How to use get_sparkline method in yandex-tank

Best Python code snippet using yandex-tank

smoothing.py

Source: smoothing.py Github

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...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

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test_spark.py

Source: test_spark.py Github

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...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 = ' _▁▂▃▄▅▆▇ ▃▄▅▆▇ _'22 expected_short = '▆▇ _'23 expected_long = ' _▁▂▃▄▅▆▇ ▃▄▅▆▇ _'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 = '__▁▁▂▂▃▃▄▄▅▅▆▆▇▇'38 expected_spotty = '_ ▁ ▂ ▃ ▄ ▅ ▆ ▇ '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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