Uploaded LakeBeD-US: Ecology Edition to LakeBeD-US: Computer Science Edition conversion source code

src/lakedata.py

@@ -0,0 +1,542 @@
+#!/usr/bin/env python3 
+# -*- coding: utf-8 -*-
+"""
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+Filename: lakedata.py
+Author: Aanish Pradhan
+Created: 2024-09-09
+License: Creative Commons Attribution 4.0 International (CC BY 4.0)
+Description: Defines a LakeData class and methods for wrangling data from a 
+lake as well as constants with suggested default values for wrangling data.
+"""
+# IMPORT PACKAGES
+import pandas as pd # Dataframe manipulation
+import sys # System-specific parameters and functions
+import typing # Type hints
+
+# CLASSES
+class LakeData:
+	"""
+	A class that defines a lake and its corresponding datasets in different 
+	forms.
+
+	Attributes:
+		lake_id (str): The abbreviation for the name of the lake (e.g., Falling 
+			Reservoir = FCR)
+		raw_data (pandas.DataFrame): The primary dataframe for wrangling. It is 
+			recommended that all wrangling is performed on this attribute 
+			before splitting.
+		long_data (typing.Dict[str, pandas.DataFrame]): Contains the cleaned 
+			raw data in long format. One-dimensional variables (see constants) 
+			are contained in one dataframe and paired with the "1D" key and 
+			two-dimensional variables (see constants) are contained in another 
+			dataframe and paired with the "2D" key
+		wide_data (typing.Dict[str, pandas.DataFrame]): Contains the cleaned 
+			raw data in wide format. One-dimensional variables (see constants) 
+			are contained in one dataframe and paired with the "1D" key and 
+			two-dimensional variables (see constants) are contained in another 
+			dataframe and paired with the "2D" key
+	"""
+	# DEFINE CONSTANTS
+	DEDUPLICATION_GROUPS = ["datetime", "depth", "variable", "unit", "flag"]
+
+	"""See original LakeBeD-US manuscript by McAfee et. al. 2024 for details"""
+	ONE_DIMENSIONAL_VARS = ["secchi", "inflow"]
+	TWO_DIMENSIONAL_VARS = ["chla_ugl", "chla_rfu", "do", "fdom", "temp", \
+		"phyco", "tp", "drp", "tn", "no2", "no3", "no3no2", "nh4", "dic", \
+		"doc", "poc", "par"]
+
+	ONE_DIMENSIONAL_PIVOT_INDEXES = ["datetime", "flag"]
+	TWO_DIMENSIONAL_PIVOT_INDEXES = ["datetime", "depth", "flag"]
+
+	# CONSTRUCTOR
+	def __init__(self, lake_id: str, raw_data: pd.DataFrame) -> None:
+		"""
+		Creates a Lake object.
+
+		Args:
+			- lake_id (str): The abbreviation for the name of the lake (e.g., 
+				 Falling Reservoir = FCR)
+			- raw_data (pandas.DataFrame): Dataframe containing all the raw 
+					 data for a file, on which all wrangling will be done
+		"""
+		self.lake_id = lake_id
+		self.raw_data = raw_data
+		self.long_data: typing.Dict[str, typing.Optional[pd.DataFrame]] = {
+			"1D": None,
+			"2D": None
+		}
+		self.wide_data: typing.Dict[str, typing.Optional[pd.DataFrame]] = {
+			"1D": None,
+			"2D": None
+		}
+
+	# METHODS
+	def deduplicate(self, deduplication_groups: list = DEDUPLICATION_GROUPS, 
+		aggregation_function: str = "median", 
+		aggregation_variable: str = "observation") -> None:
+		"""
+		Removes "duplicate" observations in a Lake's raw data. Observations are 
+		considered duplicates if they contain identical values among 
+		DEDUPLICATION_GROUPS. Duplicates are grouped together and aggregated by 
+		a specified function on a specified variable.
+		
+		Args:
+			deduplication_groups (list): List of groups to match for 
+				deduplication (default LakeData.DEDUPLICATION_GROUPS)
+			aggregation_function (str): Aggregation function (default "median")
+			aggregation_variable (str): Variable to aggregate (default 
+				"observation")
+		"""
+		self.raw_data = (self.raw_data.groupby(deduplication_groups, dropna = False)
+			.agg({aggregation_variable: aggregation_function}).reset_index())
+
+	def separate_variables(self, 
+		one_dimensional_vars: list = ONE_DIMENSIONAL_VARS, 
+		two_dimensional_vars: list = TWO_DIMENSIONAL_VARS) -> None:
+		"""
+		Separates raw data into one-dimensional (temporally dependent) and 
+		two-dimensional (spatiotemporally-dependent) variables. The default 1D 
+		and 2D variables are defined in the LakeBeD-US manuscript by 
+		McAfee et. al. 2024
+
+		Args:
+			one_dimensional_vars (list): List of variables changing with time 
+				(default LakeData.ONE_DIMENSIONAL_VARS)
+			two_dimensional_vars (list): List of variables changing with space 
+				and time (default LakeData.TWO_DIMENSIONAL_VARS)
+		"""
+
+		if self.raw_data is None:
+			print(f"Lake's raw data is empty")
+			sys.exit(1)
+
+		self.long_data["1D"] = self.raw_data[self.raw_data["variable"].isin(one_dimensional_vars)]
+		self.long_data["2D"] = self.raw_data[self.raw_data["variable"].isin(two_dimensional_vars)]
+
+	def long_to_wide(self, pivot_value: str = "observation",
+		one_dimensional_pivot_indexes: list = ONE_DIMENSIONAL_PIVOT_INDEXES,
+		two_dimensional_pivot_indexes: list = TWO_DIMENSIONAL_PIVOT_INDEXES, 
+		pivot_columns = "variable") -> None:
+		"""
+		Pivot's a lake's long data into a wide, tabular format.
+
+		Args:
+			pivot_value (str): Column from the long format, raw data containing 
+				the values to fill the columns various variables in the wide 
+				format (default "observation")
+			pivot_index (list): List of variables to be used as pivots
+			pivot_column (str): Column in the long format containing the 
+				columns to be used in the wide format (default "variable")
+		"""
+
+		if self.long_data["1D"] is None or self.long_data["2D"] is None:
+			print(f"Lake's long data is empty")
+			sys.exit(1)
+
+		if type(self.long_data["1D"]) is not pd.DataFrame or \
+			type(self.long_data["2D"]) is not pd.DataFrame:
+			
+			print(f"Lake's long data is not of type pandas.DataFrame")
+			sys.exit(1)
+
+		self.wide_data["1D"] = (self.long_data["1D"]
+			.pivot_table(pivot_value, one_dimensional_pivot_indexes, pivot_columns)
+			.reset_index())
+		self.wide_data["2D"] = (self.long_data["2D"]
+			.pivot_table(pivot_value, two_dimensional_pivot_indexes, pivot_columns)
+			.reset_index())

src/preprocess.py

@@ -0,0 +1,733 @@
+#!/usr/bin/env python3 
+# -*- coding: utf-8 -*-
+"""
+Attribution 4.0 International
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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)