diff --git a/README.md b/README.md
index 060994a..c11d4d5 100644
--- a/README.md
+++ b/README.md
@@ -1,40 +1,90 @@
-# FIXME Lesson title
+# Tetris Game (俄罗斯方块)
 
-[![Create a Slack Account with us](https://img.shields.io/badge/Create_Slack_Account-The_Carpentries-071159.svg)](https://swc-slack-invite.herokuapp.com/)
+A simple, classic Tetris game implementation in Python using pygame.
 
-This repository generates the corresponding lesson website from [The Carpentries](https://carpentries.org/) repertoire of lessons. 
+## Description
 
-## Contributing
+This is a fully functional Tetris game featuring:
+- Classic Tetris gameplay with all 7 tetromino shapes
+- Score tracking
+- Smooth piece movement and rotation
+- Line clearing mechanics
+- Game over detection
 
-We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any
-questions, concerns, or experience any difficulties along the way.
+## Requirements
 
-We'd like to ask you to familiarize yourself with our [Contribution Guide](CONTRIBUTING.md) and have a look at
-the [more detailed guidelines][lesson-example] on proper formatting, ways to render the lesson locally, and even
-how to write new episodes.
+- Python 3.6 or higher
+- pygame 2.0.0 or higher
 
-Please see the current list of [issues][FIXME] for ideas for contributing to this
-repository. For making your contribution, we use the GitHub flow, which is
-nicely explained in the chapter [Contributing to a Project](http://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) in Pro Git
-by Scott Chacon.
-Look for the tag ![good_first_issue](https://img.shields.io/badge/-good%20first%20issue-gold.svg). This indicates that the maintainers will welcome a pull request fixing this issue.  
+## Installation
 
+1. Clone this repository:
+```bash
+git clone <repository-url>
+cd introduction-to-python
+```
 
-## Maintainer(s)
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
 
-Current maintainers of this lesson are 
+Or install pygame directly:
+```bash
+pip install pygame
+```
 
-* FIXME
-* FIXME
-* FIXME
+## How to Play
 
+Run the game with:
+```bash
+python tetris.py
+```
 
-## Authors
+### Controls
+
+- **LEFT/RIGHT Arrow Keys**: Move piece horizontally
+- **DOWN Arrow Key**: Soft drop (move piece down faster)
+- **UP Arrow Key**: Rotate piece clockwise
+- **SPACE**: Hard drop (instantly drop piece to the bottom)
+
+### Objective
+
+- Arrange falling tetromino pieces to create complete horizontal lines
+- Completed lines are cleared and you earn points
+- The game ends when pieces stack up to the top of the screen
+- Try to achieve the highest score possible!
+
+## Scoring
+
+- Each cleared line awards 100 points
+- Clear multiple lines at once for maximum efficiency
 
-A list of contributors to the lesson can be found in [AUTHORS](AUTHORS)
+## Game Features
 
-## Citation
+- 7 unique tetromino shapes with different colors:
+  - I-piece (Cyan): 4 blocks in a line
+  - O-piece (Yellow): 2x2 square
+  - T-piece (Magenta): T-shaped
+  - L-piece (Orange): L-shaped
+  - J-piece (Blue): Reverse L-shaped
+  - S-piece (Green): S-shaped
+  - Z-piece (Red): Z-shaped
 
-To cite this lesson, please consult with [CITATION](CITATION)
+- Automatic piece falling
+- Collision detection
+- Piece rotation
+- Score display
+- Game over screen
+
+## License
+
+This project maintains the original LICENSE.md from the repository.
+
+## Contributing
+
+Contributions are welcome! Feel free to submit issues or pull requests.
+
+## Authors
 
-[lesson-example]: https://carpentries.github.io/lesson-example
+See [AUTHORS](AUTHORS) file for contributors.
diff --git a/_episodes/01-intro.md b/_episodes/01-intro.md
deleted file mode 100644
index d50a7a3..0000000
--- a/_episodes/01-intro.md
+++ /dev/null
@@ -1,313 +0,0 @@
----
-title: Python Fundamentals
-teaching: 20
-exercises: 10
-questions:
-- "What basic data types can I work with in Python?"
-- "How can I create a new variable in Python?"
-- "How do I use a function?"
-- "Can I change the value associated with a variable after I create it?"
-objectives:
-- "Assign values to variables."
-keypoints:
-- "Basic data types in Python include integers, strings, and floating-point numbers."
-- "Use `variable = value` to assign a value to a variable in order to record it in memory."
-- "Variables are created on demand whenever a value is assigned to them."
-- "Use `print(something)` to display the value of `something`."
-- "Built-in functions are always available to use."
----
-
-## Variables
-
-Any Python interpreter can be used as a calculator:
-~~~
-3 + 5 * 4
-~~~
-{: .language-python}
-~~~
-23
-~~~
-{: .output}
-
-This is great but not very interesting.
-To do anything useful with data, we need to assign its value to a _variable_.
-In Python, we can [assign]({{ page.root }}/reference.html#assign) a value to a
-[variable]({{ page.root }}/reference.html#variable), using the equals sign `=`.
-For example, we can track the weight of a patient who weighs 60 kilograms by
-assigning the value `60` to a variable `weight_kg`:
-
-~~~
-weight_kg = 60
-~~~
-{: .language-python}
-
-From now on, whenever we use `weight_kg`, Python will substitute the value we assigned to
-it. In layman's terms, **a variable is a name for a value**.
-
-In Python, variable names:
-
- - can include letters, digits, and underscores
- - cannot start with a digit
- - are [case sensitive]({{ page.root }}/reference.html#case-sensitive).
-
-This means that, for example:
- - `weight0` is a valid variable name, whereas `0weight` is not
- - `weight` and `Weight` are different variables
-
-## Types of data
-Python knows various types of data. Three common ones are:
-
-* integer numbers
-* floating point numbers, and
-* strings.
-
-In the example above, variable `weight_kg` has an integer value of `60`.
-If we want to more precisely track the weight of our patient,
-we can use a floating point value by executing:
-
-~~~
-weight_kg = 60.3
-~~~
-{: .language-python}
-
-To create a string, we add single or double quotes around some text.
-To identify and track a patient throughout our study,
-we can assign each person a unique identifier by storing it in a string:
-
-~~~
-patient_id = '001'
-~~~
-{: .language-python}
-
-## Using Variables in Python
-
-Once we have data stored with variable names, we can make use of it in calculations.
-We may want to store our patient's weight in pounds as well as kilograms:
-
-~~~
-weight_lb = 2.2 * weight_kg
-~~~
-{: .language-python}
-
-We might decide to add a prefix to our patient identifier:
-
-~~~
-patient_id = 'inflam_' + patient_id
-~~~
-{: .language-python}
-
-## Built-in Python functions
-
-To carry out common tasks with data and variables in Python,
-the language provides us with several built-in [functions]({{ page.root }}/reference.html#function).
-To display information to the screen, we use the `print` function:
-
-~~~
-print(weight_lb)
-print(patient_id)
-~~~
-{: .language-python}
-
-~~~
-132.66
-inflam_001
-~~~
-{: .output}
-
-When we want to make use of a function, referred to as calling the function,
-we follow its name by parentheses. The parentheses are important:
-if you leave them off, the function doesn't actually run!
-Sometimes you will include values or variables inside the parentheses for the function to use.
-In the case of `print`,
-we use the parentheses to tell the function what value we want to display.
-We will learn more about how functions work and how to create our own in later episodes.
-
-We can display multiple things at once using only one `print` call:
-
-~~~
-print(patient_id, 'weight in kilograms:', weight_kg)
-~~~
-{: .language-python}
-~~~
-inflam_001 weight in kilograms: 60.3
-~~~
-{: .output}
-
-We can also call a function inside of another
-[function call]({{ page.root }}/reference.html#function-call).
-For example, Python has a built-in function called `type` that tells you a value's data type:
-
-~~~
-print(type(60.3))
-print(type(patient_id))
-~~~
-{: .language-python}
-
-~~~
-<class 'float'>
-<class 'str'>
-~~~
-{: .output}
-
-Moreover, we can do arithmetic with variables right inside the `print` function:
-
-~~~
-print('weight in pounds:', 2.2 * weight_kg)
-~~~
-{: .language-python}
-
-~~~
-weight in pounds: 132.66
-~~~
-{: .output}
-
-The above command, however, did not change the value of `weight_kg`:
-~~~
-print(weight_kg)
-~~~
-{: .language-python}
-
-~~~
-60.3
-~~~
-{: .output}
-
-To change the value of the `weight_kg` variable, we have to
-**assign** `weight_kg` a new value using the equals `=` sign:
-
-~~~
-weight_kg = 65.0
-print('weight in kilograms is now:', weight_kg)
-~~~
-{: .language-python}
-
-~~~
-weight in kilograms is now: 65.0
-~~~
-{: .output}
-
-> ## Variables as Sticky Notes
->
-> A variable in Python is analogous to a sticky note with a name written on it:
-> assigning a value to a variable is like putting that sticky note on a particular value.
->
-> ![Value of 65.0 with weight_kg label stuck on it](../fig/python-sticky-note-variables-01.svg)
->
-> Using this analogy, we can investigate how assigning a value to one variable
-> does **not** change values of other, seemingly related, variables.  For
-> example, let's store the subject's weight in pounds in its own variable:
->
-> ~~~
-> # There are 2.2 pounds per kilogram
-> weight_lb = 2.2 * weight_kg
-> print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> weight in kilograms: 65.0 and in pounds: 143.0
-> ~~~
-> {: .output}
->
-> ![Value of 65.0 with weight_kg label stuck on it, and value of 143.0 with weight_lb label
-stuck on it](../fig/python-sticky-note-variables-02.svg)
->
-> Similar to above, the expression `2.2 * weight_kg` is evaluated to `143.0`,
-> and then this value is assigned to the variable `weight_lb` (i.e. the sticky
-> note `weight_lb` is placed on `143.0`). At this point, each variable is
-> "stuck" to completely distinct and unrelated values.
->
-> Let's now change `weight_kg`:
->
-> ~~~
-> weight_kg = 100.0
-> print('weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> weight in kilograms is now: 100.0 and weight in pounds is still: 143.0
-> ~~~
-> {: .output}
->
-> ![Value of 100.0 with label weight_kg stuck on it, and value of 143.0 with label weight_lb
-stuck on it](../fig/python-sticky-note-variables-03.svg)
->
-> Since `weight_lb` doesn't "remember" where its value comes from,
-> it is not updated when we change `weight_kg`.
-{: .callout}
-
-
-> ## Check Your Understanding
->
-> What values do the variables `mass` and `age` have after each of the following statements?
-> Test your answer by executing the lines.
->
-> ~~~
-> mass = 47.5
-> age = 122
-> mass = mass * 2.0
-> age = age - 20
-> ~~~
-> {: .language-python}
->
-> > ## Solution
-> > ~~~
-> > `mass` holds a value of 47.5, `age` does not exist
-> > `mass` still holds a value of 47.5, `age` holds a value of 122
-> > `mass` now has a value of 95.0, `age`'s value is still 122
-> > `mass` still has a value of 95.0, `age` now holds 102
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-> ## Sorting Out References
->
-> Python allows you to assign multiple values to multiple variables in one line by separating
-> the variables and values with commas. What does the following program print out?
->
-> ~~~
-> first, second = 'Grace', 'Hopper'
-> third, fourth = second, first
-> print(third, fourth)
-> ~~~
-> {: .language-python}
->
-> > ## Solution
-> > ~~~
-> > Hopper Grace
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-> ## Seeing Data Types
->
-> What are the data types of the following variables?
->
-> ~~~
-> planet = 'Earth'
-> apples = 5
-> distance = 10.5
-> ~~~
-> {: .language-python}
->
-> > ## Solution
-> > ~~~
-> > print(type(planet))
-> > print(type(apples))
-> > print(type(distance))
-> > ~~~
-> > {: .language-python}
-> >
-> > ~~~
-> > <class 'str'>
-> > <class 'int'>
-> > <class 'float'>
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-{% include links.md %}
diff --git a/_episodes/02-numpy.md b/_episodes/02-numpy.md
deleted file mode 100644
index bb3b819..0000000
--- a/_episodes/02-numpy.md
+++ /dev/null
@@ -1,801 +0,0 @@
----
-title: Analyzing Patient Data
-teaching: 45
-exercises: 20
-questions:
-- "How can I process tabular data files in Python?"
-objectives:
-- "Explain what a library is and what libraries are used for."
-- "Import a Python library and use the functions it contains."
-- "Read tabular data from a file into a program."
-- "Select individual values and subsections from data."
-- "Perform operations on arrays of data."
-keypoints:
-- "Import a library into a program using `import libraryname`."
-- "Use the `numpy` library to work with arrays in Python."
-- "The expression `array.shape` gives the shape of an array."
-- "Use `array[x, y]` to select a single element from a 2D array."
-- "Array indices start at 0, not 1."
-- "Use `low:high` to specify a `slice` that includes the indices from `low` to `high-1`."
-- "Use `# some kind of explanation` to add comments to programs."
-- "Use `numpy.mean(array)`, `numpy.max(array)`, and `numpy.min(array)` to calculate simple statistics."
-- "Use `numpy.mean(array, axis=0)` or `numpy.mean(array, axis=1)` to calculate statistics across the specified axis."
----
-
-Words are useful, but what's more useful are the sentences and stories we build with them.
-Similarly, while a lot of powerful, general tools are built into Python,
-specialized tools built up from these basic units live in
-[libraries]({{ page.root }}/reference.html#library)
-that can be called upon when needed.
-
-## Loading data into Python
-
-To begin processing inflammation data, we need to load it into Python.
-We can do that using a library called
-[NumPy](http://docs.scipy.org/doc/numpy/ "NumPy Documentation"), which stands for Numerical Python.
-In general, you should use this library when you want to do fancy things with lots of numbers,
-especially if you have matrices or arrays. To tell Python that we'd like to start using NumPy,
-we need to [import]({{ page.root }}/reference.html#import) it:
-
-~~~
-import numpy
-~~~
-{: .language-python}
-
-Importing a library is like getting a piece of lab equipment out of a storage locker and setting it
-up on the bench. Libraries provide additional functionality to the basic Python package, much like
-a new piece of equipment adds functionality to a lab space. Just like in the lab, importing too
-many libraries can sometimes complicate and slow down your programs - so we only import what we
-need for each program.
-
-Once we've imported the library, we can ask the library to read our data file for us:
-
-~~~
-numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-~~~
-{: .language-python}
-
-~~~
-array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],
-       [ 0.,  1.,  2., ...,  1.,  0.,  1.],
-       [ 0.,  1.,  1., ...,  2.,  1.,  1.],
-       ...,
-       [ 0.,  1.,  1., ...,  1.,  1.,  1.],
-       [ 0.,  0.,  0., ...,  0.,  2.,  0.],
-       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])
-~~~
-{: .output}
-
-The expression `numpy.loadtxt(...)` is a
-[function call]({{ page.root }}/reference.html#function-call)
-that asks Python to run the [function]({{ page.root }}/reference.html#function) `loadtxt` which
-belongs to the `numpy` library.
-This [dotted notation]({{ page.root }}/reference.html#dotted-notation)
-is used everywhere in Python: the thing that appears before the dot contains the thing that
-appears after.
-
-As an example, John Smith is the John that belongs to the Smith family.
-We could use the dot notation to write his name `smith.john`,
-just as `loadtxt` is a function that belongs to the `numpy` library.
-
-`numpy.loadtxt` has two [parameters]({{ page.root }}/reference.html#parameter): the name of the file
-we want to read and the [delimiter]({{ page.root }}/reference.html#delimiter) that separates values
-on a line. These both need to be character strings
-(or [strings]({{ page.root }}/reference.html#string) for short), so we put them in quotes.
-
-Since we haven't told it to do anything else with the function's output,
-the [notebook]({{ page.root }}/reference.html#notebook) displays it.
-In this case,
-that output is the data we just loaded.
-By default,
-only a few rows and columns are shown
-(with `...` to omit elements when displaying big arrays).
-Note that, to save space when displaying NumPy arrays, Python does not show us trailing zeros,
-so `1.0` becomes `1.`.
-
-Our call to `numpy.loadtxt` read our file
-but didn't save the data in memory.
-To do that,
-we need to assign the array to a variable. In a similar manner to how we assign a single
-value to a variable, we can also assign an array of values to a variable using the same syntax.
-Let's re-run `numpy.loadtxt` and save the returned data:
-
-~~~
-data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-~~~
-{: .language-python}
-
-This statement doesn't produce any output because we've assigned the output to the variable `data`.
-If we want to check that the data have been loaded,
-we can print the variable's value:
-
-~~~
-print(data)
-~~~
-{: .language-python}
-
-~~~
-[[ 0.  0.  1. ...,  3.  0.  0.]
- [ 0.  1.  2. ...,  1.  0.  1.]
- [ 0.  1.  1. ...,  2.  1.  1.]
- ...,
- [ 0.  1.  1. ...,  1.  1.  1.]
- [ 0.  0.  0. ...,  0.  2.  0.]
- [ 0.  0.  1. ...,  1.  1.  0.]]
-~~~
-{: .output}
-
-Now that the data are in memory,
-we can manipulate them.
-First,
-let's ask what [type]({{ page.root }}/reference.html#type) of thing `data` refers to:
-
-~~~
-print(type(data))
-~~~
-{: .language-python}
-
-~~~
-<class 'numpy.ndarray'>
-~~~
-{: .output}
-
-The output tells us that `data` currently refers to
-an N-dimensional array, the functionality for which is provided by the NumPy library.
-These data correspond to arthritis patients' inflammation.
-The rows are the individual patients, and the columns
-are their daily inflammation measurements.
-
-> ## Data Type
->
-> A Numpy array contains one or more elements
-> of the same type. The `type` function will only tell you that
-> a variable is a NumPy array but won't tell you the type of
-> thing inside the array.
-> We can find out the type
-> of the data contained in the NumPy array.
->
-> ~~~
-> print(data.dtype)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> float64
-> ~~~
-> {: .output}
->
-> This tells us that the NumPy array's elements are
-> [floating-point numbers]({{ page.root }}/reference.html#floating-point number).
-{: .callout}
-
-With the following command, we can see the array's [shape]({{ page.root }}/reference.html#shape):
-
-~~~
-print(data.shape)
-~~~
-{: .language-python}
-
-~~~
-(60, 40)
-~~~
-{: .output}
-
-The output tells us that the `data` array variable contains 60 rows and 40 columns. When we
-created the variable `data` to store our arthritis data, we did not only create the array; we also
-created information about the array, called [members]({{ page.root }}/reference.html#member) or
-attributes. This extra information describes `data` in the same way an adjective describes a noun.
-`data.shape` is an attribute of `data` which describes the dimensions of `data`. We use the same
-dotted notation for the attributes of variables that we use for the functions in libraries because
-they have the same part-and-whole relationship.
-
-If we want to get a single number from the array, we must provide an
-[index]({{ page.root }}/reference.html#index) in square brackets after the variable name, just as we
-do in math when referring to an element of a matrix.  Our inflammation data has two dimensions, so
-we will need to use two indices to refer to one specific value:
-
-~~~
-print('first value in data:', data[0, 0])
-~~~
-{: .language-python}
-
-~~~
-first value in data: 0.0
-~~~
-{: .output}
-
-~~~
-print('middle value in data:', data[30, 20])
-~~~
-{: .language-python}
-
-~~~
-middle value in data: 13.0
-~~~
-{: .output}
-
-The expression `data[30, 20]` accesses the element at row 30, column 20. While this expression may
-not surprise you,
- `data[0, 0]` might.
-Programming languages like Fortran, MATLAB and R start counting at 1
-because that's what human beings have done for thousands of years.
-Languages in the C family (including C++, Java, Perl, and Python) count from 0
-because it represents an offset from the first value in the array (the second
-value is offset by one index from the first value). This is closer to the way
-that computers represent arrays (if you are interested in the historical
-reasons behind counting indices from zero, you can read
-[Mike Hoye's blog post](http://exple.tive.org/blarg/2013/10/22/citation-needed/)).
-As a result,
-if we have an M×N array in Python,
-its indices go from 0 to M-1 on the first axis
-and 0 to N-1 on the second.
-It takes a bit of getting used to,
-but one way to remember the rule is that
-the index is how many steps we have to take from the start to get the item we want.
-
-!["data" is a 3 by 3 numpy array containing row 0: ['A', 'B', 'C'], row 1: ['D', 'E', 'F'], and
-row 2: ['G', 'H', 'I']. Starting in the upper left hand corner, data[0, 0] = 'A', data[0, 1] = 'B',
-data[0, 2] = 'C', data[1, 0] = 'D', data[1, 1] = 'E', data[1, 2] = 'F', data[2, 0] = 'G',
-data[2, 1] = 'H', and data[2, 2] = 'I',
-in the bottom right hand corner.](../fig/python-zero-index.svg)
-
-> ## In the Corner
->
-> What may also surprise you is that when Python displays an array,
-> it shows the element with index `[0, 0]` in the upper left corner
-> rather than the lower left.
-> This is consistent with the way mathematicians draw matrices
-> but different from the Cartesian coordinates.
-> The indices are (row, column) instead of (column, row) for the same reason,
-> which can be confusing when plotting data.
-{: .callout}
-
-## Slicing data
-An index like `[30, 20]` selects a single element of an array,
-but we can select whole sections as well.
-For example,
-we can select the first ten days (columns) of values
-for the first four patients (rows) like this:
-
-~~~
-print(data[0:4, 0:10])
-~~~
-{: .language-python}
-
-~~~
-[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]
- [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]
- [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]
- [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]
-~~~
-{: .output}
-
-The [slice]({{ page.root }}/reference.html#slice) `0:4` means, "Start at index 0 and go up to,
-but not including, index 4". Again, the up-to-but-not-including takes a bit of getting used to,
-but the rule is that the difference between the upper and lower bounds is the number of values in
-the slice.
-
-We don't have to start slices at 0:
-
-~~~
-print(data[5:10, 0:10])
-~~~
-{: .language-python}
-
-~~~
-[[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]
- [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]
- [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]
- [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]
- [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]
-~~~
-{: .output}
-
-We also don't have to include the upper and lower bound on the slice.  If we don't include the lower
-bound, Python uses 0 by default; if we don't include the upper, the slice runs to the end of the
-axis, and if we don't include either (i.e., if we use ':' on its own), the slice includes
-everything:
-
-~~~
-small = data[:3, 36:]
-print('small is:')
-print(small)
-~~~
-{: .language-python}
-The above example selects rows 0 through 2 and columns 36 through to the end of the array.
-
-~~~
-small is:
-[[ 2.  3.  0.  0.]
- [ 1.  1.  0.  1.]
- [ 2.  2.  1.  1.]]
-~~~
-{: .output}
-
-## Analyzing data
-
-NumPy has several useful functions that take an array as input to perform operations on its values.
-If we want to find the average inflammation for all patients on
-all days, for example, we can ask NumPy to compute `data`'s mean value:
-
-~~~
-print(numpy.mean(data))
-~~~
-{: .language-python}
-
-~~~
-6.14875
-~~~
-{: .output}
-
-`mean` is a [function]({{ page.root }}/reference.html#function) that takes
-an array as an [argument]({{ page.root }}/reference.html#argument).
-
-> ## Not All Functions Have Input
->
-> Generally, a function uses inputs to produce outputs.
-> However, some functions produce outputs without
-> needing any input. For example, checking the current time
-> doesn't require any input.
->
-> ~~~
-> import time
-> print(time.ctime())
-> ~~~
-> {: .language-python}
->
-> ~~~
-> Sat Mar 26 13:07:33 2016
-> ~~~
-> {: .output}
->
-> For functions that don't take in any arguments,
-> we still need parentheses (`()`)
-> to tell Python to go and do something for us.
-{: .callout}
-
-Let's use three other NumPy functions to get some descriptive values about the dataset.
-We'll also use multiple assignment,
-a convenient Python feature that will enable us to do this all in one line.
-
-~~~
-maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)
-
-print('maximum inflammation:', maxval)
-print('minimum inflammation:', minval)
-print('standard deviation:', stdval)
-~~~
-{: .language-python}
-
-Here we've assigned the return value from `numpy.max(data)` to the variable `maxval`, the value
-from `numpy.min(data)` to `minval`, and so on.
-
-~~~
-maximum inflammation: 20.0
-minimum inflammation: 0.0
-standard deviation: 4.61383319712
-~~~
-{: .output}
-
-> ## Mystery Functions in IPython
->
-> How did we know what functions NumPy has and how to use them?
-> If you are working in IPython or in a Jupyter Notebook, there is an easy way to find out.
-> If you type the name of something followed by a dot, then you can use
-> [tab completion]({{ page.root }}/reference.html#tab-completion)
-> (e.g. type `numpy.` and then press <kbd>Tab</kbd>)
-> to see a list of all functions and attributes that you can use. After selecting one, you
-> can also add a question mark (e.g. `numpy.cumprod?`), and IPython will return an
-> explanation of the method! This is the same as doing `help(numpy.cumprod)`.
-> Similarly, if you are using the "plain vanilla" Python interpreter, you can type `numpy.`
-> and press the <kbd>Tab</kbd> key twice for a listing of what is available. You can then use the
-> `help()` function to see an explanation of the function you're interested in,
-> for example: `help(numpy.cumprod)`.
-{: .callout}
-
-When analyzing data, though,
-we often want to look at variations in statistical values,
-such as the maximum inflammation per patient
-or the average inflammation per day.
-One way to do this is to create a new temporary array of the data we want,
-then ask it to do the calculation:
-
-~~~
-patient_0 = data[0, :] # 0 on the first axis (rows), everything on the second (columns)
-print('maximum inflammation for patient 0:', numpy.max(patient_0))
-~~~
-{: .language-python}
-
-~~~
-maximum inflammation for patient 0: 18.0
-~~~
-{: .output}
-
-Everything in a line of code following the '#' symbol is a
-[comment]({{ page.root }}/reference.html#comment) that is ignored by Python.
-Comments allow programmers to leave explanatory notes for other
-programmers or their future selves.
-
-We don't actually need to store the row in a variable of its own.
-Instead, we can combine the selection and the function call:
-
-~~~
-print('maximum inflammation for patient 2:', numpy.max(data[2, :]))
-~~~
-{: .language-python}
-
-~~~
-maximum inflammation for patient 2: 19.0
-~~~
-{: .output}
-
-What if we need the maximum inflammation for each patient over all days (as in the
-next diagram on the left) or the average for each day (as in the
-diagram on the right)? As the diagram below shows, we want to perform the
-operation across an axis:
-
-![Per-patient maximum inflammation is computed row-wise across all columns using
-numpy.max(data, axis=1). Per-day average inflammation is computed column-wise across all rows using
-numpy.mean(data, axis=0).](../fig/python-operations-across-axes.png)
-
-To support this functionality,
-most array functions allow us to specify the axis we want to work on.
-If we ask for the average across axis 0 (rows in our 2D example),
-we get:
-
-~~~
-print(numpy.mean(data, axis=0))
-~~~
-{: .language-python}
-
-~~~
-[  0.           0.45         1.11666667   1.75         2.43333333   3.15
-   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9
-   8.35         7.73333333   8.36666667   9.5          9.58333333
-  10.63333333  11.56666667  12.35        13.25        11.96666667
-  11.03333333  10.16666667  10.           8.66666667   9.15         7.25
-   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6
-   3.3          3.56666667   2.48333333   1.5          1.13333333
-   0.56666667]
-~~~
-{: .output}
-
-As a quick check,
-we can ask this array what its shape is:
-
-~~~
-print(numpy.mean(data, axis=0).shape)
-~~~
-{: .language-python}
-
-~~~
-(40,)
-~~~
-{: .output}
-
-The expression `(40,)` tells us we have an N×1 vector,
-so this is the average inflammation per day for all patients.
-If we average across axis 1 (columns in our 2D example), we get:
-
-~~~
-print(numpy.mean(data, axis=1))
-~~~
-{: .language-python}
-
-~~~
-[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525
-  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55
-  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55
-  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75
-  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7
-  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]
-~~~
-{: .output}
-
-which is the average inflammation per patient across all days.
-
-
-> ## Slicing Strings
->
-> A section of an array is called a [slice]({{ page.root }}/reference.html#slice).
-> We can take slices of character strings as well:
->
-> ~~~
-> element = 'oxygen'
-> print('first three characters:', element[0:3])
-> print('last three characters:', element[3:6])
-> ~~~
-> {: .language-python}
->
-> ~~~
-> first three characters: oxy
-> last three characters: gen
-> ~~~
-> {: .output}
->
-> What is the value of `element[:4]`?
-> What about `element[4:]`?
-> Or `element[:]`?
->
-> > ## Solution
-> > ~~~
-> > oxyg
-> > en
-> > oxygen
-> > ~~~
-> > {: .output}
-> {: .solution}
->
-> What is `element[-1]`?
-> What is `element[-2]`?
->
-> > ## Solution
-> > ~~~
-> > n
-> > e
-> > ~~~
-> > {: .output}
-> {: .solution}
->
-> Given those answers,
-> explain what `element[1:-1]` does.
->
-> > ## Solution
-> > Creates a substring from index 1 up to (not including) the final index,
-> > effectively removing the first and last letters from 'oxygen'
-> {: .solution}
->
-> How can we rewrite the slice for getting the last three characters of `element`,
-> so that it works even if we assign a different string to `element`?
-> Test your solution with the following strings: `carpentry`, `clone`, `hi`.
->
-> > ## Solution
-> > ~~~
-> > element = 'oxygen'
-> > print('last three characters:', element[-3:])
-> > element = 'carpentry'
-> > print('last three characters:', element[-3:])
-> > element = 'clone'
-> > print('last three characters:', element[-3:])
-> > element = 'hi'
-> > print('last three characters:', element[-3:])
-> > ~~~
-> > {: .language-python}
-> > ~~~
-> > last three characters: gen
-> > last three characters: try
-> > last three characters: one
-> > last three characters: hi
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-> ## Thin Slices
->
-> The expression `element[3:3]` produces an
-> [empty string]({{ page.root }}/reference.html#empty-string),
-> i.e., a string that contains no characters.
-> If `data` holds our array of patient data,
-> what does `data[3:3, 4:4]` produce?
-> What about `data[3:3, :]`?
->
-> > ## Solution
-> > ~~~
-> > array([], shape=(0, 0), dtype=float64)
-> > array([], shape=(0, 40), dtype=float64)
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-> ## Stacking Arrays
->
-> Arrays can be concatenated and stacked on top of one another,
-> using NumPy's `vstack` and `hstack` functions for vertical and horizontal stacking, respectively.
->
-> ~~~
-> import numpy
->
-> A = numpy.array([[1,2,3], [4,5,6], [7, 8, 9]])
-> print('A = ')
-> print(A)
->
-> B = numpy.hstack([A, A])
-> print('B = ')
-> print(B)
->
-> C = numpy.vstack([A, A])
-> print('C = ')
-> print(C)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> A =
-> [[1 2 3]
->  [4 5 6]
->  [7 8 9]]
-> B =
-> [[1 2 3 1 2 3]
->  [4 5 6 4 5 6]
->  [7 8 9 7 8 9]]
-> C =
-> [[1 2 3]
->  [4 5 6]
->  [7 8 9]
->  [1 2 3]
->  [4 5 6]
->  [7 8 9]]
-> ~~~
-> {: .output}
->
-> Write some additional code that slices the first and last columns of `A`,
-> and stacks them into a 3x2 array.
-> Make sure to `print` the results to verify your solution.
->
-> > ## Solution
-> >
-> > A 'gotcha' with array indexing is that singleton dimensions
-> > are dropped by default. That means `A[:, 0]` is a one dimensional
-> > array, which won't stack as desired. To preserve singleton dimensions,
-> > the index itself can be a slice or array. For example, `A[:, :1]` returns
-> > a two dimensional array with one singleton dimension (i.e. a column
-> > vector).
-> >
-> > ~~~
-> > D = numpy.hstack((A[:, :1], A[:, -1:]))
-> > print('D = ')
-> > print(D)
-> > ~~~
-> > {: .language-python}
-> >
-> > ~~~
-> > D =
-> > [[1 3]
-> >  [4 6]
-> >  [7 9]]
-> > ~~~
-> > {: .output}
-> {: .solution}
->
-> > ## Solution
-> >
-> > An alternative way to achieve the same result is to use Numpy's
-> > delete function to remove the second column of A.
-> >
-> > ~~~
-> > D = numpy.delete(A, 1, 1)
-> > print('D = ')
-> > print(D)
-> > ~~~
-> > {: .language-python}
-> >
-> > ~~~
-> > D =
-> > [[1 3]
-> >  [4 6]
-> >  [7 9]]
-> > ~~~
-> > {: .output}
-> {: .solution}
-{: .challenge}
-
-> ## Change In Inflammation
->
-> The patient data is _longitudinal_ in the sense that each row represents a
-> series of observations relating to one individual.  This means that
-> the change in inflammation over time is a meaningful concept.
-> Let's find out how to calculate changes in the data contained in an array
-> with NumPy.
->
-> The `numpy.diff()` function takes an array and returns the differences
-> between two successive values. Let's use it to examine the changes
-> each day across the first week of patient 3 from our inflammation dataset.
->
-> ~~~
-> patient3_week1 = data[3, :7]
-> print(patient3_week1)
-> ~~~
-> {: .language-python}
->
-> ~~~
->  [0. 0. 2. 0. 4. 2. 2.]
-> ~~~
-> {: .output}
->
-> Calling `numpy.diff(patient3_week1)` would do the following calculations
->
-> ~~~
-> [ 0 - 0, 2 - 0, 0 - 2, 4 - 0, 2 - 4, 2 - 2 ]
-> ~~~
-> {: .language-python}
->
-> and return the 6 difference values in a new array.
->
-> ~~~
-> numpy.diff(patient3_week1)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> array([ 0.,  2., -2.,  4., -2.,  0.])
-> ~~~
-> {: .output}
->
-> Note that the array of differences is shorter by one element (length 6).
->
-> When calling `numpy.diff` with a multi-dimensional array, an `axis` argument may
-> be passed to the function to specify which axis to process. When applying
-> `numpy.diff` to our 2D inflammation array `data`, which axis would we specify?
->
-> > ## Solution
-> > Since the row axis (0) is patients, it does not make sense to get the
-> > difference between two arbitrary patients. The column axis (1) is in
-> > days, so the difference is the change in inflammation -- a meaningful
-> > concept.
-> >
-> > ~~~
-> > numpy.diff(data, axis=1)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
->
-> If the shape of an individual data file is `(60, 40)` (60 rows and 40
-> columns), what would the shape of the array be after you run the `diff()`
-> function and why?
->
-> > ## Solution
-> > The shape will be `(60, 39)` because there is one fewer difference between
-> > columns than there are columns in the data.
-> {: .solution}
->
-> How would you find the largest change in inflammation for each patient? Does
-> it matter if the change in inflammation is an increase or a decrease?
->
-> > ## Solution
-> > By using the `numpy.max()` function after you apply the `numpy.diff()`
-> > function, you will get the largest difference between days.
-> >
-> > ~~~
-> > numpy.max(numpy.diff(data, axis=1), axis=1)
-> > ~~~
-> > {: .language-python}
-> >
-> > ~~~
-> > array([  7.,  12.,  11.,  10.,  11.,  13.,  10.,   8.,  10.,  10.,   7.,
-> >          7.,  13.,   7.,  10.,  10.,   8.,  10.,   9.,  10.,  13.,   7.,
-> >         12.,   9.,  12.,  11.,  10.,  10.,   7.,  10.,  11.,  10.,   8.,
-> >         11.,  12.,  10.,   9.,  10.,  13.,  10.,   7.,   7.,  10.,  13.,
-> >         12.,   8.,   8.,  10.,  10.,   9.,   8.,  13.,  10.,   7.,  10.,
-> >          8.,  12.,  10.,   7.,  12.])
-> > ~~~
-> > {: .language-python}
-> >
-> > If inflammation values *decrease* along an axis, then the difference from
-> > one element to the next will be negative. If
-> > you are interested in the **magnitude** of the change and not the
-> > direction, the `numpy.absolute()` function will provide that.
-> >
-> > Notice the difference if you get the largest _absolute_ difference
-> > between readings.
-> >
-> > ~~~
-> > numpy.max(numpy.absolute(numpy.diff(data, axis=1)), axis=1)
-> > ~~~
-> > {: .language-python}
-> >
-> > ~~~
-> > array([ 12.,  14.,  11.,  13.,  11.,  13.,  10.,  12.,  10.,  10.,  10.,
-> >         12.,  13.,  10.,  11.,  10.,  12.,  13.,   9.,  10.,  13.,   9.,
-> >         12.,   9.,  12.,  11.,  10.,  13.,   9.,  13.,  11.,  11.,   8.,
-> >         11.,  12.,  13.,   9.,  10.,  13.,  11.,  11.,  13.,  11.,  13.,
-> >         13.,  10.,   9.,  10.,  10.,   9.,   9.,  13.,  10.,   9.,  10.,
-> >         11.,  13.,  10.,  10.,  12.])
-> > ~~~
-> > {: .language-python}
-> >
-> {: .solution}
-{: .challenge}
-
-{% include links.md %}
diff --git a/_episodes/03-matplotlib.md b/_episodes/03-matplotlib.md
deleted file mode 100644
index 55c56c1..0000000
--- a/_episodes/03-matplotlib.md
+++ /dev/null
@@ -1,288 +0,0 @@
----
-title: Visualizing Tabular Data
-teaching: 35
-exercises: 20
-questions:
-- "How can I visualize tabular data in Python?"
-- "How can I group several plots together?"
-objectives:
-- "Plot simple graphs from data."
-- "Plot multiple graphs in a single figure."
-keypoints:
-- "Use the `pyplot` module from the `matplotlib` library for creating simple visualizations."
----
-
-## Visualizing data
-The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and
-the best way to develop insight is often to visualize data.  Visualization deserves an entire
-lecture of its own, but we can explore a few features of Python's `matplotlib` library here.  While
-there is no official plotting library, `matplotlib` is the _de facto_ standard.  First, we will
-import the `pyplot` module from `matplotlib` and use two of its functions to create and display a
-[heat map]({{ page.root }}/reference.html#heat-map) of our data:
-
-~~~
-import matplotlib.pyplot
-image = matplotlib.pyplot.imshow(data)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Heat map representing the `data` variable. Each cell is colored by value along a color gradient
-from blue to yellow.](../fig/inflammation-01-imshow.svg)
-
-Blue pixels in this heat map represent low values, while yellow pixels represent high values.  As we
-can see, inflammation rises and falls over a 40-day period.  Let's take a look at the average
-inflammation over time:
-
-~~~
-ave_inflammation = numpy.mean(data, axis=0)
-ave_plot = matplotlib.pyplot.plot(ave_inflammation)
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![A line graph showing the average inflammation across all patients over a 40-day period.](../fig/inflammation-01-average.svg)
-
-Here, we have put the average inflammation per day across all patients in the variable
-`ave_inflammation`, then asked `matplotlib.pyplot` to create and display a line graph of those
-values.  The result is a roughly linear rise and fall, which is suspicious: we might instead expect
-a sharper rise and slower fall.  Let's have a look at two other statistics:
-
-~~~
-max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![A line graph showing the maximum inflammation across all patients over a 40-day period.](../fig/inflammation-01-maximum.svg)
-
-~~~
-min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![A line graph showing the minimum inflammation across all patients over a 40-day period.](../fig/inflammation-01-minimum.svg)
-
-The maximum value rises and falls smoothly, while the minimum seems to be a step function.  Neither
-trend seems particularly likely, so either there's a mistake in our calculations or something is
-wrong with our data.  This insight would have been difficult to reach by examining the numbers
-themselves without visualization tools.
-
-### Grouping plots
-You can group similar plots in a single figure using subplots.
-This script below uses a number of new commands. The function `matplotlib.pyplot.figure()`
-creates a space into which we will place all of our plots. The parameter `figsize`
-tells Python how big to make this space. Each subplot is placed into the figure using
-its `add_subplot` [method]({{ page.root }}/reference.html#method). The `add_subplot` method takes 3
-parameters. The first denotes how many total rows of subplots there are, the second parameter
-refers to the total number of subplot columns, and the final parameter denotes which subplot
-your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a
-different variable (`axes1`, `axes2`, `axes3`). Once a subplot is created, the axes can
-be titled using the `set_xlabel()` command (or `set_ylabel()`).
-Here are our three plots side by side:
-
-~~~
-import numpy
-import matplotlib.pyplot
-
-data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-
-fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-
-axes1 = fig.add_subplot(1, 3, 1)
-axes2 = fig.add_subplot(1, 3, 2)
-axes3 = fig.add_subplot(1, 3, 3)
-
-axes1.set_ylabel('average')
-axes1.plot(numpy.mean(data, axis=0))
-
-axes2.set_ylabel('max')
-axes2.plot(numpy.max(data, axis=0))
-
-axes3.set_ylabel('min')
-axes3.plot(numpy.min(data, axis=0))
-
-fig.tight_layout()
-
-matplotlib.pyplot.savefig('inflammation.png')
-matplotlib.pyplot.show()
-~~~
-{: .language-python}
-
-![Three line graphs showing the daily average, maximum and minimum inflammation over a 40-day period.](../fig/inflammation-01-group-plot.svg)
-
-The [call]({{ page.root }}/reference.html#function-call) to `loadtxt` reads our data,
-and the rest of the program tells the plotting library
-how large we want the figure to be,
-that we're creating three subplots,
-what to draw for each one,
-and that we want a tight layout.
-(If we leave out that call to `fig.tight_layout()`,
-the graphs will actually be squeezed together more closely.)
-
-The call to `savefig` stores the plot as a graphics file. This can be
-a convenient way to store your plots for use in other documents, web
-pages etc. The graphics format is automatically determined by
-Matplotlib from the file name ending we specify; here PNG from
-'inflammation.png'. Matplotlib supports many different graphics
-formats, including SVG, PDF, and JPEG.
-
-> ## Importing libraries with shortcuts
->
-> In this lesson we use the `import matplotlib.pyplot`
-> [syntax]({{ page.root }}/reference.html#syntax)
-> to import the `pyplot` module of `matplotlib`. However, shortcuts such as
-> `import matplotlib.pyplot as plt` are frequently used.
-> Importing `pyplot` this way means that after the initial import, rather than writing
-> `matplotlib.pyplot.plot(...)`, you can now write `plt.plot(...)`.
-> Another common convention is to use the shortcut `import numpy as np` when importing the
-> NumPy library. We then can write `np.loadtxt(...)` instead of `numpy.loadtxt(...)`,
-> for example.
->
-> Some people prefer these shortcuts as it is quicker to type and results in shorter
-> lines of code - especially for libraries with long names! You will frequently see
-> Python code online using a `pyplot` function with `plt`, or a NumPy function with
-> `np`, and it's because they've used this shortcut. It makes no difference which
-> approach you choose to take, but you must be consistent as if you use
-> `import matplotlib.pyplot as plt` then `matplotlib.pyplot.plot(...)` will not work, and
-> you must use `plt.plot(...)` instead. Because of this, when working with other people it
-> is important you agree on how libraries are imported.
-{: .callout}
-
-> ## Plot Scaling
->
-> Why do all of our plots stop just short of the upper end of our graph?
->
-> > ## Solution
-> > Because matplotlib normally sets x and y axes limits to the min and max of our data
-> > (depending on data range)
-> {: .solution}
->
-> If we want to change this, we can use the `set_ylim(min, max)` method of each 'axes',
-> for example:
->
-> ~~~
-> axes3.set_ylim(0,6)
-> ~~~
-> {: .language-python}
->
-> Update your plotting code to automatically set a more appropriate scale.
-> (Hint: you can make use of the `max` and `min` methods to help.)
->
-> > ## Solution
-> > ~~~
-> > # One method
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> > axes3.set_ylim(0,6)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
->
-> > ## Solution
-> > ~~~
-> > # A more automated approach
-> > min_data = numpy.min(data, axis=0)
-> > axes3.set_ylabel('min')
-> > axes3.plot(min_data)
-> > axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Drawing Straight Lines
->
-> In the center and right subplots above, we expect all lines to look like step functions because
-> non-integer value are not realistic for the minimum and maximum values. However, you can see
-> that the lines are not always vertical or horizontal, and in particular the step function
-> in the subplot on the right looks slanted. Why is this?
->
-> > ## Solution
-> > Because matplotlib interpolates (draws a straight line) between the points.
-> > One way to do avoid this is to use the Matplotlib `drawstyle` option:
-> >
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
-> >
-> > axes1 = fig.add_subplot(1, 3, 1)
-> > axes2 = fig.add_subplot(1, 3, 2)
-> > axes3 = fig.add_subplot(1, 3, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid')
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid')
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> ![Three line graphs, with step lines connecting the points, showing the daily average, maximum
- and minimum inflammation over a 40-day period.](../fig/inflammation-01-line-styles.svg)
-> {: .solution}
-{: .challenge}
-
-> ## Make Your Own Plot
->
-> Create a plot showing the standard deviation (`numpy.std`)
-> of the inflammation data for each day across all patients.
->
-> > ## Solution
-> > ~~~
-> > std_plot = matplotlib.pyplot.plot(numpy.std(data, axis=0))
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Moving Plots Around
->
-> Modify the program to display the three plots on top of one another
-> instead of side by side.
->
-> > ## Solution
-> > ~~~
-> > import numpy
-> > import matplotlib.pyplot
-> >
-> > data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
-> >
-> > # change figsize (swap width and height)
-> > fig = matplotlib.pyplot.figure(figsize=(3.0, 10.0))
-> >
-> > # change add_subplot (swap first two parameters)
-> > axes1 = fig.add_subplot(3, 1, 1)
-> > axes2 = fig.add_subplot(3, 1, 2)
-> > axes3 = fig.add_subplot(3, 1, 3)
-> >
-> > axes1.set_ylabel('average')
-> > axes1.plot(numpy.mean(data, axis=0))
-> >
-> > axes2.set_ylabel('max')
-> > axes2.plot(numpy.max(data, axis=0))
-> >
-> > axes3.set_ylabel('min')
-> > axes3.plot(numpy.min(data, axis=0))
-> >
-> > fig.tight_layout()
-> >
-> > matplotlib.pyplot.show()
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-{% include links.md %}
diff --git a/_episodes/04-lists.md b/_episodes/04-lists.md
deleted file mode 100644
index 6df7fbe..0000000
--- a/_episodes/04-lists.md
+++ /dev/null
@@ -1,531 +0,0 @@
----
-title: Storing Multiple Values in Lists
-teaching: 35
-exercises: 15
-questions:
-- "How can I store many values together?"
-objectives:
-- "Explain what a list is."
-- "Create and index lists of simple values."
-- "Change the values of individual elements"
-- "Append values to an existing list"
-- "Reorder and slice list elements"
-- "Create and manipulate nested lists"
-keypoints:
-- "`[value1, value2, value3, ...]` creates a list."
-- "Lists can contain any Python object, including lists (i.e., list of lists)."
-- "Lists are indexed and sliced with square brackets (e.g., list[0] and
-list[2:9]), in the same way as strings and arrays."
-- "Lists are mutable (i.e., their values can be changed in place)."
-- "Strings are immutable (i.e., the characters in them cannot be changed)."
----
-
-In the previous episode, we analyzed a single file with inflammation data. Our goal, however, is to
-process all the inflammation data we have, which means that we still have eleven more files to go!
-
-The natural first step is to collect the names of all the files that we have to process. In Python,
-a list is a way to store multiple values together. In this episode, we will learn how to store
-multiple values in a list as well as how to work with lists.
-
-## Python lists
-
-Unlike NumPy arrays, lists are built into the language so we do not have to load a library
-to use them.
-We create a list by putting values inside square brackets and separating the values with commas:
-
-~~~
-odds = [1, 3, 5, 7]
-print('odds are:', odds)
-~~~
-{: .language-python}
-
-~~~
-odds are: [1, 3, 5, 7]
-~~~
-{: .output}
-
-We can access elements of a list using indices -- numbered positions of elements in the list.
-These positions are numbered starting at 0, so the first element has an index of 0.
-
-~~~
-print('first element:', odds[0])
-print('last element:', odds[3])
-print('"-1" element:', odds[-1])
-~~~
-{: .language-python}
-
-~~~
-first element: 1
-last element: 7
-"-1" element: 7
-~~~
-{: .output}
-
-Yes, we can use negative numbers as indices in Python. When we do so, the index `-1` gives us the
-last element in the list, `-2` the second to last, and so on.
-Because of this, `odds[3]` and `odds[-1]` point to the same element here.
-
-There is one important difference between lists and strings:
-we can change the values in a list,
-but we cannot change individual characters in a string.
-For example:
-
-~~~
-names = ['Curie', 'Darwing', 'Turing']  # typo in Darwin's name
-print('names is originally:', names)
-names[1] = 'Darwin'  # correct the name
-print('final value of names:', names)
-~~~
-{: .language-python}
-
-~~~
-names is originally: ['Curie', 'Darwing', 'Turing']
-final value of names: ['Curie', 'Darwin', 'Turing']
-~~~
-{: .output}
-
-works, but:
-
-~~~
-name = 'Darwin'
-name[0] = 'd'
-~~~
-{: .language-python}
-
-~~~
----------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-<ipython-input-8-220df48aeb2e> in <module>()
-      1 name = 'Darwin'
-----> 2 name[0] = 'd'
-
-TypeError: 'str' object does not support item assignment
-~~~
-{: .error}
-
-does not.
-
-> ## Ch-Ch-Ch-Ch-Changes
->
-> Data which can be modified in place is called [mutable]({{ page.root }}/reference.html#mutable),
-> while data which cannot be modified is called
-> [immutable]({{ page.root }}/reference.html#immutable).
-> Strings and numbers are immutable. This does not mean that variables with string or number values
-> are constants, but when we want to change the value of a string or number variable, we can only
-> replace the old value with a completely new value.
->
-> Lists and arrays, on the other hand, are mutable: we can modify them after they have been
-> created. We can change individual elements, append new elements, or reorder the whole list. For
-> some operations, like sorting, we can choose whether to use a function that modifies the data
-> in-place or a function that returns a modified copy and leaves the original unchanged.
->
-> Be careful when modifying data in-place. If two variables refer to the same list, and you modify
-> the list value, it will change for both variables!
->
-> ~~~
-> salsa = ['peppers', 'onions', 'cilantro', 'tomatoes']
-> my_salsa = salsa        # <-- my_salsa and salsa point to the *same* list data in memory
-> salsa[0] = 'hot peppers'
-> print('Ingredients in my salsa:', my_salsa)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> Ingredients in my salsa: ['hot peppers', 'onions', 'cilantro', 'tomatoes']
-> ~~~
-> {: .output}
->
-> If you want variables with mutable values to be independent, you
-> must make a copy of the value when you assign it.
->
-> ~~~
-> salsa = ['peppers', 'onions', 'cilantro', 'tomatoes']
-> my_salsa = list(salsa)        # <-- makes a *copy* of the list
-> salsa[0] = 'hot peppers'
-> print('Ingredients in my salsa:', my_salsa)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> Ingredients in my salsa: ['peppers', 'onions', 'cilantro', 'tomatoes']
-> ~~~
-> {: .output}
->
-> Because of pitfalls like this, code which modifies data in place can be more difficult to
-> understand. However, it is often far more efficient to modify a large data structure in place
-> than to create a modified copy for every small change. You should consider both of these aspects
-> when writing your code.
-{: .callout}
-
-> ## Nested Lists
-> Since a list can contain any Python variables, it can even contain other lists.
->
-> For example, we could represent the products in the shelves of a small grocery shop:
->
-> ~~~
-> x = [['pepper', 'zucchini', 'onion'],
->      ['cabbage', 'lettuce', 'garlic'],
->      ['apple', 'pear', 'banana']]
-> ~~~
-> {: .language-python}
->
-> Here is a visual example of how indexing a list of lists `x` works:
->
-> [![x is represented as a pepper shaker containing several packets of pepper. [x[0]] is represented
-> as a pepper shaker containing a single packet of pepper. x[0] is represented as a single packet of
-> pepper. x[0][0] is represented as single grain of pepper.  Adapted
-> from @hadleywickham.](../fig/indexing_lists_python.png)][hadleywickham-tweet]
->
-> Using the previously declared list `x`, these would be the results of the
-> index operations shown in the image:
->
-> ~~~
-> print([x[0]])
-> ~~~
-> {: .language-python}
->
-> ~~~
-> [['pepper', 'zucchini', 'onion']]
-> ~~~
-> {: .output}
->
-> ~~~
-> print(x[0])
-> ~~~
-> {: .language-python}
->
-> ~~~
-> ['pepper', 'zucchini', 'onion']
-> ~~~
-> {: .output}
->
-> ~~~
-> print(x[0][0])
-> ~~~
-> {: .language-python}
->
-> ~~~
-> 'pepper'
-> ~~~
-> {: .output}
->
-> Thanks to [Hadley Wickham][hadleywickham-tweet]
-> for the image above.
-{: .callout}
-
-> ## Heterogeneous Lists
-> Lists in Python can contain elements of different types. Example:
-> ~~~
-> sample_ages = [10, 12.5, 'Unknown']
-> ~~~
-> {: .language-python}
-{: .callout}
-
-There are many ways to change the contents of lists besides assigning new values to
-individual elements:
-
-~~~
-odds.append(11)
-print('odds after adding a value:', odds)
-~~~
-{: .language-python}
-
-~~~
-odds after adding a value: [1, 3, 5, 7, 11]
-~~~
-{: .output}
-
-~~~
-removed_element = odds.pop(0)
-print('odds after removing the first element:', odds)
-print('removed_element:', removed_element)
-~~~
-{: .language-python}
-
-~~~
-odds after removing the first element: [3, 5, 7, 11]
-removed_element: 1
-~~~
-{: .output}
-
-~~~
-odds.reverse()
-print('odds after reversing:', odds)
-~~~
-{: .language-python}
-
-~~~
-odds after reversing: [11, 7, 5, 3]
-~~~
-{: .output}
-
-While modifying in place, it is useful to remember that Python treats lists in a slightly
-counter-intuitive way.
-
-As we saw earlier, when we modified the `salsa` list item in-place, if we make a list, (attempt to)
-copy it and then modify this list, we can cause all sorts of trouble. This also applies to modifying
-the list using the above functions:
-
-~~~
-odds = [1, 3, 5, 7]
-primes = odds
-primes.append(2)
-print('primes:', primes)
-print('odds:', odds)
-~~~
-{: .language-python}
-
-~~~
-primes: [1, 3, 5, 7, 2]
-odds: [1, 3, 5, 7, 2]
-~~~
-{: .output}
-
-This is because Python stores a list in memory, and then can use multiple names to refer to the
-same list. If all we want to do is copy a (simple) list, we can again use the `list` function, so we
-do not modify a list we did not mean to:
-
-~~~
-odds = [1, 3, 5, 7]
-primes = list(odds)
-primes.append(2)
-print('primes:', primes)
-print('odds:', odds)
-~~~
-{: .language-python}
-
-~~~
-primes: [1, 3, 5, 7, 2]
-odds: [1, 3, 5, 7]
-~~~
-{: .output}
-
-Subsets of lists and strings can be accessed by specifying ranges of values in brackets,
-similar to how we accessed ranges of positions in a NumPy array.
-This is commonly referred to as "slicing" the list/string.
-
-~~~
-binomial_name = 'Drosophila melanogaster'
-group = binomial_name[0:10]
-print('group:', group)
-
-species = binomial_name[11:23]
-print('species:', species)
-
-chromosomes = ['X', 'Y', '2', '3', '4']
-autosomes = chromosomes[2:5]
-print('autosomes:', autosomes)
-
-last = chromosomes[-1]
-print('last:', last)
-~~~
-{: .language-python}
-
-~~~
-group: Drosophila
-species: melanogaster
-autosomes: ['2', '3', '4']
-last: 4
-~~~
-{: .output}
-
-> ## Slicing From the End
->
-> Use slicing to access only the last four characters of a string or entries of a list.
->
-> ~~~
-> string_for_slicing = 'Observation date: 02-Feb-2013'
-> list_for_slicing = [['fluorine', 'F'],
->                     ['chlorine', 'Cl'],
->                     ['bromine', 'Br'],
->                     ['iodine', 'I'],
->                     ['astatine', 'At']]
-> ~~~
-> {: .language-python}
->
-> ~~~
-> '2013'
-> [['chlorine', 'Cl'], ['bromine', 'Br'], ['iodine', 'I'], ['astatine', 'At']]
-> ~~~
-> {: .output}
->
-> Would your solution work regardless of whether you knew beforehand
-> the length of the string or list
-> (e.g. if you wanted to apply the solution to a set of lists of different lengths)?
-> If not, try to change your approach to make it more robust.
->
-> Hint: Remember that indices can be negative as well as positive
->
-> > ## Solution
-> > Use negative indices to count elements from the end of a container (such as list or string):
-> >
-> > ~~~
-> > string_for_slicing[-4:]
-> > list_for_slicing[-4:]
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-> ## Non-Continuous Slices
->
-> So far we've seen how to use slicing to take single blocks
-> of successive entries from a sequence.
-> But what if we want to take a subset of entries
-> that aren't next to each other in the sequence?
->
-> You can achieve this by providing a third argument
-> to the range within the brackets, called the _step size_.
-> The example below shows how you can take every third entry in a list:
->
-> ~~~
-> primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
-> subset = primes[0:12:3]
-> print('subset', subset)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> subset [2, 7, 17, 29]
-> ~~~
-> {: .output}
->
-> Notice that the slice taken begins with the first entry in the range,
-> followed by entries taken at equally-spaced intervals (the steps) thereafter.
-> If you wanted to begin the subset with the third entry,
-> you would need to specify that as the starting point of the sliced range:
->
-> ~~~
-> primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
-> subset = primes[2:12:3]
-> print('subset', subset)
-> ~~~
-> {: .language-python}
->
-> ~~~
-> subset [5, 13, 23, 37]
-> ~~~
-> {: .output}
->
-> Use the step size argument to create a new string
-> that contains only every other character in the string
-> "In an octopus's garden in the shade". Start with
-> creating a variable to hold the string:
->
-> ~~~
-> beatles = "In an octopus's garden in the shade"
-> ~~~
-> {: .language-python}
->
-> What slice of `beatles` will produce the
-> following output (i.e., the first character, third
-> character, and every other character through the end
-> of the string)?
-> ~~~
-> I notpssgre ntesae
-> ~~~
-> {: .output}
->
-> > ## Solution
-> > To obtain every other character you need to provide a slice with the step
-> > size of 2:
-> >
-> > ~~~
-> > beatles[0:35:2]
-> > ~~~
-> > {: .language-python}
-> >
-> > You can also leave out the beginning and end of the slice to take the whole string
-> > and provide only the step argument to go every second
-> > element:
-> >
-> > ~~~
-> > beatles[::2]
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-If you want to take a slice from the beginning of a sequence, you can omit the first index in the
-range:
-
-~~~
-date = 'Monday 4 January 2016'
-day = date[0:6]
-print('Using 0 to begin range:', day)
-day = date[:6]
-print('Omitting beginning index:', day)
-~~~
-{: .language-python}
-
-~~~
-Using 0 to begin range: Monday
-Omitting beginning index: Monday
-~~~
-{: .output}
-
-And similarly, you can omit the ending index in the range to take a slice to the very end of the
-sequence:
-
-~~~
-months = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
-sond = months[8:12]
-print('With known last position:', sond)
-sond = months[8:len(months)]
-print('Using len() to get last entry:', sond)
-sond = months[8:]
-print('Omitting ending index:', sond)
-~~~
-{: .language-python}
-
-~~~
-With known last position: ['sep', 'oct', 'nov', 'dec']
-Using len() to get last entry: ['sep', 'oct', 'nov', 'dec']
-Omitting ending index: ['sep', 'oct', 'nov', 'dec']
-~~~
-{: .output}
-
-> ## Overloading
->
-> `+` usually means addition, but when used on strings or lists, it means "concatenate".
-> Given that, what do you think the multiplication operator `*` does on lists?
-> In particular, what will be the output of the following code?
->
-> ~~~
-> counts = [2, 4, 6, 8, 10]
-> repeats = counts * 2
-> print(repeats)
-> ~~~
-> {: .language-python}
->
-> 1.  `[2, 4, 6, 8, 10, 2, 4, 6, 8, 10]`
-> 2.  `[4, 8, 12, 16, 20]`
-> 3.  `[[2, 4, 6, 8, 10],[2, 4, 6, 8, 10]]`
-> 4.  `[2, 4, 6, 8, 10, 4, 8, 12, 16, 20]`
->
-> The technical term for this is *operator overloading*:
-> a single operator, like `+` or `*`,
-> can do different things depending on what it's applied to.
->
-> > ## Solution
-> >
-> > The multiplication operator `*` used on a list replicates elements of the list and concatenates
-> > them together:
-> >
-> > ~~~
-> > [2, 4, 6, 8, 10, 2, 4, 6, 8, 10]
-> > ~~~
-> > {: .output}
-> >
-> > It's equivalent to:
-> >
-> > ~~~
-> > counts + counts
-> > ~~~
-> > {: .language-python}
-> {: .solution}
-{: .challenge}
-
-[hadleywickham-tweet]: https://twitter.com/hadleywickham/status/643381054758363136
-
-{% include links.md %}
diff --git a/_episodes/05-code-migration-01.md b/_episodes/05-code-migration-01.md
deleted file mode 100644
index 974127f..0000000
--- a/_episodes/05-code-migration-01.md
+++ /dev/null
@@ -1,532 +0,0 @@
----
-title: Code migration to HPC systems
-teaching: 40
-exercises: 0
-questions:
-- "I would like to try Hawk, can I keep working with Jupyter Notebooks? 
-  (yes, but ...)"
-- "How to access Jupyter Notebooks on Hawk?"
-- "How to transition from interactive Jupyter Notebooks to automated Python scripts?"
-objectives:
-- "Learn how to set up your work environment (transfer files, install libraries)."
-- "Understand the differences and trade-offs between Jupyter Notebooks and 
-  Python scripts."
-keypoints:
-- "It is possible to use Jupyter Notebooks on Hawk via OnDemand and ssh tunnels."
-- "The recommended method to setup your work environment (installing libraries)
-  is using Anaconda virtual environments."
-- "Include the library ipykernel to make the environment reachable from Jupyter
-   Notebooks"
-- "Use Jupyter Notebooks in the early (development) stages of your project when
-  lots of debugging are necessary. Move towards automated Python scripts in later
-  stages and submit them via SLURM job scripts."
-
----
-
-## Running Jupyter Notebooks on a remote HPC systems
-
-Although the most traditional way to interact with remote HPC and cloud systems 
-is through the command line (via the `ssh` and `scp` commands) some systems also
-offer graphical user interfaces for some services. Specifically, on Hawk you can 
-deploy
-Jupyter Notebooks via [OnDemand](https://openondemand.org/) (a web portal that
-allow you to work with HPC systems interactively). The notes below provide
-instructions for both methods of access: through OnDemand and through a `ssh` 
-*tunnel*.
-
-{::options parse_block_html="true" /}
-<div>
-  <ul class="nav nav-tabs" role="tablist">
-   <li role="presentation" class="active"><a data-os="ssh-tunnel" href="#SSH" 
-       aria-controls="SSH" role="tab" data-toggle="tab">SSH tunnel</a></li>
-   <li role="presentation"><a data-os="ondemand" href="#OnDemand" 
-       aria-controls="OnDemand" role="tab" data-toggle="tab">OnDemand</a></li>
-  </ul>
-
- <div class="tab-content">
-  <article role="tabpanel" class="tab-pane active" id="SSH">
-
-  To access a Jupyter Notebook server via an `ssh` tunnel you need first to 
-  login to Hawk:
-
-  ```
-  $ ssh hawk-username@hawklogin.cf.ac.uk
-  ```
-
-  Once logged in, confirm that Python 3 is accessible:
-
-  ```
-  $ module load compiler/gnu/9
-  $ module load python/3.7.0
-  $ python3 --version
-  ```
-  ~~~
-  Python 3.7.0
-  ~~~
-  {: .output}
-
-  We need to install Jupyter Notebooks on our user account on the remote server
-  (we will discuss more about installing Python packages later on):
-
-  ```
-  $ python3 -m venv create my_venv
-  $ . my_venv/bin/activate
-  $ python3 -m pip install jupyterlab
-  ```
-
-  The installation process will try to download several dependencies from the 
-  internet. Be patient, it shouldn't take more than a couple of minutes.
-
-  Now, this is important, the Jupyter Notebook server must be run on a *compute
-  node*, please take a look at our best practices guidelines in the 
-  [SCW portal](https://portal.supercomputing.wales/index.php/best-practice/).
-  If the concept of login and compute nodes is still not clear at this point 
-  don't worry too much (but you can find out more in out 
-  [Supercomputing for Beginners training course](https://arcca.github.io/hpc-intro/).
-  For now, run the following command to instruct the Hawk job scheduler to run 
-  a Jupyter Notebook server on a compute node:
-
-  ```
-  $ srun -n 1 -p htc --account=scwXXXX -t 1:00:00 jupyter-lab --ip=0.0.0.0
-  ```
-
-  ~~~
-  http://ccs1015:8888/?token=77777add13ab93a0c408c287a630249c2dba93efdd3fae06
-  or http://127.0.0.1:8888/?token=77777add13ab93a0c408c287a630249c2dba93efdd3fae06
-  ~~~
-  {: .output}
-
-  Next, open a new terminal and create a ssh tunnel using the node and port 
-  obtained in the previous step (e.g. ccs1015:8888):
-  
-  ```
-  $ ssh -L8888:ccs1015:8888 hawk-username@hawklogin.cf.ac.uk
-  ```
-
-  You should be able to navigate to http://localhost:8888 in your web browser 
-  (use the token provided in the output if needed). If everything went well, you
-  should see something like: 
-
-  <img src="{{ page.root }}/fig/jupyter-lab-home.png" alt="Jupyter Lab Home" 
-   width="50%" height="50%" />
-
-  Where you should be able to access the files stored your Hawk user account.
-
-
-  </article>
-
-  <article role="tabpanel" class="tab-pane" id="OnDemand">
-  1.  Go to [ARCCA OnDemand](https://arcondemand.cardiff.ac.uk) portal (this
-      requires access to [Cardiff University VPN](https://intranet.cardiff.ac.uk/staff/supporting-your-work/it-support/wireless-and-remote-access/off-campus-access/virtual-private-network-vpn) ).
-  2.  Enter your details: Hawk username and password. Once logged in you should
-      land on a page with useful information including the usual Message of the
-      Day (MOD) commonly seen when logging in to Hawk via the terminal.
-
-      |          |          |
-      |:--------:|:--------:|
-      | <img src="{{ page.root }}/fig/ondemand-login.png" alt="ARCCA OnDemand login page" width="400px" /> | <img src="{{ page.root }}/fig/ondemand-landing-page.png" alt="ARCCA landing page" width="400px" /> |
-      | | |
-
-  3.  Go to "Interactive Apps" in the top menu and select "Jupyter Notebook/Lab". 
-      This will bring you to a form where you can specify for how much time the
-      desktop is required, number of CPUs, partition, etc. You can also choose
-      to receive an email once the desktop is ready for you. Click the *Launch*
-      button to submit the request.
-
-      |          |          |
-      |:--------:|:--------:|
-      | <img src="{{ page.root }}/fig/ondemand-select-jupyter-notebook-lab.png" alt="ARCCA OnDemand login page" width="500px" /> | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-requirements.png" alt="OnDemand JN requirements" width="500px" /> |
-      | | |
-
-  4.  After submission you request will be placed on the queue and will wait
-      for resources, hopefully for a short period, but this *depends on the 
-      number of cores as well as time requested*, so please be patient. At this
-      point you can close the OnDemand website and come back at a later point 
-      to check progress or wait for the email notification if the option was 
-      selected.
-
-      Once your request is granted you should be able to see a *Running* message, 
-      the amount of resources granted and the time remaining.
-
-      Click *Connect to Jupyter* to launch the Jupyter in a new web browser tab.
-
-      |          |          |
-      |:--------:|:--------:|
-      | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-queued.png" alt="OnDemand JN queued" width="500px" /> | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-running.png" alt="OnDemand JN running" width="500px" /> |
-      | | |
-
-  5.  You should now have the familiar interface of Jupyter Notebooks in front of
-      you. It will show the documents and directories in your user account on 
-      Hawk. To create a new Notebook, go to the dropdown menu *New* on the right
-      side and click on *Python 3 (ipykernel)*. A new tab will open with a new
-      notebook ready for you to start working.
-
-      |          |          |
-      |:--------:|:--------:|
-      | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-main.png" alt="OnDemand JN main" width="500px" /> | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-new-notebook.png" alt="OnDemand JN new notebook" width="500px" /> |
-      | | |
-
-  </article>
- </div>
-</div>
-
-{% include links.md %}
-
-
-## Copying data
-
-To keep working on Hawk with the Notebooks we have written locally in our 
-Desktop computer we need to transfer them over. Depending on our platform we
-can do this in a couple of ways:
-
-{::options parse_block_html="true" /}
-<div>
-  <ul class="nav nav-tabs" role="tablist">
-   <li role="presentation" class="active"><a href="#copy-Windows"  aria-controls="copy-Windows"  role="tab" data-toggle="tab">Windows    </a></li>
-   <li role="presentation"               ><a href="#copy-MacOS"    aria-controls="copy-MacOS"    role="tab" data-toggle="tab">MacOS/Linux</a></li>
-   <li role="presentation"               ><a href="#copy-OnDemand" aria-controls="copy-OnDemand" role="tab" data-toggle="tab">OnDemand   </a></li>
-  </ul>
- 
- <div class="tab-content">
-  <article role="tabpanel" class="tab-pane active" id="copy-Windows">
-  On Windows you can use [MobaXterm](https://mobaxterm.mobatek.net/) to 
-  transfer files to Hawk from your local computer.
-
-  |                  |                  |
-  |:----------------:|:----------------:|
-  | <figure> <img src="{{ page.root }}/fig/MobaXterm-01-arrows.png" alt="Open SCP session on MobaXterm" width=500px> </figure> <br /> Click on **Session** to open the different connection methods available in MobaXterm | <img src="{{ page.root }}/fig/mobaxterm-scp-setup.png" alt="Enter details to start SPC session on MobaXterm" width="500px"/> <br /> Select **SFTP** and enter the Remote Host (*hawklogin.cf.ac.uk*) and your **Hawk username** |
-  | | |
-  | <figure> <img src="{{ page.root }}/fig/mobaxterm-scp-copy-directory.png" alt="Open SCP session on MobaXterm" width=500px> </figure> <br /> Locate the directory in your local computer and drag and drop to the remote server on the right pane. ||
-
-  </article>
- 
-  <article role="tabpanel" class="tab-pane" id="copy-MacOS">
-  MacOS and Linux provide the command scp -r that can be used to recursively 
-  copy your work directory over to your home directory in Hawk:
-  
-  ```
-  $ scp -r arcca-python hawk-username@hawklogin.cf.ac.uk:/home/hawk-username
-  python-novice-inflammation-code.zip           100% 7216   193.0KB/s   00:00
-  Untitled.ipynb                                100%   67KB 880.2KB/s   00:00
-  inflammation.png                              100%   13KB 315.6KB/s   00:00
-  argv_list.py                                  100%   42     0.4KB/s   00:00
-  readings_08.py                                100% 1097    10.6KB/s   00:00
-  readings_09.py                                100%  851    24.8KB/s   00:00
-  ```
-
-  </article>
-
-  <article role="tabpanel" class="tab-pane" id="copy-OnDemand">
-  With OnDemand you can also download and upload files to Hawk. In this 
-  example we will upload the directory with the Jupyter Notebooks we have 
-  created so far. Go to `Files` and select the directory where you wish to 
-  upload the files (our home directory in this case), then select `Upload`
-  and locate the directory in your local computer. Once uploaded, the files
-  should be available on Hawk:
-
-  |                  |                  |
-  |:----------------:|:----------------:|
-  | <img src="{{ page.root }}/fig/ondemand-upload-files-select-directory.png" alt="Go to Files and select directory where to upload." width=500px> | <img src="{{ page.root }}/fig/ondemand-upload-files-click-upload.png" alt="Click Upload" width="500px"/> |
-  | <img src="{{ page.root }}/fig/ondemand-upload-files-locate-local-directory.png" alt="Locate files in your local computer." width=500px> | <img src="{{ page.root }}/fig/ondemand-upload-files-uploaded.png" alt="Locate files in your local computer." width=500px> |
-
-  </article>
-
- </div>
-</div>
-
-## Setup your Jupyter Notebook work environment
-
-Depending on how you started your Jupyter Notebook you should have access to
-some default packages. But these are not guaranteed to be the same (this also
-applies for the version of Python) between the OnDemand and the `ssh` tunnel 
-methods. Moreover, it is unlikely that the remote HPC system would provide 
-every package you need by default.
-
-### Installing Python libraries
-
-The **recommended approach** is to create a conda virtual environment with a 
-`environemnt.yml` file which includes a list of all packages (and versions)
-needed for your work. This file can be created and used in your local computer
-and then copied to Hawk to reproduce the same environment. An example file is:
-
-~~~
-name: my-conda-env
-dependencies:
-  - python=3.9.2
-  - numpy
-  - pandas
-  - ipykernel
-~~~
-{: .language-yaml}
-
-The package `ipykernel` is required here to make the environment reachable from
-Jupyter Notebooks. You can find more about creating an `environment.yml` file 
-in the [Anaconda documentation](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually).
-
-On Hawk you need to first load Anaconda:
-
-```
-$ module load anaconda/2020.02
-$ source activate
-$ which conda
-```
-
-~~~
-/apps/languages/anaconda/2020.02/bin/conda
-~~~
-{: .output}
-
-And then proceed to install the virtual environment:
-
-```
-$ conda env create -f environment.yml
-```
-
-~~~
-...
-libffi-3.3           | 50 KB     | ##################################### | 100%
-numpy-1.21.2         | 23 KB     | ##################################### | 100%
-pandas-1.3.4         | 9.6 MB    | ##################################### | 100%
-mkl-2021.4.0         | 142.6 MB  | ##################################### | 100%
-six-1.16.0           | 18 KB     | ##################################### | 100%
-Preparing transaction: done
-Verifying transaction: done
-Executing transaction: done
-#
-# To activate this environment, use
-#
-#     $ conda activate my-conda-env
-#
-# To deactivate an active environment, use
-#
-#     $ conda deactivate
-~~~
-{: .output}
-
-We can then follow the instructions printed at the end of the installation 
-process to activate our environment:
-
-```
-$ conda activate my-conda-env
-$ which python
-```
-
-~~~
-~/.conda/envs/my-conda-env/bin/python
-~~~
-{: .output}
-
-We can further confirm the version of Python used by the environment:
-
-```
-$ python --version
-```
-
-~~~
-Python 3.9.2
-~~~
-{: .output}
-
-To deactivate the environment and return to the default Python provided by the
-system (or the loaded module):
-
-```
-$ conda deactivate
-$ python --version
-```
-
-~~~
-Python 3.7.6
-~~~
-{: .output}
-
-### Using an Anaconda virtual environment from Jupyter Notebooks
-
-We can access our newly installed Anaconda environment from Jupyter Notebooks
-on OnDemand. For this, create a new session and when the resources are granted
-click on `Connect to Jupyter`. On Jupyter Lab you might be asked to choose 
-which kernel to start, if so, select the name given to your virtual environment
-(*my-conda-env*) in this example:
-
-<img src="{{ page.root }}/fig/ondemand-jupyter-notebook-select-kernel.png"
-alt="Change JN kernel when automatically prompted." width=500px> 
-
-If another kernel is loaded by default, you can still change it by clicking on
-the top right corner of your Notebook, a similar menu should appear:
-
-  |                  |                  |
-  |:----------------:|:----------------:|
-  | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-change-kernel-manually.png" alt="Change JN kernel manually." width=500px> | <img src="{{ page.root }}/fig/ondemand-jupyter-notebook-select-kernel.png" alt="Select JN kernel from menu" width="500px"/> |
-
-If all goes well you should be able to confirm the Python versions and path, as
-well as the location of the installed libraries:
-
-<img src="{{ page.root }}/fig/ondemand-jupyter-notebook-confirm-environment.png"
-alt="Confirm the Python version, and paths to libraries included in
-environment." width="500px"/>
-
-At this point you should have all the packages required to continue working on 
-Hawk as if you were working on your local computer.
-
-
-### A more efficient approach
-
-During these examples we have been requesting only 1 CPU when we launch our 
-Jupyter Notebook and that, hopefully, has caused our request to be fulfilled
-fairly quickly. However, there will be a point where 1 CPU is no longer enough
-(maybe the application has become more complex or there is more data to analyse,
-and memory requirements have increased). At that point you can modify the 
-requirements and increase the number of CPUS,  memory, time or devices (GPUs).
-One point to keep in mind when increasing requirements is that this will impact
-the time it takes for the system scheduler to deliver your request and allocate
-you the resources, the higher the requirements, the longer it will take.
-
-When the time spent waiting in queue becomes excessive it is worth considering
-moving away from the Jupyter Notebook workflow towards a more traditional 
-Python script approach (**recommended for HPC systems**). The main difference
-between them is that while Jupyter Notebooks is ideal for the development 
-stages of a project (since you can test things out in real time and debug if 
-needed), the Python script approach is better suited for the production stages
-where the needed for supervision and debugging is reduced. Python scripts also 
-have the advantage, on HPC systems, of being able to be queued for resources
-and automatically executed when these are granted without you needing to be 
-logged in the system.
-
-So, how do we actually transfer our Jupyter Notebook to a Python script?
-Fortunately, Jupyter Notebook developers thought of this requirement and added
-a convenient export method to the Notebooks (the menus might be different
-depending on if Jupyter Notebooks or Jupyter Lab was launched from OnDemand):
-
-|                  |                  |
-|:----------------:|:----------------:|
-| ![Download Notebook from JN as Py script](../fig/jupyter-notebook-download-as-python-script.png) | ![Download Notebook from JL as Py script](../fig/jupyter-lab-download-as-python-script.png) |
-| Download from Jupyter Notebook | Download from Jupyter Lab |
-
-After choosing an appropriate name and saving the file, we should have a Python 
-script (a text file) with entries similar to:
-
-~~~
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-3 + 5 * 8
-
-
-# In[2]:
-
-
-weight_kg=60
-
-~~~
-{: .language-python}
-
-Notice the `# In[X]:` that mark the position of corresponding cells in our
-Jupyter Notebook and are kept for reference. We can keep them in place, they
-won't cause any trouble as they are included as comments (due to the initial
-`#`), but if we wanted to remove them we could do it by hand or more 
-efficiently by using the command line tool `sed` to find and delete lines that
-start with the characters `# [` and to delete empty lines (`^` is used by `sed`
-to indicate the beginning of a line and `$` to indicate the end):
-
-**(from this point onwards we move away from Jupyter Notebooks and start typing
-commands on a terminal connected to Hawk)**
-
-```
-sed -e '/# In/d' -e '/^$/d' lesson1.py > lesson1_cleaned.py
-```
-
-This will produce the `lesson1_cleaned.py` file with entries similar to:
-
-~~~
-#!/usr/bin/env python
-# coding: utf-8
-3+5+8
-weight_kg=60
-~~~
-{: .language-python}
-
-Now that we have our Python script, we need to create an additional file (job
-script) to place it in the queue (submit the job). Make sure to remove any
-commands from the Python script that might need additional confirmation or user
-interaction as you won't be able to provide it with this method of execution.
-The following is the content a job script that is equivalent to how we have 
-been requesting resources through OnDemand:
-
-~~~
-#!/bin/bash
-
-#SBATCH -J test             # job name
-#SBATCH -n 1                # number of tasks needed
-#SBATCH -p htc              # partition
-#SBATCH --time=01:00:00     # time limit
-#SBATCH -A scwXXXX          # account number
-
-set -eu
-
-module purge
-module load anaconda/2020.02
-module list
-
-# Load conda
-source activate
-
-# Load our environment
-conda activate my-conda-env
-
-which python
-python --version
-
-python my-python-script.py
-
-~~~
-{: .language-bash}
-
-To submit (put it queue) the above script, on Hawk:
-
-```
-$ sbatch my-job-script.sh
-```
-
-~~~
-Submitted batch job 25859860
-~~~
-{: .output}
-
-You can query the current state of this job with:
-
-```
-$ squeue -u $USER
-```
-
-~~~
-    JOBID       PARTITION    NAME    USER      ST    TIME  NODES NODELIST(REASON)
-    25860025    htc          test    c.xxxxxxx PD    0:00      1 ccs3004
-~~~
-{: .output}
-
-This particular job might not spend a long time in queue and the above output
-might not show it, but on completion there should be a `slurm-<job-id>.out`
-created in the current directory with the output produced by our script.
-
-There is a lot more to know about working with HTC systems and job schedulers,
-once you are ready to go this route, take a look at our documentation and 
-training courses on these topics:
-
-- [**Supercomputing for Beginners**](https://arcca.github.io/hpc-intro): Why 
-  use HPC? Accessing systems, using SLURM, loading software, file transfer and
-  optimising resources.
-- [**Slurm: Advanced Topics**](https://arcca.github.io/slurm_advanced_topics):
-  Additional material to interface with HPC more effectively.
-
-> ## Need help?
-> 
-> If during the above steps you found any issues or have doubts regarding your
-> specific work environment, get in touch with us at arcca-help@cardiff.ac.uk.
-{: .callout}
-
-
-
-{% include links.md %}
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----
-title: About
----
-{% include carpentries.html %}
-{% include links.md %}
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----
-title: Discussion
----
-FIXME
-
-{% include links.md %}
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+++ /dev/null
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----
-title: Figures
----
-
-{% include base_path.html %}
-{% include manual_episode_order.html %}
-
-<script>
-  window.onload = function() {
-    var lesson_episodes = [
-    {% for lesson_episode in lesson_episodes %}
-      {% if site.episode_order %}
-        {% assign episode = site.episodes | where: "slug", lesson_episode | first %}
-      {% else %}
-        {% assign episode = lesson_episode %}
-      {% endif %}
-    "{{ episode.url }}"{% unless forloop.last %},{% endunless %}
-    {% endfor %}
-    ];
-
-    var xmlHttp = [];  /* Required since we are going to query every episode. */
-    for (i=0; i < lesson_episodes.length; i++) {
-
-      xmlHttp[i] = new XMLHttpRequest();
-      xmlHttp[i].episode = lesson_episodes[i];  /* To enable use this later. */
-      xmlHttp[i].onreadystatechange = function() {
-
-        if (this.readyState == 4 && this.status == 200) {
-          var parser = new DOMParser();
-          var htmlDoc = parser.parseFromString(this.responseText,"text/html");
-          var htmlDocArticle = htmlDoc.getElementsByTagName("article")[0];
-
-          var article_here = document.getElementById(this.episode);
-          var images = htmlDocArticle.getElementsByTagName("img");
-
-          if (images.length > 0) {
-            var h1text = htmlDocArticle.getElementsByTagName("h1")[0].innerHTML;
-
-            var htitle = document.createElement('h2');
-            htitle.innerHTML = h1text;
-            article_here.appendChild(htitle);
-
-            var image_num = 0;
-            for (let image of images) {
-              image_num++;
-
-              var title = document.createElement('p');
-              title.innerHTML = "<strong>Figure " + image_num + ".</strong> " + image.alt;
-              article_here.appendChild(title);
-
-              article_here.appendChild(image.cloneNode(false));
-
-              if (image_num < images.length) {
-                var hr = document.createElement('hr');
-                article_here.appendChild(hr);
-              }
-            }
-          }
-        }
-      }
-      episode_url = "{{ relative_root_path }}" + lesson_episodes[i];
-      xmlHttp[i].open("GET", episode_url);
-      xmlHttp[i].send(null);
-    }
-  }
-</script>
-
-{% comment %} Create anchor for each one of the episodes.  {% endcomment %}
-
-{% for lesson_episode in lesson_episodes %}
-  {% if site.episode_order %}
-    {% assign episode = site.episodes | where: "slug", lesson_episode | first %}
-  {% else %}
-    {% assign episode = lesson_episode %}
-  {% endif %}
-<article id="{{ episode.url }}" class="figures"></article>
-{% endfor %}
-
-{% include links.md %}
diff --git a/_extras/guide.md b/_extras/guide.md
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--- a/_extras/guide.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: "Instructor Notes"
----
-FIXME
-
-{% include links.md %}
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-<svg width="460.8pt" height="345.6pt" version="1.1" viewBox="0 0 460.8 345.6" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-  <defs>
-    <style type="text/css">*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}</style>
-  </defs>
-  <path d="m0 345.6h460.8v-345.6h-460.8z" fill="#ffffff"/>
-  <path d="m57.6 307.6h357.1v-266.1h-357.1z" fill="#ffffff"/>
-  <defs>
-    <path id="a" d="m0 0v3.5" stroke="#000000" />
-  </defs>
-  <use x="73.832727" y="307.584" stroke="#000000"  xlink:href="#a"/>
-  <text x="73.832727" y="322.182437"  font-size="10px" text-anchor="middle">0</text>
-  <use x="115.455105" y="307.584" stroke="#000000"  xlink:href="#a"/>
-  <text x="115.455105" y="322.182437"  font-size="10px" text-anchor="middle">5</text>
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index 7ebe2ce..0000000
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diff --git a/files/.gitkeep b/files/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/index.md b/index.md
index 43cbdef..1a7a9c1 100644
--- a/index.md
+++ b/index.md
@@ -1,86 +1,32 @@
 ---
 layout: lesson
-root: .  # Is the only page that doesn't follow the pattern /:path/index.html
-permalink: index.html  # Is the only page that doesn't follow the pattern /:path/index.html
+root: .
+permalink: index.html
 ---
-Python is becoming a dominant language to perform many different types of work
-across many disciplines.  This course is based on the Software Carpentry material
-and will cover topics such as fundamentals of Python to get a basic understanding
-and then focussing on storing and handling data within Python using common packages
-such as numpy and matplotlib.  Python has an extensive set of packages to extend
-its abilities and will be highlighted within the course.  Once an understanding of
-Python is provided, migrating Python code onto a supercomputer has a few topics to
-cover such as how to copy files, different versions of Python and installing
-packages.  Advice will be provided on running and storing data on our supercomputer,
-“Hawk” to get the attendee started using Python efficiently within a supercomputer
-environment. 
 
-This introductory course to Python is based on the freely available material 
-produced by the **Software Carpentry**. Due to time restrictions we are only able
-to cover a few lessons, but if you find the course interesting we encourage you
-to continue your learning journey with [them](https://swcarpentry.github.io/python-novice-inflammation/).
+# Tetris Game (俄罗斯方块)
 
-<!-- this is an html comment -->
+Welcome to a classic Tetris game implementation in Python!
 
-{% comment %} This is a comment in Liquid {% endcomment %}
+This project features a fully functional Tetris game built with pygame, offering the classic block-stacking puzzle experience.
 
----
-
-The best way to learn how to program is to do something useful,
-so this introduction to Python is built around a common scientific task:
-**data analysis**.
-
-### Arthritis Inflammation
-We are studying **inflammation in patients** who have been given a new treatment for arthritis.
-
-There are 60 patients, who had their inflammation levels recorded for 40 days.
-We want to analyze these recordings to study the effect of the new arthritis treatment.
-
-To see how the treatment is affecting the patients in general, we would like to:
-
-1. Calculate the average inflammation per day across all patients.
-2. Plot the result to discuss and share with colleagues.
-
-![3-step flowchart shows inflammation data records for patients moving to the Analysis step
-where a heat map of provided data is generated moving to the Conclusion step that asks the
-question, How does the medication affect patients?](
-fig/lesson-overview.svg "Lesson Overview")
-
-
-### Data Format
-The data sets are stored in
-[comma-separated values]({{ page.root }}/reference.html#comma-separated-values) (CSV) format:
+## Quick Start
 
-- each row holds information for a single patient,
-- columns represent successive days.
+1. Install pygame: `pip install pygame`
+2. Run the game: `python tetris.py`
+3. Enjoy!
 
-The first three rows of our first file look like this:
-~~~
-0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
-0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
-0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1
-~~~
-{: .source}
-Each number represents the number of inflammation bouts that a particular patient experienced on a
-given day.
+## Game Controls
 
-For example, value "6" at row 3 column 7 of the data set above means that the third
-patient was experiencing inflammation six times on the seventh day of the clinical study.
+- **Arrow Keys (Left/Right)**: Move pieces horizontally
+- **Arrow Key (Down)**: Soft drop
+- **Arrow Key (Up)**: Rotate piece
+- **Space Bar**: Hard drop (instant drop)
 
-In order to analyze this data and report to our colleagues, we'll have to learn a little bit
-about programming.
+## About Tetris
 
-> ## Prerequisites
->
-> You need to understand the concepts of **files** and **directories** and how to start a Python
-> interpreter before tackling this lesson. This lesson sometimes references Jupyter
-> Notebook although you can use any Python interpreter mentioned in the [Setup][lesson-setup].
->
-> The commands in this lesson pertain to **Python 3**.
-{: .prereq}
+Tetris is a classic tile-matching puzzle game where players arrange falling tetrominoes (geometric shapes composed of four square blocks) to create complete horizontal lines, which then disappear and award points.
 
-### Getting Started
-To get started, follow the directions on the "[Setup][lesson-setup]" page to download data
-and install a Python interpreter.
+The goal is to prevent the pieces from stacking up to the top of the playing field. The game ends when no more pieces can enter the play area.
 
-{% include links.md %}
+Good luck and have fun!
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5d2caa2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+pygame>=2.0.0
diff --git a/tetris.py b/tetris.py
new file mode 100644
index 0000000..1de9c67
--- /dev/null
+++ b/tetris.py
@@ -0,0 +1,239 @@
+#!/usr/bin/env python3
+"""
+Simple Tetris Game
+A classic Tetris implementation in Python using pygame.
+"""
+
+import pygame
+import random
+import sys
+
+# Initialize pygame
+pygame.init()
+
+# Constants
+SCREEN_WIDTH = 300
+SCREEN_HEIGHT = 600
+BLOCK_SIZE = 30
+GRID_WIDTH = SCREEN_WIDTH // BLOCK_SIZE
+GRID_HEIGHT = SCREEN_HEIGHT // BLOCK_SIZE
+
+# Colors
+BLACK = (0, 0, 0)
+WHITE = (255, 255, 255)
+GRAY = (128, 128, 128)
+RED = (255, 0, 0)
+GREEN = (0, 255, 0)
+BLUE = (0, 0, 255)
+CYAN = (0, 255, 255)
+MAGENTA = (255, 0, 255)
+YELLOW = (255, 255, 0)
+ORANGE = (255, 165, 0)
+
+# Tetromino shapes
+SHAPES = [
+    [[1, 1, 1, 1]],  # I
+    [[1, 1], [1, 1]],  # O
+    [[1, 1, 1], [0, 1, 0]],  # T
+    [[1, 1, 1], [1, 0, 0]],  # L
+    [[1, 1, 1], [0, 0, 1]],  # J
+    [[1, 1, 0], [0, 1, 1]],  # S
+    [[0, 1, 1], [1, 1, 0]]   # Z
+]
+
+SHAPE_COLORS = [CYAN, YELLOW, MAGENTA, ORANGE, BLUE, GREEN, RED]
+
+
+class Tetromino:
+    def __init__(self):
+        self.shape_index = random.randint(0, len(SHAPES) - 1)
+        self.shape = SHAPES[self.shape_index]
+        self.color = SHAPE_COLORS[self.shape_index]
+        self.x = GRID_WIDTH // 2 - len(self.shape[0]) // 2
+        self.y = 0
+
+    def rotate(self):
+        """Rotate the tetromino 90 degrees clockwise"""
+        self.shape = list(zip(*self.shape[::-1]))
+
+
+class Tetris:
+    def __init__(self):
+        self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
+        pygame.display.set_caption('Tetris')
+        self.clock = pygame.time.Clock()
+        self.grid = [[0 for _ in range(GRID_WIDTH)] for _ in range(GRID_HEIGHT)]
+        self.current_piece = Tetromino()
+        self.game_over = False
+        self.score = 0
+        self.fall_time = 0
+        self.fall_speed = 500  # milliseconds
+
+    def check_collision(self, piece, offset_x=0, offset_y=0):
+        """Check if the piece collides with the grid or boundaries"""
+        for y, row in enumerate(piece.shape):
+            for x, cell in enumerate(row):
+                if cell:
+                    new_x = piece.x + x + offset_x
+                    new_y = piece.y + y + offset_y
+
+                    # Check boundaries
+                    if new_x < 0 or new_x >= GRID_WIDTH or new_y >= GRID_HEIGHT:
+                        return True
+
+                    # Check grid collision
+                    if new_y >= 0 and self.grid[new_y][new_x]:
+                        return True
+        return False
+
+    def lock_piece(self):
+        """Lock the current piece into the grid"""
+        for y, row in enumerate(self.current_piece.shape):
+            for x, cell in enumerate(row):
+                if cell:
+                    grid_x = self.current_piece.x + x
+                    grid_y = self.current_piece.y + y
+                    if grid_y >= 0:
+                        self.grid[grid_y][grid_x] = self.current_piece.color
+
+    def clear_lines(self):
+        """Clear completed lines and update score"""
+        lines_cleared = 0
+        y = GRID_HEIGHT - 1
+        while y >= 0:
+            if all(self.grid[y]):
+                del self.grid[y]
+                self.grid.insert(0, [0 for _ in range(GRID_WIDTH)])
+                lines_cleared += 1
+            else:
+                y -= 1
+
+        if lines_cleared > 0:
+            self.score += lines_cleared * 100
+
+    def move(self, dx, dy):
+        """Move the current piece"""
+        if not self.check_collision(self.current_piece, dx, dy):
+            self.current_piece.x += dx
+            self.current_piece.y += dy
+            return True
+        return False
+
+    def rotate_piece(self):
+        """Rotate the current piece"""
+        old_shape = self.current_piece.shape
+        self.current_piece.rotate()
+
+        if self.check_collision(self.current_piece):
+            self.current_piece.shape = old_shape
+
+    def drop_piece(self):
+        """Drop the piece all the way down"""
+        while self.move(0, 1):
+            pass
+
+    def draw_grid(self):
+        """Draw the grid and locked pieces"""
+        for y in range(GRID_HEIGHT):
+            for x in range(GRID_WIDTH):
+                rect = pygame.Rect(x * BLOCK_SIZE, y * BLOCK_SIZE, BLOCK_SIZE, BLOCK_SIZE)
+                if self.grid[y][x]:
+                    pygame.draw.rect(self.screen, self.grid[y][x], rect)
+                pygame.draw.rect(self.screen, GRAY, rect, 1)
+
+    def draw_piece(self, piece):
+        """Draw the current piece"""
+        for y, row in enumerate(piece.shape):
+            for x, cell in enumerate(row):
+                if cell:
+                    rect = pygame.Rect(
+                        (piece.x + x) * BLOCK_SIZE,
+                        (piece.y + y) * BLOCK_SIZE,
+                        BLOCK_SIZE,
+                        BLOCK_SIZE
+                    )
+                    pygame.draw.rect(self.screen, piece.color, rect)
+                    pygame.draw.rect(self.screen, GRAY, rect, 1)
+
+    def draw_text(self, text, size, color, x, y):
+        """Draw text on screen"""
+        font = pygame.font.Font(None, size)
+        text_surface = font.render(text, True, color)
+        text_rect = text_surface.get_rect()
+        text_rect.midtop = (x, y)
+        self.screen.blit(text_surface, text_rect)
+
+    def run(self):
+        """Main game loop"""
+        while not self.game_over:
+            self.fall_time += self.clock.get_rawtime()
+            self.clock.tick()
+
+            # Handle events
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    pygame.quit()
+                    sys.exit()
+
+                if event.type == pygame.KEYDOWN:
+                    if event.key == pygame.K_LEFT:
+                        self.move(-1, 0)
+                    elif event.key == pygame.K_RIGHT:
+                        self.move(1, 0)
+                    elif event.key == pygame.K_DOWN:
+                        self.move(0, 1)
+                    elif event.key == pygame.K_UP:
+                        self.rotate_piece()
+                    elif event.key == pygame.K_SPACE:
+                        self.drop_piece()
+
+            # Auto fall
+            if self.fall_time >= self.fall_speed:
+                self.fall_time = 0
+                if not self.move(0, 1):
+                    self.lock_piece()
+                    self.clear_lines()
+                    self.current_piece = Tetromino()
+
+                    # Check game over
+                    if self.check_collision(self.current_piece):
+                        self.game_over = True
+
+            # Draw everything
+            self.screen.fill(BLACK)
+            self.draw_grid()
+            self.draw_piece(self.current_piece)
+            self.draw_text(f'Score: {self.score}', 30, WHITE, SCREEN_WIDTH // 2, 10)
+            pygame.display.flip()
+
+        # Game over screen
+        self.screen.fill(BLACK)
+        self.draw_text('GAME OVER', 50, RED, SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 - 50)
+        self.draw_text(f'Final Score: {self.score}', 30, WHITE, SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2)
+        self.draw_text('Press any key to exit', 25, WHITE, SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 + 50)
+        pygame.display.flip()
+
+        # Wait for key press to exit
+        waiting = True
+        while waiting:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    waiting = False
+                if event.type == pygame.KEYDOWN:
+                    waiting = False
+
+        pygame.quit()
+        sys.exit()
+
+
+if __name__ == '__main__':
+    print("Starting Tetris...")
+    print("Controls:")
+    print("  LEFT/RIGHT: Move piece")
+    print("  DOWN: Soft drop")
+    print("  UP: Rotate piece")
+    print("  SPACE: Hard drop")
+    print()
+
+    game = Tetris()
+    game.run()