diff --git a/ImageD11/nbGui/S3DXRD/select_for_index_unknown.ipynb b/ImageD11/nbGui/S3DXRD/select_for_index_unknown.ipynb
index efa07775..9e11d6cc 100644
--- a/ImageD11/nbGui/S3DXRD/select_for_index_unknown.ipynb
+++ b/ImageD11/nbGui/S3DXRD/select_for_index_unknown.ipynb
@@ -115,18 +115,9 @@
    "outputs": [],
    "source": [
     "# powder patterns\n",
-    "hsI, dsbinedges = np.histogram( cf_4d.ds, weights=cf_4d.sum_intensity, bins=np.linspace(0, cf_4d.ds.max(), 5000))\n",
-    "hs1, dsbinedges = np.histogram( cf_4d.ds, bins=np.linspace(0, cf_4d.ds.max(), 5000))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "pbins = 5000\n",
+    "hsI, dsbinedges = np.histogram( cf_4d.ds, weights=cf_4d.sum_intensity, bins=np.linspace(0, cf_4d.ds.max(), pbins))\n",
+    "hs1, dsbinedges = np.histogram( cf_4d.ds, bins=np.linspace(0, cf_4d.ds.max(), pbins))\n",
     "dsbincens = 0.5*(dsbinedges[1:]+dsbinedges[:-1])"
    ]
   },
@@ -166,7 +157,9 @@
    "outputs": [],
    "source": [
     "# These peaks are already indexed by the main phase\n",
-    "ringmask = ImageD11.peakselect.rings_mask( cf_4d, 0.005, 1.0, cell = ucell )"
+    "dstar_tol = 0.005  # range in dstar to mask\n",
+    "dsmax = 1.0        # max dstar (highest angle)\n",
+    "ringmask = ImageD11.peakselect.rings_mask( cf_4d, dstar_tol, 1.0, cell = ucell )"
    ]
   },
   {
@@ -178,7 +171,7 @@
    "outputs": [],
    "source": [
     "# cring = columnfile of things not indexed by the main rings\n",
-    "cring = cf_4d.copyrows( (cf_4d.ds < 1.)&( ~ringmask ))"
+    "cring = cf_4d.copyrows( (cf_4d.ds < dsmax)&( ~ringmask ))"
    ]
   },
   {
@@ -193,7 +186,7 @@
     "ax.plot( dsbincens, hsI/hsI.max() , '-')\n",
     "ax.plot( ucell.ringds, np.full( len(ucell.ringds), 1e-5) , \"|\", ms=20, lw =1 )\n",
     "ax.plot( cring.ds, cring.sum_intensity/cring.sum_intensity.max(), \".\", ms=1 )\n",
-    "ax.set(yscale='log', xlim=(0.1,1), ylim=(1e-5,1))"
+    "ax.set(yscale='log', xlim=(0.1,1), ylim=(1e-5,1));"
    ]
   },
   {
@@ -240,6 +233,20 @@
    },
    "outputs": [],
    "source": [
+    "# Position where the beam goes through the rotation axis\n",
+    "y0 = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Interactive plot to select a peak\n",
+    "\n",
     "fig, ax = plt.subplots(1,2)\n",
     "ax[0].scatter( cpk.omega, cpk.dty, \n",
     "            c=np.log(cpk.sum_intensity) * np.sign( cpk.yl ), \n",
@@ -247,7 +254,6 @@
     "\n",
     "selected, = ax[0].plot( [], [], \"o\", mfc = 'none')\n",
     "\n",
-    "\n",
     "ax[1].scatter( cring.omega, cring.dty,\n",
     "            c=np.log(cring.sum_intensity) * np.sign( cring.yl ), \n",
     "            s=4, cmap='RdBu')\n",
@@ -255,7 +261,8 @@
     "\n",
     "def find_pair( idx, cf ):\n",
     "    \"\"\"\n",
-    "    Given a peak idx, locate the Friedel pair in cf\n",
+    "    Given a peak idx, locate the Friedel pair in colfile\n",
+    "    Matches on gvector only (not intensity, etc)\n",
     "    \"\"\"\n",
     "    dg2 = (cf.gx[idx] + cf.gx)**2 + (cf.gy[idx] + cf.gy)**2 + (cf.gz[idx] + cf.gz)**2 \n",
     "    pair = np.argmin( dg2 )\n",
@@ -264,32 +271,42 @@
     "om = np.linspace( cring.omega.min(), cring.omega.max(), 90 )\n",
     "fitline, = ax[1].plot( om, np.zeros_like(om), '-')\n",
     "\n",
-    "### GLOBALS\n",
-    "p1 = None\n",
-    "p2 = None\n",
-    "xy = None\n",
-    "peak_ycalc = None\n",
+    "### GLOBALS, also y0\n",
+    "p1 = None   # index of first peak in cring\n",
+    "p2 = None   # index of second peak in cring\n",
+    "xy = None   # fitted position\n",
+    "peak_ycalc = None   # dty computed\n",
     "\n",
     "def fit_pair( cf, i, j ):\n",
     "    # fits a sine wave\n",
-    "    global xy, p1, p2, peak_ycalc\n",
+    "    global xy, p1, p2, peak_ycalc, y0\n",
     "    p1 = i\n",
     "    p2 = j\n",
+    "    # sin/cos omega\n",
     "    ci = np.cos( np.radians( cf.omega[i] ) )\n",
     "    cj = np.cos( np.radians( cf.omega[j] ) )\n",
     "    si = np.sin( np.radians( cf.omega[i] ) )\n",
     "    sj = np.sin( np.radians( cf.omega[j] ) )\n",
-    "    yi = cf.dty[i]\n",
-    "    yj = cf.dty[j]\n",
-    "    # yi = xp.ci + yp.si\n",
-    "    # yj = xp.cj + yp.sj\n",
     "    #\n",
+    "    # ImageD11.sinograms.geometry:\n",
+    "    # def dtycalc_sincos(sinomega, cosomega, x, y, y0):\n",
+    "    #     ....\n",
+    "    #     return y0 - x * sinomega - y * cosomega\n",
+    "    #\n",
+    "    # yi = y0 - xp.si - yp.ci\n",
+    "    # yj = y0 - xp.sj - yp.cj\n",
+    "    #\n",
+    "    yi = cf.dty[i] - y0\n",
+    "    yj = cf.dty[j] - y0\n",
+    "    #\n",
+    "    # Only two peaks, cannot fit x/y/y0\n",
     "    # Y = R.x\n",
-    "    R = np.array( [ [ ci, si ], [cj, sj] ] )\n",
-    "    xy = np.linalg.inv(R).dot( [ yi, yj ] )\n",
-    "    ycalc = np.cos(np.radians(om))*xy[0]+np.sin(np.radians(om))*xy[1]\n",
+    "    R = np.array( [ [ -si, -ci ], \n",
+    "                    [ -sj, -cj] ] )\n",
+    "    xy = x, y = np.linalg.inv(R).dot( [ yi, yj ] )\n",
+    "    ycalc = ImageD11.sinograms.geometry.x_y_y0_omega_to_dty(om, x, y, y0)\n",
     "    fitline.set_ydata( ycalc )\n",
-    "    peak_ycalc = np.cos(np.radians(cring.omega))*xy[0]+np.sin(np.radians(cring.omega))*xy[1]\n",
+    "    peak_ycalc = ImageD11.sinograms.geometry.x_y_y0_omega_to_dty(cring.omega, x, y, y0)\n",
     "    \n",
     "def onpick( evt ):\n",
     "    art = evt.artist\n",
@@ -300,6 +317,7 @@
     "    selected.set_ydata( [cpk.dty[ind[-1]],cpk.dty[pair]] )\n",
     "    ax[0].set( title = f'{ind} {pair}' )\n",
     "    fig.canvas.draw_idle()\n",
+    "\n",
     "ax[1].set(title=\"Pick a peak on the other plot\")\n",
     "fig.canvas.mpl_connect( 'pick_event', onpick );"
    ]
@@ -312,12 +330,13 @@
    },
    "outputs": [],
    "source": [
+    "dtyrange = 5.\n",
     "print(\"Selected points are\",p1,p2)\n",
     "print(\"Omega, dty\")\n",
     "print(cpk.omega[p1],cpk.dty[p1])\n",
     "print(cpk.omega[p2],cpk.dty[p2])\n",
     "f,ax = plt.subplots()\n",
-    "ax.hist( cring.dty - peak_ycalc, bins = np.arange(-2,2,ds.ystep) );\n",
+    "ax.hist( cring.dty - peak_ycalc, bins = np.arange(-dtyrange,dtyrange,ds.ystep) );\n",
     "ax.set(title='dty error histogram', xlabel='dty', ylabel='frequency');"
    ]
   },
@@ -330,8 +349,9 @@
    "outputs": [],
    "source": [
     "# now we select the peaks from the sinogram based on that position in space\n",
+    "dty_cut = 0.4 # from the last plot\n",
     "\n",
-    "ymask = abs(peak_ycalc - cring.dty)<0.4\n",
+    "ymask = abs(peak_ycalc - cring.dty)<dty_cut\n",
     "cgrain = cring.copyrows( ymask )\n",
     "fig = plt.figure()\n",
     "ax = fig.add_subplot( projection='3d', proj_type='ortho')\n",
@@ -448,7 +468,8 @@
    },
    "outputs": [],
    "source": [
-    "pks, dists = find_overlaps_3d( cgrain, (cpk.spot3d_id[p1], cpk.spot3d_id[p2] ), gtol=0.002 )\n",
+    "gtol = 0.002 # from the last plot\n",
+    "pks, dists = find_overlaps_3d( cgrain, (cpk.spot3d_id[p1], cpk.spot3d_id[p2] ), gtol=gtol )\n",
     "\n",
     "fig = plt.figure(constrained_layout=True)\n",
     "a = fig.add_subplot(1,1,1, projection='3d', proj_type='ortho')\n",
@@ -486,7 +507,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Position selectiion on the sinogram"
+    "## Position selection on the sinogram"
    ]
   },
   {
@@ -498,10 +519,12 @@
    "outputs": [],
    "source": [
     "# make a sinogram and recon\n",
+    "nbins_angle = min( len( ds.obincens), int(len(ds.ybincens)*1.5) )\n",
     "sino, obinedge, ybinedge = np.histogram2d( cring.omega, cring.dty, weights = np.log(cring.sum_intensity),\n",
-    "                      bins = (np.linspace(-90, 90, 180), ds.ybinedges) )\n",
+    "                      bins = (np.linspace(ds.obinedges.min(), ds.obinedges.max(), nbins_angle), ds.ybinedges) )\n",
     "obincen = 0.5*(obinedge[:-1] + obinedge[1:])\n",
-    "recon = ImageD11.sinograms.roi_iradon.iradon( sino.T, obincen, filter_name='shepp-logan', workers=10)"
+    "recon = ImageD11.sinograms.roi_iradon.iradon( sino.T, obincen, filter_name='shepp-logan', \n",
+    "                                             workers=ImageD11.cImageD11.cores_available())"
    ]
   },
   {
@@ -518,10 +541,12 @@
     "                  ybinedge,\n",
     "                  sino.T);\n",
     "ax[1].pcolormesh( -ds.ybinedges,ds.ybinedges, recon, vmin=0, vmax = recon.max()/2 )\n",
+    "location, = ax[1].plot( [],[], 'ow', mfc='none', ms=20)\n",
     "ax[1].set(xlim=(ds.ybinedges[-1],ds.ybinedges[0]),\n",
     "          xlabel='laboratory Y', ylabel='laboratory X')\n",
     "ax[0].set(title='sinogram')\n",
     "ax[1].set(title='click on the place you want')\n",
+    "\n",
     "om = np.linspace( cring.omega.min(), cring.omega.max(), 90 )\n",
     "fitline1, = ax[0].plot( om, np.zeros_like(om), 'w-')\n",
     "fitline2, = ax[0].plot( om, np.zeros_like(om), 'w-')\n",
@@ -538,6 +563,8 @@
     "        ycalcall = ImageD11.sinograms.geometry.dtycalc( cring.omega, x, y, 0 )\n",
     "        fitline1.set_ydata( ycalc + 1 )\n",
     "        fitline2.set_ydata( ycalc - 1 )\n",
+    "        location.set_xdata( [y,] )\n",
+    "        location.set_ydata( [x,] )\n",
     "        fig.canvas.draw_idle()\n",
     "    \n",
     "fig.canvas.mpl_connect( 'button_press_event', onclick );"
diff --git a/ImageD11/nbGui/peaksearch_CeO2_powder.ipynb b/ImageD11/nbGui/peaksearch_CeO2_powder.ipynb
new file mode 100644
index 00000000..5ce524cd
--- /dev/null
+++ b/ImageD11/nbGui/peaksearch_CeO2_powder.ipynb
@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07afc3da-8ce0-4717-ac4c-f2d746148cd4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "exec(open('/data/id11/nanoscope/install_ImageD11_from_git.py').read())\n",
+    "PYTHONPATH = setup_ImageD11_from_git( )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed9d32b5-bf86-4633-9928-afae3df0226d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib widget\n",
+    "import h5py, hdf5plugin\n",
+    "import numpy as np, matplotlib.pyplot as plt\n",
+    "import silx.io\n",
+    "import scipy.ndimage, fabio\n",
+    "import ImageD11.frelon_peaksearch\n",
+    "import ImageD11.blobcorrector\n",
+    "import ipywidgets\n",
+    "import ImageD11.transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbfde13c-0b61-440a-b105-27a30194cf0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "masterfile = \"/data/id11/3dxrd/ihma233/id11/CeO2/CeO2_align/CeO2_align.h5\"\n",
+    "scan = \"39.1\"\n",
+    "detector = \"frelon3\"\n",
+    "energy = 43.35\n",
+    "print(energy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc515a1e-23f9-4e71-a298-143fa872ad1c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "darkscan = \"34.1\" # first 7 frames:\n",
+    "dark = silx.io.get_data( f\"silx:{masterfile}::{darkscan}/measurement/{detector}\" )[0:7].mean(axis=0, dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e4ff2dc-5bd6-4ae2-9258-c19330d90c3c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "frames = silx.io.get_data( f\"silx:{masterfile}::{scan}/measurement/{detector}\" )\n",
+    "frames.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39f5d07a-328a-4f0f-be5a-210222657c5a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "if detector.find('frelon')>=0:\n",
+    "    \n",
+    "    serial = silx.io.get_data( f\"silx:{masterfile}::{darkscan}/instrument/{detector}/camera_settings/camera_serial\" )\n",
+    "    print(\"Detector serial\", serial )\n",
+    "    if serial == \"21\":\n",
+    "        flat = silx.io.get_data( f\"fabio:/data/id11/3dxrd/inhouse/Frelon21/F21_flat_july18.edf\" )\n",
+    "        e2dxfile = \"/data/id11/3dxrd/inhouse/Frelon21/F21dxnew.edf\"\n",
+    "        e2dyfile = \"/data/id11/3dxrd/inhouse/Frelon21/F21dynew.edf\"\n",
+    "        pixelsize = float(fabio.open(e2dxfile).header['PSize_1'].split()[0])*1e6 # microns\n",
+    "        print('Pixel size', pixelsize )\n",
+    "    if serial == \"36\": \n",
+    "        flat = silx.io.get_data( f\"fabio:/data/id11/3dxrd/inhouse/Frelon36/F36_Nov2023.edf\" )\n",
+    "        flat = flat / flat[500:-500, 500:-500].mean()\n",
+    "        e2dxfile = \"fabio:/data/id11/3dxrd/inhouse/Frelon36/f36dx.edf\"\n",
+    "        e2dyfile = f\"fabio:/data/id11/3dxrd/inhouse/Frelon36/f36dy.edf\"\n",
+    "        pixelsize = float(fabio.open(e2dxfile).header['PSize_1'].split()[0])*1e6 # microns\n",
+    "    if serial == \"29\":\n",
+    "        flat = silx.io.get_data( f\"fabio:/data/id11/3dxrd/inhouse/Frelon4M/c29_Agfilt_flat-dark.edf\" )\n",
+    "        e2dxfile = \"fabio:/data/id11/3dxrd/inhouse/Frelon4M/F4M_EO_dx.edf\"\n",
+    "        e2dyfile = \"fabio:/data/id11/3dxrd/inhouse/Frelon4M/F4M_EO_dy.edf\"\n",
+    "        pixelsize = float(fabio.open(e2dxfile).header['PSize_1'].split()[0])*1e6 # microns\n",
+    "        \n",
+    "    print( \"Average flat, should be close to 1 : \",flat[100:-100, 100:-100].mean() )\n",
+    "        \n",
+    "    cor = ( frames - dark[ None, :, :] ).mean( axis = 0, dtype = np.float32 ) / flat\n",
+    "    \n",
+    "else: # eiger\n",
+    "    \n",
+    "    cor = frames.mean( axis = 0, dtype = np.float32 )\n",
+    "    \n",
+    "    e2dxfile = \"/data/id11/nanoscope/Eiger/e2dx_E-08-0144_20240205.edf\"\n",
+    "    e2dyfile = \"/data/id11/nanoscope/Eiger/e2dy_E-08-0144_20240205.edf\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4d3e39-6615-41da-9a49-7c3687306957",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def guess_median_bg( img, size=91 ):\n",
+    "    m0 = scipy.ndimage.median_filter( cor, (size,1))\n",
+    "    m1 = scipy.ndimage.median_filter( cor, (1,size))\n",
+    "    bg = np.where(m0<m1,m0,m1)\n",
+    "    return bg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "573b0d4f-6a00-43d1-bd30-cb2d1db87a0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "lowcut = 200 # low cutoff for masking, check next plot\n",
+    "mask = ( cor < 4e9 ) & (cor > lowcut )\n",
+    "\n",
+    "bg = guess_median_bg( cor )\n",
+    "\n",
+    "signal = ((cor - bg )*mask).clip(0,None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "631b7295-1b53-4fac-994d-62967e242662",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "f,(a,b) = plt.subplots( 2, 2, figsize=(8, 6), constrained_layout=True )\n",
+    "f.colorbar( a[1].imshow( cor * mask ) )\n",
+    "f.colorbar( b[1].imshow( signal ) )\n",
+    "a[0].plot( cor[ 1024, : ] )\n",
+    "a[0].plot( cor[ :, 1024 ] )\n",
+    "a[0].set(xlabel='pixel', ylabel='intensity')\n",
+    "b[0].plot( signal[ 1024, : ] )\n",
+    "b[0].plot( signal[ :, 1024 ] )\n",
+    "b[0].set(xlabel='pixel', ylabel='intensity', ylim=(0, None) );"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dd443d3-9be7-4c84-a1cc-502d3c4aab0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Old way, using command line:\n",
+    "# !powderimagetopeaks.py CeO2_for_S5_blue_rot_clean.edf CeO2_for_S5_blue_rot_clean0000.edf CeO2_for_S5_blue_rot_clean0001.edf 1024 1024\n",
+    "def powderimagetopeaks( img, ci = 1024, cj = 1024 ):\n",
+    "    # cuts into 1 degree bins\n",
+    "    si, sj = img.shape\n",
+    "    i, j = np.mgrid[0:si,0:sj]\n",
+    "    phi = np.arctan2( i - ci, j - cj ) * 361 / np.pi\n",
+    "    return (phi%2).astype(int) == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbc62cc4-03aa-4f33-a4a8-431b4e6fa00d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# old way:\n",
+    "# !peaksearch.py -n CeO2_for_S5_blue_rot_clean -f 0 -l 1 -p N -s /data/id11/3dxrd/inhouse/Frelon36/frelon36.spline -t 5000 -t 10000 -t 20000 -t 40000 -o CeO2_for_S5_blue_rot_clean.spt "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebe6849c-0b51-4ec2-95b8-2caefc9b75c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "worker = ImageD11.frelon_peaksearch.worker(None, None)\n",
+    "pkmsk = powderimagetopeaks( signal )\n",
+    "pks = np.concatenate( (\n",
+    "     worker.peaksearch( signal * pkmsk ),     # even degrees\n",
+    "     worker.peaksearch( signal * (1-pkmsk)), # odd degrees\n",
+    "    ) , axis = 0 )\n",
+    "pks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d7cefae-e1f3-4bad-9a07-454b592a8ccc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from ImageD11.cImageD11 import s_1, s_I, s_sI, s_fI\n",
+    "colfile = ImageD11.columnfile.colfile_from_dict( {\n",
+    "    \"s_raw\" : pks[ :, s_sI ] / pks[ :, s_I ],\n",
+    "    \"f_raw\" : pks[ :, s_fI ] / pks[ :, s_I ],\n",
+    "    \"Number_of_pixels\" : pks[ :, s_1 ], \n",
+    "    \"sum_intensity\" : pks[ :, s_I ],\n",
+    "    \"omega\" : np.zeros( len(pks), float ),\n",
+    "} )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fa38247-7d08-45cd-aeec-4618ac178917",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "f, a = plt.subplots(1,2, figsize=(8,5), constrained_layout=True)\n",
+    "a[0].plot( colfile.f_raw, colfile.s_raw, \".\", ms = 1)\n",
+    "a[0].set( ylabel=\"detector slow direction\", xlabel=\"detector fast direction\", aspect='equal')\n",
+    "a[1].plot( colfile.Number_of_pixels, colfile.sum_intensity, \".\", ms =1 )\n",
+    "a[1].set( xlabel=\"Number of pixels\", ylabel=\"sum intensity\", yscale='log', xscale='log');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a543865-9505-4b25-bab3-f130546bcbff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# old way\n",
+    "# !spatialfix.py frelon36_spline_dx.edf frelon36_spline_dy.edf CeO2_for_S5_blue_rot_clean_t5000.flt CeO2_for_S5_blue_rot_clean_spline_t5000.flt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abec9195-69d1-44f8-9238-e549ee68fbf6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "colfile = ImageD11.blobcorrector.eiger_spatial( e2dxfile, e2dyfile )(colfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bad00d35-5618-4a79-969b-ef2328e74447",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# input data from peaksearch and starting parameter file:\n",
+    "trans=ImageD11.transformer.transformer()\n",
+    "trans.colfile = colfile\n",
+    "trans.setxyomcols(\"sc\", \"fc\", \"omega\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62bc178a-d49a-4242-b4a6-9566c324fd6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "distance = 210 * 1e3 # micron\n",
+    "wavelength = 12.3984 / energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce7a07c4-b353-4be7-989f-22a8150848f9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "wavelength, energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9ba5882-5236-47bf-b85c-0982c4db89c0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "trans.parameterobj.set( 'y_size', pixelsize )\n",
+    "trans.parameterobj.set( 'z_size', pixelsize )\n",
+    "trans.parameterobj.set( 'distance', distance )\n",
+    "trans.parameterobj.set( 'wavelength', wavelength )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "639cd3aa-b35d-41a7-acb5-335042c68fc0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# unit cell\n",
+    "a = 5.4115260\n",
+    "[ trans.parameterobj.set( f'cell__{x}', a ) for x in 'abc']\n",
+    "[ trans.parameterobj.set( f'cell_{x}', 90 ) for x in ('alpha','beta','gamma')]\n",
+    "trans.parameterobj.set('cell_lattice_[P,A,B,C,I,F,R]', \"F\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20a04c19-5065-4edd-8241-df851a686c6d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def parCallBack( arg ):\n",
+    "    trans.parameterobj.parameters.update( { arg['owner'].description : arg['new'] } )\n",
+    "    drawPlot()\n",
+    "    \n",
+    "def fixVaryCallBack( arg ):\n",
+    "    name = arg['owner'].description.split(\" \")[1]\n",
+    "    vars = trans.getvars()\n",
+    "    if arg.new and not (name in vars):\n",
+    "        vars.append( name )\n",
+    "    if name in vars and not arg.new:\n",
+    "        vars.remove(name)\n",
+    "    trans.parameterobj.set_varylist(vars)\n",
+    "    \n",
+    "def fitCallBack(arg):\n",
+    "    \"\"\" fit call back - runs fit \"\"\"\n",
+    "    lo, hi = ax1.get_xlim()\n",
+    "    trans.fit( lo, hi )\n",
+    "    for i, pname in enumerate(vars):\n",
+    "        layout[i,0].value = trans.parameterobj.get(pname)\n",
+    "    drawPlot()\n",
+    "    \n",
+    "def drawPlot():\n",
+    "    tth, eta = trans.compute_tth_eta()\n",
+    "    pt1.set_data( tth, eta )\n",
+    "    fig.canvas.update\n",
+    "\n",
+    "# Things to be edited in the UI\n",
+    "vars = \"y_center z_center distance tilt_y tilt_z\".split()\n",
+    "steps = (1,         1,       100,     0.01, 0.01, 0)\n",
+    "nv = len(vars)\n",
+    "\n",
+    "# Draw the widgets:\n",
+    "layout = ipywidgets.GridspecLayout(nv+1,2)\n",
+    "for i,( pname, pstep ) in enumerate( zip( vars, steps ) ) :\n",
+    "    layout[i,0] = ipywidgets.FloatText( description=pname, \n",
+    "        value = trans.parameterobj.parameters.get(pname),\n",
+    "        step=pstep)\n",
+    "    layout[i,0].observe( parCallBack , names='value' )\n",
+    "    layout[i,1] = ipywidgets.ToggleButton( description=\"Vary \"+pname, \n",
+    "        value = pname in trans.getvars() )\n",
+    "    layout[i,1].observe( fixVaryCallBack, names='value' )\n",
+    "    \n",
+    "layout[nv,0] = ipywidgets.FloatText( description='fit_tolerance', \n",
+    "        value = trans.parameterobj.parameters.get(\"fit_tolerance\"), step=0,)\n",
+    "layout[nv,0].observe( parCallBack , names='value' )\n",
+    "\n",
+    "layout[nv,1] = ipywidgets.Button(description=\"Run Fit (blocks)\")\n",
+    "layout[nv,1].on_click( fitCallBack )\n",
+    "\n",
+    "\n",
+    "# Draw plot\n",
+    "fig1 = plt.figure(figsize=(9,6))\n",
+    "ax1 = fig1.add_subplot()\n",
+    "tth, eta = trans.compute_tth_eta()\n",
+    "trans.addcellpeaks()\n",
+    "pt1, = ax1.plot( tth, eta, \",\")\n",
+    "ax1.set(xlabel=\"tth\", ylabel=\"eta\")\n",
+    "ax1.plot( trans.theorytth, [0,]*len(trans.theorytth), \"r|\", ms=360, alpha=0.2 )\n",
+    "# Add controls\n",
+    "display(layout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36bbea15-894b-4424-abe9-1e34702da583",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pprint\n",
+    "pprint.pprint( trans.parameterobj.parameters )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0c4bd4e-b7d6-4234-8442-283f314c6a54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trans.parameterobj.saveparameters('CeO2_fitted.par')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a17baf-133e-4a87-b900-8448bccc0eee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "12.3985 / trans.parameterobj.get('wavelength')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a29f6ee3-95f5-4e97-b03b-4fdd6deb4c71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (main)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}