diff --git a/Urumbu_short_z/README.md b/Urumbu_short_z/README.md
index e8ce24300ab81371d4e8683a664b4b7123b2f3e0..a7a302e14b791e2a3a30f3522fe9986e20271147 100644
--- a/Urumbu_short_z/README.md
+++ b/Urumbu_short_z/README.md
@@ -9,3 +9,7 @@ Below is a short z-axis integrated into the xy machine. The z axis has about 50
String up and measure z-axis alone. First horizontal, with fishing line as belt. Necessary to adjust bearing position to ease friction on z-travel. Once that feels good, the plot below is obtained, moving in increments of 32*(1/32) steps eight times forward, then reversing. Each full step is calculated, using the radius of the capstan, to be 0.31 mm. Hysteresis is about 0.05 mm.
<img src=./img/z_mar08.svg width=75%><br>
+
+Now do the same with axis held vertically. Result below. Looks like hysteresis is now about 0.13 mm.
+
+<img src=./img/z_mar11.svg width=75%><br
diff --git a/Urumbu_short_z/img/z_mar11.svg b/Urumbu_short_z/img/z_mar11.svg
new file mode 100644
index 0000000000000000000000000000000000000000..9c7cc91eb39711da8efd3bba8ceccd582eab9fdf
--- /dev/null
+++ b/Urumbu_short_z/img/z_mar11.svg
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diff --git a/Urumbu_short_z/jupyter_notebooks/.ipynb_checkpoints/z_axis_Mar11-checkpoint.ipynb b/Urumbu_short_z/jupyter_notebooks/.ipynb_checkpoints/z_axis_Mar11-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f1fc288d547930ba3ec9e37136d8a2f772719e2d
--- /dev/null
+++ b/Urumbu_short_z/jupyter_notebooks/.ipynb_checkpoints/z_axis_Mar11-checkpoint.ipynb
@@ -0,0 +1,72 @@
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GV25mo1otE8oUYHPJ9pZUVrFOROwEngQmVXnsAuDaku0jI+Kh9Plh4MhKQUlql9QlqWvbtm3Vn40N37Rpwys3s1FtVA7KS9oPOAP4eqX9ERFADLBvSUS0RkRrS0tLDaM0OjspTjiXAhtoYhcFNlCccC50dtY7MjOrgfE1bHsrcHTJ9tRUVqnOFknjgUOAx6o49lTgzoh4pKTsEUmTI+IhSZOBR/M5DdtTRdpo17voTf/MeijQrqXAeNrqG5qZ1UAtr1DuAGZKmpGuKBYAq8rqrALOSZ/PAm5NVxergAXpLrAZwEzg9pLjFtK/u6u8rXOAG3I7E9sjHR3Qu6P/3yy9O8Z7TN5sH1WzK5SI2CnpAuBmYBxwVUTcJ+lioCsiVgFXAl+T1A08TpZ0SPVWAGuBncD5EbELQNJE4G3Ae8u+8jPACknnAT3AO2t1blYdj8mbjS3KLgjGptbW1ujq6qp3GPusQiF79KTc9OmwceNIR2NmeZG0JiJay8tH5aC8jQ6dndDc3L+sudlj8mb7KicUq5m2NliyJLsikbL3JUuycjPb99TyLi8z2tqcQMzGCl+hmJlZLpxQzMwsF04oZmaWCycUMzPLhROKmZnlwgnFzMxy4YRiZma5GPQ5FElTyebX+n3gKOBZ4F7gO8B3I+L5mkdoZmajwoAJRdJXyBa1+jbZ0ryPAgcAryBblrdD0qKI+M+RCNTMzBrbYFcoX4iIeyuU3wt8M01J76X3zMwMGGQMZYBkUrp/R0R05x+S2QgoFrPpkJuasvdisd4RmY16Qw7KSzpd0l2SHpf0lKSnJT01EsGZ1USxCO3t2dz6Edl7e7uTitlequYury+RrYA4KSIOjoiDIuLg2oZlVkMdHdDb27+stxcvJWm2d6pJKJuBe2MPVuKSNE/SOkndkhZV2L+/pOvS/tWSCiX7FqfydZLmlpQfKmmlpAck3S/pDan8k5K2Sro7vU4bbrw2RngpSbOaqGb6+o8AN0r6IfBcX2FEfHGwgySNAy4jW653C3CHpFURsbak2nnA9og4RtICsrvJ3iVpFtntyseR3a78A0mvSMsA/yNwU0SclW4MKF3C6dKI+HwV52Rj2bRplZeSnOZ7TMz2RjVXKJ1AL9ktwweVvIZyItAdEesjYgewHJhfVmc+sCx9XgnMkaRUvjwinouIDUA3cKKkQ4A3k61F33djwBNVxGL2os5OihPOpcAGmthFgQ0UJ5zrpSTN9lI1VyhHRcQr96DtKWTdZX22AK8bqE5E7JT0JDAplf+07NgpZA9WbgO+IunVwBrggxHxTKp3gaQ/BbqAD0XE9vKgJLUD7QDT/BfpmFSkjXa9i970z7+HAu1aCozHa4GZ7blqrlBulHRKzSOpznjgeOCKiHgN8AzQNzZzBfByYDbwEPCFSg1ExJKIaI2I1paWltpHbA2nowN6d/T/W6p3x3iPyZvtpWoSyvuAmyQ9O8zbhrcCR5dsT01lFetIGg8cAjw2yLFbgC0RsTqVryRLMETEIxGxK00Hs5Ssy81sNx6TN6uNIRNKuk24KSIOHOZtw3cAMyXNSIPnC4BVZXVWkd2SDHAWcGu6m2wVsCDdBTYDmAncHhEPA5slHZuOmQOsBZA0uaTdd5A90W+2m4F6Ot0DarZ3qhlDQdKrgEJp/Yj45mDHpDGRC4CbgXHAVRFxn6SLga6IWEU2uP41Sd3A42RJh1RvBVmy2Amcn+7wAvgAUExJaj3wnlT+WUmzgQA2Au+t5txs7OnszJ5jLH0UpbnZY/Jme0tDPV4i6SrgVcB9QN/swhER/7fGsdVca2trdHV11TsMq4NiMRtL2bQpuzLp7IQ2j8ibVUXSmohoLS+v5grl9RExqwYxmdVNW5sTiFneqhmU/0l60NDMzGxA1Vyh/BtZUnmY7El5kXV5vaqmkZmZ2ahSTUK5Eng3cA8vjqGYmZn1U01C2ZbuyDIzMxtQNQnlLknXAN+i/+SQg942bGZmY0s1CeVAskRSOv1KAE4oZmb2giETSkS8Z6g6ZmZmA942LOnjkg4fZP/Jkk6vTVhmZjbaDHaFcg/wLUm/Ae4kmzb+ALJ5tWYDPwA+XesAzcxsdBgwoUTEDcANkmYCbwImA08BVwPtEfHsyIRoZmajQTVjKL8AfjECsZiZ2ShWzdQrZmZmQ3JCMTOzXDihmDWSYhEKBWhqyt6LxXpHZFa1IcdQJLUAf87uC2yN+vVQzBpKsdh/5a+enmwbPNe+jQrVXKHcQLbW+w+A75S8hiRpnqR1krolLaqwf39J16X9qyUVSvYtTuXrJM0tKT9U0kpJD0i6X9IbUvnhkr4v6Rfp/bBqYjRrGB0d/ZeRhGy7o6M+8ZgNUzVTrzRHxEeH27CkccBlwNuALcAdklZFxNqSaucB2yPiGEkLgEuAd6X1VxYAxwFHAT+Q9Iq0DPA/AjdFxFlpGeDm1NYi4JaI+ExKXouAYcdtVjebNg2v3KzBVHOF8m1Jp+1B2ycC3RGxPiJ2AMuB+WV15gPL0ueVwBxJSuXLI+K5iNgAdAMnSjoEeDPZlPpExI6IeKJCW8uAM/cgZrP6mTZteOVmDaaahPJBsqTyG0lPp9dTVRw3Bdhcsr0llVWsExE7gSeBSYMcO4Psif2vSLpL0pclTUx1joyIh9Lnh4EjKwUlqV1Sl6Subdu2VXEaZiOks5PihHMpsIEmdlFgA8UJ52YL3puNAkMmlIg4KCKaIuKA9PmgiDh4JIKrYDxwPHBFRLwGeIasa6ufiAiyGZF3ExFLIqI1IlpbWlpqGqzZcBRpo11L6aFA0EQPBdq1lCIekLfRoarbhiWdIenz6VXthJBbgaNLtqemsop1JI0nG/x/bJBjtwBbImJ1Kl9JlmAAHpE0ObU1GXi0yjjNGkJHB/Tu6D+s2btjvMfkbdQYMqFI+gxZt9fa9PqgpH+oou07gJmSZqTB8wVA+cqPq4Bz0uezgFvT1cUqYEG6C2wG2YSUt0fEw8BmScemY+akmMrbOofs7jSzUcNj8jbaVXOX12nA7Ih4HkDSMuAuYPFgB0XETkkXADcD44CrIuI+SRcDXWlZ4SuBr0nqBh4nSzqkeivIksVO4Px0hxfAB4BiSlLrgb71Wj4DrJB0HtADvLOqn4BZg5g2LXv0pFK52Wig7IJgkArSz4E/iIjH0/bhwH9ExKtGIL6aam1tja6urnqHYQbs/lwjQHMzLFni5xqtsUhaExGt5eXVXKH8A9m68rcBIrttd7eBcDPbO31Jo6Mj6+aaNi27wcvJxEaLIa9Q4IVB7hPSZt9YxqjnKxQzs+Eb6AplsCWAfye9H0+2uNaW9DoqlZmZmb1gsC6vi4B24AsV9gVwck0iMjOzUWmwJYDTNKecGhG/Kd0n6YCaRmVmZqNONQ82/neVZWZmNoYNeIUi6X+RzZ91oKTXkN3hBXAwL87wa2ZmBgw+hjIXOJds2pMvlpQ/DXyshjGZmdkoNNgYyjJgmaT/ExHfGMGYzMxsFBqsy+vsiLgaKEi6qHx/RHyxwmFmZjZGDdbl1bfOyEtGIhAzMxvdBuvy+tf0/ncjF46ZmY1W1Uxf/1lJB0uaIOkWSdsknT0SwZlZgygWoVCApqbsvVisd0TWgKp5DuWUiHgKOB3YCBwDfLiWQZlZA+mbBrmnByKy9/Z2JxXbTTUJpa9b7O3A1yPiyRrGY2aNpqOj/5z6kG17KUkrU8309d+W9ADwLPA+SS3Ab4Y4xsz2FV5K0qo05BVKRCwC3gi0RsRvgWeA+dU0LmmepHWSuiXttoZKWuL3urR/taRCyb7FqXydpLkl5Rsl3SPpbkldJeWflLQ1ld8t6bRqYjSzIQy0ZKSXkrQy1QzKTwDOBq6TtBI4D3isiuPGAZcBpwKzgIWSZpVVOw/YHhHHAJcCl6RjZ5EtB3wcMA+4PLXX5y0RMbvCfPyXpvLZEXHjUDGaWRU6OylOOJcCG2hiFwU2UJxwbrb6l1mJasZQrgBeC1yeXsensqGcCHRHxPqI2AEsZ/crm/nAsvR5JTBHklL58oh4LiI2AN2pPTMbYUXaaNdSeigQNNFDgXYtpYiXkrT+qkkoJ0TEORFxa3q9hxdXbxzMFGBzyfaWVFaxTkTsBJ4EJg1xbADfk7RGUjv9XSDp55KuknRYFTGa2RA6OqB3R//h1t4d4z0mb7upJqHskvTyvg1JLwN21S6kIZ0UEceTdaWdL+nNqfwK4OXAbOAhKi8MhqR2SV2SurZt2zYS8ZqNah6Tt2pVk1A+DNwm6T8k/RC4FfhQFcdtBY4u2Z6ayirWkTQeOIRsfGbAYyOi7/1R4HpSV1hEPBIRuyLieWApA3SRRcSSiGiNiNaWlpYqTsNsbPOYvFWrmru8bgFmAn8JfAA4NiJuq6LtO4CZkmZI2o9skH1VWZ1VwDnp81nArRERqXxBugtsRvr+2yVNlHQQgKSJwCnAvWl7ckm77+grN7O909kJzWUrIDU3e0zedjfkcyhpud/3AyeRjV/8SNK/lC8LXC4idkq6ALgZGAdcFRH3SboY6IqIVcCVwNckdQOPkyUdUr0VwFpgJ3B+ROySdCRwfTZuz3jgmoi4KX3lZyXNTjFuBN47jJ+DmQ2gLY29d3Rk3VzTpmXJpM1j8lZG2QXBIBWyX+xPA1enoj8BDo2IP65xbDXX2toaXV1dQ1c0M7MXSFpT4bGNqp6Uf2VElD4/cpuktfmFZmZm+4JqBuXvlPT6vg1JrwP8Z72ZmfVTzRXKa4H/ltR3k+A0YJ2ke4CIiFfVLDozMxs1qkko82oehZmZjXpDJpSI6BmJQMzMbHSrZgzFzMxsSE4oZmaWCycUMzPLhROKmZnlwgnFzMxy4YRiZma5cEIxM7NcOKGYmVkunFDMzCwXTihmNnoVi1AoQFNT9l4s1juiMa2aubzMzBpPsQjt7dDbm2339GTb4NW/6sRXKGY2OnV0vJhM+vT2ZuVWFzVNKJLmSVonqVvSogr795d0Xdq/WlKhZN/iVL5O0tyS8o2S7pF0t6SukvLDJX1f0i/S+2G1PDczq7NNm4ZXbjVXs4QiaRxwGXAqMAtYKGlWWbXzgO0RcQxwKXBJOnYW2fryx5FNn395aq/PWyJidtkSlIuAWyJiJnBL2jazfdW0aRRZSIENNLGLAhsosjBb9N7qopZXKCcC3RGxPiJ2AMuB+WV15gPL0ueVwBxJSuXLI+K5iNgAdKf2BlPa1jLgzL0/BTNrVMXTrqadpfRQIGiihwLtLKV42tX1Dm3MqmVCmQJsLtneksoq1omIncCTwKQhjg3ge5LWSGovqXNkRDyUPj8MHFkpKEntkrokdW3btm34Z2VmDaHjxpPoZWK/sl4m0nHjSXWKyEbjoPxJEXE8WVfa+ZLeXF4hIoIs8ewmIpZERGtEtLa0tNQ4VDOrFQ+hNJ5aJpStwNEl21NTWcU6ksYDhwCPDXZsRPS9Pwpcz4tdYY9Impzamgw8muO5mFmDGWioxEMo9VPLhHIHMFPSDEn7kQ2yryqrswo4J30+C7g1XV2sAhaku8BmADOB2yVNlHQQgKSJwCnAvRXaOge4oUbnZWYNoLMTmpv7lzU3Z+VWHzV7sDEidkq6ALgZGAdcFRH3SboY6IqIVcCVwNckdQOPkyUdUr0VwFpgJ3B+ROySdCRwfTZuz3jgmoi4KX3lZ4AVks4DeoB31urczKz++p5d7OjIurmmTcuSiZ9prB9lFwRjU2tra3R1dQ1d0czMXiBpTdljG8DoHJQ3M7MG5IRiZma5cEIxM7NcOKGYmVkunFDMzCwXTihmZpYLJxQzM8uFE4qZmeXCCcXMzHLhhGJmZrlwQjEzs1w4oZiZ5a1YhEIBmpqy92Kx3hGNiJrNNmxmNiYVi9DeDr292XZPT7YN+/xUyL5CMTPLU0fHi8mkT29vVr6Pc0IxM8vTGF6b2AnFzCxP06ZRZCEFNtDELgpsoMjCMbE2cU0TiqR5ktZJ6pa0qML+/SVdl/avllQo2bc4la+TNLfsuHGS7pL07ZKyr0raIOnu9Jpdy3MzM6ukeNrVtLOUHgoETfRQoJ2lFE+7ut6h1VzNEoqkccBlwKnALGChpFll1c4DtkfEMcClwCXp2FlkywEfB8wDLk/t9fkgcH+Fr/1wRMxOr7vzPB8zs2p03HgSvUzsV9bLRDpuPKlOEY2cWl6hnAh0R8T6iNgBLAfml9WZDyxLn1cCc5QtGD8fWB4Rz0XEBqA7tYekqcDbgS/XMHYzsz0yhodQappQpgCbS7a3pLKKdSJiJ/AkMGmIY78EfAR4vsJ3dkr6uaRLJe1fKShJ7ZK6JHVt27ZteGdkZjaEgYZKxsAQyugalJd0OvBoRKypsHsx8DvACcDhwEcrtRERSyKiNSJaW1paahesmY1JnZ3Q3Ny/rLk5K9/X1TKhbAWOLtmemsoq1pE0HjgEeGyQY98EnCFpI1kX2smSrgaIiIci8xzwFVIXmZnZSGprgyVLYPp0kLL3JUv2+WcagdomlDuAmZJmSNqPbJB9VVmdVcA56fNZwK0REal8QboLbAYwE7g9IhZHxNSIKKT2bo2IswEkTU7vAs4E7q3huZmZDaitDTZuhOefz97HQjKBGk69EhE7JV0A3AyMA66KiPskXQx0RcQq4Erga5K6gcfJkgSp3gpgLbATOD8idg3xlUVJLYCAu4G/qMV5mZlZZcouCMam1tbW6OrqqncYZmajiqQ1EdFaXj6qBuXNzKxxOaGYmVkunFDMzCwXTihmZpYLJxQzM8uFE4qZmeXCCcXMzHLhhGJmZrlwQjEzs1w4oZiZWS6cUMzMLBdOKGZmlgsnFDOzMaT4/h9TGL+FJj1PYfwWiu//cW5t12z6ejMzayzF9/+Y9iteQy8TAejZNZX2Kw4Dfkzb5Sftdfu+QjEzGyM6lhReSCZ9eplIx5JCLu07oZiZjRGbdh01rPLhqmlCkTRP0jpJ3ZIWVdi/v6Tr0v7Vkgol+xan8nWS5pYdN07SXZK+XVI2I7XRndrcr5bnZmY22kwb9+CwyoerZglF0jjgMuBUYBawUNKssmrnAdsj4hjgUuCSdOwssuWAjwPmAZen9vp8ELi/rK1LgEtTW9tT22ZmlnS2b6SZZ/qVNfMMne0bc2m/llcoJwLdEbE+InYAy4H5ZXXmA8vS55XAHElK5csj4rmI2AB0p/aQNBV4O/DlvkbSMSenNkhtnlmLkzIzG63aLj+JJe+7i+njtiCeZ/q4LSx53125DMhDbe/ymgJsLtneArxuoDoRsVPSk8CkVP7TsmOnpM9fAj4CHFSyfxLwRETsrFC/H0ntQDvAtGnThnVCZmajXdvlJ9F2ed/W1PTKx6galJd0OvBoRKzZ0zYiYklEtEZEa0tLS47RmZmNbbVMKFuBo0u2p6ayinUkjQcOAR4b5Ng3AWdI2kjWhXaypKvTMYemNgb6LjMzq6FaJpQ7gJnp7qv9yAbZV5XVWQWckz6fBdwaEZHKF6S7wGYAM4HbI2JxREyNiEJq79aIODsdc1tqg9TmDTU8NzMzK1OzhJLGMy4Abia7I2tFRNwn6WJJZ6RqVwKTJHUDFwGL0rH3ASuAtcBNwPkRsWuIr/wocFFqa1Jq28zMRoiyP+7HptbW1ujq6qp3GGZmo4qkNRHRulv5WE4okrYBPXt4+BHAr3IMJy+Oa3gc1/A4ruFp1Lhg72KbHhG73dU0phPK3pDUVSlD15vjGh7HNTyOa3gaNS6oTWyj6rZhMzNrXE4oZmaWCyeUPbek3gEMwHENj+MaHsc1PI0aF9QgNo+hmJlZLnyFYmZmuXBCMTOzXDih7IGhFg6rB0lXSXpU0r31jqWUpKMl3SZpraT7JH2w3jEBSDpA0u2Sfpbi+rt6x1Sq0iJy9SZpo6R7JN0tqWGeCJZ0qKSVkh6QdL+kNzRATMemn1Pf6ylJF9Y7LgBJf5X+zd8r6VpJB+TWtsdQhict9PU/wNvIpsm/A1gYEWvrHNebgV8D/xYRr6xnLKUkTQYmR8Sdkg4C1gBnNsDPS8DEiPi1pAnAj4EPRsRPhzh0REi6CGgFDo6I0+sdD2QJBWiNiIZ6UE/SMuBHEfHlNG9gc0Q8UeewXpB+Z2wFXhcRe/ogdV6xTCH7tz4rIp6VtAK4MSK+mkf7vkIZvmoWDhtxEfGfwOP1jqNcRDwUEXemz0+TzetWca2akRSZX6fNCenVEH9dVVpEziqTdAjwZtLcfRGxo5GSSTIH+GW9k0mJ8cCBaXb2ZiCf9X9xQtkTlRYOq/svyNFAUgF4DbC6zqEAL3Qr3Q08Cnw/IhoiLl5cRO75OsdRLoDvSVqTFqprBDOAbcBXUhfhlyVNrHdQZRYA19Y7CICI2Ap8HtgEPAQ8GRHfy6t9JxQbEZJeAnwDuDAinqp3PAARsSsiZpOtn3OipLp3FeaxiFwNnRQRxwOnAuenbtZ6Gw8cD1wREa8BniHNWt4IUhfcGcDX6x0LgKTDyHpUZgBHARMlnZ1X+04ow1fNwmFWIo1RfAMoRsQ36x1PudRFchswr86hwMCLyNVd+uuWiHgUuJ6s+7fetgBbSq4uV5IlmEZxKnBnRDxS70CStwIbImJbRPwW+Cbwxrwad0IZvmoWDrMkDX5fCdwfEV+sdzx9JLVIOjR9PpDsJosH6hoUMNAicnUOC0kT000VpC6lU4C631EYEQ8DmyUdm4rmkK2j1CgW0iDdXckm4PWSmtP/m3PIxjVzMX7oKlYqInZK6ls4bBxwVVoQrK4kXQv8AXCEpC3AJyKiERYZexPwbuCeNF4B8LGIuLF+IQEwGViW7sBpIlsArmFu0W1ARwLXZ7+DGA9cExE31TekF3wAKKY/8NYD76lzPMALifdtwHvrHUufiFgtaSVwJ7ATuIscp2DxbcNmZpYLd3mZmVkunFDMzCwXTihmZpYLJxQzM8uFE4qZmeXCCcWsTiRdKKm53nGY5cW3DZvVSaPO3mu2p3yFYjYC0pPm30nrr9wr6RNkcyndJum2VOcUST+RdKekr6f5z/rWIflsWovkdknHpPI/Tm39TNJ/1u/szDJOKGYjYx7wYES8Oq1X8yWyacPfEhFvkXQE8HHgrWkCxi7gopLjn4yI3wP+OR0L8LfA3Ih4NdkEhGZ15YRiNjLuAd4m6RJJvx8RT5btfz0wC/ivNEXNOcD0kv3Xlrz3rUj4X8BXJf052TRAZnXlubzMRkBE/I+k44HTgE9JuqWsisjWZFk4UBPlnyPiLyS9jmwxrjWSXhsRj+Udu1m1fIViNgIkHQX0RsTVwOfIplh/GjgoVfkp8KaS8ZGJkl5R0sS7St5/kuq8PCJWR8Tfki0yVbqsgtmI8xWK2cj4PeBzkp4Hfgu8j6zr6iZJD6ZxlHOBayXtn475OPA/6fNhkn4OPEc2JTqpvZlkVze3AD8bmVMxq8y3DZs1ON9ebKOFu7zMzCwXvkIxM7Nc+ArFzMxy4YRiZma5cEIxM7NcOKGYmVkunFDMzCwX/x9A9mkOVQJmaAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "steps=np.array([0,1,2,3,4,5,6,7,8])\n",
+ "pos=np.array([.2032,.1921,.1782,.1668,.1537,.1419,.1285,.1170,.1038])\n",
+ "pos_mm = 1000*pos/25.4\n",
+ "steps_dec=np.array([8,7,6,5,4,3,2,1,0])\n",
+ "pos_dec=np.array([.1038,.1150,.1266,.1396,.1512,.1642,.1759,.1892,.2009])\n",
+ "pos_dec_mm = 1000*pos_dec/25.4\n",
+ "\n",
+ "plt.title('position vs steps (1/32)')\n",
+ "plt.xlabel('steps')\n",
+ "plt.ylabel('position (mm)')\n",
+ "plt.plot(steps,pos_mm,'or')\n",
+ "plt.plot(steps_dec,pos_dec_mm,'ob')\n",
+ "\n",
+ "plt.savefig('z_mar08.svg')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a85d3e67",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Urumbu_short_z/jupyter_notebooks/z_axis_Mar11.ipynb b/Urumbu_short_z/jupyter_notebooks/z_axis_Mar11.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..742ef7fe2453459e5a37c6b553d66329ebca69ff
--- /dev/null
+++ b/Urumbu_short_z/jupyter_notebooks/z_axis_Mar11.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "34236bd4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "steps=np.array([0,1,2,3,4,5,6,7,8])\n",
+ "pos=np.array([.4636,.4510,.4391,.4261,.4152,.4026,.3910,.3779,.3669])\n",
+ "pos_mm = 1000*pos/25.4\n",
+ "steps_dec=np.array([8,7,6,5,4,3,2,1,0])\n",
+ "pos_dec=np.array([.3669,.3731,.3855,.3972,.4097,.4209,.4335,.4457,.4586])\n",
+ "pos_dec_mm = 1000*pos_dec/25.4\n",
+ "\n",
+ "plt.title('position vs steps (1/32)')\n",
+ "plt.xlabel('steps')\n",
+ "plt.ylabel('position (mm)')\n",
+ "plt.plot(steps,pos_mm,'or')\n",
+ "plt.plot(steps_dec,pos_dec_mm,'ob')\n",
+ "\n",
+ "plt.savefig('z_mar11.svg')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a85d3e67",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}