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How to use bundle_length method in hypothesis

Best Python code snippet using hypothesis

test_qbx.py

Source:test_qbx.py

1import itertools2import numpy as np3from dipy.segment.clustering import QuickBundlesX4from dipy.segment.metric import AveragePointwiseEuclideanMetric5from dipy.tracking.streamline import set_number_of_points6from dipy.data import get_data7import nibabel.trackvis as tv8def straight_bundle(nb_streamlines=1, nb_pts=30, step_size=1,9                    radius=1, rng=np.random.RandomState(42)):10    bundle = []11    bundle_length = step_size * nb_pts12    Z = -np.linspace(0, bundle_length, nb_pts)13    for k in range(nb_streamlines):14        theta = rng.rand() * (2*np.pi)15        r = radius * rng.rand()16        Xk = np.ones(nb_pts) * (r * np.cos(theta))17        Yk = np.ones(nb_pts) * (r * np.sin(theta))18        Zk = Z.copy()19        bundle.append(np.c_[Xk, Yk, Zk])20    return bundle21def bearing_bundles(nb_balls=6, bearing_radius=2):22    bundles = []23    for theta in np.linspace(0, 2*np.pi, nb_balls, endpoint=False):24        x = bearing_radius * np.cos(theta)25        y = bearing_radius * np.sin(theta)26        bundle = np.array(straight_bundle(nb_streamlines=100))27        bundle += (x, y, 0)28        bundles.append(bundle)29    return bundles30def streamlines_in_circle(nb_streamlines=1, nb_pts=30, step_size=1,31                          radius=1):32    bundle = []33    bundle_length = step_size * nb_pts34    Z = np.linspace(0, bundle_length, nb_pts)35    theta = 036    for theta in np.linspace(0, 2*np.pi, nb_streamlines, endpoint=False):37        Xk = np.ones(nb_pts) * (radius * np.cos(theta))38        Yk = np.ones(nb_pts) * (radius * np.sin(theta))39        Zk = Z.copy()40        bundle.append(np.c_[Xk, Yk, Zk])41    return bundle42def streamlines_parallel(nb_streamlines=1, nb_pts=30, step_size=1,43                         delta=1):44    bundle = []45    bundle_length = step_size * nb_pts46    Z = np.linspace(0, bundle_length, nb_pts)47    for x in delta*np.arange(0, nb_streamlines):48        Xk = np.ones(nb_pts) * x49        Yk = np.zeros(nb_pts)50        Zk = Z.copy()51        bundle.append(np.c_[Xk, Yk, Zk])52    return bundle53def simulated_bundle(no_streamlines=10, waves=False, no_pts=12):54    t = np.linspace(-10, 10, 200)55    # parallel waves or parallel lines56    bundle = []57    for i in np.linspace(-5, 5, no_streamlines):58        if waves:59            pts = np.vstack((np.cos(t), t, i * np.ones(t.shape))).T60        else:61            pts = np.vstack((np.zeros(t.shape), t, i * np.ones(t.shape))).T62        pts = set_number_of_points(pts, no_pts)63        bundle.append(pts)64    return bundle65def fornix_streamlines(no_pts=12):66    fname = get_data('fornix')67    streams, hdr = tv.read(fname)68    streamlines = [set_number_of_points(i[0], no_pts) for i in streams]69    return streamlines70def test_3D_segments():71    points = np.array([[[1, 0, 0],72                        [1, 1, 0]],73                       [[3, 1, 0],74                        [3, 0, 0]],75                       [[2, 0, 0],76                        [2, 1, 0]],77                       [[5, 1, 0],78                        [5, 0, 0]],79                       [[5.5, 0, 0],80                        [5.5, 1, 0]]], dtype="f4")81    thresholds = [4, 2, 1]82    qbx_class = QuickBundlesX(thresholds)83    qbx = qbx_class.cluster(points)84    print qbx85def test_3D_points():86    points = np.array([[[1, 0, 0]],87                       [[3, 0, 0]],88                       [[2, 0, 0]],89                       [[5, 0, 0]],90                       [[5.5, 0, 0]]], dtype="f4")91    thresholds = [4, 2, 1]92    qbx_class = QuickBundlesX(thresholds)93    qbx = qbx_class.cluster(points)94    print qbx95def test_with_simulated_bundles():96    streamlines = simulated_bundle(3, False, 2)97    from dipy.viz import actor, window98    renderer = window.Renderer()99    bundle_actor = actor.line(streamlines)100    renderer.add(bundle_actor)101    window.show(renderer)102    thresholds = [10, 3, 1]103    qbx_class = QuickBundlesX(thresholds)104    qbx = qbx_class.cluster(streamlines)105    print qbx106    renderer.clear()107    for level in range(len(thresholds) + 1):108        clusters = qbx.get_clusters(level)109        clusters_actor = actor.line(clusters.centroids)110        renderer.add(clusters_actor)111        window.show(renderer)112        renderer.clear()113    from ipdb import set_trace114    set_trace()115def test_with_simulated_bundles2():116    # Generate synthetic streamlines117    bundles = bearing_bundles(4, 2)118    bundles.append(straight_bundle(1))119    streamlines = list(itertools.chain(*bundles))120    from dipy.viz import actor, window121    renderer = window.Renderer()122    bundle_actor = actor.line(streamlines)123    renderer.add(bundle_actor)124    window.show(renderer)125    thresholds = [10, 2, 1]126    qbx_class = QuickBundlesX(thresholds)127    print "Adding streamlines..."128    qbx = qbx_class.cluster(streamlines)129    renderer.clear()130    # for level in range(len(thresholds) + 1):131    #     clusters = qbx.get_clusters(level)132    #     clusters_actor = actor.line(clusters.centroids)133    #     renderer.add(clusters_actor)134    #     window.show(renderer)135    #     renderer.clear()136    from dipy.viz.clustering import show_hierarchical_clusters137    tree = qbx.get_tree_cluster_map()138    tree.refdata = streamlines139    show_hierarchical_clusters(tree, show_circles=True)140    from ipdb import set_trace141    set_trace()142def color_tree(tree, bg=(1, 1, 1)):143    import colorsys144    from dipy.viz.colormap import distinguishable_colormap145    global colormap146    colormap = iter(distinguishable_colormap(bg=bg, exclude=[(1., 1., 0.93103448)]))147    def _color_subtree(node, color=None, level=0):148        global colormap149        node.color = color150        max_luminosity = 0151        if color is not None:152            hls = np.asarray(colorsys.rgb_to_hls(*color))153            max_luminosity = hls[1]154        #luminosities = np.linspace(0.3, 0.8, len(node.children))155        children_sizes = map(len, node.children)156        indices = np.argsort(children_sizes)[::-1]157        luminosities = np.linspace(max_luminosity, 0.2, len(node.children))158        offsets = np.linspace(-0.2, 0.2, len(node.children))159        #for child, luminosity, offset in zip(node.children, luminosities, offsets):160        for idx, luminosity, offset in zip(indices, luminosities, offsets):161            child = node.children[idx]162            if level == 0:163                color = next(colormap)164                _color_subtree(child, color, level+1)165            else:166                hls = np.asarray(colorsys.rgb_to_hls(*color))167                #if hls[1] > 0.8:168                #    hls[1] -= 0.3169                #elif hls[1] < 0.3:170                #    hls[1] += 0.3171                rbg = colorsys.hls_to_rgb(hls[0], luminosity, hls[2])172                _color_subtree(child, np.asarray(rbg), level+1)173    _color_subtree(tree.root)174def test_show_qbx_tree():175    filename = "/home/marc/research/dat/streamlines/MPI_Camille/myBrain.trk"176    import nibabel as nib177    print "Loading streamlines..."178    import os179    tmp_filename = "/tmp/streamlines.npz"180    if os.path.isfile(tmp_filename):181        streamlines = nib.streamlines.compact_list.load_compact_list(tmp_filename)182    else:183        streamlines = nib.streamlines.load(filename).streamlines184        nib.streamlines.compact_list.save_compact_list(tmp_filename, streamlines)185    streamlines = streamlines[::10].copy()186    streamlines._data -= np.mean(streamlines._data, axis=0)187    print "Displaying {} streamlines...".format(len(streamlines))188    #from dipy.viz import actor, window189    #renderer = window.Renderer()190    #bundle_actor = actor.line(streamlines)191    #renderer.add(bundle_actor)192    #window.show(renderer)193    thresholds = [40, 30, 25]#, 20, 15]194    qbx_class = QuickBundlesX(thresholds)195    print "Clustering {} streamlines ({})...".format(len(streamlines), thresholds)196    qbx = qbx_class.cluster(streamlines)197    print "Displaying clusters graph..."198    tree = qbx.get_tree_cluster_map()199    tree.refdata = streamlines200    color_tree(tree)201    from dipy.viz.clustering import show_clusters_graph202    show_clusters_graph(tree)203def test_show_qbx():204    filename = "/home/marc/research/dat/streamlines/MPI_Camille/myBrain.trk"205    import nibabel as nib206    print "Loading streamlines..."207    import os208    tmp_filename = "/tmp/streamlines.npz"209    if os.path.isfile(tmp_filename):210        streamlines = nib.streamlines.compact_list.load_compact_list(tmp_filename)211    else:212        streamlines = nib.streamlines.load(filename).streamlines213        nib.streamlines.compact_list.save_compact_list(tmp_filename, streamlines)214    streamlines = streamlines[::10].copy()215    streamlines._data -= np.mean(streamlines._data, axis=0)216    # Rotate brain to see a sagital view.217    from nibabel.affines import apply_affine218    from dipy.core.geometry import rodrigues_axis_rotation219    R1 = np.eye(4)220    R1[:3, :3] = rodrigues_axis_rotation((0, 1, 0), theta=90)221    R2 = np.eye(4)222    R2[:3, :3] = rodrigues_axis_rotation((0, 0, 1), theta=90)223    R = np.dot(R2, R1)224    streamlines._data = apply_affine(R, streamlines._data)225    #print "Displaying {} streamlines...".format(len(streamlines))226    #from dipy.viz import actor, window227    #renderer = window.Renderer()228    #bundle_actor = actor.line(streamlines)229    #renderer.add(bundle_actor)230    #window.show(renderer)231    thresholds = [40, 30, 25]#, 20, 15]232    qbx_class = QuickBundlesX(thresholds)233    print "Clustering {} streamlines ({})...".format(len(streamlines), thresholds)234    qbx = qbx_class.cluster(streamlines)235    clusters = qbx.get_clusters(len(thresholds))236    clusters.refdata = streamlines237    from dipy.viz.clustering import show_clusters238    print "Displaying {} clusters...".format(len(clusters))239    tree = qbx.get_tree_cluster_map()240    tree.refdata = streamlines241    color_tree(tree)242    for level in range(1, len(thresholds) + 1):243        print level, thresholds[level-1]244        clusters = tree.get_clusters(level)245        clusters.refdata = streamlines246        show_clusters(clusters)247if __name__ == '__main__':248    #test_with_simulated_bundles2()249    #test_show_qbx_tree()...

second_round_bundle_generator.py

Source:second_round_bundle_generator.py

...70        bundle_length = 071        if self.student.b_m_timetable_student.exists():72            bundle_length = len(list(self.student.b_m_timetable_student.get().get_tutorials_list()))73        else:74            bundle_length = self.student.preferences.get_bundle_length()75        violating_dict = self.__get_violating_timeslot_dict(bundle_length)76        rank = 177        for num_violations in violating_dict:78            all_bundles_num_violation = self.create_valid_bundles(num_violations,violating_dict)79            for bundle in all_bundles_num_violation:80                new_pref_list.add_item(rank,frozenset(bundle))81            rank += 182        return new_pref_list83    def __additional_rounds_in_second_round(self,with_course_constraint):84        new_pref_list = PreferenceList()85        bundles = self.create_no_constraint_prefs(self.interested_tutorials,with_course_constraint)86        rank = 187        for bundle in bundles:88            new_pref_list.add_item(rank, frozenset(bundle))...

HexagonalBundle.py

Source:HexagonalBundle.py

1from Molecule import Molecule2from ..geometry import shapes3import numpy as np4from math import ceil5class HexagonalBundle(Molecule):6  def __init__(self):7    super(HexagonalBundle,self).__init__() #Need to call parent class' constructor8    self.name = 'HexagonalBundle'9  # def build(self,nstems,nlayers,diameter=1.0,topType=0,bottomType=2,middleType=1):10  def build(self,bundle_radius,bundle_length,bead_diameter=1.0,topType=0,bottomType=2,middleType=1,base_gridsize=500):11    # pos = shapes.hexagonalspiral.positions(nstems,diameter)12    kwargs = {}13    kwargs['nx'] = base_gridsize14    kwargs['ny'] = base_gridsize15    kwargs['nz'] = 116    kwargs['diameter'] = bead_diameter17    kwargs['topType']    = topType18    kwargs['middleType'] = topType19    kwargs['bottomType'] = topType20    kwargs['alternate_z'] = True21    molData = shapes.hexagonal.surface(**kwargs)22    pos = np.array([molData['x'], molData['y'], molData['z']]).T23    idex = pos.shape[0]/224    idex = base_gridsize*base_gridsize/2 + base_gridsize/225    cpos = pos[idex]26    dist = np.sqrt(np.sum(np.square(cpos - pos),axis=1))27    28    mask = ( dist<=bundle_radius )29    pos = pos[mask]30    pos -= cpos31    new_pos = []32    bonds = []33    types = [topType for i in range(pos.shape[0])]34    beadj = pos.shape[0]35    bundle_length = int(bundle_length)36    for pi,p in enumerate(pos):37      beadi = pi38      for l in range(1,bundle_length):39        new_pos.append([p[0],p[1],p[2]+l*bead_diameter])40        bonds.append([beadi,beadj])41        types.append(middleType)42        beadi = beadj43        beadj = beadj + 144      types[-1] = bottomType45    pos = np.append(pos,new_pos,axis=0)46    types = np.array(types)47    48    49    molData = {}50    molData['x'] = pos[:,0]51    molData['y'] = pos[:,1]52    molData['z'] = pos[:,2]53    molData['types'] = types54    molData['bonds'] = bonds...

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