Commit 427d64cd authored by Michiel Cottaar's avatar Michiel Cottaar Committed by Michiel Cottaar
Browse files

move to data_visualisation directory

parent fbd8ae16
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "dda775e0",
"id": "fa095385",
"metadata": {},
"source": [
"# Matplotlib tutorial\n",
......@@ -48,7 +48,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "5ccc9a73",
"id": "41578cdc",
"metadata": {},
"outputs": [],
"source": [
......@@ -58,7 +58,7 @@
},
{
"cell_type": "markdown",
"id": "ed561bcb",
"id": "1a9a5f55",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"line\"></a>\n",
......@@ -69,7 +69,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "80e15ee1",
"id": "2531bb20",
"metadata": {},
"outputs": [],
"source": [
......@@ -78,7 +78,7 @@
},
{
"cell_type": "markdown",
"id": "c2923739",
"id": "9ef51d5c",
"metadata": {},
"source": [
"To adjust how the line is plotted, check the documentation:"
......@@ -87,7 +87,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0ce4a966",
"id": "9a768ab3",
"metadata": {},
"outputs": [],
"source": [
......@@ -96,7 +96,7 @@
},
{
"cell_type": "markdown",
"id": "e7263d90",
"id": "d2e6a4d1",
"metadata": {},
"source": [
"As you can see there are a lot of options.\n",
......@@ -109,7 +109,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "dfbbb093",
"id": "85ed5f73",
"metadata": {},
"outputs": [],
"source": [
......@@ -123,7 +123,7 @@
},
{
"cell_type": "markdown",
"id": "c5cf861d",
"id": "a359e01a",
"metadata": {},
"source": [
"Because these keywords are so common, you can actually set one or more of them by passing in a string as the third argument."
......@@ -132,7 +132,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "9446bd5b",
"id": "0e69e842",
"metadata": {},
"outputs": [],
"source": [
......@@ -145,7 +145,7 @@
},
{
"cell_type": "markdown",
"id": "f17ba1d2",
"id": "891a9f9e",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"scatter\"></a>\n",
......@@ -156,7 +156,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "31c06d59",
"id": "8cb13b7e",
"metadata": {},
"outputs": [],
"source": [
......@@ -168,7 +168,7 @@
},
{
"cell_type": "markdown",
"id": "777734ae",
"id": "df311f5c",
"metadata": {},
"source": [
"The third argument is the variable determining the size, while the fourth argument is the variable setting the color.\n",
......@@ -180,7 +180,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1fd95cae",
"id": "f9bb4e76",
"metadata": {},
"outputs": [],
"source": [
......@@ -190,7 +190,7 @@
},
{
"cell_type": "markdown",
"id": "72a015c6",
"id": "141bf7e8",
"metadata": {},
"source": [
"where it also returns the number of elements in each bin, as `n`, and\n",
......@@ -207,7 +207,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0c410bcd",
"id": "951bd53e",
"metadata": {},
"outputs": [],
"source": [
......@@ -222,7 +222,7 @@
},
{
"cell_type": "markdown",
"id": "75c96456",
"id": "2ae38282",
"metadata": {},
"source": [
"> If you want more advanced distribution plots beyond a simple histogram, have a look at the seaborn [gallery](https://seaborn.pydata.org/examples/index.html) for (too?) many options.\n",
......@@ -236,7 +236,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "00caf192",
"id": "3f440fd0",
"metadata": {},
"outputs": [],
"source": [
......@@ -249,7 +249,7 @@
},
{
"cell_type": "markdown",
"id": "dd9fb30f",
"id": "1405cf82",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"shade\"></a>\n",
......@@ -260,7 +260,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "bb53b679",
"id": "c0f12a0d",
"metadata": {},
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"source": [
......@@ -270,7 +270,7 @@
},
{
"cell_type": "markdown",
"id": "e47aefc6",
"id": "71d7bc82",
"metadata": {},
"source": [
"This can be nicely combined with a polar projection, to create 2D orientation distribution functions:"
......@@ -279,7 +279,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "84538d49",
"id": "e337ced8",
"metadata": {},
"outputs": [],
"source": [
......@@ -290,7 +290,7 @@
},
{
"cell_type": "markdown",
"id": "91a936ab",
"id": "12c4eee6",
"metadata": {},
"source": [
"The area between two lines can be shaded using `fill_between`:"
......@@ -299,7 +299,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "ebb0f958",
"id": "54c6b838",
"metadata": {},
"outputs": [],
"source": [
......@@ -313,7 +313,7 @@
},
{
"cell_type": "markdown",
"id": "8ae52787",
"id": "3a1d3815",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"image\"></a>\n",
......@@ -324,7 +324,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0fe3f185",
"id": "ed051029",
"metadata": {},
"outputs": [],
"source": [
......@@ -340,7 +340,7 @@
},
{
"cell_type": "markdown",
"id": "dabed6ef",
"id": "156b0628",
"metadata": {},
"source": [
"Note that matplotlib will use the **voxel data orientation**, and that\n",
......@@ -352,7 +352,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "65e1381a",
"id": "a65cf0d6",
"metadata": {},
"outputs": [],
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......@@ -365,7 +365,7 @@
},
{
"cell_type": "markdown",
"id": "0e20f40f",
"id": "7c8a01a8",
"metadata": {},
"source": [
"> It is easier to produce informative brain images using nilearn or fsleyes\n",
......@@ -377,7 +377,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c5101a5b",
"id": "3f9f4fad",
"metadata": {},
"outputs": [],
"source": [
......@@ -390,7 +390,7 @@
},
{
"cell_type": "markdown",
"id": "6c91efd8",
"id": "d2fb44b4",
"metadata": {},
"source": [
"By default the locations of the arrows and text will be in data coordinates (i.e., whatever is on the axes),\n",
......@@ -425,7 +425,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "994a4e47",
"id": "43229971",
"metadata": {},
"outputs": [],
"source": [
......@@ -436,7 +436,7 @@
},
{
"cell_type": "markdown",
"id": "0c244a1a",
"id": "8d4bee33",
"metadata": {},
"source": [
"Note that here we explicitly create the figure and add a single sub-plot to the figure.\n",
......@@ -450,7 +450,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "87b60efe",
"id": "2cc5123a",
"metadata": {},
"outputs": [],
"source": [
......@@ -459,7 +459,7 @@
},
{
"cell_type": "markdown",
"id": "9e8785b0",
"id": "37251f4a",
"metadata": {},
"source": [
"When going through this list carefully you might have spotted that the plotted line is stored in the `lines` property.\n",
......@@ -469,7 +469,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1e9372b7",
"id": "db290a0a",
"metadata": {},
"outputs": [],
"source": [
......@@ -478,7 +478,7 @@
},
{
"cell_type": "markdown",
"id": "afd6a54e",
"id": "ae053e0c",
"metadata": {},
"source": [
"This shows us all the properties stored about this line,\n",
......@@ -495,7 +495,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "5bff872f",
"id": "8bd710d5",
"metadata": {},
"outputs": [],
"source": [
......@@ -511,7 +511,7 @@
},
{
"cell_type": "markdown",
"id": "510185fb",
"id": "28b82718",
"metadata": {},
"source": [
"For such a simple example, this works fine. But for longer examples you would find yourself constantly looking back through the\n",
......@@ -523,7 +523,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "52ffc81d",
"id": "89a20086",
"metadata": {},
"outputs": [],
"source": [
......@@ -536,7 +536,7 @@
},
{
"cell_type": "markdown",
"id": "48464ab0",
"id": "852c2d46",
"metadata": {},
"source": [
"Here we use `plt.subplots`, which creates both a new figure for us and a grid of sub-plots. \n",
......@@ -551,7 +551,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d047679b",
"id": "5c14ec50",
"metadata": {},
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"source": [
......@@ -565,7 +565,7 @@
},
{
"cell_type": "markdown",
"id": "c8fe247b",
"id": "338c7239",
"metadata": {},
"source": [
"Uncomment `fig.tight_layout` and see how it adjusts the spacings between the plots automatically to reduce the whitespace.\n",
......@@ -576,7 +576,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c82a0837",
"id": "5df7361f",
"metadata": {},
"outputs": [],
"source": [
......@@ -591,7 +591,7 @@
},
{
"cell_type": "markdown",
"id": "09b2b281",
"id": "ff58c930",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"grid-spec\"></a>\n",
......@@ -603,7 +603,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "b4f4a54b",
"id": "c1651d0c",
"metadata": {},
"outputs": [],
"source": [
......@@ -623,7 +623,7 @@
},
{
"cell_type": "markdown",
"id": "37bc60de",
"id": "5676c42d",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"styling\"></a>\n",
......@@ -637,7 +637,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c4b8f402",
"id": "b6841514",
"metadata": {},
"outputs": [],
"source": [
......@@ -650,7 +650,7 @@
},
{
"cell_type": "markdown",
"id": "7c6e0fc1",
"id": "c27500eb",
"metadata": {},
"source": [
"You can also set any of these properties by calling `Axes.set` directly:"
......@@ -659,7 +659,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "ff0502fc",
"id": "4aa8461b",
"metadata": {},
"outputs": [],
"source": [
......@@ -674,7 +674,7 @@
},
{
"cell_type": "markdown",
"id": "0db1eb83",
"id": "e69e0f4b",
"metadata": {},
"source": [
"> To match the matlab API and save some typing the equivalent commands in the procedural interface do not have the `set_` preset. So, they are `plt.xlabel`, `plt.ylabel`, `plt.title`. This is also true for many of the `set_` commands we will see below.\n",
......@@ -685,7 +685,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "369c02d6",
"id": "d9958b2e",
"metadata": {},
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"source": [
......@@ -698,7 +698,7 @@
},
{
"cell_type": "markdown",
"id": "6095ba78",
"id": "111da8e1",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"axis\"></a>\n",
......@@ -717,7 +717,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c8f5a0dd",
"id": "4e402140",
"metadata": {},
"outputs": [],
"source": [
......@@ -738,7 +738,7 @@
},
{
"cell_type": "markdown",
"id": "2dbbad8d",
"id": "9bd34f1c",
"metadata": {},
"source": [
"As illustrated earlier, we can get a more complete list of the things we could change about the x-axis by looking at its properties:"
......@@ -747,7 +747,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "20ca99eb",
"id": "db2b0e6e",
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......@@ -756,7 +756,7 @@
},
{
"cell_type": "markdown",
"id": "35fe5da3",
"id": "48b79b04",
"metadata": {},
"source": [
"<a class=\"anchor\" id=\"faq\"></a>\n",
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{
"cell_type": "code",
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"id": "1620a4da",
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......@@ -797,7 +797,7 @@
},
{
"cell_type": "markdown",
"id": "b0024423",
"id": "68b0aac8",
"metadata": {},
"source": [
"> If you are using Jupyterlab (new version of the jupyter notebook) the `nbagg` backend will not work. Instead you will have to install `ipympl` and then use the `widgets` backend to get an interactive backend (this also works in the old notebooks).\n",
......@@ -808,7 +808,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "b67ee344",
"id": "b81eb924",
"metadata": {},
"outputs": [],
"source": [
......@@ -818,7 +818,7 @@
},
{
"cell_type": "markdown",
"id": "36ca2dfc",
"id": "14663014",
"metadata": {},
"source": [
"Usually, the default backend will be fine, so you will not have to set it. \n",
......
%% Cell type:markdown id:dda775e0 tags:
%% Cell type:markdown id:fa095385 tags:
# Matplotlib tutorial
The main plotting library in python is `matplotlib`.
It provides a simple interface to just explore the data,
while also having a lot of flexibility to create publication-worthy plots.
In fact, the vast majority of python-produced plots in papers will be either produced
directly using matplotlib or by one of the many plotting libraries built on top of
matplotlib (such as [seaborn](https://seaborn.pydata.org/) or [nilearn](https://nilearn.github.io/)).
Like everything in python, there is a lot of help available online (just google it or ask your local pythonista).
A particularly useful resource for matplotlib is the [gallery](https://matplotlib.org/gallery/index.html).
Here you can find a wide range of plots.
Just find one that looks like what you want to do and click on it to see (and copy) the code used to generate the plot.
## Contents
- [Basic plotting commands](#basic-plotting-commands)
- [Line plots](#line)
- [Scatter plots](#scatter)
- [Histograms and bar plots](#histograms)
- [Adding error bars](#error)
- [Shading regions](#shade)
- [Displaying images](#image)
- [Adding lines, arrows, text](#annotations)
- [Using the object-oriented interface](#OO)
- [Multiple plots (i.e., subplots)](#subplots)
- [Adjusting plot layouts](#layout)
- [Advanced grid configurations (GridSpec)](#grid-spec)
- [Styling your plot](#styling)
- [Setting title and labels](#labels)
- [Editing the x- and y-axis](#axis)
- [FAQ](#faq)
- [Why am I getting two images?](#double-image)
- [I produced a plot in my python script, but it does not show up](#show)
- [Changing where the image appears: backends](#backends)
<a class="anchor" id="basic-plotting-commands"></a>
## Basic plotting commands
Let's start with the basic imports:
%% Cell type:code id:5ccc9a73 tags:
%% Cell type:code id:41578cdc tags:
```
import matplotlib.pyplot as plt
import numpy as np
```
%% Cell type:markdown id:ed561bcb tags:
%% Cell type:markdown id:1a9a5f55 tags:
<a class="anchor" id="line"></a>
### Line plots
A basic lineplot can be made just by calling `plt.plot`:
%% Cell type:code id:80e15ee1 tags:
%% Cell type:code id:2531bb20 tags:
```
plt.plot([1, 2, 3], [1.3, 4.2, 3.1])
```
%% Cell type:markdown id:c2923739 tags:
%% Cell type:markdown id:9ef51d5c tags:
To adjust how the line is plotted, check the documentation:
%% Cell type:code id:0ce4a966 tags:
%% Cell type:code id:9a768ab3 tags:
```
plt.plot?
```
%% Cell type:markdown id:e7263d90 tags:
%% Cell type:markdown id:d2e6a4d1 tags:
As you can see there are a lot of options.
The ones you will probably use most often are:
- `linestyle`: how the line is plotted (set to '' to omit the line)
- `marker`: how the points are plotted (these are not plotted by default)
- `color`: what color to use (defaults to cycling through a set of 7 colors)
%% Cell type:code id:dfbbb093 tags:
%% Cell type:code id:85ed5f73 tags:
```
theta = np.linspace(0, 2 * np.pi, 101)
plt.plot(np.sin(theta), np.cos(theta))
plt.plot([-0.3, 0.3], [0.3, 0.3], marker='o', linestyle='', markersize=20)
plt.plot(0, -0.1, marker='s', color='black')
x = np.linspace(-0.5, 0.5, 5)
plt.plot(x, x ** 2 - 0.5, linestyle='--', marker='+', color='red')
```
%% Cell type:markdown id:c5cf861d tags:
%% Cell type:markdown id:a359e01a tags:
Because these keywords are so common, you can actually set one or more of them by passing in a string as the third argument.
%% Cell type:code id:9446bd5b tags:
%% Cell type:code id:0e69e842 tags:
```
x = np.linspace(0, 1, 11)
plt.plot(x, x)
plt.plot(x, x ** 2, '--') # sets the linestyle to dashed
plt.plot(x, x ** 3, 's') # sets the marker to square (and turns off the line)
plt.plot(x, x ** 4, '^y:') # sets the marker to triangles (i.e., '^'), linestyle to dotted (i.e., ':'), and the color to yellow (i.e., 'y')
```
%% Cell type:markdown id:f17ba1d2 tags:
%% Cell type:markdown id:891a9f9e tags:
<a class="anchor" id="scatter"></a>
### Scatter plots
The main extra feature of `plt.scatter` over `plt.plot` is that you can vary the color and size of the points based on some other variable array:
%% Cell type:code id:31c06d59 tags:
%% Cell type:code id:8cb13b7e tags:
```
x = np.random.rand(30)
y = np.random.rand(30)
plt.scatter(x, y, x * 30, y)
plt.colorbar() # adds a colorbar
```
%% Cell type:markdown id:777734ae tags:
%% Cell type:markdown id:df311f5c tags:
The third argument is the variable determining the size, while the fourth argument is the variable setting the color.
<a class="anchor" id="histograms"></a>
### Histograms and bar plots
For a simple histogram you can do this:
%% Cell type:code id:1fd95cae tags:
%% Cell type:code id:f9bb4e76 tags:
```
r = np.random.rand(1000)
n,bins,_ = plt.hist((r-0.5)**2, bins=30)
```
%% Cell type:markdown id:72a015c6 tags:
%% Cell type:markdown id:141bf7e8 tags:
where it also returns the number of elements in each bin, as `n`, and
the bin centres, as `bins`.
> The `_` in the third part on the left
> hand side is a shorthand for just throwing away the corresponding part
> of the return structure.
There is also a call for doing bar plots:
%% Cell type:code id:0c410bcd tags:
%% Cell type:code id:951bd53e tags:
```
samp1 = r[0:10]
samp2 = r[10:20]
bwidth = 0.3
xcoord = np.arange(10)
plt.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1')
plt.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2')
plt.legend(loc='upper left')
```
%% Cell type:markdown id:75c96456 tags:
%% Cell type:markdown id:2ae38282 tags:
> If you want more advanced distribution plots beyond a simple histogram, have a look at the seaborn [gallery](https://seaborn.pydata.org/examples/index.html) for (too?) many options.
<a class="anchor" id="error"></a>
### Adding error bars
If your data is not completely perfect and has for some obscure reason some uncertainty associated with it,
you can plot these using `plt.error`:
%% Cell type:code id:00caf192 tags:
%% Cell type:code id:3f440fd0 tags:
```
x = np.arange(5)
y1 = [0.3, 0.5, 0.7, 0.1, 0.3]
yerr = [0.12, 0.28, 0.1, 0.25, 0.6]
xerr = 0.3
plt.errorbar(x, y1, yerr, xerr, marker='s', linestyle='')
```
%% Cell type:markdown id:dd9fb30f tags:
%% Cell type:markdown id:1405cf82 tags:
<a class="anchor" id="shade"></a>
### Shading regions
An area below a plot can be shaded using `plt.fill`
%% Cell type:code id:bb53b679 tags:
%% Cell type:code id:c0f12a0d tags:
```
x = np.linspace(0, 2, 100)
plt.fill(x, np.sin(x * np.pi))
```
%% Cell type:markdown id:e47aefc6 tags:
%% Cell type:markdown id:71d7bc82 tags:
This can be nicely combined with a polar projection, to create 2D orientation distribution functions:
%% Cell type:code id:84538d49 tags:
%% Cell type:code id:e337ced8 tags:
```
plt.subplot(projection='polar')
theta = np.linspace(0, 2 * np.pi, 100)
plt.fill(theta, np.exp(-2 * np.cos(theta) ** 2))
```
%% Cell type:markdown id:91a936ab tags:
%% Cell type:markdown id:12c4eee6 tags:
The area between two lines can be shaded using `fill_between`:
%% Cell type:code id:ebb0f958 tags:
%% Cell type:code id:54c6b838 tags:
```
x = np.linspace(0, 10, 1000)
y = 5 * np.sin(5 * x) + x - 0.1 * x ** 2
yl = x - 0.1 * x ** 2 - 5
yu = yl + 10
plt.plot(x, y, 'r')
plt.fill_between(x, yl, yu)
```
%% Cell type:markdown id:8ae52787 tags:
%% Cell type:markdown id:3a1d3815 tags:
<a class="anchor" id="image"></a>
### Displaying images
The main command for displaying images is `plt.imshow` (use `plt.pcolor` for cases where you do not have a regular grid)
%% Cell type:code id:0fe3f185 tags:
%% Cell type:code id:ed051029 tags:
```
import nibabel as nib
import os.path as op
nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)
imdat = nim.get_data().astype(float)
imslc = imdat[:,:,70]
plt.imshow(imslc, cmap=plt.cm.gray)
plt.colorbar()
plt.axis('off')
```
%% Cell type:markdown id:dabed6ef tags:
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Note that matplotlib will use the **voxel data orientation**, and that
configuring the plot orientation is **your responsibility**. To rotate a
slice, simply transpose the data (`.T`). To invert the data along along an
axis, you don't need to modify the data - simply swap the axis limits around:
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```
plt.imshow(imslc.T, cmap=plt.cm.gray)
plt.xlim(reversed(plt.xlim()))
plt.ylim(reversed(plt.ylim()))
plt.colorbar()
plt.axis('off')
```
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> It is easier to produce informative brain images using nilearn or fsleyes
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### Adding lines, arrows, and text
Adding horizontal/vertical lines, arrows, and text:
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```
plt.axhline(-1) # horizontal line
plt.axvline(1) # vertical line
plt.arrow(0.2, -0.2, 0.2, -0.8, length_includes_head=True, width=0.01)
plt.text(0.5, 0.5, 'middle of the plot', transform=plt.gca().transAxes, ha='center', va='center')
plt.annotate("line crossing", (1, -1), (0.8, -0.8), arrowprops={}) # adds both text and arrow; need to set the arrowprops keyword for the arrow to be plotted
```
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By default the locations of the arrows and text will be in data coordinates (i.e., whatever is on the axes),
however you can change that. For example to find the middle of the plot in the last example we use
axes coordinates, which are always (0, 0) in the lower left and (1, 1) in the upper right.
See the matplotlib [transformations tutorial](https://matplotlib.org/stable/tutorials/advanced/transforms_tutorial.html)
for more detail.
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## Using the object-oriented interface
In the examples above we simply added multiple lines/points/bars/images
(collectively called [artists](https://matplotlib.org/stable/tutorials/intermediate/artists.html) in matplotlib) to a single plot.
To prettify this plots, we first need to know what all the features are called:
![anatomy of a plot](https://matplotlib.org/stable/_images/anatomy.png)
Using the terms in this plot let's see what our first command of `plt.plot([1, 2, 3], [1.3, 4.2, 3.1])`
actually does: