From 53ab1dd57df08a33f5ad014249e140bf9f4515ce Mon Sep 17 00:00:00 2001
From: Claudio Scafuri <claudio.scafuri@elettra.eu>
Date: Tue, 3 Dec 2024 14:58:41 +0100
Subject: [PATCH] testing

---
 elettra2test.py                   |  10 +-
 elettratest.py                    |   2 +-
 esrfworkshop/1_Introduction.ipynb |  93 ++++++----
 esrfworkshop/3_SimpleOptics.ipynb | 286 +++++++++---------------------
 esrfworkshop/3_SimpleOptics.py    |  19 +-
 esrfworkshop/arc.mat              | Bin 18336 -> 18336 bytes
 esrfworkshop/dba.mat              | Bin 472424 -> 472424 bytes
 7 files changed, 158 insertions(+), 252 deletions(-)

diff --git a/elettra2test.py b/elettra2test.py
index 00b3c82..e273be7 100755
--- a/elettra2test.py
+++ b/elettra2test.py
@@ -56,22 +56,22 @@ mp.title('orb_x' )
 mp.show()
 print('tune H:',tuneH,'tune V:',tuneV)
 print('chrom H:',chromH,' chromV:', chromV)
-prinpt('E:', e_eV/1e9,' GeV')
+print('E:', e_eV/1e9,' GeV')
 
 # find index in ring strucutre a specific quadrupole
 
-iq = ring.uint32_refpts('QD_S05_02')[0]  # use also pattern matching idf needed
+iq = ring.uint32_refpts('QD_S05.02')[0]  # use also pattern matching idf needed
 
 # get transport matrxi of 'QD_S05_02')
 
 TM = at.find_elem_m66(ring[iq]) # get 6x6 transport materix
-
+print(TM)
 
 # get global ring parameters
 ring.radiation_on()
 ringparams = ring.radiation_parameters()
-ringparams = ring.envelope_parameters(params=ringparams)
-print(ringparams)
+ringparams2 = ring.envelope_parameters(params=ringparams)
+print(ringparams2)
 
 
 
diff --git a/elettratest.py b/elettratest.py
index a43710e..468b25d 100755
--- a/elettratest.py
+++ b/elettratest.py
@@ -4,7 +4,7 @@ import at
 import matplotlib.pylab as mp
 
 ring=at.load_lattice("../../machine/lattice/elettra/elettra_strS4.m",energy=2.4e9)
-
+#ring=at.load_lattice("../../machine/lattice/elettra/srlattice_with_aper_SCW.m",energy=2.0e9)
 refpts = range(len(ring) + 1)
 ring.radiation_off()
 elemdata0, ringdata,elemdata=at.linopt6(ring,refpts,0.001)
diff --git a/esrfworkshop/1_Introduction.ipynb b/esrfworkshop/1_Introduction.ipynb
index 7f9da0e..183c77c 100644
--- a/esrfworkshop/1_Introduction.ipynb
+++ b/esrfworkshop/1_Introduction.ipynb
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -398,6 +398,16 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
@@ -406,13 +416,14 @@
        " <AxesSubplot:>)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import at.plot\n",
+    "arc.plot_beta()\n",
     "arc.plot_beta()"
    ]
   },
@@ -425,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +564,7 @@
        "0.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -565,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -607,7 +618,7 @@
        "       -0.2708 ,  0.38041])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -673,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +711,7 @@
        " 'qe']"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -738,7 +749,7 @@
        "      dtype=uint32)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -749,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -758,7 +769,7 @@
        "array([91], dtype=uint32)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -793,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -816,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -842,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -851,7 +862,7 @@
        "-100.0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "scrolled": true
    },
@@ -899,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -926,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -961,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -970,7 +981,7 @@
        "Lattice([Drift('DR_01', 2.25), Quadrupole('QF1', 0.75, 0.38041), Drift('DR_02', 0.75), Quadrupole('QD2', 0.75, -0.2708), Drift('DR_03', 0.75), Dipole('Bend', 3.0, 0.12566370614359174, 0.0), Drift('DR_04', 0.1875), Quadrupole('QD3', 0.75, -0.33319), Drift('DR_05', 0.1875), Sextupole('SD', 0.1875, -0.1), Drift('DR_06', 0.5625), Quadrupole('QF4', 0.75, 0.4588), Drift('DR_07', 0.1875), Sextupole('SF', 0.75, 0.1), Monitor('BPM_CellCenter'), Sextupole('SF', 0.75, 0.1), Drift('DR_07', 0.1875), Quadrupole('QF4', 0.75, 0.4588), Drift('DR_06', 0.5625), Sextupole('SD', 0.1875, -0.1), Drift('DR_05', 0.1875), Quadrupole('QD3', 0.75, -0.33319), Drift('DR_04', 0.1875), Dipole('Bend', 3.0, 0.12566370614359174, 0.0), Drift('DR_03', 0.75), Quadrupole('QD2', 0.75, -0.2708), Drift('DR_02', 0.75), Quadrupole('QF1', 0.75, 0.38041), Drift('DR_01', 2.25)], name='', energy=3000000000.0, particle=Particle('relativistic'), periodicity=25, beam_current=0.0, nbunch=1)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -988,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1050,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1075,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1115,7 +1126,7 @@
        "array([-8.42941077e-08,  7.40233657e-10, -1.70283305e-03])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1127,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1137,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1169,7 @@
        " <AxesSubplot:>)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1167,6 +1178,20 @@
     "ring.plot_beta()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/esrfworkshop/3_SimpleOptics.ipynb b/esrfworkshop/3_SimpleOptics.ipynb
index 8a3ab38..c61becf 100644
--- a/esrfworkshop/3_SimpleOptics.ipynb
+++ b/esrfworkshop/3_SimpleOptics.ipynb
@@ -4,32 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '/home/claudio/src/gitlab/dt/exercises/pyatworkshop/dba.mat'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "File \u001b[0;32m/usr/lib/python3/dist-packages/scipy/io/matlab/_mio.py:39\u001b[0m, in \u001b[0;36m_open_file\u001b[0;34m(file_like, appendmat, mode)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;66;03m# Probably \"not found\"\u001b[39;00m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/claudio/src/gitlab/dt/exercises/pyatworkshop/dba.mat'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m ring \u001b[38;5;241m=\u001b[39m \u001b[43mat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_mat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./dba.mat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmat_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRING\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m arc \u001b[38;5;241m=\u001b[39m at\u001b[38;5;241m.\u001b[39mload_mat(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./arc.mat\u001b[39m\u001b[38;5;124m'\u001b[39m, mat_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mARC\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/at/load/matfile.py:169\u001b[0m, in \u001b[0;36mload_mat\u001b[0;34m(filename, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkey\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:  \u001b[38;5;66;03m# process the deprecated 'key' keyword\u001b[39;00m\n\u001b[1;32m    168\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmat_key\u001b[39m\u001b[38;5;124m'\u001b[39m, kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkey\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m--> 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mLattice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mringparam_filter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatfile_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mabspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[43m               \u001b[49m\u001b[43miterator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams_filter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/at/lattice/lattice_object.py:186\u001b[0m, in \u001b[0;36mLattice.__init__\u001b[0;34m(self, iterator, scan, *args, **kwargs)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    184\u001b[0m     elems \u001b[38;5;241m=\u001b[39m iterator(kwargs, \u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mLattice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43melems\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;66;03m# removing excluded attributes\u001b[39;00m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_excluded_attributes:\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/at/lattice/lattice_object.py:1474\u001b[0m, in \u001b[0;36mparams_filter\u001b[0;34m(params, elem_filter, *args)\u001b[0m\n\u001b[1;32m   1471\u001b[0m cavities \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m   1472\u001b[0m cell_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1474\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, elem \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(elem_filter(params, \u001b[38;5;241m*\u001b[39margs)):\n\u001b[1;32m   1475\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, elt\u001b[38;5;241m.\u001b[39mRFCavity):\n\u001b[1;32m   1476\u001b[0m         cavities\u001b[38;5;241m.\u001b[39mappend(elem)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/at/load/matfile.py:115\u001b[0m, in \u001b[0;36mringparam_filter\u001b[0;34m(params, elem_iterator, *args)\u001b[0m\n\u001b[1;32m    113\u001b[0m ringparams \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    114\u001b[0m radiate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m elem \u001b[38;5;129;01min\u001b[39;00m elem_iterator(params, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (elem\u001b[38;5;241m.\u001b[39mPassMethod\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRadPass\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m    117\u001b[0m             elem\u001b[38;5;241m.\u001b[39mPassMethod\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCavityPass\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[1;32m    118\u001b[0m         radiate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/at/load/matfile.py:67\u001b[0m, in \u001b[0;36mmatfile_generator\u001b[0;34m(params, mat_file)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;66;03m# Remove any surplus dimensions in arrays.\u001b[39;00m\n\u001b[1;32m     65\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m numpy\u001b[38;5;241m.\u001b[39msqueeze(data)\n\u001b[0;32m---> 67\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloadmat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetdefault\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmat_file\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmat_file\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     68\u001b[0m matvars \u001b[38;5;241m=\u001b[39m [varname \u001b[38;5;28;01mfor\u001b[39;00m varname \u001b[38;5;129;01min\u001b[39;00m m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m varname\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[1;32m     69\u001b[0m default_key \u001b[38;5;241m=\u001b[39m matvars[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(matvars) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRING\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[0;32m/usr/lib/python3/dist-packages/scipy/io/matlab/_mio.py:224\u001b[0m, in \u001b[0;36mloadmat\u001b[0;34m(file_name, mdict, appendmat, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;124;03mLoad MATLAB file.\u001b[39;00m\n\u001b[1;32m     89\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;124;03m    3.14159265+3.14159265j])\u001b[39;00m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    223\u001b[0m variable_names \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvariable_names\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _open_file_context(file_name, appendmat) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    225\u001b[0m     MR, _ \u001b[38;5;241m=\u001b[39m mat_reader_factory(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    226\u001b[0m     matfile_dict \u001b[38;5;241m=\u001b[39m MR\u001b[38;5;241m.\u001b[39mget_variables(variable_names)\n",
-      "File \u001b[0;32m/usr/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
-      "File \u001b[0;32m/usr/lib/python3/dist-packages/scipy/io/matlab/_mio.py:17\u001b[0m, in \u001b[0;36m_open_file_context\u001b[0;34m(file_like, appendmat, mode)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;129m@contextmanager\u001b[39m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_file_context\u001b[39m(file_like, appendmat, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m---> 17\u001b[0m     f, opened \u001b[38;5;241m=\u001b[39m \u001b[43m_open_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mappendmat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m f\n",
-      "File \u001b[0;32m/usr/lib/python3/dist-packages/scipy/io/matlab/_mio.py:45\u001b[0m, in \u001b[0;36m_open_file\u001b[0;34m(file_like, appendmat, mode)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m appendmat \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m file_like\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.mat\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     44\u001b[0m         file_like \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.mat\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 45\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mReader needs file name or open file-like object\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     49\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/claudio/src/gitlab/dt/exercises/pyatworkshop/dba.mat'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import at\n",
     "import at.plot\n",
@@ -82,21 +57,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/mnt/multipath-shares/machfs/carver/pyat_dev/at/pyat/at/tracking/particles.py:107: AtWarning: Monochromatic beam: no energy spread\n",
+      "/home/claudio/.local/lib/python3.10/site-packages/at/tracking/particles.py:107: AtWarning: Monochromatic beam: no energy spread\n",
       "  warn(AtWarning('Monochromatic beam: no energy spread'))\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "array([[ 2.45937189e-09, -2.37902221e-21,  0.00000000e+00,\n",
+       "array([[ 2.45937187e-09, -2.38004590e-21,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
-       "       [-2.37902221e-21,  4.06607883e-12,  0.00000000e+00,\n",
+       "       [-2.38004590e-21,  4.06607887e-12,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
-       "       [ 0.00000000e+00,  0.00000000e+00,  1.67372947e-11,\n",
-       "        -4.04065923e-23,  0.00000000e+00,  0.00000000e+00],\n",
-       "       [ 0.00000000e+00,  0.00000000e+00, -4.04065923e-23,\n",
-       "         5.97468119e-14,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  1.67372973e-11,\n",
+       "        -4.03895454e-23,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00, -4.03895454e-23,\n",
+       "         5.97468029e-14,  0.00000000e+00,  0.00000000e+00],\n",
        "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
        "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
@@ -128,18 +103,18 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.82796706e-07,  8.80029073e-13, -9.03634961e-27,\n",
-       "        -2.23193015e-27, -6.45406656e-12, -1.10736402e-11],\n",
-       "       [ 8.80029502e-13,  3.02682564e-10, -3.93185281e-28,\n",
-       "         4.28789015e-29, -1.37026502e-12,  1.60304356e-13],\n",
-       "       [ 1.14458611e-27,  2.36795948e-28,  1.67373110e-10,\n",
-       "         2.00352563e-16, -4.85449966e-23, -1.27782072e-23],\n",
-       "       [-1.30926147e-28, -2.70856465e-29,  2.00352563e-16,\n",
-       "         5.97467540e-13,  5.55276393e-24,  1.46211048e-24],\n",
-       "       [-6.45942538e-12, -1.35267982e-12,  8.54658540e-23,\n",
-       "        -5.46753954e-24,  2.76400648e-07,  3.98317397e-08],\n",
-       "       [-1.17777339e-11,  1.84183232e-13,  1.67970673e-21,\n",
-       "         7.49294951e-23,  3.98317397e-08,  6.20209387e-06]])"
+       "array([[ 1.82796669e-07,  8.80028812e-13, -4.43787738e-27,\n",
+       "        -3.86153903e-27, -6.45302620e-12, -1.10733585e-11],\n",
+       "       [ 8.80029240e-13,  3.02682509e-10, -1.35987098e-27,\n",
+       "         1.32432811e-29, -1.37026500e-12,  1.60296677e-13],\n",
+       "       [ 3.50100764e-27,  7.24397447e-28,  1.67373135e-10,\n",
+       "         2.00351684e-16, -1.48507006e-22, -3.91018949e-23],\n",
+       "       [ 4.48139737e-29,  9.27306504e-30,  2.00351684e-16,\n",
+       "         5.97467449e-13, -1.90105090e-24, -5.00210745e-25],\n",
+       "       [-6.45838519e-12, -1.35267980e-12,  2.55547359e-22,\n",
+       "         3.23386449e-24,  2.76400648e-07,  3.98317402e-08],\n",
+       "       [-1.17774761e-11,  1.84183510e-13,  6.11805186e-22,\n",
+       "         2.71890778e-22,  3.98317402e-08,  6.20209394e-06]])"
       ]
      },
      "execution_count": 4,
@@ -188,7 +163,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.002487854833857263\n"
+      "0.0024878548474715633\n"
      ]
     },
     {
@@ -211,32 +186,32 @@
        "        4.000e+01, 2.400e+01, 1.700e+01, 1.800e+01, 1.700e+01, 5.000e+00,\n",
        "        1.200e+01, 5.000e+00, 5.000e+00, 4.000e+00, 3.000e+00, 2.000e+00,\n",
        "        0.000e+00, 0.000e+00, 1.000e+00, 1.000e+00]),\n",
-       " array([-1.09313451e-02, -1.07209302e-02, -1.05105153e-02, -1.03001004e-02,\n",
-       "        -1.00896855e-02, -9.87927058e-03, -9.66885567e-03, -9.45844075e-03,\n",
-       "        -9.24802584e-03, -9.03761093e-03, -8.82719602e-03, -8.61678110e-03,\n",
-       "        -8.40636619e-03, -8.19595128e-03, -7.98553637e-03, -7.77512145e-03,\n",
-       "        -7.56470654e-03, -7.35429163e-03, -7.14387671e-03, -6.93346180e-03,\n",
-       "        -6.72304689e-03, -6.51263198e-03, -6.30221706e-03, -6.09180215e-03,\n",
-       "        -5.88138724e-03, -5.67097233e-03, -5.46055741e-03, -5.25014250e-03,\n",
-       "        -5.03972759e-03, -4.82931268e-03, -4.61889776e-03, -4.40848285e-03,\n",
-       "        -4.19806794e-03, -3.98765303e-03, -3.77723811e-03, -3.56682320e-03,\n",
-       "        -3.35640829e-03, -3.14599338e-03, -2.93557846e-03, -2.72516355e-03,\n",
-       "        -2.51474864e-03, -2.30433373e-03, -2.09391881e-03, -1.88350390e-03,\n",
-       "        -1.67308899e-03, -1.46267408e-03, -1.25225916e-03, -1.04184425e-03,\n",
-       "        -8.31429338e-04, -6.21014426e-04, -4.10599513e-04, -2.00184601e-04,\n",
-       "         1.02303120e-05,  2.20645225e-04,  4.31060137e-04,  6.41475050e-04,\n",
-       "         8.51889962e-04,  1.06230487e-03,  1.27271979e-03,  1.48313470e-03,\n",
-       "         1.69354961e-03,  1.90396452e-03,  2.11437944e-03,  2.32479435e-03,\n",
-       "         2.53520926e-03,  2.74562418e-03,  2.95603909e-03,  3.16645400e-03,\n",
-       "         3.37686891e-03,  3.58728383e-03,  3.79769874e-03,  4.00811365e-03,\n",
-       "         4.21852856e-03,  4.42894348e-03,  4.63935839e-03,  4.84977330e-03,\n",
-       "         5.06018821e-03,  5.27060313e-03,  5.48101804e-03,  5.69143295e-03,\n",
-       "         5.90184786e-03,  6.11226278e-03,  6.32267769e-03,  6.53309260e-03,\n",
-       "         6.74350751e-03,  6.95392243e-03,  7.16433734e-03,  7.37475225e-03,\n",
-       "         7.58516716e-03,  7.79558208e-03,  8.00599699e-03,  8.21641190e-03,\n",
-       "         8.42682681e-03,  8.63724173e-03,  8.84765664e-03,  9.05807155e-03,\n",
-       "         9.26848646e-03,  9.47890138e-03,  9.68931629e-03,  9.89973120e-03,\n",
-       "         1.01101461e-02]),\n",
+       " array([-1.09313452e-02, -1.07209303e-02, -1.05105154e-02, -1.03001005e-02,\n",
+       "        -1.00896855e-02, -9.87927063e-03, -9.66885572e-03, -9.45844080e-03,\n",
+       "        -9.24802589e-03, -9.03761098e-03, -8.82719606e-03, -8.61678115e-03,\n",
+       "        -8.40636624e-03, -8.19595132e-03, -7.98553641e-03, -7.77512150e-03,\n",
+       "        -7.56470658e-03, -7.35429167e-03, -7.14387675e-03, -6.93346184e-03,\n",
+       "        -6.72304693e-03, -6.51263201e-03, -6.30221710e-03, -6.09180219e-03,\n",
+       "        -5.88138727e-03, -5.67097236e-03, -5.46055744e-03, -5.25014253e-03,\n",
+       "        -5.03972762e-03, -4.82931270e-03, -4.61889779e-03, -4.40848288e-03,\n",
+       "        -4.19806796e-03, -3.98765305e-03, -3.77723813e-03, -3.56682322e-03,\n",
+       "        -3.35640831e-03, -3.14599339e-03, -2.93557848e-03, -2.72516357e-03,\n",
+       "        -2.51474865e-03, -2.30433374e-03, -2.09391883e-03, -1.88350391e-03,\n",
+       "        -1.67308900e-03, -1.46267408e-03, -1.25225917e-03, -1.04184426e-03,\n",
+       "        -8.31429343e-04, -6.21014429e-04, -4.10599516e-04, -2.00184602e-04,\n",
+       "         1.02303117e-05,  2.20645225e-04,  4.31060139e-04,  6.41475053e-04,\n",
+       "         8.51889967e-04,  1.06230488e-03,  1.27271979e-03,  1.48313471e-03,\n",
+       "         1.69354962e-03,  1.90396454e-03,  2.11437945e-03,  2.32479436e-03,\n",
+       "         2.53520928e-03,  2.74562419e-03,  2.95603910e-03,  3.16645402e-03,\n",
+       "         3.37686893e-03,  3.58728384e-03,  3.79769876e-03,  4.00811367e-03,\n",
+       "         4.21852859e-03,  4.42894350e-03,  4.63935841e-03,  4.84977333e-03,\n",
+       "         5.06018824e-03,  5.27060315e-03,  5.48101807e-03,  5.69143298e-03,\n",
+       "         5.90184790e-03,  6.11226281e-03,  6.32267772e-03,  6.53309264e-03,\n",
+       "         6.74350755e-03,  6.95392246e-03,  7.16433738e-03,  7.37475229e-03,\n",
+       "         7.58516721e-03,  7.79558212e-03,  8.00599703e-03,  8.21641195e-03,\n",
+       "         8.42682686e-03,  8.63724177e-03,  8.84765669e-03,  9.05807160e-03,\n",
+       "         9.26848651e-03,  9.47890143e-03,  9.68931634e-03,  9.89973126e-03,\n",
+       "         1.01101462e-02]),\n",
        " <BarContainer object of 100 artists>)"
       ]
      },
@@ -246,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -305,145 +280,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `ring.track` not found.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m\n",
-       "\u001b[0mring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mr_in\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mnturns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mrefpts\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Refpts'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m<\u001b[0m\u001b[0mRefptsCode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEnd\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'End'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0min_place\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\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;31mDocstring:\u001b[0m\n",
-       ":py:func:`track_function` tracks particles through each element of a\n",
-       "lattice or throught a single Element calling the element-specific\n",
-       "tracking function specified in the Element's *PassMethod* field.\n",
-       "\n",
-       "Usage:\n",
-       "  >>> lattice_track(lattice, r_in)\n",
-       "  >>> lattice.track(r_in)\n",
-       "\n",
-       "Parameters:\n",
-       "    lattice: list of elements\n",
-       "    r_in: (6, N) array: input coordinates of N particles.\n",
-       "      *r_in* is modified in-place only if *in_place* is \n",
-       "      :py:obj:`True` and reports the coordinates at\n",
-       "      the end of the element. For the best efficiency, *r_in*\n",
-       "      should be given as F_CONTIGUOUS numpy array.\n",
-       "\n",
-       "Keyword arguments:\n",
-       "    nturns: number of turns to be tracked\n",
-       "    refpts: Selects the location of coordinates output.\n",
-       "      See \":ref:`Selecting elements in a lattice <refpts>`\"\n",
-       "    in_place (bool): If True *r_in* is modified in-place and\n",
-       "      reports the coordinates at the end of the element.\n",
-       "      (default: False)\n",
-       "    keep_lattice (bool):    Use elements persisted from a previous\n",
-       "      call. If :py:obj:`True`, assume that the lattice has not changed\n",
-       "      since the previous call.\n",
-       "    keep_counter (bool):    Keep the turn number from the previous\n",
-       "      call.\n",
-       "    turn (int):             Starting turn number. Ignored if\n",
-       "      *keep_counter* is :py:obj:`True`. The turn number is necessary to\n",
-       "      compute the absolute path length used in RFCavityPass.\n",
-       "    losses (bool):          Boolean to activate loss maps output\n",
-       "    omp_num_threads (int):  Number of OpenMP threads\n",
-       "      (default: automatic)\n",
-       "\n",
-       "The following keyword arguments overload the lattice values\n",
-       "\n",
-       "Keyword arguments:\n",
-       "\n",
-       "    particle (Optional[Particle]): circulating particle.\n",
-       "      Default: :code:`lattice.particle` if existing,\n",
-       "      otherwise :code:`Particle('relativistic')`\n",
-       "    energy (Optiona[float]): lattice energy. Default 0.\n",
-       "    unfold_beam (bool): Internal beam folding activate, this\n",
-       "      assumes the input particles are in bucket 0, works only\n",
-       "      if all bucket see the same RF Voltage.\n",
-       "      Default: :py:obj:`True`\n",
-       "\n",
-       "If *energy* is not available, relativistic tracking if forced,\n",
-       "*rest_energy* is ignored.\n",
-       "\n",
-       "Returns:\n",
-       "    r_out: (6, N, R, T) array containing output coordinates of N particles\n",
-       "      at R reference points for T turns\n",
-       "    trackparam: A dictionary containing tracking input parameters with the\n",
-       "      following keys:\n",
-       "\n",
-       "      ==============    ===================================================\n",
-       "      **npart**         number of particles\n",
-       "      **rout**          final particle coordinates\n",
-       "      **turn**          starting turn\n",
-       "      **refpts**        array of index where particle coordinate are saved\n",
-       "                        (only for lattice tracking)\n",
-       "      **nturns**        number of turn\n",
-       "\n",
-       "    trackdata: A dictionary containinf tracking data with the following\n",
-       "      keys:\n",
-       "\n",
-       "      ==============    ===================================================\n",
-       "      **loss_map**: recarray containing the loss_map (only for lattice\n",
-       "                    tracking)\n",
-       "\n",
-       "\n",
-       "    The **loss_map** is filled only if *losses* is :py:obj:`True`,\n",
-       "      it contains the following keys:\n",
-       "      ==============    ===================================================\n",
-       "      **islost**        (npart,) bool array indicating lost particles\n",
-       "      **turn**          (npart,) int array indicating the turn at\n",
-       "                        which the particle is lost\n",
-       "      **element**       ((npart,) int array indicating the element at\n",
-       "                        which the particle is lost\n",
-       "      **coord**         (npart, 6) float array giving the coordinates at\n",
-       "                        which the particle is lost (zero for surviving\n",
-       "                        particles)\n",
-       "      ==============    ===================================================\n",
-       "\n",
-       "\n",
-       ".. note::\n",
-       "\n",
-       "   * :pycode:`track_function(lattice, r_in, refpts=len(line))` is the same\n",
-       "     as :pycode:`track_function(lattice, r_in)` since the reference point\n",
-       "     len(line) is the exit of the last element.\n",
-       "   * :pycode:`track_function(lattice, r_in, refpts=0)` is a copy of *r_in*\n",
-       "     since the reference point 0 is the entrance of the first element.\n",
-       "   * To resume an interrupted tracking (for instance to get intermediate\n",
-       "     results), one must use one of the *turn* or *keep_counter*\n",
-       "     keywords to ensure the continuity of the turn number.\n",
-       "   * For multiparticle tracking with large number of turn the size of\n",
-       "     *r_out* may increase excessively. To avoid memory issues\n",
-       "     :pycode:`track_function(lattice, r_in, refpts=None, in_place=True)`\n",
-       "     can be used. An empty list is returned and the tracking results of\n",
-       "     the last turn are stored in *r_in*.\n",
-       "   * To model buckets with different RF voltage :pycode:`unfold_beam=False`\n",
-       "     has to be used. The beam can be unfolded using the function\n",
-       "     :py:func:`.unfold_beam`. This function takes into account\n",
-       "     the true voltage in each bucket and distributes the particles in the\n",
-       "     bunches defined by :code:`ring.fillpattern` using a 6D orbit search.\n",
-       "\u001b[0;31mFile:\u001b[0m      /mnt/multipath-shares/machfs/carver/pyat_dev/at/pyat/at/tracking/track.py\n",
-       "\u001b[0;31mType:\u001b[0m      method\n"
+       "array([0.44998061, 0.84998489])"
       ]
      },
+     "execution_count": 12,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ring.track?"
+    "ring.track?\n",
+    "ring.get_tune()\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Lattice' object has no attribute 'track'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Method 1, the output of the tracking is a big array with shape (6, N, R, T)\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# where 6 is the particle coordinate, N is the particle index, R is the element index, \u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# and T is the turn number\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m pout, \u001b[38;5;241m*\u001b[39m_ \u001b[38;5;241m=\u001b[39m \u001b[43mring\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack\u001b[49m(p_in, refpts\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mlen\u001b[39m(ring)), nturns\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Lattice' object has no attribute 'track'"
+     ]
+    }
+   ],
    "source": [
     "# Method 1, the output of the tracking is a big array with shape (6, N, R, T)\n",
     "# where 6 is the particle coordinate, N is the particle index, R is the element index, \n",
@@ -457,14 +336,15 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'pout' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m tunex, tuney \u001b[38;5;241m=\u001b[39m ring\u001b[38;5;241m.\u001b[39mget_tune()\n\u001b[0;32m----> 2\u001b[0m xdat \u001b[38;5;241m=\u001b[39m \u001b[43mpout\u001b[49m[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m,:]\n\u001b[1;32m      3\u001b[0m fig, (ax1,ax2) \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      4\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(xdat)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'pout' is not defined"
+     ]
     }
    ],
    "source": [
diff --git a/esrfworkshop/3_SimpleOptics.py b/esrfworkshop/3_SimpleOptics.py
index f2b9adf..42d4756 100644
--- a/esrfworkshop/3_SimpleOptics.py
+++ b/esrfworkshop/3_SimpleOptics.py
@@ -6,6 +6,7 @@
 
 import at
 import at.plot
+import at.tracking
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -76,7 +77,7 @@ p_in.shape
 # In[8]:
 
 
-get_ipython().run_line_magic('pinfo', 'ring.track')
+#get_ipython().run_line_magic('pinfo', 'ring.track')
 
 
 # In[9]:
@@ -85,14 +86,14 @@ get_ipython().run_line_magic('pinfo', 'ring.track')
 # Method 1, the output of the tracking is a big array with shape (6, N, R, T)
 # where 6 is the particle coordinate, N is the particle index, R is the element index, 
 # and T is the turn number
-pout, *_ = ring.track(p_in, refpts=np.arange(len(ring)), nturns=200)
+pout, *_ = at.lattice_pass(ring,p_in, refpts=np.arange(len(ring)), nturns=200)
 
 
 # In[10]:
 
 
 tunex, tuney = ring.get_tune()
-xdat = pout[0,0,0,:]
+xdat = pout[0,0,:]
 fig, (ax1,ax2) = plt.subplots(2,1)
 ax1.plot(xdat)
 ax2.plot(np.fft.rfftfreq(len(xdat)), np.abs(np.fft.rfft(xdat)))
@@ -111,7 +112,7 @@ n_turns=200
 x_data = np.zeros((p_in.shape[1], n_turns))
 xp_data = np.zeros((p_in.shape[1], n_turns))
 for i in np.arange(n_turns):
-    _ = ring.track(p_in, nturns=1, in_place=True, refpts=None) #in_place means modify input particle, refpts=None stops the track function from generating big arrays for unneeded output
+    _ = at.lattice_pass(ring,p_in, nturns=1, refpts=None) #in_place means modify input particle, refpts=None stops the track function from generating big arrays for unneeded output
     x_data[:, i] = p_in[0,:]
     xp_data[:, i] = p_in[1,:]
 
@@ -162,7 +163,7 @@ plt.show()
 # In[15]:
 
 
-get_ipython().run_line_magic('pinfo', 'at.get_optics')
+#get_ipython().run_line_magic('pinfo', 'at.get_optics')
 
 
 # In[16]:
@@ -192,7 +193,7 @@ plt.show()
 # In[18]:
 
 
-get_ipython().run_line_magic('pinfo', 'at.find_orbit4')
+#get_ipython().run_line_magic('pinfo', 'at.find_orbit4')
 
 
 # # Orbit Correction
@@ -386,7 +387,7 @@ half_arc.plot_beta(twiss_in=ld0)
 # In[34]:
 
 
-get_ipython().run_line_magic('pinfo', 'at.Variable')
+#get_ipython().run_line_magic('pinfo', 'at.Variable')
 
 
 # In[35]:
@@ -449,7 +450,7 @@ new_lat.plot_beta(twiss_in=ld0)
 # In[41]:
 
 
-get_ipython().run_line_magic('pinfo', 'constr.add')
+#get_ipython().run_line_magic('pinfo', 'constr.add')
 
 
 # ## Dynamic Aperture
@@ -465,7 +466,7 @@ get_ipython().run_line_magic('pinfo', 'constr.add')
 # In[42]:
 
 
-get_ipython().run_line_magic('pinfo', 'at.get_acceptance')
+#get_ipython().run_line_magic('pinfo', 'at.get_acceptance')
 
 
 # In[43]:
diff --git a/esrfworkshop/arc.mat b/esrfworkshop/arc.mat
index 7ceb1515a1cbe75bf0f00731e42d2c7b1a65c4f4..a22f224e50a04662da2f5d163d1bd0996a549808 100644
GIT binary patch
delta 43
ycmZ3`&$ytUae|?QZ+@PFS81Mtk%@w#ft8Vgm5HH(k%5uP#6X>i2^<@1mE8dyO$>Sf

delta 43
ycmZ3`&$ytUae|>lNNK8qe{zX}g0X_3iIu6bm7%GEk%5u%#6X>i2^<@1mE8dw_Y7_T

diff --git a/esrfworkshop/dba.mat b/esrfworkshop/dba.mat
index c2a13cc9f3999ad899ca83291a6bc6c15c7510d1..06e5defa59ed1b86b63748a5348bd9ebe252e2f0 100644
GIT binary patch
delta 64
zcmaFyO6J8YnF)pxzWI3yUZr^oMkWe|23AG}Rwf1tMg~SE69aW7CU7*?w$?JX)-tu$
UGPl;UwAQk=*0OD_Wq)k~0MtDd)Bpeg

delta 64
zcmaFyO6J8YnF)pxA*HDb{>dc@3dRbCCRV1#R)!`DMg~U469aW7CU7*?w$?JX)-tu$
UGPl;UwAQk=*0OD_Wq)k~0MY9e$N&HU

-- 
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