gnuplot_test

gnuplot_test, a Python code which call gnuplot(), which reads, converts, and displays graphics information.

Ordinarily, the code is used interactively; that is, the user invokes the program, and then describes the plot to be created. The data for the plot is created either by a formula, or by reference to some data file created earlier.

It is often extremely important to be able to create graphical images of the results of an executable program. If the user is familiar with GNUPLOT, then the program can be modified to create a suitable data file, and, once the program has completed, the user can run GNUPLOT and use that data file to create a graph.

However, if the program is going to create the data file, it can just as easily create a file containing the appropriate commands to GNUPLOT that will create the desired image. In this way, the user can avoid having to recall the correct commands in the correct order to create the plot.

Thus, the program might create two files, called, perhaps data.txt and commands.txt. The command file would contain GNUPLOT commands, including one that refers to the data file itself. To create the graphics image, the user would simply type

        gnuplot < commands.txt
      

This directory includes some sample programs which carry out numerical calculations, creating data and command files that are used in exactly this way.

Some common plot types include:

• animation, a sequential display of images which are meant to suggest motion or evolution.
• bar, a display of values y(1:n) in which every value y(i) is represented by a bar of that height.
• bar3d, a display of values z(1:m,1:n) over a 2D grid, in which every value z(i,j) is represented by a bar of that height.
• barh, a display of values x(1:n) in which every value x(i) is represented by a horizontal bar of that length. The horizontal format can be more suitable for labeling bars when there are many of them.
• binary_tree, a connected directed graph with no circuits, in which each node has no more than 2 descendants.
• contour fill, a display of values z(x,y) in which the values of z are suggested by regions filled with color.
• contour line, a display of values z(x,y) in which the values of z are suggested by colored contour lines.
• decision_tree, a connected directed graph with no circuits, in which labeled nodes represent current states to be tested, and edges represent the choice or decision to move from one state to another.
• dendrogram, a connected directed graph with no circuits, which generally has a single root node, and illustrates the genealogy or relatedness of a set of objects.
• digraph, a "directed graph", nodes (possibly with defined locations, labels or colors), connected by directed edges (possibly with defined weights, labels, colors or linestyles).
• fill, a shape composed of polygons, each of which is filled with a particular color.
• graph, nodes (possibly with defined locations, labels or colors), connected by undirected edges (possibly with defined weights, labels, colors or linestyles).
• heatmap, cells (typically logical squares or cubes) with an associated scalar value, such as temperature. Each cell is filled in with a color on a scale of "cold" (blue) to "hot" (red). This is related to, but different than, a colorbar or a color-filled contour map.
• histogram, a display of values x in which the x range is divided into intervals, and the value y(i) represents the number of x values in the i-th interval, and a bar plot of intervals versus count is displayed.
• plot, a display of a single curve defined by a sequence of functional pairs (x,y(x)), or a function y=f(x) sampled over an interval, or a sequence of related pairs (x,y).
• plot3d, a 3D display of a single curve defined by a sequence of triples (x(i),y(i),z(i)), or a parametric function (x(t),y(t),z(t)) sampled over an interval.
• plots, several curves on a single display.
• scatter, a display of unconnected pairs (x,y), whose positions are indicated by markers.
• scatter3d, a 3D display of unconnected triples (x,y,z), whose positions are indicated by markers.
• scatters, a scatter plot involving several distinct sets of (x,y) pairs are displayed, distinguished by markers of different size, shape or color.
• subplots, a display involving two or more separate plots, shown as a single figure.
• surface, a 3D display of values z(x,y), represented as a surface.
• vector, a 2D display of vectors (u,v)(x,y), representing a gradient field, an ODE direction field, or a flow of some kind.

Licensing:

The information on this web page is distributed under the MIT license.

Languages:

gnuplot_test is available in a non-language specific version and a C version and a C++ version and a Fortran77 version and a Fortran90 version and a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

Reference:

1. Philipp Janert,
   Gnuplot in Action: Understanding Data with Graphs,
   Manning, 2010,
   ISBN13: 978-1-933988-39-8,
   LC: QA276.4.J37.
2. Thomas Williams, Colin Kelley,
   gnuplot 4.4, An Interactive Plotting Program,
   The Free Software Foundation, 2010.
3. The GNUPLOT Website.

Source Code:

• gnuplot_test.sh, runs all the tests;
• gnuplot_test.txt, the output file.

album_bar lists the year, and total number of music albums (LP's, cassettes, CD's and downloads) sold each year from 2007 to 2017. This data is plotted as a bar graph.

• album_commands.txt, the gnuplot commands.
• album_data.txt, a data file
• album_bar.py, reads and plots the data.
• album_bar.sh, runs the test.
• album_bar.txt, the output file.
• album_bar.png, the plot.

automobile_scatter contains prices and weights of cars available in 1985. A scatter plot is to be made.

• automobile_commands.txt, the gnuplot() command file.
• automobile_data.txt, a data file.
• automobile_scatter.py, reads the data and creates the gnuplot command file.
• automobile_scatter.sh, runs the test.
• automobile_scatter.txt, the output file.
• automobile_scatter.png, the plot.

brownian_2d_plot plots data representing 1000 steps of Brownian motion in two dimensions. Unlike a typical y=f(x) plot, this data wanders around the page.

• brownian_2d_commands.txt, the gnuplot() command file.
• brownian_2d_data.txt, the data file.
• brownian_2d_plot.py, plots the data
• brownian_2d_plot.sh, runs the test.
• brownian_2d_plot.txt, the output file.
• brownian_2d_plot.png, the plot.

bulgaria_plot takes data about the Bulgarian population over time and makes a plot of (time,population).

• bulgaria_commands.txt, the command file.
• bulgaria_data.txt, a data file; each line is a year and population count.
• bulgaria_plot.py, creates a gnuplot() command file to plot the data.
• bulgaria_plot.sh, runs the test.
• bulgaria_plot.txt, the output file.
• bulgaria_plot.png, the plot.

caffeine_scatter seeks a relationship between the percentage of blindness due to cataracts, and the daily intake of caffeine, in a number of countries. A scatter plot is produced.

• caffeine_commands.txt, the gnuplot() command file.
• caffeine_data.txt, cataract percentage, and milligrams of caffeine.
• caffeine_scatter.py, creates a gnuplot() command file to plot the data.
• caffeine_scatter.sh, runs the test.
• caffeine_scatter.txt, the output file.
• caffeine_scatter.png, the plot.

corkscrew_plot3d generates (X,Y,Z) points that lie along a 3D curve looking something like a corkscrew.

• corkscrew_commands.txt, commands to gnuplot that will create a plot.
• corkscrew_data.txt, the X, Y, Z coordinates.
• corkscrew_plot3d.py, creates the data, and writes the GNUPLOT files.
• corkscrew_plot3d.sh, runs the test.
• corkscrew_plot3d.txt, the output file.
• corkscrew_plot3d.png, the plot.

correlation_heat_map takes an 8x8 correlation matrix and draws an 8x8 grid of squares, colored white for the highest values, yellow, then red, and black for the lowest.

• correlation_commands.txt, gnuplot() commands that can create an image from the data file.
• correlation_data.txt, the data file.
• correlation_heat_map.py, creates a heat map from a correlation matrix.
• correlation_heat_map.sh, runs the test.
• correlation_heat_map.txt, the output file.
• correlation_heat_map.png, the plot.

corvette_scatter considers the resale price for Corvettes by model year, displaying the results as a scatter plot.

• corvette_commands.txt, commands to gnuplot that will create a plot.
• corvette_data.txt, the data.
• corvette_scatter.py, reads the data and writes the gnuplot command file.
• corvette_scatter.sh, runs the test.
• corvette_scatter.txt, the output file.
• corvette_scatter.png, the plot.

damped_sine evaluates the damped sine correlation function for a range of values of its argument. A plot of the function is desired.

• damped_sine_commands.txt, gnuplot() commands that can create an image from the data file.
• damped_sine_data.txt, a file containing the data values.
• damped_sine_plot.py, the source code.
• damped_sine_plot.sh, runs the test.
• damped_sine_plot.txt, the output file.
• damped_sine_plot.png, an image of the data.

drug_dosage_plots depicts measurements over 48 hours of the blood level concentration of a medicinal drug. The drug needs to reach a certain level to have an effect, but must not exceed the toxic level. A graphic is created which shows, on one plot, the concentration over time, the minimal effective level, and the maximum nonlethal leval.

• drug_dosage_commands.txt, commands to gnuplot that will create a plot.
• drug_dosage_data.txt, a data file of times and concentrations;
• drug_dosage_plots.py, reads the data, and plots the concentration level and two limiting straight lines.
• drug_dosage_plots.sh, runs the test.
• drug_dosage_plots.txt, the output file.
• drug_dosage_plots.png, the plot.

faithful_histogram contains the waiting time in minutes between successive eruptions of the Old Faithful geyser.

• faithful_commands.txt, the gnuplot command file.
• faithful_data.txt, waiting times and eruption durations.
• faithful_histogram.py, reads the data and writes a gnuplot command file.
• faithful_histogram.sh, runs the test.
• faithful_histogram.txt, the output file.
• faithful_histogram.png, the plot.

geyser_scatter looks for relations between the duration in minutes of the eruption and following waiting times for the Old Faithful geyser.

• geyser_commands.txt, the gnuplot command file.
• geyser_data.txt, waiting times and eruption durations.
• geyser_scatter.py, reads the data and writes a gnuplot command file.
• geyser_scatter.sh, runs the test.
• geyser_scatter.txt, the output file.
• geyser_scatter.png, the plot.

grid_surface records, on a 41x41 grid over [-2,2]x[-2,+2], the values z = exp(-(x^2+y^2)) * cos(0.25*x) * sin(y) * cos(2*(x^2+y^2)). The data is plotted as a surface.

• grid_commands.txt, commands to gnuplot that will create a plot.
• grid_data.txt, a table of x, y, z(x,y) values.
• grid_surface.py, creates the data, and writes the GNUPLOT files.
• grid_surface.sh, runs the test.
• grid_surface.txt, the output file.
• grid_surface.png, a PNG image of the plot.

humps_plot plots the function y=humps(x) over [0,2]. Instead of a file of data, gnuplot() works directly with a formula for the function.

• humps_commands.txt, the gnuplot() command file.
• humps_plot.py, creates a gnuplot() command file to plot the data.
• humps_plot.sh, runs the test.
• humps_plot.txt, the output file.
• humps_plot.png, the plot.

insect_scatter3d involves 3 data sets, each containing 10 measurements of 3 quantities for each of 3 species of insect. The quantities are first tarsus width, second tarsus width, and maximum width of the aedeagus. It is of interest to know whether these three measurements are enough to differentiate between members of the three species. A 3D scatter plot is created.

• insect_commands.txt, commands to gnuplot that will create a plot.
• insect_a_data.txt;
• insect_b_data.txt;
• insect_c_data.txt;
• insect_scatter3d.py, reads the data, writes the gnuplot command file.
• insect_scatter3d.sh, runs the test.
• insect_scatter3d.txt, the output file.
• insect_scatter3d.png, the plot.

least_squares_plots compares 15 pairs of (x,y) data, the least squares line that approximates their behavior, and a quadratic curve that is much closer to the data.

• least_squares_commands.txt, commands to gnuplot() that will create a plot.
• least_squares1_data.txt, coordinates of the 15 sample points;
• least_squares2_data.txt, coordinates of two points on the least squares approximating line;
• least_squares3_data.txt, coordinates of points on the exact curve;
• least_squares_plots.py, reads the data, writes the gnuplot command file.
• least_squares_plots.sh, runs the test.
• least_squares_plots.txt, the output file.
• least_squares_plots.png, the plot.

mario_fill makes a simple image of Mario, by constructing a grid of squares filled with color. Instead of a data file, gnuplot() is given a list of squares and the appropriate colors to assign them.

• mario_commands.txt, the gnuplot command file.
• mario_fill.py, makes a command file for input to gnuplot.
• mario_fill.sh, runs the test.
• mario_fill.txt, the output file.
• mario_fill.png, the plot.

mark_points adds green and red dots to mark the first and last data points in a plot.

• mark_points_commands.txt, the gnuplot command file.
• mark_points_data.txt, the main data file.
• mark_points_start_data.txt, the data file identifying the first point.
• mark_points_end_data.txt, the data file identifying the last point.
• mark_points.py, calls all the tests.
• mark_points.sh, runs all the tests.
• mark_points.txt, the output file.
• mark_points.png, an image of the damped sine data with the first data point marked by a green circle, the last one by a red circle.

nile_histogram makes a histogram of the yearly measurement of the height of the Nile at maximum flood. By lumping the data into bins, it is easier to see the range of flood heights, and the probability of various values in the range.

• nile_commands.txt, the gnuplot command file.
• nile_data.txt, a list of index and flood height for each year.
• nile_histogram.c, reads the data and writes a gnuplot command file.
• nile_histogram.sh, runs the test.
• nile_histogram.txt, the output file.
• nile_histogram.png, the plot.

orbital_fill makes a color contour plot of orbital data.

• orbital_commands.txt
• orbital_data.txt, the gnuplot data file, formatted with blank lines between rows, to follow the gnuplot table data convention.
• orbital_fill_contour.py, calls all the tests.
• orbital_fill_contour.sh, runs all the tests.
• orbital_fill_contour.txt, the output file.
• orbital_fill_contour.png, an image of the orbital data.

predator_plot3d models the populations of predators (foxes) and prey (rabbits) over time. A 3D plot of (time, rabbits, foxes) is created.

• predator_commands.txt, commands to gnuplot that will create a plot.
• predator_data.txt, a list of time, prey, and predator.
• predator_plot3d.py, creates the data, and writes the GNUPLOT files.
• predator_plot3d.sh, runs the test.
• predator_plot3d.txt, the output file.
• predator_plot3d.png, the plot.

president_heights_bar plots the heights of US presidents in inches, as a bar plot.

• president_heights_commands.txt, the gnuplot() command file.
• president_heights_data.txt, a data file;
• president_heights_bar.py, creates a gnuplot() command file to plot the data.
• president_heights_bar.sh, runs the test.
• president_heights_bar.txt, the output file.
• president_heights_bar.png, the plot.

price_plots reads a table of average monthly prices for 11 consumer products, between February 2008 and February 2018, as compiled by the Bureau of Labor Statistics, and plots together the prices of three of the items.

• price_commands.txt, the gnuplot() command file.
• price_data.txt, a data file;
• price_plots.py, creates a gnuplot() command file to plot the data.
• price_plots.sh, runs the test.
• price_plots.txt, the output file.
• price_plots.png, the plot.

snowfall_plot makes a line plot of the snowfall at Michigan Tech from 1890 to 2017.

• snowfall_commands.txt, the gnuplot() command file.
• snowfall_data.txt, the data.
• snowfall_plot.py, creates a gnuplot() command file to plot the data.
• snowfall_plot.sh, runs the test.
• snowfall_plot.txt, the output file.
• snowfall_plot.png, the plot.

string_simulation simulates the behavior of a string that obeys the wave equation. The shape of the string is to be plotted over successive times.

• string_simulation_commands.txt
• string_simulation_data.txt
• string_simulation.py, calls all the tests.
• string_simulation.sh, runs all the tests.
• string_simulation.txt, the output file.
• string_simulation.png

temperature_scatter3d shows temperatures and locations in the US where the January temperature was recorded.

• temperature_commands.txt, the gnuplot() command file.
• temperature_data.txt, the data file, containing temperature, -longitude and latitude.
• temperature_scatter3d.py, creates a gnuplot() command file to plot the data.
• temperature_scatter3d.sh, runs the test.
• temperature_scatter3d.txt, the output file.
• temperature_scatter3d.png, the plot.

track_bar considers an eye-tracking experiment in which the eye was focused on different regions for different durations. Each region has a text label. A bar graph is desired, in which the bar for each duration is given the appropriate label.

• track_commands.txt, the gnuplot() command file.
• track_data.txt, a data file including a region name, and a duration;
• track_bar.py, creates a gnuplot() command file to plot the data.
• track_bar.sh, runs the test.
• track_bar.txt, the output file.
• track_bar.png, the plot.

volcano_fill creates a color-filled contour plot of a volcano's height, using an MxN table of data.

• volcano_commands.txt
• volcano_data.txt
• volcano_fill.sh
• volcano_fill.png

vortex_subplots displays a pair of subplots of the pressure and velocity vectors associated with a vortex flow.

• vortex_commands.txt
• vortex_pressure_data.txt
• vortex_velocity_data.txt
• vortex_subplots.py, calls all the tests.
• vortex_subplots.sh, runs all the tests.
• vortex_subplots.txt, the output file.
• vortex_subplots.png