@@ -109,15 +109,15 @@ def compute_asynchronousshaper_icct_endtoend_delay_from_simulation_results(**kwa
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filter_expression = """type =~ scalar AND name =~ meanBitLifeTimePerPacket:histogram:max"""
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df = read_result_files (inet_project .get_full_path ("tests/validation/tsn/trafficshaping/asynchronousshaper/icct/results/*.sca" ), filter_expression = filter_expression , include_fields_as_scalars = True )
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df = get_scalars (df )
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- df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max)" , "\\ 1" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N6.app\\ [[0-4]\ \].*" , "Flow 4, Class A" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N6.app\\ [[5-9]\ \].*" , "Flow 5, Class B" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [[0-9]\ \].*" , "Flow 1, CDT" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [1[0-9]\ \].*" , "Flow 2, Class A" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [2[0-9]\ \].*" , "Flow 3, Class B" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [3[0-4]\ \].*" , "Flow 6, Class A" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [3[5-9]\ \].*" , "Flow 7, Class B" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\\ [40\ \].*" , "Flow 8, Best Effort" , name ))
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+ df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max)" , "\1 " , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N6.app\[[0-4]\].*" , "Flow 4, Class A" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N6.app\[[5-9]\].*" , "Flow 5, Class B" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[[0-9]\].*" , "Flow 1, CDT" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[1[0-9]\].*" , "Flow 2, Class A" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[2[0-9]\].*" , "Flow 3, Class B" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[3[0-4]\].*" , "Flow 6, Class A" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[3[5-9]\].*" , "Flow 7, Class B" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*N7.app\[40\].*" , "Flow 8, Best Effort" , name ))
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df = pd .pivot_table (df , index = "module" , columns = "name" , values = "value" , aggfunc = "max" )
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return df * 1000000
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@@ -155,11 +155,11 @@ def compute_asynchronousshaper_core4inet_endtoend_delay_from_simulation_results(
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filter_expression = """type =~ scalar AND (name =~ meanBitLifeTimePerPacket:histogram:min OR name =~ meanBitLifeTimePerPacket:histogram:max OR name =~ meanBitLifeTimePerPacket:histogram:mean OR name =~ meanBitLifeTimePerPacket:histogram:stddev)"""
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df = read_result_files (inet_project .get_full_path ("tests/validation/tsn/trafficshaping/asynchronousshaper/core4inet/results/*.sca" ), filter_expression = filter_expression , include_fields_as_scalars = True )
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df = get_scalars (df )
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- df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max|mean|stddev)" , "\\ 1" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[0\ \].*" , "Best effort" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[1\ \].*" , "Medium" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[2\ \].*" , "High" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[3\ \].*" , "Critical" , name ))
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+ df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max|mean|stddev)" , "\1 " , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[0 \].*" , "Best effort" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[1 \].*" , "Medium" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[2 \].*" , "High" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[3 \].*" , "Critical" , name ))
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df = df .loc [df ["module" ]!= "Best effort" ]
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df = pd .pivot_table (df , index = "module" , columns = "name" , values = "value" )
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return df * 1000000
@@ -222,11 +222,11 @@ def compute_creditbasedshaper_endtoend_delay_from_simulation_results(**kwargs):
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filter_expression = """type =~ scalar AND (name =~ meanBitLifeTimePerPacket:histogram:min OR name =~ meanBitLifeTimePerPacket:histogram:max OR name =~ meanBitLifeTimePerPacket:histogram:mean OR name =~ meanBitLifeTimePerPacket:histogram:stddev)"""
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df = read_result_files (inet_project .get_full_path ("tests/validation/tsn/trafficshaping/creditbasedshaper/results/*.sca" ), filter_expression = filter_expression , include_fields_as_scalars = True )
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df = get_scalars (df )
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- df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max|mean|stddev)" , "\\ 1" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[0\ \].*" , "Best effort" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[1\ \].*" , "Medium" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[2\ \].*" , "High" , name ))
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- df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\\[3\ \].*" , "Critical" , name ))
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+ df ["name" ] = df ["name" ].map (lambda name : re .sub (r".*(min|max|mean|stddev)" , "\1 " , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[0 \].*" , "Best effort" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[1 \].*" , "Medium" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[2 \].*" , "High" , name ))
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+ df ["module" ] = df ["module" ].map (lambda name : re .sub (r".*app\[3 \].*" , "Critical" , name ))
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df = df .loc [df ["module" ]!= "Best effort" ]
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df = pd .pivot_table (df , index = "module" , columns = "name" , values = "value" )
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return df * 1000000
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