我需要自动检测二维图中的倾角,如下图中用红色圆圈标记的区域。我只对“主要”倾角感兴趣,这意味着倾角必须在 x 轴上跨越最小长度。倾角的数量是未知的,即不同的地块将包含不同数量的倾角。有任何想法吗?
Update:
根据要求,这里是示例数据,以及按照 vine 的建议使用中值过滤对其进行平滑的尝试。
看起来我现在需要一种稳健的方法来近似每个点的导数,从而忽略数据中残留的小信号。有没有标准的方法?
y <- c(0.9943,0.9917,0.9879,0.9831,0.9553,0.9316,0.9208,0.9119,0.8857,0.7951,0.7605,0.8074,0.7342,0.6374,0.6035,0.5331,0.4781,0.4825,0.4825,0.4879,0.5374,0.4600,0.3668,0.3456,0.4282,0.3578,0.3630,0.3399,0.3578,0.4116,0.3762,0.3668,0.4420,0.4749,0.4556,0.4458,0.5084,0.5043,0.5043,0.5331,0.4781,0.5623,0.6604,0.5900,0.5084,0.5802,0.5802,0.6174,0.6124,0.6374,0.6827,0.6906,0.7034,0.7418,0.7817,0.8311,0.8001,0.7912,0.7912,0.7540,0.7951,0.7817,0.7644,0.7912,0.8311,0.8311,0.7912,0.7688,0.7418,0.7232,0.7147,0.6906,0.6715,0.6681,0.6374,0.6516,0.6650,0.6604,0.6124,0.6334,0.6374,0.5514,0.5514,0.5412,0.5514,0.5374,0.5473,0.4825,0.5084,0.5126,0.5229,0.5126,0.5043,0.4379,0.4781,0.4600,0.4781,0.3806,0.4078,0.3096,0.3263,0.3399,0.3184,0.2820,0.2167,0.2122,0.2080,0.2558,0.2255,0.1921,0.1766,0.1732,0.1205,0.1732,0.0723,0.0701,0.0405,0.0643,0.0771,0.1018,0.0587,0.0884,0.0884,0.1240,0.1088,0.0554,0.0607,0.0441,0.0387,0.0490,0.0478,0.0231,0.0414,0.0297,0.0701,0.0502,0.0567,0.0405,0.0363,0.0464,0.0701,0.0832,0.0991,0.1322,0.1998,0.3146,0.3146,0.3184,0.3578,0.3311,0.3184,0.4203,0.3578,0.3578,0.3578,0.4282,0.5084,0.5802,0.5667,0.5473,0.5514,0.5331,0.4749,0.4037,0.4116,0.4203,0.3184,0.4037,0.4037,0.4282,0.4513,0.4749,0.4116,0.4825,0.4918,0.4879,0.4918,0.4825,0.4245,0.4333,0.4651,0.4879,0.5412,0.5802,0.5126,0.4458,0.5374,0.4600,0.4600,0.4600,0.4600,0.3992,0.4879,0.4282,0.4333,0.3668,0.3005,0.3096,0.3847,0.3939,0.3630,0.3359,0.2292,0.2292,0.2748,0.3399,0.2963,0.2963,0.2385,0.2531,0.1805,0.2531,0.2786,0.3456,0.3399,0.3491,0.4037,0.3885,0.3806,0.2748,0.2700,0.2657,0.2963,0.2865,0.2167,0.2080,0.1844,0.2041,0.1602,0.1416,0.2041,0.1958,0.1018,0.0744,0.0677,0.0909,0.0789,0.0723,0.0660,0.1322,0.1532,0.1060,0.1018,0.1060,0.1150,0.0789,0.1266,0.0965,0.1732,0.1766,0.1766,0.1805,0.2820,0.3096,0.2602,0.2080,0.2333,0.2385,0.2385,0.2432,0.1602,0.2122,0.2385,0.2333,0.2558,0.2432,0.2292,0.2209,0.2483,0.2531,0.2432,0.2432,0.2432,0.2432,0.3053,0.3630,0.3578,0.3630,0.3668,0.3263,0.3992,0.4037,0.4556,0.4703,0.5173,0.6219,0.6412,0.7275,0.6984,0.6756,0.7079,0.7192,0.7342,0.7458,0.7501,0.7540,0.7605,0.7605,0.7342,0.7912,0.7951,0.8036,0.8074,0.8074,0.8118,0.7951,0.8118,0.8242,0.8488,0.8650,0.8488,0.8311,0.8424,0.7912,0.7951,0.8001,0.8001,0.7458,0.7192,0.6984,0.6412,0.6516,0.5900,0.5802,0.5802,0.5762,0.5623,0.5374,0.4556,0.4556,0.4333,0.3762,0.3456,0.4037,0.3311,0.3263,0.3311,0.3717,0.3762,0.3717,0.3668,0.3491,0.4203,0.4037,0.4149,0.4037,0.3992,0.4078,0.4651,0.4967,0.5229,0.5802,0.5802,0.5846,0.6293,0.6412,0.6374,0.6604,0.7317,0.7034,0.7573,0.7573,0.7573,0.7772,0.7605,0.8036,0.7951,0.7817,0.7869,0.7724,0.7869,0.7869,0.7951,0.7644,0.7912,0.7275,0.7342,0.7275,0.6984,0.7342,0.7605,0.7418,0.7418,0.7275,0.7573,0.7724,0.8118,0.8521,0.8823,0.8984,0.9119,0.9316,0.9512)
yy <- runmed(y, 41)
plot(y, type="l", ylim=c(0,1), ylab="", xlab="", lwd=0.5)
points(yy, col="blue", type="l", lwd=2)