class Connection:
"""
Holds connection and authentication data for the Plexus server and
provides a method for uploading files.
Mandatory constructor arguments are <user> and <password> for
authentication with Plexus. Optional argument are
<server> - the base URL for the Plexus web application
<manager> - the manager for newly created projects,
<interactive> - if True, asks for missing login and/or password
"""
def __init__(self,
user,
password,
server = "https://plexus.anu.edu.au",
manager = None,
interactive = False):
# -- if interactive, ask for authentication data with the service
if interactive:
import getpass
while user in (None, ""):
user = raw_input("Server login: "******""):
password = getpass.getpass("Server password: "******""
# -- set default retry parameters
self.retry_limit = 10 # number of upload attempts
self.retry_wait = 300 # waiting period between upload attempts (sec)
self.log = Logger()
def post_form(self, selector, fields, files):
"""
Composes an multipart/form-data message, posts it to the URL on
the server given by the relative path <selector> and returns a
tuple consisting of the HTTP status, reason and response text
as received from the server.
Normal fields are specified as a sequence <fields> of
(name, value) pairs. Attached file data is specified as a
sequence <files> of (name, filename, value) elements.
"""
content_type, body = encode_formdata(fields, files)
headers = {
'User-Agent': 'python',
'Content-Type': content_type,
'Accept': 'application/json'
}
count = 0
while True:
try:
if self.server.startswith("https://"):
host = re.sub('^https:\/\/', '', self.server)
self.log.writeln("Connecting to %s using https" % host)
h = httplib.HTTPSConnection(host)
else:
host = re.sub('^http:\/\/', '', self.server)
self.log.writeln("Connecting to %s using http" % host)
h = httplib.HTTPConnection(host)
h.request('POST', selector, body, headers)
res = h.getresponse()
return res.status, res.reason, res.read()
except KeyboardInterrupt, ex:
raise ex
except Exception, ex:
self.log.writeln("> %s: %s <" % (ex.__class__.__name__, ex),
LOGGER_ERROR)
count += 1
if count < self.retry_limit:
self.log.writeln("(will retry in %d seconds)"
% self.retry_wait)
time.sleep(self.retry_wait)
self.log.writeln("retrying...")
else:
self.log.writeln("too many retries - giving up",
LOGGER_ERROR)
raise ex
def slices(path,
existing = [],
replace = False,
dry_run = False,
sizes = (None,),
info = {}):
"""
A generator which extracts slice images from a Mango volume data set
stored in a collection of NetCDF files.
The parameter <path> specifies a single NetCDF file or a directory
containing a single volume split into several NetCDF files.
Basic usage:
for (data, name, action) in slices(path):
fp = file(name, 'wb')
fp.write(data)
fp.close()
"""
path = re.sub("/$", "", path)
name = re.sub("[._]nc$", "", os.path.basename(path))
basename = re.sub("^tomo", "tom", re.sub("^segmented", "seg", name))
img_mode = None
log = Logger()
# -- collect the list of files to be processed
entries = datafiles(path)
if not entries:
return
# -- find a useable volume variable and analyse it
filename = entries[0]
log.writeln("slices(): looking for a volume variable in %s..." %
os.path.basename(filename))
var = find_variable(filename)
if var is None :
log.writeln("No appropriate volume data found.")
return
# -- determine the appropriate mask value
mask_value = { numpy.uint8: 0xff,
numpy.uint16: 0xffff,
numpy.int32: 0x7fffffff,
numpy.float32: 1.0e30 }[var['dtype']]
# -- initialize the slice set to be created
slices = default_slice_set(var, 10, basename)
r_or_s = 'REPLACE' if replace else 'SKIP'
actions = list((r_or_s if n in existing else 'ADD')
for (s, n) in slices)
slices = list(slices[i] + (actions[i],)
for i in range(len(slices))
if actions[i] != 'SKIP')
if len(slices) == 0:
log.writeln("No slices are to be made.")
return
if not dry_run:
# -- initialize the histogram
if var['dtype'] == numpy.float32:
log.writeln("Determining the data range...")
(minval, maxval) = data_range(var, entries)
hist = Histogram(mask_value, minval, maxval)
else:
hist = Histogram(mask_value)
# -- loop through files and copy data into slice arrays
for filename in entries:
log.writeln("Processing %s..." % os.path.basename(filename))
for tmp in z_slices(var, filename):
z, data = tmp[:2]
if data is None:
log.writeln(tmp[2] + " at z = %d" % z, LOGGER_WARNING)
else:
hist.update(data)
for (s, n, a) in slices:
s.update(data, z)
# -- analyse histogram to determine 'lo' and 'hi' values
log.writeln("Analysing the histogram...")
if name.startswith("tom"):
# -- determine 0.1 and 99.9 percentile for contrast stretching
lo = bottom_percentile(hist, 0.1)
hi = top_percentile(hist, 0.1)
else:
lo = 0
hi = hist.counts.size - 1
# -- encode slices as PNG images
log.writeln("Making the images...")
for (s, n, a) in slices:
for sz in sizes:
if dry_run:
data = make_image.make_dummy(n, 256, 256, sz)
else:
data = image_data(s, lo, hi, mask_value, info, sz)
prefix = ("__%sx%s__" % sz) if sz else ""
yield (data, prefix + n, a)
# -- report success
log.writeln("Slice image generation finished.")
