From c2ae429cbe4bae97e221f3afe42cb446b1e62419 Mon Sep 17 00:00:00 2001
From: Juan Marulanda <jmaruland@bnl.gov>
Date: Wed, 30 Aug 2023 17:59:47 -0400
Subject: [PATCH] Added scripts to run a backup between tiled servers running
 different versions

---
 aimmdb/ingest/aimmdb_backup.py | 102 +++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 aimmdb/ingest/aimmdb_backup.py

diff --git a/aimmdb/ingest/aimmdb_backup.py b/aimmdb/ingest/aimmdb_backup.py
new file mode 100644
index 0000000..685816e
--- /dev/null
+++ b/aimmdb/ingest/aimmdb_backup.py
@@ -0,0 +1,102 @@
+import json
+import os
+import pandas as pd
+
+def download_aimmdd_data(c):
+    """
+    Navigate through a tiled client and download all the entries that are found
+
+    Parameters
+    ----------
+    c : tiled.client
+
+    Returns
+    -------
+    None.
+
+    """
+    
+    if not os.path.exists("files/"):
+        os.mkdir("files/")
+    
+    for key in c['dataset'].keys():
+        print(f"Downloading {key} dataset...")
+        if not os.path.exists(f"files/{key}/"):
+            os.mkdir(f"files/{key}/")
+            
+        for node_value in c['dataset'][key]['uid'].values():
+            uid = node_value.item['id']
+            print(f"---- Saving node {uid}...")
+            metadata = dict(node_value.metadata)
+            del metadata['_tiled'] # it's not necessary to keep the uid generated by the server. The new server will generate a new uid
+            specs = list(node_value.specs)
+                
+            data_dict = node_value.read().to_dict()
+            
+            meta_content = {"metadata": metadata, "specs": specs, "data": data_dict}
+            
+            with open(f"files/{key}/{uid}.json", 'w') as file:
+                json.dump(meta_content, file)
+
+    print("Download completed!")
+    
+def upload_aimmdb_data(c, folder_path):
+    """
+    Walks through a path in the local machine and writes the data into a remote instance of tiled
+
+    Parameters
+    ----------
+    c : tiled.client
+    folder_path : pathlib.Path
+        Path to folder contianing backup data.
+
+    Returns
+    -------
+    None.
+
+    """
+    
+    filepaths = sorted(folder_path.iterdir())
+    
+    for i in range(len(filepaths)):
+        if filepaths[i].name.startswith("."):
+            # Skip hidden files.
+            continue
+        if not filepaths[i].is_file():
+            # Explore subfolder for more labview files recursively
+            upload_aimmdb_data(c, filepaths[i])
+        if filepaths[i].suffix == ".json":
+            if filepaths[i].stem not in c:
+                with open(filepaths[i]) as f:
+                    sample = json.load(f)
+                    
+                metadata = sample["metadata"]
+                specs = sample["specs"]
+                data = pd.DataFrame(sample["data"])
+                
+                node = c.write_dataframe(data, key=filepaths[i].stem, metadata=metadata, specs=specs)
+                print(f"dataset: {folder_path.name} - node {node.item['id']} was created")
+            else:
+                print(f"dataset: {folder_path.name} - node {filepaths[i].stem} already exists")
+            
+        
+def delete_dataset(c, dataset):
+    """
+    Deletes all the entries of a given dataset
+
+    Parameters
+    ----------
+    c : tiled.client    
+    dataset : str
+        name of a data set in a remote tiled instance.
+
+    Returns
+    -------
+    None.
+
+    """
+    
+    if dataset in c:
+        for key in c[dataset].keys():
+            c[dataset].delete(key)
+            print(f"Node {key} has been deleted")
\ No newline at end of file