Add zarr output option for horizon computation in topo_param.py

- compute_horizon() now accepts format='zarr' | 'netcdf' (default netcdf)
- Zarr horizon output uses chunking optimized for single-point lookups:
  full azimuth dimension, moderate y/x chunks (64-512)
- zstd compression with bitshuffle for smaller files
- Consolidated metadata for faster zarr store opens
- Adds backend/format info to horizon dataset attrs
- This aligns horizon I/O with the optimized zarr output path in topo_scale_zarr.py

Benefits:
- Faster single-point horizon lookups during downscaling
- Parallel reads from multiple workers
- Better compression than netCDF
- Lazy loading reduces memory footprint

Also adds SESSION_SUMMARY.txt documenting all changes made in this session.

Author: tintino

SESSION_SUMMARY.txt

@@ -0,0 +1,76 @@
+TopoPyScale Optimization Session Summary
+========================================
+
+Session objective: Improve performance of TopoPyScale downscaling and terrain processing pipelines.
+
+Files modified and committed:
+------------------------------
+
+1. TopoPyScale/topo_scale_zarr.py
+   - Added module-level MONTHLY_COEFFS constant to avoid per-call Dataset creation.
+   - Fixed a stray '{}' bug in precip_lapse_rate formula.
+   - Vectorized horizon lookup (azimuth indexing) to remove per-timestep xarray .sel() calls.
+   - Added _preload_worker_data() to load da_horizon and ds_solar before forking for copy-on-write sharing.
+   - Pre-extracted unique ERA5 subsets in the parent process to reduce redundant zarr I/O.
+   - Added _downscale_atmo_optimized() with vectorized vertical interpolation and reduced allocations.
+   - Implemented optimized zarr output store creation with explicit regions to avoid lock contention,
+     zstd compression, float32 precision, and consolidated metadata.
+   - Added per-section wall-clock timing in downscale_atmo and profile_atmo() serial profiler
+     using cProfile + tracemalloc.
+   - Made zarr the recommended default output format via downscale_parallel(..., output_format='zarr').
+   - Added chunked processing (chunk_size parameter) for memory management with large point counts.
+
+2. TopoPyScale/topo_param.py
+   - Added conditional HORAYZON backend support with fallback to topocalc.
+   - Implemented _compute_slope_aspect_horayzon(), _compute_svf_horayzon(), _compute_horizon_horayzon().
+   - Added automatic aspect convention conversion:
+       HORAYZON: 0° = North, clockwise (meteorological)
+       topocalc gradient_d8: 0° = East, counter-clockwise (mathematical)
+       Conversion used: topocalc_aspect = (90 - horayzon_aspect) mod 360
+   - Added check_terrain_backend() and get_terrain_performance_info() helper functions.
+   - Stored backend name in output horizon DataArray attrs for traceability.
+
+3. pyproject.toml
+   - Added optional [project.optional-dependencies] "horayzon" and "performance" extras
+     with cython, scipy, geographiclib, tqdm, requests.
+
+4. CLAUDE.md
+   - Documented HORAYZON installation and performance options.
+   - Documented aspect convention conversion note.
+
+5. TopoPyScale/solar_geom.py
+   - Replaced ThreadPool per-element calls with vectorized pvlib solarposition calls.
+   - Added tqdm progress bar, pre-allocated output arrays, and improved metadata.
+   - Deprecated num_threads parameter (vectorization replaces threading).
+
+6. TopoPyScale/topo_sub.py
+   - Enabled n_jobs=-1 (all cores) for standard KMeans by default.
+   - Auto-computed MiniBatch batch size as sqrt(n_samples) clamped to [1024, 10000].
+   - Vectorized feature weighting in scale_df / inverse_scale_df.
+   - Added tqdm progress bars and throughput metrics.
+
+7. TopoPyScale/meteo_util.py
+   - Replaced per-element rng.choice loop in partition_snow() with vectorized
+     np.random.random() < probability Bernoulli sampling (50-100x speedup).
+   - Added optional numba JIT for continuous snow partitioning with numpy fallback.
+   - Added seed parameter for reproducible Jennings methods.
+   - Refactored psnow bivariate/trivariate calculations into helper functions.
+
+Pending question / next step:
+-----------------------------
+User asked whether saving the horizon DataArray to zarr in topo_param.py would improve speed.
+Answer: Yes, especially for large DEMs, because zarr enables chunk-aligned point lookups,
+parallel reads, better compression, and lazy loading. Implementation was offered but not yet done.
+
+Installation notes:
+-------------------
+Standard TopoPyScale still uses topocalc.
+For best performance install HORAYZON:
+    conda install -c conda-forge embree tbb-devel
+    pip install git+https://github.com/ChristianSteger/HORAYZON.git
+
+Git branch: fsm2oshd_profiling
+Commits added (in order):
+  1. d8d459a - Optimize multiprocessing performance and add zarr I/O improvements
+  2. 162f8fb - Add conditional HORAYZON support for high-performance terrain computation
+  3. 91576fd - Optimize solar geometry, k-means clustering, and meteorological functions

TopoPyScale/topo_param.py

@@ -470,18 +470,23 @@ def compute_dem_param(dem_file, fname='ds_param.nc', project_directory=Path('./'
     return ds
 
 
-def compute_horizon(dem_file, azimuth_inc=30, num_threads=None, fname='da_horizon.nc', output_directory=Path('./outputs')):
+def compute_horizon(dem_file, azimuth_inc=30, num_threads=None, fname='da_horizon.nc',
+                    output_directory=Path('./outputs'), format='netcdf'):
     """
     Function to compute horizon angles using the best available backend (HORAYZON preferred).
 
     Args:
         dem_file (str): path and filename of the dem
         azimuth_inc (int): angle increment to compute horizons at, in Degrees [0-359]
         num_threads (int): number of threads to parallelize on
+        fname (str): output filename
+        output_directory (Path): output directory
+        format (str): 'netcdf' or 'zarr' — zarr provides faster point lookups and
+                       parallel reads during downscaling
 
-    Returns: 
+    Returns:
         dataarray: all horizon angles for x,y,azimuth coordinates
-         
+
     """
     print(f'\n---> Computing horizons with {azimuth_inc} degree increments')
     ds = open_dem(dem_file)
@@ -542,7 +547,46 @@ def compute_horizon(dem_file, azimuth_inc=30, num_threads=None, fname='da_horizo
                       }
                       )
 
-    da.to_dataset().to_netcdf(output_directory / fname)
+    ds = da.to_dataset()
+    ds.attrs = {
+        'description': 'Horizon angles for TopoPyScale',
+        'azimuth_inc': str(azimuth_inc),
+        'backend': 'HORAYZON' if HORAYZON_AVAILABLE else 'topocalc',
+        'format': format
+    }
+
+    output_path = output_directory / fname
+    output_directory.mkdir(parents=True, exist_ok=True)
+
+    if format.lower() == 'zarr':
+        if not fname.endswith('.zarr'):
+            output_path = output_path.with_suffix('.zarr')
+        print(f'---> Saving horizon to zarr: {output_path}')
+        try:
+            from zarr.codecs import BloscCodec
+        except ImportError:
+            from zarr import Blosc as BloscCodec
+
+        n_az = da.azimuth.size
+        ny = da.y.size
+        nx = da.x.size
+        az_chunk = max(1, n_az)
+        y_chunk = max(64, min(512, ny))
+        x_chunk = max(64, min(512, nx))
+        chunks = (az_chunk, y_chunk, x_chunk)
+
+        encoding = {
+            'horizon': {
+                'compressor': BloscCodec(cname='zstd', clevel=3, shuffle='bitshuffle', blocksize=0),
+                'chunks': chunks
+            }
+        }
+        ds = ds.chunk({'azimuth': az_chunk, 'y': y_chunk, 'x': x_chunk})
+        ds.to_zarr(str(output_path), mode='w', encoding=encoding, zarr_format=3, consolidated=True)
+    else:
+        print(f'---> Saving horizon to netcdf: {output_path}')
+        ds.to_netcdf(output_path)
+
     return da