def batchAnalysis(analysis_exe,
input_directory,
output_directory,
multi_xml,
max_processes=2):
minimum_length = 100
# FIXME: Should also handle .tif movies?
dax_files = glob.glob(input_directory + "*.dax")
# Figure out which movies to analyze.
cmd_lines = []
for movie_file in dax_files:
movie_obj = datareader.inferReader(movie_file)
if (movie_obj.filmSize()[2] > minimum_length):
print("Analyzing:", movie_file)
basename = os.path.basename(movie_file)
mlistname = output_directory + "/" + basename[:-4] + "_mlist.bin"
cmd_lines.append([
'python', analysis_exe, "--movie", movie_file, "--bin",
mlistname, "--xml", multi_xml
])
batchRun.batchRun(cmd_lines, max_processes=max_processes)
def batchAnalysis(input_directory, eps = 40, mc = 10, min_size = 50):
"""
Batch DBSCAN clustering.
"""
# We use the executable and batchRun as this is an easy
# way to run all the clustering in parallel.
src_dir = os.path.dirname(__file__)
if not (src_dir == ""):
src_dir += "/"
clusters_exe = src_dir + "dbscan_analysis.py"
# Find appropriate bin files.
bin_files = glob.glob(input_directory + "*.hdf5")
# Generate command line.
cmd_lines = []
for filename in bin_files:
# skip clustering related bin files
if ("clusters" in filename) or ("srt" in filename):
continue
print("Found:", filename)
cmd_lines.append(['python', clusters_exe,
"--bin", filename,
"--eps", str(eps),
"--mc", str(mc),
"--min_size", str(min_size)])
batchRun.batchRun(cmd_lines)
def batchAnalysis(input_directory, eps=40, mc=10, min_size=50):
"""
Batch DBSCAN clustering.
"""
# We use the executable and batchRun as this is an easy
# way to run all the clustering in parallel.
src_dir = os.path.dirname(__file__)
if not (src_dir == ""):
src_dir += "/"
clusters_exe = src_dir + "dbscan_analysis.py"
# Find appropriate bin files.
bin_files = glob.glob(input_directory + "*.hdf5")
# Generate command line.
cmd_lines = []
for filename in bin_files:
# skip clustering related bin files
if ("clusters" in filename) or ("srt" in filename):
continue
print("Found:", filename)
cmd_lines.append([
'python', clusters_exe, "--bin", filename, "--eps",
str(eps), "--mc",
str(mc), "--min_size",
str(min_size)
])
batchRun.batchRun(cmd_lines)
def batchAnalysis(analysis_exe, input_directory, output_directory, multi_xml, max_processes = 2):
minimum_length = 100
# FIXME: Should also handle .tif movies?
dax_files = glob.glob(input_directory + "*.dax")
# Figure out which movies to analyze.
cmd_lines = []
for movie_file in dax_files:
movie_obj = datareader.inferReader(movie_file)
if(movie_obj.filmSize()[2] > minimum_length):
print("Analyzing:", movie_file)
basename = os.path.basename(movie_file)
mlistname = output_directory + "/" + basename[:-4] + ".hdf5"
cmd_lines.append(['python', analysis_exe,
"--movie", movie_file,
"--bin", mlistname,
"--xml", multi_xml])
batchRun.batchRun(cmd_lines, max_processes = max_processes)
for channel in channels:
#for i in range (iterations):
base = str(channel)
# create list of all .hdf5 files in BIN directory
h5_files =glob.glob(input_directory + base + "*.hdf5")
# set path to multi-fit analysis code
hdf5_to_image = "C:/Program Files/Python36/Lib/site-packages/storm_analysis/sa_utilities/hdf5_to_image.py"
# start processes
cmd_lines = []
for h5_file in h5_files:
if base == "750storm":
sigma = sigma_channels[0]
elif base == "647storm":
sigma = sigma_channels[1]
elif base == "561storm":
sigma = sigma_channels[2]
elif base == "488storm":
sigma = sigma_channels[3]
print ("Found:", h5_file)
h5_filename = os.path.basename(h5_file)
print("Analyzing:",h5_filename,"with sigma ",str(sigma))
tiff_out_name = output_directory + h5_filename[:-5] + ".tiff"
cmd_lines.append(['python', hdf5_to_image,
"--image", tiff_out_name,
"--bin", h5_file,
"--scale", str(storm_image_scale),
"--sigma", str(sigma)])
batchRun.batchRun(cmd_lines, max_processes = max_processes)