From 8e56673bc24138af8879c8ef134b9262a46925c2 Mon Sep 17 00:00:00 2001
From: giumas <giuseppemasetti@gmail.com>
Date: Thu, 28 Nov 2024 20:42:54 +0100
Subject: [PATCH] initial work on a script to reduce WOA23

---
 scripts/reduce_netcdf_woa23.py | 82 ++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 scripts/reduce_netcdf_woa23.py

diff --git a/scripts/reduce_netcdf_woa23.py b/scripts/reduce_netcdf_woa23.py
new file mode 100644
index 00000000..f72fe324
--- /dev/null
+++ b/scripts/reduce_netcdf_woa23.py
@@ -0,0 +1,82 @@
+import logging
+import os
+
+from netCDF4 import Dataset
+
+from hyo2.ssm2.app.gui.soundspeedmanager import AppInfo
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+input_path = r"C:\Users\gmasetti\AppData\Local\HydrOffice\Sound Speed\atlases\woa23_orig"
+output_path = r"C:\Users\gmasetti\AppData\Local\HydrOffice\Sound Speed\atlases\woa23"
+
+#  """ USED TO REDUCE THE SIZE OF THE FULL WOA13 DATABASE """
+
+# temp annual
+file = "woa18_decav_t00_04.nc"
+i_path = os.path.join(input_path, "temp", file)
+i = Dataset(i_path, mode='r')
+o_path = os.path.join(output_path, "temp", file)
+if os.path.exists(o_path):
+    os.remove(o_path)
+o = Dataset(o_path, mode='w')
+
+for name, dim in i.dimensions.items():
+    o.createDimension(name, len(dim) if not dim.isunlimited() else None)
+
+for name, var_i in i.variables.items():
+
+    if name in ['lat', 'lon', 'depth', 't_an']:
+        # create variable
+        var_o = o.createVariable(name, var_i.datatype, var_i.dimensions)
+        # copy attributes
+        var_o.setncatts({k: var_i.getncattr(k) for k in var_i.ncattrs()})
+        # copy data
+        var_o[:] = var_i[:]
+
+# temp monthly/seasonal
+for id in range(1, 17):
+    file = "woa18_decav_t%02d_04.nc" % id
+    i_path = os.path.join(input_path, "temp", file)
+    i = Dataset(i_path, mode='r')
+    o_path = os.path.join(output_path, "temp", file)
+    if os.path.exists(o_path):
+        os.remove(o_path)
+    o = Dataset(o_path, mode='w')
+
+    for name, dim in i.dimensions.items():
+        o.createDimension(name, len(dim) if not dim.isunlimited() else None)
+
+    for name, var_i in i.variables.items():
+
+        if name in ['lon', 'lat', 't_an', 's_an', 't_sd', 's_sd', 'depth']:
+            # create variable
+            var_o = o.createVariable(name, var_i.datatype, var_i.dimensions)
+            # copy attributes
+            var_o.setncatts({k: var_i.getncattr(k) for k in var_i.ncattrs()})
+            # copy data
+            var_o[:] = var_i[:]
+
+# temp monthly/seasonal
+for id in range(1, 17):
+    file = "woa18_decav_s%02d_04.nc" % id
+    i_path = os.path.join(input_path, "sal", file)
+    i = Dataset(i_path, mode='r')
+    o_path = os.path.join(output_path, "sal", file)
+    if os.path.exists(o_path):
+        os.remove(o_path)
+    o = Dataset(o_path, mode='w')
+
+    for name, dim in i.dimensions.items():
+        o.createDimension(name, len(dim) if not dim.isunlimited() else None)
+
+    for name, var_i in i.variables.items():
+
+        if name in ['lon', 'lat', 't_an', 's_an', 't_sd', 's_sd', 'depth']:
+            # create variable
+            var_o = o.createVariable(name, var_i.datatype, var_i.dimensions)
+            # copy attributes
+            var_o.setncatts({k: var_i.getncattr(k) for k in var_i.ncattrs()})
+            # copy data
+            var_o[:] = var_i[:]