Mercurial > repos > recetox > table_scipy_interpolate

annotate table_pandas_transform.py @ 0:0112f08c95ed draft default tip

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/tables commit d0ff40eb2b536fec6c973c3a9ea8e7f31cd9a0d6
author recetox
date Wed, 29 Jan 2025 15:36:02 +0000
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0112f08c95ed planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/tables commit d0ff40eb2b536fec6c973c3a9ea8e7f31cd9a0d6
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1 import argparse
0112f08c95ed planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/tables commit d0ff40eb2b536fec6c973c3a9ea8e7f31cd9a0d6
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2 import logging
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3 from typing import Callable, List, Tuple
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4
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6 import numpy as np
0112f08c95ed planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/tables commit d0ff40eb2b536fec6c973c3a9ea8e7f31cd9a0d6
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7 import pandas as pd
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8 from utils import LoadDataAction, SplitColumnIndicesAction, StoreOutputAction
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9
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10
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11 # Define the available transformations
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12 TRANSFORMATIONS = {
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13 "log": np.log,
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14 "log10": np.log10,
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15 "ln": np.log,
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16 "sqrt": np.sqrt,
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17 "exp": np.exp,
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18 "abs": np.abs,
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19 "floor": np.floor,
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20 "ceil": np.ceil,
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21 }
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24 def apply_transformation(
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25 df: pd.DataFrame, columns: List[int], transformation: str
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26 ) -> pd.DataFrame:
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27 """
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28 Apply the specified transformation to the given columns of the dataframe.
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29
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30 Parameters:
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31 df (pd.DataFrame): The input dataframe.
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32 columns (List[int]): The 0-based indices of the columns to transform.
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33 transformation (str): The transformation to apply.
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34
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35 Returns:
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36 pd.DataFrame: The dataframe with the transformation applied.
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37 """
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38 try:
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39 transform_func = TRANSFORMATIONS[transformation]
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40 for column_index in columns:
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41 column_name = df.columns[column_index]
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42 df[column_name] = transform_func(df[column_name])
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43 return df
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44 except KeyError as e:
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45 logging.error(f"Invalid transformation: {e}")
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46 raise
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47 except IndexError as e:
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48 logging.error(f"Invalid column index: {e}")
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49 raise
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50 except Exception as e:
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51 logging.error(f"Error applying transformation: {e}")
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52 raise
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53
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54
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55 def main(
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56 input_dataset: pd.DataFrame,
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57 columns: List[int],
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58 transformation: str,
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59 output_dataset: Tuple[Callable[[pd.DataFrame, str], None], str],
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60 ) -> None:
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61 """
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62 Main function to load the dataset, apply the transformation, and save the result.
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63
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64 Parameters:
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65 input_dataset (pd.DataFrame): The input dataset.
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66 columns (List[int]): The 0-based indices of the columns to transform.
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67 transformation (str): The transformation to apply.
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68 output_dataset (Tuple[Callable[[pd.DataFrame, str], None], str]): The output dataset and its file extension.
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69 """
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70 try:
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71 df = apply_transformation(input_dataset, columns, transformation)
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72 write_func, file_path = output_dataset
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73 write_func(df, file_path)
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74 except Exception as e:
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75 logging.error(f"Error in main function: {e}")
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76 raise
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77
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78
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79 if __name__ == "__main__":
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80 logging.basicConfig(level=logging.INFO)
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81 parser = argparse.ArgumentParser(
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82 description="Apply mathematical transformations to dataframe columns."
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83 )
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84 parser.add_argument(
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85 "--input_dataset",
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86 nargs=2,
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87 action=LoadDataAction,
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88 required=True,
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89 help="Path to the input dataset and its file extension (csv, tsv, parquet)",
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90 )
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91 parser.add_argument(
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92 "--columns",
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93 action=SplitColumnIndicesAction,
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94 required=True,
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95 help="Comma-separated list of 1-based indices of the columns to apply the transformation on",
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96 )
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97 parser.add_argument(
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98 "--transformation",
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99 type=str,
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100 choices=TRANSFORMATIONS.keys(),
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101 required=True,
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102 help="Transformation to apply",
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103 )
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104 parser.add_argument(
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105 "--output_dataset",
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106 nargs=2,
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107 action=StoreOutputAction,
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108 required=True,
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109 help="Path to the output dataset and its file extension (csv, tsv, parquet)",
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110 )
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111
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112 args = parser.parse_args()
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113 main(args.input_dataset, args.columns, args.transformation, args.output_dataset)