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From: John Hunter <jdhunter@ac...>  20060215 14:37:15

>>>>> "Eric" == Eric Firing <efiring@...> writes: Eric> Starting from CVS mpl, I tested examples/quadmesh_demo.py Eric> and found that it worked only with numarray; Numeric and Eric> numpy refused to cast the Float64 X and Y arrays to Float32. Eric> So I fixed that in CVS by using the astype method. Now the Eric> demo works with numpy and numarray, but segfaults with Eric> Numeric. I have not tried to track the problem down; it is Eric> very low priority for me. Given the momentum towards numpy, Eric> it might not be worth tracking down at all. I'm a little hesitant to leave in a segfault because they can be very difficult to track down. Perhaps Alex and/or Paul can address this. If it proves too difficult, we should at least check for Numeric in calls to quadmesh and generate a warning before the segfault occurs. JDH 
From: John Hunter <jdhunter@ac...>  20060215 14:31:46

>>>>> "Sajec," == Sajec, Mike TQO <msajec@...> writes: Sajec> I would like to propose modifying the boxplot function as Sajec> follows: Instead of only accepting an (mxn) matrix (x) and Sajec> creating (n) boxplots from the columns of (x), optionally Sajec> in place of the (mxn) matrix accept a list of numeric Sajec> arrays which can be any length, and create boxplots for Sajec> each of the arrays in the list. Since I have never used boxplot I don't feel qualified to comment on this, so I suggest you bring it up on the user's list. If noone objects, and you send me a patch against CVS, I'll include it. Thanks, JDH 
From: David TREMOUILLES <david.trem@gm...>  20060215 10:15:00

Hello, I'm using matplotlib figures in several places at a time in my GTK base= d app (means different page in a notebook). I have trace a bug I partially solved but still doubting if it is a matplotlib bug or a bad design of my app. Matplotlib 0.86.2 pygtk2.8.4 gtk+2.8.6 Here is the problem: in matplotlib/backends/backend_gtk.py file, in class FigureCanvasGTK (gtk.DrawingArea, FigureCanvasBase): ... in: def draw(self): # synchronous window redraw (like GTK+ 1.2 used to do) # Note: this does not follow the usual way that GTK redraws, # which is asynchronous redraw using calls to gtk_widget_queue_draw(), # which triggers an exposeevent # GTK+ 2.x style draw() 1 self._need_redraw =3D True 1 self.queue_draw() # synchronous draw (needed for animation) 2 x, y, w, h =3D self.allocation 2 if w<3 or h<3: return # empty fig 2 self._pixmap_prepare (w, h) 2 self._render_figure(self._pixmap, w, h) 2 self._need_redraw =3D False 2 self.window.draw_drawable (self.style.fg_gc[self.state], 2 self._pixmap, 0, 0, 0, 0, w, h) If I use method 1 (and comment 2) no problem, all run smoothly... But using the default method, switching on method 2 (and comment 1) I get the followig error message when trying to redraw one of the figure (the figure is plotted correctly the first time. No change made to the figure, just redrawing...) ***/python2.4/sitepackages/matplotlib/backends/backend_gtk.py:298: GtkWarning: gdk_pixmap_new: assertion `(drawable !=3D NULL)  (depth !=3D = 1)' failed self._pixmap_height) Traceback (most recent call last): ... self.canvas_leak.draw() File "***/python2.4/sitepackages/matplotlib/backends/backend_gtk.py", line 250, in draw self._pixmap_prepare (w, h) File "***/python2.4/sitepackages/matplotlib/backends/backend_gtk.py", line 298, in _pixmap_prepare self._pixmap_height) RuntimeError: could not create GdkPixmap object Somebody has a clue ??? 
From: Sajec, Mike TQO <msajec@tq...>  20060215 01:30:27

I would like to propose modifying the boxplot function as follows: Instead of only accepting an (mxn) matrix (x) and creating (n) boxplots from the columns of (x), optionally in place of the (mxn) matrix accept a list of numeric arrays which can be any length, and create boxplots for each of the arrays in the list. The obvious benefit in doing this is that one can easily create boxplots to compare datasets with differing numbers of datapoints. For example: >>from RandomArray import normal >>x1 =3D normal(10,3,100) >>x2 =3D normal(10,5,150) >>x3 =3D normal(10,1,1000) >> >>boxplot([x1, x2, x3]) The code below implements the proposed change by modifying the boxplot function axes.py. It works and is proof of concept if nothing else.=20 #### ## modifications to the boxplot function in axes.py ## #### def boxplot(self, x, notch=3D0, sym=3D'b+', vert=3D1, whis=3D1.5, positions=3DNone, widths=3DNone): """ boxplot(x, notch=3D0, sym=3D'+', vert=3D1, whis=3D1.5, positions=3DNone, widths=3DNone) Make a box and whisker plot for each column of x. Or Make a box and whisker plot for each array in list x. =20 The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. notch =3D 0 (default) produces a rectangular box plot. notch =3D 1 will produce a notched box plot sym (default 'b+') is the default symbol for flier points. Enter an empty string ('') if you don't want to show fliers. vert =3D 1 (default) makes the boxes vertical. vert =3D 0 makes horizontal boxes. This seems goofy, but that's how Matlab did it. whis (default 1.5) defines the length of the whiskers as a function of the inner quartile range. They extend to the most extreme data point within ( whis*(75%25%) ) data range. positions (default 1,2,...,n) sets the horizontal positions of the boxes. The ticks and limits are automatically set to match the positions. widths is either a scalar or a vector and sets the width of each box. The default is 0.5, or 0.15*(distance between extreme positions) if that is smaller. x is either:=20 (1) a Numeric array=20 (2) a list of 1dimension Numeric arrays of any length Returns a list of the lines added """ =20 =20 if not self._hold: self.cla() holdStatus =3D self._hold whiskers, caps, boxes, medians, fliers =3D [], [], [], [], [] # CASE #1: x is a numeric array if type(x) =3D=3D type(array([0])): x =3D asarray(x) rank =3D len(x.shape) if 1 =3D=3D rank: x.shape =3D 1, 1 row, col =3D x.shape # CASE #2: x is a list of numeric arrays if type(x) =3D=3D type(list([0])): col =3D len(x) # one column for each array #reshape the vectors in list x if necessary =20 for ii in range(len(x)): rank =3D len(x[ii].shape) if 1 =3D=3D rank: x[ii].shape =3D 1, 1 =20 # get some plot info if positions is None: positions =3D range(1, col + 1) if widths is None: distance =3D max(positions)  min(positions) widths =3D min(0.15*max(distance,1.0), 0.5) if isinstance(widths, float) or isinstance(widths, int): widths =3D ones((col,), 'd') * widths # loop through columns, adding each to plot self.hold(True) for i,pos in enumerate(positions): # CASE #1: x is a numeric array=20 if type(x)=3D=3Dtype(array([0])): d =3D x[:,i] # CASE #2: x is a list of numeric arrays =20 if type(x)=3D=3Dtype(list([0])): d =3D x[i][:,0] row =3D len(d) # get median and quartiles q1, med, q3 =3D prctile(d,[25,50,75]) # get high extreme iq =3D q3  q1 hi_val =3D q3 + whis*iq wisk_hi =3D compress( d <=3D hi_val , d ) if len(wisk_hi) =3D=3D 0: wisk_hi =3D q3 else: wisk_hi =3D max(wisk_hi) # get low extreme lo_val =3D q1  whis*iq wisk_lo =3D compress( d >=3D lo_val, d ) if len(wisk_lo) =3D=3D 0: wisk_lo =3D q1 else: wisk_lo =3D min(wisk_lo) # get fliers  if we are showing them flier_hi =3D [] flier_lo =3D [] flier_hi_x =3D [] flier_lo_x =3D [] if len(sym) !=3D 0: flier_hi =3D compress( d > wisk_hi, d ) flier_lo =3D compress( d < wisk_lo, d ) flier_hi_x =3D ones(flier_hi.shape[0]) * pos flier_lo_x =3D ones(flier_lo.shape[0]) * pos # get x locations for fliers, whisker, whisker cap and box sides box_x_min =3D pos  widths[i] * 0.5 box_x_max =3D pos + widths[i] * 0.5 wisk_x =3D ones(2) * pos cap_x_min =3D pos  widths[i] * 0.25 cap_x_max =3D pos + widths[i] * 0.25 cap_x =3D [cap_x_min, cap_x_max] # get y location for median med_y =3D [med, med] # calculate 'regular' plot if notch =3D=3D 0: # make our box vectors box_x =3D [box_x_min, box_x_max, box_x_max, box_x_min, box_x_min ] box_y =3D [q1, q1, q3, q3, q1 ] # make our median line vectors med_x =3D [box_x_min, box_x_max] # calculate 'notch' plot else: notch_max =3D med + 1.57*iq/sqrt(row) notch_min =3D med  1.57*iq/sqrt(row) if notch_max > q3: notch_max =3D q3 if notch_min < q1: notch_min =3D q1 # make our notched box vectors box_x =3D [box_x_min, box_x_max, box_x_max, cap_x_max, box_x_max, box_x_max, box_x_min, box_x_min, cap_x_min, box_x_min, box_x_min ] box_y =3D [q1, q1, notch_min, med, notch_max, q3, q3, notch_max, med, notch_min, q1] # make our median line vectors med_x =3D [cap_x_min, cap_x_max] med_y =3D [med, med] # vertical or horizontal plot? if vert: def doplot(*args): return self.plot(*args) else: def doplot(*args): shuffled =3D [] for i in range(0, len(args), 3): shuffled.extend([args[i+1], args[i], args[i+2]]) return self.plot(*shuffled) whiskers.extend(doplot(wisk_x, [q1, wisk_lo], 'b', wisk_x, [q3, wisk_hi], 'b')) caps.extend(doplot(cap_x, [wisk_hi, wisk_hi], 'k', cap_x, [wisk_lo, wisk_lo], 'k')) boxes.extend(doplot(box_x, box_y, 'b')) medians.extend(doplot(med_x, med_y, 'r')) fliers.extend(doplot(flier_hi_x, flier_hi, sym, flier_lo_x, flier_lo, sym)) # fix our axes/ticks up a little if 1 =3D=3D vert: setticks, setlim =3D self.set_xticks, self.set_xlim else: setticks, setlim =3D self.set_yticks, self.set_ylim newlimits =3D min(positions)0.5, max(positions)+0.5 setlim(newlimits) setticks(positions) =20 # reset hold status self.hold(holdStatus) return dict(whiskers=3Dwhiskers, caps=3Dcaps, boxes=3Dboxes, medians=3Dmedians, fliers=3Dfliers) 