program first_scatter
use fortplot
implicit none
! Sample data: city population vs average temperature
real(wp) :: temperature(5) = [15.2_wp, 22.1_wp, 8.3_wp, 28.7_wp, 18.9_wp]
real(wp) :: population(5) = [2.1_wp, 5.3_wp, 1.2_wp, 8.7_wp, 3.4_wp] ! millions
call figure()
call scatter(temperature, population, label='Cities')
call title('Population vs Average Temperature')
call xlabel('Average Temperature (°C)')
call ylabel('Population (millions)')
call savefig('tutorial_step1.png')
end program
Key Points:
- Basic scatter(x, y) syntax
- Always include axis labels and title
- Use label parameter for legends
program bubble_chart
use fortplot
implicit none
real(wp) :: temperature(5) = [15.2_wp, 22.1_wp, 8.3_wp, 28.7_wp, 18.9_wp]
real(wp) :: population(5) = [2.1_wp, 5.3_wp, 1.2_wp, 8.7_wp, 3.4_wp]
real(wp) :: city_area(5) = [100.0_wp, 300.0_wp, 50.0_wp, 500.0_wp, 200.0_wp] ! km²
! Size mapping: larger areas = larger markers
real(wp) :: marker_sizes(5)
marker_sizes = city_area * 0.2_wp ! Scale for visibility
call figure(600, 400)
call scatter(temperature, population, s=marker_sizes, &
marker='circle', label='Cities by Area')
call title('Population vs Temperature (bubble size = area)')
call xlabel('Average Temperature (°C)')
call ylabel('Population (millions)')
call legend()
call savefig('tutorial_step2.png')
end program
Key Points:
- s parameter for size mapping
- Scale size values for visual clarity
- Bubble size represents third dimension of data
program color_scatter
use fortplot
implicit none
real(wp) :: temperature(5) = [15.2_wp, 22.1_wp, 8.3_wp, 28.7_wp, 18.9_wp]
real(wp) :: population(5) = [2.1_wp, 5.3_wp, 1.2_wp, 8.7_wp, 3.4_wp]
real(wp) :: happiness_index(5) = [7.2_wp, 8.1_wp, 6.8_wp, 7.9_wp, 8.3_wp] ! 1-10 scale
call figure(800, 600)
call scatter(temperature, population, c=happiness_index, &
colormap='viridis', marker='diamond', &
alpha=0.8_wp, label='Cities by Happiness')
call title('Population vs Temperature (color = happiness index)')
call xlabel('Average Temperature (°C)')
call ylabel('Population (millions)')
call legend()
call savefig('tutorial_step3.png')
end program
Key Points:
- c parameter for color mapping
- colormap chooses color scheme
- alpha adds transparency
- Colorbar automatically generated
program multidimensional_scatter
use fortplot
implicit none
real(wp) :: temperature(8) = [15.2_wp, 22.1_wp, 8.3_wp, 28.7_wp, &
18.9_wp, 12.4_wp, 31.2_wp, 5.8_wp]
real(wp) :: population(8) = [2.1_wp, 5.3_wp, 1.2_wp, 8.7_wp, &
3.4_wp, 1.8_wp, 6.2_wp, 0.9_wp]
real(wp) :: city_area(8) = [100.0_wp, 300.0_wp, 50.0_wp, 500.0_wp, &
200.0_wp, 80.0_wp, 400.0_wp, 30.0_wp]
real(wp) :: gdp_per_capita(8) = [45000.0_wp, 55000.0_wp, 38000.0_wp, 62000.0_wp, &
48000.0_wp, 41000.0_wp, 58000.0_wp, 35000.0_wp]
! Normalize for visualization
real(wp) :: marker_sizes(8), gdp_normalized(8)
marker_sizes = city_area * 0.15_wp + 10.0_wp ! Minimum size + scaling
gdp_normalized = (gdp_per_capita - 30000.0_wp) / 35000.0_wp ! 0-1 range
type(figure_t) :: fig
call fig%initialize(900, 700)
call fig%scatter(temperature, population, s=marker_sizes, c=gdp_normalized, &
colormap='plasma', marker='circle', alpha=0.7_wp, &
label='Cities (size=area, color=GDP/capita)')
call fig%set_title('Multi-dimensional City Analysis')
call fig%set_xlabel('Average Temperature (°C)')
call fig%set_ylabel('Population (millions)')
call fig%legend()
call fig%savefig('tutorial_step4.png')
end program
Key Points:
- Combine s and c for 4D visualization
- Normalize color data to 0-1 range
- Choose marker size range for readability
- Object-oriented API for complex plots
program marker_styling
use fortplot
implicit none
real(wp) :: x(3) = [1.0_wp, 2.0_wp, 3.0_wp]
real(wp) :: y(3) = [1.0_wp, 2.0_wp, 3.0_wp]
real(wp) :: sizes(3) = [50.0_wp, 60.0_wp, 55.0_wp]
type(figure_t) :: fig
call fig%initialize(600, 500)
! Custom face and edge colors
call fig%scatter([x(1)], [y(1)], s=[sizes(1)], marker='star', &
facecolor=[1.0_wp, 0.8_wp, 0.0_wp], & ! Golden
edgecolor=[0.0_wp, 0.0_wp, 0.0_wp], & ! Black outline
linewidth=2.0_wp, label='Gold Star')
call fig%scatter([x(2)], [y(2)], s=[sizes(2)], marker='hexagon', &
facecolor=[0.2_wp, 0.8_wp, 0.2_wp], & ! Green
edgecolor=[0.1_wp, 0.4_wp, 0.1_wp], & ! Dark green outline
linewidth=1.5_wp, label='Green Hex')
call fig%scatter([x(3)], [y(3)], s=[sizes(3)], marker='pentagon', &
facecolor=[0.8_wp, 0.2_wp, 0.8_wp], & ! Magenta
alpha=0.6_wp, label='Transparent Pentagon')
call fig%set_title('Custom Marker Styling')
call fig%legend()
call fig%savefig('tutorial_step5.png')
end program
Key Points:
- facecolor and edgecolor for custom colors
- linewidth controls edge thickness
- Mix opaque and transparent markers
- Different marker shapes for different categories
program scientific_example
use fortplot
implicit none
integer, parameter :: n_experiments = 50
real(wp) :: pressure(n_experiments), yield(n_experiments), &
temperature(n_experiments), reaction_time(n_experiments)
integer :: i
! Generate realistic experimental data
do i = 1, n_experiments
! Pressure: 1-10 bar
pressure(i) = 1.0_wp + 9.0_wp * real(i-1, wp) / real(n_experiments-1, wp)
! Temperature: 300-400 K (with some variation)
temperature(i) = 320.0_wp + 60.0_wp * sin(real(i, wp) * 0.2_wp) + &
10.0_wp * (real(i, wp) / real(n_experiments, wp) - 0.5_wp)
! Reaction time depends on pressure and temperature
reaction_time(i) = 10.0_wp + 20.0_wp / pressure(i) + &
0.1_wp * (temperature(i) - 350.0_wp)
! Yield depends on all factors with some noise
yield(i) = 60.0_wp + 20.0_wp * pressure(i) / 10.0_wp - &
0.1_wp * abs(temperature(i) - 360.0_wp) + &
real(mod(i * 7, 21) - 10, wp)
end do
type(figure_t) :: fig
call fig%initialize(900, 600)
call fig%scatter(pressure, yield, s=reaction_time, c=temperature, &
colormap='coolwarm', marker='circle', alpha=0.7_wp, &
label='Chemical Experiments')
call fig%set_title('Chemical Reaction Analysis')
call fig%set_xlabel('Pressure (bar)')
call fig%set_ylabel('Product Yield (%)')
call fig%legend()
call fig%savefig('tutorial_scientific.png')
end program
Key Points:
- Realistic scientific data generation
- Multiple correlated variables
- Use coolwarm colormap for temperature data
- Size represents reaction time, color represents temperature
program multiple_datasets
use fortplot
implicit none
! Dataset 1: Material A
real(wp) :: stress_a(4) = [100.0_wp, 200.0_wp, 300.0_wp, 400.0_wp]
real(wp) :: strain_a(4) = [0.001_wp, 0.003_wp, 0.006_wp, 0.012_wp]
real(wp) :: temp_a(4) = [25.0_wp, 25.0_wp, 25.0_wp, 25.0_wp] ! Room temp
! Dataset 2: Material B
real(wp) :: stress_b(4) = [120.0_wp, 180.0_wp, 280.0_wp, 350.0_wp]
real(wp) :: strain_b(4) = [0.0008_wp, 0.002_wp, 0.005_wp, 0.009_wp]
real(wp) :: temp_b(4) = [100.0_wp, 100.0_wp, 100.0_wp, 100.0_wp] ! Elevated temp
type(figure_t) :: fig
call fig%initialize(800, 600)
! Material A at room temperature
call fig%scatter(strain_a, stress_a, c=temp_a, &
colormap='coolwarm', marker='circle', &
s=[30.0_wp, 30.0_wp, 30.0_wp, 30.0_wp], &
label='Material A (25°C)')
! Material B at elevated temperature
call fig%scatter(strain_b, stress_b, c=temp_b, &
colormap='coolwarm', marker='square', &
s=[35.0_wp, 35.0_wp, 35.0_wp, 35.0_wp], &
label='Material B (100°C)')
call fig%set_title('Material Comparison: Stress vs Strain')
call fig%set_xlabel('Strain')
call fig%set_ylabel('Stress (MPa)')
call fig%legend()
call fig%savefig('tutorial_comparison.png')
end program
Key Points: - Multiple scatter series on same plot - Different markers distinguish datasets - Shared colormap for temperature comparison - Clear labeling for interpretation
Integration with contour plots
Performance Optimization:
Rendering speed optimization
Scientific Applications:
Publication-quality formatting
Backend Exploration:
Try modifying these examples with your own data to master scatter plot visualization!