From 42826218ba8a706fe6f02bfb55765b3582910b5f Mon Sep 17 00:00:00 2001 From: Elina Date: Mon, 21 Aug 2023 03:59:29 -0400 Subject: [PATCH 1/4] changed formatting --- index.Rmd | 34 ++++++++++++++++++++-------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/index.Rmd b/index.Rmd index 7c0b981..2c08bd5 100644 --- a/index.Rmd +++ b/index.Rmd @@ -1,23 +1,29 @@ --- -pagetitle: "Tutorial 8: Python refresher" +pagetitle: "Python refresher" author: "Jan Verbesselt, Jorge Mendes de Jesus, Aldo Bergsma, Johannes Eberenz, Dainius Masiliunas, David Swinkels, Judith Verstegen, Corné Vreugdenhil, Arno Timmer" -date: "`r format(Sys.time(), '%d %B, %Y')`" +date: "`r format(Sys.time(), '%Y-%m-%d')`" output: - knitrBootstrap::bootstrap_document: - title: "Tutorial 8: Python refresher" - theme: "simplex" - highlight: Tomorrow Night Bright - menu: FALSE - theme.chooser: TRUE - highlight.chooser: TRUE + rmdformats::html_clean: + highlight: zenburn --- - +```{css, echo=FALSE} +@import url("https://netdna.bootstrapcdn.com/bootswatch/3.0.0/simplex/bootstrap.min.css"); +.main-container {max-width: none;} +div.figcaption {display: none;} +pre {color: inherit; background-color: inherit;} +code[class^="sourceCode"]::before { + content: attr(class); + display: block; + text-align: right; + font-size: 70%; +} +code[class^="sourceCode r"]::before { content: "R Source";} +code[class^="sourceCode python"]::before { content: "Python Source"; } +code[class^="sourceCode bash"]::before { content: "Bash Source"; } +``` -# [WUR Geoscripting](https://geoscripting-wur.github.io/) WUR logo +[WUR Geoscripting](https://geoscripting-wur.github.io/) WUR logo # Week 3, Tutorial 8: Python refresher From b5aa9855506675abf07e48a46cee118923abf852 Mon Sep 17 00:00:00 2001 From: Elina Date: Mon, 21 Aug 2023 09:00:14 -0400 Subject: [PATCH 2/4] added question, updated learning outcomes --- index.Rmd | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/index.Rmd b/index.Rmd index 2c08bd5..8c735a9 100644 --- a/index.Rmd +++ b/index.Rmd @@ -25,7 +25,7 @@ code[class^="sourceCode bash"]::before { content: "Bash Source"; } [WUR Geoscripting](https://geoscripting-wur.github.io/) WUR logo -# Week 3, Tutorial 8: Python refresher +# Python refresher ## Introduction @@ -36,16 +36,10 @@ Good afternoon! Today we will start working with Python for geoscripting and do ## Today’s Learning objectives +- Be able to visualize data using Python and matplotlib +- Familiarize yourself with Python objects and inheritance - Know how to work with virtual environments: *Conda* + *Mamba* -- Know how to run a Python script from the terminal -- Get introduced to Python editors and IDEs -- Refresh Python programming knowledge - -- Know how to work with virtual environments: Conda + Mamba - Get introduced to Python editors and IDEs -- Familiarize yourself with python Objects and inheritance -- Be able to visualize data using python and matplotlib - # Introduction to Python @@ -70,12 +64,12 @@ Python is a jack-of-all-trades programming language that is free, flexible, open * PyTorch (Deep Learning) ## 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 your system Python interpreter](https://askubuntu.com/questions/95037/what-is-the-best-way-to-install-python-packages). +A set of tools exist for installing and managing Python packages. Although it is possible to install packages on your base Python interpreter, sooner or later you will get conflicts between Python packages since packages may have varying dependencies. This can even [break your system Python interpreter](https://askubuntu.com/questions/95037/what-is-the-best-way-to-install-python-packages). -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. +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 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 collaborators or users of your code. ## Mamba installation -For this course, we will make use of *Mamba*, a fast drop-in reimplementation of the *Conda* package manager. It has its core parts implemented in C++ for maximum efficiency, makes use of parallel downloading of repository data and package files using multi-threading, and uses `libsolv` for (much) faster dependency solving. +For this course, we will make use of *Mamba*, a fast drop-in re-implementation of the *Conda* package manager. It has its core parts implemented in C++ for maximum efficiency, makes use of parallel downloading of repository data and package files using multi-threading, and uses `libsolv` for (much) faster dependency solving. To install *Mamba* in your Linux environment, we have prepared a short *Bash* script for you. Just run the following lines of code, line by line, in a new terminal window. ```{bash, eval=FALSE} From 0b2f9a2f27ae4050584211951adcc4dd6abf3079 Mon Sep 17 00:00:00 2001 From: Elina Date: Tue, 22 Aug 2023 00:37:39 -0400 Subject: [PATCH 3/4] grammar fixes --- index.Rmd | 53 +- index.html | 3022 ++++++++++++++++++++++++---------------------------- 2 files changed, 1444 insertions(+), 1631 deletions(-) diff --git a/index.Rmd b/index.Rmd index 8c735a9..a39c59e 100644 --- a/index.Rmd +++ b/index.Rmd @@ -36,10 +36,11 @@ Good afternoon! Today we will start working with Python for geoscripting and do ## Today’s Learning objectives -- Be able to visualize data using Python and matplotlib -- Familiarize yourself with Python objects and inheritance - Know how to work with virtual environments: *Conda* + *Mamba* - Get introduced to Python editors and IDEs +- Familiarize yourself with Python objects and inheritance +- Be able to visualize data using Python and matplotlib + # Introduction to Python @@ -68,6 +69,10 @@ A set of tools exist for installing and managing Python packages. Although it is 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 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 collaborators or users of your code. +```{block, type="alert alert-success"} +> **Question 1**: Why do you not need to worry about environments for scripting in R? Hint: Look back at [R & Python Basics](https://geoscripting-wur.github.io/RPythonBasics/#what-are-r-and-python). +``` + ## Mamba installation For this course, we will make use of *Mamba*, a fast drop-in re-implementation of the *Conda* package manager. It has its core parts implemented in C++ for maximum efficiency, makes use of parallel downloading of repository data and package files using multi-threading, and uses `libsolv` for (much) faster dependency solving. To install *Mamba* in your Linux environment, we have prepared a short *Bash* script for you. Just run the following lines of code, line by line, in a new terminal window. @@ -95,13 +100,13 @@ Scroll down the license with enter. Accept License (i.e. type yes). Use default This will install *Mamba* into `~/mamba`. Finally, restart your terminal to be able to use *Mamba* and *Conda* in the terminal. Next, let's see how to use *Mamba* in case you want make new virtual environments by yourself, or install packages after creating the environment. ## 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* 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: ```{r, eval=FALSE, engine='bash'} mamba create --name geotest python numpy ``` -This would create 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). +This would create 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: @@ -124,10 +129,10 @@ 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 instance, if you close the shell window and open a new one you will have to activate it again. -After creating a conda environment, (additional) Python packages can be installed. There are three possible ways to install packages, which we list below. +After creating a Conda environment, (additional) Python packages can be installed. There are three possible ways to install packages, which we list below. -* Using *Mamba* to install and manage conda packages. This downloads conda packages using conda channels, which are URLs to directories containing the conda packages. **Generally, installing conda packages using *Mamba* is the preferred method.** -* Using *pip* to install packages and *Mamba* to manage these packages. *pip* is available for Windows, macOS and Linux. *pip* can also install [binary wheels on Windows](https://www.lfd.uci.edu/~gohlke/Pythonlibs). You should generally not install packages from *pip* in a conda environment unless it's the last resort. This is because after you use *pip* to modify an environment, you can no longer use `conda`/`mamba` to do so (trying that will break your environment, because *pip* does not communicate its changes to *Conda*). Hence install packages with `mamba` that you can first, and only then use `pip`, and then never touch the environment with `mamba` again (delete and start fresh if you need to). +* Using *Mamba* to install and manage Conda packages. This downloads Conda packages using Conda channels, which are URLs to directories containing the Conda packages. **Generally, installing Conda packages using *Mamba* is the preferred method.** +* Using *pip* to install packages and *Mamba* to manage these packages. *pip* is available for Windows, macOS and Linux. *pip* can also install [binary wheels on Windows](https://www.lfd.uci.edu/~gohlke/Pythonlibs). You should generally not install packages from *pip* in a Conda environment unless it's the last resort. This is because after you use *pip* to modify an environment, you can no longer use `Conda`/`mamba` to do so (trying that will break your environment, because *pip* does not communicate its changes to *Conda*). Hence install packages with `mamba` that you can first, and only then use `pip`, and then never touch the environment with `mamba` again (delete and start fresh if you need to). * Using the distribution's package manager (only on Ubuntu, that is `sudo apt-get install python-*`). 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: @@ -156,7 +161,7 @@ Some additional helpful utilities for package management in this context are: * `mamba list` to check which packages are installed in `root` or in the active environment; * `python --version` or `gdal-config --version` to check which Python or GDAL version is used in the environment; -* `which spyder` or `type spyder` to find out which *Spyder* executable is used either from system or conda environment. +* `which spyder` or `type spyder` to find out which *Spyder* executable is used either from system or Conda environment. Removing packages is just as simple: @@ -186,7 +191,7 @@ 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: +Within a Conda environment, Python can be started directly, or can be called to run a script file. To start Python directly: ```{r, eval=FALSE, engine='bash'} python @@ -265,7 +270,7 @@ conda deactivate ### 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: +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: ```{r, eval=FALSE,engine='bash'} spyder @@ -285,7 +290,7 @@ Open a new file and save it somewhere as `main.py` (File -- > New File --> Save ## Setting up the environment -In the second part of this tutorial we will refresh your python 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. +In the second part of this tutorial we will refresh your Python 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: @@ -296,7 +301,7 @@ 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 preferred text editor, e.g. `gedit`. Then, (re)name it (to) `refresher.yaml`, and copy the following content into the file: +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 preferred text editor, e.g. `gedit`. Then, (re)name it (to) `refresher.yaml`, and copy the following content into the file: ``` name: refresher @@ -308,7 +313,7 @@ dependencies: - spyder ``` -Now, create a new conda environment based on this file: +Now, create a new Conda environment based on this file: ```{r, eval=FALSE, engine='bash'} mamba env create --file refresher.yaml @@ -327,12 +332,12 @@ Important to note: for compatibility, it is best to install packages from the sa ## Quick refresher -In the tutorial about R and Python we have gone over the differences and similarities of python and R. This tutorial also contains some basic python syntax, in this tutorial we assume you know this content, but we will go over a few basics here as well. The example below are mostly meant for reference purposes, we assume you understand most of this refresher already. +In the tutorial about R and Python we have gone over the differences and similarities of Python and R. This tutorial also contains some basic Python syntax, in this tutorial we assume you know this content, but we will go over a few basics here as well. The example below are mostly meant for reference purposes, we assume you understand most of this refresher already. ### Printing and basic data types -In python we assign variable using the equals sign (`=`): +In Python we assign variable using the equals sign (`=`): -Printing in python is done using the `print` function. We can print variables directly: +Printing in Python is done using the `print` function. We can print variables directly: ```{r, engine = 'Python', eval=FALSE} # Integer @@ -351,7 +356,7 @@ is_student = True 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. +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. ```{r, engine = 'Python', eval=FALSE} # String formatting and printing @@ -451,15 +456,15 @@ del person["is_student"] ### Importing packages -Python is used by a very large community, as is said before. One of the reasons for this is that this entire community builds a lot of (open source) packages. It is therefor very useful to be able to build upon these packages. In R you have worked a with *dataframes* and *spatial dataframes*. In Python these are not standard datatypes, but they are implemented in very well known packages called `Pandas` and its spatial counterpart `GeoPandas.` We will go in much more detail during the Python-Vector tutorial but we will introduce them quickly here. +Python is used by a very large community, as is said before. One of the reasons for this is that this entire community builds a lot of (open source) packages. It is therefore very useful to be able to build upon these packages. In R you have worked a with *dataframes* and *spatial dataframes*. In Python these are not standard datatypes, but they are implemented in very well known packages called `Pandas` and its spatial counterpart `GeoPandas.` We will go in much more detail during the Python-Vector tutorial but we will introduce them quickly here. -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 +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 ```{r, engine = 'Python', eval=FALSE} 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 pandas we we call `pd.read_csv`. This function is also implemented in other packages, but now we are sure we use the pandas version of this function. Importing pandas is a convention, used very widely in the python community. +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 pandas we we call `pd.read_csv`. This function is also implemented in other packages, but now we are sure we use the pandas version of this function. Importing pandas is a convention, used very widely in the Python community. We can create a `dataframe` as follows: ```{r, engine = 'Python', eval=FALSE} @@ -545,7 +550,7 @@ person2.greet() # Output: Hello, my name is Bob and I'm 30 years old. This example is straightforward, but keep in mind that classes can become more complex. ```{block, type="alert alert-success"} -> **Question 1**: Take a look at the creation of a GeoPandas `GeoSeries` [here](https://github.com/geopandas/geopandas/blob/main/geopandas/geoseries.py#L83). It may seem complicated and hard to read, but it is well-documented, so try to understand some of its functionality. The `to_json` method, for example, provides straightforward functionality (though it may still be challenging to read due to its complexity). +> **Question 2**: Take a look at the creation of a GeoPandas `GeoSeries` [here](https://github.com/geopandas/geopandas/blob/main/geopandas/geoseries.py#L83). It may seem complicated and hard to read, but it is well-documented, so try to understand some of its functionality. The `to_json` method, for example, provides straightforward functionality (though it may still be challenging to read due to its complexity). ``` You may have noticed that class definitions differ from what we have learned. In the `GeoPandas` example, the class is defined as follows: @@ -593,7 +598,7 @@ student.study() # Output: Eve is studying. In this example, `student` is an instance of the `Student` class. It can access the inherited properties from the `Person` class, such as `name`, as well as the newly added property `student_id` and `is_studying`, which defaults to `False`. Similarly, it can invoke both the inherited method `greet` and the additional method `study`, which are specific to the `Student` class. The method `study` prints a message and sets the `is_studying` property to `True`. ```{block, type="alert alert-success"} -> **Question 2**: Create a new class called `Teacher`. This new class also inherits from `Person`. Define a method for the teacher that checks whether a student is studying. The student should be an input to the method. +> **Question 3**: Create a new class called `Teacher`. This new class also inherits from `Person`. Define a method for the teacher that checks whether a student is studying. The student should be an input to the method. ``` ## Visualization @@ -712,7 +717,7 @@ plt.show() matplotlib plot type examples ```{block, type="alert alert-success"} -> **Question 3**: In the upper right subplot, why is there no point at x=3, y=8?. +> **Question 4**: In the upper right subplot, why is there no point at x=3, y=8?. ``` When working with spatial data, it is crucial that the spatial scale (aspect) in the x and y direction is the same. Because Matplotlib can be used for plotting any kind of data, not necessarily spatial, it does not automatically do this. We can use `plt.axis('equal')`, or `ax.set_aspect('equal')` on the axis, to ensure equal scales in both directions. Let's test this on the non-spatial data in the plots above, see what happens (make sure to adjust the code above properly, 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. @@ -739,7 +744,7 @@ help(sys) See how the objects and functions in the `sys` package got listed. ```{block, type="alert alert-success"} -> **Question 4**: What kind of functionality does the `sys` package provide? +> **Question 5**: What kind of functionality does the `sys` package provide? ``` # What have we learned? diff --git a/index.html b/index.html index 54929cd..7b39a14 100644 --- a/index.html +++ b/index.html @@ -1,1059 +1,982 @@ - + - - - - - - Tutorial 8: Python refresher - - - - - - - - - - - - - - - - - - - - - - + + + + + - + + + + + + + + + + + - @media screen and (max-width: 768px) { - .row-offcanvas .collapsing { - -webkit-transition: none 0; - -moz-transition: none 0; - transition: none 0; - } - .row-offcanvas .navbar { - position: absolute; - z-index: 2; - right:0; - height:100%; - width:55px; - border:0; - background-color:transparent; - } - .row-offcanvas .navbar-toggle { - margin-right: 5px; - margin-left: 5px; - } - .row-offcanvas { - position: relative; - } - .row-offcanvas-right.active .navbar { - position: absolute; - z-index: 2; - right: -28.4%; - width:40%; - background-color:#eee; - border:0 solid #ddd; - border-left-width:1px; - } - .row-offcanvas-right.active { - left: -30%; - } - .row-offcanvas-right.active .navbar-collapse { - position: relative; - width: 100%; - } - .row-offcanvas .content { - /*width:calc(100% - 60px);*/ - } - } - + + + + + + + + - - -Tutorial 8: Python refresher - -
-
-
-
+ + + + +
+
+
+ + + + + + + + +

Jan Verbesselt, Jorge Mendes de Jesus, +Aldo Bergsma, Johannes Eberenz, Dainius Masiliunas, David Swinkels, +Judith Verstegen, Corné Vreugdenhil, Arno Timmer

+

2023-08-21

+ + + + +
-

WUR Geoscripting -WUR logo

-

Week 3, Tutorial 8: Python -refresher

-

Introduction

+

WUR +Geoscripting +WUR logo

+
+

Python refresher

+
+

Introduction

Good afternoon! Today we will start working with Python for geoscripting and do a refresher of functions in Python. If you are unfamiliar with Python and/or feel that you need more training, follow one of the Datacamp courses as introduction into Python before today:

-

Today’s Learning objectives

+
+
+

Today’s Learning objectives

    -

  • Know how to work with virtual environments: Conda + -Mamba

    • -

  • Know how to run a Python script from the terminal

    • -

  • Get introduced to Python editors and IDEs

    • -

  • Refresh Python programming knowledge

    • -

  • Know how to work with virtual environments: Conda + -Mamba

    • -

  • Get introduced to Python editors and IDEs

    • -

  • Familiarize yourself with python Objects and inheritance

    • -

  • Be able to visualize data using python and matplotlib

    • +
  • Know how to work with virtual environments: Conda + +Mamba
    • +
  • Get introduced to Python editors and IDEs
    • +
  • Familiarize yourself with Python objects and inheritance
    • +
  • Be able to visualize data using Python and matplotlib
-

Introduction to Python

+
+
+
+

Introduction to Python

Python is a jack-of-all-trades programming language that is free, flexible, open-source, cross-platform and has a very large community behind it. If you ask Python programmers what they like most about @@ -1088,49 +1011,57 @@

Introduction to Python

  • PyTorch (Deep Learning)
    • -

    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 +

    +

    Python package management with Conda

    +

    A set of tools exist for installing and managing Python packages. +Although it is possible to install packages on your base Python +interpreter, sooner or later you will get conflicts between Python +packages since packages may have varying dependencies. This 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 +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 for installing Python packages. Packages installed in one environment do not -interfere with your base Python or with other conda environments. +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.

    -

    Mamba installation

    +your (open source) project with collaborators or users of your code.

    +
    +
    +

    +Question 1: Why do you not need to worry about +environments for scripting in R? Hint: Look back at R +& Python Basics. +

    +
    +
    +
    +
    +

    Mamba installation

    For this course, we will make use of Mamba, a fast drop-in -reimplementation of the Conda package manager. It has its core +re-implementation of the Conda package manager. It has its core parts implemented in C++ for maximum efficiency, makes use of parallel downloading of repository data and package files using multi-threading, and uses libsolv for (much) faster dependency solving. To install Mamba in your Linux environment, we have prepared a short Bash script for you. Just run the following lines of code, line by line, in a new terminal window.

    -
    - -
    git clone https://github.com/GeoScripting-WUR/InstallLinuxScript.git
-cd InstallLinuxScript/user
-chmod u+x ./install.sh
-./install.sh
    -
    +
    git clone https://github.com/GeoScripting-WUR/InstallLinuxScript.git
+cd InstallLinuxScript/user
+chmod u+x ./install.sh
+./install.sh

    This will install Mamba into ~/mamba. Finally, @@ -1138,69 +1069,55 @@

    Mamba installation

    Conda in the terminal. Next, let’s see how to use Mamba in case you want make new virtual environments by yourself, or install packages after creating the environment.

    -

    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
    +
    +

    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 Python, NumPy and Spyder installed into the -conda environment. Another option is to create an environment from a +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
    -
    +
    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 instance, if you close the shell window and open a new one you will have to activate it again.

    -

    After creating a conda environment, (additional) Python packages can +

    After creating a Conda environment, (additional) Python packages can be installed. There are three possible ways to install packages, which we list below.

      -
    • Using Mamba to install and manage conda packages. This -downloads conda packages using conda channels, which are URLs to -directories containing the conda packages. Generally, installing -conda packages using Mamba is the preferred +
    • Using Mamba to install and manage Conda packages. This +downloads Conda packages using Conda channels, which are URLs to +directories containing the Conda packages. Generally, installing +Conda packages using Mamba is the preferred method.
    • Using pip to install packages and Mamba to manage these packages. pip is available for Windows, macOS and Linux. -pip can also install binary wheels on +pip can also install binary wheels on Windows. You should generally not install packages from pip -in a conda environment unless it’s the last resort. This is because +in a Conda environment unless it’s the last resort. This is because after you use pip to modify an environment, you can no longer -use conda/mamba to do so (trying that will +use Conda/mamba to do so (trying that will break your environment, because pip does not communicate its changes to Conda). Hence install packages with mamba that you can first, and only then use @@ -1212,31 +1129,16 @@

      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 @@ -1250,67 +1152,38 @@

      Mamba usage

    • python --version or gdal-config --version to check which Python or GDAL version is used in the environment;
    • which spyder or type spyder to find out -which Spyder executable is used either from system or conda +which Spyder executable is used either from system or Conda 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 +

    +

    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)
    -
    -To go back, type: -
    - -
    exit()
-# or 
-quit()
    -
    +
    import sys
+print('Good morning, you are running Python:', sys.version)
    +

    To go back, type:

    +
    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 @@ -1318,19 +1191,16 @@

    Running a Python script (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 the script has finished running. Therefore, running a script from the terminal is a good final test before submitting an exercise or assignment.

    -

    Python editors and IDEs

    +
    +
    +

    Python editors and IDEs

    There are many Integrated Development Environments [IDE] for Python, and every programmer has their own preference. An IDE is a software application that provides facilities for software development.

    @@ -1343,8 +1213,7 @@

    Python editors and IDEs

    Spark or PySpark.
  • Spyder is a lightweight IDE. In this course, Spyder is the recommended Python IDE.
    • -
  • PyCharm +
  • PyCharm Community Edition is a free professional Python IDE with a lot of advanced functionality, such as integrated GIT version control, code completion, code checking, debugging and navigation. This IDE can @@ -1352,17 +1221,13 @@

    Python editors and IDEs

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

    Jupyter Notebooks

    +
    +

    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 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 @@ -1389,20 +1254,15 @@

    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 @@ -1412,25 +1272,17 @@

    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

    +
    +

    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 +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
    -
    +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:

      @@ -1443,24 +1295,24 @@

      Spyder

      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.

      -

      Python refresher

      -

      Setting up the environment

      -

      In the second part of this tutorial we will refresh your python +

    +
    +
    +
    +

    Python refresher

    +
    +

    Setting up the environment

    +

    In the second part of this tutorial we will refresh your Python 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.

    +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 +Conda environment from a file. First create a text file in your preferred text editor, e.g. gedit. Then, (re)name it (to) refresher.yaml, and copy the following content into the file:

    @@ -1471,251 +1323,201 @@

    Setting up the environment

    - matplotlib - geopandas - spyder -

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

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

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

    +
    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 file refresher.yaml are installed from the conda-forge channel, it is wise to use this same channel when you want to install additional packages in your environment.

    -

    Quick refresher

    +
    +
    +

    Quick refresher

    In the tutorial about R and Python we have gone over the differences -and similarities of python and R. This tutorial also contains some basic -python syntax, in this tutorial we assume you know this content, but we +and similarities of Python and R. This tutorial also contains some basic +Python syntax, in this tutorial we assume you know this content, but we will go over a few basics here as well. The example below are mostly meant for reference purposes, we assume you understand most of this refresher already.

    -

    Printing and basic data -types

    -

    In python we assign variable using the equals sign +

    +

    Printing and basic data types

    +

    In Python we assign variable using the equals sign (=):

    -

    Printing in python is done using the print function. We +

    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.)
    +Python code.

    +
    # 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)
    +
    +

    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)
    -

    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")
    +
    +

    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")
    -

    Loops (for and while)

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

    Loops (for and while)

    +
    # 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
    +
    +

    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
    -

    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
    +
    +

    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
    -

    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"]
    +
    +

    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"]
    -

    Importing packages

    +
    +

    Importing packages

    Python is used by a very large community, as is said before. One of the reasons for this is that this entire community builds a lot of (open -source) packages. It is therefor very useful to be able to build upon +source) packages. It is therefore very useful to be able to build upon these packages. In R you have worked a with dataframes and spatial dataframes. In Python these are not standard datatypes, but they are implemented in very well known packages called Pandas and its spatial counterpart GeoPandas. We will go in much more detail during the Python-Vector tutorial but we will introduce them quickly here.

    -

    In python we import a package using the import statement +

    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 pandas we we call pd.read_csv. This function is also implemented in other packages, but now we are sure we use the pandas 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)
    -
    +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)

    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)
    +
    +

    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)
    -

    Object-Oriented -Programming in Python

    +
    +
    +

    Object-Oriented Programming in Python

    We have now gone over most of the more basic basic functionality of Python, a lot of similar things you have used in R. A concept we have not used before is the concept of objects. They have shortly been @@ -1734,8 +1536,8 @@

    Object-Oriented organized programming. Especially when working on projects containing lots of code OOP will make your work a lot easier to understand for you and others and it is easier to re-use parts of the code.

    -

    How to work with objects in -Python

    +
    +

    How to work with objects in Python

    In Python, objects are created and manipulated using classes. A class serves as a blueprint that defines the structure and behavior of objects. It brings together data (properties) and functions (methods) @@ -1743,18 +1545,13 @@

    How to work with objects in class keyword, followed by the name of the class. Let’s take a look at an example of a simple class called Person:

    -
    - -
    class Person:
-    def __init__(self, name, age):
-        self.name = name  # < this is a property
-        self.age = age    # < this is also a property
-    
-    def greet(self):  # < This "function" is a method
-        print(f"Hello, my name is {self.name} and I'm {self.age} years old.")
    -
    +
    class Person:
+    def __init__(self, name, age):
+        self.name = name  # < this is a property
+        self.age = age    # < this is also a property
+    
+    def greet(self):  # < This "function" is a method
+        print(f"Hello, my name is {self.name} and I'm {self.age} years old.")

    In the provided code, the __init__ method is a special method known as a constructor. It is automatically called when an object is created from the class. The self parameter @@ -1769,35 +1566,23 @@

    How to work with objects in

    To create an instance of the Person class, you simply call the class as if it were a function and assign the result to a variable:

    -
    - -
    person1 = Person("Alice", 25)
-person2 = Person("Bob", 30)
    -
    +
    person1 = Person("Alice", 25)
+person2 = Person("Bob", 30)

    We have created two objects, person1 and person2, which are instances of the Person class. Now we can access the properties and call the methods of these objects:

    -
    - -
    print(person1.name)  # Output: Alice
-print(person2.age)   # Output: 30
-person1.greet()      # Output: Hello, my name is Alice and I'm 25 years old.
-person2.greet()      # Output: Hello, my name is Bob and I'm 30 years old.
    -
    +
    print(person1.name)  # Output: Alice
+print(person2.age)   # Output: 30
+person1.greet()      # Output: Hello, my name is Alice and I'm 25 years old.
+person2.greet()      # Output: Hello, my name is Bob and I'm 30 years old.

    This example is straightforward, but keep in mind that classes can become more complex.

    -

    -Question 1: Take a look at the creation of a GeoPandas -GeoSeries here. +Question 2: Take a look at the creation of a GeoPandas +GeoSeries here. It may seem complicated and hard to read, but it is well-documented, so try to understand some of its functionality. The to_json method, for example, provides straightforward functionality (though it @@ -1805,29 +1590,17 @@

    How to work with objects in

    -

    You may have noticed that class definitions differ from what we have learned. In the GeoPandas example, the class is defined as follows:

    -
    - -
    class GeoSeries(GeoPandasBase, Series):
    -
    +
    class GeoSeries(GeoPandasBase, Series):

    However, we previously learned to define a class like this:

    -
    - -
    class GeoSeries:
    -
    +
    class GeoSeries:

    The difference lies in the code within the parentheses, which represents inheritance. The class will inherit all the functionality from the classes specified as arguments, in this case, GeoPandasBase and Series. The -Series object refers to the Pandas.Series, +Series object refers to the Pandas.Series, which contains thousands of lines of code with various functionality. Therefore, the GeoPandas GeoSeries contains all the functionality implemented in Pandas, as well as those from @@ -1845,20 +1618,15 @@

    How to work with objects in object called Student, which will inherit all the properties and methods from the Person class we defined earlier.

    -
    - -
    class Student(Person):
-    def __init__(self, name, age, student_id):
-        super().__init__(name, age)
-        self.student_id = student_id
-        self.is_studying = False
-    
-    def study(self):
-        self.is_studying = True
-        print(f"{self.name} is studying.")
    -
    +
    class Student(Person):
+    def __init__(self, name, age, student_id):
+        super().__init__(name, age)
+        self.student_id = student_id
+        self.is_studying = False
+    
+    def study(self):
+        self.is_studying = True
+        print(f"{self.name} is studying.")

    In the provided code, the Student class inherits from the Person class, which we will refer to as the superclass. By doing so, it extends the functionality of the superclass by adding a @@ -1869,16 +1637,11 @@

    How to work with objects in

    As a result, the Student class contains both the methods and properties inherited from the Person class, as well as the additional ones defined within the Student class:

    -
    - -
    student = Student("Eve", 22, "123456")
-print(student.name)         # Output: Eve
-print(student.student_id)   # Output: 123456
-student.greet()             # Output: Hello, my name is Eve and I'm 22 years old.
-student.study()             # Output: Eve is studying.
    -
    +
    student = Student("Eve", 22, "123456")
+print(student.name)         # Output: Eve
+print(student.student_id)   # Output: 123456
+student.greet()             # Output: Hello, my name is Eve and I'm 22 years old.
+student.study()             # Output: Eve is studying.

    In this example, student is an instance of the Student class. It can access the inherited properties from the Person class, such as name, as well as the @@ -1889,11 +1652,10 @@

    How to work with objects in Student class. The method study prints a message and sets the is_studying property to True.

    -

    -Question 2: Create a new class called +Question 3: Create a new class called Teacher. This new class also inherits from Person. Define a method for the teacher that checks whether a student is studying. The student should be an input to the method. @@ -1901,7 +1663,9 @@

    How to work with objects in

    -

    Visualization

    +
    +
    +

    Visualization

    Visualization is essential to make concepts understandable and patterns recognizable. In Python, Matplotlib is a general plotting package. It is used as a base for many other, more tailored, @@ -1914,39 +1678,32 @@

    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 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. +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:

    @@ -1967,113 +1724,99 @@

    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

    -Question 3: In the upper right subplot, why is there no +Question 4: In the upper right subplot, why is there no point at x=3, y=8?.

    -

    When working with spatial data, it is crucial that the spatial scale (aspect) in the x and y direction is the same. Because Matplotlib can be used for plotting any kind of data, not necessarily spatial, it does not -automatically do this. We can use plt.axis('equal'), or -ax.set_aspect('equal') on the axis, to ensure equal scales +automatically do this. We can use plt.axis('equal'), or +ax.set_aspect('equal') on the axis, to ensure equal scales in both directions. Let’s test this on the non-spatial data in the plots above, see what happens (make sure to adjust the code above properly, 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')
    +
    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

    -

    matplotlib plot example with equal scale

    -

    Python help

    +
    +
    +

    Python help

    There are several ways to find help with programming in Python. Searching the internet typically solves your problem the quickest, because it finds answers on multiple platforms, such as StackOverflow @@ -2081,26 +1824,21 @@

    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.

    -

    -Question 4: What kind of functionality does the +Question 5: What kind of functionality does the sys package provide?

    -

    What have we learned?

    +
    +

    What have we learned?

    1. Python package management with Mamba & Conda
      2. @@ -2111,7 +1849,9 @@

      What have we learned?

    2. Visualization
    3. Python help
    -

    More info

    +
    +
    +

    More info

    +
  • Overview +Python package Cheatsheets
    • +
    -
    -
    - - -
    - + + +
    +
    + +
    +
    +
    + + + + + + + + + + From d2ff300204867599adb3fb2d24569cb98fbc038c Mon Sep 17 00:00:00 2001 From: Elina Date: Tue, 22 Aug 2023 07:22:01 -0400 Subject: [PATCH 4/4] fixed capital --- index.Rmd | 2 +- index.html | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/index.Rmd b/index.Rmd index a39c59e..c766bc8 100644 --- a/index.Rmd +++ b/index.Rmd @@ -132,7 +132,7 @@ After this, the current environment is shown in parentheses in front of your pro After creating a Conda environment, (additional) Python packages can be installed. There are three possible ways to install packages, which we list below. * Using *Mamba* to install and manage Conda packages. This downloads Conda packages using Conda channels, which are URLs to directories containing the Conda packages. **Generally, installing Conda packages using *Mamba* is the preferred method.** -* Using *pip* to install packages and *Mamba* to manage these packages. *pip* is available for Windows, macOS and Linux. *pip* can also install [binary wheels on Windows](https://www.lfd.uci.edu/~gohlke/Pythonlibs). You should generally not install packages from *pip* in a Conda environment unless it's the last resort. This is because after you use *pip* to modify an environment, you can no longer use `Conda`/`mamba` to do so (trying that will break your environment, because *pip* does not communicate its changes to *Conda*). Hence install packages with `mamba` that you can first, and only then use `pip`, and then never touch the environment with `mamba` again (delete and start fresh if you need to). +* Using *pip* to install packages and *Mamba* to manage these packages. *pip* is available for Windows, macOS and Linux. *pip* can also install [binary wheels on Windows](https://www.lfd.uci.edu/~gohlke/Pythonlibs). You should generally not install packages from *pip* in a Conda environment unless it's the last resort. This is because after you use *pip* to modify an environment, you can no longer use `conda`/`mamba` to do so (trying that will break your environment, because *pip* does not communicate its changes to *Conda*). Hence install packages with `mamba` that you can first, and only then use `pip`, and then never touch the environment with `mamba` again (delete and start fresh if you need to). * Using the distribution's package manager (only on Ubuntu, that is `sudo apt-get install python-*`). 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: diff --git a/index.html b/index.html index 7b39a14..116f4a4 100644 --- a/index.html +++ b/index.html @@ -7,7 +7,7 @@ - + Python refresher @@ -906,7 +906,7 @@

    Jan Verbesselt, Jorge Mendes de Jesus, Aldo Bergsma, Johannes Eberenz, Dainius Masiliunas, David Swinkels, Judith Verstegen, Corné Vreugdenhil, Arno Timmer

    -

    2023-08-21

    +

    2023-08-22

    @@ -1117,7 +1117,7 @@

    Mamba usage

    Windows. You should generally not install packages from pip in a Conda environment unless it’s the last resort. This is because after you use pip to modify an environment, you can no longer -use Conda/mamba to do so (trying that will +use conda/mamba to do so (trying that will break your environment, because pip does not communicate its changes to Conda). Hence install packages with mamba that you can first, and only then use