diff --git a/README.md b/README.md
index baa448c09f2f59d0c83a8cd2ffc48fbfc2c4087b..4a238e0a75063fc6667107768907284b596ac80d 100755
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
 ## Current build status
 [![PyPI](https://img.shields.io/pypi/v/pyrost?color=brightgreen)](https://pypi.org/project/pyrost/)
 [![Documentation Status](https://readthedocs.org/projects/robust-speckle-tracking/badge/?version=latest)](https://robust-speckle-tracking.readthedocs.io/en/latest/?badge=latest)
+![conda-forge](https://img.shields.io/conda/vn/conda-forge/pyrost?color=brightgreen)
 
 # pyrost
 Python Robust Speckle Tracking (**pyrost**) is a library for wavefront metrology
diff --git a/dev.ipynb b/dev.ipynb
old mode 100644
new mode 100755
index 6adb3a72141d43e17787b64e0396cd22d12d9639..32e63dcebd517b3102a7e2521f5ed0b4880e4091
--- a/dev.ipynb
+++ b/dev.ipynb
@@ -8,7 +8,7 @@
     {
      "data": {
       "text/plain": [
-       "(None, <pyximport.pyximport.PyxImporter at 0x7fcda09c02d0>)"
+       "(None, <pyximport.pyximport.PyxImporter at 0x7ff5702489d0>)"
       ]
      },
      "execution_count": 1,
@@ -50,7 +50,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['__builtins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__pyx_unpickle_Enum', '__spec__', '__test__', 'barcode_steps', 'bnprd_var', 'bprd_var', 'ct_integrate', 'init_newton', 'krig_data', 'make_frames', 'make_reference', 'mse_2d', 'np', 'subpixel_refinement_1d', 'subpixel_refinement_2d', 'total_mse', 'update_pixel_map_gs', 'upm_newton_1d']\n"
+      "['__builtins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__pyx_unpickle_Enum', '__spec__', '__test__', 'bnprd_var', 'bprd_var', 'ct_integrate', 'gaussian_filter', 'init_newton', 'krig_data', 'make_frames', 'make_reference', 'mse_2d', 'np', 'pixel_translations', 'st', 'st_update', 'str_update', 'subpixel_refinement_1d', 'subpixel_refinement_2d', 'total_mse', 'update_pixel_map_gs', 'upm_newton_1d']\n"
      ]
     }
    ],
@@ -68,90 +68,6 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def st_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss, sw_fs, ls, n_iter=5):\n",
-    "    \"\"\"\n",
-    "    Andrew's speckle tracking update algorithm\n",
-    "    \n",
-    "    I_n - measured data\n",
-    "    W - whitefield\n",
-    "    basis - detector plane basis vectors\n",
-    "    x_ps, y_ps - x and y pixel sizes\n",
-    "    z - distance between the sample and the detector\n",
-    "    df - defocus distance\n",
-    "    sw_max - pixel mapping search window size\n",
-    "    n_iter - number of iterations\n",
-    "    \"\"\"\n",
-    "    M = np.ones((I_n.shape[1], I_n.shape[2]), dtype=bool)\n",
-    "    u, dij_pix, res = st.generate_pixel_map(W.shape, dij, basis, x_ps,\n",
-    "                                            y_ps, z, df, verbose=False)\n",
-    "    I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls)\n",
-    "\n",
-    "    es = []\n",
-    "    for i in range(n_iter):\n",
-    "\n",
-    "        # calculate errors\n",
-    "        error_total = st.calc_error(I_n, M, W, dij_pix, I0, u, n0, m0, subpixel=True, verbose=False)[0]\n",
-    "\n",
-    "        # store total error\n",
-    "        es.append(error_total)\n",
-    "\n",
-    "        # update pixel map\n",
-    "        u = st.update_pixel_map(I_n, M, W, I0, u, n0, m0, dij_pix,\n",
-    "                                sw_ss, sw_fs, ls)[0]\n",
-    "\n",
-    "        # make reference image\n",
-    "        I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls)\n",
-    "    return {'u':u, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}\n",
-    "\n",
-    "def pixel_translations(basis, dij, df, z):\n",
-    "    dij_pix = (basis * dij[:, None]).sum(axis=-1)\n",
-    "    dij_pix /= (basis**2).sum(axis=-1) * df / z\n",
-    "    dij_pix -= dij_pix.mean(axis=0)\n",
-    "    return np.ascontiguousarray(dij_pix[:, 0]), np.ascontiguousarray(dij_pix[:, 1])\n",
-    "\n",
-    "def str_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_max=100, n_iter=5, l_scale=2.5):\n",
-    "    \"\"\"\n",
-    "    Robust version of Andrew's speckle tracking update algorithm\n",
-    "    \n",
-    "    I_n - measured data\n",
-    "    W - whitefield\n",
-    "    basis - detector plane basis vectors\n",
-    "    x_ps, y_ps - x and y pixel sizes\n",
-    "    z - distance between the sample and the detector\n",
-    "    df - defocus distance\n",
-    "    sw_max - pixel mapping search window size\n",
-    "    n_iter - number of iterations\n",
-    "    \"\"\"\n",
-    "    I_n = I_n.astype(np.float64)\n",
-    "    W = W.astype(np.float64)\n",
-    "    u0 = np.indices(W.shape, dtype=np.float64)\n",
-    "    di, dj = pixel_translations(basis, dij, df, z)\n",
-    "    I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_fs=0, sw_ss=0)\n",
-    "\n",
-    "    es = []\n",
-    "    for i in range(n_iter):\n",
-    "\n",
-    "        # calculate errors\n",
-    "        es.append(total_mse(I_n=I_n, W=W, I0=I0, u=u0, di=di - n0, dj=dj - m0, ls=l_scale))\n",
-    "\n",
-    "        # update pixel map\n",
-    "        u = update_pixel_map_gs(I_n=I_n, W=W, I0=I0, u0=u0, di=di - n0, dj=dj - m0,\n",
-    "                                sw_ss=0, sw_fs=sw_max, ls=l_scale)\n",
-    "        sw_max = int(np.max(np.abs(u - u0)))\n",
-    "        u0 = u0 + gaussian_filter(u - u0, (0, 0, l_scale))\n",
-    "\n",
-    "        # make reference image\n",
-    "        I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_ss=0, sw_fs=0)\n",
-    "        I0 = gaussian_filter(I0, (0, l_scale))\n",
-    "    return {'u':u0, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def ab_model(pix, coeff):\n",
     "    return coeff[0] + coeff[1] * (pix - coeff[3]) + coeff[2] * (pix - coeff[3])**2\n",
@@ -866,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -887,10 +803,10 @@
     {
      "data": {
       "text/plain": [
-       "-0.05065844450615439"
+       "-0.050667038898925924"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -912,104 +828,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['__builtins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__pyx_unpickle_Enum', '__spec__', '__test__', 'bnprd_var', 'bprd_var', 'ct_integrate', 'gaussian_filter', 'init_newton', 'krig_data', 'make_frames', 'make_reference', 'mse_2d', 'np', 'pixel_translations', 'st', 'st_update', 'str_update', 'subpixel_refinement_1d', 'subpixel_refinement_2d', 'total_mse', 'update_pixel_map_gs', 'upm_newton_1d']\n"
+     ]
+    }
+   ],
    "source": [
-    "def st_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss, sw_fs, ls, n_iter=5):\n",
-    "    \"\"\"\n",
-    "    Andrew's speckle tracking update algorithm\n",
-    "    \n",
-    "    I_n - measured data\n",
-    "    W - whitefield\n",
-    "    basis - detector plane basis vectors\n",
-    "    x_ps, y_ps - x and y pixel sizes\n",
-    "    z - distance between the sample and the detector\n",
-    "    df - defocus distance\n",
-    "    sw_max - pixel mapping search window size\n",
-    "    n_iter - number of iterations\n",
-    "    \"\"\"\n",
-    "    M = np.ones((I_n.shape[1], I_n.shape[2]), dtype=bool)\n",
-    "    u, dij_pix, res = st.generate_pixel_map(W.shape, dij, basis, x_ps,\n",
-    "                                            y_ps, z, df, verbose=False)\n",
-    "    I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls)\n",
-    "\n",
-    "    es = []\n",
-    "    for i in range(n_iter):\n",
-    "\n",
-    "        # calculate errors\n",
-    "        error_total = st.calc_error(I_n, M, W, dij_pix, I0, u, n0, m0, subpixel=True, verbose=False)[0]\n",
-    "\n",
-    "        # store total error\n",
-    "        es.append(error_total)\n",
-    "\n",
-    "        # update pixel map\n",
-    "        u = st.update_pixel_map(I_n, M, W, I0, u, n0, m0, dij_pix,\n",
-    "                                sw_ss, sw_fs, ls)[0]\n",
-    "\n",
-    "        # make reference image\n",
-    "        I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls)\n",
-    "    return {'u':u, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}\n",
-    "\n",
-    "def pixel_translations(basis, dij, df, z):\n",
-    "    dij_pix = (basis * dij[:, None]).sum(axis=-1)\n",
-    "    dij_pix /= (basis**2).sum(axis=-1) * df / z\n",
-    "    dij_pix -= dij_pix.mean(axis=0)\n",
-    "    return np.ascontiguousarray(dij_pix[:, 0]), np.ascontiguousarray(dij_pix[:, 1])\n",
-    "\n",
-    "def str_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_max=100, n_iter=5, l_scale=2.5):\n",
-    "    \"\"\"\n",
-    "    Robust version of Andrew's speckle tracking update algorithm\n",
-    "    \n",
-    "    I_n - measured data\n",
-    "    W - whitefield\n",
-    "    basis - detector plane basis vectors\n",
-    "    x_ps, y_ps - x and y pixel sizes\n",
-    "    z - distance between the sample and the detector\n",
-    "    df - defocus distance\n",
-    "    sw_max - pixel mapping search window size\n",
-    "    n_iter - number of iterations\n",
-    "    \"\"\"\n",
-    "    I_n = I_n.astype(np.float64)\n",
-    "    W = W.astype(np.float64)\n",
-    "    u0 = np.indices(W.shape, dtype=np.float64)\n",
-    "    di, dj = pixel_translations(basis, dij, df, z)\n",
-    "    I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_fs=0, sw_ss=0)\n",
-    "\n",
-    "    es = []\n",
-    "    for i in range(n_iter):\n",
-    "\n",
-    "        # calculate errors\n",
-    "        es.append(total_mse(I_n=I_n, W=W, I0=I0, u=u0, di=di - n0, dj=dj - m0, ls=l_scale))\n",
-    "\n",
-    "        # update pixel map\n",
-    "        u = update_pixel_map_gs(I_n=I_n, W=W, I0=I0, u0=u0, di=di - n0, dj=dj - m0,\n",
-    "                                sw_ss=0, sw_fs=sw_max, ls=l_scale)\n",
-    "        sw_max = int(np.max(np.abs(u - u0)))\n",
-    "        u0 = u0 + gaussian_filter(u - u0, (0, 0, l_scale))\n",
-    "\n",
-    "        # make reference image\n",
-    "        I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_ss=0, sw_fs=0)\n",
-    "        I0 = gaussian_filter(I0, (0, l_scale))\n",
-    "    return {'u':u0, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}"
+    "if sys.modules.get('dev'): # Maybe sys.modules is better?\n",
+    "    dev = sys.modules.get('dev')\n",
+    "    dev = reload(dev)\n",
+    "else:\n",
+    "    import dev\n",
+    "print(dir(dev))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AssertionError",
-     "evalue": "",
+     "ename": "TypeError",
+     "evalue": "calc_error() got an unexpected keyword argument 'roi'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-ee5d7db2dd6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrst_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefocus_fs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mst_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mst_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mI_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdij\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_ps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_ps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_ss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_fs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-47-027f2184dbbf>\u001b[0m in \u001b[0;36mst_update\u001b[0;34m(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss, sw_fs, ls, n_iter)\u001b[0m\n\u001b[1;32m     15\u001b[0m     u, dij_pix, res = st.generate_pixel_map(W.shape, dij, basis, x_ps,\n\u001b[1;32m     16\u001b[0m                                             y_ps, z, df, verbose=False)\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mI0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_object_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mI_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdij_pix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/speckle_tracking-2020.1-py3.7-macosx-10.9-x86_64.egg/speckle_tracking/make_object_map.pyx\u001b[0m in \u001b[0;36mspeckle_tracking.make_object_map.make_object_map\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-747928e0812a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mst_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mI_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdij\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_ps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_ps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_ss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_fs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/OneDrive/programming/speckle-tracking/pyrost/dev.pyx\u001b[0m in \u001b[0;36mdev.st_update\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;31m# calculate errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         error_total = st.calc_error(I_n, M, W, dij_pix, I0, u, n0, m0, ls=ls,\n\u001b[0m\u001b[1;32m     47\u001b[0m                                     roi=roi, subpixel=True, verbose=False)[0]\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: calc_error() got an unexpected keyword argument 'roi'"
      ]
     }
    ],
@@ -1022,18 +875,31 @@
     "y_ps = rst_data.y_pixel_size\n",
     "z = rst_data.distance\n",
     "df = rst_data.defocus_fs\n",
+    "roi = rst_data.roi\n",
     "\n",
-    "st_res = st_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss=0, sw_fs=10, ls=5, n_iter=5)"
+    "\n",
+    "st_res = dev.st_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss=0, sw_fs=10, ls=5, n_iter=10, roi=roi)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (2,1,2000) (2,1,930) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-570de3ac565a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Reference image'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrst_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpixel_map\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mrst_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpixel_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst_res\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'u'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mrst_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpixel_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pixel mapping'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,1,2000) (2,1,930) "
+     ]
+    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -1049,8 +915,10 @@
     "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
     "axes[0].plot(np.arange(rst_res.reference_image.shape[1]) - rst_res.m0,\n",
     "             rst_res.reference_image[0])\n",
+    "axes[0].plot(np.arange(st_res['I0'].shape[1]) - st_res['m0'], st_res['I0'][0])\n",
     "axes[0].set_title('Reference image', fontsize=20)\n",
     "axes[1].plot((rst_res.pixel_map - rst_obj.pixel_map)[1, 0])\n",
+    "axes[1].plot((st_res['u'][] - rst_obj.pixel_map)[1, 0])\n",
     "axes[1].set_title('Pixel mapping', fontsize=20)\n",
     "for ax in axes:\n",
     "    ax.tick_params(labelsize=15)\n",
diff --git a/dev.pxd b/dev.pxd
old mode 100644
new mode 100755
index e01e5a1ad78d1fa7df4031f14db0727177cb10a5..836e3709b073ab87e42145ff09ee197c34051eff
--- a/dev.pxd
+++ b/dev.pxd
@@ -1,4 +1,4 @@
-#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True
+#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True, embedsignature=True
 cdef extern from "gsl/gsl_math.h":
 
     ctypedef struct gsl_function:
diff --git a/dev.pyx b/dev.pyx
index 9ca9ed463244f04bae7f82d7853eb1f503bb33a4..a7f63cddb8c87b50e2a3d22d0fcde57906fb7842 100755
--- a/dev.pyx
+++ b/dev.pyx
@@ -1,10 +1,13 @@
-#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True
+#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True, embedsignature=True
 cimport numpy as np
 import numpy as np
 cimport openmp
 from libc.math cimport sqrt, cos, sin, exp, pi, erf, sinh, floor, ceil
 from libc.time cimport time, time_t
-from cython.parallel import prange, parallel
+from cython.parallel import prange
+from scipy.ndimage import gaussian_filter
+from pyrost.bin import update_pixel_map_gs, make_reference, total_mse
+import speckle_tracking as st
 
 ctypedef fused float_t:
     np.float64_t
@@ -18,20 +21,83 @@ DEF FLOAT_MAX = 1.7976931348623157e+308
 DEF MU_C = 1.681792830507429
 DEF NO_VAR = -1.0
 
-def barcode_steps(double x0, double x1, double br_dx, double rd):
-    cdef:
-        int br_n = <int>((x1 - x0) / 2 / br_dx) * 2 if x1 - x0 > 0 else 0, i
-        gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937)
-        double bs_min = max(1 - rd, 0), bs_max = min(1 + rd, 2)
-        double[::1] bx_arr = np.empty(br_n, dtype=np.float64)
-        time_t t = time(NULL)
-    gsl_rng_set(r, t)
-    if br_n:
-        bx_arr[0] = x0 + br_dx * ((bs_max - bs_min) * gsl_rng_uniform_pos(r) - 1)
-        for i in range(1, br_n):
-            bx_arr[i] = bx_arr[i - 1] + br_dx * (bs_min + (bs_max - bs_min) * gsl_rng_uniform_pos(r))
-    gsl_rng_free(r)
-    return np.asarray(bx_arr)
+def st_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_ss, sw_fs, ls, roi=None, n_iter=5):
+    """
+    Andrew's speckle tracking update algorithm
+    
+    I_n - measured data
+    W - whitefield
+    basis - detector plane basis vectors
+    x_ps, y_ps - x and y pixel sizes
+    z - distance between the sample and the detector
+    df - defocus distance
+    sw_max - pixel mapping search window size
+    n_iter - number of iterations
+    """
+    M = np.ones((I_n.shape[1], I_n.shape[2]), dtype=bool)
+    u, dij_pix, res = st.generate_pixel_map(W.shape, dij, basis, x_ps,
+                                            y_ps, z, df, verbose=False)
+    I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls, roi=roi)
+
+    es = []
+    for i in range(n_iter):
+
+        # calculate errors
+        error_total = st.calc_error(I_n, M, W, dij_pix, I0, u, n0, m0, ls=ls,
+                                    roi=roi, subpixel=True, verbose=False)[0]
+
+        # store total error
+        es.append(error_total)
+
+        # update pixel map
+        u = st.update_pixel_map(I_n, M, W, I0, u, n0, m0, dij_pix,
+                                sw_ss, sw_fs, ls, roi=roi)
+
+        # make reference image
+        I0, n0, m0 = st.make_object_map(I_n, M, W, dij_pix, u, ls, roi=roi)
+    return {'u':u, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}
+
+def pixel_translations(basis, dij, df, z):
+    dij_pix = (basis * dij[:, None]).sum(axis=-1)
+    dij_pix /= (basis**2).sum(axis=-1) * df / z
+    dij_pix -= dij_pix.mean(axis=0)
+    return np.ascontiguousarray(dij_pix[:, 0]), np.ascontiguousarray(dij_pix[:, 1])
+
+def str_update(I_n, W, dij, basis, x_ps, y_ps, z, df, sw_max=100, n_iter=5, l_scale=2.5):
+    """
+    Robust version of Andrew's speckle tracking update algorithm
+    
+    I_n - measured data
+    W - whitefield
+    basis - detector plane basis vectors
+    x_ps, y_ps - x and y pixel sizes
+    z - distance between the sample and the detector
+    df - defocus distance
+    sw_max - pixel mapping search window size
+    n_iter - number of iterations
+    """
+    I_n = I_n.astype(np.float64)
+    W = W.astype(np.float64)
+    u0 = np.indices(W.shape, dtype=np.float64)
+    di, dj = pixel_translations(basis, dij, df, z)
+    I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_fs=0, sw_ss=0)
+
+    es = []
+    for i in range(n_iter):
+
+        # calculate errors
+        es.append(total_mse(I_n=I_n, W=W, I0=I0, u=u0, di=di - n0, dj=dj - m0, ls=l_scale))
+
+        # update pixel map
+        u = update_pixel_map_gs(I_n=I_n, W=W, I0=I0, u0=u0, di=di - n0, dj=dj - m0,
+                                sw_ss=0, sw_fs=sw_max, ls=l_scale)
+        sw_max = int(np.max(np.abs(u - u0)))
+        u0 = u0 + gaussian_filter(u - u0, (0, 0, l_scale))
+
+        # make reference image
+        I0, n0, m0 = make_reference(I_n=I_n, W=W, u=u0, di=di, dj=dj, ls=l_scale, sw_ss=0, sw_fs=0)
+        I0 = gaussian_filter(I0, (0, l_scale))
+    return {'u':u0, 'I0':I0, 'errors':es, 'n0': n0, 'm0': m0}
 
 cdef float_t bprd_varc(float_t br_dx, float_t sgm, float_t atn) nogil:
     cdef:
@@ -258,63 +324,6 @@ def krig_data(float_t[:, :, ::1] I_n, float_t[:, ::1] W, float_t[:, :, ::1] u,
         I[a] = rss / w0**2
     return np.asarray(I)
 
-cdef void frame_reference(float_t[:, ::1] I0, float_t[:, ::1] w0, float_t[:, ::1] I, float_t[:, ::1] W,
-                          float_t[:, :, ::1] u, float_t di, float_t dj, float_t ls) nogil:
-    cdef:
-        int b = I.shape[0], c = I.shape[1], j, k, jj, kk, j0, k0
-        int aa = I0.shape[0], bb = I0.shape[1], jj0, jj1, kk0, kk1
-        int dn = <int>(ceil(4 * ls))
-        float_t ss, fs, r
-    for j in range(b):
-        for k in range(c):
-            ss = u[0, j, k] - di
-            fs = u[1, j, k] - dj
-            j0 = <int>(ss) + 1
-            k0 = <int>(fs) + 1
-            jj0 = j0 - dn if j0 - dn > 0 else 0
-            jj1 = j0 + dn if j0 + dn < aa else aa
-            kk0 = k0 - dn if k0 - dn > 0 else 0
-            kk1 = k0 + dn if k0 + dn < bb else bb
-            for jj in range(jj0, jj1):
-                for kk in range(kk0, kk1):
-                    r = rbf((jj - ss)**2 + (kk - fs)**2, ls)
-                    I0[jj, kk] += I[j, k] * W[j, k] * r
-                    w0[jj, kk] += W[j, k]**2 * r
-
-def make_reference(float_t[:, :, ::1] I_n, float_t[:, ::1] W, float_t[:, :, ::1] u, float_t[::1] di,
-                   float_t[::1] dj, float_t ls, int sw_ss, int sw_fs, bool_t return_nm0=True):
-    dtype = np.float64 if float_t is np.float64_t else np.float32
-    cdef:
-        int a = I_n.shape[0], b = I_n.shape[1], c = I_n.shape[2], i, j, k, t
-        float_t n0 = -min_float(&u[0, 0, 0], b * c) + max_float(&di[0], a) + sw_ss
-        float_t m0 = -min_float(&u[1, 0, 0], b * c) + max_float(&dj[0], a) + sw_fs
-        int aa = <int>(max_float(&u[0, 0, 0], b * c) - min_float(&di[0], a) + n0) + 1 + sw_ss
-        int bb = <int>(max_float(&u[1, 0, 0], b * c) - min_float(&dj[0], a) + m0) + 1 + sw_fs
-        int max_threads = openmp.omp_get_max_threads()
-        float_t[:, :, ::1] I = np.zeros((max_threads, aa, bb), dtype=dtype)
-        float_t[:, :, ::1] w = np.zeros((max_threads, aa, bb), dtype=dtype)
-        float_t[::1] Is = np.empty(max_threads, dtype=dtype)
-        float_t[::1] ws = np.empty(max_threads, dtype=dtype)
-        float_t[:, ::1] I0 = np.zeros((aa, bb), dtype=dtype)
-    for i in prange(a, schedule='guided', nogil=True):
-        t = openmp.omp_get_thread_num()
-        frame_reference(I[t], w[t], I_n[i], W, u, di[i] - n0, dj[i] - m0, ls)
-    for k in prange(bb, schedule='guided', nogil=True):
-        t = openmp.omp_get_thread_num()
-        for j in range(aa):
-            Is[t] = 0; ws[t] = 0
-            for i in range(max_threads):
-                Is[t] = Is[t] + I[i, j, k]
-                ws[t] = ws[t] + w[i, j, k]
-            if ws[t]:
-                I0[j, k] = Is[t] / ws[t]
-            else:
-                I0[j, k] = 0
-    if return_nm0:
-        return np.asarray(I0), <int>(n0), <int>(m0)
-    else:
-        return np.asarray(I0)
-
 def subpixel_refinement_2d(float_t[::1] I, float_t[:, ::1] I0, float_t[:] u0,
                            float_t[::1] di, float_t[::1] dj, float_t l1):
     dtype = np.float64 if float_t is np.float64_t else np.float32
@@ -399,117 +408,6 @@ def subpixel_refinement_1d(float_t[::1] I, float_t[:, ::1] I0, float_t[:] u0,
     u[1] += dfs
     return np.asarray(u)
 
-cdef void subpixel_ref_2d(float_t[::1] I, float_t[:, ::1] I0, float_t[::1] u,
-                          float_t[::1] di, float_t[::1] dj, float_t l1) nogil:
-    cdef:
-        float_t dss = 0, dfs = 0, det, mu, dd
-        float_t f22, f11, f00, f21, f01, f12, f10
-        float_t mv_ptr[2]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1])
-    f11 = mv_ptr[0]
-    mu = MU_C * mv_ptr[1]**0.25 / sqrt(l1)
-    mu = mu if mu > 2 else 2
-    mv_ptr[1] = NO_VAR
-
-    mse_bi(mv_ptr, I, I0, di, dj, u[0] - mu / 2, u[1] - mu / 2)
-    f00 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0] - mu / 2, u[1])
-    f01 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1] - mu / 2)
-    f10 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1] + mu / 2)
-    f12 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0] + mu / 2, u[1])
-    f21 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0] + mu / 2, u[1] + mu / 2)
-    f22 = mv_ptr[0]
-
-    det = 4 * (f21 + f01 - 2 * f11) * (f12 + f10 - 2 * f11) - \
-          (f22 + f00 + 2 * f11 - f01 - f21 - f10 - f12)**2
-    if det != 0:
-        dss = ((f22 + f00 + 2 * f11 - f01 - f21 - f10 - f12) * (f12 - f10) - \
-               2 * (f12 + f10 - 2 * f11) * (f21 - f01)) / det * mu / 2
-        dfs = ((f22 + f00 + 2 * f11 - f01 - f21 - f10 - f12) * (f21 - f01) - \
-               2 * (f21 + f01 - 2 * f11) * (f12 - f10)) / det * mu / 2
-        dd = sqrt(dfs**2 + dss**2)
-        if dd > 1:
-            dss /= dd; dfs /= dd
-    
-    u[0] += dss; u[1] += dfs
-
-cdef void subpixel_ref_1d(float_t[::1] I, float_t[:, ::1] I0, float_t[::1] u,
-                          float_t[::1] di, float_t[::1] dj, float_t l1) nogil:
-    cdef:
-        float_t dfs = 0, det, mu, dd
-        float_t f11, f12, f10
-        float_t mv_ptr[2]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1])
-    f11 = mv_ptr[0]
-    mu = MU_C * mv_ptr[1]**0.25 / sqrt(l1)
-    mu = mu if mu > 2 else 2
-    mv_ptr[1] = NO_VAR
-
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1] - mu / 2)
-    f10 = mv_ptr[0]
-    mse_bi(mv_ptr, I, I0, di, dj, u[0], u[1] + mu / 2)
-    f12 = mv_ptr[0]
-
-    det = 4 * (f12 + f10 - 2 * f11)
-    if det != 0:
-        dfs = (f10 - f12) / det * mu
-        dd = sqrt(dfs**2)
-        if dd > 1:
-            dfs /= dd
-
-    u[1] += dfs
-
-cdef void mse_min_c(float_t[::1] I, float_t[:, ::1] I0, float_t[::1] u,
-                    float_t[::1] di, float_t[::1] dj, int* bnds) nogil:
-    cdef:
-        int sslb = -bnds[0] if bnds[0] < u[0] - bnds[2] else <int>(bnds[2] - u[0])
-        int ssub = bnds[0] if bnds[0] < bnds[3] - u[0] else <int>(bnds[3] - u[0])
-        int fslb = -bnds[1] if bnds[1] < u[1] - bnds[4] else <int>(bnds[4] - u[1])
-        int fsub = bnds[1] if bnds[1] < bnds[5] - u[1] else <int>(bnds[5] - u[1])
-        int ss_min = sslb, fs_min = fslb, ss_max = sslb, fs_max = fslb, ss, fs
-        float_t mse_min = FLOAT_MAX, mse_max = -FLOAT_MAX, l1
-        float_t mv_ptr[2]
-    mv_ptr[1] = NO_VAR
-    for ss in range(sslb, ssub):
-        for fs in range(fslb, fsub):
-            mse_bi(mv_ptr, I, I0, di, dj, u[0] + ss, u[1] + fs)
-            if mv_ptr[0] < mse_min:
-                mse_min = mv_ptr[0]; ss_min = ss; fs_min = fs
-            if mv_ptr[0] > mse_max:
-                mse_max = mv_ptr[0]; ss_max = ss; fs_max = fs
-    u[0] += ss_min; u[1] += fs_min
-    l1 = 2 * (mse_max - mse_min) / ((ss_max - ss_min)**2 + (fs_max - fs_min)**2)
-    if ssub - sslb > 1:
-        subpixel_ref_2d(I, I0, u, di, dj, l1)
-    else:
-        subpixel_ref_1d(I, I0, u, di, dj, l1)
-    
-def update_pixel_map_gs(float_t[:, :, ::1] I_n, float_t[:, ::1] W, float_t[:, ::1] I0,
-                        float_t[:, :, ::1] u0, float_t[::1] di, float_t[::1] dj,
-                        int sw_ss, int sw_fs, float_t ls):
-    dtype = np.float64 if float_t is np.float64_t else np.float32
-    cdef:
-        int a = I_n.shape[0], b = I_n.shape[1], c = I_n.shape[2]
-        int aa = I0.shape[0], bb = I0.shape[1], j, k, t
-        int max_threads = openmp.omp_get_max_threads()
-        float_t[::1, :, :] u = np.empty((2, b, c), dtype=dtype, order='F')
-        float_t[:, ::1] I = np.empty((max_threads, a + 1), dtype=dtype)
-        int bnds[6] # sw_ss, sw_fs, di0, di1, dj0, dj1
-    bnds[0] = sw_ss if sw_ss >= 1 else 1; bnds[1] = sw_fs if sw_fs >= 1 else 1
-    bnds[2] = <int>(min_float(&di[0], a)); bnds[3] = <int>(max_float(&di[0], a)) + aa
-    bnds[4] = <int>(min_float(&dj[0], a)); bnds[5] = <int>(max_float(&dj[0], a)) + bb
-    for k in prange(c, schedule='guided', nogil=True):
-        t = openmp.omp_get_thread_num()
-        for j in range(b):
-            krig_data_c(I[t], I_n, W, u0, j, k, ls)
-            u[:, j, k] = u0[:, j, k]
-            mse_min_c(I[t], I0, u[:, j, k], di, dj, bnds)
-    return np.asarray(u, order='C')
-
 cdef void mse_surface_c(float_t[:, ::1] mse_m, float_t[:, ::1] mse_var, float_t[::1] I, float_t[:, ::1] I0,
                         float_t[::1] di, float_t[::1] dj, float_t u_ss, float_t u_fs, int* bnds) nogil:
     cdef:
@@ -653,24 +551,6 @@ def init_newton(float_t[:, :, ::1] I_n, float_t[:, ::1] W, float_t[:, ::1] I0,
             l1[j, k] = sptr[t, 2]
     return np.asarray(l1)
 
-def total_mse(float_t[:, :, ::1] I_n, float_t[:, ::1] W, float_t[:, ::1] I0,
-              float_t[:, :, ::1] u, float_t[::1] di, float_t[::1] dj, float_t ls):
-    dtype = np.float64 if float_t is np.float64_t else np.float32
-    cdef:
-        int a = I_n.shape[0], b = I_n.shape[1], c = I_n.shape[2]
-        int aa = I0.shape[0], bb = I0.shape[1], j, k, t
-        int max_threads = openmp.omp_get_max_threads()
-        float_t err = 0
-        float_t[:, ::1] mptr = NO_VAR * np.ones((max_threads, 2), dtype=dtype)
-        float_t[:, ::1] I = np.empty((max_threads, a + 1), dtype=dtype)
-    for k in prange(c, schedule='static', nogil=True):
-        t = openmp.omp_get_thread_num()
-        for j in range(b):
-            krig_data_c(I[t], I_n, W, u, j, k, ls)
-            mse_bi(&mptr[t, 0], I[t], I0, di, dj, u[0, j, k], u[1, j, k])
-            err += mptr[t, 0]
-    return err / b / c
-
 def ct_integrate(float_t[:, ::1] sx_arr, float_t[:, ::1] sy_arr):
     dtype = np.float64 if float_t is np.float64_t else np.float32
     cdef: