TEND-EVALCLAMP(1)

💖 tend-evalclamp(1) — the manual page, but make it make sense 💖

okay bestie, so this is tend-evalclamp, living its best life in section 1 (User Commands) of the manual. it's a tend subcommand, and it's here to ✨squish your eigenvalues into a range✨. buckle up.

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🌸 NAME 🌸

tend-evalclamp — modify shape by clamping eigenvalues in some range

like literally that's it, that's the whole job description. she clamps. she slays. 💅

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🎀 SYNOPSIS 🎀

tend evalclamp -min <min> [-max <max>] [-i <nin>] [-o <nout>]

so -min is the non-negotiable one (no brackets, no escape), everything else is optional. iykyk.

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🦋 DESCRIPTION 🦋

okay so picture this: you've got a tensor, and you want to modify its shape by clamping eigenvalues into some range. that's the vibe. that's the whole assignment.

the orientation of the tensor? unchanged. not touched. not even a little. she keeps her directions, she just changes her shape, and honestly? relatable. 🙈

⚠️ but bestie, listen — unlike tend-anscale(1), this operation can completely change the shape of the tensor. not a glow-up, a full transformation. so know what you're doing before you run it. fr fr.

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✨ OPTIONS ✨

• -min <min> — eigenvalues get clamped from below by this value (it's the minimum output eigenvalue). and if you don't want any minimum clamping at all? just hit it with "nan" and she'll skip that part entirely. (float)
• -max <max> — eigenvalues get clamped from above by this value (the maximum output eigenvalue). same deal: use "nan" and no maximum clamping happens. (float)
• -i <nin> — the input diffusion tensor volume. default: "-"
• -o <nout> — the output image, and yes she's floating point, that's just how she is. (string) default: "-"

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👑 SEE ALSO 👑

• tend(1)
• tend-anscale(1)

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and that's the whole page, bestie. no cap. go clamp something. 💜

mwah 💋 — this one's dated April 2021, and it came to you straight from the User Commands section of the manual. take care of her. 🫶