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...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.