def format_data(input_dir, output_dir):
primary_image_dimensions = {
Axes.ROUND: 4,
Axes.CH: 4,
Axes.ZPLANE: 1,
}
aux_name_to_dimensions = {
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
'dots': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
}
}
write_experiment_json(
path=output_dir,
fov_count=16,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=ISSCroppedBreastPrimaryTileFetcher(input_dir),
aux_tile_fetcher={
'nuclei': ISSCroppedBreastAuxTileFetcher(input_dir, 'nuclei'),
'dots': ISSCroppedBreastAuxTileFetcher(input_dir, 'dots'),
},
)
def format_data(input_dir, output_dir):
def add_codebook(experiment_json_doc):
experiment_json_doc['codebook'] = "codebook.json"
return experiment_json_doc
num_fovs = 496
primary_image_dimensions = {
Axes.ROUND: 8,
Axes.CH: 2,
Axes.ZPLANE: 1,
}
aux_name_to_dimensions = {
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1
}
}
write_experiment_json(output_dir,
num_fovs,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=MERFISHTileFetcher(
input_dir, is_dapi=False),
aux_tile_fetcher={
'nuclei':
MERFISHTileFetcher(input_dir, is_dapi=True),
},
postprocess_func=add_codebook,
default_shape=SHAPE)
def format_data(image_dir: Path,
primary_image_dimensions: Mapping[Union[Axes, str], int],
aux_name_to_dimensions: Mapping[str, Mapping[Union[Axes, str],
int]], num_fovs):
def add_codebook(experiment_json_doc):
experiment_json_doc['codebook'] = "codebook.json"
return experiment_json_doc
enable_inplace_mode()
write_experiment_json(
path=os.fspath(image_dir),
fov_count=num_fovs,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=InplaceFetcher(image_dir,
FieldOfView.PRIMARY_IMAGES),
aux_tile_fetcher={
aux_img_name: InplaceFetcher(image_dir, aux_img_name)
for aux_img_name in aux_name_to_dimensions.keys()
},
postprocess_func=add_codebook,
default_shape=SHAPE,
fov_path_generator=fov_path_generator,
tile_opener=inplace_tile_opener,
)
def cli(input_dir: str, output_dir: str, codebook_csv: str) -> int:
"""CLI entrypoint for spaceTx format construction for SeqFISH data
Parameters
----------
input_dir : str
directory containing input multi-page TIFF files for a single field of view, separated by
the imaging round they were acquired in and named <1-index round>.tif
output_dir : str
directory containing output files. Will be created if it does not exist.
codebook_csv : str
name of the codebook csv file containing barcode information for this field of view.
Notes
-----
- each round is organized as [z, ch, [x|y], [x|y]] -- the order of x and y are not known, but
since this script uses dummy coordinates, this distinction is not important.
- The spatial organization of the field of view is not known to the starfish developers,
so they are filled by dummy coordinates
- Raw data (input for this tool) for this experiment can be found at:
s3://spacetx.starfish.data.public/browse/raw/seqfish/
- Processed data (output of this tool) can be found at:
s3://spacetx.starfish.data.public/browse/formatted/20181211/seqfish/ and accessed in
`starfish.data.SeqFISH`
Returns
-------
int :
Returns 0 if successful
"""
os.makedirs(output_dir, exist_ok=True)
primary_tile_fetcher = SeqFISHTileFetcher(os.path.expanduser(input_dir))
# This is hardcoded for this example data set
primary_image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND: 5,
Axes.CH: 12,
Axes.ZPLANE: 29,
}
write_experiment_json(
path=output_dir,
fov_count=1,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions={},
primary_tile_fetcher=primary_tile_fetcher,
tile_format=ImageFormat.TIFF,
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE)
)
# Note: this must trigger AFTER write_experiment_json, as it will clobber the codebook with
# a placeholder.
codebook = parse_codebook(codebook_csv)
codebook.to_json("codebook.json")
return 0
def format_data(input_dir, output_dir) -> None:
"""Format a BaristaSeq Tile
Parameters
----------
input_dir : str
Input directory containing data. Example data for a single FoV can be downloaded from
s3://spacetx.starfish.data.public/browse/raw/20181231/barista-seq-mouse-cortex-cropped
output_dir : str
Output directory containing formatted data in SpaceTx format. Example output data can be
downloaded from
https://d2nhj9g34unfro.cloudfront.net/browse/formatted/20181028/ \
BaristaSeq/cropped_formatted/experiment.json"
"""
num_fovs = 1
primary_image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND: 3,
Axes.CH: 4,
Axes.ZPLANE: 17,
}
aux_name_to_dimensions: Mapping[str, Mapping[Union[str, Axes], int]] = {
"nuclei": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 17,
}
}
os.makedirs(output_dir, exist_ok=True)
write_experiment_json(
path=output_dir,
fov_count=num_fovs,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=BaristaSeqTileFetcher(input_dir),
aux_tile_fetcher={
"nuclei": BaristaSeqNucleiTileFetcher(input_dir, "nuclei"),
},
tile_format=ImageFormat.TIFF,
default_shape=DEFAULT_TILE_SHAPE
)
shutil.copyfile(
src=os.path.join(input_dir, "codebook.json"),
dst=os.path.join(output_dir, "codebook.json")
)
def format_data(input_dir, output_dir):
input_dir = os.path.abspath(input_dir)
output_dir = os.path.abspath(output_dir)
def add_scale_factors(experiment_json_doc):
filename = os.path.join(input_dir, "scale_factors.json")
with open(filename, 'r') as f:
data = json.load(f)
experiment_json_doc['extras'] = {"scale_factors": data}
return experiment_json_doc
num_fovs = 496
primary_image_dimensions = {
Axes.ROUND: 8,
Axes.CH: 2,
Axes.ZPLANE: 1,
}
aux_name_to_dimensions = {
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1
}
}
write_experiment_json(output_dir,
num_fovs,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=MERFISHTileFetcher(
input_dir, is_dapi=False),
aux_tile_fetcher={
'nuclei':
MERFISHTileFetcher(input_dir, is_dapi=True),
},
postprocess_func=add_scale_factors,
default_shape=SHAPE)
def cli(input_dir, output_dir):
"""CLI entrypoint for spaceTx format construction for Imaging Mass Cytometry
Raw data (input for this tool) for this experiment can be found at:
s3://spacetx.starfish.data.public/browse/raw/20181015/imaging_cytof/\
BodenmillerBreastCancerSamples/
Processed data (output of this tool) can be found at:
s3://spacetx.starfish.data.public/browse/formatted/20181023/imaging_cytof/\
BodenmillerBreastCancerSamples/
"""
os.makedirs(output_dir, exist_ok=True)
primary_tile_fetcher = ImagingMassCytometryTileFetcher(
os.path.expanduser(input_dir))
primary_image_dimensions = {
Axes.ROUND: 1,
Axes.CH: len(primary_tile_fetcher._ch_dict),
Axes.ZPLANE: 1
}
def postprocess_func(experiment_json_doc):
experiment_json_doc["codebook"] = "codebook.json"
return experiment_json_doc
with open(os.path.join(output_dir, "codebook.json"), 'w') as f:
codebook = primary_tile_fetcher.generate_codebook()
json.dump(codebook, f)
write_experiment_json(
path=output_dir,
fov_count=len(primary_tile_fetcher._fov_map),
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions={},
primary_tile_fetcher=primary_tile_fetcher,
postprocess_func=postprocess_func,
)
def format_data(input_dir, output_dir, num_fovs):
def add_codebook(experiment_json_doc):
experiment_json_doc['codebook'] = "codebook.json"
return experiment_json_doc
primary_image_dimensions = {
Axes.ROUND: 4,
Axes.CH: 4,
Axes.ZPLANE: 1,
}
aux_name_to_dimensions = {
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
'dots': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
}
}
write_experiment_json(
path=output_dir,
fov_count=num_fovs,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=ISSCroppedBreastPrimaryTileFetcher(input_dir),
aux_tile_fetcher={
'nuclei': ISSCroppedBreastAuxTileFetcher(input_dir, 'nuclei'),
'dots': ISSCroppedBreastAuxTileFetcher(input_dir, 'dots'),
},
postprocess_func=add_codebook,
default_shape={
Axes.Y: 1044,
Axes.X: 1390
})
def cli(input_dir, metadata_yaml, output_dir):
"""Reads osmFISH images from <input-dir> and experiment metadata from <metadata-yaml> and writes
spaceTx-formatted data to <output-dir>.
Raw data (input for this tool) for this experiment can be found at:
s3://spacetx.starfish.data.upload/simone/
Processed data (output of this tool) can be found at:
s3://spacetx.starfish.data.public/20181031/osmFISH/
"""
os.makedirs(output_dir, exist_ok=True)
primary_tile_fetcher = osmFISHTileFetcher(os.path.expanduser(input_dir),
metadata_yaml)
# This is hardcoded for this example data set
primary_image_dimensions = {
Axes.ROUND: 13,
Axes.CH: len(primary_tile_fetcher.channel_map),
Axes.ZPLANE: primary_tile_fetcher.num_z
}
def postprocess_func(experiment_json_doc):
experiment_json_doc["codebook"] = "codebook.json"
return experiment_json_doc
with open(os.path.join(output_dir, "codebook.json"), "w") as f:
codebook = primary_tile_fetcher.generate_codebook()
json.dump(codebook, f)
write_experiment_json(path=output_dir,
fov_count=len(primary_tile_fetcher.fov_map),
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions={},
primary_tile_fetcher=primary_tile_fetcher,
postprocess_func=postprocess_func,
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE))
pass
def format_data(input_dir: str, output_dir: str, gene_name: str) -> None:
primary_image_dimensions: Mapping[Axes, int] = {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1
}
aux_name_to_dimensions: Mapping[str, Mapping[Union[str, Axes], int]] = {
"nissl": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1
}
}
write_experiment_json(
path=output_dir,
fov_count=1,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=StarMapTileFetcher(input_dir),
aux_tile_fetcher={"nissl": StarMapTileFetcher(input_dir)},
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE))
codebook = [{
Features.CODEWORD: [{
Axes.ROUND.value: 0,
Axes.CH.value: 0,
Features.CODE_VALUE: 1
}],
Features.TARGET:
gene_name
}]
Codebook.from_code_array(codebook).to_json(
os.path.join(output_dir, "codebook.json"))
def cli(input_dir, output_dir, metadata_yaml):
"""CLI entrypoint for spaceTx format construction for osmFISH data
Raw data (input for this tool) for this experiment can be found at:
s3://spacetx.starfish.data.upload/simone/
Processed data (output of this tool) can be found at:
s3://spacetx.starfish.data.public/20181031/osmFISH/
"""
os.makedirs(output_dir, exist_ok=True)
primary_tile_fetcher = osmFISHTileFetcher(os.path.expanduser(input_dir),
metadata_yaml)
# This is hardcoded for this example data set
primary_image_dimensions = {
Indices.ROUND: 13,
Indices.CH: len(primary_tile_fetcher.channel_map),
Indices.Z: primary_tile_fetcher.num_z
}
def postprocess_func(experiment_json_doc):
experiment_json_doc["codebook"] = "codebook.json"
return experiment_json_doc
with open(os.path.join(output_dir, "codebook.json"), "w") as f:
codebook = primary_tile_fetcher.generate_codebook()
json.dump(codebook, f)
write_experiment_json(path=output_dir,
fov_count=len(primary_tile_fetcher.fov_map),
tile_format=ImageFormat.NUMPY,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions={},
primary_tile_fetcher=primary_tile_fetcher,
postprocess_func=postprocess_func,
dimension_order=(Indices.ROUND, Indices.CH,
Indices.Z))
pass
def cli(input_dir, output_dir) -> None:
"""CLI entrypoint for spaceTx format construction for osmFISH data
Raw data (input for this tool) for this experiment can be found at:
s3://spacetx.starfish.data.public/browse/raw/20181031/starmap/
Processed data (output of this tool) can be found at:
s3://spacetx.starfish.data.public/browse/formatted/20190111/starmap/
Parameters
----------
input_dir : str
directory containing input data. See TileFetcher classes for expected directory structures.
output_dir : str
directory that 2-d images and SpaceTx metadata will be written to.
"""
abs_output_dir = os.path.expanduser(output_dir)
abs_input_dir = os.path.expanduser(input_dir)
os.makedirs(abs_output_dir, exist_ok=True)
primary_tile_fetcher = StarMapTileFetcher(abs_input_dir)
dapi_tile_fetcher = StarMapDapiTileFetcher(abs_input_dir)
nissl_00_tile_fetcher = StarMapNisslTileFetcher(abs_input_dir,
channels=(0, 0))
nissl_01_tile_fetcher = StarMapNisslTileFetcher(abs_input_dir,
channels=(0, 1))
nissl_02_tile_fetcher = StarMapNisslTileFetcher(abs_input_dir,
channels=(0, 2))
# This is hardcoded for this example data set
primary_image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND: 6,
Axes.CH: 4,
Axes.ZPLANE: 28,
}
aux_images_dimensions: Mapping[str, Mapping[Union[str, Axes], int]] = {
"nuclei": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
"nissl_channels_0_0": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
"nissl_channels_0_1": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
"nissl_channels_0_2": {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
}
write_experiment_json(path=output_dir,
fov_count=1,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_images_dimensions,
primary_tile_fetcher=primary_tile_fetcher,
aux_tile_fetcher={
"nuclei": dapi_tile_fetcher,
"nissl_channels_0_0": nissl_00_tile_fetcher,
"nissl_channels_0_1": nissl_01_tile_fetcher,
"nissl_hcannels_0_2": nissl_02_tile_fetcher
},
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE))
primary_tile_fetcher.generate_codebook(abs_output_dir)
def format_data(input_dir, output_dir, d):
if not input_dir.endswith("/"):
input_dir += "/"
if not output_dir.endswith("/"):
output_dir += "/"
if d:
url = "http://d1zymp9ayga15t.cloudfront.net/content/Examplezips/ExampleInSituSequencing.zip"
download(input_dir, url)
input_dir += "ExampleInSituSequencing/"
print("Data downloaded to: {}".format(input_dir))
else:
input_dir += "ExampleInSituSequencing/"
print("Using data in : {}".format(input_dir))
def add_codebook(experiment_json_doc):
experiment_json_doc['codebook'] = "codebook.json"
return experiment_json_doc
# the magic numbers here are just for the ISS example data set.
write_experiment_json(
output_dir,
1,
ImageFormat.TIFF,
primary_image_dimensions={
Axes.ROUND: 4,
Axes.CH: 4,
Axes.ZPLANE: 1,
},
aux_name_to_dimensions={
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
},
'dots': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: 1,
}
},
primary_tile_fetcher=ISSPrimaryTileFetcher(input_dir),
aux_tile_fetcher={
'nuclei': ISSAuxTileFetcher(os.path.join(input_dir, "DO", "c1.TIF")),
'dots': ISSAuxTileFetcher(os.path.join(input_dir, "DO", "c2.TIF")),
},
postprocess_func=add_codebook,
default_shape=SHAPE
)
codebook_array = [
{
Features.CODEWORD: [
{Axes.ROUND.value: 0, Axes.CH.value: 3, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 1, Axes.CH.value: 3, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 2, Axes.CH.value: 1, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 3, Axes.CH.value: 2, Features.CODE_VALUE: 1}
],
Features.TARGET: "ACTB_human"
},
{
Features.CODEWORD: [
{Axes.ROUND.value: 0, Axes.CH.value: 3, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 1, Axes.CH.value: 1, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 2, Axes.CH.value: 1, Features.CODE_VALUE: 1},
{Axes.ROUND.value: 3, Axes.CH.value: 2, Features.CODE_VALUE: 1}
],
Features.TARGET: "ACTB_mouse"
},
]
codebook = Codebook.from_code_array(codebook_array)
codebook_json_filename = "codebook.json"
codebook.to_json(os.path.join(output_dir, codebook_json_filename))
Z_NUM = stitched_img.shape[0]
for z in range(Z_NUM):
Z_FILE_NAME = f"{BASE_NAME}_CH{channel_num}_Z{z+1:03}.tif"
Z_FILE = os.path.join(TX_ORIGINAL_DIR, Z_FILE_NAME)
print(Z_FILE)
io.imsave(Z_FILE, stitched_img[z, :, :])
print("Time to read and split image:" + str(time.time() - start_time))
# Create spacetx format for starfish
primary_image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND:
1, # only change this if processing rounds together, otherwise, keep at 1
Axes.CH: CHANNEL_NUM,
Axes.ZPLANE: Z_NUM,
}
start_time = time.time()
write_experiment_json(
path=TX_FORMATTED_DIR,
fov_count=FOV_NUM,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
primary_tile_fetcher=ImageTileFetcher(TX_ORIGINAL_DIR),
aux_name_to_dimensions={},
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE))
print("Time to convert all channels to spaceTx:" +
str(time.time() - start_time))
def cli(
input_dir: str,
output_dir: str,
file_format: str,
file_vars: list,
cache_read_order: List[str],
counts: dict,
aux_names: List[str] = [],
aux_file_formats: List[str] = [],
aux_file_vars: List[List[str]] = [],
aux_cache_read_order: List[str] = [],
aux_channel_count: List[int] = [],
aux_channel_slope: List[float] = [],
aux_channel_intercept: List[int] = [],
locs: List[Mapping[Axes, float]] = None,
shape: Mapping[Axes, int] = None,
voxel: Mapping[Axes, float] = None,
) -> int:
"""CLI entrypoint for spaceTx format construction for SeqFISH data
Parameters
----------
input_dir : str
Directory containing folders for fovs.
output_dir : str
Directory containing output files. Will be created if it does not exist.
file_format: str
String format for individual image files of primary view. Appended to input_dir.
Each "{}" within this string will be replaced the tile-specific values, as specified in the order of "file_vars"
file_vars: list
Variables to insert in file_format. The following values are accepted:
- channel
- offset_channel (channel + channel_offset)
- round
- offset_round (round + round_offset)
- fov
- offset_fov (fov + fov_offset)
- zplane
- offset_zplane (zplane + zplane_offset)
cache_read_order: list
Description of the order of the axes of the images. Each item in the list is one dimension in the image.
counts: dict
Dict with the counts for each dimension of the data. Expects values that correspond
to keys of ["rounds","channels","zplanes","fovs"]
aux_names: list
A list containing the names of any auxilliary tile views.
aux_file_formats: list
The same as file_format, but for each individual aux view. Items within each list entry are semicolon (;) delimited.
aux_file_vars: list
The same as file_vars, but for each individual aux view. Items within each list entry are semicolon (;) delimited.
aux_cache_read_order: list
The same as cache_read_order, but for each individual aux view. Items within each list entry are semicolon (;) delimited.
aux_channel_count: list
The total number of channels per aux view.
aux_channel_slope: list
The slope for converting 0-indexed channel IDs to the channel ID within the image.
aux_channel_intercept: list
The intercept for converting 0-index channel IDs to the channel ID within the image.
locs: List[Mapping[Axes, float]]
Each list item refers to the fov of the same index. The start location of the image, mapped to the corresponding Axes object (X, Y, or ZPLANE)
shape: Mapping[Axes, int]
The offset for the size of the image, mapped to the corresponding Axes object (X, Y, ZPLANE)
voxel: Mapping[Axes, float]
The size of each image, mapped to the corresponding Axes object (X, Y, ZPLANE)
Returns
-------
int :
Returns 0 if successful
"""
t0 = time()
os.makedirs(output_dir, exist_ok=True)
reportFile = os.path.join(output_dir, datetime.now().strftime("%Y%m%d_%H%M_TXconversion.log"))
sys.stdout = open(reportFile, "w")
image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND: counts["rounds"],
Axes.CH: counts["channels"],
Axes.ZPLANE: counts["zplanes"],
}
cache_read_order_formatted = []
for item in cache_read_order:
if item == "Z":
cache_read_order_formatted.append(Axes.ZPLANE)
elif item == "CH":
cache_read_order_formatted.append(Axes.CH)
else:
cache_read_order_formatted.append("other")
primary_tile_fetcher = PrimaryTileFetcher(
os.path.expanduser(input_dir),
file_format,
file_vars,
cache_read_order_formatted,
counts["zplane_offset"],
counts["fov_offset"],
counts["round_offset"],
counts["channel_offset"],
locs,
shape,
voxel,
)
aux_name_to_dimensions = {}
aux_tile_fetcher = {}
if aux_names:
for i in range(len(aux_names)):
name = aux_names[i]
aux_image_dimensions: Mapping[Union[str, Axes], int] = {
Axes.ROUND: counts["rounds"],
Axes.CH: int(aux_channel_count[i]),
Axes.ZPLANE: counts["zplanes"],
}
aux_name_to_dimensions[name] = aux_image_dimensions
aux_cache_read_order_raw = aux_cache_read_order[i].split(";")
aux_cache_read_order_formatted = []
for item in aux_cache_read_order_raw:
if item == "Z":
aux_cache_read_order_formatted.append(Axes.ZPLANE)
elif item == "CH":
aux_cache_read_order_formatted.append(Axes.CH)
else:
aux_cache_read_order_formatted.append("other")
aux_tile_fetcher[name] = AuxTileFetcher(
os.path.expanduser(input_dir),
aux_file_formats[i],
aux_file_vars[i],
aux_cache_read_order_formatted,
aux_channel_slope[i],
aux_channel_intercept[i],
counts["zplane_offset"],
counts["fov_offset"],
counts["round_offset"],
counts["channel_offset"],
locs,
shape,
voxel,
)
# aux_tile_fetcher = {"DAPI": AuxTileFetcher(os.path.expanduser(input_dir), file_format, file_vars, counts["fov_offset"], counts["round_offset"],3)}
# aux_name_to_dimensions = {"DAPI": aux_image_dimensions}
t1 = time()
print("Elapsed time to make experiment", t1 - t0)
write_experiment_json(
path=output_dir,
fov_count=counts["fovs"],
aux_tile_fetcher=aux_tile_fetcher,
primary_tile_fetcher=primary_tile_fetcher,
primary_image_dimensions=image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
tile_format=ImageFormat.TIFF,
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE),
)
os.remove(output_dir + "/codebook.json")
t2 = time()
print("Elapsed time for .json manipulation", t2 - t1)
print("Operation complete, total elapsed time", t2 - t0)
sys.stdout = sys.__stdout__
return 0
Axes.CH: 1,
Axes.ZPLANE: 3,
}
aux_images_dimensions: Mapping[str, Mapping[Union[str, Axes], int]] = {
"nuclei": {
Axes.ROUND: 2,
Axes.CH: 1,
Axes.ZPLANE: 3,
},
}
write_experiment_json(
path=outputdir.name,
fov_count=2,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_images_dimensions,
primary_tile_fetcher=primary_tile_fetcher,
aux_tile_fetcher={"nuclei": nuclei_tile_fetcher},
dimension_order=(Axes.ROUND, Axes.CH, Axes.ZPLANE)
)
###################################################################################################
# Don't forget to replace the fake codebook.json
# ----------------------------------------------
# There are no starfish tools for creating a codebook. You can write the JSON manually or write a
# script to do it for you. Be sure the format matches the examples in
# :ref:`SpaceTx Format<sptx_codebook_format>`.
# this is the placeholder codebook.json
with open(os.path.join(outputdir.name, "codebook.json"), "r") as fh:
print(fh.read())
def format_data(input_dir, output_dir, root_dir_name, nameBeforeDot, fileType, seriesName, num_zplanes):
def add_codebook(experiment_json_doc):
experiment_json_doc['codebook'] = "codebook.json"
return experiment_json_doc
primary_image_dimensions = {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: num_zplanes,
}
aux_name_to_dimensions = {
'nuclei': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: num_zplanes,
},
'channel2': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: num_zplanes,
},
'dots': {
Axes.ROUND: 1,
Axes.CH: 1,
Axes.ZPLANE: num_zplanes,
}
}
write_experiment_json(
path=output_dir,
fov_count=1,
tile_format=ImageFormat.TIFF,
primary_image_dimensions=primary_image_dimensions,
aux_name_to_dimensions=aux_name_to_dimensions,
primary_tile_fetcher=RNAScopePrimaryTileFetcher(input_dir, root_dir_name, nameBeforeDot, fileType, seriesName, num_zplanes),
aux_tile_fetcher={
'nuclei': RNAScopeAuxTileFetcher(input_dir, 'nuclei', root_dir_name, nameBeforeDot, fileType, seriesName, num_zplanes),
'channel2': RNAScopeAuxTileFetcher(input_dir, 'channel2', root_dir_name, nameBeforeDot, fileType, seriesName, num_zplanes),
'dots': RNAScopeAuxTileFetcher(input_dir, 'dots', root_dir_name, nameBeforeDot, fileType, seriesName, num_zplanes),
},
postprocess_func=add_codebook,
default_shape=(1024, 1024)
)
# mappings_array = [
# {
# "codeword": [
# {"r": 0, "c": 0, "v": 1},
# ],
# "target": "GFP"
# },
# ]
# codebook = {
# "version": "0.0.0",
# "mappings": mappings_array
# }
# codebook = Codebook.from_code_array(codebook)
# codebook_json_filename = "codebook.json"
# codebook.to_json(os.path.join(output_dir, codebook_json_filename))
# At some point, somebody might need to make a more copmlex codebook (with all the target genes, called Features.TARGET here,
# and what round and channel they're in). When that time comes, model your codebook according to below,
# based on what ROUND and CH (channel) your target gene was imaged in
"""