def cli(paths, mode, name_regex, pixel_size, dry_run, strict, name, mmap, lazy,
no_show):
"""
PeepingTom command line interface.
Opens files in napari and lands in an interactive ipython shell
with peepingtom imported as `pt` and the initialised Peeper available as `p`.
PATHS: any number of files or globs [default='./*']
\b
MODE choices:
- lone: each datablock in a separate volume
- zip_by_type: one of each datablock type per volume
- bunch: all datablocks in a single volume
\b
EXAMPLES:
Open a .star file as particles:
peep particles.star
Open particles and images from a directory:
peep /dir/with/star_and_mrc_files/
Match files such as MyProtein_10.star and MyProtein_001.mrc,
and name the respective DataBlocks Protein_10 and Protein_001:
peep /path/to/dir/MyProtein* -n 'Protein_\d+'
""" # noqa: W605
if not paths:
paths = ['./*']
if dry_run:
files = pt.io_.find_files(paths)
print('Files found:')
print(*(str(file) for file in files), sep='\n')
sys.exit()
peeper = pt.read(
*paths, # noqa: F841
name=name,
mode=mode,
name_regex=name_regex,
pixel_size=pixel_size,
strict=strict,
mmap=mmap,
lazy=lazy,
)
# set up ipython shell nicely
banner = '''=== PeepingTom ===
initialised variables:
- peeper
- viewer
'''
sh = InteractiveShellEmbed(banner2=banner)
sh.enable_gui('qt')
sh.push('peeper')
if not no_show:
sh.run_cell('peeper.show()', silent=True)
viewer = peeper.napari_viewer # noqa: F841
sh.push('viewer')
sh()
def cli(paths, mode, name_regex, pixel_size, dry_run, strict, name, lazy,
no_show):
"""
Blik command line interface.
Opens files in napari and lands in an interactive ipython shell
with blik imported and the initialised DataSet available as `dataset`.
PATHS: any number of files or globs [default='./*']
\b
MODE choices:
- lone: each datablock in a separate volume
- zip_by_type: one of each datablock type per volume
- bunch: all datablocks in a single volume
\b
EXAMPLES:
Open a .star file as particles:
blik particles.star
Open particles and images from a directory:
blik /dir/with/star_and_mrc_files/
Match files such as MyProtein_10.star and MyProtein_001.mrc,
and name the respective DataBlocks Protein_10 and Protein_001:
blik /path/to/dir/MyProtein* -n 'Protein_\d+'
""" # noqa: W605
if not paths:
paths = ['./*']
if dry_run:
from blik.io_.reading.main import find_files
files = [str(file) for file in find_files(paths)]
if files:
click.echo('Files found:\n' + '\n'.join(files))
else:
click.echo('No files found.')
click.get_current_context().exit()
import blik
from IPython.terminal.embed import InteractiveShellEmbed
dataset = blik.read(
*paths, # noqa: F841
name=name,
mode=mode,
name_regex=name_regex,
pixel_size=pixel_size,
strict=strict,
lazy=lazy,
)
# set up ipython shell nicely
banner = '''=== Blik ===
initialised variables:
- dataset
- viewer
'''
sh = InteractiveShellEmbed(banner2=banner)
sh.enable_gui('qt')
sh.push('dataset')
if not no_show:
sh.run_cell('dataset.show()', silent=True)
viewer = dataset.napari_viewer # noqa: F841
sh.push('viewer')
sh()
"""
Start napari and land directly in an embedded ipython console with qt event loop.
A similar effect can be achieved more simply with `viewer.update_console(locals())`,
such as shown in https://github.com/napari/napari/blob/main/examples/update_console.py.
However, differently from `update_console`, this will start an independent
ipython console which can outlive the viewer.
"""
import napari
from IPython.terminal.embed import InteractiveShellEmbed
# any code
text = 'some text'
# initalize viewer
viewer = napari.Viewer()
# embed ipython and run the magic command to use the qt event loop
sh = InteractiveShellEmbed()
sh.enable_gui('qt') # equivalent to using the '%gui qt' magic
sh() # open the embedded shell
# From there, you can access the script's scope, such as the variables `text` and `viewer`
layout = QVBoxLayout()
dialog.setLayout(layout)
layout.addWidget(table)
dialog.setWindowTitle(title)
dialog.exec_()
def __get_table_model(self, data):
if isinstance(data, DataFrame):
return PandasDataframeModel(data)
elif isinstance(data, Series):
return PandasSeriesModel(data)
def __console(self):
df = self.__dataframe # noqa
shell()
def __action(self, label, shortcut, callback):
action = QAction(QIcon("icon.ico"), label, self)
action.setShortcut(shortcut)
action.triggered.connect(callback)
return action
if __name__ == "__main__":
shell = InteractiveShellEmbed()
shell.enable_gui("qt5")
appctxt = AppContext()
exit_code = appctxt.run()
sys.exit(exit_code)
set_option("precision", 1)
