def saveExp(source_dir: str, save_dir: str, exp: Experiment = None):
# go through and save all images, if an experiment is provided
if exp:
for fov in exp.keys():
for view in exp[fov].image_types:
img = exp[fov].get_image(view)
prefix = f"{view}-{fov}"
saveImg(save_dir, prefix, img)
# copy the non-tiff files to the new directory
cp_files = [x for x in os.listdir(source_dir) if x[-5:] != ".tiff" and x[-4:] != ".log"]
for file in cp_files:
if "fov" in file:
# if file contains images, we need to update sha's
data = json.load(open(str(source_dir) + "/" + file))
for i in range(len(data["tiles"])):
abspath = str(save_dir) + "/" + data["tiles"][i]["file"]
with open(os.fspath(abspath), "rb") as fh:
hsh = hashlib.sha256(fh.read()).hexdigest()
data["tiles"][i]["sha256"] = hsh
print(f"\tupdated hash for {data['tiles'][i]['file']}")
with open(str(save_dir) + "/" + file, "w") as f:
json.dump(data, f)
print(f"saved {file} with modified hashes")
else:
# we can just copy the rest of the files
shutil.copyfile(f"{source_dir}/{file}", f"{save_dir}/{file}")
print(f"copied {file}")
def run(
output_dir: str,
experiment: Experiment,
blob_based: bool,
use_ref: bool,
blobRunnerKwargs: dict,
decodeRunnerKwargs: dict,
pixelRunnerKwargs: dict,
):
"""
Main method for executing runs. Sets up directories and calls appropriate driver methods.
Parameters
----------
output_dir: str
Location to put all output from this tool. Dir will be created if not present.
experiment: Experiment
Experiment object with corresponding images and codebook.
blob_based: bool
If true, use blob-detection and decoding methods. Else, use pixel-based methods.
use_ref: bool
If true, a reference image will be used and created by flattening the fov.
blobRunnerKwargs: dict
Dictionary with arguments for blob detection. Refer to blobRunner.
decodeRunnerKwargs: dict
Dictionary with arguments for spot-based decoding. Refer to decodeRunner.
pixelRunnerKwargs: dict
Dictionary with arguments for pixel-based detection and decoding. Refer to starfish PixelSpotDecoder.
"""
if not path.isdir(output_dir):
makedirs(output_dir)
if not path.isdir(output_dir + "csv/"):
makedirs(output_dir + "csv")
if not path.isdir(output_dir + "cdf/"):
makedirs(output_dir + "cdf")
if blob_based and not path.isdir(output_dir + "spots/"):
makedirs(output_dir + "spots")
reporter = open(
path.join(output_dir,
datetime.now().strftime("%Y%m%d_%H%M_starfish_runner.log")),
"w")
sys.stdout = reporter
sys.stderr = reporter
print(
"output_dir: {}\nexp: {}\nblob_based: {}\nuse_ref: {}\nblobrunner: {}\ndecoderunner: {}\npixelrunner: {}\n"
.format(
output_dir,
experiment,
blob_based,
use_ref,
blobRunnerKwargs,
decodeRunnerKwargs,
pixelRunnerKwargs,
))
# disabling tdqm for pipeline runs
tqdm.__init__ = partialmethod(tqdm.__init__, disable=True)
imgs = {}
for fov in experiment.keys():
imgs[fov] = experiment[fov].get_image("primary")
ref_imgs = None
if use_ref:
ref_imgs = {}
for fov in experiment.keys():
ref_imgs[fov] = imgs[fov].reduce(
{Axes.CH, Axes.ROUND, Axes.ZPLANE}, func="max")
decoded = {}
if blob_based:
blobs, decoded = blobDriver(imgs, ref_imgs, experiment.codebook,
blobRunnerKwargs, decodeRunnerKwargs,
output_dir)
else:
decoded = pixelDriver(imgs, experiment.codebook, pixelRunnerKwargs)
# saving
for fov in decoded.keys():
saveTable(decoded[fov], output_dir + "csv/" + fov + "_decoded.csv")
# decoded[fov].to_decoded_dataframe().save_csv(output_dir+fov+"_decoded.csv")
decoded[fov].to_netcdf(output_dir + "cdf/" + fov + "_decoded.cdf")
sys.stdout = sys.__stdout__
return 0