ChartDirector Ver 5.0 (Python Edition)

Surface Wireframe


          

This example demonstrates the rectangular and triangular wireframes of a surface at different interpolation levels, configured using SurfaceChart.setShadingMode and SurfaceChart.setInterpolation.

Source Code Listing

[Standalone Version] pythondemo\surfacewireframe.py
#!/usr/bin/python
from pychartdir import *
import math

def createChart(img) :

    # The x and y coordinates of the grid
    dataX = [-2, -1, 0, 1, 2]
    dataY = [-2, -1, 0, 1, 2]

    # The values at the grid points. In this example, we will compute the values
    # using the formula z = square_root(15 - x * x - y * y).
    dataZ = [0] * (len(dataX) * len(dataY))
    for yIndex in range(0, len(dataY)) :
        y = dataY[yIndex]
        for xIndex in range(0, len(dataX)) :
            x = dataX[xIndex]
            dataZ[yIndex * len(dataX) + xIndex] = math.sqrt(15 - x * x - y * y)

    # Create a SurfaceChart object of size 380 x 340 pixels, with white (ffffff)
    # background and grey (888888) border.
    c = SurfaceChart(380, 340, '0xffffff', '0x888888')

    # Demonstrate various wireframes with and without interpolation
    if img == "0" :
        # Original data without interpolation
        c.addTitle("5 x 5 Data Points\nStandard Shading", "arialbd.ttf", 12)
        c.setContourColor('0x80ffffff')
    elif img == "1" :
        # Original data, spline interpolated to 40 x 40 for smoothness
        c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nStandard Shading",
            "arialbd.ttf", 12)
        c.setContourColor('0x80ffffff')
        c.setInterpolation(40, 40)
    elif img == "2" :
        # Rectangular wireframe of original data
        c.addTitle("5 x 5 Data Points\nRectangular Wireframe")
        c.setShadingMode(RectangularFrame)
    elif img == "3" :
        # Rectangular wireframe of original data spline interpolated to 40 x 40
        c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nRectangular Wireframe")
        c.setShadingMode(RectangularFrame)
        c.setInterpolation(40, 40)
    elif img == "4" :
        # Triangular wireframe of original data
        c.addTitle("5 x 5 Data Points\nTriangular Wireframe")
        c.setShadingMode(TriangularFrame)
    else :
        # Triangular wireframe of original data spline interpolated to 40 x 40
        c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nTriangular Wireframe")
        c.setShadingMode(TriangularFrame)
        c.setInterpolation(40, 40)

    # Set the center of the plot region at (200, 170), and set width x depth x height
    # to 200 x 200 x 150 pixels
    c.setPlotRegion(200, 170, 200, 200, 150)

    # Set the plot region wall thichness to 5 pixels
    c.setWallThickness(5)

    # Set the elevation and rotation angles to 20 and 30 degrees
    c.setViewAngle(20, 30)

    # Set the data to use to plot the chart
    c.setData(dataX, dataY, dataZ)

    # Output the chart
    c.makeChart("surfacewireframe%s.jpg" % img)


createChart("0")
createChart("1")
createChart("2")
createChart("3")
createChart("4")
createChart("5")

[CGI Version] pythondemo_cgi\surfacewireframe.py
#!/usr/bin/python
from pychartdir import *
import cgi, math

# Get HTTP query parameters
query = cgi.FieldStorage()

# The x and y coordinates of the grid
dataX = [-2, -1, 0, 1, 2]
dataY = [-2, -1, 0, 1, 2]

# The values at the grid points. In this example, we will compute the values using
# the formula z = square_root(15 - x * x - y * y).
dataZ = [0] * (len(dataX) * len(dataY))
for yIndex in range(0, len(dataY)) :
    y = dataY[yIndex]
    for xIndex in range(0, len(dataX)) :
        x = dataX[xIndex]
        dataZ[yIndex * len(dataX) + xIndex] = math.sqrt(15 - x * x - y * y)

# Create a SurfaceChart object of size 380 x 340 pixels, with white (ffffff)
# background and grey (888888) border.
c = SurfaceChart(380, 340, '0xffffff', '0x888888')

# Demonstrate various wireframes with and without interpolation
if query["img"].value == "0" :
    # Original data without interpolation
    c.addTitle("5 x 5 Data Points\nStandard Shading", "arialbd.ttf", 12)
    c.setContourColor('0x80ffffff')
elif query["img"].value == "1" :
    # Original data, spline interpolated to 40 x 40 for smoothness
    c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nStandard Shading",
        "arialbd.ttf", 12)
    c.setContourColor('0x80ffffff')
    c.setInterpolation(40, 40)
elif query["img"].value == "2" :
    # Rectangular wireframe of original data
    c.addTitle("5 x 5 Data Points\nRectangular Wireframe")
    c.setShadingMode(RectangularFrame)
elif query["img"].value == "3" :
    # Rectangular wireframe of original data spline interpolated to 40 x 40
    c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nRectangular Wireframe")
    c.setShadingMode(RectangularFrame)
    c.setInterpolation(40, 40)
elif query["img"].value == "4" :
    # Triangular wireframe of original data
    c.addTitle("5 x 5 Data Points\nTriangular Wireframe")
    c.setShadingMode(TriangularFrame)
else :
    # Triangular wireframe of original data spline interpolated to 40 x 40
    c.addTitle("5 x 5 Points - Spline Fitted to 40 x 40\nTriangular Wireframe")
    c.setShadingMode(TriangularFrame)
    c.setInterpolation(40, 40)

# Set the center of the plot region at (200, 170), and set width x depth x height to
# 200 x 200 x 150 pixels
c.setPlotRegion(200, 170, 200, 200, 150)

# Set the plot region wall thichness to 5 pixels
c.setWallThickness(5)

# Set the elevation and rotation angles to 20 and 30 degrees
c.setViewAngle(20, 30)

# Set the data to use to plot the chart
c.setData(dataX, dataY, dataZ)

# Output the chart
print("Content-type: image/jpeg\n")
binaryPrint(c.makeChart2(JPG))