7

Numpy を使用して Meshgrid を生成していますが、多くのメモリとかなりの時間がかかります。

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

基になるサイトマップ画像と同じ解像度のメッシュグリッドを生成していますが、3000px のサイズの場合もあります。数ギガのメモリを使用する場合があり、ページ ファイルに書き込むときに 10 ～ 15 秒以上かかります。

私の質問は; サーバーをアップグレードせずにこれを高速化できますか? これは、私のアプリケーション ソース コードの完全なコピーです。

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
4

3 に答える 3

2

どうですかxi, yi = np.meshgrid(xi, yi, copy=False)。そうすれば、すべてのデータをコピーするのではなく、元の配列のビューのみを返します。

于 2013-08-13T04:16:58.187 に答える