Skip to content

Instantly share code, notes, and snippets.

@cdeil

cdeil/gist:11374384

Created April 28, 2014 14:48
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save cdeil/11374384 to your computer and use it in GitHub Desktop.
Save cdeil/11374384 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
gistfile1.txt
$ python3.2 setup.py test
/Users/deil/Library/Python/3.2/lib/python/site-packages/astropy-0.4.dev8186-py3.2-macosx-10.9-x86_64.egg/astropy/setup_helpers.py:1023: AstropyDeprecationWarning: astropy.setup_helpers.update_package_files is deprecated. Update your setup.py to use astropy.setup_helpers.get_package_info instead.
AstropyDeprecationWarning)
running test
running build
running build_py
regenerating default gammapy.cfg file
copying gammapy/gammapy.cfg -> build/lib.macosx-10.9-x86_64-3.2/gammapy
running build_scripts
==================================================================== test session starts ====================================================================
platform darwin -- Python 3.2.5 -- pytest-2.5.1
Running tests with Astropy version 0.4.dev8186.
Running tests in gammapy /Users/deil/code/gammapy/docs.
Platform: Darwin-13.1.0-x86_64-i386-64bit
Executable: /opt/local/Library/Frameworks/Python.framework/Versions/3.2/Resources/Python.app/Contents/MacOS/Python
Full Python Version:
3.2.5 (default, Nov 26 2013, 10:28:25)
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.2.79)]
encodings: sys: utf-8, locale: UTF-8, filesystem: utf-8, unicode bits: 15
byteorder: little
float info: dig: 15, mant_dig: 15
Numpy: 1.8.1
Scipy: 0.13.3
Matplotlib: not available
h5py: 2.2.1
Running doctests in .rst files is not supported on Python 3.x
collected 126 items
gammapy/astro/population/tests/test_spatial.py .
gammapy/astro/population/tests/test_velocity.py .
gammapy/astro/source/tests/test_pulsar.py ..
gammapy/astro/source/tests/test_pwn.py .
gammapy/astro/source/tests/test_snr.py ..
gammapy/background/tests/test_maps.py x
gammapy/background/tests/test_reflected.py x
gammapy/background/tests/test_ring.py ....
gammapy/catalog/tests/test_analysis.py .
gammapy/catalog/tests/test_utils.py ...
gammapy/datasets/tests/test_all.py ....s
gammapy/detect/tests/test_blob.py .
gammapy/detect/tests/test_cwt.py .
gammapy/detect/tests/test_iterfind.py .
gammapy/detect/tests/test_matched_filter.py .x
gammapy/detect/tests/test_sex.py .
gammapy/detect/tests/test_test_statistic.py xxx
gammapy/detect/tests/test_utils.py .
gammapy/image/tests/test_healpix.py s
gammapy/image/tests/test_measure.py .
gammapy/image/tests/test_plotting.py ss
gammapy/image/tests/test_profile.py .
gammapy/image/tests/test_utils.py ....x.xFFFF.F
gammapy/irf/tests/test_effective_area.py .
gammapy/irf/tests/test_energy_dispersion.py x
gammapy/morphology/tests/test_fermi_psf.py .
gammapy/morphology/tests/test_gauss.py ............
gammapy/morphology/tests/test_model.py .
gammapy/morphology/tests/test_psf.py ...
gammapy/morphology/tests/test_theta.py ....
gammapy/obs/tests/test_run.py ..
gammapy/shower/tests/test_hillas.py .
gammapy/shower/tests/test_image.py ..
gammapy/spectrum/tests/test_cosmic_ray.py ...
gammapy/spectrum/tests/test_crab.py .
gammapy/spectrum/tests/test_diffuse.py x
gammapy/spectrum/tests/test_flux_point.py xx
gammapy/spectrum/tests/test_inverse_compton.py .
gammapy/spectrum/tests/test_isrf.py xx.
gammapy/spectrum/tests/test_pion.py x
gammapy/spectrum/tests/test_powerlaw.py x.....
gammapy/spectrum/tests/test_sed.py xxxx
gammapy/spectrum/tests/test_utils.py .
gammapy/stats/tests/test_data.py ...
gammapy/stats/tests/test_fit_statistics.py ...
gammapy/stats/tests/test_fitting.py .
gammapy/stats/tests/test_poisson.py .x
gammapy/stats/tests/test_utils.py .
gammapy/utils/coordinates/tests/test_celestial.py ......
gammapy/utils/coordinates/tests/test_other.py .
gammapy/utils/distributions/tests/test_general_random.py x
gammapy/utils/root/tests/test_convert.py sss
gammapy/utils/tests/test_const.py .
gammapy/utils/tests/test_random.py ..
gammapy/utils/tests/test_region.py .
========================================================================= FAILURES ==========================================================================
_________________________________________________________ TestBlockReduceHDU.test_image[operation0] _________________________________________________________
self = <gammapy.image.tests.test_utils.TestBlockReduceHDU object at 0x108335f90>, operation = <function sum at 0x103b64b78>
@pytest.mark.parametrize(('operation'), list([np.sum, np.mean]))
def test_image(self, operation):
> image_1 = utils.block_reduce_hdu(self.image, (2, 4), func=operation)
gammapy/image/tests/test_utils.py:130:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
input_hdu = <astropy.io.fits.hdu.image.ImageHDU object at 0x108335250>, block_size = (4, 2), func = <function sum at 0x103b64b78>, cval = 0
def block_reduce_hdu(input_hdu, block_size, func, cval=0):
"""Provides block reduce functionality for image HDUs.
See http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.block_reduce
Parameters
----------
image_hdu : `astropy.io.fits.ImageHDU`
Original image HDU, unscaled
block_size : array_like
Array containing down-sampling integer factor along each axis.
func : callable
Function object which is used to calculate the return value for each local block.
This function must implement an axis parameter such as `numpy.sum` or `numpy.mean`.
cval : float (optional)
Constant padding value if image is not perfectly divisible by the block size. Default 0.
Returns
-------
image_hdu : `astropy.io.fits.ImageHDU`
Rebinned Image HDU
"""
from skimage.measure import block_reduce
header = input_hdu.header.copy()
data = input_hdu.data
# Define new header values for new resolution
header['CDELT1'] = header['CDELT1'] * block_size[0]
header['CDELT2'] = header['CDELT2'] * block_size[1]
header['CRPIX1'] = ((header['CRPIX1'] - 0.5) / block_size[0]) + 0.5
header['CRPIX2'] = ((header['CRPIX2'] - 0.5) / block_size[1]) + 0.5
if len(input_hdu.data.shape) == 3:
block_size = (1, block_size[1], block_size[0])
elif len(input_hdu.data.shape) == 2:
block_size = (block_size[1], block_size[0])
> data_reduced = block_reduce(data, block_size, func, cval)
gammapy/image/utils.py:871:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
image = array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., ...1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
block_size = (4, 2), func = <function sum at 0x103b64b78>, cval = 0
def block_reduce(image, block_size, func=np.sum, cval=0):
"""Down-sample image by applying function to local blocks.
Parameters
----------
image : ndarray
N-dimensional input image.
block_size : array_like
Array containing down-sampling integer factor along each axis.
func : callable
Function object which is used to calculate the return value for each
local block. This function must implement an ``axis`` parameter such as
``numpy.sum`` or ``numpy.min``.
cval : float
Constant padding value if image is not perfectly divisible by the
block size.
Returns
-------
image : ndarray
Down-sampled image with same number of dimensions as input image.
Examples
--------
>>> from skimage.measure import block_reduce
>>> image = np.arange(3*3*4).reshape(3, 3, 4)
>>> image # doctest: +NORMALIZE_WHITESPACE
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]],
[[24, 25, 26, 27],
[28, 29, 30, 31],
[32, 33, 34, 35]]])
>>> block_reduce(image, block_size=(3, 3, 1), func=np.mean)
array([[[ 16., 17., 18., 19.]]])
>>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=np.max)
>>> image_max1 # doctest: +NORMALIZE_WHITESPACE
array([[[11]],
[[23]],
[[35]]])
>>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max)
>>> image_max2 # doctest: +NORMALIZE_WHITESPACE
array([[[27],
[31],
[35]]])
"""
if len(block_size) != image.ndim:
raise ValueError("`block_size` must have the same length "
"as `image.shape`.")
pad_width = []
for i in range(len(block_size)):
if image.shape[i] % block_size[i] != 0:
after_width = block_size[i] - (image.shape[i] % block_size[i])
else:
after_width = 0
pad_width.append((0, after_width))
image = pad(image, pad_width=pad_width, mode='constant',
constant_values=cval)
> out = view_as_blocks(image, block_size)
/Users/deil/Library/Python/3.2/lib/python/site-packages/scikit_image-0.10dev-py3.2-macosx-10.9-x86_64.egg/skimage/measure/block.py:70:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
arr_in = array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., ...1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
block_shape = array([4, 2])
def view_as_blocks(arr_in, block_shape):
"""Block view of the input n-dimensional array (using re-striding).
Blocks are non-overlapping views of the input array.
Parameters
----------
arr_in : ndarray
N-d input array.
block_shape : tuple
The shape of the block. Each dimension must divide evenly into the
corresponding dimensions of `arr_in`.
Returns
-------
arr_out : ndarray
Block view of the input array. If `arr_in` is non-contiguous, a copy
is made.
Examples
--------
>>> import numpy as np
>>> from skimage.util.shape import view_as_blocks
>>> A = np.arange(4*4).reshape(4,4)
>>> A
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> B = view_as_blocks(A, block_shape=(2, 2))
>>> B[0, 0]
array([[0, 1],
[4, 5]])
>>> B[0, 1]
array([[2, 3],
[6, 7]])
>>> B[1, 0, 1, 1]
13
>>> A = np.arange(4*4*6).reshape(4,4,6)
>>> A # doctest: +NORMALIZE_WHITESPACE
array([[[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23]],
[[24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35],
[36, 37, 38, 39, 40, 41],
[42, 43, 44, 45, 46, 47]],
[[48, 49, 50, 51, 52, 53],
[54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65],
[66, 67, 68, 69, 70, 71]],
[[72, 73, 74, 75, 76, 77],
[78, 79, 80, 81, 82, 83],
[84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95]]])
>>> B = view_as_blocks(A, block_shape=(1, 2, 2))
>>> B.shape
(4, 2, 3, 1, 2, 2)
>>> B[2:, 0, 2] # doctest: +NORMALIZE_WHITESPACE
array([[[[52, 53],
[58, 59]]],
[[[76, 77],
[82, 83]]]])
"""
if not isinstance(block_shape, tuple):
raise TypeError('block needs to be a tuple')
block_shape = np.array(block_shape)
if (block_shape <= 0).any():
raise ValueError("'block_shape' elements must be strictly positive")
if block_shape.size != arr_in.ndim:
raise ValueError("'block_shape' must have the same length "
"as 'arr_in.shape'")
arr_shape = np.array(arr_in.shape)
if (arr_shape % block_shape).sum() != 0:
raise ValueError("'block_shape' is not compatible with 'arr_in'")
# -- restride the array to build the block view
if not arr_in.flags.contiguous:
warn(RuntimeWarning("Cannot provide views on a non-contiguous input "
"array without copying."))
arr_in = np.ascontiguousarray(arr_in)
new_shape = tuple(arr_shape / block_shape) + tuple(block_shape)
new_strides = tuple(arr_in.strides * block_shape) + arr_in.strides
> arr_out = as_strided(arr_in, shape=new_shape, strides=new_strides)
/Users/deil/Library/Python/3.2/lib/python/site-packages/scikit_image-0.10dev-py3.2-macosx-10.9-x86_64.egg/skimage/util/shape.py:100:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
x = array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., ...1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
shape = (2.0, 6.0, 4, 2), strides = (384, 16, 96, 8)
def as_strided(x, shape=None, strides=None):
""" Make an ndarray from the given array with the given shape and strides.
"""
interface = dict(x.__array_interface__)
if shape is not None:
interface['shape'] = tuple(shape)
if strides is not None:
interface['strides'] = tuple(strides)
> array = np.asarray(DummyArray(interface, base=x))
/opt/local/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/site-packages/numpy/lib/stride_tricks.py:30:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a = <numpy.lib.stride_tricks.DummyArray object at 0x10840bbd0>, dtype = None, order = None
def asarray(a, dtype=None, order=None):
"""
Convert the input to an array.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples
of lists and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major ('C') or column-major ('F' for FORTRAN)
memory representation. Defaults to 'C'.
Returns
-------
out : ndarray
Array interpretation of `a`. No copy is performed if the input
is already an ndarray. If `a` is a subclass of ndarray, a base
class ndarray is returned.
See Also
--------
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])
Existing arrays are not copied:
>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True
If `dtype` is set, array is copied only if dtype does not match:
>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False
Contrary to `asanyarray`, ndarray subclasses are not passed through:
>>> issubclass(np.matrix, np.ndarray)
True
>>> a = np.matrix([[1, 2]])
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
"""
> return array(a, dtype, copy=False, order=order)
E DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
/opt/local/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/site-packages/numpy/core/numeric.py:460: DeprecationWarning
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment