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ImageD11_gui fixes and some scanning notebooks updates #226

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Feb 26, 2024
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ImageD11_gui fixes and some scanning notebooks updates #226

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c057251
Minor phase scanning notebooks now use nb_utils, needs testing
jadball Feb 22, 2024
478b22e
Correct ImageD11_gui bugs - broken log buttons and tth_histogram
jadball Feb 26, 2024
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218 changes: 41 additions & 177 deletions ImageD11/nbGui/S3DXRD/1_S3DXRD_index_z_slice_minor_phase.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
"source": [
"# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n",
"# Written by Haixing Fang, Jon Wright and James Ball\n",
"## Date: 19/02/2024"
"## Date: 21/02/2024"
]
},
{
Expand Down Expand Up @@ -53,7 +53,7 @@
"%matplotlib widget\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import utils\n",
"import ImageD11.nbGui.nb_utils as utils\n",
"\n",
"import ImageD11.grain\n",
"import ImageD11.unitcell\n",
Expand All @@ -76,21 +76,19 @@
},
"outputs": [],
"source": [
"# OLD DATASETS\n",
"\n",
"# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n",
"# In this case, use this cell to specify where your experimental folder is, and do not run the cell below\n",
"# e.g /data/visitor/4752/id11/20210513\n",
"# e.g /data/visitor/ma4752/id11/20210513\n",
"\n",
"### USER: specify your experimental directory\n",
"\n",
"rawdata_path = \"/home/esrf/james1997a/Data/ma4752/id11/20210618\"\n",
"rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n",
"\n",
"!ls -lrt {rawdata_path}\n",
"\n",
"### USER: specify where you want your processed data to go\n",
"\n",
"processed_data_root_dir = \"/home/esrf/james1997a/Data/ma4752/id11/20240118/James\""
"processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240221\""
]
},
{
Expand All @@ -103,8 +101,8 @@
"source": [
"# USER: pick a sample and a dataset you want to segment\n",
"\n",
"sample = \"MA4752_S4_2_XRD\"\n",
"dataset = \"DTL1z90\""
"sample = \"FeAu_0p5_tR_nscope\"\n",
"dataset = \"top_100um\""
]
},
{
Expand All @@ -119,8 +117,12 @@
"\n",
"dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n",
"\n",
"e2dx_path = \"/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dx.edf\"\n",
"e2dy_path = \"/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dy.edf\""
"# USER: specify the path to the parameter file\n",
"\n",
"par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n",
"\n",
"e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n",
"e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')"
]
},
{
Expand All @@ -139,17 +141,6 @@
"print(ds.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"par_path = 'nickel.par'"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -345,61 +336,32 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# now we are indexing!\n",
"\n",
"# indexing will select all rings with a multiplicity below max_multiplity to search\n",
"max_multiplicity = 11\n",
"\n",
"min_counts_on_ring = 30\n",
"\n",
"n_peaks_expected = 0\n",
"rings = []\n",
"for i, dstar in enumerate(indexer.unitcell.ringds):\n",
" multiplicity = len(indexer.unitcell.ringhkls[indexer.unitcell.ringds[i]])\n",
" counts_on_this_ring = (indexer.ra == i).sum()\n",
" if counts_on_this_ring > min_counts_on_ring:\n",
" n_peaks_expected += multiplicity\n",
" if multiplicity < max_multiplicity:\n",
" rings.append((counts_on_this_ring, multiplicity, i))\n",
"\n",
"rings.sort()\n",
"\n",
"print(f\"{n_peaks_expected} peaks expected\")\n",
"print(f\"Trying these rings (counts, multiplicity, ring number): {rings}\")\n",
"\n",
"# USER: specify the HKL tolerances you want to use for indexing\n",
"hkl_tols_seq = [0.01, 0.02, 0.03]\n",
"\n",
"# USER: specify the fraction of the total expected peaks\n",
"fracs = [0.9, 0.8, 0.7]\n",
"\n",
"# ImageD11.cImageD11.cimaged11_omp_set_num_threads(1)\n",
"ImageD11.indexing.loglevel=3\n",
"\n",
"# indexer.uniqueness = 0.3\n",
"indexer.cosine_tol = np.cos(np.radians(90.25))\n",
"indexer.max_grains = 1000\n",
"\n",
"# iterate over HKL tolerances\n",
"for frac in fracs:\n",
" for tol in hkl_tols_seq:\n",
" indexer.minpks = n_peaks_expected*frac\n",
" indexer.hkl_tol = tol\n",
" \n",
" # iterate over rings\n",
" for i in range(len(rings)):\n",
" for j in range(i, len(rings)):\n",
" indexer.ring_1 = rings[i][2]\n",
" indexer.ring_2 = rings[j][2]\n",
" \n",
" indexer.find()\n",
" indexer.scorethem()\n",
" \n",
" print(frac, tol, len(indexer.ubis))"
"# the minimum number of peaks on a ring for a ring to be indexed on\n",
"min_counts_on_ring = 0\n",
"# the sequence of hkl tolerances the indexer will iterate through\n",
"hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.1]\n",
"# the sequence of minpks fractions the indexer will iterate through\n",
"fracs = [0.9, 0.8, 0.7, 0.6, 0.5]\n",
"# the tolerance in g-vector angle\n",
"cosine_tol = np.cos(np.radians(90.25))\n",
"# the max number of UBIs we can find per pair of rings\n",
"max_grains = 1000\n",
"\n",
"grains, indexer = utils.do_index(cf=cf_strong,\n",
" dstol=indexer.ds_tol,\n",
" max_mult=max_multiplicity,\n",
" min_ring_count=min_counts_on_ring,\n",
" hkl_tols=hkl_tols_seq,\n",
" fracs=fracs,\n",
" cosine_tol=cosine_tol,\n",
" max_grains=max_grains\n",
")"
]
},
{
Expand All @@ -410,9 +372,6 @@
},
"outputs": [],
"source": [
"# create grain objects\n",
"grains = [ImageD11.grain.grain(ubi, translation=np.array([0., 0., 0.])) for ubi in indexer.ubis]\n",
"\n",
"# set grain GIDs (useful if we ever delete a grain)\n",
"for i, g in enumerate(grains):\n",
" g.gid = i\n",
Expand Down Expand Up @@ -453,127 +412,32 @@
"\n",
"tol=0.05\n",
"\n",
"# column to store the grain labels\n",
"labels = np.zeros(cf_strong.nrows, 'i')\n",
"# get all g-vectors from columnfile\n",
"gv = np.transpose((cf_strong.gx,cf_strong.gy,cf_strong.gz)).astype(float)\n",
"# column to store drlv2 (error in hkl)\n",
"drlv2 = np.ones(cf_strong.nrows, 'd')\n",
"# iterate over all grains\n",
"print(f\"Scoring and assigning {len(grains)} grains\")\n",
"for g in tqdm(grains):\n",
" n = ImageD11.cImageD11.score_and_assign(g.ubi, gv, tol, drlv2, labels, g.gid)\n",
"\n",
"# add the labels column to the columnfile\n",
"cf_strong.addcolumn(labels, 'grain_id')\n",
"utils.assign_peaks_to_grains(grains, cf_strong, tol=tol)\n",
"\n",
"print(\"Storing peak data in grains\")\n",
"# iterate through all the grains\n",
"for g in tqdm(grains):\n",
" # store this grain's peak indices so we know which 4D peaks we used for indexing\n",
" g.mask_4d = cf_strong.grain_id == g.gid\n",
" g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"def plot_index_results(ind, colfile, title):\n",
" # Generate a histogram of |drlv| for a ubi matrix\n",
" indexer.histogram_drlv_fit()\n",
" indexer.fight_over_peaks()\n",
" \n",
" fig, axs = plt.subplots(3, 2, layout=\"constrained\", figsize=(9,12))\n",
" axs_flat = axs.ravel()\n",
" \n",
" # For each grain, plot the error in hkl vs the number of peaks with that error\n",
" \n",
" for grh in ind.histogram:\n",
" axs_flat[0].plot(ind.bins[1:-1], grh[:-1], \"-\")\n",
" \n",
" axs_flat[0].set(ylabel=\"number of peaks\",\n",
" xlabel=\"error in hkl (e.g. hkl versus integer)\",\n",
" title=title)\n",
" \n",
" # set a mask of all non-assigned g-vectors\n",
" \n",
" m = ind.ga == -1\n",
" \n",
" # plot the assigned g-vectors omega vs dty (sinograms)\n",
" \n",
" axs_flat[1].scatter(colfile.omega[~m],\n",
" colfile.dty[~m],\n",
" c=ind.ga[~m],\n",
" s=2,\n",
" cmap='tab20')\n",
" \n",
" axs_flat[1].set(title=f'Sinograms of {ind.ga.max()+1} grains',\n",
" xlabel='Omega/deg',\n",
" ylabel='dty/um')\n",
" \n",
" # Define weak peaks as all non-assigned peaks with intensity 1e-4 of max\n",
" cut = colfile.sum_intensity[m].max() * 1e-4\n",
" weak = colfile.sum_intensity[m] < cut\n",
" \n",
" # Plot unassigned peaks in omega vs dty\n",
" \n",
" axs_flat[2].scatter(colfile.omega[m][weak], colfile.dty[m][weak], s=2, label='weak')\n",
" axs_flat[2].scatter(colfile.omega[m][~weak], colfile.dty[m][~weak], s=2, label='not weak')\n",
" \n",
" axs_flat[2].set(title='Sinograms of unassigned peaks',\n",
" xlabel='Omega/deg',\n",
" ylabel='dty/um')\n",
" axs_flat[2].legend()\n",
" \n",
" # Plot d-star vs intensity for all assigned peaks\n",
" \n",
" axs_flat[3].scatter(colfile.ds[~m], colfile.sum_intensity[~m], s=2)\n",
" axs_flat[3].set(title='Intensity of all assigned peaks',\n",
" xlabel='d-star',\n",
" ylabel='Intensity',\n",
" yscale='log')\n",
" \n",
" # Plot d-star vs intensity for all unassigned peaks\n",
" \n",
" axs_flat[4].scatter(colfile.ds[m][weak], colfile.sum_intensity[m][weak], s=2, label='weak')\n",
" axs_flat[4].scatter(colfile.ds[m][~weak], colfile.sum_intensity[m][~weak], s=2, label='not weak')\n",
" \n",
" axs_flat[4].set(title='Intensity of all unassigned peaks',\n",
" xlabel='d-star',\n",
" ylabel='Intensity',\n",
" yscale='log')\n",
" axs_flat[4].legend()\n",
" \n",
" # Get the number of peaks per grain\n",
" \n",
" npks = [(ind.ga == i).sum() for i in range(len(ind.ubis))]\n",
" \n",
" # Plot histogram of number of peaks per grain\n",
" \n",
" axs_flat[5].hist(npks, bins=64)\n",
" axs_flat[5].set(title='Hist of peaks per grain',\n",
" xlabel='Number of peaks',\n",
" ylabel='Number of grains')\n",
" \n",
" for ax in axs_flat:\n",
" ax.set_box_aspect(0.7)\n",
" \n",
" plt.show()"
"utils.plot_index_results(indexer, cf_strong, 'First attempt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"plot_index_results(indexer, cf_strong, 'First attempt')"
"utils.plot_grain_sinograms(grains, cf_strong, 25)"
]
},
{
Expand Down
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