curve fitting examples
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Hello everyone,
I retrieve some data, which can have missing data points even in the beginning or at the end. So what I needed was both; interpolation as well as extrapolation, to fill these positions. Typically the deviation over the data is not that huge and a straight fitting works well under this circumstance. Otherwise on more fluctuating/arbitrary data I got no satisfying results with any of the available fitting functions (see example makro below). Maybe my expectations are wrong about this and I thought that there is a way to adjusted methods in how close a resulting fitting would be applied. So I used median/mean as prefilter-methods. What wonders me is that sometimes a certain method works very well. In my case Rodbard, Error or a Gaussian. But sometimes, like in the given example, it makes no sense to use these at all. Is this typical or is my data strange :) Regards, Rainer // START (makro) ############################################# // Curve Fitting Demo // // This macro demonstates how to use the Fit.* functions, // which were added to the macro language in v1.41k. // modified to see some larger data sets x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339); y = newArray(96, 97, 98, 101, 101, 102, 101, 101, 101, 101, 101, 101, 100, 100, 101, 101, 102, 101, 100, 100, 101, 100, 99, 99, 100, 101, 100, 100, 101, 101, 100, 99, 99, 99, 99, 99, 99, 98, 99, 99, 98, 97, 96, 96, 96, 96, 95, 94, 95, 95, 94, 94, 93, 90, 89, 89, 88, 87, 87, 87, 87, 84, 85, 84, 85, 84, 83, 81, 81, 81, 80, 80, 78, 77, 77, 77, 76, 75, 75, 75, 75, 74, 73, 73, 73, 73, 72, 71, 71, 71, 70, 69, 68, 68, 68, 67, 66, 66, 65, 66, 65, 66, 64, 64, 64, 64, 64, 65, 65, 66, 66, 65, 65, 65, 65, 66, 66, 66, 67, 68, 68, 68, 68, 69, 69, 70, 69, 69, 69, 70, 70, 71, 70, 72, 72, 72, 71, 71, 72, 73, 73, 73, 73, 75, 75, 75, 74, 75, 75, 76, 76, 77, 76, 76, 77, 78, 77, 77, 77, 78, 79, 80, 79, 79, 80, 81, 80, 80, 81, 81, 81, 80, 81, 81, 82, 82, 82, 81, 82, 83, 83, 83, 82, 83, 83, 83, 82, 84, 84, 84, 84, 84, 83, 83, 84, 84, 83, 84, 84, 84, 83, 83, 83, 83, 85, 85, 84, 83, 84, 84, 84, 83, 83, 84, 84, 85, 84, 83, 83, 84, 83, 83, 83, 83, 84, 83, 83, 84, 84, 84, 84, 84, 83, 84, 84, 84, 83, 83, 83, 84, 84, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 85, 84, 83, 83, 84, 84, 84, 84, 85, 85, 84, 83, 84, 84, 83, 84, 83, 84, 85, 84, 84, 84, 84, 84, 85, 84, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 84, 84, 83, 83, 84, 84, 83, 83, 84, 84, 84, 83, 83, 84, 84, 84, 84, 83, 83, 84, 84, 83, 82, 83, 83, 83, 83, 83, 83, 84, 84, 83, 83, 83, 84, 84, 83, 83, 83, 83); doAllCurveFittings("raw", x, y); //pretty dense x-range x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339); y = newArray(100.8147, 100.7783, 100.7480, 100.6839, 100.6290, 100.6147, 100.6335, 100.6794, 100.6647, 100.6252, 100.5897, 100.5647, 100.5397, 100.4647, 100.3897, 100.3647, 100.3647, 100.3147, 100.2897, 100.2897, 100.3147, 100.2897, 100.2147, 100.1647, 100.0897, 100.0147, 99.9147, 99.7897, 99.6647, 99.5897, 99.5397, 99.4147, 99.2647, 99.1147, 98.9397, 98.7147, 98.4397, 98.1897, 97.8897, 97.5897, 97.2647, 96.9647, 96.7147, 96.3897, 96.0730, 95.7147, 95.3397, 94.9480, 94.5897, 94.1897, 93.7147, 93.2397, 92.7897, 92.3397, 91.8897, 91.4147, 90.9647, 90.4397, 89.9647, 89.4397, 88.9647, 88.4647, 87.9147, 87.3647, 86.8064, 86.2397, 85.6647, 85.0814, 84.4397, 83.7647, 83.1647, 82.5897, 81.9897, 81.3897, 80.8147, 80.2397, 79.6647, 79.1147, 78.5147, 77.9397, 77.3147, 76.7397, 76.1897, 75.6397, 75.1147, 74.6147, 74.1147, 73.5647, 73.0397, 72.5647, 72.0647, 71.5397, 71.0147, 70.5397, 70.0147, 69.5397, 69.0397, 68.6397, 68.1647, 67.6897, 67.2397, 66.7897, 66.3897, 66.0897, 65.7897, 65.5397, 65.3147, 65.1397, 64.9897, 64.8647, 64.7397, 64.6897, 64.7147, 64.7147, 64.7897, 64.9147, 65.0397, 65.1397, 65.3647, 65.6147, 65.8647, 66.1647, 66.3897, 66.6147, 66.8397, 67.0647, 67.3147, 67.6147, 67.9147, 68.2647, 68.6147, 68.9147, 69.1647, 69.4397, 69.7147, 69.9647, 70.2397, 70.5147, 70.7647, 71.0897, 71.4147, 71.6897, 71.9897, 72.3147, 72.6147, 72.9147, 73.1897, 73.4647, 73.7147, 73.9147, 74.1647, 74.4397, 74.7147, 74.9897, 75.2397, 75.4647, 75.7397, 76.0647, 76.3397, 76.5397, 76.7897, 77.0647, 77.3397, 77.5397, 77.8147, 78.0647, 78.3147, 78.4897, 78.7397, 78.9897, 79.2397, 79.4647, 79.7147, 79.9397, 80.1897, 80.4647, 80.6897, 80.8397, 81.0147, 81.2147, 81.3647, 81.4897, 81.6147, 81.8397, 82.0147, 82.1647, 82.3147, 82.5147, 82.6397, 82.7647, 82.8647, 82.9647, 83.0647, 83.2147, 83.3147, 83.3647, 83.3647, 83.4147, 83.4897, 83.5147, 83.6147, 83.7397, 83.8397, 83.8147, 83.8147, 83.8147, 83.8397, 83.7897, 83.8147, 83.8397, 83.8397, 83.8897, 83.9147, 83.8647, 83.8397, 83.8397, 83.8397, 83.8397, 83.8397, 83.8397, 83.7897, 83.6897, 83.6147, 83.6397, 83.6397, 83.6397, 83.6147, 83.6647, 83.6397, 83.6397, 83.6647, 83.6147, 83.5647, 83.5647, 83.5397, 83.5397, 83.5897, 83.5647, 83.5647, 83.5897, 83.6147, 83.6647, 83.7147, 83.7147, 83.7147, 83.7397, 83.7647, 83.7897, 83.8397, 83.8897, 83.8897, 83.9397, 83.9897, 84.0147, 84.0397, 84.0647, 84.0897, 84.1647, 84.2147, 84.2647, 84.2897, 84.2897, 84.2897, 84.2897, 84.2897, 84.2397, 84.2397, 84.1897, 84.2147, 84.2147, 84.2397, 84.2397, 84.2897, 84.3397, 84.3897, 84.4397, 84.4397, 84.3897, 84.3647, 84.3397, 84.3147, 84.3397, 84.3647, 84.3897, 84.4147, 84.4647, 84.4897, 84.5397, 84.5397, 84.5397, 84.5397, 84.5147, 84.4897, 84.4397, 84.4147, 84.3647, 84.3647, 84.3897, 84.3647, 84.3647, 84.3397, 84.3147, 84.2647, 84.2147, 84.2147, 84.2147, 84.1897, 84.1647, 84.1147, 84.0647, 84.0397, 84.0397, 83.9897, 83.9647, 83.9397, 83.9147, 83.8647, 83.8397, 83.8397, 83.7897, 83.8147, 83.8147, 83.8147, 83.8147, 83.7647, 83.7147, 83.6647, 83.6397, 83.6647, 83.6647, 83.6647, 83.6252, 83.6369, 83.6794, 83.6960, 83.6814, 83.7004, 83.7224, 83.7480, 83.7783); doAllCurveFittings("pre-filtered", x, y); //full x-range function doAllCurveFittings(stackTitle, xpoints, ypoints) { // Do a straight line fit Fit.doFit("Straight Line", xpoints, ypoints); //print("a="+d2s(Fit.p(0),6)+", b="+d2s(Fit.p(1),6)); // Do all possible fits, plot them and add the plots to a stack setBatchMode(true); for (i=0; i<Fit.nEquations; i++) { Fit.doFit(i, xpoints, ypoints); Fit.plot(); if (i==0) stack = getImageID; else { run("Copy"); close(); selectImage(stack); run("Add Slice"); run("Paste"); } Fit.getEquation(i, name, formula); //print(""); print("index: "+i+", "+name+ " ["+formula+"]"); /*print(" R^2="+d2s(Fit.rSquared,3)); for (j=0; j<Fit.nParams; j++) print(" p["+j+"]="+d2s(Fit.p(j),6));*/ } setBatchMode(false); run("Select None"); rename(stackTitle); } // END ################################################## -- Rainer M. Engel Berlin -- ImageJ mailing list: http://imagej.nih.gov/ij/list.html |
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Good day Rainer,
if I remember correctly you've discussed a similar topic before... Please understand that curve fitting or general regression, to be more precise, needs a "null hypothesis" or "model", i.e. you need to know the type of function that underlies your data. If you don't have an idea about the data generating process, you are lost or the results become arbitrary. There are very few exceptions from this rule and two of them are interpolation and smoothing (they are no regression techniques). Interpolation is well-defined if the data is correctly sampled from a continuous process. Smoothing and related operations are less well-defined and they are more an art than hard science. Please keep this in mind and consider the proper approaches: Curve fitting provided by ImageJ is based on "general regression" and requires a "null hypothesis" or "model". The most general "model" is a polynomial of high degree but I don't recommend to use it without profound reason. If your data doesn't fit a mathematically formulated "model", then "general regression" according to an arbitrary chosen "model"-function is like a shot in the dark. I fear that the "general regression"-approach is not what you looking for. Regards Herbie ::::::::::::::::::::::::::::::::::::::::::::: Am 07.05.19 um 11:40 schrieb Rainer M. Engel: > Hello everyone, > > I retrieve some data, which can have missing data points even in the > beginning or at the end. So what I needed was both; interpolation as > well as extrapolation, to fill these positions. > > Typically the deviation over the data is not that huge and a straight > fitting works well under this circumstance. Otherwise on more > fluctuating/arbitrary data I got no satisfying results with any of the > available fitting functions (see example makro below). > > Maybe my expectations are wrong about this and I thought that there is a > way to adjusted methods in how close a resulting fitting would be > applied. So I used median/mean as prefilter-methods. > > What wonders me is that sometimes a certain method works very well. In > my case Rodbard, Error or a Gaussian. But sometimes, like in the given > example, it makes no sense to use these at all. > > Is this typical or is my data strange :) > > Regards, > Rainer > > > > > // START (makro) ############################################# > // Curve Fitting Demo > // > // This macro demonstates how to use the Fit.* functions, > // which were added to the macro language in v1.41k. > > // modified to see some larger data sets > > x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, > 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, > 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, > 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, > 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, > 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, > 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, > 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, > 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, > 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, > 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, > 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, > 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, > 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, > 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, > 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, > 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, > 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, > 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, > 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, > 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, > 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, > 333, 334, 335, 336, 337, 338, 339); > y = newArray(96, 97, 98, 101, 101, 102, 101, 101, 101, 101, 101, 101, > 100, 100, 101, 101, 102, 101, 100, 100, 101, 100, 99, 99, 100, 101, 100, > 100, 101, 101, 100, 99, 99, 99, 99, 99, 99, 98, 99, 99, 98, 97, 96, 96, > 96, 96, 95, 94, 95, 95, 94, 94, 93, 90, 89, 89, 88, 87, 87, 87, 87, 84, > 85, 84, 85, 84, 83, 81, 81, 81, 80, 80, 78, 77, 77, 77, 76, 75, 75, 75, > 75, 74, 73, 73, 73, 73, 72, 71, 71, 71, 70, 69, 68, 68, 68, 67, 66, 66, > 65, 66, 65, 66, 64, 64, 64, 64, 64, 65, 65, 66, 66, 65, 65, 65, 65, 66, > 66, 66, 67, 68, 68, 68, 68, 69, 69, 70, 69, 69, 69, 70, 70, 71, 70, 72, > 72, 72, 71, 71, 72, 73, 73, 73, 73, 75, 75, 75, 74, 75, 75, 76, 76, 77, > 76, 76, 77, 78, 77, 77, 77, 78, 79, 80, 79, 79, 80, 81, 80, 80, 81, 81, > 81, 80, 81, 81, 82, 82, 82, 81, 82, 83, 83, 83, 82, 83, 83, 83, 82, 84, > 84, 84, 84, 84, 83, 83, 84, 84, 83, 84, 84, 84, 83, 83, 83, 83, 85, 85, > 84, 83, 84, 84, 84, 83, 83, 84, 84, 85, 84, 83, 83, 84, 83, 83, 83, 83, > 84, 83, 83, 84, 84, 84, 84, 84, 83, 84, 84, 84, 83, 83, 83, 84, 84, 83, > 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 85, 84, 83, 83, 84, > 84, 84, 84, 85, 85, 84, 83, 84, 84, 83, 84, 83, 84, 85, 84, 84, 84, 84, > 84, 85, 84, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 84, > 84, 83, 83, 84, 84, 83, 83, 84, 84, 84, 83, 83, 84, 84, 84, 84, 83, 83, > 84, 84, 83, 82, 83, 83, 83, 83, 83, 83, 84, 84, 83, 83, 83, 84, 84, 83, > 83, 83, 83); > > doAllCurveFittings("raw", x, y); //pretty dense x-range > > x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, > 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, > 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, > 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, > 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, > 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, > 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, > 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, > 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, > 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, > 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, > 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, > 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, > 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, > 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, > 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, > 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, > 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, > 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, > 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, > 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, > 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, > 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339); > y = newArray(100.8147, 100.7783, 100.7480, 100.6839, 100.6290, 100.6147, > 100.6335, 100.6794, 100.6647, 100.6252, 100.5897, 100.5647, 100.5397, > 100.4647, 100.3897, 100.3647, 100.3647, 100.3147, 100.2897, 100.2897, > 100.3147, 100.2897, 100.2147, 100.1647, 100.0897, 100.0147, 99.9147, > 99.7897, 99.6647, 99.5897, 99.5397, 99.4147, 99.2647, 99.1147, 98.9397, > 98.7147, 98.4397, 98.1897, 97.8897, 97.5897, 97.2647, 96.9647, 96.7147, > 96.3897, 96.0730, 95.7147, 95.3397, 94.9480, 94.5897, 94.1897, 93.7147, > 93.2397, 92.7897, 92.3397, 91.8897, 91.4147, 90.9647, 90.4397, 89.9647, > 89.4397, 88.9647, 88.4647, 87.9147, 87.3647, 86.8064, 86.2397, 85.6647, > 85.0814, 84.4397, 83.7647, 83.1647, 82.5897, 81.9897, 81.3897, 80.8147, > 80.2397, 79.6647, 79.1147, 78.5147, 77.9397, 77.3147, 76.7397, 76.1897, > 75.6397, 75.1147, 74.6147, 74.1147, 73.5647, 73.0397, 72.5647, 72.0647, > 71.5397, 71.0147, 70.5397, 70.0147, 69.5397, 69.0397, 68.6397, 68.1647, > 67.6897, 67.2397, 66.7897, 66.3897, 66.0897, 65.7897, 65.5397, 65.3147, > 65.1397, 64.9897, 64.8647, 64.7397, 64.6897, 64.7147, 64.7147, 64.7897, > 64.9147, 65.0397, 65.1397, 65.3647, 65.6147, 65.8647, 66.1647, 66.3897, > 66.6147, 66.8397, 67.0647, 67.3147, 67.6147, 67.9147, 68.2647, 68.6147, > 68.9147, 69.1647, 69.4397, 69.7147, 69.9647, 70.2397, 70.5147, 70.7647, > 71.0897, 71.4147, 71.6897, 71.9897, 72.3147, 72.6147, 72.9147, 73.1897, > 73.4647, 73.7147, 73.9147, 74.1647, 74.4397, 74.7147, 74.9897, 75.2397, > 75.4647, 75.7397, 76.0647, 76.3397, 76.5397, 76.7897, 77.0647, 77.3397, > 77.5397, 77.8147, 78.0647, 78.3147, 78.4897, 78.7397, 78.9897, 79.2397, > 79.4647, 79.7147, 79.9397, 80.1897, 80.4647, 80.6897, 80.8397, 81.0147, > 81.2147, 81.3647, 81.4897, 81.6147, 81.8397, 82.0147, 82.1647, 82.3147, > 82.5147, 82.6397, 82.7647, 82.8647, 82.9647, 83.0647, 83.2147, 83.3147, > 83.3647, 83.3647, 83.4147, 83.4897, 83.5147, 83.6147, 83.7397, 83.8397, > 83.8147, 83.8147, 83.8147, 83.8397, 83.7897, 83.8147, 83.8397, 83.8397, > 83.8897, 83.9147, 83.8647, 83.8397, 83.8397, 83.8397, 83.8397, 83.8397, > 83.8397, 83.7897, 83.6897, 83.6147, 83.6397, 83.6397, 83.6397, 83.6147, > 83.6647, 83.6397, 83.6397, 83.6647, 83.6147, 83.5647, 83.5647, 83.5397, > 83.5397, 83.5897, 83.5647, 83.5647, 83.5897, 83.6147, 83.6647, 83.7147, > 83.7147, 83.7147, 83.7397, 83.7647, 83.7897, 83.8397, 83.8897, 83.8897, > 83.9397, 83.9897, 84.0147, 84.0397, 84.0647, 84.0897, 84.1647, 84.2147, > 84.2647, 84.2897, 84.2897, 84.2897, 84.2897, 84.2897, 84.2397, 84.2397, > 84.1897, 84.2147, 84.2147, 84.2397, 84.2397, 84.2897, 84.3397, 84.3897, > 84.4397, 84.4397, 84.3897, 84.3647, 84.3397, 84.3147, 84.3397, 84.3647, > 84.3897, 84.4147, 84.4647, 84.4897, 84.5397, 84.5397, 84.5397, 84.5397, > 84.5147, 84.4897, 84.4397, 84.4147, 84.3647, 84.3647, 84.3897, 84.3647, > 84.3647, 84.3397, 84.3147, 84.2647, 84.2147, 84.2147, 84.2147, 84.1897, > 84.1647, 84.1147, 84.0647, 84.0397, 84.0397, 83.9897, 83.9647, 83.9397, > 83.9147, 83.8647, 83.8397, 83.8397, 83.7897, 83.8147, 83.8147, 83.8147, > 83.8147, 83.7647, 83.7147, 83.6647, 83.6397, 83.6647, 83.6647, 83.6647, > 83.6252, 83.6369, 83.6794, 83.6960, 83.6814, 83.7004, 83.7224, 83.7480, > 83.7783); > > doAllCurveFittings("pre-filtered", x, y); //full x-range > > function doAllCurveFittings(stackTitle, xpoints, ypoints) { > // Do a straight line fit > Fit.doFit("Straight Line", xpoints, ypoints); > //print("a="+d2s(Fit.p(0),6)+", b="+d2s(Fit.p(1),6)); > > // Do all possible fits, plot them and add the plots to a stack > setBatchMode(true); > for (i=0; i<Fit.nEquations; i++) { > Fit.doFit(i, xpoints, ypoints); > Fit.plot(); > if (i==0) > stack = getImageID; > else { > run("Copy"); > close(); > selectImage(stack); > run("Add Slice"); > run("Paste"); > } > Fit.getEquation(i, name, formula); > //print(""); > print("index: "+i+", "+name+ " ["+formula+"]"); > /*print(" R^2="+d2s(Fit.rSquared,3)); > for (j=0; j<Fit.nParams; j++) > print(" p["+j+"]="+d2s(Fit.p(j),6));*/ > } > setBatchMode(false); > run("Select None"); > rename(stackTitle); > } > // END ################################################## > > -- ImageJ mailing list: http://imagej.nih.gov/ij/list.html |
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Good day Herbie,
you are right about that. Some 1-2 years ago, but right now I'm refining my code and I have a better overview now, since the underlying data retrieval is more robust. > more an art than hard science I think I tended to 'misuse' it, even though it worked in 90% of the cases without the need to set filtering windows. So I wanted to check on the limits again. One thought: The data I get is very stable and missing data positions are marked to be filled. Right now I'm linear interpolating these, but by this these positions have the same "weight" like the former analysed ones, which then tend to be more deviating and hence will be smoothed stronger. This is one reason why I came back and tested the fitting methods, which are doing a nice job at least in some regions.. Thank you for your fast reply and thoughts on this.. Regards, Rainer Am 07.05.2019 um 12:06 schrieb Herbie: > Good day Rainer, > > if I remember correctly you've discussed a similar topic before... > > Please understand that curve fitting or general regression, to be more > precise, needs a "null hypothesis" or "model", i.e. you need to know the > type of function that underlies your data. If you don't have an idea > about the data generating process, you are lost or the results become > arbitrary. > > There are very few exceptions from this rule and two of them are > interpolation and smoothing (they are no regression techniques). > Interpolation is well-defined if the data is correctly sampled from a > continuous process. Smoothing and related operations are less > well-defined and they are more an art than hard science. > > Please keep this in mind and consider the proper approaches: > > Curve fitting provided by ImageJ is based on "general regression" and > requires a "null hypothesis" or "model". The most general "model" is a > polynomial of high degree but I don't recommend to use it without > profound reason. > > If your data doesn't fit a mathematically formulated "model", then > "general regression" according to an arbitrary chosen "model"-function > is like a shot in the dark. > > I fear that the "general regression"-approach is not what you looking for. > > Regards > > Herbie > > ::::::::::::::::::::::::::::::::::::::::::::: > Am 07.05.19 um 11:40 schrieb Rainer M. Engel: >> Hello everyone, >> >> I retrieve some data, which can have missing data points even in the >> beginning or at the end. So what I needed was both; interpolation as >> well as extrapolation, to fill these positions. >> >> Typically the deviation over the data is not that huge and a straight >> fitting works well under this circumstance. Otherwise on more >> fluctuating/arbitrary data I got no satisfying results with any of the >> available fitting functions (see example makro below). >> >> Maybe my expectations are wrong about this and I thought that there is a >> way to adjusted methods in how close a resulting fitting would be >> applied. So I used median/mean as prefilter-methods. >> >> What wonders me is that sometimes a certain method works very well. In >> my case Rodbard, Error or a Gaussian. But sometimes, like in the given >> example, it makes no sense to use these at all. >> >> Is this typical or is my data strange :) >> >> Regards, >> Rainer >> >> >> >> >> // START (makro) ############################################# >> // Curve Fitting Demo >> // >> // This macro demonstates how to use the Fit.* functions, >> // which were added to the macro language in v1.41k. >> >> // modified to see some larger data sets >> >> x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, >> 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, >> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, >> 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, >> 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, >> 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, >> 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, >> 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, >> 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, >> 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, >> 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, >> 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, >> 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, >> 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, >> 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, >> 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, >> 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, >> 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, >> 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, >> 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, >> 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, >> 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, >> 333, 334, 335, 336, 337, 338, 339); >> y = newArray(96, 97, 98, 101, 101, 102, 101, 101, 101, 101, 101, 101, >> 100, 100, 101, 101, 102, 101, 100, 100, 101, 100, 99, 99, 100, 101, 100, >> 100, 101, 101, 100, 99, 99, 99, 99, 99, 99, 98, 99, 99, 98, 97, 96, 96, >> 96, 96, 95, 94, 95, 95, 94, 94, 93, 90, 89, 89, 88, 87, 87, 87, 87, 84, >> 85, 84, 85, 84, 83, 81, 81, 81, 80, 80, 78, 77, 77, 77, 76, 75, 75, 75, >> 75, 74, 73, 73, 73, 73, 72, 71, 71, 71, 70, 69, 68, 68, 68, 67, 66, 66, >> 65, 66, 65, 66, 64, 64, 64, 64, 64, 65, 65, 66, 66, 65, 65, 65, 65, 66, >> 66, 66, 67, 68, 68, 68, 68, 69, 69, 70, 69, 69, 69, 70, 70, 71, 70, 72, >> 72, 72, 71, 71, 72, 73, 73, 73, 73, 75, 75, 75, 74, 75, 75, 76, 76, 77, >> 76, 76, 77, 78, 77, 77, 77, 78, 79, 80, 79, 79, 80, 81, 80, 80, 81, 81, >> 81, 80, 81, 81, 82, 82, 82, 81, 82, 83, 83, 83, 82, 83, 83, 83, 82, 84, >> 84, 84, 84, 84, 83, 83, 84, 84, 83, 84, 84, 84, 83, 83, 83, 83, 85, 85, >> 84, 83, 84, 84, 84, 83, 83, 84, 84, 85, 84, 83, 83, 84, 83, 83, 83, 83, >> 84, 83, 83, 84, 84, 84, 84, 84, 83, 84, 84, 84, 83, 83, 83, 84, 84, 83, >> 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 85, 84, 83, 83, 84, >> 84, 84, 84, 85, 85, 84, 83, 84, 84, 83, 84, 83, 84, 85, 84, 84, 84, 84, >> 84, 85, 84, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 84, >> 84, 83, 83, 84, 84, 83, 83, 84, 84, 84, 83, 83, 84, 84, 84, 84, 83, 83, >> 84, 84, 83, 82, 83, 83, 83, 83, 83, 83, 84, 84, 83, 83, 83, 84, 84, 83, >> 83, 83, 83); >> >> doAllCurveFittings("raw", x, y); //pretty dense x-range >> >> x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, >> 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, >> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, >> 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, >> 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, >> 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, >> 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, >> 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, >> 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, >> 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, >> 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, >> 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, >> 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, >> 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, >> 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, >> 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, >> 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, >> 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, >> 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, >> 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, >> 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, >> 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, >> 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339); >> y = newArray(100.8147, 100.7783, 100.7480, 100.6839, 100.6290, 100.6147, >> 100.6335, 100.6794, 100.6647, 100.6252, 100.5897, 100.5647, 100.5397, >> 100.4647, 100.3897, 100.3647, 100.3647, 100.3147, 100.2897, 100.2897, >> 100.3147, 100.2897, 100.2147, 100.1647, 100.0897, 100.0147, 99.9147, >> 99.7897, 99.6647, 99.5897, 99.5397, 99.4147, 99.2647, 99.1147, 98.9397, >> 98.7147, 98.4397, 98.1897, 97.8897, 97.5897, 97.2647, 96.9647, 96.7147, >> 96.3897, 96.0730, 95.7147, 95.3397, 94.9480, 94.5897, 94.1897, 93.7147, >> 93.2397, 92.7897, 92.3397, 91.8897, 91.4147, 90.9647, 90.4397, 89.9647, >> 89.4397, 88.9647, 88.4647, 87.9147, 87.3647, 86.8064, 86.2397, 85.6647, >> 85.0814, 84.4397, 83.7647, 83.1647, 82.5897, 81.9897, 81.3897, 80.8147, >> 80.2397, 79.6647, 79.1147, 78.5147, 77.9397, 77.3147, 76.7397, 76.1897, >> 75.6397, 75.1147, 74.6147, 74.1147, 73.5647, 73.0397, 72.5647, 72.0647, >> 71.5397, 71.0147, 70.5397, 70.0147, 69.5397, 69.0397, 68.6397, 68.1647, >> 67.6897, 67.2397, 66.7897, 66.3897, 66.0897, 65.7897, 65.5397, 65.3147, >> 65.1397, 64.9897, 64.8647, 64.7397, 64.6897, 64.7147, 64.7147, 64.7897, >> 64.9147, 65.0397, 65.1397, 65.3647, 65.6147, 65.8647, 66.1647, 66.3897, >> 66.6147, 66.8397, 67.0647, 67.3147, 67.6147, 67.9147, 68.2647, 68.6147, >> 68.9147, 69.1647, 69.4397, 69.7147, 69.9647, 70.2397, 70.5147, 70.7647, >> 71.0897, 71.4147, 71.6897, 71.9897, 72.3147, 72.6147, 72.9147, 73.1897, >> 73.4647, 73.7147, 73.9147, 74.1647, 74.4397, 74.7147, 74.9897, 75.2397, >> 75.4647, 75.7397, 76.0647, 76.3397, 76.5397, 76.7897, 77.0647, 77.3397, >> 77.5397, 77.8147, 78.0647, 78.3147, 78.4897, 78.7397, 78.9897, 79.2397, >> 79.4647, 79.7147, 79.9397, 80.1897, 80.4647, 80.6897, 80.8397, 81.0147, >> 81.2147, 81.3647, 81.4897, 81.6147, 81.8397, 82.0147, 82.1647, 82.3147, >> 82.5147, 82.6397, 82.7647, 82.8647, 82.9647, 83.0647, 83.2147, 83.3147, >> 83.3647, 83.3647, 83.4147, 83.4897, 83.5147, 83.6147, 83.7397, 83.8397, >> 83.8147, 83.8147, 83.8147, 83.8397, 83.7897, 83.8147, 83.8397, 83.8397, >> 83.8897, 83.9147, 83.8647, 83.8397, 83.8397, 83.8397, 83.8397, 83.8397, >> 83.8397, 83.7897, 83.6897, 83.6147, 83.6397, 83.6397, 83.6397, 83.6147, >> 83.6647, 83.6397, 83.6397, 83.6647, 83.6147, 83.5647, 83.5647, 83.5397, >> 83.5397, 83.5897, 83.5647, 83.5647, 83.5897, 83.6147, 83.6647, 83.7147, >> 83.7147, 83.7147, 83.7397, 83.7647, 83.7897, 83.8397, 83.8897, 83.8897, >> 83.9397, 83.9897, 84.0147, 84.0397, 84.0647, 84.0897, 84.1647, 84.2147, >> 84.2647, 84.2897, 84.2897, 84.2897, 84.2897, 84.2897, 84.2397, 84.2397, >> 84.1897, 84.2147, 84.2147, 84.2397, 84.2397, 84.2897, 84.3397, 84.3897, >> 84.4397, 84.4397, 84.3897, 84.3647, 84.3397, 84.3147, 84.3397, 84.3647, >> 84.3897, 84.4147, 84.4647, 84.4897, 84.5397, 84.5397, 84.5397, 84.5397, >> 84.5147, 84.4897, 84.4397, 84.4147, 84.3647, 84.3647, 84.3897, 84.3647, >> 84.3647, 84.3397, 84.3147, 84.2647, 84.2147, 84.2147, 84.2147, 84.1897, >> 84.1647, 84.1147, 84.0647, 84.0397, 84.0397, 83.9897, 83.9647, 83.9397, >> 83.9147, 83.8647, 83.8397, 83.8397, 83.7897, 83.8147, 83.8147, 83.8147, >> 83.8147, 83.7647, 83.7147, 83.6647, 83.6397, 83.6647, 83.6647, 83.6647, >> 83.6252, 83.6369, 83.6794, 83.6960, 83.6814, 83.7004, 83.7224, 83.7480, >> 83.7783); >> >> doAllCurveFittings("pre-filtered", x, y); //full x-range >> >> function doAllCurveFittings(stackTitle, xpoints, ypoints) { >> // Do a straight line fit >> Fit.doFit("Straight Line", xpoints, ypoints); >> //print("a="+d2s(Fit.p(0),6)+", b="+d2s(Fit.p(1),6)); >> >> // Do all possible fits, plot them and add the plots to a stack >> setBatchMode(true); >> for (i=0; i<Fit.nEquations; i++) { >> Fit.doFit(i, xpoints, ypoints); >> Fit.plot(); >> if (i==0) >> stack = getImageID; >> else { >> run("Copy"); >> close(); >> selectImage(stack); >> run("Add Slice"); >> run("Paste"); >> } >> Fit.getEquation(i, name, formula); >> //print(""); >> print("index: "+i+", "+name+ " ["+formula+"]"); >> /*print(" R^2="+d2s(Fit.rSquared,3)); >> for (j=0; j<Fit.nParams; j++) >> print(" p["+j+"]="+d2s(Fit.p(j),6));*/ >> } >> setBatchMode(false); >> run("Select None"); >> rename(stackTitle); >> } >> // END ################################################## >> >> > > -- > ImageJ mailing list: http://imagej.nih.gov/ij/list.html > -- ImageJ mailing list: http://imagej.nih.gov/ij/list.html |
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Again,
what do you mean by: "it worked in 90% of the cases" How can you know that the missing data is correctly interpolated. Did you make tests with all kinds of data and have you cut out parts of them and after interpolation compared the cut-out data with the reconstructed ones? What criterion do you use when comparing both? I think such a comparison with a suitable criterion is a reasonable way to show that your method works correctly. Regards Herbie ::::::::::::::::::::::::::::::::::::::::::::: Am 07.05.19 um 12:48 schrieb Rainer M. Engel: > Good day Herbie, > > you are right about that. Some 1-2 years ago, but right now I'm refining > my code and I have a better overview now, since the underlying data > retrieval is more robust. > >> more an art than hard science > > I think I tended to 'misuse' it, even though it worked in 90% of the > cases without the need to set filtering windows. So I wanted to check on > the limits again. > > One thought: > The data I get is very stable and missing data positions are marked to > be filled. Right now I'm linear interpolating these, but by this these > positions have the same "weight" like the former analysed ones, which > then tend to be more deviating and hence will be smoothed stronger. This > is one reason why I came back and tested the fitting methods, which are > doing a nice job at least in some regions.. > > Thank you for your fast reply and thoughts on this.. > > Regards, > Rainer > > > Am 07.05.2019 um 12:06 schrieb Herbie: >> Good day Rainer, >> >> if I remember correctly you've discussed a similar topic before... >> >> Please understand that curve fitting or general regression, to be more >> precise, needs a "null hypothesis" or "model", i.e. you need to know the >> type of function that underlies your data. If you don't have an idea >> about the data generating process, you are lost or the results become >> arbitrary. >> >> There are very few exceptions from this rule and two of them are >> interpolation and smoothing (they are no regression techniques). >> Interpolation is well-defined if the data is correctly sampled from a >> continuous process. Smoothing and related operations are less >> well-defined and they are more an art than hard science. >> >> Please keep this in mind and consider the proper approaches: >> >> Curve fitting provided by ImageJ is based on "general regression" and >> requires a "null hypothesis" or "model". The most general "model" is a >> polynomial of high degree but I don't recommend to use it without >> profound reason. >> >> If your data doesn't fit a mathematically formulated "model", then >> "general regression" according to an arbitrary chosen "model"-function >> is like a shot in the dark. >> >> I fear that the "general regression"-approach is not what you looking for. >> >> Regards >> >> Herbie >> >> ::::::::::::::::::::::::::::::::::::::::::::: >> Am 07.05.19 um 11:40 schrieb Rainer M. Engel: >>> Hello everyone, >>> >>> I retrieve some data, which can have missing data points even in the >>> beginning or at the end. So what I needed was both; interpolation as >>> well as extrapolation, to fill these positions. >>> >>> Typically the deviation over the data is not that huge and a straight >>> fitting works well under this circumstance. Otherwise on more >>> fluctuating/arbitrary data I got no satisfying results with any of the >>> available fitting functions (see example makro below). >>> >>> Maybe my expectations are wrong about this and I thought that there is a >>> way to adjusted methods in how close a resulting fitting would be >>> applied. So I used median/mean as prefilter-methods. >>> >>> What wonders me is that sometimes a certain method works very well. In >>> my case Rodbard, Error or a Gaussian. But sometimes, like in the given >>> example, it makes no sense to use these at all. >>> >>> Is this typical or is my data strange :) >>> >>> Regards, >>> Rainer >>> >>> >>> >>> >>> // START (makro) ############################################# >>> // Curve Fitting Demo >>> // >>> // This macro demonstates how to use the Fit.* functions, >>> // which were added to the macro language in v1.41k. >>> >>> // modified to see some larger data sets >>> >>> x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, >>> 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, >>> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, >>> 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, >>> 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, >>> 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, >>> 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, >>> 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, >>> 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, >>> 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, >>> 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, >>> 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, >>> 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, >>> 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, >>> 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, >>> 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, >>> 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, >>> 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, >>> 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, >>> 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, >>> 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, >>> 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, >>> 333, 334, 335, 336, 337, 338, 339); >>> y = newArray(96, 97, 98, 101, 101, 102, 101, 101, 101, 101, 101, 101, >>> 100, 100, 101, 101, 102, 101, 100, 100, 101, 100, 99, 99, 100, 101, 100, >>> 100, 101, 101, 100, 99, 99, 99, 99, 99, 99, 98, 99, 99, 98, 97, 96, 96, >>> 96, 96, 95, 94, 95, 95, 94, 94, 93, 90, 89, 89, 88, 87, 87, 87, 87, 84, >>> 85, 84, 85, 84, 83, 81, 81, 81, 80, 80, 78, 77, 77, 77, 76, 75, 75, 75, >>> 75, 74, 73, 73, 73, 73, 72, 71, 71, 71, 70, 69, 68, 68, 68, 67, 66, 66, >>> 65, 66, 65, 66, 64, 64, 64, 64, 64, 65, 65, 66, 66, 65, 65, 65, 65, 66, >>> 66, 66, 67, 68, 68, 68, 68, 69, 69, 70, 69, 69, 69, 70, 70, 71, 70, 72, >>> 72, 72, 71, 71, 72, 73, 73, 73, 73, 75, 75, 75, 74, 75, 75, 76, 76, 77, >>> 76, 76, 77, 78, 77, 77, 77, 78, 79, 80, 79, 79, 80, 81, 80, 80, 81, 81, >>> 81, 80, 81, 81, 82, 82, 82, 81, 82, 83, 83, 83, 82, 83, 83, 83, 82, 84, >>> 84, 84, 84, 84, 83, 83, 84, 84, 83, 84, 84, 84, 83, 83, 83, 83, 85, 85, >>> 84, 83, 84, 84, 84, 83, 83, 84, 84, 85, 84, 83, 83, 84, 83, 83, 83, 83, >>> 84, 83, 83, 84, 84, 84, 84, 84, 83, 84, 84, 84, 83, 83, 83, 84, 84, 83, >>> 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 85, 84, 83, 83, 84, >>> 84, 84, 84, 85, 85, 84, 83, 84, 84, 83, 84, 83, 84, 85, 84, 84, 84, 84, >>> 84, 85, 84, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 84, 84, >>> 84, 83, 83, 84, 84, 83, 83, 84, 84, 84, 83, 83, 84, 84, 84, 84, 83, 83, >>> 84, 84, 83, 82, 83, 83, 83, 83, 83, 83, 84, 84, 83, 83, 83, 84, 84, 83, >>> 83, 83, 83); >>> >>> doAllCurveFittings("raw", x, y); //pretty dense x-range >>> >>> x = newArray(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, >>> 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, >>> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, >>> 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, >>> 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, >>> 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, >>> 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, >>> 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, >>> 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, >>> 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, >>> 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, >>> 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, >>> 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, >>> 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, >>> 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, >>> 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, >>> 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, >>> 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, >>> 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, >>> 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, >>> 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, >>> 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, >>> 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339); >>> y = newArray(100.8147, 100.7783, 100.7480, 100.6839, 100.6290, 100.6147, >>> 100.6335, 100.6794, 100.6647, 100.6252, 100.5897, 100.5647, 100.5397, >>> 100.4647, 100.3897, 100.3647, 100.3647, 100.3147, 100.2897, 100.2897, >>> 100.3147, 100.2897, 100.2147, 100.1647, 100.0897, 100.0147, 99.9147, >>> 99.7897, 99.6647, 99.5897, 99.5397, 99.4147, 99.2647, 99.1147, 98.9397, >>> 98.7147, 98.4397, 98.1897, 97.8897, 97.5897, 97.2647, 96.9647, 96.7147, >>> 96.3897, 96.0730, 95.7147, 95.3397, 94.9480, 94.5897, 94.1897, 93.7147, >>> 93.2397, 92.7897, 92.3397, 91.8897, 91.4147, 90.9647, 90.4397, 89.9647, >>> 89.4397, 88.9647, 88.4647, 87.9147, 87.3647, 86.8064, 86.2397, 85.6647, >>> 85.0814, 84.4397, 83.7647, 83.1647, 82.5897, 81.9897, 81.3897, 80.8147, >>> 80.2397, 79.6647, 79.1147, 78.5147, 77.9397, 77.3147, 76.7397, 76.1897, >>> 75.6397, 75.1147, 74.6147, 74.1147, 73.5647, 73.0397, 72.5647, 72.0647, >>> 71.5397, 71.0147, 70.5397, 70.0147, 69.5397, 69.0397, 68.6397, 68.1647, >>> 67.6897, 67.2397, 66.7897, 66.3897, 66.0897, 65.7897, 65.5397, 65.3147, >>> 65.1397, 64.9897, 64.8647, 64.7397, 64.6897, 64.7147, 64.7147, 64.7897, >>> 64.9147, 65.0397, 65.1397, 65.3647, 65.6147, 65.8647, 66.1647, 66.3897, >>> 66.6147, 66.8397, 67.0647, 67.3147, 67.6147, 67.9147, 68.2647, 68.6147, >>> 68.9147, 69.1647, 69.4397, 69.7147, 69.9647, 70.2397, 70.5147, 70.7647, >>> 71.0897, 71.4147, 71.6897, 71.9897, 72.3147, 72.6147, 72.9147, 73.1897, >>> 73.4647, 73.7147, 73.9147, 74.1647, 74.4397, 74.7147, 74.9897, 75.2397, >>> 75.4647, 75.7397, 76.0647, 76.3397, 76.5397, 76.7897, 77.0647, 77.3397, >>> 77.5397, 77.8147, 78.0647, 78.3147, 78.4897, 78.7397, 78.9897, 79.2397, >>> 79.4647, 79.7147, 79.9397, 80.1897, 80.4647, 80.6897, 80.8397, 81.0147, >>> 81.2147, 81.3647, 81.4897, 81.6147, 81.8397, 82.0147, 82.1647, 82.3147, >>> 82.5147, 82.6397, 82.7647, 82.8647, 82.9647, 83.0647, 83.2147, 83.3147, >>> 83.3647, 83.3647, 83.4147, 83.4897, 83.5147, 83.6147, 83.7397, 83.8397, >>> 83.8147, 83.8147, 83.8147, 83.8397, 83.7897, 83.8147, 83.8397, 83.8397, >>> 83.8897, 83.9147, 83.8647, 83.8397, 83.8397, 83.8397, 83.8397, 83.8397, >>> 83.8397, 83.7897, 83.6897, 83.6147, 83.6397, 83.6397, 83.6397, 83.6147, >>> 83.6647, 83.6397, 83.6397, 83.6647, 83.6147, 83.5647, 83.5647, 83.5397, >>> 83.5397, 83.5897, 83.5647, 83.5647, 83.5897, 83.6147, 83.6647, 83.7147, >>> 83.7147, 83.7147, 83.7397, 83.7647, 83.7897, 83.8397, 83.8897, 83.8897, >>> 83.9397, 83.9897, 84.0147, 84.0397, 84.0647, 84.0897, 84.1647, 84.2147, >>> 84.2647, 84.2897, 84.2897, 84.2897, 84.2897, 84.2897, 84.2397, 84.2397, >>> 84.1897, 84.2147, 84.2147, 84.2397, 84.2397, 84.2897, 84.3397, 84.3897, >>> 84.4397, 84.4397, 84.3897, 84.3647, 84.3397, 84.3147, 84.3397, 84.3647, >>> 84.3897, 84.4147, 84.4647, 84.4897, 84.5397, 84.5397, 84.5397, 84.5397, >>> 84.5147, 84.4897, 84.4397, 84.4147, 84.3647, 84.3647, 84.3897, 84.3647, >>> 84.3647, 84.3397, 84.3147, 84.2647, 84.2147, 84.2147, 84.2147, 84.1897, >>> 84.1647, 84.1147, 84.0647, 84.0397, 84.0397, 83.9897, 83.9647, 83.9397, >>> 83.9147, 83.8647, 83.8397, 83.8397, 83.7897, 83.8147, 83.8147, 83.8147, >>> 83.8147, 83.7647, 83.7147, 83.6647, 83.6397, 83.6647, 83.6647, 83.6647, >>> 83.6252, 83.6369, 83.6794, 83.6960, 83.6814, 83.7004, 83.7224, 83.7480, >>> 83.7783); >>> >>> doAllCurveFittings("pre-filtered", x, y); //full x-range >>> >>> function doAllCurveFittings(stackTitle, xpoints, ypoints) { >>> // Do a straight line fit >>> Fit.doFit("Straight Line", xpoints, ypoints); >>> //print("a="+d2s(Fit.p(0),6)+", b="+d2s(Fit.p(1),6)); >>> >>> // Do all possible fits, plot them and add the plots to a stack >>> setBatchMode(true); >>> for (i=0; i<Fit.nEquations; i++) { >>> Fit.doFit(i, xpoints, ypoints); >>> Fit.plot(); >>> if (i==0) >>> stack = getImageID; >>> else { >>> run("Copy"); >>> close(); >>> selectImage(stack); >>> run("Add Slice"); >>> run("Paste"); >>> } >>> Fit.getEquation(i, name, formula); >>> //print(""); >>> print("index: "+i+", "+name+ " ["+formula+"]"); >>> /*print(" R^2="+d2s(Fit.rSquared,3)); >>> for (j=0; j<Fit.nParams; j++) >>> print(" p["+j+"]="+d2s(Fit.p(j),6));*/ >>> } >>> setBatchMode(false); >>> run("Select None"); >>> rename(stackTitle); >>> } >>> // END ################################################## >>> >>> >> >> -- >> ImageJ mailing list: http://imagej.nih.gov/ij/list.html >> > > -- > ImageJ mailing list: http://imagej.nih.gov/ij/list.html > -- ImageJ mailing list: http://imagej.nih.gov/ij/list.html |
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