From 83af36b008f6dd5c4d98b2f90134386f8ff7c77c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joan=20H=C3=A9risson?= Date: Fri, 19 Jul 2024 11:09:15 +0200 Subject: [PATCH 1/6] feat(experimenter): add Data Extractor --- icfree/experimenter/__init__.py | 0 icfree/experimenter/data_extractor.py | 177 +++++++++ .../input/plate1_initial_data.xlsx | Bin 0 -> 365024 bytes .../data_extractor/input/plate1_sampling.tsv | 343 ++++++++++++++++++ .../data_extractor/output/plate1.csv | 58 +++ .../data_extractor/output/plate1.xlsx | Bin 0 -> 9925 bytes tests/experimenter/test_data_extractor.py | 86 +++++ 7 files changed, 664 insertions(+) create mode 100644 icfree/experimenter/__init__.py create mode 100644 icfree/experimenter/data_extractor.py create mode 100644 tests/data/experimenter/data_extractor/input/plate1_initial_data.xlsx create mode 100644 tests/data/experimenter/data_extractor/input/plate1_sampling.tsv create mode 100644 tests/data/experimenter/data_extractor/output/plate1.csv create mode 100644 tests/data/experimenter/data_extractor/output/plate1.xlsx create mode 100644 tests/experimenter/test_data_extractor.py diff --git a/icfree/experimenter/__init__.py b/icfree/experimenter/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/icfree/experimenter/data_extractor.py b/icfree/experimenter/data_extractor.py new file mode 100644 index 0000000..ecb1814 --- /dev/null +++ b/icfree/experimenter/data_extractor.py @@ -0,0 +1,177 @@ +import pandas as pd +import argparse + +def find_n_m(sampling_file_path): + """ + Find the number of unique combinations (n) and the number of replicates (m) from a sampling file. + + Parameters: + sampling_file_path (str): Path to the sampling file (Excel, CSV, or TSV). + + Returns: + tuple: A tuple containing the number of unique combinations (n) and the number of replicates (m). + """ + # Load the sampling file + df_sampling = pd.read_csv(sampling_file_path, sep='\t') + + # Drop the unnamed column if it exists + if df_sampling.columns[0].startswith('Unnamed'): + df_sampling = df_sampling.drop(columns=df_sampling.columns[0]) + + # Identify unique combinations (n) and replicates (m) + unique_combinations = df_sampling.drop_duplicates() + n = unique_combinations.shape[0] + m = df_sampling.shape[0] // n + + return n, m + +def process_data(file_path, num_samples, num_replicates): + """ + Process the initial data file to reshape the fluorescence data. + + Parameters: + file_path (str): Path to the initial data file (Excel, CSV, or TSV). + num_samples (int): Number of samples. + num_replicates (int): Number of replicates. + + Returns: + tuple: A tuple containing the reshaped DataFrame and the sheet name (if applicable). + """ + # Determine the file extension + file_extension = file_path.split('.')[-1].lower() + + # Load the data based on file extension + if file_extension == 'xlsx': + excel_data = pd.ExcelFile(file_path) + sheet_name = excel_data.sheet_names[0] + df = pd.read_excel(file_path, sheet_name=sheet_name) + elif file_extension == 'csv': + df = pd.read_csv(file_path) + sheet_name = None + elif file_extension == 'tsv': + df = pd.read_csv(file_path, sep='\t') + sheet_name = None + else: + raise ValueError("Unsupported file type. Please provide an Excel (.xlsx), CSV (.csv), or TSV (.tsv) file.") + + # Keep only the last row and remove the first two columns + df_last_row = df.iloc[[-1]].drop(columns=df.columns[:2]) + + # Keep only the required number of values + total_values = num_samples * num_replicates + values_to_keep = df_last_row.values.flatten()[:total_values] + + # Reshape the values into the specified number of columns and rows + reshaped_values = values_to_keep.reshape((num_samples, num_replicates), order='F') + + # Create a new DataFrame with the reshaped values + df_reshaped = pd.DataFrame(reshaped_values) + + # Add headers to the reshaped DataFrame + headers = [f"Fluorescence Value {i+1}" for i in range(num_replicates)] + df_reshaped.columns = headers + + # Add "Fluorescence Average" column + df_reshaped["Fluorescence Average"] = df_reshaped.mean(axis=1) + + return df_reshaped, sheet_name + +def load_sampling_file(file_path, num_samples): + """ + Load the sampling file and take only the first num_samples lines. + + Parameters: + file_path (str): Path to the sampling file (Excel, CSV, or TSV). + num_samples (int): Number of samples to load. + + Returns: + DataFrame: A DataFrame containing the first num_samples lines of the sampling file. + """ + # Determine the file extension + file_extension = file_path.split('.')[-1].lower() + + # Load the data based on file extension + if file_extension == 'xlsx': + df = pd.read_excel(file_path) + elif file_extension == 'csv': + df = pd.read_csv(file_path) + elif file_extension == 'tsv': + df = pd.read_csv(file_path, sep='\t') + else: + raise ValueError("Unsupported file type. Please provide an Excel (.xlsx), CSV (.csv), or TSV (.tsv) file.") + + # Take only the first num_samples lines + df = df.head(num_samples) + + return df + +def clean_sampling_file(df_sampling): + """ + Clean the sampling file by removing the unnamed first column if it contains incrementing integers. + + Parameters: + df_sampling (DataFrame): The sampling DataFrame to clean. + + Returns: + DataFrame: The cleaned sampling DataFrame. + """ + first_column = df_sampling.iloc[:, 0] + if first_column.name.startswith('Unnamed') and pd.api.types.is_integer_dtype(first_column): + if (first_column == range(len(first_column))).all(): + df_sampling = df_sampling.drop(columns=first_column.name) + return df_sampling + +def process(initial_data_file, output_file_path, sampling_file, num_samples=None, num_replicates=None, display=True): + """ + Process the initial data file and sampling file to combine the data and save the output. + + Parameters: + initial_data_file (str): Path to the initial data file (Excel, CSV, or TSV). + output_file_path (str): Path for the output file. + sampling_file (str): Path to the sampling file (Excel, CSV, or TSV). + num_samples (int, optional): Number of samples. If not specified, it will be detected from the sampling file. + num_replicates (int, optional): Number of replicates. If not specified, it will be detected from the sampling file. + display (bool, optional): Whether to display the combined data. Default is True. + + Returns: + DataFrame: The combined DataFrame. + """ + if num_samples is None or num_replicates is None: + n, m = find_n_m(sampling_file) + num_samples = num_samples if num_samples is not None else n + num_replicates = num_replicates if num_replicates is not None else m + + df_reshaped, sheet_name = process_data(initial_data_file, num_samples, num_replicates) + + df_sampling = load_sampling_file(sampling_file, num_samples) + df_sampling_cleaned = clean_sampling_file(df_sampling) + df_combined = pd.concat([df_sampling_cleaned, df_reshaped], axis=1) + + if display: + print(df_combined) + + # Save the combined dataframe with headers and averages in the specified format + if output_file_path.endswith('.xlsx'): + df_combined.to_excel(output_file_path, index=False, sheet_name=sheet_name) + elif output_file_path.endswith('.csv'): + df_combined.to_csv(output_file_path, index=False) + elif output_file_path.endswith('.tsv'): + df_combined.to_csv(output_file_path, index=False, sep='\t') + else: + raise ValueError("Unsupported output file type. Please provide an Excel (.xlsx), CSV (.csv), or TSV (.tsv) file.") + + print(f"Processed data saved to {output_file_path}") + return df_combined + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Process and reshape fluorescence data.") + parser.add_argument("--initial_data_file", type=str, required=True, help="Path to the initial Excel, CSV, or TSV file.") + parser.add_argument("--output_file", type=str, required=True, help="Path for the output file.") + parser.add_argument("--sampling_file", type=str, required=True, help="Path to the sampling Excel, CSV, or TSV file.") + parser.add_argument("--num_samples", type=int, help="Number of samples.") + parser.add_argument("--num_replicates", type=int, help="Number of replicates.") + parser.add_argument("--no_display", action="store_true", help="Do not display the result.") + + args = parser.parse_args() + + process(args.initial_data_file, args.output_file, args.sampling_file, args.num_samples, args.num_replicates, 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zp)c)V?`mQ1YNYAmXyNkVFCLY}PyEHBexv+fpFi;kI!YX{N3a}rq6PGR$lb{@b#Qfv zUaD4rRAD44=JsD*yWG>4G?*cd3R{Y4dr0F`1xn2M#_I;5WYT?>SHXQM$OzG>b4K3$ z+T@MR><+JrbV>T#vks(p%w{n`;gE~kK8&Y3T2shXWvSH^F-1xry0y7}iKTER!`S3J z*wz2Wz)>nQv?he+f@MSV#?v<;BFL5nuT6fyI;RGH-wJMIgw(Pb{j(kZ9^@)YQ6T~~ zum6QFm`!e~kAU|q#^io=%-%SrNgg_D2gLbVhxlzb!*;(Ud{8Q8&_DJ43<+bLpS Date: Fri, 19 Jul 2024 11:15:51 +0200 Subject: [PATCH 2/6] build(environment.yaml): add openpyxl for Data Extractor --- environment.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yaml b/environment.yaml index bca5d8b..618a287 100644 --- a/environment.yaml +++ b/environment.yaml @@ -6,3 +6,4 @@ dependencies: - pandas - numpy - pyDOE2 + - openpyxl From 58444646905fe599e2dfb442329be0014229db76 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joan=20H=C3=A9risson?= Date: Fri, 19 Jul 2024 11:16:24 +0200 Subject: [PATCH 3/6] trigger --- icfree/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/icfree/__init__.py b/icfree/__init__.py index bacb2f4..e80a8da 100644 --- a/icfree/__init__.py +++ b/icfree/__init__.py @@ -2,4 +2,5 @@ from icfree.sampler import main as sampler from icfree.plate_designer import main as plate_designer + __all__ = ['instructor', 'sampler', 'plate_designer'] From c518895d8974662309c1b2334b19f3413732594f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joan=20H=C3=A9risson?= Date: Fri, 26 Jul 2024 16:58:24 +0200 Subject: [PATCH 4/6] feat(sampler): add --ratios --- icfree/sampler.py | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/icfree/sampler.py b/icfree/sampler.py index 8b685ed..bb42d02 100644 --- a/icfree/sampler.py +++ b/icfree/sampler.py @@ -5,7 +5,7 @@ from pyDOE2 import lhs import ast -def generate_lhs_samples(input_file, num_samples, step, fixed_values=None, seed=None): +def generate_lhs_samples(input_file, num_samples, step=None, ratios=None, fixed_values=None, seed=None): """ Generates Latin Hypercube Samples for components based on discrete ranges. @@ -13,6 +13,7 @@ def generate_lhs_samples(input_file, num_samples, step, fixed_values=None, seed= - input_file: Path to the input file containing components and their max values. - num_samples: Number of samples to generate. - step: Step size for creating discrete ranges. + - ratios: List of ratios for creating discrete ranges. - fixed_values: Dictionary of components with fixed values (optional). - seed: Random seed for reproducibility. @@ -33,11 +34,17 @@ def generate_lhs_samples(input_file, num_samples, step, fixed_values=None, seed= # Generate discrete ranges for each component for index, row in components_df.iterrows(): component_name = row['Component'] + max_value = row['maxValue'] if fixed_values and component_name in fixed_values: # If the component has a fixed value, use a single-element array component_range = np.array([fixed_values[component_name]]) else: - component_range = np.arange(0, row['maxValue'] + step, step) + if ratios is not None: + # Use ratios to create the discrete range + component_range = np.array([r * max_value for r in ratios]) + else: + # Use step to create the discrete range + component_range = np.arange(0, max_value + step, step) discrete_ranges.append(component_range) # Determine the number of components @@ -55,7 +62,7 @@ def generate_lhs_samples(input_file, num_samples, step, fixed_values=None, seed= samples_df = pd.DataFrame(samples, columns=components_df['Component']) return samples_df -def main(input_file, output_file, num_samples, step=2.5, fixed_values=None, seed=None): +def main(input_file, output_file, num_samples, step=None, ratios=None, fixed_values=None, seed=None): """ Main function to generate LHS samples and save them to a CSV file. @@ -63,7 +70,8 @@ def main(input_file, output_file, num_samples, step=2.5, fixed_values=None, seed - input_file: Path to the input file containing components and their max values. - output_file: Path to the output CSV file where samples will be written. - num_samples: Number of samples to generate. - - step: Step size for creating discrete ranges (default: 2.5). + - step: Step size for creating discrete ranges (optional). + - ratios: List of ratios for creating discrete ranges (optional). - fixed_values: Dictionary of components with fixed values (optional). - seed: Random seed for reproducibility (optional). """ @@ -80,7 +88,7 @@ def main(input_file, output_file, num_samples, step=2.5, fixed_values=None, seed print(f"Warning: Component '{component}' not found in the input file.") # Generate LHS samples - samples_df = generate_lhs_samples(input_file, num_samples, step, fixed_values, seed) + samples_df = generate_lhs_samples(input_file, num_samples, step, ratios, fixed_values, seed) # Write the samples to a CSV file samples_df.to_csv(output_file, index=False) @@ -92,7 +100,12 @@ def main(input_file, output_file, num_samples, step=2.5, fixed_values=None, seed parser.add_argument('input_file', type=str, help='Input file path with components and their max values.') parser.add_argument('output_file', type=str, help='Output CSV file path for the samples.') parser.add_argument('num_samples', type=int, help='Number of samples to generate.') - parser.add_argument('--step', type=float, default=2.5, help='Step size for creating discrete ranges (default: 2.5).') + + # Create a mutually exclusive group for step and ratios + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument('--step', type=float, help='Step size for creating discrete ranges.') + group.add_argument('--ratios', type=str, help='Comma-separated list of ratios for creating discrete ranges (e.g., "0,0.2,0.4,0.6,0.8,1").') + parser.add_argument('--fixed_values', type=str, default=None, help='Fixed values for components as a dictionary (e.g., \'{"Component1": 10, "Component2": 20}\')') parser.add_argument('--seed', type=int, default=None, help='Seed for random number generation for reproducibility (optional).') @@ -102,5 +115,8 @@ def main(input_file, output_file, num_samples, step=2.5, fixed_values=None, seed # Convert fixed_values argument from string to dictionary if provided fixed_values = ast.literal_eval(args.fixed_values) if args.fixed_values else None + # Convert ratios argument from comma-separated string to list of floats if provided + ratios = [float(r) for r in args.ratios.split(',')] if args.ratios else None + # Run the main function with the parsed arguments - main(args.input_file, args.output_file, args.num_samples, args.step, fixed_values, args.seed) + main(args.input_file, args.output_file, args.num_samples, args.step, ratios, fixed_values, args.seed) From ad69e2bdf259045afc4bc7fd2d02587ab17f9464 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joan=20H=C3=A9risson?= Date: Fri, 26 Jul 2024 17:00:34 +0200 Subject: [PATCH 5/6] test(sampler): fit to add --ratios --- tests/test_sampler.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/test_sampler.py b/tests/test_sampler.py index 7976a26..5de58dd 100644 --- a/tests/test_sampler.py +++ b/tests/test_sampler.py @@ -19,7 +19,7 @@ def setUp(self): def test_generate_lhs_samples_normal(self, mock_read_csv): mock_read_csv.return_value = self.components_df - result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, self.seed) + result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, None, self.seed) self.assertEqual(result.shape, (self.num_samples, 3)) self.assertListEqual(list(result.columns), ['A', 'B', 'C']) @@ -28,7 +28,7 @@ def test_generate_lhs_samples_normal(self, mock_read_csv): def test_generate_lhs_samples_no_seed(self, mock_read_csv): mock_read_csv.return_value = self.components_df - result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, None) + result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, None, None) self.assertEqual(result.shape, (self.num_samples, 3)) self.assertListEqual(list(result.columns), ['A', 'B', 'C']) @@ -39,7 +39,7 @@ def test_generate_lhs_samples_edge_case_zero_maxValue(self, mock_read_csv): edge_case_df.loc[0, 'maxValue'] = 0 # Set maxValue of component 'A' to 0 mock_read_csv.return_value = edge_case_df - result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, self.seed) + result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, None, self.seed) self.assertEqual(result.shape, (self.num_samples, 3)) self.assertTrue((result['A'] == 0).all()) # All values in column 'A' should be zero @@ -49,20 +49,20 @@ def test_generate_lhs_samples_invalid_step(self, mock_read_csv): mock_read_csv.return_value = self.components_df with self.assertRaises(IndexError): - generate_lhs_samples("fake_path.csv", self.num_samples, -2.5, None, self.seed) # Negative step size should raise an error + generate_lhs_samples("fake_path.csv", self.num_samples, -2.5, None, None, self.seed) # Negative step size should raise an error @patch("icfree.sampler.pd.read_csv") def test_generate_lhs_samples_invalid_input_file(self, mock_read_csv): mock_read_csv.side_effect = FileNotFoundError with self.assertRaises(FileNotFoundError): - generate_lhs_samples("invalid_path.csv", self.num_samples, self.step, None, self.seed) + generate_lhs_samples("invalid_path.csv", self.num_samples, self.step, None, None, self.seed) @patch("icfree.sampler.pd.read_csv") def test_generate_lhs_samples_fix_component_value(self, mock_read_csv): mock_read_csv.return_value = self.components_df - result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, {'A': 5}, self.seed) + result = generate_lhs_samples("fake_path.csv", self.num_samples, self.step, None, {'A': 5}, self.seed) self.assertEqual(result.shape, (self.num_samples, 3)) self.assertTrue((result['A'] == 5).all()) From 4f11075ab524c097bb74544b2f9ec5c726ec6797 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joan=20H=C3=A9risson?= Date: Mon, 16 Sep 2024 10:26:00 +0200 Subject: [PATCH 6/6] chore(experimenter): rename extractor --- icfree/experimenter/{data_extractor.py => extractor.py} | 0 .../experimenter/{test_data_extractor.py => test_extractor.py} | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename icfree/experimenter/{data_extractor.py => extractor.py} (100%) rename tests/experimenter/{test_data_extractor.py => test_extractor.py} (96%) diff --git a/icfree/experimenter/data_extractor.py b/icfree/experimenter/extractor.py similarity index 100% rename from icfree/experimenter/data_extractor.py rename to icfree/experimenter/extractor.py diff --git a/tests/experimenter/test_data_extractor.py b/tests/experimenter/test_extractor.py similarity index 96% rename from tests/experimenter/test_data_extractor.py rename to tests/experimenter/test_extractor.py index e4a3b6b..0881301 100644 --- a/tests/experimenter/test_data_extractor.py +++ b/tests/experimenter/test_extractor.py @@ -2,7 +2,7 @@ import pandas as pd import sys from os import path as os_path -from icfree.experimenter.data_extractor import find_n_m, process_data, load_sampling_file, clean_sampling_file, process +from icfree.experimenter.extractor import find_n_m, process_data, load_sampling_file, clean_sampling_file, process class TestDataExtractor(unittest.TestCase):