初始化提交

boards/default_src/python_skulpt/templates/html/statusbar-image.html

@@ -0,0 +1,26 @@
+<style>
+    div[m-id="{{d.mId}}"] {
+        display: flex;
+        flex-direction: row;
+        justify-content: center;
+        align-items: center;
+        overflow-y: auto;
+    }
+
+    html[data-bs-theme=light] div[m-id="{{d.mId}}"] {
+        background-color: #ffffff;
+    }
+
+    html[data-bs-theme=dark] div[m-id="{{d.mId}}"] {
+        background-color: #1e1e1e;
+    }
+
+    div[m-id="{{d.mId}}"] > #output-img {
+        width: 500px;
+        height: 500px;
+        position: relative;
+    }
+</style>
+<div m-id="{{d.mId}}" class="page-item mixly-scrollbar">
+    <div id="output-img"></div>
+</div>

boards/default_src/python_skulpt/templates/python/mixpy.py

@@ -0,0 +1,59 @@
+import math
+
+def math_map(v, al, ah, bl, bh):
+    return bl +  (bh - bl) * (v - al) / (ah - al)
+
+def math_mean(myList):
+    localList = [e for e in myList if type(e) == int or type(e) == float]
+    if not localList: return
+    return float(sum(localList)) / len(localList)
+
+def math_median(myList):
+    localList = sorted([e for e in myList if type(e) == int or type(e) == float])
+    if not localList: return
+    if len(localList) % 2 == 0:
+        return (localList[len(localList) // 2 - 1] + localList[len(localList) // 2]) / 2.0
+    else:
+        return localList[(len(localList) - 1) // 2]
+
+def math_modes(some_list):
+    modes = []
+    # Using a lists of [item, count] to keep count rather than dict
+    # to avoid "unhashable" errors when the counted item is itself a list or dict.
+    counts = []
+    maxCount = 1
+    for item in some_list:
+        found = False
+        for count in counts:
+            if count[0] == item:
+                count[1] += 1
+                maxCount = max(maxCount, count[1])
+                found = True
+        if not found:
+            counts.append([item, 1])
+    for counted_item, item_count in counts:
+        if item_count == maxCount:
+            modes.append(counted_item)
+    return modes
+
+def math_standard_deviation(numbers):
+    n = len(numbers)
+    if n == 0: return
+    mean = float(sum(numbers)) / n
+    variance = sum((x - mean) ** 2 for x in numbers) / n
+    return math.sqrt(variance)
+
+def lists_sort(my_list, type, reverse):
+    def try_float(s):
+        try:
+            return float(s)
+        except:
+            return 0
+    key_funcs = {
+        "NUMERIC": try_float,
+        "TEXT": str,
+        "IGNORE_CASE": lambda s: str(s).lower()
+    }
+    key_func = key_funcs[type]
+    list_cpy = list(my_list)
+    return sorted(list_cpy, key=key_func, reverse=reverse)