Add single-point restart extraction doc and nc comparison script

docs/single_point_restart_extraction.md

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+# Single Point Restart File Extraction
+
+This guide shows how to extract single point restart files:
+1. How to Get Single Point 20-year Ad-Spin Up Restart File
+2. How to Get Single Point AI-Updated Ad-Spin Up Restart File
+3. How to Validate
+## Target Site Coordinates
+
+The Amazon site coordinates in TRENDY coordinate system:
+- **Longitude**: 303.75°
+- **Latitude**: -17.4246°
+
+## 1. How to Get Single Point 20-year Ad-Spin Up Restart File
+
+Extract single point from the original 20-year ad-spin up restart file:
+
+```bash
+python scripts/extract_elm_restart_point.py \
+  --restart-file <path/to/input_20year_ad_spinup_restart_file.nc> \
+  --lat -17.4246 \
+  --lon 303.75 \
+  --output-file <path/to/output_single_point_file.nc>
+```
+
+This command extracts the single point data and saves it to the specified output directory.
+
+## 2. How to Get Single Point AI-Updated Ad-Spin Up Restart File
+
+**Step 1:** Follow `CNP_pipeline_runbook.md` to train a model and generate an AI-updated ad-spin up restart file across all sites.
+
+**Step 2:** Extract single point from the AI-updated ad-spin up restart file:
+
+```bash
+python scripts/extract_elm_restart_point.py \
+  --restart-file <path/to/AI-updated_input_20year_ad_spinup_restart_file.nc> \
+  --lat -17.4246 \
+  --lon 303.75 \
+  --output-file <path/to/output_AI-updated_single_point_file.nc>
+```
+
+This command extracts the single point data from the AI-predicted restart file and saves it to the specified output directory.
+
+## 3. How to Validate
+
+Compare the original single point restart file with the AI-updated single point restart file to validate the differences:
+
+```bash
+python3 scripts/compare_nc2.py \
+  --file_name1 <path/to/output_single_point_file.nc> \
+  --file_name2 <path/to/output_AI-updated_single_point_file.nc>
+```
+
+This command compares the two files and reports differences in variables, data types, shapes, and values.

scripts/compare_nc2.py

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+import numpy as np
+import netCDF4 as nc
+import sys
+import argparse
+
+def compare_variables(var1, var2):
+    if var1.dtype != var2.dtype:
+        print(f'Different data types: {var1.dtype} vs {var2.dtype}')
+    if var1.shape != var2.shape:
+        print(f'Different shapes: {var1.shape} vs {var2.shape}')
+    if (np.issubdtype(var1.dtype, np.number) and var1.shape == var2.shape and var1.dtype != 'short'):
+
+        if len(var1.shape) <= 2:
+            compare_data(var1[:], var2[:])
+        elif len(var1.shape) == 3:
+            for i in range(var1.shape[0]):
+                compare_data(var1[i, :, :], var2[i, :, :])
+        elif len(var1.shape) == 4:
+            for i in range(var1.shape[0]):
+                for j in range(var1.shape[1]):
+                    compare_data(var1[i, j, :, :], var2[i, j, :, :])
+
+def compare_data(data1, data2):
+    if not np.allclose(data1, data2):
+        print(f'Difference in data:')
+        print(f'Sum: {np.sum(data1)} vs {np.sum(data2)}')
+        print(f'Mean: {np.mean(data1)} vs {np.mean(data2)}')
+        print(f'Max: {np.max(data1)} vs {np.max(data2)}')
+        print(f'Min: {np.min(data1)} vs {np.min(data2)}')
+
+def main():
+    parser = argparse.ArgumentParser(description='Compare two NetCDF files')
+    parser.add_argument('--file_name1', type=str, 
+                        default='/global/cfs/cdirs/m4814/daweigao/15_code_Landsim/LandSim/1_single_point/AI_updated_single_point_303_17_20251201_TRENDY2024_default_ICB1850CNRDCTCBC_ad_spinup.elm.r.0021-01-01-00000.nc',
+                        help='Path to the first NetCDF file')
+    parser.add_argument('--file_name2', type=str,
+                        default='/global/cfs/cdirs/m4814/daweigao/15_code_Landsim/LandSim/1_single_point/single_point_303_17_20251201_TRENDY2024_default_ICB1850CNRDCTCBC_ad_spinup.elm.r.0021-01-01-00000.nc',
+                        help='Path to the second NetCDF file')
+
+    args = parser.parse_args()
+    file_name1 = args.file_name1
+    file_name2 = args.file_name2
+
+
+
+    file1 = nc.Dataset(file_name1)
+    file2 = nc.Dataset(file_name2)
+
+    variables1 = file1.variables
+    variables2 = file2.variables
+
+    for var in variables1:
+        if var in variables2:
+            print(var)
+            compare_variables(variables1[var], variables2[var])
+        else:
+            print(f'Variable {var} is not in the second file')
+
+    for var in variables2:
+        if var not in variables1:
+            print(f'Variable {var} is not in the first file')
+
+    file1.close()
+    file2.close()
+
+if __name__ == '__main__':
+    main()