sklearn实战-乳腺癌细胞数据挖掘（博主亲自录制视频）
QQ：231469242
读取下载美国在研新药PDF内数据：unii，分子式，分子重量，药品名，who，编码，。。。。
PDF无逻辑规则，不能百分之百提取，只能部分提取
几个默认字段为空
# -*- coding: utf-8 -*-
""" io.open() is the preferred, higher-level interface to file I/O. It wraps the OS-level file descriptor in an object that you can use to access the file in a Pythonic manner. os.open() is just a wrapper for the lower-level POSIX syscall. It takes less symbolic (and more POSIX-y) arguments, and returns the file descriptor (a number) that represents the opened file. It does not return a file object; the returned value will not have read() or write() methods.
"""
import re
from pdfminer.pdfinterp import PDFResourceManager, process_pdf
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams # pip3 install pdfminer3k from io import StringIO
from io import open #pdf文件名
pdfFilename="atesidorsen sodium.pdf"
#文件名前缀
frontName="usan/2016/"
#商标文件名
trademark_filename="trademarks.txt"
#赞助商文件名
sponsor_filename="sponsor.txt"
#读取PDF数据
def readPDF(pdfFile):
rsrcmgr = PDFResourceManager()
retstr = StringIO()
laparams = LAParams()
device = TextConverter(rsrcmgr, retstr, laparams=laparams)
process_pdf(rsrcmgr, device, pdfFile)
device.close()
content = retstr.getvalue()
retstr.close()
return content #规范PDF数据
def Format(str1):
list2=[]
#分割成列表
list1=str1.split("\n")
for i in list1:
#if i=="/n":
if i=='' or i==' 'or i==' ':
continue
list2.append(i) return list2 #提取me_usan，药品名
def Get_me_usan(the_list_data):
return the_list_data[0] #提取me_therapeutic
def Get_me_therapeutic(the_list_data):
for i in the_list_data:
if "Treatment of" in i:
return i #提取me_chemical1 分子式1
def Get_me_chemical1(the_list_data):
for i in the_list_data:
if "1. " in i:
return i
return "" #提取me_chemical2 分子式2
def Get_me_chemical2(the_list_data):
for i in the_list_data:
if "2. " in i:
return i
return "" #匹配分子式
def Re_formula(str1):
#匹配正在表达式
re_formula=re.compile(r'C(\d)+H(\d)+')
mo1=re_formula.search(str1)
if mo1!=None:
return True
return False #提取me_mo_formula，特征包含碳氢CH元素
def Get_me_mo_formula(the_list_data):
for i in the_list_data:
#转换为大写
i=i.upper()
value=Re_formula(i)
if value==True:
return i return "" #提取分子质量me_mo_weight，如果出现MOLECULAR WEIGHT，且下一个值是数字或浮点数，就提取下一个值
def Get_me_mo_weight(the_list_data):
for count in range(len(the_list_data)):
#如果出现MOLECULAR WEIGHT，则提取下一个值
if 'MOLECULAR WEIGHT' in the_list_data[count]:
value=the_list_data[count+1]
if type(eval(value)) == int or type(eval(value)) == float:
return value
return "" #从trademarks.txt搜索数据
def Get_txt_contents(filename):
file=open(filename)
content=file.readlines()
content1=[i.replace("\n","") for i in content]
return content1 #提取me_trademark，从trademarks.txt搜索数据
def Get_me_trademark(the_list_data):
for i in the_list_data:
i=i.strip(" ")
for k in list_trademarks:
if k in i:
return i
return "" #提取me_sponsor，从sponsor.txt搜索数据
def Get_me_sponsor(the_list_data):
for i in the_list_data:
i=i.strip(" ")
for k in list_sponsors:
if k in i:
return i
return "" #匹配CAS正则表达式
def Re_CAS(str1):
re_CAS=re.compile(r'(\d)+-(\d)+-(\d)+')
mo1=re_CAS.search(str1)
if mo1!=None:
return True
return False #提取CAS
def Get_CAS(the_list_data):
for i in the_list_data:
value=Re_CAS(i)
if value==True:
return i return "" #匹配WHO正则表达式
def Re_WHO(str1):
re_WHO=re.compile(r'(\d)+')
mo1=re_WHO.search(str1)
if mo1!=None:
return True
return False #提取WHO
def Get_WHO(the_list_data):
for count in range(len(the_list_data)):
#如果出现MOLECULAR WEIGHT，则提取下一个值
if 'WHO NUMBER' in the_list_data[count]:
value=the_list_data[count+1]
if type(eval(value)) == int:
return value
return "" #匹配UNII正则表达式
def Re_UNII(str1):
#{10}表示出现10次
re_UNII=re.compile(r'[A-Za-z0-9]{10}')
mo1=re_UNII.search(str1)
if mo1!=None:
return True
return False #提取UNII
def Get_UNII(the_list_data):
for count in range(len(the_list_data)):
#如果出现MOLECULAR WEIGHT，则提取下一个值
if 'UNII' in the_list_data[count]:
value=the_list_data[count+1]
if Re_UNII(value)==True:
return value
return "" #获取me_down数据
def Get_me_down(the_list_data):
name=frontName+pdfFilename
return name pdfFile = open(pdfFilename, 'rb')
outputString = readPDF(pdfFile) list_data=Format(outputString) me_source=2016 #提取me_usan，药品名
me_usan=Get_me_usan(list_data)
#提取me_therapeutic 治疗疾病
me_therapeutic=Get_me_therapeutic(list_data)
#提取me_therapeutic
me_chemical1=Get_me_chemical1(list_data) #提取me_chemical2 分子式2
me_chemical2=Get_me_chemical2(list_data) #提取me_mo_formula，特征包含碳氢CH元素
me_mo_formula=Get_me_mo_formula(list_data) #提取分子质量me_mo_weight
me_mo_weight=Get_me_mo_weight(list_data) #商标名数据库
list_trademarks=Get_txt_contents(trademark_filename)
#提取商标名
me_trademark=Get_me_trademark(list_data)
#赞助商数据库
list_sponsors=Get_txt_contents(sponsor_filename)
#提取赞助商，新公司则找不到
me_sponsor=Get_me_sponsor(list_data)
#提取CAS
me_CAS=Get_CAS(list_data)
#提取WHO
me_WHO=Get_WHO(list_data)
#提取UNII
me_UNII=Get_UNII(list_data)
#获取me_down
me_down=Get_me_down(list_data)
#me_bianma数据默认为空
me_bianma=""
#me_ylbm数据默认为空
me_ylbm=""
