Hoja de repaso: Python Data Import and Plotting

📋 Course Outline

1. Python Programming Context
2. Data File Organization
3. NumPy Data Import
4. Matplotlib Plot Construction
5. Points Vectors and Annotations

📖 1. Python Programming Context

★ Must-know

• Python is a programming language developed since 1989 whose simple syntax is based on indentation rather than symbols.

• This course focuses on two Python applications in physics and chemistry: importing data and representing data graphically.

Further detail

• Python can be used online with the Python laboratory or through distributions such as E-dypthon and Anaconda.

📖 2. Data File Organization

🔑 Key Concepts & Definitions

• Data table : Organized into rows and columns and commonly contains two columns for the data used in the course.

★ Must-know

• A data file may use a comma or a point to separate the integer and decimal parts of numerical values, so the separator format must be identified before import.

Further detail

• Before importing a file, the user must identify the number of lines to ignore, including possible column headings or units, and the columns to import.

• The example data file is named donnees.txt and contains measurements arranged in columns such as t, x, and y, with units shown in a separate line.

📖 3. NumPy Data Import

🔑 Key Concepts & Definitions

• numpy.loadtxt : () is a NumPy function used to retrieve numerical data from a text file and place it into an array.

★ Must-know

• The delimiter parameter specifies the character separating columns in the data file.

📌 The skiprows parameter specifies the number of lines to skip when importing the file.

• The usecols parameter selects one or more columns to import from the file.

Further detail

• The dtype parameter specifies the chosen data type for imported values.

• The loadtxt parameters comments, converters, unpack, and encoding respectively identify comment-starting characters, convert character strings into numbers, return data in separate columns when set to True, and specify the file encoding.

📖 4. Matplotlib Plot Construction

🔑 Key Concepts & Definitions

• matplotlib.pyplot : A Python library module that provides functions for representing data graphically, including point clouds.

★ Must-know

• A basic graphing program imports NumPy and matplotlib.pyplot, loads x and y data with numpy.loadtxt(), plots the data, adds labels, and displays the graph.

• The axis() function sets the graph axes, title() adds a graph title, and xlabel() and ylabel() label the horizontal and vertical axes.

Further detail

• The legend() function displays the graph legend and show() displays the graphs on screen.

• In the example, plt.plot(x, y, 'o-', color="red", marker="o", linewidth=1, label="vitesse") creates a red point-and-line plot labelled vitesse.

📖 5. Points Vectors and Annotations

★ Must-know

• The plot() function places points or connects abscissas and ordinates from the x and y lists, and its color, label, and marker parameters customize the display.

• The quiver() function draws vectors using the origin coordinates x and y, the vector components, and additional display parameters.

• The completed matplotlib graph represents the trajectory of a falling ball and includes several velocity vectors.

Further detail

• The example uses vx=[0.007, 0.013, 0.019], vy=[0.002, 0.002, -0.001], ax=[0.0, 0.02, 0.04], and ay=[1.503, 1.507, 1.505] to draw three velocity vectors.