feat(data): implement damage files index generation and data processing
Closes #38
This commit is contained in:
nuluh
@@ -1,25 +1,263 @@
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import pandas as pd
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import os
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import re
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import sys
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from colorama import Fore, Style, init
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from typing import TypedDict, Dict, List
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from joblib import load
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from pprint import pprint
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# class DamageFilesIndex(TypedDict):
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# damage_index: int
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# files: list[int]
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DamageFilesIndex = Dict[int, List[str]]
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def generate_damage_files_index(**kwargs) -> DamageFilesIndex:
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prefix = kwargs.get("prefix", "zzzAD")
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extension = kwargs.get("extension", ".TXT")
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num_damage = kwargs.get("num_damage")
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file_index_start = kwargs.get("file_index_start")
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col = kwargs.get("col")
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base_path = kwargs.get("base_path")
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damage_scenarios = {}
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a = file_index_start
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b = col + 1
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for i in range(1, num_damage + 1):
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damage_scenarios[i] = range(a, b)
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a += col
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b += col
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# return damage_scenarios
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x = {}
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for damage, files in damage_scenarios.items():
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x[damage] = [] # Initialize each key with an empty list
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for i, file_index in enumerate(files, start=1):
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if base_path:
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x[damage].append(
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os.path.normpath(
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os.path.join(base_path, f"{prefix}{file_index}{extension}")
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)
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)
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# if not os.path.exists(file_path):
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# print(Fore.RED + f"File {file_path} does not exist.")
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# continue
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else:
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x[damage].append(f"{prefix}{file_index}{extension}")
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return x
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# file_path = os.path.join(base_path, f"zzz{prefix}D{file_index}.TXT")
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# df = pd.read_csv( file_path, sep="\t", skiprows=10) # Read with explicit column names
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class DataProcessor:
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def __init__(self, file_index: Dict[int, List[str]], cache_path: str = None):
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self.file_index = file_index
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if cache_path:
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self.data = load(cache_path)
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else:
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self.data = self._load_all_data()
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def _extract_column_names(self, file_path: str) -> List[str]:
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"""
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Extracts column names from the header of the given file.
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Assumes the 6th line contains column names.
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:param file_path: Path to the data file.
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:return: List of column names.
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"""
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with open(file_path, "r") as f:
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header_lines = [next(f) for _ in range(12)]
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# Extract column names from the 6th line
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channel_line = header_lines[10].strip()
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tokens = re.findall(r'"([^"]+)"', channel_line)
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if not channel_line.startswith('"'):
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first_token = channel_line.split()[0]
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tokens = [first_token] + tokens
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return tokens # Prepend 'Time' column if applicable
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def _load_dataframe(self, file_path: str) -> pd.DataFrame:
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"""
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Loads a single data file into a pandas DataFrame.
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:param file_path: Path to the data file.
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:return: DataFrame containing the numerical data.
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"""
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col_names = self._extract_column_names(file_path)
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df = pd.read_csv(
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file_path, delim_whitespace=True, skiprows=11, header=None, memory_map=True
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)
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df.columns = col_names
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return df
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def _load_all_data(self) -> List[List[pd.DataFrame]]:
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"""
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Loads all data files based on the grouping dictionary and returns a nested list.
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:return: A nested list of DataFrames where the outer index corresponds to group_idx - 1.
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"""
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data = []
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# Find the maximum group index to determine the list size
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max_group_idx = max(self.file_index.keys()) if self.file_index else 0
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# Initialize empty lists
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for _ in range(max_group_idx):
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data.append([])
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# Fill the list with data
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for group_idx, file_list in self.file_index.items():
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# Adjust index to be 0-based
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list_idx = group_idx - 1
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data[list_idx] = [self._load_dataframe(file) for file in file_list]
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return data
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def get_group_data(self, group_idx: int) -> List[pd.DataFrame]:
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"""
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Returns the list of DataFrames for the given group index.
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:param group_idx: Index of the group.
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:return: List of DataFrames.
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"""
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return self.data.get([group_idx, []])
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def get_column_names(self, group_idx: int, file_idx: int = 0) -> List[str]:
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"""
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Returns the column names for the given group and file indices.
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:param group_idx: Index of the group.
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:param file_idx: Index of the file in the group.
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:return: List of column names.
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"""
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if group_idx in self.data and len(self.data[group_idx]) > file_idx:
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return self.data[group_idx][file_idx].columns.tolist()
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return []
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def get_data_info(self):
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"""
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Print information about the loaded data structure.
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Adapted for when self.data is a List instead of a Dictionary.
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"""
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if isinstance(self.data, list):
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# For each sublist in self.data, get the type names of all elements
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pprint(
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[
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(
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[type(item).__name__ for item in sublist]
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if isinstance(sublist, list)
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else type(sublist).__name__
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)
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for sublist in self.data
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]
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)
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else:
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pprint(
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{
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key: [type(df).__name__ for df in value]
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for key, value in self.data.items()
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}
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if isinstance(self.data, dict)
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else type(self.data).__name__
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)
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def _create_vector_column_index(self):
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vector_col_idx = []
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y = 0
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for data_group in self.data: # len(data_group[i]) = 5
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for j in data_group: # len(j[i]) =
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c = [] # column vector c_{j}
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x = 0
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for _ in range(6): # TODO: range(6) should be dynamic and parameterized
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c.append(x + y)
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x += 5
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vector_col_idx.append(c)
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y += 1
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return vector_col_idx
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def create_vector_column(self, overwrite=True):
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"""
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Create a vector column from the loaded data.
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:param overwrite: Overwrite the original data with vector column-based data.
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"""
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idx = self._create_vector_column_index()
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# if overwrite:
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for i in range(len(self.data)):
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for j in range(len(self.data[i])):
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# Get the appropriate indices for slicing from idx
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indices = idx[j]
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# Get the current DataFrame
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df = self.data[i][j]
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# Keep the 'Time' column and select only specified 'Real' columns
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# First, we add 1 to all indices to account for 'Time' being at position 0
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real_indices = [index + 1 for index in indices]
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# Create list with Time column index (0) and the adjusted Real indices
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all_indices = [0] + real_indices
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# Apply the slicing
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self.data[i][j] = df.iloc[:, all_indices]
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# TODO: if !overwrite:
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def create_limited_sensor_vector_column(self, overwrite=True):
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"""
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Create a vector column from the loaded data.
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:param overwrite: Overwrite the original data with vector column-based data.
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"""
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idx = self._create_vector_column_index()
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# if overwrite:
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for i in range(len(self.data)):
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for j in range(len(self.data[i])):
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# Get the appropriate indices for slicing from idx
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indices = idx[j]
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# Get the current DataFrame
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df = self.data[i][j]
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# Keep the 'Time' column and select only specified 'Real' columns
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# First, we add 1 to all indices to account for 'Time' being at position 0
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real_indices = [index + 1 for index in indices]
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# Create list with Time column index (0) and the adjusted Real indices
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all_indices = [0] + [real_indices[0]] + [real_indices[-1]]
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# Apply the slicing
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self.data[i][j] = df.iloc[:, all_indices]
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# TODO: if !overwrite:
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def create_damage_files(base_path, output_base, prefix):
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# Initialize colorama
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init(autoreset=True)
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# Generate column labels based on expected duplication in input files
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columns = ['Real'] + [f'Real.{i}' for i in range(1, 30)] # Explicitly setting column names
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columns = ["Real"] + [
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f"Real.{i}" for i in range(1, 30)
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] # Explicitly setting column names
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sensor_end_map = {1: 'Real.25', 2: 'Real.26', 3: 'Real.27', 4: 'Real.28', 5: 'Real.29'}
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sensor_end_map = {
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1: "Real.25",
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2: "Real.26",
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3: "Real.27",
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4: "Real.28",
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5: "Real.29",
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}
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# Define the damage scenarios and the corresponding original file indices
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damage_scenarios = {
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1: range(1, 6), # Damage 1 files from zzzAD1.csv to zzzAD5.csv
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2: range(6, 11), # Damage 2 files from zzzAD6.csv to zzzAD10.csv
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3: range(11, 16), # Damage 3 files from zzzAD11.csv to zzzAD15.csvs
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4: range(16, 21), # Damage 4 files from zzzAD16.csv to zzzAD20.csv
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3: range(11, 16), # Damage 3 files from zzzAD11.csv to zzzAD15.csvs
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4: range(16, 21), # Damage 4 files from zzzAD16.csv to zzzAD20.csv
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5: range(21, 26), # Damage 5 files from zzzAD21.csv to zzzAD25.csv
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6: range(26, 31) # Damage 6 files from zzzAD26.csv to zzzAD30.csv
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6: range(26, 31), # Damage 6 files from zzzAD26.csv to zzzAD30.csv
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}
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damage_pad = len(str(len(damage_scenarios)))
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test_pad = len(str(30))
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@@ -27,29 +265,36 @@ def create_damage_files(base_path, output_base, prefix):
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for damage, files in damage_scenarios.items():
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for i, file_index in enumerate(files, start=1):
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# Load original data file
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file_path = os.path.join(base_path, f'zzz{prefix}D{file_index}.TXT')
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df = pd.read_csv(file_path, sep='\t', skiprows=10) # Read with explicit column names
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file_path = os.path.join(base_path, f"zzz{prefix}D{file_index}.TXT")
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df = pd.read_csv(
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file_path, sep="\t", skiprows=10
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) # Read with explicit column names
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top_sensor = columns[i-1]
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top_sensor = columns[i - 1]
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print(top_sensor, type(top_sensor))
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output_file_1 = os.path.join(output_base, f'DAMAGE_{damage}', f'DAMAGE{damage}_TEST{i}_01.csv')
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output_file_1 = os.path.join(
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output_base, f"DAMAGE_{damage}", f"DAMAGE{damage}_TEST{i}_01.csv"
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)
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print(f"Creating {output_file_1} from taking zzz{prefix}D{file_index}.TXT")
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print("Taking datetime column on index 0...")
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print(f"Taking `{top_sensor}`...")
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os.makedirs(os.path.dirname(output_file_1), exist_ok=True)
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df[['Time', top_sensor]].to_csv(output_file_1, index=False)
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df[["Time", top_sensor]].to_csv(output_file_1, index=False)
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print(Fore.GREEN + "Done")
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bottom_sensor = sensor_end_map[i]
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output_file_2 = os.path.join(output_base, f'DAMAGE_{damage}', f'DAMAGE{damage}_TEST{i}_02.csv')
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output_file_2 = os.path.join(
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output_base, f"DAMAGE_{damage}", f"DAMAGE{damage}_TEST{i}_02.csv"
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)
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print(f"Creating {output_file_2} from taking zzz{prefix}D{file_index}.TXT")
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print("Taking datetime column on index 0...")
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print(f"Taking `{bottom_sensor}`...")
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os.makedirs(os.path.dirname(output_file_2), exist_ok=True)
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df[['Time', bottom_sensor]].to_csv(output_file_2, index=False)
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df[["Time", bottom_sensor]].to_csv(output_file_2, index=False)
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print(Fore.GREEN + "Done")
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print("---")
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def main():
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if len(sys.argv) < 2:
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print("Usage: python convert.py <path_to_csv_files>")
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@@ -66,5 +311,6 @@ def main():
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create_damage_files(base_path, output_base, prefix)
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print(Fore.YELLOW + Style.BRIGHT + "All files have been created successfully.")
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if __name__ == "__main__":
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main()
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7
data/QUGS/test.py
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7
data/QUGS/test.py
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@@ -0,0 +1,7 @@
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from convert import *
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from joblib import dump
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a = generate_damage_files_index(
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num_damage=6, file_index_start=1, col=5, base_path="D:/thesis/data/dataset_A"
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)
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dump(DataProcessor(file_index=a), "D:/cache.joblib")
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