def run(self, data, output_dir, **run_args):
results = {}
infile = data.infile
f = tools.Filename(infile)
filepath, filename, filebase, ext = f.split()
results['infile'] = infile
results['filepath'] = filepath
results['filename'] = filename
results['filebase'] = filebase
results['fileext'] = ext
results['sample_name'] = data.name
results['file_ctime'] = os.path.getctime(infile)
results['file_modification_time'] = os.path.getmtime(infile)
results['file_access_time'] = os.path.getatime(infile)
results['file_size'] = os.path.getsize(infile)
patterns = [
['theta', '.+_th(\d+\.\d+)_.+'] ,
['annealing_temperature', '.+_T(\d+\.\d\d\d)C_.+'] ,
['annealing_time', '.+_(\d+\.\d)s_T.+'] ,
['exposure_time', '.+_(\d+\.\d+)c_\d+_saxs.+'] ,
['sequence_ID', '.+_(\d+)_saxs.+'] ,
]
for pattern_name, pattern_string in patterns:
pattern = re.compile(pattern_string)
m = pattern.match(filename)
if m:
if run_args['verbosity']>=5:
print(' matched: {} = {}'.format(pattern_name, m.groups()[0]))
results[pattern_name] = float(m.groups()[0])
#outfile = self.get_outfile(data.name, output_dir)
return results
def plot_results(x_vals,
y_vals,
z_vals,
outfile,
plot2d=True,
plot3d=False,
grid=None,
dgrid=None,
title=None,
cmap='viridis',
alpha=1.0,
x_label='$x$',
y_label='$y$',
z_label=None,
interpolate_cyclic=None,
verbosity=3):
if verbosity >= 3:
print("Plotting for {}".format(outfile))
val_stats(z_vals, name='z_vals')
# Define grid for interpolation
if grid is None:
grid = [np.min(x_vals), np.max(x_vals), np.min(y_vals), np.max(y_vals)]
if dgrid is None:
dgrid = [(grid[1] - grid[0]) / 200, (grid[3] - grid[2]) / 200]
if isinstance(dgrid, float):
dgrid = [dgrid, dgrid]
xi = np.arange(grid[0], grid[1] + dgrid[0], dgrid[0])
yi = np.arange(grid[2], grid[3] + dgrid[1], dgrid[1])
XI, YI = np.meshgrid(xi, yi)
# Interpolation
import scipy.interpolate
POINTS = np.column_stack((x_vals, y_vals))
VALUES = z_vals
if verbosity >= 4:
print("Interpolating {:,} points to {:,}×{:,} = {:,} points".format(
len(VALUES), len(xi), len(yi),
len(xi) * len(yi)))
if interpolate_cyclic is not None:
xlike = np.cos(VALUES * 2 * np.pi / interpolate_cyclic)
ylike = np.sin(VALUES * 2 * np.pi / interpolate_cyclic)
XLIKE = scipy.interpolate.griddata(POINTS,
xlike, (XI, YI),
method='linear')
YLIKE = scipy.interpolate.griddata(POINTS,
ylike, (XI, YI),
method='linear')
ZI = (np.arctan2(YLIKE, XLIKE) / (2 * np.pi)) * interpolate_cyclic
ZI_mask = np.ma.masked_where(np.isnan(ZI), ZI)
else:
ZI = scipy.interpolate.griddata(
POINTS, VALUES, (XI, YI),
method='linear') # method='nearest' 'linear' 'cubic'
ZI_mask = np.ma.masked_where(np.isnan(ZI), ZI)
if verbosity >= 4:
val_stats(ZI, name='ZI')
val_stats(ZI_mask, name='ZI_mask')
d = Data2D_current()
d.data = ZI_mask
d.x_axis = xi
d.y_axis = yi
d.x_vals = x_vals
d.y_vals = y_vals
d.z_vals = z_vals
d.set_z_display([None, None, 'linear', 1.0])
d.x_rlabel = x_label
d.y_rlabel = y_label
d.z_rlabel = z_label
if plot2d:
d.plot_args['rcParams'] = {
'axes.labelsize': 50,
'xtick.labelsize': 40,
'ytick.labelsize': 40,
}
d.alpha = alpha
d.plot(save=outfile,
show=False,
cmap=cmap,
zmin=zmin,
zmax=zmax,
title=title,
plot_buffers=[0.21, 0.12, 0.18, 0.10],
plot_range=grid,
plot_2D_type='pcolormesh',
dpi=150,
transparent=False)
if plot3d:
d.plot_args['rcParams'] = {
'axes.labelsize': 40,
'xtick.labelsize': 20,
'ytick.labelsize': 20,
}
d.X = XI
d.Y = YI
elev = 30
azim = 30
azim = 30 - 90
#outfile = outfile[:-4]+'-3D.png'
outfile = tools.Filename(outfile).path_append('3D')
d.plot3D(save=outfile,
show=False,
cmap=cmap,
zmin=zmin,
zmax=zmax,
title=title,
plot_buffers=[0.05, 0.10, 0.05, 0.05],
plot_range=grid,
elev=elev,
azim=azim,
dpi=150,
transparent=False)
data_dir = 'LDRD_Meso_correlation/2015_10Oct-ref_patterns/21/'
protocol = 'view'
source_dir = root_dir+'data/'+data_dir
output_dir = root_dir+'analysis/'+data_dir+'/'+protocol+'/'
tools.make_dir(output_dir)
infile = 'AgBH_22_master.h5'
infile = 'Coral_porous_glass_0.3s_30_master.h5'
infile = 'chip411_found_star_x-0.356_y13.468_922_master.h5'
f = tools.Filename(source_dir+infile)
if f.get_ext()=='.h5':
# Check if this is a HDF5 data (as opposed to master) file
m = re.compile('(.+)_data_\d{5,20}\.h5').match(f.get_filename())
if m:
# Switch to corresponding master file
f = tools.Filename(f.get_path() + m.groups()[0] + '_master.h5')
data = Data2DScattering(f.get_filepath())
data.threshold_pixels(4294967295-1) # Eiger inter-module gaps
data.set_z_display( [None, None, 'gamma', 0.3] )
data.plot(show=True)