From 1c43d09fce050c94e716fb0bad2bf0ff88a4c10b Mon Sep 17 00:00:00 2001 From: anushka255 Date: Wed, 16 Oct 2024 09:10:00 -0600 Subject: [PATCH 01/12] added io sub module to process files and respective test cases for it --- src/paste3/io.py | 77 +++++++++++++++++++++++++++++++++++ tests/data/input/slice1.h5ad | Bin 0 -> 8485168 bytes tests/data/input/slice2.h5ad | Bin 0 -> 8098396 bytes tests/data/input/slice3.h5ad | Bin 0 -> 8221232 bytes tests/data/input/slice4.h5ad | Bin 0 -> 8287080 bytes tests/test_paste_cmd_line.py | 37 +++++++++++++++++ 6 files changed, 114 insertions(+) create mode 100644 src/paste3/io.py create mode 100644 tests/data/input/slice1.h5ad create mode 100644 tests/data/input/slice2.h5ad create mode 100644 tests/data/input/slice3.h5ad create mode 100644 tests/data/input/slice4.h5ad diff --git a/src/paste3/io.py b/src/paste3/io.py new file mode 100644 index 0000000..f4f1032 --- /dev/null +++ b/src/paste3/io.py @@ -0,0 +1,77 @@ +import scanpy as sc +import numpy as np + +from pathlib import Path +from collections import defaultdict +import logging + + +logger = logging.getLogger(__name__) + + +def process_files(g_fpath, s_fpath, w_fpath=None): + """Returns a list of AnnData objects.""" + + ext = Path(g_fpath[0]).suffix + + if ext == ".csv": + if not (len(s_fpath) == len(g_fpath)): + ValueError("Length of spatial files doesn't equal number of gene files") + _slices = defaultdict() + for file in g_fpath: + # The header of this file is alphanumeric, so this file has to be imported as a string + _slices[get_shape(file)[0]] = sc.read_csv(file) + + for file in s_fpath: + try: + _slice = _slices[get_shape(file)[0]] + except KeyError: + raise ValueError("Incomplete information for a slice") + else: + _slice.obsm["spatial"] = np.genfromtxt( + file, delimiter=",", dtype="float64" + ) + + if w_fpath: + if not (len(w_fpath) == len(g_fpath)): + ValueError("Length of weight files doesn't equal number of gene files") + for file in w_fpath: + _slice = _slices[get_shape(file)[0]] + _slice.obsm["weights"] = np.genfromtxt( + file, delimiter=",", dtype="float64" + ) + else: + for k, v in _slices.items(): + v.obsm["weights"] = np.ones((v.shape[0],)) / v.shape[0] + + slices = list(_slices.values()) + elif ext == ".h5ad": + slices = [sc.read_h5ad(file) for file in g_fpath] + + else: + raise ValueError("Incorrect file type provided ") + + return slices + + +def get_shape(file_path): + """Determines the shapes of the csv without opening the files""" + + def is_numeric(value): + try: + float(value) + return True + except ValueError: + return False + + with open(file_path, "r") as file: + first_line = file.readline().strip() + num_columns = len(first_line.split(",")) + + num_rows = sum(1 for _ in file) + + # Determine if the first row is a header + if all(is_numeric(val) for val in first_line.split(",")): + num_rows += 1 + + return num_rows, num_columns diff --git a/tests/data/input/slice1.h5ad b/tests/data/input/slice1.h5ad new file mode 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