The Wayback Machine - http://web.archive.org/web/20200703194611/https://github.com/topics/transformations?l=python
Skip to content
#

transformations

Here are 42 public repositories matching this topic...

fatboyzz
fatboyzz commented Oct 2, 2019
  1. Create 'Cube', 'Cube.001', 'Cube.002'. Create collection 'Collection 1'.
  2. Move 'Cube.002' into 'Collection 1'.
  3. Exclude 'Collection 1' by clicking the check box on its left.
  4. Goto 'Quick Align Planes' Grab Destination of 'Cube'.
  5. Select 'Cube.001' select three points and hit 'Apply to Object' button.
  6. Get error message "Object Cube.002 not in View layer 'View layer'".
mojodo
mojodo commented Apr 24, 2020

The y=arccos(x) function results in numerical imprecision when y should be close to 0 or pi, i.e. when x is close to 1 or x is close to -1.

To circumvent this you could for example write the following:

# determine angle
angle = np.arccos((np.trace(R) - 1.0) / 2.0)
    
if angle < 0.001: # get numerical precision at low angular values
   angle = np.arcsin(np.linalg.n
bug
spydrnet
thunder-hammer
thunder-hammer commented Feb 26, 2020

This is a list of different shortcuts that would be super useful to include in the API. There is currently a long way to do all of these things. It would be useful to add the following shortcuts into the API to make the tools easier to use and work with.

  • set the top instance
    Long way:
    top = Instance()
    top.reference = top_definition
    netlist.top_instance = top
    could be simplif
paulmelnikow
paulmelnikow commented Oct 19, 2019

This library is close to being ready for a 1.0 release!

The 1.0 release should include:

  • 100% test coverage (#77 #78 #79 #80 #102)
  • Hosted documentation (#82)
  • At least one sentence of documentation for each function (#134)
  • An updated readme

Ideally I'd spend some time polishing the API before releasing 1.0, though I don't want to block on that. I think it's

Undergraduate level computational physics solutions. Includes euler-method, linear equations, multi-dimensional vector manipulation, non-linear equations, rk4, and integration techniques in Python

  • Updated Jan 15, 2020
  • Python

Two Sigma Financial Modeling Challenge - The goal of the competition is to predict a variable ‘y’ which depends on 110 anonymized features pertaining to a time-varying value for a financial instrument, No further information are provided on the meaning of the features, the transformations that were applied to them, the timescale, or the type of instruments that are included in the data. Prototyped predictive models, using linear regression and extra-trees regressor to develop a predictive model for predicting the output variable ‘y’.

  • Updated Mar 30, 2018
  • Python

Improve this page

Add a description, image, and links to the transformations topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the transformations topic, visit your repo's landing page and select "manage topics."

Learn more

You can’t perform that action at this time.