Source code for yt.testing

"""
Utilities to aid testing.


"""
from __future__ import print_function

#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------

import hashlib
from yt.extern.six import string_types
from yt.extern.six.moves import cPickle
import itertools as it
import numpy as np
import functools
import importlib
import os
import unittest
from yt.funcs import iterable
from yt.config import ytcfg
# we import this in a weird way from numpy.testing to avoid triggering
# flake8 errors from the unused imports. These test functions are imported
# elsewhere in yt from here so we want them to be imported here.
from numpy.testing import assert_array_equal, assert_almost_equal  # NOQA
from numpy.testing import assert_approx_equal, assert_array_almost_equal  # NOQA
from numpy.testing import assert_equal, assert_array_less  # NOQA
from numpy.testing import assert_string_equal  # NOQA
from numpy.testing import assert_array_almost_equal_nulp  # NOQA
from numpy.testing import assert_allclose, assert_raises  # NOQA
from numpy.random import RandomState
from yt.convenience import load
from yt.units.yt_array import YTArray, YTQuantity
from yt.utilities.exceptions import YTUnitOperationError

# Expose assert_true and assert_less_equal from unittest.TestCase
# this is adopted from nose. Doing this here allows us to avoid importing
# nose at the top level.
class _Dummy(unittest.TestCase):
    def nop():
        pass
_t = _Dummy('nop')

assert_true = getattr(_t, 'assertTrue')
assert_less_equal = getattr(_t, 'assertLessEqual')


[docs]def assert_rel_equal(a1, a2, decimals, err_msg='', verbose=True): # We have nan checks in here because occasionally we have fields that get # weighted without non-zero weights. I'm looking at you, particle fields! if isinstance(a1, np.ndarray): assert(a1.size == a2.size) # Mask out NaNs assert((np.isnan(a1) == np.isnan(a2)).all()) a1[np.isnan(a1)] = 1.0 a2[np.isnan(a2)] = 1.0 # Mask out 0 ind1 = np.array(np.abs(a1) < np.finfo(a1.dtype).eps) ind2 = np.array(np.abs(a2) < np.finfo(a2.dtype).eps) assert((ind1 == ind2).all()) a1[ind1] = 1.0 a2[ind2] = 1.0 elif np.any(np.isnan(a1)) and np.any(np.isnan(a2)): return True if not isinstance(a1, np.ndarray) and a1 == a2 == 0.0: # NANS! a1 = a2 = 1.0 return assert_almost_equal(np.array(a1)/np.array(a2), 1.0, decimals, err_msg=err_msg, verbose=verbose)
[docs]def amrspace(extent, levels=7, cells=8): """Creates two numpy arrays representing the left and right bounds of an AMR grid as well as an array for the AMR level of each cell. Parameters ---------- extent : array-like This a sequence of length 2*ndims that is the bounds of each dimension. For example, the 2D unit square would be given by [0.0, 1.0, 0.0, 1.0]. A 3D cylindrical grid may look like [0.0, 2.0, -1.0, 1.0, 0.0, 2*np.pi]. levels : int or sequence of ints, optional This is the number of AMR refinement levels. If given as a sequence (of length ndims), then each dimension will be refined down to this level. All values in this array must be the same or zero. A zero valued dimension indicates that this dim should not be refined. Taking the 3D cylindrical example above if we don't want refine theta but want r and z at 5 we would set levels=(5, 5, 0). cells : int, optional This is the number of cells per refinement level. Returns ------- left : float ndarray, shape=(npoints, ndims) The left AMR grid points. right : float ndarray, shape=(npoints, ndims) The right AMR grid points. level : int ndarray, shape=(npoints,) The AMR level for each point. Examples -------- >>> l, r, lvl = amrspace([0.0, 2.0, 1.0, 2.0, 0.0, 3.14], levels=(3,3,0), cells=2) >>> print l [[ 0. 1. 0. ] [ 0.25 1. 0. ] [ 0. 1.125 0. ] [ 0.25 1.125 0. ] [ 0.5 1. 0. ] [ 0. 1.25 0. ] [ 0.5 1.25 0. ] [ 1. 1. 0. ] [ 0. 1.5 0. ] [ 1. 1.5 0. ]] """ extent = np.asarray(extent, dtype='f8') dextent = extent[1::2] - extent[::2] ndims = len(dextent) if isinstance(levels, int): minlvl = maxlvl = levels levels = np.array([levels]*ndims, dtype='int32') else: levels = np.asarray(levels, dtype='int32') minlvl = levels.min() maxlvl = levels.max() if minlvl != maxlvl and (minlvl != 0 or set([minlvl, maxlvl]) != set(levels)): raise ValueError("all levels must have the same value or zero.") dims_zero = (levels == 0) dims_nonzero = ~dims_zero ndims_nonzero = dims_nonzero.sum() npoints = (cells**ndims_nonzero - 1)*maxlvl + 1 left = np.empty((npoints, ndims), dtype='float64') right = np.empty((npoints, ndims), dtype='float64') level = np.empty(npoints, dtype='int32') # fill zero dims left[:,dims_zero] = extent[::2][dims_zero] right[:,dims_zero] = extent[1::2][dims_zero] # fill non-zero dims dcell = 1.0 / cells left_slice = tuple([slice(extent[2*n], extent[2*n+1], extent[2*n+1]) if \ dims_zero[n] else slice(0.0,1.0,dcell) for n in range(ndims)]) right_slice = tuple([slice(extent[2*n+1], extent[2*n], -extent[2*n+1]) if \ dims_zero[n] else slice(dcell,1.0+dcell,dcell) for n in range(ndims)]) left_norm_grid = np.reshape(np.mgrid[left_slice].T.flat[ndims:], (-1, ndims)) lng_zero = left_norm_grid[:,dims_zero] lng_nonzero = left_norm_grid[:,dims_nonzero] right_norm_grid = np.reshape(np.mgrid[right_slice].T.flat[ndims:], (-1, ndims)) rng_zero = right_norm_grid[:,dims_zero] rng_nonzero = right_norm_grid[:,dims_nonzero] level[0] = maxlvl left[0,:] = extent[::2] right[0,dims_zero] = extent[1::2][dims_zero] right[0,dims_nonzero] = (dcell**maxlvl)*dextent[dims_nonzero] + extent[::2][dims_nonzero] for i, lvl in enumerate(range(maxlvl, 0, -1)): start = (cells**ndims_nonzero - 1)*i + 1 stop = (cells**ndims_nonzero - 1)*(i+1) + 1 dsize = dcell**(lvl-1) * dextent[dims_nonzero] level[start:stop] = lvl left[start:stop,dims_zero] = lng_zero left[start:stop,dims_nonzero] = lng_nonzero*dsize + extent[::2][dims_nonzero] right[start:stop,dims_zero] = rng_zero right[start:stop,dims_nonzero] = rng_nonzero*dsize + extent[::2][dims_nonzero] return left, right, level
[docs]def fake_random_ds( ndims, peak_value = 1.0, fields = ("density", "velocity_x", "velocity_y", "velocity_z"), units = ('g/cm**3', 'cm/s', 'cm/s', 'cm/s'), particle_fields=None, particle_field_units=None, negative = False, nprocs = 1, particles = 0, length_unit=1.0, unit_system="cgs", bbox=None): from yt.frontends.stream.api import load_uniform_grid prng = RandomState(0x4d3d3d3) if not iterable(ndims): ndims = [ndims, ndims, ndims] else: assert(len(ndims) == 3) if not iterable(negative): negative = [negative for f in fields] assert(len(fields) == len(negative)) offsets = [] for n in negative: if n: offsets.append(0.5) else: offsets.append(0.0) data = {} for field, offset, u in zip(fields, offsets, units): v = (prng.random_sample(ndims) - offset) * peak_value if field[0] == "all": data['number_of_particles'] = v.size v = v.ravel() data[field] = (v, u) if particles: if particle_fields is not None: for field, unit in zip(particle_fields, particle_field_units): if field in ('particle_position', 'particle_velocity'): data['io', field] = ( prng.random_sample((int(particles), 3)), unit) else: data['io', field] = ( prng.random_sample(size=int(particles)), unit) else: for f in ('particle_position_%s' % ax for ax in 'xyz'): data['io', f] = (prng.random_sample(size=particles), 'code_length') for f in ('particle_velocity_%s' % ax for ax in 'xyz'): data['io', f] = (prng.random_sample(size=particles) - 0.5, 'cm/s') data['io', 'particle_mass'] = (prng.random_sample(particles), 'g') data['number_of_particles'] = particles ug = load_uniform_grid(data, ndims, length_unit=length_unit, nprocs=nprocs, unit_system=unit_system, bbox=bbox) return ug
_geom_transforms = { # These are the bounds we want. Cartesian we just assume goes 0 .. 1. 'cartesian' : ( (0.0, 0.0, 0.0), (1.0, 1.0, 1.0) ), 'spherical' : ( (0.0, 0.0, 0.0), (1.0, np.pi, 2*np.pi) ), 'cylindrical': ( (0.0, 0.0, 0.0), (1.0, 1.0, 2.0*np.pi) ), # rzt 'polar' : ( (0.0, 0.0, 0.0), (1.0, 2.0*np.pi, 1.0) ), # rtz 'geographic' : ( (-90.0, -180.0, 0.0), (90.0, 180.0, 1000.0) ), # latlonalt 'internal_geographic' : ( (-90.0, -180.0, 0.0), (90.0, 180.0, 1000.0) ), # latlondep }
[docs]def fake_amr_ds(fields = ("Density",), geometry = "cartesian", particles=0): from yt.frontends.stream.api import load_amr_grids prng = RandomState(0x4d3d3d3) LE, RE = _geom_transforms[geometry] LE = np.array(LE) RE = np.array(RE) data = [] for gspec in _amr_grid_index: level, left_edge, right_edge, dims = gspec left_edge = left_edge * (RE - LE) + LE right_edge = right_edge * (RE - LE) + LE gdata = dict(level = level, left_edge = left_edge, right_edge = right_edge, dimensions = dims) for f in fields: gdata[f] = prng.random_sample(dims) if particles: for i, f in enumerate('particle_position_%s' % ax for ax in 'xyz'): pdata = prng.random_sample(particles) pdata /= (right_edge[i] - left_edge[i]) pdata += left_edge[i] gdata['io', f] = (pdata, 'code_length') for f in ('particle_velocity_%s' % ax for ax in 'xyz'): gdata['io', f] = (prng.random_sample(particles) - 0.5, 'cm/s') gdata['io', 'particle_mass'] = (prng.random_sample(particles), 'g') gdata['number_of_particles'] = particles data.append(gdata) bbox = np.array([LE, RE]).T return load_amr_grids(data, [32, 32, 32], geometry=geometry, bbox=bbox)
[docs]def fake_particle_ds( fields = ("particle_position_x", "particle_position_y", "particle_position_z", "particle_mass", "particle_velocity_x", "particle_velocity_y", "particle_velocity_z"), units = ('cm', 'cm', 'cm', 'g', 'cm/s', 'cm/s', 'cm/s'), negative = (False, False, False, False, True, True, True), npart = 16**3, length_unit=1.0): from yt.frontends.stream.api import load_particles prng = RandomState(0x4d3d3d3) if not iterable(negative): negative = [negative for f in fields] assert(len(fields) == len(negative)) offsets = [] for n in negative: if n: offsets.append(0.5) else: offsets.append(0.0) data = {} for field, offset, u in zip(fields, offsets, units): if "position" in field: v = prng.normal(loc=0.5, scale=0.25, size=npart) np.clip(v, 0.0, 1.0, v) v = (prng.random_sample(npart) - offset) data[field] = (v, u) bbox = np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]) ds = load_particles(data, 1.0, bbox=bbox) return ds
[docs]def fake_tetrahedral_ds(): from yt.frontends.stream.api import load_unstructured_mesh from yt.frontends.stream.sample_data.tetrahedral_mesh import \ _connectivity, _coordinates prng = RandomState(0x4d3d3d3) # the distance from the origin node_data = {} dist = np.sum(_coordinates**2, 1) node_data[('connect1', 'test')] = dist[_connectivity] # each element gets a random number elem_data = {} elem_data[('connect1', 'elem')] = prng.rand(_connectivity.shape[0]) ds = load_unstructured_mesh(_connectivity, _coordinates, node_data=node_data, elem_data=elem_data) return ds
[docs]def fake_hexahedral_ds(): from yt.frontends.stream.api import load_unstructured_mesh from yt.frontends.stream.sample_data.hexahedral_mesh import \ _connectivity, _coordinates prng = RandomState(0x4d3d3d3) # the distance from the origin node_data = {} dist = np.sum(_coordinates**2, 1) node_data[('connect1', 'test')] = dist[_connectivity-1] # each element gets a random number elem_data = {} elem_data[('connect1', 'elem')] = prng.rand(_connectivity.shape[0]) ds = load_unstructured_mesh(_connectivity-1, _coordinates, node_data=node_data, elem_data=elem_data) return ds
[docs]def small_fake_hexahedral_ds(): from yt.frontends.stream.api import load_unstructured_mesh _coordinates = np.array([[-1., -1., -1.], [ 0., -1., -1.], [ -0., 0., -1.], [-1., -0., -1.], [-1., -1., 0.], [ -0., -1., 0.], [ -0., 0., -0.], [-1., 0., -0.]]) _connectivity = np.array([[1, 2, 3, 4, 5, 6, 7, 8]]) # the distance from the origin node_data = {} dist = np.sum(_coordinates**2, 1) node_data[('connect1', 'test')] = dist[_connectivity-1] ds = load_unstructured_mesh(_connectivity-1, _coordinates, node_data=node_data) return ds
[docs]def fake_vr_orientation_test_ds(N = 96, scale=1): """ create a toy dataset that puts a sphere at (0,0,0), a single cube on +x, two cubes on +y, and three cubes on +z in a domain from [-1*scale,1*scale]**3. The lower planes (x = -1*scale, y = -1*scale, z = -1*scale) are also given non-zero values. This dataset allows you to easily explore orientations and handiness in VR and other renderings Parameters ---------- N : integer The number of cells along each direction scale : float A spatial scale, the domain boundaries will be multiplied by scale to test datasets that have spatial different scales (e.g. data in CGS units) """ from yt.frontends.stream.api import load_uniform_grid xmin = ymin = zmin = -1.0 * scale xmax = ymax = zmax = 1.0 * scale dcoord = (xmax - xmin)/N arr = np.zeros((N,N,N), dtype=np.float64) arr[:,:,:] = 1.e-4 bbox = np.array([ [xmin, xmax], [ymin, ymax], [zmin, zmax] ]) # coordinates -- in the notation data[i, j, k] x = (np.arange(N) + 0.5)*dcoord + xmin y = (np.arange(N) + 0.5)*dcoord + ymin z = (np.arange(N) + 0.5)*dcoord + zmin x3d, y3d, z3d = np.meshgrid(x, y, z, indexing="ij") # sphere at the origin c = np.array( [0.5*(xmin + xmax), 0.5*(ymin + ymax), 0.5*(zmin + zmax) ] ) r = np.sqrt((x3d - c[0])**2 + (y3d - c[1])**2 + (z3d - c[2])**2) arr[r < 0.05] = 1.0 arr[abs(x3d - xmin) < 2*dcoord] = 0.3 arr[abs(y3d - ymin) < 2*dcoord] = 0.3 arr[abs(z3d - zmin) < 2*dcoord] = 0.3 # single cube on +x xc = 0.75 * scale dx = 0.05 * scale idx = np.logical_and(np.logical_and(x3d > xc-dx, x3d < xc+dx), np.logical_and(np.logical_and(y3d > -dx, y3d < dx), np.logical_and(z3d > -dx, z3d < dx)) ) arr[idx] = 1.0 # two cubes on +y dy = 0.05 * scale for yc in [0.65 * scale, 0.85 * scale]: idx = np.logical_and(np.logical_and(y3d > yc-dy, y3d < yc+dy), np.logical_and(np.logical_and(x3d > -dy, x3d < dy), np.logical_and(z3d > -dy, z3d < dy)) ) arr[idx] = 0.8 # three cubes on +z dz = 0.05 * scale for zc in [0.5 * scale, 0.7 * scale, 0.9 * scale]: idx = np.logical_and(np.logical_and(z3d > zc-dz, z3d < zc+dz), np.logical_and(np.logical_and(x3d > -dz, x3d < dz), np.logical_and(y3d > -dz, y3d < dz)) ) arr[idx] = 0.6 data = dict(density = (arr, "g/cm**3")) ds = load_uniform_grid(data, arr.shape, bbox=bbox) return ds
[docs]def expand_keywords(keywords, full=False): """ expand_keywords is a means for testing all possible keyword arguments in the nosetests. Simply pass it a dictionary of all the keyword arguments and all of the values for these arguments that you want to test. It will return a list of kwargs dicts containing combinations of the various kwarg values you passed it. These can then be passed to the appropriate function in nosetests. If full=True, then every possible combination of keywords is produced, otherwise, every keyword option is included at least once in the output list. Be careful, by using full=True, you may be in for an exponentially larger number of tests! Parameters ---------- keywords : dict a dictionary where the keys are the keywords for the function, and the values of each key are the possible values that this key can take in the function full : bool if set to True, every possible combination of given keywords is returned Returns ------- array of dicts An array of dictionaries to be individually passed to the appropriate function matching these kwargs. Examples -------- >>> keywords = {} >>> keywords['dpi'] = (50, 100, 200) >>> keywords['cmap'] = ('arbre', 'kelp') >>> list_of_kwargs = expand_keywords(keywords) >>> print list_of_kwargs array([{'cmap': 'arbre', 'dpi': 50}, {'cmap': 'kelp', 'dpi': 100}, {'cmap': 'arbre', 'dpi': 200}], dtype=object) >>> list_of_kwargs = expand_keywords(keywords, full=True) >>> print list_of_kwargs array([{'cmap': 'arbre', 'dpi': 50}, {'cmap': 'arbre', 'dpi': 100}, {'cmap': 'arbre', 'dpi': 200}, {'cmap': 'kelp', 'dpi': 50}, {'cmap': 'kelp', 'dpi': 100}, {'cmap': 'kelp', 'dpi': 200}], dtype=object) >>> for kwargs in list_of_kwargs: ... write_projection(*args, **kwargs) """ # if we want every possible combination of keywords, use iter magic if full: keys = sorted(keywords) list_of_kwarg_dicts = np.array([dict(zip(keys, prod)) for prod in \ it.product(*(keywords[key] for key in keys))]) # if we just want to probe each keyword, but not necessarily every # combination else: # Determine the maximum number of values any of the keywords has num_lists = 0 for val in keywords.values(): if isinstance(val, string_types): num_lists = max(1.0, num_lists) else: num_lists = max(len(val), num_lists) # Construct array of kwargs dicts, each element of the list is a different # **kwargs dict. each kwargs dict gives a different combination of # the possible values of the kwargs # initialize array list_of_kwarg_dicts = np.array([dict() for x in range(num_lists)]) # fill in array for i in np.arange(num_lists): list_of_kwarg_dicts[i] = {} for key in keywords.keys(): # if it's a string, use it (there's only one) if isinstance(keywords[key], string_types): list_of_kwarg_dicts[i][key] = keywords[key] # if there are more options, use the i'th val elif i < len(keywords[key]): list_of_kwarg_dicts[i][key] = keywords[key][i] # if there are not more options, use the 0'th val else: list_of_kwarg_dicts[i][key] = keywords[key][0] return list_of_kwarg_dicts
[docs]def requires_module(module): """ Decorator that takes a module name as an argument and tries to import it. If the module imports without issue, the function is returned, but if not, a null function is returned. This is so tests that depend on certain modules being imported will not fail if the module is not installed on the testing platform. """ def ffalse(func): return lambda: None def ftrue(func): return func try: importlib.import_module(module) except ImportError: return ffalse else: return ftrue
[docs]def requires_file(req_file): path = ytcfg.get("yt", "test_data_dir") def ffalse(func): return lambda: None def ftrue(func): return func if os.path.exists(req_file): return ftrue else: if os.path.exists(os.path.join(path,req_file)): return ftrue else: return ffalse
[docs]def disable_dataset_cache(func): @functools.wraps(func) def newfunc(*args, **kwargs): restore_cfg_state = False if ytcfg.get("yt", "skip_dataset_cache") == "False": ytcfg["yt","skip_dataset_cache"] = "True" rv = func(*args, **kwargs) if restore_cfg_state: ytcfg["yt","skip_dataset_cache"] = "False" return rv return newfunc
@disable_dataset_cache
[docs]def units_override_check(fn): units_list = ["length","time","mass","velocity", "magnetic","temperature"] ds1 = load(fn) units_override = {} attrs1 = [] attrs2 = [] for u in units_list: unit_attr = getattr(ds1, "%s_unit" % u, None) if unit_attr is not None: attrs1.append(unit_attr) units_override["%s_unit" % u] = (unit_attr.v, str(unit_attr.units)) del ds1 ds2 = load(fn, units_override=units_override) assert(len(ds2.units_override) > 0) for u in units_list: unit_attr = getattr(ds2, "%s_unit" % u, None) if unit_attr is not None: attrs2.append(unit_attr) assert_equal(attrs1, attrs2)
# This is an export of the 40 grids in IsolatedGalaxy that are of level 4 or # lower. It's just designed to give a sample AMR index to deal with. _amr_grid_index = [ [ 0, [0.0,0.0,0.0], [1.0,1.0,1.0], [32,32,32], ], [ 1, [0.25,0.21875,0.25], [0.5,0.5,0.5], [16,18,16], ], [ 1, [0.5,0.21875,0.25], [0.75,0.5,0.5], [16,18,16], ], [ 1, [0.21875,0.5,0.25], [0.5,0.75,0.5], [18,16,16], ], [ 1, [0.5,0.5,0.25], [0.75,0.75,0.5], [16,16,16], ], [ 1, [0.25,0.25,0.5], [0.5,0.5,0.75], [16,16,16], ], [ 1, [0.5,0.25,0.5], [0.75,0.5,0.75], [16,16,16], ], [ 1, [0.25,0.5,0.5], [0.5,0.75,0.75], [16,16,16], ], [ 1, [0.5,0.5,0.5], [0.75,0.75,0.75], [16,16,16], ], [ 2, [0.5,0.5,0.5], [0.71875,0.71875,0.71875], [28,28,28], ], [ 3, [0.5,0.5,0.5], [0.6640625,0.65625,0.6796875], [42,40,46], ], [ 4, [0.5,0.5,0.5], [0.59765625,0.6015625,0.6015625], [50,52,52], ], [ 2, [0.28125,0.5,0.5], [0.5,0.734375,0.71875], [28,30,28], ], [ 3, [0.3359375,0.5,0.5], [0.5,0.671875,0.6640625], [42,44,42], ], [ 4, [0.40625,0.5,0.5], [0.5,0.59765625,0.59765625], [48,50,50], ], [ 2, [0.5,0.28125,0.5], [0.71875,0.5,0.71875], [28,28,28], ], [ 3, [0.5,0.3359375,0.5], [0.671875,0.5,0.6640625], [44,42,42], ], [ 4, [0.5,0.40625,0.5], [0.6015625,0.5,0.59765625], [52,48,50], ], [ 2, [0.28125,0.28125,0.5], [0.5,0.5,0.71875], [28,28,28], ], [ 3, [0.3359375,0.3359375,0.5], [0.5,0.5,0.671875], [42,42,44], ], [ 4, [0.46484375,0.37890625,0.50390625], [0.4765625,0.390625,0.515625], [6,6,6], ], [ 4, [0.40625,0.40625,0.5], [0.5,0.5,0.59765625], [48,48,50], ], [ 2, [0.5,0.5,0.28125], [0.71875,0.71875,0.5], [28,28,28], ], [ 3, [0.5,0.5,0.3359375], [0.6796875,0.6953125,0.5], [46,50,42], ], [ 4, [0.5,0.5,0.40234375], [0.59375,0.6015625,0.5], [48,52,50], ], [ 2, [0.265625,0.5,0.28125], [0.5,0.71875,0.5], [30,28,28], ], [ 3, [0.3359375,0.5,0.328125], [0.5,0.65625,0.5], [42,40,44], ], [ 4, [0.40234375,0.5,0.40625], [0.5,0.60546875,0.5], [50,54,48], ], [ 2, [0.5,0.265625,0.28125], [0.71875,0.5,0.5], [28,30,28], ], [ 3, [0.5,0.3203125,0.328125], [0.6640625,0.5,0.5], [42,46,44], ], [ 4, [0.5,0.3984375,0.40625], [0.546875,0.5,0.5], [24,52,48], ], [ 4, [0.546875,0.41796875,0.4453125], [0.5625,0.4375,0.5], [8,10,28], ], [ 4, [0.546875,0.453125,0.41796875], [0.5546875,0.48046875,0.4375], [4,14,10], ], [ 4, [0.546875,0.4375,0.4375], [0.609375,0.5,0.5], [32,32,32], ], [ 4, [0.546875,0.4921875,0.41796875], [0.56640625,0.5,0.4375], [10,4,10], ], [ 4, [0.546875,0.48046875,0.41796875], [0.5703125,0.4921875,0.4375], [12,6,10], ], [ 4, [0.55859375,0.46875,0.43359375], [0.5703125,0.48046875,0.4375], [6,6,2], ], [ 2, [0.265625,0.28125,0.28125], [0.5,0.5,0.5], [30,28,28], ], [ 3, [0.328125,0.3359375,0.328125], [0.5,0.5,0.5], [44,42,44], ], [ 4, [0.4140625,0.40625,0.40625], [0.5,0.5,0.5], [44,48,48], ], ]
[docs]def check_results(func): r"""This is a decorator for a function to verify that the (numpy ndarray) result of a function is what it should be. This function is designed to be used for very light answer testing. Essentially, it wraps around a larger function that returns a numpy array, and that has results that should not change. It is not necessarily used inside the testing scripts themselves, but inside testing scripts written by developers during the testing of pull requests and new functionality. If a hash is specified, it "wins" and the others are ignored. Otherwise, tolerance is 1e-8 (just above single precision.) The correct results will be stored if the command line contains --answer-reference , and otherwise it will compare against the results on disk. The filename will be func_results_ref_FUNCNAME.cpkl where FUNCNAME is the name of the function being tested. If you would like more control over the name of the pickle file the results are stored in, you can pass the result_basename keyword argument to the function you are testing. The check_results decorator will use the value of the keyword to construct the filename of the results data file. If result_basename is not specified, the name of the testing function is used. This will raise an exception if the results are not correct. Examples -------- >>> @check_results ... def my_func(ds): ... return ds.domain_width >>> my_func(ds) >>> @check_results ... def field_checker(dd, field_name): ... return dd[field_name] >>> field_checker(ds.all_data(), 'density', result_basename='density') """ def compute_results(func): def _func(*args, **kwargs): name = kwargs.pop("result_basename", func.__name__) rv = func(*args, **kwargs) if hasattr(rv, "convert_to_base"): rv.convert_to_base() _rv = rv.ndarray_view() else: _rv = rv mi = _rv.min() ma = _rv.max() st = _rv.std(dtype="float64") su = _rv.sum(dtype="float64") si = _rv.size ha = hashlib.md5(_rv.tostring()).hexdigest() fn = "func_results_ref_%s.cpkl" % (name) with open(fn, "wb") as f: cPickle.dump( (mi, ma, st, su, si, ha), f) return rv return _func from yt.mods import unparsed_args if "--answer-reference" in unparsed_args: return compute_results(func) def compare_results(func): def _func(*args, **kwargs): name = kwargs.pop("result_basename", func.__name__) rv = func(*args, **kwargs) if hasattr(rv, "convert_to_base"): rv.convert_to_base() _rv = rv.ndarray_view() else: _rv = rv vals = (_rv.min(), _rv.max(), _rv.std(dtype="float64"), _rv.sum(dtype="float64"), _rv.size, hashlib.md5(_rv.tostring()).hexdigest() ) fn = "func_results_ref_%s.cpkl" % (name) if not os.path.exists(fn): print("Answers need to be created with --answer-reference .") return False with open(fn, "rb") as f: ref = cPickle.load(f) print("Sizes: %s (%s, %s)" % (vals[4] == ref[4], vals[4], ref[4])) assert_allclose(vals[0], ref[0], 1e-8, err_msg="min") assert_allclose(vals[1], ref[1], 1e-8, err_msg="max") assert_allclose(vals[2], ref[2], 1e-8, err_msg="std") assert_allclose(vals[3], ref[3], 1e-8, err_msg="sum") assert_equal(vals[4], ref[4]) print("Hashes equal: %s" % (vals[-1] == ref[-1])) return rv return _func return compare_results(func)
[docs]def periodicity_cases(ds): # This is a generator that yields things near the corners. It's good for # getting different places to check periodicity. yield (ds.domain_left_edge + ds.domain_right_edge)/2.0 dx = ds.domain_width / ds.domain_dimensions # We start one dx in, and only go to one in as well. for i in (1, ds.domain_dimensions[0] - 2): for j in (1, ds.domain_dimensions[1] - 2): for k in (1, ds.domain_dimensions[2] - 2): center = dx * np.array([i,j,k]) + ds.domain_left_edge yield center
[docs]def run_nose(verbose=False, run_answer_tests=False, answer_big_data=False, call_pdb=False, module=None): from yt.utilities.on_demand_imports import _nose import sys from yt.utilities.logger import ytLogger as mylog orig_level = mylog.getEffectiveLevel() mylog.setLevel(50) nose_argv = sys.argv nose_argv += ['--exclude=answer_testing','--detailed-errors', '--exe'] if call_pdb: nose_argv += ["--pdb", "--pdb-failures"] if verbose: nose_argv.append('-v') if run_answer_tests: nose_argv.append('--with-answer-testing') if answer_big_data: nose_argv.append('--answer-big-data') if module: nose_argv.append(module) initial_dir = os.getcwd() yt_file = os.path.abspath(__file__) yt_dir = os.path.dirname(yt_file) if os.path.samefile(os.path.dirname(yt_dir), initial_dir): # Provide a nice error message to work around nose bug # see https://github.com/nose-devs/nose/issues/701 raise RuntimeError( """ The yt.run_nose function does not work correctly when invoked in the same directory as the installed yt package. Try starting a python session in a different directory before invoking yt.run_nose again. Alternatively, you can also run the "nosetests" executable in the current directory like so: $ nosetests """ ) os.chdir(yt_dir) try: _nose.run(argv=nose_argv) finally: os.chdir(initial_dir) mylog.setLevel(orig_level)
[docs]def assert_allclose_units(actual, desired, rtol=1e-7, atol=0, **kwargs): """Raise an error if two objects are not equal up to desired tolerance This is a wrapper for :func:`numpy.testing.assert_allclose` that also verifies unit consistency Parameters ---------- actual : array-like Array obtained (possibly with attached units) desired : array-like Array to compare with (possibly with attached units) rtol : float, optional Relative tolerance, defaults to 1e-7 atol : float or quantity, optional Absolute tolerance. If units are attached, they must be consistent with the units of ``actual`` and ``desired``. If no units are attached, assumes the same units as ``desired``. Defaults to zero. Notes ----- Also accepts additional keyword arguments accepted by :func:`numpy.testing.assert_allclose`, see the documentation of that function for details. """ # Create a copy to ensure this function does not alter input arrays act = YTArray(actual) des = YTArray(desired) try: des = des.in_units(act.units) except YTUnitOperationError: raise AssertionError("Units of actual (%s) and desired (%s) do not have " "equivalent dimensions" % (act.units, des.units)) rt = YTArray(rtol) if not rt.units.is_dimensionless: raise AssertionError("Units of rtol (%s) are not " "dimensionless" % rt.units) if not isinstance(atol, YTArray): at = YTQuantity(atol, des.units) try: at = at.in_units(act.units) except YTUnitOperationError: raise AssertionError("Units of atol (%s) and actual (%s) do not have " "equivalent dimensions" % (at.units, act.units)) # units have been validated, so we strip units before calling numpy # to avoid spurious errors act = act.value des = des.value rt = rt.value at = at.value return assert_allclose(act, des, rt, at, **kwargs)
[docs]def assert_fname(fname): """Function that checks file type using libmagic""" if fname is None: return with open(fname, 'rb') as fimg: data = fimg.read() image_type = '' # see http://www.w3.org/TR/PNG/#5PNG-file-signature if data.startswith(b'\211PNG\r\n\032\n'): image_type = '.png' # see http://www.mathguide.de/info/tools/media-types/image/jpeg elif data.startswith(b'\377\330'): image_type = '.jpeg' elif data.startswith(b'%!PS-Adobe'): data_str = data.decode("utf-8", "ignore") if 'EPSF' in data_str[:data_str.index('\n')]: image_type = '.eps' else: image_type = '.ps' elif data.startswith(b'%PDF'): image_type = '.pdf' return image_type == os.path.splitext(fname)[1]