src/preprocess.py

#!/usr/bin/env python3 
# -*- coding: utf-8 -*-
"""
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File: preprocess.py
Author: Aanish Pradhan
Created: 2024-09-09
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Description: Converts the LakeBeD-US: Ecology Edition dataset (McAfee et.al. 
2024) into the LakeBeD-US: Computer Science Edition dataset.
"""
# IMPORT PACKAGES
from collections import defaultdict # Default value for absent keys in dict
import lakedata # LakeBeD-US Lake class, constants and methods
import numpy as np # Numerical operations
import os # Operating system interfaces
import pandas as pd # Dataframe manipulation
import re # Regular expressions
import sys # System-specific parameters and functions
import typing # Type hints

# METHODS
def parse(lakebed_path: str) -> None:
	"""
	Parses through the files in the high frequency and low frequency 
	subdirectories of the specified LakeBeD-US data directory and processes 
	each raw data file.

	Args:
		lakebed_path (str): Path of the unzipped LakeBeD-US directory
	"""
	
	os.chdir(lakebed_path)
	
	# Process low frequency data
	print("Processing low frequency data...\n")
	os.chdir("LowFrequency")
	low_frequency_data_files = [file for file in os.listdir() if os.path.isfile(file)]
	low_frequency_data_files.sort()
	print(f"Low frequency data files: {low_frequency_data_files}\n")
	for low_frequency_data_file in low_frequency_data_files:
		print(f"Processing {low_frequency_data_file}...")
		wrangle(pd.read_parquet(low_frequency_data_file), low_frequency_data_file)
		print(f"Processed {low_frequency_data_file}\n")
	print("Processed low frequency data.\n")

	os.chdir("..") # Navigate back to parent LakeBeD-US directory

	# Process the high frequency data
	print("Processing high frequency data...\n")
	os.chdir("HighFrequency")
	concatenate_partitions(os.listdir())
	high_frequency_data_files = [file for file in os.listdir() if os.path.isfile(file)]
	high_frequency_data_files.sort()
	print(f"High frequency data files: {high_frequency_data_files}\n")
	for high_frequency_data_file in high_frequency_data_files:
		print(f"Processing {high_frequency_data_file}...")
		wrangle(pd.read_parquet(high_frequency_data_file), high_frequency_data_file)
		print(f"Processed {high_frequency_data_file}\n")
	print("Processed high frequency data.\n")

	os.chdir("..")
	print("Processed all files. Done")

def concatenate_partitions(files: list) -> None:
	"""
	Determines if any partitions of raw files in the unzipped LakeBeD-US data 
	directory need to be concatenated into a single data file. Partitioned 
	files are detected via RegEx matching if their filename matches the 
	following patterns (omitting the brackets): [FILENAME]_[LETTER PARTITION] 
	or [FILENAME]_[LETTER PARTITION]_[NUMBER PARTITION]. Data files with letter 
	and number partitions are concatenated into a single, lettered partition 
	file first. Lettered partitions are then concatenated into a single 
	file. For example, "Lake_A_1", "Lake_A_2", etc., "Lake_B_1", "Lake_B_2", 
	etc., are first concatenated into "Lake_A" and "Lake_B" and then 
	concatenated into "Lake". The original partitions and intermediate files 
	are deleted after concatenation.
	
	Args:
		files (list): A list of partitioned data files in the current working 
			directory
	"""

	lake_partitions = defaultdict(lambda: defaultdict(list))
	pattern = re.compile(r"([A-Za-z]+)(_([A-Z])(_(\d+))?)?\.parquet")

	for file in files:
		if file.endswith('.parquet'):
			match = pattern.match(file)
			if match:
				lake = match.group(1)
				lettered_partition = match.group(3) or ""
				numbered_sub_partition = match.group(5)

				if numbered_sub_partition:
					lake_partitions[lake][lettered_partition].append(file)
				else:
					lake_partitions[lake][lettered_partition].append(file)

	actions = []

	for lake, partitions in lake_partitions.items():
		for partition_letter, files in partitions.items():
			if partition_letter:
				if len(files) > 1:
					output_file = f"{lake}_{partition_letter}.parquet"
					actions.append((files, output_file))
					concatenate(files, output_file)
					for f in files:
						if os.path.exists(f):
							print(f"Deleting {f} after concatenation.")
							os.remove(f)
						else:
							print(f"File {f} not found. Skipping deletion.")

	for lake, partitions in lake_partitions.items():
		if len(partitions) > 1 or "" not in partitions:
			output_file = f"{lake}.parquet"
			partition_files = [f"{lake}_{letter}.parquet" for letter in partitions if letter]
			if partition_files:
				actions.append((partition_files, output_file))
				concatenate(partition_files, output_file)
				for pf in partition_files:
					if os.path.exists(pf):
						print(f"Deleting {pf} after concatenation.")
						os.remove(pf)
					else:
						print(f"File {pf} not found. Skipping deletion.")

	print("Concatenation Actions:")
	for files, output in actions:
		print(f"Concatenated {files} into {output}")

def concatenate(files: list, output_name: str) -> None:
	"""
	Concatenates a list of Apache Parquet files into a single file and then 
	writes the file to disk.

	Args:
		files (list): List of Apache Parquet files to concatenate
		output_name (str): Name of the output file
	"""

	files.sort()
	print(f"Concatenating {files} into {output_name}")

	partitions = []
	for file in files:
		partitions.append(pd.read_parquet(file))
	concatenated_data = pd.concat(partitions, axis=0)
	concatenated_data.to_parquet(output_name)

	for partition in partitions:
		del(partition)
	del(partitions)

def wrangle(data: pd.DataFrame, filename: str) -> None:
	"""
	Performs wrangling of dataframe from an Apache Parquet file.

	Args:
		data (pandas.DataFrame): Dataframe containing raw data of, potentially, 
			multiple lakes
		filename (str): Name of the file from which "data" came
	"""
	
	# Data Imputation
	"""
	Some dataframes may have missing values in the "flag" column. In cases 
	where the "flag" code is missing, we assume that there is no error with the 
	observation and impute the missing values with a '0' indicating a normal 
	observation.
	"""
	data["flag"] = data["flag"].fillna(0) # 0 = No error with observation
	print(f"Finished imputing {filename}")

	# Data Formatting
	"""
	This step verifies that the columns in the original dataframe are all of 
	correct type. Missing values in the dataframe might be coded as "None" 
	values but this doesn't let us meaningfully perform operations on the data 
	(e.g. grouping and aggregating). Replacing "None" with numpy.nan values 
	allows for such operations to function.
	"""
	data.fillna(np.nan) # NaN permits arithmetic operations on the missing data 
	data["source"] = data["source"].astype(str)
	data["datetime"] = pd.to_datetime(data["datetime"], utc = True)
	data["lake_id"] = data["lake_id"].astype(str)
	data["depth"] = data["depth"].astype(float)
	data["variable"] = data["variable"].astype(str)
	data["unit"] = data["unit"].astype(str)
	data["observation"] = data["observation"].astype(float)
	data["flag"] = data["flag"].astype(int)
	print(f"Finished formatting {filename}")

	# Data Cleaning
	"""
	Some erroneous observations may be coded such that their depth value is 
	"-99". We omit these values. It should be noted that negative depth values 
	are not necessarily incorrect. Hence we only omit observations with this 
	specific value.

	Chlorophyll a (chla) can be reported in two units: micrograms per liter and 
	relative fluoresence units. We split observations measuring chlorophyll a 
	into two variables by their unit.
	"""
	data[data["depth"] != -99.0]

	data.loc[(data["variable"] == "chla") & (data["unit"] == "MicroGM-PER-L"), "variable"] = "chla_ugl"
	data.loc[(data["variable"] == "chla") & (data["unit"] == "RFU"), "variable"] = "chla_rfu"
	print(f"Finished cleaning {filename}\n")

	# Data Structuring
	"""
	A single dataframe could contain data from multiple lakes. We split these 
	dataframes into a list of dataframes such that each element contains data 
	from only one lake.
	
	The data for each lake is then converted into a LakeData object (see 
	LakeData.py). This LakeData object holds the data for the lake in different 
	forms and for one-dimensional and two-dimensional variables. Depending on 
	what kind of output data is desired, we provide an easy way to extend the 
	functionality of this script to fit new purposes.

	Data within a dataframe can be duplicated. We combine duplicates into a 
	single observation by some predefined criteria (see LakeData.py).

	A dataframe can contain data for variables that change with time or depth 
	and time. We separate these variables into two dataframes.

	The data originally comes in a long format where different columns for 
	different features are represented in one columns with multiple rows. We 
	pivot this form to a wide, tabular format where different columns 
	represent different variables and each row represents a different datetime 
	and depth. The pivoting is done by some predefined criteria 
	(see LakeData.py).
	
	The one- and two-dimensional wide formats of the data are written out as 
	Apache Parquet files into a folder containing the data for a single lake. 
	The output files' names match the following pattern (omitting the 
	brackets): [LAKE ABBREVIATION]_[PARENT FILENAME]_[DIMENSIONALITY]
	"""
	data = data.sort_values(["lake_id", "datetime", "depth", "flag"])
	lakes = split_by_lake(data)
	lakes = [convert(lake) for lake in lakes]

	for lake in lakes:
		print(f"Deduplicating {lake.lake_id} in {filename}...")
		lake.deduplicate()
		print(f"Deduplicated {lake.lake_id} in {filename}")

		print(f"Separating variables for {lake.lake_id} in {filename}...")
		lake.separate_variables()
		print(f"Separated variables for {lake.lake_id} in {filename}")

		print(f"Pivoting {lake.lake_id} in {filename}...")
		lake.long_to_wide()
		print(f"Finished pivoting {lake.lake_id} in {filename}\n")

	print(f"Finished structuring {filename}\n")

	# Data Output
	for lake in lakes:
		lake_dir = lake.lake_id

		# Create directory based on lake_id if it doesn't exist
		if not os.path.exists(lake_dir):
			os.makedirs(lake_dir)

		# Sanitize parent_filename to remove file extensions and any unwanted characters
		parent_filename_clean = os.path.splitext(os.path.basename(filename))[0]
		parent_filename_clean = parent_filename_clean.replace(" ", "_")  # Replace spaces with underscores for safety

		# Generate output filenames using parent filename
		output_1d_filename = f"{lake.lake_id}_{parent_filename_clean}_1D.parquet"
		output_2d_filename = f"{lake.lake_id}_{parent_filename_clean}_2D.parquet"

		# Save CSV files in the created directory with the unique names
		print(f"Writing {lake_dir} in {filename} to disk...")
		lake.wide_data["1D"].to_parquet(os.path.join(lake_dir, output_1d_filename))
		lake.wide_data["2D"].to_parquet(os.path.join(lake_dir, output_2d_filename))
		print(f"Wrote {lake_dir} in {filename} to disk\n")


def split_by_lake(data: pd.DataFrame) -> typing.List[pd.DataFrame]:
	"""
	Splits a single dataframe containing data from multiple lakes into a list 
	of multiple dataframes with unique lakes.

	Args:
		data (pandas.DataFrame): Dataframe (potentially) containing data from 
			multiple lakes

	Returns:
		lakes (list): List containing dataframes with data for each unique lake
	"""	

	lake_ids = data["lake_id"].unique()
	lakes = []
	for lake_id in lake_ids:
		lake = data[data["lake_id"] == lake_id].reset_index(drop = True)
		lakes.append(lake)
		
	return lakes

def convert(data: pd.DataFrame) -> lakedata.LakeData:
	"""
	Converts a Pandas dataframe with data for a lake into a LakeData object.

	Args:
		data (pandas.DataFrame): Dataframe containing data for a single lake

	Returns:
		lake (LakeData): LakeData object with lake_id and raw_data fields 
			populated
	"""
	lake_id = data["lake_id"].unique()
	if len(lake_id) > 1:
		print(f"Lake dataframe contains more than 1 lake in it.")
		sys.exit(1)

	raw_data = data.drop("lake_id", axis = 1)
	lake = lakedata.LakeData(lake_id[0], raw_data)

	return lake

if __name__ == "__main__":

	if len(sys.argv) != 2:
		print(f"Usage: {sys.argv[0]} <PATH TO LAKEBED-US DATA DIRECTORY>/")
		sys.exit(1)
	else:
		lakebed_path = sys.argv[1]
		if not os.path.isdir(lakebed_path):
			print(f"Error: The provided path '{lakebed_path}' is not a directory.")
			sys.exit(1)
		  
		print(f"LakeBeD-US path: {lakebed_path}/\n")
		parse(lakebed_path)