minor edits and changes to matplotlib

Author: Jahn, Kalle L

notebooks/part0_python_intro/06_matplotlib.ipynb

@@ -192,6 +192,33 @@
     "    ax.set_title(\"title\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For complex subplots on a non-uniform grid, use `subplot_mosaic`: https://matplotlib.org/stable/users/explain/axes/mosaic.html#mosaic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplot_mosaic(\n",
+    "    \"\"\"\n",
+    "    A.D\n",
+    "    CBD\n",
+    "    \"\"\"\n",
+    "    )# There is no internal meaning to the letters used, they could be anything.\n",
+    "# `axes`` now is a dictionary with the letters as keys, Axes objects as values.\n",
+    "for i, ax in axes.items():\n",
+    "    ax.plot(x, y, \"r\")\n",
+    "    ax.set_xlabel(\"x\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    ax.set_title(i);\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -249,7 +276,7 @@
    "source": [
     "The advantage with this method is that if curves are added or removed from the figure, the legend is automatically updated accordingly.\n",
     "\n",
-    "The `legend` function takes an optional keyword argument `loc` that can be used to specify where in the figure the legend is to be drawn. The allowed values of `loc` are keywords for the various places the legend can be drawn. See https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend for details. Some of the most common `loc` values are:\n",
+    "The `legend` function takes an optional keyword argument `loc` that can be used to specify where in the figure the legend is to be drawn. The allowed values of `loc` are keywords for the various places the legend can be drawn. See https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend for details. Some common `loc` values are:\n",
     "\n",
     "```python\n",
     "ax.legend(loc='best')\n",
@@ -285,7 +312,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Colors can also be defined by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments:"
+    "Colors can also be defined by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments.\n",
+    "\n",
+    "There is also a [large list of named colors](https://matplotlib.org/stable/gallery/color/named_colors.html) available."
    ]
   },
   {
@@ -449,7 +478,7 @@
     "\n",
     "That was easy, but it isn't so pretty with overlapping figure axes and labels, right?\n",
     "\n",
-    "Overlapping figures can be dealt with using the `fig.tight_layout` method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
+    "Overlapping figures can be dealt with by passing `layout=\"constrained\"` keyword argument when creating a `fig` object. The `constrained` layout automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content. See https://matplotlib.org/stable/users/explain/axes/constrainedlayout_guide.html for more details"
    ]
   },
   {
@@ -458,15 +487,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, layout=\"constrained\")\n",
     "\n",
     "for ax in axes:\n",
     "    ax.plot(x, y, \"r\")\n",
     "    ax.set_xlabel(\"x\")\n",
     "    ax.set_ylabel(\"y\")\n",
-    "    ax.set_title(\"title\")\n",
-    "\n",
-    "fig.tight_layout();"
+    "    ax.set_title(\"title\")"
    ]
   },
   {
@@ -487,7 +514,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`matplotlib` allows the aspect ratio, DPI and figure size to be specified when the `Figure` object is created, using the `figsize` and `dpi` keyword arguments. `figsize` is a tuple of the width and height of the figure in inches, and `dpi` is the dots-per-inch (pixel per inch). To create an 800x400 pixel, 100 dots-per-inch figure, we can do: "
+    "`matplotlib` allows the aspect ratio, DPI and figure size to be specified when the `Figure` object is created, using the `figsize` and `dpi` keyword arguments. `figsize` is a tuple of the width and height of the figure in inches, and `dpi` is the dots-per-inch (pixel per inch). To create an 400x400 pixel, 100 dots-per-inch figure, we can do: "
    ]
   },
   {
@@ -496,7 +523,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(figsize=(12, 3), dpi=100)\n",
+    "fig, axes = plt.subplots(figsize=(4, 4), dpi=100)\n",
     "\n",
     "axes.plot(x, y, \"r\")\n",
     "axes.set_xlabel(\"x\")\n",
@@ -555,7 +582,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig.savefig(output_path / \"filename.svg\")"
+    "fig.savefig(output_path / \"filename.pdf\")"
    ]
   },
   {
@@ -616,8 +643,8 @@
     "axes[1].set_title(\"tight axes\")\n",
     "\n",
     "axes[2].plot(x, x**2, x, x**3)\n",
-    "axes[2].set_ylim([0, 60])\n",
-    "axes[2].set_xlim([2, 3])\n",
+    "axes[2].set_ylim(0, 60)\n",
+    "axes[2].set_xlim(2, 3)\n",
     "axes[2].set_title(\"custom axes range\");"
    ]
   },
@@ -650,8 +677,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Logarithmic scales can also be specified using `semilogx()`, `semilogy()`, or `loglog()`. What are the differences in the figures plotted using `.set_yscale(\"log\")` and `.semilogy()`?\n",
-    "\n",
+    "Logarithmic scales can also be specified using `semilogx()`, `semilogy()`, or `loglog()`. What are the differences in the figures plotted using `.set_yscale(\"log\")` and `.semilogy()`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "### Placement of ticks and custom tick labels\n",
     "\n",
     "The axis ticks can be explicitly set using with `set_xticks` and `set_yticks`, which both take a list of values for where on the axis the ticks are to be placed. The `set_xticklabels` and `set_yticklabels` methods can be used to provide a list of custom text labels for each tick location:"
@@ -683,7 +722,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are a number of more advanced methods for controlling major and minor tick placement in `matplotlib` figures, such as automatic placement according to different policies. See http://`matplotlib`.org/api/ticker_api.html for details.\n",
+    "There are a number of more advanced methods for controlling major and minor tick placement in `matplotlib` figures, such as automatic placement according to different policies. See http://matplotlib.org/api/ticker_api.html for details.\n",
     "\n",
     "### Axis grid\n",
     "\n",
@@ -727,8 +766,15 @@
     "ax1.plot(x, x**2, lw=2, color=\"blue\")\n",
     "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n",
     "for label in ax1.get_yticklabels():\n",
-    "    label.set_color(\"blue\")\n",
-    "\n",
+    "    label.set_color(\"blue\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "ax2 = ax1.twinx()\n",
     "ax2.plot(x, x**3, lw=2, color=\"red\")\n",
     "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n",
@@ -742,7 +788,7 @@
    "source": [
     "## Other 2D plot styles\n",
     "\n",
-    "In addition to the regular `plot` method, there are a number of other functions for generating different kind of plots. See the `matplotlib` plot gallery for a complete list of available plot types: http://`matplotlib`.org/gallery.html. Some of the more useful ones are show below:"
+    "In addition to the regular `plot` method, there are a number of other functions for generating different kind of plots. See the `matplotlib` plot gallery for a complete list of available plot types: http://matplotlib.org/gallery.html. Some of the more useful ones are show below:"
    ]
   },
   {
@@ -801,7 +847,7 @@
    "source": [
     "## Text annotation\n",
     "\n",
-    "Annotating text in `matplotlib` figures can be done using the `text` function. It supports LaTeX formatting just like axis label texts and titles:"
+    "Annotating text in `matplotlib` figures can be done using the `annotate` function. It supports LaTeX formatting just like axis label texts and titles:"
    ]
   },
   {
@@ -814,8 +860,9 @@
     "\n",
     "ax.plot(xx, xx**2, xx, xx**3)\n",
     "\n",
-    "ax.text(0.15, 0.2, r\"$\\alpha y=x^2$\", fontsize=20, color=\"blue\")\n",
-    "ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=20, color=\"green\");"
+    "ax.annotate(text=r'$\\alpha y=x^2$', xy=(0.5, 0.2), fontsize=20, color='tab:blue', va='bottom', ha='right')\n",
+    "ax.annotate(r'$y=x^3$', xy=(xx[25], (xx**4)[25]),  xytext=(0.7, -0.2), fontsize=20, color='tab:orange',\n",
+    "            arrowprops=dict(arrowstyle='->'));"
    ]
   },
   {
@@ -824,7 +871,7 @@
    "source": [
     "## Colormap and contour figures\n",
     "\n",
-    "Colormaps and contour figures are useful for plotting functions of two variables. A colormap will be used to encode one dimension of the data in most of these functions. There are a number of predefined colormaps. It is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see: http://www.scipy.org/Cookbook/`matplotlib`/Show_colormaps"
+    "Colormaps and contour figures are useful for plotting functions of two variables. A colormap will be used to encode one dimension of the data in most of these functions. There are a number of predefined colormaps. It is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see: http://www.scipy.org/Cookbook/matplotlib/Show_colormaps"
    ]
   },
   {
@@ -862,7 +909,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### pcolor"
+    "### pcolormesh"
    ]
   },
   {
@@ -873,11 +920,11 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
-    "p = ax.pcolor(\n",
-    "    X / (2 * np.pi),\n",
-    "    Y / (2 * np.pi),\n",
+    "p = ax.pcolormesh(\n",
+    "    X / (2 * np.pi), # corners of quadrilaterals\n",
+    "    Y / (2 * np.pi), # corners of quadrilaterals\n",
     "    Z,\n",
-    "    cmap=plt.cm.RdBu,\n",
+    "    cmap='viridis',\n",
     "    vmin=abs(Z).min(),\n",
     "    vmax=abs(Z).max(),\n",
     ")\n",
@@ -899,14 +946,14 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
-    "im = plt.imshow(\n",
+    "im = ax.imshow(\n",
     "    Z,\n",
     "    cmap=plt.cm.RdBu,\n",
     "    vmin=abs(Z).min(),\n",
     "    vmax=abs(Z).max(),\n",
     "    extent=[0, 1, 0, 1],\n",
+    "    interpolation=\"bilinear\",\n",
     ")\n",
-    "im.set_interpolation(\"bilinear\")\n",
     "\n",
     "cb = fig.colorbar(im, ax=ax)"
    ]
@@ -1121,23 +1168,16 @@
     "        ax.plot(\n",
     "            xx, yy, lw=0, marker=\"o\", ms=4, mfc=\"red\", mec=\"red\", clip_on=False\n",
     "        )\n",
-    "\n",
+    "        \n",
+    "        ax.set_title(f'Page {idx + 1} - {xx:6.2f} $\\pi$')\n",
     "        pdf.savefig()  # saves the current figure into a pdf page\n",
     "        plt.close()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pyclass",
+   "display_name": "lisus_p2",
    "language": "python",
    "name": "python3"
   },
@@ -1151,7 +1191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,