Added scatterpoints=1 to legend where applicable.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3629e2fdae20e09b4bc3545d5894210b005707cdba2c96a26450fcac7a6df90e"
+  "signature": "sha256:baea7682f826fcbcca71a2693a7d43c4f2b9b361029d9bab12908795ea315c61"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f6d1ef1ac50>"
+        "<IPython.core.display.HTML at 0x7f12aedfe1d0>"
        ]
       }
      ],
@@ -323,7 +323,7 @@
       "plt.errorbar([170], [1.05], xerr=8, fmt='o', label='B', capthick=2, capsize=10)\n",
       "plt.ylim(0, 2)\n",
       "plt.xlim(145, 185)\n",
-      "plt.legend()\n",
+      "plt.legend(scatterpoints=3)\n",
       "plt.gca().axes.yaxis.set_ticks([])\n",
       "plt.show()"
      ],
@@ -335,7 +335,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06a4c160>"
+        "<matplotlib.figure.Figure at 0x7f1295f1b320>"
        ]
       }
      ],
@@ -372,7 +372,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGGCAYAAAANcKzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d067203c8>"
+        "<matplotlib.figure.Figure at 0x7f1295ed3a20>"
        ]
       }
      ],
@@ -406,7 +406,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06a4c1d0>"
+        "<matplotlib.figure.Figure at 0x7f1295f1b1d0>"
        ]
       }
      ],
@@ -448,7 +448,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 165.0019\n"
+        "Average of measurements is 164.9904\n"
        ]
       }
      ],
@@ -484,7 +484,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9496\n"
+        "Average of measurements is 164.9898\n"
        ]
       }
      ],
@@ -527,7 +527,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzsAAAGkCAYAAAAWgDuQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066a7198>"
+        "<matplotlib.figure.Figure at 0x7f1295ddda20>"
        ]
       }
      ],
@@ -563,7 +563,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06689240>"
+        "<matplotlib.figure.Figure at 0x7f1295da9e10>"
        ]
       }
      ],
@@ -598,7 +598,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0677d4a8>"
+        "<matplotlib.figure.Figure at 0x7f1295eca8d0>"
        ]
       }
      ],
@@ -636,7 +636,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066d03c8>"
+        "<matplotlib.figure.Figure at 0x7f1295f106d8>"
        ]
       }
      ],
@@ -674,7 +674,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0653e0b8>"
+        "<matplotlib.figure.Figure at 0x7f1295cb6b00>"
        ]
       }
      ],
@@ -713,7 +713,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066e2d68>"
+        "<matplotlib.figure.Figure at 0x7f1295de8358>"
        ]
       }
      ],
@@ -740,7 +740,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06430dd8>"
+        "<matplotlib.figure.Figure at 0x7f1295ba4a90>"
        ]
       }
      ],
@@ -780,7 +780,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0652d278>"
+        "<matplotlib.figure.Figure at 0x7f1295ca4048>"
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@@ -848,7 +848,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064437b8>"
+        "<matplotlib.figure.Figure at 0x7f1295ee3278>"
        ]
       }
      ],
@@ -877,7 +877,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06682dd8>"
+        "<matplotlib.figure.Figure at 0x7f1295d9e470>"
        ]
       }
      ],
@@ -946,9 +946,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0662dcf8>"
+        "<matplotlib.figure.Figure at 0x7f1295de53c8>"
        ]
       }
      ],
@@ -1012,7 +1012,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06769320>"
+        "<matplotlib.figure.Figure at 0x7f1295c923c8>"
        ]
       }
      ],
@@ -1149,9 +1149,9 @@
       {
        "metadata": {},
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066eab70>"
+        "<matplotlib.figure.Figure at 0x7f1295cfd400>"
        ]
       }
      ],
@@ -1228,9 +1228,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06623828>"
+        "<matplotlib.figure.Figure at 0x7f1295f418d0>"
        ]
       }
      ],
@@ -1284,9 +1284,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0650ba58>"
+        "<matplotlib.figure.Figure at 0x7f1295e3def0>"
        ]
       }
      ],
@@ -1347,9 +1347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064c3278>"
+        "<matplotlib.figure.Figure at 0x7f1295ce2470>"
        ]
       }
      ],
@@ -1423,7 +1423,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0651d1d0>"
+        "<matplotlib.figure.Figure at 0x7f1295cc1d68>"
        ]
       }
      ],
@@ -1496,25 +1496,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06764438>"
+        "<matplotlib.figure.Figure at 0x7f1295d21908>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d065450b8>"
+        "<matplotlib.figure.Figure at 0x7f1295c7a710>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064db2b0>"
+        "<matplotlib.figure.Figure at 0x7f1295c804e0>"
        ]
       }
      ],
@@ -1553,7 +1553,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d0651d160>"
+        "<matplotlib.figure.Figure at 0x7f1295bad518>"
        ]
       },
       {
@@ -1561,7 +1561,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d065c19e8>"
+        "<matplotlib.figure.Figure at 0x7f1295bad9e8>"
        ]
       },
       {
@@ -1569,7 +1569,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064c8dd8>"
+        "<matplotlib.figure.Figure at 0x7f1295e7d5c0>"
        ]
       }
      ],
@@ -1604,7 +1604,7 @@
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        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064302b0>"
+        "<matplotlib.figure.Figure at 0x7f1295c88630>"
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       {
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d04be41d0>"
+        "<matplotlib.figure.Figure at 0x7f1295bf7f28>"
        ]
       }
      ],
@@ -1660,7 +1660,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d04bf65c0>"
+        "<matplotlib.figure.Figure at 0x7f1294302160>"
        ]
       },
       {
@@ -1668,7 +1668,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d04b378d0>"
+        "<matplotlib.figure.Figure at 0x7f1294302860>"
        ]
       },
       {
@@ -1676,7 +1676,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d04bdd668>"
+        "<matplotlib.figure.Figure at 0x7f1294382be0>"
        ]
       }
      ],
@@ -1753,9 +1753,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06705b70>"
+        "<matplotlib.figure.Figure at 0x7f1295de43c8>"
        ]
       }
      ],
@@ -1784,9 +1784,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d06638908>"
+        "<matplotlib.figure.Figure at 0x7f1295e8f0f0>"
        ]
       }
      ],
@@ -1813,9 +1813,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066e8c50>"
+        "<matplotlib.figure.Figure at 0x7f1295de5278>"
        ]
       }
      ],
@@ -1844,9 +1844,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d064c5c88>"
+        "<matplotlib.figure.Figure at 0x7f1295e83400>"
        ]
       }
      ],
@@ -1892,9 +1892,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d04b2b470>"
+        "<matplotlib.figure.Figure at 0x7f1295cccac8>"
        ]
       }
      ],
@@ -1921,9 +1921,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6d066342b0>"
+        "<matplotlib.figure.Figure at 0x7f1295ccdc50>"
        ]
       }
      ],