368 lines
14 KiB
Python
368 lines
14 KiB
Python
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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import numpy as np
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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 DamageFilesIndices(TypedDict):
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# damage_index: int
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# files: list[int]
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OriginalSingleDamageScenarioFilePath = str
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DamageScenarioGroupIndex = int
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OriginalSingleDamageScenario = pd.DataFrame
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SensorIndex = int
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VectorColumnIndex = List[SensorIndex]
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VectorColumnIndices = List[VectorColumnIndex]
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DamageScenarioGroup = List[OriginalSingleDamageScenario]
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GroupDataset = List[DamageScenarioGroup]
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class DamageFilesIndices(TypedDict):
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damage_index: int
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files: List[str]
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def generate_damage_files_index(**kwargs) -> DamageFilesIndices:
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"""
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Generate a dictionary of damage scenarios with file indices.
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:param kwargs: Keyword arguments to specify parameters.
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- prefix: Prefix for the file names (default: "zzzAD").
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- extension: File extension (default: ".TXT").
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- num_damage: Number of damage scenarios.
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- file_index_start: Starting index for file names.
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- col: Number of files per damage scenario.
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- base_path: Base path for the files.
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- undamage_file: Name of the undamaged file with extension.
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:return: A dictionary where keys are damage scenario indices and values are lists of file paths.
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"""
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prefix: str = kwargs.get("prefix", "zzzAD")
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extension: str = kwargs.get("extension", ".TXT")
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num_damage: int = kwargs.get("num_damage")
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file_index_start: int = kwargs.get("file_index_start")
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col: int = kwargs.get("col")
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base_path: str = kwargs.get("base_path")
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undamage_file: str = kwargs.get("undamage_file")
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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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if undamage_file:
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try:
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x[0] = []
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if base_path:
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x[0].append(
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os.path.normpath(os.path.join(base_path, f"{undamage_file}"))
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)
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else:
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x[0].append(f"{prefix}{undamage_file}")
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except Exception as e:
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print(Fore.RED + f"Error processing undamaged file: {e}")
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sys.exit(1)
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else:
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print(Fore.RED + "No undamaged file specified, terminating.")
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sys.exit(1)
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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: DamageFilesIndices, 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 _load_dataframe(self, file_path: str) -> OriginalSingleDamageScenario:
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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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df = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True)
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return df
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def _load_all_data(self) -> GroupDataset:
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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 = len(self.file_index) if self.file_index else 0
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# Handle case when file_index is empty
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if max_group_idx == 0:
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raise ValueError("No file index provided; file_index is empty.")
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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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data[group_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) -> VectorColumnIndices:
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vector_col_idx: VectorColumnIndices = []
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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: VectorColumnIndex = [] # 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) -> List[List[List[pd.DataFrame]]]:
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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)): # damage(s)
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for j in range(len(self.data[i])): # col(s)
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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 specifid 'Real' colmns
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# First, we add 1 to all indices to acount for 'Time' being at positiion 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 adjustedd 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 export_to_csv(self, output_dir: str, file_prefix: str = "DAMAGE"):
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"""
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Export the processed data to CSV files in the required folder structure.
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:param output_dir: Directory to save the CSV files.
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:param file_prefix: Prefix for the output filenames.
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"""
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for group_idx, group in enumerate(self.data, start=1):
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group_folder = os.path.join(output_dir, f"{file_prefix}_{group_idx}")
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os.makedirs(group_folder, exist_ok=True)
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for test_idx, df in enumerate(group, start=1):
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# Ensure columns are named uniquely if duplicated
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df = df.copy()
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df.columns = ["Time", "Real_0", "Real_1"] # Rename
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# Export first Real column
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out1 = os.path.join(
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group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_01.csv"
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)
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df[["Time", "Real_0"]].rename(columns={"Real_0": "Real"}).to_csv(
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out1, index=False
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)
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# Export last Real column
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out2 = os.path.join(
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group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_02.csv"
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)
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df[["Time", "Real_1"]].rename(columns={"Real_1": "Real"}).to_csv(
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out2, index=False
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)
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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"] + [
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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 = {
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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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# 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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# }
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# damage_pad = len(str(len(damage_scenarios)))
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# test_pad = len(str(30))
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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(
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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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# print(top_sensor, type(top_sensor))
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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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# print(Fore.GREEN + "Done")
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# bottom_sensor = sensor_end_map[i]
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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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# 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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sys.exit(1)
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base_path = sys.argv[1]
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output_base = sys.argv[2]
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prefix = sys.argv[3] # Define output directory
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# Create output folders if they don't exist
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# for i in range(1, 7):
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# os.makedirs(os.path.join(output_base, f'DAMAGE_{i}'), exist_ok=True)
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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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