From ef5f083656d5289d81bcd31a12a745ea6bd3d9fe Mon Sep 17 00:00:00 2001 From: Jochen Hanisch-Johannsen Date: Sun, 21 Sep 2025 16:00:54 +0200 Subject: [PATCH] =?UTF-8?q?git=20commit=20-m=20"Promotion:=20fix(tag-match?= =?UTF-8?q?ing)=20|=20Fehlerhafte=20Z=C3=A4hlung=20durch=20unvollst=C3=A4n?= =?UTF-8?q?diges=20Matching=20von=20zusammengesetzten=20Tags=20behoben=20(?= =?UTF-8?q?Nummer:Typ:Begriff),=20semantische=20Inkonsistenzen=20(z.?= =?UTF-8?q?=E2=80=AFB.=20'digitales:lernen'=20statt=20'digital:lernen')=20?= =?UTF-8?q?korrigiert"?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- analyse_netzwerk.py | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/analyse_netzwerk.py b/analyse_netzwerk.py index 8817e9f..0f24db9 100644 --- a/analyse_netzwerk.py +++ b/analyse_netzwerk.py @@ -166,13 +166,6 @@ def visualize_network(bib_database): if tag in keyword: tag_counts[tag] += 1 - fundzahlen = defaultdict(int) - for tag, count in tag_counts.items(): - search_term = tag.split(':')[-1] - for key, value in search_terms.items(): - if search_term == value: - fundzahlen[value] += count - search_terms_network = { "Primäre Begriffe": { "learning:management:system": [ @@ -181,7 +174,7 @@ def visualize_network(bib_database): "online:lernplattform", "online:lernumgebung", "digital:learning", - "digitales:lernen" + "digital:lernen" ] }, "Sekundäre Begriffe": { @@ -191,15 +184,15 @@ def visualize_network(bib_database): ], "bildung:technologie": [ "digital:learning", - "digitales:lernen", + "digital:lernen", "blended:learning" ], "digital:learning": [ - "digitale:medien", + "digital:medien", "online:learning" ], - "digitales:lernen": [ - "digitale:medien", + "digital:lernen": [ + "digital:medien", "online:lernen" ], "blended:learning": ["mooc"] @@ -210,6 +203,14 @@ def visualize_network(bib_database): } } + # Fundzählung exakt entlang der search_terms-Definition + fundzahlen = defaultdict(int) + + for number, suchbegriff in search_terms.items(): + for typ in types: + tag = f'#{number}:{typ}:{suchbegriff}'.lower() + fundzahlen[suchbegriff.lower()] += tag_counts.get(tag, 0) + G = nx.Graph() hierarchy_colors = {