def _cli(cls, args, print_help=False):
"""Runs the pixel finder component based on parsed arguments."""
if args.pixel_finder_algorithm_class is None or print_help:
cls.pixel_finder_group.print_help()
cls.pixel_finder_group.exit(status=2)
print('Detecting Pixels...')
s = Stack()
s.read(args.input)
instance = args.pixel_finder_algorithm_class(**vars(args))
# TODO does this work?
pixel_attributes, encoded_pixels = instance.find(s)
# TODO ambrosejcarr: this still needs to be added back.
# if args.show:
# encoded_pixels.show(figsize=(10, 10))
path = os.path.join(args.output, 'pixels.geojson')
print(f"Writing | pixels geojson to: {path}")
pixel_attributes.save_geojson(path)
path = os.path.join(args.output, 'pixels.json')
print(f"Writing | spot_id | x | y | z | to: {path}")
pixel_attributes.save(path)
path = os.path.join(args.output, 'encoder_table.json')
print(f"Writing | spot_id | hyb | ch | val | to: {path}")
encoded_pixels.save(path)
def _cli(cls, args, print_help=False):
"""Runs the filter component based on parsed arguments."""
if args.filter_algorithm_class is None or print_help:
cls.filter_group.print_help()
cls.filter_group.exit(status=2)
print('Filtering images ...')
s = Stack()
s.read(args.input)
instance = args.filter_algorithm_class(**vars(args))
instance.filter(s)
s.write(args.output)
def merfish_stack() -> Stack:
"""retrieve MERFISH testing data from cloudfront and expose it at the module level
Notes
-----
Because download takes time, this fixture runs once per session -- that is, the download is run only once.
Returns
-------
Stack :
starfish.io.Stack object containing MERFISH data
"""
s = Stack()
s.read('https://s3.amazonaws.com/czi.starfish.data.public/20180607/test/MERFISH/fov_001/experiment_new.json')
return deepcopy(s)
def _cli(cls, args, print_help=False):
"""Runs the registration component based on parsed arguments."""
if args.registration_algorithm_class is None or print_help:
cls.register_group.print_help()
cls.register_group.exit(status=2)
instance = args.registration_algorithm_class(**vars(args))
from starfish.io import Stack
print('Registering ...')
s = Stack()
s.read(args.input)
instance.register(s)
s.write(args.output)
from showit import image, tile # EPY: END code # EPY: START markdown # ## Raw Data # # The raw data can be downloaded and formatted for analysis by running: ```python examples/get_iss_data.py ><raw data directory> <output directory> --d 1``` from the Starfish directory # EPY: END markdown # EPY: START code from starfish.io import Stack # replace <output directory> with where you saved the formatted data to with the above script in_json = '<output directory>/org.json' s = Stack() s.read(in_json) tile(s.squeeze(), size=10) # EPY: END code # EPY: START code image(s.aux_dict['dots'], size=10) # EPY: END code # EPY: START markdown # ## Register # EPY: END markdown # EPY: START code from starfish.registration._fourier_shift import compute_shift, shift_im