How to use brainrender - 10 common examples

def test_custom_video():
    from brainrender.animation.video import CustomVideoMaker

    # --------------------------------- Variables -------------------------------- #
    N_FRAMES = 20

    # Variables to specify camera position at each frame
    zoom = np.linspace(1, 1.35, N_FRAMES)
    frac = np.zeros_like(
        zoom
    )  # for camera transition, interpolation value between cameras
    frac[:10] = np.linspace(0, 1, 10)
    frac[10:] = np.linspace(1, 0, len(frac[10:]))

    # ------------------------------- Create scene ------------------------------- #
    scene = Scene(display_inset=True, use_default_key_bindings=True)

    filepaths, data = scene.atlas.download_streamlines_for_region("TH")
    scene.add_brain_regions(["TH"], alpha=0.2)

    # Create new cameras
    cam1 = buildcam(sagittal_camera)
    cam2 = buildcam(top_camera)
    cam3 = buildcam(
        dict(
            position=[1862.135, -4020.792, -36292.348],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.185, -0.97, 0.161],
            distance=42972.44,
            clipping=[29629.503, 59872.10],
        )
    )

brainrender.DEFAULT_STRUCTURE_ALPHA
    brainrender.INJECTION_VOLUME_SIZE
    brainrender.TRACTO_RADIUS
    brainrender.TRACTO_ALPHA
    brainrender.TRACTO_RES
    brainrender.TRACT_DEFAULT_COLOR
    brainrender.INJECTION_DEFAULT_COLOR
    brainrender.STREAMLINES_RESOLUTION
    brainrender.INJECTION_VOLUME_SIZE
    brainrender.TRACTO_RADIUS
    brainrender.TRACTO_ALPHA
    brainrender.TRACTO_RES
    brainrender.TRACT_DEFAULT_COLOR
    brainrender.INJECTION_DEFAULT_COLOR
    brainrender.STREAMLINES_RESOLUTION
    brainrender.SHADER_STYLE
    brainrender.VERBOSE
    brainrender.HDF_SUFFIXES
    brainrender.DEFAULT_HDF_KEY

    brainrender.reset_defaults()

getColor("#ffffff")
    getColor(7)
    getColor(-7)

    getColorName("#ffffff")

    cols = colorMap([0, 1, 2])
    if not isinstance(cols, (list, np.ndarray)):
        raise ValueError
    if len(cols) != 3:
        raise ValueError

    c = colorMap(3, vmin=-3, vmax=4)

    check_colors(cols)
    check_colors(c)

getColor("k")
    getColor("#ffffff")
    getColor(7)
    getColor(-7)

    getColorName("#ffffff")

    cols = colorMap([0, 1, 2])
    if not isinstance(cols, (list, np.ndarray)):
        raise ValueError
    if len(cols) != 3:
        raise ValueError

    c = colorMap(3, vmin=-3, vmax=4)

    check_colors(cols)
    check_colors(c)

filterby="soma", filter_regions=["MOs"]
    )

    neurons_files = ml_api.download_neurons(neurons_metadata[:N_neurons])

    # ------------------------------- Create scene ------------------------------- #
    scene = Scene(display_inset=False, use_default_key_bindings=True)

    neurons_actors = scene.add_neurons(
        neurons_files, neurite_radius=12, alpha=0
    )

    # Create new cameras
    cam1 = buildcam(sagittal_camera)

    cam2 = buildcam(
        dict(
            position=[-16624.081, -33431.408, 33527.412],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.634, -0.676, -0.376],
            distance=51996.653,
            clipping=[34765.671, 73812.327],
        )
    )

    cam3 = buildcam(
        dict(
            position=[1862.135, -4020.792, -36292.348],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.185, -0.97, 0.161],
            distance=42972.44,
            clipping=[29629.503, 59872.10],

)

    # Create new cameras
    cam1 = buildcam(sagittal_camera)

    cam2 = buildcam(
        dict(
            position=[-16624.081, -33431.408, 33527.412],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.634, -0.676, -0.376],
            distance=51996.653,
            clipping=[34765.671, 73812.327],
        )
    )

    cam3 = buildcam(
        dict(
            position=[1862.135, -4020.792, -36292.348],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.185, -0.97, 0.161],
            distance=42972.44,
            clipping=[29629.503, 59872.10],
        )
    )

    # ------------------------------- Create frames ------------------------------ #
    # Create frames
    prev_neurons = []
    for step in track(
        np.arange(N_FRAMES), total=N_FRAMES, description="Generating frames..."
    ):
        if step % N_frames_for_change == 0:  # change neurons every N framse

zoom = np.linspace(1, 1.35, N_FRAMES)
    frac = np.zeros_like(
        zoom
    )  # for camera transition, interpolation value between cameras
    frac[:10] = np.linspace(0, 1, 10)
    frac[10:] = np.linspace(1, 0, len(frac[10:]))

    # ------------------------------- Create scene ------------------------------- #
    scene = Scene(display_inset=True, use_default_key_bindings=True)

    filepaths, data = scene.atlas.download_streamlines_for_region("TH")
    scene.add_brain_regions(["TH"], alpha=0.2)

    # Create new cameras
    cam1 = buildcam(sagittal_camera)
    cam2 = buildcam(top_camera)
    cam3 = buildcam(
        dict(
            position=[1862.135, -4020.792, -36292.348],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.185, -0.97, 0.161],
            distance=42972.44,
            clipping=[29629.503, 59872.10],
        )
    )

    # Iniziale camera position
    scene.plotter.moveCamera(cam1, cam2, frac[0])

    # ------------------------------- Create frames ------------------------------ #
    def frame_maker(scene=None, video=None, videomaker=None):
        for step in track(

frac = np.zeros_like(
        zoom
    )  # for camera transition, interpolation value between cameras
    frac[:10] = np.linspace(0, 1, 10)
    frac[10:] = np.linspace(1, 0, len(frac[10:]))

    # ------------------------------- Create scene ------------------------------- #
    scene = Scene(display_inset=True, use_default_key_bindings=True)

    filepaths, data = scene.atlas.download_streamlines_for_region("TH")
    scene.add_brain_regions(["TH"], alpha=0.2)

    # Create new cameras
    cam1 = buildcam(sagittal_camera)
    cam2 = buildcam(top_camera)
    cam3 = buildcam(
        dict(
            position=[1862.135, -4020.792, -36292.348],
            focal=[6587.835, 3849.085, 5688.164],
            viewup=[0.185, -0.97, 0.161],
            distance=42972.44,
            clipping=[29629.503, 59872.10],
        )
    )

    # Iniziale camera position
    scene.plotter.moveCamera(cam1, cam2, frac[0])

    # ------------------------------- Create frames ------------------------------ #
    def frame_maker(scene=None, video=None, videomaker=None):
        for step in track(
            np.arange(N_FRAMES),

def test_default():
    brainrender.DISPLAY_INSET
    brainrender.DISPLAY_ROOT
    brainrender.WHOLE_SCREEN
    brainrender.BACKGROUND_COLOR
    brainrender.SHOW_AXES
    brainrender.WINDOW_POS
    brainrender.CAMERA
    brainrender.DEFAULT_SCREENSHOT_NAME
    brainrender.DEFAULT_SCREENSHOT_TYPE
    brainrender.DEFAULT_SCREENSHOT_SCALE
    brainrender.SCREENSHOT_TRANSPARENT_BACKGROUND
    brainrender.ROOT_COLOR
    brainrender.ROOT_ALPHA
    brainrender.DEFAULT_STRUCTURE_COLOR
    brainrender.DEFAULT_STRUCTURE_ALPHA
    brainrender.INJECTION_VOLUME_SIZE
    brainrender.TRACTO_RADIUS
    brainrender.TRACTO_ALPHA
    brainrender.TRACTO_RES
    brainrender.TRACT_DEFAULT_COLOR
    brainrender.INJECTION_DEFAULT_COLOR
    brainrender.STREAMLINES_RESOLUTION
    brainrender.INJECTION_VOLUME_SIZE
    brainrender.TRACTO_RADIUS
    brainrender.TRACTO_ALPHA
    brainrender.TRACTO_RES
    brainrender.TRACT_DEFAULT_COLOR
    brainrender.INJECTION_DEFAULT_COLOR
    brainrender.STREAMLINES_RESOLUTION

def test_io():
    fld = os.getcwd()
    if not isinstance(listdir(fld), list):
        raise ValueError(f"listdir returned: {listdir(fld)}")
    if not isinstance(get_subdirs(fld), list):
        raise ValueError(f"get_subdirs returned: {get_subdirs(fld)}")