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No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. ======================================================================= Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” The text of the Creative Commons public licenses is dedicated to the public domain under the CC0 Public Domain Dedication. Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark "Creative Commons" or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. Creative Commons may be contacted at creativecommons.org. 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]} /") 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)