I have a ndarray a, corresponding to the acquired data. I need to split it into many single scans and then average over them. The original data and function along with it is plotted in the following.

I am looking to find a function related to profs 1 and profs 2 ot expression that will chip away a single scan from it. And display the average plot. The function or expression should result to change the figure into one averaged figure of the rewinding scans;

Will thumbs up!

profs1 = np.zeros((1600, len(fns))) profs2 = np.zeros((1600, len(fns))) for ind, fn_ in enumerate(fns): print(ind, end=',') img = CV2.imread(fn_, -1).astype(np.float64) - 2**15 img -= np.median(img) #plt.figure() #plt. imshow(img) profs1[:, ind] = np. sum(img[400:500, :), axis=0) profs2[:, ind] = np. sum(img[600:700,:], axis=0) 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,4 5,46,47,48,49,50,51,52,53,54,55,56,57,58,59, 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, 86,8 7,88,89,90,91,92,93,94,95,96,97,98,99, 100, 101, 102,103,104,105,106,107, 108, 109, 110, 111, 112, 113, 114, 115, 116,117,118,119,120,121,1 22,123, 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, 184,18 5,186, 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, 216,2 17,218, 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, 28 0,281, 282,283, 284,285,286,287,288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300,301,302,303,304,305,306,307,308,309,310,311,3 12,313, 314, 315, 316, 317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340, 341,342,343, 344,345,346,347,348,349,350, 351, 352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371, 372, 373,374,37 5,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,4 07,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438, 439,440,441,442,443,444,445,446,447,448,449,450,451, 452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,47 0,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,5 02,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533, 534,535,536,537,538,539,540,541,542,543, 544,545,546,547,548,549,550,551,552,553,554,555,556,557, 01 200 400 600 800 1000 1200 1400 0 200 400 0 200 400 600 800 1000 1200 1400 0 250 500 750 profs1 = np.zeros((1600, len(fns))) profs2 = np.zeros((1600, len(fns))) for ind, fn_ in enumerate(fns): print(ind, end=',') img = CV2.imread(fn_, -1).astype(np.float64) - 2**15 img -= np.median(img) #plt.figure() #plt. imshow(img) profs1[:, ind] = np. sum(img[400:500, :), axis=0) profs2[:, ind] = np. sum(img[600:700,:], axis=0) 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,4 5,46,47,48,49,50,51,52,53,54,55,56,57,58,59, 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, 86,8 7,88,89,90,91,92,93,94,95,96,97,98,99, 100, 101, 102,103,104,105,106,107, 108, 109, 110, 111, 112, 113, 114, 115, 116,117,118,119,120,121,1 22,123, 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, 184,18 5,186, 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, 216,2 17,218, 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, 28 0,281, 282,283, 284,285,286,287,288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300,301,302,303,304,305,306,307,308,309,310,311,3 12,313, 314, 315, 316, 317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340, 341,342,343, 344,345,346,347,348,349,350, 351, 352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371, 372, 373,374,37 5,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,4 07,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438, 439,440,441,442,443,444,445,446,447,448,449,450,451, 452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,47 0,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,5 02,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533, 534,535,536,537,538,539,540,541,542,543, 544,545,546,547,548,549,550,551,552,553,554,555,556,557, 01 200 400 600 800 1000 1200 1400 0 200 400 0 200 400 600 800 1000 1200 1400 0 250 500 750