adler-j

C:\Users\Jonas\Desktop> python -m line_profiler gadgetzan.py.lprof
Timer unit: 3.00191e-07 s

Total time: 8.63965 s
File: d:\github\odl\odl\util\phantom.py
Function: _phantom_3d at line 231

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   231                                           @profile

adler-j

   277                                               # Blank image
   278         1           71     71.0      0.0      p = np.zeros(space.shape)
   279
   280                                               # Create the pixel grid
   281         1          308    308.0      0.0      grid_in = space.grid.meshgr
id()
   282         1           25     25.0      0.0      minp = space.grid.min()
   283         1           18     18.0      0.0      maxp = space.grid.max()
   284
   285                                               # move points to [-1, 1]

adler-j

   277                                               # Blank image
   278         1           71     71.0      0.0      p = np.zeros(space.shape)
   279
   280                                               # Create the pixel grid
   281         1          318    318.0      0.0      grid_in = space.grid.meshgr
id()
   282         1           26     26.0      0.0      minp = space.grid.min()
   283         1           18     18.0      0.0      maxp = space.grid.max()
   284
   285                                               # move points to [-1, 1]

adler-j

import odl
import numpy as np

# Discrete reconstruction space: discretized functions on the rectangle
# [-20, 20]^2 with 300 samples per dimension.
reco_space = odl.uniform_discr(
    min_corner=[-1, -1], max_corner=[1, 1], nsamples=[256, 256],
    dtype='float32')

# Make a parallel beam geometry with flat detector

adler-j

import odl
import numpy as np

# Discrete reconstruction space: discretized functions on the rectangle
# [-20, 20]^2 with 300 samples per dimension.
reco_space = odl.uniform_discr(
    min_corner=[-1, -1], max_corner=[1, 1], nsamples=[256, 256],
    dtype='float32')

# Make a parallel beam geometry with flat detector

adler-j

import odl
import numpy as np

# Discrete reconstruction space: discretized functions on the rectangle
# [-20, 20]^2 with 300 samples per dimension.
reco_space = odl.uniform_discr(
    min_pt=[-1, -1], max_pt=[1, 1], shape=[256, 256])

# Make a parallel beam geometry with flat detector

adler-j

"""Example of using the boolean ray transform with ODL.

This example is with the 2d ray transform, but you could easily change the
geometry to 3d and get the example you gave. See

github.com/odlgroup/odl/blob/master/examples/tomo/ray_trafo_parallel_3d.py

for an example.

Note that some rounding errors may occur.

adler-j

# Copyright 2014-2016 The ODL development group
#
# This file is part of ODL.
#
# ODL is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ODL is distributed in the hope that it will be useful,

adler-j

import fnmatch
import os

matches = []
for root, dirnames, filenames in os.walk('E:/Github/ODL/odl'):
    for filename in fnmatch.filter(filenames, '*.py'):
        matches.append(os.path.join(root, filename))
        
for match in matches:
    with open(match) as f:

adler-j

#!/bin/bash
#set -x 
 
# Shows you the largest objects in your repo's pack file.
# Written for osx.
#
# @see https://stubbisms.wordpress.com/2009/07/10/git-script-to-show-largest-pack-objects-and-trim-your-waist-line/
# @author Antony Stubbs
 
export LC_ALL=C