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Merged
marcodallavecchia merged 3 commits into from
Mar 25, 2026
Merged

Measuring Root Mass updated results #362

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1cffe91
updated root mass values based on fresh new environment
Mar 23, 2026
b958160
added callout block to highlight the importance of reproducible envir…
Mar 23, 2026
ad79637
more updated values after re-running with fresh updated environment
Mar 24, 2026
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32 changes: 20 additions & 12 deletions episodes/07-thresholding.md
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Original file line number Diff line number Diff line change
Expand Up @@ -383,7 +383,7 @@ print("Found automatic threshold t = {}.".format(t))
```

```output
Found automatic threshold t = 0.4172454549881862.
Found automatic threshold t = 0.4116003928683858.
```

For this root image and a Gaussian blur with the chosen sigma of 1.0,
Expand Down Expand Up @@ -512,7 +512,7 @@ measure_root_mass(filename="data/trial-016.jpg", sigma=1.5)
```

```output
0.0482436835106383`
0.04907247340425532
```

Now we can use the function to process the series of four images shown above.
Expand All @@ -525,20 +525,28 @@ and the filenames all start with the **trial-** prefix and
end with the **.jpg** suffix.

```python
all_files = glob.glob("data/trial-*.jpg")
all_files = sorted(glob.glob("data/trial-*.jpg"))
for filename in all_files:
density = measure_root_mass(filename=filename, sigma=1.5)
# output in format suitable for .csv
print(filename, density, sep=",")
```

```output
data/trial-016.jpg,0.0482436835106383
data/trial-020.jpg,0.06346941489361702
data/trial-216.jpg,0.14073969414893617
data/trial-293.jpg,0.13607895611702128
data/trial-016.jpg,0.04907247340425532
data/trial-020.jpg,0.06381366356382978
data/trial-216.jpg,0.14205152925531914
data/trial-293.jpg,0.13665791223404256
```

::::::::::::::::::::::::::::::::::::::::: callout

Compare your results with the values above. Do they match exactly? You may find that certain decimal values differ slightly, even when using identical input parameters.

This variation often stems from the specific versions of your installed packages (such as `numpy` or `scikit-image`). As these libraries evolve, updates can introduce subtle changes in numerical handling, underlying algorithms, or rounding logic. This highlights why reproducible environments, as well as reproducible code, are essential for consistent scientific computing.

:::::::::::::::::::::::::::::::::::::::::

::::::::::::::::::::::::::::::::::::::: challenge

## Ignoring more of the images -- brainstorming (10 min)
Expand Down Expand Up @@ -650,7 +658,7 @@ def enhanced_root_mass(filename, sigma):
return density


all_files = glob.glob("data/trial-*.jpg")
all_files = sorted(glob.glob("data/trial-*.jpg"))
for filename in all_files:
density = enhanced_root_mass(filename=filename, sigma=1.5)
# output in format suitable for .csv
Expand All @@ -661,10 +669,10 @@ The output of the improved program does illustrate that the white circles
and labels were skewing our root mass ratios:

```output
data/trial-016.jpg,0.046250166223404256
data/trial-020.jpg,0.05886968085106383
data/trial-216.jpg,0.13712117686170214
data/trial-293.jpg,0.13190342420212767
data/trial-016.jpg,0.046261136968085106
data/trial-020.jpg,0.05887167553191489
data/trial-216.jpg,0.13712067819148935
data/trial-293.jpg,0.1319044215425532
```
:::::::::::::::::::::::::::::::::::::::::: spoiler

Expand Down
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