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"cell_type" : " markdown" ,
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- "id" : " 1c06b3e6-9d38-4ce5-9b1e-55283b8eec4c " ,
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+ "id" : " 27 " ,
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"metadata" : {},
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"source" : [
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" The full table covers a range of distances, and looks like"
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " cd779e2b-7a69-47f8-a7a2-ad4fa2f3fccc " ,
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+ "id" : " 28 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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{
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"attachments" : {},
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"cell_type" : " markdown" ,
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- "id" : " 27 " ,
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+ "id" : " 29 " ,
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"metadata" : {},
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"source" : [
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" ### Computing a time-of-flight coordinate\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 28 " ,
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+ "id" : " 30 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 29 " ,
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+ "id" : " 31 " ,
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"metadata" : {},
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"source" : [
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" Histogramming the data for a plot should show a profile with 6 bumps that correspond to the frames:"
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 30 " ,
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+ "id" : " 32 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 31 " ,
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+ "id" : " 33 " ,
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"metadata" : {},
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"source" : [
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" ### Converting to wavelength\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 32 " ,
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+ "id" : " 34 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 33 " ,
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+ "id" : " 35 " ,
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"metadata" : {},
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"source" : [
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" ### Comparing to the ground truth\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 34 " ,
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+ "id" : " 36 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 35 " ,
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+ "id" : " 37 " ,
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"metadata" : {},
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"source" : [
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" ## Multiple detector pixels\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 36 " ,
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+ "id" : " 38 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 37 " ,
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+ "id" : " 39 " ,
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"metadata" : {},
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"source" : [
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" Our raw data has now a `detector_number` dimension of length 2.\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 38 " ,
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+ "id" : " 40 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 39 " ,
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+ "id" : " 41 " ,
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"metadata" : {},
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"source" : [
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" Computing time-of-flight is done in the same way as above.\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 40 " ,
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+ "id" : " 42 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 41 " ,
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+ "id" : " 43 " ,
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"metadata" : {},
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"source" : [
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" ## Handling time overlap between subframes\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 42 " ,
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+ "id" : " 44 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 43 " ,
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+ "id" : " 45 " ,
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"metadata" : {},
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"source" : [
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" We can now see that there is no longer a gap between the two frames at the center of each pulse (green region).\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 44 " ,
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+ "id" : " 46 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 45 " ,
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+ "id" : " 47 " ,
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"metadata" : {},
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"source" : [
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" The data in the lookup table contains both the mean time-of-flight for each distance and time-of-arrival bin,\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 46 " ,
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+ "id" : " 48 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 47 " ,
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+ "id" : " 49 " ,
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"metadata" : {},
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"source" : [
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" The workflow has a parameter which is used to mask out regions where the standard deviation is above a certain threshold.\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 48 " ,
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+ "id" : " 50 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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},
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{
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"cell_type" : " markdown" ,
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- "id" : " 49 " ,
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+ "id" : " 51 " ,
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"metadata" : {},
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"source" : [
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" We can now see that the central region is masked out.\n " ,
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{
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"cell_type" : " code" ,
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"execution_count" : null ,
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- "id" : " 50 " ,
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+ "id" : " 52 " ,
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"metadata" : {},
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"outputs" : [],
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"source" : [
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