Add type hints. (927efbf) - make_chart.git

make_chart.py

@@ -7,6 +7,7 @@ import operator
 import subprocess
 import platform
 import webbrowser
+from typing import List, Tuple
 
 
 html = """<html>
@@ -47,7 +48,7 @@ html = """<html>
 </html>"""
 
 
-def calculate_median_mean_stddev_from_samples(values):
+def calculate_median_mean_stddev_from_samples(values: List[int]) -> Tuple[float, float, float]:
     """ returns the median, mean, and standard deviation of values """
     # Calculate the median
     values.sort()
@@ -70,7 +71,7 @@ def calculate_median_mean_stddev_from_samples(values):
     return median, mean, std_dev
 
 
-def calculate_median_mean_stddev(x, y):
+def calculate_median_mean_stddev(x: List[float], y: List[int]) -> Tuple[float, float, float]:
     """ returns the median, mean, and standard deviation given
     an array of values, x, and an array of counts of those values, y. """
     # Median: Walk the sample counts (y) halfway to the sum of sample counts.
@@ -91,7 +92,7 @@ def calculate_median_mean_stddev(x, y):
     return median, mean, std_dev
 
 
-def remove_outliers_by_idx(x, y, outlier_count, idx):
+def remove_outliers_by_idx(x: List[float], y: List[int], outlier_count: int, idx: int) -> None:
     """Removes outlier_count samples from idx side of the buckets."""
     cur_count = 0
     while cur_count + y[idx] < outlier_count:
@@ -102,7 +103,7 @@ def remove_outliers_by_idx(x, y, outlier_count, idx):
         y[idx] = y[idx] - (outlier_count - cur_count)
 
 
-def remove_outliers(x, y, outlier_percentile):
+def remove_outliers(x: List[float], y: List[int], outlier_percentile: int) -> None:
     """Removes outlier_percentile samples from the beginning and end
     of the sample set."""
     outlier_count = sum(y) * outlier_percentile // 100
@@ -110,7 +111,8 @@ def remove_outliers(x, y, outlier_percentile):
     remove_outliers_by_idx(x, y, outlier_count, -1)
 
 
-def acquire_data(buckets):
+def acquire_data(buckets: int) -> Tuple[List[float], List[float], List[int]]:
+    """Manufactures some fake data."""
     x = [i*10 for i in range(1,buckets+1)]
     y = []
     for i in range(0, buckets // 4):
@@ -125,7 +127,7 @@ def acquire_data(buckets):
     return samples, x, y
 
 
-def main(renderer):
+def main(renderer: str) -> None:
     buckets = 20
     samples, x, y = acquire_data(buckets)
 


...	...	@@ -7,6 +7,7 @@ import operator
7	7	import subprocess
8	8	import platform
9	9	import webbrowser
	10	+from typing import List, Tuple
10	11
11	12
12	13	html = """<html>
...	...	@@ -47,7 +48,7 @@ html = """<html>
47	48	</html>"""
48	49
49	50
50		-def calculate_median_mean_stddev_from_samples(values):
	51	+def calculate_median_mean_stddev_from_samples(values: List[int]) -> Tuple[float, float, float]:
51	52	""" returns the median, mean, and standard deviation of values """
52	53	# Calculate the median
53	54	values.sort()
...	...	@@ -70,7 +71,7 @@ def calculate_median_mean_stddev_from_samples(values):
70	71	return median, mean, std_dev
71	72
72	73
73		-def calculate_median_mean_stddev(x, y):
	74	+def calculate_median_mean_stddev(x: List[float], y: List[int]) -> Tuple[float, float, float]:
74	75	""" returns the median, mean, and standard deviation given
75	76	an array of values, x, and an array of counts of those values, y. """
76	77	# Median: Walk the sample counts (y) halfway to the sum of sample counts.
...	...	@@ -91,7 +92,7 @@ def calculate_median_mean_stddev(x, y):
91	92	return median, mean, std_dev
92	93
93	94
94		-def remove_outliers_by_idx(x, y, outlier_count, idx):
	95	+def remove_outliers_by_idx(x: List[float], y: List[int], outlier_count: int, idx: int) -> None:
95	96	"""Removes outlier_count samples from idx side of the buckets."""
96	97	cur_count = 0
97	98	while cur_count + y[idx] < outlier_count:
...	...	@@ -102,7 +103,7 @@ def remove_outliers_by_idx(x, y, outlier_count, idx):
102	103	y[idx] = y[idx] - (outlier_count - cur_count)
103	104
104	105
105		-def remove_outliers(x, y, outlier_percentile):
	106	+def remove_outliers(x: List[float], y: List[int], outlier_percentile: int) -> None:
106	107	"""Removes outlier_percentile samples from the beginning and end
107	108	of the sample set."""
108	109	outlier_count = sum(y) * outlier_percentile // 100
...	...	@@ -110,7 +111,8 @@ def remove_outliers(x, y, outlier_percentile):
110	111	remove_outliers_by_idx(x, y, outlier_count, -1)
111	112
112	113
113		-def acquire_data(buckets):
	114	+def acquire_data(buckets: int) -> Tuple[List[float], List[float], List[int]]:
	115	+ """Manufactures some fake data."""
114	116	x = [i*10 for i in range(1,buckets+1)]
115	117	y = []
116	118	for i in range(0, buckets // 4):
...	...	@@ -125,7 +127,7 @@ def acquire_data(buckets):
125	127	return samples, x, y
126	128
127	129
128		-def main(renderer):
	130	+def main(renderer: str) -> None:
129	131	buckets = 20
130	132	samples, x, y = acquire_data(buckets)
131	133
132	134