Update pyPDAF/mcp_output/mcp_plugin/mcp_service.py

pyPDAF/mcp_output/mcp_plugin/mcp_service.py

@@ -8,23 +8,30 @@ This module provides two MCP tools:
 
 import sys
 import os
-# Add source path to import pyPDAF
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../source/src'))
-
 import numpy as np
 import netCDF4 as nc
 from fastmcp import FastMCP
 from typing import Dict, Any, Tuple
 import glob
 
+# Try to add source path to import pyPDAF
+_current_dir = os.path.dirname(os.path.abspath(__file__))
+_source_paths = [
+    os.path.join(_current_dir, '../../source/src'),  # Local development
+    os.path.join(_current_dir, '../../../source/src'),  # Alternative path
+    '/app/pyPDAF',  # HuggingFace Spaces path
+]
+
+for _path in _source_paths:
+    if os.path.exists(_path) and _path not in sys.path:
+        sys.path.insert(0, _path)
+
 try:
     import pyPDAF
-    import pyPDAF.PDAF as PDAF
-    import pyPDAF.PDAFomi as PDAFomi
     PYPDAF_AVAILABLE = True
-except ImportError:
+except ImportError as e:
     PYPDAF_AVAILABLE = False
-    print("Warning: pyPDAF not available, run_enoi_pipeline will not work")
+    print(f"Warning: pyPDAF not available: {e}")
 
 # Initialize FastMCP server
 mcp = FastMCP("pyPDAF MCP Server")
@@ -178,10 +185,7 @@ def _gaspari_cohn(r: float, c: float) -> float:
 # MCP Tool 1: Generate Demo Ocean Data
 # ============================================================================
 
-@mcp.tool(
-    name="generate_demo_ocean_data",
-    description="Generate synthetic ocean data for EnOI testing. Creates restart.nc (background state), ensemble members, and temperature observations at surface."
-)
+@mcp.tool(name="generate_demo_ocean_data", description="Generate synthetic ocean data for EnOI testing. Creates restart.nc (background state), ensemble members, and temperature observations at surface.")
 def generate_demo_ocean_data(
     nx: int = 20,
     ny: int = 20,
@@ -310,10 +314,7 @@ def generate_demo_ocean_data(
 # MCP Tool 2: Run EnOI Pipeline with Localization
 # ============================================================================
 
-@mcp.tool(
-    name="run_enoi_pipeline",
-    description="Run Ensemble Optimal Interpolation (EnOI) data assimilation pipeline with optional Gaspari-Cohn localization. Computes analysis state from background, ensemble, and observations."
-)
+@mcp.tool(name="run_enoi_pipeline", description="Run Ensemble Optimal Interpolation (EnOI) data assimilation pipeline with optional Gaspari-Cohn localization. Computes analysis state from background, ensemble, and observations.")
 def run_enoi_pipeline(
     restart_path: str = "restart.nc",
     ensemble_dir: str = "ensemble_data",
@@ -417,7 +418,7 @@ def run_enoi_pipeline(
     # ========================================================================
     
     # Initialize PDAFomi with 1 observation type
-    PDAFomi.init(1)
+    pyPDAF.PDAFomi.init(1)
     
     # Setup localization if requested
     if localization_radius > 0:
@@ -425,10 +426,10 @@ def run_enoi_pipeline(
         radius_deg = localization_radius / 111.0
         
         # Initialize local analysis
-        PDAFomi.init_local()
+        pyPDAF.PDAFomi.init_local()
         
         # Set localization
-        PDAF.set_localfilter(1)  # Enable local filter
+        pyPDAF.PDAF.set_localfilter(1)  # Enable local filter
     
     # Observation class to handle PDAFomi callbacks
     class ObsHandler:
@@ -438,9 +439,9 @@ def run_enoi_pipeline(
         def init_dim_obs_pdafomi(self, step, dim_obs):
             """Initialize observation dimension"""
             # Set OMI parameters
-            PDAFomi.set_doassim(self.i_obs, 1)  # Assimilate this obs type
-            PDAFomi.set_disttype(self.i_obs, 0)  # Cartesian distance
-            PDAFomi.set_ncoord(self.i_obs, 2)   # 2D coordinates
+            pyPDAF.PDAFomi.set_doassim(self.i_obs, 1)  # Assimilate this obs type
+            pyPDAF.PDAFomi.set_disttype(self.i_obs, 0)  # Cartesian distance
+            pyPDAF.PDAFomi.set_ncoord(self.i_obs, 2)   # 2D coordinates
             
             # Create coordinate array for observations
             ocoord_p = np.zeros((2, n_obs), order='F')
@@ -455,12 +456,12 @@ def run_enoi_pipeline(
             id_obs_p[0, :] = obs_indices + 1  # Fortran 1-indexing
             
             # Gather observation information
-            PDAFomi.set_id_obs_p(self.i_obs, 1, n_obs, id_obs_p)
-            PDAFomi.set_ivar_obs_p(self.i_obs, n_obs, ivar_obs)
-            PDAFomi.set_ocoord_p(self.i_obs, 2, n_obs, ocoord_p)
+            pyPDAF.PDAFomi.set_id_obs_p(self.i_obs, 1, n_obs, id_obs_p)
+            pyPDAF.PDAFomi.set_ivar_obs_p(self.i_obs, n_obs, ivar_obs)
+            pyPDAF.PDAFomi.set_ocoord_p(self.i_obs, 2, n_obs, ocoord_p)
             
             # Set observation values
-            PDAFomi.set_obs_p(self.i_obs, n_obs, obs_temp)
+            pyPDAF.PDAFomi.set_obs_p(self.i_obs, n_obs, obs_temp)
             
             return n_obs
     
@@ -581,7 +582,7 @@ def run_enoi_pipeline(
     rms_innovation = float(np.sqrt(np.mean(innovation**2)))
     
     # Finalize PDAF
-    PDAF.deallocate()
+    pyPDAF.PDAF.deallocate()
     
     return {
         "status": "success",