markovmodel / pyemma_tutorials

How to analyze molecular dynamics data with PyEMMA
Creative Commons Attribution 4.0 International
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Review #156

Closed marscher closed 5 years ago

marscher commented 5 years ago

addresses some open issues.

marscher commented 5 years ago

Thanks I've added your requested changes.

brookehus commented 5 years ago

Got it!

Am 05.09.2018 um 08:15 schrieb Martin K. Scherer notifications@github.com:

@marscher commented on this pull request.

In notebooks/00-pentapeptide-showcase.ipynb:

@@ -231,7 +231,11 @@ "\n", "### TICA\n", "\n",

  • "The goal of the next step is to find a function that maps the usually high-dimensional input space into some lower dimensional space that captures the important dynamics. The recommended way of doing so is a time-lagged independent component analysis (TICA), <a id=\"ref-4\" href=\"#cite-tica2\">molgedey-94, <a id=\"ref-5\" href=\"#cite-tica\">perez-hernandez-13. We perform TICA (with kinetic map scaling) using the lag time obtained from the VAMP-2 score. \n",
  • "The goal of the next step is to find a function that maps the usually high-dimensional input space into some lower dimensional space that captures the important dynamics. The recommended way of doing so is a time-lagged independent component analysis (TICA), <a id=\"ref-4\" href=\"#cite-tica2\">molgedey-94, <a id=\"ref-5\" href=\"#cite-tica\">perez-hernandez-13. We perform TICA (with kinetic map scaling) using the lag time obtained from the VAMP-2 score.\n",
  • "\n",
  • "By using the tica() functions default parameters, we will use as many dimensions in order to preserve $95\%$ of the kinetic variance. By default, tica also applies a kinetic map scaling.\n", the code names the instance "tica", so this is correct, as the text refers to the instance

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