Differences between revisions 38 and 117 (spanning 79 versions)

Sage Interactions - Calculus

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Contents

Sage Interactions - Calculus

Root Finding Using Bisection

by William Stein

Newton's Method

Note that there is a more complicated Newton's method below.

by William Stein

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2824-Double%20Precision%20Root%20Finding%20Using%20Newton's%20Method.sagews

A contour map and 3d plot of two inverse distance functions

by William Stein

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2823.sagews

A simple tangent line grapher

by Marshall Hampton

Numerical integrals with the midpoint rule

by Marshall Hampton

Numerical integrals with various rules

by Nick Alexander (based on the work of Marshall Hampton)

Some polar parametric curves

by Marshall Hampton. This is not very general, but could be modified to show other families of polar curves.

Function tool

Enter symbolic functions f, g, and a, a range, then click the appropriate button to compute and plot some combination of f, g, and a along with f and g. This is inspired by the Matlab funtool GUI.

Newton-Raphson Root Finding

by Neal Holtz

This allows user to display the Newton-Raphson procedure one step at a time. It uses the heuristic that, if any of the values of the controls change, then the procedure should be re-started, else it should be continued.

Coordinate Transformations

by Jason Grout

Taylor Series

by Harald Schilly

Illustration of the precise definition of a limit

by John Perry

I'll break tradition and put the image first. Apologies if this is Not A Good Thing.

A graphical illustration of sin(x)/x -> 1 as x-> 0

by Wai Yan Pong

Quadric Surface Plotter

by Marshall Hampton. This is pretty simple, so I encourage people to spruce it up. In particular, it isn't set up to show all possible types of quadrics.

The midpoint rule for numerically integrating a function of two variables

by Marshall Hampton

Gaussian (Legendre) quadrature

by Jason Grout

The output shows the points evaluated using Gaussian quadrature (using a weight of 1, so using Legendre polynomials). The vertical bars are shaded to represent the relative weights of the points (darker = more weight). The error in the trapezoid, Simpson, and quadrature methods is both printed out and compared through a bar graph. The "Real" error is the error returned from scipy on the definite integral.

Vector Calculus, 2-D Motion

By Rob Beezer

A fast_float() version is available in a worksheet

Vector Calculus, 3-D Motion

by Rob Beezer

Available as a worksheet

Multivariate Limits by Definition

by John Travis

http://sagenb.mc.edu/home/pub/97/

Directional Derivatives

This interact displays graphically a tangent line to a function, illustrating a directional derivative (the slope of the tangent line).

directional derivative.png

3D graph with points and curves

By Robert Marik

This sagelet is handy when showing local, constrained and absolute maxima and minima in two variables. Available as a worksheet

Approximating function in two variables by differential

by Robert Marik

Taylor approximations in two variables

by John Palmieri

This displays the nth order Taylor approximation, for n from 1 to 10, of the function sin(x² + y²) cos(y) exp(-(x²+y²)/2).

Volumes over non-rectangular domains

by John Travis

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2829.sagews

Lateral Surface Area

by John Travis

http://sagenb.mc.edu/home/pub/89/

Note that this works in Sage cell, but causes a zip file error in Jupyter

Parametric surface example

by Marshall Hampton

Note that this works in Sage cell, but causes a zip file error in Jupyter.

Line Integrals in 3D Vector Field

by John Travis

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2827-$%20%5Cint_%7BC%7D%20%5Cleft%20%5Clangle%20M,N,P%20%5Cright%20%5Crangle%20dr%20$%20=%20$%20%25s%20$.sagews

-  ⇤ ← Revision 38 as of 2011-06-16 18:10:11 → 
  Size: 45735
  Editor: was
  Comment:
+   ← Revision 117 as of 2020-08-11 14:10:09 → ⇥
  Size: 63144
  Editor: kcrisman
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 6:
-Line 9:
+Line 10:
-{{{
+{{{#!sagecell
-Line 26:
+Line 27:
-            raise ValueError, "f must have a sign change in the interval (%s,%s)"%(a,b)
+            raise ValueError("f must have a sign change in the interval (%s,%s)"%(a,b))
-Line 28:
+Line 29:
-html("<h1>Double Precision Root Finding Using Bisection</h1>")
@interact
def _(f = cos(x) - x, a = float(0), b = float(1), eps=(-3,(-16..-1))):
+pretty_print(html("<h1>Double Precision Root Finding Using Bisection</h1>"))
@interact
def _(f = cos(x) - x, a = float(0), b = float(1), eps=(-3,(-16, -1))):
-Line 32:
+Line 33:
-     print "eps = %s"%float(eps)
+     print("eps = %s" % float(eps))
-Line 34:
+Line 35:
-         time c, intervals = bisect_method(f, a, b, eps)
+         c, intervals = bisect_method(f, a, b, eps)
-Line 36:
+Line 37:
-         print "f must have opposite sign at the endpoints of the interval"
+         print("f must have opposite sign at the endpoints of the interval")
-Line 39:
+Line 40:
-         print "root =", c
         print "f(c) = %r"%f(c)
         print "iterations =", len(intervals)
+         print("root =", c)
         print("f(c) = %r" % f(x=c))
         print("iterations =", len(intervals))
-Line 56:
+Line 57:
-{{{
+https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2824-Double%20Precision%20Root%20Finding%20Using%20Newton's%20Method.sagews 

{{{#!sagecell
-Line 66:
+Line 69:
-    for i in xrange(maxiter):
+    for i in range(maxiter):
-Line 72:
+Line 75:
-html("<h1>Double Precision Root Finding Using Newton's Method</h1>")
@interact
def _(f = x^2 - 2, c = float(0.5), eps=(-3,(-16..-1)), interval=float(0.5)):
+     var('x')    
pretty_print(html("<h1>Double Precision Root Finding Using Newton's Method</h1>"))
@interact
def _(f = x^2 - 2, c = float(0.5), eps=(-3,(-16, -1)), interval=float(0.5)):
-Line 76:
+Line 81:
-     print "eps = %s"%float(eps)
     time z, iterates = newton_method(f, c, eps)
     print "root =", z
     print "f(c) = %r"%f(z)
+     print("eps = %s"%float(eps))
     z, iterates = newton_method(f, c, eps)
     print("root = {}".format(z))
     print("f(c) = %r" % f(x=z))
-Line 81:
+Line 86:
-     print "iterations =", n
     html(iterates)
     P = plot(f, z-interval, z+interval, rgbcolor='blue')
+     print("iterations = {}".format(n))
     pretty_print(html(iterates))
     P = plot(f, (x,z-interval, z+interval), rgbcolor='blue')
-Line 94:
+Line 99:
-http://sagenb.org/home/pub/2823/

{{{
+https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2823.sagews

{{{#!sagecell
-Line 112:
+Line 117:
-{{{
html('<h2>Tangent line grapher</h2>')
+{{{#!sagecell
pretty_print(html('<h2>Tangent line grapher</h2>'))
-Line 120:
+Line 125:
-    tanf = f(x0i) + df(x0i)*(x-x0i)
+    tanf = f(x=x0i) + df(x=x0i)*(x-x0i)
-Line 122:
+Line 127:
-    print 'Tangent line is y = ' + tanf._repr_()
+    print('Tangent line is y = ' + tanf._repr_())
-Line 124:
+Line 129:
-    fmax = f.find_maximum_on_interval(prange[0], prange[1])[0]
    fmin = f.find_minimum_on_interval(prange[0], prange[1])[0]
+    fmax = f.find_local_maximum(prange[0], prange[1])[0]
    fmin = f.find_local_minimum(prange[0], prange[1])[0]
-Line 132:
+Line 137:
-{{{
+{{{#!sagecell
-Line 141:
+Line 146:
-    midys = [func(x_val) for x_val in midxs]
+    midys = [func(x=x_val) for x_val in midxs]
-Line 147:
+Line 152:
-    min_y = find_minimum_on_interval(func,a,b)[0]
    max_y = find_maximum_on_interval(func,a,b)[0]
    html('<h3>Numerical integrals with the midpoint rule</h3>')
    html('$\int_{a}^{b}{f(x) dx} {\\approx} \sum_i{f(x_i) \Delta x}$')
    print "\n\nSage numerical answer: " + str(integral_numerical(func,a,b,max_points = 200)[0])
    print "Midpoint estimated answer: " + str(RDF(dx*sum([midys[q] for q in range(n)])))
+    min_y = min(0, find_local_minimum(func,a,b)[0])
    max_y = max(0, find_local_maximum(func,a,b)[0])
    pretty_print(html('<h3>Numerical integrals with the midpoint rule</h3>'))
    pretty_print(html(r'$\int_{a}^{b}{f(x) dx} {\approx} \sum_i{f(x_i) \Delta x}$'))
    print("\n\nSage numerical answer: " + str(integral_numerical(func,a,b,max_points = 200)[0]))
    print("Midpoint estimated answer: " + str(RDF(dx*sum([midys[q] for q in range(n)]))))
-Line 160:
+Line 165:
-{{{
# by Nick Alexander (based on the work of Marshall Hampton)
+{{{#!sagecell
-Line 171:
+Line 174:
-    t = sage.calculus.calculus.var('t')
+    t = var('t')
-Line 185:
+Line 188:
-            x = find_maximum_on_interval(func, q*dx + a, q*dx + dx + a)[1]
+            x = find_local_maximum(func, q*dx + a, q*dx + dx + a)[1]
-Line 188:
+Line 191:
-            x = find_minimum_on_interval(func, q*dx + a, q*dx + dx + a)[1]
+            x = find_local_minimum(func, q*dx + a, q*dx + dx + a)[1]
-Line 199:
+Line 202:
-    min_y = min(0, find_minimum_on_interval(func,a,b)[0])
    max_y = max(0, find_maximum_on_interval(func,a,b)[0])

    # html('<h3>Numerical integrals with the midpoint rule</h3>')
+    min_y = min(0, find_local_minimum(func,a,b)[0])
    max_y = max(0, find_local_maximum(func,a,b)[0])

    pretty_print(html('<h3>Numerical integral with the {} rule</h3>'.format(endpoint_rule)))
-Line 210:
+Line 213:
-    sum_html = "%s \cdot \\left[ %s \\right]" % (dx, ' + '.join([ "f(%s)" % cap(i) for i in xs ]))
    num_html = "%s \cdot \\left[ %s \\right]" % (dx, ' + '.join([ str(cap(i)) for i in ys ]))
+    sum_html = "%s \\cdot \\left[ %s \\right]" % (dx, ' + '.join([ "f(%s)" % cap(i) for i in xs ]))
    num_html = "%s \\cdot \\left[ %s \\right]" % (dx, ' + '.join([ str(cap(i)) for i in ys ]))
-Line 216:
+Line 219:
-    html(r'''
    <div class="math">
    \begin{align*}
      \int_{a}^{b} {f(x) \, dx} & = %s \\\
      \sum_{i=1}^{%s} {f(x_i) \, \Delta x}
      & = %s \\\
      & = %s \\\
      & = %s .
    \end{align*}
    </div>
    ''' % (numerical_answer, number_of_subdivisions, sum_html, num_html, estimated_answer))
+    pretty_print(html(r'''
    <div class="math"> 
    \begin{align*}      \int_{a}^{b} {f(x) \, dx} & = %s \\\      \sum_{i=1}^{%s} {f(x_i) \, \Delta x} & = %s \\\      & = %s \\\      & = %s . \end{align*} </div>''' 
                      % (numerical_answer, number_of_subdivisions, sum_html, num_html, estimated_answer)))
 Line 233:
-{{{
+{{{#!sagecell
 Line 237:
-    html('$r=' + latex(b+sin(a1*t)^n1 + cos(a2*t)^n2)+'$')
+    pretty_print(html('$r=' + latex(b+sin(a1*t)^n1 + cos(a2*t)^n2)+'$'))
 Line 246:
-{{{
+{{{#!sagecell
 Line 257:
-    except TypeError, msg:
        print msg[-200:]
        print "Unable to make sense of f,g, or a as symbolic expressions."
+    except TypeError as msg:
        print(msg[-200:])
        print("Unable to make sense of f,g, or a as symbolic expressions.")
 Line 324:
-    html('<center><font color="red">$f = %s$</font></center>'%latex(f))
    html('<center><font color="green">$g = %s$</font></center>'%latex(g))
    html('<center><font color="blue"><b>$h = %s = %s$</b></font></center>'%(lbl, latex(h)))
+    pretty_print(html('<center><font color="red">$f = %s$</font></center>'%latex(f)))
    pretty_print(html('<center><font color="green">$g = %s$</font></center>'%latex(g)))
    pretty_print(html('<center><font color="blue"><b>$h = %s = %s$</b></font></center>'%(lbl, latex(h))))
 Line 344:
-{{{
+{{{#!sagecell
 Line 374:
-                     vertical_alignment="bottom" if f(x0) < 0 else "top" )
+                     vertical_alignment="bottom" if f(x=x0) < 0 else "top" )
 Line 390:
-        fi = RR(f(xi))
        fpi = RR(df(xi))
+        fi = RR(f(x=xi))
        fpi = RR(df(x=xi))
 Line 426:
-                             vertical_alignment="bottom" if f(xip1) < 0 else "top" )
+                             vertical_alignment="bottom" if f(x=xip1) < 0 else "top" )
 Line 442:
-            html( t )
+            pretty_print(html( t ))
 Line 450:
-{{{
+{{{#!sagecell
 Line 452:
+# polar coordinates
#(x,y)=(u*cos(v),u*sin(v)); (u_range,v_range)=([0..6],[0..2*pi,step=pi/12])

# weird example
(x,y)=(u^2-v^2,u*v+cos(u*v)); (u_range,v_range)=([-5..5],[-5..5])

thickness=4
square_length=.05
-Line 455:
+Line 464:
-def trans(x=input_box(u^2-v^2, label="x=",type=SR), \
         y=input_box(u*v+cos(u*v), label="y=",type=SR), \
         t_val=slider(0,10,0.2,6, label="Length of curves"), \
         u_percent=slider(0,1,0.05,label="<font color='red'>u</font>", default=.7),
         v_percent=slider(0,1,0.05,label="<font color='blue'>v</font>", default=.7),
         u_range=input_box(range(-5,5,1), label="u lines"),
         v_range=input_box(range(-5,5,1), label="v lines")):
     thickness=4
     u_val = min(u_range)+(max(u_range)-min(u_range))*u_percent
     v_val = min(v_range)+(max(v_range)-min(v_range))*v_percent
     t_min = -t_val
     t_max = t_val
     g1=sum([parametric_plot((i,v), (v,t_min,t_max), rgbcolor=(1,0,0)) for i in u_range])
     g2=sum([parametric_plot((u,i), (u,t_min,t_max), rgbcolor=(0,0,1)) for i in v_range])
     vline_straight=parametric_plot((u,v_val), (u,t_min,t_max), rgbcolor=(0,0,1), linestyle='-',thickness=thickness)
     uline_straight=parametric_plot((u_val, v), (v,t_min,t_max),rgbcolor=(1,0,0), linestyle='-',thickness=thickness)
 
     (g1+g2+vline_straight+uline_straight).save("uv_coord.png",aspect_ratio=1, figsize=[5,5], axes_labels=['$u$','$v$'])
     xuv = fast_float(x,'u','v')
     yuv = fast_float(y,'u','v')
     xvu = fast_float(x,'v','u')
     yvu = fast_float(y,'v','u')
     g3=sum([parametric_plot((partial(xuv,i),partial(yuv,i)), (v,t_min,t_max), rgbcolor=(1,0,0)) for i in u_range])
     g4=sum([parametric_plot((partial(xvu,i),partial(yvu,i)), (u,t_min,t_max), rgbcolor=(0,0,1)) for i in v_range])
     uline=parametric_plot((partial(xuv,u_val),partial(yuv,u_val)),(v,t_min,t_max),rgbcolor=(1,0,0), linestyle='-',thickness=thickness)
     vline=parametric_plot((partial(xvu,v_val),partial(yvu,v_val)), (u,t_min,t_max), rgbcolor=(0,0,1), linestyle='-',thickness=thickness)
     (g3+g4+vline+uline).save("xy_coord.png", aspect_ratio=1, figsize=[5,5], axes_labels=['$x$','$y$'])
     print jsmath("x=%s, \: y=%s"%(latex(x), latex(y)))
     print "<html><table><tr><td><img src='cell://uv_coord.png'/></td><td><img src='cell://xy_coord.png'/></td></tr></table></html>"
+def trans(x=input_box(x, label="x",type=SR),
         y=input_box(y, label="y",type=SR),
         u_percent=slider(0,1,0.05,label="u", default=.7),
         v_percent=slider(0,1,0.05,label="v", default=.7),
         t_val=slider(0,10,0.2,6, label="Length"),
         u_range=input_box(u_range, label="u lines"),
         v_range=input_box(v_range, label="v lines")):

    x(u,v)=x
    y(u,v)=y
    u_val = min(u_range)+(max(u_range)-min(u_range))*u_percent
    v_val = min(v_range)+(max(v_range)-min(v_range))*v_percent
    t_min = -t_val
    t_max = t_val
    uvplot=sum([parametric_plot((i,v), (v,t_min,t_max), color='red',axes_labels=['u','v'],figsize=[5,5]) for i in u_range])
    uvplot+=sum([parametric_plot((u,i), (u,t_min,t_max), color='blue',axes_labels=['u','v']) for i in v_range])
    uvplot+=parametric_plot((u,v_val), (u,t_min,t_max), rgbcolor=(0,0,1), linestyle='-',thickness=thickness)
    uvplot+=parametric_plot((u_val, v), (v,t_min,t_max),rgbcolor=(1,0,0), linestyle='-',thickness=thickness)
    pt=vector([u_val,v_val])
    du=vector([(t_max-t_min)*square_length,0])
    dv=vector([0,(t_max-t_min)*square_length])
    uvplot+=polygon([pt,pt+dv,pt+du+dv,pt+du],color='purple',alpha=0.7)
    uvplot+=line([pt,pt+dv,pt+du+dv,pt+du],color='green')

    T(u,v)=(x,y)
    xuv = fast_float(x,'u','v')
    yuv = fast_float(y,'u','v')
    xvu = fast_float(x,'v','u')
    yvu = fast_float(y,'v','u')
    xyplot=sum([parametric_plot((partial(xuv,i),partial(yuv,i)), (v,t_min,t_max), color='red', axes_labels=['x','y'],figsize=[5,5]) for i in u_range])
    xyplot+=sum([parametric_plot((partial(xvu,i),partial(yvu,i)), (u,t_min,t_max), color='blue') for i in v_range])
    xyplot+=parametric_plot((partial(xuv,u_val),partial(yuv,u_val)),(v,t_min,t_max),color='red', linestyle='-',thickness=thickness)
    xyplot+=parametric_plot((partial(xvu,v_val),partial(yvu,v_val)), (u,t_min,t_max), color='blue', linestyle='-',thickness=thickness)
    jacobian(u,v)=abs(T.diff().det()).simplify_full()
    t_vals=[0..1,step=t_val*.01]
    vertices=[(x(*c),y(*c)) for c in [pt+t*dv for t in t_vals]]
    vertices+=[(x(*c),y(*c)) for c in [pt+dv+t*du for t in t_vals]]
    vertices+=[(x(*c),y(*c)) for c in [pt+(1-t)*dv+du for t in t_vals]]
    vertices+=[(x(*c),y(*c)) for c in [pt+(1-t)*du for t in t_vals]]
    xyplot+=polygon(vertices,color='purple',alpha=0.7)
    xyplot+=line(vertices,color='green')
    pretty_print(html("$T(u,v)=%s$"%(latex(T(u,v)))))
    pretty_print(html("Jacobian: $%s$"%latex(jacobian(u,v))))
    pretty_print(html("A very small region in $xy$ plane is approximately %0.4g times the size of the corresponding region in the $uv$ plane"%jacobian(u_val,v_val).n()))
    show(graphics_array([uvplot,xyplot]))
-Line 492:
+Line 517:
-{{{
+{{{#!sagecell
-Line 497:
+Line 522:
-dot = point((x0,f(x0)),pointsize=80,rgbcolor=(1,0,0))
@interact
def _(order=(1..12)):
+dot = point((x0,f(x=x0)),pointsize=80,rgbcolor=(1,0,0))
@interact
def _(order=[1..12]):
-Line 502:
+Line 527:
-    html('$f(x)\;=\;%s$'%latex(f))
    html('$\hat{f}(x;%s)\;=\;%s+\mathcal{O}(x^{%s})$'%(x0,latex(ft),order+1))
+    pretty_print(html(r'$f(x)\;=\;%s$'%latex(f)))
    pretty_print(html(r'$\hat{f}(x;%s)\;=\;%s+\mathcal{O}(x^{%s})$'%(x0,latex(ft),order+1)))
-Line 515:
+Line 540:
-{{{
html("<h2>Limits: <i>ε-δ</i></h2>")
html("This allows you to estimate which values of <i>δ</i> guarantee that <i>f</i> is within <i>ε</i> units of a limit.")
html("<ul><li>Modify the value of <i>f</i> to choose a function.</li>")
html("<li>Modify the value of <i>a</i> to change the <i>x</i>-value where the limit is being estimated.</li>")
html("<li>Modify the value of <i>L</i> to change your guess of the limit.</li>")
html("<li>Modify the values of <i>δ</i> and <i>ε</i> to modify the rectangle.</li></ul>")
html("If the blue curve passes through the pink boxes, your values for <i>δ</i> and/or <i>ε</i> are probably wrong.")
@interact
def delta_epsilon(f = input_box(default=(x^2-x)/(x-1)), a=input_box(default=1), L = input_box(default=1), delta=input_box(label="δ",default=0.1), epsilon=input_box(label="ε",default=0.1), xm=input_box(label="<i>x</i><sub>min</sub>",default=-1), xM=input_box(label="<i>x</i><sub>max</sub>",default=4)):
+{{{#!sagecell
pretty_print(html("<h2>Limits: <i>ε-δ</i></h2>"))
pretty_print(html("This allows you to estimate which values of <i>δ</i> guarantee that <i>f</i> is within <i>ε</i> units of a limit."))
pretty_print(html("<ul><li>Modify the value of <i>f</i> to choose a function.</li>"))
pretty_print(html("<li>Modify the value of <i>a</i> to change the <i>x</i>-value where the limit is being estimated.</li>"))
pretty_print(html("<li>Modify the value of <i>L</i> to change your guess of the limit.</li>"))
pretty_print(html("<li>Modify the values of <i>δ</i> and <i>ε</i> to modify the rectangle.</li></ul>"))
pretty_print(html("If the blue curve passes through the pink boxes, your values for <i>δ</i> and/or <i>ε</i> are probably wrong."))
@interact
def delta_epsilon(f = input_box(default=(x^2-x)/(x-1), label="$f$"), a=input_box(default=1, label="$a$"), L = input_box(default=1, label="$L$"), delta=input_box(label=r"$\delta$",default=0.1), epsilon=input_box(label=r"$\varepsilon$",default=0.1), xm=input_box(label=r"$x_{min}$",default=-1), xM=input_box(label=r"$x_{max}$",default=4)):
-Line 542:
+Line 567:
-{{{
+{{{#!sagecell
-Line 546:
+Line 571:
-    html('<h3>A graphical illustration of $\lim_{x -> 0} \sin(x)/x =1$</h3>')
    html('Below is the unit circle, so the length of the <font color=red>red line</font> is |sin(x)|')
    html('and the length of the <font color=blue>blue line</font> is |tan(x)| where x is the length of the arc.') 
    html('From the picture, we see that |sin(x)| $\le$ |x| $\le$ |tan(x)|.')
    html('It follows easily from this that cos(x) $\le$ sin(x)/x $\le$ 1 when x is near 0.')
    html('As $\lim_{x ->0} \cos(x) =1$, we conclude that $\lim_{x -> 0} \sin(x)/x =1$.')
+    pretty_print(html(r'<h3>A graphical illustration of $\lim_{x -> 0} \sin(x)/x =1$</h3>'))
    pretty_print(html(r'Below is the unit circle, so the length of the <font color=red>red line</font> is |sin(x)|'))
    pretty_print(html(r'and the length of the <font color=blue>blue line</font> is |tan(x)| where x is the length of the arc.'))
    pretty_print(html(r'From the picture, we see that |sin(x)| $\le$ |x| $\le$ |tan(x)|.'))
    pretty_print(html(r'It follows easily from this that cos(x) $\le$ sin(x)/x $\le$ 1 when x is near 0.'))
    pretty_print(html(r'As $\lim_{x ->0} \cos(x) =1$, we conclude that $\lim_{x -> 0} \sin(x)/x =1$.'))
-Line 568:
+Line 593:
-{{{
+{{{#!sagecell
-Line 572:
+Line 597:
-def quads(q = selector(quadrics.keys()), a = slider(0,5,1/2,default = 1)):
+def quads(q = selector(list(quadrics)), a = slider(0,5,1/2,default = 1)):
-Line 574:
+Line 599:
-    if a==0 or q=='Cone': html('<center>$'+latex(f)+' \ $'+ '(degenerate)</center>')
    else: html('<center>$'+latex(f)+'$ </center>')
+    if a==0 or q=='Cone': pretty_print(latex(f), "    (degenerate)")
    else: pretty_print(latex(f))
-Line 583:
+Line 608:
-{{{
+{{{#!sagecell
-Line 602:
+Line 627:
-sin,cos = math.sin,math.cos 
html("<h1>The midpoint rule for a function of two variables</h1>")
+pretty_print(html(r"<h1>The midpoint rule for a function of two variables</h1>"))
-Line 618:
+Line 643:
-    html("$$\int_{"+str(R16(y_start))+"}^{"+str(R16(y_end))+"} "+ "\int_{"+str(R16(x_start))+"}^{"+str(R16(x_end))+"} "+func+"\ dx \ dy$$")
    html('<p style="text-align: center;">Numerical approximation: ' + str(num_approx)+'</p>')
+    pretty_print(html(r"$\int_{"+str(R16(y_start))+r"}^{"+str(R16(y_end))+r"} "+ r"\int_{"+str(R16(x_start))+r"}^{"+str(R16(x_end))+r"} "+latex(SR(func))+r"\ dx \ dy$"))
    pretty_print(html(r'<p style="text-align: center;">Numerical approximation: ' + str(num_approx)+r'</p>'))
-Line 629:
+Line 654:
-{{{
from scipy.special.orthogonal import p_roots
+{{{#!sagecell
import scipy
import numpy
from scipy.special.orthogonal import p_roots, t_roots, u_roots
-Line 633:
+Line 660:
-from numpy import linspace
+from numpy import linspace, asanyarray, diff
-Line 639:
+Line 666:
-                'Chebyshev': {'w': 1/sqrt(1-x**2), 'xmin': -1, 'xmax': 1, 'func': t_roots},
                'Chebyshev2': {'w': sqrt(1-x**2), 'xmin': -1, 'xmax': 1, 'func': u_roots},
                'Trapezoid': {'w': 1, 'xmin': -1, 'xmax': 1, 'func': lambda n: (linspace(-1r,1,n), numpy.array([1.0r]+[2.0r]*(n-2)+[1.0r])*1.0r/n)},
                'Simpson': {'w': 1, 'xmin': -1, 'xmax': 1, 'func': lambda n: (linspace(-1r,1,n), numpy.array([1.0r]+[4.0r,2.0r]*int((n-3.0r)/2.0r)+[4.0r,1.0r])*2.0r/(3.0r*n))}}
+     'Chebyshev': {'w': 1/sqrt(1-x**2), 'xmin': -1, 'xmax': 1, 'func': t_roots},
     'Chebyshev2': {'w': sqrt(1-x**2), 'xmin': -1, 'xmax': 1, 'func': u_roots},
     'Trapezoid': {'w': 1, 'xmin': -1, 'xmax': 1,          'func': lambda n: (linspace(-1r,1,n), numpy.array([1.0r]+[2.0r]*(n-2)+[1.0r])*1.0r/n)},
     'Simpson': {'w': 1, 'xmin': -1, 'xmax': 1,          'func': lambda n: (linspace(-1r,1,n), 
            numpy.array([1.0r]+[4.0r,2.0r]*int((n-3.0r)/2.0r)+[4.0r,1.0r])*2.0r/(3.0r*n))}}
-Line 646:
+Line 676:
-    return polygon([(center-width2,0),(center+width2,0),(center+width2,height),(center-width2,height)],**kwds)
+    return polygon([(center-width2,0),
        (center+width2,0),(center+width2,height),(center-width2,height)],**kwds)
-Line 650:
+Line 681:
-def weights(n=slider(1,30,1,default=10),f=input_box(default=3*x+cos(10*x)),show_method=["Legendre", "Chebyshev", "Chebyshev2", "Trapezoid","Simpson"]):
+def weights(n=slider(1,30,1,default=10),f=input_box(default=3*x+cos(10*x),type=SR),
    show_method=["Legendre", "Chebyshev", "Chebyshev2", "Trapezoid","Simpson"]):
-Line 659:
+Line 691:
-    scaled_ff = fast_float(scaled_func)
+    scaled_ff = fast_float(scaled_func, 'x')
-Line 667:
+Line 699:
-    stems = sum(line([(x,0),(x,scaled_ff(x))],rgbcolor=(1-y,1-y,1-y),thickness=2,markersize=6,alpha=y) for x,y in coords_scaled)
    points = sum([point([(x,0),(x,scaled_ff(x))],rgbcolor='black',pointsize=30) for x,_ in coords])
+    stems = sum(line([(x,0),(x,scaled_ff(x))],rgbcolor=(1-y,1-y,1-y),
        thickness=2,markersize=6,alpha=y) for x,y in coords_scaled)
    points = sum([point([(x,0),
        (x,scaled_ff(x))],rgbcolor='black',pointsize=30) for x,_ in coords])
-Line 673:
+Line 707:
-    show(graph,xmin=plot_min,xmax=plot_max)
+    show(graph,xmin=plot_min,xmax=plot_max,aspect_ratio="auto")
-Line 678:
+Line 712:
-    y_val = map(scaled_ff,x_val)
+    y_val = [*map(scaled_ff,x_val)]
-Line 681:
+Line 715:
-    html("$$\sum_{i=1}^{i=%s}w_i\left(%s\\right)= %s\\approx %s =\int_{-1}^{1}%s \,dx$$"%(n,latex(f.subs(x="x_i")), approximation, integral, latex(scaled_func)))
+    pretty_print(html(r"$$\sum_{i=1}^{i=%s}w_i\left(%s\right)= %s\approx %s =\int_{-1}^{1}%s \,dx$$"%(n,
        latex(f), approximation, integral, latex(scaled_func))))
-Line 683:
+Line 718:
-    print "Trapezoid: %s, Simpson: %s, \nMethod: %s, Real: %s"%tuple(error_data)
+    print("Trapezoid: %s, Simpson: %s, \nMethod: %s, Real: %s" % tuple(error_data))
-Line 694:
+Line 729:
-{{{
+{{{#!sagecell
-Line 721:
+Line 756:
-path = parametric_plot( position(t).list(), (t, start, stop), color = "black" )
+path = parametric_plot( position.list(), (t, start, stop), color = "black" )
-Line 725:
+Line 760:
-velocity = derivative( position(t) )
acceleration = derivative(velocity(t))
+velocity = derivative(position, t)
acceleration = derivative(velocity, t)
-Line 728:
+Line 763:
-speed_deriv = derivative(speed)
+speed_deriv = derivative(speed, t)
-Line 730:
+Line 765:
-dT = derivative(tangent(t))
+dT = derivative(tangent, t)
-Line 751:
+Line 786:
-    pos_tzero = position(t0)
+    pos_tzero = position(t=t0)
-Line 755:
+Line 790:
-    speed_component = speed(t0)
    tangent_component = speed_deriv(t0)
    normal_component = sqrt( acceleration(t0).norm()^2 - tangent_component^2 )
+    speed_component = speed(t=t0)
    tangent_component = speed_deriv(t=t0)
    normal_component = sqrt( acceleration(t=t0).norm()^2 - tangent_component^2 )
-Line 763:
+Line 798:
-    tan = arrow(pos_tzero, pos_tzero + tangent(t0), rgbcolor=(0,1,0) )
    vel = arrow(pos_tzero, pos_tzero + velocity(t0), rgbcolor=(0,0.5,0))
    nor = arrow(pos_tzero, pos_tzero + normal(t0), rgbcolor=(0.5,0,0))
    acc = arrow(pos_tzero, pos_tzero + acceleration(t0), rgbcolor=(1,0,1))
    tancomp = arrow(pos_tzero, pos_tzero + tangent_component*tangent(t0), rgbcolor=(1,0,1) )
    norcomp = arrow(pos_tzero, pos_tzero + normal_component*normal(t0), rgbcolor=(1,0,1))
+    tan = arrow(pos_tzero, pos_tzero + tangent(t=t0), rgbcolor=(0,1,0) )
    vel = arrow(pos_tzero, pos_tzero + velocity(t=t0), rgbcolor=(0,0.5,0))
    nor = arrow(pos_tzero, pos_tzero + normal(t=t0), rgbcolor=(0.5,0,0))
    acc = arrow(pos_tzero, pos_tzero + acceleration(t=t0), rgbcolor=(1,0,1))
    tancomp = arrow(pos_tzero, pos_tzero + tangent_component*tangent(t=t0), rgbcolor=(1,0,1) )
    norcomp = arrow(pos_tzero, pos_tzero + normal_component*normal(t=t0), rgbcolor=(1,0,1))
-Line 790:
+Line 825:
-    print "Position vector defined as r(t)=", position(t)
    print "Speed is ", N(speed(t0))
    print "Curvature is ", N(curvature)
+    print("Position vector defined as r(t)={}".format(position))
    print("Speed is {}".format(N(speed(t=t0))))
    print("Curvature is {}".format(N(curvature)))
-Line 806:
+Line 841:
-{{{
+{{{#!sagecell
-Line 822:
+Line 857:
+assume(t, 'real')
-Line 839:
+Line 875:
-path = parametric_plot3d( position(t).list(), (t, start, stop), color = "black" )
+path = parametric_plot3d( position.list(), (t, start, stop), color = "black" )
-Line 843:
+Line 879:
-velocity = derivative( position(t) )
acceleration = derivative(velocity(t))
+velocity = derivative( position, t)
acceleration = derivative(velocity, t)
-Line 846:
+Line 882:
-speed_deriv = derivative(speed)
+speed_deriv = derivative(speed, t)
-Line 848:
+Line 884:
-dT = derivative(tangent(t))
+dT = derivative(tangent, t)
-Line 851:
+Line 887:
-## dB = derivative(binormal(t))
+## dB = derivative(binormal, t)
-Line 872:
+Line 908:
-    pos_tzero = position(t0)
+    pos_tzero = position(t=t0)
-Line 876:
+Line 912:
-    speed_component = speed(t0)
    tangent_component = speed_deriv(t0)
    normal_component = sqrt( acceleration(t0).norm()^2 - tangent_component^2 )
+    speed_component = speed(t=t0)
    tangent_component = speed_deriv(t=t0)
    normal_component = sqrt( acceleration(t=t0).norm()^2 - tangent_component^2 )
-Line 885:
+Line 921:
-    tan = arrow3d(pos_tzero, pos_tzero + tangent(t0), rgbcolor=(0,1,0) )
    vel = arrow3d(pos_tzero, pos_tzero + velocity(t0), rgbcolor=(0,0.5,0))
    nor = arrow3d(pos_tzero, pos_tzero + normal(t0), rgbcolor=(0.5,0,0))
    bin = arrow3d(pos_tzero, pos_tzero + binormal(t0), rgbcolor=(0,0,0.5))
    acc = arrow3d(pos_tzero, pos_tzero + acceleration(t0), rgbcolor=(1,0,1))
    tancomp = arrow3d(pos_tzero, pos_tzero + tangent_component*tangent(t0), rgbcolor=(1,0,1) )
    norcomp = arrow3d(pos_tzero, pos_tzero + normal_component*normal(t0), rgbcolor=(1,0,1))
+    tan = arrow3d(pos_tzero, pos_tzero + tangent(t=t0), rgbcolor=(0,1,0) )
    vel = arrow3d(pos_tzero, pos_tzero + velocity(t=t0), rgbcolor=(0,0.5,0))
    nor = arrow3d(pos_tzero, pos_tzero + normal(t=t0), rgbcolor=(0.5,0,0))
    bin = arrow3d(pos_tzero, pos_tzero + binormal(t=t0), rgbcolor=(0,0,0.5))
    acc = arrow3d(pos_tzero, pos_tzero + acceleration(t=t0), rgbcolor=(1,0,1))
    tancomp = arrow3d(pos_tzero, pos_tzero + tangent_component*tangent(t=t0), rgbcolor=(1,0,1) )
    norcomp = arrow3d(pos_tzero, pos_tzero + normal_component*normal(t=t0), rgbcolor=(1,0,1))
-Line 915:
+Line 951:
-    print "Position vector: r(t)=", position(t)
    print "Speed is ", N(speed(t0))
    print "Curvature is ", N(curvature)
    ## print "Torsion is ", N(torsion)
    print
    print "Right-click on graphic to zoom to 400%"
    print "Drag graphic to rotate"
+    print("Position vector: r(t)=", position)
    print("Speed is ", N(speed(t=t0)))
    print("Curvature is ", N(curvature))
    ## print("Torsion is ", N(torsion))
    print()
    print("Right-click on graphic to zoom to 400%")
    print("Drag graphic to rotate")
-Line 929:
+Line 965:
+== Multivariate Limits by Definition ==
by John Travis

http://sagenb.mc.edu/home/pub/97/

{{{#!sagecell
##  An interactive way to demonstrate limits of multivariate functions of the form z = f(x,y)
##
##  John Travis
##  Mississippi College
##  
##  Spring 2011
##

#  Starting point for radius values before collapsing in as R approaches 0.
#  Functions ought to be "reasonable" within a circular domain of radius R surrounding 
#  the desired (x_0,y_0).
var('x,y,z')
Rmin=1/10
Rmax=2
@interact(layout=dict(top=[['f'],['x0'],['y0']], 
bottom=[['in_3d','curves','R','graphjmol']]))
def _(f=input_box((x^2-y^2)/(x^2+y^2),width=30,label='$f(x)$'),
        R=slider(Rmin,Rmax,1/10,Rmax,label=',   $R$'),
        x0=input_box(0,width=10,label='$x_0$'),
        y0=input_box(0,width=10,label='$y_0$'),
        curves=checkbox(default=false,label='Show curves'),
        in_3d=checkbox(default=false,label='3D'),
        graphjmol=checkbox(default=true,label='Interactive graph')):
    if graphjmol:
        view_method = 'jmol'
    else:
        view_method = 'tachyon'

#   converting f to cylindrical coordinates.  
    g(r,t) = f(x=r*cos(t)+x0,y=r*sin(t)+y0)

#   Sage graphing transformation used to see the original surface.
    cylinder = (r*cos(t)+x0,r*sin(t)+y0,z)
    surface = plot3d(g,(t,0,2*pi),(r,1/100,Rmax),transformation=cylinder,opacity=0.2)
    
#   Regraph the surface for smaller and smaller radii controlled by the slider.
    collapsing_surface = plot3d(g,(t,0,2*pi),(r,1/100,R),transformation=cylinder,rgbcolor=(0,1,0))
    
    G = surface+collapsing_surface
    pretty_print(html('Enter $(x_0 ,y_0 )$ above and see what happens as $ R \\rightarrow 0 $.'))
    pretty_print(html('The surface has a limit as $(x,y) \\rightarrow $ ('+str(x0)+','+str(y0)+') if the green region collapses to a point.'))

#   If checked, add a couple of curves on the surface corresponding to limit as x->x0 for y=x^(3/5),
#   and as y->y0 for x=y^(3/5).  Should make this more robust but perhaps using 
#   these relatively obtuse curves could eliminate problems.

    if curves:
        curve_x = parametric_plot3d([x0-t,y0-t^(3/5),f(x=x0-t,y=y0-t^(3/5))],(t,Rmin,Rmax),color='red',thickness=10)
        curve_y = parametric_plot3d([x0+t^(3/5),y0+t,f(x=x0+t^(3/5),y=y0+t)],(t,Rmin,Rmax),color='red',thickness=10)
        R2 = Rmin/4
        G += arrow((x0-Rmin,y0-Rmin^(3/5),f(x=x0-Rmin,y=y0-Rmin^(3/5))),(x0-R2,y0-R2^(3/5),f(x=x0-R2,y=y0-R2^(3/5))),size=30 )
        G += arrow((x0+Rmin^(3/5),y0+Rmin,f(x=x0+Rmin^(3/5),y=y0+Rmin)),(x0+R2^(3/5),y0+R2,f(x=x0+R2^(3/5),y=y0+R2)),size=30 )  

        limit_x = limit(f(x=x0-t,y=y0-t^(3/5)),t=0)
        limit_y = limit(f(x=x0+t^(3/5),y=y0+t),t=0)
        text_x = text3d(limit_x,(x0,y0,limit_x))
        text_y = text3d(limit_y,(x0,y0,limit_y))
        G += curve_x+curve_y+text_x+text_y 
 
    
        pretty_print(html('The red curves represent a couple of trajectories on the surface.  If they do not meet, then'))
        pretty_print(html('there is also no limit.  (If computer hangs up, likely the computer can not do these limits.)'))
        pretty_print(html(r'<center><font color="red">$\lim_{(x,?)\rightarrow(x_0,y_0)} f(x,y) =%s$</font>'%str(limit_x)+r'  and <font color="red">$\lim_{(?,y)\rightarrow(x_0,y_0)} f(x,y) =%s$</font></center>'%str(limit_y)))
        
    if in_3d:
        show(G,stereo="redcyan",viewer=view_method)
    else:
        show(G,perspective_depth=true,viewer=view_method)
}}}
{{attachment:3D_Limit_Defn.png}}


{{{#!sagecell
##  An interactive way to demonstrate limits of multivariate functions of the form z = f(x,y)
##  This one uses contour plots and so will work with functions that have asymptotic behavior.
##
##  John Travis
##  Mississippi College
##  
##  Spring 2011
##

#  An increasing number of contours for z = f(x,y) are utilized surrounding a desired (x_0,y_0).
#  A limit can be shown to exist at (x_0,y_0) provided the point stays trapped between adjacent 
#  contour lines as the number of lines increases.  If the contours change wildly near the point,
#  then a limit does not exist.
#  Looking for two different paths to approach (x_0,y_0) that utilize a different selection of colors
#  will help locate paths to use that exhibit the absence of a limit.

var('x,y,z,u')
@interact(layout=dict(top=[['f'],['x0'],['y0']], 
bottom=[['N'],['R']]))
def _(f=input_box(default=(x*y^2)/(x^2+y^4),width=30,label='$f(x)$'),
        N=slider(5,100,1,10,label='Number of Contours'),
        R=slider(0.1,1,0.01,1,label='Radius of circular neighborhood'),
        x0=input_box(0,width=10,label='$x_0$'),
        y0=input_box(0,width=10,label='$y_0$')):

    pretty_print(html(r'Enter $(x_0 ,y_0 )$ above and see what happens as the number of contour levels $\rightarrow \infty $.'))
    pretty_print(html('A surface will have a limit in the center of this graph provided there is not a sudden change in color there.'))

#   Need to make certain the min and max contour lines are not huge due to asymptotes.  If so, clip and start contours at some reasonable
#   values so that there are a nice collection of contours to show around the desired point.

    surface = contour_plot(f,(x,x0-1,x0+1),(y,y0-1,y0+1),cmap=True,colorbar=True,fill=False,contours=N)
    surface += parametric_plot([R*cos(u),R*sin(u)],[0,2*pi],color='black')
#      Nice to use if f=x*y^2/(x^2 + y^4)    
#    var('u')
#    surface += parametric_plot([u^2,u],[u,-1,1],color='black')    
    limit_point = point((x0,y0),color='red',size=30)
#    show(limit_point+surface)
    show(surface)
}}}
{{attachment:3D_Limit_Defn_Contours.png}}
-Line 933:
+Line 1092:
-{{{
+{{{#!sagecell
-Line 986:
+Line 1145:
-{{{
%hide
%auto
+{{{#!sagecell
-Line 1003:
+Line 1160:
- html(r'Function $ f(x,y)=%s$ '%latex(f(x,y)))
+ pretty_print(html(r'Function $ f(x,y)=%s$ '%latex(f(x,y))))
-Line 1017:
+Line 1174:
-              html(r'<tr><td>$\quad f(%s,%s)\quad $</td><td>$\quad %s$</td>\
              </tr>'%(latex(x0),latex(y0),z0.n()))
+              pretty_print(html(r'<tr><td>$\quad f(%s,%s)\quad $</td><td>$\quad %s$</td>\
              </tr>'%(latex(x0),latex(y0),z0.n())))
-Line 1047:
+Line 1204:
-{{{
+{{{#!sagecell
-Line 1051:
+Line 1208:
-html('Points x0 and y0 are values where the exact value of the function \
+pretty_print(html('Points x0 and y0 are values where the exact value of the function \
-Line 1053:
+Line 1210:
-and approximation by differential at shifted point are compared.')
+and approximation by differential at shifted point are compared.'))
-Line 1071:
+Line 1228:
-  html(r'Function $ f(x,y)=%s \approx %s $ '%(latex(f(x,y)),latex(tangent(x,y))))
  html(r' $f %s = %s$'%(latex((x0,y0)),latex(exact_value_ori)))
  html(r'Shifted point $%s$'%latex(((x0+deltax),(y0+deltay))))
  html(r'Value of the function in shifted point is $%s$'%f(x0+deltax,y0+deltay))
  html(r'Value on the tangent plane in shifted point is $%s$'%latex(approx_value))
  html(r'Error is $%s$'%latex(abs_error))
+  pretty_print(html(r'Function $ f(x,y)=%s \approx %s $ '%(latex(f(x,y)),latex(tangent(x,y)))))
  pretty_print(html(r' $f %s = %s$'%(latex((x0,y0)),latex(exact_value_ori))))
  pretty_print(html(r'Shifted point $%s$'%latex(((x0+deltax),(y0+deltay)))))
  pretty_print(html(r'Value of the function in shifted point is $%s$'%f(x0+deltax,y0+deltay)))
  pretty_print(html(r'Value on the tangent plane in shifted point is $%s$'%latex(approx_value)))
  pretty_print(html(r'Error is $%s$'%latex(abs_error)))
-Line 1085:
+Line 1242:
-{{{
+{{{#!sagecell
-Line 1094:
+Line 1251:
-      order=(1..10)):
+      order=[1..10]):
-Line 1113:
+Line 1270:
-    html('$F(x,y) = e^{-(x^2+y^2)/2} \\cos(y) \\sin(x^2+y^2)$')
+    pretty_print(html('$F(x,y) = e^{-(x^2+y^2)/2} \\cos(y) \\sin(x^2+y^2)$'))
-Line 1117:
+Line 1274:
+== Volumes over non-rectangular domains ==

by John Travis

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2829.sagews

{{{#!sagecell
##  Graphing surfaces over non-rectangular domains 
##  John Travis
##  Spring 2011
##
##
##  An updated version of this worksheet may be available at http://sagenb.mc.edu
##
##  Interact allows the user to input up to two inequality constraints on the
##  domain when dealing with functional surfaces
##
##  User inputs:
##    f = "top" surface with z = f(x,y)
##    g = "bottom" surface with z = g(x,y)
##    condition1 = a single boundary constraint.  It should not include && or | to join two conditions.
##    condition2 = another boundary constraint.  If there is only one constraint, just enter something true
##        or even just an x (or y) in the entry blank.
##
## 

var('x,y')

#  f is the top surface
#  g is the bottom surface
global f,g

#  condition1 and condition2 are the inequality constraints.  It would be nice
#  to have any number of conditions connected by $$ or | 
global condition1,condition2

@interact
def _(f=input_box(default=(1/3)*x^2 + (1/4)*y^2 + 5,label='$f(x)=$'),
        g=input_box(default=-1*x+0*y,label='$g(x)=$'),
        condition1=input_box(default= x^2+y^2<8,label='$Constraint_1=$'),
        condition2=input_box(default=y<sin(3*x),label='$Constraint_2=$'),
        show_3d=('Stereographic',false), show_vol=('Shade volume',true), 
        dospin = ('Spin?',true), 
        clr = color_selector('#faff00', label='Volume Color', widget='colorpicker', hide_box=True), 
        xx = range_slider(-5, 5, 1, default=(-3,3), label='X Range'),
        yy = range_slider(-5, 5, 1, default=(-3,3), label='Y Range'),
        auto_update=false):
    
    #  This is the top function actually graphed by using NaN outside domain
    def F(x,y):
        if condition1(x=x,y=y):
            if condition2(x=x,y=y):
                return f(x=x,y=y)
            else:
                return -NaN
        else:
            return -NaN

    # This is the bottom function actually graphed by using NaN outside domain
    def G(x,y):
        if condition1(x=x,y=y):
            if condition2(x=x,y=y):
                return g(x=x,y=y)
            else:
                return -NaN
        else:
            return -NaN
        
    P = Graphics()      
      
#  The graph of the top and bottom surfaces
    P_list = []
    P_list.append(plot3d(F,(x,xx[0],xx[1]),(y,yy[0],yy[1]),color='blue',opacity=0.9))
    P_list.append(plot3d(G,(x,xx[0],xx[1]),(y,yy[0],yy[1]),color='gray',opacity=0.9))
    
#  Interpolate "layers" between the top and bottom if desired

    if show_vol:
        ratios = range(10)

        def H(x,y,r):
            return (1-r)*F(x=x,y=y)+r*G(x=x,y=y)
        P_list.extend([
        plot3d(lambda x,y: H(x,y,ratios[1]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[2]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[3]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[4]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[5]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[6]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[7]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[8]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr),
        plot3d(lambda x,y: H(x,y,ratios[9]/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2,color=clr)
        ])
#            P = plot3d(lambda x,y: H(x,y,ratio/10),(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.1)
             
           
#  Now, accumulate all of the graphs into one grouped graph.        
    P = sum(P_list[i] for i in range(len(P_list)))


    if show_3d:
        show(P,frame=true,axes=false,xmin=xx[0],xmax=xx[1],ymin=yy[0],ymax=yy[1],stereo='redcyan',figsize=(6,9),viewer='jmol',spin=dospin)
    else:
        show(P,frame=true,axes=false,xmin=xx[0],xmax=xx[1],ymin=yy[0],ymax=yy[1],figsize=(6,9),viewer='jmol',spin=dospin)
}}}
{{attachment:3D_Irregular_Volume.png}}

== Lateral Surface Area ==

by John Travis

http://sagenb.mc.edu/home/pub/89/

Note that this works in Sage cell, but causes a zip file error in Jupyter

{{{#!sagecell
##  Display and compute the area of the lateral surface between two surfaces
##  corresponding to the (scalar) line integral
##  John Travis
##  Spring 2011
##

var('x,y,t,s')
@interact(layout=dict(top=[['f','u'],['g','v']], 
left=[['a'],['b'],['in_3d'],['smoother']],
bottom=[['xx','yy']]))
def _(f=input_box(default=6-4*x^2-y^2*2/5,label='Top = $f(x,y) = $',width=30),
        g=input_box(default=-2+sin(x)+sin(y),label='Bottom = $g(x,y) = $',width=30),
        u=input_box(default=cos(t),label='   $ x = u(t) = $',width=20),
        v=input_box(default=2*sin(t),label='   $ y = v(t) = $',width=20),
        a=input_box(default=0,label='$a = $',width=10),
        b=input_box(default=3*pi/2,label='$b = $',width=10),
        xx = range_slider(-5, 5, 1, default=(-1,1), label='x view'),
        yy = range_slider(-5, 5, 1, default=(-2,2), label='y view'),
        in_3d = checkbox(default=true,label='3D'),
        smoother=checkbox(default=false),
        auto_update=true):
        
    ds = sqrt(derivative(u,t)^2+derivative(v,t)^2)
    
#   Set up the integrand to compute the line integral, making all attempts
#   to simplify the result so that it looks as nice as possible.    
    A = (f(x=u,y=v)-g(x=u,y=v))*ds.simplify_trig().simplify()
    
#   It is not expected that Sage can actually perform the line integral calculation.
#   So, the result displayed may not be a numerical value as expected.
#   Creating a good but harder example that "works" is desirable.
#   If you want Sage to try, uncomment the lines below.

#    line_integral = integrate(A,t,a,b)
#    html(r'<align=center size=+1>Lateral Surface Area = $ %s $ </font>'%latex(line_integral))

    line_integral_approx = numerical_integral(A,a,b)[0]

    pretty_print(html(r'<font align=center size=+1>Lateral Surface $ \approx $ %s</font>'%str(line_integral_approx)))

#   Plot the top function z = f(x,y) that is being integrated.
    G = plot3d(f,(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2)
    G += plot3d(g,(x,xx[0],xx[1]),(y,yy[0],yy[1]),opacity=0.2)

#   Add space curves on the surfaces "above" the domain curve (u(t),v(t)) 
    G += parametric_plot3d([u,v,g(x=u,y=v)],(t,a,b),thickness=2,color='red')
    G += parametric_plot3d([u,v,f(x=u,y=v)],(t,a,b),thickness=2,color='red')
    k=0
    if smoother:
        delw = 0.025
        lat_thick = 3
    else:
        delw = 0.10
        lat_thick = 10
    for w in (a,a+delw,..,b):
        G += parametric_plot3d([u(t=w),v(t=w),s*f(x=u(t=w),y=v(t=w))+(1-s)*g(x=u(t=w),y=v(t=w))],(s,0,1),thickness=lat_thick,color='yellow',opacity=0.9)
        
    if in_3d:
        show(G,stereo='redcyan',spin=true)
    else:
        show(G,perspective_depth=true,spin=true)
}}}
{{attachment:Lateral_Surface.png}}
-Line 1120:
+Line 1459:
-{{{
+Note that this works in Sage cell, but causes a zip file error in Jupyter.
{{{#!sagecell
-Line 1134:
+Line 1475:
+== Line Integrals in 3D Vector Field ==

by John Travis

https://cloud.sagemath.com/projects/19575ea0-317e-402b-be57-368d04c113db/files/pub/2801-2901/2827-$%20%5Cint_%7BC%7D%20%5Cleft%20%5Clangle%20M,N,P%20%5Cright%20%5Crangle%20dr%20$%20=%20$%20%25s%20$.sagews

{{{#!sagecell
##  This worksheet interactively computes and displays the line integral of a 3D vector field 
##  over a given smooth curve C
##  
##  John Travis
##  Mississippi College
##  06/16/11
##
##  An updated version of this worksheet may be available at http://sagenb.mc.edu
##

var('x,y,z,t,s')

@interact
def _(M=input_box(default=x*y*z,label="$M(x,y,z)$"),
        N=input_box(default=-y*z,label="$N(x,y,z)$"),
        P=input_box(default=z*y,label="$P(x,y,z)$"),
        u=input_box(default=cos(t),label="$x=u(t)$"),
        v=input_box(default=2*sin(t),label="$y=v(t)$"),
        w=input_box(default=t*(t-2*pi)/pi,label="$z=w(t)$"),
        tt = range_slider(-2*pi, 2*pi, pi/6, default=(0,2*pi), label='t Range'),
        xx = range_slider(-5, 5, 1, default=(-1,1), label='x Range'),
        yy = range_slider(-5, 5, 1, default=(-2,2), label='y Range'),
        zz = range_slider(-5, 5, 1, default=(-3,1), label='z Range'),
        in_3d=checkbox(true)):

#   setup the parts and then compute the line integral
    u(t) = u
    v(t) = v
    w(t) = w
    dr = [derivative(u(t),t),derivative(v(t),t),derivative(w(t),t)]
    A = (M(x=u(t),y=v(t),z=w(t))*dr[0]
        +N(x=u(t),y=v(t),z=w(t))*dr[1]
        +P(x=u(t),y=v(t),z=w(t))*dr[2])
    global line_integral
    line_integral = integral(A(t=t),t,tt[0],tt[1])
    
    pretty_print(html(r'<h2 align=center>$ \int_{C} \left \langle M,N,P \right \rangle dr $ = $ %s $ </h2>'%latex(line_integral)))
    G = plot_vector_field3d((M,N,P),(x,xx[0],xx[1]),(y,yy[0],yy[1]),(z,zz[0],zz[1]),plot_points=6)
    G += parametric_plot3d([u,v,w],(t,tt[0],tt[1]),thickness='5',color='yellow')
    if in_3d:
        show(G,stereo='redcyan',spin=true)
    else:
        show(G,perspective_depth=true)
}}}
{{attachment:3D_Line_Integral.png}}

Diff for "interact/calculus"

Sage Interactions - Calculus

Root Finding Using Bisection

Newton's Method

A contour map and 3d plot of two inverse distance functions

A simple tangent line grapher

Numerical integrals with the midpoint rule

Numerical integrals with various rules

Some polar parametric curves

Function tool

Newton-Raphson Root Finding

Coordinate Transformations

Taylor Series

Illustration of the precise definition of a limit

A graphical illustration of sin(x)/x -> 1 as x-> 0

Quadric Surface Plotter

The midpoint rule for numerically integrating a function of two variables

Gaussian (Legendre) quadrature

Vector Calculus, 2-D Motion

Vector Calculus, 3-D Motion

Multivariate Limits by Definition

Directional Derivatives

3D graph with points and curves

Approximating function in two variables by differential

Taylor approximations in two variables

Volumes over non-rectangular domains

Lateral Surface Area

Parametric surface example

Line Integrals in 3D Vector Field