Commit 4e99b883 authored by NUNEZ Arturo's avatar NUNEZ Arturo

Automatic commit mercredi 1 août 2018, 16:30:02 (UTC+0200)

parent 3b98370c
This source diff could not be displayed because it is too large. You can view the blob instead.
This source diff could not be displayed because it is too large. You can view the blob instead.
This source diff could not be displayed because it is too large. You can view the blob instead.
This source diff could not be displayed because it is too large. You can view the blob instead.
This source diff could not be displayed because it is too large. You can view the blob instead.
......@@ -64,7 +64,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {
"collapsed": false,
"hide_input": false,
......@@ -77,12 +77,22 @@
"text": [
"loading Dark matter..\n",
"loading Stars..\n",
"loading Gas..\n",
"| r_200 = 212.70\n",
"| Diagonal matrix computed \n",
"| | 20, 0, 0|\n",
"| D =| 0, 14, 0|\n",
"| | 0, 0, 4|\n"
"loading Gas..\n"
]
},
{
"ename": "LinAlgError",
"evalue": "Array must not contain infs or NaNs",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-f763ddbd9374>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mmyhydro\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwkbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGalaxy_Hound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhydro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmyhydro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcenter_shift\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhydro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_dm_com\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmyhydro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr_virial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/arturo/Documents/git/WKBL/wkbl/astro/galaxy_peeker.pyc\u001b[0m in \u001b[0;36mr_virial\u001b[0;34m(self, r_max, r_min, rotate, n, bins, quiet)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrotate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;32mand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotate_galaxy\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 85\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mredefine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_T\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_P\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_T\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/home/arturo/Documents/git/WKBL/wkbl/astro/galaxy_peeker.pyc\u001b[0m in \u001b[0;36mrotate_galaxy\u001b[0;34m(self, rmin, rmax)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0msecond\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos_ring\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos_ring\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\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 121\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirst\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0meigen_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mevecs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0morder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meigen_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36meig\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[0m_assertRankAtLeast2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1142\u001b[0m \u001b[0m_assertNdSquareness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1143\u001b[0;31m \u001b[0m_assertFinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1144\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_commonType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36m_assertFinite\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\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--> 216\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Array must not contain infs or NaNs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isEmpty2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mLinAlgError\u001b[0m: Array must not contain infs or NaNs"
]
}
],
......@@ -90,25 +100,16 @@
"hydro = wkbl.astro.halo_info.AdicoraHydro() \n",
"myhydro = wkbl.Galaxy_Hound(hydro.path)\n",
"myhydro.center_shift(hydro.c_dm_com)\n",
"myhydro.r_virial(600,n=25)\n"
"myhydro.r_virial(600,n=2.5)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/data/OWN/SF1test/SF0/mstar1_T3600/output_00041\n",
"[20418.88714 17567.72033 17124.40448]\n"
]
}
],
"outputs": [],
"source": [
"print hydro.path\n",
"print hydro.c_dm_com"
......@@ -116,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {
"collapsed": true
},
......@@ -132,31 +133,12 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8.215e+11\n",
"1.175e+11\n"
]
},
{
"data": {
"text/plain": [
"0.281102"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"print \"{0:.3e}\".format(np.sum(myhydro.dm.mass[myhydro.dm.r<myhydro.r200]))\n",
"print \"{0:.3e}\".format(np.sum(myhydro.st.mass[myhydro.st.r<myhydro.r200]))\n",
......@@ -5203,7 +5185,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 5,
"metadata": {
"collapsed": true
},
......@@ -5214,7 +5196,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 6,
"metadata": {
"collapsed": false,
"hide_input": false
......@@ -5245,7 +5227,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 7,
"metadata": {
"collapsed": false
},
......@@ -5256,7 +5238,7 @@
"0.308899998664856"
]
},
"execution_count": 14,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
......@@ -5267,7 +5249,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 10,
"metadata": {
"collapsed": false
},
......@@ -5275,21 +5257,21 @@
{
"data": {
"text/plain": [
"275.9765625"
"205.6640625"
]
},
"execution_count": 15,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myDMO.r97"
"myDMO.r200"
]
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 9,
"metadata": {
"collapsed": false,
"hide_input": false
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {
"collapsed": true
},
......@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {
"collapsed": true
},
......@@ -64,7 +64,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {
"collapsed": true
},
......@@ -74,6 +74,28 @@
"hydro = wkbl.astro.halo_info.AdicoraHydro()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/data/OWN/Adicora/SF0/Stable/output_00041'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hydro.path"
]
},
{
"cell_type": "code",
"execution_count": 5,
......@@ -88,11 +110,11 @@
"loading Dark matter..\n",
"loading Stars..\n",
"loading Gas..\n",
"36844.594\n",
"| r_200 = 212.70\n",
"36839.426\n",
"| r_200 = 211.52\n",
"| Diagonal matrix computed \n",
"| | 20, 0, 0|\n",
"| D =| 0, 14, 0|\n",
"| | 19, 0, 0|\n",
"| D =| 0, 15, 0|\n",
"| | 0, 0, 4|\n"
]
}
......@@ -102,9 +124,9 @@
"simname=hydro.name\n",
"myhydro = wkbl.Galaxy_Hound(hydro.path)\n",
"print myhydro.dm.pos3d[:,0].max()\n",
"#zoom_reg = np.where(myhydro.dm.mass==myhydro.dm.mass.min())\n",
"#nucenter = nbe.real_center(myhydro.dm.pos3d[zoom_reg], myhydro.dm.mass[zoom_reg])\n",
"myhydro.center_shift(hydro.c_dm_com)\n",
"zoom_reg = np.where(myhydro.dm.mass==myhydro.dm.mass.min())\n",
"nucenter = nbe.real_center(myhydro.dm.pos3d[zoom_reg], myhydro.dm.mass[zoom_reg])\n",
"myhydro.center_shift(nucenter)\n",
"myhydro.r_virial(600,n=2.3)\n",
"\n"
]
......@@ -135,7 +157,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"9508326 9508326\n"
"7679691 7679691\n"
]
}
],
......@@ -162,7 +184,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 11,
"metadata": {
"collapsed": true
},
......@@ -171,17 +193,26 @@
"get_mat = np.vectorize(nbe.m_matrix_for_r)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
"collapsed": false
},
"outputs": [],
"source": [
"r = np.linspace(myhydro.gs.hsml.min(),2*myhydro.r200,300)\n",
"M_dm = np.array([nbe.m_matrix_for_r(myhydro,'halo',i) for i in r])\n",
"M_st = np.array([nbe.m_matrix_for_r(myhydro,'stars',i) for i in r])\n",
"M_dm = np.array([nbe.m_matrix_for_r(myhydro,'halo',i)[0] for i in r])\n",
"M_st = np.array([nbe.m_matrix_for_r(myhydro,'stars',i)[0] for i in r])\n",
"a_dm, b_dm, c_dm = np.sqrt(M_dm[:,0,0]), np.sqrt(M_dm[:,1,1]), np.sqrt(M_dm[:,2,2])\n",
"a_st, b_st, c_st = np.sqrt(M_st[:,0,0]), np.sqrt(M_st[:,1,1]), np.sqrt(M_st[:,2,2])\n",
"S_dm = c_dm/a_dm\n",
......@@ -211,7 +242,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 12,
"metadata": {
"collapsed": false
},
......@@ -983,7 +1014,7 @@
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\">"
"<img src=\"data:image/png;base64,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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
......@@ -1032,7 +1063,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {
"collapsed": true
},
......@@ -1047,7 +1078,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {
"collapsed": true
},
......@@ -1066,7 +1097,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {
"collapsed": true
},
......@@ -1077,7 +1108,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {
"collapsed": true
},
......@@ -1096,788 +1127,11 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",