+
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+
+
+
+
+
+
+
+
+This notebook will convert the (potentially) multi-valued results of a point-by-point strain refinement process to a single-valued 'TensorMap' with many useful export formats, like H5, Paraview XDMF, and MTEX CTF.
+
+
+
+
+
+
+
+In [1]:
+
+
+
+
+
+import os
+
+os.environ['OMP_NUM_THREADS'] = '1'
+os.environ['OPENBLAS_NUM_THREADS'] = '1'
+os.environ['MKL_NUM_THREADS'] = '1'
+
+
+
+
+
+
+
+In [2]:
+
+
+
+
+
+exec(open('/data/id11/nanoscope/install_ImageD11_from_git.py').read())
+
+
+
+
+
+
+
+In [3]:
+
+
+
+
+
+# this cell is tagged with 'parameters'
+# to view the tag, select the cell, then find the settings gear icon (right or left sidebar) and look for Cell Tags
+
+PYTHONPATH = setup_ImageD11_from_git( ) # ( os.path.join( os.environ['HOME'],'Code'), 'ImageD11_git' )
+
+# dataset file to import
+dset_file = 'si_cube_test/processed/Si_cube/Si_cube_S3DXRD_nt_moves_dty/Si_cube_S3DXRD_nt_moves_dty_dataset.h5'
+
+# which phase to index
+phase_str = 'Si'
+
+# the minimum number of peaks you want a pixel to have to be counted
+min_unique = 400
+
+
+
+
+
+
+
+
+
+
+
+# Setting path via: +sys.path.insert(0, /home/esrf/james1997a/Code/ImageD11 ) +# Running from: /home/esrf/james1997a/Code/ImageD11/ImageD11/__init__.py ++
+
+
+
+
+
+
+In [4]:
+
+
+
+
+
+# Parameters
+PYTHONPATH = "../"
+dset_file = "tomo_route/processed/Si_cube/Si_cube_S3DXRD_nt_moves_dty/Si_cube_S3DXRD_nt_moves_dty_dataset.h5"
+phase_str = "Si"
+min_unique = 400
+
+
+
+
+
+
+
+In [5]:
+
+
+
+
+
+import numba
+import numpy as np
+import scipy.ndimage as ndi
+from matplotlib import pyplot as plt
+import matplotlib.cm as cm
+from matplotlib.colors import Normalize
+
+from ImageD11.grain import grain
+from ImageD11 import unitcell
+import ImageD11.sinograms.dataset
+from ImageD11.sinograms.point_by_point import PBPMap, nb_inv, PBPRefine
+from ImageD11.sinograms.tensor_map import TensorMap
+from ImageD11.nbGui import nb_utils as utils
+
+%matplotlib ipympl
+
+
+
+
+
+
+
+In [6]:
+
+
+
+
+
+# USER: Pass path to dataset file
+
+ds = ImageD11.sinograms.dataset.load(dset_file)
+
+sample = ds.sample
+dataset = ds.dsname
+rawdata_path = ds.dataroot
+processed_data_root_dir = ds.analysisroot
+
+print(ds)
+print(ds.shape)
+
+
+
+
+
+
+
+
+
+
+
+dataroot = "/home/esrf/james1997a/Code/ImageD11/test/tomo_route/raw" +analysisroot = "/home/esrf/james1997a/Code/ImageD11/test/tomo_route/processed" +sample = "Si_cube" +dset = "S3DXRD_nt_moves_dty" +shape = ( 41, 3620) +# scans 41 from 1.1 to 41.1 +(41, 3620) ++
+
+
+
+
+
+
+In [7]:
+
+
+
+
+
+# load phases from parameter file
+
+ds.phases = ds.get_phases_from_disk()
+ds.phases.unitcells
+
+
+
+
+
+
+
+
+Out[7]:
+
+
+{'Si': <ImageD11.unitcell.unitcell at 0x14cd6dd4f010>}
+
+
+
+
+
+
+
+In [8]:
+
+
+
+
+
+# now let's select a phase to index from our parameters json
+
+ref_ucell = ds.phases.unitcells[phase_str]
+
+print(ref_ucell.lattice_parameters, ref_ucell.spacegroup)
+
+
+
+
+
+
+
+
+
+
+
+[ 5.43 5.43 5.43 90. 90. 90. ] 227 ++
+
+
+
+
+
+
+In [9]:
+
+
+
+
+
+# import refinement manager
+
+refine = PBPRefine.from_h5(ds.refmanfile)
+
+
+
+
+
+
+
+
+
+
+
+Loading peaks +Loading input map +Loading output map ++
+
+
+
+
+
+
+In [10]:
+
+
+
+
+
+# now we inspect the results of the refined map
+
+# plot a histogram of unique peaks per ubi
+
+refine.refinedmap.plot_nuniq_hist()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [11]:
+
+
+
+
+
+# choose the minimum number of peaks you want a pixel to have to be counted
+
+refine.refinedmap.choose_best(min_unique)
+
+# refine.refinedmap.choose_best(min_unique)
+
+
+
+
+
+
+
+In [12]:
+
+
+
+
+
+# let's plot the result of your choice
+
+refine.refinedmap.plot_best(min_unique)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [13]:
+
+
+
+
+
+# look at the fancy strain results
+# this is from refine.refinedmap.best_eps
+
+fig, axs = plt.subplots(3,3, sharex=True, sharey=True, layout='constrained', figsize=(10,10))
+
+cmap = cm.get_cmap('RdBu_r')
+normalizer = Normalize(-1e-3, 1e-3)
+im = cm.ScalarMappable(norm=normalizer, cmap=cmap)
+
+for i in range(3):
+ for j in range(3):
+ axs[i,j].imshow(refine.refinedmap.best_eps[:, :, i, j], origin="lower", cmap=cmap, norm=normalizer)
+ axs[i,j].set_title(f'eps_{i+1}{j+1}')
+fig.supxlabel('< Lab Y axis')
+fig.supylabel('Lab X axis')
+fig.colorbar(im, ax=axs.ravel().tolist())
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+/tmp/ipykernel_3901222/4072199474.py:6: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+ cmap = cm.get_cmap('RdBu_r')
+
+
+
+
+
+
+
+
+
+
+
+
+In [14]:
+
+
+
+
+
+# now make a TensorMap from our refined map so we can plot and export
+
+# first let's work out what phase we have
+phases = {0: ref_ucell}
+
+# let's make a phase id map from our pbpmap
+phase_ids = TensorMap.recon_order_to_map_order(np.where(refine.refinedmap.best_nuniq > min_unique, 0, -1))
+
+# reshape the fancy strain map too
+eps_sample = TensorMap.recon_order_to_map_order(refine.refinedmap.best_eps)
+
+tmap = TensorMap.from_pbpmap(refine.refinedmap, steps=(1, ds.ystep, ds.ystep), phases=phases)
+tmap['phase_ids'] = phase_ids
+tmap['eps_sample'] = eps_sample
+
+
+
+
+
+
+
+In [15]:
+
+
+
+
+
+# Plot the tensormap unique peaks
+
+tmap.plot('nuniq')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [16]:
+
+
+
+
+
+# compute the IPF colours from the UBIs and phase
+
+tmap.get_ipf_maps()
+
+
+
+
+
+
+
+In [17]:
+
+
+
+
+
+tmap.plot('ipf_x')
+tmap.plot('ipf_y')
+tmap.plot('ipf_z')
+
+
+
+
+
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+
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+
+
+
+
+
+
+In [18]:
+
+
+
+
+
+fig, axs = plt.subplots(3,3, sharex=True, sharey=True, layout='constrained', figsize=(10,10))
+
+cmap = cm.get_cmap('RdBu_r')
+normalizer = Normalize(-1e-3, 1e-3)
+im = cm.ScalarMappable(norm=normalizer, cmap=cmap)
+
+for i in range(3):
+ for j in range(3):
+ axs[i,j].imshow(tmap.eps_sample[0, ..., i, j], origin="lower", cmap=cmap, norm=normalizer)
+ axs[i,j].set_title(f'eps_{i+1}{j+1}')
+fig.supxlabel('Lab X axis --->')
+fig.supylabel('Lab Y axis --->')
+fig.colorbar(im, ax=axs.ravel().tolist())
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+/tmp/ipykernel_3901222/655200093.py:3: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+ cmap = cm.get_cmap('RdBu_r')
+
+
+
+
+
+
+
+
+
+
+
+
+In [19]:
+
+
+
+
+
+# look at unit cells - mean of a, b, c for cubic for now
+
+fig, ax = plt.subplots()
+ax.hist(tmap.unitcell[0, :, :, :3].mean(axis=-1).ravel(), bins=1000)
+ax.set_xlabel('unitcell of pixel')
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [20]:
+
+
+
+
+
+# mean unitcell?
+
+print(np.nanmean(tmap.unitcell[0, :, :, :3]))
+
+
+
+
+
+
+
+
+
+
+
+5.4324073368541335 ++
+
+
+
+
+
+
+In [21]:
+
+
+
+
+
+# trigger calculation of all the maps
+
+eul = tmap.euler
+
+
+
+
+
+
+
+In [22]:
+
+
+
+
+
+# save the refined TensorMap to disk
+
+refined_tmap_path = os.path.join(ds.analysispath, f'{ds.sample}_{ds.dset}_refined_tmap.h5')
+
+tmap.to_h5(refined_tmap_path)
+tmap.to_paraview(refined_tmap_path)
+
+
+
+
+
+
+
+In [23]:
+
+
+
+
+
+# you can also do an MTEX export if you like:
+
+refined_ctf_path = os.path.join(ds.analysispath, f'{ds.sample}_{ds.dset}_refined_tmap.ctf')
+
+tmap.to_ctf_mtex(refined_ctf_path, z_index=0)
+
+
+
+
+
+
+
+
+
+
+
+CTF exported!
+In MTEX, run the command:
+import_wizard('EBSD')
+Click the '+', choose file /home/esrf/james1997a/Code/ImageD11/test/tomo_route/processed/Si_cube/Si_cube_S3DXRD_nt_moves_dty/Si_cube_S3DXRD_nt_moves_dty_refined_tmap.ctf and click 'Open'
+Click 'Next >>'
+Click 'Next >>' though the phases, changing if necessary (should be right though)
+Choose 'apply rotation to Euler angles and spatial coordinates' with an angle of [0,0,0]
+Choose the default MTEX plotting convention (X east, Y north)
+Click 'Next >>' then 'script (m-file)' for the 'Import to', then 'Finish'
+Click 'Run' at the top, save the import script somewhere, then it should run
+Your EBSD should now be imported into MTEX. You can try to plot it with 'plot(ebsd)'
+
+
+
+
+
+
+
+
+In [24]:
+
+
+
+
+
+ds.save()
+
+
+
+
+
+
+
+In [25]:
+
+
+
+
+
+# We can run the below cell to do this in bulk for many samples/datasets
+# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix
+# you can add samples and datasets to skip in skips_dict
+
+skips_dict = {
+ "FeAu_0p5_tR_nscope": ["top_-50um", "top_-100um"]
+}
+
+dset_prefix = "top"
+
+sample_list = ["FeAu_0p5_tR_nscope"]
+
+samples_dict = utils.find_datasets_to_process(ds.dataroot, skips_dict, dset_prefix, sample_list)
+
+# manual override:
+# samples_dict = {"FeAu_0p5_tR_nscope": ["top_100um", "top_150um"]}
+
+# now we have our samples_dict, we can process our data:
+
+for sample, datasets in samples_dict.items():
+ for dataset in datasets:
+ print(f"Processing dataset {dataset} in sample {sample}")
+ dset_path = os.path.join(ds.analysisroot, sample, f"{sample}_{dataset}", f"{sample}_{dataset}_dataset.h5")
+ if not os.path.exists(dset_path):
+ print(f"Missing DataSet file for {dataset} in sample {sample}, skipping")
+ continue
+
+ print("Importing DataSet object")
+
+ ds = ImageD11.sinograms.dataset.load(dset_path)
+ print(f"I have a DataSet {ds.dset} in sample {ds.sample}")
+
+ if not os.path.exists(ds.refoutfile):
+ print(f"Couldn't find PBP refinement output file for {dataset} in sample {sample}, skipping")
+ continue
+
+ if os.path.exists(os.path.join(ds.analysispath, 'pbp_tensormap_refined.h5')):
+ print(f"Already have refined TensorMap output file for {dataset} in sample {sample}, skipping")
+ continue
+
+ refine = PBPRefine.from_h5(ds.refmanfile)
+ refine.refinedmap.choose_best(min_unique)
+
+ # first let's work out what phase we have
+ phases = {0: ref_ucell}
+
+ # let's make a phase id map from our pbpmap
+ phase_ids = TensorMap.recon_order_to_map_order(np.where(refine.refinedmap.best_nuniq > min_unique, 0, -1))
+
+ # reshape the fancy strain map too
+ eps_sample = TensorMap.recon_order_to_map_order(refine.refinedmap.best_eps)
+
+ tmap = TensorMap.from_pbpmap(refine.refinedmap, steps=(1, ds.ystep, ds.ystep), phases=phases)
+ tmap['phase_ids'] = phase_ids
+ tmap['eps_sample'] = eps_sample
+
+ tmap.get_ipf_maps()
+ eul = tmap.euler
+
+ tmap.to_h5(os.path.join(ds.analysispath, 'pbp_tensormap_refined.h5'))
+ tmap.to_paraview(os.path.join(ds.analysispath, 'pbp_tensormap_refined.h5'))
+ ctf_path = os.path.join(ds.analysispath, 'pbp_tensormap_refined.ctf')
+ tmap.to_ctf_mtex(ctf_path, z_index=0)
+
+ ds.save()
+
+print("Done!")
+
+
+
+
+
+
+
+
+
+
+
+Done! ++
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+