Environment summary for Single-Cell to Spatial Mapping (Human fetal heart -> MERFISH human heart 3D)

Timestamp: 2025-11-25
Host: lenovo-03
Project root: /home/wzxu/software/pantheon-agents/examples/single_cell_spatial_analysis

Operating system
- Ubuntu 22.04.5 LTS (Jammy)
- Kernel: 6.8.0-87-generic

CPU and memory
- CPU: AMD EPYC 9224 24-Core Processor
  - Sockets: 2; Cores/socket: 24; Threads/core: 2; Total logical CPUs: 96
- RAM: 188 GiB total; ~167 GiB available at audit time
- Swap: 2.0 GiB (mostly used)

GPU
- 2 x NVIDIA H100 PCIe (80 GiB each)
- Driver: 570.172.08; CUDA: 12.8
- PyTorch CUDA: available (torch 2.9.0+cu128)

Disk
- Root filesystem (/): 879G total, 53G free (94% used)
- Note: Free space is modest; avoid creating large intermediate files.

Python environment
- Python: 3.10.18 (conda-forge build)
- Executable: /home/wzxu/.local/share/mamba/envs/pantheon/bin/python
- pip: 25.3
- conda/mamba CLIs: not found in PATH (environment appears to be mamba-managed, but mamba/conda commands are unavailable)

Key Python packages (requested set)
- anndata 0.11.4 (>=0.9) OK
- scanpy 1.11.5 (>=1.10) OK
- squidpy 1.6.2 (>=1.4) OK
- numpy 1.26.4 OK
- scipy 1.15.3 OK
- pandas 2.3.3 OK
- scikit-learn 1.7.2 OK
- umap-learn 0.5.9.post2 OK
- matplotlib 3.8.4 OK
- seaborn 0.13.2 OK
- scvi-tools 1.3.3 (>=1.1) OK
- tangram-sc 1.0.4 (1.*) OK
- plotly 6.3.0 OK
- pyarrow 21.0.0 OK
- tqdm 4.67.1 OK
- h5py 3.11.0 (>=3.10) OK
Additional relevant packages
- numba 0.61.2; scikit-image 0.25.2; scikit-misc 0.5.1; harmonypy 0.0.10; moscot 0.4.3; torch 2.9.0+cu128; jax 0.6.2

Large dataset readiness
- Available datasets:
  - human_fetal_heart.h5ad ~ 5.58 GiB (reported size 5,979,855,150 bytes)
  - merfish_human_heart_3d.h5ad ~ 9.75 GiB (10,463,377,848 bytes)
  - merfish_human_heart_2d.h5ad ~ 0.34 GiB (370,287,574 bytes)
- Memory check: ~167 GiB RAM available at audit time; loading either dataset in memory should be feasible, assuming sparse matrices are preserved and operations avoid densifying X.

Caveats and recommendations for large IO
- Prefer backed mode when inspecting or subsetting: ad = anndata.read_h5ad(path, backed='r')
- Avoid operations that densify sparse matrices (e.g., .X.toarray()); use .layers/.raw thoughtfully.
- For scanpy, work on highly-variable genes to reduce memory: sc.pp.highly_variable_genes(...); subset before PCA/UMAP.
- For scvi-tools, use minibatches and DataLoaders (batch_size, num_workers) to control memory.
- Given only ~53G free disk on /, keep temporary outputs small; set a writable tmp directory with ample space if needed (e.g., TMPDIR).

Conclusion
- Environment READY for single-cell to spatial mapping with scanpy/squidpy and optional scvi-tools/tangram. GPUs are available for acceleration. No missing packages detected and version constraints satisfied.
