa+uuXTkg+6kO4o4ueIRGbMwR0je9hDGluEs2UyT54Tq9Ezyu4dWB808r2adLWFCpA/Gh99/u9U7/tzuO+GCvrLQR7oni0+n2YXC2FUi9De8EGcPdPT6uEyYaHzhcHMlk0WaXUXyYcqzS4TL+a/iAiXGBcdzcqoU2vIfNTx0bw99rnncnMlhEnz/kCJKnHvEoY1kFJ5brcFh7n8wnJX9KC2p/CX7qMGbD1P9QBHvZB5RdzD6MvP0cPnco4twPrWG9evDBF7Ft7gvq486yXgfCjnWimPI70/WRGE8GSdU1Y7jwBrmlwzf6A4X8uKSepLkNUVxelrSKQc/UGLhEae2+LNdQ78dPl5zbKTadW/Z2O4s788pq2qpHdJ/D5vI1l/3gueSC1Rsi0uE6/yt6E6yLmULuq3HbOGdv/7Jqa+T4zcu+OxmjtVRl0F1PfWfLezyLd1GzjQxh17XYy5JxdTNLwwzsOhdyvUm90GqpefbnbykLJqME/qLLOXNseHSos/1TH4wTJk0OZcrl0/1u65d3TF1sOrJSOfj7DHsGMj7H4dk7i27btfIMk2LzvPUx+G51dLyqg/d7tCDLN4dag8LdwVoX4aMVrw+g7qjsBFlePPOMEr06CmMYzm8cV1N32u/zrLZNJrCt7u94zJzTVp0g7ubD/Sxk6xwrqffb8u4TH6xTLi++VfEuCh0/J5HfVlKC8FRF3XITlywapGNqvmVn5qjrY8U8/Bl/cMYVA6zSEYWpGvGpVjFGXVDzcoS7FOweR52HQpP8L6j1+JFfabtosPCFcnfqxz/Mq/f4zlUoRgX0VH/Wlx85RuRRf+K4mQ25MdZPM0o/65fcq3X14blbB3aFjzsRWHGuAtOZ8bgy6mJMsLM8fpw/+Cu42hMVXR7X7haaMPlqxeFC5PdwsYugzCW5UOyVZm7069qdAlL8mZgFGn3xBPtQ5/cD2EzJ125T41a6Mjj+3vQt6FwuR2XbERftgw0oH9p2elyvSblGlBzvCYTaHId60czo8LlljvP73fzxFJeTbY4BW/vJJ3Dugsr2Tk908NL+zOZrDIYgok8fF5/HytqTdrE+z3KajrpJRs6llfivSO29Cyj0ZiCo7iMWV1aK57JKd0wiXc/xjTaDcGF4byrcy3OTbPdMXWw7+9/wby+odX0/X1JFyXhhOu4qlKPvp5ChdT5aWLv3Aoq5e02/p6rnZpeTj8GT7vDuqmVlF+/z5vJSyG3sIoki14P50rHsfaq6k55hhJtjtizjwXNm4S2IL2FqvTh7NlBk1YFbzmZV7rVltWcSXrq5375q9GLau+/FRK013CfAfSmc7/JDvUSNvQbG2qXli3FOvSg1yjO3kqbUTNpw8tjw2PLPUj5ZJFmXWgzaybPLggH/7WrJom+foqjNbk1nH9vnn87OIX6tLs+TBKUGHfEktXM2i2cwjzG4rEcuocbl4RL2xeSfd6cz45J4dSpRNMjtqZNrB1RnbHhMrjH9eRPQw1UpsZCtqRZrObqouM/s6B1eb+wnPz1XjQawU3XDw/ZrX78uMRUCh87jMa9X7M/vUDDCpSftk+JWUfsaMeuOd8mi9zR3NrmZrnXdbCx71lmzWmtSvud5u+9iIpsX1fSDz2v4gTkk9+X+3rzwUjMYX6Li8IkiqX+9YRt9M1QWF1bBV35j25K7vu2t/6zmsvnLgqb5B2g11bUZ8R60hqJ4Tl3OXK4JB2pnF4gu+hL3hWSo/f3ZU4nZIVvVHFsGFfe1TuoezhD/vKqflezqwXT6OwJnUpQNi3DRjL3F3HWPnbuDkd3fjJPuWv4SZmQhnpYb/Dr3bSsweb0fEXSyxUVKnbVCO+ptjC2dUqd/SQ93mf8JOxna2/+O1lHu4JTr3v4N1NMOu8GdtPTJLeNHWmiXupVWRkWQCmuoIFDFP2LCqvWywrftE6UHEDvbq5N/9tD6TXSxom8U1tYlJyl5TlLPHB6jod79/dBHnrz/vKyzGL4AzfZPeZk+w4UtSZtZ+r6K701r5r0nUT6fmLl+rMM/OvdwhnV+0JclxU6offAAE69kR69hFgfIiSO+wjBcmbBrazjfQO+KPomiknbr6Xi/I4HpvWULkkMf/guh1A3m/sfRpNwOp3OEK4kemISHbs/zRy2X1eSSSQ3feiNRjUdmIhxPJ72ZS112r0k7UmSkere6hGD3rorJGY7ojf/pbu0Mb+QIx3NljFlbOrKugphV8WeZeidhoaf4ypez3B+taaLzLnc/MAwYTCIJzbRl60slRq5/JczdPdfjGbeemZd15o9oeJphXNV6r5b6zZht/sOI2g9hnfSppLG1Lxmi5c0ChXlmfxu1+Ue79fJSbX2sYOOyaMafbxMle47be3FDqdJ5qYqTdtNCfLP4+WltF80z4cHhNe5ioYVWPBeE9rx4lX19OpC8pvUeb1WK5WMlLkwHEdxtEf3Ox4JG2pE0f9ZRVnpmZY92MzLnZjZjD45j7vy1KfZxsg3bgtVsFPo856QhJ2LouzpmSF5j/JFN5jZjD1LQ+uOlmct4TZevL4eL6M0rx44X95z50uqpxotWewjJxu08C5/bNbCU3U6ydqKJWycfZYt55ZhFF3Oe9K4Ntd6rGQP83YzMwfPcThtrVE3xs261h01BnttYBY7mVunA0MLLsAxr3eYANGQuZs7CJX2MWEb/U+K41IGZUlPSTyWNFNi0hEm0WjcYu0wYhBNltN5FTsqI4cLd5FRUP0N7XoUFN9+TLMRzFlGs1OXqNV9g9xRHXTOxmyyzsUMFi/FmDBuLG5PuTo0G0LrflR8kfMuW63GdQW7AdTimmSIJXOxjXae0i6bdqOwn+UTsjW/8wVaCRPzO3nqY/Qn/7qqSuOuQfdaqT7raZ87z/jZNMpcYl5v3vEdFWxjGMvWkjUmnKU9tR/t+TeP8wuOJmuPj96d0RehOM6nSS+d0v+WXp3YO6eCrIqb2EjF5DU+5tkKTR3oQk4N5kwLe2xMS/Yxlj8lLyg3nxvbHm2s0DAta31f9qZ99LiQ6qu3S/LJL3rInmST3PW07Enljzf71b5rVbptt4mjuinR84jM/mGyY8Sk25UuhsPM2U3jtLzhK++ioEXVE+vZkrzkylOf1mEiNUawfmT43cTLyF3E7oaJyVeQX7mqPm+hNxWv4uK0KsO4OH1J0i11x/7b7VklJHqT3ZRjlTpe29CQ3+0Qdifbe2z+L4qOA4W9aCsKEVAbVwl9aW/g9h6GpxOlf0rsHneySUtvcEbyOIfJaMjD6V2efbWpeRvZ9DCnvMjdgweq0ninJ/pz8+ljlL7zkBJ3HlEy2aCajR52HZewoApvJ/lGNbmVZ8vhYs3T7/HbPkxuyaSKWqezPPLX7tKsxO/SB81Lf+ildLDy6S7ebc2POwhJ3MJNkQurc6Po/6q4EBcVhYmB2sIkRu1P3Up/6lY42REnDz4tJm2/li607ZJSpuhORXZcH/78p3nc+Co6hj+lk3Z0c6AaW2tiDdOmXOHAqwUv0Z30+eJ+60cyh2MgxXz0yV5hs8u2NuK7N3jw4+uNuvPW0O98GzuG8cCgHN8tu9JbD9b37k4qrd+t6nJqbtuiKPJ388ehLdx3DX3yH5d/VVU/ypxGDj1NFGp6q4iBFn25zqcWTy3t5DQ7mFyQBJ09T/ocdXZwldnqT3khVKPnoRh7crh0UJ5kWio5M/WcSzhM0ivVcePT2ppjbcVaXE/FLEpfdYj6VOxCg5w10jUJmSzqeYGsNgVbAtxJ6YXseE/YNOMqWs5Y4uXGrE1e4yrSqQmHGZCFyizpz4A1VG1DZZu5m+OnrUj09VQaPUJF64FQg9qhOaaRkcX9LUjfSaxfLVTV9WZ7tZLkkL+U0j0OmXMO5bqHJd2vHjifC9mUT/oif/Jd6+6oxFge00PTuS9JNySWPdxM4ztf09XU0K4nR5iEyOPQNVS6ZreXu7JkBX1HPyp/X5Z2TenQAz1pP2OeGVMu17fJo26bNFK9kfkW1brgk9ZuNw5EDVq/SuO0uj1Dw7HLrituxhH9Y4XbItVm8tne/c8KrAlLqUf0RQ3y9jezP62kEWHyYgdly2AsGU2EPplrwt/5QY/dpZzwJ14W7frxym4OPUW31TPlr6rKq0Kf6KY0e5L355TVvNcLmjUSVkAWRR57mqIVyTWpqtl4MGx62WQq5vPTpdPkr6rqjYE1nfyD3c67ZLW1w6qzgsmXcWhomK5YvJk7DdPuDqyif3Iru3m9Oz85nKH0e+H0Lqvoa26+ZIwR28IC7zn96ZUVPlfXao/aklTCPKFKKzpxlMLFDGppyh9+5In2PRjN1q5CcV4+Nbqw9U4uzc2TOQFFw3eqQTFunIoh9LlO6AgwkD5jwoK+KpN2qtGWAe3Gf9LCbXJjGjZnZ9pEtzZYyLyih5zkMKXptSjXglyso97p+Sxl2UG0ocN1VO+6nfasX3F0O6YSaXNFT0JNluVQoxE9JtBrMN0WM/tAJz/ZFeJEO2xm/WyeSjbZ8F55qx4+X9or8VyJVkp3wVyhIr0ac7Xh0gyf9EqJos8oTEY1w1DOvJFKffhRL9Z2Njid4a30Eum6ItI+iZvvHOPQ2ZTeeohOBZPQjblnEaNyb3Vpbp7qKF2MLdllDN052pvLWJ3e7Iz31nASS+5pYEeapUmVV/Q0iatoVoYOjVi+JFuaJFanV1nYoq13O1SQ/iKRTk+MSjrYrDLncsXk+S7Zmee8sattm1/dBdX/wG+2Cv1yzhDaN1QU/j7ExFn0/yPD0eRrYYV5H+r3YUIfJnfjxc7k9pC5qyVvd+LpzqECXW+cSaWhnJkjXExc6mhS98T+Lsak7ddOWH5TYcNOyx5sBiZ9TI33SPLRF3lhrvdwkitzDRUr8OL0erpsflLmZJ7S1s7sTPs7FtHMYm3vme6mnsP1q/OIrefwrEu1P32ewevHKb3wEOXYMyxDzzseUK47b47mrUvqs40aldGO3POYXDGcsmzA/mFFDOjJuqxKsuZvUnosy9bzsaI0zghP+uSUKoq+aBXJbCjr+dfozi6nsiqcAL12VZb3d5KMo3Hj16zcfAGHuXdCfxP7dVO6C4du5d2GFcwfepFf9h/itXuydPru40yg7GUHnFluE+fgAHvmc+guPphDzxEPyJ3Qwa+u6qJ5rxeMe+ZaZ08UNquYEyqwXl7JzgmZnu7YXNE0S21Ci8AuWM+v3urCXJpcyI5sdGTox/eEZFcU/a8Vx8VM4PKlixhbMEd3Lrkb+WDN0SmBGtUYMj0kd8rP3Sd3G1lXYS3tuqAYrZ+kaeOXPH5uJ6vTpnbu5upe09V8eAunM3n2z0PAVWbddZWYzzLnm1irm1916xKSU03JWIJWYSqvSUc8SL0m+fIXV+V0oUdIUTr2ftqmpUzuhSya13yB2YxfwX2jGdGJJ86h1vANSldAJ1QS3iRWBUafU/idqMhprVW9Ot8kPc0Ycrkz0gZy7mDnnEybS1RSItliB9KxwiZHhaahPtZzUVreqNm3ataPdmtCC5FXxtTWqAfzdvN+dllnPbPRKxVqM4PXpme5st1UVdrvNHbiz2hB/Rdf4CoOLWbzgeru69dHOiSxdbXQdkE58sIYsqQJHznZ9+av83q5ug5Np1bOBhrTYzpP7A6bOzUbx5TTf8ZkXBguuZ9e0lzdUWwtGqrQq04IVb7pipBMG1ymIDW7n3aruMZj/PgUoZdpPG/7ZitcmVAKTTn1RtnpEQ/8Z0/dVswMm0+u4NdpH4rywQRGTOPXhHOU2RhBjRqUPpf7uwrXJaeHyYTXm4V/V31M2KC1N5OfYkdW2Oyyx3M8sYiWc5aEHiPPhW9cn0seJ4/lzbO1nIp8FmzEalp24/GOnbw/oaxZU1szLmyk92D6O52ncq6XJY8xs0XB9/p09vco4tDD7G9cxHe87Z0y1Q3yn9JtuCMsHofqVbazgtcrhkSW3ULP9SWMb3q1MW/cHHo92yocaRxrosJEbR1hT43eWqW7rUnPcFf+IF3+/CvNZzwtvTYx4rbbZeVtMqcmCyqSXJjKWIXhvLmZAeMwjaEXMqI77uSVtIN3D3KpZyWPcXbl0LpjqQt5iiYVX1EvP5+enJRsYCcZo1Cb7378J6awL1lnRx5Hku3M5775oV/0d5KRnrgEOxhZDvkcuJC9SnFqRVY04cVmnNpNSLZVEc4kY8uE6NOKO5qULdxIr6xQRVsFN3DlACrdyMoePtpVRtookU5IpG8mmjd6Wror8WGdctJSRaQVE+mvE+XTv3gonSX9YyIdnrgrHUTVhmR2xk1C8vZvv48nzncyjj5fO4dRw47qxZnBPXnh/6R5p3NhMUpXZvE1tJ7FkqsaWO4j5y1crfFtr4WTjR38dNU06XCSHjRd+5LvL+8kYwZrV1VXa/IGTt/plfdqa9BkDfVJ6qTmn3SRSXk3WPtYdWefu8Hrfal7IePfv1qfuY/rNoP9E4ooMfYIB7iv2HUGLByv5ulbQgxl0ege2hgrnZpImqe8W1ys2oi+HGfQhNnam7meAWPHm9efPpXJTfJ1e0zYHKkfcnh/SlkfKO5kH3F6WPJafeh21etvpzb1VuR7YnYPhx5kzz7KreGsrJXuee8WV576tIxiZPwg9F4rZa/ZrqIGfas8GpKxd2ICFedScRKmHXBS10U2ps1Jq3nNRvXG5jOAn26cFjYWyKbcftTnjyd9PyR+P9n1NYr+f2TgDKrWpTFb61NmH0rywYPhAnXCfnIGsvWKsOx0eOO7TF5Gj9F0ayW02bmNWVNbu2rnPAeLsatdGVdPns4c9hQVKqneC5MN5brzo8emuMNtKtimzZKZuvsvXfo/GYax01nSvIEmZ71CLSpW5v3pZVVpvNOmpWQ12xQSYiPC+ypL1efokS0koEYzuX04/WtXQdgQcxhtm073VF4nS7o1CJs2/b6iWAEVfV7h8s6Ldds+0ZSHf2bJdQ285bs6zg7bFpU9/YCyI7awmA3NCp5WlAk7KZpHRp3wChdizpCr9Kv9CAM5NJla4zfwMWMf+5kb5zwiabxTKnGoCUZQb2y+343u6t73+rt57hiuZ+WkC1hLRiNqNd6g1tjx3lhY0/fWrlPxGbb4mN7UzmXpfvYm63zvDmqt2MAanp3SVItT80yaTJ/HMIWZfekwRlhJMpuGI9B+ETmhojarCZ7BbioO3mBbp+rsoFt3ZIc2Wa3NChthOuzo8u8YU99MGUIVXS9+i7XM0l5W9012Tsk0JzmgOJZpxPrxHtgfnlUU+UuqyjpvU2iFuRAPcuFSITl7kNb4UTrfg0krdcdgNYuuu8Cf00bKjR6j3Fh0p3Ux3Me8FbQey/lpGXOS3WHTvWtW2/JYGZUq7tZyGksm0eTboW3b1YOma19sngXDabU6JbnH/gNFlC65W27Bcf45vdbH3rZXKR1HPS2jzRFN9r8i/8Wq6uXl++Agp9wZNkJrMjhMBpoUnruiRxc/LTPN2quqq33ru2Fl++8IG4SUE2KiqLD3QIyPE09xoX3A+ZQsF/aCeZdWlzzpueFXenZIU23NcUuz0VzP+KVh4+HxzejTFgOZ3rStiUn4BmXg7FXYypNrWslI5rsvH8lMi7Gy3QVsDPsHZLXbFFZGLS+p/Hn73H8WNw4pmGzPQw1yHrvdMo0sLtFa23SDbSOr+zAHm7kYOrK3f8Fkfk2uTsu4J9mt6IMsK3G+U0azpWEZHc3wb3/9Lz9rPSU8cZzwtX97hzBTnyGsNynuaBx8KF7vf1N9egM7QtIHp7YMRbC/FQrNT8NfKPm77fYuO5kKrMuupGbuFuvr85c023nnrbZ1JAuLtrVjRChKVJGcWqxbU8m2FtWpyY7HwoqnlR3rSx9M3N0ubAx5+7+PYBRsEr6LhdfMW4Vzlw8LjvWwr2Zz4q9WrLT9WlrgHWfyWDhnyKnFxbso3ZPX11M3zXTfVX18pJjz8ldbnp1tyx1lvD+mrEVTLuB0kvooielk7GFBzyZ6m+CDvkJVkxmhyukwaa1Ey2lLwMkOGnL9req+Svdhj+gz53Hqc8NjI5RYGhK2G4aV18Lv3buuPxNY+1x1+eOq0r2gB2ELBSdBsa9t9GXIEJbz8b0m6xB2G279GKoVVFCcS96w813UZT5DqfLwTnd1utfVi6Z/UtmX9nO0B/RTmMubu7KUe5BDFXhreH0db3taRgmUYMmcBirbrHH+a0bMvt2BfuS+34Hrw+tYiiyWNGZPpwytx3CaHS6fvUi9afmh+qpdVuhJ2A6L8Fx47dbvP8uSfGGgOXFmDaMvQuFFeGcNNi7xh3oNFS/G70s0l4FTepMzgQELeXFUPb9O+yjqY9rR41wUZd58lGHBCtp3mie5hMy5vO074Xq1Jx8eDMvA0wmUm4IW/HZtdx84RS+PmNu+gy6XPElTDuViCk26v2LPe6x/CrUolblTsxfnqdqULYvLsJKtlzFnBTPnYzgbKpeX9saLXLMydVZa3fatJcnkvqZ9PLWyEy3ChmdKcvQkLYo+7UKa3EhuVZd4jjl8pJhrb53KuYy4DR+T25X7mjHgepI2PDm1lT79wjVI3bS2ZqgxlX7rH2ESi9eTcZ1w/lQ2rPL4rx2d3P3iQK7hgTJ9vHkO+f2qksl1JoQetJvJ6Xm7N0bVNK7jtXQMKz46mhFO9F6m+aAXdK3/qOLv0bJVwSVSG7RgxzRaP7rYR1uL6LOKJ65BbxqnZe3JYeesTItX4lzWzqquTqOX1IF7uO8sjGXb3OqsYdMi5LH1PO767iBneDdcA4XFutE3VlGhmrozmw5Y3KGRdEEia9ImFaZsUHboAe8LqZcnTu9B5ZCIzelBj2L8Ptnk58tHh4m2O1GfTWmr0LKzFRXv4A+XtVL3XJ7ojzxqJC/4yMmhef8UFKN4JgvyaN2KPZNCQra4glTAQa7wJK/yysTamjTHcO5I2obzpRa0HMi+M0/yh3SWUyofMW8/3RrR7Un6XvOoS8/J07HT02EDzD3YzdJkE4M4ZRw5E24Piahh5DxGzovkvMdPV0yjBLVWbtDlrl+FiYxx6FmDks2Eysp4fnZiKVzqXQdt6dvalPQ6V+8dL/1BYvUl33GH25jCpbXz7E3yzVrcmsl/k8Jsh0mcb5leY7ihRHjFWRNaS5dyxfD5BlzHgJ4MqMWN3YQYu4dfH8RqXr+MCmP3St5O3fic0M4nk9dezbK1PyMm327x6tb05j/9O3l8lFayoDFnFGNc5WvDmFDgJ3INbR5WbJwyHQupNHm3B5NWes3Ole5NpA8npqz/kXRy4up0On2b8JtGjLqRU3sJm5g1FVdpfFMU9pIt/N6X9Ul/5tNupPGN5LY0PJ1rxF9vMGXaj2w7VEp6aaL5tKelVyb27qzgV83DitKaZ22heWixc96c1eyjYjVkMf1jciqQM47Fa6l55xYOcv8klhxuJRnOb0d2V6fdSy72extV41xhU+SVVTVJuTz9MytbktuNTdeweoAw2PwEVwuVvxUdrQg+vr+jMWn7tVOF3JZK2Ute+IqpzclXpnQMeaG9ShlQc7wWG/KYxnnnrTZVV5tV1nzzC+ECoV94ta3nYAItH15i8cOtnVKfteuq6+rXtk8tacmEBmEJ4Gwso+bpWwxff5f15zBl7c8oyd2VB7reQ6GicCXVm21Xb2y+s5Ixci+jVpUNzti9ySvVarONu9cNDP0Mr+yGweJJTvTFqsCVGWH5RME1ZrlyjLgGG8PscdfsR12+/2l/6N2K0RjNghksaSGc+Ndg644yrKH85H1sZs+KjFAN+x4Zd2FhQWK3NIrRZNArzjp1XUjQ1iBzMz/aPzP8XA5zyJt1viYdKb7vEDtpXPM15vJalyyKUq9Ffhg/GvPsW03tKZfBZbglU7igKiUmoKL/PxXI7KVm+oabjNFk7StKV+bykYu0rsF1o8ZQDa/SeNFrBuSNV6noulCdMQT9wsWzHBqchBW8uQKTKZ284s070Z+Kr5JRmmSuUCayBnOov3u13z3YlXyWPNeALDJqoQa20fHA7xxOqzo0Kxzt/UlrL+dR6eHdDjwTKoLTtJX2O9CW6nO3S9pz04jh1B+h9q/fDTF6gOdcYvE5aMP9H/cjE94Wx5joqKK4isWNXPuHcdJ9iW4jZ7KO5pNekFZNTDy94KHXh3YBN+3AUibmcsU1880Yc7kzd9Bg6RpVdnBHl8HhPKkNzZbwyojaTCO/TVW3LR3pT75r1MFBnryulQGrx6t6OEPW6k00ouycA9ZeWN2V5041ov/tvrdtnb6DHpXmk3EWb9ZsaN2aSqFqqxZTa1/rpXI4PYwGL5+HO2lzeDE9D3m32BmMpnM1LKPyzp3yD1K25gHNhuAxajXZYNXQ832YNrGsE5ekNenCjnbYFpauT5zMBxi0f7TxeQPYNFOoVvlmVaZEhYqjAdnd+H1pFmc6w3rWsa5nJdtqVpd7TwdDn6NXc/Rj8mjqPoaFYZXfJWklD7UYaOIVwt/3Wlxx53zvDysbvr+ZvDwX2WxPr2UEVcsU9Oxv4ZM2Ub/ddUX4i12Z1w+y0MVa3sPZg9GIBZnNvDYwS4N2a8ImgO/RZBjLVuMwPUY95MfFfmNn8oqndobax3tf7G/BFcyczM5XM9nNm5cJk+rl6DEQ9bn/GgYXvd2bjYWs2lAGNbqLJYw4jw/a07X+o6auv9a8V5vZ3ftkP5s4lh8TusR/KFbZfpMVJq0qCunMnhjAkg4sOZvVdMuZafI5P3ffNr43eZ1TktdozNo11TW5ijb75zm0lF5ppqevb65PNoN63kULXku2U4JF+5o7Ow2bzByuiYVhX40bJo5gGJoy8RwWX8ZPiqFy+Ouc1kioxYZW5b3xak3KUm9QvqVpa8qwNrs6w7m62XT3zaXmpC2hG+h0zvS2NUJhy46OXJO0pSaz09HSxkJMD6Vux/DvBUvD+57jVdYweezPdXrgcWmDxFsDq+n715HkEqZb3hfj4nhU+H2vgoKestk5XDmUzAG4EU25u527tg/S4IUlHv1JV/9Rbow6yTjdZsxUfuQ+8wZRzUbbry/JhLCiNB1B7lsd3FOFe6YJCazThdBqQZ9ZpGt4sy9Nd+Bg+PnGIVyxcX5YLXsVq247X+NzXjPp4RtofMiaKWdIlyT+0KuVJ0p28od6DW37SSlpn+LS6xKr06b8tqLq6T5FtlxFsz7UurHg81U5Fv+RvzAxafu1UpzGPaSlErUmbbDpGnanNVnJS8/XoW+4HHk+2W7xetJNJ1t2G9owoMp4541dHS7E84TlSo0oXowdI5h4XbeQrMqi1tINzvamKbqb67Jw4VtGmMFoj1m8mF4RZtPXcUu70WrN2cBADkzhpCf2yet3vksPFNESanDfqTTotIYW3DJoNIcZPvsmrjxFqAKLoi/KGfrPvpcuPLAzpGxGbCNnHRPf72aA+0ydfa29nSqY/HDBU8ZQLq2tyXLerIPGVGq/O1xIrGH5xGylex+S269DmL3ugYosK1uPLKF/ZkMyhuBj9tcuwlw2l6ikTb+ZTGH5qmzLnC93egcfFw0bd9yxbjCNqHdWPmUZtPAummMEbyZ5SucdogZpv8RLaTsye331/zmj41hx/ISnaeMZ3av9VrIw/aT9hvZ8N+nv/stIh2McM5vx7a27/eq5Ls64ag2rObSMPXkZym5DjTBPMe6Za9Vdzk3p7zhJGCfqC7GxPzzOuoIJjqvYsqqMJhtfsT+rCOdyYBpO5+ndV5qqq5N7popvpe4UGlbDGjJXkzmQH378vKQOzw5sGsavc/nlyCE4074fnWTONUxbeIXncq503oEivEjppodCokAVMdEUBQV9nfuf7UidxD3JDe7vzYgcIem6mvt6h0V0OaOYNzTsB588hnH0Kqg26tj1ackK3EmyjttGjvSjIVPCWNCTBuetsXNcplXqKFV/m+HT7rKr5re1LDnfouwLlH7ukJ3ZmaGU6lxqPbjB70Z3tXWsMPn9A5Iy6IhywsqqHjzds7nla7KdgfxJIQ4b3kOdZ16yLL8ZgzJ8b/Y6snjjvZrSx3i7bFUNmyKLycNRhj0rsJ+Wk5eoggPJOocm8e7HLNnaIEzQoMYSij+bMoiw9WC86P5mKuhfe2VLg1fdYWWLszz0kx4ecCN38k4SKpy6NZlpwSXYw8RB/KQM7/coa39+EbKp2X4L/ej1jHC9sBR5VGm/M/SnHUfDVbw8mb7nPMoOMtoUtFrowgeTwnNucY9mHcORZeC5SVfqOuTR0IekC1sPUm9pfijg+zMfFJx/NXofw5jc/eeeWt1JI2HCL6sbN68c47DwLS7b/gCNGZuOMajHXWG1SEfh3AvvfxzmHTu/P5k7QrIhv0tVA8pw+DBT514rLRM2rC3d75Bmng9jnyqO7mgefTMUJq0KN0wqjgu5tA/PttMn/bWfpWPtuLC4tH3i5efPpgwjVjKgOYpR9y1en8x3Tt3ACDKH8sBuyl5ywFu+a/mqbKN63aprj0e1uwqnU9mfzU12alp0nsm7unEOadnEiJI5lCG3VziSZvdQek3YKLblVqzn3RcqqL5xu+f9kLk8O6qp37vYt696R62HNzjwGIvzGNAKs6m6HJlcOjTvk+Y3JXbT7TrunjjQQ5cNtL5spXCpvpz8Gcij5URM4HsV17mvV9i88omhPWhHVt9NKtpW0D7kFWFUjedhx5fiwknKDVzai7Wl1Uy3SIcl0rMS6Q8SadOE39QwfOhNbqky2suLLvLTudMkq0Kxx32dMIXWWdzoAc0tChNlbUiuoVvFmYpi6FR2jsikKFtXYBj3teeX5cJUWNKTLfeUkZwrXIMXrALUF/nC9cYs0odODhtKliHpmTrlOZo8+IryD++z+Cm0Y70zpL9I5O+u7T8r/rsuz//Kn9ecWlAPdXwXeMSk7ddKRthY5SAWMTwdLfu1dxjHu86gDL0Wcj6arWFxE2oXY9mdwhKeNqjD1hnC+FOO1w80UG4zvWrnhk35LsRQukx+0ln+5Af+W4sxT4WTo3HcMWawPUPpMv9JinGgC+lkZLJucCWZK1hbqaTzSr6kRPcjKp6L3qG83YssmYbraJ0zyw89r+xv3y84mOM7UKKvi7Jo6ZcrhsitHWoeKgpL+N6oUVOv+bmmzrmWYmG3+/aHM0LV97k0qL3GoSzObord3DArzGgfGMZ5zVazlY+dFCaNN6MCjee+xlkcGiC0cKqBqyhxzRHyqDl7i7nTOjh0Pec9uNq/7xynWTJT5jlhwmSFc9nNpnzGz7raqGtuZW34JIegGsmNqQ+aMMB9BdfMMVaif0UGavObU3RrMdEDK3OkVyQWX9/IhuXlTWzTjcPhT/uNL5LUYfHs8MyP9pTxhM429KnljsGDZcxiwEn3mVD2ajaHnYv7tnjUHecOtuxgI+lSoSp3NGpiLvu7F6EMO0dl2lMxQ6WVu91XrY8S046YMe5y/UqMYQh3lrnZDe734eRExjZ2dsu0bCNvjhUupHM4VOwQWbwpVLJsGo2VpC/+yOzMI9qNES4Q5lDikiPhJI5wYvfJljLRia04mjKuoT/8sqHZZVtbgxuzwm/T03GAAReGc/fxg8L/dj1QxCUDf8dk5nSiznsveX9q2TAG1GDPORmU5bctuofv2znYTNnbDmjffZ5BJUbRBKM4pQI/LPcCt4VWDF5EQQVT94GPKFVC+Ps/H6czbty1tKFlryWczuXdF2m89UU56UOysjmzLOYwTl83ZQ1n1HpJ5VSbITN978F1kiyy+m9y3+I+jAif51Aucw6SDmNBjyaqvkiltKyM56hbhiZVXtFm60wNh5BkpmHiY8ViBU1to2+cskKzjwYunz3D3btv90ff16fK40Y9fCstwkXziM1sWhomweetoNdENu8qr5+xJhXrGSbhYKjwRSvK9n4lPb2wuddmZTFLuP4fTcPKjHy1L7WZNvUKk3rd4PF7OjmlMm8u5Z3Z2fZPKUJ+uIZxG1PbXcsMXqlQW9b14a22DkIjTinD/kuKMJKzXlxJE5ZkN1BxVtisb1kum87h4jLhvGrObObcycMV+xv14K2hB+JmDrXhxlnUrUyT5aECeEkvrtv/iDN3brJqV22lm4bPmNQXrpdWCO2ExhHONgsXvccVUce3gvMnV1Eyh1tuVObApTQeQO8m0vMT6cbEDz3v4W39lc0/QG0anLXG2CE/cwbksKjLBWSGyeiMVmw9C3nhvGtPHrWT8b6drLZ4ElM3X+uVWbUpSoPVa/TqR97h1or7kCk8UZOn9jPzsnBk3VphPR/UIWuYkMBaSfUZ27menv+PvbuP87lO+8b//MZkQsY5s27WbdQUInLTKLPXWELkZhEtjaUilKJ0mjNCKVqWopRE0ZplEXITIZbdUcZdbiasaUlu1s2a08hoNPT9/fEe2eu8zt917Xld5+625znH4zGPMV+f7+f7+Xy+75vjeB2v43Xkva3/wcnqRjaaUnZokAEqxmelad4OQ5mxkhlNCm85PjQ+e6oxsevC/xdzWfkPDpvvp/Y+zcuVw1ReNgRHCpslp4R5lYSnx77Am6RP7Wrkjyfy3slww0WA7T+gxaCAxiVJpcct73jHQ7K7VfHqBPQKfZVO/DTimZsnW3scG+nT7g3bKyMtiGLIZeGB9m5P2OfZyB08zcksfIqxpLVjRY8W4tPzvfTBEBVS0ZKn5l5tX6YpSyK538kf7D4V1nDdUIozy7iwiZcnMHfcw2RQO2kHPcjvg7o0r8HJNP5H7HrqElOHeyLTvFb8YXsiuUF71z4hOZMkzLIy/pFi7iLQ9ntlxQNoUxvZ7NRA9EzE8HYjdZu3IjCn5nAYW2vV1bwWcbuiDkY7Ba2oQURTqDBMcJ6KhQyywhIHyyh4WgCKfh+10r3+5AfWrezASdosft9z68b7ef5IEjk3O4iTRw5iMTXHnZDdnN9HWyi5D1+yY2ttM3qxexCzj4TJF23KyoVdNW25S7tiKwWHsciK7D/DbgiL+kbujyMtI0yX30fbq5NzMAC0M3m3XXeRPZwsVoFmZJevwjIOxNUMih1xvDY7TXQnZ0vFBX3N4vTeuUDO3FjRxrw4eSh5ZE+sYm5cd59Nrmlpt9b6JL4R2IZTONY5nmxGx410+LFyvqoUgOR3D3RXZifzS3fnj6yKphp477tmzObVeUgkLQmbeKN9RMk3SfPzQtC2SMy/yP5PVuhkFO/otfv7+rl/oR85U2OlbNqi+vHTyjtFZxrGCyzDzqG8rusHtKyx3OKLXUSrRnSOTGAMM+99XAWnLN7X1t78+jRm9PEJzjb+ocgYVCM7sYr1H9zp8Q3jXSxxrV3jE030z2YVe9C5ejF2q0ci9ewxvckQu2okGpPzkvia+fLiuHA7H7tL3fxrgvRPU5wJGrktNyz3VPY081eGBkrWIY3UT5FLi3qf+OG+P4T525SNGXcofc9pRdrpRfZd6XdyI2881kdy5g5dmq5yMNqDiUE/8GIJjk4nJyPW/eFoX0XbKjX7WydVII+Ow9lz7x0uRHICO28TZUYVhMql2wUm0kHBn+rA1DkP62smJVjcoy29OHSmIlMLKzlSMJ5e7d4yZ80jSh4U2LqDwvuX6xBih+eFUsCOLK7QJZxzHSPOjGRZAJdeuXe46NSaDjSNWHixGw34bHNNksJ8cynsievOpriEXyXQakmGHUm1Vb49h2XELKDqsQNKumD/2OoebjS1sP/AleaXRUDUfx0rjlq4hw236RT9teXNuzseV87A0e+GQ2ahGU8dIO0xjkbrG7iYtpVCrBAbOe290b0MPvWWD0r7bngsaxP+XW76eS1Kr7dd49A8cjBGkX2cEr5RUJdhfkE27YsvIJX8aG1mUqrHtw5mVFRmm8CAHYcRNFy4L8QvM7kUjVcwloI8WpRYb+vkug482MC5wSSP3sEZutVb4Y4zVKnBzLO9dZx8VZFx/8nqYGuPup7vPMy5BBZ1YcbxcP5X1gyXXJfY19mTQII/eXUNGTuJDI6K/DJKA7pNX2HRvnY8mygI+BZVD/7jWoyw6I7g1/eL+VMVG75KEr0m4myDH8r65EbRUhEZzzd0um9p3XqtYBwzElP9ZMNcHx5IcYMvdI9nb5sg2bSsGgolDCo05uROGiVSZg5t36RK60C0khXkDdUSsKMWdBm3ygMJi+jD/e24fzFdF9PoTdqtXmTtTHbkEZ2KPaTP6upcL1QLhXnTFw6xLtpdzGpmb3tUTt9YjWaw+YP6dreh7xn6jQrvtZ+1g4Qq2gRuSNznmQmTnWpa3TPTJ/tQiKc6oGNVvHn1qaVNpkJdJi4ZGVj5QxaxYbuwgXztHwn4KrIrdg772ZZO6klPeFVKmy1yIkc9kYI8WkfrysChbFqVR1Vmd39Uoz282pKujVn4ZXsDLr9p9ZkUFWAfFRoI5IpTaEyH1etYx7MnXySW2b2Y3TNcRavySGHg2FAZriW3taZjM2b0C+dLqE1OHk99yfwRWMPe2Y2YRLHKTGn2iPSDXVWYSpkx2MauY4nqHAys8laTiW6JMPM5VaLNxPypmdBl7ylhxP9jWBFo+72yGMpyum5pBdl8XK2l/CRWay2/I7LZO5u2i2lyPIsRRDdELNfR7iVox/EzhdpSU3i8x3jV650Of49j/dQ7fXPyGh/OSfHCS08bZYyOl5cRR4ehC6QZb1jL511WTHaNKsoMIGXAFhJ48s1xTo6gcik6bF9nbdVkeoQA/LfRtyyJDpOCOn2JJKI459bE2KMeZRMUBdZF9v9uFdDRs1uHW/Y0MVOZ1qy34rg1skJkM/PTkELvhQvYyOeRo/LHckPuUfMSO7l1WaEmbW3kEhlMxZ65Ko7LJTFomZ3xA5GptLHa/m7VJaYd1fv4Ard2P+hjd7nbOgdbVySP6y9/RZeg+VQ9+7QyC4gvFoLsgqZszOPCm9wQSQ+Nn+IKb6UHHqRDnwUGjsVQfud/CN1gipyfIvs/2XXooH7BZoOmvy0hL4difK2kaEfMpkpkjcMp5USGojyLhhRqpGex7kgHl4sXkzGC26oWnjKRLhtXucXvjTFKzvhY71TqwRRGTn5G/lgqXD7pPfd5bU2a1dqofzzbj/3G4FNvKZNRYPbCR4njN5oHfU4PiAwm52CshMXEFOfuvPVKNf5Wla1sX4jRGMG62R08nzhMZSFmP3c8xtoNyUEHMRmDOJ5wIxeZ17mTj92lQ6nllG7kO8ZAkf03tEKJkA3NRedGDJz0rhlNkcBD/eY5153P85hVOiQt4pPzVegTEsxdXl9l9gB+EblTwbTCgOESiR2QREYWZrN3QCH7eygvbx7ID9GTQR3fDoBoG7rMXuXF54d6xs/1TXot7DH7kM2clY8EacQx6EN2rSo0Zs3hdta2S5Yfh17h/J0mrQkaoG0YN/oFBpGYfdRnH9RkH4kn+UPst6IduXXSQWt70ur1DEebsCyXu2I3gtQOWFi4Fx0SAOR9/MQSm9zlCzdIkslkQrOOyor2nv8qVh7dmdhV++g20ZUR7w/paeuGuv7gJu8834MFAlBaj2U3s/11EiK7AriTSswBKgwObKdobf4pemdgPY0KwXRGS4ynZApNI/PIwAim1nhY1fPsUc/ouJFe8gyXKLgUJ38oDWvuc24dcqg55ERgo28LcY94JJLTP1bfWa+psuiMU3HxYlqw5ccpMiX54aw/KLOSHc/X9m7f7swjMpPsQzwQ+66Dgytq9WbYDU5EDouOosnCLKOXTFCmNF1nFaYoMgvvPxdTuG0q30ZOayZM22hixO+WNAokgWZ03biSFwuEUvAi/c5/LLtOWJB7U3yEO6IlLYq2E/0w4vc/iFMpsoVclu5r7dZ6B6lF5cgO5WqepzGPTx6v34B0c0v3dM/0jTqNWCMylV3RTp70io5DhURcCWZvCyGGRKQwof8gmWuEOHxAaDx5phqZ3dncsb4dw2v7MIe1E8hYifJkdiF7AL+O7Rp4XMWILOSdfT10jF2kzCQMpmAli7rTe8wC2UlVFNwdNNSNpumkXW7rxvIErGbH0NrE0awUec2ukZnMq5HaNOZoZpANgnviqdMHPdh48g47LnN0IRlDyNjT0NEumMnRKTwXXRBuSsO/4XdZZP85dkW3uTc3pDK7gqZjdtGMpEqkbwxjsEnNLG1LsQXbT5HZD63Jr1EIddalbGSFlcWruGfmRvswe6egyXxEAP7fJJoZoTVLKpY2f2aojv1BtIX7q5J9sgoz2TuCry+TnyjI0TSjfLQ1xZmRRZXBTKg6yP3d2HywfiBp1SZmPIN7vaVVZFF4LZvP9tTU4FcH2ExsvXAN+cOZ1TdiYuRm3/SLk3vph5ZHW6oSDfFUKK24qfC5fD99oSLQ9ntlBdTllPJi4lGKP5Zm+5Ef2Vuqttnrg7sgDbPZ3Qvnmbv/YQOi6xT04Z9K54TypeG8Ni8tCJpXom/Sa5b4ifkl7rdaG738Um/visv4xrRmvS3P7K6qI57wqvHNn5NY72jQwB2F13ll9HAV1rEvj2j5iFavZ0gfws9i3zZn9iNGT5kgh+DHHMFULhQrGRqq3U0hXFBkRfb/YA35KS+8/pKOLXCcs/5Jo25UKUHk1m+0iJZ2Jo1V3QO7ou2X/K40MQ+GMxzsWDHoN49BVmBKKU/28CpcZm76wxKHHKVa6Apea+Vh+aPYUam24QtGGp/5nJ0aOK6SE7XilDlZwHi6Ja9w4XaMJqExv3eLr/MDMaXksAAWmEnMR+E6dh9i9gCWT+keDtrIhOmjhUiiCHwqsv+dxaC7ctGyPnCvVQM4WBpNqZyeI/IgJ4bHabSa6jNPOzqCxcPbShZYH2aytGprZQ4WuA4vfTnEXRvWheB1XpD0WNmrq8uKK++k8i0Oe2HKSy6UilXmZIERxtrcur6nvCyyJOqiay0t3zpUyCXwWd2aHu01m/GMb/ocOQFM7tXxLTEtCsvyEii4O7CAz61j2SHM4XGvSu5Bo76U6VWg1cwMajChxSDOs/pMiu1dKBtZ6plek/3BjZzfoIid/t/RivtOk+2mCnqnTAuSBsULFScvMW1Gb2V2ctvQsKo2miwkzJoGj2RZYZ+A2xCTSZnSzFid6tiyeJaQ3A7jw4yrshkNeOrmaaKzOXYgPgTqxTFDYJGsmWT+oD5mTno8sExGMXXUw+a16+TEl3Hendrd0j6tnVVWn5Q3zK3eTevD6+0tVTuwFDdybGi8muknQuSfL8iS4NZTB7nI0vKtncOlQvbvTQJ7KlO4pzIb6dOfl5YNMW9uJyeVtzhXkAzqxWuvp/ljxxsdimzU1Xt6PzCNPomF7y7wfQ1WiuwvtXg0JKOG5UNbWn6ku/QJqM3vIllKRT7x0MZ55iezvSUXNtJxLI1aB31ncWRP4HT50rZPCaNizJlhGsd+Ym/3ADZlbCI5Bc1C47E6DQQ2+mIGpb8tdhszNz1u3JoXpDZfxCUqZuWGtb+ZIEEwGcO5vscpxlNu5XkTyg9SUCP4XjOPPC76SkTlnTk0INo7YlDHt31+MdGNLbI0PL7PTg1CzHEwzPkyzag5/YQLQwOM1Hwz287UZR09O79tVS4SeWKfMN/OCPqJfcNaMDb6mo3RgfpVJaNjQzvdzuvCutL8nMAq/FwRYPuPYhUEsLavutGaJkV/4Y2CPjIPNdcssoo+1EihUv419A1N9bbuqcsRauwJyeajQ5h7+QEZ0yk5FZ3DT3ZPesxcqmJ2bsiRrA7AazP0q8HzHwyTXb6K+80PEiCFY+3MyQRlzpCUQtOsXaDtB39WpN0rrMKJSbyaP1TbBiQsRjGu9Y0yCcLnZfHK2UE6xiGRxHFHLc5lb6fATDcBjbkumiy6kguRfY5OovH57UolfitpBh1n4DxVVodtrBleyRGqnNqRMn2LetE4VT4guS9fRHbYA9VYEB3ouV3jaZwgzLYinefvt13Rbr6i31z4fTWtIrokIto2Enz4alhK6mYSNwv9Kc7HaluCRo+R1IBD/dhWqqEaw0Pjx8+FRNfRfkG+sy0ypwTop9GXv7P0ZGtWsrYnzUsEnyUD7VeuZzw3lD1KC2qfocpYNpdGM+ZPoJhL9KFfLXZPYViFqfJn0fTQLteOzA39NTKYlk5+tJxzc/Alt1Y7KFouIq/LNYYfHOncoZC4P6YwWmjGmeIFZntQH7M8HP2Eza2omIp7hCR2GVfrNr4fVgTafq8shqPcOvqgGYeYtp8as8iPp2GXfVd73vVg94iwHZmOZfzI78S8QMkMAdRdjTjmdetEVWYOedxr6WkeOjXPcZXc8Nkpf5IgMWWXJFtsTLpD4sqjKk/KoRTpe7oq2Ex6pa6ha+pJfEmjBrxbtTtxhWogfcLrEmm0QXBuNvDyuoEqTsr1u/Wtg6xDUdldkf0/WQwVk9w5bz2VqLrugFVpPLNpsr0LKTOU6IfXynSHhDm0ncs1kSx7q4XldveSwAqvWPpEAG27YSeDBrxNKnP1pAEdUhf4cHKK9X3uNNrzRrZ7RuwSrveVl94eI1I8auKmkZLX71Dx6Vz1Km3x+JzxXswYqmQSiz9tGwKXQ2/bcbHQtS9O6gKM5HDjcuJRPhqnT2fksTcNk8jtey3ftQkosiL79ywGlXm2ilNLqqvcJUfbBuyIdjV+CvN7oQwVt+Va2zqwVA9E79Sh7KrgqFRlxoFUHXPWMIVGm3k6d7J5fhqCke30brmAEZQbdF6Hwx86Nbu6c4Ni/FElJlB5XI6m43b5tR66Ppaunj2OqEpLXmoxxK2bDho253mbK9U3dfPDzi2Lca1vTPGEcwtjwvyrSkwZXj3ziDJTr7pE8W3yGRvAtr5zXwtN1cYxbMJUi57mntc3ajSKfdGBWs5ZbstnKYXvLAqi/3taedTWKXueWQmPSm/K7CGk1hDK7R581+zaWCCU+3UU/JViLIw+rOOnpO6hwihsJGYP/balq7wth1SOrgyvJ84R9owpGEVkAJWzcqjN4a3lbG5cP/QiWMa5KTGmDn04SOhk8mO/sVxHxVy2WhtnlZUpyezXH1XDFyZXH6Ssfw1lhKjcNCeUrR8PeusHG1QMCcEvQ9OYNnlrQGZuOL7G3DD6k9BqHbuSEk1982HPNJmsx71L1Z+Z7f5EbkzJElmNmYxfHt472vPmnPxZoZztv22wVOSz/eNZefTl141Ub7bfvTnrmUfq85jJU3VJ2kdOSqxvo508Gt3gWF74P03DOrxqSCCHfBs5r9Hk4OeP3jRBmY4hqP5aSGbs2FCbaqFRnqkKm0IKuv+jhcR4oZ60eVhHTsdYUii77I9k8U75Hr46VN7hjuU83e4FwzKnirmXmodOOFcpJpwrlpdGDfFon0nEUWr2t/4wpa5zFWJMPvYk89g7PQT7kV9+zUFKjiK+FDIoFclS583trveVjp+ytVld9WptsehBohvpU/cNzz8/TEO8/dkgTw2a5kIOLxhlm0bu/3S2SP+oooT6P4pdJ8BCj7N/oJRonG/O/sCeane4OzLNDZF3vVqzELjJRg9Kffmt+fVQirORrLBHlCd+Yb7PcSSuiuTFNOrzO0+Xf0GHxgt0iW6hKfmVBHZ6d1q1ICEaq+XB5UYPmUDkqD9FTitTDXE8XesFlx8qLWYneauvkVD7aLjkSiStLqxHHU+NaLwJmwcZ+cFEGZ82FE3mXBshATIek8jo3FAbq8Vc0Ul/M0CndVpz6mx86D9QlQRn/Db+Dsm1gi/4u+KNwj54UWguu5+jbWgbrSJpTyFndlThOUdRsXuuQ/eiGKktqBGtLjqL5jaEfWMbYW4U930Ct4rsz+06YW9oiD6UfoLYJ2gwUPSZiA23s6gCO9bVDof3JL0pG5qiNYci+a6LxT4W7QzyZcnJO8gN+0E9QSW2yrqgn186/xpJHagxke1NfqTTwjXWZtIqkbgpUY2G0rYUctndk5O5DO8x0i/juzs6guaD0ThAy+13rqcUGfu5LQOT2Vaa9Bpd5d0Y9x3+VA8HIqeV6Ru0bzVFbZbEfuu1vMeVqcbA8eH2uhRDcX4b7e69cb28kPWSmQ8+7rmkNG//saf4S9fSuA83PSXIe1b3fQFvI9FoNPr3voi/pZ07d05cXBz+RRCh+T5ZPAOe8MW08qpXPu3wsXKqjzsdyurWsCs1Uf152TJ6Bmin7WKe7vyCidtGksfClPbaXF7tSLGqbrj4hVKDvw3vjRU0b1N4ud1Ar3jS0YGJKk47qLHtSrjoLf185Xr71HGjP0jcf9TmWvV95Xq/0tMf3OR32a1Zw6FBVIkj5rHQrbgyjke76z1mgZOjw/IdL2Rfike76vX4e0xNL3zl+2YX8XO5ubnKlPnvywb+fs8LqMzMfqJfRshm8dy2unRZpcHiT+zsfqdXF17NI/YZLwQLA8g4FTLE9hWepilrJyZrdW+Gc8tilBlQQGMKfkqruFXu857p+nvSK9pZqeKp3JB9TCTyz1Fr1v5IWWfdsW6Pt1r2Us8eTbftkl+b2KaIZcbWVP0eTC9skkSkdFS0UYSD7OqYGJqAbHzXyJRnvFDvJaf3lFZu5XmyiESj/EumwHn52t/PiuYF39d5cRPpqaJVIvTDY0H64JTg9Kc+z5kxJLzJ1r51NSibZXduYXBdQsj2vSk4O4NYOKy97tOXu+YneS6/WzrsFauFqreb6TPxDbPTH3UwtaJn/NxwY9Vfn21Gi1SNbFfMJV+oYa86nlk4+bsGTpoKLKW+FJThcnGqlTrs1JTq0gY/Z/yS55zrQZnFZNwbpmyBEP+0TQ3X2X/iZA18amDNd80/xP3DBXRqCgWLuTY9yqDZQv78bxFQF80L/nxePCs4GH9ru8KyHYjdeke3SIw8ihCuf4G0qkJAmSswYvtypgd7LgcXvMY6lOLdpO56L1xgWXc69sUebti8zxeDarMQfeg5/m1zxzxMuqBnS3BymqIE+wdXd62LTqmg6eu7aMe8Gp0kyVQz84THk8Z7bUQa7djfrLpaKw9b3K6tss5qOf1j+/rfoFby4XCteVhAtA2PnZnkG9dKizzucrS6WuMOOzmCCnNQlTMtSZjNub6h6WzjvB1it7G3eeh9UICODYQmtUl81rGmFZGD2uJQtLV2uWsMipvsrR8NJiNDgOuu7Dt/aSIkHy/+t58T/L3nRS0qdlX/j5vt3H9nYJL2Qi4Zg6gQrSLx5qNBEzyWDb1o3pipWx82aMrbFg9uq8v6VeRyphs7LiWrHclQZRY7+tTWsPI+R4+HlbayoCGbfbCKxElHA3v9daaOfdigfm+HhEUlAWDqy32N5yjvlL5muuny58psLghT+EvadHtfOysNfv2tACbN5MXnQ1OlO361B0Q/iCiYRswLwgZxEXOJLI+KJkUcvZ0qXyKfvTdTZ24AA74glK63E7SoZ6I2L88a6KkK06w9RasaodS26bhdVo0I9/ar6HMmbBwtuiQiMiWKRULUVbzw9zn/5/lRNC+u2N/GjyojpBgKyOpozq33Oe6HhjWfavfG0Nol/sr1oHW0nNjIabtxfbS+phN2BYZpF6LDBR3/UtgXGLQ3ILEuL+0Z4pmbJ7OYjHpMjs7xuZvsbHKnkVuf8cLKlxy6lxqNhRLvFmiJ40QiUUPrv2jiypGUIq/pNfJLfSuhXvh/iwsfTy+Bud6g8JZ6YCrvTO3hpAqKuWRY7anksP7knX6U+4kdZUmao1CjXACkG7D2g2Q3RDJkRrtKrbbI/CNXV/g7hPl8Uqg2uR41ZrC2XwiZnljN2jYBjK63botpBmiWsENkHe826K5PZL7AmdwjnPUvqXgq8qGu2N8uvogRvt3m3HCbHofe8aRX1MvLEptJeouu2hZfJKGLEO8Oxzxabl5uXfMOYSxVFfyhL5mWzPHoM154/SXbB9FoOBnjKB6tr2nNXQxnWb+rn3yTgP7Eo290iz3pdwTfLAW5RIcS2URm7TCKUuLJPFNf09t3yd5Jl+gWW/PuMLbUM37gjMHH33KuJjPzB3pq3rTvGmM6xdoBJF2KUWZfAV2Yn82K6Fva+UCpyFL7kLZPiHN6sLYerTZjTxj3x9DneVzk3bHd9YmkuxqZZAtH/DVi879sXhQxbb9XdomKVD9yOmT8lCXHd1o59Wtmcy5MgrYTMYCJtUeK1sQQut28wqVixdw676CZJfoGnzGTR+tO0mj87zzZbpxf+pmjf7iJjzixqKY/uFEj283U1zp3u6CkxDVHba1VV9NBu8x3v9nrH/W75q3tT6xu8WNtlcTRXMRTLXqnVgvoPWWBd0b1cF2JsAHsEH53jF0UgOMiK7L/J7tN8sNrSWXF3BZu9DmX2Jl1J+XDnCgvOFaqsqoLBpO8mJwZsTZn1BcZGGUYrWZmSPvgOcuKdQwbUXmuPRH1ldKu95Ws7U2s1M5F14JHu01yQ4N9TGaWB73nPuNbPq6qI1rlrXWicZzYewsvYKnQ1KadwDSpyvLeLZWve1j0Qeo/mO0WB+Q1vcYLm16iEqcj5716L9HxDEqbQJUk34eMXpF9H+06tKIxkUtRhuI4HeMY+Bip6wJge+kyjvBZJMumXBpNRCIZTxeeZgHPfzmMU3Q7tEL0UsS5smUC8jsUa8hbeI3ocB4wV3pqV5mSLBjb2ykVfNgixSMb5/idH3nNEzodWuOZ/ZPDHKjGux27e7H80BAEjeJsXGknS5Vzqk11pjA++zlq8/VF8lNIHkWFxVRZQNsavDRniI0T7zB92xAD27wrEo3Kj3bnFNs7cde6dYbFjQvYiBxFDKj/TlZcCNAfEH/plN7RLWZ3edSIxwIY2zRazsASvkt2n7y5kHlRIgBRzZtROlraopbsbhoaJJ3sHmbW9pns2FzbF2Nqu2/qHDtO1qZ40OdcPKptYImXR2t6Ln5bxrCg5Vdr3GE1p5zQdNwu6x+707Ea8c673ikVTE162JyLP7NibAsLm7W3Ujsz2qW63lcS/Mkn/RuoNeTw1duLw3YiLXmj5lD3eU/iUmoNOuzMqMKRXimwJRNWohhlFpC8fofMUneQGJgwbbvR8VNhPsdT8DNuTT8oLSkwVjqtXCMmjWtdDDpwRTqd/8B2Pcldtf7jUjvH3cmXbFiDlVxIC8zYxJuP0oBjfeMDYLsVNRi08m1nhoZqosMtyok8FXVdLM1KZwQC9ikaNt3HNCrEkfQpVcoTzSWx49GQZJhZCNgmvx2C8UlE44RmflOY4J+9njNUwzb7lBla4FzTGB2SFrCNP0kwePZb0h57LvTKwIicSWZ50PYH6oj+U4RSxOwPd9pm9fuB5XuEb34aSnqrjOeGqvt4nToZQiNlIT+5e5KQhByHQaydTUpkmsxTAUhYdoiZ+soYEYCq4hif9ZwFKR0MnzwyANDFu1KxD1IVeplF9r2xm9AFzVjRlvc62nfrDVL7LZI26TVrNyS7bXz41u5/jNhoa/0+paSvfRpNcXc8TdvsCrFAR7xJZKXADi+BbrQaRuJ4rGb4P71CPudqx0juzHsLe3neaDu21pYk09F7qdFOkFCYieWsKN/CyAbPiA6O+EXOSMqHhNvluG8lVMJizuWS34AVjVtwLxeOCzjRlQR6Rx7qOc/P/FJJXweMZxJxkU8UVC7cF+Yhnw0bOXysHC24IZIhcQ6pKxfZeyQ8scpC6fqX0RStUmklkK1ejY6ztm+yVgd4oioHW1fUqjN6safJHZJn7hAZR3aDKj5yN/dxlS7z9ySZFNn/2b7Cckozt9rDmkzPEjs6/M99eYv84PEomzl8slyQ1ujLuswO9C9s3DiJzGqsSg5r5Au1XwoV1sNwkeSJNJ2yi4tE00ICvGNKgIpPCrM0aRaZpe+wtxcnpwTt5oIhnMrBHhrGhbEZWUhSwi6SyIx2tWfMHb5J4IU2Lxmc/RZTKDOLp7ZNc+5BjnbHJdL7d9VqMmW2FbCQ6Bnu38CcJY/4acJSJxUKdfbhs7E19ar7llazsJP9fat/F0nMH828sZ0cUdUn0SZqR/fz8yRBQOTvK5lQBNp+r6w4v6Zq1QPmZ1N/UraMSby8YKBD3ZEYhKGTluJeIVMwjshqpn3a2+kDpW3wYw16fGKbxnZMrm3zY/VNm/+UHeuSfer2oDF70xfBW9nAvlYNrdTOGT+wVx03+tyO1rU1WZklOiboUhmP7FBm0TlnlanRZ9SYg9sF8KwSIwc/46Hb5/nVRfot5om+YZMs8yXaE4KBIiCqyP5vLGTQyzpr0c38KbLe/kg2Q8io25CFDJxM17Ehk3eiR5y745g3vBOniB+Tr+myXao8kG1G+VQ28oRXpW5bJHJL1MbOd8i65UYLdTPLgzo1mqeYy75W0ovlh5q26ClnL5YVWyXHvKUP+ca1hm2a6oKSvppQXsUlud7d0D1oOXeigpOhnONkQy7Tft56p9ZU9/VFPpyVokXLT7wd+630Zl1pQJ2xYXZEVgeNXkcpcoCK7H+1GNzAfRW8dktfB++OONc3hmWhAZFNFDRi+KXJKoxHh+AoNZ+KlxFXmMRIjaUNo9dMoAQf1kiR8VhDpZK/Dd7VzvBTauW3Il/yo7wMvQ7P0/P59zUckeH3bvGEKRqmZHg270VvfzZInRrbTavV29MpLyjf+bA+q+d7tsmkEPRcolz6edVPnQ7s3lK8mDiUblTIILYmJ8eQ3YXd3dGHZ1ZO9pXSVGD2GqI3R/Set0DGTBp14ON+LVV1hLMnXS3LK9pf/uvbFYZtCm8mWF/sx2YlPBoYG7HsjY5Uvd9pq/Pb+2xUTZG8qIobohbkoR/LdKAz5W4+7/ZoRbdNDkzVCuVpNZVGc0OjpM9G1fS5m7znPrvGJtoz6A5d0lZRnv0LqtOMlRfb+UINKwa3kDG8of6DJ9s8vL4LrlPMZbvVs0xHX7le7hcVbfBjd8j0ZM40e9XxSz9zWfEQ6AwQNoHyRNcJklK1KcjhnuyNhnccSWt2XbrTc9HXRLuxO5LvaBtXyU2ruavsFu9U6uGJBWjJ9tvZmlqX5czPFdhcxwXCyBg2v1nfBj8u1IUucFUSoQi8/cew4ribp5/wwu+etnrQTywc3t6LrYcqwLR53H9+gXPLYoKEwRgq35tjN84lk72gihPt4sQU54iqqq887eusiGN5fHa+ruRmvDxsYACILnLt/KiWDZaLzI3KPFM/lFBfRu1CqamZSOHdxd19Gl/bwdUVnZsd44BbvBnfO5RsF6fYpctu8jmD2NEt2ek+pRVz2TId5A2/Rsf4Bd5YOVTDIftYg7ECeLWN5412e84+MlgZ19rawckU44t6tRXMFhpxXg57XdKb3NaXvRNIP4TFNCwWmGG1L8UY+CkdyzNzxOO24KnBoeHO7np0e3qFcUtesGpWc6kFM9T+4w5+ABf+xt9xkf2vdp0A73SnT6o7ov/qVPRu0V9FfPtIxLeRw+bPJNor4mQkw7Q02g5n0et0Sl/DJX6QcN5dsRsdOlORmezoUVga3lIYa72Ecb+Sc1PYOqxuWDd/zYtfDg1MvmbIo9Pra+zUQIPIelXaYXiIPxJP4jHaL1zvhdtfohS3xO9kAvFxlKmEzaEqo0wPYvNo32Y9P6XkZmxk/+bqQf+5NhPmDlIpJ8c3rjXl4COhKqRY0NpNnhX6A5zMDEBZtZzT0oY+J7EHy3oFPfON0d7io8labQil7DdGNpqfToXn6ZfEDb7QalBG6ItTU5CT60JOn1iHtvF03xfk9bnGGQnSf9WvUAS7QFG88n2164T4+SYBpk+xc8/NdA+M1K0T61rbkuOlKvr6xYh5X3ZSfclpkTVR0nm1Kc4Fjeb9zarbh5RS9BsukAnj0ZhDkziThljyDl4jsoyDMyqKTIqKF8ZmYgbG8EUedWqFJpdJc4n5H7wbHaRBt0/ElCj0QA4S2cbR6aQ2XaTvqNfIC/595s1kTCCzJ7pTphdVZiCR1JsXOTqECy1JH0OkqUBEGcfeMzX1q8uAeMRx67KD5ox4JGj4JlJr0mGfCwz8+zPo8fpSzSOTHFfJ3jWN/iyx/feVSSgCbb9XdolYjuy/2f3D2DU00RZ0j0yzA3vXFB6Wh2V81rqmn3Sey+sMHPKucmnndRm3ys4hd5qz5BHz3e+4Svbdf4MWLVfY2OEeGZEYYYGNsiJ85O/doqQLyjqr/rhs9XL3sZNIY0HbJo2cY7HKZZ13LIEX6r0USlTTgqOnPH3NlP1pFX3jyOiCdkyI/i7oaR2ZIXTmK9JIK7L/G4vnJ2Usn9Rd18GBV/cF1CI5a4cLeTw+eLyM4Q3dtpiKXXJl5tKjwlInBzBh1CBqc2Tmzfplpls7J9lXrlev8RZH709wg0OKuWy+nyrrX32hhgV/+JmHvGPk9okMIndbRSVLf61Hp3f8q7I+a1bTBSVpwYnOcXpPWhBKSmbQzkqqkbxphwu9wuaS3YZNebTJ2UhHnqhBw8giMpk6/GEpeLzxeOnv9sPuv+OzLrLvr13nCstnj3p+eJ61xUMjvK6JiGdZWXpEhoSgYzZnoy1kD2LZcTa0QSXis/Kd2SfQik5xz4MbrdaGPuyfU13PyW8Tz8LO7d3QYJ+nSk3Stvpyk0Y/6h0PecOjstvWt+fMbc5/VI7Z7LuzoUcPz1DHXst09HCbqV7eOjCU9lUQKuh6CWynN+lgmZNZoSt5ZEzUpWi8k9GG6u+PcioEGF8ryUb6pIT3ROpExeDkcvRl6JQ3Ck9eWdHe8t/BrgC28bSvY1H/duovyzYzR2jatYBxs19wbg7dZq5w686Dopsjohsj+rTj6CleG51GF9YeSFYj50QAe9Nod3KRRx+b5PEe44NDP/OgnRvvNG7TC7ZrHErN65H+QVf3+sDLKQOll3jAZcWU8I0frdvurUhHI7zosuL+xUteO57m+dwXghzOLS/rYLmHvKNf/Gt6muud3EdcVkzO4FjHasV/12U5Uqhtu+v5RDElMJt6djOBFpM+MTP7cZGhIV660lBDXPgdM6tw/ymByaGh30XX2ryhvtXRt3w2rCZxLOzTntjg+13vK0G3jSKw9h/JiqNCqMw5y7O3T6Ied0ZWKBmZ5HMM7Mazke7GFhthYev2zAnAzhMZlBkTmLIlY3NdFyvsAcuI3UbiB/zOj2jKkwnTHN0ZpEWiEyLaWK1cyy81HbcraC9vxmxhMC5j+OSReu9f4BvXqnn8hN8W+xEo4RvRSugfNESPq+RcpRg9Fr6j3JLzRuW94HY7nSxR3g0OWdiuvZzJsWGg56EdhzeU03TNrhCb5NBp3hqtumdQm3p7togZT+RNHCS9c9fAnswOcXsZrFjcQnwch8axr3gBubx6ivSxXcMjXUefvtw2mZOTQizTJmejOTsfCfPqB4TAvcj+flaYvHY3Zeu45ud5LirhK6VdWEakOEtwf132l68utQ8DxzJ/XOAazu7FoSacPBPnX/PLqTn6BH34ZxNkPNYwJLUKmbKW4UHKrKNJdhZZ1G2zVT17GMDjQ8cHSZuZPHRknuuKFV7iGXpMWhp8nl7C2j4n/D4wrgHZRCr5Lr7usXMpjdlY/g5TVz9sSuNHbKx7B12olX2YjbxTtYdhT08V+ZKn+k3zL7kvkUnCtnDetX2SlWlMhVmFJJDaPGgWSSEx8d6Z9ga2edeP8jI4TnbL4ALe31lgGM/jwcuzLJza3vyFGE7JFvRPnexoJN8CPFN8pFJzvtV0zK6QWPzTle+kQFG10/fJrrCfY9CdrPv1ju6QGL3eWf8ke1I4qsnTWfah5ogTYm8P43BDF6KfRhzbEO+JdZwbjI7U2nlYn25MOT+EPbz82MCQUDsUtGsT5mAWpfZ9a2qzh9VceELFRgfV6eaqHFsidYa60hvQyB7PMJRhHaf6NCdUzdYYL8iCzKbKvvCemSsfV2ZPAYNJjMYqg6REnj74QvDhxpFxO7pRZTIlB5NaHpM40S2Ok9w676AZWfwqp7Dy6o8UvM7BFhXDRIgLO2pu5LQzKewdFKQVr4usMrsNd8zbyE+SmdiHik8JmY2GrjZ1u/K8/7qAbhFo+32znSf5ANuovzHbU5WoMofmxajzJomtcYr8x7i1+0FzS/cMm8IyYaPJwmDu6zzHHvXM1dPH7grnXpEpDKxj2MEX2Ww46vSUai4oqYFP9Rn+hpj00G28z8E3nFgXhJ5LXPyGfVT5gI177qANz28d5kfzt3spcYiZ+krseFTMNI5EO9naua6FupFD+qZ+FE/4uzzOIvtHt0ItnvfPBCbrJZ7aF16dVhlxLM+jVSRNYmSHZV1YtoTkbijOfdE1hq2fanNifQf7VnQ4qZxGtnnWi9pZ6T332aeO3t41fOQrlu7qYdexBjwb8UlmC04Ijkk6Ofsru+A6D5jrMa/box6lqJiZy0569XnL4gZtXVfIxMhvQMltNM2ISixPq/58VYknB4/z6MFJPsL4jeRF3la7FPda6Zp78vzt9DmL7B/LvqZBkjsXrjf90BAfl7rT53i1E4oxdd3DulYqlG8ewLLXSbLF5Wh1HYeF0nE72V+3unmXHg7BST4G88LKl6jLEp094VWq0e3UCn3NdMF1Pr54l6HvvqHBdQfsi1zgIwpWlAnZ5/24h/bV39czb56mM3eZOeFxx1WSPbUK7Sj/5mGmsr9zdc83G6Z+82wV3uRCqWtEP42oPCIHRL+KSJq6wYKcUNlxfedTfMmJGnHG1X/SwugLPoj2oBiGnAyaorq6qlZXZP817QooX5vmqf64vKwLSprQcZDa0YbM5Mkvx1nf585w2Dx0YcZsDo8qRwpfR6tY+nxr02r01qpahk0JWMbSY62lGe+N2UO91jQtdPmuxDspPdzYLMtDWfNCY6UvQ2C/WVMJzmh/fL12Vobk3SXEBl3nL9zgn5yVVuk5r8UNVMwlR1T1vp8o6WtvDxxkvDQxC2kyKcs6d/uln3mxwVDHEgvB2xLUH5Id7qU1bax2dBNmsjWxLnO47QNWRAcFXd6U7sxkRecWNkYKOyrXRDbJzXdomrzLmMgjbs0+KGNPQz/JXREa4fiTT6a3EIpjrxQHFgG3338rjtoU76PtkcW2zKhn7U4so0pqSGMN7IA+QapsfPpzbvZ7+cMpk4YaHBsWHwCpecR8wPyNfYKkSCWebzfMV65nD5GpLIo+IuF1Nq67wz/nTPWZWynF4mZtRSZEffZBTefmxMh/jHHbXnCwVkVNd+4im/Yz12uZu9HD6+b6IL6FlxKH6JX4lgfN0rjYVts08mTnca770zfKOmu1Nl47nqaRbeI35js4KjAhDWGwKU60jhNpGSWFC/04sxgP0tNceX2u8WG3FOe6x0hNXkQCaRuecxM6NqP9pvUi1YJsY9KCUKLeBc96MawwtdGAaUOoMLawlDaRpQ1amzT2WfZnCN5n0Rz529sVIKo8bqJ0je+k93a2vNOnkROW57GqMOmVnkWtzMNmzMbmwDUsFW2vz+IADFUckav6ptMi5aOy11Wx7lQHyU132DuPVU3QRmCnpwlOVRusppLjOs1cw2Je65dGAiM/fYa+ocopOge/JX8Ax1bHO3Yw3ofrUqytm8xJcobHKthQeO5SLJ7T1okGcawmZfoWJVw0OOstKfO2BBZgNumDu3ro+Dz6klf7mrCfTU+wf0N1Hgzs2lbHM2hKQq+jkhqz9hRHIoetHcLCk+39pOwK4vhTaWb0SFW5FB0Xs3hxWxeSOVmTi8VK6HbvCjmwkEXLmT5uiAKk9Rf8xpr0GfWGrvXTqajw4eT89b/+Ivvf2JW5Uabwp7KwmD1F8Sqc5Q9ucmBAAynjtkjcE4rq1OWJzuwdR3o2r95eSIgqxq/0dKY1ZbLZP7m6HQ1q67PgDcPXvUJN+l+cHuTPsOhpNvRkQ2ao7inpQsicEKbrJI71iOdL3p3Y3enFpUUr8TNzbE2pyykihXO5oD+mC0mOPYLETqKAbV1kugFuS0VfJjYfKa/bNUYefEbyRN4Z2yMcXwIz0I5BpoosibKefqO4KVpfyR5MG0DM2JBMNIcn+45zG24bT8Jm6tRFH9oOo2M01pZdKaL/EhHdHxHtFBFdHNEwGs+LT9AgLVyQhgLP/a8H3BY1IvteWZBsjo76KbNI/7KrH/mtlZHTiqPflZKNFLYuqOvbSFZoCNBN2AAm8dn4mm6dcJCk4MjP8qAzEmQtbRL0/7IyhEX2itBxc/pW0GPGO+BpEzUcvY/WZDRrKHnCjsDaSiF9VFf3l10kpjNaFjYyqCQ8xhrh83Un50CsI6o6FcnWKpFdBxI1uPMAm1/1/Vvci0TR+T7Pi3j0UT163OxIbc3nCJeXKzQJWILibJx7h5TpW6jLZ81qurXjwaDL84Hglb9OxsSGktfvCMyQBixs195v/Uhxl03e9EyQJbhB0Gk6ukrYvh4QoviSvFhB6xFL3SFTcZdDeflCQeZspuDcneJCR0rOIjqAHTnUiMb6TSRf10poQUY6yZ3DsS9vCmFy2gJcFJpebJvh78/mKJoXfJ/mRQyqszlVdHzEySVBWkANIQIVnKbmG4TmZGN5sdtQX7jB1LzHxXbBeFo2WO7XeijX67xdcxLV75htxrJUlxRzu50h0N5HTo9Yj5jhT35gY497wrzYho8u4GtKJ1Cr8NJKC033LjHplUc9NWGatGHP6WCZ5Ok7aMRdjdf5+HhLunM4o5wSvhGJ5KrQDCmhiUFyOwEEnihk8LM4ua2wScY+jtWKlx3J0XwDu5tzY/41Sn9+mbpnsMDfRtu2aF7wt264dEX+4nrczU01vJ/dxgXXqRpZKvlT1je407WRTyQ/xu7XuW0uM3rSrwHi2b6eRsd4t1J3lSILtFqKSuxvXB3UqnzY0eOUKUGZqcjis8k1HVdJq4UZXCKvyzVKHf820PZq0Kf8GxrbboxRTmdWk5i0yz856y4f+40fO3K5qpx7KofS1vMoS6db5ll55l5vJgyQ4IxTyivmsun6e95o9/TcGAZ8NfShoFMoH7xv2RzvjevlpeFDPFNtsswjYaQnt0M3Mh5kTnSyN3OGWJzA+Wh3vWsuYAHTmhQCeNsDQF0lssZtZ9kQl2yMUTKub8X5Ga76Zv9RQKqo4dIV+9vMi0Lg6p5Uj6yaYvroIezh8OJyqnc5LfJwVLRChEoUlCImGXPYfTs3lQpMqWcenGz37EKOUDESxnEyjRPRRPWzs32YmOKeXhsD4ymTHQtCIzITsZ2FE9vr9vQKT04cp4Gdeo9bYPHwtm70uQ1+7CvXu8vHnvWiDy/f48fF1vvicg2diy3xles1ss10A5R0wVll/Sk3wdNxEx1R1d3Wud5XOu1cEx7hwcJHmYuF7Jhb2z0+9KnbVR6dQyXu6z/HS5Fe4ouR0JcVb7bQPnm9DzNS3JO+0fxedCjFsfNVtLDekTE3k87TB14wsV6hbm1p1ve/U4t7P/HqygB7dO2Lvlx7U66CH8BawTf76i/4normxRX7z/Oj4gUEJ4XmJckgmhGxOKmtLtVWGX/k6pFXYJPieKAYiy+Hle06dC9FTh5VGjDh00HS5r9m0f3t3B5ZpUZjlm5trdPGUNq6vXl4X8OzxOafl17hAT2eXso8VhxrEZIMyWk8FoCpVz1h/M7nrGjQQvsJ600d9rASLvqh49rPW+/xHuOdUkFzvzFwyrumDH7EkHrTRWdFAjGlUFJqYcf2fu8WDXzqgFs8NW6a7OFVvOc+z6RNDrHPUEzmwhEuXWJZfmCM98p8TzQ3Ynub8AwaNQjHrk1Ndp0LLisupfKW75qkpffpKrXLItuX0OhTHOLxzuP1i6RZEh3mJ5aon5XNvMBKT226iFgi9aJMHf9nT/tr/2cfrMiHumL/OfPiSgO+63iuefDVVyCW8zOLuVCipH7e8n5mzyDvUQn72PFYbQ3r7XNmH5su03GWQP67ApLmoTwtZy23blwHEjCKM2fYcDk0rktszdo1ISHyevQFN/jCoOlvK/gpf4irrla/w6bOeFg7H6i58kRIplcjZ2ys+KfzPTpxklHGqNgxlwSGzxqprH81rPlUKza0cIsDnvGS93r2cmEZJVORjCnoRsLQo04kVBHzQ47ti1d5WQ6XifbloxxafSrMw+nrTe1f2CRzHfl7KHaJmCaoxYzl9JtYeO+xhftbvRUkELk+Sl2Wv9RS+/3rza8d1pAKuCOeWWd6+Bc/d7pDNVZkCEjz/00s8pfNiyKm7ffKqvMv94tOZe8RUnsuUtLXepei7xmhHGE4StBkY5Y7zvBM9C2NFvwuAKZrQllfpFNUXtNrVHXEEVVlNWkivv2xkKmQI2xBJ4UFdgeYf/J+d/lYwzX7QsKgFO0vfmD9sDvNW9fJ1lF1/d7Nyp38o4OzKpLCj49+rd6xLaHj5bxATx954Bnxt+ern55tBzZkU39JdmFGrkj3psj+o3Ybk8uYoZ/mLcjrdo3+3SY71jc+OPLtODE3TsqILab0f4Ti3NrloCeXjZNTK5bitKuxyI8mrlHJcdEGQY5AzdAw7GN3BUH9j/DTKE3XcnS3gOBeJzjpG1Gej/jK9e610uj9E0LuI1/wCkcJiZOVlFyGSaFMr9E6/ikhX9cNQol4zbCk6xEA27ui9Q28FMNF7kudUxgvF2lzFtm/teJIEI2L2L6E+didjE6CXu04mi9mc0p90c0s6k5/042IPC62Jeohj3VDOihb9rzoFOqvCYBtvyHpRhuj6cJdxIau9U95WTGXbdx4j5ip54J22UfnhH3jGOd3c1Zg2W44F/aWswxd94ZIo6g69toiiRKsbZysr5msDNdQvcJpJWNzg0vTFK1JTsXzQcbBBPbPqu7w1nLWCtPsdK3SLihpSPQTcrltM3+I/TaU5bqkSB7hv7pdh7bMrqF99kKdmqzRY/TSsFyuo3HsJ/ZEe2s5dbnbjjGtZ9DWn70Tp8iIPmJzpfp671+garQ6Q4IczXT91br5MBuoksj1pQQJj43cOu5g0OxfgixK7fnWyzUGapT0O/XKb7HO3R7/1Uynb69GKtmR+rZ0TjH5V8/4fe7NcrIqhwTgRLxH6Sqn3W6nnyX8UiPbbNBcv8x0lxWzyj3uGbQxgLsXhQTJPGKewpshme5UkKG60kzmEuyhT583JGdwUnlL4tvqupTexxfYcIiNje+wPfqa6KZwHe3KrrERX1fgZ96V0bYV5+f7y0CoIvt+WDyac4m3pg8OAyGRbyKnzVhC9HSE7aRXJmahIIWTG1bIX+XxdNnJ9OK2qVTpQUI2eYOvUaFbaMh18mbuGbdRwXJs4sJKGlbbZ8KxQSGofYwHzvzKhImDvHJouOZ+Q2O67F+lxuUvzPKgkYsmGmOUT15vIa7PN75yvZznKnvv4n0WHO5pjp85+KtbZU1v4ujhG3yTX8L7OtujnhIumu9+zzcYJqnWBpvb1acM8zp3YhQN0/Y5dai6yitzeAyTeW9ZL4kbSL/0CLFU8kd6cc+pANjeP5G08+Ml9jvqyPGbg5zIAiaOG6nPnjeCHuMcflT2ExcKk6A5ODOLjKYUTLzCYEtQxLL9e1iM8PyboZn60d3W/eYu1xzNoy9dmqxiCmmppI0PRz81h+6oHm3t/KVy+jUOZ3rgLNeVCLUFxnKf90Sfi4Bj0YbUoGRkjcUpbe1uHr7tpA0h+XF5e2n3xi4N0glzaN9lvdfGpYWYOZvKA3Lc5WMmcYsDNCDBn/Sbma6qI+b16OTnF58xP7OPgZnvSh/c1eDpb2m9Z6kXGw81vPNIU9o94t2O3f3eLY6oaroBnjo1jQYkZh01/FevhMFZI1wDlIynzEGaRxZJ7bhI9HiElWHXbHRFJz2eVukZkmfvkFJzi4zjyOJ0n9JSRy9iXEADpt0ezvlahTQnMXrABPWrZdOGseNoW3yRjM0Nbd5QX3RYhJvShCd9k6sJ1iL729h1QjL7em9F3/b10xHnf1pMNBIR7RHxy9hvzYucd2ekZ1jzapHdogqTqFd2nxlZLLgcvPrMB6mzeHuIaXdS9YMD1GBd7Q4KJnCif5xVp8i/FK/rlYao3Wh1kEPROz1TfKRBTd+2ewB74mqrteQwmxnU5G01008Ev+ZBdo1NtMh9HOdnfhkqVevRfNYqlxUzbM1UqtImd73EhUf91v8giZIzBDLHNkGuqhZnMqooXgxz+I0fB/JIKSKx4bqU53Y7HR3AoOy3XZiHusTeTMzK0OyvwbJPJGLG08KcasQtkRW27qlLD+Yu/4lolYj2bdY7VJu8aA+lo3dK+iBIkJR3UiXHC5XrKhd+H3898kgRaPu9sjIavpQh0o063cicR7kB5314vq1r9kStqkzmECHQvUhkJ3OWPRJkCGoLHVMfjNHjlnd8VOJuH7vL8dwfUpqc4lEsEkLgcwKAei78zKRzhSXOSDC89chALIzj5RJPucEhPTYt1WR9lheOvOTsiB8q6Wt9q74mGrlOB8tlNGgovUI4c6XIS17dyeOp43URhvCKzi0KRZyLFvMi+49aM9G8iFaTMogLgXM7Kx1R1fDOIylBhZxcevFo7lusZvcShkaGi8/OV+yn5yXJ1M5KNTueEJkSnLTTtUo7oqrrnZfVr0kAbS0XvKFNrs6Pz4VF+Gv+xCfrWhhjlB21als7OFn2nCqB8TsBPbjwJltb1CWHjO6sbcnynMCY2l2TGWMYuJhXu4e7OxXZ5WTxAi/34kXPFpJzipIbRfZvrQw/aEt+0Kl8PJ5fRN/Sa+tbIWkWx7QulInsEukWNG7/GDnvmmg5csmZGBvKrvOJSWVWfA8SaW21XpPf0s4H8jpeY1qt3u6OX+7ddQMtqNeb5hT84AybZwvI7eeCa7+Jz5dxvrCE+6yQ3R/Esy2H67N9vqcyp3ExNKvsd3JGiHfj2XjyDmUGU6USDiCBrXPq8nToNut5/offqt7ztByB11Sq9Hk7NfBx6TsZgjGFme5fbAwn/YsYHkX2j2spVKwi2jZi+ZHuDm1j8fNtNZ+FNWy6yMCm71rXr4ODlSpKwYwu3BatzUwGL3vLfPezkVoTDtOX15aleWXTcJ4nci7qswM1RRoIwU1HPhyeoubGEzbOvYMetGu8yG/82C0OyPpDY0c3JvKe4Nt8fhJreT+b9wsveYhQgTEbGZzfXM7nbvITSxxwi3ZWSkt6Tr/j6Urm5Xtp6pDQ1GkABRlEFxae5xxNn97l0BTKtTmvzliShtM8haNH+LlnvNhsqPd79tTcb3zYMUVm5eCP7YpsMbPa40Hn82Vezg2zZNNFTmyqWbjvnXN17hQBUt9vK2zCN7GKlLUfiu97LFT7ZJHYnw7RuMBmmklqjfCOhENID7B8v4lB91hVLOPQPCFJATV4rXmaCuuwhJiPMICSKYjjB85QnPzy/Cjht+rYC2Z70OnWpW2sdYe4E+fsmt6UyWy87h6eRvpu2ZE65JPbtyJ1Y2RFmoR5MQDJMb6tW0pWpInjKrnBF+72kdELJ8hc39zNfk/poLe4uFZbepFW4znN262SXwo92NUxkSMMfv0tEgNQlp9Kwc2kRONkPE3zSFqQkFvIZ4NrBlbZbKo64thlZLP3bKKScayPznUDEtpRKVpR65eWBsa84n/2U2R/G6uAFBo8pX60QPRYnJ3N79Qi8xOXO5V2NAuN8CXz5nSyO42n9lAwKEgLdmq+RvVBp0XSowYu5qu4WJcuBx/p0L3M1M+ZbLqsWeXryA4bFnJ3PF2mr3Jb30LhpZ2CfvNKbs/PYg2Rl6IBJCpMsu0YVZs0Oo0LDPHEI0e5xPs6W9/3Tqu18UjeDB+VuNt9SXM8mTTOP5vg+tRTirnsjAR/cKOxRjiiqpHzJ3pr02DX+8qx8vF6tnvb4rptPfvA8NCYLw6lyN8WPt+gwBw+tFyoBlnJ1Ogk+09WF1ks7EeVhJL24YVRxlDej5x3YRJy6beUgc0whpdPBYLJhulCM/JijKhLQgWST+3QdMAuT1d9ISQmVRagv0uK/LC/tl1JYNxE8TRq9eMHyfplpoudR6le3xq/EA8ycG5Y99MaYxsNGn/i5sQjyu77o1/lBrbs19GB+vUNcjF7JzUKa3J/jtx+c2C11iamERW752rbn3/xcxcmUacZ1uMiLdZ8IqY4Z7Zx2zD2quNE57hAZFqKPKZ17i2nRayyzqptL2NpemiXHUm1RYeyYWFbPf2KM7w8Z6DLxTnYraLGtgVpkstCBdJmgaR4ioIG/PJMdzZyg0PuqzTHS62HOHm88Lg8RnhRp+jvOELMMcG/ysMESs5lZ5M7nUEVhZ8xmttacL2vZAygU+ml7OPJ1eO8Hn3BQ7Xn+STyibH3IpsOb6+z67Omoc/bDTUKz1TBXwvvKgJtv1eWY0eN5MBI2lm49PWiy/pVdqbc7IzC1xYztfXDrOZCT2rOO+FcrxiacW1+gY/c7bJizkiQvy0+DN7vKNtcdcyvAFPTLMpM9Vy58V7qNca5ejHs4aFN8+xxm/7NJrOYgjKoRdm8XGnGk01HyySv3CF1FF2KMbAHT8zheucl9kW0ii8Ueo9Fjk6R/YftiyBv0IWMJejJx+7StM0u425/wdIerUUacLQ2MZmoyW3tqPIYBU34UYXfuc4FzxyfHOQLHgvN81ZrY0Kv0Tb+oU1weDIOCfPjSgB7SdjuYgpfPxZeKs1lxTTcuU+r0RkuuC6AYfFYEzL4TVZmKThF0lla1SqUJt/KZ9FO+vUluwtP9OGppUSjrcUXCzmX99wXNHSLnJ4i+5+sDLobePpl0jkxI04kjjmHHvGV0jKrsSsjUaVoa7dmRRnAwgPtLYsOVX3eabv3cyiSH8pL1/DS1CEemj4PFHfZ8osdjDJGkxJblfCNDSd/LL75MbIKsF1gmp8T5sE+V5MKBQoVsALj9k8F9OG1i094odHT4fXzVMo74YMK93q883hqU8JF8ojms305mfXIi2TxPEYxvxOnWla3f2519wsdvUvWpVvLFUoOF8q2LlHhS7YsShGQpyKm4H9di6dxHb3/OI0BnK5a2jXRchr4NASuJagTLReSZ5co5rI6E8O6uzeyL6z7U3il9nCRmVGRrt+yj6OdBKbSdqIdI27ddjA0ZGkQznnPuI02ptzhjASRdVGrft/FWWXNa/UQN12i+dGgtW6ZkNA4h0wyyG8eH9jn287hDDt5uOVU6ZF+Ojy+zlNedtG1luvgs0o1fVzqTs9kT1bsnfPqpW5xPK6cSBtBl3Yb+lCjL1NXP6zgsXBKqaG0veLMXM9OmUQH4lfmu+f2jd/tIE+0CL+f7DaOp0ibE55L2xpUbHYw3KsqihLq/whWHPfwkxp2Dr3ZhjFtvVi8Cq2FSp/1YexL48TWOMcOxod1uRlPz3jBdYSKi1GCzFoJarTDSUp1+zYk1fqzo0Vt4tnauC7jOLcsxuN7xnswZx6liN3HDP18oYZHa0zyI79T/vdfaZ6ZSUZsSN5tRv4Z8neHD/AFEzNIn835RdjNR4fC76MF30nsnLizpkxJ4MNuKXa1SBT/er70Wl1FHi9U8tvH+CHP+eDivWKnoKbQLPCkkMCcw0PeEduPzFwqNsl1S7S0rt0E8CmB/MhBr47GMDp730dC2W/9zGxbj9X1joe+e+TbNZZ5MSkAEL4SHMGi5MbfxmKEcZ/swKdV7ax2pw2VmbGRlws7w38t/PY6X0aWysFP6s4V84Ggp9mX7a8TfSLCKeKP54cGj5eoMZVx2S8oc4b1re/UqlugaEQaCJr/l64KCT7d+AXW8Gs/pTPRX0VkDG8YEiC1aXhon+waVWwdXpd2TKva2652iS661kfudtY/ebTUG6brb9GuVJN/9IwTkfPOly5rVY8uJk96xoJ3eyvrX43NHS7r/htlNbvRvD88qLGt3sl7WJf0VerYq2X55RYvaCt9dVexbZBSCCKnUuMx7u8/27l9MZ70ilrND1vVj3NfCizKpqT3o9VceqW+pV8GJXtQUKuwP0JuOO6pBcHDOye8Z9mRwv8rTPg4zku5I/k1AV8o6sXx17cr86F2+HmP3+1r5P3TbYKbPob5CwtXp6GYXYi65GIlO0fcaV32Xb6M+6EOxUg6wF0+9viM8X7SbW5IpOUxZegjlu1kWWGVXuTRqJcWDOEUc+o94tKV5W8j+2tVZzHFi5NQjfXj75S6ZJH57vdOSg8nKsU53L+cBy7PdVlx1XJOm+9+fWq84bMaNX3jWj+PH+J0t9Lq5Gbr2eNt5Z0Sm81xlfzI7wK5Y5/AcB+Fbr6Thes9aQHnQuPidy4/5JlDk1U4iGrsvpknveJXUuUnUbwmGhDzvBCzv4xhdH0z/JkzLDZUtB/hWhdtiQ5UcjWO88rC4SbuH2n3/qvfxsuniP4wIlouYlGndkHlQoGrTXP/8/2qItD2e2UFgWJdD6VIHiVoOLWorX7lbKnlmRydY/O6+gaNeDscB2v4uNhdCvowvdQjPnSPwaZ4M7c/zyH/6J99xqU/+31J2O3O0XQ8f5pGerq45G/M69iJEnQ6tSZ0kS1FzGxkEdudb1zLEZr22sU8lo3hxKWaNONQL8ZlvcBQEm8+atDrbweJr+A2FlmR/YUWg+UiZ6JMCvp947MZP/u5oM1TlU7pIau9CcvaoAaLP2hLHDEt2NC0rbutC8fXCN1kZ3nQdP35ED+NFLLAr7Brv/Lvs/aOhXV4ImvWdSKOGc+nqj89mx4sHt+WSkQSebndQAWXiOmOSrQ9yLzETnrcvpRSXIgmhgz9QpJkShhEsxL8RvPCbqxFAUGRXbGgc65Kgh/7DVWpuCnXqkPYxjsesgkbI9nqRNb4+oaI7O50G7DCs2MmsZ/bFtPoA0EvbRnPDJgcAtxSVN6Y48ESs4z3L/qaaZvGvt1WSk7xWGFOfO4qSHtFWufK38cE0HYtZy/wgxg+p2eJufqb7mBSRWuHJovtxT3JG702IY19odmZbUTasCXaW9Iwmg/G65gcpPyXrmut1qHDKtTiwkw+3Jxixnrmj8Awlq0h8k60cF/54q/+LRTZ38OKC1F3P+W2fmmsZ61Y3EK5bedVb3pazaYnaED6cs4qSw7TZlMt4TR/vFqouWijAO7OJNouIvrJNQ6lUyVDYG4sFhh4aaHhi8GC455HyuwtumSuUrrvaRVvOShjZKsgcyNHmERbXE30Fe4fJ+azM4M/bRfm0CbyM71dYxA/wQ84frKSde7W10x1F/1By3Ufk8XlzNL2TLkj3M9gPkutGYDlQi33QRPeBrveTCSXyZeeCR8Rx9qeQizXjeQahTtYdri3p01kPz9KXeOpPuFZnBhfk22HCg+6kqgssu+v1UYdUqk/M5uVhTSMQnCpzoHtytU8b1VLKk7IVXlcznfB+qQHn3Uu2jAEuqXYurkueeQvEObGKIGhi4YD9pFIk9lZem54W5mhBV5LTxPZJ8gjXA4J5n/OnVDIDBwewOFkgaW14gyXtgsVGdnCVW4o/DtH8LG+EAZuIaD7xcnABBwSrmGPerZprP6EbM6QOnqR6MSILt1XUSnoTZfa/G1gDqbwUNY8h4eWk7DmqHObYqxb34F4kmcgkS/c4PSC0pTiw9QUm6480pY0rL3PPdhyGVVp0iTLbyL5WpXn0BJu9nu5syvy+RV5oKJKqL+dFSBe7NmcMM7GhFHTbymPxzH2UFi+xy8RfH40P8hYzwZ/ohSaERutGSQMJqETuwublZnHyZvJLEupyCfE0Wg4a9cl2/V8Is2CZE50DBMrjwRNOmZ5qcEQvcq/FXpkxKE1BfF8pKW5elraubX3ddbg3QO+UcJcPb20bowJH4y26s4uNFhLxt7CizgZYpFf4yjZm+qrGnfETH3V3fQHd974Gyeer+m69lE/Sl2jjdWSbFHPHqnrF4VYYh7R6yNB67Md85v0UaZuQaguOUPbDbySPyw8k5WkzmBZT+bMfoQxpM/kjbhHPFFJYBTnIYdy0R5qQ8fgCY49wvhMYXPtTswLwp7mBkWJv7+2xQiM5ob8pK1noktldbrRPnUcj6xhZvh+vlAI1K5E06vfSsE0rKHx5W3KzCFhE4bSdOYur61P82MbmMr64XcanPWWjp3pWEnA42sFaSY1OZTFmYtCY9RL1FpzmDWUHMbjB8e7xe95k15+6aFl81TMzvWssZ4q9rL57ndP/PsOuMW76wbqaJmZ+hr+wSue8rJBcZN94QZ71DOjQarpBrjPe451jNdy7HJVBx9wol1cmMs9iMlDFk8+P06rzAy3FNvPStbXuJMz3FaeLZIs00HsPCJDfcdCFydkZMpzrH+8/Ogd9qpDHOf2xai58ER4cJt4eYlQTfsptxXKrIyoFDDkyNqoSLOortevZMAq4Rv4/8MR/t+tqBHZ98qac7Sp6NvXGTuaEV+SVvU547c9Fxbb4RxMrKhmvxMuzKPknMDauymeSwc5FRev8v4cMujT9w3vrh7Iiwo3h3047H91OApc7Tx4HW6gbFuepsqIbEf238w6sh+rIlOS1DWLnOsYOtDueL62hjfvoxTbd9JosZBhr8eO1NoaTt9nR//aSrqg9qYvSE4Xlv7vkxWJovN9nhcxGCD65Q94kw3jaD5UIUMIZ/jJ4LneX9/T7pah0UapdVGxtXJ8vTwhMJKWMGXDI4a8O11K7w+dUt6+dQ25OyoE3TuEefHni+yVra64sLLHU/r+INt0N2rx1sO99NuYLjuliut95fdukVJti61f1tXk5izGsrBbe91eX6EgNWhiZWSRXFdwHqcw/IORxk14gRr07zbZW5HBQu3T3ztjXTQv+D7Mi3g8zoqIaJOIaHEivVi2kinR5R40W+rsRUxhxk6WRReYX7q7kjUEqY0f4sHC0+RxJVrtMHaB5Su7W9su2XGVNLZNR8sc/OzWUNL64VEhHOJ/3TOuZPv5bm5IonQFGlP9N/tt1US5necDDrRGKMlrijNcSA9NM8psFJpcxqIF0ZpB8udkSypsxkqmPP+I5MhbqkVLB0DiEIuir5k55HFlx/9RbmxF/C0bXBbNC/4WDZeuQK61xZ5N8vWRhMCYHYplZE+hWLSikr5W8eZcsD2bRgs42p0qQ/lsYk231jtoVRZtGwsNI48LAO1G6o3aYs/GO+jH5gP1Xe8rt2YfDBq2qYJj34aWVZe7y8dePDaK1Ngg1O9zYXO5ksQo7n+eJ1f8Kd/dB7cFMPjnVLk32/3me9Tras4+EaQaZwqAcWY4/Mla47xSebiCPM6dJ+fy1dqor6PJWlXLCGDbaNKPkzqWkyOoMJdrW+f6JiuObI72o0oS5zbFKPNmgb2DWBt9xJDIdKS72vTy/xa0LWq4dMX+evMiHvfwdA2Jv9jlQMcGCn4bylbHrmfEDKJpvHmmt4ET3uUSh0ZQYxbnBrDvIkkzONY3XuXmOQFL7YZ27G5T6PnHEZPEhU2U3MC522OUSSvwzsQeHto5LzDHy3OwdUUpfutoZmJgyGYJcUbWXmGEHhNG6VfCvLgyRwpcnRfXCdTe6kikeEwArj5nXP8n9fQrA71p5aGuJtQYZNiEqXwpgMLjyZ5TReLxo05XCjJXDVvu02fdG2Zve1Tkn74V/ewauzsF7XNvMn/21bREn4NIZvOx+gigxRWGmbGMvZdhcaw7myIa2ej2aJwuFvskctp/LH4pmhdX7P/Nj6rg4ehlM9MfN7tXGF0jymMoTw97wcTZIxnBq8d5YizW8eS6ccaWHq7kAY5W5ptoRTXbnGAAee2uUWrSt9Tl0Y6TTCk71O7cMCLrdMAf2by1vjqxu5TJxiQWT26ry/5VjOfDWSnuGbTR5qn13dl2J6nMfeAnTqlgSK/pIT74QtAyb1z4c1ZYDqYWCH7VOVf9qAqFbzhF6RGB0d0gvFT67tOal9qgrLO+cIMGPvWkV9TMOiGnbqz4jflh32gqvK+NsHflYQr1Fm8xP3KHOimc21zoc+3DJc4MIDbvGv1KzPAL/6xSQo5IPaE5WQ6ZU2gYR0wzHOTo/tDutbwgvbL4YFt71PNc5AGs8pcnM4p8qCv2l8+LKxhNZcrez7ao3BtKKDO2gASio3glhwGlKDkVhzg3njJ7kMyrp/7niDYG/yNaUc3ZJ0zoM8iwflM5SfayKhLTj7KOvDevUSrl2zCWFgrNGgdTMLDwPMWI1CPSKurOEet9vKZl8NGKF97KZMo3PuxlT+n1+nvcE1X/xky7Hm9Ke6Eio4owN65sE4Q9ZSa+KHBH9GM/dNxlxd1vvo/d5SvX+0hLH7pH/YXZcrrFim+ZTwLtFiyyckxXEnjpsSGe6TiZ1zlZjQqPFd7DfkwlM/PqrtSqszCwU8jsSVIzHs8Y77XX00jCvYFV++dReVoPoXJjJZH8aLiXF7GzwFUpuZN/6VBQ1IjsH9KqS678Oy1HLTeiLquqMb7NcyY0HuTDWSn049PICTZTsjFWU6EEkc3EbKLymJwABhXnvOuDBlMGYZs75eoQ/fOfK3YlHDjD2aPcQB+zQkxenMRDR6W2XCTamDLdcInqxfd590B3+oTlJL0L0ZkoQcOa+0zp/4iGPfepdfthqvy9Qagi+8e0Arzmh1X/YOy44OI7HtgSlqEa7y/rKaMlty0I8gTdk9719f6EEOBXQgcGr3xL7E9yPOBXLigZnH/HhIZjfz4v/vxz/82/8wXHqxY9Hn5HV+9Zm5IscfRRFeflqmc3m2myPivMvZV027/CuQExYp5Gy5AnPbOP/Gboxrj0F7ycRnZ3yjor3FTRXCmyK9aW9yO231uHbE7Gxzm3LEan96LWR9rrded77CNzJ2Wj7ZV0QcnB1NmznWGkLXtOducqjqXEk8jLYweSFGRtmrdb5WfeNd/9/uQHDr59a3D4NxCCiOv8+0DOv8fK+4Lz2znPLX7vfT8JQO0SQdw/Fp25dmyukgMo01Qo+17PvGGdWM+2+LrMoXT+NSKxUYfGMLj2W2JQLu085YMLNDPtcecmxchdUVFAuIoYgv+17EpY0ZAX2/rX4gmy6wmBwDL2TglD9NrICR9FcqnKjGxql2J2d6p8QP+Jk9265iDNCguSOqBNALai9cgY1VB/021NqUstfqVnWH8vI5fFldqSSEbVhqo6YroBPBvLhu2uOuPfQaiuMiuuVDBdea2wkknx0Iy1NKbyuFd95XqXFSeX7MQqoQlgLI92nmRjrTu80nM4lzh1Nl5CpdCp+bNoJ9BqTYaMI+zuh7rhE2ePCG0z8zuyLKFj+OhYqjyPHMp0LJA+iDqjGLJueuGzLpIV+cewr0muoe4vtrrNHi8vJyYR4xnRmZf7EVnNeP8iI41pI6gxiuwHKZNI0mZcpvKSkNw6eVzoEL4sAJu3nT3jD2erMyD4UHZSZlIBw+hgOV+SkxorL+UaN/7+j06eqaBKUjax0cB6yjoqULL2+Q6A+k5S51zhPcS4Kjt1iitKgitiQgB/UwFlA1vxjB9YeLGbGTVS3eVjU4Y9QjMikaitc+pKrHaU7pRbf17DXvvkrIv1+sVB/JroV9fY0KnwUzteBWyhdzwzajLtOE2X7NK03q4AYNdFEtvvDcctzuWeJhtdQMUluUYZw033C+B5kczb39ZOenv7IAdTKzomYCvGcmEME+eN9OqDXMgtPLQZigtrJ0ynSnlq1j5BIu907qHUnm+lD+/KH3k5b6iv82k0izpzhLG8OFSQlklAN/LH0sCnXq410GezavpAO7umJmqaucszq0ap/8BmPTe9b6wRoZFkljCeiwsb1cQCZm5n6iK8JuwdOYU/p1yVLizO+aMBWLob6ZzPKmflyXZWXm5n7+U6XluSZrx/cbpuSFbILfy8OKKbeHnywPC5pZBLZuk7bI12t3hDW9eXYmtS3cC2TyZhIXtL1JHm585EcryZw6sbuTABK0kaS0wDQfP9bFTVFVFP1SD1MRoc/ETXSHfPRSr7jwG2RfYftyuAbbwA8HO+SnFl+hY4Pap06JF9JtlTB5h6fhBjcImZFwUQtVnogVFc6KfSry67ouPUHHdCyz7LDRs9lZUsW05il6OcZMNssmK/BbuzhDxDJYq1OG/82aFiyhNJwBmi/SIW6xJC18fYtTWR4RQk8o4H3e2jwvEcsev2pkHN7J4oU6P8ywV+vpsXs/l5Bj/fwLN7+SIIjm+JpFj64x5WdOimV7f3TBv/lPR3+zkxvaaHvEMe8fPy6c7mBfW94yFPj3qBSTwzZnJgx06iQmGiwnShsvZNkhqzL9o7aFb3wiBsJ+lTZHOTP1jxWAv3NZ4j78trPFUjfBNpc0nLwEGMZ/cA3hgSEf1DhJ/jzRgBW8hRJI/wX9piXGELrevVgTTalkc3hs2b6p4BG+lL1y+J/DwqfUNX1lBmNYcTyzGbcyNiGMXLfQY6rlJA/XHVafr/s38L3sZziRd3jVNQCsWEZgVNiQxm/Zw7FbxOwiB6py1QMJo65UP27dMztcnkXHaM37slvDePa2K/cZXRUWRF9pdYTPhJf85vIzciZHr1JVOStQsFZ/sci6LjQkY7O5QGFbQRWN/j0IX+7SbrGLdcB8uVdKGwccwXwpy7Emz/e3YlAD/Gpd1BvmAz8371kIn+WasxGYEBks1rnginG09Ou1hSMJMyBwtCti6PGikktAy6cFaGT6iMxKFMqDpacOCKrMgghoqJjnZKcC6yT3YyyyO5yswpML7r43weFa0boUFoTLRbPfPr9bFoHHvXNJLZnfFrnlMh9qg96jGuMACfyVeu96YBfivFy54KeoQ3CeBq/m5hrb6SzLj0Zz9/bpdcVT37AqeoQnmn/NLPSGH/2OoW9mgfxv9AvukX552JPcLfbxJ5+1s9uixlJx+5m42UGvqt6IyI/di9P8Q9r05gQyZ99hH5WVTchm8K9aOyFQUM/5XsCiByHY2bSx6xVuxMEhcI63sNap+hXzuqtA7L7avr6TeMkuPDaJx/L9OfHmJVG3ShynCyR1VhFpeK8UF8C8nZOwyq97Ym6Vmis3mtY5rP3eS+WnOoRIfcVfKaXuMLNWzX2OlnqgU2ni8EsOlKZcaVOfBvE22XhHlxhW17mBMFIVGxgRfyRpl+aIjEQ0dNGfxIOCSbSB7PGy0pbwtl6H9yckgyjmP7GlpElko+RnYbbitRePaWVz81dSl/LM099TYGPKAYLrMqm5dXkhSt4tjz8YEFZq2rc6co8fH9tq+ZHbVnzB1+EenlJgL20wkd6V2MM015NVJbBcGnUJXEDziXzU+S5rKO/Z2r8xgVEmn52HKLp7ZlH/lfxBvt+eDLINqFz4bVZCHlFp63tmOyP7jJKyWe1OOWd9yV8LFLivGnCOcLhEj8SqD65/sG//McuTJPCgTw9vowFt/E+zHcxCkVjPa8r0pcr+fFuZKX7NA/7y3WED0f0eT1LFu/DAm+R1tM4gjXl833+xK3BJLJuHAFyYmYxP2VaFuM1MFE1tGvUvj0V7uwfs+dHOfl6WyfHpp8jkgtnBVV6TqD4Z1Hatt2A5+v9e8nLIvsr2MxGCgl2lT0+Yia1U7oE40PT784H55v60wvnqjFu+d7eyJRYATWZvY8Sn4qgJp98SC7X+eh0fNEzkSNMdLeARS7RJl6aMZdqetC4UFTAcAZjnrEngq6/E+dmuakCl7LStOgyQGR96Ia2+YGh/g1p1+vFtb3iWfYtpezGzBN2Di2CMm+Alf3jT+v7osRQLlMFMYZK/AmL1d4SrFil40qNobyQV5qpr7q98wOVVE1yOt2jUgb+l+cHqquGqdKW/eckon0Tl/gczeKLKRJclaoNJmDBhyMZKk/KFvlYuEqnphDh/PLTTvQ28HhFUV+HBUZGaUPD9871emDpfWc+rZdHZr6n+OnIvvrWYGgtHwTzZszgFdKPEkC5Vqe50tiIhnO1GZYl6lePsTRcTz1AVMWP0Ii16yM6teXQ12wjFcmDbdoBB8ldLB3DMbS8SAuseFpmg8nKQkdKR2tyE7SF3d1+ePSnr15UsictEAlTpcvreKYXNlTq9jct77687I92m6SFnFr9Mj7tR/uOhuSGe0F927/KiGSfxPvCpWuq4TYN1vIKnxReO+z2bCMFZm8dyg0fs0n5r5z7rbOo30m2dGjNnVD4r3i9FzLdDDh4CBWklfjmpDsPyDg3YNRjEO3c24PA2u+q1F5enV+K6wThXG6StwceUv7leu9t7OX/FLfmnGo0N8azcvNBgZprePcVouBn5KZRnRJRIv+K4Qjqwtzuoz/TPC2CLT93tj1NG1khn6MY34vtGZG31RyyJtyTZgovfjmgYjU9EWhinoJ1U+d9uKCococLxAtRTsrg/7IrwmbAP9n4JawkVyHrzlKzfqfuVxcKNVozImxcX4yZ64LrhOzmdOTSyuYzs6zdS082V50G+Ol6TnxbWWaFXij49BAc2nBt1+UElCBIiuyv9RqMXuE2Q/cL3FPIEulrePVloxOm6A2diWGrN4rLYfrWeNt1yyM8hQxNdCdkZ8+w8YgRj5/Wx/9zPCvygqB9+f+90yjK0HGn5W/fiHoBN1zzk0+t2JUC+/O6k4PtmkcMnbdBC2pxmyYxNLE1qJpaBEaKBiO9exeyKu9+DbaSeSHUY5uV8SyLbJgMejACirl5LhO0ELOQeaD3BiZKvraNSExkEn6OOpGXjI/K7w7o03hSGpAmS+5Z9PGUDGx7CjVeOr4NLXGHDZdf7fn7ggBwnvYfNJVHcJ/Lzj989eK/9lrZXBHaHIxsp/7vOdHjdeo/foXyjvlxKi4wOwaz0M157ExaGNFf3FNyHwX45kJk80/RPrrzH6dts34fbS9J/qH4vLmU1lcq63o7oi6LbcW+nVFTMH/OvbnkhvNgl6tB6wY3MJn3Woyj4wh7Eng8AflpK8JRz8xFAtZNoh+bwaQRvkgibChDRnjSKx91Obx9cXcS/us9d8BnYtT2/ooPpl6pOzfYohXFKQyPe5hH5e4y3T9ZXVuEhgUVglI6J8n+v7tev3vgVNlfOdXXbrARK6N/cb+GtW9W6O7nuZKnHmUJE5UjVOu53mxLbGdX1z+Z2941MLU9hqto0JdZIYzHsmv6QucTKPPZH56nqkdH1ZjohCUj8N+jj0fr+2eEPYlHj8aGCo7udo4pgiE+n7bddQdIXr9NcRRow8dx2Il448z/0HOXypn66WUwBqKVgnBZU3kseoi76f3lL2gih9bz0Uysll3pIOyzlrcp63o1oj5S/qExMFiPo+v4lVPOPFYnD7d3jDYFHd8tkeCP33X5PjEopqFmuJHXY0v/m0S4M9///k4u04Ye4tDVu68gPvm8ycJ7jffD+efVXrIZY4T+6BQHTWHg49V1GRJFoeYkjvUixuGOnC2poaz94kOQh/aLhAaW9biyWPjJBRKq9zYIEt0T7iSJx4L2rnWhCv5EONn8nJ6YVOaxbEiP/7WS5ExfDhfAN6K9pu/vhXK9MWOUD96yIuRtqYtR19+bL2vowMt7tNW9cgqCeO5bvMZD8S+q9eBtyyc2p7V9JlLQXkKZvuu6d5tm9kwhuj2iAPLGgRKyH6Wbm1NRz5Obhmq6WphDxmPNQx/b+NMqXjvlO+h8eVtYNzWJ732i75+7xZLM3uEKTDoAkczhXGyEbuF+XCFVftvk99X9ogr+8ilP/v3BfILyGDIrumSZLqhEMhKkumZBydTKWhvRn7xrddLPMpPKdXjW3tnh+ZS4zs+FxyndQzrODUk8AYLe8Nj7KhRO+jVXuLS5cJLKcZPIx2IvKtmhRNyn71W9O6I6I8iZg56XLmd581b91BhNdZ/LhhVZP87y8EmhhD9QcSz2yZZNomM9dhP8wUkdGH7knD0H6J3SGoXZDUkEC0RsXBGezUak1kTCeHbi0xiSnRyID/tw+uF1awr8SZHRwjarmOo5Liend8Ojeg68uKbQx1cXdEPcs7ThkoXj2uUu8u57jF+5pcuuM75P5XlBNUb7dewUYbWW5cKoNAXrs6LY8K6eoV5fqV6ie/8JkdRg6m06L/C3Qkf2auOJ73iY3dxnNemp3GS3XkNPGAu1XisxFQbx98RkjBvEi2Ml2p8SpkaaErGKeaMeYQBDO82kmos/rStMwT2cB4J9UKy7wZI4qmO07y8EN3I3B++muLQknVLOjD7ft5PpdZAoUvH9f6zGpMVgbbfG/uKzYXAzx7un8vjc8a7LZLu0ccmOVuiLHvI2RBrRy7RwcL4HooUnt05iSlE0pjo6eCMXCKk40/5ywbLlaBpDxvobInLxa+xtXFdO/rXdq2LnjbRH1USbUa5yufFfESTplm6jVnhq0o8EOlj7oCHndvJ8GUjRW6MhmucSBHTtsj+coshuavohojeLRewjtuGYyrNsXdC8PHrV8i29MvW9GBu9sOiLSKM4Z1Pe5DCCyNecqJPnE81MKNxqpIuODGypqtMqD8Prv8310I4/lIBZynYX8ZDmfO037Ze720LGMHybd1ZHHTj7jeffFLi6dRkjY9ysIR+5bGSV9O47bGwcXbOW+oPQ3+oKHgusqt2HT+5TfRMROTNAKN+HMlGyIAnYvuUkNzbMIXU4dyfEuZG1w4kDyX5GFPLPxw0ZbsLXcabojztKi2iR5DkqBp3hD7RoCP1nVzIv2VL/bn92yCj8HqvvL1P1FMnX3a/+bY8Vk/K7C0qHsr9/9i79zif67x//PdPTMYh2pkY6xCjphARcsi0YwmRQxSWHYuNIopizUZRKu24EDUW0dLFshFyiLAs2ygKEaFm1yGHddiZq5FjQ5/fH6+ZtPu9rmt3r+919eva7zxut89tZj6H9+f9+czr+X49D4/n4xnqJGeFCnX/8MpzczGYD+bUogjdUqkcbRjaWDfRZf4KWuQ7Sw3ovGQVI9l9tDaXTvhLGy7E/37E+NrBvcBMfbV/a51ab/3RtVv/JDk9DNar0vmUqoIdaI9adMgQAu5U3h6e4qOtYWUkD0QKlSI7Hc8sQxwZnz3ASjoPW2WJTvKGYy8HJfq8TCkNbPWix2Q2aZkfoG5xxTb+I3btv/dZCnQLr8m/r4TkgWstLdJB9WOHXOMLZSefIYVoz8CA15vFm9tQn9In8ry4ZoQu169gF6d3xOjZ6RVbcEvj/Vp+yKZoG0eGEDuaeyKvBnufz4FjOEvF5BzKBRt6rMJYayIdOb5M8A0L95zvLoqiOnc9ruuu1wITaB2Zs+pxlrzK4Vk1BDm0eNkSkDT3iJbPYw57uoaQ8WyXqyQNOeJD9ZhM8gZOJ9E84T2d4le50J3THWJMzejFGKrmHjF9whBb1XdZUZUdtvuWG2zRyIIBvexucjv347cFsggF+Pc6Mv4aBXtHgV+VHWRDmlGq7ilFXXan30vqtlOTqevDU7IEksomEuOPO90dI4iZzGVF3NJvvy19iMwRWsNTBP3qTQIjeB2Rc1F/3FdLpGcYYqUk5SOv2JI/lCoFaePD7wfl2+KNR/2l7nMh/udQwDatR7PeBpyfaJjxmsYxYCjpoxkfqauyw1KKrlL/BCqw7Np4sy8yZ8mDuvRbEYbZLQ+SgatyBfZ4a9Y3aqLZaizjo47EFeGFRkN0HBmGOD2WOdbHy6q5sAwtSO6w3fo5TdzfZY6fh6qdN4rcL6tWJe+6w0ptjZjwYtCS3UAYGFAwfK8g8fTNLr48/35RjyvxSEXBlzpB9RieI7nOWtf4wh/d4LWmXU30OK1oP36B3xX5oa++d5XhYzOCFNxZalbnltv3B9mE9vmn05/jKWWozfxlHZlNVmSv81g0ndcLznIm/erm71bPU3pWnhPDaN92gUhMVOSHUe6ay5l0hb7Xt4mqVE3zQMcM1w76k50NknSoTPLwUGia2pWpCykfjfP4JFIOvG9LWjOtrQ5M085hoOLarTSqy1O9n9ByNXlDmL5ySFgjg2hfeYHE3oz7cJAP6tZS6US43wKudjEMpR+LjTw5ZoLufqNp3DrrGzXRr9gMw8uMVebIWZ0stie3Jgdj2M2hpOq2t0y2ZlFH/Nq/L9X5TZLUeUEktrZA9usQzuMM69PbWX2itRV3dpFw+YRrfOFXXboHGba6xC7hmAraLlhk9oSH3Xb5Q6cPxBBHZBD7NlQhnnOHyZxP8gLBdFsxtuKzXqvc1Rvul1pLiJeKIpXnjg7VZqnA2I3l8cU4S6Px2Bws3zJcIFonIrfd1ZL3rqV/MzwqRGdxrnRf/ddQmLT9LuF+UgctChWPfby8L02jEaGyVnFhDtfzu8gFOfhddhPHB5cxufKDLCSt7tPBWUniX7N/EvRo7yZcteP8/RfXS6jBvbzrDncXe9u77nCNL1xWVPKO7fp1nivSQNA5iWXp5lbsCxMFT0BtjlxkbM9nRf8cCed1hMIqdSH+frT34DuTZc8hfb0repwjuDWDmru4NVrDqpOUiawJRbo0RiQ9xTR+enI+gwM7vKW17rLOg6/OseCPPwnOjL3+8arX6fC6BpRpcNyFWjzSIJ31GI6JHEosq9If/yzu2AUTGwwQqcDaD5LVK0LWQjzPxHE8OolDGWX1HsXWkvX0M0OhVlohAmJQJSSgFvPSyDBCsh7SMti+uYZbk8KU42a4Plpe1vOVLNvIxOjTdBb0ZI8xaP6rQeNqnjC0aCzzR3XUwzwuB0mCW+0iM8KFDQL7vMCh+s/wTWmEHF9rfF6g3g2bDEsYr6oDbl+/24jeT9meWMPx1WVCYJGLsyS1p8QIxOYHyAs5PTtGMRdd2p8fRGwJ530umhSmGu9n8/46VMoVQo3C1rx/PsTjNHOjirosujMiejkiI/b7PkrDfNYuIXkUbfayMaVhWPNtyUypp3L6p9o8tcGt09gaHWJixgDDpj2r0iiheNCaQf1eDR0PJ3jUSz4tU832TjWkHl6ksc1+4jWrXu8cFuHnBRqEf82Q+lsouJ4XzBMozRC6ed3NPmUfnaevcnxwGR8nVRMZQVLPIxwILbByhMTTWXI+i7VvcBWlp+WZc+zBsOrbYhOdJ6+yF2qROAMlyZtOYlt2jk8KGonzqRstFUgB5fnHBmQU4ttHcdxK9ftMWPuw1/v3dmI5q5YTE9nupbHE3E5ap1C8XhfZ79rIbh/hQE+WjWT27LDqKly4Ssn0r2ROZl8k14Gt5HXkwwsN6UKkLrHzKV03z4Dk15jNrjI1vDa0q2t9bs7hB33fMR0s89qvB/CmID1ljxDtFug7/712UVDIKOpruyqFzylS9LKqDlqprX8x3LvLWnhm4HBqMXVZL/qGocule1N/8zteGDXE6LnjZM7M5/rGC+aWlf97LvdYac8WojMidGLuSmJaYVlICTQaHyw7eRqzh4Vo6dFnqPrMyfz/wxf/wGcrxP8dEnArP+ewylLHLdIpe54Xxg+Rlh5WToPICosvs79ceeO6D9IyhUebc6hTWZJY1l8Ie3fTYTPGsL16DXde+15I7Ezm1reIn8AToyfRHX0YYaxbkvfbVbKWtYnJNKD5uPe8MbOnOWse1O3i67Zq4AkvePfyHbZcbhQWzhv4/HVXOhf+mm3+twp830xWbRSYuufZ9xFvhM7Zefse8NDF6e70e++4U+TLqBv9QcfJa0TOYh2vde/KKCbvfdDRD+LC/rZJYNZepHSpXAurt/OlYjaMpFsGjRaEwuejk3h8PFPXI5sfZzOjH5ZxNlreishFJs3m84mu+IiF+FZxJMwZ+jzp++rMzZK3i53pSXoLhbuq2BzJIYsXEoeYnP5gSLJWYOf+JB0t06wMRgUyk0vEVEdJ1ndvwjSW9+jKZoZXzJAtPmjCJmILyc22OzzhprCsU/l4VDXPedK7x1povvI9v855wCVF1Kiyy/FfV3PhN3FhptIw/GEZv13G/S/5+9dOnhD5vI0NZJzgrmx+zlf9S4KWRdY6p7g33G97qxp6d/ilx1LH+ty1Vg65L3QlzcpT+rM8pvH2Mylujj/Efk6fKaNeyTBP5oXVQzxSId2vjnZ3UFXzrn+AERzoIwwb68CTYyfI6RDLBS7MwjQO9OP0kBhT5waJro/mojYbbuN80TztLVNl6r7ggxVt5D+eE/L3ozBp+51BuSDWPJT5Qzt+TUGfMZafxC8gl0i1aGAcofn695TfnWvwTa94rtZQ6SOfJpt9w6uYEj/QLrdyHKUi+a/4e7P7+Vqfn/Nhbl2/z2nlmAoqnz2ibIczcurGirSOikyMmt+oIztoe+0aSoYNNQEWUnMvGgSR5rUVktmcJ7hDhe0UhfhbKI7aOlliU37bzsdtq2lrpW4NZjvdP8bc2tRrtlebwfnOei2MoIhLYZhMa3QX2lt/drsiLhn6wHPEXsxP2v7H0xn/fZxHEkNu1SZlsb7FZorN5eWTaVSnSOUznpr3hCrHTom2uYpNPH77VGt3Jfu1HjZcJul5lvULVmAJ55WQN5n3NbJ+SzuFbI5CBBRFVa5jxbTmKmLkQzRKxViKRvbSl6ljKX4pRrXWxyXNPqLDDNJXPi2zj9DWtyNMej13jFMppTy0d5JIk6gefd6U4IRx1Qd5N/uOoD3+dsH7nnaluPa3nIsCttQ1SKBuJX4e1cdsX7hGtuvIZoe6irkoISeXXOZ/2JHNPLdsaJD8OUG8P9uyhe1F8yzTwdRrg8WlT2bqPhJjs6jAQ0MnaXLoA6H1sLAI+M+DosJaqs2TbVSKlhb99CqjO4+TORIXSa3FrZ1YtTCsjbVjyKxeT8rc90kn2oCKke0+jr3J4eciJPJE8iSPJ0z1mBdZwCOJ6Szm9LSg/+8s1Ucecsv0/eot2+uDyrXsP3qD7MvXhcTU1jxB+7WAZfv3ooBle17Yz5KoRKnnToXWVWnWN29CLuWH5KqZsx+MmzOIRsx9q19w9GejP3HVLqhe+xDHyKwYAu5lo5HNsiG0nEXkmqiP+jF3CTH10TzorOf0jbVzcJJfelhmv5Ycz/Sf67gX4v9fFBcYd3cxl8c3TqUFCeu4O45h0TUebSr8+3KvvCIpIwTvn0RTdFhA7xnUH0jJal/ZPqqG5CSarSMxNUyETznwfmiXzsKU/Lcuhmnheny1i8Ya6YbKuxV12f7XbyH1QNBmlikwbAuKdn8Pw/avEeNrP2wznvtI7qDyfp/TyoBNrynikr4dXjZ69jjSeHjb7KBnmIWRbLvtTk9Um8RMkpfScgYrUpvL6kq0A/YydzTXRzaqOZ65E9A9aNtmLqjHPWyM9rpyOp+FT9I7hcisKE+fFrQXT/8XPlsh/msoypMJhrZ+TidL9Bz+iqWRxq7zZyfSaDOCxKXhv7E6ctzw2Rn2byhPSaqsOUUFml2KIZbNw+tQkuzNfC+y15zPu4dtZqswsLuFkNwdgdWUvecMRfN9EY0o8KOqMbVVL2OKjbJFQwdUlXOwgpxpFYOu/oWC6+k394d/pMAX4y+7Ms4LpZhNvMGITi+6UJmXij2q2tbjtmpgQ69GXhw9gg68XTnF0XVxeh1ewBQG93lFxSE5xLJhChuqoQElhtMkskKvhQsUlS//UDucwaoh4e26FuGDz2qJrKbfeCK3RN0Q+b2/LMoUxvDfHgrY51X5DY1ytzM4FOZixlGnYpasaD3N5lEhmuS+Crw+hSf6TDJ48iuhSHuMLxVzcF8Nr+cKSdgUnCRt89Mco3nF90S7ozdv702hOj8otTEUAzZ3pYNAuDiJs2zvXsNAU9S1Q0aFB5xuHePRuHSzz/axt3294Ls8mX8zV0j0/6PJ/l3y+atC70Nm/jGO8OY5yvP+6yke3jnbqgmd9fOKn5jjxdkjtByUaeGkdh5aMMnOvknBj9rESx4V6cmdKWuUn56rxOawdz7ReZJ5l3/spzfNN6joOGsP4xiJ6WxZwqqbwnDPuBoXnMooJXYy0kNhvPSYPEUxdxy3phNf44hm1cOuMbxnRpCoOC5IY4kT4qX/ug0VJm2/SzgYdGZuiiz1VN8nGBGW+K9zMBJFqV8uXNazW5FVmxNZPLlvAgNZn9EEoeX1Zp8EnahY/r4EVcEFOZ++XYoKZf7ks7iybvQHt5d8nxzihl3w4EOTRZ+I6D5/qSM9iemL5lSqQIdWSOFcA0wI045b3pZpUbSjMNqzEIX4e7BYf9N0aE7a89yycL8bcg9pH+lte9E8nUuiC0qGQsFTbZ+QdxfPLnshMAsv4BKjd4/z5ZMRu9xqwrYnORMqZVdae/49XcJ/D/kabNWDhMm/5DwVWr1zON6hjM0JjT1b+wVOhiTZ211SXNhAy4RMh1V2X3PsDVp0vTOQzc3xh8TMCBuJYVwRXy/E/7uIEdZaaX50Qbqfa4SPprNhLkYEjviytPCs14vmhQ1hvjCE4yLJqfmHusiJ/KD+RY+ZPnuIId1eEB0VUdsu8/zYM/Gj7U6/nd/kCUf+R4LTAiZhAlIoStkbDrvT7y3XXq8lCwpma7ql//7QnTGE7seW2jynjieHTLCtH+tHNVFnS5Yq0TKa1WJs12dVdKVRsFdJSncnewyvbBlM1U3551mQ6CvE/24UrKPALlz0bFuH199EC/LWUzMaSw0+3lWN3bQZT4fh4RXJQ7bL7InOgYG3DwcvhpWsL4cyyzr72VVBLqor91sESu/NY7Ig1XERxdjYoaEuFoorny3njYpM2iMUB745bOUfZdxVFNr74jhyWs+ScyQ4Yfzhp3ziJseHl+ESkf44EOQZDCH6L5HwIVIwidr737dlN1qRPAcVgtVtGR0YNnlDUDcqT4irXl/PqmHBF4zbekGduVkhgPsNhUyp7zryh878KMaA+hNDUnU+25vXMCu7u7l+7FeZ3Uln4noef55+SdgbeHq3RTaCmn23BUmFIlQputeeLHKax5JM1kqOJsaZm3Qfadzx4bowu6IxF0ZQZcIpNe2x+Ox91rrL/d4IftN1iUIgfdSVFvCCZNN/BbWREFpG7r+Vpy+InGRc00He1MnMtEfCvjaKZ+sPYw0HbudXlbs79GFZJnF0Qxy7A+upXYv1kiZxW/Z7pATbqDkKHYIVbxlNj0mvOinBjAkMmPuaRcOCjuPUsTz+KZE7oxxMF/SC/iNt90L896EgeV8dSWo9+4Hx/Z9yk0/cG3lQdHM1/a6f62i0BteH6/SA/QwYz4w+VOt8PPyrplE/9R2/K/JDNtK4x072Er+GxHn8dNn8QOaYKdhUusDGnubrAUULN7RzXokw/HGWUAVJDBqxw4y3J7em7T9LDpf0QeeERNIJYf1/k2X7j6Kor/0+8GN+PoAjFyQvWSux5B+96DHdGsz2E//qDfeFVuxd/EY3FXvkhK4Mws9W7OlJs0nh7F6r3NWFoRyM1nO6J8mDSV6ynU8pEq2mTQUylxA/O8ylUVu4NUC7pHA+gW5SiG8V1wiLsCG/pWWZVXQPycSpY4V1G9nu4+7VTNffiWN0G8WM2cweQvrMp60dwu3rd8uuFQQH1t4mkJxKUtOeICcyNUjLTG3Vy92dNzKBEuU4vTVGI1soRs68WPvSqzjVqpR6G/f6od/ZoqFzivtZkX+RsXG4M/eWDfMxLh3hzxuEgnfB5yjA3+uzf9PX+kLwbPbm377gjSPh3OviWv5VL3V9GD5bClUddKd31BmUFex9M5MNpjfvHGvFW0QiUQllsJvsE5VU/vRT8etouZTXh4W3ajQtFEuLwlnKjj3DGj66jS0rMYd+D4VLibPMKlqJZJJmIZmdExrz9mlX/K5/pCvl/0Rh0va7hGl0tFT9cjw79wUqhMmY57HoJKl1ZnjqxBNaPs8fLn/j356CAxRxWfUlh3TZvUIJ57j3Qr6/8U1h5/8IMd/4Gcef+YWfq5J1Sr/5c+0a1jAc6zOu8QVpqJB/1ETh4p7OsjUcf6aMEuV46TAXI0tJpfPsVfJn2haiEH8HKinisnNbcABDOHhtcKGblqFEI7YNwi6qRMu4xhdi/lVoszuLJVxYhzRWl2keWj0yhQL2Ef5+plGBXXxB9frUZaApoSVpKz6j/LHcYG/xZDfgi7PcXXGjl0oOYjXrJreXt43iGdls5vjAMqxjeQ6vdenqUORLMmcoZA4W4msUTVKr4i7vHGul0mJuncdX0SZUptsIfhFdp/dDISht/dab2q5eJGsNj3UaGwZvxKIYcSUpUTn/mr2DR7xkf2J55RfmqupAmHyMYBjfLGD8vbaRL65fKUJjTt1+vcMqO7z1pmAfHwq2d5HoViJ7onIqxPqtu1hG/aE0v/49jzRKd0I54nlpIfdNotuHPPohJTtFLZpNfDrRyhEym7niBBa2rf7vxjcTthVpl2CLRtKaP81lYkryeuSCl2pzy5b9Mj+tJzvtystOTA4yCavG8m6ZhtqU49YFYWVO/GyAKj1OebHYY4HtMItr/RtlMIGdGUkiA6LUIpIUNd1DnvOknNkVg06ho/m3gj3gH1lrxfNv+az5BjHMLK2Ec2bqy15q2qvE5XNOZZRyakEpPuOXJ4c6tKGsc1vRnezuOM2ulQ01Wk37lAXh8BdoNIsaxfi3aEMxg4luv0r9SeFdu6XTpm2QYjCM091jrHiiC2dmK5RG+C6jwB4O8gZTuzxOWyov/lT1Unv9dPd8VRqf8tPZ8/M1yHGSGz7dDQbkF/VO9wzX+rM1rrL0s1bij3FVtIr4t86zNd/aGucEzcPZvDu7hXFzBsl5PlbsbJSizuEsvyw5wCBTvOgxdXpt5mn+spBRsGf8PftFQZGtIDlVNfydirt5cuEIX5Yq7nRSjLRFL+ttlrwRQhJqR5CJ04DSReiZO1+VPqf4ExV353AxWOpH6zkwhA/jm5BEcjrLxuAwj6YzNfpL83o+4HRkVagnjuK+DG6M1jHgeSKvRHkuy5VBaYX4n0VxIWXeiXb3SY6eMNlgq6azPbLdH6AYmz+rIyuy16r+pIx8n2Sczp8R0VZgzXZm2/Q7dTy8JhhBksCo3Y+JiCfyu/OBGDuGEXOeohhHK8SRzNvLUnRZskKtox9rYKvjo8rY2KWhEYlPqfvWp8pmnfHlhWKMzxbe4Kgrw4UK4ut/tIhcUPCIE9qOyuX/HU8qzSv+VidL/NAGJw9UsfJsW0027vDymDQmYTKVHQ7MxiLCpb0vq+4JKSIb6daJXvMXKHaR5MnblR4hJLr6B23fY5H9th+toWY01vbUGt7Y19OhWmVFEqLB3ldsExjn+xTq2H7b+EJYa9OYtsHGyA/cUWGdiWn5HaaJJI/nVGS/X84fKmEB6gdibB7k0rI5JnDhUpzvRZOUxpaKnOjKHZEFtp1EUY62jTOgw2uiGzGIjfsberfIHapPOUQ54sZccLWLyo4+QxmWae82OxyUaKW23HghhBHOCfZQ4KPHuDLknv8a2SLPFUmdhPz7KobiyT7qPLDZMRWcVyJkT6/Pl1eZuyjkertgMkvcG4awdmbpslbUOicmXei6up19F6tzkezO+eWTWuGrjwyl31HydgnSc3HUjuN4tJVH9qfbOS3JXmSOCf+tqTOFrqj+RJMjSp25yJBbie2GW/3fDPErTNp+Z3CJZrTwW4qFATMusGof/Vpx32e8EHnQs2NfCAyRv8ZkUia/b2Onhuznal+yNZZr+fsWSMGFOLRnxA7LUdy5wEI54Iqfv4PxY54ytWkvepI0FImMSxrkXH/ORjsqPzaXoTw6nB+X4aP8ikV+LaIQhfgbuJHdjfzeDyw4G4TWleRn0XkGlCEmCVPIjTZxYX5ICv0sPiMIlfdk4wcNRctxfclD1KXdzPU65axSpn9+Rd4q/1i1qyiuCT7LvsBe2l+5vLOdr3K6RYz9Fcqbqa/I9Kj41iQsJusYCU46l4ws8i5x/nA891D+plzzy3XU4WjYWFx3q0LdtEIEFEVFLuXpY5bFFdrI7Iz5NJ/7HmfZ+HxD7/Zp4drJf1I3Wsrq2vdaWe0+SZlMjB8R1ngHbGT9meac5YkOk0RHUW3JcdWWHCeRz33POneFFr+/0D3/e9ZhgQNVm+vakMoTL48y6YOHtH9tHdtwko3TGobq9vMci4vT8MBG1+Re8ORNE2QeQFPWHublTWmu8YXjG8qEw2dxunaMSJOoXnOmOo+sYeyvUD6/5WqXQrbgPxOK40aaMeriGOlTng7teOUYcIJH4oiUiqppj/hBiONctJVrSqIRTYuRG3nfhpNkdqVZBo/XnkppnlwzQeMeO+Xtos64LDPKpZJGnc5Zol9EDOv9LMcD62SDZsFR/7zgnAqChYL21b+FgqRUQSBeD5W4n2oPfOx13cDEVgOkbHnfgSJVXXP2jIrZRwPrNzcMVioxWJhaPIq8ND66BxNYvq+r9alNgtZcHAcuJEnp/L7T6bjIniEMaEBWWjib2+/ZTXt+UuS1/ER0Ib7buORrduelE2ylWYVVDne9yW/OdPd8bV7P1/n2WT73rRHzIrXMnYJWNBsf2OYDkl9Tcv5XXvIIY6i+45DoqxF54yleBLUpt+mLwKBtyvBhGeKWXNB34MvO9r5Kz8qvmKWPVVM6W9Olo52REgz6SHjXguTyP6rvXNzX7b4aMiRJ3OyjZLLI/WK2UXpBnjn33e9z12pc5p3QRXKRlo0zKUN8Sr5EwkpkcKhWWS88M0TyZm49GmQ8f53j6wnoHQZjI+OGD9I78rDoZDoXI20gyrB+YBMXFQtJqvGEqVJfKGTYfhu4JOhW7mNFVDevaz79PSkleTQl/I8+uo3GaTvlCGHkR2PRndfHCCzZmegvMGMzmFv5Pspx6plSHtmcTizbtpJ3D9F/Ky72Nvb0oV/kWSZScXQOJbmoGGlEzxUXL1v5sbmu8YWxK58VvS2ib9LLvvq8pBBP1xQyRgVxRIFU1F/LEP49e0acwIHsTOP76N+PaZSqesr6pe0Ud868JQ/YnljDFzPLieZGgob7TAzKL2b0F+Qc9mErbRqFszy6OI66nO5DZBrZQ5kxmm190DtYZZFoE1+6WlzCBecUF9kSVefiR/wCuzcIFnVQYXzybaNAeqw01QewtZnU6K9VdtjjKaEDVQ420Wwg4rmpyw6n74mRkJ5f0MgRSBx9qbgkR519WRoNDKs2YRRJb4WVl9eDiltyHFhOpA+KUtUBDW3hMza3reORUen2qMkuttetYdPZOx1Q1UFVHTtRQa2Ku0LxoHwJSiWhvivFueLf+Ex/q7v1mwTCgjij4HX5j1VP4MkIs6OG3vycnasb26W2ilk5tifW4AR1fWh/avkwAHkyBgVJQo2RFmTbol1KyupP3iA8Q8n5X5FLfM985ZAs4bryGUajikAk201kBx2XrHGbHepMyVIbFaLlpQ0N9MQZI/k82pzJnL5YLnw39xI6Cv7rsUth0vY7g/OU5y7r6BMukdmdabMZZcipHKvSQGEy8FAaDf3G9lBMGITUmZQx7yO/okC+3/H3tn/nVwybJbm1zC7XOGNyrQfDIu8u2M46LAkO4UeHycvfOFZrrcQCut+z1LgRg0J1PJZlufl1kUHyB2D8V9uoCvH/BmKQLP2WR1Scm+NG+eu8Ka2tFvO4cBGdQ/N974ndzU3X7hdJE9iFs0gZ8r5fxA1x8liVsA5z6RC3QOVih4NutIaurPe/haLCxlMj/PlnLrradZez3VPsLfCpm714YIQjN8eH6nddkmbQa9MCJeZgEyWGogMH+rMoi+6bltI6bByhjaQwAVUIwpqsZ2g03eO3TVU7ssoubFnO1J4c6UFKn/edmlXK0mIdlbh4TtauSqFtsGlDm3IEncOBRPpH/eDyO+Gwf8pvwd6IbOo2eE9tH+ltllrRD9DIXzpG/9l1+ptJrBoMIuWFt7X1ltp2Gd7rGVkPVeJ5GlzcqvGOnTTje6VyrI1tJubklSOt7UzLxcxN5lTkuPJbcj3alyNTKN09T3RDRL3Iw0FRuikpfs+Gbb7WXSjEPwnOE1tTraEfgNmDmLiVl3agO6uzU0SnRiyKXPDSZLYMo+OaNUo8j1z+cKGGDk1ptpjkvkgha1cldae9RzFem9fVL8s8yGL6TZ4bWEkTeLtBivaWE8tTt4/36tJB+cHqESFxU9D98E0f6j9L4BYwbBME171E2HNqsX/jLZbq6BEvhUGxWdyevVXsbN6KvyewQXK5xhmnx8SwngOjianM1dEqnGRRDY5F3rNsBw93mKDO7CwfLaF0BmmpT6s5ii1bSVrAa2919cFbtcwYmmrpE905s8yV6nthQuq7iQImUTfuTTDowDgbDrTx/EJ+OnN+YFH3ZtFh7t87x6LoAnN7BO5r6izWNkMytz6D3vTo/ap1nds7MQU5fLC4lpgc4ruik5AAmozpHB9fxuZOdQwz3vfO/Jvr/NlFV4fk0AqCD1QQdXzh/xy49J99JoIfFRc+37XNmFlCnRc3u7/IG15+sa/HvGhn8yQfdK+lpj3ajVxv2447PVd9KFNdkQG6jc0N6mh7YpHtu2qoMuWU+yOTRNtyouKVM5o9hLkHAhv/xBi6RDI0q8Uv4oZwNsaBKcz4MFXzZu/53LVhoJS5AjWrEN8O8oQ1dQ3tIu7wLpMoUZf1G5qI9Im6tTfnpjBgVHjmJmRNoFuq4OsU5cQgPl5XjbN0u3aRtXM5qKr7LfL91D+KE6SiXqvV1dRPeymOxIECSzeVC70DGcNgfpXUXcrC90mh3pi95rftKBrLGxfvF3PdafrHCBYXEfyQEgJDtkA+oHT4PP9pAvebe0gNnGNQicA670/zh1YoXvK8JzqOskdNOzslqeCYyOYoW7iQiAlkdaqkgW2Kf5jNTO48uiZ8JyNIjJaVGckxdzSld6EB8dPC1aV+K7aMo+YJmo99T+P1OylKco/tWvVaKjf2EL+Zq3B2wP9fKPDBS6M9d1Oq+ilz9j1oZKR38BUSWdSf0yvRhRmteTNSV+kRebalsfREKxbyeh8yOj1gaqdeVtUgOp9mQ7GO061jJCSx/PM2ZjRKlVhZIFV3YaW24mZfYCVN3trh5ZVpWueu5xL1eu71RclSkjdt18gWzySMtlAXHWfND/5OhnzJv0b5t4au6PsXfL5vJmf91e8Fn79AOiX/GqF4YKyuiKr27Mfuu+HXLitiQOuJYbjySSGxfDro+FabcDyw7Xuz760qgSR1BrsYPXMce0kaHK4Nh/qW9VTvJ8Lm0Z1KC4ScwiYcxl5iirHpLNuykMXOTkl+On2+PYNomUq1isdpFVjQMSgVWU86H8bVUKXOvvytsBJfZzb+cbZtYdL2u4QzpE5e5HQ6abM4dalKSMgmcS5ygd7cuWGNQ8PL8hmJmSRMQjprGyTLqlyJnkzu8KBuXpfccW3QtJLgPxcR/+biqUrVsIEt1159W7Vovlz7tgusyGzOLnp++Arp4YgbctEgtIBbgjiGz82QnY7EUPhL2MXEygM4nvU/9c0V4p8GxQUHCJND8eLROTjLgJ6vhZbrhewZK2hSzQ/M20iVKNM41xmVeaLrpFB9vsRzQ4caZnxoBTz+773nfxaAF1Q7E6hOraEf2OVWXYos9As/V3pznhv8kTQqdsixNKkVRXmm73AfN60Wqnx1Q9VfFxJrcV8nLtTlxO58R7FgGkAhCqEoLoXgJSV0MgxoG7qOBgzNv4o35ZziUjqEAl1SiyPMIaXx+5IH8qvV3R1JIDoqovS6PMaiFmkLnvbMpOE2961jqQ6GGS/bdXavvt0VXbYCbba/5Uxcg4bUiqconSzRKHe75dq7xheSthxhDCXTvxL5WdRDn05SIokfXVhEUSp/+qlrokla7scgUmfQaDhTG5M9i0pzMIrMRvU8eiJ0OhrFkQlJAsu2kPXxz4PiuIsn+Rc/827sV3rP4vHNPJrBiHVPuXv6RhkZD/giOsCj3cO+sLa1MFSyNvVu2xsk8ycQXYwdJJ084l/8TPuUBXrvfN3gzq+wmMqDP2U0CxPbuXvrRkVcsvyeFsp/sD/sGW9zRV+8IOH0DSmQr/HXgUfBkImCpO2tFE3gOYbc84INKY184RpxWy74wjUksjm+MQsDi/Dann8iKwQdpZPynOgfOGgHdlPOCQd2XEmT5eCXC4c6NyjwAhf1I/3w06KDadSe9K70um2By4r4N9eGQU+FhcHvOPK7LPQO3QT9eXl0Wmj9hvW0j1aiZFgXb1zf0y/8XOoCuhXjeO8yikSbBIbpbLRgXtoD2i5eJOEt9jWv4vZhu4N8VFNat33Tx22rBSLGMf7gBo0P7/SOO42KH2PSlCdkta8T5KSq4sYYYV3n+Mvrb8G+8dd/f/P+S75ObN3bLAT21/GM0f7l8s+86w4Pfn+OYr5Uzgn1Ou9lDfvrltfM74KLVFQgj3Si8bKdRnjeuchePiNpNZFZJAwltXuwxN5taVOEo9EaEoYHHcis3YyY8KI/F4mXOJ5+4+ZavKGNexNX8/YqhcXAbxsF/vUXbGWmvjL2PuDsuqs0H/ueVvWXspkStVg1JujY5gm9craS1QdJ4e9bmu1nHYtzaXmU2yfv9o47/WnHDf4geA29blpgwMbXJH4qsFVXsjCpndjufOlqxwcGqTXlBDvpwzt+4KoPo3KfK69C/DGevECtW/PnxTQVuinihURMOV/HC3/xGb+ZhCrYS6oLyax6XHcfN1Jj4HbD6zxj/Sft3OwTnSzx8sY0CU4qPz1XtE+EHcQORQv+6Abl78l1/mQ8e4VZNktQjSr3nNJtHqm9mXqTwBaMDd/fuU1Eo3UsLdfKubFCkq0TjrF6/r0Cp7kg4fZNPdJCfDv45vX1ND9iWMnxnAmeyb0D5+nR/VWXsOEisuiXSc3xGEyNkhyLrBHNpla0mkeemOnhP87SZjWR7hyYwMLMdv61yE/oTuf1q/QbM5eRnPqsFJcYsOa1sPfUZs09d7KRmLFCR1B6fnJ0E6O3jvPksQmq7z7kDu8aO+cxbXotVu1fPg4JoLn4RU08LthLgZ+UzyL+Ws+5+F/9Hpd/qxhOQj10CNIoRS/Zv+4Wf3adT9zsmO8rvztXTtNYqWMWsYPq/Q6xjUN1y5JI9SGHtPBb3RrMDnW5auG7MphKg6my6ZRnN71gWw9hKFsudrFoEAvfaudUZimaBsuoP4+lzVup0zPL1P4sR9ZcXjqGaaTuCoNAm3VBMeon7nEoUoq5i4Qq6EH/VW3bwqTtreij8AABAABJREFUdwZxNGP74BpKfxbuqd7nEFNo8fxy66Jdieed+a084ReUIytZqAI0puWSTD8yX15cEGB+1ig7ztbNT1JV9B+zpwqCjvwWcEHUf83R1m72iebZv7NubHvL13R1XgkXUpgz8kFHkqnfl5YPIZdDg8sGh6oP5pB1qY6jvUPlcW1tfp79C4Wi/oX42ziPiMdyM2zYGoITRdCBR+akh3aFRGp2wiWWTeajrUS/ijjSgkuXsAUHeHjZBDvnJHlq3XirtTbrcp/899jryubwHzEKC+yiIFA/TbugGz3QFKt33Kt+7k77m5aXLV7eGqTQsd8aL1Qe4gvXeNcd1vZNtn1WDWXHnAnLfyY1F28T2y8EGK9WHiS4k4UoRAwq8kaSztNX0YgPxtdiRlilcyeEgRqn+pZSpcYpM5alKjnqK+e2sL9yeYrSO+OX7r/8hkrdyd7I0lat/Cq1O9mkT3ja6CnjlHPC5661TQOz9Fa+9X5hYMBfV77/o2C8QJMwIWij3c1dfitmNy8uGWHY2QlBRy6R46PK0JjpzYYwgce8yEwOL7lJnSVZdGbuMSb2HWDLuPAuv74s2HkfkkdvZ05o+Y3ERxm2TNCSK8Q/D2Jw0lV9zzqnhPrRWOl9yG7K84MY2/hZMGj3q671uehqurUPeaRfzegeBhX1xTHydnM0BxnsSaDluEzLb+tqb52qHls8likcTruJJGrbRU5IFFTwp1DUqyV/UGVBC18By6Oqv2wL/2ZSqiDAyE/Uasl1jRgWw8ELajTarrZdirnotssfUoYqK09RLj/ZnF/Lzo1NMLn7g1IS3jd/f0dZ+WcQhEBKeF+Y15Q6kN4PkdePEinhne+riwVsj0cfumLZDoo7J236y2z4SCHL9ruMoqhKcqqk6E5vPttadHTER2OYfSB/1R0jadARE6eEv6ceDl1GUig9h/ILczXv/B59yTqAzZweG2PllvucbXGV6kMOOTIBc3CS1SPvdcuS/ZQhmkHyxu3GVR5kpOfDgJq7KL98f2CeN8PdaFwa/VwJrPnLRNRfJ6aKCzbRMBzkupYMIXnhWv7ME15QenOekZ736Z8q+8I1qrQ+FZhk40PCLHnHdqYI92WiHKe7IrJdcnPU5fXWvk5GKxc0303iuj9+aYCpDMVibvptVFTE637k3qHzjBs+yH13ruTgBl8PuCnEt4zzOGLdn+4w+dqhboi8quT1X5k4ktWT75WdJeguR5+gdkgjPloL6SRlYiW94ti5IYm++aWp27A7tEjbFK54bboInXDj+Dipmp3Nk2RNoOsny5lMy/6Zyt+e6w9uNDFlgIdaTTKs8rP+5eLPpLd4xLxn7/Wol8Iw4zPC/IwG8ZRKENZ2fdyHlsJecN6V/aEg8VmwV+QP4FODmSV4k0GDx8kwyJ1+r9bNH0jzC7fP3G19ShPl++eyjIxWD1CGFdOaU4G7O2+UvZqMpAec7hkTBga2Ct+N/cK8mw4M6IJlzO4ZdrQS02g8bqd9kTVKlEMSd2asEXkiSg6DoifZkOSKfnUhvn3ECB7AIdpxryVO3E6Htswr1UODyAO6NaDDUiGROoa5w5hakd+fSdE/ju053DJ2v/NPRkR/cBUXOJ5RRuJwuty0wqCerwaDypfLkc3nrtV28CLGMKzysw7NK6vVx++E6+7YGDszkpyqUErK2PdF6kedalBK1Qp7zaiV6k8qKOaitlb6iX9V/ub9kn68M8QJP+IKQSnBlaRs6W/cKgrM8/b5j6WgjZCprQma3Lfe01We9GSLEZ4x2kSPe3NjD0drxYnbekHGqAe8PT7FqRmlGEiVsadYj9V0n7DU6816B13bWSjDrxK7s5rs/NlQOXg7h7xhQSP9vrfoMneFc4pziUfb81IPfnj5d6Irw9mmJQWC8qOd2Le4SpCkSOHhBRNE6kQ5+DoWC0rTJ/zfDCMr7FX/ziCPN9jwQDOnE/ba5Mo/Z13v9oxBbU6NL2V+5Icei9Zye8/d1jZKVtVBP0scZ9vCO93QZbff+JFXIz9QK3rQ7rvKMu2Ev9Rn+yYKHK8EtGdQCb1enmpe9o9d63Mfxd9q2Ihn9TDPQFN0mbKCS/nU8Qkox/bVNUQje/0OPzrD7avft2tMw2BvOBbt6jYfel/u/+xXWIh/AlThYJ6Y6YGkOrKLMOSuJy+fTaNBqK6/FE338qY05uaHolOotEG4gq7BZJ4zUtz8C3K7Xu1Dt/nlpYe5ruB94oXL819p5fwFy7Ag8KjNtTWpzheuUdVB99adp7Zdnu33gmorjzt9gceHpnspPs0TzSexnEivKHc/r3v0++Y1eEDeRGL2smdxfdEMro/7lAlZCsX9CxFQFB0MuG8i13M26yqfRHa7fQado5WcjhxhOmXXn6ExMZG5TiAhhWrTj1u7iQGRhxUvg38l/hk61l4THPiijBs6yB41zR72sKPjv/S+Rn7gHcV8aYFYV9b9aVcSO5e+cW5csYmq1K1EVRrW3+iWw/sDk2MfsYsxiLrd37PjZBPRP0Yc2EjiOq5u/qUTY0moy6Id4eipS1FtKktpdBm5RK4JwbULKMavRnQnUpiw/edCwZrKQ5yvflNS542rOBvWRXwaPcaSubme7ZHtuuGnvecHv2Msn0Sbhy6HWpjNspXB7W+2GXOomcEHDWq5/cJuK7X1YtYIkZ9+JXrNVYyi+pRDjg6M8y+GGyTDH9yQX+Q+l39eFb/xs6BQsUuwj9Pf+BznBf+pHLG3hjX7JDUGb/dvrtXDPPVtVcGflJ6Ux2X2Da/ioKruLroxxCYziW66KshIFaV7s6U8hA4suofMSI5u1cPnsgOxxLQgp0us+MgFp/eyeFg4q/pjA1l4QAaR1Z/SP1vg4xYybb+bKIqmtEs2YPlEfczyVWS354VV93iGIPO0FSk8fpGXZjJgMTM6068x2w7wfrSXel1fc2RJuEof7EHLiXnMo+TWr+Q9Q6UGwhrqgJLU7vS+tVo6rLLbL+5W0x7X+bMpHpZ881qZS1sq9aNTzhwvG8hBmwvW/q1CteGvOzJCp0hAVVcKHqeZGc8lXknpqYXfKvHAee+40/GmZdwyZb/5Azsq6rKay/YomfsVE7gj5V22MHV8Lw9/Mlv6wEcM75GhdAWa5rJoPZfWhyKO+didP0CnQwvtly0wxHgvnhyhb7mXzez/iOiBiMxhPJE0SbkOhyyt3Z3dmULCttA+vl0UJDBrcG2y5isftCWXNgswn7wl5I0OQ7fjl/HsmBesnc+L0UVWbryPs2xL5ky0oZS099XpkUXRkLd3PeNmDDJ8fQYXaJPJqaallN1xhh2U8oUK155yugjR8RE+47HVY1V2WLZ448c+5d4R89zvDSWzv3JdhWyTPOYL12h480bvZzbieGxY6rFCMaEgkftGApsTuHDElfi6YG0VFAFbBqLt3TzwQIarfelWu/zMOPdbpL3lEpxkE81z3hMdy2dxZQ3KetWMeamKuBx0rfty9HIoaDrJb5p3c/fJjaFIcRGtmbub1Mrh3fPkp2CLuTLIcBqR+VHRKZGQV5oWGP4ZY2or9Lf+/0Yg29U5tVmd9VkhFh7BgpU8Xpfofl7uyKMj0D10GWT1YXlkoyLRZC0rZnI9J0qWVaXWKTZRflCutM+elt766cCwHiOwSs9y/LMybnrqsOjBiOzNjL/tKRM/HCD6VoSVlF6fp05GlqmJvQxY9ppofITDHEyp4ePEatpbbpfafueHirjkRn/Uw689PGR22Br6RnhjAJ+fxnZ/yaaNhM83SEjyPp0UfLtSgm09F37vY5YWfusdP/C5a23RUPWkDBVyckSeiaqyfJ+Da2oEQuMlLOTCh8QW4VdDu7s49GoD0l6jM3mt+em4+U7sI6E3ZtKyE6uWsCqXDtU5cA/vo15qMVqjDMWX83qRbvpNmKvpENZ/2kT1yHsWLm6nedEVtl3m1aQJpj7yOMc3CHZfQFr8W5q+/zkKmbbfGXzB2we8rtvXyjgFON1CSIAuoOygM3ZH73L7kt0ONOYGf5DU84g3+/dwtEucf/EzW9VXL3paBceUve8zwSi+WRUvoKcX3J9/K1qCQTT1rk3xTXXdtFwRl9W1Q+3cveHldckaF1gej20eyxjq3bTXJfRuRGwfdm1qKGPUA46nlDFyAZ0uL/H+r1OEhVqYnCrEf4QYVFKmfDa9ua8R+xZUkX4TE7fwUn+mzg3h58t90hxvWuZrHuyWTXzUjNPX4zOmftDLT/3K291TvF6kmzGe8lyxJ8OAmRuTXWlh+utCxjdZhsWFzST+63a+/W/dIiEnV7brPDvzBStmNCeD0g3CpO7ns4eKDkIXotsi5kQ/DNX+0bycG87NSN6Ia+fj2Js4UlmhTRTi63VXNMZqrelOydpfqYqzPa+yWmvno/XCJbsk4undnYTqnN6McrTMd8xjOrGiQ3MXKgjB+dDwmmt8EYZWXKLixhz3elMzG0KySjd/2dJXwJj6a3ZtQet3Qxpw1aCz7vWmfZWrhFaqTuHhRo02qOqAzeXq8CGJq7GXlNbvOyGwAKHbeGFoWQs04KPO2Ez0sQiz2J5eg2X8dM18ruugcA/5Z0S+Zni7qIcXTwja/NCFD6NtJC/c7pLA+SmYJD81i0aCPIiNaB7qdUUJrJG+ZN3O7cN2y5sYhozJJfqvVzFECATiOKeEk8rZopGTEsIBGpcgthlF6wsteVXD+akk7BsFhb0Cm8hnEsbWD4H4JDQOU72Pb6vmWp87r4QiLlEGF6i+9ZC7q20MwVJTdKDmQ9tMzBgQbOh5Vk0X5lEJRMezO67invB5j/TEJuKmXNCmKaXrhnbwR2cglwGb6TnwlRDo2O7/LMIU4ruBa3AXbyaLvhrxy4pDfRzZrdFngS2NkE/ci97s6cq2mTy6GHN49PNsiw6Epw1Ifk2jukFh82sebF+hPbU7MTN5IXVIkADJxgR29WkoISfX7cN2m9zqQeeVUDVfGuQL16jUMUv9ktvCAJVLKF9aSNoWMKQKpt0XvGNBu2vt/NutSCK1vjoPbPb0Q2n6HZhrgGnKn8w1xijHVGAZ3RsvBSVnfhUSSpdJ6nmEMQzY+Jro9ojzStg3r4r7988RSeK+1fm7Qfeg+64Yn7rJouUkR7p6MW2EVQnM7PNISOrmkNydox3inJp+Pbs/EuyjkE347aLAz45Dcph3cjb/+l2D9YubSOtEzHgafcbx4WVsmB94aitH3heYbEOo/wEpNd4PyZTSmBA0Kh/5IF1Rl0ljxdDmFKNs/zMcYO3RZFVGn5KTS/FYYUDQUF6cP8LjK6d6yHT3j5jjzSk9tFyW6Wz8VXrmzrfK3Va4xwuesKPirWzlgUYZhtZ5Tpm+x8Psl4IkbnXEpgrrP0EQq21JrQ7h59uCbfammQ0GmuI+b/i5dOcUN3b9s26fuTt8zrZEcqmy+5SNSQ1d63M/3Tc/MAMrULGIIPUwgWlnHw7J3JPh+Ht28+M4pDP38Df4fZuYnd/hMbE10TKRUIOJJ+92rEaDNv8T//hC/F34Sxmmndc0FjkfFekbFdkX1XsgD304yWfZZcXg+bE83yfcklJ5vBMtd2eK7sJGqiw8ZfHqNtLSn6Yv6VlPm9q8F4dZu0XgMA2l/OxcXw6LWD+nifiN4b5Z+gS/ax7KMT+xoz5nX2MhLR5aHgbGZnHL1v3KD8rVcmWmsbOf9QPvqOCYATte4zdC4vXP2BplZmkmNSP1VgZV4t5IkHD6Bam9ZniyzggTljzsyWdHGJv2mNRnZxgafc6ELx52tS9VO3Bcr/kL3OFd/S9PZyORUXRd/ppDH1dXt9V7ohnkTeHcMb6MJ3sMP+0334CZr7GFPY3ZmxuGFCYk0mzWKtFdYXhfm960z8ZtJDYNfSKbIkeYTUbqA+pGa+k3dy4Xg85t8wnvSUKX0Stsukz9t/hlj6GiwyMejO7k7jZCEqGc/9v4pTBp+51B+Ee+/3yKc9E6YV5SPmZfFILaWFzilun7re0cHnvD/ezm3NwwBXOgDA9HfuyHNnjKGIkOUDXRlQTUXztXcb7WEL0WfwjtgrP0MbTpc652UWrnRWLmcqrP9aElZRbbc3lx4QhHbsNwGh3F5XB+JoeJluV75DKG0rvygrZJYdWuEH8T51UtdjBoFZaheu1DcqNPqR9tqOCy1xSPzRqr/PW5qiIpGqfRZm7tTdGimMKA6a9Z2r67fmYo56RnjLZc+xB8VCI4U99khXwTRQVHq6CwkcWkC8Z2fEydezaL7Oc3fqRb39nWucvxTmVkbyYust2TnSeITGBDZzaM5E6/Vy6S8ZeExRS63LZC6Z64O1bh4ItCBMSEZBJc5PinZRxFyaSv3O8Nyf22e2kJcyejKXPn8/o+So8SmBP5iC4j0UGxk4WW0qKsmNPcgE2v6eZ1cyfdR9GgSfWG+22/MzmfgV7A+C7+l+f09X5RDwNwF6kR2hGfkO1Gf1D54mGPlEsPRYnvs2V3M2/26aHx7J22763BJbIWV+IkZaJlv057jRs6iCwyZ9TzXIWhfnjpiMxp9Zw+EGPpZ63Uqx2KhXVbvceftyhsX/1nQUGhrDhqMCRG+g2PhvbTmXwa7e5o3Tidq60ysWtYeS0HM3UNUyeHpG7ZuWekzHw/rLnZ4YjJ1fGbMHE86VMUIWYwv/eDsLy7I5WN4xs62j3O5661R02HPqkuTbp6H2YGacILeWGv+FGE5+IZHwnJ3mbJQoRckKi6EeW4Pz4EJkO46kdnuRAGZw6p/4JBm151gz961x0e7jshJBh2MGN/qn2fVnHTtB0e2Z9uqgEeHzPVwox2XKLNOjI71NOtQeD+Xij5lakn2TCX0sVQi7mDUIztm2uo/NanMvvWoxMvNBpi7lP9yNwjZPzyFCZsv2sojnoMqm9Dx0ZOJDD1WPA6oiVJqhw89uxpwpqZf0V44KPOaMpHZeq4rxz1h6MC+z8s7+44Wi7If15/gQVYlMlDH3SbD+3slOThURMc31VG1qxKIrnozeDdr+iyZIWJHvcLT9hxsokjS5NsfOvusLb/IOwT19YU1lM9YV9IEhK0VfN/PkqDliQn0i6BIbw8p687vKu3WdYnNrEi915GsatzQ/H+zEnmbr7PW9p6ZuBw+gYbFU/eWSG2WM7oTeNUr3jIGyt7yt7K9lY1pNblke7pGi3lyD7iZftetImfRkuxhaaXYtjNx6urMZN986qotCWb/kcEOlbhnvLtI09IIR7FbPa9JDIxamr0ZfE1jmix7V1LF7fyWt+urKT89FzNPuBkdHhoBa+AppxODkc72/QqGnCiNlZys0893mIqbWl3bL0jt+MYBzrTsk8mcUyLDleig1BIKyPEqLvpbLE3TvYMSczaPF9spHfLNBS35IJfGqj57vfUWZMl5aG3vbppkLv81oxi/VxV/WywkR8JptAADZLYlyz28xxtops8sCvDoOg4C1q312Toetvq1HStf3PL1v2+l3PBVg08u/4FWc0rBabsVmSzOLENcfxWC10SVjCU85dxlvgW+d/HBXaVrCVaI8ggzGidLzaXr4mdmi/7nroUI+g9nrQPebyC0CLfhUMLytryeT0zNqeGGOy/MCipEP8dyBMclqPYxJmptNsQumZ+y70Z80zfOESF3FMGdKKK4JGMHBh+ObKEDbWJrMRZFndpo/OOVdKnPM1ZspIqecP9NmwNHszmzDoitaIye9cTc4DmQ94LMcVKdvVryAWOVERuKIDHjsWlQATxGWrxqwbdwQdta1GUll0zvXa2t1N1S/lTnWvJvOC9JXVtu+EWSQ/slD74EbEZOd58ubWhS54zvNEzerWY6maf+Ny1fueH+phl4OUpbvQHde1Q1UGDzmY4lFiW9WzQTJciCw3r/qzWGW+q4JjoAxE7+jQRGUtM/aCHXfp6ci4LBJZcNKfmKG5tFWzk9DE2bGzjs+yyLinig1m1RMb6entLnBESt498kO5+b7jaReLCYHFbEcut1VGZDpvJ64ELbKzc0CufDObtbeH/+N+QAytM2n5nEGjT9UZmarxpp+JIjcZ9fck80VhgTXVHI34ffUJib4Y3y2Bw0KjZ+UySTt40JzrdMON96mbxsoM1X9dNyFYVdWVYRozA4aiE2iFhcJD3V6co5qLx+55SZeupEGyvJpoWYQoH+lApGud0z9AsuKxfOMyMrajGtoXcvnB3eHCwwAwrReEGUIi/Bzv7NLa0eavQInGSPWo4r7ikaSTVDdfRF6ePcPyzMmZFn1Jxdo5tjdk8q44SlZFK5Mao6KsRM/TTccIaq7UOmoVV5Q8QKCo468X/nTOIQQJFUylfn6eT1aq4yw/9zjNGc5YXPeZ+b3jxwAjll+Q6fzlc/NcvbkJKOHo2qjQ7pfdDnN7L460wgud75k9Fzy44l8K2vEJAbZ4Lsh5SiL821wlMPMy+SK7Ik1969Gj+allJ6gm6dRKmf+9ACo0+YFN2Pbdk7Q9byhpcZLkOHms6VtzIC7rlLiKJ8YZ5YcIYMW+eDsyQazsIjMJvdmAUFSyuaji/duhdgreJa3fUbT5U1w6/LXaXX1x8wtoOybQms1Y9nkcWJ5VzujPHVBBdFwZk3JdIhw8ZviyDJOb5sadeHS/7w0qSx2xXen6efZE1Nu5qSAY7IzUFp6eQZfvPhRg0UuvFDwxfmaF6z0PE8dOE+SouyyEjpIaatUJfBswLf9csx+s9BT3byyga4mXxjBs/yK27OHITzjL1mV7Gzn42MJAmcKptKV8oZYtG/uAGx1RQ5+bNfpY7TgnnwxrPjOF+gZWYmqf80P1in85h7gVXHY8KzKlbKVWfdjVVWpjl6Y5p/tjr++omfCg1ZYbHD0z14pQR7OWas2e027feL4cMDcyuWPqNnqt6v0M+3VHXyxvT/CD+fZbQZdiKMNkik11qM57EpcQvDN9Ys1qUXkk0LQTih9aVVW/sXiMiN0nuvN3G9IZGvPUiz50TRhieV5iw/S6iNtclq/XyBw6rbK0Qpp/EFxVwmW61eOXSEGZyekmMpL5XSskWElf0iLkneX0cRjHcvziRXYY5QiH7MyGIP8Dgnq9oce1GX7jGL/sNVb5xrqR7jgT/PL/oN7VTL++6I7Btz+ICVe7Zp+yzn4VW2kpCt5L6xCYJ8UPt8Ldm4edvKP/Bfk3eWa/s8s+8/2JtN/tEvD/7pYEqOwyOToujS2AQGkHq3EV+Zrwe5jmQwA2R99kR2FBmCsnnzUIMNCYE4fWW7WUGL09Po1S4BnROW2WHusp2PeP1jTxeZKJ7P5jnln77WUyNXx+k8QHBNr4pc1KIbx9fCImMS2xe5NXEQXKmVbS3flUdE9bIjiwgg/UPNdG3wctBs7U96jJxNr++iHWU3PQV+4LskjKCVud8ZJJZMXgNx5eVkVhO6GTozlYNQoJ1jsBoPYbPaG+5j8tVowF3JK4zdtCz2p1dYWmnVsYfeyrsOTv4uV+IFo24++RG55RQO2EXDeje61eumnZWw3c2KpN5XKubl3q+zJOm6m/mpke8vCNN24srvTu9hXpj9mo3fz2x/CTuFelZT7OfpOlHtB24SObwetanNPGOO0VrU9RlD5+YQOWwctduQYswfHPFuuaqOiinHL3fCrSU+u1RNGhw6kn9VpzqUIrZzB4mJGafJ6d77NdbxJ0/2+bBSBcuvPQt/P8L8Z+jIHl7QugxXcxvMi2NdBSZG3X1G1GR0VE3R2vpjcwpOBlULragd+ov5U2l8+2rPH9bmBEQnUlS1yM+d61mTUOapvHsnaILI5Lnb6cP2Rlk3o6BGMy26VRqjy7UaZ3FMV5IHOLNrj3U7/sO6wWpqpz84dpbkUjsSspOOOOai19YVfFujbfuVG/+Xp8urGv4gQwDy/zSDy//Tl07fOIm/U1X14denp7mnOKK+VLpE3n6mik1bZHO61f5Ymw5e9Ukmy5jVng7516PedHqfff6eWSEc7tpP2sB89myngvrMCr/Sj9cKKb0QSeOro5zPpqs9Ey2NKPKlFPg9tq73Tt+ntOzmTGbe/vOU3M8L7dIU/76XHWGZbGXE9E6YZ8shkHk9I2V1ZiYpTy0eJJm07eE4r+j/rtmOhUmbb9TOGr7pmT6BJ+94sIcj6/j0XQS5vH6BOY2w0h+EnnB5ll1gpgyclJj1WmW5Zeth0o9sEj5mbkeeGaeG/yBFUIA8nVAHp//88Z8EfUaVEpkLikD37agdXsvHh7BGfY3KC9p65FALumE3iT2DQzf0qtps5r2cWz+tI7iWD++ifr7OdeHubvuc24I45IGhetNYcBdiL8Hv2Gr+t5OT2EX/xrbwx/c6IOHatGKSq2Y25/yLXKNHfKsxb3bqL+BmrE7ndiHJGq02O5UuVJu8gk4KNGHbhNX62goIBQtIYT/N/rLYkI5gcdVL2ifV2fO6Ps18zvLdHCHd6kQdHW6TF7hVGIp5lNpP/UbcFXkPRuqkTyc+5YK1f/plO4gXK/zddjbRCuFZFsphShEPlpaN/AO3W9fymBiqoeV+fjecOX+tEo19oXa3YaZaMzplYLGZ47ggMezWmsTkwbIm0jeWOQyfeYQLy4c4d7n54nZh5Es39dVvaGZro3/PATjNxJYU9/UXYoRErZVuLtEcHj60/XUayYXGayck5L2HdFx3xqri7V2UFX2kjxsu3M3cWAsFSIblV5Nyvz3RbKEfNf1Qj64XxiC88uxQ0UzIsEBWom94V2/dLVHGqQLgoqFAfY/H8pRl91Ha9vXtoq85ejCjBOpJnZkwz00ay6su9bM6EHZaPl84UK2rWHqW72YT8sPydzE3ZEMiobVe2QKn7o5JKYqYBRz9NRuwnqdD6/yvkbgk9ybFC16GTzRbZSrbjyrUrcsHVPmiyl1XqKDKpc5zJuxvnqjJLLonUQG9y2fa5Qx7vSOalnHPWaSOTMfNCzxWVrQvu8Cscc4m3iVFyYNCUmn1b5mmCuKyezNEdyyzVQe/ylnGVDxNXYzoyMGM2AO0pjagkhTXqveVZUtp2wfUcOAusxf3NGb7g2JZ79WWBD8LqMq7djVtaFKkUVSK5DWngEVKJ1C9gk05Yd+J/tDjhTNkzkzpBpX4cgW4jeSOoo2xTCKN6r1dE5xHy+rFpJWy5DD8mgvuhCTTvKY7SH52QUjMA6xbKzVUP+c14I8D3ISY23o1kgjWww0xdN10jgiaA72DqfvFzHcGM8Q2kUXeiCa4dluw/xaql/5qR/4vdun79ZyTKZHvSx999N+6WGXi9LYZq6nWa1V8lphKx+6TdKOIyp9TqXPmLqhl0rR2HC+a9CVQ+PLUiYU8NVgz+1M7Y90mnXi7JirPN5iqsyFdNvPOD8zJtKDE0RORHmSkBovtI3vBgq6FmKozssD+yrissdOjPX482Tt5r3IexpFHnFLs/1sZNl0Hk+laxFGVHgqrPVPhfVeF2MFrc7TJDcisRPl++WGwkMaPmN20a6BTdtBcPnbh9/HLnzWLbftB5tyWnCMO0q+G2TO1qAImcPr2aOmEY2eEvldVL/sGV7zE8/2Gmaixw1LGO8pY5y68H1fuMbjk6eqMvuUFk2Xs4X+xaZZ/1AT2uJsKHLf7BMOC75cNit73if5tu2ar3/PC2dHiFwfJEsmXztUi2nLnY3W0rIu84d3tDiei5H1tqnvaH7Ha6PF6Mqy+aFDcfZWJq9+UNl9Z8L2VZkDPVGGbNeJ/CaqatmTjJ/oimRIYcz+3UI+89ZMZi6jbxaDaPjrXcpvjToTTfHUW0+4KvqAS5g9+WHJZTZ4fmt49cg5RDowccEA22660/bMGjqskz9Ni8zu9WhL/EqSa+ESmbVDNHBufXiOVkye9aAnGk9iFNuW3Sk6OP/0sqlw8Zj9k8Jg5Gs7/IkylDzwlbt3b3S29lVu6L7b/V3mMIXxQ55Sem+e2j4y0liNN+3Ubtl6ttLBcpUOHTe3wn1+54dkcah5WbK58+I7X8fTf44LBXh7SahMiZKk+YVzW2m0gdhN5M+cdWI5p1uHz2AT93hLy3GZXu+ZvxuspkrFU/TkzbE9/Poi/cqFzsWsYUJhZxQ2kz6Mxj13XumCP8ncyAVbsDGloVceGUz/DexbJJQTvxYn+b/Ct5q0/f3vf699+/YqVKggEol48803/+LxaDTq6aefVqFCBcWLF9esWTMff/zxXzzn4sWLHnnkEdddd52SJUvq0KGDI0eOfIuf4n8Spz3QNMOefK2Z9K5CDL2YrB7fCFlHkLSOxkN2hirhJSH46B5+NxYNeHl0X8OMV+/mzKC344gQhFcRElZJITF1XenAKLmWjd+7OySm5uMsFc4e5xiP9E5nDM9sHm7GjFSDJ7xCDtHubMyh/rU7nUfzue8xOUw1Hm+YEq3o5M38Ey9sQ/prFNrEX6Oi1PMzPOplH6ktcv5LWRcZNPtVFRwLTk1dUmdhDocmldW5wyrbmlF6GQkVAgNpT3J92eKt1traocm6+Y3DKksociLkfy5xRces6DducbiV60Jb37O/G+ZmnyjhvNt8aLXW5ibd55ba+8lijKdCa1F/RnzwlHqXYjRbgD7kpaBvEDg/vYycdbEsY2QGRVwS2RNlwzKFwcP/if837WKRO86+Z9tWth0mfUv+NX9gWLJJU45QhOei6Zo9jzKUziSvPZoy8cMBIg2jnt3ygh9Hpjp9Jv+wRdGCnl1e8eaEHmY0SqUtH1evZqSxTv2xcpgMvo/wjgXSOcWFyPxGbkzkSaqM3mdNozu9fri31DWLzBn3oMzq9RhF5zGr9Gs9184uSSaOH+D8Ba6OxrkGB5oJibfeZPYQtLZaYBK/z27IlMA+v9CAPVvwWWhHarXtHRlJw4WqX2Gnxj+HXRRII8RR9D5JH+4UPVhc9YRDYj5FSfp1nSsPX0WbhITrGtSn3wyq9TguOjOsj/q9GTDoNWcTryItBBq3jmBz9ToSPyWuJC/2HGFhl3bEsblyncAIqcbHlat5cdwIgw+/4ldlfqp3yVkmG2yLhionHJYm3Uhj9Ymfpa4d7rKOu7jq/rPSoy8ZPusZKoVA+k7v6Hh2qbPXX+U6fzajb6pyTrCL5a27OpVUSsmRX3niwKTgc/Vn6rxekhtvD4mDxtQcHpgh5vDn3HjRNLzFjEH0+4DsY8JE8BEhZ31uPb0jr1vRqLkSzrGAP7rRpNVPCEKm3xx88c+N/312EYejfM7RBXGarcZAji6Loy6r1uS3PSfwOz8Ud5KaH5I8ih4XrtIsWkmlVljGB8/UUno125bgLNW2HHdT7n7rJzUJyc50Xvcj4tn4UMMQoCcKjMGtgWhxqHJZt13+0PVxn1pwtJtt6nvHDzxugpt8or6tlmmv7K7PlH35M1f94qzye/erkbad3gx/8RkPmeZ+bzinhOb73lN97iFvTO+p9UNvmj+qo7K7z9hfq7y2VsouGefw/JusbZpsw6A2Pi1Tjb1cVsSpuqVcLsqR6xnQ5zVxHS44cEBIMHemyrhTdGHfh1WYzcfRdiB7HS5RMvsr+uRHG7OJ63xB1WJCgXwrDu5xJSn1z20b3327KJBtOY2D3EhVBySNPGJoZASfheT8yOeDYFnWRp7vEQRfIKYoXyhl9kyBPbeLZTPJq83OjKSQnBnM3MX3iaazrSsn9pHTIFZ8WzYObcg01GJzhzqeazXU8S5lKMnm1Doil7gwh4kel3D2VOB0ZJHcYbuqDupgmT91u9ZH8be6NSfLkwcmiM/N9VLuI9odWy9mB495ke6c6l3KQL/k+6HF/EtXG9dgkFN9S7mz9jZPTplAb6LpnBvL5DkPMp8DLYjtzKkPSxmf9pSYWawb295v3eXeD+fpXnup+zK5rxM3+cSxaIqzra9yqlMglHQYzvroA4Zc+JN5eog8EtVjxqt6f/ZL1XZEXf2DXDd97zDTVvHn5/2/UBz/7tvFf4bzwrVrF5aTOZXU2TR4XZvILz0XqeyRyEwjB/P0ELZsaebeaDUPRvMZQjV4PG0qtajXLN+SEpHGicj2YI59BQLFRZI3hBVRIk4ofN9Dn8uzQtGvGHYQSeL2Uu/LXkck/ivVuh5nNp+v/j6xIdawkJJ9vjJDP29M6Onj8dVCsSSOOv2zXHR1kC64jWYzVjkhQaUqB0Fq10VcosqSU+yg5LSge57VqZKym87ofNsq+zpVYSUfnSR59nbbz9SjHwdaozUtJ5Ewnn+7UDZ0Z+1iSaQJy8I3mk0gUqUK57GEAcNZeyLZiwkjLMfavckU5fTGGMOzhdpfOgtT22n9zJseHc7N0VqaRWaQMUPYZ/b57xw2/q0mbc+ePatOnToyMjL+3cfHjRtn4sSJMjIyfPDBB8qXL69ly5a++OJKsm/IkCGWLFniN7/5jczMTGfOnNGuXTuXL1/+tj7G/xDycNSrkYfUTA/3pM3ipXxGUv/ocv2aszw6mwN81IIek15lCcZx9/UbmcD2dTWczbhK67pvemTjTMV86Yc2qNbrY2rVFwLxRMTROyFUzOE6xt7ymOb/tsKP/Ca0y+5jZskHyOHlrmkiX0UVd068bK8PY1HnoJdTThh+Uxw1U7fJm40d7KjYxNQl3JhzJLQ2/cXAp0JQaBN/ieLE3qqDZcoOOSMayTC56tU2QjEq9swJAW4F1OJExRCkahHSTDKwgEhj5HJzziEvekyr598xXX8jPe9qX4a3Kk+IXgoSQTEYwP33UT2Gp0nquFMDWw022R41dRm5wh02uctvw4T7x76S0W94qMDt5g6bbC3SwEddWVWDmNFC0FSU0k3ZE7lAd341sLsb3vpT/vTWAr3BQnwT/2/ZRQxu5Mn7xC6kfnPKR+Ok9c1/OOUbV85jvJSTFiq+w1GUdWVSmMzjPaaKropY2yhZwjzi09jwebKjR+O4xJzdD/pgaC39Zs+lHLfs2O9f9RR73b8Flu69fC0XUqkfd/cTinuVeJrkpmu9ppcOuUuDs3aJicMHSJ68PXg8+YMxDqvssfipisdScWWOL5DYSBgYVZtbi/Gz7GctPNHOxO4DpEx+386jSWGGx5Cwpa2Y01xiCt7AH+b676pS/2/HP49dxCCOvnx6sq4tyUISvy/n+rFqYWjuaT73PUaiXGgPNzswhyILSexCZFiUFlyd8FVYw/OxMrT7WUjxYuRl0GX3Ch9Pq6aIy+66+FvqcsuE/VQm8tWXrvGFUZ51W85ev81p74vL1/jJ5X9V8+IezfxOml+4xhea3Lze5oTGbrNDghOitSPaW260Z5wp9bE/F4t397CN+ubMdaM/WtGlOScpe9MZPuX09TFOtMAgBmS9JmPzAwzlozQMpVF1HGNimccdi4tjFP3q4iLxXdGa+Z91dGstXj5LtEtEk6LrVR95yNqkZE9tGc/dBU32/+/gf5dd5GvZ3t9M1yWvedxE97eaY9tIKjbOMXElbRYLvnlbrotMMjMek8l+niXFOtkSORIYP0u4fdluT6U8oX4Dlp5o5VwLYk7TfOF7bCK7Fb+Lb8NCUma/b32XJt5eliJncywbaXB5qyoHTik9M8/b7hb9fXF379joJY/4wjXWucu7mvqhDUZ63qlXr3dnwjumGeApY7w6sofncp92mx2KuByGXe7n49RqXGB1j3vDffMZbYwXPaZijxyup+U9mSzhlj77HV9dRq+sBcpuPCN2E+2i7wV5tVGULhKSZwZiPueGUL3iITufT9LMBjUE+3hpOUajVpCRsBWDQkG/3LxD/Py0wFT7509M8b/FLgo6epKYSfvb14kfc0Sb6Ps+mFbLRzvQlg5JJLW9Qq3QgtcvhoGUvZvTo9OrLAyewvZc6izJkp2O6aSuXGRU3BPqf0BCEnG7L4SBlxvfd2AuJtC4z059zfQHN4j8+ktvacs9YSblG+4XOwG1yZpTyfplTSzRyR/cqPyOXDV2HjQq7gmKErOcxDIHZVR4wHMpQ3Vdt9zxcmWUbXFG5x2rTO7woCc6T3L3wo2GV8xQ9p4zyu/az7JQmIvEUqIGg4e84lfVuzuPe1fPU3bfGUvTW9GIEyND4re2XYGsNRLdWR057u7+G50oVk5HS8Njx2hvmdxm5b0fk8JvN5gfud9rMQOou1bedWv5fKLQUP//ho/1v8Mu/ha+KZ1wUGBz/jb/sQ0iZ6Oe7kt6Y265fr9PImdM7cnUNJ5LH2rtEkZseIpizC/X0YGFodC1aBx5p8nZH0scq5rRMlVgsVfDKLYUacQ4PrqJrNFIo0QD4lMosVe4bqeQ9xMM5ZYe+4OCDmrbZe3QZLf0229Ep6fsrJAkZ1qs5JXbQyGxVdCXHdv1WVP1lzpkkbQFT3ts2Vj28vHmahwg7yaSZh7hAGpRfdkhzvJhtCuN2aYBbUkoybIWHB9chgpsjZySvZ+saZVsR3RvYKLfVw5jmD2OE2NRkqXprbRcn2niyfBtNy2VKW8Ipdfl+SSuivarFzjb5SpdDq+wJhIvMu6whkm7XJFfzPPfbVORaDQa/W894t/7xpGIJUuWuPfeexEqGxUqVDBkyBBpaWkIlYyEhATp6ekeeughubm5ypYta86cObp16waOHTumcuXKVq5cqXXr1v/H+1y8eNHFixe//vv06dMqV66Mn/uL6S3fCSTQbIBow0igUsSyv1V51aYfDxORd+Msq2aGJdGynBCk5Ars2oFE7vzKOzc0kDx/u7nd79PIFh0t1dAWP7RB756vB/veit9StulnTv2xsuQbfmuk5929b6Pt1Ws45vva9Vtv/oyOuk9fShaRR74Uff/qkPPdxIkhJCxlT8fgBvXrjs/CIJyrZkTtSLtJJJLl42hHPSJDfDc1CS/iF3Jzc5UuXfr/1zP5tmyi4DjfPbuI4+ePWvPCnRIimZYL4c3j0zjSn0pHOV6hjNGeMX3NEHbx2tCuup1dYHTJp4PIegvBHvrieqJzmBL3gEHHXpVZoZ69anr87ERnSpUVNruPGNSSxgz68TgZvx5OdWJvzHFtmc9tdbuRnnOjP2pki5YbM4N+6FbhvcqxsG07XaqtCEnjSyzs0E6XhBVeP3mFQ9t7EvMHd3SbHW7IPeTq/lF+cxov++7ZBIV2EfDt2EVxDNAr+muzJzzMFqYuDN16BVzwDXi0LRLJmhKGQdotFMO6YDqqB/ZdTFFiWrF+QRPN17wXmIrxGErWW5UkjT3ijhHrbMxtYV2ZFB2yl8nbUDqs6+f2IMmQ6HiTNj4RWCjHSfrdTllb6mjYaKMW1hloit+703kl9MydL2Zc+Hrmj+qogj9JGfu+zBH1rHOX0SvHhUv/unAOaguyPuWEtqILmMmifbQsRukK4fHXUrrq/cnrVF+L93037KTQLvimXTwpX5j770RB+aE0UniyplrPfmBZpKHELtgVmFAJm1nbKFnLfZl0Ytk+6kTLqpJ2yvb0GupN3uuxwWNN+uPPRf/tKnl3cT47xqUiRcRNucAmxs0bZHjtjLD+J5DVtJKkZkeM2PCUsUOeZTCRX0U5TnRwvjRHPvHudOMYxc/kSS0zW5p0RVxSt8unoj0jRnR4ytj5QfrAxXDO29vWUP97e1T6tyyHs26SlVRJ0pIjtneqod7JveF53TmyKewJSRu4P2WOZjYYNPbVwEJPE34OFvy6NUgXCjRb+WgyN1y4SskdX3m+caiRJD+Pkkwd3Mvg7JfkXVcaU125cnwbuIDnvhM2wf8Wu7ib5PqGv/OMtlZK2f2+rNokTRLW4G5OzA5dOs4Kw14a8NIUakWbaN7hva8H/+5ZE8L1PBSLNtfI+y4rovw9uQ6szB8lWYS8S2WUz8oNTO+Ca+8zTG3by43+qKqDrnbRIFMsX9LV2k7JWk1/x6cPVZZ07IjKFT41wvNOSlDfVu2y1nskKd3LK9Ocbh1jZJHndPM6SB69nbo812moJ5tNYBIv1B3isbOTTC/5oMHrX6F0kF+r1v+4pdNaqeywberr13MunZnRKdVNPrVDXfGypW5ZJK81W3Kv9OwVRaOCPWMBRxvEqTglh5VMXMnj63iu+VDvusOqyF143bfHsC20iwL8535UjOADFag117Mg+qh33eHF2SOCn1NM8OvzZcZeuifsIOfzX9FtgRADn2VtFi175z93l2AnG5ESih7xm2nUYIMtyc0c2ETiHPalVvEHN6gbWa9Sez5eVk3ViweVTP/K6ZExSvfNM3fWfVKHLRIdQaQ7a1cn+5l/sWNCE5J5rNFYL84eoX3vBZ4z0kx9vdwsTc6GWEUvX1Z6fx5DeWrZE57t+YLoSiLpKMehDmV9GTklDzXnUKTlGZf7lZK1PEQpyeNRgfXdm2g+5j3bRnNrGb48cZWSjb8ihdmTQwqvYbSOD9U1oMVrpHGuM+vPNPek5+2M1BfEVXKEIxfIYH2b+O74UHyX7eK/goqoSmpLzeessG5Jey915tHVglbtCRZc6mVA2mvMZuOJhk4qp8uYFaE5Z56QOE0Rcvgpgv+RHY6+cXBDf46875Xom1aPvNfpCZTeT0aFB9S0R/Mp7wUN6r1NzNPDY150XWS/hP2CbvT1fDCvlltK7VYiSShMrhHstChGkTckDI+1UtCcrkazTqtsaNbGiY0kfMqBm8LKTaorSIzUYk8Pai5lfYcmfhj/no+yk9RJyApxObp1ma2mPUYfGGd2NXrFBakIvTmUUla263wvslcMKg0M51u/0Tu2XX8nk3m+MyOnsa0/9Suw/2h5vfyrzG0tw3c36YigJlwQ9f+jtvX32cV3RtP2wIEDjh8/rlWrVl/fV6xYMSkpKd59912wbds2eXl5f/GcChUqqFWr1tfP+Wu88MILypQp8/UtGMh3FedJ5oX0IUE3aiy73Gpuf2b3C1OTZ88M0oMtNwuSBhsxmbwsjCT69FWSR2+3v3t5qVMWSTp2xPcdk+igCo6Jm300lBU24BLX+bNBN/yLR70UBgScoN7YvbrkLqQT3acsDWvvB0QbXu105xh2kDeahGewiZpDaQMDWZHZ3Im4MqIVI+oMylJndlSPpDcFmvh3Iej+34P/KZvgu2oXMWKGndYyOdMJof21uKBfmx1Noivlk3M96iVzW7NlGL1WLvCnUgwv+rQDg4QEVhGhsJDBi3EDDFr4qrcrpNjgh77vmDMZZcNm0SyBmS2termZ939c25eKmffjez1QP8OtZXZp6l0V9+WYvfJhT06eoOWazCBX0oCP51SzvXcNeT3octMKmQfyJ0beRJf1K1z72Z9020XvE/TuwmuDu0r3czdce8jLZQbkF0SXKbSJfxz/XHYRgwQal3abHexm9sLAAZ8Ufdatk/hztIlH1zF3JWrkp73GsHl8nZCMbcCJLJylxAhi2oeEbZ2i+QnbvsJ6r0Hls0eowLtbWmhZJrQg5f2mNL+l+bMrlLkQZ0f0Fn3N5MYLPBkStgP90iuNeirlC229BbpvWuqnLeaLWc7C59uxhO79lkqp8T59uTV2u9Fbx7GFPWOFpNT1LK7exoxxWC+fMkN2FveN4vcXmlOGD1Jq6TV3Qb6szy6FdvK38b/LLq4hNiRsJxusBD5ayKG9ZZ2OVmIZRyOZZtRg276gQFOl5ynmk3J2Iyd5ccwIx2+8iovELCCraJ4DkQuhG6MvlxUNSdAkbKRq7hGqB53ktZOS5STG2vZsTWtm3Gl+rY4mN3qQz/igaS0/LvJrw8uMNVV/77hTnX1Z3llY3/wOHd3vDfO7dzS33H2yKlfycdtq6i3cK/rjiO3qsTDfzkZQ77a9TsfHyCtN78xfqpTIwWgyu3ljX0+DDrwazu8CT334RGjNm0Z0aP7XlCh0lnTh1qGUnP2VqY3DQzuiD4THknj40Ax512Vjom83Yfvdx3fTLjaROde4KaOVcM6i2mGOxYwhzBiGxBCczp4ttHhPQFMerUvz6e+RTKRDlO7U7B3qYCXw/ch6b7rXcu2Ne2uQxOpUWhASVe+6I9DXy6Eu+z8oz0gGNH5NVQdVPnvEZUUsn92VrbScnKnJQ+v9q55sYbPGjqngJp8E3cGN/MQctlJ6dZ7Pfc/3fC553PaQZK7Mk30mhGRbSZ64Z5LYKQxe8wqVMZZI5Ljo2CAXVTSyV7+Kc30wp1b+N3SHZpFMg2u/InXMIkcbxVmVGyRQ2iTSphbx0RpKTyD9AA81mOR3fmhuvubuY9lMbv6gp24fb1WksyvJqn9uSYS/F989u6hB3fs8GF2my/oVXuw5gjnsHJ9k7VgyMh4wudWD5IY5L3kYMC3/alcUg5mRRdOS5E0SSB2juopm4HGsJD6/GDYj0owOfBJNoSTVrz+kXc/1Kg0Mhe+rI/uVTP/K4lFtLCnSiXWktl5EXyK9kUOXiwvt2NEk2NRqXkwewWSWj+5qkCle3prGJeIqXlD6szyZSfVI4E9hKEboCByLQVS56ZQb40gqgxNsTPiBrOWhGPMFJLChB5cj7zk3jvqziHmGkulfaf/hAjoFVypOmGkwYN9rodliFyX6Bl33nZFLQnvfLoG5Vdi99O/hu2cX/wiOYjvthMHb80MsIQ11KZ3NHjXlpMcyggqR9zUvusLs0eHxzQ3qsJ499wi5pd5IInMIWhMbeV89rJ5+r7wp7LlQx4mKNI+8qnzkvVCnwg51PR15ROXY/RJGUTVxr+OZZWjO7Vt3K7GavA2Cf/88L3w2hC5M7d1LTDlOjGH+Bx3tH1peVmfmRtowPqza7Boktsonrkwg0irK9dT8jHM9aL71PW/ncGt8FtNY36UJ46hrh9Fjxzlb4Sq9JzEzh7mz2dOMKrNPqTdmr12oNEnYcy+wNHJnIJw0YmQFzKRitAxNScw5HjpIfiN0BMoTLPC8/0nb+s4kbY8fPw4SEhL+4v6EhISvHzt+/Lirr77a9773vf/wOX+NJ554Qm5u7te3w4cP/w+c/X8X8jhCA1uDDs+G+3ScskYCemcEx6z3ApKSeKbR8ND6UBIDiUnkXC5qsP2ZGqFt/CSWs25+e/d7wy61pRX5BalRrmVIyguqOqiCP/mDGy3RKQQQFzmfFi+vqVB1GStofcxnSZFOPurHwVyhYl8X5ai0AdNot3C9x01kMh9k1PJKr5784YRC3c5/HP9TNsF31S5OyLu3NNm07EvLEfy4WNCyqjMzKzj/n3Essl9qBRplMKNtqsRdwRlLrCBMrO8pJKpW8vixqSRSwx7pZ9MUddm6tDt4Oo/+1Hsg0+MmWq212na5rKj6tkqTrpkNFAnTxrMGVwrFipIhqP+Nburt2yumPRaEwWMx6Yyo/hSD+Xzy951ugGMsWkivsQvsONlETBxDX/8lf84UNthC/KP457OLotRi8PpXbJgdgpAz0ebGb3mKAzQfF5yhDsWINIu6Ohona38ldc/u5CFU4I7o7hC4JKI7zW96T/FYwVmbiSRyxseKnRa6MVo3elNtH6lpD6VoNXWpBrb5/OT31VmfZZY+alTco80tiz3qZeeU0G/LXAt1kTx2u5XaWtg0aAnKDQ6RBuybUYUEppbr5eBFNtzOqjHUXMyKTs0daZyvfVuL11fSs/srbGHmZRaNESYpd+CWUrtlpVbKH45WGFj8PfjfYxfF0YjnWKqD5i3eswG3zqHqYycl9Tli4thgB+dR/61A+jgyF8l8VKpcKAJcDvfbgTIhFZMbbRIKd5t5Yt8k5xaycU5DKhBzFzYyfvpTmuVmOilBva17tVyW6TY7DF4WdPp3qKu2XXqY50vF9DDP1Oq9NM3ZbofbxPuz7pOXutknkiYfccu4/Yr84Ayt6OcVFgYNwgtb0YrSz+eJGcvs9Q+H4Tg4MoiHqk/yQuKQMITpLM/OfsHSlFbmZ3QUaSr4/3vZ2TwpJMHKhQJmDh4rmf9VTmNp21ZU3SKk/Qr9rL/Gd88u8td/aqonB45w+5LdzgtF6n4Z9NuAzkSaR7WMxlnULzC/DRFaxYuhHNFakeCXnyQxnTYfUn8p/VbO1W/KXMOnBIrR4i5tDGvwrPvWrTRi11NMY+ecJP3MCCzeeJKaHRE7NugzF+iAPjx4gl/5qWe3vMAlrr34uXl+rPuWpWxDNW7fsjsUSXYz4+yDbjm8PxRKRgjso575H7kD+96qAk60Fl7TP4Qo2+Npt3W988g6xsXIbrKZcnGQZ6NPmL2bA6PDYeoREgnL0J168/eyibQGTN8yROq4RXJwuic/jnvVkHXT2ZqNGUIKrDBhW4Dvhl38f+zdf5zPdbo//vurTIRMZ2b9WD8S7bSIEorWdDiESFOxWHZ8sBFKsbFslKKlHYeWVkuN1nTMZiNEIpRjPo2NQn6FbYrkR37szGHys1Gv7x/PUZ1zvud8ds+PXe2+H7fb+za83+95vV7v9zyv1/O6Htd1Pa5LhSxCSzak2/nulZ4ZNozR5M9pYv4bnV33TIGqSPUHQ+c+GxJV00KoumoQg1sKs11yuDOuqPwU9l9O/qP0WTTPPxV2D8ntkajPjNF9XDubVSPTdYymhIq+8cTLKJhe08cnaktbweFH6TJguT6r55FFyTziKlycfcKZNRSUTQvV6mXDcXUS7tkn+T/+yZn6GE3xx0mK6pTT7vgq2jNr5f3UYvSrj5DBOx83dPT9iqKJIYY4Oryi9NabpPWn46ul3k/Zr6qKy09BCxYPbS+eFgYVOknGQqrHN7k+mhR4gTReHMGL03iw0QzhCIcl/Kn/HBeGXfxXcSmu5AeMqvNLo+c9ot1OYYh8HS7ZEPvlrFFSlp0Jwxyns/pcZz9MRitaPL1F7gYajCU/T5DcbF5f+gvsrldNAZ6OH7djUOCAkqItNpWe9QghBpnIg4NmqJpM4VmmjbvHrpP1HVSdVA7fwKkOJBVQ3Iqt1/PA2adcPv4Tgw8+74n3h6nahu9fvljd1YektaHmcLKbZWqeT+pBoQOwPnqS3OIQY4i6xC46zPPNuus4mgcKs5S0Cf6cEzSLpsoeQ4UrvtBo6NvOIXNeKCd8sR8vPkrGHBYO7Sg3oyup1LyduIKwX3XCq/yT3qIxsYuOxqZE7Zicy/4cwf/a+z/89/z3uGBI2/OIouhf/T+O43/33L/Ff/aesmXLqlSp0r96XLgImm3/7B8sGEHm1QsUD0rSbhu33zdPsxM4S1xaqh6tjZnOnu44QvkVmE+T+jtd6aPwpnOowFUnd5ulv/JOy7xqlue+28vPzj7sZm+q6rAX9dDfLL2qPxe0Rw6S9DRaBTK4eC3G0ufpeYqRliXsmlXD8R0s/X8ZXqh/N7hh5fagC/SlbHwC/xX8T9sEF7Bd5OcoWYfj5E+kUgvaZeNRcrezah/t2qAN+UPoUiZXXF2YRn8+kfw0JhGlx6yn+PoktfccdX+FX+pQlOcyn+pc+2UXtT7p5x7S3yzf95L/8/k/6b3xJd9S6Ednf62WfeJULj9+Qtqi/eSRdEcglB5v9ARlKchlz/VCgFLIxH2Pq7XtfQWjqNSXxY3bh0xnKrurVPPE7mH8ga9akxL4r+Kvwy7K4Fq1s3cxj9Yz6Y7OI1Z7qgVaMX3k3VZ1Srf/LPHhyDaNVIr2O5ksBN37+HB7Q/krSw83BqMp35K5+XeQg3KkHDlDBvHvL1LFEf9gjb8/nqdun/f87uT3gj7abs405zelrU1nXeJ2S5R3yqbm9aU8eoY9DBifq1v3pXRn1321XXn5foWzqbd+r8N5vOCH/iW+0XeEEGFrFypHq23D0rFt/HpbTwXxcHNa3CN7PkNP0HWkYLuVKH8XV7+4jw25Enbyp+HCtYvz0gjnkEZN6s49RJdShcmPeeMX33NqfnjXg7VCZeElzY9bFz8SPJpx1LmvVHm5PRkzkczhFqExsM0Vb4kz0I359Tor341W49+mJdPfuTsE751Iepx6i/aKolhBRk31huwNVR+1+LaDJo563Hc//71qGcdVnnVCWZ85d3EYKlN7wFFShST3Hk4Ovcjnv63okYyH/KORHAmts69XaGNGVh8jxj4e7K4bhX2pH+UrxiPRMA/Vnyorebi4AzZwx9UrXR0tdmBnCp14vlP3kLAcxZ5RIey+DOVnhoqqvHU3ujNaIciHJAjb/wwXjl2UoAof8bPeE1W5a6/Md8LAF+eETEQHVCwlUVFpXomnjnDtZMwhr++NYZ33RB0WjCr93RYCaVqF9UNYv4suU5abPOwRJ9IvNrH143Th2qICG882C4MhX23ix2smso0jqnqi+jDG84Cn1Ju1V0Hzmgq61VQh6wudvOpA8zAszRzBte+ChZQ7IgTTlTBJIMnOCR2BZai3eq9xI0eqOk/wl5bQvGdppeRJ0uJy0u4Lkh97BoSq8oe3T9F3Hefimo5HRWq2xNNsaljacrKdvS9UtmQDdlMykZpxR5WWUCiVxwhthX9bGs9/Cv5ydpHkS21zzZX7TpF6eXttmlpf8WbSF23SbdhSuYNCeUPPZxZb30v4U95OUdzRd7BnLbsXVuMwn0QnrBlEnQOkD0QWfdbPC8VH23GGwcOep37Qp/zsWDNbZ1HQj3cL60vrt1+9XXtDKzlBQ7kCB3qmSNqHND7/l4rKNaPy9hOByBlUek2t2PVGbcoyYFeuzRWus+M2KvUskbLkjNPzU63vzv4OKGLiosdpHuLkAZ61ahDmUvbzs/TkUHZyIGMnh2rBU0KFrZb8ul5PydFKvyiifjSDDeR0CZW42zDjCnSix10sjp+ze1s1bmmndPpHAn8ELpz94k/BaaEe9Sk+etET0ThR/Vg0N9Z7+rM+6xoxn4JONT3ft7ul97VxVllJUykZg49ph5zxfCdOVtCtpibTdoYBlzmHnMPPTj6iQWaQ8jmfSDgcX6d1GjuGYShVZu51yYJYnRcY9uIzLl0Xm2wEs9iKuPAi2c0y/eZMH9cupMLcLxyt+m17avBQjan0C7rUeW1uDD5bGwZUzfVey7pWVUm3py6/bt7TxiMcK/w2A4m/HXmlQmd96s6zfiJPXj7KHckLVHFE7q6QshvwAk8dYUZ0o8G7iT6OZQ4M39ilcI5WZZbLzF0Q5NwKiHpjA6OzHxHdERsVPUjjjdSbIVC+B0ofRf4cCZELhrStVq0a/LssxZEjR77MeFSrVs1nn33mX/7lX/7D93yzcRrZnvi78bpORxqFZUrkNbzRCJNdXAP1aFb4pkd7TfL43SPs2EedsZTsEKoL5+Jz0nrtt3tcNavuS5edkelghWru9Ss7NHC9dy1yl0Vl73K9d/1Bqs0rb/K5i72w4e6QeewukLEzKd+KSv1xkLz7bnSpUidxjqC/lkJh75CZfOeuhoEk6Mv09nfb5lqJAWT/Nfzt2UQS6rukcax4SejOeDGv9KU6ZDbm5hM8+cbgMKAjri21Ey1T3hB3ouhwOU8sGRY0bGaSeV223XdVUymvhLK8oJfZKT2t11yqP7j8W8fcfDLfg6tnuNQplTaUkB8E/meUHaRQqot2xpIKgr7allfTKAyaoXu3VXay+kXSdlPlzEU2HaivYAwvXsG+uldLK7Xfg9FKr1A6dIwPXcXPCRMlExnv/wr+euwiSXCiU3zfS6xl6yBqduL7k+eAwm4MmfucS6N822A63yuX51CcplKhMLVrCsaTno2P2b+Z4t5JzAxDwXplPxcCgOOMSnvM9zvN8X0v6TJruZ8nP6SX35hb4QcKpbq51UrbKjT0G5nGGmf13g4+Ud2wBc9oMn4n72ICvcc+q8O8l5lCvdv2On2G059jDlVH8uYV7bVq9Laa28iYzrVZgZgrROeqqzWL5nrkvckcDNUxxyoky5/EjLUcHsGMOX2CnpUjEnbyx+HCt4vz5PulNKZuj/cUd08K99TSV9rkvuU3J0tlcfaFX/ns0WQTez3uW3Ft96ZNkf80dV4VEhYTwuTeqqXdPps+ri+ajT10m7SUQZyahOM8fPZnYXhTT96bXNfRuyqK10VhUCqM56qM7X5ktneyGlpxcYdAQp3lR7fNlZRDtbXHlUzmUGZy0MUdxKdlL2Mt31IobcN+TxwYpt2YfJ03rDZ4Wmg9d47fFHa1/1yamnNoMK70A3fh4Zwpfp4yLGjkZtG0JzVyiswf2lmfKfO+bC9PjssZ0IYe0JLfu1rrceuFsseEj/Uf4cKzi3N4jfxTfMSnxysGEnMDiigYhduJT0bqTdmraze29EzzwFRyRrBgNeWdEjUu9cPPd+gW4Taer9KdSTSfF+6c+0cgmQoffxF8+oVctCi2tmxL5oR1O/UnDzGLdrvyPTRqqt3Tq/mpJ/ys/3Bpc/dLW7tfyVB+uWSUsy7x/F3dg95yGYruKhek/AZxdELFkEDvJkj0NaRgEKf2EK2KPbp6kuLeQiVuK3a/UE27ThxolWKB79OYgjHUyRa6Rk6S3TzTzmi/Bp04tRlVaLJnZ9BFX0btXUe1Ppfk1AA+PVbOg550SdPjVibdQX6O4Gsl8G/xl7eLEiHm3YsnnTlRnkk0eWanTWdZ3oXimWTeV3p3S6H5SDZ1q0/dQMrXeYc4ruYyJ0jm2oa0TBZi0GKmrbtHcbMk7qJgXE2KeXLqYGcaBtmAN5NvcklcW9oLNNmwMyRBsjA5yI5Mbxzi4ZkGqtXwfdEcQSt3uEAEz+LbSz4M9+gjVIz2BiJ3N1G0Jexrd4W5L5K5svQTOy4UOk0KmrPPlekFCvKodLjE0YEVVcs4bnl3CkbQpupbPsWOfpjLj3Lnaj02VOfvx5OPBtrobWHv7BBXc6h/shELH9fcelfd/wmvZ0v4Uv9v/OXt4r+L08JmsFPInmWx60m5UZJLjv9BrxXPSWu7X4+T83TOW61jmQVy+5HUjZJnqDo0yF1WyzguLX2//GHkDmBjP/pOoNzV4fC5w0LNXvPSOGPh+x01yMYc1kS1xbsicQfiyyLx6sjcrB+RFpLrFTZ/YcANuTa7PhQ/9SNpJHWqCImSFTRJ5lT0NoNYchsHDqe4pvtu7R7NV6cvP6o/19G4Fa8wqudjLs4+oVuHpXSh+WSSVrCsd1eZdRdIVbry6/PAO5yIW7GHKcPvteqZULR7U1zR4r7tpW5mVWa6wuFEU2PRpFhUMfbEt8ezbiteE/QNz+tC/3llRi4Y0rZOnTqqVatm1apVXz732WefycvL873vfQ80bdpUUlLSv3rPJ598Yvv27V++55uNEhRxbLmoYmzpq23UqUXrrPVaLXlb0mieaDbMxkY3u/gXJ9xuiQYvcGoKxamc2idsKCiek2SkfzTUNANG5XpFhpv9X0+eHO5qv1fLPpmbF1giI2jZluGyzz8N/v/nyKfH0JxwsG0UT0uSu7urVjlvazo1GGvumHC+wt7kneuo6k5+H20Pm954hrR+zu4PG3ztsyXwp+BvzyYu5fLm4kmRSkMY04ke7wuJiAziN/jnijSJZtjYiytP7rXxFX7Xoq1oGam/Oe3/+KeQvFjCCP+oTtEhJ1texC4+2lzfj/bMVd1Btezzh+M1LapwBxeX6kztJmdoD9fs2+07PtTADvGiiKcZ0DvXdd0LWBg2t9rTjjpS7guG81LZ72uyZ6ciwbl8cQ/ZQ9gxIlRF9YmT+ZhZBnjuriHsX/MX/I6/+fjrsoti7PQbvRjPtaNRgbmX93YOyz9na68Q5vcYR/4iLj5+kTf9vaTtHJ1XkUyiu2Pj+o+kOzVHU2l8iU116mtmo8t8qtqg4wwna9JjJvmJ7/jAI/0fskIH/czWeUnQLnzt7K2gzdq3LHGHtNo77NDAs117h31hIruq1Dan0T1WLLuTF0Kl1umz1FzMnqfZm1U5SOq0RSd+fN9EMmg3MAQtJnDtVN5rGJn/cWeva6va/OM+jLvrf4yq2dyblIMFEtWDfzy+GXZxKW4hPdyfK40uUTyxdNJ7MqYxoBWDq5M5OnRXFM7kvRfqqrdvr4udkz6VJzoN42OWftxGt7VLrTlCcXqSss4qaYU5oTJV/9LTjuLYxG+HYLlO0PfcppG+9/3K71Nq+9n04Qqm17TQXY4crO2ftQ5k62r63verEP+0xViSHg3a6rZhAtWuPy5+g6HLnrW3WWUP7ZlKT0Y0e9zPhg4PEiQng+zVdaMK+DhoLdZ8IVyLAh4aNjX8eyj6hgDp+6lL2cw7wxsy/qtK+aoz2VInTcGE63gsX0hvnpaoSP//x4VpFyV4nQ8481qK6Ntx0D+bRdpYpDPt43v4RGgHx9KhbfQdGciZG57ZzjRaX4wng57tix1Ys4HOZebZv4FJ3YZIn06NFLaOF0jhWqxueZN4ZmSPK+lOvSN7PfePvUJ1bwr6UbffIS+P6OXhnCl+3HNiWPe3kJvRVd2cQ/psmBdaRueGauA7q79AcmlS+mDpR+yCoqA9uPXEdeL7I8Wtkqw9y96GlckJ55FJjQ1FBjydK2r3Wai6H4914fvoUS43PNeG35xkwXpy6grSC+cwlUoZJaadGCZ1xWl7a9VT8q39nHtSQoLqP8aFYRclgg9UTM2fa/rqmzTkTNxKxyyKS2c2VYKz5E+iyaydSiYy4OpcC5t1VHftIZV3nQhSBdVJShGq8zKpZZ9KY0s4S1rGfi7mwSkzlJsSCOE2a99y1eV7rerF0WYVw610J/IonMPAy5+zYzyPV33CvrZXB1ucTkk3tCG3TVefDLqKhqhFzW7IYc1tIRZIW8z6XqHKz6Rw796KBR2EfPQEys9m2+e0W8y2uKMrq+9UOf2EVa/QsQrl4xTG03cODSbz1EQh+90qXOqA28PQ2hKMqRW0nK+q9Ylv33/MlGiwYdF0pi/xleBQAv8ZLgy7+J/CaSEl/h20pGGquYt/xEz+UJHDrSk4d53McWTNCvMw1OIUlr/Cofxk155LkpkVakoLxuAdVA9HXoMXB4W5LreUW045VrUOMrAWEu1EOQ5MSPHFpEj21ExPxM+GKtbCYJ9SA/XpLLLDObbmknQXHdeR3SZTMdZFRezkiXHDaMWhnclu7Z1HJbJyHvN2tYqyVzJjCntGsLxFOLfhfBT3cX8KxY2SOMg5F8tty63RDO16hvtL5bknpEUrZTei3ZB8m86lc2s2DRcwKJtD2YIu+p+fqP06/qzp+RMnTvjggw++/P+ePXts3rxZSkqKK664wrBhw0ycOFFaWpq0tDQTJ05Uvnx5vXqFLFRycrK7777b8OHDpaamSklJMWLECI0aNXLLLbf8OT/K/yLO4VYd+yzUeclqzT9eI86JvHgHParzUMpUhlC+4inX7Suwvlf4I1ZC2hTyBzaRvnaTfRfX8tIzveUO7MpBLnfMG26xrMJtDvq29W5kJ99v/JI2499SPCbJqxd3cnpgeQcHVvfjs78w3ZBQTZtHpVElMrstCNMEK1B/OOk9KZjIh3ErXaYtZydr4l9Kje7X8X1+ljacuyJfzXpN4N8iYRNfx3fU/JeC0B44hrz3b9TqyNsc4dDIZNWuP67jcNzHtDr3KIqeBfnr+aR5Z/HMiBOYQMm2QMRet7BAhT1fBN2p6qjLXcnL7Uhp4Ocpwzx8+Ge0+mEQFC9HllGq1zrocxfboJkGWTtU6lQS/J2FFKdRaRY7eofb9qULk/VZMo/Paf4uzd/ASXY8SoPpNNhJ9PwxcavIMZeHtLj9EkmM/xx/O3YRSKxDgyOrZqRrNytf1jJGTeXWYTS4T2h3hbmkD0XaF4YMeo4WfCv1BEOJz0ZKWrC0VhudT67WYOpGO25oSi0aL3zXJROO+2xSstEjH3HapX6xb7TH854IG8cefjZ0uGY22la2kRa5W2jIjSu2OZ0eebTCY7ImPmbauHsMHf+sZxoOVHHbp/qZrW7eIW/6ezVs0jvjWXPW3GOLy72f+V3t5uYr2Ua/aHQgzxoK2lg9qzkYHbINg2ctDcHLWhp126Zn8hwLBvxQEC1MVKP/W3zz7eI01rIrzXd8SFsOT6JOXJ/NO8OfvbHgZxwOmmMLutO1YDfLeHjYKBbz0NqpoPPVqzkTVkml6iWumbfbr1v19KMjc9U8VkoJJCcrv/a4d1o2VMs+1cYcN2BaruyhmXKm3Ou14a0M9IyLnVPr5BmKGVXjYZb+Ujw8ktP+3nDpWZgYpoQ/UipO+4xBHto1VZTPpE5DNPc2dai9/ah+ZrvcMQVREdl8N3UvaylpQZ+V8/x4zUS/uGK0woNcWo4fT53q2VVDHaoXadoytP3WKccN/babkENt3BxX9rbmul//CpvXCJtlgqz95tnFpzjMIeTz5i+bUpUFR/hgPK0xtOyzgUQaSl5UoHLcwJasNJdOKmADkwYOMbLVdHrTcSDLe9E6m4IBgSS6MZquJJnDx0upmrOMmPm4yV0eIY3Oj66mHnrzoy5zGceTVQZ7cNoMxnOoVrJTLvWzsw+L5xNtI3PWAhpzqFmyakeOk03tqke9PLwXPWnRawsbGPf+SFe2/8hhVYxcNl2LvC3iLhwuKtFxHDuio/Rl9OxHjPCPlkVn3HXmIsp9pOlkofRpGVpRMIvMoejAkRFhu7gUriBnO31TqbJmr6M3XRGIXi8K30CJr2r4/zbxTbOLTdHrIvtoWJNB1IwLzPUD6cmbPNU7/DXTCyg5x7L320uJlhsYT/VMzrBAnMJB1g28Tmq0RZc6y23Znea6UQUKXuHqX3zB65G4c2TVo0in62jaFWDECTZQsK6mF/XwcP8p3EGDqTzft7s+i+Y5fD1V1/CL5CFGPjNdpgXemdnQMp08OmwSJ3lnXkOnZ23XejGSaT4cF4cB49/1e1UHLXcOo8Y9JuvIY4yhxQl2VKSl5fItt15Ywe0mcCoq8hQeqI7+pYnvk/g43CckByrpSpjCRXPisIdOPyXobBYL95uEL3Ue3zS7+O/htODo1+ByKt5yVKcKC/w6/pGqvY87HG1hbOkw+bNMGBE4pSR0zDmuqG85kksMKC0o0QvdGDCc3CmhclZP1o6nY2PaTWfGELSiQ8uXrXz+DvGuiJ4MaJ1rQMtcynLz7pXevLo99Ur3p0osuSOs0q73CVLXFeh19gXz0bVxGF5WN5pKPtX6HffenLq+Fe22RrjmAZnk5rI1bq+BHV5zlT9EeQanPc/YMDDT9bzk+57/6DUf1r5C9OFB8bqLmBbi9QZZGM4P/QZvCYm/80nxv7wNRXEcx3+uk61Zs8Y//MM//Lvn+/TpIycnRxzHxo0b55lnnvEv//Ivmjdv7umnn9awYcMv33vmzBk/+clPvPDCC06fPq1t27Z+9atf/dFT+IqLiyUnJ+OnApNzoSEJP9Y5ftWTHpS2Zz8TmTErLJsHu5E/n/RO7F9GzYVBL/Dagfx45sQwcbO/kGksK+jtJKMcq1+4SZsjbwWv52PWdbrOD/zWRzn1bemb5tqiAtES8vs2kX79JnPfvcP3/E7tWUdDu0knVo2hXRZFI8tJ6XDGOysa+na0PWhNTQ2VwPeVm6rSFZS8wyU1Y07kcF5j94LDWfzc8ePH/yK6rheCTXAh2EUSenk8ftqAaIqqmWhB1hBGDRe01xrR/BgTk0d67KEsnzxxub3RcZfi2jUooKh/OSmtz4RkwzKhdXAQT64YrJNlPnCVl91l1tP3Byfvcza1qe8jV+oyYnn46M05kJGixtoiVpI/rom/c8w1A3aHgTAtsRrjwu/LYuGKjhrZJq3t/pDdqx+Ok9U9TD5vfR/RgZiX4SkX/oTvhF3wv20XSc4P4qgc/70jo2rbMSm42ecEJ+TSr72zFRq8in7CAKK72rtj0Uo2sGcidQ6H1wpXkHQ2yWVVSkQbKKhTU9qo/XKzusqcuMC60ddpfHKLcsuwlpNZF6kw6gtHp1ZUedQJr2W1clB1x1yur9lSJp1x5j7KPU3eyBtt1th3/d6tG/JYKRBtqYI9ZbC7ZTW/8GO/7DVK4TxSny597RwFrUl7la23hSz97aWfbRs6ZvNI/4f87PaJLL1QbSRhF3zdLh4Wbnh/DM7XCDThpXZe6HqnnnMX8zl7Myur3f1oeLkKNrBrXW1nXeLtqECGsMwGDKf35GfNyb0HlAxh27H66lXcqfyrGIK+ocuhQTeK5yY5eHF19WrsDffrctgtyIqswChGzH7c5ImPBM3DE2F6eIUtX9AwMuf092XmLAjXNAz5RCWn3V1jlllH7nemAhfXIOdYprPKutIehb6lT9t5Nq4OofJp3JpC1IXp2Xe7InpOc/w2vsftlqj7zCG5g6iJt+MhRg6YLvrpF+KfX6RgFrVOUPYK/qWwnN/5nttvf4OlL/oqmPhL4wx+9hezCb6pdpGCG6nXlJeI/y6SW72rStGCL6dAjBobpmPHb0RcwaG+yarNPx5evAIbiHvyemqpLjS6zsQJbh6+0i1eN7ZoktdSQ7iZMZ3m962xrqi1qil77TlbR4UhX4ThXh8jPQxeiU4KVeldBAIoT6iOWkje2Bu1WvK2kv9D0jZBMueK8J6CYeE8DV7lZNuLVOj2hWlL7tH/7CwPl/2ZXywaHeyugJvfWOnNae1DbDGWk50uklF2seej282LB3vwmRmeHBSSFXPjOV56unco4TonDJxNxiB2LaytfvSRQF195MLZNxJ2cR5/vB913ieqTcWuqn2626s6a3L9zlBdmoqWzJ1+h56zFiseQqXe4bn9/cIRfoMfCoodD+SH2Rbx+5HsqxnwvhD/TuS1V1u59em84JtsF9b7zPCL65pd5yofqrzohK1duLIsa84G6qt1Q86s4/cV0lzXuyB0YExjyWYyRrNgYriOc7i9Qvh3+XeZkdbH4Cuet35fKZHblOJ+YfZF9jPcHierNvF4sLl3mdEr2NJ5X/BKXBdXti86Kr0NKvD8ku76DJkXbLQC0fulMcbmAsFYDrgQiKZ/jb+sD8U30S7+O0gSeplKiVvteJjNj18dukenhFOvrnKTNkve8tQdPJCGxiyd10bntqvD0PmBFFUvJ+WGM4FjasOLVwfd5PxFpO9kfX2aZ5aetkM47rRupX7ObYdkv5ppQJfc8Ptj0IkdE0MVb49k3j9W1zXP7OZiTGPXttr2qaXd1fnh6+nC0XEVbdPIt6K3XDucGZP7+NRlro+ma/cqBbeFAWtbh4Sq9r0Y01PY30pn39w/L8v0BSP17PprH7rK9/zOBs3kT2gXbGkmlp7CPF/Nn/nftqM/zi7+rKTthYC/PDn1/0Igr+rGp3zYoWFoQerL6rE3ORi9JbMe0nhvSV3XbNhNdxbvbq+WfUFzsBzZIzMNGJbLSY5mV1R5Q2nryEnGNR/pXY29PL/Xlzf60dUf8b6rvTStd9Ci2iMQA9mh5XDAxdleTO8rN7+rtGiB5g15Z1tD3y23XaUJzBhRSig0I39D8O+qYMBYok0xS5/0lUt5oeEvv4FcCPjL28WleJAPyogvuUhhHVKHlV7KNqQST2FJSnt3LFsZfiWHGfP6WK+5T1S3IuPOIO8xFLPJ3dlV7wEviYdHGtTbaMeepkFb9iRFU8tJGXCGssTjiY4ITttCYUlUEViCs8x4t4/Bi5434q7Hfc/vdBm/nDKlCYwVpeebSckdoaqlZnVyD3RVP1qgaRsmrA7NKZPiN22KrsTzLjxH6t8iYRf8OUjbMrjd4/GvPVxjCq14cu5Xq2PUivDzUPtkb0fHZYwMlYlVaxHNjsX1o+Dkt0AVFo9u72LnVPeJJkN2spKfvT88HLsl8+d11m31UnFjPk6prOrJo8ptx2h6vJGjk2X65M1zWbMjqlQ44sNhDYP9FQnrvDrms38Wp+OaIUnRitFjHzFxxOPmTr4D9Jy/mPEUbKsprcZ+mvLiK9wQV/OZsrZEe/XoKWTuh/NkfR5sTnQ2ZvNywY27EG0kYRf8V0jb84RtCvrz88jmUVe77pkCRQPLmWiMyW0fcXg1VWezru91WgzaotfM5zzgKe9GW0IlxTpsYPkQ/uEE5SYKOoS7yc7INGB1roI2NZ2O9ru2L1fO3mmT652KzqhZXUi4ncNO8nbeqNXKtwPROzQM4jh9hkpdyH+hiYf83L2e1jN9sen5d/uOD9zaKy8MpslOUaNGUSCg1mI2hRtI7S0skfZM6xvIqgpDvgjnrMJrWa0cj/K+HCjWtT3rV/Lj+A2/W9s2tIX3RjN21autXtu9pr9xtyETnzNu9EiPPZPFoHxBvfB8SP+Xxl+enLpQ8MfbxaX4Dt/K0ORovo2zbrZ/QKAc05szYT1j2nD5sk8cG/9tCybSdQ4Oc2B4ihp5RZ5oNcxAMy2Lzsg8LJCfm4V7an5Y35+5RLt++Rbk0CyurHbvo1bNSVfLPvWW7OUIr/Vv5dZleQo61ZR22/4vdZ9VIHXzfoXtawYZtOGkTsE+LKFwN6nLBL9pp5DAPheC8AbzMIQth9MkRQW2ocd9SKYwK8j+DDhWaEzyRA9Pm8IeDk/jlvhtj0U36nq7EPsM4d77pphV2N8faiRL7hfrPGO+V6Z199rQVjr+eA1TS4T9Yo0Lwx7OI2EX5/HH+1GXCuJ79flWczqjGvrHnIs0+W6+08rbubiJ+CdRWKuNBVnACuhA8XTKlOHAiZoe9jPzHunjlcfbuqnMakXnakq7fn8gjZJZ0zskBZbE92gXPes0mvZlTQ5l4ibSB2wKGrXL2DGLBreT/wrpw3ly8mCz9Lfj6aakBXmSNnFFlW84EeLozUJCYyymsXrdTdrc9haZFPUsJ2XDGYYwet0jMqPHg855J042ukiFul+EJEklTqZepMLwL2Q/HbavbegxAec4OeoiFbK+oAOjmj9m0l2P8vJTLuzK2oQPdR5/vrj7fDLkMqVKtHxQhf2RwlaXSsk78+VA+YUZHXXZtZw8Vg+8SZu2bynM49znJJ+g3BTMKh0Knil0IL3C0YEVVV52gokUruP5z2ka36jVM29zl5AonyNo1s5nxqJ/vUIfmByeb7vuFW+MuJ0UisdTqT6qh2KU5z+nQdxK5ShP03cEnipLqDx/Q0i+NGPHNMbFOV5s1FfWdkbdzpOvcH8ySe2pMm+vz85e4vhPq4V981gsaNXu91VV7fmE+J/Ljv44u7hgNG0TOI8SvG133jVMofggVtPs8w0yXxAyAKms19yaG1izhztuWOniUmfl1yN7GpCRq3gmUjiiirnN7nCoYTJL+L6XvHywl93dqoUqqQ3huZc29A4k1T4mtRwSJr/mUGlniezPB1iVny4zfYHkuLbD27khfbtKrVCRwdNLXaU6paXyGPCq4MxV+3N+dwl8c3Epkjx81RgFV4Sb8/4pFEyoGZyeAqLG3FFjJWXC9MuTcy7yjEFyptxrxa47uRqLKR6epP/OX8rMXWBe9u2sYMeApiFT3xbHGekfQwlsGp8mJ4X1nsekrCG0pPXo5eGyTjL44POcoYMVPnexe8eGIKNdvaBNtWMXe1tVllSBqsd48iDtogWaZoVDjGkVnK1Ne5v7qm0vgQQghWoNPPD5U6FVtRIPzqZVfJ1RnYTs8DSqVT0uoyXFE5NUrReei58LessL1qMhM0b3cUfblTp3X63JxJ2BjDpTOoF8KEbzB6nkERXyd58fU/YsM5r3ceUbO714sK9mNjCfTxdV0d8sFz90gmbc/24WZYleiXXKXqBmY065lOuRx8T0xzlBz0aL9bx6cQjie5O2fb/eB55lCD3WUfeZQ+p12OtQfI+CuYL+4RshcGq87i02rxfy4wkb+evEaeTx0/Vm6a/kB3yujH5m0yLwNFpwbcUt1jzDC1Xv1uK2LV/peB3HRDqOpNxamk54M2SJR3FtlMtujrlcclzZodnJlkT1pUw8o2Z7DMRU4ln03/lLreq/bVf72kwNA1aTHuGSwjAcqaoj3uzSXs8Ri5nNkO3PuXVZnqMvVLQ6+yZ/V7HI/oMcboQ57F1XWWqBkOz7GA0ZesOz9pb7AuTPbmJu1h3aHs/T4/bwUboOxX2kxeX8bk9bBemow57eWEe9UXu5niF1n6Mlj23MYlCBC4uwTeBPRxlfVj79oVhZn5ERauLKx/U5Gd4Rb2ZL2WtJo2s2G3ujiBqbi+jNQ49OlTLxjMw6yGPUyMdEd8dsY8ku9kVvazcpn/F0vYva/Y5SQEqU72S0V6eMBfb2r+wXfkx1ykT7g+7tbEqKBKI2o6bDq7GS4s/DTxXYU0DqZrZ2wLTwXivC6w3SeL5bd1pyXesCDV6lx2jun57FNj74PNwFTi9LlRpNoYDpU+9WtRbf8zsZx4T5OclYyF0WSU39g0pjGTzjSQPN9MjQh3S8YQ1TlwhR9xoJe/hrwGmhsm09f8glZwk/38h33qbeVmV95navWHNHcwsKcI4Jjdi/Byv49eSeKm1g3YmbpNXf78VH+4qXRi53TOpK0gr2fznzYlNmfa0bhpFC3/eSKyuwP24vej3W+n3Sl2wK/slYNAtXFq8t1d+sE652x76m5LC0fRs96vFqdCJw9W1wF/t3kd0Lw2k77XeiB2Prel6nTLkzX1Yajrv8cXsJCfHNVBj+hcMH0ZP7q2d5vewtfj29p6posIIeQzk0Oln+2Ca6lZ0vOhqL+sYmRY/y8gJ/rin2CXyTUCIUzh0QElwz+M4kWm+VesNp0cxYNCX2XkZdXQqW21ifjYOoFr1l6RttpB6kanPKLUGLMPC13Tj25ApDNL9N5e4nFHdIMir/MXvP1Xcardq+7fmB3b1WpZVVgwRiOEeowhXu2OeEVL7cEMusntJZ28mvsJlK7QWf6q6g+3wrbu2XF/q2xwjScSdLhxPuQ3uyppUOMd/X16rtjHoV/XlwOElp4XjXe9fxn1VjKo5tFOrzdwpl+EWl31WJC9GOEpW2FySqSou/5/3xje14NNA8V8V1XbNot+fv6q5hNE/TNBYU0HWy4OCkYBFRYayw5aVShpyRPT3TgBG5pAnBzgYhA5gnrM1uwqaUE85a9G45Kd3PhLW7TKj4OF95exfvtGzohiu2h/aO6gIRMB8/YOsIrp2D0UzYFyoLe2QT3R9zZoILcfEHJLJ+XAh2cSmaq3jiGp/uqeLJRuGZb8Wdddu+lNGsfyXQORlYEme6Z+McG5s20KT7TofnU7UNdnHzgZXeTG/PEUa8/7jJzzxi1cB07TLyw9TXp9GfX7fv6fufv6TSoJLw/EGKWyWp1LskrO3N+LawGazj+TmltjcS6ynZTFJztqxI0/iN973VtrEWdbdQho0FVItTrIqKTIo3mq+bhmkf8sGFXHX+dSTsgj9HpW1tLs/kZXq2+rWfRnd7BWOaCS2rU8LP5Y/SqgLlR7N/DDVrMenjIe7ysrQx+0OUX0G4txeE9dc0n70tK8vyUw94Sr0bSlvE6wrZ6aGlx/+cAy+kqJFT5Mm+gz346IxweTOFKd8d0J5VH6f7jV4a2GHktOn2Dq2s9m1HrV9G83UCWVWA6jzS9yE/OzBW/NilYf+ZjdsEB2w6uftC21Iv1GlPPJeLZsQ8PMOFHXQk7IL/TqXtecGPNKZ3lHXf/UbmThd34g8pFVXOOSGrX2hvPS00PJ+Jh2hgR2jTO4gMOmS9bH65O316JsU/+T/uKTNV6lph/ZWlabc3bZx4M5040DhFjVFFHOTUIsqPY9Pw+pqs3Kk4g0o90Ynm3db41GV2rG9qU/P6mmzeGTozBlGr4fvm62aRu2R1eEy8gagVW0urRZpOp3g4lVLZepBr72Pr0xTHTaQf2fSlj2WuYHcrMZrsRvRN5jfHw6r/cQqvFIVqqkvxYCYH5qSoeUMhG3YI1SAXgiTC15GoKDyPP94uzovfnEMNHeMkt1ticOvnqRIGTB6NTqgWB5/85JyLVGj1hd3rqql79SHZBXSNy9nmWhON9qPoTj3uYvXCm2zQ1K3R9OCPzxKS1bPxDIdnsS5uLzla6cYKlG8pVNW2ZccVNBhOweSa0q7YzzRO9aboJPfECyzr15Wy5M28UcXobU1vR1XWzAo20G5y+FjxeDIK53mld3e6kNPlX9/Nz2HwOOaOvUNz69Udcoj57DlCnXlYy5pptD5cev1z2bOdOu9yZ+MXLF7RM1RgnssVSIgLlaxN2MV5/Ol+1Hkt4ksFW2nC9HRxmYi5xAuJhrDuhetEpV0Yn6L1XRjOVS23+/CGhrRkxtQ+atnnmmi11HNJZpUJq7F/WSplcGoZH5zk2uGcGUe5gwIh1JI1wzget3fn+hXebBF9KVvSN5mkoRweL0i5rST7CAMWM+MOBtdCM87ModwYQZInnVPjg1SCWezKqh1ke6oLCcXjKGD+zM66dVnKWKK/+0y89BLRw3FgrH6by3cypew6oCinBtOxeYdAxO114fpNX0fChzqPCyPu/prud5kHwzqryU0zVktVaOlvugX/pRy1X9ll73P1xH8fiVsQTcRc8vNIzya3f1eZXRa4d+EUv6o/3OFdVO2EWkLB1Hi2bEtz3bAC702t651ot771eHFXkBL5xUn6CCm4Axg1QegkzGfCAMbMxOzQiQI942p6+q31y1oruI3fxd3dVW6erWdJ301JCkmf0KNejnc19pmy9q6ox2+RU+grzefT/vJ+VaLS9huM0woeuU7Bo4FbzajDLVYxnFPRPEegHr+KXwmEUjaTdg5hRRhIk3LFGfOnd3a19/148sQgHHiQQ/OSWcTe6ZVtmZfmULdkC4d2tPfdylQgpe6ZoMU5XsiaH0cevSY/p6BlTfDiPpq/j3E8P7K7wnd5ckTIVj7ZG124ET16ktf/Rs4s983YSBL4y+Icaruk3Gf0DBNQP4ofc1O0NESwvcPIlQGLqXofA/rlig9HfuVe4+aNVPV2dOK1A628ubZ9WMdTmdzvEZrTbnt+2DT2CKzvEX40ca5KbUsoy6F6yXa3qeb/XnxzIAaWhGEGpoXj9p3zK2+6WbPc2KqsdKvyghSC41ybWuCXbftrcXAL84h+EWv6MT/1c31fYLSJHjWOD85vDgkkcB6nOXaKnzM3+oFrJzOmDgs2CARnlzBp/tYUyk/B8SBLcGYnI1tPtzXa78xomo9b48x9AiHUkKaNcY6KZY76Ve5w1cvtDW2EG4RkXJ6QvCgK//65UYp7JwXCtmzphNa+nBqDVsQnaTcg3yDPGLkntFaU9Zn8ZTSfSt/mvwo+Z33M4qGKT4i/uFTxzCTFw5MU7EJjFo7saM2+4JCNqUOdsUTPfubvKnwSAhCHJfaLv2acbz3rqOZ9BT5RnbJsSuXX0QnGMyqLmlmk9eeB2YxcNt2+aHVYq2eZn9XZiil3uqwCP/JrD7WdKrUDh1ugkHXdrrOxw81hKNF4anQvUpBV0+FcyjdGXZrM3SluRqXzieydrF/d2pbLm9rbvLIm03ba1Lg+fdnTiH1rrxZFW2Rd/xgTiGajS6ATms7BWgrOsuogr8eDWc+1o/mWQjqQPSo81m8WdNUqsbERi+IFkpqHb6YERcdLp4Cn8WAVfjxnopoDCtmw3oVJ2CbwX8P59ssq6GH5e11scy39aTTvbZVzTliEQt8igwo9vxAXsDQ6ZE8BtyCl9xmtGr1t5Xt36HEAZ2kz6y395Lh2cThL8RtJigtYckOQSqs6nDs6BHmpTSeRTMkwdCkdF7wT0X7FR9h7V2Wfngz36mXXd3VqPhrRKuPt0EU0CocDudruPo4Or0gObxdxs/8byKjC0gExDb+qqiqBuvScFS6yw/SXjTj8uDovCFWNy2idFq5JMoZQ532ubLzT4jo9uTWfc08KJFXCHv46UeJLbVs/5Pvpptx3r6sGbhf1i5UpOeHmF1ZqsWuLynE1TecwL57CFRxO58PxDVmMnQze9bx90Wp13uXpi+9DaJOutJtoTGz1iTZ+H3emYdCpzU7LpDGpQ/YrQBSt9EWnyDZhtQ0Yx9bjKKTqcO6fk8UKBszBqHDlT+0LOp+fpWI4PZbk0JO8kzyf1p2drNDBjoM4QXb/TLuHV6OY3dHSEIPcRnzoklBNuCHmJbiWD7YqKrOD/tlsflLoO0908CXwX8FpgbQsfZx7kqXZzMzxVvQvlkbHyMzl9QUsXWVvtIf+O0RjYw8UZtk9sJqBa6ZKn8no/o+43L8Yt3CkX20YThFVm1H0ajnFTycxEX25bmUBy/id74UVW4YebSi/O+yG85BZK9SsqMCq+phUmvbPY8967i/Lb+O3vegH+ptld6dq0g7wsJ+59sxOD8UrRcWxSx6ORb1j86IqCqJP7Y3+wK1LyMkVqmsPCI7lN2cfSZC2FyTKkE5avdKF2pC/c4wyDP6Y6+NkPmHJ2TsoS8Ed/KRoeqiMmi04PdZoVfVtv6g/2q+r9wSvu4Uz1B5z1D61VDtyXKeTy33oO1Qn3sCBcSmiZTErKdiMxhRKlZa+3w23bQc5V3Oof7I+q+dJHcuDtb6SRVCFbfFgDV7YqPW09YJGSAIJ/L9QBuV9/+KXxAdDFdP90WPWI6sXT3bngYUhiw2Fc3jxNrJT79fDi3Tg3uFTbNBMcVvh7t8Ya3mncUPvNGxIffL63shx5mbeQXX2rqlMGar1Pu4jdbzgh868Gi6nxa4tdr9RzZkq3OINHawQt4u0K8jXLoua3TCK54soGz1HixAcfZYe2XMF/+gnbOZBT1qwIFOYapxwrBL4Os4hj9dKaFze3OF32L27mg/Ov7SOPo/O85siFgziaFZFlzvm4hp4MLylXDrrC1r7VYXBmrdfQ32ef7c7V1B0rqbCvhSfFSpx+3PgnRSrd9/kyezBSl4g3sYvj4xSaV1J0J6qzkP7pjKU8ndx6mQpmTSfFhu2eKdOQ8tHsCs6Ln0C7wxt6KnPh2qU8baSNhjLvJPYSaXGJSqNLQmjYXbTpUaQHflRXDFU4NbBB0mOl9vLoQl/1m8+gT8Xyvyb/1+G2P4BaaY+/RBPcyK+0ajROMv+UWwcJZCu9ZnRqY9b4pqcIX8P3VosdWYQO4tYkXunjatxMNxZL8k8rsXBLX69oidXM33h3YrnJkkbtV8xNq4lv4vQldSfUy042qliiFlmkvQue6KjzKfR5Ts90WyYOneF15q3L7381eTfgbk0yMc8FswN7n+70Ty4ZEbQRs+hXv29Zmz+irBaT6i66hfyJsuu7spJ+rakbtzZB5+HmR/6UvI+U595iFkFgnDuNyewSOCPRSVaU/+aTcGPacydFjGTQReTdv1++ZlN2M4vinhgHq/FfVwUV1aQy1PbiTMjnuR7r75B9dCmKpmCzJouPve5knNh5VTufYKmrCodCSBuQhmSamFhWN8bl4W02daz7IuOKkDz7KDrX34o2fdlWr3kJltGpilqWS5I8IzlwPQUx6ITNAzvHzl/OuuZMaC0DrYcg/tSJ26liVIJkFbUnXvIitvuNHnPI57v2V3jnW8xnVHvP6bkVarct1engQtE82J7o8p8lC1Yzvn21SQJ/DXjCBby0lOGp/3K7uhb9OeLH1SQf0M70dOxq27/RNT7pBU6cJyq/TGIM8kC8zOewZORwUN1p3pwJElZWMbw637m7z9/U7cWS8UZDPWUMSZQhcLdNWUgoxZRXQbX44GPsZqmDdGfpZPb+OXqUaGDAu/trOucYKfpLak0h6wreDDqyzQ6ZtEnZ56trzD06WctwlO7aGqjpdEhToTBy6PWPebQgWRRuVjU4jjfeaU0UfG6UB14fsjYeTtIIIH/CZyXUPhIqJY6ICQEdgmyTFvDz99uNT0a6aqen3i291BRfuyJH493+w1veOzbWaK1sVGHH7PqnXQfqePcxReb/u7doagD6jBgX64Bo9m4XXB6WoWG8cugKlV7EjWMPRyv4SSj7kNT6tShUif6m+VX7nXPxjmu6v2JqHZsf5Rib7RPfnSYxkuY/iIbnhKyka+XPjaVfqYLuaPvP0ZCHuGCRApaSjlXzcoyNUPbRxXkcbJOaJPKWR/Wf5NjJM3HBtY8wyfxHZa5TVlnzVp5f/BvCnGWe2dP8atHhwdx9ElCUHSOgv41pS3arzgjiQolKp0frrGWi399wudPVZQ/MeQ961egfLIweeycoMWVmSL1ZJEvqjLzJA+uI795EzfftJF1F3o7eKJVgwvBLi5Ff4/Fk9wVTbIco/IF4fLePJkeQv8kdIirqXvboVA1OAfbKBxFagcM5L2Muq5ptJuTvLO7oat8ILXjaR8tr6L2pKPBvDoI0cl4vrfkDb+b1dY7/Ru6Ycx2V07Y6aMj9RlC/AZRP5xgyTNkdCLv1Ru1mvt2qEifQu5tpRn4+4Rq3t3hemZ+zpg1RK3/IGwW36RseMIu+HMNIktBX7XjD33UoT4fB53kBp2EycareXLDV9TXA0MF4f2GWMbSFW10Xrva3paV1T5yVPEVVGpEvIJot+CbTKFkPUn34eNQwdfXbA0uL3D6DP905m79Tz6nXC7epWhmOSlHzniiyjAPjZjKOfZOraz2sKO8wfLt4Xo+j1u59Ugejcg93FXm/AVyugd7GNxf6NhYSH6zJtKnbVIwjI/idO265DOURq3etv2yGzgxQ6la3AWOhF3w35FHgBSaDeC3sfjli/gtu96p7TKfqjGpyMZRXHuMpF0UN0tSqbDE3iqV1e5w1KqVNC9LpZasWh0sp0kh21LZEPf0o5y5PM3972T5ZY1RYQrxUHyCDezPo9K5JPvLlGiQiarkTb5R9ehtg+JXvNH69tBh1FfwnToRbYzF347C/I6p6MmOR2lQh4I9pB0mLkPUW6gUTC710z7+wjtpDd1w/XZG81T3rxq5H8wmewA9ylKpCkc/DkM88js1kT5rk03962v6xg5uyReCjE//u3+y/0Uk2sDP40+TR0jBtZRpzq5Yzas+sFobB6L9ZsQ5XlzdV16bG002ws+Mcd2QAs7yTnZDcEP97Uo+oczFHCii5mz29q3sMp9KaX2G6hTPSVKpU0mQrlmELGbM7mPwbc8zkg6tXrZi7p1BCqotynKyxUUqZHxBf1Z1D7tU60xW5Yb1e8uxYJsyhDigp5CIaMWp1nx6kopnLlJh+heeGvHVJz6HQRXYeTJ4Q6NakZXHqJZsyU9zq9cs0kWLzVuMbvyIl91p581NyCckvD/yVa3uN4GsTdjFefz3/ajzg5TKoJIQU6YIpC6c5uUx4vci00bfY+ijz4aE8FphD7hemNnSn053LfA9v/NwrylhlkWp5kFJJz66nLT3BdmoNGGwcePS4d635Yaq13z2vlHZ8eiotbg+vs7voi1aCXfp0s5tP4orqtzvBMjJoXsFLi0b4ondk6upO/+Q4i5JtpYpkT4OqXS6b4GPXGnn0014DH+40IeK/VeQ8KHO4y8fd/9XcD5uSfLVvfi87FWRr6R/aqArFYWtsLUga9MQ1c4wvZxfPtHfkILnbElLc93mApfWKXT6SCoHibbHDMlBFz66VHrtNU651BFV7V+RRqZwiz1RIAhp8ZcZIPY/hT/OLhKk7QWJJNRjcldxaiSrX6mY8njy14XgN39Y0BCxCO3pPfRZc4bcwxss2MWJuLs+0+bJHdrV908uUG4f3hfaw4sER2sQplLYjNThYbLxzWffVGH4F6pM36u8U3r7Jx2iJ9RAnTWsa3WdFuO3mDG2jyuj5xWjx3SeGsIDhwUt3LmUzOOSZvE3QMMzsYFwIdhFJfRhVqp4XkR/CnuS2onCZSSVIedssIxBhUSNhPtzXyHYGCX0VTzDa5NbuTU9jzkcrVNR5fUndGj+sg98x4d5DekuOHAL0Z/eac+as+eeQAIXhKsZPfoRE7c/Hkjh9ZgQ2q4K0WAO0WWx+Iso2FCGMGmsBVaQM4y+zclaz6iRROVjHssWspbfFCTsgj8Xaduaj5qI+1wSAoXmQhBdn+/fN0d/s7Q+mafcfPb0o85kohOx+F+iUFG+SAjKZwtabFks70DH9kF6IE66hNWoRWFbUmfhXOiWKF/uuB1nrtOi+5bQtv1tPMOm9+tb70aDBz0frqmRoMP5bSEA6s/yPXSsI3R49BekROYKZFdLipex5CzL4udc5lM3RcOUYMA63mne0GQjzOvWh5dyJfTYvln400lbviJu61Mmgw9KrLzyEjvxwO3EORRV4dJybDrRxJXRJp/FYZJp3SGHaMa6vtf5ve/qcXKe2RX6aBI9r3krNuaFu2vGSKLusfi+yPfXzfHSqN50YX0L3onvNqTtc4F8zUcOm9rUN8R0r5Vra/3ZcHWn4vbuaLTS0W0VDTbDWt/zyYCr2MyqDeE97d4NHRzfLbfdB2fqa7JkZ7j/t8BodvevZrCZHoru1LoeUnlyLfcfI2mIoCm9XSB5K3BqPdtOsjR+SB0f+amfO3rpFZzJEZIZF6puJwly6iv86Zq216Il9ZJoTeUZH/uoYm3l52Ef0dJY+1cWW1H/zuBbDyEaG6vY8KhPc6sEsnRIOOKp+lz6MdEg5s67Q89+iwNxlc7Jlhd5s+zNbt2Xx3QWTCoVKSnLwTO11eu1lxTypt+oUKouY5YH32cIcRazC3u60yIpk86EIUuDeXFDuGNn9kR7tGVTrfqapO8MPlkKcil8l9TevpwbYL5QMDKNvm1+5VcV71WhXWzIoklus8yDnrRzQBNmHRactGJfE1bwzSBsSdjFV/hzxRet4hb6mW2zxhpEo30qCCx0bYZsll9PxzaCT7OWVdtpt5h3Mhq6Itqu6jt4lFWvpmu3K1//er80a/79VOCp23igWZCuao7nMaYnh+eGsCE9jYKC0CGrKvlrQgLu1LBA1l7069jGOxp41DjVHTTCZJ+6zDMGet0tdv/+GurFggP1kbCjfRN8oj8VCR/qPP7ycff/Js7HNwQitZIvB3AqIzhilXAl1SqF7fKjYipWCrfOczuEat/zCZokX+0Flwp+0Xm5rb8GO/nj7OLf9q0lcEGgBOV1H/48LRjVl6JO5aSsPCM9dxPnSF/H1hbUT+azhRc55nJLniajJ13LYMw8ayaSOXdBKD3vxpaMNNd1KQgE1xlObeB0M1LT2ZRV362D8hyYmaLCqCJHRtQOxJaQw7iykHdSGmrxzBb7H2VwhecdRpUUbr5vpSfu+6kZGmnS73k3prAiuQ0fbJRo6Uvgj0MJdtI/RfRaLD4VSb2Co0sqqnzkBBs4d1sIcTal0rR6aB9NWuvLicdWoxs3n30z3P/aUnnJCY4zwmTtrsgPp3pXILf6El39WzfFleXVuVGrwrdDAJRGxuglFjbsqMuQ5ZQjP53qcTUNJh1y5i5ertBBU2/auPBmkN0vcLcfoe8EdowJecbXslqFBMmXmcgEEvg6knBj8DuyBeIzl+i5LxgUibMi93+c5dacPCpQZ2B4PW4WyZ4VVtWl6Fql9HBzkMzfn7mI4194v0pdZ05SbpRQefW5MLApj2pnjzt8llr2eW9eXdcM2x3ihXo8Y6BmNtg0s74mLXaGgpYN9F7zrOyT9yjXk5KJaE9BB9ImMP+uzv7QZalBKUTnqNSX708hs/XddAq1UjdCT278503kJPHSEt8cwjaB/zrOu5qVUMK5BVxZ3or4cd2iR/iEaCwpRzhblvRem6xCu0aHzNjO4OpkT8+0Rx2/PHm/TytcZsiy58iiYFTYD5pOF0imRRHNudRpR7Mqqtz7hOZTSbFCyUKSRpH/cRPpuzYZ7xG/m9iWZE4fIWMhloT+8cr9Trhz9ste6tI7JOUKaddJIJxu44Z3t5NPk0k7qUDOHeFtaUXUffpQINrWhGtavpYmWHI5Xd8ROj1GC8mOPZRPDdrQLaZNDPeCEfCkhP/014rzghlbcZpdLZlcSXmnlJ9Nj0457vUrCwZ20mX+coUFpPbHcI63ilTqgoMcXRPW94w5fVQ9ccQSt+s//149ey/+Ktl2mPLVv/DdojwlyUGP8zQyG/LatlYqRnmhGr0Nx1yuS4vl9qxnSXyPobueJYsfjZgbMtbJeJqT+RfpsewLdtJr9HMmRHer8w5lrthJT/LHNZG+ehP1SB2IcqxqmK5li3wdTqz0j36iisNyltxLdbYvusps/XRcvyZUZJ1Z4N9r1n5TyNoE/vy4FCnyolvluRVUjn/o45O1lVvGgW4p9rjSZ/G3GLTSpJlD/Pj4dO1WsjrjJv+stbHHtgfdzdlsi/Jtw6xu9yuYzyVxZQ/0P8p9nL6emm2wmqy5gcBt3RNteLh/jnnj+ojfjqSv32Rg/6kG9n/GQd/WxwwDPGtTXjqv8ey6oWF/sEBoP3/HV/qaCX8ogW86SvzrdXxeO/d88dKur146xJc+4onTvhrSef73v0kFT/+7SGjaXpBIwmUOqu7werbmkDLiDMcpyiwXdBGmhaa5pL+nwsEv1LJPxjxenMuC7cQzaT2aLevS6IRlYWiHDshnaeM2Tp8hNQ0P8p3PP1CcQ43tRZRl3OSRFhRQuJuOaUSjuOGZ7awIZrdqBFX78ovCwfKjIjdHfdwbdbEwfkw0ltsXvCFooCSQwB+Dc0KfUpLHO4xgBa/tbqXy3BP2VPVlF9SNFXg77kNjkiaG5/LHsOp6NKd18+UqLPnClnfSaMXRhhUpoN2RfLkfd/Xrj3taVT2d8TzZarBP4oF+l9dWqxZvK2yBttTKf99B1UO1yUI2jasv/XbqjjikYGRN5d4IQwRGRzcbOGcqVQKZXLVnIG3NYkt8hx+mUMWR0va+IxJI4N/jUrzOsYhhxHNRyLCrfi6+JjJjH2nRKA6ysR/7n0FPCmaFPHVme7reLsgQbBcqmXCk3Bc0Iu3gfpcujcMwyttDOLBjlpDJLkPVNtSYW+SaJbu1nrrcE0uGsY9//PwnWke5ri/aae66O+yZhMb8+vg9ys3CslJ3qhVpaZyaSLdFS9UQ9oSiJeXox6IKd5DGqlH0yKLBOs5sw4gkHtsoZNITAcrfDk4LDvhedDS23CO+czEWs+ZpfpHKnIpoQ8sKmFYawu7g975rYt3HfZpTxZAbnjO60yPU4cpkVh1It6oLBw6khMTFSiZH9/giOkE/tGWbRpLO4iTnok1KWjAs6kV9sg9nyhhLwV01xf3YsT28L3PRApsW1g8DX4dTnCUkCHtiCvk3sGYUztF3KJ/G9TUYudGpUUL1+9ygO9oIrRvTNU3gYvszvefdob18EDLpUP3lQNaO2ChkcBKE7V83zlcJQSXWsHfvVe7tNsXVfq+6g/apxUS+OFeRVOLeLD+LNBau6ejl6AQF3PvhbF26L9cyutc27MjlxUdZ04WFPTuKelLnPl4+1lnTNTSLa1uwnVu350lvxovTWHIHd8xdySjqVKepDVbVL5X9aI5UISvRnkXlvpDbhYvvPmHuQz9SHi/ewLXVWfpCG+eiTZxhf24YSFzQm3aN8l1alp9E7bWoukW1ioeU/B8K3q+p4d5dpkQP02IVZ54SAvrE+k/gj8VpQUZghpAe3uro313h0u/HohdiNesUuvnvNrqz0QrR2dio53/pkhOnRdfGfuC3xhc9IekTlma1ISc04/Uvi2SqlqX2oqN+lj3ca41bycxHh1D7N2omrfsST+dMT35b1Ncdj86V/2oTUX7s2QMDNf3wPbff9Ibn6w+2KbqW1vn8PJc1M4TNYJuw3j+S0KhN4G8HJf/mcdpX9/xE4uI/QkIe4YJFEyZn6Dh8oVu87sHuM0I2/Eh4bB1EjYtJzRP8vjSyq2caUD9X7i4ys4TBM7MwQSg9L51uabfQajWc5+t09z2/k5azP7TqlROqP+YLej/b0ZfCNF7/nNsrUL4VW5eFq5wZTzEjOl++Xp/9ofJQi3Lsz3LhO16JVg0uFLtIwf2ei3/ommiu5sNZPyU0QWTs5HD94I490ExIRKShDetrhN+8FG/HHXW5enmIeQdgstD2XUbotGtBbvuuMmssCMP3drOtiGtX8Ej7h1xvsy7XL2dqqRRIry1ufmGl/PfaiQ9E4mZEGRTnlWrFVaF4PpV6C62IDcnPaCJ94ibTR9/t/vtnMX2N0pT6NwgJu+DPIY9QBQNYw0etqujjn9zidY/8ZLItkyPXjqVgPGmZgrRNT+HencbhaWFV9ajHnTtf8PLmXizh8KMkxeWkDDkTpBNeENZ/F4FwepTV79ykTfe3PD+vu9stkdL2DBWCFElqf6HNuwqmcfWKzd6/orGt+0iOK6t9/VFGktsr2FyRsAVdhoyyJD8Se3jMaFdFT+g7lJxpQc/t4xO11b/9I76DqeuFq7/Q94d/i4Rd8N/VtIUkfvCgF+beqdC3dIueU6iUyn2f7KsZkMZr77dy6zN5QZdwGXsXVvYr98ma9pjVQ2/SpsZbcg9+pXKYWnr0jhM4MDpFjaeLQi5wrTCYslCIyLMwi40tvkqnlcH3zlykwsQvuI8lVcloLxBVc8nbeaM/RG87jY4XkzqT3AFkNmb+u521KbPUW+fa6Jy32qhWj+kfPSZtJlmDwvG7IK1v6cmaUXRfOamvng7xei42lwikw9fJvG8CEm3g5/Ffkw1pJNwUS9tF00srSjvjpyV+GQ92/94Zdl95iSNoPhplmfFouP8ej++RHj3r2mRWHGujc/3VtuxMc931BV7cTI855PSm7wuYS4clL1vR9s6wl6Ty2l2tgmzCNJZMISOfH7ecaLSJKl99wosF9HiB7F4MGC5wTHsEbcK2pf/+e+7PyNJPjuJop9a1uOrj7WZGDR3GLXGyalcf/6rAeAgyicbEQSd0yF9jVXnCLs7jzxdffF1fs8zXnjv9tefPT0MqbdWensa30Czm55E3s5vq6bd2H79aUg6asbtlNU3Ovmtp2du0LNokqk/+kRCCVP2Y6bXudv/zs7hc6MRYKsTO9vtK6qDIv5f6+FtEwoc6jwsj7k7gwkBCHuEbjmJeZ1nbrtZcH1z5wfXZOj68eu1IoeWoZbpjLtftyFIDJuWGATVYP4rmnRjx7uMm73qEE8IOcw5zaTrnTRuX3axPxjybttVnGgX7SMtHlfD7p7uXjkqoHlpqe1yM6pjFtcPDsWb8/kGs8uUky1nlgv+5v/DP+WUl8FeBczSO9D4+V9IECsbQfCjeQB6r4zs8MGkxCzk6rqKn3K+DFdKbb3LzupWejtqH6fTjMIrL3j1iXYUWrsndrbhnkuJHS9TMpHeFl3z3QCOvu8VDs6YqHkBJdx6f/kTQxR3KxtZcW2ELK1h9eXsbj13n+Wu667Nonty1pJYp0TEbs3jpTE8/6j5XPIVoCoejTYrL8r3RvwvB+JejCRJI4Os4r2uI6VzpCJPJu/JWFX9+1LUXkT+e9P7oSdT2pLvzf23WmfvpQNWd9DiIRhxRlbFcNveIEWMn+41e3m/bODBFo4SE3wpBl/ZjroneYix9us8jjeiB2JE7LnM0OiE1PVxSwW0h3Hg1amx1fJPryrzl7eio2nPC65eiayfB1xzCi22pNIWXhkSOPBySLU9N44F3WNUs3W2Fr1INU08JfSLfJGIqgf85JKE2v6VXuZe5harxYd3mLtXgSeam3WFA88UWrKeFbWxj+dN0bEXtaUdlFT3GxTQr9xZZnBv2lWp+MXrchbXU6FIU1n8roa17HQbx62491crcp7qDztmtCapmkTOKveW+0KAbhpRSSB+zeEV7d7Rd6Zfu91LP3rbOpehzUjcEmu29d+vqNmup7M9pEq0WbY7FAyJKJaxGzWFr77ALzMthzGLWZVynxcEt6t72nt2drxE2ivOVJgm7+NvCB77SLb6M/NM4P4CrqUKphtWebBEenM1T/XigMYPbsGc1dUY8ayOSWnJNtNqhOFmlqICpnB7Ggt6lFFEx+vNQdKcC7FxNRhrfvuug/VeEK8l4lWkt7/GLDaPNb9bZqffL67N6HvPC0LzXJrdyic+0WfsWO8kZQJ9Cov78cs8oC4aFBMrSj9v4sGpDTwpNHmuj47rOQQH3j8syvdZIRhWHz52zyl8fYZvAXwb/tiX76ygW9p7z/vilqMqQrcKqrYRL3fzbjVTkknMxNQU7nIx0Jg8foWPZWyw/fKtXZPjUZWZEPxTI2SWl5y7yr4eH/a2TtAkkkMD/FBLyCBcsijjEk40Ha72Ny+M7OBnI0p14chLLx9Nubr4PfMeTVQYHQnUmmbVIi8vZ/Wo1k5c8YnS9R+Q3axKGBayg5BU2pt+s4DZk0WT9TlqSVgt72J1WTfN6tG7IleeSZA8ipwUlt+AVVo+9KWxke4jficQL2zOzL6ryW2SeEqQREsFHAn8KylCNpLPsGVNaXdgsPH1oYLKe6YstGEXueiovO+HxGk9In7uJobx5RXvXVsGD7BnA/vV8urmKa+rudmoQlQ6XqNkTx3mrZWOFUj2UMZUNpC8OE5iV5fL5n7Az6COWn80jLR8y51hPOzTQZ9k8hpDZhl/GC3To/zKD+FHbuWQQdcLHXIlKm7nVaxxbLhGQJPDvcV6f71Ns5aVibsHSAvLpVeEFWpI+msOzmN7mbl4v77CqXhzBnkbMX9HZoW3Jdr1Q21tT2tCBT1dWMdAzXnOrrV2ECRkTMYznG3enG3uOUHWeIKWwE/2Ja0RORCcChTw7/N4HaJcd9oU2dd9y+HM6Ti297CN0zcJmji6s6GibinqMY82Q8IkGjwvB+gNjid6LDZDtsss/ZdYeXw2XSQQyfzv4ui/wtQngv6XJD/N1m7vUjl6oQM89ixWvTdJ1JDWuLqIxt6awaU19ZtFg3EaSKT6LfSGxnJkVqrx7ZLNqEe4qPU3pwLGofez5N7qzkn+I5joQ5Xsn2q351FJbqBeO02ACq+Yzet4jeswMx2huPXt4Ka+3/XPZGndVowKncmmXz1llrRkQ4vu30eS6fM2z15jfvLNrX8A8DsatZM4Jms6FXfjAd0Q1PrM7Ohou0AGh5jfhM/3t4TzRU+yrNfARmvJaWH+/uGK0GrAn3F8dQdXQSKETTYeyallIhb0QHfcKCobW1HcbXe+iIw4MTGEUrW/nO4VhxZ06yHXjC9ScwPa4FeVIj561/IYgj9Cn7jx2UpwThkrempcXJBv24Dh9Z/N0yt3hY6TRNTPUMb7vu6YdvseDVegSv63rQuZndnb56E9MHzyytKhjldA7tffP8zUnkMC/asP+VHCAPhAsZ014nJjBoSf5w5NsfpKXsnl5DSPWWBy1caLiUTdH60yKBpkR3SiUVL3iK5mD0gKmL8+T8HMSSCCB/xkkKm0vWJxmc7bhg3/lwYtnaPH0YtaQ3TpsAy1RMa5L3m4PNZpKJ6Zn3e1Tl3mo7lQp3c9IOXeIPfx9xv+Vvn4T1Tk8guQTlDtxwue3VjS30x161l9s/y5qLmbHHeztfci76NqcSm1LnBMqSnKOh47zNjXe8uLBsDVVQeZY/D18yq7lwoZVJLFZJfCnIYVy7K1SWZ3ZR8XD+WBOTVcv3meqgYb2fNbetaHaRH0aH3jLC9FNGlRHJhpzasBXIcBH6aSv4b1WDW3Q1OAfPO9ARooW/baQxtZXuLYNNlN0nC3dbnJs87flZnWVWXeBVbvTPV7whMNXl87kmy601VZn2YCuvp394Vctt+fIzg2rvgmW1mvj6IAr8Oqf8wtM4BuDkq/9LAiPc+VRW+a72Z5ZO0yjjLfdnPGmXw0dbsiS55TPOOW+49NNQJ33qZv3in9uHekdvyNeGVmwMlBh7eYcZ/xxBhKPJhrOmiP0GT+PVtSpgmGYKlQhjkcGdeZgIZMWDrHGP1h2rivzif75C8k1D5tWdqi6J+fZUaG+y6KdPkDH9lQeEwYFLllJRnWhbbZ+2B8a1GJ5n9b6+bWimjWEisIDEomMv2WcnwJcwg/YOOlmxWMDD7U3j8vq8oESfSeTVcD9wyg/nKoO058dNZpShw/jG9Wc+LaM0ewYxXtxZ90aLVUVSwaUSht0whhiURhw9CB1zvFpThgBVTAsrMbWWaFJ/TxnOnHl46SxZjtVouNeF2wrYyqZVyzwzomGbti83aTGQ4zsMj3sCyms7n+TjatvFqXGur3Q2vyszrqlLzXeWLcuaev1+DFN/Vzvn7wkjHgtkKiu/VvHv/3bV0E6M3mzQ1PpAzYxjR6Nya4bkgtZBxnVmBpzmdGWwR/Tvn6s3A+KfHR5qqoDWR/tD9I4PXl9ETWiIjUac3L+RS4+94XB9dCfBSNKB5N1yrOpTX1NZ++0sG9HdZctV3yQGn2P+GBIFUVQxCJ36vPxPMowre89hg57Vu4iMneybud1Wgza4sHuM8QzMYdtBTeKDsahwPFhvL5e+M+nf56vN4EE/n9x3gf7ui/y/+eXnBY8ewK5S0iX/Nv4NhHvJpBAAv+7SFTaXrAodeR3kT+9iZrJvNOqIULs0Xwm1+Tupognt4fK2yHLnvPQ2qkOrUnmOI5QvCHJrQfzQpvgu2FbKdeFe6s+LffdrtqXWWzrLmrOw9V8P96o41S61sLnuJ3Bo6kf10TQ0vUCtePr3I7M0VhGxRZHBXfycOmjWAIJ/Gn4lEMM8TQ7+Unh49KG7Hdk/mWqO2jVfeke3IY0FtTl/5a7yTYsP0jxNCRTfkJY46VqVVa1SndDo+26RM8r7h4qt9bnUDw+rNJfv9HT8kd5+3PaXPEWS7g2WkBbakT5Vl3Ngbi+DMwYwjuZDd3Z5gWG88kzV9nVuLYnRzChHwMGhoFk7eZw+97XmFUiYQcJ/Oc456squ0K+1cAKHaxqmW7f2Vp+dWQ4pWt7wOFsZ9ak+E4FVl3Ny606aP1u6WGmBz3PdpNDO6yxWEs0hI1jwm6y5FFWtWbrESHJUQVzhWr22eEwexdWNnLldMsyuobOjVHEey8yrexQ3SrOU244TWrslDYy1IItWcn+iYxe8YiMqZScxBTyut3osvg6R/tXNFs/h5LqcmiCfz8RPIG/PZT4spowZ6PGI99SaTOtR4aE1wfoO9yXArUzT4auohrTisgLQ8ncxd+nvh3K+hoGGriWfeKDnI0bypiJzUJ2uyerrqBoTTkLMzran0Nx3ERzpI2m9VQ2juI1bH2UdjPJ6YA5tK5D/RQytwWtz+xhbN3HDcu2e6dxQyPnTtd44VsOjwrna/vG77zXpq73G0denETZaCnNGGu8HvNynHKp1KzTTF4vVHklCNsE/i1qoirf4uZ+Gy3Obh8SYUMY0JzBtRiVhZY8G7/iMljCgoGdnMlNUXUyq54J6QDlkBwop21YsJl3yn3hk4rCALz5dJ1AZkuevJ7aZXbyMV1mLedcqGb/dFEVKclhlb7YhZfTezGX3FE0iJ6lOplDMZcWG7YY0fJxufO6uqhrLNoZuyTleCBrWzzF6zPwugRhm8A3B1+vzi2W6BJKIIEE/lJIkLYXNA6zZo8GdlhzLN170XZFuHYOzw/sTj0UhLjkwfswHBVK27Jnlg5L+rhENCMOQwd2UbM6Mvjl3FEyVy+QOpdrR7OqO0ay40jTkAQfRf4GrKZ4fJJTLnVJ3N21B7CWFjds8Xlc21MTcZzUCoX+teB6IhBJ4E/FpWxm6eJutOSf/B/G8nF0wr5oqXZV88WtsISuI0O73i0XszkeZv2ZdLazfFiI4z+J27h2Mv/sH5gVwuP9Z9n7fmXNGzP7zD0uxY8WzXUpOr4qDO3rFAJ660KrbLtsmqzcaY3gpn3389+b4GHeRR3qDdqrJca0oeAZbi0kuiHmyp2lB0wQVAn8ZzjfplcUHn9Y7mjPK7TPetPxQdX0rvKs/AlNnGxxkS+WVmAzxSeSbcMl0UqWsW/Y1aSTFqfIH95EeWjGqu3oxBdxQx3vC1LjNRDHaaEju4iNywTjOInhlPVZ6L8Zztz77gha6IPos2ieTSfDcY1EYwbfF66+ME4z8cjjNAoV6/L4rt87q6w5epsX9eHchNLPmwh2EjgnBL4HaNjU9/yOFPZMoupw+mazZwrr+wXVwcvQcTd2smAR6y9nxgiiaYy66zHGk5ZFi9u2eLWwjWap24NvNJKSG7CQdvVIKTijy23L1RxO+qxN0vqSP5GFQztqOpLBw0sVpgvoO4HsHJ7awytFgm81J6z3rdCYG6Zsl9uLf4pushAznibeHrlmyG5r4kw9xpKRyeLD7XXcssa8jn1MbzSSn64XJqOdJ68TSOA8kpSmw0JTwk+50p6gS54iSNr0J+4ffr5R43bl4472D+EjV3rzvqZmjKDd7UGmyUEOd+DBoTw4MqTMWmcH2tRcFLFgDPlrg52t/Zw1j2I96++g5gTMJmkxD9ajxzxmrOWp7eFYa5E1CnnYzoFmKaY0eljvG15iTQHDFij51lqOPeWrfS6xBySQQAIJJJDAn4oEaXvB43mpjU77uVFy4uXqY1Vvvn/2pRD3tAwS6IYgjaLG5bzqNjZQXKaEQt56vHEIzN8QKqxaogxxN2Fg3Ru0G41+wvvSwiO9JYZTaXeJ6/IKlHfK1ho8OYZVG6g3Zq+qsJC96+v51xnIhGOWwJ+K05wp4M49ooLYZ2cv8bMqw+2P2+sQ1zXt8D2iiQKpVJ1j8d2KPuehlVNVd9DhUXQcGCq1OtddTXX6RY+zIQQq2fFEtXsdZWTQa/u7uK7intSIa4oqxFa3v0nBDWGo09JtbUJr7VqUahE+UIuMi5e4Zt9ukvl++zmOzqyo+VjOLCGtHrNTelLvlFDnUvQff9QEEvhX98rzBM4pXsLP8Drr3Wikf3S4bBXx4sh7j0WqdTjuwUxqw8fMmIYq1JhSJH3QJh0XsqR+adB+khumbacxDarQ4AWuq1pAf0rahBTbjqfDVXz/8BzV6h8PJeoH6TlqMYtYUMDGLqTPxBhGDX0sVHCtpVp8o+vyCuyoit1BFe69++r6wFV2qG94rV8pFReR2BMSCDi/1lPYzrYgTKDOmkDWyiX1XJI1eGAgPc4lhcrxPP4hLucDgWAtuZ2W0WOW72LcyJF8TOf5q0X9Odzal/NmilOT5O8SOo+WJMmd3JV0XEH6OLrMXx58osaB/JXH9NF3G1CdwclkrAjv3f8MD9QLNfHGUNKfzOlhKOzgeQyeirYow4BHc8VDiVrE7rx+BY3389oOtudIELYJ/Mc4f4+sRDXafHep6j5RMpu9cyozi0Njk0WzOLOB/IPcWnG5A2jrdWnRJoNnYlC4X88YQlJcjjZ0yloQ9PDWM2C24N9kkHGMOnGKAe9yNu7sMlz8sxMaVRCq1a+goDUnN1/ELAZn8+24sxQhEfhgMkbzs8zhag4oZPtWNjwp6HzuFdIcX6+sTZJAAgkkkEACCfxpSJC23wRsn2H14M7GmCBjDofjrir0/IJt7E8v1fDMwnhe8EM1Hi2iOlWT8TRX+TAQsbPJzsoMxOyw0gqS0UJAhIV3dZTdKtOqcemK2yZ5L78uH6MCS1pzo/WuHciDU2k3j3gmPTIZ0fDxMF0zgQT+WygWHPxdjChwvGY1j+RNdkfuStc02u0SZ4MOJ8xlyJDnpLVHY67pvttHKMlid9yTObzYKyz1cfeNtHEAv+gwmt24nvQlm1ziM5XySTu4X7w20mbDW9IW0+Xq5TqvXh3aB6uzak66OrNRlwejjh6p9RDn2KCZyqOCnme5iczY2cfdz70g0MZ7JUiqBP7fOL9GkoRsRBLn8jmRy/5TCvpd563BbbwigytCC7cuOBhWmc8Z3I1TewR92vq4mIzhoV5LPcznZ/2Hs4IFvVh1BM1IGhWmh+8svYKXJvWWu4v8GwRJhiK0CK81nczSgW3II2vIYxxn9bs3aTX3bXEjql5M4aBwzt/qIdNv3Nsvh/0vCleasIUEvo7SltMryT9wM0cC0foasvIok1zih1j+DL+/+LsUsX4XKcPO6DsSdUjqEnaMj1AlmhTm29Vh66Swi6yfT9K3w7GaJ2Mnq8qUuOvsIkZTMB6rWdKdU72wTCCymvNZ9JwnD5I0FMkUzqbmG2zdFa5+f06o+PUxL04KP/cPY30jNGbauHtcVBAzpJDNC4Taxtd9VWmYIGwT+M9wmpms/ofOqh0+5JL9sSvXHvHt9h+6zVKjRj6msEKK9LG8eqKz/biuUYGqU1k+CAWsG3idweNIGXEGLBvT1QPbmJ/dWVY/8sc2IYWkHA5FRSZcT7cOS72NbdUqev0kS+e14Y2g5LmvbC3LV7J8AN3ylrp3Q6z3y7HBx34pqhJ7pPJkZuUKWs2nv/ZIrPUEEkgggQQS+O8iQdp+I1DCzAK/8z05vdkbLbBxEVcO3anm4lAFqAyOMGTJc1aNZ316aGnSlsodTjg1C10YcH2uAz1TaB+O/OI+XryNNRP5hR+r6FOVonwLy5T4XbSbPBZW7yhjHLWHHJX1jDB86eMw4EZdZp3tz0urJCoLE/jv4ZxAdu7EGv5wmNYlosoxVRh82/MhwbCOaevu4TjvrGhIB16cHyrOk0Zw99oX6EePlmRM4F6/0jSNwyuFCsF5WMnljnGQwiuEtsMRYRDfpvfrU5YD61LoSfu8N7mCeBvN8fjVT1CXj+rXtyUrjfbMmNDHvYtz6L9fqKRKyCIk8KfitEBwbiv9987QIvsSQzs8S4XSe31ZlCHjdgzl+XndlV9Bzg1Cm2pqkOi4pZA9NTCSp91LFbrm0y4zJOiMwmS6ZqNfGOZ0DukfI0OoPiyNt58aQecrVrOOJ5+moB9trn7LyS4XiQpJHca3fv+FUVOZfHyEvf9Qj5xVpZ8nYQsJ8K/Jm6Tw/48Kebncl8XYgxcyqj1x4UVqtqfjHG6oup1mNG9GwTTh9voxrqBZXNvgqcEu9lyNXqHau3Uy78R3M4zyg8KxZ/Tvo2savyv7PSWrSWuJg2Qc4MoTeznIjFLStQwe7ETx2CQWcuZcipIubIh76hpXVnM26Z1YMokezYJcw2k0n0xq7/2GRe1psUrYbD4QFNTPTxJPkFgJ/Ec4J6yVnXiKNRt9Ue0EDU/RmkO16tr0dLptGqmxsoj76LZkqa5tUIUZw+g4gZK+tOi9RfROTGniwRUYRAM7dEH6yk3yx3B4WJCTGjOH1StuUgUNGpIxls6NVlOOHiOp9+heHcdxRVxXtDlmA4bwXNQtTPL7Q5bgvyXWdwIJJJBAAgn8TyNB2n4jcBqbPDYtS9+ejJnJifhGZ10iOyNT+lRyZwnT7VfSLivMmFnVKh2UzCPvJAXrkUWNPUVWzU53VVxXDSGMKMSbj7bXc8RiW9F3MQMWow5dNiy35FEKni51x7ZTPIajoysqGlfO8b7VfDVUI4EE/qv4uuD/p0J9x3L1O2yiu7D4HsX1/EGqR+Y85IZHt4vfCHJv52Ac8fiI5kQ/ienGseiESe8PsR8nV1wUuvaO8LI7xemktuK96XU5TIPmNNm1k1lhaJks3mrVmMNEy6j6rsDcHqfKzr2y/FTUMHZvuxx+SrCDxKCCBP5UnF/7xULyq1T/7xzpR1dxnHgW6YtxfshSMbsa1tY3etGdLV/QtxOHFiYzjfiXkbNlqfMutnNoby0Wsbhle7bzoh6hMrER+wegCg2mll7KeOSwqW99joerqQ9DeLJf0EcEKVTo9gUTUY/4xxfZMjTNmUEprDklkM/npR8SSODrOD+ksVjSD4rpTZWUMOjIdirM+cKElWT1xlTeG1eX/qTNZvlarMRw6tXdyxs0bVZahT483J6T7uP+xbPIY9rke1xx7EODWzxvTwHtpuX76DhnVmA6l6d+4sj62r635g2D12E9B+LHGMisMiXMDftN4bFkP7phri+ioyb0Q3bQiFaWI/FIV2+ORR/FisrUEKzm/F7wdSQIrQT+XzjnKz9orXCj/iXnsthfwM9Y/psu4mZYQkFGTR3eeJkWIelhJUmvoD/vv1IrJLqfIX9gE8vXUj7aLa0Wpnx19L///E0jMh/XZu1bbjvB4e1kjWf5dszj5PiLrBt3nV5jn9Ow/ocMK2bQklByLqf0mhNrO4EEEkgggQT+t5Agbb8ROIcyvEbjF97SeOBbWl+/3qFpdf3GDy0d2kbmZOIcQVMtI4QM7ern251ZTVIeHYfy+7gNBeTVuVG7RfmueWa3JGS2p+tCSqbh/dJTThIIsqr4nIzZIWAaM4fsIWGqbJVpn4ZJyPl/ga8kgb9SnCd5TmM/t2bYcVtTawahL1kF5E/hc2U8/swTTk3h+VTa7aTHbLJL4+VJc4Z47o5eTl0fbGFko+nSylKh8Avr3rnO1vkMmJ9ra0oaVblm0O4wrfygUIWeF7Q8DaXFoC3h0max8XpezGVay3sc3fttc5/+Ef2FztddWyWSFwn8z+Ec/enlNyxhYhFP3oF9PHkEH1NvwF5xz8jLS3p579W6qg07bvW8m5jONRW2h+VYlkNXXuLX9/WUHK3kOA9GfQPxO5ear2I+uUO76lsH3VhVQJMrdpLDgPa0G4czPDgnVDumzQ7XkfUKBbNrMgVX0HjK+/z2MJ6XsIME/mOUDiP7Th3fSf3A0VoVRWn0mI5MDvVPNqYOtyDuwIpotycHoZCO7+Ik+Y0oLkjiExwM1bHah1WXN+FG8e8jWXM5ET3rkyuuUriBOmP59dCe1mNaRRSwpey1bOB3Ldpa0gLr6BM9xrCgODJhH9flFjjlUh4MOb9bYRlNh/L/sXfncVGV7R/HP8MOKhhu4Iao5BLmgoqaplmiZtpiZYvmkpaamtlTZj7lUmn6lFk/S8t9q6y00jKVNM1931dUFBcQBQUVwQHO748RFEEFWWYGv+/Xi5fOmXPOXPfhXMyZa+5z3xXWHGJ44zGWO5AmzMEyVlU0GW8NV1FLciqZ65N3gWWw8VMQdRKKwofeQ6jUcz/3mw6yzPQIOMHGZ4AiwAKY3wICypyE0xD2fXmC47bRtjQkGJWJivACJyhivEqfCuAZYOaz6A9gB7ifu0ppb7hkDKHtfqhTZz1FD6fQ2LSdH0w94MAMLJOs7rbEo/NaREQk36loaxfMQDQsgZ2mi+ys1Qh2nISBZlYNacMMusOXMKkE4AWmUwZBPS3/X26KsvSEWgBPnF7Bwn5QltPwMzDbckv55YUOmFuC8/dwYKEfvULg6BofSLaMY9g++CdOdfPmZHcY1wV6LYAjRkNLz9735sDJOWScaEAkt8zARSgKBFqWbH0JnjN8aHoKRhT/CP6EmZehW1k4W70oeMFB4yPu37yDd5+ZgJfpB05dKk9wN5i5+3k8PwfaQaM5O3E0KsNlqB0exqPfL7KMZxgA8eHOLIhoCxOg40MwrwF88i188hLQDQKSnXnMEb7ldXjW2TJm4bGtwGQsN+YqDyQvmIEEvCedwpWrHC9diobAoPdh8KnhPA+MCweexjKMwa/wQI2jHBjvR6Oi66EsHPlPIDNeAmZYxvjsMfYHWtQHSkBwMOycHQAvwpl2sG8KXDTNZ2s4GPWh1WbgcfA9dITYpW4YX8K8YUCMpWfWV93h91Mh9C8CAStOYqwF01Lj2rTkK9Es4XJ714YJOAb7f69HCo6WYlMHSPgaok1xrAm3lIMOl7AMVfAyWCZOTQY+g6YTwLOimTlbICEOen0Gk/07sw0oZdrEmsEw+HvLq4WegJ3JjeF1y4RNncta0qbmG1uJpCzshq0br40TPQkqFQE2QNCLlu0/6QI+RaPgV8vjoGBgGfwxviUnnQNgwz4sY5ncWMS6+V+R3Lh2J0b98vAHfFx3FMdNcViG4DgJb0Dwcnj2z9mYuhr8ZfwfJg8D0zKD+xufwOUXgx/OPMnXvMFn/IfH/5zP0/zKWxGjMD1qYCpjYFpjQH1nHDal8nGDUZg+N9hpagSBa7AUasdhOcf1hZyIiEhBcrJ2AJJd8Vg+FDhfm4HYHfCGTz2Z37kzByL+S8/ix+FF2HOmCvTC8ts1OsPpOSwsB/HloPNRYMZJCAKehoC18L1bKs5AtzVQ/cvjhC2DgClRMAFqsZuhfEK5YbF8bLzNf5/5nKNP+9Bi50Y4fIbrH1L0AV3y2hUa/7yChKLwkBc4ewO1ojBWwa44CPjVmVpOZvCEUs9f4uxPRfns5w/Y/VwtqA8PLfDC5/RJ8IKuX/5E2EAIWABhz8ADq45axkW8DE/2aw+TIP5NODy9Kh03Lsb40gRhlix73xtMzYGBMGzzCL4Y/D77l9eDLfO43is4+lrMygPJC55AC2KjirCwXHtScMTFuEoUSxnTfTgn01YbCf03jCGYTXQ+PZ/qLY4TnwyEA53B/DlM3HNteANX4BKsPAAtykLtkWGQAn8aL/KI6QcaAkELoIX3X3xcoi1N60NkhyrwKFw+7YC7WypjBkL/IuCxAOi9zDIOz3RwCDTgcAwcPoZlaijlgdyJt+VLuUqwlNa0Xr4Un2FxbLt8bdgBLFMa1QOK/1mUMgcu8Vb1UfQ1vU/AGeAE4AnJp8HDG/gZeu2Yw+vGeAaNHAgdYEGdtrzb5y+cB0G823oYDH6PnwVvOHUa9g0OYsZYaFQWgrZDUDQQBh494bKXA0UupaZFikcFiF8IAyZDt57fUJUjfOD+GSQv5PpkeyrQSn5I66m9G7a4W37Yj+VLYjOwG9Now7JqJ+DcRqZyFcvQBc0Bf3CCl778zTKOyBOWp8onPwM7/gK+suzrx2svV9UJ8IQtaee0zm0RERFrUk9bu3EFyyD/sVgKuKew3J60HwLBkRSc34C3zoziOX623CoVBr1Oz4G112/Um1H52u42Qvjz8OAZy/JnXGHmQ8+TMBQCesKqng0B8Ot+FncSYDb89z+fgxNUXhx1bfzOjVyfXEMkr5lZb0qiyO8G714Yxb5w+GQPjC0BQXXAM8RM0zFACkz8GUq1uwTLYGm7p9j7fmV8VsSxsByw3zJBxxWAn6/d2l0HEtbC+298QCcAVyh2GuqN3Y8x2ASuMDEa4o2OmHbAHwtaQnP4ovX77PwkAPqBZcKQU1zvVag8kLzgRPqXchPcqM9WTlOWbqa2+Ja6QNR0r/SzbcxG+L92g+ncbj4Tyr4KH4LnSPiqEUysa/nb3rUItHgaxr3Zh8kHIAzgcaAyUB16dPgB/84QNBvaP/0Tfya1o2kwMALoAmMGwiy3VDo8DoO8YMWllpa7N4pCk6PLqTR7/7X3g5VoEj65vRuHCTgDF8LgBdhIMD414mALbMLyV/UMloJtqw/hCFVgIXzR5X0CvoeEyjCmH+AF3bpZJlSlCPAofDt4IOZxwNvwTPRfOP8NH3/4NmFJsHlEIJMbAT3hMUeoMmYP3f6EM6eBdrCmNUR186Lv+M8p0j0VhsLQT6DPh3D//h14rTJ4quf3zJzXhw/8P4PEGVz/kkJFLSkI27D8nY3F8rfW3fIzPhTGfwXnxmH5W5w2bvQi2DADusXDwK2wZCH0Wwlb5sCOr7Dch3HzHULJXB9b/cZhPkRERMQaVLS1K2YyXkilXZSd5P6tJ+Bp+ICRjOUdcITgQFhZDsKev772Q2DpcdUBfjX6wGnoMx08B0PXLT8x5TJsnhxI88qbYA/8Mb0ltU+HMT8c/vociIM5j3eEJSe5djOhSD5yJ7WuiRdM71PzRRi63PJBnveBIrBwMISFQZ/mwHg4O7koKxfDA8uOEtayPB384atllnP/wWBgg+XWbpbD2ktNGTXjIzYYIczvDqYJsHUw0Bloatmmc4v5fFBhCE90WAE/AJOgzteH4MBGruehilSSV5yv/XutCOQG3ZnO8COfAvFw7hN83S+w3WjLuzHX/p4fhXGL4ZWUWVAB+BJedry+F4+HYPSCgQzaMxFvoKc3GGNgYhdI6AVfLYL4nyG+Jyxq/TyfuA61dBz3AsZaPq73mQQ7/wzAeRA8sXYFc1Z2pNiIaNa3bcnxgOpY7gI5joYHkTtLu8HLGdgGByYz0VQaBgI9oRgQ7AWtAiw/LUb8RaPBOwkfjGV4BC/wCLbsYd5GODu9KE7AJyuAhUANcJ6O5e/1cmAZ/Hfo5wT9CQ0+30Ms8NVAKLEQjvwcCDXAZ4bBxtOW8usvPMs3B97m5++fwDTDwPSPgSneIMxUGxqd5HfnF+GFhXBsMpbS8kVU1JKCk0zGLz+uYOnAsYmMRda0MXEvYjlP52Ip9h7D0uEi7e+1zl0RERFbp6Kt3TGTcbKmi8A2qG+GUeDdLpEnpqzg1H5vWA5VgYDx0O0Ty9YBp7AMTeUP/YtP5I86LS13UCUCOyyXbw2677GMlxgO7/I/2AhNgYdcYfPSQLoM+YXrM4OL5Bdn4AoO/xgEvw4EweiWA2n1BpYP+HugFhAwGWaufJ7QgKb8zaO0CIb7Q3YQUPkkc66NizigJSQuB9PqKwwIBPMK2E4dwrvDkzOWUQZgseVjT1gvCBt87Zby7+FBdjNi4bswHZ71n32tl+02dP5L/nEHjkEUlF8VA71NpBd0E+fQse5iTGHwkDdM3N+VQSGw0clMfGVncIUS18biDAbeX/oBQ4aNB6DjSmgXM59fvdvSZ7NlmINngClJ4Fkadi4NYFT3jxgTDuOawq4tMPRPIADqHDzEzg8DSKwDwxjBpUqlYMkYODwZSwFAd11ITlzh+p0KZdjwem0YA73eBecJwBtAMqxc3BbWgr8/zPzpeSgBHy9/m3JYvrQoNfYSyUCQ0Rz2ANGw5ul6uLvGwKMQPxLLF9UngF8tA48MOAqshWbPLeMl/6lwzvIld+fm0G/jVMZW78fznyyCSaHw93wYvxFLRXgjJE/G8oV1LCp4ifVl5xy8seesCrUiIiL2RkVbu2cGTsGPzpbrsBdhYi8o1y+W0NJN2Q+cGQhd3v+OXuOBRfD78hAoDfsuBPDE8yugJ5YJNjbCoJbAacsoWF99DitoSejTTUk2vPHam0rDxrvhF7B8W68ehpLfqtOw4yrL7dw/wpBHx7NiQmPM++Cv8GurbIGrpp84bFrDi/1+h0Zw6Ic60Bs6n4FBnYGhEFPEm2XlWrFwDzi/CWWJZKgxlfje0HQ80MgyRGdAZ0gwAmjVHIiGkXzI8DFjMLkazG/WGctIi7FZBSuSRy4C+2FSGLSIh783Yrmrwh1oBYfhg+AhzI2FPj/MxLwRmiQ64LnUzNGjPnw1x/LX2RsYNeUjCISwwPKcbV6UxQs7Utf0F/SDt0JGsdp40vKSIy3j3C6cYXkrGTQZHlyDZTwFR1hU7VFW8gjucwyOuj8A5+ZzvfCm4UEkp8xYet22hwOtCI7dydgN/VgxpjEcBZbDyXBgGNAbeBG6PvMT/AzBbOQKUP4nCH23KVWBNpVXwZuw5t16NNu7lWlePWAPeK6xDI9AdWAB9GkJLfz/wmVQHGsatOKHGj3gP39h+tjg8ZXzMZVMZXCF/4P/bsQybmg013so3jgcjgpfIiIiIpL/VLQtFMzwQgKtF/4GwdDnRTC9aNAu5k9aBcBJYPba12AtfNUbAkzLYCx4mML446eWUAT+ONQSTkPCRqAOBI2xzGfg0yWOd/gf5U0/QtURsOErODwPFa0k/zkBJRjIF5ztUJQZW4CK0LL1epxjoaQRiP8G4MT1kZ5xg4VfAiug07szYA+cnANMgpeZiyMpJBlP0GzEMp5O+pWynGZeEpwcCKyCjmuADlD78zBar/wNR99L7KnRwDJEXDdgzRgstxaqQCX54caJX+KxnHhzgb+vLa8HB8pgDDHxNL/RyQvYCuPioMjPqYxpB5WXRTHgacveAtJO1zAoV/QkpZtdhIHgbnjBM/BFufd5cdXvDPoJZnQH9kCHQ5YejMZgLBOa/QB/NG/JSh5h4MZvofeua2N5HijA4yKFR1qx0xnL1wolYBI4/GPwPS9zgeIc/dCHZguX8aSxmrGb+2E8DtSHVQsaQnVotWwNvSrAiuca8wlDCfoMth2tgamSQbNWW+ExeKnCb7jUjsN03OAXnsW0xsD0q4Fpl8Gqz9tgDvSELVvhQKglnGfhr/uegaq74eQYLLl3BUsept3ZdOWmNoiIiIiI5C+nO68itu8K8BfLDnaEc0AAGL1NzNsDjIcgfyACNv4MfbzgyAU/GHWcgM8g4P4VnAyDVFYw+dqeBoQBbrDA6Eqf6Jns/LMRMP+G1zqDilaS/64AE3mTr4g+7Uc9oMn05axb8Sg8Cg0m7YEtYF4L3kZn3E1zmPE5dHsdNn4L3SdPZ3DL4fQ0psDQk4QWb8vEOHgQ+Nf7D0yr4PO1/8WY/AHUgvj6zniGmWEtzHm3I8tM13ogMh8ORGP5c6ne5VIQ0sYmdOb631pvcGvBb9VaQ2mo12A/Z+JgxueWEtKcLpa15rSGVte2iP8QhiWO4otV77P7MjABeBz+NsXReTqWsaEHA19Ctzqw8mdoMQbcjNqYnt+JaalBwIadhJ2qBsPdYEo4lt6Heg+QvLIfxpshqjw7dzSi45rF0AbLUAeBsK93TY54V6XC0yfoznQm9+xMEq5Uigin/efL4T8rMTU1LDXWP/ZhGbqpDFAD82NlwA129msEUXOASkAA/Gcblq+z0/6el4Hkv+DCGa5/IZ1M5ktkFWtFREREpGCpaFsoWHqAlKoWAVdgzMgbPm6UACoCX0LwCOB5mEcnhgWOhX4wLeJFmrGaeqaTLLi2Jx6Fef2gz9KZ/L4hBJ5IwHJb4PXXEsl/zkAlzj5dEb6C80ZDgtloeepDmBbyIj0q/sCh9yvz2szZGJ/M4ZOhsPJb8DH8CB61ijYdVsHPQGmIv2Q5c08CpvrQP3AM1IrBRBxPGEvxIIGfkroy+91n6bL8FyxdFM9w/ZZYkYJ042QzAPGQaOZ7XuLJscuYHHZ9VE1voHN9IBHC90C8UZ7HTCdxPwPjTb5UMl7jzfe/w7hkgunQORz2dYeay4FoGB08kCGJ4wkzOtOi8hwa9d7J2Z+KwlwIe7o2/AEkT7TEoMKV5MqNl50XsQw7sB9+rIdlFOZwWOmMZdzYeBLfK8d3T70JW+CDfp9Rb/Aatm1tahmLf0IoEA9rtmL5O33s2n5jLfvcYcYyim1aD/ZtWEYuN3P9CxFnrv+dv3nYA53rIiIiImJdKtoWGlc426siFIHBgUAX4HPY1wVqfgg8BAt7QQcvGMZYKGt5vsevP1iKWpOh/2D4v1jL8k51gFrwVJ+lwBo0K7gUPDNwDH5biam4Qefpk1lHExJaevBt2EAeMf1wrax6FGOziX3dLCN+7gLquR1nZRLUGAofGuP5dthA1qZAOa5NMDYSJnR5F/gKiOUPUxnw6QNRu+jy31/gY7AUbWNJnwBKpEBk1YPVGSgDRZ1pzVK+PPQab+74joV1LV9EdCoNdTav5z+mxpwCejqdZJnxJL9SDTZ0o7tbdxiJpZNsEaAO1PwMuATzw+EVZgHQa8oc2ABHS/tQZX4kdA67ttGNY3mK5IW0cyltAru0cWPTCqdpQxPsh9+u9Tp/ryrbhjeFxHAsk9/tv7aPU1wfVsSZjF8spy2/1etrPGYRERERsV0q2hYaV2DKVkw/GhjNTbz19Ci+mP6+pWB7Gmr23Mq+0kHsexJqPgef/AxD38YyOUcIjO3Wj8GtxhFVycUya/OjwGXgv3C994qINRyDGfOZU6kXFIWab38FXcC/JVTuYWBUNRFUfzVbRzTjyjDLR/YXEuezeE9HWA4jTQOhG3QYD7gBR+Hx4PnQaCXXv4xwh6h9QCx8vAtLMSCtd60+0IstKAaVoCb7aPT5ToxR4GU0JIjD0C+WCabGrAUGvwEEQJLpd4Z9Bu9/NBbnfrBxMLQ3jhNdw48f9j/J2Ua/4wl0qw+0i4UfoH+dMUxo8C5sScAyoO1KlAeSf24+p85wvXdr2nPOWIY88AZiIfEMlmLure74uXmfNz7OasiDG58TEREREbEtmois0LgCREMdIA6+aPo+oQfgZD9Y9UlD9q0KYkmH5tSsD7//FMLQnsDjwA4gCd7dOIGoSi4sBMtYcgMh9KGmlufVw0qsJhnLB/lYGL4RdoALSZbiaxngHJguGWx9phmsgEtGQz40vmfxgY7wHMwZCGXqg6mqwdE3fcAVTD0M/jKl9exKO6/PYCkMHMNyC+2pmwMRsbJYSIRZvAKrYG0svMAPvMxcTn5tuel78OtAI/hq4PWJ+ZxfALwh+E/oyRTCD8CLG39nwNNwxegKybDgz7aYUgwmLH8XtuwC/gKWcH0ABhW0JK/cfD7dWKRN63V74/VG2qR8p4AwLH+rb1WwTb7h35v/n9Xjm58TEREREbEtKtoWGslAGEwBvODMWrjfKEX5p6H5xk2caQFtuqyCN6Gp0zLOTAHGw5mvgQ3AzzDZeBtfoyVnZgB14IWUH+FCDBoaQawn7VbZi8DfsAcucB+0BEYAJQEfLJMpPQQeJFCWSH6obplErJgRQuJKYAO0YQmmPw2oPhxLT9obCwNXsBRs08Y2jC+IxonkwEU4bGZ6XDcmL+zMfUZlItdWYWW7tpRfAF0d4dQkb0519sYdGHAKJr7R1fIlXBdgI4x+biT+1WFiI6AR9Fk7E3pDx+cWQ/2J8Fgolt61acMhZFVEE8kLdyqW3vxcWlH3xm2zKrxqTFoRERERKTxUtC00zMBF+OwMVIAy28GJFMb9CubqMBcYNweYDT+lWPqrvL5wPJuNlpj+Nnj0s0V88OJn7KMm7q5Qafx+Yp3KATPRB3axrht6YB2A2XRhzYf1GBvQDz4DnoL3gz8g9JOmNDy1mYkHB7GDuiScgCf/swz3qFSoCmEv1oZfJt7mda5cfx0Rm+MOnCSx+C5eC5hNO/6EPRC2GBgMJbpDud6xlPs8lnPGQNaUrUefXjPZuhaYDWNH9GPIzx/C8/BKogPPvjsb02cGpieuwC8xWM79TVi+ILmI8kAKTnZ7vqpXrIiIiIjcWzSmbaFjxvSWAU+BsdzEoAVAd8vH78GTgYXgZHQm1jSHb6MHYipjABtZ8fsTcAE6MY/ziaU4bqqOpcfVrW5DFClI1wpIifPZ79+RZk23whwDy23c1Rk9dySjqwKN4qGpG2N/HMyRS1WY/2VnaAEkAufSJhYTsUdpQ3pcgcP18CABQiDgDeBt2OZfg3ob93M2uCgj4z4g3KsylIWfjOFsIpy67CBk7WqOjKjCT590tYxnvgeo5AbM5vrfehVrxVakFWidULFWRERERO5FKtoWKmbgL1jjDGu68aixiHYsptYzE6kHJAyEY5eh17I58DvMLP08sA/YBv8Nhj1QaWY0bbsuuLY8DH2AF9tyCo7Nh2OtgHlYCk0xMNwfLgCEwpryUCmY+cllgBlYzmF3IBqdz2K/0nrAugMr2W+qQR1jPRMmvEEMJdlIMJ2Dn2K/qR78B9753/9oMmItY38fZhkCpyjwGJyhDERxbZLJeVhyQrkhtkwFWxERERG5N6loW+hEX/v3DCt+f4ImT66j1UNgLITkypaRPGuuAtOoy8AkYBVwBvZMBjyhG/w1oROWXrYay1ZszUUs5/jKa/+/Nvbs4a1YCrinLM8nb8MyPi1YilzJqCgl9ivt3HW+9v9jwCl2mp6h2YGtcBjL9xO/nATWwOGmzDnejTl/97IMIXLgJFQtD3NgVZ028GM818eu1Zi1IiIiIiIitkhF20LHjOWD/Rr4uCP/12YAHy0YzUUvZ7ymXiXuKRc4Y4bFHrADrk+4dBFL0csTtpwETqKhEcQ2xV77SSs0XQG2cf0chsyTjIkUFmkT85UG3KEzlr/lyWuwDJ9QFX5bA7+VAcoAG7EMqWAGDsOBesACLDmkHowiIiIiIiK2SkXbQskMxMKWeOJ8fHA5HId5gie8B17jr/LE6z/Djl1knHQpHkuxNxlLAUy3y4otyuqcTOb6TPcihdmNPW6Tgb9gi/u15aeuLTt87afStXVjsfQ2/5vrX3CA8kVERERERMS2qWhbaEUDf8GFK5h7doNzQGI8vOfJH5eew3Jr7M0FsLTH2xCxH7q9W+5FV7AMAXLl2k/a0Alpd1uc4fqwIGlf0GmYEBEREREREXuhom2hlTbuoTP8FnNt2X644A7/qcStC7P6QC8iYvvSCrTJNzy+0Y1jkmsYBBEREREREXvjYO0A7tY333yDv78/bm5uBAUFsXr1amuHZIPSelWtxDLW4X5gN5bxDFWcLYyUFyIZFd6cSObWPWfNZOyBfvNjudcV3rwQuXvKC5HMlBcimSkvpCDZZdF23rx5DBw4kKFDh7J9+3aaNWtG27ZtiYiIsHZoNibttthTWHrdxt7wo55XhY3yQiSjwpsTOSnCqlArGRXevBC5e8oLkcyUFyKZKS+koJkMwzCsHUROBQcHU69ePSZOnJi+rEaNGjz11FOMHj36ttvGx8fj5eUFvAe45m+gNsP92r+aeCazJOBT4uLi8PT0tHYwuaK8kLxTOPIiNzkBygu5mfICbsyL/wJu+Reo2IFE4GO7zwlQXkheUl6k0XWUXFc4rqFAeSF5KXt5YXdj2l69epWtW7fy3nvvZVgeEhLCunXrMq2flJREUlJS+uO4uLi0Z/IzTBtzL7U1pyzHxg6/u8hAeSF5y/7zIqc5AcoLuRPlBSgv5Eb2nxOgvJC8prxIo7yQ65QXaZQXcl328sLuirbnzp0jJSWFMmXKZFhepkwZoqKiMq0/evRoRowYkcWevsinCMUeXbx48do3XvZJeSH5wZ7zIqc5AcoLyR7lRZr/5UOEYo/sOSdAeSH5Q3lxI11HiYXy4kbKC7G4U17YXdE2jclkyvDYMIxMywCGDBnCoEGD0h+npqZy/Phx6tSpw4kTJ+y+e358fDwVKlRQW+6SYRhcvHiRsmXLFsjr5TflhYXyIncKU15kNycgc15cuHABPz8/IiIi7PoCM43yIneUFxbKC9tV0G0pTDkByos0hSknQHmRW8oLC+VF7igvlBf2wFbzwu6KtiVLlsTR0THTNxnR0dGZvvEAcHV1xdU141ghDg6W+dc8PT0LxckFaktu2PsfS1Be3IracvfsPS9ymhOQdV6A5VgUlvMIlBe5oby4TnlhuwqyLfaeE6C8uJXClBOgvMgp5UXWlBd3T3mRkfLCdtlaXjgUQBx5ysXFhaCgIEJDQzMsDw0NpUmTJlaKSsS6lBciGSknRDJTXohkprwQyUx5IZKZ8kKswe562gIMGjSILl26UL9+fRo3bsx3331HREQEvXv3tnZoIlajvBDJSDkhkpnyQiQz5YVIZsoLkcyUF1LQ7LJo26lTJ2JiYhg5ciSRkZEEBgayePFi/Pz8srW9q6srw4YNy7Kbur1RWySN8uI6tUVAOXGzwtSewtSWgqa8yKgwtacwtaWgKS+uK0xtgcLXnoKkvLiuMLUFCl97CpLy4rrC1Baw3faYDMMwrB2EiIiIiIiIiIiIiFjY3Zi2IiIiIiIiIiIiIoWZirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhtxzRdtvvvkGf39/3NzcCAoKYvXq1dYOKZN///2X9u3bU7ZsWUwmE7/99luG5w3DYPjw4ZQtWxZ3d3datGjB3r17M6yTlJRE//79KVmyJEWKFKFDhw6cPHmyAFthMXr0aBo0aECxYsUoXbo0Tz31FAcPHsywjj21p7BSXhQs5YV9UF4ULOWFfVBeFCzlhX1QXhQc5YR9sIecAOWFLbalMLOHvCgsOQGFKC+Me8iPP/5oODs7G5MnTzb27dtnvPnmm0aRIkWM48ePWzu0DBYvXmwMHTrUmD9/vgEYv/76a4bnP/30U6NYsWLG/Pnzjd27dxudOnUyfH19jfj4+PR1evfubZQrV84IDQ01tm3bZjzyyCNG7dq1jeTk5AJtS+vWrY3p06cbe/bsMXbs2GG0a9fOqFixonHp0iW7bE9hpLxQXkhmygvlhWSmvFBeSGbKi4I9j5QTts9ecsIwlBe22JbCyl7yorDkhGEUnry4p4q2DRs2NHr37p1hWfXq1Y333nvPShHd2c2Jkpqaavj4+Biffvpp+rLExETDy8vLmDRpkmEYhnHhwgXD2dnZ+PHHH9PXOXXqlOHg4GAsWbKkwGLPSnR0tAEYq1atMgzD/ttTGCgvrH8eKS9sj/LC+ueR8sL2KC+sfx4pL2yP8sK655FywvbYY04YhvLCVttSWNhjXhSmnDAM+82Le2Z4hKtXr7J161ZCQkIyLA8JCWHdunVWiirnwsPDiYqKytAOV1dXmjdvnt6OrVu3YjabM6xTtmxZAgMDrd7WuLg4ALy9vQH7b4+9U17YxnmkvLAtygvbOI+UF7ZFeWEb55HywrYoL6x/HiknbEthyQmw73NJeWFbCkte2Pt5ZK95cc8Ubc+dO0dKSgplypTJsLxMmTJERUVZKaqcS4v1du2IiorCxcWF++6775brWINhGAwaNIimTZsSGBgI2Hd7CgPlhfXbqrywPcoL67dVeWF7lBfWb6vywvYoL6zbVuWE7SksOQH2ey4pL2xPYckLez6P7DkvnArkVWyIyWTK8NgwjEzL7MHdtMPabe3Xrx+7du1izZo1mZ6zx/YUJsoL5YVkprxQXkhmygvlhWSmvLBOW5UTtquw5ATY37mkvLBdhSUv7PE8sue8uGd62pYsWRJHR8dM1fDo6OhMlXVb5uPjA3Dbdvj4+HD16lXOnz9/y3UKWv/+/Vm4cCH//PMP5cuXT19ur+0pLJQXygvJTHmhvJDMlBfKC8lMeWG9tionbFNhyQmwz3NJeWGbCkte2Ot5ZO95cc8UbV1cXAgKCiI0NDTD8tDQUJo0aWKlqHLO398fHx+fDO24evUqq1atSm9HUFAQzs7OGdaJjIxkz549Bd5WwzDo168fCxYsYMWKFfj7+2d43t7aU9goL5QXkpnyQnkhmSkvlBeSmfKi4M8j5YRtKyw5AfZ1LikvbFthyQt7O48KTV7k+dRmNuzHH380nJ2djalTpxr79u0zBg4caBQpUsQ4duyYtUPL4OLFi8b27duN7du3G4Axbtw4Y/v27cbx48cNwzCMTz/91PDy8jIWLFhg7N6923jxxRcNX19fIz4+Pn0fvXv3NsqXL2/8/fffxrZt24yWLVsatWvXNpKTkwu0LX369DG8vLyMlStXGpGRkek/CQkJ6evYU3sKI+WF8kIyU14oLyQz5YXyQjJTXhTseaScsH32khOGobywxbYUVvaSF4UlJwyj8OTFPVW0NQzD+Prrrw0/Pz/DxcXFqFevnrFq1Sprh5TJP//8YwCZfrp27WoYhmGkpqYaw4YNM3x8fAxXV1fj4YcfNnbv3p1hH1euXDH69etneHt7G+7u7sYTTzxhREREFHhbsmoHYEyfPj19HXtqT2GlvChYygv7oLwoWMoL+6C8KFjKC/ugvCg4ygn7YA85YRjKC1tsS2FmD3lRWHLCMApPXpiuNUZEREREREREREREbMA9M6atiIiIiIiIiIiIiD1Q0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIirYiIiIiIiIiIiIiNkRFWxEREREREREREREboqKtiIiIiIiIiIiIiA1R0VZERERERERERETEhqhoKyIiIiIiIiIiImJDVLQVERERERERERERsSEq2oqIiIiIiIiIiIjYEBVtRURERERERERERGyIk7UDKGipqamcPn2aYsWKYTKZrB2OiIjNMQyDixcvUrZsWRwc7t3v9vR+ISJya3qvuE7vFyIiIpIT2b2OuueKtqdPn6ZChQrWDkNExOadOHGC8uXLWzsMq9H7hYjInd3r7xWg9wsRERG5O3e6jrrnirbFihUDLAfG09MzR9uazWaWLVtGSEgIzs7O+RFegVA7bIvaYVvUDoiPj6dChQrpfy/vVXq/UDtsjdphWwpDO/RekTdy836RHYXhXLMmHb/c0fG7ezp2uaPjlzs6frmT38cvu9dR91zRNu2WJU9Pz7v6EO7h4YGnp6ddn/Rqh21RO2yL2nHdvX6Lp94v1A5bo3bYlsLQDr1X5I3cvF9kR2E416xJxy93dPzuno5d7uj45Y6OX+4U1PG703XUvT0AlYiIiIiIiIiIiIiNUdFWRERERERERERExIaoaCsiIiIiIiIiIiJiQ1S0FREREREREREREbEhKtqKiIiIiIiIiIiI2BAVbUVERERERERERERsiIq2IlaUkmqwMTyWredMbAyPJSXVsHZIInbj4sWLDBw4ED8/P9zd3WnSpAmbN29Of94wDIYPH07ZsmVxd3enRYsW7N2714oRi4jYDl2DiIjko9QUTMfXUC52PabjayA1xdoRiYgdUtFWxEqW7Imk6ZgVdJ62hVlhjnSetoWmY1awZE+ktUMTsQs9e/YkNDSU2bNns3v3bkJCQnjsscc4deoUAGPHjmXcuHFMmDCBzZs34+PjQ6tWrbh48aKVIxcRsa7CfA3y77//0r59e8qWLYvJZOK333674zarVq0iKCgINzc3KleuzKRJk/I/0OxS4Sd3dPxyR8fv7uxbCOMDcZrzFPWPT8RpzlMwPtCyXLJH517u6Pjljg0dPxVtRaxgyZ5I+szZRmRcYoblUXGJ9JmzrVB8aBLJT1euXGH+/PmMHTuWhx9+mKpVqzJ8+HD8/f2ZOHEihmEwfvx4hg4dyjPPPENgYCAzZ84kISGB77//3trhi4hYTWG/Brl8+TK1a9dmwoQJ2Vo/PDycxx9/nGbNmrF9+3bef/99BgwYwPz58/M50mxQ4Sd3dPxyR8fv7uxbCD+9AvGnMy6Pj7Qs1/G7M517uaPjlzs2dvycrPKqIvewlFSDEYv2kdVNiAZgAkYs2kermj44OpgKODoR+5CcnExKSgpubm4Zlru7u7NmzRrCw8OJiooiJCQk/TlXV1eaN2/OunXreP311zPtMykpiaSkpPTH8fHxAJjNZsxmc47iS1s/p9vZGrXDtqgdtsUe25GSajB84d47XIPspUVAiTteg9hqu9u2bUvbtm2zvf6kSZOoWLEi48ePB6BGjRps2bKFzz77jI4dO2a5TV6+X9yK6cAfOM7vDhjc+JswrhV+UjpOx6j+RJ68VmGk45c7On53KTUFp78Gc/NxszAsS5e8R3KVEHBwLPj47IDOvdzR8cudgjx+2b1eUNFWpIBtCo/N1LvlRgYQGZfIpvBYGlcpUXCBidiRYsWK0bhxYz766CNq1KhBmTJl+OGHH9i4cSMBAQFERUUBUKZMmQzblSlThuPHj2e5z9GjRzNixIhMy5ctW4aHh8ddxRkaGnpX29katcO2qB22xZ7aERZnIir+1oUCyzVIEhPmLSHA6/Zj3CYkJORxdNaxfv36DF/wAbRu3ZqpU6diNptxdnbOtE1+vF9kYKQSsncQjlkUfkwYGEDy72+ybs8JTKYb1sjwKzPS17/Fi2TY5622tzyf1QsYmf5rutXzN7/ObV/rVl8pXHveuP3zYICRStDx7257/FJ+68f2itstx8+4+VilHY+05WnLbtifcdPjtONtZNyH6dp23LDdzcfJdMNrXd/nDY+N669/YzuuPzQyxZ45jhv2keH3dfM+LT+Vz4Xe1L7r6xiA8Wtvjns3BxMZ4k3fx437N4zMxzDD8TNuaucNbTIyty/z9qlgZGzPLbe91e8ny+0znhcmI/WGbTP+3tK2dUxJwtl8jlsxYUD8KZI+r0Wyo7tlicl07RkThskh/f+Yrv17bXnmZSbA4dp2ppu2c8hi39fWz7Ru2v8dbtr3TTHduF0uY8q0ftq6hkGdE9PvkLv92eq3G0y5uWnctjtGGaa7jM9Ipf6xSXd47+jPlh0HLMfv2koZ1zZdf2y6vuzmTE/75/q2N/41vGFZpte46bUyvEba69+wzJTF694Yi+kWr5u+C9NNz93UxhvWM4xUHjn4wW2P39WFgwg9Qi7PP4vsXkepaCtSwKLjb12wvdGMteGkGgZBfvfh5qxvYkVuNnv2bHr06EG5cuVwdHSkXr16vPTSS2zbti19HdNNFz2GYWRalmbIkCEMGjQo/XF8fDwVKlQgJCQET0/PHMVmNpsJDQ2lVatWWX7gtxdqh21RO2yLPbZj0a5I2Lf7jutVfqAOjz/oe9t10nqX2ruoqKgsv+BLTk7m3Llz+PpmPg55+X6RFdPxNTjtiL3184BbchwtD36Q69e6F5kA15RLNAr/0tqh2CUT4JyaSNVzS60dit0qejXa2iHYJUvuXqTJ0c+tHYpdsrx3XKTpkU+tHYpdMgEe5ljaBRbH8Gua6/1l9zpKRVuRAmIYBisORPN//4Rla/2l+86wdN8ZXBwdqFOxOI0ql6Bx5RLUrVhcRVwRoEqVKqxatYrLly8THx+Pr68vnTp1wt/fHx8fH8DyYfzGD9zR0dGZPpyncXV1xdXVNdNyZ2fnuy7I5GZbW6J22Ba1w7bYUzt8ixfJ9np3apO9tDk7svqCL6vlafLj/SKDKzHZW8/VE5zcMvQ0yqqHUdbLsrMumdfNt9fKYt27jfXKBYg7wR3d5w8eJa7tz3T93/TXMGV8LdPNcWV3m1ssu3FfebKfm5dxd/uJOQJHlt/5+N3fBkpVz/zad/yXa73UcrLNTXHf1XZ32E+OY8rimEbtgWVD73zsHhsJZR4AI5W03uFZ/6T1fr5peaZtjJv+vdX6WT1/i33dMq6cxnS7uMj4+PJZiD165+PnWQHci995vRy5/d0leftS+fRaiXEQf/LO6xXzBddicFPP87Qe7Lf//83rX3uc0//n6DXy4fVywelKDOTBe312rxdUtBXJZympBot3R/L1P4c5EJW9Weu93J1pcX9JNoTHciY+iU3hsWwKj+Wr5WG4ODlQ74Yibp2KxXF1UhFX7l1FihShSJEinD9/nqVLlzJ27Nj0wm1oaCh169YF4OrVq6xatYoxY8ZYOWIREetoUOk+iro6cSkpOcvnTYCPlxsN/b0LNjAr8vHxSR9SJ010dDROTk6UKGGlYaqKZv3lYiYvfA/+zfI3FnsUvhpmZmPMwQ7/p+OXlfDV2SvaNu6n43ezSs1gw9eWSceyLA6ZwLMsNOmnMW2zkt3cfXqizr2sZPf4PTNZxw+uF3XTirnH1sCsDnfeLrvv0XlERVuRfGJOSeXX7aeYtPIIR89dBqCIiyNdGlfCv2QR3pu/C8j4dp72nfeYjrVoE+iLYRgci0lgw9EY1h+JYf3RGM5eTGLD0Vg2HI1lPGG4OjkQ5HefpYhbpQS1yxfHxSn3Y6yI2LqlS5diGAbVqlXj8OHDvPPOO1SrVo3u3btjMpkYOHAgo0aNIiAggICAAEaNGoWHhwcvvfSStUMXESlwhmEwZsmB2xZsAYa1r3lPTYTauHFjFi1alGHZsmXLqF+/vvV6E/s1sRR27lT48WtS0JHZBx2/3NHxu3sOjtBmDPz0Cpa/qll80mvzqQq2t6JzL3d0/HLm5rsnKjW1yeNn1crOv//+S/v27Slbtiwmk4nffvvtjtusWrWKoKAg3NzcqFy5MpMmTcr/QEVyINGcwqz1x2jxv5W8+8sujp67THEPZ9567H7Wvfco77WtTqcGFZjYuR4+Xm4ZtvXxcmNi53q0CbTczm0ymfAvWYQXG1bkqxfrsun9R1n+dnM+eTqQJx70pWRRV5KSU1l3JIZxoYd4btJ6HhyxlM5TNjJhRRhbj8dyNTk1qzBF7F5cXBxvvPEG1atX55VXXqFp06YsW7Ys/UP2u+++y8CBA+nbty/169fn1KlTLFu2jGLFilk5chGRgpWSavD+r3uYvDocgOeCyuN7h2sQe3Xp0iV27NjBjh07AAgPD2fHjh1EREQAlvFoX3nllfT1e/fuzfHjxxk0aBD79+9n2rRpTJ06lf/85z/WCN8irfADXC+nk/GxCj+3puOXOzp+uVOzAzw/Czxv+lvqWdayvGY2evLdq3Tu5Y6OX+7Y6PGzak/by5cvU7t2bbp3707Hjh3vuH54eDiPP/44vXr1Ys6cOaxdu5a+fftSqlSpbG0vkp8uJSUzZ8NxpqwO59ylJABKFXOlVzN/Xg72o4hrxnRrE+hLq5o+rD8czbLVGwlpFkzjqqVv27vFZDJRpVRRqpQqysvBfhiGwZGzl1h/NJYNR2LYcDSGmMtXWXP4HGsOW2YudXd2pH6l6z1xa5XzwtlRPXHF/j3//PM8//zzt3zeZDIxfPhwhg8fXnBBiYjYGHNKKv/5eSe/7ziNyQRjnnmQ5xtUICXVyNE1iL3YsmULjzzySPrjtAnDunbtyowZM4iMjEwv4AL4+/uzePFi3nrrLb7++mvKli3LV199Zf3PFmmFnyWDIf709eWeZS0fGlX4uT0dv9zR8cudmh2gejuSj/7LjtVLqdOsNU6VH1axLDt07uWOjl/u2ODxs2rRtm3btrRt2zbb60+aNImKFSsyfvx4AGrUqMGWLVv47LPPrH9hJfes85evMmPdMWasO0bcFTMA5Yq707tFFZ4LKn/bScMcHUwE+3sTs98g2N87xx+WTCYTVUsXo2rpYnRpZCnihkVfSh9OYcPRGM4nmFkddo7VYZYibhEXR+pX8k4v4gaW9cRJRVwREZFCJ9GcQv8fthO67wxODia+6FSH9rXLArm/BrFVLVq0SJ9ILCszZszItKx58+Zs27YtH6O6Syr85I6OX+7o+OWOgyOGX1NO7Y2ntl9THbec0LmXOzp+uWNjx8+uxrRdv349ISEhGZa1bt2aqVOnYjabsxx3KikpiaSkpPTH8fHxAJjNZsxmc45eP239nG5na9SOvBF9MYlpa4/xw+aTJFxNAaBySQ9ef9if9g/6XuvNmorZfPvhCfK6Hf7ebvh7l+PF+uVITbUUcTceO8+Go7FsPnaeC1fMrDp0llWHzgJQxNWR+n730cjfm2D/+6jp63lXH9ys/fvIK2qH/bddREQg4Woyr83ayprD53BxcmBS53q0rF6wk2dIHlDhJ3d0/HJHx0+sRede7uj45Y4NHT+7KtpGRUVRpkzGi80yZcqQnJzMuXPn8PXNPAbX6NGjGTFiRKbly5Ytw8PD467iCA0NvavtbI3acXdiEmHFaQc2RJtINizFzXIeBiHlU3nQOx6HyJ2ERu7M8X7zsx0lgSeKw+O1ITIBwuJNHI4zcTjexOWkFFYdOseqQ5aeuG6OBlU8DQI8Dap6GpQrAneq4aYacCTeRLzZRNgvf1PF07jjNrbuXs6PhISEfIhEREQKStwVMz1mbGbr8fN4uDgypWt9mlQpae2wRERERCQH7KpoC5bbwW+UdvvTzcvTDBkyJH0sK7D0tK1QoQIhISF4enrm6LXNZjOhoaG0atXKerPJ5gG14+4cOXuZb1eHs2hnJMmplvOuXsXi9GnuT/OAkrc8B+/Emr+PlFSDA1EX2ZTWE/f4eS4mJrP3vIm95y3reLo50aDSfQRf64lbvUwxHG6oyC7de4bRiw8QFX+9R7uPpyv/fbw6rR+wvx49yo/rdySIiIj9ibmUxCvTNrH3dDyebk7M6NGQehXvs3ZYIiIiIpJDdlW09fHxISoqKsOy6OhonJycKFGiRJbbuLq64urqmmm5s7PzXRdkcrOtLVE7smfPqTi+WXmYv/ZEkTZEWrOAkrzxSFWC/b3vulh7M2v8PpyBOn4lqONXgteaW4q4+07HW8bEPRrDpvBY4hOTWX7gLMsPWIZT8HJ3Jtjfm8ZVSpCSavDJn/u5eeS4M/FJ9P9xp13PQn0v50dhaLeIyL0oKi6RzlM3cjj6EiWLujCrRzA1y+ask4KIiIiI2Aa7Kto2btyYRYsWZVi2bNky6tevryKD5Lmtx2OZsOIw/xw8m74spGYZ3nikKrUrFLdeYPnI0cFErfJe1CrvRa+HK5OcksreG4q4m8NjibtiZtm+Myzbd+aW+zEAEzBi0T5a1fQpNJObiIiI2KqImARenrqBE7FX8PVyY07PYKqUKmrtsERERETkLlm1aHvp0iUOHz6c/jg8PJwdO3bg7e1NxYoVGTJkCKdOnWLWrFkA9O7dmwkTJjBo0CB69erF+vXrmTp1Kj/88IO1miCFjGEYrDl8jgkrDrMxPBawjOfavnZZ+raoSjWfYlaOsGA5OTpQu0JxalcozuvNq5CcksruU3FsOBrLX3si2XUy7pbbGkBkXCKbwmNpXCXrnvAiIiKSe4ejL/LylI2ciU/Cr4QHc14NpoL33c3dICIiIiK2wapF2y1btvDII4+kP04be7Zr167MmDGDyMhIIiIi0p/39/dn8eLFvPXWW3z99deULVuWr776io4dOxZ47FK4pKYa/L3/DF//c5id1wqRzo4mOtYrT+/mVahUsoiVI7QNTo4O1K14H3Ur3kfZ4m68+eOOO24THZ+Y/4GJiIjco/aciuOVaZuIvXyV+8sUZc6rwZT2dLN2WCIiIiKSS1Yt2rZo0SJ9IrGszJgxI9Oy5s2bs23btnyMSu4lySmp/Lk7km/+OcLBMxcBcHN24MWGFXnt4cr4erlbOULbVbpY9j4QfrbsIAbQ7kFfnB0d8jcoERGRe8iWY7F0n7GZi4nJPFjei5ndG3JfERdrhyUiIiIiecCuxrQVyStXk1NZsO0kE1cd4XhMAgDFXJ3o0tiPHk39KVk08+R1klFDf298vdyIikvMNBFZGhNw4vwVBs7bwf+WHuTVpv680LACHi760yMiIpIba8LO0WvWFq6YU2hYyZup3epTzE1zPIiIiIgUFqqcyD3lytUUftgUweTVR4mMs9y2f5+HM6829adL40p4uevDTnY5OpgY1r4mfeZswwQZCrdp04599lxtIuOuMGPdMU5duMLIP/bx1YowXmnkxytNKqk4LiIicheW7Y2i3/fbuZqSysP3l+LbzkG4uzhaOywRERERyUMq2so9IT7RzOz1x5m2JpyYy1cBKOPpSq9mlXkpuKJ6ft6lNoG+TOxcjxGL9qUXwQF8vNwY1r4mbQJ9AejZrDLzt51k8r9HORaTwFcrDvPtv0d5vn4Fejbzx6+ExgwWERHJjt93nGLQTztJSTVo84APX75YB1cnFWxFREREChtVqqRQi718lWlrwpm5/hgXE5MBqOjtQe/mVegYVE4fcvJAm0BfWtX0Yf3haJat3khIs2AaVy2No4MpfR03Z0deDvbjhQYVWbo3ikmrjrDrZByzNxxn7sbjtK3lS++Hq1CrvJcVWyIiImLbvt8YwdDfdmMY8Ezdcox99kGcNF68iIiISKGkoq3YpZRUg43hsWw9Z6JEeGymImFUXCKTVx/l+40RXDGnABBQuihvPFKVJx701QecPOboYCLY35uY/QbB/t4Zfhc3r/d4LV/aBvqw/mgM3646yqpDZ/lzVyR/7orkoaoleP3hKjQLKInJlPU+RERE7kWT/z3KJ4v3A9ClkR8jOjyAwy3eb0VERETE/qloK3ZnyZ7IG27Hd2RW2BZ8r92OX9PXi4mrjjB/60mupqQCUKucF288UpWQmmX04cZGmEwmmlQpSZMqJdl3Op7v/j3Col2RrD0cw9rDMdT09eT15pVpV0sFdhERubcZhsH4v8P4cnkYAL2bV2Fwm2r6clNERESkkFPRVuzKkj2R9JmzLcOkVwCRcYn0nrMNBxOkXnuyob83/R6pql6bNq5mWU/Gv1CX/7SuxtQ14fy46QT7IuN588cd/G/pQXo1q8xz9ctr3GEREbnnGIbBJ3/uZ8qacADeaV2NNx6pauWoRERERKQgqAoidiMl1WDEon2ZCrY3SjXg4YCS9H80gAaVvAssNsm98vd5MKz9AwxoGcDsDceZse4YJ89fYdjCvYz/+xCvNK5E1yaV8C7iYu1QRURE8l1KqsHQX3fz4+YTAAxvX5NuD/lbOSoRERERKSi671jswpWrKczbHHFtSITb69Oiqgq2duy+Ii4MeDSAtYNb8tFTgVT09uB8gpkvl4fR5NPlDPt9DydiE6wdpoiISL4xp6Ty1rwd/Lj5BA4mGPvsgyrYioiIiNxj1NNWbMr5y1c5cvYSh6Ov/Vz7/6kLVzBu18X2BtEX71zYFdvn7uJIl0Z+vNigAkv2RjFp1RH2nIpn5vrjzNkYQbtavrz2cGUCy3lZO1QREZE8k2hOod/32/h7fzRODia+fKEu7R70tXZYIiIiIlLAVLSVAmcYBpFxiZkKs0eiLxFz+eottyvq6silpJQ77r90Mbe8DFeszMnRgSceLEu7Wr6sOxLDpFVHWB12joU7T7Nw52maBZSkd/MqNKlSQmMXi4iIXbuclMxrs7ew9nAMrk4OTOocxCPVS1s7LBERERGxAhVtJd+YU1I5HpOQ3nP2yLUC7ZHoS1y+euvia7ni7lQpXZSqpYpStfT1Hy93Z5qOWUFUXGKW49qaAB8vNxr6a2iEwshkMvFQ1ZI8VLUke07F8d2/R/lj12lWh51jddg5Ast58vrDVWgb6IOTo0Z+ERER+xJ3xUz36ZvYFnGBIi6OTOnagMZVSlg7LBERERGxEhVt7zEpqQYbw2PZes5EifBYGlctjaND7nonJlxN5ujZy9d7zl4rzh6PuYw5JesxDZwcTPiV8MhQlK1aqhiVSxWhiOutT8th7WvSZ842TJChcGu64fnctkdsX2A5L756sS7vtK7GlNVHmbflBHtOxdP/h+1U9PagVzN/ng2qgLuLo7VDFRERuaNzl5J4Zeom9kXG4+XuzIzuDahb8T5rhyUiIiIiVqSi7T1kyZ5IRizad20yL0dmhW3B18uNYe1r0ibwzmOlnb98NX0ogxt/Tl24csttPFwcqXJDj9kqpYpQtXRR/EoUwfkuekO2CfRlYud6N7TDwicH7ZDCo4K3ByOeDOTNx+5n1vpjzFx3jIjYBD74fS9f/B1GtyaV6NLIj/uKuFg7VBERkSxFxl2h85SNHDl7mZJFXZn9akNq+HpaOywRERERsTIVbe8RS/ZE0mfOtkzDCkTFJdJnzjYmdq5Hm0BfDMPg9I3jzV4b1uDI2duPN+tdxIWqpYpahjW44cfX0w2HPO752ibQl1Y1fVh/OJplqzcS0iw4T3oMi/3yLuLCwMfu57WHK/PzlpNMXn2Uk+evMC70EBNXHuGFhhV4tak/5e/zsHaoIiIi6SJiEnhpygZOnr9CWS835vQMpnKpotYOS0RERERsgIq294CUVIMRi/ZlOQ5s2rK35u1kworDHD13mYS7GG/Wu4B7Mjo6mAj29yZmv0Gwv7cKtgKAh4sTXZtU4uXgivy5O5JvVx1lX2Q809ceY9b647R/0JfXm1dRDyYREbG6sDMXeXnKRqIvJlGphAdzegbry0URERERSaei7T1gU3hshqEEsnLFnMKe0/GAZbzZSiWLZCrMVi5VBA8XnTJi+5wcHXiyTjk61C7LmsPnmLTqCGsPx/DbjtP8tuM0ze8vxevNK9O4cglMpusF//wY81lERORme07F0WXqRs4nmKlWphizezakdDE3a4clIiIiIjZEFbh7QPTF2xds07z6kD8vBlfEr4THXY03K2JrTCYTzQJK0SygFLtPxvHtv0dYvDuSVYfOsurQWWqX9+L15lVo/YAPofuicjXms4iISHZsORZL9+mbuZiUTO3yXszs0ZDiHhp7XUREREQyUtG2kNt18gIz1x7L1rqP1SxD1dIaR00Kp1rlvZjwUj2Ox1xmyupwftpygp0n4+g7dxulirpw9lLmMZtvHvNZREQkN1aHneW1WVu5Yk6hob83U7vWp5ibs7XDEhEREREbpKJtIbXzxAW+XB7GigPRd1zXBPh4udHQ3zv/AxOxMr8SRfjoqUDefCyAWeuOMWPdsSwLtmAZ89kEjFi0j1Y1fTRUgoiI3LWle6Po//12rqak0vz+UkzqHIS7i6O1wxIRERERG6WibSGzPeI8Xy4PY+XBswA4mOCpOuV4sIIXIxbuA8gwIVlaCWpY+5oqSMk9pWRRVwaFVKNuxfvoPmPzLdczgMi4RDaFx9K4SomCC1BERAqN37af4u2fd5KSatA20IcvX6iLi5OGohIRERGRW1PRtpDYFnGeL/8OY9UhS7HW0cHEU3XK0a9lVfxLFgHAx9PthjE7LXw0Zqfc4+ITzdlaL7tjQ4uIiNxo7sbj/Pe3PRgGdKxXnjEda+GkuQNERERE5A5UtLVzW49betb+e0Ox9um65ej3SFUqXSvWpmkT6Eurmj6sPxzNstUbCWkWTOOqpdXDVu5p2Z2te86G41T09qBuxfvyOSIRESksvvv3CKMWHwCga2M/hrV/AAddd4mIiIhINqhoa6e2HIvly+VhrA47B1iKtc/UtfSs9StR5JbbOTqYCPb3Jma/QbC/twq2cs9r6O+Nr5cbUXGJGYYOudnmY+d5+pt1NKrsTZ8WVXk4oCQmk/JHREQyMwyDL0IP8dWKwwD0bVGFd1pX0/uGiIiIiGSbirZ2ZvOxWL78O4w1hy3FWicHEx3rleeNR6pSsYSHlaMTsT+ODiaGta9JnznbMJH1mM8fPFGTfZHx/Lb9FBuOxrLh6CZq+nrSu0UVHg/00W2uIiKSzjAMPvpjP9PWhgPwTutqvPFIVStHJSIiIiL2RkVbO7EpPJYvlx9i7eEYwFKsfTbIUqyt4K1irUhutAn0ZWLnencc83lQq/uZsjqcHzdHsC8yngE/bOczbw96PVyZ54LK4+asWcBFRO5lKakGQ3/dzY+bTwAw8skHeKVxJesGJSIiIiJ2SUVbG7fhaAxf/h3G+qPXi7XP1a9A3xZVVKwVyUPZGfO5bHF3Pmxfk/4tqzJr/XFmrAsnIjaBD37bw5d/H6L7Q/50buSHl7uzFVsiIiLWYE5J5a15O/hjVyQOJhj7bG2eDSpv7bBERERExE6paGuj1h+J4cvlh9hwNBYAZ8frxdry96lYK5Ifsjvm831FXHjzsQB6PezPT5tPMHl1OKcuXOF/Sw8yceURXgquyKtN/Snjmb1JzkRExL6kpBpsDI9l6zkTJcJjqetXggE/bGf5gWicHU18+UJdHq/la+0wRURERMSOqWhrQwzDYP21nrUbw68Xa5+vX4G+j1SlXHF3K0coIjfycHGi20P+vNzIjz92nWbSyqMcPHOR7/49yoy1x3imXjlee7gylUsVtXaoIiKSR5bsibxhOB1HZoVtwcXRgaspqbg6OTCpSxCPVCtt7TBFRERExM5p9hwbYBgG6w6fo9O3G3hp8kY2hsfi4uhAl0Z+rHrnET55upYKtiI2zNnRgafrlmfJwGZM61afBpXu42pKKj9uPsGj41bRZ85Wdp64YO0wC5VKlSphMpky/bzxxhsAdOvWLdNzjRo1snLUImLvluyJpM+cbRnGPwe4mpIKwBuPVFXBVkRERETyhHraWpFhGKw7EsP4vw+x+dh5AFwcHXihYQX6tKiCr5cKtSL2xGQy0bJ6GVpWL8OWY7FMWnWEv/dH89eeKP7aE0WTKiXo06IKTauWxGTKeugFyZ7NmzeTkpKS/njPnj20atWK5557Ln1ZmzZtmD59evpjFxeXAo1RRAqXlFSDEYv2YdxmnR82RfDGI1VvObyOiIiIiEh2qWhrBYZhsObwOb78O4wtx68Va50ceLFBBXqrWCtSKNSv5M2USt4cjLrIt/8eYeGO06w7EsO6IzE8UNaTPi2q0DbQVx/s71KpUqUyPP7000+pUqUKzZs3T1/m6uqKj49PQYcmIoXUpvDYTD1sbxYZl8im8FgaVylRQFGJiIiISGGlom0BMgyD1WHnGP/3IbZFXAAsxdqXGlakd/Mq+Hhp0iKRwqaaTzHGPV+HQa3uZ8rqcOZtPsHe0/H0+347lUocpNfDlelYrzxuzo7WDtVuXb16lTlz5jBo0KAMPZhXrlxJ6dKlKV68OM2bN+eTTz6hdOlb37aclJREUlJS+uP4+HgAzGYzZrM5RzGlrZ/T7WyN2mFb1A7rirxwOdvrmc2e+RxN3sjN78Lefn8iIiIi9kZF2wJgGAb/XivWbr9WrHV1cuClYEuxVjPMixR+5e/zYHiHBxjwaAAz1x1j5vpjHItJYOive/giNIxXm/rzcqOKeLo5WztUu/Pbb79x4cIFunXrlr6sbdu2PPfcc/j5+REeHs4HH3xAy5Yt2bp1K66urlnuZ/To0YwYMSLT8mXLluHh4XFXsYWGht7VdrZG7bAtaod1HI0zAXf+gu3o3h0sPrk9/wPKQ3fzu0hISMiHSEREREQkjYq2+cgwDFYeOsuXf4ex49okRK5ODrwc7Efv5pUprWKtyD3Hu4gLb7W6n9cersy8zSeYsvoop+MSGbPkAN/8c5iXG/nR46FK+vuQA1OnTqVt27aULVs2fVmnTp3S/x8YGEj9+vXx8/Pjzz//5JlnnslyP0OGDGHQoEHpj+Pj46lQoQIhISF4euas15zZbCY0NJRWrVrh7Gy/hXi1w7aoHdYVf8XMtwdXYk7JelRbE+Dj5Uq/Tg/bzdA3ufldpN2NICIiIiL5Q0XbfGAYBisPnmX88rD0GePdnC3F2tebV6Z0MRVjRO51RVyd6NHUn86N/Fi48zTfrjpCWPQlJq06wrQ14XQMKs/rD1emUski1g7Vph0/fpy///6bBQsW3HY9X19f/Pz8CAsLu+U6rq6uWfbCdXZ2vuvCUm62tSVqh21ROwpeojmF/vN23bZgCzCs/QO4udrfpId387uwl9+diIiIiL1S0TYPGYbBigPRfLk8jF0n4wBLsbZLIz9ee7gKpYplfUuuiNy7XJwceDaoPM/ULcfyA9FMXHmYbREX+GFTBPM2R9A20JfezatQq7yXtUO1SdOnT6d06dK0a9futuvFxMRw4sQJfH19CygyESkszCmp9Pt+G+uOxFDExZF+LQOYtf5YhknJfLzcGNa+Jm0C9TdGRERERPKGirbZlJJqsDE8lq3nTJQIj6Vx1dLpt74ZhsHy/ZZi7e5TlmKtu7MjXRr70atZZRVrReSOHBxMtKpZhsdqlGbzsfNMWnWEFQei+XN3JH/ujqRp1ZL0aVGFJlVKZJhs616WmprK9OnT6dq1K05O19/OLl26xPDhw+nYsSO+vr4cO3aM999/n5IlS/L0009bMWIRsTcpqQaDftrJ3/ujcXVyYGq3BjSqXILXHq7M+sPRLFu9kZBmwRmuC0VERERE8oKKttmwZE8kIxbtu9ajwpFZYVvw9XLjwydq4uTowJfLD7HnlGVcL3dnR15p7EevhytTsqiKtSKSMyaTiYb+3jT092Z/ZDzfrjrCol2RrDl8jjWHz/FgeS96N69C6wd87vkCwd9//01ERAQ9evTIsNzR0ZHdu3cza9YsLly4gK+vL4888gjz5s2jWLFiVopWROyNYRgM/XU3i3aextnRxKQuQTSqXAIARwcTwf7exOw3CPb3vuf/HouIiIhI3nOwdgC2bsmeSPrM2ZbhFjiAyLhE+szdRq9ZW9hzKh4PF0d6N6/CmsGPMOTxGirYikiu1fD1ZPwLdVn5nxZ0beyHm7MDu07G0XfuNh4bt4ofNkWQlJySabsb7wzYGB5LSmrWYzDau5CQEAzD4P7778+w3N3dnaVLlxIdHc3Vq1c5fvw4M2bMoEKFClaKVETsjWEYfPTHfn7cfAIHE3z5Ql0eqVba2mFJNn3zzTf4+/vj5uZGUFAQq1evvu36c+fOpXbt2nh4eODr60v37t2JiYkpoGhFREREsmb1om1OL6q+/vpratSogbu7O9WqVWPWrFn5FltKqsGIRfu4XbnDBLzevDJrBrfkvbbVKaFirYjksQreHox4MpC1g1syoGVVvNydCT93mSELdtNszD98u+oIFxPNgOWLpqZjVtB52hZmhTnSedoWmo5ZwZI9kVZuhYiI/fji7zCmrQ0HYOyztXm8lsaqtRfz5s1j4MCBDB06lO3bt9OsWTPatm1LREREluuvWbOGV155hVdffZW9e/fy888/s3nzZnr27FnAkYuIiIhkZNWibU4vqiZOnMiQIUMYPnw4e/fuZcSIEbzxxhssWrQoX+LbFB6bqYftzQygxf2l8S5ifzMFi4h9KVHUlUEh1Vj3Xkv+264GPp5uRF9MYvRfB2jy6Qpen72F3lncGRAVl0ifOdtUuBURyYbv/j3CV8vDABjR4QGeDSpv5YgkJ8aNG8err75Kz549qVGjBuPHj6dChQpMnDgxy/U3bNhApUqVGDBgAP7+/jRt2pTXX3+dLVu2FHDkIiIiIhlZdUzbGy+qAMaPH8/SpUuZOHEio0ePzrT+7Nmzef311+nUqRMAlStXZsOGDYwZM4b27dtn+RpJSUkkJSWlP46Pt4w9azabMZvNt40v8sLlbLUj8sJlzGbPbK1rC9Lafaf22zq1w7aoHQXHxQG6NqrAi/XLsWhXJN+tPsbRc5dZuvdMlusbWO4KGLFoLy0CStxx7EVbbruISH6au/E4oxYfAOCd1tXo2qSSdQOSHLl69Spbt27lvffey7A8JCSEdevWZblNkyZNGDp0KIsXL6Zt27ZER0fzyy+/0K5du1u+Tm4+X9wNe7g2sWU6frmj43f3dOxyR8cvd3T8cie/j19292u1ou3dXFQlJSXh5uaWYZm7uzubNm3CbDbj7OycaZvRo0czYsSITMuXLVuGh4fHbWM8GmcCHO/QEji6dweLT26/43q2JjQ01Noh5Am1w7aoHQXLHehfFULdTSw+ceu/VwYQGZfEhHlLCPC6/Ri3CQkJeRukiIgd+HX7Sf772x4A+raowhuPVLVyRJJT586dIyUlhTJlymRYXqZMGaKiorLcpkmTJsydO5dOnTqRmJhIcnIyHTp04P/+7/9u+Tq5+XyRG/ZybWKrdPxyR8fv7unY5Y6OX+7o+OVOfh2/7H7mtlrR9m4uqlq3bs2UKVN46qmnqFevHlu3bmXatGmYzWbOnTuHr2/m8caGDBnCoEGD0h/Hx8dToUIFQkJC8PS8fe/YlFSDXz7/lzPxSVmOa2sCfLxc6dfpYbuaNdhsNhMaGkqrVq2yLHTbC7XDtqgd1mXsimTxid13XK/yA3V4/MHbj82Y1mNIROResXRvFP/5eReGAV0b+/FO62rWDklywWTKeF1uGEamZWn27dvHgAED+PDDD2ndujWRkZG888479O7dm6lTp2a5TW4+X9wNe702sRU6frmj43f3dOxyR8cvd3T8cie/j192P3NbdXgEyNlF1QcffEBUVBSNGjXCMAzKlClDt27dGDt2LI6OWfcwc3V1xdU18+Rgzs7OdzzwzsDwDg/QZ842TJChcJsW4bD2D+Dmap/j2WbnGNgDtcO2qB3W4Vu8SLbXu+PfPjtqt4hIbq0OO0v/77eTkmrQsV55hrV/4JbXomLbSpYsiaOjY6YOINHR0Zk6iqQZPXo0Dz30EO+88w4ADz74IEWKFKFZs2Z8/PHHWXYKyc3ni9ywt2sTW6Pjlzs6fndPxy53dPxyR8cvd/Lr+GV3n1abiOxuLqrc3d2ZNm0aCQkJHDt2jIiICCpVqkSxYsUoWbJkvtr79WYAAIazSURBVMTZJtCXiZ3r4eOVcVgGHy83JnauR5tAzSYsItbX0N8bXy83blVmMAG+Xm409PcuyLBERGza5mOx9Jq1haspqbQN9GFMx1o42NHdU5KRi4sLQUFBmW5lDA0NpUmTJlluk5CQgINDxo9EaZ1BDOP2wwmJiIiI5CerFW3v5qIqjbOzM+XLl8fR0ZEff/yRJ554ItPFVl5qE+jLmsEtmdOjPq8EpDCnR33WDG6pgq2I2AxHBxPD2tcEyFS4vX5nQE27GspFRCQ/7T4ZR4/pm0k0p9L8/lJ8+UJdnBytdmkseWTQoEFMmTKFadOmsX//ft566y0iIiLo3bs3YBna4JVXXklfv3379ixYsICJEydy9OhR1q5dy4ABA2jYsCFly5a1VjNERERErDs8wqBBg+jSpQv169encePGfPfdd5kuqk6dOsWsWbMAOHToEJs2bSI4OJjz588zbtw49uzZw8yZM/M9VkcHE8H+3sTsNwj291bhQ0RsTtqdASMW7SMyLjF9uY+XG8Pa19QXTSIi1xw6c5FXpm3kYlIyDf29mdQ5CBcnFWwLg06dOhETE8PIkSOJjIwkMDCQxYsX4+fnB0BkZCQRERHp63fr1o2LFy8yYcIE3n77bYoXL07Lli0ZM2aMtZogIiIiAli5aJvTi6qUlBQ+//xzDh48iLOzM4888gjr1q2jUqVKVmqBiIhtaRPoS6uaPqw/HM2y1RsJaRZM46ql9UWTiMg1x2Mu03nKRs4nmKld3oupXevj7pL13Ahin/r27Uvfvn2zfG7GjBmZlvXv35/+/fvnc1QiIiIiOWP1ichyclFVo0YNtm/fXgBRiYjYL90ZICKStci4K7w0eSPRF5OoVqYYM3s0pJibJucQEREREduj+8BEREREpNA7dymJl6ds5NSFK/iXLMLsng0p7uFi7bBERERERLKkoq2IiIiIFGpxCWa6TN3E0bOXKevlxpyewZQu5mbtsEREREREbklFWxEREREptC4nJdNtxib2R8ZTsqgrc3s1olxxd2uHJSIiIiJyWyraioiIiEihlGhOodesLWyPuICXuzNzejbEv2QRa4clIiIiInJHKtqKiIiISKFjTknljbnbWHckhiIujszs0ZDqPp7WDktEREREJFtUtBURERGRQiUl1eCteTtYfiAaVycHpnZrQJ0Kxa0dloiIiIhItqloKyIiIiKFRmqqwfsLdvPHrkicHU182yWIRpVLWDssEREREZEcUdFWRERERAoFwzD46M99zNtyAgcTfPVCXVpUK23tsEREREREckxFWxEREREpFL4IPcT0tccAGPtsbdrW8rVuQCIiIiIid0lFWxERERGxe9+uOsJXKw4DMPLJB3g2qLyVIxIRERERuXsq2oqIiIiIXZuz4Tij/zoAwLttqvFK40rWDUhEREREJJdUtBURERERu/Xr9pN88PseAPq2qELfFlWtHJGIiIiISO6paCsiIiIidmnJnij+8/MuDAO6NanEO62rWTskEREREZE8oaKtiIiIiNidfw+dZcAP20lJNXg2qDwfPlETk8lk7bBERERERPKEirYiIiIiYlc2hcfy2uwtXE1J5fFaPnz6TC0cHFSwFREREZHCQ0VbEREREbEbu05eoMeMzSSaU2lRrRTjO9XFyVGXtCIiIiJSuOgKV0RERETswqEzF+k6bROXkpIJ9vdmUucgXJx0OSsiIiIihY+uckVERETE5h07d5mXp2zkfIKZ2hWKM7VbA9ycHa0dloiIiIhIvlDRVkRERERs2ukLV3h5ykbOXkyiuk8xZnZvQFFXJ2uHJSIiIiKSb1S0FRERERGbde5SEp2nbOTUhSv4lyzC7FeDKe7hYu2wRERERETylYq2IiIiImKT4hLMdJm6iaPnLlOuuDtzegZTqpirtcMSEREREcl3KtqKiIiIiM25lJRM1+mb2B8ZT8mirszpGUy54u7WDktEREREpECoaCsiIiIiNiXRnEKvmVvYceICxT2cmdszGP+SRawdloiIiIhIgVHRVkRERERsxtXkVPrO3cb6ozEUdXViZveGVPMpZu2wREREREQKlIq2IiIiImITUlIN3vppBysOROPq5MDUrvWpXaG4tcMSERERESlwKtqKiIiIiNWlphoMWbCLP3dF4uxo4tsuQQRXLmHtsERERERErEJFWxERERGxKsMwGPnHPn7achIHE3z1Ql1aVCtt7bBERERERKxGRVsRERERsapxoYeYse4YAP97tjZta/laNyAREREREStT0VZEROzSqVOn6Ny5MyVKlMDDw4M6deqwdevW9OcNw2D48OGULVsWd3d3WrRowd69e60YsYhkZdKqI/zfisMAfPTkA3QMKm/liERERERErE9FWxERsTvnz5/noYcewtnZmb/++ot9+/bx+eefU7x48fR1xo4dy7hx45gwYQKbN2/Gx8eHVq1acfHiResFLnKPS0k12Bgey9ZzJjaGxzJzXTif/nUAgMFtqtOlcSXrBigiIiIiYiOcrB2AiIhITo0ZM4YKFSowffr09GWVKlVK/79hGIwfP56hQ4fyzDPPADBz5kzKlCnD999/z+uvv55pn0lJSSQlJaU/jo+PB8BsNmM2m3MUX9r6Od3O1qgdtsXe27F07xk+XnyAqPgkwJFZYVvSn+vzsD89H6poV22z998H5K4N9txuEREREXugoq2IiNidhQsX0rp1a5577jlWrVpFuXLl6Nu3L7169QIgPDycqKgoQkJC0rdxdXWlefPmrFu3Lsui7ejRoxkxYkSm5cuWLcPDw+Ou4gwNDb2r7WyN2mFb7LEdO2NMTDuUdoOX6aZnDRKjDrN4cVhBh5Un7PH3cbO7aUNCQkI+RCIiIiIiaVS0FRERu3P06FEmTpzIoEGDeP/999m0aRMDBgzA1dWVV155haioKADKlCmTYbsyZcpw/PjxLPc5ZMgQBg0alP44Pj6eChUqEBISgqenZ47iM5vNhIaG0qpVK5ydnXPYOtuhdtgWe21HSqrB6M//BZKyfN6EiSVnPBj88sM4Otxc0LVd9vr7uFFu2pB2N4KIiIiI5A8VbUVExO6kpqZSv359Ro0aBUDdunXZu3cvEydO5JVXXklfz2TKWAAyDCPTsjSurq64urpmWu7s7HzXBZncbGtL1A7bYm/t2HIk5tqQCFkzgMi4JLafvEjjKiUKLrA8Ym+/j6zcTRvsvc0iIiIitk4TkYmIiN3x9fWlZs2aGZbVqFGDiIgIAHx8fADSe9ymiY6OztT7VkTyV/TFxDxdT0RERETkXqCirYiI2J2HHnqIgwcPZlh26NAh/Pz8APD398fHxyfDOI1Xr15l1apVNGnSpEBjFbnX3eeRvR6ZpYu55XMkIiIiIiL2Q8MjiIiI3Xnrrbdo0qQJo0aN4vnnn2fTpk189913fPfdd4BlWISBAwcyatQoAgICCAgIYNSoUXh4ePDSSy9ZOXqRe8fZi0l8EXrotuuYAB8vNxr6exdMUCIiIiIidkBFWxERsTsNGjTg119/ZciQIYwcORJ/f3/Gjx/Pyy+/nL7Ou+++y5UrV+jbty/nz58nODiYZcuWUaxYMStGLnLv2Hs6jl4zt3A6LhF3ZweumFMxYRnDNk3aCNPD2te0q0nIRERERETym4q2IiJil5544gmeeOKJWz5vMpkYPnw4w4cPL7igRASAJXsieWveTq6YU6hcsghTutbn0JmLjFi0j8i462PX+ni5Max9TdoE+loxWhERERER26OirYiIiIjkCcMw+Pqfw3y2zDIkQrOAkkx4sR5eHs5ULlWUVjV9WH84mmWrNxLSLJjGVUurh62IiIiISBasPhHZN998g7+/P25ubgQFBbF69erbrj937lxq166Nh4cHvr6+dO/enZiYmAKKVkRERESykmhOYcCPO9ILtt2aVGJ6twZ43TARmaODiWB/b4JKGgT7e6tgKyIiIiJyC1Yt2s6bN4+BAwcydOhQtm/fTrNmzWjbti0RERFZrr9mzRpeeeUVXn31Vfbu3cvPP//M5s2b6dmzZwFHLiIiIiJpzsQn8vy361m08zRODiZGPV2L4R0ewMnR6v0DRERERETsklWHRxg3bhyvvvpqetF1/PjxLF26lIkTJzJ69OhM62/YsIFKlSoxYMAAAPz9/Xn99dcZO3bsLV8jKSmJpKSk9Mfx8fEAmM1mzGZzjuJNWz+n29katcO2qB22Re2w/7aLSMHaeeICr83ewpn4JIp7ODPx5SAaVylh7bBEREREROya1Yq2V69eZevWrbz33nsZloeEhLBu3bost2nSpAlDhw5l8eLFtG3blujoaH755RfatWt3y9cZPXo0I0aMyLR82bJleHh43FXsoaGhd7WdrVE7bIvaYVvu5XYkJCTkQyQiUhgt2nma//y8k6TkVAJKF2Vq1wZULHF311ciIiIiInKd1Yq2586dIyUlhTJlymRYXqZMGaKiorLcpkmTJsydO5dOnTqRmJhIcnIyHTp04P/+7/9u+TpDhgxh0KBB6Y/j4+OpUKECISEheHp65ihms9lMaGgorVq1wtnZ+c4b2Ci1w7aoHbZF7bh+R4KIyK2kphqM//sQX604DEDL6qX58oU6FHOz37+bIiIiIiK2xKrDIwCYTBknoDAMI9OyNPv27WPAgAF8+OGHtG7dmsjISN555x169+7N1KlTs9zG1dUVV1fXTMudnZ3vuiCTm21tidphW9QO23Ivt6MwtFtE8k/C1WQGzdvJkr2WL9lff7gy77aprknFRERERETykNVmhyhZsiSOjo6ZetVGR0dn6n2bZvTo0Tz00EO88847PPjgg7Ru3ZpvvvmGadOmERkZWRBhi4iIiNyzTl24wrMT17NkbxQujg7879kHGfJ4DRVsxaZ88803+Pv74+bmRlBQEKtXr77t+klJSQwdOhQ/Pz9cXV2pUqUK06ZNK6BoRURERLJmtaKti4sLQUFBmcZbDA0NpUmTJlluk5CQgINDxpAdHR0BSw9dERGxbWfOnKFLly6ULVsWJycnHB0dM/yIiO3aevw8T05Yy77IeEoWdeH7XsE8V7+CtcMSyWDevHkMHDiQoUOHsn37dpo1a0bbtm2JiIi45TbPP/88y5cvZ+rUqRw8eJAffviB6tWrF2DUIiIiIplZdXiEQYMG0aVLF+rXr0/jxo357rvviIiIoHfv3oBlPNpTp04xa9YsANq3b0+vXr2YOHFi+vAIAwcOpGHDhpQtW9aaTRERkWzo1q0bERERfPDBB/j6+t5yOBwRsS0Ltp3kvfm7uZqSSg1fTya/EkT5+zThmNiecePG8eqrr9KzZ08Axo8fz9KlS5k4cSKjR4/OtP6SJUtYtWoVR48exdvbG4BKlSoVZMgiIiIiWbJq0bZTp07ExMQwcuRIIiMjCQwMZPHixfj5+QEQGRmZ4Vvxbt26cfHiRSZMmMDbb79N8eLFadmyJWPGjLFWE0REJAfWrFnD6tWrqVOnjrVDEZFsSEk1+N/Sg0xadQSA1g+UYdzzdSjiavVpEUQyuXr1Klu3buW9997LsDwkJIR169Zluc3ChQupX78+Y8eOZfbs2RQpUoQOHTrw0Ucf4e7unuU2SUlJJCUlpT9Om8DTbDZjNpvzqDXXpe0zP/Z9L9Dxyx0dv7unY5c7On65o+OXO/l9/LK7X6tfcfft25e+fftm+dyMGTMyLevfvz/9+/fP56hERCQ/VKhQQcPZiNiJS0nJDPxxO3/vjwagf8uqvPXY/Tho/FqxUefOnSMlJSXT/BhlypTJNI9GmqNHj7JmzRrc3Nz49ddfOXfuHH379iU2NvaW49qOHj2aESNGZFq+bNkyPDzyrwf6zcPKSc7o+OWOjt/d07HLHR2/3NHxy538On4JCQnZWu+uirYXLlxg6tSp7N+/H5PJRI0aNXj11Vfx8vK6m92JiMg9Yvz48bz33nt8++23uv1UxIadiE2g58wtHDxzEVcnB8Y++yBP1iln7bBEsuXmoXcMw7jlcDypqamYTCbmzp2b/llm3LhxPPvss3z99ddZ9rYdMmQIgwYNSn8cHx9PhQoVCAkJwdPTMw9bYmE2mwkNDaVVq1Y4Ozvn+f4LOx2/3NHxu3s6drmj45c7On65k9/HL+0unTvJcdF2y5YttG7dGnd3dxo2bIhhGHzxxReMGjWKZcuWUa9evRwHKyIi94ZOnTqRkJBAlSpV8PDwyPQGGBsba6XIRCTNxqMx9J6zlfMJZkoXc+W7V+pTp0Jxa4clckclS5bE0dExU6/a6OjoTL1v0/j6+lKuXLkMnU9q1KiBYRicPHmSgICATNu4urri6uqaabmzs3O+fjDO7/0Xdjp+uaPjd/d07HJHxy93dPxyJ7+OX3b3meOi7VtvvUWHDh2YPHkyTk6WzZOTk+nZsycDBw7k33//zekuRUTkHjF+/HhrhyAit/Hjpgj++9seklMNapXzYvIr9fHxcrN2WCLZ4uLiQlBQEKGhoTz99NPpy0NDQ3nyySez3Oahhx7i559/5tKlSxQtWhSAQ4cO4eDgQPny5QskbhEREZGs3FVP2xsLtgBOTk68++671K9fP0+DExGRwqVr167WDkFEspCcksqoxQeYtjYcgCce9OV/z9bG3cXRypHJvWLv3r088MADWT63ZMkS2rRpk639DBo0iC5dulC/fn0aN27Md999R0REBL179wYsQxucOnWKWbNmAfDSSy/x0Ucf0b17d0aMGMG5c+d455136NGjxy0nIhMREREpCDku2np6ehIREUH16tUzLD9x4gTFihXLs8BERKRwSklJ4bfffksfF71mzZp06NABR0cVh0SsIe6Kmf4/bOffQ2cBGNTqfvq3rHrLMUBF8kP9+vUZO3ZshgmHk5KSePvtt5k6dSpXrlzJ1n46depETEwMI0eOJDIyksDAQBYvXoyfnx8AkZGRREREpK9ftGhRQkND6d+/P/Xr16dEiRI8//zzfPzxx3nbQBEREZEcynHRtlOnTrz66qt89tlnNGnSBJPJxJo1a3jnnXd48cUX8yNGEREpJA4fPszjjz/OqVOnqFatGoZhcOjQISpUqMCff/5JlSpVrB2iyD0l/NxlXp25maNnL+Pu7Mi452vTtpavtcOSe9DcuXN57bXXWLx4MdOnTycqKoqXXnoJgLVr1+ZoX3379qVv375ZPjdjxoxMy6pXr67ZtUVERMTm5Lho+9lnn2EymXjllVdITk4GLAPo9unTh08//TTPAxSRwi8lJQWz2WztMADLLJFOTk4kJiaSkpJi7XDu2u3a4ejoiJOTk1V60Q0YMIAqVaqwYcMGvL29AYiJiaFz584MGDCAP//8s8BjErlXrQk7xxvfbyPuihlfLzcmv1KfwHJed95QJB8888wzNGrUiK5duxIYGMjly5fp3r07n3/+uYYpEBERkXtSjou2Li4ufPnll4wePZojR45gGAZVq1bFw8MjP+ITkULu0qVLnDx5EsMwrB0KAIZh4OPjw4kTJ+z61uA7tcPDwwNfX19cXFwKNK5Vq1ZlKNgClChRgk8//ZSHHnqoQGMRuZfNXn+M4Yv2kZJqULdicb7tEkTpYppwTKwrJSWFq1evkpKSQkpKCj4+Pri6ulo7LBERERGryHHRNo2Hhwe1atXKy1hE5B6TkpLCyZMn8fDwoFSpUjZRJE1NTU2fQdrBwcHa4dy1W7XDMAyuXr3K2bNnCQ8PJyAgoEDb6erqysWLFzMtv3TpUoEXkEXuReaUVEYs2sucDZYxPZ+pW45Rz9TCzVljSot1/fjjj/Tp04dmzZpx6NAhduzYQffu3Vm6dCmzZ8+mcuXK1g5RREREpEBlq2j7zDPPMGPGDDw9PXnmmWduu+6CBQvyJDARKfzMZjOGYVCqVCmbufUxNTWVq1ev4ubmZvdF21u1w93dHWdnZ44fP56+TkF54okneO2115g6dSoNGzYEYOPGjfTu3ZsOHToUWBwi96ILCVfpO3cb647EYDLB4DbVef3hyjbxhZlI2pwZffr0AaBVq1bs3r2b119/nTp16hAfH2/lCEVEREQKVraKtl5eXukX9J6enrq4F5E8pb8pBc9aBemvvvqKrl270rhxY5ydnQFITk6mQ4cOfPnll1aJSeRecDj6Iq/O3MLxmASKuDjy5Qt1eaxmGWuHJZJu27ZtVKtWLcOy++67j59++onZs2dbKSoRERER68lW0Xb69Onp/89qxlUREZHsKF68OL///jthYWEcOHAAwzCoWbMmVatWtXZoIoXWPwejGfD9di4mJVP+Pnemdm1ANZ9i1g5LJIObC7Y36tKlSwFGIiIiImIbcjymbcuWLVmwYAHFixfPsDw+Pp6nnnqKFStW5FVsIiJSSAUEBBAQEGDtMEQKNcMwmLomnFGL95NqQMNK3kzsXI8SRTWxk9imkydPsnDhQiIiIrh69WqG58aNG2elqERERESsI8dF25UrV2a6iAJITExk9erVeRKUiEhOpKQabAqPJfpiIqWLudHQ3xtHB+sNubBy5UoeeeQRzp8/n+kLrnvRoEGD+OijjyhSpAiDBg267br6UC6SN64mp/LBb3uYt+UEAJ3qV+CjpwJxcbLfsbqlcFu+fDkdOnTA39+fgwcPEhgYyLFjxzAMg3r16lk7PBEREZECl+2i7a5du9L/v2/fPqKiotIfp6SksGTJEsqVK5e30YmI3MGSPZGMWLSPyLjE9GW+Xm4Ma1+TNoG++fra69ato1mzZrRq1YolS5bk62vZs+3bt2M2m9P/LyL5K+ZSEn3mbGPTsVgcTPDfdjXp/lAljR8uNm3IkCG8/fbbjBw5kmLFijF//nxKly7Nyy+/TJs2bawdnoiIiEiBy3bRtk6dOphMJkwmEy1btsz0vLu7O//3f/+Xp8GJiNzOkj2R9JmzDeOm5VFxifSZs42Jnevla+F22rRp9O/fnylTphAREUHFihXz7bXs2T///JPl/0Uk7x2IiufVGVs4deEKxdycmPBSPZrfX8raYYnc0f79+/nhhx8AcHJy4sqVKxQtWpSRI0fy5JNP0qdPHytHKCIiIlKwsn2PXHh4OEeOHMEwDDZt2kR4eHj6z6lTp4iPj6dHjx75GauIFHKGYZBwNTlbPxcTzQxbuDdTwRZIXzZ84T4uJpqztT/DyGpPt3b58mV++ukn+vTpwxNPPHHHSRrnz5/PAw88gKurK5UqVeLzzz/P8HylSpUYNWoUPXr0oFixYlSsWJHvvvsuwzrr1q2jTp06uLm5Ub9+fX777TdMJhM7duzIUezW1KNHDy5evJhp+eXLl/UeIpJLofvO0PGbdZy6cIVKJTz4te9DKtiK3ShSpAhJSUkAlC1bliNHjqQ/d+7cOWuFJSIiImI12e5p6+fnB0Bqamq+BSMi97Yr5hRqfrg0T/ZlAFHxidQavixb6+8b2RoPl+wP8z1v3jyqVatGtWrV6Ny5M/379+eDDz7I8vbjrVu38vzzzzN8+HA6derEunXr6Nu3LyVKlKBbt27p633++ed89NFHvP/++/zyyy/06dOHhx9+mOrVq3Px4kXat2/P448/zvfff8/x48cZOHBgtuO1FTNnzuTTTz+lWLGMM9dfuXKFWbNmMW3aNCtFJmK/DMNg4qoj/G/pQQwDHqpagq9fqkdxDxdrhyaSbY0aNWLt2rXUrFmTdu3a8fbbb7N7924WLFhAo0aNrB2eiIiISIHL8URkafbt25flzK4dOnTIdVAiIrZu6tSpdO7cGYA2bdpw6dIlli9fzmOPPZZp3XHjxvHoo4/ywQcfAHD//fezb98+/ve//2Uo2j7++OP07dsXgMGDB/PFF1+wcuVKqlevzty5czGZTEyePBk3Nzdq1qzJqVOn6NWrV/43Ng/Ex8djGAaGYXDx4kXc3NzSn0tJSWHx4sWULl3aihGK2KdEcwpDFuzm1+2nAOjSyI8P29fE2VETjol9GTduHJcuXQJg+PDhXLp0iXnz5lG1alW++OILK0cnIiIiUvByXLQ9evQoTz/9NLt378ZkMqXfUpzWuywlJSVvIxSRe4a7syP7RrbO1rqbwmPpNn3zHdeb0b0BDf29s/Xa2XXw4EE2bdrEggULAMvYe506dWLatGlZFm3379/Pk08+mWHZQw89xPjx40lJScHR0fLaDz74YPrzJpMJHx8foqOj01/zwQcfzFDsbNiwYbZjtrbixYunj4t+//33Z3reZDIxYsQIK0QmYh9SUg02hsey9ZyJEuGxNK5ampjLSbw2ays7TlzA0cHE8PY16dK4krVDFbkrlStXTv+/h4cH33zzjRWjEREREbG+HBdt33zzTfz9/fn777+pXLkymzZtIiYmhrfffpvPPvssP2IUkXuEyWTK9hAFzQJK4evlRlRcYpbj2poAHy83mgWUwtEhb2dMnzp1KsnJyZQrVy59mWEYODs7c/78+UzrG4aRadiErMbQdXZ2zvDYZDKlD0mT3X3Yqn/++QfDMGjZsiXz58/H2/t6Id3FxQU/Pz/Kli1rxQhFbNeSPZGMWLSPyLhEwJFZYVsoWdSF5FSDCwlmvNydmfhyPZpULWntUEXyxKVLlzINyebp6WmlaERERESsI8dF2/Xr17NixQpKlSqFg4MDDg4ONG3alNGjRzNgwAC2b9+eH3GKiGTg6GBiWPua9JmzDRNkKNymlTaHta+Z5wXb5ORkZs2axeeff05ISEiG5zp27MjcuXMJDAzMsLxmzZqsWbMmw7J169Zx//33p/eyvZO0IRKSkpJwdXUFYMuWLbloScFq3rw5YJnUskKFCjg46NZtkexYsieSPnO2Zfpy6twly/BUZTxdmfdaYyqVLFLwwYnkofDwcPr168fKlStJTExMX572paXu5hMREZF7TY6LtikpKRQtWhSAkiVLcvr0aapVq4afnx8HDx7M8wBFRG6lTaAvEzvXu6EHmoWPlxvD2tekTaBvnr/mH3/8wfnz53n11Vfx8vLK8Nyzzz7L1KlTM4299/bbb9OgQQM++ugjOnXqxPr165kwYUKObv186aWXGDp0KK+99hrvvfceERER6Xc3ZDX5ma1Km9QyISEhy3HRbxwiQuRel5JqMGLRvizvJkhjAip4exRUSCL55uWXXwZg2rRplClTxq7e20RERETyQ46LtoGBgezatYvKlSsTHBzM2LFjcXFx4bvvvsswFpWISEFoE+hLq5o+bAqPJfpiIqWLudHQ3zvPe9immTp1Ko899limgi1YetqOGjWKbdu2ZVher149fvrpJz788EM++ugjfH19GTlyZIZJyO7E09OTRYsW0adPH+rUqUOtWrX48MMPeemllzKMc2vrzp49S/fu3fnrr7+yfF49qUSu2xQem+ELqaxExSexKTyWxlVKFFBUIvlj165dbN26lWrVqlk7FBERERGbkOOi7X//+18uX74MwMcff8wTTzxBs2bNKFGiBPPmzcvzAEVE7sTRwVRgBYtFixbd8rl69eqljzM7aNCgDM917NiRjh073nLbY8eOZVq2Y8eODI+bNGnCzp070x/PnTsXZ2dnKlasmI3IbcPAgQM5f/48GzZs4JFHHuHXX3/lzJkzfPzxx3z++efWDk/EpkRfvH3BNqfridiyBg0acOLECRVtRURERK7JcdG2devrM7tXrlyZffv2ERsby3333afbmERE8tGsWbOoXLky5cqVY+fOnQwePJjnn38ed3d3a4eWbStWrOD333+nQYMGODg44OfnR6tWrfD09GT06NG0a9fO2iGK2IzSxbLXiz6764nYsilTptC7d29OnTpFYGBgpsk5NXyOiIiI3GtyNBNMcnIyTk5O7NmzJ8Nyb29vFWxFRPJZVFQUnTt3pkaNGrz11ls899xzfPfdd9YOK0cuX75M6dKlAct7x9mzZwGoVatWpmElsmv06NGYTCYGDhyYvqxbt26YTKYMP40aNcp1/CIFqaG/NyWLutzyeRPg62UZEkbE3p09e5YjR47QvXt3GjRoQJ06dahbt276vyIiIiL3mhz1tHVycsLPz09jDoqIWMG7777Lu+++a+0wcqVatWocPHiQSpUqUadOHb799lsqVarEpEmT8PXN+cRxmzdv5rvvvsuyB1abNm2YPn16+mMXl1sXv0RsUVJyCs6OWX+/nvZV+bD2NfNtDG+RgtSjRw/q1q3LDz/8oInIRERERLjLMW2HDBnCnDlz8PZWzw4REcm+gQMHEhkZCcCwYcNo3bo1c+fOxcXFhRkzZuRoX5cuXeLll19m8uTJfPzxx5med3V1xcfHJy/CFilwhmHw/oLdRMYl4unmhJuzI9EXk9Kf9/FyY1j7mrQJzPmXHSK26Pjx4yxcuJCqVataOxQRERERm5Djou1XX33F4cOHKVu2LH5+fhQpUiTD83d7e6uIiBR+L7/8cvr/69aty7Fjxzhw4AAVK1akZMmSOdrXG2+8Qbt27XjssceyLNquXLmS0qVLU7x4cZo3b84nn3ySPjRDVpKSkkhKul4Ui4+PB8BsNmM2m3MUW9r6Od3O1qgd1vPD5hP8tuM0jg4mJr5ch6CK97HhyFlWrN9Ky8ZBNKpSCkcHk121KY09/j6yUhjakZs25HW7W7Zsyc6dO1W0FREREbkmx0Xbp556Kh/CEBGRe8HIkSP5z3/+g4eHBwAeHh7Uq1ePK1euMHLkSD788MNs7efHH39k27ZtbN68Ocvn27Zty3PPPYefnx/h4eF88MEHtGzZkq1bt+Lq6prlNqNHj2bEiBGZli9btiw93pwKDQ29q+1sjdpRsCIuwfg9joCJduWTObdvA0v3WZ4LKglxYVtYGmbVEPOEvfw+7qQwtONu2pCQkJCnMbRv35633nqL3bt3U6tWrUwTkXXo0CFPX09ERETE1uW4aDts2LD8iENERO4BI0aMoHfv3pmKoAkJCYwYMSJbRdsTJ07w5ptvsmzZMtzc3LJcp1OnTun/DwwMpH79+vj5+fHnn3/yzDPPZLnNkCFDGDRoUPrj+Ph4KlSoQEhICJ6entlpXjqz2UxoaCitWrXKVHiwJ2pHwbuQYGbsxPWkGIm0qlGaz16snT62pz2143bUDtuRmzak3Y2QV3r37g1Yvty7mclk0pwaIiIics/JcdFWRETkbhmGkeXkMjt37sz2OOlbt24lOjqaoKCg9GUpKSn8+++/TJgwgaSkJBwdHTNs4+vri5+fH2Fht+6e6OrqmmUvXGdn57suyORmW1uidhSM1FSD937dwakLiVT09uCz5+vg4pI5XltvR3apHbbjbtqQ121OTU3N0/2JiIiI2DsVbUVEJN/dd999mEwmTCYT999/f4bCbUpKCpcuXUrvZXUnjz76KLt3786wrHv37lSvXp3BgwdnKtgCxMTEcOLECXx9NWmT2K5J/x5h+YFoXJwc+Obleni523chUCQ7XnrpJZ566inatm1LsWLFrB2OiIiIiM1Q0VZE7F9qChxfB5fOQNEy4NcEHDIX7sR6xo8fj2EY9OjRgxEjRuDl5ZX+nIuLC5UqVaJx48bZ2lexYsUIDAzMsKxIkSKUKFGCwMBALl26xPDhw+nYsSO+vr4cO3aM999/n5IlS/L000/nabtE8sq6I+f4bOlBAD568gECy3ndYQuR/2/vzuOiqvc/jr9mhl0FtwRURNwX1FRuhqaWBmqmZqXeNq20X2abeSs166Z1DcvyUrc0K83UFtvTNJEWl9JyLxMzUxJTlFxBkW3m/P4YQRFQFuHMwPv5eJyHzDnfc+bz/R7ke+Yz3/M9lUPLli15/vnnGT58OD169GDQoEEMHDiQkJAQs0MTERERMZWStiLi3hIWw/LxkHrg7Dr/+tD3eWhTvg8tWbt2Ld27dycqKorly5eX63u5uxEjRgAQFhZG165dy/VWYpvNxrZt25g/fz7Hjx8nODiYa665hkWLFmkUl7ikQ6kZPPT+FhwG3Ny5IUMjlKySquPpp5/m6aef5q+//mLx4sV88cUX/Otf/6JNmzYMHDiQQYMG0bFjR7PDFBEREalwZU7a2u12tm3bRmhoKLVq1boUMYmIFE/CYvhwOGDkX5+a7Fw/dH65Jm7nzp3Lgw8+yFtvvUVSUhKNGjUq1XGysrLw8vK6xNG5pp49e+JwOPj9999JSUkpMIdhjx49SnXclStX5v3s6+tLXFxcWcIUqTA5dgcPvreFwyezaBVUg2cHhRc677NIZdewYUPGjBnDmDFjSEtL46uvvuKLL76gd+/e1KhRgwEDBnDffffRtm1bs0MVERERqRDWku4wduxY5syZAzgTtj179qRTp06EhITk+9AsIlJihgFZp4q3ZKTCV49TIGHrPJDzn+XjneWKczyjsOMU7dSpU3z44Yfcd999XH/99cybNy/f9sWLFxMREYGPjw9169blxhtvzNvWuHFj/vOf/3DnnXcSEBDAPffcA8Ann3xC27Zt8fb2pnHjxrz00kv5jjlz5kyaN2+Oj48PgYGB3HzzzXnbPv74Y9q1a4evry916tQhOjqaU6dOlahOFeHHH3+kWbNmtG7dmh49enD11VfnLddcc43Z4YlUuOlxO1n/51Gqe3sw87ZO+HppaheRGjVqMHToUN59913+/vtv5s6di81mY926dWaHJiIiIlJhSjzS9uOPP+b2228HYMmSJSQmJvLbb78xf/58Jk2axA8//HDJgxSRKiI7HZ6rf4kOZjinTJhWzNuMnzgAXtWKffRFixbRsmVLWrZsye23386DDz7IU089hcViYenSpdx4441MmjSJBQsWkJWVxdKlS/PtP336dJ566imefPJJADZt2sTQoUOZPHkyw4YNY+3atYwZM4Y6depw5513snHjRh566CEWLFhA165dOXr0KGvWrAEgOTmZW265hRdeeIHBgweTlpbG6tWrMUqYiK4Io0ePJiIigqVLlxIcHKwRhVKlxW0/yOzVewCYfnN7mlxW3eSIRFyPzWajd+/e9O7d2+xQRERERCpUiZO2hw8fJigoCIBly5YxZMgQWrRowciRI3nllVdKHMDMmTOZPn06ycnJtG3bltjYWLp3715o2TvvvJN33nmnwPo2bdqwffv2Er+3iEhpzZkzJ+8LrL59+3Ly5Em++eYbrr32WqZOnco///lPpkyZkle+Q4cO+fbv1asXjz76aN7r2267jd69e/PUU08B0KJFCxISEpg+fTp33nknSUlJVKtWjeuvv54aNWoQGhqaN8dfcnIyOTk53HjjjYSGhgLQtm1bUlNTy7UNSmPXrl18/PHHNGvWzOxQREy198gpHv3oZwBGXhVGv3bBJkckYq5Dhw7x6KOP8s0335CSklLgi0e73W5SZCIiIiLmKHHSNjAwkISEBIKDg1m+fDkzZ84EID09HZutZLf0LVq0iLFjxzJz5ky6devG7Nmz6devHwkJCYXODfnyyy8zbdq0vNc5OTl06NCBIUOGlLQaIuKKPP2cI16LY+9aePfmi5e77WMI7Vq89y6mnTt3sn79ej799FMAPDw8GDZsGHPnzuXaa69l69ateVMeFCUiIiLf6x07djBo0KB867p160ZsbCx2u52oqChCQ0Np0qQJffv2pW/fvgwePBg/Pz86dOhA7969adeuHX369CE6Opobb7yxxH+TK0KXLl34448/lLSVKi0j2859CzeTlpFD59BaTOjXyuyQREyX+wXlU089pTsxRERERChF0vauu+5i6NCheRdTUVFRAPz000+0alWyDx0zZsxg5MiRjBo1CoDY2Fji4uKYNWsWMTExBcoHBAQQEBCQ9/rzzz/n2LFj3HXXXSWthoi4Ioul+FMUNO0F/vWdDx0rdF5bi3N7015gvbTJyzlz5pCTk0ODBg3y1hmGgaenJ8eOHcPX1/eix6hWLX89DcMo8AH13FFGNWrUYPPmzaxcuZIVK1bw73//m8mTJ7NhwwZq1qxJfHw8a9euZcWKFfzvf/9j0qRJxMfH065duzLW9tJ68MEH+de//sXBgwdp164dnp6e+ba3b9/epMhEKs7kxdtJSE6lTjUvXru1E562Ej9iQKTS+f7771mzZg2XX3652aGIiIiIuIQSJ20nT55MeHg4+/btY8iQIXh7ewPO+aYmTJhQ7ONkZWWxadOmAvtER0ezdu3aYh1jzpw5XHvttXm3AxcmMzOTzMzMvNe5twtnZ2eTnZ1d7Hhz9zn3X3eleriWqlyP7OxsDMPA4XDgcDhK+I4W6DMNy0cjAAuWcxK3Bs7kp9EnxlmuBMfOTZTmxnW+nJwc5s+fz4svvpj3pVWuIUOGsHDhQtq3b8/XX3/NiBEjLvg+5x6/devWrFmzJt+6H374gRYtWmCxWHA4HFitVnr16kWvXr146qmnqF27Nl9//XXeQ84iIyOJjIzkySefJCwsjC+//JLw8PBC6+FwODAMg+zs7AIjcsvzd/Gmm24C4O67785bZ7FY8pLWuv1VKruPNu7jgw37sFjg5X92JCjAx+yQRFxCSEiIS87FLiIiImKWEidtgXxPLM91oeREYQ4fPozdbicwMDDf+sDAQA4ePHjR/ZOTk/nqq6947733LlguJiYm37ySuVasWIGfX/Fvhz5XfHx8qfZzNaqHa6mK9fDw8CAoKIiTJ0+SlZVV8jdr0BPP62fhu3IKlpPJeauN6kGcvvppshv0hFLO65qWllbo+qVLl3Ls2DFuvvnmfCP/Aa6//nrefPNNnnvuOQYNGkTDhg258cYbycnJ4euvv+bhhx8GnAnTjIyMfHPO3nvvvXnJ2MGDB7NhwwZee+01XnzxRVJTU1m+fDl79+6la9euBAQEEB8fj8PhoEGDBnz77besWrWKXr16UbduXTZt2sTff/9NixYtiqxHVlYWp0+fZvXq1eTk5OTblp6eXqo2K47ExMRyO7aIq9uRnMpTX/wKwCPXtuCq5nVNjkjEdcTGxjJhwgRmz55N48aNzQ5HRERExHTFStq+8sor/N///R8+Pj4XfdjYQw89VKIACrsduDhzWM2bN4+aNWtyww03XLDcxIkTGTduXN7r1NRUQkJCiI6Oxt/fv0SxZmdnEx8fT1RUVIFbet2J6uFaqnI9MjIy2LdvH9WrV8fHp5SjzToNg8tvxpG0Dk4ehOpB0CgSX6uNi09SUJBhGKSlpVGjRo1C/xa9//779O7dm5CQkALbbrnlFmbMmEFQUBCLFi1i6tSpxMbG4u/vT/fu3fP+5litVnx8fPL9DerevTsffPABkydPZvr06QQHBzNlyhRGjx4NQP369Xn99dd5/vnnycjIoHnz5rz77rt06dKFHTt2sH79embPnk1qaiqhoaFMnz6dqKioIuuRkZGBr68vPXr0KND25fkAswvdGSFSmaVmZDPm3c1kZDvo2eIyHrhG8zqLnGvYsGGkp6fTtGlT/Pz8ClxLHD161KTIRERERMxRrKTtf//7X2677TZ8fHz473//W2Q5i8VS7KRt3bp1sdlsBUbVpqSkFBh9ez7DMJg7dy533HEHXl5eFyzr7e2dN4XDuTw9PUudICvLvq5E9XAtVbEedrsdi8WC1WrFai3DnI5WKzTpUfr9z5E7lUBuXOf78ssvi9w3IiIi79bOiIiIQu9KAPjzzz8LXT9kyJAiH6zYo0cPVq5cWei2tm3bEhcXl2+dw+EgNTW1yHpYrVYsFkuh56u8fw8XLFjA66+/TmJiIuvWrSM0NJTY2FjCwsIKPIxNpDIwDIPxH/9C4uFT1A/wIXbY5VitesiSyLliY2PNDkFERETEpRQraXvu7ayX6tZWLy8vOnfuTHx8PIMHD85bHx8ff9EP7atWreKPP/5g5MiRlyQWERGpGLNmzeLf//43Y8eOZerUqXlz2NasWZPY2FglbaVSmvvDn3z160E8bRZeu60Ttapd+AtnkaqopFOtiYiIiFR2JR7advr06SK3JScnF7mtMOPGjeOtt95i7ty57Nixg0ceeYSkpKS824EnTpzI8OHDC+w3Z84cunTpQnh4eMmCFxERU/3vf//jzTffZNKkSfkegBYREcG2bdtMjEykfGzae5SYZTsAeLJ/Gzo2qmVyRCKu49zpeFJTUy+4iIiIiFQ1JX4QWceOHXnvvffo1KlTvvUff/wx9913H3///XexjzVs2DCOHDnCM888Q3JyMuHh4SxbtixvzsPk5GSSkpLy7XPixAk++eQTXn755ZKGLiIiJktMTKRjx44F1nt7e3Pq1CkTIhIpP4dPZnL/u1vIcRgM6FCf4ZGa01nkXLVq1SI5OZl69epRs2bNQudgz33eRe6dGSIiIiJVRYmTtlFRUXTt2pXJkyczfvx4Tp06xQMPPMBHH33EtGnTShzAmDFjGDNmTKHb5s2bV2BdQEBAuT7ZXEREyk9YWBhbt24t8ECyr776ijZt2pgUlcilZ3cYjP1gKwdTM2h6WTVibmxXrAetilQl3377LbVr1wbgu+++MzkaEREREddS4qTt//73P/r3789dd93F0qVLOXDgAP7+/mzYsEEfuEWkVHIf3iUVx6w2f+yxx7j//vvJyMjAMAzWr1/P+++/T0xMDG+99ZYpMYmUh5e/2cX3fxzG19PGrNs7U927xJdcIpVez5498/2ckZHBL7/8QkpKSt6DQUVERESqqlJ9goiOjubGG29k1qxZeHh4sGTJEiVsRaTEcuc0zcrKwtfX1+RoqpbcOxY8PT0r9H3vuusucnJyePzxx0lPT+fWW2+lQYMGvPzyy/zzn/+s0FhEysvKnSn879tdAMTc2I4WgTVMjkjE9S1fvpzhw4dz+PDhAts0PYKIiIhURSVO2u7evZtbb72VgwcPEhcXx6pVqxg0aBAPPfQQU6dOrfAEgIi4Lw8PD/z8/Pj777/x9PTEai3xsxEvOYfDQVZWFhkZGS4RT2kVVQ/DMEhPTyclJYWaNWvmexhYRbnnnnu45557OHz4MA6Hg3r16lV4DCLlZf/x04xdtBXDgNuvbMQNHRuYHZKIW3jggQcYMmQI//73vwkMDDQ7HBERERHTlThpe/nll9O/f3/i4uKoWbMmUVFRXHfddQwfPpz4+Hi2bNlSHnGKSCVksVgIDg4mMTGRvXv3mh0O4Exqnj59Gl9fX7eef/Ji9ahZsyZBQUEmRHZW3bp1TX1/kUstK8fBmHc3czw9m/YNA3jqet2FJFJcKSkpjBs3TglbERERkTNKnLSdOXMmd9xxR751Xbt2ZcuWLYwdO/ZSxSUiVYSXlxfNmzcnKyvL7FAAyM7OZvXq1fTo0cOt7xy4UD08PT0rdIRtp06d+Oabb6hVqxYdO3a8YDJ88+bNFRaXyKU2dWkCP+87ToCvJ6/d2glvj4ofyS7irm6++WZWrlxJ06ZNy3ysmTNnMn36dJKTk2nbti2xsbF07979ovv98MMP9OzZk/DwcLZu3VrmOERERETKosRJ2/MTtrlq1KjBnDlzyhyQiFQ9VqsVHx8fs8MAnPPs5uTk4OPj49ZJW1eqx6BBg/D29gbghhtuMDUWkfKy+OcDvLPOecfAf4d1IKS2n8kRibiXV199lSFDhrBmzRratWtXoO966KGHinWcRYsWMXbsWGbOnEm3bt2YPXs2/fr1IyEhgUaNGhW534kTJxg+fDi9e/fm0KFDZaqLiIiIyKVQ6kcZJyQkkJSUlG90nMViYcCAAZckMBERqRyefvppAOx2O1dffTXt27enVq1aJkclcun8kZLGhE9+AeD+a5rSq5Vu7xYpqffee4+4uDh8fX1ZuXJlvrsyLBZLsZO2M2bMYOTIkYwaNQqA2NhY4uLimDVrFjExMUXud++993Lrrbdis9n4/PPPL/gemZmZZGZm5r1OTU0FnHe5ZGdnFyvOksg9ZnkcuypQ+5WN2q/01HZlo/YrG7Vf2ZR3+xX3uCVO2u7Zs4fBgwezbds2LBYLhmEA5F1Y6cmuIiJSGJvNRp8+fdixY4eStlJppGflcN/CzaRn2YlsUodHrm1hdkgibunJJ5/kmWeeYcKECaV+EGhWVhabNm1iwoQJ+dZHR0ezdu3aIvd7++232b17NwsXLuQ///nPRd8nJiaGKVOmFFi/YsUK/PzKb5R9fHx8uR27KlD7lY3ar/TUdmWj9isbtV/ZlFf7paenF6tciZO2Dz/8MGFhYXz99dc0adKE9evXc+TIEf71r3/x4osvljhQERGpOtq1a8eePXsICwszOxSRMjMMgyc+3caulJPUq+HNK7d0xMNWumSTSFWXlZXFsGHDSp2wBTh8+DB2u73Aw8wCAwM5ePBgofvs2rWLCRMmsGbNGjw8ivfRaOLEiYwbNy7vdWpqKiEhIURHR+Pv71/q+IuSnZ1NfHw8UVFRpk955I7UfmWj9is9tV3ZqP3KRu1XNuXdfrl36VxMiZO269at49tvv+Wyyy7DarVitVq56qqriImJ4aGHHmLLli0lDlZERKqGqVOn8uijj/Lss8/SuXNnqlWrlm97eXzYFSkv7/6UxOdbD2CzWnj11k5cVsPb7JBE3NaIESNYtGgRTzzxRJmPdf4DLw3DKPQhmHa7nVtvvZUpU6bQokXxR8l7e3vnzdV+Lk9Pz3L9YFzex6/s1H5lo/YrPbVd2aj9ykbtVzbl1X7FPWaJk7Z2u53q1asDULduXQ4cOEDLli0JDQ1l586dJT2ciIhUIX379gVg4MCB+T5A536g1hQ74i5++es4zyxJAGB835ZcEVbb5IhE3JvdbueFF14gLi6O9u3bF/gwM2PGjIseo27duthstgKjalNSUgqMvgVIS0tj48aNbNmyhQceeAAAh8OBYRh4eHiwYsUKevXqVYZaiYiIiJReiZO24eHh/PLLLzRp0oQuXbrwwgsv4OXlxRtvvEGTJk3KI0YREakkvvvuO7NDECmz4+lZ3LdwM1l2B9FtArmnu65/RMpq27ZtdOzYEYBff/0137bCRskWxsvLi86dOxMfH8/gwYPz1sfHxzNo0KAC5f39/dm2bVu+dTNnzuTbb7/l448/1lQ+IiIiYqoSJ22ffPJJTp06BcB//vMfrr/+erp3706dOnVYtGjRJQ9QREQqj549e3L8+HHmzJnDjh07sFgstG7dmpEjRxIQEGB2eCIX5XAYjPvwZ/YfP01oHT+mD+lQ7ISSiBTtUn2pN27cOO644w4iIiKIjIzkjTfeICkpidGjRwPO+Wj379/P/PnzsVqthIeH59u/Xr16+Pj4FFgvIiIiUtFKnLTt06dP3s9NmjQhISGBo0ePUqtWLX1oERGRC9q4cSN9+/bFx8eHK664AsMw+O9//8tzzz3HihUr6NSpk9khilzQrFW7+fa3FLw8rMy8rRMBvpojTMSVDBs2jCNHjvDMM8+QnJxMeHg4y5YtIzQ0FIDk5GSSkpJMjlJERETk4kqctC1M7dqax01ERC7ukUceYcCAAbz55pt5T+nOyclh1KhRjB07ltWrV5scoUjR1u4+zEsrnPP3PzuoLW3ra3S4iCsaM2YMY8aMKXTbvHnzLrjv5MmTmTx58qUPSkRERKSELknSVkREpDg2btyYL2EL4OHhweOPP05ERISJkYlc2KHUDB56fwsOA4Z0bsiwfzQyOyQREREREanErGYHICIiVYe/v3+ht6Xu27ePGjVqmBCRyMVl2x088N5mDp/MolVQDZ4ZpLkuRURERESkfClpKyIiFWbYsGGMHDmSRYsWsW/fPv766y8++OADRo0axS233GJ2eCKFejFuJxv+PEZ1bw9m3d4ZXy+b2SGJiIiIiEglp+kRRESkwrz44otYLBaGDx9OTk4OAJ6entx3331MmzbN5OhECorbfpDZq/cA8OKQ9oTVrWZyRCIiIiIiUhUoaSsiIhXGy8uLl19+mZiYGHbv3o1hGDRr1gw/Pz+zQxMpYO+RUzz64c8AjLoqjL7hwSZHJCIiIiIiVYWStiIiUuH8/Pxo166d2WGIFCkj2859CzeTlplD59BajO/XyuyQRERERESkCtGctiIiIiLnefqL7SQkp1Knmhev3doJT5sumUREREREpOLoE4iIiIjIOT7cuI9FG/dhscArt3QkKMDH7JBERERERKSKUdJWRERE5IyEA6k89fmvAIy7tgXdmtU1OSIREREREamKlLQVERERAVIzshnz7iYycxxc3fIy7r+mmdkhiYiIiIhIFaWkrYiIuJ1Zs2bRvn17/P398ff3JzIykq+++ipvu2EYTJ48mfr16+Pr68vVV1/N9u3bTYxYXJ1hGDz+0S/8eSSdBjV9+e/Qy7FaLWaHJSIiIiIiVZSStiIi4nYaNmzItGnT2LhxIxs3bqRXr14MGjQoLzH7wgsvMGPGDF599VU2bNhAUFAQUVFRpKWlmRy5uKo53yeyfPtBPG0WXrutE7WqeZkdkoiIiIiIVGFK2oqIiNsZMGAA1113HS1atKBFixZMnTqV6tWr8+OPP2IYBrGxsUyaNIkbb7yR8PBw3nnnHdLT03nvvffMDl1c0MY/jzLtq98AeOr6NlweUtPcgEREREREpMrzMDsAERGRsrDb7Xz00UecOnWKyMhIEhMTOXjwINHR0XllvL296dmzJ2vXruXee+8t9DiZmZlkZmbmvU5NTQUgOzub7OzsEsWUW76k+7maqlCPIyczuf/dzeQ4DPq3C+Kfneu7bH2rwvlwJ5WhHmWpgzvXW0RERMQdKGkrIiJuadu2bURGRpKRkUH16tX57LPPaNOmDWvXrgUgMDAwX/nAwED27t1b5PFiYmKYMmVKgfUrVqzAz8+vVDHGx8eXaj9XU1nr4TBg1g4rh9KsBPoa9PT9i6+++suk6Iqvsp4Pd1UZ6lGaOqSnp5dDJCIiIiKSS0lbERFxSy1btmTr1q0cP36cTz75hBEjRrBq1aq87RZL/odIGYZRYN25Jk6cyLhx4/Jep6amEhISQnR0NP7+/iWKLTs7m/j4eKKiovD09CzRvq6kstcj9ps/+P3EHnw9rbw96kqa16tuYpQXV9nPh7upDPUoSx1y70YQERERkfKhpK2IiLglLy8vmjVrBkBERAQbNmzg5ZdfZvz48QAcPHiQ4ODgvPIpKSkFRt+ey9vbG29v7wLrPT09S52QKcu+rqQy1uO7nSm8tnIPANNuak+bBrXMDK1EKuP5cGeVoR6lqYO711lERETE1elBZCIiUikYhkFmZiZhYWEEBQXlu903KyuLVatW0bVrVxMjFFfx17F0Hlm0FYDbr2zEoMsbmBuQiIiIiIjIeTTSVkRE3M4TTzxBv379CAkJIS0tjQ8++ICVK1eyfPlyLBYLY8eO5bnnnqN58+Y0b96c5557Dj8/P2699VazQxeTZebYuf+9LRxPz6Z9wwCeur6N2SGJiIiIiIgUoKStiIi4nUOHDnHHHXeQnJxMQEAA7du3Z/ny5URFRQHw+OOPc/r0acaMGcOxY8fo0qULK1asoEaNGiZHLmZ7bukOft53nABfT167tRPeHjazQxIRERERESlASVsREXE7c+bMueB2i8XC5MmTmTx5csUEJC7L7jD4KfEomw5b+P3rXbyzbi8A/x3WgZDafiZHJyIiIiIiUjglbUVERKRSWv5rMlOWJJB8IgOwwa5EAPqFB9GrVdEPpRMRERERETGbHkQmIiIilc7yX5O5b+HmMwnb87cdZPmvySZEJSIiIiIiUjxK2oqIiEilYncYTFmSgHGBMlOWJGB3XKiEiIiIiIiIeZS0FRERkUplfeLRQkfY5jKA5BMZrE88WnFBiYiIiIiIlIDpSduZM2cSFhaGj48PnTt3Zs2aNRcsn5mZyaRJkwgNDcXb25umTZsyd+7cCopWREREXF3yidPFKpeSVnRiV0RERERExEymPohs0aJFjB07lpkzZ9KtWzdmz55Nv379SEhIoFGjRoXuM3ToUA4dOsScOXNo1qwZKSkp5OTkVHDkIiIi4oq2HzjBy1/vKlbZejV8yjkaERERERGR0jE1aTtjxgxGjhzJqFGjAIiNjSUuLo5Zs2YRExNToPzy5ctZtWoVe/bsoXbt2gA0bty4IkMWERERF5SZY+e1b/9g5srd5DgMLBYwipiy1gIEBfhwRVjtCo1RRERERESkuExL2mZlZbFp0yYmTJiQb310dDRr164tdJ/FixcTERHBCy+8wIIFC6hWrRoDBw7k2WefxdfXt9B9MjMzyczMzHudmpoKQHZ2NtnZ2SWKObd8SfdzNaqHa1E9XIvq4f51l6rn533Heezjn/n90EkArmsXRM8W9ZjwyS8A+R5IZjnz79MD2mCzWhAREREREXFFpiVtDx8+jN1uJzAwMN/6wMBADh48WOg+e/bs4fvvv8fHx4fPPvuMw4cPM2bMGI4ePVrkvLYxMTFMmTKlwPoVK1bg5+dXqtjj4+NLtZ+rUT1ci+rhWqpyPdLT08shEpFLLyPbTuzXu3hj9W4cBtSp5sWzN4RzXbtgAAJ8PZiyJCHfQ8mCAnx4ekAb+oYHmxW2iIiIiIjIRZk6PQKAxZJ/lIthGAXW5XI4HFgsFt59910CAgIA5xQLN998M6+99lqho20nTpzIuHHj8l6npqYSEhJCdHQ0/v7+JYo1Ozub+Ph4oqKi8PT0LNG+rkT1cC2qh2tRPc7ekSDiyjbtPcbjH//M7r9PATCwQ30mD2xL7WpeeWX6hgcT1SaIdX+ksGLNT0R370Jks3oaYSsiIiIiIi7PtKRt3bp1sdlsBUbVpqSkFBh9mys4OJgGDRrkJWwBWrdujWEY/PXXXzRv3rzAPt7e3nh7exdY7+npWeqETFn2dSWqh2tRPVxLVa5HZai3VF6ns+y8uGInc39IxDDgshreTL0hnOi2QYWWt1ktdAmrzZEdBl3CaithKyIiIiIibsFq1ht7eXnRuXPnArfuxsfH07Vr10L36datGwcOHODkyZN5637//XesVisNGzYs13hFRETEXD/tOUK/l1cz53tnwvamTg35+pGeRSZsRURERERE3JVpSVuAcePG8dZbbzF37lx27NjBI488QlJSEqNHjwacUxsMHz48r/ytt95KnTp1uOuuu0hISGD16tU89thj3H333UU+iExERETc26nMHJ7+4leGvfEjfx5JJzjAh7fv+gcvDe1AgJ9GhouIiIiISOVj6py2w4YN48iRIzzzzDMkJycTHh7OsmXLCA0NBSA5OZmkpKS88tWrVyc+Pp4HH3yQiIgI6tSpw9ChQ/nPf/5jVhVERESkHP3wx2HGf/ILfx07DcAtV4Qw8brW+PsoWSsiIiIiIpWX6Q8iGzNmDGPGjCl027x58wqsa9WqVaV5qruIiIgULi0jm+eW/cb7651f3jao6cvzN7XnquZ1TY5MRERERESk/JmetBURERE518qdKUz8dBvJJzIAuOPKUMb3a0V1b122iIiIiIhI1aBPPyIiIuISTqRn8+zSBD7e9BcAjWr78fxN7YlsWsfkyERERERERCqWkrYiIiJiuq8TDvHEZ9tIScvEYoG7uobxaJ8W+HnpUkVERERERKoefRISMZPDjmXv9zQ4ug7LXn9o0gOsNrOjEhGpMMdOZTF5yXa+2HoAgCZ1q/HCze2JaFzb5MhERERERETMo6StiFkSFsPy8XikHiACYO8s8K8PfZ+HNgPNjk5EpNx9tS2Zp774lcMns7Ba4J7uTXgkqgU+nvrySkREREREqjYlbUXMkLAYPhwOGPnXpyY71w+dr8StiFRah09m8vQX21m6LRmA5vWq88LN7enYqJbJkYmIiIiIiLgGJW1FKprDDsvHUyBhC2fWWWD5BGjVX1MliEilYhgGi38+wOTF2zmWno3NauG+nk15sHczvD30905ERERERCSXkrYiFW3vWkg9cIECBqTud5YL615hYYmIlKeU1Awmff4r8QmHAGgVVIMXh3QgvEGAyZGJiIiIiIi4HiVtRSpK2iH4/SvY8Fbxyq94Ei6/FcJ6wmUtwWIp3/hERMqBYRh8unk/z3yZwInT2XhYLTzQqxljrm6Gl4fV7PBERERERERckpK2IuXp8C747Uv4bRn8tYHCp0QoQvJW5wJQPRDCejgTuE16Qs1G5RCsiMillXziNE98uo3vdv4NQHgDf6bf3IHWwf4mRyYiIiIiIuLalLQVuZQcDti/EX5b6lyO7Mq/vUFnaNEX1r8Jp/6m8CSuBarVhS73wp/fQ9KPcPIQbPvIuQDUCnMmb8N6OpO51eqWd81ERIrNMAwWbdjH1KU7SMvMwctm5eFrm3NvjyZ42DS6VkRERERE5GKUtBUpq+wMSFztHFH7+3JngjWX1dOZVG3VH1r2A//6zvWXtYIPhwMW8iduz0yB0H8GtBkIPR5zHv+v9bBnFSSugv2b4VgibEqETfOc5QPbnU3ihnYF7+rlX28RkUL8dSydiZ9uY82uwwBcHlKT6Te3p3lgDZMjE5GqYubMmUyfPp3k5GTatm1LbGws3bsX/pyATz/9lFmzZrF161YyMzNp27YtkydPpk+fPhUctYiIiEh+StqKlMbpY7Ar3pmo/eMbyDp5dpu3PzSPciZqm10LPoU8ZKfNQBg6H5aPz/9QMv/60Heac3suT58zUyP0AJ6CjFTY+8OZJO5qSNkOh7Y5l3WvgtXDOaI3dyqFhv8AD+9yawoREQCHw+Dd9UlMW7aDU1l2vD2sPBrdkruvCsNm1ZzcIlIxFi1axNixY5k5cybdunVj9uzZ9OvXj4SEBBo1Kji91OrVq4mKiuK5556jZs2avP322wwYMICffvqJjh07mlADERERESclbUWK6/g+2LnMOe3B3h/AkXN2W4360Oo6aHkdNO4OHl4XP16bgdCqPzl7VrN1TRyXd++DR5MeYLVdeD8ff+eo3Zb9nK9PpjiTt4mrnInc43th30/OZfUL4OELoZFnk7hB7S/+HiIiJbD3yCnGf/ILP+45CsA/Gtfi+Zva0+QyjfoXkYo1Y8YMRo4cyahRowCIjY0lLi6OWbNmERMTU6B8bGxsvtfPPfccX3zxBUuWLFHSVkREREylpK1IUQwDDv3qfIjYb1/CwV/yb6/XxpmkbdUf6ncESylGklltGKFXsX97Kh1CrypdMrV6PWh3s3MBOPbn2akUElc7587d/a1zAfCpCWHdzyRxr4Y6zUoXu4hUeQ6Hwby1fzI9biens+34etp4vG9LRkQ2xqrRtSJSwbKysti0aRMTJkzItz46Opq1a9cW6xgOh4O0tDRq165dZJnMzEwyMzPzXqempgKQnZ1NdnZ2KSK/sNxjlsexqwK1X9mo/UpPbVc2ar+yUfuVTXm3X3GPq6StyLnsOZC0zjmadudSOJ50dpvFCiFXOpO0ra6D2k3Mi/NCajWGzo2h8whn4jklwZm83bPK+WCzjOOwY4lzAeco4bAeZ+fEDWhgYvAi4i72/H2Sxz/+hY17jwFwZZPaPH9Te0LrVDM5MhGpqg4fPozdbicwMDDf+sDAQA4ePFisY7z00kucOnWKoUOHFlkmJiaGKVOmFFi/YsUK/Pz8ShZ0CcTHx5fbsasCtV/ZqP1KT21XNmq/slH7lU15tV96enqxyilpK5J1yjkv7c5lzgeJnT52dpuHLzTt5UzStugL1eqaF2dpWCwQ2Na5XHmfMyl9YAskrnQmcff9BGkH4JcPnAs4R97mTqXQuDv4FT3SJI/DjmXv9zQ4ug7LXn8ozjQPIuLS7A6DnxKPsumwhTqJR4lsVg+b1YLdYfDWmj3MiP+dzBwH1bxsTLyuNbde0Uija0XEJVjOu4PIMIwC6wrz/vvvM3nyZL744gvq1atXZLmJEycybty4vNepqamEhIQQHR2Nv79/6QMvQnZ2NvHx8URFReHp6XnJj1/Zqf3KRu1Xemq7slH7lY3ar2zKu/1y79K5GCVtpWo6+Tf8/pVzRO2elZCTcXabb23nfLGt+kOTa8Cr/EZMVDibB4T8w7n0eAyyT0PSj2fnw03eCkf+cC4b5wAWCG5/NonbKBK8zhtFl7AYlo/HI/UAEQB7Z515oNrz+R+oJiJuY/mvyUxZkkDyiQzAxvxdGwkO8OGe7k344ucD/LzvOADdm9cl5sZ2NKxVif5Oiojbqlu3LjabrcCo2pSUlAKjb8+3aNEiRo4cyUcffcS11157wbLe3t54exd8yKunp2e5fjAu7+NXdmq/slH7lZ7armzUfmWj9iub8mq/4h5TSVtxT6UZ2Xlkt3Nu2t+WOUeYYpzdVqsxtLreOUdtSBdncrMq8PSFptc4F4DTx50PWcudE/fv3yD5Z+ey9hWwekLIFc4kblgPSDsIH99FvrYESE2GD4fD0PlK3Iq4meW/JnPfws3n/68m+UQGz3yZAEANbw+evL41QyNCijV6TUSkInh5edG5c2fi4+MZPHhw3vr4+HgGDRpU5H7vv/8+d999N++//z79+/eviFBFRERELqqKZKakUinuyE6HwzkVwG9fOkfUHt6Z/zj1O0LL/s4RtfVa62FcAL41z8zZe+YDS9rBs/PhJq6CE/ucSd29P8DK5wALBRK2cGadBZZPcB5LUyWIuAW7w2DKkoRC/1fn8vaw8tXY7hpdKyIuady4cdxxxx1EREQQGRnJG2+8QVJSEqNHjwacUxvs37+f+fPnA86E7fDhw3n55Ze58sor80bp+vr6EhAQYFo9RERERJS0FfeSsNg5grOokZ03zwHvAOdDxH5bBifPuT3O6uGco7VVf+eIWj1w6+JqBEH7oc7FMODoHmcSN3GVcx7gzAvNw2JA6n7YuxbCuldYyCJSeusTj56ZEqFomTkO9h09raStiLikYcOGceTIEZ555hmSk5MJDw9n2bJlhIaGApCcnExS0tkHzc6ePZucnBzuv/9+7r///rz1I0aMYN68eRUdvoiIiEgeJW3FfTjssHw8RY/sBD4emX+7Vw1oHuVM1Da71jmSVErHYoE6TZ1LxF3wy4fw6T0X3+/kofKPTUQuiZS0CydsS1pORMQMY8aMYcyYMYVuOz8Ru3LlyvIPSERERKQUlLQV97F3LaQeuEghA3xqQfhg59QHYd3Bo+CDIuQSqBFcvHLVL/zgDxFxHfVq+FzSciIiIiIiIlI6StqK+yjuiM3rXnDezi/lK7Srcy7h1GQKH/2M88FlNYIqNCwRKb0rwmoT4OvJidPZhW63AEEBPlwRVrtiAxMREREREalirGYHIFJsNs/ilSvuCFApG6vN+fA3wJnKKYQjG97sDTu+rLCwpGpYvXo1AwYMoH79+lgsFj7//PN82++8804sFku+5corrzQnWDfy9Y5DpGUUnbAFeHpAG2xWPbhRRERERESkPClpK64vOwPWvASf3XeRghbwb+AcASoVo81AGDof/M9LlPs3gOtfhpAukHkCFt0GcZPAXngySKSkTp06RYcOHXj11VeLLNO3b1+Sk5PzlmXLllVghO5n7R+HefC9LTgMiGxShyD//FMgBAX4MOv2TvQN1xdjIiIiIiIi5U3TI4jrMgz47Utnsu/4Xue62k3h6G6cY77OvSX/zKivvtOcI0Cl4rQZCK36k7NnNVvXxHF59z54NOnhPA8db4OvJ8O6V53LXxthyNvOaRVEyqBfv37069fvgmW8vb0JCir+9ByZmZlkZmbmvU5NTQUgOzub7OySfeGQW76k+5nll79OcM/8jWTZHUS1rscrw9pjsVj4cffffLtuE70iO3Nl08uwWS1uU6dzudv5KIrq4VoqQz3KUgd3rreIiIiIO1DSVlzTwV9h+QT4c43zdY1guHYKtBviTOQuH5//oWT+9Z0J2zYDzYm3qrPaMEKvYv/2VDqEXnU2cW7zhD5TnSNuv7gf9v0Ir3eHm96CpteYG7NUeitXrqRevXrUrFmTnj17MnXqVOrVq1dk+ZiYGKZMmVJg/YoVK/Dz8ytVDPHx8aXaryIdTIdXtts4lWOhub+DPv4HWBF39u9r57pwYtdG4naZGOQl4g7nozhUD9dSGepRmjqkp6eXQyQiIiIikktJW3Etp47Ad1Nh09tgOMDmDd0egm5jwbu6s8yFRnaKa2ozEALbwkcj4OA2WDAYrp4APR7TeZNy0a9fP4YMGUJoaCiJiYk89dRT9OrVi02bNuHt7V3oPhMnTmTcuHF5r1NTUwkJCSE6Ohp/f/8SvX92djbx8fFERUXh6VnM+bhN8Nex0zz31npO5WTSvqE/79wZQXXvs5cG7lKPi1E9XIvq4TrKUofcuxFEREREpHwoaSuuwZ4NG96ClTGQccK5rs0giHoWaoUWLF/UyE5xXXWawsh4+Go8bH7Hea73/QQ3vgnV6podnVQyw4YNy/s5PDyciIgIQkNDWbp0KTfeeGOh+3h7exea0PX09Cx1QqYs+5a3lLQM7npnE4dSM2lerzrv3NWFWtW8Ci3ryvUoCdXDtagerqM0dXD3OouIiIi4Oj2ITMy362uY1dU5HULGCQhsB3cudT7gqrCErbgvT18Y+Arc8Dp4+MLub53TJST9ZHZkUskFBwcTGhrKrl2V4B7/S+DE6WyGz1nPn0fSaVjLlwUji07YioiIiIiISMVT0lbMc/gPeHcovHsTHP4d/OrA9bFw7ypofJXZ0Ul5uvwWuOdbqNMc0g7AvOtg7avOh8+JlIMjR46wb98+goODzQ7FdOlZOdw9bwO/HUyjbnVvFo7sQlCAj9lhiYiIiIiIyDk0PYJUvIwTsOoF+Gk2OLLB6gFdRjvnN/WtaXZ0UlEC28D/fQdLHoZfP4EVkyBpHQx6Tb8HclEnT57kjz/+yHudmJjI1q1bqV27NrVr12by5MncdNNNBAcH8+eff/LEE09Qt25dBg8ebGLU5svKcXDfws1s2nsMfx8PFoy8gsZ1q5kdloiIiIiIiJxHSVupOA47bFkA3zwL6Yed65pHQ5/noG5zc2MTc3jXgJvmQKNIWD4RfvsSDm13To0R3N7s6MSFbdy4kWuuuSbvde4DxEaMGMGsWbPYtm0b8+fP5/jx4wQHB3PNNdewaNEiatSoYVbIprM7DMZ9uJVVv/+Nj6eVt+/6B62DS/aANREREREREakYStpKxfjzB1g+Hg5uc76u0xz6xkDzKHPjEvNZLHDFPdCgE3x4JxxLhLeuhetegE4jnNtFznP11VdjXGA6jbi4uAqMxvUZhsFTX/zKl78k42mzMPuOCDqH1jY7LBERERERESmC5rSV8nU8CT4c4Zyz9OA28A6APjEwZp0StpJfg87O+Yxb9AV7pnPahM9GQ9YpsyMTcXvT43by3k9JWCzw32GX07PFZWaHJCIiIiIiIhegpK2Uj6xT8O1UePUfkPA5WKwQcTc8tBkix4DN0+wIxRX51YZ/vg+9n3b+zvzyAbzZG/7+3ezIRNzW7FW7mblyNwDPDW7H9e3rmxyRiIiIiIiIXIyStnJpGQb88iH8LwJWvwA5GdC4O9y7Gq7/L1Sra3aE4uqsVug+DkYsgeqB8PcOePMa2Pax2ZGJuJ0P1icR89VvAEzo14pbrmhkckQiIiIiIiJSHErayqXz1yaYEwWf3gNpB6BmIxi6wJl8C2pndnTibhpfBfeucSb9s07CJyNh6b8gJ9PsyETcwtJfkpn4mXMe8dE9mzK6Z1OTIxIREREREZHiUtJWyi7tIHx2H7zVC/7aAJ7VoPe/4f4N0GagHiQlpVcjEO74HLr/y/l6w1swtw8c22tqWCKubvXvfzN20RYMA265IoTxfVuaHZKIiIiIiIiUgOlJ25kzZxIWFoaPjw+dO3dmzZo1RZZduXIlFoulwPLbb79VYMSSJzsD1rwE/+sMP7/nXNfhFnhwkzPJ5uljbnxSOdg8nF8C3PoR+NaCA1tgdg/YudzsyERc0qa9x7h3wSay7Qb92wXznxvaYdGXZyIiIiIiIm7F1KTtokWLGDt2LJMmTWLLli10796dfv36kZSUdMH9du7cSXJyct7SvHnzCopYAOe8tTuWwGtXwDfPOG9dbxABo76Bwa+Df7DZEUpl1CLaOV1Cg86QcRzeHwbxT4M9x+zIRFzGjuRU7np7Paez7XRvXpf/Drscm1UJWxEREREREXdjatJ2xowZjBw5klGjRtG6dWtiY2MJCQlh1qxZF9yvXr16BAUF5S02m62CIhYObYf5A2HR7XB8L9QIhsFvwMh4aBhhdnRS2dUMgbuWwxX3Ol//EOv8fUw7aGpYIq5g75FTDJ+7ntSMHDo1qsnsOzrj5WH6DTUiIiIiIiJSCh5mvXFWVhabNm1iwoQJ+dZHR0ezdu3aC+7bsWNHMjIyaNOmDU8++STXXHNNkWUzMzPJzDz74KLU1FQAsrOzyc7OLlHMueVLup+rKVU90o9gXTUN65Z3sBgODJs3jisfwNH1QfCqDna7c6lAVfp8uKCKq4cFoqZiaXgFti8fxrL3B4zXu2O/YTZG4+5lPrrOh/vXvSo6lJrB7XN+4u+0TFoF1eDtO6/Az8u0Ll5ERERERETKyLRPdIcPH8ZutxMYGJhvfWBgIAcPFj5qLjg4mDfeeIPOnTuTmZnJggUL6N27NytXrqRHjx6F7hMTE8OUKVMKrF+xYgV+fn6lij0+Pr5U+7ma4tTDYuQQ9vc3tDz4GTZ7OgD7a/6D7fX/yen0y+Dr1eUd5kVVpfPhDiquHp5Ua/oU/0h8lYBT+7C9eyO/Bd/E74HXg6Xsowur8vlIT08vh0ikvBxPz+KOOT+x7+hpQuv4MX/kFQT4eZodloiIiIiIiJSB6cNwzn84imEYRT4wpWXLlrRsefYJ2JGRkezbt48XX3yxyKTtxIkTGTduXN7r1NRUQkJCiI6Oxt/fv0SxZmdnEx8fT1RUFJ6e7vuBuLj1sOz+Blv8s1iO7ALAqBeOPXoq9UK7Ua+igr2AqnY+XJ1p9ci+BcfyCVh/eY/WyR/T0u849oEzwa926Q6n85F3R4K4vlOZOdz59gZ+P3SSQH9vFo7sQr0aegikiIiIiIiIuzMtaVu3bl1sNluBUbUpKSkFRt9eyJVXXsnChQuL3O7t7Y23t3eB9Z6enqVOyJRlX1dSZD0O/wFxT8CuOOdrvzrQ6yksnYbjYXW9+YMr/flwMxVeD88AuHEWNO4Kyx7FuvtrrHN7w5B5ZZpnuSqfj8pQ76ogM8fO/y3YyNZ9x6np58mCkV0IqV26O0hERERERETEtZj2hBIvLy86d+5c4Nbd+Ph4unbtWuzjbNmyheDg4EsdXuXlsGPZ+z0Njq7Dsvd7cJwzD23GCYibBDOvdCZsrR4Q+QA8uBki7gIXTNiK5Ol0B4z6Gmo3gRP7YG5f+PF1MAyzIxO55HLsDh5+fys//HEEPy8b8+66ghaBNcwOS0RERERERC4RU6dHGDduHHfccQcRERFERkbyxhtvkJSUxOjRowHn1Ab79+9n/vz5AMTGxtK4cWPatm1LVlYWCxcu5JNPPuGTTz4xsxruI2ExLB+PR+oBIgD2zgL/+tAnBjKOwzfPQvphZ9nm0dDnOajb3MSARUooqB383ypY/AAkfAHLx0PSOhj4P/Ap2XQoIq7KMAwmfrqN5dsP4mWz8ubwCC4PqWl2WCIiIiIiInIJmZq0HTZsGEeOHOGZZ54hOTmZ8PBwli1bRmhoKADJyckkJSXllc/KyuLRRx9l//79+Pr60rZtW5YuXcp1111nVhXcR8Ji+HA4cN6ow9QD8NGIs6/rNIe+MdA8qkLDE7lkfPxhyDvw02xYMQkSPoeD22DofAgKNzs6kTIxDIOpS3fw0aa/sFrglVs60q1ZXbPDEhERERERkUvM9AeRjRkzhjFjxhS6bd68efleP/744zz++OMVEFUl47A7Rxyen7DNxwLR/4Eu94JN81mKm7NY4MrR0KAzfHQnHN0Nb/WG/i9Bx9vNjk6k1Gau3M1b3ycCMO2m9vQNDzI5IhERERERESkPps1pKxVo71rniNoLMiC4gxK2UrmE/APuXQ3NroWcDPjifueSfdrsyERKbMGPe5ketxOAJ/u3ZmhEiMkRiYiIiIiISHlR0rayO5oIW98tXtmTh8o3FhEzVKsDt34EvZ4EixW2LIS3roUju82OTKTYvti6n39/8SsAD/ZqxqjuTUyOSERERERERMqT6dMjyCVmz4Z9P8Hvcc7l8M7i71s9sPziEjGT1Qo9HoOGV8AnI+HQrzC7Jwx6FdreYHZ0Ihf03W8p/OvDnzEMGB4ZyrioFmaHJCIiIiIiIuVMSdvK4NQR+ONr+H057P4GMk6c3WaxQaNIOPgLZKYWcQAL+NeH0K4VEq6IaZr0hHvXwMd3Q9Ja50P4ku6DqGfAwwscdix7v6fB0XVY9vpDkx5gtZkdtVRh6xOPMnrhJnIcBoMur8/kAW2xWCxmhyUiIiIiIiLlTElbd2QYkJLgTNL+Hgd/bQDDcXa7b21oHg0toqFpb/CtCQmL4cPhuQc452BnPvz3nabklFQN/sEwYgl8+yz8EAs/zYL9G+HyW2H1dDxSDxABsHeW88uMvs9Dm4EmBy1V0a/7TzBy3gYycxz0alWPF4d0wGpVwlZERERERKQqUNLWXWSfhsQ1ZxO1qX/l3x4YDi36QPM+0DCiYAK2zUAYOh+Wj8//UDL/+s6ErZJSUpXYPCBqCoR0gc9HO7/4+GtDwXKpyc4vO4bO1/8RqVB7/j7JiLnrScvM4Yqw2sy8rROeNk1DLyIiIiIiUlUoaevKTuyHXWfmpt2zCnLOeeK9hw+E9TyTqI2GmsV4inibgdCqPzl7VrN1TRyXd++Dh27/lqqs1XVwz3fwWhdwZBdSwAAssHwCtOqv/ytSIQ4cP80dc9Zz5FQWbev789aICHw89bsnIiIiIiJSlShp60ocdti/6cxo2hVwaFv+7f4NnVMetOgLjbuDl1/J38Nqwwi9iv3bU+kQepWSUCKpB4pI2OYyIHU/7F0LYd0rLCypmo6czOSOOT+x//hpmtStxjt3X4G/j6fZYYmIiIiIiEgFU9LWbBkn4I9vYNcK55J+5JyNFgi54uy0B4FtQQ+gEbm0Th4qXrmkdRDaDay6RV3KR1pGNne+vYHdf58iOMCHBaO6ULe6t9lhiYiIiIiIiAmUtK1ohgFH/jg7N23SOnDknN3uHQDNejtH0za7FqrVMS9WkaqgemDxyn03FTa+Da2vh9YDoFFX59y4IpdARradUe9sZNv+E9Su5sWCkV1oUNPX7LBERERERETEJMo4VIScLNj7gzNJ+/tyOJaYf3vdlmenPQjpAjbdCitSYUK7Oh/Il5qMcw7bQnj6AlZIOwDr33AuvrWdc+K2HgRNeoKHRkRK6WTbHTzw3mZ+SjxKdW8P5t99Bc3qVTc7LBERERERETGRkrbF5bBj2fs9DY6uw7LXHy72AK+0Q2emPIiD3d9B1smz22xe0Pgq55QHLaKhdpPyj19ECme1Qd/n4cPhgIX8idsz05EMfsP5wL/EVbBjMfy2DE4fhS0LnYtXDec0Jq0HOEfIeyvhJsXjcBiM//gXvt6RgreHlbdGRBDeIMDssERERERERMRkStoWR8JiWD4ej9QDRADsneUcmdf3eWgz0FnG4YCDP58ZTRsHBzbnP0b1QGfSp0UfaHI1eNeo4EqISJHaDISh82H5eOeDyXL514e+087+P2/Rx7lcn+McPb9jCfz2JaQlw68fOxcPH2ja27lPiz7gW8ucOonLMwyDZ75M4NMt+7FZLcy8rRNXNtGUOCIiIiIiIqKk7cUlLD4zAu+826ZTk53ruz3sfHjYrng4eTB/mfodnVMetOgDQR30ACMRV9ZmILTqT86e1WxdE8fl3fvgUdSIepuHc0qEJj2h3wuwf5NzBO6OxXDsT9i51LlYPSCsh3MEbsv+UKOY8+dKlRD79S7mrf0TgJeGdKB3a/1+iIiIiIiIiJOSthfisDtH3hU6z+WZdT/Enl3lVd05irZFX2geBTWCyj9GEbl0rDaM0KvYvz2VDqFXXXgKlLx9rBDyD+cS9Qwc2n4mgbsEUhJg97fO5ctx0OhKZwK39QCo2aj86yMua+73ibz8zS4Apgxsyw0dG5gckYiIiIiIiLgSJW0vZO/a/LdKF6X1AIi4G0K76WFEIlWZxQJB4c7lmifg8B/w2xJnAnf/Jkha51zinoDgy88kcAfCZS3Mjlwq0Ceb/uKZLxMAGBfVghFdG5sbkIiIiIiIiLgcJW0v5OSh4pVrcwM07VWuoYiIG6rbDK56xLmc+At+W+qcciVpLSRvdS7fPgt1WzqnZ2g9AILaO5O/Uimt2H6Qxz/5BYC7u4XxYK9mJkckIiIiIiIirkiTrF5I9WLOL1jcciJSdQU0hC73wl1L4V+/w4BXoFkUWD3h8E5YPR1m94CX20PcJEj60fmAQ6k01u4+zAPvb8HuMLipU0Oe7N8aixL0IiJyAXaHwU+JR9l02MJPiUexOwqbtk2KovYTkapIf/vKxpXaTyNtLyS0q/Pp8anJFD6vrcW5PbRrRUcmIu6s+mXQeYRzyTgBv69wzoP7x9dwPAnWvepcqgdCq+udI3AbXwU2T7Mjl1L65a/j3PPORrJyHES3CeT5m9phtSphKyJSHmbOnMn06dNJTk6mbdu2xMbG0r179yLLr1q1inHjxrF9+3bq16/P448/zujRoysw4sIt/zWZKUsSSD6RAdiYv2sjwQE+PD2gDX3Dg80Oz+Wp/cru3MRFncSjRDarh03XL1IB9LtXevrbVzau1n4aaXshVhv0ff7Mi/P/QJx53Xda8R5WJCJSGJ8AaD8Ehi2Ax3bDsIXQfhh4BzinaNk4BxbcANObwWf3wW/LIPv0hY/psGPZ+z0Njq7Dsvd750MVpcKc/83szoOpjJi7nlNZdro2rcMrt3TEw6buV0SkPCxatIixY8cyadIktmzZQvfu3enXrx9JSUmFlk9MTOS6666je/fubNmyhSeeeIKHHnqITz75pIIjz2/5r8nct3DzmQ+NZx08kcF9Czez/NdkkyJzD2q/slv+azJXPf8tt8/dyPxdNm6fu5Grnv9WbVdMrjRSz93od6/09LevbFyx/TTS9mLaDISh82H5+PwPJfOv70zYthloXmwiUrl4+Z15ONkAyMmCP1c7H2L221I49Tf8/J5z8awGzaOcf3+aR4N3jbPHSFgMy8fjkXqACIC9s878vXpef68qQGHfzFot4DCgQ8MA3hgegY+nvugTESkvM2bMYOTIkYwaNQqA2NhY4uLimDVrFjExMQXKv/766zRq1IjY2FgAWrduzcaNG3nxxRe56aabKjL0PHaHwZQlCYXe55e77snPfyU4wFcjzwphdxg8+fmvF2y/f3+xnVZB/nh6WLFZLFgtYLVanD9bLdjyfgabxfm6Kk1plJu4OL8NcxMXs27vpBF7F+BqI/XciX73Su9ifYcFmLw4ga5N6+bd8WcYRt52wzinMGCc+cEw8q3Ot0/+7cZ5+xe+vcjjnRd4sfc7rzxFli/ieGf+zXE4Lth3WIApSxKIahNUoX2vkrbF0WYgtOpPzp7VbF0Tx+Xd++DRpIdG2IpI+fHwgmbXOpf+M5xz3O5Y4lxS/4KEz52LzRuaXuNM9Fqs8PkYCkznkpoMHw53fgGlxG25KeoiM3dgxR1XhlLdW92uiEh5ycrKYtOmTUyYMCHf+ujoaNauXVvoPuvWrSM6Ojrfuj59+jBnzhyys7Px9Cw4NVFmZiaZmZl5r1NTUwHIzs4mOzu7rNXgp8SjBUb5nO/wySwGvfZDmd+rqkpJy+TqF1eWeD+b1ZngtZ2b4D2T3LVazl13XhL43MSw1eIse96xLBYLttzjnFPm3OSx5czrwspZLeevK6LMeXGdXw4Mnl2684JJ70mf/cpl1Tzx8rBis1rwsFrwsFnwsDpf26wWPM/869xmxWqhSiS+47Yf4sEPfi4y6fi/f3agT1s9E6cwdofB5MXbL5g0e3rxdi5v4A8WZ3m7w8BhGOQ4DBxnXtsdBnbDwOFwJuIcBuetP6e8ce4+nN1mnH+sc/cBu8OB3UH+9z5nn/wxUfS2fOvPxOsoIl6jkHLn7J9zJqaiGMDB1AzaT1lRPiewkjOA5BMZrPsjhS5htct8vOJeL+jTY3FZbRihV7F/eyodQq9SwlZEKo7VBo27OZe+MXBgy5kE7mI48gf8vty5FOnMZc7yCdCqf5X6+1XSeQ1L60LfbOd6Kf53BndqqFFRIiLl5PDhw9jtdgID8ydEAgMDOXjwYKH7HDx4sNDyOTk5HD58mODggiO6YmJimDJlSoH1K1aswM/Prww1cNp02AJcvK/2sxl4VZ0uvdiy7JBuv3hfa7MYWAAHzpFWRoHp8AqyOwzsQLa9at/qfuRUFjfN/qnE+1ktBjZwJqHhTKIZZ5K7kH/PbjcK307h++RfZ1xg23nvBdgKxFbEe5/zOncdwLSfbWeuB/P/PuWOCXzy062c2m3HcuZOLIfh/B3M+9lwlrUbzt/L87ef3WYpfH+c++Xtf96xc1/nlQEcRRyrwP7nbTu7//nbLXmxFvbehdbRgGwDsh1F/z80gEOpmVz5/MoS/+5J+bKc9ynIct4PRUw2Wuxy528vbrmSHCfbAaeL0XesWPMTR3aUvQ9IT08vVjklbUVE3InFAg06OZfe/4a/f3MmcLe+B8cSL7CjAan7Ye9aCLv0SUtXlDuv4cyZM+nWrRuzZ8+mX79+JCQk0KhRo0v6XuuLMSoq+UQG6xOPEtm0ziV9bxERye/80XyGYVxwhF9h5Qtbn2vixImMGzcu73VqaiohISFER0fj7+9f2rDz1Ek8yvxdGy9a7o0R/7gko30qm58Sj3L73Iu33zt35W8/wzDyRuSdHdFm5I2mO3eU27kj4OwO40wCyzhv3/OPxdmfzxlhl1vOMHLXcd77F7Yvecc491i5cZxdl38/x3nbz/57Zr1hkJKayR9/n7po+wX4euBls5JzZhSg/cy/OXZH3l1G53MYFhwAJX7cQmX5wtvC8SwYv0FpmEshb6T6OaPHPc4dpX7OqPdz11stzpHhFytX4Lh5o9OdI9Rt55Qrep+C5QoeyzkK3sNqdZY/5zhF70OBevzy1wkeWvTLRdtt7vBO/KNxrXMSmc6fLOe8PPtzwW3nrq9Mitt3RHfvckn63ty7dC5Gfy1ERNyVxQL1WjuX2k3gk5EX3+fkofKPy0WUdF7DskhJu3DCtqTlRESk5OrWrYvNZiswqjYlJaXAaNpcQUFBhZb38PCgTp3Cv2Tz9vbG29u7wHpPT89Cp1Moqchm9QgO8OHgiYxC7+CwAEEBPnqaehHUfmWzbvcRbnnzx4uWe/32iCK/iM5NBufYc2/Zzk3oFv06237uLd652wzsDsc5ZQt/fXbfM9vt5yaSz3/tTCznvs62O87ZN/+2gmXPvr/9nPiLSlKXRG4CMl+CrrAkYoFkX2EJyPzlc49ps56f2ASb1XpOAtL5c26ZohOWhScoC00w2i4Szznxb/vrOI98+PNF22rhyCvo1qxupUwclkVInRrELP/9on/7eraq2DlZ3UVF9x3FvV5Q0lZEpDKoXsy5sYpbzs2VZl7DssxRWMeveN1pHT+PSzLfYUXJjdWdYi6M6uFaVA/XUZY6uGK9vby86Ny5M/Hx8QwePDhvfXx8PIMGDSp0n8jISJYsWZJv3YoVK4iIiLgkCdjSsFktPD2gDfct3IxzhtGzcj8mPj2gjT50F0HtVzZXhNUuVuLiiguMNLNaLVix4Hz2auWfwyM3Sb32j8OMeHvDRcvPHRHBlU3r5EuwKgEJYXWr8ULczosnzZoqYVsY/e0rG1dtPyVtRUQqg9Cu4F/f+dCxoi5z/Os7y1UBpZnXsCxzFDoMqOll43gWFH4Ln0FNL/g74UeW7ShmJVxIfHy82SFcEqqHa1E9XEdp6lDcudgq2rhx47jjjjuIiIggMjKSN954g6SkJEaPHg04pzbYv38/8+fPB2D06NG8+uqrjBs3jnvuuYd169YxZ84c3n//fTOrQd/wYGbd3umcJ9A7BekJ9MWi9is9V01cuLLcJPVVzS8rVsK7Z0uN8i6MfvfKTn/7ysYV209JWxGRysBqg77Pw4fDoajLnL7TqtRDyKBk8xqWdY5Cz8bOpwVDYa1v4T83ut/TgrOzs4mPjycqKsq0EWeXgurhWlQP11GWOhR3LraKNmzYMI4cOcIzzzxDcnIy4eHhLFu2jNDQUACSk5NJSkrKKx8WFsayZct45JFHeO2116hfvz6vvPIKN910k1lVyNM3PJioNkGs+yOFFWt+Irp7F93SXwJqv9JzxcSFO1DSsez0u1d2+ttXNq7WfkraiohUFm0GwtD5sHw8pB44u96/vjNh22agebFVsNLMa1jWOQqvv7whHh62SnmReanmaTSb6uFaVA/XUZo6uHKdx4wZw5gxYwrdNm/evALrevbsyebNm8s5qtKxWS10CavNkR0GXcJq60N3Can9Ss/VEhfuQknHstPvXtnpb1/ZuFL7KWkrIlKZtBkIrfqTs2c1W9fEcXn3Png06VHlRtiWZl7DS0EXmSIiIlJZuFLiwp3oerDs9Lsn4qSkrYhIZWO1YYRexf7tqXQIvarKJWxzXWxew/Kii0wRERGRqk3XgyJyKShpKyIildLF5jUUERERERERcVVK2oqISKV1oXkNRURERERERFyV1ewAREREREREREREROQsJW1FREREREREREREXIiStiIiIiIiIiIiIiIuRElbEREREREREREREReipK2IiIiIiIiIiIiIC/EwO4CKZhgGAKmpqSXeNzs7m/T0dFJTU/H09LzUoVUY1cO1qB6uRfU4+/cx9+9lVaX+QvVwNaqHa6kM9VBfcWmUpb8ojsrwu2YmtV/ZqP1KT21XNmq/slH7lU15t19xr6OqXNI2LS0NgJCQEJMjERFxbWlpaQQEBJgdhmnUX4iIXFxV7ytA/YWIiIiUzsWuoyxGFft63OFwcODAAWrUqIHFYinRvqmpqYSEhLBv3z78/f3LKcLyp3q4FtXDtagezm/70tLSqF+/PlZr1Z1FR/2F6uFqVA/XUhnqob7i0ihLf1EcleF3zUxqv7JR+5We2q5s1H5lo/Yrm/Juv+JeR1W5kbZWq5WGDRuW6Rj+/v6V4pde9XAtqodrqer1qOqjpkD9xblUD9eieriWylAP9RVlcyn6i+KoDL9rZlL7lY3ar/TUdmWj9isbtV/ZlGf7Fec6qmp/LS4iIiIiIiIiIiLiYpS0FREREREREREREXEhStqWgLe3N08//TTe3t5mh1ImqodrUT1ci+ohl0JlaX/Vw7WoHq6lMtSjMtShKtB5Khu1X9mo/UpPbVc2ar+yUfuVjau0X5V7EJmIiIiIiIiIiIiIK9NIWxEREREREREREREXoqStiIiIiIiIiIiIiAtR0lZERERERERERETEhShpKyIiIiIiIiIiIuJClLQtgZkzZxIWFoaPjw+dO3dmzZo1Zod0QatXr2bAgAHUr18fi8XC559/nm/7nXfeicViybdceeWV5gRbhFmzZtG+fXv8/f3x9/cnMjKSr776Km+7YRhMnjyZ+vXr4+vry9VXX8327dtNjLh4YmJisFgsjB07Nm+dO5wPgP3793P77bdTp04d/Pz8uPzyy9m0aVPednc4J40bNy7Q1haLhfvvvx9wn3MBkJaWxtixYwkNDcXX15euXbuyYcOGvO3ucD4qI/UXFU/9hWudD1B/4UrUV7gvd+tPzFIZ+jEzVdY+1Azu3G+bpTJcL5ilslynmMnVr5GUtC2mRYsWMXbsWCZNmsSWLVvo3r07/fr1IykpyezQinTq1Ck6dOjAq6++WmSZvn37kpycnLcsW7asAiO8uIYNGzJt2jQ2btzIxo0b6dWrF4MGDcr7T/LCCy8wY8YMXn31VTZs2EBQUBBRUVGkpaWZHHnRNmzYwBtvvEH79u0LbHP183Hs2DG6deuGp6cnX331FQkJCbz00kvUrFkzr4w7nJMNGzbka+f4+HgAhgwZklfG1c9FrlGjRhEfH8+CBQvYtm0b0dHRXHvttezfvx9wj/NR2ai/MIf6C9c6H+ovXIv6Cvfkjv2JWSpDP2amytiHmsGd+22zVJbrBbNUlusUM7n8NZIhxXLFFVcYo0ePzreuVatWxoQJE0yKqGQA47PPPsu3bsSIEcagQYNMiacsatWqZbz11luGw+EwgoKCjGnTpuVty8jIMAICAozXX3/dxAiLlpaWZjRv3tyIj483evbsaTz88MN529zhfIwfP9646qqritzujufEMAzj4YcfNpo2bWo4HA7DMNzjXBiGYaSnpxs2m8348ssv863v0KGDMWnSJLc9H+5O/YXrUH9hHvUXrkN9hfty9/7ELJWpHzOTO/ehZnD3ftsslfV6wSzueJ1iJne4RtJI22LIyspi06ZNREdH51sfHR3N2rVrTYrq0li5ciX16tWjRYsW3HPPPaSkpJgdUpHsdjsffPABp06dIjIyksTERA4ePJjvvHh7e9OzZ0+XPS/3338//fv359prry10u6ufj8WLFxMREcGQIUOoV68eHTt25M0338zb7o7nJCsri4ULF3L33XdjsVjy1rv6uQDIycnBbrfj4+OTb72vry/ff/+9W54Pd6f+wjWovzCf+gvXob7CPVXm/sQsrv5/1VVUhj7UDO7eb5ulMl4vmMVdr1PM5A7XSEraFsPhw4ex2+0EBgbmWx8YGMjBgwdNiqrs+vXrx7vvvsu3337LSy+9xIYNG+jVqxeZmZlmh5bPtm3bqF69Ot7e3owePZrPPvuMNm3a5LW9u5yXDz74gM2bNxMTE1Podnc4H3v27GHWrFk0b96cuLg4Ro8ezUMPPcT8+fMB3O6cAHz++eccP36cO++8M2+dO5wLgBo1ahAZGcmzzz7LgQMHsNvtLFy4kJ9++onk5GS3PB/uTv2FudRfuM75UH/hOudCfYV7qqz9iVnc4f+q2SpLH2qGytBvm6UyXi+YxV2vU8zkDtdIHhXyLpXEud9WgHNC4vPXuZNhw4bl/RweHk5ERAShoaEsXbqUG2+80cTI8mvZsiVbt27l+PHjfPLJJ4wYMYJVq1blbXeH87Jv3z4efvhhVqxYUeBbnFzucD4cDgcRERE899xzAHTs2JHt27cza9Yshg8fnlfOHc5Jrjlz5tCvXz/q16+ft84dzkWuBQsWcPfdd9OgQQNsNhudOnXi1ltvZfPmzXll3Ol8VBaVrc3d5f+E+gvXOR/qL1znXID6Cnem83JpuMv/VTNVhj7UDJWl3zZLZbxeMIs7X6eYydWvkTTSthjq1q2LzWYrkElPSUkpkHF3Z8HBwYSGhrJr1y6zQ8nHy8uLZs2aERERQUxMDB06dODll18mKCgIwC3Oy6ZNm0hJSaFz5854eHjg4eHBqlWreOWVV/Dw8MButxfYxxXPR3BwMG3atMm3rnXr1nkPxHCncwKwd+9evv76a0aNGnXBcq54LnI1bdqUVatWcfLkSfbt28f69evJzs4mLCzM7c5HZaD+wlzqL1znfKi/cJ1zAeor3FFV6U/M4qr/V81UGfpQM1SWftssle16wSzufp1iJle/RlLSthi8vLzo3Llz3pP4csXHx9O1a1eTorr0jhw5wr59+wgODjY7lAsyDIPMzMy8/0TnnpesrCxWrVrlcueld+/ebNu2ja1bt+YtERER3HbbbWzduhWbzVZgH1c8H926dWPnzp351v3++++EhoYCuNU5AXj77bepV68e/fv3v2A5VzwX56tWrRrBwcEcO3aMuLg4Bg0a5HbnozJQf+Fa1F+YR/2F65yLc6mvcB9VpT8xi6v/X3UF7tiHmqGy9NtmqWzXC2apLNcpZnLZa6QKedxZJfDBBx8Ynp6expw5c4yEhARj7NixRrVq1Yw///zT7NCKlJaWZmzZssXYsmWLARgzZswwtmzZYuzdu9dIS0sz/vWvfxlr1641EhMTje+++86IjIw0GjRoYKSmppodep6JEycaq1evNhITE41ffvnFeOKJJwyr1WqsWLHCMAzDmDZtmhEQEGB8+umnxrZt24xbbrnFCA4Odqk6FOXcp4q6y/lYv3694eHhYUydOtXYtWuX8e677xp+fn7GwoUL88q4yzmx2+1Go0aNjPHjx+db7y7nItfy5cuNr776ytizZ4+xYsUKo0OHDsYVV1xhZGVlGYbhPuejMlF/YQ71F651PtRfuFYd1Fe4J3fsT8xSGfoxM1XmPtQM7thvm6UyXS+YpTJcp5jJ1a+RlLQtgddee80IDQ01vLy8jE6dOhmrVq0yO6QL+u677wygwDJixAgjPT3diI6ONi677DLD09PTaNSokTFixAgjKSnJ7LDzufvuu/Pa/LLLLjN69+6dd/FgGIbhcDiMp59+2ggKCjK8vb2NHj16GNu2bTMx4uI7tzN3l/NhGIaxZMkSIzw83PD29jZatWplvPHGG/m2u8s5iYuLMwBj586d+da707kwDMNYtGiR0aRJE8PLy8sICgoy7r//fuP48eN5293lfFQ26i8qnvoL1zofhqH+wpWor3Bf7tafmKUy9GNmqsx9qBnctd82S2W5XjBLZbhOMZOrXyNZDMMwKmZMr4iIiIiIiIiIiIhcjOa0FREREREREREREXEhStqKiIiIiIiIiIiIuBAlbUVERERERERERERciJK2IiIiIiIiIiIiIi5ESVsRERERERERERERF6KkrYiIiIiIiIiIiIgLUdJWRERERERERERExIUoaSsiIiIiIiIiIiLiQpS0FSlHf/75JxaLha1btxZ7n3nz5lGzZs1yi0lERFyL+goRERH3or5bRCqCkrYiIiIiIiIiIiIiLkRJWxEREREREREREREXoqStSBktX76cq666ipo1a1KnTh2uv/56du/eXWjZlStXYrFYWLp0KR06dMDHx4cuXbqwbdu2AmXj4uJo3bo11atXp2/fviQnJ+dt27BhA1FRUdStW5eAgAB69uzJ5s2by62OIiJSNuorRERE3Iv6bhExm5K2ImV06tQpxo0bx4YNG/jmm2+wWq0MHjwYh8NR5D6PPfYYL774Ihs2bKBevXoMHDiQ7OzsvO3p6em8+OKLLFiwgNWrV5OUlMSjjz6atz0tLY0RI0awZs0afvzxR5o3b851111HWlpaudZVRERKR32FiIiIe1HfLSKmM0TkkkpJSTEAY9u2bUZiYqIBGFu2bDEMwzC+++47AzA++OCDvPJHjhwxfH19jUWLFhmGYRhvv/22ARh//PFHXpnXXnvNCAwMLPI9c3JyjBo1ahhLliwpn0qJiMglpb5CRETEvajvFpGKppG2ImW0e/dubr31Vpo0aYK/vz9hYWEAJCUlFblPZGRk3s+1a9emZcuW7NixI2+dn58fTZs2zXsdHBxMSkpK3uuUlBRGjx5NixYtCAgIICAggJMnT17wPUVExDzqK0RERNyL+m4RMZuH2QGIuLsBAwYQEhLCm2++Sf369XE4HISHh5OVlVWi41gslryfPT09C2wzDCPv9Z133snff/9NbGwsoaGheHt7ExkZWeL3FBGRiqG+QkRExL2o7xYRsylpK1IGR44cYceOHcyePZvu3bsD8P333190vx9//JFGjRoBcOzYMX7//XdatWpV7Pdds2YNM2fO5LrrrgNg3759HD58uBQ1EBGR8qa+QkRExL2o7xYRV6CkrUgZ1KpVizp16vDGG28QHBxMUlISEyZMuOh+zzzzDHXq1CEwMJBJkyZRt25dbrjhhmK/b7NmzViwYAERERGkpqby2GOP4evrW4aaiIhIeVFfISIi4l7Ud4uIK9CctiJlYLVa+eCDD9i0aRPh4eE88sgjTJ8+/aL7TZs2jYcffpjOnTuTnJzM4sWL8fLyKvb7zp07l2PHjtGxY0fuuOMOHnroIerVq1eWqoiISDlRXyEiIuJe1HeLiCuwGOdOoCIi5WrlypVcc801HDt2jJo1a5odjoiIuCD1FSIiIu5FfbeIlAeNtBURERERERERERFxIUraioiIiIiIiIiIiLgQTY8gIiIiIiIiIiIi4kI00lZERERERERERETEhShpKyIiIiIiIiIiIuJClLQVERERERERERERcSFK2oqIiIiIiIiIiIi4ECVtRURERERERERERFyIkrYiIiIiIiIiIiIiLkRJWxEREREREREREREXoqStiIiIiIiIiIiIiAv5f4XiySHd3VhGAAAAAElFTkSuQmCC"",
      ""text/plain"": [
       ""<Figure size 1400x700 with 10 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""cv_along = out_fib['major_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""cv_across = out_fib['minor_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""\n"",
    ""fig, axs = plt.subplot_mosaic([[f'{i}' for i in range(7)],\n"",
    ""                               ['axis_ratio'] * 2 + ['orientation']*2 +\n"",
    ""                               ['min_max'] * 3],\n"",
    ""                               figsize=(14, 7))\n"",
    ""\n"",
    ""mins = []\n"",
    ""maxs = []\n"",
    ""for i in range(len(alphas)):\n"",
    ""    ax = axs[f'{i}']\n"",
    ""    ax.imshow(images_fib[i], cmap='jet', origin='lower')\n"",
    ""    ax.set_title(f'{np.degrees(alphas[i]):.0f}')\n"",
    ""\n"",
    ""    mins.append(images_fib[i].min())\n"",
    ""    maxs.append(images_fib[i].max())\n"",
    ""    \n"",
    ""\n"",
    ""ax = axs['axis_ratio']\n"",
    ""ax.plot(out_fib['alpha'], cv_along, 'o-', label='Along')\n"",
    ""ax.plot(out_fib['alpha'], cv_across, 'o-', label='Across')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('CV')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""ax.legend()\n"",
    ""\n"",
    ""out_fib.loc[out_fib['orientation'] < 0, 'orientation'] += 180\n"",
    ""\n"",
    ""orientation = out_fib['orientation'].values\n"",
    ""orientation[orientation > 90] = 180 - orientation[orientation > 90]\n"",
    ""\n"",
    ""ax = axs['orientation']\n"",
    ""ax.plot(out_fib['alpha'], orientation, 'o-')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('orientation')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.set_yticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""\n"",
    ""ax = axs['min_max']\n"",
    ""ax.plot(np.degrees(alphas), mins, 'o-', label='min')\n"",
    ""ax.plot(np.degrees(alphas), maxs, 'o-', label='max')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('min/max')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Using TP06 Model\n"",
    ""\n"",
    ""When using ionic model using smaller step (compared to the calculation without fibers) can be nessesary otherwise the calculation can be unstable.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:24<00:00, 237.89it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:18<00:00, 254.19it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:19<00:00, 252.75it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:17<00:00, 256.73it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:21<00:00, 244.26it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:21<00:00, 246.77it/s]\n"",
      ""Running TP062D: 100%|█████████▉| 19999/20000 [01:22<00:00, 243.64it/s]\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import skimage as ski\n"",
    ""import pandas as pd\n"",
    ""\n"",
    ""import finitewave as fw\n"",
    ""\n"",
    ""# number of nodes on the side\n"",
    ""n = 256\n"",
    ""dt = 0.001\n"",
    ""dr = 0.1\n"",
    ""t_max = 20\n"",
    ""\n"",
    ""alphas = np.radians(np.arange(0, 91, 15))\n"",
    ""d = 0.2\n"",
    ""out_lr_fib = []\n"",
    ""images_lr_fib = []\n"",
    ""for alpha in alphas:\n"",
    ""    tissue = fw.CardiacTissue2D([n, n])\n"",
    ""    tissue.add_pattern(fw.Diffuse2DPattern(d))\n"",
    ""    # add fibers orientation vectors\n"",
    ""    tissue.fibers = np.zeros([n, n, 2])\n"",
    ""    tissue.fibers[..., 0] = np.cos(alpha)\n"",
    ""    tissue.fibers[..., 1] = np.sin(alpha)\n"",
    ""\n"",
    ""    # set up stimulation parameters:\n"",
    ""    stim = fw.StimCurrentArea2D(0, 200, 1)\n"",
    ""    stim.add_stim_point([n//2, n//2], tissue.mesh, 5)\n"",
    ""    stim_sequence = fw.StimSequence()\n"",
    ""    stim_sequence.add_stim(stim)\n"",
    ""\n"",
    ""    # create model object:\n"",
    ""    model = fw.TP062D()\n"",
    ""    # set up numerical parameters:\n"",
    ""    model.dt = dt\n"",
    ""    model.dr = dr\n"",
    ""    model.t_max = t_max\n"",
    ""    model.cardiac_tissue = tissue\n"",
    ""    model.stim_sequence = stim_sequence\n"",
    ""\n"",
    ""    model.run()\n"",
    ""\n"",
    ""    labeled = (model.u > 0.).astype(int)\n"",
    ""    props = ski.measure.regionprops_table(\n"",
    ""        labeled, properties=('orientation', 'major_axis_length',\n"",
    ""                             'minor_axis_length'))\n"",
    ""    props['orientation'] = np.degrees(props['orientation'])\n"",
    ""    props['axis_ratio'] = props['major_axis_length'] / props['minor_axis_length']\n"",
    ""    props['alpha'] = np.degrees(alpha)\n"",
    ""    props['density_calc'] = (np.sum(tissue.mesh[-1:1, -1:1] == 2) \n"",
    ""                                / ((n - 2) * (n - 2)))\n"",
    ""    images_lr_fib.append(model.u.copy())\n"",
    ""\n"",
    ""    out_lr_fib.append(pd.DataFrame(props))\n"",
    ""\n"",
    ""out_lr_fib = pd.concat(out_lr_fib)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": ""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"",
      ""text/plain"": [
       ""<Figure size 1400x700 with 10 Axes>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""cv_along = out_lr_fib['major_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""cv_across = out_lr_fib['minor_axis_length'] / 2 * model.dr / model.t_max\n"",
    ""\n"",
    ""fig, axs = plt.subplot_mosaic([[f'{i}' for i in range(7)],\n"",
    ""                               ['axis_ratio'] * 2 + ['orientation']*2 +\n"",
    ""                               ['min_max'] * 3],\n"",
    ""                               figsize=(14, 7))\n"",
    ""\n"",
    ""mins = []\n"",
    ""maxs = []\n"",
    ""for i in range(len(alphas)):\n"",
    ""    ax = axs[f'{i}']\n"",
    ""    ax.imshow(images_lr_fib[i], cmap='jet', origin='lower')\n"",
    ""    ax.set_title(f'{np.degrees(alphas[i]):.0f}')\n"",
    ""\n"",
    ""    mins.append(images_lr_fib[i].min())\n"",
    ""    maxs.append(images_lr_fib[i].max())\n"",
    ""    \n"",
    ""\n"",
    ""ax = axs['axis_ratio']\n"",
    ""ax.plot(out_lr_fib['alpha'], cv_along, 'o-', label='Along')\n"",
    ""ax.plot(out_lr_fib['alpha'], cv_across, 'o-', label='Across')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('CV, mm/ms')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""ax.legend()\n"",
    ""\n"",
    ""out_lr_fib.loc[out_lr_fib['orientation'] < 0, 'orientation'] += 180\n"",
    ""\n"",
    ""orientation = out_lr_fib['orientation'].values\n"",
    ""orientation[orientation > 90] = 180 - orientation[orientation > 90]\n"",
    ""\n"",
    ""ax = axs['orientation']\n"",
    ""ax.plot(out_lr_fib['alpha'], orientation, 'o-')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('orientation')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.set_yticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""\n"",
    ""ax = axs['min_max']\n"",
    ""ax.plot(np.degrees(alphas), mins, 'o-', label='min')\n"",
    ""ax.plot(np.degrees(alphas), maxs, 'o-', label='max')\n"",
    ""ax.set_xlabel('alpha')\n"",
    ""ax.set_ylabel('min/max')\n"",
    ""ax.set_xticks(np.degrees(alphas))\n"",
    ""ax.grid(True)\n"",
    ""\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"": ""3.11.0""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 4
}
","Unknown"
"Cell biology","finitewave/Finitewave","tests/test_trackers_2d.py",".py","9282","306","import os
import shutil
import numpy as np
import pytest
import finitewave as fw

@pytest.fixture
def cable_model():
    ni = 12
    nj = 3
    tissue = fw.CardiacTissue2D([ni, nj])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord2D(0, 5, 0.5, 0, 5, 0, nj))
    
    model = fw.AlievPanfilov2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 3
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.fixture
def spiral_model():
    ni = 100
    nj = 100
    tissue = fw.CardiacTissue2D([ni, nj])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimVoltageCoord2D(0, 1, 0, ni, 0, 3))
    stim_sequence.add_stim(fw.StimVoltageCoord2D(5, 1, 0, ni//2, 0, nj))
    
    model = fw.Barkley2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 20
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.fixture
def planar_model():
    ni = 50
    nj = 5
    tissue = fw.CardiacTissue2D([ni, nj])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord2D(5, 5, 0.5, 0, 5, 0, nj))
    
    model = fw.AlievPanfilov2D()
    model.dt = 0.0015
    model.dr = 0.25
    model.t_max = 15
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.mark.action_potential_2d_tracker
def test_action_potential_tracker(cable_model):
    tracker = fw.ActionPotential2DTracker()
    tracker.cell_ind = [10, 1]
    tracker.step = 1

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.t_max = 30
    cable_model.run()

    u = tracker.output

    # Check if the output is not empty
    assert u is not None
    assert len(u) > 0

    # Check if the Aliev-Panfilov model maximal amplitude is within expected range
    assert np.max(u) == pytest.approx(1.0, abs=0.02)

    threshold = 0.1
    up_idx = np.where((u[:-1] < threshold) & (u[1:] >= threshold))[0]
    down_idx = np.where((u[:-1] > threshold) & (u[1:] <= threshold))[0]

    assert len(up_idx) > 0, ""Action potential upstroke not found""
    assert len(down_idx) > 0, ""Action potential downstroke not found""

    ap_start = up_idx[0]
    ap_end = down_idx[down_idx > ap_start][0]

    apd = (ap_end - ap_start) * cable_model.dt
    # without prebeats:
    assert 20 <= apd <= 30, f""APD90 is out of expected range {apd}""

@pytest.mark.animation_2d_tracker
def test_animation_2d_tracker(spiral_model):
    tracker = fw.Animation2DTracker()
    tracker.variable_name = ""u""
    tracker.dir_name = ""test_frames""
    tracker.step = 100 # write every 100th step
    tracker.overwrite = True

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    # Check if the animation files are created
    assert os.path.exists(tracker.dir_name), ""Output directory was not created.""
    files = sorted(os.listdir(tracker.dir_name))
    expected_frames = (spiral_model.t_max/spiral_model.dt) // tracker.step
    assert len(files) == expected_frames, f""Expected {expected_frames} frames, got {len(files)}""

    # Check if the frames are not empty
    for fname in files:
        frame = np.load(os.path.join(tracker.dir_name, fname))
        assert np.any(frame > 0), f""Frame {fname} appears to be empty."" 

    shutil.rmtree(tracker.dir_name)

@pytest.mark.activation_time_2d_tracker
def test_activation_time_2d_tracker(cable_model):
    # TODO:
    # Edge cases: start time - end time, values rewriting (should not work) 
    tracker = fw.ActivationTime2DTracker()
    tracker.threshold = 0.5
    tracker.step = 1
    tracker.start_time = 0

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.run()

    ats = tracker.output

    # Check if the output is not empty
    assert ats is not None
    assert len(ats) > 0
    assert np.any(~np.isnan(ats)), ""AT array is entirely NaN""

    # Check if the wavefront speed (distance/activation time) value is within expected range
    speed = 5*cable_model.dr/ats[10, 1] # 5 - number of nodes on the way
    assert 1.5 <= speed <= 2, f""Wavefront speed is out of expected range {speed}"" 

@pytest.mark.local_activation_time_2d_tracker
def test_local_activation_time_2d_tracker(cable_model):
    tracker = fw.LocalActivationTime2DTracker()
    tracker.threshold = 0.5
    tracker.step = 1
    tracker.start_time = 0

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.stim_sequence.add_stim(fw.StimVoltageCoord2D(45, 1, 0, 5, 0, 10))
    
    cable_model.t_max = 50
    cable_model.run()

    lats = tracker.output

    # Check if the output is not empty
    assert lats is not None
    assert len(lats) > 0
    assert np.any(~np.isnan(lats)), ""LAT array is entirely NaN""

    # Values at the center cell should have two LAT values
    assert len(lats) == 2, ""Every cell should have two LAT values""
    LAT1, LAT2 = lats[:, 10, 1]

    # Check if the wavefront speed (distance/activation time)  values are within expected range
    assert LAT1 < LAT2, ""LAT values should be in ascending order""
    speed_1 = 5*cable_model.dr/LAT1 # 5 - number of nodes on the way
    speed_2 = 5*cable_model.dr/(LAT2 - 45) # 45 - second wave start time
    assert 1.5 <= speed_1 <= 2, f""Wavefront speed for the first wave is out of expected range {speed_1}"" 
    assert 1.5 <= speed_2 <= 2, f""Wavefront speed for the second wave is out of expected range {speed_2}""

@pytest.mark.activation_time_2d_tracker
def test_multi_variable_2d_tracker(cable_model):
    tracker = fw.MultiVariable2DTracker()
    tracker.cell_ind = [10, 1]
    tracker.var_list = [""v""]

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq
    
    cable_model.t_max = 30
    cable_model.run()

    v = tracker.output[""v""]

    # Check if the output is not empty
    assert v is not None
    assert len(v) > 0

    # Check if the Aliev-Panfilov model 'v' maximal amplitude is within expected range
    assert np.max(v) == pytest.approx(2, abs=0.1)

@pytest.mark.spiral_wave_core_2d_tracker
def test_spiral_wave_core_2d_tracker(spiral_model):
    tracker = fw.SpiralWaveCore2DTracker()
    tracker.threshold = 0.5
    tracker.start_time = 12
    tracker.step = 10  # Record the spiral wave core every 10 step

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    sw_core = tracker.output

    x, y =  sw_core['x'], sw_core['y']

    # Check if the output is not empty
    assert x is not None
    assert y is not None
    assert len(x) > 0
    assert len(y) > 0

    # Check if the spiral wave core is within expected range
    assert np.min(x) >= 32
    assert np.max(x) <= 38
    assert np.min(y) >= 47
    assert np.max(y) <= 53

@pytest.mark.spiral_wave_period_2d_tracker
def test_spiral_wave_period_2d_tracker(spiral_model):
    tracker = fw.Period2DTracker()
    # Here we create an int array of detectors as a list of positions in which we want to calculate the period.
    positions = np.array([[80, 80], [20, 70], [40, 10], [25, 90]])
    tracker.cell_ind = positions
    tracker.threshold = 0.5
    tracker.start_time = 10
    tracker.step = 10

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    periods = tracker.output

    period_mean = np.mean(np.array([np.mean(x) if len(x) > 0 else np.nan for x in periods]))

    # Check if the output is not empty
    assert periods is not None
    assert len(periods) > 0

    # Check if the spiral wave period is within expected range
    assert period_mean == pytest.approx(3.5, abs=0.2)

@pytest.mark.ecg_2d_tracker
def test_ecg_2d_tracker(planar_model):
    tracker = fw.ECG2DTracker()
    tracker.start_time = 0
    tracker.step = 10
    tracker.measure_coords = np.array([[25, 2, 0]])

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)

    planar_model.tracker_sequence = seq

    planar_model.run()

    ecg = tracker.output.T[0]

    assert ecg.max() > 0.001
    assert ecg.min() < -0.001
    assert np.argmax(ecg) > 100  # Check if the peak occurs not at the beginning

def test_period_animation_2d_tracker(spiral_model):
    tracker = fw.PeriodAnimation2DTracker()
    tracker.dir_name = ""test_frames""
    tracker.threshold = 0.5
    tracker.step = 100  # write every 100th step
    tracker.overwrite = True

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    # Check if the animation files are created
    assert os.path.exists(tracker.dir_name), ""Output directory was not created.""
    files = sorted(os.listdir(tracker.dir_name))
    expected_frames = (spiral_model.t_max/spiral_model.dt) // tracker.step
    assert len(files) == expected_frames, f""Expected {expected_frames} frames, got {len(files)}""

    # Check if the frames are not empty
    frame = np.load(os.path.join(tracker.dir_name, files[-1]))
    assert np.any(frame > 0), f""Frame {frame} appears to be empty.""

    shutil.rmtree(tracker.dir_name)


","Python"
"Cell biology","finitewave/Finitewave","tests/test_fibrosis_3d.py",".py","1594","51","import random
import numpy as np
from finitewave.cpuwave3D.fibrosis.diffuse_3d_pattern import Diffuse3DPattern
from finitewave.cpuwave3D.fibrosis.structural_3d_pattern import Structural3DPattern

def test_diffuse_fibrosis_3d():
    shape = (100, 100, 100)
    x1, x2 = 10, 90
    y1, y2 = 20, 80
    z1, z2 = 30, 70
    density = 0.3

    random.seed(0)

    pattern = Diffuse3DPattern(density=density, x1=x1, x2=x2, y1=y1, y2=y2, z1=z1, z2=z2)
    result = pattern.generate(shape=shape)

    assert result.shape == shape, ""Diffuse: shape mismatch""

    assert np.all(np.isin(result, [1, 2])), ""Diffuse: invalid values in result""

    subregion = result[x1:x2, y1:y2, z1:z2]
    fibrosis_ratio = np.sum(subregion == 2) / subregion.size

    assert abs(fibrosis_ratio - density) < 0.01, ""Diffuse: fibrosis density mismatch""

def test_structural_fibrosis_3d():
    shape = (100, 100, 100)
    x1, x2 = 10, 90
    y1, y2 = 20, 80
    z1, z2 = 30, 70
    density = 0.4
    length_i = 5
    length_j = 4
    length_k = 3

    random.seed(0)

    pattern = Structural3DPattern(
        density=density, length_i=length_i, length_j=length_j, length_k=length_k,
        x1=x1, x2=x2, y1=y1, y2=y2, z1=z1, z2=z2
    )
    result = pattern.generate(shape=shape)

    assert result.shape == shape, ""Structural: shape mismatch""
    assert np.all(np.isin(result, [1, 2])), ""Structural: invalid values in result""

    subregion = result[x1:x2, y1:y2, z1:z2]
    fibrosis_ratio = np.sum(subregion == 2) / subregion.size

    assert abs(fibrosis_ratio - density) < 0.05, ""Structural: fibrosis density mismatch""","Python"
"Cell biology","finitewave/Finitewave","tests/test_basics.py",".py","1936","76","import os
import shutil
import numpy as np
import pytest
import finitewave as fw


def test_state_loading():
    n = 5
    tissue = fw.CardiacTissue2D([n, n])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord2D(0, 10, 0.5, 1, n//2, n//2 + 1,
                                                 n//2, n//2 + 1))

    state_saver = fw.StateSaverCollection()
    state_saver.savers.append(fw.StateSaver(""state_0"", time=3))

    model = fw.FentonKarma2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 5

    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    model.state_saver = state_saver

    model.run()

    u_before = model.u.copy()
    v_before = model.v.copy()
    w_before = model.w.copy()

    # recreate the model
    model = fw.FentonKarma2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 5

    model.cardiac_tissue = tissue
    model.state_loader = fw.StateLoader(""state_0"")

    model.run()
    u_after = model.u.copy()
    v_after = model.v.copy()
    w_after = model.w.copy()

    assert np.allclose(u_before, u_after, atol=1e-5), ""u states are not equal""
    assert np.allclose(v_before, v_after, atol=1e-5), ""v states are not equal""
    assert np.allclose(w_before, w_after, atol=1e-5), ""w states are not equal""

def test_commands():
    n = 5
    tissue = fw.CardiacTissue2D([n, n])

    stim_sequence = fw.StimSequence()

    model = fw.FentonKarma2D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 10

    class ExcitationCommand(fw.Command):
        def execute(self, model):
            model.u[1:-1, 1:-1] = 1

    command_sequence = fw.CommandSequence()
    command_sequence.add_command(ExcitationCommand(5))

    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    model.command_sequence = command_sequence

    model.run()   

    assert np.mean(model.u[1:-1, 1:-1]) > 0.5, ""Command did not work""","Python"
"Cell biology","finitewave/Finitewave","tests/test_models_3d.py",".py","9016","297","import os
import shutil
import numpy as np
import pytest
import finitewave as fw


def prepare_model(model_class, curr_value, curr_dur, t_calc, t_prebeats):
    """"""
    Prepares a 3D cardiac model with a stimulation protocol.

    Parameters
    ----------
    model_class : Callable
        The cardiac model class to be instantiated.
    curr_value : float
        Amplitude of the stimulus current (μA/cm² or model units).
    curr_dur : float
        Duration of each stimulus pulse (ms or model units).
    t_calc : float
        Time after the last preconditioning beat to continue recording (ms or model units).
    t_prebeats : float
        Interval between preconditioning stimuli (ms or model units).

    Returns
    -------
    model : CardiacModel
        Configured and initialized model ready for simulation.
    """"""
    ni = 5
    nj = 3
    nk = 3
    tissue = fw.CardiacTissue3D([ni, nj, nk])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord3D(0, curr_value, curr_dur, 0, 2, 0, nj, 0, nk))
    stim_sequence.add_stim(fw.StimCurrentCoord3D(t_prebeats, curr_value, curr_dur, 0, 2, 0, nj, 0, nk))
    stim_sequence.add_stim(fw.StimCurrentCoord3D(2*t_prebeats, curr_value, curr_dur, 0, 2, 0, nj, 0, nk))
    stim_sequence.add_stim(fw.StimCurrentCoord3D(3*t_prebeats, curr_value, curr_dur, 0, 2, 0, nj, 0, nk))

    model = model_class()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 3*t_prebeats + t_calc
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence

    return model

def run_model(model):
    """"""
    Runs a cardiac model with a membrane potential tracker.

    Parameters
    ----------
    model : CardiacModel
        A configured model with stimulation and tissue already assigned.

    Returns
    -------
    output : np.ndarray
        Time series of membrane potential for a specific cell.
    """"""
    tracker = fw.ActionPotential3DTracker()
    tracker.cell_ind = [3, 1, 1]
    tracker.step = 1

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    model.tracker_sequence = seq

    model.run()
    return tracker.output

def calculate_apd(u, dt, threshold, beat_index=3):
    """"""
    Calculates the action potential duration (APD) for a single beat.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential time series.
    dt : float
        Time step of the simulation (ms).
    threshold : float
        Voltage threshold to define APD90 (e.g., -70 mV or 0.1 for normalized models).
    beat_index : int, optional
        Index of the beat to analyze (default is 3).

    Returns
    -------
    apd : float or None
        Duration of the action potential (ms or model units), or None if no complete AP was found.
    """"""
    up_idx = np.where((u[:-1] < threshold) & (u[1:] >= threshold))[0]
    down_idx = np.where((u[:-1] > threshold) & (u[1:] <= threshold))[0]

    if len(up_idx) <= beat_index or len(down_idx) == 0:
        return None

    ap_start = up_idx[beat_index]
    ap_end_candidates = down_idx[down_idx > ap_start]
    if len(ap_end_candidates) == 0:
        return None

    ap_end = ap_end_candidates[0]
    return (ap_end - ap_start) * dt

@pytest.mark.aliev_panfilov_3d
def test_aliev_panfilov():
    """"""
    Test the Aliev-Panfilov 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within expected range [21, 23] ms.

    Stimulation:
    - 4 current pulses at intervals of 60 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.AlievPanfilov3D, curr_value=5, curr_dur=0.5, t_calc=80, t_prebeats=60)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.02)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 20 <= apd <= 25, f""Aliev-Panfilov APD90 is out of expected range {apd}""

@pytest.mark.barkley_3d
def test_barkley():
    """"""
    Test the Barkley 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within expected range [1, 2] ms.

    Stimulation:
    - 4 current pulses at intervals of 60 ms.
    - Amplitude: 1
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.Barkley3D, curr_value=5, curr_dur=0.1, t_calc=80, t_prebeats=60)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.02)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 1 <= apd <= 4, f""Barkley APD90 is out of expected range {apd}""

@pytest.mark.mitchell_schaeffer_3d
def test_mitchell_schaeffer():
    """"""
    Test the Mitchell-Schaeffer 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [280, 320] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.MitchellSchaeffer3D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(0.95, abs=0.02)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)

    assert 250 <= apd <= 350, f""Mitchell-Schaeffer APD90 is out of expected range {apd}""

@pytest.mark.fenton_karma_3d
def test_fenton_karma():
    """"""
    Test the Fenton-Karma 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [100, 200] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.FentonKarma3D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.02)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 100 <= apd <= 200, f""Fenton-Karma APD90 is out of expected range {apd}""

@pytest.mark.bueno_orovio_3d
def test_bueno_orovio_3d():
    """"""
    Test the Bueno-Orovio 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [200, 300] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.BuenoOrovio3D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.4, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 200 <= apd <= 300, f""Bueno-Orovio APD90 is out of expected range {apd}""


@pytest.mark.luo_rudy91_3d
def test_luo_rudy():
    """"""
    Test the Luo-Rudy 1991 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - APD90 is within [350, 400] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.LuoRudy913D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 20
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 350 <= apd <= 400, f""Luo-Rudy APD90 is out of expected range {apd}""

@pytest.mark.tp06_3d
def test_tp06():
    """"""
    Test the Ten Tusscher-Panfilov 2006 (TP06) 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [280, 320] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.TP063D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 20
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 280 <= apd <= 320, f""TP06 APD90 is out of expected range {apd}""

@pytest.mark.courtemanche_3d
def test_courtemanche():
    """"""
    Test the Courtemanche 3D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [200, 300] ms.

    Note: Slightly elevated plateau potential is expected in some parameterizations.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.Courtemanche3D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 10
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 200 <= apd <= 300, f""Courtemanche APD90 is out of expected range {apd}""

","Python"
"Cell biology","finitewave/Finitewave","tests/test_models_2d.py",".py","8986","295","import os
import shutil
import numpy as np
import pytest
import finitewave as fw


def prepare_model(model_class, curr_value, curr_dur, t_calc, t_prebeats):
    """"""
    Prepares a 2D cardiac model with a stimulation protocol.

    Parameters
    ----------
    model_class : Callable
        The cardiac model class to be instantiated.
    curr_value : float
        Amplitude of the stimulus current (μA/cm² or model units).
    curr_dur : float
        Duration of each stimulus pulse (ms or model units).
    t_calc : float
        Time after the last preconditioning beat to continue recording (ms or model units).
    t_prebeats : float
        Interval between preconditioning stimuli (ms or model units).

    Returns
    -------
    model : CardiacModel
        Configured and initialized model ready for simulation.
    """"""
    ni = 5
    nj = 3
    tissue = fw.CardiacTissue2D([ni, nj])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord2D(0, curr_value, curr_dur, 0, 2, 0, nj))
    stim_sequence.add_stim(fw.StimCurrentCoord2D(t_prebeats, curr_value, curr_dur, 0, 2, 0, nj))
    stim_sequence.add_stim(fw.StimCurrentCoord2D(2*t_prebeats, curr_value, curr_dur, 0, 2, 0, nj))
    stim_sequence.add_stim(fw.StimCurrentCoord2D(3*t_prebeats, curr_value, curr_dur, 0, 2, 0, nj))

    model = model_class()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 3*t_prebeats + t_calc
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence

    return model

def run_model(model):
    """"""
    Runs a cardiac model with a membrane potential tracker.

    Parameters
    ----------
    model : CardiacModel
        A configured model with stimulation and tissue already assigned.

    Returns
    -------
    output : np.ndarray
        Time series of membrane potential for a specific cell.
    """"""
    tracker = fw.ActionPotential2DTracker()
    tracker.cell_ind = [3, 1]
    tracker.step = 1

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    model.tracker_sequence = seq

    model.run()
    return tracker.output

def calculate_apd(u, dt, threshold, beat_index=3):
    """"""
    Calculates the action potential duration (APD) for a single beat.

    Parameters
    ----------
    u : np.ndarray
        Membrane potential time series.
    dt : float
        Time step of the simulation (ms).
    threshold : float
        Voltage threshold to define APD90 (e.g., -70 mV or 0.1 for normalized models).
    beat_index : int, optional
        Index of the beat to analyze (default is 3).

    Returns
    -------
    apd : float or None
        Duration of the action potential (ms or model units), or None if no complete AP was found.
    """"""
    up_idx = np.where((u[:-1] < threshold) & (u[1:] >= threshold))[0]
    down_idx = np.where((u[:-1] > threshold) & (u[1:] <= threshold))[0]

    if len(up_idx) <= beat_index or len(down_idx) == 0:
        return None

    ap_start = up_idx[beat_index]
    ap_end_candidates = down_idx[down_idx > ap_start]
    if len(ap_end_candidates) == 0:
        return None

    ap_end = ap_end_candidates[0]
    return (ap_end - ap_start) * dt

@pytest.mark.aliev_panfilov_2d
def test_aliev_panfilov_2d():
    """"""
    Test the Aliev-Panfilov 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within expected range [21, 23] ms.

    Stimulation:
    - 4 current pulses at intervals of 60 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.AlievPanfilov2D, curr_value=5, curr_dur=0.5, t_calc=80, t_prebeats=60)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 20 <= apd <= 25, f""Aliev-Panfilov APD90 is out of expected range {apd}""

@pytest.mark.barkley_2d
def test_barkley_2d():
    """"""
    Test the Barkley 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within expected range [1, 2] ms.

    Stimulation:
    - 4 current pulses at intervals of 60 ms.
    - Amplitude: 1
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.Barkley2D, curr_value=5, curr_dur=0.1, t_calc=80, t_prebeats=60)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 1 <= apd <= 4, f""Barkley APD90 is out of expected range {apd}""

@pytest.mark.mitchell_schaeffer_2d
def test_mitchell_schaeffer_2d():
    """"""
    Test the Mitchell-Schaeffer 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [280, 320] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.MitchellSchaeffer2D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(0.95, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)

    assert 250 <= apd <= 350, f""Mitchell-Schaeffer APD90 is out of expected range {apd}""

@pytest.mark.fenton_karma_2d
def test_fenton_karma_2d():
    """"""
    Test the Fenton-Karma 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [100, 200] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 10
    - Duration: 0.5 ms
    """"""
    model = prepare_model(fw.FentonKarma2D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.0, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 100 <= apd <= 200, f""Fenton-Karma APD90 is out of expected range {apd}""

@pytest.mark.bueno_orovio_2d
def test_bueno_orovio_2d():
    """"""
    Test the Bueno-Orovio 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [200, 300] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.BuenoOrovio2D, curr_value=5, curr_dur=0.5, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) == pytest.approx(1.4, abs=0.1)
    assert np.min(u) == pytest.approx(0.0, abs=0.01)

    apd = calculate_apd(u, model.dt, threshold=0.1)
    assert 200 <= apd <= 300, f""Bueno-Orovio APD90 is out of expected range {apd}""

@pytest.mark.luo_rudy91_2d
def test_luo_rudy_2d():
    """"""
    Test the Luo-Rudy 1991 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - APD90 is within [350, 400] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.LuoRudy912D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 20
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 350 <= apd <= 400, f""Luo-Rudy APD90 is out of expected range {apd}""

@pytest.mark.tp06_2d
def test_tp06_2d():
    """"""
    Test the Ten Tusscher-Panfilov 2006 (TP06) 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [280, 320] ms.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.TP062D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 20
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 280 <= apd <= 320, f""TP06 APD90 is out of expected range {apd}""

@pytest.mark.courtemanche_2d
def test_courtemanche_2d():
    """"""
    Test the Courtemanche 2D model.

    This test checks:
    - Correct range of membrane potential values after stimulation.
    - Action potential duration (APD90) within [200, 300] ms.

    Note: Slightly elevated plateau potential is expected in some parameterizations.

    Stimulation:
    - 4 current pulses at intervals of 1000 ms.
    - Amplitude: 100
    - Duration: 1 ms
    """"""
    model = prepare_model(fw.Courtemanche2D, curr_value=100, curr_dur=1, t_calc=1000, t_prebeats=1000)
    u = run_model(model)

    assert np.max(u) > 10
    assert np.min(u) < -80

    apd = calculate_apd(u, model.dt, threshold=-70)
    assert 200 <= apd <= 300, f""Courtemanche APD90 is out of expected range {apd}""

","Python"
"Cell biology","finitewave/Finitewave","tests/test_fibrosis_2d.py",".py","1478","48","import random
import numpy as np
from finitewave.cpuwave2D.fibrosis.diffuse_2d_pattern import Diffuse2DPattern
from finitewave.cpuwave2D.fibrosis.structural_2d_pattern import Structural2DPattern

def test_diffuse_fibrosis_2d():
    shape = (1000, 1000)
    x1, x2 = 100, 900
    y1, y2 = 200, 800
    density = 0.3

    random.seed(0)

    pattern = Diffuse2DPattern(density=density, x1=x1, x2=x2, y1=y1, y2=y2)
    result = pattern.generate(shape=shape)

    assert result.shape == shape, ""Diffuse: shape mismatch""

    assert np.all(np.isin(result, [1, 2])), ""Diffuse: invalid values in result""

    subregion = result[x1:x2, y1:y2]
    fibrosis_ratio = np.sum(subregion == 2) / subregion.size

    assert abs(fibrosis_ratio - density) < 0.01, ""Diffuse: fibrosis density mismatch""

def test_structural_fibrosis_2d():
    shape = (1000, 1000)
    x1, x2 = 100, 900
    y1, y2 = 200, 800
    density = 0.4
    length_i = 5
    length_j = 4

    random.seed(0)

    pattern = Structural2DPattern(
        density=density, length_i=length_i, length_j=length_j,
        x1=x1, x2=x2, y1=y1, y2=y2
    )
    result = pattern.generate(shape=shape)

    assert result.shape == shape, ""Structural: shape mismatch""
    assert np.all(np.isin(result, [1, 2])), ""Structural: invalid values in result""

    subregion = result[x1:x2, y1:y2]
    fibrosis_ratio = np.sum(subregion == 2) / subregion.size

    assert abs(fibrosis_ratio - density) < 0.05, ""Structural: fibrosis density mismatch""","Python"
"Cell biology","finitewave/Finitewave","tests/test_trackers_3d.py",".py","10644","345","import os
import shutil
from pathlib import Path
from tempfile import TemporaryDirectory
import numpy as np
import pytest
import finitewave as fw

@pytest.fixture
def cable_model():
    ni = 12
    nj = 3
    nk = 3
    tissue = fw.CardiacTissue3D([ni, nj, nk])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord3D(0, 5, 0.5, 0, 5, 0, nj, 0, nk))
    
    model = fw.AlievPanfilov3D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 3
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.fixture
def spiral_model():
    ni = 100
    nj = 100
    nk = 3
    tissue = fw.CardiacTissue3D([ni, nj, nk])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimVoltageCoord3D(0, 1, 0, ni, 0, 3, 0, nk))
    stim_sequence.add_stim(fw.StimVoltageCoord3D(5, 1, 0, ni//2, 0, nj, 0, nk))
    
    model = fw.Barkley3D()
    model.dt = 0.01
    model.dr = 0.25
    model.t_max = 20
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.fixture
def planar_model():
    ni = 50
    nj = 5
    nk = 3
    tissue = fw.CardiacTissue3D([ni, nj, nk])

    stim_sequence = fw.StimSequence()
    stim_sequence.add_stim(fw.StimCurrentCoord3D(5, 5, 0.5, 0, 5, 0, nj, 0, nk))
    
    model = fw.AlievPanfilov3D()
    model.dt = 0.0015
    model.dr = 0.25
    model.t_max = 15
    model.cardiac_tissue = tissue
    model.stim_sequence = stim_sequence
    return model

@pytest.mark.action_potential_3d_tracker
def test_action_potential_tracker(cable_model):
    tracker = fw.ActionPotential3DTracker()
    tracker.cell_ind = [10, 1, 1]
    tracker.step = 1

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.t_max = 30
    cable_model.run()

    u = tracker.output

    # Check if the output is not empty
    assert u is not None
    assert len(u) > 0

    # Check if the Aliev-Panfilov model maximal amplitude is within expected range
    assert np.max(u) == pytest.approx(1.0, abs=0.02)

    threshold = 0.1
    up_idx = np.where((u[:-1] < threshold) & (u[1:] >= threshold))[0]
    down_idx = np.where((u[:-1] > threshold) & (u[1:] <= threshold))[0]

    assert len(up_idx) > 0, ""Action potential upstroke not found""
    assert len(down_idx) > 0, ""Action potential downstroke not found""

    ap_start = up_idx[0]
    ap_end = down_idx[down_idx > ap_start][0]

    apd = (ap_end - ap_start) * cable_model.dt
    # without prebeats:
    assert 20 <= apd <= 30, f""APD90 is out of expected range {apd}""

@pytest.mark.animation_3d_tracker
def test_animation_3d_tracker(spiral_model):
    tracker = fw.Animation3DTracker()
    tracker.variable_name = ""u""
    tracker.dir_name = ""test_frames""
    tracker.step = 100 # write every 100th step
    tracker.overwrite = True

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    # Check if the animation files are created
    assert os.path.exists(tracker.dir_name), ""Output directory was not created.""
    files = sorted(os.listdir(tracker.dir_name))
    expected_frames = (spiral_model.t_max/spiral_model.dt) // tracker.step
    assert len(files) == expected_frames, f""Expected {expected_frames} frames, got {len(files)}""

    # Check if the frames are not empty
    for fname in files:
        frame = np.load(os.path.join(tracker.dir_name, fname))
        assert np.any(frame > 0), f""Frame {fname} appears to be empty."" 

    shutil.rmtree(tracker.dir_name)

@pytest.mark.activation_time_3d_tracker
def test_activation_time_3d_tracker(cable_model):
    # TODO:
    # Edge cases: start time - end time, values rewriting (should not work) 
    tracker = fw.ActivationTime3DTracker()
    tracker.threshold = 0.5
    tracker.step = 1
    tracker.start_time = 0

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.run()

    ats = tracker.output

    # Check if the output is not empty
    assert ats is not None
    assert len(ats) > 0
    assert np.any(~np.isnan(ats)), ""AT array is entirely NaN""

    # Check if the wavefront speed (distance/activation time) value is within expected range
    speed = 5*cable_model.dr/ats[10, 1, 1] # 5 - number of nodes on the way
    assert 1.5 <= speed <= 2, f""Wavefront speed is out of expected range {speed}"" 

@pytest.mark.local_activation_time_3d_tracker
def test_local_activation_time_3d_tracker(cable_model):
    tracker = fw.LocalActivationTime3DTracker()
    tracker.threshold = 0.5
    tracker.step = 1
    tracker.start_time = 0

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq

    cable_model.stim_sequence.add_stim(fw.StimVoltageCoord3D(45, 1, 0, 5, 0, 10, 0, 3))
    
    cable_model.t_max = 50
    cable_model.run()

    lats = tracker.output

    # Check if the output is not empty
    assert lats is not None
    assert len(lats) > 0
    assert np.any(~np.isnan(lats)), ""LAT array is entirely NaN""

    # Values at the center cell should have two LAT values
    assert len(lats) == 2, ""Every cell should have two LAT values""
    LAT1, LAT2 = lats[:, 10, 1, 1]

    # Check if the wavefront speed (distance/activation time)  values are within expected range
    assert LAT1 < LAT2, ""LAT values should be in ascending order""
    speed_1 = 5*cable_model.dr/LAT1 # 5 - number of nodes on the way
    speed_2 = 5*cable_model.dr/(LAT2 - 45) # 45 - second wave start time
    assert 1.5 <= speed_1 <= 2, f""Wavefront speed for the first wave is out of expected range {speed_1}"" 
    assert 1.5 <= speed_2 <= 2, f""Wavefront speed for the second wave is out of expected range {speed_2}""

@pytest.mark.activation_time_3d_tracker
def test_multi_variable_3d_tracker(cable_model):
    tracker = fw.MultiVariable3DTracker()
    tracker.cell_ind = [10, 1, 1]
    tracker.var_list = [""v""]

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    cable_model.tracker_sequence = seq
    
    cable_model.t_max = 30
    cable_model.run()

    v = tracker.output[""v""]

    # Check if the output is not empty
    assert v is not None
    assert len(v) > 0

    # Check if the Aliev-Panfilov model 'v' maximal amplitude is within expected range
    assert np.max(v) == pytest.approx(2, abs=0.1)

@pytest.mark.spiral_wave_core_3d_tracker
def test_spiral_wave_core_3d_tracker(spiral_model):
    tracker = fw.SpiralWaveCore3DTracker()
    tracker.threshold = 0.5
    tracker.start_time = 12
    tracker.step = 10  # Record the spiral wave core every 10 step

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    sw_core = tracker.output

    x, y, z =  sw_core['x'], sw_core['y'], sw_core['z']

    # Check if the output is not empty
    assert x is not None
    assert y is not None
    assert z is not None
    assert len(x) > 0
    assert len(y) > 0
    assert len(z) > 0

    # Check if the spiral wave core is within expected range
    assert np.min(x) >= 32
    assert np.max(x) <= 38
    assert np.min(y) >= 47
    assert np.max(y) <= 53
    assert np.min(z) >= 0
    assert np.max(z) <= 2

@pytest.mark.spiral_wave_period_3d_tracker
def test_spiral_wave_period_3d_tracker(spiral_model):
    tracker = fw.Period3DTracker()
    # Here we create an int array of detectors as a list of positions in which we want to calculate the period.
    positions = np.array([[80, 80, 1], [20, 70, 1], [40, 10, 1], [25, 90, 1]])
    tracker.cell_ind = positions
    tracker.threshold = 0.5
    tracker.start_time = 10
    tracker.step = 10

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    periods = tracker.output

    period_mean = np.mean(np.array([np.mean(x) if len(x) > 0 else np.nan for x in periods]))

    # Check if the output is not empty
    assert periods is not None
    assert len(periods) > 0

    # Check if the spiral wave period is within expected range
    assert period_mean == pytest.approx(3.5, abs=0.2)

@pytest.mark.ecg_3d_tracker
def test_ecg_3d_tracker(planar_model):
    tracker = fw.ECG3DTracker()
    tracker.start_time = 0
    tracker.step = 10
    tracker.measure_coords = np.array([[25, 2, 1]])

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)

    planar_model.tracker_sequence = seq

    planar_model.run()

    ecg = tracker.output.T[0]

    assert ecg.max() > 0.001
    assert ecg.min() < -0.001
    assert np.argmax(ecg) > 100  # Check if the peak occurs not at the beginning

def test_animation_slice_3d_tracker():
    
    class MockModel:
        def __init__(self):
            self.V = np.random.rand(5, 5, 5)
            self.cardiac_tissue = type(""Tissue"", (), {})()
            self.cardiac_tissue.mesh = np.ones((5, 5, 5), dtype=np.int8)

    tracker = fw.AnimationSlice3DTracker()
    tracker.variable_name = 'V'
    tracker.slice_z = 2  # only one of slice_x, slice_y, slice_z must be set
    tracker.dir_name = ""test_frames""
    tracker.file_name = ""test_animation""

    with TemporaryDirectory() as tmpdir:
        tracker.path = tmpdir
        model = MockModel()
        tracker.initialize(model)

        for _ in range(3):
            tracker._track()

        output_dir = Path(tmpdir) / ""test_frames""
        files = sorted(output_dir.glob(""*.npy""))
        assert len(files) == 3, ""Should create exactly 3 frame files""

        for file in files:
            frame = np.load(file)
            assert frame.shape == (5, 5), ""Each frame should have shape (5, 5)""
            assert frame.dtype == np.float32 or frame.dtype == np.float64

def test_period_animation_3d_tracker(spiral_model):
    tracker = fw.PeriodAnimation3DTracker()
    tracker.dir_name = ""test_frames""
    tracker.threshold = 0.5
    tracker.step = 100  # write every 100th step
    tracker.overwrite = True

    seq = fw.TrackerSequence()
    seq.add_tracker(tracker)
    spiral_model.tracker_sequence = seq

    spiral_model.run()

    # Check if the animation files are created
    assert os.path.exists(tracker.dir_name), ""Output directory was not created.""
    files = sorted(os.listdir(tracker.dir_name))
    expected_frames = (spiral_model.t_max/spiral_model.dt) // tracker.step
    assert len(files) == expected_frames, f""Expected {expected_frames} frames, got {len(files)}""

    # Check if the frames are not empty
    frame = np.load(os.path.join(tracker.dir_name, files[-1]))
    assert np.any(frame > 0), f""Frame {frame} appears to be empty.""

    shutil.rmtree(tracker.dir_name)

","Python"