removed old code (orangebook and old rxnorm_mappings) and adjusted the AACT_downloader to use a local directory when launching
youainti
3 years ago
parent
016a449258
commit
f6f687fff5
@ -0,0 +1 @@
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This is where the db is stored.
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@ -1,93 +0,0 @@
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# Adobe Pdf Character ID (cid:\d+) parser
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# The purpose is to allow someone to create their own table equivalent to the "\toUnicode" that
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# should be provided in every PDF using cid's (but is often mangled).
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def get_digits(string):
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"""
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Extract leading the digits from a cid tag.
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"""
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splat = string.split(")")
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num = splat[0]
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l = len(num)
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return int(num),l
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def token_generator(string):
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"""
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An iterable that returns tokens describing a string in a pdf.
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Tokens take two forms:
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- Integers: these represend CID codes
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- Characters: these represent the arbitrary characters often returned amidst cid's.
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It is a python generator becasue that simplifies the ordering and allows us to avoid recursion.
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"""
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start = 0
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str_len = len(string)
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while start < str_len:
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substring = string[start:]
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#check for cid
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if (str_len - start > 6) and (substring[0:5] == "(cid:"):
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num,length = get_digits(substring[5:])
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start += length + 6
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yield num
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elif (str_len - start > 1):
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start += 1
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yield substring[0]
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else:
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start += 1
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yield substring
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class UnknownSymbol():
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"""
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Represents a token that is not in the parser's dictionary.
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"""
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def __init__(self, symbol):
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self.symbol = symbol
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def __repr__(self):
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return "UnknownSymbol: {} of type {}".format(self.symbol, type(self.symbol))
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def __str__(self):
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return "\uFFFD"
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class Parser:
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"""
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Translates from tokens to character arrays or strings, handling errors as it goes.
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It requires a dictionary during instantiation.
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This dictionary is what is used to perform lookups.
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It exposes 3 methods
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- convert attempts to convert a single token
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- convert_stream will try to convert an iterable of tokens into an iterable of text.
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- check_list_of_strings will try to convert a list of strings containing cids and other symbols into
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- strings, if there are no Unknown symbols.
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- lists, containing characters and Unknown symbols.
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"""
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def __init__(self, lookup_table):
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self._lookup_table = lookup_table
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def convert(self,token):
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try:
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return self._lookup_table[token]
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except:
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return UnknownSymbol(token)
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def convert_list(self,token_stream):
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for token in token_stream:
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yield self.convert(token)
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def convert_list_of_strings(self, list_of_strings):
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for token_stream in list_of_stings:
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arr = [x for x in ob2020.convert_stream(token_generator(token_stream))]
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try:
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print("".join(arr))
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except:
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print(arr)
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if __name__ == "__main__":
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print("Plan was to accept and proceess a symbol table and text. Apparently it has not been implemented."
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@ -1,371 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "40358f02-c376-4431-be39-cdd477f17e7a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import polars as pl"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "8fb27ee2-72c1-4e80-9d00-de54f2834fe8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"polars.datatypes.Datetime"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pl.datatypes.Datetime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"id": "2c0edd77-c2d0-4184-a094-8c01783d2f0e",
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"metadata": {},
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"outputs": [],
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"source": [
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"products = pl.scan_csv(file=\"./EOBZIP_2022_04/products.txt\", sep=\"~\")\n",
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"patents = pl.scan_csv(file=\"./EOBZIP_2022_04/patent.txt\", sep=\"~\")\n",
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"exclusivity = pl.scan_csv(file=\"./EOBZIP_2022_04/exclusivity.txt\", sep=\"~\", parse_dates=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"id": "023f211d-23aa-4a2c-843d-1b60cec91079",
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"metadata": {},
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"outputs": [],
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"source": [
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"def set_exclusivity_types(df):\n",
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" return df.with_columns([\n",
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" pl.col(\"Exclusivity_Date\").str.strptime(pl.Date, fmt=\"%b %-d, %Y\")\n",
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" ])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"id": "a1da42c9-e47a-4437-b089-e9b91f789a0c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1 \"class=\"dataframe \">\n",
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"<thead>\n",
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"<tr>\n",
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"<th>\n",
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"Appl_Type\n",
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"</th>\n",
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"<th>\n",
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"Appl_No\n",
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"</th>\n",
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"<th>\n",
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"Product_No\n",
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"</th>\n",
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"<th>\n",
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"Exclusivity_Code\n",
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"</th>\n",
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"<th>\n",
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"Exclusivity_Date\n",
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"</th>\n",
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"</tr>\n",
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"<tr>\n",
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"<td>\n",
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"str\n",
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"</td>\n",
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"<td>\n",
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"i64\n",
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"</td>\n",
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"<td>\n",
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"i64\n",
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"</td>\n",
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"<td>\n",
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"str\n",
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"</td>\n",
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"<td>\n",
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"date\n",
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"</td>\n",
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"</tr>\n",
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"</thead>\n",
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"<tbody>\n",
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"<tr>\n",
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"<td>\n",
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"\"N\"\n",
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"</td>\n",
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"<td>\n",
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"11366\n",
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"</td>\n",
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"<td>\n",
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"2\n",
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"</td>\n",
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"<td>\n",
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"\"ODE-96\"\n",
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"</td>\n",
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"<td>\n",
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"2022-08-07\n",
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"</td>\n",
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"</tr>\n",
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"<tr>\n",
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"<td>\n",
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"\"N\"\n",
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"</td>\n",
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"<td>\n",
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"20287\n",
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"</td>\n",
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"<td>\n",
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"11\n",
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"</td>\n",
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"<td>\n",
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"\"NPP\"\n",
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"</td>\n",
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"<td>\n",
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"2022-05-16\n",
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"</td>\n",
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"</tr>\n",
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"<tr>\n",
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"<td>\n",
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"\"N\"\n",
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"</td>\n",
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"<td>\n",
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"20287\n",
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"</td>\n",
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"<td>\n",
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"10\n",
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"</td>\n",
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"<td>\n",
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"\"NPP\"\n",
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"</td>\n",
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"<td>\n",
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"2022-05-16\n",
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"</td>\n",
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"</tr>\n",
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"<tr>\n",
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"<td>\n",
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"\"N\"\n",
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"</td>\n",
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"<td>\n",
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"20287\n",
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"</td>\n",
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"<td>\n",
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"9\n",
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"</td>\n",
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"<td>\n",
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"\"NPP\"\n",
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"</td>\n",
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"<td>\n",
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"2022-05-16\n",
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"</td>\n",
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"</tr>\n",
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"<tr>\n",
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"<td>\n",
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"\"N\"\n",
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"</td>\n",
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"<td>\n",
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"20287\n",
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"</td>\n",
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"<td>\n",
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"8\n",
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"</td>\n",
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"<td>\n",
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"\"NPP\"\n",
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"</td>\n",
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"<td>\n",
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"2022-05-16\n",
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"</td>\n",
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"</tr>\n",
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"</tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"shape: (5, 5)\n",
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"┌───────────┬─────────┬────────────┬──────────────────┬──────────────────┐\n",
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"│ Appl_Type ┆ Appl_No ┆ Product_No ┆ Exclusivity_Code ┆ Exclusivity_Date │\n",
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"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
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"│ str ┆ i64 ┆ i64 ┆ str ┆ date │\n",
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"╞═══════════╪═════════╪════════════╪══════════════════╪══════════════════╡\n",
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"│ N ┆ 11366 ┆ 2 ┆ ODE-96 ┆ 2022-08-07 │\n",
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"├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
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"│ N ┆ 20287 ┆ 11 ┆ NPP ┆ 2022-05-16 │\n",
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"├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
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"│ N ┆ 20287 ┆ 10 ┆ NPP ┆ 2022-05-16 │\n",
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"├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
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"│ N ┆ 20287 ┆ 9 ┆ NPP ┆ 2022-05-16 │\n",
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"├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
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"│ N ┆ 20287 ┆ 8 ┆ NPP ┆ 2022-05-16 │\n",
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"└───────────┴─────────┴────────────┴──────────────────┴──────────────────┘"
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]
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},
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"execution_count": 61,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"exclusivity.pipe(set_exclusivity_types).head(5).collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"id": "92fe99fa-1963-460c-99ea-7f614b4b2e25",
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"metadata": {},
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"outputs": [],
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"source": [
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"def set_patent_types(df):\n",
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" return df.with_columns([\n",
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" pl.col(\"Patent_Expire_Date_Text\").str.strptime(pl.Date, fmt=\"%b %-d, %Y\"),\n",
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" pl.col(\"Submission_Date\").str.strptime(pl.Date, fmt=\"%b %-d, %Y\"),\n",
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" pl.col(\"Drug_Substance_Flag\") == \"Y\",\n",
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" pl.col(\"Drug_Product_Flag\") == \"Y\",\n",
|
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" pl.col(\"Delist_Flag\") == \"Y\"\n",
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" ])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"id": "13707ca6-094f-4ed7-94cb-824087e97874",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
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" }\n",
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"\n",
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||||
" .dataframe thead th {\n",
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" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
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"<table border=\"1 \"class=\"dataframe \">\n",
|
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"<thead>\n",
|
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"<tr>\n",
|
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"<th>\n",
|
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"Patent_Expire_Date_Text\n",
|
||||
"</th>\n",
|
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"</tr>\n",
|
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"<tr>\n",
|
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"<td>\n",
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"date\n",
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"</td>\n",
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||||
"</tr>\n",
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"</thead>\n",
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||||
"<tbody>\n",
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"<tr>\n",
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"<td>\n",
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||||
"2022-01-02\n",
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||||
"</td>\n",
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||||
"</tr>\n",
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||||
"</tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
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||||
],
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||||
"text/plain": [
|
||||
"shape: (1, 1)\n",
|
||||
"┌─────────────────────────┐\n",
|
||||
"│ Patent_Expire_Date_Text │\n",
|
||||
"│ --- │\n",
|
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"│ date │\n",
|
||||
"╞═════════════════════════╡\n",
|
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"│ 2022-01-02 │\n",
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||||
"└─────────────────────────┘"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"patents.pipe(set_patent_types).select(\"Patent_Expire_Date_Text\").min().collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"id": "18ad8df7-45d5-4454-8955-c5f28a7d7f1e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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||||
"polars.datatypes.Null"
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]
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},
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"execution_count": 81,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pl.datatypes.Null"
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]
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||||
},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"id": "79e4b3d9-29ae-4302-bee1-4be02e0ba654",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
}
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||||
],
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"metadata": {
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"kernelspec": {
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||||
"display_name": "Python 3 (ipykernel)",
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"language": "python",
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||||
"name": "python3"
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},
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||||
"language_info": {
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||||
"codemirror_mode": {
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"name": "ipython",
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||||
"version": 3
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||||
},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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@ -1 +0,0 @@
|
||||
https://www.fda.gov/media/76860/download
|
||||
@ -1 +0,0 @@
|
||||
Most of these are related to potentially parsing orangebook data from the pdfs.
|
||||
@ -1,145 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "51bf48a1-920a-4e64-ac5f-323ff3a27ebf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Will use tool 'Tesseract (sh)'\n",
|
||||
"Available languages: eng, osd\n",
|
||||
"Will use language 'eng'\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Import the required libraries\n",
|
||||
"from wand.image import Image\n",
|
||||
"from PIL import Image as PI\n",
|
||||
"import pyocr\n",
|
||||
"import pyocr.builders\n",
|
||||
"import io, sys\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Get the handle of the OCR library (in this case, tesseract)\n",
|
||||
"tools = pyocr.get_available_tools()\n",
|
||||
"if len(tools) == 0:\n",
|
||||
"\tprint(\"No OCR tool found!\")\n",
|
||||
"\tsys.exit(1)\n",
|
||||
"tool = tools[0]\n",
|
||||
"print(\"Will use tool '%s'\" % (tool.get_name()))\n",
|
||||
"\n",
|
||||
"# Get the language\n",
|
||||
"langs = tool.get_available_languages()\n",
|
||||
"print(\"Available languages: %s\" % \", \".join(langs)) \n",
|
||||
"lang = langs[0] # For English\n",
|
||||
"print(\"Will use language '%s'\" % (lang))\n",
|
||||
"\n",
|
||||
"# Setup two lists which will be used to hold our images and final_text\n",
|
||||
"req_image = []\n",
|
||||
"final_text = []\n",
|
||||
"\n",
|
||||
"# Open the PDF file using wand and convert it to jpeg\n",
|
||||
"image_pdf = Image(filename=\"/home/will/research/ClinicalTrialsDataProcessing/Orangebook/Orangebooks/testprint.pdf\", resolution=300)\n",
|
||||
"image_jpeg = image_pdf.convert('pdf')\n",
|
||||
"\n",
|
||||
"# wand has converted all the separate pages in the PDF into separate image\n",
|
||||
"# blobs. We can loop over them and append them as a blob into the req_image\n",
|
||||
"# list.\n",
|
||||
"for img in image_jpeg.sequence:\n",
|
||||
"\timg_page = Image(image=img)\n",
|
||||
"\treq_image.append(img_page.make_blob('jpeg'))\n",
|
||||
"\n",
|
||||
"# Now we just need to run OCR over the image blobs and store all of the \n",
|
||||
"# recognized text in final_text.\n",
|
||||
"for img in req_image:\n",
|
||||
"\ttxt = tool.image_to_string(\n",
|
||||
"\t\tPI.open(io.BytesIO(img)),\n",
|
||||
"\t\tlang=lang,\n",
|
||||
"\t\tbuilder=pyocr.builders.TextBuilder()\n",
|
||||
"\t)\n",
|
||||
"\tfinal_text.append(txt)\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f0d5f1d6-7e15-4ee6-b4ee-cbd41c5afb99",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"The final text is: \n",
|
||||
"\n",
|
||||
"40TH EDITION - 2020 - APPROVED DRUG PRODUCT LIST\n",
|
||||
"\n",
|
||||
"PRESCRIPTION DRUG PRODUCT LIST\n",
|
||||
"\n",
|
||||
"ABACAVIR SULFATE\n",
|
||||
"SOLUTION; ORAL\n",
|
||||
"ABACAVIR SULFATE\n",
|
||||
"\n",
|
||||
"EQ 2 5 /ML\n",
|
||||
"\n",
|
||||
"EQ 2 Ee /ML\n",
|
||||
"\n",
|
||||
"EQ 300MG BASE\n",
|
||||
"EQ 300MG BASE\n",
|
||||
"EQ 300MG BASE\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"\\nThe final text is: \\n\")\n",
|
||||
"print(final_text[0][0:200])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1cac17e7-079d-4e32-bdbf-ae49194b2078",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"it appears taht this does not have the required precision. I'll need to do this some other way."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2283e290-fab3-4cda-8ce9-55a0b3533c98",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue