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pf2helpers.py
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pf2helpers.py
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import csv
from bs4 import BeautifulSoup
import requests
import re
headers = {
'User-Agent': 'PF2 data to rest builder',
'From': 'jimbarnesrtp' # This is another valid field
}
class Pf2Helpers():
def load_csv(self, file_name):
with open(file_name, encoding='utf-8-sig') as csv_file:
csv_reader = csv.DictReader(csv_file)
data_list = []
for row in csv_reader:
data = {}
for field in csv_reader.fieldnames:
data[field.lower()] = row[field]
data_list.append(data)
#print(tl_data)
return data_list
def load_html(self, link):
res2 = requests.get(link, headers, verify=False)
res2.raise_for_status()
soup2 = BeautifulSoup(res2.text, 'html5lib')
main = soup2.find("span", {'id':'ctl00_RadDrawer1_Content_MainContent_DetailedOutput'})
return main
def parse_text_from_html(self, html, blacklist: list):
data_to_parse = ""
if hasattr(html, "find_all"):
data_to_parse = html
else:
data_to_parse = BeautifulSoup(html, 'html5lib')
text_holder = []
text = data_to_parse.find_all(text=True)
for t in text:
if t.parent.name not in blacklist:
#print("t:",t," parent:", t.parent.name)
text_holder.append(t)
#this was done to remove extraspaces between words and create a better reading space
return " ".join(" ".join(text_holder).split())
def norm_link(self, name):
#print("norm link:", name)
if name == "—" or name == "":
return name
else:
soup = BeautifulSoup(name, 'html5lib')
href = soup.find_all("a")
return href[0].text
def norm_multi(self, multi):
#print("Multi:", multi)
if multi == "—" or multi == "":
return multi
ret_multi = []
found_list = re.finditer("<u>(.*?)</u>", multi)
for match in found_list:
ret_multi.append(self.norm_link(match.group()))
return ret_multi
def norm_url(self, url):
#print("URL:", url)
soup = BeautifulSoup(url, 'html5lib')
href = soup.find_all("a")
return "https://2e.aonprd.com/"+href[0]['href']
def split_children_by_rule(self, children, rule):
string_holder = []
split_children = []
string = " "
for child in children:
string_contents = str(child)
if string_contents.startswith(rule):
split_children.append(string.join(string_holder))
string_holder = []
string_holder.append(string_contents)
split_children.append(string.join(string_holder))
return split_children
def split_children(self, children):
return self.split_children_by_rule(children, "<h2")
def norm_prereqs(self, prereq):
soup = BeautifulSoup(prereq, 'html5lib')
text = ''.join(soup.html.findAll(text=True))
#print("prereqs:", text)
return text
def norm_pfs(self, pfs):
soup = BeautifulSoup(pfs, 'html5lib')
img = soup.find_all("img")
if(len(img) == 0):
return "Excluded"
else:
return img[0]['alt']
def objectify_attributes(self, attrs, key_words: list):
attr_locs = []
for key_word in key_words:
index = attrs.find(key_word)
if index > -1:
attr_loc = {}
attr_loc['keyword'] = key_word
attr_loc['start'] = index
attr_locs.append(attr_loc)
new_locs = sorted(attr_locs, key=lambda loc: loc['start'])
slices = self.get_slices_from_locs(new_locs)
attributes = {}
for piece in slices:
start_loc = piece['start'] + len(piece['keyword'])
if piece['end'] == -1:
attributes[piece['keyword'].lower()] = attrs[start_loc:]
else:
attributes[piece['keyword'].lower()] = attrs[start_loc:piece['end']]
attributes['raw'] = attrs
return attributes
# will pull out the exact slices to request from the text
def get_slices_from_locs(self, new_locs: list):
slices = []
i = 0
while i < len(new_locs):
attr = {}
t = i +1
attr['start'] = new_locs[i]['start']
if t == len(new_locs):
attr['end'] = -1
else:
attr['end'] = new_locs[i+1]['start']
attr['keyword'] = new_locs[i]['keyword']
slices.append(attr)
i += 1
return slices