python - Speed up Numpy Meshgrid Command -

i generating meshgrid numpy , it's taking lot of memory , quite bit of time well.

xi, yi = np.meshgrid(xi, yi) 

i generating meshgrid same resolution underlying sitemap image, 3000px dimensions. uses several gigs of memory , takes 10-15 seconds or more while when it's writing page file.

my question is; can speed without upgrading server? here full copy of application source code.

def generatecontours(date_collected, substance_name, well_arr, site_id, sitemap_id, image, title_wildcard='', label_over_well=false, crop_contours=false, groundwater_contours=false, flow_lines=false, site_image_alpha=1, status_token=""):     #create empty arrays fill up!     x_values = []     y_values = []     z_values = []      #iterate on wells , fill arrays data     in well_arr:         x_values.append(well['xpos'])         y_values.append(well['ypos'])         z_values.append(well['value'])      #initialize numpy array required interpolation functions     x = np.array(x_values, dtype=np.float)     y = np.array(y_values, dtype=np.float)     z = np.array(z_values, dtype=np.float)      #create list of x, y coordinate tuples     points = zip(x, y)      #create grid on interpolate data     start_time = time.time()     xi, yi = np.linspace(0, image['width'], image['width']), np.linspace(0, image['height'], image['height'])      xi, yi = np.meshgrid(xi, yi)      #interpolate data matlab griddata function (http://matplotlib.org/api/mlab_api.html#matplotlib.mlab.griddata)     zi = griddata(x, y, z, xi, yi, interp='nn')      #create matplotlib figure , adjust width , heights output contours resolution close original sitemap     fig = plt.figure(figsize=(image['width']/72, image['height']/72))      #create single subplot, takes on whole figure if 1 specified     ax = fig.add_subplot(111, frameon=false, xticks=[], yticks=[])      #read database image , save temporary variable     im = image.open(image['tmpfile'])      #place sitemap image on top of figure     ax.imshow(im, origin='upper', alpha=site_image_alpha)      #figure out linewidth     if image['width'] > 2000:         linewidth = 3     else:         linewidth = 2      #create contours (options here http://cl.ly/2x0c311v2y01)     kwargs = {}     if groundwater_contours:         kwargs['colors'] = 'b'      cs = plt.contour(xi, yi, zi, linewidths=linewidth, **kwargs)     key, value in enumerate(cs.levels):         if value == 0:             cs.collections[key].remove()      #add streamplot     if flow_lines:         dy, dx = np.gradient(zi)         plt.streamplot(xi, yi, dx, dy, color='c', density=1, arrowsize=3, arrowstyle='<-')      #add labels locations     label_kwargs = {}     if label_over_well true:         label_kwargs['manual'] = points      plt.clabel(cs, cs.levels[1::1], inline=5, fontsize=math.floor(image['width']/100), fmt="%.1f", **label_kwargs)      #add scatterplot show data read     scatter_size = math.floor(image['width']/20)     plt.scatter(x, y, s=scatter_size, c='k', facecolors='none', marker=(5, 1))      try:         site_name = db_session.query(sites).filter_by(site_id=site_id).first().title     except:         site_name = "site map #%i" % site_id      sitemap = sitemaps.query.get(sitemap_id)     if sitemap.title != 'sitemap':         sitemap_wildcard = " - " + sitemap.title     else:         sitemap_wildcard = ""      if title_wildcard != '':         filename_wildcard = "-" + slugify(title_wildcard)         title_wildcard = " - " + title_wildcard     else:         filename_wildcard = ""         title_wildcard = ""      #add descriptive title top of contours     title_font_size = math.floor(image['width']/72)     plt.title(parsedate(date_collected) + " - " + site_name + " " + substance_name + " contour" + sitemap_wildcard + title_wildcard, fontsize=title_font_size)      #generate unique filename , save temp directory     filename = slugify(site_name) + str(int(time.time())) + filename_wildcard + ".pdf"     temp_dir = tempfile.gettempdir()     tempfileobj = temp_dir + "/" + filename     savefig(tempfileobj)  # bbox_inches='tight' tightens white border      #clears matplotlib memory     clf()      #send temporary file user     resp = make_response(send_file(tempfileobj, mimetype='application/pdf', as_attachment=true, attachment_filename=filename))      #set users status token javascript workaround check if file done being generated     resp.set_cookie('status_token', status_token)      return resp 

if meshgrid what's slowing down, don't call it... according griddata docs:

xi , yi must describe regular grid, can either 1d or 2d, must monotonically increasing.

so call griddata should work same if skip call meshgrid , do:

xi = np.linspace(0, image['width'], image['width']) yi = np.linspace(0, image['height'], image['height']) zi = griddata(x, y, z, xi, yi, interp='nn') 

this said, if x , y vectors large, actual interpolation, i.e. call griddata going take quite time, delaunay triangulation computationally intensive operation. sure performanc issues coming meshgrid, not griddata?