Python 2.7 compatibility.

list does not have copy in 2.7, so I created a new tuple where
I was using list.copy().

10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d27738d4ebd41dd3efed105a08061fdfd276fd46d733ea481d2547b3fae667b0"
+  "signature": "sha256:91d07763ffcce70ef7d6684c1c101ac01415d586c71de3856e92f1be5f1ed905"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f59ed6af128>"
+        "<IPython.core.display.HTML at 0x7fc3f0500fd0>"
        ]
       }
      ],
@@ -309,9 +309,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAGGCAYAAAD8e/dDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d1249208>"
+        "<matplotlib.figure.Figure at 0x7fc3f052ad10>"
        ]
       }
      ],
@@ -347,9 +347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d1223128>"
+        "<matplotlib.figure.Figure at 0x7fc3f0519cd0>"
        ]
       }
      ],
@@ -396,7 +396,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d121cc88>"
+        "<matplotlib.figure.Figure at 0x7fc40a478450>"
        ]
       }
      ],
@@ -494,16 +494,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc9ef940>"
+        "<matplotlib.figure.Figure at 0x7fc40a478450>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Difference in mean x=0.089, y=43.503\n"
+        "Difference in mean x=-0.021, y=43.404\n"
        ]
       }
      ],
@@ -542,7 +542,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc779780>"
+        "<matplotlib.figure.Figure at 0x7fc3e8458ed0>"
        ]
       }
      ],
@@ -582,7 +582,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc997828>"
+        "<matplotlib.figure.Figure at 0x7fc3f0409d90>"
        ]
       }
      ],
@@ -637,7 +637,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc918240>"
+        "<matplotlib.figure.Figure at 0x7fc3f039a290>"
        ]
       }
      ],
@@ -978,9 +978,9 @@
       {
        "metadata": {},
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc9bc240>"
+        "<matplotlib.figure.Figure at 0x7fc3efc20150>"
        ]
       }
      ],
@@ -1010,9 +1010,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc987be0>"
+        "<matplotlib.figure.Figure at 0x7fc3f0261810>"
        ]
       }
      ],
@@ -1059,7 +1059,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0c870f0>"
+        "<matplotlib.figure.Figure at 0x7fc3f026b750>"
        ]
       }
      ],
@@ -1146,7 +1146,7 @@
       "    for i in range(300):\n",
       "        target_pos[0] += 1 + randn()*0.0001\n",
       "        target_pos[1] += 1 + randn()*0.0001\n",
-      "        txs.append(target_pos.copy())\n",
+      "        txs.append((target_pos[0], target_pos[1]))\n",
       "\n",
       "        z = measurement(sa_pos, sb_pos, target_pos)\n",
       "        z[0] += randn() * std_noise\n",
@@ -1182,7 +1182,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAqkAAAJ9CAYAAAAWiev+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc93f588>"
+        "<matplotlib.figure.Figure at 0x7fc3e8458310>"
        ]
       }
      ],
@@ -1214,7 +1214,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAJ9CAYAAAA2ZbQDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0c577b8>"
+        "<matplotlib.figure.Figure at 0x7fc3f0134990>"
        ]
       }
      ],
@@ -1267,7 +1267,7 @@
       "    for i in range(300):\n",
       "        target_pos[0] = math.cos(i/10)*radius + randn()*0.0001\n",
       "        target_pos[1] = math.sin(i/10)*radius + randn()*0.0001\n",
-      "        txs.append(target_pos.copy())\n",
+      "        txs.append((target_pos[0], target_pos[1]))\n",
       "\n",
       "        z = measurement(sa_pos, sb_pos, target_pos)\n",
       "        z[0] += randn() * std_noise\n",
@@ -1302,7 +1302,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAArQAAAJ2CAYAAACuHhFlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0cd53c8>"
+        "<matplotlib.figure.Figure at 0x7fc3e6b6e5d0>"
        ]
       }
      ],
@@ -1347,7 +1347,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc8b94a8>"
+        "<matplotlib.figure.Figure at 0x7fc3e6bd1490>"
        ]
       }
      ],
@@ -1405,7 +1405,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc890a20>"
+        "<matplotlib.figure.Figure at 0x7fc3f02be4d0>"
        ]
       }
      ],
@@ -1504,7 +1504,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc8abac8>"
+        "<matplotlib.figure.Figure at 0x7fc3e6bb2350>"
        ]
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@@ -1563,7 +1563,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0c2df60>"
+        "<matplotlib.figure.Figure at 0x7fc3efc77790>"
        ]
       }
      ],
@@ -1663,7 +1663,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc919dd8>"
+        "<matplotlib.figure.Figure at 0x7fc3f032d990>"
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       }
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@@ -1698,7 +1698,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc9ef668>"
+        "<matplotlib.figure.Figure at 0x7fc3f04c92d0>"
        ]
       }
      ],
@@ -2405,7 +2405,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc89dbe0>"
+        "<matplotlib.figure.Figure at 0x7fc3f02f5c10>"
        ]
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@@ -2523,7 +2523,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0cbdf28>"
+        "<matplotlib.figure.Figure at 0x7fc3f02b6b10>"
        ]
       }
      ],
@@ -2621,7 +2621,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f59d0ca8240>"
+        "<matplotlib.figure.Figure at 0x7fc3f02b6850>"
        ]
       }
      ],
@@ -2679,7 +2679,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f59cc720630>"
+        "<matplotlib.figure.Figure at 0x7fc3f01da2d0>"
        ]
       }
      ],