refactor for big version 0.13.0

__back/target_seq/get_target-seq_info.py_bak

@@ -0,0 +1,181 @@
+# import argparse
+# import logging
+# import os
+# import sys
+
+# import numpy as np
+# import pandas as pd
+
+# """initialize"""
+# pd.set_option("max_colwidth", 60)  # column最大宽度
+# pd.set_option("display.width", 150)  # dataframe宽度
+# pd.set_option("display.max_columns", None)  # column最大显示数
+# pd.set_option("display.max_rows", 100)  # row最大显示数
+
+# dt_type = {
+#     "chr_name": str,
+#     "chr_index": str,
+#     "ref_base": str,
+#     "A": int,
+#     "G": int,
+#     "C": int,
+#     "T": int,
+#     "del_count": int,
+#     "insert_count": int,
+#     "ambiguous_count": int,
+#     "deletion": str,
+#     "insertion": str,
+#     "ambiguous": str,
+#     "mut_num": int,
+# }
+
+
+# def logging_setting():
+#     # log setting
+#     logging.basicConfig(
+#         level=logging.DEBUG,
+#         format="%(levelname)-5s @ %(asctime)s: %(message)s ",
+#         datefmt="%Y-%m-%d %H:%M:%S",
+#         stream=sys.stderr,
+#         filemode="w",
+#         force=True,
+#     )
+
+
+# def argparse_setting():
+#     # argparse setting
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument(
+#         "--bmat_folder",
+#         type=str,
+#         required=True,
+#         help="""
+#             All the bmat file should be formated as:
+#             TargetSeq-TREATMENT_x_REP_x_REGIONID_x_CUTOFF_x.bmat.gz
+#             and put in this folder.
+#         """,
+#     )
+#     parser.add_argument(
+#         "--out",
+#         type=str,
+#         default=None,
+#         help="""
+#             The path to put out csv file.
+#         """,
+#     )
+#     return parser.parse_args()
+
+
+# def return_base_ratio(x):
+#     if x["ref_base"] == "C":
+#         return x["T"] / x["depth"]
+#     elif x["ref_base"] == "G":
+#         return x["A"] / x["depth"]
+#     else:
+#         return np.NaN
+
+
+# if __name__ == "__main__":
+#     # logging_setting
+#     logging_setting()
+#     # argparse_setting
+#     args = argparse_setting()
+#     pt_bmat = args.bmat_folder
+#     pt_out = args.out
+#     ls_bmat = [os.path.join(pt_bmat, i) for i in os.listdir(pt_bmat) if "bmat" in i]
+#     # # TargetSeq-TREATMENT_x_REP_x_REGIONID_x_CUTOFF_x.bmat.gz
+
+#     # """ load bmat and merge """
+#     df_merge = pd.DataFrame()
+#     for path in ls_bmat:
+#         df = pd.read_csv(path, header=0, index_col=False, sep="\t", dtype=dt_type)
+#         df["bmat_name"] = path.replace("bmat/", "")
+#         df_merge = pd.concat([df_merge, df], ignore_index=True)
+
+#     # fix region_id for ND4 ND5.1 ND6
+#     # df_merge.chr_name = df_merge.chr_name.map(lambda x: x.replace("SHARE", "share"))
+#     # df_merge.chr_name = df_merge.chr_name.map(lambda x: x.replace("ONLY", "only"))
+#     # """ fix rep info """
+#     # remove no need cols
+#     df = df_merge[
+#         [
+#             "chr_name",
+#             "chr_index",
+#             "ref_base",
+#             "A",
+#             "G",
+#             "C",
+#             "T",
+#             "mut_num",
+#             "bmat_name",
+#         ]
+#     ].copy()
+
+#     # form rep info
+#     df["rep"] = df.bmat_name.map(
+#         lambda x: "rep" + x.split("_REP_")[1].split("_CUTOFF_")[0]
+#     )
+#     # form treatment info
+#     df["treatment"] = df.bmat_name.map(
+#         lambda x: x.split("-TREATMENT_")[1].split("_REP_")[0]
+#     )
+#     # form cutoff info
+#     df["cutoff"] = df.bmat_name.map(
+#         lambda x: int(x.split("_CUTOFF_")[1].split("_REGIONID_")[0])
+#     )
+
+#     if df.isna().sum().sum() != 0:
+#         raise ValueError("bmat file name should be formated")
+
+#     # select
+#     df_select = df[
+#         [
+#             "chr_name",
+#             "chr_index",
+#             "ref_base",
+#             "A",
+#             "G",
+#             "C",
+#             "T",
+#             "bmat_name",
+#             "rep",
+#             "treatment",
+#             "cutoff",
+#         ]
+#     ].copy()
+
+#     # df_select
+#     df_select["depth"] = df_select[["A", "G", "C", "T"]].apply(sum, axis=1)
+#     # 防止分母为0
+#     df_select.depth = df_select.depth.map(lambda x: x if x != 0 else 1)
+
+#     df_bmat_melt = df_select.melt(
+#         id_vars=[
+#             "treatment",
+#             "rep",
+#             "cutoff",
+#             "chr_name",
+#             "ref_base",
+#             "chr_index",
+#             "bmat_name",
+#             "depth",
+#         ],
+#         value_vars=["A", "G", "C", "T"],
+#         var_name="mut_base",
+#         value_name="mut_count",
+#     )
+
+#     df_bmat_melt.columns = [
+#         "treatment",
+#         "rep",
+#         "cutoff",
+#         "region_id",
+#         "ref_base",
+#         "relative_pos",
+#         "bmat_name",
+#         "total_count",
+#         "mut_base",
+#         "mut_count",
+#     ]
+#     df_bmat_melt.to_csv(pt_out, index=False, header=True)
+#     logging.info("script done.")