Exercise5 5

exercise5/A5.5.py

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+"""
+application for solving Exercise 5.5
+"""
+import math
+import scipy.stats
+
+import pandas
+
+
+def main(filepath, datasetname):
+    """
+    main application
+    """
+    dataframe = pandas.read_csv(filepath)
+    dataframe["FLOPS"] = dataframe["FLOPS"].apply(lambda x: math.log10(x))
+    print(f"\n=== Exercise a) {datasetname} ===")
+    correlation_efficiency, p_value = scipy.stats.pearsonr(dataframe["Jahr"], dataframe["FLOPS"])
+    print(f"Korrelationseffizient: {correlation_efficiency}")
+
+
+if __name__ == "__main__":
+    main("./data/GrosseKisten.csv", "Große Kisten")
+    main("./data/Prozessoren.csv", "Prozessoren")

exercise5/data/GrosseKisten.csv

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+Jahr,FLOPS,Rechner
+1941,0.3,Zuse Z3
+1950,682,Univac 1101
+1956,10670,IBM 704
+1959,97350,IBM 7090
+1961,371700,IBM 7030
+1969,3.30E+06,CDC 7600
+1976,1.10E+08,Cray-1S
+1985,1.22E+09,Cray-2s/4-128
+1990,2.32E+10,NEC SX-3/44
+1996,1.06E+12,Intel ASCI Red
+2002,3.59E+13,NEC Earth-Simulator
+2008,1.03E+15,Roadrunner
+2016,9.30E+16,TaihuLight
+2018,1.22E+17,Summit

exercise5/data/Prozessoren.csv

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+Jahr,FLOPS,Rechner
+1978,1.20E+04,8086/87
+1985,1.70E+05,80386/87
+1989,2.00E+06,80486
+1996,6.20E+07,Pentium Pro
+2007,6.54E+09,Pentium Woodcrest
+2007,4.26E+10,Core 2 Q6600
+2014,3.20E+10,i5-4300U
+2016,9.60E+11,Xeon E7-8890

requirements.txt

@@ -1 +1,3 @@
-numpy>=1.21.2
+numpy>=1.21.2
+pandas>=1.3.5
+scipy>=1.7.3