From c4247332561712a87f19fb4489ce65fea8f19b09 Mon Sep 17 00:00:00 2001
From: "Mark A. Miller" <MAM@lbl.gov>
Date: Mon, 28 Oct 2024 20:18:36 -0400
Subject: [PATCH] some redundancy with linkml-run-examples

---
 .../scripts/check_examples.py                 | 105 ++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 src/nmdc_submission_schema/scripts/check_examples.py

diff --git a/src/nmdc_submission_schema/scripts/check_examples.py b/src/nmdc_submission_schema/scripts/check_examples.py
new file mode 100644
index 00000000..6e8ad35b
--- /dev/null
+++ b/src/nmdc_submission_schema/scripts/check_examples.py
@@ -0,0 +1,105 @@
+# could check globally, targeted in slot usages, or in all induced slots from all classes
+# check though validation of an instance? requires population of all other required slots too
+from pathlib import Path
+
+from jsonasobj2 import JsonObj
+from linkml_runtime import SchemaView
+from collections import defaultdict
+import csv
+
+#
+# tsv_file = Path("../schema/nmdc_submission_schema.tsv")
+#
+# # how to load schema? hardcoded file path, or pathlib?
+
+schema_file = Path("../nmdc_submission_schema/schema/nmdc_submission_schema.yaml")
+schema_view = SchemaView(schema_file)  # time it
+
+# # print(schema_view.schema.name)
+#
+# all_slots = schema_view.all_slots()
+#
+# attribute_usage_count = defaultdict(int)
+#
+# # Assume `slot_dict` is your dictionary with slot names as keys and SlotDefinitions as values
+# for slot_name, slot_def in sorted(all_slots.items()):
+#     # Use vars() to get the dictionary of attributes for the SlotDefinition object
+#     attributes = vars(slot_def)
+#
+#     # Filter the attributes to only show the populated ones (excluding None, empty lists, etc.)
+#     populated_attributes = {k: v for k, v in sorted(attributes.items()) if v not in [None, '', [], {}, ()]}
+#
+#     # Print or log the results as needed
+#     # print(f"Slot Name: {slot_name}")
+#     for attr_name, attr_value in populated_attributes.items():
+#         # print(f"  {attr_name}: {attr_value}")
+#         attribute_usage_count[attr_name] += 1
+#
+# # Convert defaultdict to a regular dict if needed
+# attribute_usage_count = dict(attribute_usage_count)
+#
+# # Print or log the attribute usage count
+# for attr_name, count in attribute_usage_count.items():
+#     print(f"{attr_name}: {count}")
+#
+# # Prepare data for TSV file
+# # Using an explicit type conversion to str for keys and values
+# rows = [{'Attribute Name': str(attr_name), 'Usage Count': int(count)} for attr_name, count in
+#         attribute_usage_count.items()]
+#
+# # Define the TSV file name
+# tsv_file_name = "attribute_usage_count.tsv"
+#
+# # Write the results to a TSV file using DictWriter
+# with open(tsv_file_name, mode='w', newline='') as tsv_file:
+#     writer = csv.DictWriter(tsv_file, fieldnames=['Attribute Name', 'Usage Count'], delimiter='\t')
+#     writer.writeheader()  # Write the header row
+#     writer.writerows(rows)  # Write the data rows
+#
+# print(f"Attribute usage count has been saved to {tsv_file_name}")
+
+all_classes = schema_view.all_classes()
+all_class_names = all_classes.keys()
+
+attribute_usage_count = defaultdict(int)
+
+for class_name in sorted(all_class_names):
+    print(f"Class Name: {class_name}")
+    current_class = schema_view.induced_class(class_name)
+    current_slots = current_class.attributes
+    for slot_name, slot_def in sorted(current_slots.items()):
+        # Use vars() to get the dictionary of attributes for the SlotDefinition object
+        attributes = vars(slot_def)
+
+        # Filter the attributes to only show the populated ones (excluding None, empty lists, etc.)
+        populated_attributes = {k: v for k, v in sorted(attributes.items()) if
+                                v not in [None, '', [], {}, (), JsonObj()]}
+
+        # Print or log the results as needed
+        # print(f"Slot Name: {slot_name}")
+        for attr_name, attr_value in sorted(populated_attributes.items()):
+            print(f"{class_name}:{slot_name} ... {attr_name} = {attr_value}")
+            attribute_usage_count[attr_name] += 1
+
+# Convert defaultdict to a regular dict if needed
+attribute_usage_count = dict(attribute_usage_count)
+
+# Print or log the attribute usage count
+for attr_name, count in attribute_usage_count.items():
+    print(f"{attr_name}: {count}")
+
+# Prepare data for TSV file
+# Using an explicit type conversion to str for keys and values
+rows = [{'Attribute Name': str(attr_name), 'Usage Count': int(count)} for attr_name, count in
+        attribute_usage_count.items()]
+
+# Define the TSV file name
+tsv_file_name = "attribute_usage_count.tsv"
+
+# Write the results to a TSV file using DictWriter
+with open(tsv_file_name, mode='w', newline='') as tsv_file:
+    writer = csv.DictWriter(tsv_file, fieldnames=['Attribute Name', 'Usage Count'], delimiter='\t')
+    writer.writeheader()  # Write the header row
+    writer.writerows(rows)  # Write the data rows
+
+print(f"Attribute usage count has been saved to {tsv_file_name}")