A small Python library to read and write SerialEM navigator files (.nav), and process grid maps.
Install using pip install pyserialem.
The main use of pyserialem is to manipulate coordinates in a .nav file written by SerialEM (specification).
Reading a .nav file:
import pyserialem
from pathlib import Path
p = Path('C:/path/to/data/') / 'nav.nav'
items = pyserialem.read_nav_file(p) # listYou can set the acquire_only toggle to return only the items with the Acquire tag set:
items = pyserialem.read_nav_file(p, acquire_only=True) # listThis returns a list of MapItem and NavItem. A MapItem is associated with an image in the corresponding .mrc file, and a NavItem is a marker or point on that image.
map_items = [item.kind == 'Map' for item in items]
nav_items = [item.kind == 'Marker' for item in items]All of the tags associated with the MapItem or NavItem can be accessed as an attribute using the same name as in the .nav file, i.e. with the key defined here. This is also how the values should be updated:
nav_item = nav_items[0]
stage_position = nav_item.StageXYZ # tuple
map_item.StageXYZ = (100, 200, 0) # overwrite valuesAlternatively, the stage position can be accessed directly through:
x = map_item.stage_x
y = map_item.stage_y
z = map_item.stage_z
xy = map_item.stage_xyA MapItem has all the functions of a NavItem, and then some. Each MapItem can have a list of markers associated with it:
map_item = map_items[0]
markers = map_item.markers # listTo visualize them, call:
map_item.plot()To just load the image associated with the MapItem:
img = map_item.load() # np.arrayThey can be extracted as a dictionary:
d = map_item.to_dict() # dict...and restored:
new_map_item = pysem.from_dict(d, tag='new_mapitem')This is also the easiest way of constructing a new MapItem, because some keys can be autogenerated. Otherwise, all the required keys have to be specified to the MapItem constructor. The tag specifies the name of the item when displayed in SerialEM. If omitted, one will be generated.
It is easy to add new markers to a MapItem. As a pixel coordinate (i.e. from segmentation) is the default. PySerialEM calculates the corresponding stage position. The acquire argument sets the acquire flag (default=True):
pixel_position = (0, 0)
new_nav_item = map_item.add_marker(
pixel_position,
tag='pixel_item',
acquire=True) # NavItemYou can also add a marker as a stage coordinate (although this is a bit more tricky to calculate the corresponding pixel coordinate):
stage_positionion = (1000, 1000)
new_nav_item = map_item.add_marker(
pixel_position,
kind='stage',
tag='stage_item',
acquire=False) # NavItemTo add many markers:
pixel_coordinates = ((0, 0), (100, 100), (200, 200))
nav_item_group = map_item.add_marker_group(pixel_coordinates) # tupleSpecify replace=True to replace the current list of markers associated with the MapItem.
If the MapItem has a set of markers associated with it map_item.markers, the coordinates be retrieved as followed:
map_item.markers_as_pixel_coordinates() # np.array (Nx2)
map_item.markers_as_stage_coordinates() # np.array (Nx2)To just convert between stage and pixel coordinates:
pixel_coord = (1024, 1024)
stage_coord = map_item.pixel_to_stagecoords(pixel_coord) # tuple
new_pixel_coord = map_item.stage_to_pixelcoords(stagecoord) # tuple
assert new_pixel_coord == pixel_coordTo write a new file:
pyserialem.write_nav_file('out.nav', map_item, *nav_item_group)Note the *. This function captures arguments in a list (*args, so they must be unpacked when supplied.
A basic stitching algorithm is available to get an overview of the location of all map items:
map_items = [item for item in items if item.kind == 'Map']
pyserialem.stitch_map_items(map_items)For more advanced stitching and montaging, use the pyserialem.montage module. A demo notebook is available to demonstrate its usage.
There is also a simple function to read .mdoc files (link). This returns a list of python objects where each key can be accessed as an attribute.
p = Path('C:/path/to/data') / 'gm.mrc.mdoc'
mdoc = pyserialem.read_mdoc_file(p)