From bd8e9f0b2d8cc785a49e1cdaa8e886fc2d2d6f58 Mon Sep 17 00:00:00 2001
From: shuaizhou <lihaicheng7003@163.com>
Date: Tue, 16 Dec 2025 11:19:26 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=20share.py?=
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---
 share.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 share.py

diff --git a/share.py b/share.py
new file mode 100644
index 0000000..dcf7fc9
--- /dev/null
+++ b/share.py
@@ -0,0 +1,46 @@
+    def qc_charr_score(self, df: pd.DataFrame) -> float:
+        """
+        calculate charr score
+        Args:
+            df(pd.DataFrame): dataframe of parser vcf
+        Returns:
+            float, charr score
+        """
+
+        def charr_score(row: pd.Series) -> float:
+            """
+            charr score for one site
+            Args:
+                row(pd.Series): one site
+            Returns:
+                float, charr score
+            """
+            ad = [int(i) for i in row["ad"].split(",")]  # allele depth
+            freq = float(row["freq"])
+            if (ad[0] + ad[1]) == 0 or freq == 0:
+                return 0  # The denominator cannot be zero
+            return ad[0] / (freq * (ad[0] + ad[1]))
+
+        # autosome
+        data = df[df["chrom"].isin([f"chr{i}" for i in range(1, 23)])]
+
+        data = data[data["gt"].isin(["1/1", "1|1"])]  # Homogenous variant sites
+        data = data[(data["dp"] > 30) & (data["gq"] > 20)]  # filter
+
+        def get_freq(row: pd.Series) -> float:
+            """
+            get reference freq for one site in population
+            Args:
+                row(pd.Series): one site
+            Returns:
+                float, reference freq
+            """
+            key = (row["chrom"], row["position"])
+            return self.charr_required_freq.get(key, 0.0001)  # If not, fill in the minimum value to avoid division by 0
+
+        data["freq"] = data.apply(get_freq, axis=1)
+        if data.shape[0] == 0:
+            return 0.0  # no site after filter
+        score = round(data.apply(charr_score, axis=1).sum() / data.shape[0], 4)  # mean and retain four decimal places
+
+        return score
\ No newline at end of file