How to use get_sparkline method in yandex-tank

Best Python code snippet using yandex-tank

smoothing.py

Source:smoothing.py

...135            four_24mo_units_back.append(smooth_rating)136        ylpline_ratings.append([publish_since_epoch, round(smooth_rating, 2)])137        review_ratings.append([publish_since_epoch, actual_rating])138        prev_smooth_rating = smooth_rating139    sparkline = get_sparkline([five_spark_units_back, four_spark_units_back, three_spark_units_back, two_spark_units_back, one_spark_unit_back])140    sparkline_6mo = get_sparkline([four_6mo_units_back, three_6mo_units_back, two_6mo_units_back, one_6mo_unit_back])141    sparkline_12mo = get_sparkline([four_12mo_units_back, three_12mo_units_back, two_12mo_units_back, one_12mo_unit_back])142    sparkline_24mo = get_sparkline([four_24mo_units_back, three_24mo_units_back, two_24mo_units_back, one_24mo_unit_back])143    return ylpline_ratings, review_ratings, smooth_rating, sparkline, sparkline_6mo, sparkline_12mo, sparkline_24mo144def get_sparkline(ratings):145    """146    Create a list of averaged ratings data to be used to render a sparkline.147    :param ratings: List of ratings148    :return: List of averaged ratings data149    """150    sparkline = []151    for week in ratings:152        rating_count = len(week)153        rating_sum = 0154        for rating in week:155            rating_sum += rating;156        if rating_count > 0:157            sparkline.append(round(float(rating_sum)/float(rating_count), 2))158    return sparkline

test_spark.py

Source:test_spark.py

...8        sparkline = Sparkline(len(data))9        start = int(time.time()) - len(data)10        for num, val in enumerate(data):11            sparkline.add(start + num, 'data', val)12        spark = ''.join(sparkline.get_sparkline('data'))13        assert (len(spark) == len(data))14        assert (spark == expected)15        zero = sparkline.get_sparkline('continous', spark_len=0)16        assert (len(zero) == 0)17        negative = sparkline.get_sparkline('continous', spark_len=-1)18        assert (len(negative) == 0)19    def test_non_continuos(self):20        data = range(20)21        expected = ' _âââââââ    âââââ _'22        expected_short = 'ââ _'23        expected_long = '     _âââââââ    âââââ _'24        spark_len = 2425        sparkline = Sparkline(spark_len)26        start = int(time.time()) - len(data)27        for num, val in enumerate(data):28            if val <= 8 or val > 12:29                sparkline.add(start + num, 'data', val % 9)30        spark = ''.join(sparkline.get_sparkline('data', spark_len=len(data)))31        assert (spark == expected)32        short_spark = ''.join(sparkline.get_sparkline('data', spark_len=4))33        assert (short_spark == expected_short)34        long_spark = ''.join(sparkline.get_sparkline('data'))35        assert (long_spark == expected_long)36    def test_multi_graphs(self):37        expected_continous = '__ââââââââââââââ'38        expected_spotty = '_ â â â â â â â '39        continous_vals = range(1, 17)40        sparkline = Sparkline(len(continous_vals))41        start = int(time.time()) - len(continous_vals)42        for val in continous_vals:43            sparkline.add(start + val, 'continous', val)44            if val % 2 == 1:45                sparkline.add(start + val, 'spotty', val)46        continous = ''.join(sparkline.get_sparkline('continous'))47        spotty = ''.join(sparkline.get_sparkline('spotty'))48        assert (continous == expected_continous)...

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