week3.ipynb

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    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
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    "# Week 3: The data science ecosystem\n",
    "\n",
    "<img width = \"250\" src=\"./images/hazel.png\" align=\"right\" style=\"padding-left:10px\">\n",
    "\n",
    "Congrats. You have learned the foundations of Python! So far, we have studied the Python standard library, but have not looked at all at the external libraries that people typically use for data analysis.\n",
    "\n",
    "This week you will start this journey, going through a crash course on the most essential tools you will need to analyze and visualize data. Since we are done with ATBS, it is time to delegate our flipped teaching responsibilities to multiple sources. I basically scoured the internet for readable, beginner-friendly, but practical introductions to the topics we are covering. I think I have found some really good resources, but if you find any additional material that you like, please let me know!\n",
    "\n",
    "<a id=\"toc\"></a>\n",
    "- [1: Overview of Python's data science ecosystem](#intro)\n",
    "- [2: Virtual Environments](#virtual)\n",
    "- [3: Numpy for numerical computing](#numpy)\n",
    "- [4: Matplotlib for plotting](#matplotlib)\n",
    "\n",
    "Note each of these libraries could fill multiple weeks of discussion. Our goal isn't to become an expert in matplotlib. Rather, I want you to become comfortable loading the libraries and running basic commands. Building up this muscle memory will be crucial when you are running things on your own.  \n",
    "\n",
    "Incidentally, there is a good book on Python for data science that is free and available online: [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/). The material in that book is a bit too dense for a rapid crash course. However, when you get stuck or have questions, I encourage you to use it as a reference (along with Google).\n",
    "\n"
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    "<a id =\"intro\"></a>\n",
    "# 1: Overview of Python's data science ecosystem\n",
    "You will often hear about Python *data science stack*, which is the main set of Python libraries that people use to analyze and visualize their data. It includes libraries for numerical computing, plotting, statistical tests, and much more. Basically, if you want to analyze data, there is a core set of libraries you'll end up using. This does not mean you need to be an expert: it is more about being comfortable enough to install and use the library's basic functionality (one secret with these libraries is that just a handful of commands is enough). \n",
    "\n",
    "The data science stack isn't some crisply defined list defined by some committee of developers. Indeed, *it really isn't a stack at all*. While we often talk about data science stack, this metaphor implies some foundation where one piece builds on another. The data analysis tools form more of an **ecosystem**, an interconnected web of software packages all geared toward helping you analyze your data. Here is a cross-section through some of the resources available in Python:\n",
    "\n",
    "<img width = \"650\" src=\"./images/ds_ecosystem.jpg\" style=\"padding-top:30px; padding-bottom:30px\">\n",
    "\n",
    "As you can see there are a few core packages in the center (e.g., Numpy): these are the packages with which most people are familiar, and that many other packages depend upon. Then there are more peripheral packages located more at the edges of the web. These are the ones that you will pick up when you have a particular need (e.g., you don't need to learn Tensorflow unless you are doing a particular type of machine learning project). The more peripheral packages aren't lower quality, they are just *specialized* for particular tasks that many people will never need. All the packages are typically very high quality, that's how they make it into the ecosystem.\n",
    "\n",
    "I can't stress enough: you **do not need** to learn all these packages. That would be a waste of time, especially when starting out. My plan is to help you navigate this web, cut through the complexity, and find your bearings.\n",
    "\n",
    "Our specific goals this week are threefold:\n",
    "- Informally discuss the main packages in the data science ecosystem. That way, in the future, you can have an idea what tool to use when you have a specific need. \n",
    "- Discuss *virtual environments*, which are extremely important in any serious analysis project, and create your first virtual environment.\n",
    "- Learn in more detail about two important tools in the data science ecosystem (`numpy` and `matplotlib`).\n",
    "\n",
    "Next week we will learn about `pandas` and `scikit-learn`."
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    " ## Synopsis of specific packages\n",
    "Before jumping into learning more specifics, let's do a quick overview of the main packages in the ecosystem.\n",
    "\n",
    "- **numpy**: NumPy is a numerical computing library that supports extremely efficient array computations (as we will see, arrays are basically containers for numbers and are extremely useful for representing data). It also has basic mathematical operations and statistical calculations on those numerical arrays. NumPy forms the basis for almost all packages that perform operations on numbers.\n",
    "- **matplotlib**: Matlplotlib is the core plotting library for Python. \n",
    "- **scipy**: Scipy is a scientific computing package that is basically one abstraction level up from NumPy. It includes basic statistics, optimization, numerical integration,  and other scientific computing tools. It is a sort of grab-bag for useful computations that you will use across multiple projects. \n",
    "\n",
    "The above tools are sort of the core of the Python data science ecosystem. If you do any heavy data analysis, I can pretty much *guarantee* you will use the above libraries. Because most other  libraries typically build on the above, I would classify them as more peripheral in the sense that you may or may not use them depending on your specialization and the type of data you use. They include:"
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    "- **Pandas**: an extremely user-friendly package for handling tabular data (basically, data arranged in something like an Excel spreadsheet). Pandas includes tools for analysis as well as plotting such data. It builds extensively on numpy and Matplotlib. Next week, we will learn the basics of Pandas, partly because it is used extensively in Neuroscience, and because a lot of data in neuroscience is stored in the `csv` format.\n",
    "- **Pillow**: A simple image processing library (initially, there was PIL (Python Image Library) and Pillow is based on it). Allows for reading/writing images, converting between image types, and applying simple filters (e.g., contrast/smoothing) to images. \n",
    "- **OpenCV**: An extremely powerful computer vision library. Written in C++, a Python wrapper lets you use it using pure Python. It allows you to capture video from your web camera, read and write movies, and has tools for extensive processing and filtering of your images and movies, including machine vision in real time (e.g., facial recognition). \n",
    "- **scikit-learn**: The main machine learning library when you want to do traditional (not-neural network based) machine learning. It has tools for classification, clustering, and regression. It has a consistent interface for these problems, and is fairly intuitive to use.\n",
    "- **tensorflow/pytorch**: These are the two main *deep learning* packages that use artificial neural networks to let you tap into the power of cutting edge machine learning algorithms. Tensorflow was developed by Google and has been around longer, while Pytorch was developed by Facebook and is a bit easier to use and learn. Such approaches tend to work great for complex machine vision problems, but are not typically necessary (i.e., `scikit-learn` is often fine). \n",
    "- **Plotly / Bokeh / Seaborn (etc)**: while Matplotlib is a great plotting library, there are some limitations. Because of this, there has been [a proliferation of plotting libraries in Python](https://geo-python-site.readthedocs.io/en/stable/lessons/L7/python-plotting.html). I've just listed some of the more popular ones here. *Seaborn* is built on Matplotlib and provides an intuitive interface to generate beautiful plots even with complex data, plots that would take many many lines of code using Matplotlib.  *Plotly* and *Bokeh* allow you to embed beautiful interactive plots in Jupyter notebooks: they are basically Python interfaces with a Javascript (web-page friendly) back end. Plotly and Bokeh can also be integrated into dashboards that you can share online, or even in the cloud. \n",
    "- **sympy**: the symbolic mathematics library for Python. This is not used that much by people tussling with real messy data. However, if you need an *exact* solution to an equation, then you can use `sympy`. \n",
    "- **statsmodels**: for specialized or advanced statistical analysis: if SciPy doesn't have what you need, check statsmodels. This used to be part of SciPy, but branched off into its own specialized package.\n",
    "- **PyMC3**: A tool specialized for Bayesian modeling and inference. "
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    "## How to navigate the ecosystem\n",
    "The above is a brief tour of the main components of the Python data science ecosystem. Note do *not* worry about memorizing everything in there: I'd just try to remember the general types of tools available. Then when you need one, you know you will be able to find it. \n",
    "\n",
    "Honestly, there is a good chance you will *never* use some of the libraries in the secondary stack, especially if you are not a full-time programmer. I *am* a full-time programmer and I have never explicitly imported `PyMC3` or `sympy`. They are perfectly good packages: I just haven't needed them yet.  \n",
    "\n",
    "Below, we will focus on learning the basics of just two of the packages in the data science stack. This will give you a sense for how to install, import, and work with different libraries. Once you've done it a couple of times, your skill will generalize to new libraries when they are needed.\n",
    "\n",
    "In general with Python, for important tasks (e.g., web scraping) there will typically be a canonical framework out there that does it really well. Finding it is typically easy with Google. There are also curated lists of great Python packages: my favorite is the [awesome python](https://github.com/vinta/awesome-python) list, which is fairly comprehensive and organized by general category. "
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    "### What about Jupyter, github, and IDEs?\n",
    "While Jupyter notebooks aren't part of your first-order analysis workflow (i.e., you don't typically `import` Jupyter in your code), they have become the de-facto scaffolding for workflows, so I thought it was important to include it in the figure above. \n",
    "\n",
    "With their extremely convenient web-interface for coding, they have become absorbed into all the major cloud computing platforms (AWS, Google Colabs, and Azure). Further, Jupyter notebooks are the tool of choice for book authors, and creators of new libraries that want to get people on board quickly. It used to be you had to worry about what IDE someone was using when you shared code -- now you know you can just provide a Jupyter notebook, and things will be fine. \n",
    "\n",
    "If this class were for software developers, **git** and **github** would also be in the diagram. By making it extremely easy to track and share code, such tools have been crucial for speeding up development of analysis software, and removing arbitrariness from the process. Since this class is for neuroscientists, who often will not use a version control system, I decided to leave it out. \n",
    "\n",
    "Similarly, as discussed in `week0.ipynb`,  *integrated development environments* (IDEs) are extremely important components of any programmer's workflow: Jupyter notebooks simply cannot compare in terms of power/speed/efficiency for writing and refactoring code. Jupyter notebooks have been a boon for *communicating* code. Our purpose here is more didactic, and Jupyter notebooks are often sufficient for most analysis purposes: if you ever feel the need to go beyond the notebook, to something more powerful, then there are plenty available (see Section 2 of `week0.ipynb` where we went over some options)."
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    "<a id =\"virtual\"></a>\n",
    "[Back to Table of Contents](#toc)\n",
    "<img width = \"250\" src=\"./images/sandbox.png\" align=\"right\" style=\"padding-left:10px\">\n",
    "# 2: Virtual environments: your Python sandbox\n",
    "\n",
    "Before we start with numpy, we need to learn about *virtual environments*. This will be a brief tour of anaconda virtual environments. We'll start with an introduction and some background material before building our own virtual environment."
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    "## Virtual environments: what and why?\n",
    "In the final two classes we will go beyond the standard Python library, forging fairly deeply into the data science jungle. Hence, we need to start thinking about how to  manage the contexts in which we carry out our work in Python. We need to talk about *virtual environments*.\n",
    "\n",
    "A virtual environment is an isolated playground or sandbox made up of a Python version and a set of software packages. You typically construct a virtual environment to carry out a particular task. That task might be extremely general (\"Practice doing things in the data science ecosystem\") or specific (\"Set up Deep Lab Cut and run it on my fly data\"). Each virtual environment will have its own unique name and can easily be activated within conda at the command line. \n",
    "\n",
    "Why do we need virtual environments?\n",
    "\n",
    "<img width=\"180\" src=\"./images/virt_env_thumbsup.jpg\" align=\"right\" style=\"padding-left:10px\">\n",
    "\n",
    "As we work on different projects, we will find that they frequently depend on different, often incompatible versions of software packages. For instance, you might have one project from 2020 that uses `Python 3.6` and `Tensorflow v1`. Another more recent project may use `Python 3.9` and `Tensorflow v2`. It would be agonizing to have to build up and tear down such environments each time you need to use a new one on the same machine, to reproduce the same results you got with the different software versions. \n",
    "\n",
    "It is much easier to build up temporary isolated *virtual* environments that you can activate and work within, on an as needed basis, for these different projects. That way, you don't have to worry about making breaking changes to your carefully constructed architecture every time you want to switch projects.\n",
    "\n",
    "Also, what if, you have a carefully constructed combination of software packages that works really well for a particular analysis pipeline (maybe a pipeline used in a paper)? Wouldn't it be nice if you could share this, such that someone could easily reconstruct it on their own computer? This would not only make things convenient, but make for more *reproducible science*!\n",
    "\n",
    "Virtual environments let you do all the above things in an intuitive and easy way, without having to send a bunch of error-prone step-by-step installation instructions. Also, since you have been using Anaconda this whole time, it will be very easy to get it working."
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    "## Virtual environments in conda\n",
    "In fact, while we haven't discussed it, you have been working within a virtual environment in conda this whole time: you have been working from your anaconda `base` environment! To see this, open up your conda prompt (whatever command line you use to open Jupyter notebooks). You should see the word `base` in parentheses before the command line prompt. This means you are in your base anaconda environment. \n",
    "\n",
    "We can see more details about conda virtual environments in the following image, created by User Interface Designer [Kristztina Szerovay](https://krisztina.szerovay.hu/):\n",
    "\n",
    "<img width = \"650\" src=\"./images/virtual_environments.jpg\">\n",
    "\n",
    "This figure deserves close study. At the top, we have the conda package manager which handles *everything*. You installed this (in the form of miniconda) in the first class. On the lower left is the `base` environment (called the `root` environment in the figure) which is the default environment that contains the Python standard library (you can also install any packages you want available in your base environment). You have been working in your base environment since Day 1.\n",
    "\n",
    "On the right hand side are the additional environments. Each environment contains a version of Python that can be different from that in the base, and software packages that are molded to the environment's purpose. The name of each environment should be something that clearly reminds you of its purpose, but it can be anything you like."
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    "## Creating and activating your first environment\n",
    "The above discussion is fairly abstract and theoretical. Let's get some practical experience and see how all this works. Creating new environments is really easy in conda. In your anaconda prompt, just use the command:\n",
    "\n",
    "    conda create -n <env_name> \n",
    "    \n",
    "Where `<env_name>` is the name of the environment you want to create. I try to pick environment names so that it is pretty clear what that environment is for. For this class, let's create a data science virtual environment:\n",
    "\n",
    "    conda create -n datasci\n",
    "    \n",
    "You will see a bunch of stuff, and be asked if you want to proceed, enter `y` and hit your ENTER key, and you now have your virtual environment!  \n",
    "\n",
    "Now we are ready to play, right? **Not yet**. You must first *activate* the environment: \n",
    "\n",
    "    conda activate datasci\n",
    "\n",
    "Now you will notice that the name in parentheses before your command line prompt has switched from `(base)` to `(datasci)`:\n",
    "\n",
    "<img width = \"400\" src=\"./images/conda_venv_activate.jpg\">\n",
    "\n",
    "In general, this is how you know what virtual environment you are in: conda wants this to be very easy to keep track of!\n",
    "\n",
    "I think the most common mistake people make when starting out with virtual environments is forgetting to activate an environment, and then wondering \"What is wrong with my software?\". I think this is so common it should basically be elevated to a rite of passage."
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    "## Building up your environment\n",
    "Once inside of our activated `datasci` environment, it isn't quite time to play yet. Right now it is just an empty placeholder with a name: we first have to *build* the environment by installing the packages that we want. \n",
    "\n",
    "Let's install five packages in our environment: jupyter, numpy, matplotlib, pandas, and scikit-learn. At your conda prompt, enter the following commands:\n",
    "\n",
    "    conda activate datasci # just in case you didn't already do this\n",
    "    conda install -c conda-forge numpy matplotlib pandas scikit-learn notebook\n",
    " \n",
    "You will be prompted to confirm you want to install these libraries (and their many, many dependencies). Enter `y` and sit back for a few minutes while conda installs everything.\n",
    "\n",
    "> Note: the `-c conda-forge` part of the install command is telling conda to install these packages from the `conda-forge` channel, which is where software developers maintain the installers for their software packages. \n",
    "\n",
    "Once you've done the above, you've successfully finished building your datasci environment! You can now enter `jupyter notebook` at the command line prompt as you have been doing all along, and your Jupyter server will start. The cool thing is that now the notebook will start *within the context of your new virtual environment*, so you will be able to import numpy, matplotlib, and pandas in your Jupyter notebook. In your base environment, if you try to import those packages, you will get an error because they have not been installed.  \n",
    "\n",
    "Congratulations, you've just created and built up your first virtual environment! This is a very important step. If you ever take on independent projects of any complexity, you will want to consider working on them in a new virtual environments. When I start any new project, it is always the first thing I do."
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    "## Deactivating environments, and a few loose ends\n",
    "What about when you are done working within a virtual environment? When you are ready to *deactivate* an environment and go back to your base environment, just enter:\n",
    "\n",
    "    conda deactivate\n",
    "    \n",
    "And your environment will go back to base.\n",
    "\n",
    "To see what packages you have installed in your virtual environment, use the following:\n",
    "\n",
    "    conda list\n",
    "\n",
    "To see a list of virtual environments you have in your system:\n",
    "\n",
    "    conda env list\n",
    "    \n",
    "When you run this, you should now have `base` and `datasci` environment (as well as any others you might have created in the past).\n",
    "\n",
    "Finally, what if your virtual environment is fubar and you want to delete it? Maybe you are getting errors that you can't fix, and are ready to just start over and install things in a different order. To remove a virtual environment from your computer, use the following command:\n",
    "\n",
    "    conda remove -n <env_name> --all\n",
    "    \n",
    "Just be sure you really want to do this!"
   ]
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    "## To learn more \n",
    "Virtual environments is a big topic, and there are lots of resources, but you really don't need to know much more than the above to get started in practical terms. \n",
    "\n",
    "If you do want to learn about conda virtual environments in more depth (like how to save/share an environment), see the following):\n",
    "- https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html\n",
    "- https://www.freecodecamp.org/news/why-you-need-python-environments-and-how-to-manage-them-with-conda-85f155f4353c/\n",
    "\n",
    "\n",
    "Our main goal now is to learn by doing, so let's put our `datasci` virtual environment to work by learning some numpy!"
   ]
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    "<a id =\"numpy\"></a>\n",
    "[Back to Table of Contents](#toc)\n",
    "# 3: Numpy for numerical computing\n",
    "NumPy stands for *Numerical Python*, and as mentioned above in the [section on the data science ecosystem](#intro), it is a numerical computing library that is central to just about every other package in the data science stack. If you do anything with arrays of numbers in Python, you will use numpy. It includes optimizations for working with arrays that help it approach C and C++ in terms of speed. "
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    "## How to work with external packages\n",
    "Before jumping in with NumPy, I should give a couple of notes on how to work with external packages more generally. How do we use packages that are not part of the standard Python library? You will do this *all the time*, so it is important. Basically there are three steps:\n",
    "1. **Install**   \n",
    " The first step with any package is *installing* it. Luckily with anaconda this is very easy as we say in [Section 2](#virtual) when we created our `datasci` virtual environment (be sure you are running this notebook from your activated `datasci` enviromment, as discussed in [Section 2](#virtual))."
   ]
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    "2. **Import**   \n",
    " Once a package is installed in your virtual environment, then you can import things in your actual Python code (however you choose to run it: in this case we are using Jupyter). We covered imports in Week 2 (Chapter 2 of ATBS), but let's do a bit more now. The commands in base Python are already in your namespace (the namespace is the names of all the variables, functions, other objects available for you to work with).  In other words, keywords like `int` and `and` are already available in your namespace, without you having to do any work to set it up. But if you try to use commands from external libraries like NumPy before importing them, you will get a `NameError`. Such resources need to be *imported*.\n",
    " \n",
    " There are different ways to import libraries. For instance, with numpy, you *could* use `import numpy` and then just start using numpy functions (e.g., `numpy.array()`). But nobody actually does that, because with NumPy (and many other packages) there is a convention that people use an *alias* or nickname for the package in their code: they call it by the abbreviation `np`. The syntax for importing a library under an alias is `import <package_name> as <alias>`. In this case we would import numpy using `import numpy as np`. \n",
    " \n",
    " Note there are additional wrinkles we will see as we traverse the ecosystem. You can import individual functions from a package using syntax like `from numpy import array`. This is pretty common with larger sprawling libraries where you don't want to import every single item into your namespace (you don't want to *pollute your namespace*). But with numpy you typicaly just `import numpy as np`. \n",
    " \n",
    " Finally, you will sometimes see people use the following \"wildcard\" import: `from <package> import *`. That asterisk means they have just imported every single function and class from the package, and can now use it without explicitly referencing which package it came from. **Do not do this!** Such an import is a code smell: it is not not Pythonic, and should make you reflexively recoil in horror when you see it. There are *some* rare cases when it is useful, but we will never run into them in this class. It is a namespace-polluting nightmare that makes it very hard to understand and debug code: explicit is better than implicit, and wildcard imports leaves everything implicit."
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    "3. **Use**    \n",
    " Once you've imported your library, you can finally use it: the fun begins! The syntax after importing is `package_name.function()`, a construction that we will see a great deal in the code examples this week. This may seem cumbersome at first, but it is something that makes Python elegant and easy to read: when you are 4000 lines deep in the fifteenth file of a complex code base, it is really nice to know exactly where a particular function came from! It makes debugging and understanding much easier. \n",
    " \n",
    "Contrast all of this import stuff with Matlab, where you often have several overloaded functions (i.e., functions with the same name from different toolboxes), no import system, all drawn from a single heap. It's like a `from Matlab import *` Wild West situation with one big polluted namespace. \n",
    "\n",
    "I realize that imports take a little getting used to if you are coming from Matlab, and might seem like an unnecessary complexity. However, you will quickly start to appreciate the clean code that results when you start to track and manage where your code is coming from within the code itself."
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    "## Suggested strategy with NumPy\n",
    "For your introduction to NumPy, please read and follow along with the following tutorial at Analytics Vidhya (a site that generally has some very good tutorials on data science topics):    \n",
    "https://www.analyticsvidhya.com/blog/2020/04/the-ultimate-numpy-tutorial-for-data-science-beginners/\n",
    "\n",
    "The practical notebook sections below will exactly match the sections of that tutorial, though in some places I will bring in things early just because they are so useful. Note that NumPy is a huge library with lots of amazing resources, and we will only scratch the surface here. \n",
    "\n",
    "There are some secondary resources I recommend if you want to triangulate references, or want additional future reference:\n",
    "\n",
    "- https://numpy.org/doc/stable/user/absolute_beginners.html\n",
    "- https://github.com/leriomaggio/numpy-euroscipy  (notebooks 1 and 2)\n",
    "- https://www.freecodecamp.org/news/the-ultimate-guide-to-the-numpy-scientific-computing-library-for-python/ \n",
    "\n",
    "My recommendation for picking up the material this week is the same as when you were learning with `Automate the Boring Stuff`. Make lots of cells, mess around with code, and don't be scared to break things and make mistakes. Tweak and twiddle with the examples in the code to see what happens. Coding is largely muscle memory, so it is really important to literally *type code* rather than just read and understand the examples. If you run into error messages, first inspect the guilty code for obvious mistakes (e.g., an unclosed paren). Second, read the error message: they are sometimes helpful. Barring that, Google the error message there are great resources online. As you've probably learned by now, debugging is itself a skill that you will get better at over time.\n",
    "\n",
    "> Now that you have learned the basics of the standard library in the first three weeks, I am going to start using jargon from previous weeks. If there is a technical term from a previous week below (e.g., the expression `keyword argument`), I don't necessarily expect you to remember it, but I have no doubt you can understand it quickly with a little Googling or using the search command with previous notebooks.\n",
    "\n",
    "Also, if you are ever confused about what a function means, it is often very helpful to enter `np.func_name?` in a Jupyter notebook cell to see the documentation for that function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc31c9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sin?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b362f7",
   "metadata": {},
   "source": [
    "### What is the Numpy library?\n",
    "As mentioned at the site, numpy is mainly a package for working with *numerical arrays*. Usually these consist of 1d or 2d arrays (to store voltage traces and images, respectively). However, arrays can be higher dimensions (e.g., a 3d array can store an image stack). We have talked enough: let's import numpy now!\n",
    "\n",
    "> Note: I am also going to have you import Matplotlib here, which is a plotting library you will learn about next. This is an intentional cheat on my part: I don't want to send you down a rabbit hole of learning about numerical arrays without giving you *some* simple way to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a67a2834",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # the standard import for numpy\n",
    "import matplotlib.pyplot as plt  # cheat!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01e2885",
   "metadata": {},
   "source": [
    "### Python lists vs NumPy Arrays\n",
    "This is technically an optional section, but it contains a really important point about why numpy is so blazingly fast (it approaches c in terms of speed, which is what makes it so useful for numerical computing in scientific applications). One of the optimizations that helps make it so fast: instead of letting you throw any old thing into an array, as you do with lists, it strives to pack arrays with elements of the same data type (e.g., `int`s). Such seemingly minor tweaks allow NumPy to take advantage of optimizations in how the arrays are stored, accessed, and manipulated in your computer's memory, so operations on the arrays take place much faster. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e90d03",
   "metadata": {},
   "source": [
    "## Creating a NumPy Array\n",
    "Before we do things with arrays, we must *have* arrays. This first substantive section of the tutorial shows you how to make many simple arrays using one-line numpy commands. There are actually a *lot* of ways to make numpy arrays, and some are more important than others. For instance, one method in the tutorial (`full()`) I have literally never used, but it is in most tutorials. Others, like `array()` and `linspace()` are extremely important. The latter is important enough that I have bumped it up in the order of presentation.\n",
    "\n",
    "### Basic n-d array: array()\n",
    "The `np.array()` command lets you create an array from a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "865057c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for playing with the np.array() function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc74594",
   "metadata": {},
   "source": [
    "Some things you might try in your workspace:\n",
    "- Check what type your array is with `type(array)`.\n",
    "- Use the `dtype` keyword argument to specify the data type of your array.t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42651438",
   "metadata": {},
   "source": [
    "#### Aside 1: what shape is your array?\n",
    "I am going to cheat a little bit and introduce the `shape` attribute from the next section of the tutorial, but it is so important we just shouldn't wait. \n",
    "\n",
    "The `shape` attribute of an array returns a tuple that contains the lengths of the array along each dimension. For instance, if you have a 1d array with 1000 elements, it will return (1000,). If you have a 2d array (see next section) that has 10 rows and 1000 columns, it will return `(2, 1000)`. Test it out with with a 7 element 1d array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "58f57878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7,)\n"
     ]
    }
   ],
   "source": [
    "foo = np.array([1,2,3,4,5,6,7])\n",
    "print(foo.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0f2c90",
   "metadata": {},
   "source": [
    "When working with real data (which will often be in numpy arrays in practice), when errors star popping up, checking the shape is often the first step in debugging (making sure you haven't accidentally shaved off rows, columns, etc, just determining whether things are the shapes you expect them to be often provides a useful initial check). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212ac2c4",
   "metadata": {},
   "source": [
    "#### Aside 2: Plotting 1d arrays with matplotlib\n",
    "The arrays we have created are very simple and easy to visualize. Things will not always be so easy, so we will want simple tools to visualize arrays. Matplotlib provides a bunch of simple functions for visualizing arrays. The simplest for visualizing 1d arrays is `plot()`. If you want to see the values in an array you can just send them to `plot()` (recall we imported our matplotlib as `import matplotlib.pyplot as plt`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b93100f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(foo);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd2d0d4",
   "metadata": {},
   "source": [
    "Obviously this isn't a particularly interesting example, but things will heat up below. And you will learn a lot more about Matplotlib in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26f2e9b",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "94e36f35",
   "metadata": {},
   "source": [
    "#### Using array() to make 2d arrays\n",
    "Creating 2-d arrays is *relatively* straightforward: you feed `np.array()` a comma-deliminted list of lists. For example, to create a 2x3 array (2 rows and 3 columns), you would create an outer list that contains two inner nested lists that each contain three elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a243d6f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "Array shape: (2, 3)\n"
     ]
    }
   ],
   "source": [
    "# outer list encloses two lists that contain 3 elements each\n",
    "arr_2x3 = np.array([[1,2,3],\n",
    "                    [4,5,6]]);\n",
    "print(arr_2x3)\n",
    "print(f\"Array shape: {arr_2x3.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52eb3297",
   "metadata": {},
   "source": [
    "If you liked to create work for yourself you can even create an image by hand (let's create a 10x10 array):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f609c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_image = np.array([\n",
    "   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,], # row 0\n",
    "   [0, 0, 1, 1, 0, 0, 1, 1, 0, 0,], # row 1\n",
    "   [0, 0, 1, 1, 0, 0, 1, 1, 0, 0,], # row 2\n",
    "   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,], # row 3\n",
    "   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0,], # row 4\n",
    "   [0, 1, 0, 0, 1, 1, 0, 0, 1, 0,], # row 5\n",
    "   [0, 0, 1, 0, 0, 0, 0, 1, 1, 0,], # row 6\n",
    "   [0, 0, 1, 1, 1, 1, 1, 1, 0, 0,], # row 7 \n",
    "   [0, 0, 0, 1, 1, 1, 1, 0, 0, 0,], # row 8\n",
    "   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,], # row 9 \n",
    "]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9a74308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_image.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a2a4ee",
   "metadata": {},
   "source": [
    "#### Visualizing 2d arrays with matplotlib: imshow()\n",
    "The above is hard to visualize so we now see why I cheated and imported the matplotlib library. It has a bunch of simple functions for visualizing arrays, for instance `imshow` lets you visualize 2d arrays as images, so we can visualize the 10x10 array we just created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7c71749",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(arr_image, cmap='gray');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7f5e5f",
   "metadata": {},
   "source": [
    "Feel free to adjust that li'l funkmeister as you see fit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2da204c",
   "metadata": {},
   "source": [
    "#### Workspace for creating/visualizing arrays\n",
    "You can create higher-dimensional arrays, like 3d arrays, but that is a bit more complex and will come later in the tutorial when it goes over slicing arrays.\n",
    "\n",
    "For now, here is some space to play with 1d and 2d arrays (and you have some basic matplotlib functionality so you can visualize them using `plt.plot()` and `plt.imshow()` respectively)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b534291a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  workspace for array(): 1d, 2d, and plotting them\n",
    "#\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fc2ada",
   "metadata": {},
   "source": [
    "### Array of zeros and ones: zeros()/ones()\n",
    "The section on creating arrays of zeros and ones using `np.zeros()` and `np.ones()` is pretty clear. They are pretty useful when you quickly need to create an array. When I need to create an array with shape `(n,m)` with a constant value, I typically just use `value*np.ones((n,m))`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf92615",
   "metadata": {},
   "source": [
    "### Random numbers: random.rand()\n",
    "It is extremely common to create random \"dummy\" data to test out algorithms (e.g., algorithms for calculating the mean/sem of an array or whatever. `np.random.rand(n,m)` creates an array of shape `(n,m)` of random values uniformly distributed between 0 and 1 (not including 0). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74834b3d",
   "metadata": {},
   "source": [
    "### Fill an array with a number: full()\n",
    "You can create an `(n,m)`  array with some arbitrary number using `np.full((n, m), number)`. Frankly I didn't know this function existed before I started to prepare this class. As I mentioned above, I typically just use `ones()`. But it is in the tutorial so let's include it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e74c39b1",
   "metadata": {},
   "source": [
    "### Identity matrix: eye()\n",
    "In linear algebra, the identity matrix is a square with ones on the main diagonal (element [0,0] to [n,n]). You can create such an array in numpy using `eye()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7d522d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "id_2 = np.eye(2)\n",
    "print(id_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c07e64",
   "metadata": {},
   "source": [
    "You can easily visualize larger identity matrices using `imshow()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c4e67e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_10 = np.eye(10);\n",
    "plt.imshow(id_10, cmap='gray');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d259e61",
   "metadata": {},
   "source": [
    "The tutorial points out that you can actually shift the diagonal along which the 1's will appear, using the `k` keyword. This is a useful trick to be familiar with, so check it out. Note such matrices are no longer technically an identity matrix, and I frankly rarely use them, but this idea of setting an offset value in NumPy using a `k` parameter comes up in other contexts, so is useful to be familiar with.\n",
    "\n",
    "Let's visualize two examples of such shifted diagonals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "723c6985",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_10_kdown = np.eye(10, k=-3)\n",
    "id_10_kup = np.eye(10, k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da77156",
   "metadata": {},
   "source": [
    "We can visualize them in one plot using *subplots*, which you will learn about in the Matplotlib section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d914d74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = plt.figure(figsize=(8,5))\n",
    "plt.subplot(1,3,1)\n",
    "plt.imshow(id_10, cmap='gray');\n",
    "plt.title('id')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.imshow(id_10_kdown, cmap='gray');\n",
    "plt.title('k = -3')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.imshow(id_10_kup, cmap='gray');\n",
    "plt.title('k = 3')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248f9a94",
   "metadata": {},
   "source": [
    "#### workspace for built-in array creation methods ... so far\n",
    "We've quickly gone over a bunch of useful ways to create arrays. I recommend practicing them (though you can honestly skip `full()`. I basically never see it used in practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637f41eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for zeros()  ones() random.rand() and eye())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91d6002",
   "metadata": {},
   "source": [
    "### Evenly spaced arrays of numbers: arange() and linspace()\n",
    "These are really important functions. I was tempted to put them first in the list of specialized array creation functions, but I didn't want to mess up the order of things compared to the tutorial. They are extremely important in data applications as they are used for creating space and time axes, bin edges for histograms, and many other numerical cases. Also, it is important to practice using them because it is surprisingly easy to make mistakes: it is really important to test your code when you *think* you are building simple increasing arrays of numbers. \n",
    "\n",
    "`arange()` works a lot like `range()` in the basic Python library, which we covered in some detail in Week 1 when discussing for loops. Recall it takes in a start number, stop number, and step size, and returns those values (excluding the final number).\n",
    "\n",
    "`linspace()` is related, but has a few key differences. First, you specify the starting and stopping number, while the third argument is the number of samples between the first two numbers.  Linspace tends to be more easy to control in some ways, partly because the resulting array *includes both end points*. For instance:\n",
    "\n",
    "    np.arange(0,10,1)\n",
    "    \n",
    "Will create a numpy array with the ten integers from 0 to 9 (not including 10).  On the other hand:\n",
    "\n",
    "    np.linspace(0, 10, 10)\n",
    "\n",
    "Will create a numpy array containing ten numbers evenly spaced from 0 to 10. Note you might be surprised at the output! I recommend you check it out below to check your intuitions: what should you enter using `linspace()` if you want the numbers `[0, 1, 2....10]`?\n",
    "\n",
    "If you want to learn more about `linspace` and `arange`, here is a nice little article about them:     \n",
    "https://www.sharpsightlabs.com/blog/numpy-linspace/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad6e84f",
   "metadata": {},
   "source": [
    "#### Plotting fun with linspace\n",
    "These tools are useful because now we can *build and plot functions* of time (or space) in a natural way.\n",
    "\n",
    "For example, run the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28ea20e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the time dimension using linspace\n",
    "t = np.linspace(1, 100, 1000);\n",
    "\n",
    "# calculate the sin \n",
    "y = np.sin(t/2)\n",
    "\n",
    "# plot y vs t\n",
    "plt.plot(t,y);\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Voltage (mV)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639506b5",
   "metadata": {},
   "source": [
    "Note that example exploits the fact that if you want to create a `sin()` or `cos()` or some other familiar mathematical function in NumPy, chances are it exists and you will be able to just use it. To find out just try `np.function_name()`. We will talk about this more below, but to see a list of such functions, see:    \n",
    "https://numpy.org/doc/stable/reference/routines.math.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16631c78",
   "metadata": {},
   "source": [
    "#### Workspace for linspace and arange\n",
    "As mentioned above, these little guys can be tricky so they deserve their own workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21f02345",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for linspace and arange\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d03f17",
   "metadata": {},
   "source": [
    "# The Shape and Reshaping of NumPy Arrays\n",
    " NumPy contains lots of tools for examining how many elements are in arrays, and also *rearranging* the elements that are in an array (for instance it is fairly common that you want to *flatten* a 2d array and convert it to a 1d array that contains all the same elements). This section of the tutorial goes over these kinds of operations. We have already discussed the `shape` properties of arrays (it returns the number of elements along each dimension of an array). This section will introduce a few more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fd6bff",
   "metadata": {},
   "source": [
    "## Dimensions of an array: ndim\n",
    "Do you have a 1d array, 2d array, etc? Check with the `ndim` attribute.\n",
    "\n",
    "This is different from the number of *elements* in the array. Let's look at a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5205c3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n"
     ]
    }
   ],
   "source": [
    "z1 = np.zeros((200,))\n",
    "z2 = np.zeros((3,3))\n",
    "print(z1.ndim, z2.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42b66a7",
   "metadata": {},
   "source": [
    "## Shape of arrays: shape\n",
    "We have already gone over this quite a bit. It is extremely useful. It doesn't just reveal the number of dimensions, but how many elements are in each dimension. Let's apply it to the arrays we just created.\n",
    "\n",
    "The one thing to remember is that for 2d arrays, shape returns `(num_rows, num_columns)`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59ec3e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,) (3, 3)\n"
     ]
    }
   ],
   "source": [
    "print(z1.shape, z2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4e996c",
   "metadata": {},
   "source": [
    "## Total number of elements in an array: size\n",
    "This returns the *total* number of elements in an array. So, for a 2d array, it returns `num_rows*num_columns`. Continuing with our example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90d338dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 9\n"
     ]
    }
   ],
   "source": [
    "print(z1.size, z2.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ca8a50",
   "metadata": {},
   "source": [
    "## Reshaping  arrays: reshape()\n",
    "Here we are moving away from *attributes* and toward *methods* (recall the difference from Week 2). We are going to start *doing things to our arrays* instead of just checking out their attributes.\n",
    "\n",
    "The first, `reshape()`, lets you change the shape of an array. The first argument to `reshape()` is the array you want to reshape, and the second is the final shape you want it to have. For instance, if you have a 1d array with 100 elements, and you want to convert it to 2d array with shape 10x10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0d3e654",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "flat_array = np.random.rand(100)\n",
    "square_array = np.reshape(flat_array, (10,10), order='F')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3584329",
   "metadata": {},
   "source": [
    "View the square array as an image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6e57ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(square_array, cmap='gray');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85c5313",
   "metadata": {},
   "source": [
    "How is the data inserted? That is, are the first ten elements of `flat_array` inserted into the first *row* of `square_array` or the first *column*? The default behavior is to insert by row, but this can be changed using the `order` parameter of `reshape()`. The default order is called the `C` order (standing for the programming language `C` for don't ask why). If you want the data to be inserted column-wise, then you can change the order parameter to `F` (for Fortran, also don't ask). I do *not* recommend reading the documentation for the order parameter for `np.reshape()` it is incomprehensible unless you are a software engineer.\n",
    "\n",
    "Frankly, because this can be confusing, whenever I use `reshape()` I test things out first with simple data to be sure my data was reshaped as expected: I don't assume it worked as I expected. Even with supposedly easy things, it is easy to make mistakes, so always test your code with arrays where you know the right answer, and be sure things are working properly.\n",
    "\n",
    "You might have noticed some weird code above: `np.random.seed(42)`? With that, I set the seed of the random number generator so that subsequent runs of random number generator will produce the exact same result every time it is run. This can be useful for testing things out."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b3b721",
   "metadata": {},
   "source": [
    "## Flattening arrays: flatten() \n",
    "While above using `reshape()` we went from a 1d array to a 2d array, you could also use `reshape()` to *flatten* a 2d array and create a 1d array. But NumPy has a special built-in function for this called, surprisingly enough, `flatten()`. \n",
    "\n",
    "There is also a function called called `ravel()`, which points to the original array in memory. This is discussed in some detail in the tutorial. We also discussed this topic in Week 2 in the section **A note on variable reference and scope**. Basically, `flatten()` makes a copy of the array before flattening it, while `ravel()` does not. \n",
    "\n",
    "I almost always use `flatten()` just to avoid the pitfalls of accidentally changing things accidentally, but `ravel()` does have its uses. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668bb3a6",
   "metadata": {},
   "source": [
    "## Transpose \n",
    "In linear algebra, when you swap the rows and columns of a matrix, that is known as taking the transpose. So basically if your first row was [1,2,3] that now is your first column. Do this for every row, and you now have the transpose of your array. So if you start with an array with n rows and m columns, you end up with an array with m rows and n columns. \n",
    "\n",
    "This can be done a few ways in numpy. Let's create a random 2x3 array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "649f402f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.41038292 0.75555114 0.22879817]\n",
      " [0.07697991 0.28975145 0.16122129]]\n"
     ]
    }
   ],
   "source": [
    "original = np.random.rand(2,3)\n",
    "print(original)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f65ab3",
   "metadata": {},
   "source": [
    "The following are all equivalent ways to get the transpose of the original array (thought the first, using the capital T without parentheses, is the most common in practice):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8085bef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.41038292 0.07697991]\n",
      " [0.75555114 0.28975145]\n",
      " [0.22879817 0.16122129]]\n",
      "[[0.41038292 0.07697991]\n",
      " [0.75555114 0.28975145]\n",
      " [0.22879817 0.16122129]]\n",
      "[[0.41038292 0.07697991]\n",
      " [0.75555114 0.28975145]\n",
      " [0.22879817 0.16122129]]\n"
     ]
    }
   ],
   "source": [
    "print(original.T) # most common\n",
    "print(original.transpose()) #  I rarely see this\n",
    "print(np.transpose(original)) # I almost never see this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbde7fd4",
   "metadata": {},
   "source": [
    "Let's apply it to an image for fun, a greyscale image of Hazel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66914f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((570, 741), (741, 570))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hazel = plt.imread('images/hazel_grey.png')\n",
    "hazel_transpose = hazel_array.T\n",
    "hazel.shape, hazel_transpose.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8413108b",
   "metadata": {},
   "source": [
    "Notice the swapping of row and column dimensions in the transpose of Hazel's image. Let's take a look at what she looks like. Original on left, transpose on right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1f05c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(hazel_array, cmap='bone');\n",
    "plt.xticks([]); plt.yticks([]);\n",
    "plt.title('Original')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(hazel_transpose, cmap='bone')\n",
    "plt.xticks([]); plt.yticks([]);\n",
    "plt.title('Transpose');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93772f2c",
   "metadata": {},
   "source": [
    "Geometrically, what did the transpose do to the image? What type of rotation?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07343a4c",
   "metadata": {},
   "source": [
    "### Workspace for shapefinding and shapeshifting\n",
    "The ever-important space to tinker and tweak. In this section we learned about the `ndim`, `shape`, and `size` attributes of arrays. We also learned about the `reshape()`, `flatten()`, and transpose methods on arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8ba93023",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for shapefinding and shapeshifting\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcc7f87",
   "metadata": {},
   "source": [
    "## Expanding/squeezing arrays \n",
    "These functions are a little bit esoteric, and frankly you can safely skip this section on first reading. \n",
    "\n",
    "### Expanding with expand_dims()\n",
    "Expanding arrays is a sort of strange operation that you will typically only run into when a function requires an array with n dimensions, and your array is `n-1` dimensions, so you have to literally just add a dimension to make the function happy. We will see an example of this below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854c4d7f",
   "metadata": {},
   "source": [
    "### Squeezing an array: squeeze()\n",
    "Sometimes you will find yourself with an array that has no entries along one dimension (e.g., a `(300,1)` array is 2d, but there are no entries in the second dimension). To get rid of that dimension, you can use `squeeze()` to make a 1d array. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd2d340",
   "metadata": {},
   "source": [
    "### Workspace for expand_dims() and squeeze()\n",
    "Some space to mess around with these functions: note you will have a more natural spot to play with these below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2aad9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for expand_dims() and squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c713ed1b",
   "metadata": {},
   "source": [
    "## Indexing and slicing arrays\n",
    "This section is *extremely* important. It is similar to indexing lists, which was discussed quite a bit in the beginning of [Chapter 4](https://automatetheboringstuff.com/2e/chapter4/) of Automate the Boring Stuff. I recommend revisiting that bit of the book, as most of it also applies to arrays. \n",
    "\n",
    "### Slicing 1d  arrays\n",
    "This section of the tutorial is well-written and tracks what you find in lists pretty closely (and even goes into a little more detail, such as showing the ability to add a *third* step size to a slice, and showing what happens when you leave out an argument).\n",
    "\n",
    "As an aside, I don't love code when people leave out arguments in their slices (`foo[::2]`) because in Python `Explicit is better than implicit`, and when you end up with a bunch of nested arrays with a lot of colons, code can become very hard to read. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f6bc1d",
   "metadata": {},
   "source": [
    "## Slicing 2d arrays\n",
    "Unfortunatley this section of the tutorial leaves quite a bit to be desired. Luckily, slicing a 2d array is basically like slicing a 1d array, except now you have to indicate both the rows and columns:\n",
    "\n",
    "    sliced_array = original_array[row_ind1:row_ind2, col_ind1:col_ind2]\n",
    "    \n",
    "That is, you slide the 2d array just like you do a 1d array, except now you need to slice rows (always listed first), and then columns (always listed second), with the row and column slices separated by a comma. Always bear in mind that the last index is *not* included because Python is weird like that.\n",
    "\n",
    "If you want to just pick a single row of a 2d array you would do this (this is very common, for instance if you have a bunch of calcium traces row-wise in an array, and want to pick one to plot):\n",
    "\n",
    "    single_row = original_array[row_index, :]\n",
    "    \n",
    "This picks the index of the row, and the bare semicolon in the column position says to just pick all the columns. A similar syntax applies if you want to pick a single column:\n",
    "\n",
    "    single_column = original_array[:, column_index]\n",
    "    \n",
    "Let's look at a slicing example with our friend Hazel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d199a55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(570, 741)\n"
     ]
    }
   ],
   "source": [
    "hazel = plt.imread('images/hazel_grey.png')\n",
    "hazel_sliced = hazel[80:300, 230:450]\n",
    "print(hazel.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce651fc",
   "metadata": {},
   "source": [
    "Now let's see what we just did. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0873d6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(hazel, cmap='bone');\n",
    "plt.title('Hazel')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(hazel_sliced, cmap='bone')\n",
    "plt.title('Sliced Hazel');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ec41cb",
   "metadata": {},
   "source": [
    "This is how you can zoom in on an image. I left in the x and y coordinates so you can mess around with slicing Hazel in the workspace and have a better sense of what indices to enter.\n",
    "\n",
    "If you want more on slicing 2d arrays, I recommend [this tutorial section](https://www.pluralsight.com/guides/working-numpy-arrays-indexing-slicing-guide#module-slicing) for a little more insight."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e943b9f",
   "metadata": {},
   "source": [
    "### Workspace for slicing 1d and 2d arrays\n",
    "I recommend you realy take the time to work on this *especially* if you have programmed in Matlab before, because this will be one of the toughest things to get used to. I recommend playing with negative indexing, 0-based indexing, and slicing 2d arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3fb7dccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for 1d/2d slicing \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a07ea84",
   "metadata": {},
   "source": [
    "### Introducing 3d arrays\n",
    "If this was not a neuroscience-specific class I would probably leave this out. But in neuroscience, it is *very* important. Individual color images are 3d arrays. Greyscale image stacks are 3d arrays. So basically if you ever deal with color, or image stacks, you deal with 3d arrays. Luckily, the move from 2d to 3d is not that hard.\n",
    "\n",
    "A 3d array is just a stack of 2d arrays. Let's consider one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07f7bb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 (10, 10, 3)\n"
     ]
    }
   ],
   "source": [
    "my_3array = np.random.rand(10,10,3);\n",
    "print(my_3array.ndim, my_3array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6e20dc",
   "metadata": {},
   "source": [
    "We have created a stack of three 2d arrays (each of the 2d arrays is 10x10), with the third dimension being the R/G/B values, respectively. We can plot this using Matplotlib's `imshow()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9156318",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(my_3array);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224605cd",
   "metadata": {},
   "source": [
    "We can access the individual **R/G/B** components via indexing and plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cd60d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "array1 = my_3array[:,:,0]\n",
    "plt.imshow(array1, cmap='gray');\n",
    "plt.title('Array of Red Values');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32da90ce",
   "metadata": {},
   "source": [
    "It might be a fun exercise to plot the other components in your workspace (or plot all three in subplots)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5acf60",
   "metadata": {},
   "source": [
    "## Conventions with 3d arrays\n",
    "When dealing with 3D volumes, or movies with stacks of images over time, the 3d arrays typically have dimensions `(frames, row, column)`. For such data in RGB it will typically have dimensions `(frames, row, column, channels)`.\n",
    "\n",
    "In that case, if you have a 1000-frame movie with RGB channels, and each image has height 600 and width 400, you will end up with a 4d array with the following shape:\n",
    "\n",
    "    (1000, 600, 400, 3)\n",
    "\n",
    "Calcium imaging applications typically deal with greyscale image data, which is simpler: we only have to handle 3d arrays with dimensions `num_frames x rows x columns`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca90700b",
   "metadata": {},
   "source": [
    "### Workspace for 3d arrays\n",
    "Have at it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82e24bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for 3d arrays and 3d array slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2c895e",
   "metadata": {},
   "source": [
    "## Stacking and concatenating arrays\n",
    "We have seen a few ways to create new arrays from an old array: reshaping, transposing, slicing, etc. What if you want to pack together two (or more) arrays into a final, third, array? The stack/concatenate operations on arrays let you do just that! Before getting into it, let's create some 1d arrays that we want to stack together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccc19b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.zeros((10,))\n",
    "arr2 = np.ones((10,))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7048adc",
   "metadata": {},
   "source": [
    "### Row-wise combinations: vstack()\n",
    "The most common use of `vstack()` is if you have 1d arrays and want to pack them together as separate rows (creating an array with two rows):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9c9ba43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstacked = np.vstack((arr1, arr2))\n",
    "vstacked.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b4160d",
   "metadata": {},
   "source": [
    "### Column-wise combinations: hstack()\n",
    "As discussed in the tutorial, this will add elements column-wise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6719380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hstacked1 = np.hstack((arr1, arr2))\n",
    "hstacked1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a4799a",
   "metadata": {},
   "source": [
    "Be careful with `hstack()` and note the asymmetry with `vstack()`. Most people expect it to create a `10x2` array rather than a 20-element 1d array. If you want to do that with `hstack()`, you have to reshape the arrays, making `10x1` 2d arrays and applying `hstack()` to those: This somewhat counterintuitive behavior can be fixed by reshaping the arrays to (10,1), either using `reshape()` or `expand_dims()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd9b41cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hstacked2 = np.hstack((arr1.reshape(10,1), arr2.reshape(10,1)))\n",
    "hstacked2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46841d1",
   "metadata": {},
   "source": [
    "I'm not sure why things are not symmetrical between `vstack()` and `hstack()`, but it is something to watch out for.\n",
    "\n",
    "In practice many people end up using `vstack()`, with its intuitive behavior, and transpose the result instead of messing around with `hstack()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519400c8",
   "metadata": {},
   "source": [
    "### Depth stacking: dstack()\n",
    "This provides an extremely useful way to make 3d arrays. Say you have a couple of images as 2d arrays, and want to put them into a 3d array (e.g., for a confocal stack or a movie):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ebb528a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image1 = np.random.rand(10,10)\n",
    "image2 = np.random.rand(10,10)\n",
    "stack = np.dstack((image1, image2))\n",
    "stack.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2323e94b",
   "metadata": {},
   "source": [
    "We see this created a movie with 2 frames (where frame number is the third dimension of this stack). Let's put some more images into this stack and create a movie with 12 frames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95c54300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_stack = stack\n",
    "for frame_num in range(10):\n",
    "    movie_stack = np.dstack((movie_stack, np.random.rand(10,10)))\n",
    "movie_stack.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e4c0c7",
   "metadata": {},
   "source": [
    "Now we have a stack of 12 random movie frame. Let's make a function to view frame N. You have studied functions, 3d arrays, and seen the `imshow()` function, so should be able to read throught his function and understand how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b836f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded func def\n"
     ]
    }
   ],
   "source": [
    "def view_frame(stack, frame_ind):\n",
    "    plt.imshow(stack[:,:,frame_ind], cmap='gray')\n",
    "    plt.title(f\"Frame {frame_ind}\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    return\n",
    "print('loaded func def')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425b6f25",
   "metadata": {},
   "source": [
    "Let's use it to view one frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "032e36bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view_frame(movie_stack, frame_ind=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89653c35",
   "metadata": {},
   "source": [
    "What if we want to watch all the images in a stack as a movie in Jupyter? Then we can wrap that `view_frame()` function in a for loop, index the third element of the image stack, and watch them. \n",
    "\n",
    "I made a function to do this, with a couple of bells and whistles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8abac2b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded\n"
     ]
    }
   ],
   "source": [
    "def display_movie(movie_stack, pause_time=0.25):\n",
    "    \"\"\"\n",
    "    Uses the view_frame() function defined earlier in this notebook.\n",
    "    pause_time is time to pause between frames, in seconds\n",
    "    \n",
    "    Hack to display movie_stack in Jupyter. Assumes movie_stack \n",
    "    contains frame as third dimension.\n",
    "    \n",
    "    movie_stack is a (height x width x frames) numpy array\n",
    "    \n",
    "    Adapted from https://stackoverflow.com/a/29675706/1886357\n",
    "    \n",
    "    Note: not tested in qt mode\n",
    "    \"\"\"\n",
    "    from IPython import display\n",
    "    import time\n",
    "    \n",
    "    num_frames = movie_stack.shape[2]\n",
    "    \n",
    "    # Check to see how long it will take\n",
    "    if num_frames * pause_time > 180:\n",
    "        reply = input(\"This will take more than 3 minutes. Exit function? (y)\")\n",
    "        if reply.lower() == 'y':\n",
    "            print(\"Exiting\")\n",
    "            return\n",
    "    \n",
    "    for frame_ind in range(num_frames):\n",
    "        view_frame(movie_stack, frame_ind);\n",
    "        display.display(plt.gcf())\n",
    "        display.clear_output(wait=True)\n",
    "        time.sleep(pause_time)\n",
    "        \n",
    "    return\n",
    "print('loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4fde70",
   "metadata": {},
   "source": [
    "Let's apply the function to our little random image stack:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e56b4161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_movie(movie_stack, pause_time=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0bc5776",
   "metadata": {},
   "source": [
    "### Generic concatenation: concatenate()\n",
    "Instead of using a separate `stack` method for rows, columns, and depth concatenation, you can use one method to do everything: `concatenate()`. In this case you can use a keyword argument to specify the axis along which you want to glue together your arrays:\n",
    "- Axis 0 means you want to append row-wise (this will behave like `vstack()`\n",
    "- Axis 1 is columnar concatenation (like `hstack()`).\n",
    "- Axis 2 is the third dimension (like `dstack()`). \n",
    "\n",
    "You might wonder why people don't always do this? if your arrays only have 2 dimensions and you try to concatenate with `axis=2` then you will get an error. For instance, try to concatenate two 2d arrays along the third dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "903d094b",
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = np.concatenate((image1, image2), axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f022acf",
   "metadata": {},
   "source": [
    "Before this will work, you will have to use something like `expand_dims()` (or `reshape()`) to add a third dimension to the images (a useful exercise for the workspace). This restriction can make `concatenate()` a bit inconvenient, and is why the *stack* convenience functions are often used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501c81c3",
   "metadata": {},
   "source": [
    "### Append to end of array: append()\n",
    "With lists, `append()` is extremely common, and fast. It is very slow with arrays, so is not used often. I would be wary of using it, it is not considered very NumPythonic. It is much more standard, when you are adding items to an empty array-like object in Python, to add items to a *list*, and then at the end convert the list to an array, which can be done easily with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "678291b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_values = [] # initialize empty list\n",
    "for ind in np.arange(1,100, 0.1):\n",
    "    my_value = np.random.rand()+np.cos(ind)\n",
    "    my_values.append(my_value)\n",
    "my_values = np.array(my_values);    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9fe34a",
   "metadata": {},
   "source": [
    "Note just to be clear, the above is just a silly example and could be done more efficiently with *list comprehension*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16f9d8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_values = [np.random.rand()+np.cos(ind) for ind in np.arange(1,100,0.1)]\n",
    "my_values = np.array(my_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8879bf33",
   "metadata": {},
   "source": [
    "But the general point is the same. List comprehension is a little advanced for this class, but in general it is extremely fast and efficient and awesome, so google it if it looks awesome. If you want to learn about list comprehension, see:\n",
    "https://www.pythonforbeginners.com/basics/list-comprehensions-in-python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177e7108",
   "metadata": {},
   "source": [
    "### Workspace for stacking/concatenating arrays\n",
    "I recommend you play around with these functions, and use `expand_dims()` to get `concatenate()` working with 2d arrays to make an image stack, and think about how you can use `display_movie()` to view what you have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8fe2f365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for stacking/concatenating arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60df45cd",
   "metadata": {},
   "source": [
    "## Broadcasting and Ufuncs: SKIP\n",
    "I recommend you skip these sections, it is a bit advanced for a beginner introduction, and frankly some of the \"tricks\" the author suggests are simply asking for trouble and I would recommend steering clear for now: e.g., multiplying a 5-element array and a 2 element array is technically allowed, but for now I'd stick with more intuitive operations. \n",
    "\n",
    "The other bits that are important we will cover in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7241862e",
   "metadata": {},
   "source": [
    "## Math with arrays\n",
    "This section is important for data analysis, so be sure you work through the examples and tweak/twiddle in the workspace! \n",
    "\n",
    "We have discussed basic mathematical functions in numpy above, and [here is the link to all of the basic mathematical functions available in NumPy](https://numpy.org/doc/stable/reference/routines.math.html). This section goes into a bit more detail about it. \n",
    "\n",
    "Of particular importance is the `index` key word. This confused me for a while, so let's look at an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d9dad0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.text?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "82377cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_example = np.zeros((3,3))\n",
    "max_example[1,0] = 0.8\n",
    "max_example[0,1] = 0.7\n",
    "max_example[2,1] = 0.5\n",
    "max_example[1,2] = 0.6\n",
    "\n",
    "# show as image\n",
    "plt.imshow(max_example, cmap='gray');\n",
    "\n",
    "# Draw some text to show values\n",
    "\"\"\" \n",
    "Note x/y are reversed compared to image locations.\n",
    "Image coordinates are row/column (y/x).\n",
    "These coordinates are x/y like standard math\n",
    "\"\"\"\n",
    "plt.text(0,1, \"0.8\")\n",
    "plt.text(1,0, \"0.7\")\n",
    "plt.text(1,2, \"0.5\")\n",
    "plt.text(2,1, \"0.6\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a59cf2c",
   "metadata": {},
   "source": [
    "There are *three* ways to take the max of this 2d array. Either overall, the max value in each *column*, or the max value in each *row*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "548e4abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "max_overall = np.max(max_example)\n",
    "print(max_overall)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d018231",
   "metadata": {},
   "source": [
    "Here is where things get a *little* counterintuitive. Since for 2d arrays, the rows are the first axis and columns are the second axis, you might think you would do something like `np.max(array, axis=1)` to get the maximum value along the columns. \n",
    "\n",
    "That is *not* how it works: to get the max value in each column you do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bcb79469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8 0.7 0.6]\n"
     ]
    }
   ],
   "source": [
    "column_max = np.max(max_example, axis=0)\n",
    "print(column_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c151482a",
   "metadata": {},
   "source": [
    "The  way to think about the `axis` keyword argument when finding the maximum value in each column is: along which axis do you have to collect values? In this case, you have to collect values across rows to find out the max value in a column. The row axis is axis 0, so that is the argument.\n",
    "\n",
    "This is not some idiosyncratic feature of `np.max()`, but is a general feature of the functions in numpy that aggregate data across arrays (`sum()`, `mean()`, `median()`). Hence, be sure you understand this simple example with max, and I recommend you play around with it and similar aggregating functions in the workspace. For instance, what what if you want to find out the maximum value in each row? What about the mean values of elements in columns of an array?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0edd23",
   "metadata": {},
   "source": [
    "### Workspace for math\n",
    "This is an important section you will use these functions a lot. Be sure to check the max of each row, but also test out the other functions as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8a59f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace for mathematics in numpy\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5bc0c6b",
   "metadata": {},
   "source": [
    "## Sorting numpy arrays\n",
    "Sorting lists was covered in Week 2 (Chapter 4 of ATBS). It isn't surprising that NumPy has built-in methods to sort arrays as well. The tutorial goes into some detail about types of sorting algorithms, but frankly you shouldn't worry about that. Just call `np.sort()` and you should be fine: NumPy will handle the details, and sort in increasing order (if you want to sort in decreasing order, just take the negative of your array). \n",
    "\n",
    "There is a really useful function that the tutorial leaves out: `argsort()`. This is a very useful convenience function that I use fairly frequently in practice: it returns the *indices* of the sorting operation rather than the sorted *values*. Let's see an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba0fdaa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 2 0 1]\n"
     ]
    }
   ],
   "source": [
    "foo = np.array([3, 4, 2, 1])\n",
    "sort_inds = np.argsort(foo)\n",
    "print(sort_inds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fb236e",
   "metadata": {},
   "source": [
    "Make sure you understand the output. \n",
    "\n",
    "One, you can verify it worked, as with these indices you can easily reconstruct the original data sorted in ascending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f27fee0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print(foo[sort_inds])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb71ab2",
   "metadata": {},
   "source": [
    "Why would you want to do this? There are a few reasons. One, efficiency: if you have a bunch of 32 bit floats, or it can be much more efficient to sort the indices of the elements than to store the floating point values. Also, more importantly, often you will have multiple paired observations (e.g., weight, height, performance on some task) in separate arrays, and you might want to sort on one feature (e.g., performance on the task), and then re-examine your data after rearranging *all* your arrays using the same argsort output. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a310c6e2",
   "metadata": {},
   "source": [
    "### Workspace for sorting\n",
    "I suggest also playing with argsort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50ab344b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b76876e",
   "metadata": {},
   "source": [
    "## Numpy arrays as images\n",
    "We have already done quite a bit with this topic, treating 2d arrays as images for visualization purposes. This part of the tutorial goes into a little more depth on 8-bit (256 values, between 0 and 255 at each pixel) image representations.\n",
    "\n",
    "One thing it does that we haven't discussed is the `flip()` operation, which reverses data along the specified axis (here we will do left-right -- the columnar axis). Note the second line of the following I am adding a small square to the top left of the figure so you can better track the flipping -- make sure you understand how I am doing this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "929e9798",
   "metadata": {},
   "outputs": [],
   "source": [
    "hazel = plt.imread('images/hazel.png')\n",
    "hazel[10:30, 10:30, :]=1  # add a square to the top left of hazel representation\n",
    "hazel_flipped = np.flip(hazel, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38be695c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(hazel);\n",
    "plt.xticks([]); plt.yticks([]);\n",
    "plt.title('Hazel')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(hazel_flipped)\n",
    "plt.xticks([]); plt.yticks([]);\n",
    "plt.title('Hazel Flipped');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853128e0",
   "metadata": {},
   "source": [
    "### Workspace for images\n",
    "You can do a lot more with images, and I encourage you to explore using the functions we have learned. E.g., what is the mean value in the blue channel and how does that compare to the green channel? You can start doing lots of calculations using the small set of tools we have picked up thus far with NumPy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b33bc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imagbe workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414ccd4c",
   "metadata": {},
   "source": [
    "## Congrats! Moving on from NumPy\n",
    "We have finished our brief and selective tour of NumPy: don't worry if you don't remember every bit of it, or even the majority. That will come with experience, and applying what you have learned in practical situations. Just google things when you aren't sure, or come here or the tutorial/resources I linked to above. There is a certain *numpythonic* way of doing things in NumPy that you will get used to over time. \n",
    "\n",
    "One of the first things you will want to do is *plot* arrays. This is so crucial that I've already introduced a good deal of plotting functionality already, because it just felt strang to throw around a bunch of arrays without visualizing them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87dd473d",
   "metadata": {},
   "source": [
    "<a id =\"matplotlib\"></a>\n",
    "[Back to Table of Contents](#toc)\n",
    "# 4: Plotting with matplotlib\n",
    "Now that we've got a handle on how to represent numerical data, what about *plotting* it? We've seen a bunch of examples above of how to plot arrays in Python. In this short section I will send you to a YouTube video from an excellent Python instructor, Corey Schafer. \n",
    "\n",
    "This is a short video, just 35 minutes, that goes over how to plot line plots using Matplotlib. It is a lot of fun, as by the end it shows you how to recreate [xkcd](https://xkcd.com/)-style plots using Python!\n",
    "\n",
    "First let's make sure we have our imports squared away. As before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cd0121b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0a620e",
   "metadata": {},
   "source": [
    "## Notes before starting\n",
    "Note at this point in the class, you should be able to follow along with the video, entering the code that Corey does, tweak and play with it as you've been doing all along in your own workspace. But there are a few things that make plotting in Jupyter a bit different than in the video. As with ATBS, when Corey is entering code in his IDE, you should enter and run it in code cells in Jupyter.\n",
    "\n",
    "#### Inline vs Qt mode for plotting\n",
    "But an additional wrinkle is that in Jupyter notebooks you can view matplotlib plots in different modes. You can view it inside the notebook: this is the default, which we've been doing all along. But you can also view plots in separate windows, which gives you much greater control over the view. We want to do that for this section. \n",
    "\n",
    "To switch to that mode, we have to enter the expression:\n",
    "\n",
    "    %matplotlib qt\n",
    "    \n",
    "into a code cell. Qt is a GUI programming framework that is used to draw the plots as separate windows. To see plots in the notebook we would use `%matplotlib inline`, which we have been doing all along.\n",
    "\n",
    "> Note it sometimes doesn't work to switch from `qt` to `inline` mode, so you often have to restart your kernel to go back to inline mode.\n",
    "\n",
    "Let's go to the mode where plotting functions will spawn separate windows, so your plots will look like Corey's plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "660e9880",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ff2800",
   "metadata": {},
   "source": [
    "Now test this to make sure it worked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fd7564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.arange(10), np.arange(10));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cc4dd1",
   "metadata": {},
   "source": [
    "If you ran `%matplotlib qt` above, you won't see a plot in your notebook. Rather, a plot will open as a separate popup window. It may be behind your browser window, so check if you don't see it. To close the plot you can either click `q` on your keyboard, or the standard way (click the `x` on the plot window). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585b0052",
   "metadata": {},
   "source": [
    "## Video: Matlab Tutorial (Part 1)\n",
    "Notice that this is the first of many tutorials from Corey Schafer. To save time (you are already doing a lot this week), I am only going to have you work through the first video in the series. At the end, I'll add a couple of things to that.\n",
    "\n",
    "But to help you follow along, here are the topics covered with rough time marks:\n",
    "\n",
    "1. Intro (0-1:00)\n",
    "2. Install (you have already done this) (1:00-1:45)\n",
    "3. Making a simple plot (1:50 - 6:00)\n",
    "4. Explore the plot (6:00-7:27)\n",
    "5. Add labels to plot (7:30-9:00)\n",
    "6. Add another line (9:05 - 11:30)\n",
    "7. Add a legend (11:35-14:05)\n",
    "8. Change line formatting (14:10-24:30)\n",
    "9. Change figure style (24:30-29:40)\n",
    "10. Save figure (29:44-30:00)\n",
    "11. Expand age range (30:00-32:30)\n",
    "12. Sponsor promotional material (the rest)\n",
    "\n",
    "I recommend you create workspaces for these sections and enter code. But note that Corey enters code snippets from this site during the video:    \n",
    "https://github.com/CoreyMSchafer/code_snippets/blob/master/Python/Matplotlib/01-Introduction/snippets.py \n",
    "\n",
    "Note he enters the data as lists: one useful exerecise for you would be to convert to NumPy arrays. :) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30db8762",
   "metadata": {},
   "source": [
    "### Link to video\n",
    "\n",
    "[Video: Matplotlib Tutorial (Part 1)](https://youtu.be/UO98lJQ3QGI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36cb579",
   "metadata": {},
   "source": [
    "## Workspace for video\n",
    "As mentioned above, there are about 8 substantive topics covered in the video, so you will be entering a lot of code on your own here. At this point in the class you should be comfortable doing this, and I recommend taking notes in markdown.\n",
    "\n",
    "Instead of creating all the different titles/cells for you I'm leaving that as an exercise for the student:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0159a562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace for Corey Shafer Matplotlib video\n",
    "# Making a simple plot (1:50 - 6:00)\n",
    "\n",
    "# Explore the plot (6:00-7:27)\n",
    "\n",
    "# Add labels to plot (7:30-9:00)\n",
    "\n",
    "# Add another line (9:05 - 11:30)\n",
    "\n",
    "# Add a legend (11:35-14:05)\n",
    "\n",
    "# Change line formatting (14:10-24:30)\n",
    "\n",
    "# Change figure style (24:30-29:40)\n",
    "\n",
    "# Make sure to check out xkcd style!\n",
    "\n",
    "# Save figure (29:44-30:00)\n",
    "\n",
    "# Expand age range (30:00-32:30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b10b82a",
   "metadata": {},
   "source": [
    "# More plotting\n",
    "If you followed the video, and wrote out all the code, then you know a lot about making line plots in Python! You surely know more than I do about styles, as I always just use the vanilla default style. \n",
    "\n",
    "There is obviously a *lot* more we can do with plots, and in the remaining sections I am going to just cover a few essential topics briefly: bar plots, histograms, and subplots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f3f9e2",
   "metadata": {},
   "source": [
    "## Scatter plots\n",
    "So far we have just done line plots (though Corey Schafer discussed how to add points to line plots with the `marker` keyword argument). \n",
    "\n",
    "What if you just want a good old fashioined scatterplot? This is simple: instead of `plot()`, just use the `scatter()` function in matplotlib:\n",
    "\n",
    "https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\n",
    "\n",
    "All the other bells and whistles are largely the same as with line plot. One of the main differences is that the *first* (optional) argument is the size of the marker.\n",
    "\n",
    "Let's learn via a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce236c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_dat = np.arange(10)\n",
    "y_dat1 = np.random.rand(10)+0.5\n",
    "y_dat2 = np.random.rand(10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ae7bf9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x_dat, y_dat1, 6, color='red')\n",
    "plt.scatter(x_dat, y_dat2, 20, color='blue');\n",
    "plt.xlabel('x', fontsize=12)\n",
    "plt.ylabel('y', fontsize=12)\n",
    "plt.title('Scatter Example', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe531d81",
   "metadata": {},
   "source": [
    "If you want a more detailed deep dive into scatter plots, Corey Schafer has an excellent video:    \n",
    "https://www.youtube.com/watch?v=zZZ_RCwp49g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25497059",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scatter plot workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3e43d6",
   "metadata": {},
   "source": [
    "## Bar plots \n",
    "Quite often in neuroscience, we need bar plots. This is, shockingly, done with the `bar()` function:\n",
    "\n",
    "https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html\n",
    "\n",
    "Let's see a simple example using some of the data we just used for scatter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8d0ea49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Firing Rate (Hz)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.bar(x_dat, y_dat1, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=12)\n",
    "plt.ylabel('Firing Rate (Hz)', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d1a9b8",
   "metadata": {},
   "source": [
    "Note the first line `plt.figure()` opens a new figure window, so as not to overwrite already open figures. This can be convenient if you are working with lots of figures. If you want to close all open figures because things are getting cluttered, use `plt.close('all')`.\n",
    "\n",
    "What if you have error bars because you have run multiple trials and have a standard error of the mean? You can see in the documentation there is a useful `yerr` keyword you can use to add this to the bar plot. Let's generate some random standard error data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7adb94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dat1_err = 0.25*np.random.rand(y_dat1.shape[0]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2fb0e2",
   "metadata": {},
   "source": [
    "And then these error bars into the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3d7bf76",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.bar(x_dat, y_dat1, yerr=y_dat1_err, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=12)\n",
    "plt.ylabel('Firing Rate (Hz)', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7dafca05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bar plot workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14df878",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "A histogram is a type of bar plot that shows a *distribution* of data: the value on the x axis and the count (or frequency) on the y axis. A count histogram can be generated using the `hist()` function in matplotlib:\n",
    "\n",
    "https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\n",
    "\n",
    "The main parameter for a histogram is the number of bins (or the bin edges) you want to use to bin your data. It also takes in a boolean keyword `density` that determines whether it will generate a *count* or *probability* density that sums to 1. Let's generate some random data and view it using a histogram with 100 bins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d82b56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Data Points')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_dat = np.random.rand(1000)\n",
    "plt.hist(rand_dat, bins=100, color='black');\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Number of Data Points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056fe776",
   "metadata": {},
   "source": [
    " `random.rand()` generates a uniform distribution between 0 and 1. What if we want to generate a normal distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0dfa225",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = 0\n",
    "std_dev = 1\n",
    "num_points = 1000\n",
    "nrand_dat = np.random.normal(loc=mean, scale=std_dev, size=num_points);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999c8839",
   "metadata": {},
   "source": [
    "Now let's plot it (this time using a probability density):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "025fb723",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(nrand_dat, bins=100, color='black', density=True);\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e54f528",
   "metadata": {},
   "source": [
    "You should see something a bit much more interesting now.\n",
    "\n",
    "One thing you might try is looking at the documentation for `hist()` and figuring out how to specify the *bin edges* instead of just the number of bins. This is often extremely helpful, especially if you ever create PSTHs for neural data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "432b45ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# histogram workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc3e872",
   "metadata": {},
   "source": [
    "## Subplots \n",
    "When going through examples of images for NumPy, I often posted multiple images in the same overall plot. This is known as using *subplots*:\n",
    "\n",
    "https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.pyplot.subplot.html\n",
    "\n",
    "Subplots provide a useful way to display related data in separate panels within one plot. Let's start with an example and then discuss it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b42957e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.subplot(2,2,1)\n",
    "plt.hist(rand_dat, bins=100, color='red')\n",
    "plt.title(\"Red Uniform\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hist(rand_dat, bins=100, color='blue')\n",
    "plt.title(\"Blue Uniform\")\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.hist(nrand_dat, bins=100, color='red')\n",
    "plt.title(\"Red Normal\")\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.hist(nrand_dat, bins=100, color='blue')\n",
    "plt.title(\"Blue Normal\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c34bf5",
   "metadata": {},
   "source": [
    "Subplot works as follows -- to plot in an array of plots with `nrows` and `ncols`, and plot in the panel with `index`, you use:\n",
    "\n",
    "    plt.subplot(nrows, ncols, index)\n",
    "    \n",
    "A couple of things to note: the indexing starts at the top left and moves across the top row, moves to the second row, and so on. Also, the column and row counts (and the index numer) *do not use zero-based indexing*. This is a strange vestige of the fact that `matplotlib` started as an attempt to copy Matlab.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86838be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# subplot workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfc7c7a",
   "metadata": {},
   "source": [
    "## Images\n",
    "We have already seen how to load (`imread()`) and plot (`imshow()`) images using matplotlib.\n",
    "\n",
    "https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html\n",
    "\n",
    "To review this, please see the examples in the section on Numpy. Basically, images are represented as 2d (greyscale) or 3d (RGB) arrays, and are displayed using the `imshow()` function, as we saw above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e48566b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# workspace for imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd31511",
   "metadata": {},
   "source": [
    "## The object-oriented interface (an aside)\n",
    "There is another style of programming in Matplotlab you should be familiar with, in case you run into it. It is discussed in [this blog post](https://matplotlib.org/matplotblog/posts/pyplot-vs-object-oriented-interface/) (don't worry about reading it now, just have it handy as a reference). It is called the object-oriented API. That is a really good Matplotlib tutorial in general, and goes over both the basic pyplot interface, and the object-oriented interface.\n",
    "\n",
    "It is a very powerful and useful interface, but more complicated than what we have done. Basically, if you ever see code like this that explicitly call `ax` or `axes`, you have entered the object-oriented API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53b1934e",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, axes = plt.subplots()\n",
    "axes.plot([1, 2, 3], [1, 3, 2], marker='.', markersize=10)\n",
    "axes.set_xlabel('Time')\n",
    "axes.set_title('Object Oriented API');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c62928",
   "metadata": {},
   "source": [
    "Notice that instead of setting a title with the method `plt.title()`, it sets it with `axes.set_title()`. Please don't worry about the object oriented API right now. Just know it exists, it is very powerful, and if you ever get really into data visualization in Matplotlib you will use it. But for now, just use the standard basic api (called either the 'pyplot' or 'matlab' or 'basic' api or interface) that was made initially to mimic Matlab. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f3d0aa",
   "metadata": {},
   "source": [
    "## Huzzahh, you can plot!\n",
    "Congrats on learning the basics of Matplotlib. You should be able to visualize your data, whether it be signals, bars, or images. It was admittedly a fast tour, but frankly Matplotlib is well-enough designed, documented, and simple compared to NumPy, I trust that if you ever need to do anything complex, it is pretty easy to learn to do it via Google. One exception is 3d plotting, which can get somewhat gnarly, so I have a link to that below. \n",
    "\n",
    "If you want more, here are some good resources:\n",
    "- [Corey Schafer's 10-part Matplotlib series](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_)\n",
    "- [Discussion of 3d plotting](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83462149",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">\n",
    "    <h1>Congratulations!!!</h1>\n",
    "</span>\n",
    "<img width = \"150\" src=\"./images/yippee.jpg\" align=\"right\" style=\"padding-right:10px\"/>\n",
    "\n",
    "You learned *a lot* this week.   \n",
    "\n",
    "- Why it is important to work in the context of a virtual environment, especially when working on more complex projects with multiple dependencies? \n",
    "- How to use the NumPy library: this is probably the most important package in the data science ecosystem. Pretty much every library in the ecosystem depends on NumPy, from Pandas to Tensorflow.\n",
    "- How to plot data. This is crucial. Without figures, we can't publish. If we can't publish, we perish. \n",
    "\n",
    "Frankly, you could stop now and go do Python for the next year and be fine. \n",
    "\n",
    "I would recommend taking a break from Python for a day or two to let this information bubble from your hippocampus into your cortical fossil record and get embedded there. The final week is going to be another roller coaster: we are going to move outside of the central web in the data science ecosystem to mingle with Pandas and enter the machine learning forest. "
   ]
  }
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