diff --git a/00. Anaconda Install.ipynb b/00. Anaconda Install.ipynb
deleted file mode 100644
index 420639a..0000000
--- a/00. Anaconda Install.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Anaconda Install\n",
- "### If you're reading this from the downloaded version off github and you're running this with Jupyter, you can skip this lesson.\n",
- "\n",
- "## [Download Anaconda to use Jupyter](https://docs.anaconda.com/anaconda/install/) or [watch a video on how to do it](https://youtu.be/LrMOrMb8-3s).\n",
- "### (Please use the two links above if you want to use the downloaded files, from [github](https://github.com/BaconBomber/BeginnersTCLab), but can't run Jupyter. Jupyter is a program run from [Anaconda](https://www.anaconda.com/))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\eric\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning: Consider using IPython.display.IFrame instead\n",
- " warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "def video(key):\n",
- " display(HTML(''))\n",
- "\n",
- "video('LrMOrMb8-3s')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/00. Introduction.ipynb b/00. Introduction.ipynb
new file mode 100644
index 0000000..e753c42
--- /dev/null
+++ b/00. Introduction.ipynb
@@ -0,0 +1,58 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Introduction to Python\n",
+ "\n",
+ "\n",
+ "\n",
+ "Welcome to this introductory course on Python! This course is intended to help you start programming in Python from little or no prior experience. There are video tutorials for each exercise if you have questions along the way.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=EO_YpBs8cs0&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=EO_YpBs8cs0&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Introduction to Python\")\n",
+ "\n",
+ "One of the unique things about this course is that you work on basic elements to help you with a temperature control project. You will see your Python code have a real effect by adjusting heaters to maintain a target temperature, just like a thermostat in a home or office.\n",
+ "\n",
+ "\n",
+ "\n",
+ "One of the best ways to start or review a programming language is to work on a simple project. These exercises are designed to teach basic Python programming skills to help you design a temperature controller. Temperature control is found in many applications such as home or office HVAC, manufacturing processes, transportation, and life sciences. Even our bodies regulate to a specific temperature.\n",
+ "\n",
+ "\n",
+ "\n",
+ "This project is to regulate the temperature of the TCLab (Temperature Control Lab). Each TCLab has thermochromic (changes color with temperature) paint that turns from black to purple when the temperature reaches the target temperature of 37°C (99°F).\n",
+ "\n",
+ "\n",
+ "\n",
+ "The first thing that you'll need is to install Anaconda and run the Jupyter notebook. [Download Anaconda to use Jupyter](https://www.anaconda.com/distribution/) or [watch a video on how to install it](https://youtu.be/LrMOrMb8-3s).\n",
+ "\n",
+ "[](https://www.youtube.com/watch?v=LrMOrMb8-3s \"Install Anaconda\")\n",
+ "\n",
+ "The video shows how to install Anaconda and a few things you can do with the Jupyter Notebook. Examples are with Jupyter Notebook and Spyder. There are [additional instructions on installing Python and managing modules](https://apmonitor.com/pdc/index.php/Main/InstallPython)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/01. Overview.ipynb b/01. Overview.ipynb
index 963d02a..640400e 100644
--- a/01. Overview.ipynb
+++ b/01. Overview.ipynb
@@ -4,31 +4,78 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Python Knowledge Interactive\n",
+ "## 1. Overview\n",
"\n",
- "## ***Background:***\n",
- "You have a chicken egg you really want to hatch, but you don't want to sit around forever, checking the temperture and adjusting heaters. The best way to do this you found, was control it using python. Unfortunately, you only get one try to get this right. But, you have a simulator of the incubator so you can practice python, without having to worry about mistakes. \n",
+ "### Background\n",
"\n",
- "## ***Introduction:***\n",
- "Every programmer must get some background knowledge before using the language more complexly. So if you want to do well in the second, complex part, that will better monitor the egg you should for sure work through the basic information. For the people who already have experience with python and think this doesn’t apply to you, this background knowledge is different because it uses the temperature control lab to help teach basics and if you don't do it part two won't be so easy.\n",
+ "\n",
"\n",
- "### ***Some things this background knowledge interactive will go through are:***\n",
+ "You have eggs that need to hatch in an incubator. One option is to constantly check the temperature and adjust the heaters manually. Another way is to automate the temperature control by constantly checking the temperature and adjusting the heaters with Python. Unfortunately, you only get three eggs for the test and one attempt to get it right. You do have a simulator of the incubator (TCLab) so you can practice Python, without having to worry about mistakes. The purpose of this lab is to develop a temperature controller (like a thermostat) that could be used for an egg incubator. There are other factors such as humidity and turning the eggs that are important with incubators but we'll only focus on the temperature for this project.\n",
"\n",
- "#### -Debugging\n",
- "#### -Variables\n",
- "#### -Printing\n",
- "#### -Objects\n",
- "#### -Functions\n",
- "#### -Loops\n",
- "#### -Input\n",
- "#### -If Statements\n",
- "#### -Lists and Tuples\n",
- "#### -Arrays\n",
- "#### -Plotting\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=wuWLVbBFPuc&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
"\n",
- "(It is best to follow the lessons in those steps. Some things, such as __equations__, and __tclab functions__, you aren't required to learn but can be useful. If you wish to learn those, __useful for tclab__ and __basic python knowledge__, are on seperate files in the same folder. Other general python info, such as equations, can be found online.)\n",
+ "[](https://www.youtube.com/watch?v=wuWLVbBFPuc&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Course Overview\")\n",
"\n",
- "*Installation of TCLab is on the Functions Lesson, number 5. Or in the file, Useful For TCLab*"
+ "### Introduction\n",
+ "\n",
+ "Every programmer must have basic background knowledge before using the language in more complex projects. This tutorial steps through basic programming skills to build the more complex incubator temperature control. The introductory 12 exercises are designed to be completed in 2-3 hours (15-20 minutes each) but sections can be skipped if you already have the background knowledge.\n",
+ "\n",
+ "If you want to do well in monitoring and controlling the egg temperature, you should work through the basic information. For those who already have experience with Python, this background knowledge also teaches the `tclab` package functions.\n",
+ "\n",
+ "1. Overview (this lesson)\n",
+ "2. Debugging\n",
+ "3. Variables\n",
+ "4. Printing\n",
+ "5. Classes and Objects\n",
+ "6. Functions\n",
+ "7. Loops\n",
+ "8. Input\n",
+ "9. If Statements\n",
+ "10. Lists and Tuples\n",
+ "11. Dictionaries\n",
+ "12. Plotting\n",
+ "\n",
+ "It is best to follow the lessons in these steps because the later lessons build upon the information from the prior lessons.\n",
+ "\n",
+ "### Install TCLab Module and Test LED\n",
+ "\n",
+ "\n",
+ "\n",
+ "You need the TCLab to do the exercises. As a first step, plug in the TCLab (USB blue cable only) and install the package with `pip install tclab` or by running the cell below (`Ctrl+Enter`). Restart the Python kernal with `Kernel...Restart & Run All` from the menu if there is an error importing `tclab` after the installation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# install tclab\n",
+ "try:\n",
+ " import tclab\n",
+ "except:\n",
+ " # Needed to communicate through usb port\n",
+ " !pip install --user pyserial\n",
+ " # The --user is put in for accounts without admin privileges\n",
+ " !pip install --user tclab \n",
+ " # restart kernel if this doesn't import\n",
+ " import tclab\n",
+ " \n",
+ "import time\n",
+ "\n",
+ "# tclab test\n",
+ "lab = tclab.TCLab()\n",
+ "lab.LED(100) # turn on LED\n",
+ "time.sleep(5) # wait 5 seconds\n",
+ "lab.LED(0) # turn off LED\n",
+ "lab.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are seperate Jupyter notebook files to help with TCLab installation. There are also [Frequently Asked Questions](https://apmonitor.com/pdc/index.php/Main/ArduinoSetup) for setup and troubleshooting. More information on installation of TCLab package is in **5. Objects Lesson** and also in the IPython notebook __TCLab Help__."
]
}
],
@@ -48,7 +95,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.5"
}
},
"nbformat": 4,
diff --git a/02. Debugging.ipynb b/02. Debugging.ipynb
index 17145a5..324ffb8 100644
--- a/02. Debugging.ipynb
+++ b/02. Debugging.ipynb
@@ -4,21 +4,65 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Debugging\n",
+ "## 2. Debugging\n",
"\n",
- "One of the biggest time consumers in programming is debugging, or mistakes in your programming. This is true for every language because the computer needs exact commands. To limit this mistake, a couple of steps can limit the time you are just looking for mistakes, instead of just programming. \n",
+ "\n",
+ "\n",
+ "One of the biggest time consuming parts of programming is debugging, or resolving mistakes in the program. This is true for every language because the computer needs exact commands, which is very important for precise measurements and temperature control. A few steps can limit the time you are searching for mistakes, instead of completing the project. \n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=n1BlQkoWbdM&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=n1BlQkoWbdM&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Pseudo-Code and Debugging\")\n",
"\n",
"### Steps for more effective programming\n",
- "#### _First_\n",
- "You should start with understanding the big picture. It seems silly, but once you start going over the whole thing you find a lot of gaps. Do this in whichever way suits you best, we’ll leave it up to you.\n",
- "#### _Second_\n",
- "Start with outlining your code, writing in English what you want the code to do, then break it into more direct tasks.\n",
- "#### _Third_\n",
- "Program the specific tasks and connect them together. The explained direct tasks make it significantly easier to program. It’s a lot harder to program something all in one go if the program is bigger.\n",
- "#### _Fourth_\n",
- "Test and fix problems. Basically debugging, but don’t only test the whole project all in one go. It’s much easier to find a problem if you test every once in a while, when you are programming.\n",
- "\n",
- "The better you follow these steps the less time you will have to spend fixing problems in your code."
+ "\n",
+ "\n",
+ "\n",
+ "1. Start with understanding the big picture. Go over the whole program architecture to find high-level gaps. Do this in whichever way suits you best, such as drawing a flowchart. For the egg incubator, the program scope is to diagram what you actually need to do to help an egg hatch.\n",
+ "\n",
+ "2. Start by outlining your code, writing **high-level instructions (pseudo-code)** what you want each section of the code to do. Break it into more specific tasks. You can do this even without understanding the basics of Python. Once you learn Python basics, you can translate these high level instructions into code. Organizing the outline helps to make sure your programming isn't more complex than it needs to be.\n",
+ "\n",
+ "3. Program the specific tasks and connect them together. Direct tasks make it significantly easier to program because the program is modular. It is a lot harder to program something if the program is large and complex. For the incubator, this may be programming something specific like how hard the heater should work, based on a low temperature reading.\n",
+ "\n",
+ "4. Test and fix problems. This is debugging, but don’t only test the whole project after you've completed all of the programming. It is much easier to find a problem if you test every once in a while, when you are programming smaller parts. A good way to do this is grab a specific piece of code, run it on a separate file, and see if it does the job you want. An example would be fixing when the heater should stop working, so the egg doesn't go over temperature.\n",
+ "\n",
+ "The better you follow these steps the less time you will have to spend fixing problems in your code for your incubator, or just Python programs in general.\n",
+ "\n",
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fix the errors in the following code cells. Use the error messages to help you locate the problems."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('Program to Hatch an Egg) # hint: look for a missing ' character"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target temperature = 37 # hint: variable names cannot have spaces"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "current_temperature = 30\n",
+ "target_temperature = 37\n",
+ "difference = target_temperature - current_temperature\n",
+ "print(difference + 'degC') # hint: use str(difference) to make it a string"
]
}
],
@@ -38,7 +82,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/03. Variables.ipynb b/03. Variables.ipynb
index 8209c74..3102d96 100644
--- a/03. Variables.ipynb
+++ b/03. Variables.ipynb
@@ -4,14 +4,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Variables\n",
+ "## 3. Variables\n",
"\n",
- "Variables are something that stores information in python. For example, if you wanted to keep someone’s age, you would type ```jim = 7```. The first part tells what the variable will be called, and the value after the ```=``` tells what is being stored. \n",
+ "\n",
"\n",
- "## Values in Variables\n",
- "There are many types of values but for now, we’ll just go through the basics. You can run the code using the symbol to the left, but in this case the computer won't tell you anything, because you're just storing values.\n",
+ "Variables store information and are objects in Python. For example, if you wanted to keep a set temperature for an egg, you would type ```egg = 37.5``` (°C) or ```egg = 99.5``` (°F). The first part tells what the variable will be called, and the value after the ```=``` tells what is being stored. \n",
"\n",
- "(Pro Tip: Another way to run a cell, select the cell you want to run, it should turn blue, then holding ```Ctrl``` and pressing ```Enter```.)"
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=q6QOsauDyPg&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=q6QOsauDyPg&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Variable Types\")\n",
+ "\n",
+ "### Types of Variables\n",
+ "\n",
+ "\n",
+ "\n",
+ "There are many types of value types but for now, we’ll just go through the basics. You can run the code, but in this case the computer won't tell you anything, because it is only storing values. \n",
+ "\n",
+ "While you can run a program by clicking the run button towards the top of the screen, a better way to run cells individually is to click a cell, then type ```Ctrl``` and ```Enter```. Running this way means you're running one cell at a time, instead of all cell. Jupyter Notebooks can also have the run button to the left of the program cell."
]
},
{
@@ -19,16 +28,16 @@
"metadata": {},
"source": [
"#### Integers\n",
- "Integers, or ```int```, are the value of a basic, whole number like ```9```. "
+ "Integers, or ```int```, are the value of a basic, whole number like ```99```. You can use the function `int(99.5)` to convert a number to an integer `99` as it removes any numbers after the decimal."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "temp = 9"
+ "temp = 99"
]
},
{
@@ -36,16 +45,16 @@
"metadata": {},
"source": [
"#### Floats\n",
- "Floats, or ```float```, are basically the same thing, but with decimals like ```4.72```."
+ "Floating point numbers, or ```float```, are also numbers, but with decimals like ```4.72```."
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "price = 4.72"
+ "price = 34.09"
]
},
{
@@ -53,12 +62,12 @@
"metadata": {},
"source": [
"#### Strings\n",
- "Strings, or ```str```, are words or text, usually in english typed like ```\"hi\"```. You can use ```\"\"``` or ```''```."
+ "Strings, or ```str```, are words or text, usually typed like ```\"hi\"```. You can use double ```\"\"``` or single ```''``` quotes."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -70,12 +79,12 @@
"metadata": {},
"source": [
"#### Boolean\n",
- "Boolean, or ```bool```, is a ```True``` or ```False``` Statement."
+ "Boolean, or ```bool```, is a ```True``` or ```False``` value. They both need to start with a capital letter."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -86,29 +95,72 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Rules of Variables\n",
+ "### Comments \n",
+ "Comments are ignored by Python and are words in a program to explain the code. They are created with the ```#``` symbol and can appear to the right of a statement or on a separate line."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "variable = \"random\" # This is a comment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Rules of Variables\n",
+ "\n",
+ "\n",
+ "\n",
+ "#### Variable Naming\n",
+ "In order to properly name a variable, it can only start with a __letters__ or an __underscore__. After the first character you can use __letters, __numbers__, and __underscores__. \n",
+ "\n",
+ "##### Python variables can start with:\n",
+ "\n",
+ "```_``` or **letters** (Recommended to start with lowercase)\n",
+ "\n",
+ "##### And are continued with:\n",
+ "\n",
+ "**Numbers** ```0-9``` ,**letters**, and __underscores__. Example:\n",
+ "\n",
+ "```python\n",
+ "_this_is_a_variable_27 = \"Incubator Temperature Control\"```\n",
+ "\n",
+ "If you want to check your understanding, run incorrect variables and then fix the variable names to whatever you want. Make sure the variable name follows the naming rules."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "375 = \"Important Egg Data\" # Can't start with a number, change it to something else\n",
"\n",
- "### Variable Naming\n",
- "There are a couple of rules though because python still needs exact commands. In order to properly name a variable, it can only start with a __lowercase letter__ or an __underscore__. Even if you start a variable name right though, you need to continue it the right way. Anything after the first character you can have __any letter__ (including uppercase), __numbers__, and __underscores__. Try running incorrect variables and then fix them to whatever you want."
+ "badtemp$ = 6 # Look for the symbol, delete it"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variable Properties\n",
+ "Variables can be assigned a value such as a string, integer, or float. If you assign them again later in the code it simply changes to that new assignment. For example, the code below would first use ```3``` for ```test```, but after would use ```egg``` instead. Unlike other programming languages, Python variables are mutable (can change type). You can see the variable type with `type(test)`."
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "SyntaxError",
- "evalue": "invalid syntax (, line 2)",
- "output_type": "error",
- "traceback": [
- "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m ^&@ = 6\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "375 = \"Important Data\"\n",
- "^&@ = 6"
+ "test = 3\n",
+ "test = \"egg\"\n",
+ "type(test)"
]
},
{
@@ -116,38 +168,64 @@
"metadata": {},
"source": [
"### Variable Usage\n",
- "Variables can be used in math or when you are trying to show a result, which is very useful but they do have restrictions. Different types of value typically can't be combined, such as ```5 + \"degC\"```. This can sometimes be fixed if you really need __5degC__ to be output. All you do is change the value type of ```5``` manually into a __string__, ```str(5) + \"degC\"```. But, you can't turn a __string__ into a __interger__ which makes sense, the letter _n_ for example doesn't have a number that goes with it."
+ "Variables can be used in math or when you are trying to show a result, which is very useful but they do have restrictions. Different types of values typically can't be added together, such as ```5 + \"degC\"```. This can be fixed if you need __5degC__ to be output. Change the value type of ```5``` manually into a __string__, ```str(5) + \"degC\"```."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "combined = str(5) + \"degC\""
+ "combined_str = \"right\" + 'left'\n",
+ "combined_int = 4 + 2\n",
+ "combined_int_str = str(5) + \"degC\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Try making your own variable to store a random piece of information, your siblings name, your age, whatever you like. Then combine the variables, ```x``` and ```y```, below by changing their types to __strings__.\n",
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Make one variable for every variable type, string, integer, float, and boolean.\n",
"\n",
"(The ```#``` symbol makes the text behind it a comment. Those words are just for discribing the program, they don't do anything to the code.)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "string = \n",
+ "integer = \n",
+ "float1 = # \"float\" in code is already assigned so it's changed to something else, to avoid errors\n",
+ "boolean = "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Try making your own variable to store your chicken's name. Make sure to change the assignment name from ```yourVariable``` to what the information is about so you remember. \n",
+ "\n",
+ "Then combine the variables, ```x``` and ```y```, below by changing their types to __strings__. If you get stuck, the example just above changes ```integer``` 5 into a string. Use the same code on x and y."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Your variable\n",
- "x = True\n",
- "y = 236.4\n",
- "combined = x + y"
+ "ChickenName = \n",
+ "x = True # This is a Boolean\n",
+ "y = 236.4 # This is a float\n",
+ "combined = x + y # Make x and y strings"
]
}
],
@@ -167,7 +245,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/04. Printing.ipynb b/04. Printing.ipynb
index fac391c..d59cc34 100644
--- a/04. Printing.ipynb
+++ b/04. Printing.ipynb
@@ -4,9 +4,33 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Printing\n",
+ "## 4. Printing\n",
"\n",
- "Printing isn't what it means to most people who don't program. It's outputting values, not putting ink on paper. You use the built in python __function__ ```print()``` to output values. Try putting something in the function and running the program."
+ "\n",
+ "\n",
+ "Printing is displaying values to the screen. The word `print` comes from the time when programs previously put ink on paper. You use the built-in python __function__ ```print()``` to output values.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=CCPNIocw0_Y&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=CCPNIocw0_Y&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Print\")\n",
+ "\n",
+ "You could use this to tell you what the current temperature of the egg is and when to display it. Get more practice by putting something different in the function and running the program."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Chicken Hatches: 'Hello World!'\") # Put something in between the \"\" symbols"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also print functions, this function is one that will tell the information type that is stored in a variable. The `type` function will return ``````, `str`, `bool`, `float`, whatever is stored in the variable."
]
},
{
@@ -15,57 +39,47 @@
"metadata": {},
"outputs": [],
"source": [
- "print(\"\") # Put something in between the \"\" symbols"
+ "eggs = 200 # integer variable\n",
+ "print(type(eggs))\n",
+ "\n",
+ "egg_type = \"Platypus\" # string variable\n",
+ "print(type(egg_type))\n",
+ "\n",
+ "yes = True # Boolean variable\n",
+ "print(type(yes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now using from the previous lesson combining different value types, print a ```number``` and a ```string``` together. You'll have to change the value type for it to work like the previous activity."
+ "Now using information from the previous lesson, combine different value types by printing a ```number``` and a ```string``` together. This is similar but instead of putting them in a variable, you are printing them to the screen. You'll have to change the value type for it to work like in the previous activity. Go back a lesson if you get stuck."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "SyntaxError",
- "evalue": "invalid syntax (, line 1)",
- "output_type": "error",
- "traceback": [
- "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m print( + )\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "print( + )"
+ "print( + ) # Print a string and number together, correctly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can even use two __variables__ in a ```print``` function by doing something like this ```print(var1 + var2)```. Try putting something into the __variables__."
+ "You can even use two __variables__ in a ```print``` function by doing something like this ```print(var1 + var2)```. If you want more practice put different strings into the __variables__."
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Put something here. And put something here.\n"
- ]
- }
- ],
- "source": [
- "var1 = 'Put something here.'\n",
- "var2 = \" And put something here.\"\n",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "var1 = 'Put something here'\n",
+ "var2 = \" and put something here.\"\n",
"print(var1 + var2) # Prints both"
]
},
@@ -73,8 +87,112 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity \n",
- "Make two __variables__ that will turn into a sentence when you print them together."
+ "You can also use commas to seperate things when ```printing```. They will automatically put a **space** between the values, and it doesn't matter if they are *numbers, strings, or both*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "var1 = \"You are cooking the egg at\"\n",
+ "var2 = 45\n",
+ "print(var1, var2, \"°C, cool down!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Printing Equations\n",
+ "\n",
+ "\n",
+ "\n",
+ "Jupyter Notebook automatically prints the last expression in a cell. One disadvatage of this feature is it only gives the result for the last line in the code. This method is mostly useful when you need to do a quick one line calculation. Run the code below and put in different equations to check your understanding."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "5*2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The typical way in other Python modules is to simply put it in a ```print()``` function and you can get output for all lines."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(14/2)\n",
+ "print(7*2)\n",
+ "print(3+11)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Activity \n",
+ "\n",
+ "\n",
+ "\n",
+ "Make two __variables__ that will add two numbers when you print them together. This is kind of like the example above, but use numbers in your variables. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " # First number variable\n",
+ " # Second number variable\n",
+ "print() # Print together"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In lesson 3 it mentions variable properties. This was saying if you assign the same variable twice, it simply changes values.\n",
+ "\n",
+ "```python\n",
+ "test = 3\n",
+ "test = \"egg\"```\n",
+ "\n",
+ "Do the same here, except now print the variable in between variable assignments so you can see how it works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = 3\n",
+ " # print here\n",
+ "test = \"egg\"\n",
+ " # print here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use the variable with an equation to ```print``` 500. You need to make the ```print()``` have an equation in order to change the output value."
]
},
{
@@ -83,9 +201,8 @@
"metadata": {},
"outputs": [],
"source": [
- "var1\n",
- "var2\n",
- "print()"
+ "equation = 25*4\n",
+ "print(equation ) # Make an equation to equal 500"
]
}
],
@@ -105,7 +222,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/05. Classes and Objects.ipynb b/05. Classes and Objects.ipynb
new file mode 100644
index 0000000..3cb0de7
--- /dev/null
+++ b/05. Classes and Objects.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Classes and Objects\n",
+ "\n",
+ "\n",
+ "\n",
+ "Classes are collections of objects and functions. Many Python packages such as `time`, `tclab`, `numpy`, `scipy`, `gekko`, and others are distributed as `classes`. A class is imported with the `import` statement such as ```import time```.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=-rvIRWf7eGc&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=-rvIRWf7eGc&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Classes and Objects\")\n",
+ "\n",
+ "Time is a package that has timing functions that we will use to pause the program for a specified amount of time. TCLab package has functions created with ```tclab.TCLab()```. The next lesson shows how to use the `tclab` functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Objects\n",
+ "\n",
+ "\n",
+ "\n",
+ "Variables such as `int`, `float`, and `str` types are objects. Objects may also be created from a class template to create a new custom variable. You use a variable assignment to create an object, assigned to a name, such as ```lab = tclab.TCLab()```. This is called a parent (`tclab.TCLab()`) to child (`lab`) relationship because `lab` is created from the class `tclab`. The child object `lab` inherits all the functions of the parent. The child object is modified and customized in your code. You can give objects a name that you will remember and that are easy to type. For `tclab`, we prefer to use ```lab```, but it can be a different name. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Explanation of Objects\n",
+ "```python\n",
+ "lab = tclab.TCLab()\n",
+ "```\n",
+ "\n",
+ "The first part of the expression is the child object name. For the Temperature Control Lab, we often use ```lab``` to as the name of our object. For something like your incubator, you could make an object to connect with it, called something like ```incubator```. \n",
+ "\n",
+ "The second part after ```=```, opens the installed package called ```tclab```. Then it looks for and uses ```TCLab``` to connect with your tclab. \n",
+ "\n",
+ "The ```.``` makes the program look for a sub-object, and the ```()``` tells python it is a function. This a function with no input arguments, because no value is put in the parentheses. Functions will be taught next lesson. \n",
+ "\n",
+ "So ```lab``` or whatever you decide to call it, is assigned to the programming already made found in the package:\n",
+ "```python\n",
+ "tclab.TCLab()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Try and Except\n",
+ "\n",
+ "\n",
+ "\n",
+ "`Try` and `Except` methods can catch program errors. The program attempts to run the code under ```try```, and if it can't it runs the ```except``` section of the code. This is useful in a number of situations, such as importing packages you may not have installed and printing a message that something is wrong."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " print('Temperature = ' + 30)\n",
+ "except:\n",
+ " print('There is an error')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Install Temperature Control Lab\n",
+ "\n",
+ "This code will first ```try``` and import tclab. If it can't do that for some reason it uses ```except``` and installs the program that will let you use your Temperature Control Kit. Run the program to install TCLab. If installing TCLab is not working for you and the connection test below isn't working, try going [here](https://apmonitor.com/pdc/index.php/Main/ArduinoSetup) for additional instructions. If you want to install this package just for your user, not with admin privileges, keep the code ```--user``` in lines 6 and 8."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# install tclab\n",
+ "try:\n",
+ " import tclab\n",
+ "except:\n",
+ " # Needed to communicate through usb port\n",
+ " !pip install --user pyserial\n",
+ " # The --user is put in for accounts without admin privileges\n",
+ " !pip install --user tclab \n",
+ " # restart kernel if this doesn't import\n",
+ " import tclab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Connection Test\n",
+ "\n",
+ "This object is what allows you to connect with the kit, read current temperatures, adjust heaters, or change the LED brightness. In the next lesson we will go over the basics of what this temperature control kit can really do. \n",
+ "\n",
+ "Plug in your TCLab if it's available. Now try running and the program will create the object from below. If the `tclab` package is installed and imported, it should connect, tell you additional information about your TCLab, then disconnect. If it doesn't work, or comes up with an error, go to the file __TCLab Help__.\n",
+ "\n",
+ "Don't worry if this is confusing, most of it will be explained in the next lesson."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab # Imported Package\n",
+ "lab = tclab.TCLab() # Object that connects with the kit\n",
+ "lab.close() # Disconnects with the Lab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Connect the blue USB cable and plug in the white power cable to the power adapter. Run the TCLab exercise below that imports and tests the `TCLab` functions. Modify the name of the object from `lab` to `incubator` and add comments to the code. All these functions are covered in depth in lesson `6: Functions`. Don't worry if you don't understand each step of the program yet. Your task is to only change the name of the child object from `lab` to `incubator` and add comments such as `# this is a comment` to the code on the purpose of each section.\n",
+ "\n",
+ "\n",
+ "\n",
+ "**TCLab Function Help**\n",
+ "\n",
+ "***Connect/Disconnect:*** ```lab = tclab.TCLab()``` Connect and create new lab object, ```lab.close()``` disconnects lab.\n",
+ "\n",
+ "***LED:*** ```lab.LED()``` Percentage of output light for __Hot__ Light.\n",
+ "\n",
+ "***Heaters:*** ```lab.Q1()``` and ```lab.Q2()``` Percentage of power to heaters (0-100).\n",
+ "\n",
+ "***Temperatures:*** ```lab.T1``` and ```lab.T2``` Value of current heater temperatures."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab\n",
+ "import time\n",
+ "\n",
+ "lab = tclab.TCLab()\n",
+ "\n",
+ "print('Turn on heaters and LED for 20 seconds')\n",
+ "lab.Q1(50)\n",
+ "lab.Q2(50)\n",
+ "lab.LED(50)\n",
+ "\n",
+ "for i in range(21):\n",
+ " print('Time:',i,'T1:',lab.T1,'T2:',lab.T2)\n",
+ " time.sleep(1)\n",
+ " \n",
+ "lab.Q1(0); lab.Q2(0); lab.LED(0)\n",
+ "lab.close()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05. Objects.ipynb b/05. Objects.ipynb
deleted file mode 100644
index 278c509..0000000
--- a/05. Objects.ipynb
+++ /dev/null
@@ -1,106 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Objects\n",
- "Objects are created to represent something. For the Temperture Control Kit, it's object is usually named _lab_. You can name objects the name that you will remember and that are easy to type. You use a variable to create an object.\n",
- "\n",
- "```python\n",
- "lab = tclab.TCLab()\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Explanation \n",
- "The first part of an object is what you are naming it. The second part, ```tclab```, goes into the downloaded package called tclab. Then it looks for and uses ```TCLab``` to connect with your tclab. The ```.``` makes it look in the folder, and the ```()``` tells the computer it's an object to create."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Install Temperature Control Lab\n",
- "This code installs a program that will let you use your Temperature Control Kit. Run the program to install TCLab. If installing TCLab is not working for you and the connection test below isn't working, try going [here](https://apmonitor.com/pdc/index.php/Main/ArduinoSetup) for additional instruction.\n",
- "\n",
- "(If you want to run the program with admin privileges, delete the code ```--user``` in lines 5 and 6)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "# install tclab\n",
- "try:\n",
- " import tclab\n",
- "except:\n",
- " # Needed to communicate through usb port\n",
- " !pip install --user pyserial\n",
- " # The --user is put in for accounts without admin privileges\n",
- " !pip install --user tclab \n",
- " # restart kernel if this doesn't import\n",
- " import tclab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This object is what allows you to connect with the kit, make it do things. In the next lesson we will go over the basics of what this temperture control kit can really do. Now try running and creating the object from below. Now if tclab is installed and imported, it should connect, tell you additional information, then disconnect. Don't worry if this is confusing, all of it will be explained, next lesson."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TCLab version 0.4.9\n",
- "Could not connect at high speed, but succeeded at low speed.\n",
- "This may be due to an old TCLab firmware.\n",
- "New Arduino TCLab firmware available at:\n",
- "https://github.com/jckantor/TCLab-sketch\n",
- "NHduino connected on port COM6 at 9600 baud.\n",
- "TCLab Firmware Version 1.01.\n",
- "TCLab disconnected successfully.\n"
- ]
- }
- ],
- "source": [
- "import tclab # Imported Package\n",
- "lab = tclab.TCLab() # Object that connects with the kit\n",
- "lab.close() # Disconnects with the Lab"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/06. Functions.ipynb b/06. Functions.ipynb
index f8a45e9..c126c93 100644
--- a/06. Functions.ipynb
+++ b/06. Functions.ipynb
@@ -4,15 +4,45 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Functions\n",
- "Functions exist to do the same task repeatedly without you having to type out the same code everytime. You can create your own, but also some are built in to python or in certain packages. One built in function you have already seen is the ```print()``` function. "
+ "## 6. Functions\n",
+ "\n",
+ "\n",
+ "\n",
+ "Functions create modular code that can do the same task repeatedly without you having to type out the same code each time. Functions make complex code accessible with a single statement. You also can create your own function, but there are also some are built in to Python or in many packages. One built in function you have already seen is the ```print()``` function.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=dP3e2jIHqXw&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=dP3e2jIHqXw&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Functions\")\n",
+ "\n",
+ "For the incubation example, a display function could be used such as the function `ctemp()` to print the current temperature. This re-uses the print statement so that you don't need to type it out each time for temperatures 1 and 2.\n",
+ "\n",
+ "```python\n",
+ "# create the function\n",
+ "def ctemp(temperature):\n",
+ " print('Current temperature: ' + str(temperature) + '°C')\n",
+ " return\n",
+ "\n",
+ "# use the function twice\n",
+ "ctemp(20.51)\n",
+ "ctemp(22.57)\n",
+ "```\n",
+ "\n",
+ "This prints the two temperatures as:\n",
+ "\n",
+ "```\n",
+ "Current temperature: 20.51°C\n",
+ "Current temperature: 22.57°C\n",
+ "```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Creating your own\n",
+ "### Creating a custom function\n",
+ "\n",
+ "\n",
+ "\n",
"The way to tell the computer you are starting your own function is ```def``` followed by the function name. You need the ```():``` for the computer to recognize it as a function. \n",
"\n",
"```python\n",
@@ -24,22 +54,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The code is then put into the function on what it's supposed to do. But you need to call the function for it to actually use that code. The way you do that is the function name with ```()```."
+ "The code is then added into the function scope. Once the function code is complete, you need to call the function for it to run that code. The way you do that is the function name with ```()``` as `myFunction()`."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Function\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def myFunction(): # Defining the Function\n",
" print(\"Function\")\n",
@@ -51,28 +73,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can also put in values in the ```()``` to the function that it needs to run. You should also use the ```return``` phrase that gives the calling a value to print."
+ "You can also put in values in the ```()``` as inputs for the function. Sometimes a function needs multiple inputs and those can be separated with commas. You should also use the ```return``` statement that gives back results from `myFunction()`.\n",
+ "\n",
+ "This is a confusing concept, and if you need more help with it, play around with the code below or research information about Python functions online."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "12"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def myFunction(a, b): \n",
- " return a*b # Gives the finished value back to be output\n",
+ " return a*b # Gives the multiplied value back to be output\n",
"\n",
"myFunction(3, 4) # Enters 3 into a and 4 into b"
]
@@ -81,8 +94,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Make a function that adds two values together, you can choose the two values or make them an user input."
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Make a function that adds two values together, you can choose the two values or make them an user input. Use the example above for help if you need it."
]
},
{
@@ -101,28 +117,38 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## TCLab Functions\n",
- "In the last lesson on objects, it mentioned the ```lab``` object. Objects can have functions that are used with a ```.``` and then the function name. Using functions, we can change things on our temperture kit values such as __light brightness__, __heater power__, and get __output tempertures__.\n",
+ "### TCLab Functions\n",
+ "\n",
+ "\n",
+ "\n",
+ "In the last lesson on objects, it mentioned the ```lab``` object. Objects can have functions that are used with a ```.``` and then the function name. Functions can change things on the TCLab such as __light brightness__, __heater power__, and get __temperatures__.\n",
"\n",
- "### LED\n",
- "```lab.LED()``` This function will set the light, that is labeled __Hot__, on your tclab turn on at a certain brightness. If you want the light brightest you would use ```lab.LED(100)``` and all the way off ```lab.LED(0)```.\n",
+ "***LED***\n",
"\n",
- "### Heaters\n",
- "```lab.Q1()``` or ```lab.Q2()``` will set the power percentage of heater one or two. Same as the ```LED``` function, ```Q1``` is set highest at ```lab.Q1(100)``` and off at ```lab.Q1(0)```. The other heater works the same way, ```Q2``` is set highest at ```lab.Q2(100)``` and off at ```lab.Q2(0)```\n",
+ "```lab.LED()``` This function sets the LED power, that is labeled __Hot__, on your tclab turn on at a certain brightness. If you want the light brightest use ```lab.LED(100)``` and ```lab.LED(0)``` to turn the LED off.\n",
"\n",
- "### Temperture Readers\n",
- "```lab.T1()``` or ```lab.T2()``` gives the current reading of the heater it corresponds to. It will give the values in __Celcius__.\n",
+ "***Heaters***\n",
"\n",
- "### Disconnecting\n",
- "We also use ```lab.close()``` at the end of the program to disconnect the kit to make sure it doesn't keep running the heaters. You don't want your heaters to keep going when you don't want them to. This function does _not_ turn off the LED however."
+ "```lab.Q1()``` or ```lab.Q2()``` sets the power percentage of heater one or two. Same as the ```LED``` function, ```Q1``` is set highest at ```lab.Q1(100)``` and off at ```lab.Q1(0)```. The other heater works the same way, ```Q2``` is set highest at ```lab.Q2(100)``` and off at ```lab.Q2(0)```\n",
+ "\n",
+ "***Temperatures***\n",
+ "\n",
+ "```lab.T1``` or ```lab.T2``` gives the current reading of the temperature for the first or second heater. It gives the values in __Celsius__.\n",
+ "\n",
+ "***Disconnecting***\n",
+ "\n",
+ "We also use ```lab.close()``` at the end of the program to disconnect the kit to make sure it doesn't keep the heaters on. This function does _not_ turn off the LED, however."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Use the LED at a percent of (your choice) and then turn it off after (your choice) seconds. "
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Use the LED at 70% and then turn it off after 10 seconds. "
]
},
{
@@ -147,9 +173,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Use heater one, ```Q1```, at 40 percent and heater two, ```Q2```, at 80 percent. Use ```print()``` to record the tempertures with ```T1``` and ```T2``` after 10 seconds, with ```time.sleep()```. Make sure to disconnect the heaters after you record the tempertures. Use the LED activity and the discriptions above if you don't remember some of the functions. \n",
- "\n",
- "(If the heaters don't turn off because of a bug in the code, unplug the cables connected to the lab to stop heat up)"
+ "Use heater one, ```lab.Q1```, at 40 percent and heater two, ```lab.Q2```, at 80 percent. Use ```print()``` to record the temperatures with ```lab.T1``` and ```lab.T2``` after 10 seconds, with ```time.sleep()```. Make sure to disconnect the heaters after you record the temperatures. Use the LED activity and the discriptions above if you don't remember some of the functions. If the heaters don't turn off because of a bug in your code, unplug the cables connected to the lab to stop the heaters or restart the kernel to run the lab again. If you get errors when you run your code, check out the __TCLab Help__ IPython notebook for common errors or search the [Frequently Asked Questions for Troubleshooting](https://apmonitor.com/pdc/index.php/Main/ArduinoSetup). You can also read the error descriptions Jupyter gives you below your code to diagnose the problem."
]
},
{
@@ -176,7 +200,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/07. Loops.ipynb b/07. Loops.ipynb
index 9794c61..a2c4edd 100644
--- a/07. Loops.ipynb
+++ b/07. Loops.ipynb
@@ -4,37 +4,93 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Loops\n",
- "There are two basic types of loops.\n",
+ "## 7. Loops\n",
+ "\n",
+ "\n",
+ "\n",
+ "There are two basic types of loops including `for` and `while`. An example of a loop is to check the temperature of the egg every second and adjust the heater.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=qR0njzQvvOA&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=qR0njzQvvOA&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Loops\")\n",
"\n",
"### While Loops\n",
- "These loops work by repeating until their condition becomes _false_. These loops often get you stuck in an infinite loop if you aren't careful. You can stop the infinite loop by setting the value of true to become false in many ways. In the code below, the infinite loop is broken by making x one less each time it goes through the loop, with ```x = x - 1```. Try changing the value of ```x```."
+ "\n",
+ "\n",
+ "\n",
+ "While loops work by repeating until the condition becomes ```False```. The way they are coded can almost be said as a sentence. ```while x > 0:``` While x is greater than 0.\n",
+ "\n",
+ "While loops can get stuck in an infinite loop if the condition for termination is never met. You can stop the infinite loop by setting the value of ```True``` to become ```False``` in different ways. In the code below, the infinite loop is broken by making ```x``` one less each time it goes through the loop, with ```x = x - 1```. \n",
+ "\n",
+ "See how the loop changes by switching the value of ```x```."
]
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Are you done yet?\n",
- "Almost done\n",
- "Almost done\n",
- "Almost done\n",
- "Almost done\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"print('Are you done yet?')\n",
- "x = 8 # Try changing this number\n",
"\n",
- "while x >= 5: # Code stops when x is less than 5\n",
- " x = x - 1 # Makes x one less each loop \n",
- " print(\"Almost done\")"
+ "x = 8 # Switch this number\n",
+ "\n",
+ "while x >= 5: # Code stops when x is less than or equal to 5\n",
+ " print(\"Almost done \" + str(x)) # Uses the value in the loop and changes it to a string\n",
+ " x = x - 1 # Makes x one less each loop "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tip: ```x = x - 1``` can be simplified into ```x -= 1```. They mean the same thing, it's just a little shorter. This is true for most other math operators. ```+=```, ```*=```, ```/=```, are all working equations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = 5\n",
+ "x *= 3\n",
+ "print(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another thing used in `while` loops is comparitive statements such as ```==``` symbols. Just one ```=``` means you are assigning a variable, but this shouldn't be done to check ```while``` loop conditions. Instead use the ```==``` to tell the `while` loop that you are just comparing the two values. For example:\n",
+ "\n",
+ "```python\n",
+ "while x == 0:\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = 0\n",
+ "\n",
+ "while x == 0:\n",
+ " print('First While Loop')\n",
+ " print(x)\n",
+ " x += 1\n",
+ " while x == 1:\n",
+ " print('Second (Nested) While Loop')\n",
+ " print(x)\n",
+ " x += 1\n",
+ "print('Third While Loop')\n",
+ "while x <= 5:\n",
+ " print(x)\n",
+ " x += 1"
]
},
{
@@ -42,37 +98,55 @@
"metadata": {},
"source": [
"### For Loops\n",
- "For loops can be very useful but are a little more complicated to understand. They are formatted ```for i in ```\\___```:```. The i is just traditional, you can enter any name you like right there. You can enter a number of things into the blank spot to complete the loop. Make sure to try new things into the loops.\n",
"\n",
+ "\n",
+ "\n",
+ "A `for` loop is needed when you know how many times the loop should cycle. A `while` loop is typically used when the number of cycles is unknown beforehand. A `for` loop is formatted ```for i in ```[1,2,3]```:```. The ```i``` is just a typical iterator. You can enter any variable name you like instead of `i`. The ```i```, is used to keep track of the current loop count. The ```in``` is saying what values the `for` loop iterates through, and the ```[1,2,3]``` is a list of the values that the `for` loop will use.\n",
+ "\n",
+ "You can enter a number of things besides the `[1,2,3]` to define the loop, and we'll go over those below. You can practice `for` loops by putting new values into that spot such as a list or range operator."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"#### Range\n",
- "The first thing that can go in for loops is range. It's formatted as ```range()``` and what ever number is in the ```()``` is how many times it will repeat. Try putting in a different number. You can also step for loops pattern by using 3 different numbers. The __first__ is the starting number, the __second__ is the last number, and the __third__ is what steps it takes. It looks like this: \n",
+ "One thing that can go in for loops is `range`. It's formatted as ```range()```, and the number is in the ```()``` is how many times it repeats. See what `range` does differently by putting in a different number. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(7): # Enter a new number\n",
+ " print('Cycle', i)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Range can have up to 3 numbers if you need more control. The __first__ is the starting number, the __second__ is the ending number, and the __third__ is what steps it will take. It looks something like this: \n",
"```python\n",
"for i in range(0,8,3):\n",
- "```"
+ "```\n",
+ "If you only put one value into a ```range()``` it will assume it is the __second__ number. So the example you did above, ```for i in range(7):```, really looks like this:\n",
+ "```python\n",
+ "for i in range(0,7,1):\n",
+ "```\n",
+ "Practice this concept by changing the numbers. Make the stepping number negative, but the first number bigger than the second. This should make the loop count __backwards__."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "for i in range(7): # Enter a new number\n",
- " print(\"This many times\")"
+ "for i in range(0,8,3): # Enter new numbers, then enter 8,0,-3\n",
+ " print('Cycle', i)"
]
},
{
@@ -80,29 +154,17 @@
"metadata": {},
"source": [
"#### String\n",
- "If you enter a string, like ```\"hello\"``` it will loop however many characters are in the string. "
+ "If you enter a string, like ```\"hello\"``` it will loop however many characters are in the string. Enter any new word you like."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "for i in \"hello\":\n",
- " print('This many times')"
+ "for i in \"hello\": # Loops for number of characters, practice by entering a new word\n",
+ " print('Cycle', i)"
]
},
{
@@ -110,34 +172,22 @@
"metadata": {},
"source": [
"#### Variable\n",
- "You can also enter a variable into a for loop. All it does is put in the for loop what was in the variable. So using ```for i in range(9):``` is the same as ```x = 9``` ```for i in range(x):```."
+ "You can also enter a variable to control the number of `for` loop cycles. The variable is used in the `for` loop to determine the number of cycles. The variable can be numbers, strings, or a list. Using ```for i in range(9):``` is the same as ```x = 9``` ```for i in range(x):```."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n",
- "This many times\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "x = \"not sure\"\n",
- "\n",
+ "x = \"chicken\"\n",
"for i in x:\n",
- " print(\"This many times\")"
+ " print(i)\n",
+ "\n",
+ "y = 9\n",
+ "for i in range(y):\n",
+ " print(i)"
]
},
{
@@ -145,59 +195,32 @@
"metadata": {},
"source": [
"#### Using ```i```\n",
- "Now here's the twist. In a for loop the ```i``` variable, in ```for i in ```\\___```:```, can be used during the repeated loop. Everytime the for loop goes again, it goes to the next value. For numbers, it starts at 0 so if the loop had a ```range(3)```, it would count 0, 1, 2. Strings it just prints the characters in order so ```\"hi\"``` would just be h, i. This can be used in a number of useful ways you may use later in this course. Now using stepping in range, we can print numbers in steps."
+ "Now here's the twist. In a `for` loop the ```i``` variable, in ```for i in ```(variable, range, string)```:```, can be used during the repeating loop. Everytime the `for` loop repeats again, it goes to the next value, and assigns it to `i`. You can use `i` in a loop to do different things based on the cycle number. \n",
+ "\n",
+ "This is similar to the ```while``` loop example where we used this code \n",
+ "```python\n",
+ "print(\"Almost done \" + str(x))```\n",
+ "\n",
+ "For numbers, ```for loops``` start counting at `0`. If the loop had a ```range(3)```, it would count 0, 1, 2. For strings, it just prints the individual characters in order so ```\"hi\"``` would just output __h__ then __i__. This can be used in a number of useful ways you may use later in this course. Now using stepping in range, we can print numbers in steps."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "c\n",
- "h\n",
- "e\n",
- "e\n",
- "s\n",
- "e\n",
- " \n",
- "0\n",
- "1\n",
- "2\n",
- "3\n",
- "4\n",
- " \n",
- "0\n",
- "2\n",
- "4\n",
- "6\n",
- "8\n",
- " \n",
- "10\n",
- "8\n",
- "6\n",
- "4\n",
- "2\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for i in 'cheese':\n",
" print(i)\n",
"\n",
- "print(\" \")\n",
+ "print(\" \") # Just to space it out, same for the 2 other print lines below\n",
" \n",
"for i in range(5):\n",
" print(i)\n",
- " \n",
"print(\" \")\n",
"\n",
- "for i in range(0,10,2): # See the explanation in the range section above\n",
+ "for i in range(0,10,2): # See the explanation in the range section above if this is confusing\n",
" print(i)\n",
- " \n",
"print(' ')\n",
"\n",
"for i in range(10,0,-2): # Backwards version of the loop above\n",
@@ -208,8 +231,44 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Make a while loop that blinks on the __Hot__ LED light, every half second. If the temperture gets above 28 degC, then turn on the light on, disconnect the lab after 3 seconds. You don't have to install tclab again, just use ```import tclab```. It's also useful to print what the temperture is to see if the program works."
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Create a ```for``` loop that prints out each letter of your name, __backwards__.\n",
+ "\n",
+ "```python\n",
+ "string[number]\n",
+ "``` \n",
+ "\n",
+ "This function finds the index of a string. ```\"hi\"[0]``` would be ```\"h\"``` and ```\"hi\"[1]``` would be ```\"i\"```.\n",
+ "\n",
+ "```python\n",
+ "len(string)\n",
+ "```\n",
+ "\n",
+ "This function gives the length of a string. ```len(\"waffles\")``` would be ```7```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "name = \"enter your name\"\n",
+ "\n",
+ "for i in range((len(name) - 1), \"end value\", \"step backwards\"): # Remember that python starts at 0 for indexing\n",
+ " print(name[i]) # Brackets [] refer to the index of a string"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you run code that turns on the heaters, make sure they work by putting your hand over them or observing the thermochromic paint turn pink when they are over 37°C. Go over Lesson 6 Functions TCLab again, if you don't remember them.\n",
+ "\n",
+ "Make a `while` loop that blinks on and off the __Hot__ LED light. If the temperature gets above 28°C, then turn on the light on, disconnect the lab after 3 seconds. You don't need to install `tclab` again once it is installed the first time, just use ```import tclab```. It's also useful to print the temperature to see if the program works and the temperature is changing."
]
},
{
@@ -218,15 +277,20 @@
"metadata": {},
"outputs": [],
"source": [
- "import tclab\n",
- "# Code usually goes after imports"
+ "import tclab # Imports usually come first\n",
+ "import time\n",
+ "lab = tclab.TCLab()\n",
+ "\n",
+ "# Set both heater powers on before the while loop\n",
+ "\n",
+ "# Make a while loop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Make a for loop that makes the LED percent go all the way from 100 to 0 in steps of 1."
+ "Make a `for` loop that makes the LED percent go all the way from 100 to 0 in steps of 1. Include a 0.1 second pause in between each step with `time.sleep(0.1)`."
]
},
{
@@ -234,7 +298,13 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "import tclab\n",
+ "import time\n",
+ "\n",
+ "for i in range(): # Needs to go from 100 to 0\n",
+ " time.sleep(0.1)"
+ ]
}
],
"metadata": {
@@ -253,7 +323,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/08. Input.ipynb b/08. Input.ipynb
index c28aa89..dd9fb9b 100644
--- a/08. Input.ipynb
+++ b/08. Input.ipynb
@@ -4,59 +4,53 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Input\n",
- "Input simply asks the person using the computer to enter a value. By default they will be stored as a __string__ so if you're doing a math equation you need to turn them into an __interger__. If you run the code below, it asks you for a number, then comes up as an error. You need to turn ```money``` __variable__ into a __interger__. \n",
+ "## 8. Input\n",
"\n",
- "(Reference the bottom of variables lesson, if you're stuck)"
+ "\n",
+ "\n",
+ "Input simply requests information from the person using the computer. By default the input value is stored as a __string__ so if you're doing a math equation you need to turn it into an __integer__ or __float__. In an egg temperature controller, certain types of eggs may require a different input and you could put that in your code to change the desired temperature. You could also use `input` to manually change power to the heaters.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=63P8tLh-j5o&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=63P8tLh-j5o&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Input\")\n",
+ "\n",
+ "If you run the code below, it asks you for a number, then comes up as an error. You need to turn ```eggs``` __variable__ into an __int__. \n",
+ "\n",
+ "Use ```int()``` and ```str()``` to change types.\n",
+ "(Reference the bottom of the **variables lesson**, if you get stuck)\n",
+ "\n",
+ ""
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "How much money do you have: 40\n",
- "After buying you have 20 dollars left\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "money = input(\"How much money do you have: \")\n",
+ "eggs = input(\"How many eggs do you have? \")\n",
"\n",
- "# Change money into an interger\n",
+ "# Change eggs into an integer\n",
"\n",
- "money = money - 20\n",
+ "eggs = eggs - 2\n",
"\n",
- "print(\"After spending you have \" + (money) + ' dollars left') # Change money back to a string"
+ "print(\"Your hatchlings are \" + (eggs) + ' because two are still developing') # Change eggs back to a string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can also make the input change into an __interger__ straight out of the input."
+ "You can also make the input change into an __int__ on the same line as the input."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enter a number: 4\n",
- "9\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"number = int(input('Enter a number: '))\n",
"\n",
@@ -67,35 +61,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Use a while loop to keep asking the user for input for the __Hot__ light power percentage. Make the code so if the entered value is 0, the program stops."
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Make a function that uses `input` for two values. The function should take two values, add the second number to the first number 20 times, then return the new value. Make sure to use a ```for``` loop in your function. Go back to the lesson on **Loops** or **Functions** if you get stuck."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define a function\n",
+ " # Use a for loop\n",
+ "\n",
+ "print() # Call the function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use a ```while``` loop to keep asking the user for input for the __Hot__ light power percentage. Make the code so if the entered value is 0, the program stops.\n",
+ "\n",
+ "(This is similar to something used in a real incubator, you could keep turning it to different temperatures, or turn off the system completely)"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TCLab version 0.4.6\n",
- "Could not connect at high speed, but succeeded at low speed.\n",
- "This may be due to an old TCLab firmware.\n",
- "New Arduino TCLab firmware available at:\n",
- " https://github.com/jckantor/TCLab-sketch\n",
- "NHduino connected on port COM5 at 9600 baud.\n",
- "TCLab Firmware Version 1.01.\n",
- "What percent: 4\n",
- "What percent: 80\n",
- "What percent: 50\n",
- "What percent: 2\n",
- "What percent: 0\n",
- "TCLab disconnected successfully.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tclab\n"
]
@@ -117,7 +115,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/09. If Statements.ipynb b/09. If Statements.ipynb
index f64ed52..ff4f237 100644
--- a/09. If Statements.ipynb
+++ b/09. If Statements.ipynb
@@ -4,72 +4,66 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# ```If``` Statements\n",
- "If and else statements tell the computer what to do based on the circumstances. \n",
+ "## 9. ```if``` Statements\n",
"\n",
- "## ```If```\n",
- "If is a certain condition, in this case if the number is greater than 16, it will print something."
+ "\n",
+ "\n",
+ "If and else statements tell the computer what to do based on a `True` condition.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=aNB2MQecPNk&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=aNB2MQecPNk&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Conditionals: if\")\n",
+ "\n",
+ "This could be used for the temperature of the egg by telling the heaters to turn on, if the egg is too cold.\n",
+ "\n",
+ "\n",
+ "\n",
+ "### ```if```\n",
+ "```if``` is a certain condition, in this case ```if``` the number is greater than 21, it will print something. Practice using ```if``` by running the code and putting in a value _more_ than 21."
]
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enter an age 30\n",
- "You can go on this ride\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "age = int(input(\"Enter an age \")) # Asks person to type in a number that will be assigned to x\n",
+ "days = int(input(\"Enter how long the egg has been in the incubator \"))\n",
"\n",
- "if age > 16: # Executes code if age is greater than 16\n",
- " print(\"You can go on this ride\")"
+ "if days < 21: # Print if days is less than 21 days\n",
+ " print(\"The chick is still developing\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## ```Else```\n",
- "Else statements will execute code if no ```if``` statements are executed. "
+ "### ```else```\n",
+ "Else statements will execute code if no ```if``` statements are executed. Check your understanding by running the code and putting in a value _less_ than 21."
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enter an age 20\n",
- "You can go on this ride\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "age = int(input(\"Enter an age \")) # Asks person to type in a number that will be assigned to x\n",
+ "days = int(input(\"Enter the days \"))\n",
"\n",
- "if age > 16: # Executes code if age is greater than 16\n",
- " print(\"You can go on this ride\") \n",
+ "if days > 21:\n",
+ " print(\"Chick should have hatched out of egg\") \n",
"\n",
- "else: # Executes only when the first if doesn't go\n",
- " print(\"Nope not old enough\")"
+ "else:\n",
+ " print(\"The chick is still developing\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Make your own\n",
- "Try making your own if and else statement using user input, with your own idea. Make sure it prints something when it's done."
+ "**Make your own `if` statement**\n",
+ "\n",
+ "Practice `if` statements by making your own `if` and `else` statement using user input, with your own idea. Make sure it prints something when it is done."
]
},
{
@@ -83,44 +77,33 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Elif\n",
- "```Elif```, or else if, statements are a continued if statement. When the first if doesn't work, it will go down to the next ```elif``` and so on. You can have as many ```elif``` statements as you need."
+ "## elif\n",
+ "```elif```, or else if, statements are a continued if statement. When the first condition isn't true, it will go down to the next ```elif``` and so on. You can have as many ```elif``` statements as you need. Check your understanding by using the right input value to get ```elif``` statement to run."
]
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Enter an age 10\n",
- "Nope not old enough\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "age = int(input('Enter an age ')) # Asks person to type in a number that will be assigned to x\n",
+ "days = int(input('Enter days '))\n",
"\n",
- "if age > 16: # Executes code if age is greater than 16\n",
- " print(\"You can go on this ride\")\n",
+ "if days > 21:\n",
+ " print('Chick should have hatched out of egg')\n",
" \n",
- "elif age > 12: # Executes when first doesn't work and age is greater than 14\n",
- " print('You can ride with an adult')\n",
+ "elif days > 18:\n",
+ " print('Chick should hatch soon')\n",
"\n",
"else: # Executes only when everything else doesn't work\n",
- " print(\"Nope not old enough\")"
+ " print('The chick is still developing')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- " You can also have an ```if``` statement inside another ```if``` statement. You can also use the __comparative__ statements from the _Python Basic Equations (Cheat Sheet)_. \n",
- " \n",
- " Try modifying the code to what you like and add other ```if``` statements for the missing ages."
+ "You can also have an ```if``` statement inside another ```if``` statement. You can also use the __comparative__ statements from the _Python Basic Knowledge_. These include things like ```and```, ```or```, and lots more. Go to that file if you want to see common ones and what they do. Practice modifying the code to what you like and add other ```if``` statements for the missing days."
]
},
{
@@ -129,57 +112,86 @@
"metadata": {},
"outputs": [],
"source": [
- "x = int(input(\"What's your age \"))\n",
+ "x = int(input(\"How long has the egg been in the incubator? \"))\n",
"\n",
- "if x < 20 and x > 12: # Uses comparative statement, must be true for both statements to execute code\n",
- " print(\"You're just a teen\")\n",
+ "if x < 21 and x > 18:\n",
+ " print('Chick should hatch soon')\n",
"\n",
- "elif x >= 50:\n",
- " if x > 100:\n",
- " print(\"You're REALLY old\")\n",
+ "elif x >= 21:\n",
+ " if x > 25:\n",
+ " print(\"Chick probably won't hatch\")\n",
" else:\n",
- " print(\"That's old\")\n",
+ " print('Chick should have hatched out of egg')\n",
"\n",
"else:\n",
- " print(\"Unknown Age Group\")"
+ " print('The chick is still developing')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Using ```if``` statements and a ```while``` or ```for``` loop, create code that turns on the __Hot__ LED when the temperture goes above 30 degC, and disconnects the kit when it goes above 35 degC, but also output the tempertures. Set the heater powers to what you feel is necessary. "
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Use ```if``` and ```elif``` statements to create a calculator using basic operators. Have the user input two values to be operated on. Have an option to use addition, subtraction, multiplication, and division on those two numbers. Make sure you print the finished answer. "
]
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TCLab version 0.4.9\n",
- "Could not connect at high speed, but succeeded at low speed.\n",
- "This may be due to an old TCLab firmware.\n",
- "New Arduino TCLab firmware available at:\n",
- "https://github.com/jckantor/TCLab-sketch\n",
- "NHduino connected on port COM6 at 9600 baud.\n",
- "TCLab Firmware Version 1.01.\n",
- "TCLab disconnected successfully.\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = float(input(\"Enter your first value: \"))\n",
+ "y = float(input(\"Enter your second value: \"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use ```if``` statements in a ```while``` loop. Create code that turns on the __LED__ when the temperature goes above 30°C, and disconnects the kit and turns off the __LED__ when the temperature goes above 35°C, and output the temperature every time the program loops. Set the heater powers with user ```input```. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import tclab\n",
"lab = tclab.TCLab()\n",
"\n",
- "\n",
+ "#while temp < 35\n",
+ " #if temp < 30\n",
"\n",
"lab.close()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make a guessing game that makes a random LED percent and the user has to type inputs to guess it. Make the program say higher or lower depending on the guess. When the guess is correct, flash the LED for a few seconds and disconnect.\n",
+ "\n",
+ "You can use ```while``` loops, ```for``` loops, ```if``` and ```else``` statements, ```input```, ```print```. Use any combination to create the program."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random # You need this for the random LED percent\n",
+ "\n",
+ "randomLED = random.randint(1,101) # This makes a random value between 1 and 100\n",
+ "\n",
+ "import tclab\n",
+ "import time\n"
+ ]
}
],
"metadata": {
@@ -198,7 +210,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/10. Lists and Tuples.ipynb b/10. Lists and Tuples.ipynb
index 9361c43..e2f6fc3 100644
--- a/10. Lists and Tuples.ipynb
+++ b/10. Lists and Tuples.ipynb
@@ -4,23 +4,26 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Lists\n",
- "Lists are a way of storing lots of information, and different types of information, in just one __variable__."
+ "## 10. Lists\n",
+ "\n",
+ "\n",
+ "\n",
+ "Lists are a way of storing a sequence of values and possibly different types of information in just one __variable__.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=1CPP8aB-8WU&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=1CPP8aB-8WU&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Lists and Tuples\")\n",
+ "\n",
+ "This is shown below, by using brackets \\[ ]. You could use lists, for an incubator, to keep information on which chickens are in different eggs. You could also use them to store a previous temperature of the incubator.\n",
+ "\n",
+ ""
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['Leghorn', 'Barnevelder', 'Plymouth Rock', 23, False]\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = [\"Leghorn\", 'Barnevelder', 'Plymouth Rock', 23, False]\n",
"print(chickens)"
@@ -30,22 +33,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can have a list in a list."
+ "You can also have a list in a list."
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['Leghorn', [True, 42], 'Barnevelder', 'Plymouth Rock']\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = ['Leghorn', [True, 42],'Barnevelder', \"Plymouth Rock\"]\n",
"print(chickens)"
@@ -55,23 +50,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can change one of the values in a list or print just one value. Remember how ```for``` loops start at 0? These do too."
+ "You can change one of the values in a list or print just one value. Remember how ```for``` loops start at `0`? A list index also starts at `0`."
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['Ancona', 'Plymouth Rock']\n",
- "Plymouth Rock\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = ['Leghorn', \"Barnevelder\", 'Plymouth Rock']\n",
"chickens[0] = 'Ancona' # Replaces Leghorn with Ancona\n",
@@ -84,24 +70,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can also use them in a ```for``` loop."
+ "You can also use a list to iterate with a ```for``` loop."
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Leghorn\n",
- "Barnevelder\n",
- "Plymouth Rock\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = [\"Leghorn\", 'Barnevelder', 'Plymouth Rock']\n",
"\n",
@@ -113,26 +89,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can even do a for loop of a list with if statements inside.\n",
+ "You can even do a `for` loop of a list with `if` statements inside.\n",
"\n",
"(The == symbol is comparing two values. If they are the same it will be ```True```.)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ancona\n",
- "Barnevelder\n",
- "Plymouth Rock\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = [\"Leghorn\", 'Barnevelder', 'Plymouth Rock']\n",
"\n",
@@ -147,17 +113,58 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "There are many more ways to use lists than these examples and all of them are online."
+ "**Useful List Functions**\n",
+ "\n",
+ "There are many more ways to use lists than these examples and all of them are online. Some of these include ```listName.append()```, ```listName.extend()```, and much more. Run this code to see what the ```.append()``` function does."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "myList = [\"yes\", \"no\"]\n",
+ "\n",
+ "myList.append(\"maybe\")\n",
+ "\n",
+ "print(myList)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Tuples\n",
- "Tuples are pretty much the same thing as lists, but can't change just one value as the list just did above. They use ```()``` to surrond their values in stead of ```[]``` for lists.\n",
+ "**Assigning New Values**\n",
"\n",
- "So you can't do something like this to them or you will get an error.\n",
+ "After you get all of your data points in a list, one way to execute the same equation to all values in a list is shown below. This can be very useful, for example if you wanted to turn your data points from Celcius to Fahrenheit. The example below shows a way to convert a whole list of values from Celcius to Fahrenheit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_celsius = [24, 21, 26, 30, 20] # List of Celsius temperature values\n",
+ "\n",
+ "data_fahrenheit = [i*(9/5) + 32 for i in data_celsius] # This converts to Fahrenheit\n",
+ "\n",
+ "formatted_data = [\"%.2f\" % i for i in data_fahrenheit] # This formats the data points to 2 decimal places\n",
+ "\n",
+ "print(formatted_data)\n",
+ "\n",
+ "for i in data_fahrenheit:\n",
+ " print(\"%.1f\" % i) # Prints out each data point to 1 decimal place"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Tuples**\n",
+ "\n",
+ "Tuples similar to lists, but they aren't designed to change values like in a list. They use ```()``` to surround their values instead of ```[]``` for lists. You can't add a value like this or you will get an error.\n",
"```python\n",
"chickens[0] = 'Ancona'\n",
"```"
@@ -165,17 +172,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "('Leghorn', 'Barnevelder', 'Plymouth Rock')\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"chickens = (\"Leghorn\", 'Barnevelder', 'Plymouth Rock')\n",
"print(chickens)"
@@ -185,15 +184,49 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can see this gets very complicated, pretty fast, with almost endless examples of what you can combine with what you've learned so far from previous lessons. You don't want a ton of lessons going over every example, so the best way to use this knowledge of when to use them. So if you get stuck, review the basics again, even on another python course, and break it down into smaller tasks like we talked about in the __Debugging__ lesson."
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Use user input to add temperature values to a list with the ```.append()``` function. One temperature value is collected each second for 20 seconds. If you see an error `You already have an open connection` then restart your Python kernel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab\n",
+ "import time\n",
+ "\n",
+ "lab = tclab.TCLab()\n",
+ "\n",
+ "# initialize lists\n",
+ "tm = []\n",
+ "T1 = []\n",
+ "\n",
+ "# turn on heater 1\n",
+ "lab.Q1(60)\n",
+ "\n",
+ "for i in range(20):\n",
+ " tm.append(i)\n",
+ " print(i,lab.T1)\n",
+ " # store the new temperature in the list T1 with .append()\n",
+ "\n",
+ " time.sleep(1)\n",
+ "\n",
+ "print(T1)\n",
+ "lab.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Activity\n",
- "Make a for loop that uses at least 10 values, stored in a list, of your choice to turn on the LED light and both heaters. It should end with 0 to make sure the light and heaters are off. Make sure their is an one second break in between each light change, and print out the tempertures."
+ "Make a ```for``` loop that uses at least **5 values**, stored in a ```list```, of your choice to turn on the __LED light__ and __both heaters__. It should end with **0** to make sure the light **and** heaters are off. \n",
+ "\n",
+ "Make sure there is a **3 second** break in between each light and heater changes, and print out the temperatures from the heaters every time the program loops."
]
},
{
@@ -201,7 +234,9 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "my_List = [\"5 different numbers\", 0] # Put 5 numbers in this list, last one should be zero"
+ ]
}
],
"metadata": {
@@ -220,7 +255,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/11. Dictionaries.ipynb b/11. Dictionaries.ipynb
new file mode 100644
index 0000000..f9dc7cd
--- /dev/null
+++ b/11. Dictionaries.ipynb
@@ -0,0 +1,129 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 11. Dictionaries\n",
+ "\n",
+ "\n",
+ "\n",
+ "Dictionaries are a list with key and value pairs.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=klEt6bU0bYg&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=klEt6bU0bYg&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Dictionaries\")\n",
+ "\n",
+ "The format for this method of storing values is curly brackets ```{}``` as seen below. After you assign a dictionary a name, you can enter values by putting a value followed by a colon to indicate the corresponding value. Multiple corresponding values in a dictionary are separated by commas. You can also print the corresponding value of the key, using brackets ```[]```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "dictionary = {} # An empty dictionary\n",
+ "print(dictionary)\n",
+ "\n",
+ "new_Dictionary = {1: 'First value', 2: 'Second value', 3: 'Third value'} # Dictionary with 3 keys / values\n",
+ "print(new_Dictionary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Many things can be done to dictionaries, including changing values, adding new keys, and more."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "new_Dictionary = {1: 'First value', 2: 'Second value', 3: 'Third value'}\n",
+ "print(new_Dictionary)\n",
+ "\n",
+ "print(new_Dictionary[2]) # Prints corresponding value for 2\n",
+ "\n",
+ "new_Dictionary[2] = \"This is different\" # Changes corresponding value for 2\n",
+ "\n",
+ "print(new_Dictionary[2])\n",
+ "\n",
+ "print(new_Dictionary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Creating another key is like changing a value, but instead of using the same key name, like ```new_Dictionary[2]``` you make a new one, such as ```new_Dictionary[4]```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "definitions = {'egg': 'an oval or round object laid by a female bird, reptile, fish, or invertebrate',\\\n",
+ " 'chick': 'a young bird, especially one newly hatched'}\n",
+ "print(definitions)\n",
+ "\n",
+ "definitions['hatchling'] = 'a young animal that has recently emerged from its egg'\n",
+ "print(definitions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Create a dictionary of animals that lay eggs (at least three) with a discription of them as the value. \n",
+ "\n",
+ "\n",
+ "\n",
+ "Make a while loop with an input to ask for the description of an animal. The `while` loop should stop if equal to ```\"none\"```. Make sure the loop prints the discription of the animal entered."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "animals = {}"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11. Plotting.ipynb b/11. Plotting.ipynb
deleted file mode 100644
index f3646af..0000000
--- a/11. Plotting.ipynb
+++ /dev/null
@@ -1,389 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting\n",
- "Plotting is putting values, mostly numbers, into a visual that makes it easier to see the full picture. You can plot with matlab. ```x``` for now is going to be __time__ and ```y``` or ```z``` will be the __data__.\n",
- "\n",
- "You can see all available functions using the code ```help(plt.plot)``` or use the file _Python Basic Equations_. You can also use the ```help()``` for any other function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt # Allows you to graph with a package\n",
- "\n",
- "x = [1, 2, 3, 4] # Time Spacing\n",
- "y = [3, 4, 2, 6] # List with line data\n",
- "z = [5, 1, 8, 3] # List with line data\n",
- "\n",
- "plt.plot(x,y) # Plots a graph with the list\n",
- "plt.plot(x,z,'r--') # 'r--' changes line to red, and to a dashed line"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Help on function plot in module matplotlib.pyplot:\n",
- "\n",
- "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
- " Plot y versus x as lines and/or markers.\n",
- " \n",
- " Call signatures::\n",
- " \n",
- " plot([x], y, [fmt], data=None, **kwargs)\n",
- " plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
- " \n",
- " The coordinates of the points or line nodes are given by *x*, *y*.\n",
- " \n",
- " The optional parameter *fmt* is a convenient way for defining basic\n",
- " formatting like color, marker and linestyle. It's a shortcut string\n",
- " notation described in the *Notes* section below.\n",
- " \n",
- " >>> plot(x, y) # plot x and y using default line style and color\n",
- " >>> plot(x, y, 'bo') # plot x and y using blue circle markers\n",
- " >>> plot(y) # plot y using x as index array 0..N-1\n",
- " >>> plot(y, 'r+') # ditto, but with red plusses\n",
- " \n",
- " You can use `.Line2D` properties as keyword arguments for more\n",
- " control on the appearance. Line properties and *fmt* can be mixed.\n",
- " The following two calls yield identical results:\n",
- " \n",
- " >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
- " >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
- " ... linewidth=2, markersize=12)\n",
- " \n",
- " When conflicting with *fmt*, keyword arguments take precedence.\n",
- " \n",
- " **Plotting labelled data**\n",
- " \n",
- " There's a convenient way for plotting objects with labelled data (i.e.\n",
- " data that can be accessed by index ``obj['y']``). Instead of giving\n",
- " the data in *x* and *y*, you can provide the object in the *data*\n",
- " parameter and just give the labels for *x* and *y*::\n",
- " \n",
- " >>> plot('xlabel', 'ylabel', data=obj)\n",
- " \n",
- " All indexable objects are supported. This could e.g. be a `dict`, a\n",
- " `pandas.DataFame` or a structured numpy array.\n",
- " \n",
- " \n",
- " **Plotting multiple sets of data**\n",
- " \n",
- " There are various ways to plot multiple sets of data.\n",
- " \n",
- " - The most straight forward way is just to call `plot` multiple times.\n",
- " Example:\n",
- " \n",
- " >>> plot(x1, y1, 'bo')\n",
- " >>> plot(x2, y2, 'go')\n",
- " \n",
- " - Alternatively, if your data is already a 2d array, you can pass it\n",
- " directly to *x*, *y*. A separate data set will be drawn for every\n",
- " column.\n",
- " \n",
- " Example: an array ``a`` where the first column represents the *x*\n",
- " values and the other columns are the *y* columns::\n",
- " \n",
- " >>> plot(a[0], a[1:])\n",
- " \n",
- " - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
- " groups::\n",
- " \n",
- " >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
- " \n",
- " In this case, any additional keyword argument applies to all\n",
- " datasets. Also this syntax cannot be combined with the *data*\n",
- " parameter.\n",
- " \n",
- " By default, each line is assigned a different style specified by a\n",
- " 'style cycle'. The *fmt* and line property parameters are only\n",
- " necessary if you want explicit deviations from these defaults.\n",
- " Alternatively, you can also change the style cycle using the\n",
- " 'axes.prop_cycle' rcParam.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " x, y : array-like or scalar\n",
- " The horizontal / vertical coordinates of the data points.\n",
- " *x* values are optional. If not given, they default to\n",
- " ``[0, ..., N-1]``.\n",
- " \n",
- " Commonly, these parameters are arrays of length N. However,\n",
- " scalars are supported as well (equivalent to an array with\n",
- " constant value).\n",
- " \n",
- " The parameters can also be 2-dimensional. Then, the columns\n",
- " represent separate data sets.\n",
- " \n",
- " fmt : str, optional\n",
- " A format string, e.g. 'ro' for red circles. See the *Notes*\n",
- " section for a full description of the format strings.\n",
- " \n",
- " Format strings are just an abbreviation for quickly setting\n",
- " basic line properties. All of these and more can also be\n",
- " controlled by keyword arguments.\n",
- " \n",
- " data : indexable object, optional\n",
- " An object with labelled data. If given, provide the label names to\n",
- " plot in *x* and *y*.\n",
- " \n",
- " .. note::\n",
- " Technically there's a slight ambiguity in calls where the\n",
- " second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
- " could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
- " the former interpretation is chosen, but a warning is issued.\n",
- " You may suppress the warning by adding an empty format string\n",
- " `plot('n', 'o', '', data=obj)`.\n",
- " \n",
- " \n",
- " Other Parameters\n",
- " ----------------\n",
- " scalex, scaley : bool, optional, default: True\n",
- " These parameters determined if the view limits are adapted to\n",
- " the data limits. The values are passed on to `autoscale_view`.\n",
- " \n",
- " **kwargs : `.Line2D` properties, optional\n",
- " *kwargs* are used to specify properties like a line label (for\n",
- " auto legends), linewidth, antialiasing, marker face color.\n",
- " Example::\n",
- " \n",
- " >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
- " >>> plot([1,2,3], [1,4,9], 'rs', label='line 2')\n",
- " \n",
- " If you make multiple lines with one plot command, the kwargs\n",
- " apply to all those lines.\n",
- " \n",
- " Here is a list of available `.Line2D` properties:\n",
- " \n",
- " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n",
- " alpha: float\n",
- " animated: bool\n",
- " antialiased: bool\n",
- " clip_box: `.Bbox`\n",
- " clip_on: bool\n",
- " clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None] \n",
- " color: color\n",
- " contains: callable\n",
- " dash_capstyle: {'butt', 'round', 'projecting'}\n",
- " dash_joinstyle: {'miter', 'round', 'bevel'}\n",
- " dashes: sequence of floats (on/off ink in points) or (None, None)\n",
- " drawstyle: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}\n",
- " figure: `.Figure`\n",
- " fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
- " gid: str\n",
- " in_layout: bool\n",
- " label: object\n",
- " linestyle: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
- " linewidth: float\n",
- " marker: unknown\n",
- " markeredgecolor: color\n",
- " markeredgewidth: float\n",
- " markerfacecolor: color\n",
- " markerfacecoloralt: color\n",
- " markersize: float\n",
- " markevery: unknown\n",
- " path_effects: `.AbstractPathEffect`\n",
- " picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
- " pickradius: float\n",
- " rasterized: bool or None\n",
- " sketch_params: (scale: float, length: float, randomness: float) \n",
- " snap: bool or None\n",
- " solid_capstyle: {'butt', 'round', 'projecting'}\n",
- " solid_joinstyle: {'miter', 'round', 'bevel'}\n",
- " transform: matplotlib.transforms.Transform\n",
- " url: str\n",
- " visible: bool\n",
- " xdata: 1D array\n",
- " ydata: 1D array\n",
- " zorder: float\n",
- " \n",
- " Returns\n",
- " -------\n",
- " lines\n",
- " A list of `.Line2D` objects representing the plotted data.\n",
- " \n",
- " \n",
- " See Also\n",
- " --------\n",
- " scatter : XY scatter plot with markers of varying size and/or color (\n",
- " sometimes also called bubble chart).\n",
- " \n",
- " \n",
- " Notes\n",
- " -----\n",
- " **Format Strings**\n",
- " \n",
- " A format string consists of a part for color, marker and line::\n",
- " \n",
- " fmt = '[color][marker][line]'\n",
- " \n",
- " Each of them is optional. If not provided, the value from the style\n",
- " cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
- " the data will be a line without markers.\n",
- " \n",
- " **Colors**\n",
- " \n",
- " The following color abbreviations are supported:\n",
- " \n",
- " ============= ===============================\n",
- " character color\n",
- " ============= ===============================\n",
- " ``'b'`` blue\n",
- " ``'g'`` green\n",
- " ``'r'`` red\n",
- " ``'c'`` cyan\n",
- " ``'m'`` magenta\n",
- " ``'y'`` yellow\n",
- " ``'k'`` black\n",
- " ``'w'`` white\n",
- " ============= ===============================\n",
- " \n",
- " If the color is the only part of the format string, you can\n",
- " additionally use any `matplotlib.colors` spec, e.g. full names\n",
- " (``'green'``) or hex strings (``'#008000'``).\n",
- " \n",
- " **Markers**\n",
- " \n",
- " ============= ===============================\n",
- " character description\n",
- " ============= ===============================\n",
- " ``'.'`` point marker\n",
- " ``','`` pixel marker\n",
- " ``'o'`` circle marker\n",
- " ``'v'`` triangle_down marker\n",
- " ``'^'`` triangle_up marker\n",
- " ``'<'`` triangle_left marker\n",
- " ``'>'`` triangle_right marker\n",
- " ``'1'`` tri_down marker\n",
- " ``'2'`` tri_up marker\n",
- " ``'3'`` tri_left marker\n",
- " ``'4'`` tri_right marker\n",
- " ``'s'`` square marker\n",
- " ``'p'`` pentagon marker\n",
- " ``'*'`` star marker\n",
- " ``'h'`` hexagon1 marker\n",
- " ``'H'`` hexagon2 marker\n",
- " ``'+'`` plus marker\n",
- " ``'x'`` x marker\n",
- " ``'D'`` diamond marker\n",
- " ``'d'`` thin_diamond marker\n",
- " ``'|'`` vline marker\n",
- " ``'_'`` hline marker\n",
- " ============= ===============================\n",
- " \n",
- " **Line Styles**\n",
- " \n",
- " ============= ===============================\n",
- " character description\n",
- " ============= ===============================\n",
- " ``'-'`` solid line style\n",
- " ``'--'`` dashed line style\n",
- " ``'-.'`` dash-dot line style\n",
- " ``':'`` dotted line style\n",
- " ============= ===============================\n",
- " \n",
- " Example format strings::\n",
- " \n",
- " 'b' # blue markers with default shape\n",
- " 'ro' # red circles\n",
- " 'g-' # green solid line\n",
- " '--' # dashed line with default color\n",
- " 'k^:' # black triangle_up markers connected by a dotted line\n",
- " \n",
- " .. note::\n",
- " In addition to the above described arguments, this function can take a\n",
- " **data** keyword argument. If such a **data** argument is given, the\n",
- " following arguments are replaced by **data[]**:\n",
- " \n",
- " * All arguments with the following names: 'x', 'y'.\n",
- " \n",
- " Objects passed as **data** must support item access (``data[]``) and\n",
- " membership test (`` in data``).\n",
- "\n"
- ]
- }
- ],
- "source": [
- "help(plt.plot) # Run if you want more functions"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Add Legend, x label, y label, title, adjust limits, save figure"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Activity\n",
- "Make a plot that comes from the lab's temperture"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/12. Plotting.ipynb b/12. Plotting.ipynb
new file mode 100644
index 0000000..698dab3
--- /dev/null
+++ b/12. Plotting.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 12. Plotting\n",
+ "\n",
+ "\n",
+ "\n",
+ "A plot is a visual representation of the data and is especially valuable to analyze data graphically. You can plot with the ```matplotlib``` package.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=yyLqmP1AEuU&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=yyLqmP1AEuU&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Plots\")\n",
+ "\n",
+ "In the incubator example, we may want to see how the temperature changes with time. The ```x```-axis may be __time__ and ```y```-axis may be the __data__. In an incubator, graphs could be used for a number of things. Some could be heater percentage, egg temperature, and much more.\n",
+ "\n",
+ "In the code below, graphs can have lines that are formatted as dashed lines:\n",
+ "```python\n",
+ "plt.plot(x,y,'r--')```\n",
+ "\n",
+ "Changing values for ```x``` and ```y``` will change the points that are plotted for the lines. The ```r``` states that a certain line is red, and the ```--``` tells that the same line is also dashed. You can get more line modifiers in the file *Basic Python Equations*, or scroll down to the help command."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt # shorten to plt\n",
+ "%matplotlib inline \n",
+ "\n",
+ "x = [1, 2, 3, 4] # Times\n",
+ "\n",
+ "y = [3, 4, 2, 6] # List with data\n",
+ "z = [5, 1, 8, 3] # List with data\n",
+ "\n",
+ "plt.plot(x,y) # Plots a graph with the list\n",
+ "plt.plot(x,z,'r--') # 'r--' changes line to red, and to a dashed line\n",
+ "plt.show() # Outputs graph"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Help Command"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "You can see all available functions by running the code ```help(plt.plot)``` (run after you import matplotlib) or find functions in the file *Python Basic Knowledge*. You can also use the ```help()``` for any other function in python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "help(plt.plot)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Make a plot using 3 lines with 5 data points each.\n",
+ "\n",
+ "```python\n",
+ "time = [1, 2, 3, 4, 5]\n",
+ "x = [5,4,0,2,3]\n",
+ "y = [3,-1,4,6,8]\n",
+ "z = [1,2,4,8,9]\n",
+ "```\n",
+ "\n",
+ "Use this data to plot (time, x), (time,y), and (time,z). Use the plotting examples or the plotting help above if you need help."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "time = [1, 2, 3, 4, 5]\n",
+ "x = [5,4,0,2,3]\n",
+ "y = [3,-1,4,6,8]\n",
+ "z = [1,2,4,8,9]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Customizing Plots**\n",
+ "\n",
+ "Plots are customized with labels (x-label, y-label, title, legend) with more lines of code such as:\n",
+ "\n",
+ "```python\n",
+ "plt.xlabel() # add x-label\n",
+ "plt.ylabel() # add y-label\n",
+ "plt.title() # add title\n",
+ "plt.legend() # add legend\n",
+ "```\n",
+ "\n",
+ "There are other ways to modify the plot such as adjusting the range of `y` values (`plt.ylim()`) or range of `x` values (`plt.xlim()`). Each function modifies the plot and they can be used in any order. \n",
+ "\n",
+ "You can also create sub-plots by first telling what grid size you'd like to use. In this case, we have 2 rows and 1 column with:\n",
+ "\n",
+ "```python\n",
+ "plt.subplot(2,1,1)\n",
+ "```\n",
+ "\n",
+ "The third number (1) indicates that we are now plotting in the first sub-plot on top. When we give another command:\n",
+ "\n",
+ "```python\n",
+ "plt.subplot(2,1,2)\n",
+ "```\n",
+ "\n",
+ "to begin making modifications to the bottom subplot. There are many examples that can be used as a template for creating [custom plots](https://matplotlib.org/gallery.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Exporting Plots**\n",
+ "\n",
+ "Plots can be exported as a static figure or animated to create a movie.\n",
+ "\n",
+ "\n",
+ "\n",
+ "A plot is exported by using the function:\n",
+ "\n",
+ "```python\n",
+ "plt.savefig('FileName.png')\n",
+ "```\n",
+ "\n",
+ "(Common file extensions for export are `.png`, `.jpg`, and `.eps`. Changing the name of the file extension changes the type and size of the file export. Portable Network Graphics(`.png`) files work well for import into a presentation or into a report document.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Where did it save the file?\n",
+ "\n",
+ "Sometimes it can be hard to find the directory where Python saved the figure. You can display your current run folder by running the following Python cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "print(\"Figure saved in current directory: \" + os.getcwd())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Activity\n",
+ "\n",
+ "\n",
+ "\n",
+ "Try the code below to add a title, x-label, and y-label. \n",
+ "\n",
+ "Adjust the `y` range to be between `-1` and `1.2`. Adjust the `x` range to be between `0` and `6`.\n",
+ "\n",
+ "The legend (`plt.legend`) gets values to run from lines `plt.plot(label='Your label')`.\n",
+ "\n",
+ "**Bonus:** Split the two plots into two subplots. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "x = np.linspace(0,6,100) # First value is starting point, next is end point, and last is how many points in between\n",
+ "y = np.sin(x)\n",
+ "z = np.cos(x)\n",
+ "\n",
+ "plt.title('Put title here') # Add a title\n",
+ "\n",
+ "plt.plot(x,y, 'r--', linewidth=3, label='y=sin(x)') # linewidth modifies the width of a line\n",
+ "plt.plot(x,z, 'k:', linewidth=2, label='What is a good label?') # label gives the line a label\n",
+ "\n",
+ "plt.legend() # Values given to legend comes from label='Your label' in lines above\n",
+ "\n",
+ "plt.xlim([0, 3]) # Set the x range: 0 to 6\n",
+ "plt.ylim([-1.5, 1.5]) # Set the y range: -1 to 1.2\n",
+ "\n",
+ "plt.ylabel('Change y label')\n",
+ "plt.xlabel('Change x label')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make a plot that comes from the lab's temperatures, 30 seconds of data. The code below sets heater one to 70 percent and heater two to 100 percent. It collects data for 30 seconds in the lists `tm`=Time, `T1`=Temperature 1, and `T2`=Temperature 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab\n",
+ "import time\n",
+ "\n",
+ "lab = tclab.TCLab()\n",
+ "\n",
+ "# initialize lists\n",
+ "tm = []\n",
+ "T1 = []\n",
+ "T2 = []\n",
+ "\n",
+ "# turn on heater 1\n",
+ "lab.Q1(70); lab.Q2(100)\n",
+ "\n",
+ "# collect data for 30 seconds\n",
+ "for i in range(30):\n",
+ " tm.append(i)\n",
+ " T1.append(lab.T1)\n",
+ " T2.append(lab.T2)\n",
+ " print(tm[i],T1[i],T2[i])\n",
+ " time.sleep(1)\n",
+ "lab.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make the plotting lines modified to your liking. Because you're using two heaters, you should have two lines to represent the two temperatures."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "plt.plot(tm,T1,'r--',label='T1')\n",
+ "# add plot for temperature 2\n",
+ "\n",
+ "plt.legend()\n",
+ "plt.xlabel('Add x-label')\n",
+ "plt.ylabel('Add y-label')\n",
+ "plt.savefig('temperatures.png')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Basic Python Knowledge.ipynb b/Basic Python Knowledge.ipynb
index 6c7479f..8c63794 100644
--- a/Basic Python Knowledge.ipynb
+++ b/Basic Python Knowledge.ipynb
@@ -1,13 +1,29 @@
{
"cells": [
{
+ "attachments": {
+ "search.png": {
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+ }
+ },
"cell_type": "markdown",
"metadata": {},
"source": [
- "## SHORTCUTS\n",
- "Press h to see the list of all shortcuts in Jupyter\n",
+ "## Internet Resource\n",
+ "\n",
+ "\n",
"\n",
- "## MATH\n",
+ "The internet is an important resource when it comes to general programming information. Learn to search information from tutorial sites and quickly find the information that you need. Specific websites that can be helpful are StackOverflow, Geeks for Geeks, and many more. Below are some helps for math, comparison, logical, plotting, and turtle methods as examples of the types of resources that you'll find. The hardest part about using the Internet as a resource is how to quickly sort through the information to find the specific answer that you need. Sometimes the hardest part is formulating the correct question.\n",
+ "\n",
+ "## Shortcuts\n",
+ "Press h to see or edit the list of every shortcut in Jupyter Notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Math\n",
"\n",
"\\+\tAddition: adds two operands\tx + y\n",
"\n",
@@ -21,7 +37,7 @@
"\n",
"%\tModulus: returns the remainder when first operand is divided by the second\tx % y\n",
"\n",
- "## COMPARE\n",
+ "## Compare\n",
"\n",
"\\>\tGreater than: True if left operand is greater than the right\tx > y\n",
"\n",
@@ -35,15 +51,27 @@
"\n",
"<=\tLess than or equal to: True if left operand is less than or equal to the right\tx <= y\n",
"\n",
- "## LOGICAL \n",
+ "## Logical \n",
"\n",
"and\tLogical AND: True if both the operands are true\tx and y\n",
"\n",
"or\tLogical OR: True if either of the operands is true\tx or y\n",
"\n",
- "not\tLogical NOT: True if operand is false\tnot x\n",
+ "not\tLogical NOT: True if operand is false\tnot x"
+ ]
+ },
+ {
+ "attachments": {
+ "plot.png": {
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+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## For matplotlib plotting\n",
"\n",
- "## FOR MATLAB PLOTTING\n",
+ "\n",
"\n",
"**Colors**\n",
" \n",
@@ -118,7 +146,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.5"
}
},
"nbformat": 4,
diff --git a/BeginPython.png b/BeginPython.png
new file mode 100644
index 0000000..6539ebe
Binary files /dev/null and b/BeginPython.png differ
diff --git a/README.md b/README.md
index 7820b83..7c901db 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,62 @@
-# Beginners TCLab
-### Simplified Version of the TCLab. No background knowledge needed. These lessons teach you python interactively from beginners level using the [Temperture Control Kit](http://apmonitor.com/pdc/index.php/Main/ArduinoTemperatureControl). Purchase is available at that link.
+### Begin Python 🐍 with the TCLab
-## [Download Anaconda to use Jupyter](https://docs.anaconda.com/anaconda/install/) or [watch a video on how to do it](https://youtu.be/LrMOrMb8-3s).
-### (Please use the two links above if you want to use the downloaded files, from this github, but can't run Jupyter.)
+Welcome to this introductory course on Python! This course is intended to help you start programming in Python from little or no prior experience. There are video tutorials for each exercise if you have questions along the way. One of the unique things about this course is that you work on basic elements to help you with a temperature control project. You will see your Python code have a real effect by adjusting heaters to maintain a target temperature, just like a thermostat in a home or office.
-[About Temperture Control Kit](http://apmonitor.com/pdc/index.php/Main/ArduinoTemperatureControl)
+[](https://www.youtube.com/watch?v=EO_YpBs8cs0 "Begin Python")
-[More on Temperture Control Kit](https://github.com/APMonitor/arduino/blob/master/README.md)
+One of the best ways to start or review a programming language is to work on a simple project. These exercises are designed to teach basic Python programming skills to help you design a temperature controller. Temperature control is found in many applications such as home or office HVAC, manufacturing processes, transportation, and life sciences. Even our bodies regulate temperature to a specific set point. This project is to regulate the temperature of the TCLab. Each TCLab has thermochromic (changes color with temperature) paint that turns from black to purple when the temperature reaches the target temperature of 37°C (99°F).
-[Original Programs](https://github.com/APMonitor/arduino)
+**[Final Project](https://github.com/APMonitor/begin_python/blob/master/XX.%20Final%20Project.ipynb) Objective**: Program the TCLab to maintain the temperature at 37°C. Display the heater level with an LED indicator as the program is adjusting the temperature. Create a plot of the temperature and heater values over a 10 minute evaluation period.
-[College Course](https://github.com/APMonitor/learn_python)
+To make the problem more concrete, suppose that you are designing a chicken egg incubator. Temperature, humidity, and egg rotation are all important to help the chicks develop. For this exercise, you will only focus on temperature control by adjusting the heater.
+**Topics**
+
+There are 12 lessons to help you with the objective of designing the temperature control for the incubator. The first thing that you will need is to [install Anaconda](https://github.com/APMonitor/begin_python/blob/master/00.%20Introduction.ipynb) to open and run the IPython notebook files in Jupyter. Any Python distribution or Integrated Development Environment (IDE) can be used (IDLE (python.org), Spyder, PyCharm, and others) but Jupyter notebook is required to open and run the IPython notebook (`.ipynb`) files. All of the IPython notebook (`.ipynb`) files can be [downloaded at this link](https://github.com/APMonitor/begin_python/archive/master.zip). Don't forget to unzip the folder (extract the archive) and copy it to a convenient location before starting.
+
+1. [Overview](https://github.com/APMonitor/begin_python/blob/master/01.%20Overview.ipynb)
+2. [Debugging](https://github.com/APMonitor/begin_python/blob/master/02.%20Debugging.ipynb)
+3. [Variables](https://github.com/APMonitor/begin_python/blob/master/03.%20Variables.ipynb)
+4. [Printing](https://github.com/APMonitor/begin_python/blob/master/04.%20Printing.ipynb)
+5. [Classes and Objects](https://github.com/APMonitor/begin_python/blob/master/05.%20Classes%20and%20Objects.ipynb)
+6. [Functions](https://github.com/APMonitor/begin_python/blob/master/06.%20Functions.ipynb)
+7. [Loops](https://github.com/APMonitor/begin_python/blob/master/07.%20Loops.ipynb)
+8. [Input](https://github.com/APMonitor/begin_python/blob/master/08.%20Input.ipynb)
+9. [If Statements](https://github.com/APMonitor/begin_python/blob/master/09.%20If%20Statements.ipynb)
+10. [Lists and Tuples](https://github.com/APMonitor/begin_python/blob/master/10.%20Lists%20and%20Tuples.ipynb)
+11. [Dictionaries](https://github.com/APMonitor/begin_python/blob/master/11.%20Dictionaries.ipynb)
+12. [Plotting](https://github.com/APMonitor/begin_python/blob/master/12.%20Plotting.ipynb)
+
+**Get TCLab**
+
+You will need a [TCLab kit](https://apmonitor.com/heat.htm) to complete the exercises and they are available for [purchase on Amazon](https://www.amazon.com/TCLab-Temperature-Control-Lab/dp/B07GMFWMRY).
+
+
+
+**Install Python**
+
+[Download and install Python](https://www.anaconda.com/products/individual) or watch a video on how to install Python.
+
+**[Install Python on Windows](https://youtu.be/_BHsM452vK0)**
+
+ [](https://www.youtube.com/watch?v=_BHsM452vK0 "Install Python on Windows")
+
+**[Install Python on MacOS](https://youtu.be/2VECcPofhP8)**
+
+ [](https://www.youtube.com/watch?v=2VECcPofhP8 "Install Python on MacOS")
+
+**[Install Python on Linux](https://youtu.be/eUq-6ZuwC_A)**
+
+ [](https://www.youtube.com/watch?v=eUq-6ZuwC_A "Install Python on Linux")
+
+There are additional instructions on how to [install Python and manage modules](https://apmonitor.com/pdc/index.php/Main/InstallPython).
+
+**Support**
+
+We would love to hear any feedback or problems you would like to send us! We are always trying to improve this course and would like to hear about your experience. We can be contacted at _eric@apmonitor.com_ or _john@apmonitor.com_.
+
+**Additional Resources**
+
+- [Temperature Control Lab (TCLab) Kit](https://apmonitor.com/pdc/index.php/Main/ArduinoTemperatureControl)
+- [Engineering Programming Course](https://apmonitor.com/pdc) with [Source Code](https://github.com/APMonitor/learn_python)
+- [Jupyter as interactive environment for Python, Julia, R](https://jupyter.org/)
diff --git a/TCLab Help.ipynb b/TCLab Help.ipynb
new file mode 100644
index 0000000..aa8443d
--- /dev/null
+++ b/TCLab Help.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "connections.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## TCLab Function Help\n",
+ "\n",
+ "\n",
+ "\n",
+ "#### Connect/Disconnect\n",
+ "```lab = tclab.TCLab()``` Connect and create new lab object, ```lab.close()``` disconnects lab.\n",
+ "\n",
+ "#### LED\n",
+ "```lab.LED()``` Percentage of output light for __Hot__ Light.\n",
+ "\n",
+ "#### Heaters\n",
+ "```lab.Q1()``` and ```lab.Q2()``` Percentage of power to heaters.\n",
+ "\n",
+ "#### Temperatures\n",
+ "```lab.T1``` and ```lab.T2``` Value of current heater tempertures in Celsius.\n",
+ "\n",
+ "_(All these functions are covered in depth in Lessons 5 and 6)_"
+ ]
+ },
+ {
+ "attachments": {
+ "debug.png": {
+ "image/png": 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+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Errors\n",
+ "\n",
+ "\n",
+ "\n",
+ "Submit an error to us at support@apmonitor.com so we can fix the problem and add it to this list. There is also a list of [troubleshooting items with frequently asked questions](https://apmonitor.com/pdc/index.php/Main/ArduinoSetup). If you get the error about already having an open connection to the TCLab, try restarting the **kernel**. Go to the top of the page to the **Kernel** tab, click it, then click restart."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How to Run a Cell\n",
+ "Use the symbol to run the needed cell, left of the program. If you don't see a symbol, try selecting the cell and holding `Ctrl`, then pressing `Enter`. Another option is clicking the \"Run\" button at the top of the page when the cell is selected."
+ ]
+ },
+ {
+ "attachments": {
+ "temperature.png": {
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+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install Temperature Control Lab\n",
+ "\n",
+ "\n",
+ "\n",
+ "This code trys to import `tclab`. If it if it fails to find the package, it installs the program with `pip`. The `pip` installation is required once to let you use the Temperature Control Lab for all of the exercises. It does not need to be installed again, even if the IPython session is restarted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# install tclab\n",
+ "try:\n",
+ " import tclab\n",
+ "except:\n",
+ " # Needed to communicate through usb port\n",
+ " !pip install --user pyserial\n",
+ " # The --user is put in for accounts without admin privileges\n",
+ " !pip install --user tclab \n",
+ " # restart kernel if this doesn't import\n",
+ " import tclab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Update Temperature Control Lab\n",
+ "Updates package to latest version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install tclab --upgrade --user"
+ ]
+ },
+ {
+ "attachments": {
+ "usb.png": {
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+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Connection Check\n",
+ "\n",
+ "\n",
+ "\n",
+ "The following code connects, reads temperatures 1 and 2, sets heaters 1 and 2, turns on the LED to 45%, and then disconnects from the TCLab. If an error is shown, try unplugging your lab and/or restarting Jupyter's kernel from the Jupyter notebook menu."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab\n",
+ "lab = tclab.TCLab()\n",
+ "print(lab.T1,lab.T2)\n",
+ "lab.Q1(50); lab.Q2(40)\n",
+ "lab.LED(45)\n",
+ "lab.close()"
+ ]
+ },
+ {
+ "attachments": {
+ "expert.png": {
+ "image/png": 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+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Another way to connect to the TCLab is the `with` statement. The advantage of the `with` statement is that the connection automatically closes if there is a fatal error (bug) in the code and the program never reaches the `lab.close()` statement. This prevents a kernel restart to reset the USB connection to the TCLab Arduino."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tclab\n",
+ "import time\n",
+ "with tclab.TCLab() as lab:\n",
+ " print(lab.T1,lab.T2)\n",
+ " lab.Q1(50); lab.Q2(40)\n",
+ " lab.LED(45)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Useful For TCLab.ipynb b/Useful For TCLab.ipynb
deleted file mode 100644
index c52c5c8..0000000
--- a/Useful For TCLab.ipynb
+++ /dev/null
@@ -1,125 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# TCLab Cheat Sheet\n",
- "\n",
- "#### Connect/Disconnect\n",
- "```lab = tclab.TCLab()``` to connect and create new lab object, ```lab.close()```\n",
- "\n",
- "#### LED\n",
- "```lab.LED()``` Percentage of output light for __Hot__ Light\n",
- "\n",
- "#### Heaters\n",
- "```lab.Q1()``` and ```lab.Q2()``` Percentage of power to heaters\n",
- "\n",
- "#### Temperture Readers\n",
- "```lab.T1``` and ```lab.T2``` Value of current heater tempertures\n",
- "\n",
- "_(All these functions are gone through in depth in lesson 6)_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to Use Programs Below\n",
- "Use the symbol to run the needed cell, left of the program."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Install Temperature Control Lab\n",
- "This code installs a program that will let you use your Temperature Control Kit."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# install tclab\n",
- "try:\n",
- " import tclab\n",
- "except:\n",
- " # Needed to communicate through usb port\n",
- " !pip install --user pyserial\n",
- " # The --user is put in for accounts without admin privileges\n",
- " !pip install --user tclab \n",
- " # restart kernel if this doesn't import\n",
- " import tclab"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Update Temperature Control Lab\n",
- "Updates package to latest version."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already up-to-date: tclab in c:\\users\\eric\\anaconda3\\lib\\site-packages (0.4.9)\n",
- "Requirement already satisfied, skipping upgrade: pyserial in c:\\users\\eric\\anaconda3\\lib\\site-packages (from tclab) (3.4)\n"
- ]
- }
- ],
- "source": [
- "!pip install tclab --upgrade"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Connection Check\n",
- "Connects then disconnects the tclab. If an error is shown, try unplugging your lab and/or restarting jupyter's kernal located above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import tclab\n",
- "lab = tclab.TCLab()\n",
- "lab.close()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/XX. Final Project.ipynb b/XX. Final Project.ipynb
new file mode 100644
index 0000000..12eebbe
--- /dev/null
+++ b/XX. Final Project.ipynb
@@ -0,0 +1,53 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Final Project\n",
+ "\n",
+ "The final project is to put together all of the basic parts of the course to help you complete a project to design the temperature control for an egg incubator. This is an opportunity for you to review the course material as you use many of the elements that you learned in Lessons 1-12.\n",
+ "\n",
+ "[Python Playlist on YouTube](https://www.youtube.com/watch?v=5Ry5fKdxNAw&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46)",
+ "\n",
+ "[](https://www.youtube.com/watch?v=5Ry5fKdxNAw&list=PLLBUgWXdTBDi3J05aMVG1knUpqRhkbc46 \"Incubator Project\")\n",
+ "\n",
+ "A good place to start is to create high-level instructions (pseudo-code) on what you want the program to do. Next, start filling in those parts of the code and test each part as you go. The final test is for 10 minutes but use a shorter time (maybe 10 seconds) as you test your code. Good luck!\n",
+ "\n",
+ "\n",
+ "\n",
+ "**Objective**: Program the TCLab to maintain the temperature (`T1`) at 37°C by adjusting the heater (`Q1`). Display the heater level (`Q1`) with an LED indicator as the program is adjusting the temperature. Create a plot of the temperature and heater values over a 10 minute evaluation period.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_config.yml b/_config.yml
new file mode 100644
index 0000000..2f7efbe
--- /dev/null
+++ b/_config.yml
@@ -0,0 +1 @@
+theme: jekyll-theme-minimal
\ No newline at end of file
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