Develop Reference

Convert XML file to a list of lists in Python

I have a dataset from HMDB the Saliva Metabolites data.
This data is an XML file. What I want to do is to convert this XML file to a list of lists (nested lists) in Python, however, I don't want all the nodes in the list.
EDITED: AND THIS IS EXAMPLE OF PARTIAL DATA FOR ONE METABOLITE
<?xml version="1.0" encoding="UTF-8"?>
<hmdb xmlns="http://www.hmdb.ca">
<metabolite>
<version>4.0</version>
<creation_date>2005-11-16 15:48:42 UTC</creation_date>
<update_date>2019-01-11 19:13:56 UTC</update_date>
<accession>HMDB0000001</accession>
<status>quantified</status>
<secondary_accessions>
<accession>HMDB00001</accession>
<accession>HMDB0004935</accession>
<accession>HMDB0006703</accession>
<accession>HMDB0006704</accession>
<accession>HMDB04935</accession>
<accession>HMDB06703</accession>
<accession>HMDB06704</accession>
</secondary_accessions>
<name>1-Methylhistidine</name>
<cs_description>1-Methylhistidine, also known as 1-mhis, belongs to the class of organic compounds known as histidine and derivatives. Histidine and derivatives are compounds containing cysteine or a derivative thereof resulting from reaction of cysteine at the amino group or the carboxy group, or from the replacement of any hydrogen of glycine by a heteroatom. 1-Methylhistidine has been found in human muscle and skeletal muscle tissues, and has also been detected in most biofluids, including cerebrospinal fluid, saliva, blood, and feces. Within the cell, 1-methylhistidine is primarily located in the cytoplasm. 1-Methylhistidine participates in a number of enzymatic reactions. In particular, 1-Methylhistidine and Beta-alanine can be converted into anserine; which is catalyzed by the enzyme carnosine synthase 1. In addition, Beta-Alanine and 1-methylhistidine can be biosynthesized from anserine; which is mediated by the enzyme cytosolic non-specific dipeptidase. In humans, 1-methylhistidine is involved in the histidine metabolism pathway. 1-Methylhistidine is also involved in the metabolic disorder called the histidinemia pathway.</cs_description>
<description>One-methylhistidine (1-MHis) is derived mainly from the anserine of dietary flesh sources, especially poultry. The enzyme, carnosinase, splits anserine into b-alanine and 1-MHis. High levels of 1-MHis tend to inhibit the enzyme carnosinase and increase anserine levels. Conversely, genetic variants with deficient carnosinase activity in plasma show increased 1-MHis excretions when they consume a high meat diet. Reduced serum carnosinase activity is also found in patients with Parkinson's disease and multiple sclerosis and patients following a cerebrovascular accident. Vitamin E deficiency can lead to 1-methylhistidinuria from increased oxidative effects in skeletal muscle. 1-Methylhistidine is a biomarker for the consumption of meat, especially red meat.</description>
<synonyms>
<synonym>(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoic acid</synonym>
<synonym>1-Methylhistidine</synonym>
<synonym>Pi-methylhistidine</synonym>
<synonym>(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoate</synonym>
<synonym>1 Methylhistidine</synonym>
<synonym>1-Methyl histidine</synonym>
</synonyms>
<chemical_formula>C7H11N3O2</chemical_formula>
<smiles>CN1C=NC(C[C#H](N)C(O)=O)=C1</smiles>
<inchikey>BRMWTNUJHUMWMS-LURJTMIESA-N</inchikey>
<diseases>
<disease>
<name>Kidney disease</name>
<omim_id/>
<references>
<reference>
<reference_text>McGregor DO, Dellow WJ, Lever M, George PM, Robson RA, Chambers ST: Dimethylglycine accumulates in uremia and predicts elevated plasma homocysteine concentrations. Kidney Int. 2001 Jun;59(6):2267-72.</reference_text>
<pubmed_id>11380830</pubmed_id>
</reference>
<reference>
<reference_text>Ehrenpreis ED, Salvino M, Craig RM: Improving the serum D-xylose test for the identification of patients with small intestinal malabsorption. J Clin Gastroenterol. 2001 Jul;33(1):36-40.</reference_text>
<pubmed_id>11418788</pubmed_id>
</reference>
<reference>
</reference>
</references>
</disease>
<disease>
Importing the file:
import xml.etree.ElementTree as et
data1 = et.parse('D:/path/to/Tal/my/HMDB/DataSets/saliva_metabolites/saliva_metabolites.xml')
root = data1.getroot()
Now, not sure how to select specific nodes. Meaning, my goal is to create a list of metabolites and each metabolite from the list will contain a list of nodes (say, <accession>, <name>, <synonyms> and <diseases_name>)
In turn, those elements will contain another list (say, inside <synonyms> there will be a list of values names, or inside <diseases_name> will be the list of names of diseases and each disease will contain a list of pub_id values).
# To access the 4'th node of the first metabolit
>> root[0][3].text
'HMDB0000001'
where root[0][3] represents the <accession> node.
Tried to run loop with print so i'll understand the output of the loop but recieved list of None
for node in root:
print(node.find('accession'))
None
None
None
None
None
.
.
.
Also tried
>> root.findall('./metabolite/accession')
[]
But received empty brackets
for list of synonyms of the first metbolite i tried:
>> root[0][9].text
'\n '
# This gave the first value of synonyms
root[0][9][0].text
'\n '
I used those questions to find an answer:
How do I parse XML in Python?
how to create a list of elements from an XML file in python
Python: XML file to pandas dataframe
Convert XML into Lists of Tags and Values with Python
Generating nested lists from XML doc
Any hints, ideas would be a help, thank you for your time

You are ignoring the namespace in the XML.
<hmdb xmlns="http://www.hmdb.ca">
means that there is no <hmdb> element. There is a <hmdb> in the http://www.hmdb.ca namespace. And since it's the default namespace for this element, all descendant elements are in the same namespace, unless they override that.
So this
root.findall('./metabolite/accession')
will not return anything because you're searching in the wrong namespace.
Let's search in the http://www.hmdb.ca namespace by giving it the handle h, for convenience:
ns = {
"h": "http://www.hmdb.ca"
}
accession = root.findall('./h:metabolite/h:accession', ns)
print(accession)
This finds one element (see how it explicitly denotes the namespace when you print it):
[<Element '{http://www.hmdb.ca}accession' at 0x03E6E7B0>]
You can use the same explicit syntax in ElementTree, but it gets unwieldy very quickly:
t.findall('./{http://www.hmdb.ca}metabolite/{http://www.hmdb.ca}accession')
The shorter (and standard) syntax with the prefix: is a lot nicer to work with.

How to extract URL from a redirect URL using regex in Python?

I have the following test_string from which I need to obtain the actual URL.
Test string (partly shown):
An experimental and modeling study of autoignition characteristics of
butanol/diesel blends over wide temperature ranges
<http://scholar.google.com/scholar_url?url=3Dhttps://www.sciencedirect.com/=
science/article/pii/S0010218020301346&hl=3Den&sa=3DX&d=3D448628313728630325=
1&scisig=3DAAGBfm26Wh2koXdeGZkQxzZbenQYFPytLQ&nossl=3D1&oi=3Dscholaralrt&hi=
st=3Dv2Y_3P0AAAAJ:17949955323429043383:AAGBfm1nUe-t2q_4mKFiHSHFEAo0A4rRSA>
Y Qiu, W Zhou, Y Feng, S Wang, L Yu, Z Wu, Y Mao=E2=80=A6 - Combustion and =
Flame,
2020
Desired output for part of test_string
https://www.sciencedirect.com/science/article/pii/S0010218020301346
I have been trying to obtain this with the MWE given below applied to many strings, but it gives only one URL.
MWE
from urlparse import urlparse, parse_qs
import re
from re import search
test_string = '''
Production, Properties, and Applications of ALPHA-Terpineol
<http://scholar.google.com/scholar_url?url=https://link.springer.com/content/pdf/10.1007/s11947-020-02461-6.pdf&hl=en&sa=X&d=12771069332921982368&scisig=AAGBfm1tFjLUm7GV1DRnuYCzvR4uGWq9Cg&nossl=1&oi=scholaralrt&hist=v2Y_3P0AAAAJ:17949955323429043383:AAGBfm1nUe-t2q_4mKFiHSHFEAo0A4rRSA>
A Sales, L de Oliveira Felipe, JL Bicas
Abstract ALPHA-Terpineol (CAS No. 98-55-5) is a tertiary monoterpenoid
alcohol widely
and commonly used in the flavors and fragrances industry for its sensory
properties.
It is present in different natural sources, but its production is mostly
based on ...
Save
<http://scholar.google.com/citations?update_op=email_library_add&info=oB2z7uTzO7EJ&citsig=AMD79ooAAAAAYLfmix3sQyUWnFrHeKYZxuK31qlqlbCh&hl=en>
Twitter
<http://scholar.google.com/scholar_share?hl=en&oi=scholaralrt&ss=tw&url=https://link.springer.com/content/pdf/10.1007/s11947-020-02461-6.pdf&rt=Production,+Properties,+and+Applications+of+%CE%B1-Terpineol&scisig=AAGBfm0yXFStqItd97MUyPT5nRKLjPIK6g>
Facebook
<http://scholar.google.com/scholar_share?hl=en&oi=scholaralrt&ss=fb&url=https://link.springer.com/content/pdf/10.1007/s11947-020-02461-6.pdf&rt=Production,+Properties,+and+Applications+of+%CE%B1-Terpineol&scisig=AAGBfm0yXFStqItd97MUyPT5nRKLjPIK6g>
An experimental and modeling study of autoignition characteristics of
butanol/diesel blends over wide temperature ranges
<http://scholar.google.com/scholar_url?url=3Dhttps://www.sciencedirect.com/=
science/article/pii/S0010218020301346&hl=3Den&sa=3DX&d=3D448628313728630325=
1&scisig=3DAAGBfm26Wh2koXdeGZkQxzZbenQYFPytLQ&nossl=3D1&oi=3Dscholaralrt&hi=
st=3Dv2Y_3P0AAAAJ:17949955323429043383:AAGBfm1nUe-t2q_4mKFiHSHFEAo0A4rRSA>
Y Qiu, W Zhou, Y Feng, S Wang, L Yu, Z Wu, Y Mao=E2=80=A6 - Combustion and =
Flame,
2020
Butanol/diesel blend is considered as a very promising alternative fuel
with
agreeable combustion and emission performance in engines. This paper
intends to
further investigate its autoignition characteristics with the combination
of a heated =E2=80=A6
[image: Save]
<http://scholar.google.com/citations?update_op=3Demail_library_add&info=3DE=
27Gd756Qj4J&citsig=3DAMD79ooAAAAAYImDxwWCwd5S5xIogWp9RTavFRMtTDgS&hl=3Den>
[image:
Twitter]
<http://scholar.google.com/scholar_share?hl=3Den&oi=3Dscholaralrt&ss=3Dtw&u=
rl=3Dhttps://www.sciencedirect.com/science/article/pii/S0010218020301346&rt=
=3DAn+experimental+and+modeling+study+of+autoignition+characteristics+of+bu=
tanol/diesel+blends+over+wide+temperature+ranges&scisig=3DAAGBfm19DOLNm3-Fl=
WaO0trAxZkeidxYWg>
[image:
Facebook]
<http://scholar.google.com/scholar_share?hl=3Den&oi=3Dscholaralrt&ss=3Dfb&u=
rl=3Dhttps://www.sciencedirect.com/science/article/pii/S0010218020301346&rt=
=3DAn+experimental+and+modeling+study+of+autoignition+characteristics+of+bu=
tanol/diesel+blends+over+wide+temperature+ranges&scisig=3DAAGBfm19DOLNm3-Fl=
WaO0trAxZkeidxYWg>
Using NMR spectroscopy to investigate the role played by copper in prion
diseases.
<http://scholar.google.com/scholar_url?url=3Dhttps://europepmc.org/article/=
med/32328835&hl=3Den&sa=3DX&d=3D16122276072657817806&scisig=3DAAGBfm1AE6Kyl=
jWO1k0f7oBnKFClEzhTMg&nossl=3D1&oi=3Dscholaralrt&hist=3Dv2Y_3P0AAAAJ:179499=
55323429043383:AAGBfm1nUe-t2q_4mKFiHSHFEAo0A4rRSA>
RA Alsiary, M Alghrably, A Saoudi, S Al-Ghamdi=E2=80=A6 - =E2=80=A6 and of =
the Italian
Society of =E2=80=A6, 2020
Prion diseases are a group of rare neurodegenerative disorders that develop
as a
result of the conformational conversion of normal prion protein (PrPC) to
the disease-
associated isoform (PrPSc). The mechanism that actually causes disease
remains =E2=80=A6
[image: Save]
<http://scholar.google.com/citations?update_op=3Demail_library_add&info=3Dz=
pCMKavUvd8J&citsig=3DAMD79ooAAAAAYImDx3r4gltEWBAkhl0g2POsXB9Qn4Lk&hl=3Den>
[image:
Twitter]
<http://scholar.google.com/scholar_share?hl=3Den&oi=3Dscholaralrt&ss=3Dtw&u=
rl=3Dhttps://europepmc.org/article/med/32328835&rt=3DUsing+NMR+spectroscopy=
+to+investigate+the+role+played+by+copper+in+prion+diseases.&scisig=3DAAGBf=
m1RidyRD-x2FOemP6iqCsr-6GAVKA>
[image:
Facebook]
<http://scholar.google.com/scholar_share?hl=3Den&oi=3Dscholaralrt&ss=3Dfb&u=
rl=3Dhttps://europepmc.org/article/med/32328835&rt=3DUsing+NMR+spectroscopy=
+to+investigate+the+role+played+by+copper+in+prion+diseases.&scisig=3DAAGBf=
m1RidyRD-x2FOemP6iqCsr-6GAVKA>
'''
regex = re.compile('(http://scholar.*?)&')
url_all = regex.findall(test_string)
citation_url = []
for i in url_all:
if search('scholar.google.com',i):
qs = parse_qs(urlparse(i).query).values()
if search('http',str(qs[0])):
citation_url.append(qs[0])
print citation_url
Present output
https://link.springer.com/content/pdf/10.1007/s11947-020-02461-6.pdf
Desired output
https://link.springer.com/content/pdf/10.1007/s11947-020-02461-6.pdf
https://www.sciencedirect.com/science/article/pii/S0010218020301346
https://europepmc.org/article/med/3232883
How to get handle URL text wrapping with equal sign and extracting the redirect URL in Python?

You could match either a question mark or ampersand [&?] using a character class. Looking at the example data, for the url= part, you can add optional newlines and an optional equals sign and adjust accordingly.
Some urls start with 3D, you can make that part optional using a non capturing group (?:3D)?
Then capture in group 1 matching http followed by matching all chars except &
\bhttp://scholar\.google\.com.*?[&?]\n?u=?\n?r\n?l\n?=(?:3D)?(http[^&]+)
Regex demo

see this regex pattern i think it might help to extract redirect uri
(http:\/\/scholar[\w.\/=&?]*)[?]?u[=]?rl=([\w\:.\/\-=]+)
also see this example here https://regex101.com/r/dmkF3h/3

Data Truncated: Cant scroll to Capture all Data Selenium Python

I have got the first 50 dataset from the table but can seem to get all:
url='https://definitivehc.maps.arcgis.com/home/item.html?id=1044bb19da8d4dbfb6a96eb1b4ebf629&view=list&showFilters=false#data'
browser = webdriver.Chrome(r"C:\Users\scrape\chromedriver")
browser.get(url)
time.sleep(25)
rows_in_table = browser.find_elements_by_xpath('//table[#class="dgrid-row-table"]//tr[td]')
for element in rows_in_table:
print(element.text.replace('\n', '||'))
result:Snippet of first 50 data points
Atmore Community Hospital||Short Term Acute Care Hospital||Atmore||AL||36502||Escambia||Alabama||01||053||01053||51||49||6||6||0.00||0.36||2||2.00||46
Gadsden Regional Medical Center||Short Term Acute Care Hospital||Gadsden||AL||35903||Etowah||Alabama||01||055||01055||346||222||40||40||0.00||0.73||124||8.00||47
Riverview Regional Medical Center||Short Term Acute Care Hospital||Gadsden||AL||35901||Etowah||Alabama||01||055||01055||281||256||45||45||0.00||0.26||25||6.00||48
Fayette Medical Center (FKA Weimer Medical Center)||Short Term Acute Care Hospital||Fayette||AL||35555||Fayette||Alabama||01||057||01057||61||45||8||8||0.00||0.23||16||2.00||49
Russellville Hospital||Short Term Acute Care Hospital||Russellville||AL||35653||Franklin||Alabama||01||059||01059||100||49||7||7||0.00||0.40||51||2.00||50
Thanks for your help.

How to solve Unicoder problem when reading csv

I am totally new to python. I am using a package that takes medical text and annotates it with classifiers called pyConTextNLP
It basically takes some natural language text, adds some 'modifiers' to it and classifies it whilst removing negative findings.
The problem I am having is how to add the list of modifiers as a csv or a yaml file. I have been following the basic setup instructions here:
The problem is the line here:
modifiers = itemData.get_items("https://raw.githubusercontent.com/chapmanbe/pyConTextNLP/master/KB/lexical_kb_05042016.yml")
itemData.get_items doesn't look like it exists anymore and there is a function instead called itemData.get_fileobj(). This takes a csv file as far as I understand and the csv is passed to the function markup.markItems(modifiers, mode="modifier") which looks at the text and 'marks up' any concepts in the raw text that match the modifiers.
The error that I get when trying to run the example code is:
if not `item.getLiteral() in compiledRegExprs:`
and this gives me the error:
AttributeError: 'UnicodeReader' object has no attribute 'getLiteral'
The whole code is here: but I have also written it below
import networkx as nx
import pyConTextNLP.itemData as itemData
import pyConTextNLP.pyConTextGraph as pyConText
reports = [
"""IMPRESSION: Evaluation limited by lack of IV contrast; however, no evidence of
bowel obstruction or mass identified within the abdomen or pelvis. Non-specific interstitial opacities and bronchiectasis seen at the right
base, suggestive of post-inflammatory changes.""",
"""DIAGNOSIS: NO SIGNIFICANT PATHOLOGY
MICRO These biopsies of large bowel mucosa show oedema of the lamina propriabut no architectural abnormality
There is no dysplasia or malignancy
There is no evidence of active inflammation
There is no increase in the inflammatory cell content of the lamina propria""" ,
"""IMPRESSION:
1. 2.0 cm cyst of the right renal lower pole. Otherwise, normal appearance
of the right kidney with patent vasculature and no sonographic evidence of
renal artery stenosis.
2. Surgically absent left kidney.""",
"""IMPRESSION: No definite pneumothorax""",
"""IMPRESSION: New opacity at the left lower lobe consistent with pneumonia."""
]
modifiers = itemData.get_fileobj("/Applications/anaconda3/lib/python3.7/site-packages/pyConTextNLP-0.6.2.0-py3.7.egg/pyConTextNLP/CSV_Modifiers.csv")
targets = itemData.get_fileobj("/Applications/anaconda3/lib/python3.7/site-packages/pyConTextNLP-0.6.2.0-py3.7.egg/pyConTextNLP/CSV_targets.csv")
def markup_sentence(s, modifiers, targets, prune_inactive=True):
"""
"""
markup = pyConText.ConTextMarkup()
markup.setRawText(s)
markup.cleanText()
markup.markItems(modifiers, mode="modifier")
markup.markItems(targets, mode="target")
markup.pruneMarks()
markup.dropMarks('Exclusion')
# apply modifiers to any targets within the modifiers scope
markup.applyModifiers()
markup.pruneSelfModifyingRelationships()
if prune_inactive:
markup.dropInactiveModifiers()
return markup
reports[3]
markup = pyConText.ConTextMarkup()
isinstance(markup,nx.DiGraph)
markup.setRawText(reports[4].lower())
print(markup)
print(len(markup.getRawText()))
markup.cleanText()
print(markup)
print(len(markup.getText()))
markup.markItems(modifiers, mode="modifier")
print(markup.nodes(data=True))
print(type(list(markup.nodes())[0]))
markup.markItems(targets, mode="target")
for node in markup.nodes(data=True):
print(node)
markup.pruneMarks()
for node in markup.nodes(data=True):
print(node)
print(markup.edges())
markup.applyModifiers()
for edge in markup.edges():
print(edge)
markItems function is here:
def markItems(self, items, mode="target"):
"""tags the sentence for a list of items
items: a list of contextItems"""
if not items:
return
for item in items:
self.add_nodes_from(self.markItem(item, ConTextMode=mode),
category=mode)
The question is, how can I get the code to read the list in the csv file without throwing this error?

2 different blocks of text are merging together. Can I separate them if i know what 1 is?

I've used a number of pdf-->text methods to extract text from pdf documents. For one particular type of PDF I have, neither pyPDF or pdfMiner are doing a good job extracting the text. However, http://www.convertpdftotext.net/ does it (almost) perfectly.
I discovered that the pdf I'm using has some transparent text in it, and it is getting merged into the other text.
Some examples of the blocks of text I get back are:
12324 35th Ed. 01-MAR-12 Last LNM: 14/12 NAD 83 14/12 Corrective Object of Corrective
ChartTitle: Intracoastal Waterway Sandy Hook to Little Egg Harbor Position
C HAActRionT N Y -NJ - S A N D Y H OO K ATcO tionLI T TLE EGG HARBOR. Page/Side: N/A
(Temp) indicates that the chart correction action is temporary in nature. Courses and bearings are givCGenD 0in 1 degrees clockwise from 000 true.
Bearings RoEf LlighOCtAT seEc tors aSrehre towwsbuardry th Re ivligher Ct fhroanmn seel Lawighartde.d B Theuoy 5no minal range of lights is expressedf roin mna 4u0tic-24al -mi46les.56 0(NNM ) unless othe0r7w4is-00e n-o05te.d8.8 0 W
to 40-24-48.585N 074-00-05.967W
and
12352 33rd Ed. 01-MAR-11 Last LNM: 03/12 NAD 83 04/12 . . l . . . . Corrective Object of Corrective ChartTitle: Shinnecock Bay to East Rockaway Inlet Position C HAActRionT S H IN N E C OC K B A Y TO AcEtionAS T ROCKAWAY INLET. Page/Side: N/A (Temp) indicates that the chart correction action is temporary in nature. Courses and bearings are givCGenD 0in 1 degrees clockwise from 000 true. (BTeeamringp) s DoEf LlighETtE s ectors aSretat toew Baoratd Ctheh anlighnet lf Droaym beseacoawanr 3d. The nominal range of lights is expressedf roin mna 4u0tic-37al -mi11les.52 0(NNM ) unless othe0r7w3is-29e n-5o3te.d76. 0 W
and I have discovered that the "ghost text" is ALWAYS the following:
Corrective Object of Corrective Position
Action Action
(Temp) indicates that the chart correction action is temporary in nature. Courses and bearings are given in degrees clockwise from 000 true.
Bearings of light sectors are toward the light from seaward. The nominal range of lights is expressed in nautical miles (NM) unless otherwise noted.
In the 2nd example I posted, the text I want (with the ghost text removed) is:
12352 33rd Ed. 01-Mar-11 Last LNM:03/12 NAD 83 04/12
Chart Title:Shinnecock Bay to East Rockaway Inlet. Page/Side:N/A
CGD01
(Temp) DELETE State Boat Channel Daybeacon 3 from 40-37-11.520N 073-29-53.760W
This problem occurs just once per document, and does not appear to be totally consistent (as seen above). I am wondering if one of you wizards could think of a way to remove the ghosted text (I don't need/want it) using python. If I had been using pyPDF, I would have used a regex to rip it out during the conversion to text. Unfortunately, since I'm starting out with a text file from the website listed above, the damage has already been done. I'm at a bit of a loss.
Thanks for reading.
EDIT:
The solution to this problem looks like it be more complex than the rest of the application, so I'm going to withdraw my request for help.
I very much appreciate the thought put into it by those who have contributed.

Given that the ghost text can be split up in seemingly unpredictable ways, I don't think there is a simple automatic way of removing it that would not have false positives. What you need is almost human-level pattern recognition. :-)
What you could try is exploiting the format of these kinds of messages. Roughly;
<number> <number>[rn]d Ed. <date> Last LNM:<mm>/<yy> NAD <date2>
Chart Title:<text>. Page/Side:<N/A or number(s)> CGD<number> <text>
<position>
Using this you could pluck out the nonsense from the predictable elements, and then if you have a list of chart names ('Shinnecock Bay to East Rockaway Inlet') and descriptive words (like 'State', 'Boat', 'Daybeacon') you might be able to reconstruct the original words by finding the smallest levenshtein distance between mangled words in the two text blocks and those in your word lists.
If you can install the poppler software, you could try and use pdftotext with the -layout option to keep the formatting from the original PDF as much as possible. That might make your problem disappear.

You could recursively find all possible ways that your Pattern
"Corrective Object of Corrective Position Action ..." can be contained within your mangled text,
Then you can unmangle the text for each of these possible paths, run some sort of spellcheck over them, and choose the one with the fewest spelling mistakes. Or since you know roughly where each substring should appear, you can use that as a heuristic.
Or you could simply use the first path.
some pseudocode (untested):
def findPaths(mangledText, pattern, path)
if len(pattern)==0: # end of pattern
return [path]
else:
nextLetter= pattern[0]
locations = findAllOccurences (mangledText, nextLetter) # get all indices in mangledText that contain nextLetter
allPaths = []
for loc in locations:
paths = findPaths( mangledText[loc+1:], pattern[1:], path + (loc,) )
allPaths.Extend(paths)
return allPaths # if no locations for the next letters exist, allPaths will be emtpy
Then you can call it like this (optionally remove all spaces from your search pattern, unless you are certain they are all included in the mangled text)
allPossiblePaths = findPaths ( YourMangledText, "Corrective Object...", () )
then allPossiblePaths should contain a list of all possible ways your pattern could be contained in your mangled text.
Each entry is a tuple with the same length as the pattern, containing the index at which the corresponding letter of the pattern occurs in the search text.