Add SymLogNorm example

Author: maxnoe

matplotlib.ipynb

@@ -122,7 +122,7 @@
    },
    "outputs": [],
    "source": [
-    "x = np.linspace(0, 1, 100) # 100 numbers from 0 to 1\n",
+    "x = np.linspace(0, 1, 250) # 250 numbers from 0 to 1\n",
     "\n",
     "plt.plot(x, x**2)\n",
     "# If not interactive, e.g. in a script: \n",
@@ -144,7 +144,7 @@
    },
    "outputs": [],
    "source": [
-    "t = np.linspace(0, 2 * np.pi)   # 50 points between 0 and 2π\n",
+    "t = np.linspace(0, 2 * np.pi, 50)   # 50 points between 0 and 2π\n",
     "plt.plot(t, np.sin(t));"
    ]
   },
@@ -373,6 +373,33 @@
     "**Remember**: Legend entries are only generated for plot objects that have a label (note x⁴ is missing)!"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Legend outside the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(t, np.sin(t), label=r'$\\sin(t)$')\n",
+    "plt.plot(t, np.cos(t), label=r'$\\cos(t)$')\n",
+    "plt.legend()\n",
+    "\n",
+    "# put the legend in the center above the plot\n",
+    "# coordinates are relative to the axes size.\n",
+    "plt.legend(\n",
+    "    bbox_to_anchor=(0.5, 1.01),\n",
+    "    loc='lower center',  # this no sets the anchor of the legend\n",
+    "    ncol=2,\n",
+    ")\n",
+    "plt.tight_layout()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -494,7 +521,7 @@
     "errX = np.random.normal(0, 0.4, 10)\n",
     "errY = np.random.normal(0, 0.4, 10)\n",
     "\n",
-    "plt.errorbar(x + errX, x + errY, xerr=0.4, yerr=errY, fmt='o');"
+    "plt.errorbar(x + errX, x + errY, xerr=0.4, yerr=errY, linestyle='');"
    ]
   },
   {
@@ -699,6 +726,29 @@
     "### Using Norms – e.g. Logarithmic colorscale"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.colors import LogNorm, DivergingNorm, SymLogNorm\n",
+    "\n",
+    "plt.hist2d(x, y, bins=50, norm=LogNorm())\n",
+    "plt.colorbar(label='Counts');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SymLogNorm\n",
+    "\n",
+    "The SymLogNorm uses two logscales, one for the negative, one for the positive numbers.\n",
+    "Around 0, a linear scale is used. The threshold value for the linear scale\n",
+    "has to be given."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -707,9 +757,22 @@
    },
    "outputs": [],
    "source": [
-    "from matplotlib.colors import LogNorm, DivergingNorm, SymLogNorm\n",
+    "x2, y2 = np.random.multivariate_normal(mean, cov, size=10000).T\n",
     "\n",
-    "plt.hist2d(x, y, bins=50, norm=LogNorm())\n",
+    "\n",
+    "hist1, xedges, yedges = np.histogram2d(x, y, bins=100)\n",
+    "hist2, _, _ = np.histogram2d(x2, y2, bins=[xedges, yedges])\n",
+    "\n",
+    "\n",
+    "plt.pcolormesh(\n",
+    "    xedges,\n",
+    "    yedges,\n",
+    "    hist1 - hist2,\n",
+    "    cmap='RdBu',\n",
+    "    norm=SymLogNorm(0.1),\n",
+    "    vmin=-15,\n",
+    "    vmax=15,\n",
+    ")\n",
     "\n",
     "plt.colorbar();"
    ]