Python_Tutorial.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Python_Tutorial.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"3N27Nl9jswsG","colab_type":"text"},"source":["# Python Tutorial for CEE 4530\n","\n","The goal of this tutorial is to help you learn some of the essential tools for data analysis and presentation that you will need throughout the semester.\n","\n","You will need to save this file in your google drive.\n","\n","Select the flask in the upper right corner and opt in to the new editor for Colab. This will make it a whole lot easier to write python code!\n","\n","For a more detailed tutorial see the [python tutorial](https://aguaclara.github.io/Textbook/Introduction/Python_Tutorial.html) used for CEE 4520.\n","\n","## Objectives\n","\n","1. illustrate how to load data from a file\n","1. learn about dataframes\n","1. perform a linear regression\n","1. create a well formatted graph\n","1. create an equation\n","1. add the graph to your document as a figure.\n","\n","See [Selecting Subsets of Data in Pandas](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c) for a good background on working with a pandas dafaframe."]},{"cell_type":"code","metadata":{"id":"puPJxn8suWz0","colab_type":"code","outputId":"d7d38a7c-39a6-494d-b66f-84f3a2316527","executionInfo":{"status":"ok","timestamp":1577895053818,"user_tz":300,"elapsed":18371,"user":{"displayName":"Monroe Weber-Shirk","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDmYNDq6ij0468RSHe1goXE_t9gbSPdq5OAsU4-ejQ=s64","userId":"08369668289863895493"}},"colab":{"base_uri":"https://localhost:8080/","height":681}},"source":["!pip install aguaclara\n","from aguaclara.core.units import unit_registry as u\n","import aguaclara as ac\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","from scipy import stats"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting aguaclara\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/96/d95acf30fa089f7f9c4ba879844e40df382d92f6af64e900c8af944cff50/aguaclara-0.1.14.tar.gz (85kB)\n","\u001b[K     |████████████████████████████████| 92kB 2.8MB/s \n","\u001b[?25hCollecting matplotlib==3.0.3\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/69/f5e05f578585ed9935247be3788b374f90701296a70c8871bcd6d21edb00/matplotlib-3.0.3-cp36-cp36m-manylinux1_x86_64.whl (13.0MB)\n","\u001b[K     |████████████████████████████████| 13.0MB 8.2MB/s \n","\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from aguaclara) (1.24.3)\n","Collecting pint==0.8.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/40/6938f7d544eef208a8183c2c80624289e8a4f4e0aea43f4658b9527077de/Pint-0.8.1.tar.gz (162kB)\n","\u001b[K     |████████████████████████████████| 163kB 46.3MB/s \n","\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from aguaclara) (0.25.3)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from aguaclara) (1.3.3)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.0.3->aguaclara) (1.1.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.0.3->aguaclara) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.0.3->aguaclara) (2.4.5)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.0.3->aguaclara) (2.6.1)\n","Requirement already satisfied: numpy>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.0.3->aguaclara) (1.17.4)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->aguaclara) (2018.9)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.0.3->aguaclara) (42.0.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib==3.0.3->aguaclara) (1.12.0)\n","Building wheels for collected packages: aguaclara, pint\n","  Building wheel for aguaclara (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for aguaclara: filename=aguaclara-0.1.14-cp36-none-any.whl size=97010 sha256=620328527cb22baa9ecfa44a476e9d2598a207593ea78a63474ecc175278c69f\n","  Stored in directory: /root/.cache/pip/wheels/1b/6f/d0/19b9938f102549db3fa7a030ad03f9153cd7f1cbc7b1c94101\n","  Building wheel for pint (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pint: filename=Pint-0.8.1-py2.py3-none-any.whl size=134155 sha256=8fa17e5f43682bf76aba3a87434e2fb38a2ff1fc1b6e0c064b9131deb8b656d6\n","  Stored in directory: /root/.cache/pip/wheels/b4/56/36/7c4d6a4e3660e233b95e3b0482551915cb184abca0f93ee1dd\n","Successfully built aguaclara pint\n","\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n","Installing collected packages: matplotlib, pint, aguaclara\n","  Found existing installation: matplotlib 3.1.2\n","    Uninstalling matplotlib-3.1.2:\n","      Successfully uninstalled matplotlib-3.1.2\n","Successfully installed aguaclara-0.1.14 matplotlib-3.0.3 pint-0.8.1\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["matplotlib","mpl_toolkits"]}}},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"gvRc7ZstslRo","colab_type":"code","outputId":"8e80c27c-c435-4fee-e8bd-5b41100d9af0","executionInfo":{"status":"ok","timestamp":1577895054274,"user_tz":300,"elapsed":18805,"user":{"displayName":"Monroe Weber-Shirk","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDmYNDq6ij0468RSHe1goXE_t9gbSPdq5OAsU4-ejQ=s64","userId":"08369668289863895493"}},"colab":{"base_uri":"https://localhost:8080/","height":500}},"source":["#The data file path is the raw data url on github. Happily python can read directly from a web page.\n","data_file_path = \"https://raw.githubusercontent.com/monroews/CEE4530/master/linear_regression.tsv\"\n","\n","#Now we create a pandas dataframe with the data in the file\n","df = pd.read_csv(data_file_path,delimiter='\\t')\n","#if you want to see what is in the dataframe you can print it!\n","print(df)\n","# The column headers can be accessed by using the list command\n","list(df)\n","columns = df.columns\n","print(columns)\n","#Below are three equally fine methods of extracting a column of data from the pandas dataframe.\n","\n","# 1) We can select a column by using the column header. Here we use the column header by selecting one array element from the list command.\n","x = df[list(df)[0]].values * u.mg/u.L\n","x\n","# 2) We can use the loc command to select all of the rows (: command) and the column with the label given by list(df)[0].\n","x = df.loc[:, list(df)[0]].values * u.mg/u.L\n","x\n","# 3) We can use the iloc command and select all of the rows in column 0.\n","x = df.iloc[:,0].values * u.mg/u.L\n","x\n","#The iloc method is simple and efficient, so I'll use that to get the y values.\n","y = df.iloc[:,1].values * u.mg/u.L\n","\n","# We will use the stats package to do the linear regression.\n","# It is important to note that the units are stripped from the x and y arrays when processed by the stats package.\n","slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n","\n","#We can add the units to intercept by giving it the same units as the y values.\n","intercept = intercept * y.units\n","# Note that slope is dimensionless for this case, but not in general!\n","# For the general case we can attach the correct units to slope.\n","slope = slope * y.units/x.units\n","\n","# Now create a figure and plot the data and the line from the linear regression.\n","fig, ax = plt.subplots()\n","# plot the data as red circles\n","ax.plot(x, y, 'ro', )\n","\n","#plot the linear regression as a black line\n","ax.plot(x, slope * x + intercept, 'k-', )\n","\n","# Add axis labels using the column labels from the dataframe\n","ax.set(xlabel=list(df)[0])\n","ax.set(ylabel=list(df)[1])\n","ax.legend(['Measured', 'Linear regression'])\n","ax.grid(True)\n","plt.show()"],"execution_count":2,"outputs":[{"output_type":"stream","text":["    standard (mg/L)  measured (mg/L)\n","0                 0           -0.176\n","1                 1             2.44\n","2                 2            3.571\n","3                 3            5.596\n","4                 4            8.363\n","5                 5            10.18\n","6                 6            11.79\n","7                 7            13.64\n","8                 8            16.13\n","9                 9            17.67\n","10               10            19.96\n","Index(['standard (mg/L)', 'measured (mg/L)'], dtype='object')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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EzNZVHybD0o63qZi6RJ11Mg4EknCk2SqZ7GWPygZUrV1K9enXeeustnnjiCXaOHk3bYsUu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\nQF555RWuvPLKzC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L4ZJxnJYxxK6AflDP+wzUR3Syc6VIKAW/iTFhWAYgRkSOq2lhExgC3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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"cjGGKNRCteyV","colab_type":"text"},"source":["Now we will display our figure in Colab. To have the figure show up for anyone who opens this Colab file we copy the image above and paste it into a text box and add a caption. To have the caption appear on a new line we insert an extra return below the figure.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"ioe4g9TuvHl_","colab_type":"text"},"source":["![image.png](data:image/png;base64,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)\n","\n","Figure 1: Captions are very important for figures. Captions go below figures on a new line. Place figures in their own text boxes to make it easier to edit the text without seeing the long code used to store the image.\n"]},{"cell_type":"markdown","metadata":{"id":"byIQf1kAvVgH","colab_type":"text"},"source":["Equations can be copied directly from the lab manual by clicking on the equation and requesting that it be displayed in Latex. Below is equation 2 from the [Laboratory Manual](https://monroews.github.io/EnvEngLabTextbook/Laboratory_Measurements/Laboratory_Measurements.html)\n","\n","$$A=\\log \\left(\\frac{P_{o} }{P} \\right)$$\n","\n","\n","You can also build your own beautiful Latex equations using [codecogs](https://www.codecogs.com/latex/eqneditor.php)."]},{"cell_type":"markdown","metadata":{"id":"c2g4SAp_vYJk","colab_type":"text"},"source":["# Assignment\n","\n","1.   Find a set of data that includes units (or make one up!) that could reasonably be fit with linear regression.\n","1.   Save the data to a tab delimited file in your atom project workspace.\n","1.   Load the data from the file into a Pandas dataframe.\n","1.   Plot the data and the linear regression line.\n","1.   Make sure to handle units carefully and to attach units to the linear regression line.\n","1.   Add a figure in Markdown showing the graph you produced.\n","1.   Show the linear regression equation that you obtained using latex."]}]}