diff --git "a/data/dataset_Cell_biology.csv" "b/data/dataset_Cell_biology.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_Cell_biology.csv"
@@ -0,0 +1,22592 @@
+"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'(.*?)', 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'(.*?)', 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('')
+ if len(parts) == 2:
+ return parts[0] + enhancement + '' + 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'(.*?)',
+ 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('')
+ if len(parts) == 2:
+ return parts[0] + enhancement + '' + 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: {json.dumps(contextual_data)}""
+
+ 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 [--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 | 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 [options]
+```
+
+**Options:**
+- `--agent minisweagent` - Use mini-swe-agent to run the evaluation
+- `--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 [options]
+```
+
+**Options:**
+- `--agent minisweagent` - Use mini-swe-agent for all evaluations
+- `--model ` - Model name for agent
+- `--output, -o ` - Output directory for results
+- `--parallel, -p ` - 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 `/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
+```
+
+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 ]
+```
+
+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: ${result['metadata']['total_cost']:.4f}"")
+print(f""Steps: {result['metadata']['n_steps']}"")
+```
+
+## Monitoring Batch Runs
+
+See [BATCH_MONITORING.md](../BATCH_MONITORING.md) for detailed instructions on:
+- Real-time progress monitoring
+- Identifying stuck evaluations
+- Viewing agent logs
+- Performance metrics
+- Troubleshooting common issues
+
+## Benchmark Script
+
+For running multi-model benchmarks:
+
+```bash
+./scripts/benchmark_models.sh [workers] [keep_workspace]
+```
+
+This script:
+- Runs multiple models on the same eval set
+- Organizes results by timestamp: `results/run_TIMESTAMP/`
+- Produces separate results for each model
+- Includes timing and cost comparisons
+
+**Example:**
+
+```bash
+./scripts/benchmark_models.sh evals_full/seeker 6 true
+```
+
+Results will be saved to:
+```
+results/
+ run_20250112_213045/
+ sonnet45/
+ batch_results.json
+ batch_log.txt
+ gpt5codex/
+ batch_results.json
+ batch_log.txt
+```
+","Markdown"
+"Cell biology","latchbio/spatialbench","scripts/cleanup_orphaned_processes.sh",".sh","877","32","#!/bin/bash
+set -e
+
+echo ""==========================================""
+echo ""Cleaning up orphaned multiprocessing workers""
+echo ""==========================================""
+echo """"
+
+ORPHANED_COUNT=$(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 "")
+ 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"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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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AA8BkFJWVavnGfFq5N04HjRZKkkACzbukXpymDExUTHuThCAGgfnk0aXvZZZedsZbb6YnYr7/+umEDAgAAaCQTJkzwdAgAAHi9I/klWrouXUtTM3SiyCZJatMiQJMGJmpc/3iFB3MPAABNk8/VtAUAAGgq7Ha7PvzwQ+3cuVMmk0ndu3fX9ddfL7OZu1sDAJq3zCOFemXNXr29aZ9KyhySpITWwbpjSAeN7tVegRZeKwE0bSRtAQAAPGD37t265pprdODAAXXt2lWGYeiXX35RbGysPv30U3Xs2NHTIQIA0Oi2Hzihl1bt0WfbsuQ4+cHci9qHa9rQjrr6gmiZ/UyeDRAAGglJWwAAvJhhGCorK5Pdbvd0KD7BZrPJ399fxcXF59RnZrNZ/v7+Mpka/g3hH/7wB3Xs2FHr169XRITzDtdHjhzRuHHj9Ic//EGffvppg8cAAIA3MAxD3+w+ogWr92jNrsOu9iFdztO0oR2U1KF1o7w2A4A3IWkLAICXKi0tVVZWlgoLCz0dis8wDEPR0dHat2/fOb+5Cw4OVkxMjAICAuo5OnerVq1yS9hKUuvWrfXkk09q4MCBDXpuAAC8QZndoc+3Z2vB6j3afiBXkmT2M+m6i2L0uyEddEHbcA9HCACeQ9IWAAAv5HA4lJ6eLrPZrLZt2yogIIAZJjXgcDiUn5+vFi1ayM/Pr1b7Goah0tJSHTp0SGlpaercuXOtj1EbVqtVeXl5ldrz8/MbPGEMAIAnFdvsemfTPr2yJk2ZR51/nA60+Glsn1hNHdxBsRHBHo4QADyPpC0AAF7IZrPJ4XAoNjZWwcG8cakph8Oh0tJSBQYGnlPCNSgoSBaLRRkZGa7jNJTrrrtOv/vd77Rw4UL169dPkrRhwwZNmzZN119/fYOdFwAATzleWKrXUjO0ZF26jhSUSpJaBVs0PilBEwYkKCKEP1oCQDmStgAAeCHDcN55oyFneqJqjdXn//d//6cJEyYoKSlJFotFklRWVqbrr79e//jHPxolBgAAGsOB40VauCZNb23MVGGps+Z8u5ZBumNwosb0jVVwAKkJADgdPxkBAAA8oGXLlvroo4+0a9cu/fTTTzIMQ927d1enTp08HRoAAPXip+xcvbxqrz7+/leVOZx/kO4WE6ZpQzvomgtjZDHzx2kAqA5JWwAAAA/q3LmzOnfu7OkwAACoF4Zh6Nu0o3pp1R797+dDrvakDq017bKOGtK5DXX6AaAGSNoCANCE2R3ON045ecWKDA1Uv8QImf08+0bp66+/1uWXX65jx46pZcuWHo2lsc2YMUOPPfaYQkJCNGPGjDNu+9xzzzVSVAAA1J3DYWjljoN6adUebd13XJJkMkkjekTrziEd1TO2pUfjAwBfQ9IWAIAmasX2LM3+ZIeyThS72mLCA/XIyO4a3iOmwc+/bt06DR48WMOGDdOKFSsa/Hy+4LvvvpPNZnN9DwCArysps+uDLQf08uq92nu4QJIU4O+nm3q31x2DOyixTYiHIwQA30TSFgCAJmjF9izd9foWGae1Z58o1l2vb9H8cb0aPHG7aNEi3XvvvXr11VeVmZmpuLi4Bj2fL/jf//5X5fcAAPia3GKb3lifqUXfpOlQXokkKSzQX7cnxWvCgARFhgZ6OEIA8G1U/QYAwEcYhqHC0rKzfuUV2/TIxz9WSthKcrXN+niH8optNTqeYVR1pDMrKCjQ22+/rbvuukvXXXedlixZcsbt33vvPV1wwQWyWq1KSEjQs88+67Y+ISFBc+bM0eTJkxUaGqq4uDi9/PLLbtusW7dOvXr1UnR0tPr166cPP/xQJpNJW7durXX8jWHy5MnKy8ur1F5QUKDJkyd7ICIAAM7uYG6x5n62UwPmfqWnVvykQ3klig4L1IPXdNO6mVfq/qvPJ2ELAPWAmbYAAPiIIptd3R/+os7HMSRl5xbrwlkra7T9jkevVnBA7X5lWL58ubp27aquXbtq3Lhxuvfee/XQQw9VeeORzZs3a8yYMZo1a5bGjh2rdevW6e6771br1q01ceJE13bPPvusHnvsMf31r3/Vu+++q7vuuktDhgzR+eefr7y8PI0cOVIjRozQSy+9pCNHjpy1ZqynLV26VE8++aRCQ0Pd2ouKirRs2TItWrTIQ5EBAFDZ7px8vbJ6rz747oBK7Q5JUufIFvrdkA76zcXtFODPnDAAqE8kbQEAQL1buHChxo0bJ0kaPny48vPz9eWXX+qqq66qtO1zzz2nK6+8Ug899JAkqUuXLtqxY4eeeeYZt6TtNddco7vvvluS9Oc//1nPP/+8vv76a51//vl64403ZDKZ9PLLL6u0tFRhYWHKysrSHXfc0fAPtpZyc3NlGIYMw1BeXp4CA0/NRrLb7frss88UGRnpwQgBADhlc8YxLVi1Ryk7D6r8wzd94ltp2tCOuuL8SPl5+AanANBUkbQFAMBHBFnM2vHo1Wfd7tu0o5q4eONZt1syqa/6JUbU6Ly18fPPP+vbb7/V+++/L0ny9/fX2LFjtWjRoiqTtjt37tRvfvMbt7aBAwdq3rx5stvtMpud57/oootc600mk6Kjo5WTk+M650UXXaTAwECVlpZKkvr161eruBtLy5YtZTKZZDKZ1KVLl0rrTSaTZs+e7YHIAABwcjgM/e/nHC1YtVffph91tV/VLUrThnZQn4Sz//4AAKgbkrYAAPgIk8lUozIFgzufp5jwQGWfKK6yrq1JUnR4oAZ3Pk/mBpgds3DhQpWVlaldu3auNsMwZLFYdOzYsUrbG4ZRqWxCVXV0LRaL27LJZJLD4ajVMbzB//73PxmGoSuuuELvvfeeIiJOvfENCAhQfHy82rZt68EIAQDNVWmZQ598/6sWrN6jXw7mS5IsZpNuuLid7hzaQZ0iQ89yBABAfSFpCwBAE2P2M+mRkd111+tbZJLcErflac1HRnZvkIRtWVmZli1bpmeffVbJyclu60aPHq033nhDPXr0cGvv3r271q5d69a2bt06denSxTXL9mzKSySUlJS42jZt2nSOj6JhDR06VJKUlpam2NhY+flRAxAA4FkFJWV689tMLVybpqwTxZKkFlZ/3do/TpMHJio6nBuLAUBjq1XStqysTP7+5HkBAPB2w3vEaP64Xpr9yQ7Xmy/JOcP2kZHdNbxHTIOc9z//+Y+OHTumKVOmKDw83G3dTTfdpIULF+r55593a//Tn/6kvn376rHHHtPYsWOVmpqqF154QS+++GKNz3vrrbfqwQcf1J133qnf//73Onr0qP7+979LUpU3P/MG8fHxkqTCwkJlZma6yjqUq1gOAgCAhnA4v0RLvknXa+szdKLIJklq08KqyYMSdFv/eIUHWc5yBABAQ6lVBjYmJkYTJkzQlClT1K1bt4aKCQAA1IPhPWI0rHu0vk07qpy8YkWGBqpfYkSDzLAtt3DhQl111VWVEraSc6btnDlztGXLFrf2Xr166e2339bDDz+sxx57TDExMXr00UfdbkJ2NmFhYfrkk0901113aciQIbrwwgv18MMP69Zbb3W70Zc3OXTokCZNmqTPP/+8yvV2u72RIwIANBcZRwr08uq9enfzfpWUOUsNJbYJ0e+GdNCNl7RTYC3r2QMA6l+tkrYzZszQkiVL9Pzzz6tfv36aOnWqxo4dqxYtWjRUfAAAoA7MfiYldWzdaOf75JNPql3Xq1cvV53ZGTNmuK0bPXq0Ro8eXe2+6enpldq2bt3qtjxgwAB99913ys3NVVhYmN58801ZLBbFxcXV/AE0ounTp+vYsWNav369Lr/8cn3wwQc6ePCgHn/8cT377LOeDg8A0ARt239CL63ao8+3Z8lxsn5Sz/bhmja0o5IviG7QP+wCAGqnVknbmTNnaubMmVqzZo0WLVqk6dOna/r06brppps0depUDRw4sKHiBAAAOKNly5YpISFB4eHh2rNnj/785z9rzJgxCgoK8nRoVfrqq6/00UcfqW/fvvLz81N8fLyGDRumsLAwzZ07V9dee62nQwQA+BC7w9CGtKPafNik1mlHldQpUmY/kwzD0Jpdh7Vg9R59s/uIa/vLup6nO4d01KUdIry2lBAANGfndOeLwYMHa/HixcrOzta8efO0e/duDR48WF27dtXTTz9d3zECAACcVXZ2tsaPH6/+/fvrT3/6k26++Wa9/PLLng6rWgUFBYqMjJQkRURE6NChQ5KkCy+8sFIJiZqaO3euTCaTpk+f7mqbOHGiTCaT29ell15a5/gBAN5jxfYsDXrqK41btEnLdpk1btEmDXzyKz36yY+69v/Wavyib/XN7iMy+5l0w8Vt9fn/G6wlk/opqWNrErYA4KXqdFexkJAQTZkyRVOmTNGnn36q8ePHa+bMmXrggQfqKz4AAIAaeeCBB3Tfffe5yiP4+Z3T36YbTdeuXfXzzz8rISFBF198sRYsWKCEhAS99NJLiomp/Y3iNm7cqJdffrnKG5gNHz5cixcvdi0HBATUKXYAgPdYsT1Ld72+RcZp7dm5xVr0TbokKchi1ti+sZo6OFHtWwU3eowAgNqrU9K2sLBQy5cv1+LFi/XNN9+oY8eOuv/+++srNgAAgCZr+vTpysrKkiQ98sgjuvrqq/XGG28oICBAS5YsqdWx8vPzddttt+mVV17R448/Xmm91WpVdHR0fYQNAPAidoeh2Z/sqJSwraiF1V9f33eZ2oRaGy0uAEDdnVPSds2aNVq8eLHeffdd2e123XTTTXr88cc1ZMiQ+o4PAACgSbrttttc319yySVKT0/XTz/9pLi4OLVp06ZWx/r973+va6+9VldddVWVSduvv/5akZGRatmypYYOHaonnnjCVZqhKiUlJSopKXEt5+bmSpJsNptsNlutYqsv5ef11Plxbrhuvonr5js2pB1V1oniM26TX1Kmn7KOq39gRCNFhdpizPkmrptv8obrVtNz1yppO2fOHC1ZskS7d+9W37599cwzz+iWW25RWFjYOQUJAADQXD366KO67777FBzs/JhqcHCwevXqpaKiIj366KN6+OGHa3Sct956S1u2bNHGjRurXD9ixAjdfPPNio+PV1pamh566CFdccUV2rx5s6zWqmddzZ07V7Nnz67UvnLlSle8npKSkuLR8+PccN18E9fNux0okN5J81NNblWzcs0GHdl5pvm48AaMOd/EdfNNnrxuhYWFNdquVknb559/XrfffrsmT56sHj16nFNgAAAAkGbPnq1p06ZVSoIWFhZq9uzZNUra7tu3T//v//0/rVy5UoGBgVVuM3bsWNf3PXr0UJ8+fRQfH69PP/1Uo0aNqnKfmTNnasaMGa7l3NxcxcbGKjk52WN/rLfZbEpJSdGwYcNksVg8EgNqj+vmm7hu3sswDH2bfkwvr0nT6l1Harxf8uD+6p/ITFtvxZjzTVw33+QN1638U2xnU6uk7RtvvKHp06dr1qxZldadOHFCAwYM0EsvvaTBgwfX5rAAAADNjmEYVd6x+/vvv1dERM3eWG/evFk5OTnq3bu3q81ut2v16tV64YUXVFJSIrPZ7LZPTEyM4uPjtWvXrmqPa7Vaq5yFa7FYPP6mxBtiQO1x3XwT18172B2GVv6YrZdW7dH3+09IkvxM0vAe0dqQdlRH80urrGtrkhQdHqikTpEy+1V+zYF3Ycz5Jq6bb/LkdavpeWuVtP3Xv/6l3/3ud1XOsAgPD9edd96p5557jqQtAABANVq1aiWTySSTyaQuXbq4JW7tdrvy8/M1bdq0Gh3ryiuv1LZt29zaJk2apPPPP19//vOfKyVsJenIkSPat2+fYmJi6vZAAAANrthm1/tbDuiVNXuVdrhAkmT199PNfdpr6qAOSmgTohXbs3TX61tkktwSt+WvLo+M7E7CFgB8UK2Stt99952efPLJatcnJyfr73//e52DAgAA9cRhlzLWSfkHpRZRUvwAya9yIg+NZ968eTIMQ5MnT9bs2bMVHh7uWhcQEKCEhAQlJSXV6FihoaGVSlaFhISodevW6tGjh/Lz8zVr1iyNHj1aMTExSk9P11//+le1adNGN954Y70+LgBA/TlRZNPr6zO0+Jt0Hc533hgyLNBf45MSNHFggtq0OPVpiOE9YjR/XC/N/mSH203JosMD9cjI7hregz/SAYAvqlXSNicn54xTeP39/XXo0KE6BwUAAOrBjo+lFX+Wcn891RbWVhr+lNT9+gY//bp16zR48GANGzZMK1asaPDz+YoJEyZIkhITEzVgwIAG/ViW2WzWtm3btGzZMh0/flwxMTG6/PLLtXz5coWGhjbYeQEA5yb7RLEWrt2rf2/IVEGpXZIUEx6oKYMS9dt+cWphrfot/PAeMRrWPVqpu3O0cs0GJQ/uT0kEAPBxtUratmvXTtu2bVOnTp2qXP/DDz/wUTsAALzBjo+lt8dLp1e4y81yto9Z1uCJ20WLFunee+/Vq6++qszMTMXFxZ3TcUpLSxUQEFDP0Xne0KFD5XA49MsvvygnJ0cOh8Nt/ZAhQ87puF9//bXr+6CgIH3xxRd1CRMA0Ah25+Rpwaq9+nDrAdnsztfuLlEtdOeQjrr+4raymP3Oegyzn0n9EyN0ZKeh/okRJGwBwMed/Sd/Bddcc40efvhhFRcXV1pXVFSkRx55RNddd129BQcAACowDKm04OxfxbnS5w+oUsLWeRDnfyv+7NyuJsczqjrOmRUUFOjtt9/WXXfdpeuuu05LlixxW//xxx+rT58+CgwMVJs2bTRq1CjXuoSEBD3++OOaOHGiwsPDdccdd0iS3nvvPV1wwQWyWq1KSEjQs88+63bMF198UV27dlV0dLRiYmJ00003uda9++67uvDCCxUUFKTWrVvrqquuUkFBQa0fV31av369OnXqpG7dumnIkCG67LLLXF+XX365R2MDADSOzRlHNXXpJl313Gq9s3m/bHZD/RIitGhiH30xfYhG925fo4QtAKDpqdVM27/97W96//331aVLF91zzz3q2rWrTCaTdu7cqX/961+y2+168MEHGypWAACaN1uhNKdtPRzIcJZMeDK2Zpv/9VcpIKRWZ1i+fLm6du2qrl27aty4cbr33nv10EMPyWQy6dNPP9WoUaP04IMP6rXXXlNpaak+/fRTt/2feeYZPfTQQ/rb3/4mSdq8ebPGjBmjWbNmaezYsVq3bp3uvvtutW7dWhMnTtSmTZv0hz/8QUuXLtWFF14om82mb775RpKUlZWlW265RU8//bRuvPFG5eXlac2aNTLOIRldn6ZNm6Y+ffro008/VUxMjNsNyQAATZfDYeirn3L00qo92pRxTJJkMknDukXpzqEd1Tu+lYcjBAB4g1olbaOiorRu3TrdddddmjlzpuvNjslk0tVXX60XX3xRUVFRDRIoAADwHQsXLtS4ceMkScOHD1d+fr6+/PJLXXXVVXriiSf029/+VrNnz3Zt37NnT7f9r7jiCt13332u5dtuu01XXnmlHnroIUlSly5dtGPHDj3zzDOaOHGiMjMzFRISouuuu06GYSgsLEy9e/eW5EzalpWVadSoUYqPj5ckXXjhhQ36+Gti165devfdd6stOwUAaFpKyxz6aOsBvbx6r3bl5EuSAsx+uvGSdrpjSAd1imzh4QgBAN6kVklbSYqPj9dnn32mY8eOaffu3TIMQ507d1arVvw1EACABmUJds56PZuMddIbN519u9veleIH1Oy8tfDzzz/r22+/1fvvvy/JeaPSsWPHatGiRbrqqqu0detWV8mD6vTp08dteefOnfrNb37j1jZw4EDNmzdPdrtdw4YNU3x8vDp16qQrrrhC1113nUaPHq3g4GD17NlTV155pS688EJdffXVSk5O1k033eTx31369++v3bt3k7QFgCYuv6RMb27I1MK1acrOdZYaDLX669ZL4zR5YKKiwgI9HCEAwBvVOmlbrlWrVurbt299xgIAAM7EZKpZmYKOV0hhbZ03Hauyrq3Jub7jFZKfub6j1MKFC1VWVqZ27dq52gzDkMVi0bFjxxQUFHTWY4SEuD9OwzAqlQ+oWN4gNDRUW7Zs0VdffaX//Oc/mjVrlh599FFt3LhRLVu2VEpKitatW6eVK1fqn//8px588EFt2LBBiYmJdXy05+7ee+/Vn/70J2VnZ+vCCy+UxWJxW3/RRRd5KDIAQH04lFeixd+k6bX1GcorLpMkRYZaNXlQom7tH6ewQMtZjgAAaM7OOWkLAAC8lJ9ZGv6U9PZ4SSa5J25PJj6HP9kgCduysjItW7ZMzz77rJKTk93WjR49Wm+88YYuuugiffnll5o0aVKNj9u9e3etXbvWrW3dunXq0qWLzGbn4/D399dVV12lfv366YknnlBERIS++uorjRo1SiaTSQMHDtTAgQP18MMPKz4+Xh988IFmzJhR9wd9jkaPHi1Jmjx5sqvNZDK5EtR2u91ToQEA6iDtcIFeXr1X723Zr9IyhySpQ5sQ/W5IB93Yq52s/vX/+gsAaHpI2gIA0BR1v14as0xa8WfnTcfKhbV1Jmy7X98gp/3Pf/6jY8eOacqUKQoPD3dbd9NNN2nhwoV6/vnndeWVV6pjx4767W9/q7KyMn3++ed64IEHqj3un/70J/Xt21ePPfaYxo4dq9TUVL3wwgt68cUXXefdu3evBg0aJH9/f61Zs0YOh0Ndu3bVhg0b9OWXXyo5OVmRkZHasGGDDh06pG7dujVIH9RUWlqaR88PAKhf3+87rgWr9+jz7dkq/zDIxbEtNW1oRyV3j5KfHzecBADUHElbAACaqu7XS+df66xxm39QahHlrGHbADNsyy1cuFBXXXVVpYSt5JxZOmfOHIWFhemdd97RY489pieffFJhYWEaMmTIGY/bq1cvvf3223r44Yf12GOPKSYmRo8++qgmTpwoSWrZsqXef/99zZo1S8XFxercubPefPNNXXDBBdq5c6dWr16tefPmKTc3V/Hx8Xr22Wc1YsSIhuiCGiu/KRoAwHcZhqFVvxzSglV7lbr3iKv9ivMjdeeQDuqXGFGpvA8AADVB0hYAgKbMzywlDm60033yySfVruvVq5erDm2vXr00atSoKrdLT0+vsn306NGukgKnGzRokL7++ms5HA7l5uYqLCxMfn5+kqRu3bppxYoVtXgUjee1117TSy+9pLS0NKWmpio+Pl7z5s1TYmJipRuvAQC8R5ndoU+3ZemlVXu1MytXkuTvZ9L1Pdvqd0M76PzoMA9HCADwdX6eDgAAAKA5mj9/vmbMmKFrrrlGx48fd9WwbdmypebNm+fZ4AAAVSosLdOSb9I09Jmv9f/e2qqdWbkKDjBr8sBErXrgcj039mIStgCAesFMWwAAAA/45z//qVdeeUU33HCDnnzySVd7nz59dN9993kwMgDA6Y4WlGrpunQtS03XsUKbJKl1SIAmDkjQ7Unxahkc4OEIAQBNjUdn2q5evVojR45U27ZtZTKZ9OGHH551n1WrVql3794KDAxUhw4d9NJLLzV8oAAAAPUsLS1Nl1xySaV2q9WqgoICD0QEADjdvqOFeuSj7Rrw5Jf6x5e7dKzQpriIYD12Qw9985crdO+VnUnYAgAahEdn2hYUFKhnz56aNGlStTXqKkpLS9M111yjO+64Q6+//rq++eYb3X333TrvvPNqtD8AAIC3SExM1NatWyvdkOzzzz9X9+7dPRQVAECSfvz1hBas2qtPt2XJ7nDWY+/RLkzThnbU8Aui5W+m0iAAoGF5NGk7YsSIWt25+aWXXlJcXJyrzlu3bt20adMm/f3vf682aVtSUqKSkhLXcm6us0i8zWaTzWY79+CrUX7Mhjh2c0Ef1g39Vzf0X93Qf3VX3ndlZWUyDEN2u10Oh8PDUfmO8hudGYZxzv1mt9tlGIbKysoqPZfr87l9//336/e//72Ki4tlGIa+/fZbvfnmm5o7d65effXVejsPAKBmDMNQ6p4jmr9qj9bsOuxqH9y5jaYN7agBHVvLZDJ5MEIAQHPiUzVtU1NTlZyc7NZ29dVXa+HChbLZbLJYLJX2mTt3rmbPnl2pfeXKlQoODm6wWFNSUhrs2M0FfVg39F/d0H91Q//V3Zo1axQTE6OjR48qNDTU0+H4nLy8vDrtW1BQoK+++sqVBC5XWFhY19BcJk2apLKyMj3wwAMqLCzUrbfeqnbt2ukf//iHfvvb39bbeQAAZ2Z3GFqxPVsLVu/RD/tPSJL8TNK1F7XVnUM6qEe7cA9HCABojnwqaZudna2oqCi3tqioKJWVlenw4cOKiYmptM/MmTM1Y8YM13Jubq5iY2OVnJyssLD6v6unzWZTSkqKhg0bVmUSGWdHH9YN/Vc39F/d0H91V96HV111lY4eParc3FwFBgYqODiY2T01YBiGCgoKFBISUuv+MgxDhYWFysvLU0xMjC6++OJK25R/Yqe+3HHHHbrjjjt0+PBhORwORUZG1uvxAQDVK7bZ9e7m/XplzV5lHHH+Uc7q76exfWM1dVAHxbVuuEk+AACcjU8lbSVVegNWPgOmujdmVqtVVqu1UrvFYmnQhEJDH785oA/rhv6rG/qvbui/urNYLGrXrp3MZrMOHz589h0gyfl7QVFRkYKCgs45yd2qVStFR0dXuX9DPa/btGnTIMcFAFR2otCm1zdkaPE3aTqcXypJahls0fikBE1IilfrFpXfPwIA0Nh8KmkbHR2t7Oxst7acnBz5+/urdevWHooKAICGYTKZFBMTo8jISOoE15DNZtPq1as1ZMiQc0qwWiwWmc3mBojMqVevXvryyy/VqlUrXXLJJWdMLG/ZsqXB4gCA5ujX40VatDZNb36bqYJSuySpXcsgTRmUqLF9YxVi9am3xwCAJs6nXpWSkpL0ySefuLWtXLlSffr0YUYXAKDJMpvNDZpIbErMZrPKysoUGBjolb8b/OY3v3F9AuiGG27wbDAA0Ez8cjBPC1bt1UdbD6jM4fyk5vnRobpzaAddd1FbWcx+Ho4QAIDKPJq0zc/P1+7du13LaWlp2rp1qyIiIhQXF6eZM2fqwIEDWrZsmSRp2rRpeuGFFzRjxgzdcccdSk1N1cKFC/Xmm2966iEAAADU2COPPCJJstvtuuyyy3TRRRepVatWHo4KAJqmjelH9dLXe/TlTzmutv6JEZp2WUdd1uU8asUDALyaR5O2mzZt0uWXX+5aLr9h2IQJE7RkyRJlZWUpMzPTtT4xMVGfffaZ/vjHP+pf//qX2rZtq//7v//T6NGjGz12AACAc2U2m3X11Vdr586dJG0BoB45HIb+u/OgFqzeq80ZxyRJJpN0dfdo3Tm0gy6J42cuAMA3eDRpe9lll7luJFaVJUuWVGobOnQoNd4AAIDPu/DCC7V3714lJiZ6OhQA8HklZXZ99N2vWrB6j/YcKpAkBZj9NLp3O90xuIM6nNfCwxECAFA7PlXTFgAAoKl44okndN999+mxxx5T7969FRIS4rY+LCzMQ5EBgO/IK7bpzW8ztXBtmg7mlkiSQq3+GpcUr0kDEhQZFujhCAEAODckbQEAADxg+PDhkqTrr7/era6iYRgymUyy2+2eCg0AvF5ObrEWr0vX6+szlFdcJkmKCrNqyqBE3dIvTqGB3nczSgAAaoOkLQAAgAf873//83QIAOBz9h7K1ytr9uq9zQdUandIkjqeF6I7h3bUby5uK6u/2cMRAgBQP0jaAgAAeMDQoUN1/PhxLVy4UDt37pTJZFK3bt00ZcoUhYeHezo8APAqW/cd10tf79EXO7JVfluUXnEtNW1oR13VLUp+fqYzHwAAAB9D0hYAAMADNm3apOHDhyswMFD9+vWTYRh6/vnnNWfOHK1cuVK9evXydIgA4FGGYejrXw7ppa/3aEPaUVf7Vd0idefQjuqbEOHB6AAAaFgkbQEAADzgj3/8o0aOHKlXXnlF/v7OX8nKyso0depUTZ8+XatXr/ZwhADgGTa7Q//54VctWLVXP2XnSZL8/Uy64ZJ2+t2QDuoSFerhCAEAaHgkbQEAADxg06ZNbglbSfL399cDDzygPn36eDAyAPCMwtIyvfXtPi1cm6YDx4skSSEBZt3SL05TBicqJjzIwxECANB4SNoCAAB4QFhYmDIzM3X++ee7te/bt0+hocwiA9B8HMkv0dJ16Vq2PkPHC22SpDYtAjRpYKLG9Y9XeLDFwxECAND4SNoCAAB4wNixYzVlyhT9/e9/14ABA2QymbR27Vrdf//9uuWWWzwdHgA0uMwjhXp17V69vWmfim0OSVJC62DdMaSDRvdqr0CL2cMRAgDgOSRtAQAAPODvf/+7TCaTxo8fr7KyMkmSxWLRXXfdpSeffNLD0QFAw9l+4IQWrN6rT3/4VQ7D2XZR+3BNG9pRV18QLbOfybMBAgDgBUjaAgAAeEBAQID+8Y9/aO7cudqzZ48Mw1CnTp0UHBzs6dAAoN4ZhqFvdh/RgtV7tGbXYVf7kC7nadrQDkrq0FomE8laAADKkbQFAADwoODgYF144YWeDgMAGkSZ3aEVP2Zrwaq92nbghCTJ7GfSdRfF6HdDOuiCtuEejhAAAO9E0hYAAAAAUK+KbXa9s3m/Xlm9V5lHCyVJgRY/je0Tq6mDOyg2gk8VAABwJiRtAQAAAAD14nhhqV5LzdCSdek6UlAqSWoVbNH4pARNGJCgiJAAD0cIAIBvIGkLAAAAAKiTA8eLtHBNmt7amKnCUrskqV3LIN0xOFFj+sYqOIC3ngAA1AavnAAAAACAc/Jzdp4WrNqjj7//VWUOQ5LULSZM04Z20DUXxshi9vNwhAAA+CZeQQEAAHzY/PnzddFFFyksLExhYWFKSkrS559/7lpvGIZmzZqltm3bKigoSJdddpl+/PFHD0YMwNcZhqENe49o8pKNunrear3/3QGVOQwldWitpZP76bM/DNJvLm5HwhYAgDpgpi0AAIAPa9++vZ588kl16tRJkrR06VL95je/0XfffacLLrhATz/9tJ577jktWbJEXbp00eOPP65hw4bp559/VmhoqIejB+Bt7A5DG9KOavNhk1qnHVVSp0iZ/UySJIfD0ModB7Vg9R59l3lckmQySSN6ROvOIR3VM7al5wIHAKCJIWkLAADgw0aOHOm2/MQTT2j+/Plav369unfvrnnz5unBBx/UqFGjJDmTulFRUfr3v/+tO++8s8pjlpSUqKSkxLWcm5srSbLZbLLZbA30SM6s/LyeOj/ODdfNt3zx40E9/tlPys4tkWTWsl2bFB1m1Z+Hd1VhSZleXZuutCOFkqQAfz+NuqStpgyMV0LrEElcZ2/AmPNNXDffxHXzTd5w3Wp6bpK2AAAATYTdbtc777yjgoICJSUlKS0tTdnZ2UpOTnZtY7VaNXToUK1bt67apO3cuXM1e/bsSu0rV65UcHBwg8VfEykpKR49P84N1837fX/EpEW/lJczMLnas3OL9ce3v3e1BZkNDYo2NCS6TGH+6dqxIV07Gj9cnAVjzjdx3XwT1803efK6FRYW1mg7krYAAAA+btu2bUpKSlJxcbFatGihDz74QN27d9e6deskSVFRUW7bR0VFKSMjo9rjzZw5UzNmzHAt5+bmKjY2VsnJyQoLC2uYB3EWNptNKSkpGjZsmCwWi0diQO1x3XyD3WFo7rOrJZVUsdaZrPUzSfcnd9Zv+8aqhZW3kd6KMeebuG6+ievmm7zhupV/iu1seLUFAADwcV27dtXWrVt1/Phxvffee5owYYJWrVrlWm8ymdy2NwyjUltFVqtVVqu1UrvFYvH4mxJviAG1x3Xzbpv2HDlZEqF6DkO6OK61WrUIaqSoUBeMOd/EdfNNXDff5MnrVtPzcjtPAAAAHxcQEKBOnTqpT58+mjt3rnr27Kl//OMfio6OliRlZ2e7bZ+Tk1Np9i2A5snhMPTVTwdrtG1OXnEDRwMAAMqRtAUAAGhiDMNQSUmJEhMTFR0d7Vazq7S0VKtWrdKAAQM8GCEATyssLdNrqem68rlVemVNWo32iQwNbOCoAABAOcojAAAA+LC//vWvGjFihGJjY5WXl6e33npLX3/9tVasWCGTyaTp06drzpw56ty5szp37qw5c+YoODhYt956q6dDB+ABB3OLtXRdut7YkKkTRc67V7ewmmUYUkGpvcp9TJKiwwPVLzGiESMFAKB5I2kLAADgww4ePKjbb79dWVlZCg8P10UXXaQVK1Zo2LBhkqQHHnhARUVFuvvuu3Xs2DH1799fK1euVGhoqIcjB9CYth84oUVr0/TJD7/KZjckSfGtgzVpQIJu7hOrNbsO6a7Xt0iSjAr7lVe/fmRkd5n9qq+FDQAA6hdJWwAAAB+2cOHCM643mUyaNWuWZs2a1TgBAfAaznq1OXp17V6t33vU1d4vIUJTBifqqm5RrkTs8B4xmj+ul2Z/skNZJ07Vro0OD9QjI7treI+YRo8fAIDmjKQtAAAAADQhhaVlem/LAS1am6a0wwWSJLOfSddeGKMpgxLVM7ZllfsN7xGjYd2jlbo7RyvXbFDy4P5K6hTJDFsAADyApC0AAAAANAEHc4u1LNVZr/Z4obNebWigv27tH6cJSQlq2zLorMcw+5nUPzFCR3Ya6p8YQcIWAAAPIWkLAAAAAD6sqnq1cRHBmjzQWa82xMrbPgAAfA2v3gAAAADgY8rr1S5cm6bUvUdc7f0SIjR5UKKGdY9iliwAAD6MpC0AAAAA+IiiUrve3bJfi9emaW8t6tUCAADfQtIWAAAAALxctfVq+8VpwoCa1asFAAC+g6QtAAAAAHipH389oYVr0/TJ95Xr1d7UJ1YtqFcLAECTxCs8AAAAAHgRh8PQ/37O0atr3OvV9k1opSmDOlCvFgCAZoCkLQAAAAB4gaJSu97bsl+LqFcLAECzR9IWAAAAADyIerUAAOB0JG0BAAAAwAOqqlcbGxGkyQMTdTP1agEAaNb4LQAAAAAAGonDYejrX5z1atftoV4tAACoGklbAAAAAGhgrnq136Rp76FT9WqvOVmv9mLq1QIAgApI2gIAAABAA8nJLday1Ay9viGjUr3a8QMS1I56tQAAoAokbQEAAACgnlGvFgAA1AW/KQAAAABAPaiuXm2f+FaaOjhRw7pHU68WAADUiJ+nA3jxxReVmJiowMBA9e7dW2vWrDnj9m+88YZ69uyp4OBgxcTEaNKkSTpy5MgZ9wEAAACAhlJUatcbGzJ01fOrNHnJJq3bc0RmP5NG9myrD38/UO/eNUDDe8SQsAUAADXm0Zm2y5cv1/Tp0/Xiiy9q4MCBWrBggUaMGKEdO3YoLi6u0vZr167V+PHj9fzzz2vkyJE6cOCApk2bpqlTp+qDDz7wwCMAAAAA0FyV16t9Y0OGjpXXq7X665b+cZpAvVoAAFAHHk3aPvfcc5oyZYqmTp0qSZo3b56++OILzZ8/X3Pnzq20/fr165WQkKA//OEPkqTExETdeeedevrppxs1bgAAAADN145fc7VwbZo+/v6AW73aSQMSNaYv9WoBAEDdeey3idLSUm3evFl/+ctf3NqTk5O1bt26KvcZMGCAHnzwQX322WcaMWKEcnJy9O677+raa6+t9jwlJSUqKSlxLefm5kqSbDabbDZbPTwSd+XHbIhjNxf0Yd3Qf3VD/9UN/Vd39GHdNHT/cV2A5svhMLTql0N6de1efbOberUAAKBheSxpe/jwYdntdkVFRbm1R0VFKTs7u8p9BgwYoDfeeENjx45VcXGxysrKdP311+uf//xnteeZO3euZs+eXal95cqVCg4OrtuDOIOUlJQGO3ZzQR/WDf1XN/Rf3dB/dUcf1k1D9V9hYWGDHBeA9yoqtev97/Zr4do07T1UIEky+5k0oke0pgxK1CVxrTwcIQAAaIo8/rkdk8n9r9GGYVRqK7djxw794Q9/0MMPP6yrr75aWVlZuv/++zVt2jQtXLiwyn1mzpypGTNmuJZzc3MVGxur5ORkhYWF1d8DOclmsyklJUXDhg2TxWKp9+M3B/Rh3dB/dUP/1Q39V3f0Yd00dP+Vf2IHQNOXk1es11Iz9Pp66tUCAIDG57GkbZs2bWQ2myvNqs3Jyak0+7bc3LlzNXDgQN1///2SpIsuukghISEaPHiwHn/8ccXExFTax2q1ymq1Vmq3WCwN+ma4oY/fHNCHdUP/1Q39Vzf0X93Rh3XTUP3HNQGavvJ6tZ98/6tK7Q5JUvtWQZo8kHq1AACg8XjsN46AgAD17t1bKSkpuvHGG13tKSkp+s1vflPlPoWFhfL3dw/ZbDZLcs7QBQAAAIDaqq5ebe/4Vpo6KFHJF1CvFgAANC6P/pl4xowZuv3229WnTx8lJSXp5ZdfVmZmpqZNmybJWdrgwIEDWrZsmSRp5MiRuuOOOzR//nxXeYTp06erX79+atu2rScfCgAAAAAfU2yz6/0tB7Rw7V7toV4tAADwIh5N2o4dO1ZHjhzRo48+qqysLPXo0UOfffaZ4uPjJUlZWVnKzMx0bT9x4kTl5eXphRde0J/+9Ce1bNlSV1xxhZ566ilPPQQAAAAAPqa6erW/7RerCQMS1L5Vw92wGAAAoCY8XpDp7rvv1t13313luiVLllRqu/fee3Xvvfc2cFQAAAAAmpqdWc56tR9vpV4tAADwbvxWAgAAAKDJOlu92mHdo+Rv9vNghAAAAJWRtAUAAADQ5FRXr3b4yXq1vahXCwAAvBhJWwAAAABNRk5esV5PzdDrGzJ1tKBUEvVqAQCA7yFpCwAAAMDnVVevdtLARI3p016hgRYPRwgAAFBzJG0BAAAA+CSHw9CqXYe0cE2a1u4+7GrvHd9KUwYlKpl6tQAAwEeRtAUAAADgU4ptdn3w3QEtXJum3Tn5kiQ/kzTiwhjq1QIAgCaBpC0AAAAAn1BdvdqxfZ31amMjqFcLAACaBpK2AAAAALzaT9m5WrgmTR9VqFfbrmWQJg+iXi0AAGiaSNoCAAAA8Drl9WoXrU3Tml2n6tX2imupqYM7UK8WAAA0aSRtAQAAAHgN6tUCAACQtAUAAADgBQ7llei19Rl6fX2Gq15tC6u/fku9WgAA0AzxeSIAAAAftnr1ao0cOVJt27aVyWTShx9+6LZ+4sSJMplMbl+XXnqpZ4IFqvBTdq7uf+d7DXzyK/3fl7t0tKBU7VoG6W/XdlPqzCv0t+u6k7AFAADNDjNtAQAAfFhBQYF69uypSZMmafTo0VVuM3z4cC1evNi1HBAQ0FjhoRmzOwxtSDuqzYdNap12VEmdImX2M0mSDMPQql8OaSH1agEAAKpE0hYAAMCHjRgxQiNGjDjjNlarVdHR0TU+ZklJiUpKSlzLubm5kiSbzSabzXZugdZR+Xk9dX7Uzhc/HtTjn/2k7NwSSWYt27VJ0WFW/fnqLiootWvJugztPlQgyVmv9uruUZo0IF6XxLWUJBkOu2wOu+ceQDPHePNdXDvfxHXzTVw33+QN162m5yZpCwAA0MR9/fXXioyMVMuWLTV06FA98cQTioyMrHb7uXPnavbs2ZXaV65cqeBgz35MPSUlxaPnx9l9f8SkRb+Uz5I1udqzc4v1x3d+cLVZzYaSIg0NiXaodeABZW0/9CsrjAAAKAtJREFUoKztjR8vqsd4811cO9/EdfNNXDff5MnrVlhYWKPtSNoCAAA0YSNGjNDNN9+s+Ph4paWl6aGHHtIVV1yhzZs3y2q1VrnPzJkzNWPGDNdybm6uYmNjlZycrLCwsMYK3Y3NZlNKSoqGDRsmi8XikRhwdnaHobnPrpZUUsVaZ7LWbJLuv7qLxvRur9BA3o54I8ab7+La+Saum2/iuvkmb7hu5Z9iOxt+SwIAAGjCxo4d6/q+R48e6tOnj+Lj4/Xpp59q1KhRVe5jtVqrTOhaLBaPvynxhhhQvU17jpwsiVA9uyH1jI1QRGhQI0WFc8V4811cO9/EdfNNXDff5MnrVtPzUt0fAACgGYmJiVF8fLx27drl6VDQxOTkFWtZanqNtwUAAED1mGkLAADQjBw5ckT79u1TTEyMp0NBE2AYhrZkHtPSdRn6fHuWbHajRvtFhgY2cGQAAAC+jaQtAACAD8vPz9fu3btdy2lpadq6dasiIiIUERGhWbNmafTo0YqJiVF6err++te/qk2bNrrxxhs9GDV8XbHNro+3/qqlqen68ddTddl6xbXU3kMFOlFkU1XpW5Ok6PBA9UuMaLRYAQAAfBFJWwAAAB+2adMmXX755a7l8huITZgwQfPnz9e2bdu0bNkyHT9+XDExMbr88su1fPlyhYaGeipk+LB9Rwv1+voMLd+0T8cLbZIkq7+ffnNxW41PSlCPduFasT1Ld72+RSbJLXFrOvn/IyO7y+xnOv3QAAAAqICkLQAAgA+77LLLZBjVfyT9iy++aMRo0BQ5HIbW7j6sZanp+vKnHJU/3dq3CtLtl8ZrTJ9YtQoJcG0/vEeM5o/rpdmf7FDWiVO1a6PDA/XIyO4a3oPSHAAAAGdD0hYAAABAJbnFNr23eb9eS83Q3sMFrvbBndtoQlKCLj8/stoZs8N7xGhY92il7s7RyjUblDy4v5I6Vb89AAAA3JG0BQAAAODyy8E8LUtN1/tbDqiw1C5JCrX6a3Tv9ro9KV4dz2tRo+OY/UzqnxihIzsN9U+MIGELAABQCyRtAQAAgGauzO7Qf3ce1NJ1GUrde8TV3iWqhW5PStCNl7RTCytvHQAAABoLv3kBAAAAzdTh/BIt37hPr6/PcNWfNfuZlNw9SuOTEnRphwiZTMyQBQAAaGwkbQEAAIBmZuu+41q2Ll3/+SFLpXaHJKl1SIBu6RenW/vHqW3LIA9HCAAA0LyRtAUAAACagWKbXZ/+kKVlqen6fv8JV/vFsS01YUC8rrkwRlZ/swcjBAAAQDmStgAAAEATduB4kd5Yn6G3Nu7T0YJSSVKAv59GXtRW45Pi1TO2pWcDBAAAQCUkbQEAAIAmxjAMpe45oqWp6UrZcVAOw9neNjxQ45LiNbZPrFq3sHo2SAAAAFSLpC0AAADQROSXlOmDLfu1NDVDu3PyXe0DO7XW+KQEXXl+pPzNfh6MEAAAADVB0hYAAADwcbtz8vX6+gy9u3m/8kvKJEkhAWaN7t1et18ar85RoR6OEAAAALVB0hYAAADwQXaHoS93HtSy1Ayt3X3Y1d7hvBBNSErQqF7tFBpo8WCEAAAAOFckbQEAAAAfcrSgVMs37tPr6zN04HiRJMnPJF3ZLUoTkhI0sFNrmUwmD0cJAACAuiBpCwAAAPiAbftPaGlquj7+/leVljkkSa2CLRrbN0639Y9TbESwhyMEAABAfSFpCwAAAHipkjK7Pt+WraWp6fou87irvUe7ME1IStDInm0VaDF7LkAAAAA0CJK2AAAAgJfJOlGkf2/I1JvfZupwfqkkyWI26doLYzR+QIIuiW1JCQQAAIAmjKQtAAAA4AUMw9CGtKNalpquL348KLvDkCRFhwXqtv5x+m2/OJ0XavVwlAAAAGgMJG0BAAAADyooKdOHWw9o2boM/Xwwz9V+aYcITUhK0FXdo2Qx+3kwQgAAADQ2krYAAACAB6QdLtBrqRl6Z/M+5RWXSZKCLGaN6tVO45MS1DU61MMRAgAAwFNI2gIAAACNxO4wtOqXHC1dl6FVvxxytSe2CdHtl8ZrdO/2Cg+yeDBCAAAAeAOStgBQzmGXKWOt2h1NlSkjTOowRPLjjtwAgLo7Xliqdzbt12vrM5R5tFCSZDJJV3SN1PgBCRrcqY38/LixGAAAAJxI2gKAJO34WFrxZ/nn/qo+kpQxXwprKw1/Sup+vaejAwD4qB9/PaHXUjP04dYDKrY5JEnhQRaN7Rurcf3jFdc62MMRAgAAwBuRtAWAHR9Lb4+XZLi352Y528csI3ELAKix0jKHvvgxW8tS07Ux/ZirvVtMmCYOiNf1PdspKIBPcgAAAKB6Hr8N7YsvvqjExEQFBgaqd+/eWrNmzRm3Lykp0YMPPqj4+HhZrVZ17NhRixYtaqRoAS/n9vH+tZLD7umIvI/dJhUdl3J/lQ7vlvZvlv7zR1VK2Eon2wzp0xlS1jbpWIZUcFgqLZSMqrZv5nj+AWjmcnKLNe+/v2jQU1/p3je/08b0Y/L3M2lkz7Z6d1qSPvvDII3tG0fCFgAAAGfl0Zm2y5cv1/Tp0/Xiiy9q4MCBWrBggUaMGKEdO3YoLi6uyn3GjBmjgwcPauHCherUqZNycnJUVlbWyJEDXqgpfbzfYZdshVJpgfPLVuhMlJbmn/rednKd6/uT25d/X2n/k//bS2sfT8EhacGg0xpNUkCIZAmWAoIlS4jz/4CQU99bgqWAFhW+D6mwT0jVbQEhkjnAWejQlzSl5x8A1IJhGNqUcUxL16VrxfZslTmcf9Q7L9Sq2/rH6dZ+cYoMC/RwlAAAAPA1Hk3aPvfcc5oyZYqmTp0qSZo3b56++OILzZ8/X3Pnzq20/YoVK7Rq1Srt3btXERERkqSEhIQznqOkpEQlJSWu5dzcXEmSzWaTzWarp0dySvkxG+LYzQV9WHumn/4j83uTJBmqmOozTn683z56sYzzr6vfkxqGMwlqO5UgNZ2eULUVVmirsI3NPdlqqnAM2QplKiuu31irCt9kdiZIZZKp5MTZtw9o4ZxJWlZU3uJMIpfmSwUNEVuFRK8lRMZpyWHDcnJ9xW3c9gmusE+FxLG5/u9I7pHnXxPGz8C6aej+47qgXFGpXR9tPaClqRnamZXrau+b0ErjkxJ09QXRCvD3+IfaAAAA4KM8lrQtLS3V5s2b9Ze//MWtPTk5WevWratyn48//lh9+vTR008/rddee00hISG6/vrr9dhjjykoKKjKfebOnavZs2dXal+5cqWCgxvuxg8pKSkNduzmgj6sIcOh5B9nyHxawkySTDJkSLJ9/Eet2XlY/g6bzI4S+TuKZXaUnPz+5P/205ZPbnN6e/n3/o6SqqKp34cmk+x+VpX5WU/9b664HHjy/wDn/2bncvm25evt5gD3ZT+rHCZ/yWRS67ydGrS78h+JTvdN3L06EtpNMhzufVFdv51Dv5oN56cGTIZdKslzfp1UX/NuHSZzhX472Vdm62n9XPX6StuYrSozWTT0l9lnfP6VfjxDKXskmUhe1AY/A+umofqvsLCwQY4L35F5pFCvrU/X25v260SRM4kfaPHTDRe30+1J8bqgbbiHIwQAAEBT4LGk7eHDh2W32xUVFeXWHhUVpezs7Cr32bt3r9auXavAwEB98MEHOnz4sO6++24dPXq02rq2M2fO1IwZM1zLubm5io2NVXJyssLCwurvAZ1ks9mUkpKiYcOGyWKp/xltzQF9eBaGw1lXNS9LprwsmdLXyWw7Wu3mJklBtmNK3nFfw4XkH1T1LNDyj/1bgmWcVjbAcCsREHxypmhIhdmjwZJ/kGQyySzJLCmgIYJ3XC3jhaXO/qyirq0hkxTWVv1vni75NVwNQockh93mXv7BVihTeVmHk+2m8hIQVc5wPsNsZsNZX9bPsCvAXiDZ63l6cDVMkoJtR3VdyPcyEi+TERojhcZIlqr/0AZ+BtZVQ/df+Sd20Lw4HIZW7zqkZakZ+t/POa6y5nERwbr90njd3Ke9WgY3yKsUAAAAmimPlkeQJNNpdRsNw6jUVs7hcMhkMumNN95QeLhzFsNzzz2nm266Sf/617+qnG1rtVpltVortVsslgZ9M9zQx28OmmUf2oqlvCznV+6vzq/y7/OypNyT6xzn8PFck1kKDKtDvdWT27t9TN+5zuTnPoPSt6qxWqQRT0lvj5cz8oqJW5PzsQx/UhZrI9QjtFikwAb4BIBhOGv5VlXjt851g/NrFIL5m+elb54/1RDUSgptK4WdTOKGtXV+udraSsERvlfbtx41y5+B9aih+o9r0rycKLLp3c379VpqutKPnJplPbTLeZowIF5Du0TK7Nd8f04BAACg4XgsadumTRuZzeZKs2pzcnIqzb4tFxMTo3bt2rkStpLUrVs3GYah/fv3q3Pnzg0aM3DODEMqOlZFEvbAqURs7q9SUfUzZt2ZpJDznMkts1Xa/+3Zdxn/kZQ4uE4Po8nqfr00Zpm04s/O61AurK00/Enfv5GWyST5W51fwRH1e+y01dLSkWffLqqHM8mbmyWVFTnHQ9ExKefH6vcxW6XQaCmsnXtyt2KSt0W05M/sNgD166fsXC1LzdAHWw6oyOb8pEJooL/G9InVuEvjldgmxMMRAgAAoKnzWNI2ICBAvXv3VkpKiv5/e/ceHWV173/8M5PLJCHkQiA3LgMCipgjAjlguJQWJQdEpLZVlngJpXT98oNWkJYF1j+o2BZOVRbtsSAoVgHXgnZVXFq5pa1cPChGLsccYQlHLuFAMIDBDKQkYbLPH0PG3CaZkIR5nsn7tdasTPYzlz3P3vPM9/nOnr0ffPBBf3lBQYGmTp3a5H1Gjx6tP//5z7p8+bLi4+MlSUePHpXT6VSvXr1uSr2BRrzVkudcgyTs2frJWE+JFOziWpExDZJTGd+MPkzo6Svrmv7NglI1XmlFlu/5mvh5v67/vF/uUe31isPT4AekQZN17fhuHdqzXXeN/TdF3vKtDp0SISy4R/v6V0v97//t9u3L2i8w/CPHz9b/W37Wd73iouStlC6d8l2a06VH0wndumUxiZ161C6AllV7a1Rw+Eu9sfek9p345kvU29K6Km9UX313aKbiokP+IzUAAAB0EiGNPOfPn6/HH39c2dnZysnJ0Zo1a1RcXKz8/HxJvvloz5w5o3Xr1kmSpk+frueee04//OEP9eyzz+rChQtasGCBZs6cGXAhMuCGGSNVljeRWGqQZLpyXk0nq5oQ263phFLdv7HJrUsuOSOkiYF/3i/JN1qU5GPLnBEy7jE681m5hrjHsM+C0dr+53D4RvvGdZPS7gj8uNcqGyd2axO6tWWec75pH66c913OfRr48aLiAid0a8u6pEoRJGSAzua8p1IbPy7Wm/uKda7c9wVrhNOhiXek64kct0b06xZw6i4AAACgo4T07HTatGm6ePGilixZopKSEmVlZWnLli1yu92SpJKSEhUXF/tvHx8fr4KCAv30pz9Vdna2UlJS9PDDD+tXv/pVqF4C2luNV45TH6jnVx/KcSpB6qiRjjVe6XJp00nYugmi6iAXa3JG1RkVGyAx1DVDiuqgeVHD/ef9sLaO6H+RLim5r+8SiDG+Ebn++Z8bvpevv4+vXvJNzfDVF75LIA6nFJ/W8qhdV3zrX0+wbtYxEOjkjDE6ePqS1u09qfeKSlTt9X3h1D0+WtNH9NEjI/soI5EBAQAAAAidkA8pmj17tmbPnt3kttdff71R2aBBg1RQUNDBtUJIHH5H2rZQkeVnlS1Jp1ZdT/r8e+uSPlVXAiRhz3wzcu/yl5LxBvd4MYn1F0dqauGkuBSpwWJcNx0/70cohaL/ORxSl+6+S8adgW9XVdFgLuk6f2uve875jgm1CwGePRD48VwJTSR0G0xjEte99ceE9joGAgjoarVX7/7XWa378JSKznztLx/WJ0l5o/pqYla6XJF8bgIAACD0Qp60BST5khV/ekKNphkoL/GVP7xOGnS/b1Sdf3Ts2aZ/Pl35dZNP0YgjwjeqLiHjm+RrvcTs9b/RNlpshJ/3I5Ss2v+i46SU/r5LIDVe3xQLTSV06x5rqjy+aVMqy6ULnwd+PGeUb+7pegndJpK8taPvgzkGkrgFbtjpryr05r5ibSosVllFtSQpOtKpqUMy9UROX/1Lr8QWHgEAAAC4uUjaIrS813yJ2C0/U9Pzwl4v+/MMSQ7JXAvucaPjAy/iVVsWn2qdpBKA0HJGXE+ypjd/u0pPg5H7TXx5dPlLqaZa+vq079Kc2GQpPuP6tA2BjoEOaetC6bb7mHMXaAVjjP7zfy7qjQ9P6u9HvlTN9bdYz6RYPZ7j1sPZvdWtS3RoKwkAAAAEwNkf2q7GK1392rci/NVL0j8vXf9bVud6nb91r1d5gnsO/1QGDl+ytd6ck00kZmMS2vc1AoAkubpKPbpKPW4NfBtvtS9xG2iu7Nrr1/55/ThZ1sKTGt99fpXqW8AtJkmKTfIlfGuvt1QWxdyc6Dw8V6v11oEzeuPDkzp+/pu56ccO7K4ncvpq/KBURThZWAwAAADWRtK2Pdl5AZmaGt/PfZtKtjZKxtYt+zr46QjaauJvpX+dKUVE3ZznA4AbERElJfbyXQIxxncMLS+RPt0k/eeKlh/XXJ/C4cr5G6iTq04iN7l+UrelskhX658vVOz8OYxmeWuM9p34SvsvOJRy4ivlDGiceD32pUfrPjyltw78r65U+b7sjXdF6gfDe+mxu90akNqBiwgCAAAA7YykbXuxwgIyxvh+uhsw2drUyNfrZZXlkqlp2/NHxzcxwispQGIg+ZsRYCWfSuuntvz4aYNJ2AIIDw7H9eNhsjTg3uCStj/4o9T91uaP6019yWa8krfSN/r38petr2tkbOtH9tZev5nHbCt8DlvcypUr9fzzz6ukpER33HGHVqxYobFjx4a6Wi3a9t8levbdwyr5+qqkCK079okyEmO0eMpg3Xt7mv52pFTrPjypvV9c9N9nQGq88nLcenBYL8W7CHcBAABgP0Sx7aE9F5AxRqq60vqT8n+W+aYo8E8jcIMiYxuPuArmRD0mUYq8wXnh+o31nViXl6jpOR0dvu3uUTf2+ABgZe5RwR0DB09t/ajRhl/mtfQFXsPPFhnfNA6ef/rm7m2tqC4BRvEm1f8yr+Fo35jE1s3fy0JuLdq0aZPmzZunlStXavTo0Vq9erUmTZqkw4cPq0+fPqGuXkDb/rtE/3/DgUbvjJKvryp/wwElx0X5FxZzOqQJg9OUl9NXOf1T5HAwBQIAAADsi6RtW9V4pW0L1ewiWu/Nl+T0TSMQcJqBOmU11W2rU+3PYIMaFdXgZDoUP4N1RvhGQv3pCUkO1d+X10+4Ji7jJ64AwlNHHgMdDt8c3zEJUlIrE3P1ps251MJUOZfqJH/rTJtTfcV3Kf/f1tfdlXD9symx+S8RXQnXP2ebWc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+ """"
+ ]
+ },
+ ""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"": 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"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""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"": [
+ """"
+ ]
+ },
+ ""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"