@@ -383,7 +383,7 @@ print("Found automatic threshold t = {}.".format(t))
383383```
384384
385385``` output
386- Found automatic threshold t = 0.4172454549881862 .
386+ Found automatic threshold t = 0.4116003928683858 .
387387```
388388
389389For this root image and a Gaussian blur with the chosen sigma of 1.0,
@@ -658,7 +658,7 @@ def enhanced_root_mass(filename, sigma):
658658 return density
659659
660660
661- all_files = glob.glob(" data/trial-*.jpg" )
661+ all_files = sorted ( glob.glob(" data/trial-*.jpg" ) )
662662for filename in all_files:
663663 density = enhanced_root_mass(filename = filename, sigma = 1.5 )
664664 # output in format suitable for .csv
@@ -669,10 +669,10 @@ The output of the improved program does illustrate that the white circles
669669and labels were skewing our root mass ratios:
670670
671671``` output
672- data/trial-016.jpg,0.046250166223404256
673- data/trial-020.jpg,0.05886968085106383
674- data/trial-216.jpg,0.13712117686170214
675- data/trial-293.jpg,0.13190342420212767
672+ data/trial-016.jpg,0.046261136968085106
673+ data/trial-020.jpg,0.05887167553191489
674+ data/trial-216.jpg,0.13712067819148935
675+ data/trial-293.jpg,0.1319044215425532
676676```
677677:::::::::::::::::::::::::::::::::::::::::: spoiler
678678
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