From 05a51cd5e6dcd62f32d50fd8447b00a41aa3403e Mon Sep 17 00:00:00 2001
From: Dominik Roth <dominik.roth.dev@gmail.com>
Date: Fri, 11 Feb 2022 17:50:07 +0100
Subject: [PATCH] Better newBooks

---
 caliGraph.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/caliGraph.py b/caliGraph.py
index a2de9b4..724d4d6 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -992,7 +992,7 @@ def progress(G, minimum=3.5):
     print('Progress: '+str(perc)+'%')
 
 
-def analyze(G, books, type_name, name, dist=2.1):
+def analyze(G, books, mu, type_name, name, dist=2.1):
     from fuzzywuzzy import fuzz
     type_ident = type_name[0]
     full_name = type_ident + "/" + name
@@ -1009,7 +1009,7 @@ def analyze(G, books, type_name, name, dist=2.1):
     if bestRatio < 70:
         print("Best Match: "+match['label'])
 
-    findNewBooks(G, books, num=-1, minRecSco=1)
+    findNewBooks(G, books, mu, num=-1, minRecSco=1)
 
     menge = set()
     waveFlow(G, match, n, dist, menge)
@@ -1095,8 +1095,8 @@ def shell(G, books, mu, std):
 
 def newBooks(G, books, num, mu, std):
     removeBad(G, mu-std*2)
+    findNewBooks(G, books, mu, num, minRecSco = mu-std)
     removeThinRecs(G, 2)
-    findNewBooks(G, books, num, minRecSco = mu-std)
     removeUnread(G)
     removeUselessReadBooks(G)
     removeTags(G)
@@ -1110,7 +1110,7 @@ def newBooks(G, books, num, mu, std):
     addScoreToLabels(G)
 
 
-def findNewBooks(G, books, num=-1, minRecSco=5):
+def findNewBooks(G, books, mu, num=-1, minRecSco=5):
     mrbdf = pd.read_csv('mrb_db.csv')
     recs = []
     for n in list(G.nodes):
@@ -1148,9 +1148,10 @@ def findNewBooks(G, books, num=-1, minRecSco=5):
                 G.remove_node(n)
             else:
                 ses.append(min(ses))
-                node['fake_se'] = sum(ses)/(len(ses)**1.2) + 0.5 + 0.5 * (len(scores)==1) # This is not how SE works. DILLIGAF?
+                scores.append(mu)
+                node['fake_se'] = sum(ses)/(len(ses)**1.2) + 0.5 + 0.5 * (len(scores)==2) # This is not how SE works. DILLIGAF?
                 node['score'] = sum(scores)/len(scores)*1.2 - node['fake_se']*2 + 0.5 - 0.1/math.sqrt(len(scores))
-                if len(scores)==1:
+                if len(scores)==2:
                     node['score']*=0.80
                 node['value'] = 20 + 5 * float(node['score'])
                 node['label'] += " ({:.2f}±{:.1f})".format(node['score'], node['fake_se'])
@@ -1342,7 +1343,7 @@ def cliInterface():
         if args.new==-1:
             args.new = int(args.n / 5)
         if args.new != 0:
-            findNewBooks(G, books, args.new, minRecSco = mu-std)
+            findNewBooks(G, books, mu, args.new, minRecSco = mu-std)
         if args.tag_based:
             if args.recommender_based:
                 raise Exception('tag-based and recommender-based can not be be combined')
@@ -1356,7 +1357,7 @@ def cliInterface():
     elif args.cmd=="read":
         readBooksAnalysis(G, args.min_rating, args.all_tags, args.only_connected, not args.keep_top_lists)
     elif args.cmd=="analyze":
-        analyze(G, books, args.type, args.name, args.d)
+        analyze(G, books, mu, args.type, args.name, args.d)
     elif args.cmd=="full":
         fullGraph(G, not args.keep_top_lists)
     elif args.cmd=="competence":