Commit 2c7dcf47 authored by Loic Huder's avatar Loic Huder
Browse files

Added some info elements and examples to matplotlib tuto

parent b0fbe1b9
......@@ -121,22 +121,22 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'y')"
"<matplotlib.legend.Legend at 0x7f7646bf5208>"
]
},
"execution_count": 7,
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -149,9 +149,10 @@
],
"source": [
"plt.figure()\n",
"plt.plot(x,y)\n",
"plt.plot(x,y, label='Square function')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')"
"plt.ylabel('y')\n",
"plt.legend()"
]
},
{
......@@ -163,7 +164,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 55,
"metadata": {},
"outputs": [
{
......@@ -172,7 +173,7 @@
"Text(0, 0.5, '$\\\\Pi / \\\\epsilon$')"
]
},
"execution_count": 8,
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
......@@ -192,27 +193,107 @@
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(x, y, color=\"red\", linestyle='dashed', linewidth=3, marker='o',\n",
"res = ax.plot(x, y, color=\"red\", linestyle='dashed', linewidth=3, marker='o',\n",
" markerfacecolor='blue', markersize=5)\n",
"\n",
"ax.set_xlabel('$Re$')\n",
"ax.set_ylabel('$\\Pi / \\epsilon$')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also recover the plotted matplotlib object to get info on it."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'matplotlib.lines.Line2D'>\n",
"Color of the line is red\n",
"X data of the plot: [0. 0.10526316 0.21052632 0.31578947 0.42105263 0.52631579\n",
" 0.63157895 0.73684211 0.84210526 0.94736842 1.05263158 1.15789474\n",
" 1.26315789 1.36842105 1.47368421 1.57894737 1.68421053 1.78947368\n",
" 1.89473684 2. ]\n"
]
}
],
"source": [
"line_object = res[0]\n",
"print(type(line_object))\n",
"print('Color of the line is', line_object.get_color())\n",
"print('X data of the plot:', line_object.get_xdata())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example of multiple subplots"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Angle (rad)')"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(211) # First, number of subplots along X (2), then along Y (1), then the id of the subplot (1)\n",
"ax2 = fig.add_subplot(212, sharex=ax1) # It is possible to share axes between subplots\n",
"X = np.arange(0, 2*np.pi, 0.1)\n",
"ax1.plot(X, np.cos(2*X), color=\"red\")\n",
"ax2.plot(X, np.sin(2*X), color=\"magenta\")\n",
"ax2.set_xlabel('Angle (rad)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anatomy of a Matplotlib figure\n",
"\n",
"![Anatomy of a figure](images/anatomy.png)"
"![Anatomy of a figure](images/anatomy.png)\n",
"\n",
"For consistent figure changes, define your own stylesheets that are basically a list of parameters to tune the aspect of the figure elements.\n",
"See https://matplotlib.org/tutorials/introductory/customizing.html for more info."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also plot array of data."
"We can also plot 2D data arrays."
]
},
{
......@@ -290,6 +371,34 @@
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Choose your colormaps wisely !\n",
"When doing such colorplots, it is easy to lose the interesting features by setting a colormap that is not adapted to the data. \n",
"\n",
"Also, when producing scientific figures, think about how will your plot will look like to colorblind people or in greyscales (as it can happen in printed articles...).\n",
"\n",
"See the interesting discussion on matplotlib website: https://matplotlib.org/users/colormaps.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other plot types\n",
"Matplotlib also allows to plot:\n",
"- Histograms\n",
"- Plots with error bars\n",
"- Box plots\n",
"- Contours\n",
"- in 3D\n",
"- ...\n",
"\n",
"See the [gallery](http://matplotlib.org/gallery.html) to see what suits you the most."
]
},
{
"cell_type": "markdown",
"metadata": {
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