ants

XRStools/tools_sequencer_esynth.py

@@ -119,6 +119,76 @@ def extract_sample_givenrois(
         ) 
         process_input(input_string, exploit_slurm_mpi = 0)
         
+def synthetise_response(scan_address=None, target_address=None,             response_fit_options = None
+):
+    input_string = """
+    superR_fit_responses :
+       foil_scan_address : "{scan_address}"
+       nref : 7                 # the number of subdivision per pixel dimension used to 
+                                # represent the optical response function at higher resolution
+       niter_optical  :  {niter_optical}    # the number of iterations used in the optimisation of the optical
+                                # response
+       beta_optical  :  {beta_optical}     # The L1 norm factor in the regularisation 
+                                #  term for the optical functions
+       pixel_dim : 1            # The pixel response function is represented with a 
+                                #  pixel_dim**2 array
+       niter_pixel : 10        # The number of iterations in the pixel response optimisation
+                                # phase. A negative number stands for ISTA, positive for FISTA
+       beta_pixel  :  0.0    # L1 factor for the pixel response regularisation
+
+       ## The used trajectories are always written whith the calculated response 
+       ## They can be reloaded and used as initialization(and freezed with do_refine_trajectory : 0 )
+       ## Uncomment the following line if you want to reload a set of trajectories
+       ## without this options trajectories are initialised from the spots drifts
+       ##
+       #   reload_trajectories_file : "response.h5"
+
+       filter_rois : 0
+
+
+       ######
+       ## The method first find an estimation of the foil scan trajectory on each roi
+       ## then, based on this, obtain a fit of the optical response function
+       ## assuming a flat pixel response. Finally the pixel response is optimised
+       ##
+       ## There is a final phase where a global optimisation
+       ## is done in niter_global steps.
+       ##
+       ## Each step is composed of optical response fit, followed by a pixel response fit.
+       ## If do_refine_trajectory is different from zero, the trajectory is reoptimised at each step
+       ## 
+       niter_global  :  {niter_global}
+
+       ## if do_refine_trajectory=1 the start and end point of the trajectory are free
+       ##  if =2 then the start and end point are forced to a trajectory which is obtained
+       ##  from a reference scan : the foil scan may be short, then one can use the scan of
+       ##   an object to get another one : key *trajectory_reference_scan_address*
+       ##
+
+       do_refine_trajectory : 1
+
+       ## optional: only if do_refine_trajectory = 2
+
+       trajectory_reference_scansequence_address : "demo_newrois.h5:/ROI_FOIL/images/scans/"
+       trajectory_threshold   : 0.1
+
+       ## if the pixel response function is forced to be symmetrical 
+
+       simmetrizza : 1
+
+       ## where the found responses are written
+
+       target_file : {target_address}
+       # target_file : "fitted_responses.h5"
+
+    """ 
+    s=input_string.format(  scan_address=scan_address ,
+                           target_address=target_address,
+                           niter_optical = response_fit_options[ "niter_optical"],
+                           beta_optical=response_fit_options["beta_optical"],
+                           niter_global=response_fit_options["niter_global"]
+    )
+    process_input( s , exploit_slurm_mpi = 1, stop_omp = True) 
 
 def    resynthetise_scan(
         old_scan_address= None,

nonregressions/volumes/esrf_scans/esynth/batch_extraction_esynth1.py

@@ -10,10 +10,8 @@ from XRStools import tools_sequencer_esynth
 from XRStools import xrs_read,  xrs_rois
 
 import os
-def main():
-    
+def main():    
     os.system("xz -dk mask.h5.xz --stdout > mask.h5")
-
     filter_path = "mask.h5:/FILTER_MASK/filter"
 
     roi_scan_num   = [245,246,247]
@@ -32,36 +30,23 @@ def main():
     
     ### custom_components_file = "abc.h5"
     custom_components_file = None
-
     
     datadir =  "/data/id20/inhouse/data/run3_20/run3_es949"
-
-    # If reference_clip is not None, then a smaller part of the reference scan is considered
-    # This may be usefule to obtain smaller volumes containing the interesting part
-    # The used reference scan  will the correspond to the positions from reference_clip[0] to reference_clip[1]-1 included
-    ###########
     #    reference_clip = None
     reference_clip = [ 90, 180 ]
 
-    ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than
-    ## such radius from the maximum
     isolate_spot_by = 6
 
-
-    #### For the fit of the response function based on reference scans
     response_fit_options = dict( [
         ["niter_optical" , 100],
         ["beta_optical"  , 0.1],
         ["niter_global"  , 3  ]
     ])
                                  
-    # selected_rois = list(range(0,24)) + list( range(36,60) ) 
-    selected_rois = list( range(24,36) ) + list( range(60,72) ) 
-    # selected_rois = list(range(0,72))
+    selected_rois = list(range(0,72))
     volume_retrieval_beta  = 6.0e-20
     volume_retrieval_niter = 100 
     
-
     steps_to_do = {
         "do_step_make_roi":                      False,
         "do_step_sample_extraction":             False,
@@ -69,7 +54,7 @@ def main():
         "do_step_fit_reference_response":               False,
         "do_step_resynthetise_reference":               False,
 
-        "do_step_scalars"               :    True,
+        "do_step_scalars"               :    False,
     
         "do_step_interpolation_coefficients":    True,
         
@@ -105,8 +90,7 @@ def main():
     elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum')
     el_dict = elastic.cenom_dict
     Enominal = np.median(  list( el_dict.values() ) )
-    peaks_shift = np.array([    el_dict["ROI%02d"%i] if ("ROI%02d"%i) in  el_dict else nan    for i in range 72  ] ) 
-    Enominal = np.median(peaks_shifts)
+    peaks_shifts = np.array([    el_dict["ROI%02d"%i] if ("ROI%02d"%i) in  el_dict else nan    for i in range( 72)  ] ) 
     peaks_shifts-= Enominal
     
     ##############################################################

nonregressions/volumes/esrf_scans/fourmis/interp/batch_extraction_interp.py

+import numpy as np
+import h5py
+import glob
+import json
+import os
+import h5py
+import math
+
+from XRStools import tools_sequencer_interp
+
+import os
+def main():
+    
+    ## os.system("xz -dk ../mask.h5.xz --stdout > mask.h5 ")
+
+    filter_path = None
+    # filter_path = "mask.h5:/FILTER_MASK/filter"
+
+    roi_scan_num   = [592]
+    reference_scan_list = [245, 246, 247]
+
+    
+    monitor_column = "izero/0.000001"
+    
+    first_scan_num = 651 
+    Ydim        = 25
+    Zdim        = 10
+    Edim        = 7
+
+    elastic_scan_for_peaks_shifts = 69
+
+    datadir =   "/data/id20/inhouse/data/run5_17/run7_ihr/"
+
+    # If reference_clip is not None, then a smaller part of the reference scan is considered
+    # This may be usefule to obtain smaller volumes containing the interesting part
+    # The used reference scan  will the correspond to the positions from reference_clip[0] to reference_clip[1]-1 included
+    ###########
+    reference_clip = None
+    # reference_clip = [ 90, 180 ]
+
+    ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than
+    ## such radius from the maximum
+    isolate_spot_by = 7
+
+
+    #### For the fit of the response function based on reference scans
+    response_fit_options = dict( [
+        ["niter_optical" , 100],
+        ["beta_optical"  , 0.1],
+        ["niter_global"  , 3  ]
+    ])
+                                 
+
+    selected_rois = list(range(0,24)) + list( range(36,60) ) 
+
+
+    scal_prod_use_optional_solution = True 
+
+
+    volume_retrieval_beta  = 6.0e-20
+    volume_retrieval_niter = 100 
+    
+    rois_are_ready = False
+    
+    steps_to_do = {
+        "do_step_make_roi":                      True,
+        "do_step_sample_extraction":             False,
+        "do_step_interpolation":                 False,
+        "do_step_extract_reference_scan":             False,
+                
+        "do_step_fit_reference_response":               False,
+        "do_step_resynthetise_reference":               False,
+        "do_step_scalars"               :    False,
+        "do_step_volume_retrieval"      :    False,
+    }
+
+    
+    os.system("mkdir results")
+
+
+    scalar_products_and_volume_target_file  = "results/scalar_products_and_volume.h5"
+
+    roi_target_path    = "results/myrois.h5:/ROIS"
+        
+    signals_target_file = "results/signals.h5"
+    
+    interpolated_signals_target_file = "results/interpolated_signals.h5"
+
+    extracted_reference_target_file = "results/reference.h5"
+    
+    response_target_file = "results/response.h5"
+
+    resynthetised_reference_and_roi_target_file = "results/resynthetised_roi_and_scan.h5"
+
+    ###########################################################################################
+    ######  LOADING PEAKS SHIFTS
+    ###### peaks_shifts = h5py.File("../peaks_positions_for_analysers.h5","r")["peaks_positions"][()]
+    ###### assert( len(peaks_shifts) == 72)
+
+    if rois_are_ready:
+        roiob = xrs_rois.roi_object()
+        roiob.loadH5( roi_target_path  )
+        elastic = xrs_read.Hydra( datadir )
+        elastic.set_roiObj( roiob )
+        elastic.get_compensation_factor( elastic_scan_for_peaks_shifts , method='sum')
+        el_dict = elastic.cenom_dict
+        Enominal = np.median(  list( el_dict.values() ) )
+        peaks_shift = np.array([    el_dict["ROI%02d"%i] if ("ROI%02d"%i) in  el_dict else nan    for i in range( 72)  ] ) 
+        Enominal = np.median(peaks_shifts)
+        peaks_shifts-= Enominal
+    else:
+        peaks_shifts = None
+    
+    ##############################################################
+    ##########################################################################
+    
+    tools_sequencer_interp.tools_sequencer(  peaks_shifts          = peaks_shifts          ,
+                      datadir               = datadir               ,
+                      filter_path           = filter_path           ,
+                      roi_scan_num          = roi_scan_num          ,
+                      roi_target_path       = roi_target_path       ,
+                      
+                      steps_to_do = steps_to_do,
+                      
+                      first_scan_num =  first_scan_num,
+                      Ydim        =  Ydim       ,
+                      Zdim        =  Zdim       ,
+                      Edim        =  Edim       ,
+
+                      monitor_column = monitor_column,
+                      signals_target_file = signals_target_file,
+                      interpolated_signals_target_file = interpolated_signals_target_file,
+
+                      reference_scan_list = reference_scan_list,
+                      reference_clip = reference_clip,
+                      extracted_reference_target_file = extracted_reference_target_file ,
+                      isolate_spot_by =  isolate_spot_by,
+                      response_target_file = response_target_file,
+                      response_fit_options = response_fit_options,
+                      
+                      resynthetised_reference_and_roi_target_file = resynthetised_reference_and_roi_target_file,
+
+                      selected_rois = selected_rois,
+                      
+                      scal_prod_use_optional_solution = scal_prod_use_optional_solution ,
+                      scalar_products_and_volume_target_file      = scalar_products_and_volume_target_file ,
+
+                      volume_retrieval_beta  = volume_retrieval_beta ,
+                      volume_retrieval_niter = volume_retrieval_niter 
+                      
+    )
+    
+    
+
+            
+            
+main()
+
+

nonregressions/volumes/esrf_scans/interpolation/batch_extraction_interp.py

@@ -5,12 +5,9 @@ import json
 import os
 import h5py
 import math
-
 from XRStools import tools_sequencer_interp
-
 import os
-def main():
-    
+def main():    
     os.system("xz -dk ../mask.h5.xz --stdout > mask.h5 ")
 
     filter_path = "mask.h5:/FILTER_MASK/filter"
@@ -18,7 +15,6 @@ def main():
     roi_scan_num   = [245,246,247]
     reference_scan_list = [245, 246, 247]
 
-    
     monitor_column = "izero/0.000001"
     
     first_scan_num = 651 
@@ -30,36 +26,24 @@ def main():
 
     datadir =  "/data/id20/inhouse/data/run3_20/run3_es949"
 
-    # If reference_clip is not None, then a smaller part of the reference scan is considered
-    # This may be usefule to obtain smaller volumes containing the interesting part
-    # The used reference scan  will the correspond to the positions from reference_clip[0] to reference_clip[1]-1 included
-    ###########
     #    reference_clip = None
     reference_clip = [ 90, 180 ]
 
-    ## in the reference scan for each position there is a spot with a maximum. We set zero the background which is further than
-    ## such radius from the maximum
     isolate_spot_by = 6
 
-
-    #### For the fit of the response function based on reference scans
     response_fit_options = dict( [
         ["niter_optical" , 100],
         ["beta_optical"  , 0.1],
         ["niter_global"  , 3  ]
-    ])
-                                 
+    ])                                 
 
     selected_rois = list(range(0,24)) + list( range(36,60) ) 
 
-
     scal_prod_use_optional_solution = True 
 
-
     volume_retrieval_beta  = 6.0e-20
     volume_retrieval_niter = 100 
     
-
     
     steps_to_do = {
         "do_step_make_roi":                      False,