Upload assemble_data.py with huggingface_hub

assemble_data.py

@@ -0,0 +1,79 @@
+import argparse
+import os
+from netCDF4 import Dataset
+
+
+def assemble_data(input_dir, output_file):
+    nc_files = [f for f in os.listdir(input_dir) if f.endswith(".nc") and not f.startswith("c")]
+    c_files = [f for f in os.listdir(input_dir) if f.endswith(".nc") and f.startswith("c")]
+    nc_files.sort()
+    c_files.sort()
+
+    samples = [0]
+    with Dataset(os.path.join(input_dir, nc_files[0]), "r") as first_nc:
+        samples.append(first_nc.dimensions["sample"].size)
+        num_times = first_nc.dimensions["time"].size
+        try:
+            num_channels = first_nc.dimensions["channel"].size
+        except:
+            num_channels = None
+        x_size = first_nc.dimensions["x"].size
+        y_size = first_nc.dimensions["y"].size
+        dtype = first_nc.variables[nc_files[0].split("_")[0]].dtype
+
+    for nc_file in nc_files[1:]:
+        with Dataset(os.path.join(input_dir, nc_file), "r") as nc:
+            samples.append(nc.dimensions["sample"].size)
+
+    num_samples = sum(samples)
+    for i in range(1, len(samples)):
+        samples[i] += samples[i - 1]
+    with Dataset(output_file, "w") as out_nc:
+        out_nc.createDimension("sample", num_samples)
+        out_nc.createDimension("time", num_times)
+        if num_channels is not None:
+            out_nc.createDimension("channel", num_channels)
+        out_nc.createDimension("x", x_size)
+        out_nc.createDimension("y", y_size)
+        if num_channels is not None:
+            out_nc.createVariable(
+                nc_files[0].split("_")[0], dtype, ("sample", "time", "channel", "x", "y")
+            )
+        else:
+            out_nc.createVariable(
+                nc_files[0].split("_")[0], dtype, ("sample", "time", "x", "y")
+            )
+            
+        out_nc.createVariable(
+            "c", dtype, ("sample", "x", "y")
+        )
+
+        for i, nc_file in enumerate(nc_files):
+            with Dataset(os.path.join(input_dir, nc_file), "r") as nc:
+                print(f"Processing {os.path.join(input_dir, nc_file)}")
+                variable = nc.variables[nc_file.split("_")[0]]
+                out_nc[nc_file.split("_")[0]][samples[i] : samples[i + 1]] = variable[:]
+        
+    samples = [0]
+    for nc_file in c_files[]:
+        with Dataset(os.path.join(input_dir, nc_file), "r") as nc:
+            samples.append(nc.dimensions["sample"].size)
+    for i in range(1, len(samples)):
+        samples[i] += samples[i - 1]
+    
+    for i, c_file in enumerate(c_files):
+        with Dataset(os.path.join(input_dir, c_file), "r") as nc:
+            print(f"Processing {os.path.join(input_dir, c_file)}")
+            variable = nc.variables[c_file.split("_")[0]]
+            out_nc[c_file.split("_")[0]][samples[i] : samples[i + 1]] = variable[:]
+
+    print(f"Saved data to {output_file}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", type=str, required=True)
+    parser.add_argument("--output_file", type=str, required=True)
+    args = parser.parse_args()
+
+    assemble_data(args.input_dir, args.output_file)