From ea8c8b8f43fe8ce07b0e82c01632c176afc3028b Mon Sep 17 00:00:00 2001 From: Arno Timmer Date: Tue, 20 Aug 2024 17:06:33 +0200 Subject: [PATCH] work on envs --- images/WUR_Basic_Folium_Map.png | Bin 0 -> 575521 bytes images/mpl1.png | Bin 0 -> 25581 bytes images/mpl2.png | Bin 0 -> 27806 bytes images/mpl3.png | Bin 0 -> 27233 bytes images/mpl4.png | Bin 0 -> 13768 bytes images/mpl_simple_plot.png | Bin 0 -> 21128 bytes images/mpl_structure.png | Bin 0 -> 22690 bytes images/numpy_slicing.png | Bin 0 -> 45307 bytes images/numpy_structure.png | Bin 0 -> 174315 bytes index.Rmd | 78 ++-- index.html | 645 +++++++++++++++++--------------- 11 files changed, 403 insertions(+), 320 deletions(-) create mode 100644 images/WUR_Basic_Folium_Map.png create mode 100644 images/mpl1.png create mode 100644 images/mpl2.png create mode 100644 images/mpl3.png create mode 100644 images/mpl4.png create mode 100644 images/mpl_simple_plot.png create mode 100644 images/mpl_structure.png create mode 100644 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z#;l2kaOqZQ%hetv8QDc~kmN8S_CHrY6v@lWbK0IGr{(Q&e+Z-=JU~T@hy-(4I}>vExFx8gA;l8VK$?tB?UYk~H@`1|Y@m@NFcpTSAyi$jIoiSU zC9$7#TX+fbRP_dGFtDJ7djApXAE59_r7Qh<<8JWeMA6R#=TfW0JiVsUZ;f7OGySY7^nnM{UPdb7g&%ntgFKo#4 zYg?aC%=ix0Y@+{smU`n_#d2kBZ!+W-&A^ZlLtL|KT@bf1r New File --> Save As). Test writing a few lines of code and running the script. + + ### Jupyter Notebooks -Jupyter Notebooks integrate code and visualization, and are therefore helpful for demonstration purposes. Install `jupyter` and the module `folium` in an existing or new environment that includes Python and start Jupyter: +Jupyter Notebooks is actually not a IDE but it is very useful for writing code. Jupyter stands for the languages that once can use (*JU*lia, *PY*thon and *R*) and notebooks means that they are actually files instead of an IDE (such as Rstudio or Spyder). The notebooks can be interpreted and run by varying interpreters of which we will cover 2 later on. Jupyter Notebooks integrate code and visualization, and are therefore very helpful for demonstration purposes and to be run by online interperters (such as google colab). first we will show how to run Jupyter Notebooks locally. To do thi install `jupyter` and the module `folium` in an existing or new environment that includes Python and start Jupyter: ```{r, eval=FALSE, engine='bash'} jupyter notebook ``` -Jupyter should pop up in your browser. You will see a menu with all files in your working directory. The Jupyter Notebook will only see files that are accessible from the working directory in which you launched the notebook! +Jupyter should pop up in your browser. Note that although jupyter is opened in your browser, internet is not used, the code is interpreted and run locally. You will see a menu with all files in your working directory. The Jupyter Notebook will only see files that are accessible from the working directory in which you launched the notebook! Make a new folder: *New* → *Folder*, rename the folder (check the box next to the new 'Untitled Folder' and click **'Rename'** in the top) and, in this folder, create a new Python3 Jupyter Notebook *New* → *Python 3*. Give your notebook a name by clicking on *untitled*. Note that this creates a file with the extension *.ipynb*, which stands for Jupyter Notebook. @@ -267,24 +301,14 @@ To exit a notebook properly, use *File* → *Close and Halt*. After that, by pre ```{r, engine='bash', eval=FALSE} conda deactivate ``` +### Google Colab +As said before, jupyter is locally opened in your browser. It does not connect to the internet, but it does show the possibilities, one could create something online that can run your notebooks for you on the cloud. This is exactly what Google does with Google Colab. Google Colab is a cloud service that allows you to run your jupyter notebooks on the Google cloud for free. Let's see what this looks like: -### Spyder - -The [Spyder IDE](https://docs.spyder-ide.org/) can be started in a terminal when the *Spyder* package is installed in the active conda environment. So, using *Mamba*, make an environment and install Spyder to that environment. Activate the environment. Spyder will automatically make use of the Python interpreter of the active conda environment. To start Spyder: +* Go to https://colab.research.google.com/notebooks/empty.ipynb (note the similaritie and differences between jupyter locally and on google colab) +* type `!pip install folium` and press ctrl+enter to run and install folium +* In a new cell run the same python code as locally to create and show a new folium map -```{r, eval=FALSE,engine='bash'} -spyder -``` - -In Spyder you should see an editor, a file explorer and a console. Have a look at the toolbar. Some important shortcuts are: - -* F5 to run your script -* CTRL + S to save your script -* CTRL + 1 to comment/uncomment your code -* TAB to indent your code -* SHIFT + TAB to unindent your code - -Open a new file and save it somewhere as `main.py` (File -- > New File --> Save As). Test writing a few lines of code and running the script. +For this course we will rarely use jupyter notebooks and or google colab, but it is good to know they exist. Especially google colab is being used more and more in the scientific community and you are likely to come across these during other courses. # Python refresher diff --git a/index.html b/index.html index dd044fb..24fe784 100644 --- a/index.html +++ b/index.html @@ -949,11 +949,11 @@

20 augustus, 2024

Tutorial 8: Python Environments

@@ -981,7 +981,8 @@

Introduction to Python

behind it. If you ask Python programmers what they like most about Python, they will often cite its high readability and high availability of good packages. There are many Python packages out there for -geoscripting, data wrangling, visualization and machine learning. For +geoscripting, data wrangling, visualization, machine learning and for +almost everything else. Relevant packages for this course are for example:

  • Geoscripting @@ -1012,20 +1013,29 @@

    Introduction to Python

Python package management with Conda

-

A set of tools co-exist for installing and managing Python packages. -It is possible to install packages on your base Python interpreter, but -sooner or later you will get conflicting Python packages since packages -have varying dependencies. It can even break +

The high availability of packages is also a threat sometimes. If a +piece of software is developed depending on a package, but this packages +changes later on, the code might not work anymore. Also, different +packages require different dependencies that they are built upon. It is +important to make sure all these dependencies are working together and +that the right versions are used. Luckily, a set of tools exist for +installing and managing Python packages. It is possible to install +packages on your main Python installation (called the +base python interpreter), but sooner or later you will +get conflicting Python packages since packages have varying dependencies +and you might have installed several versions of the same package. It +can even break your system Python interpreter.

-

Instead, we recommend to use a Python package manager that can make -use of virtual environments, such as Conda or Mamba. -That way, you can create a conda environment on your machine for each -project. In conda environments, basically anything, such as software, C -libraries or R packages can be installed. Here we use them here for -installing Python packages. Packages installed in one environment do not -interfere with your base Python or with other conda environments. -Additionally, it is possible to export and share the requirements for -your (open source) project with colaborators or users of your code.

+

Therefor, we recommend to use a Python package manager that uses of +virtual environments, such as Conda or Mamba. That +way, you can create a separate environment on your machine for each +project. In these environments, any dependencie of the project, such as +software, C libraries or R packages can be installed. We will use them +here for installing Python packages. Packages installed in one +environment do not interfere with your base Python or with other +environments. Additionally, it is possible to export and share the +requirements for your (open source) project with collaborators or users +of your code.

Mamba installation

@@ -1065,28 +1075,41 @@

Mamba usage

Mamba creates isolated conda environments with sets of packages, that do not interfere with your base Python or with other conda environments. To create an environment:

-
mamba create --name geotest python numpy
-

This would create a new environment called geotest with +

mamba create --name geotest python numpy spyder
+

This creates a new environment called geotest with Python, NumPy and Spyder installed into the conda environment. Another option is to create an environment from a -.yaml file, in which all required modules are listed. You -will see an example of this later on in this tutorial. To create an -environment from such a file, you can use the argument ---file (or --f in short).

-

Let’s first list the currently available environments:

-
mamba info --envs
+file, a YAML file. In this file all required packages are listed and if +required which version should be used. An example of a YAML file is the +following:

+
name: geotest
+dependencies:
+  - python
+  - numpy
+  - spyder
+

The first line defines what the evnironment will be called +(geotest in this example) and what packages should be +installed (python, numpy and spyder). As you can see, this definition of +the geotest environment is the exact same as the geotest +environment as defined before. To create the geotest environment from +such a file, save this yaml to a new file named env.yaml, +or however you want to call it and use the argument --file +(or --f in short):

+
mamba creat env -f env.yml
+

Let’s list the currently available environments:

+
mamba info --envs

Mamba puts an asterisk (*) in front of the active environment. Now we activate the environment. While Mamba replaces Conda for most commands, this is not the case for (de)activating environments:

-
# Cross-platform (but not always working, like in our VM, so we use the next option)
-conda activate geotest
-
-# Linux, macOS
-source activate geotest
-
-# Windows
-activate geotest
+
# Cross-platform (but not always working, like in our VM, so we use the next option)
+conda activate geotest
+
+# Linux, macOS
+source activate geotest
+
+# Windows
+activate geotest

After this, the current environment is shown in parentheses in front of your prompt ((geotest)$). Note that the activated environment is only valid for the shell in which you activated it. For @@ -1119,16 +1142,16 @@

Mamba usage

The mamba search command searches a set of channels. By default, packages are automatically downloaded and updated from the default channel. To search for a package, type:

-
mamba search pandas
+
mamba search pandas

This gives a list of all packages that have “pandas” in the name and lists all available versions. To install:

-
mamba install pandas
+
mamba install pandas

This installs the latest compatible version of Pandas. Note that this would install it into your currently activated environment.

Note that you can also install multiple packages at the same time:

-
mamba install geopandas matplotlib
+
mamba install geopandas matplotlib

As you saw with Spyder (which is an IDE, more on that later), Mamba is also able to install some non-Python packages that have Python bindings. This is useful for making sure your Python @@ -1146,34 +1169,34 @@

Mamba usage

environment.

Removing packages is just as simple:

-
mamba remove geopandas pandas folium
+
mamba remove geopandas pandas folium

Now, we deactivate the environment and return to base environment.

-
# Cross-platform
-conda deactivate
-
-# Linux, macOS
-source deactivate
-
-# Windows
-deactivate
+
# Cross-platform
+conda deactivate
+
+# Linux, macOS
+source deactivate
+
+# Windows
+deactivate

When we are finished, and do not need the environment for next time, we can remove the environment geotest.

-
mamba remove --name geotest --all
+
mamba remove --name geotest --all

Running a Python script in the terminal

Within a conda environment, Python can be started directly, or can be called to run a script file. To start Python directly:

-
python
+
python

Now, you can type Python expressions that will be executed one by one:

-
import sys
-print('Good morning, you are running Python:', sys.version)
+
import sys
+print('Good morning, you are running Python:', sys.version)

To go back, type:

-
exit()
-# or 
-quit()
+
exit()
+# or 
+quit()

Usually, we do not want to run expressions one by one, but build scripts instead, to ensure transferability and reproducibilty. Create a new text file and (re)name it (to) test.py. Open it, for @@ -1181,7 +1204,7 @@

Running a Python script in the terminal

(import sys etc.), and save the script. Navigate in the terminal to the location where this script is stored, using cd. Finally, run the script with:

-
python test.py
+
python test.py

The output is printed to the terminal. Running a script from the terminal is less error-prone than running it from an IDE (see the next section), such as Spyder, as IDEs often keep variables in memory after @@ -1195,14 +1218,14 @@

Python editors and IDEs

and every programmer has their own preference. An IDE is a software application that provides facilities for software development.

    -
  • Jupyter Notebook integrates +
  • Spyder is a lightweight +IDE. In this course, Spyder is the recommended Python IDE.
  • +
  • Jupyter notebooks integrates visualization with code and is suitable to make tutorials, simple dashboards, quick visualizations, and do prototype testing. Jupyter Notebooks run in your browser on a localhost server or on a web server. They allow for various programming languages, e.g. Python, R, Julia, Spark or PySpark.
  • -
  • Spyder is a lightweight -IDE. In this course, Spyder is the recommended Python IDE.
  • PyCharm Community Edition is a free professional Python IDE with a lot of advanced functionality, such as integrated GIT version control, code @@ -1211,17 +1234,53 @@

    Python editors and IDEs

    of Spyder, but do know that you will not be assisted for solving IDE-related issues.
+
+

Spyder

+

Spyder is a IDE for developing python mainly for scientific purposes. +Fun fact, it is completely written in +python! Spyder is a very complete IDE that looks a bit like Rstudio. +It shows the variables present in the current session, it has a code +editor, a console and a figures pain in the main view.

+

The Spyder IDE can be +started in a terminal when the Spyder package is installed in +the active conda environment. So, using Mamba, make an +environment and install Spyder to that environment. Activate the +environment. Spyder will automatically make use of the Python +interpreter of the active conda environment. To start Spyder:

+
spyder
+

In Spyder you should see an editor, a file explorer and a console. +Have a look at the toolbar. Some important shortcuts are:

+
    +
  • F5 to run your script
  • +
  • CTRL + S to save your script
  • +
  • CTRL + 1 to comment/uncomment your code
  • +
  • TAB to indent your code
  • +
  • SHIFT + TAB to unindent your code
  • +
+

Open a new file and save it somewhere as main.py (File – +> New File –> Save As). Test writing a few lines of code and +running the script.

+

Jupyter Notebooks

-

Jupyter Notebooks integrate code and visualization, and are therefore -helpful for demonstration purposes. Install jupyter and the -module folium in an existing or new environment that -includes Python and start Jupyter:

-
jupyter notebook
-

Jupyter should pop up in your browser. You will see a menu with all -files in your working directory. The Jupyter Notebook will only see -files that are accessible from the working directory in which you -launched the notebook!

+

Jupyter Notebooks is actually not a IDE but it is very useful for +writing code. Jupyter stands for the languages that once can use +(JUlia, PYthon and R) and notebooks means +that they are actually files instead of an IDE (such as Rstudio or +Spyder). The notebooks can be interpreted and run by varying +interpreters of which we will cover 2 later on. Jupyter Notebooks +integrate code and visualization, and are therefore very helpful for +demonstration purposes and to be run by online interperters (such as +google colab). first we will show how to run Jupyter Notebooks locally. +To do thi install jupyter and the module +folium in an existing or new environment that includes +Python and start Jupyter:

+
jupyter notebook
+

Jupyter should pop up in your browser. Note that although jupyter is +opened in your browser, internet is not used, the code is interpreted +and run locally. You will see a menu with all files in your working +directory. The Jupyter Notebook will only see files that are accessible +from the working directory in which you launched the notebook!

Make a new folder: NewFolder, rename the folder (check the box next to the new ‘Untitled Folder’ and click ‘Rename’ in the top) and, in this folder, create a new @@ -1244,15 +1303,15 @@

Jupyter Notebooks

markdown. Enter some documentation for your code (e.g. your team name, exercise and date). Leave the other cell on code.

Type the following Python code in the code cell:

-
import folium
-
-m = folium.Map(location=[51.9700000, 5.6666700], zoom_start=13)
-m
+
import folium
+
+m = folium.Map(location=[51.9700000, 5.6666700], zoom_start=13)
+m

Run the code cell by selecting it and pressing the Run button, or press CTRL + Enter or Shift + Enter. You’ll see a map visualized below your code, similar to the one below. Try to drag the map to play around with it.

-

Wageningen University Basic Folium map

+

Wageningen University Basic Folium map

Your Jupyter Notebook is automatically saved as an .ipynb file on your computer (the file extension comes from the historic name “IPython Notebook”). The notebook can be downloaded as @@ -1262,29 +1321,29 @@

Jupyter Notebooks

Jupyter Notebook server is running, you cancel the running process. The terminal goes back to command line and you can exit the virtual environment by typing conda deactivate.

-
conda deactivate
+
conda deactivate
-
-

Spyder

-

The Spyder IDE can be -started in a terminal when the Spyder package is installed in -the active conda environment. So, using Mamba, make an -environment and install Spyder to that environment. Activate the -environment. Spyder will automatically make use of the Python -interpreter of the active conda environment. To start Spyder:

-
spyder
-

In Spyder you should see an editor, a file explorer and a console. -Have a look at the toolbar. Some important shortcuts are:

+
+

Google Colab

+

As said before, jupyter is locally opened in your browser. It does +not connect to the internet, but it does show the possibilities, one +could create something online that can run your notebooks for you on the +cloud. This is exactly what Google does with Google Colab. Google Colab +is a cloud service that allows you to run your jupyter notebooks on the +Google cloud for free. Let’s see what this looks like:

    -
  • F5 to run your script
  • -
  • CTRL + S to save your script
  • -
  • CTRL + 1 to comment/uncomment your code
  • -
  • TAB to indent your code
  • -
  • SHIFT + TAB to unindent your code
  • +
  • Go to https://colab.research.google.com/notebooks/empty.ipynb +(note the similaritie and differences between jupyter locally and on +google colab)
  • +
  • type !pip install folium and press ctrl+enter to run +and install folium
  • +
  • In a new cell run the same python code as locally to create and show +a new folium map
-

Open a new file and save it somewhere as main.py (File – -> New File –> Save As). Test writing a few lines of code and -running the script.

+

For this course we will rarely use jupyter notebooks and or google +colab, but it is good to know they exist. Especially google colab is +being used more and more in the scientific community and you are likely +to come across these during other courses.

@@ -1296,10 +1355,10 @@

Setting up the environment

knowledge and build upon it. Again, we advise you to code in Spyder, as this IDE is the recommended IDE for the python part of this course.

First, make a directory structure for this tutorial:

-
cd ~/Documents/
-mkdir PythonRefresher #or give the directory a name to your liking
-cd ./PythonRefresher
-mkdir output
+
cd ~/Documents/
+mkdir PythonRefresher #or give the directory a name to your liking
+cd ./PythonRefresher
+mkdir output

We only make a directory for output, because no input data or separate scripts are created in this tutorial. Next, we will create a conda environment from a file. First create a text file in your @@ -1314,11 +1373,11 @@

Setting up the environment

- geopandas - spyder

Now, create a new conda environment based on this file:

-
mamba env create --file refresher.yaml
+
mamba env create --file refresher.yaml

Once everything is installed, activate the environment and start Spyder:

-
source activate refresher
-spyder
+
source activate refresher
+spyder

Create a new Python script and save it.

Important to note: for compatibility, it is best to install packages from the same channel as much as possible. Given that packages in the @@ -1340,108 +1399,108 @@

Printing and basic data types

(=):

Printing in python is done using the print function. We can print variables directly:

-
# Integer
-age = 25
-
-# Float
-height = 1.75
-
-# String
-name = "John Doe"
-
-# Boolean
-is_student = True
-
-# Print a name
-print(name)
+
# Integer
+age = 25
+
+# Float
+height = 1.75
+
+# String
+name = "John Doe"
+
+# Boolean
+is_student = True
+
+# Print a name
+print(name)

We can use string formatting to use flexible strings, for example for printing. to start a formatted string, we put a f before the string. We can use curly brackets {} in this formatted string. The text between these curly brackets is executed as regular python code.

-
# String formatting and printing 
-print(f'{name} is {age} years old and is {height} meters tall.')
+
# String formatting and printing 
+print(f'{name} is {age} years old and is {height} meters tall.')

Basic arithmetic operations:

-
a = 10
-b = 5
-
-addition = a + b
-subtraction = a - b
-multiplication = a * b
-division = a / b
-modulo = a % b
-exponentiation = a ** b
-
-print(addition, subtraction, multiplication, division, modulo, exponentiation)
+
a = 10
+b = 5
+
+addition = a + b
+subtraction = a - b
+multiplication = a * b
+division = a / b
+modulo = a % b
+exponentiation = a ** b
+
+print(addition, subtraction, multiplication, division, modulo, exponentiation)

Conditional statements

-
x = 15
-
-if x > 10:
-    print("x is greater than 10")
-elif x == 10:
-    print("x is equal to 10")
-else:
-    print("x is less than 10")
+
x = 15
+
+if x > 10:
+    print("x is greater than 10")
+elif x == 10:
+    print("x is equal to 10")
+else:
+    print("x is less than 10")

Loops (for and while)

-
# For loop
-for i in range(5):
-    print(i)
-
-# While loop
-count = 0
-while count < 5:
-    print(count)
-    count += 1
+
# For loop
+for i in range(5):
+    print(i)
+
+# While loop
+count = 0
+while count < 5:
+    print(count)
+    count += 1

Lists and basic list operations

-
# Creating a list
-fruits = ["apple", "banana", "orange"]
-
-# Accessing elements
-print(fruits[0])  # Output: "apple"
-
-# Adding elements
-fruits.append("grape")
-
-# Removing elements
-fruits.remove("banana")
-
-# Length of the list
-print(len(fruits))  # Output: 3
+
# Creating a list
+fruits = ["apple", "banana", "orange"]
+
+# Accessing elements
+print(fruits[0])  # Output: "apple"
+
+# Adding elements
+fruits.append("grape")
+
+# Removing elements
+fruits.remove("banana")
+
+# Length of the list
+print(len(fruits))  # Output: 3

Functions

-
# Function to add two numbers and return the result
-def add_numbers(a, b):
-    return a + b
-
-result = add_numbers(5, 3)
-print(result)  # Output: 8
+
# Function to add two numbers and return the result
+def add_numbers(a, b):
+    return a + b
+
+result = add_numbers(5, 3)
+print(result)  # Output: 8

Dictionaries

-
# Creating a dictionary
-person = {
-    "name": "Alice",
-    "age": 30,
-    "is_student": False
-}
-
-# Accessing values
-print(person["name"])  # Output: "Alice"
-
-# Adding a new key-value pair
-person["occupation"] = "Engineer"
-
-# Removing a key-value pair
-del person["is_student"]
+
# Creating a dictionary
+person = {
+    "name": "Alice",
+    "age": 30,
+    "is_student": False
+}
+
+# Accessing values
+print(person["name"])  # Output: "Alice"
+
+# Adding a new key-value pair
+person["occupation"] = "Engineer"
+
+# Removing a key-value pair
+del person["is_student"]

Importing packages

@@ -1457,7 +1516,7 @@

Importing packages

In python we import a package using the import statement (instead of th the library function in R) . For example importing the pandas package goes as follows

-
import pandas as pd
+
import pandas as pd

As you can see we can import a package as something. We use this if we want to point at specific functionality of this package. If we want to point at for example the read_csv function from @@ -1466,44 +1525,44 @@

Importing packages

version of this function. Importing pandas is a convention, used very widely in the python community.

We can create a dataframe as follows:

-
data = {
-    'Name': ['Alice', 'Bob', 'Charlie'],
-    'Age': [25, 30, 22],
-    'City': ['New York', 'San Francisco', 'Chicago']
-}
-
-df = pd.DataFrame(data)
-print(df)
+
data = {
+    'Name': ['Alice', 'Bob', 'Charlie'],
+    'Age': [25, 30, 22],
+    'City': ['New York', 'San Francisco', 'Chicago']
+}
+
+df = pd.DataFrame(data)
+print(df)

We can access some information from this dataframe as follows:

-
# Display the first few rows of the DataFrame
-print(df.head())
-
-# Get statistical information about the DataFrame
-print(df.describe())
-
-# Access a specific column
-print(df['Age'])
+
# Display the first few rows of the DataFrame
+print(df.head())
+
+# Get statistical information about the DataFrame
+print(df.describe())
+
+# Access a specific column
+print(df['Age'])

GeoDataFrame

The spatial counterpart of a dataframe is a ‘GeoDataFrame’, which we normally import as gpd:

-
import geopandas as gpd
-
-# Dummy data for the GeoDataFrame
-data = {
-    'Name': ['Location A', 'Location B', 'Location C'],
-    'Latitude': [40.7128, 34.0522, 41.8781],
-    'Longitude': [-74.0060, -118.2437, -87.6298]
-}
-
-# Create the GeoDataFrame with a single line of code
-gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data['Longitude'], data['Latitude']))
-
-# Display the GeoDataFrame
-print(gdf)
+
import geopandas as gpd
+
+# Dummy data for the GeoDataFrame
+data = {
+    'Name': ['Location A', 'Location B', 'Location C'],
+    'Latitude': [40.7128, 34.0522, 41.8781],
+    'Longitude': [-74.0060, -118.2437, -87.6298]
+}
+
+# Create the GeoDataFrame with a single line of code
+gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data['Longitude'], data['Latitude']))
+
+# Display the GeoDataFrame
+print(gdf)
@@ -1520,32 +1579,32 @@

Visualization

and text. This hierarchy is important to understand when you want to edit a plot:

-matplotlib class structure +matplotlib class structure
Matplotlib hierarchy of figure elements, source https://www.aosabook.org/en/matplotlib.html.
-matplotlib simple plot +matplotlib simple plot
Elements in an example Matplotlib figure, source https://www.aosabook.org/en/matplotlib.html.

Let’s create a simple line figure, based on data in NumPy.arrays.

-
import numpy as np
-from matplotlib import pyplot as plt
-
-# Create some data
-x = np.arange(-np.pi, np.pi, 0.2)
-y = np.sin(x)
-
-# Plot x against y
-plt.plot(x, y)
-
-# Show the plot
-plt.show()
-

matplotlib simple figure

+
import numpy as np
+from matplotlib import pyplot as plt
+
+# Create some data
+x = np.arange(-np.pi, np.pi, 0.2)
+y = np.sin(x)
+
+# Plot x against y
+plt.plot(x, y)
+
+# Show the plot
+plt.show()
+

matplotlib simple figure

Note that the behavior of plt.show() depends on how you run the script. If you are using Spyder and want plt.show() to work:

@@ -1566,71 +1625,71 @@

Visualization

meaningful in the context of trigonometric functions and customize the labels with regular text or lateX. Check if you understand what object in the hierarchy is edited and why.

-
import numpy as np
-from matplotlib import pyplot as plt
-
-# Create some data
-x1 = np.arange(-np.pi, np.pi, 0.2)
-y1 = np.sin(x1)
-x2 = np.arange(-np.pi, 0, 0.2)
-y2 = np.cos(x2)
-
-# Initiate a figure with two subplots 
-f, axarr = plt.subplots(2, sharex=True)
-
-# Subplots are stored in an array
-line = axarr[0].plot(x1, y1)
-axarr[0].set_title('plot 1')
-axarr[1].plot(x2, y2)
-axarr[1].set_title('plot 2')
-f.suptitle('super title', fontsize=16)
-
-# Axis label
-axarr[1].set_xlabel('x')
-axarr[0].set_ylabel('sin(x)')
-axarr[1].set_ylabel('cos(x)')
-
-# Axis ticks and tick labels
-xticks = axarr[1].get_xticks()
-print(xticks)
-new_ticks = np.arange(-np.pi, np.pi + 0.1, 0.25 * np.pi)
-new_labels = [r"$-\pi$", r"$-\frac{3}{4}\pi$",
-              r"$-\frac{1}{2}\pi$", r"$-\frac{1}{4}\pi$",
-              "$0$", r"$\frac{1}{4}\pi$",
-              r"$\frac{1}{2}\pi$", r"$\frac{3}{4}\pi$",
-              r"$2\pi$"]
-axarr[1].set_xticks(new_ticks)
-axarr[1].set_xticklabels(new_labels)
-plt.show()
-

two subplots with shared x-axis

+
import numpy as np
+from matplotlib import pyplot as plt
+
+# Create some data
+x1 = np.arange(-np.pi, np.pi, 0.2)
+y1 = np.sin(x1)
+x2 = np.arange(-np.pi, 0, 0.2)
+y2 = np.cos(x2)
+
+# Initiate a figure with two subplots 
+f, axarr = plt.subplots(2, sharex=True)
+
+# Subplots are stored in an array
+line = axarr[0].plot(x1, y1)
+axarr[0].set_title('plot 1')
+axarr[1].plot(x2, y2)
+axarr[1].set_title('plot 2')
+f.suptitle('super title', fontsize=16)
+
+# Axis label
+axarr[1].set_xlabel('x')
+axarr[0].set_ylabel('sin(x)')
+axarr[1].set_ylabel('cos(x)')
+
+# Axis ticks and tick labels
+xticks = axarr[1].get_xticks()
+print(xticks)
+new_ticks = np.arange(-np.pi, np.pi + 0.1, 0.25 * np.pi)
+new_labels = [r"$-\pi$", r"$-\frac{3}{4}\pi$",
+              r"$-\frac{1}{2}\pi$", r"$-\frac{1}{4}\pi$",
+              "$0$", r"$\frac{1}{4}\pi$",
+              r"$\frac{1}{2}\pi$", r"$\frac{3}{4}\pi$",
+              r"$2\pi$"]
+axarr[1].set_xticks(new_ticks)
+axarr[1].set_xticklabels(new_labels)
+plt.show()
+

two subplots with shared x-axis

One can also create multiple subplots (axes) and use different plotting styles, changing e.g. the marker style, line style, marker size, and colors. Furthermore, for the upper left subplot it is demonstrated how to add a legend; adding a label to the plotted line is essential for this.

-
from matplotlib import pyplot as plt
-
-x = [1, 2, 3, 4, 5]
-y = [6, 7, 8, 9, 10]
-
-# New: define number of rows and columns of subplots and unpack them directly 
-# into variables that then each contain one axes object
-f, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2, 2)
-
-# Dashed line, label for legend, and show the legend on the subplot
-ax0.plot(x, y, 'r--', label='red dashed line')
-ax0.legend(loc='lower right')
-
-# Scatter plot, using a colormap based on the y-value, changing the marker size to 35
-ax1.scatter(x, y, c=y, cmap='bwr', s=35)
-
-# Bar chart, changing the bar color to black
-ax2.bar(x, y, color='k')
-
-# Horizontal bar chart, changing the bar color to yellow
-ax3.barh(x, y, color='y')
-plt.show()
-

matplotlib plot type examples

+
from matplotlib import pyplot as plt
+
+x = [1, 2, 3, 4, 5]
+y = [6, 7, 8, 9, 10]
+
+# New: define number of rows and columns of subplots and unpack them directly 
+# into variables that then each contain one axes object
+f, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2, 2)
+
+# Dashed line, label for legend, and show the legend on the subplot
+ax0.plot(x, y, 'r--', label='red dashed line')
+ax0.legend(loc='lower right')
+
+# Scatter plot, using a colormap based on the y-value, changing the marker size to 35
+ax1.scatter(x, y, c=y, cmap='bwr', s=35)
+
+# Bar chart, changing the bar color to black
+ax2.bar(x, y, color='k')
+
+# Horizontal bar chart, changing the bar color to yellow
+ax3.barh(x, y, color='y')
+plt.show()
+

matplotlib plot type examples

@@ -1649,12 +1708,12 @@

Visualization

and replace plt.show() with the code below). Adjust the Spyder working directory by clicking on the directory icon in the top right pane if necessary.

-
ax0.set_aspect('equal')
-ax1.set_aspect('equal')
-ax2.set_aspect('equal')
-ax3.set_aspect('equal')
-plt.savefig('output/equal_scale.png')
-

matplotlib plot example with equal scale

+
ax0.set_aspect('equal')
+ax1.set_aspect('equal')
+ax2.set_aspect('equal')
+ax3.set_aspect('equal')
+plt.savefig('output/equal_scale.png')
+

matplotlib plot example with equal scale

@@ -1666,8 +1725,8 @@

Python help

Asking your friends or colleagues in person is also a great way to learn and fix programming problems. Another good option is get documentation from the package website or inside Python:

-
import sys
-help(sys)
+
import sys
+help(sys)

See how the objects and functions in the sys package got listed.

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