mozilla / trend-getter - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Conditional Complexity Overall

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 25 most complex units

Unit	# lines	McCabe index	# params
def predict_impacts() in trend_getter/holidays.py	32	10	4
def _get_scenario_forecasts() in trend_getter/scenarios.py	50	9	1
def fetch_data() in trend_getter/scenarios.py	81	7	2
def _populate() in trend_getter/holidays.py	6	5	2
def _query_() in trend_getter/scenarios.py	8	4	1
def _populate() in trend_getter/holidays.py	9	3	2
def fit() in trend_getter/holidays.py	46	3	1
def __post_init__() in trend_getter/scenarios.py	14	3	1
def predict() in trend_getter/holidays.py	9	2	1
def plot_countries() in trend_getter/holidays.py	29	2	1
def __post_init__() in trend_getter/scenarios.py	9	2	1
def plot() in trend_getter/plotting.py	20	2	4
def get_calendar() in trend_getter/holidays.py	5	1	0
def detrend() in trend_getter/holidays.py	7	1	0
def scaled_sigmoid() in trend_getter/holidays.py	2	1	5
def estimate_impacts() in trend_getter/holidays.py	6	1	0
def __post_init__() in trend_getter/holidays.py	5	1	1
def plot_overall() in trend_getter/holidays.py	19	1	1
def plot_future_impacts() in trend_getter/holidays.py	7	1	1
def sample() in trend_getter/scenarios.py	2	1	2
def plot_distribution() in trend_getter/scenarios.py	32	1	1
def column_names_map() in trend_getter/scenarios.py	2	1	1
def _get_historical_forecasts() in trend_getter/scenarios.py	6	1	0
def moving_average() in trend_getter/metric_calculations.py	2	1	2
def year_over_year() in trend_getter/metric_calculations.py	8	1	3