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

de:nähester Prototyp

de:mische _

de:initialisiere

de:platziere _ Daten

de:erzeuge _ Prototypen

de:Kundendaten

de:lasse _ _ tun ToDo: Max und Min im csv finden, davon abhängig normalisieren

de:platziere _daten auf der Leinwand

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==