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tables.py
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tables.py
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import camelot
import pandas as pd
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import Callable
from columns import Columns
from taco import TACO
class TableSide(Enum):
MAIN = 'M'
CONTINUATION = 'C'
@dataclass
class Table(ABC):
start_page: int
end_page: int
columns: list[str]
data: pd.DataFrame = None
correction_functions: list[Callable[[int, pd.DataFrame], pd.DataFrame]] = None
FOOD_ID_ORIGINAL_COLUMN: int = 0
def __post_init__(self):
self.data = pd.DataFrame()
def get_page_range(self) -> str:
return f'{self.start_page}-{self.end_page}'
def extract(self) -> None:
dataframes_by_page = self._get_dataframes_by_page()
self._apply_fixes(dataframes_by_page)
main_df = pd.concat(dataframes_by_page[TableSide.MAIN].values())
continuation_df = pd.concat(dataframes_by_page[TableSide.CONTINUATION].values())
merged_df = self._merge_dataframes(main_df, continuation_df)
merged_df = self._rename(merged_df)
self.data = merged_df
def _get_dataframes_by_page(self) -> dict[TableSide, dict[int, pd.DataFrame]]:
tables = self._parse_tables()
dataframes_by_page = {
TableSide.MAIN: {table.page: table.df for table in tables[0::2]},
TableSide.CONTINUATION: {table.page: table.df for table in tables[1::2]},
}
return dataframes_by_page
def _parse_tables(self) -> camelot.core.TableList:
return camelot.read_pdf(filepath=TACO.FILENAME, pages=self.get_page_range(), flavor='stream')
def save(self) -> None:
self.data.to_csv(f'_{self.__class__.__name__}.csv', encoding='utf-8')
def _apply_fixes(self, tables: dict[TableSide, dict[int, pd.DataFrame]]) -> None:
for side in tables:
for page in tables[side]:
df = tables[side][page]
# Remove first n-rows based on the table side
df = df.tail(df.shape[0] - 4)
# Convert Food ID column to int
series = pd.to_numeric(df[Table.FOOD_ID_ORIGINAL_COLUMN], errors='coerce').dropna().astype('int64')
df = df.drop(labels=[Table.FOOD_ID_ORIGINAL_COLUMN], axis=1)
df[Table.FOOD_ID_ORIGINAL_COLUMN] = series
# Drop rows without an integer Food ID
df = df.dropna()
# Set index to Food ID
df = df.set_index(Table.FOOD_ID_ORIGINAL_COLUMN, drop=True)
# Hook for specific correction functions
if self.correction_functions:
for function in self.correction_functions:
df = function(page, df)
tables[side][page] = df
def _rename(self, df: pd.DataFrame) -> pd.DataFrame:
df.index = df.index.rename(Columns.General.FOOD_ID)
df.columns = self.columns
return df
@staticmethod
def _merge_dataframes(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
return left.join(right, lsuffix='_caller', rsuffix='_other')
class MacroAndMicronutrientsTable(Table):
def __init__(self):
super().__init__(start_page=29, end_page=68,
columns=[Columns.General.DESCRIPTION, Columns.MicroAndMacronutrients.HUMIDITY,
Columns.MicroAndMacronutrients.KCAL, Columns.MicroAndMacronutrients.KJ,
Columns.MicroAndMacronutrients.PROTEIN, Columns.MicroAndMacronutrients.LIPID,
Columns.MicroAndMacronutrients.CHOLESTEROL,
Columns.MicroAndMacronutrients.CARBOHYDRATE,
Columns.MicroAndMacronutrients.DIETARY_FIBER, Columns.MicroAndMacronutrients.ASH,
Columns.MicroAndMacronutrients.CALCIUM, Columns.MicroAndMacronutrients.MAGNESIUM,
Columns.MicroAndMacronutrients.MANGANESE, Columns.MicroAndMacronutrients.PHOSPHORUS,
Columns.MicroAndMacronutrients.IRON, Columns.MicroAndMacronutrients.SODIUM,
Columns.MicroAndMacronutrients.POTASSIUM, Columns.MicroAndMacronutrients.COPPER,
Columns.MicroAndMacronutrients.ZINC, Columns.MicroAndMacronutrients.RETINOL,
Columns.MicroAndMacronutrients.RETINOL_EQUIVALENTS,
Columns.MicroAndMacronutrients.RETINOL_ACTIVITY_EQUIVALENTS,
Columns.MicroAndMacronutrients.THIAMINE, Columns.MicroAndMacronutrients.RIBOFLAVIN,
Columns.MicroAndMacronutrients.PYRIDOXINE, Columns.MicroAndMacronutrients.NIACIN,
Columns.MicroAndMacronutrients.VITAMIN_C],
correction_functions=[
self._fix_misread_columns,
]
)
@staticmethod
def _fix_misread_columns(page: int, df: pd.DataFrame):
if page == 64:
df = df.drop(columns=[13, 14])
df = df.rename(columns={15: 13, 16: 14, 17: 15})
return df
class FattyAcidsTable(Table):
def __init__(self):
super().__init__(start_page=71, end_page=100,
columns=[Columns.General.DESCRIPTION, Columns.FattyAcids.SATURATED,
Columns.FattyAcids.MONO_UNSATURATED, Columns.FattyAcids.POLY_UNSATURATED,
Columns.FattyAcids.LAURIC, Columns.FattyAcids.MYRISTIC, Columns.FattyAcids.PALMITIC,
Columns.FattyAcids.STEARIC, Columns.FattyAcids.ARACHIDIC, Columns.FattyAcids.BEHENIC,
Columns.FattyAcids.LIGNOCERIC, Columns.FattyAcids.MYRISTOLEIC,
Columns.FattyAcids.PALMITOLEIC, Columns.FattyAcids.OLEIC, Columns.FattyAcids.GADOLEIC,
Columns.FattyAcids.LINOLEIC, Columns.FattyAcids.ALPHA_LINOLENIC,
Columns.FattyAcids.ARACHIDONIC, Columns.FattyAcids.TIMNODONIC,
Columns.FattyAcids.CLUPANODONIC, Columns.FattyAcids.CERVONIC,
Columns.FattyAcids.ELAIDIC, Columns.FattyAcids.LINOLELAIDIC])
class AminoAcidsTable(Table):
def __init__(self):
super().__init__(start_page=103, end_page=104,
columns=[Columns.General.DESCRIPTION, Columns.AminoAcids.TRYPTOPHAN,
Columns.AminoAcids.THREONINE, Columns.AminoAcids.ISOLEUCINE,
Columns.AminoAcids.LEUCINE, Columns.AminoAcids.LYSINE, Columns.AminoAcids.METHIONINE,
Columns.AminoAcids.CYSTINE, Columns.AminoAcids.PHENYLALANINE,
Columns.AminoAcids.TYROSINE, Columns.AminoAcids.VALINE, Columns.AminoAcids.ARGININE,
Columns.AminoAcids.HISTIDINE, Columns.AminoAcids.ALANINE,
Columns.AminoAcids.ASPARTIC_ACID, Columns.AminoAcids.GLUTAMIC_ACID,
Columns.AminoAcids.GLYCINE, Columns.AminoAcids.PROLINE, Columns.AminoAcids.SERINE])