napari: Segmentation fault wen trying to start Napari viewer from script

🐛 Bug

When I try to start the Napari Viewer (latest version) from a script on Ubunto 18.04.3 LTS I recently got: Segmentation fault

To Reproduce

The relevant lines of code are here and I think the error occue

def show_napari(array, metadata,
                blending='additive',
                gamma=0.45,
                verbose=True):

    with napari.gui_qt():

        # create scalefcator with all ones
        scalefactors = [1] * len(array.shape)

        # initialize the napari viewer
        viewer = napari.Viewer()

        if metadata['ImageType'] == 'ometiff':

            # find position of dimensions
            posZ = metadata['DimOrder BF Array'].find('Z')
            posC = metadata['DimOrder BF Array'].find('C')
            posT = metadata['DimOrder BF Array'].find('T')

            # get the scalefactors from the metadata
            scalef = get_scalefactor(metadata)
            # modify the tuple for the scales for napari
            scalefactors[posZ] = scalef['zx']

            if verbose:
                print('Dim PosT : ', posT)
                print('Dim PosC : ', posC)
                print('Dim PosZ : ', posZ)
                print('Scale Factors : ', scalefactors)

            # add all channels as layers
            for ch in range(metadata['SizeC']):

                try:
                    # get the channel name
                    chname = metadata['Channels'][ch]
                except:
                    # or use CH1 etc. as string for the name
                    chname = 'CH' + str(ch + 1)

                # cutout channel
                channel = array.take(ch, axis=posC)
                print('Shape Channel : ', ch, channel.shape)

                # actually show the image array
                print('Scaling Factors: ', scalefactors)

                # get min-max values for initial scaling
                clim = [channel.min(), np.round(channel.max() * 0.85)]
                if verbose:
                    print('Scaling: ', clim)
                viewer.add_image(channel,
                                 name=chname,
                                 scale=scalefactors,
                                 contrast_limits=clim,
                                 blending=blending,
                                 gamma=gamma)

        if metadata['ImageType'] == 'czi':

            # find position of dimensions
            posZ = metadata['Axes'].find('Z')
            posC = metadata['Axes'].find('C')
            posT = metadata['Axes'].find('T')

            # get the scalefactors from the metadata
            scalef = get_scalefactor(metadata)
            # modify the tuple for the scales for napari
            scalefactors[posZ] = scalef['zx']

            if verbose:
                print('Dim PosT : ', posT)
                print('Dim PosZ : ', posZ)
                print('Dim PosC : ', posC)
                print('Scale Factors : ', scalefactors)

            if metadata['SizeC'] > 1:
                # add all channels as layers
                for ch in range(metadata['SizeC']):

                    try:
                        # get the channel name
                        chname = metadata['Channels'][ch]
                    except:
                        # or use CH1 etc. as string for the name
                        chname = 'CH' + str(ch + 1)

                    # cut out channel
                    channel = array.take(ch, axis=posC)
                    print('Shape Channel : ', ch, channel.shape)

                    # actually show the image array
                    print('Adding Channel: ', chname)
                    print('Scaling Factors: ', scalefactors)

                    # get min-max values for initial scaling
                    clim = [channel.min(), np.round(channel.max() * 0.85)]
                    if verbose:
                        print('Scaling: ', clim)
                    viewer.add_image(channel,
                                     name=chname,
                                     scale=scalefactors,
                                     contrast_limits=clim,
                                     blending=blending,
                                     gamma=gamma)

            if metadata['SizeC'] == 1:

                ch = 0
                # just add one channel as a layer
                try:
                        # get the channel name
                    chname = metadata['Channels'][ch]
                except:
                    # or use CH1 etc. as string for the name
                    chname = 'CH' + str(ch + 1)

                # actually show the image array
                print('Adding Channel: ', chname)
                print('Scaling Factors: ', scalefactors)

                # get min-max values for initial scaling
                clim = [channel.min(), np.round(channel.max() * 0.85)]
                if verbose:
                    print('Scaling: ', clim)
                viewer.add_image(array,
                                 name=chname,
                                 scale=scalefactors,
                                 contrast_limits=clim,
                                 blending=blending,
                                 gamma=gamma)

This is the output when I call the script:

Expected behavior

No Segmentation fault

Environment

I used the latest version (24.11.2019) of Napari from GitHub and installed it via

pip install -e .