calendar_image.py 文件源码

def test():
    from io import BytesIO
    x = [datetime.datetime(2017, 4, 6, 0, 0), datetime.datetime(2017, 4, 7, 0, 0), datetime.datetime(2017, 4, 8, 0, 0), datetime.datetime(2017, 4, 11, 0, 0), datetime.datetime(2017, 4, 12, 0, 0), datetime.datetime(2017, 4, 13, 0, 0), datetime.datetime(2017, 4, 14, 0, 0), datetime.datetime(2017, 4, 16, 0, 0), datetime.datetime(2017, 4, 17, 0, 0),
         datetime.datetime(2017, 4, 18, 0, 0), datetime.datetime(2017, 4, 19, 0, 0), datetime.datetime(2017, 4, 20, 0, 0), datetime.datetime(2017, 4, 22, 0, 0), datetime.datetime(2017, 4, 23, 0, 0)]
    y = [[0.0, 15.0, 9.0, 0.0, 9.0, 5.0, 6.0, 0.0, 11.0, 9.0, 5.0, 6.0, 0.0, 11.0],
         [15.0, 17.0, 0.0, 20.0, 20.0, 19.0, 30.0, 32.0, 23.0, 20.0, 19.0, 30.0, 32.0, 23.0]]
    grid = [
        [{'date': datetime.datetime(2017, 4, 3, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 4, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 5, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 6, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  0.,  15.]}, {'date': datetime.datetime(2017, 4, 7, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 15.,  17.]}, {'date': datetime.datetime(2017, 4, 8, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 9.,  0.]}, {'date': datetime.datetime(2017, 4, 9, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}],
        [{'date': datetime.datetime(2017, 4, 10, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 11, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  0.,  20.]}, {'date': datetime.datetime(2017, 4, 12, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  9.,  20.]}, {'date': datetime.datetime(2017, 4, 13, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  5.,  19.]}, {'date': datetime.datetime(2017, 4, 14, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  6.,  30.]}, {'date': datetime.datetime(2017, 4, 15, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 16, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [  0.,  32.]}],
        [{'date': datetime.datetime(2017, 4, 17, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 11.,  23.]}, {'date': datetime.datetime(2017, 4, 18, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 19, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 20, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 21, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 22, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 23, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}],
        [{'date': datetime.datetime(2017, 4, 24, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 11.,  23.]}, {'date': datetime.datetime(2017, 4, 25, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 26, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 27, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 28, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 29, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}, {'date': datetime.datetime(2017, 4, 30, 0, 0, tzinfo=tzoffset(None, 10800)), 'values': [ 0.,  0.]}]
            ]
    dashboard = {
        "summary": "Anna work-out",
        "empty_image": "../amazon-dash-private/images/old-woman.png",
        "images_folder": "../amazon-dash-private/images/"
    }
    labels = [
        {"summary": "Morning work-out", "image": "../amazon-dash-private/images/morning4.png"},
        {"summary": "Physiotherapy", "image": "../amazon-dash-private/images/evening2.png"}
    ]
    absent_labels = [
        {'image_grid': '../amazon-dash-private/images/absent_ill_grid.png',
         'image_plot': '../amazon-dash-private/images/absent_ill_plot.png',
        'summary': 'Sick'},
        {'image_grid': '../amazon-dash-private/images/absent_vacation_grid.png',
         'image_plot': '../amazon-dash-private/images/absent_vacation_plot.png',
         'summary': 'Vacation'}
    ]
    weather = {'day': [datetime.datetime(2017, 4, 22, 0, 0),
                       datetime.datetime(2017, 4, 23, 0, 0),
                       datetime.datetime(2017, 4, 24, 0, 0),
                       datetime.datetime(2017, 4, 25, 0, 0)],
               'icon': ['sct', 'ovc', 'hi_shwrs', 'sn'],
               'temp_max': [6.64, 6.38, 4.07, 6.91],
               'temp_min': [-0.58, -2.86, -1.87, -1.91],
               'images_folder': '../amazon-dash-private/images/'}
    t0 = datetime.datetime.now()
    image_data = draw_calendar(grid, x, y, weather, dashboard, labels, absent_labels,
                               ImageParams(
                                   dashboard='',
                                   format='gif',
                                   style='seaborn-talk',
                                   xkcd=1,
                                   rotate=0
                               )
                               )
    t1 = datetime.datetime.now()
    print(t1 - t0)
    image_file = BytesIO(image_data)
    image = PIL.Image.open(image_file)
    image.show()
    # with open('test.png', 'wb') as png_file:
    #     png_file.write(image)
    #plt.show()
    #todo speed it up. too many rescalings as I see from profiling.
    # may be using artists (http://stackoverflow.com/questions/41453902/is-it-possible-to-patch-an-image-in-matplotlib)
    # will reduce number of rescaling?
    # now it looks like matplotlib rescales after each operation