Stretch plot rather than zero padding

pybadges/__init__.py

@@ -199,11 +199,8 @@ def badge(
         raise ValueError('right-image and trend cannot be used together.')
 
     if show_trend:
-        samples = show_trend if len(show_trend) <= 10 else show_trend[:10]
-        if len(samples) < 10:
-            samples = [0] * (10 - len(samples)) + samples
         right_image = trend(
-            samples=samples,
+            samples=show_trend,
             stroke_color=(trend_color or right_color),
             stroke_width=trend_width,
         )

pybadges/badge-template-full.svg

@@ -1,6 +1,6 @@
 {% set logo_width = 14 if logo else 0 %}
 {% set logo_padding = 3 if (logo and left_text) else 0 %}
-{% set image_width = 110 if right_image else 0 %}
+{% set image_width = 107 if right_image else 0 %}
 {% set left_width = image_width + left_text_width + 10 + logo_width + logo_padding %}
 {% set right_width = right_text_width + 10 %}
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="{{ left_width + right_width }}" height="20">

pybadges/trend.py

@@ -1,13 +1,12 @@
 from typing import Optional, List, Tuple
 
 import drawSvg as draw
-import itertools
 import numpy as np
 
 import pybadges
 
 HEIGHT = 13
-WIDTH = 110
+WIDTH = 107
 X_OFFSET = 7
 Y_OFFSET = 1
 
@@ -19,23 +18,11 @@ def normalize(arr: np.ndarray) -> np.ndarray:
     return arr
 
 
-def repeat(samples: List[int], n: int) -> List[int]:
-    """Repeats a value n times in an array.
-
-    Args:
-        samples: The list of all elements to be repeated.
-        n: Number of times to repeat each element in samples.
-    """
-    return list(
-        itertools.chain.from_iterable(
-            itertools.repeat(sample, n) for sample in samples))
-
-
 def fit_data(samples: List[int]) -> Tuple[List[int], List[int]]:
-    y = list(
-        itertools.chain.from_iterable(
-            itertools.repeat(sample, 10) for sample in samples))
-    xp = np.arange(len(y))
+    width = WIDTH - X_OFFSET
+    N = int(width / len(samples))
+    y = np.repeat(samples, N)
+    xp = np.linspace(start=X_OFFSET, stop=width, num=len(y))
     yp = normalize(np.poly1d(np.polyfit(xp, y, 15))(xp))
     yp[yp > 0] *= (HEIGHT - 2)
     return xp, yp
@@ -51,9 +38,9 @@ def trend(samples: List[int], stroke_color: str, stroke_width: int) -> str:
     )
 
     xp, yp = fit_data(samples)
-    path.M(X_OFFSET + xp[0], yp[0])
+    path.M(xp[0], yp[0])
     for x, y in zip(xp[1:], yp[1:]):
-        path.L(X_OFFSET + x, y)
+        path.L(x, y)
     canvas.append(path)
 
     return canvas.asDataUri()