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# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
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# See COPYING file distributed along with the NiBabel package for the
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# copyright and license terms.
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### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
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''' Test for scaling / rounding in volumeutils module '''
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from __future__ import with_statement
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from ..py3k import BytesIO
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from ..volumeutils import (calculate_scale, scale_min_max, finite_range,
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apply_read_scaling, array_to_file, array_from_file)
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from ..casting import type_info
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from numpy.testing import (assert_array_almost_equal, assert_array_equal)
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from nose.tools import (assert_true, assert_equal, assert_raises,
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# Debug print statements
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def test_scale_min_max():
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mx_dt = np.maximum_sctype(np.float)
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for tp in np.sctypes['uint'] + np.sctypes['int']:
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# Need to pump up to max fp type to contain python longs
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imin = np.array(info.min, dtype=mx_dt)
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imax = np.array(info.max, dtype=mx_dt)
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for mn, mx in value_pairs:
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scale, inter = scale_min_max(mn, mx, tp, True)
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assert_array_almost_equal, (mx-inter) / scale, imax
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assert_array_almost_equal, (mn-inter) / scale, imin
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assert_equal, (scale, inter), (1.0, mn)
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if imin == 0 and mn < 0 and mx > 0:
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(assert_raises, ValueError,
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scale_min_max, mn, mx, tp, False)
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scale, inter = scale_min_max(mn, mx, tp, False)
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assert_equal, inter, 0.0
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if mn == 0 and mx == 0:
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assert_equal, scale, 1.0
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assert_true, sc_mn >= imin
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assert_true, sc_mx <= imax
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if mx > 0: # numbers all +ve
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assert_array_almost_equal, mx / scale, imax
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else: # numbers all -ve
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assert_array_almost_equal, mn / scale, imax
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if abs(mx) >= abs(mn):
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assert_array_almost_equal, mx / scale, imax
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assert_array_almost_equal, mn / scale, imin
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def test_finite_range():
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# Finite range utility function
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a = np.array([[-1, 0, 1],[np.inf, np.nan, -np.inf]])
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assert_equal(finite_range(a), (-1.0, 1.0))
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a = np.array([[np.nan],[np.nan]])
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assert_equal(finite_range(a), (np.inf, -np.inf))
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a = np.array([[-3, 0, 1],[2,-1,4]], dtype=np.int)
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assert_equal(finite_range(a), (-3, 4))
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a = np.array([[1, 0, 1],[2,3,4]], dtype=np.uint)
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assert_equal(finite_range(a), (0, 4))
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assert_raises(TypeError, finite_range, a)
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a = np.array([0., 1, 2, 3])
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assert_equal(finite_range(a), (0,3))
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def test_calculate_scale():
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# Test for special cases in scale calculation
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# Here the offset handles it
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res = calculate_scale(npa([-2, -1], dtype=np.int8), np.uint8, True)
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assert_equal(res, (1.0, -2.0, None, None))
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# Not having offset not a problem obviously
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res = calculate_scale(npa([-2, -1], dtype=np.int8), np.uint8, 0)
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assert_equal(res, (-1.0, 0.0, None, None))
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# Case where offset handles scaling
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res = calculate_scale(npa([-1, 1], dtype=np.int8), np.uint8, 1)
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assert_equal(res, (1.0, -1.0, None, None))
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# Can't work for no offset case
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assert_raises(ValueError,
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calculate_scale, npa([-1, 1], dtype=np.int8), np.uint8, 0)
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# Offset trick can't work when max is out of range
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res = calculate_scale(npa([-1, 255], dtype=np.int16), np.uint8, 1)
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assert_not_equal(res, (1.0, -1.0, None, None))
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def test_a2f_mn_mx():
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# Test array to file mn, mx handling
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for out_type in (np.int16, np.float32):
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arr = np.arange(6, dtype=out_type)
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arr_orig = arr.copy() # safe backup for testing against
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# Basic round trip to warm up
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array_to_file(arr, str_io)
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data_back = array_from_file(arr.shape, out_type, str_io)
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assert_array_equal(arr, data_back)
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array_to_file(arr, str_io, mn=2)
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data_back = array_from_file(arr.shape, out_type, str_io)
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assert_array_equal(arr, arr_orig)
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# returned value clipped low
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assert_array_equal(data_back, [2,2,2,3,4,5])
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array_to_file(arr, str_io, mx=4)
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data_back = array_from_file(arr.shape, out_type, str_io)
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assert_array_equal(arr, arr_orig)
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# returned value clipped high
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assert_array_equal(data_back, [0,1,2,3,4,4])
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array_to_file(arr, str_io, mn=2, mx=4)
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data_back = array_from_file(arr.shape, out_type, str_io)
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assert_array_equal(arr, arr_orig)
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# returned value clipped high
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assert_array_equal(data_back, [2,2,2,3,4,4])
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def test_a2f_nan2zero():
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# Test conditions under which nans written to zero
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arr = np.array([np.nan, 99.], dtype=np.float32)
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array_to_file(arr, str_io)
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data_back = array_from_file(arr.shape, np.float32, str_io)
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assert_array_equal(np.isnan(data_back), [True, False])
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# nan2zero ignored for floats
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array_to_file(arr, str_io, nan2zero=True)
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data_back = array_from_file(arr.shape, np.float32, str_io)
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assert_array_equal(np.isnan(data_back), [True, False])
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# Integer output with nan2zero gives zero
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array_to_file(arr, str_io, np.int32, nan2zero=True)
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data_back = array_from_file(arr.shape, np.int32, str_io)
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assert_array_equal(data_back, [0, 99])
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# Integer output with nan2zero=False gives whatever astype gives
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array_to_file(arr, str_io, np.int32, nan2zero=False)
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data_back = array_from_file(arr.shape, np.int32, str_io)
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assert_array_equal(data_back, [np.array(np.nan).astype(np.int32), 99])
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def test_array_file_scales():
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# Test scaling works for max, min when going from larger to smaller type,
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# and from float to integer.
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for in_type, out_type, err in ((np.int16, np.int16, None),
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(np.int16, np.int8, None),
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(np.uint16, np.uint8, None),
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(np.int32, np.int8, None),
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(np.float32, np.uint8, None),
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(np.float32, np.int16, None)):
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out_dtype = np.dtype(out_type)
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arr = np.zeros((3,), dtype=in_type)
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info = type_info(in_type)
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arr[0], arr[1] = info['min'], info['max']
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assert_raises(err, calculate_scale, arr, out_dtype, True)
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slope, inter, mn, mx = calculate_scale(arr, out_dtype, True)
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array_to_file(arr, bio, out_type, 0, inter, slope, mn, mx)
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arr2 = array_from_file(arr.shape, out_dtype, bio)
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arr3 = apply_read_scaling(arr2, slope, inter)
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# Max rounding error for integer type
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max_miss = slope / 2.
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assert_true(np.all(np.abs(arr - arr3) <= max_miss))
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def test_scaling_in_abstract():
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# Confirm that, for all ints and uints as input, and all possible outputs,
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# for any simple way of doing the calculation, the result is near enough
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for category0, category1 in (('int', 'int'),
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for in_type in np.sctypes[category0]:
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for out_type in np.sctypes[category1]:
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check_int_a2f(in_type, out_type)
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# Converting floats to integer
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for category0, category1 in (('float', 'int'),
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for in_type in np.sctypes[category0]:
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for out_type in np.sctypes[category1]:
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check_int_a2f(in_type, out_type)
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def check_int_a2f(in_type, out_type):
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# Check that array to / from file returns roughly the same as input
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big_floater = np.maximum_sctype(np.float)
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info = type_info(in_type)
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this_min, this_max = info['min'], info['max']
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if not in_type in np.sctypes['complex']:
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data = np.array([this_min, this_max], in_type)
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else: # Funny behavior with complex256
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data = np.zeros((2,), in_type)
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data[0] = this_min + 0j
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data[1] = this_max + 0j
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scale, inter, mn, mx = calculate_scale(data, out_type, True)
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print in_type, out_type, sys.exc_info()[1]
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array_to_file(data, str_io, out_type, 0, inter, scale, mn, mx)
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data_back = array_from_file(data.shape, out_type, str_io)
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data_back = apply_read_scaling(data_back, scale, inter)
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assert_true(np.allclose(big_floater(data), big_floater(data_back)))
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# Try with analyze-size scale and inter
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scale32 = np.float32(scale)
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inter32 = np.float32(inter)
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if scale32 == np.inf or inter32 == np.inf:
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data_back = array_from_file(data.shape, out_type, str_io)
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data_back = apply_read_scaling(data_back, scale32, inter32)
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# Clip at extremes to remove inf
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info = type_info(in_type)
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out_min, out_max = info['min'], info['max']
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assert_true(np.allclose(big_floater(data),
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big_floater(np.clip(data_back, out_min, out_max))))
added
removed
nibabel/tests/test_scaling.py
