Fixed allowed range for g-h filter in interactive

01-g-h-filter.ipynb

@@ -28,6 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,9 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
        "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-size: 115%;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -329,7 +330,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x462c438>"
+       "<matplotlib.figure.Figure at 0x443c828>"
       ]
      },
      "metadata": {},
@@ -363,7 +364,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6eac080>"
+       "<matplotlib.figure.Figure at 0x74dc860>"
       ]
      },
      "metadata": {},
@@ -394,7 +395,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7453dd8>"
+       "<matplotlib.figure.Figure at 0x7ae0e80>"
       ]
      },
      "metadata": {},
@@ -429,7 +430,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9799\n"
+      "Average of measurements is 164.9700\n"
      ]
     }
    ],
@@ -460,7 +461,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9642\n"
+      "Average of measurements is 165.0224\n"
      ]
     }
    ],
@@ -494,7 +495,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74cae10>"
+       "<matplotlib.figure.Figure at 0x7b6e5f8>"
       ]
      },
      "metadata": {},
@@ -525,7 +526,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7554e48>"
+       "<matplotlib.figure.Figure at 0x7bf79e8>"
       ]
      },
      "metadata": {},
@@ -554,7 +555,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75bc780>"
+       "<matplotlib.figure.Figure at 0x7c6b208>"
       ]
      },
      "metadata": {},
@@ -583,9 +584,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7634e10>"
+       "<matplotlib.figure.Figure at 0x7ce1710>"
       ]
      },
      "metadata": {},
@@ -615,9 +616,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76b20b8>"
+       "<matplotlib.figure.Figure at 0x7d57630>"
       ]
      },
      "metadata": {},
@@ -652,7 +653,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7621c18>"
+       "<matplotlib.figure.Figure at 0x7cde7b8>"
       ]
      },
      "metadata": {},
@@ -681,7 +682,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e8ae10>"
+       "<matplotlib.figure.Figure at 0x7c0d588>"
       ]
      },
      "metadata": {},
@@ -724,7 +725,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76d8eb8>"
+       "<matplotlib.figure.Figure at 0x74d6b00>"
       ]
      },
      "metadata": {},
@@ -781,7 +782,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7429668>"
+       "<matplotlib.figure.Figure at 0x7c17ac8>"
       ]
      },
      "metadata": {},
@@ -845,7 +846,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7501f28>"
+       "<matplotlib.figure.Figure at 0x7b1ada0>"
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      },
      "metadata": {},
@@ -887,7 +888,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7519be0>"
+       "<matplotlib.figure.Figure at 0x7ab5d30>"
       ]
      },
      "metadata": {},
@@ -980,7 +981,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74ed588>"
+       "<matplotlib.figure.Figure at 0x7a99588>"
       ]
      },
      "metadata": {},
@@ -1389,7 +1390,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x745f4a8>"
+       "<matplotlib.figure.Figure at 0x7afca20>"
       ]
      },
      "metadata": {},
@@ -1483,9 +1484,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7506d30>"
+       "<matplotlib.figure.Figure at 0x7c05a20>"
       ]
      },
      "metadata": {},
@@ -1520,9 +1521,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7502cc0>"
+       "<matplotlib.figure.Figure at 0x7abbb70>"
       ]
      },
      "metadata": {},
@@ -1574,9 +1575,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x751b7f0>"
+       "<matplotlib.figure.Figure at 0x74d1cf8>"
       ]
      },
      "metadata": {},
@@ -1637,9 +1638,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x763a2e8>"
+       "<matplotlib.figure.Figure at 0x8ea5320>"
       ]
      },
      "metadata": {},
@@ -1704,7 +1705,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89670f0>"
+       "<matplotlib.figure.Figure at 0x900bc18>"
       ]
      },
      "metadata": {},
@@ -1782,7 +1783,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89d19e8>"
+       "<matplotlib.figure.Figure at 0x9058358>"
       ]
      },
      "metadata": {},
@@ -1824,7 +1825,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a1f0f0>"
+       "<matplotlib.figure.Figure at 0x90ca828>"
       ]
      },
      "metadata": {},
@@ -1869,7 +1870,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7597780>"
+       "<matplotlib.figure.Figure at 0x9074080>"
       ]
      },
      "metadata": {},
@@ -1916,7 +1917,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x761b6a0>"
+       "<matplotlib.figure.Figure at 0x7cef668>"
       ]
      },
      "metadata": {},
@@ -1964,7 +1965,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAEWCAYAAADrZBh7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e75470>"
+       "<matplotlib.figure.Figure at 0x7d5fd30>"
       ]
      },
      "metadata": {},
@@ -1973,7 +1974,7 @@
    ],
    "source": [
     "from IPython.html.widgets import interact, interactive, fixed\n",
-    "from IPython.html.widgets import FloatSliderWidget\n",
+    "from IPython.html.widgets import FloatSlider\n",
     "\n",
     "zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
     "\n",
@@ -1985,10 +1986,10 @@
     "    plt.show()\n",
     "\n",
     "interact (interactive_gh,           \n",
-    "          x=FloatSliderWidget(value=0., min=-50, max=50.), \n",
-    "          dx=FloatSliderWidget(value=5., min=-50., max=50.), \n",
-    "          g=FloatSliderWidget(value=0.1, min=0.01, max=2, step=.02), \n",
-    "          h=FloatSliderWidget(value=0.02, min=0.0, max=0.5, step=0.01));"
+    "          x=FloatSlider(value=0., min=-50, max=50.), \n",
+    "          dx=FloatSlider(value=5., min=-50., max=50.), \n",
+    "          g=FloatSlider(value=0.1, min=0.01, max=2, step=.02), \n",
+    "          h=FloatSlider(value=0.02, min=0.0, max=0.5, step=0.01));"
    ]
   },
   {
@@ -2011,7 +2012,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8965d68>"
+       "<matplotlib.figure.Figure at 0x7cb2978>"
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      },
      "metadata": {},
@@ -2047,7 +2048,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89efe48>"
+       "<matplotlib.figure.Figure at 0x74dd080>"
       ]
      },
      "metadata": {},
@@ -2113,7 +2114,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x741b2e8>"
+       "<matplotlib.figure.Figure at 0x7bed9b0>"
       ]
      },
      "metadata": {},
@@ -2164,7 +2165,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8970dd8>"
+       "<matplotlib.figure.Figure at 0x7d0fef0>"
       ]
      },
      "metadata": {},
@@ -2195,7 +2196,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x898eb00>"
+       "<matplotlib.figure.Figure at 0x90e8550>"
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      "metadata": {},
@@ -2228,7 +2229,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87e8630>"
+       "<matplotlib.figure.Figure at 0x8ec2860>"
       ]
      },
      "metadata": {},
@@ -2278,7 +2279,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8808358>"
+       "<matplotlib.figure.Figure at 0x90ed358>"
       ]
      },
      "metadata": {},
@@ -2309,7 +2310,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74f7710>"
+       "<matplotlib.figure.Figure at 0x8e9f828>"
       ]
      },
      "metadata": {},
@@ -2431,7 +2432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -2440,7 +2441,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(2.136, 0.339)\n"
+      "(1.965, 0.375)\n"
      ]
     }
    ],
@@ -2457,7 +2458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false
    },
@@ -2466,10 +2467,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 2.136   0.339 ]\n",
-      " [ 2.895   0.444 ]\n",
-      " [ 3.8678  0.5762]\n",
-      " [ 4.8888  0.6874]]\n"
+      "[[ 1.965   0.375 ]\n",
+      " [ 2.868   0.507 ]\n",
+      " [ 3.875   0.632 ]\n",
+      " [ 4.9014  0.7306]]\n"
      ]
     }
    ],
@@ -2486,7 +2487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
@@ -2495,8 +2496,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " x = [   3.8    13.2   101.64]\n",
-      "dx = [ 8.2   9.8   0.56]\n"
+      " x = [[ 1.  2.]\n",
+      " [ 3.  4.]]\n",
+      "dx = [ 10.   12.    0.2]\n"
      ]
     }
    ],
@@ -2507,7 +2509,7 @@
     "f_air = GHFilter (x=x_0, dx=dx_0, dt=1., g=.8, h=.2)\n",
     "f_air.update(z=np.array((2., 11., 102.)))\n",
     "print(' x =', x)\n",
-    "print('dx =', dx)"
+    "print('dx =', dx_0)"
    ]
   },
   {