2-ExportQRES Template for multiple q bins all together¶
The following template is meant to create the output that can be loaded by Mumott.
Import packages & libraries
[1]:
%matplotlib ipympl
from tqdm import tqdm
import numpy as np
import os
import matplotlib.pyplot as plt
import h5py
import pickle
import json
from ipywidgets import interact
from matplotlib import cm, colors
from matplotlib.patches import Rectangle
from data_processing.dataset import Dataset
Step 1 - Load args from previous step.¶
Note: This includes already the q_rois chosen in the first template
[2]:
#--- User Input to load the exported args file --------------------#
filepath_args = '/data/visitors/formax/20220566/2023031408/process/work_dir_Christian/big_brain/args_big_brain.pickle'
#--- End Input to load the exported args file ---------------------#
## ---------- Don't change this if not needed ----------------- ##
with open(filepath_args,'rb') as fid:
args = pickle.load(fid)
frame_id_range = args['frame_id_range']
for key in args.keys():
print(key)
base_path
work_directory
integration_folder
frameID
sample_name
air_id
proposal
visit
bkg_scan
detector_name
normkey
valid_pixels
fast_scan_direction
slow_scan_direction
projection_direction
detector_angle_origin
detector_angle_pos_dir
norm_transmission
flat_field_level
correct_background
tomodata
beamline
inner_rotation_axis
inner_rotation_key
first_rotation_indexes
tilt_axis
tilt_key
data_sorting
data_index_origin
principal_rotation_right_handed
secondary_rotation_right_handed
detector_angle_0
detector_angle_right_handed
offset_positive
air_transmission
snake
phi_det
q
norm_sum
air_scattering
multiprocessing
frame_id_range
q_rois
Load beamline specific loader functions based on args input
[3]:
#do not modify
print(f"Loading beamline specific functions from {args['beamline']}_utils")
# Import the cSAXS loader functions
if args['beamline'] == 'csaxs':
from data_processing.cSAXS_utils import metadata_reader_complete as metadata_reader
from data_processing.cSAXS_utils import transmission_loader_mcs as transmission_loader
from data_processing.cSAXS_utils import scattering_loader as scattering_loader_eiger
from data_processing.cSAXS_utils import create_args
# Import the PX loader functions
elif args['beamline'] == 'px':
from data_processing.PX_utils import metadata_reader_json as metadata_reader
from data_processing.PX_utils import transmission_loader_eiger as transmission_loader
from data_processing.PX_utils import scattering_loader_eiger
from data_processing.PX_utils import create_args
# Import the PX loader functions
elif args['beamline'] == 'formax':
from data_processing.ForMAX_utils import metadata_reader
from data_processing.ForMAX_utils import transmission_loader
from data_processing.ForMAX_utils import scattering_loader_eiger
from data_processing.ForMAX_utils import create_args
Loading beamline specific functions from formax_utils
Load the dataset
Loading of data, might take a couple of minutes depending on the allocation (recommended 8 cores or more).
GPUs are a bit slower here. With 8-12 cores it takes around 1min for full dataset
[4]:
%%time
## ---------- Don't change this if not needed ----------------- ##
ds = Dataset(frame_id_range, metadata_reader, transmission_loader, scattering_loader_eiger, **args)
CPU times: user 347 ms, sys: 395 ms, total: 742 ms
Wall time: 2.9 s
Step 2 - Choose number of q bins¶
Choose the lowest q bin, the highest q bin and how many should be created in total
The following cell is meant to help you with your selection
Avoid detector gaps in the symmetric cake plot of the data
Step 2.1 Check data with projections and full q range
[8]:
#--- User Input which projection to show --------------------#
proj_nr = 0
qmin = 0.015 # Choose range for q resolved
qmax = 0.42 # Choose range for q resolved
qbins = 160
#Pick a roi from the args list
which_q_roi = 0
#--- End Input which projection to show --------------------#
roi = args['q_rois'][which_q_roi]
print(f"Chosen q range for plot {roi[:2]}")
q_mask = np.logical_and(args['q'] > qmin, args['q'] < qmax)
#np.searchsorted would also work to get indizes
q_ranges = np.geomspace(args['q'][q_mask][0], args['q'][q_mask][-1], qbins+1)
fig, axs = plt.subplots(2,3, figsize=(10,8))
# load data
f1amp, f2amp, f2phase, colorfulplot = ds.stack[proj_nr].colorful_image_plot(q_range=roi[:2], symmetric = True)
# Draw plots
axs[0,0].imshow(colorfulplot, picker=True)
axs[0,0].set_title('Click me')
axs[0,1].imshow(f1amp)
axs[0,1].set_title('symmetric intensity')
axs[0,2].imshow(f2amp)
axs[0,2].set_title('assymmetric intensity')
# Load cake image
cake_img = ds.stack[proj_nr].scaled_scattering_data_symmetric
# draw average curves first
axs[1,0].loglog(ds.stack[proj_nr].metadata['q'], np.nansum(cake_img, axis=(0,1)).T)
axs[1,0].set_title('Average 1D curves for projection')
axs[1,0].set_xlabel('q / Ang-1')
axs[1,0].set_ylabel('I / a.u.')
# Add plot of q bins
axs[1,0].axvspan(args['q'][q_mask][0], args['q'][q_mask][-1], alpha=0.3, color='red')
rect = Rectangle((args['q'][q_mask][0], 0), args['q'][q_mask][-1]-args['q'][q_mask][0], cake_img.shape[0], fill = False, color = "red", linewidth = 2)
#Cake plot from scan
vmin, vmax = np.nanpercentile(np.nansum(cake_img, axis=(0,1)), [20,87])
axs[1,1].imshow(np.nansum(cake_img, axis=(0,1)), norm=colors.LogNorm(vmin = vmin, vmax= vmax), aspect = 'auto', extent=[np.min(args['q']), np.max(args['q']), 0, cake_img.shape[0]])
axs[1,1].set_title('cake plot')
axs[1,1].add_patch(rect)
axs[1,2].axis('off')
def onpick(event):
mouseevent = event.mouseevent
xcord = int(mouseevent.xdata)
ycord = int(mouseevent.ydata)
#Redraw colorful plot
axs[0,0].clear()
axs[0,0].imshow(colorfulplot, picker=True)
axs[0,0].set_title('Click me')
axs[0,0].plot(xcord, ycord, '+', ms=12, color ='r')
# Redraw Scattering plot
axs[1,0].clear()
axs[1,0].loglog(ds.stack[proj_nr].metadata['q'], cake_img[ycord, xcord,...].T)
axs[1,0].set_title('Average 1D curves for projection')
axs[1,0].set_xlabel('q / A$^{-1}$')
axs[1,0].set_ylabel('I / a.u.')
axs[1,0].loglog(q_ranges, np.repeat(np.max(cake_img[ycord, xcord,...]), len(q_ranges)), 'k.')
axs[1,0].axvspan(args['q'][q_mask][0], args['q'][q_mask][-1], alpha=0.3, color='red')
# update cake plot
axs[1,1].clear()
vmin = None
vmax = None
# This line might create problems in case of negative and nan data, change [x,x] to adjust colorscale
vmin, vmax = np.nanpercentile(cake_img[ycord, xcord,...], [20,87])
axs[1,1].imshow(cake_img[ycord, xcord,...], norm=colors.LogNorm(vmin = vmin, vmax= vmax), aspect = 'auto', extent=[np.min(args['q']), np.max(args['q']), 0, cake_img.shape[0]])
axs[1,1].add_patch(rect)
axs[1,1].set_title('cake plot')
axs[1,1].set_xlabel('q / A$^{-1}$')
axs[1,1].set_ylabel('azimuthal bin')
axs[1,2].axis('off')
fig.canvas.mpl_connect('pick_event', onpick)
Chosen q range for plot (0.01594954951718868, 0.018899687067514187)
[8]:
9
Step 3 - Load scattering data¶
[9]:
#------------------------------------------------- User Input for number of azimuthal bins -----------------------------------------------#
# Chose how many azimuthal bins should be used for the reconstruction
# 8 is typically our minimum, more will be more time consuming but might be needed for higher anisotropic data
n_directions = 8
#------------------------------------------------- User Input for number of azimuthal bins -----------------------------------------------#
# Check if n_direction is possible
if not args['norm_sum'].shape[0]/2%n_directions == 0:
print("This number of segments doesn't work out.")
else:
print(f"The current selection of n_directions {n_directions:d} works perfectly fine with shape of norm_sum {args['norm_sum'].shape}")
The current selection of n_directions 8 works perfectly fine with shape of norm_sum (32, 1000)
Loading of data
[10]:
# ----------- do not modify this ---------------- #
import multiprocessing
def load_data(projection, n_directions=8, q_range=None):
"""Simple loading function for colorful_image_plot
Returns
f1amp
f2amp
f2phase
colorfulplot"""
# directly return the projection
data_q, data_norm = projection.bin_qranges(q_range, normalized = True, symmetric = True)
phi_range = list(range(0,data_norm.shape[0]+1,data_norm.shape[0]//n_directions))
data, _ = projection.bin_det_phi(phi_range = phi_range, data=data_q, norm=data_norm, normalized = False, symmetric=False)
data = data.squeeze()
# This is a bit ackward
projection.clear_data()
return data
def load_data_parallel(dataset, n_directions=8, array=None, q_range=None):
"""
Parallel loading:
Output needs to be precreated
"""
# Create args for output
if array is None:
shp = (*ds.stack[0].metadata['padded_shape'], n_directions, qbins)
array = np.zeros((shp[0], shp[1], len(ds.stack), shp[2], shp[3]))
#data = []
args_pool = [(projection, n_directions, q_range) for projection in ds.stack]
num_cores = multiprocessing.cpu_count()
if num_cores > 16:
num_cores = 16
pool = multiprocessing.Pool(processes=num_cores)
results = []
for i, arg in enumerate(args_pool):
result = pool.apply_async(load_data, args = arg)
results.append(result)
with tqdm(total=len(results)) as pbar:
for i,result in enumerate(results):
pbar.update()
array[...,i,:,:] = result.get()
pool.close()
pool.join()
return array
print(f'Bin into {n_directions} directions')
print(f'Using the q_range from {q_ranges[0]} to {q_ranges[-1]} with {qbins} qbins')
shp = (*ds.stack[0].metadata['padded_shape'], n_directions, qbins)
data_array = np.zeros((shp[0], shp[1], len(ds.stack), shp[2], shp[3]))
if not args['norm_sum'].shape[0]/2%n_directions == 0:
print("The current selection of n_directions does not work out, nothing no data loading is performed!! - please correct n_direction in the cell above")
else:
if 'multiprocessing' in args:
if args['multiprocessing']:
data_array = load_data_parallel(ds, n_directions=n_directions, array = data_array, q_range=q_ranges)#f1amp, f2amp, colorfulplot
else:
for ii, projection in tqdm(enumerate(ds.stack)):
data_array[:,:,ii,:,:] = load_data(projection, n_directions=n_directions, q_range=q_ranges)
else:
for ii, projection in tqdm(enumerate(ds.stack)):
data_array[:,:,ii,:,:] = load_data(projection, n_directions=n_directions, q_range=q_ranges)
#Create detector angle vector from first projection
_, data_norm = ds.stack[0].bin_qranges(q_ranges, normalized = True, symmetric = True)
max_angle = data_norm.shape[0]
stepping = data_norm.shape[0]//n_directions
angles = (args['phi_det'][:max_angle:stepping] +
args['phi_det'][stepping//2:max_angle:stepping])/2
Bin into 8 directions
Using the q_range from 0.015212015129607303 to 0.4193808595242018 with 160 qbins
100%|██████████| 419/419 [05:30<00:00, 1.27it/s]
Step 4 Import alignment and export data¶
Step 4.1 Import mask and absorption tomogram
[11]:
# ----------- do not modify this ---------------- #
# Load mask
with open(args['work_directory'] + '/' + args['sample_name'] + '/mask_arrays.npy','rb') as fid:
masks = pickle.load(fid)
# Load absorption tomo
with open(args['work_directory'] + '/' + args['sample_name'] + '/tomogram.npy','rb') as fid:
tomogram = pickle.load(fid)
# plt.figure()
# plt.imshow(masks[:,:,proj_nr])
# plt.show()
Step 4.2 Import shifts
[12]:
# ----------- do not modify this ---------------- #
# Load aligment values
with open(args['work_directory'] + '/' + args['sample_name'] + '/shift_values.json','r') as fid:
shifts = json.load(fid)
plt.figure()
plt.plot(frame_id_range, shifts['offsets_j'], '.-')
plt.plot(frame_id_range, shifts['offsets_k'], '.-')
plt.legend(['offsets_j', 'offsets_k'])
plt.ylabel('pixels')
plt.xlabel('frameid')
plt.show()
Step 4.3 last check before data is written to file for mumott
Important, also check that there are no Nans in the data. Mumott can not deal with them, they should be set to 0 the section below has some example. However, check that the data has not an unreasonable large amount of Nans included
There will be a red printout in case your data or mask has Nan values
It also plots you once again the raw data, mask and masked data one last time before the export
[15]:
# Choose option to show histograms
show_histograms = True #True
# ----------- do not modify this ---------------- #
fig, axs = plt.subplots(2,3, figsize=(10,8))
qbin = len(q_ranges)//2
if np.isnan(data_array).any():
#print(f"The data has {len(np.isnan(data_array)==True)} NaN values")
print(f"\x1b[31mThe data array has Nan values, this needs to be accounted for in the next template --> Mumott!!! Please run the step that mentions NaN values to avoid crashing reconstructions\x1b[0m")
elif np.isnan(masks).any():
#print(f"The mask has {len(np.isnan(masks)==True)} NaN values")
print(f"\x1b[31mThe mask has Nan values, this needs to be accounted for in the next template --> Mumott!!! Please run the step that mentions NaN values to avoid crashing reconstructions\x1b[0m")
@interact(proj_nr=(0,data_array.shape[2]-1))
def plot(proj_nr):
for array in axs:
for axis in array:
axis.clear()
axs[0, 0].imshow(np.nansum(data_array[:,:,proj_nr,:,qbin], axis=-1))
axs[0, 0].set_title(f"Sum over all azimuthals, proj {proj_nr:03d}, qbin {qbin}")
axs[0, 1].imshow(np.nansum(data_array[:,:,proj_nr,:,qbin], axis=-1)*masks[:,:,proj_nr])
axs[0, 1].set_title('Sum times mask')
axs[0, 2].imshow(masks[:,:,proj_nr],vmin = 0, vmax = 1)
axs[0, 2].set_title('Mask')
axs[1, 0].imshow(ds.stack[proj_nr].transmission_factor, cmap = 'Greys_r')
axs[1, 0].set_title('normalized transmission')
if show_histograms:
axs[1, 1].hist(ds.stack[proj_nr].transmission_factor.flatten(), 500)
axs[1, 1].set_title('histogram transmission')
axs[1, 2].hist(np.nansum(data_array[:,:,proj_nr,:,qbin], axis=-2).flatten(), 500)
axs[1, 2].set_yscale('log')
axs[1, 2].set_title('histogram scattering')
The data array has Nan values, this needs to be accounted for in the next template --> Mumott!!! Please run the step that mentions NaN values to avoid crashing reconstructions
Step 4.4 Output data for Mumott
[16]:
# ----------- do not modify this ---------------- #
image_shape = ds.stack[0].metadata['padded_shape']
volume_shape = tomogram.shape
# Hard coded path to subfolder, can be adapted for the future
dir_path = os.path.join(args['work_directory'], args['sample_name'], 'dataset_qresolved')
try:
os.makedirs(dir_path)
except:
print('Folder already exists')
pass
print(data_array.shape)
for jj in range(data_array.shape[-1]):
q_range = (q_ranges[jj], q_ranges[jj+1])
print(f'Exporting {jj+1}/{(data_array.shape[-1])} for the q_range from {q_range[0]} to {q_range[1]}')
fname = f"{dir_path}/dataset_q_{q_range[0]:.3f}_{q_range[1]:.3f}.h5"
with h5py.File(fname,'w') as file:
#Assign global parameters
file.create_dataset('detector_angles', data = np.array(angles)*np.pi/180)
file.create_dataset('volume_shape', data = volume_shape)
# Make data group
grp = file.create_group('projections')
# Loop through projections
for ii, projection in enumerate(ds.stack):
# Make a group for each projection
subgrp = grp.create_group(str(ii))
#Choose here between using reconstructed transmission or raw data
diode = projection.transmission_factor
# Mask of values where data is nan!
mask = ~masks[:,:,ii]
data = data_array[:,:,ii,:, jj]
#weights = projection.needle_mask()[args['valid_pixels']]
weights = masks[...,ii].astype(float)
# Make sure to mask the parts where data was nan
weights[mask] = 0
offset_j = shifts['offsets_j'][ii]
offset_k = shifts['offsets_k'][ii]
rotations = projection.metadata[args['inner_rotation_key']]
tilts = projection.metadata[args['tilt_key']]
# Assign data
#
subgrp.create_dataset('data', data = np.ascontiguousarray(data))
subgrp.create_dataset('diode', data = diode)
subgrp.create_dataset('weights', data = weights)
subgrp.create_dataset('offset_j', data = np.array([offset_j]))
subgrp.create_dataset('offset_k', data = np.array([offset_k]))
subgrp.create_dataset('rotations', data = np.array([rotations*np.pi/180]))
subgrp.create_dataset('tilts', data = np.array([tilts*np.pi/180]))
(131, 85, 419, 8, 160)
Exporting 1/160 for the q_range from 0.015212015129607303 to 0.015530641158918466
Exporting 2/160 for the q_range from 0.015530641158918466 to 0.015855941027671
Exporting 3/160 for the q_range from 0.015855941027671 to 0.016188054523982594
Exporting 4/160 for the q_range from 0.016188054523982594 to 0.016527124363928412
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