upstream/ipython Files · docs/examples/parallel/rmt/rmtkernel.py

new completer for qtconsole....

new completer for qtconsole. add a completer to the qtconsole that is navigable by arraow keys and tab. One need to call it twice to get it on focus and be able to select completion with Return. looks like zsh completer, not the gui drop down list of --gui-completer. This also try to split the completion logic from console_widget, and try to keep the old completer qui around. The plain completer that never takes focus back, and the QlistWidget completer. to switch between the 3, the --gui-completion flag as been changed to take an argument (plain, droplist, ncurses).

Fernando Perez - - Load All Authors

File last commit:

r4910:0dc49390


                r7389:1422d277

Download file

             rmtkernel.py
        
                    42 lines
            
             | 1.2 KiB
            
                | text/x-python
            
             |
                PythonLexer
            
             / docs / examples / parallel / rmt / rmtkernel.py
          
                    History
                
                 |
                  Annotation
                 | Raw
                 |Copy content
                 |Copy permalink

      #-------------------------------------------------------------------------------

      # Core routines for computing properties of symmetric random matrices.

      #-------------------------------------------------------------------------------

      import numpy as np

      ra = np.random

      la = np.linalg

      def GOE(N):

          """Creates an NxN element of the Gaussian Orthogonal Ensemble"""

          m = ra.standard_normal((N,N))

          m += m.T

          return m/2

      def center_eigenvalue_diff(mat):

          """Compute the eigvals of mat and then find the center eigval difference."""

          N = len(mat)

          evals = np.sort(la.eigvals(mat))

          diff = np.abs(evals[N/2] - evals[N/2-1])

          return diff

      def ensemble_diffs(num, N):

          """Return num eigenvalue diffs for the NxN GOE ensemble."""

          diffs = np.empty(num)

          for i in xrange(num):

              mat = GOE(N)

              diffs[i] = center_eigenvalue_diff(mat)

          return diffs

      def normalize_diffs(diffs):

          """Normalize an array of eigenvalue diffs."""

          return diffs/diffs.mean()

      def normalized_ensemble_diffs(num, N):

          """Return num *normalized* eigenvalue diffs for the NxN GOE ensemble."""

          diffs = ensemble_diffs(num, N)

          return normalize_diffs(diffs)

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages

				#-------------------------------------------------------------------------------
				# Core routines for computing properties of symmetric random matrices.
				#-------------------------------------------------------------------------------

				import numpy as np
				ra = np.random
				la = np.linalg

				def GOE(N):
				"""Creates an NxN element of the Gaussian Orthogonal Ensemble"""
				m = ra.standard_normal((N,N))
				m += m.T
				return m/2


				def center_eigenvalue_diff(mat):
				"""Compute the eigvals of mat and then find the center eigval difference."""
				N = len(mat)
				evals = np.sort(la.eigvals(mat))
				diff = np.abs(evals[N/2] - evals[N/2-1])
				return diff


				def ensemble_diffs(num, N):
				"""Return num eigenvalue diffs for the NxN GOE ensemble."""
				diffs = np.empty(num)
				for i in xrange(num):
				mat = GOE(N)
				diffs[i] = center_eigenvalue_diff(mat)
				return diffs


				def normalize_diffs(diffs):
				"""Normalize an array of eigenvalue diffs."""
				return diffs/diffs.mean()


				def normalized_ensemble_diffs(num, N):
				"""Return num normalized eigenvalue diffs for the NxN GOE ensemble."""
				diffs = ensemble_diffs(num, N)
				return normalize_diffs(diffs)