Repository: FABLE-3DXRD/ImageD11

Issue:
pbp indexer - y0 not added in grid and units for diffty not matching distance
Around line 2000 in point_by_point:
"""
        nlo = np.floor((self.ybincens.min()) / self.ystep).astype(int)
        nhi = np.ceil((self.ybincens.max()) / self.ystep).astype(int)

        rng = range(nlo, nhi + 1, gridstep)
"""
It needs to subtract y0 if there was one.

Also noted: the distance correction in get_local_gv assumes dty and distance have the same units.  This is harder to fix automagically unless everything comes with units.

Hints from issue comments:
@jonwright: Also the indexing loglevel does not work when changed. Something about the multiprocessing pool and global state perhaps. No idea how to fix that.@jadball: For the first issue: here's the commit to blame: https://github.com/FABLE-3DXRD/ImageD11/commit/32dfd946ab5ee80f1a285606b41f65824e8fbc32

It seems I was correcting a `y0` bug for a dataset with a big y0 by modifying the code in two places - I remember things definitely got better after the bug fix, but I suspect the second fix wasn't needed. To me this raises deeper questions about how the `geometry.dty_to_dtyi` function works/should work. TODO after Christmas for sure!@jadball: I think the underlying problem is that we assume that a perfectly aligned diffractometer would have a y0 value of 0?@jadball: The simplest fix for this (without possibly introducing more bugs by modifying `geometry.py`) would be to have an option to shift `self.dty` in the `DataSet` class when the motor values are read/imported, to account for where the rotation axis should be in the lab frame assuming perfect alignment (~14 for the TDXRD station, 0 for the NSCOPE station). @jonwright what do you think? The alternative is I think some rather drastic additions to `geometry.py` to introduce a new static reference frame centered on the diffractometer.@jonwright: I didn't understand why the 14 is different to y0 yet? I will try to go through the code soon.

Just adding some known offset to the silicon testcase should give something small to look at numbers and debug...@jonwright: Is it just:

Sinogram scan step space != Recon step space

?@jadball: > Is it just:
> 
> Sinogram scan step space != Recon step space
> 
> ?

@jonwright you're right.
Continuous `(dty, omega)` space should have its own discretisation.
By convention we use `(ystep, ostep)`.
As the 'centre' of the `dty` scan can be arbitrary (e.g NSCOPE vs 3DXRD), I propose that `dtyi = 0` happens at the lowest-value `dty` scan (e.g `ds.ymin`) such that:

```python
def dty_to_dtyi(dty, ystep, ymin):
    """
    We take continuous dty space and discretise it
    We do this by counting the number of ysteps away from ymin
    ymin should probably be ds.ymin
    This should be equivalent to determining the closest bin in ds.ybincens
    But this is neater as it accounts for values outside the ds.ybincens range

    dtyi = (dty - ymin) / ystep
    """
    dtyi = np.round((dty - ymin) / ystep).astype(int)
    return dtyi
```

Merge coming soon for this bug fix, including standardised ways of producing grids in `step` space which is I think where this issue appeared@jonwright: Ymin is probably dataset.ybincens[0] with dataset.ystep for the step. That should work for now, but for a sinogram that is not on a regular grid we might have to come back to this.

Thanks for fixing this!@jadball: @jonwright looking at `point_by_point.py`, we often need a method to construct a grid in step space `(si, sj)` to reconstruct on.
What do you think of the proposal below?
It means that even for an unsymmetric scan we get symmetric reconstruction grids:

```python
def step_grid_from_ybincens(ybincens, step_size, gridstep, y0):
    """
    Produce a grid in (si, sj) step space to reconstruct on.
    Constructs a symmetric grid with the origin on y0
    Therefore the step space is truly aligned to the rotation axis.
    The size of the grid is determined by the extremes of ybincens.
    The grid will always be symmetric such that (0, 0) is the centre of an image formed from the grid
    :param ybincens: An evenly sampled dty space
    :type ybincens: np.ndarray
    :param step_size: the step size of the grid (same units as ybincens and y0)
    :type step_size: float
    :param gridstep: the sampling resolution of the grid (e.g 2 = [0, 2, 4, 6, ...])
    :type gridstep: int
    :return: (si, sj) for si in ints for sj in ints
    :rtype: tuple(int, int)
    """
    # centre y range on y0
    y_relative = ybincens - y0
    # find minimum and maximum values of y
    y_min = y_relative.min()
    y_max = y_relative.max()
    # work out which is larger, so our grid is always symmetric
    # i.e never go from (-65, 65) to (100, 100)
    # always go from (-100, -100) to (100, 100)
    y_largest = np.max(np.abs([y_min, y_max]))
    y_min = -y_largest
    y_max = y_largest
    y_min_int = np.floor(y_min/step_size).astype(int)
    y_max_int = np.ceil(y_max/step_size).astype(int)
    ints = range(y_min_int, y_max_int + 1, gridstep)
    step_points = [(si, sj) for si in ints for sj in ints]
    return step_points
```@jadball: ^ per my previous comment, this tweak is already paying dividends when we have a significant `(y0-ybeam)` error:

![Image](https://github.com/user-attachments/assets/4b52f19f-09af-4129-b9b0-56896b263dd6)

On the right image, the step grid we have reconstructed on is automatically large enough to account for the effects of the scan not being well centered to the beam, because we are considering the true y0 value when constructing our step grid.@jadball: Resolved by #379 :)

Solution patch:
diff --git a/ImageD11/nbGui/S3DXRD/pbp_3_refinement.ipynb b/ImageD11/nbGui/S3DXRD/pbp_3_refinement.ipynb
index cfad395..146c307 100755
--- a/ImageD11/nbGui/S3DXRD/pbp_3_refinement.ipynb
+++ b/ImageD11/nbGui/S3DXRD/pbp_3_refinement.ipynb
@@ -201,14 +201,13 @@
     "# set up a refinement manager object\n",
     "\n",
     "y0 = 0.0\n",
-    "fpks = 0.95\n",
     "hkl_tol_origins = 0.05\n",
     "hkl_tol_refine = 0.1\n",
     "hkl_tol_refine_merged = 0.05\n",
     "ds_tol = 0.006\n",
     "ifrac = 6e-3\n",
     "\n",
-    "refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)"
+    "refine = PBPRefine(dset=ds, y0=y0, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)"
    ]
   },
   {
@@ -394,7 +393,7 @@
     "        pmap = PBPMap(ds.pbpfile)\n",
     "        pmap.choose_best(min_unique)\n",
     "        \n",
-    "        refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)\n",
+    "        refine = PBPRefine(dset=ds, y0=y0, ybeam=ybeam, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)\n",
     "        \n",
     "        refine.setmap(pmap)\n",
     "        refine.setpeaks(cf_2d)\n",
diff --git a/ImageD11/nbGui/S3DXRD/tomo_2_map.ipynb b/ImageD11/nbGui/S3DXRD/tomo_2_map.ipynb
index 7918dc4..3e5acc6 100644
--- a/ImageD11/nbGui/S3DXRD/tomo_2_map.ipynb
+++ b/ImageD11/nbGui/S3DXRD/tomo_2_map.ipynb
@@ -63,6 +63,7 @@
     "from ImageD11.sinograms.sinogram import GrainSinogram, build_slice_arrays, write_slice_recon, read_h5, write_h5, get_2d_peaks_from_4d_peaks\n",
     "from ImageD11.sinograms.roi_iradon import run_iradon\n",
     "from ImageD11.sinograms.tensor_map import TensorMap\n",
+    "from ImageD11.sinograms.geometry import sino_shift_and_pad\n",
     "import ImageD11.sinograms.dataset\n",
     "import ImageD11.nbGui.nb_utils as utils\n",
     "\n",
@@ -133,18 +134,6 @@
     "is_half_scan = False"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if is_half_scan:\n",
-    "    ds.correct_bins_for_half_scan()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -318,8 +307,8 @@
     "a[3].scatter(y, x, c=s)\n",
     "a[3].set(title='Number of 4D peaks', aspect='equal')\n",
     "\n",
-    "fig.supxlabel(\"<- Lab y (transverse)\")\n",
-    "fig.supylabel(\"Lab x (beam) ->\")\n",
+    "fig.supxlabel(\"<- Sample y (transverse)\")\n",
+    "fig.supylabel(\"Sample x (beam) ->\")\n",
     "\n",
     "for a in ax.ravel():\n",
     "    a.invert_xaxis()\n",
@@ -351,16 +340,19 @@
     "\n",
     "print('y0 is', y0)\n",
     "\n",
-    "# update the y0 for each grain with the median y0:\n",
+    "if is_half_scan:\n",
+    "    ds.correct_bins_for_half_scan(y0=y0)\n",
     "\n",
-    "for gs in grainsinos:\n",
-    "    gs.update_recon_parameters(y0=y0)\n",
+    "# try to automatically determine the sinogram shift and the padding from the y0 values\n",
+    "shift, pad = sino_shift_and_pad(y0, len(ds.ybincens), min(ds.ybincens), ds.ystep)\n",
     "\n",
-    "# the shift we have to apply to the reconstructions is equal to -y0/ystep (in integer coords)\n",
+    "print('shift is', shift)\n",
+    "print('pad is', pad)\n",
     "\n",
-    "shift = -y0/ds.ystep\n",
+    "# update the grainsinogram parameters accordingly:\n",
     "\n",
-    "print('shift is', shift)"
+    "for gs in grainsinos:\n",
+    "    gs.update_recon_parameters(y0=y0, shift=shift, pad=pad)"
    ]
   },
   {
@@ -392,7 +384,6 @@
    "source": [
     "# \"quick\" whole-sample reconstruction\n",
     "\n",
-    "pad = 30\n",
     "nthreads = len(os.sched_getaffinity(os.getpid()))\n",
     "\n",
     "whole_sample_recon = run_iradon(whole_sample_sino, ds.obincens, pad, shift, workers=nthreads, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)"
@@ -446,8 +437,8 @@
     "axs[1].set_title(\"Binarised threshold\")\n",
     "axs[2].set_title(\"Convex hull\")\n",
     "\n",
-    "fig.supxlabel(\"<-- Y axis\")\n",
-    "fig.supylabel(\"Beam >\")\n",
+    "fig.supxlabel(\"<-- Sample Y axis\")\n",
+    "fig.supylabel(\"Sample x axis (Beam) >\")\n",
     "\n",
     "plt.show()"
    ]
@@ -592,8 +583,8 @@
     "axs[0].set_title(\"ssino\")\n",
     "axs[1].imshow(gs.recons[\"iradon\"], vmin=0, origin=\"lower\")\n",
     "axs[1].set_title(\"ID11 iradon\")\n",
-    "axs[1].set_xlabel(\"<-- Lab Y\")\n",
-    "axs[1].set_ylabel(\"Lab X\")\n",
+    "axs[1].set_xlabel(\"<-- Sample Y\")\n",
+    "axs[1].set_ylabel(\"Sample X\")\n",
     "\n",
     "plt.show()"
    ]
@@ -644,8 +635,8 @@
     "fig, a = plt.subplots(1,2,figsize=(10,5))\n",
     "rec = a[0].imshow(grainsinos[0].recons[\"iradon\"], vmin=0, origin=\"lower\")\n",
     "sin = a[1].imshow(grainsinos[0].ssino, aspect='auto')\n",
-    "a[0].set_xlabel(\"<-- Lab Y\")\n",
-    "a[0].set_ylabel(\"Lab X\")\n",
+    "a[0].set_xlabel(\"<-- Sample Y\")\n",
+    "a[0].set_ylabel(\"Sample X\")\n",
     "\n",
     "# Function to update the displayed image based on the selected frame\n",
     "def update_frame(i):\n",
@@ -948,8 +939,6 @@
     "                continue\n",
     "        \n",
     "        ds.phases = ds.get_phases_from_disk()\n",
-    "        if is_half_scan:\n",
-    "            ds.correct_bins_for_half_scan()\n",
     "        \n",
     "        print(\"Peaks\")\n",
     "        # cf_4d = ds.get_cf_4d_from_disk()\n",
@@ -968,9 +957,8 @@
     "        for grain_label, gs in enumerate(grainsinos):\n",
     "            gs.update_lab_position_from_peaks(cf_strong, grain_label)\n",
     "        \n",
-    "        sample_y0s = [gs.recon_y0 for gs in grainsinos]\n",
-    "        y0 = np.median(sample_y0s)\n",
-    "        shift = -y0/ds.ystep\n",
+    "        if is_half_scan:\n",
+    "            ds.correct_bins_for_half_scan(y0)\n",
     "        \n",
     "        print(\"Determining RGB colours\")\n",
     "        utils.get_rgbs_for_grains(grains)\n",
diff --git a/ImageD11/nbGui/S3DXRD/tomo_2_map_minor_phase.ipynb b/ImageD11/nbGui/S3DXRD/tomo_2_map_minor_phase.ipynb
index bc0bdde..7fca414 100755
--- a/ImageD11/nbGui/S3DXRD/tomo_2_map_minor_phase.ipynb
+++ b/ImageD11/nbGui/S3DXRD/tomo_2_map_minor_phase.ipynb
@@ -139,18 +139,6 @@
     "is_half_scan = False"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if is_half_scan:\n",
-    "    ds.correct_bins_for_half_scan()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -300,6 +288,16 @@
     "print(shift, y0, pad)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if is_half_scan:\n",
+    "    ds.correct_bins_for_half_scan(y0)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -994,8 +992,7 @@
     "        \n",
     "        ds.phases = ds.get_phases_from_disk()\n",
     "        \n",
-    "        if is_half_scan:\n",
-    "            ds.correct_bins_for_half_scan()\n",
+    "\n",
     "        \n",
     "        print(\"Importing peaks\")\n",
     "        cf_4d = ds.get_cf_4d_from_disk()\n",
@@ -1011,6 +1008,9 @@
     "        whole_sample_mask = major_phase_grainsinos[0].recon_mask\n",
     "        shift = major_phase_grainsinos[0].recon_shift\n",
     "        pad = major_phase_grainsinos[0].recon_pad\n",
+    "        y0 = major_phase_grainsinos[0].recon_y0\n",
+    "        if is_half_scan:\n",
+    "            ds.correct_bins_for_half_scan(y0)\n",
     "        \n",
     "        utils.assign_peaks_to_grains(grains, cf_strong, tol=peak_assign_tol)\n",
     "        for grain_label, g in enumerate(grains):\n",
diff --git a/ImageD11/nbGui/S3DXRD/tomo_3_refinement.ipynb b/ImageD11/nbGui/S3DXRD/tomo_3_refinement.ipynb
index e62d74f..96cdc9d 100755
--- a/ImageD11/nbGui/S3DXRD/tomo_3_refinement.ipynb
+++ b/ImageD11/nbGui/S3DXRD/tomo_3_refinement.ipynb
@@ -69,7 +69,6 @@
     "from ImageD11.sinograms.point_by_point import PBPRefine\n",
     "from ImageD11.peakselect import select_ring_peaks_by_intensity\n",
     "from ImageD11.sinograms import properties, roi_iradon\n",
-    "from ImageD11.sinograms import geometry\n",
     "from ImageD11.sinograms.sinogram import GrainSinogram, build_slice_arrays, write_slice_recon, read_slice_recon, write_h5, read_h5, write_pbp_strain\n",
     "from ImageD11.grain import grain\n",
     "from ImageD11 import cImageD11\n",
@@ -231,14 +230,13 @@
     "# set up a refinement manager object\n",
     "\n",
     "y0 = grainsinos[0].recon_y0\n",
-    "fpks = 0.9\n",
     "hkl_tol_origins = 0.05\n",
     "hkl_tol_refine = 0.1\n",
     "hkl_tol_refine_merged = 0.05\n",
     "ds_tol = 0.004\n",
     "ifrac = 7e-3\n",
     "\n",
-    "refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)"
+    "refine = PBPRefine(dset=ds, y0=y0, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)"
    ]
   },
   {
@@ -442,7 +440,7 @@
     "        pmap = tensor_map.to_pbpmap(z_layer=0, default_npks=20, default_nuniq=20)\n",
     "        pmap.choose_best(1)\n",
     "\n",
-    "        refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)\n",
+    "        refine = PBPRefine(dset=ds, y0=y0, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str)\n",
     "        \n",
     "        refine.setmap(pmap)\n",
     "        refine.setpeaks(cf_2d)\n",
diff --git a/ImageD11/nbGui/S3DXRD/tomo_3_refinement_minor_phase.ipynb b/ImageD11/nbGui/S3DXRD/tomo_3_refinement_minor_phase.ipynb
index 18d9434..35de7f7 100755
--- a/ImageD11/nbGui/S3DXRD/tomo_3_refinement_minor_phase.ipynb
+++ b/ImageD11/nbGui/S3DXRD/tomo_3_refinement_minor_phase.ipynb
@@ -69,7 +69,6 @@
     "from ImageD11.sinograms.point_by_point import PBPRefine\n",
     "from ImageD11.peakselect import select_ring_peaks_by_intensity\n",
     "from ImageD11.sinograms import properties, roi_iradon\n",
-    "from ImageD11.sinograms import geometry\n",
     "from ImageD11.sinograms.sinogram import GrainSinogram, build_slice_arrays, write_slice_recon, read_slice_recon, write_h5, read_h5, write_pbp_strain\n",
     "from ImageD11.grain import grain\n",
     "from ImageD11 import cImageD11\n",
@@ -319,7 +318,6 @@
     "# set up a refinement manager object\n",
     "\n",
     "y0 = grainsinos[0].recon_y0\n",
-    "fpks = 0.9\n",
     "hkl_tol_origins = 0.05\n",
     "hkl_tol_refine = 0.1\n",
     "hkl_tol_refine_merged = 0.05\n",
@@ -328,7 +326,7 @@
     "forref = [0, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13]\n",
     "phase_str = minor_phase_str\n",
     "\n",
-    "refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str, forref=forref)"
+    "refine = PBPRefine(dset=ds, y0=y0, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=phase_str, forref=forref)"
    ]
   },
   {
@@ -562,7 +560,7 @@
     "        pmap = tensor_map.to_pbpmap(z_layer=0, default_npks=20, default_nuniq=20)\n",
     "        pmap.choose_best(1)\n",
     "\n",
-    "        refine = PBPRefine(dset=ds, y0=y0, fpks=fpks, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=minor_phase_str, forref=forref)\n",
+    "        refine = PBPRefine(dset=ds, y0=y0, hkl_tol_origins=hkl_tol_origins, hkl_tol_refine=hkl_tol_refine, hkl_tol_refine_merged=hkl_tol_refine_merged, ds_tol=ds_tol, ifrac=ifrac, phase_name=minor_phase_str, forref=forref)\n",
     "        \n",
     "        refine.own_filename = os.path.splitext(refine.own_filename)[0] + f'_{phase_str}.h5'\n",
     "        refine.icolf_filename = os.path.splitext(refine.icolf_filename)[0] + f'_{phase_str}.h5'\n",
diff --git a/ImageD11/nbGui/segmenter_gui.py b/ImageD11/nbGui/segmenter_gui.py
index c5c5a8d..0e8de3b 100644
--- a/ImageD11/nbGui/segmenter_gui.py
+++ b/ImageD11/nbGui/segmenter_gui.py
@@ -1,6 +1,3 @@
-
-
-
 import os
 import numpy as np
 import matplotlib.pyplot as plt
@@ -10,35 +7,37 @@ import h5py
 import ImageD11.sinograms.lima_segmenter
 import ImageD11.sparseframe
 
+
 def guess_ESRF_paths():  # This should be in silx somewhere?
     """ Locates:
     dataroot     holds raw data       in folders dataroot     + {sample}/{sample}_{dataset}
     analysisroot holds output results in folders analysisroot + {sample}/{sample}_{dataset}
     """
     path_items = os.getcwd().split('/')
-    if 'visitor' in path_items:  
+    if 'visitor' in path_items:
         idx = path_items.index('visitor')
-        experiment, session = path_items[ idx + 1 ], path_items[ idx + 3 ]
-        path = os.path.join( "/data", "visitor", experiment, "id11", session )
-        return [os.path.join( path, folder ) for folder in
-                ("RAW_DATA", "PROCESSED_DATA") ]
+        experiment, session = path_items[idx + 1], path_items[idx + 3]
+        path = os.path.join("/data", "visitor", experiment, "id11", session)
+        return [os.path.join(path, folder) for folder in
+                ("RAW_DATA", "PROCESSED_DATA")]
     return "", ""
 
-def printsamples( dataroot ):
-    samples = sorted( [ name for name in os.listdir( dataroot ) 
-             if os.path.isdir( os.path.join( dataroot, name ) ) ] )
-    print("Samples:\n\t"+"\n\t".join(sorted( samples ) ))
-    
-def printdatasets( dataroot, sample):
-    sroot = os.path.join(dataroot, sample)
-    print("Datsets:\n\t"+"\n\t".join(sorted( 
-        [ name[len(sample)+1:] for name in os.listdir( sroot ) 
-         if os.path.isdir( os.path.join( sroot, name ) ) 
-         and name.startswith( sample ) ] ) ) )
 
+def printsamples(dataroot):
+    samples = sorted([name for name in os.listdir(dataroot)
+                      if os.path.isdir(os.path.join(dataroot, name))])
+    print("Samples:\n\t " + "\n\t".join(sorted(samples)))
 
 
-def chooseframe(self, dset, scan=None, idx=None, counter="_roi1", fetch_raw_image=False):
+def printdatasets(dataroot, sample):
+    sroot = os.path.join(dataroot, sample)
+    print("Datsets:\n\t " + "\n\t".join(sorted(
+        [name[len(sample) + 1:] for name in os.listdir(sroot)
+         if os.path.isdir(os.path.join(sroot, name))
+         and name.startswith(sample)])))
+
+
+def chooseframe(dset, scan=None, idx=None, counter="_roi1", fetch_raw_image=False):
     """
     Locate a busy frame from the dataset and optionally fetch the raw image.
 
@@ -78,28 +77,28 @@ def chooseframe(self, dset, scan=None, idx=None, counter="_roi1", fetch_raw_imag
     return scan, idx, raw_image
 
 
-
 class SegmenterGui:
-    
     """ UI for a jupyter notebook to set the segmentation parameters
     From @jadball notebook, @jonwright refactored to put in a python file
     """
-    
-    def __init__(self, dset, counter="_roi1", scan=None, frame=None, cut=1, pixels_in_spot=3, howmany=100000,):
+
+    def __init__(self, dset, counter="_roi1", scan=None, frame=None, cut=1, pixels_in_spot=3, howmany=100000, ):
         self.dset = dset
         self.fig = None
-        self.options = { "maskfile" : dset.maskfile ,
-                        "cut" : cut,
-                        "pixels_in_spot" : pixels_in_spot,
-                        "howmany": howmany }
+        self.options = {"maskfile": dset.maskfile,
+                        "cut": cut,
+                        "pixels_in_spot": pixels_in_spot,
+                        "howmany": howmany}
         self.scan = scan
         self.idx = frame
         self.chooseframe(counter)
         cut_slider = widgets.IntSlider(value=cut, min=0, max=200, step=1, description='Cut:')
-        pixels_in_spot_slider = widgets.IntSlider(value=pixels_in_spot, min=0, max=20, step=1, description='Pixels in Spot:')
+        pixels_in_spot_slider = widgets.IntSlider(value=pixels_in_spot, min=0, max=20, step=1,
+                                                  description='Pixels in Spot:')
         howmany_slider = widgets.IntSlider(value=np.log10(howmany), min=1, max=15, step=1, description='log(howmany):')
-        self.widget = widgets.interactive(self.update_image, cut=cut_slider, pixels_in_spot=pixels_in_spot_slider, howmany=howmany_slider)
-        display( self.widget )
+        self.widget = widgets.interactive(self.update_image, cut=cut_slider, pixels_in_spot=pixels_in_spot_slider,
+                                          howmany=howmany_slider)
+        display(self.widget)
 
     def chooseframe(self, counter):
         # Use the base class method without fetching the raw image
@@ -113,40 +112,43 @@ class SegmenterGui:
         frame_num = which frame in the file
         """
         ds = self.dset
-        opts = ImageD11.sinograms.lima_segmenter.OPTIONS = ImageD11.sinograms.lima_segmenter.SegmenterOptions(**self.options)
+        opts = ImageD11.sinograms.lima_segmenter.OPTIONS = ImageD11.sinograms.lima_segmenter.SegmenterOptions \
+            (**self.options)
         opts.setup()
-        with h5py.File( ds.masterfile, 'r' ) as hin:
+        with h5py.File(ds.masterfile, 'r') as hin:
             frms = hin[self.scan]['measurement'][ds.detector]
-            for i, spf in enumerate( ImageD11.sinograms.lima_segmenter.reader( frms, opts.mask, opts.cut, start = self.idx ) ): 
+            for i, spf in enumerate(
+                    ImageD11.sinograms.lima_segmenter.reader(frms, opts.mask, opts.cut, start=self.idx)):
                 if spf is None:
-                    print("Warning: no pixels found",self.scan,i,self.idx)
-                ref = frms[i+self.idx]
-                break    
+                    print("Warning: no pixels found", self.scan, i, self.idx)
+                ref = frms[i + self.idx]
+                break
         if spf is None:
             spi = np.zeros_like(ref)
             npeaks = 0
         else:
             spi = spf.to_dense('intensity')
-            npeaks = ImageD11.sparseframe.sparse_localmax( spf )
+            npeaks = ImageD11.sparseframe.sparse_localmax(spf)
         if opts.mask is not None:
             ref = ref * opts.mask
-            spi = spi * opts.mask    
+            spi = spi * opts.mask
         return ref, spi, npeaks
 
     def display(self):
         # Display the image initially
         self.fig, self.axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 8), constrained_layout=True)
-        self.im1 = self.axs[0].imshow(self.raw_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
-        self.im2 = self.axs[1].imshow(self.segmented_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
+        self.im1 = self.axs[0].imshow(self.raw_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
+                                      interpolation="nearest")
+        self.im2 = self.axs[1].imshow(self.segmented_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
+                                      interpolation="nearest")
         self.axs[0].set_title("Raw image")
         self.axs[1].set_title("Segmented image")
-        self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d"%
-                          (self.nblobs,self.options['cut'],self.options['pixels_in_spot'],self.options['howmany']))
+        self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d " %
+                          (self.nblobs, self.options['cut'], self.options['pixels_in_spot'], self.options['howmany']))
         # self.fig.show()
 
-
     def update_image(self, cut, pixels_in_spot, howmany):
-        howmany_exp = 10**howmany
+        howmany_exp = 10 ** howmany
         self.options["cut"] = cut
         self.options["pixels_in_spot"] = pixels_in_spot
         self.options["howmany"] = howmany_exp
@@ -155,23 +157,21 @@ class SegmenterGui:
             self.display()
         self.im1.set_data(self.raw_image)
         self.im2.set_data(self.segmented_image)
-        self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d"%
-                          (self.nblobs,cut,pixels_in_spot,howmany))
+        self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d " %
+                          (self.nblobs, cut, pixels_in_spot, howmany))
         self.fig.canvas.draw()
 
-
     def getopts(self):
-        opts = { name: self.options[name] for name in ('cut','pixels_in_spot','howmany') }
-        print("options = ",repr(opts))
+        opts = {name: self.options[name] for name in ('cut', 'pixels_in_spot', 'howmany')}
+        print("options = ", repr(opts))
         return opts
 
 
 class FrelonSegmenterGui:
-
     """ UI for a jupyter notebook to set the segmentation parameters
     From @jadball notebook, @jadball refactored to put in a python file
     """
-    
+
     def __init__(self, dset, worker_func, process_func, counter="_roi1", scan=None, frame=None, **options):
         self.dset = dset
         self.worker_func = worker_func
@@ -181,12 +181,16 @@ class FrelonSegmenterGui:
         self.scan = scan
         self.idx = frame
         self.chooseframe(counter)
-        thresh_slider = widgets.IntSlider(value=options["threshold"], min=0, max=10000, step=1, description='Threshold:')
-        smsig_slider = widgets.FloatSlider(value=options["smoothsigma"], min=0.0, max=3.0, step=0.05, description='Smoothsigma:')
+        thresh_slider = widgets.IntSlider(value=options["threshold"], min=0, max=10000, step=1,
+                                          description='Threshold:')
+        smsig_slider = widgets.FloatSlider(value=options["smoothsigma"], min=0.0, max=3.0, step=0.05,
+                                           description='Smoothsigma:')
         bgc_slider = widgets.FloatSlider(value=options["bgc"], min=0.0, max=1.0, step=0.05, description='bgc:')
         minpx_slider = widgets.IntSlider(value=options["minpx"], min=0, max=50, step=1, description='minpx:')
-        mofft_slider = widgets.IntSlider(value=options["m_offset_thresh"], min=0, max=200, step=1, description='m_offset_thresh:')
-        mratt_slider = widgets.IntSlider(value=options["m_ratio_thresh"], min=0, max=2000, step=1, description='m_ratio_thresh:')
+        mofft_slider = widgets.IntSlider(value=options["m_offset_thresh"], min=0, max=200, step=1,
+                                         description='m_offset_thresh:')
+        mratt_slider = widgets.IntSlider(value=options["m_ratio_thresh"], min=0, max=2000, step=1,
+                                         description='m_ratio_thresh:')
         self.widget = widgets.interactive(self.update_image,
                                           threshold=thresh_slider,
                                           smoothsigma=smsig_slider,
@@ -194,7 +198,7 @@ class FrelonSegmenterGui:
                                           minpx=minpx_slider,
                                           m_offset_thresh=mofft_slider,
                                           m_ratio_thresh=mratt_slider)
-        display( self.widget )
+        display(self.widget)
 
     def chooseframe(self, counter):
         self.scan, self.idx, self.raw_image = chooseframe(self.dset, self.scan, self.idx, counter, fetch_raw_image=True)
@@ -205,41 +209,41 @@ class FrelonSegmenterGui:
         goodpeaks = image_worker.peaksearch(img=self.raw_image, omega=0)
         fc, sc = goodpeaks[:, 23:25].T  # 23 and 24 are the columns for fc and sc from blob properties
         return image_worker, fc, sc, len(fc)
-    
+
     def display(self):
         self.fig, self.axs = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 8), layout="constrained")
         self.im1 = self.axs[0].imshow(self.raw_image, norm='log', vmin=100, vmax=1000)
         self.axs[0].set_title("Original image")
-        self.im2 = self.axs[1].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
+        self.im2 = self.axs[1].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
+                                      interpolation="nearest")
         self.axs[1].set_title("Background corrected")
-        self.im3 = self.axs[2].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
+        self.im3 = self.axs[2].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
+                                      interpolation="nearest")
         self.axs[2].set_title(str(self.npeaks) + " peaks")
         self.sc1, = self.axs[2].plot(self.fc, self.sc, marker='+', c="r", ls="")
         self.axs[2].set_aspect(1)
-    
+
     def update_image(self, threshold, smoothsigma, bgc, minpx, m_offset_thresh, m_ratio_thresh):
-        
         self.options["threshold"] = threshold
         self.options["smoothsigma"] = smoothsigma
         self.options["bgc"] = bgc
         self.options["minpx"] = minpx
         self.options["m_offset_thresh"] = m_offset_thresh
         self.options["m_ratio_thresh"] = m_ratio_thresh
-        
+
         image_worker, self.fc, self.sc, self.npeaks = self.segment_frame()
         self.smoothed_image = image_worker.smoothed
         if self.fig is None:
             self.display()
-        
+
         self.im1.set_data(self.raw_image)
         self.im2.set_data(self.smoothed_image)
         self.im3.set_data(self.smoothed_image)
         self.sc1.set_data(self.fc, self.sc)
         self.fig.canvas.draw()
-    
-        
+
     def getopts(self):
-        opts = { name: self.options[name] for name in ('bgfile','maskfile','threshold','smoothsigma','bgc','minpx','m_offset_thresh','m_ratio_thresh') }
-        print("options = ",repr(opts))
+        opts = {name: self.options[name] for name in
+                ('bgfile', 'maskfile', 'threshold', 'smoothsigma', 'bgc', 'minpx', 'm_offset_thresh', 'm_ratio_thresh')}
+        print("options = ", repr(opts))
         return opts
-    
\ No newline at end of file
diff --git a/ImageD11/sinograms/dataset.py b/ImageD11/sinograms/dataset.py
index c692739..672bcf2 100644
--- a/ImageD11/sinograms/dataset.py
+++ b/ImageD11/sinograms/dataset.py
@@ -398,6 +398,7 @@ class DataSet:
                     ]
                     bad.append(i)
                 # this can be an array or a scalar
+                # read from h5:
                 dty = hin[scan]["instrument/positioners"][self.dtymotor]
                 if len(dty.shape) == 0:
                     self.dty[i] = np.full(self.frames_per_scan[i], dty[()])
diff --git a/ImageD11/sinograms/geometry.py b/ImageD11/sinograms/geometry.py
index 71ddff2..ba984eb 100644
--- a/ImageD11/sinograms/geometry.py
+++ b/ImageD11/sinograms/geometry.py
@@ -1,13 +1,27 @@
 """Sinogram-related geometric functions pulled from various places.
-In this file there are four reference frames:
-
-1. The lab frame that we all know and love
-
-2. The sample frame - this has the rotation axis at its origin, but could be translated by the dty motor
-It could also be rotated by an angle omega, CCW about the rotation axis (looking top down)
-
-3. Step space:
-   step from the rotation axis center,  coords are (si, sj)
+The choice of reference frames is tricky.
+In regular 3DXRD, the origin of the lab reference frame is defined as the intersection between the middle of the beam
+and the rotation axis.
+However, because the rotation axis is moving, this gets more difficult for scanning 3DXRD.
+I have therefore defined the reference frames in the following way:
+
+1. Static lab frame - vectors defined by the beam vector and the dty translation axis vector.
+   Its origin is defined directly as (0,0,0):
+   x: The intersection of the beam vector and the dty translation axis vector
+   y: The centre of the beam horizontally
+   z: The centre of the beam vertically
+   dtyi is a discretisation of the dty motor values.
+
+2. Sample frame
+   Origin is the rotation axis
+   The rotation axis is translated by dty.
+   The sample frame rotates with omega
+   This translates with dty and rotates with omega, CW about the rotation axis (looking top down).
+   This has the rotation axis at its origin.
+   y0 is the true value of dty when the rotation axis intersects the beam.
+
+3. Step space
+   This is simply an integer discretisation of the sample frame.
 
 
 4. Reconstruction space:
@@ -22,11 +36,11 @@ lab frame:
          |
          | x
          |
-<------- O (0, 0)
+<------- O (0, 0) centre of beam
     y
 
 
-sample frame (could be rotated about omega then translated along lab y):
+sample frame (could be rotated about omega then translated along lab y by dty):
 
          ^
          |
@@ -61,6 +75,7 @@ Reconstruction space (iradon output when plotted with origin="lower"):
 
 
 """
+from functools import partial
 
 import numpy as np
 from scipy.optimize import curve_fit
@@ -68,47 +83,85 @@ from skimage.feature import blob_log
 
 
 def sample_to_lab_sincos(sx, sy, y0, dty, sinomega, cosomega):
-    """Same as sample_to_lab but using sinomega and cosomega"""
+    """
+    Converts position in sample frame (sx, sy) to position in lab frame (lx, ly).
+    The units of sx, sy, y0, and dty must agree.
+
+    :param sx: X-coordinate in sample reference frame
+    :type sx: (float, np.ndarray)
+    :param sy: Y-coordinate in sample reference frame
+    :type sy: (float, np.ndarray)
+    :param y0: the true value of dty when the rotation axis intersects the beam
+    :type y0: float
+    :param dty: the dty motor underneath the rotation axis
+    :type dty: (float, np.ndarray)
+    :param sinomega: the sine of the omega value of the rotation axis
+    :type sinomega: (float, np.ndarray)
+    :param cosomega: the cosine of the omega value of the rotation axis
+    :type cosomega: (float, np.ndarray)
+    :return: lx, ly: the x and y coordinates in the lab reference frame
+    :rtype: ((float, np.ndarray), (float, np.ndarray))
+    """
+    # first, rotate sx and sy by omega about its origin (rotation axis)
     sxr = sx * cosomega - sy * sinomega
     syr = sx * sinomega + sy * cosomega
-    
+
+    # then translate about the rotated frame
     lx = sxr
-    ly = syr - y0 + dty
-    
+    ly = syr + dty - y0
+
     return lx, ly
 
 
 def sample_to_lab(sx, sy, y0, dty, omega):
-    """Converts sample (sx, sy) position to the lab frame (lx, ly)
-    The sample reference frame could be rotated by an angle omega (degrees) CCW about the rotation axis
-    The rotation axis is defined as the origin of the sample reference frame
-    The sample reference frame could also be translated by dty along ly after rotation"""
+    """Calls sample_to_lab_sincos after converting omega (degrees) into sinomega, cosomega"""
 
-    # first, rotate sx and sy by omega about its origin (rotation axis)
     omega_rad = np.radians(omega)
-    sxr = sx * np.cos(omega_rad) - sy * np.sin(omega_rad)
-    syr = sx * np.sin(omega_rad) + sy * np.cos(omega_rad)
-
-    # then translate about the rotated frame
-
-    lx = sxr
-    ly = syr - y0 + dty
+    sinomega = np.sin(omega_rad)
+    cosomega = np.cos(omega_rad)
+    lx, ly = sample_to_lab_sincos(sx, sy, y0, dty, sinomega, cosomega)
 
     return lx, ly
 
 
-def lab_to_sample(lx, ly, y0, dty, omega):
-    """Converts lab (lx, ly) fixed reference frame position
-    to a possibly rotated and translated sample frame (sx, sy)"""
+def lab_to_sample_sincos(lx, ly, y0, dty, sinomega, cosomega):
+    """
+    Converts position in lab frame (lx, ly) to position in lab frame (sx, sy).
+    The units of sx, sy, y0, and dty must agree.
+
+    :param lx: X-coordinate in lab reference frame
+    :type lx: (float, np.ndarray)
+    :param ly: Y-coordinate in lab reference frame
+    :type ly: (float, np.ndarray)
+    :param y0: the true value of dty when the rotation axis intersects the beam
+    :type y0: float
+    :param dty: the dty motor underneath the rotation axis
+    :type dty: (float, np.ndarray)
+    :param sinomega: the sine of the omega value of the rotation axis
+    :type sinomega: (float, np.ndarray)
+    :param cosomega: the cosine of the omega value of the rotation axis
+    :type cosomega: (float, np.ndarray)
+    :return: sx, su: the x and y coordinates in the sample reference frame
+    :rtype: ((float, np.ndarray), (float, np.ndarray))
+    """
     # first, translate
-
     sxr = lx
-    syr = ly + y0 - dty
+    syr = ly - dty + y0
+
+    # then unrotate
+    sx = sxr * cosomega + syr * sinomega
+    sy = -sxr * sinomega + syr * cosomega
+
+    return sx, sy
+
+
+def lab_to_sample(lx, ly, y0, dty, omega):
+    """Calls lab_to_sample_sincos after converting omega (degrees) into sinomega, cosomega"""
 
-    # then rotate
     omega_rad = np.radians(omega)
-    sx = sxr * np.cos(-omega_rad) - syr * np.sin(-omega_rad)
-    sy = sxr * np.sin(-omega_rad) + syr * np.cos(-omega_rad)
+    sinomega = np.sin(omega_rad)
+    cosomega = np.cos(omega_rad)
+    sx, sy = lab_to_sample_sincos(lx, ly, y0, dty, sinomega, cosomega)
 
     return sx, sy
 
@@ -141,64 +194,91 @@ def recon_to_step(ri, rj, recon_shape):
     return si, sj
 
 
-def sample_to_recon(x, y, recon_shape, ystep):
-    """Converts sample space (x, y) to reconstruction space (ri, rj)"""
-    si, sj = sample_to_step(x, y, ystep)
+def sample_to_recon(sx, sy, recon_shape, ystep):
+    """Converts sample space (sx, sy) to reconstruction space (ri, rj)"""
+    si, sj = sample_to_step(sx, sy, ystep)
     ri, rj = step_to_recon(si, sj, recon_shape)
     return ri, rj
 
 
 def recon_to_sample(ri, rj, recon_shape, ystep):
-    """Converts reconstruction space (ri, rj) to sample space (x, y)"""
+    """Converts reconstruction space (ri, rj) to sample space (sx, sy)"""
     si, sj = recon_to_step(ri, rj, recon_shape)
-    x, y = step_to_sample(si, sj, ystep)
-    return x, y
+    sx, sy = step_to_sample(si, sj, ystep)
+    return sx, sy
+
 
+def lab_to_step(lx, ly, y0, dty, omega, ystep):
+    """Converts lab space (lx, ly) to step space (si, sj)"""
+    sx, sy = lab_to_sample(lx, ly, y0, dty, omega)
+    si, sj = sample_to_step(sx, sy, ystep)
+    return si, sj
 
-def dtycalc_sincos(sinomega, cosomega, x, y, y0):
-    """This is the dty position where the grain should be in the middle of the beam
-    For a given omega value
-    A grain on the centre of rotation has a = b = 0
-    To get the grain into the beam, dty should equal y0
-    So the offset equals y0.
-    Phased sine function with vertical offset using linear combination of sin and cos
-    A grain at (0, +y), you need to move dty negative to compensate
-    At omega = 0, cos(0) = 1
-    So cosine term is multiplied by -y
-    A grain at (+x, 0) when omega = +90, sin(90) = 1
-    So b goes to -x
 
-    This version avoids recomputing sin,cos(radians(x)) many times
-    """
-    return y0 - x * sinomega - y * cosomega
+def step_to_lab(si, sj, y0, dty, omega, ystep):
+    """Converts step space (si, sj) to lab space (lx, ly)"""
+    sx, sy = step_to_sample(si, sj, ystep)
+    lx, ly = sample_to_lab(sx, sy, y0, dty, omega)
+    return lx, ly
+
+
+def lab_to_recon(lx, ly, y0, dty, omega, recon_shape, ystep):
+    """Converts lab space (lx, ly) to recon space (ri, rj)"""
+    si, sj = lab_to_step(lx, ly, y0, dty, omega, ystep)
+    ri, rj = step_to_recon(si, sj, recon_shape)
+    return ri, rj
+
+
+def recon_to_lab(ri, rj, y0, dty, omega, recon_shape, ystep):
+    """Converts recon space (ri, rj) to lab space (lx, ly)"""
+    si, sj = recon_to_step(ri, rj, recon_shape)
+    lx, ly = step_to_lab(si, sj, y0, dty, omega, ystep)
+    return lx, ly
 
 
-def x_y_y0_omega_to_dty(omega, x, y, y0):
-    """This is the dty position where the grain should be in the middle of the beam
-    For a given omega value
-    A grain on the centre of rotation has a = b = 0
-    To get the grain into the beam, dty should equal y0
-    So the offset equals y0.
-    Phased sine function with vertical offset using linear combination of sin and cos
-    A grain at (0, +y), you need to move dty negative to compensate
-    At omega = 0, cos(0) = 1
-    So cosine term is multiplied by -y
-    A grain at (+x, 0) when omega = +90, sin(90) = 1
-    So b goes to -x"""
-    s = np.sin(np.radians(omega))
-    c = np.cos(np.radians(omega))
-    # y0 - x * np.sin(np.radians(omega)) - y * np.cos(np.radians(omega))
-    return dtycalc_sincos(s, c, x, y, y0)
+def dty_values_grain_in_beam_sincos(sx, sy, y0, sinomega, cosomega):
+    """
+    Take a grain positioned at (sx, sy) in the sample reference frame
+    Determine the dty values needed to bring the grain into the beam as you rotate by omega.
+    Equivalently, solve the sample<->lab conversion where ly = 0 (grain is in-beam).
+    From sample_to_lab_sincos:
+
+    sxr = sx * cosomega - sy * sinomega
+    syr = sx * sinomega + sy * cosomega
+    
+    lx = sxr
+    ly = syr + dty - y0
+    
+    Therefore:
+    ly = sx * sinomega + sy * cosomega + dty - y0
+    
+    Solving for ly = 0:
+    0 = sx * sinomega + sy * cosomega + dty - y0
+    dty = y0 - sx * sinomega - sy * cosomega
+    """
+    dty = y0 - sx * sinomega - sy * cosomega
+    return dty
 
 
-dtycalc = x_y_y0_omega_to_dty
+def dty_values_grain_in_beam(sx, sy, y0, omega):
+    """Calls dty_values_grain_in_beam_sincos after converting omega (degrees) into sinomega, cosomega"""
+    omega_rad = np.radians(omega)
+    sinomega = np.sin(omega_rad)
+    cosomega = np.cos(omega_rad)
+    return dty_values_grain_in_beam_sincos(sx, sy, y0, sinomega, cosomega)
 
 
 def fit_sine_wave(omega, dty, initial_guess, weights=None):
-    """Fits a sine wave to omega and dty data
-    Returns directly x, y, y0 in sample frame"""
+    """
+    Take a series of (omega, dty) values that originate from the same point in the sample.
+    Determine the point in sample coordinates (sx, sy) by fitting a sine wave to the data.
+    Includes a possible shift of the rotation axis in y (y0).
+    In practice, we perform a curve_fit on dty_values_grain_in_beam to determine sx, sy, y0
+    """
+    partial_function = partial(lambda _om, _sx, _sy, _y0: dty_values_grain_in_beam(_sx, _sy, _y0, _om))
+
     popt, _ = curve_fit(
-        x_y_y0_omega_to_dty,
+        partial_function,
         omega,
         dty,
         p0=initial_guess,
@@ -208,135 +288,220 @@ def fit_sine_wave(omega, dty, initial_guess, weights=None):
         sigma=weights,
     )
 
-    x, y, y0 = popt
+    sx_out, sy_out, y0_out = popt
 
-    return x, y, y0
+    return sx_out, sy_out, y0_out
 
 
-def dty_omega_to_x_y_y0(dty, omega, weights=None):
-    """Fits sine wave to dty vs omega plot, extracts x, y, y0"""
+def sx_sy_y0_from_dty_omega(dty, omega, weights=None):
+    """Fits sine wave to dty vs omega plot, extracts sx, sy, y0"""
     initial_guess = (0.5, 0.5, 0)
 
-    x, y, y0 = fit_sine_wave(omega, dty, initial_guess, weights=weights)
-
-    return x, y, y0
+    sx, sy, y0 = fit_sine_wave(omega, dty, initial_guess, weights=weights)
 
-
-def dty_to_dtyi(dty, ystep):
-    """Converts dty value (lab frame) to step space, then rounds to integer
-    Note that this will invert the sign of the values"""
-    _, dty_step = sample_to_step(0, dty, ystep)
-    dtyi = np.round(dty_step).astype(int)
-    return dtyi
+    return sx, sy, y0
 
 
-def dtyi_to_dty(dtyi, ystep):
-    """Converts dtyi value (step space) to dty value (lab frame)"""
-    _, sy = step_to_sample(0, dtyi, ystep)
-    return sy
-
+def dty_to_dtyi(dty, ystep, ymin):
+    """
+    We take continuous dty space and discretise it
+    We do this by counting the number of ysteps away from ymin
+    ymin should probably be ds.ymin
+    This should be equivalent to determining the closest bin in ds.ybincens
+    But this is neater as it accounts for values outside the ds.ybincens range
 
-def dty_to_dtyi_for_sinogram(dty, ystep, ymin):
-    """Converts dty value (lab frame) to something like scan number"""
+    dtyi = (dty - ymin) / ystep
+    """
     dtyi = np.round((dty - ymin) / ystep).astype(int)
     return dtyi
 
 
+def dtyi_to_dty(dtyi, ystep, ymin):
+    """
+    Simply:
+    dty = (dtyi * ystep) + ymin
+    """
+    dty = dtyi * ystep + ymin
+    return dty
+
+
 def step_omega_to_dty(si, sj, omega, y0, ystep):
-    """Convert step value (si, sj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega"""
-    x, y = step_to_sample(si, sj, ystep)
-    dty = x_y_y0_omega_to_dty(omega, x, y, y0)
+    """
+    Convert step space (si, sj) to sample space (sx, sy)
+    Then get corresponding dty values which puts (sx, sy) into the beam given omega
+    """
+    sx, sy = step_to_sample(si, sj, ystep)
+    dty = dty_values_grain_in_beam(sx, sy, y0, omega)
     return dty
 
 
-def step_omega_to_dtyi(si, sj, omega, y0, ystep):
-    """Convert step value (si, sj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega
-    Compute dtyi using dty_to_dtyi then return"""
-    x, y = step_to_sample(si, sj, ystep)
-    dty = x_y_y0_omega_to_dty(omega, x, y, y0)
-    dtyi = dty_to_dtyi(dty, ystep)
+def step_omega_to_dtyi(si, sj, omega, y0, ystep, ymin):
+    """
+    Convert step space (si, sj) to sample space (sx, sy)
+    Then get corresponding dty values which puts (sx, sy) into the beam given omega
+    Then converts dty to dtyi
+    """
+    sx, sy = step_to_sample(si, sj, ystep)
+    dty = dty_values_grain_in_beam(sx, sy, y0, omega)
+    dtyi = dty_to_dtyi(dty, ystep, ymin)
     return dtyi
 
 
 def recon_omega_to_dty(ri, rj, omega, y0, recon_shape, ystep):
-    """Convert recon value (ri, rj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega"""
-    x, y = recon_to_sample(ri, rj, recon_shape, ystep)
-    dty = x_y_y0_omega_to_dty(omega, x, y, y0)
+    """
+    Convert recon space (ri, rj) to step space (si, sj)
+    Then get corresponding dty values which puts (si, sj) into the beam given omega
+    """
+    sx, sy = recon_to_sample(ri, rj, recon_shape, ystep)
+    dty = dty_values_grain_in_beam(sx, sy, y0, omega)
     return dty
 
 
-def recon_omega_to_dtyi(ri, rj, omega, y0, recon_shape, ystep):
-    """Convert recon value (ri, rj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega
-    Compute dtyi using dty_to_dtyi then return"""
-    x, y = recon_to_sample(ri, rj, recon_shape, ystep)
-    dty = x_y_y0_omega_to_dty(omega, x, y, y0)
-    dtyi = dty_to_dtyi(dty, ystep)
+def recon_omega_to_dtyi(ri, rj, omega, y0, recon_shape, ystep, ymin):
+    """
+    Convert recon space (ri, rj) to step space (si, sj)
+    Then get corresponding dtyi values which puts (si, sj) into the beam given omega
+    Then converts dty to dtyi
+    """
+    sx, sy = recon_to_sample(ri, rj, recon_shape, ystep)
+    dty = dty_values_grain_in_beam(sx, sy, y0, omega)
+    dtyi = dty_to_dtyi(dty, ystep, ymin)
     return dtyi
 
 
-def dtyimask_from_step(si, sj, omega, dtyi, y0, ystep):
-    """Convert step value (si, sj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega
-    Compute dtyi_calc using dty_to_dtyi
-    Compare dtyi_calc to supplied dtyi and produce a mask"""
-    dtyi_calc = step_omega_to_dtyi(si, sj, omega, y0, ystep)
-    return dtyi == dtyi_calc
-
-
-def dtyimask_from_sincos(si, sj, sinomega, cosomega, dtyi, y0, ystep):
-    """Convert step value (si, sj) to (x, y)
-    Determine corresponding dty values from (x, y) given omega
-    Compute dtyi_calc using dty_to_dtyi
-    Compare dtyi_calc to supplied dtyi and produce a mask"""
-    # Old code
-    # dtyi_calc = step_omega_to_dtyi(si, sj, omega, y0, ystep)
-    #  ->      x, y = step_to_sample(si, sj, ystep)
-    #  -->         x = si * ystep
-    #  -->         y = -sj * ystep    #
-    #  ->      dty = x_y_y0_omega_to_dty(omega, x, y, y0)
-    #  -->         dty = y0 - x * sinomega - y * cosomega
-    #  ->      dtyi = dty_to_dtyi(dty, ystep)
-    #  -->          _, dty_step = sample_to_step(0, dty, ystep)
-    #  -->          si = x / ystep
-    #  -->           sj = -y / ystep
-    #  --> dtyi = np.round(dty_step).astype(int)
-
-    x, y = step_to_sample(si, sj, ystep)  # flips sj sign (y = -sj * ystep)
-    dty = dtycalc_sincos(sinomega, cosomega, x, y, y0)  # computes dty
-    dtyi_calc = dty_to_dtyi(dty, ystep)  # flips sj sign again (sj = -y / ystep)
-    return dtyi == dtyi_calc
-
-
-def dtyimask_from_recon(ri, rj, omega, dtyi, y0, ystep, recon_shape):
-    """Convert recon value (ri, rj) to (x, y)
-    Determine ccorresponding dty values from (x, y) given omega
-    Compute dtyi_calc using dty_to_dtyi
-    Compare dtyi_calc to supplied dtyi and produce a mask"""
-    dtyi_calc = recon_omega_to_dtyi(ri, rj, omega, y0, recon_shape, ystep)
-    return dtyi == dtyi_calc
+def dtyimask_from_sample(sx, sy, omega, dtyi, y0, ystep, ymin):
+    """
+    Given a position in the sample (sx, sy) and arrays of (omega, dtyi) values
+    Mask each (omega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    dty_calc = dty_values_grain_in_beam(sx, sy, y0, omega)
+    dtyi_calc = dty_to_dtyi(dty_calc, ystep, ymin)
+    mask = dtyi_calc == dtyi
+    return mask
+
+
+def dtyimask_from_sample_sincos(sx, sy, sinomega, cosomega, dtyi, y0, ystep, ymin):
+    """
+    Given a position in the sample (sx, sy) and arrays of (sinomega, cosomega, dtyi) values
+    Mask each (sinomega, cosomega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    dty_calc = dty_values_grain_in_beam_sincos(sx, sy, y0, sinomega, cosomega)
+    dtyi_calc = dty_to_dtyi(dty_calc, ystep, ymin)
+    mask = dtyi_calc == dtyi
+    return mask
+
+
+def dtyimask_from_step(si, sj, omega, dtyi, y0, ystep, ymin):
+    """
+    Given a position in step space (si, sj) and arrays of (omega, dtyi) values
+    Convert step space (si, sj) to sample space (sx, sy)
+    Mask each (omega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    sx, sy = step_to_sample(si, sj, ystep)
+    return dtyimask_from_sample(sx, sy, omega, dtyi, y0, ystep, ymin)
+
+
+def dtyimask_from_step_sincos(si, sj, sinomega, cosomega, dtyi, y0, ystep, ymin):
+    """
+    Given a position in step space (si, sj) and arrays of (sinomega, cosomega, dtyi) values
+    Convert step space (si, sj) to sample space (sx, sy)
+    Mask each (sinomega, cosomega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    sx, sy = step_to_sample(si, sj, ystep)
+    return dtyimask_from_sample_sincos(sx, sy, sinomega, cosomega, dtyi, y0, ystep, ymin)
+
+
+def dtyimask_from_recon(ri, rj, omega, dtyi, y0, ystep, ymin, recon_shape):
+    """
+    Given a position in recon space (ri, rj) and arrays of (omega, dtyi) values
+    Convert recon space (ri, rj) to sample space (sx, sy)
+    Mask each (omega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    sx, sy = recon_to_sample(ri, rj, recon_shape, ystep)
+    return dtyimask_from_sample(sx, sy, omega, dtyi, y0, ystep, ymin)
+
+
+def dtyimask_from_recon_sincos(ri, rj, sinomega, cosomega, dtyi, y0, ystep, ymin, recon_shape):
+    """
+    Given a position in recon space (ri, rj) and arrays of (sinomega, cosomega, dtyi) values
+    Convert recon space (ri, rj) to sample space (sx, sy)
+    Mask each (sinomega, cosomega, dtyi) value if they put the position (sx, sy) into the beam
+    """
+    sx, sy = recon_to_sample(ri, rj, recon_shape, ystep)
+    return dtyimask_from_sample_sincos(sx, sy, sinomega, cosomega, dtyi, y0, ystep, ymin)
 
 
 def fit_sample_position_from_recon(recon, ystep):
-    """Fits grain position by doing a LoG search with skimage on the reconstruction image
+    """
+    Fits grain position in sample space by doing a LoG search with skimage on the reconstruction image
     Useful if grain is very small (close to pointlike)
     Returns position in sample frame (unit matches ystep)
-    Returns None if we couldn't find any blobs"""
+    Returns None if we couldn't find any blobs
+    """
     blobs = blob_log(recon, min_sigma=1, max_sigma=10, num_sigma=10, threshold=0.01)
-    blobs_sorted = sorted(blobs, key=lambda x: x[2], reverse=True)
+    blobs_sorted = sorted(blobs, key=lambda x: x[2], reverse=False)
     try:
         largest_blob = blobs_sorted[0]
 
         ri, rj, sigma = largest_blob
         # we now have the blob position in recon space
         # we need to go back to sample space
-        x, y = recon_to_sample(ri, rj, recon.shape, ystep)
+        sx, sy = recon_to_sample(ri, rj, recon.shape, ystep)
 
-        return x, y
+        return sx, sy
     except IndexError:
         # didn't find any blobs
         # if we didn't find a blob, normally indicates recon is bad
         return None
+
+
+def sino_shift_and_pad(y0, ny, ymin, ystep):
+    """
+    Determine the difference in sinogram pixels between
+    the central dtyi value of the sinogram and the dtyi value of the rotation axis (from y0)
+    To do this, we convert y0 into dtyi without rounding
+    Also determine the minimum pad to get the whole sample in the frame
+    Which should be double the shift
+    """
+    # get the middle row of the sinogram in pixel space
+    ymid_px = ny/2
+    # get the value of y0 in pixel space
+    # this is the same as dty_to_dtyi without rounding
+    y0_px = (y0 - ymin)/ystep
+    shift = ymid_px - y0_px
+    pad = np.ceil(np.abs(shift) * 2).astype(int) + 1
+    return shift, pad
+
+
+def step_grid_from_ybincens(ybincens, step_size, gridstep, y0):
+    """
+    Produce a grid in (si, sj) step space to reconstruct on.
+    Constructs a symmetric grid with the origin on y0
+    Therefore the step space is truly aligned to the rotation axis.
+    The size of the grid is determined by the extremes of ybincens.
+    The grid will always be symmetric such that (0, 0) is the centre of an image formed from the grid
+    :param ybincens: An evenly sampled dty space
+    :type ybincens: np.ndarray
+    :param step_size: the step size of the grid (same units as ybincens and y0)
+    :type step_size: float
+    :param gridstep: the sampling resolution of the grid (e.g 2 = [0, 2, 4, 6, ...])
+    :type gridstep: int
+    :return: (si, sj) for si in ints for sj in ints
+    :rtype: tuple(int, int)
+    """
+    # centre y range on y0
+    y_relative = ybincens - y0
+    # find minimum and maximum values of y
+    y_min = y_relative.min()
+    y_max = y_relative.max()
+    # work out which is larger, so our grid is always symmetric
+    # i.e never go from (-65, 65) to (100, 100)
+    # always go from (-100, -100) to (100, 100)
+    y_largest = np.max(np.abs([y_min, y_max]))
+    y_min = -y_largest
+    y_max = y_largest
+    y_min_int = np.floor(y_min/step_size).astype(int)
+    y_max_int = np.ceil(y_max/step_size).astype(int)
+    ints = range(y_min_int, y_max_int + 1, gridstep)
+    step_points = [(si, sj) for si in ints for sj in ints]
+    return step_points
diff --git a/ImageD11/sinograms/point_by_point.py b/ImageD11/sinograms/point_by_point.py
index d8ddb3f..4d60659 100644
--- a/ImageD11/sinograms/point_by_point.py
+++ b/ImageD11/sinograms/point_by_point.py
@@ -40,8 +40,7 @@ from ImageD11 import sym_u, unitcell, parameters
 import ImageD11.sinograms.dataset
 import ImageD11.indexing
 from ImageD11.columnfile import columnfile
-from ImageD11.sinograms.geometry import dty_to_dtyi, dtyimask_from_sincos, step_to_sample, sample_to_lab_sincos, \
-    dtyi_to_dty, step_to_recon
+from ImageD11.sinograms import geometry
 from ImageD11.sinograms.roi_iradon import run_iradon
 from ImageD11.sinograms.tensor_map import unitcell_to_b
 from ImageD11.sinograms.sinogram import save_array
@@ -97,6 +96,44 @@ def detector_rotation_matrix(tilt_x, tilt_y, tilt_z):
     return r2r1
 
 
+@numba.njit(cache = True)
+def compute_grain_origins(omega, wedge, chi, t_x, t_y, t_z):
+    w = np.radians(wedge)
+    WI = np.array([[np.cos(w),         0, -np.sin(w)],
+                   [0,           1.0,         0],
+                   [np.sin(w),         0,  np.cos(w)]], np.float64)
+    c = np.radians(chi)
+    CI = np.array([[1,            0.0,         0],
+                   [0,     np.cos(c), -np.sin(c)],
+                   [0,     np.sin(c),  np.cos(c)]], np.float64)
+    t = np.zeros((3, omega.shape[0]), np.float64)  # crystal translations
+    # Rotations in reverse order compared to making g-vector
+    # also reverse directions. this is trans at all zero to
+    # current setting. gv is scattering vector to all zero
+    om_r = np.radians(omega)
+    # This is the real rotation (right handed, g back to k)
+    t[0, :] = np.cos(om_r) * t_x - np.sin(om_r) * t_y
+    t[1, :] = np.sin(om_r) * t_x + np.cos(om_r) * t_y
+    t[2, :] = t_z
+    if chi != 0.0:
+        c = np.cos(np.radians(chi))
+        s = np.sin(np.radians(chi))
+        u = np.zeros(t.shape, np.float64)
+        u[0, :] = t[0, :]
+        u[1, :] = c * t[1, :] + -s * t[2, :]
+        u[2, :] = s * t[1, :] + c * t[2, :]
+        t = u
+    if wedge != 0.0:
+        c = np.cos(np.radians(wedge))
+        s = np.sin(np.radians(wedge))
+        u = np.zeros(t.shape, np.float64)
+        u[0, :] = c * t[0, :] + -s * t[2, :]
+        u[1, :] = t[1, :]
+        u[2, :] = s * t[0, :] + c * t[2, :]
+        t = u
+    return t
+
+
 @numba.njit(cache = True)
 def compute_xyz_lab(sc, fc,
                     y_center=0., y_size=0., tilt_y=0.,
@@ -133,12 +170,12 @@ def compute_xyz_lab(sc, fc,
 
 
 @numba.njit(cache=True)
-def compute_tth_eta_from_xyz(peaks_xyz,
+def compute_tth_eta_from_xyz(peaks_xyz, omega,
                              t_x=0.0, t_y=0.0, t_z=0.0,
                              wedge=0.0,  # Wedge == theta on 4circ
                              chi=0.0):  # last line is for laziness -
 
-    s1 = peaks_xyz
+    s1 = peaks_xyz - compute_grain_origins(omega, wedge, chi, t_x, t_y, t_z)
 
     # CHANGED to HFP convention 4-9-2007
     eta = np.degrees(np.arctan2(-s1[1, :], s1[2, :]))
@@ -148,7 +185,7 @@ def compute_tth_eta_from_xyz(peaks_xyz,
 
 
 @numba.njit(cache=True)
-def compute_tth_eta(sc, fc,
+def compute_tth_eta(sc, fc, omega,
                     y_center=0., y_size=0., tilt_y=0.,
                     z_center=0., z_size=0., tilt_z=0.,
                     tilt_x=0.,
@@ -167,7 +204,7 @@ def compute_tth_eta(sc, fc,
         o11=o11, o12=o12, o21=o21, o22=o22)
 
     tth, eta = compute_tth_eta_from_xyz(
-        peaks_xyz,
+        peaks_xyz, omega,
         t_x=t_x, t_y=t_y, t_z=t_z,
         wedge=wedge,
         chi=chi)
@@ -322,7 +359,7 @@ def merge(hkl, etasign, dtyi, sum_intensity, sc, fc, omega, dty, xpos_refined, e
 def get_voxel_idx(y0, xi0, yi0, sinomega, cosomega, dty, ystep):
     """
     get peaks at xi0, yi0
-    basically just geometry.dtycalc_sincos
+    basically just geometry.dty_values_grain_in_beam_sincos
     """
 
     ydist = np.abs(y0 - xi0 * sinomega - yi0 * cosomega - dty)
@@ -338,7 +375,7 @@ def compute_gve(sc, fc, omega, xpos,
                 t_x, t_y, t_z, wedge, chi, wavelength):
     this_distance = distance - xpos
 
-    tth, eta = compute_tth_eta(sc, fc,
+    tth, eta = compute_tth_eta(sc, fc, omega,
                                y_center=y_center,
                                y_size=y_size,
                                tilt_y=tilt_y,
@@ -622,7 +659,6 @@ class PBPRefine:
                  hkl_tol_origins=0.05,
                  hkl_tol_refine=0.1,
                  hkl_tol_refine_merged=0.05,
-                 fpks=0.7,
                  ds_tol=0.005,
                  etacut=0.1,
                  ifrac=None,
@@ -633,7 +669,6 @@ class PBPRefine:
         self.dset = dset
         self.phase_name = phase_name
         # peak selection parameters
-        self.fpks = fpks
         self.forref = forref
         self.ds_tol = ds_tol
 
@@ -652,6 +687,7 @@ class PBPRefine:
         # geometry stuff
         self.ystep = self.dset.ystep
         self.y0 = y0
+        self.ybincens = self.dset.ybincens
 
         # set default paths
         self.pbpmap_filename = self.dset.refmapfile  # pbp map input
@@ -673,7 +709,7 @@ class PBPRefine:
         sj = np.arange(self.pbpmap.j.min(), self.pbpmap.j.max() + 1)
 
         # convert these arrays to sample space
-        sx, sy = step_to_sample(si, sj, self.ystep)
+        sx, sy = geometry.step_to_sample(si, sj, self.ystep)
 
         # make a grid of these
         self.sx_grid, self.sy_grid = np.meshgrid(sx, sy, indexing='ij')
@@ -774,7 +810,7 @@ class PBPRefine:
         isel = isel & ((np.abs(np.sin(np.radians(colf.eta)))) > self.etacut)
 
         colf.addcolumn(isel, "isel")  # peaks selected for refinement
-        dtyi = dty_to_dtyi(colf.dty, self.ystep)
+        dtyi = geometry.dty_to_dtyi(colf.dty, self.ystep, self.ybincens.min())
         colf.addcolumn(dtyi, "dtyi")
 
         # cache these to speed up selections later
@@ -786,10 +822,6 @@ class PBPRefine:
         self.icolf = colf.copy()
         self.icolf.filter(sel)
         self.npks = npks
-        if self.fpks < 1:
-            self.minpks = int(npks * self.fpks)
-        else:
-            self.minpks = self.fpks
         print(
             "Using for refinement:",
             self.icolf.nrows,
@@ -858,7 +890,7 @@ class PBPRefine:
             omega = self.colf.omega
         whole_sample_sino, xedges, yedges = np.histogram2d(dty, omega,
                                                            bins=[self.dset.ybinedges, self.dset.obinedges])
-        shift = -self.y0 / self.ystep
+        shift, _ = geometry.sino_shift_and_pad(self.y0, len(self.ybincens), self.ybincens.min(), self.ystep)
         nthreads = len(os.sched_getaffinity(os.getpid()))
         # make sure the shape is the same as sx_grid
         pad = self.sx_grid.shape[0] - whole_sample_sino.shape[0]
@@ -962,7 +994,7 @@ class PBPRefine:
                     continue
 
             # other pars we need for refinement
-            pars = ['phase_name', 'hkl_tol_origins', 'hkl_tol_refine', 'hkl_tol_refine_merged', 'fpks', 'ds_tol',
+            pars = ['phase_name', 'hkl_tol_origins', 'hkl_tol_refine', 'hkl_tol_refine_merged', 'ds_tol',
                     'etacut', 'ifrac', 'forref', 'y0', 'min_grain_npks']
 
             for par in pars:
@@ -999,7 +1031,7 @@ class PBPRefine:
                 except (AttributeError, KeyError):
                     continue
 
-            pars = ['phase_name', 'hkl_tol_origins', 'hkl_tol_refine', 'hkl_tol_refine_merged', 'fpks', 'ds_tol',
+            pars = ['phase_name', 'hkl_tol_origins', 'hkl_tol_refine', 'hkl_tol_refine_merged', 'ds_tol',
                     'etacut', 'ifrac', 'forref', 'y0', 'min_grain_npks']
             pars_dict = {}
             for par in pars:
@@ -1123,7 +1155,7 @@ class PBPRefine:
             # get columns for refine.pbpmap
             # which contain the ri, rj points
             # right now it's indexed in step space
-            ri_col, rj_col = step_to_recon(self.pbpmap.i, self.pbpmap.j, self.mask.shape)
+            ri_col, rj_col = geometry.step_to_recon(self.pbpmap.i, self.pbpmap.j, self.mask.shape)
 
             if points_step_space is None:
                 # get the list of peak indices masks in reconstruction space
@@ -1133,7 +1165,7 @@ class PBPRefine:
                     # print(points_recon_space)
             else:
                 # convert input points to reconstruction space, then pass to the function
-                points_recon_space = [step_to_recon(si, sj, self.mask.shape) for (si, sj) in points_step_space]
+                points_recon_space = [geometry.step_to_recon(si, sj, self.mask.shape) for (si, sj) in points_step_space]
 
             # columnfile by [3, 3, (ri, rj)]
             all_pbpmap_ubis = self.pbpmap.ubi
@@ -1326,6 +1358,10 @@ def compute_origins(singlemap, sample_mask,
             so = sinomega[omega_idx]
             co = cosomega[omega_idx]
 
+            # geometry.dty_values_grain_in_beam_sincos
+            # for each grid point (sx, sy) get a dty value that brings the grid point
+            # into the beam at this omega angle
+
             ygrid = y0 - sx_grid * so - sy_grid * co
 
             # now within this group we need to iterate over dty
@@ -1619,12 +1655,20 @@ def refine_map(refine_points, all_pbpmap_ubis, ri_col, rj_col, sx_grid, sy_grid,
     return final_ubis, final_eps, final_npks, final_nuniq
 
 
-def get_local_gv(i, j, ystep, omega, sinomega, cosomega, xl, yl, zl):
-    """now we correct g-vectors for origin point"""
+def get_local_gv(si, sj, ystep, omega, sinomega, cosomega, xl, yl, zl):
+    """
+    Given a point (si, sj) in step space, compute the x values in the lab frame for each omega angle
+    Then subtract them from xl to account for the change in the diffraction origin point.
+    Then re-compute the g-vectors.
+    """
     # compute the sample coordinates for this pixel
-    sx, sy = step_to_sample(i, j, ystep)
+    sx, sy = geometry.step_to_sample(si, sj, ystep)
 
+    # compute the position in the lab frame
     # geometry.sample_to_lab_sincos
+    # lx = sxr
+    # sxr = sx * cosomega - sy * sinomega
+    # lx = sx * cosomega - sy * sinomega
     # we only care about the x axis
     x_offset = sx * cosomega - sy * sinomega
     # compute local offset
@@ -1650,8 +1694,8 @@ def get_local_gv(i, j, ystep, omega, sinomega, cosomega, xl, yl, zl):
 
 
 def idxpoint(
-        i,
-        j,
+        si,
+        sj,
         isel,
         omega,
         sinomega,
@@ -1663,6 +1707,7 @@ def idxpoint(
         eta,
         ystep=2.00,
         y0=0.0,
+        ymin=-2.00,
         minpks=1000,
         hkl_tol=0.1,
         ds_tol=0.005,
@@ -1676,7 +1721,7 @@ def idxpoint(
     Selects peaks from the sinogram and attempts to index.
     More of this could be indexing.py I guess.
 
-    i,j = integer step from the rotation axis center
+    si, sj = position in step space (origin is rotation axis)
 
     Effectively a columnfile:
         isel = column of rings to use
@@ -1703,7 +1748,8 @@ def idxpoint(
     # ring mask
     mr = isel
     # mask all the peaks by dtyi and omega (for scoring)
-    m = dtyimask_from_sincos(i, j, sinomega, cosomega, dtyi, y0, ystep)
+
+    m = geometry.dtyimask_from_step_sincos(si, sj, sinomega, cosomega, dtyi, y0, ystep, ymin)
     # combine masks together (for indexing)
     local_mask = m & mr
 
@@ -1713,7 +1759,7 @@ def idxpoint(
     # then mask those for indexing via [mr]
 
     # compute g-vectors for peaks[m]
-    gv, gx, gy, gz = get_local_gv(i, j, ystep, omega[m], sinomega[m], cosomega[m], xl[m], yl[m], zl[m])
+    gv, gx, gy, gz = get_local_gv(si, sj, ystep, omega[m], sinomega[m], cosomega[m], xl[m], yl[m], zl[m])
     eta_local = eta[m]
 
     # mask g-vectors to [m & mr]
@@ -1854,6 +1900,7 @@ class PBP:
             self.ifrac = ifrac
         self.ystep = dset.ystep
         self.y0 = y0
+        self.ymin = self.ybincens.min()
         self.uniqcut = uniqcut
         self.cosine_tol = cosine_tol
         self.loglevel = loglevel
@@ -1914,9 +1961,7 @@ class PBP:
 
         colf.addcolumn(isel, "isel")  # peaks selected for indexing
 
-        # colf.addcolumn(np.round((colf.dty - self.y0) / self.ystep).astype(int), "dtyi")
-        # dtyi = dty_to_dtyi(colf.dty - self.y0, self.ystep)
-        dtyi = dty_to_dtyi(colf.dty, self.ystep)
+        dtyi = geometry.dty_to_dtyi(colf.dty, self.ystep, self.ymin)
         colf.addcolumn(dtyi, "dtyi")
 
         # cache these to speed up selections later
@@ -1990,6 +2035,7 @@ class PBP:
         idxopt = {
             "ystep": self.ystep,
             "y0": self.y0,
+            "ymin": self.ymin,
             "minpks": self.minpks,
             "hkl_tol": self.hkl_tol,
             "ds_tol": self.ds_tol,
@@ -2000,16 +2046,8 @@ class PBP:
         }
         pprint.pprint(idxopt)
 
-        # nlo = np.floor((self.ybincens.min() - self.y0) / self.ystep).astype(int)
-        # nhi = np.ceil((self.ybincens.max() - self.y0) / self.ystep).astype(int)
-
-        nlo = np.floor((self.ybincens.min()) / self.ystep).astype(int)
-        nhi = np.ceil((self.ybincens.max()) / self.ystep).astype(int)
-
-        rng = range(nlo, nhi + 1, gridstep)
-
         if debugpoints is None:
-            args = [(i, j, idxopt) for i in rng for j in rng]
+            args = [(i, j, idxopt) for (i, j) in geometry.step_grid_from_ybincens(self.ybincens, self.ystep, gridstep, self.y0)]
             random.shuffle(args)
         else:
             args = [(i, j, idxopt) for i, j in debugpoints]
diff --git a/ImageD11/sinograms/sinogram.py b/ImageD11/sinograms/sinogram.py
index 7aee985..590056f 100644
--- a/ImageD11/sinograms/sinogram.py
+++ b/ImageD11/sinograms/sinogram.py
@@ -189,8 +189,8 @@ class GrainSinogram:
         mask_4d = cf_4d.grain_id == grain_label
         omega = cf_4d.omega[mask_4d]
         dty = cf_4d.dty[mask_4d]
-        x, y, y0 = ImageD11.sinograms.geometry.dty_omega_to_x_y_y0(dty, omega)
-        self.grain.translation = np.array([x, y, 0])
+        sx, sy, y0 = ImageD11.sinograms.geometry.sx_sy_y0_from_dty_omega(dty, omega)
+        self.grain.translation = np.array([sx, sy, 0])
         self.recon_y0 = y0
 
     def update_lab_position_from_recon(self, method="iradon"):
diff --git a/sandbox/scanning_geometry.ipynb b/sandbox/scanning_geometry.ipynb
index 488d10f..02b56f1 100644
--- a/sandbox/scanning_geometry.ipynb
+++ b/sandbox/scanning_geometry.ipynb
@@ -34,8 +34,10 @@
    "source": [
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
+    "from matplotlib.lines import Line2D\n",
     "\n",
     "from ImageD11.sinograms import geometry\n",
+    "from ImageD11.sinograms.roi_iradon import run_iradon\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -63,7 +65,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -80,11 +82,40 @@
     "ax.set_aspect(1)\n",
     "ax.set_xlabel(\"Sample Y\")\n",
     "ax.set_ylabel(\"Sample X\")\n",
-    "ax.legend()\n",
+    "\n",
     "ax.set_title(\"Top-down\")\n",
     "plt.grid(axis='x', color='0.95')\n",
     "plt.grid(axis='y', color='0.95')\n",
     "circle1 = plt.Circle((0, 0), sample_radius, color='r', fill=False)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
     "ax.add_patch(circle1)\n",
     "plt.show()"
    ]
@@ -99,13 +130,11 @@
    "outputs": [],
    "source": [
     "# we have 1 grain in our sample\n",
-    "# grain centre-of-mass position is [-300, 400, 0] um in the sample frame\n",
-    "\n",
-    "sample_x = 300.0\n",
-    "sample_y = 400.0\n",
-    "sample_z = 0\n",
+    "# grain centre-of-mass position is [300, 400, 0] um in the sample frame\n",
     "\n",
-    "pos_sample = np.array([sample_x, sample_y, sample_z])"
+    "sx = 300.0\n",
+    "sy = 400.0\n",
+    "sz = 0"
    ]
   },
   {
@@ -118,7 +147,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -129,19 +158,47 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.scatter(pos_sample[1], pos_sample[0], label='grain COM', c='g')\n",
+    "ax.scatter(sy, sx, label='grain COM', c='g')\n",
     "ax.scatter(0, 0, label='Rotation axis', c='r')\n",
     "ax.set_xlim(1000, -1000)\n",
     "ax.set_ylim(-1000, 1000)\n",
     "ax.set_aspect(1)\n",
-    "ax.set_xlabel(\"Sample Y\")\n",
+    "ax.set_xlabel(\"< Sample Y\")\n",
     "ax.set_ylabel(\"Sample X\")\n",
-    "ax.legend()\n",
     "ax.set_title(\"Top-down\")\n",
     "plt.grid(axis='x', color='0.95')\n",
     "plt.grid(axis='y', color='0.95')\n",
     "circle1 = plt.Circle((0, 0), sample_radius, color='r', fill=False)\n",
     "ax.add_patch(circle1)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
     "plt.show()"
    ]
   },
@@ -154,28 +211,45 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(-0.000893208512337651, 499.9999999992022)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.000893208512337651 499.99999999920146\n"
+     ]
     }
    ],
    "source": [
-    "# assume no dty problems for now, y0 is 0 and dty is 0\n",
+    "# assume no misalignment between beam and rotation axis\n",
+    "# also assume that we're at the correct dty position\n",
+    "# therefore we only consider rotation at the moment\n",
+    "\n",
+    "y0 = dty = 14.0*1000  # um\n",
+    "\n",
     "# then put omega = 36.87 degrees\n",
     "# grain should end up at (0, 500) in the lab frame\n",
     "\n",
-    "pos_lab = geometry.sample_to_lab(pos_sample[0], pos_sample[1], y0=0, dty=0, omega=36.87)\n",
-    "pos_lab"
+    "omega = 36.87\n",
+    "lx, ly = geometry.sample_to_lab(sx, sy, y0=y0, dty=dty, omega=omega)\n",
+    "print(lx, ly)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "25d9c811-a4e1-49cb-8027-1514737ec305",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# where is the rotation axis in the lab frame?\n",
+    "\n",
+    "lx_rot, ly_rot = geometry.sample_to_lab(0, 0, y0=y0, dty=dty, omega=omega)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "cb9bbe31-2209-48f8-8835-35d1d2032351",
    "metadata": {
     "tags": []
@@ -183,7 +257,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -194,26 +268,84 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.scatter(pos_lab[1], pos_lab[0], label='grain COM', c='g')\n",
-    "ax.scatter(0, 0, label='lab origin', c='k')\n",
+    "ax.scatter(ly, lx, label='grain COM', c='g')\n",
+    "ax.scatter(ly_rot, lx_rot, label='rotation axis')\n",
+    "# ax.scatter(0, 0, label='lab origin', c='k')\n",
     "# ax.scatter(-y0, 0, label='sample centre', c='r')\n",
-    "ax.set_xlim(1000, -1000)\n",
+    "ax.set_xlim(+1000,  -1000)\n",
     "ax.set_ylim(-1000, 1000)\n",
     "ax.set_aspect(1)\n",
-    "ax.set_xlabel(\"Lab Y\")\n",
+    "ax.set_xlabel(\"< Lab Y\")\n",
     "ax.set_ylabel(\"Lab X (beam)\")\n",
-    "ax.legend()\n",
-    "ax.set_title(\"Top-down, dty = 0, omega = 36.87\")\n",
+    "\n",
+    "ax.set_title(f\"Top-down, dty = {dty}, omega = {omega}\")\n",
     "plt.grid(axis='x', color='0.95')\n",
     "plt.grid(axis='y', color='0.95')\n",
-    "circle1 = plt.Circle((0, 0), sample_radius, color='r', fill=False)\n",
+    "circle1 = plt.Circle((ly_rot, lx_rot), sample_radius, color='r', fill=False)\n",
     "ax.add_patch(circle1)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
+   "id": "d977ead9-abdc-467b-9ff2-f1e1171cd71f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Now let's assume an alignment problem of 65 um\n",
+    "# therefore y0 = -65\n",
+    "y0 = -65\n",
+    "dty = 0\n",
+    "# at dty = 0, the centre of the sample does not intercept the beam\n",
+    "# this instead happens at dty = y0\n",
+    "\n",
+    "# what happens when we try to rotate the grain into the beam?\n",
+    "# need to rotate 90 degrees more\n",
+    "omega = omega + 90\n",
+    "\n",
+    "# now where is the grain in the lab frame?\n",
+    "lx, ly = geometry.sample_to_lab(sx, sy, y0=y0, dty=dty, omega=omega)\n",
+    "\n",
+    "# now where is the rotation axis in the lab frame?\n",
+    "\n",
+    "lx_rot, ly_rot = geometry.sample_to_lab(0, 0, y0=y0, dty=dty, omega=omega)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "aedb2576-8ed8-4e51-ba58-140b86c63b18",
    "metadata": {
     "tags": []
@@ -221,7 +353,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -231,41 +363,56 @@
     }
    ],
    "source": [
-    "# let's assume that we didn't reset the position of dty\n",
-    "# so there's an offset\n",
-    "# at dty = 0, the centre of the sample does not intercept the beam\n",
-    "# this instead happens at dty = -65 um\n",
-    "# therefore y0 = -65\n",
-    "\n",
-    "# what happens when we try to rotate the grain into the beam?\n",
-    "\n",
-    "y0 = -65\n",
-    "\n",
-    "pos_lab = geometry.sample_to_lab(pos_sample[0], pos_sample[1], y0, 0, omega=36.87+90)\n",
-    "pos_rotaxis_lab = geometry.sample_to_lab(0, 0, y0, 0, omega=36.87+90)\n",
-    "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.scatter(pos_lab[1], pos_lab[0], label='grain COM', c='g')\n",
-    "ax.scatter(0, 0, label='lab origin', c='k')\n",
-    "ax.scatter(pos_rotaxis_lab[1], pos_rotaxis_lab[0], label='sample centre', c='r')\n",
-    "ax.set_xlim(1000, -1000)\n",
+    "ax.scatter(ly, lx, label='grain COM', c='g')\n",
+    "ax.scatter(ly_rot, lx_rot, label='rotation axis')\n",
+    "ax.set_xlim(y0+1000,  y0-1000)\n",
     "ax.set_ylim(-1000, 1000)\n",
     "ax.set_aspect(1)\n",
     "ax.set_xlabel(\"Lab Y\")\n",
     "ax.set_ylabel(\"Lab X (beam)\")\n",
-    "ax.legend()\n",
-    "ax.set_title(f\"Top-down, dty = 0, omega = {36.87+90}\")\n",
+    "\n",
+    "ax.set_title(f\"Top-down, dty = {dty} (beam), omega = {omega}\")\n",
     "plt.grid(axis='x', color='0.95')\n",
     "plt.grid(axis='y', color='0.95')\n",
-    "circle1 = plt.Circle((pos_rotaxis_lab[1], pos_rotaxis_lab[0]), sample_radius, color='r', fill=False)\n",
+    "circle1 = plt.Circle((ly_rot, lx_rot), sample_radius, color='r', fill=False)\n",
     "ax.add_patch(circle1)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "2a19bcc9-a219-45b3-ae07-e9d4feb64993",
+   "execution_count": 12,
+   "id": "ac42c9d7-0f7c-44be-9f9d-42ed391dc0e6",
    "metadata": {
     "tags": []
    },
@@ -274,12 +421,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(300.0, 0.0)\n"
+      "-65\n",
+      "-65\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# To get the grain into the beam at the current omega value?\n",
+    "# the same as getting the rotation axis into the beam\n",
+    "# which we know is dty = y0\n",
+    "\n",
+    "dty = y0\n",
+    "\n",
+    "# now where is the grain in the lab frame?\n",
+    "lx, ly = geometry.sample_to_lab(sx, sy, y0=y0, dty=dty, omega=omega)\n",
+    "\n",
+    "# now where is the rotation axis in the lab frame?\n",
+    "\n",
+    "lx_rot, ly_rot = geometry.sample_to_lab(0, 0, y0=y0, dty=dty, omega=omega)\n",
+    "\n",
+    "print(dty)\n",
+    "print(y0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2a19bcc9-a219-45b3-ae07-e9d4feb64993",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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fLU+sABTxyo/f+PHjMX78eLWxyG/bysjIgKurq8rrby7Lzs5Gq1atYGlpia+//lpxW4z8+nxZx8PCwgKNGzcutYycqampRuXKgzGGIUOGYN26dVi7di26d++u9Lr8/6NFixawtbVVLLe2tkZISEiZt4rt2bMHMTEx2LJlC3r27KlYHh4eDh8fH8yaNQuxsbEq77t48SLOnj2L0aNHQywWv8Uecijp6pCjoyPOnDkDxphS4k1PT0dhYSGcnJyUyqelpamsQ75M/oEr3rEJgOIL3NHRsdT3F48JAFJTU1XKPnr0SCmmZs2awcbGBvv370dKSgratm0LkUiEtm3bYtGiRYiPj8e9e/fUJl1tkseclpYGT09PxfLCwkKVL8GyjBo1Cr169cK5c+fwww8/oFatWggLCyvzfWvXrsWnn36q0TYYYyW+5uHhofI3rF27NgCuQ1ppHec06ZjCGNOonJOTE54+fVri6yUda/nfQv45+eOPPxS3WaiTmZmJXbt2YebMmZg8ebJieV5eXqnbfxvy9b75/6UJR0dHFBYW4t9//1VKvIwxpKWlvd2tIsXIY5syZQp69Oihtoz8c+Ho6KhI0sW9+b998OBBPHr0CIcPH0ZISIhi+ZsdwEqi7oSwJPITRW2RJ9zY2FjExMTg448/VinTsGHDUt9f1uc+MTERgOqJop2dHfz8/JCUlKT2fTExMQCAIUOGlLp+TVHS1aG2bdti8+bN2L59Oz744APF8l9//VXxenEHDhzA48ePFWewRUVF2LRpE2rUqIGqVasCAIKCgtRuKzQ0FAsWLMCFCxfQqFEjxfLff/9dqVxwcDCsrKywbt06RU9fAHjw4AEOHjyodAZobm6O9957D3Fxcbh//75iEIVWrVrBzMwM06ZNUyRhXZL/c69fvx6BgYGK5Zs3b0ZhYaFSWfmZaEln9R988AGqVauGcePG4ciRI1iyZIlGPYW7du2qkiwrwsLCosS/4QcffICpU6fif//7n9Ln5X//+x8YY2jevHmp6z59+jRu3LiBUaNGlRlHnTp1cOvWrRJfL+lYy/8W7du3h5mZGW7duqXUc/1NIpEIjDGVGsLq1atRVFRUZpwVcfv2bQAVuz++bdu2WLBgAdatW4exY8cqlv/555/IycnR2me9du3aqFmzJi5cuIC5c+eWWjYkJAS7d+/GkydPFMlaJpNhy5YtSuXkn+M3j7UmLTmA+hPC0uLXFsYYhg4ditjYWPz8888lnty6u7sjODgYJ06cQFZWFqpUqQKA69V85MiRMv8/PDw8AHD/J8VPFDMyMnD9+nW1f9u8vDysW7cOTZs2hb+/f0V3UdlbN1AThZJ6L9va2rLFixezuLg4NnPmTGZubl6u3stlXYthjLHU1FTm7Oys0nvZy8urxN7LAwYMYLt372a//fYb8/PzU+q9LLdo0SLFNaHi1zxCQ0MVnRDeBICNGDFCZfmb99TKy2pyrefjjz9mIpGITZw4UdF72cPDQ6n3MmOM3b59mwFg77//Pjt27BiLj49nT548UVqXvCObRCJhz58/L3Pb+hQZGclMTExYVFQUi4uLYz/++COzt7dnAQEBSh3AGjZsyBYsWMB27tzJ4uLi2Jw5c5idnR3z8PBgjx49KnM70dHRzMzMTKmzD2OqvZf37dvHlixZwmxsbFijRo2UYpg7dy4zMzNjn3/+Odu2bRs7fPgw27RpExs3bhybMWOGotx7773HHBwc2KpVq1hcXBybNm0ac3d3Z3Z2dkp/O/k13Tevi5d073BJ98WPHDmSOTo6KvWlkHcSevPz9yZ572Vzc3M2a9YsFhcXxxYtWsRsbGzU9l7u3LmzyjrUff7l2//2228Vyw4ePMjEYjELDw9nv//+Ozty5Ajbtm0bmzt3LuvZs6eiXGJioqL38qZNmxS9l729vRkARY/1J0+eMHt7e9aoUSO2detWtnPnTtanTx9Ws2ZNlR7R+hAfH8+2bNnCtmzZovhukz8v/l0SGRnJALBBgwYp7jiQP86dO6e0zhMnTjALCwvWvHlztm3bNrZ9+3bWqlUrZm5uzk6ePKkol5KSwkxNTdmgQYMUy168eMG8vb2Zvb09W7hwITt48CBbv349a9y4MTM1NVXbD2Tjxo0qHejeFiVdLVL3JZCRkcGGDRvG3N3dmZmZGfP29mZTpkxRuSAv/0ddvnw5q1GjBjM3N2d16tQp103+ycnJLCwsjFlaWjIHBwc2ePBg9tdff6ntWLR69WrWsGFDZmFhwaRSKevevbvSbR5y8k4pNWvWVFo+Z84cBoBFRUWpvEfTpPvixYsSO129KS8vj40bN465uLgwS0tL1rx5c3bq1Cm1iXzp0qXM19dX0WnnzS8beaewinTq0rXCwkI2f/585ufnx8zNzZm7uzv74osvVG5/6dOnD/Pz82MSiYSZm5szb29vNmzYMI0SLmOM3bx5k4lEIpUOdfIEl5CQwLp27cpsbGyYra0t69u3r9pBDLZv385CQ0NZlSpVmFgsZt7e3qxnz55s//79ijIPHjxgH374IbO3t2e2trasQ4cOLCkpSeVvp42kK5PJmLe3t0oP6EuXLjEAKoOBqPPq1Ss2adIk5u3tXerf4G2TLmPc/9dHH33EXFxcmLm5OXNzc2Nt2rRhK1asUCp37Ngx1qxZMyYWi5mbmxubMGGC4uSx+InjyZMnWXBwMLO2tmbOzs5syJAhig6E+k66AwcOLLH3c/FY5CcP6h7e3t4q6z127BgLCQlh1tbWzNramrVp04adOHFCqUxJJ1mpqaksMjKS+fn5MUtLS+bh4cE6d+7MTp06pXYfwsLCmEQiKXfHsdJQ0jUQJSUqY/b3338zkUjELl68qNftfvfddwwAS0pK0ut2DU2XLl3U3jYkZPv372cmJibsypUrSst//PFHJpFIlG7JE7qwsDCVk2Fi+OiaLuHNoUOH0KdPHzRo0EAv2zt//jzu3LmD6OhodO/eXWlM18po3rx5CAgIQHx8vNY6CPHt66+/xqBBg1CnTh2l5YcOHcKoUaPU9gIWgqioKAQEBMDLywtPnz7F+vXrERcXp+jkQ4SDki7hzbfffqvX7X3wwQdIS0tTTCZQ2fn7+yM2NlZtr3chevbsGUJCQlRmZAKg0ulIaIqKijBjxgykpaVBJBKhXr16+O2339T28iWGTcRYKfc3EEIIIURrBDXhwdGjR9G1a1d4eHhAJBKp3AzNGMOsWbPg4eEBKysrtG7dWmWexLy8PIwcORJOTk6QSCTo1q0bHjx4oFTm2bNnGDBgAKRSKaRSKQYMGKDxvW6EEEJISQSVdHNyctCoUSP88MMPal9fsGABFi9ejB9++AHx8fFwc3NDWFiY0kw7Y8aMwbZt27Bx40YcP34c2dnZ6NKli9I9g/369UNiYiL27NmDPXv2IDExscTZWAghhBCN8dyRq8IAsG3btimey2Qy5ubmxubPn69Ylpuby6RSqaL7/fPnz5m5ubnSfa8PHz5kJiYmisGyk5OTGQB2+vRpRZlTp04xAOzq1as63itCCCHGzGg6Ut25cwdpaWkIDw9XLBOLxQgJCcHJkyfx+eefIyEhAQUFBUplPDw84O/vj5MnT6J9+/Y4deoUpFIpmjVrpijTvHlzSKVSnDx5Uu1ILHl5eUrz48pkMjx9+hSOjo46nxeVEEKI9jHG8OLFC3h4eGg8N7AmjCbpyntgvnlLgKurq2Ky7LS0NFhYWMDe3l6ljPz9aWlpaidVdnFxKbGX57x58zB79uy33gdCCCGG5f79+4pheLXBaJKu3Js1S/bGZAPqvFlGXfnS1jNlyhRERUUpnmdmZqJatWq4d++eYnxQvjDGkJOTA4lEIqhaN8WtXxS3/gk19soSd1ZWFqpVq6Y0o5E2GE3SlU9FlpaWpjRtXXp6uqL26+bmhvz8fDx79kyptpueno4WLVooyqib0ePff/8t8cZ6sVisdsonqVRqEEnX1NQUNjY2gvsHobj1h+LWP6HGXlnilpfR9j4KqvdyaXx9feHm5qY0H2x+fj6OHDmiSKiBgYEwNzdXKpOamoqkpCRFmeDgYGRmZuKff/5RlDlz5gwyMzMVZQghhJCKEFRNNzs7Gzdv3lQ8v3PnDhITE+Hg4IBq1aphzJgxmDt3LmrWrImaNWti7ty5sLa2Rr9+/QBwNc/Bgwdj3LhxcHR0hIODA8aPH48GDRoo5oStW7cuOnTogKFDhyqmxPrss8/QpUsXrU5nRYgh6dsXOH5cgsREoALT0BJCNKXfztJv59ChQ2pnopDPJCGTydjMmTOZm5sbE4vF7L333mOXLl1SWserV69YZGQkc3BwYFZWVqxLly7s3r17SmUyMjJY//79ma2tLbO1tWX9+/dXmWGkNJmZmQwAy8zMfNtdfmsymYxlZWUpTXMmBBS3/ty6xRggYwBjQ4YIJ27GhHm85YQae2WJW1ff4zQMpA5kZWVBKpUiMzPTIK7pZmdnC/L6C8WtH0OGAPJx821sGB48EEEq5TcmTWl6vIuKilBQUKDHyMrGGMPLly9hbW0tmM8KYDxxm5ubw9TUtMTyuvoeF1TzMiFEu27fBtauff08O1uE774Dpk/nLyZtYowhLS3NYIdxlclkWr0HVF+MJW47Ozu4ubnp9eSBki4hldjcuUBhIWBtzfDyJffFs3gxMGoUBFPbLY084bq4uBhczYwxpkgChhRXWYwhbgB4+fIl0tPTAUDpjhddo6RLSCVVvJbr4AC8fAn4+DCkpBhHbbeoqEiRcB0dHfkOR4UxJC8hx21lZQWAu2XUxcWl1KZmbRJe+wAhRCtiY7labvv2wH/fPxg4kPv5X8d9QZNfw7W2tuY5EmKo5J8NfV7vp5ouIZVUaChw5gywdCnQrRu3rHVrIC0N4Ln/n1YJqTZG9IuPzwYlXUIqqTZtuEdxpqbAihX8xENIZUDNy4QQUonMmjULjRs35juMSouSLiGEVCLjx4/HgQMH3no9+fn5WLBgARo1agRra2s4OTmhZcuWiI2NVbpGev/+fQwePBgeHh6wsLCAt7c3Ro8ejYyMDKX1tW7dGiKRCPPnz1fZVqdOnSASiTBr1qy3jptvlHQJIaQMRbIiHE45jA2XNuBwymEUyYr4DklFfn6+RuVsbGzeujd3fn4+OnTogPnz5+Ozzz7DyZMn8c8//2DEiBH4/vvvcfnyZQDA7du3ERQUhOvXr2PDhg24efMmVqxYgQMHDiA4OBhPnz5VWq+XlxdiY2OVlj169AgHDx7U6209ukRJlxBCSrH1ylb4LPNB6NpQ9NvaD6FrQ+GzzAdbr2zV2TZfvHiB/v37QyKRwN3dHUuWLEHr1q0xZswYRRkfHx98/fXXiIiIgFQqxdChQwEAkyZNQq1atWBtbY3q1atj+vTpSjXPN5uXIyIi8P7772PhwoVwd3eHo6MjRowYUWqP3mXLluHo0aM4cOAARowYgcaNG6N69ero168fzpw5g5o1awIARowYAQsLC+zbtw8hISGoVq0aOnbsiP379+Phw4eYOnWq0nq7dOmCjIwMnDhxQrFszZo1CA8PVzvPuRBR0iWEkBJsvbIVPTf3xIOsB0rLH2Y9RM/NPXWWeKOionDixAns2LEDcXFxOHbsGM6dO6dS7ttvv4W/vz8SEhIw/b8bq21tbbFmzRokJydj2bJlWLVqFZYsWVLq9g4dOoRbt27h0KFDWLt2LdasWYM1a9aUWH7Dhg1o164dAgICVF4zNzeHRCLB06dPsXfvXgwfPlxxT6ycm5sb+vfvj02bNqH4SMQWFhbo37+/Um13zZo1GDRoUKnxCwklXUIIUaNIVoTRe0aDQXV4evmyMXvGaL2p+cWLF1i7di0WLlyItm3bwt/fH7GxsSgqUt1OmzZtMH78ePj5+cHPzw8AMG3aNLRo0QI+Pj7o2rUrxo0bh82bN5e6TXt7e/zwww+oU6cOunTpgs6dO5d63ffGjRtlzrp248YNMMZQt25dta/XrVsXz549w7///qu0fPDgwdi8eTNycnJw9OhRZGZmonPnzqVuS0joliFCCFHj2L1jKjXc4hgY7mfdx7F7x9Dap7XWtnv79m0UFBSgadOmimVSqVRtkgsKClJZ9scff2Dp0qW4efMmsrOzUVhYWOaA/fXr11cakcnd3R2XLl0qsTxj7K3vcZXXcN9cT8OGDVGzZk388ccfOHToEAYMGABzc/O32pYhoZouIYSokfoiVavlNFVSMlI3IZxEIlF6fvr0afTp0wcdO3bErl27cP78eUydOrXMTlZvJjWRSASZTFZi+Vq1auHq1aulrtPPzw8ikQjJyclqX7969Srs7e3hpGYC50GDBuHHH3/EH3/8YVRNywAlXUIIUcvdVrPespqW01SNGjVgbm6Of/75R7EsKysLN27cKPO9J06cgLe3N6ZOnYqgoCDUrFkTd+/e1Wp8ANCnTx/s378f58+fV3mtsLAQOTk5cHR0RFhYGJYvX45Xr14plUlLS8P69evRu3dvtTXmfv364dKlS/D390e9evW0Hj+fKOkSQogaraq1QtUqVSGC+mZUEUTwquKFVtVaaXW7tra2GDhwICZMmIBDhw7h8uXLGDRokEYTDPj5+eHevXvYuHEjbt26he+++w7btm3TanwAMHr0aLRs2RJt27bFjz/+iAsXLuD27dvYvHkzmjVrpjhB+OGHH5CXl4f27dvj6NGjuH//Pvbs2YOwsDB4enpizpw5atdvb2+P1NRUrdxPbGgo6RJCiBqmJqZY1mEZAKgkXvnzpR2WwtRE+7PTLF68GMHBwejSpQvatWuHli1bom7durC0tCz1fd27d8fYsWMRGRmJxo0b4+TJk4pezdokFouxb98+TJw4ET///DOaN2+Od955B9999x1GjRoFf39/AEDNmjVx9uxZ1KhRA71790aNGjXw2WefITQ0FKdOnYKDg0OJ27Czs1NpPjcGIqbuQgF5K1lZWZBKpcjMzCyzA4OuMcaQnZ0NGxsbQQ38TnHrV61aDDduiHD0KEOrVsKJu7TjnZubizt37sDX17fMZFWarVe2YvSe0UqdqryqeGFph6XoUbfHW8Wu6RR5OTk58PT0xKJFizB48OAKb1MbjGVqP6D0z4iuvsep9zIhhJSiR90e6F67O47dO4bUF6lwt3VHq2qtdFLDlTt//jyuXr2Kpk2bIjMzE9HR0QC4miwRNkq6hBBSBlMTU63eFqSJhQsX4tq1a7CwsEBgYCCOHTumtqcvERZKuoQQYmACAgKQkJDAdxhEB6gjFSGEEKInlHQJIYQQPaGkSwghhOgJJV1CCCFETyjpEkIIIXpCSZcQQgjRE0q6hBBCVKSkpEAkEiExMZHvUMrUunVrjBkzhu8wNEL36RJCiJFo3bo1GjdujKVLl5brfREREXj+/Dm2b9+uWObl5YXU1FRBDMixdetWwcy5S0mXEELKUCRj+OfOU6S/yIWLrSWa+jrA1ER/4w7n5+fDwsJCb9sDAFNTU7i5uel1mxVV2sQJhoaalwkhpBR7klLx7jcH0XfVaYzemIi+q07j3W8OYk+SdievL65169aIjIxEVFQUnJycEBYWBgA4cuQImjZtCrFYDHd3d0yePBmFhYUAuNrqkSNHsGzZMohEIohEIqSkpKCoqAiDBw+Gr68vrKysULt2bSxbtkyxrVmzZmHt2rX466+/FO87fPiw2ublI0eOoFmzZrC2toaHh4fS9uVxjxo1ChMnToSDgwPc3Nwwa9asUvc1Pj4eYWFhcHJyglQqRUhICM6dO6d4/fDhw7CwsMCxY8cUyxYtWgQnJyekpqYqtlu8eXn58uWoWbMmLC0t4erqip49e5b7b6ArlHQJIaQEe5JS8cW6c0jNzFVanpaZiy/WndNp4l27di3MzMxw4sQJ/Pzzz3j48CE6deqEd955BxcuXMBPP/2EmJgYfP311wCAZcuWITg4GEOHDkVqaipSU1Ph5eUFmUyGqlWrYvPmzUhOTsaMGTPw5ZdfYvPmzQCA8ePH46OPPkKHDh0U72vRooVKPPLtBwUF4dy5c1i+fLnS9ovHLZFIcObMGSxYsADR0dGIi4srcT9fvHiBgQMH4tixYzh9+jRq1qyJTp064cWLFwBeJ9QBAwYgMzMTFy5cwNSpU7Fq1Sq4u7urrO/s2bMYNWoUoqOjce3aNezZswfvvfdehf8O2kbNy4QQokaRjGH2zmSom/uUARABmL0zGWH13HTS1Ozn54cFCxYonk+dOhVeXl744YcfIBKJUKdOHTx69AiTJk3CjBkzIJVKYWFhAWtra6VmYVNTU8yePVvx3NfXFydPnsTmzZvx0UcfwcbGBlZWVsjLyyu1OXn58uWK7TPGUK9ePaSmpiq2b2LC1eEaNmyImTNnAuDm0/3hhx9w4MABRW39TW3atFF6/vPPP8Pe3h5HjhxBly5dAABff/019u/fj88++wyXL1/GgAED8MEHH6hd37179yCRSNClSxfY2trC29sbAQEBMJRZbKmmSwghavxz56lKDbc4BiA1Mxf/3Hmqk+0HBQUpPb9y5QqCg4OV5rBt2bIlsrOz8eDBgzffrmTFihUICgqCs7MzbGxssGrVKty7d69c8Wi6/YYNGyq9z93dHenp6SWuNz09HcOGDUOtWrUglUohlUqRnZ2tFJ+FhQXWrVuHP//8E69evSq1o1hYWBi8vb1RvXp1DBgwAOvXr8fLly/Lta+6REmXEELUSH9RcsKtSLnykkgkSs8ZYyqTxstrb6VNJr9582aMHTsWgwYNwr59+5CYmIhPP/0U+fn55YpH0+2/2YtYJBJBJpOVuN6IiAgkJCRg6dKlOHnyJBITE+Ho6KgS38mTJwEAT58+xdOnJZ/o2Nra4ty5c9iwYQPc3d0xY8YMNGrUCM+fP9doP3XNqJKuj4+PoiNA8ceIESMAcH/cN19r3ry50jry8vIwcuRIODk5QSKRoFu3bmWeRRJCjI+LraVWy72tevXq4eTJk0rNpCdPnoStrS08PT0BcDXCoqIipfcdO3YMLVq0wPDhwxEQEAA/Pz/cunVLqYy691Vk+xVx7NgxjBo1Cp06dUL9+vUhFovx5MkTpTK3bt3C2LFjsWrVKjRv3hyffPJJqYnczMwM7dq1w4IFC3Dx4kWkpKTg4MGDFY5Rm4wq6cbHxys6AqSmpiou3vfq1UtRpnhngdTUVOzevVtpHWPGjMG2bduwceNGHD9+HNnZ2ejSpUuZH0hCiHFp6usAd6klSqpDigC4S7nbh/Rh+PDhuH//PkaOHImrV6/ir7/+wsyZMxEVFaW4nurj44MzZ84gJSUFT548gUwmg5+fH86ePYu9e/fi+vXrmD59OuLj45XW7ePjg4sXL+LatWt48uQJCgoKKrT9ivDz88Nvv/2GK1eu4MyZM+jfvz+srKwUrxcVFWHAgAEIDw/Hp59+itjYWCQlJWHRokVq17dr1y589913SExMxN27d/Hrr79CJpOhdu3aFY5Rm4wq6To7O8PNzU3x2LVrF2rUqIGQkBBFGbFYrFSm+P1dmZmZiImJwaJFi9CuXTsEBARg3bp1uHTpEvbv38/HLhFCeGJqIsLMrvUAQCXxyp/P7FpPb/frenp6Yvfu3fjnn3/QqFEjDBs2DIMHD8a0adMUZcaPHw9TU1PUq1cPzs7OuHfvHoYNG4YePXqgd+/eaNasGTIyMjB8+HCldQ8dOhS1a9dWXPc9ceJEiduPj49HkyZN8MUXX6hsvyJ++eUXPHv2DAEBARgwYABGjRoFFxcXxetz5sxBSkoKVq5cCQBwc3PD6tWrMW3aNLWjZdnZ2WHr1q1o06YN6tatixUrVmDDhg2oX7/+W8WpLSJmKF26tCw/Px8eHh6IiorCl19+CYBrXt6+fTssLCxgZ2eHkJAQzJkzR/EHPnjwINq2bYunT5/C3t5esa5GjRrh/fffV+oBWFxeXh7y8vIUz7OysuDl5YXnz5+jSpUqOtzLsjHGkJOTA4lEUup1H0NDcetX7drAjRsiHDkiQ6tWwom7tOOdm5uLlJQU+Pj4wNKy4k3Ae5LSMHtXMtKKdapyl1piRpd66OD/doNHyGSyt6ol8sVY4i7tM5KVlQU7OztkZmZq9XvcaG8Z2r59O54/f46IiAjFso4dO6JXr17w9vbGnTt3MH36dLRp0wYJCQkQi8VIS0uDhYWFUsIFAFdXV6SlpZW4rXnz5qlNyNnZ2QbzwczJyeE7hAqhuPVDJpMAECE3NxfZ2cK7lKLueOfn50MmkykeFRVezwVt6zgjPuUp0l/kwcVWjHd8uBGp3ma9ctpYBx+MIW75Z+Ply5dKg3wA3Pe3Lhht0o2JiUHHjh3h4eGhWNa7d2/F7/7+/ggKCoK3tzf+/vtv9OjRo8R1qeu1V9yUKVMQFRWleC6v6drY2MDGxuYt9+TtCLXmRXHrl/zc0NLSEjY2wom7rJquiYmJ4vE2TEyAFn7Ob7UOdYylxigUb8Yt/2xYW1ur1HR1dVJhlEn37t272L9/P7Zu3VpqOXd3d3h7e+PGjRsAuGsF+fn5ePbsmVJtNz09Xe0ILXJisRhisVhlubyHtCEwpFjKg+LWl9e3fggrbo66uOXPDXWfil/ZM8T4SmJMcZf2GdHVvgnvVEUDsbGxcHFxQefOnUstl5GRgfv37yuGEgsMDIS5ubnSkGWpqalISkoqNekSQgghmjC6mq5MJkNsbCwGDhwIM7PXu5ednY1Zs2bhww8/hLu7O1JSUvDll1/CyclJMZyYVCrF4MGDMW7cODg6OsLBwQHjx49HgwYN0K5dO752iRBCiJEwuqS7f/9+3Lt3D4MGDVJabmpqikuXLuHXX3/F8+fP4e7ujtDQUGzatAm2traKckuWLIGZmRk++ugjvHr1Cm3btsWaNWtgamqq710hhBBiZIwu6YaHh6sd2NrKygp79+4t8/2Wlpb4/vvv8f333+siPEIIIZWYUV7TJYSQisrLA4xz9AJiCCjpEkLIf+7fB7y9gWbNgL17KfkS7aOkSwgh//n3X+DxYyAhAejQgZIv0T5KuoQQ8gb5uAjnzvGTfN+cEc3R0REdOnTAxYsX9RMA0RlKuoQQUgL55GJ8JN/iM6IdOHAAZmZm6NKli+43THSKki4hhJSBj+RbfEa0xo0bY9KkSbh//z7+/fdfAMDDhw/Ru3dv2Nvbw9HREd27d0dKSori/fHx8QgLC4OTkxOkUilCQkJw7tw5pW2IRCL8/PPP6NKlC6ytrVG3bl2cOnUKN2/eROvWrSGRSBAcHKwy/y6pOEq6hJBKgzEgJ6fkx6tXpb//zeQbFAT89ReQnV36et82OWdnZ2P9+vXw8/ODo6MjXr58idDQUNjY2ODo0aM4fvw4bGxs0KFDB+Tn5wMAXrx4gYEDB+LYsWM4ffo0atasiU6dOuHFixdK6/7qq6/wySefIDExEXXq1EG/fv3w+eefY8qUKTh79iwAIDIy8u12gLzGiNZlZmYyACwzM5PvUJhMJmNZWVlMJpPxHUq5UNz6VbOmjAGMHT0qrLhLO96vXr1iycnJ7NWrV4pl2dmMcSlQv4/sbPWxFxYWqo194MCBzNTUlEkkEiaRSBgA5u7uzhISEhhjjMXExLDatWsrvTcvL49ZWVmxvXv3qj1WhYWFzNbWlu3cuVOxDACbNm2a4vmpU6cYABYTE6NYtmHDBmZpaalR3IZMXdzqPiNyuvoep5ouIYQYoNDQUCQmJiIxMRFnzpxBeHg4OnbsiLt37yIhIQE3b96Era2tYjYzBwcH5ObmKpqC09PTMWzYMNSqVQtSqRRSqRTZ2dm4d++e0nYaNmyo+N3V1RUA0KBBA6Vlubm5yMrK0sNeGz+jG5GKEEJKYm3NNQWXJDERePddzddnaso1OTdpAsyeDYSGlrzd8pJIJPDz81M8DwwMhFQqxapVqyCTyRAYGIj169ervM/ZmZuCMCIiAv/++y+WLl0Kb29viMViBAcHK5qf5czNzRW/y2fWUbdMqPPnGhpKuoSQSkMkAiSSkl+3stJsPcWT7VdfAeHh3Lp1SSQSwcTEBK9evUKTJk2wadMmuLi4oEqVKmrLHzt2DMuXL0enTp0AAPfv38eTJ090GyQpEzUvE0KIhuTznjRpAuzZA5w5A7Rvr5uEm5eXh7S0NKSlpeHKlSsYOXIksrOz0bVrV/Tv3x9OTk7o3r07jh07hjt37uDIkSMYPXo0Hjx4AADw8/PDb7/9hitXruDMmTPo378/rDQ9qyA6Q0mXEELKoM9kK7dnzx64u7vD3d0dzZo1Q3x8PLZs2YLWrVvD2toaR48eRbVq1dCjRw/UrVsXgwYNwqtXrxQ1319++QXPnj1DQEAABgwYgFGjRsHFxUV3ARONUPMyIYSUgI9mZABYs2YN1qxZU2oZNzc3rF27tsTXAwICEB8fr7SsZ8+eSs/ZG/cy+fj4qCxr3bq12pnbSMVQ0iWEkDeYmHBDQeo72RLjR0mXEEL+4+ICuLkBXl6UbIluUNIlhJD/VK0KpKQAFhaUbIluUNIlhJBixGK+IyDGjHovE0KMGnUCIiXh47NBSZcQYpTkoyq9fPmS50iIoZJ/NoqPwKVr1LxMiDF78QJ49AhITeUejx4BaWlAbi5QUAAUFnKPx/MBuAHffgvEXgXMzQEzM+6nkxPg7g54eHA/3d0BZ2eui68BMzU1hZ2dHdLT0wEA1tbWiiENDQFjDDKZDCYmJgYVV1mMIW6AS7jp6emws7ODqfxGbD2gpEuIkGVncwMGnzvH9QCSJ1b5z5wc5fI2Nlz3XImES6ryR2Eh93paGvDk6uuEnJ8PPHkC/Je4FMzMAFdX5UTs6Qk0aAAEBnI9kgzgC9nNzQ0AFInX0BRPAkJiLHHb2dkpPiP6QkmXEKHIzgbOnwcSEl4/rl7lZo8TiwEfHy4JVq0KNG36OhkWT4y2turXXYsBNwAsWgS0UpMsCwqAx49Va83yn//8A9y7B2RkcOWdnbnJZgMDXz94SMQikQju7u5wcXFBQUGBXrddFsYYXr58aXA18LIYS9zm5uZ6reHKUdIlxBAxBly4ABw+zCXXs2eBa9deJ9hGjbgpbcaP5xJavXpcU7CumJtzSbNq1dJjfvhQ+aRg5crXtWRn59cJuHlzoE2bik2/UwGmpqa8fMGWhjGGwsJCWFpaCi55UdwVR0mXEEORl8cl2Z07gR07gPv3XyfYNm2ACRP0k2ArSiR6nZi7d+eWqUvEq1YBc+YAlpZAWBjQrRvQpQvX7E2IkaOkSwifMjKA3bu5JLtnD9eE7O0NvP8+l4zee48bqUGoSkrE1669Prn4/HNuzMVmzbh97tYNqF/fIK4JE6JtlHQJ0bfbt4Ft27iEc/w4l3CaNgUmT+YSjr+/cScckQioU4d7TJjAddSSn3jMnQtMncpdn5Yn4NatX0/zQ4jAUdIlRB8KC4Fdu4Dly4G4OK5ptV07YMUKrmnV3Z3vCPnj5AR88gn3yM193cS+dSvw3XdczX/YMGDwYO66MCECJrw+34QISVoa8PXXgK8v8MEH3H2zv/7K1e527gSGDq3cCfdNlpZAhw7Ajz9yvaFPn+Y6jM2ezTVRf/wxcPIk10RNiABR0iVEF06cAPr04aarmTuXSyQJCcCpU8CAAdx9sqR0IhF3nTc2FnjwgDuOp08DLVsCAQFcz+jsbL6jJKRcKOkSoi1ZWcDq1dzvnTpx99QuXMjdx7pqFTc5K6kYR0dg3Djg+nWuw5m3N/DFF9x1YYDrmEWIAFDSJeRtvXwJfPMNlwgmTuSW/fUXN3DF6NGAnR2v4RkVExOgfXvu+N6+DXz2Gbe8aVOgVy9KvsTgUdIlpKIKCrgmzpo1gWnTgP79gUuXuNdatzbuHsiGwNsbmDGD+/3HH4EzZ7hbjT77jLs3mBADREmXkPJiDNiyhbu15/PPuQR79Srwww/c+MNE/z7+mGt6/vZbrteznx8waRLw7BnfkRGihJIuIeWxfz/wzjvARx8B1atz123Xrwdq1OA7MmJpCYwdC9y6xQ2P+cMP3N/om2+4SwCEGACjSrqzZs2CSCRSehSfQYIxhlmzZsHDwwNWVlZo3bo1Ll++rLSOvLw8jBw5Ek5OTpBIJOjWrRsePHig710hhiYhgRuyMCyMm2Hn0CHgf/8DGjfmOzLyJqkU+OorLvn27881/desyV0KkM+mRAhPjCrpAkD9+vWRmpqqeFySX2MDsGDBAixevBg//PAD4uPj4ebmhrCwMLx48UJRZsyYMdi2bRs2btyI48ePIzs7G126dEFRUREfu0P4lpkJDBnCzZjz4AHXdHnqFNekTAybmxtX2716FQgJ4S4FNGrEzYhECE+MLumamZnBzc1N8XD+bwQbxhiWLl2KqVOnokePHvD398fatWvx8uVL/P777wCAzMxMxMTEYNGiRWjXrh0CAgKwbt06XLp0Cfv37+dztwgf9uzhrttu3gz89BPXSeqDD6iDlNDUqAH8/js357CVFRAcDEyZwo1+RYieGV3SvXHjBjw8PODr64s+ffrg9u3bAIA7d+4gLS0N4eHhirJisRghISE4efIkACAhIQEFBQVKZTw8PODv768oQyqBzExuyMGOHYG6dYGkJG4YQjMaNVXQAgK4wTW++oqbNzgwkGq9RO+M6lukWbNm+PXXX1GrVi08fvwYX3/9NVq0aIHLly8jLS0NAODq6qr0HldXV9y9excAkJaWBgsLC9jb26uUkb9fnby8POTl5SmeZ2VlAeBq14zn4erk2+c7jvLiLe79+4GRI7mBLn75BRg4kKvZahiHUI+3HPeZ5TsKzZX7eJuacrXc7t25wTXCw7l7qSdP5jpi6ZFQPyuVJW5d7Z9RJd2OHTsqfm/QoAGCg4NRo0YNrF27Fs2bNwcAlcmLGWNlTmhcVpl58+Zh9uzZKsuzs7NhYmIYjQk5OTl8h1Aheo+7eXMgPr54ABVajdCOt0wmASBCbm4usrOF13+h3Me7WjXg779fPy8s5G1ISaF9VuSMPe5sHX0ejCrpvkkikaBBgwa4ceMG3n//fQBcbda92ADz6enpitqvm5sb8vPz8ezZM6Xabnp6Olq0aFHidqZMmYKoqCjF86ysLHh5ecHGxgY2NjZa3qvyYYwhJycHEomkzJMLQ6LXuIvXbufMeV27rQChHm/5uaGlpSVsbIQTt1aOd3IyV+u9eFGvtV6hflYqS9wymUwncRhGNUxH8vLycOXKFbi7u8PX1xdubm6Ii4tTvJ6fn48jR44oEmpgYCDMzc2VyqSmpiIpKanUpCsWi1GlShWlBwCV25f4ehhSLAYVd1ERRGPHQhQeDlG1ahCdPg3Rp59CZGJi2HHr6FgLMW6tHO/69SE6eBCiiRMhmjcPoqZNIbpzRxixC/WYCyRuXTCqpDt+/HgcOXIEd+7cwZkzZ9CzZ09kZWVh4MCBEIlEGDNmDObOnYtt27YhKSkJERERsLa2Rr9+/QAAUqkUgwcPxrhx43DgwAGcP38eH3/8MRo0aIB27drxvHdEq54+5TpK/fgjN2fr3r1ckyOpnMzMgC+/5O7Hzs3lBkA5eJDvqIgRMqrm5QcPHqBv37548uQJnJ2d0bx5c5w+fRre3t4AgIkTJ+LVq1cYPnw4nj17hmbNmmHfvn2wtbVVrGPJkiUwMzPDRx99hFevXqFt27ZYs2YNTE1N+dotom3JyUC3bsDz58C+fUBoKN8REUPRoAHXo7l3b66T1bJlwPDhdJsY0RoRE1oXNAHIysqCVCpFZmamoqmZL4wxZGdnw8bGRmfNJbqgs7h37uRGKfL25maqqV5de+uGcI93rVoMN26IcPQoQ6tWwolbZ8e7sBCYMAFYuhQYOpQbZMPCQnvrh3A/K5Ulbl19jxtV8zIhJWIMmD+fu1WkbVvg5EmtJ1xiRMzMgCVLgJgYYM0a7jOTns53VMQIUNIlxu/VK652O2UKNw7vn38CxS4pEFKiQYOAw4eBGze467yJiXxHRASOki4xbg8eAK1aAdu3A5s2AdHRr++PIUQTLVpw9247OQEtWwJ//MF3RETA6NuHGK/kZKBpU65Z8MQJbjo+QirCyws4dgzo2hXo1QtYvJjviIhAGVXvZUIULlwA2rUD3N25HsrFpngkpEKsrYENGwBfX2DcOO7Woi+/5DsqIjCUdInxOXuWu92jenXu/ltHR74jIsZCJALmzuUS8NSpXOKdPZtuKSIao6RLjMupU0CHDkC9etwk83Z2fEdEjI1IBEyfDojFwKRJXOL95htKvEQjlHSJ8Th9mqvhNmkC7NpFPZSJbk2cyI3RPHo0d0vaggWUeEmZKOkS43DuHFfDbdwY2L0bkEj4johUBqNGcb3hR44ErKy43vGElIKSLhG+S5eAsDCgTh1uujZKuESfIiO5e8EnTuSanKdO5TsiYsAo6RJhu3KFGy3I2xvYswfgedhNUklNmMBd2502jWtyHjeO74iIgaKkS4QrPZ1rUnZ15W4Lok5ThE/TpnE13vHjAQ8PoG9fviMiBoiSLhGm/Hzgww+BvDyul7KTE98RkcpOJALmzAHu3+eGj6xVCwgM5DsqYmBoRCoiPIxx19H++QfYtg2oWpXviAjhiETAypXcFIHduwNpaXxHRAwMJV0iPMuXA6tWAStWAMHBfEdDiDIrK+5kUCYDevTgWmMI+Q8lXSIsBw9y90WOHQt8+inf0RCinqcnl3jPnQOGDeNaZwgBJV0iJLducYPNt2nDDURAiCFr1oxrkVmzBli2jO9oiIGgjlREGLKygG7dAAcHboo+M/roEgEYMIC7j3zcOG5o0vBwviMiPKOaLjF8Mhnw8cfc3Lg7dgD29nxHRIjm5s0D2rcHevcGbtzgOxrCM0q6xPBFR3NjKW/YANSty3c0hJSPqSn32XV15VprcnL4jojwiJIuMWznzwNffw3MnAl06sR3NIRUjFQKbN8OpKTQHLyVHCVdYtiGDQMaNaIvKiJ8depwc/F+9x1w9Cjf0RCeUNIlhu3WLa73p7k535EQ8vZGjQJatuRGrKJm5kqJki4xTOfPcz8nTeJG9yHEGJiaArGxwMOHNA1gJUVJlxievDyuWRkAxozhNRRCtK5mTa6ZecUKviMhPKCkSwxPdDTXrAxQszIxTqNGAc2bc79TM3OlQkmXGJazZ4FvvuGalQkxVqam3BjiADUzVzKUdInhyMsDIiK43srUrEyMXY0a3M8VK6g3cyVCSZcYjjlzgOvXqbcyqVyaN+d6M+fm8h0J0QNKusQwPHgAfPstMHEi9VYmlcv333ODZvz4I9+RED2gpEsMQ3Q0YGPDJV1CKpNatYDBg7kezZmZfEdDdIySLuHftWvAL78AU6cCVarwHQ0h+jdzJvDqFdfaQ4waJV3Cv6lTuUm/5ffmElLZeHgAo0cDS5YAqal8R0N0iJIu4Vd8PPDnn1zzsqUl39EQwp+JEwELC26CD2K0KOkS/jAGTJ4M1K/PzZdLSGVmbw9MmQKsXAncvMl3NERHjCrpzps3D++88w5sbW3h4uKC999/H9euXVMqExERAZFIpPRoLh8Z5j95eXkYOXIknJycIJFI0K1bNzx48ECfu1I5xMUBBw9yHUhMTfmOhhD+jRwJuLgA06fzHQnREaNKukeOHMGIESNw+vRpxMXFobCwEOHh4ch5Y5i1Dh06IDU1VfHYvXu30utjxozBtm3bsHHjRhw/fhzZ2dno0qULioqK9Lk7xk0m487qW7QAunblOxpCDIOVFTBrFrBx4+tJP4hRMeM7AG3as2eP0vPY2Fi4uLggISEB7733nmK5WCyGm5ub2nVkZmYiJiYGv/32G9q1awcAWLduHby8vLB//360b99edztQmWzZApw7x43EIxLxHQ0hhuPTT4FFi7iT0je+04jwGVVN902Z/93z5uDgoLT88OHDcHFxQa1atTB06FCkp6crXktISEBBQQHCw8MVyzw8PODv74+TJ0/qJ3BjxxgwezbQqRPQqhXf0RBiWMzMuNHZ9u4FTp/mOxqiZUZV0y2OMYaoqCi8++678Pf3Vyzv2LEjevXqBW9vb9y5cwfTp09HmzZtkJCQALFYjLS0NFhYWMDe3l5pfa6urkhLS1O7rby8POTl5SmeZ2VlKWJgjOlg7zQn3z7fcSg5epQbgWrFCi4Bq2GQcWtAqHHLcZ9ZvqPQnJCPd6mxv/8+4O/P3b/erJl+AyuDUI95eePW1f4ZbdKNjIzExYsXcfz4caXlvXv3Vvzu7++PoKAgeHt74++//0aPHj1KXB9jDKISmkHnzZuH2bNnqyzPzs6GiYlhNCa8eV2bV02acJN4A0B2dqlFDSruchBa3DKZBIAIubm5yM4WXt8FoR3v4kqMXd6yVsb/CF+Eesw1jTtbR8e9XEmXMYYjR47g2LFjSElJwcuXL+Hs7IyAgAC0a9cOXl5eOgmyvEaOHIkdO3bg6NGjqFq1aqll3d3d4e3tjRs3bgAA3NzckJ+fj2fPninVdtPT09GiRQu165gyZQqioqIUz7OysuDl5QUbGxvY2NhoYY8qjjGGnJwcSCSSEk8a9Co1lbtFaP584LPPSixmcHFrSKhxy88NLS0tYWMjnLiFerwBDWLPyADq1OEGjzGgWbeEeszLG7dMJtNJHBol3VevXmHJkiVYvnw5MjIy0KhRI3h6esLKygo3b97E9u3bMXToUISHh2PGjBkqt+DoC2MMI0eOxLZt23D48GH4+vqW+Z6MjAzcv38f7u7uAIDAwECYm5sjLi4OH330EQAgNTUVSUlJWLBggdp1iMViiMVileXyW5IMgcHEsmoVUFAA9OmjUQcqg4m7nIQXN9eUJry4OUKNGygldicnoHNnbkKEUaMM7rY6oR5zTePW1b5plHRr1aqFZs2aYcWKFWjfvj3M1Uy7dvfuXfz+++/o3bs3pk2bhqFDh2o92LKMGDECv//+O/766y/Y2toqrsFKpVJYWVkhOzsbs2bNwocffgh3d3ekpKTgyy+/hJOTEz744ANF2cGDB2PcuHFwdHSEg4MDxo8fjwYNGih6M5MKKijgbvz/+GNAKuU7GkIM3/DhwK+/cp2qOnXiOxqiDUwDly5d0qQYY4yxvLw8dv36dY3LaxO403WVR2xsLGOMsZcvX7Lw8HDm7OzMzM3NWbVq1djAgQPZvXv3lNbz6tUrFhkZyRwcHJiVlRXr0qWLSpnSZGZmMgAsMzNTm7tXITKZjGVlZTGZTMZ3KIz98QdjAGOJiWUWNai4y0GocdesKWMAY0ePCituoR5vxjSMXSZjrEkTxjp31l9gZRDqMS9v3Lr6HhcxJrAuaAKQlZUFqVSKzMxMVOF51hzGGLKzs2FjY8N/U1DbtkBeHvBG5zZ1DCruchBq3LVqMdy4IcLRowytWgknbqEeb6AcscfEAEOHArduARpcMtM1oR7z8satq+/xCvVezs3NxcWLF5Genq5ysblbt25aCYwYmStXuCEf16/nOxJChKVvX2D8eODnn7kOiETQyp109+zZg08++QRPnjxReU0kEtFQiUS9FSu4MWU//JDvSAgRFmtrbpSqmBhuiEiajUvQyn0TaWRkJHr16oXU1FTIZDKlByVcolZ+PtcZZNAgQE0vb0JIGYYNA548AXbs4DsS8pbKnXTT09MRFRUFV1dXXcRDjNGxY8Dz50CxgUkIIeVQqxbQqBElXSNQ7qTbs2dPHD58WAehEKO1Ywfg5cV9aRBCKqZbN+Dvv7lb74hglfua7g8//IBevXrh2LFjaNCggco9u6NGjdJacMQIMMYl3a5daTYhQt5Gt27AV18BJ04ArVvzHQ2poHIn3d9//x179+6FlZUVDh8+rNT1WiQSUdIlypKSgJQU7guDEFJxTZoAHh7Azp2UdAWs3M3L06ZNQ3R0NDIzM5GSkoI7d+4oHrdv39ZFjETIduwAbGzoS4KQt2ViAnTpAvz1V4mzcxHDV+6km5+fj969exvM7DnEwO3cCXToQL2WCdGGbt24QTKuXuU7ElJB5c6cAwcOxKZNm3QRCzE2aWnAmTPUtEyItrRpw923S72YBavc13SLioqwYMEC7N27Fw0bNlTpSLV48WKtBUcEbtcurkmMBmonRDusrIDwcK4FadIkvqMhFVDupHvp0iUEBAQAAJKSkpReE9I4nEQPduwAWrYEHB35joQQ49G1KzBkCPDvv4CzM9/RkHIqd9I9dOiQLuIgxubVKyAujrvFgRCiPZ07cz937wYGDuQ3FlJu1BuK6EZiIpCby12DIoRoj6sr4O8PnDzJdySkAio0y1B8fDy2bNmCe/fuIT8/X+m1rVu3aiUwInAJCYCFBfflQAjRrsBA7n+MCE65a7obN25Ey5YtkZycjG3btqGgoADJyck4ePAgpFKpLmIkQpSQADRowCVeQoh2BQYCFy9y81MTQSl30p07dy6WLFmCXbt2wcLCAsuWLcOVK1fw0UcfoVq1arqIkQjR2bPcFwMhRPuCgrgxmN/ozEoMX7mT7q1bt9D5vwv5YrEYOTk5EIlEGDt2LFauXKn1AIkAvXwJJCdT0iVEVxo1AkxNqYlZgMqddB0cHPDixQsAgKenp+K2oefPn+Ply5fajY4I04ULgExGSZcQXbGyAurVo6QrQOXuSNWqVSvExcWhQYMG+OijjzB69GgcPHgQcXFxaNu2rS5iJEKTkACYm1MnKkJ0iTpTCVKFpvbLzc0FAEyZMgXm5uY4fvw4evTogenTp2s9QCJACQlAw4Y03jIhuhQYCKxfz3Wmov81wSh30nVwcFD8bmJigokTJ2LixIlaDYoI3NmzQIsWfEdBiHEr3pmKLuUIRoUGx7h16xamTZuGvn37Ij09HQCwZ88eXL58WavBEQGiTlSE6Ad1phKkcifdI0eOoEGDBjhz5gy2bt2K7OxsAMDFixcxc+ZMrQdIBObyZa4T1X/jcxNCdMTKCqhbl+u4SASj3El38uTJ+PrrrxEXFweLYgMfhIaG4tSpU1oNjgjQgwfcT29vfuMgpDKoVu31/xwRhHIn3UuXLuGDDz5QWe7s7IyMjAytBEUELDUVMDMDnJz4joQQ4+fuzv3PEcEod9K1s7NDqpo/8vnz5+Hp6amVoIiAPXoEuLlx8+gSQnTLw4P7nyOCUe5vxn79+mHSpElIS0uDSCSCTCbDiRMnMH78eHzyySe6iJEISWoqd/ZNCNE9d3fg8WOuHwURhHIn3Tlz5qBatWrw9PREdnY26tWrh/feew8tWrTAtGnTdBEjEZLUVO7smxCiex4eQGEh8OQJ35EQDZX7Pl1zc3OsX78e0dHROH/+PGQyGQICAlCzZk1dxEeE5tEjIDiY7ygIqRzkrUqPHgEuLvzGQjRSofl0AaBGjRqoXr06AEAkEmktICJwVNMlRH/k/2upqUDjxryGQjRTod4uMTEx8Pf3h6WlJSwtLeHv74/Vq1drOzYiNAUFQHo6XdMlRF9cXQGRiDpTCUi5a7rTp0/HkiVLMHLkSAT/14x46tQpjB07FikpKfj666+1HiQRiMePuZ+UdAnRD3Nz7vY8um1IMMqddH/66SesWrUKffv2VSzr1q0bGjZsiJEjR1LSrczk//jUvEyI/nh4UNIVkHI3LxcVFSEoKEhleWBgIAoLC7USFBGo/8bhpg4dhOiRi8vr/z1i8MqddD/++GP89NNPKstXrlyJ/v37ayUoIlAFBdxPmmZMUIpkDLkF3H2eyalZKJIxniMi5SIWA/n5fEdBNKRR83JUVJTid5FIhNWrV2Pfvn1o3rw5AOD06dO4f/++0Q2OsXz5cnz77bdITU1F/fr1sXTpUrRq1YrvsAyXvKXDrMKd4ome7UlKxeydyXic1RyABPN3X8GaWzmY2bUeOvjTtXlBMDOjpCsgGn07nj9/Xul54H/Ttt26dQsAN+6ys7OzUU3tt2nTJowZMwbLly9Hy5Yt8fPPP6Njx45ITk5GtWrV+A7PMMlruubm/MZBNLInKRVfrDuHN+u1aZm5+GLdOfz0cRNKvEJgZsZNqUkEQaOke+jQIV3HYXAWL16MwYMHY8iQIQCApUuXYu/evfjpp58wb948nqMzUFTTFYwiGcPsnckqCRcAGAARgNk7kxFWzw2mJnQfvkEzM3t9wksMHn07qpGfn4+EhARMnjxZaXl4eDhOnjypUj4vLw95eXmK51lZWQAAxhgY4/f6mHz7eovD1pabWPstt6f3uLVEKHGfTXmKrFcFkFiYAuBu9QQAsbkpTP9blvWqAGdTnqKprwNfYZZJKMdbHa3FbmsLZGS89f+cpoR6zMsbt672T6OkO2zYMEydOhVeXl5llt20aRMKCwsF3anqyZMnKCoqgqurq9JyV1dXpKWlqZSfN28eZs+erbI8OzsbJgYy205OTo7uN/L++9zj1SutrVIvceuAocddz9kCpye0VDwP+N0KtzKA73rVR3BwkVLZ7OxsfYdXboZ+vEvz1rEvWsT91PPfSajHXNO4dfW51yjpOjs7w9/fHy1atEC3bt0QFBQEDw8PWFpa4tmzZ0hOTsbx48exceNGeHp6YuXKlToJVt/eHN6SMaZ2yMspU6YodTbLysqCl5cXbGxsYGNjo/M4S8MYQ05ODiQSie6H69y0CfjsM26QDEvLt1qVXuPWIqHE/c+dpxi0Jl7x/MHzVgAkGLXlMkyPv54X+5eIdwy+piuE462O1mIfNAj4919g507tBVcKoR7z8sYt09HMTRol3a+++gojR45ETEwMVqxYgaSkJKXXbW1t0a5dO6xevRrh4eE6CVSfnJycYGpqqlKrTU9PV6n9AoBYLIZYzW0yIpHIYD6UeovlxQugqOh1e+VbMqRjWB6GHneQjwOqWJkjLTMXDK9bJvMKisDyiyAC4Ca1RJCPg0Hvh5yhH+/SvHXs2dncNV09779Qj7mmcetq3zRu+3RxccGUKVNw4cIFZGRk4Ny5czhx4gSuXbuGZ8+e4Y8//jCKhAsAFhYWCAwMRFxcnNLyuLg4tGjRgqeoBEDegYoGSTF4piYizOxaDwDXaao4+fOZXetRJyohKCigOwYEpEIdqezs7GBnZ6flUAxLVFQUBgwYgKCgIAQHB2PlypW4d+8ehg0bxndohkuedKknpSB08HfHTx83weydySg+XL6b1JLu0xWSwkK6Y0BA6C9Vgt69eyMjIwPR0dFITU2Fv78/du/eDW9vb75DM1zy67ha7EhFdKuDvzvC6rnBd6UM958CkzvVxZCeVaiGKyS5uYCjI99REA1R0i3F8OHDMXz4cL7DEA759e7HjwEaQEQwTE1EsDTnrjTVc6eEKzhpaUD9+nxHQTRkGPezEOMgn9KP5vYkRH8ePaLpNAWEki7RHmdnwMSEphkjRF9yc4FnzyjpCojGSTc5ObnMMuvWrXurYIjAmZoCbm6UdAnRF/ltjTSHtWBonHQDAwOxcOFCtUNjPX78GN26dcMXX3yh1eCIALm7U/MyIfoi/1+jmq5gaJx0161bhwULFuC9995TzC4kX16vXj1kZmYiMTFRFzESIfHwoJouIfoi/1+jmq5gaJx0P/zwQyQlJcHJyQmNGjXCwoUL0b17d3z++eeYMWMGDh8+jBo1augyViIEVNMlRH8ePeImsbe35zsSoqFy3TLk4uKCbdu2oX///pg4cSIkEglOnz6NBg0a6Co+IjTu7lTTJURfUlO5fhQCHI6xsipX7+Vnz56hX79+2L59OyZPngwXFxf07t0b8fHxZb+ZVA4eHkB6Og0FSYg+pKZS07LAaJx0d+3ahXr16uHWrVtISEjA3LlzcfHiRYSEhODdd9/FtGnTUEhftMTdHZDJuMRLCNEtukdXcDROuj179sTIkSNx6tQp1KlTBwAgkUjw008/YdeuXfjtt98QFBSks0CJQMhHorp9m984CKkM7tyh0d8ERuOk+88//+DLL79UOyl7WFgYLl26hMDAQK0GRwSobl3AwgI4d47vSAgxbpmZwI0bQEAA35GQctA46TZs2LDU16tUqYKYmJi3DogInIUF0LAhkJDAdySEGLfz57mfVNkRFBoGkmhfUBAlXUJ07exZwNoa+O9yHxEGSrpE+wIDgStXgJwcviMhxHglJHBNy6amfEdCyoGSLtG+wECuBzONUEaI7iQkUNOyAFHSJdpXvz53bZeamAnRDXknKkq6glPhSezPnj2LK1euQCQSoU6dOnS7EHmNOlMRolvUiUqwyp10Hzx4gL59++LEiROws7MDADx//hwtWrTAhg0b4OXlpe0YiRAFBQHHjvEdBSHGiTpRCVa5m5cHDRqEgoICXLlyBU+fPsXTp09x5coVMMYwePBgXcRIhIg6UxGiO9SJSrDKnXSPHTuGn376CbVr11Ysq127Nr7//nsco5oNkaPOVIToDnWiEqxyJ91q1aqhoKBAZXlhYSE8PT21EhQxAv7+gFQKxMXxHQkhxiUlhetE1aoV35GQCih30l2wYAFGjhyJs2fPgjEGgOtUNXr0aCxcuFDrARKBMjcHOnUCduzgOxJCjMvOnVxnxfbt+Y6EVIBGHans7e0hKjZfY05ODpo1awYzM+7thYWFMDMzw6BBg/D+++/rJFAiQN26ARs2AA8eAFWr8h0NIcZhxw4gNBSwteU7ElIBGiXdpUuX6jgMYpQ6dADMzLgz8y++4DsaQoQvMxM4fBhYtozvSEgFaZR0Bw4cqOs4iDGyswNCQrgzc0q6hLy9PXuAwkKgSxe+IyEV9FYjUr169QpZWVlKD0KUdOsGHDwIvHjBdySECN+OHUDjxjSHroCVO+nm5OQgMjISLi4usLGxgb29vdKDECVduwL5+cC+fXxHQoiwFRQAu3dzJ7JEsMqddCdOnIiDBw9i+fLlEIvFWL16NWbPng0PDw/8+uuvuoiRCJmvL3f7EPViJuTtnDgBPH9OSVfgyj0M5M6dO/Hrr7+idevWGDRoEFq1agU/Pz94e3tj/fr16N+/vy7iJELWrRvw88/ctSizCg/3TUjltmMH4OEBNGnCdyTkLZS7pvv06VP4+voCAKpUqYKnT58CAN59910cPXpUu9ER49CtG5CRAZw6xXckhAgTY1zS7doVKHb7JhGecifd6tWrIyUlBQBQr149bN68GQBXA5ZPgECIknfe4c7Qf/+d70gIEaazZ4FbtwAaB0Hwyp10P/30U1y4cAEAMGXKFMW13bFjx2LChAlaD5AYARMTYMgQYN06gHq4E1J+y5cD3t5AWBjfkZC3VO4LbGPHjlX8HhoaiqtXr+Ls2bOoUaMGGjVqpNXgiBEZOhSYM4dLvMOH8x0NIcKRkQFs3AjMnEmzChmBt7pPF+AmQOjRowccHBwwaNAgbcREjFHVqkD37twZ+39jdhNCNLBmDTdjF32/GoW3TrpyT58+xdq1a7W1OmKMhg8HLl+mye0J0ZRMBvz0E9CrF+Diwnc0RAu0lnT5lpKSgsGDB8PX1xdWVlaoUaMGZs6cifz8fKVyIpFI5bFixQqlMpcuXUJISAisrKzg6emJ6OhoxYxK5C20aQPUrs3VdgkhZYuL4zpQ0SUZo2E0N01evXoVMpkMP//8M/z8/JCUlIShQ4ciJydHZcrB2NhYdOjQQfFcKpUqfs/KykJYWBhCQ0MRHx+P69evIyIiAhKJBOPGjdPb/hglkYgbg3n8eCAtDXBz4zsiQgzb8uVAo0ZAcDDfkRAtMZqk26FDB6VEWr16dVy7dg0//fSTStK1s7ODWwlf+OvXr0dubi7WrFkDsVgMf39/XL9+HYsXL0ZUVJTSFIekAgYOBKZMAVavBqZN4zsaQgzX3bvArl1c8zJ97xgNjZNujx49Sn39+fPnbxuL1mVmZsLBwUFleWRkJIYMGQJfX18MHjwYn332GUxMuJb2U6dOISQkBGKxWFG+ffv2mDJlClJSUhQDgxSXl5eHvLw8xXP5xA+MMd6bpeXb5zsOBamU6xCybh0waVKJI1QZXNwaEmrcctxnlu8oNCfk411m7GvWcK1BffsaVOdDoR7z8satq/3TOOkWb4It6fVPPvnkrQPSllu3buH777/HokWLlJZ/9dVXaNu2LaysrHDgwAGMGzcOT548wbT/al1paWnw8fFReo+rq6viNXVJd968eZg9e7bK8uzsbEUy51tOTg7fIbw2bx73yM0ts6hBxV0OQotbJpMAECE3NxfZ2UV8h1NuQjvexZUYe1QU9wCA7Gz9BaQhoR5zTePO1tEx1zjpxsbG6iSAssyaNUttQisuPj4eQUFBiuePHj1Chw4d0KtXLwwZMkSp7LRiTZqNGzcGAERHRystf7MJWX7GU1LT8pQpUxAl/+cAV9P18vKCjY0NbGxsSo1d1xhjyMnJgUQiMaym8fBwbizmAwfUNp0ZbNxlEGrc8nNDS0tL2NgIJ26hHm+gjNhjYoBx44B//gFq1eInwBII9ZiXN26ZTKaTOAz+mm5kZCT69OlTapniNdNHjx4hNDQUwcHBWLlyZZnrb968ObKysvD48WO4urrCzc0NaWlpSmXS09MBvK7xvkksFis1R8vJe0cbAkOKBQB3XbdtW2D7dqCUSxcGF7eGhBf36xNLYcXNEWrcgJrYc3KA6dO5Mctr1+YvsDII9ZhrGreu9s3gk66TkxOcnJw0Kvvw4UOEhoYiMDAQsbGxGjXtnj9/HpaWlopxo4ODg/Hll18iPz8fFhYWAIB9+/bBw8NDpdmZvIU2bbja7pdfcl8uNPsQIZxly7hRqKKj+Y6E6IBhXHDUgkePHqF169bw8vLCwoUL8e+//yItLU2p1rpz506sWrUKSUlJuHXrFlavXo2pU6fis88+U9RU+/XrB7FYjIiICCQlJWHbtm2YO3cu9VzWhXnzgGvXABpUhRBORgbwzTfcrXVq+o8Q4TOa6sW+fftw8+ZN3Lx5E1WrVlV6TX5N1tzcHMuXL0dUVBRkMhmqV6+O6OhojBgxQlFWKpUiLi4OI0aMQFBQEOzt7REVFaV0zZZoSZMmQO/e3Jiy/foBVlZ8R0QIv+bP50ahmjqV70iIjoiY0Pp9C0BWVhakUikyMzNRpUoVXmNhjCE7Oxs2NjaGWVO/eROoW5er9Y4fr1hs8HGXQKhx16rFcOOGCEePMrRqJZy4hXq8ATWx378P1KwJTJ4MzJrFd3glEuoxL2/cuvoeN5rmZSJQfn7ctH9z5wIGeK83IXozaxZga/v6NiFilCjpEv7NmAHk5QHffst3JITw48oVbjCMadMAnlvHiG5R0iX8c3cHxowBliwBUlP5joYQ/Zs6FfDyAoYN4zsSomOUdIlhmDABsLTkar2EVCanTwPbtnG3CKm5358YF0q6xDDY2QFff81NhHDwIN/REKI/I0YATZsC/fvzHQnRA0q6xHAMGwa0bs1NiGCAY80SohP37nHXc01N+Y6E6AElXWI4TEyAX34BnjyhZmZi/P75h/s5dSp32xypFCjpEsPi68uNyBMTw3ckhOjOq1fcqFMAEBnJbyxEryjpEsPzxRfAu+9yv1MzMzFGM2ZwzcoAjTteyVDSJYbHxARYvpz7ffp0fmMhRNtOnQIWLaKhHispSrrEMHl7cz9/+YWbc5cQY/DqFRARAbzzDjUrV1KUdIlhe/ddYPBg4MULviMh5O3NmAHcvcv1VqZm5UqJki4xbMuXc72Zx47lOxJC3s7Ro8DixdwgGNRbudKipEsMm7c38N13XG/mn37iOxpCKubePaBnT6BVK5rQoJKj9g1i+AYNAhITgVGjuBpC69Z8R0SI5nJygO7dAYkE2LKFmpUrOarpEmFYvBgICeFqC3fu8B0NIZphjOs4deMG8NdfgLMz3xERnlHSJcJgZgZs2sSN0dy9O92/S4Th66+BP/4AfvsNaNiQ72iIAaCkS4TD0ZGrLaSkAJ98AshkfEdESMm2beN6K0dHAx98wHc0xEBQ0iXCUr8+sH49sH07MHs239EQot6lS8CAAdzlkGnT+I6GGBBKukR4unYF5szhahBbtvAdDSHKnjwBunUD/Py4+3FFIr4jIgaEutERYZo8Gbh4keuk4ucHBATwHREhQEEBV7vNyQEOH+Z6LBNSDNV0iTCJRNy9u3XrAh07Atev8x0RqewKC7mJ6E+eBP788/VQpoQUQ0mXCJe1NfC//3EdrNq0AW7f5jsiUlnJZNz95Fu3Aps3c4NgEKIGJV0ibM7OwP79XAJu2/b1dGmE6ItMBnz+OdfBb/164P33+Y6IGDBKukT43N1fz0TUti3w6BG/8ZDKgzFg9GjuUkdsLNC7N98REQNHSZcYBy8v4OBBIC+PG7nq/n2+IyLGTibjpuf74QdgxQru3nFCykBJlxgPX1/gyBGuQ8t779FwkUR3ioqAoUO5SThWrwY++4zviIhAUNIlxsXXl5tCzcyM68xCvZqJthUWcrXaNWuAX3/l5nsmREOUdInx8fLiEm+VKlyN9+JFviMixiI3F+jTh+uhvHEj8PHHfEdEBIaSLjFO7u7c4AQeHkCLFtywkYS8jUePuP4Cf//N3YfbqxffEREBoqRLjJeLC3DsGNChAzfg/Fdfcb1NCSmvf/4BgoKAhw+5z1S3bnxHRASKki4xbhIJ1xQ4ezY340vv3twQfYRoat067jKFtzcQH88lX0IqiJIuMX4mJlzC3boV2L0bePddGkSDlK2oCJg4kZstqG9f7nKFuzvfURGBo6RLKo8PPuDGxX3+nKutHD/Od0TEUGVmck3IixYBixcDv/wCiMV8R0WMACVdUrk0bMg1Edarx43XvHo13xHxrkhWhFeFrwAAFx5fQJGsiOeIeHb9OtCsGXeC9r//AWPH0vR8RGuMKun6+PhAJBIpPSZPnqxU5t69e+jatSskEgmcnJwwatQo5OfnK5W5dOkSQkJCYGVlBU9PT0RHR4NRBxzj4eQExMVx91cOHQqMGAG8fMl3VLzYemUrfJb54GEWN3TmxLiJ8Fnmg61XtvIcGU927OASLgCcOQOEh/MbDzE6RjefbnR0NIYOHap4bmNjo/i9qKgInTt3hrOzM44fP46MjAwMHDgQjDF8//33AICsrCyEhYUhNDQU8fHxuH79OiIiIiCRSDBu3Di97w/REXNzbjShRo2AMWO4JBwbC7RsyXdkerP1ylb03NwTDAzF63EPsx6i5+ae+OOjP9Cjbg/e4tOrZ8+4MZR/+41rVv71V0Aq5TsqYoSMqqYLALa2tnBzc1M8iifdffv2ITk5GevWrUNAQADatWuHRYsWYdWqVcjKygIArF+/Hrm5uVizZg38/f3Ro0cPfPnll1i8eDHVdo3RsGFAYiI3PWCrVsC4cZWi1lskK8LoPaPBoPqZli8bs2dM5Whq3rkTqF+fq+WuWcPd000Jl+iI0SXdb775Bo6OjmjcuDHmzJmj1HR86tQp+Pv7w8PDQ7Gsffv2yMvLQ0JCgqJMSEgIxMU6TbRv3x6PHj1CSkqK2m3m5eUhKytL6QEAjDGDeBhSLAYZd+3aYMeOgS1ZArZ2LVjLlmCnThl+3G/xOHH/BDJzM2FrYQtbC1uY/HfN0trMGrYWtrCxsMHz3Oc4cf8E77Hq7Hg/ewb22Wdg/ftzf/PLl8E++QRMj38/IXxWKnPcumBUzcujR49GkyZNYG9vj3/++QdTpkzBnTt3sPq/zjJpaWlwdXVVeo+9vT0sLCyQlpamKOPj46NURv6etLQ0+Pr6qmx33rx5mD17tsry7OxsmJgYxnlNjkDvTdVr3IMGcQ+57OwKr8rQj3eAQwAeRj58/TxGgltPgN+7/47gYOXabfZbHAd9qdDxNjPjeicvWvR6GQ/7auiflZIYe9y6+twbfNKdNWuW2oRWXHx8PIKCgjB27FjFsoYNG8Le3h49e/ZU1H4BQKSmFyJjTGn5m2XkZzzq3gsAU6ZMQVRUlOJ5VlYWvLy8YGNjo9S8zQfGGHJyciCRSEqM3xDxGndREfDjj9wIVl5ewPLlQPPmGr1VKMf72L1j6PJ7F8Xzl5nnAdRAv7/6IT/hoGL5rn670KpaKx4i1Ey5j/fz58CkSdy4ye3bA0uXckOF8kAon5U3VZa4ZTKZTuIw+KQbGRmJPn36lFrmzZqpXPP/vihv3rwJR0dHuLm54cyZM0plnj17hoKCAkVt1s3NTVHrlUtPTwcAlVqynFgsVmqOlpP3oDYEhhRLefASt5kZ16mmfXvg00+5zlWjRnEDbDg4aLQKQz/eLb1aQmopxcOsh1xHqv9OLF8WvsSr/BcQQYSqVaqipVdLg94PuTKPt0wGbNnC3f7z8iXw/ffcTEEGsG+G/lkpibHHrat9M4y2z1I4OTmhTp06pT4sLS3Vvvf8+fMAAPf/RpEJDg5GUlISUlNTFWX27dsHsViMwMBARZmjR48qXQvet28fPDw8SkzuxEjVqcMNoLFgAXc/b/XqwLx5RtHRytTEFMs6LAMAiKD85SJ/vrTDUpiamOo9Nq2LiwOaNuVmB2raFLh8GRg40CASLql8DD7paurUqVNYsmQJEhMTcefOHWzevBmff/45unXrhmrVqgEAwsPDUa9ePQwYMADnz5/HgQMHMH78eAwdOhRVqlQBAPTr1w9isRgRERFISkrCtm3bMHfuXERFRQnyrI68JVNTYPx44PZtrmY0cybg5wesWAEUFPAd3VvpUbcH/vjoD3hW8VRaXrVKVeO4XSg+HmjXjrvX1sKCm+5x+3bA07PMtxKiM8xIJCQksGbNmjGpVMosLS1Z7dq12cyZM1lOTo5Subt377LOnTszKysr5uDgwCIjI1lubq5SmYsXL7JWrVoxsVjM3Nzc2KxZs5hMJtM4lszMTAaAZWZmamXf3oZMJmNZWVnlit8QGGzct24x9vHHjIlEjNWsydimTYwVFSleNti4S1FYVMiq+uYwgLHvt5xnhUWFfIekMbXH++pVxnr2ZAxgrH59xv76izED/HsI8bPCWOWJW1ff4yLGdNQvuhLLysqCVCpFZmamogbNF8YYsrOzYWNjI6iausHHfeEC8OWX3AQKgYFcs3NYmOHHXYJatRhu3BDh6FGGVq2EE7fS8X70iJtN6pdfuM5R0dHcZAWmhtlELtTPSmWJW1ff40bTvEyIXjVqxE1mfuQI13QZHs41ZR49yndkldP06Vyz/9atwLffcuMnR0QYbMIllRclXULexnvvASdOAH/9BTx+DHTtyi1fuZKbqYbohkwG7N8P9O/PPY+JASZMAG7d4nool9C5khC+UdIl5G2JRNx4vRcvckMKAsDkyVyHnWHDuKZooh3PnnH31tatC4SFcUkW4Gq20dE0fCMxeJR0CdEWkYir+QLcbSkTJnBJuHFj4N13gd9/B/LyeA1RsM6dA4YM4U5kJk4EmjThmvJPneJe53kQGkI0RUmXEF1wd+duL0pJAf74g5sAvX9/boSrL78ErlwBqA9j6Z494yYgaN6c66y2bx8wdSpw7x6wYQM3QYWAOvIQAlDSJUS3zM2BDz8EDhwAkpOBvn25ISbr1QNq1eJmNTp8GCgs5DtSw3DrFtd83KYN4OzMjQgmlXL3196+zSVdNze+oySkwijpEqIvdesCy5YBaWlcz+e2bbkaW2go4OICfPwxsHlz5eqAVVTENRFPmQL4+3M9kCdPBqysgB9+AO7fB/buBbp354bnJETg6FNMiL5ZWQGdOnGP5cu565U7dnCP9eu52nHr1lznrPbtuURkTM2oz55x12N37AB27QLS0wEnJ6BLF26SibAwukZLjBYlXUL4ZGICBAVxj+ho4O5dLhHt2AFERXFDTUqlXMehoCDu2mZgIFCjhjAS8dOn3ElFQsLrx+3b3Gt16nD30nbrxl23pXtqSSVASZcQQ+LtDYwYwT2ysrim17NnuWS1aRM38APwOhHLk3BgIODry18TLGNARgZw/rxygr1zh3vdxoaLt3t3LtbmzbkTB0IqGUq6hBiqKlW45uX27V8v+/dfruYoT8SbNwMLF3KvmZhw14bd3blhEN3dlX+X/3R15ZqwNcEY1xz86BGQmvr6Z/Hf5T9zc7n3yBPs+++/PiGoVYuLj5BKjpIuIULi7Kw+EZ8/z92eVDwJnj/PjQ39+DHXYak4CwuuVmxmxiXgrHgAvkDv3sCrOK43dUEB93hzMm87u9cJvHp1br5hd3fuHtoGDSjBElIKSrqECJ2zMzf2c0mKirjELE/IaWlcrbSw8HVyXSQFngD44APA553XCdnMjFu/vKbs5sZ1BCOEVAglXUKMnakplyzd3ICAAPVlfmFc0u3TBxDQLEOECA21ARFCCCF6QkmXEEII0RNKuoQQQoieUNIlhBBC9ISSLiGEEKInlHQJIYQQPaGkSwghhOgJJV1CCCFETyjpEkIIIXpCSZcQQgjRE0q6hBBCiJ5Q0iWEEEL0hJIuIYQQoieUdAkhhBA9oaRLCCGE6AklXUIIIURPKOkSQgghekJJlxBCCNETSrqEEEKInlDSJYQQQvSEki4hhBCiJ0aTdA8fPgyRSKT2ER8fryin7vUVK1YorevSpUsICQmBlZUVPD09ER0dDcaYvneJEEKIkTHjOwBtadGiBVJTU5WWTZ8+Hfv370dQUJDS8tjYWHTo0EHxXCqVKn7PyspCWFgYQkNDER8fj+vXryMiIgISiQTjxo3T7U4QQggxakaTdC0sLODm5qZ4XlBQgB07diAyMhIikUiprJ2dnVLZ4tavX4/c3FysWbMGYrEY/v7+uH79OhYvXoyoqCiVdRFCCCGaMprm5Tft2LEDT548QUREhMprkZGRcHJywjvvvIMVK1ZAJpMpXjt16hRCQkIgFosVy9q3b49Hjx4hJSVFD5ETQggxVkZT031TTEwM2rdvDy8vL6XlX331Fdq2bQsrKyscOHAA48aNw5MnTzBt2jQAQFpaGnx8fJTe4+rqqnjN19dXZVt5eXnIy8tTPM/KygIAMMZ4vxYs3z7fcZQXxc0P7jPLdxSaE/LxFmrslSVuXe2fwSfdWbNmYfbs2aWWiY+PV7pu++DBA+zduxebN29WKStPrgDQuHFjAEB0dLTS8jebkOUHv6Sm5Xnz5qmNMTs7GyYmhtGYkJOTw3cIFUJx64dMJgEgQm5uLrKzi/gOp9yEdryLE2rsxh53dna2TrZv8Ek3MjISffr0KbXMmzXT2NhYODo6olu3bmWuv3nz5sjKysLjx4/h6uoKNzc3pKWlKZVJT08H8LrG+6YpU6YgKipK8TwrKwteXl6wsbGBjY1NmTHoEmMMOTk5kEgkgroeTXHrl/zc0NLSEjY2wolbqMcbEG7slSXu4pcdtcngk66TkxOcnJw0Ls8YQ2xsLD755BOYm5uXWf78+fOwtLSEnZ0dACA4OBhffvkl8vPzYWFhAQDYt28fPDw8VJK7nFgsVroGLCe/JckQGFIs5UFx68vr1hxhxc0RatyAcGM39rh1tW+G0fapRQcPHsSdO3cwePBgldd27tyJVatWISkpCbdu3cLq1asxdepUfPbZZ4qk2a9fP4jFYkRERCApKQnbtm3D3LlzqecyIYSQt2bwNd3yiomJQYsWLVC3bl2V18zNzbF8+XJERUVBJpOhevXqiI6OxogRIxRlpFIp4uLiMGLECAQFBcHe3h5RUVFKzceEEEJIRRhd0v39999LfK1Dhw5Kg2KUpEGDBjh69Kg2wyKEEEKMr3mZEEIIMVSUdAkhhBA9oaRLCCGE6AklXUIIIURPKOkSQgghekJJlxBCCNETSrqEEEKInlDSJYQQQvSEki4hhBCiJ5R0CSGEED2hpEsIIYToCSVdQgghRE8o6RJCCCF6QkmXEEII0RNKuoQQQoieUNIlhBBC9ISSLiGEEKInlHQJIYQQPaGkSwghhOgJJV1CCCFETyjpEkIIIXpCSZcQQgjRE0q6hBBCiJ5Q0iWEEEL0hJIuIYQQoieUdAkhhBA9oaRLCCGE6AklXUIIIURPKOkSQgghekJJlxBCCNETSrqEEEKInlDSJYQQQvSEki4hhBCiJ5R0CSGEED2hpEsIIYToiWCS7pw5c9CiRQtYW1vDzs5ObZl79+6ha9eukEgkcHJywqhRo5Cfn69U5tKlSwgJCYGVlRU8PT0RHR0NxphSmSNHjiAwMBCWlpaoXr06VqxYoavdIoQQUomY8R2ApvLz89GrVy8EBwcjJiZG5fWioiJ07twZzs7OOH78ODIyMjBw4EAwxvD9998DALKyshAWFobQ0FDEx8fj+vXriIiIgEQiwbhx4wAAd+7cQadOnTB06FCsW7cOJ06cwPDhw+Hs7IwPP/xQr/tMCCHEyDCBiY2NZVKpVGX57t27mYmJCXv48KFi2YYNG5hYLGaZmZmMMcaWL1/OpFIpy83NVZSZN28e8/DwYDKZjDHG2MSJE1mdOnWU1v3555+z5s2baxxjZmYmA6DYLp9kMhnLyspS7J9QUNz6NXGijNWqVciePxdW3EI93owJN/bKEreuvscF07xcllOnTsHf3x8eHh6KZe3bt0deXh4SEhIUZUJCQiAWi5XKPHr0CCkpKYoy4eHhSutu3749zp49i4KCAt3vCCE8mD8fOHv2JapU4TsSQoybYJqXy5KWlgZXV1elZfb29rCwsEBaWpqijI+Pj1IZ+XvS0tLg6+urdj2urq4oLCzEkydP4O7urrLtvLw85OXlKZ5nZmYqfrI3rhfrG2MMOTk5KCoqgkgk4jWW8qC49Yvi1j+hxl5Z4s7KylK8T5t4TbqzZs3C7NmzSy0THx+PoKAgjdan7kAyxpSWv1lGfkDLW6a4efPmqd2PatWqaRQ3IYQQw/TixQtIpVKtrY/XpBsZGYk+ffqUWubNmmlJ3NzccObMGaVlz549Q0FBgaLm6ubmpqj1yqWnpwNAmWXMzMzg6OiodttTpkxBVFSU4rlMJsPTp0/h6OjI+5lgVlYWvLy8cP/+fVQRUNshxa1fFLf+CTX2yhI3YwwvXrxQumSpDbwmXScnJzg5OWllXcHBwZgzZw5SU1MVTcD79u2DWCxGYGCgosyXX36J/Px8WFhYKMp4eHgokntwcDB27typtO59+/YhKCgI5ubmarctFouVrhMDKPG2Jr5UqVJFUP8gchS3flHc+ifU2CtD3Nqs4coJpiPVvXv3kJiYiHv37qGoqAiJiYlITExEdnY2ACA8PBz16tXDgAEDcP78eRw4cADjx4/H0KFDFQe4X79+EIvFiIiIQFJSErZt24a5c+ciKipKUSMdNmwY7t69i6ioKFy5cgW//PILYmJiMH78eN72nRBCiJHQal9oHRo4cCADoPI4dOiQoszdu3dZ586dmZWVFXNwcGCRkZFKtwcxxtjFixdZq1atmFgsZm5ubmzWrFkqXcgPHz7MAgICmIWFBfPx8WE//fSTPnZRJwzp9qXyoLj1i+LWP6HGTnG/HcH0Xl6zZg3WrFlTaplq1aph165dpZZp0KABjh49WmqZkJAQnDt3rrwhGiSxWIyZM2eqNH8bOopbvyhu/RNq7BT32xExxvM9LYQQQkglIZhruoQQQojQUdIlhBBC9ISSLiGEEKInlHQJIYQQPaGkKzA+Pj4QiUQqjxEjRgAAIiIiVF5r3ry50jry8vIwcuRIODk5QSKRoFu3bnjw4IHOY581a5ZKbG5uborXGWOYNWsWPDw8YGVlhdatW+Py5cu8xz5v3jy88847sLW1hYuLC95//31cu3ZNqYwhH3dNLF++HL6+vrC0tERgYCCOHTvGd0hKUlJSMHjwYPj6+sLKygo1atTAzJkzVebLVve/8eZ82JrMqa1N6v5nJ0+erFRGW3OBa8vhw4fVHkuRSIT4+HhFOUM43oKba53H25VIBaSnp7PU1FTFIy4uTul+5YEDB7IOHToolcnIyFBax7Bhw5inpyeLi4tj586dY6GhoaxRo0assLBQp7HPnDmT1a9fXym29PR0xevz589ntra27M8//2SXLl1ivXv3Zu7u7iwrK4vX2Nu3b89iY2NZUlISS0xMZJ07d2bVqlVj2dnZijKGfNzLsnHjRmZubs5WrVrFkpOT2ejRo5lEImF3797lNa7i/ve//7GIiAi2d+9eduvWLfbXX38xFxcXNm7cOKVyAFhsbKzS3+Hly5eK1zMzM5mrqyvr06cPu3TpEvvzzz+Zra0tW7hwoc5i9/b2ZtHR0UoxvXjxQvF6YWEh8/f3Z6GhoezcuXMsLi6OeXh4sMjISN7izsvLU4o3NTWVDRkyhPn4+CiNa2AIx3vGjBls8eLFLCoqSu20r9o6vrdv32bW1tZs9OjRLDk5ma1atYqZm5uzP/74o1zxUtIVuNGjR7MaNWoo/hEGDhzIunfvXmL558+fM3Nzc7Zx40bFsocPHzITExO2Z88encY6c+ZM1qhRI7WvyWQy5ubmxubPn69Ylpuby6RSKVuxYgXvsReXnp7OALAjR44olhnycS9L06ZN2bBhw5SW1alTh02ePJmniDSzYMEC5uvrq7QMANu2bVuJ79FkTm1t8/b2ZkuWLCnxdW3NBa5L+fn5zMXFhUVHRystN6TjLYS51hkzovl0K6P8/HysW7cOgwYNUppY4fDhw3BxcUGtWrUwdOhQxaQOAJCQkICCggKlOYM9PDzg7++PkydP6jzmGzduwMPDA76+vujTpw9u374NALhz5w7S0tKU4hKLxQgJCVHExXfscvKpGx0cHJSWG/JxL0l+fj4SEhJU5pAODw/nNS5NZGZmqvwNAG4iFScnJ7zzzjtYsWIFZDKZ4jVN5tTWhW+++QaOjo5o3Lgx5syZo9S0qa25wHVpx44dePLkCSIiIlReM8TjXZyhzbUumBGpiKrt27fj+fPnSv8IHTt2RK9eveDt7Y07d+5g+vTpaNOmDRISEiAWi5GWlgYLCwvY29srrcvV1VVldiVta9asGX799VfUqlULjx8/xtdff40WLVrg8uXLim2rm8v47t27AMBr7HKMMURFReHdd9+Fv7+/YrkhH/fSPHnyBEVFRWqPO59xleXWrVv4/vvvsWjRIqXlX331Fdq2bQsrKyscOHAA48aNw5MnTzBt2jQAms2prW2jR49GkyZNYG9vj3/++QdTpkzBnTt3sHr1asV2tTEXuC7FxMSgffv28PLyUlpuiMf7TXzOta4OJV0Bi4mJQceOHZXO4Hr37q343d/fH0FBQfD29sbff/+NHj16lLgu9sa8w7rQsWNHxe8NGjRAcHAwatSogbVr1yo6Hamby7isuPQRu1xkZCQuXryI48ePKy035OOuiYocd22oyJzajx49QocOHdCrVy8MGTJEqaz8yx4AGjduDACIjo5WWl7e+bLfNu6xY8cqljVs2BD29vbo2bOnovZb0rbf/BvoO265Bw8eYO/evdi8ebNKWV0db2OZa10dSroCdffuXezfvx9bt24ttZy7uzu8vb1x48YNANx8wfn5+Xj27JlSrSs9PR0tWrTQacxvkkgkaNCgAW7cuIH3338fAHdWWfyMMT09XWmuYz5jHzlyJHbs2IGjR4+iatWqpZY15ONenJOTE0xNTdXOIf3mWb0ulHdO7UePHiE0NBTBwcFYuXJlmetv3rw5srKy8PjxY7i6umo0p7Yu4n4zJgC4efMmHB0dtTYXuK7ijo2NhaOjI7p161bm+rV1vI1lrnW1ynUFmBiMmTNnMjc3N1ZQUFBquSdPnjCxWMzWrl3LGHvdoWfTpk2KMo8ePeKlQ09ubi7z9PRks2fPVnSk+uabbxSv5+Xlqe1Ipe/YZTIZGzFiBPPw8GDXr1/X6D2GfNzf1LRpU/bFF18oLatbt67BdaR68OABq1mzJuvTp4/GPb6///57Zmlpqeggs3z5cmZnZ8fy8vIUZebPn6+3DkmMMbZz504GQNE7XN7R59GjR4oyGzduVOnow0fcMpmM+fr6qvQSLwmfx7usjlRve3wnTpzI6tatq7TuYcOGlbsjFSVdASoqKmLVqlVjkyZNUlr+4sULNm7cOHby5El2584ddujQIRYcHMw8PT1VbrupWrUq279/Pzt37hxr06aNXm5dGTduHDt8+DC7ffs2O336NOvSpQuztbVlKSkpjDHuQy6VStnWrVvZpUuXWN++fdXeMqTv2L/44gsmlUrZ4cOH1d4aYejHvSzyW4ZiYmJYcnIyGzNmDJNIJIq/iyF4+PAh8/PzY23atGEPHjxQ+jvI7dixg61cuZJdunSJ3bx5k61atYpVqVKFjRo1SlHm+fPnzNXVlfXt25ddunSJbd26lVWpUkVnt96cPHmSLV68mJ0/f57dvn2bbdq0iXl4eLBu3bopyshvaWnbti07d+4c279/P6tatarSLS36jltu//79DABLTk5Wec1Qjvfdu3fZ+fPn2ezZs5mNjQ07f/48O3/+vOK2LG0dX/ktQ2PHjmXJycksJiaGbhmqLPbu3csAsGvXriktf/nyJQsPD2fOzs7M3NycVatWjQ0cOJDdu3dPqdyrV69YZGQkc3BwYFZWVqxLly4qZXRBft+tubk58/DwYD169GCXL19WvC6TyRQ1eLFYzN577z126dIl3mOHmnmc8d/9iYwZ/nHXxI8//si8vb2ZhYUFa9KkidLtUIYgNja2xL+D3P/+9z/WuHFjZmNjw6ytrZm/vz9bunSpSmuQJnNqa0tCQgJr1qwZk0qlzNLSktWuXZvNnDmT5eTkKJXT1lzg2ta3b1/WokULta8ZyvEW2lzrNLUfIYQQoid0ny4hhBCiJ5R0CSGEED2hpEsIIYToCSVdQgghRE8o6RJCCCF6QkmXEEII0RNKuoQQQoieUNIlhJTLmjVrYGdnx3cYhAgSJV1CKpmIiAjFBBP6cP36dVhbW+P3339XWi6TydCiRQt88MEHeouFEL5R0iWE6FStWrUwf/58jBw5EqmpqYrlixYtws2bN/Hzzz/zGB0h+kVJlxCiZPHixWjQoAEkEgm8vLwwfPhwZGdnq5Tbvn07atWqBUtLS4SFheH+/fslrnPkyJFo3Lgxhg4dCgC4evUqZsyYgZUrV8LFxUVn+0KIoaGkSwhRYmJigu+++w5JSUlYu3YtDh48iIkTJyqVefnyJebMmYO1a9fixIkTyMrKKnX+U5FIhNjYWBw7dgyrVq1CREQEevfurddmbkIMAU14QEglExERgefPn2P79u0ald+yZQu++OILPHnyBADXkerTTz/F6dOn0axZMwBczbVu3bo4c+YMmjZtWuK6YmNjMWTIEHh6euLSpUuQSqVvvT+ECAnVdAkhSg4dOoSwsDB4enrC1tYWn3zyCTIyMpCTk6MoY2ZmhqCgIMXzOnXqwM7ODleuXCl13Z9++inc3d0xatQoSrikUqKkSwhRuHv3Ljp16gR/f3/8+eefSEhIwI8//ggAKCgoUCorEolU3q9u2ZvMzMxgZmamnYAJERhKuoQQhbNnz6KwsBCLFi1C8+bNUatWLTx69EilXGFhIc6ePat4fu3aNTx//hx16tTRZ7iECA6dbhJSCWVmZiIxMVFpmYODA2rUqIHCwkJ8//336Nq1K06cOIEVK1aovN/c3BwjR47Ed999B3Nzc0RGRqJ58+alXs8lhFBNl5BK6fDhwwgICFB6zJgxA40bN8bixYvxzTffwN/fH+vXr8e8efNU3m9tbY1JkyahX79+CA4OhpWVFTZu3MjDnhAiLNR7mRBCCNETqukSQgghekJJlxBCCNETSrqEEEKInlDSJYQQQvSEki4hhBCiJ5R0CSGEED2hpEsIIYToCSVdQgghRE8o6RJCCCF6QkmXEEII0RNKuoQQQoieUNIlhBBC9OT/BZXdzV/xrIMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -288,46 +463,188 @@
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(ly, lx, label='grain COM', c='g')\n",
+    "ax.scatter(ly_rot, lx_rot, label='rotation axis')\n",
+    "ax.set_xlim(y0+1000,  y0-1000)\n",
+    "ax.set_ylim(-1000, 1000)\n",
+    "ax.set_aspect(1)\n",
+    "ax.set_xlabel(\"Lab Y\")\n",
+    "ax.set_ylabel(\"Lab X (beam)\")\n",
+    "\n",
+    "ax.set_title(f\"Top-down, dty = {dty} (beam), omega = {omega}\")\n",
+    "plt.grid(axis='x', color='0.95')\n",
+    "plt.grid(axis='y', color='0.95')\n",
+    "circle1 = plt.Circle((ly_rot, lx_rot), sample_radius, color='r', fill=False)\n",
+    "ax.add_patch(circle1)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1e59f801-6b49-4704-89de-3228b8635138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-465.0\n",
+      "-65\n"
+     ]
+    }
+   ],
    "source": [
     "# To get the grain into the beam at omega = 0?\n",
+    "# dty needs to be at the true intersection position\n",
+    "# then subtract sy\n",
     "\n",
-    "dty = -465\n",
+    "dty = y0 - sy\n",
+    "omega = 0\n",
     "\n",
-    "pos_lab = geometry.sample_to_lab(pos_sample[0], pos_sample[1], y0, dty=dty, omega=0)\n",
-    "pos_rotaxis_lab = geometry.sample_to_lab(0, 0, y0, dty=dty, omega=0)\n",
-    "print(pos_lab)\n",
+    "# now where is the grain in the lab frame?\n",
+    "lx, ly = geometry.sample_to_lab(sx, sy, y0=y0, dty=dty, omega=omega)\n",
     "\n",
+    "# now where is the rotation axis in the lab frame?\n",
+    "\n",
+    "lx_rot, ly_rot = geometry.sample_to_lab(0, 0, y0=y0, dty=dty, omega=omega)\n",
+    "\n",
+    "print(dty)\n",
+    "print(y0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d9025cdb-da18-4c92-9c97-fbc113b4bd9d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-400.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ly_rot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "861dfeb7-b43f-44bb-b644-f356f5cc1c59",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.scatter(pos_lab[1], pos_lab[0], label='grain COM', c='g')\n",
-    "ax.scatter(0, 0, label='lab origin', c='k', s=100)\n",
-    "ax.scatter(pos_rotaxis_lab[1], pos_rotaxis_lab[0], label='sample centre', c='r')\n",
-    "ax.set_xlim(1000, -1000)\n",
+    "ax.scatter(ly, lx, label='grain COM', c='g')\n",
+    "ax.scatter(ly_rot, lx_rot, label='rotation axis')\n",
+    "ax.set_xlim(y0+1000,  y0-1000)\n",
     "ax.set_ylim(-1000, 1000)\n",
     "ax.set_aspect(1)\n",
     "ax.set_xlabel(\"Lab Y\")\n",
     "ax.set_ylabel(\"Lab X (beam)\")\n",
-    "ax.legend()\n",
-    "ax.set_title(f\"Top-down, dty = {dty}, omega = 0\")\n",
+    "\n",
+    "ax.set_title(f\"Top-down, dty = {dty}, omega = {omega}\")\n",
     "plt.grid(axis='x', color='0.95')\n",
     "plt.grid(axis='y', color='0.95')\n",
-    "circle1 = plt.Circle((pos_rotaxis_lab[1], pos_rotaxis_lab[0]), sample_radius, color='r', fill=False)\n",
+    "circle1 = plt.Circle((ly_rot, lx_rot), sample_radius, color='r', fill=False)\n",
     "ax.add_patch(circle1)\n",
+    "plt.annotate(\n",
+    "    '',        # Text to display\n",
+    "    xy=(0, 1000),                # Arrow's head position\n",
+    "    xytext=(0, -1000),            # Text position\n",
+    "    arrowprops=dict(\n",
+    "        arrowstyle=\"->\",       # Arrow style\n",
+    "        lw=1.5,               # Line width\n",
+    "        color='blue'            # Arrow color\n",
+    "    ),\n",
+    "    ha='center',               # Horizontal alignment of the text\n",
+    "    va='center',                # Vertical alignment of the text\n",
+    ")\n",
+    "\n",
+    "# Create a custom legend handle for the arrow\n",
+    "arrow_legend = Line2D(\n",
+    "    [0], [0],                  # Dummy data\n",
+    "    color='blue',              # Arrow color\n",
+    "    lw=1.5,                    # Line width\n",
+    "    linestyle='-',             # Line style\n",
+    "    marker='>',                # Arrow marker\n",
+    "    markersize=10,             # Marker size\n",
+    ")\n",
+    "\n",
+    "# Add the custom legend handle to the legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles.append(arrow_legend)\n",
+    "labels.append('Beam')\n",
+    "# Update the legend with the new handles and labels\n",
+    "ax.legend(handles=handles, labels=labels)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "id": "4cae0b1b-7314-4dbf-b363-23d528abbb6c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# check the co-ordinate systems\n",
     "\n",
-    "# let's assume we measured this sample from -750 to +750 um in dty, with a dty step of 10\n",
+    "# to make things harder, let's assume we did a half-acquisition\n",
     "ystep = 10  # microns\n",
     "ymin = -750\n",
-    "ymax = 750\n",
+    "ymax = +100\n",
     "\n",
     "yrange = ymax - ymin\n",
     "ny = int(yrange // ystep) + 1\n",
@@ -336,7 +653,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
+   "id": "bc25c343-c512-4d50-8369-d323950578de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "si, sj = geometry.sample_to_step(sx, sy, ystep=ystep)\n",
+    "ri, rj = geometry.sample_to_recon(sx, sy, ystep=ystep, recon_shape=(200, 200))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "e43e951d-bee2-4874-a013-0ece0ad2bb8a",
    "metadata": {
     "tags": []
@@ -354,11 +684,8 @@
     }
    ],
    "source": [
-    "pos_step = geometry.sample_to_step(*pos_sample[0:2], ystep=ystep)\n",
-    "pos_recon = geometry.sample_to_recon(*pos_sample[0:2], ystep=ystep, recon_shape=(200, 200))\n",
-    "\n",
     "fig, axs = plt.subplots(1, 3, figsize=(16, 5), layout=\"constrained\")\n",
-    "axs[0].scatter(pos_sample[1], pos_sample[0], label='grain COM', c='g')\n",
+    "axs[0].scatter(sy, sx, label='grain COM', c='g')\n",
     "axs[0].set_xlim(1000, -1000)\n",
     "axs[0].set_ylim(-1000, 1000)\n",
     "axs[0].set_aspect(1)\n",
@@ -369,7 +696,7 @@
     "axs[0].grid(axis='x', color='0.95')\n",
     "axs[0].grid(axis='y', color='0.95')\n",
     "\n",
-    "axs[1].scatter(pos_step[1], pos_step[0], label='grain COM', c='g')\n",
+    "axs[1].scatter(sj, si, label='grain COM', c='g')\n",
     "axs[1].set_xlim(-1000/ystep, 1000/ystep)\n",
     "axs[1].set_ylim(-1000/ystep, 1000/ystep)\n",
     "axs[1].set_aspect(1)\n",
@@ -379,7 +706,7 @@
     "axs[1].set_title(\"Step frame\")\n",
     "\n",
     "\n",
-    "axs[2].scatter(pos_recon[1], pos_recon[0], label='grain COM', c='g')\n",
+    "axs[2].scatter(rj, ri, label='grain COM', c='g')\n",
     "axs[2].set_xlim(0, 200)\n",
     "axs[2].set_ylim(0, 200)\n",
     "axs[2].set_aspect(1)\n",
@@ -393,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "id": "e7d82abd-f750-4206-9ee6-90599d9a4d88",
    "metadata": {
     "tags": []
@@ -419,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "id": "d83ce30f-7825-471d-8ece-14dbe1e4cd34",
    "metadata": {
     "tags": []
@@ -431,35 +758,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "4acaaf81-093f-47f5-a2e5-812f307fb3ce",
+   "execution_count": 22,
+   "id": "6022e73e-2496-4ffe-8349-df24c1b5b4ff",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-32.0 65\n"
+     ]
+    }
+   ],
    "source": [
-    "# how would our sinogram look?\n",
+    "y0 = ( 14 * 1000 ) # um\n",
+    "# we thought y0 was 14 mm which is what we scanned across\n",
+    "# in a weird sorta-half-acquisition way\n",
     "\n",
-    "omega = np.arange(0, 180, 1)  # 1 degree step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "207ed86a-79d6-4a87-ab49-5bc6377263d5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# compute our dty values from omega and the grain position\n",
+    "ystep = 10  # microns\n",
+    "ymin = 14 * 1000 - 750\n",
+    "ymax = 14 * 1000 + 100\n",
+    "\n",
+    "yrange = ymax - ymin\n",
+    "ny = int(yrange // ystep) + 1\n",
+    "ybincens = np.linspace(ymin, ymax, ny)\n",
+    "\n",
+    "omega = np.arange(0, 361, 1)  # 1 degree step\n",
+    "\n",
+    "sx = 530.6\n",
+    "sy = -530\n",
     "\n",
-    "dty = geometry.x_y_y0_omega_to_dty(omega, pos_sample[0], pos_sample[1], y0)"
+    "dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)\n",
+    "dtyi = geometry.dty_to_dtyi(dty, ystep, ybincens[0])\n",
+    "\n",
+    "# construct a sinogram\n",
+    "sino = np.zeros((ny, len(omega)), dtype=float)\n",
+    "\n",
+    "for i in range(sino.shape[0]):  # ny\n",
+    "    for j in range(sino.shape[1]):  # omega\n",
+    "        # use dtyi_calc as a lookup table from omega index\n",
+    "        this_dtyi = dtyi[j]\n",
+    "        sino[i, j] = 1/(50*np.cbrt((np.abs(i - this_dtyi)))+0.01)\n",
+    "\n",
+    "shift, pad = geometry.sino_shift_and_pad(y0, ny, ymin, ystep)\n",
+    "print(shift, pad)\n",
+    "recon = run_iradon(sino, omega, pad=pad, shift=shift)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "c16e796e-887c-4e97-82c5-e7d7db6f637d",
    "metadata": {
     "tags": []
@@ -467,7 +817,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -481,54 +831,39 @@
     "ax.scatter(omega, dty)\n",
     "ax.set_xlabel(\"Omega (deg)\")\n",
     "ax.set_ylabel(\"dty (um)\")\n",
-    "ax.set_ylim(-750, 750)\n",
+    "ax.set_ylim(ybincens.min(), ybincens.max())\n",
+    "plt.axhline(y0)\n",
     "\n",
-    "# grain is a 3,4,5 triangle\n",
-    "# at omega = 0, sample should be in the beam when dty is at (-sample_y) + y0\n",
-    "\n",
-    "plt.axhline(y0 - sample_y)  # dty\n",
-    "plt.axvline(0)  # omega\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "8cef9b44-fe9c-420d-9df8-cd6a751298d8",
+   "execution_count": 24,
+   "id": "8d2051bd-a75f-481f-bc76-f9599adf5a2b",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
-   "source": [
-    "# can we recover the lab position from the sinogram?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "ab3afbe9-5cf4-44a4-821d-1a3948386a6f",
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-65.00000000005464 299.99999999983055 400.00000000000205\n",
-      "-65 [300. 400.   0.]\n"
+      "530.6000000000004 -530.0 14000.0\n",
+      "530.6 -530 14000\n"
      ]
     }
    ],
    "source": [
-    "x_calc, y_calc, y0_calc = geometry.dty_omega_to_x_y_y0(dty, omega)\n",
-    "\n",
-    "\n",
-    "print(y0_calc, x_calc, y_calc)\n",
-    "print(y0, pos_sample)"
+    "# can we get the sample position from the dty curve?\n",
+    "sx_calc, sy_calc, y0_calc = geometry.sx_sy_y0_from_dty_omega(dty, omega)\n",
+    "print(sx_calc, sy_calc, y0_calc)\n",
+    "print(sx, sy, y0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "id": "16cd83e2-1593-46c6-8c99-c7d7f57d4eb4",
    "metadata": {
     "tags": []
@@ -536,7 +871,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -547,69 +882,55 @@
    ],
    "source": [
     "# let's investigate dtyi\n",
-    "# negative values of dty get scanned first\n",
-    "# flip vertical axis to match sinogram\n",
-    "\n",
-    "dtyi = geometry.dty_to_dtyi_for_sinogram(dty, ystep, ybincens[0])\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(omega, dtyi)\n",
     "ax.set_xlabel(\"Omega (deg)\")\n",
     "ax.set_ylabel(\"dtyi\")\n",
-    "ax.set_ylim(ny, 0)\n",
+    "ax.set_ylim(dtyi.max(), dtyi.min())\n",
+    "\n",
+    "plt.axhline(geometry.dty_to_dtyi(y0, ystep, ybincens[0]))\n",
+    "plt.axhline(0, c='g')\n",
+    "plt.axhline(ny, c='g')\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "b719211f-f41e-4588-8da9-c77da2543083",
+   "execution_count": 26,
+   "id": "c93c0695-84ee-4b57-88f7-0bf35333aa5f",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "150"
       ]
      },
+     "execution_count": 26,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# now let's come up with a sinogram image\n",
-    "# we will determine it from ystep and omega\n",
-    "\n",
-    "sino = np.zeros((ny, len(omega)), dtype=float)\n",
-    "\n",
-    "for i in range(sino.shape[0]):  # ny\n",
-    "    for j in range(sino.shape[1]):  # omega\n",
-    "        # use dtyi_calc as a lookup table from omega index\n",
-    "        this_dtyi = dtyi[j]\n",
-    "        sino[i, j] = 1/(50*np.cbrt((np.abs(i - this_dtyi)))+0.01)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.imshow(sino, aspect=1)\n",
-    "ax.set_xlabel(\"Omega step\")\n",
-    "ax.set_ylabel(\"dty step\")\n",
-    "plt.show()\n"
+    "dtyi.min()\n",
+    "dtyi.max()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "c56aea60-4c85-4815-8ab1-90ad96618287",
+   "execution_count": 27,
+   "id": "b719211f-f41e-4588-8da9-c77da2543083",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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wu5gxGo0wGOo++yY/Px9arbZFQpF9U/n7oeTPMaiqcYBbpgTNdwfljkQ2xGFHOjyOCZRWOuHik1FQdQiWOxIR3SGzi5mhQ4di6dKlpmVJknDt2jXMnTsX999/f0tmIzskOahxNToAW+b+H4JeqUH7VRwjQy3PdV0a/KaU4ovZ76B4oC8kzk1FZNXMvjW7oKAAgwYNglKpRFZWFqKjo5GVlQVPT0/s3bvX4h4AyVuzrcuZtfcgWncOV8c6QF98iU/AprtHkqDy9sLl1TfvwHSNPyNzICL6tbt6a7afnx8OHz6M9evXIz09HUajEU899RT++Mc/1hoQTGQOhaMjTq2IgOqcA37e2RkeRTwjQ3eZENAXXQD+E4KSCAkXPnZD+MyzMJRcljsZEZnJ7DMze/fuRWxsLFSq2nWQXq9HSkoKBgwY0KIB7xTPzFg+pbcXbkQGovc76dizqB/cPuHMvtS6qh7ojfvm78P2lwfA5fB56PN5MwOR3Mw5M2P2mJlBgwbh8uW6/3IpKyvDoEGDzP04IhT8IQxr/v0PHBvkxkKGZKH59iD2R7ni7WWr8PNfguSOQ0RmMvsykxCi3scQlJSUwNnZuUVCkf0oSOqKyorrmDguEVL5cbnjkB0TNdV4/ZHxCCs4B04EQGRdmlzM3HoatiRJmDBhQq0nUxsMBhw9ehSxsbEtn5BsWvVxN7iUSJBSU+SOQgQcOIaK+3uj7EEdvFayTxJZiyYXM25ubgBunpnRarW1Bvuq1Wr069cPkyZNavmEZJsUSqi8PBHy2kFOiEcW5dwwJR4buA8ZG/1guFTC/klkBZpczKxevRoAEBwcjFmzZvGSEt0RZXgoPt66Gg9PmgH1Fk6KR5aj06wfcaB3L2w4+B7+8FgiFPsy5I5ERI0w+26m69evQwiBNm3aALj54MmkpCR07doVcXFxdyXkneDdTJbn4pQYVMeVw3GzK7y/zYG+sEjuSES1KN3dUTw2HOX3VUB92AX+C3jJiai13dW7mRISErBmzRoAQGlpKfr06YNFixYhISEB7733XvMSk92oiu+Nss4CGocaePwzlYUMWSTDlSvw+GcqICRUdKjBjRF9gHpufCAiy2B2MfPjjz+if//+AIAvvvgCPj4+yM3NxZo1a7Bs2bIWD0i2Q1KpMGPZOkgGwHPkabnjEDUq5NEjcD7jgOXvLoPCxUXuOER0G2YXM5WVlaYHSm7btg1jxoyBQqFAv379kJub2+IByTbU3BeFp0/+hOVTxqHTgiy54xA1WdD7J/DXP0zC+EPHUf5oP7njEFE9zC5mwsLCsGnTJuTl5WHr1q2mcTLFxcWNXtMi+1QyKQa5Dzhg5ldPQHPkLAyXSuSORNRkhtIyKE5kY86mR1DcByiawSkoiCyN2cXMq6++ilmzZiE4OBh9+/ZFTEwMgJtnaXr27NniAcmKSRKUncNwbeg1eIVfROisNBYyZJWMFRXo8LdUSN43oBjEZzcRWRqz72YCgKKiIhQWFqJHjx5QKG7WQwcOHICrqyvCw8NbPOSd4N1M8lF6tMMHGV9j5IIX4PVeKmB+VyOyLLcGAbMvE911d/Wp2QDg4+MDHx+fWuv69OnTnI8iG2YsK8fjU5+D39Fz0PMvf7IFQkA/OAr+b2Wh5CEX6M8XyJ2IiNDMYoaoMcpunXGxbzt4rDkIPWdQJRuivKHHyRIfVD/kCd8UD4iDx+SORGT3zB4zQ9QYhaMj8uI9sGLOMij+N7kika2QUo7AY1Q2Xk38BNljXNjHiSwAixlqce47nVAVfQ3z+twPQ3m53HGIWpzQ6/Gv2N6oaWtAVMpVueMQ2T2zi5mKiooW+/J58+ZBkqRar1+PxRFCYN68efDz84OTkxPuvfdenDhxosW+n1qWKjAAZ9begx93dIH3BicYLl6UOxLRXWO4VILgTUZ8ubE/zqy9B8ouHeWORGS3zC5mvL29MXHiROzfv79FAnTr1g2FhYWm17Fjv1x/XrhwIRYvXox3330XBw8ehI+PD4YOHYqrV/kvIUuj6hCMK7EBmBCRhqCtFWiT9IPckYjuOvXWQwj5/CImRKThYj9PKDuHyR2JyC6ZXcysW7cOZWVlGDJkCDp16oQFCxagoKD5I/pVKpXp7igfHx+0b98ewM2zMkuXLsXs2bMxZswYRERE4OOPP0ZlZSXWrl3b7O+ju+Pk895IfP1z7OvuCCnliNxxiFqN4WQW9nV3xLBn9+PM6xw/QyQHs4uZkSNH4ssvv0RBQQGmTp2KdevWQafTYcSIEdi4caPZd65kZWXBz88PISEheOSRR5CdnQ0AyMnJQVFRUa0ncWs0GgwcOBApKbd/gm1VVRXKy8trveju67KwAP95ZLjcMYhkk/5EN4TO5lljIjk0ewCwh4cHnnvuORw5cgSLFy/Gjh078NBDD8HPzw+vvvoqKisrG/2Mvn37Ys2aNdi6dSs+/PBDFBUVITY2FiUlJSgquvk0ZW9v71rv8fb2Nm2rz/z58+Hm5mZ6BQYGNvcQqSkkCYUzY3Ej1Asig+OZyH4Zj56CwVOL83+LhaTirBdEranZxUxRUREWLlyILl264MUXX8RDDz2EnTt3YsmSJUhKSsKoUaMa/Yz4+HiMHTsWkZGRuO+++/Dtt98CAD7++GPTPtKtGTf/RwhRZ92vvfTSSygrKzO98vLymneA1CjJQQ1VgD8mPfUtCgZo5I5DJLsLvV3w5p/XQBEcCIWjo9xxiOyG2f982LhxI1avXo2tW7eia9euSExMxOOPP462bdua9rnnnnua9ZwmZ2dnREZGIisry1QMFRUVwdfX17RPcXFxnbM1v6bRaKDR8Bdra6h84B5sXL4ETwyfCF3mAXCOX7J33it+wIdJA7H8+/9g3Jt/hceHqXJHIrILZp+ZefLJJ+Hn54fvv/8ehw8fxrRp02oVMgDQoUMHzJ492+wwVVVVOHnyJHx9fRESEgIfHx9s377dtL26uhp79uxBbCyfWiu3s2/FoODhGtw/ZxbE2XwIzvJLBBgNMFy4iMfm/BUl/WqQtayv3ImI7ILZZ2YKCwvRppEZL52cnDB37txGP2vWrFkYOXIkgoKCUFxcjDfffBPl5eUYP348JEnCjBkz8Pbbb6Njx47o2LEj3n77bbRp0waPPfaYubGphUgOalSM7ImawCqIChXcP0qFUe5QRBZE1FSj7ZpUlETGwCH4Gioe6gvt5mMwNmEcIRE1j9lnZrRaLYqLi+usLykpgVKpNOuz8vPz8eijj6Jz584YM2YM1Go10tLSoNPpAAAvvPACZsyYgaeffhrR0dE4f/48tm3bBq1Wa25saiEKNy3WLV2Edns16DT5oNxxiCxW6F9T0f4TJ2xbuhySv0/jbyCiZpOEMO9xxgqFAkVFRfDy8qq1vqCgAKGhobh+/XqLBrxT5eXlcHNzw71IgEpykDuO9ZMkKEODIS5cgpGTFxI1SOHoCCnAF8azebwUS2QmvahBMr5CWVkZXF1dG9y3yZeZli1bBuDm3UX//Oc/4eLiYtpmMBiwd+9ehIeHNzMyWQP94CjkxqsR9nI6RE213HGILJ7xxg3gzFnkvNUPPgcMcNp0QO5IRDapycXMkiVLANy8NXrVqlW1Limp1WoEBwdj1apVLZ+QLIIyLAR5MRpMGr4dyXPbs5ghMsPwuEPY7BCNTj+FwnD6jNxxiGxOk4uZnJwcAMCgQYOwceNGuLu737VQZHnu+fwMfs7UYld3F0BwICNRkwmBzN56GJcZ8cdvkrGmMyfyJGppZg8A3r17NwsZO6LsFIrQg47Y8v7vELpAD5g3xIqIAEAIhK+8jGVv/QGhBx2Bft3lTkRkU5p0ZmbmzJlN/sDFixc3OwxZFmP/nijo54SiQhW891+B8fgpuSMRWS3DySx4Vt5A2kM6VA12gVfb3lBv4R2BRC2hScVMRkZGreX09HQYDAZ07twZAHD69GkolUpERUW1fEKSzZk/qDF10FbsiNByLhmiFqDPzUP7BwG/NC2+j+yAkC1yJyKyDU0qZnbv3m36efHixdBqtfj4449Nl5uuXLmCJ598Ev379787KUkWnf92DLs1QQCuyB2FyKZcGK5AqCGb/0ggaiFmzzPj7++Pbdu2oVu3brXWHz9+HHFxcSgoKGjRgHeK88w0gyTh9PvR8NmthOu6NLnTENmkqvt749wjBnSe/NPNW7iJqBZz5pkxewBweXk5Lly4UGd9cXExrnISNaun9GgH/eBe+F1EFiq9m/1QdSJqxHUPFe7vcgI37o2Eyuf2D88losaZ/dtq9OjRePLJJ/HFF18gPz8f+fn5+OKLL/DUU09hzJgxdyMjtaKyIZ2wec0qXHm8LXyWpsgdh8hmtf1PKrLj2mDtB0tQMCZU7jhEVs3sy0yVlZWYNWsW/v3vf6OmpgYAoFKp8NRTT+Gdd96Bs7PzXQnaXLzM1HRZH/eCh8c1eLymAQ5ncmI8ortNoYTUqwuyn1fC0akavqNOyp2IyGLclccZ3NKmTRusXLkS77zzDs6cOQMhBMLCwiyuiCHzuRx1RKXaEe0O8owMUaswGiAOHUeb1FhUu/HvUKLmMvvMjLXhmZkmkCSo/P1gvFTCgYhEMpEc1FB6t4e+oAgwGuSOQyS7uzoAmGyPytsL76VsQMm4nnJHIbJbN+J64JPUz6HsGCJ3FCKrw2LGzpU/2g+XV7tg7Ny/ov1ey7qtnsieOP+Yh2GvPI+id5S4ODVG7jhEVoXFjB2riu+NyxESFJKAx9ofoc/JlTsSkd3SFxbBfU0a9EYFyjoK3BjZR+5IRFaDxYydklQqDFyQAn3QDbgMz4aoqpI7EhEJYbqjafL/fQlIksyBiKwDixk7pLinK17M/BG75/4OnZ87J3ccIvqNzm9kYm1cLJ9ST9RELGbskOJiKSZ//hdoMwphuFQidxwi+g3DlStAVTVOr+wDZZeOcschsngsZuyMyt8PwqUNQl5MhT43T+44RHQbQuuM1wZvRElvT6h0gXLHIbJoLGbszE/z/PFA0g9yxyCiRhiysvFpeAB6T/8R+f9wkTsOkUVjMWMnJJUKyt1+cChRYdNf7pM7DhE10elnwlFz0B3tU9pCodXKHYfIIrGYsQOqDsEoeroPsi96wOOIgGJfhtyRiKiJpNQj8MqoQUahPwqeioQiIlzuSEQWR9ZiJjg4GJIk1XklJiYCACZMmFBnW79+/eSMbJVKo7zx2fPvIPT5K3BdlyZ3HCIyk2bzQeiezMP7zyxH0YB2cschsjhmP2iyJR08eBAGwy/PIDl+/DiGDh2Khx9+2LRu+PDhWL16tWlZrVa3akZboE1Kx4wdI2AoPS93FCJqJkN5OV6Pug/e1w6BN2wT1SZrMdO+fftaywsWLEBoaCgGDhxoWqfRaODj49Pa0WzGuVdjIVQCuldT5Y5CRHfIcOUKCv4aixvtBTq8wD/TRLdYzJiZ6upqfPLJJ5g4cSKkX816mZycDC8vL3Tq1AmTJk1CcXFxg59TVVWF8vLyWi97JKlU0A+Ogoi4iuqgarnjEFELqfQzwr3bJeiHREHh6Ch3HCKLYDHFzKZNm1BaWooJEyaY1sXHx+PTTz/Frl27sGjRIhw8eBCDBw9GVQNT78+fPx9ubm6mV2Cgfc7PoPT2wsY178Ltvy7oOCFd7jhE1ELCnkuD8/K22LbmQyAsWO44RBZBEsIy5sseNmwY1Go1vvnmm9vuU1hYCJ1Oh/Xr12PMmDH17lNVVVWr2CkvL0dgYCDuRQJUkkOL57ZExdNi8cCf9+HglJ5QnsqFobRM7khE1IIUzs4QXUIQuCIH+zf3QNBrKXJHImpxelGDZHyFsrIyuLq6NrivrGNmbsnNzcWOHTuwcePGBvfz9fWFTqdDVlbWbffRaDTQaDQtHdGquOXUYO2e36HToUMw6PVyxyGiFmasqAAOHUdycgw8TxvljkMkO4soZlavXg0vLy888MADDe5XUlKCvLw8+Pr6tlIy66Py9YEy7Qw6fnuZdzwQ2bgOf0uF0tUVkr8f9OcL5I5DJBvZx8wYjUasXr0a48ePh0r1S2117do1zJo1C6mpqTh79iySk5MxcuRIeHp6YvTo0TImtmx9tpzDyf8LkTsGEbWSrNndMH7398Cvbpwgsjeyn5nZsWMHzp07h4kTJ9Zar1QqcezYMaxZswalpaXw9fXFoEGDsGHDBmg5pXcdyi4dgZUVSPpXMDoerJA7DhG1krB1Zfh79mNov/McjK94Qko5InckolYnezETFxeH+sYgOzk5YevWrTIkskL9uqMw2gX+0jX47SyB4USm3ImIqJUYD/8En0v+EI8InI91ho9DTyj28JElZF9kL2bozkgqFU5PccBforZjV6QzgEK5IxFRK9PnnwcGAT1SKnGivw+89sidiKh1yT5mhppP0mgw8ugFaM5qsHd4R7njEJHMrozRwPfPJXLHIGp1LGasmKjRY8WnI+GffIN3MhAR9EUXUBMegKx3+0LRpo3ccYhaDYsZK6Vs6wZF984InP8DlMk/yh2HiCzEVZ0G0+7dDn1UZyg9PeSOQ9QqWMxYqZKRXfH5f/8Nla+33FGIyIK4fZKGHbEBWPXJcpz/U2e54xC1ChYzVuj0v6NRNvIaHpyQCEPxJbnjEJGFMVyrwOQnn0V59yqc+zxS7jhEdx2LGSuicHbGhWdiAYME6SctHHakQ9TwidhE9BtGA1S70uFyQoOqCjWKZsRC6dFO7lREd43N35p9aw4bPWpg7fP7q9q2wZapi/Hgq1Og/SwFfOoSETXE6509qBnSE/9970OM3fEQjJcuyB2JqMn0qAGAeuei+y2LeWr23ZKfn4/AwEC5YxAREVEz5OXlISAgoMF9bL6YMRqNyMzMRNeuXZGXl9foY8TtQXl5OQIDA9kev8I2qYttUhfbpDa2R11sk7qa2yZCCFy9ehV+fn5QKBoeFWPzl5kUCgX8/f0BAK6uruxcv8L2qIttUhfbpC62SW1sj7rYJnU1p03c3NyatB8HABMREZFVYzFDREREVs0uihmNRoO5c+dCo9HIHcUisD3qYpvUxTapi21SG9ujLrZJXa3RJjY/AJiIiIhsm12cmSEiIiLbxWKGiIiIrBqLGSIiIrJqLGaIiIjIqtl8MbNy5UqEhITA0dERUVFR2Ldvn9yRWs28efMgSVKtl4+Pj2m7EALz5s2Dn58fnJyccO+99+LEiRMyJm5Ze/fuxciRI+Hn5wdJkrBp06Za25ty/FVVVZg+fTo8PT3h7OyMBx98EPn5+a14FC2rsTaZMGFCnT7Tr1+/WvvYUpvMnz8fvXv3hlarhZeXF0aNGoXMzMxa+9hbP2lKm9hbP3nvvffQvXt306RvMTEx+O6770zb7a2PNNYecvQPmy5mNmzYgBkzZmD27NnIyMhA//79ER8fj3PnzskdrdV069YNhYWFptexY8dM2xYuXIjFixfj3XffxcGDB+Hj44OhQ4fi6tWrMiZuORUVFejRowfefffderc35fhnzJiBpKQkrF+/Hvv378e1a9cwYsQIGAyG1jqMFtVYmwDA8OHDa/WZzZs319puS22yZ88eJCYmIi0tDdu3b4der0dcXBwqKipM+9hbP2lKmwD21U8CAgKwYMECHDp0CIcOHcLgwYORkJBgKljsrY801h6ADP1D2LA+ffqIKVOm1FoXHh4uXnzxRZkSta65c+eKHj161LvNaDQKHx8fsWDBAtO6GzduCDc3N7Fq1apWSth6AIikpCTTclOOv7S0VDg4OIj169eb9jl//rxQKBRiy5YtrZb9bvltmwghxPjx40VCQsJt32PrbVJcXCwAiD179ggh2E+EqNsmQrCfCCGEu7u7+Oc//8k+8j+32kMIefqHzZ6Zqa6uRnp6OuLi4mqtj4uLQ0pKikypWl9WVhb8/PwQEhKCRx55BNnZ2QCAnJwcFBUV1WofjUaDgQMH2kX7NOX409PTUVNTU2sfPz8/RERE2HQbJScnw8vLC506dcKkSZNQXFxs2mbrbVJWVgYAaNeuHQD2E6Bum9xir/3EYDBg/fr1qKioQExMjN33kd+2xy2t3T9s9kGTly5dgsFggLe3d6313t7eKCoqkilV6+rbty/WrFmDTp064cKFC3jzzTcRGxuLEydOmNqgvvbJzc2VI26rasrxFxUVQa1Ww93dvc4+ttqH4uPj8fDDD0On0yEnJwdz5szB4MGDkZ6eDo1GY9NtIoTAzJkz8fvf/x4REREA2E/qaxPAPvvJsWPHEBMTgxs3bsDFxQVJSUno2rWr6ZevvfWR27UHIE//sNli5hZJkmotCyHqrLNV8fHxpp8jIyMRExOD0NBQfPzxx6bBWPbcPkDzjt+W22jcuHGmnyMiIhAdHQ2dTodvv/0WY8aMue37bKFNpk2bhqNHj2L//v11ttlrP7ldm9hjP+ncuTMOHz6M0tJSfPnllxg/fjz27Nlj2m5vfeR27dG1a1dZ+ofNXmby9PSEUqmsU+UVFxfXqaDthbOzMyIjI5GVlWW6q8le26cpx+/j44Pq6mpcuXLltvvYOl9fX+h0OmRlZQGw3TaZPn06vv76a+zevRsBAQGm9fbcT27XJvWxh36iVqsRFhaG6OhozJ8/Hz169MA//vEPu+0jt2uP+rRG/7DZYkatViMqKgrbt2+vtX779u2IjY2VKZW8qqqqcPLkSfj6+iIkJAQ+Pj612qe6uhp79uyxi/ZpyvFHRUXBwcGh1j6FhYU4fvy4XbQRAJSUlCAvLw++vr4AbK9NhBCYNm0aNm7ciF27diEkJKTWdnvsJ421SX1svZ/URwiBqqoqu+wj9bnVHvVplf7RrGHDVmL9+vXCwcFB/Otf/xI//fSTmDFjhnB2dhZnz56VO1qreP7550VycrLIzs4WaWlpYsSIEUKr1ZqOf8GCBcLNzU1s3LhRHDt2TDz66KPC19dXlJeXy5y8ZVy9elVkZGSIjIwMAUAsXrxYZGRkiNzcXCFE045/ypQpIiAgQOzYsUP8+OOPYvDgwaJHjx5Cr9fLdVh3pKE2uXr1qnj++edFSkqKyMnJEbt37xYxMTHC39/fZttk6tSpws3NTSQnJ4vCwkLTq7Ky0rSPvfWTxtrEHvvJSy+9JPbu3StycnLE0aNHxcsvvywUCoXYtm2bEML++khD7SFX/7DpYkYIIVasWCF0Op1Qq9WiV69etW4vtHXjxo0Tvr6+wsHBQfj5+YkxY8aIEydOmLYbjUYxd+5c4ePjIzQajRgwYIA4duyYjIlb1u7duwWAOq/x48cLIZp2/NevXxfTpk0T7dq1E05OTmLEiBHi3LlzMhxNy2ioTSorK0VcXJxo3769cHBwEEFBQWL8+PF1jteW2qS+tgAgVq9ebdrH3vpJY21ij/1k4sSJpt8j7du3F0OGDDEVMkLYXx9pqD3k6h+SEEI075wOERERkfxsdswMERER2QcWM0RERGTVWMwQERGRVWMxQ0RERFaNxQwRERFZNRYzREREZNVYzBAREZFVYzFDREREVo3FDBE1KC8vD0899RT8/PygVquh0+nw7LPPoqSkRO5od01wcDCWLl0qdwwiaiIWM0R0W9nZ2YiOjsbp06exbt06/Pzzz1i1ahV27tyJmJgYXL58We6IREQsZojo9hITE6FWq7Ft2zYMHDgQQUFBiI+Px44dO3D+/HnMnj3btG9wcDDefPNNPPHEE3BxcYFOp8NXX32FixcvIiEhAS4uLoiMjMShQ4dqfUdKSgoGDBgAJycnBAYG4plnnkFFRYVpe2FhIR544AE4OTkhJCQEa9eurXPmZPHixYiMjISzszMCAwPx9NNP49q1aw0e27x58xAUFASNRgM/Pz8888wzAIB7770Xubm5eO655yBJEiRJanLW4OBgvPHGG3jsscfg4uICPz8/LF++vFltT0RmuINnTRGRDSspKRGSJIm333673u2TJk0S7u7uwmg0CiGE0Ol0ol27dmLVqlXi9OnTYurUqUKr1Yrhw4eLzz77TGRmZopRo0aJLl26mN5z9OhR4eLiIpYsWSJOnz4tvv/+e9GzZ08xYcIE0/fcd9994p577hFpaWkiPT1dDBw4UDg5OYklS5aY9lmyZInYtWuXyM7OFjt37hSdO3cWU6dOve2xff7558LV1VVs3rxZ5Obmih9++EF88MEHpuMOCAgQr7/+uumJ0U3NqtPphFarFfPnzxeZmZli2bJlQqlU1nooIRG1PBYzRFSvtLQ0AUAkJSXVu33x4sUCgLhw4YIQ4uYv8scff9y0vbCwUAAQc+bMMa1LTU0VAEwFwp/+9CcxefLkWp+7b98+oVAoxPXr18XJkycFAHHw4EHT9qysLAGgVjHzW5999pnw8PC47fZFixaJTp06ierq6nq363S6Op/fWNZb7xs+fHitfcaNGyfi4+Nvm4WI7hwvMxFRswghAKDWZZju3bubfvb29gYAREZG1llXXFwMAEhPT8dHH30EFxcX02vYsGEwGo3IyclBZmYmVCoVevXqZfqMsLAwuLu718qye/duDB06FP7+/tBqtXjiiSdQUlJS6xLQrz388MO4fv06OnTogEmTJiEpKQl6vb7B420s6y0xMTG13hcTE4OTJ082+NlEdGdYzBBRvcLCwiBJEn766ad6t586dQru7u7w9PQ0rXNwcDD9fKvIqW+d0Wg0/fcvf/kLDh8+bHodOXIEWVlZCA0NNRVMv/Xr9bm5ubj//vsRERGBL7/8Eunp6VixYgUAoKampt73BwYGIjMzEytWrICTkxOefvppDBgw4Lb7NyVrQ35d8BFRy1PJHYCILJOHhweGDh2KlStX4rnnnoOTk5NpW1FRET799FM88cQTd/SLulevXjhx4gTCwsLq3R4eHg69Xo+MjAxERUUBAH7++WeUlpaa9jl06BD0ej0WLVoEheLmv88+++yzRr/byckJDz74IB588EEkJiYiPDwcx44dQ69evaBWq2EwGMzKektaWlqd5fDw8EbzEFHz8cwMEd3Wu+++i6qqKgwbNgx79+5FXl4etmzZYrqk89Zbb93R5//tb39DamoqEhMTcfjwYWRlZeHrr7/G9OnTAdwsZu677z5MnjwZBw4cQEZGBiZPngwnJydTERUaGgq9Xo/ly5cjOzsb//nPf7Bq1aoGv/ejjz7Cv/71Lxw/ftz0HicnJ+h0OgA370rau3cvzp8/j0uXLjUp6y3ff/89Fi5ciNOnT2PFihX4/PPP8eyzz95ROxFRw1jMENFtdezYEYcOHUJoaCjGjRuH0NBQTJ48GYMGDUJqairatWt3R5/fvXt37NmzB1lZWejfvz969uyJOXPmwNfX17TPmjVr4O3tjQEDBmD06NGYNGkStFotHB0dAQD33HMPFi9ejL///e+IiIjAp59+ivnz5zf4vW3btsWHH36I3/3ud+jevTt27tyJb775Bh4eHgCA119/HWfPnkVoaCjat2/f5KwA8PzzzyM9PR09e/bEG2+8gUWLFmHYsGF31E5E1DBJ3O6iNBGRBcrPz0dgYCB27NiBIUOGyB2nluDgYMyYMQMzZsyQOwqRXeGYGSKyaLt27cK1a9cQGRmJwsJCvPDCCwgODsaAAQPkjkZEFoLFDBFZtJqaGrz88svIzs6GVqtFbGwsPv3001p3SRGRfeNlJiIiIrJqHABMREREVo3FDBEREVk1FjNERERk1VjMEBERkVVjMUNERERWjcUMERERWTUWM0RERGTVWMwQERGRVWMxQ0RERFbt/wELMIDNxWdTyQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -619,48 +940,45 @@
     }
    ],
    "source": [
-    "from ImageD11.sinograms.roi_iradon import run_iradon\n",
-    "\n",
-    "# projection shifts is in units of pixels\n",
-    "\n",
-    "shift = -y0/ystep\n",
-    "\n",
-    "recon = run_iradon(sino, omega, pad=13, shift=shift)\n",
-    "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.imshow(recon, vmin=0, origin=\"lower\")\n",
-    "ax.set_ylabel(\"i\")\n",
-    "ax.set_xlabel(\"j\")\n",
+    "ax.imshow(sino, aspect=1)\n",
+    "ax.set_xlabel(\"Omega step\")\n",
+    "ax.set_ylabel(\"dty step\")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "fb59ed5c-a7da-4393-8e95-4e061dcd1139",
+   "execution_count": 28,
+   "id": "c56aea60-4c85-4815-8ab1-90ad96618287",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "(164, 164)"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "recon.shape"
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(recon, vmin=0, origin=\"lower\")\n",
+    "ax.scatter(*geometry.sample_to_recon(sx, sy, recon.shape, ystep), marker='+', c='r')\n",
+    "ax.set_ylabel(\"i\")\n",
+    "ax.set_xlabel(\"j\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "a12eb778-b597-4eb1-8581-1532e0c1b4e9",
+   "execution_count": 29,
+   "id": "1f3eddb8-0b73-4947-9766-803283918e4f",
    "metadata": {
     "tags": []
    },
@@ -669,24 +987,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "112 42\n"
+      "(530.0, -530.0)\n",
+      "530.6 -530\n"
      ]
     }
    ],
    "source": [
-    "# can we now go from recon space back to real space?\n",
-    "\n",
-    "ri, rj = np.where(recon == recon.max())\n",
-    "ri = ri[0]\n",
-    "rj = rj[0]\n",
-    "\n",
-    "print(ri, rj)"
+    "# can we recover the sample position from the reconstruction?\n",
+    "print(geometry.fit_sample_position_from_recon(recon, ystep))\n",
+    "print(sx, sy)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "74349cdf-8256-49c5-8717-88702917446a",
+   "execution_count": 30,
+   "id": "a5023b54-dc85-4bb1-9b61-15209158d4df",
    "metadata": {
     "tags": []
    },
@@ -695,49 +1010,64 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(300, 400)\n",
-      "300.0 400.0\n"
+      "(151, 151)\n",
+      "151.0\n"
      ]
     }
    ],
    "source": [
-    "print(geometry.recon_to_sample(ri, rj, recon.shape, ystep))\n",
-    "print(sample_x, sample_y)"
+    "# does the reconstruction by default match the step space?\n",
+    "print(recon.shape)\n",
+    "print(np.sqrt(len(geometry.step_grid_from_ybincens(ybincens, ystep, 1, y0))))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "3781cf99-ab3a-4a4f-b209-32a00bf06b98",
+   "execution_count": 31,
+   "id": "dfbacda9-6e24-4e77-8fc8-c6d3c3e5e9b9",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(112.0, 42.0)\n",
-      "112 42\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "print(geometry.sample_to_recon(sample_x, sample_y, recon.shape, ystep))\n",
-    "print(ri, rj)"
+    "# convert reconstruction coordinates to (sx, sy)\n",
+    "# then calulate dty from that\n",
+    "\n",
+    "ri, rj = np.array(np.where(recon == recon.max())).flatten()\n",
+    "\n",
+    "dty_calc = geometry.recon_omega_to_dty(ri, rj, omega, y0, recon.shape, ystep)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(omega, dty, label='dty original')\n",
+    "ax.scatter(omega, dty_calc, label='dty calculated from recon', s=3)\n",
+    "ax.legend()\n",
+    "ax.set_xlabel('omega (deg)')\n",
+    "ax.set_ylabel('dty')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "dfbacda9-6e24-4e77-8fc8-c6d3c3e5e9b9",
+   "execution_count": 32,
+   "id": "347360e9-3379-4df3-9a83-df135b9d6fd8",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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iaqxpg0IQ7usKbWU13t96RnQcm8cyRERko/79v9PQGUwY2NETo3v6iY5DN0GpkOPf4yIAAOsOZeFoVrHYQDaOZYiIyAbtTCnAttP5UMplmDumO2QyrilkbfqHeGB8n/aQJOCNn07CaOLF1C2FZYiIyMboDEbMrVlT6NGBIejkwy03rNUrd4fDVa3E8Wwt1h/KEh3HZlldGVqwYAFCQ0Ph6OiIyMhI7Nmzp1GP27dvH5RKJXr37t2yAYmIBFu+Nx1pheXwaqPGCyM6iY5Dt8Db1REv1lxM/d7WMyjixdQtwqrK0Pr16zFr1iy89tprSEpKwpAhQzBq1ChkZmZe93FarRZTpkzBHXfc0UpJiYjEyNVW4r9/nAMAvHp3OFx50bTVmxITjHBfVxRXVOODbdzItSVYVRn66KOP8Pjjj+OJJ55A165d8cknnyAwMBALFy687uOeeuopTJ48GTExMa2UlIhIjP9sPoMKvRH9gtvivj7tRcehZqBUyDF3THcAwNqDmTiTVyI4ke2xmjKk1+tx+PBhxMbG1rs/NjYW8fHx13zcihUrcOHCBbz55puN+j46nQ4lJSX1bkRE1iAh9TJ+PpYDuQyYO5YXTduS6A6eGBXhC5ME/L9fkrkydTOzmjJUWFgIo9EIHx+fevf7+PggLy+vwcecO3cOr7zyCtasWQOlUtmo7zNv3jxoNJq6W2Bg4C1nJyJqaSaThHd+MV80/VBUELr7awQnouY2Z1RXOCjk2HOuEDtTLomOY1OspgzV+vv/dCRJavB/P0ajEZMnT8bcuXPRuXPnRn/9OXPmQKvV1t2ysnj1PhFZvh+PXsTJiyVwVSvrVi8m2xLk6Yxpg0IAAO/8chrVRpPYQDakccMlFsDLywsKheKqUaCCgoKrRosAoLS0FImJiUhKSsLMmTMBACaTCZIkQalUYtu2bRg+fPhVj1Or1VCr1S1zEkRELaBSb8T7W80X1j4zLAyebfg7zFY9OzwM3x/OxoVL5fjmQCamDgwRHckmWM3IkIODAyIjI7F9+/Z692/fvh0DBw686ng3NzecOHECR48erbvNmDEDXbp0wdGjRxEdHd1a0YmIWtSyvanI1VahvbtT3cgB2SY3R1XdVPuPfzsLbUW14ES2wWpGhgBg9uzZiIuLQ79+/RATE4Mvv/wSmZmZmDFjBgDzW1wXL17EqlWrIJfLERERUe/x3t7ecHR0vOp+IiJrVVBahYU7LwAA/jmyCxxVCsGJqKVN6h+IVfvTcTa/DJ/9cQ6vj+4mOpLVs5qRIQCYOHEiPvnkE7z99tvo3bs3du/ejc2bNyM4OBgAkJube8M1h4iIbMknv51Dud6IXgEa3NvTX3QcagVKhRz/d4+5AK3ab15gk26NTOL8vOsqKSmBRqOBVquFm5ub6DhERHXO5pdi5Ce7YZKA72bEoH+Ih+hI1IqmrTiIHSmXcFd3HyyO6yc6jsW5mddvqxoZIiKiP/1nczJMEjCyuy+LkB169e6ukMuArafycSSzSHQcq8YyRERkhfbWrDWjlMvwr1HhouOQAJ18XPFAZAAA4N1fz3AhxlvAMkREZGUkScJ7W88AAB4ZEIxQLxfBiUiUWSM6w0Epx4G0K9h5lgsxNhXLEBGRldlyMg/Hs7VwdlBg5vAw0XFIIH93J0yNMU8iem9LCkwmjg41BcsQEZEVMRhNeL9m5/InhnSAFxdYtHvP3B4GV7USybkl2HQsR3Qcq8QyRERkRTYcuYjUS+Vo66zC9CGhouOQBWjr4oAZt3cEAHy4PQV6A7fpuFksQ0REVqKq2ohPfjsLoGY0wFElOBFZimmDQtDOVY2sK5VYe5Dr7d0sliEiIiux5kAmcrRV8HVzRFzNdSJEAODsoMQLd3QCAPz3j3Mo1xkEJ7IuLENERFagTGfAFzvOAwBeGNGJ227QVSb2D0SolwsKy/RYuidNdByrwjJERGQFlu5JxZVyPTp4ueDBmrVliP5KpZBjds0mrkv3pHIT15vAMkREZOGulP/5P/3ZsZ2hVPBXNzXsnh5+CPd1RanOgCV7UkXHsRr8F0VEZOEW77qAMp0B3f3dcHeEn+g4ZMHkchlerBkdWrEvDVfK9YITWQeWISIiC1ZYpsOq/RkAgJdiO0MulwlORJYutpsPuvu7oVxvxOLdF0THsQosQ0REFmzxrguorDaiV6A7hnXxFh2HrIBMJqu7dmhVfAYuleoEJ7J8LENERBaqoLQKqxPMo0KzRnSCTMZRIWqc4eHe6BXojspqIxbt4ujQjbAMERFZqMW7UlFVbULvQHfc3rmd6DhkRf46OvR1QgbyS6oEJ7JsLENERBaooKQKX9eMCr14Z2eOCtFNu62TF/oFt4XOYMLCnRwduh6WISIiC7Rw1wXoDCb0DXLHbZ28RMchK/TX0aFvDmQip7hScCLLxTJERGRh8kuqsOaAeX8pjgrRrRgY5oUBHTygN5rqVjCnq7EMERFZmIU7L0BvMKFfcFsMDuOoEN2aF0eYR4e+S8xGnpbXDjWEZYiIyILkaavwzUGOClHzie7giahQ8+gQ1x1qGMsQEZEFWbTLPCoUFeKBgR09RcchG/H8cPOO9t8cyOS6Qw1gGSIishCXSnVYWzMq9PwdXFeIms+gME/0DnSHzmDC0r3cs+zvWIaIiCzEsr1p0BlM6BXojkFhHBWi5iOTyfD8HWEAgNX7M1DEPcvqYRkiIrIAxRV6rN6fDgCYOSyMo0LU7IZ18UZ3fzdU6I1YsS9NdByLwjJERGQBvopPR7neiHBfV9wRzj3IqPnJZDI8N9w8OrQiPh0lVdWCE1kOliEiIsHKdAas2JcOAHh2WBh3pqcWE9vNF5192qC0yoBV8emi41gMliEiIsHWJGRAW1mNDl4uuLuHn+g4ZMPkchmeHWYeHVq2Nw3lOoPgRJaBZYiISKCqaiOW7DFfv/H07R2h4KgQtbDRPf0R6uWCoopqrDmQITqORWAZIiISaP2hLBSW6dDe3Qnj+rQXHYfsgEIuwzO3dwQAfLk7DVXVRsGJxGMZIiISRG8wYfEu84rAM27vCJWCv5KpdYzr0x4BbZ1QWKbDupq1rewZ/+UREQmyMSkbOdoqeLuq8WBkgOg4ZEdUCjmerhkdWrw7FXqDSXAisViGiIgEMBhNWLjTPCo0fUgHOKoUghORvXkgMgA+bmrkaqvw09GLouMIxTJERCTALydykX65Am2dVZgcHSQ6DtkhtVKBxwaFAjCPDplMkuBE4rAMERG1MkmS6kaFHhsUChe1UnAisleTo4Pg6qjE+YIy/JacLzqOMCxDREStbOfZSziTVwoXBwWmxISIjkN2zNVRhUcGBAMAFu26AEmyz9EhliEiolZWO4PsoaggaJxVgtOQvZs2KAQOSjmOZBbjUHqR6DhCsAwREbWio1nFSEi9AqVchscGh4qOQwRvV0c8UDObcVFNUbc3LENERK2odlRobO/28Hd3EpyGyOzJIR0glwF/nCnAmbwS0XFaHcsQEVErSSssx5ZTeQCAp4Z2EJyG6E8hXi4YFWHeF2/xrlTBaVofyxARUSv5cncqJAm4I9wbnX1cRcchqmfGUPMijJuO5SC7qEJwmtbFMkRE1AoKSqvww5FsAMBTNS86RJakR4AGg8O8YDRJWFqzebC9YBkiImoFK+PToTeY0CfIHf1D2oqOQ9Sg2tGhdYcycaVcLzhN62EZIiJqYWU6A1bvzwBgfrGRyWSCExE1bFCYJyLau6Gq2oSV8emi47QaliEioha27mAmSqoM6ODlgju7+oiOQ3RNMpmsbnRo5f50VOgNghO1DqsrQwsWLEBoaCgcHR0RGRmJPXv2XPPYDRs24M4770S7du3g5uaGmJgYbN26tRXTEpG90xtMdddfPHlbB8jlHBUiyzYqwg/Bns4orqjGd4nZouO0CqsqQ+vXr8esWbPw2muvISkpCUOGDMGoUaOQmZnZ4PG7d+/GnXfeic2bN+Pw4cMYNmwY7r33XiQlJbVyciKyV5uO5SCvpArtXNW4r2970XGIbkghl+HxmgVBl+1Ng9EONnCVSVa0EUl0dDT69u2LhQsX1t3XtWtXjBs3DvPmzWvU1+jevTsmTpyIN954o1HHl5SUQKPRQKvVws3NrUm5icg+SZKEuz7ZjbP5ZfjXyHA8fTtnkZF1qNAbEDPvD2grq7Hokb4YWbMGkTW5mddvqxkZ0uv1OHz4MGJjY+vdHxsbi/j4+EZ9DZPJhNLSUnh4eFzzGJ1Oh5KSkno3IqKm2HX2Es7ml6GNWonJ0UGi4xA1mrODEo8MMP/MLrGDafZWU4YKCwthNBrh41P/4kMfHx/k5eU16mt8+OGHKC8vx4QJE655zLx586DRaOpugYGBt5SbiOxX7bVCE/sHQuPEDVnJukyNCYGDQo7DGUU4kmnbG7haTRmq9fcpqZIkNWqa6tq1a/HWW29h/fr18Pb2vuZxc+bMgVarrbtlZWXdcmYisj/JuSXYe74Qchnw6MAQ0XGIbpq3myPG9vYHACzdY9tbdFhNGfLy8oJCobhqFKigoOCq0aK/W79+PR5//HF8++23GDFixHWPVavVcHNzq3cjIrpZy/aaR4VGRfgh0MNZcBqipnliiHkPvS0n85B52Xa36LCaMuTg4IDIyEhs37693v3bt2/HwIEDr/m4tWvX4tFHH8U333yDe+65p6VjEhGhoKQKPx29CAB4Ykio4DRETdfF1xW3dW4HkwQs32e71w5ZTRkCgNmzZ2Pp0qVYvnw5kpOT8eKLLyIzMxMzZswAYH6La8qUKXXHr127FlOmTMGHH36IAQMGIC8vD3l5edBqtaJOgYjswKr9Gag2SogMbos+Qdx6g6zb9JpC/21iFrQV1YLTtAyrKkMTJ07EJ598grfffhu9e/fG7t27sXnzZgQHBwMAcnNz6605tHjxYhgMBjz77LPw8/Oru73wwguiToGIbFyl3oivD5i33pjOUSGyAYPDvBDu64oKvRFrDmaIjtMirGqdIRG4zhAR3YzVCRl4/ceTCPJwxo6Xb4eCK06TDfj+cDZe/u4YfNzU2PPP4XBQWv5Yik2uM0REZOlMJgnLay6cfmxQCIsQ2Ywxvfzh7apGfokOPx/LER2n2bEMERE1k9/PFCCtsByujko82I9rlJHtcFDKMbVmiYgle1Jha28qsQwRETWT2rVYJkcHwUWtFJyGqHk9HB0EJ5UCZ/JKse/8ZdFxmhXLEBFRMziRrcWBtCtQymVcZJFskruzAyb0CwBgHh2yJSxDRETNYOle84vD6J5+8NM4CU5D1DIeGxwKmax2371S0XGaDcsQEdEtyimuxC/HcwH8uWIvkS0K9nTBXd18AdjWFh0sQ0REt2jl/nQYTBIGdPBARHuN6DhELWr6beb1s348moPCMp3gNM2DZYiI6BaU6Qz45oB5sdfpHBUiO9A3qC16BWigN5jqfvatHcsQEdEt+C4xC6VVBnTwcsGwLt6i4xC1OJlMhscGm0eHVidkQG8wCU5061iGiIiayGSSsDI+HQAwbXAo5FxkkezEqAg/+LipcalUh19OWP8ijCxDRERNtOvsJaRfroCroxL3920vOg5Rq3FQyhE3wLwv6PK96Va/CCPLEBFRE62oGRWa1D8Qzg5cZJHsy0NRQVAr5ThxUYvDGUWi49wSliEioiY4X1CG3WcvQSYDpsSEiI5D1Oo826hxXx/ziOjyfWmC09waliEioiaovVZoRFcfBHo4iw1DJMijg0IAAFtO5iG7qEJsmFvAMkREdJO0ldX44Ug2AGAat94gOxbu64ZBYZ4wScDq/Rmi4zQZyxAR0U36LjELFXojuvi4Iqajp+g4REI9Nsg8zX7twUxU6A2C0zQNyxAR0U0wmiSsqvkf8KODQiCTcTo92bdhXbwR4umMkioDfjhyUXScJmEZIiK6CTvOFCDzSgU0TiqM683p9ERyuQyP1rxdvGJfGkwm65tmzzJERHQTVsSbZ81MigqEk4NCcBoiy/BAv0C4qpVIvVSOXecuiY5z01iGiIga6Wx+Kfadvwy5DHULzhER0EatxIT+gQCAFfvSxYZpApYhIqJG+qpmOv1d3X0R0JbT6Yn+6tGBIZDLgN1nL+FcfqnoODeFZYiIqBG0FdXYUDOd/lFOpye6SqCHM+7s5gPgz9XZrQXLEBFRI6xPzERVtQld/dwQFeohOg6RRZpWM81+w5FsFFfoBadpPJYhIqIbMBhNWBlvnk4/bSCn0xNdS3SoB7r5uaGq2oS1B7NEx2k0liEiohv4LbkAF4sr0dZZhTG9/UXHIbJYMpkMjw02jw6t2p+OaqNJcKLGYRkiIrqBr2qm00+ODoKjitPpia7n3l5+8GrjgFxtFbaczBMdp1GUjT2wT58+jR4aPnLkSJMDERFZkuTcEiSkXoFCLsMjnE5PdENqpQIPRwfj09/PYWV8Ou7tZfmjqY0uQ+PGjWvBGERElql2d/qREb7w0ziJDUNkJR6ODsKCneeRmFGEE9la9AjQiI50XY0uQ2+++WZL5iAisjhXyvXYmGTea4m70xM1nrebI+7u4Yefjubgq/h0fDihl+hI18VrhoiIrmHdoUzoDCZEtHdDZHBb0XGIrErtelw/H8tBYZlObJgbaFIZMhqN+OCDDxAVFQVfX194eHjUuxERWTuD0YTV+2un04dyOj3RTeoT1Ba9At2hN5qw7mCm6DjX1aQyNHfuXHz00UeYMGECtFotZs+ejfHjx0Mul+Ott95q5ohERK1v2+l85Gqr4NXGAaN7+YmOQ2SVat9eXp2QYdHT7JtUhtasWYMlS5bg5ZdfhlKpxEMPPYSlS5fijTfeQEJCQnNnJCJqdSv21UynjwqCWsnp9ERNcXcPP3i1USO/RGfR0+ybVIby8vLQo0cPAECbNm2g1WoBAKNHj8Yvv/zSfOmIiAQ4eVGLQ+lFUMpleJjT6YmazEEpx8PRQQD+nJlpiZpUhgICApCbmwsACAsLw7Zt2wAAhw4dglqtbr50REQC1O5Of3cPP/i4OYoNQ2TlHo4Ogkohq5tmb4maVIbuu+8+/P777wCAF154Aa+//jo6deqEKVOm4LHHHmvWgEREramwTIdNR3MAAI8OChEbhsgG1E6zB/78j4alafQ6Q381f/78uj8/8MADCAgIQHx8PMLCwjBmzJhmC0dE1NrWHcyE3mhCr0B39A3idHqi5vDowBD8dDQHPx/LwZy7w+HVxrLeRWpSGfq7AQMGYMCAAc3xpYiIhKk2mrA64c/d6YmoedROsz+WVYx1BzMxc3gn0ZHqaVIZWrVq1XU/P2XKlCaFISIS6deTecgv0aGdq7puWJ+ImsejA4Px4vpirE7IwFNDO0KlsJx1n5tUhl544YV6H1dXV6OiogIODg5wdnZmGSIiq/RVzXT6h6OD4KC0nF/URLbg7h5++H+/nKmbZm9JG7g26V97UVFRvVtZWRlSUlIwePBgrF27trkzEhG1uGNZxTiSWQyVQobJNVOBiaj5mHezN//bsrQLqZvtvz6dOnXC/Pnzrxo1IiKyBrVroNzb0x/erpxOT9QSHo4OglIuw2ELm2bfrOPACoUCOTk5zfkliYhaXEFpFX4+bv7dNZUXThO1GG83R9zT0/Km2TfpmqFNmzbV+1iSJOTm5uLzzz/HoEGDmiUYEVFr+eZAJqqNEvoGuaNXoLvoOEQ2zRKn2TepDI0bN67exzKZDO3atcPw4cPx4YcfNkcuIqJWoTeY8HWCeUftRweFCk5DZPv6BLVFrwANjmVrsfZAJp67Q/w0+ya9TWYymerdjEYj8vLy8M0338DPr2Wnoy5YsAChoaFwdHREZGQk9uzZc93jd+3ahcjISDg6OqJDhw5YtGhRi+YjIuuy+UQuCst08HFTY1SEr+g4RHahdnX3rw9Yxm72VjV3dP369Zg1axZee+01JCUlYciQIRg1ahQyMzMbPD4tLQ133303hgwZgqSkJLz66qt4/vnn8cMPP7RyciKyVCtqrluIGxBsUeueENkyS9vNXiZJktSYA2fPnt3oL/rRRx81OdD1REdHo2/fvli4cGHdfV27dsW4ceMwb968q47/17/+hU2bNiE5ObnuvhkzZuDYsWPYv39/o75nSUkJNBoNtFot3Nzcbv0kiMhiHMkswvgF8XBQyrH/leHwtIBrF4jsxcfbz+LT388hMrgtfnh6YLN//Zt5/W70NUNJSUn1Pj58+DCMRiO6dOkCADh79iwUCgUiIyObEPnG9Ho9Dh8+jFdeeaXe/bGxsYiPj2/wMfv370dsbGy9++666y4sW7YM1dXVUKlUVz1Gp9NBp9PVfVxSUtIM6a928qIWy/elYVzv9ritc7sW+R5EdH1f7UsHAIzp5c8iRNTKHo4Owhc7ztdNs+8RoBGWpdFjwjt27Ki73Xvvvbj99tuRnZ2NI0eO4MiRI8jKysKwYcNwzz33tEjQwsJCGI1G+Pj41Lvfx8cHeXkND7Hl5eU1eLzBYEBhYWGDj5k3bx40Gk3dLTAwsHlO4G82Jl3EhiMXsWxvWot8fSK6vvySKmw+kQvAPLuFiFqXJU2zb9Ib5B9++CHmzZuHtm3/3NG5bdu2eOedd1p8NplMJqv3sSRJV913o+Mbur/WnDlzoNVq625ZWVm3mLhhU2KCIZMBu85ewoVLZS3yPYjo2tYkZMBgktA/pC0i2ov7HymRPZs2KBQPRQVi+m1iZ3I2qQyVlJQgPz//qvsLCgpQWlp6y6Ea4uXlBYVCcdUoUEFBwVWjP7V8fX0bPF6pVMLT07PBx6jVari5udW7tYRgTxfcEe4NAFhlQQtPEdkDncGINQdqptMP5HR6IlF6B7pj3vieCPcVe01uk8rQfffdh2nTpuH7779HdnY2srOz8f333+Pxxx/H+PHjmzsjAMDBwQGRkZHYvn17vfu3b9+OgQMbvvAqJibmquO3bduGfv36NXi9UGur/SX8/eFslFRVC05DZD9+PpaLy+V6+GkccVf3hv8zRUT2o0llaNGiRbjnnnvwyCOPIDg4GMHBwXj44YcxatQoLFiwoLkz1pk9ezaWLl2K5cuXIzk5GS+++CIyMzMxY8YMAOa3uKZMmVJ3/IwZM5CRkYHZs2cjOTkZy5cvx7Jly/Dyyy+3WMabMSjME52826Bcb8R3idmi4xDZBUmSsKJmd/q4mGAoOZ2eyO41aQVqZ2dnLFiwAO+//z4uXLgASZIQFhYGFxeX5s5Xz8SJE3H58mW8/fbbyM3NRUREBDZv3ozg4GAAQG5ubr01h0JDQ7F582a8+OKL+OKLL+Dv74/PPvsM999/f4vmbCyZTIZHB4XgtY0nsTI+HY8ODIFCfu3rn4jo1h3OKMKpnBKolXJM6s/d6YnoJtYZslctvc5Qhd6AAf/5HSVVBiyb2g93dOWQPVFLevabI/jleC4m9gvEuw/0FB2HiFpIi6wzNH78eHz11Vdwc3O74XVBGzZsaOyXtXvODkpMigrCl7tT8VV8OssQUQvK1VbWrXZbux0AEVGjy5BGo6mbjq7RcBpqc4obEIyle1Kx51whzuWXopOPq+hIRDZp9f4MGE0SBnTwQFc/rihPRGaNLkMrVqxo8M906wI9nDGiqw+2nc7Hyv3peGdcD9GRiGxOVbURaw9yOj0RXa1J0ygqKytRUVFR93FGRgY++eQTbNu2rdmC2ZvaIfsfDl+EtoLT7Ima26ajOSiqqEZ7dyeM6OotOg4RWZAmlaGxY8di1apVAIDi4mJERUXhww8/xNixY+ttokqNF9PBE+G+rqisNuLbxJZZ9ZrIXkmSVLc7/RROpyeiv2nSb4QjR45gyJAhAIDvv/8evr6+yMjIwKpVq/DZZ581a0B7IZPJ6vZHWrk/HUYTJ/kRNZeDaVeQnFsCR5UcE/u3zH6DRGS9mlSGKioq4Opqvsh327ZtGD9+PORyOQYMGICMjIxmDWhPxvZuD3dnFbKLKvF78tXbnRBR06yo2Z1+fN8AuDs7iA1DRBanSWUoLCwMP/74I7KysrB161bExsYCMO/71VJ7edkDJwdF3SJwonfwJbIV2UUV2Ha6Zjo9d6cnogY0qQy98cYbePnllxESEoKoqCjExMQAMI8S9enTp1kD2pu4mGAo5DLEX7iMlLyW2fSWyJ6s3p8BkwQM7OiJzly2goga0KQy9MADDyAzMxOJiYnYunVr3f133HEHPv7442YLZ4/auzvVbRz5VXya4DRE1q1Cb6ibTj9tEKfTE1HDmjylwtfXF66urti+fTsqKysBAP3790d4eHizhbNXtWugbEy6iKJyveA0RNZrY9JFlFQZEOThjOHhnE5PRA1rUhm6fPky7rjjDnTu3Bl33303cnNzAQBPPPEEXnrppWYNaI/6h7RFNz83VFWbsJ7T7ImaRJIkfFVz4fRUboJMRNfRpDL04osvQqVSITMzE87OznX3T5w4EVu2bGm2cPaqdjd7wHy9g8FoEhuIyArtPV+IcwVlcHFQ4MF+AaLjEJEFa1IZ2rZtG959910EBNT/BdOpUydOrW8mY3r5w8PFAReLK/Ebp9kT3bTa6fQP9guEm6NKbBgismhNKkPl5eX1RoRqFRYWQq1W33IoAhxVCjwUZV4cbnnNL3Uiapy0wnL8caYAgPktMiKi62lSGbrtttvqtuMAzG/rmEwmvP/++xg2bFizhbN3cQPM1zkcTLuCUzla0XGIrMbKmnW6hnVph1AvF7FhiMjiNXrX+r/64IMPMHToUCQmJkKv1+Of//wnTp06hStXrmDfvn3NndFu+WocMSrCF/87nouV8el474FeoiMRWbzSqmp8fzgbAKfTE1Hj3PTIUHV1NZ555hls2rQJUVFRuPPOO1FeXo7x48cjKSkJHTt2bImcdmtazYXUPx7NwRVOsye6oe8Ss1GmMyDMuw2GdPISHYeIrMBNjwypVCqcPHkSnp6emDt3bktkor/oG9QWPdprcOKiFmsPZuLZYWGiIxFZLKNJwsr96QDMW2/IZJxOT0Q31qRrhqZMmYJly5Y1dxZqwF93s/86IQPVnGZPdE07UwqQcbkCbo5KjO/bXnQcIrISTbpmSK/XY+nSpdi+fTv69esHF5f6Fyh+9NFHzRKOzEb38sO8X5ORq63C1lN5GN3TX3QkIotUO51+UlQQnB2a9OuNiOxQk35bnDx5En379gUAnD17tt7nOCzd/NRKBSZHB+Oz38/hq33pLENEDTibX4q95wshlwFTYoJFxyEiK9KkMrRjx47mzkE38Eh0EBbsOI/EjCKcyNaiR4BGdCQii1I7KhTbzRcBba9eB42I6FqavFErtS5vN0fc09MPAPBVzRoqRGRWXKHHxqTa6fQhYsMQkdVhGbIitRdS/3wsB4VlOrFhiCzIukNZqKo2oZufG6JCPUTHISIrwzJkRfoEtUWvQHfojSasPZApOg6RRTAYTVhVM1r66CBOpyeim8cyZGWm1YwOrU7IgN7AafZE207nI0dbBU8XB4zpxckFRHTzWIaszN09/NDOVY2CUh1+PZkrOg6RcCv2pQEAJkcHwVGlEJyGiKwRy5CVcVDK8Ui0edrw8n3pkCRJcCIicU5e1OJQehGUchkeGcDp9ETUNCxDVmhydBAclHIcyyrGkcwi0XGIhKmdTn9PTz/4uDmKDUNEVotlyAq1c1Xjvt7mrQaW7kkTnIZIjMIyHX4+lgOAu9MT0a1hGbJSjw02//LfeioPWVcqBKchan1fJ2RAbzShd6A7ege6i45DRFaMZchKdfF1xZBOXjBJf75VQGQvqqqNWL0/AwDwxBCOChHRrWEZsmKP14wOfZuYhZKqasFpiFrPT0cv4nK5Hu3dnTCyu6/oOERk5ViGrNjQzu3QybsNynQGfHsoS3QcolYhSVLdtXKPDgyBUsFfY0R0a/hbxIrJZLK6a4dW7EuHwchFGMn27T5XiHMFZXBxUGBiVKDoOERkA1iGrNx9fdrDw8UBF4srsfVUvug4RC1u6Z5UAMDE/kFwc1QJTkNEtoBlyMo5qhR1i80t3ZsqOA1Ry0rJK8Wec4WQy7g7PRE1H5YhGxA3IBgOCjmSMotxOIOLMJLtWr7XfK3QyAhfBHo4C05DRLaCZcgGtHNVY2xv8waVtS8WRLbmUqkOG49eBAA8PriD4DREZEtYhmzE4zVrrfx6MpeLMJJN+johA3qDeZHFyOC2ouMQkQ1hGbIR4b5uGBxmXoRxZXy66DhEzaqq2oivE7jIIhG1DJYhG1K7COP6Q1ko5SKMZEO4yCIRtSSWIRsytHM7dGznglKdAd8mZouOQ9Qs/rrI4rRBXGSRiJoff6vYELn8r4swpsFokgQnIrp1tYsstlErMaE/F1kkouZnNWWoqKgIcXFx0Gg00Gg0iIuLQ3Fx8TWPr66uxr/+9S/06NEDLi4u8Pf3x5QpU5CTk9N6oQUY3ycAbZ1VyC6qxLZTeaLjEN2y2kUWJ/QL5CKLRNQirKYMTZ48GUePHsWWLVuwZcsWHD16FHFxcdc8vqKiAkeOHMHrr7+OI0eOYMOGDTh79izGjBnTiqlbn5PDn4swLuM0e7JyXGSRiFqDUnSAxkhOTsaWLVuQkJCA6OhoAMCSJUsQExODlJQUdOnS5arHaDQabN++vd59//3vfxEVFYXMzEwEBQW1SnYR4gYEY9GuC0jMKEJSZhH6BHEaMlmnZTWrqnORRSJqSVYxMrR//35oNJq6IgQAAwYMgEajQXx8fKO/jlarhUwmg7u7+zWP0el0KCkpqXezNt5ujhjTqz0A1F14SmRtCkqq8GOS+W3t2pmSREQtwSrKUF5eHry9va+639vbG3l5jbsupqqqCq+88gomT54MNze3ax43b968uuuSNBoNAgOt84LN6bf9uQhjemG54DREN29FfDr0RhP6BbdFZLCH6DhEZMOElqG33noLMpnsurfExEQAgEwmu+rxkiQ1eP/fVVdXY9KkSTCZTFiwYMF1j50zZw60Wm3dLSsrq2knJ1i4rxuGdWkHk8QNXMn6lOkMdYssPjW0o+A0RGTrhF4zNHPmTEyaNOm6x4SEhOD48ePIz8+/6nOXLl2Cj4/PdR9fXV2NCRMmIC0tDX/88cd1R4UAQK1WQ61W3zi8FXjyto7YkXIJ3yVm48URneHZxjbOi2zfuoOZKK0yoGM7F9wRfvWoMBFRcxJahry8vODl5XXD42JiYqDVanHw4EFERUUBAA4cOACtVouBAwde83G1RejcuXPYsWMHPD09my27NRjQwQO9AjQ4lq3Fyv0ZmH1nZ9GRiG6o2miqmwn55G0dIJffePSXiOhWWMU1Q127dsXIkSMxffp0JCQkICEhAdOnT8fo0aPrzSQLDw/Hxo0bAQAGgwEPPPAAEhMTsWbNGhiNRuTl5SEvLw96vV7UqbQqmUyGJ28zv8Wwen86KvQGwYmIbuznYznI1Vahnasa4/q0Fx2HiOyAVZQhAFizZg169OiB2NhYxMbGomfPnli9enW9Y1JSUqDVagEA2dnZ2LRpE7Kzs9G7d2/4+fnV3W5mBpq1GxnhiyAPZxRVVOM7btFBFk6SJCzeZb7GbdqgEKiVCsGJiMgeWMU6QwDg4eGBr7/++rrHSNKf20+EhITU+9heKeQyTL+tA17/8SSW7EnFw9FB3NuJLNbOs5eQkl8KFwcFHo4OFh2HiOwEXxXtwIORAfBwcUB2USV+PcktOshyfVkzKvRQVBA0Ttx6g4haB8uQHXBUKTA1JgQAsHj3BY6YkUU6nl2M/amXofzLhsNERK2BZchOxMUEw1Elx8mLJdh/4bLoOERXWbzbPCo0ppc//N2dBKchInvCMmQnPFwcMLGfeTXtRbu5CCNZlozL5fj1RC4A4MmhHQSnISJ7wzJkR54Y0gFyGbD77CWczrG+PdfIdi3dkwaTBNzepR3Cfa+/MCoRUXNjGbIjgR7OuLuHHwDztUNElqCwTIfvDpu3vXnyNo4KEVHrYxmyMzNq9nn6+VgOMi9XCE5DBKzYl4aqahN6BWgQ08G+VoknIsvAMmRnItprMLSzeQPXRRwdIsFKqqqxKt68Ieszw8IatfEyEVFzYxmyQ88OCwMAfJ+YjfySKsFpyJ6t3p+BUp0BnX3a4M6u1990mYiopbAM2aGoUA9EhXhAbzRhCWeWkSCVeiOW12zI+sztYdyQlYiEYRmyU88MM187tOZAJorK7WPjWrIs6w5l4nK5HoEeThjd0090HCKyYyxDdmpo53bo7u+GymojVsSni45DdkZvMOHLmlHJGUM7cr88IhKKv4HslEwmq7t26Kt9aSjTGQQnInvyY9JF5Gqr4O2qxv19A0THISI7xzJkx+7q7osO7VxQUmXAmoQM0XHIThhNEhbuMs9knD6kAxxVCsGJiMjesQzZMYVchmduN48OLdmThqpqo+BEZA82n8hFWmE53J1VmBwdJDoOERHLkL0b29sf7d2dzKsAJ2aJjkM2TpIkfLHjPABg2sBQuKiVghMREbEM2T2VQo6najbGXLQrFdVGk+BEZMt2pBTgTF4pXBwUmDowWHQcIiIALEMEYEK/QHi1UeNicSU2Hc0RHYdslCRJ+PwP86jQIwOC4e7sIDgREZEZyxDBUaXAE0NCAQALdp6H0SQJTkS2KCH1Co5kFsNBKcfjNT9vRESWgGWIAAAPRwdB46TChUvl+OVErug4ZIM+/f0sAGBiv0B4uzoKTkNE9CeWIQIAuDqq8MRg8//WP/v9HEeHqFntv3AZCalX4KCQ4+nbO4qOQ0RUD8sQ1Zk6KAQaJxXOF5RxdIiaVd2oUP9A+Ls7CU5DRFQfyxDVcXNU4fGa0aH/cnSImklCqnlUSKWQcVSIiCwSyxDV8+igELg5KnGuoAybOTpEzeDT384B4KgQEVkuliGqx81RhSeGmNcd+uz3czBxdIhuwYHUy9ifehkqxZ+rnRMRWRqWIbpKvdGhkxwdoqb79HfzqNCEfhwVIiLLxTJEVzFfO2QeHfr0N44OUdMcSL2M+As1o0LDOCpERJaLZYgaxNEhulV/HRVqz1EhIrJgLEPUII2TCo9x3SFqooNpVzgqRERWg2WIrmnaoFC4OSpxNr8MPx/jnmXUOJIk4YOtKQCABzkqRERWgGWIrknjpMJTQ83rwnz821nuaE+NsvtcIQ6mX4GDUo7nh3cSHYeI6IZYhui6pg0KgVcbB2RcrsB3idmi45CF++uo0JQBwfDVcA8yIrJ8LEN0Xc4OSjxbc83HZ7+fQ1W1UXAismRbT+XhxEUtXBwUXG2aiKwGyxDd0OToIPhrHJFXUoWvEzJExyELZTRJ+GCbeQ+yxweHwrONWnAiIqLGYRmiG1IrFXhhhPnaj4U7L6BMZxCciCzRT0cv4nxBGTROKjxxWwfRcYiIGo1liBrl/r4BCPVyweVyPVbsTRMdhyyM3mDCx7+ZR4VmDO0IN0eV4ERERI3HMkSNolTI8eKdnQEAX+5JRXGFXnAisiTfJmYh60ol2rmqMXVgsOg4REQ3hWWIGm10Dz+E+7qitMqAxbtTRcchC1FVbcRnNatNzxwWBmcHpeBEREQ3h2WIGk0ul+Hl2C4AgBX70pCnrRKciCzBin3pKCjVob27EyZFBYqOQ0R001iG6Kbc0dUb/YLboqrahI+3nxUdhwS7Uq7Hgh3nAQCz7+wMtVIhOBER0c1jGaKbIpPJMOfurgCA7w5nISWvVHAiEum/f5xDqc6Arn5uuK9Pe9FxiIiahGWIblpkcFuMivCFSQLm/5osOg4JknG5vG7dqVfvDodcLhOciIioaViGqEn+OTIcSrkMO1IuIf58oeg4JMB7W1NQbZRwW+d2GNKpneg4RERNxjJETRLq5YJHBpinUP+/zckwmSTBiag1JWUW4ZfjuZDJgDmjwkXHISK6JSxD1GTPDQ+Dq1qJUzkl2HQsR3QcaiWSJGHe5jMAzItxdvVzE5yIiOjWWE0ZKioqQlxcHDQaDTQaDeLi4lBcXNzoxz/11FOQyWT45JNPWiyjvfFso8aMms0439+awk1c7cT20/k4mH4Fjio5XortLDoOEdEts5oyNHnyZBw9ehRbtmzBli1bcPToUcTFxTXqsT/++CMOHDgAf3//Fk5pfx4fHAo/jSMuFldiZXy66DjUwqqNJszfYh4VMj/3ToITERHdOqsoQ8nJydiyZQuWLl2KmJgYxMTEYMmSJfjf//6HlJSU6z724sWLmDlzJtasWQOVivslNTdHlQKza7bp+HzHeVwu0wlORC1p7cFMpF4qh4eLA54a2lF0HCKiZmEVZWj//v3QaDSIjo6uu2/AgAHQaDSIj4+/5uNMJhPi4uLwj3/8A927d2/U99LpdCgpKal3o+sb3zcA3f3dUFplwAfbrl9OyXoVlevx4TbzQpsvjujEzViJyGZYRRnKy8uDt7f3Vfd7e3sjLy/vmo979913oVQq8fzzzzf6e82bN6/uuiSNRoPAQG4vcCMKuQxvjTGXzXWHsnDyolZwImoJH25PgbayGuG+rpgczc1Yich2CC1Db731FmQy2XVviYmJAMwrH/+dJEkN3g8Ahw8fxqeffoqvvvrqmsc0ZM6cOdBqtXW3rKyspp2cnekf4oGxvf0hScBbm05BkjjV3paczinBNwcyAQBvjekOBRdYJCIbInR76ZkzZ2LSpEnXPSYkJATHjx9Hfn7+VZ+7dOkSfHx8Gnzcnj17UFBQgKCgoLr7jEYjXnrpJXzyySdIT09v8HFqtRpqtbrxJ0F1XhkVjm2n8pGYUYRNx3Iwtje3Z7AFkiThrZ9PwSQBo3v6YUAHT9GRiIialdAy5OXlBS8vrxseFxMTA61Wi4MHDyIqKgoAcODAAWi1WgwcOLDBx8TFxWHEiBH17rvrrrsQFxeHadOm3Xp4uoqfxgkzh4fh/a0p+M/mZIzo6gMXtdAfMWoG/zuei4Np5qn0r9bsS0dEZEus4pqhrl27YuTIkZg+fToSEhKQkJCA6dOnY/To0ejSpUvdceHh4di4cSMAwNPTExEREfVuKpUKvr6+9R5DzevxwaEI8nBGfokOX9TsZk7Wq0JvwH82m/efe+b2MPi7cyo9EdkeqyhDALBmzRr06NEDsbGxiI2NRc+ePbF69ep6x6SkpECr5cW7IjmqFPi/e8yjB0v3pCHjcrngRHQrFu28gFxtFQLaOuHJ2zqIjkNE1CJkEq90va6SkhJoNBpotVq4uXHbgcaQJAlTlh/EnnOFGNHVG0un9hcdiZog60oF7vhoF/QGExY90hcjI/xERyIiarSbef22mpEhsh4ymQxv3tsNSrkMvyUXYPvpqy9+J8smSRLe+Okk9AYTBoV54q7uvqIjERG1GJYhahFh3q54Yoj5bZU3fzqJcp1BcCK6Gb+ezMOOlEtwUMjx9tiIm1qegojI2rAMUYt54Y5OCGjrhBxtFT79/ZzoONRIpVXVmPvzKQDAjNs7omO7NoITERG1LJYhajFODgr8e2wEAGDZ3jSczuHWJtbgw21nkV+iQ4inM565nfuPEZHtYxmiFjUs3Bt39/CF0SThtR9PwGTi9fqW7Hh2MVbuTwcAvDOuBxxVCrGBiIhaAcsQtbg37+2ONmolkjKL8c3BTNFx6BoMRhNe3XgCkgSM7e2PwZ1uvCAqEZEtYBmiFufj5oiXYzsDAOb/egY5xZWCE1FDlu1Nw8mLJXBzVOL/7ukmOg4RUathGaJWERcTgr5B7ijTGWpGH/h2mSW5cKkMH24/CwD4v9Hd0M6V+/MRkf1gGaJWoZDL8N4DveCglGNnyiVsOHJRdCSqYTRJ+Nf3x6E3mDCkkxcejAwQHYmIqFWxDFGrCfNug1kjOgEA5v58CgUlVYITEQCs2p+OxIwiuDgoMP/+nlxTiIjsDssQtaonh3RAj/YalFQZ8NqPJ/l2mWCZlyvw3pYUAMCcu7uiPTdiJSI7xDJErUqpkOP9B3tCpZBh++l8bDqWIzqS3TKZJPzzh2OorDZiQAcPTI4KEh2JiEgIliFqdeG+bpg5zPx22f/9eJKzywRZtjcNCalX4KRS4N37e0Iu59tjRGSfWIZIiGeGdUTvQHeUVhnw0rfHuBhjK0vOLcH7W81vj71xbzcEe7oITkREJA7LEAmhUsjx8cTecFIpsD/1MpbtTRMdyW5UVRsxa91R6I0mjOjqg0n9A0VHIiISimWIhAn1csEb95oX93t/awqSc7l3WWt4f2sKUvJL4dXGAfPv78HZY0Rk91iGSKhJ/QMxoqsP9EYTZq07iqpqo+hINm3f+cK6Ubj3HugJrzZcXJGIiGWIhJLJZHj3/h7waqNGSn4p3v7fadGRbFZBaRVeWHcUAPDIgCAMD/cRG4iIyEKwDJFwnm3U+HhiL8hkwDcHMvHTUa5O3dyMJgkvrD2KwjIduvi44rW7ufcYEVEtliGyCEM6tcNzw8IAAK9uOIELl8oEJ7Itn/5+DvtTL8PZQYEvHu4LJweF6EhERBaDZYgsxgsjOiOmgyfK9UY8u+YIKvW8fqg57Dl3Cf/94xwAYN74HgjzbiM4ERGRZWEZIouhkMvw6UO94dVGjTN5pXhzE7fruFV52irMWncUkgQ8FBWEsb3bi45ERGRxWIbIoni7OuKzSb0hlwHfJmbj64QM0ZGsVlW1EU99fRiXy/Xo6ueGN+/ldUJERA1hGSKLMzDMC/8cGQ4AmPvzacRfKBScyPpIkoRXN5zAsaxiuDursOiRvnBU8TohIqKGsAyRRXrqtg4Y19sfBpOEZ9ccQdaVCtGRrMqSPanYkHQRCrkMCyb35XYbRETXwTJEFkkmk2H+/T3RM0CDoopqTF+ViHKdQXQsq7AzpQDzfz0DAHj9nq4YGOYlOBERkWVjGSKL5ahSYHFcJNq5mi+ofmFdEgxGk+hYFi0lrxTPrU2CSTKv7j11YIjoSEREFo9liCyan8YJi+Mi4aCU47fkArz+0ynOMLuGnOJKTF1+EKVVBvQPaYu3x0Zw3zEiokZgGSKL1zeoLT6b1BsyGbD2YCY++/286EgWR1tRjanLDyKvpAph3m2wZEo/OCj5z5uIqDH425KswsgIP7w9pjsA4OPfzmLdwUzBiSxHVbUR01cl4lxBGXzc1Fj5WBTcnR1ExyIishosQ2Q14mJC8OywjgCAVzeewJaTeYITiVdtNOH5tUk4mH4Fro5KrHwsCu3dnUTHIiKyKixDZFVeju2CByIDYJKA59YewfbT+aIjCVNtNOGFdUnYdjofDgo5vozrh3BfN9GxiIisDssQWRWZTIb543vg3l7+qDZKeGbNYfyebH+FyGA0Ydb6o9h8Ig8OCjkWx0UipqOn6FhERFaJZYisjlIhx8cTeuGenn6oNkp4+usj+OOM/RQig9GEF789hl+O50KlkGHhI30xLNxbdCwiIqvFMkRWSamQ49OJvXFPDz/ojSbMWH0Em0/kio7V4qqqjXh+XRJ+PpZjLkIPR+KOrj6iYxERWTWWIbJaSoUcn0z6sxA9+80RrIxPFx2rxWgrqzFl+cG6t8a+mNwXI7qxCBER3SqWIbJqKoUcnz3UB3EDgiFJwJubTuHdLWdsbmHGXG0lJizaj4NpV+CqVuKrx/ojtruv6FhERDaBZYisnkIuw9tju+Pl2M4AgIU7L+Clb4+hqtooOFnzSM4twfgF8UjJL4W3qxrfzojBwI7cb4yIqLmwDJFNkMlkmDm8E967vycUchk2JF3ExC8TkKutFB3tlvzveA7GL4hHrrYKHdq5YMMzA9HVj9PniYiaE8sQ2ZQJ/QPx1bT+cHdW4VhWMe797z7sv3BZdKybVm004T+bkzHzmyRUVhsxpJMXNjw9EAFtnUVHIyKyOSxDZHOGdGqHTc8ORrivKwrLdJi8NAEfbE1BtZXseJ9xuRwPLIzHl7tTAQBPDe2Ar6Zxiw0iopbCMkQ2KcjTGRueGYhJ/QMhScDnO87jwUX7cS6/VHS0azKZJKw5kIG7P92DY9laaJxUWPRIX8wZ1RUKOXefJyJqKTLJ1qbdNLOSkhJoNBpotVq4ufFaDWv0v+M5mLPhBEqrDFApZHh2WBievr0j1EqF6Gh10grL8coPx3Eg7QoAICrUA59M7A1/7jNGRNQkN/P6zTJ0AyxDtiFXW4nXfzyJ35ILAADBns6YMyocd3X3hUwmbtRFW1mNz/84h6/i01FtlOCkUuAfd3XB1IEhHA0iIroFLEPNiGXIdkiShP8dz8W//3caBaU6AED/kLZ4/o5OGBzm1aqlqExnwNcJGVi86wKKKqoBAEM7t8M74yIQ6MGLpImIbtXNvH5bzTVDRUVFiIuLg0ajgUajQVxcHIqLi2/4uOTkZIwZMwYajQaurq4YMGAAMjMzWz4wWRyZTIZ7e/ljx8u347nhYVAr5TiUXoS4ZQdx34J4/HwsB3pDy15knVNciY+2pWDQ/D8w/9czKKqoRph3G3w1rT9WPhbFIkREJIDVjAyNGjUK2dnZ+PLLLwEATz75JEJCQvDzzz9f8zEXLlxAVFQUHn/8cTz00EPQaDRITk5G//794e3duI0tOTJku/K0VVi06wLWHsyErqYEebVxwPi+Abi7hx96BWiaZbSoTGfAjjMF+OloDv44kw9Tzb+4Dl4ueGZYGMb19odSYTX/LyEisgo29zZZcnIyunXrhoSEBERHRwMAEhISEBMTgzNnzqBLly4NPm7SpElQqVRYvXp1k783y5Dtu1Sqw+r96Vh3KKvu7TMA8Nc4YnAnL0SHeqJPkDuCPV0adR1Pmc6AlLwSHEi7ggOpV7A/9XK9EafoUA/ExQRjVIQfrwsiImohNleGli9fjtmzZ1/1tpi7uzs+/vhjTJs27arHmEwmaDQa/POf/8TevXuRlJSE0NBQzJkzB+PGjbvm99LpdNDp/nxBLCkpQWBgIMuQHag2mvB7cgH+dzwHf5wpQIW+/nYeDko5Qj1d4O2mhqeLA5zVSsgAmCQJReXVuFKuR3ZRBXK0VVd97VAvF4yM8MX9fdsjzNu1lc6IiMh+3UwZUrZSpluSl5fX4Nta3t7eyMvLa/AxBQUFKCsrw/z58/HOO+/g3XffxZYtWzB+/Hjs2LEDQ4cObfBx8+bNw9y5c5s1P1kHlUKOkRG+GBnhi6pqI/ZfuIyEtMs4kHoFZ/JKUFVtQkp+KVIasVZRO1c1+ga5IzrUEwPDPNHFx1XorDUiIro2oWXorbfeumHxOHToEAA0+EIiSdI1X2BMJvPbEmPHjsWLL74IAOjduzfi4+OxaNGia5ahOXPmYPbs2XUf144MkX1xVCkwLNwbw8LNJdxkknCxuBIXLpWhsEyPonJ93ciRTAa4O6vg4eIAP40jwtq5QuOsEhmfiIhugtAyNHPmTEyaNOm6x4SEhOD48ePIz8+/6nOXLl2Cj49Pg4/z8vKCUqlEt27d6t3ftWtX7N2795rfT61WQ61WNyI92RO5XIZAD2fO9iIiskFCy5CXlxe8vLxueFxMTAy0Wi0OHjyIqKgoAMCBAweg1WoxcODABh/j4OCA/v37IyUlpd79Z8+eRXBw8K2HJyIiIptgFfN5u3btipEjR2L69OlISEhAQkICpk+fjtGjR9ebSRYeHo6NGzfWffyPf/wD69evx5IlS3D+/Hl8/vnn+Pnnn/HMM8+IOA0iIiKyQFZRhgBgzZo16NGjB2JjYxEbG4uePXteNWU+JSUFWq227uP77rsPixYtwnvvvYcePXpg6dKl+OGHHzB48ODWjk9EREQWyiqm1ovEdYaIiIisj01ux0FERETUEliGiIiIyK6xDBEREZFdYxkiIiIiu8YyRERERHaNZYiIiIjsGssQERER2TWWISIiIrJrLENERERk14Ru1GoNahfoLikpEZyEiIiIGqv2dbsxG22wDN1AaWkpACAwMFBwEiIiIrpZpaWl0Gg01z2Ge5PdgMlkQk5ODlxdXSGTyZr1a5eUlCAwMBBZWVl2te+ZvZ43YL/nbq/nDfDc7fHc7fW8Acs6d0mSUFpaCn9/f8jl178qiCNDNyCXyxEQENCi38PNzU34D40I9nregP2eu72eN8Bzt8dzt9fzBizn3G80IlSLF1ATERGRXWMZIiIiIrvGMiSQWq3Gm2++CbVaLTpKq7LX8wbs99zt9bwBnrs9nru9njdgvefOC6iJiIjIrnFkiIiIiOwayxARERHZNZYhIiIismssQ0RERGTXWIYEWbBgAUJDQ+Ho6IjIyEjs2bNHdKRm9dZbb0Emk9W7+fr61n1ekiS89dZb8Pf3h5OTE26//XacOnVKYOKm2717N+699174+/tDJpPhxx9/rPf5xpyrTqfDc889By8vL7i4uGDMmDHIzs5uxbNomhud+6OPPnrVz8GAAQPqHWON5z5v3jz0798frq6u8Pb2xrhx45CSklLvGFt83htz3rb6nC9cuBA9e/asW0wwJiYGv/76a93nbfH5rnWjc7eF55xlSID169dj1qxZeO2115CUlIQhQ4Zg1KhRyMzMFB2tWXXv3h25ubl1txMnTtR97r333sNHH32Ezz//HIcOHYKvry/uvPPOur3grEl5eTl69eqFzz//vMHPN+ZcZ82ahY0bN2LdunXYu3cvysrKMHr0aBiNxtY6jSa50bkDwMiRI+v9HGzevLne563x3Hft2oVnn30WCQkJ2L59OwwGA2JjY1FeXl53jC0+7405b8A2n/OAgADMnz8fiYmJSExMxPDhwzF27Ni6wmOLz3etG507YAPPuUStLioqSpoxY0a9+8LDw6VXXnlFUKLm9+abb0q9evVq8HMmk0ny9fWV5s+fX3dfVVWVpNFopEWLFrVSwpYBQNq4cWPdx4051+LiYkmlUknr1q2rO+bixYuSXC6XtmzZ0mrZb9Xfz12SJGnq1KnS2LFjr/kYWzn3goICCYC0a9cuSZLs53n/+3lLkv0855IkSW3btpWWLl1qN8/3X9WeuyTZxnPOkaFWptfrcfjwYcTGxta7PzY2FvHx8YJStYxz587B398foaGhmDRpElJTUwEAaWlpyMvLq/d3oFarMXToUJv7O2jMuR4+fBjV1dX1jvH390dERIRN/H3s3LkT3t7e6Ny5M6ZPn46CgoK6z9nKuWu1WgCAh4cHAPt53v9+3rVs/Tk3Go1Yt24dysvLERMTYzfPN3D1udey9uecG7W2ssLCQhiNRvj4+NS738fHB3l5eYJSNb/o6GisWrUKnTt3Rn5+Pt555x0MHDgQp06dqjvPhv4OMjIyRMRtMY0517y8PDg4OKBt27ZXHWPtPxOjRo3Cgw8+iODgYKSlpeH111/H8OHDcfjwYajVaps4d0mSMHv2bAwePBgREREA7ON5b+i8Adt+zk+cOIGYmBhUVVWhTZs22LhxI7p161b3gm7Lz/e1zh2wjeecZUgQmUxW72NJkq66z5qNGjWq7s89evRATEwMOnbsiJUrV9ZdWGfrfwd/1ZRztYW/j4kTJ9b9OSIiAv369UNwcDB++eUXjB8//pqPs6ZznzlzJo4fP469e/de9Tlbft6vdd62/Jx36dIFR48eRXFxMX744QdMnToVu3btqvu8LT/f1zr3bt262cRzzrfJWpmXlxcUCsVVbbigoOCq/1XYEhcXF/To0QPnzp2rm1VmD38HjTlXX19f6PV6FBUVXfMYW+Hn54fg4GCcO3cOgPWf+3PPPYdNmzZhx44dCAgIqLvf1p/3a513Q2zpOXdwcEBYWBj69euHefPmoVevXvj0009t/vkGrn3uDbHG55xlqJU5ODggMjIS27dvr3f/9u3bMXDgQEGpWp5Op0NycjL8/PwQGhoKX1/fen8Her0eu3btsrm/g8aca2RkJFQqVb1jcnNzcfLkSZv7+7h8+TKysrLg5+cHwHrPXZIkzJw5Exs2bMAff/yB0NDQep+31ef9RufdEFt5zhsiSRJ0Op3NPt/XU3vuDbHK57zVL9kmad26dZJKpZKWLVsmnT59Wpo1a5bk4uIipaeni47WbF566SVp586dUmpqqpSQkCCNHj1acnV1rTvH+fPnSxqNRtqwYYN04sQJ6aGHHpL8/PykkpISwclvXmlpqZSUlCQlJSVJAKSPPvpISkpKkjIyMiRJaty5zpgxQwoICJB+++036ciRI9Lw4cOlXr16SQaDQdRpNcr1zr20tFR66aWXpPj4eCktLU3asWOHFBMTI7Vv397qz/3pp5+WNBqNtHPnTik3N7fuVlFRUXeMLT7vNzpvW37O58yZI+3evVtKS0uTjh8/Lr366quSXC6Xtm3bJkmSbT7fta537rbynLMMCfLFF19IwcHBkoODg9S3b996U1NtwcSJEyU/Pz9JpVJJ/v7+0vjx46VTp07Vfd5kMklvvvmm5OvrK6nVaum2226TTpw4ITBx0+3YsUMCcNVt6tSpkiQ17lwrKyulmTNnSh4eHpKTk5M0evRoKTMzU8DZ3JzrnXtFRYUUGxsrtWvXTlKpVFJQUJA0derUq87LGs+9oXMGIK1YsaLuGFt83m903rb8nD/22GN1v7PbtWsn3XHHHXVFSJJs8/mudb1zt5XnXCZJktR641BEREREloXXDBEREZFdYxkiIiIiu8YyRERERHaNZYiIiIjsGssQERER2TWWISIiIrJrLENERERk11iGiIiIyK6xDBEREZFdYxkiIiIiu8YyRERERHaNZYiIiIjs2v8H4oXmKchs64AAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -747,21 +1077,17 @@
     }
    ],
    "source": [
-    "# convert reconstruction coordinates to (x, y)\n",
-    "# calulate dty from that\n",
-    "\n",
-    "dty_calc = geometry.recon_omega_to_dty(ri, rj, omega, y0, recon.shape, ystep)\n",
+    "# plot residual\n",
     "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.scatter(omega, dty, label='dty original')\n",
-    "ax.scatter(omega, dty_calc, label='dty calculated from recon', s=3)\n",
-    "ax.legend()\n",
+    "ax.plot(dty - dty_calc)\n",
+    "ax.set_ylabel('residual')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "id": "6469278a-5007-47e1-91b1-d9b8c3279949",
    "metadata": {
     "tags": []
@@ -769,7 +1095,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -779,16 +1105,52 @@
     }
    ],
    "source": [
-    "dtyi_desired = geometry.dty_to_dtyi(dty, ystep)\n",
-    "dtyi_calc = geometry.recon_omega_to_dtyi(ri, rj, omega, y0, recon.shape, ystep)\n",
+    "dtyi_desired = geometry.dty_to_dtyi(dty, ystep, ybincens[0])\n",
+    "dtyi_calc = geometry.recon_omega_to_dtyi(ri, rj, omega, y0, recon.shape, ystep, ybincens[0])\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(omega, dtyi_desired, label='dtyi original')\n",
     "ax.scatter(omega, dtyi_calc, label='dtyi calculated from recon', s=3)\n",
     "ax.legend()\n",
-    "# ax.set_ylim(ny, 0)\n",
+    "ax.set_xlabel('omega (deg)')\n",
+    "ax.set_ylabel('dtyi')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d4fa9328-09be-403f-9c67-87da74c7fff3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot residual\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(dtyi_desired - dtyi_calc)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ef5f4c5-99b7-4a3b-9d7d-add286ddd482",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {


Test patch:
diff --git a/test/test_geometry.py b/test/test_geometry.py
index f8cf6cf..230c0e2 100644
--- a/test/test_geometry.py
+++ b/test/test_geometry.py
@@ -1,3 +1,5 @@
+import sys
+
 import numpy as np
 import unittest
 
@@ -6,41 +8,60 @@ from ImageD11.sinograms import geometry
 
 class TestSampleToLab(unittest.TestCase):
     def test_sample_to_lab(self):
+        # set up a grain in the sample reference frame
+        # such that assuming no alignment problems, rotating it by 30 degrees should bring it onto the y-axis
+        # with (lx, ly) = (0, 1 + dty)
+
         sx = 0.5  # um
         sy = np.sqrt(3) / 2  # um
-        y0 = 35  # um
-        dty = 34  # um
         omega = 30  # degrees
+
+        # however we have an alignment problem with y0
+
+        y0 = 101  # um - dty value where the rotation axis hits the beam
+        dty = 100  # um
+
+        # such that the grain actually ends up in the beam (ly = ybeam)
+
         desired_lx = 0.0  # um
         desired_ly = 0.0  # um
 
-        # assuming no y0/dty problems, rotating by 30 degrees should bring the grain
-        # onto the x-axis
-        # with coords (1, 0)
-        # however because y0 != dty
-        # rotation axis is further negative y than expected
-        # shift the sample to the right (negative y)
-        # puts the grain on the origin
-
         lx, ly = geometry.sample_to_lab(sx, sy, y0, dty, omega)
 
         self.assertAlmostEqual(desired_lx, lx)
         self.assertAlmostEqual(desired_ly, ly)
 
     def test_lab_to_sample(self):
-        # let's define a grain that's in the beam at 90 degrees
+        # for now, assume no alignment problems
+        y0 = 0  # um
+        dty = 0  # um
+        # set up a grain that's in the beam at 90 degrees
         lx = -1.0  # um
-        ly = 0.0  # um
-        y0 = 35  # um
-        dty = 34  # um
+        ly = 0  # um
         omega = 90.0  # degrees
-        # dty is less than y0, so the rotation axis is further to the right (negative lab y) than expected
-        desired_sx = 1.0
+        # in the sample reference frame, should be (0, 1)
+        desired_sx = 0.0
         desired_sy = 1.0
 
-        # (-1, 0) in the lab
-        # goes to (-1, 1) in the translated frame
-        # then we rotate reference frame to match
+        sx, sy = geometry.lab_to_sample(lx, ly, y0, dty, omega)
+
+        self.assertAlmostEqual(desired_sx, sx)
+        self.assertAlmostEqual(desired_sy, sy)
+
+        # now set up alignment problem
+        # put dty at where we think the beam is
+        # beam actually intersects at dty = y0
+        # so rotation axis is slightly to the right (negative y)
+
+        y0 = 36  # um
+        dty = 35  # um
+
+        # set up a grain that's in the beam at 90 degrees
+        lx = -1.0  # um
+        ly = 0.0  # um
+
+        desired_sx = 1.0
+        desired_sy = 1.0
 
         sx, sy = geometry.lab_to_sample(lx, ly, y0, dty, omega)
 
@@ -80,17 +101,17 @@ class TestSampleToStep(unittest.TestCase):
         desired_x = 100.0  # um
         desired_y = 200.0  # um
 
-        x, y = geometry.step_to_sample(si, sj, ystep)
+        sx, sy = geometry.step_to_sample(si, sj, ystep)
 
-        self.assertSequenceEqual((desired_x, desired_y), (x, y))
+        self.assertSequenceEqual((desired_x, desired_y), (sx, sy))
 
     def test_cycle(self):
         ystep = 2.0  # um/px
         si = 50.0  # px
         sj = -100.0  # px
 
-        x, y = geometry.step_to_sample(si, sj, ystep)
-        si_out, sj_out = geometry.sample_to_step(x, y, ystep)
+        sx, sy = geometry.step_to_sample(si, sj, ystep)
+        si_out, sj_out = geometry.sample_to_step(sx, sy, ystep)
 
         self.assertSequenceEqual((si, sj), (si_out, sj_out))
 
@@ -129,10 +150,10 @@ class TestStepToRecon(unittest.TestCase):
         self.assertSequenceEqual((si, sj), (si_out, sj_out))
 
 
-class TestXYY0OmegaToDty(unittest.TestCase):
+class TestDtyValuesGrainInBeam(unittest.TestCase):
     def test_grain_position1(self):
-        x = 1.0  # um
-        y = 0.0  # um
+        sx = 1.0  # um
+        sy = 0.0  # um
         omega = 90.0  # degrees
 
         # a grain is at (1, 0) at 0 degrees
@@ -142,13 +163,13 @@ class TestXYY0OmegaToDty(unittest.TestCase):
         desired_dty = -1.0
         y0 = 0.0
 
-        dty = geometry.x_y_y0_omega_to_dty(omega, x, y, y0)
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
 
         self.assertEqual(desired_dty, dty)
 
     def test_grain_position2(self):
-        x = 0.0  # um
-        y = 1.0  # um
+        sx = 0.0  # um
+        sy = 1.0  # um
         omega = 0.0  # degrees
 
         # a grain is at (0, 1) at 0 degrees
@@ -157,13 +178,13 @@ class TestXYY0OmegaToDty(unittest.TestCase):
         desired_dty = -1.0
         y0 = 0.0
 
-        dty = geometry.x_y_y0_omega_to_dty(omega, x, y, y0)
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
 
         self.assertEqual(desired_dty, dty)
 
     def test_grain_position3(self):
-        x = -1.0  # um
-        y = 0.0  # um
+        sx = -1.0  # um
+        sy = 0.0  # um
         omega = 90.0  # degrees
 
         # a grain is at (-1, 0) at 0 degrees
@@ -173,13 +194,13 @@ class TestXYY0OmegaToDty(unittest.TestCase):
         desired_dty = 1.0
         y0 = 0.0
 
-        dty = geometry.x_y_y0_omega_to_dty(omega, x, y, y0)
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
 
         self.assertEqual(desired_dty, dty)
 
     def test_grain_position4(self):
-        x = 0.0  # um
-        y = -1.0  # um
+        sx = 0.0  # um
+        sy = -1.0  # um
         omega = 0.0  # degrees
 
         # a grain is at (0, -1) at 0 degrees
@@ -188,13 +209,13 @@ class TestXYY0OmegaToDty(unittest.TestCase):
         desired_dty = 1.0
         y0 = 0.0
 
-        dty = geometry.x_y_y0_omega_to_dty(omega, x, y, y0)
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
 
         self.assertEqual(desired_dty, dty)
 
     def test_grain_position_with_y0(self):
-        x = 0.0  # um
-        y = 1.0  # um
+        sx = 0.0  # um
+        sy = 1.0  # um
         omega = 0.0  # degrees
 
         # a grain is at (0, 1) at 0 degrees
@@ -206,12 +227,12 @@ class TestXYY0OmegaToDty(unittest.TestCase):
         desired_dty = 4.0
         y0 = 5.0
 
-        dty = geometry.x_y_y0_omega_to_dty(omega, x, y, y0)
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
 
         self.assertEqual(desired_dty, dty)
 
 
-class TestDtyOmegaToXYY0(unittest.TestCase):
+class TestSxSyY0FromDtyOmega(unittest.TestCase):
     def test_sin(self):
         # define a sine wave
         omega = np.arange(0, 180, 1)  # degrees
@@ -223,13 +244,13 @@ class TestDtyOmegaToXYY0(unittest.TestCase):
         # therefore position should be (-1, 0)
         # as sample is translated in +y, need to rotate +ve to bring grain back into beam
 
-        desired_x, desired_y = (-1, 0)
+        desired_sx, desired_sy = (-1, 0)
         desired_y0 = 0
 
-        x, y, y0 = geometry.dty_omega_to_x_y_y0(dty, omega)
+        sx, sy, y0 = geometry.sx_sy_y0_from_dty_omega(dty, omega)
 
-        self.assertAlmostEqual(desired_x, x)
-        self.assertAlmostEqual(desired_y, y)
+        self.assertAlmostEqual(desired_sx, sx)
+        self.assertAlmostEqual(desired_sy, sy)
         self.assertAlmostEqual(desired_y0, y0)
 
     def test_cos(self):
@@ -239,53 +260,50 @@ class TestDtyOmegaToXYY0(unittest.TestCase):
         # dty starts at 1
         # so grain is at (0, -1) at omega = 0
 
-        desired_x, desired_y = (0, -1)
+        desired_sx, desired_sy = (0, -1)
         desired_y0 = 0
 
-        x, y, y0 = geometry.dty_omega_to_x_y_y0(dty, omega)
+        sx, sy, y0 = geometry.sx_sy_y0_from_dty_omega(dty, omega)
 
-        self.assertAlmostEqual(desired_x, x)
-        self.assertAlmostEqual(desired_y, y)
+        self.assertAlmostEqual(desired_sx, sx)
+        self.assertAlmostEqual(desired_sy, sy)
         self.assertAlmostEqual(desired_y0, y0)
 
     def test_cos_with_y0(self):
         # define a cos wave
         omega = np.arange(0, 180, 1)  # degrees
+        # we have a shift in the 'centre' of the cos wave
         desired_y0 = 5
+        # shift cos graph in dty accordingly
         dty = desired_y0 + np.cos(np.radians(omega))
         # dty starts at 1
         # so grain is at (0, -1) at omega = 0
-        # offset in dty should not affect this
+        # offset in dty should not be affected by y0
 
-        desired_x, desired_y = (0, -1)
+        desired_sx, desired_sy = (0, -1)
 
-        x, y, y0 = geometry.dty_omega_to_x_y_y0(dty, omega)
+        sx, sy, y0 = geometry.sx_sy_y0_from_dty_omega(dty, omega)
 
-        self.assertAlmostEqual(desired_x, x)
-        self.assertAlmostEqual(desired_y, y)
+        self.assertAlmostEqual(desired_sx, sx)
+        self.assertAlmostEqual(desired_sy, sy)
         self.assertAlmostEqual(desired_y0, y0)
 
 
 class TestDtyToDtyi(unittest.TestCase):
     def test_simple_sequence(self):
-        dty = np.array([0., 2., 4., 6., 8])  # um
-        ystep = 2.0  # um/px
-        desired_dtyi = np.array([0, -1, -2, -3, -4])
-        dtyi = geometry.dty_to_dtyi(dty, ystep)
+        ystep = 0.1  # um/px
+        ymin = 13.5  # um
+        ymax = 14.5  # um
+        dty = np.arange(ymin, ymax + ystep, ystep)  # um
 
-        self.assertTrue(np.allclose(desired_dtyi, dtyi))
-
-
-class TestDtyToDtyiForSinogram(unittest.TestCase):
-    def test_simple_sequence(self):
-        dty = np.array([0., 2., 4., 6., 8])  # um
-        ystep = 2.0  # um/px
-        ymin = -2.0  # um
-        desired_dtyi = np.array([1, 2, 3, 4, 5])
-        dtyi = geometry.dty_to_dtyi_for_sinogram(dty, ystep, ymin)
+        desired_dtyi = np.arange(0, len(dty))
+        dtyi = geometry.dty_to_dtyi(dty, ystep, ymin)
 
         self.assertTrue(np.allclose(desired_dtyi, dtyi))
 
+        dty_back = geometry.dtyi_to_dty(dtyi, ystep, ymin)
+        self.assertTrue(np.allclose(dty, dty_back))
+
 
 class TestFitSamplePositionFromRecon(unittest.TestCase):
     def test_simple_recon(self):
@@ -296,21 +314,60 @@ class TestFitSamplePositionFromRecon(unittest.TestCase):
         ri, rj = (recon_centre[0] + centre_offset, recon_centre[1] - centre_offset)  # recon position of hot pixel
         recon[ri, rj] = 1.0
         ystep = 2.0  # um/px
-        desired_x, desired_y = 20.0, 20.0  # 20 px away from centre
+        desired_sx, desired_sy = 20.0, 20.0  # 20 px away from centre
         # remember y sign is flipped!
         # we are slightly up and to the left of centre in recon space
         # slightly +ve in both x and y
         # centre_offset px worth - that's 20 um
         # x, y should be (20, 20)
-        x, y = geometry.fit_sample_position_from_recon(recon, ystep)
-
-        self.assertSequenceEqual((desired_x, desired_y), (x, y))
+        sx, sy = geometry.fit_sample_position_from_recon(recon, ystep)
+
+        self.assertSequenceEqual((desired_sx, desired_sy), (sx, sy))
+
+
+class TestDtyiMaskFromSample(unittest.TestCase):
+    def test_simple_value(self):
+        y0 = 14.0
+        sx = 1.0
+        sy = 0.0
+        # omega = 0 and omega = 180 should hit, no others
+        # if we keep dty at 0
+        omega = np.arange(0, 181, 1)
+        dty = np.zeros_like(omega) + 14.0
+        # make ystep small so only omega values very close to desired are accepted
+        ystep = 0.01
+        ymin = 14.0
+        dtyi = geometry.dty_to_dtyi(dty, ystep, ymin)
+        # true values should be where omega is a multiple of 180
+        desired_mask = np.mod(omega, 180) == 0
+        mask = geometry.dtyimask_from_sample(sx, sy, omega, dtyi, y0, ystep, ymin)
+
+        self.assertTrue(np.allclose(desired_mask, mask))
+
+    def test_weird_angle(self):
+        y0 = 14.0
+        sx = 1.0
+        sy = 1.0
+        # omega = 135 degrees should hit, no others
+        # if we keep dty at 0
+        omega = np.arange(0, 181, 1)
+        dty = np.zeros_like(omega) + 14.0
+        # make ystep small so only omega values very close to desired are accepted
+        ystep = 0.01
+        ymin = 14.0
+        dtyi = geometry.dty_to_dtyi(dty, ystep, ymin)
+        # true values should be where omega is a multiple of 180
+        desired_mask = omega == 135
+        mask = geometry.dtyimask_from_sample(sx, sy, omega, dtyi, y0, ystep, ymin)
+
+        self.assertTrue(np.allclose(desired_mask, mask))
 
 
 class TestDtyMask(unittest.TestCase):
     def setUp(self):
         om = np.arange(180)
         self.ystep = 0.1
+        self.ymin = -5
         y = np.arange(-5, 5.1, self.ystep)
         self.shape = (len(y), len(om))
         self.omega = np.empty(self.shape, float)
@@ -327,10 +384,53 @@ class TestDtyMask(unittest.TestCase):
         for y0 in (-10.05, 0):
             for si in (-12, 0, 13):
                 for sj in (-11, 0, 2):
-                    m1 = geometry.dtyimask_from_step(si, sj, self.omega, self.dtyi, y0, self.ystep)
-                    m2 = geometry.dtyimask_from_sincos(si, sj, self.sinomega, self.cosomega, self.dtyi, y0, self.ystep)
+                    m1 = geometry.dtyimask_from_step(si, sj, self.omega, self.dtyi, y0, self.ystep, self.ymin)
+                    m2 = geometry.dtyimask_from_step_sincos(si, sj, self.sinomega, self.cosomega, self.dtyi, y0,
+                                                            self.ystep, self.ymin)
                     self.assertTrue((m1 == m2).all())
 
 
+@unittest.skipIf(int(sys.version_info.major) == 2, "can't test iradon on Python 2")
+class TestFullLoop(unittest.TestCase):
+    def test_full_loop(self):
+        from ImageD11.sinograms.roi_iradon import run_iradon
+        # tricky almost-half-acquisition scan
+        # we scanned around 14 mm
+        # but the true y0 is actually around 14.5 mm
+        # also we didn't scan symmetrically
+        y0 = 13.5 * 1000  # um
+
+        ystep = 10.0
+        ymin = 14 * 1000 - 750
+        ymax = 14 * 1000 + 100
+
+        yrange = ymax - ymin
+        ny = int(yrange // ystep) + 1
+        ybincens = np.linspace(ymin, ymax, ny)
+
+        omega = np.arange(0, 361, 1)
+
+        sx = 539.86
+        sy = -510.25
+
+        dty = geometry.dty_values_grain_in_beam(sx, sy, y0, omega)
+        dtyi = geometry.dty_to_dtyi(dty, ystep, ybincens[0])
+
+        # fill sinogram image
+        sino = np.zeros((ny, len(omega)), dtype=float)
+
+        for i in range(sino.shape[0]):
+            for j in range(sino.shape[1]):
+                this_dtyi = dtyi[j]
+                sino[i, j] = 1 / (50 * np.cbrt((np.abs(i - this_dtyi))) + 0.01)
+
+        shift, pad = geometry.sino_shift_and_pad(y0, ny, ymin, ystep)
+        recon = run_iradon(sino, omega, pad=pad, shift=shift)
+        ri_calc, rj_calc = np.array(np.where(recon == recon.max())).flatten()
+        sx_calc, sy_calc = geometry.recon_to_sample(ri_calc, rj_calc, recon.shape, ystep)
+        self.assertTrue(np.abs(sx - sx_calc) < ystep)
+        self.assertTrue(np.abs(sy - sy_calc) < ystep)
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/test_point_by_point.py b/test/test_point_by_point.py
index 617ba55..1b35d5a 100644
--- a/test/test_point_by_point.py
+++ b/test/test_point_by_point.py
@@ -72,10 +72,14 @@ class TestComputeTthEtaFromXYZ(unittest.TestCase):
             peaks_xyz = np.random.random((3, npeaks)) * 10000 - 5000
             wedge = np.random.random()
             chi = np.random.random()
+            t_x = np.random.random()
+            t_y = np.random.random()
+            t_z = np.random.random()
+            omega = np.random.random(npeaks) * 380 - 180
             
-            tth_id11, eta_id11 = transform.compute_tth_eta_from_xyz(peaks_xyz=peaks_xyz, omega=None, wedge=wedge, chi=chi)
+            tth_id11, eta_id11 = transform.compute_tth_eta_from_xyz(peaks_xyz=peaks_xyz, omega=omega, wedge=wedge, chi=chi, t_x=t_x, t_y=t_y, t_z=t_z)
             
-            tth_numba, eta_numba = point_by_point.compute_tth_eta_from_xyz(peaks_xyz=peaks_xyz, wedge=wedge, chi=chi)
+            tth_numba, eta_numba = point_by_point.compute_tth_eta_from_xyz(peaks_xyz=peaks_xyz, omega=omega, wedge=wedge, chi=chi, t_x=t_x, t_y=t_y, t_z=t_z)
 
             self.assertTrue(np.allclose(tth_id11, tth_numba))
             self.assertTrue(np.allclose(eta_id11, eta_numba))
@@ -97,6 +101,10 @@ class TestComputeTthEta(unittest.TestCase):
             distance = np.random.random() * 1500
             wedge = np.random.random()
             chi = np.random.random()
+            t_x = np.random.random()
+            t_y = np.random.random()
+            t_z = np.random.random()
+            omega = np.random.random(npeaks) * 380 - 180
             
             peaks = np.vstack((sc, fc))
             
@@ -110,7 +118,11 @@ class TestComputeTthEta(unittest.TestCase):
                                                     tilt_z=tilt_z,
                                                     distance=distance,
                                                     wedge=wedge,
-                                                    chi=chi)
+                                                    chi=chi,
+                                                    t_x=t_x,
+                                                    t_y=t_y,
+                                                    t_z=t_z,
+                                                    omega=omega)
             
             tth_numba, eta_numba = point_by_point.compute_tth_eta(sc=sc,
                                                             fc=fc,
@@ -123,7 +135,11 @@ class TestComputeTthEta(unittest.TestCase):
                                                             tilt_z=tilt_z,
                                                             distance=distance,
                                                             wedge=wedge,
-                                                            chi=chi)
+                                                            chi=chi,
+                                                            t_x=t_x,
+                                                            t_y=t_y,
+                                                            t_z=t_z,
+                                                            omega=omega)
 
             self.assertTrue(np.allclose(tth_id11, tth_numba))
             self.assertTrue(np.allclose(eta_id11, eta_numba))