python - 加速Numpy Meshgrid命令

我正在用Numpy生成一个Meshgrid，它占用了大量内存，并且花费了很多时间。

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

我正在生成与底层站点地图图像相同的分辨率的网状网格，有时尺寸为3000px。有时，它会使用几段内存，而将其写入页面文件时则需要10-15秒或更长时间。

我的问题是；我可以在不升级服务器的情况下加快速度吗？这是我的应用程序源代码的完整副本。

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 to fill up!
    x_values = []
    y_values = []
    z_values = []

    #iterate over wells and fill the arrays with well data
    for well in well_arr:
        x_values.append(well['xpos'])
        y_values.append(well['ypos'])
        z_values.append(well['value'])

    #initialize numpy array as required for 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 a list of x, y coordinate tuples
    points = zip(x, y)

    #create a grid on which to 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 the data with the matlab griddata function (http://matplotlib.org/api/mlab_api.html#matplotlib.mlab.griddata)
    zi = griddata(x, y, z, xi, yi, interp='nn')

    #create a matplotlib figure and adjust the width and heights to output contours to a resolution very close to the original sitemap
    fig = plt.figure(figsize=(image['width']/72, image['height']/72))

    #create a single subplot, just takes over the whole figure if only one is specified
    ax = fig.add_subplot(111, frameon=False, xticks=[], yticks=[])

    #read the database image and save to a temporary variable
    im = Image.open(image['tmpfile'])

    #place the sitemap image on top of the figure
    ax.imshow(im, origin='upper', alpha=site_image_alpha)

    #figure out a good linewidth
    if image['width'] > 2000:
        linewidth = 3
    else:
        linewidth = 2

    #create the contours (options here http://cl.ly/2X0c311V2y01)
    kwargs = {}
    if groundwater_contours:
        kwargs['colors'] = 'b'

    CS = plt.contour(xi, yi, zi, linewidths=linewidth, **kwargs)
    for key, value in enumerate(CS.levels):
        if value == 0:
            CS.collections[key].remove()

    #add a streamplot
    if flow_lines:
        dy, dx = np.gradient(zi)
        plt.streamplot(xi, yi, dx, dy, color='c', density=1, arrowsize=3, arrowstyle='<-')

    #add labels to well locations
    label_kwargs = {}
    if label_over_well is 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 to show where well data was 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 to the top of the 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 a unique filename and save to a 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 the white border

    #clears the matplotlib memory
    clf()

    #send the temporary file to the user
    resp = make_response(send_file(tempFileObj, mimetype='application/pdf', as_attachment=True, attachment_filename=filename))

    #set the users status token for javascript workaround to check if file is done being generated
    resp.set_cookie('status_token', status_token)

    return resp

最佳答案

如果meshgrid是让您减速的原因，请不要调用它...根据 griddata docs:



因此，如果您跳过对griddata的调用并执行以下操作，则对meshgrid的调用应与之相同:

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')

这就是说，如果您的x和y向量很大，则实际的插值(即对griddata的调用)可能会花费相当长的时间，因为Delaunay三角剖分是一项计算密集型操作。您确定您的性能问题来自meshgrid而不是griddata吗？

关于python - 加速Numpy Meshgrid命令，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/18200541/