diff --git a/python/model.csv b/python/model.csv index 2c57d2a..549f6f6 100644 --- a/python/model.csv +++ b/python/model.csv @@ -1,5001 +1,5101 @@ -,n_trials,n_repeated,trial_type,n_features,external_noise_std,kernel,internal_noise_std,criteria,run,metric -0,100,100,,1,1,[1],0.0,-5,0,"(1.0, 1.0)" -1,100,100,,1,1,[1],0.0,-5,1,"(1.0, 1.0)" -2,100,100,,1,1,[1],0.0,-5,2,"(1.0, 1.0)" -3,100,100,,1,1,[1],0.0,-5,3,"(1.0, 1.0)" -4,100,100,,1,1,[1],0.0,-5,4,"(1.0, 1.0)" -5,100,100,,1,1,[1],0.0,-5,5,"(1.0, 1.0)" -6,100,100,,1,1,[1],0.0,-5,6,"(1.0, 1.0)" -7,100,100,,1,1,[1],0.0,-5,7,"(1.0, 1.0)" -8,100,100,,1,1,[1],0.0,-5,8,"(1.0, 1.0)" -9,100,100,,1,1,[1],0.0,-5,9,"(1.0, 1.0)" -10,100,100,,1,1,[1],0.0,-4,0,"(1.0, 1.0)" -11,100,100,,1,1,[1],0.0,-4,1,"(1.0, 1.0)" -12,100,100,,1,1,[1],0.0,-4,2,"(1.0, 1.0)" -13,100,100,,1,1,[1],0.0,-4,3,"(1.0, 1.0)" -14,100,100,,1,1,[1],0.0,-4,4,"(1.0, 1.0)" -15,100,100,,1,1,[1],0.0,-4,5,"(1.0, 0.99)" -16,100,100,,1,1,[1],0.0,-4,6,"(1.0, 1.0)" 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-1897,100,100,,1,1,[1],1.8,4,7,"(0.91, 0.045)" -1898,100,100,,1,1,[1],1.8,4,8,"(0.9, 0.05)" -1899,100,100,,1,1,[1],1.8,4,9,"(0.94, 0.03)" -1900,100,100,,1,1,[1],1.9000000000000001,-5,0,"(0.96, 0.98)" -1901,100,100,,1,1,[1],1.9000000000000001,-5,1,"(0.98, 0.99)" -1902,100,100,,1,1,[1],1.9000000000000001,-5,2,"(0.98, 0.99)" -1903,100,100,,1,1,[1],1.9000000000000001,-5,3,"(0.98, 0.98)" -1904,100,100,,1,1,[1],1.9000000000000001,-5,4,"(0.94, 0.97)" -1905,100,100,,1,1,[1],1.9000000000000001,-5,5,"(0.96, 0.98)" -1906,100,100,,1,1,[1],1.9000000000000001,-5,6,"(0.98, 0.99)" -1907,100,100,,1,1,[1],1.9000000000000001,-5,7,"(0.96, 0.98)" -1908,100,100,,1,1,[1],1.9000000000000001,-5,8,"(0.98, 0.99)" -1909,100,100,,1,1,[1],1.9000000000000001,-5,9,"(0.96, 0.98)" -1910,100,100,,1,1,[1],1.9000000000000001,-4,0,"(0.92, 0.96)" -1911,100,100,,1,1,[1],1.9000000000000001,-4,1,"(0.95, 0.955)" -1912,100,100,,1,1,[1],1.9000000000000001,-4,2,"(0.91, 0.955)" -1913,100,100,,1,1,[1],1.9000000000000001,-4,3,"(0.92, 0.94)" -1914,100,100,,1,1,[1],1.9000000000000001,-4,4,"(0.9, 0.95)" -1915,100,100,,1,1,[1],1.9000000000000001,-4,5,"(0.93, 0.955)" -1916,100,100,,1,1,[1],1.9000000000000001,-4,6,"(0.95, 0.955)" -1917,100,100,,1,1,[1],1.9000000000000001,-4,7,"(0.92, 0.95)" -1918,100,100,,1,1,[1],1.9000000000000001,-4,8,"(0.95, 0.945)" -1919,100,100,,1,1,[1],1.9000000000000001,-4,9,"(0.95, 0.965)" -1920,100,100,,1,1,[1],1.9000000000000001,-3,0,"(0.91, 0.945)" -1921,100,100,,1,1,[1],1.9000000000000001,-3,1,"(0.83, 0.895)" -1922,100,100,,1,1,[1],1.9000000000000001,-3,2,"(0.84, 0.86)" -1923,100,100,,1,1,[1],1.9000000000000001,-3,3,"(0.85, 0.925)" -1924,100,100,,1,1,[1],1.9000000000000001,-3,4,"(0.85, 0.895)" -1925,100,100,,1,1,[1],1.9000000000000001,-3,5,"(0.81, 0.865)" -1926,100,100,,1,1,[1],1.9000000000000001,-3,6,"(0.82, 0.88)" -1927,100,100,,1,1,[1],1.9000000000000001,-3,7,"(0.83, 0.895)" -1928,100,100,,1,1,[1],1.9000000000000001,-3,8,"(0.83, 0.885)" -1929,100,100,,1,1,[1],1.9000000000000001,-3,9,"(0.8, 0.89)" -1930,100,100,,1,1,[1],1.9000000000000001,-2,0,"(0.77, 0.845)" -1931,100,100,,1,1,[1],1.9000000000000001,-2,1,"(0.78, 0.85)" -1932,100,100,,1,1,[1],1.9000000000000001,-2,2,"(0.73, 0.775)" -1933,100,100,,1,1,[1],1.9000000000000001,-2,3,"(0.79, 0.835)" -1934,100,100,,1,1,[1],1.9000000000000001,-2,4,"(0.75, 0.845)" -1935,100,100,,1,1,[1],1.9000000000000001,-2,5,"(0.71, 0.795)" -1936,100,100,,1,1,[1],1.9000000000000001,-2,6,"(0.71, 0.805)" -1937,100,100,,1,1,[1],1.9000000000000001,-2,7,"(0.8, 0.77)" -1938,100,100,,1,1,[1],1.9000000000000001,-2,8,"(0.81, 0.805)" -1939,100,100,,1,1,[1],1.9000000000000001,-2,9,"(0.76, 0.82)" -1940,100,100,,1,1,[1],1.9000000000000001,-1,0,"(0.59, 0.645)" -1941,100,100,,1,1,[1],1.9000000000000001,-1,1,"(0.62, 0.67)" -1942,100,100,,1,1,[1],1.9000000000000001,-1,2,"(0.6, 0.66)" -1943,100,100,,1,1,[1],1.9000000000000001,-1,3,"(0.63, 0.675)" -1944,100,100,,1,1,[1],1.9000000000000001,-1,4,"(0.66, 0.66)" -1945,100,100,,1,1,[1],1.9000000000000001,-1,5,"(0.7, 0.64)" -1946,100,100,,1,1,[1],1.9000000000000001,-1,6,"(0.68, 0.66)" -1947,100,100,,1,1,[1],1.9000000000000001,-1,7,"(0.68, 0.71)" -1948,100,100,,1,1,[1],1.9000000000000001,-1,8,"(0.7, 0.64)" -1949,100,100,,1,1,[1],1.9000000000000001,-1,9,"(0.65, 0.715)" -1950,100,100,,1,1,[1],1.9000000000000001,0,0,"(0.64, 0.51)" -1951,100,100,,1,1,[1],1.9000000000000001,0,1,"(0.57, 0.515)" -1952,100,100,,1,1,[1],1.9000000000000001,0,2,"(0.56, 0.55)" -1953,100,100,,1,1,[1],1.9000000000000001,0,3,"(0.55, 0.495)" -1954,100,100,,1,1,[1],1.9000000000000001,0,4,"(0.62, 0.45)" -1955,100,100,,1,1,[1],1.9000000000000001,0,5,"(0.63, 0.445)" -1956,100,100,,1,1,[1],1.9000000000000001,0,6,"(0.64, 0.47)" -1957,100,100,,1,1,[1],1.9000000000000001,0,7,"(0.51, 0.455)" -1958,100,100,,1,1,[1],1.9000000000000001,0,8,"(0.62, 0.54)" -1959,100,100,,1,1,[1],1.9000000000000001,0,9,"(0.66, 0.57)" -1960,100,100,,1,1,[1],1.9000000000000001,1,0,"(0.64, 0.28)" -1961,100,100,,1,1,[1],1.9000000000000001,1,1,"(0.67, 0.335)" -1962,100,100,,1,1,[1],1.9000000000000001,1,2,"(0.63, 0.325)" -1963,100,100,,1,1,[1],1.9000000000000001,1,3,"(0.61, 0.395)" -1964,100,100,,1,1,[1],1.9000000000000001,1,4,"(0.58, 0.35)" -1965,100,100,,1,1,[1],1.9000000000000001,1,5,"(0.74, 0.35)" -1966,100,100,,1,1,[1],1.9000000000000001,1,6,"(0.65, 0.355)" -1967,100,100,,1,1,[1],1.9000000000000001,1,7,"(0.67, 0.325)" -1968,100,100,,1,1,[1],1.9000000000000001,1,8,"(0.73, 0.365)" -1969,100,100,,1,1,[1],1.9000000000000001,1,9,"(0.65, 0.355)" -1970,100,100,,1,1,[1],1.9000000000000001,2,0,"(0.76, 0.16)" -1971,100,100,,1,1,[1],1.9000000000000001,2,1,"(0.7, 0.21)" -1972,100,100,,1,1,[1],1.9000000000000001,2,2,"(0.83, 0.135)" -1973,100,100,,1,1,[1],1.9000000000000001,2,3,"(0.68, 0.2)" -1974,100,100,,1,1,[1],1.9000000000000001,2,4,"(0.71, 0.215)" -1975,100,100,,1,1,[1],1.9000000000000001,2,5,"(0.74, 0.19)" -1976,100,100,,1,1,[1],1.9000000000000001,2,6,"(0.8, 0.22)" -1977,100,100,,1,1,[1],1.9000000000000001,2,7,"(0.75, 0.215)" -1978,100,100,,1,1,[1],1.9000000000000001,2,8,"(0.71, 0.215)" -1979,100,100,,1,1,[1],1.9000000000000001,2,9,"(0.79, 0.205)" -1980,100,100,,1,1,[1],1.9000000000000001,3,0,"(0.82, 0.1)" -1981,100,100,,1,1,[1],1.9000000000000001,3,1,"(0.75, 0.145)" -1982,100,100,,1,1,[1],1.9000000000000001,3,2,"(0.9, 0.08)" -1983,100,100,,1,1,[1],1.9000000000000001,3,3,"(0.83, 0.125)" -1984,100,100,,1,1,[1],1.9000000000000001,3,4,"(0.88, 0.1)" -1985,100,100,,1,1,[1],1.9000000000000001,3,5,"(0.84, 0.09)" -1986,100,100,,1,1,[1],1.9000000000000001,3,6,"(0.92, 0.09)" -1987,100,100,,1,1,[1],1.9000000000000001,3,7,"(0.83, 0.115)" -1988,100,100,,1,1,[1],1.9000000000000001,3,8,"(0.88, 0.1)" -1989,100,100,,1,1,[1],1.9000000000000001,3,9,"(0.81, 0.145)" -1990,100,100,,1,1,[1],1.9000000000000001,4,0,"(0.9, 0.05)" -1991,100,100,,1,1,[1],1.9000000000000001,4,1,"(0.92, 0.04)" -1992,100,100,,1,1,[1],1.9000000000000001,4,2,"(0.86, 0.07)" -1993,100,100,,1,1,[1],1.9000000000000001,4,3,"(0.93, 0.035)" -1994,100,100,,1,1,[1],1.9000000000000001,4,4,"(0.94, 0.05)" -1995,100,100,,1,1,[1],1.9000000000000001,4,5,"(0.91, 0.055)" -1996,100,100,,1,1,[1],1.9000000000000001,4,6,"(0.96, 0.03)" -1997,100,100,,1,1,[1],1.9000000000000001,4,7,"(0.89, 0.055)" -1998,100,100,,1,1,[1],1.9000000000000001,4,8,"(0.96, 0.03)" -1999,100,100,,1,1,[1],1.9000000000000001,4,9,"(0.97, 0.015)" -2000,100,100,,1,1,[1],2.0,-5,0,"(0.99, 0.995)" -2001,100,100,,1,1,[1],2.0,-5,1,"(0.97, 0.985)" -2002,100,100,,1,1,[1],2.0,-5,2,"(1.0, 1.0)" -2003,100,100,,1,1,[1],2.0,-5,3,"(0.95, 0.975)" -2004,100,100,,1,1,[1],2.0,-5,4,"(0.97, 0.985)" -2005,100,100,,1,1,[1],2.0,-5,5,"(0.95, 0.975)" -2006,100,100,,1,1,[1],2.0,-5,6,"(0.92, 0.96)" -2007,100,100,,1,1,[1],2.0,-5,7,"(0.97, 0.985)" -2008,100,100,,1,1,[1],2.0,-5,8,"(0.96, 0.98)" -2009,100,100,,1,1,[1],2.0,-5,9,"(0.95, 0.975)" -2010,100,100,,1,1,[1],2.0,-4,0,"(0.82, 0.91)" -2011,100,100,,1,1,[1],2.0,-4,1,"(0.92, 0.96)" -2012,100,100,,1,1,[1],2.0,-4,2,"(0.9, 0.93)" -2013,100,100,,1,1,[1],2.0,-4,3,"(0.91, 0.955)" -2014,100,100,,1,1,[1],2.0,-4,4,"(0.88, 0.91)" -2015,100,100,,1,1,[1],2.0,-4,5,"(0.94, 0.95)" -2016,100,100,,1,1,[1],2.0,-4,6,"(0.91, 0.935)" -2017,100,100,,1,1,[1],2.0,-4,7,"(0.95, 0.975)" -2018,100,100,,1,1,[1],2.0,-4,8,"(0.91, 0.955)" -2019,100,100,,1,1,[1],2.0,-4,9,"(0.94, 0.97)" -2020,100,100,,1,1,[1],2.0,-3,0,"(0.84, 0.89)" -2021,100,100,,1,1,[1],2.0,-3,1,"(0.86, 0.91)" -2022,100,100,,1,1,[1],2.0,-3,2,"(0.91, 0.935)" -2023,100,100,,1,1,[1],2.0,-3,3,"(0.84, 0.89)" -2024,100,100,,1,1,[1],2.0,-3,4,"(0.87, 0.925)" -2025,100,100,,1,1,[1],2.0,-3,5,"(0.87, 0.915)" -2026,100,100,,1,1,[1],2.0,-3,6,"(0.86, 0.88)" -2027,100,100,,1,1,[1],2.0,-3,7,"(0.78, 0.88)" -2028,100,100,,1,1,[1],2.0,-3,8,"(0.7, 0.84)" -2029,100,100,,1,1,[1],2.0,-3,9,"(0.84, 0.88)" -2030,100,100,,1,1,[1],2.0,-2,0,"(0.81, 0.825)" -2031,100,100,,1,1,[1],2.0,-2,1,"(0.75, 0.815)" -2032,100,100,,1,1,[1],2.0,-2,2,"(0.75, 0.825)" -2033,100,100,,1,1,[1],2.0,-2,3,"(0.74, 0.8)" -2034,100,100,,1,1,[1],2.0,-2,4,"(0.72, 0.77)" -2035,100,100,,1,1,[1],2.0,-2,5,"(0.68, 0.78)" -2036,100,100,,1,1,[1],2.0,-2,6,"(0.69, 0.795)" -2037,100,100,,1,1,[1],2.0,-2,7,"(0.71, 0.785)" -2038,100,100,,1,1,[1],2.0,-2,8,"(0.75, 0.785)" -2039,100,100,,1,1,[1],2.0,-2,9,"(0.66, 0.78)" -2040,100,100,,1,1,[1],2.0,-1,0,"(0.72, 0.64)" -2041,100,100,,1,1,[1],2.0,-1,1,"(0.62, 0.65)" -2042,100,100,,1,1,[1],2.0,-1,2,"(0.63, 0.665)" -2043,100,100,,1,1,[1],2.0,-1,3,"(0.67, 0.685)" -2044,100,100,,1,1,[1],2.0,-1,4,"(0.56, 0.67)" -2045,100,100,,1,1,[1],2.0,-1,5,"(0.65, 0.625)" -2046,100,100,,1,1,[1],2.0,-1,6,"(0.67, 0.665)" -2047,100,100,,1,1,[1],2.0,-1,7,"(0.7, 0.66)" -2048,100,100,,1,1,[1],2.0,-1,8,"(0.65, 0.665)" -2049,100,100,,1,1,[1],2.0,-1,9,"(0.63, 0.665)" -2050,100,100,,1,1,[1],2.0,0,0,"(0.66, 0.47)" -2051,100,100,,1,1,[1],2.0,0,1,"(0.73, 0.555)" -2052,100,100,,1,1,[1],2.0,0,2,"(0.52, 0.5)" -2053,100,100,,1,1,[1],2.0,0,3,"(0.61, 0.525)" -2054,100,100,,1,1,[1],2.0,0,4,"(0.62, 0.48)" -2055,100,100,,1,1,[1],2.0,0,5,"(0.67, 0.475)" -2056,100,100,,1,1,[1],2.0,0,6,"(0.72, 0.48)" -2057,100,100,,1,1,[1],2.0,0,7,"(0.62, 0.52)" -2058,100,100,,1,1,[1],2.0,0,8,"(0.59, 0.475)" -2059,100,100,,1,1,[1],2.0,0,9,"(0.59, 0.495)" -2060,100,100,,1,1,[1],2.0,1,0,"(0.68, 0.36)" -2061,100,100,,1,1,[1],2.0,1,1,"(0.7, 0.34)" -2062,100,100,,1,1,[1],2.0,1,2,"(0.63, 0.335)" -2063,100,100,,1,1,[1],2.0,1,3,"(0.62, 0.35)" -2064,100,100,,1,1,[1],2.0,1,4,"(0.59, 0.345)" -2065,100,100,,1,1,[1],2.0,1,5,"(0.67, 0.425)" -2066,100,100,,1,1,[1],2.0,1,6,"(0.6, 0.35)" -2067,100,100,,1,1,[1],2.0,1,7,"(0.66, 0.36)" -2068,100,100,,1,1,[1],2.0,1,8,"(0.63, 0.365)" -2069,100,100,,1,1,[1],2.0,1,9,"(0.63, 0.305)" -2070,100,100,,1,1,[1],2.0,2,0,"(0.67, 0.235)" -2071,100,100,,1,1,[1],2.0,2,1,"(0.76, 0.28)" -2072,100,100,,1,1,[1],2.0,2,2,"(0.78, 0.16)" -2073,100,100,,1,1,[1],2.0,2,3,"(0.76, 0.19)" -2074,100,100,,1,1,[1],2.0,2,4,"(0.78, 0.18)" -2075,100,100,,1,1,[1],2.0,2,5,"(0.73, 0.295)" -2076,100,100,,1,1,[1],2.0,2,6,"(0.75, 0.185)" -2077,100,100,,1,1,[1],2.0,2,7,"(0.74, 0.22)" -2078,100,100,,1,1,[1],2.0,2,8,"(0.71, 0.195)" -2079,100,100,,1,1,[1],2.0,2,9,"(0.75, 0.215)" -2080,100,100,,1,1,[1],2.0,3,0,"(0.86, 0.12)" -2081,100,100,,1,1,[1],2.0,3,1,"(0.87, 0.105)" -2082,100,100,,1,1,[1],2.0,3,2,"(0.8, 0.15)" -2083,100,100,,1,1,[1],2.0,3,3,"(0.83, 0.085)" -2084,100,100,,1,1,[1],2.0,3,4,"(0.82, 0.13)" -2085,100,100,,1,1,[1],2.0,3,5,"(0.87, 0.075)" -2086,100,100,,1,1,[1],2.0,3,6,"(0.88, 0.12)" -2087,100,100,,1,1,[1],2.0,3,7,"(0.82, 0.16)" -2088,100,100,,1,1,[1],2.0,3,8,"(0.82, 0.13)" -2089,100,100,,1,1,[1],2.0,3,9,"(0.86, 0.1)" -2090,100,100,,1,1,[1],2.0,4,0,"(0.89, 0.065)" -2091,100,100,,1,1,[1],2.0,4,1,"(0.92, 0.06)" -2092,100,100,,1,1,[1],2.0,4,2,"(0.89, 0.055)" -2093,100,100,,1,1,[1],2.0,4,3,"(0.94, 0.03)" -2094,100,100,,1,1,[1],2.0,4,4,"(0.94, 0.04)" -2095,100,100,,1,1,[1],2.0,4,5,"(0.96, 0.02)" -2096,100,100,,1,1,[1],2.0,4,6,"(0.88, 0.07)" -2097,100,100,,1,1,[1],2.0,4,7,"(0.96, 0.04)" -2098,100,100,,1,1,[1],2.0,4,8,"(0.95, 0.025)" -2099,100,100,,1,1,[1],2.0,4,9,"(0.91, 0.055)" -2100,100,100,,1,1,[1],2.1,-5,0,"(0.94, 0.97)" -2101,100,100,,1,1,[1],2.1,-5,1,"(0.94, 0.96)" -2102,100,100,,1,1,[1],2.1,-5,2,"(0.93, 0.965)" -2103,100,100,,1,1,[1],2.1,-5,3,"(0.96, 0.98)" -2104,100,100,,1,1,[1],2.1,-5,4,"(0.96, 0.98)" -2105,100,100,,1,1,[1],2.1,-5,5,"(0.98, 0.99)" -2106,100,100,,1,1,[1],2.1,-5,6,"(0.96, 0.98)" -2107,100,100,,1,1,[1],2.1,-5,7,"(0.94, 0.97)" -2108,100,100,,1,1,[1],2.1,-5,8,"(0.99, 0.995)" -2109,100,100,,1,1,[1],2.1,-5,9,"(0.96, 0.98)" -2110,100,100,,1,1,[1],2.1,-4,0,"(0.9, 0.94)" -2111,100,100,,1,1,[1],2.1,-4,1,"(0.9, 0.94)" -2112,100,100,,1,1,[1],2.1,-4,2,"(0.95, 0.975)" -2113,100,100,,1,1,[1],2.1,-4,3,"(0.91, 0.945)" -2114,100,100,,1,1,[1],2.1,-4,4,"(0.9, 0.94)" -2115,100,100,,1,1,[1],2.1,-4,5,"(0.92, 0.94)" -2116,100,100,,1,1,[1],2.1,-4,6,"(0.92, 0.96)" -2117,100,100,,1,1,[1],2.1,-4,7,"(0.83, 0.915)" -2118,100,100,,1,1,[1],2.1,-4,8,"(0.92, 0.96)" -2119,100,100,,1,1,[1],2.1,-4,9,"(0.92, 0.95)" -2120,100,100,,1,1,[1],2.1,-3,0,"(0.78, 0.88)" -2121,100,100,,1,1,[1],2.1,-3,1,"(0.73, 0.835)" -2122,100,100,,1,1,[1],2.1,-3,2,"(0.88, 0.91)" -2123,100,100,,1,1,[1],2.1,-3,3,"(0.8, 0.87)" -2124,100,100,,1,1,[1],2.1,-3,4,"(0.79, 0.845)" -2125,100,100,,1,1,[1],2.1,-3,5,"(0.86, 0.93)" -2126,100,100,,1,1,[1],2.1,-3,6,"(0.78, 0.88)" -2127,100,100,,1,1,[1],2.1,-3,7,"(0.82, 0.88)" -2128,100,100,,1,1,[1],2.1,-3,8,"(0.82, 0.89)" -2129,100,100,,1,1,[1],2.1,-3,9,"(0.77, 0.865)" -2130,100,100,,1,1,[1],2.1,-2,0,"(0.72, 0.8)" -2131,100,100,,1,1,[1],2.1,-2,1,"(0.73, 0.765)" -2132,100,100,,1,1,[1],2.1,-2,2,"(0.72, 0.81)" -2133,100,100,,1,1,[1],2.1,-2,3,"(0.74, 0.75)" -2134,100,100,,1,1,[1],2.1,-2,4,"(0.77, 0.785)" -2135,100,100,,1,1,[1],2.1,-2,5,"(0.76, 0.83)" -2136,100,100,,1,1,[1],2.1,-2,6,"(0.75, 0.805)" -2137,100,100,,1,1,[1],2.1,-2,7,"(0.72, 0.81)" -2138,100,100,,1,1,[1],2.1,-2,8,"(0.76, 0.76)" 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-2538,100,100,,1,1,[1],2.5,-2,8,"(0.56, 0.73)" -2539,100,100,,1,1,[1],2.5,-2,9,"(0.7, 0.79)" -2540,100,100,,1,1,[1],2.5,-1,0,"(0.64, 0.7)" -2541,100,100,,1,1,[1],2.5,-1,1,"(0.68, 0.67)" -2542,100,100,,1,1,[1],2.5,-1,2,"(0.64, 0.66)" -2543,100,100,,1,1,[1],2.5,-1,3,"(0.5, 0.61)" -2544,100,100,,1,1,[1],2.5,-1,4,"(0.54, 0.66)" -2545,100,100,,1,1,[1],2.5,-1,5,"(0.54, 0.57)" -2546,100,100,,1,1,[1],2.5,-1,6,"(0.71, 0.615)" -2547,100,100,,1,1,[1],2.5,-1,7,"(0.63, 0.615)" -2548,100,100,,1,1,[1],2.5,-1,8,"(0.56, 0.65)" -2549,100,100,,1,1,[1],2.5,-1,9,"(0.59, 0.645)" -2550,100,100,,1,1,[1],2.5,0,0,"(0.67, 0.535)" -2551,100,100,,1,1,[1],2.5,0,1,"(0.53, 0.455)" -2552,100,100,,1,1,[1],2.5,0,2,"(0.61, 0.515)" -2553,100,100,,1,1,[1],2.5,0,3,"(0.56, 0.53)" -2554,100,100,,1,1,[1],2.5,0,4,"(0.52, 0.5)" -2555,100,100,,1,1,[1],2.5,0,5,"(0.6, 0.56)" -2556,100,100,,1,1,[1],2.5,0,6,"(0.53, 0.545)" -2557,100,100,,1,1,[1],2.5,0,7,"(0.59, 0.505)" -2558,100,100,,1,1,[1],2.5,0,8,"(0.53, 0.465)" -2559,100,100,,1,1,[1],2.5,0,9,"(0.59, 0.475)" -2560,100,100,,1,1,[1],2.5,1,0,"(0.58, 0.31)" -2561,100,100,,1,1,[1],2.5,1,1,"(0.65, 0.335)" -2562,100,100,,1,1,[1],2.5,1,2,"(0.55, 0.395)" -2563,100,100,,1,1,[1],2.5,1,3,"(0.55, 0.405)" -2564,100,100,,1,1,[1],2.5,1,4,"(0.58, 0.33)" -2565,100,100,,1,1,[1],2.5,1,5,"(0.65, 0.425)" -2566,100,100,,1,1,[1],2.5,1,6,"(0.68, 0.36)" -2567,100,100,,1,1,[1],2.5,1,7,"(0.62, 0.36)" -2568,100,100,,1,1,[1],2.5,1,8,"(0.62, 0.36)" -2569,100,100,,1,1,[1],2.5,1,9,"(0.54, 0.41)" -2570,100,100,,1,1,[1],2.5,2,0,"(0.62, 0.27)" -2571,100,100,,1,1,[1],2.5,2,1,"(0.67, 0.255)" -2572,100,100,,1,1,[1],2.5,2,2,"(0.65, 0.265)" -2573,100,100,,1,1,[1],2.5,2,3,"(0.73, 0.245)" -2574,100,100,,1,1,[1],2.5,2,4,"(0.68, 0.24)" -2575,100,100,,1,1,[1],2.5,2,5,"(0.77, 0.185)" -2576,100,100,,1,1,[1],2.5,2,6,"(0.7, 0.25)" -2577,100,100,,1,1,[1],2.5,2,7,"(0.74, 0.26)" -2578,100,100,,1,1,[1],2.5,2,8,"(0.69, 0.215)" -2579,100,100,,1,1,[1],2.5,2,9,"(0.82, 0.2)" -2580,100,100,,1,1,[1],2.5,3,0,"(0.79, 0.155)" -2581,100,100,,1,1,[1],2.5,3,1,"(0.74, 0.17)" -2582,100,100,,1,1,[1],2.5,3,2,"(0.72, 0.19)" -2583,100,100,,1,1,[1],2.5,3,3,"(0.84, 0.14)" -2584,100,100,,1,1,[1],2.5,3,4,"(0.76, 0.14)" -2585,100,100,,1,1,[1],2.5,3,5,"(0.82, 0.14)" -2586,100,100,,1,1,[1],2.5,3,6,"(0.68, 0.17)" -2587,100,100,,1,1,[1],2.5,3,7,"(0.74, 0.15)" -2588,100,100,,1,1,[1],2.5,3,8,"(0.75, 0.175)" -2589,100,100,,1,1,[1],2.5,3,9,"(0.72, 0.16)" -2590,100,100,,1,1,[1],2.5,4,0,"(0.86, 0.08)" -2591,100,100,,1,1,[1],2.5,4,1,"(0.91, 0.055)" -2592,100,100,,1,1,[1],2.5,4,2,"(0.87, 0.075)" -2593,100,100,,1,1,[1],2.5,4,3,"(0.85, 0.095)" -2594,100,100,,1,1,[1],2.5,4,4,"(0.88, 0.07)" -2595,100,100,,1,1,[1],2.5,4,5,"(0.88, 0.1)" -2596,100,100,,1,1,[1],2.5,4,6,"(0.93, 0.035)" -2597,100,100,,1,1,[1],2.5,4,7,"(0.87, 0.075)" -2598,100,100,,1,1,[1],2.5,4,8,"(0.85, 0.085)" -2599,100,100,,1,1,[1],2.5,4,9,"(0.91, 0.055)" -2600,100,100,,1,1,[1],2.6,-5,0,"(0.9, 0.94)" -2601,100,100,,1,1,[1],2.6,-5,1,"(0.93, 0.965)" -2602,100,100,,1,1,[1],2.6,-5,2,"(0.91, 0.945)" -2603,100,100,,1,1,[1],2.6,-5,3,"(0.96, 0.98)" -2604,100,100,,1,1,[1],2.6,-5,4,"(0.88, 0.94)" -2605,100,100,,1,1,[1],2.6,-5,5,"(0.92, 0.95)" -2606,100,100,,1,1,[1],2.6,-5,6,"(0.95, 0.965)" -2607,100,100,,1,1,[1],2.6,-5,7,"(0.95, 0.975)" -2608,100,100,,1,1,[1],2.6,-5,8,"(0.96, 0.98)" -2609,100,100,,1,1,[1],2.6,-5,9,"(0.94, 0.96)" -2610,100,100,,1,1,[1],2.6,-4,0,"(0.85, 0.925)" -2611,100,100,,1,1,[1],2.6,-4,1,"(0.89, 0.945)" -2612,100,100,,1,1,[1],2.6,-4,2,"(0.82, 0.9)" -2613,100,100,,1,1,[1],2.6,-4,3,"(0.79, 0.875)" -2614,100,100,,1,1,[1],2.6,-4,4,"(0.78, 0.85)" -2615,100,100,,1,1,[1],2.6,-4,5,"(0.81, 0.895)" -2616,100,100,,1,1,[1],2.6,-4,6,"(0.88, 0.93)" -2617,100,100,,1,1,[1],2.6,-4,7,"(0.89, 0.925)" -2618,100,100,,1,1,[1],2.6,-4,8,"(0.85, 0.915)" -2619,100,100,,1,1,[1],2.6,-4,9,"(0.84, 0.89)" -2620,100,100,,1,1,[1],2.6,-3,0,"(0.7, 0.84)" -2621,100,100,,1,1,[1],2.6,-3,1,"(0.75, 0.815)" -2622,100,100,,1,1,[1],2.6,-3,2,"(0.78, 0.86)" -2623,100,100,,1,1,[1],2.6,-3,3,"(0.69, 0.815)" -2624,100,100,,1,1,[1],2.6,-3,4,"(0.81, 0.855)" -2625,100,100,,1,1,[1],2.6,-3,5,"(0.8, 0.89)" -2626,100,100,,1,1,[1],2.6,-3,6,"(0.82, 0.89)" -2627,100,100,,1,1,[1],2.6,-3,7,"(0.78, 0.86)" -2628,100,100,,1,1,[1],2.6,-3,8,"(0.76, 0.85)" -2629,100,100,,1,1,[1],2.6,-3,9,"(0.73, 0.835)" -2630,100,100,,1,1,[1],2.6,-2,0,"(0.8, 0.79)" -2631,100,100,,1,1,[1],2.6,-2,1,"(0.68, 0.74)" -2632,100,100,,1,1,[1],2.6,-2,2,"(0.67, 0.795)" -2633,100,100,,1,1,[1],2.6,-2,3,"(0.59, 0.715)" -2634,100,100,,1,1,[1],2.6,-2,4,"(0.66, 0.72)" -2635,100,100,,1,1,[1],2.6,-2,5,"(0.71, 0.765)" -2636,100,100,,1,1,[1],2.6,-2,6,"(0.64, 0.74)" -2637,100,100,,1,1,[1],2.6,-2,7,"(0.64, 0.77)" -2638,100,100,,1,1,[1],2.6,-2,8,"(0.7, 0.72)" -2639,100,100,,1,1,[1],2.6,-2,9,"(0.64, 0.75)" -2640,100,100,,1,1,[1],2.6,-1,0,"(0.63, 0.625)" -2641,100,100,,1,1,[1],2.6,-1,1,"(0.63, 0.665)" -2642,100,100,,1,1,[1],2.6,-1,2,"(0.54, 0.58)" -2643,100,100,,1,1,[1],2.6,-1,3,"(0.59, 0.625)" -2644,100,100,,1,1,[1],2.6,-1,4,"(0.54, 0.6)" -2645,100,100,,1,1,[1],2.6,-1,5,"(0.64, 0.61)" -2646,100,100,,1,1,[1],2.6,-1,6,"(0.54, 0.61)" -2647,100,100,,1,1,[1],2.6,-1,7,"(0.64, 0.67)" -2648,100,100,,1,1,[1],2.6,-1,8,"(0.63, 0.615)" -2649,100,100,,1,1,[1],2.6,-1,9,"(0.59, 0.655)" -2650,100,100,,1,1,[1],2.6,0,0,"(0.49, 0.535)" -2651,100,100,,1,1,[1],2.6,0,1,"(0.65, 0.465)" -2652,100,100,,1,1,[1],2.6,0,2,"(0.54, 0.55)" -2653,100,100,,1,1,[1],2.6,0,3,"(0.56, 0.5)" -2654,100,100,,1,1,[1],2.6,0,4,"(0.57, 0.535)" -2655,100,100,,1,1,[1],2.6,0,5,"(0.49, 0.515)" -2656,100,100,,1,1,[1],2.6,0,6,"(0.59, 0.525)" -2657,100,100,,1,1,[1],2.6,0,7,"(0.54, 0.45)" -2658,100,100,,1,1,[1],2.6,0,8,"(0.62, 0.54)" -2659,100,100,,1,1,[1],2.6,0,9,"(0.54, 0.56)" -2660,100,100,,1,1,[1],2.6,1,0,"(0.59, 0.345)" -2661,100,100,,1,1,[1],2.6,1,1,"(0.65, 0.355)" -2662,100,100,,1,1,[1],2.6,1,2,"(0.57, 0.395)" -2663,100,100,,1,1,[1],2.6,1,3,"(0.57, 0.385)" -2664,100,100,,1,1,[1],2.6,1,4,"(0.64, 0.35)" -2665,100,100,,1,1,[1],2.6,1,5,"(0.57, 0.385)" -2666,100,100,,1,1,[1],2.6,1,6,"(0.61, 0.355)" -2667,100,100,,1,1,[1],2.6,1,7,"(0.6, 0.41)" -2668,100,100,,1,1,[1],2.6,1,8,"(0.61, 0.325)" -2669,100,100,,1,1,[1],2.6,1,9,"(0.62, 0.32)" -2670,100,100,,1,1,[1],2.6,2,0,"(0.67, 0.195)" -2671,100,100,,1,1,[1],2.6,2,1,"(0.69, 0.245)" -2672,100,100,,1,1,[1],2.6,2,2,"(0.59, 0.245)" -2673,100,100,,1,1,[1],2.6,2,3,"(0.72, 0.18)" -2674,100,100,,1,1,[1],2.6,2,4,"(0.69, 0.285)" -2675,100,100,,1,1,[1],2.6,2,5,"(0.64, 0.33)" -2676,100,100,,1,1,[1],2.6,2,6,"(0.74, 0.2)" -2677,100,100,,1,1,[1],2.6,2,7,"(0.72, 0.26)" -2678,100,100,,1,1,[1],2.6,2,8,"(0.67, 0.275)" -2679,100,100,,1,1,[1],2.6,2,9,"(0.61, 0.265)" -2680,100,100,,1,1,[1],2.6,3,0,"(0.77, 0.135)" -2681,100,100,,1,1,[1],2.6,3,1,"(0.72, 0.19)" -2682,100,100,,1,1,[1],2.6,3,2,"(0.73, 0.185)" -2683,100,100,,1,1,[1],2.6,3,3,"(0.81, 0.145)" -2684,100,100,,1,1,[1],2.6,3,4,"(0.86, 0.13)" -2685,100,100,,1,1,[1],2.6,3,5,"(0.79, 0.135)" -2686,100,100,,1,1,[1],2.6,3,6,"(0.75, 0.165)" -2687,100,100,,1,1,[1],2.6,3,7,"(0.7, 0.2)" -2688,100,100,,1,1,[1],2.6,3,8,"(0.74, 0.14)" -2689,100,100,,1,1,[1],2.6,3,9,"(0.8, 0.16)" -2690,100,100,,1,1,[1],2.6,4,0,"(0.9, 0.05)" -2691,100,100,,1,1,[1],2.6,4,1,"(0.89, 0.055)" -2692,100,100,,1,1,[1],2.6,4,2,"(0.82, 0.09)" -2693,100,100,,1,1,[1],2.6,4,3,"(0.81, 0.115)" -2694,100,100,,1,1,[1],2.6,4,4,"(0.9, 0.05)" -2695,100,100,,1,1,[1],2.6,4,5,"(0.84, 0.08)" -2696,100,100,,1,1,[1],2.6,4,6,"(0.81, 0.105)" -2697,100,100,,1,1,[1],2.6,4,7,"(0.89, 0.065)" -2698,100,100,,1,1,[1],2.6,4,8,"(0.89, 0.055)" -2699,100,100,,1,1,[1],2.6,4,9,"(0.88, 0.09)" -2700,100,100,,1,1,[1],2.7,-5,0,"(0.94, 0.97)" -2701,100,100,,1,1,[1],2.7,-5,1,"(0.88, 0.94)" -2702,100,100,,1,1,[1],2.7,-5,2,"(0.93, 0.965)" -2703,100,100,,1,1,[1],2.7,-5,3,"(0.91, 0.955)" -2704,100,100,,1,1,[1],2.7,-5,4,"(0.88, 0.93)" -2705,100,100,,1,1,[1],2.7,-5,5,"(0.93, 0.955)" -2706,100,100,,1,1,[1],2.7,-5,6,"(0.89, 0.945)" -2707,100,100,,1,1,[1],2.7,-5,7,"(0.91, 0.955)" -2708,100,100,,1,1,[1],2.7,-5,8,"(0.96, 0.98)" -2709,100,100,,1,1,[1],2.7,-5,9,"(0.87, 0.935)" -2710,100,100,,1,1,[1],2.7,-4,0,"(0.8, 0.88)" -2711,100,100,,1,1,[1],2.7,-4,1,"(0.83, 0.895)" -2712,100,100,,1,1,[1],2.7,-4,2,"(0.86, 0.9)" -2713,100,100,,1,1,[1],2.7,-4,3,"(0.83, 0.905)" -2714,100,100,,1,1,[1],2.7,-4,4,"(0.78, 0.88)" -2715,100,100,,1,1,[1],2.7,-4,5,"(0.84, 0.91)" -2716,100,100,,1,1,[1],2.7,-4,6,"(0.87, 0.905)" -2717,100,100,,1,1,[1],2.7,-4,7,"(0.77, 0.855)" -2718,100,100,,1,1,[1],2.7,-4,8,"(0.82, 0.89)" -2719,100,100,,1,1,[1],2.7,-4,9,"(0.85, 0.905)" -2720,100,100,,1,1,[1],2.7,-3,0,"(0.87, 0.875)" -2721,100,100,,1,1,[1],2.7,-3,1,"(0.74, 0.79)" -2722,100,100,,1,1,[1],2.7,-3,2,"(0.76, 0.83)" -2723,100,100,,1,1,[1],2.7,-3,3,"(0.78, 0.86)" -2724,100,100,,1,1,[1],2.7,-3,4,"(0.77, 0.855)" -2725,100,100,,1,1,[1],2.7,-3,5,"(0.77, 0.835)" -2726,100,100,,1,1,[1],2.7,-3,6,"(0.76, 0.82)" -2727,100,100,,1,1,[1],2.7,-3,7,"(0.82, 0.85)" -2728,100,100,,1,1,[1],2.7,-3,8,"(0.76, 0.87)" -2729,100,100,,1,1,[1],2.7,-3,9,"(0.74, 0.8)" -2730,100,100,,1,1,[1],2.7,-2,0,"(0.63, 0.695)" -2731,100,100,,1,1,[1],2.7,-2,1,"(0.69, 0.735)" -2732,100,100,,1,1,[1],2.7,-2,2,"(0.59, 0.725)" -2733,100,100,,1,1,[1],2.7,-2,3,"(0.71, 0.675)" -2734,100,100,,1,1,[1],2.7,-2,4,"(0.67, 0.725)" -2735,100,100,,1,1,[1],2.7,-2,5,"(0.71, 0.765)" -2736,100,100,,1,1,[1],2.7,-2,6,"(0.54, 0.7)" -2737,100,100,,1,1,[1],2.7,-2,7,"(0.71, 0.765)" -2738,100,100,,1,1,[1],2.7,-2,8,"(0.66, 0.72)" -2739,100,100,,1,1,[1],2.7,-2,9,"(0.65, 0.785)" -2740,100,100,,1,1,[1],2.7,-1,0,"(0.64, 0.69)" -2741,100,100,,1,1,[1],2.7,-1,1,"(0.59, 0.615)" -2742,100,100,,1,1,[1],2.7,-1,2,"(0.5, 0.61)" -2743,100,100,,1,1,[1],2.7,-1,3,"(0.56, 0.59)" -2744,100,100,,1,1,[1],2.7,-1,4,"(0.6, 0.66)" -2745,100,100,,1,1,[1],2.7,-1,5,"(0.69, 0.625)" -2746,100,100,,1,1,[1],2.7,-1,6,"(0.56, 0.62)" -2747,100,100,,1,1,[1],2.7,-1,7,"(0.62, 0.66)" -2748,100,100,,1,1,[1],2.7,-1,8,"(0.57, 0.715)" -2749,100,100,,1,1,[1],2.7,-1,9,"(0.63, 0.625)" -2750,100,100,,1,1,[1],2.7,0,0,"(0.51, 0.485)" -2751,100,100,,1,1,[1],2.7,0,1,"(0.54, 0.44)" -2752,100,100,,1,1,[1],2.7,0,2,"(0.57, 0.495)" -2753,100,100,,1,1,[1],2.7,0,3,"(0.63, 0.525)" -2754,100,100,,1,1,[1],2.7,0,4,"(0.5, 0.49)" -2755,100,100,,1,1,[1],2.7,0,5,"(0.57, 0.545)" -2756,100,100,,1,1,[1],2.7,0,6,"(0.57, 0.465)" -2757,100,100,,1,1,[1],2.7,0,7,"(0.56, 0.51)" -2758,100,100,,1,1,[1],2.7,0,8,"(0.58, 0.47)" -2759,100,100,,1,1,[1],2.7,0,9,"(0.58, 0.53)" -2760,100,100,,1,1,[1],2.7,1,0,"(0.6, 0.35)" -2761,100,100,,1,1,[1],2.7,1,1,"(0.57, 0.425)" -2762,100,100,,1,1,[1],2.7,1,2,"(0.6, 0.37)" -2763,100,100,,1,1,[1],2.7,1,3,"(0.51, 0.395)" -2764,100,100,,1,1,[1],2.7,1,4,"(0.52, 0.37)" -2765,100,100,,1,1,[1],2.7,1,5,"(0.57, 0.405)" -2766,100,100,,1,1,[1],2.7,1,6,"(0.64, 0.36)" -2767,100,100,,1,1,[1],2.7,1,7,"(0.55, 0.365)" -2768,100,100,,1,1,[1],2.7,1,8,"(0.65, 0.405)" 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-3567,100,100,,1,1,[1],3.5,1,7,"(0.56, 0.36)" -3568,100,100,,1,1,[1],3.5,1,8,"(0.6, 0.38)" -3569,100,100,,1,1,[1],3.5,1,9,"(0.5, 0.38)" -3570,100,100,,1,1,[1],3.5,2,0,"(0.65, 0.265)" -3571,100,100,,1,1,[1],3.5,2,1,"(0.57, 0.275)" -3572,100,100,,1,1,[1],3.5,2,2,"(0.6, 0.28)" -3573,100,100,,1,1,[1],3.5,2,3,"(0.55, 0.335)" -3574,100,100,,1,1,[1],3.5,2,4,"(0.63, 0.275)" -3575,100,100,,1,1,[1],3.5,2,5,"(0.64, 0.28)" -3576,100,100,,1,1,[1],3.5,2,6,"(0.61, 0.305)" -3577,100,100,,1,1,[1],3.5,2,7,"(0.61, 0.335)" -3578,100,100,,1,1,[1],3.5,2,8,"(0.58, 0.32)" -3579,100,100,,1,1,[1],3.5,2,9,"(0.59, 0.305)" -3580,100,100,,1,1,[1],3.5,3,0,"(0.65, 0.225)" -3581,100,100,,1,1,[1],3.5,3,1,"(0.71, 0.215)" -3582,100,100,,1,1,[1],3.5,3,2,"(0.7, 0.23)" -3583,100,100,,1,1,[1],3.5,3,3,"(0.75, 0.195)" -3584,100,100,,1,1,[1],3.5,3,4,"(0.67, 0.235)" -3585,100,100,,1,1,[1],3.5,3,5,"(0.7, 0.2)" -3586,100,100,,1,1,[1],3.5,3,6,"(0.63, 0.235)" -3587,100,100,,1,1,[1],3.5,3,7,"(0.6, 0.26)" -3588,100,100,,1,1,[1],3.5,3,8,"(0.66, 0.21)" -3589,100,100,,1,1,[1],3.5,3,9,"(0.69, 0.205)" -3590,100,100,,1,1,[1],3.5,4,0,"(0.73, 0.145)" -3591,100,100,,1,1,[1],3.5,4,1,"(0.79, 0.145)" -3592,100,100,,1,1,[1],3.5,4,2,"(0.78, 0.15)" -3593,100,100,,1,1,[1],3.5,4,3,"(0.76, 0.13)" -3594,100,100,,1,1,[1],3.5,4,4,"(0.76, 0.15)" -3595,100,100,,1,1,[1],3.5,4,5,"(0.8, 0.12)" -3596,100,100,,1,1,[1],3.5,4,6,"(0.72, 0.19)" -3597,100,100,,1,1,[1],3.5,4,7,"(0.73, 0.145)" -3598,100,100,,1,1,[1],3.5,4,8,"(0.7, 0.17)" -3599,100,100,,1,1,[1],3.5,4,9,"(0.79, 0.135)" -3600,100,100,,1,1,[1],3.6,-5,0,"(0.79, 0.895)" -3601,100,100,,1,1,[1],3.6,-5,1,"(0.81, 0.905)" -3602,100,100,,1,1,[1],3.6,-5,2,"(0.83, 0.915)" -3603,100,100,,1,1,[1],3.6,-5,3,"(0.87, 0.915)" -3604,100,100,,1,1,[1],3.6,-5,4,"(0.83, 0.915)" -3605,100,100,,1,1,[1],3.6,-5,5,"(0.88, 0.92)" -3606,100,100,,1,1,[1],3.6,-5,6,"(0.81, 0.895)" -3607,100,100,,1,1,[1],3.6,-5,7,"(0.9, 0.94)" -3608,100,100,,1,1,[1],3.6,-5,8,"(0.77, 0.855)" -3609,100,100,,1,1,[1],3.6,-5,9,"(0.84, 0.9)" -3610,100,100,,1,1,[1],3.6,-4,0,"(0.68, 0.83)" -3611,100,100,,1,1,[1],3.6,-4,1,"(0.81, 0.875)" -3612,100,100,,1,1,[1],3.6,-4,2,"(0.72, 0.83)" -3613,100,100,,1,1,[1],3.6,-4,3,"(0.84, 0.91)" -3614,100,100,,1,1,[1],3.6,-4,4,"(0.78, 0.81)" -3615,100,100,,1,1,[1],3.6,-4,5,"(0.71, 0.815)" -3616,100,100,,1,1,[1],3.6,-4,6,"(0.83, 0.865)" -3617,100,100,,1,1,[1],3.6,-4,7,"(0.73, 0.845)" -3618,100,100,,1,1,[1],3.6,-4,8,"(0.82, 0.86)" -3619,100,100,,1,1,[1],3.6,-4,9,"(0.78, 0.85)" -3620,100,100,,1,1,[1],3.6,-3,0,"(0.65, 0.775)" -3621,100,100,,1,1,[1],3.6,-3,1,"(0.71, 0.795)" -3622,100,100,,1,1,[1],3.6,-3,2,"(0.72, 0.79)" -3623,100,100,,1,1,[1],3.6,-3,3,"(0.75, 0.805)" -3624,100,100,,1,1,[1],3.6,-3,4,"(0.67, 0.785)" -3625,100,100,,1,1,[1],3.6,-3,5,"(0.7, 0.77)" -3626,100,100,,1,1,[1],3.6,-3,6,"(0.7, 0.81)" -3627,100,100,,1,1,[1],3.6,-3,7,"(0.76, 0.82)" -3628,100,100,,1,1,[1],3.6,-3,8,"(0.62, 0.78)" -3629,100,100,,1,1,[1],3.6,-3,9,"(0.75, 0.805)" -3630,100,100,,1,1,[1],3.6,-2,0,"(0.51, 0.695)" -3631,100,100,,1,1,[1],3.6,-2,1,"(0.68, 0.7)" -3632,100,100,,1,1,[1],3.6,-2,2,"(0.6, 0.64)" -3633,100,100,,1,1,[1],3.6,-2,3,"(0.62, 0.69)" -3634,100,100,,1,1,[1],3.6,-2,4,"(0.64, 0.76)" -3635,100,100,,1,1,[1],3.6,-2,5,"(0.54, 0.68)" -3636,100,100,,1,1,[1],3.6,-2,6,"(0.6, 0.72)" -3637,100,100,,1,1,[1],3.6,-2,7,"(0.67, 0.735)" -3638,100,100,,1,1,[1],3.6,-2,8,"(0.62, 0.71)" -3639,100,100,,1,1,[1],3.6,-2,9,"(0.63, 0.725)" -3640,100,100,,1,1,[1],3.6,-1,0,"(0.57, 0.595)" -3641,100,100,,1,1,[1],3.6,-1,1,"(0.62, 0.58)" -3642,100,100,,1,1,[1],3.6,-1,2,"(0.59, 0.645)" -3643,100,100,,1,1,[1],3.6,-1,3,"(0.57, 0.595)" -3644,100,100,,1,1,[1],3.6,-1,4,"(0.58, 0.57)" -3645,100,100,,1,1,[1],3.6,-1,5,"(0.54, 0.66)" -3646,100,100,,1,1,[1],3.6,-1,6,"(0.5, 0.64)" -3647,100,100,,1,1,[1],3.6,-1,7,"(0.49, 0.595)" -3648,100,100,,1,1,[1],3.6,-1,8,"(0.5, 0.59)" -3649,100,100,,1,1,[1],3.6,-1,9,"(0.6, 0.59)" -3650,100,100,,1,1,[1],3.6,0,0,"(0.53, 0.545)" -3651,100,100,,1,1,[1],3.6,0,1,"(0.52, 0.54)" -3652,100,100,,1,1,[1],3.6,0,2,"(0.5, 0.5)" -3653,100,100,,1,1,[1],3.6,0,3,"(0.57, 0.485)" -3654,100,100,,1,1,[1],3.6,0,4,"(0.61, 0.505)" -3655,100,100,,1,1,[1],3.6,0,5,"(0.57, 0.445)" -3656,100,100,,1,1,[1],3.6,0,6,"(0.53, 0.435)" -3657,100,100,,1,1,[1],3.6,0,7,"(0.58, 0.52)" -3658,100,100,,1,1,[1],3.6,0,8,"(0.58, 0.45)" -3659,100,100,,1,1,[1],3.6,0,9,"(0.55, 0.515)" -3660,100,100,,1,1,[1],3.6,1,0,"(0.5, 0.44)" -3661,100,100,,1,1,[1],3.6,1,1,"(0.58, 0.35)" -3662,100,100,,1,1,[1],3.6,1,2,"(0.51, 0.405)" -3663,100,100,,1,1,[1],3.6,1,3,"(0.62, 0.39)" -3664,100,100,,1,1,[1],3.6,1,4,"(0.59, 0.345)" -3665,100,100,,1,1,[1],3.6,1,5,"(0.57, 0.355)" -3666,100,100,,1,1,[1],3.6,1,6,"(0.61, 0.395)" -3667,100,100,,1,1,[1],3.6,1,7,"(0.64, 0.29)" -3668,100,100,,1,1,[1],3.6,1,8,"(0.52, 0.42)" -3669,100,100,,1,1,[1],3.6,1,9,"(0.51, 0.385)" -3670,100,100,,1,1,[1],3.6,2,0,"(0.62, 0.31)" -3671,100,100,,1,1,[1],3.6,2,1,"(0.59, 0.315)" -3672,100,100,,1,1,[1],3.6,2,2,"(0.68, 0.27)" -3673,100,100,,1,1,[1],3.6,2,3,"(0.66, 0.24)" -3674,100,100,,1,1,[1],3.6,2,4,"(0.57, 0.345)" -3675,100,100,,1,1,[1],3.6,2,5,"(0.71, 0.285)" -3676,100,100,,1,1,[1],3.6,2,6,"(0.58, 0.34)" -3677,100,100,,1,1,[1],3.6,2,7,"(0.72, 0.31)" -3678,100,100,,1,1,[1],3.6,2,8,"(0.6, 0.36)" -3679,100,100,,1,1,[1],3.6,2,9,"(0.61, 0.345)" -3680,100,100,,1,1,[1],3.6,3,0,"(0.7, 0.21)" -3681,100,100,,1,1,[1],3.6,3,1,"(0.66, 0.22)" -3682,100,100,,1,1,[1],3.6,3,2,"(0.74, 0.17)" -3683,100,100,,1,1,[1],3.6,3,3,"(0.69, 0.185)" -3684,100,100,,1,1,[1],3.6,3,4,"(0.7, 0.21)" -3685,100,100,,1,1,[1],3.6,3,5,"(0.64, 0.25)" -3686,100,100,,1,1,[1],3.6,3,6,"(0.7, 0.18)" -3687,100,100,,1,1,[1],3.6,3,7,"(0.69, 0.235)" -3688,100,100,,1,1,[1],3.6,3,8,"(0.75, 0.225)" -3689,100,100,,1,1,[1],3.6,3,9,"(0.77, 0.175)" -3690,100,100,,1,1,[1],3.6,4,0,"(0.79, 0.115)" -3691,100,100,,1,1,[1],3.6,4,1,"(0.75, 0.155)" -3692,100,100,,1,1,[1],3.6,4,2,"(0.85, 0.115)" -3693,100,100,,1,1,[1],3.6,4,3,"(0.74, 0.18)" -3694,100,100,,1,1,[1],3.6,4,4,"(0.76, 0.17)" -3695,100,100,,1,1,[1],3.6,4,5,"(0.7, 0.18)" -3696,100,100,,1,1,[1],3.6,4,6,"(0.78, 0.14)" -3697,100,100,,1,1,[1],3.6,4,7,"(0.83, 0.135)" -3698,100,100,,1,1,[1],3.6,4,8,"(0.8, 0.12)" -3699,100,100,,1,1,[1],3.6,4,9,"(0.83, 0.105)" -3700,100,100,,1,1,[1],3.7,-5,0,"(0.85, 0.895)" -3701,100,100,,1,1,[1],3.7,-5,1,"(0.85, 0.915)" -3702,100,100,,1,1,[1],3.7,-5,2,"(0.84, 0.87)" -3703,100,100,,1,1,[1],3.7,-5,3,"(0.8, 0.88)" -3704,100,100,,1,1,[1],3.7,-5,4,"(0.79, 0.875)" -3705,100,100,,1,1,[1],3.7,-5,5,"(0.82, 0.89)" -3706,100,100,,1,1,[1],3.7,-5,6,"(0.78, 0.89)" -3707,100,100,,1,1,[1],3.7,-5,7,"(0.84, 0.89)" -3708,100,100,,1,1,[1],3.7,-5,8,"(0.89, 0.945)" -3709,100,100,,1,1,[1],3.7,-5,9,"(0.75, 0.865)" -3710,100,100,,1,1,[1],3.7,-4,0,"(0.69, 0.805)" -3711,100,100,,1,1,[1],3.7,-4,1,"(0.73, 0.855)" -3712,100,100,,1,1,[1],3.7,-4,2,"(0.69, 0.815)" -3713,100,100,,1,1,[1],3.7,-4,3,"(0.78, 0.84)" -3714,100,100,,1,1,[1],3.7,-4,4,"(0.8, 0.86)" -3715,100,100,,1,1,[1],3.7,-4,5,"(0.76, 0.84)" -3716,100,100,,1,1,[1],3.7,-4,6,"(0.8, 0.88)" -3717,100,100,,1,1,[1],3.7,-4,7,"(0.78, 0.87)" -3718,100,100,,1,1,[1],3.7,-4,8,"(0.79, 0.875)" -3719,100,100,,1,1,[1],3.7,-4,9,"(0.79, 0.845)" -3720,100,100,,1,1,[1],3.7,-3,0,"(0.65, 0.755)" -3721,100,100,,1,1,[1],3.7,-3,1,"(0.71, 0.785)" -3722,100,100,,1,1,[1],3.7,-3,2,"(0.74, 0.82)" -3723,100,100,,1,1,[1],3.7,-3,3,"(0.61, 0.735)" -3724,100,100,,1,1,[1],3.7,-3,4,"(0.71, 0.785)" -3725,100,100,,1,1,[1],3.7,-3,5,"(0.71, 0.815)" -3726,100,100,,1,1,[1],3.7,-3,6,"(0.66, 0.81)" -3727,100,100,,1,1,[1],3.7,-3,7,"(0.63, 0.735)" -3728,100,100,,1,1,[1],3.7,-3,8,"(0.65, 0.775)" -3729,100,100,,1,1,[1],3.7,-3,9,"(0.73, 0.745)" -3730,100,100,,1,1,[1],3.7,-2,0,"(0.61, 0.695)" -3731,100,100,,1,1,[1],3.7,-2,1,"(0.58, 0.63)" -3732,100,100,,1,1,[1],3.7,-2,2,"(0.53, 0.665)" -3733,100,100,,1,1,[1],3.7,-2,3,"(0.67, 0.715)" -3734,100,100,,1,1,[1],3.7,-2,4,"(0.49, 0.665)" -3735,100,100,,1,1,[1],3.7,-2,5,"(0.61, 0.685)" -3736,100,100,,1,1,[1],3.7,-2,6,"(0.51, 0.685)" -3737,100,100,,1,1,[1],3.7,-2,7,"(0.46, 0.66)" -3738,100,100,,1,1,[1],3.7,-2,8,"(0.67, 0.745)" -3739,100,100,,1,1,[1],3.7,-2,9,"(0.59, 0.705)" -3740,100,100,,1,1,[1],3.7,-1,0,"(0.58, 0.57)" -3741,100,100,,1,1,[1],3.7,-1,1,"(0.61, 0.675)" -3742,100,100,,1,1,[1],3.7,-1,2,"(0.44, 0.6)" -3743,100,100,,1,1,[1],3.7,-1,3,"(0.62, 0.6)" -3744,100,100,,1,1,[1],3.7,-1,4,"(0.49, 0.595)" -3745,100,100,,1,1,[1],3.7,-1,5,"(0.44, 0.59)" -3746,100,100,,1,1,[1],3.7,-1,6,"(0.55, 0.605)" -3747,100,100,,1,1,[1],3.7,-1,7,"(0.46, 0.59)" -3748,100,100,,1,1,[1],3.7,-1,8,"(0.6, 0.67)" -3749,100,100,,1,1,[1],3.7,-1,9,"(0.46, 0.55)" -3750,100,100,,1,1,[1],3.7,0,0,"(0.6, 0.45)" -3751,100,100,,1,1,[1],3.7,0,1,"(0.54, 0.51)" -3752,100,100,,1,1,[1],3.7,0,2,"(0.51, 0.415)" -3753,100,100,,1,1,[1],3.7,0,3,"(0.54, 0.52)" -3754,100,100,,1,1,[1],3.7,0,4,"(0.57, 0.525)" -3755,100,100,,1,1,[1],3.7,0,5,"(0.49, 0.565)" -3756,100,100,,1,1,[1],3.7,0,6,"(0.55, 0.455)" -3757,100,100,,1,1,[1],3.7,0,7,"(0.53, 0.485)" -3758,100,100,,1,1,[1],3.7,0,8,"(0.59, 0.395)" -3759,100,100,,1,1,[1],3.7,0,9,"(0.51, 0.465)" -3760,100,100,,1,1,[1],3.7,1,0,"(0.61, 0.375)" -3761,100,100,,1,1,[1],3.7,1,1,"(0.55, 0.415)" -3762,100,100,,1,1,[1],3.7,1,2,"(0.54, 0.39)" -3763,100,100,,1,1,[1],3.7,1,3,"(0.59, 0.455)" -3764,100,100,,1,1,[1],3.7,1,4,"(0.58, 0.42)" -3765,100,100,,1,1,[1],3.7,1,5,"(0.54, 0.39)" -3766,100,100,,1,1,[1],3.7,1,6,"(0.55, 0.415)" -3767,100,100,,1,1,[1],3.7,1,7,"(0.58, 0.44)" -3768,100,100,,1,1,[1],3.7,1,8,"(0.6, 0.41)" -3769,100,100,,1,1,[1],3.7,1,9,"(0.51, 0.405)" -3770,100,100,,1,1,[1],3.7,2,0,"(0.59, 0.285)" -3771,100,100,,1,1,[1],3.7,2,1,"(0.51, 0.315)" -3772,100,100,,1,1,[1],3.7,2,2,"(0.61, 0.315)" -3773,100,100,,1,1,[1],3.7,2,3,"(0.54, 0.29)" -3774,100,100,,1,1,[1],3.7,2,4,"(0.71, 0.245)" -3775,100,100,,1,1,[1],3.7,2,5,"(0.61, 0.245)" -3776,100,100,,1,1,[1],3.7,2,6,"(0.66, 0.35)" -3777,100,100,,1,1,[1],3.7,2,7,"(0.54, 0.34)" -3778,100,100,,1,1,[1],3.7,2,8,"(0.57, 0.315)" -3779,100,100,,1,1,[1],3.7,2,9,"(0.62, 0.32)" -3780,100,100,,1,1,[1],3.7,3,0,"(0.64, 0.28)" -3781,100,100,,1,1,[1],3.7,3,1,"(0.69, 0.255)" -3782,100,100,,1,1,[1],3.7,3,2,"(0.65, 0.255)" -3783,100,100,,1,1,[1],3.7,3,3,"(0.66, 0.26)" -3784,100,100,,1,1,[1],3.7,3,4,"(0.65, 0.225)" -3785,100,100,,1,1,[1],3.7,3,5,"(0.72, 0.19)" -3786,100,100,,1,1,[1],3.7,3,6,"(0.73, 0.215)" -3787,100,100,,1,1,[1],3.7,3,7,"(0.67, 0.215)" -3788,100,100,,1,1,[1],3.7,3,8,"(0.7, 0.21)" -3789,100,100,,1,1,[1],3.7,3,9,"(0.75, 0.235)" -3790,100,100,,1,1,[1],3.7,4,0,"(0.75, 0.145)" -3791,100,100,,1,1,[1],3.7,4,1,"(0.73, 0.185)" -3792,100,100,,1,1,[1],3.7,4,2,"(0.75, 0.145)" -3793,100,100,,1,1,[1],3.7,4,3,"(0.77, 0.145)" -3794,100,100,,1,1,[1],3.7,4,4,"(0.7, 0.18)" -3795,100,100,,1,1,[1],3.7,4,5,"(0.73, 0.145)" -3796,100,100,,1,1,[1],3.7,4,6,"(0.78, 0.16)" -3797,100,100,,1,1,[1],3.7,4,7,"(0.81, 0.115)" 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0.76)" -3926,100,100,,1,1,[1],3.9000000000000004,-3,6,"(0.69, 0.745)" -3927,100,100,,1,1,[1],3.9000000000000004,-3,7,"(0.64, 0.79)" -3928,100,100,,1,1,[1],3.9000000000000004,-3,8,"(0.72, 0.76)" -3929,100,100,,1,1,[1],3.9000000000000004,-3,9,"(0.71, 0.785)" -3930,100,100,,1,1,[1],3.9000000000000004,-2,0,"(0.59, 0.725)" -3931,100,100,,1,1,[1],3.9000000000000004,-2,1,"(0.61, 0.645)" -3932,100,100,,1,1,[1],3.9000000000000004,-2,2,"(0.61, 0.655)" -3933,100,100,,1,1,[1],3.9000000000000004,-2,3,"(0.57, 0.715)" -3934,100,100,,1,1,[1],3.9000000000000004,-2,4,"(0.6, 0.73)" -3935,100,100,,1,1,[1],3.9000000000000004,-2,5,"(0.63, 0.685)" -3936,100,100,,1,1,[1],3.9000000000000004,-2,6,"(0.64, 0.72)" -3937,100,100,,1,1,[1],3.9000000000000004,-2,7,"(0.67, 0.745)" -3938,100,100,,1,1,[1],3.9000000000000004,-2,8,"(0.56, 0.68)" -3939,100,100,,1,1,[1],3.9000000000000004,-2,9,"(0.59, 0.715)" -3940,100,100,,1,1,[1],3.9000000000000004,-1,0,"(0.6, 0.56)" -3941,100,100,,1,1,[1],3.9000000000000004,-1,1,"(0.53, 0.615)" -3942,100,100,,1,1,[1],3.9000000000000004,-1,2,"(0.58, 0.62)" -3943,100,100,,1,1,[1],3.9000000000000004,-1,3,"(0.55, 0.605)" -3944,100,100,,1,1,[1],3.9000000000000004,-1,4,"(0.54, 0.59)" -3945,100,100,,1,1,[1],3.9000000000000004,-1,5,"(0.54, 0.61)" -3946,100,100,,1,1,[1],3.9000000000000004,-1,6,"(0.53, 0.585)" -3947,100,100,,1,1,[1],3.9000000000000004,-1,7,"(0.55, 0.635)" -3948,100,100,,1,1,[1],3.9000000000000004,-1,8,"(0.52, 0.58)" -3949,100,100,,1,1,[1],3.9000000000000004,-1,9,"(0.51, 0.575)" -3950,100,100,,1,1,[1],3.9000000000000004,0,0,"(0.58, 0.49)" -3951,100,100,,1,1,[1],3.9000000000000004,0,1,"(0.51, 0.565)" -3952,100,100,,1,1,[1],3.9000000000000004,0,2,"(0.56, 0.53)" -3953,100,100,,1,1,[1],3.9000000000000004,0,3,"(0.56, 0.44)" -3954,100,100,,1,1,[1],3.9000000000000004,0,4,"(0.54, 0.47)" -3955,100,100,,1,1,[1],3.9000000000000004,0,5,"(0.45, 0.495)" -3956,100,100,,1,1,[1],3.9000000000000004,0,6,"(0.51, 0.445)" -3957,100,100,,1,1,[1],3.9000000000000004,0,7,"(0.52, 0.49)" 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+5097,100,100,,1,1,[1],5.0,4.6999999999999655,0,0.8,0.16 +5098,100,100,,1,1,[1],5.0,4.799999999999965,0,0.73,0.175 +5099,100,100,,1,1,[1],5.0,4.899999999999965,0,0.71,0.165 diff --git a/python/new_sim.ipynb b/python/new_sim.ipynb index 9ee7e2a..9f9f808 100644 --- a/python/new_sim.ipynb +++ b/python/new_sim.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e99fe799", + "id": "2dd1ed60", "metadata": {}, "source": [ "# Simulation Experiment" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "25ef725a", + "id": "58a4c6ba", "metadata": { "ExecuteTime": { "end_time": "2024-01-18T13:37:38.559326Z", @@ -38,7 +38,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "12b04338", + "id": "25579328", "metadata": { "ExecuteTime": { "end_time": "2024-01-18T13:37:49.595623Z", @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "b2a8e69b", + "id": "fd7b1e24", "metadata": {}, "source": [ "## generate trials and responses" @@ -102,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "93deb009", + "id": "e2254d3c", "metadata": {}, "source": [ "### with IN" @@ -111,7 +111,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "c63479f5", + "id": "56ec6410", "metadata": { "ExecuteTime": { "end_time": "2024-01-09T11:51:05.326379Z", @@ -407,7 +407,7 @@ }, { "cell_type": "markdown", - "id": "95614290", + "id": "299bc835", "metadata": {}, "source": [ "### without IN" @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "600f94be", + "id": "eb167922", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T13:48:42.038219Z", @@ -734,7 +734,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "d7e6f0ef", + "id": "54b03eab", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T13:56:41.339132Z", @@ -802,7 +802,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "df82ef23", + "id": "0fe51e96", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T14:13:00.797724Z", @@ -1058,7 +1058,7 @@ }, { "cell_type": "markdown", - "id": "dcd35e0e", + "id": "c41676a5", "metadata": {}, "source": [ "## CI kernel computaion with palin" @@ -1067,7 +1067,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "a1c2acec", + "id": "bac4b454", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T10:48:37.990321Z", @@ -1191,7 +1191,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "0d581ad6", + "id": "84292476", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T10:50:16.700639Z", @@ -1388,7 +1388,7 @@ }, { "cell_type": "markdown", - "id": "e669651c", + "id": "226365e2", "metadata": {}, "source": [ "## Simulate participant w/ CI kernel and IN 0 (graphs)" @@ -1397,7 +1397,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "c60393c8", + "id": "388a033c", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T13:33:09.670157Z", @@ -1507,7 +1507,7 @@ }, { "cell_type": "markdown", - "id": "7a218f2c", + "id": "2b1d7155", "metadata": {}, "source": [ "## Graphs " @@ -1515,7 +1515,7 @@ }, { "cell_type": "markdown", - "id": "b0045be8", + "id": "8fe92b13", "metadata": {}, "source": [ "### random kernel visualization" @@ -1524,7 +1524,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "da3bfa43", + "id": "47d88d7c", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T14:00:48.775953Z", @@ -1620,7 +1620,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "2a59b2a0", + "id": "2f72fecd", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T13:33:34.410827Z", @@ -1650,7 +1650,7 @@ }, { "cell_type": "markdown", - "id": "8e391534", + "id": "90d8c41c", "metadata": {}, "source": [ "### Kernel value Evolution" @@ -1659,7 +1659,7 @@ { "cell_type": "code", "execution_count": 95, - "id": "59a5af6e", + "id": "7d4ab019", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T13:34:39.290061Z", @@ -1847,7 +1847,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "83d9d01a", + "id": "255f7d5d", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T14:00:29.686409Z", @@ -2030,7 +2030,7 @@ { "cell_type": "code", "execution_count": 97, - "id": "12951875", + "id": "98d79a54", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T13:35:06.438482Z", @@ -2226,7 +2226,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "eead7e97", + "id": "41919892", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T12:56:27.559484Z", @@ -2407,7 +2407,7 @@ }, { "cell_type": "markdown", - "id": "1160fd60", + "id": "3e18dd39", "metadata": {}, "source": [ "### correlation with participant random kernel" @@ -2416,7 +2416,7 @@ { "cell_type": "code", "execution_count": 102, - "id": "3d440806", + "id": "7ceff7bd", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T14:04:33.150155Z", @@ -2492,7 +2492,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "c5ff4a70", + "id": "c954b35d", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T13:55:15.633617Z", @@ -2530,7 +2530,7 @@ { "cell_type": "code", "execution_count": 70, - "id": "ad3c31b7", + "id": "da9f630f", "metadata": { "ExecuteTime": { "end_time": "2024-01-11T12:34:32.110896Z", @@ -2579,7 +2579,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "b7cf4832", + "id": "5b134ada", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T14:38:54.912560Z", @@ -2618,7 +2618,7 @@ }, { "cell_type": "markdown", - "id": "2c09a8f3", + "id": "e935609b", "metadata": {}, "source": [ "## different values of IN and Criteria" @@ -2627,7 +2627,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "7a46844b", + "id": "3f12ad2b", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T10:46:43.760310Z", @@ -2696,7 +2696,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29009e30", + "id": "c4cbf32d", "metadata": {}, "outputs": [], "source": [ @@ -2759,7 +2759,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "5e9a3d7c", + "id": "a1713c77", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T10:46:51.758548Z", @@ -2991,7 +2991,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "6e4fa100", + "id": "17cf5ec9", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T10:47:14.696339Z", @@ -3189,7 +3189,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "14cfea33", + "id": "5f8eea4b", "metadata": { "ExecuteTime": { "end_time": "2024-01-10T11:13:25.877519Z", @@ -3267,7 +3267,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "a95aa8e0", + "id": "0e0922c0", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T14:58:54.514751Z", @@ -3285,7 +3285,7 @@ { "cell_type": "code", "execution_count": 147, - "id": "ed8bf8d5", + "id": "8e715ca8", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:04:45.627539Z", @@ -3472,7 +3472,7 @@ { "cell_type": "code", "execution_count": 148, - "id": "8cbba7ef", + "id": "0657ae75", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:04:57.636887Z", @@ -3511,7 +3511,7 @@ { "cell_type": "code", "execution_count": 149, - "id": "86bc796d", + "id": "7de45c91", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:05:03.546637Z", @@ -3689,7 +3689,7 @@ { "cell_type": "code", "execution_count": 178, - "id": "55f1c3a0", + "id": "138348de", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:29:43.229712Z", @@ -3776,7 +3776,7 @@ { "cell_type": "code", "execution_count": 137, - "id": "dbd37577", + "id": "60504c48", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T15:36:50.667399Z", @@ -3794,7 +3794,7 @@ { "cell_type": "code", "execution_count": 138, - "id": "82fe1272", + "id": "7bbbdb44", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T15:36:54.065249Z", @@ -3820,7 +3820,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "2d995dc4", + "id": "3d24da73", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:16:30.123735Z", @@ -4061,7 +4061,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95f1f085", + "id": "39cdd026", "metadata": {}, "outputs": [], "source": [ @@ -4143,7 +4143,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "3ac37b58", + "id": "7e69bb8a", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:19:29.722626Z", @@ -4432,7 +4432,7 @@ { "cell_type": "code", "execution_count": 59, - "id": "30fa36bf", + "id": "19e5f4c0", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T09:57:26.120796Z", @@ -4457,7 +4457,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "17efce54", + "id": "8f224379", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T09:57:26.951351Z", @@ -4486,7 +4486,7 @@ { "cell_type": "code", "execution_count": 88, - "id": "da7d3dac", + "id": "3092d4fa", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:20:56.141439Z", @@ -4552,7 +4552,7 @@ { "cell_type": "code", "execution_count": null, - "id": "235143ae", + "id": "6b098b91", "metadata": {}, "outputs": [], "source": [ @@ -4684,7 +4684,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "44e7690d", + "id": "2d699e4f", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T09:57:40.477337Z", @@ -4817,7 +4817,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "1fda96e6", + "id": "caa4272f", "metadata": { "ExecuteTime": { "end_time": "2023-12-21T21:31:26.672299Z", @@ -4998,7 +4998,7 @@ { "cell_type": "code", "execution_count": 186, - "id": "a38e4822", + "id": "4f78c81d", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:32:49.165346Z", @@ -5036,7 +5036,7 @@ { "cell_type": "code", "execution_count": 187, - "id": "a3b326aa", + "id": "e5afe516", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T16:32:49.908428Z", @@ -5244,7 +5244,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "5a09b1c4", + "id": "99afc16c", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:21:19.872108Z", @@ -5420,7 +5420,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "bdce1492", + "id": "fd7ed80e", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T09:57:51.619598Z", @@ -5579,7 +5579,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fd8f2de7", + "id": "7308b3fa", "metadata": {}, "outputs": [], "source": [ @@ -5638,7 +5638,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9768764e", + "id": "c1a8f7af", "metadata": {}, "outputs": [], "source": [ @@ -5648,7 +5648,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7f71bf08", + "id": "8da80e92", "metadata": {}, "outputs": [], "source": [ @@ -5665,7 +5665,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a31c7ec0", + "id": "578871f8", "metadata": {}, "outputs": [], "source": [ @@ -5721,7 +5721,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "4513b1da", + "id": "a0926d68", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:21:52.587637Z", @@ -5830,7 +5830,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "5f1dc0ae", + "id": "ae56dee6", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:09:46.311424Z", @@ -5911,7 +5911,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "c727988c", + "id": "4f504fb9", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:23:53.589811Z", @@ -5999,7 +5999,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "a3a9af36", + "id": "b9c20834", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:22:00.580704Z", @@ -6028,7 +6028,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "cecf7185", + "id": "6ee4dec9", "metadata": { "ExecuteTime": { "end_time": "2023-12-22T10:21:32.745907Z", @@ -6060,7 +6060,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ec589710", + "id": "6c1bd6ae", "metadata": {}, "outputs": [], "source": [] diff --git a/python/palin/internal_noise/double_pass.py b/python/palin/internal_noise/double_pass.py index 6fdec7a..acfa0c1 100644 --- a/python/palin/internal_noise/double_pass.py +++ b/python/palin/internal_noise/double_pass.py @@ -18,7 +18,7 @@ from ..simulation.double_pass_experiment import DoublePassExperiment from ..simulation.trial import Int2Trial, Int1Trial from ..simulation.linear_observer import LinearObserver -from ..simulation.double_pass_statistics import DoublePassStatistics +from ..simulation import double_pass_statistics as dps from ..simulation.simulation import Simulation as Sim @@ -149,12 +149,12 @@ def build_model(cls,internal_noise_range=np.arange(0,5,.1),criteria_range=np.ara sim = Sim(DoublePassExperiment, experiment_params, LinearObserver, observer_params, - DoublePassStatistics, analyser_params) + dps.DoublePassStatistics, analyser_params) sim_df = sim.run_all(n_runs=n_runs, verbose=True) # average measures over all runs - sim_df.groupby(['internal_noise_std','criteria'])[DoublePassStatistics.get_metric_names()].mean() + sim_df.groupby(['internal_noise_std','criteria'])[dps.DoublePassStatistics.get_metric_names()].mean() return sim_df \ No newline at end of file diff --git a/python/palin/internal_noise/internal_noise_extractor.py b/python/palin/internal_noise/internal_noise_extractor.py index be6fbb6..1365dec 100644 --- a/python/palin/internal_noise/internal_noise_extractor.py +++ b/python/palin/internal_noise/internal_noise_extractor.py @@ -18,10 +18,10 @@ def extract_single_internal_noise(cls,data_df, trial_id = 'trial_id', feature_id raise NotImplementedError() @classmethod - def extract_internal_noise(cls,data_df, group_ids, trial_id, feature_id, value_id, response_id, normalize = True): + def extract_internal_noise(cls,data_df, group_ids, trial_id, feature_id, value_id, response_id, model_file): # for each level in group, extract internal_noise return data_df.groupby(group_ids).apply(lambda group: cls.extract_single_internal_noise(group, - trial_id, feature_id, value_id, response_id)).reset_index() + trial_id, feature_id, value_id, response_id, model_file)).reset_index() \ No newline at end of file diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc index 1db8caf..701fe27 100644 Binary files a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc and b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc differ diff --git a/python/palin/simulation/kernel_distance.py b/python/palin/simulation/kernel_distance.py index 6140879..87734f9 100644 --- a/python/palin/simulation/kernel_distance.py +++ b/python/palin/simulation/kernel_distance.py @@ -9,7 +9,7 @@ def __init__(self, kernel_extractor, distance='CORR'): self.distance = distance def get_metric_names(self): - return [distance.lower()] + return [self.distance.lower()] def analyse(self, experiment, participant, participant_responses): @@ -36,7 +36,7 @@ def normalize_kernel(self, kernel): return self.kernel_extractor.normalize_kernel(kernel) def compute_distance(self, kernel_1, kernel_2): - return me.kernel_distance(kernel_1, kernel_2, type=self.distance) + return [me.kernel_distance(kernel_1, kernel_2, type=self.distance)] \ No newline at end of file diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py index d0cb543..2a39dc4 100644 --- a/python/palin/simulation/simulation.py +++ b/python/palin/simulation/simulation.py @@ -115,7 +115,7 @@ def run(self, config_param): metrics = ana.get_metric_names() values = ana.analyse(exp, obs, responses) - + # return the metrics as a dict of name:value pairs results = {} for metric,value in zip(metrics,values): diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb index 40c5f81..fc307f4 100644 --- a/python/sandbox.ipynb +++ b/python/sandbox.ipynb @@ -2,24 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 51, - "id": "87dd4fb9", + "execution_count": 1, + "id": "dd024a9f", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T12:37:25.098785Z", - "start_time": "2024-04-10T12:37:25.041938Z" + "end_time": "2024-04-22T14:17:28.832662Z", + "start_time": "2024-04-22T14:17:28.792726Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -27,12 +18,12 @@ }, { "cell_type": "code", - "execution_count": 179, - "id": "ab35c09e", + "execution_count": 2, + "id": "af003d60", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T04:19:40.595606Z", - "start_time": "2024-04-11T04:19:40.530803Z" + "end_time": "2024-04-22T14:17:39.480077Z", + "start_time": "2024-04-22T14:17:38.354348Z" } }, "outputs": [], @@ -45,12 +36,12 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "6a76823c", + "execution_count": 3, + "id": "c00cdd7b", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T12:37:27.663173Z", - "start_time": "2024-04-10T12:37:27.618292Z" + "end_time": "2024-04-22T14:17:42.091684Z", + "start_time": "2024-04-22T14:17:42.039629Z" } }, "outputs": [], @@ -60,12 +51,12 @@ }, { "cell_type": "code", - "execution_count": 208, - "id": "29b09a91", + "execution_count": 6, + "id": "1d4cca24", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T11:24:47.119855Z", - "start_time": "2024-04-11T11:24:47.063798Z" + "end_time": "2024-04-22T14:56:26.656143Z", + "start_time": "2024-04-22T14:56:26.596523Z" } }, "outputs": [], @@ -85,7 +76,7 @@ }, { "cell_type": "markdown", - "id": "31bb6aee", + "id": "a54e142f", "metadata": {}, "source": [ "## Simulate with internal noise" @@ -93,7 +84,7 @@ }, { "cell_type": "markdown", - "id": "cd63729b", + "id": "0296d613", "metadata": {}, "source": [ "Single run" @@ -101,34 +92,34 @@ }, { "cell_type": "code", - "execution_count": 189, - "id": "63e53f77", + "execution_count": 20, + "id": "9802f955", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T04:53:29.899499Z", - "start_time": "2024-04-11T04:53:29.371264Z" + "end_time": "2024-04-22T14:59:47.009015Z", + "start_time": "2024-04-22T14:59:46.740731Z" } }, "outputs": [ { "data": { "text/plain": [ - "(0.1, 1)" + "(2.1, 1.7999999999999758)" ] }, - "execution_count": 189, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# single run: \n", - "exp = DoublePassExperiment(n_trials = 100, n_repeated=50,\n", + "exp = DoublePassExperiment(n_trials = 1000, n_repeated=50,\n", " trial_type = Int2Trial, \n", " n_features = 5, \n", " external_noise_std = 100)\n", - "obs = Obs.with_random_kernel(n_features = exp.n_features, \n", - " internal_noise_std = 0, \n", + "obs = LinearObserver.with_random_kernel(n_features = exp.n_features, \n", + " internal_noise_std = 1, \n", " criteria = 1)\n", "responses = obs.respond_to_experiment(exp)\n", "ana = InternalNoiseValue(internal_noise_extractor = DoublePass, model_file='model.csv')\n", @@ -137,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "d90d2366", + "id": "01c2f4d3", "metadata": {}, "source": [ "Simulation" @@ -145,12 +136,12 @@ }, { "cell_type": "code", - "execution_count": 200, - "id": "fc511049", + "execution_count": 26, + "id": "b0d2e495", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T06:57:34.621971Z", - "start_time": "2024-04-11T06:00:58.596353Z" + "end_time": "2024-04-22T15:10:31.367464Z", + "start_time": "2024-04-22T15:10:01.475677Z" }, "scrolled": false }, @@ -159,138 +150,79 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running 1 configs\n", - "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model_large.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n", - " 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n", - " 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n", - " 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]), 'criteria_range': array([-5.00000000e+00, -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n", - " -4.60000000e+00, -4.50000000e+00, -4.40000000e+00, -4.30000000e+00,\n", - " -4.20000000e+00, -4.10000000e+00, -4.00000000e+00, -3.90000000e+00,\n", - " -3.80000000e+00, -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n", - " -3.40000000e+00, -3.30000000e+00, -3.20000000e+00, -3.10000000e+00,\n", - " -3.00000000e+00, -2.90000000e+00, -2.80000000e+00, -2.70000000e+00,\n", - " -2.60000000e+00, -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n", - " -2.20000000e+00, -2.10000000e+00, -2.00000000e+00, -1.90000000e+00,\n", - " -1.80000000e+00, -1.70000000e+00, -1.60000000e+00, -1.50000000e+00,\n", - " -1.40000000e+00, -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n", - " -1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n", - " -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n", - " -2.00000000e-01, -1.00000000e-01, -1.77635684e-14, 1.00000000e-01,\n", - " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01, 5.00000000e-01,\n", - " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01,\n", - " 1.00000000e+00, 1.10000000e+00, 1.20000000e+00, 1.30000000e+00,\n", - " 1.40000000e+00, 1.50000000e+00, 1.60000000e+00, 1.70000000e+00,\n", - " 1.80000000e+00, 1.90000000e+00, 2.00000000e+00, 2.10000000e+00,\n", - " 2.20000000e+00, 2.30000000e+00, 2.40000000e+00, 2.50000000e+00,\n", - " 2.60000000e+00, 2.70000000e+00, 2.80000000e+00, 2.90000000e+00,\n", - " 3.00000000e+00, 3.10000000e+00, 3.20000000e+00, 3.30000000e+00,\n", - " 3.40000000e+00, 3.50000000e+00, 3.60000000e+00, 3.70000000e+00,\n", - " 3.80000000e+00, 3.90000000e+00, 4.00000000e+00, 4.10000000e+00,\n", - " 4.20000000e+00, 4.30000000e+00, 4.40000000e+00, 4.50000000e+00,\n", - " 4.60000000e+00, 4.70000000e+00, 4.80000000e+00, 4.90000000e+00]), 'n_runs': 2}\n", - ".Rebuilding double-pass model\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[200], line 19\u001b[0m\n\u001b[0;32m 9\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_extractor\u001b[39m\u001b[38;5;124m'\u001b[39m:[DoublePass], \n\u001b[0;32m 10\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_file\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_large.csv\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[0;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrebuild_model\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;28;01mFalse\u001b[39;00m],\n\u001b[0;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5.1\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m 14\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_runs\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m2\u001b[39m]}\n\u001b[0;32m 16\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params, \n\u001b[0;32m 17\u001b[0m LinearObserver, observer_params, \n\u001b[0;32m 18\u001b[0m InternalNoiseValue, analyser_params)\n\u001b[1;32m---> 19\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m 43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m 44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run}) \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m 47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m 64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m 66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m 70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:21\u001b[0m, in \u001b[0;36mInternalNoiseValue.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant, participant_responses): \n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparticipant_responses\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:27\u001b[0m, in \u001b[0;36mInternalNoiseValue.estimate_internal_noise\u001b[1;34m(self, experiment, participant_responses)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mestimate_internal_noise\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant_responses): \n\u001b[0;32m 25\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_df(experiment, participant_responses)\n\u001b[1;32m---> 27\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_extractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_single_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstim_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfeature\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise, criteria\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:46\u001b[0m, in \u001b[0;36mDoublePass.extract_single_internal_noise\u001b[1;34m(cls, data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m 44\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_prob_first(data_df, trial_id\u001b[38;5;241m=\u001b[39mtrial_id, response_id\u001b[38;5;241m=\u001b[39mresponse_id, stim_id\u001b[38;5;241m=\u001b[39mstim_id, double_pass_id\u001b[38;5;241m=\u001b[39mdouble_pass_id)\n\u001b[1;32m---> 46\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_noise_criteria\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprob_agree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprob_first\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrebuild_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise,criteria\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:122\u001b[0m, in \u001b[0;36mDoublePass.estimate_noise_criteria\u001b[1;34m(cls, prob_agree, prob_first, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 120\u001b[0m model_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(model_file, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 122\u001b[0m model_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 123\u001b[0m model_df\u001b[38;5;241m.\u001b[39mto_csv(model_file)\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first \u001b[39;00m\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:154\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m 148\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 150\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params,\n\u001b[0;32m 151\u001b[0m LinearObserver, observer_params, \n\u001b[0;32m 152\u001b[0m DoublePassStatistics, analyser_params)\n\u001b[1;32m--> 154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[0;32m 157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[DoublePassStatistics\u001b[38;5;241m.\u001b[39mget_metric_names]\u001b[38;5;241m.\u001b[39mmean()\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m 43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m 44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run}) \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m 47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m 64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m 66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m 70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\double_pass_statistics.py:19\u001b[0m, in \u001b[0;36mDoublePassStatistics.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m 15\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mindex_double_pass_trials(data_df \u001b[38;5;241m=\u001b[39m responses_df, \n\u001b[0;32m 16\u001b[0m trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m,value_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# compute probability of agreement over double pass\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m prob_agree \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_prob_agreement\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdouble_pass_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m 21\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mcompute_prob_first(responses_df, trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m, response_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m, stim_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstim\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement\u001b[1;34m(cls, data_df, trial_id, response_id, double_pass_id)\u001b[0m\n\u001b[0;32m 90\u001b[0m d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m \u001b[43mdata_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1567\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1559\u001b[0m new_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe operation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_func\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed on a column. If any error is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraised, this will raise an exception in a future version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1562\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof pandas. Drop these columns to avoid this warning.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1563\u001b[0m )\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[0;32m 1565\u001b[0m old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[0;32m 1566\u001b[0m ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[1;32m-> 1567\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 1569\u001b[0m \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[0;32m 1570\u001b[0m \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[0;32m 1575\u001b[0m \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n\u001b[0;32m 1577\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_selection_context():\n\u001b[0;32m 1578\u001b[0m \u001b[38;5;66;03m# GH#50538\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m 1592\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m 1594\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1599\u001b[0m is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 1600\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m 1601\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1602\u001b[0m \u001b[38;5;124;03m Apply function f in python space\u001b[39;00m\n\u001b[0;32m 1603\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1627\u001b[0m \u001b[38;5;124;03m data after applying f\u001b[39;00m\n\u001b[0;32m 1628\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1629\u001b[0m values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1631\u001b[0m not_indexed_same \u001b[38;5;241m=\u001b[39m mutated \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmutated\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m 837\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m 838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m 841\u001b[0m mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement..\u001b[1;34m(group)\u001b[0m\n\u001b[0;32m 90\u001b[0m d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m data_df\u001b[38;5;241m.\u001b[39mgroupby(double_pass_id)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n", - "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:90\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement..same_answer\u001b[1;34m(group, trial_id, response_id)\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msame_answer\u001b[39m(group, trial_id, response_id): \n\u001b[1;32m---> 90\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:895\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 892\u001b[0m func \u001b[38;5;241m=\u001b[39m maybe_mangle_lambdas(func)\n\u001b[0;32m 894\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args, kwargs)\n\u001b[1;32m--> 895\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 897\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(arg):\n\u001b[1;32m--> 172\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(arg):\n\u001b[0;32m 174\u001b[0m \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[0;32m 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:504\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 501\u001b[0m results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 503\u001b[0m \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m results \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 505\u001b[0m key: obj\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 506\u001b[0m }\n\u001b[0;32m 508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m 509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:505\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 501\u001b[0m results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 503\u001b[0m \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[0;32m 504\u001b[0m results \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m--> 505\u001b[0m key: \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gotitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 506\u001b[0m }\n\u001b[0;32m 508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m 509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:297\u001b[0m, in \u001b[0;36mSeriesGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_agg_general(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 297\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_agg_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 298\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 299\u001b[0m \u001b[38;5;66;03m# TODO: KeyError is raised in _python_agg_general,\u001b[39;00m\n\u001b[0;32m 300\u001b[0m \u001b[38;5;66;03m# see test_groupby.test_basic\u001b[39;00m\n\u001b[0;32m 301\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_named(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1673\u001b[0m, in \u001b[0;36mGroupBy._python_agg_general\u001b[1;34m(self, func, raise_on_typeerror, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1670\u001b[0m \u001b[38;5;66;03m# iterate through \"columns\" ex exclusions to populate output dict\u001b[39;00m\n\u001b[0;32m 1671\u001b[0m output: \u001b[38;5;28mdict\u001b[39m[base\u001b[38;5;241m.\u001b[39mOutputKey, ArrayLike] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m-> 1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1674\u001b[0m \u001b[38;5;66;03m# agg_series below assumes ngroups > 0\u001b[39;00m\n\u001b[0;32m 1675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj, is_agg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 1677\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterate_slices()):\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:677\u001b[0m, in \u001b[0;36mBaseGroupBy.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 674\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 675\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:982\u001b[0m, in \u001b[0;36mBaseGrouper.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 979\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 982\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m)\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:993\u001b[0m, in \u001b[0;36mBaseGrouper.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 990\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m 991\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresult_index\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m 992\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 993\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupings\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 995\u001b[0m codes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreconstructed_codes\n\u001b[0;32m 996\u001b[0m levels \u001b[38;5;241m=\u001b[39m [ping\u001b[38;5;241m.\u001b[39mresult_index \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings]\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:647\u001b[0m, in \u001b[0;36mGrouping.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 645\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(group_idx, CategoricalIndex)\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recode_from_groupby(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_grouper, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sort, group_idx)\n\u001b[1;32m--> 647\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_index\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:655\u001b[0m, in \u001b[0;36mGrouping.group_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 652\u001b[0m \u001b[38;5;66;03m# _group_index is set in __init__ for MultiIndex cases\u001b[39;00m\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index\n\u001b[1;32m--> 655\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_codes_and_uniques\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(uniques, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:692\u001b[0m, in \u001b[0;36mGrouping._codes_and_uniques\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 685\u001b[0m uniques \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouping_vector\u001b[38;5;241m.\u001b[39mresult_index\u001b[38;5;241m.\u001b[39m_values \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[0;32m 687\u001b[0m )\n\u001b[0;32m 688\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 689\u001b[0m \u001b[38;5;66;03m# GH35667, replace dropna=False with use_na_sentinel=False\u001b[39;00m\n\u001b[0;32m 690\u001b[0m \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"Union[\u001b[39;00m\n\u001b[0;32m 691\u001b[0m \u001b[38;5;66;03m# ndarray[Any, Any], Index]\", variable has type \"Categorical\")\u001b[39;00m\n\u001b[1;32m--> 692\u001b[0m codes, uniques \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[0;32m 693\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouping_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dropna\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:832\u001b[0m, in \u001b[0;36mfactorize\u001b[1;34m(values, sort, na_sentinel, use_na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m 829\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 830\u001b[0m \u001b[38;5;66;03m# TODO: Can remove when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m 831\u001b[0m na_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 832\u001b[0m uniques, codes \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43muniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_sentinel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43massume_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[0;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m sort:\n\u001b[0;32m 837\u001b[0m \u001b[38;5;66;03m# TODO: Can remove entire block when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m 838\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:1875\u001b[0m, in \u001b[0;36msafe_sort\u001b[1;34m(values, codes, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m 1873\u001b[0m ordered \u001b[38;5;241m=\u001b[39m original_values\u001b[38;5;241m.\u001b[39mtake(sorter)\n\u001b[0;32m 1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m ordered \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1876\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 1877\u001b[0m \u001b[38;5;66;03m# Previous sorters failed or were not applicable, try `_sort_mixed`\u001b[39;00m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;66;03m# which would work, but which fails for special case of 1d arrays\u001b[39;00m\n\u001b[0;32m 1879\u001b[0m \u001b[38;5;66;03m# with tuples.\u001b[39;00m\n\u001b[0;32m 1880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mtuple\u001b[39m):\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Running 3 configs\n", + "0 : {'n_trials': 100, 'n_repeated': 1000, 'trial_type': , 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model.csv', 'rebuild_model': False}\n", + "..........;\n", + "1 : {'n_trials': 500, 'n_repeated': 1000, 'trial_type': , 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model.csv', 'rebuild_model': False}\n", + "..........;\n", + "2 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': , 'model_file': 'model.csv', 'rebuild_model': False}\n", + "..........;\n" ] } ], "source": [ - "observer_params = {'kernel':[[1]],\n", + "observer_params = {'kernel':[[0,0,0,0,10]],\n", " 'internal_noise_std':[0], \n", " 'criteria':[0]}\n", - "experiment_params = {'n_trials':[1000],#np.arange(1,1000,100),\n", + "experiment_params = {'n_trials':[100,500,1000], #np.arange(1,1000,100),\n", " 'n_repeated':[1000],\n", " 'trial_type': [Int2Trial],\n", - " 'n_features': [1],\n", + " 'n_features': [5],\n", " 'external_noise_std': [100]}\n", "analyser_params = {'internal_noise_extractor':[DoublePass], \n", - " 'model_file': ['model_large.csv'], \n", - " 'rebuild_model': [False],\n", - " 'internal_noise_range':[np.arange(0,5.1,0.1)],\n", - " 'criteria_range':[np.arange(-5,5,0.1)],\n", - " 'n_runs':[2]}\n", + " 'model_file': ['model.csv'], \n", + " 'rebuild_model': [False]}\n", + " #'internal_noise_range':[np.arange(0,5.1,0.1)],\n", + " #'criteria_range':[np.arange(-5,5,0.1)],\n", + " #'n_runs':[2]}\n", " \n", "sim = Sim(DoublePassExperiment, experiment_params, \n", " LinearObserver, observer_params, \n", " InternalNoiseValue, analyser_params)\n", - "sim_df = sim.run_all(n_runs=1)\n", + "sim_df = sim.run_all(n_runs=10)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 216, - "id": "d36ad464", + "execution_count": 29, + "id": "126511b3", "metadata": { "ExecuteTime": { - "end_time": "2024-04-11T11:28:06.869895Z", - "start_time": "2024-04-11T11:28:06.817000Z" + "end_time": "2024-04-22T15:12:06.333448Z", + "start_time": "2024-04-22T15:12:06.055674Z" } }, "outputs": [ { "data": { "text/plain": [ - "palin.simulation.simple_experiment.SimpleExperiment" + "" ] }, - "execution_count": 216, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "SimpleExperiment" + "sns.pointplot(data=sim_df, x='n_trials', y='estimated_internal_noise')" ] }, { "cell_type": "code", "execution_count": 219, - "id": "7aea1f12", + "id": "b64a77c2", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T20:09:16.840733Z", @@ -1749,13 +1681,7 @@ "..........;\n", "670 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.0}\n", "..........;\n", - "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n", "..........;\n", "672 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.0}\n", "..........;\n", @@ -2311,13 +2237,7 @@ "..........;\n", "930 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.0}\n", "..........;\n", - "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n", "..........;\n", "932 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': , 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.0}\n", "..........;\n", @@ -2485,7 +2405,7 @@ { "cell_type": "code", "execution_count": null, - "id": "94a3c61d", + "id": "da967fc0", "metadata": {}, "outputs": [], "source": [ @@ -2494,7 +2414,7 @@ }, { "cell_type": "markdown", - "id": "f217823b", + "id": "47c4c0c5", "metadata": {}, "source": [ "## Simulate with kernels" @@ -2502,7 +2422,7 @@ }, { "cell_type": "markdown", - "id": "26ee88e2", + "id": "b6d62245", "metadata": {}, "source": [ "Single run" @@ -2510,48 +2430,48 @@ }, { "cell_type": "code", - "execution_count": 104, - "id": "986cc8b8", + "execution_count": 32, + "id": "17c7e065", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T12:41:07.781836Z", - "start_time": "2024-04-10T12:41:07.728949Z" + "end_time": "2024-04-22T15:16:15.983628Z", + "start_time": "2024-04-22T15:16:15.907873Z" } }, "outputs": [ { "data": { "text/plain": [ - "0.948432263142621" + "0.9799297710374408" ] }, - "execution_count": 104, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# single run: \n", - "exp = Exp(n_trials = 100,\n", + "exp = SimpleExperiment(n_trials = 100,\n", " trial_type = Int2Trial, \n", " n_features = 5, \n", " external_noise_std = 100)\n", - "obs = Obs.with_random_kernel(n_features = 5, \n", + "obs = LinearObserver.with_random_kernel(n_features = 5, \n", " internal_noise_std = 1, \n", " criteria = 0)\n", "responses = obs.respond_to_experiment(exp)\n", - "ka = Analyser(ClassificationImage)\n", + "ka = KernelDistance(ClassificationImage)\n", "ka.analyse(exp, obs, responses)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "85ac8a46", + "execution_count": 41, + "id": "dc9c6a70", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T04:42:23.899935Z", - "start_time": "2024-04-10T04:42:23.752329Z" + "end_time": "2024-04-22T15:23:19.385125Z", + "start_time": "2024-04-22T15:23:19.185663Z" }, "scrolled": false }, @@ -2560,23 +2480,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "generated 8 runs\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 3, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 4, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 5, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 6, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 7, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 8, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n", - "{'n_trials': 100, 'trial_type': , 'n_features': 9, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", - ".\n" + "Running 8 configs\n", + "0 : {'n_trials': 100, 'trial_type': , 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[1.0]\n", + ";\n", + "1 : {'n_trials': 100, 'trial_type': , 'n_features': 3, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9997584847350102]\n", + ";\n", + "2 : {'n_trials': 100, 'trial_type': , 'n_features': 4, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9948042864707103]\n", + ";\n", + "3 : {'n_trials': 100, 'trial_type': , 'n_features': 5, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9713583847649068]\n", + ";\n", + "4 : {'n_trials': 100, 'trial_type': , 'n_features': 6, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.8998782407812661]\n", + ";\n", + "5 : {'n_trials': 100, 'trial_type': , 'n_features': 7, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9226774771188183]\n", + ";\n", + "6 : {'n_trials': 100, 'trial_type': , 'n_features': 8, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9693352107369663]\n", + ";\n", + "7 : {'n_trials': 100, 'trial_type': , 'n_features': 9, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': , 'distance': 'CORR'}\n", + ".['corr']\n", + "[0.9457780982693152]\n", + ";\n" ] } ], @@ -2594,10 +2530,10 @@ " 'distance':['CORR']}\n", "\n", "\n", - "sim = Sim(Exp, experiment_params, \n", - " Obs, observer_params, \n", - " Analyser, analyser_params)\n", - "sim_df = sim.run_all(n_samples=1)\n", + "sim = Sim(SimpleExperiment, experiment_params, \n", + " LinearObserver, observer_params, \n", + " KernelDistance, analyser_params)\n", + "sim_df = sim.run_all(n_runs=1)\n", "\n", "\n", "\n", @@ -2606,12 +2542,12 @@ }, { "cell_type": "code", - "execution_count": 147, - "id": "1e0c8a43", + "execution_count": 42, + "id": "2115ae57", "metadata": { "ExecuteTime": { - "end_time": "2024-04-10T15:36:24.243659Z", - "start_time": "2024-04-10T15:36:24.180798Z" + "end_time": "2024-04-22T15:23:23.748491Z", + "start_time": "2024-04-22T15:23:23.682656Z" } }, "outputs": [ @@ -2637,246 +2573,168 @@ " \n", " \n", " n_trials\n", - " n_repeated\n", " trial_type\n", " n_features\n", " external_noise_std\n", " kernel\n", " internal_noise_std\n", " criteria\n", - " sample\n", - " metric\n", - " dist\n", - " prob_agree\n", - " prob_first\n", + " kernel_extractor\n", + " distance\n", + " run\n", + " corr\n", " \n", " \n", " \n", " \n", " 0\n", " 100\n", - " 50\n", " <class 'palin.simulation.trial.Int2Trial'>\n", - " 1\n", + " 2\n", " 100\n", - " [1]\n", - " 0.0\n", - " -5\n", + " random\n", + " 1\n", " 0\n", - " (1.0, 1.0)\n", - " 0.2900\n", - " 0.8\n", - " 0.5\n", + " <class 'palin.kernels.classification_images.Cl...\n", + " CORR\n", + " 0\n", + " 1.000000\n", " \n", " \n", " 1\n", " 100\n", - " 50\n", " <class 'palin.simulation.trial.Int2Trial'>\n", - " 1\n", + " 3\n", " 100\n", - " [1]\n", - " 0.0\n", - " -4\n", + " random\n", + " 1\n", + " 0\n", + " <class 'palin.kernels.classification_images.Cl...\n", + " CORR\n", " 0\n", - " (1.0, 1.0)\n", - " 0.2900\n", - " 0.8\n", - " 0.5\n", + " 0.999758\n", " \n", " \n", " 2\n", " 100\n", - " 50\n", " <class 'palin.simulation.trial.Int2Trial'>\n", - " 1\n", + " 4\n", " 100\n", - " [1]\n", - " 0.0\n", - " -3\n", + " random\n", + " 1\n", + " 0\n", + " <class 'palin.kernels.classification_images.Cl...\n", + " CORR\n", " 0\n", - " (1.0, 1.0)\n", - " 0.2900\n", - " 0.8\n", - " 0.5\n", + " 0.994804\n", " \n", " \n", " 3\n", " 100\n", - 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500 rows × 13 columns

\n", "" ], "text/plain": [ - " n_trials n_repeated trial_type \\\n", - "0 100 50 \n", - "1 100 50 \n", - "2 100 50 \n", - "3 100 50 \n", - "4 100 50 \n", - ".. ... ... ... \n", - "495 100 50 \n", - "496 100 50 \n", - "497 100 50 \n", - "498 100 50 \n", - "499 100 50 \n", - "\n", - " n_features external_noise_std kernel internal_noise_std criteria \\\n", - "0 1 100 [1] 0.0 -5 \n", - "1 1 100 [1] 0.0 -4 \n", - "2 1 100 [1] 0.0 -3 \n", - "3 1 100 [1] 0.0 -2 \n", - "4 1 100 [1] 0.0 -1 \n", - ".. ... ... ... ... ... \n", - "495 1 100 [1] 4.9 0 \n", - "496 1 100 [1] 4.9 1 \n", - "497 1 100 [1] 4.9 2 \n", - "498 1 100 [1] 4.9 3 \n", - "499 1 100 [1] 4.9 4 \n", + " n_trials trial_type n_features \\\n", + "0 100 2 \n", + "1 100 3 \n", + "2 100 4 \n", + "3 100 5 \n", + "4 100 6 \n", + "5 100 7 \n", + "6 100 8 \n", + "7 100 9 \n", "\n", - " sample metric dist prob_agree prob_first \n", - "0 0 (1.0, 1.0) 0.2900 0.8 0.5 \n", - "1 0 (1.0, 1.0) 0.2900 0.8 0.5 \n", - "2 0 (1.0, 1.0) 0.2900 0.8 0.5 \n", - "3 0 (1.0, 0.88) 0.1844 0.8 0.5 \n", - "4 0 (1.0, 0.68) 0.0724 0.8 0.5 \n", - ".. ... ... ... ... ... \n", - "495 0 (0.56, 0.5) 0.0576 0.8 0.5 \n", - "496 0 (0.56, 0.34) 0.0832 0.8 0.5 \n", - "497 0 (0.54, 0.41) 0.0757 0.8 0.5 \n", - "498 0 (0.62, 0.23) 0.1053 0.8 0.5 \n", - "499 0 (0.72, 0.16) 0.1220 0.8 0.5 \n", + " external_noise_std kernel internal_noise_std criteria \\\n", + "0 100 random 1 0 \n", + "1 100 random 1 0 \n", + "2 100 random 1 0 \n", + "3 100 random 1 0 \n", + "4 100 random 1 0 \n", + "5 100 random 1 0 \n", + "6 100 random 1 0 \n", + "7 100 random 1 0 \n", "\n", - "[500 rows x 13 columns]" + " kernel_extractor distance run corr \n", + "0 " + "" ] }, - "execution_count": 17, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -2920,13 +2778,13 @@ "source": [ "sns.lineplot(data=sim_df, \n", " x='n_features',\n", - " y='metric')#, hue='n_features')" + " y='corr')#, hue='n_features')" ] }, { "cell_type": "code", "execution_count": 168, - "id": "81eef14d", + "id": "7183e87f", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:08:49.331441Z", @@ -2964,7 +2822,7 @@ { "cell_type": "code", "execution_count": 174, - "id": "caf9471b", + "id": "5fa9b058", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:10:11.894810Z", @@ -2989,7 +2847,7 @@ { "cell_type": "code", "execution_count": 173, - "id": "ed2de38c", + "id": "87afe0c9", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:09:56.002651Z", @@ -3015,7 +2873,7 @@ { "cell_type": "code", "execution_count": 175, - "id": "48a2e99c", + "id": "09e9a387", "metadata": { "ExecuteTime": { "end_time": "2024-04-11T04:10:14.250903Z", @@ -3171,7 +3029,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f578f871", + "id": "d5a96a45", "metadata": {}, "outputs": [], "source": []