William Stein wrote: > I did try pasting that example into sagenb.org and it gives > some weird errors involving _fast_float. Jason Grout -- maybe > you could look at why your interact appears broken?
Robert Bradshaw: I've asked a question at the bottom of this email to you about partial function evaluation of fast_float functions... Okay, I've updated the code to be smarter. The code ended up calling maxima a *lot* for what basically was partial function evaluation. Instead, I switched it to use the functools.partial class to partially evaluate a fast_float function. Apparently I triggered the surge protection on the wiki and so cannot post the update there. It is here: http://sagenb.org:8000/home/pub/69 and also just in case sometime in the future, the public notebook server goes down, here is the code so it's archived on the list: var('u v') from sage.ext.fast_eval import fast_float from functools import partial @interact 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), t_min,t_max, rgbcolor=(1,0,0)) for i in u_range]) g2=sum([parametric_plot((u,i), t_min,t_max, rgbcolor=(0,0,1)) for i in v_range]) vline_straight=parametric_plot((u,v_val), t_min,t_max, rgbcolor=(0,0,1), linestyle='-',thickness=thickness) uline_straight=parametric_plot((u_val, 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)), t_min,t_max, rgbcolor=(1,0,0)) for i in u_range]) g4=sum([parametric_plot((partial(xvu,i),partial(yvu,i)), t_min,t_max, rgbcolor=(0,0,1)) for i in v_range]) vline=parametric_plot((partial(xvu,v_val),partial(yvu,v_val)), t_min,t_max, rgbcolor=(0,0,1), linestyle='-',thickness=thickness) uline=parametric_plot((partial(xuv,u_val),partial(yuv,u_val)), t_min,t_max,rgbcolor=(1,0,0), 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>" Robert, can we make partial function evaluation part of fast_float? That way, given the following: var("u,v") x=u^2+v^2 xuv = fast_float(x,'u','v') the following are equivalent: xuv(2)(3) and xuv(2,3) Of course, right now, we can do this (with a slight performance penalty) by doing: import functools.partial functools.partial(xuv,2)(3) My whole reason for doing this (to avoid expensive maxima calls) is disappearing soon, so maybe it's not worth the effort, especially since functools.partial provides a standard python way to get this. Thanks, Jason --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to [email protected] To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/sage-support URLs: http://www.sagemath.org -~----------~----~----~----~------~----~------~--~---
