added main

dict_test.py

@@ -3,25 +3,25 @@ Development Notes
 JSON is a common format used to represent structured text.   
 
 The essence of the program is to introduce noise to disrupt the 
-mapping relation of the dictionary. 
+mapping relation present in the dictionary. 
 
-Learning rules is also essential to machine learning. 
+Rules are also present in machine learning. 
 
-When the rule is disrupted, when I query the dictionary again, 
+The mapping rule is disrupted, when I query the dictionary again, 
 the message is disrupted. 
 
 The rule is disrupted by linear arithemetic operation, 
-are there more disruptive and complex rules? 
+are there more non-linear and nuanced rules? 
 
 Prototype to translate a system to text into dictionary
-as a type of structured text
+as a type of structured text. 
 
 str int conversion is important to debug the program
 
-why is it that noise is perceived as adverse?  
+I am still hostile to the concept and etymology of noise, rename
+the concept into something else. 
 
 '''
-
 import numpy as np
 import json
 
@@ -34,49 +34,77 @@ import json
 
 # disrupted message idenfified by "no" field
 
-data = json.load(open('seed.json', 'r'))
 
-print("before disrupting, the message is: ")
+def parse_json(filename):
+    data = json.load(open(filename,'r'))
+    return data
+
+def in_msg(data):
+    message_i = []
+    message_full_i = []
 
     for i in data:
-    print(i)
         if i["no"] == "2":
-        print(i["glyph"] + " " + i["radical"] )
+            message_i.append(i["glyph"])
+            message_full_i.append(i)
         if i["no"] == "3":
-        print(i["glyph"] + " " + i["radical"] )
+            message_i.append(i["glyph"])
+            message_full_i.append(i)
 
-# write as a function, input are codes, output are a {} of glyphs
+    print("message prior to disruption contains: ")
+    for s in message_i:
+        print(s)
     
+def disrupt(data):
     noise = np.random.randint(1,3)
-
     for i in data:
         i["no"] = int(i["no"]) 
         i["no"] += noise
+    return data
 
-with open('noised.json','w') as w_file:
-    json.dump(data,w_file, indent=4)
+def save_json(data):
+    with open('noised.json','w', encoding='utf-8') as w_file:
+        json.dump(data,w_file, indent=4, ensure_ascii = False)
 
-print("after disrupting, the message is: ")
+def out_msg(noise_data):
+    message_o = []
+    message_full_o = []
 
-# use noised json to decrypt 
-
-noise_data = json.load(open('noised.json','r')) 
     for i in noise_data:
-    print(i)
-    print(type(i["no"]))
         #comparing integers, the noise_data no fields are previously
         #dumped as integers
         if i["no"] == 2:
-        print(i["glyph"] + " " + i["radical"] )
+            message_o.append(i["glyph"])
+            message_full_o.append(i)
         if i["no"] == 3:
-        print(i["glyph"] + " " + i["radical"] )
+            message_o.append(i["glyph"])
+            message_full_o.append(i)
+    
+    print("message after disruption contains: ")
 
-# at this point the interferences are not so apparent 
+    for s in message_o:
+        print(s)
 
-# test with a large corpus 
+# at this point the interferences are somewhat apparent 
+# how can i present the interference to be more apparent? 
+    # todo 
+# input chinese blocks in here
+
+
+if __name__ == "__main__":
+    parsed_json = parse_json(filename = "seed.json")
+    in_msg(data = parsed_json)
+    disrupted_data = disrupt(data = parsed_json)
+    save_json(data = disrupted_data)
+    parsed_noise_json = parse_json(filename = "noised.json")
+    out_msg(noise_data = parsed_noise_json)
+
+# test with a large corpus in separate program
 
 # try with corpuses of different language
 
 # try a chinese dictionary and a latin dictionary
 
 # and any other types of dictionary structures, remix!
+
+# main section looks really ugly

noised.json

@@ -3,24 +3,24 @@
         "glyph": "hong",
         "dept-no": "silk-1",
         "radical": "silk",
-        "no": 2
+        "no": 3
     },
     {
         "glyph": "jiao",
         "dept-no": "silk-2",
         "radical": "silk",
-        "no": 3
+        "no": 4
     },
     {
         "glyph": "zhu",
         "dept-no": "silk-3",
         "radical": "silk",
-        "no": 4
+        "no": 5
     },
     {
         "glyph": "du",
         "dept-no": "earth-1",
         "radical": "earth",
-        "no": 5
+        "no": 6
     }
 ]