def generate_mask_func(data, store, signal, context):
logger.debug("Generating mask")
md = data['md']
mask = generate_mask(**md)['mask']
data['mask'] = mask
# pass the stream name
data['md']['stream_name'] = context.task_name
def to_thumb_func(data, store, signal, context):
logger.debug("Making thumb of image")
data_dict = dict(img=data['img'])
attrs = data['md']
store_results_mpl(data_dict, attrs, images=['img'])
# pass the stream name
data['md']['stream_name'] = context.task_name
def parse_attributes_func(data, store, signal, context):
logger.debug("Parsing attributes")
md = data['md']
md = normalize_calib_dict(**md)
md = add_detector_info(**md)
#print("parse attributes, final metadata: {}".format(md))
data['md'] = md
# pass the stream name
data['md']['stream_name'] = context.task_name
def make_calibration_func(data, store, signal, context):
logger.debug("Making calibration")
md = data['md']
#print("making calibration from metadata {}".format(md))
calibration = make_calibration(**md)
calibration.generate_maps()
#print("done")
data['calibration'] = calibration
# pass the stream name
data['md']['stream_name'] = context.task_name
def qphiavg_plot_func(data, store, signal, context):
logger.debug("Plotting qphiavg")
data_dict = dict(sqphi=data['sqphi'])
attr = data['md']
attr['stream_name'] = 'qphiavg'
store_results_mpl(
data_dict,
attr,
img_norm=normalizer,
aspect='auto',
images=['sqphi'],
xlabel="$\phi\,$(radians)",
ylabel="$q\,$(pixel)",
)
def qphiavg_func(data, store, signal, context):
logger.debug("making qphiavg")
image = data['img']
calibration = data.get_by_alias('calibration')['calibration']
q_map = calibration.q_map
phi_map = calibration.angle_map
mask = data.get_by_alias('mask')['mask']
data['sqphi'] = qphiavg(image,
q_map=None,
phi_map=None,
mask=None,
bins=(800, 360),
origin=None,
range=None,
statistic='mean')
data['md']['stream_name'] = context.task_name
def circavg_plot_func(data, store, signal, context):
logger.debug("Plotting circular average")
data_dict = dict()
data_dict['sqx'] = data['sqx']
data_dict['sqy'] = data['sqy']
attrs = data['md']
xlbl = "$q\,(\AA^\{-1\})$"
ylbl = "$I(q)$"
store_results_mpl(
data_dict,
attrs,
lines=[('sqx', 'sqy')],
scale='loglog',
xlabel=xlbl,
ylabel=ylbl,
)
def peakfind_func(data, signal, store, context):
logger.debug("Finding peaks")
from SciStreams.processing.peak_finding import peak_finding
sqx = data['sqx']
sqy = data['sqy']
res = peak_finding(intensity=sqy, frac=0.0001).peak_position()
model = res[0]
y_origin = res[1]
inds_peak = res[2]
xdata = res[3]
ratio = res[4]
ydata = res[5]
wdata = res[6]
bkgd = res[7]
variance = res[8]
variance_mean = res[9]
peaksx = list()
peaksy = list()
for ind in inds_peak:
peaksx.append(sqx[ind])
peaksy.append(sqy[ind])
res_dict = dict(
model=model,
y_origin=y_origin,
inds_peak=inds_peak,
xdata=xdata,
ratio=ratio,
ydata=ydata,
wdata=wdata,
bkgd=bkgd,
variance=variance,
variance_mean=variance_mean,
peaksx=peaksx,
peaksy=peaksy,
)
for key in res_dict.keys():
data[key] = res_dict[key]
data['md']['stream_name'] = context.task_name
def peakfind_plot_func(data, store, signal, context):
logger.debug("Plotting found peaks")
data['md']['stream_name'] = 'peakfind'
data_dict = dict()
data_dict['sqx'] = data['sqx']
data_dict['sqy'] = data['sqy']
data_dict['peaksx'] = data['peaksx']
data_dict['peaksy'] = data['peaksy']
xlbl = "$q,(\AA^{-1})$"
ylbl = "I(q)"
store_results_mpl(
data_dict,
data['md'],
lines=[
dict(x='sqx', y='sqy'),
dict(x='peaksx', y='peaksy', marker='o', color='r', linewidth=0)
],
xlabel=xlbl,
ylabel=ylbl,
scale='loglog',
)
def circavg_func(data, store, signal, context):
logger.debug("Computing circular average")
image = data.get_by_alias('image')['img']
calibration = data.get_by_alias('calibration')['calibration']
q_map = calibration.q_map
r_map = calibration.r_map
mask = data.get_by_alias('mask')['mask']
#print("computing circavg")
#print("q_map: {}".format(q_map))
#print("r_map: {}".format(r_map))
#print("image: {}".format(image))
#print("mask: {}".format(mask))
res = circavg(image, q_map=q_map, r_map=r_map, mask=mask)
#print("done")
data['sqx'] = res['sqx']
data['sqxerr'] = res['sqxerr']
data['sqy'] = res['sqy']
data['sqyerr'] = res['sqyerr']
# pass the stream name
data['md']['stream_name'] = context.task_name
def input_func(data, store, signal, context):
# pass the stream name
logger.debug("Beginning of one image pipeline")
data['md']['stream_name'] = context.task_name