matshow

Author: rasbt

ipynb/heatmaps.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0a65e4dbd0c5d614618a27422709b43217b06c4e543ea837f53c43e8993a0cc3"
+  "signature": "sha256:e67f934cd7d306469b4248cb5d5e4344927f9567aa21d0cc4547d520275174a2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -33,17 +33,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "The watermark extension is already loaded. To reload it, use:\n",
-        "  %reload_ext watermark\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
+     "outputs": [],
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -58,17 +49,17 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 15/07/2014 \n",
+        "Last updated: 21/08/2014 \n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.0.0\n",
         "\n",
         "matplotlib 1.3.1\n",
-        "numpy 1.8.1\n"
+        "numpy 1.8.2\n"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -86,7 +77,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -110,8 +101,9 @@
      "source": [
       "- [Simple heat maps](#Simple-heat-map)\n",
       "    - [Using NumPy's histogram2d](#Using-NumPy's-histogram2d)\n",
-      "    - [Using hist2d from matplotlib pyplot](#Using-hist2d-from-matplotlib-pyplot)\n",
-      "    - [Using pcolor from matplotlib pyplot](#Using-pcolor-from-matplotlib-pyplot)\n",
+      "    - [Using hist2d from matplotlib](#Using-hist2d-from-matplotlib)\n",
+      "    - [Using pcolor from matplotlib](#Using-pcolor-from-matplotlib)\n",
+      "    - [Using matshow from matplotlib](#Using-matshow-from-matplotlib)\n",
       "- [Using different color maps](#Using-different-color-maps)\n",
       "    - [Available color maps](#Available-color-maps)"
      ]
@@ -251,7 +243,7 @@
      "level": 3,
      "metadata": {},
      "source": [
-      "Using hist2d from matplotlib pyplot"
+      "Using hist2d from matplotlib"
      ]
     },
     {
@@ -322,7 +314,7 @@
      "level": 3,
      "metadata": {},
      "source": [
-      "Using pcolor from matplotlib pyplot"
+      "Using pcolor from matplotlib"
      ]
     },
     {
@@ -363,6 +355,67 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Using matshow from matplotlib"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "columns = ['A', 'B', 'C', 'D']\n",
+      "rows = ['1', '2', '3', '4']\n",
+      "\n",
+      "data = np.random.random((4,4))\n",
+      "\n",
+      "fig = plt.figure()\n",
+      "\n",
+      "ax = fig.add_subplot(111)\n",
+      "\n",
+      "cax = ax.matshow(data, interpolation='nearest')\n",
+      "fig.colorbar(cax)\n",
+      "\n",
+      "ax.set_xticklabels([''] + columns)\n",
+      "ax.set_yticklabels([''] + rows)\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAD7CAYAAAAsJIKcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106388710>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,