Initial commit

ods_to_json.py

@@ -0,0 +1,116 @@
+from datetime import datetime
+import pandas as pd # type: ignore
+import json
+import os
+
+# Load ODS file
+df = pd.read_excel('data.ods', engine='odf')
+
+# Create node list (convert each row to a dictionary)
+nodes = []
+
+# Function to clean dictionary values
+def clean_dict(d):
+    """Remove keys with None values from a dictionary."""
+    # Ensure that we are dealing with single values or lists (no arrays)
+    cleaned_dict = {}
+    for k, v in d.items():
+        if isinstance(v, list):  # If the value is a list, check each item for validity
+            v = [item for item in v if pd.notna(item) and item is not None]
+            if v:  # Only include non-empty lists
+                cleaned_dict[k] = v
+        elif pd.notna(v) and v is not None:
+            cleaned_dict[k] = v
+    return cleaned_dict
+
+# Iterate over rows to create nodes
+for _, row in df.iterrows():
+    # Handle the "image" field to check for multiple images
+    image_value = str(row["image"]).strip() if pd.notna(row["image"]) else None
+    
+    if image_value:
+        if ',' in image_value:
+            # Split multiple image paths into a list
+            images = [image.strip() for image in image_value.split(',')]
+        else:
+            # Single image, store as a string
+            images = image_value
+    else:
+        images = None  # No images if the field is empty or NaN
+
+    node = {
+        "id": str(row["id"]),  # Ensure ID is a string
+        "name": row["name"],
+        "description": row["description"],
+        "category": row["category"],
+        "location": {
+            "country": row["country"],
+            "state": row["state"],
+            "city": row["city"],
+        },
+        "group": row["country"].split()[0],  # Only get the 3 letter code without emoji
+        "image": images,  # Store image(s) (single or list)
+        "link": row["link"],
+    }
+
+    # Clean the node dictionary before appending
+    nodes.append(clean_dict(node))
+
+# Create a dictionary to store nodes by location for linking
+location_groups = {}
+
+for node in nodes:
+    country = node["location"]["country"].split()[0] # Only get the 3 letter code without emoji
+    state = node["location"]["state"]
+    city = node["location"]["city"]
+    node_id = node["id"]
+
+    # Create hierarchical keys for grouping
+    country_key = country
+    state_key = f"{country}|{state}"
+    city_key = f"{country}|{state}|{city}"
+
+    #  Initialize lists if they don't exist
+    location_groups.setdefault(country_key, []).append(node_id)
+    location_groups.setdefault(state_key, []).append(node_id)
+    location_groups.setdefault(city_key, []).append(node_id)
+
+# Generate links based on hierarchical grouping
+links = []
+
+def create_links(node_list, strength):
+    """Create links between nodes in the same group"""
+    for i in range(len(node_list)):
+        for j in range(i + 1, len(node_list)):
+            links.append({
+                "source": node_list[i],
+                "target": node_list[j],
+                "strength": strength
+            })
+
+# Apply links at different levels
+for city_nodes in location_groups.values():
+    if len(city_nodes) > 1:
+        create_links(city_nodes, strength=3) # Strongest links
+
+for state_nodes in location_groups.values():
+    if len(state_nodes) > 1:
+        create_links(state_nodes, strength=2) # Medium links
+
+for country_nodes in location_groups.values():
+    if len(country_nodes) > 1:
+        create_links(country_nodes, strength=1) # Weakest links
+
+# Final JSON structure
+graph_data = {"nodes": nodes, "links": links}
+
+# Save to JSON
+# Format: graph_data_YYYYMMDD_HHMMSS.json
+timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+output_file = f"graph_data__{timestamp}.json"
+with open(output_file, "w") as f:
+    json.dump(graph_data, f, indent=4)
+
+# Print success message and run macOS sound command when script finishes
+print(f"\n✅ JSON file '{output_file}' generated successfully!")
+os.system("afplay /System/Library/Sounds/Glass.aiff")