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Sign in to get your referral linkFrom velocity vector fields and streamline plots to boundary layer profiles and turbulence spectra - create publication-ready CFD figures with AI-generated Python code.
Computational fluid dynamics generates massive datasets - velocity fields, pressure distributions, vorticity maps, and turbulence statistics. Each requires specialized visualization techniques to communicate flow physics clearly in publications and reports.
2D/3D quiver plots showing velocity direction and magnitude across grids
Particle path visualization revealing flow patterns and recirculation zones
Boundary layer comparisons - Blasius, power law, and Poiseuille solutions
Pressure and temperature distribution fields with colormaps
Vortex identification and strength visualization with colormapped contours
Energy spectrum density versus wavenumber in log-log scaling
Quiver, streamline, and contour plots from simulation or experimental PIV data.
Side-by-side comparison of different Reynolds numbers or mesh refinements.
Export to SVG/PDF sized for Journal of Fluid Mechanics or Physics of Fluids.
Overlay profiles across conditions with automated legends and annotations.
Potential flow around a cylinder with velocity magnitude contours, quiver arrows, and streamlines. The doublet solution produces the characteristic flow separation pattern used in introductory CFD.
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Click any dataset to load it in the editor and generate a visualization instantly.
Temperature distribution and thermal conductivity data
Frequency-domain signal data for spectral analysis
Structural vibration modes and damping coefficients
Controller step response with rise time and overshoot
Voltage-capacity curves at various C-rates
Three canonical boundary layer profiles compared side by side - Blasius laminar solution, the turbulent 1/7th power law, and Poiseuille parabolic pipe flow. Essential for any CFD validation study.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Interactive examples with ready-to-run code
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced styling
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],
Displays three-dimensional data in two dimensions using contour lines connecting points of equal value.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Create electromagnetic field distribution in a rectangular waveguide
x = np.linspace(0, 10, 200)
y = np.linspace(0, 6, 120)
X, Y = np.meshgrid(x, y)
# TE10 mode in rectangular waveguide - dominant mode
# Electric field pattern
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30Upload your CSV, describe the plot you need, and get publication-ready figures with full Python code in seconds.
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