From 8e983313804ddf76b60565fc93c819833584bde8 Mon Sep 17 00:00:00 2001
From: Rafael Andrei Vinasco Soler <vinascor@jupyterMiLAB>
Date: Fri, 19 Feb 2021 13:01:32 -0500
Subject: [PATCH] limpieza de archivo

---
 solucion.ipynb | 404 ++++---------------------------------------------
 1 file changed, 26 insertions(+), 378 deletions(-)

diff --git a/solucion.ipynb b/solucion.ipynb
index 763f0d9..ce17254 100644
--- a/solucion.ipynb
+++ b/solucion.ipynb
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1542,
+   "execution_count": 1573,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -747,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1543,
+   "execution_count": 1574,
    "metadata": {
     "scrolled": false
    },
@@ -755,10 +755,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7feab8641b00>"
+       "<matplotlib.colorbar.Colorbar at 0x7feab77ccac8>"
       ]
      },
-     "execution_count": 1543,
+     "execution_count": 1574,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -794,16 +794,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1544,
+   "execution_count": 1575,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7feab850a4e0>"
+       "<matplotlib.colorbar.Colorbar at 0x7feab7504b00>"
       ]
      },
-     "execution_count": 1544,
+     "execution_count": 1575,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -842,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1545,
+   "execution_count": 1576,
    "metadata": {},
    "outputs": [
     {
@@ -864,7 +864,9 @@
     "plt.ylim(bb[3],bb[4]), plt.yticks([])\n",
     "plt.scatter(ui,uj,s=70, c = ulum, alpha=5)\n",
     "plt.colorbar()\n",
-    "plt.show()"
+    "plt.show()\n",
+    "\n",
+    "##NOTA, LA IMAGEN ESTA ROTADA"
    ]
   },
   {
@@ -877,22 +879,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1509,
+   "execution_count": 1568,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7feab9375240>"
+       "<matplotlib.colorbar.Colorbar at 0x7feab797a828>"
       ]
      },
-     "execution_count": 1509,
+     "execution_count": 1568,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -904,7 +906,7 @@
     }
    ],
    "source": [
-    "for x in range (2,3,1):\n",
+    "for x in range (2,3,1): ## cuando termine funcion de fitear hay si pongo x en el rango que es\n",
     "    for y in range (2,3,1):\n",
     "        AA_PART= img_part(x,y,imgttt_copy)\n",
     "        a=[0,0]\n",
@@ -924,25 +926,6 @@
     "### Funcion para analizar cada estrella"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_array = np.full([bb[2]- bb[1], bb[4]- bb[3] ], None)\n",
-    "ui=[]\n",
-    "uj=[]\n",
-    "ulum=[]\n",
-    "for i in range (bb[1],bb[2],1):\n",
-    "    for j in range (bb[3],bb[4],1):\n",
-    "        if AA_PART[i,j]==2:\n",
-    "            ui.append(i)\n",
-    "            uj.append(j)\n",
-    "            ulum.append( imgtt_copy[minn(x*part_elevation)+i,minn(y*part_wide)+j] )\n",
-    "            data_array[i-bb[1],j-bb[3]] = imgtt_copy[minn(x*part_elevation)+i,minn(y*part_wide)+j]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -952,17 +935,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1438,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "ui\n",
+    "uj\n",
+    "ulum"
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1209,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## :("
+   ]
   },
   {
    "cell_type": "code",
@@ -980,49 +967,6 @@
     "    return z\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = np.meshgrid(UI, )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1409,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "object too deep for desired array",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
-     ]
-    },
-    {
-     "ename": "error",
-     "evalue": "Result from function call is not a proper array of floats.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1409-eb088119fb9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minitial_guess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurve_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgauss2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mulum\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_guess\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~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mcurve_fit\u001b[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0;31m# Remove full_output from kwargs, otherwise we're passing it in twice.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0mreturn_full\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'full_output'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleastsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDfun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    785\u001b[0m         \u001b[0mpopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfodict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m         \u001b[0mysize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfodict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fvec'\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~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mleastsq\u001b[0;34m(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)\u001b[0m\n\u001b[1;32m    421\u001b[0m             \u001b[0mmaxfev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m         retval = _minpack._lmdif(func, x0, args, full_output, ftol, xtol,\n\u001b[0;32m--> 423\u001b[0;31m                                  gtol, maxfev, epsfcn, factor, diag)\n\u001b[0m\u001b[1;32m    424\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcol_deriv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31merror\u001b[0m: Result from function call is not a proper array of floats."
-     ]
-    }
-   ],
-   "source": [
-    "initial_guess = (3,2,3,2,2)\n",
-    "popt, pcov = opt.curve_fit(gauss2D, (x, y), ulum , p0=initial_guess)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1080,28 +1024,6 @@
     "    return z"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1352,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-1352-b5d4cad1a37c>, line 4)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1352-b5d4cad1a37c>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    plt.contourf(x,y,z)\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "xx,yy = np.meshgrid(ui,uj,sparse=True)\n",
-    "zz = np.sin(xx**2 + yy**2) / (xx**2 + yy**2\n",
-    "                             \n",
-    "plt.contourf(x,y,z)                    \n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1382,
@@ -1132,51 +1054,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1390,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-1390-1819500bda63>, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1390-1819500bda63>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    res, cov = curve_fit(gauss2D,  , po)\u001b[0m\n\u001b[0m                                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "po= [1.,1.,1.,1.,1.]\n",
-    "res, cov = curve_fit(gauss2D,  , po)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1397,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-1397-bd1f04f45f83>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1397-bd1f04f45f83>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def twoD_Gaussian((x, y), amplitude, xo, yo, sigma_x, sigma_y, theta, offset):\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "def twoD_Gaussian((x, y), amplitude, xo, yo, sigma_x, sigma_y, theta, offset):\n",
-    "    xo = float(xo)\n",
-    "    yo = float(yo)    \n",
-    "    a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2)\n",
-    "    b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2)\n",
-    "    c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_y**2)\n",
-    "    g = offset + amplitude*np.exp( - (a*((x-xo)**2) + 2*b*(x-xo)*(y-yo) \n",
-    "                            + c*((y-yo)**2)))\n",
-    "    return g.ravel()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1209,235 +1086,6 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1355,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[73, 74, 72, 73, 74, 75, 72, 73, 74, 75, 72, 73, 74, 75, 73, 74, 75]"
-      ]
-     },
-     "execution_count": 1355,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "uj"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1356,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 37, 38, 38, 38]"
-      ]
-     },
-     "execution_count": 1356,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ui"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1287,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "curve_fit?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1249,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "gauss2D() takes 7 positional arguments but 18 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1249-5e11c9aa2575>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurve_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgauss2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mulum\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~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mcurve_fit\u001b[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0;31m# Remove full_output from kwargs, otherwise we're passing it in twice.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0mreturn_full\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'full_output'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleastsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDfun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    785\u001b[0m         \u001b[0mpopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfodict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m         \u001b[0mysize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfodict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fvec'\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~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mleastsq\u001b[0;34m(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)\u001b[0m\n\u001b[1;32m    408\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\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[1;32m    409\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0margs\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--> 410\u001b[0;31m     \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'leastsq'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'func'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\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    411\u001b[0m     \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape\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[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36m_check_func\u001b[0;34m(checker, argname, thefunc, x0, args, numinputs, output_shape)\u001b[0m\n\u001b[1;32m     22\u001b[0m def _check_func(checker, argname, thefunc, x0, args, numinputs,\n\u001b[1;32m     23\u001b[0m                 output_shape=None):\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthefunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnuminputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\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[0m\u001b[1;32m     25\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0moutput_shape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0moutput_shape\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[1;32m     26\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0moutput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\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~/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mfunc_wrapped\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m    482\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\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--> 484\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mydata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    485\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\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;31mTypeError\u001b[0m: gauss2D() takes 7 positional arguments but 18 were given"
-     ]
-    }
-   ],
-   "source": [
-    "res, cov = curve_fit(gauss2D, ui, uj, ulum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1196,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "curve_fit?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1069,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7feadb23f828>"
-      ]
-     },
-     "execution_count": 1069,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for x in range (3,5,1):\n",
-    "    for y in range (3,5,1):\n",
-    "        AA_PART= img_part(x,y,imgttt_copy)\n",
-    "        a=[0,0]\n",
-    "        while a[0]>-1:\n",
-    "            a=tanteo(x,y,0,0)\n",
-    "            ubicar_estrella(x,y,a[0],a[1],AA_PART)\n",
-    "\n",
-    "plt.imshow(AA_PART, cmap = 'gray')\n",
-    "plt.title(\"parte blanco y negro\")\n",
-    "plt.colorbar() \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1073,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7feabfda1320>"
-      ]
-     },
-     "execution_count": 1073,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x560 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')\n",
-    "plt.imshow(imgttt_copy, cmap = 'gray')\n",
-    "plt.title(\"parte blanco y negro\")\n",
-    "plt.colorbar() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 1207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "uo=[]\n",
-    "uo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1204,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uo.append(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1205,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5]"
-      ]
-     },
-     "execution_count": 1205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "uo"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-- 
GitLab