From a12506b8bea21574f5a4f3b7972b1068c1585fafb0f207d1632876a818bb48f3 Mon Sep 17 00:00:00 2001
From: PioApocalypse <pioapocalypse@proton.me>
Date: Fri, 13 Feb 2026 00:01:24 +0100
Subject: [PATCH] MAJOR: main.py successfully produces JSON following
 NeXus-schema

takes API key and elabid of the "entrypoint" sample as input
returns indented JSON with the reconstructed dataset!
currently lacks instruments_used data (matter of minutes)
and all the layer data (already present in Layer-class objects)
---
 src/main.py | 70 ++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 53 insertions(+), 17 deletions(-)

diff --git a/src/main.py b/src/main.py
index dcca231..619e1a3 100644
--- a/src/main.py
+++ b/src/main.py
@@ -98,9 +98,44 @@ def chain_layer_to_target(layer_object):
     material_object = call_material_from_elabid(target_elabid)
     return material_object
 
-#sample_object = call_entrypoint_from_elabid(elabid)
-#from_entrypoint_to_material(sample_object)
-    
+def make_nexus_schema_dictionary(substrate_object, layers):
+    pld_fabrication = {
+        "sample": {
+            "substrate": {
+                "name": substrate_object.name,
+                "chemical_formula" : substrate_object.get_compound_formula(apikey),
+                "orientation" : substrate_object.orientation,
+                "miscut_angle" : substrate_object.miscut_angle,
+                "miscut_direction" : substrate_object.miscut_direction,
+                "thickness" : substrate_object.thickness,
+                "dimensions" : substrate_object.dimensions,
+                "surface_treatment" : substrate_object.surface_treatment,
+                "manufacturer" : substrate_object.manufacturer,
+                "batch_id" : substrate_object.batch_id,
+            },
+            "multilayer": {},
+        },
+    }
+    multilayer = pld_fabrication["sample"]["multilayer"]
+    for layer in layers:
+        name = "layer_" + layer.layer_number
+        target_object = chain_layer_to_target(layer)
+        target_dict = {
+            "name": target_object.name,
+            "chemical_formula" : target_object.get_compound_formula(apikey),
+            "description" : target_object.description,
+            "shape" : target_object.shape,
+            "dimensions" : target_object.dimensions,
+            "thickness" : target_object.thickness,
+            "solid_form" : target_object.solid_form,
+            "manufacturer" : target_object.manufacturer,
+            # TO-DO: currently not available:
+            # "batch_id" : target_object.batch_id,
+        }
+        multilayer[name] = {
+            "target": target_dict
+        }
+    return json.dumps(pld_fabrication, indent=2)
 
 if __name__=="__main__":
     print(f"=======================\n===== DEBUG MODE! =====\n=======================\n")
@@ -109,22 +144,23 @@ if __name__=="__main__":
     elabid = input("Enter elabid of your starting sample [default= 1111]: ") or 1111
     data = APIHandler(apikey).get_entry_from_elabid(elabid)
     sample = Entrypoint(data)
-    batch = chain_entrypoint_to_batch(sample) # Material-class object
-    bd = batch.__dict__
-    bd.pop("extra")
+    substrate_object = chain_entrypoint_to_batch(sample) # Substrate-class object
+    # bd = substrate_object.__dict__ # debug
+    # bd.pop("extra") # debug
     layers = chain_entrypoint_to_layers(sample) # list of Layer-class objects
-    print(f"Sample name:\n{sample.name}\n")
-    print(f"Substrate data:\n{bd}\n")
-    print(f"Layers data:")
+    # print(f"Sample name:\n{sample.name}\n") # debug
+    # print(f"Substrate data:\n{bd}\n") # debug
+    # print(f"Layers data:") # debug
+    print(make_nexus_schema_dictionary(substrate_object, layers)) # debug
     for layer in layers:
-        ld = layer.__dict__
-        ld.pop("extra")
-        tgt = chain_layer_to_target(layer)
-        td = tgt.__dict__
-        td.pop("extra")
-        print(ld)
-        print(td)
-        print()
+        # ld = layer.__dict__ # debug
+        # ld.pop("extra") # debug
+        tgt = chain_layer_to_target(layer) # Target-class object
+        # td = tgt.__dict__ # debug
+        # td.pop("extra") # debug
+        # print(ld) # debug
+        # # print(td) # debug
+        # print() # debug
 
     # entryType = None
     # while entryType not in ["items", "experiments"]: