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import os
import sys
import time
from functools import reduce, wraps
from shutil import copy2
import pandas as pd
import pymongo
from bson.json_util import loads
g_set = {'A', 'C', 'T', 'G'}
gg_set = {'AC', 'AG', 'AT', 'CG', 'CT', 'GT'}
summary = {
"ok": 0,
"skip": 0,
"error": 0,
"tmp": 0
}
db_name = "test"
collection_name = "snp_info"
index_name = "Sample_ID"
def bak_file(fp, ds="tmp", ext="bak", rename=True):
fn = os.path.basename(fp)
if not os.path.exists(ds):
os.mkdir(ds)
# 复制到临时目录
copy2(fp, os.path.join(ds, fn))
# 原路径加.bak后缀名
if rename:
os.rename(fp, "{}.{}".format(fp, ext))
def pick_rules(fp):
# 文件名中包含LH-R及LH-I的均为其他项目, 跳过不处理
fp_base = os.path.basename(fp)
if "LH-R" in fp_base or "LH-I" in fp_base:
bak_file(fp, "skip")
summary["skip"] += 1
print("{}\t0\t0\tskip".format(fp_base))
return False
elif fp_base.startswith("~"): # 跳过office临时文件
bak_file(fp, rename=False)
summary["tmp"] += 1
print("{}\t0\t0\ttmp".format(fp_base))
return False
else:
return True
def get_files(dp, ext="xlsx"):
"""
获取目标目录下指定后缀名文件列表
:param dp: 目标目录
:param ext: 后缀名, 默认为xlsx
:return: 指定后缀名文件路径列表
"""
# 绝对路径中有空格的情况会有bug, 所以暂时还是用相对路径
# abspath = os.path.abspath(dp)
lst = []
for root, dirs, files in os.walk(dp):
for file in files:
if file.endswith(".{}".format(ext)):
lst.append(os.path.join(root, file))
return lst
def xlsx2df(fp, sn="Sample_ID"):
"""
将xlsx内带有检测信息的结果转化为dataframe返回
可以处理以下四种情况
1. Sample_ID在首行, 紧接着是样本信息, 中间没有空行
2. Sample_ID在首行, 紧接着两行首行为空, 第四行开始才是正常样本信息
3. 1, 2两行首列为空, Sample_ID在第三行, 后面才是样本信息
4. 没有Sample_ID, 首行首列为空, 紧接着就是样本信息
:param fp: Excel文件路径
:param sn: header name默认是Sample_ID
:return: 整理后的dataframe
"""
df = pd.read_excel(fp, header=None)
# 添加header name
is_format_ok = False
for i in range(df.shape[0]):
_t = str(df.iloc[i, 0])
if _t == sn:
# Sample_ID在首行的情况(大多数情况)
df.columns = [i for i in df.iloc[i].values]
df = df.iloc[(i + 1):] # 跳过Sample_ID行
is_format_ok = True
break
if _t.startswith("B"):
# 没有Sample_ID的情况(极少)
# 以B开头即表示正常ID, 则取上一行内容为header name, 第一个应该是sn即Sample_ID
df.columns = [sn] + [i for i in df.iloc[i - 1].values][1:]
is_format_ok = True
break
if not is_format_ok:
# 文件无法识别, 备份源文件并返回空df
print("{}:\t0\t0\terror".format(os.path.basename(fp)))
bak_file(fp, "error")
summary["error"] += 1
return pd.DataFrame()
# 删除Sample_ID为空的行
df = df[pd.notnull(df[sn])]
# 删除空列
df = df.dropna(axis=1, how="all")
# 删除重复rs号
df = df.loc[:, ~df.columns.duplicated()]
rows, cols = df.shape
for i in range(rows):
for j in range(1, cols): # 跳过第一列Sample_ID不作处理
v = df.iloc[i, j]
if isinstance(v, str):
v = v.strip().upper() # 去空格&转大写
if v in g_set:
# 纯合子加倍
df.iloc[i, j] = v * 2
elif len(v) == 2:
# 杂合子排序
_v = "".join(sorted(v))
df.iloc[i, j] = _v if _v in gg_set else None
elif v == "DEL":
# 单个碱基缺失, 用DD表示
df.iloc[i, j] = "DD"
elif "." in v:
# 杂合缺失, 将DEL替换为D
_s = sorted(v.split("."), key=lambda x: len(x))[0] # 提取出单个碱基
df.iloc[i, j] = "".join(sorted((_s, "D"))) if _s in g_set else None
else:
# 空值或者超长字符, 填Nan
df.iloc[i, j] = None
else:
# 非string值填Nan
df.iloc[i, j] = None
summary["ok"] += 1
print("{}\t{}\t{}\tok".format(os.path.basename(fp), rows, cols))
return df
def insert_many(collection, docs=None, update=True):
if not docs:
return
# $set 的时候, 会更新数据, setOnInsert只插入不更新
update_key = "$set" if update else "$setOnInsert"
bulk = pymongo.bulk.BulkOperationBuilder(collection, ordered=False)
for i in docs:
if i.get(index_name):
bulk.find({index_name: i[index_name]}).upsert().update_one({update_key: i})
else:
bulk.insert(i)
result = bulk.execute()
return result
def df2db(df):
# 写入数据库
client = pymongo.MongoClient()
db = client[db_name]
collection = db[collection_name]
try:
collection.index_information()
except pymongo.errors.OperationFailure:
# 索引Sample_ID
collection.create_index(index_name, unique=True)
data = loads(df.T.to_json()).values()
rs = insert_many(collection, data)
# collection.insert_many(loads(df.T.to_json()).values())
print("-" * 30)
print("summary(files):")
print("\tok:\t\t{}".format(summary['ok']))
print("\terror:\t\t{}".format(summary['error']))
print("\ttmp:\t\t{}".format(summary['tmp']))
print("\tskip:\t\t{}".format(summary['skip']))
print("\ttotal:\t\t{}".format(sum(summary.values())))
print("-" * 30)
print("summary(samples):")
print("\tupdate:\t\t{}".format(rs.get("nModified")))
print("\tinsert:\t\t{}".format(rs.get("nUpserted") + rs.get("nInserted")))
print("\ttotal:\t\t{}".format(collection.count()))
client.close()
def time_it(func):
@wraps(func)
def wrapper(*args, **kwargs):
t1 = time.time()
rt = func(*args, **kwargs)
print("cost: {}s".format(round((time.time() - t1), 2)))
return rt
return wrapper
@time_it
def main(dp):
# 读入Excel, 处理后合并输出到df
df = reduce(
lambda df1, df2: df1.append(df2, ignore_index=True),
map(
xlsx2df, filter(
pick_rules,
get_files(dp)
)
)
)
# 删除重复ID
df = df.drop_duplicates(subset="Sample_ID")
# 删除空列
df = df.dropna(axis=1, how="all")
df2db(df)
print("-" * 30)
print("data importing complete!")
if __name__ == "__main__":
if len(sys.argv) != 2:
sys.exit("Usage: {} <samples_dir>".format(os.path.basename(sys.argv[0])))
main(sys.argv[1])
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