class AllAttributes:
def __init__(self, prop_name, txt_name, text_path, triple_path, out_path,
c_path):
self.txt_name = txt_name
self.text_path = text_path
self.triple_path = triple_path
self.prop_name = prop_name
self.out_path = out_path
self.c_path = c_path
self.log_wp = LogWp()
def get_toexcel(self):
xls = xlwt.Workbook()
sheet = xls.add_sheet("all_attributes")
sheet.write(0, 0, 'txt_name')
sheet.write(0, 1, 'Full_text')
sheet.write(0, 2, 'Target_sentences')
sheet.write(0, 3, 'alloy')
sheet.write(0, 4, 'property')
sheet.write(0, 5, 'value')
# triples_tuple
data_triples = xlrd.open_workbook(self.triple_path)
table_triples = data_triples.sheet_by_index(0)
for x in range(0, len(table_triples.col_values(0))):
sheet.write(x + 1, 1, str(table_triples.col_values(0)[x]))
sheet.write(x + 1, 2, str(table_triples.col_values(1)[x]))
sheet.write(x + 1, 3, str(table_triples.col_values(2)[x]))
sheet.write(x + 1, 4, str(table_triples.col_values(3)[x]))
sheet.write(x + 1, 5, str(table_triples.col_values(4)[x]))
# txt_name+doi_list
k = 0
for i in range(0, len(table_triples.col_values(0))):
if table_triples.col_values(0)[i]:
sheet.write(i + 1, 0, str(self.txt_name[0][k]))
k += 1
# text + filtered_text_1 + Target sentences
txt_name = os.listdir(self.text_path)
process_text = []
for i in range(0, len(txt_name)):
file = open(self.text_path + '/' + str(txt_name[i]),
'r',
encoding='utf-8')
sole_text = file.read()
pre_processor = PreProcessor(sole_text, self.c_path)
filter_txt = pre_processor.pre_processor()
path_2 = self.text_path + '/proprecess'
is_exists = os.path.exists(path_2)
if is_exists:
text_path = path_2 + '/' + str(txt_name[i])
file = open(text_path, 'w', encoding='utf-8')
file.write(filter_txt)
process_text.append(text_path)
else:
os.makedirs(path_2)
text_path = path_2 + '/' + txt_name[i]
file = open(text_path, 'w', encoding='utf-8')
file.write(filter_txt)
process_text.append(text_path)
self.log_wp.excel_save(xls, self.out_path)
def get_extraction_outcome(xml_path, save_path, config_path):
TableExtractor_m = TableExtractorModifiedtoalloy(xml_path, save_path, config_path)
all_error_file = []
xml_name = os.listdir(xml_path)
log_wp = LogWp()
for file_i in range(len(os.listdir(xml_path))):
tables = None
all_tables = []
doi = xml_name[file_i].replace(".xml", "")
doi = doi.replace("-", "/", 1)
xml_n = xml_name[file_i]
file = xml_path + '/' + str(xml_n)
try:
tables, captions = TableExtractor_m.get_xml_tables(doi, file)
except:
all_error_file.append(doi)
if tables:
cols, rows, col_inds, row_inds = TableExtractor_m.get_headers(tables, doi)
tab = []
for table, row_ind, col_ind in zip(tables, row_inds, col_inds):
curr, error_file = (TableExtractor_m.construct_table_object(doi, table, row_ind, col_ind))
if curr:
tab.append(curr)
if error_file:
all_error_file.append(str(doi))
for i, (t, caption) in enumerate(zip(tab, captions)):
if t is not None:
t['order'] = i
t['_id'] = ObjectId()
t['caption'] = caption
t['paper_doi'] = doi
all_tables.append(t)
log_wp.print_log('Success: Extracted Tables from : %s', doi)
xls = openpyxl.Workbook()
sheet_id = 1
if all_tables:
for table in all_tables:
sht_new = xls.create_sheet(str(sheet_id))
act_table = table['act_table']
caption = table['caption']
row_len = len(act_table[0])
doi = table['paper_doi']
sht_new.cell(1, 1, str(doi))
sht_new.cell(2, 1, str(caption))
start_row = 3
for row in act_table:
len_row = len(row)
for index in range(len_row):
sht_new.cell(start_row, index + 1, row[index])
start_row += 1
sheet_id += 1
del xls['Sheet']
log_wp.excel_save(xls, save_path + '/' + str(file_i) + "end.xlsx")
return all_error_file, len(all_error_file)
class File_IO:
def __init__(self, target_sents, out_path, txt_name):
self.target_sents = target_sents
self.out_path = out_path
self.txt_name = txt_name
self.log_wp = LogWp()
def out_to_excel(self):
length_sent = len(self.target_sents)
xls = xlwt.Workbook()
sht1 = xls.add_sheet("Sheet1")
for w in range(0, length_sent):
sht1.write(w, 0, str(w) + '.txt')
if self.target_sents[w] == '{}':
out = 'no target sentence'
sht1.write(w, 1, out)
else:
sht1.write(w, 1, self.target_sents[w])
self.log_wp.excel_save(xls, self.out_path)
def data_from_excel(self):
all_data = []
file = xlrd.open_workbook(self.out_path)
sheet = file.sheet_by_index(0)
col_value = sheet.col_values(1)
k = len(col_value)
for k in range(0, k):
unit_data = []
str_sent = col_value[k]
if str_sent == 'no target sentence':
all_data.append([])
else:
dict_sent = eval(str_sent)
for i, sent in dict_sent.items():
unit_data.append(sent)
all_data.append(unit_data)
return all_data
class GetTargetInfo:
def __init__(self, all_info, out_path, c_path):
self.related_allinfo = all_info
self.out_path = out_path
self.log_wp = LogWp()
self.dict_info = Dictionary(c_path)
self.prop_pattern = self.dict_info.table_prop_pattern
def structure_ele(self):
xls = openpyxl.Workbook()
sht = xls.create_sheet(index=0)
sht.cell(1, 1, "File_name")
sht.cell(1, 2, "DOIs")
sht.cell(1, 3, "Material")
sht.cell(1, 4, "Percentage")
sht.cell(1, 5, "Element_info and other_info")
start_row = 2
for file, ele in self.related_allinfo.items():
for material in ele:
sht.cell(start_row, 1, file)
if 'doi' in material.keys():
sht.cell(start_row, 2, material['doi'])
material.pop('doi')
if 'material' in material.keys():
material_name = material['material']
noisy = re.findall('\s*\[.+\]', str(material_name))
if noisy:
for puc in noisy:
material_name = str(material_name).replace(puc, '')
sht.cell(start_row, 3, material_name)
material.pop('material')
if 'percentage' in material.keys():
sht.cell(start_row, 4, material['percentage'])
material.pop('percentage')
if material:
sht.cell(start_row, 5, str(material))
start_row += 1
self.log_wp.excel_save(xls, self.out_path)
def structure_prop(self, prop_name_s):
xls = openpyxl.Workbook()
sht = xls.create_sheet(0)
sht.cell(1, 1, "File_name")
sht.cell(1, 2, "DOIs")
sht.cell(1, 3, "Table_topic")
sht.cell(1, 4, "Material")
sht.cell(1, 5, "unit")
sht.cell(1, 6, "Property_name")
sht.cell(1, 7, "Property_value")
sht.cell(1, 8, "Child_tag")
sht.cell(1, 9, "Other_info")
start_row = 2
for file, ele in self.related_allinfo.items():
for material in ele:
get_prop = None
sht.cell(start_row, 1, file)
if 'doi' in material.keys():
sht.cell(start_row, 2, material['doi'])
material.pop('doi')
if 'table_topic' in material.keys():
sht.cell(start_row, 3, material['table_topic'])
material.pop('table_topic')
if 'material' in material.keys():
material_name = material['material']
noisy = re.findall('\s*\[.+\]', str(material_name))
if noisy:
for puc in noisy:
material_name = str(material_name).replace(puc, '')
sht.cell(start_row, 4, material_name)
material.pop('material')
if 'unit' in material.keys():
unit_replace = material['unit'].replace('degC', '°C')
sht.cell(start_row, 5, unit_replace)
material.pop('unit')
if 'child_tag' in material.keys():
sht.cell(start_row, 8, str(material['child_tag']))
material.pop('child_tag')
if 'other_info' in material.keys():
sht.cell(start_row, 9, str(material['other_info']))
material.pop('other_info')
if len(material) == 1:
for prop_name, value in material.items():
sht.cell(start_row, 6, str(prop_name))
sht.cell(start_row, 7, str(value))
elif len(material) >= 1:
for prop_name, value in material.items():
for pattern in self.prop_pattern[prop_name_s]:
prop_search = re.findall(pattern, str(prop_name))
if prop_search:
sht.cell(start_row, 6, str(prop_name))
sht.cell(start_row, 7, str(value))
get_prop = True
break
if get_prop:
break
start_row += 1
self.log_wp.excel_save(xls, self.out_path)
triple_lines += 1
n_triple = 0
for index, v in all_outcome.items():
out_unit.append(v)
n_triple += 1
for n in range(0, n_triple):
sht2.write(num_of_lines + n, 1, sent)
num_of_lines = num_of_lines + n_triple
for s in range(0, len(out_unit)):
sht2.write(triple_lines + s, 2, out_unit[s][0])
sht2.write(triple_lines + s, 3, out_unit[s][1])
sht2.write(triple_lines + s, 4, out_unit[s][2])
if out_unit:
triple_lines = triple_lines + len(out_unit)
else:
triple_lines += 1
file_index += 1
else:
out = 'no target triples'
sht2.write(triple_lines, 2, out)
sht2.write(triple_lines, 3, 'None')
sht2.write(triple_lines, 4, 'None')
sht2.write(num_of_lines, 1, 'no target sentence')
num_of_lines += 1
triple_lines += 1
file_index += 1
log_wp.excel_save(xls, triple_path)
attributes = AllAttributes(prop_name, txt_name, text_path, triple_path, out_path, c_path)
attributes.get_toexcel()
def relation_extraction(self, C_path, origin_text_path, prop_name,
triple_path, out_path, m_path):
log_wp = LogWp()
# The path to the folder where the full-text text is stored
text_path = os.path.join(m_path, "full_text")
# Locate the obtained target corpus
TS_path = os.path.join(m_path, "sent.xls")
# Filter to get the full text
FT = FilterText(origin_text_path, text_path)
txt_name, dois = FT.process()
# Get the target corpus
all_x = []
txt_name2 = []
length = len(os.listdir(text_path))
for i in range(0, length):
n_path = text_path + '/' + str(os.listdir(text_path)[i])
with open(n_path, 'r', encoding='utf-8') as file:
data = file.read()
pre_processor = PreProcessor(data, C_path)
filter_data = pre_processor.pre_processor()
processor = TPreProcessor(filter_data, prop_name, C_path)
filter_data = processor.processor()
positioner = SentencePositioner(filter_data, prop_name, C_path)
target_sents = positioner.target_sent()
# print(target_sents)
all_x.append(str(target_sents))
txt_name2.append(n_path)
FI_out = FI(all_x, TS_path, txt_name2)
FI_out.out_to_excel()
# Extraction of triples
data = FI_out.data_from_excel()
xls = xlwt.Workbook()
sht2 = xls.add_sheet("triple_extracion")
triple_lines = 0 # the number of "triple"
file_index = 0 # document indexing
num_of_lines = 0 # the number of sentences
for item in data:
doi = dois[file_index].replace("doi:", "")
sht2.write(triple_lines, 0, doi)
if item != []:
out_unit = []
sent_out = {}
l_sent = []
for sent in item:
processor = TPreProcessor(sent, prop_name, C_path)
filter_data = processor.processor()
parse = PhraseParse(filter_data, prop_name, C_path)
sub_order, sub_id, object_list = parse.alloy_sub_search()
RE = RelationExtraciton(prop_name, filter_data, sub_order,
sub_id, object_list, C_path)
all_outcome = RE.triple_extraction()
if not all_outcome:
out = 'no target triples'
sht2.write(triple_lines, 2, out)
sht2.write(triple_lines, 3, 'None')
sht2.write(triple_lines, 4, 'None')
sht2.write(num_of_lines, 1, 'no target sentence')
num_of_lines += 1
triple_lines += 1
n_triple = 0
for index, v in all_outcome.items():
out_unit.append(v)
n_triple += 1
for n in range(0, n_triple):
sht2.write(num_of_lines + n, 1, sent)
num_of_lines = num_of_lines + n_triple
for s in range(0, len(out_unit)):
sht2.write(triple_lines + s, 2, out_unit[s][0])
sht2.write(triple_lines + s, 3, out_unit[s][1])
sht2.write(triple_lines + s, 4, out_unit[s][2])
if out_unit:
triple_lines = triple_lines + len(out_unit)
else:
triple_lines += 1
file_index += 1
else:
out = 'no target triples'
sht2.write(triple_lines, 2, out)
sht2.write(triple_lines, 3, 'None')
sht2.write(triple_lines, 4, 'None')
sht2.write(num_of_lines, 1, 'no target sentence')
num_of_lines += 1
triple_lines += 1
file_index += 1
log_wp.excel_save(xls, triple_path)
attributes = AllAttributes(prop_name, txt_name, text_path, triple_path,
out_path, C_path, dois)
attributes.get_toexcel()
class AcquireTargetInfo:
def __init__(self, c_path, origin_text_path, prop_list, excels_path,
out_path):
self.c_path = c_path
self.prop_list = prop_list
self.origin_text_path = origin_text_path
self.excels_path = excels_path
self.dict_info = Dictionary(self.c_path)
self.out_path = out_path
self.log_wp = LogWp()
def mkdir(self, file_name):
pathd = os.getcwd() + '\\' + file_name
if os.path.exists(pathd):
for root, dirs, files in os.walk(pathd, topdown=False):
for name in files:
os.remove(os.path.join(root, name))
for name in dirs:
os.rmdir(os.path.join(root, name))
os.rmdir(pathd)
os.mkdir(pathd)
def get_doi_fromtxt(self, txt_path):
text_name = txt_path.replace(".txt", "")
doi = text_name.replace("-", "/", 1)
return doi
def get_abrre(self, text, prop_name):
processor = TPreProcessor(text, prop_name, self.c_path)
text = processor.processor()
sentences = nltk.sent_tokenize(text)
sentences_split = text.split(" ")
alloy_write_type = self.dict_info.alloy_writing_type
len_type = len(alloy_write_type)
abbre_to_alloy = {}
for sent in sentences:
processor = TPreProcessor(sent, prop_name, self.c_path)
filter_data = processor.processor()
words = nltk.word_tokenize(filter_data)
for word in words:
for type_i in range(0, len_type):
outcome = re.findall(alloy_write_type[type_i], word)
outcome_alloy = None
if outcome:
abbre = "(" + word + ")"
if abbre in sentences_split:
index_alloy = sentences_split.index(abbre) - 1
alloy = sentences_split[index_alloy]
for type_j in range(0, len_type):
outcome_alloy = re.findall(
alloy_write_type[type_j], alloy)
if outcome_alloy:
abbre_to_alloy[word] = alloy
break
if outcome_alloy:
break
return abbre_to_alloy
def get_text_triple(self, prop_name):
self.mkdir('output_tt')
text_path = r"output_tt\full_text"
self.mkdir(text_path)
ft = FilterText(self.origin_text_path, text_path)
txt_name = ft.process()
length = len(os.listdir(self.origin_text_path))
all_txt_info = []
for i in range(0, length):
n_path = os.listdir(self.origin_text_path)[i]
doi = self.get_doi_fromtxt(n_path)
file = open(text_path + '/' + n_path, 'r', encoding='utf-8')
data = file.read()
pre_processor = PreProcessor(data, self.c_path)
filter_txt = pre_processor.pre_processor()
file_origin = open(self.origin_text_path + '/' + n_path,
'r',
encoding='utf-8')
data_origin = file_origin.read()
abbre_pairs = self.get_abrre(data_origin, prop_name)
positioner = SentencePositioner(filter_txt, prop_name, self.c_path)
target_sents = positioner.target_sent()
for index, sent in target_sents.items():
processor = TPreProcessor(sent, prop_name, self.c_path)
filter_data = processor.processor()
parse = PhraseParse(filter_data, prop_name, self.c_path)
sub_order, sub_id, object_list = parse.alloy_sub_search()
ree = RelationExtraciton(prop_name, filter_data, sub_order,
sub_id, object_list, self.c_path,
abbre_pairs)
all_outcome = ree.triple_extraction()
if all_outcome:
for id_m, info in all_outcome.items():
sole_info = dict()
sole_info['doi'] = doi
sole_info['material'] = info[0]
sole_info['prop_name'] = info[1]
sole_info['prop_value'] = info[2]
all_txt_info.append(sole_info)
return all_txt_info
def gather_tableinfo_textinfo(self, all_txt_info, table_info, prop_name,
prop_pattern, unit_pattern_text):
gather_outcome = list()
for file_name, t_info in table_info.items():
sole_info = dict()
all_triple_info = list()
sole_doi = list()
for sole_m_info in t_info:
triple_info = dict()
triple_info['source'] = 'table'
if 'doi' in sole_m_info.keys():
plus_doi = sole_m_info['doi']
sole_doi.append(plus_doi)
sole_m_info.pop('doi')
if 'material' in sole_m_info.keys():
sole_material = sole_m_info['material']
noisy = re.findall('\s*\[.+\]', str(sole_material))
if noisy:
for puc in noisy:
sole_material = str(sole_material).replace(puc, '')
triple_info['material'] = sole_material
sole_m_info.pop('material')
if 'unit' in sole_m_info.keys():
sole_unit = sole_m_info['unit']
triple_info['unit'] = sole_unit
sole_m_info.pop('unit')
if 'other_info' in sole_m_info.keys():
sole_other_info = sole_m_info['other_info']
triple_info['other_prop_info'] = sole_other_info
sole_m_info.pop('other_info')
if 'child_tag' in sole_m_info.keys():
sole_tag_info = sole_m_info['child_tag']
triple_info['child_tag'] = sole_tag_info
sole_m_info.pop('child_tag')
if 'table_topic' in sole_m_info.keys():
sole_tag_info = sole_m_info['table_topic']
triple_info['table_topic'] = sole_tag_info
sole_m_info.pop('table_topic')
if len(sole_m_info) == 1:
for prop_name_t, value in sole_m_info.items():
sole_propname = str(prop_name_t)
triple_info['prop_name'] = sole_propname
sole_value = str(value)
triple_info['value'] = sole_value
elif len(sole_m_info) >= 1:
get_prop = None
for prop_name_t, value in sole_m_info.items():
for pattern in prop_pattern[prop_name]:
prop_search = re.findall(pattern, str(prop_name_t))
if prop_search:
sole_propname = str(prop_name_t)
triple_info['prop_name'] = sole_propname
sole_value = str(value)
triple_info['value'] = sole_value
get_prop = True
break
if get_prop:
break
all_triple_info.append(triple_info)
if list(set(sole_doi)):
sole_info[list(set(sole_doi))[0]] = all_triple_info
gather_outcome.append(sole_info)
gather = 0
for q in gather_outcome:
k = tuple(q.keys())[0]
i = q[k]
for n in i:
for w, v in n.items():
if w == 'value':
gather += 1
self.log_wp.print_log("gather number :%s", gather)
copy_all_txt_info = copy.copy(all_txt_info)
if copy_all_txt_info:
all_text = 0
all_gather_doi = []
for info_one in gather_outcome:
all_gather_doi.append(tuple(info_one.keys())[0])
for triple_info_sole in copy_all_txt_info:
if triple_info_sole['doi'] in all_gather_doi:
all_text += 1
plus_info = dict()
plus_info['source'] = 'text'
plus_info['prop_name'] = triple_info_sole['prop_name']
prop_value = triple_info_sole['prop_value']
plus_info['material'] = triple_info_sole['material']
unit_search = re.findall(unit_pattern_text[prop_name],
str(prop_value))
if unit_search:
plus_info['unit'] = unit_search[0]
prop_value = prop_value.replace(unit_search[0], '')
plus_info['value'] = prop_value
else:
plus_info['unit'] = ""
plus_info['value'] = prop_value
for get_info in gather_outcome:
if tuple(
get_info.keys())[0] == triple_info_sole['doi']:
get_info[triple_info_sole['doi']].append(plus_info)
if triple_info_sole['doi'] not in all_gather_doi:
all_text += 1
plus_info = {}
full_info = {}
sole_triple = []
plus_info['source'] = 'text'
plus_info['prop_name'] = triple_info_sole['prop_name']
prop_value = triple_info_sole['prop_value']
plus_info['material'] = triple_info_sole['material']
unit_search = re.findall(unit_pattern_text[prop_name],
str(prop_value))
if unit_search:
plus_info['unit'] = unit_search[0]
prop_value = prop_value.replace(unit_search[0], '')
plus_info['value'] = prop_value
else:
plus_info['unit'] = ""
plus_info['value'] = prop_value
if plus_info:
sole_triple.append(plus_info)
full_info[triple_info_sole['doi']] = sole_triple
gather_outcome.append(full_info)
all_gather_doi.append(triple_info_sole['doi'])
self.log_wp.print_log("all_text number :%s", all_text)
return gather_outcome
def transform_comp_outcome(self, all_composition):
ele_list = self.dict_info.ele_list
gather_outcome = []
for file_name, t_info in all_composition.items():
sole_info = {}
all_triple_info = []
for sole_m_info in t_info:
triple_info = {}
sole_doi = sole_m_info['doi']
sole_m_info.pop('doi')
if 'material' in sole_m_info.keys():
sole_material = sole_m_info['material']
noisy = re.findall('\[.+\]', str(sole_material))
if noisy:
for puc in noisy:
sole_material = str(sole_material).replace(puc, '')
triple_info['material'] = sole_material
sole_m_info.pop('material')
for element in ele_list:
if element in sole_m_info.keys():
triple_info[element] = sole_m_info[element]
sole_m_info.pop(element)
if sole_m_info:
triple_info["other_eleinfo"] = sole_m_info
all_triple_info.append(triple_info)
sole_info[sole_doi] = all_triple_info
gather_outcome.append(sole_info)
return gather_outcome
def allinfo_dependencyparse(self, comp_info, prop_info):
all_ele_doi = []
all_prop_doi = []
outcome = []
for doi_info_ele in comp_info:
ele_doi = tuple(doi_info_ele.keys())[0]
all_ele_doi.append(ele_doi)
for doi_info_prop in prop_info:
prop_doi = tuple(doi_info_prop.keys())[0]
all_prop_doi.append(prop_doi)
prop_info_modified = copy.copy(prop_info)
for doi_info_ele in comp_info:
ele_doi = tuple(doi_info_ele.keys())[0]
if ele_doi in all_prop_doi:
for doi_info_prop in prop_info:
prop_doi = tuple(doi_info_prop.keys())[0]
plus_info = {}
all_doi_info = []
if ele_doi == prop_doi:
if doi_info_prop in prop_info_modified:
prop_info_modified.remove(doi_info_prop)
ele_doi_fullinfo = doi_info_ele[ele_doi]
ele_allname = []
prop_allname = []
pop_name = []
for one_material_ele in ele_doi_fullinfo:
if 'material' in one_material_ele.keys():
ele_m_name = one_material_ele['material']
ele_allname.append(ele_m_name)
modified_ele_allname = []
for name in ele_allname:
space_search = re.findall('\s', str(name))
if space_search:
name_list = str(name).split()
modified_ele_allname.append(str(name))
for name_sepe in name_list:
modified_ele_allname.append(name_sepe)
else:
modified_ele_allname.append(name)
for one_material_prop in doi_info_prop[prop_doi]:
if 'material' in one_material_prop.keys():
prop_m_name = one_material_prop['material']
prop_allname.append(prop_m_name)
if prop_m_name not in modified_ele_allname and len(
ele_doi_fullinfo) == 1:
if one_material_prop['source'] == 'table':
combine_info = {}
for prop_name, prop_value in one_material_prop.items(
):
combine_info[
prop_name] = prop_value
for ele_name, ele_value in ele_doi_fullinfo[
0].items():
combine_info[ele_name] = ele_value
all_doi_info.append(combine_info)
else:
all_doi_info.append(one_material_prop)
if prop_m_name not in modified_ele_allname and len(
ele_doi_fullinfo) != 1:
all_doi_info.append(one_material_prop)
if prop_m_name in modified_ele_allname:
for one_material_ele in ele_doi_fullinfo:
if 'material' in one_material_ele.keys(
):
ele_m_name = one_material_ele[
'material']
space_search = re.findall(
'\s', str(ele_m_name))
if space_search:
ele_m_name_split = ele_m_name.split(
)
if prop_m_name in ele_m_name_split or prop_m_name == ele_m_name:
pop_name.append(ele_m_name)
combine_info = {}
for prop_name, prop_value in one_material_prop.items(
):
combine_info[
prop_name] = prop_value
for ele_name, ele_value in one_material_ele.items(
):
combine_info[
ele_name] = ele_value
all_doi_info.append(
combine_info)
else:
if prop_m_name == ele_m_name:
combine_info = {}
for prop_name, prop_value in one_material_prop.items(
):
combine_info[
prop_name] = prop_value
for ele_name, ele_value in one_material_ele.items(
):
combine_info[
ele_name] = ele_value
all_doi_info.append(
combine_info)
for one_material_ele in ele_doi_fullinfo:
if 'material' in one_material_ele.keys():
ele_m_name = one_material_ele['material']
if ele_m_name not in pop_name:
if ele_m_name not in prop_allname:
all_doi_info.append(one_material_ele)
if all_doi_info:
plus_info[ele_doi] = all_doi_info
outcome.append(plus_info)
else:
outcome.append(doi_info_ele)
for extra_prop in prop_info_modified:
outcome.append(extra_prop)
return outcome
def structureinfo_toexcel(self, all_structureinfo, out_path):
ele_list = self.dict_info.ele_list
xls = openpyxl.Workbook()
sht = xls.create_sheet("0")
sht = xls.create_sheet(index=0)
sht.cell(1, 1, "Source")
sht.cell(1, 2, "DOIs")
sht.cell(1, 3, "table_topic")
sht.cell(1, 4, "material")
sht.cell(1, 5, "Property_name")
sht.cell(1, 6, "Property_value")
sht.cell(1, 7, "Unit")
col_n = 8
row_now = 2
sht.cell(1, col_n, str("other_element_info"))
col_n += 1
sht.cell(1, col_n, str("other_property_info"))
col_n += 1
sht.cell(1, col_n, str("child_tag"))
col_n += 1
for ele in ele_list:
sht.cell(1, col_n, ele)
col_n += 1
for m_info in all_structureinfo:
doi = tuple(m_info.keys())[0]
length_m_info = m_info[doi]
for index_m in range(len(length_m_info)):
sht.cell(row_now, 2, doi)
material_now = length_m_info[index_m]
if 'source' in material_now.keys():
sht.cell(row_now, 1, str(material_now['source']))
if 'table_topic' in material_now.keys():
sht.cell(row_now, 3, str(material_now['table_topic']))
if 'material' in material_now.keys():
sht.cell(row_now, 4, str(material_now['material']))
if 'prop_name' in material_now.keys():
sht.cell(row_now, 5, str(material_now['prop_name']))
if 'value' in material_now.keys():
sht.cell(row_now, 6, str(material_now['value']))
if 'unit' in material_now.keys():
sht.cell(row_now, 7, str(material_now['unit']))
if "other_eleinfo" in material_now.keys():
sht.cell(row_now, 8, str(material_now['other_eleinfo']))
if "other_prop_info" in material_now.keys():
sht.cell(row_now, 9, str(material_now['other_prop_info']))
if "child_tag" in material_now.keys():
sht.cell(row_now, 10, str(material_now["child_tag"]))
col_ele = 11
for ele in ele_list:
if ele in material_now.keys():
sht.cell(row_now, col_ele, material_now[ele])
col_ele += 1
row_now += 1
del xls['Sheet']
self.log_wp.excel_save(xls, out_path)
def run(self):
prop_pattern = self.dict_info.table_prop_pattern
unit_pattern_text = self.dict_info.table_unit_pattern_text
for prop_name in self.prop_list:
self.mkdir('output_tt')
text_path = r"output_tt\full_text"
self.mkdir(text_path)
all_txt_info = self.get_text_triple(prop_name)
target_property = prop_name # 'density' 'liquidus' 'solidus' 'solvus'
te = TableExtraction(self.excels_path,
self.c_path,
prop_name=target_property)
info_all = te.property_info_extraction()
i_l = 0
for k, v in info_all.items():
i_l += len(v)
all_composition = te.composition_triple_extraction()
gather_outcome = self.gather_tableinfo_textinfo(
all_txt_info, info_all, prop_name, prop_pattern,
unit_pattern_text)
gather = 0
for q in gather_outcome:
k = tuple(q.keys())[0]
i = q[k]
gather += len(i)
ele_transform = self.transform_comp_outcome(all_composition)
all_structureinfo = self.allinfo_dependencyparse(
ele_transform, gather_outcome)
b = 0
for a in all_structureinfo:
k = tuple(a.keys())[0]
i = a[k]
for n in i:
for w, v in n.items():
if w == 'value':
b += 1
out_path = self.out_path + '/' + str(prop_name) + '.xlsx'
self.structureinfo_toexcel(all_structureinfo, out_path)